def _check_transform(coords):
# Coords is a shape.Subinterval
wcsprm = Wcsprm()
coord_array = np.array([[coords.lower, coords.upper]])
sky_transform = wcsprm.p2s(coord_array, ORIGIN)
pix_transform = wcsprm.s2p(sky_transform['world'], ORIGIN)
transformed_coords = pix_transform['pixcrd']
if not (transformed_coords[0][0] == coords.lower
and transformed_coords[0][1] == coords.upper):
raise ValueError(
"Could not transform coordinates pixel to sky, sky to pixel")
def _check_transform(coords):
# Coords is a shape.Subinterval
wcsprm = Wcsprm()
coord_array = np.array([[coords.lower, coords.upper]])
sky_transform = wcsprm.p2s(coord_array, ORIGIN)
wcsprm.s2p(sky_transform['world'], ORIGIN)
def _check_transform(lower, upper):
wcsprm = Wcsprm()
coord_array = np.array([[lower, upper]])
sky_transform = wcsprm.p2s(coord_array, ORIGIN)
wcsprm.s2p(sky_transform['world'], ORIGIN)
def add_wcs_coordinates(objects, catParNames, catParFormt, catParUnits, Parameters):
try:
hdulist = fits.open(Parameters["import"]["inFile"])
header = hdulist[0].header
hdulist.close()
# Fix headers where "per second" is written "/S" instead of "/s"
# (assuming they mean "per second" and not "per Siemens").
if "cunit3" in header and "/S" in header["cunit3"]:
err.warning("Converting '/S' to '/s' in CUNIT3.")
header["cunit3"] = header["cunit3"].replace("/S","/s")
# Check if there is a Nmap/GIPSY FITS header keyword value present
gipsyKey = [k for k in ["FREQ-OHEL", "FREQ-OLSR", "FREQ-RHEL", "FREQ-RLSR"] if (k in [header[key] for key in header if ("CTYPE" in key)])]
if gipsyKey:
err.message("GIPSY header found. Trying to convert to FITS standard.")
from astropy.wcs import Wcsprm
header = fix_gipsy_header(header)
wcsin = Wcsprm(str(header))
wcsin.sptr("VOPT-F2W")
#if header["naxis"] == 4:
# objects = np.concatenate((objects, wcsin.p2s(np.concatenate((objects[:, catParNames.index("x"):catParNames. index("x") + 3], np.zeros((objects.shape[0], 1))), axis=1), 0)["world"][:,:-1]), axis=1)
#else:
# objects = np.concatenate((objects, wcsin.p2s(objects[:, catParNames.index("x"):catParNames.index("x") + 3], 0)["world"]), axis=1)
objects = np.concatenate((objects, wcsin.p2s(objects[:, catParNames.index("x"):catParNames.index("x") + 3], 0)["world"]), axis=1)
catParUnits = tuple(list(catParUnits) + [str(cc).replace(" ", "") for cc in wcsin.cunit])
catParNames = tuple(list(catParNames) + [(cc.split("--")[0]).lower() for cc in wcsin.ctype])
catParFormt = tuple(list(catParFormt) + ["%15.7e", "%15.7e", "%15.7e"])
else:
# Constrain the RA axis reference value CRVAL_ to be between 0 and 360 deg
rafound = 0
for kk in range(header["naxis"]):
if header["ctype1"][:2] == "RA":
rafound = 1
break
if rafound:
if header["crval%i" % (kk + 1)] < 0:
err.warning("Adding 360 deg to RA reference value.")
header["crval%i" % (kk + 1)] += 360
elif header["crval%i" % (kk + 1)] > 360:
err.warning("Subtracting 360 deg from RA reference value.")
header["crval%i" % (kk + 1)] -= 360
#if header["naxis"] == 4: wcsin = wcs.WCS(header, naxis=[wcs.WCSSUB_CELESTIAL, wcs.WCSSUB_SPECTRAL, wcs.WCSSUB_STOKES])
#else: wcsin = wcs.WCS(header, naxis=[wcs.WCSSUB_CELESTIAL, wcs.WCSSUB_SPECTRAL])
wcsin = wcs.WCS(header, naxis=[wcs.WCSSUB_CELESTIAL, wcs.WCSSUB_SPECTRAL])
xyz = objects[:, catParNames.index("x"):catParNames.index("x") + 3].astype(float)
if "cellscal" in header and header["cellscal"] == "1/F":
err.warning(
"CELLSCAL keyword with value of 1/F found.\n"
"Will account for varying pixel scale in WCS coordinate calculation.")
x0, y0 = header["crpix1"] - 1, header["crpix2"] - 1
# Will calculate the pixscale factor of each channel as:
# pixscale = ref_frequency / frequency
if header["ctype3"] == "VELO-HEL":
pixscale = (1 - header["crval3"] / scipy.constants.c) / (1 - (((xyz[:, 2] + 1) - header["crpix3"]) * header["cdelt3"] + header["crval3"]) / scipy.constants.c)
else:
err.warning("Cannot convert 3rd axis coordinates to frequency. Ignoring the effect of CELLSCAL = 1/F.")
pixscale = 1.0
xyz[:, 0] = (xyz[:, 0] - x0) * pixscale + x0
xyz[:, 1] = (xyz[:, 1] - y0) * pixscale + y0
#if header["naxis"] == 4: objects = np.concatenate((objects, wcsin.wcs_pix2world(np.concatenate((xyz, np.zeros((objects.shape[0], 1))), axis=1), 0)[:, :-1]), axis=1)
#else: objects = np.concatenate((objects, wcsin.wcs_pix2world(xyz, 0)), axis=1)
objects = np.concatenate((objects, wcsin.wcs_pix2world(xyz, 0)), axis=1)
catParUnits = tuple(list(catParUnits) + [str(cc).replace(" ", "") for cc in wcsin.wcs.cunit])
catParNames = tuple(list(catParNames) + [(cc.split("--")[0]).lower() for cc in wcsin.wcs.ctype])
catParFormt = tuple(list(catParFormt) + ["%15.7e", "%15.7e", "%15.7e"])
#if header["naxis"] == 4:
# catParUnits = catParUnits[:-1]
# catParNames= catParNames[:-1]
err.message("WCS coordinates added to catalogue.")
