def add_is_weekend_feature(main_table):
# Extracting relevant columns from main table
table = main_table.groupby("display_id").first().reset_index()[[
"display_id", "click_tstamp"
]]
timestamps = table["click_tstamp"]
# Initializing empty is_weekend array
is_weekend_boolean = np.zeros(shape=len(timestamps))
for i in range(len(timestamps)):
# Format conversions to allow use of is_busday function
as_datetime64 = np.datetime64(int(timestamps[i]), 's')
as_datetime = pd.to_datetime(as_datetime64)
# Using the is_busday to return an is_weekend boolean - True for Saturday and Sunday, False otherwise
boolean = np.is_busday(as_datetime, weekmask='0000011')
is_weekend_boolean[i] = boolean
# Creating result Dataframe with two columns - display_id and is_weekend
res_frame = pd.DataFrame()
res_frame["display_id"] = table["display_id"]
res_frame["is_weekend"] = is_weekend_boolean
return res_frame
def Sergipe(self):
holidays2020 = np.array(['2020-01-01','2020-01-02','2020-01-03','2020-01-04','2020-01-05','2020-01-06','2020-02-24','2020-02-25','2020-02-26','2020-03-16','2020-03-17','2020-04-09','2020-04-10','2020-04-21','2020-05-01','2020-05-01','2020-05-22','2020-05-25','2020-05-26','2020-06-11','2020-09-07','2020-10-12','2020-11-02','2020-11-15','2020-12-24','2020-12-25','2020-12-30'],dtype='datetime64')
self.__log("Data de inicio e fim da pesquisa:","green")
dataInit,dataFim = self.__askDate()
start = np.datetime64(dataInit)
end = np.datetime64(dataFim)
date = start
e='SE'
while date <= end:
if np.is_busday(date,holidays=holidays2020):
edicao = self.__EdicaoSE(date)
nomeArquivo = "Diario-"+str(date)+".pdf"
fileUrl ="http://www.diario.tjse.jus.br/diario/diarios/"+edicao+".pdf"
diretorio = e
downloadFile(fileUrl,nomeArquivo,diretorio)
print("Download:"+nomeArquivo)
date = date + np.timedelta64(1,'D')
print("Arquivos Baixados")
def _filter_to_business_days(dates):
# only weekdays
dates = dates[np.where(np.is_busday(dates))]
# only US trading days
dates = dates[np.where(
np.isin(dates, _US_HOLIDAYS, invert=True, assume_unique=True))]
return dates
def Amazonas(self):
holidays2020 = np.array(['2020-01-01','2020-02-24','2020-02-25','2020-02-26','2020-04-08','2020-04-09','2020-04-10','2020-04-21','2020-05-01','2020-06-11','2020-06-24','2020-06-29','','2020-09-07','2020-09-16','2020-10-12','2020-10-28','2020-11-02','2020-11-15','2020-11-20','2020-11-30','2020-12-08','2020-12-24','2020-12-25','2020-12-30'],dtype='datetime64')
self.__log("Data de inicio e fim da pesquisa:","green")
dataInit,dataFim = self.__askDate()
start = np.datetime64(dataInit)
end = np.datetime64(dataFim)
e='AM'
date =start
while date <= end:
dia = str(date).rsplit('-')[2]
mes = str(date).rsplit('-')[1]
ano = str(date).rsplit('-')[0]
dateStr = dia+"/"+mes+"/"+ano
if np.is_busday(date,holidays=holidays2020):
nomeArquivo = "Diario-"+str(date)+".pdf"
fileUrl = "https://consultasaj.tjam.jus.br/cdje/downloadCaderno.do?dtDiario="+dateStr+"&cdCaderno=2&tpDownload=D"
downloadFile(fileUrl,nomeArquivo,e)
print("Download:"+nomeArquivo)
date = date + np.timedelta64(1,'D')
print("Arquivos Baixados")
def Roraima(self):
holidays2020 = np.array(['2020-01-01','2020-01-20','2020-02-24','2020-02-25','2020-02-26','2020-04-09','2020-04-10','2020-04-20','2020-04-21','2020-05-01','2020-06-11','2020-06-12','2020-06-29','2020-07-09','2020-07-10','2020-08-10','2020-09-07','2020-09-16','2020-10-12','2020-10-28','2020-11-02','2020-11-15','2020-11-20','2020-11-30','2020-12-08','2020-12-24','2020-12-25','2020-12-31'],dtype='datetime64')
