def make_private_public(*args, **kwargs):
translation.activate(settings.LANGUAGE_CODE)
num_days_after_due_date = settings.FROIDE_CONFIG.get(
'make_public_num_days_after_due_date', 365)
now = timezone.now()
due_date_everything_should_be_made_private = now - timedelta(num_days_after_due_date)
for foirequest in FoiRequest.objects.filter(Q(visibility=0) | Q(visibility=1), is_foi=True, due_date__lte=due_date_everything_should_be_made_private):
foirequest.visibility = 2
foirequest.save()
def detect_private_will_be_made_public(*args, **kwargs):
translation.activate(settings.LANGUAGE_CODE)
num_days_after_due_date = settings.FROIDE_CONFIG.get(
'make_public_num_days_after_due_date', 365)
num_days_after_due_date = num_days_after_due_date - NOTIFICATION_SENT_BEFORE_NUM_DAYS
now = timezone.now()
due_date_everything_should_be_made_private = now - timedelta(num_days_after_due_date)
for foirequest in FoiRequest.objects.filter(Q(visibility=0) | Q(visibility=1), is_foi=True, due_date=due_date_everything_should_be_made_private):
send_mail(u'{0}'.format(
_("%(site_name)s: Reminder that your request is being made public in 7 days") % {
"site_name": settings.SITE_NAME
},
),
render_to_string("foirequest/emails/became_public.txt", {
"site_name": settings.SITE_NAME,
"request": foirequest,
}),
settings.DEFAULT_FROM_EMAIL,
[foirequest.user.email]
)
In [1]: from datetime import timedelta In [2]: a = timedelta(days=2, hours=6) In [3]: a Out[3]: datetime.timedelta(2, 21600) In [4]: b = timedelta(hours=4.5) In [5]: b Out[5]: datetime.timedelta(0, 16200) In [6]: c = a + b In [7]: c Out[7]: datetime.timedelta(2, 37800) In [8]: 16200/4.5 Out[8]: 3600.0 In [9]: c.days Out[9]: 2 In [10]: c.seconds Out[10]: 37800 In [11]: c.seconds/3600 Out[11]: 10.5 In [12]: c.total_seconds()/3600 Out[12]: 58.5
def cleanup_namespace_resources(namespace_pattern, cleanup_older_than, k8s_context, kube_config_file):
"""Delete resources within target namespaces which have exceeded some AGE threshold."""
for ctx in k8s_context:
print("Processing Kubernetes context {context}...".format(context=ctx))
config.load_kube_config(context=ctx, config_file=kube_config_file)
v1 = client.CoreV1Api()
response = v1.list_namespace()
for pattern in namespace_pattern:
print(
"Checking cluster namespaces against pattern: {p}".format(p=pattern))
regexp = re.compile(pattern)
for ns in response.items:
print("Namespace: {namespace}, creation_time: {createdAt}".format(
namespace=ns.metadata.name, createdAt=ns.metadata.creation_timestamp))
# cleanup all namespace resources which match pattern and whose creation time is older than threshold
if regexp.search(ns.metadata.name) and (ns.metadata.creation_timestamp <= now-timedelta(seconds=cleanup_older_than)):
print("Namespace [{namespace}] exceeds age of {age} seconds. Cleaning up resources...".format(
namespace=ns.metadata.name, age=cleanup_older_than))
command = "kubectl delete all --all -n {namespace}".format(
namespace=ns)
process = subprocess.Popen(
command.split(), stdout=subprocess.PIPE)
output, error = process.communicate()
if error:
print(error)
if output:
print(output)
else:
print("Skipping Namespace {namespace}.".format(
namespace=ns.metadata.name))
def seconds_ago(s,n):
f = "%Y-%m-%d %H:%M:%S"
s_date = dt.strptime(s, f) - timedelta(seconds = n)
return str(s_date)
def update_time(self):
return timedelta(hours=1)
def screensaver_time(self):
return timedelta(days=2)
