def load(self, fList):
from Gaugi import load
from Gaugi import csvStr2List, expandFolders, progressbar
fList = csvStr2List(fList)
fList = expandFolders(fList)
from saphyra import TunedData_v1
self._obj = TunedData_v1()
for inputFile in progressbar(fList,
len(fList),
prefix="Reading tuned data collection...",
logger=self._logger):
raw = load(inputFile)
# get the file version
version = raw['__version']
# the current file version
if version == 1:
obj = TunedData_v1.fromRawObj(raw)
self._obj.merge(obj)
else:
# error because the file does not exist
self._logger.fatal('File version (%d) not supported in (%s)',
version, inputFile)
# return the list of keras models
return self._obj
def __init__(self, fList):
Logger.__init__(self)
from Gaugi import csvStr2List
from Gaugi import expandFolders
self.fList = csvStr2List(fList)
self.fList = expandFolders(fList)
self.process_pipe = []
self.output_stack = []
import random
import time
random.seed(time.time())
self._base_id = random.randrange(100000)
def __init__(self, fList, reader, nFilesPerJob, nthreads):
Logger.__init__(self)
from Gaugi import csvStr2List
from Gaugi import expandFolders
fList = csvStr2List(fList)
self._fList = expandFolders(fList)
def chunks(l, n):
"""Yield successive n-sized chunks from l."""
for i in range(0, len(l), n):
yield l[i:i + n]
self._fList = [l for l in chunks(self._fList, nFilesPerJob)]
self.process_pipe = []
self._outputs = []
self._nthreads = nthreads
self._reader = reader