self.__log("Data de inicio e fim da pesquisa:","green")
dataInit,dataFim = self.__askDate()
start = np.datetime64(dataInit)
end = np.datetime64(dataFim)
date = start
e='RR'
while date <= end:
dia = str(date).rsplit('-')[2]
mes = str(date).rsplit('-')[1]
ano = str(date).rsplit('-')[0]
if np.is_busday(date,holidays=holidays2020):
nomeArquivo = "Diario-"+str(date)+".pdf"
fileUrl = "http://diario.tjrr.jus.br/dpj/dpj-"+ano+mes+dia+".pdf"
downloadFile(fileUrl,nomeArquivo,e)
print("Download:"+nomeArquivo)
date = date + np.timedelta64(1,'D')
print("Arquivos Baixados")
def is_trading_day(
self, dates: Union[np.datetime64, pd.Series, np.ndarray, str,
datetime.datetime, datetime.date]
) -> Union[bool, np.ndarray]:
'''
Returns whether the date is not a holiday or a weekend
Args:
dates: date times to check
Return:
Whether this date is a trading day
>>> import datetime
>>> eurex = Calendar.get_calendar(Calendar.EUREX)
>>> eurex.is_trading_day('2016-12-25')
False
>>> eurex.is_trading_day(datetime.date(2016, 12, 22))
True
>>> nyse = Calendar.get_calendar(Calendar.NYSE)
>>> nyse.is_trading_day('2017-04-01') # Weekend
False
>>> nyse.is_trading_day(np.arange('2017-04-01', '2017-04-09', dtype = np.datetime64)) # doctest:+ELLIPSIS
array([False, False, True, True, True, True, True, False]...)
'''
if isinstance(dates, str) or isinstance(dates, datetime.date):
dates = np.datetime64(dates, 'D')
if isinstance(
dates.astype(datetime.datetime), int
): # User can pass in a string like 20180101 which gets parsed as a year
raise Exception(f'invalid date: {dates}')
if isinstance(dates, pd.Series): dates = dates.values
return np.is_busday(dates.astype('M8[D]'), busdaycal=self.bus_day_cal)
def MatoGrosso(self):
holidays2020 = np.array(['2020-01-01','2020-02-24','2020-02-25','2020-02-26','2020-04-09','2020-04-10','2020-04-20','2020-04-21','2020-05-01','2020-06-11','2020-06-12','2020-09-07','2020-10-12','2020-11-02','2020-11-15','2020-11-20','2020-12-08','2020-12-24','2020-12-25','2020-12-30'],dtype='datetime64')
self.__log("Data de inicio e fim da pesquisa:","green")
dataInit,dataFim = self.__askDate()
start = np.datetime64(dataInit)
end = np.datetime64(dataFim)
date = start
e='MT'
while date <= end:
if np.is_busday(date,holidays=holidays2020):
edicao = self.__definirEdicaoMT(date)
nomeArquivo = "Diario-"+str(edicao)+".pdf"
fileUrl = "http://dje.tjmt.jus.br/publicacoes/"+str(edicao)+"-2020%20CADERNO%20JUDICIAL%20DO%20TRIBUNAL%20DE%20JUSTICA.pdf"
downloadFile(fileUrl,nomeArquivo,e)
print("Download:"+nomeArquivo)
date = date + np.timedelta64(1,'D')
print("Arquivos Baixados")
def get_trading_days(self,
start: Union[np.datetime64, pd.Series, np.ndarray,
str, datetime.datetime, datetime.date],
end: Union[np.datetime64, pd.Series, np.ndarray, str,
datetime.datetime, datetime.date],
include_first: bool = False,
include_last: bool = True) -> Union[int, np.ndarray]:
'''
Get back a list of numpy dates that are trading days between the start and end
>>> nyse = Calendar.get_calendar(Calendar.NYSE)
>>> nyse.get_trading_days('2005-01-01', '2005-01-08')
array(['2005-01-03', '2005-01-04', '2005-01-05', '2005-01-06', '2005-01-07'], dtype='datetime64[D]')
>>> nyse.get_trading_days(datetime.date(2005, 1, 1), datetime.date(2005, 2, 1))
array(['2005-01-03', '2005-01-04', '2005-01-05', '2005-01-06',
