def fromCMGCrabDirectory(cls, name, baseDirectory, treeFilename = 'tree.root', treeName = 'tree', maxN = None, \
selectionString = None, weightString = None,
isData = False, color = 0, texName = None):
'''Load a CMG crab output directory
'''
import tarfile
from cmg_helpers import read_cmg_normalization
maxN = maxN if maxN is not None and maxN>0 else None
# Walk through all subdirectories and pick up pairs of files '..._n.root and ..._n.tgz where n is the job number'
treeFiles = {}
zipFiles = {}
for root, subFolders, filenames in os.walk( baseDirectory ):
for filename in filenames:
base, ext = os.path.splitext( filename )
try:
n = int(base.split('_')[-1])
except:
# filename is not of the form 'xyz_n' where n is the job number
continue
# add the tgz and files to the dict.
filename_ = os.path.join(root, filename)
if ext=='.root':
treeFiles[n] = filename_
if ext=='.tgz':
zipFiles[n] = filename_
# Find pairs of zip and root files
pairs = set(zipFiles.keys()) & set(treeFiles.keys())
n_jobs = len( set(zipFiles.keys()) | set(treeFiles.keys()) )
normalization = 0
files = []
failedJobs = []
for n in pairs:
sumW = None
tf = tarfile.open( zipFiles[n], 'r:gz' )
for f in tf.getmembers():
if "SkimReport.txt" in f.name:
sumW = read_cmg_normalization(tf.extractfile(f))
if sumW is not None: break
if sumW is None:
logger.warning( "No normalization found when reading tar file %s", zipFiles[n] )
tf.close()
# Check treefile for whether the tree 'treeName' can be found.
# This is an implicit check for broken, recovered or otherwise corrupted root files.
treeFile = treeFiles[n] if helpers.checkRootFile(treeFiles[n], checkForObjects = [treeName] ) else None
if treeFile is None: logger.warning( "File %s looks broken. Checked for presence of tree %s.", treeFiles[n] , treeName )
# If both, normalization and treefile are OK call it successful.
if sumW and treeFile:
files.append( treeFile )
normalization += sumW
logger.debug( "Successfully read job %i and incremented normalization by %7.2f", n, sumW )
else:
failedJobs.append( n )
# Don't allow empty samples
if len(files) == 0:
raise helpers.EmptySampleError("Could not find valid crab CMG output for sample {0}. Total number of jobs: {1}. baseDirectory: {2}"\
.format(name, len(pairs), baseDirectory))
# Log statements
eff = 100*len(failedJobs)/float( n_jobs )
logger.debug("Loaded CMGOutput sample %s. Total number of jobs: %i, both tgz and root: %i. Normalization: %7.2f Bad: %i. Inefficiency: %3.3f", \
name, len(pairs), n_jobs, normalization, len(failedJobs), eff)
logger.debug( "Read %i chunks and total normalization of %f", len(files), normalization )
return cls( name = name, treeName = treeName, files = files, normalization = normalization,
selectionString = selectionString, weightString = weightString,
isData = isData, color = color, texName = texName )
def fromCMGCrabDirectory(cls, name, baseDirectory, treeFilename = 'tree.root', treeName = 'tree', maxN = None, xSection = -1,\
selectionString = None, weightString = None,
isData = False, color = 0, texName = None):
'''Load a CMG crab output directory
'''
import tarfile
from cmg_helpers import read_cmg_normalization
maxN = maxN if maxN is not None and maxN>0 else None
# Walk through all subdirectories and pick up pairs of files '..._n.root and ..._n.tgz where n is the job number'
treeFiles = {}
zipFiles = {}
for root, subFolders, filenames in os.walk( baseDirectory ):
for filename in filenames:
base, ext = os.path.splitext( filename )
try:
n = int(base.split('_')[-1])
except:
# filename is not of the form 'xyz_n' where n is the job number
continue
# add the tgz and files to the dict.
filename_ = os.path.join(root, filename)
if ext=='.root':
treeFiles[n] = filename_
if ext=='.tgz':
zipFiles[n] = filename_
# Find pairs of zip and root files
pairs = set(zipFiles.keys()) & set(treeFiles.keys())
n_jobs = len( set(zipFiles.keys()) | set(treeFiles.keys()) )
normalization = 0
files = []
failedJobs = []
for n in pairs:
sumW = None
tf = tarfile.open( zipFiles[n], 'r:gz' )
for f in tf.getmembers():
if "SkimReport.txt" in f.name:
sumW = read_cmg_normalization(tf.extractfile(f))
if sumW is not None: break
if sumW is None:
logger.warning( "No normalization found when reading tar file %s", zipFiles[n] )
tf.close()
