if len(args) == 1:
dojson = True
else:
parser.print_help()
sys.exit(-1)
import logging
logformat = '%(message)s'
from astrometry.util.util import log_init #, LOG_VERB, LOG_MSG
loglvl = 2
if opt.verbose:
logging.basicConfig(level=logging.DEBUG, format=logformat)
loglvl += 1
else:
logging.basicConfig(level=logging.INFO, format=logformat)
log_init(loglvl)
wcsfn = args[0]
if dojson:
from astrometry.util.util import anwcs
wcs = anwcs(wcsfn,0)
jobjs = get_annotations_for_wcs(wcs, opt)
import json
j = json.dumps(jobjs)
print(j)
sys.exit(0)
fmt = PLOTSTUFF_FORMAT_JPG
s = outfn.split('.')
if len(s):
def main():
import optparse
import logging
import sys
parser = optparse.OptionParser()
parser.add_option('--threads', dest='threads', default=1, type=int, help='Use this many concurrent processors')
parser.add_option('-v', '--verbose', dest='verbose', action='count', default=0,
help='Make more verbose')
parser.add_option('--grid', '-g', dest='gridn', type=int, default=5, help='Dust parameter grid size')
parser.add_option('--steps', '-s', dest='steps', type=int, default=10, help='# Optimization step')
parser.add_option('--suffix', dest='suffix', default='', help='Output file suffix')
parser.add_option('--no-100', dest='no100', action='store_true', default=False,
help='Omit PACS-100 data?')
parser.add_option('--callgrind', dest='callgrind', action='store_true', default=False, help='Turn on callgrind around tractor.optimize()')
parser.add_option('--resume', '-r', dest='resume', type=int, default=-1, help='Resume from a previous run at the given step?')
parser.add_option('--zoom', dest='zoom', type=float, default=1, help='Scale down the model to only touch the (1/zoom x 1/zoom) central region of the images')
parser.add_option('--damp', dest='damp', type=float, default=1., help='LSQR damping')
opt,args = parser.parse_args()
if opt.verbose == 0:
lvl = logging.INFO
log_init(2)
else:
lvl = logging.DEBUG
log_init(3)
logging.basicConfig(level=lvl, format='%(message)s', stream=sys.stdout)
if opt.threads > 1 and False:
global dpool
import debugpool
dpool = debugpool.DebugPool(opt.threads)
Time.add_measurement(debugpool.DebugPoolMeas(dpool))
mp = multiproc(pool=dpool)
else:
print('N threads', opt.threads)
mp = multiproc(opt.threads)#, wrap_all=True)
if opt.callgrind:
import callgrind
else:
callgrind = None
np.seterrcall(np_err_handler)
np.seterr(all='call')
#np.seterr(all='raise')
if opt.resume > -1:
pfn = 'herschel-%02i%s.pickle' % (opt.resume, opt.suffix)
print('Reading from', pfn)
tractor = unpickle_from_file(pfn)
tractor.mp = mp
ds = tractor.getCatalog()[0]
print('DustSheet:', ds)
# derivs = ds.getParamDerivatives(tim)
# dim = np.zeros(tim.shape)
# #for k,deriv in enumerate(derivs[:40]):
# for k,deriv in enumerate(derivs[::10]):
# dim[:,:] = 0
# deriv.addTo(dim)
# plt.clf()
# plt.imshow(dim, interpolation='nearest', origin='lower')
# plt.savefig('deriv-%04i.png' % k)
#tim = tractor.getImages()[0]
# for it,tim in enumerate(tractor.getImages()):
# X = ds._getTransformation(tim)
# # #print 'X', X
# keys = X.keys()
# keys.sort()
# # for k in keys[::10]:
# # for k in keys[:40]:
# for k in keys[::202]:
# I,G,nil,nil = X[k]
# rim = np.zeros_like(tim.getImage())
# rim.ravel()[I] = G
# plt.clf()
# plt.imshow(rim, interpolation='nearest', origin='lower')
# plt.colorbar()
# plt.savefig('rim-%i-%04i.png' % (it,k))
# print 'pix', k
# sys.exit(0)
makeplots(tractor, opt.resume, opt.suffix)
step0 = opt.resume + 1
else:
step0 = 0
tractor = create_tractor(opt)
tractor.mp = mp
# zero out invvar outside the model bounds.
ds = tractor.getCatalog()[0]
rd = ds.getRaDecCorners(margin=0.5)
for i,tim in enumerate(tractor.getImages()):
poly = np.array([tim.getWcs().positionToPixel(
RaDecPos(rdi[0], rdi[1])) for rdi in rd])
poly = poly[:-1,:]
print('Model bounding box in image', tim.name, 'coordinates:')
#print poly.shape
print(poly)
H,W = tim.shape
xx,yy = np.meshgrid(np.arange(W), np.arange(H))
inside = point_in_poly(xx, yy, poly)
tim.inverr[inside == 0] = 0.
plt.clf()
for i,tim in enumerate(tractor.images):
h,w = tim.shape
rd = [tim.getWcs().pixelToPosition(x,y)
for x,y in [(-0.5,-0.5),(w-0.5,-0.5),(w-0.5,h-0.5),
(-0.5,h-0.5),(-0.5,-0.5)]]
plt.plot([p.ra for p in rd], [p.dec for p in rd], '-',
label=tim.name)
rd = ds.getRaDecCorners(margin=0.5)
plt.plot(rd[:,0], rd[:,1], 'k-', label='Grid')
mh,mw = ds.shape
r,d = ds.wcs.pixelxy2radec(1 + np.arange(mw), np.ones(mw))
plt.plot(r, d, 'k.')
r,d = ds.wcs.pixelxy2radec(np.ones(mh), 1 + np.arange(mh))
plt.plot(r, d, 'k.')
r,d = ds.wcs.pixelxy2radec(1 + np.arange(mw), np.zeros(mw)+mh)
plt.plot(r, d, 'k.')
r,d = ds.wcs.pixelxy2radec(np.zeros(mh)+mw, 1 + np.arange(mh))
plt.plot(r, d, 'k.')
plt.legend()
plt.savefig('radec.png')
print('Precomputing transformations...')
ds = tractor.getCatalog()[0]
# Split the grid-spread matrix into strips...
async_results = []
for im in tractor.getImages():
args = []
H,W = ds.shape
dy = 10
y = 0
while y <= H:
args.append((ds, im, y, min(H, y+dy)))
y += dy
async_results.append(mp.map_async(_map_trans, args))
# Glue to strips back together...
XX = []
for ar in async_results:
Xblocks = ar.get()
X = Xblocks[0]
for xi in Xblocks[1:]:
X.update(xi)
XX.append(X)
for im,X in zip(tractor.getImages(), XX):
ds._normalizeTransformation(im, X)
ds._setTransformation(im, X)
print('done precomputing.')
# Plot the grid-spread functions.
for itim,tim in enumerate(tractor.images):
T = ds._getTransformation(tim)
(I,G,nz,NZI) = T[0]
plt.clf()
g = np.zeros(tim.shape, np.float32)
g.flat[I] = G
plt.imshow(g, interpolation='nearest', origin='lower', cmap='hot')
plt.colorbar()
plt.title('Grid-spread function for cell 0, image %s' % tim.name)
plt.savefig('gsf-%i.png' % itim)
makeplots(tractor, 0, opt.suffix)
pfn = 'herschel-%02i%s.pickle' % (0, opt.suffix)
pickle_to_file(tractor, pfn)
print('Wrote', pfn)
for im in tractor.getImages():
im.freezeAllBut('sky')
for i in range(step0, opt.steps):
print('Step', i)
if callgrind:
callgrind.callgrind_start_instrumentation()
tractor.optimize(damp=opt.damp, alphas=[1e-3, 1e-2, 0.1, 0.3, 1., 3., 10., 30., 100.])
if callgrind:
callgrind.callgrind_stop_instrumentation()
makeplots(tractor, 1 + i, opt.suffix)
pfn = 'herschel-%02i%s.pickle' % (1 + i, opt.suffix)
pickle_to_file(tractor, pfn)
print('Wrote', pfn)
def check_priors():
np.seterrcall(np_err_handler)
np.seterr(all='call')
import logging
import sys
lvl = logging.DEBUG
log_init(3)
logging.basicConfig(level=lvl, format='%(message)s', stream=sys.stdout)
if True:
# Check two-pixel priors: smoothness.
H,W = 1,2
logsa = np.zeros((H,W)) + np.log(1e-3)
logt = np.zeros((H,W)) + np.log(17.)
emis = np.zeros((H,W)) + 2.
dwcs = Tan(11.2, 41.9, 1, 1, 1e-3, 0, 0, 1e-3, W, H)
ds = DustSheet(logsa, logt, emis, dwcs)
cat = Catalog()
cat.append(ds)
tractor = Tractor()
tractor.setCatalog(cat)
p0 = tractor.getParams()
print('lnp0', tractor.getLogProb())
if True:
# check getLogProb()
for j,xx in enumerate([
np.linspace(np.log(1e-5), np.log(1e-1), 20),
np.linspace(np.log(1e-5), np.log(1e-1), 20),
np.linspace(np.log(10.), np.log(20.), 20),
np.linspace(np.log(10.), np.log(20.), 20),
np.linspace(0., 4., 20),
np.linspace(0., 4., 20),
]):
pp = []
for x in xx:
tractor.setParam(j, x)
p = tractor.getLogProb()
pp.append(p)
tractor.setParam(j, p0[j])
plt.clf()
plt.plot(xx, pp, 'ro-')
plt.title(ds.getParamNames()[j])
plt.savefig('p%i.png' % (20 + j))
# set the absolute priors to have little effect and repeat.
ds.prior_logt_std = np.log(100.)
ds.prior_emis_std = np.log(100.)
for j,xx in enumerate([
np.linspace(np.log(1e-5), np.log(1e-1), 20),
np.linspace(np.log(1e-5), np.log(1e-1), 20),
np.linspace(np.log(10.), np.log(20.), 20),
np.linspace(np.log(10.), np.log(20.), 20),
np.linspace(0., 4., 20),
np.linspace(0., 4., 20),
]):
pp = []
for x in xx:
tractor.setParam(j, x)
p = tractor.getLogProb()
pp.append(p)
tractor.setParam(j, p0[j])
plt.clf()
plt.plot(xx, pp, 'ro-')
plt.title(ds.getParamNames()[j])
plt.savefig('p%i.png' % (30 + j))
# revert the priors
ds.prior_logt_std = np.log(1.2)
ds.prior_emis_std = np.log(0.5)
# check getLogPriorChi.
for j,(ip,val) in enumerate([
(0, np.log(1e-1)),
(1, np.log(1e-5)),
(2, np.log(5.)),
(3, np.log(5.)),
(2, np.log(30.)),
(3, np.log(30.)),
(4, 1.),
(5, 1.),
(4, 3.),
(5, 3.),
]):
print()
print()
print('Setting', ds.getParamNames()[ip], 'from', p0[ip], 'to', val)
tractor.setParams(p0)
tractor.setParam(ip, val)
xx = [val]
xxall = [tractor.getParams()]
pp = [tractor.getLogProb()]
for i in range(10):
tractor.optimize()#damp=1e-3)
xx.append(tractor.getParams()[ip])
pp.append(tractor.getLogProb())
xxall.append(tractor.getParams())
plt.clf()
plt.plot(xx, pp, 'ro-')
plt.axvline(val, color='r', lw=2, alpha=0.5)
plt.title(ds.getParamNames()[ip])
plt.savefig('p%i.png' % (j+40))
plt.clf()
xxall = np.vstack(xxall)
print('xxall', xxall.shape)
for i in range(6):
#plt.subplot(1,3,(i/2)+1)
#plt.plot(xxall[:,i], pp, 'ro-')
#if i == ip:
# plt.axvline(xxall[0,i], color='r', lw=2, alpha=0.5)
#plt.title(ds.getParamNames()[i])
plt.subplot(3,1,(i/2)+1)
c = 'b'
if i == ip:
c = 'r'
plt.plot(xxall[:,i], 'o-', color=c)
plt.title(ds.getParamNames()[i])
plt.savefig('p%i.png' % (j+50))
if False:
# Check single-pixel priors: getLogPrior()
N = 1
H,W = N,N
logsa = np.zeros((H,W)) + np.log(1e-3)
logt = np.zeros((H,W)) + np.log(17.)
emis = np.zeros((H,W)) + 2.