'2005-01-07', '2005-01-10', '2005-01-11', '2005-01-12',
'2005-01-13', '2005-01-14', '2005-01-18', '2005-01-19',
'2005-01-20', '2005-01-21', '2005-01-24', '2005-01-25',
'2005-01-26', '2005-01-27', '2005-01-28', '2005-01-31', '2005-02-01'], dtype='datetime64[D]')
>>> nyse.get_trading_days(datetime.date(2016, 1, 5), datetime.date(2016, 1, 29), include_last = False)
array(['2016-01-06', '2016-01-07', '2016-01-08', '2016-01-11',
'2016-01-12', '2016-01-13', '2016-01-14', '2016-01-15',
'2016-01-19', '2016-01-20', '2016-01-21', '2016-01-22',
'2016-01-25', '2016-01-26', '2016-01-27', '2016-01-28'], dtype='datetime64[D]')
>>> nyse.get_trading_days('2017-07-04', '2017-07-08', include_first = False)
array(['2017-07-05', '2017-07-06', '2017-07-07'], dtype='datetime64[D]')
>>> nyse.get_trading_days(np.datetime64('2017-07-04'), np.datetime64('2017-07-08'), include_first = False)
array(['2017-07-05', '2017-07-06', '2017-07-07'], dtype='datetime64[D]')
'''
s, e = _normalize(start, end, include_first, include_last)
dates = np.arange(s, e, dtype='datetime64[D]')
dates = dates[np.is_busday(dates, busdaycal=self.bus_day_cal)]
return dates
def get_statistics(cls):
# For now not using this to improve performance
# response_values = [cls.get_approved_names_counter(),
# cls.get_waiting_time_priority_queue(unit=UnitTime.HR.value),
# cls.get_waiting_time_regular_queue(unit=UnitTime.DAY.value)]
oldest_draft = Request.get_oldest_draft()
todays_date = get_utc_now().date()
submitted_date = oldest_draft.submittedDate.date()
# note that busday_count does not count the end date provided
delta = np.busday_count(submitted_date, todays_date)
delta = int(delta)
# add one to waiting time to account for specific scenarios
if np.is_busday(todays_date) or delta == 0:
delta += 1
response_values = [
0,
0, #cls.get_waiting_time_priority_queue(unit=UnitTime.HR.value),
delta
]
response = query_result_to_dict(response_keys, response_values)
return response
def _get_ticker_array(self):
self.start_time = self.config.start_time
self.end_time = self.config.end_time
current_day = self.start_time
while current_day != self.end_time:
current_day_str = current_day.strftime("%Y-%m-%d")
if _np.is_busday(current_day_str):
start_day = current_day + _timedelta(hours=9, minutes=30)
end_day = current_day + _timedelta(hours=16)
start_day_str = start_day.strftime("%Y-%m-%dT%H:%M:%S" + self._daylight_savings_offset(current_day))
end_day_str = end_day.strftime("%Y-%m-%dT%H:%M:%S" + self._daylight_savings_offset(current_day))
for t in self.config.tickers:
barset = self._api.get_barset(t, 'minute',
start=start_day_str,
end=end_day_str)
bars = barset[t]
bar_set = [ self._create_bar_backtest(b, t) for b in bars]
self.buffer_bar.extend(bar_set)
_time.sleep(0.5)
current_day += AlpacaDataSource.DELTA
self.buffer_bar.sort(key=lambda x: x.timestamp)
def biz_days_offset(date, count):
date1 = np.busday_offset(date, 0, roll='forward', holidays=US_HOLIDAY_LIST)
if not np.is_busday(date, holidays=US_HOLIDAY_LIST):
print('\nWARNING: the date "' + str(date) + '" is not a business day; ' \
+ 'using the first business day immediately following that date,' \
+ 'namely, "' + str(date1) + '"\n')
biz_date = np.busday_offset(date1, count, holidays=US_HOLIDAY_LIST)
# print('inside utils_general we have value: ' + str(biz_date))
return str(biz_date)
def count_days_mounth(self, month, year):
"""
função que recebe o ano e o mês e retorne a quantidade de dias úteis que ele possui.