# Create IAU-compliant source name:
# WARNING: This currently assumes a regular, ≥ 2-dim. data cube where the first two axes are longitude and latitude.
n_src = objects.shape[0]
n_par = objects.shape[1]
iau_names = np.empty([n_src, 1], dtype=object)
if header["ctype1"][:4] == "RA--":
# Equatorial coordinates; try to figure out equinox:
iau_coord = "equ"
if "equinox" in header:
if int(header["equinox"]) >= 2000: iau_equinox = "J"
else: iau_equinox = "B"
elif "epoch" in header:
# Assume that EPOCH has been abused to record the equinox:
if int(header["epoch"]) >= 2000: iau_equinox = "J"
else: iau_equinox = "B"
else:
# Equinox undefined:
iau_equinox = "X"
elif header["ctype1"][:4] == "GLON":
# Galactic coordinates:
iau_coord = "gal"
iau_equinox = "G"
else:
# Unsupported coordinate system:
iau_coord = ""
iau_equinox = ""
for src in xrange(n_src):
lon = objects[src][n_par - 3]
lat = objects[src][n_par - 2]
if iau_coord == "equ":
ra = Longitude(lon, unit=u.deg)
dec = Latitude(lat, unit=u.deg)
iau_pos = ra.to_string(unit=u.h, decimal=False, sep="", precision=2, alwayssign=False, pad=True, fields=3)
iau_pos += dec.to_string(unit=u.deg, decimal=False, sep="", precision=1, alwayssign=True, pad=True, fields=3)
else:
iau_pos = "{0:08.4f}".format(lon)
if lat < 0.0: iau_pos += "-"
else: iau_pos += "+"
iau_pos += "{0:07.4f}".format(abs(lat))
iau_names[src][0] = "SoFiA " + iau_equinox + iau_pos
objects = np.concatenate((objects, iau_names), axis = 1)
catParUnits = tuple(list(catParUnits) + ["-"])
catParNames = tuple(list(catParNames) + ["name"])
catParFormt = tuple(list(catParFormt) + ["%30s"])
except:
err.warning("WCS conversion of parameters failed.")
return (objects, catParNames, catParFormt, catParUnits)
def add_wcs_coordinates(objects, catParNames, catParFormt, catParUnits,
Parameters):
try:
hdulist = fits.open(Parameters["import"]["inFile"])
header = hdulist[0].header
hdulist.close()
# Fix headers where "per second" is written "/S" instead of "/s"
# (assuming they mean "per second" and not "per Siemens").
if "cunit3" in header and "/S" in header["cunit3"]:
err.warning("Converting '/S' to '/s' in CUNIT3.")
header["cunit3"] = header["cunit3"].replace("/S", "/s")
# Check if there is a Nmap/GIPSY FITS header keyword value present
gipsyKey = [
k for k in ["FREQ-OHEL", "FREQ-OLSR", "FREQ-RHEL", "FREQ-RLSR"]
if (k in [header[key] for key in header if ("CTYPE" in key)])
]
if gipsyKey:
err.message(
"GIPSY header found. Trying to convert to FITS standard.")
from astropy.wcs import Wcsprm
header = fix_gipsy_header(header)
wcsin = Wcsprm(str(header))
wcsin.sptr("VOPT-F2W")
#if header["naxis"] == 4:
# objects = np.concatenate((objects, wcsin.p2s(np.concatenate((objects[:, catParNames.index("x"):catParNames. index("x") + 3], np.zeros((objects.shape[0], 1))), axis=1), 0)["world"][:,:-1]), axis=1)
#else:
# objects = np.concatenate((objects, wcsin.p2s(objects[:, catParNames.index("x"):catParNames.index("x") + 3], 0)["world"]), axis=1)
objects = np.concatenate(
(objects,
wcsin.p2s(
objects[:,
catParNames.index("x"):catParNames.index("x") +
3], 0)["world"]),
axis=1)
catParUnits = tuple(
list(catParUnits) +
[str(cc).replace(" ", "") for cc in wcsin.cunit])
catParNames = tuple(
list(catParNames) + [(cc.split("--")[0]).lower()
for cc in wcsin.ctype])
catParFormt = tuple(
list(catParFormt) + ["%15.7e", "%15.7e", "%15.7e"])
else:
# Constrain the RA axis reference value CRVAL_ to be between 0 and 360 deg
rafound = 0
for kk in range(header["naxis"]):
if header["ctype1"][:2] == "RA":
rafound = 1
break
if rafound:
if header["crval%i" % (kk + 1)] < 0:
err.warning("Adding 360 deg to RA reference value.")
header["crval%i" % (kk + 1)] += 360
elif header["crval%i" % (kk + 1)] > 360:
err.warning("Subtracting 360 deg from RA reference value.")
header["crval%i" % (kk + 1)] -= 360
#if header["naxis"] == 4: wcsin = wcs.WCS(header, naxis=[wcs.WCSSUB_CELESTIAL, wcs.WCSSUB_SPECTRAL, wcs.WCSSUB_STOKES])
#else: wcsin = wcs.WCS(header, naxis=[wcs.WCSSUB_CELESTIAL, wcs.WCSSUB_SPECTRAL])
wcsin = wcs.WCS(header,
naxis=[wcs.WCSSUB_CELESTIAL, wcs.WCSSUB_SPECTRAL])
xyz = objects[:,
catParNames.index("x"):catParNames.index("x") +
3].astype(float)
if "cellscal" in header and header["cellscal"] == "1/F":
err.warning(
"CELLSCAL keyword with value of 1/F found.\n"
"Will account for varying pixel scale in WCS coordinate calculation."
