def get_healpixes_touching_wcs(tan, nside=None, topscale=256.):
'''
tan: TanWCS database object.
'''
from astrometry.util.starutil_numpy import degrees_between
if nside is None:
# print 'Pixscale', tan.get_pixscale()
nside = 2 ** int(np.round(np.log2(topscale / tan.get_pixscale())))
# print 'Nside', nside
nside = int(np.clip(nside, 1, 2**10))
# print 'Nside', nside
r1,d1 = healpix_to_radecdeg(0, nside, 0., 0.)
r2,d2 = healpix_to_radecdeg(0, nside, 0.5, 0.5)
hpradius = degrees_between(r1,d1, r2,d2)
# HACK -- padding for squished parallelograms
hpradius *= 1.5
r,d,radius = tan.get_center_radecradius()
radius = np.hypot(radius, hpradius)
hh = healpix_rangesearch_radec(r, d, radius, nside)
hh.sort()
#print 'Healpixes:', hh
return (nside, hh)
def get_healpixes_touching_wcs(tan, nside=None, topscale=256.):
'''
tan: TanWCS database object.
'''
from astrometry.util.starutil_numpy import degrees_between
if nside is None:
# print 'Pixscale', tan.get_pixscale()
nside = 2**int(np.round(np.log2(topscale / tan.get_pixscale())))
# print 'Nside', nside
nside = int(np.clip(nside, 1, 2**10))
# print 'Nside', nside
r1, d1 = healpix_to_radecdeg(0, nside, 0., 0.)
r2, d2 = healpix_to_radecdeg(0, nside, 0.5, 0.5)
hpradius = degrees_between(r1, d1, r2, d2)
# HACK -- padding for squished parallelograms
hpradius *= 1.5
r, d, radius = tan.get_center_radecradius()
radius = np.hypot(radius, hpradius)
hh = healpix_rangesearch_radec(r, d, radius, nside)
hh.sort()
#print 'Healpixes:', hh
return (nside, hh)
def ccds_touching_wcs(targetwcs, T, ccdrad=0.17, polygons=True):
'''
targetwcs: wcs object describing region of interest
T: fits_table object of CCDs
ccdrad: radius of CCDs, in degrees. Default 0.17 is for DECam.
#If None, computed from T.
Returns: index array I of CCDs within range.
'''
trad = targetwcs.radius()
if ccdrad is None:
ccdrad = max(np.sqrt(np.abs(T.cd1_1 * T.cd2_2 - T.cd1_2 * T.cd2_1)) *
np.hypot(T.width, T.height) / 2.)
rad = trad + ccdrad
#r,d = targetwcs.crval
r,d = targetwcs.radec_center()
#print len(T), 'ccds'
#print 'trad', trad, 'ccdrad', ccdrad
I = np.flatnonzero(np.abs(T.dec - d) < rad)
#print 'Cut to', len(I), 'on Dec'
I = I[degrees_between(T.ra[I], T.dec[I], r, d) < rad]
#print 'Cut to', len(I), 'on RA,Dec'
if not polygons:
return I
# now check actual polygon intersection
tw,th = targetwcs.imagew, targetwcs.imageh
targetpoly = [(0.5,0.5),(tw+0.5,0.5),(tw+0.5,th+0.5),(0.5,th+0.5)]
cd = targetwcs.get_cd()
tdet = cd[0]*cd[3] - cd[1]*cd[2]
#print 'tdet', tdet
if tdet > 0:
targetpoly = list(reversed(targetpoly))
targetpoly = np.array(targetpoly)
keep = []
for i in I:
W,H = T.width[i],T.height[i]
wcs = Tan(*[float(x) for x in
[T.crval1[i], T.crval2[i], T.crpix1[i], T.crpix2[i], T.cd1_1[i],
T.cd1_2[i], T.cd2_1[i], T.cd2_2[i], W, H]])
cd = wcs.get_cd()
wdet = cd[0]*cd[3] - cd[1]*cd[2]
#print 'wdet', wdet
poly = []
for x,y in [(0.5,0.5),(W+0.5,0.5),(W+0.5,H+0.5),(0.5,H+0.5)]:
rr,dd = wcs.pixelxy2radec(x,y)
ok,xx,yy = targetwcs.radec2pixelxy(rr,dd)
poly.append((xx,yy))
if wdet > 0:
poly = list(reversed(poly))
poly = np.array(poly)
if polygons_intersect(targetpoly, poly):
keep.append(i)
I = np.array(keep)
#print 'Cut to', len(I), 'on polygons'
return I
def ccds_touching_wcs(targetwcs, ccds, ccdrad=0.17, polygons=True):
'''
targetwcs: wcs object describing region of interest
ccds: fits_table object of CCDs
ccdrad: radius of CCDs, in degrees. Default 0.17 is for DECam.
#If None, computed from T.
Returns: index array I of CCDs within range.
'''
trad = targetwcs.radius()
if ccdrad is None:
ccdrad = max(
np.sqrt(np.abs(ccds.cd1_1 * ccds.cd2_2 - ccds.cd1_2 * ccds.cd2_1))
* np.hypot(ccds.width, ccds.height) / 2.)
rad = trad + ccdrad
r, d = targetwcs.radec_center()
I, = np.nonzero(np.abs(ccds.dec - d) < rad)
I = I[np.atleast_1d(degrees_between(ccds.ra[I], ccds.dec[I], r, d) < rad)]
if not polygons:
return I
# now check actual polygon intersection
tw, th = targetwcs.imagew, targetwcs.imageh
targetpoly = [(0.5, 0.5), (tw + 0.5, 0.5), (tw + 0.5, th + 0.5),
(0.5, th + 0.5)]
cd = targetwcs.get_cd()
tdet = cd[0] * cd[3] - cd[1] * cd[2]
if tdet > 0:
targetpoly = list(reversed(targetpoly))
targetpoly = np.array(targetpoly)
keep = []
for i in I:
W, H = ccds.width[i], ccds.height[i]
wcs = Tan(*[
float(x) for x in [
ccds.crval1[i], ccds.crval2[i], ccds.crpix1[i], ccds.crpix2[i],
ccds.cd1_1[i], ccds.cd1_2[i], ccds.cd2_1[i], ccds.cd2_2[i], W,
H
]
])
cd = wcs.get_cd()
wdet = cd[0] * cd[3] - cd[1] * cd[2]
poly = []
for x, y in [(0.5, 0.5), (W + 0.5, 0.5), (W + 0.5, H + 0.5),
(0.5, H + 0.5)]:
rr, dd = wcs.pixelxy2radec(x, y)
ok, xx, yy = targetwcs.radec2pixelxy(rr, dd)
poly.append((xx, yy))
if wdet > 0:
poly = list(reversed(poly))
poly = np.array(poly)
if polygons_intersect(targetpoly, poly):
keep.append(i)
I = np.array(keep)
return I
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 ccds_touching_wcs(targetwcs, ccds, ccdrad=0.17, polygons=True):
'''
targetwcs: wcs object describing region of interest
ccds: fits_table object of CCDs
ccdrad: radius of CCDs, in degrees. Default 0.17 is for DECam.
#If None, computed from T.
Returns: index array I of CCDs within range.
'''
trad = targetwcs.radius()
if ccdrad is None:
ccdrad = max(np.sqrt(np.abs(ccds.cd1_1 * ccds.cd2_2 -
ccds.cd1_2 * ccds.cd2_1)) *
np.hypot(ccds.width, ccds.height) / 2.)
rad = trad + ccdrad
r,d = targetwcs.radec_center()
I, = np.nonzero(np.abs(ccds.dec - d) < rad)
I = I[np.atleast_1d(degrees_between(ccds.ra[I], ccds.dec[I], r, d) < rad)]
if not polygons:
return I
# now check actual polygon intersection
tw,th = targetwcs.imagew, targetwcs.imageh
targetpoly = [(0.5,0.5),(tw+0.5,0.5),(tw+0.5,th+0.5),(0.5,th+0.5)]
cd = targetwcs.get_cd()
tdet = cd[0]*cd[3] - cd[1]*cd[2]
if tdet > 0:
targetpoly = list(reversed(targetpoly))
targetpoly = np.array(targetpoly)
keep = []
for i in I:
W,H = ccds.width[i],ccds.height[i]
wcs = Tan(*[float(x) for x in
[ccds.crval1[i], ccds.crval2[i], ccds.crpix1[i], ccds.crpix2[i],
ccds.cd1_1[i], ccds.cd1_2[i], ccds.cd2_1[i], ccds.cd2_2[i], W, H]])
cd = wcs.get_cd()
wdet = cd[0]*cd[3] - cd[1]*cd[2]
poly = []
for x,y in [(0.5,0.5),(W+0.5,0.5),(W+0.5,H+0.5),(0.5,H+0.5)]:
rr,dd = wcs.pixelxy2radec(x,y)
ok,xx,yy = targetwcs.radec2pixelxy(rr,dd)
poly.append((xx,yy))
if wdet > 0:
poly = list(reversed(poly))
poly = np.array(poly)
if polygons_intersect(targetpoly, poly):
keep.append(i)
I = np.array(keep)
return I
def unwise_tiles_touching_wcs(wcs, polygons=True):
'''
Returns a FITS table (with RA,Dec,coadd_id) of unWISE tiles
'''
from astrometry.util.miscutils import polygons_intersect
from astrometry.util.starutil_numpy import degrees_between
from pkg_resources import resource_filename
atlasfn = resource_filename('legacypipe', 'data/wise-tiles.fits')
T = fits_table(atlasfn)
trad = wcs.radius()
wrad = np.sqrt(2.) / 2. * 2048 * 2.75 / 3600.
rad = trad + wrad
r, d = wcs.radec_center()
I, = np.nonzero(np.abs(T.dec - d) < rad)
I = I[degrees_between(T.ra[I], T.dec[I], r, d) < rad]
if not polygons:
return T[I]
# now check actual polygon intersection
tw, th = wcs.imagew, wcs.imageh
targetpoly = [(0.5, 0.5), (tw + 0.5, 0.5), (tw + 0.5, th + 0.5),
(0.5, th + 0.5)]
cd = wcs.get_cd()
tdet = cd[0] * cd[3] - cd[1] * cd[2]
if tdet > 0:
targetpoly = list(reversed(targetpoly))
targetpoly = np.array(targetpoly)
keep = []
for i in I:
wwcs = unwise_tile_wcs(T.ra[i], T.dec[i])
cd = wwcs.get_cd()
wdet = cd[0] * cd[3] - cd[1] * cd[2]
H, W = wwcs.shape
poly = []
for x, y in [(0.5, 0.5), (W + 0.5, 0.5), (W + 0.5, H + 0.5),
(0.5, H + 0.5)]:
rr, dd = wwcs.pixelxy2radec(x, y)
_, xx, yy = wcs.radec2pixelxy(rr, dd)
poly.append((xx, yy))
if wdet > 0:
poly = list(reversed(poly))
poly = np.array(poly)
if polygons_intersect(targetpoly, poly):
keep.append(i)
I = np.array(keep)
return T[I]
def unWISE2BOSS(tilename, channelNumber, BOSSra, BOSSdec, plot=True, vmin=-50,vmax=300):
# Input tile name
RA = BOSSra.copy()
DEC =BOSSdec.copy()
tileName = tilename
channel = channelNumber
# Contructing file address
fileaddress = get_unwise_filename(tileName, channel)
tol = 2.00
if 'm' in tileName:
ra = float(tileName.split('m')[0])/10.0
dec = float(tileName.split('m')[1])/10.0
else:
ra = float(tileName.split('p')[0])/10.0
dec = float(tileName.split('p')[1])/10.0
iBool = degrees_between(ra, dec, RA,DEC) <tol
# Getting x,y positions of the objects near by the center of the tile.
wcs = Tan(fileaddress)
ok, x, y = wcs.radec2pixelxy(RA[np.where(iBool==True)], DEC[np.where(iBool==True)])
a = np.isnan(x)
b = np.isnan(y)
x[a] = 0
y[b] = 0
iBool = (1<x)&(x<2048)&(1<y)&(y<2048)
x -= 1
y -= 1
x=x[iBool]
y=y[iBool]
#From the result, I choose to use 1504p196
if plot:
objs1 = fitsio.FITS(fileaddress)
blockImage =objs1[0][:,:]
plt.imshow(blockImage, cmap='gray', vmin=vmin, vmax=vmax, origin='lower',interpolation='nearest') # 10/8/2015: Becareful about the orientation of the matrix.
plt.scatter(x, y, facecolors='none', edgecolors='r',s=100)
plt.show()
#Return RA/DEC
return x, y
def unwise_tiles_touching_wcs(wcs, polygons=True):
'''
Returns a FITS table (with RA,Dec,coadd_id) of unWISE tiles
'''
atlasfn = os.path.join(os.path.dirname(__file__), 'allsky-atlas.fits')
T = fits_table(atlasfn)
trad = wcs.radius()
wrad = np.sqrt(2.) / 2. * 2048 * 2.75 / 3600.
rad = trad + wrad
r, d = wcs.radec_center()
I, = np.nonzero(np.abs(T.dec - d) < rad)
I = I[degrees_between(T.ra[I], T.dec[I], r, d) < rad]
if not polygons:
return T[I]
# now check actual polygon intersection
tw, th = wcs.imagew, wcs.imageh
targetpoly = [(0.5, 0.5), (tw + 0.5, 0.5), (tw + 0.5, th + 0.5),
(0.5, th + 0.5)]
cd = wcs.get_cd()
tdet = cd[0] * cd[3] - cd[1] * cd[2]
if tdet > 0:
targetpoly = list(reversed(targetpoly))
targetpoly = np.array(targetpoly)
keep = []
for i in I:
wwcs = unwise_tile_wcs(T.ra[i], T.dec[i])
cd = wwcs.get_cd()
wdet = cd[0] * cd[3] - cd[1] * cd[2]
H, W = wwcs.shape
poly = []
for x, y in [(0.5, 0.5), (W + 0.5, 0.5), (W + 0.5, H + 0.5),
(0.5, H + 0.5)]:
rr, dd = wwcs.pixelxy2radec(x, y)
ok, xx, yy = wcs.radec2pixelxy(rr, dd)
poly.append((xx, yy))
if wdet > 0:
poly = list(reversed(poly))
poly = np.array(poly)
if polygons_intersect(targetpoly, poly):
keep.append(i)
I = np.array(keep)
return T[I]
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 unwise_tiles_touching_wcs(wcs, polygons=True):
'''
Returns a FITS table (with RA,Dec,coadd_id) of unWISE tiles
'''
atlasfn = os.path.join(os.path.dirname(__file__), 'allsky-atlas.fits')
T = fits_table(atlasfn)
trad = wcs.radius()
wrad = np.sqrt(2.)/2. * 2048 * 2.75/3600.
rad = trad + wrad
r,d = wcs.radec_center()
I, = np.nonzero(np.abs(T.dec - d) < rad)
I = I[degrees_between(T.ra[I], T.dec[I], r, d) < rad]
if not polygons:
return T[I]
# now check actual polygon intersection
tw,th = wcs.imagew, wcs.imageh
targetpoly = [(0.5,0.5),(tw+0.5,0.5),(tw+0.5,th+0.5),(0.5,th+0.5)]
cd = wcs.get_cd()
tdet = cd[0]*cd[3] - cd[1]*cd[2]
if tdet > 0:
targetpoly = list(reversed(targetpoly))
targetpoly = np.array(targetpoly)
keep = []
for i in I:
wwcs = unwise_tile_wcs(T.ra[i], T.dec[i])
cd = wwcs.get_cd()
wdet = cd[0]*cd[3] - cd[1]*cd[2]
H,W = wwcs.shape
poly = []
for x,y in [(0.5,0.5),(W+0.5,0.5),(W+0.5,H+0.5),(0.5,H+0.5)]:
rr,dd = wwcs.pixelxy2radec(x,y)
ok,xx,yy = wcs.radec2pixelxy(rr,dd)
poly.append((xx,yy))
if wdet > 0:
poly = list(reversed(poly))
poly = np.array(poly)
if polygons_intersect(targetpoly, poly):
keep.append(i)
I = np.array(keep)
return T[I]
def bricks_touching_radec_box(self, bricks,
ralo, rahi, declo, dechi):
'''
Returns an index vector of the bricks that touch the given RA,Dec box.
'''
if bricks is None:
bricks = self.get_bricks_readonly()
if self.cache_tree and bricks == self.bricks:
from astrometry.libkd.spherematch import tree_build_radec, tree_search_radec
# Use kdtree
if self.bricktree is None:
self.bricktree = tree_build_radec(bricks.ra, bricks.dec)
# brick size
radius = np.sqrt(2.)/2. * self.bricksize
# + RA,Dec box size
radius = radius + degrees_between(ralo, declo, rahi, dechi) / 2.
dec = (dechi + declo) / 2.
c = (np.cos(np.deg2rad(rahi)) + np.cos(np.deg2rad(ralo))) / 2.
s = (np.sin(np.deg2rad(rahi)) + np.sin(np.deg2rad(ralo))) / 2.
ra = np.rad2deg(np.arctan2(s, c))
J = tree_search_radec(self.bricktree, ra, dec, radius)
I = J[np.nonzero((bricks.ra1[J] <= rahi ) * (bricks.ra2[J] >= ralo) *
(bricks.dec1[J] <= dechi) * (bricks.dec2[J] >= declo))[0]]
return I
if rahi < ralo:
# Wrap-around
print('In Dec slice:', len(np.flatnonzero((bricks.dec1 <= dechi) *
(bricks.dec2 >= declo))))
print('Above RAlo=', ralo, ':', len(np.flatnonzero(bricks.ra2 >= ralo)))
print('Below RAhi=', rahi, ':', len(np.flatnonzero(bricks.ra1 <= rahi)))
print('In RA slice:', len(np.nonzero(np.logical_or(bricks.ra2 >= ralo,
bricks.ra1 <= rahi))))
I, = np.nonzero(np.logical_or(bricks.ra2 >= ralo, bricks.ra1 <= rahi) *
(bricks.dec1 <= dechi) * (bricks.dec2 >= declo))
print('In RA&Dec slice', len(I))
else:
I, = np.nonzero((bricks.ra1 <= rahi ) * (bricks.ra2 >= ralo) *
(bricks.dec1 <= dechi) * (bricks.dec2 >= declo))
return I
def bricks_touching_radec_box(self, bricks,
ralo, rahi, declo, dechi):
'''
Returns an index vector of the bricks that touch the given RA,Dec box.
