def save(self, obj):
desc = "object of type %s" % (describe_type(obj))
# , describe_value(obj, 100))
# self.stack.append(describe_value(obj, 120))
self.stack.append(desc)
Pickler.save(self, obj)
self.stack.pop()
def __init__(self, file, protocol=None):
Pickler.__init__(self, file, protocol)
self.lazywrites = deque()
self.realwrite = file.write
# Pickler.__init__ overwrites self.write, we do not want that
del self.write
def picklify(objs): from StringIO import StringIO from pickle import Pickler sio = StringIO() pickler = Pickler(sio) pickler.dump(objs) return sio.getvalue()
class Environment(object):
handle = None
loader = None
writer = None
data = None
def __init__(self):
self.handle = open("environment.pickle", 'w+')
self.loader = Unpickler(self.handle)
self.writer = Pickler(self.handle)
try:
self.data = self.loader.load()
except EOFError:
print "WARNING: Empty environment, creating environment file."
self.data = {}
self.write(self.data)
def write(self, data):
self.writer.dump(data)
def save_type(pickler, obj):
if obj in _typemap:
pickler.save_reduce(_load_type, (_typemap[obj],), obj=obj)
elif obj.__module__ == '__main__':
if type(obj) == type:
# we are pickling the interpreter
if is_dill(pickler) and pickler._session:
_dict = _dict_from_dictproxy(obj.__dict__)
else: # otherwise punt to StockPickler
StockPickler.save_global(pickler, obj)
return
else:
_dict = obj.__dict__
#print _dict
#print "%s\n%s" % (type(obj), obj.__name__)
#print "%s\n%s" % (obj.__bases__, obj.__dict__)
pickler.save_reduce(_create_type, (type(obj), obj.__name__,
obj.__bases__, _dict), obj=obj)
else:
#print obj.__dict__
#print "%s\n%s" % (type(obj), obj.__name__)
#print "%s\n%s" % (obj.__bases__, obj.__dict__)
StockPickler.save_global(pickler, obj)
return
def __setitem__(self, key, value):
if self.writeback:
self.cache[key] = value
f = BytesIO()
p = Pickler(f, self._protocol)
p.dump(value)
self.dict[key.encode(self.keyencoding)] = f.getvalue()
def parseLog(self):
logData = {}
logRE = re.compile('^.*/([1-9][0-9]*(\.[0-9]+|))_[^/]+/step([1-9])_.*\.log$')
max_steps = 0
for logFile in glob.glob(self.basedir+'/[1-9]*/step[0-9]*.log'):
m = logRE.match(logFile)
if not m: continue
wf = m.group(1)
step = int(m.group(3))
if step>max_steps: max_steps=step
if not logData.has_key(wf):
logData[wf] = {'steps': {}, 'events' : [], 'failed' : [], 'warning' : []}
if not logData[wf]['steps'].has_key(step):
logData[wf]['steps'][step]=logFile
cache_read=0
log_processed=0
for wf in logData:
for k in logData[wf]:
if k == 'steps': continue
for s in range(0, max_steps):
logData[wf][k].append(-1)
index =0
for step in sorted(logData[wf]['steps']):
data = [0, 0, 0]
logFile = logData[wf]['steps'][step]
json_cache = os.path.dirname(logFile)+"/logcache_"+str(step)+".json"
if (not os.path.exists(json_cache)) or (os.path.getmtime(logFile)>os.path.getmtime(json_cache)):
try:
es_parse_log(logFile)
except Exception as e:
print "Sending log information to elasticsearch failed" , str(e)
inFile = open(logFile)
for line in inFile:
if '%MSG-w' in line: data[1]=data[1]+1
if '%MSG-e' in line: data[2]=data[2]+1
if 'Begin processing the ' in line: data[0]=data[0]+1
inFile.close()
jfile = open(json_cache,"w")
json.dump(data,jfile)
jfile.close()
log_processed+=1
else:
jfile = open(json_cache,"r")
data = json.load(jfile)
jfile.close()
cache_read+=1
logData[wf]['events'][index] = data[0]
logData[wf]['failed'][index] = data[2]
logData[wf]['warning'][index] = data[1]
index+=1
del logData[wf]['steps']
print "Log processed: ",log_processed
print "Caches read:",cache_read
from pickle import Pickler
outFile = open(os.path.join(self.outdir,'runTheMatrixMsgs.pkl'), 'w')
pklFile = Pickler(outFile)
pklFile.dump(logData)
outFile.close()
return
def __init__(self, file, protocol=None):
Pickler.__init__(self, file, protocol)
# set of modules to unpickle
self.modules = set()
# map ids to dictionary. used to ensure that functions can share
# global env
self.globals_ref = {}
def save_function(pickler, obj):
if not _locate_function(obj):
pickler.save_reduce(FunctionType, (obj.func_code, obj.func_globals,
obj.func_name, obj.func_defaults,
obj.func_closure), obj=obj)
else:
Pickler.save_global(pickler, obj)
def realsave(self, obj):
def _name(obj):
try:
name = getattr(obj, '__name__', None)
if name is not None:
return ': %s' % name
except Exception:
pass
return ''
def _loc(obj):
try:
fn = getattr(obj, '__file__', None)
if fn is not None:
return ' @%s' % (fn,)
obj = getattr(obj, 'im_func', obj)
code = getattr(obj, '__code__', None)
if code is not None:
return ' @%s:%s' % (code.co_filename, code.co_firstlineno)
except Exception:
pass
return ''
try:
Pickler.save(self, obj)
except TypeError:
logger.error('Failed to serialize %s%s%s',
type(obj), _name(obj), _loc(obj))
raise
def parseLog(self):
logData = {}
logRE = re.compile('^([1-9][0-9]*\.[0-9]+)[^/]+/step([1-9])_.*\.log$')