)
x0, y0 = header["crpix1"] - 1, header["crpix2"] - 1
# Will calculate the pixscale factor of each channel as:
# pixscale = ref_frequency / frequency
if header["ctype3"] == "VELO-HEL":
pixscale = (1 - header["crval3"] / scipy.constants.c) / (
1 - (((xyz[:, 2] + 1) - header["crpix3"]) *
header["cdelt3"] + header["crval3"]) /
scipy.constants.c)
else:
err.warning(
"Cannot convert 3rd axis coordinates to frequency. Ignoring the effect of CELLSCAL = 1/F."
)
pixscale = 1.0
xyz[:, 0] = (xyz[:, 0] - x0) * pixscale + x0
xyz[:, 1] = (xyz[:, 1] - y0) * pixscale + y0
#if header["naxis"] == 4: objects = np.concatenate((objects, wcsin.wcs_pix2world(np.concatenate((xyz, np.zeros((objects.shape[0], 1))), axis=1), 0)[:, :-1]), axis=1)
#else: objects = np.concatenate((objects, wcsin.wcs_pix2world(xyz, 0)), axis=1)
objects = np.concatenate((objects, wcsin.wcs_pix2world(xyz, 0)),
axis=1)
catParUnits = tuple(
list(catParUnits) +
[str(cc).replace(" ", "") for cc in wcsin.wcs.cunit])
catParNames = tuple(
list(catParNames) + [(cc.split("--")[0]).lower()
for cc in wcsin.wcs.ctype])
catParFormt = tuple(
list(catParFormt) + ["%15.7e", "%15.7e", "%15.7e"])
#if header["naxis"] == 4:
# catParUnits = catParUnits[:-1]
# catParNames= catParNames[:-1]
err.message("WCS coordinates added to catalogue.")
# Create IAU-compliant source name:
# WARNING: This currently assumes a regular, ≥ 2-dim. data cube where the first two axes are longitude and latitude.
n_src = objects.shape[0]
n_par = objects.shape[1]
iau_names = np.empty([n_src, 1], dtype=object)
if header["ctype1"][:4] == "RA--":
# Equatorial coordinates; try to figure out equinox:
iau_coord = "equ"
if "equinox" in header:
if int(header["equinox"]) >= 2000: iau_equinox = "J"
else: iau_equinox = "B"
elif "epoch" in header:
# Assume that EPOCH has been abused to record the equinox:
if int(header["epoch"]) >= 2000: iau_equinox = "J"
else: iau_equinox = "B"
else:
# Equinox undefined:
iau_equinox = "X"
elif header["ctype1"][:4] == "GLON":
# Galactic coordinates:
iau_coord = "gal"
iau_equinox = "G"
else:
# Unsupported coordinate system:
iau_coord = ""
iau_equinox = ""
for src in xrange(n_src):
lon = objects[src][n_par - 3]
lat = objects[src][n_par - 2]
if iau_coord == "equ":
ra = Longitude(lon, unit=u.deg)
dec = Latitude(lat, unit=u.deg)
iau_pos = ra.to_string(unit=u.h,
decimal=False,
sep="",
precision=2,
alwayssign=False,
pad=True,
fields=3)
iau_pos += dec.to_string(unit=u.deg,
decimal=False,
sep="",
precision=1,
alwayssign=True,
pad=True,
fields=3)
else:
iau_pos = "{0:08.4f}".format(lon)
if lat < 0.0: iau_pos += "-"
else: iau_pos += "+"
iau_pos += "{0:07.4f}".format(abs(lat))
iau_names[src][0] = "SoFiA " + iau_equinox + iau_pos
objects = np.concatenate((objects, iau_names), axis=1)
catParUnits = tuple(list(catParUnits) + ["-"])
catParNames = tuple(list(catParNames) + ["name"])
catParFormt = tuple(list(catParFormt) + ["%30s"])
except:
err.warning("WCS conversion of parameters failed.")
return (objects, catParNames, catParFormt, catParUnits)
def _check_transform(lower, upper):
wcsprm = Wcsprm()
coord_array = np.array([[lower, upper]])
sky_transform = wcsprm.p2s(coord_array, ORIGIN)
wcsprm.s2p(sky_transform['world'], ORIGIN)
def add_wcs_coordinates(objects,catParNames,catParFormt,catParUnits,Parameters):
import imp
import sys
import numpy as np
c = 299792458.0 # speed of light in m/s
try:
imp.find_module('astropy')
found = True
except ImportError: found = False
if found:
try:
from astropy import wcs
from astropy.io import fits
hdulist = fits.open(Parameters['import']['inFile'])
header = hdulist[0].header
hdulist.close()
# Fix headers where "per second" is written "/S" instead of "/s"
# (assuming they mean "per second" and not "per Siemens").
if 'cunit3' in header and '/S' in header['cunit3']:
sys.stderr.write('WARNING: Converting "/S" to "/s" in CUNIT3.\n')
header['cunit3']=header['cunit3'].replace('/S','/s')
## check if there is a Nmap/GIPSY FITS header keyword value present
gipsyKey = [k for k in ['FREQ-OHEL','FREQ-OLSR','FREQ-RHEL','FREQ-RLSR'] if (k in [header[key] for key in header if ('CTYPE' in key)])]
if gipsyKey:
print ('GIPSY header found. Trying to convert it.')
from astropy.wcs import Wcsprm
header = fix_gipsy_header(header)
wcsin = Wcsprm(str(header))
wcsin.sptr('VOPT-F2W')
#if header['naxis'] == 4:
# objects = np.concatenate((objects, wcsin.p2s(np.concatenate((objects[:, catParNames.index('x'):catParNames.index('x')+3], np.zeros((objects.shape[0], 1))), axis=1),0)['world'][:,:-1]), axis=1)
#else:
# objects = np.concatenate((objects, wcsin.p2s(objects[:, catParNames.index('x'):catParNames.index('x')+3], 0)['world']), axis=1)
objects = np.concatenate((objects, wcsin.p2s(objects[:, catParNames.index('x'):catParNames.index('x')+3], 0)['world']), axis=1)
catParUnits = tuple(list(catParUnits) + [str(cc).replace(' ','') for cc in wcsin.cunit])
catParNames = tuple(list(catParNames) + [(cc.split('--')[0]).lower() for cc in wcsin.ctype])
catParFormt = tuple(list(catParFormt) + ['%15.7e', '%15.7e', '%15.7e'])
else:
# constrain the RA axis reference value CRVAL_ to be between 0 and 360 deg
rafound = 0
for kk in range(header['naxis']):
if header['ctype1'][:2] == 'RA':
rafound = 1
break
if rafound:
if header['crval%i'%(kk + 1)] < 0:
sys.stderr.write("WARNING: adding 360 deg to RA reference value.\n")
header['crval%i'%(kk + 1)] += 360
elif header['crval%i'%(kk + 1)] > 360:
sys.stderr.write("WARNING: subtracting 360 deg from RA reference value.\n")
header['crval%i'%(kk + 1)] -= 360
#if header['naxis'] == 4: wcsin = wcs.WCS(header, naxis=[wcs.WCSSUB_CELESTIAL, wcs.WCSSUB_SPECTRAL,wcs.WCSSUB_STOKES])
#else: wcsin = wcs.WCS(header, naxis=[wcs.WCSSUB_CELESTIAL, wcs.WCSSUB_SPECTRAL])
wcsin = wcs.WCS(header, naxis=[wcs.WCSSUB_CELESTIAL, wcs.WCSSUB_SPECTRAL])
xyz = objects[:,catParNames.index('x'):catParNames.index('x')+3].astype(float)
if 'cellscal' in header and header['cellscal'] == '1/F':
sys.stderr.write('WARNING: CELLSCAL keyword with value 1/F found.\n')
sys.stderr.write(' Will account for varying pixel scale in WCS coordinate calculation.\n')
x0, y0 = header['crpix1'] - 1, header['crpix2'] - 1
# Will calculate the pixscale factor of each channel as:
# pixscale = ref_frequency / frequency
if header['ctype3'] == 'VELO-HEL':
pixscale = (1 - header['crval3'] / c) / (1 - (((xyz[:,2] + 1) - header['crpix3']) * header['cdelt3'] + header['crval3']) / c)
else:
sys.stderr.write("WARNING: Cannot convert 3rd axis coordinates to frequency. Will ignore the effect of CELLSCAL = 1/F.\n")
pixscale = 1.
xyz[:,0] = (xyz[:,0] - x0) * pixscale + x0
xyz[:,1] = (xyz[:,1] - y0) * pixscale + y0
#if header['naxis'] == 4: objects = np.concatenate((objects, wcsin.wcs_pix2world(np.concatenate((xyz, np.zeros((objects.shape[0], 1))), axis=1), 0)[:,:-1]), axis=1)
#else: objects = np.concatenate((objects, wcsin.wcs_pix2world(xyz, 0)), axis=1)
objects = np.concatenate((objects, wcsin.wcs_pix2world(xyz, 0)), axis=1)
catParUnits = tuple(list(catParUnits) + [str(cc).replace(' ','') for cc in wcsin.wcs.cunit])
catParNames = tuple(list(catParNames) + [(cc.split('--')[0]).lower() for cc in wcsin.wcs.ctype])
catParFormt = tuple(list(catParFormt) + ['%15.7e', '%15.7e', '%15.7e'])
#if header['naxis'] == 4:
# catParUnits = catParUnits[:-1]
# catParNames= catParNames[:-1]
print ('WCS coordinates added to catalogue.')
# Create IAU-compliant source name:
# WARNING: This currently assumes a regular, ≥ 2-dim. data cube where the first two axes are longitude and latitude.
n_src = objects.shape[0]
n_par = objects.shape[1]
iau_names = np.empty([n_src, 1], dtype=object)
if header["ctype1"][:4] == "RA--":
# Equatorial coordinates try to figure out equinox:
iau_coord = "equ"
if "equinox" in header:
if int(header["equinox"]) >= 2000: iau_equinox = "J"
else: iau_equinox = "B"
elif "epoch" in header:
# Assume that EPOCH has been abused to record the equinox:
if int(header["epoch"]) >= 2000: iau_equinox = "J"
else: iau_equinox = "B"
else:
# Equinox undefined:
iau_equinox = "X"
elif header["ctype1"][:4] == "GLON":
# Galactic coordinates:
iau_coord = "gal"
iau_equinox = "G"
else:
# Unsupported coordinate system:
iau_coord = ""
iau_equinox = ""
for src in xrange(n_src):
lon = objects[src][n_par - 3]
lat = objects[src][n_par - 2]
if iau_coord == "equ":
ra = lon / 15.0 # convert assumed degrees to hours
ra_h = int(ra)
ra_m = int((ra - ra_h) * 60.0)
ra_s = (ra - ra_h - (ra_m / 60.0)) * 3600.0
dec_sgn = np.sign(lat)
dec = abs(lat)
dec_d = int(dec)
dec_m = int((dec - dec_d) * 60.0)
dec_s = (dec - dec_d - (dec_m / 60.0)) * 3600.0
iau_pos = "{0:02d}{1:02d}{2:05.2f}".format(ra_h, ra_m, ra_s)
if dec_sgn < 0: iau_pos += "-"
else: iau_pos += "+"
iau_pos += "{0:02d}{1:02d}{2:04.1f}".format(dec_d, dec_m, dec_s)
else:
iau_pos = "{0:08.4f}".format(lon)
if lat < 0.0: iau_pos += "-"
else: iau_pos += "+"
iau_pos += "{0:07.4f}".format(abs(lat))
iau_names[src][0] = "SoFiA " + iau_equinox + iau_pos
objects = np.concatenate((objects, iau_names), axis = 1)
catParUnits = tuple(list(catParUnits) + ["-"])
catParNames = tuple(list(catParNames) + ["name"])
catParFormt = tuple(list(catParFormt) + ["%30s"])
except:
sys.stderr.write("WARNING: WCS conversion of parameters failed.\n")
return(objects, catParNames, catParFormt, catParUnits)
def add_wcs_coordinates(objects,catParNames,catParFormt,catParUnits,Parameters):
import imp
import sys
import numpy as np
try:
imp.find_module('astropy')
found = True
except ImportError: found = False
if found:
try:
from astropy import wcs
from astropy.io import fits
hdulist = fits.open(Parameters['import']['inFile'])
header = hdulist[0].header
hdulist.close()
## check if there is a Nmap/GIPSY FITS header keyword value present
gipsyKey = [k for k in ['FREQ-OHEL','FREQ-OLSR','FREQ-RHEL','FREQ-RLSR'] if (k in [header[key] for key in header if ('CTYPE' in key)])]
if gipsyKey:
print 'GIPSY header found. Trying to convert it.'
from astropy.wcs import Wcsprm
header = fix_gipsy_header(header)
wcsin = Wcsprm(str(header))
wcsin.sptr('VOPT-F2W')
if header['naxis']==4:
objects = np.concatenate((objects,wcsin.p2s(np.concatenate((objects[:,catParNames.index('Xm'):catParNames.index('Xm')+3],np.zeros((objects.shape[0],1))),axis=1),0)['world'][:,:-1]),axis=1)
else:
objects = np.concatenate((objects,wcsin.p2s(objects[:,catParNames.index('Xm'):catParNames.index('Xm')+3],0)['world']),axis=1)
catParUnits = tuple(list(catParUnits) + [str(cc).replace(' ','') for cc in wcsin.cunit])
catParNames = tuple(list(catParNames) + [cc.split('--')[0]+'m' for cc in wcsin.ctype])
catParFormt = tuple(list(catParFormt) + ['%15.7e', '%15.7e', '%15.7e'])
else:
# constrain the RA axis reference value CRVAL_ to be between 0 and 360 deg
rafound=0
for kk in range(header['naxis']):
if header['ctype1'][:2]=='RA':
rafound=1
break
if rafound:
if header['crval%i'%(kk+1)]<0:
print 'WARNING: adding 360 deg to RA reference value'
header['crval%i'%(kk+1)]+=360
elif header['crval%i'%(kk+1)]>360:
print 'WARNING: subtracting 360 deg from RA reference value'
header['crval%i'%(kk+1)]-=360
wcsin = wcs.WCS(header)
if header['naxis']==4:
objects=np.concatenate((objects,wcsin.wcs_pix2world(np.concatenate((objects[:,catParNames.index('Xm'):catParNames.index('Xm')+3],np.zeros((objects.shape[0],1))),axis=1),0)[:,:-1]),axis=1)
else:
objects=np.concatenate((objects,wcsin.wcs_pix2world(objects[:,catParNames.index('Xm'):catParNames.index('Xm')+3],0)),axis=1)
catParUnits = tuple(list(catParUnits) + [str(cc).replace(' ','') for cc in wcsin.wcs.cunit])
catParNames = tuple(list(catParNames) + [cc.split('--')[0]+'m' for cc in wcsin.wcs.ctype])
catParFormt = tuple(list(catParFormt) + ['%15.7e', '%15.7e', '%15.7e'])
if header['naxis']==4:
catParUnits = catParUnits[:-1]
catParNames= catParNames[:-1]
print "WCS coordinates added to the catalog."
except:
print "WARNING: WCS conversion of parameters could not be executed!\n"
return(objects, catParNames, catParFormt, catParUnits)
def main():
"""Perform photometry for the given file."""
print("Program version: 0.2")
StartTime = datetime.now()
args = parseArguments()
if (args.ignore_warnings):
warnings.simplefilter('ignore', UserWarning)
fits_image_filenames = args.input
#print(fits_image_filenames)
#for directories search for appropriate fits files
if (os.path.isdir(fits_image_filenames[0])):
print(
"detected a directory. Will search for fits files in it that already have astrometry calibration and therefor contain _astro"
)
path = fits_image_filenames[0]
fits_image_filenames = []
for file in os.listdir(path):
if file.endswith(".fits") and "_astro" in file:
fits_image_filenames.append(path + "/" + file)
print(fits_image_filenames)
for fits_image_filename in fits_image_filenames:
print("")
print(">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>")
print("> Photometry for {} ".format(fits_image_filename))
with fits.open(fits_image_filename) as hdul:
#print(hdul.info())
if (args.verbose):
print(
"if image is not at first position in the fits file the program will break later on"
)
#print(hdul[0].header)
hdu = hdul[0]
hdr = hdu.header
image_or = hdul[0].data.astype(float)
image = image_or - np.median(image_or)
wcsprm = Wcsprm(hdr.tostring().encode(
'utf-8')) #everything else gave me errors with python 3
#tranlating aperture into pixel:
on_sky = wcsprm.p2s([[0, 0], [1, 1]], 0)["world"]
px_scale = np.sqrt((on_sky[0, 0] - on_sky[1, 0])**2 +
(on_sky[0, 1] - on_sky[1, 1])**2)
px_scale = px_scale * 60 * 60 #in arcsec
aperture = args.aperture / px_scale #aperture n pixel
print("aperture")
observation = find_sources(image, aperture)
#print(observation)
positions = (observation['xcenter'], observation['ycenter'])
apertures = CircularAperture(positions, r=4.)
#get rough coordinates
coord = SkyCoord(wcsprm.crval[0],
wcsprm.crval[1],
unit=(u.deg, u.deg),
frame="icrs")
#put in nice wrapper! with repeated tries and maybe try synchron!
print(">Dowloading catalog data")
#WCS.calc_footprint(header=None, undistort=True, axes=None, center=True)
radius = u.Quantity(5, u.arcmin) #should be enough for all images
catalog_data, band_name, catalog_name, mag_sys = query.get_photometry_data(
coord, radius, args.band, args.catalog)
#throwing out blended sources (should be improved, TODO)
apertures_catalog = CircularAperture(wcsprm.s2p(
catalog_data[["ra", "dec"]], 1)['pixcrd'],
r=5.)
obs_matched, cat_matched, distances = register.find_matches_keep_catalog_info(
observation, catalog_data, wcsprm, threshold=3)
print("Found {} matches".format(obs_matched.shape[0]))
MAG_CALC = True
if (obs_matched.shape[0] == 0):
MAG_CALC = False
obs_matched["aperture_sum"]
cat_matched[band_name]
#mag = -2.5 log10(cts) + ZP
ZP = -1 * (-2.5 * np.log10(obs_matched["aperture_sum"].values) -
cat_matched[band_name].values)
#FIGURE OUT LINEAR RANGE TOTO
ZP_median = np.median(ZP[~np.isnan(ZP)])
new_magnitudes = -2.5 * np.log10(
observation["aperture_sum"].values) + ZP_median
ra = args.ra
dec = args.dec
if (args.name):
targets = pd.read_csv("targets.csv")
if ((targets["NAME"] == args.name).sum()):
ra = targets.loc[targets["NAME"] == args.name, "RA"].values[0]
dec = targets.loc[targets["NAME"] == args.name,
"DEC"].values[0]
print("For {} the following coordinates where found".format(
args.name))
print("ra: {} dec: {}".format(ra, dec))
else:
print("{} not found".format(args.name))
if (ra and dec):
c_unknown = SkyCoord(ra, dec, unit=(u.deg, u.deg), frame="icrs")
#print(observation[["xcenter", "ycenter"]].values)
coordinats_obs = wcsprm.p2s(
observation[["xcenter", "ycenter"]].values, 1)["world"]
#print(coordinats_obs[17])
c_obs = SkyCoord(coordinats_obs[:, 0],
coordinats_obs[:, 1],
unit=(u.deg, u.deg),
frame="icrs")
#print(coordinats_obs)
from astropy.coordinates import match_coordinates_sky
idx, d2d, d3d = match_coordinates_sky(c_unknown, c_obs)
cand_dups = d2d < 5 * u.arcsec
mean, median, std = sigma_clipped_stats(image, sigma=3.0)
ap_area = CircularAperture((2, 2), r=aperture)
sig3_limiting_mag = -2.5 * np.log10(
3 * std * np.sqrt(ap_area.area())) + ZP_median
sig5_limiting_mag = -2.5 * np.log10(
5 * std * np.sqrt(ap_area.area())) + ZP_median
#######################
#estimating the error via random apertures
# print(std*np.sqrt(aperture_obj.area()))
ran_x = 2 * aperture + np.random.random(1000) * (image.shape[0] -
4 * aperture)
ran_y = 2 * aperture + np.random.random(1000) * (image.shape[0] -
4 * aperture)
apertures_error = CircularAperture((ran_x, ran_y), r=aperture)
phot_table = aperture_photometry(image, apertures_error)
phot_table[
'aperture_sum'].info.format = '%.8g' # for consistent table output
random_extractions = Table(phot_table).to_pandas()
#print(np.mean(np.abs(random_extractions["aperture_sum"])))
# print(np.median(np.abs(random_extractions["aperture_sum"])))
# sig5_limiting_mag_apertures = -2.5*np.log10(5*np.median(np.abs(random_extractions["aperture_sum"]))) + ZP_median
# print(sig5_limiting_mag_apertures)
m, _, std_apertures = sigma_clipped_stats(
random_extractions["aperture_sum"], sigma=3)
print(std_apertures)
sig5_limiting_mag_apertures = -2.5 * np.log10(
5 * std_apertures) + ZP_median
# print(sig5_limiting_mag_apertures)
# print(np.mean(random_extractions.loc[random_extractions["aperture_sum"]<0,"aperture_sum"]))
###########################
mag = 0
if (cand_dups.sum() > 0):
print(
"Position was given. Found {} sources in a 5 arcsec radius. Here is the magnitude:"
.format(cand_dups.sum()))
print(new_magnitudes[idx])
print("----")
aperture_obj = CircularAperture(
((observation["xcenter"].values)[idx],
(observation["ycenter"].values)[idx]),
r=aperture)
#noise = (observation["aperture_sum"].values)[idx] /(std*np.sqrt(aperture_obj.area()))
#https://en.wikipedia.org/wiki/Sum_of_normally_distributed_random_variables
#the std**2 is added, so we get a square root for the area!
