def test_compare_specter(self):
#- specter version
psf = specter.psf.load_psf(self.psffile)
for ispec in np.linspace(0, 499, 20).astype(int):
spots, corners, psfparams = get_spots(ispec, 1, self.wavelengths,
self.psfdata)
xc, yc = corners
ny, nx = spots.shape[2:4]
for iwave, w in enumerate(self.wavelengths):
xslice, yslice, pix = psf.xypix(ispec, w)
self.assertEqual(pix.shape, (ny, nx))
self.assertEqual(xc[0, iwave], xslice.start)
self.assertEqual(yc[0, iwave], yslice.start)
self.assertTrue(np.allclose(spots[0, iwave], pix))
def compute_fiber_bundle_trace_shifts_using_psf(fibers,
line,
psf,
image,
maxshift=2.):
"""
Computes trace shifts along x and y from a preprocessed image, a PSF (with trace coords), and a given emission line,
by doing a forward model of the image.
Args:
fibers : 1D array with list of fibers
line : float, wavelength of an emission line (in Angstrom)
psf : specter psf object
image : DESI preprocessed image object
Optional:
maxshift : float maximum shift in pixels for 2D chi2 scan
Returns:
x : 1D array of x coordinates on CCD (axis=1 in numpy image array, AXIS=0 in FITS, cross-dispersion axis = fiber number direction)
y : 1D array of y coordinates on CCD (axis=0 in numpy image array, AXIS=1 in FITS, wavelength dispersion axis)
dx : 1D array of shifts along x coordinates on CCD
dy : 1D array of shifts along y coordinates on CCD
sx : 1D array of uncertainties on dx
sy : 1D array of uncertainties on dy
"""
log = get_logger()
#log.info("compute_fiber_bundle_offsets fibers={} line={}".format(fibers,line))
# get central coordinates of bundle for interpolation of offsets on CCD
x, y = psf.xy([
int(np.median(fibers)),
], line)
try:
nfibers = len(fibers)
# compute stamp coordinates
xstart = None
xstop = None
ystart = None
ystop = None
xs = []
ys = []
pix = []
xx = []
yy = []
for fiber in fibers:
txs, tys, tpix = psf.xypix(fiber, line)
xs.append(txs)
ys.append(tys)
pix.append(tpix)
if xstart is None:
xstart = txs.start
xstop = txs.stop
ystart = tys.start
ystop = tys.stop
else:
xstart = min(xstart, txs.start)
xstop = max(xstop, txs.stop)
ystart = min(ystart, tys.start)
ystop = max(ystop, tys.stop)
# load stamp data, with margins to avoid problems with shifted psf
margin = int(maxshift) + 1
stamp = np.zeros(
(ystop - ystart + 2 * margin, xstop - xstart + 2 * margin))
stampivar = np.zeros(stamp.shape)
stamp[margin:-margin, margin:-margin] = image.pix[ystart:ystop,
xstart:xstop]
stampivar[margin:-margin, margin:-margin] = image.ivar[ystart:ystop,
xstart:xstop]
# will use a fixed footprint despite changes of psf stamps
# so that chi2 always based on same data set
footprint = np.zeros(stamp.shape)
for i in range(nfibers):
footprint[margin - ystart + ys[i].start:margin - ystart +
ys[i].stop, margin - xstart + xs[i].start:margin -
xstart + xs[i].stop] = 1
#plt.imshow(footprint) ; plt.show() ; sys.exit(12)
# define grid of shifts to test
res = 0.5
nshift = int(maxshift / res)
dx = res * np.tile(
np.arange(2 * nshift + 1) - nshift, (2 * nshift + 1, 1))
dy = dx.T
original_shape = dx.shape
dx = dx.ravel()
dy = dy.ravel()
chi2 = np.zeros(dx.shape)
A = np.zeros((nfibers, nfibers))
B = np.zeros((nfibers))
mods = np.zeros(np.zeros(nfibers).shape + stamp.shape)
debugging = False
if debugging: # FOR DEBUGGING KEEP MODELS
models = []
# loop on possible shifts
# refit fluxes and compute chi2
for d in range(len(dx)):
# print(d,dx[d],dy[d])
A *= 0
B *= 0
mods *= 0
for i, fiber in enumerate(fibers):
# apply the PSF shift
psf._cache = {} # reset cache !!