'''
if bricks is None:
bricks = self.get_bricks_readonly()
if self.cache_tree and bricks == self.bricks:
from astrometry.libkd.spherematch import tree_build_radec, tree_search_radec
# Use kdtree
if self.bricktree is None:
self.bricktree = tree_build_radec(bricks.ra, bricks.dec)
# brick size
radius = np.sqrt(2.)/2. * self.bricksize
# + RA,Dec box size
radius = radius + degrees_between(ralo, declo, rahi, dechi) / 2.
dec = (dechi + declo) / 2.
c = (np.cos(np.deg2rad(rahi)) + np.cos(np.deg2rad(ralo))) / 2.
s = (np.sin(np.deg2rad(rahi)) + np.sin(np.deg2rad(ralo))) / 2.
ra = np.rad2deg(np.arctan2(s, c))
J = tree_search_radec(self.bricktree, ra, dec, radius)
I = J[np.nonzero((bricks.ra1[J] <= rahi ) * (bricks.ra2[J] >= ralo) *
(bricks.dec1[J] <= dechi) * (bricks.dec2[J] >= declo))[0]]
return I
if rahi < ralo:
# Wrap-around
print('In Dec slice:', len(np.flatnonzero((bricks.dec1 <= dechi) *
(bricks.dec2 >= declo))))
print('Above RAlo=', ralo, ':', len(np.flatnonzero(bricks.ra2 >= ralo)))
print('Below RAhi=', rahi, ':', len(np.flatnonzero(bricks.ra1 <= rahi)))
print('In RA slice:', len(np.nonzero(np.logical_or(bricks.ra2 >= ralo,
bricks.ra1 <= rahi))))
I, = np.nonzero(np.logical_or(bricks.ra2 >= ralo, bricks.ra1 <= rahi) *
(bricks.dec1 <= dechi) * (bricks.dec2 >= declo))
print('In RA&Dec slice', len(I))
else:
I, = np.nonzero((bricks.ra1 <= rahi ) * (bricks.ra2 >= ralo) *
(bricks.dec1 <= dechi) * (bricks.dec2 >= declo))
return I
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 BOSS2unWISE(ra, dec, tileID, tileRA, tileDEC,plot=True): tol = 1.56 iBool = degrees_between(ra, dec, tileRA,tileDEC) <tol tilesCandidates = tileID[iBool] tiles = [] for tName in tilesCandidates: channel = 'w1' fileaddress = get_unwise_filename(tName, channel) wcs = Tan(fileaddress) ok, x, y = wcs.radec2pixelxy(ra, dec) x -= 1 y -= 1 iTile= (0<x) &(x<2047)&(0<y)&(y<2047) if iTile: tiles = np.append(tiles, [tName]) if plot: objs1 = fitsio.FITS(fileaddress) blockImage =objs1[0][:,:] plt.imshow(blockImage, cmap='gray', vmin=-50, vmax=300, origin='lower',interpolation='nearest') # 10/8/2015: Becareful about the orientation of the matrix. plt.scatter(x, y, facecolors='none', edgecolors='r',s=100) plt.show() return tiles
def read_star_clusters(targetwcs):
"""The code to generate the NGC-star-clusters-fits catalog is in
legacypipe/bin/build-cluster-catalog.py.
"""
from pkg_resources import resource_filename
from astrometry.util.starutil_numpy import degrees_between
clusterfile = resource_filename('legacypipe',
'data/NGC-star-clusters.fits')
debug('Reading {}'.format(clusterfile))
clusters = fits_table(clusterfile,
columns=['ra', 'dec', 'radius', 'type', 'ba', 'pa'])
clusters.ref_id = np.arange(len(clusters))
radius = 1.
rc, dc = targetwcs.radec_center()
d = degrees_between(rc, dc, clusters.ra, clusters.dec)
clusters.cut(d < radius)
if len(clusters) == 0:
return None
debug('Cut to {} star cluster(s) within the brick'.format(len(clusters)))
clusters.ref_cat = np.array(['CL'] * len(clusters))
# Radius in degrees
clusters.radius = clusters.radius
clusters.radius[np.logical_not(np.isfinite(clusters.radius))] = 1. / 60.
# Set isbright=True
clusters.isbright = np.zeros(len(clusters), bool)
clusters.iscluster = np.ones(len(clusters), bool)
clusters.sources = np.array([None] * len(clusters))
return clusters
def distanceFrom(self, pos):
from astrometry.util.starutil_numpy import degrees_between
return degrees_between(self.ra, self.dec, pos.ra, pos.dec)
H, W = 100, 100
## WCS.
targetwcs = Tan(ra, dec, W / 2. + 0.5, H / 2. + 0.5, -ps, 0., 0., ps,
float(W), float(H))
wcs = tractor.ConstantFitsWcs(targetwcs)
targetrd = np.array([
targetwcs.pixelxy2radec(x, y)
for x, y in [(1, 1), (W, 1), (W, H), (1, H), (1, 1)]
])
## Bricks.
B = survey.get_bricks_readonly()
B.about()
B.cut(np.argsort(degrees_between(ra, dec, B.ra, B.dec)))
brick = B[0]
tims = []
for band in bands:
sky_level_sig, psf_fwhm, zpt, psf_theta, psf_ell = unpack_ccds(
band=band, index=0)
## Gaussian approx.
psf_sigma = psf_fwhm / (2. * np.sqrt(2. * np.log(2.)))
psf_sigma2 = psf_sigma**2.
psfnorm = 1. / (2. * np.sqrt(np.pi) * psf_sigma)
def annotate_one_ccd(X):
ccd, survey, normalizePsf, carryOn = X
print('Annotating CCD', ccd.image_filename.strip(), 'expnum', ccd.expnum,
'CCD', ccd.ccdname)
result = {}
try:
im = survey.get_image_object(ccd)
except:
print('Failed to get_image_object()')
import traceback
traceback.print_exc()
if carryOn:
return result
else:
raise
X = im.get_good_image_subregion()
reg = [-1, -1, -1, -1]
for i, x in enumerate(X):
if x is not None:
reg[i] = x
result.update(good_region=reg)
kwargs = dict(pixPsf=True,
splinesky=True,
subsky=False,
pixels=False,
dq=False,
invvar=False,
normalizePsf=normalizePsf)
psf = None
wcs = None
sky = None
if ccd.ccdnastrom == 0: # something went terribly wrong
print('ccdnastrom == 0; bailing on annotation')
return result
try:
tim = im.get_tractor_image(**kwargs)
except:
print('Failed to get_tractor_image')
import traceback
traceback.print_exc()
if carryOn:
return result
else:
raise
if tim is None:
print('Failed to get_tractor_image; bailing on annotation')
return result
psf = tim.psf
wcs = tim.wcs.wcs
sky = tim.sky
hdr = tim.primhdr
result.update(humidity=hdr.get('HUMIDITY'),
outtemp=hdr.get('OUTTEMP'),
plver=tim.plver,
procdate=tim.procdate,
plprocid=tim.plprocid)
# parse 'DECaLS_15150_r' to get tile number
obj = ccd.object.strip()
words = obj.split('_')
if len(words) == 3 and words[0] in ('DECaLS', 'MzLS', 'MOSAIC'):
try:
tileid = int(words[1])
result.update(tileid=tileid)
except:
import traceback
traceback.print_exc()
# Instantiate PSF on a grid
S = 32
W, H = ccd.width, ccd.height
xx = np.linspace(1 + S, W - S, 5)
yy = np.linspace(1 + S, H - S, 5)
xx, yy = np.meshgrid(xx, yy)
psfnorms = []
galnorms = []
for x, y in zip(xx.ravel(), yy.ravel()):
tim.psf = psf.constantPsfAt(x, y)
try:
p = im.psf_norm(tim, x=x, y=y)
g = im.galaxy_norm(tim, x=x, y=y)
psfnorms.append(p)
galnorms.append(g)
except:
pass
tim.psf = psf
result.update(psfnorm_mean=np.mean(psfnorms),
psfnorm_std=np.std(psfnorms),
galnorm_mean=np.mean(galnorms),
galnorm_std=np.std(galnorms))
# PSF in center of field
cx, cy = (W + 1) / 2., (H + 1) / 2.
p = psf.getPointSourcePatch(cx, cy).patch
ph, pw = p.shape
px, py = np.meshgrid(np.arange(pw), np.arange(ph))
psum = np.sum(p)
p /= psum
# centroids
cenx = np.sum(p * px)
ceny = np.sum(p * py)
# second moments
x2 = np.sum(p * (px - cenx)**2)
y2 = np.sum(p * (py - ceny)**2)
xy = np.sum(p * (px - cenx) * (py - ceny))
# semi-major/minor axes and position angle
theta = np.rad2deg(np.arctan2(2 * xy, x2 - y2) / 2.)
theta = np.abs(theta) * np.sign(xy)
s = np.sqrt(((x2 - y2) / 2.)**2 + xy**2)
a = np.sqrt((x2 + y2) / 2. + s)
b = np.sqrt((x2 + y2) / 2. - s)
ell = 1. - b / a
result.update(psf_mx2=x2,
psf_my2=y2,
psf_mxy=xy,
psf_a=a,
psf_b=b,
psf_theta=theta,
psf_ell=ell)
# Galaxy norm using Gaussian approximation of PSF.
realpsf = tim.psf
tim.psf = im.read_psf_model(0, 0, gaussPsf=True, psf_sigma=tim.psf_sigma)
result.update(gaussgalnorm=im.galaxy_norm(tim, x=cx, y=cy))
tim.psf = realpsf
has_skygrid = hasattr(sky, 'evaluateGrid')
# Sky -- evaluate on a grid (every ~10th pixel)
if has_skygrid:
skygrid = sky.evaluateGrid(
np.linspace(0, ccd.width - 1, int(1 + ccd.width / 10)),
np.linspace(0, ccd.height - 1, int(1 + ccd.height / 10)))
result.update(meansky=np.mean(skygrid),
stdsky=np.std(skygrid),
maxsky=skygrid.max(),
minsky=skygrid.min())
else:
skyval = sky.getConstant()
result.update(meansky=skyval, stdsky=0., maxsky=skyval, minsky=skyval)
# WCS
r0, d0 = wcs.pixelxy2radec(1, 1)
r1, d1 = wcs.pixelxy2radec(1, H)
r2, d2 = wcs.pixelxy2radec(W, H)
r3, d3 = wcs.pixelxy2radec(W, 1)
result.update(ra0=r0,
dec0=d0,
ra1=r1,
dec1=d1,
ra2=r2,
dec2=d2,
ra3=r3,
dec3=d3)
midx, midy = (W + 1) / 2., (H + 1) / 2.
rc, dc = wcs.pixelxy2radec(midx, midy)
ra, dec = wcs.pixelxy2radec([1, W, midx, midx], [midy, midy, 1, H])
result.update(dra=max(degrees_between(ra, dc + np.zeros_like(ra), rc, dc)),
ddec=max(
degrees_between(rc + np.zeros_like(dec), dec, rc, dc)),
ra_center=rc,
dec_center=dc)
# Compute scale change across the chip
# how many pixels to step
step = 10
xx = np.linspace(1 + step, W - step, 5)
yy = np.linspace(1 + step, H - step, 5)
xx, yy = np.meshgrid(xx, yy)
pixscale = []
for x, y in zip(xx.ravel(), yy.ravel()):
sx = [x - step, x - step, x + step, x + step, x - step]
sy = [y - step, y + step, y + step, y - step, y - step]
sr, sd = wcs.pixelxy2radec(sx, sy)
rc, dc = wcs.pixelxy2radec(x, y)
# project around a tiny little TAN WCS at (x,y), with 1" pixels
locwcs = Tan(rc, dc, 0., 0., 1. / 3600, 0., 0., 1. / 3600, 1., 1.)
ok, lx, ly = locwcs.radec2pixelxy(sr, sd)
A = polygon_area((lx, ly))
pixscale.append(np.sqrt(A / (2 * step)**2))
result.update(pixscale_mean=np.mean(pixscale),
pixscale_std=np.std(pixscale),
pixscale_min=min(pixscale),
pixscale_max=max(pixscale))
result.update(annotated=True)
print('Finished annotation')
return result
def main(outfn='ccds-annotated.fits', ccds=None):
decals = Decals()
if ccds is None:
ccds = decals.get_ccds()
# File from the "observing" svn repo:
# https://desi.lbl.gov/svn/decam/code/observing/trunk
tiles = fits_table('decam-tiles_obstatus.fits')
#ccds.cut(np.arange(100))
#print("HACK!")
#ccds.cut(np.array([name in ['N15', 'N16', 'N21', 'N9']
# for name in ccds.ccdname]) *
# ccds.expnum == 229683)
I = decals.photometric_ccds(ccds)
ccds.photometric = np.zeros(len(ccds), bool)
ccds.photometric[I] = True
I = decals.apply_blacklist(ccds)
ccds.blacklist_ok = np.zeros(len(ccds), bool)
ccds.blacklist_ok[I] = True
ccds.good_region = np.empty((len(ccds), 4), np.int16)
ccds.good_region[:,:] = -1
ccds.ra0 = np.zeros(len(ccds), np.float64)
ccds.dec0 = np.zeros(len(ccds), np.float64)
ccds.ra1 = np.zeros(len(ccds), np.float64)
ccds.dec1 = np.zeros(len(ccds), np.float64)
ccds.ra2 = np.zeros(len(ccds), np.float64)
ccds.dec2 = np.zeros(len(ccds), np.float64)
ccds.ra3 = np.zeros(len(ccds), np.float64)
ccds.dec3 = np.zeros(len(ccds), np.float64)
ccds.dra = np.zeros(len(ccds), np.float32)
ccds.ddec = np.zeros(len(ccds), np.float32)
ccds.ra_center = np.zeros(len(ccds), np.float64)
ccds.dec_center = np.zeros(len(ccds), np.float64)
ccds.sig1 = np.zeros(len(ccds), np.float32)
ccds.meansky = np.zeros(len(ccds), np.float32)
ccds.stdsky = np.zeros(len(ccds), np.float32)
ccds.maxsky = np.zeros(len(ccds), np.float32)
ccds.minsky = np.zeros(len(ccds), np.float32)
ccds.pixscale_mean = np.zeros(len(ccds), np.float32)
ccds.pixscale_std = np.zeros(len(ccds), np.float32)
ccds.pixscale_max = np.zeros(len(ccds), np.float32)
ccds.pixscale_min = np.zeros(len(ccds), np.float32)
ccds.psfnorm_mean = np.zeros(len(ccds), np.float32)
ccds.psfnorm_std = np.zeros(len(ccds), np.float32)
ccds.galnorm_mean = np.zeros(len(ccds), np.float32)
ccds.galnorm_std = np.zeros(len(ccds), np.float32)
gaussgalnorm = np.zeros(len(ccds), np.float32)
# 2nd moments
ccds.psf_mx2 = np.zeros(len(ccds), np.float32)
ccds.psf_my2 = np.zeros(len(ccds), np.float32)
ccds.psf_mxy = np.zeros(len(ccds), np.float32)
#
ccds.psf_a = np.zeros(len(ccds), np.float32)
ccds.psf_b = np.zeros(len(ccds), np.float32)
ccds.psf_theta = np.zeros(len(ccds), np.float32)
ccds.psf_ell = np.zeros(len(ccds), np.float32)
ccds.humidity = np.zeros(len(ccds), np.float32)
ccds.outtemp = np.zeros(len(ccds), np.float32)
ccds.tileid = np.zeros(len(ccds), np.int32)
ccds.tilepass = np.zeros(len(ccds), np.uint8)
ccds.tileebv = np.zeros(len(ccds), np.float32)
plvers = []
for iccd,ccd in enumerate(ccds):
im = decals.get_image_object(ccd)
print('Reading CCD %i of %i:' % (iccd+1, len(ccds)), im)
X = im.get_good_image_subregion()
for i,x in enumerate(X):
if x is not None:
ccds.good_region[iccd,i] = x
W,H = ccd.width, ccd.height
psf = None
wcs = None
sky = None
try:
tim = im.get_tractor_image(pixPsf=True, splinesky=True,
subsky=False, pixels=False)
except:
import traceback
traceback.print_exc()
plvers.append('')
continue
if tim is None:
plvers.append('')
continue
psf = tim.psf
wcs = tim.wcs.wcs
sky = tim.sky
hdr = tim.primhdr
# print('Got PSF', psf)
# print('Got sky', type(sky))
# print('Got WCS', wcs)
ccds.humidity[iccd] = hdr.get('HUMIDITY')
ccds.outtemp[iccd] = hdr.get('OUTTEMP')
ccds.sig1[iccd] = tim.sig1
plvers.append(tim.plver)
obj = hdr.get('OBJECT')
# parse 'DECaLS_15150_r'
words = obj.split('_')
tile = None
if len(words) == 3 and words[0] == 'DECaLS':
try:
tileid = int(words[1])
tile = tiles[tileid - 1]
if tile.tileid != tileid:
I = np.flatnonzero(tile.tileid == tileid)
tile = tiles[I[0]]
except:
pass
if tile is not None:
ccds.tileid [iccd] = tile.tileid
ccds.tilepass[iccd] = tile.get('pass')
ccds.tileebv [iccd] = tile.ebv_med
# Instantiate PSF on a grid
S = 32
xx = np.linspace(1+S, W-S, 5)
yy = np.linspace(1+S, H-S, 5)
xx,yy = np.meshgrid(xx, yy)
psfnorms = []
galnorms = []
for x,y in zip(xx.ravel(), yy.ravel()):
p = im.psf_norm(tim, x=x, y=y)
g = im.galaxy_norm(tim, x=x, y=y)
psfnorms.append(p)
galnorms.append(g)
ccds.psfnorm_mean[iccd] = np.mean(psfnorms)
ccds.psfnorm_std [iccd] = np.std (psfnorms)
ccds.galnorm_mean[iccd] = np.mean(galnorms)
ccds.galnorm_std [iccd] = np.std (galnorms)
# PSF in center of field
cx,cy = (W+1)/2., (H+1)/2.
