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 galex_tractor_image(tile, band, galex_dir, radecbox, bandname):
from tractor import (NanoMaggies, Image, LinearPhotoCal,
ConstantFitsWcs, ConstantSky)
assert(band in ['n','f'])
#nicegbands = ['NUV', 'FUV']
#zps = dict(n=20.08, f=18.82)
#zp = zps[band]
imfn = os.path.join(galex_dir, tile.tilename.strip(),
'%s-%sd-intbgsub.fits.gz' % (tile.visitname.strip(), band))
gwcs = Tan(*[float(f) for f in
[tile.crval1, tile.crval2, tile.crpix1, tile.crpix2,
tile.cdelt1, 0., 0., tile.cdelt2, 3840., 3840.]])
(r0,r1,d0,d1) = radecbox
H,W = gwcs.shape
ok,xx,yy = gwcs.radec2pixelxy([r0,r0,r1,r1], [d0,d1,d1,d0])
#print('GALEX WCS pixel positions of RA,Dec box:', xx, yy)
if np.any(np.logical_not(ok)):
return None
x0 = np.clip(np.floor(xx-1).astype(int).min(), 0, W-1)
x1 = np.clip(np.ceil (xx-1).astype(int).max(), 0, W)
if x1-x0 <= 1:
return None
y0 = np.clip(np.floor(yy-1).astype(int).min(), 0, H-1)
y1 = np.clip(np.ceil (yy-1).astype(int).max(), 0, H)
if y1-y0 <= 1:
return None
debug('Reading GALEX subimage x0,y0', x0,y0, 'size', x1-x0, y1-y0)
gwcs = gwcs.get_subimage(x0, y0, x1 - x0, y1 - y0)
twcs = ConstantFitsWcs(gwcs)
roislice = (slice(y0, y1), slice(x0, x1))
fitsimg = fitsio.FITS(imfn)[0]
hdr = fitsimg.read_header()
img = fitsimg[roislice]
inverr = np.ones_like(img)
inverr[img == 0.] = 0.
zp = tile.get('%s_zpmag' % band)
photocal = LinearPhotoCal(NanoMaggies.zeropointToScale(zp), band=bandname)
tsky = ConstantSky(0.)
name = 'GALEX ' + hdr['OBJECT'] + ' ' + band
psfimg = galex_psf(band, galex_dir)
tpsf = PixelizedPSF(psfimg)
tim = Image(data=img, inverr=inverr, psf=tpsf, wcs=twcs,
sky=tsky, photocal=photocal, name=name)
tim.roi = [x0,x1,y0,y1]
return tim
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 sdss_image(req, calid=None, size='full'):
cal = get_object_or_404(Calibration, pk=calid)
key = 'sdss_size%s_cal%i' % (size, cal.id)
df = CachedFile.get(key)
if df is None:
wcsfn = cal.get_wcs_file()
from astrometry.util.util import Tan
wcs = Tan(wcsfn)
if size == 'display':
image = cal.job.user_image
scale = float(
image.image.get_display_image().width) / image.image.width
wcs = wcs.scale(scale)
else:
scale = 1.0
urlargs = urlencode(
dict(crval1='%.6f' % wcs.crval[0],
crval2='%.6f' % wcs.crval[1],
crpix1='%.2f' % wcs.crpix[0],
crpix2='%.2f' % wcs.crpix[1],
cd11='%.6g' % wcs.cd[0],
cd12='%.6g' % wcs.cd[1],
cd21='%.6g' % wcs.cd[2],
cd22='%.6g' % wcs.cd[3],
imagew='%i' % int(wcs.imagew),
imageh='%i' % int(wcs.imageh)))
url = 'http://legacysurvey.org/viewer/cutout-wcs/?layer=sdssco&' + urlargs
return HttpResponseRedirect(url)
#print('Retrieving:', url)
#f = urlopen(url)
#plotfn = get_temp_file()
#plotfn, headers = urlretrieve(url, plotfn)
#print('Headers:', headers)
#plot_sdss_image(wcsfn, plotfn, scale)
# cache
# logmsg('Caching key "%s"' % key)
# df = CachedFile.add(key, plotfn)
else:
logmsg('Cache hit for key "%s" -> %s' % (key, df.get_path()))
f = open(df.get_path(), 'rb')
res = HttpResponse(f)
res['Content-Type'] = 'image/jpeg'
return res
def __init__(self, name, **kwargs):
## HACK -- don't call the PhatLayer constructor!
#super(M33Layer, self).__init__(name, **kwargs)
super(PhatLayer, self).__init__(name, **kwargs)
self.nativescale = 17
self.pixscale = 0.035
#fn = self.get_base_filname(None, None)
#self.fits = fitsio.FITS(fn)[0]
#fn = os.path.join(settings.DATA_DIR, 'm33', 'F475W_wcs.fits')
#fn = os.path.join(settings.DATA_DIR, 'm33', 'm33-wcs.fits')
fn = os.path.join(settings.DATA_DIR, 'm33', 'm33-wcs814.fits')
#self.wcs = anwcs_open_wcslib(fn, 0)
from astrometry.util.util import Tan
self.wcs = Tan(fn, 0)
print('M33 WCS: center', self.wcs.radec_center())
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 __init__(self, wcs, hdu=0):
'''
Creates a new ``FitsWcs`` given a :class:`~astrometry.util.util.Tan`
object. To create one of these from a filename and FITS HDU extension,
::
from astrometry.util.util import Tan
fn = 'my-file.fits'
ext = 0
TanWcs(Tan(fn, ext))
To create one from WCS parameters,
tanwcs = Tan(crval1, crval2, crpix1, crpix2,
cd11, cd12, cd21, cd22, imagew, imageh)
TanWcs(tanwcs)
'''
if hasattr(self, 'x0'):
print('TanWcs has an x0 attr:', self.x0)
self.x0 = 0
self.y0 = 0
if isinstance(wcs, basestring):
from astrometry.util.util import Tan
wcs = Tan(wcs, hdu)
super(TanWcs, self).__init__(wcs)
# ParamList keeps its params in a list; we don't want to do that.
del self.vals
def read_tan_wcs(sourcefn, ext, hdr=None, W=None, H=None, fitsfile=None):
from astrometry.util.util import Tan
if not os.path.exists(sourcefn):
return None
wcs = read_tansip_wcs(sourcefn, ext, hdr=hdr, W=W, H=H, tansip=Tan)
if wcs is None:
import fitsio
# maybe gzipped; try fitsio header.
if hdr is None:
hdr = fitsio.read_header(sourcefn, ext)
if W is None or H is None:
if fitsfile is None:
F = fitsio.FITS(sourcefn)
else:
F = fitsfile
info = F[ext].get_info()
H,W = info['dims']
# PS1 wonky WCS
if (not 'CD1_1' in hdr) and ('PC001001' in hdr):
cdelt1 = hdr['CDELT1']
cdelt2 = hdr['CDELT2']
# ????
cd11 = hdr['PC001001'] * cdelt1
cd12 = hdr['PC001002'] * cdelt1
cd21 = hdr['PC002001'] * cdelt2
cd22 = hdr['PC002002'] * cdelt2
else:
cd11,cd12,cd21,cd22 = hdr['CD1_1'], hdr['CD1_2'], hdr['CD2_1'], hdr['CD2_2'],
wcs = Tan(*[float(x) for x in [
hdr['CRVAL1'], hdr['CRVAL2'], hdr['CRPIX1'], hdr['CRPIX2'],
cd11, cd12, cd21, cd22, W, H]])
return wcs
def plot_into_wcs(wcsfn, plotfn, wcsext=0, basedir='.', scale=1.0):
wcs = Tan(wcsfn, wcsext)
ra, dec = wcs.radec_center()
radius = wcs.radius()
# The "index.fits" table has RA,Dec center and file paths.
T = fits_table(os.path.join(basedir, 'index.fits'))
# MAGIC 1: the GALEX fields are all smaller than 1 degree (~0.95) in radius,
# so add that to the search
r = radius + 1.
# find rows in "T" that are within range.
J = points_within_radius(ra, dec, r, T.ra, T.dec)
T = T[J]
debug(len(T), 'GALEX fields within range of RA,Dec = ', ra, dec, 'radius',
radius)
size = [int(scale * wcs.imagew), int(scale * wcs.imageh)]
plot = ps.Plotstuff(outformat='png', wcsfn=wcsfn, wcsext=wcsext, size=size)
plot.scale_wcs(scale)
#debug('WCS:', str(plot.wcs))
#plot.wcs.write_to('/tmp/wcs.fits')
img = plot.image
img.format = ps.PLOTSTUFF_FORMAT_JPG
img.resample = 1
if len(T):
paths = [fn.strip() for fn in T.path]
else:
paths = []
plot.color = 'black'
plot.plot('fill')
for jpegfn in paths:
jpegfn = os.path.join(basedir, jpegfn)
imwcsfn = jpegfn.replace('.jpg', '.wcs')
debug(' Plotting GALEX fields', jpegfn, imwcsfn)
#debug('jpeg "%s"' % jpegfn)
#debug('wcs "%s"' % imwcsfn)
img.set_wcs_file(imwcsfn, 0)
img.set_file(jpegfn)
# convert black to transparent
ps.plot_image_read(plot.pargs, img)
ps.plot_image_make_color_transparent(img, 0, 0, 0)
plot.plot('image')
plot.write(plotfn)
debug('wrote', plotfn)
def sdss_image(req, calid=None, size='full'):
cal = get_object_or_404(Calibration, pk=calid)
key = 'sdss_size%s_cal%i' % (size, cal.id)
df = CachedFile.get(key)
if df is None:
wcsfn = cal.get_wcs_file()
plotfn = get_temp_file()
from astrometry.util.util import Tan
wcs = Tan(wcsfn)
if size == 'display':
image = cal.job.user_image
scale = float(image.image.get_display_image().width)/image.image.width
wcs = wcs.scale(scale)
else:
scale = 1.0
urlargs = urllib.urlencode(dict(crval1='%.6f' % wcs.crval[0],
crval2='%.6f' % wcs.crval[1],
crpix1='%.2f' % wcs.crpix[0],
crpix2='%.2f' % wcs.crpix[1],
cd11='%.6g' % wcs.cd[0],
cd12='%.6g' % wcs.cd[1],
cd21='%.6g' % wcs.cd[2],
cd22='%.6g' % wcs.cd[3],
imagew='%i' % int(wcs.imagew),
imageh='%i' % int(wcs.imageh)))
url = 'http://legacysurvey.org/viewer-dev/sdss-wcs/?' + urlargs
return HttpResponseRedirect(url)
#print('Retrieving:', url)
#f = urllib.urlopen(url)
plotfn,headers = urllib.urlretrieve(url, plotfn)
#print('Headers:', headers)
#plot_sdss_image(wcsfn, plotfn, scale)
# cache
logmsg('Caching key "%s"' % key)
df = CachedFile.add(key, plotfn)
else:
logmsg('Cache hit for key "%s" -> %s' % (key, df.get_path()))
f = open(df.get_path())
res = HttpResponse(f)
res['Content-Type'] = 'image/jpeg'
return res
class M33Layer(PhatLayer):
'''
# Image files:
tifftopnm data/m33/M33_F814W_F475W_mosaic_181216_MJD.tif | ppmtorgb3 -
pamflip -tb -- -.red | an-pnmtofits -o data/m33/m33-R.fits -v &
pamflip -tb -- -.grn | an-pnmtofits -o data/m33/m33-G.fits -v &
pamflip -tb -- -.blu | an-pnmtofits -o data/m33/m33-B.fits -v &
# WCS header:
hdr = fitsio.read_header('/Users/dstn/Downloads/F475W_wcs.fits')
outhdr = fitsio.FITSHDR()
for key in ['SIMPLE', 'BITPIX', 'NAXIS', 'WCSAXES', 'CRPIX1', 'CRPIX2', 'CTYPE1', 'CTYPE2', 'CRVAL1', 'CRVAL2']:
v = hdr[key]
outhdr[key] = v
outhdr
outhdr['CD1_1'] = hdr['CDELT1'] * hdr['PC1_1']
outhdr['CD1_2'] = hdr['CDELT1'] * hdr['PC1_2']
outhdr['CD2_1'] = hdr['CDELT2'] * hdr['PC2_1']
outhdr['CD2_2'] = hdr['CDELT2'] * hdr['PC2_2']
outhdr['IMAGEW'] = 32073
outhdr['IMAGEH'] = 41147
fitsio.write('/tmp/m33-wcs.fits', None, header=outhdr)
'''
def __init__(self, name, **kwargs):
## HACK -- don't call the PhatLayer constructor!
#super(M33Layer, self).__init__(name, **kwargs)
super(PhatLayer, self).__init__(name, **kwargs)
self.nativescale = 17
self.pixscale = 0.035
#fn = self.get_base_filname(None, None)
#self.fits = fitsio.FITS(fn)[0]
#fn = os.path.join(settings.DATA_DIR, 'm33', 'F475W_wcs.fits')
#fn = os.path.join(settings.DATA_DIR, 'm33', 'm33-wcs.fits')
fn = os.path.join(settings.DATA_DIR, 'm33', 'm33-wcs814.fits')
#self.wcs = anwcs_open_wcslib(fn, 0)
from astrometry.util.util import Tan
self.wcs = Tan(fn, 0)
print('M33 WCS: center', self.wcs.radec_center())
def read_wcs(self, brick, band, scale, fn=None):
if scale == 0:
return self.wcs
return super(M33Layer, self).read_wcs(brick, band, scale, fn=fn)
def get_bands(self):
return 'RGB'
def get_base_filename(self, brick, band, **kwargs):
return os.path.join(settings.DATA_DIR, 'm33', 'm33-%s.fits' % band)
def get_scaled_filename(self, brick, band, scale):
return os.path.join(settings.DATA_DIR, 'm33',
'm33-%s-scale%i.fits' % (band, scale))
def get_rgb(self, imgs, bands, **kwargs):
import numpy as np
#for img in imgs:
# print('Image', img.shape, img.dtype, img.min(), img.max())
return np.dstack(imgs) / 255.
def read_pv_wcs(self):
'''extract wcs from fits header directly'''
hdr = fitsio.read_header(self.imgfn, self.hdu)
H,W = self.get_image_shape()
wcs= Tan(hdr['CRVAL1'], hdr['CRVAL2'],hdr['CRPIX1'],hdr['CRPIX2'],\
hdr['CD1_1'],hdr['CD1_2'],hdr['CD2_1'],hdr['CD2_2'],\
float(W),float(H))
return wcs
def halfsize(sourcefn, halffn):
''' Reads and image and WCS from the given source file,
Bins down by a factor of 2, and writes to the given output file.
'''
im,hdr = fitsio.read(sourcefn, header=True)
H,W = im.shape
# make even size; smooth down
if H % 2 == 1:
im = im[:-1,:]
if W % 2 == 1:
im = im[:,:-1]
# bin (excluding NaNs)
q1 = im[::2,::2]
q2 = im[1::2,::2]
q3 = im[1::2,1::2]
q4 = im[::2,1::2]
f1 = np.isfinite(q1)
f2 = np.isfinite(q2)
f3 = np.isfinite(q3)
f4 = np.isfinite(q4)
im = (np.where(f1, q1, 0) +
np.where(f2, q2, 0) +
np.where(f3, q3, 0) +
np.where(f4, q4, 0)) / np.maximum(1, f1+f2+f3+f4)
#im = (im[::2,::2] + im[1::2,::2] + im[1::2,1::2] + im[::2,1::2])/4.
im = im.astype(np.float32)
# shrink WCS too
wcs = Tan(sourcefn, 0)
# include the even size clip; this may be a no-op
H,W = im.shape
wcs = wcs.get_subimage(0, 0, W, H)
subwcs = wcs.scale(0.5)
hdr = fitsio.FITSHDR()
subwcs.add_to_header(hdr)
dirnm = os.path.dirname(halffn)
f,tmpfn = tempfile.mkstemp(suffix='.fits.tmp', dir=dirnm)
os.close(f)
# To avoid overwriting the (empty) temp file (and fitsio
# printing "Removing existing file")
os.unlink(tmpfn)
fitsio.write(tmpfn, im, header=hdr, clobber=True)
os.rename(tmpfn, halffn)
print('Wrote', halffn)
def unwise_tile_wcs(ra, dec, W=2048, H=2048, pixscale=2.75):
'''
Returns a Tan WCS object at the given RA,Dec center, axis aligned, with the
given pixel W,H and pixel scale in arcsec/pixel.
'''
cowcs = Tan(ra, dec, (W + 1) / 2., (H + 1) / 2., -pixscale / 3600., 0., 0.,
pixscale / 3600., W, H)
return cowcs
def sdss_image(req, calid=None, size='full'):
cal = get_object_or_404(Calibration, pk=calid)
key = 'sdss_size%s_cal%i' % (size, cal.id)
df = CachedFile.get(key)
if df is None:
wcsfn = cal.get_wcs_file()
#plotfn = get_temp_file()
from astrometry.util.util import Tan
wcs = Tan(wcsfn)
if size == 'display':
image = cal.job.user_image
scale = float(
image.image.get_display_image().width) / image.image.width
wcs = wcs.scale(scale)
else:
scale = 1.0
url = 'http://legacysurvey.org/viewer-dev/sdss-wcs/?crval1=%.6f&crval2=%.6f&crpix1=%.2f&crpix2=%.2f&cd11=%.6g&cd12=%.6g&cd21=%.6g&cd22=%.6g&imagew=%i&imageh=%i' % (
wcs.crval[0], wcs.crval[1], wcs.crpix[0], wcs.crpix[1], wcs.cd[0],
wcs.cd[1], wcs.cd[2], wcs.cd[3], int(wcs.imagew), int(wcs.imageh))
print('Retrieving:', url)
import urllib
plotfn, headers = urllib2.urlretrieve(url)
# jwcs = json.dumps(dict(
# crval1=wcs.crval[0], crval2=wcs.crval[1],
# crpix1=wcs.crpix[0], crpix2=wcs.crpix[1],
# cd11=wcs.cd[0], cd12=wcs.cd[1], cd21=wcs.cd[2], cd22=wcs.cd[3],
# imagew=wcs.imagew, imageh=wcs.imageh))
# plot_sdss_image(wcsfn, plotfn, scale)
# cache
logmsg('Caching key "%s"' % key)
df = CachedFile.add(key, plotfn)
else:
logmsg('Cache hit for key "%s"' % key)
f = open(df.get_path())
res = HttpResponse(f)
res['Content-Type'] = 'image/jpeg'
return res
def forwards(self, orm):
"Write your forwards methods here."
for calib in orm.Calibration.objects.all():
wcsfn = os.path.join(JOBDIR, '%08i' % calib.job.id)
wcsfn = os.path.join(wcsfn, 'wcs.fits')
wcs = Tan(str(wcsfn), 0)
ra,dec = wcs.radec_center()
radius = (wcs.pixel_scale() *
math.hypot(wcs.imagew, wcs.imageh)/2. / 3600.)
