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planet_fit.py
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planet_fit.py
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import numpy as np, argparse, sys, os
from enlib import enmap, utils
from scipy import optimize, ndimage
#from matplotlib.pylab import *
parser = argparse.ArgumentParser()
parser.add_argument("ifiles", nargs="+", help="map map ... div div ...")
parser.add_argument("odir")
parser.add_argument("--slice", type=str, default="")
parser.add_argument("-d", "--downgrade", type=int, default=1)
parser.add_argument("-T", "--transpose", action="store_true")
parser.add_argument("-L", "--ref-nloop", type=int, default=2)
parser.add_argument("-b", "--beam", type=float, default=1.5)
parser.add_argument("-i", "--individual", action="store_true")
parser.add_argument("-D", "--deglitch", action="count", default=1)
args = parser.parse_args()
deglitch = args.deglitch % 2 == 1
ref_nloop = args.ref_nloop
utils.mkdir(args.odir)
nfile = len(args.ifiles)/2
if not args.transpose:
mapfiles = args.ifiles[:nfile]
divfiles = args.ifiles[nfile:]
else:
mapfiles = args.ifiles[0::2]
divfiles = args.ifiles[1::2]
beam_sigma = args.beam*utils.arcmin*utils.fwhm
def read_map(fname):
m = enmap.read_map(fname)
m = m[...,1:-1,1:-1]
#m = eval("m" + args.slice)
m = enmap.downgrade(m, args.downgrade)
return m
def apply_params(maps, params, inverse=False, nooff=False):
"""Given maps[nmap,...,ny,nx] and params[nmap,{dy,dx,A,o}],
return maps shifted, offset and scaled by those parameters."""
omaps = maps.copy()
shape = omaps.shape
omaps = omaps.reshape((-1,)+shape[-3:])
params= np.array(params)
params= params.reshape(-1,params.shape[-1])
for i in range(omaps.shape[0]):
dy,dx,A = params[i,:3]
dy = int(np.round(dy))
dx = int(np.round(dx))
o = params[i,3:]
if inverse:
omaps[i] = np.roll(omaps[i], dy, -2)
omaps[i] = np.roll(omaps[i], dx, -1)
omaps[i] *= A
if not nooff:
omaps[i] += o[:,None,None]
else:
if not nooff:
omaps[i] -= o[:,None,None]
omaps[i] /= A
omaps[i] = np.roll(omaps[i],-dx, -1)
omaps[i] = np.roll(omaps[i],-dy, -2)
omaps = omaps.reshape(shape)
return omaps
def solve(div, rhs):
if rhs.ndim == 3: return rhs/div
elif rhs.ndim == 4: return rhs/div[:,None]
raise NotImplementedError
def remove_offset(m):
ny,nx = m.shape[-2:]
s = m.shape[:-2]+(-1,)
samps = np.concatenate([
m[...,:ny/4].reshape(s),
m[...,-ny/4:].reshape(s),
m[...,ny/4:-ny/4,:nx/4].reshape(s),
m[...,ny/4:-ny/4,-nx/4:].reshape(s)],-1)
samps = np.ma.array(samps, mask=samps==0)
return m - np.asarray(np.ma.median(samps,-1)[...,None,None])
class SingleFitter:
def __init__(self, ref, map, div):
self.ref = ref
self.map = map
self.div = div
self.ncomp = ref.shape[0]
self.verbose = False
self.i = 0
def calc_chisq(self, params):
model = apply_params(self.ref, params, inverse=True)
residual = self.map-model
chisq = np.sum(residual**2*self.div)
if self.verbose:
print ("%4d" + " %9.4f"*params.size + " %15.7e") % (
(self.i,)+tuple(params)+(chisq,))
self.i += 1
return chisq
def fit(self, verbose=False):
self.verbose = verbose
self.i = 0
params = np.zeros([3+self.ncomp])
params[2] = 1
params = optimize.fmin_powell(self.calc_chisq, params, disp=False)
return params
def build_gauss(posmap, sigma):
r2 = np.sum(posmap**2,0)
return np.exp(-0.5*r2/sigma**2)
# Ok, read in the maps
maps, divs, ids = [], [], []
for i, (rfile,dfile) in enumerate(zip(mapfiles, divfiles)):
print "Reading %s" % rfile
map = read_map(rfile)
print "Reading %s" % dfile
div = read_map(dfile).preflat[0]
maps.append(map)
divs.append(div)
ids.append(os.path.basename(rfile)[:-9])
maps = enmap.samewcs(np.asarray(maps),maps[0])
divs = enmap.samewcs(np.asarray(divs),divs[0])
nmap = maps.shape[0]
ncomp= maps.shape[1]
ref, refdiv = maps[0], divs[0]
ref_small = eval("ref"+args.slice)
refdiv_small = eval("refdiv"+args.slice)
# Fit gaussian
fitter = SingleFitter(build_gauss(ref_small.posmap(), beam_sigma)[None], ref_small[:1], refdiv_small)
p_ref = fitter.fit(verbose=True)
# Don't override amplitude or offset
p_ref[2:] = [1,0]
ref_small = apply_params(ref_small, p_ref)
# Choose a reference map. Fit all maps to it. Coadd to find
# new reference map. Repeat.
for ri in range(ref_nloop):
print "Loop %d" % ri
params = []
for mi in range(nmap):
print "Map %2d" % mi
map_small = eval("maps[mi]"+args.slice)
div_small = eval("divs[mi]"+args.slice)
#fitter = SingleFitter(ref, maps[mi], divs[mi,:1,:1])
fitter = SingleFitter(ref_small, map_small, div_small)
p = fitter.fit(verbose=True)
params.append(p)
params = np.array(params)
rhss = divs[:,None]*maps
mrhss = apply_params(rhss, params, nooff=True)
mdivs = apply_params(divs, params, nooff=True)
mrhs = np.sum(mrhss,0)
mdiv = np.sum(mdivs,0)
ref = solve(mdiv, mrhs)
# Compute median too. This is more robust to glitches, but not optimally weighted.
smaps = apply_params(maps, params)
mask = np.logical_or(np.repeat(mdivs[:,None]==0,smaps.shape[1],1), smaps==0)
medmap= enmap.enmap(np.ma.median(np.ma.array(smaps,mask=mask),0),ref.wcs)
ref = remove_offset(ref)
medmap = remove_offset(medmap)
with open(args.odir + "/fit_%03d.txt"%ri, "w") as f:
for id, p in zip(ids,params):
f.write(("%7.4f %7.4f %7.4f" + " %12.4f"*(params.shape[-1]-3) + " %s\n") %
(tuple(p)+(id,)))
enmap.write_map(args.odir + "/tot_map_%03d.fits" % ri, ref)
enmap.write_map(args.odir + "/tot_div_%03d.fits" % ri, mdiv)
enmap.write_map(args.odir + "/tot_med_%03d.fits" % ri, medmap)
# Output the individual best fits too
if args.individual:
for i, id in enumerate(ids):
enmap.write_map(args.odir + "/%s_map_%03d.fits" % (id,ri), smaps[i])
enmap.write_map(args.odir + "/%s_div_%03d.fits" % (id,ri), mdivs[i])
enmap.write_map(args.odir + "/%s_resid_%03d.fits" % (id,ri), smaps[i]-ref)
del smaps, mdivs, mrhs, mdiv, ref, mrhss