p = psf.getPointSourcePatch(cx, cy).patch
ph,pw = p.shape
px,py = np.meshgrid(np.arange(pw), np.arange(ph))
psum = np.sum(p)
# print('psum', psum)
p /= psum
# centroids
cenx = np.sum(p * px)
ceny = np.sum(p * py)
# print('cenx,ceny', cenx,ceny)
# second moments
x2 = np.sum(p * (px - cenx)**2)
y2 = np.sum(p * (py - ceny)**2)
xy = np.sum(p * (px - cenx)*(py - ceny))
# semi-major/minor axes and position angle
theta = np.rad2deg(np.arctan2(2 * xy, x2 - y2) / 2.)
theta = np.abs(theta) * np.sign(xy)
s = np.sqrt(((x2 - y2)/2.)**2 + xy**2)
a = np.sqrt((x2 + y2) / 2. + s)
b = np.sqrt((x2 + y2) / 2. - s)
ell = 1. - b/a
# print('PSF second moments', x2, y2, xy)
# print('PSF position angle', theta)
# print('PSF semi-axes', a, b)
# print('PSF ellipticity', ell)
ccds.psf_mx2[iccd] = x2
ccds.psf_my2[iccd] = y2
ccds.psf_mxy[iccd] = xy
ccds.psf_a[iccd] = a
ccds.psf_b[iccd] = b
ccds.psf_theta[iccd] = theta
ccds.psf_ell [iccd] = ell
# Galaxy norm using Gaussian approximation of PSF.
realpsf = tim.psf
tim.psf = im.read_psf_model(0, 0, gaussPsf=True,
psf_sigma=tim.psf_sigma)
gaussgalnorm[iccd] = im.galaxy_norm(tim, x=cx, y=cy)
tim.psf = realpsf
# Sky
mod = np.zeros((ccd.height, ccd.width), np.float32)
sky.addTo(mod)
ccds.meansky[iccd] = np.mean(mod)
ccds.stdsky[iccd] = np.std(mod)
ccds.maxsky[iccd] = mod.max()
ccds.minsky[iccd] = mod.min()
# WCS
ccds.ra0[iccd],ccds.dec0[iccd] = wcs.pixelxy2radec(1, 1)
ccds.ra1[iccd],ccds.dec1[iccd] = wcs.pixelxy2radec(1, H)
ccds.ra2[iccd],ccds.dec2[iccd] = wcs.pixelxy2radec(W, H)
ccds.ra3[iccd],ccds.dec3[iccd] = wcs.pixelxy2radec(W, 1)
midx, midy = (W+1)/2., (H+1)/2.
rc,dc = wcs.pixelxy2radec(midx, midy)
ra,dec = wcs.pixelxy2radec([1,W,midx,midx], [midy,midy,1,H])
ccds.dra [iccd] = max(degrees_between(ra, dc+np.zeros_like(ra),
rc, dc))
ccds.ddec[iccd] = max(degrees_between(rc+np.zeros_like(dec), dec,
rc, dc))
ccds.ra_center [iccd] = rc
ccds.dec_center[iccd] = dc
# Compute scale change across the chip
# how many pixels to step
step = 10
xx = np.linspace(1+step, W-step, 5)
yy = np.linspace(1+step, H-step, 5)
xx,yy = np.meshgrid(xx, yy)
pixscale = []
for x,y in zip(xx.ravel(), yy.ravel()):
sx = [x-step, x-step, x+step, x+step, x-step]
sy = [y-step, y+step, y+step, y-step, y-step]
sr,sd = wcs.pixelxy2radec(sx, sy)
rc,dc = wcs.pixelxy2radec(x, y)
# project around a tiny little TAN WCS at (x,y), with 1" pixels
locwcs = Tan(rc, dc, 0., 0., 1./3600, 0., 0., 1./3600, 1., 1.)
ok,lx,ly = locwcs.radec2pixelxy(sr, sd)
#print('local x,y:', lx, ly)
A = polygon_area((lx, ly))
pixscale.append(np.sqrt(A / (2*step)**2))
# print('Pixel scales:', pixscale)
ccds.pixscale_mean[iccd] = np.mean(pixscale)
ccds.pixscale_min[iccd] = min(pixscale)
ccds.pixscale_max[iccd] = max(pixscale)
ccds.pixscale_std[iccd] = np.std(pixscale)
ccds.plver = np.array(plvers)
sfd = tractor.sfd.SFDMap()
allbands = 'ugrizY'
filts = ['%s %s' % ('DES', f) for f in allbands]
wisebands = ['WISE W1', 'WISE W2', 'WISE W3', 'WISE W4']
ebv,ext = sfd.extinction(filts + wisebands, ccds.ra_center,
ccds.dec_center, get_ebv=True)
ext = ext.astype(np.float32)
ccds.ebv = ebv.astype(np.float32)
ccds.decam_extinction = ext[:,:len(allbands)]
ccds.wise_extinction = ext[:,len(allbands):]
# Depth
detsig1 = ccds.sig1 / ccds.psfnorm_mean
depth = 5. * detsig1
# that's flux in nanomaggies -- convert to mag
ccds.psfdepth = -2.5 * (np.log10(depth) - 9)
detsig1 = ccds.sig1 / ccds.galnorm_mean
depth = 5. * detsig1
# that's flux in nanomaggies -- convert to mag
ccds.galdepth = -2.5 * (np.log10(depth) - 9)
# Depth using Gaussian FWHM.
psf_sigma = ccds.fwhm / 2.35
gnorm = 1./(2. * np.sqrt(np.pi) * psf_sigma)
detsig1 = ccds.sig1 / gnorm
depth = 5. * detsig1
# that's flux in nanomaggies -- convert to mag
ccds.gausspsfdepth = -2.5 * (np.log10(depth) - 9)
# Gaussian galaxy depth
detsig1 = ccds.sig1 / gaussgalnorm
depth = 5. * detsig1
# that's flux in nanomaggies -- convert to mag
ccds.gaussgaldepth = -2.5 * (np.log10(depth) - 9)
ccds.writeto(outfn)
def update_for_image(self, M, forcepass=None, now=None):
# M: measurements for the image that's we're going to use to update
# exposure times.
# now: for testing purposes only; defaults to ephem.now()
# filename patterns for the exposure and slew scripts
expscriptpat = os.path.join(self.scriptdir, self.expscriptpattern)
slewscriptpat = os.path.join(self.scriptdir, self.slewscriptpattern)
if M is not None:
# Choose the pass, based on...
trans = M['transparency']
seeing = M['seeing']
skybright = M['skybright']
# eg, nominal = 20, sky = 19, brighter is 1 mag brighter than nom.
meas_band = M['band']
nomsky = self.nom.sky(meas_band)
brighter = nomsky - skybright
print('Transparency: %6.02f' % trans)
print('Seeing : %6.02f' % seeing)
print('Sky : %6.02f' % skybright)
print('Nominal sky : %6.02f' % nomsky)
print('Sky over nom: %6.02f (positive means brighter than nominal)' %
brighter)
nextpass = choose_pass(trans, seeing, skybright, nomsky,
forcedir=self.scriptdir)
elif forcepass is not None:
nextpass = forcepass
else:
nextpass = self.opt.passnum
print()
print('Selected pass:'******'%s: reading sequence number from %s' %
(str(ephem.now()), self.seqnumpath))
f = open(self.seqnumpath, 'r')
s = f.read()
f.close()
seqnum = int(s)
else:
# Tonight.sh may not have started yet -- pretend that
# we're currently taking seqnum=0, so the next one to be
# planned is seqnum=1.
seqnum = 0
print('%s: sequence number: %i' % (str(now), seqnum))
# 'iplan': the tile index we will use for exposure # 'seqnum'
iplan = None
if self.opt.sequence:
iplan = seqnum + self.sequence_offset
# Check whether the next tile will be observed more than an hour
# later than its scheduled time -- if so, skip a block of tiles.
if iplan < len(J):
j = J[iplan]
tstart = ephem.Date(str(j['approx_datetime']))
if now > (tstart + 3600./86400.):
print('The next tile will be observed more than an hour late. Skipping a block of observations...')
for i,j in enumerate(J[iplan:]):
tstart = ephem.Date(str(j['approx_datetime']))
if tstart <= now:
continue
print('Found tile', j['object'], 'which starts at', str(tstart))
print('Skipping', i, 'tiles')
self.sequence_offset += i
iplan += i
break
elif self.opt.cut_before_now:
# The usual case -- find the next tile scheduled for after now.
for i,j in enumerate(J):
tstart = ephem.Date(str(j['approx_datetime']))
if tstart <= now:
continue
print('Found tile', j['object'], 'which starts at', str(tstart))
iplan = i
break
if iplan is None:
print('Could not find a JSON observation in pass', nextpass,
'with approx_datetime after now =', str(now),
'-- latest one', str(tstart))
return False
else:
# For when we want to observe every tile in the plan:
# 'seqnum' is the exposure currently running;
# seqnum is 1-indexed, so that's the index we want for iplan.
iplan = seqnum
# Set observing conditions for computing exposure time
self.obs.date = now
# Planned exposures:
P = fits_table()
P.type = []
P.tilename = []
P.filter = []
P.exptime = []
P.ra = []
P.dec = []
P.passnumber = []
lasttile = None
lastdate = None
iahead = 0
for ii,jplan in enumerate(J[iplan:]):
from astrometry.util.starutil_numpy import degrees_between
if iahead >= self.Nahead:
break
tilename = str(jplan['object'])
nextseq = seqnum + 1 + iahead
if self.n_exposures > 0 and nextseq > self.n_exposures:
print('Planned tile is beyond the exposure number in ',
'tonight.sh -- RESTART MOSBOT')
return False
print('Considering planning tile %s for exp %i'%(tilename,nextseq))
# Check all planned tiles before this one for a duplicate tile.
dup = False
for s in range(nextseq-1, 0, -1):
if tilename == self.planned_tiles[s]:
dup = True
print('Wanted to plan tile %s (pass %i element %i) for exp %i'
% (tilename, nextpass, iplan+ii, nextseq),
'but it was already planned for exp %i' % s)
break
if dup:
continue
self.planned_tiles[nextseq] = tilename
iahead += 1
# Find this tile in the tiles table.
tile = get_tile_from_name(tilename, self.tiles)
ebv = tile.ebv_med
nextband = str(jplan['filter'])[0]
print('Selected tile:', tile.tileid, nextband)
rastr = ra2hms (jplan['RA' ])
decstr = dec2dms(jplan['dec'])
ephemstr = str('%s,f,%s,%s,20' % (tilename, rastr, decstr))
etile = ephem.readdb(ephemstr)
etile.compute(self.obs)
airmass = get_airmass(float(etile.alt))
print('Airmass of planned tile:', airmass)
print('Time of observation:', self.obs.date)
# HACK -- try setting the date to the planned datetime from the plan file.
#self.obs.date = ephem.Date(str(jplan['approx_datetime']))
lst = np.rad2deg(float(self.obs.sidereal_time()))
ha = lst - jplan['RA']
if ha < -180:
ha += 360.
print('LST:', lst, 'Tile RA:', jplan['RA'], 'Dec', jplan['dec'])
print('HA of planned tile:', ha)
readout_during_move = True
# Check for large slew
slew = 0.
if lasttile is None:
# Look up the tile we previously planned
prevname = self.planned_tiles.get(nextseq - 1, None)
Jall = self.J1 + self.J2 + self.J3
I, = np.nonzero([str(j['object']) == prevname for j in Jall])
if len(I) == 0:
print('Could not find previous tile "%s"' % prevname)
else:
jprev = Jall[I[0]]
lasttile = jprev
lastdate = ephem.Date(str(jprev['approx_datetime']))
if lasttile is not None:
slew = degrees_between(lasttile['RA'], lasttile['dec'],
jplan['RA'], jplan['dec'])
print('Slew: %.1f degrees' % slew)
# Compute HA for previous tile
thedate = self.obs.date
self.obs.date = lastdate
last_lst = np.rad2deg(float(self.obs.sidereal_time()))
self.obs.date = thedate
last_ha = last_lst - lasttile['RA']
if last_ha < -180:
last_ha += 360.
print('Last tile LST:', last_lst, 'Tile RA:', lasttile['RA'], 'Dec', lasttile['dec'])
print('Last HA:', last_ha)
fuzz = 3.
maybe_flip = maybe_ha_flip(ha, last_ha, fuzz)
print('Over-the-pole flip possible:', maybe_flip)
if maybe_flip:
readout_during_move = False
if iahead == 1 and slew > 10.:
# Beep the terminal -- OA has to okay it (?)
print()
print()
print('Large slew from current exposure to next: ' +
'RA,Dec %.1f, %.1f to %.1f, %.1f ==> %.1f degrees' %
(jprev['RA'], jprev['dec'], jplan['RA'],
jplan['dec'], slew))
print()
print()
os.system('tput bel; sleep 0.2; ' * 5)
if slew > 35:
# We risk a timeout -- modify the "slewread"
# script we write out so that we read out before
# commanding the telescope to move.
print('Large slew from current exposure to next: reading out before moving the telescope')
readout_during_move = False
lasttile = jplan
lastdate = self.obs.date
if M is not None:
if M['band'] == nextband:
nextsky = skybright
else:
# Guess that the sky is as much brighter than canonical
# in the next band as it is in this one!
nextsky = ((skybright - nomsky) + self.nom.sky(nextband))
fid = self.nom.fiducial_exptime(nextband)
expfactor = exposure_factor(fid, self.nom,
airmass, ebv, seeing, nextsky, trans)
print('Exposure factor:', expfactor)
expfactor_orig = expfactor
# Adjust for previous exposures?
if self.opt.adjust:
debug=True
adjfactor,others = self.adjust_for_previous(
tile, nextband, fid, debug=debug, get_others=True)
# Don't adjust exposure times down, only up.
adjfactor = max(adjfactor, 1.0)
expfactor *= adjfactor
else:
adjfactor = 0.
others = []
exptime = expfactor * fid.exptime
### HACK -- safety factor!
print('Exposure time:', exptime)
exptime *= 1.1
print('Exposure time with safety factor:', exptime)
exptime_unclipped = exptime
exptime = np.clip(exptime, fid.exptime_min, fid.exptime_max)
print('Clipped exptime', exptime)
exptime_satclipped = 0.
if nextband == 'z':
# Compute cap on exposure time to avoid saturation /
# loss of dynamic range.
t_sat = self.nom.saturation_time(nextband, nextsky)
if exptime > t_sat:
exptime_satclipped = t_sat
exptime = t_sat
print('Reduced exposure time to avoid z-band saturation:',
'%.1f' % exptime)
exptime = int(np.ceil(exptime))
print('Changing exptime from', jplan['expTime'], 'to', exptime)
jplan['expTime'] = exptime
# Update the computed exposure-time database.
if self.opt.db:
try:
# NOTE, these kwargs MUST match the names in models.py
self.update_exptime_db(
nextseq, others, tileid=tile.tileid,
passnumber=nextpass, band=nextband, airmass=airmass,
ebv=ebv, meas_band=meas_band,
zeropoint=M['zp'], transparency=trans,
seeing=seeing, sky=skybright, expfactor=expfactor_orig,
adjfactor=adjfactor,
exptime_unclipped=exptime_unclipped,
exptime_satclipped=exptime_satclipped,
exptime=exptime)
except:
print('Failed to update computed-exptime database.')
import traceback
traceback.print_exc()
# carry on
print('Predict tile will be observed at', str(self.obs.date),
'vs approx_datetime', jplan.get('approx_datetime',None))
status = ('Exp %i: Tile %s, Pass %i, RA %s, Dec %s' %
(nextseq, tilename, nextpass, rastr, decstr))
print('%s: updating exposure %i to tile %s' %
(str(ephem.now()), nextseq, tilename))
expscriptfn = expscriptpat % (nextseq)
exptmpfn = expscriptfn + '.tmp'
f = open(exptmpfn, 'w')
f.write(('# Exp %i Tile: %s, set at Seq %i, %s\n' %
(nextseq, tilename, seqnum, str(ephem.now()))) +
expscript_for_json(jplan, status=status))
f.close()
slewscriptfn = slewscriptpat % (nextseq)
slewtmpfn = slewscriptfn + '.tmp'
f = open(slewtmpfn, 'w')
f.write(slewscript_for_json(jplan, readout_during_move=readout_during_move))
f.close()
os.rename(exptmpfn, expscriptfn)
print('Wrote', expscriptfn)
os.rename(slewtmpfn, slewscriptfn)
print('Wrote', slewscriptfn)
exptime = jplan['expTime']
self.obs.date += (exptime + self.nom.overhead) / 86400.
