Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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133 lines
4.6 KiB
133 lines
4.6 KiB
'''
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$Id: tzfile.py,v 1.8 2004/06/03 00:15:24 zenzen Exp $
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'''
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from datetime import datetime
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from struct import unpack, calcsize
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from pytz.tzinfo import StaticTzInfo, DstTzInfo, memorized_ttinfo
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from pytz.tzinfo import memorized_datetime, memorized_timedelta
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def _byte_string(s):
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"""Cast a string or byte string to an ASCII byte string."""
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return s.encode('ASCII')
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_NULL = _byte_string('\0')
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def _std_string(s):
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"""Cast a string or byte string to an ASCII string."""
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return str(s.decode('ASCII'))
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def build_tzinfo(zone, fp):
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head_fmt = '>4s c 15x 6l'
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head_size = calcsize(head_fmt)
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(magic, format, ttisgmtcnt, ttisstdcnt, leapcnt, timecnt,
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typecnt, charcnt) = unpack(head_fmt, fp.read(head_size))
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# Make sure it is a tzfile(5) file
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assert magic == _byte_string('TZif'), 'Got magic %s' % repr(magic)
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# Read out the transition times, localtime indices and ttinfo structures.
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data_fmt = '>%(timecnt)dl %(timecnt)dB %(ttinfo)s %(charcnt)ds' % dict(
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timecnt=timecnt, ttinfo='lBB' * typecnt, charcnt=charcnt)
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data_size = calcsize(data_fmt)
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data = unpack(data_fmt, fp.read(data_size))
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# make sure we unpacked the right number of values
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assert len(data) == 2 * timecnt + 3 * typecnt + 1
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transitions = [memorized_datetime(trans)
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for trans in data[:timecnt]]
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lindexes = list(data[timecnt:2 * timecnt])
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ttinfo_raw = data[2 * timecnt:-1]
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tznames_raw = data[-1]
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del data
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# Process ttinfo into separate structs
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ttinfo = []
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tznames = {}
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i = 0
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while i < len(ttinfo_raw):
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# have we looked up this timezone name yet?
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tzname_offset = ttinfo_raw[i + 2]
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if tzname_offset not in tznames:
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nul = tznames_raw.find(_NULL, tzname_offset)
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if nul < 0:
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nul = len(tznames_raw)
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tznames[tzname_offset] = _std_string(
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tznames_raw[tzname_offset:nul])
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ttinfo.append((ttinfo_raw[i],
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bool(ttinfo_raw[i + 1]),
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tznames[tzname_offset]))
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i += 3
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# Now build the timezone object
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if len(ttinfo) == 1 or len(transitions) == 0:
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ttinfo[0][0], ttinfo[0][2]
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cls = type(zone, (StaticTzInfo,), dict(
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zone=zone,
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_utcoffset=memorized_timedelta(ttinfo[0][0]),
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_tzname=ttinfo[0][2]))
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else:
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# Early dates use the first standard time ttinfo
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i = 0
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while ttinfo[i][1]:
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i += 1
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if ttinfo[i] == ttinfo[lindexes[0]]:
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transitions[0] = datetime.min
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else:
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transitions.insert(0, datetime.min)
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lindexes.insert(0, i)
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# calculate transition info
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transition_info = []
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for i in range(len(transitions)):
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inf = ttinfo[lindexes[i]]
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utcoffset = inf[0]
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if not inf[1]:
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dst = 0
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else:
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for j in range(i - 1, -1, -1):
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prev_inf = ttinfo[lindexes[j]]
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if not prev_inf[1]:
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break
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dst = inf[0] - prev_inf[0] # dst offset
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# Bad dst? Look further. DST > 24 hours happens when
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# a timzone has moved across the international dateline.
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if dst <= 0 or dst > 3600 * 3:
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for j in range(i + 1, len(transitions)):
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stdinf = ttinfo[lindexes[j]]
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if not stdinf[1]:
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dst = inf[0] - stdinf[0]
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if dst > 0:
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break # Found a useful std time.
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tzname = inf[2]
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# Round utcoffset and dst to the nearest minute or the
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# datetime library will complain. Conversions to these timezones
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# might be up to plus or minus 30 seconds out, but it is
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# the best we can do.
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utcoffset = int((utcoffset + 30) // 60) * 60
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dst = int((dst + 30) // 60) * 60
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transition_info.append(memorized_ttinfo(utcoffset, dst, tzname))
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cls = type(zone, (DstTzInfo,), dict(
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zone=zone,
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_utc_transition_times=transitions,
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_transition_info=transition_info))
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return cls()
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if __name__ == '__main__':
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import os.path
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from pprint import pprint
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base = os.path.join(os.path.dirname(__file__), 'zoneinfo')
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tz = build_tzinfo('Australia/Melbourne',
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open(os.path.join(base, 'Australia', 'Melbourne'), 'rb'))
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tz = build_tzinfo('US/Eastern',
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open(os.path.join(base, 'US', 'Eastern'), 'rb'))
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pprint(tz._utc_transition_times)
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