if __name__ == '__main__':

import matplotlib

matplotlib.use('Agg')

import logging

import pylab as plt

import numpy as np

from astrometry.util.fits import *

from astrometry.util.file import *

from astrometry.util.plotutils import *

from astrometry.util.starutil_numpy import *

from astrometry.util.multiproc import *

from astrometry.util.sdss_radec_to_rcf import *

from astrometry.libkd.spherematch import *

from astrometry.sdss import *

from wise3 import *

from tractor import *

class myopts(object):

pass

basedir = 'wise-frames'

wisedatadirs = [(basedir, 'merged'),]

#indexfn = None

#indexfn = os.path.join(basedir, 'WISE-index-L1b.fits')

indexfn = 'WISE-index-L1b.fits'

datadir = 'strauss-data'

def _run_one((dataset, band, rlo, rhi, dlo, dhi, T, sfn)):

return None

'''

Type II quasars in SDSS to forced-photometer in WISE, from Michael

Strauss

email of 2013-08-23 from Strauss: attach dustin.lis

text2fits.py -H "plate fiber mjd something ra dec" -f jjjddd dustin.lis strauss.fits

+ from Jenny Greene 2013-09-12:

>> SDSS1309_0205.dat 2.2325 197.8264 2.09655

>> SDSS2252_0108.dat 2.537 343.21960 1.14157

cat > strauss2.txt <<EOF

# z ra dec

2.2325 197.8264 2.09655

2.537 343.21960 1.14157

EOF

text2fits.py -f ddd strauss2.txt strauss2.fits

'''

if __name__ == '__main__':

#sfn = 'strauss.fits'

#fulldataset = 'strauss'

sfn = 'strauss2.fits'

fulldataset = 'strauss2'

TT = fits_table(sfn)

#mp = multiproc(8)

mp = multiproc(1)

margin = 0.003

#lvl = logging.INFO

lvl = logging.DEBUG

logging.basicConfig(level=lvl, format='%(message)s', stream=sys.stdout)

# Cut to single objects

args = []

for i in range(len(TT)):

T = TT[np.array([i])]

dataset = '%s-%03i' % (fulldataset, i)

r0,r1 = T.ra.min(), T.ra.max()

d0,d1 = T.dec.min(), T.dec.max()

dr = margin / np.cos(np.deg2rad((d0+d1)/2.))

rlo = r0 - dr

rhi = r1 + dr

dlo = d0 - margin

dhi = d1 + margin

if True:

# HACK -- got the WISE catalogs on riemann and the WISE exposures on NERSC...

from wisecat import wise_catalog_radecbox

cols=['cntr', 'ra', 'dec', 'sigra', 'sigdec', 'cc_flags',

'ext_flg', 'var_flg', 'moon_lev', 'ph_qual',

'w1mpro', 'w1sigmpro', 'w1sat', 'w1nm', 'w1m',

'w1snr', 'w1cov', 'w1mag', 'w1sigm', 'w1flg',

'w2mpro', 'w2sigmpro', 'w2sat', 'w2nm', 'w2m',

'w2snr', 'w2cov', 'w2mag', 'w2sigm', 'w2flg',

'w3mpro', 'w3sigmpro', 'w3sat', 'w3nm', 'w3m',

'w3snr', 'w3cov', 'w3mag', 'w3sigm', 'w3flg',

'w4mpro', 'w4sigmpro', 'w4sat', 'w4nm', 'w4m',

'w4snr', 'w4cov', 'w4mag', 'w4sigm', 'w4flg', ]

W = wise_catalog_radecbox(rlo, rhi, dlo, dhi, cols=cols)

if W is None:

W = fits_table()

for c in cols:

W.set(c, np.array([]))

wfn = os.path.join(datadir, 'wise-objs-%s.fits' % dataset)

W.writeto(wfn)

print 'Wrote', wfn

continue

for band in [1,2,3,4]:

pfn = os.path.join(datadir, '%s-w%i-stage108.pickle' % (dataset, band))

if os.path.exists(pfn):

print 'Output exists:', pfn, '; skipping'

continue

args.append((dataset, band, rlo, rhi, dlo, dhi, T, sfn))

mp.map(_run_one, args)

# Collate results

results = []

for i in range(len(TT)):

T = TT[np.array([i])]

dataset = '%s-%03i' % (fulldataset, i)

resfn = os.path.join(datadir, '%s.fits' % dataset)

gotall = True

for band in [1,2,3,4]:

pfn = os.path.join(datadir, '%s-w%i-stage106.pickle' % (dataset, band))

if not os.path.exists(pfn):

print 'File does not exist:', pfn, '; skipping'

gotall = False

break

X = unpickle_from_file(pfn)

R = X['R']

nwise = 0

nsdss = 0

mpro = np.nan

if len(R) > len(T):

# print 'T:'

# T.about()

# print 'R:'

# R.about()

# print 'R.sdss?', R.sdss

UW = X['UW']

# print 'UW:'

# UW.about()

S = X['S']

# print 'S:'

# S.about()

nwise = len(UW)

nsdss = len(R) - len(UW) - len(T)

if nwise:

mpro = UW.get('w%impro' % band)[0]

# UW items are at the end

R = R[:len(R)-nwise]

if nsdss:

# Which one is the target T?

assert(len(T) == 1)

ir = np.argmin(np.hypot(T.ra[0] - R.ra, T.dec[0] - R.dec))

R = R[np.array([ir])]

T.set('wise_n_near_w%i' % band, np.zeros(len(T), int) + nwise)

T.set('other_sdss_near_w%i' % band, np.zeros(len(T), int) + nsdss)

T.set('wise_near0_w%impro' % band, np.zeros(len(T), np.float32) + mpro)

assert(len(R) == len(T))

nm = R.get('w%i' % band)

nm_ivar = R.get('w%i_ivar' % band)

T.set('w%i_nanomaggies' % band, nm)

T.set('w%i_nanomaggies_invvar' % band, nm_ivar)

dnm = 1./np.sqrt(nm_ivar)

mag = NanoMaggies.nanomaggiesToMag(nm)

dmag = np.abs((-2.5 / np.log(10.)) * dnm / nm)

T.set('w%i_mag' % band, mag)

T.set('w%i_mag_err' % band, dmag)

if not gotall:

continue

T.writeto(resfn)

print 'Wrote', resfn

results.append((i,T))

I = np.array([i for i,T in results])

R = merge_tables([T for i,T in results])

origcols = TT.get_columns()

for k in R.get_columns():

if k in origcols:

print 'Skipping original column', k

continue

print 'Adding column', k

r = R.get(k)

X = np.zeros(len(TT), r.dtype)

X[I] = r

TT.set(k, X)

TT.writeto('%s-results.fits' % fulldataset)