Obs: Utilize o Numpy.
"""
days_of_month = self.get_all_dates(month, year)
workdays = np.is_busday(
np.array(days_of_month).astype("datetime64[D]"))
return np.count_nonzero(workdays)
def adicionar_dias_uteis(data, dias):
for dia in range(dias):
data = adicionar_dias_corridos(data=data, dias=1)
while not np.is_busday(
dates=data, weekmask='1111100', holidays=feriados):
data = adicionar_dias_corridos(data=data, dias=1)
return data
def get_market_holidays(start='2000-01-01', end='2019-01-01'):
temp = web.get_data_yahoo('^GDAXI', start=start, end=end)
temp = temp.loc[temp.Volume>0,:]
opens=set(temp.index.values)
opens = pd.to_datetime(list(opens))
weekmask = [1, 1, 1, 1, 1, 0, 0]
busdays = np.arange(start, end, dtype='datetime64[D]')
busdays = pd.to_datetime(busdays[np.is_busday(busdays, weekmask=weekmask)])
holidays = set(busdays).difference(opens)
df = pd.to_datetime(list(holidays)).sort_values()
return df
def meanvec(mean_busday, mean_holiday, t):
num_days = int(t.shape[0]/24)
mu = np.empty((1, t.shape[0]))
for d in range(num_days):
if np.is_busday(t[d*24].astype('datetime64[D]')):
mu[0, d*24:(d+1)*24] = mean_busday
else:
mu[0, d*24:(d+1)*24] = mean_holiday
return mu
def next_n_business_days(start_date, n, include_start=False):
'''
@return: A list of the next n valid business days.
'''
dates = []
if include_start and np.is_busday(start_date):
dates.append(np.datetime64(start_date))
curr_date = start_date
for _ in range(n):
curr_date = next_business_day(curr_date)
dates.append(curr_date)
return dates
def trading_day(self, aDatetime):
"""
Check if a datetime is a trading day based on marketName
output:
True: It is a trading day.
"""
if isinstance(aDatetime, dt.datetime):
aDatetime = aDatetime.date()
if np.is_busday(aDatetime):
return not (np.datetime64(aDatetime)
in self.marketName.holidays().holidays)
else:
return False
def is_trade_day(days):
"""
交易日判断。
"""
if is_non_string_iterable(days):
datetimes = pd.to_datetime(days)
else:
datetimes = pd.DatetimeIndex([days])
is_bdays = np.is_busday(datetimes.to_numpy(dtype='datetime64[D]'),
busdaycal=CHN_A_Calendar)
if not is_non_string_iterable(days):
return is_bdays[0]
return is_bdays
def dateExtractor(x):
years = x.astype('datetime64[Y]').astype(int) + 1970
months = x.astype('datetime64[M]').astype(int) % 12 + 1
daysOfTheYear = x.astype('datetime64[D]').astype(int) - 365 * (years - 1970) - 12
daysOfTheYear = np.digitize(daysOfTheYear, [0, 60, 120, 180, 240, 300, 360])
businessDays = np.is_busday(x.astype('datetime64[D]'))
businessDays = businessDays.astype(int)
hours = np.mod(x.astype('datetime64[h]').astype(int) - 395659, 24)
dayOfTheWeek = np.mod((x.astype('datetime64[D]').astype(int)), 7)
years = years.astype('int64')
months = months.astype('int64')
hourPeriod = np.digitize(hours, [0, 12, 24])