print('%s: updated exposure %i to tile %s' %
(str(ephem.now()), nextseq, tilename))
P.tilename.append(tilename)
P.filter.append(nextband)
P.exptime.append(exptime)
P.ra.append(jplan['RA'])
P.dec.append(jplan['dec'])
P.passnumber.append(nextpass)
P.type.append('P')
if iahead < self.Nahead:
# We ran out of tiles.
# Overwrite the default planned exposures with blanks to avoid
# running the defaults (eg, at end of night).
seqstart = seqnum + 1 + iahead
seqend = self.n_exposures
print('Overwriting remaining exposure scripts (%i to %i inclusive) with blanks.' % (seqstart, seqend))
for nextseq in range(seqstart, seqend+1):
print('Blanking out exposure %i' % nextseq)
fn = self.expscriptpattern % nextseq
path = os.path.join(self.scriptdir, fn)
f = open(path, 'w')
f.write('\n')
f.close()
fn = self.slewscriptpattern % nextseq
path = os.path.join(self.scriptdir, fn)
f = open(path, 'w')
f.write('\n')
f.close()
for i,J in enumerate([self.J1,self.J2,self.J3]):
passnum = i+1
for j in J:
tstart = ephem.Date(str(j['approx_datetime']))
if self.opt.cut_before_now and tstart < now:
continue
P.tilename.append(str(j['object']))
filt = str(j['filter'])[0]
P.filter.append(filt)
P.exptime.append(j['expTime'])
P.ra.append(j['RA'])
P.dec.append(j['dec'])
P.passnumber.append(passnum)
P.type.append('%i' % passnum)
P.to_np_arrays()
fn = 'mosbot-plan.fits'
tmpfn = fn + '.tmp'
P.writeto(tmpfn)
os.rename(tmpfn, fn)
print('Wrote', fn)
return True
def main():
import optparse
import sys
parser = optparse.OptionParser(usage='%prog <json>')
parser.add_option('--base', default='plan', help='Plot base filename')
parser.add_option('-t', '--obstatus', help='Show already-observed tiles?')
parser.add_option(
'--bands',
help='Plot only already-observed tiles in the given bands',
default='g,r,z')
parser.add_option('--sgc', action='store_true', help='Center on SGC?')
parser.add_option('--ralo', type=float, default=None)
parser.add_option('--rahi', type=float, default=None)
parser.add_option('--declo', type=float, default=None)
parser.add_option('--dechi', type=float, default=None)
parser.add_option(
'--scaled',
action='store_true',
default=False,
help='Scale plot so that 1 deg RA = 1 deg Dec (no COS term)')
parser.add_option('--wide',
action='store_true',
default=False,
help='Make wider plots?')
parser.add_option('--also',
action='append',
default=[],
help='Also plot the plan from the given filename.')
parser.add_option('--mosaic',
action='store_true',
help='Set defaults for Mosaic survey')
parser.add_option(
'--start-time',
help=
'Start time for this plan, HH:MM:SS UTC. Default: 12-degree twilight tonight.'
)
parser.add_option('--start-date',
help='Start date for this plan, YYYY-MM-DD UTC.')
parser.add_option(
'--stop-time',
help='Stop time for this plan, HH:MM:SS UTC. Default: no limit.')
parser.add_option(
'--second-half',
action='store_true',
help='This plan starts at the start of the second half-night.')
parser.add_option('--skip',
type=int,
default=1,
help='Write every Nth plot only')
parser.add_option('--threads', type=int, help='Multi-processing?')
opt, args = parser.parse_args()
if len(args) != 1:
parser.print_help()
sys.exit(-1)
if opt.mosaic:
dd = dict(ralo=0, rahi=360, declo=30, dechi=88)
else:
dd = dict(ralo=0, rahi=360, declo=-10, dechi=35)
for k in dd.keys():
if getattr(opt, k, None) is None:
setattr(opt, k, dd[k])
start_date_specified = (opt.start_date is not None)
if opt.start_date is None:
# Get date at start of night, where we define a new day as
# starting at noon UTC.
now = datetime.datetime.utcnow()
# noon
nightstart = now - datetime.timedelta(0, 12 * 3600)
d = nightstart.date()
opt.start_date = '%04i-%02i-%02i' % (d.year, d.month, d.day)
print('Set start date to', opt.start_date)
if opt.mosaic:
from camera_mosaic import ephem_observer
else:
from camera_decam import ephem_observer
obs = ephem_observer()
obs.temp = 10.0 # deg celsius; average temp for August
obs.pressure = 780.0 # mbar
### HACK
obs.date = ephem.Date(opt.start_date + ' 8:00:00')
#print('Obs date:', obs.date)
daystart = obs.date
obs.horizon = -ephem.degrees('12:00:00.0')
sun = ephem.Sun()
eve_twi = obs.next_setting(sun)
obs.date = eve_twi
morn_twi = obs.next_rising(sun)
print('Evening twilight:', eve_twi)
print('Morning twilight:', morn_twi)
assert (morn_twi > eve_twi)
obs.horizon = 0.
print('Eve twi:', eve_twi, 'Morning:', morn_twi)
if opt.second_half:
# Set start-time to the midpoint between 12-degree twilights.
obs.date = ephem.Date((eve_twi + morn_twi) / 2.)
print('Second half starts at', obs.date)
elif opt.start_time is None:
# 12-degree twilight on start_date
obs.date = eve_twi
else:
obs.date = ephem.Date(opt.start_date + ' ' + opt.start_time)
if not start_date_specified and obs.date < daystart:
# If --start-date is, eg, 2am, assume it's during the night starting on daystart.
obs.date = ephem.Date(float(obs.date) + 1.)
print('Start date:', obs.date)
if opt.stop_time is not None:
# The date should be unambiguous -- try the same as obs.date =
# start time, add one day if necessary.
date = obs.date.datetime()
stopdate = ephem.Date('%04i-%02i-%02i' %
(date.year, date.month, date.day) + ' ' +
opt.stop_time)
if stopdate < obs.date:
stopdate = ephem.Date(float(stopdate) + 1.)
print('Stop date:', stopdate)
jfn = args[0]
print('Reading JSON file', jfn)
J = json.loads(open(jfn, 'rb').read())
print(len(J), 'entries')
Jalso = [json.loads(open(fn, 'rb').read()) for fn in opt.also]
# Get times when exposures should occur.
times = []
LSTs = []
# If the JSON files include estimated times, use those
if 'approx_datetime' in J[0]:
for i, j in enumerate(J):
obs.date = ephem.Date(str(j['approx_datetime']))
if opt.stop_time is not None and obs.date > stopdate:
print('Tile', i, 'is after --stopdate')
J = J[:i]
assert (len(J) == len(times))
break
times.append(ephem.Date(obs.date))
LSTs.append(np.rad2deg(float(obs.sidereal_time())))
print('Date', obs.date)
print('LST', obs.sidereal_time())
else:
# Predict overheads
lastra, lastdec = None, None
for i in range(len(J)):
print('Exposure', i, 'should start at', str(obs.date))
if opt.stop_time is not None and obs.date > stopdate:
print('Tile', J[i], 'is after --stopdate')
break
times.append(ephem.Date(obs.date))
LSTs.append(np.rad2deg(float(obs.sidereal_time())))
overhead = 30.
if lastra is not None:
slew = degrees_between(lastra, lastdec, ras[i], decs[i])
lastra = ras[i]
lastdec = decs[i]
# Add 3 seconds per degree for slews longer than 2 degrees
overhead += np.maximum(0, slew - 2.) * 3.
# Add overhead
print('Adding', exptime[i], 'seconds exptime plus', overhead,
'seconds overhead')
obs.date += (exptime[i] + overhead) / (24 * 3600.)
tiles = None
if opt.obstatus is not None:
from astrometry.util.fits import fits_table
tiles = fits_table(opt.obstatus)
print('Read', len(tiles), 'tiles')
tiles = tiles[(tiles.in_des == 0) * np.logical_or(
(tiles.in_sdss == 1), (tiles.in_sdss == 0) * (tiles.in_desi == 1))]
print(len(tiles), 'in footprint')
fcmap = dict(g='g', r='r', z='m', zd='m')
ddecmap = dict(g=-0.2, r=0, z=0.2, zd=0.2)
ras = np.array([j['RA'] for j in J])
decs = np.array([j['dec'] for j in J])
filts = np.array([j['filter'] for j in J])
exptime = np.array([j['expTime'] for j in J])
fieldname = [j['object'] for j in J]
passnum = np.zeros(len(J), int)
filtcc = np.array([fcmap[f] for f in filts])
ddecs = np.array([ddecmap[f] for f in filts])
# passmap = { 1: dict(marker='.'),
# 2: dict(marker='o', mfc='none'),
# 3: dict(marker='x') }
passmap = {
1: dict(marker='.'),
2: dict(marker='.'),
3: dict(marker='.'),
}
opt.bands = opt.bands.split(',')
if len(opt.bands) == 1:
filtddec = {'g': 0, 'r': 0, 'z': 0}
else:
ddec = 0.4
filtddec = {'g': -ddec, 'r': 0, 'z': ddec}
seqmap = ['r', 'y', 'g', 'b', 'm']
#seqcc = np.array([seqmap[s % len(seqmap)] for s in seqnum])
#seqcc = np.array([seqmap[s % len(seqmap)] for s in seqid])
ax = [
transform_ra(opt.rahi, opt),
transform_ra(opt.ralo, opt), opt.declo, opt.dechi
]
alsocolors = 'kbr'
also = []
for Ja in Jalso:
# We assume the --also plan files contain approx_datetime...
atimes = np.array([ephem.Date(str(j['approx_datetime'])) for j in Ja])
aras = np.array([j['RA'] for j in Ja])
adecs = np.array([j['dec'] for j in Ja])
afilts = np.array([j['filter'] for j in Ja])
aexptime = np.array([j['expTime'] for j in Ja])
afieldname = [j['object'] for j in Ja]
apassnum = np.zeros(len(Ja), int)
if tiles is not None:
for i, f in enumerate(afieldname):
tile = get_tile_from_name(f, tiles)
if tile is None:
continue
pa = tile.get('pass')
apassnum[i] = pa
also.append(
(atimes, aras, adecs, afilts, aexptime, afieldname, apassnum))
# Try to get the pass number via parsing the field name to get tile id
# and looking up the pass number in the tiles table.
if tiles is not None:
for i, f in enumerate(fieldname):
tile = get_tile_from_name(f, tiles)
if tile is None:
continue
pa = tile.get('pass')
passnum[i] = pa
print('Field', f, 'tileid', tile.tileid, 'pass', pa)
allargs = []
for i in reversed(range(0, len(J), opt.skip)):
#print('Exposure', i, 'of', len(J))
fn = '%s-%03i.png' % (opt.base, i)
fn = os.path.join(os.path.dirname(args[0]), fn)
pargs = (opt, ax, tiles, filtddec, fcmap, passmap, also, LSTs, times,
ras, decs, ddecs, fieldname, passnum, exptime, i, filtcc,
alsocolors, ddecmap, fn)
allargs.append(pargs)
if opt.threads:
from astrometry.util.multiproc import multiproc
mp = multiproc(opt.threads, init=plot_init)
mp.map(plot_one, allargs)
else:
plot_init()
map(plot_one, allargs)
#plot_one(pargs)
print()
cmd = 'avconv -r 4 -i %s-%%03d.png -y %s.mov' % (opt.base, opt.base)
print(cmd)
os.system(cmd)
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 map_decals_wl(req, ver, zoom, x, y):
tag = 'decals-wl'
ignoreCached = False
filename = None
forcecache = False
from decals import settings
savecache = settings.SAVE_CACHE
zoom = int(zoom)
zoomscale = 2.**zoom
x = int(x)
y = int(y)
if zoom < 0 or x < 0 or y < 0 or x >= zoomscale or y >= zoomscale:
raise RuntimeError('Invalid zoom,x,y %i,%i,%i' % (zoom,x,y))
ver = int(ver)
if not ver in tileversions[tag]:
raise RuntimeError('Invalid version %i for tag %s' % (ver, tag))
basedir = settings.DATA_DIR
tilefn = os.path.join(basedir, 'tiles', tag,
'%i/%i/%i/%i.jpg' % (ver, zoom, x, y))
if os.path.exists(tilefn) and not ignoreCached:
print('Cached:', tilefn)
return send_file(tilefn, 'image/jpeg', expires=oneyear,
modsince=req.META.get('HTTP_IF_MODIFIED_SINCE'),
filename=filename)
else:
print('Tile image does not exist:', tilefn)
from astrometry.util.resample import resample_with_wcs, OverlapError
from astrometry.util.util import Tan
from astrometry.libkd.spherematch import match_radec
from astrometry.util.fits import fits_table
from astrometry.util.starutil_numpy import degrees_between
import numpy as np
import fitsio
try:
wcs, W, H, zoomscale, zoom,x,y = get_tile_wcs(zoom, x, y)
except RuntimeError as e:
return HttpResponse(e.strerror)
mydir = os.path.join(basedir, 'coadd', 'weak-lensing')
rlo,d = wcs.pixelxy2radec(W, H/2)[-2:]
rhi,d = wcs.pixelxy2radec(1, H/2)[-2:]
r,d1 = wcs.pixelxy2radec(W/2, 1)[-2:]
r,d2 = wcs.pixelxy2radec(W/2, H)[-2:]
#dlo = min(d1, d2)
#dhi = max(d1, d2)
r,d = wcs.pixelxy2radec(W/2, H/2)[-2:]
rad = degrees_between(r, d, rlo, d1)
fn = os.path.join(mydir, 'index.fits')
if not os.path.exists(fn):
#
ii,rr,dd = [],[],[]
for i in range(1, 52852+1):
imgfn = os.path.join(mydir, 'map%i.fits' % i)
hdr = fitsio.read_header(imgfn)
r = hdr['CRVAL1']
d = hdr['CRVAL2']
ii.append(i)
rr.append(r)
dd.append(d)
T = fits_table()
T.ra = np.array(rr)
T.dec = np.array(dd)
T.i = np.array(ii)
T.writeto(fn)
T = fits_table(fn)
I,J,d = match_radec(T.ra, T.dec, r, d, rad + 0.2)
T.cut(I)
print(len(T), 'weak-lensing maps in range')
if len(I) == 0:
from django.http import HttpResponseRedirect
if forcecache:
# create symlink to blank.jpg!
trymakedirs(tilefn)
src = os.path.join(settings.STATIC_ROOT, 'blank.jpg')
if os.path.exists(tilefn):
os.unlink(tilefn)
os.symlink(src, tilefn)
print('Symlinked', tilefn, '->', src)
return HttpResponseRedirect(settings.STATIC_URL + 'blank.jpg')
r,d = wcs.pixelxy2radec([1,1,1,W/2,W,W,W,W/2],
[1,H/2,H,H,H,H/2,1,1])[-2:]
foundany = False
rimg = np.zeros((H,W), np.float32)
rn = np.zeros((H,W), np.uint8)
for tilei in T.i:
fn = os.path.join(mydir, 'map%i.fits' % tilei)
try:
bwcs = _read_tan_wcs(fn, 0)
except:
print('Failed to read WCS:', fn)
savecache = False
import traceback
import sys
traceback.print_exc(None, sys.stdout)
continue
foundany = True
print('Reading', fn)
ok,xx,yy = bwcs.radec2pixelxy(r, d)
xx = xx.astype(np.int)
yy = yy.astype(np.int)
imW,imH = int(bwcs.get_width()), int(bwcs.get_height())
M = 10
xlo = np.clip(xx.min() - M, 0, imW)
xhi = np.clip(xx.max() + M, 0, imW)
ylo = np.clip(yy.min() - M, 0, imH)
yhi = np.clip(yy.max() + M, 0, imH)
if xlo >= xhi or ylo >= yhi:
continue
subwcs = bwcs.get_subimage(xlo, ylo, xhi-xlo, yhi-ylo)
slc = slice(ylo,yhi), slice(xlo,xhi)
try:
f = fitsio.FITS(fn)[0]
img = f[slc]
del f
except:
print('Failed to read image and WCS:', fn)
savecache = False
import traceback
import sys
traceback.print_exc(None, sys.stdout)
continue
try:
Yo,Xo,Yi,Xi,nil = resample_with_wcs(wcs, subwcs, [], 3)
except OverlapError:
print('Resampling exception')
continue
rimg[Yo,Xo] += img[Yi,Xi]
rn [Yo,Xo] += 1
rimg /= np.maximum(rn, 1)
if forcecache:
savecache = True
if savecache:
trymakedirs(tilefn)
else:
import tempfile
f,tilefn = tempfile.mkstemp(suffix='.jpg')
os.close(f)
import pylab as plt
# S/N
#lo,hi = 1.5, 5.0
lo,hi = 0, 5.0
rgb = plt.cm.hot((rimg - lo) / (hi - lo))
plt.imsave(tilefn, rgb)
print('Wrote', tilefn)
return send_file(tilefn, 'image/jpeg', unlink=(not savecache),
filename=filename)
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 map_sdss(req,
ver,
zoom,
x,
y,
savecache=None,
tag='sdss',
get_images=False,
ignoreCached=False,
wcs=None,
forcecache=False,
forcescale=None,
bestOnly=False,
bands='gri',
**kwargs):
from decals import settings
if savecache is None:
savecache = settings.SAVE_CACHE
zoom = int(zoom)
zoomscale = 2.**zoom
x = int(x)
y = int(y)
if zoom < 0 or x < 0 or y < 0 or x >= zoomscale or y >= zoomscale:
raise RuntimeError('Invalid zoom,x,y %i,%i,%i' % (zoom, x, y))
ver = int(ver)
if not ver in tileversions[tag]:
raise RuntimeError('Invalid version %i for tag %s' % (ver, tag))
basedir = settings.DATA_DIR
tilefn = os.path.join(basedir, 'tiles', tag,
'%i/%i/%i/%i.jpg' % (ver, zoom, x, y))
if os.path.exists(tilefn) and not ignoreCached:
if get_images:
return None
return send_file(tilefn,
'image/jpeg',
expires=oneyear,
modsince=req.META.get('HTTP_IF_MODIFIED_SINCE'))
if not savecache:
import tempfile
f, tilefn = tempfile.mkstemp(suffix='.jpg')
os.close(f)
if wcs is None:
try:
wcs, W, H, zoomscale, zoom, x, y = get_tile_wcs(zoom, x, y)
except RuntimeError as e:
if get_images:
return None
return HttpResponse(e.strerror)
else:
W = wcs.get_width()
H = wcs.get_height()
from astrometry.util.fits import fits_table
import numpy as np
from astrometry.libkd.spherematch import tree_build_radec, tree_search_radec
from astrometry.util.starutil_numpy import degrees_between, arcsec_between
from astrometry.util.resample import resample_with_wcs, OverlapError
from astrometry.util.util import Tan, Sip
import fitsio
print('Tile wcs: center', wcs.radec_center(), 'pixel scale',
wcs.pixel_scale())
global w_flist
global w_flist_tree
if w_flist is None:
w_flist = fits_table(
os.path.join(settings.DATA_DIR, 'sdss', 'window_flist.fits'),
columns=['run', 'rerun', 'camcol', 'field', 'ra', 'dec', 'score'])
print('Read', len(w_flist), 'window_flist entries')
w_flist.cut(w_flist.rerun == '301')
print('Cut to', len(w_flist), 'in rerun 301')
w_flist_tree = tree_build_radec(w_flist.ra, w_flist.dec)
# SDSS field size
radius = 1.01 * np.hypot(10., 14.) / 2. / 60.
# leaflet tile size
ra, dec = wcs.pixelxy2radec(W / 2., H / 2.)[-2:]
r0, d0 = wcs.pixelxy2radec(1, 1)[-2:]
r1, d1 = wcs.pixelxy2radec(W, H)[-2:]
radius = radius + max(degrees_between(ra, dec, r0, d0),
degrees_between(ra, dec, r1, d1))
J = tree_search_radec(w_flist_tree, ra, dec, radius)
print(len(J), 'overlapping SDSS fields found')
if len(J) == 0:
if get_images:
return None
if forcecache:
# create symlink to blank.jpg!
trymakedirs(tilefn)
src = os.path.join(settings.STATIC_ROOT, 'blank.jpg')
if os.path.exists(tilefn):
os.unlink(tilefn)
os.symlink(src, tilefn)
print('Symlinked', tilefn, '->', src)
from django.http import HttpResponseRedirect
return HttpResponseRedirect(settings.STATIC_URL + 'blank.jpg')
ww = [1, W * 0.25, W * 0.5, W * 0.75, W]
hh = [1, H * 0.25, H * 0.5, H * 0.75, H]
r, d = wcs.pixelxy2radec(
[1] * len(hh) + ww + [W] * len(hh) + list(reversed(ww)),
hh + [1] * len(ww) + list(reversed(hh)) + [H] * len(ww))[-2:]
scaled = 0
scalepat = None
scaledir = 'sdss'
if zoom <= 13 and forcescale is None:
# Get *actual* pixel scales at the top & bottom
r1, d1 = wcs.pixelxy2radec(W / 2., H)[-2:]
r2, d2 = wcs.pixelxy2radec(W / 2., H - 1.)[-2:]
r3, d3 = wcs.pixelxy2radec(W / 2., 1.)[-2:]
r4, d4 = wcs.pixelxy2radec(W / 2., 2.)[-2:]
# Take the min = most zoomed-in
scale = min(arcsec_between(r1, d1, r2, d2),
arcsec_between(r3, d3, r4, d4))
native_scale = 0.396
scaled = int(np.floor(np.log2(scale / native_scale)))
print('Zoom:', zoom, 'x,y', x, y, 'Tile pixel scale:', scale,
'Scale step:', scaled)
scaled = np.clip(scaled, 1, 7)
dirnm = os.path.join(basedir, 'scaled', scaledir)
scalepat = os.path.join(
dirnm, '%(scale)i%(band)s', '%(rerun)s', '%(run)i', '%(camcol)i',
'sdss-%(run)i-%(camcol)i-%(field)i-%(band)s.fits')
if forcescale is not None:
scaled = forcescale
rimgs = [np.zeros((H, W), np.float32) for band in bands]
rns = [np.zeros((H, W), np.float32) for band in bands]
from astrometry.sdss import AsTransWrapper, DR9
sdss = DR9(basedir=settings.SDSS_DIR)
sdss.saveUnzippedFiles(settings.SDSS_DIR)
#sdss.setFitsioReadBZ2()
if settings.SDSS_PHOTOOBJS:
sdss.useLocalTree(photoObjs=settings.SDSS_PHOTOOBJS,
resolve=settings.SDSS_RESOLVE)
for jnum, j in enumerate(J):
print('SDSS field', jnum, 'of', len(J), 'for zoom', zoom, 'x', x, 'y',
y)
im = w_flist[j]
if im.score >= 0.5:
weight = 1.
else:
weight = 0.001
for band, rimg, rn in zip(bands, rimgs, rns):
if im.rerun != '301':
continue
tmpsuff = '.tmp%08i' % np.random.randint(100000000)
basefn = sdss.retrieve('frame',
im.run,
im.camcol,
field=im.field,
band=band,
rerun=im.rerun,
tempsuffix=tmpsuff)
if scaled > 0:
fnargs = dict(band=band,
rerun=im.rerun,
run=im.run,
camcol=im.camcol,
field=im.field)
fn = get_scaled(scalepat,
fnargs,
scaled,
basefn,
read_base_wcs=read_astrans,
read_wcs=read_sip_wcs)
print('get_scaled:', fn)
else:
fn = basefn
frame = None
if fn == basefn:
frame = sdss.readFrame(im.run,
im.camcol,
im.field,
band,
filename=fn)
h, w = frame.getImageShape()
# Trim off the overlapping top of the image
# Wimp out and instead of trimming 128 pix, trim 124!
trim = 124
subh = h - trim
astrans = frame.getAsTrans()
fwcs = AsTransWrapper(astrans, w, subh)
fullimg = frame.getImage()
fullimg = fullimg[:-trim, :]
else:
fwcs = Sip(fn)
fitsimg = fitsio.FITS(fn)[0]
h, w = fitsimg.get_info()['dims']
fullimg = fitsimg.read()
try:
#Yo,Xo,Yi,Xi,nil = resample_with_wcs(wcs, fwcs, [], 3)
Yo, Xo, Yi, Xi, [resamp
] = resample_with_wcs(wcs, fwcs, [fullimg], 2)
except OverlapError:
continue
if len(Xi) == 0:
#print 'No overlap'
continue
if sdssps is not None:
x0 = Xi.min()
x1 = Xi.max()
y0 = Yi.min()
y1 = Yi.max()
slc = (slice(y0, y1 + 1), slice(x0, x1 + 1))
if frame is not None:
img = frame.getImageSlice(slc)
else:
img = fitsimg[slc]
#rimg[Yo,Xo] += img[Yi-y0, Xi-x0]
if bestOnly:
K = np.flatnonzero(weight > rn[Yo, Xo])
print('Updating', len(K), 'of', len(Yo), 'pixels')
if len(K):
rimg[Yo[K], Xo[K]] = resamp[K] * weight
rn[Yo[K], Xo[K]] = weight
else:
rimg[Yo, Xo] += resamp * weight
rn[Yo, Xo] += weight
if sdssps is not None:
# goodpix = np.ones(img.shape, bool)
# fpM = sdss.readFpM(im.run, im.camcol, im.field, band)
# for plane in [ 'INTERP', 'SATUR', 'CR', 'GHOST' ]:
# fpM.setMaskedPixels(plane, goodpix, False, roi=[x0,x1,y0,y1])
plt.clf()
#ima = dict(vmin=-0.05, vmax=0.5)
#ima = dict(vmin=-0.5, vmax=2.)
ima = dict(vmax=np.percentile(img, 99))
plt.subplot(2, 3, 1)
dimshow(img, ticks=False, **ima)
plt.title('image')
rthis = np.zeros_like(rimg)
#rthis[Yo,Xo] += img[Yi-y0, Xi-x0]
rthis[Yo, Xo] += resamp
plt.subplot(2, 3, 2)
dimshow(rthis, ticks=False, **ima)
plt.title('resampled')
# plt.subplot(2,3,3)
# dimshow(goodpix, ticks=False, vmin=0, vmax=1)
# plt.title('good pix')
plt.subplot(2, 3, 4)
dimshow(rimg / np.maximum(rn, 1), ticks=False, **ima)
plt.title('coadd')
plt.subplot(2, 3, 5)
dimshow(rn, vmin=0, ticks=False)
plt.title('coverage: max %i' % rn.max())
plt.subplot(2, 3, 6)
rgb = sdss_rgb(
[rimg / np.maximum(rn, 1) for rimg, rn in zip(rimgs, rns)],
bands)
dimshow(rgb)
plt.suptitle('SDSS %s, R/C/F %i/%i/%i' %
(band, im.run, im.camcol, im.field))
sdssps.savefig()
for rimg, rn in zip(rimgs, rns):
rimg /= np.maximum(rn, 1e-3)
del rns
if get_images:
return rimgs
rgb = sdss_rgb(rimgs, bands)
trymakedirs(tilefn)
save_jpeg(tilefn, rgb)
print('Wrote', tilefn)
return send_file(tilefn, 'image/jpeg', unlink=(not savecache))
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 run_tiles(X):
tiles, tag = X
print('Running', tag, '-', len(tiles), 'tiles')
# Aaron's file has all images share the boresight CRVAL, so they have large CRPIX values.
T = fits_table(
'/global/cfs/cdirs/desi/users/ameisner/GFA/gfa_reduce_etc/gfa_wcs+focus.bigtan-zenith.fits'
)
Nbright = 10
tiles_ann = fits_table()
tiles_ann.index = tiles.index
gfa_regions = []
maxr = 0.
#t.cd[0,0], t.cd[0,1], t.cd[1,0], t.cd[1,1],
for t in T:
wcs = Tan(0., 0., t.crpix[0], t.crpix[1], t.cd[0, 0], t.cd[1, 0],
t.cd[0, 1], t.cd[1, 1], float(t.naxis[0]), float(t.naxis[1]))
ctype = t.extname[:5]
cnum = int(t.extname[5])
h, w = wcs.shape
x, y = [1, 1, w, w, 1], [1, h, h, 1, 1]
r, d = wcs.pixelxy2radec(x, y)
dists = degrees_between(0., 0., r, d)
maxr = max(maxr, max(dists))
# x0, y0, x1, y1
rois = []
if ctype == 'FOCUS':
# add the two half-chips.
# wcs.get_subimage moves the CRPIX, but leave CRVAL unchanged, so tx,ty still work unchanged.
# Aaron's WCS templates correct for the overscans
#wcs_subs.append((cstr, cnum, 'a', wcs.get_subimage(0, 0, 1024, h)))
#wcs_subs.append((cstr, cnum, 'b', wcs.get_subimage(1024, 0, 1024, h)))
#all_sub_wcs[(cstr, cnum, 1)] = (tx, ty, wcs.get_subimage(50, 0, 1024, 1032))
#all_sub_wcs[(cstr, cnum, 2)] = (tx, ty, wcs.get_subimage(1174, 0, 1024, 1032))
# Add (negative) margin for donut size and telescope pointing uncertainty.
# ~10" for donuts and ~10" for telescope pointing
#margin = 100
#wcs_subs.append((cstr, cnum, 'a_margin', wcs.get_subimage(margin, margin, 1024-2*margin, h-2*margin)))
#wcs_subs.append((cstr, cnum, 'b_margin', wcs.get_subimage(1024+margin, margin, 1024-2*margin, h-2*margin)))
# Also add a positive margin for bright-star reflections off filters
#margin = 125
#wcs_subs.append((cstr, cnum, 'expanded', wcs.get_subimage(-margin, -margin, w+2*margin, h+2*margin)))
rois.append(('a', 0, 0, 1024, h))
rois.append(('b', 1024, 0, 2048, h))
margin = 100
rois.append(
('a_margin', margin, margin, 1024 - margin, h - margin))
rois.append(
('b_margin', 1024 + margin, margin, 2048 - margin, h - margin))
margin = 125
rois.append(('expanded', -margin, -margin, w + margin, h + margin))
else:
# Guide chips include overscan pixels -- including a blank region in the middle.
#print(cstr,cnum, 'shape', wcs.shape)
#wcs_subs.append((cstr, cnum, 'ccd', wcs))
rois.append(('ccd', 0, 0, w, h))
# Add expanded GUIDE chips -- 25" margin / 0.2"/pix = 125 pix
margin = 125
#wcs_subs.append((cstr, cnum, 'expanded', wcs.get_subimage(-margin, -margin, w+2*margin, h+2*margin)))
rois.append(('expanded', -margin, -margin, w + margin, h + margin))
margin = 125
expwcs = wcs.get_subimage(-margin, -margin, w + 2 * margin,
h + 2 * margin)
newrois = []
for tag, x0, y0, x1, y1 in rois:
name = '%s_%i_%s' % (ctype.lower(), cnum, tag)
arr = np.zeros(len(tiles), (np.float32, Nbright))
tiles_ann.set('brightest_' + name, arr)
# (the rois have zero-indexed x0,y0, and non-inclusive x1,y1!)
newrois.append((name, arr, 1 + x0, 1 + y0, x1, y1))
gfa_regions.append((ctype, cnum, wcs, expwcs, newrois))
# DEBUG WCS
# s = []
# for ctype,cnum,wcs,expwcs,rois in gfa_regions:
# WCS = wcs
# h,w = WCS.shape
# #print('Expwcs:', w, 'x', h)
# x = [1,1,w,w,1]
# y = [1,h,h,1,1]
# r,d = WCS.pixelxy2radec(x, y)
# p = ','.join(['%.4f,%.4f' % (rr,dd) for rr,dd in zip(r,d)])
# s.append(p)
# s = ';'.join(s)
# print('http://legacysurvey.org/viewer/?ra=0&dec=0&poly='+s)
# sys.exit(0)
gaia = CachingGaiaCatalog(columns=[
'ra', 'dec', 'phot_g_mean_mag', 'phot_bp_mean_mag', 'phot_rp_mean_mag',
'astrometric_excess_noise', 'astrometric_params_solved', 'source_id',
'pmra_error', 'pmdec_error', 'parallax_error', 'ra_error', 'dec_error',
'pmra', 'pmdec', 'parallax', 'ref_epoch'
])
tyc2fn = '/global/cfs/cdirs/cosmo/staging/tycho2/tycho2.kd.fits'
tycho_kd = tree_open(tyc2fn)
tycho_cat = fits_table(tyc2fn)
maxrad = maxr * 1.05
for itile, tile in enumerate(tiles):
#if not tile.in_imaging:
# continue
#if tile.centerid % 10 == 0:
#print('tile program', tile.program, 'pass', tile.get('pass'), 'id', tile.centerid, gaia.get_healpix_tree.cache_info())
I = tree_search_radec(tycho_kd, tile.ra, tile.dec, maxrad)
tycstars = tycho_cat[I]
fix_tycho(tycstars)
for cstr, cname, chipwcs, bigwcs, rois in gfa_regions:
h, w = chipwcs.shape
chipwcs.set_crval(tile.ra, tile.dec)
bigwcs.set_crval(tile.ra, tile.dec)
gstars = gaia.get_catalog_in_wcs(bigwcs, step=1032, margin=0)
fix_gaia(gstars)
bh, bw = bigwcs.shape
ok, x, y = bigwcs.radec2pixelxy(tycstars.ra, tycstars.dec)
tstars = tycstars[(x >= 1) * (y >= 1) * (x <= bw) * (y <= bh)]
#print('Tile', tile.program, 'p', tile.get('pass'), tile.centerid,
# 'GFA', cstr, cname, ':', len(gstars), 'Gaia stars', len(tstars), 'Tycho-2 stars')
if len(gstars) + len(tstars) == 0:
print('No stars in tile centerid', tile.centerid, 'chip', name)
continue
if len(gstars) > 0 and len(tstars) > 0:
merge_gaia_tycho(gstars, tstars)
stars = merge_tables([gstars, tstars], columns='fillzero')
elif len(tstars) > 0:
stars = tstars
else:
stars = gstars
ok, x, y = chipwcs.radec2pixelxy(stars.ra, stars.dec)
for name, arr, x0, y0, x1, y1 in rois:
J = np.flatnonzero(
(x >= x0) * (x <= x1) * (y >= y0) * (y <= y1))
mags = stars.mag[J]
#print(' ', len(mags), 'in name')
K = np.argsort(mags)
K = K[:Nbright]
arr[itile, :len(K)] = mags[K]
#tiles.add_columns_from(tiles_ann)
return tiles_ann
def main(outfn='ccds-annotated.fits', ccds=None):
decals = Decals()
if ccds is None:
ccds = decals.get_ccds()
# File from the "observing" svn repo:
# https://desi.lbl.gov/svn/decam/code/observing/trunk
tiles = fits_table('decam-tiles_obstatus.fits')
#ccds.cut(np.arange(100))
#print("HACK!")
