def makequads2(starlist, f=5.0, n=6, s=0, d=50.0, verbose=True): """ Similar, but fxf in subareas roughly f times smaller than the full frame. s allows to skip the brightest stars in each region :param f: smallness of the subareas :type f: float :param n: number of stars to consider in each subarea :type n: int :param d: minimal distance between stars :type d: float :param s: number of brightest stars to skip in each subarea :type s: int """ quadlist = [] sortedstars = star.sortstarlistbyflux(starlist) (xmin, xmax, ymin, ymax) = star.area(sortedstars) r = 2.0 * max(xmax - xmin, ymax - ymin) / f for xc in np.linspace(xmin, xmax, f+2)[1:-1]: for yc in np.linspace(ymin, ymax, f+2)[1:-1]: cstar = star.Star(x=xc, y=yc) das = cstar.distanceandsort(sortedstars) #closest = [s["star"] for s in das[0:4]] brightestwithinr = star.sortstarlistbyflux([element["star"] for element in das if element["dist"] <= r])[s:s+n] for fourstars in itertools.combinations(brightestwithinr, 4): if mindist(fourstars) > d: quadlist.append(Quad(fourstars)) if verbose: print "Made %4i quads from %4i stars (combi sub f=%.1f n=%i s=%i d=%.1f)" % (len(quadlist), len(starlist), f, n, s, d) return quadlist
def makequads1(starlist, n=7, s=0, d=50.0, verbose=True):
"""
First trivial quad maker.
Makes combis of the n brightest stars.
:param n: number of stars to consider (brightest ones).
:type n: int
:param s: how many of the brightest stars should I skip ?
This feature is useful to avoid building quads with nearly saturated stars that are not
available in other exposures.
:type s: int
:param d: minimal distance between stars
:type d: float
"""
quadlist = []
sortedstars = star.sortstarlistbyflux(starlist)
for fourstars in itertools.combinations(sortedstars[s:s + n], 4):
if mindist(fourstars) > d:
quadlist.append(Quad(fourstars))
if verbose:
print "Made %4i quads from %4i stars (combi n=%i s=%i d=%.1f)" % (
len(quadlist), len(starlist), n, s, d)
return quadlist
def makequads1(starlist, n=7, s=0, d=50.0, verbose=True):
"""
First trivial quad maker.
Makes combis of the n brightest stars.
:param n: number of stars to consider (brightest ones).
:type n: int
:param s: how many of the brightest stars should I skip ?
This feature is useful to avoid building quads with nearly saturated stars that are not
available in other exposures.
:type s: int
:param d: minimal distance between stars
:type d: float
"""
quadlist = []
sortedstars = star.sortstarlistbyflux(starlist)
for fourstars in itertools.combinations(sortedstars[s:s + n], 4):
if mindist(fourstars) > d:
quadlist.append(Quad(fourstars))
if verbose:
print("Made %4i quads from %4i stars (combi n=%i s=%i d=%.1f)" % (len(quadlist), len(starlist), n, s, d))
return quadlist
def makequads2(starlist, f=5.0, n=6, s=0, d=50.0, verbose=True):
"""
Similar, but fxf in subareas roughly f times smaller than the full frame.
s allows to skip the brightest stars in each region
:param f: smallness of the subareas
:type f: float
:param n: number of stars to consider in each subarea
:type n: int
:param d: minimal distance between stars
:type d: float
:param s: number of brightest stars to skip in each subarea
:type s: int
"""
quadlist = []
sortedstars = star.sortstarlistbyflux(starlist)
(xmin, xmax, ymin, ymax) = star.area(sortedstars)
r = 2.0 * max(xmax - xmin, ymax - ymin) / f
for xc in np.linspace(xmin, xmax, f + 2)[1:-1]:
for yc in np.linspace(ymin, ymax, f + 2)[1:-1]:
cstar = star.Star(x=xc, y=yc)
das = cstar.distanceandsort(sortedstars)
#closest = [s["star"] for s in das[0:4]]
brightestwithinr = star.sortstarlistbyflux([
element["star"] for element in das if element["dist"] <= r
])[s:s + n]
for fourstars in itertools.combinations(brightestwithinr, 4):
if mindist(fourstars) > d:
quadlist.append(Quad(fourstars))
if verbose:
print "Made %4i quads from %4i stars (combi sub f=%.1f n=%i s=%i d=%.1f)" % (
len(quadlist), len(starlist), f, n, s, d)
return quadlist
def makestarlist(self, skipsaturated=False, n=200, verbose=True):
if self.cat:
if skipsaturated:
maxflag = 3
else:
maxflag = 7
self.starlist = star.sortstarlistbyflux(star.readsexcat(self.cat, hdu=self.hdu, maxflag=maxflag, verbose=verbose))[:n]
(xmin, xmax, ymin, ymax) = star.area(self.starlist, border=0.01)
self.xlim = (xmin, xmax)
self.ylim = (ymin, ymax)