# Check treefile for whether the tree 'treeName' can be found.
# This is an implicit check for broken, recovered or otherwise corrupted root files.
treeFile = treeFiles[n] if helpers.checkRootFile(treeFiles[n], checkForObjects = [treeName] ) else None
if treeFile is None: logger.warning( "File %s looks broken. Checked for presence of tree %s.", treeFiles[n] , treeName )
# If both, normalization and treefile are OK call it successful.
if sumW and treeFile:
files.append( treeFile )
normalization += sumW
logger.debug( "Successfully read job %i and incremented normalization by %7.2f", n, sumW )
else:
failedJobs.append( n )
# Don't allow empty samples
if len(files) == 0:
raise helpers.EmptySampleError("Could not find valid crab CMG output for sample {0}. Total number of jobs: {1}. baseDirectory: {2}"\
.format(name, len(pairs), baseDirectory))
# Log statements
eff = 100*len(failedJobs)/float( n_jobs )
logger.debug("Loaded CMGOutput sample %s. Total number of jobs: %i, both tgz and root: %i. Normalization: %7.2f Bad: %i. Inefficiency: %3.3f", \
name, len(pairs), n_jobs, normalization, len(failedJobs), eff)
logger.debug( "Read %i chunks and total normalization of %f", len(files), normalization )
return cls( name = name, treeName = treeName, files = files, normalization = normalization, xSection = xSection,
selectionString = selectionString, weightString = weightString,
isData = isData, color = color, texName = texName )
def fromCMGOutput(cls, name, baseDirectory, treeFilename = 'tree.root', chunkString = None, treeName = 'tree', maxN = None, \
selectionString = None, weightString = None,
isData = False, color = 0, texName = None):
'''Load a CMG output directory from e.g. unzipped crab output in the 'Chunks' directory structure.
Expects the presence of the tree root file and the SkimReport.txt
'''
from cmg_helpers import read_cmg_normalization
maxN = maxN if maxN is not None and maxN>0 else None
# Reading all subdirectories in base directory. If chunkString != None, require cmg output name formatting
chunkDirectories = []
# FIXME: Better to loop only over subdirectories in base directory?
for x in os.listdir(baseDirectory):
if os.path.isdir(os.path.join(baseDirectory, x)):
if not chunkString or (x.startswith(chunkString) and x.endswith('_Chunk')) or x==chunkString:
chunkDirectories.append(os.path.join(baseDirectory, x))
if len(chunkDirectories)==maxN:break
logger.debug( "Found %i chunk directories with chunkString %s in base directory %s", \
len(chunkDirectories), chunkString, baseDirectory )
normalization = 0
files = []
failedChunks=[]
goodChunks =[]
for i, chunkDirectory in enumerate( chunkDirectories ):
success = False
logger.debug("Reading chunk %s", chunkDirectory)
# Find normalization
sumW = None
for root, subFolders, filenames in os.walk( chunkDirectory ):
# Determine normalization constant
if 'SkimReport.txt' in filenames:
skimReportFilename = os.path.join(root, 'SkimReport.txt')
with open(skimReportFilename, 'r') as fin:
sumW = read_cmg_normalization(fin)
if not sumW:
logger.warning( "Read chunk %s and found report '%s' but could not read normalization.",
chunkDirectory, skimReportFilename )
# Find treefile
treeFile = None
for root, subFolders, filenames in os.walk( chunkDirectory ):
# Load tree file
if treeFilename in filenames:
treeFile = os.path.join(root, treeFilename)
# Checking whether root file is OG and contains a tree
if not helpers.checkRootFile(treeFile, checkForObjects=[treeName] ):
logger.warning( "Read chunk %s and found tree file '%s' but file looks broken.", chunkDirectory, treeFile )
