def todolist(request, interval='all'):
assert isinstance(request, HttpRequest)
if request.method == 'GET' and 'search' in request.GET:
search = request.GET['search']
if not search:
tasks = {}
else:
tasks = Task.objects.filter(Q(name__icontains=search) |
Q(description__icontains=search) |
Q(date__icontains=search))
return render(request,
'app/todolist.html',
{
'title': 'ToDoList',
'year':datetime.now().year,
'message': "Hello",
'tasks': tasks,
})
now = date.today()
step = request.GET.get('step', timedelta(days=1))
tasks = False
gap = now
if interval == 'today':
gap = now
elif interval == 'tomorrow':
now += timedelta(days=1)
gap = now
elif interval == 'week':
gap = now + timedelta(days=7)
elif interval == 'month':
gap = now + timedelta(days=31)
elif interval == 'year':
gap = now + timedelta(days=365)
else:
tasks = Task.objects.all()
if not tasks:
currentDay = str(now.year) + '-' + str(now.month) + '-' + str(now.day)
nextDay = str(gap.year) + '-' + str(gap.month) + '-' + str(gap.day)
tasks = Task.objects.filter(date__range=[currentDay, nextDay])
return render(
request,
'app/todolist.html',
{
'title': 'ToDoList',
'year':datetime.now().year,
'message': "Hello",
'tasks': tasks,
}
)
def DateCreator(date):
mydate = date.today()
todaysName = calendar.day_name[mydate.weekday()]
yesterday = mydate - timedelta(1)
yesterdayName = calendar.day_name[yesterday.weekday()]
# This portion of the code grabs the last day and prepares it for the url
workingDate = date - timedelta(1)
yesterdayYear = '20' + workingDate.strftime('%y')
yesterdayDay = workingDate.strftime('%d')
yesterdayMonth = workingDate.strftime('%m')
urlCreator(yesterdayDay, yesterdayMonth, yesterdayYear, city, state)
def __init__(self, search):
self.solr_searcher = search
self.graphPool = dict()
last_time_stamp = datetime.now(pytz.utc) - timedelta(minutes=.5)
# self.time_slice = timedelta(minutes = 5).total_seconds()
self.start_app_time = last_time_stamp
self.last_time_stamp = last_time_stamp
def getAverages(time, key, location, typeOfLocation, longTime, weekly, filter, timeWindow, resolution, sortOperators):
#datetime.date object
#key: if set, db query by uid: values can be uid or 0
#weekly: true = week, false = day
#filter: true/false
#typeOfLocation gets its values as follows:
# 0 = db query w/o area restriction
# 1 = db query by postal code
# 2 = db query by city name
# more to be added
#timeWindow: how many days of data is fetched (if filter=true, this doesn't matter)
#resolution: averages are calculated for every [resolution] hours, values 1-24
#sortOperators: true/false, whether separation of operators is desired.
#some data alteration needed if key is postal code
if typeOfLocation == 1:
location = location + "%"
#time is altered to include the given day
time = time + timedelta(days = 1)
#Different functions are called according to the parameters
if sortOperators:
data = getAveragesSortOperators(time, key, location, typeOfLocation, longTime, weekly, filter, timeWindow, resolution, sortOperators)
elif longTime:
data = getAveragesLongTime(time, key, location, typeOfLocation, sortOperators)
elif filter:
data = getAveragesWeekFilter(weekly, time, key, location, typeOfLocation, timeWindow, resolution, sortOperators)
elif weekly:
data = calculateAveragesWeekly(time, key, location, typeOfLocation, timeWindow, resolution, sortOperators,0,0)
else:
data = calculateAveragesDaily(time, key, location, typeOfLocation, timeWindow, resolution, sortOperators,0,0)
return data
def DateCreator(date,deltaDate):
workingDate = date - timedelta(deltaDate)
yesterdayYear = '20' + workingDate.strftime('%y')
yesterdayDay = workingDate.strftime('%d')
yesterdayMonth = workingDate.strftime('%m')
return(yesterdayDay,yesterdayMonth, yesterdayYear)
def _customer_cancel(self):
# 当前订单状态 ，是否可以直接取消的操作
# 判断当前预定的时间是否是免费的
if self.hotelPackageOrder.process_state == HotelPackageOrder.CUSTOMER_REQUIRE: # 如果商家未接单,全额退还
self.hotelPackageOrder.process_state = HotelPackageOrder.CUSTOMER_CANCEL # 标记为用户取消
self.customer.add_customer_points(self.hotelPackageOrder.amount)
# warn 全额退款 生成账单
elif self.hotelPackageOrder.process_state == HotelPackageOrder.SELLER_ACCEPT: # 商家已接单
# warn 商家已接单
hotelPackageOrderItems = self.hotelPackageOrder.items.select_subclasses()
checkin_time = self.hotelPackageOrder.checkin_time
# 入住当天6点的时间
deduct_all_point_time = datetime(checkin_time.year, checkin_time.month, checkin_time.day,
hour=18) # 晚于这个时间扣除全部积分 (相当于默认的入住时间)
# 入住前一天2点的时间
dedcut_halt_point_time = deduct_all_point_time - timedelta(hours=28) # 入住前一天的2点之后扣除一半
self.hotelPackageOrder.process_state = HotelPackageOrder.CUSTOMER_BACK
if self.cur_datetime < dedcut_halt_point_time: # 未到扣除积分的时间
self.hotelPackageOrder.process_state = HotelPackageOrder.CUSTOMER_CANCEL
self.customer.add_customer_points(self.hotelPackageOrder.amount)
# warn 同样的 也是i用户取消
else:
now = datetime.now()
if self.cur_datetime < deduct_all_point_time: # 扣除一半
need_deduct_poins = hotelPackageOrderItems[0].point * 0.5
if len(hotelPackageOrderItems) > 1 and now.hour >= 14:
need_deduct_poins += hotelPackageOrderItems[1].point * 0.5
need_back_to_customer_point = int(self.hotelPackageOrder.amount - need_deduct_poins)
orderBill = OrderBill.create_for_roomOrder_cancel(roomOrder=self.hotelPackageOrder,
refund_amount=need_back_to_customer_point)
self.orderBill = orderBill
else: # 扣除当天全部 改代理商 %75
need_deduct_poins = hotelPackageOrderItems[0].point
if len(hotelPackageOrderItems) > 1 and now.hour >= 14:
need_deduct_poins += hotelPackageOrderItems[1].point * 0.5
need_back_to_customer_point = int(self.hotelPackageOrder.amount - need_deduct_poins)
orderBill = OrderBill.create_for_roomOrder_cancel(roomOrder=self.hotelPackageOrder,
refund_amount=need_back_to_customer_point)
self.orderBill = orderBill
self.hotelPackageOrder.success = True
self.hotelPackageOrder.settled = True
# TODO 如果
self.hotelPackageOrder.closed = True
self.hotelPackageOrder.success = True
self.hotelPackageOrder.settled = True
def main():
CurrentDate()
print(holdTemp)
print(holdDegreeDays)
mydate = date.today()
test33 = mydate - timedelta(1)
capture = calendar.day_name[mydate.weekday()]
capture2 = calendar.day_name[test33.weekday()]
print(capture)
print(capture2)
def _is_freetime_for_cancel(self):
if self.hotelPackageOrder.process_state == HotelPackageOrder.CUSTOMER_REQUIRE: # 如果商家未接单
return True
max_later_hours = app_settings.hotelOrder_free_cancel_hours
checkin_time = self.hotelPackageOrder.checkin_time
checkin_time = datetime(checkin_time.year, checkin_time.month, checkin_time.day, hour=14)
delay_date = checkin_time - timedelta(hours=max_later_hours)
if self.cur_datetime < delay_date: # 未到扣除积分的时间
return True
else:
return False
def convfdat(a):
"""
Convert the date in the Excel document in a datetime that can be used by DJango.
:param a: the date express as a float
:type a: Float
:example:
>> convfdat(45135.45987)
Convert the date 45135.45987 into the date 2023-07-28 11:02:13.
"""
exord = datetime.toordinal(date(1900,1,1))-2
d = datetime.fromordinal(floor(a)+exord)
sec=round(((a-floor(a))*10**9)/11574)
d4=d+timedelta(seconds=sec)
return(d4)
def generateChallenge(username, entries=Global.DEFAULT_KEY_NUMBER, output_folder="users", expiration_date=None):
if expiration_date is None:
expiration_date = datetime.now() + timedelta(days=Global.EXPIRATION_DAYS)
alphabet = string.ascii_letters
keys = [generateKeyword(alphabet, Global.KEY_LENGHT) for k in range(entries)]
challenges = [generateKeyword(alphabet, Global.CHALLENGE_LENGHT) for k in range(entries)]
if not os.path.exists(output_folder):
os.makedirs(output_folder)
with open(output_folder+"/" + username + ".txt", encoding="UTF-8", mode="w") as file:
file.write(expiration_date.strftime(Global.DATE_FORMAT) + "\n")
file.write("\n")
for (key, challenge) in zip(keys, challenges):
file.write(key + Global.USER_FILE_DELIMITER + challenge + "\n")
def propStartDiff(self, daysOffset=7):
"""
Devuelve la diferencia en segundos entre el tiempo de comienzo del evento
de el mismo contra mismo evento propagado daysOffset dias despues
Como saber que es el mismo evento?
Por aproximacion de fechas es lo mejor que se puede hacer...
"""
md = self._propagation._datetime + timedelta(days=daysOffset)
#busco los eventos propagados daysOffset despues
offsetPropagations = Propagation.objects.filter(_datetime__year=md.year,
_datetime__month=md.month,
_datetime__day=md.day)
#Dentro de las propagaciones ejecutadas daysOffset dias despues me quedo
#con mi mismo evento de esa propagacion
events = offsetPropagations.Event_set
for e in events:
print(e)
import time
from _datetime import datetime, timedelta
print("changes test made for git")
print(time.time())
print(time.localtime())
t = datetime.now()
print(t)
print("%s/%s/%s" % (t.year, t.day, t.month))
print("%s/%s/%s,%s:%s:%s" % (t.day, t.month, t.year, t.hour, t.minute, t.second))
date = datetime.now()
print(date)
print(date + timedelta(10))
def get_zhiji(h=1):
"""以当前之间之前的时间作为值机日期获取值机日期"""
next_time = (datetime.now() - timedelta(hours=h)).strftime("%Y%m%d%H%M%S")
return str(next_time)
def load_data(file, version):
if (version == 15):
#load csv
data = pd.read_csv(file + '.csv',
sep=',',
header=0,
usecols=[0, 1, 5, 6],
dtype={
0: np.int32,
1: np.int32,
5: str,
6: np.int32
})
#specify header names
data.columns = ['UserId', 'ItemId', 'TimeStr', 'ActionType']
buy_key = 2
elif (version == 13):
#load csv( user_id,brand_id,action_type,action_time )
data = pd.read_csv(file + '.csv',
sep=',',
header=0,
usecols=[0, 1, 2, 3],
dtype={
0: np.int32,
1: np.int32,
3: str,
2: np.int32
})
#specify header names
data.columns = ['UserId', 'ItemId', 'ActionType', 'TimeStr']
buy_key = 1
data = data[data.ActionType.isin([0, buy_key])] #click+buy
#convert time string to timestamp and remove the original column
data['SessionId'] = data.groupby([data.UserId,
data.TimeStr]).grouper.group_info[0]
data['ActionNum'] = data.groupby([data.UserId, data.TimeStr]).cumcount()
if (version == 15):
data['Time'] = data.apply(lambda x: (datetime.strptime(
x['TimeStr'] + '-2015 00:00:00.000', '%m%d-%Y %H:%M:%S.%f'
) + timedelta(seconds=x['ActionNum'])).timestamp(),
axis=1)
elif (version == 13):
data['Time'] = data.apply(lambda x: (datetime.strptime(
x['TimeStr'] + '-2015 00:00:00.000', '%m-%d-%Y %H:%M:%S.%f'
) + timedelta(seconds=x['ActionNum'])).timestamp(),
axis=1)
del (data['ActionNum'])
del (data['TimeStr'])
data.sort_values(['SessionId', 'Time'], inplace=True)
#output
data_start = datetime.fromtimestamp(data.Time.min(), timezone.utc)
data_end = datetime.fromtimestamp(data.Time.max(), timezone.utc)
buys = data[data.ActionType == buy_key]
data = data[data.ActionType == 0]
del (data['ActionType'])
print(
'Loaded data set\n\tEvents: {}\n\tSessions: {}\n\tItems: {}\n\tSpan: {} / {}\n\n'
.format(len(data), data.SessionId.nunique(), data.ItemId.nunique(),
data_start.date().isoformat(),
data_end.date().isoformat()))
return data, buys
now_second = now.timestamp()
print('now time to 1970.1.1.00.00 second =', now_second)
##### datestamp转到datetime
#### 转到当前时区的datetime
now2 = datetime.fromtimestamp(now_second)
print('now datetime =', now2)
#### 转到utc时区的datetime
utc_now2 = datetime.utcfromtimestamp(now_second)
print('now utc datetime =', utc_now2)
##### str转datetime
dt = datetime.strptime('2015-08-09 10:11:12', '%Y-%m-%d %H:%M:%S')
print('Certain datetime =', dt)
str_dt = dt.strftime('%Y-%m-%d %H:%M:%S')
print('Certain datetime str =', str_dt)
##### datetime加减
dt = dt - timedelta(days=2, hours=12)
print('Certain datetime =', dt)
#####时区转换
#先转到含有时区的datetime
utc_dt = datetime.utcnow().replace(tzinfo=timezone.utc)
print('UTC datetime =', utc_dt)
# 转到相应的时区datetime
bj_dt = utc_dt.astimezone(timezone(timedelta(hours=8)))
print('Beijin datetime =', bj_dt)
tokyo_dt = bj_dt.astimezone(timezone(timedelta(hours=9)))
print('Tokyo datetime =', tokyo_dt)
print('END')
def test_new_passpoints(request):
is_invalid = 'is-invalid'
active_user = PasswordUser.objects.get(username = request.user)
login_info = LoginInfo.objects.filter(uid=active_user).order_by('login_period')
test_period = login_info.count() + 1
oldpps = NewPpInfo.objects.filter(uid=active_user).order_by('pk')
oldpp_len = len(oldpps)
newpps = oldpps.last()
img_count = newpps.img_number
img_fin = request.POST.get('img_fin')
if img_fin == None:
img_fin = 1
else:
img_fin = int(img_fin)
if img_fin <= img_count:
img_key = NewPpInfo.objects.filter(uid=active_user, img_number=img_fin).first()
img_key = img_key.cp_id.iid.pk
img = Image.objects.get(pk=img_key)
images = Image.objects.all().order_by('?')[:8]
confirm_info = ConfirmInfo.objects.filter(uid=active_user).last()
confirm_date = confirm_info.date_confirmed
login_period = login_info.count()
test_period = login_period + 1
if LoginInfo.objects.filter(uid=active_user).exists():
last_login = login_info.latest('date_login')
last_login = last_login.date_login
today = timezone.now()
if login_period == 0:
d = 1
elif login_period == 1:
d = 3
elif login_period == 2:
d = 7
else:
try:
sus_check = SusResult.objects.get(uid=active_user)
except SusResult.DoesNotExist:
sus_check = None
if sus_check:
return render(request, 'gp/complete.html', {})
else:
return render(request, 'gp/login_success.html', {'test_period': test_period})
test_date = confirm_date + timedelta(days=d)
if today >= test_date:
if request.method == 'POST':
time_prev = request.POST.get('time_prev')
if time_prev == '':
time_prev = 0
else:
time_prev = int(time_prev)
time_create = int(request.POST.get('time'))
time_create = time_create + time_prev
m = math.floor(time_create / 60)
s = time_create % 60
time = datetime.time(0,m,s)
key = request.POST.get('image')
image = Image.objects.get(pk=key)
if image == img:
img_url = image.image.url
return render(request, 'gp/test_newpp2.html', {'img_url': img_url, 'oldpps': oldpps, 'test_period': test_period, 'oldpp_len': oldpp_len, 'image': image, 'time_prev': time_create, 'img_fin': img_fin})
else:
return render(request, 'gp/test_newpp.html', {'img': img, 'images': images, 'test_period': test_period, 'is_invalid': is_invalid, 'time_prev': time_create, 'img_fin': img_fin, 'image': image})
return render(request, 'gp/test_newpp.html', {'img': img, 'images': images, 'test_period': test_period, 'img_fin': img_fin})
else:
return render(request, 'gp/login_wait.html', {'test_date': test_date})
HIGHER_TIER_ARBITRATOR_ID, BOSS_ID
}
can_esn = {
UPDATER_ID, DEVELOPER_ID, LOWER_TIER_ARBITRATOR_ID,
HIGHER_TIER_ARBITRATOR_ID, BOSS_ID
}
can_notify = {
REPORTER_ID, UPDATER_ID, DEVELOPER_ID, LOWER_TIER_ARBITRATOR_ID,
HIGHER_TIER_ARBITRATOR_ID, BOSS_ID
}
can_movelu = {
REPORTER_ID, UPDATER_ID, DEVELOPER_ID, LOWER_TIER_ARBITRATOR_ID,
HIGHER_TIER_ARBITRATOR_ID, BOSS_ID
}
LIST_WAIT_TIME = timedelta(seconds=20)
ESN_WAIT_TIME = timedelta(minutes=40)
PING_INTERVAL = timedelta(minutes=10)
ML_WAIT_TIME = timedelta(seconds=15)
MLLU_WAIT_TIME = timedelta(seconds=45)
MMR_LU_WAIT_TIME = timedelta(seconds=30)
quick_delete = 5
medium_delete = 5
long_delete = 30
class TierMogi(object):
def __init__(self, channel: discord.channel.TextChannel):
'''
Constructor
'''
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