return years, months, daysOfTheYear, businessDays, hours, dayOfTheWeek, hourPeriod
def getdates(stock):
data = get_history(symbol=stock,
start=date.today() - relativedelta(months=+1),
end=date.today()) #specified date
retdata = dict()
#determining the high and low of the month with date
data = data.reset_index()
data = data[['Date', 'Open', 'High', 'Low', 'Close']]
index_high = data['High'].idxmax()
index_low = data['Low'].idxmin()
high_date = data.loc[index_high, :]['Date']
low_date = data.loc[index_low, :]['Date']
retdata['High'] = max(data['High'])
retdata['highDate'] = str(high_date)
retdata['Low'] = min(data['Low'])
retdata['lowDate'] = str(low_date)
retdata['Close'] = data['Close'][len(data) - 1]
price_diff = max(data['High']) - min(data['Low'])
#degree conversion
degree = ((sqrt(price_diff) * 180) - 225) % 360
#calculating number having same degree
n = 3
l = []
for i in range(1, n):
#l.append(round((2*i+2*degree/360-1.25)**2))
l.append(round((2 * i + 2 * degree / 360 + 1.25)**2))
#Predicting future date (low/high)
preddate = []
for i in l:
preddate.append(high_date + relativedelta(days=+i))
preddate.append(low_date + relativedelta(days=+i))
retdata['predictedDates'] = []
for i in preddate:
if not np.is_busday(i):
#print(i,"Not a Business day")
d = i
next = d + timedelta(days=7 -
d.weekday() if d.weekday() > 3 else 1)
retdata['predictedDates'] = retdata['predictedDates'] + [str(next)]
else:
retdata['predictedDates'] = retdata['predictedDates'] + [str(i)]
return retdata
def CheckFunction():
global dateChanged
global todayDate
global portfolio
if dateChanged == True:
#print('Date Changed!')
dateChanged = False
todayDate = todayDate + datetime.timedelta(days = 1)
#print('2020-04-23' in holidays.TR())
while not np.is_busday(todayDate.date()) or (todayDate.date() in holidays.TR()):
todayDate = todayDate + datetime.timedelta(days = 1)
#print("Date passed: " + str(todayDate.date()))
date = str(todayDate.year) + '-' + str(todayDate.month).zfill(2) + '-' + str(todayDate.day).zfill(2)
portfolio.SetDate(date)
stockDatas = []
for stockData in portfolio.stockDatas:
stockDatas.append(stockData.name)
PullStocks(stockDatas, -1)
for stockData in portfolio.stockDatas:
stock = Stocker.Stock(stockData.name)
stock.Initialize()
stock.name = stockData.name
stock.Load('./Resources/Stocks/')
CheckStocks(stock)
PortfolioToUI()
return
def num_days(self, upto_today=False):
'''The number of classes in the semester (from start date to end date
excluding weekends and ExcludedDates). '''
excluded_days = self.excluded_days()
if upto_today and date.today() < self.last_day:
last_day = date.today()
else:
last_day = self.last_day
count = numpy.busday_count(self.first_day,
last_day,
holidays=excluded_days)
if numpy.is_busday(last_day, holidays=excluded_days
): # end date is not included, so add here.