dwcs = Tan(11.2, 41.9, 1, 1, 1e-3, 0, 0, 1e-3, N, N)
ds = DustSheet(logsa, logt, emis, dwcs)
cat = Catalog()
cat.append(ds)
tractor = Tractor()
tractor.setCatalog(cat)
p0 = tractor.getParams()
print('lnp0', tractor.getLogProb())
# no prior on solid angle
# N(log(17.), log(1.2)) on T
# N(2, 0.5) on emis
for j,xx in enumerate([
np.linspace(np.log(1e-5), np.log(1e-1), 20),
np.linspace(np.log(10.), np.log(20.), 20),
np.linspace(0., 4., 20),
]):
pp = []
for x in xx:
tractor.setParam(j, x)
p = tractor.getLogProb()
pp.append(p)
tractor.setParam(j, p0[j])
plt.clf()
plt.plot(xx, pp, 'ro-')
plt.title(ds.getParamNames()[j])
plt.savefig('p%i.png' % j)
# Check single-pixel priors: getPriorChi()
for j,(ip,val) in enumerate([
(0, 1e-2),
(1, np.log(5.)),
(1, np.log(30.)),
(2, 1.),
(2, 3.),
]):
print()
print()
print('Setting', ds.getParamNames()[ip], 'to', val)
tractor.setParams(p0)
tractor.setParam(ip, val)
xx = [val]
pp = [tractor.getLogProb()]
for i in range(10):
tractor.optimize(damp=1e-3)
xx.append(tractor.getParams()[ip])
pp.append(tractor.getLogProb())
plt.clf()
plt.plot(xx, pp, 'ro-')
plt.title(ds.getParamNames()[ip])
plt.savefig('p%i.png' % (j+10))
def check_priors():
np.seterrcall(np_err_handler)
np.seterr(all='call')
import logging
import sys
lvl = logging.DEBUG
log_init(3)
logging.basicConfig(level=lvl, format='%(message)s', stream=sys.stdout)
if True:
# Check two-pixel priors: smoothness.
H,W = 1,2
logsa = np.zeros((H,W)) + np.log(1e-3)
logt = np.zeros((H,W)) + np.log(17.)
emis = np.zeros((H,W)) + 2.
dwcs = Tan(11.2, 41.9, 1, 1, 1e-3, 0, 0, 1e-3, W, H)
ds = DustSheet(logsa, logt, emis, dwcs)
cat = Catalog()
cat.append(ds)
tractor = Tractor()
tractor.setCatalog(cat)
p0 = tractor.getParams()
print 'lnp0', tractor.getLogProb()
if True:
# check getLogProb()
for j,xx in enumerate([
np.linspace(np.log(1e-5), np.log(1e-1), 20),
np.linspace(np.log(1e-5), np.log(1e-1), 20),
np.linspace(np.log(10.), np.log(20.), 20),
np.linspace(np.log(10.), np.log(20.), 20),
np.linspace(0., 4., 20),
np.linspace(0., 4., 20),
]):
pp = []
for x in xx:
tractor.setParam(j, x)
p = tractor.getLogProb()
pp.append(p)
tractor.setParam(j, p0[j])
plt.clf()
plt.plot(xx, pp, 'ro-')
plt.title(ds.getParamNames()[j])
plt.savefig('p%i.png' % (20 + j))
# set the absolute priors to have little effect and repeat.
ds.prior_logt_std = np.log(100.)
ds.prior_emis_std = np.log(100.)
for j,xx in enumerate([
np.linspace(np.log(1e-5), np.log(1e-1), 20),
np.linspace(np.log(1e-5), np.log(1e-1), 20),
np.linspace(np.log(10.), np.log(20.), 20),
np.linspace(np.log(10.), np.log(20.), 20),
np.linspace(0., 4., 20),
np.linspace(0., 4., 20),
]):
pp = []
for x in xx:
tractor.setParam(j, x)
p = tractor.getLogProb()
pp.append(p)
tractor.setParam(j, p0[j])
plt.clf()
plt.plot(xx, pp, 'ro-')
plt.title(ds.getParamNames()[j])
plt.savefig('p%i.png' % (30 + j))
# revert the priors
ds.prior_logt_std = np.log(1.2)
ds.prior_emis_std = np.log(0.5)