# If both, normalization and treefile are OK call it successful.
if sumW and treeFile:
files.append( treeFile )
normalization += sumW
logger.debug( "Successfully read chunk %s and incremented normalization by %7.2f", chunkDirectory, sumW )
success = True
goodChunks.append( chunkDirectory )
if not success:
failedChunks.append( chunkDirectory )
# Don't allow empty samples
if len(goodChunks) == 0:
raise helpers.EmptySampleError("Could not find good CMGOutput chunks for sample {0}. Total number of chunks: {1}. baseDirectory: {2}"\
.format(name, len(chunkDirectories), baseDirectory))
# Log statements
eff = 100*len(failedChunks)/float( len(chunkDirectories) )
logger.debug("Loaded CMGOutput sample %s. Total number of chunks : %i. Normalization: %7.2f Bad: %i. Inefficiency: %3.3f", \
name, len(chunkDirectories), normalization, len(failedChunks), eff)
for chunk in failedChunks:
logger.debug( "Failed to load chunk %s", chunk)
logger.debug( "Read %i chunks and total normalization of %f", len(files), normalization )
return cls( name = name, treeName = treeName, files = files, normalization = normalization,
selectionString = selectionString, weightString = weightString,
isData = isData, color = color, texName = texName )
def fromCMGOutput(cls, name, baseDirectory, treeFilename = 'tree.root', chunkString = None, treeName = 'tree', maxN = None, \
selectionString = None, xSection = -1, weightString = None,
isData = False, color = 0, texName = None):
'''Load a CMG output directory from e.g. unzipped crab output in the 'Chunks' directory structure.
Expects the presence of the tree root file and the SkimReport.txt
'''
from cmg_helpers import read_cmg_normalization
maxN = maxN if maxN is not None and maxN>0 else None
# Reading all subdirectories in base directory. If chunkString != None, require cmg output name formatting
chunkDirectories = []
# FIXME: Better to loop only over subdirectories in base directory?
for x in os.listdir(baseDirectory):
if os.path.isdir(os.path.join(baseDirectory, x)):
if not chunkString or (x.startswith(chunkString) and x.endswith('_Chunk')) or x==chunkString:
chunkDirectories.append(os.path.join(baseDirectory, x))
if len(chunkDirectories)==maxN:break
logger.debug( "Found %i chunk directories with chunkString %s in base directory %s", \
len(chunkDirectories), chunkString, baseDirectory )
normalization = 0
files = []
failedChunks=[]
goodChunks =[]
for i, chunkDirectory in enumerate( chunkDirectories ):
success = False
logger.debug("Reading chunk %s", chunkDirectory)
# Find normalization
sumW = None
for root, subFolders, filenames in os.walk( chunkDirectory ):
# Determine normalization constant
if 'SkimReport.txt' in filenames:
skimReportFilename = os.path.join(root, 'SkimReport.txt')
with open(skimReportFilename, 'r') as fin:
sumW = read_cmg_normalization(fin)
if not sumW:
logger.warning( "Read chunk %s and found report '%s' but could not read normalization.",
chunkDirectory, skimReportFilename )
# Find treefile
treeFile = None
for root, subFolders, filenames in os.walk( chunkDirectory ):
# Load tree file
if treeFilename in filenames:
treeFile = os.path.join(root, treeFilename)
# Checking whether root file is OG and contains a tree
if not helpers.checkRootFile(treeFile, checkForObjects=[treeName] ):
logger.warning( "Read chunk %s and found tree file '%s' but file looks broken.", chunkDirectory, treeFile )
# If both, normalization and treefile are OK call it successful.
if sumW and treeFile:
files.append( treeFile )
normalization += sumW
logger.debug( "Successfully read chunk %s and incremented normalization by %7.2f", chunkDirectory, sumW )
success = True
goodChunks.append( chunkDirectory )
if not success:
failedChunks.append( chunkDirectory )
# Don't allow empty samples
if len(goodChunks) == 0:
raise helpers.EmptySampleError("Could not find good CMGOutput chunks for sample {0}. Total number of chunks: {1}. baseDirectory: {2}"\
.format(name, len(chunkDirectories), baseDirectory))
# Log statements
eff = 100*len(failedChunks)/float( len(chunkDirectories) )
logger.debug("Loaded CMGOutput sample %s. Total number of chunks : %i. Normalization: %7.2f Bad: %i. Inefficiency: %3.3f", \
name, len(chunkDirectories), normalization, len(failedChunks), eff)
for chunk in failedChunks:
logger.debug( "Failed to load chunk %s", chunk)
logger.debug( "Read %i chunks and total normalization of %f", len(files), normalization )
return cls( name = name, treeName = treeName, files = files, normalization = normalization,
selectionString = selectionString, weightString = weightString, xSection = xSection,
isData = isData, color = color, texName = texName )
