Using something like PyParsing is probably better, but if you don't
want to use it you may use something like this:
raw_data = """
Field f29227: Ra=20:23:46.54 Dec=+67:30:00.0 MJD=53370.06797690 Frames
5 Set 1
Field f31448: Ra=20:24:58.13 Dec=+79:39:43.9 MJD=53370.06811620 Frames
5 Set 2
Field f31226: Ra=20:24:45.50 Dec=+78:26:45.2 MJD=53370.06823860 Frames
5 Set 3
Field f31004: Ra=20:25:05.28 Dec=+77:13:46.9 MJD=53370.06836020 Frames
5 Set 4
Field f30782: Ra=20:25:51.94 Dec=+76:00:48.6 MJD=53370.06848210 Frames
5 Set 5
Field f30560: Ra=20:27:01.82 Dec=+74:47:50.3 MJD=53370.06860400 Frames
5 Set 6
Field f30338: Ra=20:28:32.35 Dec=+73:34:52.0 MJD=53370.06872620 Frames
5 Set 7
Field f30116: Ra=20:30:21.70 Dec=+72:21:53.6 MJD=53370.06884890 Frames
5 Set 8
Field f29894: Ra=20:32:28.54 Dec=+71:08:55.0 MJD=53370.06897070 Frames
5 Set 9
Field f29672: Ra=20:34:51.89 Dec=+69:55:56.6 MJD=53370.06909350 Frames
5 Set 10"""
# from each line extract the fields: id, ra, dec, mjd
# even if they are swapped
data = []
for line in raw_data.lower().splitlines():
if line.startswith("field"):
parts = line.split()
record = {"id": int(parts[1][1:-1])}
for part in parts[2:]:
if "=" in part:
title, field = part.split("=")
record[title] = field
data.append(record)
print data
-----------------
Stefan Behnel:
>You can use named groups in a single regular expression.<
Can you show how to use them in this situation when fields can be
swapped?
Bye,
bearophile
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