#ccds.cut(np.array([name in ['N15', 'N16', 'N21', 'N9']
# for name in ccds.ccdname]) *
# ccds.expnum == 229683)
I = decals.photometric_ccds(ccds)
ccds.photometric = np.zeros(len(ccds), bool)
ccds.photometric[I] = True
I = decals.apply_blacklist(ccds)
ccds.blacklist_ok = np.zeros(len(ccds), bool)
ccds.blacklist_ok[I] = True
ccds.good_region = np.empty((len(ccds), 4), np.int16)
ccds.good_region[:, :] = -1
ccds.ra0 = np.zeros(len(ccds), np.float64)
ccds.dec0 = np.zeros(len(ccds), np.float64)
ccds.ra1 = np.zeros(len(ccds), np.float64)
ccds.dec1 = np.zeros(len(ccds), np.float64)
ccds.ra2 = np.zeros(len(ccds), np.float64)
ccds.dec2 = np.zeros(len(ccds), np.float64)
ccds.ra3 = np.zeros(len(ccds), np.float64)
ccds.dec3 = np.zeros(len(ccds), np.float64)
ccds.dra = np.zeros(len(ccds), np.float32)
ccds.ddec = np.zeros(len(ccds), np.float32)
ccds.ra_center = np.zeros(len(ccds), np.float64)
ccds.dec_center = np.zeros(len(ccds), np.float64)
ccds.sig1 = np.zeros(len(ccds), np.float32)
ccds.meansky = np.zeros(len(ccds), np.float32)
ccds.stdsky = np.zeros(len(ccds), np.float32)
ccds.maxsky = np.zeros(len(ccds), np.float32)
ccds.minsky = np.zeros(len(ccds), np.float32)
ccds.pixscale_mean = np.zeros(len(ccds), np.float32)
ccds.pixscale_std = np.zeros(len(ccds), np.float32)
ccds.pixscale_max = np.zeros(len(ccds), np.float32)
ccds.pixscale_min = np.zeros(len(ccds), np.float32)
ccds.psfnorm_mean = np.zeros(len(ccds), np.float32)
ccds.psfnorm_std = np.zeros(len(ccds), np.float32)
ccds.galnorm_mean = np.zeros(len(ccds), np.float32)
ccds.galnorm_std = np.zeros(len(ccds), np.float32)
gaussgalnorm = np.zeros(len(ccds), np.float32)
# 2nd moments
ccds.psf_mx2 = np.zeros(len(ccds), np.float32)
ccds.psf_my2 = np.zeros(len(ccds), np.float32)
ccds.psf_mxy = np.zeros(len(ccds), np.float32)
#
ccds.psf_a = np.zeros(len(ccds), np.float32)
ccds.psf_b = np.zeros(len(ccds), np.float32)
ccds.psf_theta = np.zeros(len(ccds), np.float32)
ccds.psf_ell = np.zeros(len(ccds), np.float32)
ccds.humidity = np.zeros(len(ccds), np.float32)
ccds.outtemp = np.zeros(len(ccds), np.float32)
ccds.tileid = np.zeros(len(ccds), np.int32)
ccds.tilepass = np.zeros(len(ccds), np.uint8)
ccds.tileebv = np.zeros(len(ccds), np.float32)
plvers = []
for iccd, ccd in enumerate(ccds):
im = decals.get_image_object(ccd)
print('Reading CCD %i of %i:' % (iccd + 1, len(ccds)), im)
X = im.get_good_image_subregion()
for i, x in enumerate(X):
if x is not None:
ccds.good_region[iccd, i] = x
W, H = ccd.width, ccd.height
psf = None
wcs = None
sky = None
try:
tim = im.get_tractor_image(pixPsf=True,
splinesky=True,
subsky=False,
pixels=False)
except:
import traceback
traceback.print_exc()
plvers.append('')
continue
if tim is None:
plvers.append('')
continue
psf = tim.psf
wcs = tim.wcs.wcs
sky = tim.sky
hdr = tim.primhdr
# print('Got PSF', psf)
# print('Got sky', type(sky))
# print('Got WCS', wcs)
ccds.humidity[iccd] = hdr.get('HUMIDITY')
ccds.outtemp[iccd] = hdr.get('OUTTEMP')
ccds.sig1[iccd] = tim.sig1
plvers.append(tim.plver)
obj = hdr.get('OBJECT')
# parse 'DECaLS_15150_r'
words = obj.split('_')
tile = None
if len(words) == 3 and words[0] == 'DECaLS':
try:
tileid = int(words[1])
tile = tiles[tileid - 1]
if tile.tileid != tileid:
I = np.flatnonzero(tile.tileid == tileid)
tile = tiles[I[0]]
except:
pass
if tile is not None:
ccds.tileid[iccd] = tile.tileid
ccds.tilepass[iccd] = tile.get('pass')
ccds.tileebv[iccd] = tile.ebv_med
# Instantiate PSF on a grid
S = 32
xx = np.linspace(1 + S, W - S, 5)
yy = np.linspace(1 + S, H - S, 5)
xx, yy = np.meshgrid(xx, yy)
psfnorms = []
galnorms = []
for x, y in zip(xx.ravel(), yy.ravel()):
p = im.psf_norm(tim, x=x, y=y)
g = im.galaxy_norm(tim, x=x, y=y)
psfnorms.append(p)
galnorms.append(g)
ccds.psfnorm_mean[iccd] = np.mean(psfnorms)
ccds.psfnorm_std[iccd] = np.std(psfnorms)
ccds.galnorm_mean[iccd] = np.mean(galnorms)
ccds.galnorm_std[iccd] = np.std(galnorms)
# PSF in center of field
cx, cy = (W + 1) / 2., (H + 1) / 2.
p = psf.getPointSourcePatch(cx, cy).patch
ph, pw = p.shape
px, py = np.meshgrid(np.arange(pw), np.arange(ph))
psum = np.sum(p)
# print('psum', psum)
p /= psum
# centroids
cenx = np.sum(p * px)
ceny = np.sum(p * py)
# print('cenx,ceny', cenx,ceny)
# second moments
x2 = np.sum(p * (px - cenx)**2)
y2 = np.sum(p * (py - ceny)**2)
xy = np.sum(p * (px - cenx) * (py - ceny))
# semi-major/minor axes and position angle
theta = np.rad2deg(np.arctan2(2 * xy, x2 - y2) / 2.)
theta = np.abs(theta) * np.sign(xy)
s = np.sqrt(((x2 - y2) / 2.)**2 + xy**2)
a = np.sqrt((x2 + y2) / 2. + s)
b = np.sqrt((x2 + y2) / 2. - s)
ell = 1. - b / a
# print('PSF second moments', x2, y2, xy)
# print('PSF position angle', theta)
# print('PSF semi-axes', a, b)
# print('PSF ellipticity', ell)
ccds.psf_mx2[iccd] = x2
ccds.psf_my2[iccd] = y2
ccds.psf_mxy[iccd] = xy
ccds.psf_a[iccd] = a
ccds.psf_b[iccd] = b
ccds.psf_theta[iccd] = theta
ccds.psf_ell[iccd] = ell
# Galaxy norm using Gaussian approximation of PSF.
realpsf = tim.psf
tim.psf = im.read_psf_model(0,
0,
gaussPsf=True,
psf_sigma=tim.psf_sigma)
gaussgalnorm[iccd] = im.galaxy_norm(tim, x=cx, y=cy)
tim.psf = realpsf
# Sky
mod = np.zeros((ccd.height, ccd.width), np.float32)
sky.addTo(mod)
ccds.meansky[iccd] = np.mean(mod)
ccds.stdsky[iccd] = np.std(mod)
ccds.maxsky[iccd] = mod.max()
ccds.minsky[iccd] = mod.min()
# WCS
ccds.ra0[iccd], ccds.dec0[iccd] = wcs.pixelxy2radec(1, 1)
ccds.ra1[iccd], ccds.dec1[iccd] = wcs.pixelxy2radec(1, H)
ccds.ra2[iccd], ccds.dec2[iccd] = wcs.pixelxy2radec(W, H)
ccds.ra3[iccd], ccds.dec3[iccd] = wcs.pixelxy2radec(W, 1)
midx, midy = (W + 1) / 2., (H + 1) / 2.
rc, dc = wcs.pixelxy2radec(midx, midy)
ra, dec = wcs.pixelxy2radec([1, W, midx, midx], [midy, midy, 1, H])
ccds.dra[iccd] = max(
degrees_between(ra, dc + np.zeros_like(ra), rc, dc))
ccds.ddec[iccd] = max(
degrees_between(rc + np.zeros_like(dec), dec, rc, dc))
ccds.ra_center[iccd] = rc
ccds.dec_center[iccd] = dc
# Compute scale change across the chip
# how many pixels to step
step = 10
xx = np.linspace(1 + step, W - step, 5)
yy = np.linspace(1 + step, H - step, 5)
xx, yy = np.meshgrid(xx, yy)
pixscale = []
for x, y in zip(xx.ravel(), yy.ravel()):
sx = [x - step, x - step, x + step, x + step, x - step]
sy = [y - step, y + step, y + step, y - step, y - step]
sr, sd = wcs.pixelxy2radec(sx, sy)
rc, dc = wcs.pixelxy2radec(x, y)
# project around a tiny little TAN WCS at (x,y), with 1" pixels
locwcs = Tan(rc, dc, 0., 0., 1. / 3600, 0., 0., 1. / 3600, 1., 1.)
ok, lx, ly = locwcs.radec2pixelxy(sr, sd)
#print('local x,y:', lx, ly)
A = polygon_area((lx, ly))
pixscale.append(np.sqrt(A / (2 * step)**2))
# print('Pixel scales:', pixscale)
ccds.pixscale_mean[iccd] = np.mean(pixscale)
ccds.pixscale_min[iccd] = min(pixscale)
ccds.pixscale_max[iccd] = max(pixscale)
ccds.pixscale_std[iccd] = np.std(pixscale)
ccds.plver = np.array(plvers)
sfd = tractor.sfd.SFDMap()
allbands = 'ugrizY'
filts = ['%s %s' % ('DES', f) for f in allbands]
wisebands = ['WISE W1', 'WISE W2', 'WISE W3', 'WISE W4']
ebv, ext = sfd.extinction(filts + wisebands,
ccds.ra_center,
ccds.dec_center,
get_ebv=True)
ext = ext.astype(np.float32)
ccds.ebv = ebv.astype(np.float32)
ccds.decam_extinction = ext[:, :len(allbands)]
ccds.wise_extinction = ext[:, len(allbands):]
# Depth
detsig1 = ccds.sig1 / ccds.psfnorm_mean
depth = 5. * detsig1
# that's flux in nanomaggies -- convert to mag
ccds.psfdepth = -2.5 * (np.log10(depth) - 9)
detsig1 = ccds.sig1 / ccds.galnorm_mean
depth = 5. * detsig1
# that's flux in nanomaggies -- convert to mag
ccds.galdepth = -2.5 * (np.log10(depth) - 9)
# Depth using Gaussian FWHM.
psf_sigma = ccds.fwhm / 2.35
gnorm = 1. / (2. * np.sqrt(np.pi) * psf_sigma)
detsig1 = ccds.sig1 / gnorm
depth = 5. * detsig1
# that's flux in nanomaggies -- convert to mag
ccds.gausspsfdepth = -2.5 * (np.log10(depth) - 9)
# Gaussian galaxy depth
detsig1 = ccds.sig1 / gaussgalnorm
depth = 5. * detsig1
# that's flux in nanomaggies -- convert to mag
ccds.gaussgaldepth = -2.5 * (np.log10(depth) - 9)
ccds.writeto(outfn)
def distanceFrom(self, pos):
from astrometry.util.starutil_numpy import degrees_between
return degrees_between(self.ra, self.dec, pos.ra, pos.dec)
def annotate(ccds, survey, mzls=False, normalizePsf=False):
# File from the "observing" svn repo:
if mzls:
# https://desi.lbl.gov/svn/decam/code/mosaic3/trunk
tiles = fits_table('mosaic-tiles_obstatus.fits')
else:
# https://desi.lbl.gov/svn/decam/code/observing/trunk
tiles = fits_table('decam-tiles_obstatus.fits')
# Map tile IDs back to index in the obstatus file.
tileid_to_index = np.empty(max(tiles.tileid)+1, int)
tileid_to_index[:] = -1
tileid_to_index[tiles.tileid] = np.arange(len(tiles))
#assert('ccd_cuts' in ccds.get_columns())
gaussgalnorm = np.zeros(len(ccds), np.float32)
for iccd,ccd in enumerate(ccds):
print('Reading CCD %i of %i:' % (iccd+1, len(ccds)), 'file', ccd.image_filename, 'CCD', ccd.expnum, ccd.ccdname)
try:
im = survey.get_image_object(ccd)
except:
print('Failed to get_image_object()')
import traceback
traceback.print_exc()
continue
print('Reading CCD %i of %i:' % (iccd+1, len(ccds)), im, 'file', ccd.image_filename, 'CCD', ccd.ccdname)
X = im.get_good_image_subregion()
for i,x in enumerate(X):
if x is not None:
ccds.good_region[iccd,i] = x
W,H = ccd.width, ccd.height
kwargs = dict(pixPsf=True, splinesky=True, subsky=False,
pixels=False, dq=False, invvar=False,
normalizePsf=normalizePsf)
psf = None
wcs = None
sky = None
try:
tim = im.get_tractor_image(**kwargs)
except:
print('Failed to get_tractor_image')
import traceback
traceback.print_exc()
continue
if tim is None:
continue
psf = tim.psf
wcs = tim.wcs.wcs
sky = tim.sky
hdr = tim.primhdr
# print('CCD fwhm:', ccd.fwhm)
# print('im fwhm:', im.fwhm)
# print('tim psf_fwhm', tim.psf_fwhm)
# print('tim psf_sigma:', tim.psf_sigma)
# print('Got PSF', psf)
# print('Got sky', type(sky))
# print('Got WCS', wcs)
# print('sig1', tim.sig1)
ccds.humidity[iccd] = hdr.get('HUMIDITY')
ccds.outtemp[iccd] = hdr.get('OUTTEMP')
ccds.sig1[iccd] = tim.sig1
ccds.plver[iccd] = tim.plver
# parse 'DECaLS_15150_r' to get tile number
obj = ccd.object.strip()
words = obj.split('_')
tile = None
if len(words) == 3 and (
((not mzls) and (words[0] == 'DECaLS')) or
( mzls and (words[0] in ['MzLS','MOSAIC']))):
try:
tileid = int(words[1])
tile = tiles[tileid_to_index[tileid]]
assert(tile.tileid == tileid)
except:
import traceback
traceback.print_exc()
pass
if tile is not None:
ccds.tileid [iccd] = tile.tileid
ccds.tilepass[iccd] = tile.get('pass')
ccds.tileebv [iccd] = tile.ebv_med
# Instantiate PSF on a grid
S = 32
xx = np.linspace(1+S, W-S, 5)
yy = np.linspace(1+S, H-S, 5)
xx,yy = np.meshgrid(xx, yy)
psfnorms = []
galnorms = []
for x,y in zip(xx.ravel(), yy.ravel()):
# HACK -- DR4 PSF sampling issue
tim.psf = psf.constantPsfAt(x, y)
p = im.psf_norm(tim, x=x, y=y)
g = im.galaxy_norm(tim, x=x, y=y)
psfnorms.append(p)
galnorms.append(g)
tim.psf = psf
ccds.psfnorm_mean[iccd] = np.mean(psfnorms)
ccds.psfnorm_std [iccd] = np.std (psfnorms)
ccds.galnorm_mean[iccd] = np.mean(galnorms)
ccds.galnorm_std [iccd] = np.std (galnorms)
#print('psf norm', ccds.psfnorm_mean[iccd])
#print('gal norm', ccds.galnorm_mean[iccd])
# PSF in center of field
cx,cy = (W+1)/2., (H+1)/2.
p = psf.getPointSourcePatch(cx, cy).patch
ph,pw = p.shape
px,py = np.meshgrid(np.arange(pw), np.arange(ph))
psum = np.sum(p)
# print('psum', psum)
p /= psum
# centroids
cenx = np.sum(p * px)
ceny = np.sum(p * py)
# print('cenx,ceny', cenx,ceny)
# second moments
x2 = np.sum(p * (px - cenx)**2)
y2 = np.sum(p * (py - ceny)**2)
xy = np.sum(p * (px - cenx)*(py - ceny))
# semi-major/minor axes and position angle
theta = np.rad2deg(np.arctan2(2 * xy, x2 - y2) / 2.)
theta = np.abs(theta) * np.sign(xy)
s = np.sqrt(((x2 - y2)/2.)**2 + xy**2)
a = np.sqrt((x2 + y2) / 2. + s)
b = np.sqrt((x2 + y2) / 2. - s)
ell = 1. - b/a
# print('PSF second moments', x2, y2, xy)
# print('PSF position angle', theta)
# print('PSF semi-axes', a, b)
# print('PSF ellipticity', ell)
ccds.psf_mx2[iccd] = x2
ccds.psf_my2[iccd] = y2
ccds.psf_mxy[iccd] = xy
ccds.psf_a[iccd] = a
ccds.psf_b[iccd] = b
ccds.psf_theta[iccd] = theta
ccds.psf_ell [iccd] = ell
#print('Computing Gaussian approximate PSF quantities...')
