本文整理汇总了Python中grid_control.datasets.DataProvider.bName方法的典型用法代码示例。如果您正苦于以下问题：Python DataProvider.bName方法的具体用法？Python DataProvider.bName怎么用？Python DataProvider.bName使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类grid_control.datasets.DataProvider的用法示例。
在下文中一共展示了DataProvider.bName方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: create_dbs3_proto_blocks
# 需要导入模块: from grid_control.datasets import DataProvider [as 别名]
# 或者: from grid_control.datasets.DataProvider import bName [as 别名]
def create_dbs3_proto_blocks(opts, dataset_blocks):
for dataset in dataset_blocks:
missing_info_blocks = []
dataset_types = set()
for block in dataset_blocks[dataset]:
block_dump = {'dataset_conf_list': [], 'files': [], 'file_conf_list': [], 'file_parent_list': []}
(block_size, block_dataset_types) = create_dbs3_json_files(opts, block, block_dump)
if len(block_dataset_types) > 1:
raise Exception('Data and MC files are mixed in block %s' % DataProvider.bName(block))
elif len(block_dataset_types) == 1:
yield (block, block_dump, block_size, block_dataset_types.pop())
else:
missing_info_blocks.append((block, block_dump, block_size))
dataset_types.update(block_dataset_types) # collect dataset types in this dataset for blocks with missing type information
if missing_info_blocks:
if len(dataset_types) > 1:
raise Exception('Data and MC files are mixed in dataset %s! Unable to determine dataset type for blocks without type info')
elif len(dataset_types) == 0:
if not opts.datatype:
raise Exception('Please supply dataset type via --datatype!')
dataset_type = opts.datatype
else:
dataset_type = dataset_types.pop()
for (block, block_dump, block_size) in missing_info_blocks:
yield (block, block_dump, block_size, dataset_type)示例2: create_dbs3_json_blocks
# 需要导入模块: from grid_control.datasets import DataProvider [as 别名]
# 或者: from grid_control.datasets.DataProvider import bName [as 别名]
def create_dbs3_json_blocks(opts, dataset_blocks):
for (block, block_dump, block_size, dataset_type) in create_dbs3_proto_blocks(opts, dataset_blocks):
dataset = block[DataProvider.Dataset]
try:
primary_dataset, processed_dataset, data_tier = dataset[1:].split('/')
except Exception:
raise DatasetError('Dataset name %s is not a valid DBS name!' % dataset)
# add primary dataset information
block_dump['primds'] = {'primary_ds_type': dataset_type, 'primary_ds_name': primary_dataset}
# add dataset information
block_dump['dataset'] = {
'dataset': dataset, 'processed_ds_name': processed_dataset, 'data_tier_name': data_tier,
'physics_group_name': None, 'dataset_access_type': 'VALID',
'xtcrosssection': None, # TODO: Add to meta data from FrameWorkJobReport, if possible!
}
# add block information
site_db = SiteDB()
try:
origin_site_name = site_db.se_to_cms_name(block[DataProvider.Locations][0])[0]
except IndexError:
origin_site_name = 'UNKNOWN'
block_dump['block'] = {'block_name': DataProvider.bName(block), 'block_size': block_size,
'file_count': len(block[DataProvider.FileList]), 'origin_site_name': origin_site_name}
if opts.do_close_blocks:
block_dump['block']['open_for_writing'] = 0
else:
block_dump['block']['open_for_writing'] = 1
# add acquisition_era, CRAB is important because of checks within DBS 3
block_dump['acquisition_era'] = {'acquisition_era_name': 'CRAB', 'start_date': 0}
# add processing_era
block_dump['processing_era'] = {'processing_version': 1, 'description': 'grid-control'}
yield validate_dbs3_json('blockBulk', block_dump)示例3: processBlock
# 需要导入模块: from grid_control.datasets import DataProvider [as 别名]
# 或者: from grid_control.datasets.DataProvider import bName [as 别名]
def processBlock(self, block):
if self._lumi_filter.empty() and ((self._lumi_keep == LumiKeep.RunLumi) or (DataProvider.Metadata not in block)):
return block
def getMetadataIdx(key):
if key in block.get(DataProvider.Metadata, []):
return block[DataProvider.Metadata].index(key)
idxRuns = getMetadataIdx('Runs')
idxLumi = getMetadataIdx('Lumi')
if not self._lumi_filter.empty():
lumi_filter = self._lumi_filter.lookup(block[DataProvider.Nickname], is_selector = False)
if lumi_filter and (self._lumi_strict == LumiMode.strict) and ((idxRuns is None) or (idxLumi is None)):
raise DatasetError('Strict lumi filter active but dataset %s does not provide lumi information!' % DataProvider.bName(block))
elif lumi_filter and (self._lumi_strict == LumiMode.weak) and (idxRuns is None):
raise DatasetError('Weak lumi filter active but dataset %s does not provide run information!' % DataProvider.bName(block))
block[DataProvider.FileList] = list(self._processFI(block, idxRuns, idxLumi))
if not block[DataProvider.FileList]:
return
block[DataProvider.NEntries] = sum(imap(lambda fi: fi[DataProvider.NEntries], block[DataProvider.FileList]))
# Prune metadata
if self._lumi_keep == LumiKeep.RunLumi:
return block
elif self._lumi_keep == LumiKeep.Run:
idxRuns = None
removeRunLumi(block[DataProvider.Metadata], idxRuns, idxLumi)
return block