for logFile in glob.glob(self.basedir+'/[1-9]*/step[0-9]*.log'):
m = logRE.match(logFile)
if not m: continue
wf = m.group(1)
step = int(m.group(2)) - 1
if step >= self.StepsPerJob: continue
if not logData.has_key(wf):
logData[wf] = {'events' : [], 'failed' : [], 'warning' : []}
for s in range(0, self.StepsPerJob):
for k in logData[wf].keys(): logData[wf][k].append(-1)
warn = 0
err = 0
rd = 0
inFile = open(logFile)
for line in inFile:
if '%MSG-w' in line: warn += 1
if '%MSG-e' in line: err += 1
if 'Begin processing the ' in line: rd += 1
inFile.close()
logData[wf]['events'][step] = rd
logData[wf]['failed'][step] = err
logData[wf]['warning'][step] = warn
from pickle import Pickler
outFile = open(os.path.join(self.basedir,'runTheMatrixMsgs.pkl'), 'w')
pklFile = Pickler(outFile)
pklFile.dump(logData)
outFile.close()
return
def __init__(self, writer, reducers=None, protocol=HIGHEST_PROTOCOL):
_Pickler.__init__(self, writer, protocol=protocol)
# Make the dispatch registry an instance level attribute instead of a
# reference to the class dictionary
self.dispatch = _Pickler.dispatch.copy()
for type, reduce_func in reducers.items():
self.register(type, reduce_func)
def testDeepCopyCanInvalidate(self):
"""
Tests regression for invalidation problems related to missing
readers and writers values in cloned objects (see
http://mail.zope.org/pipermail/zodb-dev/2008-August/012054.html)
"""
import ZODB.MappingStorage
database = DB(ZODB.blob.BlobStorage(
'blobs', ZODB.MappingStorage.MappingStorage()))
connection = database.open()
root = connection.root()
transaction.begin()
root['blob'] = Blob()
transaction.commit()
stream = StringIO()
p = Pickler(stream, 1)
p.dump(root['blob'])
u = Unpickler(stream)
stream.seek(0)
clone = u.load()
clone._p_invalidate()
# it should also be possible to open the cloned blob
# (even though it won't contain the original data)
clone.open()
# tearDown
database.close()
def take_snapshot(self, object):
file = open(fu.next_snapshot_file(self.basedir), "wb")
logger.debug("Taking snapshot on: " + file.name)
pickler = Pickler(file, pickle.HIGHEST_PROTOCOL)
pickler.dump(object)
file.flush()
file.close()
def save_builtin_method(pickler, obj):
if obj.__self__ is not None:
pickler.save_reduce(getattr, (obj.__self__, obj.__name__), obj=obj)
else:
StockPickler.save_global(pickler, obj)
return
def save_builtin_method(pickler, obj):
if obj.__self__ is not None:
log.info("B1: %s" % obj)
pickler.save_reduce(getattr, (obj.__self__, obj.__name__), obj=obj)
else:
log.info("B2: %s" % obj)
StockPickler.save_global(pickler, obj)
return
def __init__(self, file, protocol=None):
if protocol is None:
protocol = DEFAULT_PROTOCOL
Pickler.__init__(self, file, protocol=protocol)
# set of modules to unpickle
self.modules = set()
# map ids to dictionary. used to ensure that functions can share global env
self.globals_ref = {}
def pickle_outcomes(outcomes, fn):
fh = open(fn, 'w')
p = Pickler(fh)
p.dump(outcomes)
fh.close()
if KEEP_TIMING:
end_t = time()
time_map["pickling"] = end_t - start_t
start_t = time()
def testPickleUnpickle(self):
s = BytesIO()
p = Pickler(s)
p.dump(Allow)
s.seek(0)
u = Unpickler(s)
newAllow = u.load()
self.assertTrue(newAllow is Allow)
def save_type(pickler, obj):
if obj in _typemap:
pickler.save_reduce(_load_type, (_typemap[obj],), obj=obj)
elif obj.__module__ == '__main__':
pickler.save_reduce(_create_type, (type(obj), obj.__name__,
obj.__bases__, obj.__dict__),
obj=obj)
else:
Pickler.save_global(pickler, obj)
def save_function(pickler, obj):
if not _locate_function(obj): #, pickler._session):
log.info("F1: %s" % obj)
pickler.save_reduce(FunctionType, (obj.func_code, obj.func_globals,
obj.func_name, obj.func_defaults,
obj.func_closure), obj=obj)
else:
log.info("F2: %s" % obj)
StockPickler.save_global(pickler, obj)
return
def save_classobj(pickler, obj):
if obj.__module__ == '__main__':
pickler.save_reduce(ClassType, (obj.__name__, obj.__bases__,
obj.__dict__), obj=obj)
#XXX: or obj.__dict__.copy()), obj=obj) ?
else:
StockPickler.save_global(pickler, obj)
return
def save_module_dict(pickler, obj):
if is_dill(pickler) and obj is pickler._main_module.__dict__:
log.info("D1: %s" % "<dict ...>") # obj
pickler.write('c__builtin__\n__main__\n')
elif not is_dill(pickler) and obj is _main_module.__dict__:
log.info("D3: %s" % "<dict ...>") # obj
pickler.write('c__main__\n__dict__\n') #XXX: works in general?
else:
log.info("D2: %s" % "<dict ...>") #obj
StockPickler.save_dict(pickler, obj)
return
def save_module_dict(pickler, obj):
if is_dill(pickler) and obj is pickler._main_module.__dict__:
pickler.write('c__builtin__\n__main__\n')
elif not is_dill(pickler) and obj is _main_module.__dict__:
pickler.write('c__main__\n__dict__\n') #XXX: works in general?
else:
StockPickler.save_dict(pickler, obj)
return
def _sauvegarder(self):
"""
Sauvegarde la map.