psf.coeff['X']._coeff[fiber][0] += dx[d]
psf.coeff['Y']._coeff[fiber][0] += dy[d]
# compute pix and paste on stamp frame
xx, yy, pix = psf.xypix(fiber, line)
mods[i][margin - ystart + yy.start:margin - ystart + yy.stop,
margin - xstart + xx.start:margin - xstart +
xx.stop] = pix
# undo the PSF shift
psf.coeff['X']._coeff[fiber][0] -= dx[d]
psf.coeff['Y']._coeff[fiber][0] -= dy[d]
B[i] = np.sum(stampivar * stamp * mods[i])
for j in range(i + 1):
A[i, j] = np.sum(stampivar * mods[i] * mods[j])
if j != i:
A[j, i] = A[i, j]
Ai = np.linalg.inv(A)
flux = Ai.dot(B)
model = np.zeros(stamp.shape)
for i in range(nfibers):
model += flux[i] * mods[i]
chi2[d] = np.sum(stampivar * (stamp - model)**2)
if debugging:
models.append(model)
if debugging:
schi2 = chi2.reshape(original_shape).copy() # FOR DEBUGGING
sdx = dx.copy()
sdy = dy.copy()
# find minimum chi2 grid point
k = chi2.argmin()
j, i = np.unravel_index(k, ((2 * nshift + 1), (2 * nshift + 1)))
#print("node dx,dy=",dx.reshape(original_shape)[j,i],dy.reshape(original_shape)[j,i])
# cut a region around minimum
delta = 1
istart = max(0, i - delta)
istop = min(2 * nshift + 1, i + delta + 1)
jstart = max(0, j - delta)
jstop = min(2 * nshift + 1, j + delta + 1)
chi2 = chi2.reshape(original_shape)[jstart:jstop, istart:istop].ravel()
dx = dx.reshape(original_shape)[jstart:jstop, istart:istop].ravel()
dy = dy.reshape(original_shape)[jstart:jstop, istart:istop].ravel()
# fit 2D polynomial of deg2
m = np.array([dx * 0 + 1, dx, dy, dx**2, dy**2, dx * dy]).T
c, r, rank, s = np.linalg.lstsq(m, chi2)
if c[3] > 0 and c[4] > 0:
# get minimum
# dchi2/dx=0 : c[1]+2*c[3]*dx+c[5]*dy = 0
# dchi2/dy=0 : c[2]+2*c[4]*dy+c[5]*dx = 0
a = np.array([[2 * c[3], c[5]], [c[5], 2 * c[4]]])
b = np.array([c[1], c[2]])
t = -np.linalg.inv(a).dot(b)
dx = t[0]
dy = t[1]
sx = 1. / np.sqrt(c[3])
sy = 1. / np.sqrt(c[4])
#print("interp dx,dy=",dx,dy)
if debugging: # FOR DEBUGGING
import matplotlib.pyplot as plt
plt.figure()
plt.subplot(2, 2, 1, title="chi2")
plt.imshow(schi2,
extent=(-nshift * res, nshift * res, -nshift * res,
nshift * res),
origin=0,
interpolation="nearest")
plt.plot(dx, dy, "+", color="white", ms=20)
plt.xlabel("x")
plt.ylabel("y")
plt.subplot(2, 2, 2, title="data")
plt.imshow(stamp * footprint,
origin=0,
interpolation="nearest")
plt.grid()
k0 = np.argmin(sdx**2 + sdy**2)
plt.subplot(2, 2, 3, title="original psf")
plt.imshow(models[k0], origin=0, interpolation="nearest")
plt.grid()
plt.subplot(2, 2, 4, title="shifted psf")
plt.imshow(models[k], origin=0, interpolation="nearest")
plt.grid()
plt.show()
else:
log.warning(
"fit failed (bad chi2 surf.) for fibers [%d:%d] line=%dA" %
(fibers[0], fibers[-1] + 1, int(line)))
dx = 0.
dy = 0.
sx = 10.
sy = 10.
except LinAlgError:
log.warning(
"fit failed (masked or missing data) for fibers [%d:%d] line=%dA" %
(fibers[0], fibers[-1] + 1, int(line)))
dx = 0.
