def parse_lutkepohl_data(path): # pragma: no cover
"""
Parse data files from Lutkepohl (2005) book
Source for data files: www.jmulti.de
"""
from statsmodels.compat.pandas import datetools as dt
from collections import deque
from datetime import datetime
import pandas
import re
regex = re.compile(asbytes('<(.*) (\w)([\d]+)>.*'))
with open(path, 'rb') as f:
lines = deque(f)
to_skip = 0
while asbytes('*/') not in lines.popleft():
#while '*/' not in lines.popleft():
to_skip += 1
while True:
to_skip += 1
line = lines.popleft()
m = regex.match(line)
if m:
year, freq, start_point = m.groups()
break
data = np.genfromtxt(path, names=True, skip_header=to_skip + 1)
n = len(data)
# generate the corresponding date range (using pandas for now)
start_point = int(start_point)
year = int(year)
offsets = {
asbytes('Q'): frequencies.BQuarterEnd(),
asbytes('M'): frequencies.BMonthEnd(),
asbytes('A'): frequencies.BYearEnd()
}
# create an instance
offset = offsets[freq]
inc = offset * (start_point - 1)
start_date = offset.rollforward(datetime(year, 1, 1)) + inc
offset = offsets[freq]
from pandas import DatetimeIndex # pylint: disable=E0611
date_range = DatetimeIndex(start=start_date, freq=offset, periods=n)
return data, date_range
def parse_lutkepohl_data(path): # pragma: no cover
"""
Parse data files from Lütkepohl (2005) book
Source for data files: www.jmulti.de
"""
from collections import deque
from datetime import datetime
import re
regex = re.compile(asbytes(r'<(.*) (\w)([\d]+)>.*'))
with open(path, 'rb') as f:
lines = deque(f)
to_skip = 0
while asbytes('*/') not in lines.popleft():
#while '*/' not in lines.popleft():
to_skip += 1
while True:
to_skip += 1
line = lines.popleft()
m = regex.match(line)
if m:
year, freq, start_point = m.groups()
break
data = (pd.read_csv(path, delimiter=r"\s+",
header=to_skip + 1).to_records(index=False))
n = len(data)
# generate the corresponding date range (using pandas for now)
start_point = int(start_point)
year = int(year)
offsets = {
asbytes('Q'): frequencies.BQuarterEnd(),
asbytes('M'): frequencies.BMonthEnd(),
asbytes('A'): frequencies.BYearEnd()
}
# create an instance
offset = offsets[freq]
inc = offset * (start_point - 1)
start_date = offset.rollforward(datetime(year, 1, 1)) + inc
offset = offsets[freq]
date_range = pd.date_range(start=start_date, freq=offset, periods=n)
return data, date_range