'Nginx日志聚合'
from _datetime import datetime, timedelta
import os
import pymysql
allProductIdListFileName = "/letv/data/allProductIdList_" + (datetime.now() - timedelta(days=1)).strftime('%Y%m%d') + ".txt"
nginxMapResultFileName = "/letv/data/productIdList_" + (datetime.now() - timedelta(days=1)).strftime('%Y%m%d') + ".txt"
writeFileName = "/letv/data/allProductIdCount_" + (datetime.now() - timedelta(days=1)).strftime('%Y%m%d') + ".txt"
containerOne = "="
allProductCount = {}
dataProductList = {}
mysqlConfig = {
"host":"10.100.100.246",
"port":3322,
"user":"******",
"password":"******",
"db":"taps",
'charset':'utf8mb4'
}
queryProductSQL = "SELECT PRO_CODE, PRO_NAME FROM PS_PRODUCT"
serverList = ["10.100.54.57", "10.100.54.58", "10.110.122.107", "10.110.122.80", "10.130.208.25", "10.130.208.33",
"10.181.117.41", "10.181.117.81", "10.183.222.107", "10.183.222.135"]
if __name__ == "__main__":
# Copy the productIdList from all servers
for server in serverList:
from _datetime import datetime, timedelta
import time, timeit
#!/usr/bin/env python
# -*- coding:utf-8 -*-
_author_ = 'weixin'
dt = datetime.now()
print(dt)
print(dt.year)
print(dt.month)
print(dt.day)
# 表示一个时间间隔
t1 = datetime.now()
print(t1.strftime("%Y-%m-%d %H:%M:%S"))
td = timedelta(hours=22)
print((t1 + td).strftime("%Y-%m-%d %H:%M:%S"))
# # timeit 时间测试和工具
# def p():
# time.sleep(10.6)
# t1=time.time()
# p()
# print(time.time()-t1)
# 生成列表2种方法的比较
# c='''
# sum=[]
# for i in range(1000):
# sum.append(i)
# '''
def transform(self, dataset):
# So the first vectors are initialized correctly:
self.last_date = dataset[0]['date'] - timedelta(days=4)
return np.array([self.__vectorize(x) for x in dataset])
def make_projections(num, num2, num3):
# TODO: realistic projections
pulled_data = pull_data()
realistic_new_data_points = num
realistic_new_dates = []
realistic_cases_growth_rate = []
growth_rate_multiplier = num2
last_gr = float(pulled_data[8][-1]) - 1
last_gr *= growth_rate_multiplier
last_gr = round(last_gr, 4)
realistic_cases_growth_rate.append(last_gr)
for i in range(1, realistic_new_data_points):
x_point = i
y_point = float(
realistic_cases_growth_rate[i - 1]) * growth_rate_multiplier
y_point = round(y_point, 4)
realistic_cases_growth_rate.append(y_point)
# print(realistic_cases_growth_rate)
now = datetime.now()
for i in range(len(realistic_cases_growth_rate)):
realistic_cases_growth_rate[i] += 1
future_date = now + timedelta(days=i)
future_date_str = future_date.strftime("%m/%d")
# print(future_date_str)
realistic_new_dates.append(future_date_str)
# print(realistic_cases_growth_rate)
# print(realistic_new_dates)
# TODO: project cases based on realistic projections
last_case_number = int(pulled_data[1][-1] * realistic_cases_growth_rate[0])
cases_projections = [last_case_number]
for i in range(len(realistic_cases_growth_rate) - 1):
new_projection = cases_projections[i] * realistic_cases_growth_rate[i +
1]
cases_projections.append(int(new_projection))
# TODO: calc new cases, new deaths
new_projected_cases = [cases_projections[0] - pulled_data[1][-1]]
for i in range(1, realistic_new_data_points):
new_projected_cases_calc = cases_projections[i] - cases_projections[i -
1]
new_projected_cases.append(new_projected_cases_calc)
# print(new_cases)
# print(len(new_cases))
# new_deaths = [0]
# for i in range(1, len(dates)):
# new_deaths_calc = deaths[i] - deaths[i-1]
# new_deaths.append(new_deaths_calc)
# print(new_deaths)
# print(len(new_deaths))
# TODO: extend data for graph
for i in range(len(pulled_data[0]) - num3):
cases_projections.insert(0, " ")
for i in range(len(pulled_data[0]) - num3):
realistic_cases_growth_rate.insert(0, " ")
for i in range(len(pulled_data[0]) - num3):
new_projected_cases.insert(0, " ")
print("ran make_projections")
return realistic_new_dates, cases_projections, realistic_cases_growth_rate, new_projected_cases
def callbackBookTE(driver):
# if "Create Standard Shipment for another Carrier" in driver.page_source:
# driver.find_element_by_id("PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_Create_Standard_Shipment").send_keys(Keys.ENTER)
# elif not "Either an ID or Full Shipper information is required for a Shipper." in driver.page_source:
# while not "Either an ID or Full Shipper information is required for a Shipper." in driver.page_source:
# top = Tk()
# L2 = Label(top, text="Please navigate to shipment page the hit \"OK\"")
# L2.grid(row=0, column=0)
#
# def callbackStop():
# top.destroy()
#
# MyButton4 = Button(top, text="OK", width=10, command=callbackStop)
# MyButton4.grid(row=1, column=0)
#
# popUp(top)
driver.implicitly_wait(100)
container = Container()
setupDM(container)
try:
portCode = [""]
if container.terminal == "311":
if "PACKER" in container.extraText or "PHL" in container.extraText:
portCode[0] = "1101"
elif "PNCT" in container.extraText or "APM" in container.extraText or "MAHER" in container.extraText or "NYC" in container.extraText:
portCode[0] = "4601"
else:
top = Tk()
L1 = Label(top, text="Is the terminal Packer?")
L1.grid(row=0, column=0, columnspan=2)
L1.config(font=("Courier", 24))
def callbackPackerYes(portCode):
portCode[0] = "1101"
top.destroy()
def callbackPackerNo(portCode):
portCode[0] = "4601"
top.destroy()
MyButton4 = Button(top,
text="Yes",
width=20,
command=lambda: callbackPackerYes(portCode))
MyButton4.grid(row=2, column=0)
MyButton4.config(font=("Courier", 24))
MyButton5 = Button(top,
text="No",
width=20,
command=lambda: callbackPackerNo(portCode))
MyButton5.grid(row=2, column=1)
MyButton5.config(font=("Courier", 24))
top.lift()
top.attributes('-topmost', True)
top.after_idle(top.attributes, '-topmost', False)
# get screen width and height
ws = top.winfo_screenwidth() # width of the screen
hs = top.winfo_screenheight() # height of the screen
w = 800
h = 150
# calculate x and y coordinates for the Tk root window
x = (ws / 2) - (w / 2)
y = (hs / 2) - (h / 2)
# set the dimensions of the screen
# and where it is placed
top.geometry('%dx%d+%d+%d' % (w, h, x, y))
moveTo(946, 614)
top.mainloop()
elif container.terminal == "309":
portCode[0] = "1101"
else:
portCode[0] = "4601"
elem = driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_STANDARDSHIPMENT_SHIPMENTTYPE"
)
while "Either an ID or Full Shipper information is required for a Shipper." in driver.page_source:
try:
elem.send_keys(Keys.ENTER)
except:
pass
Select(
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_STANDARDSHIPMENT_SHIPMENTTYPE"
)).select_by_visible_text("Prefiled Inbond")
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_null"
).send_keys(Keys.ENTER)
elem = driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_userEnteredSCN")
elem.clear()
elem.send_keys("801" + str(container.PB))
Select(
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_STANDARDSHIPMENT_POINTOFLOADINGQLFR"
)).select_by_visible_text("Schedule K")
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_STANDARDSHIPMENT_POINTOFLOADING"
).send_keys("80107")
Select(
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_STANDARDSHIPMENT_FDACONFIRMATIONIND"
)).select_by_visible_text("No")
try:
driver.find_element_by_css_selector(
"input[value='Find Shipper']").send_keys(Keys.ENTER)
sleep(3)
wait = WebDriverWait(driver, 100000000)
wait.until(
lambda driver:
"Either an ID or Full Shipper information is required for a Shipper."
in driver.page_source)
except:
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_null"
).send_keys(Keys.ENTER)
wait = WebDriverWait(driver, 10)
wait.until(EC.alert_is_present())
driver.switch_to_alert().accept()
address1 = ""
address2 = ""
city = ""
country = ""
stateProv = ""
zipPost = ""
# print(container.terminal)
if container.terminal == "311":
address1 = "C/O CSX BUFFALO"
address2 = "257 LAKE AVE"
city = "BUFFALO"
country = "USA"
stateProv = "New York"
zipPost = "14206"
elif container.terminal == "305":
address1 = "C/O ASI TERMINAL"
address2 = "MARSH ST"
city = "NEWARK"
country = "USA"
stateProv = "New Jersey"
zipPost = "07100"
elif container.terminal == "306":
address1 = "C/O APM TERMINALS"
address2 = "5080 MCLESTER STEET"
city = "NEWARK"
country = "USA"
stateProv = "New Jersey"
zipPost = "07100"
elif container.terminal == "664":
address1 = "C/O NEW YORK CONTAINER TERMINAL"
address2 = "WESTERN AVE"
city = "STATEN ISLAND"
country = "USA"
stateProv = "New York"
zipPost = "10303"
elif container.terminal == "309":
address1 = "C/O PACKER TERMINAL"
address2 = "3301 S COLUMBUS BLVD"
city = "PHILADELPHIA"
country = "USA"
stateProv = "Pennsylvania"
zipPost = "19148"
elif container.terminal == "330":
address1 = "C/O MAHER TERMINAL"
address2 = "1260 CORBIN STREET"
city = "NEWARK"
country = "USA"
stateProv = "New Jersey"
zipPost = "07201"
elif container.terminal == "304":
address1 = "C/O GLOBAL TERMINAL"
address2 = "302 PORT JERSEY BLVD."
city = "BAYONNE"
country = "USA"
stateProv = "New Jersey"
zipPost = "07305"
else:
address1 = ""
address2 = ""
city = ""
country = ""
stateProv = ""
zipPost = ""
if not address1 == "":
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_STANDARDSHIPMENT_CONSIGNEE_ADDRESS_STREET"
).send_keys(address1)
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_STANDARDSHIPMENT_CONSIGNEE_ADDRESS_STREET2"
).send_keys(address2)
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_STANDARDSHIPMENT_CONSIGNEE_ADDRESS_CITY"
).send_keys(city)
Select(
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_STANDARDSHIPMENT_CONSIGNEE_ADDRESS_COUNTRY"
)).select_by_visible_text(country)
# try:
Select(
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_STANDARDSHIPMENT_CONSIGNEE_ADDRESS_REGION"
)).select_by_visible_text(stateProv)
# except:
# wait = WebDriverWait(driver, 100000000)
# wait.until(lambda driver: "Create Standard Shipment for another Carrier" in driver.page_source)
# return
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_STANDARDSHIPMENT_CONSIGNEE_ADDRESS_ZIP"
).send_keys(zipPost)
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_STANDARDSHIPMENT_CONSIGNEE_NAME"
).send_keys(Keys.ENTER)
while not GetKeyState(13) < 0:
if GetKeyState(27) < 0:
raise AssertionError
# True
Select(
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_standardShipmentEquipmentType"
)).select_by_visible_text("Create One Time")
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_standardShipmentEquipmentType"
).send_keys(Keys.ENTER)
sizeSelect = ""
if container.size == "40":
sizeSelect = "40ft ClosedTopSeaCnt"
elif container.size == "20":
sizeSelect = "20ft ClosedTopSeaCnt"
Select(
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_SHIPMENTEQUIPMENT_TYPE")
).select_by_visible_text(sizeSelect)
# print(container.containerNumber[:4] + container.containerNumber[5:11] + container.containerNumber[12:13])
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_SHIPMENTEQUIPMENT_TRANSPORTID"
).send_keys(container.containerNumber)
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_null"
).send_keys(Keys.ENTER)
# sleep(10)
driver.find_element_by_xpath(
"//form[@name='PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_createStandardShipment']/table/tbody/tr[2]/td/fieldset[6]/table/tbody/tr/td/a"
).send_keys(Keys.ENTER)
# #
container.pieces = container.pieces.replace(',', "")
elem = driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_INBONDCOMMODITY_SHIPMENTQUANTITY"
)
elem.clear()
elem.send_keys(container.pieces)
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_INBONDCOMMODITY_QUANTITYUOM"
).send_keys(Keys.ENTER)
#
while not GetKeyState(13) < 0:
if GetKeyState(27) < 0:
raise AssertionError
#
elem = driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_INBONDCOMMODITY_WEIGHT")
elem.clear()
container.weight = container.weight.replace(',', "")
index = container.weight.rfind(".")
if index > 0:
container.weight = container.weight[:index]
elem.send_keys(container.weight)
Select(
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_INBONDCOMMODITY_WEIGHTUOM"
)).select_by_visible_text('Kilograms')
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_INBONDCOMMODITY_DESCRIPTION"
).send_keys(container.description)
elem = driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_INBONDCOMMODITY_VALUE")
elem.clear()
elem.send_keys(Keys.ENTER)
while not GetKeyState(13) < 0:
if GetKeyState(27) < 0:
raise AssertionError
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_AvailableINBONDCOMMODITY_HTSNUMS"
).send_keys(Keys.ENTER)
sleep(1)
while not GetKeyState(13) < 0:
if GetKeyState(27) < 0:
raise AssertionError
HS = driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_AvailableINBONDCOMMODITY_HTSNUMS"
).get_attribute("value")
zeroes = ""
if len(HS) < 10:
for _ in range(10 - len(HS)):
zeroes += "0"
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_AvailableINBONDCOMMODITY_HTSNUMS"
).send_keys(zeroes)
driver.execute_script(
"arguments[0].click();",
driver.find_element_by_css_selector(
'img[src*="/ace1/wps/PA_Shipment/images/right_single.gif"]'))
# driver.find_element_by_css_selector('img[src*="/ace1/wps/PA_Shipment/images/right_single.gif"]').send_keys(Keys.ENTER)
# driver.find_element_by_css_selector('img[src*="/ace1/wps/PA_Shipment/images/right_single.gif"]').click()
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_null"
).send_keys(Keys.ENTER)
#
Select(
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_standardShipmentEquipmentType"
)).select_by_visible_text("Conveyance")
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_standardShipmentEquipmentType"
).send_keys(Keys.ENTER)
# didntwork = True
# while didntwork:
# try:
wait = WebDriverWait(driver, 100000000)
wait.until(lambda driver: "Conveyance</t" in driver.page_source)
Select(
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_INBONDSHIPMENT_ENTRYTYPE"
)).select_by_visible_text("Transportation and Exportation")
# while not "Transportation and Exportation" in elem.first_selected_option.text:
# failed = True
# while failed:
# try:
# Select(elem).select_by_visible_text("Transportation and Exportation")
# failed = False
# except:
# pass
# elem = Select(driver.find_element_by_id("PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_INBONDSHIPMENT_ENTRYTYPE"))
#
# didntwork=False
# except:
# pass
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_INBONDSHIPMENT_INBONDDESTINATION"
).send_keys(portCode[0])
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_INBONDSHIPMENT_BONDEDCARRIER"
).send_keys("98-066177700")
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_INBONDSHIPMENT_INBONDNUMBER"
).send_keys(container.bond)
date = datetime.now()
date = (date + timedelta(days=14)).strftime('%m/%d/%Y')
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_INBONDSHIPMENT_ESTDATEOFUSDEPARTURE"
).send_keys(date)
driver.find_element_by_id(
"PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_INBONDSHIPMENT_FOREIGNPORTOFDESTINATION"
).send_keys(Keys.ENTER)
wait.until(
lambda driver: "Create Standard Shipment for another Carrier" in
driver.page_source)
except AssertionError:
pass
except:
top = Tk()
L1 = Label(
top,
text=
"Something went wrong. Either complete the rest of this T&E manually,\n or cancel and restart."