在我们要分析的数据中，很多数据都带有时间戳，比如数据集中的每一项数据都带有一项数据项生成时的时间戳，有的时候我们需要根据这个时间戳进行运算，这在 Pandas 中非常方便。 在 Python 中，时间处理方面的相关功能主要集中在 datetime 包中，主要有的三种对象： datetime.datetime 代表时间对象； datetime.date 代表某一天； datetime.timedelta 代表两个 datetime.datetime 对象之间的时间差； In [496]: from datetime import datetime, timedelta In [497]: d1 = datetime(2017, 8, 1) In [498]: d1 Out[498]: datetime.datetime(2017, 8, 1, 0, 0) In [499]: delta = timedelta(days=10) In [500]: d1 + delta Out[500]: datetime.datetime(2017, 8, 11, 0, 0) 以上代码中，简单演示了 datetime.datetime 和 datetime.timedelta 的用法。 我们先创建一个简单的时间序列，然后演示一些基本的用法： In [539]: from datetime import datetime In [540]: dates = [datetime(2018, 1, 1), datetime(2018, 1, 2), datetime(2018, 1, 3), datetime(2018, 1,4)] In [541]: ts = Series(np.random.randn(4), index=dates) In [542]: ts Out[542]:
In [22]: bang_d = loc_d.astimezone(timezone('Asia/Kolkata'))
In [23]: print(bang_d)
2012-12-21 21:00:00+05:30
In [24]: d = datetime(2013, 3, 10, 1, 45)
In [25]: loc_d = central.localize(d)
In [26]: print(loc_d)
2013-03-10 01:45:00-06:00
In [27]: from datetime import timedelta
In [28]: later = central.normalize(loc_d + timedelta(minutes=30))
In [29]: print(later)
2013-03-10 03:15:00-05:00
In [30]: print(loc_d)
2013-03-10 01:45:00-06:00
In [33]: import pytz
In [34]: utc_d = loc_d.astimezone(pytz.utc)
In [35]: print(utc_d)
2013-03-10 07:45:00+00:00
In [36]: later_utc = utc_d + timedelta(minutes=30)
import zulu import timedelta now = zulu.now() past = now - timedelta(days - 30) print(past)
""")
stocks = cursor.fetchall()
for stock in stocks:
symbol = stock['symbol']
stock_ids[symbol] = stock['id']
# print(stock_ids)
for symbol in symbols:
start_date = datetime(2020, 10, 25).date()
end_date_range = datetime(2021, 1, 25).date()
while start_date <end_date_range:
start_date = start_date + timedelta(days=4)
print(f"=== Fetching minute bars {start_date}-{end_date} for {symbol}")
api = tradeapi.REST(config.API_KEY, config.SECRET_KEY, config.API_URL)
minutes = api.polygon.historic_agg_v2(symbol, 1, 'minute', _from='start_date', to='end_date').df
minutes = minutes.resample('1min').ffill()
for index, row in minutes.iterrows():
cursor.execute("""
INSERT INTO stock_price_minute (stock_id, datetime, open, high, low, close, volume)
VALUES (?, ?, ?, ?, ?, ?, ?)
""",(stock_ids[symbol], index.tz_localize(None).isoformat(), row['open'], row['high'],low['low'],row['close'],row['volume']))
start_date = start_date + timedelta(days=7)
def create_profile_rows(num=1):
output = []
for x in range(num):
intake_date = fake.date_between(start_date="-3y", end_date="today")
ins_status = random.choices(
('insured', 'uninsured', 'application pending'),
weights=[1.5, 1, 0.5])
if ins_status != 'uninsured':
hmo = random.choices((True, None), weights=[1.5, 0.5])
insurance = random.choices(
('Health Partners', 'Keystone First', 'Aetna Better Health',
'United Healthcare'))
ins_id = fake.credit_card_number(card_type=None)
prior_auth_date = intake_date + timedelta(
days=random.randint(90, 200))
else:
hmo = None
insurance = None
ins_id = None
prior_auth_date = None
hiv_status = random.choices((True, None), weights=[0.5, 1.5])
if hiv_status is True:
hiv_care_info = fake.sentence(nb_words=6,
variable_nb_words=True,
ext_word_list=None)
hiv_last_app = intake_date - timedelta(
days=random.randint(30, 160))
viral_load = random.choices('>1500', '>10000', '>100000',
'Undetectable')
cd4 = random.choices(True, None)
#Todo add languadge for cd4 test
else:
hiv_care_info = None
hiv_last_app = None
viral_load = None
