def main():
args = arg_parser().parse_args()
util.set_logger(args.verbose, args.logfile, args.log)
if len(args.img) > 1:
if not args.output.startswith("."):
logger.critical(
"With more than one input IMG file, the --output must start "
f"with a period, and it does not: {args.output}"
)
sys.exit()
if not args.db.startswith("."):
logger.critical(
"With more than one input IMG file, the --db must start with "
f"a period, and it does not: {args.db}"
)
sys.exit()
gainsinfo = pvl.load(args.gains)
with util.main_exceptions(args.verbose):
if len(args.img) == 1:
# No need to fire up the multiprocessing for just one image.
db_path = write_json(
EDR_Stats(
args.img[0],
util.path_w_suffix(args.output, args.img[0]),
gainsinfo,
args.histmin,
args.histmax,
keep=args.keep
),
args.db,
args.img[0],
)
logger.info(f"Wrote {db_path}")
else:
with concurrent.futures.ProcessPoolExecutor(
max_workers=args.max_workers
) as executor:
for img, histats, in zip(args.img, executor.map(
EDR_Stats,
args.img,
map(util.path_w_suffix, repeat(args.output), args.img),
repeat(gainsinfo),
repeat(args.histmin),
repeat(args.histmax),
repeat(args.keep),
)):
db_path = write_json(histats, args.db, img)
logger.info(f"Wrote {db_path}")
return
def color(obsid, conf_dir: Path, parent: Path, db_list: list, keep=False):
colors = get_cubes(f"{obsid}_COLOR*.cub", parent)
# HiColorNorm - only for color
# takes *COLOR[4|5].cub
# creates *UNFILTERED_COLOR[4|5].cub and *COLOR[4|5].HiColorNorm.cub
HiColorNorm.HiColorNorm(
colors,
"_COLOR.cub",
pvl.load(conf_dir / "HiColorNorm.conf"),
db_list=db_list,
keep=keep,
)
# HiBeautify - only for color
# takes tmp/*.HiColorNorm.cub
# creates *IRB.cub and *RGB.cub
for x in colors:
x.with_suffix(".HiColorNorm.cub")
HiBeautify.HiBeautify(
[x.with_suffix(".HiColorNorm.cub") for x in colors],
pvl.load(conf_dir / "HiBeautify.conf"),
)
return
def main():
args = arg_parser().parse_args()
util.set_logger(args.verbose, args.logfile, args.log)
oid = hirise.get_ObsID_fromfile(args.img[0])
parent = Path(args.img[0]).parent
if args.img == 1:
imgs = parent.glob(f"{oid}*.img") + parent.glob(f"{oid}*.IMG")
else:
imgs = args.img
db_list = None
with util.main_exceptions(args.verbose):
try:
get_cubes(f"{oid}*balance.cub", parent)
except FileNotFoundError:
chancubes = edr2stitch(
imgs,
args.conf_dir,
max_workers=args.max_workers,
keep=args.keep
)
db_list = [x.db for x in chancubes]
if args.color:
color(oid, args.conf_dir, parent, db_list, keep=args.keep)
if args.precision or args.jack:
jack = False
if args.jack:
jack = True
else:
slithers = list(parent.glob("*.slither.txt"))
# HiPrecisionInit to determine if you need to HiNoProj or
# HiJACK takes *slither.txt
(HiJACK_flags, _, _) = HiPrecisionInit.check(
slithers, pvl.load(args.conf_dir / "HiPrecisionInit.conf")
)
if any(HiJACK_flags):
jack = True
precision(oid, args.conf_dir, parent, hijack=jack, keep=args.keep)
return
def precision(obsid, conf_dir: Path, parent: Path, hijack=False, keep=False):
if hijack:
bal_cubs = get_cubes(f"{obsid}*balance.cub", parent)
# HiJACK - starts with balance cubes, needs all the colors
# takes all color balance.cubs
# creates a whole slow of files in HiJACK/
HiJACK.HiJACK(
bal_cubs,
conf_dir,
outdir=(parent / "HiJACK"),
plot=False,
keep=keep,
)
else:
red_bal_cubs = get_cubes(f"{obsid}_RED*balance.cub", parent)
# HiNoProj - alternate to HiccdStitch, starts with balance cubes
# takes only REDS: *RED*balance.cub
# creates PSP_010502_2090_RED.NOPROJ.cub
HiNoProj.HiNoProj(
red_bal_cubs, pvl.load(conf_dir / "HiNoProj.conf"), keep=keep
)
return
def check_lut(img: os.PathLike):
"""Checks whether a stored look up table (LUT) matches a known LUT."""