)
L1.config(font=("Courier", 30))
L1.grid(row=0, column=0)
L2 = Label(top, text=exc_info())
# L2.config(font=("Courier", 30))
L2.grid(row=1, column=0)
def callbackDM():
top.destroy()
MyButton4 = Button(top, text="OK", width=20, command=callbackDM)
MyButton4.grid(row=2, column=0)
MyButton4.config(font=("Courier", 30))
popUp(top, w=1700, h=200, widget=MyButton4)
# while not GetKeyState(13)<0 and not "Create Standard Shipment for another Carrier" in driver.page_source:
# if GetKeyState(13)<0:
# driver.find_element_by_id("PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_PC_7_CHMCHJ3VMJ3L502FK9QRJ710G2000000_null").send_keys(Keys.ENTER)
# while not "Create Standard Shipment for another Carrier" in driver.page_source:
# try:
wait = WebDriverWait(driver, 100000000)
wait.until(lambda driver: "Create Standard Shipment for another Carrier" in
driver.page_source)
{
'NAME':
'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME':
'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/2.2/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/2.2/howto/static-files/
STATIC_URL = '/static/'
SIMPLE_JWT = {
'AUTH_TOKEN_CLASSES': ('rest_framework_simplejwt.tokens.SlidingToken'),
'SLIDING_TOKEN_LIFETIME': timedelta(minutes=1)
}
# print today's date
# print yesterday's date
# ask a user to enter a date
# print the date of one week from the date entered
from _datetime import datetime, timedelta
print("Today's date is")
print(datetime.now().date())
one_day = timedelta(days=1)
Today = datetime.now()
yesterday = Today - one_day
print("date of yesterday is:")
print(yesterday.date())
one_week = timedelta(weeks=1)
next_week = Today + one_week
print("Next week is")
print(next_week)
def getDailyFundementalUpdate(runType):
start_time = time.time()
tickerPath = 'C:\\Users\\Nick\\Documents\\project MONEY\\tickers\\'
fls = pd.Series(os.listdir(tickerPath))
# tickDate = max(fls[fls.str.contains('tickers_')].replace({'tickers_':'','.csv':''}, regex= True))
# tickerFile = tickerPath + 'tickers_' + tickDate + '.csv'
#dBase = 'C:\\Users\\Nick\\Documents\\project MONEY\\data\\PJMdataBase.db'
dBase = 'C:\\Users\\Nick\\Documents\\project MONEY\\data\\yahooDaily-postAPIchange.db'
fundementalFile = 'C:\\Users\\Nick\\Documents\\project MONEY\\fundementalSymbols.csv'
#yahooFundementalHistoryFile = 'C:\\Users\\Nick\\Documents\\project MONEY\\data\\dataBase_yahoo_fundementals.csv'
tme = datetime.now()
# yahooFundementalDayFile = 'C:\\Users\\Nick\\Documents\\project MONEY\\data\\dateFiles\\dataBase_yahoo_fundementals_' + \
# str(np.datetime64(tme.date())).replace('-','') + '.csv'
#tickersBase = readTicker(dBase = dBase)
#tickersBase = readCSV(tickerFile)
exchangeMap = dBaseAction(dBase, ''' SELECT * from exchangeMap ''')[0]
#tickersBase = dBaseAction(dBase, ''' SELECT * from tickers ''')[0]
tickersBase = dBaseAction(dBase, ''' SELECT * from tickersNew ''')[0]
#tickersBase = tickersBase[ ~tickersBase.ticker.isin(tickersBase1.ticker)]
testYahooDaily = readExclude(dBase = dBase, table = 'yahooDailyExclude')
tickers = tickersBase[['category', 'exchange', 'name', 'ticker', 'type']].drop_duplicates().reset_index(drop = True)
tickers = tickers[tickers.type.isin(['stock', 'etf', 'index', 'mutual_fund','future','warrant','bond','currency'])]
tickers = tickers.drop_duplicates().reset_index(drop = True)
tickers = tickers[['exchange', 'ticker', 'type']].drop_duplicates().reset_index(drop = True)
if runType =='fundemental':
fundementals = readCSV(fundementalFile)
#yahooFundementalHistory = readCSV(yahooFundementalHistoryFile)
#url='http://finance.yahoo.com/d/quotes.csv?s=AAPL+GOOG+MSFT&f=nab'
yahooFundemental = []
couldntFind = []
track = 0
nw = np.datetime64(datetime.now().date())
sets = int(len(tickers.ticker)/199)
for k in range(int(sets+1)):
print(str(k) + ' of ' + str(sets) + ' sets')
tickerSet = tickers.ticker.iloc[(k*199):((k*199)+min(len(tickers.ticker)-k*199,199)-1)].reset_index(drop=True)
typeSet = tickers.type.iloc[(k*199):((k*199)+min(len(tickers.ticker)-k*199,199)-1)].reset_index(drop=True)
tickerJoin = '+'.join(tickerSet)
time.sleep(random.random()*5)
dataNew, track = downloadYahoo(ticker = tickerJoin, symbols=fundementals, track = track)
try:
couldntFind.append(tickerSet[ ~tickerSet.isin(dataNew.symbol)])
except:
pass
tickerSet = dataNew.symbol
dataNew['type'] = typeSet = [tickers.type[ tickers.ticker == x].iloc[0] for x in dataNew.symbol.tolist()]
dataNew['ID'] = dataNew.timestamp + '_' + dataNew.symbol
yahooFundemental.append(dataNew)
''' filter ingnore tickers'''
print('couldnt find the following tickers when calling csv:')
print(couldntFind)
dataDrop = pd.concat(yahooFundemental).drop_duplicates().reset_index(drop=True).sort_values(by = ['symbol','timestamp'])
writeDB(dBase = dBase, name = 'yahooFundementals', data = dataDrop,
createArgs= '("' + '","'.join(dataDrop.columns.tolist()) + '", PRIMARY KEY("ID"), UNIQUE("ID"))',
indexColName = 'symbol',
args ='("' + '","'.join(dataDrop.columns.tolist()) + '")' )
elif runType == 'dailyData':
''' As of 01/11/2017, yahoo API discontinued. Can only trade:
- US, UK, Canadian and German stocks
- UK ETFs
- UK indices?
Thus filter these out from relevant exchanges, and include only the 3 letter currencies
'''
ccys = tickers[ tickers.ticker.str.contains('=X')].reset_index(drop = True)
ccys = ccys[ ccys.ticker.str.len() == 5].reset_index(drop = True)
tickers = pd.merge(tickers, exchangeMap[['yahoo_exchange','Country']], left_on = 'exchange', right_on = 'yahoo_exchange')
tickers = tickers[ (tickers.type.isin(['stock','index','etf']) & (tickers.Country == 'UK')) |
(tickers.type.isin(['stock','etf']) & (tickers.Country == 'USA')) |
(tickers.type.isin(['stock']) & (tickers.Country == 'Germany')) |
(tickers.type.isin(['stock']) & (tickers.Country == 'Canada'))]
filterExchange = ['DJI','NAS','NMS','NYQ','NYS',##USA
'FGI','FSI','LSE',##UK
'DUS','FRA','GER',##Germany
'TOR','VAN']##Canada
tickers = tickers[ tickers.exchange.isin(filterExchange)].reset_index(drop = True)
tickers = tickers[ ~tickers.ticker.isin(testYahooDaily.tolist())].reset_index(drop = True)
tickers = pd.concat([tickers, ccys]).reset_index(drop = True)
''' Need market cap and volume info? '''
## format like fundementals:
base = dBaseAction(dBase, ''' Select * from yahooFundementals where symbol = "TSLA"''')[0]
base = pd.DataFrame(columns = base.columns)
track = 0
dateTo = datetime.now()
##get 10 days in case of long holiday weekends etc
dateFrom = datetime.now() - timedelta(10)
yahooDailyWrite = []
notAvailable = []
for i in range(len(tickers.ticker)):
time.sleep(random.random()*1.5)
##### YAHOO daily data
dataNew, track = downloadYahoo(ticker = tickers.ticker[i], dailyDateRange = np.array([dateFrom, dateTo]),track = track, adjust = False)
## Now we have full set of past time series, if the timeSeries available stops more than 10 days ago we will ignore
if (dataNew is None):
notAvailable.append(tickers.ticker[i])
elif (dataNew.timestamp.values.astype('datetime64[D]').max() < (np.datetime64(datetime.now()) - np.timedelta64(10,'D'))):
notAvailable.append(tickers.ticker[i])
else:
print( ' '.join([str(i),'out of',str(len(tickers.ticker)),'-',tickers.ticker[i]]) + ': - Yahoo Daily - OK')
dataNew['type'] = tickers.type[i]
dataNew = dataNew.sort_values('timestamp')
dataNew['ID'] = dataNew.timestamp + '_' + dataNew.ticker
dataNew = dataNew.rename(columns = {'ticker':'symbol','close':'previous_close','adj_close':'last_trade_price_only','high':'days_high','low':'days_low'})
dataNew = pd.concat([base,dataNew])
#dataNew = dataNew.drop('adj_close', axis =1)
yahooDailyWrite.append(dataNew)
## once get to 4000, save and take a break to calm servers
if ((i%4000 == 0) & (i > 0)) | (i == (len(tickers.ticker)-1)):
dataDrop = pd.concat(yahooDailyWrite).reset_index(drop = True)
writeDB(dBase = dBase, name = 'yahooFundementals', data = dataDrop,
createArgs= '("' + '","'.join(dataDrop.columns.tolist()) + '", PRIMARY KEY("ID"), UNIQUE("ID"))',
indexColName = 'symbol',
args ='("' + '","'.join(dataDrop.columns.tolist()) + '")' )
## add tikers that dont have data to a list we can import to ignore
writeDB(dBase = dBase, name = 'yahooDailyExclude', data = pd.DataFrame(notAvailable, columns = ['ticker']).drop_duplicates().reset_index(drop=True),
createArgs= '(ticker, PRIMARY KEY(ticker), UNIQUE(ticker))',
args ='(ticker)' )
yahooDailyWrite = []
notAvailable = []
time.sleep(60*10)
print(time.time() - start_time)
## run the incubator once data collection is finished
reportingRun('True','True')
from _datetime import timedelta
from airflow.operators.papermill_operator import PapermillOperator
from airflow.operators.python_operator import PythonOperator
import time, datetime, os
# These args will get passed on to each operator
# You can override them on a per-task basis during operator initialization
default_args = {
'owner': 'airflow',
'depends_on_past': False,
'start_date': days_ago(2),
'email': ['*****@*****.**'],
'email_on_failure': False,
'email_on_retry': False,
'retries': 0,
'retry_delay': timedelta(minutes=1),
# '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'
}
def inserirsessao(request, id):
user_check_var = user_check(request=request,
user_profile=[ProfessorUniversitario])
if user_check_var.get('exists') == False:
return user_check_var.get('render')
userId = user_check_var.get('firstProfile').utilizador_ptr_id
atividade = Atividade.objects.filter(
id=id, professoruniversitarioutilizadorid=userId)
atividadecheck = atividade.first()
sessoes = Sessao.objects.filter(atividadeid=atividadecheck)
for sessao in sessoes:
if sessao.vagas != atividadecheck.participantesmaximo:
return render(request=request,
template_name='mensagem.html',
context={
'tipo': 'error',
'm': 'Não tem permissões para esta ação!'
})
if atividade.exists():
today = datetime.now(timezone.utc)
diaaberto = Diaaberto.objects.get(
datapropostasatividadesincio__lte=today,
dataporpostaatividadesfim__gte=today)
diainicio = diaaberto.datadiaabertoinicio.date()
diafim = diaaberto.datadiaabertofim.date()
totaldias = diafim - diainicio + timedelta(days=1)
dias_diaaberto = []
for d in range(totaldias.days):
dias_diaaberto.append(diainicio + timedelta(days=d))
horariosindisponiveis = []
disp = []
atividadeid = Atividade.objects.get(id=id)
sessoes = Sessao.objects.all().filter(atividadeid=id)
check = len(sessoes)
if request.method == "POST":
if 'new' in request.POST:
diasessao = request.POST["diasessao"]
print(diasessao)
inicio = request.POST['horarioid']
splitinicio = inicio.split(":")
print(splitinicio)
duracaoesperada = atividadeid.duracaoesperada
hfim = horariofim(splitinicio, duracaoesperada)
horario = Horario.objects.filter(
inicio=request.POST['horarioid'], fim=hfim).first()
if horario is None:
new_Horario = Horario(inicio=inicio, fim=hfim)
new_Horario.save()
else:
new_Horario = horario
new_Sessao = Sessao(
vagas=Atividade.objects.get(id=id).participantesmaximo,
ninscritos=0,
horarioid=Horario.objects.get(id=new_Horario.id),
atividadeid=Atividade.objects.get(id=id),
dia=diasessao)
if atividadeid.estado != "nsub":
atividadeid.estado = "Pendente"
atividadeid.save()
new_Sessao.save()
return redirect('atividades:inserirSessao', id)
return render(request=request,
template_name='atividades/proporAtividadeSessao.html',
context={
'horarios':
"",
'sessions_activity':
Sessao.objects.all().filter(atividadeid=id),
'dias':
dias_diaaberto,
'check':
check,
"id":
id
})
else:
return render(request=request,
template_name='mensagem.html',
context={
'tipo': 'error',
'm': 'Não tem permissões para esta ação!'
})
for tz_descr in map(str.split, tz_str.split('\n')):
tz_offset = int(float(tz_descr[0]) * 3600)
for tz_code in tz_descr[1:]:
tzd[tz_code] = tz_offset
if '__main__' == __name__:
filenames = [ ]
for f in listdir(getcwd()):
if isfile(join(getcwd(),f)):
if f.split(".")[-1] == 'log':
filenames.append(f)
last_timestamp = dict()
counter = {f:0 for f in filenames}
errors = []
max_lag = 0
window_start = datetime.now(tzlocal()) + timedelta(days=-100)
linebuffer = ''
for line in fileinput.input():
if line != '\n':
linebuffer += line
else:
if linebuffer.strip():
continue
print(linebuffer)
msg = json.loads(linebuffer)
if 'note' in msg and 'at' in msg:
ts = msg['at']
time_received = dp.parse(ts, tzinfos=tzd)
time_logged = datetime.now(tzlocal())
if ((time_received - window_start)/timedelta(seconds=1) > 100):
metadata['energy'].append(np.nan)
metadata['instrumentalness'].append(np.nan)
metadata['mode'].append(np.nan)
metadata['loudness'].append(np.nan)
metadata['speechiness'].append(np.nan)
metadata['tempo'].append(np.nan)
metadata['time_signature'].append(np.nan)
metadata['valence'].append(np.nan)
except Exception:
traceback.print_exc()
done = i + 1
if (done % 100 == 0):
spent = time.time() - start
spent_r = str(timedelta(seconds=spent))
print(
'-- DONE FOR {} BATCHES! {} MORE BATCHES TO GO. TIME SPENT SO FAR: {}'
.format(done, (batches_count - done), spent_r))
if not os.path.exists(FOLDER_OUT):
os.makedirs(FOLDER_OUT)
name = FOLDER_OUT + 'all_tracks_metadata.csv'
inout.ensure_dir(name)
print('SAVING THE RESULTS: ' + name)
export = pd.DataFrame(metadata)
export = export[[
'track_id', 'popularity', 'acousticness', 'danceability', 'energy',
'instrumentalness', 'mode', 'loudness', 'speechiness', 'tempo',
'time_signature', 'valence'
def get_today_date():
return (datetime.now() - timedelta(days=1)).strftime("%Y%m%d")
cursor.execute(
'SELECT count(*) FROM saturn_fin.sovcombank_products WHERE inserted_date > %s '
'AND status_code != 2 AND status_code != 6', (DATE_START_COUNT, ))
rows = cursor.fetchall()
statistics_after['НЕ одобренные и НЕ активированные'] = rows[0][0]
print('Стало:')
print(statistics_after['Одобренные'], '\t\t',
statistics_after['Активированные'], '\t\t\t',
statistics_after['Скрытые'], '\t\t', statistics_after['Дебетовые'], '\t',
statistics_after['НЕ одобренные и НЕ активированные'])
print('ИЗМЕНЕНИЯ:')
print(
statistics_after['Одобренные'] - statistics_before['Одобренные'], '\t\t',
statistics_after['Активированные'] - statistics_before['Активированные'],
'\t\t\t', statistics_after['Скрытые'] - statistics_before['Скрытые'],
'\t\t', statistics_after['Дебетовые'] - statistics_before['Дебетовые'],
'\t', statistics_after['НЕ одобренные и НЕ активированные'] -
statistics_before['НЕ одобренные и НЕ активированные'])