"""
try:
with open(adresse_fichier_sauvegarde, 'wb') as fichier_sauvegarde:
pick = Pickler(fichier_sauvegarde)
pick.dump(self._map)
except:
print("Erreur lors de l'enregistrement du fichier")
def sync(self):
res = {}
with dbm.open(self.db, self.flag) as db:
for k, v in self.dict.items():
f = io.BytesIO()
p = Pickler(f, protocol=self._protocol)
p.dump(v)
db[k] = f.getvalue()
try:
db.sync()
except AttributeError:
pass
def pickle(obj, filename, protocol=0):
f = None
try:
f = open(filename, "wb")
p = Pickler(f, protocol)
p.dump(obj)
f.close()
f = None
# print "Pickled", filename
finally:
if f:
f.close()
def _tuer_sauvegarde(self):
"""
Efface la sauvegarde.
Devient inefficace si le mode de sauvegarde change.
"""
try:
with open(adresse_fichier_sauvegarde, 'wb') as fichier_sauvegarde:
pick = Pickler(fichier_sauvegarde)
pick.dump(None)
except:
print("Erreur lors de l'enregistrement du fichier")
def _dump_blocked(self):
dump = os.path.join(
self.get_cache_dir(), "autoqueue_block_cache")
if not self._blocked_artists:
try:
os.remove(dump)
except OSError:
pass
return
with open(dump, 'w') as pickle_file:
pickler = Pickler(pickle_file, -1)
to_dump = (self._blocked_artists, self._blocked_artists_times)
pickler.dump(to_dump)
def __init__(self, writer, reducers=None, protocol=HIGHEST_PROTOCOL):
Pickler.__init__(self, writer, protocol=protocol)
if reducers is None:
reducers = {}
if hasattr(Pickler, 'dispatch'):
# Make the dispatch registry an instance level attribute instead of
# a reference to the class dictionary under Python 2
self.dispatch = Pickler.dispatch.copy()
else:
# Under Python 3 initialize the dispatch table with a copy of the
# default registry
self.dispatch_table = copyreg.dispatch_table.copy()
for type, reduce_func in reducers.items():
self.register(type, reduce_func)
def save_memoryview(self, obj):
"""Fallback to save_string"""
Pickler.save_string(self, str(obj))
def EA_Main(locator,
building_names,
extraCosts,
extraCO2,
extraPrim,
solarFeat,
ntwFeat,
gv,
genCP=0):
"""
Evolutionary algorithm to optimize the district energy system's design
Parameters
----------
locator : string
paths to folders
finances / CO2 / Prim : float
costs [CHF] / emissions [kg CO2-eq] / primary energy needs [MJ oil]
previously calculated
solarFeat : class solarFeatures
includes data from solar files
ntwFeat : class ntwFeatures
includes data from the ntw optimization
genCP : int
generation to start the EA from (eg if there was a crash of the code)
Returns
-------
"""
t0 = time.clock()
# get number of buildings
nBuildings = len(building_names)
# set-up toolbox of DEAp library in python (containing the evolutionary algotirhtm
creator, toolbox = calc_ea_setup(nBuildings, gv)
# define objective function and register into toolbox
def evalConfig(ind):
(costs, CO2, prim) = eI.evalInd(ind, building_names, locator,
extraCosts, extraCO2, extraPrim,
solarFeat, ntwFeat, gv)
return (costs, CO2, prim)
toolbox.register("evaluate", evalConfig)
ntwList = ["1" * nBuildings]
epsInd = []
invalid_ind = []
# Evolutionary strategy
if genCP is 0:
# create population
pop = toolbox.population(n=gv.initialInd)
# Check network
for ind in pop:
eI.checkNtw(ind, ntwList, locator, gv)
# Evaluate the initial population
print "Evaluate initial population"
fitnesses = map(toolbox.evaluate, pop)
for ind, fit in zip(pop, fitnesses):
ind.fitness.values = fit
print ind.fitness.values, "fit"
# Save initial population
print "Save Initial population \n"
os.chdir(locator.pathMasterRes)
with open("CheckPointInitial", "wb") as CPwrite:
CPpickle = Pickler(CPwrite)
cp = dict(population=pop,
generation=0,
networkList=ntwList,
epsIndicator=[],
testedPop=[])
CPpickle.dump(cp)
else:
print "Recover from CP " + str(genCP) + "\n"
os.chdir(locator.pathMasterRes)
with open("CheckPoint" + str(genCP), "rb") as CPread:
CPunpick = Unpickler(CPread)
cp = CPunpick.load()
pop = cp["population"]
ntwList = cp["networkList"]
epsInd = cp["epsIndicator"]