# check getLogPriorChi.
for j,(ip,val) in enumerate([
(0, np.log(1e-1)),
(1, np.log(1e-5)),
(2, np.log(5.)),
(3, np.log(5.)),
(2, np.log(30.)),
(3, np.log(30.)),
(4, 1.),
(5, 1.),
(4, 3.),
(5, 3.),
]):
print
print
print 'Setting', ds.getParamNames()[ip], 'from', p0[ip], 'to', val
tractor.setParams(p0)
tractor.setParam(ip, val)
xx = [val]
xxall = [tractor.getParams()]
pp = [tractor.getLogProb()]
for i in range(10):
tractor.optimize()#damp=1e-3)
xx.append(tractor.getParams()[ip])
pp.append(tractor.getLogProb())
xxall.append(tractor.getParams())
plt.clf()
plt.plot(xx, pp, 'ro-')
plt.axvline(val, color='r', lw=2, alpha=0.5)
plt.title(ds.getParamNames()[ip])
plt.savefig('p%i.png' % (j+40))
plt.clf()
xxall = np.vstack(xxall)
print 'xxall', xxall.shape
for i in range(6):
#plt.subplot(1,3,(i/2)+1)
#plt.plot(xxall[:,i], pp, 'ro-')
#if i == ip:
# plt.axvline(xxall[0,i], color='r', lw=2, alpha=0.5)
#plt.title(ds.getParamNames()[i])
plt.subplot(3,1,(i/2)+1)
c = 'b'
if i == ip:
c = 'r'
plt.plot(xxall[:,i], 'o-', color=c)
plt.title(ds.getParamNames()[i])
plt.savefig('p%i.png' % (j+50))
if False:
# Check single-pixel priors: getLogPrior()
N = 1
H,W = N,N
logsa = np.zeros((H,W)) + np.log(1e-3)
logt = np.zeros((H,W)) + np.log(17.)
emis = np.zeros((H,W)) + 2.
dwcs = Tan(11.2, 41.9, 1, 1, 1e-3, 0, 0, 1e-3, N, N)
ds = DustSheet(logsa, logt, emis, dwcs)
cat = Catalog()
cat.append(ds)
tractor = Tractor()
tractor.setCatalog(cat)
p0 = tractor.getParams()
print 'lnp0', tractor.getLogProb()
# no prior on solid angle
# N(log(17.), log(1.2)) on T
# N(2, 0.5) on emis
for j,xx in enumerate([
np.linspace(np.log(1e-5), np.log(1e-1), 20),
np.linspace(np.log(10.), np.log(20.), 20),
np.linspace(0., 4., 20),
]):
pp = []
for x in xx:
tractor.setParam(j, x)
p = tractor.getLogProb()
pp.append(p)
tractor.setParam(j, p0[j])
plt.clf()
plt.plot(xx, pp, 'ro-')
plt.title(ds.getParamNames()[j])
plt.savefig('p%i.png' % j)
# Check single-pixel priors: getPriorChi()
for j,(ip,val) in enumerate([
(0, 1e-2),
(1, np.log(5.)),
(1, np.log(30.)),
(2, 1.),
(2, 3.),
]):
print
print
print 'Setting', ds.getParamNames()[ip], 'to', val
tractor.setParams(p0)
tractor.setParam(ip, val)
xx = [val]
pp = [tractor.getLogProb()]
for i in range(10):
tractor.optimize(damp=1e-3)
xx.append(tractor.getParams()[ip])
pp.append(tractor.getLogProb())
plt.clf()
plt.plot(xx, pp, 'ro-')
plt.title(ds.getParamNames()[ip])
plt.savefig('p%i.png' % (j+10))
def main():
import optparse
import logging
import sys
parser = optparse.OptionParser()
parser.add_option('--threads', dest='threads', default=1, type=int, help='Use this many concurrent processors')
parser.add_option('-v', '--verbose', dest='verbose', action='count', default=0,
help='Make more verbose')
parser.add_option('--grid', '-g', dest='gridn', type=int, default=5, help='Dust parameter grid size')
parser.add_option('--steps', '-s', dest='steps', type=int, default=10, help='# Optimization step')
parser.add_option('--suffix', dest='suffix', default='', help='Output file suffix')
parser.add_option('--no-100', dest='no100', action='store_true', default=False,
help='Omit PACS-100 data?')