# Original author of this function was Robert King, in December 2006.
# logger.info(check_lut.__doc__)
img_pvl = pvl.load(str(img))
lut_type = img_pvl["INSTRUMENT_SETTING_PARAMETERS"][
"MRO:LOOKUP_TABLE_TYPE"
]
if "STORED" in lut_type:
lut = dict()
# dec.28.06 zero-filled LUT (ie, the image was not LUT'ed)
lut["897256b6709e1a4da9daba92b6bde39ccfccd8c1"] = None
# dec.19.06 build
lut["35c8318042da3c30949f3ecb9f3876de524f158f"] = 300
lut["874f6c26fe21577bc88f8986ec5643ff43747908"] = 301
lut["02ae8c3834160614a65190cfee95fd62a2227447"] = 302
lut["919d8f16363d1161b1843783c713564110cef5cc"] = 303
lut["086adb2152c5f47741ce16e128ad3533fff1fa31"] = 304
lut["020b40e1aab312ddf9fce61d5eb7cacd33bbeaec"] = 305
lut["b45de94586e5ab539cc1c0b72d0e0267765e3b69"] = 306
lut["8e4cd2ba1646d117afb9af658b9f762fcb0bcc48"] = 307
lut["6227fbb77374afe54ba5de26a5af2cfb954f7783"] = 308
lut["31d88a70dc4a6c9cf4991eb3f7a07c3b5df624a9"] = 309
lut["166d23b2c7e71acebfaf07798a697670bff55df9"] = 310
lut["d0f29aae50c21e443d7d188e7937b249fd8ba447"] = 311
lut["a44541a295899f7fe55bf038dee9d5c130190c08"] = 312
lut["58914a9cf58f4abbf7adc60357d8daa0d8104794"] = 313
lut["bd416e8935556cd22e6caae1e9541ea42fc32fe7"] = 314
lut["332a862b5963d55716bc00185906ab0195372e03"] = 315
lut["b7d5169700fa7925adfc9ab4463c4c0c64cb9c31"] = 316
lut["de35fb5719fabf3da400de015cf2c3155bbfe1ac"] = 317
lut["55b78c9ca2822a6f87b3c3884d95034d4abbb725"] = 318
lut["2aaf567e760eb313eca50cff9b3ed74f035af0d5"] = 319
lut["43a31577737b140eecc327d09f0900c182dddbe3"] = 320
lut["79d29aa8e13dd1991f810894821ebc518bcf3ff3"] = 321
lut["dca0da083569fc25b910611f7d43a72738923d59"] = 322
lut["809c4a81505eced9203e4648d7905a3de9c71f7e"] = 323
lut["349bf694a6b11ee3e47956189aebeab234be4e9e"] = 324
lut["063a2d3ed43bb1b47e73a38fbe49091ac4183c3c"] = 325
lut["9ea3b154b83925f65c36888d0981b84eed35baa6"] = 326
lut["4ca66adf72eefe25bc53edc801c1c71e99cf4ab9"] = 327
# may.16.05 build
lut["23f9889760466065c78b070aa83097229c391ba1"] = 200
lut["d46c82527435d5b8a3b4a329f4a80ec80ed3a6e5"] = 201
lut["a814e4a5a679d1ca710c92b745f79c6d2a1c2f43"] = 202
lut["b67f6fb3adb08c25955878e5f685846c3ede6194"] = 203
lut["7c03fd37caac821554e6c9ebe593ca60200cee4b"] = 204
lut["6c21207a2e78517a8e069f99efd8f3c549ace95d"] = 205
lut["b21a8cb5dfee2be1eaf5e7c8ce2b7b30c7f385ef"] = 206
lut["801e83161358bf9a4a00a76c8970454521c7aff8"] = 207
lut["7223df1f14d33a7a57b96d2d9d39b6ae526a1f8b"] = 208
lut["90b01c7c6055ea580293771601e6f31cc966671c"] = 209
lut["a23833f7421ae17c066010b1150feb696b7dbb2c"] = 210
lut["9b20c521c050c37faa959d84e6f1031f4ad42ac6"] = 211