# Find cartesian coordinates
ra *= math.pi/180
dec *= math.pi/180
tempr = math.cos(dec)
calib.x = tempr*math.cos(ra)
calib.y = tempr*math.sin(ra)
calib.z = math.sin(dec)
calib.r = radius/180*math.pi
calib.save()
def example():
pixscale = 0.25 # [arcsec/pix]
radius = 45.0 # [arcsec]
ra, dec = 120.0, 15.0 # [degrees]
diam = np.ceil(radius / pixscale).astype('int16') # [pixels]
wcs = Tan(ra, dec, diam / 2 + 0.5, diam / 2 + 0.5, -pixscale / 3600.0, 0.0,
0.0, pixscale / 3600.0, float(diam), float(diam))
return wcs
def get_wcs(self, hdr):
# Older images have ZPX, newer TPV.
if hdr['CTYPE1'] == 'RA---TPV':
from astrometry.util.util import wcs_pv2sip_hdr
wcs = wcs_pv2sip_hdr(hdr)
else:
from astrometry.util.util import Tan
hdr['CTYPE1'] = 'RA---TAN'
hdr['CTYPE2'] = 'DEC--TAN'
wcs = Tan(hdr)
return wcs
def dither_sequence(start, ending):
dither = fits_table()
dither.expnum = []
dither.gfa_ra = []
dither.gfa_dec = []
dither.gfa05_ra = []
dither.gfa05_dec = []
dither.gfa27_ra = []
dither.gfa27_dec = []
dither.gfa38_ra = []
dither.gfa38_dec = []
dither.gfa_all_ra = []
dither.gfa_all_dec = []
dither.sky_ra = []
dither.sky_dec = []
headers = []
for expnum in range(start, ending):
fns = glob('/global/cscratch1/sd/dstn/gfa-wcs/gfa-%i-GUIDE?.wcs' %
expnum)
if len(fns) != 6:
#print('Do not have', expnum)
continue
#print('Got', expnum)
fns.sort()
rr, dd = [], []
for fn in fns:
print('Reading', fn)
wcs = Tan(fn)
rr.append(wcs.crval[0])
dd.append(wcs.crval[1])
fn = gfa_filename(expnum)
hdr = fitsio.read_header(fn, ext=1)
dither.expnum.append(expnum)
headers.append(hdr)
r, d = average_radec(rr, dd)
dither.gfa_ra.append(r)
dither.gfa_dec.append(d)
r, d = average_radec([rr[0], rr[3]], [dd[0], dd[3]])
dither.gfa05_ra.append(r)
dither.gfa05_dec.append(d)
r, d = average_radec([rr[1], rr[4]], [dd[1], dd[4]])
dither.gfa27_ra.append(r)
dither.gfa27_dec.append(d)
r, d = average_radec([rr[2], rr[5]], [dd[2], dd[5]])
dither.gfa38_ra.append(r)
dither.gfa38_dec.append(d)
dither.gfa_all_ra.append(rr)
dither.gfa_all_dec.append(dd)
dither.sky_ra.append(hdr['SKYRA'])
dither.sky_dec.append(hdr['SKYDEC'])
dither.to_np_arrays()
dither.headers = headers
return dither
def __init__(self, name, **kwargs):
import fitsio
from astrometry.util.util import Tan
#from astrometry.util.util import anwcs_open_wcslib
super(PhatLayer, self).__init__(name, **kwargs)
self.nativescale = 17
self.pixscale = 0.05
fn = os.path.join(settings.DATA_DIR, 'm31_full.fits')
self.fits = fitsio.FITS(fn)[0]
#self.wcs = anwcs_open_wcslib(fn, 0)
self.wcs = Tan(fn, 0)
def wcs_for_brick(b, W=3600, H=3600, pixscale=0.262):
'''
b: row from decals-bricks.fits file
W,H: size in pixels
pixscale: pixel scale in arcsec/pixel.
Returns: Tan wcs object
'''
pixscale = pixscale / 3600.
return Tan(b.ra, b.dec, W/2.+0.5, H/2.+0.5,
-pixscale, 0., 0., pixscale,
float(W), float(H))
def read_astrans(fn, hdu, hdr=None, W=None, H=None, fitsfile=None):
from astrometry.sdss import AsTransWrapper, AsTrans
from astrometry.util.util import Tan, fit_sip_wcs_py
from astrometry.util.starutil_numpy import radectoxyz
import numpy as np
astrans = AsTrans.read(fn, F=fitsfile, primhdr=hdr)
# Approximate as SIP.
if hdr is None and fn.endswith('.bz2'):
import fitsio
hdr = fitsio.read_header(fn, 0)
if hdr is not None:
tan = Tan(*[
float(hdr[k]) for k in [
'CRVAL1', 'CRVAL2', 'CRPIX1', 'CRPIX2', 'CD1_1', 'CD1_2',
'CD2_1', 'CD2_2', 'NAXIS1', 'NAXIS2'
]
])
else:
# Frame files include a TAN header... start there.
tan = Tan(fn)
# Evaluate AsTrans on a pixel grid...
h, w = tan.shape
xx = np.linspace(1, w, 20)
yy = np.linspace(1, h, 20)
xx, yy = np.meshgrid(xx, yy)
xx = xx.ravel()
yy = yy.ravel()
rr, dd = astrans.pixel_to_radec(xx, yy)
xyz = radectoxyz(rr, dd)
fieldxy = np.vstack((xx, yy)).T
sip_order = 5
inv_order = 7
sip = fit_sip_wcs_py(xyz, fieldxy, None, tan, sip_order, inv_order)
return sip
def get_subtile_wcs(name, x, y):
"""
pixscale: arcsec/pixel
"""
nsub = N_subtiles
pixscale = decam_pixscale
wcs = unwise_wcs_from_name(name)
W, H = wcs.get_width(), wcs.get_height()
# Tweak to DECam pixel scale and number of pixels.
D = int(np.ceil((W * wcs.pixel_scale() / pixscale) / nsub)) * nsub
DW, DH = D, D
wcs.set_crpix(DW / 2 + 1.5, DH / 2 + 1.5)
pixscale = pixscale / 3600.0
wcs.set_cd(-pixscale, 0.0, 0.0, pixscale)
wcs.set_imagesize(DW, DH)
W, H = wcs.get_width(), wcs.get_height()
subw, subh = W / nsub, H / nsub
subwcs = Tan(wcs)
subwcs.set_crpix(wcs.crpix[0] - x * subw, wcs.crpix[1] - y * subh)
subwcs.set_imagesize(subw, subh)
return subwcs
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 get_wcs(hdr, tpv_to_sip=True):
# Thanks Dstn :)
# https://github.com/legacysurvey/legacypipe/blob/master/py/legacypipe/cpimage.py#102
width = hdr['NAXIS1']
height = hdr['NAXIS2']
if not (tpv_to_sip):
# extract wcs from fits header directly
wcs= Tan(hdr['CRVAL1'], hdr['CRVAL2'],hdr['CRPIX1'],hdr['CRPIX2'],\
hdr['CD1_1'],hdr['CD1_2'],hdr['CD2_1'],hdr['CD2_2'],\
float(width),float(height))
else:
# Make sure the PV-to-SIP converter samples enough points for small
# images
stepsize = 0
if min(width, height) < 600:
stepsize = min(width, height) / 10.
wcs = wcs_pv2sip_hdr(hdr, stepsize=stepsize)
# Dstn adds an offset correction i.e.
# Correction: ccd,ccdraoff, decoff from zeropoints file
# Should I do this?
return wcs
def gettractor():
W, H = 200, 200
image = np.zeros((H, W))
invvar = np.zeros_like(image) + 1.
ra, dec = 0, 0
# arcsec/pix
pixscale = 1.
wcs1 = Tan(ra, dec, W / 2, H / 2, -pixscale / 3600., 0, 0,
-pixscale / 3600., W, H)
wcs = FitsWcs(wcs1)
photocal = NullPhotoCal()
psf = NCircularGaussianPSF([1.], [1.])
sky = ConstantSky(0.)
img = Image(data=image,
invvar=invvar,
psf=psf,
wcs=wcs,
sky=sky,
photocal=photocal)
tractor = SDSSTractor([img])
return tractor, wcs
def dojob(job,
userimage,
log=None,
solve_command=None,
solve_locally=None,
tempfiles=None):
print('dojob: tempdir:', tempfile.gettempdir())
jobdir = job.make_dir()
#print('Created job dir', jobdir)
#log = create_job_logger(job)
#jobdir = job.get_dir()
if log is None:
log = create_job_logger(job)
log.msg('Starting Job processing for', job)
job.set_start_time()
job.save()
#os.chdir(dirnm) - not thread safe (working directory is global)!
log.msg('Creating directory', jobdir)
axyfn = 'job.axy'
axypath = os.path.join(jobdir, axyfn)
sub = userimage.submission
log.msg('submission id', sub.id)
df = userimage.image.disk_file
img = userimage.image
# Build command-line arguments for the augment-xylist program, which
# detects sources in the image and adds processing arguments to the header
# to produce a "job.axy" file.
slo, shi = sub.get_scale_bounds()
# Note, this must match Job.get_wcs_file().
wcsfile = 'wcs.fits'
corrfile = 'corr.fits'
axyflags = []
axyargs = {
'--out': axypath,
'--scale-low': slo,
'--scale-high': shi,
'--scale-units': sub.scale_units,
'--wcs': wcsfile,
'--corr': corrfile,
'--rdls': 'rdls.fits',
'--pixel-error': sub.positional_error,
'--ra': sub.center_ra,
'--dec': sub.center_dec,
'--radius': sub.radius,
'--downsample': sub.downsample_factor,
# tuning-up maybe fixed; if not, turn it off with:
#'--odds-to-tune': 1e9,
# Other things we might want include...
# --invert
# -g / --guess-scale: try to guess the image scale from the FITS headers
# --crpix-x <pix>: set the WCS reference point to the given position
# --crpix-y <pix>: set the WCS reference point to the given position
# -w / --width <pixels>: specify the field width
# -e / --height <pixels>: specify the field height
# -X / --x-column <column-name>: the FITS column name
# -Y / --y-column <column-name>
}
if hasattr(img, 'sourcelist'):
# image is a source list; use --xylist
axyargs['--xylist'] = img.sourcelist.get_fits_path(tempfiles=tempfiles)
w, h = img.width, img.height
if sub.image_width:
w = sub.image_width
if sub.image_height:
h = sub.image_height
axyargs['--width'] = w
axyargs['--height'] = h
else:
axyargs['--image'] = df.get_path()
# UGLY
if sub.parity == 0:
axyargs['--parity'] = 'pos'
elif sub.parity == 1:
axyargs['--parity'] = 'neg'
if sub.tweak_order == 0:
axyflags.append('--no-tweak')
else:
axyargs['--tweak-order'] = '%i' % sub.tweak_order
if sub.use_sextractor:
axyflags.append('--use-source-extractor')
if sub.crpix_center:
axyflags.append('--crpix-center')
if sub.invert:
axyflags.append('--invert')
cmd = 'augment-xylist '
for (k, v) in list(axyargs.items()):
if v:
cmd += k + ' ' + str(v) + ' '
for k in axyflags:
cmd += k + ' '
log.msg('running: ' + cmd)
(rtn, out, err) = run_command(cmd)
if rtn:
log.msg('out: ' + out)
log.msg('err: ' + err)
logmsg('augment-xylist failed: rtn val', rtn, 'err', err)
raise Exception
log.msg('created axy file', axypath)
# shell into compute server...
logfn = job.get_log_file()
# the "tar" commands both use "-C" to chdir, and the ssh command
# and redirect uses absolute paths.
if solve_locally is not None:
cmd = (('cd %(jobdir)s && %(solvecmd)s %(jobid)s %(axyfile)s >> ' +
'%(logfile)s') % dict(jobid='job-%s-%i' %
(settings.sitename, job.id),
solvecmd=solve_locally,
axyfile=axyfn,
jobdir=jobdir,
logfile=logfn))
log.msg('command:', cmd)
w = os.system(cmd)
if not os.WIFEXITED(w):
log.msg('Solver failed (sent signal?)')
logmsg('Call to solver failed for job', job.id)
raise Exception
rtn = os.WEXITSTATUS(w)
if rtn:
log.msg('Solver failed with return value %i' % rtn)
logmsg('Call to solver failed for job', job.id, 'with return val',
rtn)
raise Exception
log.msg('Solver completed successfully.')
else:
if solve_command is None:
solve_command = 'ssh -x -T %(sshconfig)s'
cmd = ((
'(echo %(jobid)s; '
'tar cf - --ignore-failed-read -C %(jobdir)s %(axyfile)s) | ' +
solve_command + ' 2>>%(logfile)s | '
'tar xf - --atime-preserve -m --exclude=%(axyfile)s -C %(jobdir)s '
'>>%(logfile)s 2>&1') % dict(jobid='job-%s-%i' %
(settings.sitename, job.id),
axyfile=axyfn,
jobdir=jobdir,
sshconfig=settings.ssh_solver_config,
logfile=logfn))
log.msg('command:', cmd)
w = os.system(cmd)
if not os.WIFEXITED(w):
log.msg('Solver failed (sent signal?)')
logmsg('Call to solver failed for job', job.id)
raise Exception
rtn = os.WEXITSTATUS(w)
if rtn:
log.msg('Solver failed with return value %i' % rtn)
logmsg('Call to solver failed for job', job.id, 'with return val',
rtn)
raise Exception
log.msg('Solver completed successfully.')
# Solved?
wcsfn = os.path.join(jobdir, wcsfile)
log.msg('Checking for WCS file', wcsfn)
if os.path.exists(wcsfn):
log.msg('WCS file exists')
# Parse the wcs.fits file
wcs = Tan(wcsfn, 0)
# Convert to database model...
tan = TanWCS(crval1=wcs.crval[0],
crval2=wcs.crval[1],
crpix1=wcs.crpix[0],
crpix2=wcs.crpix[1],
cd11=wcs.cd[0],
cd12=wcs.cd[1],
cd21=wcs.cd[2],
cd22=wcs.cd[3],
imagew=img.width,
imageh=img.height)
tan.save()
log.msg('Created TanWCS:', tan)
# Find field's healpix nside and index
ra, dec, radius = tan.get_center_radecradius()
nside = anutil.healpix_nside_for_side_length_arcmin(radius * 60)
nside = int(2**round(math.log(nside, 2)))
nside = max(1, nside)
healpix = anutil.radecdegtohealpix(ra, dec, nside)
try:
sky_location, created = SkyLocation.objects.get_or_create(
nside=nside, healpix=healpix)
except MultipleObjectsReturned:
log.msg('Multiple SkyLocations for nside %i, healpix %i' %
(nside, healpix))
# arbitrarily take the first one.
sky_location = SkyLocation.objects.filter(nside=nside,
healpix=healpix)[0]
log.msg('SkyLocation:', sky_location)
# Find bounds for the Calibration object.
r0, r1, d0, d1 = wcs.radec_bounds()
# Find cartesian coordinates
ra *= math.pi / 180
dec *= math.pi / 180
tempr = math.cos(dec)
x = tempr * math.cos(ra)
y = tempr * math.sin(ra)
z = math.sin(dec)
r = radius / 180 * math.pi
calib = Calibration(raw_tan=tan,
ramin=r0,
ramax=r1,
decmin=d0,
decmax=d1,
x=x,
y=y,
z=z,
r=r,
sky_location=sky_location)
calib.save()
log.msg('Created Calibration', calib)
job.calibration = calib
job.save() # save calib before adding machine tags
job.status = 'S'
job.user_image.add_machine_tags(job)
job.user_image.add_sky_objects(job)
else:
job.status = 'F'
job.set_end_time()
job.save()
log.msg('Finished job', job.id)
logmsg('Finished job', job.id)
return job.id
from glob import glob
import os
from astrometry.util.util import Tan
wcsfn = "/data2/nova/BACKUP-jobs/00000046/wcs.fits"
axyfn = wcsfn.replace('wcs.fits', 'job.axy')
dirname = os.path.basename(os.path.dirname(wcsfn))
wcs = Tan(wcsfn)
r1,d1 = wcs.radec_center()
R1 = wcs.radius()
print "RA is ", r1
print "Dec is ", d1
print "radius is ", R1
inds = glob("/data1/INDEXES/4000/index-4001*")
for j, ind in enumerate(inds):
cmdline = 'query-starkd -v -o rdls4001-{0}.fits -r {1} -d {2} -R {3} {4}'.format(j, r1, d1, R1, ind)
os.system(cmdline)
def _computeTransformation(self, img, ylo=0, yhi=-1):
imwcs = img.getWcs()
# Pre-compute the "grid-spread function" transformation matrix...
H,W = self.shape
if yhi == -1:
yhi = H
Ngrid = W*H
iH,iW = img.shape
Nim = iW*iH
Lorder = 2
S = (Lorder * 2 + 3)
if False:
i0 = S/2
else:
print 'Image', img.name
print 'Image PSF', img.getPsf()
psfw = img.getPsf().getRadius()
scale = img.getWcs().pixel_scale() / self.wcs.pixel_scale()
print 'Image pixel scale', img.getWcs().pixel_scale()
print 'Model pixel scale', self.wcs.pixel_scale()
print 'PSF extent', psfw, 'pixels'
print 'pixel scale factor', scale
print '->', psfw * scale, 'model pixels'
print 'Increasing S from', S
S += int(np.ceil(psfw*scale)) * 2
print 'to', S
i0 = S/2
cmock = np.zeros((S,S), np.float32)
cmock[i0,i0] = 1.
if True:
spsf = img.getPsf().scale(scale)
print 'Scaled PSF', spsf
cmock = spsf.applyTo(cmock)
#print 'cmock'
#print cmock
cwcs = Tan(self.wcs)
cwcs.set_imagesize(S, S)
cx0,cy0 = self.wcs.crpix[0], self.wcs.crpix[1]
rim = np.zeros((iH,iW), np.float32)
X = {}
for i in range(ylo, yhi):
print 'Precomputing matrix for image', img.name, 'model row', i
for j in range(W):
#print 'Precomputing matrix for grid pixel', j,i
cwcs.set_crpix(cx0 - j + i0, cy0 - i + i0)
rim[:,:] = 0
##
weighted = 1
res = tan_wcs_resample(cwcs, imwcs.wcs, cmock, rim, weighted, Lorder)
assert(res == 0)
if False:
outimg = img.getPsf().applyTo(rim).ravel()
else:
outimg = rim.ravel()
if sum(outimg) == 0:
continue
I = np.flatnonzero((outimg > 0) * (img.getInvError().ravel() > 0))
if len(I) == 0:
continue
#if True:
# sumr.ravel()[I] += outimg[I]
xx,yy = (I % iW), (I / iW)
x0,y0 = xx.min(), yy.min()
nzh,nzw = 1 + yy.max() - y0, 1 + xx.max() - x0
NZ = ((x0, y0), (nzh, nzw))
NZI = (xx - x0) + (yy - y0) * nzw
X[i*W+j] = (I, outimg[I], NZ, NZI)
# if True:
# print 'sumr range', sumr.min(), sumr.max()
# sumr[sumr == 0] = 1.
# mn,mx = 0.,0.
# for (I, outim, NZ, NZI) in X.values():
# outim /= sumr.ravel()[I]
# mx = max(outim.max(), mx)
# mn = max(outim.min(), mn)
# print 'Min,Max grid-spread function:', mn,mx
return X
hsc.dec = []
for band in 'grz':
pat = '/global/project/projectdirs/cosmo/work/hsc/dr1/wide/deepCoadd/HSC-%s/*/*/calexp-*.fits' % (band.upper())
#pat = 'HSC-%s/*/*/calexp-*.fits' % (band.upper())
fns = glob(pat)
print('Band', band, ':', len(fns), 'files')
pat2 = '/global/project/projectdirs/cosmo/work/hsc/dr1/deep/deepCoadd/HSC-%s/*/*/calexp-*.fits' % (band.upper())
fns2 = glob(pat2)
print('Band', band, ':', len(fns2), 'files from deep')
fns = fns + fns2
for fn in fns:
hdr = fitsio.read_header(fn, ext=1)
wcs = Tan(hdr)
r,d = wcs.radec_center()
hsc.ra.append(r)
hsc.dec.append(d)
for c in wcsvals:
hsc.get(c).append(hdr.get(c.upper()))
hsc.width.append(hdr['NAXIS1'])
hsc.height.append(hdr['NAXIS2'])
hsc.band.append(band)
parts = fn.split('/')
path = '/'.join(parts[-7:])
print(path)
hsc.filename.append(path)
hsc.to_np_arrays()
def plotarea(ra, dec, radius, name, prefix, tims=None, rds=[]):
from astrometry.util.util import Tan
W,H = 512,512
scale = (radius * 60. * 4) / float(W)
print 'SDSS jpeg scale', scale
imgfn = 'sdss-mosaic-%s.png' % prefix
if not os.path.exists(imgfn):
url = (('http://skyservice.pha.jhu.edu/DR9/ImgCutout/getjpeg.aspx?' +
'ra=%f&dec=%f&scale=%f&width=%i&height=%i') %
(ra, dec, scale, W, H))
f = urllib2.urlopen(url)
of,tmpfn = tempfile.mkstemp(suffix='.jpg')
os.close(of)
of = open(tmpfn, 'wb')
of.write(f.read())
of.close()
cmd = 'jpegtopnm %s | pnmtopng > %s' % (tmpfn, imgfn)
os.system(cmd)
# Create WCS header for it
cd = scale / 3600.
