def get_nearby_transporters(self,max_distance=16093):
"""
Return a list of the transporters available near payload.
"""
base_query = Transporter.all().filter('available =',True)
return Transporter.proximity_fetch(base_query,
self.current_location,
max_results=10,
max_distance=max_distance) #within 10 miles
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)
def __init__(self):
Transporter.__init__(self)
self._channel = None
# 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