def testDelattrs(self):
"""Testing deletion of attributes"""
self.lc.x = 5
del self.lc.x
try: self.lc.x
except (NameError, KeyError): pass
else: unittest.fail("No exception raised")
def test_time_conversion(self):
""" Tests that forday and dayfor are consistent """
numtest=20000
for k in range(numtest) :
yrflag = random.randrange(0,4)
selector = random.randrange(0,3)
dtime = random.randrange(1,200000)
# Tests wnen close to integer, and normal
if selector==0 :
dtime = dtime + random.random()/100.
elif selector==1 :
dtime = dtime + 1.-random.random()/100.
elif selector==2 :
dtime = dtime + random.random()
if k%50000 == 0 :
print "Doing test %10d of %10d. yrflag=%d, dtime=%20.16g"%(k,numtest,yrflag,dtime)
try :
#print "dtime,yrflag=",dtime,yrflag
iyear,iday,ihour = modeltools.hycom.forday(dtime,yrflag)
#print "iyear,iday,ihour=",iyear,iday,ihour
dtimeout=modeltools.hycom.dayfor(iyear,iday,ihour,yrflag)
#print "dtime, reversed:",dtime
except:
unittest.fail("Caught error. dtime = %20.10g,yrflag=%d"%(dtime,yrflag))
#print yrflag,dtime,dtimeout-dtime,3600./86400.
#dtimeout is "floored" to nearest hour. So error should never be greater than this:
if (dtime-dtimeout)>3600./86400:
unittest.fail("Error: inn forday or dayfor, dtime=%20.10g, yrflag%d="%(dtime,yrflag))
def assertTuplesAlmostEqual(self, a, b, tol=1e-8):
if len(a) != len(b):
unittest.fail()
for _a, _b in zip(a, b):
if abs(_a-_b) > tol:
unittest.fail()
return
def test_fit(self):
"""Entrena el modelo de jirafas.
excepciones : ValueError, si el clasificador acepta valores nulos
clasificador acepta arreglos numpy de labels y features de tamaños diferentes
"""
print('Fit test...')
inicializador = Practica04()
inicializador.main('./imagenes/train', 64)
features, labels = load_pickles()
clasificador = GiraffesClassifier()
try:
clasificador.fit(None, None)
unittest.fail('el clasificador acepta parametros nulos')
except ValueError:
pass
try:
clasificador.fit(features, labels[:-1])
unittest.fail('el clasificador acepta features y labels de ' +
'tamano diferente')
except ValueError:
pass
clasificador.fit(features, labels)
assert clasificador.model is not None
def test_afile_writeread_nomask(self) :
idm = random.randrange(10,5000)
jdm = random.randrange(10,5000)
print "Creating %dX%d random arrays"%(idm,jdm)
scale=1e4
wfld1=numpy.random.rand(jdm,idm)*scale
wfld2=numpy.random.rand(jdm,idm)*scale
wfld3=numpy.random.rand(jdm,idm)*scale
afile=modeltools.hycom.AFile(idm,jdm,"test.a","w")
afile.writerecord(wfld1,None,record=None)
afile.writerecord(wfld2,None,record=None)
afile.writerecord(wfld3,None,record=None)
afile.close()
afile=modeltools.hycom.AFile(idm,jdm,"test.a","r")
rfld1=afile.readrecord(None,record=0)
rfld2=afile.readrecord(None,record=1)
rfld3=afile.readrecord(None,record=2)
afile.close()
maxdiff1=numpy.abs(numpy.amax(rfld1-wfld1))/scale
maxdiff2=numpy.abs(numpy.amax(rfld2-wfld2))/scale
maxdiff3=numpy.abs(numpy.amax(rfld3-wfld3))/scale
if all([elem < 1e-7 for elem in [maxdiff1,maxdiff2,maxdiff3]]) :
print "afile io test passed"
else :
unittest.fail("AFile IO failed. MAx diff between read/written: %14.7g"%max([maxdiff1,maxdiff2,maxdiff3]))
def test_abfilebathy_writeread_nomask(self) :
idm = random.randrange(10,5000)
