def place_order(self, trade_res, color, system_seller):
self.active_trades.append({
'buyer_system_id':
self.parent_id,
'buyer_planet_id':
self.planet_id,
'seller_system_id':
system_seller.parent_id,
'seller_planet_id':
system_seller.planet_id,
'seller_location': (system_seller.x_loc, system_seller.y_loc),
'resource':
trade_res,
'unit_price':
system_seller.exports[trade_res]['unit_price'],
'amount':
1
})
NEW_ORDER = Transporter(self.game_screen, system_seller.parent_id,
system_seller.planet_id,
(system_seller.x_loc, system_seller.y_loc),
self.parent_id, (self.x_loc, self.y_loc),
self.parent_system, color)
self.orders.append(NEW_ORDER)
self.order_sprites.add(NEW_ORDER)
def test_Transporter_no_args_send():
trans_obj = Transporter()
try:
trans_obj.send_it()
assert False
except ValueError as e:
assert True
def create_world(mp: MasParams):
world = World(w=mp.G, h=mp.G, torus_enabled=True)
n_ores = round(mp.G**2 * mp.D)
for _ in range(n_ores):
world.add_agent(Ore())
positions = []
for x in range(mp.G):
for y in range(mp.G):
positions.append(Vec2D(x, y))
base_positions = random.sample(positions, mp.N)
if mp.M == 1: # cooperation
company_ids = [0] * mp.N
else: # competitive
company_ids = list(range(mp.N))
for base_pos, comp_id in zip(base_positions, company_ids):
base = Base(mp, comp_id)
world.add_agent(base, base_pos)
for _ in range(mp.X):
world.add_agent(Explorer(base, mp, comp_id), pos=base_pos)
for _ in range(mp.Y):
world.add_agent(Transporter(base, mp, comp_id), pos=base_pos)
return world
def test_Transporter_field_validation():
trans_obj = Transporter(from_address='*****@*****.**',
to_address='*****@*****.**',
user_password='******',
subject='something')
assert trans_obj.msg_root['From'] == '*****@*****.**'
assert trans_obj.msg_root['To'] == '*****@*****.**'
assert trans_obj.msg_root['Subject'] == 'something'
assert trans_obj.password == 'secret'
def attachSink(self, outPortName, sinkComponent, sinkInPortName):
for index, pipe in enumerate(self.pipes):
if outPortName == pipe.sourcePort:
pipe.addSink(sinkComponent, sinkInPortName)
self.pipes[index] = pipe
break
else:
pipe = Transporter(self,
outPortName,
sinkComponent,
sinkInPortName,
name="%s:%s" % ((self.name, outPortName)),
logit=self.logit)
self.pipes.append(pipe)
def __init__(self, filepath):
with io.open(filepath, 'r', encoding='utf-8') as f:
self.raw = json.load(f)
self.compounds = [
Compound(x['name'], x['xrefs']) for x in self.raw['compounds']
]
self.remedies = [
Remedy(x['name'], x['xrefs']) for x in self.raw['remedies']
]
self.enzymes = [
Enzyme(x['name'], x['xrefs']) for x in self.raw['enzymes']
]
self.transporter = [
Transporter(x['name'], x['xrefs']) for x in self.raw['transporter']
]
self.drugs = [Drug(x['name'], x['xrefs']) for x in self.raw['drugs']]
publication = self.raw['publication']
doi = publication['doi'] if 'doi' in publication else None
self.publication = Reference(publication['pmid'], doi,
publication['citation'])
else:
if "x_train" in locals():
ae.train(AE_STEPS, x_train, x_test, lr=0.003)
else:
ae.train_iter(AE_STEPS, train_loader, test_loader, lr=0.003)
# Prepare the Latent Space Model
if not 'x_test' in locals():
# print ("EVENTUALLY CHANGE THIS BACK AS WELL!!!")
x_test = np.load("faces.npy")
# x_test = unload(test_loader)
encodings = ae.encode(x_test)
if MODEL == 'transporter':
model = Transporter(encodings, DISTR, FOLDER, BATCH_SIZE_GEN)
elif MODEL == 'generator':
model = Generator(encodings, DISTR, FOLDER, BATCH_SIZE_GEN) # I Could try L2 Loss instead of L1?
else:
raise NotImplementedError
# Train the Latent Space Model
if GEN_LOAD:
model.load_weights(MODEL)
else:
model.train(STEPS, lr=0.001) # I should try adjusting the learning rate?
#model.train(STEPS//2, lr=0.0003)
#model.train(STEPS//2, lr=0.0001)
# Display Results
fake_distr = model.generate(batches=1)
# Load the right dataset
if DATASET == 'moons':
latent, test = make_moons()
elif DATASET == 'two_cluster':
latent, test = two_cluster()
elif DATASET == 'eight_cluster':
latent, test = eight_cluster()
elif DATASET == 'circles':
latent, test = make_circles()
else:
raise NotImplementedError
# Prepare the Latent Space Model
if MODEL == 'transporter':
model = Transporter(latent, DISTR, FOLDER, BATCH_SIZE_GEN)
elif MODEL == 'generator':
model = Generator(latent, DISTR, FOLDER, BATCH_SIZE_GEN)
else:
raise NotImplementedError
# Train the Latent Space Model
if GEN_LOAD:
model.load_weights(MODEL)
else:
model.train(STEPS, lr=0.0001, images=True)
# Evaluate
model.evaluate()
# Display Results
def test_Transporter_images_is_a_text_file():
trans_obj = Transporter()
trans_obj.add_images("test_emailer.py")
# if there are no images, then no Content-ID added to msg_root
print(trans_obj.msg_root)
assert 'Content-ID' not in trans_obj.msg_root
def test_Transporter_images_is_a_string_and_is_missing():
trans_obj = Transporter()
trans_obj.add_images("missing_image.jpg")
# if there are no images, then no Content-ID added to msg_root
assert 'Content-ID' not in trans_obj.msg_root
def test_Transporter_missing_images():
trans_obj = Transporter()
trans_obj.add_images(["missing_image.jpg"])
# if there are no images, then no Content-ID added to msg_root
assert 'Content-ID' not in trans_obj.msg_root
def test_Transporter_message_text():
trans_obj = Transporter()
trans_obj.build_message_text(string_message='this is a message')
assert trans_obj.string_message == 'this is a message'
def test_Transporter_creation():
trans_obj = Transporter()
assert trans_obj is not None
