def build_phase(az, smooth=3):
""""Phase" is an angle that increases with az while az increases, but
continues to increase as az falls."""
mi, ma = utils.minmax(az)
phase = (az - mi) / (ma - mi)
falling = phase[1:] - phase[:-1] < 0
falling = np.concatenate([falling[:1], falling])
phase[falling] = 2 - phase[falling]
if smooth:
phase = signal.medfilt(phase, 2 * smooth + 1)
return phase
area = enmap.read_map(args.area)
area = enmap.zeros((ncomp, ) + area.shape[-2:], area.wcs, dtype)
# First loop through all the tods and find out which are in which scanning
# pattern.
L.info("Scanning tods")
mypatids = {}
ids = filedb.scans[args.sel]
myinds = range(comm.rank, len(ids), comm.size)
ndet_array = 0
for ind, d in scanutils.scan_iterator(ids, myinds, actscan.ACTScan,
filedb.data):
id = ids[ind]
# Determine which scanning pattern we have
el = np.mean(d.boresight[:, 2])
az1, az2 = utils.minmax(d.boresight[:, 1])
pat = np.array([el, az1, az2]) / utils.degree
pat = tuple(np.round(pat / args.bsize) * args.bsize)
ndet_array = d.layout.ndet
# And record it
if pat not in mypatids:
mypatids[pat] = []
mypatids[pat].append(id)
ndet_array = comm.allreduce(ndet_array, mpi.MAX)
# Get list of all patterns
L.info("Gathering pattern lists")
mypats = mypatids.keys()
if len(mypats) == 0: mypats = np.zeros([0, 3])
pats = utils.allgatherv(mypats, comm)
pats = list(set(sorted([tuple(pat) for pat in pats])))
abscal /= d.mce_gain
elif d.gain_mode == 'mce_compat':
abscal /= d.mce_gain * 1217.8583043
else:
raise ValueError('gain_mode {} not understood'.format(d.gain_mode))
with bench.show("calibrate"):
d = actdata.calibrate(d, exclude=["autocut"])
rel_gain = d.gain_raw.copy() # To store later on.
if d.ndet == 0 or d.nsamp < 2:
raise errors.DataMissing("no data in tod")
# Select detectors if needed.
if dbox is not None:
mid = np.mean(utils.minmax(d.point_template, 0), 0)
off = d.point_template - mid
good = np.all((off > dbox[0]) & (off < dbox[1]), -1)
d = d.restrict(dets=d.dets[good])
except errors.DataMissing as e:
print("Skipping %s (%s)" % (id, e.message))
continue
print("Processing %s" % id, d.ndet, d.nsamp)
# Very simple white noise model.
with bench.show("ivar"):
tod = d.tod
del d.tod
tod -= np.mean(tod, 1)[:, None]
tod = tod.astype(dtype)
t0 = args.t # In mjd
ibox = np.array([
[0, wt],
[args.az - args.waz / 2., args.az + args.waz / 2.],
[args.el - args.wel / 2., args.el + args.wel / 2.],
]).T + det_box / utils.degree
# units
ibox[:, 1:] *= utils.degree
wibox = ibox.copy()
wibox[:, :] = utils.widen_box(ibox[:, :])
srate = nsamp / wt
# output box
icorners = utils.box2corners(ibox)
ocorners = hor2cel(icorners.T, t0)
obox = utils.minmax(ocorners, -1)[:, :2]
wobox = utils.widen_box(obox)
# define a pixelization
shape, wcs = enmap.geometry(pos=wobox[:, ::-1],
res=args.res * utils.arcmin,
proj="cea")
nphi = int(2 * np.pi / (args.res * utils.arcmin))
map_orig = enmap.rand_gauss((ncomp, ) + shape, wcs).astype(dtype)
print "map shape %s" % str(map_orig.shape)
pbox = np.array([[0, 0], shape], dtype=int)
# define a test tod
bore = np.zeros([nsamp, 3], dtype=ptype)
bore[:, 0] = (wt * np.linspace(0, 1, nsamp, endpoint=False))
bore[:, 1] = (args.az + args.waz / 2 * utils.triangle_wave(
if not args.transpose:
imapfiles = args.ifiles[:nfile]
ilayfiles = args.ifiles[nfile:]
else:
imapfiles = args.ifiles[0::2]
ilayfiles = args.ifiles[1::2]
# Read in our focalplane layouts so we can define our output map bounds
dets, offs, boxes, imaps = [], [], [], []
for i in range(nfile):
det, off = files.read_point_template(ilayfiles[i])
imap = enmap.read_map(imapfiles[i])
if args.slice: imap = eval("imap" + args.slice)
# We want y,x-ordering
off = off[:, ::-1]
