def test_save_multivaluefield_with_empty_and_null(self):
model = TestModel()
model.multivaluefield = ["1"]
model.save()
self.assertEquals(model.multivaluefield, [u"1"])
self.assertEquals(model.blank_multivaluefield, [])
self.assertEquals(model.null_multivaluefield, [])
def index(request):
test = TestModel()
test.name = "blah"
test.save()
result = ""
for i in TestModel.objects.all():
result = result + i.name + "<br>";
return HttpResponse(result)
def test_single_select_on_db_field(self):
TestModelForm = modelform_factory(TestModel)
form = TestModelForm()
model = TestModel()
form = TestModelForm({"multivaluefield": [1,2]}, instance=model)
self.assertTrue(form.is_valid(), msg=MultiSelectFieldTest.pretty_field_errors(form))
self.assertEquals(model.multivaluefield, [u'1',u'2'])
def test_path_or_resource(self):
c = Client()
obj = TestModel()
obj.test = 'TESTING'
obj.save()
resource = resources1.Test_1_1_Resource()
list_path = resource.get_resource_list_uri()
object_path = resource.get_resource_uri(obj)
result = c._path_or_resource(list_path)
expected = list_path
self.assertEqual(
result, expected,
"Bare path.\nResult:%s\nExpected:%s" % (result, expected))
result = c._path_or_resource(list_path, obj)
expected = list_path
self.assertEqual(
result, expected,
"Bare path w/obj.\nResult:%s\nExpected:%s" % (result, expected))
result = c._path_or_resource(resource)
expected = list_path
self.assertEqual(
result, expected,
"Empty resource.\nResult:%s\nExpected:%s" % (result, expected))
result = c._path_or_resource(resource, obj)
expected = object_path
self.assertEqual(
result, expected,
"Populated resource.\nResult:%s\nExpected:%s" % (result, expected))
def generate_queries():
u1 = User.objects.filter()
new_name = str(random.randint(0, 2000000))
if u1:
u1.update(first_name=new_name)
else:
u1 = User(username=new_name)
u1.save()
u1 = User.objects.filter(username=new_name)
if u1:
u1 = u1[0]
u1.first_name = new_name + 'hello'
u1.save()
users = [User(username=get_random_text()) for i in xrange(100)]
for user in users:
user.save()
sleep(5)
u = User.objects.filter(username=user.username)
if u.exists():
username = u[0].username + 'dfas'
sleep(3)
u.update(username=username)
t = TestModel.objects.filter(user=u1)
t = list(t)
for i in xrange(100):
t = TestModel.objects.filter()
t = list(t)
for i in xrange(len(users)):
random_user = random.choice(users)
t = TestModel(user=random_user)
t.save()
sleep(5)
for i in xrange(100):
k = TestModel.objects.select_related('user')
k = list(k)
tm = TestModel.objects.all()
for t in tm:
t.delete()
sleep(5)
def setUp(self):
self.testbed = testbed.Testbed()
self.testbed.activate()
self.testbed.init_datastore_v3_stub()
self.testbed.init_memcache_stub()
model = TestModel(key_name='test_key_name')
self.setup_key = pdb.put(model,
_storage=['local', 'memcache', 'datastore'])
self.cache_key = str(self.setup_key)
def test_cascaded_cache_refresh(self): e1 = TestModel() k1 = db.put(e1) #Memcache refresh from datastore pdb.get(k1,_storage =['memcache','datastore'] ) e2 = pdb.get(k1,_storage ='memcache') self.assertEqual(e1.key(),e2.key()) #Local refresh from datastore pdb.get(k1,_storage =['local','datastore']) e2 = pdb.get(k1,_storage='local') self.assertEqual(e1.key(),e2.key()) #Local refresh from memcache e3 = TestModel(key_name='memcache_model') k3 = pdb.put(e3,_storage='memcache') pdb.get(k3, _storage=['local','memcache']) e4 = pdb.get(k3,_storage='local') self.assertEqual(e3.key(),e4.key())
def setUp(self):
self.testbed = testbed.Testbed()
self.testbed.activate()
self.testbed.init_datastore_v3_stub()
self.testbed.init_memcache_stub()
model = TestModel(key_name='test_model', name='test')
self.setup_key = db.put(model)
cache_key = str(self.setup_key)
cachepy.set(cache_key, model)
memcache.set(cache_key, _serialize(model))
async def make_new_test(new_test: TestSchemaCreate):
"""
valid, msg = validate(new_mood)
if not valid:
return HTTPException(status_code=422, detail=msg)
"""
new_mood_model = TestModel(**new_test.__dict__)
await new_mood_model.save()
return new_test
def test_sweeping(self):
tm = TestModel()
tm.boolean = False
tm.characters = 'testing'
self.assertEqual(tm.get_dirty_fields(), {
'boolean': True,
'characters': ''
})
tm.save()
self.assertEqual(tm.get_dirty_fields(), {})
def test_dirty_fields(self):
tm = TestModel()
# initial state shouldn't be dirty
self.assertEqual(tm.get_dirty_fields(), {})
# changing values should flag them as dirty
tm.boolean = False
tm.characters = 'testing'
self.assertEqual(tm.get_dirty_fields(), {
'boolean': True,
'characters': ''
})