# До какой даты ставить статус "Отрицательный результат"
# 15 сентября проставляем статусы на июньские и июльские заявки, 15 октября добавляется август...
date_end_otkaz = datetime.now() - timedelta(days=15) - relativedelta(months=1) + \
relativedelta(day=1, hour=0, minute=0, second=0, microsecond=0)
cursor = dbconn.cursor()
cursor.execute(
'UPDATE saturn_fin.sovcombank_products SET status_code = 5 WHERE status_code IN (0,1,4,200) '
'AND inserted_date < %s', (date_end_otkaz, ))
dbconn.commit()
dbconn.close()
def test_new_passpoints2(request):
is_invalid = 'is-invalid'
active_user = PasswordUser.objects.get(username = request.user)
login_info = LoginInfo.objects.filter(uid=active_user).order_by('login_period')
login_period = login_info.count()
test_period = login_period + 1
img_fin = request.POST.get('img_fin')
oldpps = NewPpInfo.objects.filter(uid=active_user, img_number=img_fin)
oldpp_len = len(oldpps)
confirm_info = ConfirmInfo.objects.filter(uid=active_user).last()
confirm_date = confirm_info.date_confirmed
if LoginInfo.objects.filter(uid=active_user).exists():
last_login = login_info.latest('date_login')
last_login = last_login.date_login
today = timezone.now()
if login_period == 0:
d = 1
elif login_period == 1:
d = 3
else:
d = 7
test_date = confirm_date + timedelta(days=d)
newpps = NewPpInfo.objects.filter(uid=active_user).order_by('pk').last()
img_count = newpps.img_number
img_id = oldpps.first().cp_id.iid.pk
img_url = oldpps.first().cp_id.iid.image.url
image = Image.objects.get(pk=img_id)
context = {}
context['img_fin'] = img_fin
context['oldpps'] = oldpps
context['test_period'] = test_period
context['oldpp_len'] = oldpp_len
context['img_url'] = img_url
context['image'] = image
if login_period < 3:
if request.method == 'POST':
time_prev = request.POST.get('time_prev')
if time_prev == '':
time_prev = 0
else:
time_prev = int(time_prev)
time_create = int(request.POST.get('time'))
time_create = time_create + time_prev
m = math.floor(time_create / 60)
s = time_create % 60
time = datetime.time(0,m,s)
operation = request.POST.get('operation')
cpCount = request.POST.get('cpCount')
if operation == 'retry':
if cpCount == '':
context['time_prev'] = time_create
return render(request, 'gp/test_newpp2.html', context)
else:
cpCount = int(cpCount)
key = request.POST.get('image')
image = Image.objects.get(pk=key)
i = 1
while i <= cpCount:
x = int(request.POST.get("x"+str(i)))
y = int(request.POST.get("y"+str(i)))
r = int(request.POST.get("r"+str(i)))
g = int(request.POST.get("g"+str(i)))
b = int(request.POST.get("b"+str(i)))
a = int(request.POST.get("a"+str(i)))
cp = int(request.POST.get("cp"+str(i)))
i+=1
new_cpfail = ClickPointFail(iid=image, uid=active_user, x_location=x, y_location=y, color=r+g+b+a, order=cp, img_number=img_fin)
new_cpfail.save()
context['time_prev'] = time_create
return render(request, 'gp/test_newpp2.html', context)
else:
if cpCount == '':
context['is_invalid'] = is_invalid
context['time_prev'] = time_create
return render(request, 'gp/test_newpp2.html', context)
else:
cpCount = int(cpCount)
if cpCount != oldpp_len:
i = 1
while i <= cpCount:
x = int(request.POST.get("x"+str(i)))
y = int(request.POST.get("y"+str(i)))
r = int(request.POST.get("r"+str(i)))
g = int(request.POST.get("g"+str(i)))
b = int(request.POST.get("b"+str(i)))
a = int(request.POST.get("a"+str(i)))
cp = int(request.POST.get("cp"+str(i)))
i+=1
new_cpfail = ClickPointFail(iid=image, uid=active_user, x_location=x, y_location=y, color=r+g+b+a, order=cp, img_number=img_fin)
new_cpfail.save()
context['is_invalid'] = is_invalid
context['time_prev'] = time_create
return render(request, 'gp/test_newpp2.html', context)
else:
image = request.POST.get('image')
image = Image.objects.get(pk=img_id)
i = 1
while i <= cpCount:
x = int(request.POST.get("x"+str(i)))
y = int(request.POST.get("y"+str(i)))
r = int(request.POST.get("r"+str(i)))
g = int(request.POST.get("g"+str(i)))
b = int(request.POST.get("b"+str(i)))
a = int(request.POST.get("a"+str(i)))
cp = int(request.POST.get("cp"+str(i)))
i+=1
check_pp = ClickPoint.objects.get(uid=active_user, order=cp, iid=image, img_number=img_fin)
x_max = int(check_pp.x_location) + 7
x_min = int(check_pp.x_location) -7
y_max = int(check_pp.y_location) + 7
y_min = int(check_pp.y_location) - 7
if x > x_max or x < x_min or y > y_max or y < y_min:
new_cpfail = ClickPointFail(iid=image, uid=active_user, x_location=x, y_location=y, color=r+g+b+a, order=cp, img_number=img_fin)
new_cpfail.save()
context['is_invalid'] = is_invalid
context['time_prev'] = time_create
return render(request, 'gp/test_newpp2.html', context)
if int(img_fin) == int(img_count):
new_submit = SubmitInfo(uid=active_user, time_submit=time)
new_submit.save()
new_login = LoginInfo(uid=active_user, time_login=time, login_period=test_period)
new_login.save()
return render(request, 'gp/login_success.html', {'active_user': active_user, 'test_period': test_period, 'test_date': test_date})
else:
img_fin = int(img_fin) + 1
img_key = NewPpInfo.objects.filter(uid= active_user, img_number=img_fin).first()
img_key = img_key.cp_id.iid.pk
img = Image.objects.get(pk=img_key)
images = Image.objects.all().order_by('?')[:8]
return render(request, 'gp/test_newpp.html', {'img': img, 'images': images, 'test_period': test_period, 'img_fin': img_fin})
else:
context['time_prev'] = time_create
return render(request, 'gp/test_newpp2.html', context)
else:
return redirect('/logout')
from collections import deque
from _datetime import datetime, timedelta
data = input().split(';')
robots = deque([])
products = deque([])
start_time = datetime.strptime(input(), '%H:%M:%S')
time_add = timedelta(seconds=1)
product_time = start_time + time_add
robot = {}
# Adding robots as dictionary to the robots deque
for el in data:
robot = {}
name, time = el.split('-')
time = int(time)
robot['name'] = name
robot['processing_time'] = time
robot['available_at'] = product_time
robots.append(robot)
product = input()
# Adding products to the products deque
while product != 'End':
products.append(product)
product = input()
# Looping through product line and robots to calculate processing time for each product
while len(products) > 0:
current_product = products.popleft()
year_val + '-05-28T00:00:00', year_val + '-05-29T00:00:00'
]
# past_dates=[year_val+'-05-28',year_val+'-05-29']
elif dt == (year_val + '-07-01T00:00:00') or dt == (
year_val + '-08-01T00:00:00') or dt == (year_val +
'-09-01T00:00:00'):
count_months -= 1
elif dt == (year_val + '-10-01T00:00:00'):
break
if dt != (year_val + '-06-01T00:00:00') and count_months != 0:
past_dates[0] = past_dates[1]
prev_date = str(past_dates[0]).replace('T', '')
#print(prev_date)
next_date = datetime.strptime(prev_date, '%Y-%m-%d%H:%M:%S')
next_date = next_date + timedelta(1)
next_date = datetime.strftime(next_date, '%Y-%m-%d')
next_date = next_date + 'T00:00:00'
past_dates[1] = next_date
lat = 5.0
longitude = 65.0
print(dt)
dict_grid[dt] = {}
list_values.append(dt)
list_values_prev1.append(past_dates[0])
list_values_prev2.append(past_dates[1])
if dt == '2013-06-01T00:00:00':
list_header.append('time')
list_header_prev1.append('past_day1_time')
os.environ["DYNAMO_TABLE"] = 'prod-visual-schedules-data-table'
def what_is_my_schedule(self, student: str, date: datetime):
calculator = Calculator(student)
print('What is the School Schedule for {} ?'.format(calculator.name))
try:
sched = calculator.is_there_school(date)
# print (sched)
print(
" ===================== {} School Schedule For {} ========================================"
.format(sched.get("name"), date.strftime("%A, %b %d, %Y")))
if sched.get("schedule") != None:
schedule = sched.get("schedule")
calculator.print_schedule(schedule)
else:
print(sched.get("message"))
print(" ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
except ValueError as e:
print("COULD NOT FIND SCHEDULE!")
print(e)
if __name__ == "__main__":
question = Questioner()
question.what_is_my_schedule("Delia", (datetime.now() + timedelta(days=1)))
question.what_is_my_schedule("Kiera", (datetime.now() + timedelta(days=1)))
question.what_is_my_schedule("Kiera",
(datetime.now() + timedelta(days=24)))
question.what_is_my_schedule("Delia", datetime.now())
question.what_is_my_schedule("Kiera", datetime.now())
def split_data_slice(data, output_file, slice_id, days_offset, days_train,
days_test):
data_start = datetime.fromtimestamp(data.Time.min(), timezone.utc)
data_end = datetime.fromtimestamp(data.Time.max(), timezone.utc)
print(
'Full data set {}\n\tEvents: {}\n\tSessions: {}\n\tItems: {}\n\tSpan: {} / {}'
.format(slice_id, len(data), data.SessionId.nunique(),
data.ItemId.nunique(), data_start.isoformat(),
data_end.isoformat()))
start = datetime.fromtimestamp(data.Time.min(),
timezone.utc) + timedelta(days_offset)
middle = start + timedelta(days_train)
end = middle + timedelta(days_test)
#prefilter the timespan
session_max_times = data.groupby('SessionId').Time.max()
greater_start = session_max_times[
session_max_times >= start.timestamp()].index
lower_end = session_max_times[session_max_times <= end.timestamp()].index
data_filtered = data[np.in1d(data.SessionId,
greater_start.intersection(lower_end))]
print(
'Slice data set {}\n\tEvents: {}\n\tSessions: {}\n\tItems: {}\n\tSpan: {} / {} / {}'
.format(slice_id, len(data_filtered),
data_filtered.SessionId.nunique(),
data_filtered.ItemId.nunique(),
start.date().isoformat(),
middle.date().isoformat(),
end.date().isoformat()))
#split to train and test
session_max_times = data_filtered.groupby('SessionId').Time.max()
sessions_train = session_max_times[
session_max_times < middle.timestamp()].index
sessions_test = session_max_times[
session_max_times >= middle.timestamp()].index
train = data[np.in1d(data.SessionId, sessions_train)]
print(
'Train set {}\n\tEvents: {}\n\tSessions: {}\n\tItems: {}\n\tSpan: {} / {}'
.format(slice_id, len(train), train.SessionId.nunique(),
train.ItemId.nunique(),
start.date().isoformat(),
middle.date().isoformat()))
train.to_csv(output_file + '_train_full.' + str(slice_id) + '.txt',
sep='\t',
index=False)
test = data[np.in1d(data.SessionId, sessions_test)]
test = test[np.in1d(test.ItemId, train.ItemId)]
tslength = test.groupby('SessionId').size()
test = test[np.in1d(test.SessionId, tslength[tslength >= 2].index)]
print(
'Test set {}\n\tEvents: {}\n\tSessions: {}\n\tItems: {}\n\tSpan: {} / {} \n\n'
.format(slice_id, len(test), test.SessionId.nunique(),
test.ItemId.nunique(),
middle.date().isoformat(),
end.date().isoformat()))
test.to_csv(output_file + '_test.' + str(slice_id) + '.txt',
sep='\t',
index=False)
def getAveragesFilter(weekly, time, key, location, typeOfLocation, timeWindow, resolution, sortOperators):
#This function calculates weekly averages, and also takes into account the previous 9 weeks
#as a simple FIR filter.
#Function acts a little different when the averages are calculated weekly.
if weekly:
val = 2
else:
val = 1
values = 0
#This for loop loops the 10 weeks, that are used to calculate averages.
for i in range(1,11):
#Data is fetched from database. 7 days for weekly aves, 1 day for daily aves.
if weekly:
data = getFromDataBase(time, key, location, typeOfLocation, -7, sortOperators)
else:
data = getFromDataBase(time, key, location, typeOfLocation, -1, sortOperators)
newData = [(item[0], item[1], item[2], item[3], item[4], item[5][:5], item[6]) for item in data]
#The functions to calculate the averages are called during the first iteration of for loop.
if i == 1 or not values:
if newData:
if weekly:
values = calculateAveragesWeekly(0,0,0,0,0,resolution,0,1,newData)
else:
values = calculateAveragesDaily(0,0,0,0,0,resolution,0,1,data)
for line in values:
line[-1] += 1
#After the first iteration, this else clause is used.
else:
if newData:
if weekly:
temp = calculateAveragesWeekly(0,0,0,0,0,resolution,0,1,newData)
else:
temp = calculateAveragesDaily(0,0,0,0,0,resolution,0,1,newData)
#More older the data, less it weighs in the average.
if i > 1 and i < 4:
weight = 0.1
elif i < 7:
weight = 0.05
else:
weight = 0.025
for line in range(len(temp)):
values[line][val] += temp[line][val] * weight
values[line][val+1] += temp[line][val+1] * weight
values[line][val+2] += temp[line][val+2] * weight
values[line][val+3] += temp[line][val+3]
if temp[line][val]:
values[line][-1] += weight
#Time is moved back 7 or 1 days for the next iteration of for loop.
if weekly:
time = time - timedelta(days = 7)
else:
time = time-timedelta(days = 1)
#Averages are calulated and the total weight is removed.
print(values)
for line in values:
if line[-1]:
line[val] = line[val] / line[-1]
line[val+1] = line[val+1] / line[-1]
line[val+2] = line[val+2] / line[-1]
line.pop(-1)
return values
def get_stockData(self, fullUpdate=False, clean=True, filter=False):
dBase = stockConfig.dBase
# cut up to most recent date, accounting for weekends
maxDate = datetime.now()
if maxDate.isoweekday() in range(2, 7):
maxDate = np.datetime64(maxDate).astype('datetime64[D]')
elif maxDate.isoweekday() == 1:
maxDate = np.datetime64(maxDate).astype('datetime64[D]') - 2
elif maxDate.isoweekday() == 7:
maxDate = np.datetime64(maxDate).astype('datetime64[D]') - 1
''' As of 01/11/2017, yahoo API discontinued. Can only trade:
- US, UK, Canadian and German stocks
- UK ETFs
- UK indices?
