def get_dir_files(directory):
"""
Make dictionary of filenames in given directory
files["filename"]["data"] = fits data
files["filename"]["lbl"] = lbl info
files["filename"]["header"] = fits header info
returns: dictionary of files
"""
files = {}
for filename in os.listdir(directory):
filepath = directory + "/" + filename
name = filename.split('.')[0]
ext = '.' + '.'.join(filename.split('.')[1:])
if name not in files:
files[name] = {}
if ext == ".fit.fz" or ext == ".fit":
with fits.open(filepath) as hdul:
files[name]["data"] = hdul[1].data
files[name]["header"] = hdul[1].header
elif ext == ".lbl":
files[name]["lbl"] = lblparser.lbl_parse((filepath))
# check for files missing their fit or lbl
missing = [name for name in files.keys() if len(files[name]) != 3]
for m in missing:
print("Missing corresponding fits or lbl for file: " + m)
del files[m]
return files # nested dictionary -> files[filename][lbl or data]
def preprocessSampleData(light_idx, FITSFiles, longid):
"""Use provided correction methods to subtract out dark images and use flats to correct for vignetting.
Write the processed file to the temporary preprocessed directory.
"""
dark_idx, flat_idx, ttd, ttf = odf_mapper(FITSFiles, light_idx)
print(ttd, ttf)
if dark_idx < 0 and flat_idx < 0:
print("NON-COMPLIANT")
return
lights_lbl = lbl_parse(FITSFiles['lights_lbl'][light_idx])
light = CCDData.read(FITSFiles['lights'][light_idx], unit='adu')
dark = CCDData.read(FITSFiles['darks'][dark_idx], unit='adu')
flat = CCDData.read(FITSFiles['flats'][flat_idx], unit='adu')
corr = flat.data
corr1 = light.data - dark.data
light.data = corr1 / corr
flat_corrected = light
path_plan = processed_volume + "/" + sample + "/"
try:
print("Attempting to build path...")
os.makedirs(path_plan)
print("Built!")
print("Writing to file " + str(longid.split('.')[0]) + ".fits")
flat_corrected.write(path_plan + str(longid.split('.')[0]) + '.fits')
except Exception as e:
print("Directory already exists. Writing to file " +
str(longid.split('.')[0]) + ".fits")
flat_corrected.write(path_plan + str(longid.split('.')[0]) + '.fits')
return flat_corrected
def getConeParams(lbl_filepath):
lab = lbl_parse(lbl_filepath)
world_ra = float(lab['RIGHT_ASCENSION'].partition('<')[0])
world_dec = float(lab['DECLINATION'].partition('<')[0])
hor_fov_arcsec = float(lab['HORIZONTAL_PIXEL_FOV'].partition('<')[0])
radius = 0.15
return (world_ra, world_dec, radius)
def odf_mapper(FITSFiles, light_idx):
"""Transforms light index into closest dark and flat indexes"""
lab = lbl_parse(FITSFiles['lights'][light_idx][:-3] + "lbl")
ttd = lab["START_TIME"].partition('T')
best_didx = 0
best_fidx = 0
min_t_d_delta = 99999999
min_t_f_delta = 99999999
for didx in range(len(FITSFiles['darks'])):
dta = lbl_parse(FITSFiles['darks'][didx][:-3] + "lbl")
ttd_dta = dta["STOP_TIME"].partition('T')
if ttd_dta[0] == ttd[0]:
tdelta = abs((datetime.strptime(ttd_dta[2], FMT) -
datetime.strptime(ttd[2], FMT)).total_seconds())
if tdelta < min_t_d_delta:
min_t_d_delta = tdelta
best_didx = didx
for fidx in range(len(FITSFiles['flats'])):
fta = lbl_parse(FITSFiles['flats'][fidx][:-3] + "lbl")
ttf_fta = fta["STOP_TIME"].partition('T')
if (ttf_fta[0] == ttd[0]):
tdelta = abs((datetime.strptime(ttf_fta[2], FMT) -
datetime.strptime(ttd[2], FMT)).total_seconds())
if tdelta < min_t_f_delta:
min_t_f_delta = tdelta
best_fidx = fidx
light_lbl = lbl_parse(FITSFiles['lights'][light_idx][:-3] + "lbl")
dark_lbl = lbl_parse(FITSFiles['darks'][best_didx][:-3] + "lbl")
flat_lbl = lbl_parse(FITSFiles['flats'][best_fidx][:-3] + "lbl")
if (flat_lbl['TARGET_NAME'] == '"FLAT FIELD"'
and dark_lbl['TARGET_NAME'] == '"DARK"'
and float(flat_lbl['EXPOSURE_DURATION'].partition('<')[0])
== float(dark_lbl['EXPOSURE_DURATION'].partition('<')[0])
and light_lbl['TARGET_NAME'] == '"ASTEROID"'
and light_lbl['FILTER_NAME'] == '"NONE"'
and float(light_lbl['EXPOSURE_DURATION'].partition('<')[0])
== float(dark_lbl['EXPOSURE_DURATION'].partition('<')[0])):
return (best_didx, best_fidx, min_t_d_delta, min_t_f_delta)
else:
return (-1, -1, 0, 0)