PROBA, SIGMAP = gv.PROBA, gv.SIGMAP
# Evolution starts !
g = genCP
stopCrit = False # Threshold for the Epsilon indictor, Not used
while g < gv.NGEN and not stopCrit and (time.clock() - t0) < gv.maxTime:
g += 1
print "Generation", g
offspring = list(pop)
# Apply crossover and mutation on the pop
print "CrossOver"
for ind1, ind2 in zip(pop[::2], pop[1::2]):
child1, child2 = cx.cxUniform(ind1, ind2, PROBA, gv)
offspring += [child1, child2]
# First half of the EA: create new un-collerated configurations
if g < gv.NGEN / 2:
for mutant in pop:
print "Mutation Flip"
offspring.append(mut.mutFlip(mutant, PROBA, gv))
print "Mutation Shuffle"
offspring.append(mut.mutShuffle(mutant, PROBA, gv))
print "Mutation GU \n"
offspring.append(mut.mutGU(mutant, PROBA, gv))
# Third quarter of the EA: keep the good individuals but modify the shares uniformly
elif g < gv.NGEN * 3 / 4:
for mutant in pop:
print "Mutation Uniform"
offspring.append(mut.mutUniformCap(mutant, gv))
# Last quarter: keep the very good individuals and modify the shares with Gauss distribution
else:
for mutant in pop:
print "Mutation Gauss"
offspring.append(mut.mutGaussCap(mutant, SIGMAP, gv))
# Evaluate the individuals with an invalid fitness
# NB: every generation leads to the reevaluation of 4n / 2n / 2n individuals
# (n being the number of individuals in the previous generation)
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
print "Update Network list \n"
for ind in invalid_ind:
eI.checkNtw(ind, ntwList, locator, gv)
print "Re-evaluate the population"
fitnesses = map(toolbox.evaluate, invalid_ind)
print "......................................."
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
print ind.fitness.values, "new fit"
print "....................................... \n"
# Select the Pareto Optimal individuals
print "Pareto Selection"
selection = sel.selectPareto(offspring)
# Compute the epsilon criteria [and check the stopping criteria]
epsInd.append(eI.epsIndicator(pop, selection))
#if len(epsInd) >1:
# eta = (epsInd[-1] - epsInd[-2]) / epsInd[-2]
# if eta < gv.epsMargin:
# stopCrit = True
# The population is entirely replaced by the best individuals
print "Replace the population \n"
pop[:] = selection
print "....................................... \n GENERATION ", g
for ind in pop:
print ind.fitness.values, "selected fit"
print "....................................... \n"
# Create Checkpoint if necessary
if g % gv.fCheckPoint == 0:
os.chdir(locator.pathMasterRes)
print "Create CheckPoint", g, "\n"
with open("CheckPoint" + str(g), "wb") as CPwrite:
CPpickle = Pickler(CPwrite)
cp = dict(population=pop,
generation=g,
networkList=ntwList,
epsIndicator=epsInd,
testedPop=invalid_ind)
CPpickle.dump(cp)
if g == gv.NGEN:
print "Final Generation reached"
else:
print "Stopping criteria reached"
# Saving the final results
print "Save final results. " + str(
len(pop)) + " individuals in final population"
print "Epsilon indicator", epsInd, "\n"
os.chdir(locator.pathMasterRes)
with open("CheckPointFinal", "wb") as CPwrite:
CPpickle = Pickler(CPwrite)
cp = dict(population=pop,
generation=g,
networkList=ntwList,
epsIndicator=epsInd,
testedPop=invalid_ind)
CPpickle.dump(cp)
print "Master Work Complete \n"
return pop, epsInd
def save(self):
logging.info('Save event database')
data = [ev.to_dict() for ev in self.events.values()]
with open(DATA_PATH, 'wb') as file:
pick = Pickler(file)
pick.dump(data)
def __setitem__(self, key, value):
"""..."""
f = BytesIO()
p = Pickler(f, self._protocol)
p.dump(value)
self.dict[key.encode(self.keyencoding)] = bz2.compress(f.getvalue())
def dumps(obj, protocol=None):
file = StringIO()
Pickler(file, protocol).dump(obj)
return file.getvalue()
def save(self, filepath):
with open(filepath, 'wb') as handle:
saver = Pickler(handle, protocol=HIGHEST_PROTOCOL)
saver.save(self.fm)
def saveMX(self, mx):
filemx = open('%s/%s' % ('db', 'matrix.mx'), 'w')
serializer = Pickler(filemx)
serializer.dump(mx)
def split(self, logFile):
self.outFile.write( "going to check "+ logFile+'\n')
subsysRe = re.compile('^>> Tests for package ([A-Z].*/[A-Z].*) ran.')
pkgTestStartRe = re.compile('^===== Test \"(.*)\" ====')
pkgTestEndRe = re.compile('^\^\^\^\^ End Test (.*) \^\^\^\^')
pkgTestResultRe = re.compile('.*---> test ([^ ]+) (had ERRORS|succeeded)')
pkgStartRe = re.compile("^>> Entering Package (.*)")
# pkgEndRe = re.compile("^>> Leaving Package (.*)")
pkgEndRe = re.compile("^>> Tests for package (.*) ran.")