Thus filter these out from relevant exchanges, and include only the 3 letter currencies
'''
# new mongodb object
mdb = MongoDB()
# stockutils object
su = StockUtils()
tickers = su.getTickers(filter=filter)
if fullUpdate:
# update all market caps once a week. interweek filter on appropriate market caps and those tickers that
# require reporting as "changes" and rereun to get recomendations, earnings dates, etc everyday
data = getTickerYahooStats(np.sort(tickers.ticker.values))
data.longName = data.longName.str.replace('\'', '')
data.longName = data.longName.str.replace('\"', '')
writeDBv2(dBase=dBase, name=stockConfig.yahooStatsTable, data=data,
createArgs='("' + '","'.join(
data.columns.tolist()) + '", PRIMARY KEY("ticker"), UNIQUE("ticker"))',
indexColName='ticker',
args='("' + '","'.join(data.columns.tolist()) + '")')
# set single symbol level for fast read from database as a single table of data
data[stockConfig.statsTable] = stockConfig.statsTable
data = mdb.getMultiIndex(data, index=[stockConfig.statsTable])
# save to mongoDB database in "tickers" library
mdb.save(data, stockConfig.statsTable, append=True)
# get unique dates so all pivots have the same index
uniqueDates = dBaseAction(dBase, ''' select distinct timestamp from %s ''' % stockConfig.dailyTable)[0]
dates = np.unique(uniqueDates).astype('datetime64[D]')
dates.sort()
dateTo = datetime.now()
# get all data for exchanges that we can actually trade, clean and update everyday
if fullUpdate:
dateFrom = datetime(1996, 1, 1)
append=False
else:
dateFrom = dateTo - timedelta(10)
append=True
yahooDailyWrite = []
notAvailable = []
track = 0
for i in range(len(tickers.ticker)):
time.sleep(random.random() * 1.5)
# -----------------
# YAHOO daily data
# -----------------
dataNew, track = self.downloadYahoo(ticker=tickers.ticker[i], dailyDateRange=np.array([dateFrom, dateTo]),
track=track, adjust=False)
# Now we have full set of past time series, if the timeSeries available stops more than 10 days
# ago we will ignore
if dataNew is None:
notAvailable.append(tickers.ticker[i])
elif (dataNew.timestamp.values.astype('datetime64[D]').max() < (
np.datetime64(datetime.now()) - np.timedelta64(10, 'D'))):
notAvailable.append(tickers.ticker[i])
else:
print(' '.join(
[str(i), 'out of', str(len(tickers.ticker)), '-', tickers.ticker[i]]) + ': - Yahoo Daily - OK')
dataNew['type'] = tickers.type[i]
dataNew = dataNew.sort_values('timestamp')
dataNew['ID'] = dataNew.timestamp + '_' + dataNew.ticker
yahooDailyWrite.append(dataNew)
# once get to 4000, save and take a break to calm servers
if ((i % 4000 == 0) & (i > 0)) | (i == (len(tickers.ticker) - 1)):
dataDrop = pd.concat(yahooDailyWrite).reset_index(drop=True)
dataToWrite = dataDrop.replace({np.nan: 'N/A', np.inf: 'N/A', -np.inf: 'N/A', 'inf': 'N/A', '-inf': 'N/A'}, regex=True)
print('writing data...')
writeDBv2(dBase=dBase, name=stockConfig.dailyTable, data=dataToWrite,
createArgs='("' + '","'.join(
dataDrop.columns.tolist()) + '", PRIMARY KEY("ID"), UNIQUE("ID"))',
indexColName='"ticker"',
args='("' + '","'.join(dataDrop.columns.tolist()) + '")')
# set ticker data with multi index, ticker then date
mongoData = copy(dataToWrite)
mongoData = mongoData.drop(['ID'], axis=1)
# set metaData as type
meta_data = mongoData[['ticker', 'type']].drop_duplicates().set_index('ticker').T.to_dict()
mongoData = mdb.getMultiIndex(mongoData.drop('type', axis=1), index=['ticker', 'timestamp'])
# save to mongoDB database in "tickers" library
mdb.save(mongoData, stockConfig.stock_daily, append=append, meta_data=meta_data)
# create a seperate table for FX rates to make reading fX data faster
fxData = dataToWrite[dataToWrite.type == 'currency'].reset_index(drop=True)
fxData = fxData[fxData.ticker.str.contains('=')].reset_index(drop=True)
dataToWrite = fxData.replace(
{np.nan: 'N/A', np.inf: 'N/A', -np.inf: 'N/A', 'inf': 'N/A', '-inf': 'N/A'}, regex=True)
if fxData.shape[0] > 0:
writeDBv2(dBase=dBase, name=stockConfig.dailyFXtable, data=dataToWrite,
createArgs='("' + '","'.join(
fxData.columns.tolist()) + '", PRIMARY KEY("ID"), UNIQUE("ID"))',
indexColName='"ticker"',
args='("' + '","'.join(fxData.columns.tolist()) + '")')
# set ticker data with multi index, ticker then date
mongoData = copy(dataToWrite)
mongoData = mongoData.drop(['ID'], axis=1)
# set metaData as type
meta_data = mongoData[['ticker', 'type']].drop_duplicates().set_index('ticker').T.to_dict()
mongoData = mdb.getMultiIndex(mongoData.drop('type', axis=1), index=['ticker', 'timestamp'])
# save to mongoDB database in "tickers" library
mdb.save(mongoData, stockConfig.fx_daily, append=append, meta_data=meta_data)
dataIgnore = pd.DataFrame(notAvailable, columns=['ticker']).drop_duplicates().reset_index(drop=True)
if len(dataIgnore) > 0:
# add tickers that dont have data to a list we can import to ignore
writeDB(dBase=dBase, name=stockConfig.tickerExcludeTable, data=dataIgnore,
createArgs='("ticker", PRIMARY KEY("ticker"), UNIQUE("ticker"))',
args='("ticker")')
# set ticker data with multi index with name of table to save all data in one table
mongoData = copy(dataIgnore)
mongoData[stockConfig.tickers_exclude] = stockConfig.tickers_exclude
mongoData = mdb.getMultiIndex(mongoData, index=[stockConfig.tickers_exclude])
# save to mongoDB database in "tickers" library
mdb.save(mongoData, stockConfig.tickers_exclude, append=True)
yahooDailyWrite = []
notAvailable = []
# dont need to clean all series every day, just once a week. during the week just insert
if fullUpdate and clean:
dataDrop.timestamp = dataDrop.timestamp.astype('datetime64[D]')
dataDrop.adj_close = dataDrop.adj_close.astype(float)
# run cleaning algorithm and update yahooDailyClean
dataPivot = dataDrop.pivot_table(index='timestamp', columns='ticker', values='adj_close', aggfunc=sum)
dataPivot.index = dataPivot.index.values.astype('datetime64[D]')
# cleanData
dataClean = Analysis(data=dataPivot)
# must be less than todays date, dont want Nans filtering in for latest date for tickers that havnt
# published yet "today"
dataClean, _ = dataClean.cleanseTimeSeries(cutMissingDates=True, dates=dates, cores=4,
maxDate=maxDate)
if len(dataClean) == 0:
break
dataClean.reset_index(inplace=True)
dataClean = dataClean.rename(columns={'index': 'timestamp'})
dataClean = pd.melt(dataClean, id_vars=['timestamp'])
dataClean = dataClean.rename(columns={'value': 'adj_close'})
# merge back volume data
dataClean = pd.merge(dataClean, dataDrop[['timestamp', 'ticker', 'volume']], on=['ticker', 'timestamp'],
how='left')
dataClean['ID'] = dataClean.timestamp.astype(str) + '_' + dataClean.ticker
dataClean = pd.merge(dataClean, tickers[['ticker', 'type']].drop_duplicates(),
on=['ticker']).reset_index(drop=True)
dataClean.timestamp = dataClean.timestamp.astype(str)
dataClean = dataClean.replace({np.nan: 'N/A', np.inf: 'N/A', -np.inf: 'N/A', 'inf': 'N/A',
'-inf': 'N/A'}, regex=True)
dataClean = dataClean[dataClean.adj_close != 'N/A'].reset_index(drop=True)
else:
dataClean = dataDrop[['timestamp', 'ticker', 'adj_close', 'volume', 'type', 'ID']]
print('writing cleaned data...')
# write clean data to its own table so can call from other functions
writeDBv2(dBase=dBase, name=stockConfig.dailyTableClean, data=dataClean,
createArgs='("' + '","'.join(
dataClean.columns.tolist()) + '", PRIMARY KEY("ID"), UNIQUE("ID"))',
indexColName='"ticker"',
args='("' + '","'.join(dataClean.columns.tolist()) + '")')
# set ticker data with multi index, ticker then date
mongoData = copy(dataClean)
mongoData = mongoData.drop(['ID'], axis=1)
# set metaData as type
meta_data = mongoData[['ticker', 'type']].drop_duplicates().set_index('ticker').T.to_dict()
mongoData = mdb.getMultiIndex(mongoData.drop('type', axis=1), index=['ticker', 'timestamp'])
# save to mongoDB database in "tickers" library
mdb.save(mongoData, stockConfig.stock_daily_clean, append=append, meta_data=meta_data)
# sleep so yahoo dont get too angry
time.sleep(60 * 3)
print('stock data fetch complete')
return tweet
def process_user(user_id):
global duplicates
global user_counter
for status in tweepy.Cursor(api.user_timeline, id=user_id).items():
try:
db[COLLECTION_NAME].insert_one(convert_date(status))
except pymongo.errors.DuplicateKeyError:
duplicates += 1
twitterStream = tweepy.Stream(auth=api.auth, listener=StreamListener())
twitterStream.sample(languages=["en"], is_async=True)
# Run for specified time
start_time = datetime.now()
end_time = start_time + timedelta(minutes=RUN_TIME)
while datetime.now() < end_time:
time.sleep(10)
twitterStream.disconnect()
for count in user_counter.most_common(10):
print("PROCESSING USER + ", count)
process_user(count[0])
print("Number of duplicates", duplicates)
from pytz import timezone
from _datetime import timedelta
d = datetime(2012, 12, 21, 9, 30, 0)
print(d)
central = timezone('US/Central')
loc_d = central.localize(d)
print(loc_d)
banf_d = loc_d.astimezone(timezone('Asia/Kolkata'))
print(banf_d)
d = datetime(2013, 3, 10, 1, 45)
loc_d = central.localize(d)
print(loc_d)
later = loc_d + timedelta(minutes=30)
print(later)
from datetime import timedelta
later = central.normalize(loc_d + timedelta(minutes=30))
print(later)
import pytz
print(loc_d)
utc_d = loc_d.astimezone(pytz.utc)#世界统一时间
print(utc_d)
later_utc = utc_d + timedelta(minutes = 30)
print(later_utc.astimezone(central))
print(pytz.country_timezones['IN'])
def izbraniPodatki():
cursor=sql_cxn()[0]
planiranje.cbUlica.clear()
seznam = seznamUlic()
danes = date.today()
leto = timedelta(days=365)
tabela = []
vsota = 0
for i in range(len(seznam)-1):
obcina = seznam[i][0]
naselje = seznam[i][1]
ulica = seznam[i][2]
obcina = obcina.replace(' ','%')
naselje = naselje.replace(' ','%')
ulica = ulica.replace(' ','%')
queryPrikazi = """
SELECT *
FROM
"eDimnikar".uporabnik
LEFT OUTER JOIN
(
SELECT
"eDimnikar".fakture_vasco.sifra_kupca,
"eDimnikar".fakture_vasco.znesek,
"eDimnikar".fakture_vasco.izvajalec,
"eDimnikar".zaposleni.priimek_ime,
"eDimnikar".fakture_vasco.datum_storitve,
rank()
OVER
(
PARTITION BY sifra_kupca
ORDER BY datum_storitve
DESC
)
FROM "eDimnikar".fakture_vasco
LEFT JOIN
"eDimnikar".zaposleni
ON
"eDimnikar".zaposleni.stara_sifra = "eDimnikar".fakture_vasco.izvajalec
) sub_query
ON uporabnik.sifra_stara = sub_query.sifra_kupca
LEFT JOIN
"eDimnikar".postne_stevilke
ON
"eDimnikar".postne_stevilke.sifra_poste = "eDimnikar".uporabnik.posta_st_fk
WHERE
"eDimnikar".uporabnik.rpe_obcina ILIKE '%{}%'
AND
"eDimnikar".uporabnik.rpe_naselje ILIKE '%{}'
AND
"eDimnikar".uporabnik.rpe_ulica ILIKE '%{}'
AND
(rank = 1 or rank IS NULL)
ORDER BY "eDimnikar".uporabnik.naslov
ASC ;""".format(obcina,naselje, ulica)
cursor.execute(queryPrikazi)
row = cursor.fetchall()
hisnaOd=planiranje.planiranjeHsOd.text()
hisnaDo=planiranje.planiranjeHsDo.text()
#print(hisnaDo, hisnaOd)
for i in row:
if hisnaDo ==''and hisnaOd=='':
tabela.append((str((i[1])),i[2],i[4],str(i[30]),str(i[31]),i[28],str(i[25]),i[26], i[17]))
rowCount=len(tabela)
elif int(i[22])>=int(hisnaOd) and int([22])<=int(hisnaDo):
tabela.append((str((i[1])),i[2],i[4],str(i[2]),str(i[28]),i[28],str(i[25]),i[26],i[17]))
rowCount=len(tabela)
planiranje.twPlaniranje.setRowCount(rowCount)
for i in range(len(tabela)):
for j in range(len(tabela[i])):
if (j==5):
if tabela[i][5] !=None:
datum = tabela[i][5]
if (danes-datum) < leto:
print(danes-datum)
datumS=str(str(tabela[i][j]))
datumS=datumS[8:]+'.'+datumS[5:-3]+'.'+datumS[:4]
value = QtGui.QTableWidgetItem(datumS)
value1 = QtGui.QTableWidgetItem("ne naroči")
planiranje.twPlaniranje.setItem(i,9,value1)
planiranje.twPlaniranje.setItem(i,j,value)
else:
datumS=str(str(tabela[i][j]))
datumS=datumS[8:]+'.'+datumS[5:-3]+'.'+datumS[:4]
value = QtGui.QTableWidgetItem(datumS)
#value = QtGui.QTableWidgetItem(str(tabela[i][j]))
planiranje.twPlaniranje.setItem(i,j,value)
elif (j==6) and tabela[i][j] != 'None' :
cena = float(tabela[i][6].strip(' "'))
zDavkom = cena*1.22
value3 = QtGui.QTableWidgetItem('{:.2f}'.format(zDavkom))
planiranje.twPlaniranje.setItem(i,j,value3)
elif (j==6) and tabela[i][j] == 'None':
value3 = QtGui.QTableWidgetItem('')
planiranje.twPlaniranje.setItem(i,j,value3)
else:
value = QtGui.QTableWidgetItem((tabela[i][j]))
planiranje.twPlaniranje.setItem(i,j,value)
planiranje.twPlaniranje.resizeColumnsToContents()
for i in tabela:
if i[6] != 'None':
cena = Decimal(i[6].strip(' "'))
vsota = vsota+cena
planiranje.planiranjeLeReal.setText('{:.2f}'.format(vsota))
return tabela
def printsol(self, sub_model=None):
if self.model.status == GRB.OPTIMAL:
for k in range(self.bus):
print('Routing for bus %g' % (k + 1))
table = BeautifulTable()
table.column_headers = self.head
for i, j in self.parameters.edges:
if self.variables.x[i, j, k].Xn > 0.5 and i not in [0, self.last] and j not in [0, self.last]:
self.row[i, j, k] = [i, j, str(timedelta(minutes=self.variables.t[i].Xn)).rsplit(':', 1)[0],
str(timedelta(minutes=self.variables.t[j].Xn)).rsplit(':', 1)[0]]
table.append_row(self.row[i, j, k])
elif self.variables.x[i, j, k].Xn > 0.5 and i == 0:
self.row[i, j, k] = [i, j, str(timedelta(minutes=self.variables.td[i, k].Xn)).rsplit(':', 1)[0],
str(timedelta(minutes=self.variables.t[j].Xn)).rsplit(':', 1)[0]]
table.append_row(self.row[i, j, k])
elif self.variables.x[i, j, k].Xn > 0.5 and j == self.last:
self.row[i, j, k] = [i, j, str(timedelta(minutes=self.variables.t[i].Xn)).rsplit(':', 1)[0],
str(timedelta(minutes=self.variables.td[j, k].Xn)).rsplit(':', 1)[0]]
table.append_row(self.row[i, j, k])
print(table)
if sub_model is not None:
for s in sub_model.parameters.xi:
for k in range(sub_model.bus):
print('Scenario %g Routing for bus %g' % (s + 1, k + 1))
table = BeautifulTable()
table.column_headers = self.head
for i, j in sub_model.parameters.edges:
if sub_model.variables.xs[i, j, k, s].Xn > 0.5 and i not in [0, sub_model.last] \
and j not in [0, sub_model.last]:
self.row[i, j, k, s] = [i, j,
str(timedelta(minutes=sub_model.variables.ts[i, s].Xn)).rsplit(
':', 1)[0],
str(timedelta(minutes=sub_model.variables.ts[j, s].Xn)).rsplit(
':', 1)[0]]
table.append_row(self.row[i, j, k, s])
elif sub_model.variables.xs[i, j, k, s].Xn > 0.5 and i == 0:
if j != self.last:
self.row[i, j, k, s] = [i, j,
str(timedelta(
minutes=sub_model.variables.tds[i, k, s].Xn)).rsplit(
':', 1)[
0],
str(timedelta(
minutes=sub_model.variables.ts[j, s].Xn)).rsplit(':',
1)[0]]
else:
self.row[i, j, k, s] = [i, j,
str(timedelta(
minutes=sub_model.variables.tds[i, k, s].Xn)).rsplit(
':', 1)[
0],
str(timedelta(
minutes=sub_model.variables.tds[j, k, s].Xn)).rsplit(
':',
1)[0]]
table.append_row(self.row[i, j, k, s])
elif sub_model.variables.xs[i, j, k, s].Xn > 0.5 and j == sub_model.last:
if i != 0:
self.row[i, j, k, s] = [i, j,
str(timedelta(
minutes=sub_model.variables.ts[i, s].Xn)).rsplit(':',
1)[0],
str(timedelta(
minutes=sub_model.variables.tds[j, k, s].Xn)).rsplit(
':', 1)[
0]]
else:
self.row[i, j, k, s] = [i, j,
str(timedelta(
minutes=sub_model.variables.tds[i, k, s].Xn)).rsplit(
':',
1)[0],
str(timedelta(
minutes=sub_model.variables.tds[j, k, s].Xn)).rsplit(
':', 1)[
0]]
table.append_row(self.row[i, j, k, s])
print(table)
def sync(client, config, catalog, state):
if 'start_date' in config:
start_date = config['start_date']
# LOGGER.info('start_date = {}'.format(start_date))
# Get datetimes for endpoint parameters
communications_dttm_str = get_bookmark(state, 'communications', 'self',
start_date)
communications_dt_str = transform_datetime(communications_dttm_str)
# LOGGER.info('communications bookmark_date = {}'.format(communications_dt_str))
deposit_transactions_dttm_str = get_bookmark(state, 'deposit_transactions',
'self', start_date)
deposit_transactions_dt_str = transform_datetime(
deposit_transactions_dttm_str)
# LOGGER.info('deposit_transactions bookmark_date = {}'.format(deposit_transactions_dt_str))
loan_transactions_dttm_str = get_bookmark(state, 'loan_transactions',
'self', start_date)
loan_transactions_dt_str = transform_datetime(loan_transactions_dttm_str)
loan_transactions_dttm = strptime_to_utc(loan_transactions_dt_str)
clients_dttm_str = get_bookmark(state, 'clients', 'self', start_date)
clients_dt_str = transform_datetime(clients_dttm_str)
groups_dttm_str = get_bookmark(state, 'groups', 'self', start_date)
groups_dt_str = transform_datetime(groups_dttm_str)
lookback_days = int(config.get('lookback_window', LOOKBACK_DEFAULT))
lookback_date = utils.now() - timedelta(lookback_days)
if loan_transactions_dttm > lookback_date:
loan_transactions_dt_str = transform_datetime(strftime(lookback_date))