noise = (
observation["aperture_sum"].values)[idx] / std_apertures
#print(noise_prob_wrong)
#print(noise_prob_wrong2)
print("We get a signal to noise of {} for the fixed aperture".
format(noise))
mag = new_magnitudes[idx]
mag_err = 2.5 * np.log10(1 + 1 / noise)
text = "found detection in {} band,\n {:.4g} +- {:.2g} {}mag, {:.3g} S/N \n 5 sig limiting mag is {:.4g}".format(
args.band, mag, mag_err, mag_sys, noise,
sig5_limiting_mag_apertures)
else:
print(
"Position was given. No object was found at that position."
)
pix_unknown = wcsprm.s2p([[ra, dec]], 1)
pix_unknown = pix_unknown['pixcrd']
aperture_obj = CircularAperture(pix_unknown, r=aperture)
phot_table = aperture_photometry(image, aperture_obj)
phot_table[
'aperture_sum'].info.format = '%.8g' # for consistent table output
#print(phot_table)
forced_phot = Table(phot_table).to_pandas()
forced_mag = -2.5 * np.log10(
forced_phot["aperture_sum"].values) + ZP_median
forced_mag = forced_mag[0]
#noise = (forced_phot["aperture_sum"].values)[0] /(std*np.sqrt(aperture_obj.area()))
noise = (forced_phot["aperture_sum"].values)[0] / std_apertures
print("Forced photometry gives a magnitude of {}".format(
forced_mag))
#local error
# pix_unknown = pix_unknown[0]
# mean_loc, median_loc, std_loc = sigma_clipped_stats(image[int(pix_unknown[0])-50:int(pix_unknown[0])+50,int(pix_unknown[1])-50:int(pix_unknown[1])+50], sigma=3.0)
# ap_area= CircularAperture((2,2), r=aperture)
# sig5_limiting_mag_local = -2.5*np.log10(5*std_loc*np.sqrt(ap_area.area())) + ZP_median
# print((std_loc*np.sqrt(aperture_obj.area())))
# print(sig5_limiting_mag_local)
print("We get a signal to noise of {} for the fixed aperture".
format(noise))
mag = forced_mag
mag_err = 2.5 * np.log10(1 + 1 / noise)
text = "forced photometry in {} band,\n {:.4g} +- {:.2g} {}mag, {:.3g} S/N \n 5 sig limiting mag is {:.4g} ".format(
args.band, mag, mag_err, mag_sys, noise,
sig5_limiting_mag_apertures)
else:
print(new_magnitudes)
mag = 0
obs_with_photometry = observation.copy()
obs_with_photometry["mag"] = new_magnitudes
#search for targeted object and print out its magnitude TODO
if (ra and dec):
plt.figure(figsize=(15, 8))
plt.subplot(1, 2, 1)
pix_unknown = wcsprm.s2p([[ra, dec]], 1)
pix_unknown = pix_unknown['pixcrd']
pix_unknown = pix_unknown[0]
plt.xlabel("pixel x direction")
plt.ylabel("pixel y direction")
plt.title(text, size=20)
#plt.imshow(image[int( pix_unknown[0])-30: int(pix_unknown[0])+30, int(pix_unknown[1])-30:int(pix_unknown[1])+30],cmap='Greys', origin='lower', norm=LogNorm())
#plt.plot(30, 30, "+", color="red", markersize=20)
plt.imshow(image, cmap='Greys', origin='lower', norm=LogNorm())
plt.xlim(int(pix_unknown[0]) - 20, int(pix_unknown[0]) + 20)
plt.ylim(int(pix_unknown[1]) - 20, int(pix_unknown[1]) + 20)
aperture_obj.plot(color='blue',
lw=1.5,
alpha=0.5,
label=str(args.aperture) + " arcsec aperture")
plt.plot(pix_unknown[0],
pix_unknown[1],
"+",
color="red",
markersize=20,
linewidth=10,
label="predicted target position")
plt.legend(bbox_to_anchor=(1.1, -0.1), ncol=2)
plt.subplot(1, 2, 2)
plt.title("Photometric calibration with {} sources from {}".format(
obs_matched.shape[0], catalog_name))
plt.plot(cat_matched[band_name].values,
ZP,
"o",
markersize=20,
label="catalog objects")
if (MAG_CALC):
plt.ylim(np.nanmin(ZP) - 1, np.nanmax(ZP) + 1)
else:
plt.ylim(10, 30)
if (mag):
plt.axvline(x=mag, linewidth=4, color="green", label="target")
plt.xlabel("catalog " + band_name + " magnitude")
plt.ylabel(
"Zeropoint (should be constant in linear part of the detector)"
)
plt.legend()
outputname = fits_image_filename.replace('.fits', '')
plt.savefig(outputname + "_ra{}dec{}.pdf".format(ra, dec))
plt.show()
else:
plt.figure()
plt.plot(cat_matched[band_name].values,
ZP,
"o",
markersize=20,
label="catalog objects")
plt.ylim(np.nanmin(ZP) - 1, np.nanmax(ZP) + 1)
if (mag):
plt.axvline(x=mag, linewidth=4, color="green", label="target")
plt.xlabel("catalog " + band_name + " magnitude")
plt.ylabel(
"Zeropoint (should be constant in linear part of the detector)"
)
plt.legend()
plt.show()
print("overall time taken")
print(datetime.now() - StartTime)
# if(args.show_images):
# plt.show()
print("-- finished --")
def add_wcs_coordinates(objects,catParNames,catParFormt,catParUnits,Parameters):
import imp
import sys
import numpy as np
try:
imp.find_module('astropy')
found = True
except ImportError: found = False
if found:
try:
from astropy import wcs
from astropy.io import fits
hdulist = fits.open(Parameters['import']['inFile'])
header = hdulist[0].header
hdulist.close()