lut["80b6d126c6ed96d1635bb0755ed701cefb001c72"] = 212
lut["598df42422e6167304db3d9e2613912a62f80931"] = 213
lut["d02cda160d162001fbd515a609837320058572c6"] = 214
lut["876a7779d8418e1a0cb9c9a4312cb65f68fd6d0e"] = 215
lut["fbc4af8337bd8e68659ffa9bd016ce3d9aa1892b"] = 216
lut["2948ffacdc179dba578fcf162234a61f3ef5e19c"] = 217
lut["010344d1afc116b92a3e5a5c3884a5c3d4f1997d"] = 218
lut["7904b0a9c14194b6209f02161be15fe6d1416926"] = 219
lut["a57657074cb1059b4529854e2b64db750b78ec93"] = 220
lut["e55530adfd7b4f7bb9715bff81870f32c528c559"] = 221
lut["e8ed81aa0c81a4d54b43d8a94fcd75133585a32c"] = 222
lut["901e6113448d3de51fff452c67d4b9f93f700a9c"] = 223
lut["258f9f498804b3bcfb4fea679df5aa528b71021b"] = 224
lut["71305d279ae2f2b2915fe33ceff59bd1038f6df5"] = 225
lut["53d91396bee5b7f8c4026343e4145b1b54269a47"] = 226
lut["07c0e1a8453a53232ccdb09d183612dd33e28ecd"] = 227
# sep.04.04 build
lut["3b2797fbf2588e6815f5cef7fa33ee4d3d9ef7db"] = 100
lut["37e4bf9213d992ce974caa936211099fbf63befc"] = 101
lut["da37672307cb95fe51b381fad59c68c537f7dff4"] = 102
lut["23f795926899e5bf1f32f0126418be4e91ac0f94"] = 103
lut["3f1835f9a832cd5c31ed4eb8276a964dcbf7ce12"] = 104
lut["95f2043d5eb69c2c5d987bccf8ffd6714cbfc333"] = 105
lut["2cd93e0418b89329ddf94daf44bfe605884d2e7f"] = 106
lut["51817ef40212588e80155d03e19fcdc586ef7781"] = 107
lut["8d2ec79837e6cb684a54068218f225262cc392e3"] = 108
lut["9a9802f68a3828a0003a302b553364fd46cc1797"] = 109
lut["f2a728884a478c0826a8c52700a6fdc53cf645e7"] = 110
lut["247b510cfc32ec50c2029fef7a9e2dc1e906f807"] = 111
lut["9d37b56fec19fecc73e90c183c74a17de7e29bd1"] = 112
lut["99e4a80b7acc14d0c903f45a379cd43d14ab471e"] = 113
lut["c9cc0c2dce9f053a1fb00fac37540c27bbe287c3"] = 114
lut["d1f7ec6a8a8ba2a249c04b8a05571bc352fbefd9"] = 115
lut["9544bb6b8c664413b76d8045f0291caf74d30654"] = 116
lut["d81ed931335a16a84c5b5335d533ea54018bcc75"] = 117
lut["e5dc08252003d7e9b4efa7ee6491387b4e0912c0"] = 118
lut["c4bfc2c546e588e3036333d485ede76d3619d407"] = 119
lut["ac3e23a5bd22d6300e46e7d30671bac3593858d5"] = 120
lut["687d908ad9821d90a5efb6a9661bce6620a21630"] = 121
lut["16df8d98efe62be7e18a1a83e29e15dcd70390d9"] = 122
lut["802b52209dda8c0d20a0889cb8dd4d1432bc08f2"] = 123
lut["b054f3a6bd8ddf33c7361b73aef1357fbf0dc894"] = 124
lut["bfdb8f8a90e3f7e1d8495a878a42cfb790bb7e88"] = 125
lut["49584c404d0eb35f96d947b7498d90f8deb9138c"] = 126
lut["386ef62256956ef1d42ab53fe5c08abdb6faadd2"] = 127
with open(img, mode="rb") as f:
f.seek(32768 + 800)
buf = f.read(16384)
return lut[hashlib.sha1(buf).hexdigest()]
return None
def edr2stitch(
images,
conf_dir,
bitflipwidth=0,