count += 1
return count
def _get_generated_visit_list(sc, sqlContext):
invoice_q = """
select d.customernumber customernumber, d.matnr matnr, d.bill_date bill_date, IF(d.units != 'CS', d.quantity * (f.umrez / f.umren), d.quantity) quantity, d.dlvry_lag dlvry_lag
from
(
select b.customernumber customernumber, b.matnr matnr, b.bill_date bill_date ,b.quantity quantity, b.units units, b.price price, c.dlvry_lag dlvry_lag
from
(
select a.kunag customernumber, a.matnr matnr, a.fkdat bill_date ,a.fklmg quantity, a.meins units, a.netwr price
from skuopt.invoices a
where a.kunag in ('0500083147','0500061438','0500067084','0500058324','0500080723','0500060033','0500068825','0500060917','0500078551','0500076115','0500071747','0500078478','0500078038','0500073982','0500064458','0500268924','0500070702','0500070336','0500076032','0500095883','0500284889')
) b
join
(
select kunnr customernumber, IF(vsbed == '01', 2, 1) dlvry_lag
from mdm.customer
where vkbur='C005'
) c
on
b.customernumber = c.customernumber
) d
join
(
select e.matnr matnr, e.meinh meinh, e.umren umren, e.umrez umrez
from mdm.dim_marm e
) f
on
d.matnr=f.matnr and d.units=f.meinh
where d.bill_date >= '20170903' and d.bill_date <= '20171007'
"""
invoice_raw = sqlContext.sql(invoice_q)
invoice_raw.cache()
data_set = invoice_raw \
.filter(col('quantity') > 0) \
.withColumn('b_date', from_unixtime(unix_timestamp(col('bill_date'), "yyyyMMdd")).cast(DateType())) \
.withColumn('dlvry_date', udf((lambda x: x.strftime('%Y-%m-%d')), StringType())(col('b_date'))) \
.withColumn('bus_day_flag',
udf((lambda x: str(np.is_busday([x])[0])), StringType())(col('dlvry_date')).cast(BooleanType())) \
.withColumn('visit_date',
udf((lambda x, y: str(np.busday_offset(x, -y, roll='backward'))), StringType())(col('dlvry_date'),
col('dlvry_lag'))) \
.select(col('customernumber'), col('visit_date')) \
.distinct()
return None
def businessDayCount(data):
dayOffs = [
'2017-01-02', '2017-01-26', '2017-01-27', '2017-01-30', '2017-01-31',
'2017-02-01', '2017-04-06', '2017-05-01', '2017-05-02', '2017-09-04',
'2018-01-01', '2018-02-12', '2018-02-13', '2018-02-14', '2018-02-15',
'2018-02-16', '2018-02-19', '2018-02-20', '2018-04-25', '2018-04-30',
'2018-05-01', '2018-09-03', '2019-01-01', '2019-02-04', '2019-02-05',
'2019-02-06', '2019-02-07', '2019-02-08', '2019-04-15', '2019-04-30',
'2019-05-01', '2019-09-02'
]
dataParameters = data.split("-")
startDate = dateFormatConverter(dataParameters[0])
endDate = dateFormatConverter(dataParameters[1])
endDayIsBusDay = np.is_busday([endDate], holidays=dayOffs)[0]
workingDayCount = np.busday_count(startDate, endDate,
holidays=dayOffs) + endDayIsBusDay
return workingDayCount
def getLastTrDay(endD):
from datetime import datetime
from datetime import date
import numpy as np
import pandas as pds
refDate = datetime.date(datetime.utcnow())
refHour = datetime.utcnow().hour
lstTDR = endD
if endD >= refDate:
if np.is_busday(refDate):
if refHour > 6: lstTDR = np.busday_offset(refDate, -1, roll='backward')
else: lstTDR = np.busday_offset(endD, -2, roll='backward')
else:
if refHour > 6: lstTDR = np.busday_offset(refDate, 0, roll='backward')
else: lstTDR = np.busday_offset(endD, -2, roll='backward')
buff = pds.to_datetime(lstTDR)
return buff
def Piaui(self):
holidays2020 = np.array(['2020-01-01','2020-01-02','2020-01-03','2020-01-04','2020-01-05','2020-05-15','2020-01-06','2020-02-03','2020-02-10','2020-02-24','2020-02-25','2020-04-09','2020-04-10','2020-04-17','2020-04-21','2020-05-01','2020-05-20','2020-08-11','2020-09-07','2020-10-12','2020-11-02','2020-11-15','2020-12-24','2020-12-25','2020-12-30'],dtype='datetime64')
self.__log("Data de inicio e fim da pesquisa:","green")
dataInit,dataFim = self.__askDate()
start = np.datetime64(dataInit)
end = np.datetime64(dataFim)
date = start
e='PI'
iniAno = np.datetime64('2020-01-01')
while date <= end:
dia = str(date).rsplit('-')[2]
mes = str(date).rsplit('-')[1]
ano = str(date).rsplit('-')[0]
ano = ano[2]+ano[3]
dateStr = ano+mes+dia
if np.is_busday(date,holidays=holidays2020):
edicao = 8819 + np.busday_count(iniAno,date + np.timedelta64(1,'D'),holidays=holidays2020)
edicaoStr = "dj" + dateStr + "_" + str(edicao)
nomeArquivo = "Diario-"+edicaoStr+".pdf"
fileUrl = "http://www.tjpi.jus.br/diarioeletronico/public/"+edicaoStr+".pdf"
downloadFile(fileUrl,nomeArquivo,e)
print("Download:"+nomeArquivo)
date = date + np.timedelta64(1,'D')
print("Arquivos Baixados")
def send_daily_notifications(self):
"""
Send daily summary mail notification. The opposite of notify_user()
:param self:
:return:
"""
# test whether today is a working day (a "business day" or "bday"), and if not then bail out;
# we don't want to bother people with emails at the weekend or on statutory holidays
today = date.today()
holiday_calendar = holidays.UK()
