def __init__(self, scan, template, sys=None):
sys = config.get("map_sys", sys)
downsamp = config.get("pmat_moby_downsamp", 20)
bore = scan.boresight.copy()
bore[:, 0] += utils.mjd2ctime(scan.mjd0)
opoint = get_moby_pointing(scan.entry,
bore,
scan.dets,
downgrade=downsamp)
# We will fit a polynomial to the pointing for each detector
box = np.array([[np.min(a), np.max(a)] for a in scan.boresight.T]).T
def scale(a, r):
return 2 * (a - r[0]) / (r[1] - r[0]) - 1
t = scale(scan.boresight[::downsamp, 0], box[:, 0])
az = scale(scan.boresight[::downsamp, 1], box[:, 1])
el = scale(scan.boresight[::downsamp, 2], box[:, 2])
basis = np.array(
[az**4, az**3, az**2, az**1, az**0, el**2, el, t**2, t, t * az]).T
denom = basis.T.dot(basis)
e, v = np.linalg.eigh(denom)
if (np.min(e) < 1e-8 * np.max(e)):
basis = np.concatenate([basis[:, :5], basis[:, 6:]], 1)
denom = basis.T.dot(basis)
# Convert from ra/dec to pixels, since we've confirmed that
# our ra/dec is the same as ninkasi to 0.01".
t1 = time.time()
pix = template.sky2pix(opoint[1::-1], safe=True)
t2 = time.time()
#rafit = np.linalg.solve(denom, basis.T.dot(opoint[0].T))
#decfit = np.linalg.solve(denom, basis.T.dot(opoint[1].T))
yfit = np.linalg.solve(denom, basis.T.dot(pix[0].T))
xfit = np.linalg.solve(denom, basis.T.dot(pix[1].T))
# Just use the same az and t as before for simplicity. The
# coefficients will be different, but the result will be
# the same.
basis = np.array([az**0, az**1, az**2, az**3, t]).T
denom = basis.T.dot(basis)
cosfit = np.linalg.solve(denom, basis.T.dot(opoint[2].T))
sinfit = np.linalg.solve(denom, basis.T.dot(opoint[3].T))
# Parameters for pmat
self.posfit = np.concatenate([yfit[:, :, None], xfit[:, :, None]], 2)
self.polfit = np.concatenate([cosfit[:, :, None], sinfit[:, :, None]],
2)
self.box = box
self.pixbox = np.array([[0, 0], template.shape[-2:]])
self.scan = scan
self.dtype = template.dtype
self.core = get_core(self.dtype)
scan = enscan.Scan(
boresight=bore,
offsets=np.concatenate([np.zeros(d.ndet)[:, None], d.point_offset], 1),
comps=np.concatenate([np.ones(d.ndet)[:, None],
np.zeros((d.ndet, 3))], 1),
mjd0=utils.ctime2mjd(d.boresight[0, 0]),
sys="hor",
site=d.site)
scan.hwp_phase = np.zeros([len(bore), 2])
bore_box = np.array([np.min(d.boresight, 1), np.max(d.boresight, 1)])
bore_corners = utils.box2corners(bore_box)
scan.entry = d.entry
# Is the source above the horizon? If not, it doesn't matter how close
# it is.
mjd = utils.ctime2mjd(
utils.mjd2ctime(scan.mjd0) + scan.boresight[::100, 0])
object_pos = coordinates.interpol_pos("cel",
"hor",
args.objname,
mjd,
site=scan.site)
visible = np.any(object_pos[1] >= margin)
if not visible:
cut = rangelist.zeros((d.ndet, d.nsamp))
else:
pmap = pmat.PmatMap(scan, mask, sys="hor:%s" % args.objname)
# Build a tod to project onto.
tod = np.zeros((d.ndet, d.nsamp), dtype=dtype)
# And project
pmap.forward(tod, mask)
# Any nonzero samples should be cut
trhs = P.backward(scan.tod, ncomp=1)
# We want the standard deviation too
scan.tod[:] = scan.boresight[None, :, 0]**2
N.white(scan.tod)
t2rhs = P.backward(scan.tod, ncomp=1)
# Get the div and hits
scan.tod[:] = 1
N.white(scan.tod)
tdiv = P.backward(scan.tod, ncomp=1)
scan.tod[:] = 1
hits = P.backward(scan.tod, ncomp=1)
with utils.nowarn():
t = trhs / tdiv
t2 = t2rhs / tdiv
trms = (t2 - t**2)**0.5
t0 = utils.mjd2ctime(scan.mjd0)
t += t0
# Get rid of nans to make future calculations easier
with utils.nowarn():
bad = ~np.isfinite(flux) | ~np.isfinite(dflux) | ~(dflux > 0) | ~(hits
> 0)
flux[bad] = t[bad] = hits[bad] = tdiv[bad] = 0
dflux[bad] = np.inf
# Cut any sources that aren't hit by anything, and also get rid of useless indices
good = np.any(np.isfinite(dflux), (1, 2, 3))
sids = np.array(sids)[good]
flux, dflux, t, hits, trms, tdiv = [
a[good, 0, :, 0] for a in [flux, dflux, t, hits, trms, tdiv]
]
