'G': 'galaxy',

'GPair': 'galaxy pair',

'GTrpl': 'galaxy triple',

'GGroup': 'galaxy group',

'Other': 'other',

'GCl': 'globular cluster',

'PN': 'planetary nebula',

'Neb': 'nebula',

'HII': 'HII region',

'Cl+N': 'cluster+nebulosity',

'RfN': 'reflection nebula',

'SNR': 'SNR?',

'EmN': 'emission nebula',

'Nova': 'nova',

import optparse

parser = optparse.OptionParser(usage='%prog')

parser.add_option('--rawdata', default=None,

help=('Directory to monitor for new images: default ' +

'$%s if set, else "rawdata"' % data_env_var))

parser.add_option('--no-show', dest='show', default=True,

action='store_false',

help='Do not show plot window, just save it.')

parser.add_option('--plotdir', help='Save plots in this directory',

default='ngcbot-plots')

parser.add_option('--tweet', default=False, action='store_true',

help='Send a tweet for each galaxy?')

parser.add_option('--coverage', type=float, help='Set coverage fraction threshold for sending tweets')

parser.add_option('--only', default=False, action='store_true',

help='Only run the command-line files, then quit')

parser.add_option('--flats', default=False, action='store_true',

help='Look for and use flats in the image directory?')

parser.add_option('--ps', default=False, action='store_true',

help='Debugging plots?')

opt,args = parser.parse_args()

global plt

if not opt.show:

import matplotlib

matplotlib.use('Agg')

import pylab

plt = pylab

from astrometry.util.plotutils import PlotSequence

global ps

ps = PlotSequence('ngcbot')

imagedir = opt.rawdata

if imagedir is None:

imagedir = os.environ.get(data_env_var, 'rawdata')

if opt.plotdir and not os.path.exists(opt.plotdir):

os.makedirs(opt.plotdir)

nom = nominal_cal

bot = NgcBot(imagedir, nom, opt)

if len(args):

for a in args:

bot.process_file(a)

if opt.only:

return 0

bot.run()

m = 0.02, clip=True):

import numpy as np

rgbscales = {'u': 1.5, #1.0,

'g': 2.5,

'r': 1.5,

'i': 1.0,

'z': 0.4, #0.3

}

if scales is not None:

rgbscales.update(scales)

b,g,r = [rimg * rgbscales[b] for rimg,b in zip(rimgs, bands)]

r = np.maximum(0, r + m)

g = np.maximum(0, g + m)

b = np.maximum(0, b + m)

I = (r+g+b)/3.

Q = 20

fI = np.arcsinh(Q * I) / np.sqrt(Q)

I += (I == 0.) * 1e-6

R = fI * r / I

G = fI * g / I

B = fI * b / I

# maxrgb = reduce(np.maximum, [R,G,B])

# J = (maxrgb > 1.)

# R[J] = R[J]/maxrgb[J]

# G[J] = G[J]/maxrgb[J]

# B[J] = B[J]/maxrgb[J]

rgb = np.dstack((R,G,B))

if clip:

rgb = np.clip(rgb, 0, 1)

def __init__(self, imagedir, nom, opt):

super(NgcBot, self).__init__(imagedir, backlog=False)

self.nom = nom

self.opt = opt

# Read catalogs

TT = []

import astrometry

catdir = os.path.join(os.path.dirname(astrometry.__file__),

'catalogs')

#fn = os.path.join(catdir, 'ngc2.fits') #'openngc-ngc.fits') #'ngc2000.fits')

fn = 'openngc-ngc-custom.fits'

print('Reading', fn)

T = fits_table(fn)

T.delete_column('ngcnum')

TT.append(T)

#fn = os.path.join(catdir, 'ic2.fits') #'openngc-ic.fits') #'ic2000.fits')

fn = 'openngc-ic-custom.fits'

print('Reading', fn)

T = fits_table(fn)

T.delete_column('icnum')

TT.append(T)

self.cat = merge_tables(TT, columns='fillzero')

del TT

self.cat.rename('type', 'classification')

print('Total of', len(self.cat), 'NGC/IC objects')

omit = [

'OC', # open cluster

'Ast', # asterism

'***', # triple star

'D*', # double star

'*', # star

'-', # called nonexistent in RNGC

'PD' # plate defect

]

