def post_random_tweet(self):
session = connect_sql()
quote = random_quote(session)
quote.used = True
session.commit()
try:
tweet(quote.to_tweet_string())
except:
logging.error("Could not tweet : %s, too long." % quote.to_tweet_string())
finally:
session.close()
def save(self, *args, **kwargs):
if self.date_published is None:
self.date_published = (
datetime.now()
) # if the date isn't set it needs to be set now or get_absolute_url() breaks
if self.is_published is True and self.is_modified is False and self.tweet is True:
absolute_url = settings.SITE_URL + self.get_absolute_url()
utils.tweet(self.title.encode(), absolute_url)
self.is_modified = True
super(Post, self).save(*args, **kwargs)
try:
ping_google(sitemap_url="/sitemap.xml")
except Exception:
# Bare 'except' because we could get a variety
# of HTTP-related exceptions.
pass
def main():
prev_Y = []
now_Y = []
diff_Y = []
incr_Y = []
decr_Y = []
for i in data_teacher:
prev_Y.append(0)
now_Y.append(0)
diff_Y.append(0)
incr_Y.append(0)
decr_Y.append(0)
# １つ前と現在のhtmlデータの入手関数
prev_student_deploy_list = []
student_deploy_list = []
parseHtml(getPrevLog(THIS_DIRECTORY + 'log_student_deploy.txt'),
prev_student_deploy_list)
parseHtml(get_student_deploy.get_html(), student_deploy_list)
# 応募人数計算関数
personNumber(prev_student_deploy_list, prev_Y)
personNumber(student_deploy_list, now_Y)
# １つ前と現在の差計算関数
setDiff_Y(prev_Y, now_Y, diff_Y)
setIncrAndDect_Y(diff_Y, incr_Y, decr_Y)
# 前回の数値が変化したか
flag = changeValue(incr_Y, decr_Y)
# print(flag)
if flag == True:
# 各リストのデバッグ
print("pre_person :", prev_Y)
print("now_person :", now_Y)
print("----> incr :", incr_Y)
print("----> decr :", decr_Y)
# グラフの色設定関数
setGraphColor(now_Y)
# グラフ描画関数
savename = drawGraph(prev_Y, now_Y, diff_Y, incr_Y, decr_Y)
tweet(savename)
else:
print("====>( No Change Value )")
return
def brew(coffee_type, db):
if bottle.request.headers.environ.get("REMOTE_ADDR") != "127.0.0.1":
return json_return(403, "Can only start a brew from the monitor host - sorry.")
data = db.execute("select id, name from coffees")
coffees = {row["id"]: row["name"] for row in data}
if coffee_type not in coffees.keys():
return json_return(403, "Invalid coffee type.")
row = db.execute("select dts, coffee from raw_log order by dts desc").fetchone()
now = datetime.datetime.now()
if row:
dt = datetime.datetime.strptime(row["dts"], "%Y-%m-%d %H:%M:%S")
delta = now - dt
if delta.seconds < BREW_TIME:
if coffee_type != row["coffee"]:
db.execute('update raw_log set coffee=%d where dts="%s"' % (coffee_type, row["dts"]))
return json_return(200, "brew type changed. Estimated completion: %s" % estimate_complete(dt))
else:
return json_return(200, "brew already in progress. Estimated completion: %s" % estimate_complete(dt))
db.execute("insert into raw_log (coffee) values (%d)" % coffee_type)
eta = estimate_complete(now)
ret_str = "Brew started: %s. Estimated completion: %s" % (coffees[coffee_type], eta)
tweet(ret_str)
publish(
json.dumps(
{
"human": ret_str,
"type": "start",
"coffee": coffees[coffee_type],
"start": now.strftime("%Y%m%d%H%M%S"),
"estimate": eta.strftime("%Y%m%d%H%M%S"),
}
)
)
os.system("(%s/followup.sh %d %s)&" % (os.path.dirname(__file__), BREW_TIME, coffees[coffee_type]))
return json_return(200, ret_str)
#!/usr/bin/env python
from utils import tweet, publish
from datetime import datetime
from time import sleep
import json
import sys
try:
cmd = sys.argv.pop(0)
delay = int(sys.argv.pop(0))
coffee = sys.argv.pop(0)
except:
print "usage: %s delay coffee" % cmd
sys.exit(1)
sleep(delay)
msg = "A pot of %s is ready for you. [%s]" % (coffee, datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
tweet(msg)
publish(
json.dumps(({"type": "complete", "coffee": coffee, "end": datetime.now().strftime("%Y%m%d%H%M%S"), "human": msg}))
)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# -- generate data set
file_list = glob.glob('./data/*.json')
raw_datasets = DataProcessor(file_list).generate_datasets()
# -- tokenize
corpus = Corpus(raw_datasets)
datasets = corpus.tokenize_all()
vocab_size = len(corpus.dictionary)
# --- tokenize data
train_data = utils.div_to_batch(datasets[0], args.batch_size).to(device)
val_data = utils.div_to_batch(datasets[1], args.batch_size).to(device)
test_data = utils.div_to_batch(datasets[2], args.batch_size).to(device)
# -- define model
model = RNN(vocab_size, args.embed_size, args.hidden_size,
args.num_layers, args.dropout).to(device)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
# -- train and validate model
trainer = Trainer(model, train_data, val_data, test_data,
optimizer, criterion, vocab_size, args)
trainer.train()
trainer.test()
# -- generate tweets
utils.tweet(corpus, 100, device)
header = f'[{store_name}, {store_city}, {store_region}]'
slot_id = f'{header}_{details}'
if store_name not in index:
index.setdefault(store_name, [])
if details not in index[store_name]:
new_slots += 1
index[store_name].append(details)
print(f'TimeSlot Available: {details}')
# tweet or clean up
# if new_slots == 1:
# # get the most recently added slot
# details = index[store_name][-1]
# tweet(header, new_slots, store_city, details)
if new_slots > 0:
tweet(header, new_slots, store_city)
write_store(store_city, full_slots)
elif counter == 0:
write_store(store_city, full_slots)
elif full_slots == 0:
if read_store(store_city) != 0:
write_store(store_city, full_slots)
index[store_name] = []
else:
# if non-200 re-authenticate
print(
f'Error: {res.status_code} response. Trying to re-authenticate'
)
headers = authy.authenticate()
except Exception as e:
