def getPositionAtTime(self, t):
from astrometry.util.starutil_numpy import radectoxyz, arcsecperrad, axistilt, xyztoradec
dt = (t - self.epoch).toYears()
# Assume "pos" is an RaDecPos
p = self.pos + dt * self.pm
suntheta = t.getSunTheta()
# print 'dt', dt, 'pos', self.pos, 'pm', self.pm, 'dt*pm:', dt * self.pm
# print 'p0: (%.8f, %.8f)' % (self.pos.ra, self.pos.dec)
# print 'p1: (%.8f, %.8f)' % (p.ra, p.dec)
xyz = radectoxyz(p.ra, p.dec)
xyz = xyz[0]
# d(celestial coords)/d(parallax)
# - takes numerical derivatives when it could take analytic ones
# output is in [degrees / arcsec]. Yep. Crazy but true.
# HACK: fmods dRA when it should do something continuous.
# rd2xyz(0,0) is a unit vector; 1/arcsecperrad is (a good approximation to)
# the distance on the unit sphere spanned by an angle of 1 arcsec.
# We take a step of that length and return the change in RA,Dec.
# It's about 1e-5 so we don't renormalize the xyz unit vector.
dxyz1 = radectoxyz(0., 0.) / arcsecperrad
dxyz1 = dxyz1[0]
# - imprecise angle of obliquity
# - implicitly assumes circular orbit
# output is in [degrees / arcsec]. Yep. Crazy but true.
dxyz2 = radectoxyz(90., axistilt) / arcsecperrad
dxyz2 = dxyz2[0]
xyz += self.parallax.getValue() * (dxyz1 * np.cos(suntheta) +
dxyz2 * np.sin(suntheta))
r,d = xyztoradec(xyz)
return RaDecPos(r,d)
def cat_sdss(req, ver):
import json
import numpy as np
from astrometry.util.starutil_numpy import degrees_between, radectoxyz, xyztoradec
from map.views import sdss_ccds_near
from astrometry.util.fits import fits_table, merge_tables
tag = 'sdss-cat'
ralo = float(req.GET['ralo'])
rahi = float(req.GET['rahi'])
declo = float(req.GET['declo'])
dechi = float(req.GET['dechi'])
ver = int(ver)
if not ver in catversions[tag]:
raise RuntimeError('Invalid version %i for tag %s' % (ver, tag))
rad = degrees_between(ralo, declo, rahi, dechi) / 2.
xyz1 = radectoxyz(ralo, declo)
xyz2 = radectoxyz(rahi, dechi)
xyz = (xyz1 + xyz2)
xyz /= np.sqrt(np.sum(xyz**2))
rc,dc = xyztoradec(xyz)
rad = rad + np.hypot(10.,14.)/2./60.
ccds = sdss_ccds_near(rc[0], dc[0], rad)
if ccds is None:
print('No SDSS CCDs nearby')
return HttpResponse(json.dumps(dict(rd=[])),
content_type='application/json')
print(len(ccds), 'SDSS CCDs')
T = []
for ccd in ccds:
# env/BOSS_PHOTOOBJ/301/2073/3/photoObj-002073-3-0088.fits
fn = os.path.join(settings.SDSS_BASEDIR, 'env', 'BOSS_PHOTOOBJ',
str(ccd.rerun), str(ccd.run), str(ccd.camcol),
'photoObj-%06i-%i-%04i.fits' % (ccd.run, ccd.camcol, ccd.field))
print('Reading', fn)
T.append(fits_table(fn, columns='ra dec objid mode objc_type objc_flags objc_flags nchild tai expflux devflux psfflux cmodelflux fracdev mjd'.split()))
T = merge_tables(T)
T.cut((T.dec >= declo) * (T.dec <= dechi))
# FIXME
T.cut((T.ra >= ralo) * (T.ra <= rahi))
# primary
T.cut(T.mode == 1)
types = ['P' if t == 6 else 'C' for t in T.objc_type]
fluxes = [p if t == 6 else c for t,p,c in zip(T.objc_type, T.psfflux, T.cmodelflux)]
return HttpResponse(json.dumps(dict(
rd=[(float(o.ra),float(o.dec)) for o in T],
sourcetype=types,
fluxes = [dict(u=float(f[0]), g=float(f[1]), r=float(f[2]),
i=float(f[3]), z=float(f[4])) for f in fluxes],
)),
content_type='application/json')
def match_radec(ra1, dec1, ra2, dec2, radius_in_deg, notself=False,
nearest=False, indexlist=False):
'''
(m1,m2,d12) = match_radec(ra1,dec1, ra2,dec2, radius_in_deg)
Cross-matches numpy arrays of RA,Dec points.
Behaves like spherematch.pro of IDL
ra1,dec1 (and 2): RA,Dec in degrees of points to match.
Must be scalars or numpy arrays.
radius_in_deg: search radius in degrees.
notself: if True, avoids returning 'identity' matches;
ASSUMES that ra1,dec1 == ra2,dec2.
nearest: if True, returns only the nearest match in (ra2,dec2)
for each point in (ra1,dec1).
indexlist: returns a list of length len(ra1), containing None or a
list of ints of matched points in ra2,dec2. Returns this list.
Returns:
m1: indices into the "ra1,dec1" arrays of matching points.
Numpy array of ints.
m2: same, but for "ra2,dec2".
d12: distance, in degrees, between the matching points.
'''
# Convert to coordinates on the unit sphere
xyz1 = radectoxyz(ra1, dec1)
#if all(ra1 == ra2) and all(dec1 == dec2):
if ra1 is ra2 and dec1 is dec2:
xyz2 = xyz1
else:
xyz2 = radectoxyz(ra2, dec2)
r = deg2dist(radius_in_deg)
if nearest:
(inds,dists2) = _nearest_func(xyz2, xyz1, r, notself=notself)
I = np.flatnonzero(inds >= 0)
J = inds[I]
d = distsq2deg(dists2[I])
else:
X = match(xyz1, xyz2, r, notself=notself, indexlist=indexlist)
if indexlist:
return X
(inds,dists) = X
dist_in_deg = dist2deg(dists)
I,J = inds[:,0], inds[:,1]
d = dist_in_deg[:,0]
return (I, J, d)
def match_radec(ra1, dec1, ra2, dec2, radius_in_deg, notself=False,
nearest=False):
'''
(m1,m2,d12) = match_radec(ra1,dec1, ra2,dec2, radius_in_deg)
Cross-matches numpy arrays of RA,Dec points.
Behaves like spherematch.pro of IDL
ra1,dec1 (and 2): RA,Dec in degrees of points to match.
Must be scalars or numpy arrays.
radius_in_deg: search radius in degrees.
notself: if True, avoids returning 'identity' matches;
ASSUMES that ra1,dec1 == ra2,dec2.
nearest: if True, returns only the nearest match in (ra2,dec2)
for each point in (ra1,dec1).
Returns:
m1: indices into the "ra1,dec1" arrays of matching points.
Numpy array of ints.
m2: same, but for "ra2,dec2".
d12: distance, in degrees, between the matching points.
'''
# Convert to coordinates on the unit sphere
xyz1 = radectoxyz(ra1, dec1)
#if all(ra1 == ra2) and all(dec1 == dec2):
if ra1 is ra2 and dec1 is dec2:
xyz2 = xyz1
else:
xyz2 = radectoxyz(ra2, dec2)
r = deg2dist(radius_in_deg)
if nearest:
(inds,dists2) = _nearest_func(xyz2, xyz1, r, notself=notself)
I = np.flatnonzero(inds >= 0)
J = inds[I]
d = distsq2deg(dists2[I])
else:
(inds,dists) = match(xyz1, xyz2, r, notself)
dist_in_deg = dist2deg(dists)
I,J = inds[:,0], inds[:,1]
d = dist_in_deg[:,0]
return (I, J, d)
def cat_kd(req, ver, tag, fn):
tag = 'spec'
ralo = float(req.GET['ralo'])
rahi = float(req.GET['rahi'])
declo = float(req.GET['declo'])
dechi = float(req.GET['dechi'])
ver = int(ver)
if not ver in catversions[tag]:
raise RuntimeError('Invalid version %i for tag %s' % (ver, tag))
import numpy as np
from astrometry.util.fits import fits_table, merge_tables
from astrometry.libkd.spherematch import tree_open, tree_search_radec
from astrometry.util.starutil_numpy import radectoxyz, xyztoradec, degrees_between
xyz1 = radectoxyz(ralo, declo)
xyz2 = radectoxyz(rahi, dechi)
xyz = (xyz1 + xyz2) / 2.
xyz /= np.sqrt(np.sum(xyz**2))
rc, dc = xyztoradec(xyz)
rc = rc[0]
dc = dc[0]
rad = degrees_between(rc, dc, ralo, declo)
kd = tree_open(fn)
I = tree_search_radec(kd, rc, dc, rad)
print('Matched', len(I), 'from', fn)
if len(I) == 0:
return None
T = fits_table(fn, rows=I)
debug(len(T), 'spectra')
if ralo > rahi:
# RA wrap
T.cut(
np.logical_or(T.ra > ralo, T.ra < rahi) * (T.dec > declo) *
(T.dec < dechi))
else:
T.cut(
(T.ra > ralo) * (T.ra < rahi) * (T.dec > declo) * (T.dec < dechi))
debug(len(T), 'in cut')
return T
def cat_targets_dr2(req, ver):
import json
tag = 'targets-dr2'
ralo = float(req.GET['ralo'])
rahi = float(req.GET['rahi'])
declo = float(req.GET['declo'])
dechi = float(req.GET['dechi'])
ver = int(ver)
if not ver in catversions[tag]:
raise RuntimeError('Invalid version %i for tag %s' % (ver, tag))
from astrometry.util.fits import fits_table, merge_tables
import numpy as np
from cat.models import DR2_Target as Target
from astrometry.util.starutil_numpy import radectoxyz, xyztoradec, degrees_between
xyz1 = radectoxyz(ralo, declo)
xyz2 = radectoxyz(rahi, dechi)
xyz = (xyz1 + xyz2) / 2.
xyz /= np.sqrt(np.sum(xyz**2))
rc, dc = xyztoradec(xyz)
rc = rc[0]
dc = dc[0]
rad = degrees_between(rc, dc, ralo, declo)
objs = Target.objects.extra(where=[
'q3c_radial_query(target.ra, target.dec, %.4f, %.4f, %g)' %
(rc, dc, rad * 1.01)
])
print('Got', objs.count(), 'targets')
print('types:', np.unique([o.type for o in objs]))
print('versions:', np.unique([o.version for o in objs]))
return HttpResponse(json.dumps(
dict(
rd=[(float(o.ra), float(o.dec)) for o in objs],
name=[o.type for o in objs],
)),
content_type='application/json')
def cat_kd(req, ver, tag, fn):
tag = 'spec'
ralo = float(req.GET['ralo'])
rahi = float(req.GET['rahi'])
declo = float(req.GET['declo'])
dechi = float(req.GET['dechi'])
ver = int(ver)
if not ver in catversions[tag]:
raise RuntimeError('Invalid version %i for tag %s' % (ver, tag))
import numpy as np
from astrometry.util.fits import fits_table, merge_tables
from astrometry.libkd.spherematch import tree_open, tree_search_radec
from astrometry.util.starutil_numpy import radectoxyz, xyztoradec, degrees_between
xyz1 = radectoxyz(ralo, declo)
xyz2 = radectoxyz(rahi, dechi)
xyz = (xyz1 + xyz2)/2.
xyz /= np.sqrt(np.sum(xyz**2))
rc,dc = xyztoradec(xyz)
rc = rc[0]
dc = dc[0]
rad = degrees_between(rc, dc, ralo, declo)
kd = tree_open(fn)
I = tree_search_radec(kd, rc, dc, rad)
print('Matched', len(I), 'from', fn)
if len(I) == 0:
return None
T = fits_table(fn, rows=I)
debug(len(T), 'spectra')
if ralo > rahi:
# RA wrap
T.cut(np.logical_or(T.ra > ralo, T.ra < rahi) * (T.dec > declo) * (T.dec < dechi))
else:
T.cut((T.ra > ralo) * (T.ra < rahi) * (T.dec > declo) * (T.dec < dechi))
debug(len(T), 'in cut')
return T
def cat_targets_dr2(req, ver):
import json
tag = 'targets-dr2'
ralo = float(req.GET['ralo'])
rahi = float(req.GET['rahi'])
declo = float(req.GET['declo'])
dechi = float(req.GET['dechi'])
ver = int(ver)
if not ver in catversions[tag]:
raise RuntimeError('Invalid version %i for tag %s' % (ver, tag))
from astrometry.util.fits import fits_table, merge_tables
import numpy as np
from cat.models import DR2_Target as Target
from astrometry.util.starutil_numpy import radectoxyz, xyztoradec, degrees_between
xyz1 = radectoxyz(ralo, declo)
xyz2 = radectoxyz(rahi, dechi)
xyz = (xyz1 + xyz2)/2.
xyz /= np.sqrt(np.sum(xyz**2))
rc,dc = xyztoradec(xyz)
rc = rc[0]
dc = dc[0]
rad = degrees_between(rc, dc, ralo, declo)
objs = Target.objects.extra(where=[
'q3c_radial_query(target.ra, target.dec, %.4f, %.4f, %g)'
% (rc, dc, rad * 1.01)])
print('Got', objs.count(), 'targets')
print('types:', np.unique([o.type for o in objs]))
print('versions:', np.unique([o.version for o in objs]))
return HttpResponse(json.dumps(dict(
rd=[(float(o.ra),float(o.dec)) for o in objs],
name=[o.type for o in objs],
)),
content_type='application/json')
def read_astrans(fn, hdu, hdr=None, W=None, H=None, fitsfile=None):
from astrometry.sdss import AsTransWrapper, AsTrans
from astrometry.util.util import Tan, fit_sip_wcs_py
from astrometry.util.starutil_numpy import radectoxyz
import numpy as np
astrans = AsTrans.read(fn, F=fitsfile, primhdr=hdr)
# Approximate as SIP.
if hdr is None and fn.endswith('.bz2'):
import fitsio
hdr = fitsio.read_header(fn, 0)
if hdr is not None:
tan = Tan(*[
float(hdr[k]) for k in [
'CRVAL1', 'CRVAL2', 'CRPIX1', 'CRPIX2', 'CD1_1', 'CD1_2',
'CD2_1', 'CD2_2', 'NAXIS1', 'NAXIS2'
]
])
else:
# Frame files include a TAN header... start there.
tan = Tan(fn)
# Evaluate AsTrans on a pixel grid...
h, w = tan.shape
xx = np.linspace(1, w, 20)
yy = np.linspace(1, h, 20)
xx, yy = np.meshgrid(xx, yy)
xx = xx.ravel()
yy = yy.ravel()
rr, dd = astrans.pixel_to_radec(xx, yy)
xyz = radectoxyz(rr, dd)
fieldxy = np.vstack((xx, yy)).T
sip_order = 5
inv_order = 7
sip = fit_sip_wcs_py(xyz, fieldxy, None, tan, sip_order, inv_order)
return sip
def read_astrans(fn, hdu, hdr=None, W=None, H=None, fitsfile=None):
from astrometry.sdss import AsTransWrapper, AsTrans
from astrometry.util.util import Tan, fit_sip_wcs_py
from astrometry.util.starutil_numpy import radectoxyz
import numpy as np
astrans = AsTrans.read(fn, F=fitsfile, primhdr=hdr)
# Approximate as SIP.
if hdr is None and fn.endswith('.bz2'):
import fitsio
hdr = fitsio.read_header(fn, 0)
if hdr is not None:
tan = Tan(*[float(hdr[k]) for k in [
'CRVAL1', 'CRVAL2', 'CRPIX1', 'CRPIX2',
'CD1_1', 'CD1_2', 'CD2_1', 'CD2_2', 'NAXIS1','NAXIS2']])
else:
# Frame files include a TAN header... start there.
tan = Tan(fn)
# Evaluate AsTrans on a pixel grid...
h,w = tan.shape
xx = np.linspace(1, w, 20)
yy = np.linspace(1, h, 20)
xx,yy = np.meshgrid(xx, yy)
xx = xx.ravel()
yy = yy.ravel()
rr,dd = astrans.pixel_to_radec(xx, yy)
xyz = radectoxyz(rr, dd)
fieldxy = np.vstack((xx, yy)).T
sip_order = 5
inv_order = 7
sip = fit_sip_wcs_py(xyz, fieldxy, None, tan, sip_order, inv_order)
return sip
def match_radec(ra1, dec1, ra2, dec2, radius_in_deg, notself=False,
nearest=False, indexlist=False, count=False):
'''
Cross-matches numpy arrays of RA,Dec points.
Behaves like spherematch.pro of IDL.
Parameters
----------
ra1, dec1, ra2, dec2 : numpy arrays, or scalars.
RA,Dec in degrees of points to match.
radius_in_deg : float
Search radius in degrees.
notself : boolean
If True, avoids returning 'identity' matches;
ASSUMES that ra1,dec1 == ra2,dec2.
nearest : boolean
If True, returns only the nearest match in *(ra2,dec2)*
for each point in *(ra1,dec1)*.
indexlist : boolean
If True, returns a list of length *len(ra1)*, containing *None*
or a list of ints of matched points in *ra2,dec2*.
Returns
-------
m1 : numpy array of integers
Indices into the *ra1,dec1* arrays of matching points.
m2 : numpy array of integers
Same, but for *ra2,dec2*.
d12 : numpy array, float
Distance, in degrees, between the matching points.
'''
# Convert to coordinates on the unit sphere
xyz1 = radectoxyz(ra1, dec1)
#if all(ra1 == ra2) and all(dec1 == dec2):
if ra1 is ra2 and dec1 is dec2:
xyz2 = xyz1
else:
xyz2 = radectoxyz(ra2, dec2)
r = deg2dist(radius_in_deg)
extra = ()
if nearest:
X = _nearest_func(xyz2, xyz1, r, notself=notself, count=count)
if not count:
(inds,dists2) = X
I = np.flatnonzero(inds >= 0)
J = inds[I]
d = distsq2deg(dists2[I])
else:
#print 'X', X
#(inds,dists2,counts) = X
J,I,d,counts = X
extra = (counts,)
print('I', I.shape, I.dtype)
print('J', J.shape, J.dtype)
print('counts', counts.shape, counts.dtype)
else:
X = match(xyz1, xyz2, r, notself=notself, indexlist=indexlist)
if indexlist:
return X
(inds,dists) = X
dist_in_deg = dist2deg(dists)
I,J = inds[:,0], inds[:,1]
d = dist_in_deg[:,0]
return (I, J, d) + extra
def cat_targets_drAB(req,
ver,
cats=[],
tag='',
bgs=False,
sky=False,
bright=False,
dark=False,
color_name_func=desitarget_color_names):
'''
color_name_func: function that selects names and colors for targets
(eg based on targeting bit values)
'''
import json
ralo = float(req.GET['ralo'])
rahi = float(req.GET['rahi'])
declo = float(req.GET['declo'])
dechi = float(req.GET['dechi'])
ver = int(ver)
if not ver in catversions[tag]:
raise RuntimeError('Invalid version %i for tag %s' % (ver, tag))
from astrometry.util.fits import fits_table, merge_tables
from astrometry.libkd.spherematch import tree_open, tree_search_radec
import numpy as np
from astrometry.util.starutil_numpy import radectoxyz, xyztoradec, degrees_between
xyz1 = radectoxyz(ralo, declo)
xyz2 = radectoxyz(rahi, dechi)
xyz = (xyz1 + xyz2) / 2.
xyz /= np.sqrt(np.sum(xyz**2))
rc, dc = xyztoradec(xyz)
rc = rc[0]
dc = dc[0]
rad = degrees_between(rc, dc, ralo, declo)
'''
startree -i /project/projectdirs/desi/target/catalogs/targets-dr4-0.20.0.fits -o data/targets-dr4-0.20.0.kd.fits -P -k -T
'''
TT = []
for fn in cats:
kd = tree_open(fn)
I = tree_search_radec(kd, rc, dc, rad)
print('Matched', len(I), 'from', fn)
if len(I) == 0:
continue
T = fits_table(fn, rows=I)
TT.append(T)
if len(TT) == 0:
return HttpResponse(json.dumps(dict(rd=[], name=[])),
content_type='application/json')
T = merge_tables(TT, columns='fillzero')
if bgs:
T.cut(T.bgs_target > 0)
if bright:
T.cut(np.logical_or(T.bgs_target > 0, T.mws_target > 0))
if dark:
T.cut(T.desi_target > 0)
names = None
colors = None
if color_name_func is not None:
names, colors = color_name_func(T)
if sky:
fluxes = [
dict(g=float(g), r=float(r), z=float(z))
for (g, r, z) in zip(T.apflux_g[:,
0], T.apflux_r[:,
0], T.apflux_z[:,
0])
]
nobs = None
else:
fluxes = [
dict(g=float(g),
r=float(r),
z=float(z),
W1=float(W1),
W2=float(W2))
for (g, r, z, W1,
W2) in zip(T.flux_g, T.flux_r, T.flux_z, T.flux_w1, T.flux_w2)
]
nobs = [
dict(g=int(g), r=int(r), z=int(z))
for g, r, z in zip(T.nobs_g, T.nobs_r, T.nobs_z)
],
rtn = dict(
rd=[(t.ra, t.dec) for t in T],
targetid=[int(t) for t in T.targetid],
fluxes=fluxes,
)
if names is not None:
rtn.update(name=names)
if colors is not None:
rtn.update(color=colors)
if nobs is not None:
rtn.update(nobs=nobs)
return HttpResponse(json.dumps(rtn), content_type='application/json')
def cat_targets_drAB(req, ver, cats=None, tag='', bgs=False, sky=False, bright=False, dark=False, color_name_func=desitarget_color_names):
'''
color_name_func: function that selects names and colors for targets
(eg based on targeting bit values)
'''
if cats is None:
cats = []
import json
ralo = float(req.GET['ralo'])
rahi = float(req.GET['rahi'])
declo = float(req.GET['declo'])
dechi = float(req.GET['dechi'])
ver = int(ver)
if not ver in catversions[tag]:
raise RuntimeError('Invalid version %i for tag %s' % (ver, tag))
from astrometry.util.fits import fits_table, merge_tables
from astrometry.libkd.spherematch import tree_open, tree_search_radec
import numpy as np
from astrometry.util.starutil_numpy import radectoxyz, xyztoradec, degrees_between
xyz1 = radectoxyz(ralo, declo)
xyz2 = radectoxyz(rahi, dechi)
xyz = (xyz1 + xyz2)/2.
xyz /= np.sqrt(np.sum(xyz**2))
rc,dc = xyztoradec(xyz)
rc = rc[0]
dc = dc[0]
rad = degrees_between(rc, dc, ralo, declo)
'''
startree -i /project/projectdirs/desi/target/catalogs/targets-dr4-0.20.0.fits -o data/targets-dr4-0.20.0.kd.fits -P -k -T
'''
TT = []
for fn in cats:
kd = tree_open(fn)
I = tree_search_radec(kd, rc, dc, rad)
print('Matched', len(I), 'from', fn)
if len(I) == 0:
continue
T = fits_table(fn, rows=I)
TT.append(T)
if len(TT) == 0:
return HttpResponse(json.dumps(dict(rd=[], name=[])),
content_type='application/json')
T = merge_tables(TT, columns='fillzero')
if bgs:
T.cut(T.bgs_target > 0)
if bright:
T.cut(np.logical_or(T.bgs_target > 0, T.mws_target > 0))
if dark:
T.cut(T.desi_target > 0)
names = None
colors = None
if color_name_func is not None:
names,colors = color_name_func(T)
if sky:
fluxes = [dict(g=float(g), r=float(r), z=float(z))
for (g,r,z) in zip(T.apflux_g[:,0], T.apflux_r[:,0], T.apflux_z[:,0])]
nobs = None
else:
fluxes = [dict(g=float(g), r=float(r), z=float(z),
W1=float(W1), W2=float(W2))
for (g,r,z,W1,W2)
in zip(T.flux_g, T.flux_r, T.flux_z, T.flux_w1, T.flux_w2)]
nobs=[dict(g=int(g), r=int(r), z=int(z)) for g,r,z
in zip(T.nobs_g, T.nobs_r, T.nobs_z)],
rtn = dict(rd=[(t.ra, t.dec) for t in T],
targetid=[int(t) for t in T.targetid],
fluxes=fluxes,
)
if names is not None:
rtn.update(name=names)
if colors is not None:
rtn.update(color=colors)
if nobs is not None:
rtn.update(nobs=nobs)
# Convert targetid to string to prevent rounding errors
rtn['targetid'] = [str(s) for s in rtn['targetid']]
return HttpResponse(json.dumps(rtn), content_type='application/json')
def cat_sdss(req, ver):
import json
import numpy as np
from astrometry.util.starutil_numpy import degrees_between, radectoxyz, xyztoradec
from map.views import sdss_ccds_near
from astrometry.util.fits import fits_table, merge_tables
tag = 'sdss-cat'
ralo = float(req.GET['ralo'])
rahi = float(req.GET['rahi'])
declo = float(req.GET['declo'])
dechi = float(req.GET['dechi'])
ver = int(ver)
if not ver in catversions[tag]:
raise RuntimeError('Invalid version %i for tag %s' % (ver, tag))
rad = degrees_between(ralo, declo, rahi, dechi) / 2.
xyz1 = radectoxyz(ralo, declo)
xyz2 = radectoxyz(rahi, dechi)
xyz = (xyz1 + xyz2)
xyz /= np.sqrt(np.sum(xyz**2))
rc, dc = xyztoradec(xyz)
rad = rad + np.hypot(10., 14.) / 2. / 60.
ccds = sdss_ccds_near(rc[0], dc[0], rad)
if ccds is None:
print('No SDSS CCDs nearby')
return HttpResponse(json.dumps(dict(rd=[])),
content_type='application/json')
print(len(ccds), 'SDSS CCDs')
T = []
for ccd in ccds:
# env/BOSS_PHOTOOBJ/301/2073/3/photoObj-002073-3-0088.fits
fn = os.path.join(
settings.SDSS_BASEDIR, 'env', 'BOSS_PHOTOOBJ', str(ccd.rerun),
str(ccd.run), str(ccd.camcol),
'photoObj-%06i-%i-%04i.fits' % (ccd.run, ccd.camcol, ccd.field))
print('Reading', fn)
T.append(
fits_table(
fn,
columns=
'ra dec objid mode objc_type objc_flags objc_flags nchild tai expflux devflux psfflux cmodelflux fracdev mjd'
.split()))
T = merge_tables(T)
T.cut((T.dec >= declo) * (T.dec <= dechi))
# FIXME
T.cut((T.ra >= ralo) * (T.ra <= rahi))
# primary
T.cut(T.mode == 1)
types = ['P' if t == 6 else 'C' for t in T.objc_type]
fluxes = [
p if t == 6 else c
for t, p, c in zip(T.objc_type, T.psfflux, T.cmodelflux)
]
return HttpResponse(json.dumps(
dict(
rd=[(float(o.ra), float(o.dec)) for o in T],
sourcetype=types,
fluxes=[
dict(u=float(f[0]),
g=float(f[1]),
r=float(f[2]),
i=float(f[3]),
z=float(f[4])) for f in fluxes
],
)),
content_type='application/json')
gridr = np.hypot(gridx, gridy)
crpixx,crpixy = (petal.ccdw+1.)/2., (petal.ccdh+1.)/2.
crx,cry = petal.gfa_pix_to_focal_mm(crpixx, crpixy)
theta = Tpsfunc(gridr)
gridu = theta * gridx / gridr
gridv = theta * gridy / gridr
crr = np.hypot(crx, cry)
crd = Tpsfunc(crr)
cru = crd * crx / crr
crv = crd * cry / crr
griddec = gridv
gridra = -gridu / np.cos(np.deg2rad(griddec))
starxyz = radectoxyz(gridra, griddec)
fieldxy = np.vstack((ccdgridpx, ccdgridpy)).T
weights = np.ones(len(gridra))
crdec = crv[0]
crra = -cru[0] / np.cos(np.deg2rad(crdec))
ps = 0.2/3600.
tan_in = Tan(crra, crdec, crpixx, crpixy, -ps, 0., 0., ps, float(petal.ccdw), float(petal.ccdh))
sip_order = inv_order = 4
sip = fit_sip_wcs_py(starxyz, fieldxy, weights, tan_in, sip_order, inv_order)
hdr = fitsio.FITSHDR()
sip.add_to_header(hdr)
for i,(x,y) in enumerate(zip(petal.gfa.gif_1_pix_x, petal.gfa.gif_1_pix_y)):
hdr.add_record(dict(name='GIF1X%i' % (i+1), value=x,
comment='Pinhole pixel pos'))
hdr.add_record(dict(name='GIF1Y%i' % (i+1), value=y))
def match_radec(ra1, dec1, ra2, dec2, radius_in_deg, notself=False,
nearest=False, indexlist=False, count=False):
'''
(m1,m2,d12) = match_radec(ra1,dec1, ra2,dec2, radius_in_deg)
Cross-matches numpy arrays of RA,Dec points.
Behaves like spherematch.pro of IDL
ra1,dec1 (and 2): RA,Dec in degrees of points to match.
Must be scalars or numpy arrays.
radius_in_deg: search radius in degrees.
notself: if True, avoids returning 'identity' matches;
ASSUMES that ra1,dec1 == ra2,dec2.
nearest: if True, returns only the nearest match in (ra2,dec2)
for each point in (ra1,dec1).
indexlist: returns a list of length len(ra1), containing None or a
list of ints of matched points in ra2,dec2. Returns this list.
Returns:
m1: indices into the "ra1,dec1" arrays of matching points.
Numpy array of ints.
m2: same, but for "ra2,dec2".
d12: distance, in degrees, between the matching points.
'''
# Convert to coordinates on the unit sphere
xyz1 = radectoxyz(ra1, dec1)
#if all(ra1 == ra2) and all(dec1 == dec2):
if ra1 is ra2 and dec1 is dec2:
xyz2 = xyz1
else:
xyz2 = radectoxyz(ra2, dec2)
r = deg2dist(radius_in_deg)
extra = ()
if nearest:
X = _nearest_func(xyz2, xyz1, r, notself=notself, count=count)
if not count:
(inds,dists2) = X
I = np.flatnonzero(inds >= 0)
J = inds[I]
d = distsq2deg(dists2[I])
else:
#print 'X', X
#(inds,dists2,counts) = X
J,I,d,counts = X
extra = (counts,)
print 'I', I.shape, I.dtype
print 'J', J.shape, J.dtype
print 'counts', counts.shape, counts.dtype
else:
X = match(xyz1, xyz2, r, notself=notself, indexlist=indexlist)
if indexlist:
return X
(inds,dists) = X
dist_in_deg = dist2deg(dists)
I,J = inds[:,0], inds[:,1]
d = dist_in_deg[:,0]
return (I, J, d) + extra
def build_groupcat_sky(parent, linking_length=2, verbose=True, groupcatfile='groupcat.fits',
parentfile='parent.fits'):
"""Build a group catalog based on just RA, Dec coordinates.
"""
from astropy.table import Column, Table
from astrometry.util.starutil_numpy import radectoxyz, xyztoradec, arcsec_between
grp, mult, frst, nxt = fof_groups(parent, linking_length=linking_length, verbose=verbose)
ngrp = max(grp) + 1
groupid = np.arange(ngrp)
groupcat = Table()
groupcat.add_column(Column(name='groupid', dtype='i4', length=ngrp, data=groupid)) # unique ID number
#groupcat.add_column(Column(name='galaxy', dtype='S1000', length=ngrp))
groupcat.add_column(Column(name='nmembers', dtype='i4', length=ngrp))
groupcat.add_column(Column(name='ra', dtype='f8', length=ngrp)) # average RA
groupcat.add_column(Column(name='dec', dtype='f8', length=ngrp)) # average Dec
groupcat.add_column(Column(name='width', dtype='f4', length=ngrp)) # maximum separation
groupcat.add_column(Column(name='d25max', dtype='f4', length=ngrp))
groupcat.add_column(Column(name='d25min', dtype='f4', length=ngrp))
groupcat.add_column(Column(name='fracmasked', dtype='f4', length=ngrp))
# Add the groupid to the input catalog.
outparent = parent.copy()
#t0 = time.time()
npergrp, _ = np.histogram(grp, bins=len(grp), range=(0, len(grp)))
#print('Time to build the histogram = {:.3f} minutes.'.format( (time.time() - t0) / 60 ) )
big = np.where( npergrp > 1 )[0]
small = np.where( npergrp == 1 )[0]
if len(small) > 0:
groupcat['nmembers'][small] = 1
groupcat['groupid'][small] = groupid[small]
groupcat['ra'][small] = parent['ra'][grp[small]]
groupcat['dec'][small] = parent['dec'][grp[small]]
groupcat['d25max'][small] = parent['d25'][grp[small]]
groupcat['d25min'][small] = parent['d25'][grp[small]]
groupcat['width'][small] = parent['d25'][grp[small]]
outparent['groupid'][grp[small]] = groupid[small]
for igrp in range(len(big)):
jj = frst[big[igrp]]
ig = list()
ig.append(jj)
while (nxt[jj] != -1):
ig.append(nxt[jj])
jj = nxt[jj]
ig = np.array(ig)
ra1, dec1 = parent['ra'][ig].data, parent['dec'][ig].data
ra2, dec2 = xyztoradec(np.mean(radectoxyz(ra1, dec1), axis=0))
groupcat['ra'][big[igrp]] = ra2
groupcat['dec'][big[igrp]] = dec2
d25min, d25max = np.min(parent['d25'][ig]), np.max(parent['d25'][ig])
groupcat['d25max'][big[igrp]] = d25max
groupcat['d25min'][big[igrp]] = d25min
groupcat['nmembers'][big[igrp]] = len(ig)
outparent['groupid'][ig] = groupcat['groupid'][big[igrp]]
# Get the distance of each object from every other object.
#diff = arcsec_between(ra1, dec1, ra2, dec2) / 60 # [arcmin] # group center
diff = list()
for _ra, _dec in zip(ra1, dec1):
diff.append(arcsec_between(ra1, dec1, _ra, _dec) / 60) # [arcmin]
#if len(ig) > 2:
# import pdb ; pdb.set_trace()
diameter = np.hstack(diff).max()
groupcat['width'][big[igrp]] = diameter
print('Writing {}'.format(groupcatfile))
groupcat.write(groupcatfile, overwrite=True)
print('Writing {}'.format(parentfile))
outparent.write(parentfile, overwrite=True)
return groupcat, outparent
def arcsec_between(ra1, dec1, ra2, dec2):
xyz1 = radectoxyz(ra1, dec1)
xyz2 = radectoxyz(ra2, dec2)
d2 = np.sum((xyz1 - xyz2)**2, axis=1)
rad = np.arccos(1. - d2 / 2.)
return 3600. * np.rad2deg(rad)