def prepare(self):
    'Prepare dataset for the usage.\n\n        Returns\n        -------\n        self\n\n        '
    scene_label = 'synthetic'
    subset_map = {'test': 'evaltest'}
    param_hash = 'bbb81504db15a03680a0044474633b67'
    Path().makedirs(path=os.path.join(self.local_path, self.evaluation_setup_folder))
    if ((not self.meta_container.exists()) and self.reference_data_present):
        meta_data = MetaDataContainer()
        for class_label in self.event_labels():
            for (subset_label, subset_name_on_disk) in iteritems(subset_map):
                subset_name_on_disk = subset_map[subset_label]
                mixture_path = os.path.join('data', 'mixture_data', subset_name_on_disk, param_hash, 'audio')
                mixture_meta_path = os.path.join(self.local_path, 'data', 'mixture_data', subset_name_on_disk, param_hash, 'meta')
                event_list_filename = os.path.join(mixture_meta_path, (((('event_list_' + subset_name_on_disk) + '_') + class_label) + '.csv'))
                if os.path.isfile(event_list_filename):
                    current_meta = MetaDataContainer(filename=event_list_filename).load(fields=['filename', 'onset', 'offset', 'event_label'])
                    for item in current_meta:
                        item.filename = os.path.join(mixture_path, item.filename)
                        item.scene_label = scene_label
                    meta_data += current_meta
        meta_data.save(filename=self.meta_file)
    test_filename = self.evaluation_setup_filename(setup_part='test', fold=None, file_extension='txt')
    evaluate_filename = self.evaluation_setup_filename(setup_part='evaluate', fold=None, file_extension='txt')
    evaluation_setup_exists = True
    if ((not os.path.isfile(test_filename)) or (not os.path.isfile(evaluate_filename))):
        evaluation_setup_exists = False
    if (not evaluation_setup_exists):
        mixture_meta_path_test = os.path.join(self.local_path, 'data', 'mixture_data', subset_map['test'], param_hash, 'meta')
        mixture_path_test = os.path.join('data', 'mixture_data', subset_map['test'], param_hash, 'audio')
        test_meta = MetaDataContainer()
        for class_label in self.event_labels():
            event_list_filename = os.path.join(mixture_meta_path_test, (((('event_list_' + subset_map['test']) + '_') + class_label) + '.csv'))
            current_meta = MetaDataContainer(filename=event_list_filename).load(fields=['filename', 'onset', 'offset', 'event_label'])
            current_meta_ = MetaDataContainer()
            for item in current_meta:
                item.filename = os.path.join(mixture_path_test, item.filename)
                current_meta_.append(MetaDataItem({'filename': item.filename, 'scene_label': scene_label}))
            test_meta += current_meta_
        test_meta.save(filename=test_filename)
        eval_meta = MetaDataContainer()
        for class_label in self.event_labels():
            event_list_filename = os.path.join(mixture_meta_path_test, (((('event_list_' + subset_map['test']) + '_') + class_label) + '.csv'))
            current_meta = MetaDataContainer(filename=event_list_filename).load(fields=['filename', 'onset', 'offset', 'event_label'])
            for item in current_meta:
                item.filename = os.path.join(mixture_path_test, item.filename)
                item.scene_label = scene_label
            eval_meta += current_meta
        eval_meta.save(filename=evaluate_filename)
        self.load()
    return self
Prepare dataset for the usage.

Returns
-------
self
def prepare(self):
    'Prepare dataset for the usage.\n\n        Returns\n        -------\n        self\n\n        '
    scene_label = 'synthetic'
    subset_map = {'test': 'evaltest'}
    param_hash = 'bbb81504db15a03680a0044474633b67'
    Path().makedirs(path=os.path.join(self.local_path, self.evaluation_setup_folder))
    if ((not self.meta_container.exists()) and self.reference_data_present):
        meta_data = MetaDataContainer()
        for class_label in self.event_labels():
            for (subset_label, subset_name_on_disk) in iteritems(subset_map):
                subset_name_on_disk = subset_map[subset_label]
                mixture_path = os.path.join('data', 'mixture_data', subset_name_on_disk, param_hash, 'audio')
                mixture_meta_path = os.path.join(self.local_path, 'data', 'mixture_data', subset_name_on_disk, param_hash, 'meta')
                event_list_filename = os.path.join(mixture_meta_path, (((('event_list_' + subset_name_on_disk) + '_') + class_label) + '.csv'))
                if os.path.isfile(event_list_filename):
                    current_meta = MetaDataContainer(filename=event_list_filename).load(fields=['filename', 'onset', 'offset', 'event_label'])
                    for item in current_meta:
                        item.filename = os.path.join(mixture_path, item.filename)
                        item.scene_label = scene_label
                    meta_data += current_meta
        meta_data.save(filename=self.meta_file)
    test_filename = self.evaluation_setup_filename(setup_part='test', fold=None, file_extension='txt')
    evaluate_filename = self.evaluation_setup_filename(setup_part='evaluate', fold=None, file_extension='txt')
    evaluation_setup_exists = True
    if ((not os.path.isfile(test_filename)) or (not os.path.isfile(evaluate_filename))):
        evaluation_setup_exists = False
    if (not evaluation_setup_exists):
        mixture_meta_path_test = os.path.join(self.local_path, 'data', 'mixture_data', subset_map['test'], param_hash, 'meta')
        mixture_path_test = os.path.join('data', 'mixture_data', subset_map['test'], param_hash, 'audio')
        test_meta = MetaDataContainer()
        for class_label in self.event_labels():
            event_list_filename = os.path.join(mixture_meta_path_test, (((('event_list_' + subset_map['test']) + '_') + class_label) + '.csv'))
            current_meta = MetaDataContainer(filename=event_list_filename).load(fields=['filename', 'onset', 'offset', 'event_label'])
            current_meta_ = MetaDataContainer()
            for item in current_meta:
                item.filename = os.path.join(mixture_path_test, item.filename)
                current_meta_.append(MetaDataItem({'filename': item.filename, 'scene_label': scene_label}))
            test_meta += current_meta_
        test_meta.save(filename=test_filename)
        eval_meta = MetaDataContainer()
        for class_label in self.event_labels():
            event_list_filename = os.path.join(mixture_meta_path_test, (((('event_list_' + subset_map['test']) + '_') + class_label) + '.csv'))
            current_meta = MetaDataContainer(filename=event_list_filename).load(fields=['filename', 'onset', 'offset', 'event_label'])
            for item in current_meta:
                item.filename = os.path.join(mixture_path_test, item.filename)
                item.scene_label = scene_label
            eval_meta += current_meta
        eval_meta.save(filename=evaluate_filename)
        self.load()
    return self