infoPkg = {}
pkgSubsysMap = {}
subsysPkgMap = {}
baseDir = os.path.split(logFile)[0]
logDirs = os.path.join( baseDir,'unitTestLogs')
print "logDirs ", logDirs
if not os.path.exists(logDirs):
os.makedirs(logDirs)
lf = open(logFile,'r')
lines = lf.xreadlines()
startTime = time.time()
nLines = 0
testNames = {}
testLines = {}
pkgLines = {}
results = {}
pkgTests = {}
actPkg = "None"
actTest = "None"
actTstLines = 0
actPkgLines = 0
actLogLines = []
startFound = False
for line in lines:
# write out log to individual log file ...
if startFound and ">> Leaving Package " not in line:
actLogLines.append(line)
nLines += 1
actTstLines += 1
actPkgLines += 1
subsysMatch = subsysRe.match(line)
if subsysMatch:
subsys, pkg = subsysMatch.group(1).split('/')
if not pkgSubsysMap.has_key(pkg) :
pkgSubsysMap[pkg] = subsys
if subsysPkgMap.has_key(subsys) :
subsysPkgMap[subsys].append(pkg)
else:
subsysPkgMap[subsys] = [pkg]
pkgStartMatch = pkgStartRe.match(line)
if pkgStartMatch:
pkg = pkgStartMatch.group(1)
actPkg = pkg
pkgTests[pkg] = 0
actPkgLines = 0
startFound = True
pkgEndMatch = pkgEndRe.match(line)
if pkgEndMatch:
pkg = pkgEndMatch.group(1)
if actPkg != pkg :
self.outFile.write( "pkgEndMatch> package mismatch: pkg found "+pkg+' actPkg='+actPkg+'\n')
pkgLines[pkg] = actPkgLines
if len(actLogLines) > 2 :
actLogDir = os.path.join(logDirs, pkg)
os.makedirs(actLogDir)
actLogFile = open(os.path.join(actLogDir, 'unitTest.log'), 'w')
actLogFile.write("".join(actLogLines))
actLogFile.close()
actLogLines = []
startFound = False
pkgTestResultMatch= pkgTestResultRe.match(line)
if pkgTestResultMatch : # this seems to only appear if there is an ERROR
tstName = pkgTestResultMatch.group(1)
results[tstName] = pkgTestResultMatch.group(2)
pkgTestStartMatch = pkgTestStartRe.match(line)
if pkgTestStartMatch:
tst = pkgTestStartMatch.group(1)
actTest = tst
actTstLines = 0
pkgTests[actPkg] += 1
if testNames.has_key(actPkg):
testNames[actPkg].append(actTest)
else:
testNames[actPkg] = [actTest]
if actTest not in results:
results[actTest] = "succeeded" # set the default, no error seen yet
pkgTestEndMatch = pkgTestEndRe.match(line)
if pkgTestEndMatch:
tst = pkgTestEndMatch.group(1)
if actTest != tst :
self.outFile.write( "pkgTestEndMatch> package mismatch: pkg found "+pkg+' actPkg='+actPkg+'\n')
testLines[tst] = actTstLines
stopTime = time.time()
lf.close()
self.outFile.write( "found a total of "+ str(nLines)+ ' lines in logfile.\n')
self.outFile.write( "analysis took "+str(stopTime-startTime)+ ' sec.\n')
self.outFile.write( "total number of tests: " +str( len(results.keys()) ) + '\n')
nMax = 1000
self.outFile.write( "tests with more than " +str(nMax) + " lines of logs:\n")
for pkg, lines in testLines.items():
if lines > nMax : self.outFile.write( " "+ pkg+ ' : ' + str(lines) +'\n')
self.outFile.write( "Number of tests for packages: \n" )
noTests = 0
nrTests = 0
indent = ' '
totalOK = 0
totalFail = 0
unitTestResults = {}
for pkg, nTst in pkgTests.items():
if nTst == 0:
noTests += 1
else:
nrTests += 1
if self.verbose: self.outFile.write( '-'*80 +'\n' )
self.outFile.write( indent+pkg+' : ' )
nOK = 0
if self.verbose: self.outFile.write( "\n" )
for tNam in testNames[pkg]:
if results[tNam] == 'succeeded':
nOK += 1
totalOK += 1
else:
totalFail += 1
if self.verbose :
self.outFile.write( indent*2 + tNam +' '+ results[tNam] + '\n')
if self.verbose: self.outFile.write( indent+ pkg+" : ")
self.outFile.write( indent + str(len(testNames[pkg]) ) + ' tests in total, OK:'+str(nOK)+ ' fail:'+str(len(testNames[pkg])-nOK) +'\n')
unitTestResults[pkg] = [testNames[pkg], nOK, len(testNames[pkg])-nOK ]
self.outFile.write( indent+str(nrTests)+" packages with tests ("+str(float(nrTests)/float(len(pkgTests.keys())) )+")\n")
self.outFile.write( indent+str(noTests)+" packages without tests ("+str(float(noTests)/float(len(pkgTests.keys())) )+")\n")
self.outFile.write( indent+"in total: tests OK : "+str(totalOK)+' tests FAIL : '+str(totalFail)+'\n')
try:
from pickle import Pickler
resFile = open(baseDir+'/unitTestResults.pkl', 'w')
pklr = Pickler(resFile)
pklr.dump(unitTestResults)
pklr.dump(results)
resFile.close()
print "Successfully pickled results for unit tests ! "
except Exception, e:
print "ERROR during pickling results for unit tests:", str(e)
# Game start
while not (game_over):
print(word_masked)
player_input = get_player_input()
if player_input in letters_guessed:
print("Vous avez déjà essayé la lettre " + player_input)
elif player_input in word_random:
letters_guessed.append(player_input)
else:
letters_guessed.append(player_input)
lives_left -= 1
print("{} ne se trouve pas dans le mot, il vous reste {} essai(s)".
