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planet9.py
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planet9.py
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# Main program for the planet 9 search. Will have three modes of operation:
# * map, which will output point source free rhs, div, ktrue, beam for each
# time chunk
# * filter, which will produce S/N maps (or chisquare maps) for each of these,
# * find, which will do the likelihood search given the chisquare maps
# Since the first step has multiple outputs, it's easiest to have it output
# a directory with standardized file names in it. Those directories can then
# be inputs and outputs for filter step. Find can take a list of directories
# or chisquare files. Filter could in theory be combined with map, but it
# wants a different parallelization (dmaps with tiles aren't good for SHTs.
# could use tile ffts, but let's keep things simple)
# TODO: Switch from B to R = BFS, where F is the flux conversion and S is the
# spectral index correction.
from __future__ import division, print_function
import argparse, sys
help_general = """Usage:
planet9 map sel area odir [prefix]
planet9 filter dirs
planet9 find dirs odir
planet9 searches for low S/N, slowly moving objects in the outer solar system by
making maps in chunks of time during which the objects won't have much
time to move ("map" operation), matched filtering these to produce a chisquare
per pixel ("filter" operation), and searching through the orbital parameter space
to find candidates ("find" operation)."""
if len(sys.argv) < 2:
sys.stderr.write(help_general + "\n")
sys.exit(1)
mode = sys.argv[1]
mjd0 = 57688
# Handle each mode. These are practically separate programs, but I keep them in one
# command to reduce clutter.
if mode == "map":
# Map mode. Process the actual time-ordered data, producing rhs.fits, div.fits and info.fits
# for each time-chunk.
import numpy as np, os, time
from enlib import utils
with utils.nowarn(): import h5py
from enlib import planet9, enmap, dmap, config, mpi, scanutils, sampcut, pmat, mapmaking
from enlib import log, pointsrcs, gapfill, ephemeris
from enact import filedb, actdata, actscan, cuts as actcuts
config.default("map_bits", 32, "Bit-depth to use for maps and TOD")
config.default("downsample", 1, "Factor with which to downsample the TOD")
config.default("map_sys", "cel", "Coordinate system for the maps")
config.default("verbosity", 1, "Verbosity")
parser = config.ArgumentParser()
parser.add_argument("map", help="dummy")
parser.add_argument("sel")
parser.add_argument("area")
parser.add_argument("odir")
parser.add_argument("prefix", nargs="?", default=None)
parser.add_argument( "--dt", type=float, default=3)
parser.add_argument("-T", "--Tref", type=float, default=40)
parser.add_argument( "--fref", type=float, default=150)
parser.add_argument( "--srcs", type=str, default=None)
parser.add_argument( "--srclim", type=float, default=500)
parser.add_argument("-S", "--corr-spacing", type=float, default=2)
parser.add_argument( "--srcsub", type=int, default=1)
parser.add_argument("-M", "--mapsub", type=str, default=None)
parser.add_argument("-I", "--inject", type=str, default=None)
parser.add_argument( "--only", type=str)
parser.add_argument( "--static", action="store_true")
parser.add_argument("-c", "--cont", action="store_true")
parser.add_argument("-D", "--dayerr", type=str, default="-1:1,-2:4")
parser.add_argument( "--srclim-day", type=float, default=150)
# These should ideally be moved into the general tod autocuts
parser.add_argument("-a", "--asteroid-file", type=str, default=None)
parser.add_argument("--asteroid-list", type=str, default=None)
args = parser.parse_args()
comm = mpi.COMM_WORLD
utils.mkdir(args.odir)
shape, wcs = enmap.read_map_geometry(args.area)
wshape = (3,)+shape[-2:]
dtype = np.float32 if config.get("map_bits") == 32 else np.float64
root = args.odir + "/" + (args.prefix + "_" if args.prefix else "")
sys = config.get("map_sys")
ym = utils.arcmin/utils.yr2days
# Bias source amplitudes 0.1% towards their fiducial value
amp_prior = 1e-3
dayerr = np.array([[float(w) for w in tok.split(":")] for tok in args.dayerr.split(",")]).T # [[x1,y1],[x2,y2]]
only = [int(word) for word in args.only.split(",")] if args.only else []
# Should we use distributed maps?
npix = shape[-2]*shape[-1]
use_dmap = npix > 5e7
utils.mkdir(root + "log")
logfile = root + "log/log%03d.txt" % comm.rank
log_level = log.verbosity2level(config.get("verbosity"))
L = log.init(level=log_level, file=logfile, rank=comm.rank)
filedb.init()
db = filedb.scans.select(args.sel)
ids = db.ids
mjd = utils.ctime2mjd(db.data["t"])
chunks = utils.find_equal_groups(mjd//args.dt)
chunks = [np.sort(chunk) for chunk in chunks]
chunks = [chunks[i] for i in np.argsort([c[0] for c in chunks])]
corr_pos = planet9.choose_corr_points(shape, wcs, args.corr_spacing*utils.degree)
if args.inject:
inject_params = np.loadtxt(args.inject,ndmin=2) # [:,{ra0,dec0,R,vy,vx,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"))
myinds = np.array(myinds, int)
# Find the cost and bbox of each successful tod
costs = np.zeros(len(chunk), int)
boxes = np.zeros([len(chunk),2,2],np.float)
for ind, scan in zip(myinds, myscans):
costs[ind] = scan.ndet*scan.nsamp
boxes[ind] = scanutils.calc_sky_bbox_scan(scan, sys)