args = (ra, dec, W/2. + 0.5, H/2. + 0.5, -cd, 0., 0., -cd, W, H)
wcs = Tan(*[float(x) for x in args])
plt.clf()
I = plt.imread(imgfn)
plt.imshow(I, interpolation='nearest', origin='lower')
x,y = wcs.radec2pixelxy(ra, dec)
R = radius * 60. / scale
ax = plt.axis()
plt.gca().add_artist(matplotlib.patches.Circle(xy=(x,y), radius=R, color='g',
lw=3, alpha=0.5, fc='none'))
if tims is not None:
print 'Plotting outlines of', len(tims), 'images'
for tim in tims:
H,W = tim.shape
twcs = tim.getWcs()
px,py = [],[]
for x,y in [(1,1),(W,1),(W,H),(1,H),(1,1)]:
rd = twcs.pixelToPosition(x,y)
xx,yy = wcs.radec2pixelxy(rd.ra, rd.dec)
print 'x,y', x, y
x1,y1 = twcs.positionToPixel(rd)
print ' x1,y1', x1,y1
print ' r,d', rd.ra, rd.dec,
print ' xx,yy', xx, yy
px.append(xx)
py.append(yy)
plt.plot(px, py, 'g-', lw=3, alpha=0.5)
# plot full-frame image outline too
# px,py = [],[]
# W,H = 2048,1489
# for x,y in [(1,1),(W,1),(W,H),(1,H),(1,1)]:
# r,d = twcs.pixelToRaDec(x,y)
# xx,yy = wcs.radec2pixelxy(r,d)
# px.append(xx)
# py.append(yy)
# plt.plot(px, py, 'g-', lw=1, alpha=1.)
if rds is not None:
px,py = [],[]
for ra,dec in rds:
print 'ra,dec', ra,dec
xx,yy = wcs.radec2pixelxy(ra, dec)
px.append(xx)
py.append(yy)
plt.plot(px, py, 'go')
plt.axis(ax)
fn = '%s.png' % prefix
plt.savefig(fn)
print 'saved', fn
def plot(self, img = None, off = [0., 0.], extra = [], extra_lines = [], grid = True, scale = 1):
field_w = self.wcs.get_width()
field_h = self.wcs.get_height()
try:
scale = float(scale)
except ValueError:
if scale.startswith('deg'):
scale = float(scale[3:]) / (self.wcs.pixel_scale() * field_w / 3600.0)
else:
raise
# the field moved by given offset pixels from the position in self.wcs
(crpix1, crpix2) = self.wcs.crpix
ra2, dec2 = self.wcs.pixelxy2radec(crpix1 - off[1], crpix2 - off[0])
# plot extra area with the original image in the center
xborder = field_w * (scale - 1) / 2.0
yborder = field_h * (scale - 1) / 2.0
nw = field_w * scale
nh = field_h * scale
new_wcs = Tan(self.wcs)
new_wcs.set_crval(ra2, dec2)
new_wcs.set_width(nw)
new_wcs.set_height(nh)
new_wcs.set_crpix(crpix1 + xborder, crpix2 + yborder)
#thumbnail size and pos
tw = int(field_w / scale)
th = int(field_h / scale)
tx = int(xborder / scale)
ty = int(yborder / scale)
plot = Plotstuff(outformat=PLOTSTUFF_FORMAT_PPM)
plot.wcs = anwcs_new_tan(new_wcs)
plot.scale_wcs(1.0 / scale)
plot.set_size_from_wcs()
plot.color = 'black'
plot.alpha = 1.0
plot.plot('fill')
plot.color = 'gray'
plot_area_deg = new_wcs.pixel_scale() * scale * field_w / 3600.0
#print "plot area", plot_area_deg
if grid:
log_step = [1, 2, 5];
log_ra = int(np.floor(np.log10(plot_area_deg / (0.01 + math.cos(math.radians(dec2)))) * 3)) - 1
log_dec = int(np.floor(np.log10(plot_area_deg) * 3)) - 1
grid_step_ra = 10 ** int(log_ra / 3) * log_step[log_ra % 3]
grid_step_dec = 10 ** int(log_dec / 3) * log_step[log_dec % 3]
grid_step_ra = min(10, grid_step_ra)
grid_step_dec = min(10, grid_step_dec)
#print "grid", plot_area_deg, log_ra, log_dec, grid_step_ra, grid_step_dec
plot.plot_grid(grid_step_ra, grid_step_dec, grid_step_ra, grid_step_dec)
ann = plot.annotations
ann.NGC = (plot_area_deg < 60)
ann.constellations = (plot_area_deg > 10)
ann.constellation_labels = (plot_area_deg > 10)
ann.constellation_labels_long = False
ann.bright = (plot_area_deg < 60)
ann.ngc_fraction = 0.05 / scale
ann.HD = False
#ann.HD = (plot_area_deg < 9)
#ann.HD_labels = (plot_area_deg < 3)
#ann.hd_catalog = "hd.fits"
if plot_area_deg < 9:
tra,tdec,I2 = match_kdtree_catalog(ra2, dec2, plot_area_deg, "tycho2.kd")
T = fits_table("tycho2.kd", hdu=6)
for r,d,t1,t2,t3 in zip(tra,tdec, T.tyc1[I2], T.tyc2[I2], T.tyc3[I2]):
if not new_wcs.is_inside(r, d):
continue
if plot_area_deg < 2:
ann.add_target(r, d, 'tyc %i-%i-%i' % (t1,t2,t3))
else:
ann.add_target(r, d, '')
for (r, d, name) in extra:
ann.add_target(r, d, name)
for (r1, d1, r2, d2) in extra_lines:
plot.move_to_radec(r1, d1)
plot.line_to_radec(r2, d2)
plot.stroke()
plot.color = 'green'
plot.lw = 2
plot.valign = 'B'
plot.halign = 'C'
plot.label_offset_x = 0;
plot.label_offset_y = -20;
plot.plot('annotations')
# frame around the image
if scale > 1:
plot.color = 'blue'
plot.polygon([(tx, ty), (tx + tw, ty), (tx + tw, ty + th), (tx, ty + th)])
plot.close_path()
plot.stroke()
plot_image = plot.get_image_as_numpy()
del plot
if img is not None and scale <= 8:
bg = np.zeros_like(plot_image)
if scale == 1:
thumb = img
else:
thumb = cv2.resize(img, (tw, th))
if thumb.ndim > 2:
bg[ty:ty+th, tx:tx+tw, 0:3] = thumb[:, :, 0:3]
else:
bg[ty:ty+th, tx:tx+tw, 0] = bg[ty:ty+th, tx:tx+tw, 1] = bg[ty:ty+th, tx:tx+tw, 2] = thumb
plot_image = cv2.add(plot_image, bg)
return plot_image
def ptf_exposure_metadata(filenames, hdus=None, trim=None):
nan = np.nan
primkeys = [('FILTER',''),
('RA', nan),
('DEC', nan),
('AIRMASS', nan),
('DATE-OBS', ''),
('EXPTIME', nan),
('EXPNUM', 0),
('MJD-OBS', 0),
('PROPID', ''),
]
hdrkeys = [('AVSKY', nan),
('ARAWGAIN', nan),
('FWHM', nan),
('CRPIX1',nan),
('CRPIX2',nan),
('CRVAL1',nan),
('CRVAL2',nan),
('CD1_1',nan),
('CD1_2',nan),
('CD2_1',nan),
('CD2_2',nan),
('EXTNAME',''),
('CCDNAME',''),
('CCDNUM',''),
('CAMERA',''),
]
otherkeys = [('IMAGE_FILENAME',''), ('IMAGE_HDU',0),
('HEIGHT',0),('WIDTH',0),
]
allkeys = primkeys + hdrkeys + otherkeys
#for each hdu and file, append ptf header info to vals
vals = dict([(k,[]) for k,d in allkeys])
for i,fn in enumerate(filenames):
print('Reading', (i+1), 'of', len(filenames), ':', fn)
F = fitsio.FITS(fn)
cpfn = fn
if trim is not None:
cpfn = cpfn.replace(trim, '')
if hdus is not None:
hdulist = hdus
else:
hdulist = range(0, len(F))
#loop through hdus
for hdu in hdulist:
for k,d in allkeys: #d is not used below
if k in F[hdu].read_header().keys():
vals[k].append(F[hdu].read_header()[k])
else:
continue #will be set below
#special handling
H,W = F[hdu].get_info()['dims']
vals['HEIGHT'].append(H)
vals['WIDTH'].append(W)
vals['IMAGE_HDU'].append(hdu)
vals['AVSKY'].append(0.) #estimate of sky level, ok to set to 0 in legacypipe
vals['EXTNAME'].append(os.path.basename(fn)[-8:-5]) #CCD id, ex) N16 for DECam
vals['CCDNAME'][-1]= os.path.basename(fn)[-8:-5]
vals['FILTER'][-1]= vals['FILTER'][-1].strip().lower() #unique ptf band name
vals['EXPNUM'].append(0) #default value givein in function "exposure_metadata"
vals['CAMERA'].append('ptf') #default value givein in function "exposure_metadata"
vals['IMAGE_FILENAME'].append(os.path.basename(fn))
#diff naming convenctions between (DECaLS,PTF)
map=[
('RA', 'OBJRA'),
('DEC', 'OBJDEC'),
('MJD-OBS', 'OBSMJD'),
('PROPID', 'PTFPID'),
('ARAWGAIN', 'GAIN'),
('FWHM', 'MEDFWHM'),
('CCDNUM','CCDID'),
]
for decal,ptf in map:
try: vals[decal].append(F[hdu].read_header()[ptf])
except AttributeError: print "WARNING, could not find ",decal,"or",ptf
#for k,d in allkeys: print "FINAL vals: k= ",k,"vals[k]= ",vals[k]
#header info now stord in val dict
T = fits_table()
for k,d in allkeys:
T.set(k.lower().replace('-','_'), np.array(vals[k]))
#finish naming conventions
T.filter = np.array([s.split()[0] for s in T.filter])
#KJB LEFT OFF HERE
T.ra_bore = np.array([hmsstring2ra (s) for s in T.ra ])
T.dec_bore = np.array([dmsstring2dec(s) for s in T.dec])
T.ra = np.zeros(len(T))
T.dec = np.zeros(len(T))
for i in range(len(T)):
W,H = T.width[i], T.height[i]
wcs = Tan(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], float(W), float(H))
xc,yc = W/2.+0.5, H/2.+0.5
rc,dc = wcs.pixelxy2radec(xc,yc)
T.ra [i] = rc
T.dec[i] = dc
#anything with type int64 crashes fits.write()
T.expnum= T.expnum.astype(np.int16)
T.image_hdu= T.image_hdu.astype(np.int16)
T.height= T.height.astype(np.int16)
T.width= T.width.astype(np.int16)
#for c in T.columns():
#if type(T.get(c)[0]) is np.int64: T.c= T.c.astype(np.int16) #answer='yes'
#else:answer='no'
#print('column= ',c,"type= ",type(T.get(c)),"type is int64?",answer)
#sanity check
for c in T.columns(): print (c,T.get(c))
return T
def cutout_jpg(req):
import tempfile
import fitsio
from astrometry.util.util import Tan
from astrometry.util.resample import resample_with_wcs
form = CutoutSearchForm(req.GET)
if not form.is_valid():
return HttpResponse('failed to parse request')
ra = form.cleaned_data['ra']
dec = form.cleaned_data['dec']
if ra is None or dec is None:
return HttpResponse('RA and Dec arguments are required')
size = form.cleaned_data['size']
if size is None:
size = 100
else:
size = min(256, size)
# Ignoring this for now...
# bandstr = form.cleaned_data['bands']
# bands = [int(c) for c in bandstr]
# bands = [b for b in bands if b in [1,2,3,4]]
version = form.cleaned_data['version']
radius = size/2. * 2.75/3600.
tiles = unwise_tiles_near_radec(ra, dec, radius)
tiles = list(tiles)
pixscale = 2.75 / 3600.
cowcs = Tan(*[float(x) for x in
[ra, dec, 0.5 + size/2., 0.5 + size/2.,
-pixscale, 0., 0., pixscale,
size, size]])
bands = [1,2]
coims = [np.zeros((size,size), np.float32) for b in bands]
con = np.zeros((size,size), int)
for tile in tiles:
coadd = tile.coadd
dirnm = os.path.join(settings.DATA_DIR, version, coadd[:3], coadd)
base = os.path.join(dirnm, 'unwise-%s' % coadd)
subwcs = None
ims = []
for band in bands:
fn = str(base + '-w%i-img-m.fits' % band)
if subwcs is None:
wcs = Tan(fn)
ok,x,y = wcs.radec2pixelxy(ra, dec)
x = int(round(x-1.))
y = int(round(y-1.))
x0 = x - size/2
x1 = x0 + size
y0 = y - size/2
y1 = y0 + size
if x1 <= 0 or y1 <= 0:
break
if x0 >= wcs.get_width() or y0 >= wcs.get_height():
break
x0 = max(x0, 0)
y0 = max(y0, 0)
x1 = min(x1, wcs.get_width())
y1 = min(y1, wcs.get_height())
subwcs = wcs.get_subimage(x0, y0, x1-x0, y1-y0)
if subwcs is None:
break
img = fitsio.FITS(fn)[0][y0:y1, x0:x1]
ims.append(img)
if subwcs is None:
continue
try:
Yo,Xo,Yi,Xi,rims = resample_with_wcs(cowcs, subwcs, ims, 3)
except:
continue
for rim,co in zip(rims, coims):
co[Yo,Xo] += rim
con[Yo,Xo] += 1
for co in coims:
co /= np.maximum(con, 1)
rgb = np.zeros((size,size,3), np.uint8)
lo,hi = -3,10
scale = 10.
rgb[:,:,2] = np.clip(255. * ((coims[1] / scale) - lo) / (hi-lo), 0, 255)
rgb[:,:,1] = np.clip(255. * (((coims[0]+coims[1])/2. / scale) - lo) / (hi-lo), 0, 255)
rgb[:,:,0] = np.clip(255. * ((coims[0] / scale) - lo) / (hi-lo), 0, 255)
f,tempfn = tempfile.mkstemp(suffix='.jpg')
os.close(f)
# import matplotlib
# matplotlib.use('Agg')
# import pylab as plt
# plt.imsave(tempfn, rgb, interpolation='nearest', origin='lower')
from PIL import Image
pic = Image.fromarray(rgb)
pic.save(tempfn)
return send_file(tempfn, 'image/jpeg', unlink=True)
def get_unwise_tractor_image(basedir, tile, band, bandname=None, masked=True,
**kwargs):
'''
masked: read "-m" images, or "-u"?
bandname: PhotoCal band name to use: default: "w%i" % band
'''
if bandname is None:
bandname = 'w%i' % band
mu = 'm' if masked else 'u'
# Allow multiple colon-separated unwise-coadd directories.
basedirs = basedir.split(':')
foundFiles = False
for basedir in basedirs:
thisdir = get_unwise_tile_dir(basedir, tile)
base = os.path.join(thisdir, 'unwise-%s-w%i-' % (tile, band))
imfn = base + 'img-%s.fits' % mu
ivfn = base + 'invvar-%s.fits.gz' % mu
# ppfn = base + 'std-%s.fits.gz' % mu
nifn = base + 'n-%s.fits.gz' % mu
nufn = base + 'n-u.fits.gz'
if not os.path.exists(imfn):
print('Does not exist:', imfn)
continue
print('Reading', imfn)
wcs = Tan(imfn)
twcs = ConstantFitsWcs(wcs)
F = fitsio.FITS(imfn)
img = F[0]
hdr = img.read_header()
H,W = img.get_info()['dims']
H,W = int(H), int(W)
roi = interpret_roi(twcs, (H,W), **kwargs)
if roi is None:
# No overlap with ROI
return None
# interpret_roi returns None or a tuple; drop the second element in the tuple.
roi,nil = roi
(x0,x1,y0,y1) = roi
wcs = wcs.get_subimage(x0, y0, x1-x0, y1-y0)
twcs = ConstantFitsWcs(wcs)
roislice = (slice(y0,y1), slice(x0,x1))
img = img[roislice]
if not os.path.exists(ivfn) and os.path.exists(ivfn.replace('.fits.gz', '.fits')):
ivfn = ivfn.replace('.fits.gz','.fits')
if not os.path.exists(nifn) and os.path.exists(nifn.replace('.fits.gz', '.fits')):
nifn = nifn.replace('.fits.gz','.fits')
if not os.path.exists(nufn) and os.path.exists(nufn.replace('.fits.gz', '.fits')):
nufn = nufn.replace('.fits.gz','.fits')
if not (os.path.exists(ivfn) and os.path.exists(nifn) and os.path.exists(nufn)):
print('Files do not exist:', ivfn, nifn, nufn)
continue
foundFiles = True
break
if not foundFiles:
raise IOError('unWISE files not found in ' + str(basedirs) + ' for tile ' + tile)
print('Reading', ivfn)
invvar = fitsio.FITS(ivfn)[0][roislice]
if band == 4:
# due to upsampling, effective invvar is smaller (the pixels
# are correlated)
invvar *= 0.25
#print 'Reading', ppfn
#pp = fitsio.FITS(ppfn)[0][roislice]
print('Reading', nifn)
nims = fitsio.FITS(nifn)[0][roislice]
if nufn == nifn:
nuims = nims
else:
print('Reading', nufn)
nuims = fitsio.FITS(nufn)[0][roislice]
#print 'Median # ims:', np.median(nims)
good = (nims > 0)
invvar[np.logical_not(good)] = 0.
sig1 = 1./np.sqrt(np.median(invvar[good]))
# Load the average PSF model (generated by wise_psf.py)
psffn = os.path.join(os.path.dirname(__file__), 'wise-psf-avg.fits')
print('Reading', psffn)
P = fits_table(psffn, hdu=band)
psf = GaussianMixturePSF(P.amp, P.mean, P.var)
sky = 0.