format(player_input, lives_left))
word_masked = gen_masked_word(word_random, letters_guessed)
if word_masked == word_random:
print("Bien joué ! Le mot était {}.".format(word_random))
game_over = True
player_score = lives_left + len(word_random)
scores = get_player_score(player_pseudo, player_score, scores)
with open("scores", "wb") as scores_file:
Pickler(scores_file).dump(scores)
elif lives_left <= 0:
print("Vous avez perdu :/ Le mot était {}.".format(word_random))
game_over = True
print(scores)
def save(self, param):
# saves the shell with all the supdirs.
with open('syncdata.bin', 'wb') as f:
Pickler(f, 3).dump(self.path)
print('Data saved...')
def __init__(self, *args, **kwargs):
Pickler.__init__(self, *args, **kwargs)
self.dispatch_table = dispatch_table.copy()
self.dispatch_table.update(self._extra_reducers)
def __init__(self, file, protocol=None):
if protocol is None:
protocol = DEFAULT_PROTOCOL
Pickler.__init__(self, file, protocol=protocol)
# map ids to dictionary. used to ensure that functions can share global env
self.globals_ref = {}
def get_trivago_datasets(columns,
percentage=1,
seed=1,
uitems_min=2,
lt_drop=0,
time=0):
"""Load train and test datasets
- percentage - how much of train could be dropped
- uitem_min - minimum number of interaction for user
- lt_drop - how many % should be dropped from long tail
- time - how many seconds from last action to keep - 0 = infinite
"""
debug_print("Loading trivago datasets", level=0)
columns = set(columns + COLUMNS)
# Compute names, must be sorted, because sets are kinda random
file_name = str(
hash_params(sorted(columns, reverse=True), percentage, seed,
uitems_min, lt_drop, time))
# Check for existence
os.makedirs(_script_relative(CACHE_FOLDER), exist_ok=True)
dataset_path = _script_relative(os.path.join(CACHE_FOLDER, file_name))
debug_print("Trying {}".format(dataset_path), level=2)
# Check cached
if not os.path.exists(dataset_path):
debug_print("Not found", level=2)
# Create dataset
train_path = _script_relative(REL_TRAIN_PATH)
test_path = _script_relative(REL_TEST_PATH)
__pandas_modify_datasets(train_path, test_path)
train = __pandas_get_dataset(train_path)
debug_print("Train shape before {}".format(train.shape))
train = __pandas_strip_columns(train, columns | {ACTION_TYPE})
train = __pandas_trivago_invalid_rows(train)
train = __pandas_strip_columns(train, columns)
train = __pandas_trivago_drop_unique(train,
USER_ID,
percentage=percentage)
train = __pandas_drop_top(train,
USER_ID,
percentage=lt_drop,
min_items=uitems_min)
train = __pandas_drop_time(train, time=time)
debug_print("Train shape after {}".format(train.shape))
test = __pandas_get_dataset(test_path)
test = __pandas_strip_columns(test, columns | {ACTION_TYPE})
test = __pandas_trivago_invalid_rows(test)
test = __pandas_strip_columns(test, columns)
debug_print("Dropping non train {}".format(test.shape), level=2)
test = test[test[USER_ID].isin(train[USER_ID].unique())]
debug_print("After non train {}".format(test.shape), level=2)
# __pandas_reindex_values(train, test, column=USER_ID)
# __pandas_reindex_values(train, test, column=REFERENCE)
# Save dataset
debug_print("Saving dataset {}".format(dataset_path))
with open(dataset_path, "wb") as f:
Pickler(f).dump((train, test))
else: # Load dataset
with open(dataset_path, "rb") as f:
debug_print("Found", level=2)
train, test = Unpickler(f).load()
print(len(set(test[USER_ID])))
print(len(set(train[USER_ID]) & set(test[USER_ID])))
__pandas_trivago_plot_density(train, USER_ID)
return __pandas_to_coo(train, test)
#!/usr/bin/env python
from __future__ import print_function
import sys
from _py2with3compatibility import run_cmd
from pickle import Pickler
def doDu(what):
error, out = run_cmd('du -k -s %s' % what)
if error:
print("Error while getting directory size.")
sys.exit(1)
results = [l.split() for l in out.split("\n")]
return dict([(pkg.strip().replace("src/", ''), int(sz.strip()*1024))
for (sz, pkg) in results])
if __name__ == '__main__':
try:
f = open('dirSizeInfo.pkl', 'wb')
pklr = Pickler(f, protocol=2)
pklr.dump(doDu("src lib bin"))
pklr.dump(doDu("src/*/*"))
f.close()
except Exception as e:
print("ERROR during pickling results for dir size:", str(e))
sys.exit(1)
print("Successfully pickled results for dir size !")