costs = utils.allreduce(costs, comm)
boxes = utils.allreduce(boxes, comm)
# Disqualify empty scans
bad = costs == 0
L.info("Rejected %d bad tods" % (np.sum(bad)))
inds = np.where(~bad)[0]
costs, boxes = costs[~bad], boxes[~bad]
ntod = len(inds)
if ntod == 0:
L.info("Chunk %d has no tods. Skipping" % (ci+1))
continue
# Redistribute
if not use_dmap:
myinds = scanutils.distribute_scans2(inds, costs, comm)
else:
myinds, mysubs, mybbox = scanutils.distribute_scans2(inds, costs, comm, boxes)
L.info("Rereading shuffled scans")
del myscans # scans do take up some space, even without the tod being read in
myinds, myscans = scanutils.read_scans(chunk_ids, myinds, actscan.ACTScan, filedb.data, downsample=config.get("downsample"))
if args.srcsub:
#### 2. Prepare our point source database and the corresponding cuts
src_override = pointsrcs.read(args.srcs) if args.srcs else None
for scan in myscans:
scan.srcparam = pointsrcs.src2param(src_override if src_override is not None else scan.pointsrcs)
scan.srcparam, nmerged = planet9.merge_nearby(scan.srcparam)
planet9.cut_srcs_rad(scan, scan.srcparam[nmerged>1])
ctime = utils.mjd2ctime(scan.mjd0) + scan.boresight[scan.nsamp//2,0]
hour = ctime/3600%24
isday = hour >= 11 and hour < 23
if isday:
planet9.cut_bright_srcs_daytime(scan, scan.srcparam, alim_include=args.srclim_day, errbox=dayerr)
else:
planet9.cut_bright_srcs(scan, scan.srcparam, alim_include=args.srclim)
if asteroids:
for scan in myscans:
planet9.cut_asteroids_scan(scan, asteroids)
#### 3. Process our tods ####
apply_window = mapmaking.FilterWindow(config.get("tod_window"))
if not use_dmap: area = enmap.zeros(wshape, wcs, dtype)
else:
geo = dmap.DGeometry(wshape, wcs, dtype=dtype, bbox=mybbox, comm=comm)
area = dmap.zeros(geo)
# Set up our signal. We do this instead of building the pmat manually
# to make it easy to support both maps and dmaps
if not use_dmap: signal = mapmaking.SignalMap(myscans, area, comm)
else: signal = mapmaking.SignalDmap(myscans, mysubs, area, comm)
# Get the input sky map that we will subtract. We do this because the CMB+CIB
# are the same from tod to tod, but we can't support intra-tod correlated noise,
# so we have to get rid of it. This is not optimal, but it shouldn't be far off.
if args.mapsub:
sshape, swcs = enmap.read_map_geometry(args.mapsub)
pixbox = enmap.pixbox_of(swcs, shape, wcs)
if not use_dmap: refmap = enmap.read_map(args.mapsub, pixbox=pixbox).astype(dtype)
else: refmap = dmap.read_map (args.mapsub, pixbox=pixbox, bbox=mybbox, comm=comm).astype(dtype)
refmap = signal.prepare(refmap)
# Get the frequency and beam for this chunk. We assume that
# this is the same for every member of the chunk, so we only need
# to do this for one scan
scan = actscan.ACTScan(filedb.data[chunk_ids[inds[0]]])
_, dets = actdata.split_detname(scan.dets)
beam = scan.beam
freq = scan.array_info.info.nom_freq[dets[0]]
barea = planet9.calc_beam_area(scan.beam)
# Get the conversion from ref-freq flux to observed amplitude. This includes
# dilution by the beam area
flux2amp = 1/utils.flux_factor(barea, args.fref*1e9, utils.T_cmb)
fref2freq = utils.planck(freq*1e9, args.Tref)/utils.planck(args.fref*1e9, args.Tref)
rfact = flux2amp * fref2freq * 1e3 # 1e3 for flux in mJy and amp in uK
# only work will be 3,ny,nx. The rest are scalar. Will copy in-out as necessary
work = signal.work()
rhs = area[0]
div = rhs.copy()
wrhs = signal.prepare(rhs)
wdiv = signal.prepare(div)
kvals = np.zeros(len(corr_pos), dtype)
freqs, bareas = np.zeros(ntod), np.zeros(ntod)
bleh = False and args.inject
if bleh:
sim_rhs = np.zeros(len(inject_params))
sim_div = np.zeros(len(inject_params))
for si, scan in zip(myinds, myscans):
L.debug("Processing %s" % scan.id)
# Read the tod
tod = scan.get_samples().astype(dtype)
tod = utils.deslope(tod)
if args.mapsub:
# Subtract the reference map. If the reference map is not source free,
# then this could reintroduce strong point sources that were cut earlier.
# To avoid this we do another round of gapfilling
signal.forward(scan, tod, refmap, tmul=1, mmul=-1)
#gapfill.gapfill_joneig(tod, scan.cut, inplace=True)
gapfill.gapfill_linear(tod, scan.cut, inplace=True)
# Inject simulated signal if requested
if args.inject:
dmjd = scan.mjd0-mjd0
earth_pos = -ephemeris.ephem_vec("Sun", scan.mjd0)[:,0]
# Set the position and amplitude uK of each simulated source
sim_srcs = np.zeros([len(inject_params),8])
# TODO: inject and analyze with no displacement to see if interpolation is the
# cause of the low bias in amplitude.
sim_srcs[:,:2] = inject_params[:,1::-1]*utils.degree
if not args.static:
sim_srcs[:,:2] = planet9.displace_pos(sim_srcs[:,:2].T, earth_pos, inject_params.T[2], inject_params.T[3:5]*ym*dmjd).T
#print("params", inject_params[:,5])
#print("rfact", rfact)
sim_srcs[:,2] = inject_params[:,5]*rfact
sim_srcs[:,5:7] = 1
psim = planet9.PsrcSimple(scan, sim_srcs)
psim.forward(tod, sim_srcs[:,2], tmul=1.0)
# Build and apply the noise model
apply_window(scan, tod)
scan.noise = scan.noise.update(tod, scan.srate)
scan.noise.apply(tod)
apply_window(scan, tod)
# Build the window into the noise model, so we don't have to drag it around
scan.noise = planet9.NmatWindowed(scan.noise, [lambda tod: apply_window(scan, tod)])
def apply_cut(tod, inplace=True): return sampcut.gapfill_const(scan.cut, tod, inplace=inplace)
if args.srcsub:
# Measure the point source amplitudes. We have ensured that they
# are reasonably independent, so this is the diagonal part of
# amps = (P'N"P)"P'N"d, where N"d is what our tod is now.
psrc = planet9.PsrcSimple(scan, scan.srcparam)
wtod = tod.copy()
src_rhs = psrc.backward(apply_cut(wtod))
# For some reason this manages to be slightly negative sometimes. Might be because the sources are
# not sufficiently separated. We avoid this by forcing it to be minimum 0
src_div = psrc.backward(apply_cut(scan.noise.apply(apply_cut(psrc.forward(wtod, src_rhs*0+1, tmul=0)))))
src_div = np.maximum(src_div, 0)
# Compute the raw source amplitudes. Since we're fitting the source amplitude we might
# as well output them.
with utils.nowarn():
src_amp_raw = src_rhs/src_div
src_amp_raw[~np.isfinite(src_amp_raw)] = 0
src_std_raw = src_div**-0.5
# Bias slightly towards input value. This helps avoid problems with sources
# that are hit by only a handful of samples, and are therefore super-uncertain.
src_amp_old = scan.srcparam[:,2]
src_div_old = planet9.defmean(src_div[src_div>0], 1e-5)*amp_prior
src_rhs += src_amp_old*src_div_old
src_div += src_div_old
with utils.nowarn():
src_amp = src_rhs/src_div
src_amp[~np.isfinite(src_amp)] = 0
# And subtract the point sources
psrc.forward(wtod, src_amp, tmul=0)
# Keep our gapfilling semi-consistent
gapfill.gapfill_linear(wtod, scan.cut, inplace=True)
scan.noise.apply(wtod)
tod -= wtod
del wtod, src_rhs, src_div
# Build our rhs map
apply_cut(tod)
signal.backward(scan, tod, work, mmul=0); wrhs += work[0]
if bleh: sim_rhs += psim.backward(tod)
# Build our div map
work[:] = 0; work[0] = 1
signal.forward(scan, tod, work, tmul=0)
scan.noise.white(tod)
apply_cut(tod)
signal.backward(scan, tod, work, mmul=0); wdiv += work[0]
# Build our sparsely sampled BPNPB
samp_srcs = np.zeros([len(corr_pos),8])
samp_srcs[:,:2] = corr_pos
samp_srcs[:,2] = 1
samp_srcs[:,5:7] = 1
psamp = planet9.PsrcSimple(scan, samp_srcs)
psamp.forward(tod, np.full(len(corr_pos),1.0), tmul=0) # PB
apply_cut(tod)
scan.noise.apply(tod) # NPB
apply_cut(tod)
kvals += psamp.backward(tod) # BPNPB
# kvals is now the samp-sum of the sample icov over the squared beam
# if iN were 1, then this would basically be the number of samples
# that hit each source. if iN were ivar, then this would be the
# inverse variance for the amplitude of each source
if bleh:
psim.forward(tod, sim_div*0+1, tmul=0)
apply_cut(tod)
scan.noise.apply(tod)
apply_cut(tod)
sim_div += psim.backward(tod)
#### 4. mpi reduce
signal.finish(rhs, wrhs)
signal.finish(div, wdiv)
kvals = utils.allreduce(kvals, comm)
del wrhs, wdiv, work, signal, myscans
if bleh:
sim_rhs = utils.allreduce(sim_rhs, comm)
sim_div = utils.allreduce(sim_div, comm)
sim_rhs *= rfact; sim_div *= rfact**2
sim_amp = sim_rhs/sim_div
sim_damp= sim_div**-0.5
if comm.rank == 0:
for i in range(len(sim_amp)):
print("%15.7e %15.7e %8.3f %8.3f %8.3f" % (sim_rhs[i], sim_div[i], sim_amp[i], sim_damp[i], sim_amp[i]/sim_damp[i]))
mean_mjd = np.mean(chunk_mjd[inds])
#### 5. Output our results
utils.mkdir(cdir)
if not use_dmap:
if comm.rank == 0:
enmap.write_map(cdir + "/rhs.fits", rhs)
enmap.write_map(cdir + "/div.fits", div)
else:
dmap.write_map(cdir + "/rhs.fits", rhs, merged=True)
dmap.write_map(cdir + "/div.fits", div, merged=True)
del rhs, div
if comm.rank == 0:
with h5py.File(cdir + "/info.hdf", "w") as hfile:
hfile["kvals"] = np.array([corr_pos[:,1], corr_pos[:,0], kvals*rfact**2]).T
hfile["beam"] = beam
hfile["barea"] = barea
hfile["freq"] = freq
hfile["fref"] = args.fref
hfile["Tref"] = args.Tref
hfile["rfact"] = rfact
hfile["ids"] = utils.encode_array_if_necessary(chunk_ids[inds])
hfile["mjd"] = mean_mjd
hfile["mjds"] = chunk_mjd[inds]
if bleh:
hfile["sim_rhs"] = sim_rhs
hfile["sim_div"] = sim_div
elif mode == "inject":