# '?', # uncertain type or may not exist

# '', # unidentified or type unknown

#Counter({'G': 9742, 'OCl': 649, '*': 546, 'Other': 433, '**': 245, 'GPair': 211, 'GCl': 205, 'PN': 129, 'Neb': 93, 'HII': 81, 'Cl+N': 65, '*Ass': 60, 'RfN': 37, 'GTrpl': 23, 'NonEx': 14, 'GGroup': 14, 'SNR': 11, 'EmN': 8, 'Nova': 3})

omit = [

'OCl',

'*',

'**',

'*Ass',

'NonEx',

]

print(Counter(self.cat.classification))

self.cat.cut(np.flatnonzero(np.array([t.strip() not in omit for t in self.cat.classification])))

print('Cut to', len(self.cat), 'NGC/IC objects')

print('Remaining classifications:', np.unique(self.cat.classification))

#self.spec = fits_table('specObj-dr12-trim-2.fits')

self.cached_flats = {}

self.read_flats = opt.flats

def try_open_file(self, path):

print('Trying to open file: %s' % path)

F = fitsio.FITS(path)

## HACK

if len(F) < min_n_exts:

print('Got', len(F), 'extensions')

raise RuntimeError('fewer extensions than expected')

def filter_new_files(self, fns):

# MzLS: ignore .dat files

return [fn for fn in fns if

fn.endswith('.fits.fz') or fn.endswith('.fits')]

def process_file(self, path):

print('Reading', path)

F = fitsio.FITS(path)

primhdr = F[0].read_header()

print(len(F), 'extensions')

# measure_raw . DECamMeasurer or Mosaic3Measurer

meas_class = get_measurer_class_for_file(path)

if meas_class is None:

print('Failed to identify camera in', path)

return

meas = meas_class(path, 0, self.nom)

# We read the WCS headers from all extensions and then spherematch

# against the catalog.

rr,dd = [],[]

exts = np.arange(1, len(F))

keep_exts = []

for ext in exts:

hdr = F[ext].read_header()

extname = hdr['EXTNAME'].strip()

# HACK -- skip DECam focus chips.

if 'F' in extname:

continue

meas.ext = ext

meas.primhdr = primhdr

wcs = meas.get_wcs(hdr)

#print('WCS:', wcs)

rc,dc = wcs.radec_center()

radius = wcs.radius()

#print('RA,Dec center', rc, dc, 'radius', radius)

rr.append(rc)

dd.append(dc)

keep_exts.append(ext)

exts = keep_exts

# we use the last extension's 'radius' here...

rr = np.array(rr)

dd = np.array(dd)

if self.match_ngc(rr, dd, radius, exts, F, primhdr, meas):

return

print('Not doing SDSS spectro jazz.')

return

# No match with NGC catalog -- look at SDSS spectro objects.

I,J,d = match_radec(rr, dd, self.spec.ra, self.spec.dec, radius)

print(len(I), 'matches to spectra')

if len(I) == 0:

return False

#meas_exts = set()

#K = []

measures = {}

specobjs = []

for k,(i,j) in enumerate(zip(I,J)):

ext = exts[i]

obj = self.spec[j]

obj.name = obj.label.strip()

hdr = F[ext].read_header()

extname = hdr['EXTNAME'].strip()

expnum = primhdr['EXPNUM']

wcs = meas.get_wcs(hdr)

ok,x,y = wcs.radec2pixelxy(obj.ra, obj.dec)

x = x - 1

y = y - 1

# Choose cutout area

pixrad = 1.4 * 50

r = pixrad

tt = '%s in exp %i ext %s (%i)' % (obj.name, expnum, extname, ext)

print(tt)

# Find the cutout region... does it actually overlap the chip?

H,W = wcs.shape

xl,xh = int(np.clip(x-r, 0, W-1)), int(np.clip(x+r, 0, W-1))

yl,yh = int(np.clip(y-r, 0, H-1)), int(np.clip(y+r, 0, H-1))

if xl == xh or yl == yh:

print('no actual overlap with image')

continue

sh,sw = yh-yl, xh-xl

if sh < 25 or sw < 25:

print('tiny overlap', sw, 'x', sh)

continue

#meas_exts.add(ext)

#K.append(k)

if ext in measures:

M = measures[ext]

else:

# Measure the image!

meas.ext = ext

meas.edge_trim = 20

try:

M = meas.run(n_fwhm=1, verbose=False, get_image=True)

except KeyboardInterrupt:

sys.exit(0)

except:

import traceback

print('Failed to measure file', path, 'ext', ext, ':')

traceback.print_exc()

continue

measures[ext] = M

raw = M['image']

# Now repeat the cutout check with the trimmed image

wcs = M['wcs']

# Trim WCS to trimmed raw image shape

trim_x0, trim_y0 = M['trim_x0'], M['trim_y0']

H,W = raw.shape

wcs = wcs.get_subimage(trim_x0, trim_y0, W, H)

ok,x,y = wcs.radec2pixelxy(obj.ra, obj.dec)

x = x - 1

y = y - 1

xl,xh = int(np.clip(x-r, 0, W-1)), int(np.clip(x+r, 0, W-1))

yl,yh = int(np.clip(y-r, 0, H-1)), int(np.clip(y+r, 0, H-1))

if xl == xh or yl == yh:

print('no actual overlap with image')

continue

subimg = raw[yl:yh, xl:xh]

sh,sw = subimg.shape

if sh < 25 or sw < 25:

print('tiny overlap', sw, 'x', sh)

continue

subwcs = wcs.get_subimage(xl, yl, sw, sh)

# Astrometric shifts

dx = M['dx']

dy = M['dy']

aff = M['affine']

x = (xl+xh)/2

y = (yl+yh)/2

#print('Affine correction terms:', aff)

adx = x - aff[0]

ady = y - aff[1]

corrx = aff[2] + aff[3] * adx + aff[4] * ady - adx

corry = aff[5] + aff[6] * adx + aff[7] * ady - ady

print('Affine correction', corrx, corry)

# Shift the 'subwcs' to account for astrometric offset

cx,cy = subwcs.get_crpix()

subwcs.set_crpix((cx - dx - corrx, cy - dy - corry))

specobjs.append((tt, subwcs, subimg))

# # What size of image are we going to request?

# scale = 1.

#

# rh,rw = int(np.ceil(sh/scale)),int(np.ceil(sw/scale))

# # make it square

# mx = max(rh, rw)

# rw = rh = mx

#

# fitsimgs = []

# # We'll resample the new image into the existing-image WCS.

# newimg = None

#

# # HACK

# imgs = []

# layer = 'sdssco'

# bands = 'gri'

# for band in bands:

# fn = 'sdssco-1679p492-%s.fits' % band

# fitsfile = fitsio.FITS(fn)

# wcs = Tan(fn, 0)

# hh,ww = wcs.shape

# ok,x,y = wcs.radec2pixelxy(obj.ra, obj.dec)

# x = x - 1

# y = y - 1

# print('x,y', x,y)

# sz = pixrad * 0.262/0.396

# xl,xh = int(np.clip(x-sz, 0, ww-1)), int(np.clip(x+sz, 0, ww-1))

# yl,yh = int(np.clip(y-sz, 0, hh-1)), int(np.clip(y+sz, 0, hh-1))

# if xl == xh or yl == yh:

# print('no overlap with SDSS image')

# continue

#

# img = fitsfile[1][yl:yh+1, xl:xh+1]

# s = (subwcs.pixel_scale() / 0.396)

# img *= s**2

# imgs.append(img)

# thiswcs = wcs

#

# if len(imgs) == 0:

# continue

#

# fitsimgs.append((layer, bands, imgs))

#

# # Resample the new image to this layer's WCS

# newimg = np.zeros((rh,rw), dtype=subimg.dtype)

# try:

# # Laczos

# Yo,Xo,Yi,Xi,rims = resample_with_wcs(thiswcs, subwcs, [subimg])

# except:

# continue

# newimg[Yo,Xo] = rims[0]

#

# if len(fitsimgs) == 0:

# # No overlap with existing surveys

# continue

#

# newband = primhdr['FILTER'][0]

# I = I[K]

# J = J[K]

# print('Cut to', len(I), 'matches in', len(meas_exts), 'extensions')

# if len(I) == 0:

# return

# measures = {}

# for ext in meas_ext:

# measures[ext] =

print(len(specobjs), 'objects')