def save_buffer(self, obj):
"""Fallback to save_string"""
Pickler.save_string(self, str(obj))
def write(self, data):
with open(self.chemin, 'wb') as f:
Pickler(f).dump(data)
def save(self, path):
grille = np.zeros((9, 9), dtype=int)
modif = np.zeros((9, 9), dtype=bool)
possibilites = []
for i in range(9):
possibilites.append([])
for j in range(9):
grille[i, j] = self.blocs[i, j].get_val()
modif[i, j] = self.blocs[i, j].is_modifiable()
possibilites[i].append(self.blocs[i, j].get_possibilites())
with open(path, "wb") as fich:
p = Pickler(fich)
p.dump(grille)
p.dump(modif)
p.dump(possibilites)
p.dump(self.chrono)
p.dump(self.level)
def ecrire(self, donnee):
with open(self.chemin, 'wb') as f:
Pickler(f).dump(donnee)
def __init__(self, file, protocol, bin=None):
Pickler.__init__(self, file, protocol)
self.bin = bin
comentario.polarity = getS(tempo2)
comentario.confidence = test.get('confidence')
comentario.subjetivity = test.get('subjectivity')
comentario.agreement = test.get('agreement')
comentario.irony = test.get('irony')
comentario.category = getCategoria(comentario.time, textfile, tags)
trama = comentario.to_String2()
file4.write(trama)
comentarios.append(comentario)
control[i] -= 1
if control[i] == 0:
i += 1
file4.close()
dbfile = open(ruta + sub + textfile, 'ab')
Pickler(dbfile).dump(comentarios)
dbfile.close()
comments.append(comentarios)
arch3 = open(ruta + "usuarios" + extension, "w", encoding="utf8")
arch3.write(users)
arch3.close()
arch2 = open(ruta + "exa" + sub + extension, "w", encoding="utf8")
mess = ""
users = sorted(usuarios.items(), key=lambda kv: (kv[1], kv[0]), reverse=True)
i = 0
for p in users:
if i < 10:
mess += "Usuario " + str(p) + "\n"
i += 1
else:
from collections import namedtuple
a = [1, 2, 3]
b = ('a', 'b', 'c')
""" pic.dump(a, open('arquivo_a.txt', 'wb'))
c = pic.dumps(b, 2)
print(c)
d = pic.loads(c)
print(d)
e = pic.load(open('arquivo_a.txt', 'rb'))
print(e) """
""" Pickler(open('arquivo_a.sav', 'wb')).dump(a)
Pickler(file=open('arquivo_txt', 'wb')).dump(b)
c = Unpickler(open('arquivo_a.sav', 'rb')).load()
print(c) """
""" user = namedtuple('User', ['nome', 'sobrenome', 'idade'])
user_1 = user('Felipe', 'Corrêa', 33)
user_2 = user('Samanta', 'Kido', 33)
user_3 = user_1 + user_2
print(user_3.count(33)) """
""" from sklearn.datasets import make_regression
a, b = make_regression(n_samples=10, n_features=5, n_targets=3)
print(a)
print(b)
def __init__(self, ip = "", port = 8081) -> None:
version = "0.1 Beta"
try:
with open("connexion.login", "rb") as login:
unpick = Unpickler(login)
logers = unpick.load()
logers : dict
logers["test"]["pwd"]
except IOError:
with open("connexion.login", "wb") as file:
tok = TokenTime(taille=20, time_out=1)
dict_base = {
"uadmin": {
"pwd": "padmin",
"admin": True,
"token": tok.getToken()
},
"test": {
"pwd": "test",
"admin": False,
"token": "ABCD0000000000000000"
}
}
picklefile = Pickler(file)
picklefile.dump(dict_base)
except:
print("Une erreur a été relevée.")
# ------------------------------------
self.accounts = None
self.accounts: dict
with open("connexion.login", "rb") as file:
pic2 = Unpickler(file)
self.accounts = pic2.load()
self.host = soc(AF_INET, SOCK_STREAM)
self.host.bind((ip, port))
self.host.listen(5)
self.connected = []
self.clients = []
print(f"Version {version}")
self.looper = True
self._thread_loop(self.demand_start)
self.open = True
self.true = True
while self.true:
if self.open:
Thread(None, self._thread_loop, None, (self, self.demand_start))
Thread.start()
self.open = False
def parseLog(self):
logData = {}
logRE = re.compile(
'^.*/([1-9][0-9]*(\.[0-9]+|))_[^/]+/step([1-9])_.*\.log$')
max_steps = 0
for logFile in glob.glob(self.basedir + '/[1-9]*/step[0-9]*.log'):
m = logRE.match(logFile)
if not m: continue
wf = m.group(1)
step = int(m.group(3))
if step > max_steps: max_steps = step
if not logData.has_key(wf):
logData[wf] = {
'steps': {},
'events': [],
'failed': [],
'warning': []
}
if not logData[wf]['steps'].has_key(step):
logData[wf]['steps'][step] = logFile
cache_read = 0
log_processed = 0
for wf in logData:
for k in logData[wf]:
if k == 'steps': continue
for s in range(0, max_steps):
logData[wf][k].append(-1)
index = 0
for step in sorted(logData[wf]['steps']):
data = [0, 0, 0]
logFile = logData[wf]['steps'][step]
json_cache = os.path.dirname(logFile) + "/logcache_" + str(
step) + ".json"
cache_ok = False
if (os.path.exists(json_cache)) and (
os.path.getmtime(logFile) <=
os.path.getmtime(json_cache)):
try:
jfile = open(json_cache, "r")
data = json.load(jfile)
jfile.close()
cache_read += 1
cache_ok = True
except:
os.remove(json_cache)
if not cache_ok:
try:
es_parse_log(logFile)
except Exception as e:
print "Sending log information to elasticsearch failed", str(
e)
inFile = open(logFile)
for line in inFile:
if '%MSG-w' in line: data[1] = data[1] + 1
if '%MSG-e' in line: data[2] = data[2] + 1
if 'Begin processing the ' in line:
data[0] = data[0] + 1
inFile.close()
jfile = open(json_cache, "w")
json.dump(data, jfile)
jfile.close()
log_processed += 1
logData[wf]['events'][index] = data[0]
logData[wf]['failed'][index] = data[2]
logData[wf]['warning'][index] = data[1]
index += 1
del logData[wf]['steps']