# LOGGER.info('loan_transactions bookmark_date = {}'.format(loan_transactions_dt_str))
# endpoints: API URL endpoints to be called
# properties:
# <root node>: Plural stream name for the endpoint
# path: API endpoint relative path, when added to the base URL, creates the full path
# api_version: v1 or v2 (default v2).
# api_method: GET or POST (default GET).
# params: Query, sort, and other endpoint specific parameters
# data_key: JSON element containing the records for the endpoint
# bookmark_query_field: Typically a date-time field used for filtering the query
# bookmark_field: Replication key field, typically a date-time, used for filtering the results
# and setting the state
# bookmark_type: Data type for bookmark, integer or datetime
# id_fields: Primary key (and other IDs) from the Parent stored when store_ids is true.
# children: A collection of child endpoints (where the endpoint path includes the parent id)
# parent: On each of the children, the singular stream name for parent element
# Details Level: https://api.mambu.com/?http#detail-level, FULL includes custom fields
endpoints = {
'branches': {
'path': 'branches',
'api_version': 'v2',
'api_method': 'GET',
'params': {
'sortBy': 'lastModifiedDate:ASC',
'detailsLevel': 'FULL',
'paginationDetails': 'ON'
},
'bookmark_field': 'last_modified_date',
'bookmark_type': 'datetime',
'id_fields': ['id']
},
'communications': {
'path':
'communications/messages:search',
'api_version':
'v2',
'api_method':
'POST',
'params': {
'detailsLevel': 'FULL'
},
'body': [{
'field': 'state',
'operator': 'EQUALS',
'value': 'SENT'
}, {
'field': 'creationDate',
'operator': 'AFTER',
'value': communications_dt_str
}],
'bookmark_field':
'creation_date',
'bookmark_type':
'datetime',
'id_fields': ['encoded_key']
},
'centres': {
'path': 'centres',
'api_version': 'v2',
'api_method': 'GET',
'params': {
'sortBy': 'lastModifiedDate:ASC',
'detailsLevel': 'FULL',
'paginationDetails': 'ON'
},
'bookmark_field': 'last_modified_date',
'bookmark_type': 'datetime',
'id_fields': ['id']
},
'clients': {
'path': 'clients:search',
'api_version': 'v2',
'api_method': 'POST',
'params': {
'detailsLevel': 'FULL'
},
'body': {
"sortingCriteria": {
"field": "lastModifiedDate",
"order": "ASC"
},
"filterCriteria": [{
"field": "lastModifiedDate",
"operator": "AFTER",
"value": clients_dt_str
}]
},
'bookmark_field': 'last_modified_date',
'bookmark_type': 'datetime',
'id_fields': ['id']
},
'credit_arrangements': {
'path': 'creditarrangements',
'api_version': 'v2',
'api_method': 'GET',
'params': {
'sortBy': 'creationDate:ASC',
'detailsLevel': 'FULL',
'paginationDetails': 'ON'
},
'bookmark_field': 'last_modified_date',
'bookmark_type': 'datetime',
'id_fields': ['id']
},
'custom_field_sets': {
'path': 'customfieldsets',
'api_version': 'v1',
'api_method': 'GET',
'params': {},
'id_fields': ['id']
},
'deposit_accounts': {
'path': 'deposits',
'api_version': 'v2',
'api_method': 'GET',
'params': {
'sortBy': 'lastModifiedDate:ASC',
'detailsLevel': 'FULL'
},
'bookmark_field': 'last_modified_date',
'bookmark_type': 'datetime',
'id_fields': ['id'],
'store_ids': True,
'children': {
'cards': {
'path': 'deposits/{}/cards',
'api_version': 'v2',
'api_method': 'GET',
'params': {
'detailsLevel': 'FULL'
},
'id_fields': ['deposit_id', 'reference_token'],
'parent': 'deposit'
}
}
},
'deposit_products': {
'path': 'savingsproducts',
'api_version': 'v1',
'api_method': 'GET',
'params': {
"fullDetails": True
},
'bookmark_field': 'last_modified_date',
'bookmark_type': 'datetime',
'id_fields': ['id']
},
'deposit_transactions': {
'path': 'deposits/transactions:search',
'api_version': 'v2',
'api_method': 'POST',
'params': {
'detailsLevel': 'FULL'
},
'body': {
"sortingCriteria": {
"field": "creationDate",
"order": "ASC"
},
"filterCriteria": [{
"field": "creationDate",
"operator": "AFTER",
"value": deposit_transactions_dt_str
}]
},
'bookmark_field': 'creation_date',
'bookmark_type': 'datetime',
'id_fields': ['encoded_key']
},
'groups': {
'path': 'groups:search',
'api_version': 'v2',
'api_method': 'POST',
'params': {
'detailsLevel': 'FULL'
},
'body': {
"sortingCriteria": {
"field": "lastModifiedDate",
"order": "ASC"
},
"filterCriteria": [{
"field": "lastModifiedDate",
"operator": "AFTER",
"value": groups_dt_str
}]
},
'bookmark_field': 'last_modified_date',
'bookmark_type': 'datetime',
'id_fields': ['id']
},
'loan_accounts': {
'path': 'loans',
'api_version': 'v2',
'api_method': 'GET',
'params': {
'sortBy': 'lastModifiedDate:ASC',
'detailsLevel': 'FULL',
'paginationDetails': 'ON'
},
'bookmark_field': 'last_modified_date',
'bookmark_type': 'datetime',
'id_fields': ['id'],
'children': {
'loan_repayments': {
'path': 'loans/{}/repayments',
'api_version': 'v1',
'api_method': 'GET',
'params': {
'detailsLevel': 'FULL',
'paginationDetails': 'ON'
},
'id_fields': ['encoded_key'],
'parent': 'loan_accounts'
}
}
},
'loan_products': {
'path': 'loanproducts',
'api_version': 'v1',
'api_method': 'GET',
'params': {
"fullDetails": True
},
'bookmark_field': 'last_modified_date',
'bookmark_type': 'datetime',
'id_fields': ['id']
},
'loan_transactions': {
'path': 'loans/transactions:search',
'api_version': 'v2',
'api_method': 'POST',
'params': {
'detailsLevel': 'FULL'
},
'body': {
"sortingCriteria": {
"field": "creationDate",
"order": "ASC"
},
"filterCriteria": [{
"field": "creationDate",
"operator": "AFTER",
"value": loan_transactions_dt_str
}]
},
'bookmark_field': 'creation_date',
'bookmark_type': 'datetime',
'id_fields': ['encoded_key']
},
'tasks': {
'path': 'tasks',
'api_version': 'v2',
'api_method': 'GET',
'params': {
'sortBy': 'lastModifiedDate:ASC',
'detailsLevel': 'FULL',
'paginationDetails': 'ON'
},
'bookmark_field': 'last_modified_date',
'bookmark_type': 'datetime',
'id_fields': ['id']
},
'users': {
'path': 'users',
'api_version': 'v2',
'api_method': 'GET',
'params': {
'sortBy': 'lastModifiedDate:ASC',
'detailsLevel': 'FULL',
'paginationDetails': 'ON'
},
'bookmark_field': 'last_modified_date',
'bookmark_type': 'datetime',
'id_fields': ['id']
},
'gl_accounts': {
'path': 'glaccounts',
'api_version': 'v1',
'api_method': 'GET',
'params': {
'type': '{sub_type}'
},
'id_fields': ['gl_code'],
'bookmark_field': 'last_modified_date',
'bookmark_type': 'datetime',
'sub_types': ['ASSET', 'LIABILITY', 'EQUITY', 'INCOME', 'EXPENSE']
},
'gl_journal_entries': {
'path': 'gljournalentries/search',
'api_version': 'v1',
'api_method': 'POST',
'body': {
"filterConstraints": [{
"filterSelection": "CREATION_DATE",
"filterElement": "BETWEEN",
"value": '{gl_journal_entries_from_dt_str}',
"secondValue": "{now_date_str}"
}]
},
'id_fields': ['entry_id'],
'bookmark_field': 'booking_date',
'bookmark_type': 'datetime'
},
'activities': {
'path': 'activities',
'api_version': 'v1',
'api_method': 'GET',
'params': {
'from': '{activities_from_dt_str}',
'to': '{now_date_str}'
},
'id_fields': ['encoded_key'],
'bookmark_field': 'timestamp',
'bookmark_type': 'datetime'
},
'index_rate_sources': {
'path': 'indexratesources',
'api_version': 'v2',
'api_method': 'GET',
'id_fields': ['encoded_key'],
'params': {}
},
'installments': {
'path': 'installments',
'api_version': 'v2',
'api_method': 'GET',
'id_fields': ['encoded_key'],
'params': {
'dueFrom': '{installments_from_dt_str}',
'dueTo': '{now_date_str}'
},
'bookmark_field': 'last_paid_date',
'bookmark_type': 'datetime'
}
}
selected_streams = get_selected_streams(catalog)
LOGGER.info('selected_streams: {}'.format(selected_streams))
if not selected_streams:
return
# last_stream = Previous currently synced stream, if the load was interrupted
last_stream = singer.get_currently_syncing(state)
LOGGER.info('last/currently syncing stream: {}'.format(last_stream))
# Start syncing from last/currently syncing stream
if last_stream in selected_streams:
selected_streams = selected_streams[selected_streams.index(
last_stream):] + selected_streams[:selected_streams.
index(last_stream)]
# For each endpoint (above), determine if the stream should be streamed
# (based on the catalog and last_stream), then sync those streams.
for stream_name in selected_streams:
endpoint_config = endpoints.get(stream_name)
if endpoint_config is None:
# null endpoint_config signifies that this is a child stream
continue
should_stream, last_stream = should_sync_stream(
selected_streams, last_stream, stream_name)
if should_stream:
# loop through each sub type
sub_types = endpoint_config.get('sub_types', ['self'])
for sub_type in sub_types:
LOGGER.info('START Syncing: {}, Type: {}'.format(
stream_name, sub_type))
# Now date
if stream_name == 'gl_journal_entries':
now_date_str = strftime(utils.now())[:10]
gl_journal_entries_from_dttm_str = get_bookmark(
state, 'gl_journal_entries', sub_type, start_date)
gl_journal_entries_from_dt_str = transform_datetime(
gl_journal_entries_from_dttm_str)[:10]
gl_journal_entries_from_param = endpoint_config.get(
'body', {}).get('filterConstraints',
{})[0].get('value')
if gl_journal_entries_from_param:
endpoint_config['body']['filterConstraints'][0][
'value'] = gl_journal_entries_from_dt_str
gl_journal_entries_to_param = endpoint_config.get(
'body', {}).get('filterConstraints',
{})[0].get('secondValue')
if gl_journal_entries_to_param:
endpoint_config['body']['filterConstraints'][0][
'secondValue'] = now_date_str
if stream_name == 'activities':
now_date_str = strftime(utils.now())[:10]
activities_from_dttm_str = get_bookmark(
state, 'activities', sub_type, start_date)
activities_from_dt_str = transform_datetime(
activities_from_dttm_str)[:10]
activities_from_param = endpoint_config.get('params',
{}).get('from')
if activities_from_param:
endpoint_config['params'][
'from'] = activities_from_dt_str
activities_to_param = endpoint_config.get('params',
{}).get('to')
if activities_to_param:
endpoint_config['params']['to'] = now_date_str
if stream_name == 'installments':
now_date_str = strftime(utils.now())[:10]
installments_from_dttm_str = get_bookmark(
state, 'installments', sub_type, start_date)
installments_from_dt_str = transform_datetime(
installments_from_dttm_str)[:10]
installments_from_param = endpoint_config.get(
'params', {}).get('dueFrom')
if installments_from_param:
endpoint_config['params'][
'dueFrom'] = installments_from_dt_str
installments_to_param = endpoint_config.get(
'params', {}).get('dueTo')
if installments_to_param:
endpoint_config['params']['dueTo'] = now_date_str
update_currently_syncing(state, stream_name)
path = endpoint_config.get('path')
sub_type_param = endpoint_config.get('params', {}).get('type')
if sub_type_param:
endpoint_config['params']['type'] = sub_type
total_records = sync_endpoint(
client=client,
catalog=catalog,
state=state,
start_date=start_date,
stream_name=stream_name,
path=path,
endpoint_config=endpoint_config,
api_version=endpoint_config.get('api_version', 'v2'),
api_method=endpoint_config.get('api_method', 'GET'),
static_params=endpoint_config.get('params', {}),
sub_type=sub_type,
bookmark_query_field=endpoint_config.get(
'bookmark_query_field'),
bookmark_field=endpoint_config.get('bookmark_field'),
bookmark_type=endpoint_config.get('bookmark_type'),
data_key=endpoint_config.get('data_key', None),
body=endpoint_config.get('body', None),
id_fields=endpoint_config.get('id_fields'))
update_currently_syncing(state, None)
LOGGER.info('Synced: {}, total_records: {}'.format(
stream_name, total_records))
LOGGER.info('FINISHED Syncing: {}'.format(stream_name))
# Format data
DailyPrices_Data = pd.read_csv("DailyPrices.csv", infer_datetime_format=True)
DailyPrices_Data.columns = ['Date', 'Price (Dollars per Million Btu)']
DailyPrices_Data = DailyPrices_Data.drop([0,1], axis=0)
DailyPrices_Data['Date'] = pd.to_datetime(DailyPrices_Data['Date'])
DailyPrices_Data = DailyPrices_Data.reset_index(drop=True)
# Save to CSV
DailyPrices_Data.to_csv('Data/DailyPrices.csv')
# Monthly Prices CSV
# Download data from site
MonthPrice_URL = 'http://www.eia.gov/dnav/ng/hist_xls/RNGWHHDm.xls'
urllib.request.urlretrieve(MonthPrice_URL, "MonthlyPrices.xls")
# Convert xls to csv
MonthPrice_XLS = pd.read_excel('MonthlyPrices.xls', 'Data 1')
MonthPrice_XLS.to_csv('MonthlyPrices.csv', index=None, header=True)
# Format Data
MonthlyPrices_Data = pd.read_csv("MonthlyPrices.csv", infer_datetime_format=True)
MonthlyPrices_Data.columns = ['Date', 'Price (Dollars per Million Btu)']
MonthlyPrices_Data = MonthlyPrices_Data.drop([0,1], axis=0)
MonthlyPrices_Data = MonthlyPrices_Data.reset_index(drop=True)
MonthlyPrices_Data['Date'] = (pd.to_datetime(MonthlyPrices_Data['Date']) + timedelta(-14))
# Save to CSV
MonthlyPrices_Data.to_csv('Data/MonthlyPrices.csv')
from resources.item import Item, ItemList
from resources.store import Store, StoreList
from _datetime import timedelta
from flask.json import jsonify
import os
app = Flask(__name__)
app.secret_key = 'email'
api = Api(app)
app.config['SQLALCHEMY_DATABASE_URI'] = os.environ.get('DATABASE_URL',
'sqlite:///data.db')
app.config[
'SQLALCHEMY_TRACK_MODIFICATIONS'] = False #turn off flask sqlalchemy modification, use sqlalchemy track
app.config['JWT_AUTH_URL_RULE'] = '/Login' #change the default /auth to /Login
app.config['JWT_EXPIRATION_DELTA'] = timedelta(
seconds=1800) #default is 5 mins
# app.config['JWT_AUTH_USERNAME_KEY']='email' # config JWT auth key name to be 'email' instead of default 'username'
jwt = JWT(app, authenticate,
identity) #JWT create an endpoint /auth to return the jwt token
# customize JWT auth response, include user_id in response body
@jwt.auth_response_handler
def customized_response_handler(access_token, identity):
return jsonify({
'access_token': access_token.decode('utf-8'),
'user_id': identity.id
})
def parse(machine = "61282649"):