box = utils.minmax(off, 0)
dets.append(det)
offs.append(off)
boxes.append(box)
imaps.append(imap)
box = utils.bounding_box(boxes)
box = utils.widen_box(box, rad * 5, relative=False)
# We assume that the two maps have the same pixelization
imaps = enmap.samewcs(np.array(imaps), imaps[0])
# Downsample by averaging
imaps = enmap.downgrade(imaps, (1, args.step))
naz = imaps.shape[-1]
# Ok, build our output geometry
shape, wcs = enmap.geometry(pos=box,
if not args.transpose:
imapfiles = args.ifiles[:nfile]
ilayfiles = args.ifiles[nfile:]
else:
imapfiles = args.ifiles[0::2]
ilayfiles = args.ifiles[1::2]
# Read in our focalplane layouts so we can define our output map bounds
dets, offs, boxes, imaps = [], [], [], []
for i in range(nfile):
det, off = files.read_point_template(ilayfiles[i])
imap = enmap.read_map(imapfiles[i])
if args.slice: imap = eval("imap"+args.slice)
# We want y,x-ordering
off = off[:,::-1]
box = utils.minmax(off,0)
dets.append(det)
offs.append(off)
boxes.append(box)
imaps.append(imap)
box = utils.bounding_box(boxes)
box = utils.widen_box(box, rad*5, relative=False)
# We assume that the two maps have the same pixelization
imaps = enmap.samewcs(np.array(imaps), imaps[0])
# Downsample by averaging
imaps = enmap.downgrade(imaps, (1,args.step))
naz = imaps.shape[-1]
# Ok, build our output geometry
shape, wcs = enmap.geometry(pos=box, res=args.res*utils.arcmin, proj="car", pre=(naz,))
try:
if not args.sim:
with bench.show("read"):
d = actdata.read(entry)
else:
sim_id = sim_ids[ind]
sim_entry = filedb.data[sim_id]
with bench.show("read"):
d = actdata.read(entry, ["boresight"])
d += actdata.read(sim_entry, exclude=["boresight"])
with bench.show("calibrate"):
d = actdata.calibrate(d, exclude=["autocut"])
if d.ndet == 0 or d.nsamp < 2: raise errors.DataMissing("no data in tod")
# Select detectors if needed
if dbox is not None:
mid = np.mean(utils.minmax(d.point_template, 0), 0)
off = d.point_template-mid
good = np.all((off > dbox[0])&(off < dbox[1]),-1)
d = d.restrict(dets=d.dets[good])
except errors.DataMissing as e:
print "Skipping %s (%s)" % (id, e.message)
continue
print "Processing %s" % id, d.ndet, d.nsamp
#broaden_beam_hor(d.tod, d, 1.35*utils.arcmin*utils.fwhm, 1.57*utils.arcmin*utils.fwhm)
# Very simple white noise model. This breaks if the beam has been tod-smoothed by this point.
with bench.show("ivar"):
tod = d.tod
del d.tod
tod -= np.mean(tod,1)[:,None]
tod = tod.astype(dtype)
diff = tod[:,1:]-tod[:,:-1]
t0 = args.t # In mjd ibox = np.array([ [0, wt], [args.az-args.waz/2., args.az+args.waz/2.], [args.el-args.wel/2., args.el+args.wel/2.], ]).T + det_box/utils.degree # units ibox[:,1:] *= utils.degree wibox = ibox.copy() wibox[:,:] = utils.widen_box(ibox[:,:]) srate = nsamp/wt # output box icorners = utils.box2corners(ibox) ocorners = hor2cel(icorners.T, t0) obox = utils.minmax(ocorners, -1)[:,:2] wobox = utils.widen_box(obox) # define a pixelization shape, wcs = enmap.geometry(pos=wobox[:,::-1], res=args.res*utils.arcmin, proj="cea") nphi = int(2*np.pi/(args.res*utils.arcmin)) map_orig = enmap.rand_gauss((ncomp,)+shape, wcs).astype(dtype) print "map shape %s" % str(map_orig.shape) pbox = np.array([[0,0],shape],dtype=int) # define a test tod bore = np.zeros([nsamp,3],dtype=ptype) bore[:,0] = (wt*np.linspace(0,1,nsamp,endpoint=False)) bore[:,1] = (args.az + args.waz/2*utils.triangle_wave(np.linspace(0,1,nsamp,endpoint=False)*20))*utils.degree bore[:,2] = (args.el + args.wel/2*utils.triangle_wave(np.linspace(0,1,nsamp,endpoint=False)))*utils.degree #bore = (ibox[None,0] + np.random.uniform(0,1,size=(nsamp,3))*(ibox[1]-ibox[0])[None,:]).astype(ptype)