# Given this starlists, what is a good minimal distance for stars in quads ?
self.mindist = min(min(xmax - xmin, ymax - ymin) / 10.0, 30.0)
else:
raise RuntimeError("No cat : call makecat first !")
def makestarlist(self, skipsaturated=False, n=200, verbose=True):
if self.cat:
if skipsaturated:
maxflag = 3
else:
maxflag = 7
self.starlist = star.sortstarlistbyflux(
star.readsexcat(self.cat,
hdu=self.hdu,
maxflag=maxflag,
verbose=verbose))[:n]
(xmin, xmax, ymin, ymax) = star.area(self.starlist, border=0.01)
self.xlim = (xmin, xmax)
self.ylim = (ymin, ymax)
# Given this starlists, what is a good minimal distance for stars in quads ?
self.mindist = min(min(xmax - xmin, ymax - ymin) / 10.0, 30.0)
else:
raise RuntimeError("No cat : call makecat first !")
print "Removing link..."
os.remove(linkpath)
os.symlink(refimgpath, linkpath)
print "I made an alias to this reference image here :"
print linkpath
print "Saturation level (in e-) of ref image : %.2f" % (
refimage["saturlevel"] * refimage["gain"])
print "Now I will need some alignment stars (write them into configdir/alistars.cat)."
proquest(askquestions)
# Load the reference sextractor catalog
refsexcat = os.path.join(alidir, refimage['imgname'] + ".cat")
refautostars = star.readsexcat(refsexcat, maxflag=16, posflux=True)
refautostars = star.sortstarlistbyflux(refautostars)
refscalingfactor = refimage['scalingfactor']
# read and identify the manual reference catalog
refmanstars = star.readmancat(
alistarscat) # So these are the "manual" star coordinates
id = star.listidentify(
refmanstars,
refautostars,
tolerance=identtolerance,
onlysingle=True,
verbose=True) # We find the corresponding precise sextractor coordinates
if len(id["nomatchnames"]) != 0:
print "Warning : the following stars could not be identified in the sextractor catalog :"
print "\n".join(id["nomatchnames"])
skyimage.rebin(3)
skyimage.makepilimage(scale = "lin", negative = False)
skyimage.upsample(2)
skyimage.writetitle("Sky", colour=(255, 0, 0))
skysubimage = f2n.fromfits(skysubimagepath)
skysubimage.setzscale("auto", "auto")
skysubimage.rebin(3)
skysubimage.makepilimage(scale = "log", negative = False)
skysubimage.upsample(2)
skysubimage.writetitle("Skysubtracted image")
# We read the 20 strongest stars from sextractor :
sexcatpath = os.path.join(alidir, image['imgname'] + ".cat")
sexcat = star.readsexcat(sexcatpath)
sexcat = star.sortstarlistbyflux(sexcat)
sexcatasdicts = [{"name":s.name, "x":s.x, "y":s.y} for s in sexcat[:20]]
skysubimage.drawstarlist(sexcatasdicts, r = 30, colour = (255, 255, 255))
skyimage.drawstarlist(sexcatasdicts, r = 30, colour = (255, 0, 0))
skysubinfo = [
"%s" % image["imgname"],
"%s UTC" % image['datet'],
image['telescopename'] + " - " + image['setname'],
"Seeing : %4.2f [arcsec]" % image['seeing'],
"Ellipticity : %4.2f" % image['ell'],
"Airmass : %4.2f" % image['airmass'],
"Sky level : %.1f" % image['skylevel'],
"Sky stddev : %.1f" % image['prealistddev'],
]