# Signal injection mode. This uses rhs, div and info to inject a fake source
# with a given flux, distance and velocity into the rhs maps. The output will
# be in a different directory, to avoid overwriting.
#
# Injecting in map space like this is an approximation compared to injecting
# directly in the TOD (with the --inject parameter in "map"), but it's pretty
# accurate, maybe a few percent off.
#
# I have tested this both with and without extra filtering in "filter". It's
# accurate to 2%.
parser = argparse.ArgumentParser()
parser.add_argument("inject", help="dummy")
parser.add_argument("paramfile")
parser.add_argument("dirs", nargs="+")
parser.add_argument("odir")
parser.add_argument("-c", "--cont", action="store_true")
parser.add_argument("-l", "--lmax", type=int, default=10000)
parser.add_argument( "--mmul", type=float, default=1)
parser.add_argument( "--lknee",type=str, default=None)
args = parser.parse_args()
import numpy as np, glob, sys, os
from enlib import mpi, planet9, utils, enmap, ephemeris, pointsrcs, bench
dirs = sorted(sum([glob.glob(dname) for dname in args.dirs],[]))
comm = mpi.COMM_WORLD
params = np.loadtxt(args.paramfile,ndmin=2) # [:,{ra0,dec0,R,vy,vx,flux}]
ym = utils.arcmin/utils.yr2days
def get_div_correction(div, info, lmax=5000):
# don't need as high lmax because div is pretty smooth
R2 = planet9.Rmat(div.shape, div.wcs, info.beam, info.rfact, lmax=lmax, pow=2)
kmap = R2.apply(div)
kmap = np.maximum(kmap, max(0,np.max(kmap)*1e-10))
approx_vals = kmap.at(info.kvals.T[1::-1], order=1)
exact_vals = info.kvals.T[2]
correction = np.sum(exact_vals*approx_vals)/np.sum(approx_vals**2)
return correction
for ind in range(comm.rank, len(dirs), comm.size):
idirpath = dirs[ind]
odirpath = args.odir + "/" + os.path.basename(idirpath)
if args.cont and os.path.isfile(odirpath + "/rhs.fits"): continue
print("Processing %s" % (idirpath))
info = planet9.hget(idirpath + "/info.hdf")
rhs = enmap.read_map(idirpath + "/rhs.fits")
div = enmap.read_map(idirpath + "/div.fits")
dmjd = info.mjd - mjd0
earth_pos = -ephemeris.ephem_vec("Sun", info.mjd)[:,0]
# Set the position and amplitude uK of each simulated source
srcs = np.zeros([len(params),3])
srcs[:,:2] = planet9.displace_pos(params.T[1::-1]*utils.degree, earth_pos, params.T[2], params.T[3:5]*ym*dmjd).T
srcs[:,2] = params[:,5]*info.rfact
with bench.show("sim"):
sim = pointsrcs.sim_srcs_dist_transform(rhs.shape, rhs.wcs, srcs, info.beam, ignore_outside=True, verbose=True)
with bench.show("filter"):
# Apply approximate mapmaker filter to sim. We use the Beam class for its
beam = planet9.Beam(rhs.shape, rhs.wcs, info.beam, lmax=args.lmax)
fit = planet9.setup_noise_fit(rhs, args.lknee, idirpath)
Falready = planet9.butterworth(beam.l, fit.lknee2, fit.alpha2)**0.5
# Our normalization assumes no Falready, so compensate for it
Falready/= np.sum(Falready*beam.lbeam**2*beam.nmode)/np.sum(beam.lbeam**2*beam.nmode)
sim = beam.apply(sim, Falready) # applies Falready instead of beam due to 2nd arg
correction = get_div_correction(div, info)
if args.mmul != 1:
rhs *= args.mmul
rhs += div*sim*correction
# And output
utils.mkdir(odirpath)
planet9.hput(odirpath + "/info.hdf", info)
enmap.write_map(odirpath + "/rhs.fits", rhs)
enmap.write_map(odirpath + "/div.fits", div)
enmap.write_map(odirpath + "/sim.fits", sim)
del rhs, div, info, sim
elif mode == "filter":
# Filter mode. This applies the harmonic part of the matched filter, as well as
# a spline prefilter for fast lookup. For each output directory from the map
# operation, read in rhs, div and info, and compute and output
# frhs = (prefilter) R rhs
# kmap = (prefilter) R**2 div * interpol(kvals/sample(R**2 div))
# With these in hand, the significance is simply frhs/sqrt(kmap)
# R = rfact * B. To apply B we can either use FFTs or SHTs, depending on the
# patch size. Should probably apply dust mask too. Or should that be in map?
# Easiest to do it here, if perhaps a bit imprecise.
parser = argparse.ArgumentParser()
parser.add_argument("filter", help="dummy")
parser.add_argument("dirs", nargs="+")
parser.add_argument("-l", "--lmax", type=int, default=10000)
parser.add_argument("-m", "--mask", type=str, default=None)
parser.add_argument("-c", "--cont", action="store_true")
parser.add_argument("-a", "--asteroid-file", type=str, default=None)
parser.add_argument("--asteroid-list", type=str, default=None)
parser.add_argument("-F", "--extra-filter", action="store_true")
parser.add_argument( "--lknee", type=str, default=None)
parser.add_argument("--planet-list", type=str, default="Mercury,Venus,Mars,Jupiter,Saturn,Uranus,Neptune")
parser.add_argument("--planet-rad", type=float, default=50)
parser.add_argument("-P", "--mask-planets", action="store_true")
parser.add_argument( "--no-noise-norm", action="store_true")
parser.add_argument( "--noiseref", type=str, default=None)
parser.add_argument("-R", "--ref", type=str, default=None, help="Reference map to keep downgrades compatible")
parser.add_argument("-d", "--downgrade", type=int, default=1)
args = parser.parse_args()
from enlib import utils
with utils.nowarn(): import h5py
import numpy as np, glob, sys, os, healpy
from scipy import ndimage
from enlib import enmap, mpi, planet9, cython, bench, bunch
comm = mpi.COMM_WORLD
scale = 1
dirs = sorted(sum([glob.glob(dname) for dname in args.dirs],[]))
# Storing this takes quite a bit of memory, but it's better than
# rereading it all the time
if args.mask: mask = enmap.read_map(args.mask).astype(bool)
asteroids = planet9.get_asteroids(args.asteroid_file, args.asteroid_list) # None if not specified
if args.downgrade > 1:
if args.ref is None:
if comm.rank == 0:
print("Downgrading requires a reference shape to ensure that the pixels remain compatible. Specify with --ref")
sys.exit(1)
else:
ref_shape, ref_wcs = enmap.read_map_geometry(args.ref)
for ind in range(comm.rank, len(dirs), comm.size):
dirpath = dirs[ind]
if args.cont and os.path.isfile(dirpath + "/kmap.fits"): continue
print("Processing %s" % (dirpath))
info = planet9.hget(dirpath + "/info.hdf")
# First we'll build frhs = F*R*rhs
rhs = enmap.read_map(dirpath + "/rhs.fits")
fit = planet9.setup_noise_fit(rhs, args.lknee, dirpath, disable=not args.extra_filter, dump=True)
R = planet9.Rmat(rhs.shape, rhs.wcs, info.beam, info.rfact, lmax=args.lmax, lknee1=fit.lknee1, alpha1=fit.alpha1, lknee2=fit.lknee2, alpha2=fit.alpha2)
R2 = planet9.Rmat(rhs.shape, rhs.wcs, info.beam, info.rfact, lmax=args.lmax, pow=2)
wmask = None
def nmul(a,b):
if a is None: return b
else: return a.__imul__(b)
if args.mask:
wmask = nmul(wmask, ~mask.extract(rhs.shape, rhs.wcs))
if asteroids:
wmask = nmul(wmask, ~planet9.build_asteroid_mask(rhs.shape, rhs.wcs, asteroids, info.mjds))
if args.mask_planets:
wmask = nmul(wmask, ~planet9.build_planet_mask(rhs.shape, rhs.wcs, args.planet_list.split(","), info.mjds, r=args.planet_rad*utils.arcmin))
if wmask is not None:
rhs *= wmask
frhs = R.apply(rhs)
unhit = rhs==0
del rhs
# Compute our approximate K
div = enmap.read_map(dirpath + "/div.fits")
if wmask is not None: div *= wmask
kmap = R2.apply(div); del div
kmap = np.maximum(kmap, max(0,np.max(kmap)*1e-10))