cd4 = None
hcv_status = random.choices((True, None), weights=[0.5, 1.5])
if hcv_status is True:
hcv_care_info = fake.sentence(nb_words=6,
variable_nb_words=True,
ext_word_list=None)
hcv_last_app = intake_date - timedelta(
days=random.randint(30, 160))
cchange_status = random.choices("waiting", 'active', 'notinvoved')
output.append({
"intake_date":
intake_date,
"first_name":
fake.first_name(),
"last_name":
fake.last_name(),
"dob":
fake.date_of_birth(tzinfo=None, minimum_age=20, maximum_age=80),
"ssn":
fake.ssn(taxpayer_identification_number_type="SSN"),
"state":
'PA',
"city":
'Philadelphia',
"address":
fake.street_address(),
"intersection":
fake.street_name() + ' & ' + fake.street_name(),
"phone_number":
fake.phone_number(),
"text_optin":
random.choices((True, None), weights=[1.75, 0.25]),
"vm_optin":
random.choices((True, None), weights=[1.5, 0.5]),
"state_id":
random.choices((True, None), weights=[1.7, 0.3]),
"insurance_status":
ins_status,
"hmo":
hmo,
"insurance":
insurance,
"insurance_id":
ins_id,
"prior_auth_date":
prior_auth_date,
"income_source":
random.choices('Full Time', 'Part Time', 'Cash Assistance'),
"monthly_income":
random.randint(300, 1900),
"housing_status":
random.choices('homeless', 'temporary housing', 'shelter',
'has home')
})
return output
def newsToday():
for i, key in enumerate(news.keys()):
if news[key].date() == utc.localize(datetime.today() - timedelta(days=0) ).date():
import from datetime import datetime
import timezone
import timedelta
tw_tz = timezone(timedelta(hours=8, minutes=16), 'Asia/Taiwan')
now = datetime.now()
today = now.strftime("%Y")+now.strftime("%m") + now.strftime("%d")
for i in 10:
print(today)
#!/usr/bin/env python# encoding: utf8 import hashlibimport jsonimport osfrom datetime import datetime, timedelta from togglapi import api from config import API_TOKEN, TIMEZONE START_MONTH = '2015-03' def get_month_start(time): return time.replace(day=1, hour=0, minute=0, second=0, microsecond=0) def get_month_end(time): month_start = get_month_start(time) next_month_start = (month_start + timedelta(days=31)).replace(day=1) return next_month_start - timedelta(seconds=1) def dump(dump_dir, year, month, verbose=False): time = datetime(year=year, month=month, day=1) month_start = get_month_start(time) month_end = get_month_end(time) month = '%d-%02d' % (year, month) if verbose: print month + ':', print month_start.isoformat(), 'to', month_end.isoformat(), '...' toggl = api.TogglAPI(API_TOKEN, TIMEZONE) data = toggl.get_time_entries(month_start.isoformat(), month_end.isoformat()) content = json.dumps(data) if not os.path.exists(dump_dir): os.makedirs(dump_dir) filename = os.path.join(dump_dir, '%s.json' % month) def md5(string): return hashlib.md5(string).digest() if os.path.exists(filename) and md5(content) == md5(open(filename).read()): if verbose: print filename, 'not changed.' else: file(filename, 'w').write(content) if verbose: print filename, 'saved.' return data def backup(backup_dir, verbose=False): time = datetime.now() while True: month = '%d-%02d' % (time.year, time.month) if month < START_MONTH: break dump(backup_dir, time.year, time.month, verbose=verbose) prev_month = (time - timedelta(days=31)).replace(day=1) time = prev_month if __name__ == '__main__': import sys if len(sys.argv) == 1: print 'Usage: %s backup_dir [-v]' % sys.argv[0] sys.exit(1) try: verbose = sys.argv[2] == '-v' except: verbose = False backup_dir = sys.argv[1] backup(backup_dir, verbose=verbose)