tsky = ConstantSky(sky)
# if opt.errfrac > 0:
# nz = (iv > 0)
# iv2 = np.zeros_like(invvar)
# iv2[nz] = 1./(1./invvar[nz] + (img[nz] * opt.errfrac)**2)
# print 'Increasing error estimate by', opt.errfrac, 'of image flux'
# invvar = iv2
tim = Image(data=img, invvar=invvar, psf=psf, wcs=twcs,
sky=tsky, photocal=LinearPhotoCal(1., band=bandname),
name='unWISE %s W%i' % (tile, band))
tim.sig1 = sig1
tim.roi = roi
tim.nims = nims
tim.nuims = nuims
tim.hdr = hdr
if 'MJDMIN' in hdr and 'MJDMAX' in hdr:
from tractor.tractortime import TAITime
tim.mjdmin = hdr['MJDMIN']
tim.mjdmax = hdr['MJDMAX']
tim.time = TAITime(None, mjd=(tim.mjdmin + tim.mjdmax)/2.)
return tim
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-o',
'--out',
dest='outfn',
help='Output filename',
default='TMP/nexp.fits')
parser.add_argument('--merge',
action='store_true',
help='Merge sub-tables')
parser.add_argument('--plot', action='store_true', help='Plot results')
parser.add_argument('files',
metavar='nexp-file.fits.gz',
nargs='+',
help='List of nexp files to process')
opt = parser.parse_args()
fns = opt.files
if opt.merge:
from astrometry.util.fits import merge_tables
TT = []
for fn in fns:
T = fits_table(fn)
print(fn, '->', len(T))
TT.append(T)
T = merge_tables(TT)
T.writeto(opt.outfn)
print('Wrote', opt.outfn)
if opt.plot:
T = fits_table(opt.files[0])
import pylab as plt
import matplotlib
ax = [360, 0, -21, 36]
def radec_plot():
plt.axis(ax)
plt.xlabel('RA (deg)')
plt.xticks(np.arange(0, 361, 45))
plt.ylabel('Dec (deg)')
gl = np.arange(361)
gb = np.zeros_like(gl)
from astrometry.util.starutil_numpy import lbtoradec
rr, dd = lbtoradec(gl, gb)
plt.plot(rr, dd, 'k-', alpha=0.5, lw=1)
rr, dd = lbtoradec(gl, gb + 10)
plt.plot(rr, dd, 'k-', alpha=0.25, lw=1)
rr, dd = lbtoradec(gl, gb - 10)
plt.plot(rr, dd, 'k-', alpha=0.25, lw=1)
plt.figure(figsize=(8, 5))
plt.subplots_adjust(left=0.1, right=0.98, top=0.93)
# Map of the tile centers we want to observe...
O = fits_table('obstatus/decam-tiles_obstatus.fits')
O.cut(O.in_desi == 1)
rr, dd = np.meshgrid(np.linspace(ax[1], ax[0], 700),
np.linspace(ax[2], ax[3], 200))
from astrometry.libkd.spherematch import match_radec
I, J, d = match_radec(O.ra, O.dec, rr.ravel(), dd.ravel(), 1.)
desimap = np.zeros(rr.shape, bool)
desimap.flat[J] = True
def desi_map():
# Show the DESI tile map in the background.
from astrometry.util.plotutils import antigray
plt.imshow(desimap,
origin='lower',
interpolation='nearest',
extent=[ax[1], ax[0], ax[2], ax[3]],
aspect='auto',
cmap=antigray,
vmax=8)
for band in 'grz':
plt.clf()
desi_map()
N = T.get('nexp_%s' % band)
I = np.flatnonzero(N > 0)
#cm = matplotlib.cm.get_cmap('jet', 6)
#cm = matplotlib.cm.get_cmap('winter', 5)
cm = matplotlib.cm.viridis
cm = matplotlib.cm.get_cmap(cm, 5)
plt.scatter(T.ra[I],
T.dec[I],
c=N[I],
s=2,
edgecolors='none',
vmin=0.5,
vmax=5.5,
cmap=cm)
radec_plot()
cax = colorbar_axes(plt.gca(), frac=0.06)
plt.colorbar(cax=cax, ticks=range(6))
#plt.colorbar(ticks=range(6))
plt.title('DECaLS DR3: Number of exposures in %s' % band)
plt.savefig('nexp-%s.png' % band)
plt.clf()
desi_map()
plt.scatter(T.ra,
T.dec,
c=T.get('nexp_%s' % band),
s=2,
edgecolors='none',
vmin=0,
vmax=2.)
radec_plot()
plt.colorbar()
plt.title('DECaLS DR3: PSF size, band %s' % band)
plt.savefig('psfsize-%s.png' % band)
return 0
for col in ['nobjs', 'npsf', 'nsimp', 'nexp', 'ndev', 'ncomp']:
plt.clf()
desi_map()
N = T.get(col)
mx = np.percentile(N, 99.5)
plt.scatter(T.ra,
T.dec,
c=N,
s=2,
edgecolors='none',
vmin=0,
vmax=mx)
radec_plot()
plt.colorbar()
plt.title('DECaLS DR3: Number of objects of type %s' % col[1:])
plt.savefig('nobjs-%s.png' % col[1:])
Ntot = T.nobjs
for col in ['npsf', 'nsimp', 'nexp', 'ndev', 'ncomp']:
plt.clf()
desi_map()
N = T.get(col) / Ntot.astype(np.float32)
mx = np.percentile(N, 99.5)
plt.scatter(T.ra,
T.dec,
c=N,
s=2,
edgecolors='none',
vmin=0,
vmax=mx)
radec_plot()
plt.colorbar()
plt.title('DECaLS DR3: Fraction of objects of type %s' % col[1:])
plt.savefig('fobjs-%s.png' % col[1:])
return 0
# fnpats = opt.files
# fns = []
# for pat in fnpats:
# pfns = glob(pat)
# fns.extend(pfns)
# print('Pattern', pat, '->', len(pfns), 'files')
#fns = glob('coadd/*/*/*-nexp*')
#fns = glob('coadd/000/*/*-nexp*')
#fns = glob('coadd/000/0001*/*-nexp*')
fns.sort()
print(len(fns), 'nexp files')
brickset = set()
bricklist = []
gn = []
rn = []
zn = []
gnhist = []
rnhist = []
znhist = []
nnhist = 6
gdepth = []
rdepth = []
zdepth = []
ibricks = []
nsrcs = []
npsf = []
nsimp = []
nexp = []
ndev = []
ncomp = []
gpsfsize = []
rpsfsize = []
zpsfsize = []
ebv = []
gtrans = []
rtrans = []
ztrans = []
bricks = fits_table('survey-bricks.fits.gz')
#sfd = SFDMap()
W = H = 3600
# H=3600
# xx,yy = np.meshgrid(np.arange(W), np.arange(H))
unique = np.ones((H, W), bool)
tlast = 0
for fn in fns:
print('File', fn)
words = fn.split('/')
dirprefix = '/'.join(words[:-4])
print('Directory prefix:', dirprefix)
words = words[-4:]
brick = words[2]
print('Brick', brick)
if not brick in brickset:
brickset.add(brick)
bricklist.append(brick)
gn.append(0)
rn.append(0)
zn.append(0)
gnhist.append([0 for i in range(nnhist)])
rnhist.append([0 for i in range(nnhist)])
znhist.append([0 for i in range(nnhist)])
index = -1
ibrick = np.nonzero(bricks.brickname == brick)[0][0]
ibricks.append(ibrick)
tfn = os.path.join(dirprefix, 'tractor', brick[:3],
'tractor-%s.fits' % brick)
print('Tractor filename', tfn)
T = fits_table(tfn,
columns=[
'brick_primary', 'type', 'decam_psfsize', 'ebv',
'decam_mw_transmission'
])
T.cut(T.brick_primary)
nsrcs.append(len(T))
types = Counter([t.strip() for t in T.type])
npsf.append(types['PSF'])
nsimp.append(types['SIMP'])
nexp.append(types['EXP'])
ndev.append(types['DEV'])
ncomp.append(types['COMP'])
print('N sources', nsrcs[-1])
gpsfsize.append(np.median(T.decam_psfsize[:, 1]))
rpsfsize.append(np.median(T.decam_psfsize[:, 2]))
zpsfsize.append(np.median(T.decam_psfsize[:, 4]))
ebv.append(np.median(T.ebv))
gtrans.append(np.median(T.decam_mw_transmission[:, 1]))
rtrans.append(np.median(T.decam_mw_transmission[:, 2]))
ztrans.append(np.median(T.decam_mw_transmission[:, 4]))
br = bricks[ibrick]
print('Computing unique brick pixels...')
#wcs = Tan(fn, 0)
#W,H = int(wcs.get_width()), int(wcs.get_height())
pixscale = 0.262 / 3600.
wcs = Tan(br.ra, br.dec, W / 2. + 0.5, H / 2. + 0.5, -pixscale, 0.,
0., pixscale, float(W), float(H))
import time
t0 = time.clock()
unique[:, :] = True
find_unique_pixels(wcs, W, H, unique, br.ra1, br.ra2, br.dec1,
br.dec2)
# for i in range(W/2):
# allin = True
# lo,hi = i, W-i-1
# # one slice per side
# side = slice(lo,hi+1)
# top = (lo, side)
# bot = (hi, side)
# left = (side, lo)
# right = (side, hi)
# for slc in [top, bot, left, right]:
# #print('xx,yy', xx[slc], yy[slc])
# rr,dd = wcs.pixelxy2radec(xx[slc]+1, yy[slc]+1)
# U = (rr >= br.ra1 ) * (rr < br.ra2 ) * (dd >= br.dec1) * (dd < br.dec2)
# #print('Pixel', i, ':', np.sum(U), 'of', len(U), 'pixels are unique')
# allin *= np.all(U)
# unique[slc] = U
# if allin:
# print('Scanned to pixel', i)
# break
t1 = time.clock()
U = np.flatnonzero(unique)
t2 = time.clock()
print(len(U), 'of', W * H, 'pixels are unique to this brick')
# #t3 = time.clock()
#rr,dd = wcs.pixelxy2radec(xx+1, yy+1)
# #t4 = time.clock()
# #u = (rr >= br.ra1 ) * (rr < br.ra2 ) * (dd >= br.dec1) * (dd < br.dec2)
# #t5 = time.clock()
# #U2 = np.flatnonzero(u)
#U2 = np.flatnonzero((rr >= br.ra1 ) * (rr < br.ra2 ) *
# (dd >= br.dec1) * (dd < br.dec2))
#assert(np.all(U == U2))
#assert(len(U) == len(U2))
# #t6 = time.clock()
# print(len(U2), 'of', W*H, 'pixels are unique to this brick')
#
#print(t0-tlast, 'other time')
#tlast = time.clock() #t2
#print('t1:', t1-t0, 't2', t2-t1)
# #print('t4:', t4-t3, 't5', t5-t4, 't6', t6-t5)
#
else:
index = bricklist.index(brick)
assert (index == len(bricklist) - 1)
index = bricklist.index(brick)
assert (index == len(bricklist) - 1)
filepart = words[-1]
filepart = filepart.replace('.fits.gz', '')
print('File:', filepart)
band = filepart[-1]
assert (band in 'grz')
nlist, nhist = dict(g=(gn, gnhist), r=(rn, rnhist),
z=(zn, znhist))[band]
upix = fitsio.read(fn).flat[U]
med = np.median(upix)
print('Band', band, ': Median', med)
nlist[index] = med
hist = nhist[index]
for i in range(nnhist):
if i < nnhist - 1:
hist[i] = np.sum(upix == i)
else:
hist[i] = np.sum(upix >= i)
assert (sum(hist) == len(upix))
print('Number of exposures histogram:', hist)
ibricks = np.array(ibricks)
print('Maximum number of sources:', max(nsrcs))
T = fits_table()
T.brickname = np.array(bricklist)
T.ra = bricks.ra[ibricks]
T.dec = bricks.dec[ibricks]
T.nexp_g = np.array(gn).astype(np.int16)
T.nexp_r = np.array(rn).astype(np.int16)
T.nexp_z = np.array(zn).astype(np.int16)
T.nexphist_g = np.array(gnhist).astype(np.int32)
T.nexphist_r = np.array(rnhist).astype(np.int32)
T.nexphist_z = np.array(znhist).astype(np.int32)
T.nobjs = np.array(nsrcs).astype(np.int16)
T.npsf = np.array(npsf).astype(np.int16)
T.nsimp = np.array(nsimp).astype(np.int16)
T.nexp = np.array(nexp).astype(np.int16)
T.ndev = np.array(ndev).astype(np.int16)
T.ncomp = np.array(ncomp).astype(np.int16)
T.psfsize_g = np.array(gpsfsize).astype(np.float32)
T.psfsize_r = np.array(rpsfsize).astype(np.float32)
T.psfsize_z = np.array(zpsfsize).astype(np.float32)
T.ebv = np.array(ebv).astype(np.float32)
T.trans_g = np.array(gtrans).astype(np.float32)
T.trans_r = np.array(rtrans).astype(np.float32)
T.trans_z = np.array(ztrans).astype(np.float32)
T.writeto(opt.outfn)
def main():
ra = 126.925
dec = 21.4833
itune1 = 5
itune2 = 5
ntune = 2
run = [4517,4576,4576]
field = [103,99,100]
camcol = [2,6,6]
bands=['r']
bandname = 'r'
flipBands = ['r']
rerun = 0
TI = []
sources = []
table = pyfits.open("J082742.02+212844.7-r.fits")
table.info()
header = table[0].header
data = table[0].data
invvar=table[1].data
skyobj = ba.ConstantSky(header['skyval'])
psffn = 'J082742.02+212844.7-r-bpsf.fits.gz'
psfimg = pyfits.open(psffn)[0].data
print 'PSF image shape', psfimg.shape
# number of Gaussian components
PS = psfimg.shape[0]
K = 3
w,mu,sig = em_init_params(K, None, None, None)
II = psfimg.copy()
II /= II.sum()
# HACK
II = np.maximum(II, 0)
print 'Multi-Gaussian PSF fit...'
xm,ym = -(PS/2), -(PS/2)
em_fit_2d(II, xm, ym, w, mu, sig)
print 'w,mu,sig', w,mu,sig
psf = GaussianMixturePSF(w, mu, sig)
sources = []
# for run,camcol,field in zip(run,camcol,field):
# sources.append(st.get_tractor_sources(run,camcol,field,bandname,bands=bands))
wcs = Tan("J082742.02+212844.7-r.fits",0)
wcs = FitsWcs(wcs)
wcs.setX0Y0(1.,1.)
photocal = ba.NasaSloanPhotoCal(bandname) #Also probably not right
TI.append(en.Image(data=data,invvar=invvar,sky=skyobj,psf=psf,wcs=wcs,photocal=photocal,name = "NASA-Sloan Test"))
lvl = logging.DEBUG
logging.basicConfig(level=lvl,format='%(message)s',stream=sys.stdout)
tims = [TI[0]]
tractor = st.SDSSTractor(tims)
for source in sources:
tractor.addSources(source)
zr = np.array([-5.,+5.])# * info['skysig']
print bands
prefix = 'ngc2595'
# saveAll('initial-'+prefix, tractor,zr,flipBands,debug=True)
bright = None
lowbright = 1000
sources=[]
for timg,sources in zip(tims,sources):
wcs = timg.getWcs()
xtr,ytr = wcs.positionToPixel(RaDecPos(ra,dec))
print xtr,ytr
xt = xtr
yt = ytr
r = 250.
for src in sources:
xs,ys = wcs.positionToPixel(src.getPosition(),src)
if (xs-xt)**2+(ys-yt)**2 <= r**2:
print "Removed:", src
print xs,ys
tractor.removeSource(src)
# saveAll('removed-'+prefix, tractor,zr,flipBands,debug=True)
newShape = sg.GalaxyShape(30.,1.,0.)
newBright = ba.Mags(r=15.0,g=15.0,u=15.0,z=15.0,i=15.0)
EG = st.ExpGalaxy(RaDecPos(ra,dec),newBright,newShape)
print EG
tractor.addSource(EG)
saveAll('added-'+prefix,tractor,zr,flipBands,debug=True)
plotInvvar('added-'+prefix,tractor)
for i in range(itune1):
if (i % 5 == 0):
tractor.optimizeCatalogLoop(nsteps=1,srcs=[EG],sky=True)
else:
tractor.optimizeCatalogLoop(nsteps=1,srcs=[EG],sky=False)
tractor.changeInvvar(9.)
tractor.clearCache()
saveAll('itune1-%d-' % (i+1)+prefix,tractor,zr,flipBands,debug=True)
plotInvvar('itune1-%d-' % (i+1)+prefix,tractor)
CGPos = EG.getPosition()
CGShape = EG.getShape()
EGBright = EG.getBrightness()
print EGBright
CGg = EGBright[0]*1.25
CGi = EGBright[1]*1.25
CGr = EGBright[2]*1.25
CGu = EGBright[3]*1.25
CGz = EGBright[4]*1.25
CGBright = ba.Mags(r=CGr,g=CGg,u=CGu,z=CGz,i=CGi)
print EGBright
print CGBright
CG = st.CompositeGalaxy(CGPos,CGBright,CGShape,CGBright,CGShape)
tractor.removeSource(EG)
tractor.addSource(CG)
for i in range(itune2):
if (i % 5 == 0):
tractor.optimizeCatalogLoop(nsteps=1,srcs=[CG],sky=True)
else:
tractor.optimizeCatalogLoop(nsteps=1,srcs=[CG],sky=False)
tractor.changeInvvar(9.)
tractor.clearCache()
saveAll('itune2-%d-' % (i+1)+prefix,tractor,zr,flipBands,debug=True)
plotInvvar('itune2-%d-' % (i+1)+prefix,tractor)
for i in range(ntune):
tractor.optimizeCatalogLoop(nsteps=1,sky=True)
saveAll('ntune-%d-' % (i+1)+prefix,tractor,zr,flipBands,debug=True)
tractor.clearCache()
makeflipbook(prefix,len(tractor.getImages()),itune1,itune2,ntune)
if not os.path.exists(qfn):
if not os.path.exists(hfn):
print('Reading', fn)
halfsize(fn, hfn)
#print('Wrote', hfn)
halfsize(hfn, qfn)
#print('Wrote', qfn)
fn = qfn
#fn = os.path.join('wise-coadds', brick[:2], brick[:4], '%s_ac51' % brick,
# '%s_ac51-w%i-int-3.fits' % (brick, band))
print('Reading', fn)
I = fitsio.read(fn)
bwcs = Tan(fn, 0)
bh,bw = I.shape
print('Image shape', bh,bw)
assert(np.all(np.isfinite(I)))
xx,yy = np.meshgrid(np.arange(bw), np.arange(bh))
rr,dd = bwcs.pixelxy2radec(xx, yy)
#print('RA,Dec range', rr.min(), rr.max(), dd.min(), dd.max())
ll,bb = radectolb(rr.ravel(), dd.ravel())
#print('L,B range', ll.min(), ll.max(), bb.min(), bb.max())
ll = ll.reshape(rr.shape)
bb = bb.reshape(rr.shape)
ok,ox,oy = wcs.radec2pixelxy(ll, bb)
#print('Unique ok:', np.unique(ok))
def exposure_metadata(filenames, hdus=None, trim=None):
'''
Creates a CCD table row object by reading metadata from a FITS
file header.