# the test is in two parts: first we check for a holiday, then for a conventional working day
# (in future perhaps allow individual users to choose their own working-day pattern).
# Annoyingly, numpy.is_busday() won't accept objects generated by the holidays module
# as a holiday calendar (it wants an array-like of datetime)
# is today a UK holiday?
if today in holiday_calendar:
return
# is today a working day?
if not is_busday(today, holidays=None):
return
# search through all active users and dispatch notifications
# we treat students and faculty slightly differently so we have different dispatchers for them
# find all students
students = db.session.query(User).filter(
User.active == True, User.roles.any(Role.name == 'student')).all()
student_tasks = group(
dispatch_student_notifications.si(r.id) for r in students
if r is not None)
# find all faculty
faculty = db.session.query(User).filter(
User.active == True, User.roles.any(Role.name == 'faculty')).all()
faculty_tasks = group(
dispatch_faculty_notifications.si(r.id) for r in faculty
if r is not None)
task = group(student_tasks, faculty_tasks)
raise self.replace(task)
def is_business_day(dates: DateOrDates, calendars: Union[str, Tuple[str, ...]] = (), week_mask: Optional[str] = None)\
-> Union[bool, Tuple[bool]]:
"""
Determine whether each date in dates is a business day
:param dates: The input date or dates
:param calendars: Calendars to use for holidays
:param week_mask: Which days are considered weekends (defaults to Saturday and Sunday)
:return: True/False if dates is a single date. A tuple indicating True/False for each date if dates is an iterable
**Examples**
>>> import datetime as dt
>>> is_business_day(dt.date.today())
>>> is_business_day(dt.date(2019, 7, 4), calendars=('NYSE',))
"""
calendar = GsCalendar.get(calendars)
res = np.is_busday(dates, busdaycal=calendar.business_day_calendar(week_mask))
return tuple(res) if isinstance(res, np.ndarray) else res
def gen_cal(start_date='1984-12-31', end_date='2025-12-31'):
busday_cal = get_cal(start_date, end_date)
s_date = np.datetime64(start_date)
e_date = np.datetime64(end_date)
day_num = -1
busday_num = -1
while s_date <= e_date:
day_num += 1
ibd = -1
if np.is_busday(s_date, busdaycal=busday_cal):
busday_num += 1
ibd = 1
res = ibd * ((busday_num << 16) | day_num)
sql_cmd = "INSERT INTO calendar VALUES ('{0:s}', {1:d})".format(
str(s_date), res)
stxdb.db_write_cmd(sql_cmd)
if day_num % 1000 == 0:
print('Inserted {0:s}'.format(str(s_date)))
s_date += np.timedelta64(1, 'D')
def isTradingDay(self, aDatetime):
"""
Check if a datetime is a trading day based on marketName
output:
True: It is a trading day.