lis_tolerance=1.0,
max_workers=None,
keep=False,
):
chids = list()
edr_conf = pvl.load(conf_dir / "EDR_Stats_gains_config.pvl")
hical_conf = HiCal.conf_setup(
pvl.load(conf_dir / "HiCal.conf"),
pvl.load(conf_dir / "NoiseFilter.conf"),
)
with concurrent.futures.ProcessPoolExecutor(
max_workers=max_workers
) as executor:
future_dbs = dict()
for i in images:
out_edr = util.path_w_suffix(".EDR_Stats.cub", i)
out_hical = util.path_w_suffix(".HiCal.cub", out_edr)
f = executor.submit(
edr_cal,
i,
out_edr,
out_hical,
edr_conf,
hical_conf,
conf_dir / "HiCal.conf",
bitflipwidth=bitflipwidth,
lis_tolerance=lis_tolerance,
keep=False,
)
future_dbs[f] = out_hical
for future in concurrent.futures.as_completed(future_dbs):
out_hical = future_dbs[future]
chids.append(ChannelCube(out_hical, future.result()))
# HiStitch
# get Channel pairs
cids = list()
stitch_conf = pvl.load(conf_dir / "HiStitch.conf")
with concurrent.futures.ProcessPoolExecutor(
max_workers=max_workers
) as executor:
future_tuples = list()
for chid1, chid2 in get_CCDpairs(chids):
f = executor.submit(
# (db, o_path) = HiStitch.HiStitch(
HiStitch.HiStitch,
chid1.nextpath,
chid2.nextpath,
chid1.db,
chid2.db,
".HiStitch.cub",
stitch_conf,
keep=keep,
)
future_tuples.append(f)
for future in concurrent.futures.as_completed(future_tuples):
(db, o_path) = future.result()
cid = HiccdStitch.HiccdStitchCube(o_path)
cid.gather_from_db(db)
cids.append(cid)
# HiccdStitch, makes balance cubes
# need to separate by color:
color_groups = get_color_groups(cids)
for color_group in color_groups.values():
db, out_stitch = HiccdStitch.HiccdStitch(
color_group,
".HiccdStitch.cub",
pvl.load(conf_dir / "HiccdStitch.conf"),
sline=None,
eline=None,
keep=keep,
)
# HiColorInit
# takes *balance.cub
# creates *[IR|BG]*.balance.precolor.cub
# Can then run JitPlot on these *.balance.precolor.cub
HiColorInit.HiColorInit(
[c.nextpath for c in cids], ".precolor.cub", keep=keep
)
# HiJitReg
# takes tmp/*balance.cub tmp/*balance.precolor.cub
# creates *regdef.pvl and *flat.tab files
for_jitreg = list()
for color, balcubes in color_groups.items():
if color == "RED":
for c in balcubes:
for_jitreg.append(c.nextpath)
else:
for c in balcubes:
for_jitreg.append(c.nextpath.with_suffix(".precolor.cub"))
HiJitReg.HiJitReg(
for_jitreg, pvl.load(conf_dir / "HiJitReg.conf"), keep=keep
)
# HiSlither
# takes same as HiJitReg (and assumes its products are available.
# creates *slither.txt, *slither.cub, and *COLOR[4|5].cub
# Can then run SlitherStats on the *slither.txt
HiSlither.HiSlither(for_jitreg)
return chids