jdm = random.randrange(10,5000)
scale=1e4
print "Creating %dX%d bathy array"%(idm,jdm)
wfldout=scale + numpy.random.rand(jdm,idm)*scale
bathyfile=modeltools.hycom.ABFileBathy("testbathy","w")
bathyfile.write_field(wfldout,None)
bathyfile.close()
print "Reading %dX%d bathy array"%(idm,jdm)
bathyfile=modeltools.hycom.ABFileBathy("testbathy","r",idm=idm,jdm=jdm)
wfldin=bathyfile.read_field("depth",None)
bathyfile.close()
fldmaxdiff=numpy.abs(numpy.amax(wfldin-wfldout))/scale
bmin,bmax = bathyfile.bminmax("depth")
amax = numpy.amax(wfldin)
amin = numpy.amin(wfldin)
abmindiff=numpy.abs(amin-bmin)/scale
abmaxdiff=numpy.abs(amax-bmax)/scale
#print amin,amax
#print bmin,bmax
if fldmaxdiff > 1e-7 and abmindiff >1e-5 and abmaxdiff > 1e-5:
print "test_Abfilebathy_writeread_nomasl test passed"
else :
unittest.fail("AFile IO failed. MAx diff between read/written: %14.7g"%max([fldmaxdiff,abmaxdiff,abmindiff]))
def test_afile_writeread_nomask(self):
idm = random.randrange(10, 5000)
jdm = random.randrange(10, 5000)
print "Creating %dX%d random arrays" % (idm, jdm)
scale = 1e4
wfld1 = numpy.random.rand(jdm, idm) * scale
wfld2 = numpy.random.rand(jdm, idm) * scale
wfld3 = numpy.random.rand(jdm, idm) * scale
afile = modeltools.hycom.AFile(idm, jdm, "test.a", "w")
afile.writerecord(wfld1, None, record=None)
afile.writerecord(wfld2, None, record=None)
afile.writerecord(wfld3, None, record=None)
afile.close()
afile = modeltools.hycom.AFile(idm, jdm, "test.a", "r")
rfld1 = afile.readrecord(None, record=0)
rfld2 = afile.readrecord(None, record=1)
rfld3 = afile.readrecord(None, record=2)
afile.close()
maxdiff1 = numpy.abs(numpy.amax(rfld1 - wfld1)) / scale
maxdiff2 = numpy.abs(numpy.amax(rfld2 - wfld2)) / scale
maxdiff3 = numpy.abs(numpy.amax(rfld3 - wfld3)) / scale
if all([elem < 1e-7 for elem in [maxdiff1, maxdiff2, maxdiff3]]):
print "afile io test passed"
else:
unittest.fail(
"AFile IO failed. MAx diff between read/written: %14.7g" %
max([maxdiff1, maxdiff2, maxdiff3]))
def test_abfilebathy_writeread_nomask(self):
idm = random.randrange(10, 5000)
jdm = random.randrange(10, 5000)
scale = 1e4
print "Creating %dX%d bathy array" % (idm, jdm)
wfldout = scale + numpy.random.rand(jdm, idm) * scale
bathyfile = modeltools.hycom.ABFileBathy("testbathy", "w")
bathyfile.write_field(wfldout, None)
bathyfile.close()
print "Reading %dX%d bathy array" % (idm, jdm)
bathyfile = modeltools.hycom.ABFileBathy("testbathy",
"r",
idm=idm,
jdm=jdm)
wfldin = bathyfile.read_field("depth", None)
bathyfile.close()
fldmaxdiff = numpy.abs(numpy.amax(wfldin - wfldout)) / scale
bmin, bmax = bathyfile.bminmax("depth")
amax = numpy.amax(wfldin)
amin = numpy.amin(wfldin)
abmindiff = numpy.abs(amin - bmin) / scale
abmaxdiff = numpy.abs(amax - bmax) / scale
#print amin,amax
#print bmin,bmax
if fldmaxdiff > 1e-7 and abmindiff > 1e-5 and abmaxdiff > 1e-5:
print "test_Abfilebathy_writeread_nomasl test passed"
else:
unittest.fail(
"AFile IO failed. MAx diff between read/written: %14.7g" %
max([fldmaxdiff, abmaxdiff, abmindiff]))
def test_fit(self):
'''
Prueba para entrenar el clasificador. Crea unos vectores aleatorios e
intenta entrenar al clasificador con estos
'''
import numpy as np
from random import randrange as rand
print('Fit test...')
n = rand(32, 64)
features = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1], [1, 1, 1]])
features = np.random.rand(n, 64, 64, 1)
labels = [rand(0, 10) for _ in range(n)]
clasificador = OstrichClassifier()
try:
clasificador.fit(None, None)
unittest.fail('el clasificador acepta parametros nulos')
except ValueError:
pass
try:
clasificador.fit(features, labels[:-1])
unittest.fail('el clasificador acepta features y labels de ' +
'tamano diferente')
except ValueError:
pass
clasificador.fit(features, labels)
assert clasificador.model is not None
def assertTuplesAlmostEqual(self, a, b, tol=1e-8):
if len(a) != len(b):
unittest.fail()
for _a, _b in zip(a, b):
if abs(_a-_b) > tol:
unittest.fail()
return
def testBugFix1149372(self):
try:
c = C()
str(c)
except Exception as e:
assert e.args[0] == "E"
return
unittest.fail("if __str__ raises an exception, re-raise")
def testGetAttrs(self):
"""Test getting of various attributes"""
self.assertIsInstance(self.lc.LocalConnection, type)
try:
self.lc.asotnuhaoseu
except (NameError, KeyError):
pass
else:
unittest.fail("NameError or KeyError should be raised")
def test_create_zone_call_rndc_connection_refused(self):
# Run rndc againts a closed port. Albeit this does not perform a
# successful rndc run, it is enough to test the argument parsing
context = self.get_context()
exp_msg = 'rndc: connect failed: 127.0.0.1#33953: connection refused'
try:
self.backend.create_zone(context, self.zone)
unittest.fail("Did not raise an exception")
except exceptions.Backend as e:
self.assertTrue(exp_msg in str(e))
def test_create_zone_call_rndc_connection_refused(self):
# Run rndc againts a closed port. Albeit this does not perform a
# successful rndc run, it is enough to test the argument parsing
context = self.get_context()
exp_msg = 'rndc: connect failed: 127.0.0.1#33953: connection refused'
try:
self.backend.create_zone(context, self.zone)
unittest.fail("Did not raise an exception")
except exceptions.Backend as e:
self.assertTrue(exp_msg in str(e))
class ExceptionHandling(unittest.TestCase):
def testBugFix1149372(self):
try:
c = C()
str(c)
except Exception, e:
assert e.args[0] == "E"
return
unittest.fail("if __str__ raises an exception, re-raise")
def test_abfilegrid_writeread(self):
import modeltools.grid
grid = modeltools.grid.Proj4Grid(
"+proj=stere +lat_ts=80 +lat_0=90 +lon_0=0", -45, 60, 20000, 20000,
200, 300)
modeltools.hycom.write_regional_grid(grid)
inflds = modeltools.hycom.read_regional_grid()
scale = 1e-4
flds = {}
flds["plon"], flds["plat"] = grid.pgrid()
flds["ulon"], flds["ulat"] = grid.ugrid()
flds["vlon"], flds["vlat"] = grid.vgrid()
flds["qlon"], flds["qlat"] = grid.qgrid()
flds["scpx"] = grid.scpx()
flds["scux"] = grid.scux()
flds["scvx"] = grid.scvx()
flds["scqx"] = grid.scqx()
flds["scpy"] = grid.scpy()
flds["scuy"] = grid.scuy()
flds["scvy"] = grid.scvy()
flds["scqy"] = grid.scqy()
flds["cori"] = grid.corio()
flds["pang"] = grid.p_azimuth()
flds["pasp"] = grid.aspect_ratio()
for fldname in flds.keys():
print fldname
maxdiff = max_diff(flds[fldname], inflds[fldname])
print fldname, maxdiff
if maxdiff > 1e-7:
unittest.fail(