def my_rgb(imgs, bands, **kwargs):

return sdss_rgb(imgs, bands,

scales=dict(g=6.0, r=3.4, i=2.5, z=2.2),

m=-0.02, clip=False, **kwargs)

def grayscale(img, band):

rgb = my_rgb([img,img,img], [band,band,band])

index = 'zrg'.index(newband)

gray = rgb[:,:,index]

return gray

newband = primhdr['FILTER'][0]

plt.figure(2)

plt.clf()

plt.subplots_adjust(left=0.03, right=0.97, bottom=0.03,

hspace=0, wspace=0)

N = len(specobjs)

C = int(np.ceil(np.sqrt(N * 1.2)))

R = int(np.ceil(N / float(C)))

print('Rows,cols, N', R,C, N)

gray = grayscale(raw, newband)

hi = np.percentile(gray, 99.9)

grayargs = dict(interpolation='nearest', origin='lower',

vmin=0., vmax=hi, cmap='gray')

print('Plotting:')

plt.clf()

for i,(tt, subwcs,subimg) in enumerate(specobjs):

plt.subplot(R, C, i+1)

print(' ', tt)

newimg = subimg

# New Image plot

newgray = grayscale(newimg, newband)

#hi = np.percentile(newgray, 99.9)

plt.imshow(newgray, **grayargs)

plt.xticks([]); plt.yticks([])

ps.savefig()

def get_flat(self, band, ext, meas):

f = self.cached_flats.get((band,ext), None)

if f is not None:

return f

fns = self.get_file_list()

fns = self.filter_new_files(fns)

fns.sort()

print(len(fns), 'files to search for flats')

flats = []

for fn in fns:

try:

F = fitsio.FITS(fn)

hdr = F[0].read_header()

obstype = hdr['OBSTYPE']

fband = meas.get_band(hdr)

print('File', fn, 'obstype', obstype, 'filter', fband)

if obstype != 'dome flat':

continue

if fband != band:

continue

flat,fhdr = meas.read_raw(F, ext)

print('Got flat:', flat.shape)

flats.append(flat)

except:

import traceback

traceback.print_exc()

pass

if len(flats) == 0:

return None

flats = np.dstack(flats)

print('Flats:', flats.shape)

f = np.median(flats, axis=2)

print('f:', f.shape)

self.cached_flats[(band,ext)] = f

return f

def match_ngc(self, rr, dd, radius, exts, F, primhdr, meas):

'''

rr, dd: np arrays: RA,Dec centers of chips/amps

radius: scalar, radius of chips/amps

exts: list (same len as rr,dd) of extension indices

F: fitsio.FITS object

meas: measurement class

'''

I,J,d = match_radec(rr, dd, self.cat.ra, self.cat.dec, radius)

# plt.clf()

# angles = np.linspace(0, 2.*np.pi, 40)

# for r,d in zip(rr, dd):

# plt.plot(r + radius * np.sin(angles) / np.cos(np.deg2rad(d)), d + radius * np.cos(angles), 'b-')

# plt.plot(rr, dd, 'bo')

# ax = plt.axis()

# plt.plot(self.cat.ra, self.cat.dec, 'r.')

# plt.axis(ax)

# ps.savefig()

print('Matched', len(I), 'NGC objects')

if len(I) == 0:

return False

for j in J:

info = ngc_typenames.get(self.cat.classification[j].strip(), '')

print(' ', self.cat.name[j], info)

# Potential tweet texts and plot filenames

tweets = []

goodplots = []

for i,j in zip(I,J):

ext = exts[i]

obj = self.cat[j]

obj.name = obj.name.strip()

hdr = F[ext].read_header()

extname = hdr['EXTNAME'].strip()

expnum = primhdr['EXPNUM']

wcs = meas.get_wcs(hdr)

ok,x,y = wcs.radec2pixelxy(obj.ra, obj.dec)

x = x - 1

y = y - 1

# Choose cutout area

pixrad = 1.4 * obj.radius * 3600. / wcs.pixel_scale()

pixrad = max(pixrad, 100)

