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when updating GFA columns record which nights changed.
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@weaverba137
weaverba137 committed Sep 17, 2024
1 parent 1896576 commit 4e7406e
Showing 1 changed file with 109 additions and 55 deletions.
164 changes: 109 additions & 55 deletions bin/desi_tsnr_afterburner
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Original file line number Diff line number Diff line change
Expand Up @@ -841,6 +841,13 @@ def add_skymags_columns(exposure_table,skymags_table) :


def main():
"""Entry-point for command-line scripts.
Returns
-------
:class:`int`
An integer suitable for passing to :func:`sys.exit`.
"""
log = get_logger()

args=parse()
Expand Down Expand Up @@ -935,8 +942,6 @@ def main():
log.warning("Couldn't find the FRAMES HDU, looking for old format name TSNR2_FRAME")
preexisting_tsnr2_frame_table = read_table(args.outfile,"TSNR2_FRAME")



# starting computing
# one night at a time

Expand All @@ -948,7 +953,7 @@ def main():
Parameters
----------
count : :class:`int`
A MPI rank number. This may be unused.
A MPI rank number. This may be unused inside this function.
night : :class:`str`
The night to analyze.
rows : :class:`list`
Expand Down Expand Up @@ -1137,6 +1142,7 @@ def main():
add_skymags_columns(tsnr2_expid_table,skymags_table)

gfa_table = None
gfa_nights = list()

if args.gfa_proc_dir is not None:
try:
Expand All @@ -1145,70 +1151,118 @@ def main():
log.error(e)
log.error("could not read some files in {}".format(args.gfa_proc_dir))
args.gfa_proc_dir = None

if args.gfa_proc_dir is not None and gfa_table is not None:
e2i = {e: i for i, e in enumerate(gfa_table["EXPID"])}
jj = []
ii = []
for j, e in enumerate(tsnr2_expid_table["EXPID"]):
if e in e2i:
jj.append(j)
ii.append(e2i[e])

cols = ("TRANSPARENCY", "FWHM_ASEC", "FIBER_FRACFLUX", "FIBER_FRACFLUX_ELG",
"FIBER_FRACFLUX_BGS", "FIBERFAC", "FIBERFAC_ELG", "FIBERFAC_BGS",
"SKY_MAG_AB", "AIRMASS")
for col in cols:
if col in gfa_table.dtype.names:
if col == "FWHM_ASEC":
col2 = "SEEING_GFA"
else:
col2 = col + "_GFA"
tsnr2_expid_table[col2] = np.zeros(len(tsnr2_expid_table), dtype=float)
if np.isfinite(gfa_table[col][ii]).all():
tsnr2_expid_table[col2][jj] = gfa_table[col][ii]
else:
log.warning("gfa_table column '%s' contains NaN or other non-finite values. Invalid values will be replaced with zero (0).", col)
good_values = gfa_table[col][ii].copy()
good_values[~np.isfinite(good_values)] = 0
tsnr2_expid_table[col2][jj] = good_values

if args.gfa_proc_dir is not None and (args.skymags is not None or args.compute_skymags):
#
# Assuming NaN are replaced with zero above, compute_efftime() will return zero for the affected rows.
# That is, there are no divide-by-zero or other errors induced by replacing NaN with zero.
# It is not unusual for GFA processing to be delayed or otherwise to be
# out of sync with the current night. Therefore, when adding GFA
# data to the exposures, make a note of any nights where the GFA
# data changed, so this can be propagated to the tiles file.
#
efftime_dark, efftime_bright, efftime_backup = compute_efftime(tsnr2_expid_table[jj])
for col in ("EFFTIME_DARK_GFA", "EFFTIME_BRIGHT_GFA", "EFFTIME_BACKUP_GFA"):
tsnr2_expid_table[col] = np.zeros(len(tsnr2_expid_table), dtype=float)
tsnr2_expid_table["EFFTIME_DARK_GFA"][jj] = efftime_dark
tsnr2_expid_table["EFFTIME_BRIGHT_GFA"][jj] = efftime_bright
tsnr2_expid_table["EFFTIME_BACKUP_GFA"][jj] = efftime_backup
goaltype = tsnr2_expid_table["GOALTYPE"]
tsnr2_expid_table["EFFTIME_GFA"] = tsnr2_expid_table["EFFTIME_DARK_GFA"] # default
tsnr2_expid_table["EFFTIME_GFA"][goaltype == "bright"] = tsnr2_expid_table["EFFTIME_BRIGHT_GFA"][goaltype == "bright"]
tsnr2_expid_table["EFFTIME_GFA"][goaltype == "backup"] = tsnr2_expid_table["EFFTIME_BACKUP_GFA"][goaltype == "backup"]