Parameters
----------
filenames : list of strings
Filenames to read
hdus : list of integers; None to read all HDUs
List of FITS extensions (HDUs) to read
trim : string
String to trim off the start of the *filenames* for the
*image_filename* table entry
Returns
-------
A table that looks like the CCDs table.
'''
nan = np.nan
primkeys = [('FILTER',''),
('RA', nan),
('DEC', nan),
('AIRMASS', nan),
('DATE-OBS', ''),
('EXPTIME', nan),
('EXPNUM', 0),
('MJD-OBS', 0),
('PROPID', ''),
('INSTRUME', ''),
('SEEING', nan),
]
hdrkeys = [('AVSKY', nan),
('ARAWGAIN', nan),
('FWHM', nan),
('CRPIX1',nan),
('CRPIX2',nan),
('CRVAL1',nan),
('CRVAL2',nan),
('CD1_1',nan),
('CD1_2',nan),
('CD2_1',nan),
('CD2_2',nan),
('EXTNAME',''),
('CCDNUM',''),
]
otherkeys = [('IMAGE_FILENAME',''), ('IMAGE_HDU',0),
('HEIGHT',0),('WIDTH',0),
]
allkeys = primkeys + hdrkeys + otherkeys
vals = dict([(k,[]) for k,d in allkeys])
for i,fn in enumerate(filenames):
print('Reading', (i+1), 'of', len(filenames), ':', fn)
F = fitsio.FITS(fn)
primhdr = F[0].read_header()
expstr = '%08i' % primhdr.get('EXPNUM')
cpfn = fn
if trim is not None:
cpfn = cpfn.replace(trim, '')
print('CP fn', cpfn)
if hdus is not None:
hdulist = hdus
else:
hdulist = range(1, len(F))
for hdu in hdulist:
hdr = F[hdu].read_header()
info = F[hdu].get_info()
#'extname': 'S1', 'dims': [4146L, 2160L]
H,W = info['dims']
for k,d in primkeys:
vals[k].append(primhdr.get(k, d))
for k,d in hdrkeys:
vals[k].append(hdr.get(k, d))
vals['IMAGE_FILENAME'].append(cpfn)
vals['IMAGE_HDU'].append(hdu)
vals['WIDTH'].append(int(W))
vals['HEIGHT'].append(int(H))
T = fits_table()
for k,d in allkeys:
T.set(k.lower().replace('-','_'), np.array(vals[k]))
#T.about()
# DECam: INSTRUME = 'DECam'
T.rename('instrume', 'camera')
T.camera = np.array([t.lower().strip() for t in T.camera])
#T.rename('extname', 'ccdname')
T.ccdname = np.array([t.strip() for t in T.extname])
T.filter = np.array([s.split()[0] for s in T.filter])
T.ra_bore = np.array([hmsstring2ra (s) for s in T.ra ])
T.dec_bore = np.array([dmsstring2dec(s) for s in T.dec])
T.ra = np.zeros(len(T))
T.dec = np.zeros(len(T))
for i in range(len(T)):
W,H = T.width[i], T.height[i]
wcs = Tan(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], float(W), float(H))
xc,yc = W/2.+0.5, H/2.+0.5
rc,dc = wcs.pixelxy2radec(xc,yc)
T.ra [i] = rc
T.dec[i] = dc
return T
def main():
'''
This function generates the plots in the paper.
Some files and directories are assumed to exist in the current directory:
* WISE atlas tiles, from http://unwise.me/data/allsky-atlas.fits
* unwise-neo1-coadds, from http://unwise.me/data/neo1/
* unwise-neo1-coadds-half, unwise-neo1-coadds-quarter: directories
'''
# First, create the WCS into which we want to render
# degrees width to render in galactic coords
# |l| < 60
# |b| < 30
width = 120
# ~2 arcmin per pixel
W = int(width * 60.) / 2
H = W/2
zoom = 360. / width
wcs = anwcs_create_hammer_aitoff(0., 0., zoom, W, H, 0)
# Select WISE tiles that overlap. This atlas table is available
# from http://unwise.me/data/allsky-atlas.fits
# Select WISE tiles that overlap.
T = fits_table('allsky-atlas.fits')
print(len(T), 'tiles total')
T.ll,T.bb = radectolb(T.ra, T.dec)
I = np.flatnonzero(np.logical_or(T.ll < width+1,
T.ll > (360-width-1)) *
(T.bb > -width/2-1) * (T.bb < width/2+1))
T.cut(I)
print(len(I), 'tiles in L,B range')
# Create a coadd for each WISE band
lbpat = 'unwise-neo1-w%i-lb.fits'
imgs = []
for band in [1,2]:
outfn = lbpat % (band)
if os.path.exists(outfn):
print('Exists:', outfn)
img = fitsio.read(outfn)
imgs.append(img)
continue
coimg = np.zeros((H,W), np.float32)
conimg = np.zeros((H,W), np.float32)
for i,brick in enumerate(T.coadd_id):
# We downsample by 2, twice, just to make repeat runs a
# little faster.
# unWISE
fn = os.path.join('unwise-neo1-coadds', brick[:3], brick,
'unwise-%s-w%i-img-u.fits' % (brick, band))
qfn = os.path.join('unwise-neo1-coadds-quarter',
'unwise-%s-w%i.fits' % (brick, band))
hfn = os.path.join('unwise-neo1-coadds-half',
'unwise-%s-w%i.fits' % (brick, band))
if not os.path.exists(qfn):
if not os.path.exists(hfn):
print('Reading', fn)
halfsize(fn, hfn)
halfsize(hfn, qfn)
fn = qfn
print('Reading', fn)
img = fitsio.read(fn)
bwcs = Tan(fn, 0)
bh,bw = img.shape
# Coadd each unWISE pixel into the nearest target pixel.
xx,yy = np.meshgrid(np.arange(bw), np.arange(bh))
rr,dd = bwcs.pixelxy2radec(xx, yy)
ll,bb = radectolb(rr.ravel(), dd.ravel())
ll = ll.reshape(rr.shape)
bb = bb.reshape(rr.shape)
ok,ox,oy = wcs.radec2pixelxy(ll, bb)
ox = np.round(ox - 1).astype(int)
oy = np.round(oy - 1).astype(int)
K = (ox >= 0) * (ox < W) * (oy >= 0) * (oy < H) * ok
#print('ok:', np.unique(ok), 'x', ox.min(), ox.max(), 'y', oy.min(), oy.max())
assert(np.all(np.isfinite(img)))
if np.sum(K) == 0:
# no overlap
print('No overlap')
continue
np.add.at( coimg, (oy[K], ox[K]), img[K])
np.add.at(conimg, (oy[K], ox[K]), 1)
img = coimg / np.maximum(conimg, 1)
# Hack -- write and then read FITS WCS header.
fn = 'wiselb.wcs'
wcs.writeto(fn)
hdr = fitsio.read_header(fn)
hdr['CTYPE1'] = 'GLON-AIT'
hdr['CTYPE2'] = 'GLAT-AIT'
fitsio.write(outfn, img, header=hdr, clobber=True)
fitsio.write(outfn.replace('.fits', '-n.fits'), conimg,
header=hdr, clobber=True)
imgs.append(img)
w1,w2 = imgs
# Get/confirm L,B bounds...
H,W = w1.shape
print('Image size', W, 'x', H)
ok,l1,b1 = wcs.pixelxy2radec(1, (H+1)/2.)
ok,l2,b2 = wcs.pixelxy2radec(W, (H+1)/2.)
ok,l3,b3 = wcs.pixelxy2radec((W+1)/2., 1)
ok,l4,b4 = wcs.pixelxy2radec((W+1)/2., H)
print('L,B', (l1,b1), (l2,b2), (l3,b3), (l4,b4))
llo,lhi = l2,l1+360
blo,bhi = b3,b4
# Set plot sizes
plt.figure(1, figsize=(10,5))
plt.subplots_adjust(left=0.1, right=0.95, bottom=0.1, top=0.95)
plt.figure(2, figsize=(5,5))
plt.subplots_adjust(left=0.11, right=0.96, bottom=0.1, top=0.95)
suffix = '.pdf'
rgb = wise_rgb(w1, w2)
xlo,ylo = 0,0
plt.figure(1)
plt.clf()
plt.imshow(rgb, origin='lower', interpolation='nearest')
lbticks(wcs, xlo, ylo, lticks=[60,30,0,330,300], bticks=[-30,-15,0,15,30])
plt.savefig('xbulge-00' + suffix)
# Compute the median of each row as a crude way of suppressing the
# Galactic plane
medy1 = np.median(w1, axis=1)
medy2 = np.median(w2, axis=1)
rgb = wise_rgb(w1 - medy1[:,np.newaxis],
w2 - medy2[:,np.newaxis])
# Zoom in a bit for Galactic plane subtracted version
lhi,llo,blo,bhi = 40, 320, -20, 20
okxy = np.array([wcs.radec2pixelxy(l,b) for l,b in [
(llo, blo), (llo, bhi), (lhi, blo), (lhi, bhi)]])
xlo = int(np.floor(min(okxy[:,-2])))
xhi = int(np.ceil (max(okxy[:,-2])))
ylo = int(np.floor(min(okxy[:,-1])))
yhi = int(np.ceil (max(okxy[:,-1])))
plt.clf()
plt.imshow(rgb[ylo:yhi, xlo:xhi, :],origin='lower', interpolation='nearest')
#lbticks(wcs, xlo, ylo, lticks=[40,20,0,340,320], bticks=[-20,-10,0,10,20])
lbticks(wcs, xlo, ylo, lticks=[30,15,0,345,330], bticks=[-20,-10,0,10,20])
plt.savefig('xbulge-01' + suffix)
# Zoom in on the core
lhi,llo,blo,bhi = 15, 345, -15, 15
ok,x1,y1 = wcs.radec2pixelxy(llo, blo)
ok,x2,y2 = wcs.radec2pixelxy(llo, bhi)
ok,x3,y3 = wcs.radec2pixelxy(lhi, blo)
ok,x4,y4 = wcs.radec2pixelxy(lhi, bhi)
xlo = int(np.floor(min(x1,x2,x3,x4)))
xhi = int(np.ceil (max(x1,x2,x3,x4)))
ylo = int(np.floor(min(y1,y2,y3,y4)))
yhi = int(np.ceil (max(y1,y2,y3,y4)))
print('xlo,ylo', xlo, ylo)
w1 = w1[ylo:yhi, xlo:xhi]
w2 = w2[ylo:yhi, xlo:xhi]
plt.figure(2)
# Apply color cut
w1mag = -2.5*(np.log10(w1) - 9.)
w2mag = -2.5*(np.log10(w2) - 9.)
cc = w1mag - w2mag
goodcolor = np.isfinite(cc)
mlo,mhi = np.percentile(cc[goodcolor], [5,95])
print('W1 - W2 color masks:', mlo,mhi)
mask = goodcolor * (cc > mlo) * (cc < mhi)
plt.clf()
rgb = wise_rgb(w1, w2)
plt.imshow(rgb, origin='lower', interpolation='nearest')
lbticks(wcs, xlo,ylo)
plt.title('Data')
plt.savefig('xbulge-fit-data' + suffix)
plt.clf()
rgb = wise_rgb(w1 * mask, w2 * mask)
plt.imshow(rgb, origin='lower', interpolation='nearest')
lbticks(wcs, xlo,ylo)
plt.title('Data (masked)')
plt.savefig('xbulge-fit-masked' + suffix)
ie = mask.astype(np.float32)
from tractor import (Image, NCircularGaussianPSF, LinearPhotoCal, Tractor,
PixPos, Fluxes)
from tractor.galaxy import ExpGalaxy, GalaxyShape
# Create Tractor images
tim1 = Image(data=w1 * mask, inverr=ie,
psf=NCircularGaussianPSF([1.],[1.]),
photocal=LinearPhotoCal(1., 'w1'))
tim2 = Image(data=w2 * mask, inverr=ie,
psf=NCircularGaussianPSF([1.],[1.]),
photocal=LinearPhotoCal(1., 'w2'))
H,W = w1.shape
gal = ExpGalaxy(PixPos(W/2, H/2), Fluxes(w1=w1.sum(), w2=w2.sum()),
GalaxyShape(200, 0.4, 90.))
tractor = Tractor([tim1, tim2],[gal])
# fitsio.write('data-w1.fits', w1 * mask, clobber=True)
# fitsio.write('data-w2.fits', w2 * mask, clobber=True)
# fitsio.write('mask.fits', mask.astype(np.uint8), clobber=True)
# Optimize galaxy model
tractor.freezeParam('images')
for step in range(50):
dlnp,x,alpha = tractor.optimize()
print('dlnp', dlnp)
print('x', x)
print('alpha', alpha)
print('Galaxy', gal)
if dlnp == 0:
break
# Get galaxy model images, compute residuals
mod1 = tractor.getModelImage(0)
resid1 = w1 - mod1
mod2 = tractor.getModelImage(1)
resid2 = w2 - mod2
rgb = wise_rgb(mod1, mod2)
plt.clf()
plt.imshow(rgb, origin='lower', interpolation='nearest')
lbticks(wcs, xlo,ylo)
plt.title('Model')
plt.savefig('xbulge-fit-model' + suffix)
rgb = resid_rgb(resid1, resid2)
plt.clf()
plt.imshow(rgb, origin='lower', interpolation='nearest')
lbticks(wcs, xlo,ylo)
plt.title('Residuals')
plt.savefig('xbulge-fit-resid' + suffix)
rgb = resid_rgb(resid1*mask, resid2*mask)
plt.clf()
plt.imshow(rgb, origin='lower', interpolation='nearest')
lbticks(wcs, xlo,ylo)
plt.title('Residuals (masked)')
plt.savefig('xbulge-fit-residmasked' + suffix)
# fitsio.write('resid1.fits', resid1, clobber=True)
# fitsio.write('resid2.fits', resid2, clobber=True)
# Compute median-smoothed residuals
fr1 = np.zeros_like(resid1)
fr2 = np.zeros_like(resid2)
median_smooth(resid1, np.logical_not(mask), 25, fr1)
median_smooth(resid2, np.logical_not(mask), 25, fr2)
rgb = resid_rgb(fr1, fr2)
plt.clf()
plt.imshow(rgb, origin='lower', interpolation='nearest')
lbticks(wcs, xlo,ylo)
plt.title('Residuals (smoothed)')
plt.savefig('xbulge-fit-smooth2' + suffix)
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 main():
ps = PlotSequence('cov')
survey = LegacySurveyData()
ra, dec = 242.0, 10.2
fn = 'coverage-ccds.fits'
if not os.path.exists(fn):
ccds = survey.get_ccds()
ccds.cut(ccds.filter == 'r')
ccds.cut(ccds.propid == '2014B-0404')
ccds.cut(np.hypot(ccds.ra_bore - ra, ccds.dec_bore - dec) < 2.5)
print(np.unique(ccds.expnum), 'unique exposures')
print('propids', np.unique(ccds.propid))
ccds.writeto(fn)
else:
ccds = fits_table(fn)
plt.clf()
for e in np.unique(ccds.expnum):
I = np.flatnonzero(ccds.expnum == e)
plt.plot(ccds.ra[I], ccds.dec[I], '.')
ps.savefig()
degw = 3.0
pixscale = 10.
W = degw * 3600 / 10.
H = W
hi = 6
cmap = cmap_discretize('jet', hi + 1)
wcs = Tan(ra, dec, W / 2. + 0.5, H / 2. + 0.5, -pixscale / 3600., 0., 0.,
pixscale / 3600., float(W), float(H))
r0, d0 = wcs.pixelxy2radec(1, 1)
r1, d1 = wcs.pixelxy2radec(W, H)
extent = [min(r0, r1), max(r0, r1), min(d0, d1), max(d0, d1)]
for expnums in [
[348666],
[348666, 348710, 348686],
[348659, 348667, 348658, 348666, 348665, 348669, 348668],
None,
[
348683, 348687, 347333, 348686, 348685, 348692, 348694, 348659,
348667, 348658, 348666, 348665, 348669, 348668, 348707, 348709,
348708, 348710, 348711, 348716, 348717
],
]:
nexp = np.zeros((H, W), np.uint8)
for ccd in ccds:
if expnums is not None and not ccd.expnum in expnums:
continue
ccdwcs = survey.get_approx_wcs(ccd)
r, d = ccdwcs.pixelxy2radec(1, 1)
ok, x0, y0 = wcs.radec2pixelxy(r, d)
r, d = ccdwcs.pixelxy2radec(ccd.width, ccd.height)
ok, x1, y1 = wcs.radec2pixelxy(r, d)
xlo = np.clip(int(np.round(min(x0, x1))) - 1, 0, W - 1)
xhi = np.clip(int(np.round(max(x0, x1))) - 1, 0, W - 1)
ylo = np.clip(int(np.round(min(y0, y1))) - 1, 0, H - 1)
yhi = np.clip(int(np.round(max(y0, y1))) - 1, 0, H - 1)
nexp[ylo:yhi + 1, xlo:xhi + 1] += 1
plt.clf()
plt.imshow(nexp,
interpolation='nearest',
origin='lower',
vmin=-0.5,
vmax=hi + 0.5,
cmap=cmap,
extent=extent)
plt.colorbar(ticks=np.arange(hi + 1))
ps.savefig()
O = fits_table('obstatus/decam-tiles_obstatus.fits')
O.cut(np.hypot(O.ra - ra, O.dec - dec) < 2.5)
for p in [1, 2, 3]:
print('Pass', p, 'exposures:', O.r_expnum[O.get('pass') == p])
O.cut(O.get('pass') == 2)
print(len(O), 'pass 2 nearby')
d = np.hypot(O.ra - ra, O.dec - dec)
print('Dists:', d)
I = np.flatnonzero(d < 0.5)
assert (len(I) == 1)
ocenter = O[I[0]]
print('Center expnum', ocenter.r_expnum)
I = np.flatnonzero(d >= 0.5)
O.cut(I)
#center = ccds[ccds.expnum == ocenter.r_expnum]
#p2 = ccds[ccds.
ok, xc, yc = wcs.radec2pixelxy(ocenter.ra, ocenter.dec)
xx, yy = np.meshgrid(np.arange(W) + 1, np.arange(H) + 1)
c_d2 = (xc - xx)**2 + (yc - yy)**2
best = np.ones((H, W), bool)
for o in O:
ok, x, y = wcs.radec2pixelxy(o.ra, o.dec)
d2 = (x - xx)**2 + (y - yy)**2
best[d2 < c_d2] = False
del d2
del c_d2, xx, yy
# plt.clf()
# plt.imshow(best, interpolation='nearest', origin='lower', cmap='gray',
# vmin=0, vmax=1)
# ps.savefig()
plt.clf()
plt.imshow(nexp * best,
interpolation='nearest',
origin='lower',
vmin=-0.5,
vmax=hi + 0.5,
cmap=cmap,
extent=extent)
plt.colorbar(ticks=np.arange(hi + 1))
ps.savefig()
plt.clf()
n, b, p = plt.hist(np.clip(nexp[best], 0, hi),
range=(-0.5, hi + 0.5),
bins=hi + 1)
plt.xlim(-0.5, hi + 0.5)
ps.savefig()
print('b', b)
print('n', n)
print('fracs', np.array(n) / np.sum(n))
print('pcts',
', '.join(['%.1f' % f for f in 100. * np.array(n) / np.sum(n)]))
def _simplewcs(gal):
'''Build a simple WCS object for a single galaxy.'''