rom datetime import datetime, timedelta
# Define default_args to be passed in to operators
default_args = {
'owner': 'airflow',
'depends_on_past': False,
'start_date': datetime.now(),
'email_on_failure': False,
'email_on_retry': False,
'retries': 3,
'retry_delay': timedelta(minutes=5),
}
# Define the EMR settings
emr_settings = {
"Applications": [
{
"Name": "Spark"
}
],
"EbsRootVolumeSize": 10,
"Instances": {
"Ec2SubnetId": "subnet-49403b67",
"EmrManagedMasterSecurityGroup": "sg-0c9ef433da846c7bf",
"EmrManagedSlaveSecurityGroup": "sg-0bf779a7d90a8d321",
"InstanceGroups": [
{
"EbsConfiguration": {
"EbsBlockDeviceConfigs": [
import urllib.request
from zipfile import ZipFile
sys.path.insert('~/Documents/git/Simple-Python-ETL/')
from airflow import DAG
from airflow.operators.python import PythonOperator
default_args = {
'owner': 'airflow',
'depends_on_past': False,
'email': ['*****@*****.**'],
'email_on_failure': False,
'email_on_retry': False,
'retries': 1,
'retry_delay': timedelta(minutes=5),
# 'queue': 'bash_queue',
# 'pool': 'backfill',
# 'priority_weight': 10,
# 'end_date': datetime(2016, 1, 1),
# 'wait_for_downstream': False,
# 'dag': dag,
# 'sla': timedelta(hours=2),
# 'execution_timeout': timedelta(seconds=300),
# 'on_failure_callback': some_function,
# 'on_success_callback': some_other_function,
# 'on_retry_callback': another_function,
# 'sla_miss_callback': yet_another_function,
# 'trigger_rule': 'all_success
}
import airflow
from airflow.models import DAG
from airflow.operators.dummy_operator import DummyOperator
import timedelta
# Setting up default arguments for DAG initialization
default_args = {
'owner': 'airflow',
'start_date': airflow.utils.dates.days_ago(2)
}
# Initializing our DAG
dag = DAG(dag_id='DAG_tmp',
default_args=default_args,
description='DAG for DSC, temporary',
schedule_interval=timedelta(days=1))
# Defining Tasks
##############
# Reading data
read_data_1 = DummyOperator(task_id='Read data', dag=dag)
read_data_2 = DummyOperator(task_id='Read data', dag=dag)
read_data_3 = DummyOperator(task_id='Read data', dag=dag)
read_data_4 = DummyOperator(task_id='Read data', dag=dag)
read_data_5 = DummyOperator(task_id='Read data', dag=dag)
def time_ticks(x, pos):
return str(timedelta(seconds=int(x))).split(".")[0]
cbar = fig.colorbar(cs, orientation='vertical')
cbar.ax.set_ylabel('$^oC$',fontsize=14)
ax.set_ylabel('Depth (m)',fontsize=14)
file = folder_fig + 'Temp_' + e.dataset_id
plt.savefig(file,bbox_inches = 'tight',pad_inches = 0.1)
#%% Temp transect POM
time_matrix_pom = np.tile(time_pom,(z_matrix_pom.shape[1],1)).T
kw = dict(levels = np.arange(6,31,3))
fig, ax = plt.subplots(figsize=(10, 3))
cs = plt.contourf(time_matrix_pom,z_matrix_pom,target_temp_pom,cmap=cmocean.cm.thermal,**kw)
plt.contour(time_matrix_pom,z_matrix_pom,target_temp_pom,[26],color='k')
plt.title('HWRF-MPIPOM Cycle ' + cycle + ' Along Track ' + e.dataset_id[0:4],fontsize=14)
plt.ylim(-70,0)
plt.xlim(timeg[0],timeg[-1])
time_vec = [datetime(2020,8,2,0),datetime(2020,8,2,12),datetime(2020,8,3,0),datetime(2020,8,3,12)]
plt.xticks(time_vec)
xfmt = mdates.DateFormatter('%b-%d-%H')
ax.xaxis.set_major_formatter(xfmt)
cbar = fig.colorbar(cs, orientation='vertical')
cbar.ax.set_ylabel('$^oC$',fontsize=14)
ax.set_ylabel('Depth (m)',fontsize=14)
file = folder_fig + 'HWRF_POM_vs_RU33_' + storm_id + '_' + cycle
plt.savefig(file,bbox_inches = 'tight',pad_inches = 0.1)
timeg[0] + timedelta(hours=6)
