def main():
from time import time
start_time = time.now()
check_pos(200, 300, 400)
end_time = time.now()
print(end_time - start_time)
def train():
from time import time
from train_ml import download_data, train_and_evaluate, serialization
start_time = time.now()
for mode in ['train', 'test']:
download_data(mode)
model, vectorizer = train_and_evaluate()
serialization(model, vectorizer)
response = time.now() - start_time
return response
def test_filter_date_range():
class Lifetime(models.NodeModel):
dob = models.DateProperty(indexed=True)
mid_life_crisis = models.DateTimeProperty(indexed=True)
tod = models.DateTimeProperty(indexed=False)
date = datetime.date
time = datetime.datetime
bdays = [date(1952, 3, 5), date(1975, 8, 11), date(1988, 7, 27)]
crises = [
time(1992, 3, 6, 2, 15, 30),
time(2007, 8, 13, 16, 10, 10),
time(2020, 8, 1, 8, 7, 59, 99)
]
tods = [
time(2022, 3, 6, 2, 15, 30),
time(2047, 10, 30, 22, 47, 1),
time(2060, 8, 15, 8, 7, 59)
]
for t in zip(bdays, crises, tods):
Lifetime.objects.create(dob=t[0], mid_life_crisis=t[1], tod=t[2])
low, high = date(1975, 9, 11), time.now()
query = Lifetime.objects.filter(dob__range=(low, high))
assert all(low < l.dob < high.date() for l in query)
nowish = date(2011, 8, 10)
query = Lifetime.objects.filter(mid_life_crisis__lt=nowish)
eq_(len(query), 2)
the_singularity = date(2032, 12, 12)
query = Lifetime.objects.filter(tod__gt=the_singularity)
eq_(len(query), 2)
def test_filter_date_range():
class Lifetime(models.NodeModel):
dob = models.DateProperty(indexed=True)
mid_life_crisis = models.DateTimeProperty(indexed=True)
tod = models.DateTimeProperty(indexed=False)
date = datetime.date
time = datetime.datetime
bdays = [date(1952, 3, 5), date(1975, 8, 11), date(1988, 7, 27)]
crises = [time(1992, 3, 6, 2, 15, 30), time(2007, 8, 13, 16, 10, 10),
time(2020, 8, 1, 8, 7, 59, 99)]
tods = [time(2022, 3, 6, 2, 15, 30), time(2047, 10, 30, 22, 47, 1),
time(2060, 8, 15, 8, 7, 59)]
for t in zip(bdays, crises, tods):
Lifetime.objects.create(dob=t[0], mid_life_crisis=t[1], tod=t[2])
low, high = date(1975, 9, 11), time.now()
query = Lifetime.objects.filter(dob__range=(low, high))
assert all(low < l.dob < high.date() for l in query)
nowish = date(2011, 8, 10)
query = Lifetime.objects.filter(mid_life_crisis__lt=nowish)
eq_(len(query), 2)
the_singularity = date(2032, 12, 12)
query = Lifetime.objects.filter(tod__gt=the_singularity)
eq_(len(query), 2)
def train_and_persist(svm_model, train_d, train_t, fp):
# your code here
# 1. train the classifier
print 'Training svm model'
from time import time
st = time.now()
svm_model.fit(train_d, train_t)
et = time.now()
duration = et - st
with open("stats.txt", "a+") as myfile:
myfile.write(duration)
with open(fp, 'wb') as output: # Overwrites any existing file.
cPickle.dump(svm_model, output)
print 'svm model trained and persisted...'
def trade_complete(self, data):
"""订单完成后，执行任务"""
event_type = data.get('event_type')
# FIXME 定时时间需要计算
eta = time.now()
# TODO 先查看是否已有订单已经完成的任务，保证不会重复创建任务
task = trade_complete(data, eta=eta)
task_id = task.id
instance_id = data.get()
key = '{}:{}'.format(event_type, instance_id)
self.push_task_id(key, task_id)
def hello_world():
print('what is now?')
print(time.now())
return 'Hello World'
from time import time print(time.now())
def capture(self):
picamera.PiCamera.capture('image-' + str(time.now()) + '.jpg')