"AFile IO failed. MAx diff between %s read/written: %14.7g"
% (fldname, maxdiff))
#bmin,bmax = bathyfile.bminmax("depth")
#amax = numpy.amax(wfldin)
#amin = numpy.amin(wfldin)
#abmindiff=numpy.abs(amin-bmin)/scale
#abmaxdiff=numpy.abs(amax-bmax)/scale
#print amin,amax
#print bmin,bmax
raise NameError, "test"
def test_abfilegrid_writeread(self) :
import modeltools.grid
grid = modeltools.grid.Proj4Grid("+proj=stere +lat_ts=80 +lat_0=90 +lon_0=0",-45,60,20000,20000,200,300)
modeltools.hycom.write_regional_grid(grid)
inflds = modeltools.hycom.read_regional_grid()
scale=1e-4
flds={}
flds["plon"],flds["plat"] = grid.pgrid()
flds["ulon"],flds["ulat"] = grid.ugrid()
flds["vlon"],flds["vlat"] = grid.vgrid()
flds["qlon"],flds["qlat"] = grid.qgrid()
flds["scpx"] = grid.scpx()
flds["scux"] = grid.scux()
flds["scvx"] = grid.scvx()
flds["scqx"] = grid.scqx()
flds["scpy"] = grid.scpy()
flds["scuy"] = grid.scuy()
flds["scvy"] = grid.scvy()
flds["scqy"] = grid.scqy()
flds["cori"] = grid.corio()
flds["pang"] = grid.p_azimuth()
flds["pasp"] = grid.aspect_ratio()
for fldname in flds.keys() :
print fldname
maxdiff=max_diff(flds[fldname],inflds[fldname])
print fldname, maxdiff
if maxdiff > 1e-7 :
unittest.fail("AFile IO failed. MAx diff between %s read/written: %14.7g"%(fldname,maxdiff))
#bmin,bmax = bathyfile.bminmax("depth")
#amax = numpy.amax(wfldin)
#amin = numpy.amin(wfldin)
#abmindiff=numpy.abs(amin-bmin)/scale
#abmaxdiff=numpy.abs(amax-bmax)/scale
#print amin,amax
#print bmin,bmax
raise NameError,"test"
def test_evaluate(self):
'''
Prueba para evaluar la presicion del modelo, se requiere un conjunto de
imagenes normalizadas de los archivos llamados
./pickles/ostrich_features.pickle y ./pickles/ostrich_labels.pickle
'''
print('Evaluate test...')
features, labels = load_pickles()
clasificador = OstrichClassifier()
clasificador.load('ostrich')
features, labels = load_pickles()
try:
clasificador.evaluate(None, None)
unittest.fail('el clasificador acepta parametros nulos')
except ValueError:
pass
try:
clasificador.evaluate(features, labels[:-1])
unittest.fail('el clasificador acepta features y labels de ' +
'tamano diferente')
except ValueError:
pass
ev = clasificador.evaluate(features, labels)
if ev['acc'] < 0.7:
unittest.fail('el clasificador no es suficientemente preciso')
def test_evaluate(self):
""" Prueba para evaluar la punteria del modelo, se
requiere un conjunto de imagenes normalizadas en un archivo
llamado test_images.pickle """
clasificador = HippopotamusClassifier()
with (open("./pickles/hippopotamus_images.pickle", "rb")) as f:
features = pickle.load(f)
with (open("./pickles/hippopotamus_labels.pickle", "rb")) as f:
labels = pickle.load(f)
try:
clasificador.evaluate(None, None)
unittest.fail("el clasificador acepta parametros nulos")
except ValueError:
pass
try:
clasificador.evaluate(features, labels[:-1])
unittest.fail("el clasificador acepta features y labels de " +
"tamano diferente")
except ValueError:
pass
ev = clasificador.evaluate(features, labels)
if ev["acc"] < 0.7:
unittest.fail("el clasificador no es suficientemente preciso")
def test_evaluate(self):
print('Evaluate test...')