# UNITS:
#
# 1. We want frhs/kmap to be a map with mJy per pixel. This will be the case if we
# include the info.kvals.T[2] information, which evaluates N" = P'Q"P at sparse points
# in the maps, and as long as we don't do any extra filtering here. Here Q" is the
# time-domain noise model.
#
# 2. However, we know that the map-maker noise model N is wrong, and underestimates
# the atmospheric noise. This means that kmap, which is supposed to describe the
# inverse variance in mJy in each pixel, underestimates the noise. We can measure
# how wrong it is by looking at the mean chisquare of the map: chi = mean(frhs**2/kmap).
# The factor by which chi is too high (higher than 1) can be robustly measured as
# A = mean(frhs**2*kmap)/mean(kmap**2). We can use this to correct kmap as long as
# we also scale frhs the same way. E.g. (frhs,kmap) -> (frhs*B, kmap*B), which
# results in chi -> chi*B. We want to divide chi by A, which means that B = 1/A.
#
# 3. Instead of just eating all that atmospheric noise, we could apply a filter F
# to get rid of it. My measurments show that a butterworth filter with
# lknee = 2000/3000/4000 at f090/f150/f220 works well. It's easy to apply this to
# frhs -> F*frhs, but to preserve the right map units we need to compensate for
# this in kmap. It is not obvious what this compensation should be. We can consider
# the filter to be an extra contibution to the noise model such that N" -> HN"H, where
# H = sqrt(F). In that case, frhs = R'N"d -> R'HN"Hd approx FR'N"d = F frhs, and
# kmap = diag(R'N"R) -> diag(R'HN"HR') = diag(HR'N"RH). But we don't have the full N",
# so the latter is hard to evaluate. Both H and R are fourier-diagonal, though, so
# in theory they could be treated the same way, and we already handle R. The problem
# with this is that we have info.kvals.T[2] to correct the approximation we make when
# handling R, but we don't have anything like this to handle H.
# A quick approximation is to assume that the original N" is a much gentler highpass
# filter than F. I think N" in practice underestimates lknee by a factor of 2, which
# would mean that it removes only 1/4 as much power as F does. Let's call this
# lknee factor alpha = 0.5. The approximate fractional loss of signal from applying
# F will be q = mean(FGB)/mean(GB), where F = butter(lknee), G = butter(alpha*lknee),
# and B is the beam. This mean should be over all modes, not just 1D ells.
# To compensate for this loss we should let kmap -> kmap*q.
# So to summarize, our approximation is (frhs,kmap) -> (F*frhs, q*kmap).
# First use info.kvals to normalize kmap. After this frhs/kmap should have
# proper mJy units
approx_vals = kmap.at(info.kvals.T[1::-1], order=1)
exact_vals = info.kvals.T[2]
correction = np.sum(exact_vals*approx_vals)/np.sum(approx_vals**2)
kmap *= correction
# Then compensate for any filter we might have applied. This factor was
# precomputed in Rmat.
print("correction %8.5f R.q %8.5f" % (correction,R.q))
kmap *= R.q
# Finally characterize any residual noise under/over-estimation. This does
# not change the flux units, just the noise and S/N ratio.
if not args.no_noise_norm:
if args.noiseref:
# Compute the normalization from a different frhs map. Useful for noiseless sims
frhs_noiseest = R.apply(enmap.read_map(args.noiseref + "/" + os.path.basename(dirpath) + "/rhs.fits"))
else: frhs_noiseest = frhs
A = planet9.get_normalization(frhs_noiseest, kmap)
print("A", A)
del frhs_noiseest
frhs /= A
kmap /= A
# Kill all unhit values. This is only necessary because kmap is approximate, and
# thus doesn't agree perfectly with frhs about how the smoothing smears out the
# signal in the holes. By setting them explicitly to zero there we avoid dividing
# by very small numbers there.
kmap[:] = np.maximum(kmap, max(np.max(kmap)*1e-4, 1e-12))
kmap[unhit] = 0
frhs[unhit] = 0
# Sharp edges from the mask appears to be causing problems, so remask masked
# areas after filtering
if wmask is not None:
kmap *= wmask
frhs *= wmask
# Kmap should contain the noise ivar in mJy at the reference frequency.
# We can compare this to an approximation based on div alone, which is
# what my forecast was based on. Given white noise with some ivar per
# pixel
if args.downgrade > 1:
frhs = planet9.downgrade_compatible(frhs, ref_wcs, args.downgrade)
kmap = planet9.downgrade_compatible(kmap, ref_wcs, args.downgrade)
#enmap.write_map(dirpath + "/norm.fits", norm)
enmap.write_map(dirpath + "/frhs.fits", frhs)
enmap.write_map(dirpath + "/kmap.fits", kmap)
sigma = frhs*0
cython.solve(frhs, kmap, sigma, klim=np.percentile(kmap, 90)*1e-3)
enmap.write_map(dirpath + "/sigma.fits", sigma)
sps, ls = (np.abs(enmap.fft(sigma))**2).lbin()
sps /= np.mean(kmap > 0)
np.savetxt(dirpath + "/sps.txt", np.array([ls,sps]).T, fmt="%15.7e")
del kmap, sigma, unhit, wmask
elif mode == "maskmore":
parser = argparse.ArgumentParser()
parser.add_argument("maskmore", help="dummy")
parser.add_argument("dirs", nargs="+")
parser.add_argument("-m", "--mask", type=str, default=None)
parser.add_argument("-a", "--asteroid-file", type=str, default=None)
parser.add_argument("--asteroid-list", type=str, default=None)
parser.add_argument("--planet-list", type=str, default="Mercury,Venus,Mars,Jupiter,Saturn,Uranus,Neptune")
parser.add_argument("--planet-rad", type=float, default=50)
parser.add_argument("-P", "--mask-planets", action="store_true")
args = parser.parse_args()
from enlib import utils
with utils.nowarn(): import h5py
import numpy as np, glob, sys, os
from scipy import ndimage
from enlib import enmap, mpi, planet9
comm = mpi.COMM_WORLD
scale = 1
dirs = sorted(sum([glob.glob(dname) for dname in args.dirs],[]))
# Storing this takes quite a bit of memory, but it's better than
# rereading it all the time
if args.mask: mask = enmap.read_map(args.mask).astype(bool)
asteroids = planet9.get_asteroids(args.asteroid_file, args.asteroid_list)
for ind in range(comm.rank, len(dirs), comm.size):
dirpath = dirs[ind]
print("Processing %s" % (dirpath))
info = planet9.hget(dirpath + "/info.hdf")
frhs = enmap.read_map(dirpath + "/frhs.fits")
wmask = None
def nmul(a,b):
if a is None: return b
else: return a.__imul__(b)
if args.mask:
wmask = nmul(wmask, ~mask.extract(frhs.shape, frhs.wcs))
if asteroids:
wmask = nmul(wmask, ~planet9.build_asteroid_mask(frhs.shape, frhs.wcs, asteroids, info.mjds))
if args.mask_planets:
wmask = nmul(wmask, ~planet9.build_planet_mask(rhs.shape, rhs.wcs, args.planet_list.split(","), info.mjds, r=args.planet_rad))
if wmask is not None:
frhs *= wmask
enmap.write_map(dirpath + "/frhs.fits", frhs)
del frhs
kmap = enmap.read_map(dirpath + "/kmap.fits")
a = np.sum(kmap!=0)
kmap *= wmask
b = np.sum(kmap!=0)
print(a-b)
enmap.write_map(dirpath + "/kmap.fits", kmap)
del kmap, wmask
elif mode == "extract":
parser = argparse.ArgumentParser()
parser.add_argument("extract", help="dummy")
parser.add_argument("box")
parser.add_argument("dirs", nargs="+")
parser.add_argument("odir")
parser.add_argument("-c", "--cont", action="store_true")
parser.add_argument("-F", "--fields", type=str, default="rhs,div,frhs,kmap")
args = parser.parse_args()
from enlib import utils
with utils.nowarn(): import h5py
import numpy as np, glob, sys, os
from scipy import ndimage
from enlib import enmap, mpi, planet9
comm = mpi.COMM_WORLD
odir = args.odir
dirs = sorted(sum([glob.glob(dname) for dname in args.dirs],[]))
# from dec1:dec2,ra1:ra2 to [[dec1,ra1],[dec2,ra2]]
box = np.array([[float(c) for c in word.split(":")] for word in args.box.split(",")]).T*utils.degree
fields = args.fields.split(",")
shape, wcs = enmap.read_map_geometry(dirs[0]+"/%s.fits" % fields[0])
shape, wcs = enmap.Geometry(shape, wcs).submap(box)
if comm.rank == 0:
utils.mkdir(args.odir)
enmap.write_map(args.odir + "/area.fits", enmap.zeros(shape, wcs, np.int16))
for ind in range(comm.rank, len(dirs), comm.size):
idirpath = dirs[ind]
odirpath = args.odir + "/" + os.path.basename(idirpath)
if args.cont and os.path.isfile(odirpath + "/info.hdf"):
continue
print("Processing %s" % (idirpath))
skip = False
for fname in ["div", "kmap", "rhs", "frhs"]:
if fname not in fields: continue
map = enmap.read_map(idirpath + "/%s.fits" % fname, geometry=(shape, wcs))
if fname in ["div", "kmap"] and np.all(map==0):
skip = True
break
utils.mkdir(odirpath)
enmap.write_map(odirpath + "/%s.fits" % fname, map)
if skip: continue
info = planet9.hget(idirpath + "/info.hdf")
planet9.hput(odirpath + "/info.hdf", info)
elif mode == "combine":