diam = np.ceil(gal['RADIUS'] / PIXSCALE).astype('int16') # [pixels]
galwcs = Tan(gal['RA'], gal['DEC'], diam / 2 + 0.5, diam / 2 + 0.5,
-PIXSCALE, 0.0, PIXSCALE, 0.0, float(diam), float(diam))
return galwcs
def cutout_fits(req):
import tempfile
import fitsio
from astrometry.util.util import Tan
form = CutoutSearchForm(req.GET)
if not form.is_valid():
return HttpResponse('failed to parse request')
ra = form.cleaned_data['ra']
dec = form.cleaned_data['dec']
if ra is None or dec is None:
return HttpResponse('RA and Dec arguments are required')
size = form.cleaned_data['size']
if size is None:
size = 100
else:
size = min(256, size)
bandstr = form.cleaned_data['bands']
bands = [int(c) for c in bandstr]
bands = [b for b in bands if b in [1,2,3,4]]
version = form.cleaned_data['version']
if version == 'neo1':
bands = [b for b in bands if b in [1,2]]
radius = size/2. * 2.75/3600.
tiles = unwise_tiles_near_radec(ra, dec, radius)
tiles = list(tiles)
# Create a temp dir in which to place cutouts
tempdir = tempfile.mkdtemp()
fns = []
filetypes = []
types = ['img_m', 'invvar_m', 'n_m', 'std_m']
for t in types:
if form.cleaned_data['file_' + t]:
filetypes.append(t)
# If none specified, include all filetypes.
if len(filetypes) == 0:
filetypes = types
for tile in tiles:
coadd = tile.coadd
dirnm = os.path.join(settings.DATA_DIR, version, coadd[:3], coadd)
base = os.path.join(dirnm, 'unwise-%s' % coadd)
w1fn = str(base + '-w1-img-m.fits')
wcs = Tan(w1fn, 0)
ok,x,y = wcs.radec2pixelxy(ra, dec)
x = int(round(x-1.))
y = int(round(y-1.))
x0 = x - size/2
x1 = x0 + size
y0 = y - size/2
y1 = y0 + size
if x1 <= 0 or y1 <= 0:
continue
if x0 >= wcs.get_width() or y0 >= wcs.get_height():
continue
x0 = max(x0, 0)
y0 = max(y0, 0)
x1 = min(x1, wcs.get_width())
y1 = min(y1, wcs.get_height())
hdr = fitsio.FITSHDR()
subwcs = wcs.get_subimage(x0, y0, x1-x0, y1-y0)
subwcs.add_to_header(hdr)
for band in bands:
for ft in filetypes:
pat = { 'img_m': '-w%i-img-m.fits',
'invvar_m': '-w%i-invvar-m.fits.gz',
'n_m': '-w%i-n-m.fits.gz',
'std_m': '-w%i-std-m.fits.gz' }[ft]
fn = str(base + pat % band)
img = fitsio.FITS(fn)[0][y0:y1, x0:x1]
basefn = os.path.basename(fn)
outfn = os.path.join(tempdir, basefn)
fitsio.write(outfn, img, header=hdr)
fns.append(basefn)
fns.extend([';', 'rm', '-R', tempdir])
return tar_files(req, fns, 'cutouts-fits_%.4f_%.4f.tar.gz' % (ra, dec),
basedir=tempdir)
def _computeTransformation(self, img, ylo=0, yhi=-1):
'''
Pre-compute the "grid-spread function" transformation matrix
for this parameter grid to the given image pixels.
The result is a home-brewed sparse matrix representation: a
dictionary mapping from model grid pixel indices (integers) to
the tuple (I, G, nz, NZI), where:
I: numpy index array (integers) in the image
G: grid weights for those pixels
nz = ((x0,y0), (h,w)): the subimage with non-zero weights
NZI: numpy index array (integers) within the "nz" subimage
'''
imwcs = img.getWcs()
H,W = self.shape
if yhi == -1:
yhi = H
Ngrid = W*H
iH,iW = img.shape
Nim = iW*iH
Lorder = 2
S = (Lorder * 2 + 3)
if False:
i0 = S/2
else:
print('Image', img.name)
print('Image PSF', img.getPsf())
psfw = img.getPsf().getRadius()
scale = img.getWcs().pixel_scale() / self.wcs.pixel_scale()
print('Image pixel scale', img.getWcs().pixel_scale())
print('Model pixel scale', self.wcs.pixel_scale())
print('PSF extent', psfw, 'pixels')
print('pixel scale factor', scale)
print('->', psfw * scale, 'model pixels')
print('Increasing S from', S)
S += int(np.ceil(psfw*scale)) * 2
print('to', S)
i0 = S/2
cmock = np.zeros((S,S), np.float32)
cmock[i0,i0] = 1.
if True:
spsf = img.getPsf().scale(scale)
print('Scaled PSF', spsf)
cmock = spsf.applyTo(cmock)
#print 'cmock'
#print cmock
cwcs = Tan(self.wcs)
cwcs.set_imagesize(S, S)
cx0,cy0 = self.wcs.crpix[0], self.wcs.crpix[1]
rim = np.zeros((iH,iW), np.float32)
X = {}
for i in range(ylo, yhi):
print('Precomputing matrix for image', img.name, 'model row', i)
for j in range(W):
#print 'Precomputing matrix for grid pixel', j,i
cwcs.set_crpix(cx0 - j + i0, cy0 - i + i0)
rim[:,:] = 0
##
weighted = 1
res = tan_wcs_resample(cwcs, imwcs.wcs, cmock, rim,
weighted, Lorder)
assert(res == 0)
if False:
outimg = img.getPsf().applyTo(rim).ravel()
else:
outimg = rim.ravel()
I = np.flatnonzero((outimg > 0) *
(img.getInvError().ravel() > 0))
if len(I) == 0:
continue
#if True:
# sumr.ravel()[I] += outimg[I]
xx,yy = (I % iW), (I / iW)
x0,y0 = xx.min(), yy.min()
nzh,nzw = 1 + yy.max() - y0, 1 + xx.max() - x0
NZ = ((x0, y0), (nzh, nzw))
NZI = (xx - x0) + (yy - y0) * nzw
X[i*W+j] = (I, outimg[I], NZ, NZI)
# if True:
# print 'sumr range', sumr.min(), sumr.max()
# sumr[sumr == 0] = 1.
# mn,mx = 0.,0.
# for (I, outim, NZ, NZI) in X.values():
# outim /= sumr.ravel()[I]
# mx = max(outim.max(), mx)
# mn = max(outim.min(), mn)
# print 'Min,Max grid-spread function:', mn,mx
return X
from legacypipe.survey import GaiaSource, GaiaPosition
from astrometry.util.util import Tan
from astrometry.util.starutil_numpy import mjdtodate
from tractor import TAITime
#ra,dec = 357.3060, 2.3957
#ccd1 = ccds[(ccds.expnum == 563212) * (ccds.ccdname == 'N17')]
ra, dec = 124.0317, 1.3028
expnum, ccdname = 393203, 'N11'
survey = LegacySurveyData()
W, H = 200, 200
pixscale = 0.262
cd = pixscale / 3600.
targetwcs = Tan(ra, dec, W / 2., H / 2., -cd, 0., 0., cd, float(W), float(H))
rr, dd = targetwcs.pixelxy2radec([1, W, W, 1, 1], [1, 1, H, H, 1])
targetrd = np.vstack((rr, dd)).T
ccds = survey.ccds_touching_wcs(targetwcs)
print(len(ccds), 'CCDs touching WCS')
print('MJDs', ccds.mjd_obs)
ccds.writeto('test-ccds.fits')
ccd1 = ccds[(ccds.expnum == expnum) * (ccds.ccdname == ccdname)]
print('CCD1:', ccd1)
im1 = survey.get_image_object(ccd1[0])
print('Im:', im1)
TT = []
for b in bricks[I]:
fn = survey.find_file('tractor', brick=b.brickname)
print('Reading', fn)
T = fits_table(fn)
print('Read', len(T))
T.cut((T.ra >= ralo) * (T.ra <= rahi) * (T.dec >= declo) * (T.dec <= dechi))
print(len(T), 'survive cut')
if len(T) == 0:
continue
TT.append(T)
T = TT = merge_tables(TT)
pixscale = dra / 1000.
W,H = int(np.ceil(2.*dra / pixscale)), int(np.ceil(2.*ddec / pixscale))
wcs = Tan(ra, dec, (W+1.)/2., (H+1.)/2., -pixscale, 0., 0., pixscale, float(W), float(H))
print('WCS radec bounds:', wcs.radec_bounds())
idmap = dict([((b,oid),i) for i,(b,oid) in enumerate(zip(T.brickname, T.objid))])
bands = 'grz'
lightcurves = dict([(b, [[] for i in range(len(T))]) for b in bands])
#ccds = survey.get_ccds_readonly()
#I = np.flatnonzero((ccds.ra1 <= rahi ) * (ccds.ra2 >= ralo) *
# (ccds.dec1 <= dechi) * (ccds.dec2 >= declo))
ccds = survey.ccds_touching_wcs(wcs)
print(len(ccds), 'ccds overlap ROI')
I = survey.photometric_ccds(ccds)
ccds.cut(I)
print('Cut to', len(ccds), 'CCDs that are photometric')
def run(self): tmp_dir = tempfile.mkdtemp() tbhdu = pyfits.BinTableHDU.from_columns([ pyfits.Column(name='X', format='E', array=self.sources_list[:, 1]), pyfits.Column(name='Y', format='E', array=self.sources_list[:, 0]), pyfits.Column(name='FLUX', format='E', array=self.sources_list[:, 2]) ]) prihdr = pyfits.Header() prihdr['IMAGEW'] = self.field_w prihdr['IMAGEH'] = self.field_h prihdr['ANRUN'] = True prihdr['ANVERUNI'] = True prihdr['ANVERDUP'] = False prihdr['ANCRPIXC'] = True prihdr['ANTWEAK'] = False prihdr['ANTWEAKO'] = 0 prihdr['ANSOLVED'] = tmp_dir + '/field.solved' #prihdr['ANMATCH'] = tmp_dir + '/field.match' prihdr['ANRDLS'] = tmp_dir + '/field.rdls' prihdr['ANWCS'] = tmp_dir + '/field.wcs' if self.field_corr is not None: prihdr['ANCORR'] = tmp_dir + '/field.corr' prihdr['ANCANCEL'] = tmp_dir + '/field.solved' prihdr['ANPOSERR'] = self.field_w / 400.0 if self.ra is not None: prihdr['ANERA'] = self.ra prihdr['ANEDEC'] = self.dec if self.radius is not None and self.radius > 0: prihdr['ANERAD'] = self.radius prihdr['ANDPL1'] = 1 prihdr['ANDPU1'] = 20 if self.field_deg is not None: prihdr['ANAPPL1'] = self.field_deg * 0.95 * 3600 / self.field_w prihdr['ANAPPU1'] = self.field_deg * 1.05 * 3600 / self.field_w if self.radius is not None and self.radius > 0 and self.radius < 5: prihdr['ANODDSSL'] = 1e6 else: prihdr['ANODDSSL'] = 1e8 prihdu = pyfits.PrimaryHDU(header=prihdr) thdulist = pyfits.HDUList([prihdu, tbhdu]) thdulist.writeto(tmp_dir + '/field.axy', clobber=True) if self.radius is not None and self.radius > 0 and self.radius < 5: conf_list = ['conf-all'] else: conf_list = ['conf-41', 'conf-42-1', 'conf-42-2' ] self.cancel_file = tmp_dir + '/field.solved' solved = None global engines for conf in conf_list: engine = engines.get(conf) engine.solve(tmp_dir + '/field.axy') self.engines.append(engine) while True: running = False for engine in self.engines: if not engine.check(): running = True if not running: break time.sleep(0.1) if os.path.exists(tmp_dir + '/field.wcs'): solved = tmp_dir + '/field' self.cancel_file = None if solved is None or not os.path.exists(solved + ".wcs"): self.ra = None self.dec = None self.field_deg = None shutil.rmtree(tmp_dir) return self.wcs = Tan(solved + ".wcs", 0) self.ra, self.dec = self.wcs.radec_center() self.field_deg = self.field_w * self.wcs.pixel_scale() / 3600 ind = pyfits.open(solved + '.rdls') tbdata = ind[1].data self.ind_sources = [] self.ind_radec = [] for l in tbdata: ok, x, y = self.wcs.radec2pixelxy(l['ra'], l['dec']) x = np.clip(int(x), 0, self.field_w - 1) y = np.clip(int(y), 0, self.field_h - 1) self.ind_sources.append((x,y)) self.ind_radec.append((l['ra'], l['dec'])) if self.field_corr is not None: corr = pyfits.open(solved + '.corr') for l in corr[1].data: self.field_corr.append((l['field_x'], l['field_y'], l['index_x'], l['field_y'])) shutil.rmtree(tmp_dir) self.solved = True
# plt.plot(x, L2, 'b-')
# plt.savefig('l1.png')
x = np.linspace(-3.5, 4.5, 8192).astype(np.float32)
L1 = np.zeros_like(x)
L2 = np.zeros_like(x)
lanczos3_filter(x, L1)
lanczos3_filter_table(x, L2, 1)
print 'L2 - L1 RMS:', np.sqrt(np.mean((L2-L1)**2))
if True:
ra,dec = 0.,0.,
pixscale = 1e-3
W,H = 10,1
cowcs = Tan(ra, dec, (W+1)/2., (H+1)/2.,
-pixscale, 0., 0., pixscale, W, H)
dx,dy = 0.25, 0.
wcs = Tan(ra, dec, (W+1)/2. + dx, (H+1)/2. + dy,
-pixscale, 0., 0., pixscale, W, H)
pix = np.zeros((H,W), np.float32)
pix[0,W/2] = 1.
Yo,Xo,Yi,Xi,(cpix,) = resample_with_wcs(cowcs, wcs, [pix], 3)
print 'C', cpix
Yo2,Xo2,Yi2,Xi2,(pypix,) = resample_with_wcs(cowcs, wcs, [pix], 3, cinterp=False, table=False)
print 'Py', pypix
print 'RMS', np.sqrt(np.mean((cpix - pypix)**2))
sys.exit(0)
def wise_cutouts(ra, dec, radius, ps, pixscale=2.75, tractor_base=".", unwise_dir="unwise-coadds"):
"""
radius in arcsec.
pixscale: WISE pixel scale in arcsec/pixel;
make this smaller than 2.75 to oversample.
"""
npix = int(np.ceil(radius / pixscale))
print("Image size:", npix)
W = H = npix
pix = pixscale / 3600.0
wcs = Tan(ra, dec, (W + 1) / 2.0, (H + 1) / 2.0, -pix, 0.0, 0.0, pix, float(W), float(H))
# Find DECaLS bricks overlapping
decals = Decals()
B = bricks_touching_wcs(wcs, decals=decals)
print("Found", len(B), "bricks overlapping")
TT = []
for b in B.brickname:
fn = os.path.join(tractor_base, "tractor", b[:3], "tractor-%s.fits" % b)
T = fits_table(fn)
print("Read", len(T), "from", b)
primhdr = fitsio.read_header(fn)
TT.append(T)
T = merge_tables(TT)
print("Total of", len(T), "sources")
T.cut(T.brick_primary)
print(len(T), "primary")
margin = 20
ok, xx, yy = wcs.radec2pixelxy(T.ra, T.dec)
I = np.flatnonzero((xx > -margin) * (yy > -margin) * (xx < W + margin) * (yy < H + margin))
T.cut(I)
print(len(T), "within ROI")
# Pull out DECaLS coadds (image, model, resid).
dwcs = wcs.scale(2.0 * pixscale / 0.262)
dh, dw = dwcs.shape
print("DECaLS resampled shape:", dh, dw)
tags = ["image", "model", "resid"]
coimgs = [np.zeros((dh, dw, 3), np.uint8) for t in tags]
for b in B.brickname:
fn = os.path.join(tractor_base, "coadd", b[:3], b, "decals-%s-image-r.fits" % b)
bwcs = Tan(fn)
try:
Yo, Xo, Yi, Xi, nil = resample_with_wcs(dwcs, bwcs)
except ResampleError:
continue
if len(Yo) == 0:
continue
print("Resampling", len(Yo), "pixels from", b)
xl, xh, yl, yh = Xi.min(), Xi.max(), Yi.min(), Yi.max()
print(
"python legacypipe/runbrick.py -b %s --zoom %i %i %i %i --outdir cluster --pixpsf --splinesky --pipe --no-early-coadds"
% (b, xl - 5, xh + 5, yl - 5, yh + 5)
+ " -P 'pickles/cluster-%(brick)s-%%(stage)s.pickle'"
)
for i, tag in enumerate(tags):
fn = os.path.join(tractor_base, "coadd", b[:3], b, "decals-%s-%s.jpg" % (b, tag))
img = plt.imread(fn)
img = np.flipud(img)
coimgs[i][Yo, Xo, :] = img[Yi, Xi, :]
tt = dict(image="Image", model="Model", resid="Resid")
for img, tag in zip(coimgs, tags):
plt.clf()
dimshow(img, ticks=False)
plt.title("DECaLS grz %s" % tt[tag])
ps.savefig()
# Find unWISE tiles overlapping
tiles = unwise_tiles_touching_wcs(wcs)
print("Cut to", len(tiles), "unWISE tiles")
# Here we assume the targetwcs is axis-aligned and that the
# edge midpoints yield the RA,Dec limits (true for TAN).
r, d = wcs.pixelxy2radec(np.array([1, W, W / 2, W / 2]), np.array([H / 2, H / 2, 1, H]))
# the way the roiradec box is used, the min/max order doesn't matter
roiradec = [r[0], r[1], d[2], d[3]]
ra, dec = T.ra, T.dec
T.shapeexp = np.vstack((T.shapeexp_r, T.shapeexp_e1, T.shapeexp_e2)).T
T.shapedev = np.vstack((T.shapedev_r, T.shapedev_e1, T.shapedev_e2)).T
srcs = read_fits_catalog(T, ellipseClass=EllipseE)
wbands = [1, 2]
wanyband = "w"
for band in wbands:
T.wise_flux[:, band - 1] *= 10.0 ** (primhdr["WISEAB%i" % band] / 2.5)
coimgs = [np.zeros((H, W), np.float32) for b in wbands]
comods = [np.zeros((H, W), np.float32) for b in wbands]
con = [np.zeros((H, W), np.uint8) for b in wbands]
for iband, band in enumerate(wbands):
print("Photometering WISE band", band)
wband = "w%i" % band
for i, src in enumerate(srcs):
# print('Source', src, 'brightness', src.getBrightness(), 'params', src.getBrightness().getParams())
# src.getBrightness().setParams([T.wise_flux[i, band-1]])
src.setBrightness(NanoMaggies(**{wanyband: T.wise_flux[i, band - 1]}))
# print('Set source brightness:', src.getBrightness())
# The tiles have some overlap, so for each source, keep the
# fit in the tile whose center is closest to the source.
for tile in tiles:
print("Reading tile", tile.coadd_id)
tim = get_unwise_tractor_image(unwise_dir, tile.coadd_id, band, bandname=wanyband, roiradecbox=roiradec)
if tim is None:
print("Actually, no overlap with tile", tile.coadd_id)
continue
print("Read image with shape", tim.shape)