# Fix headers where "per second" is written "/S" instead of "/s"
# (assuming they mean "per second" and not "per Siemens").
if 'cunit3' in header and '/S' in header['cunit3']:
print 'WARNING: Converting "/S" to "/s" in CUNIT3.'
header['cunit3']=header['cunit3'].replace('/S','/s')
## check if there is a Nmap/GIPSY FITS header keyword value present
gipsyKey = [k for k in ['FREQ-OHEL','FREQ-OLSR','FREQ-RHEL','FREQ-RLSR'] if (k in [header[key] for key in header if ('CTYPE' in key)])]
if gipsyKey:
print 'GIPSY header found. Trying to convert it.'
from astropy.wcs import Wcsprm
header = fix_gipsy_header(header)
wcsin = Wcsprm(str(header))
wcsin.sptr('VOPT-F2W')
if header['naxis']==4:
objects = np.concatenate((objects,wcsin.p2s(np.concatenate((objects[:,catParNames.index('x'):catParNames.index('x')+3],np.zeros((objects.shape[0],1))),axis=1),0)['world'][:,:-1]),axis=1)
else:
objects = np.concatenate((objects,wcsin.p2s(objects[:,catParNames.index('x'):catParNames.index('x')+3],0)['world']),axis=1)
catParUnits = tuple(list(catParUnits) + [str(cc).replace(' ','') for cc in wcsin.cunit])
catParNames = tuple(list(catParNames) + [(cc.split('--')[0]).lower() for cc in wcsin.ctype])
catParFormt = tuple(list(catParFormt) + ['%15.7e', '%15.7e', '%15.7e'])
else:
# constrain the RA axis reference value CRVAL_ to be between 0 and 360 deg
rafound=0
for kk in range(header['naxis']):
if header['ctype1'][:2]=='RA':
rafound=1
break
if rafound:
if header['crval%i'%(kk+1)]<0:
sys.stderr.write("WARNING: adding 360 deg to RA reference value.\n")
header['crval%i'%(kk+1)]+=360
elif header['crval%i'%(kk+1)]>360:
sys.stderr.write("WARNING: subtracting 360 deg from RA reference value.\n")
header['crval%i'%(kk+1)]-=360
wcsin = wcs.WCS(header)
xyz=objects[:,catParNames.index('x'):catParNames.index('x')+3].astype(float)
if 'cellscal' in header and header['cellscal'] == '1/F':
print 'WARNING: CELLSCAL keyword with value 1/F found.'
print 'Will take into account varying pixel scale when calculating wcs coordinates.'
x0,y0=header['crpix1']-1,header['crpix2']-1
# Will calculate the pixscale factor of each channel as:
# pixscale = ref_frequency / frequency
if header['ctype3']=='VELO-HEL':
pixscale=(1-header['crval3']/2.99792458e+8)/(1-(((xyz[:,2]+1)-header['crpix3'])*header['cdelt3']+header['crval3'])/2.99792458e+8)
else:
sys.stderr.write("WARNING: Cannot convert axis3 coordinates to frequency. Will ignore the effect of CELLSCAL = 1/F.\n")
pixscale=1.
xyz[:,0]=(xyz[:,0]-x0)*pixscale+x0
xyz[:,1]=(xyz[:,1]-y0)*pixscale+y0
if header['naxis']==4: objects=np.concatenate((objects,wcsin.wcs_pix2world(np.concatenate((xyz,np.zeros((objects.shape[0],1))),axis=1),0)[:,:-1]),axis=1)
else: objects=np.concatenate((objects,wcsin.wcs_pix2world(xyz,0)),axis=1)
catParUnits = tuple(list(catParUnits) + [str(cc).replace(' ','') for cc in wcsin.wcs.cunit])
catParNames = tuple(list(catParNames) + [(cc.split('--')[0]).lower() for cc in wcsin.wcs.ctype])
catParFormt = tuple(list(catParFormt) + ['%15.7e', '%15.7e', '%15.7e'])
if header['naxis']==4:
catParUnits = catParUnits[:-1]
catParNames= catParNames[:-1]
print "WCS coordinates added to the catalogue."
except:
sys.stderr.write("WARNING: WCS conversion of parameters failed.\n")
return(objects, catParNames, catParFormt, catParUnits)