dy = 0.
sx = 10.
sy = 10.
return x, y, dx, dy, sx, sy
elif cam == "z": camid = 2
expnum = int(os.path.basename(filename).split("-")[2].replace(".fits", ""))
for fiber in fibers:
tx = psf.x(fiber, args.wave)
ty = psf.y(fiber, args.wave)
dtx = tx - reftx[fiber]
dty = ty - refty[fiber]
psf._cache = {} # reset cache !!
psf.coeff['X']._coeff[fiber][0] -= dtx
psf.coeff['Y']._coeff[fiber][0] -= dty
xx, yy, img = psf.xypix(fiber, args.wave)
cx = np.sum(xpix * img) / np.sum(img)
cy = np.sum(ypix * img) / np.sum(img)
sx = np.sqrt(np.sum((xpix - cx)**2 * img) / np.sum(img))
sy = np.sqrt(np.sum((ypix - cy)**2 * img) / np.sum(img))
ebias = np.sum(refimage[fiber] * img) / np.sum(img**2) - 1.
line = "%d %d %d %d" % (expnum, camid, fiber, args.wave)
line += " %f %f %f %f %f %f %f" % (dtx, dty, cx, cy, sx, sy, ebias)
#print(line)
sys.stdout.flush()
ofile.write("%s\n" % line)
ofile.close()
def compute_fiber_bundle_trace_shifts_using_psf(fibers,line,psf,image,maxshift=2.) :
"""
Computes trace shifts along x and y from a preprocessed image, a PSF (with trace coords), and a given emission line,
by doing a forward model of the image.
Args:
fibers : 1D array with list of fibers
line : float, wavelength of an emission line (in Angstrom)
psf : specter psf object
image : DESI preprocessed image object
Optional:
maxshift : float maximum shift in pixels for 2D chi2 scan
Returns:
x : 1D array of x coordinates on CCD (axis=1 in numpy image array, AXIS=0 in FITS, cross-dispersion axis = fiber number direction)
y : 1D array of y coordinates on CCD (axis=0 in numpy image array, AXIS=1 in FITS, wavelength dispersion axis)
dx : 1D array of shifts along x coordinates on CCD
dy : 1D array of shifts along y coordinates on CCD
sx : 1D array of uncertainties on dx
sy : 1D array of uncertainties on dy
"""
log=get_logger()
#log.info("compute_fiber_bundle_offsets fibers={} line={}".format(fibers,line))
# get central coordinates of bundle for interpolation of offsets on CCD
x,y = psf.xy([int(np.median(fibers)),],line)
try :
nfibers=len(fibers)
# compute stamp coordinates
xstart=None
xstop=None
ystart=None
ystop=None
xs=[]
ys=[]
pix=[]
xx=[]
yy=[]
for fiber in fibers :
txs,tys,tpix = psf.xypix(fiber,line)
xs.append(txs)
ys.append(tys)
pix.append(tpix)
if xstart is None :
xstart =txs.start
xstop =txs.stop
ystart =tys.start
ystop =tys.stop
else :
xstart =min(xstart,txs.start)
xstop =max(xstop,txs.stop)
ystart =min(ystart,tys.start)
ystop =max(ystop,tys.stop)
# load stamp data, with margins to avoid problems with shifted psf
margin=int(maxshift)+1
stamp=np.zeros((ystop-ystart+2*margin,xstop-xstart+2*margin))
stampivar=np.zeros(stamp.shape)
stamp[margin:-margin,margin:-margin]=image.pix[ystart:ystop,xstart:xstop]
stampivar[margin:-margin,margin:-margin]=image.ivar[ystart:ystop,xstart:xstop]
# will use a fixed footprint despite changes of psf stamps
# so that chi2 always based on same data set
footprint=np.zeros(stamp.shape)
for i in range(nfibers) :
footprint[margin-ystart+ys[i].start:margin-ystart+ys[i].stop,margin-xstart+xs[i].start:margin-xstart+xs[i].stop]=1
#plt.imshow(footprint) ; plt.show() ; sys.exit(12)
# define grid of shifts to test
res=0.5
nshift=int(maxshift/res)
dx=res*np.tile(np.arange(2*nshift+1)-nshift,(2*nshift+1,1))
dy=dx.T
original_shape=dx.shape
dx=dx.ravel()
dy=dy.ravel()
chi2=np.zeros(dx.shape)
A=np.zeros((nfibers,nfibers))
B=np.zeros((nfibers))
mods=np.zeros(np.zeros(nfibers).shape+stamp.shape)
debugging=False
if debugging : # FOR DEBUGGING KEEP MODELS
models=[]
# loop on possible shifts
# refit fluxes and compute chi2
for d in range(len(dx)) :
# print(d,dx[d],dy[d])
A *= 0
B *= 0
mods *= 0
for i,fiber in enumerate(fibers) :
# apply the PSF shift
psf._cache={} # reset cache !!