"""
# print(__name__ + '::isTradingDay: %s' % (aDatetime,))
aDate = convert_datetime_to_date(aDatetime)
if aDate in self._isTradingDaySet:
return True
if aDate in self._notTradingDaySet:
return False
if np.is_busday(aDate):
ans = not (np.datetime64(aDate)
in self._marketCalendar.holidays().holidays)
else:
ans = False
if ans:
self._isTradingDaySet.add(aDate)
else:
self._notTradingDaySet.add(aDate)
return ans
def RioGrandeDoSul(self):
holidays2020 = np.array(['2020-01-01','2020-02-24','2020-02-25','2020-04-10','2020-04-21','2020-05-01','2020-06-11','2020-06-12','2020-09-07','2020-09-20','2020-10-12','2020-11-02','2020-11-15','2020-11-20','2020-12-08','2020-12-24','2020-12-25','2020-12-30'],dtype='datetime64')
cadernos = ['0','5','7','6','8']
self.__log("Data de inicio e fim da pesquisa:","green")
dataInit,dataFim = self.__askDate()
start = np.datetime64(dataInit)
end = np.datetime64(dataFim)
date = start
e='RS'
while date <= end:
if np.is_busday(date,holidays=holidays2020):
edicao = self.__EdicaoRS(date)
for c in cadernos:
nomeArquivo = "Diario-"+str(date)+"caderno-"+c+".pdf"
fileUrl ="https://www.tjrs.jus.br/servicos/diario_justica/download_edicao.php?tp="+c+"&ed="+edicao
diretorio = e+"/"+c
downloadFile(fileUrl,nomeArquivo,diretorio)
print("Download:"+nomeArquivo)
date = date + np.timedelta64(1,'D')
print("Arquivos Baixados")
#Sigma_no2_temp = functions.xcovmat(xcf_no2_temp, time[0:24*7], time[0:24*7])
#
#plt.figure(figsize=(5,4))
#plt.imshow(Sigma_no2_temp, cmap='gray')
#plt.colorbar()
#plt.xticks(np.arange(0, 24*7, 24))
#plt.yticks(np.arange(0, 24*7, 24))
#plt.xlabel(r'Time (hours)')
#plt.ylabel(r'Time (hours)')
# =============================================================================
# Meteorology data
# =============================================================================
hour = 11
time_date = time.astype('datetime64[D]')
time_busyday = np.is_busday(time_date)
this_no2 = no2[0][time_busyday]
this_no2 = this_no2[hour:-1:24]
index = this_no2 != 0
this_no2 = this_no2[index]
this_no2 = pt_no2.transform(this_no2.reshape(-1, 1))
this_temp = meteo[2][time_busyday]
this_temp = this_temp[hour:-1:24]
this_temp = this_temp[index]
plt.figure(figsize=(4, 4))
plt.hexbin(this_no2, this_temp.reshape(-1, 1), gridsize=(10, 10), cmap='gray')
plt.xlim(-2, 3)
plt.ylim(-15, 30)
def onOffset(self, dt):
day64 = self._to_dt64(dt).astype("datetime64[D]")
return np.is_busday(day64, busdaycal=self.busdaycalendar)
print shop_id
not_work_days= []
file_name = 'flow_per_shop/'+str(shop_id)+'.csv'
shop_file = open(file_name,'rb')
info = pd.read_csv(shop_file)
info['target_dates'] = info['target_dates'].astype(str)
dates = info['target_dates'].values
flow = info['count'].values
count = 0
for i in dates:
# print i
# print count
count +=1
# print ((not np.is_busday(np.datetime64(i)) and not is_exchangeday(i)) or is_holiday(i))
# print ''
not_work_days.append(((not np.is_busday(np.datetime64(i)) and not is_exchangeday(i)) or is_holiday(i)))
not_work_days = np.array(not_work_days,dtype=int)
total_not_workday = not_work_days.sum()
# print total_not_workday
total_workday = not_work_days.shape[0] - total_not_workday
# print total_workday
# show how does it change the customer flow for hoilidays (not work days)
flow_not_work_days = flow * not_work_days
print flow_not_work_days.sum()/total_not_workday
flow_work_days = flow * (1-not_work_days)
print flow_work_days.sum()/total_workday
relation = float(float(flow_not_work_days.sum())/float(total_not_workday))/float(float(flow_work_days.sum())/float(total_workday))
def filter_weekends(rows, include):
"""Either only include weekends or entirely exclude them."""
return [row for row in rows if include ^
numpy.is_busday(time.datetime_from_row(row).date())]