# Select sources in play.
wisewcs = tim.wcs.wcs
H, W = tim.shape
ok, x, y = wisewcs.radec2pixelxy(ra, dec)
x = (x - 1.0).astype(np.float32)
y = (y - 1.0).astype(np.float32)
margin = 10.0
I = np.flatnonzero((x >= -margin) * (x < W + margin) * (y >= -margin) * (y < H + margin))
print(len(I), "within the image + margin")
subcat = [srcs[i] for i in I]
tractor = Tractor([tim], subcat)
mod = tractor.getModelImage(0)
# plt.clf()
# dimshow(tim.getImage(), ticks=False)
# plt.title('WISE %s %s' % (tile.coadd_id, wband))
# ps.savefig()
# plt.clf()
# dimshow(mod, ticks=False)
# plt.title('WISE %s %s' % (tile.coadd_id, wband))
# ps.savefig()
try:
Yo, Xo, Yi, Xi, nil = resample_with_wcs(wcs, tim.wcs.wcs)
except ResampleError:
continue
if len(Yo) == 0:
continue
print("Resampling", len(Yo), "pixels from WISE", tile.coadd_id, band)
coimgs[iband][Yo, Xo] += tim.getImage()[Yi, Xi]
comods[iband][Yo, Xo] += mod[Yi, Xi]
con[iband][Yo, Xo] += 1
for img, mod, n in zip(coimgs, comods, con):
img /= np.maximum(n, 1)
mod /= np.maximum(n, 1)
for band, img, mod in zip(wbands, coimgs, comods):
lo, hi = np.percentile(img, [25, 99])
plt.clf()
dimshow(img, vmin=lo, vmax=hi, ticks=False)
plt.title("WISE W%i Data" % band)
ps.savefig()
plt.clf()
dimshow(mod, vmin=lo, vmax=hi, ticks=False)
plt.title("WISE W%i Model" % band)
ps.savefig()
resid = img - mod
mx = np.abs(resid).max()
plt.clf()
dimshow(resid, vmin=-mx, vmax=mx, ticks=False)
plt.title("WISE W%i Resid" % band)
ps.savefig()
# kwa = dict(mn=-0.1, mx=2., arcsinh = 1.)
kwa = dict(mn=-0.1, mx=2.0, arcsinh=None)
rgb = _unwise_to_rgb(coimgs, **kwa)
plt.clf()
dimshow(rgb, ticks=False)
plt.title("WISE W1/W2 Data")
ps.savefig()
rgb = _unwise_to_rgb(comods, **kwa)
plt.clf()
dimshow(rgb, ticks=False)
plt.title("WISE W1/W2 Model")
ps.savefig()
kwa = dict(mn=-1, mx=1, arcsinh=None)
rgb = _unwise_to_rgb([img - mod for img, mod in zip(coimgs, comods)], **kwa)
plt.clf()
dimshow(rgb, ticks=False)
plt.title("WISE W1/W2 Resid")
ps.savefig()
def get_unwise_tractor_image(basedir,
tile,
band,
bandname=None,
masked=True,
**kwargs):
'''
masked: read "-m" images, or "-u"?
bandname: PhotoCal band name to use: default: "w%i" % band
'''
if bandname is None:
bandname = 'w%i' % band
mu = 'm' if masked else 'u'
thisdir = get_unwise_tile_dir(basedir, tile)
base = os.path.join(thisdir, 'unwise-%s-w%i-' % (tile, band))
imfn = base + 'img-%s.fits' % mu
ivfn = base + 'invvar-%s.fits.gz' % mu
#ppfn = base + 'std-%s.fits.gz' % mu
nifn = base + 'n-%s.fits.gz' % mu
print 'Reading', imfn
wcs = Tan(imfn)
twcs = ConstantFitsWcs(wcs)
img = fitsio.FITS(imfn)[0]
H, W = img.get_info()['dims']
H, W = int(H), int(W)
roi, nil = interpret_roi(twcs, (H, W), **kwargs)
if roi is None:
# No overlap with ROI
return None
(x0, x1, y0, y1) = roi
twcs.setX0Y0(x0, y0)
roislice = (slice(y0, y1), slice(x0, x1))
img = img[roislice]
print 'Reading', ivfn
invvar = fitsio.FITS(ivfn)[0][roislice]
#print 'Reading', ppfn
#pp = fitsio.FITS(ppfn)[0][roislice]
print 'Reading', nifn
nims = fitsio.FITS(nifn)[0][roislice]
#print 'Median # ims:', np.median(nims)
good = (nims > 0)
invvar[np.logical_not(good)] = 0.
sig1 = 1. / np.sqrt(np.median(invvar[good]))
# Load the average PSF model (generated by wise_psf.py)
psffn = os.path.join(os.path.dirname(__file__), 'wise-psf-avg.fits')
print 'Reading', psffn
P = fits_table(psffn, hdu=band)
psf = GaussianMixturePSF(P.amp, P.mean, P.var)
sky = 0.
tsky = ConstantSky(sky)
# if opt.errfrac > 0:
# nz = (iv > 0)
# iv2 = np.zeros_like(invvar)
# iv2[nz] = 1./(1./invvar[nz] + (img[nz] * opt.errfrac)**2)
# print 'Increasing error estimate by', opt.errfrac, 'of image flux'
# invvar = iv2
tim = Image(data=img,
invvar=invvar,
psf=psf,
wcs=twcs,
sky=tsky,
photocal=LinearPhotoCal(1., band=bandname),
name='unWISE %s W%i' % (tile, band),
domask=False)
tim.sig1 = sig1
tim.roi = roi
tim.nims = nims
return tim
def create_tractor(opt):
"""
Brittle function to read Groves data sample and make the
things we need for fitting.
# issues:
- Need to read the WCS too.
- Need, for each image, to construct the rectangular
nearest-neighbor interpolation indices to image 0.
- Can I just blow up the SPIRE images with interpolation?
"""
dataList = [
('m31_brick15_PACS100.fits', 7.23, 7.7),
('m31_brick15_PACS160.fits', 3.71, 12.0),
('m31_brick15_SPIRE250.fits', None, 18.0),
('m31_brick15_SPIRE350.fits', None, 25.0),
('m31_brick15_SPIRE500.fits', None, 37.0),
]
# From Groves via Rix:
# PSF FWHMs: 6.97, 11.01, 18.01, 24.73, 35.98
# Within the region Ive selected I found temperatures between 15
# and 21 K, averaging 17 K, and Beta= 1.5 to 2.5 (with a larger
# uncertainty), averaging around 2.0.
if opt.no100:
dataList = dataList[1:]
print 'Reading images...'
tims = []
for i, (fn, noise, fwhm) in enumerate(dataList):
print
print 'Reading', fn
P = pyfits.open(fn)
image = P[0].data.astype(np.float32)
#wcs = Tan(fn, 0)
H,W = image.shape
hdr = P[0].header
wcs = Tan(hdr['CRVAL1'], hdr['CRVAL2'],
hdr['CRPIX1'], hdr['CRPIX2'],
hdr['CDELT1'], 0., 0., hdr['CDELT2'], W, H)
assert(hdr['CROTA2'] == 0.)
if noise is None:
noise = float(hdr['NOISE'])
print 'Noise', noise
print 'Median image value', np.median(image)
invvar = np.ones_like(image) / (noise**2)
skyval = np.percentile(image, 5)
sky = ConstantSky(skyval)
lam = float(hdr['FILTER'])
print 'Lambda', lam
# calibrated, yo
assert(hdr['BUNIT'] == 'MJy/Sr')
# "Filter" is in *microns* in the headers; convert to *m* here, to match "lam0"
pcal = DustPhotoCal(lam * 1e-6, wcs.pixel_scale())
#nm = '%s %i' % (hdr['INSTRUME'], lam)
nm = fn.replace('.fits', '')
#zr = noise * np.array([-3, 10]) + skyval
zr = np.array([np.percentile(image.ravel(), p) for p in [1, 99]])
print 'Pixel scale:', wcs.pixel_scale()
# meh
sigma = fwhm / wcs.pixel_scale() / 2.35
print 'PSF sigma', sigma, 'pixels'
psf = NCircularGaussianPSF([sigma], [1.])
twcs = FitsWcs(wcs)
tim = Image(data=image, invvar=invvar, psf=psf, wcs=twcs,
sky=sky, photocal=pcal, name=nm)
tim.zr = zr
tims.append(tim)
# plt.clf()
# plt.hist(image.ravel(), 100)
# plt.title(nm)
# plt.savefig('im%i-hist.png' % i)
radecbounds = []
for tim in tims:
H,W = tim.shape
twcs = tim.getWcs()
rds = []
for x,y in [(0.5,0.5),(W+0.5,0.5),(W+0.5,H+0.5),(0.5,H+0.5),(0.5,0.5)]:
rd = twcs.pixelToPosition(x,y)
rds.append(rd)
rds = np.array(rds)
radecbounds.append(rds)
rd = np.vstack(radecbounds)
#print 'rd', rd.shape
ramin,decmin = rd.min(axis=0)
ramax,decmax = rd.max(axis=0)
dr,dd = ramax-ramin, decmax-decmin
plotrange = (ramin - 0.05*dr, ramax + 0.05*dr, decmin - 0.05*dd, decmax + 0.05*dd)
plt.clf()
for rds,c in zip(radecbounds, ['b','g','y',(1,0.5,0),'r']):
plt.plot(rds[:,0], rds[:,1], '-', color=c, lw=2, alpha=0.5)
setRadecAxes(*plotrange)
plt.savefig('radec1%s.png' % opt.suffix)
print 'Creating dust sheet...'
N = opt.gridn
# Build a WCS for the dust sheet to match the first image
# (assuming it's square and axis-aligned)
#wcs = tims[0].getWcs().wcs
#r,d = wcs.radec_center()
#H,W = tims[0].shape
#scale = wcs.pixel_scale()
#scale *= float(W)/max(1, N-1) / 3600.
#c = float(N)/2. + 0.5
#dwcs = Tan(r, d, c, c, scale, 0, 0, scale, N, N)
# Build an axis-aligned WCS that contains all the images.
r,d = (ramin + ramax) / 2., (decmin + decmax) / 2.
# HACK -- ignore pole issues
scale = max((ramax - ramin) * np.cos(np.deg2rad(d)), decmax - decmin) / float(N)
scale *= float(N) / float(max(1, N-1))
scale *= (1. / opt.zoom)
cpix = float(N)/2. + 0.5
dwcs = Tan(r, d, cpix, cpix, scale, 0, 0, scale, N, N)
pixscale = dwcs.pixel_scale()
logsa = np.log(1e-3)
H,W = N,N
logsa = np.zeros((H,W)) + logsa
logt = np.zeros((H,W)) + np.log(17.)
emis = np.zeros((H,W)) + 2.
ds = DustSheet(logsa, logt, emis, dwcs)
rds = ds.getRaDecCorners(0.5)
plt.plot(rds[:,0], rds[:,1], 'k-', lw=1, alpha=1)
setRadecAxes(*plotrange)
plt.savefig('radec2%s.png' % opt.suffix)
# plot grid of sample points.
rds = []
H,W = N,N
for y in range(N):
for x in range(N):
r,d = dwcs.pixelxy2radec(x+1, y+1)
rds.append((r,d))
rds = np.array(rds)
plt.plot(rds[:,0], rds[:,1], 'k.', lw=1, alpha=0.5)
setRadecAxes(*plotrange)
plt.savefig('radec3%s.png' % opt.suffix)
#print 'DustSheet:', ds
#print 'np', ds.numberOfParams()
#print 'pn', ds.getParamNames()
#print 'p', ds.getParams()
# print 'PriorChi:', ds.getLogPriorChi()
# ra,ca,va,pb = ds.getLogPriorChi()
# print 'ra', ra
# print 'ca', ca
# print 'va', va
# print 'pb', pb
# for ri,ci,vi,bi in zip(ra,ca,va,pb):
# print
# print 'ri', ri
# print 'ci', ci
# print 'vi', vi
# print 'bi', bi
cat = Catalog()
cat.append(ds)
tractor = Tractor(Images(*tims), cat)
return tractor
def plotarea(ra, dec, radius, ngcnum, tims=None, rds=[]):
from astrometry.util.util import Tan
W, H = 512, 512
scale = (radius * 60.0 * 4) / float(W)
print "SDSS jpeg scale", scale
imgfn = "sdss-mosaic-ngc%04i.png" % ngcnum
if not os.path.exists(imgfn):
url = (
"http://skyservice.pha.jhu.edu/DR8/ImgCutout/getjpeg.aspx?" + "ra=%f&dec=%f&scale=%f&width=%i&height=%i"
) % (ra, dec, scale, W, H)
f = urllib2.urlopen(url)
of, tmpfn = tempfile.mkstemp(suffix=".jpg")
os.close(of)
of = open(tmpfn, "wb")
of.write(f.read())
of.close()
cmd = "jpegtopnm %s | pnmtopng > %s" % (tmpfn, imgfn)
os.system(cmd)
# Create WCS header for it
cd = scale / 3600.0
args = (ra, dec, W / 2.0 + 0.5, H / 2.0 + 0.5, -cd, 0.0, 0.0, -cd, W, H)
wcs = Tan(*[float(x) for x in args])
plt.clf()
I = plt.imread(imgfn)
plt.imshow(I, interpolation="nearest", origin="lower")
x, y = wcs.radec2pixelxy(ra, dec)
R = radius * 60.0 / scale
ax = plt.axis()
plt.gca().add_artist(matplotlib.patches.Circle(xy=(x, y), radius=R, color="g", lw=3, alpha=0.5, fc="none"))
if tims is not None:
print "Plotting outlines of", len(tims), "images"
for tim in tims:
H, W = tim.shape
twcs = tim.getWcs()
px, py = [], []
for x, y in [(1, 1), (W, 1), (W, H), (1, H), (1, 1)]:
rd = twcs.pixelToPosition(x, y)
xx, yy = wcs.radec2pixelxy(rd.ra, rd.dec)
print "x,y", x, y
x1, y1 = twcs.positionToPixel(rd)
print " x1,y1", x1, y1
print " r,d", rd.ra, rd.dec,
print " xx,yy", xx, yy
px.append(xx)
py.append(yy)
plt.plot(px, py, "g-", lw=3, alpha=0.5)
# plot full-frame image outline too
# px,py = [],[]
# W,H = 2048,1489
# for x,y in [(1,1),(W,1),(W,H),(1,H),(1,1)]:
# r,d = twcs.pixelToRaDec(x,y)
# xx,yy = wcs.radec2pixelxy(r,d)
# px.append(xx)
# py.append(yy)
# plt.plot(px, py, 'g-', lw=1, alpha=1.)
if rds is not None:
px, py = [], []
for ra, dec in rds:
print "ra,dec", ra, dec
xx, yy = wcs.radec2pixelxy(ra, dec)
px.append(xx)
py.append(yy)
plt.plot(px, py, "go")
plt.axis(ax)
fn = "ngc-%04i.png" % ngcnum
plt.savefig(fn)
print "saved", fn
def check_priors():
np.seterrcall(np_err_handler)
np.seterr(all='call')
import logging
import sys
lvl = logging.DEBUG
log_init(3)
logging.basicConfig(level=lvl, format='%(message)s', stream=sys.stdout)
if True:
# Check two-pixel priors: smoothness.
H,W = 1,2
logsa = np.zeros((H,W)) + np.log(1e-3)
logt = np.zeros((H,W)) + np.log(17.)
emis = np.zeros((H,W)) + 2.
dwcs = Tan(11.2, 41.9, 1, 1, 1e-3, 0, 0, 1e-3, W, H)
ds = DustSheet(logsa, logt, emis, dwcs)
cat = Catalog()
cat.append(ds)
tractor = Tractor()
tractor.setCatalog(cat)
p0 = tractor.getParams()
print('lnp0', tractor.getLogProb())
if True:
# check getLogProb()
for j,xx in enumerate([
np.linspace(np.log(1e-5), np.log(1e-1), 20),
np.linspace(np.log(1e-5), np.log(1e-1), 20),
np.linspace(np.log(10.), np.log(20.), 20),
np.linspace(np.log(10.), np.log(20.), 20),
np.linspace(0., 4., 20),
np.linspace(0., 4., 20),
]):
pp = []
for x in xx:
tractor.setParam(j, x)
p = tractor.getLogProb()
pp.append(p)
tractor.setParam(j, p0[j])
plt.clf()
plt.plot(xx, pp, 'ro-')
plt.title(ds.getParamNames()[j])
plt.savefig('p%i.png' % (20 + j))
# set the absolute priors to have little effect and repeat.
ds.prior_logt_std = np.log(100.)
ds.prior_emis_std = np.log(100.)
for j,xx in enumerate([
np.linspace(np.log(1e-5), np.log(1e-1), 20),
np.linspace(np.log(1e-5), np.log(1e-1), 20),
np.linspace(np.log(10.), np.log(20.), 20),
np.linspace(np.log(10.), np.log(20.), 20),
np.linspace(0., 4., 20),
np.linspace(0., 4., 20),
]):
pp = []
for x in xx:
tractor.setParam(j, x)
p = tractor.getLogProb()
pp.append(p)
tractor.setParam(j, p0[j])
plt.clf()
plt.plot(xx, pp, 'ro-')
plt.title(ds.getParamNames()[j])
plt.savefig('p%i.png' % (30 + j))
# revert the priors
ds.prior_logt_std = np.log(1.2)
ds.prior_emis_std = np.log(0.5)
# check getLogPriorChi.
for j,(ip,val) in enumerate([
(0, np.log(1e-1)),
(1, np.log(1e-5)),
(2, np.log(5.)),
(3, np.log(5.)),
(2, np.log(30.)),
(3, np.log(30.)),
(4, 1.),
(5, 1.),
(4, 3.),
(5, 3.),
]):
print()
print()
print('Setting', ds.getParamNames()[ip], 'from', p0[ip], 'to', val)
tractor.setParams(p0)
tractor.setParam(ip, val)
xx = [val]
xxall = [tractor.getParams()]
pp = [tractor.getLogProb()]
for i in range(10):
tractor.optimize()#damp=1e-3)
xx.append(tractor.getParams()[ip])
pp.append(tractor.getLogProb())
xxall.append(tractor.getParams())
plt.clf()
plt.plot(xx, pp, 'ro-')
plt.axvline(val, color='r', lw=2, alpha=0.5)
plt.title(ds.getParamNames()[ip])
plt.savefig('p%i.png' % (j+40))
plt.clf()
xxall = np.vstack(xxall)
print('xxall', xxall.shape)
for i in range(6):
#plt.subplot(1,3,(i/2)+1)
#plt.plot(xxall[:,i], pp, 'ro-')
#if i == ip:
# plt.axvline(xxall[0,i], color='r', lw=2, alpha=0.5)
#plt.title(ds.getParamNames()[i])
plt.subplot(3,1,(i/2)+1)
c = 'b'
if i == ip:
c = 'r'
plt.plot(xxall[:,i], 'o-', color=c)
plt.title(ds.getParamNames()[i])
plt.savefig('p%i.png' % (j+50))
if False:
# Check single-pixel priors: getLogPrior()
N = 1
H,W = N,N
logsa = np.zeros((H,W)) + np.log(1e-3)
logt = np.zeros((H,W)) + np.log(17.)
emis = np.zeros((H,W)) + 2.
dwcs = Tan(11.2, 41.9, 1, 1, 1e-3, 0, 0, 1e-3, N, N)
ds = DustSheet(logsa, logt, emis, dwcs)
cat = Catalog()
cat.append(ds)
tractor = Tractor()
tractor.setCatalog(cat)
p0 = tractor.getParams()
print('lnp0', tractor.getLogProb())
# no prior on solid angle
# N(log(17.), log(1.2)) on T
# N(2, 0.5) on emis
for j,xx in enumerate([
np.linspace(np.log(1e-5), np.log(1e-1), 20),
np.linspace(np.log(10.), np.log(20.), 20),
np.linspace(0., 4., 20),
]):
pp = []
for x in xx:
tractor.setParam(j, x)
p = tractor.getLogProb()
pp.append(p)
tractor.setParam(j, p0[j])
plt.clf()
plt.plot(xx, pp, 'ro-')
plt.title(ds.getParamNames()[j])
plt.savefig('p%i.png' % j)
# Check single-pixel priors: getPriorChi()
for j,(ip,val) in enumerate([
(0, 1e-2),
(1, np.log(5.)),
(1, np.log(30.)),
(2, 1.),
(2, 3.),
]):
print()
print()
print('Setting', ds.getParamNames()[ip], 'to', val)
tractor.setParams(p0)
tractor.setParam(ip, val)
xx = [val]
pp = [tractor.getLogProb()]
for i in range(10):
tractor.optimize(damp=1e-3)
xx.append(tractor.getParams()[ip])
pp.append(tractor.getLogProb())
plt.clf()
plt.plot(xx, pp, 'ro-')
plt.title(ds.getParamNames()[ip])
plt.savefig('p%i.png' % (j+10))
def main():
ps = PlotSequence('cov')
survey = LegacySurveyData()
ra,dec = 242.0, 10.2
fn = 'coverage-ccds.fits'
if not os.path.exists(fn):
ccds = survey.get_ccds()
ccds.cut(ccds.filter == 'r')
ccds.cut(ccds.propid == '2014B-0404')
ccds.cut(np.hypot(ccds.ra_bore - ra, ccds.dec_bore - dec) < 2.5)
print(np.unique(ccds.expnum), 'unique exposures')
print('propids', np.unique(ccds.propid))
ccds.writeto(fn)
else:
ccds = fits_table(fn)
plt.clf()
for e in np.unique(ccds.expnum):
I = np.flatnonzero(ccds.expnum == e)
plt.plot(ccds.ra[I], ccds.dec[I], '.')
ps.savefig()
degw = 3.0
pixscale = 10.