psf.coeff['X']._coeff[fiber][0] += dx[d]
psf.coeff['Y']._coeff[fiber][0] += dy[d]
# compute pix and paste on stamp frame
xx, yy, pix = psf.xypix(fiber,line)
mods[i][margin-ystart+yy.start:margin-ystart+yy.stop,margin-xstart+xx.start:margin-xstart+xx.stop]=pix
# undo the PSF shift
psf.coeff['X']._coeff[fiber][0] -= dx[d]
psf.coeff['Y']._coeff[fiber][0] -= dy[d]
B[i] = np.sum(stampivar*stamp*mods[i])
for j in range(i+1) :
A[i,j] = np.sum(stampivar*mods[i]*mods[j])
if j!=i :
A[j,i] = A[i,j]
Ai=np.linalg.inv(A)
flux=Ai.dot(B)
model=np.zeros(stamp.shape)
for i in range(nfibers) :
model += flux[i]*mods[i]
chi2[d]=np.sum(stampivar*(stamp-model)**2)
if debugging :
models.append(model)
if debugging :
schi2=chi2.reshape(original_shape).copy() # FOR DEBUGGING
sdx=dx.copy()
sdy=dy.copy()
# find minimum chi2 grid point
k = chi2.argmin()
j,i = np.unravel_index(k, ((2*nshift+1),(2*nshift+1)))
#print("node dx,dy=",dx.reshape(original_shape)[j,i],dy.reshape(original_shape)[j,i])
# cut a region around minimum
delta=1
istart=max(0,i-delta)
istop=min(2*nshift+1,i+delta+1)
jstart=max(0,j-delta)
jstop=min(2*nshift+1,j+delta+1)
chi2=chi2.reshape(original_shape)[jstart:jstop,istart:istop].ravel()
dx=dx.reshape(original_shape)[jstart:jstop,istart:istop].ravel()
dy=dy.reshape(original_shape)[jstart:jstop,istart:istop].ravel()
# fit 2D polynomial of deg2
m = np.array([dx*0+1, dx, dy, dx**2, dy**2, dx*dy ]).T
c, r, rank, s = np.linalg.lstsq(m, chi2)
if c[3]>0 and c[4]>0 :
# get minimum
# dchi2/dx=0 : c[1]+2*c[3]*dx+c[5]*dy = 0
# dchi2/dy=0 : c[2]+2*c[4]*dy+c[5]*dx = 0
a=np.array([[2*c[3],c[5]],[c[5],2*c[4]]])
b=np.array([c[1],c[2]])
t=-np.linalg.inv(a).dot(b)
dx=t[0]
dy=t[1]
sx=1./np.sqrt(c[3])
sy=1./np.sqrt(c[4])
#print("interp dx,dy=",dx,dy)
if debugging : # FOR DEBUGGING
import matplotlib.pyplot as plt
plt.figure()
plt.subplot(2,2,1,title="chi2")
plt.imshow(schi2,extent=(-nshift*res,nshift*res,-nshift*res,nshift*res),origin=0,interpolation="nearest")
plt.plot(dx,dy,"+",color="white",ms=20)
plt.xlabel("x")
plt.ylabel("y")
plt.subplot(2,2,2,title="data")
plt.imshow(stamp*footprint,origin=0,interpolation="nearest")
plt.grid()
k0=np.argmin(sdx**2+sdy**2)
plt.subplot(2,2,3,title="original psf")
plt.imshow(models[k0],origin=0,interpolation="nearest")
plt.grid()
plt.subplot(2,2,4,title="shifted psf")
plt.imshow(models[k],origin=0,interpolation="nearest")
plt.grid()
plt.show()
else :
log.warning("fit failed (bad chi2 surf.) for fibers [%d:%d] line=%dA"%(fibers[0],fibers[-1]+1,int(line)))
dx=0.