parser.add_option('--callgrind', dest='callgrind', action='store_true', default=False, help='Turn on callgrind around tractor.optimize()')
parser.add_option('--resume', '-r', dest='resume', type=int, default=-1, help='Resume from a previous run at the given step?')
parser.add_option('--zoom', dest='zoom', type=float, default=1, help='Scale down the model to only touch the (1/zoom x 1/zoom) central region of the images')
parser.add_option('--damp', dest='damp', type=float, default=1., help='LSQR damping')
opt,args = parser.parse_args()
if opt.verbose == 0:
lvl = logging.INFO
log_init(2)
else:
lvl = logging.DEBUG
log_init(3)
logging.basicConfig(level=lvl, format='%(message)s', stream=sys.stdout)
if opt.threads > 1 and False:
global dpool
import debugpool
dpool = debugpool.DebugPool(opt.threads)
Time.add_measurement(debugpool.DebugPoolMeas(dpool))
mp = multiproc(pool=dpool)
else:
print 'N threads', opt.threads
mp = multiproc(opt.threads)#, wrap_all=True)
if opt.callgrind:
import callgrind
else:
callgrind = None
np.seterrcall(np_err_handler)
np.seterr(all='call')
#np.seterr(all='raise')
if opt.resume > -1:
pfn = 'herschel-%02i%s.pickle' % (opt.resume, opt.suffix)
print 'Reading from', pfn
tractor = unpickle_from_file(pfn)
tractor.mp = mp
ds = tractor.getCatalog()[0]
print 'DustSheet:', ds
# derivs = ds.getParamDerivatives(tim)
# dim = np.zeros(tim.shape)
# #for k,deriv in enumerate(derivs[:40]):
# for k,deriv in enumerate(derivs[::10]):
# dim[:,:] = 0
# deriv.addTo(dim)
# plt.clf()
# plt.imshow(dim, interpolation='nearest', origin='lower')
# plt.savefig('deriv-%04i.png' % k)
#tim = tractor.getImages()[0]
# for it,tim in enumerate(tractor.getImages()):
# X = ds._getTransformation(tim)
# # #print 'X', X
# keys = X.keys()
# keys.sort()
# # for k in keys[::10]:
# # for k in keys[:40]:
# for k in keys[::202]:
# I,G,nil,nil = X[k]
# rim = np.zeros_like(tim.getImage())
# rim.ravel()[I] = G
# plt.clf()
# plt.imshow(rim, interpolation='nearest', origin='lower')
# plt.colorbar()
# plt.savefig('rim-%i-%04i.png' % (it,k))
# print 'pix', k
# sys.exit(0)
makeplots(tractor, opt.resume, opt.suffix)
step0 = opt.resume + 1
else:
step0 = 0
tractor = create_tractor(opt)
tractor.mp = mp
# zero out invvar outside the model bounds.
ds = tractor.getCatalog()[0]
rd = ds.getRaDecCorners()
for i,tim in enumerate(tractor.getImages()):
poly = np.array([tim.getWcs().positionToPixel(RaDecPos(rdi[0], rdi[1])) for rdi in rd])
poly = poly[:-1,:]
print 'Model bounding box in image', tim.name, 'coordinates:'
print poly.shape
print poly
H,W = tim.shape
xx,yy = np.meshgrid(np.arange(W), np.arange(H))
inside = point_in_poly(xx, yy, poly)
iv = tim.getInvvar()
iv[(inside == 0)] = 0.
tim.setInvvar(iv)
print 'Precomputing transformations...'
ds = tractor.getCatalog()[0]
# Split the grid-spread matrix into strips...
async_results = []
for im in tractor.getImages():
args = []
H,W = ds.shape
dy = 10
y = 0
while y <= H:
args.append((ds, im, y, min(H, y+dy)))
y += dy
async_results.append(mp.map_async(_map_trans, args))
# Glue to strips back together...
XX = []
for ar in async_results:
Xblocks = ar.get()
X = Xblocks[0]
for xi in Xblocks[1:]:
X.update(xi)
XX.append(X)
for im,X in zip(tractor.getImages(), XX):
ds._normalizeTransformation(im, X)
ds._setTransformation(im, X)
print 'done precomputing.'
