def read_sdata(ifile):
# Output thumb for this tod
with h5py.File(ifile, "r") as hfile:
sdata = [None for key in hfile]
for key in hfile:
ind = int(key)
g = hfile[key]
sdat = bunch.Bunch()
# First parse the wcs
hwcs = g["wcs"]
header = astropy.io.fits.Header()
for key in hwcs:
header[key] = hwcs[key].value
wcs = wcsutils.WCS(header).sub(2)
# Then get the site
sdat.site = bunch.Bunch(
**{key: g["site/" + key].value
for key in g["site"]})
# And the rest
for key in [
"map", "div", "srcpos", "sid", "vel", "fknee", "alpha",
"id", "ctime", "dur", "el", "az", "off"
]:
sdat[key] = g[key].value
sdat.map = enmap.ndmap(fixorder(sdat.map), wcs)
sdat.div = enmap.ndmap(fixorder(sdat.div), wcs)
sdata[ind] = sdat
return sdata
def build_grid_geometry(bounds, res): """bounds: [[y1,x1],[y2,x2]] in rads res: resolution in rads Returns shape, wcs""" bounds = np.array(bounds) ngrid = np.round((bounds[1]-bounds[0])/res).astype(int)+1 wcs = wcsutils.WCS(naxis=2) wcs.wcs.cdelt[:] = res/utils.degree wcs.wcs.crpix[:] = 1 wcs.wcs.crval = bounds[0,::-1]/utils.degree wcs.wcs.ctype = ["RA---CAR","DEC--CAR"] return tuple(ngrid), wcs
def read_thumb_data(fname): res = bunch.Bunch() hdus = astropy.io.fits.open(fname) header = hdus[0].header with warnings.catch_warnings(): wcs = wcsutils.WCS(header).sub(2) res.rhs, res.div, res.corr = enmap.fix_endian(enmap.ndmap(hdus[0].data, wcs)) res.srcinfo = hdus[1].data res.detinfo = hdus[2].data res.id = header["id"] res.off = np.array([float(header["off_x"]),float(header["off_y"])])*utils.arcmin for key in ["bore_az1","bore_az2","bore_el"]: res[key] = float(header[key])*utils.degree res.ctime = float(header["ctime"]) return res
def __exit__(self, type, value, traceback):
sys.stderr.write("%6.2f %6.3f %6.3f %s\n" %
(time.time() - self.t1, memory.current() / 1024.**3,
memory.max() / 1024.**3, self.desc))
with dprint("spec"):
ps = powspec.read_spectrum(args.powspec)[:ncomp, :ncomp]
# Construct our output coordinates, a zea system. My standard
# constructor doesn't handle pole crossing, so do it manually.
with dprint("construct omap"):
R = args.radius * deg2rad
res = args.res * min2rad
wo = wcsutils.WCS(naxis=2)
wo.wcs.ctype = ["RA---ZEA", "DEC--ZEA"]
wo.wcs.crval = [0, 90]
wo.wcs.cdelt = [res / deg2rad, res / deg2rad]
wo.wcs.crpix = [1, 1]
x, y = wo.wcs_world2pix(0, 90 - R / deg2rad, 1)
y = int(np.ceil(y))
n = 2 * y - 1
wo.wcs.crpix = [y, y]
omap = enmap.zeros((n, n), wo)
# Construct our projection coordinates this is a CAR system in order
# to make interpolation easy.
with dprint("construct imap"):
ires = np.array([1, 1. / np.sin(R)]) * res / args.supersample
shape, wi = enmap.geometry(pos=[[np.pi / 2 - R, -np.pi],
with h5py.File(infofile, "r") as hfile:
rhs = hfile["rhs"].value
hits = hfile["hits"].value
srate = hfile["srate"].value
speed = hfile["speed"].value * utils.degree
inspec = hfile["inspec"].value
offsets = hfile["offsets"].value * utils.degree
site = bunch.Bunch(
**{k: hfile["site"][k].value
for k in hfile["site"]})
pattern = hfile["pattern"].value * utils.degree
hwcs = hfile["wcs"]
header = astropy.io.fits.Header()
for key in hwcs:
header[key] = hwcs[key].value
wcs = wcsutils.WCS(header).sub(2)
# Set up our maps
rhs = enmap.ndmap(rhs, wcs)
hits = enmap.ndmap(hits, wcs)
rhs = prepare(rhs)
hits = prepare(hits, hitmap=True)
# Turn offsets into an average array offset and detector offsets relative to that,
# and use the array offset to set up the Unskew matrix.
offset_array = np.mean(offsets, 0)
offset_det = offsets - offset_array
ndet = len(offsets)
if args.unskew == "curved":
U = UnskewCurved(rhs.shape,
rhs.wcs,
pattern,