W = degw * 3600 / 10.
H = W
hi = 6
cmap = cmap_discretize('jet', hi+1)
wcs = Tan(ra, dec, W/2.+0.5, H/2.+0.5,
-pixscale/3600., 0., 0., pixscale/3600., float(W), float(H))
r0,d0 = wcs.pixelxy2radec(1,1)
r1,d1 = wcs.pixelxy2radec(W,H)
extent = [min(r0,r1),max(r0,r1), min(d0,d1),max(d0,d1)]
for expnums in [ [348666], [348666,348710, 348686],
[348659, 348667, 348658, 348666, 348665, 348669, 348668],
None,
[348683, 348687, 347333, 348686, 348685, 348692, 348694,
348659, 348667, 348658, 348666, 348665, 348669, 348668,
348707, 348709, 348708, 348710, 348711, 348716, 348717],
]:
nexp = np.zeros((H,W), np.uint8)
for ccd in ccds:
if expnums is not None and not ccd.expnum in expnums:
continue
ccdwcs = survey.get_approx_wcs(ccd)
r,d = ccdwcs.pixelxy2radec(1, 1)
ok,x0,y0 = wcs.radec2pixelxy(r, d)
r,d = ccdwcs.pixelxy2radec(ccd.width, ccd.height)
ok,x1,y1 = wcs.radec2pixelxy(r, d)
xlo = np.clip(int(np.round(min(x0,x1))) - 1, 0, W-1)
xhi = np.clip(int(np.round(max(x0,x1))) - 1, 0, W-1)
ylo = np.clip(int(np.round(min(y0,y1))) - 1, 0, H-1)
yhi = np.clip(int(np.round(max(y0,y1))) - 1, 0, H-1)
nexp[ylo:yhi+1, xlo:xhi+1] += 1
plt.clf()
plt.imshow(nexp, interpolation='nearest', origin='lower',
vmin=-0.5, vmax=hi+0.5, cmap=cmap, extent=extent)
plt.colorbar(ticks=np.arange(hi+1))
ps.savefig()
O = fits_table('obstatus/decam-tiles_obstatus.fits')
O.cut(np.hypot(O.ra - ra, O.dec - dec) < 2.5)
for p in [1,2,3]:
print('Pass', p, 'exposures:', O.r_expnum[O.get('pass') == p])
O.cut(O.get('pass') == 2)
print(len(O), 'pass 2 nearby')
d = np.hypot(O.ra - ra, O.dec - dec)
print('Dists:', d)
I = np.flatnonzero(d < 0.5)
assert(len(I) == 1)
ocenter = O[I[0]]
print('Center expnum', ocenter.r_expnum)
I = np.flatnonzero(d >= 0.5)
O.cut(I)
#center = ccds[ccds.expnum == ocenter.r_expnum]
#p2 = ccds[ccds.
ok,xc,yc = wcs.radec2pixelxy(ocenter.ra, ocenter.dec)
xx,yy = np.meshgrid(np.arange(W)+1, np.arange(H)+1)
c_d2 = (xc - xx)**2 + (yc - yy)**2
best = np.ones((H,W), bool)
for o in O:
ok,x,y = wcs.radec2pixelxy(o.ra, o.dec)
d2 = (x - xx)**2 + (y - yy)**2
best[d2 < c_d2] = False
del d2
del c_d2,xx,yy
# plt.clf()
# plt.imshow(best, interpolation='nearest', origin='lower', cmap='gray',
# vmin=0, vmax=1)
# ps.savefig()
plt.clf()
plt.imshow(nexp * best, interpolation='nearest', origin='lower',
vmin=-0.5, vmax=hi+0.5, cmap=cmap, extent=extent)
plt.colorbar(ticks=np.arange(hi+1))
ps.savefig()
plt.clf()
n,b,p = plt.hist(np.clip(nexp[best], 0, hi), range=(-0.5,hi+0.5), bins=hi+1)
plt.xlim(-0.5, hi+0.5)
ps.savefig()
print('b', b)
print('n', n)
print('fracs', np.array(n) / np.sum(n))
print('pcts', ', '.join(['%.1f' % f for f in 100. * np.array(n)/np.sum(n)]))
def create_tractor(opt):
"""
Brittle function to read Groves data sample and make the
things we need for fitting.
# issues:
- Need to read the WCS too.
- Need, for each image, to construct the rectangular
nearest-neighbor interpolation indices to image 0.
- Can I just blow up the SPIRE images with interpolation?
"""
dataList = [
('m31_brick15_PACS100.fits', 'PACS 100', 7.23, 7.7),
('m31_brick15_PACS160.fits', 'PACS 160', 3.71, 12.0),
('m31_brick15_SPIRE250.fits', 'SPIRE 250', None, 18.0),
('m31_brick15_SPIRE350.fits', 'SPIRE 350', None, 25.0),
('m31_brick15_SPIRE500.fits', 'SPIRE 500', None, 37.0),
]
# From Groves via Rix:
# PSF FWHMs: 6.97, 11.01, 18.01, 24.73, 35.98
# Within the region Ive selected I found temperatures between 15
# and 21 K, averaging 17 K, and Beta= 1.5 to 2.5 (with a larger
# uncertainty), averaging around 2.0.
if opt.no100:
dataList = dataList[1:]
print('Reading images...')
tims = []
for i, (fn, nm, noise, fwhm) in enumerate(dataList):
print()
print('Reading', fn)
P = pyfits.open(fn)
image = P[0].data.astype(np.float32)
#wcs = Tan(fn, 0)
H,W = image.shape
hdr = P[0].header
wcs = Tan(hdr['CRVAL1'], hdr['CRVAL2'],
hdr['CRPIX1'], hdr['CRPIX2'],
hdr['CDELT1'], 0., 0., hdr['CDELT2'], W, H)
assert(hdr['CROTA2'] == 0.)
if noise is None:
noise = float(hdr['NOISE'])
print('Noise', noise)
print('Median image value', np.median(image))
inverr = np.ones_like(image) / noise
skyval = np.percentile(image, 5)
sky = ConstantSky(skyval)
lam = float(hdr['FILTER'])
print('Lambda', lam)
# calibrated, yo
assert(hdr['BUNIT'] == 'MJy/Sr')
# "Filter" is in *microns* in the headers; convert to *m* here, to match "lam0"
pcal = DustPhotoCal(lam * 1e-6, wcs.pixel_scale())
#nm = '%s %i' % (hdr['INSTRUME'], lam)
#nm = fn.replace('.fits', '')
#zr = noise * np.array([-3, 10]) + skyval
zr = np.array([np.percentile(image.ravel(), p) for p in [1, 99]])
print('Pixel scale:', wcs.pixel_scale())
# meh
sigma = fwhm / wcs.pixel_scale() / 2.35
print('PSF sigma', sigma, 'pixels')
psf = NCircularGaussianPSF([sigma], [1.])
twcs = ConstantFitsWcs(wcs)
tim = Image(data=image, inverr=inverr, psf=psf, wcs=twcs,
sky=sky, photocal=pcal, name=nm)
print('created', tim)
tim.zr = zr
tims.append(tim)
# plt.clf()
# plt.hist(image.ravel(), 100)
# plt.title(nm)
# plt.savefig('im%i-hist.png' % i)
radecbounds = []
for tim in tims:
H,W = tim.shape
twcs = tim.getWcs()
rds = []
for x,y in [(0.5,0.5),(W+0.5,0.5),(W+0.5,H+0.5),(0.5,H+0.5),(0.5,0.5)]:
rd = twcs.pixelToPosition(x-1, y-1)
rds.append(rd)
rds = np.array(rds)
radecbounds.append(rds)
rd = np.vstack(radecbounds)
#print 'rd', rd.shape
ramin,decmin = rd.min(axis=0)
ramax,decmax = rd.max(axis=0)
dr,dd = ramax-ramin, decmax-decmin
plotrange = (ramin - 0.05*dr, ramax + 0.05*dr, decmin - 0.05*dd, decmax + 0.05*dd)
plt.clf()
for rds,c in zip(radecbounds, ['b','g','y',(1,0.5,0),'r']):
plt.plot(rds[:,0], rds[:,1], '-', color=c, lw=2, alpha=0.5)
setRadecAxes(*plotrange)
plt.savefig('radec1%s.png' % opt.suffix)
print('Creating dust sheet...')
N = opt.gridn
# Build a WCS for the dust sheet to match the first image
# (assuming it's square and axis-aligned)
#wcs = tims[0].getWcs().wcs
#r,d = wcs.radec_center()
#H,W = tims[0].shape
#scale = wcs.pixel_scale()
#scale *= float(W)/max(1, N-1) / 3600.
#c = float(N)/2. + 0.5
#dwcs = Tan(r, d, c, c, scale, 0, 0, scale, N, N)
# Build an axis-aligned WCS that contains all the images.
r,d = (ramin + ramax) / 2., (decmin + decmax) / 2.
# HACK -- ignore pole issues
scale = max((ramax - ramin) * np.cos(np.deg2rad(d)), decmax - decmin) / float(N)
scale *= float(N) / float(max(1, N-1))
scale *= (1. / opt.zoom)
cpix = float(N)/2. + 0.5
dwcs = Tan(r, d, cpix, cpix, scale, 0, 0, scale, N, N)
pixscale = dwcs.pixel_scale()
logsa = np.log(1e-3)
H,W = N,N
logsa = np.zeros((H,W)) + logsa
logt = np.zeros((H,W)) + np.log(17.)
emis = np.zeros((H,W)) + 2.
ds = DustSheet(logsa, logt, emis, dwcs)
rds = ds.getRaDecCorners(0.5)
plt.plot(rds[:,0], rds[:,1], 'k-', lw=1, alpha=1)
setRadecAxes(*plotrange)
plt.savefig('radec2%s.png' % opt.suffix)
# plot grid of sample points.
rds = []
H,W = N,N
for y in range(N):
for x in range(N):
r,d = dwcs.pixelxy2radec(x+1, y+1)
rds.append((r,d))
rds = np.array(rds)
plt.plot(rds[:,0], rds[:,1], 'k.', lw=1, alpha=0.5)
setRadecAxes(*plotrange)
plt.savefig('radec3%s.png' % opt.suffix)
#print 'DustSheet:', ds
#print 'np', ds.numberOfParams()
#print 'pn', ds.getParamNames()
#print 'p', ds.getParams()
# print 'PriorChi:', ds.getLogPriorChi()
# ra,ca,va,pb = ds.getLogPriorChi()
# print 'ra', ra
# print 'ca', ca
# print 'va', va
# print 'pb', pb
# for ri,ci,vi,bi in zip(ra,ca,va,pb):
# print
# print 'ri', ri
# print 'ci', ci
# print 'vi', vi
# print 'bi', bi
cat = Catalog()
cat.append(ds)
tractor = Tractor(Images(*tims), cat)
return tractor
def dojob(job, userimage, log=None):
jobdir = job.make_dir()
#print 'Created job dir', jobdir
#log = create_job_logger(job)
#jobdir = job.get_dir()
if log is None:
log = create_job_logger(job)
log.msg('Starting Job processing for', job)
job.set_start_time()
job.save()
#os.chdir(dirnm) - not thread safe (working directory is global)!
log.msg('Creating directory', jobdir)
axyfn = 'job.axy'
axypath = os.path.join(jobdir, axyfn)
sub = userimage.submission
log.msg('submission id', sub.id)
df = userimage.image.disk_file
img = userimage.image
# Build command-line arguments for the augment-xylist program, which
# detects sources in the image and adds processing arguments to the header
# to produce a "job.axy" file.
slo,shi = sub.get_scale_bounds()
# Note, this must match Job.get_wcs_file().
wcsfile = 'wcs.fits'
axyflags = []
axyargs = {
'--out': axypath,
'--scale-low': slo,
'--scale-high': shi,
'--scale-units': sub.scale_units,
'--wcs': wcsfile,
'--rdls': 'rdls.fits',
'--pixel-error': sub.positional_error,
'--ra': sub.center_ra,
'--dec': sub.center_dec,
'--radius': sub.radius,
'--downsample': sub.downsample_factor,
# tuning-up maybe fixed; if not, turn it off with:
#'--odds-to-tune': 1e9,
# Other things we might want include...
# --invert
# -g / --guess-scale: try to guess the image scale from the FITS headers
# --crpix-x <pix>: set the WCS reference point to the given position
# --crpix-y <pix>: set the WCS reference point to the given position
# -w / --width <pixels>: specify the field width
# -e / --height <pixels>: specify the field height
# -X / --x-column <column-name>: the FITS column name
# -Y / --y-column <column-name>
}
if hasattr(img,'sourcelist'):
# image is a source list; use --xylist
axyargs['--xylist'] = img.sourcelist.get_fits_path()
w,h = img.width, img.height
if sub.image_width:
w = sub.image_width
if sub.image_height:
h = sub.image_height
axyargs['--width' ] = w
axyargs['--height'] = h
else:
axyargs['--image'] = df.get_path()
# UGLY
if sub.parity == 0:
axyargs['--parity'] = 'pos'
elif sub.parity == 1:
axyargs['--parity'] = 'neg'
if sub.tweak_order == 0:
axyflags.append('--no-tweak')
else:
axyargs['--tweak-order'] = '%i' % sub.tweak_order
if sub.use_sextractor:
axyflags.append('--use-sextractor')
if sub.crpix_center:
axyflags.append('--crpix-center')
if sub.invert:
axyflags.append('--invert')
cmd = 'augment-xylist '
for (k,v) in axyargs.items():
if v:
cmd += k + ' ' + str(v) + ' '
for k in axyflags:
cmd += k + ' '
log.msg('running: ' + cmd)
(rtn, out, err) = run_command(cmd)
if rtn:
log.msg('out: ' + out)
log.msg('err: ' + err)
logmsg('augment-xylist failed: rtn val', rtn, 'err', err)
raise Exception
log.msg('created axy file', axypath)
# shell into compute server...
logfn = job.get_log_file()
# the "tar" commands both use "-C" to chdir, and the ssh command
# and redirect uses absolute paths.
cmd = ('(echo %(jobid)s; '
'tar cf - --ignore-failed-read -C %(jobdir)s %(axyfile)s) | '
'ssh -x -T %(sshconfig)s 2>>%(logfile)s | '
'tar xf - --atime-preserve -m --exclude=%(axyfile)s -C %(jobdir)s '
'>>%(logfile)s 2>&1' %
dict(jobid='job-%s-%i' % (settings.sitename, job.id),
axyfile=axyfn, jobdir=jobdir,
sshconfig=settings.ssh_solver_config,
logfile=logfn))
log.msg('command:', cmd)
w = os.system(cmd)
if not os.WIFEXITED(w):
log.msg('Solver failed (sent signal?)')
logmsg('Call to solver failed for job', job.id)
raise Exception
rtn = os.WEXITSTATUS(w)
if rtn:
log.msg('Solver failed with return value %i' % rtn)
logmsg('Call to solver failed for job', job.id, 'with return val', rtn)
raise Exception
log.msg('Solver completed successfully.')
# Solved?
wcsfn = os.path.join(jobdir, wcsfile)
log.msg('Checking for WCS file', wcsfn)
if os.path.exists(wcsfn):
log.msg('WCS file exists')
# Parse the wcs.fits file
wcs = Tan(wcsfn, 0)
# Convert to database model...
tan = TanWCS(crval1=wcs.crval[0], crval2=wcs.crval[1],
crpix1=wcs.crpix[0], crpix2=wcs.crpix[1],
cd11=wcs.cd[0], cd12=wcs.cd[1],
cd21=wcs.cd[2], cd22=wcs.cd[3],
imagew=img.width, imageh=img.height)
tan.save()
log.msg('Created TanWCS:', tan)
# Find field's healpix nside and index
ra, dec, radius = tan.get_center_radecradius()
nside = anutil.healpix_nside_for_side_length_arcmin(radius*60)
nside = int(2**round(math.log(nside, 2)))
healpix = anutil.radecdegtohealpix(ra, dec, nside)
sky_location, created = SkyLocation.objects.get_or_create(nside=nside, healpix=healpix)
log.msg('SkyLocation:', sky_location)
# Find bounds for the Calibration object.
r0,r1,d0,d1 = wcs.radec_bounds()
# Find cartesian coordinates
ra *= math.pi/180
dec *= math.pi/180
tempr = math.cos(dec)
x = tempr*math.cos(ra)
y = tempr*math.sin(ra)
z = math.sin(dec)
r = radius/180*math.pi
calib = Calibration(raw_tan=tan, ramin=r0, ramax=r1, decmin=d0, decmax=d1,
x=x,y=y,z=z,r=r,
sky_location=sky_location)
calib.save()
log.msg('Created Calibration', calib)
job.calibration = calib
job.save() # save calib before adding machine tags
job.status = 'S'
job.user_image.add_machine_tags(job)
job.user_image.add_sky_objects(job)
else:
job.status = 'F'
job.set_end_time()
job.save()
log.msg('Finished job', job.id)
logmsg('Finished job',job.id)
return job.id