makeplots(tractor, 0, opt.suffix)
pfn = 'herschel-%02i%s.pickle' % (0, opt.suffix)
pickle_to_file(tractor, pfn)
print 'Wrote', pfn
for im in tractor.getImages():
im.freezeAllBut('sky')
for i in range(step0, opt.steps):
if callgrind:
callgrind.callgrind_start_instrumentation()
tractor.optimize(damp=opt.damp, alphas=[1e-3, 1e-2, 0.1, 0.3, 1., 3., 10., 30., 100.])
if callgrind:
callgrind.callgrind_stop_instrumentation()
makeplots(tractor, 1 + i, opt.suffix)
pfn = 'herschel-%02i%s.pickle' % (1 + i, opt.suffix)
pickle_to_file(tractor, pfn)
print 'Wrote', pfn
def plot_wcs_outline(wcsfn,
plotfn,
W=256,
H=256,
width=36,
zoom=True,
zoomwidth=3.6,
grid=10,
hd=False,
hd_labels=False,
tycho2=False):
anutil.log_init(3)
#anutil.log_set_level(3)
wcs = anutil.Tan(wcsfn, 0)
ra, dec = wcs.radec_center()
plot = ps.Plotstuff(outformat='png', size=(W, H), rdw=(ra, dec, width))
plot.linestep = 1.
plot.color = 'verydarkblue'
plot.plot('fill')
plot.fontsize = 12
#plot.color = 'gray'
# dark gray
plot.rgb = (0.3, 0.3, 0.3)
if grid is not None:
plot.plot_grid(*([grid] * 4))
plot.rgb = (0.4, 0.6, 0.4)
ann = plot.annotations
ann.NGC = ann.bright = ann.HD = 0
ann.constellations = 1
ann.constellation_labels = 1
ann.constellation_labels_long = 1
plot.plot('annotations')
plot.stroke()
ann.constellation_labels = 0
ann.constellation_labels_long = 0
ann.constellation_lines = 0
ann.constellation_markers = 1
plot.markersize = 3
plot.rgb = (0.4, 0.6, 0.4)
plot.plot('annotations')
plot.fill()
ann.constellation_markers = 0
ann.bright_labels = False
ann.bright = True
plot.markersize = 2
if zoom >= 2:
ann.bright_labels = True
plot.plot('annotations')
ann.bright = False
ann.bright_labels = False
plot.fill()
if hd:
ann.HD = True
ann.HD_labels = hd_labels
ps.plot_annotations_set_hd_catalog(ann, settings.HENRY_DRAPER_CAT)
plot.plot('annotations')
plot.stroke()
ann.HD = False
ann.HD_labels = False
if tycho2 and settings.TYCHO2_KD:
from astrometry.libkd.spherematch import tree_open, tree_close, tree_build_radec, tree_free, trees_match
from astrometry.libkd import spherematch_c
from astrometry.util.starutil_numpy import deg2dist, xyztoradec
import numpy as np
import sys
kd = tree_open(settings.TYCHO2_KD)
# this is a bit silly: build a tree with a single point, then do match()
kd2 = tree_build_radec(np.array([ra]), np.array([dec]))
r = deg2dist(width * np.sqrt(2.) / 2.)