# planet9 map and planet9 filter operate on per-array, per-frequency maps because
# they need to handle the different beams etc. But the later steps don't need to
# care about that, and one can therefore save I/O by combining the different dirs
# corresponding to the same time stamp, which is what planet9 combine does. It
# can also optionally downgrade the the maps. Unlike the other commands, this one
# makes some strong assumptions about the directory name format, which is assumed to
# be season_patch_array_freq_daynight_ctime. idirs with the same season, patch and
# ctime will be combined.
parser = argparse.ArgumentParser()
parser.add_argument("combine", help="dummy")
parser.add_argument("idirs", nargs="+")
parser.add_argument("odir")
parser.add_argument("-R", "--ref", type=str, default=None, help="Reference map to keep downgrades compatible")
parser.add_argument("-d", "--downgrade", type=int, default=1)
parser.add_argument("-v", "--verbose", action="store_true")
parser.add_argument("-c", "--cont", action="store_true")
args = parser.parse_args()
import numpy as np, time, os, glob, sys
from enlib import utils
with utils.nowarn(): import h5py
from scipy import ndimage
from enlib import enmap, utils, mpi, planet9
utils.mkdir(args.odir)
comm = mpi.COMM_WORLD
dtype = np.float32
idirs = sorted(sum([glob.glob(idir) for idir in args.idirs],[]))
if args.downgrade > 1:
if args.ref is None:
if comm.rank == 0:
print("Downgrading requires a reference shape to ensure that the pixels remain compatible. Specify with --ref")
sys.exit(1)
else:
ref_shape, ref_wcs = enmap.read_map_geometry(args.ref)
# Find groups that need to be combined
dirgnames = []
for idir in idirs:
toks = os.path.basename(idir).split("_")
gname = "_".join(toks[:-4]+toks[-1:])
dirgnames.append(gname)
gnames, rinds = np.unique(dirgnames, return_inverse=True)
groups = [[] for gname in gnames]
for i, ri in enumerate(rinds):
groups[ri].append(i)
if comm.rank == 0:
print("Combining %d dirs into %d dirs" % (len(idirs), len(groups)))
# Then process the groups
for ind in range(comm.rank, len(groups), comm.size):
gname = gnames[ind]
odir = "%s/%s" % (args.odir, gname)
if args.cont and os.path.isfile(odir + "/frhs.fits") and os.path.isfile(odir + "/kmap.fits") and os.path.isfile(odir + "/info.hdf"): continue
gdirs = [idirs[i] for i in groups[ind]]
print("%3d processing %4d/%d %s %2d" % (comm.rank, ind+1, len(groups), gname, len(gdirs)))
# Read in and accumulate the individual files
frhs_tot, kmap_tot, mjds = None, None, []
for di, gdir in enumerate(gdirs):
frhs = enmap.read_map("%s/frhs.fits" % gdir)
if args.downgrade > 1:
frhs = planet9.downgrade_compatible(frhs, ref_wcs, args.downgrade) #*args.downgrade**2
kmap = enmap.read_map("%s/kmap.fits" % gdir)
if args.downgrade > 1:
kmap = planet9.downgrade_compatible(kmap, ref_wcs, args.downgrade) #*args.downgrade**2
with h5py.File("%s/info.hdf" % gdir, "r") as hfile:
mjd = hfile["mjd"][()]
if frhs_tot is None: frhs_tot = frhs
else: frhs_tot += frhs
if kmap_tot is None: kmap_tot = kmap
else: kmap_tot += kmap
mjds.append(mjd)
# And output the combined results
mjd = np.mean(mjds)
utils.mkdir(odir)
enmap.write_map(odir + "/frhs.fits", frhs_tot)
enmap.write_map(odir + "/kmap.fits", kmap_tot)
with h5py.File(odir + "/info.hdf", "w") as hfile:
hfile["mjd"] = mjd
elif mode == "find":
# planet finding mode. Takes as input the output directories from the filter step.
parser = argparse.ArgumentParser()
parser.add_argument("find", help="dummy")
parser.add_argument("idirs", nargs="+")
parser.add_argument("area")
parser.add_argument("odir")
parser.add_argument("-O", "--order", type=int, default=1)
parser.add_argument("-d", "--downgrade", type=int, default=1)
parser.add_argument("-m", "--mask", type=str, default=None)
parser.add_argument("-T", "--tsize", type=int, default=1200)
parser.add_argument("-P", "--pad", type=int, default=60)
parser.add_argument("-N", "--npertile", type=int, default=-1)
parser.add_argument("-v", "--verbose", action="count", default=1)
parser.add_argument("-q", "--quiet", action="count", default=0)
parser.add_argument( "--rref", type=float, default=300)
parser.add_argument( "--rmin", type=float, default=300)
parser.add_argument( "--rmax", type=float, default=2000)
parser.add_argument( "--dr", type=float, default=20)
parser.add_argument( "--vmax", type=float, default=6.28)
parser.add_argument( "--vmin", type=float, default=4.16)
parser.add_argument( "--dv", type=float, default=0.1)
parser.add_argument( "--static", action="store_true")
parser.add_argument( "--rinf", type=int, default=1, help="Include infinite distance in the r search. Useful for rejecting non-moving objects")
parser.add_argument("-g", "--full-geo", action="store_true", help="Read the geometry of each input data set instead of assuming that ones with the same prefix have the same geometry")
parser.add_argument("-i", "--invert", action="store_true")
parser.add_argument("-c", "--cont", action="store_true")
parser.add_argument("-f", "--fscale", type=float, default=1, help="Multiply fluxes by this factor")
parser.add_argument("-e", "--escale", type=float, default=1, help="Multiply flux errors by this factor (in addition to that implied by fscale)")
parser.add_argument( "--only", type=str, default=None)
parser.add_argument("-S", "--scratch", type=str, default=None)
args = parser.parse_args()
import numpy as np, time, os, glob
from enlib import utils
with utils.nowarn(): import h5py
from scipy import ndimage, stats
from enlib import enmap, utils, mpi, parallax, cython, ephemeris, statdist, planet9
utils.mkdir(args.odir)
comm = mpi.COMM_WORLD
dtype = np.float32
shape, wcs = planet9.get_geometry(args.area)
tsize, pad = args.tsize, args.pad
nphi = abs(utils.nint(360./wcs.wcs.cdelt[0]))
only = [int(w) for w in args.only.split(",")] if args.only else None
verbose = args.verbose - args.quiet
def build_rlist(rref, dr, rmin, rmax, rinf=False):
# r' = dr0*(r/rref)² => dr/r²[rmin:r] = dr0*dx/rref²[0:x] => 1/r1-1/r = x*dr0/rref²
# => r = 1/(1/r1 - x*dr0/rref²)
# what is xmax? xmax = (1/r1-1/r2)*rref²/dr0
xmax = int(np.ceil((1/rmin-1/rmax)*rref**2/dr))
x = np.arange(xmax+1)
r = 1/(np.maximum(1/rmin - x*dr/rref**2,1/rmax))
if rinf: r = np.concatenate([r,[1e9]])
