def train(account_id):
message = "Started training for: %s" % account_id
logging.info(message)
train_async.delay(account_id)
account = AccountHelper.get_account_object(account_id)
account['is_training'] = True
return JsonHelper.build_action_response(account, message)
def generate(account_id):
message = "Started generating bids for: %s" % account_id
logging.info(message)
train_async.delay(account_id)
account = AccountHelper.get_account_object(account_id)
account['is_generating_bids'] = True
return JsonHelper.build_action_response(account, message)
def default():
areas = {"belfast": "belfast"}
area_uris = utils.get_area_uris(areas)
utils.print_uris(area_uris)
JsonHelper("area_uris").dump(area_uris)
utils.write_csv(area_uris)
class LinkShortener:
def __init__(self):
self.url_dict = {}
self.json_helper = JsonHelper("short_links.json")
self.link_length = 10
self.port = os.getenv("PORT", 5000)
self.load_from_file()
def load_from_file(self):
self.url_dict = self.json_helper.get_json()
if not self.url_dict:
self.url_dict = {}
def save_to_file(self):
self.json_helper.save(self.url_dict)
def get_generated_code(self):
identifier = str(uuid.uuid4()).replace("-", "")
return identifier[0 : self.link_length]
def add_http_before_url(self, url):
if str(url).startswith("http://") or str(url).startswith("https://"):
return url
else:
new_url = "http://" + url
return new_url
def get_code(self, url):
url = self.add_http_before_url(url)
if not self.url_dict or not self.url_dict.get(url):
self.url_dict[url] = self.get_generated_code()
self.save_to_file()
print(self.url_dict)
return self.url_dict.get(url)
def get_code_url(self, url, to_shorten_url):
return (
self.add_http_before_url(str(url).strip("/"))
+ "/link/"
+ self.get_code(to_shorten_url)
)
def get_url(self, code):
for key, value in self.url_dict.items():
if value == code:
return key
def continue_scrape():
start_area = sys.argv[1]
start_index = sys.argv[2]
json_file = sys.argv[3]
csv_file = sys.argv[4]
area_uris = JsonHelper(json_file).read()
utils.continue_csv(area_uris, start_area, start_index, csv_file)
class Saver:
def __init__(self, ckpt_path: str, parameters_path: str):
self.ckpt_path = ckpt_path
self.parameters_path = parameters_path
self.parameters_helper = JsonHelper(self.parameters_path)
def save_models(self, episode: int, *models: DQModel):
for model in models:
try:
model.reset_metrics()
model.save_weights(
self.ckpt_path.format(type=model.get_name()) +
f"/cp-{episode:04d}",
save_format='tf')
print(
f'--- MODELLO {model.get_name()} salvato con successo ---')
except Exception as err:
print(
f"--- Non è stato possibile salvare il MODELLO {model.get_name()} ---\n",
err)
def load_models(self, *models: DQModel):
for model in models:
last_ckpt = tf.train.latest_checkpoint(
self.ckpt_path.format(type=model.get_name()))
try:
model.load_weights(last_ckpt)
print(
f"--- MODELLO {model.get_name()} (ckeckpoint: {last_ckpt}) caricato con successo ---"
)
except Exception as err:
print(
f"--- Non è stato possibile caricare il MODELLO {model.get_name()} ---\n",
err)
def save_parameters(self, total_steps: int, episode: int, eps: float,
session: str):
self.parameters_helper.save_parameters(total_steps=total_steps,
episode=episode,
eps=eps,
session=session)
def load_parameters(self):
return self.parameters_helper.load_parameters()
def write_distances(csv_file, json_file):
original_csv_file = CsvHelper(csv_file)
new_csv_file = CsvHelper(f"new_props_{time.time()}")
distance_matrix = JsonHelper(json_file).read()
try:
for index, row in enumerate(original_csv_file.read()):
key = str(index + 1)
if key in distance_matrix:
result = DistanceMatrix.to_h(distance_matrix[key])
price = int(row["Price"].replace("£",
"").replace("$", "").replace(
"€", "").replace(",", ""))
row["Price"] = price
address_index = 10
row_keys = list(row.keys())
row_values = list(row.values())
postal_info = Postal(row["Address"]).info()
postal_length = len(postal_info)
postal_keys = list(postal_info.keys())
for index, key in enumerate(postal_keys):
offset_index = address_index + index + 1
row_keys.insert(offset_index, key)
postal_values = list(postal_info.values())
for index, value in enumerate(postal_values):
offset_index = address_index + index + 1
row_values.insert(offset_index, value)
keys = list(result.keys())
for index, key in enumerate(keys):
offset_index = address_index + postal_length + index + 1
row_keys.insert(offset_index, key)
values = list(result.values())
for index, value in enumerate(values):
offset_index = address_index + postal_length + index + 1
row_values.insert(offset_index, value)
new_row = dict(zip(row_keys, row_values))
new_csv_file.write(new_row)
finally:
original_csv_file.file.close()
new_csv_file.file.close()
def get_account_json(cls, account_id):
return JsonHelper.to_json(cls.get_account_object(account_id))
def accounts():
return JsonHelper.to_json([])
def hello():
return JsonHelper.build_action_response(None, "Hello World from Flask")
cg = CommandGenerator()
command = cg.orb(filename=filename,
path_to_orb=PATH_TO_ORB_SLAM,
path_to_data=PATH_TO_TMP_DIR,
path_to_config=PATH_TO_CONFIG,
dataset=dataset,
resolution=resolution)
print("Running ORB slam on {}!".format(filename))
t1 = time.perf_counter()
process = subprocess.Popen(command, shell=True)
process.wait()
t2 = time.perf_counter()
# write the elapsed time to json file
elapsed = t2 - t1
jh = JsonHelper()
jh.add_json(
os.path.join(res_orb_data_dir, "results.txt"),
"processing_time", elapsed)
# try to copy the output in the right place
try:
shutil.move(
"KeyFrameTrajectory.txt",
os.path.join(res_orb_data_dir,
"estimated_data.txt"))
shutil.move(
"pq.ply",
os.path.join(res_orb_data_dir,
"PointCloud.txt"))
point_cloud_gt_path = os.path.join(
def __init__(self):
self.url_dict = {}
self.json_helper = JsonHelper("short_links.json")
self.link_length = 10
self.port = os.getenv("PORT", 5000)
self.load_from_file()
from json_helper import JsonHelper
from companies_comparison import CompaniesComparison
MIN_CRITERIA_NUM = 4
with open('companiesComparisonWidgetInput.json', 'r') as content_file:
content = content_file.read()
company_to_labels = JsonHelper.convert_companies_json_to_dictionary(content)
all_companies_with_all_labels_dict,max_companies_dict,all_with_max_dict = \
CompaniesComparison.find_groups_with_size(company_to_labels, MIN_CRITERIA_NUM)
print "All combinations of all companies with all combinations of shared creterias of size %s:" % MIN_CRITERIA_NUM
print JsonHelper.convert_companies_to_labels_list_to_json(
all_companies_with_all_labels_dict) + '\n'
print 'All the Maximum number of companies that have a shared creteria of %s:' % MIN_CRITERIA_NUM
print JsonHelper.convert_companies_to_labels_list_to_json(
max_companies_dict) + '\n'
print 'All the Companies combinations with their max shared cretierias that have a minimum shared creteria of %s:' % MIN_CRITERIA_NUM
print JsonHelper.convert_companies_to_labels_list_to_json(
all_with_max_dict) + '\n'
def __init__(self, ckpt_path: str, parameters_path: str):
self.ckpt_path = ckpt_path
self.parameters_path = parameters_path
self.parameters_helper = JsonHelper(self.parameters_path)