dy=0.
sx=10.
sy=10.
except LinAlgError :
log.warning("fit failed (masked or missing data) for fibers [%d:%d] line=%dA"%(fibers[0],fibers[-1]+1,int(line)))
dx=0.
dy=0.
sx=10.
sy=10.
return x,y,dx,dy,sx,sy
res_fiber=[]
res_wave=[]
res_x_rms=[]
res_y_rms=[]
use_trace = True
for fiber in fibers :
for wave in waves :
images = []
i0 = []
i1 = []
xc = []
yc = []
for psf in psfs :
xx, yy, ccdpix = psf.xypix(fiber,wave)
xc.append(psf.x(fiber,wave))
yc.append(psf.y(fiber,wave))
#print("fiber,wave",fiber,wave)
#print("xx",xx)
#print("yy",yy)
#plt.imshow(ccdpix)
#plt.show()
images.append(ccdpix)
i1.append(xx.start)
i0.append(yy.start)
default=None,
required=False,
help='path to output image (png) file')
args = parser.parse_args()
psf = specter.psf.GaussHermitePSF(args.psf)
wave = np.linspace(psf.wmin + 200, psf.wmax - 200, 15)
zoom = 18
image = np.zeros((psf.npix_y / zoom, psf.npix_x / zoom))
for fiber in range(psf.nspec):
for wavelength in wave:
print(fiber, wavelength)
x, y = psf.xy(fiber, wavelength)
xslice, yslice, pix = psf.xypix(fiber, wavelength)
xslice = slice(xslice.start + int(x / zoom - x),
xslice.stop + int(x / zoom - x))
yslice = slice(yslice.start + int(y / zoom - y),
yslice.stop + int(y / zoom - y))
try:
image[yslice, xslice] += pix
except ValueError:
print("failed for fiber wave=", fiber, wavelength)
pass
fig = pylab.figure()
pylab.imshow(image,
origin=0,
interpolation="nearest",
psf=specter.psf.GaussHermitePSF(args.psf)
wmin=max(3650,psf.wmin+500)
wmax=min(10200,psf.wmax-500)
wave=np.linspace(wmin,wmax,15)
zoom_out=32
image=np.zeros((psf.npix_y//zoom_out,psf.npix_x//zoom_out))
fibers=10+20*np.arange(25)
for fiber in fibers :
for wavelength in wave :
print(fiber,wavelength)
x,y = psf.xy(fiber,wavelength)
xslice,yslice,pix = psf.xypix(fiber,wavelength)
#dx=xslice.stop-xslice.start
#dy=yslice.stop-yslice.start
dx=(x/zoom_out-x)
dy=(y/zoom_out-y)
idx=int(dx+0.5)
idy=int(dy+0.5)
ipix=np.zeros(pix.shape)
x=np.arange(xslice.start,xslice.stop)+idx
y=np.arange(yslice.start,yslice.stop)+idy
f=scipy.interpolate.interp2d(x,y,pix)
ipix = f(x+(idx-dx), y+(idy-dy))
xslice = slice(xslice.start+idx,xslice.stop+idx)
yslice = slice(yslice.start+idy,yslice.stop+idy)
res_fiber = []
res_wave = []
res_x_rms = []
res_y_rms = []
use_trace = True
for fiber in fibers:
for wave in waves:
images = []
i0 = []
i1 = []
xc = []
yc = []
for psf in psfs:
xx, yy, ccdpix = psf.xypix(fiber, wave)
xc.append(psf.x(fiber, wave))
yc.append(psf.y(fiber, wave))
#print("fiber,wave",fiber,wave)
#print("xx",xx)
#print("yy",yy)
#plt.imshow(ccdpix)
#plt.show()
images.append(ccdpix)
i1.append(xx.start)
i0.append(yy.start)
mi0 = int(np.min(i0))
mi1 = int(np.min(i1))