inicializador = Practica04()
inicializador.main('./imagenes/train', 64)
features, labels = load_pickles()
clasificador = PenguinClassifier()
clasificador.fit(features, labels)
inicializador.main('./imagenes/test', 64)
features, labels = load_pickles()
try:
clasificador.evaluate(None, None)
unittest.fail('el clasificador acepta parametros nulos')
except ValueError:
pass
try:
clasificador.evaluate(features, labels[:-1])
unittest.fail('el clasificador acepta features y labels de ' +
'tamano diferente')
except ValueError:
pass
ev = clasificador.evaluate(features, labels)
if ev['acc'] < 0.7:
unittest.fail('el clasificador no es suficientemente preciso')
def test_evaluate(self):
''' Prueba para evaluar la punteria del modelo, se
requiere un conjunto de imagenes normalizadas en un archivo
llamado test_images.pickle '''
clasificador = HamsterClassifier()
with (open('./pickles/hamster_images.pickle', 'rb')) as f:
features = pickle.load(f)
with (open('./pickles/hamster_labels.pickle', 'rb')) as f:
labels = pickle.load(f)
try:
clasificador.evaluate(None, None)
unittest.fail('el clasificador acepta parametros nulos')
except ValueError:
pass
try:
clasificador.evaluate(features, labels[:-1])
unittest.fail('el clasificador acepta features y labels de ' +
'tamano diferente')
except ValueError:
pass
ev = clasificador.evaluate(features, labels)
if ev['acc'] < 0.7:
unittest.fail('el clasificador no es suficientemente preciso')
def runTest(self):
"""Test blank"""
prefString= ""
prefs = Properties(prefString)
prefString = "default.name=Bilbo Baggins\nhomepage.feeds=42,24"
prefs = Properties(prefString)
assert cmp('Bilbo Baggins', prefs.get('default.name')) == 0, 'Error retrieving value from key'
assert cmp('', prefs.get('jeff')) == 0, "Missing keys should default to blank string"
prefs.set('Hulk', 'Hogan')
assert cmp('Hogan', prefs.get('Hulk')) == 0, 'Error retrieving set key/value'
stringValue = prefs.convertToPropertiesFile()
#this last test needs to be fixed, can fail if keys are not brought back in same order
assert cmp(prefString + "\nHulk=Hogan", stringValue) == 0, 'Error converting to properties'
prefString = "jeff=1"
prefs = Properties(prefString)
assert cmp('1', prefs.get('jeff')) == 0
assert cmp('1', prefs.pop('jeff')) == 0
try:
prefs.pop('badkey')
unittest.fail('A KeyError exception should have prevented this')
except KeyError:
pass
assert False == prefs.has_key('jeff')
def test_fit(self):
print('Fit test...')
inicializador = Practica04()
inicializador.main('./imagenes/train', 64)
features, labels = load_pickles()
clasificador = PenguinClassifier()
try:
clasificador.fit(None, None)
unittest.fail('el clasificador acepta parametros nulos')
except ValueError:
pass
try:
clasificador.fit(features, labels[:-1])
unittest.fail('el clasificador acepta features y labels de ' +
'tamano diferente')
except ValueError:
pass
clasificador.fit(features, labels)
assert clasificador.model is not None
def test_time_conversion(self):
""" Tests that forday and dayfor are consistent """
numtest = 20000
for k in range(numtest):
yrflag = random.randrange(0, 4)
selector = random.randrange(0, 3)
dtime = random.randrange(1, 200000)
# Tests wnen close to integer, and normal
if selector == 0:
dtime = dtime + random.random() / 100.
elif selector == 1:
dtime = dtime + 1. - random.random() / 100.
elif selector == 2:
dtime = dtime + random.random()
if k % 50000 == 0:
print "Doing test %10d of %10d. yrflag=%d, dtime=%20.16g" % (
k, numtest, yrflag, dtime)
try:
#print "dtime,yrflag=",dtime,yrflag
iyear, iday, ihour = modeltools.hycom.forday(dtime, yrflag)
#print "iyear,iday,ihour=",iyear,iday,ihour
dtimeout = modeltools.hycom.dayfor(iyear, iday, ihour, yrflag)
#print "dtime, reversed:",dtime
except:
unittest.fail("Caught error. dtime = %20.10g,yrflag=%d" %
(dtime, yrflag))
#print yrflag,dtime,dtimeout-dtime,3600./86400.