return r
# Our parameter search space. Distances in AU, speeds in arcmin per year.
# smoothing from r step: 0.2' = 1au*dr/r**2/4 => dr = 20 au at r = 300 au
# vmax, vmin:
# based on 2*pi*a**2*(1-e**2)**0.5/((a/au)**1.5 * years)/r**2
# this goes as a**0.5, so only a weak function of a. Also weak function of e
# as long as e is not cose to 1. Let's assume e in [0,0.7] and a in [400,800].
# Then the highest speed is 3.5'/yr @ 400 AU and the lowest is 2.8'/yr. This translates
# to 6.2'/yr and 5.0'/yr @ 300 AU, though the actual vmax at 300 AU is a bit lower
# Let's use 6.5 to 5.0.
# Update: reading off from fig 15 in the P9 hyp paper, it looks like vref should go
# from 4.15 to 6.30 to be safe. That's about twice as expensive.sadly. And rmin should
# be around 300.
ym = utils.arcmin/utils.yr2days
# The radius at which the speed and radius steps are specified. Values at other
# radii are scaled from these
rref = args.rref
# The radial stepping
rlist = build_rlist(rref, args.dr, args.rmin, args.rmax, rinf=args.rinf)
nr = len(rlist)
# The speed bounds as a function of radius
vmaxs = args.vmax * (rlist/rref)**-2
vmins = args.vmin * (rlist/rref)**-2
vmins = np.maximum(0, vmins-args.dv)
nv = int(np.ceil(np.max(vmaxs)/args.dv))
# Convert to physical units
vmaxs *= ym; vmins *= ym; dv = args.dv*ym
if comm.rank == 0 and verbose >= 2:
print("Parameter search space:\n%8s %8s %8s" % ("r", "vmin", "vmax"))
for r, vmin, vmax in zip(rlist, vmins, vmaxs):
print("%8.2f %8.2f %8.2f" % (r, vmin/ym, vmax/ym))
#nparam = 0
#for ri, (r, vmin, vmax) in enumerate(zip(rlist, vmins, vmaxs)):
# for vy in np.arange(-nv,nv+1)*dv:
# for vx in np.arange(-nv,nv+1)*dv:
# vmag = (vy**2+vx**2)**0.5
# if vmag < vmin or vmag > vmax: continue
# nparam += 1
#print("nparam", nparam)
#1/0
# How many tiles will we have?
if tsize == 0: nty, ntx = 1, 1
else: nty, ntx = (np.array(shape[-2:])+tsize-1)//tsize
ntile = nty*ntx
idirs = sorted(sum([glob.glob(idir) for idir in args.idirs],[]))
# We can parallelize both over tiles and inside tiles. More mpi tasks
# per tile does not increase the total memory cost (much), but has
# diminishing returns due to communication overhead (and you can't go
# beyond the number of maps the tile has). More mpi tasks across tiles
# scales well computationally, but memory use goes up proportionally.
# Let's make it configurable.
if args.npertile < 0: npertile = len(idirs)//(-args.npertile)
else: npertile = args.npertile
npertile = max(1,min(comm.size,npertile))
# Build intra- and inter-tile communicators
comm_intra = comm.Split(comm.rank//npertile, comm.rank %npertile)
comm_inter = comm.Split(comm.rank %npertile, comm.rank//npertile)
if tsize > 0:
# We're using tiles, so our outputs will be directories
for name in ["sigma_plain", "param_map", "param_map_full", "sigma_eff", "sigma_eff_full", "hit_tot", "cands"]:
utils.mkdir(args.odir + "/" + name)
# Get the pixel bounding box of each input map in terms of our output area
if args.full_geo:
pboxes = planet9.read_pboxes(idirs, wcs, comm=comm, verbose=verbose>=2)
else:
# Here we assume that dirs with the same prefix have the same geometry
prefixes = np.array(["_".join(idir.split("_")[:-1]) for idir in idirs])
upres, inds, rinds = np.unique(prefixes, return_index=True, return_inverse=True)
uboxes = planet9.read_pboxes([idirs[i] for i in inds], wcs, upres, comm=comm, verbose=verbose>=2)
pboxes = uboxes[rinds]
# To speed up the likelihood search we will combine all maps that have the same
# timestamp, since they will all be displaced the same way anyway. So split our
# idirs into a list of groups with the same time
tstamps = np.array([idir.split("_")[-1] for idir in idirs])
utimes, inds, rinds = np.unique(tstamps, return_index=True, return_inverse=True)
groups = [[] for i in inds]
for i, ri in enumerate(rinds):
groups[ri].append(i)
if args.scratch:
import shutil, socket
prefix = args.scratch
hname = socket.gethostname()
# Copy over to fast local system. This node communicator lets us communicate with
# all processes inside the same node
node_comm = comm.Split_type(mpi.COMM_TYPE_SHARED)
# Figure out the full set of dirs that are used on this node
mydirs = [idirs[i] for g in groups[comm_intra.rank::comm_intra.size] for i in g]
hdirs = np.unique(node_comm.allreduce(mydirs))
if node_comm.rank == 0:
print("Host %s needs %4d/%d (%3.0f%%) files" % (hname, len(hdirs), len(idirs), 100.0*len(hdirs)/len(idirs)))
# Then copy them over. Only ncopy tasks will try copying at the same time to avoid
# trashing the file system
ncopy = min(4, node_comm.size)
if node_comm.rank < ncopy:
for hdir in hdirs[node_comm.rank::ncopy]:
#for ind in range(node_comm.rank, len(hdirs), node_comm.size):
#hdir = hdirs[ind]
utils.mkdir("%s/%s" % (prefix,hdir))
print("%3d Copying %s to %s" % (comm.rank, hdir, hname))
for fname in ["frhs.fits", "kmap.fits", "info.hdf"]:
shutil.copyfile("%s/%s" % (hdir, fname), "%s/%s/%s" % (prefix, hdir, fname))
# Finally replace the contents of idirs with the new paths
idirs = ["%s/%s" % (prefix, idir) for idir in idirs]
comm.Barrier()
# Loop over tiles
for ti in range(comm_inter.rank, ntile, comm_inter.size):
ty, tx = ti//ntx, ti%ntx
if only and (ty != only[0] or tx != only[1]): continue
if tsize > 0:
def oname(name):
fname, fext = os.path.splitext(name)
return "%s/%s/tile%03d_%03d%s" % (args.odir, fname, ty, tx, fext)
pixbox = np.array([[ty*tsize-pad,tx*tsize-pad],[(ty+1)*tsize+pad,(tx+1)*tsize+pad]])
else:
def oname(name): return args.odir + "/" + name
pixbox = np.array([[-pad,-pad],[shape[-2]+pad,shape[-1]+pad]])
if args.cont and os.path.exists(oname("cands.txt")): continue
if comm_intra.rank == 0:
print("group %3d processing tile %3d %3d" % (comm_inter.rank, ty, tx))
# Get the shape of the sliced, downgraded tiles
tshape_full, twcs_full = enmap.slice_geometry(shape, wcs, [slice(pixbox[0,0],pixbox[1,0]),slice(pixbox[0,1],pixbox[1,1])], nowrap=True)
tshape, twcs = enmap.downgrade_geometry(tshape_full, twcs_full, args.downgrade)