print "Log processed: ", log_processed
print "Caches read:", cache_read
from pickle import Pickler
outFile = open(os.path.join(self.outdir, 'runTheMatrixMsgs.pkl'), 'w')
pklFile = Pickler(outFile)
pklFile.dump(logData)
outFile.close()
return
def __delattr__(self, name: str) -> None:
if name == "accounts":
with open("connexion.login", "wb") as file:
picklefile = Pickler(file)
picklefile.dump(self.accounts)
def dump(obj, file, protocol=None):
Pickler(file, protocol).dump(obj)
def __init__(self, *args, **kargs):
Pickler.__init__(self, *args, **kargs)
self.stack = []
def dumps(self, graph, protocol):
src = StringIO()
pickler = Pickler(src)
pickler.persistent_id = self.persistent_id
pickler.dump(graph)
return src.getvalue()
def save_global(self, obj, name=None, pack=struct.pack):
if obj.__module__ == "__builtin__" or obj.__module__ == "builtins":
if obj in _BUILTIN_TYPE_NAMES:
return self.save_reduce(_builtin_type,
(_BUILTIN_TYPE_NAMES[obj], ),
obj=obj)
if name is None:
name = obj.__name__
modname = getattr(obj, "__module__", None)
if modname is None:
try:
# whichmodule() could fail, see
# https://bitbucket.org/gutworth/six/issues/63/importing-six-breaks-pickling
modname = pickle.whichmodule(obj, name)
except Exception:
modname = '__main__'
if modname == '__main__':
themodule = None
else:
__import__(modname)
themodule = sys.modules[modname]
self.modules.add(themodule)
if hasattr(themodule, name) and getattr(themodule, name) is obj:
return Pickler.save_global(self, obj, name)
typ = type(obj)
if typ is not obj and isinstance(obj, (type, types.ClassType)):
d = dict(obj.__dict__) # copy dict proxy to a dict
if not isinstance(d.get('__dict__', None), property):
# don't extract dict that are properties
d.pop('__dict__', None)
d.pop('__weakref__', None)
# hack as __new__ is stored differently in the __dict__
new_override = d.get('__new__', None)
if new_override:
d['__new__'] = obj.__new__
# workaround for namedtuple (hijacked by PySpark)
if getattr(obj, '_is_namedtuple_', False):
self.save_reduce(_load_namedtuple, (obj.__name__, obj._fields))
return
self.save(_load_class)
self.save_reduce(typ, (obj.__name__, obj.__bases__, {
"__doc__": obj.__doc__
}),
obj=obj)
d.pop('__doc__', None)
# handle property and staticmethod
dd = {}
for k, v in d.items():
if isinstance(v, property):
k = ('property', k)
v = (v.fget, v.fset, v.fdel, v.__doc__)
elif isinstance(v, staticmethod) and hasattr(v, '__func__'):
k = ('staticmethod', k)
v = v.__func__
elif isinstance(v, classmethod) and hasattr(v, '__func__'):
k = ('classmethod', k)
v = v.__func__
dd[k] = v
self.save(dd)
self.write(pickle.TUPLE2)
self.write(pickle.REDUCE)
else:
raise pickle.PicklingError("Can't pickle %r" % obj)
def __init__(self, file, protocol=None):
Pickler.__init__(self, file, protocol)
# set of modules to unpickle
self.modules = set()
# map ids to dictionary. used to ensure that functions can share global env
self.globals_ref = {}
def save_function(self, obj, name=None):
""" Registered with the dispatch to handle all function types.
Determines what kind of function obj is (e.g. lambda, defined at
interactive prompt, etc) and handles the pickling appropriately.
"""
write = self.write
if name is None:
name = obj.__name__
modname = pickle.whichmodule(obj, name)
# print('which gives %s %s %s' % (modname, obj, name))
try:
themodule = sys.modules[modname]
except KeyError:
# eval'd items such as namedtuple give invalid items for their function __module__
modname = '__main__'
if modname == '__main__':
themodule = None
if themodule:
self.modules.add(themodule)
if getattr(themodule, name, None) is obj:
return self.save_global(obj, name)
# a builtin_function_or_method which comes in as an attribute of some
# object (e.g., object.__new__, itertools.chain.from_iterable) will end
# up with modname "__main__" and so end up here. But these functions
# have no __code__ attribute in CPython, so the handling for
# user-defined functions below will fail.
# So we pickle them here using save_reduce; have to do it differently
# for different python versions.
if not hasattr(obj, '__code__'):
if PY3:
if sys.version_info < (3, 4):
raise pickle.PicklingError("Can't pickle %r" % obj)
else:
rv = obj.__reduce_ex__(self.proto)
else:
if hasattr(obj, '__self__'):
rv = (getattr, (obj.__self__, name))
else:
raise pickle.PicklingError("Can't pickle %r" % obj)
return Pickler.save_reduce(self, obj=obj, *rv)
# if func is lambda, def'ed at prompt, is in main, or is nested, then
# we'll pickle the actual function object rather than simply saving a
# reference (as is done in default pickler), via save_function_tuple.
if (islambda(obj)
or getattr(obj.__code__, 'co_filename', None) == '<stdin>'
or themodule is None):
self.save_function_tuple(obj)
return
else:
# func is nested
klass = getattr(themodule, name, None)
if klass is None or klass is not obj:
self.save_function_tuple(obj)
return
if obj.__dict__:
# essentially save_reduce, but workaround needed to avoid recursion
self.save(_restore_attr)
write(pickle.MARK + pickle.GLOBAL + modname + '\n' + name + '\n')
self.memoize(obj)
self.save(obj.__dict__)
write(pickle.TUPLE + pickle.REDUCE)
else:
write(pickle.GLOBAL + modname + '\n' + name + '\n')
self.memoize(obj)