# resetting them to original values should unflag
tm.boolean = True
self.assertEqual(tm.get_dirty_fields(), {
'characters': ''
})
def test_cascaded_cache_refresh(self):
e1 = TestModel()
k1 = db.put(e1)
#Memcache refresh from datastore
pdb.get(k1, _storage=['memcache', 'datastore'])
e2 = pdb.get(k1, _storage='memcache')
self.assertEqual(e1.key(), e2.key())
#Local refresh from datastore
pdb.get(k1, _storage=['local', 'datastore'])
e2 = pdb.get(k1, _storage='local')
self.assertEqual(e1.key(), e2.key())
#Local refresh from memcache
e3 = TestModel(key_name='memcache_model')
k3 = pdb.put(e3, _storage='memcache')
pdb.get(k3, _storage=['local', 'memcache'])
e4 = pdb.get(k3, _storage='local')
self.assertEqual(e3.key(), e4.key())
def test_result_type(self):
single_result = pdb.get(self.setup_key)
self.assertTrue(isinstance(single_result, db.Model))
dict_result = pdb.get(self.setup_key, _result_type='dict')
self.assertTrue(isinstance(dict_result, dict))
self.assertEqual(dict_result.keys()[0], str(self.setup_key))
self.assertEqual(dict_result.values()[0].name, 'test')
name_dict_result = pdb.get(self.setup_key, _result_type='name_dict')
self.assertTrue(isinstance(name_dict_result, dict))
self.assertEqual(name_dict_result.keys()[0], self.setup_key.name())
self.assertEqual(name_dict_result.values()[0].name, 'test')
#Check for integer based key
e1 = TestModel(name='integer_test')
k1 = pdb.put(e1)
name_dict_result = pdb.get(k1, _result_type='name_dict')
self.assertTrue(isinstance(name_dict_result, dict))
self.assertEqual(name_dict_result.keys()[0], str(k1.id()))
self.assertEqual(name_dict_result.values()[0].name, 'integer_test')
def generate_queries():
u1 = User.objects.filter()
new_name = str(random.randint(0, 2000000))
if u1:
u1.update(first_name=new_name)
else:
u1 = User(username=new_name)
u1.save()
u1 = User.objects.filter(username=new_name)
if u1:
u1 = u1[0]
u1.first_name = new_name + 'hello'
u1.save()
users = [User(username=get_random_text()) for i in xrange(100)]
for user in users:
user.save()
sleep(5)
u = User.objects.filter(username=user.username)
if u.exists():
username = u[0].username + 'dfas'
sleep(3)
u.update(username=username)
t = TestModel.objects.filter(user=u1)
t = list(t)
for i in xrange(100):
t = TestModel.objects.filter()
t = list(t)
for i in xrange(len(users)):
random_user = random.choice(users)
t = TestModel(user=random_user)
t.save()
sleep(5)
for i in xrange(100):
k = TestModel.objects.select_related('user')
k = list(k)
tm = TestModel.objects.all()
for t in tm:
t.delete()
sleep(5)
def test_lazy(self):
tm = TestModel()
tm.save()
def test_lazy(self):
tm = TestModel()
tm.save()
def test_put_db(self):
model = TestModel(key_name='test_key_name', name='test')
key = pdb.put(model, _storage='datastore')
self.assertEqual('test', db.get(key).name)
signal_max = 500
opt=Config()
ref = pickle.load(open('./utils/reference_peaks_dat.pkl', 'rb'))
test_input = pickle.load(open('./test_input.pkl', 'rb'))
# Pre-saved sequential PB-Net predictions on test input
#test_preds = pickle.load(open('./test_preds.pkl', 'rb'))
# Pre-saved reference-based PB-Net predictions on test input
#test_preds_ref = pickle.load(open('./test_preds_ref.pkl', 'rb'))
### sequential PB-Net predictions ###
net = TestModel(input_dim=384,
hidden_dim_lstm=128,
hidden_dim_attention=32,
n_lstm_layers=2,
n_attention_heads=8,
gpu=opt.gpu)
trainer = Trainer(net, opt, MatchLoss(), featurize=True)
trainer.load('./model-seq.pth')
preds = trainer.predict(test_input)
### reference-based PB-Net predictions ###
net = ReferenceModel(input_dim=384,
hidden_dim_lstm=128,
hidden_dim_attention=32,
n_lstm_layers=2,
n_attention_heads=8,
gpu=opt.gpu)
trainer = ReferenceTrainer(net, opt, MatchLossRaw(), featurize=True)
test_inputs = [test_input, ref]
async def get_all_tests():
all_moods = TestModel.all()
results = await TestSchema.from_queryset(all_moods)
return results
async def get_test_by_id_(id: int):
test = TestModel.filter(id=id).first()
results = await TestSchema.from_queryset_single(test)
return results
def test_put_local(self):
model = TestModel(key_name='test_key_name', name='test')
key = pdb.put(model, _storage='local')
entity = cachepy.get(str(key))
self.assertEqual('test', entity.name)
def test_put_memcache(self):
model = TestModel(key_name='test_key_name', name='test')
key = pdb.put(model, _storage='memcache')
entity = _deserialize(memcache.get(str(key)))
self.assertEqual('test', entity.name)
def setUp(self):
a = TestModel(ident=1, price=fields.Money(amount="11.11", currency="USD"))
a.save()
b = TestModel(ident=2, price=fields.Money(amount="200.02", currency="USD"))
b.save()
