def get_date_range(df, n_out):
'''
Takes in the processed dataset and returns a list of n_out
business days, beginning from tomorrow (given the next 5 days
of prediction
'''
start = (pd.Timestamp(df.index.max()) + relativedelta(days=1))
date = set()
for i in range(
n_out + 1
): # add an extra day to the set to allow for duplicates caused by Sat and Sun - then remove
date.add(start + pd.tseries.offsets.BusinessDay(n=i))
dates = list(date)
dates.sort()
return dates[:
5] # in case 6 are returned - n_out + 1 is necessary for weekly duplicating issue.
def test_isinstance_default(self):
from datetime import date
to_check = []
to_check.append(date(1999, 1, 1))
to_check.append(date.today())
date.set(2001, 1, 2)
to_check.append(date.today())
date.add(2001, 1, 3)
to_check.append(date.today())
to_check.append(date.today())
date.set(date(2001, 1, 4))
to_check.append(date.today())
date.add(date(2001, 1, 5))
to_check.append(date.today())
to_check.append(date.today())
date.set(d(2001, 1, 4))
to_check.append(date.today())
date.add(d(2001, 1, 5))
to_check.append(date.today())
to_check.append(date.today())
for inst in to_check:
self.failIf(isinstance(inst, date), inst)
self.failIf(inst.__class__ is date, inst)
self.failUnless(isinstance(inst, d), inst)
self.failUnless(inst.__class__ is d, inst)
def test_add_date_supplied(self):
from datetime import date
date.add(d(2001, 1, 2))
date.add(date(2001, 1, 3))
compare(date.today(), d(2001, 1, 2))
compare(date.today(), d(2001, 1, 3))