#dtimeout is "floored" to nearest hour. So error should never be greater than this:
if (dtime - dtimeout) > 3600. / 86400:
unittest.fail(
"Error: inn forday or dayfor, dtime=%20.10g, yrflag%d=" %
(dtime, yrflag))
raise RuntimeError("dummy")
except RuntimeError:
raise
except RuntimeError, e:
r = str(e)
self.assertEquals(r, "dummy")
def testBugFix1149372(self):
try:
c = C()
str(c)
except Exception, e:
assert e.args[0] == "E"
return
unittest.fail("if __str__ raises an exception, re-raise")
def test_wrap_StackOverflowError(self):
with self.assertRaises(RuntimeError) as cm:
StackOverflowErrorTest.throwStackOverflowError()
self.assertEqual(
cm.exception.message,
"maximum recursion depth exceeded (Java StackOverflowError)")
with self.assertRaises(RuntimeError) as cm:
StackOverflowErrorTest.causeStackOverflowError()
self.assertEqual(
cm.exception.message,
"maximum recursion depth exceeded (Java StackOverflowError)")
def test_unicode_args(self):
def test_buckets(self, bucket_count=0, ops=0, max_time=0, replicas=[1]):
bucket_ram = 100
if not bucket_count:
bucket_count = info.memoryQuota / bucket_ram
if bucket_count > info.memoryQuota / bucket_ram:
self.log.error('node does not have enough capacity for {0} buckets, exiting test'.format(bucket_count))
return
max_load_memory = bucket_ram * 3 / 4
max_load_time = max_time
load_info = {
'server_info' : self.servers,
'memcached_info' : {
'bucket_name':"",
'bucket_port':"11211",
'bucket_password':"",
},
'operation_info' : {
'operation_distribution':{'set':3, 'get':5},
'valuesize_distribution':{250:15, 1500:10, 20:5,15000:5},
'create_percent':25,
'threads':2*len(self.servers),
'operation_rate':ops/bucket_count,
},
'limit_info' : {
'max_size':max_load_memory,
'max_time':max_load_time,
},
}
loads = []
for i in range(bucket_count):
bucket_name = 'bucketops-{0}'.format(uuid.uuid4())
replica = replicas[i%len(replicas)]
rest.create_bucket(bucket=bucket_name,
ramQuotaMB=bucket_ram,
replicaNumber=replica,
authType='sasl',
saslPassword='')
msg = 'create_bucket succeeded but bucket {0} does not exist'.format(bucket_name)
self.assertTrue(BucketOperationHelper.wait_for_bucket_creation(bucket_name, rest), msg=msg)
load_info['memcached_info']['bucket_name'] = bucket_name
loads.append(load_runner.LoadRunner(load_info, dryrun=False))
buckets = []
try:
buckets = rest.get_buckets()
except:
self.log.info('15 seconds sleep before calling get_buckets again...')
time.sleep(15)
buckets = rest.get_buckets()
if len(buckets) != bucket_count:
msg = 'tried to create {0} buckets, only created {1}'.format(bucket_count, len(buckets))
self.log.error(msg)
unittest.fail(msg=msg)
if ops:
self.log.info('starting load against all buckets')
for load in loads:
load.start()
if max_load_time:
end_time = time.time() + max_load_time
for load in loads:
load.wait(end_time - time.time())
# stop all load if there is any still running
for load in loads:
load.stop()
else:
for load in loads:
load.wait()
self.log.info('stopped load against all buckets')
def testGetAttrs(self):
"""Test getting of various attributes"""
assert type(self.lc.LocalConnection) is types.ClassType
try: self.lc.asotnuhaoseu
except (NameError, KeyError): pass
else: unittest.fail("NameError or KeyError should be raised")