#r = deg2dist(wcs.radius())
I, nil, nil = trees_match(kd, kd2, r, permuted=False)
del nil
#print 'Matched', len(I)
xyz = spherematch_c.kdtree_get_positions(kd, I)
del I
tree_free(kd2)
tree_close(kd)
#print >>sys.stderr, 'Got', xyz.shape, xyz
tra, tdec = xyztoradec(xyz)
#print >>sys.stderr, 'RA,Dec', ra,dec
plot.apply_settings()
for r, d in zip(tra, tdec):
plot.marker_radec(r, d)
plot.fill()
ann.NGC = 1
plot.plot('annotations')
ann.NGC = 0
plot.color = 'white'
plot.lw = 3
out = plot.outline
out.wcs_file = wcsfn
plot.plot('outline')
if zoom:
# MAGIC width, height are arbitrary
zoomwcs = anutil.anwcs_create_box(ra, dec, zoomwidth, 1000, 1000)
out.wcs = zoomwcs
plot.lw = 1
plot.dashed(3)
plot.plot('outline')
plot.write(plotfn)
else:
parser.print_help()
sys.exit(-1)
import logging
logformat = "%(message)s"
from astrometry.util.util import log_init # , LOG_VERB, LOG_MSG
loglvl = 2
if opt.verbose:
logging.basicConfig(level=logging.DEBUG, format=logformat)
loglvl += 1
else:
logging.basicConfig(level=logging.INFO, format=logformat)
log_init(loglvl)
wcsfn = args[0]
if dojson:
from astrometry.util.util import anwcs
wcs = anwcs(wcsfn, 0)
jobjs = get_annotations_for_wcs(wcs, opt)
import simplejson
json = simplejson.dumps(jobjs)
print json
sys.exit(0)
fmt = PLOTSTUFF_FORMAT_JPG
def plot_wcs_outline(wcsfn, plotfn, W=256, H=256, width=36, zoom=True,
zoomwidth=3.6, grid=10, hd=False, hd_labels=False,
tycho2=False):
anutil.log_init(3)
#anutil.log_set_level(3)
wcs = anutil.Tan(wcsfn, 0)
ra,dec = wcs.radec_center()
plot = ps.Plotstuff(outformat='png', size=(W, H), rdw=(ra,dec,width))
plot.linestep = 1.
plot.color = 'verydarkblue'
plot.plot('fill')
plot.fontsize = 12
#plot.color = 'gray'
# dark gray
plot.rgb = (0.3,0.3,0.3)
if grid is not None:
plot.plot_grid(*([grid]*4))
plot.rgb = (0.4, 0.6, 0.4)
ann = plot.annotations
ann.NGC = ann.bright = ann.HD = 0
ann.constellations = 1
ann.constellation_labels = 1
ann.constellation_labels_long = 1
plot.plot('annotations')
plot.stroke()
ann.constellation_labels = 0
ann.constellation_labels_long = 0
ann.constellation_lines = 0
ann.constellation_markers = 1
plot.markersize = 3
plot.rgb = (0.4, 0.6, 0.4)
plot.plot('annotations')
plot.fill()
ann.constellation_markers = 0
ann.bright_labels = False
ann.bright = True
plot.markersize = 2
if zoom >= 2:
ann.bright_labels = True
plot.plot('annotations')
ann.bright = False
ann.bright_labels = False
plot.fill()
if hd:
ann.HD = True
ann.HD_labels = hd_labels
ps.plot_annotations_set_hd_catalog(ann, settings.HENRY_DRAPER_CAT)
plot.plot('annotations')
plot.stroke()
ann.HD = False
ann.HD_labels = False
if tycho2:
from astrometry.libkd.spherematch import tree_open, tree_close, tree_build_radec, tree_free, trees_match
from astrometry.libkd import spherematch_c
from astrometry.util.starutil_numpy import deg2dist, xyztoradec
import numpy as np
import sys
kd = tree_open(settings.TYCHO2_KD)
# this is a bit silly: build a tree with a single point, then do match()
kd2 = tree_build_radec(np.array([ra]), np.array([dec]))
r = deg2dist(width * np.sqrt(2.) / 2.)
#r = deg2dist(wcs.radius())
I,J,d = trees_match(kd, kd2, r, permuted=False)
del J
del d
#print 'Matched', len(I)
xyz = spherematch_c.kdtree_get_positions(kd, I)
del I
tree_free(kd2)
tree_close(kd)
#print >>sys.stderr, 'Got', xyz.shape, xyz
tra,tdec = xyztoradec(xyz)
#print >>sys.stderr, 'RA,Dec', ra,dec
plot.apply_settings()
for r,d in zip(tra,tdec):
plot.marker_radec(r,d)
plot.fill()
ann.NGC = 1
plot.plot('annotations')
ann.NGC = 0
plot.color = 'white'
plot.lw = 3
out = plot.outline
out.wcs_file = wcsfn
plot.plot('outline')
if zoom:
# MAGIC width, height are arbitrary
zoomwcs = anutil.anwcs_create_box(ra, dec, zoomwidth, 1000,1000)
out.wcs = zoomwcs
plot.lw = 1
plot.dashed(3)
plot.plot('outline')
plot.write(plotfn)