# Galaxy norm using Gaussian approximation of PSF.
realpsf = tim.psf
#print('FWHM', ccds.fwhm[i])
#print('-> sigma', ccds.fwhm[i] / 2.35)
#print('tim.PSF sigma', tim.psf_sigma)
tim.psf = im.read_psf_model(0, 0, gaussPsf=True,
psf_sigma=tim.psf_sigma)
gaussgalnorm[iccd] = im.galaxy_norm(tim, x=cx, y=cy)
psfnorm = im.psf_norm(tim)
pnorm = 1./(2. * np.sqrt(np.pi) * tim.psf_sigma)
#print('Gaussian PSF norm:', psfnorm, 'vs analytic', pnorm)
#print('Gaussian gal norm:', gaussgalnorm[iccd])
tim.psf = realpsf
has_skygrid = hasattr(sky, 'evaluateGrid')
# Sky -- evaluate on a grid (every ~10th pixel)
if has_skygrid:
skygrid = sky.evaluateGrid(np.linspace(0, ccd.width-1, int(1+ccd.width/10)),
np.linspace(0, ccd.height-1, int(1+ccd.height/10)))
ccds.meansky[iccd] = np.mean(skygrid)
ccds.stdsky[iccd] = np.std(skygrid)
ccds.maxsky[iccd] = skygrid.max()
ccds.minsky[iccd] = skygrid.min()
else:
skyval = sky.getConstant()
ccds.meansky[iccd] = skyval
ccds.stdsky[iccd] = 0.
ccds.maxsky[iccd] = skyval
ccds.minsky[iccd] = skyval
# WCS
ccds.ra0[iccd],ccds.dec0[iccd] = wcs.pixelxy2radec(1, 1)
ccds.ra1[iccd],ccds.dec1[iccd] = wcs.pixelxy2radec(1, H)
ccds.ra2[iccd],ccds.dec2[iccd] = wcs.pixelxy2radec(W, H)
ccds.ra3[iccd],ccds.dec3[iccd] = wcs.pixelxy2radec(W, 1)
midx, midy = (W+1)/2., (H+1)/2.
rc,dc = wcs.pixelxy2radec(midx, midy)
ra,dec = wcs.pixelxy2radec([1,W,midx,midx], [midy,midy,1,H])
ccds.dra [iccd] = max(degrees_between(ra, dc+np.zeros_like(ra),
rc, dc))
ccds.ddec[iccd] = max(degrees_between(rc+np.zeros_like(dec), dec,
rc, dc))
ccds.ra_center [iccd] = rc
ccds.dec_center[iccd] = dc
# Compute scale change across the chip
# how many pixels to step
step = 10
xx = np.linspace(1+step, W-step, 5)
yy = np.linspace(1+step, H-step, 5)
xx,yy = np.meshgrid(xx, yy)
pixscale = []
for x,y in zip(xx.ravel(), yy.ravel()):
sx = [x-step, x-step, x+step, x+step, x-step]
sy = [y-step, y+step, y+step, y-step, y-step]
sr,sd = wcs.pixelxy2radec(sx, sy)
rc,dc = wcs.pixelxy2radec(x, y)
# project around a tiny little TAN WCS at (x,y), with 1" pixels
locwcs = Tan(rc, dc, 0., 0., 1./3600, 0., 0., 1./3600, 1., 1.)
ok,lx,ly = locwcs.radec2pixelxy(sr, sd)
#print('local x,y:', lx, ly)
A = polygon_area((lx, ly))
pixscale.append(np.sqrt(A / (2*step)**2))
# print('Pixel scales:', pixscale)
ccds.pixscale_mean[iccd] = np.mean(pixscale)
ccds.pixscale_min[iccd] = min(pixscale)
ccds.pixscale_max[iccd] = max(pixscale)
ccds.pixscale_std[iccd] = np.std(pixscale)
ccds.annotated[iccd] = True
sfd = tractor.sfd.SFDMap()
allbands = 'ugrizY'
filts = ['%s %s' % ('DES', f) for f in allbands]
wisebands = ['WISE W1', 'WISE W2', 'WISE W3', 'WISE W4']
ebv,ext = sfd.extinction(filts + wisebands, ccds.ra_center,
ccds.dec_center, get_ebv=True)
ext[np.logical_not(ccds.annotated),:] = 0.
ebv[np.logical_not(ccds.annotated)] = 0.
ext = ext.astype(np.float32)
ccds.ebv = ebv.astype(np.float32)
ccds.decam_extinction = ext[:,:len(allbands)]
ccds.wise_extinction = ext[:,len(allbands):]
# Depth
detsig1 = ccds.sig1 / ccds.psfnorm_mean
depth = 5. * detsig1
# that's flux in nanomaggies -- convert to mag
ccds.psfdepth = -2.5 * (np.log10(depth) - 9)
detsig1 = ccds.sig1 / ccds.galnorm_mean
depth = 5. * detsig1
# that's flux in nanomaggies -- convert to mag
ccds.galdepth = -2.5 * (np.log10(depth) - 9)
# Depth using Gaussian FWHM.
psf_sigma = ccds.fwhm / 2.35
gnorm = 1./(2. * np.sqrt(np.pi) * psf_sigma)
detsig1 = ccds.sig1 / gnorm
depth = 5. * detsig1
# that's flux in nanomaggies -- convert to mag
ccds.gausspsfdepth = -2.5 * (np.log10(depth) - 9)
# Gaussian galaxy depth
detsig1 = ccds.sig1 / gaussgalnorm
depth = 5. * detsig1
# that's flux in nanomaggies -- convert to mag
ccds.gaussgaldepth = -2.5 * (np.log10(depth) - 9)
# NaN depths -> 0
for X in [ccds.psfdepth, ccds.galdepth, ccds.gausspsfdepth, ccds.gaussgaldepth]:
X[np.logical_not(np.isfinite(X))] = 0.
def read_star_clusters(targetwcs):
"""
Code to regenerate the NGC-star-clusters-fits catalog:
wget https://raw.githubusercontent.com/mattiaverga/OpenNGC/master/NGC.csv
import os
import numpy as np
import numpy.ma as ma
from astropy.io import ascii
from astrometry.util.starutil_numpy import hmsstring2ra, dmsstring2dec
import desimodel.io
import desimodel.footprint
tiles = desimodel.io.load_tiles(onlydesi=True)
names = ('name', 'type', 'ra_hms', 'dec_dms', 'const', 'majax', 'minax',
'pa', 'bmag', 'vmag', 'jmag', 'hmag', 'kmag', 'sbrightn', 'hubble',
'cstarumag', 'cstarbmag', 'cstarvmag', 'messier', 'ngc', 'ic',
'cstarnames', 'identifiers', 'commonnames', 'nednotes', 'ongcnotes')
NGC = ascii.read('NGC.csv', delimiter=';', names=names)
NGC = NGC[(NGC['ra_hms'] != 'N/A')]
ra, dec = [], []
for _ra, _dec in zip(ma.getdata(NGC['ra_hms']), ma.getdata(NGC['dec_dms'])):
ra.append(hmsstring2ra(_ra.replace('h', ':').replace('m', ':').replace('s','')))
dec.append(dmsstring2dec(_dec.replace('d', ':').replace('m', ':').replace('s','')))
NGC['ra'] = ra
NGC['dec'] = dec
objtype = np.char.strip(ma.getdata(NGC['type']))
# Keep all globular clusters and planetary nebulae
keeptype = ('PN', 'GCl')
keep = np.zeros(len(NGC), dtype=bool)
for otype in keeptype:
ww = [otype == tt for tt in objtype]
keep = np.logical_or(keep, ww)
print(np.sum(keep))
clusters = NGC[keep]
# Fill missing major axes with a nominal 0.4 arcmin (roughly works
# for NGC7009, which is the only missing PN in the footprint).
ma.set_fill_value(clusters['majax'], 0.4)
clusters['majax'] = ma.filled(clusters['majax'].data)
indesi = desimodel.footprint.is_point_in_desi(tiles, ma.getdata(clusters['ra']),
ma.getdata(clusters['dec']))
print(np.sum(indesi))
bb = clusters[indesi]
bb[np.argsort(bb['majax'])[::-1]]['name', 'ra', 'dec', 'majax', 'type']
clusters.write('NGC-star-clusters.fits', overwrite=True)
# Code to help visually check all open clusters that are in the DESI footprint.
checktype = ('OCl', 'Cl+N')
check = np.zeros(len(NGC), dtype=bool)
for otype in checktype:
ww = [otype == tt for tt in objtype]
check = np.logical_or(check, ww)
check_clusters = NGC[check] # 845 of them
# Write out a catalog, load it into the viewer and look at each of them.
check_clusters[['ra', 'dec', 'name']][indesi].write('check.fits', overwrite=True) # 25 of them
"""
from pkg_resources import resource_filename
from astrometry.util.starutil_numpy import degrees_between
clusterfile = resource_filename('legacypipe',
'data/NGC-star-clusters.fits')
debug('Reading {}'.format(clusterfile))
clusters = fits_table(clusterfile, columns=['ra', 'dec', 'majax', 'type'])
clusters.ref_id = np.arange(len(clusters))
radius = 1.
rc, dc = targetwcs.radec_center()
d = degrees_between(rc, dc, clusters.ra, clusters.dec)
clusters.cut(d < radius)
if len(clusters) == 0:
return None
debug('Cut to {} star cluster(s) within the brick'.format(len(clusters)))
# For each cluster, add a single faint star at the same coordinates, but
# set the isbright bit so we get all the brightstarinblob logic.
#clusters.ref_cat = clusters.name
clusters.ref_cat = np.array(['CL'] * len(clusters))
clusters.mag = np.array([35] * len(clusters))
# Radius in degrees (from "majax" in arcmin)
clusters.radius = clusters.majax / 60.
clusters.radius[np.logical_not(np.isfinite(clusters.radius))] = 1. / 60.
# Set isbright=True
clusters.isbright = np.zeros(len(clusters), bool)
clusters.iscluster = np.ones(len(clusters), bool)
return clusters
def map_decam_depth(req,
ver,
zoom,
x,
y,
savecache=False,
band=None,
ignoreCached=False):
global Tdepth
global Tdepthkd
if band is None:
band = req.GET.get('band')
if not band in ['g', 'r', 'z']:
raise RuntimeError('Invalid band')
tag = 'decam-depth-%s' % band
zoom = int(zoom)
zoomscale = 2.**zoom
x = int(x)
y = int(y)
if zoom < 0 or x < 0 or y < 0 or x >= zoomscale or y >= zoomscale:
raise RuntimeError('Invalid zoom,x,y %i,%i,%i' % (zoom, x, y))
ver = int(ver)
if not ver in tileversions[tag]:
raise RuntimeError('Invalid version %i for tag %s' % (ver, tag))
basedir = settings.DATA_DIR
tilefn = os.path.join(basedir, 'tiles', tag,
'%i/%i/%i/%i.jpg' % (ver, zoom, x, y))
if os.path.exists(tilefn) and not ignoreCached:
print('Cached:', tilefn)
return send_file(tilefn,
'image/jpeg',
expires=oneyear,
modsince=req.META.get('HTTP_IF_MODIFIED_SINCE'))
from astrometry.util.util import Tan
from astrometry.libkd.spherematch import match_radec
from astrometry.libkd.spherematch import tree_build_radec, tree_search_radec
from astrometry.util.fits import fits_table
from astrometry.util.starutil_numpy import degrees_between
import numpy as np
import fitsio
try:
wcs, W, H, zoomscale, zoom, x, y = get_tile_wcs(zoom, x, y)
except RuntimeError as e:
return HttpResponse(e.strerror)
rlo, d = wcs.pixelxy2radec(W, H / 2)[-2:]
rhi, d = wcs.pixelxy2radec(1, H / 2)[-2:]
r, d1 = wcs.pixelxy2radec(W / 2, 1)[-2:]
r, d2 = wcs.pixelxy2radec(W / 2, H)[-2:]
r, d = wcs.pixelxy2radec(W / 2, H / 2)[-2:]
rad = max(degrees_between(r, d, rlo, d1), degrees_between(r, d, rhi, d2))
if Tdepth is None:
T = fits_table(os.path.join(basedir, 'decals-zpt-nondecals.fits'),
columns=[
'ccdra', 'ccddec', 'arawgain', 'avsky', 'ccdzpt',
'filter', 'crpix1', 'crpix2', 'crval1', 'crval2',
'cd1_1', 'cd1_2', 'cd2_1', 'cd2_2', 'naxis1',
'naxis2', 'exptime', 'fwhm'
])
T.rename('ccdra', 'ra')
T.rename('ccddec', 'dec')
Tdepth = {}
Tdepthkd = {}
for b in ['g', 'r', 'z']:
Tdepth[b] = T[T.filter == b]
Tdepthkd[b] = tree_build_radec(Tdepth[b].ra, Tdepth[b].dec)
T = Tdepth[band]
Tkd = Tdepthkd[band]
#I,J,d = match_radec(T.ra, T.dec, r, d, rad + 0.2)
I = tree_search_radec(Tkd, r, d, rad + 0.2)
print(len(I), 'CCDs in range')
if len(I) == 0:
from django.http import HttpResponseRedirect
return HttpResponseRedirect(settings.STATIC_URL + 'blank.jpg')
depthiv = np.zeros((H, W), np.float32)
for t in T[I]:
twcs = Tan(*[
float(x) for x in [
t.crval1, t.crval2, t.crpix1, t.crpix2, t.cd1_1, t.cd1_2,
t.cd2_1, t.cd2_2, t.naxis1, t.naxis2
]
])
w, h = t.naxis1, t.naxis2
r, d = twcs.pixelxy2radec([1, 1, w, w], [1, h, h, 1])
ok, x, y = wcs.radec2pixelxy(r, d)
#print 'x,y coords of CCD:', x, y
x0 = int(x.min())
x1 = int(x.max())
y0 = int(y.min())
y1 = int(y.max())
if y1 < 0 or x1 < 0 or x0 >= W or y0 >= H:
continue
readnoise = 10. # e-; 7.0 to 15.0 according to DECam Data Handbook
skysig = np.sqrt(t.avsky * t.arawgain + readnoise**2) / t.arawgain
zpscale = 10.**((t.ccdzpt - 22.5) / 2.5) * t.exptime
sig1 = skysig / zpscale
psf_sigma = t.fwhm / 2.35
# point-source depth
psfnorm = 1. / (2. * np.sqrt(np.pi) * psf_sigma)
detsig1 = sig1 / psfnorm
#print '5-sigma point-source depth:', NanoMaggies.nanomaggiesToMag(detsig1 * 5.)
div = 1 / detsig1**2
depthiv[max(y0, 0):min(y1, H), max(x0, 0):min(x1, W)] += div
ptsrc = -2.5 * (np.log10(np.sqrt(1. / depthiv) * 5) - 9)
ptsrc[depthiv == 0] = 0.
if savecache:
trymakedirs(tilefn)
else:
import tempfile
f, tilefn = tempfile.mkstemp(suffix='.jpg')
os.close(f)
import pylab as plt
plt.imsave(tilefn, ptsrc, vmin=22., vmax=25., cmap='hot') #nipy_spectral')
return send_file(tilefn, 'image/jpeg', unlink=(not savecache))
def map_sdss(req, ver, zoom, x, y, savecache=None, tag='sdss',
get_images=False,
ignoreCached=False,
wcs=None,
forcecache=False,
forcescale=None,
**kwargs):
from decals import settings
if savecache is None:
savecache = settings.SAVE_CACHE
zoom = int(zoom)
zoomscale = 2.**zoom
x = int(x)
y = int(y)
if zoom < 0 or x < 0 or y < 0 or x >= zoomscale or y >= zoomscale:
raise RuntimeError('Invalid zoom,x,y %i,%i,%i' % (zoom,x,y))
ver = int(ver)
if not ver in tileversions[tag]:
raise RuntimeError('Invalid version %i for tag %s' % (ver, tag))
basedir = settings.DATA_DIR
tilefn = os.path.join(basedir, 'tiles', tag,
'%i/%i/%i/%i.jpg' % (ver, zoom, x, y))
if os.path.exists(tilefn) and not ignoreCached:
if get_images:
return None
return send_file(tilefn, 'image/jpeg', expires=oneyear,
modsince=req.META.get('HTTP_IF_MODIFIED_SINCE'))
if not savecache:
import tempfile
f,tilefn = tempfile.mkstemp(suffix='.jpg')
os.close(f)
if wcs is None:
try:
wcs, W, H, zoomscale, zoom,x,y = get_tile_wcs(zoom, x, y)
except RuntimeError as e:
if get_images:
return None
return HttpResponse(e.strerror)
else:
W = wcs.get_width()
H = wcs.get_height()
from astrometry.util.fits import fits_table
import numpy as np
from astrometry.libkd.spherematch import tree_build_radec, tree_search_radec
from astrometry.util.starutil_numpy import degrees_between, arcsec_between
from astrometry.util.resample import resample_with_wcs, OverlapError
from astrometry.util.util import Tan, Sip
import fitsio
print 'Tile wcs: center', wcs.radec_center(), 'pixel scale', wcs.pixel_scale()
global w_flist
global w_flist_tree
if w_flist is None:
w_flist = fits_table(os.path.join(settings.DATA_DIR, 'sdss',
'window_flist.fits'),
columns=['run','rerun','camcol','field','ra','dec','score'])
print 'Read', len(w_flist), 'window_flist entries'
w_flist.cut(w_flist.rerun == '301')
print 'Cut to', len(w_flist), 'in rerun 301'
w_flist_tree = tree_build_radec(w_flist.ra, w_flist.dec)
# SDSS field size
radius = 1.01 * np.hypot(10., 14.)/2. / 60.