#########################################################################
## izhodna datoteka
## ## leta, meseci, dnevi, ki jih preberemo iz filov. Manjkajoce izpusti.
## config = ([2015],
## {2015:[11],2016:[1]},
## {10:31, 11:27, 12:31, 1:31})
#########################################################################
product_plans = parseXmlFiles(plan_path)
print(str(product_plans))
scrap_reports = None
scrap_fname = dir_name + 'scrap_reports-fixdates-rates.p'
if os.path.isfile(scrap_fname):
pin = open(scrap_fname, 'rb')
scrap_reports = pickle.load(pin)
pin.close()
else:
scrap_reports = parseCsvFiles(showMissing=True, path=scrap_path)
pout = open(scrap_fname, 'wb')
pickle.dump(scrap_reports, pout)
pout.flush()
pout.close()
scrap_types = get_scrap_types()
header_offset = 5
row_offset = header_offset + 3
col_offset = 2
## izmene. II: 6-14h, III: 14-22h, I: 22-6h
## (v planu dela je to edino smiselno)
header_row = None
result = []
files = listdir(readings_path)
for file in files:
if not file.lower().endswith('csv'):
continue
print(file)
fin = open(readings_path + file, 'r')
reader = csv.reader(fin, delimiter=',', quotechar='"')
rows = [row for row in reader]
n_rows = len(rows)
n_cols = len(rows[0])
from_to_cell = rows[3][0]
from_to_str = from_to_cell
from_to_spl = from_to_str.split(' - ')
start_time = datetime.datetime.strptime(from_to_spl[0], "%d.%m.%y %H:%M")
end_time = datetime.datetime.strptime(from_to_spl[1], "%d.%m.%y %H:%M")
print('Parsing readings from ' + str(start_time) + ' to ' + str(end_time))
if header_row is None:
header_row = ['date', 'product', 'shift', 'timestamp', 'total parts', 'total scrap'] + scrap_types
header_row.append('total scrap (%)')
for scrap_type in scrap_types:
header_row.append(scrap_type + ' (%)')
for col_n in range(col_offset, n_cols):
h0 = rows[header_offset][col_n]
h1 = rows[header_offset+1][col_n]
h2 = rows[header_offset+2][col_n]
if h0 is None:
h0 = ''
if h1 is None:
h1 = ''
if h2 is None:
break
header_row.append(h0 + h1 + h2)
for row_n in range(row_offset, n_rows):
out_row = []
date_str = rows[row_n][0]
time_str = rows[row_n][1]
if date_str is None:
break
if date_str == 'Nastavljena vrednost':
row_n += 1
continue
reading_time = datetime.datetime.strptime(date_str + ' ' + time_str, "%d.%m.%y %H:%M:%S")
hour = reading_time.hour
timestamp = get_timestamp(reading_time)
plan_date_str = None
if hour < 6:
plan_date_str = (reading_time - timedelta(days=1)).strftime('%d.%m.%Y')
else:
plan_date_str = reading_time.strftime('%d.%m.%Y')
shift_n = get_shift(reading_time)
if not plan_date_str in product_plans:
print('Don\'t have a plan for date: ' + plan_date_str)
continue
product_plan = product_plans[plan_date_str]
# get the correct product
possible_products = product_plan[shift_n]
product = None
for product_conf in possible_products:
product_end_hour = product_conf['end']
if shift_n == 3:
if hour <= 23:
if hour <= product_end_hour or product_end_hour <= 6:
product = product_conf['product']
break
elif hour < product_end_hour:
product = product_conf['product']
break
elif hour < product_end_hour:
product = product_conf['product']
break
product = product.strip()
if product == 'prazno':
continue # TODO can I do anything better here???
if product is None:
raise ValueError('Unable to find product! Date: ' + plan_date_str)
if not product in plan_to_scrap_h:
raise ValueError('Product ' + product + ' not in hash! Date: ' + plan_date_str)
if not plan_date_str in scrap_reports:
print('Scrap report for date ' + plan_date_str + ' missing!')
continue
# extract the scrap report
scrap_report = scrap_reports[plan_date_str]
product_scrap = plan_to_scrap_h[product]
if product_scrap is None:
raise ValueError('Product ' + product + ' not found!')
if isinstance(product_scrap, list):
for ps in product_scrap:
if ps in scrap_report[shift_n]:
product_scrap = ps
break
out_row.append(date_str + ' ' + time_str)
out_row.append(product)
out_row.append(shift_n)
out_row.append(timestamp)
if not product_scrap in scrap_report[shift_n]:
print('Scrap report missing for car: ' + product_scrap + '! Date: ' + plan_date_str)
continue
scraps = scrap_report[shift_n][product_scrap]
good_parts = scraps['good_parts']
total_scrap = 0
for scrap_type in scrap_types:
total_scrap += scraps[scrap_type]
total_parts = good_parts + total_scrap
out_row.append(total_parts)
out_row.append(total_scrap)
for scrap_type in scrap_types:
out_row.append(scraps[scrap_type])
out_row.append(float(total_scrap) / total_parts if total_parts != 0 else 0)
for scrap_type in scrap_types:
out_row.append(float(scraps[scrap_type]) / total_parts if total_parts != 0 else 0)
for col_n in range(col_offset, n_cols):
value = rows[row_n][col_n]
if value is None or value == '':
value = 0
else:
value = float(value)
out_row.append(value)
result.append(out_row)
fin.close()
print('Removing duplicates ...')
timestamp_col = 3
timestamp_h = { row[timestamp_col]: row for row in result }
result = [timestamp_h[key] for key in timestamp_h]
print('Sorting ...')
result.sort(key = lambda row: row[timestamp_col])
print('Writing to output file ...')
fout = open(fout_name, 'w')
fout.write(','.join(['"' + str(val) + '"' for val in header_row]))
for row_n, out_row in enumerate(result):
line = ','.join([str(val) for val in out_row])
fout.write('\n' + line)
fout.flush()
fout.close()
print('Done!')
import sys
import glob
import numpy as np
#NX = 27
#NY = 29
NX = 36
NY = 37
dir_input = "/dados/radar/saoroque/ppi/level1_tamanduatei/2015/01"
dir_output = "/dados/radar/saoroque/ppi/level1_tamanduatei_txt/2015/01"
start = datetime.strptime("201501312310", "%Y%m%d%H%M")
end = datetime.strptime("201501312350", "%Y%m%d%H%M")
datehour = start
while datehour <= end:
pattern1 = datetime.strftime(datehour, "*%Y%m%d_%H*.dat")
files = glob.glob(os.path.join(dir_input, pattern1))
nfiles = len(files)
for file in sorted(files):
filename = os.path.basename(file)
data = np.fromfile(file.strip(), dtype=np.float32).reshape((NY, NX), order='C')
np.place(data, data==255, -99)
#np.place(data, data<0, 0.0)
txt_file = os.path.join(dir_output, filename.replace(".dat", ".txt"))
np.savetxt(txt_file, data, fmt='%03d')
#datehour = datehour + timedelta(hours=1)
datehour = datehour + timedelta(minutes=10)
def get_by_trimesters(self, year, trimester):
min_date = datetime(year, min(trimester), 1)
max_date = datetime(year, max(trimester)+1, 1)
DD = timedelta(days=1)
max_date = max_date - DD
return self.filter(date__gte=min_date, date__lte=max_date).order_by('-date')
def build_waves_by_new_cases(self, country: CovidCountry):
waves = []
wave_start_date = None
wave_start_count = 0
wave_end_date = None
wave_end_count = 0
previous_day = None
# plt.figure(figsize=(16,9))
for i, day in self.data.iterrows():
if (i != 0) and pd.notnull(day["rolling_new_cases"]):
#as we are going day by day the denominator is constant and is equal to 1
angle = np.rad2deg(
np.arctan2(
day["rolling_new_cases"] -
previous_day["rolling_new_cases"], 1))
#color = None
if angle < self.DECREASING_ANGLE:
if wave_start_date and wave_end_date is None and wave_end_count == 0:
wave_end_date = day["date"]
if wave_start_date and wave_end_date and wave_end_count < self.DECREASING_FOR:
wave_end_count += 1
if wave_start_count < self.INCREASING_FOR:
wave_start_count = 0
wave_start_date = None
elif angle > self.INCREASING_ANGLE:
if wave_start_date is None and wave_start_count == 0:
wave_start_date = day["date"]
if wave_start_date and wave_start_count < self.INCREASING_FOR:
wave_start_count += 1
if wave_end_count < self.DECREASING_FOR:
wave_end_count = 0
wave_end_date = None
if (wave_start_count == self.INCREASING_FOR
and wave_end_count == self.DECREASING_FOR):
mask_wave = ((self.data['date'] > wave_start_date) &
(self.data['date'] <= wave_end_date))
mean_case_count_during_wave = self.data[mask_wave][
"rolling_new_cases"].mean()
if mean_case_count_during_wave > self.MINIMUM_MEAN_NEW_CASES_FOR_WAVE:
mask_wave_extended = (
(self.data['date'] > wave_start_date -
timedelta(days=self.WAVE_START_OFFSET)) &
(self.data['date'] <= wave_end_date +
timedelta(days=self.WAVE_END_OFFSET)))
waves.append(self.data[
mask_wave_extended
& self.data['rolling_new_cases'].notna()])
wave_start_count = 0
wave_start_date = None
wave_end_count = 0
wave_end_date = None
if len(self.data.index) == (
i + 1
) and wave_start_count == self.INCREASING_FOR and wave_end_count < self.DECREASING_FOR:
wave_end_date = day["date"]
mask_wave = ((self.data['date'] > wave_start_date) &
(self.data['date'] <= wave_end_date))
mean_case_count_during_wave = self.data[mask_wave][
"rolling_new_cases"].mean()
if mean_case_count_during_wave > self.MINIMUM_MEAN_NEW_CASES_FOR_WAVE:
mask_wave_extended = (
(self.data['date'] > wave_start_date -
timedelta(days=self.WAVE_START_OFFSET)) &
(self.data['date'] <=
wave_end_date + timedelta(days=self.WAVE_END_OFFSET)))
waves.append(
self.data[mask_wave_extended
& self.data['rolling_new_cases'].notna()])
previous_day = day
merged_waves = Wave.merge(waves)
filtered_waves = []
for wave in merged_waves:
mean_case_count_during_wave = wave["rolling_new_cases"].mean()
if mean_case_count_during_wave > self.MINIMUM_MEAN_NEW_CASES_FOR_WAVE:
filtered_waves.append(
Wave.build_wave_from_dataframe(wave, country,
'rolling_new_cases'))
self.waves_cases = filtered_waves
# plt.title(country_iso)
# plt.savefig(FIGURES_PATH + country_iso + ".png")
# plt.close()
return filtered_waves
def downloadYahoo(ticker, intraRange=None, dailyDateRange=None, symbols=None, yahooIntra=stockConfig.yahooIntra,
yahooDaily=stockConfig.yahooDaily, yahooFundementals=stockConfig.yahooFundementals, track=0,
urlType=2, adjust=False):
dataOut = []
testFunc = None
typ = 'get'
if symbols is not None:
values = ['timeStamp'] + symbols.Description.tolist()
symbolStr = ''.join(symbols.Symbol)
url = yahooFundementals + 's=' + ticker + '&f=' + symbolStr
testFunc = 'any((lambda output=output : [any(pd.Series(output).str.contains(x)) for x in ' + str(
re.split('\+', ticker)).replace('^', '') + '])())'
elif intraRange is not None:
url = yahooIntra + ticker + '/chartdata;type=quote;range=' + intraRange + '/csv'
elif dailyDateRange is not None:
if urlType == 1:
dateFrom = dailyDateRange[0]
dateTo = dailyDateRange[1]
url = yahooDaily + ticker + '&d=' + str(dateTo.month) + '&e=' + str(dateTo.day) + '&f=' + str(
dateTo.year) + '&g=d&a=' + str(dateFrom.month) + '&b=' + str(dateFrom.day) + \
'&c=' + str(dateFrom.year) + '&ignore=.csv'
elif urlType == 2:
startEpoch = int(datetime.timestamp(dailyDateRange[0]))
endEpoch = int(datetime.timestamp(dailyDateRange[1]))
baseUrl = stockConfig.yahooBaseURL
argsHist = '%s?period1=%s&period2=%s&interval=1d&events=history' % (ticker, startEpoch, endEpoch)
url = baseUrl + argsHist
typ = 'post'
# response = requests.get(url)
data, track = tryRequest(url=url, typ=typ, track=track, testFunc=testFunc, timeOut=8)
data = np.array(pd.DataFrame(data).replace({'!"': ''}, regex=True).iloc[:, 0])
try:
if data.shape[0] == 1:
return None, track
except:
pass
try:
if ('error' in data[2]) | ('404 Not Found' in data[1]):
return None, track
except:
print(data)
input()
if symbols is not None:
startIndex = 0
elif intraRange is not None:
values = np.array(re.split(',|:', data[np.array(['values' in x for x in data])][0]))[1:]
lastValue = data[np.array([values[len(values) - 1] in x for x in data])]
startIndex = np.where(data == lastValue[len(lastValue) - 1])[0][0] + 1
else:
values = np.array(re.split(',|:', data[0]))
startIndex = 1
for i in range(startIndex, data.shape[0]):
if (len(data[i]) > 0):
splitRow1 = re.split(''',(?=(?:[^'"]|'[^']*'|"[^"]*")*$)''', data[i])
if symbols is not None:
# timeStamp = np.datetime64(datetime.now().date())
# runs at 4am, thus prices apply to timestamp day before
timeStamp = str(datetime.now().date() - timedelta(days=1))
splitRow1 = [timeStamp] + splitRow1
else:
try:
timeStamp = datetime.fromtimestamp(int(splitRow1[0]))
except:
try:
splitRow = splitRow1.copy()
timeStamp = splitRow[0]
except Exception as e:
kk = e
print(kk)
splitRow1[0] = str(timeStamp)
dataOut.append(splitRow1)
dataOut = pd.DataFrame(dataOut)
if (dataOut.shape == (0, 0)):
return None, track
values[0] = 'timestamp'
values = np.array([x.lower() for x in values])
try:
dataOut.columns = values
except:
print('columns didnt work - here')
dataOut = dataOut.replace('null', np.NaN)
if symbols is None:
dataOut = dataOut.replace('N/A', np.NaN)
dataOut = dataOut.iloc[~np.isnan(np.nansum(dataOut.iloc[:, 1:5].astype(float), axis=1)) & (
np.nansum(dataOut.iloc[:, 1:5].astype(float), axis=1) != 0), :]
cols = ['timestamp', 'open', 'close', 'high', 'low', 'volume']
try:
dataOut = dataOut[cols + values[np.array((~pd.Series(values).isin(cols)).tolist())].tolist()]
except Exception as e:
print(dataOut)
print(e)
dataOut.columns = pd.Series(dataOut.columns).replace({'adj close': 'adj_close'}, regex=True)
dataOut.iloc[:, 1:len(dataOut.columns)] = dataOut.iloc[:, 1:len(dataOut.columns)].astype(np.float)
if (dataOut.shape == (0, 0)):
return None, track
dataOut['ticker'] = ticker
try:
dataOut = dataOut.drop_duplicates(subset='timestamp', keep='first')
except Exception as e:
print(dataOut)
print(e)
if adjust & (dataOut.shape[0] > 3):
startEpoch = int(datetime.timestamp(dailyDateRange[0]))
endEpoch = int(datetime.timestamp(dailyDateRange[1]))
