def test_different_keyword_values_with_duplicate(self):
ha = Header([('A', 1), ('B', 2), ('C', 3)])
hb = ha.copy()
ha.append(('C', 4))
hb.append(('C', 5))
diff = HeaderDiff(ha, hb)
assert not diff.identical
assert diff.diff_keyword_values == {'C': [None, (4, 5)]}
def test_different_keyword_values_with_duplicate(self):
ha = Header([('A', 1), ('B', 2), ('C', 3)])
hb = ha.copy()
ha.append(('C', 4))
hb.append(('C', 5))
diff = HeaderDiff(ha, hb)
assert not diff.identical
assert diff.diff_keyword_values == {'C': [None, (4, 5)]}
def test_asymmetric_duplicate_keywords(self):
ha = Header([('A', 1), ('B', 2), ('C', 3)])
hb = ha.copy()
ha.append(('A', 2, 'comment 1'))
ha.append(('A', 3, 'comment 2'))
hb.append(('B', 4, 'comment 3'))
hb.append(('C', 5, 'comment 4'))
diff = HeaderDiff(ha, hb)
assert not diff.identical
assert diff.diff_keyword_values == {}
assert (diff.diff_duplicate_keywords ==
{'A': (3, 1), 'B': (1, 2), 'C': (1, 2)})
report = diff.report()
assert ("Inconsistent duplicates of keyword 'A' :\n"
" Occurs 3 time(s) in a, 1 times in (b)") in report
def test_asymmetric_duplicate_keywords(self):
ha = Header([('A', 1), ('B', 2), ('C', 3)])
hb = ha.copy()
ha.append(('A', 2, 'comment 1'))
ha.append(('A', 3, 'comment 2'))
hb.append(('B', 4, 'comment 3'))
hb.append(('C', 5, 'comment 4'))
diff = HeaderDiff(ha, hb)
assert not diff.identical
assert diff.diff_keyword_values == {}
assert (diff.diff_duplicate_keywords ==
{'A': (3, 1), 'B': (1, 2), 'C': (1, 2)})
report = diff.report()
assert ("Inconsistent duplicates of keyword 'A' :\n"
" Occurs 3 time(s) in a, 1 times in (b)") in report
def add_fffits_metadata(ff_filename, config, platepars_recalibrated,
fallback_platepar):
"""
Add FITS metadata and WCS to FF files generated by RMS
Args:
ff_filename (str): full or relative path to FF file
config (RMS.Config): config instance
platepars_recalibrated (dict): dictionary with recalibrated platepars
fallback_platepar (RMS.Platepar): platepar with fitted stars
Returns:
None
"""
ff_basename = os.path.basename(ff_filename)
platepar_recalibrated = Platepar()
try:
platepar_data = platepars_recalibrated[ff_basename]
with open("platepar_tmp.cal", "w") as f:
json.dump(platepar_data, f)
platepar_recalibrated.read("platepar_tmp.cal")
except (FileNotFoundError, KeyError):
platepar_recalibrated = fallback_platepar
logger.warning(f"Using non-recalibrated platepar for {ff_basename}")
fftime = getMiddleTimeFF(ff_basename, config.fps)
fit_xy = np.array(fallback_platepar.star_list)[:, 1:3]
_, fit_ra, fit_dec, _ = xyToRaDecPP([fftime] * len(fit_xy),
fit_xy[:, 0],
fit_xy[:, 1], [1] * len(fit_xy),
platepar_recalibrated,
extinction_correction=False)
x0 = platepar_recalibrated.X_res / 2
y0 = platepar_recalibrated.Y_res / 2
_, ra0, dec0, _ = xyToRaDecPP([fftime], [x0], [y0], [1],
platepar_recalibrated,
extinction_correction=False)
w = fit_wcs(fit_xy[:, 0],
fit_xy[:, 1],
fit_ra,
fit_dec,
x0,
y0,
ra0[0],
dec0[0],
5,
projection="ZEA")
hdu_list = fits.open(ff_filename, scale_back=True)
obstime = Time(filenameToDatetime(ff_basename))
header_meta = {}
header_meta["OBSERVER"] = config.stationID.strip()
header_meta["INSTRUME"] = "Global Meteor Network"
header_meta["MJD-OBS"] = obstime.mjd
header_meta["DATE-OBS"] = obstime.fits
header_meta["NFRAMES"] = 256
header_meta["EXPTIME"] = 256 / config.fps
header_meta["SITELONG"] = round(config.longitude, 2)
header_meta["SITELAT"] = round(config.latitude, 2)
for hdu in hdu_list:
if hdu.header[
"NAXIS"] == 0: # First header is not an image so should not get WCS
new_header = Header()
else:
new_header = w.to_fits(relax=True)[0].header
for key, value in header_meta.items():
new_header.append((key, value))
for key, value in new_header.items():
if key in hdu.header:
continue
hdu.header[key] = value
hdu_list.writeto(ff_filename, overwrite=True)