# leaflet tile size
ra,dec = wcs.pixelxy2radec(W/2., H/2.)[-2:]
r0,d0 = wcs.pixelxy2radec(1, 1)[-2:]
r1,d1 = wcs.pixelxy2radec(W, H)[-2:]
radius = radius + max(degrees_between(ra,dec, r0,d0), degrees_between(ra,dec, r1,d1))
J = tree_search_radec(w_flist_tree, ra, dec, radius)
print len(J), 'overlapping SDSS fields found'
if len(J) == 0:
if get_images:
return None
if forcecache:
# create symlink to blank.jpg!
trymakedirs(tilefn)
src = os.path.join(settings.STATIC_ROOT, 'blank.jpg')
if os.path.exists(tilefn):
os.unlink(tilefn)
os.symlink(src, tilefn)
print 'Symlinked', tilefn, '->', src
from django.http import HttpResponseRedirect
return HttpResponseRedirect(settings.STATIC_URL + 'blank.jpg')
ww = [1, W*0.25, W*0.5, W*0.75, W]
hh = [1, H*0.25, H*0.5, H*0.75, H]
r,d = wcs.pixelxy2radec(
[1]*len(hh) + ww + [W]*len(hh) + list(reversed(ww)),
hh + [1]*len(ww) + list(reversed(hh)) + [H]*len(ww))[-2:]
scaled = 0
scalepat = None
scaledir = 'sdss'
if zoom <= 13 and forcescale is None:
# Get *actual* pixel scales at the top & bottom
r1,d1 = wcs.pixelxy2radec(W/2., H)[-2:]
r2,d2 = wcs.pixelxy2radec(W/2., H-1.)[-2:]
r3,d3 = wcs.pixelxy2radec(W/2., 1.)[-2:]
r4,d4 = wcs.pixelxy2radec(W/2., 2.)[-2:]
# Take the min = most zoomed-in
scale = min(arcsec_between(r1,d1, r2,d2), arcsec_between(r3,d3, r4,d4))
native_scale = 0.396
scaled = int(np.floor(np.log2(scale / native_scale)))
print 'Zoom:', zoom, 'x,y', x,y, 'Tile pixel scale:', scale, 'Scale step:', scaled
scaled = np.clip(scaled, 1, 7)
dirnm = os.path.join(basedir, 'scaled', scaledir)
scalepat = os.path.join(dirnm, '%(scale)i%(band)s', '%(rerun)s', '%(run)i', '%(camcol)i', 'sdss-%(run)i-%(camcol)i-%(field)i-%(band)s.fits')
if forcescale is not None:
scaled = forcescale
bands = 'gri'
rimgs = [np.zeros((H,W), np.float32) for band in bands]
rns = [np.zeros((H,W), np.float32) for band in bands]
from astrometry.sdss import AsTransWrapper, DR9
sdss = DR9(basedir=settings.SDSS_DIR)
sdss.saveUnzippedFiles(settings.SDSS_DIR)
#sdss.setFitsioReadBZ2()
if settings.SDSS_PHOTOOBJS:
sdss.useLocalTree(photoObjs=settings.SDSS_PHOTOOBJS,
resolve=settings.SDSS_RESOLVE)
for jnum,j in enumerate(J):
print 'SDSS field', jnum, 'of', len(J), 'for zoom', zoom, 'x', x, 'y', y
im = w_flist[j]
if im.score >= 0.5:
weight = 1.
else:
weight = 0.001
for band,rimg,rn in zip(bands, rimgs, rns):
if im.rerun != '301':
continue
tmpsuff = '.tmp%08i' % np.random.randint(100000000)
basefn = sdss.retrieve('frame', im.run, im.camcol, field=im.field,
band=band, rerun=im.rerun, tempsuffix=tmpsuff)
if scaled > 0:
fnargs = dict(band=band, rerun=im.rerun, run=im.run,
camcol=im.camcol, field=im.field)
fn = get_scaled(scalepat, fnargs, scaled, basefn,
read_base_wcs=read_astrans, read_wcs=_read_sip_wcs)
print 'get_scaled:', fn
else:
fn = basefn
frame = None
if fn == basefn:
frame = sdss.readFrame(im.run, im.camcol, im.field, band,
filename=fn)
h,w = frame.getImageShape()
# Trim off the overlapping top of the image
# Wimp out and instead of trimming 128 pix, trim 124!
trim = 124
subh = h - trim
astrans = frame.getAsTrans()
fwcs = AsTransWrapper(astrans, w, subh)
fullimg = frame.getImage()
fullimg = fullimg[:-trim,:]
else:
fwcs = Sip(fn)
fitsimg = fitsio.FITS(fn)[0]
h,w = fitsimg.get_info()['dims']
fullimg = fitsimg.read()
try:
#Yo,Xo,Yi,Xi,nil = resample_with_wcs(wcs, fwcs, [], 3)
Yo,Xo,Yi,Xi,[resamp] = resample_with_wcs(wcs, fwcs, [fullimg], 2)
except OverlapError:
continue
if len(Xi) == 0:
#print 'No overlap'
continue
if sdssps is not None:
x0 = Xi.min()
x1 = Xi.max()
y0 = Yi.min()
y1 = Yi.max()
slc = (slice(y0,y1+1), slice(x0,x1+1))
if frame is not None:
img = frame.getImageSlice(slc)
else:
img = fitsimg[slc]
#rimg[Yo,Xo] += img[Yi-y0, Xi-x0]
rimg[Yo,Xo] += resamp * weight
rn [Yo,Xo] += weight
if sdssps is not None:
# goodpix = np.ones(img.shape, bool)
# fpM = sdss.readFpM(im.run, im.camcol, im.field, band)
# for plane in [ 'INTERP', 'SATUR', 'CR', 'GHOST' ]:
# fpM.setMaskedPixels(plane, goodpix, False, roi=[x0,x1,y0,y1])
plt.clf()
#ima = dict(vmin=-0.05, vmax=0.5)
#ima = dict(vmin=-0.5, vmax=2.)
ima = dict(vmax=np.percentile(img, 99))
plt.subplot(2,3,1)
dimshow(img, ticks=False, **ima)
plt.title('image')
rthis = np.zeros_like(rimg)
#rthis[Yo,Xo] += img[Yi-y0, Xi-x0]
rthis[Yo,Xo] += resamp
plt.subplot(2,3,2)
dimshow(rthis, ticks=False, **ima)
plt.title('resampled')
# plt.subplot(2,3,3)
# dimshow(goodpix, ticks=False, vmin=0, vmax=1)
# plt.title('good pix')
plt.subplot(2,3,4)
dimshow(rimg / np.maximum(rn, 1), ticks=False, **ima)
plt.title('coadd')
plt.subplot(2,3,5)
dimshow(rn, vmin=0, ticks=False)
plt.title('coverage: max %i' % rn.max())
plt.subplot(2,3,6)
rgb = sdss_rgb([rimg/np.maximum(rn,1)
for rimg,rn in zip(rimgs,rns)], bands)
dimshow(rgb)
plt.suptitle('SDSS %s, R/C/F %i/%i/%i' % (band, im.run, im.camcol, im.field))
sdssps.savefig()
for rimg,rn in zip(rimgs, rns):
rimg /= np.maximum(rn, 1e-3)
del rns
if get_images:
return rimgs
rgb = sdss_rgb(rimgs, bands)
trymakedirs(tilefn)
save_jpeg(tilefn, rgb)
print 'Wrote', tilefn
return send_file(tilefn, 'image/jpeg', unlink=(not savecache))
def map_decals_wl(req, ver, zoom, x, y):
tag = 'decals-wl'
ignoreCached = False
filename = None
forcecache = False
from decals import settings
savecache = settings.SAVE_CACHE
zoom = int(zoom)
zoomscale = 2.**zoom
x = int(x)
y = int(y)
if zoom < 0 or x < 0 or y < 0 or x >= zoomscale or y >= zoomscale:
raise RuntimeError('Invalid zoom,x,y %i,%i,%i' % (zoom, x, y))
ver = int(ver)
if not ver in tileversions[tag]:
raise RuntimeError('Invalid version %i for tag %s' % (ver, tag))
basedir = settings.DATA_DIR
tilefn = os.path.join(basedir, 'tiles', tag,
'%i/%i/%i/%i.jpg' % (ver, zoom, x, y))
if os.path.exists(tilefn) and not ignoreCached:
print('Cached:', tilefn)
return send_file(tilefn,
'image/jpeg',
expires=oneyear,
modsince=req.META.get('HTTP_IF_MODIFIED_SINCE'),
filename=filename)
else:
print('Tile image does not exist:', tilefn)
from astrometry.util.resample import resample_with_wcs, OverlapError
from astrometry.util.util import Tan
from astrometry.libkd.spherematch import match_radec
from astrometry.util.fits import fits_table
from astrometry.util.starutil_numpy import degrees_between
import numpy as np
import fitsio
try:
wcs, W, H, zoomscale, zoom, x, y = get_tile_wcs(zoom, x, y)
except RuntimeError as e:
return HttpResponse(e.strerror)
mydir = os.path.join(basedir, 'coadd', 'weak-lensing')
rlo, d = wcs.pixelxy2radec(W, H / 2)[-2:]
rhi, d = wcs.pixelxy2radec(1, H / 2)[-2:]
r, d1 = wcs.pixelxy2radec(W / 2, 1)[-2:]
r, d2 = wcs.pixelxy2radec(W / 2, H)[-2:]
#dlo = min(d1, d2)
#dhi = max(d1, d2)
r, d = wcs.pixelxy2radec(W / 2, H / 2)[-2:]
rad = degrees_between(r, d, rlo, d1)
fn = os.path.join(mydir, 'index.fits')
if not os.path.exists(fn):
#
ii, rr, dd = [], [], []
for i in range(1, 52852 + 1):
imgfn = os.path.join(mydir, 'map%i.fits' % i)
hdr = fitsio.read_header(imgfn)
r = hdr['CRVAL1']
d = hdr['CRVAL2']
ii.append(i)
rr.append(r)
dd.append(d)
T = fits_table()
T.ra = np.array(rr)
T.dec = np.array(dd)
T.i = np.array(ii)
T.writeto(fn)
T = fits_table(fn)
I, J, d = match_radec(T.ra, T.dec, r, d, rad + 0.2)
T.cut(I)
print(len(T), 'weak-lensing maps in range')
if len(I) == 0:
from django.http import HttpResponseRedirect
if forcecache:
# create symlink to blank.jpg!
trymakedirs(tilefn)
src = os.path.join(settings.STATIC_ROOT, 'blank.jpg')
if os.path.exists(tilefn):
os.unlink(tilefn)
os.symlink(src, tilefn)
print('Symlinked', tilefn, '->', src)
return HttpResponseRedirect(settings.STATIC_URL + 'blank.jpg')
r, d = wcs.pixelxy2radec([1, 1, 1, W / 2, W, W, W, W / 2],
[1, H / 2, H, H, H, H / 2, 1, 1])[-2:]
foundany = False
rimg = np.zeros((H, W), np.float32)
rn = np.zeros((H, W), np.uint8)
for tilei in T.i:
fn = os.path.join(mydir, 'map%i.fits' % tilei)
try:
bwcs = read_tan_wcs(fn, 0)
except:
print('Failed to read WCS:', fn)
savecache = False
import traceback
import sys
traceback.print_exc(None, sys.stdout)
continue
foundany = True
print('Reading', fn)
ok, xx, yy = bwcs.radec2pixelxy(r, d)
xx = xx.astype(np.int)
yy = yy.astype(np.int)
imW, imH = int(bwcs.get_width()), int(bwcs.get_height())
M = 10
xlo = np.clip(xx.min() - M, 0, imW)
xhi = np.clip(xx.max() + M, 0, imW)
ylo = np.clip(yy.min() - M, 0, imH)
yhi = np.clip(yy.max() + M, 0, imH)
if xlo >= xhi or ylo >= yhi:
continue
subwcs = bwcs.get_subimage(xlo, ylo, xhi - xlo, yhi - ylo)
slc = slice(ylo, yhi), slice(xlo, xhi)
try:
f = fitsio.FITS(fn)[0]
img = f[slc]
del f
except:
print('Failed to read image and WCS:', fn)
savecache = False
import traceback
import sys
traceback.print_exc(None, sys.stdout)
continue
try:
Yo, Xo, Yi, Xi, nil = resample_with_wcs(wcs, subwcs, [], 3)
except OverlapError:
print('Resampling exception')
continue
rimg[Yo, Xo] += img[Yi, Xi]
rn[Yo, Xo] += 1
rimg /= np.maximum(rn, 1)
if forcecache:
savecache = True
if savecache:
trymakedirs(tilefn)
else:
import tempfile
f, tilefn = tempfile.mkstemp(suffix='.jpg')
os.close(f)
import pylab as plt
# S/N
#lo,hi = 1.5, 5.0
lo, hi = 0, 5.0
rgb = plt.cm.hot((rimg - lo) / (hi - lo))
plt.imsave(tilefn, rgb)
print('Wrote', tilefn)
return send_file(tilefn,
'image/jpeg',
unlink=(not savecache),
filename=filename)
def map_decam_depth(req, ver, zoom, x, y, savecache=False, band=None,
ignoreCached=False):
global Tdepth
global Tdepthkd
if band is None:
band = req.GET.get('band')
if not band in ['g','r','z']:
raise RuntimeError('Invalid band')
tag = 'decam-depth-%s' % band
zoom = int(zoom)
zoomscale = 2.**zoom
x = int(x)
y = int(y)
if zoom < 0 or x < 0 or y < 0 or x >= zoomscale or y >= zoomscale:
raise RuntimeError('Invalid zoom,x,y %i,%i,%i' % (zoom,x,y))
ver = int(ver)
if not ver in tileversions[tag]:
raise RuntimeError('Invalid version %i for tag %s' % (ver, tag))
basedir = settings.DATA_DIR
tilefn = os.path.join(basedir, 'tiles', tag,
'%i/%i/%i/%i.jpg' % (ver, zoom, x, y))
if os.path.exists(tilefn) and not ignoreCached:
print 'Cached:', tilefn
return send_file(tilefn, 'image/jpeg', expires=oneyear,
modsince=req.META.get('HTTP_IF_MODIFIED_SINCE'))
from astrometry.util.util import Tan
from astrometry.libkd.spherematch import match_radec
from astrometry.libkd.spherematch import tree_build_radec, tree_search_radec
from astrometry.util.fits import fits_table
from astrometry.util.starutil_numpy import degrees_between
import numpy as np
import fitsio
try:
wcs, W, H, zoomscale, zoom,x,y = get_tile_wcs(zoom, x, y)
except RuntimeError as e:
return HttpResponse(e.strerror)
rlo,d = wcs.pixelxy2radec(W, H/2)[-2:]
rhi,d = wcs.pixelxy2radec(1, H/2)[-2:]
r,d1 = wcs.pixelxy2radec(W/2, 1)[-2:]
r,d2 = wcs.pixelxy2radec(W/2, H)[-2:]
r,d = wcs.pixelxy2radec(W/2, H/2)[-2:]
rad = max(degrees_between(r, d, rlo, d1),
degrees_between(r, d, rhi, d2))
if Tdepth is None:
T = fits_table(os.path.join(basedir, 'decals-zpt-nondecals.fits'),
columns=['ccdra','ccddec','arawgain', 'avsky',
'ccdzpt', 'filter', 'crpix1','crpix2',
'crval1','crval2','cd1_1','cd1_2',
'cd2_1','cd2_2', 'naxis1', 'naxis2', 'exptime', 'fwhm'])
T.rename('ccdra', 'ra')
T.rename('ccddec', 'dec')
Tdepth = {}
Tdepthkd = {}
for b in ['g','r','z']:
Tdepth[b] = T[T.filter == b]
Tdepthkd[b] = tree_build_radec(Tdepth[b].ra, Tdepth[b].dec)
T = Tdepth[band]
Tkd = Tdepthkd[band]
#I,J,d = match_radec(T.ra, T.dec, r, d, rad + 0.2)
I = tree_search_radec(Tkd, r, d, rad + 0.2)
print len(I), 'CCDs in range'
if len(I) == 0:
from django.http import HttpResponseRedirect
return HttpResponseRedirect(settings.STATIC_URL + 'blank.jpg')
depthiv = np.zeros((H,W), np.float32)
for t in T[I]:
twcs = Tan(*[float(x) for x in [
t.crval1, t.crval2, t.crpix1, t.crpix2,
t.cd1_1, t.cd1_2, t.cd2_1, t.cd2_2, t.naxis1, t.naxis2]])
w,h = t.naxis1, t.naxis2
r,d = twcs.pixelxy2radec([1,1,w,w], [1,h,h,1])
ok,x,y = wcs.radec2pixelxy(r, d)
#print 'x,y coords of CCD:', x, y
x0 = int(x.min())
x1 = int(x.max())
y0 = int(y.min())
y1 = int(y.max())
if y1 < 0 or x1 < 0 or x0 >= W or y0 >= H:
continue
readnoise = 10. # e-; 7.0 to 15.0 according to DECam Data Handbook
skysig = np.sqrt(t.avsky * t.arawgain + readnoise**2) / t.arawgain
zpscale = 10.**((t.ccdzpt - 22.5)/2.5) * t.exptime
sig1 = skysig / zpscale
psf_sigma = t.fwhm / 2.35
# point-source depth
psfnorm = 1./(2. * np.sqrt(np.pi) * psf_sigma)
detsig1 = sig1 / psfnorm
#print '5-sigma point-source depth:', NanoMaggies.nanomaggiesToMag(detsig1 * 5.)
div = 1 / detsig1**2
depthiv[max(y0,0):min(y1,H), max(x0,0):min(x1,W)] += div
ptsrc = -2.5 * (np.log10(np.sqrt(1./depthiv) * 5) - 9)
ptsrc[depthiv == 0] = 0.
if savecache:
trymakedirs(tilefn)
else:
import tempfile
f,tilefn = tempfile.mkstemp(suffix='.jpg')
os.close(f)
import pylab as plt
plt.imsave(tilefn, ptsrc, vmin=22., vmax=25., cmap='hot')#nipy_spectral')
return send_file(tilefn, 'image/jpeg', unlink=(not savecache))
