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# This file is part of the Astrometry.net suite.
# Licensed under a 3-clause BSD style license - see LICENSE
from __future__ import print_function
import matplotlib
matplotlib.use('Agg')
import pylab as plt
import numpy as np
import emcee
import triangle
from astrometry.util.util import *
from astrometry.util.plotutils import *
from astrometry.util.fits import *
from astrometry.solver.solver import *
class McTweak(object):
def __init__(self, wcs, xy, rd):
self.refra = rd.ra
self.refdec = rd.dec
self.testxy = np.vstack((xy.x, xy.y)).T
nt = len(xy)
sig2 = 1.
self.testsig2 = np.zeros(nt) + sig2
self.W = wcs.get_width()
self.H = wcs.get_height()
self.distractors = 0.25
## Accept: set to ~inf?
self.logodds_bail = -1e100
self.logodds_accept = 1e12
self.wcs = wcs
def __call__(self, args):
# plug args into wcs
# make a local copy...
wcs = Sip(self.wcs)
set_sip_args(wcs, args)
# sip.radec2pixelxy uses the *inverse* SIP polynomials... compute 'em
sip_compute_inverse_polynomials(wcs, 20, 20, 1, self.W, 1, self.H)
ok,x,y = wcs.radec2pixelxy(self.refra, self.refdec)
refxy = np.vstack((x,y)).T
logodds = verify_star_lists_np(refxy, self.testxy, self.testsig2,
self.W * self.H, self.distractors,
self.logodds_bail, self.logodds_accept)
return logodds
def set_sip_args(wcs, args):
args = list(reversed(args))
r = args.pop()
d = args.pop()
wcs.set_crval((r,d))
CD = (args.pop(), args.pop(), args.pop(), args.pop())
wcs.set_cd(CD)
order = wcs.a_order
for p in range(0, order+1):
for q in range(0, order+1-p):
if p+q <= 1:
continue
assert(p + q <= order)
wcs.set_a_term(p, q, args.pop())
order = wcs.b_order
for p in range(0, order+1):
for q in range(0, order+1-p):
if p+q <= 1:
continue
assert(p + q <= order)
wcs.set_b_term(p, q, args.pop())
assert(len(args) == 0)
def get_sip_args(wcs):
W,H = wcs.get_width(), wcs.get_height()
S = max(W, H)
args = []
sigs = []
r,d = wcs.get_crval()
pixscale = wcs.pixel_scale()
args.extend([r,d])
sigs.extend([pixscale/3600.]*2)
cd1,cd2,cd3,cd4 = wcs.get_cd()
args.extend([cd1,cd2,cd3,cd4])
sigs.extend([max(x/1000., pixscale/3600./S) for x in [cd1,cd2,cd3,cd4]])
order = wcs.a_order
for p in range(0, order+1):
for q in range(0, order+1-p):
if p+q <= 1:
continue
assert(p + q <= order)
args.append(wcs.get_a_term(p, q))
sigs.append(S**-(p+q))
order = wcs.b_order
for p in range(0, order+1):
for q in range(0, order+1-p):
if p+q <= 1:
continue
assert(p + q <= order)
args.append(wcs.get_b_term(p, q))
sigs.append(S**-(p+q))
return args, sigs
def mctweak(wcs, xy, rd):
obj = McTweak(wcs, xy, rd)
# Initial args
args,sigs = get_sip_args(wcs)
print('Args:', args)
print('Sigs:', sigs)
print('Number of arguments:', len(args))
print('Logodds:', obj(args))
ndim, nwalkers = len(args), 100
p0 = emcee.utils.sample_ball(args, sigs, size=nwalkers)
print('p0', p0.shape)
ps = PlotSequence('mctweak')
W,H = wcs.get_width(), wcs.get_height()
mywcs = Sip(wcs)
sampler = emcee.EnsembleSampler(nwalkers, ndim, obj)
lnp0, rstate = None, None
pp = []
for step in range(10000):
print('Step', step)
p0,lnp0,rstate = sampler.run_mcmc(p0, 1, lnprob0=lnp0, rstate0=rstate)
print('Best logprob:', np.max(lnp0))
i = np.argmax(lnp0)
print('Best args:', p0[i,:])
pp.extend(sampler.flatchain)
sampler.reset()
if step % 100 != 0:
continue
plt.clf()
plt.plot(obj.testxy[:,0], obj.testxy[:,1], 'r.')
for args in p0[np.random.permutation(nwalkers)[:10],:]:
set_sip_args(mywcs, args)
sip_compute_inverse_polynomials(mywcs, 20, 20, 1, W, 1, H)
ok,x,y = mywcs.radec2pixelxy(obj.refra, obj.refdec)
plt.plot(x, y, 'bo', mec='b', mfc='none', alpha=0.25)
ex = 10.
ngridx = ngridy = 10
stepx = stepy = 100
xgrid = np.linspace(0, W, ngridx)
ygrid = np.linspace(0, H, ngridy)
X = np.linspace(0, W, int(np.ceil(W/stepx)))
Y = np.linspace(0, H, int(np.ceil(H/stepy)))
for x in xgrid:
DX,DY = [],[]
xx,yy = [],[]
for y in Y:
dx,dy = mywcs.get_distortion(x, y)
xx.append(x)
yy.append(y)
DX.append(dx)
DY.append(dy)
DX = np.array(DX)
DY = np.array(DY)
xx = np.array(xx)
yy = np.array(yy)
EX = DX + ex * (DX - xx)
EY = DY + ex * (DY - yy)
#plot(xx, yy, 'k-', alpha=0.5)
plt.plot(EX, EY, 'b-', alpha=0.1)
for y in ygrid:
DX,DY = [],[]
xx,yy = [],[]
for x in X:
dx,dy = mywcs.get_distortion(x, y)
DX.append(dx)
DY.append(dy)
xx.append(x)
yy.append(y)
DX = np.array(DX)
DY = np.array(DY)
xx = np.array(xx)
yy = np.array(yy)
EX = DX + ex * (DX - xx)
EY = DY + ex * (DY - yy)
#plot(xx, yy, 'k-', alpha=0.5)
plt.plot(EX, EY, 'b-', alpha=0.1)
for x in xgrid:
plt.plot(x+np.zeros_like(Y), Y, 'k-', alpha=0.5)
for y in ygrid:
plt.plot(X, y+np.zeros_like(X), 'k-', alpha=0.5)
plt.axis([1, W, 1, H])
plt.axis('scaled')
ps.savefig()
pp = np.vstack(pp)
print('pp', pp.shape)
# plt.clf()
# triangle.corner(pp, plot_contours=False)
# ps.savefig()
pp = []
wcs = Tan('bok-01.wcs', 0)
sip = Sip(wcs)
# sip.a_order = 3
# sip.b_order = 3
# sip.ap_order = 4
# sip.bp_order = 4
sip.a_order = 2
sip.b_order = 2
sip.ap_order = 3
sip.bp_order = 3
xy = fits_table('bok-01.axy')
rd = fits_table('bok-01.rdls')
mctweak(sip, xy, rd)
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