# end of loop on nights

if gfa_table is not None:
#
# Perform a join of gfa_table & tsnr2_expid_table.
#
e2i = {e: i for i, e in enumerate(gfa_table["EXPID"])}
jj = []
ii = []
for j, e in enumerate(tsnr2_expid_table["EXPID"]):
if e in e2i:
jj.append(j)
ii.append(e2i[e])
tsnr2_nights = tsnr2_expid_table["NIGHT"][jj]
#
# For daily reductions, all of the GFA columns should already exist.
#
cols = ("TRANSPARENCY", "FWHM_ASEC", "FIBER_FRACFLUX", "FIBER_FRACFLUX_ELG",
"FIBER_FRACFLUX_BGS", "FIBERFAC", "FIBERFAC_ELG", "FIBERFAC_BGS",
"SKY_MAG_AB", "AIRMASS")
for col in cols:
if col in gfa_table.dtype.names:
if col == "FWHM_ASEC":
col2 = "SEEING_GFA"
else:
col2 = col + "_GFA"
if col2 not in tsnr2_expid_table.colnames:
tsnr2_expid_table[col2] = np.zeros(len(tsnr2_expid_table), dtype=float)
gfa_values = gfa_table[col][ii]
expid_values = tsnr2_expid_table[col2][jj]
changed_values = (expid_values != gfa_values) & np.isfinite(gfa_values)
gfa_nan = ~np.isfinite(gfa_values)
if changed_values.any():
expid_values[changed_values] = gfa_values[changed_values]
gfa_nights += tsnr2_nights[changed_values].tolist()
tsnr2_expid_table[col2][jj] = expid_values
if gfa_nan.any():
good_values = gfa_values.copy()
good_values[gfa_nan] = 0
#
# If we are working with exposure tables with historical data, chances
# are this correction has already been applied.
#
gfa_nan_changed_values = (tsnr2_expid_table[col2][jj] != good_values)
if gfa_nan_changed_values.any():
log.warning("gfa_table column '%s' contains NaN or other non-finite values. Invalid values will be replaced with zero (0).", col)
tsnr2_expid_table[col2][jj] = good_values
gfa_nights += tsnr2_nights[gfa_nan_changed_values].tolist()

if args.skymags is not None or args.compute_skymags:
#
# Assuming NaN are replaced with zero above, compute_efftime() will return zero for the affected rows.
# That is, there are no divide-by-zero or other errors induced by replacing NaN with zero.
#
efftime_dark, efftime_bright, efftime_backup = compute_efftime(tsnr2_expid_table[jj])
efftime_columns = {"EFFTIME_DARK_GFA": efftime_dark,
"EFFTIME_BRIGHT_GFA": efftime_bright,
"EFFTIME_BACKUP_GFA": efftime_backup}
goaltype = tsnr2_expid_table["GOALTYPE"][jj]
efftime_gfa = tsnr2_expid_table["GOALTYPE"][jj]
for col in efftime_columns:
if col not in tsnr2_expid_table.colnames:
tsnr2_expid_table[col] = np.zeros(len(tsnr2_expid_table), dtype=float)
gfa_values = efftime_columns[col]
expid_values = tsnr2_expid_table[col][jj]
changed_values = (expid_values != gfa_values)
if changed_values.any():
expid_values[changed_values] = gfa_values[changed_values]
gfa_nights += tsnr2_nights[changed_values].tolist()
tsnr2_expid_table[col][jj] = expid_values
if col == "EFFTIME_DARK_GFA":
# tsnr2_expid_table["EFFTIME_GFA"] = tsnr2_expid_table["EFFTIME_DARK_GFA"] # default
efftime_gfa = expid_values
elif col == "EFFTIME_BRIGHT_GFA":
# tsnr2_expid_table["EFFTIME_GFA"][goaltype == "bright"] = tsnr2_expid_table["EFFTIME_BRIGHT_GFA"][goaltype == "bright"]
efftime_gfa[goaltype == "bright"] = expid_values[goaltype == "bright"]
else:
# tsnr2_expid_table["EFFTIME_GFA"][goaltype == "backup"] = tsnr2_expid_table["EFFTIME_BACKUP_GFA"][goaltype == "backup"]
efftime_gfa[goaltype == "backup"] = expid_values[goaltype == "backup"]
tsnr2_expid_table["GOALTYPE"][jj] = efftime_gfa

gfa_nights = np.unique(np.array(gfa_nights))
#
# End of loop on nights.
#
if len(tsnr2_frame_table) == 0:
log.error("no valid exposures added")
return 1

if args.tile_completeness is not None :
if args.tile_completeness is not None:
# renaming for historical code reasons
exposure_table = tsnr2_expid_table

# get list of tiles to look at
selection = np.repeat(True,len(exposure_table))
if args.expids is not None or args.nights is not None :
if args.expids is not None :
selection = np.repeat(True, len(exposure_table))
if args.expids is not None or args.nights is not None:
#
# args.expids and args.nights should have already been parsed above.
# Is there a specific reason to reparse them here?
#
if args.expids is not None:
expids = parse_int_args(args.expids)
selection &= np.in1d(exposure_table["EXPID"],expids)
if args.nights is not None :
selection &= np.in1d(exposure_table["EXPID"], expids)
if args.nights is not None:
nights = parse_int_args(args.nights)
selection &= np.in1d(exposure_table["NIGHT"],nights)

selection &= np.in1d(exposure_table["NIGHT"], nights)
if len(gfa_nights) > 0:
selection &= np.in1d(exposure_table["NIGHT"], np.unique(np.array(gfa_nights)))
# consider only the tiles observed in this selection of exposures
tiles=np.unique(exposure_table["TILEID"][selection])
# keep all exposures of those tiles
Expand Down

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