baseUrl = 'https://query1.finance.yahoo.com/v7/finance/download/'
argsDiv = '%s?period1=%s&period2=%s&interval=1d&events=div' % (ticker, startEpoch, endEpoch)
argsSplit = '%s?period1=%s&period2=%s&interval=1d&events=split' % (ticker, startEpoch, endEpoch)
typ = 'post'
testDiv = False
cnt = 0
cnt1 = 0
while (testDiv == False) & (cnt <= 20) & (cnt1 <= 10):
dataDiv, _ = tryRequest(url=baseUrl + argsDiv, typ=typ)
if pd.Series(dataDiv).str.contains(
'No data found, symbol may be delisted|Data doesn\'t exist for startDate').any():
cnt += 1
elif pd.Series(dataDiv).str.contains('error occurred while retrieving timeseries from redis').any():
cnt1 += 1
elif 'Error occurred while retrieving timeseries from Redis' in str(dataDiv):
cnt1 += 1
elif 'duplicate key' in str(dataDiv):
cnt1 += 1
elif pd.Series(dataDiv).str.contains('error occurred').any():
cnt1 += 1
elif pd.Series(dataDiv).str.contains('HTTP ERROR: 503').any():
cnt1 += 1
elif (dataDiv.shape == (2,)):
cnt1 += 1
else:
testDiv = True
if testDiv == False:
dataDiv = np.array(['Date,Dividends', ''])
testSplit = False
cnt = 0
cnt1 = 0
cnt2 = 0
while (testSplit == False) & (cnt <= 20) & (cnt1 <= 10) & (cnt2 <= 10):
dataSplit, _ = tryRequest(url=baseUrl + argsSplit, typ=typ)
if pd.Series(dataSplit).str.contains(
'No data found, symbol may be delisted|Data doesn\'t exist for startDate').any():
cnt += 1
elif pd.Series(dataSplit).str.contains(
'error occurred while retrieving timeseries from redis').any():
cnt1 += 1
elif 'Error occurred while retrieving timeseries from Redis' in str(dataSplit):
cnt1 += 1
elif 'duplicate key' in str(dataSplit):
cnt1 += 1
elif pd.Series(dataSplit).str.contains('error occurred').any():
cnt1 += 1
elif pd.Series(dataSplit).str.contains('HTTP ERROR: 503').any():
cnt1 += 1
elif dataSplit.shape == (2,):
cnt2 += 1
else:
testSplit = True
if testSplit == False:
dataSplit = np.array(['Date,stock splits', ''])
##adjusted data effectivly ignores splits going back, so we just adjust for dividends, only relevant if trying to transform open close etc of old data,
##new data already adjusts for splits in open etc
dataExtra = {}
for k in ['dataDiv', 'dataSplit']:
cols = pd.Series(eval(k)).str.split(',', expand=True).iloc[0, :].values
cols = np.array([x.lower() for x in cols])
dataExtra[k] = pd.Series(eval(k)).str.split(',', expand=True).iloc[1:, :]
dataExtra[k].columns = cols
try:
dataExtra[k].sort_values(by='date', inplace=True) #
except:
print(dataExtra[k])
dataExtra[k].sort_values(by='date', inplace=True)
dataExtra[k] = dataExtra[k][dataExtra[k].date != ''].reset_index(drop=True)
dataOut = dataOut.reset_index(drop=True)
locDate = np.where(dataOut == np.inf)[0]
getRidDate = dataOut.timestamp.values.astype('datetime64[D]').min()
if locDate.shape[0] > 0:
getRidDate = dataOut.timestamp[np.where(dataOut == np.inf)[0].max()]
stupidSplit = dataExtra['dataSplit'].date[dataExtra['dataSplit']['stock splits'].str.contains('/0') | (
dataExtra['dataSplit']['stock splits'].str[:2] == '0/')].values
if len(stupidSplit) > 0:
getRidDate = np.max(np.array([stupidSplit.max(), getRidDate]).astype('datetime64[D]'))
uniqueDates = np.unique(np.concatenate(([dataOut.timestamp.unique(),
dataExtra['dataDiv']['date'].unique(),
dataExtra['dataSplit']['date']]))).astype('datetime64[D]')
uniqueDates.sort()
uniqueDates = uniqueDates[uniqueDates > np.array([getRidDate]).astype('datetime64[D]')]
# insert any missing dates that pay splits or dividends
dataOut = dataOut[
dataOut.timestamp.values.astype('datetime64[D]') > np.array([getRidDate]).astype('datetime64[D]')]
# if there is a split or div but no new data afterward, the getRid date will leave us with no data
if dataOut.shape[0] == 0:
return None, track
dataOut.index = dataOut.timestamp.astype('datetime64[D]').copy()
dataOut = dataOut.reindex(uniqueDates)
## need to backfill for missing dates for dividends and splits, as supposed to use tomorrows data for calculating backwards adjustment
dataOut = dataOut.fillna(method='backfill')
dataOut.timestamp = dataOut.index.values.astype('datetime64[D]').astype(str)
dataOut.reset_index(drop=True, inplace=True)
dataExtra['dataDiv'] = dataExtra['dataDiv'][
dataExtra['dataDiv']['date'].astype('datetime64[D]') > getRidDate].reset_index(drop=True)
dataExtra['dataSplit'] = dataExtra['dataSplit'][
dataExtra['dataSplit']['date'].astype('datetime64[D]') > getRidDate].reset_index(drop=True)
scalingFactor = 1
scalingFactorCum = 1
dataAdjust = dataOut.sort_values(by='timestamp', ascending=False)
div = pd.DataFrame(np.zeros(dataAdjust.shape[0]), index=dataAdjust.timestamp)
div.loc[dataExtra['dataDiv']['date']] = dataExtra['dataDiv']['dividends'].values
div.iloc[:, :] = div.iloc[:, :].astype(float)
splits = pd.DataFrame(np.zeros(dataAdjust.shape[0]), index=dataAdjust.timestamp)
splits.loc[dataExtra['dataSplit']['date']] = dataExtra['dataSplit']['stock splits'].values
adj_close = np.zeros(dataAdjust.shape[0])
try:
adj_close[0] = dataAdjust.close.values[0]
except:
broken = 1
scalar = 1
for i in range(1, dataAdjust.shape[0]):
if div.iloc[i - 1].values != 0:
scalingFactorCum = scalingFactorCum * scalingFactor
scalingFactor = (adj_close[i - 1] + div.iloc[i - 1].values) / adj_close[i - 1]
if (splits.iloc[i - 1].values != 0):
if (eval(splits.iloc[i - 1].values[0]) == 0) & (splits.iloc[i - 1].values[0] != 0):
return None, track
scalar = 1 / eval(splits.iloc[i - 1].values[0]) * scalar
closePreAdj = dataAdjust.close.iloc[i] / scalingFactorCum
adj_close[i] = closePreAdj / scalingFactor * scalar
dataOut['adj_close'] = adj_close[::-1]
dataOut = dataOut.replace(np.NaN, 'N/A')
dataOut.timestamp = dataOut.timestamp.astype('datetime64[D]')
dataOut = dataOut.sort_values(by='timestamp')
dataOut.timestamp = dataOut.timestamp.astype(str)
else:
# use dictionary to replace all instances of string in dataFrame
dataOut = dataOut.replace({'"': ''}, regex=True)
dataOut[['dividend pay date', 'ex-dividend date', 'last trade date', ]] = \
dataOut[['dividend pay date', 'ex-dividend date', 'last trade date']].apply(
lambda x: x.apply(lambda y: np.datetime64(datetime.strptime(y, '%m/%d/%Y').date()) \
if (y != 'N/A') & ('/' in y) & (len(y) <= 10) else 'N/A'))
dataOut['last trade time'] = dataOut['last trade time'].apply(
lambda x: str((datetime.strptime(pd.Series(x).replace({'pm': '', 'am': ''}, regex=True)[0], '%H:%M') + \
timedelta(hours=(12 if 'pm' in x else 0))).time()) if (x != 'N/A') & (
('am' in x) | ('pm' in x)) else 'N/A')
dataOut[['revenue', 'market capitalization', 'ebitda']] = \
dataOut[['revenue', 'market capitalization', 'ebitda']].apply(
lambda y: [float(x.replace('M', '')) * 1e6 if 'M' in x else \
float(x.replace('B', '')) * 1e9 if ('B' in x) else float(x) if (x != 'N/A') & (
x is not None) else x for x in y])
dataOut[['change in percent', 'percent change from 200 day moving average',
'percent change from 50 day moving average', 'percent change from 52 week low', \
'percent change from 52 week high']] = \
dataOut[['change in percent', 'percent change from 200 day moving average',
'percent change from 50 day moving average', \
'percent change from 52 week low', 'percent change from 52 week high']].apply(
lambda x: [float(y) / 100 if (y != 'N/A') & ('%' in y) else \
y for y in x.replace({'%': '', '\+': ''}, regex=True)])
dataOut['change'] = [np.float(x) if x != 'N/A' else x for x in dataOut.change]
dataOut[['last trade date', 'trade date']] = dataOut[['last trade date', 'trade date']].apply(
lambda x: x.apply(str))
dataOut.columns = pd.Series(dataOut.columns).replace(
{' ': '_', '-': '_', '&': 'and', '\(': '', '\)': '', '/': '_to_', '\x92': ''}, regex=True)
if dataOut.shape[0] == 0:
return None, track
return dataOut, track
def HTML_Execution_Summary_Footer(self):
try:
oFileWriter = open(self.strExecSummaryHTMLFilePath, 'a')
oFileWriter.write("</tbody>\n")
oFileWriter.write("</table>\n")
oFileWriter.write("<table>\n")
oFileWriter.write("<script type='text/javascript'>\n")
oFileWriter.write("window.onload = function () {\n")
oFileWriter.write("CanvasJS.addColorSet('chartshades',\n")
oFileWriter.write("[//colorSet Array\n")
oFileWriter.write("'lightgreen',\n")
oFileWriter.write("'red' \n")
oFileWriter.write("]);\n")
oFileWriter.write("var chart = new CanvasJS.Chart('chartContainer',\n")
oFileWriter.write("{\n")
oFileWriter.write("colorSet: 'chartshades',\n")
oFileWriter.write("zoomEnabled: true,\n")
oFileWriter.write("title:{\n")
oFileWriter.write("fontColor: '#C6FFEC',\n")
oFileWriter.write("text: 'Execution Status'\n")
oFileWriter.write("},\n")
oFileWriter.write("animationEnabled: true,\n")
oFileWriter.write("backgroundColor: 'black',\n")
oFileWriter.write("legend:{\n")
oFileWriter.write("fontColor: '#C6FFEC',\n")
oFileWriter.write("verticalAlign: 'bottom',\n")
oFileWriter.write("horizontalAlign: 'center'\n")
oFileWriter.write("},data: [{ \n")
oFileWriter.write("indexLabelFontSize: 20,\n")
oFileWriter.write("indexLabelFontFamily: 'Monospace', \n")
oFileWriter.write("indexLabelFontColor: '#C6FFEC', \n")
oFileWriter.write("indexLabelLineColor: '#C6FFEC', \n")
oFileWriter.write("indexLabelPlacement: 'auto',\n")
oFileWriter.write("type: 'pie', \n")
oFileWriter.write("showInLegend: true,\n")
oFileWriter.write(
"toolTipContent: '{y} - <strong>#percent%</strong>',\n")
oFileWriter.write("dataPoints: [\n")
if not self.intPassTCCount == 0:
oFileWriter.write("{ y: " + str(self.intPassTCCount) +
", legendText:'PASS', indexLabel: '{y}' },\n")
else:
oFileWriter.write("{ y: " + str(self.intPassTCCount) +
", legendText:'PASS'},\n")
if not self.intFailTCCount == 0:
oFileWriter.write("{ y: " + str(self.intFailTCCount) +
", legendText:'FAIL' , indexLabel: '{y}'}\n")
else:
oFileWriter.write("{ y: " + str(self.intFailTCCount) +
", legendText:'FAIL'}\n")
oFileWriter.write("]}]});chart.render();}\n")
oFileWriter.write("</script>\n")
oFileWriter.write(
"<script type='text/javascript' src='./Screenshot/canvasjs.min.js'></script>\n"
)
oFileWriter.write(
"<tr class='content' ><td><div id='chartContainer' style='height: 300px; width: 100%;'></div></td></tr></table>\n"
)
oFileWriter.write("<table id='footer'>\n")
oFileWriter.write("<colgroup>\n")
oFileWriter.write("<col style='width: 25%' />\n")
oFileWriter.write("<col style='width: 25%' />\n")
oFileWriter.write("<col style='width: 25%' />\n")
oFileWriter.write("<col style='width: 25%' />\n")
oFileWriter.write("</colgroup>\n")
oFileWriter.write("<tfoot>\n")
oFileWriter.write("<tr class='heading'>\n")
intExecEndTime = time.monotonic()
strDuration = str(
timedelta(seconds=intExecEndTime - self.intExecStartTime))
strDuration = self.getDuration(strDuration)
oFileWriter.write("<th colspan='4'>Total Duration: " + strDuration +
"</th>\n")
oFileWriter.write("</tr>\n")
oFileWriter.write("<tr class='subheading'>\n")
oFileWriter.write("<td class='pass'> Tests passed</td>\n")
oFileWriter.write("<td class='pass'> : {}</td>\n".format(
self.intPassTCCount))
oFileWriter.write("<td class='fail'> Tests failed</td>\n")
oFileWriter.write("<td class='fail'> : {}</td>\n".format(
self.intFailTCCount))
oFileWriter.write("</tr>\n")
oFileWriter.write("</tfoot>\n")
oFileWriter.write("</table>\n")
oFileWriter.write("</body>\n")
oFileWriter.write("</html>\n")
#Always close files.
oFileWriter.close()
if os.path.exists(self.strCurrentTXTFolder +
'ExecutionInProgress.txt'):
os.remove(self.strCurrentTXTFolder + 'ExecutionInProgress.txt')
except IOError as e:
print("I/O error({0}): {1}".format(e.errno, e.strerror))
from _datetime import datetime
from _datetime import timedelta
today = datetime.today()
print(today)
today = today - timedelta(hours=24)
print(datetime.today() > today)
print( (datetime.today() - today).total_seconds())
print(datetime.today() - today)
import datetime
from datetime import timedelta
# in_str = '2016-07-07'
# out_str = '2016-07-08'
# in_array = in_str.split('-')
# out_array = out_str.split('-')
# checkin_time = datetime.datetime(int(in_array[0]),int(in_array[1]),int(in_array[2])).date()
# checkout_time = datetime.datetime(int(out_array[0]),int(out_array[1]),int(out_array[2])).date()
# interval = (checkout_time-checkin_time).days
# aDay = timedelta(days=1)
#
# for i in range(1,interval+1):
# checkin_time += aDay
# print(checkin_time.day)
# str =datetime.datetime.now().strftime('%Y%m%d%H')[2:]
# print(str)
# import django.utils.timezone
# from django.utils.dates import WEEKDAYS
# now = datetime.now()
# App the REST framework url conf
ROOT_URLCONF = 'django_rest_role_jwt.urls'
# REST framework settings
REST_FRAMEWORK = {
'DEFAULT_PERMISSION_CLASSES':
('rest_framework.permissions.IsAuthenticated'),
'DEFAULT_AUTHENTICATION_CLASSES':
('rest_framework_simplejwt.authentication.JWTAuthentication',
'rest_framework.authentication.SessionAuthentication',
'rest_framework.authentication.BasicAuthentication'),
}
# Configure the JWT settings
SIMPLE_JWT = {
'ACCESS_TOKEN_LIFETIME': timedelta(minutes=5),
'REFRESH_TOKEN_LIFETIME': timedelta(days=14),
'ROTATE_REFRESH_TOKENS': True,
'BLACKLIST_AFTER_ROTATION': False,
'ALGORITHM': 'HS256',
'SIGNING_KEY': SECRET_KEY,
'VERIFYING_KEY': None,
'AUTH_HEADER_TYPES': ('JWT', ),
'USER_ID_FIELD': 'id',
'USER_ID_CLAIM': 'user_id',
'AUTH_TOKEN_CLASSES': ('rest_framework_simplejwt.tokens.AccessToken', ),
'TOKEN_TYPE_CLAIM': 'token_type',
}
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',