# annoying that scan and data objects aren't compatible.
bore = d.boresight.T.copy()
bore[:,0] -= bore[0,0]
scan = enscan.Scan(
boresight = bore,
offsets = np.concatenate([np.zeros(d.ndet)[:,None],d.point_offset],1),
comps = np.concatenate([np.ones(d.ndet)[:,None],np.zeros((d.ndet,3))],1),
mjd0 = utils.ctime2mjd(d.boresight[0,0]),
sys = "hor", site = d.site)
scan.hwp_phase = np.zeros([len(bore),2])
bore_box = np.array([np.min(d.boresight,1),np.max(d.boresight,1)])
bore_corners = utils.box2corners(bore_box)
scan.entry = d.entry
# Is the source above the horizon? If not, it doesn't matter how close
# it is.
mjd = utils.ctime2mjd(utils.mjd2ctime(scan.mjd0)+scan.boresight[::100,0])
try:
object_pos = coordinates.interpol_pos("cel","hor", args.objname, mjd, site=scan.site)
except AttributeError as e:
print("Unexpected error in interpol_pos for %s. mid time was %.5f. message: %s. skipping" % (id, mjd[len(mjd)//2], e))
continue
visible = np.any(object_pos[1] >= -margin)
if not visible:
cut = sampcut.empty(d.ndet, d.nsamp)
else:
pmap = pmat.PmatMap(scan, mask, sys="sidelobe:%s" % args.objname)
# Build a tod to project onto.
tod = np.zeros((d.ndet, d.nsamp), dtype=dtype)
# And project
pmap.forward(tod, mask)
# Any nonzero samples should be cut
args.corr_spacing * utils.degree)
if args.inject:
inject_params = np.loadtxt(args.inject,
ndmin=2) # [:,{ra0,dec0,R,vx,vy,flux}]
asteroids = planet9.get_asteroids(args.asteroid_file, args.asteroid_list)
# How to parallelize? Could do it over chunks. Usually there will be more chunks than
# mpi tasks. But there will still be many tods per chunk too (about 6 tods per hour
# and 72 hours per chunk gives 432 tods per chunk). That's quite a bit for one mpi
# task to do. Could paralellize over both... No, keep things simple. Parallelize over tods
# in a chunk, and make sure that nothing breaks if some tasks don't have anything to do.
L.info("Processing %d chunks" % len(chunks))
for ci, chunk in enumerate(chunks):
ctime0 = int(utils.mjd2ctime(mjd[chunk[0]] // args.dt * args.dt))
if only and ctime0 not in only: continue
cdir = root + str(ctime0)
if args.cont and os.path.exists(cdir + "/info.hdf"): continue
#### 1. Distribute and read in all our scans
chunk_ids = ids[chunk]
chunk_mjd = mjd[chunk]
L.info("Scanning chunk %3d/%d with %4d tods from %s" %
(ci + 1, len(chunks), len(chunk), ids[chunk[0]]))
myinds = np.arange(len(chunk))[comm.rank::comm.size]
myinds, myscans = scanutils.read_scans(
chunk_ids,
myinds,
actscan.ACTScan,
filedb.data,
downsample=config.get("downsample"))
continue
else:
print(f"found {len(sids)} sources")
# insert source into tod
entry = filedb.data[id]
try:
scan = actscan.ACTScan(entry, verbose=verbose>=2)
if scan.ndet < 2 or scan.nsamp < 1: raise errors.DataMissing("no data in tod")
except errors.DataMissing as e:
print("%s skipped: %s" % (id, e))
continue
scan = scan[:,::down]
scan.tod = scan.get_samples()
# build source lists
# ra, dec, T, Q, U, omg, phi
t0 = u.mjd2ctime(scan.mjd0)
phi0 = np.mod(phi+t0*omg, 2*np.pi)
srcs = np.array([srcpos[0], srcpos[1], amps, amps*0, amps*0, omg, phi0, D])
# srcs = np.array([srcpos[0], srcpos[1], amps, amps*0, amps*0])
# build pointing matrix
P = lib.PmatTotVar(scan, srcs, perdet=False, sys=sys)
# P = lib.PmatTot(scan, srcpos[:,sids], perdet=False, sys=sys)
# project pulsar into the given tod
# prepare source parameter: [T,Q,U,omega_c,phi_c]
tod = P.forward(scan.tod*0, amps)
# scan.tod = P.forward(scan.tod*0, amps)
# plot tod for debugging
if 1:
sel = slice(70350,70850)
plt.figure()
plt.plot(tod[:,sel].T, 'k-', alpha=0.5)