# print('radius:', obj.radius, 'pixel radius:', pixrad)

# HACK

#pixrad *= 3

rx = pixrad

ry = pixrad

#rx *= 1.5

#ry = rx

tt = '%s in exp %i ext %s (%i)' % (obj.name, expnum, extname, ext)

print(tt)

# Find the cutout region... does it actually overlap the chip?

H,W = wcs.shape

xl,xh = int(np.clip(x-rx, 0, W-1)), int(np.clip(x+rx, 0, W-1))

yl,yh = int(np.clip(y-ry, 0, H-1)), int(np.clip(y+ry, 0, H-1))

if xl == xh or yl == yh:

print('no actual overlap with image')

continue

sh,sw = yh-yl, xh-xl

if sh < 25 or sw < 25:

print('tiny overlap', sw, 'x', sh)

continue

# Measure the image!

flat = None

if self.read_flats:

band = meas.get_band(hdr)

flat = self.get_flat(band, ext, meas)

if flat is not None:

print('flat: range', flat.min(), flat.max(), 'median', np.median(flat))

meas.ext = ext

meas.edge_trim = 20

debugps = None

if self.opt.ps:

debugps = ps

try:

M = meas.run(n_fwhm=1, verbose=False, get_image=True, flat=flat, ps=debugps)

except KeyboardInterrupt:

sys.exit(0)

except:

import traceback

print('Failed to measure file', meas.fn, 'ext', ext, ':')

traceback.print_exc()

continue

#print('Measured:', M.keys())

raw = M['image']

# Now repeat the cutout check with the trimmed image

wcs = M['wcs']

# Trim WCS to trimmed raw image shape

trim_x0, trim_y0 = M['trim_x0'], M['trim_y0']

H,W = raw.shape

wcs = wcs.get_subimage(trim_x0, trim_y0, W, H)

ok,x,y = wcs.radec2pixelxy(obj.ra, obj.dec)

x = x - 1

y = y - 1

xl,xh = int(np.clip(x-rx, 0, W-1)), int(np.clip(x+rx, 0, W-1))

yl,yh = int(np.clip(y-ry, 0, H-1)), int(np.clip(y+ry, 0, H-1))

if xl == xh or yl == yh:

print('no actual overlap with image')

continue

subimg = raw[yl:yh, xl:xh]

sh,sw = subimg.shape

if sh < 25 or sw < 25:

print('tiny overlap', sw, 'x', sh)

continue

subwcs = wcs.get_subimage(xl, yl, sw, sh)

if False:

# Flat image for the same CCD region.

flatfn = '/tmp/mzls/mos3.127506.fits'

# meas_class = get_measurer_class_for_file(flatfn)

# if meas_class is None:

# print('Failed to identify camera in', flatfn)

# return

# flatmeas = meas_class(flatfn, 0, self.nom)

# print('Flat meas:', flatmeas)

# flatmeas.ext = ext

# flathdr = fitsio.read_header(flatfn)

# flatmeas.primhdr = flathdr

# flatmeas.edge_trim = 20

# FM = flatmeas.run(n_fwhm=1, verbose=False, get_image=True)

# flatraw = FM['image']

F = fitsio.FITS(flatfn)

flatraw,flathdr = meas.read_raw(F, ext)

flatsub = flatraw[yl:yh, xl:xh]

zerofn = '/tmp/mzls/mos3.127522.fits'

F = fitsio.FITS(zerofn)

zeroraw,zerohdr = meas.read_raw(F, ext)

zerosub = zeroraw[yl:yh, xl:xh]

rawfn = meas.fn

F = fitsio.FITS(rawfn)

rawimg,rawhdr = meas.read_raw(F, ext)

rawsub = rawimg[yl:yh, xl:xh]

# Astrometric shifts

dx = M['dx']

dy = M['dy']

aff = M['affine']

x = (xl+xh)/2

y = (yl+yh)/2

#print('Affine correction terms:', aff)

adx = x - aff[0]

ady = y - aff[1]

corrx = aff[2] + aff[3] * adx + aff[4] * ady - adx

corry = aff[5] + aff[6] * adx + aff[7] * ady - ady

print('Affine correction', corrx, corry)

# Shift the 'subwcs' to account for astrometric offset

cx,cy = subwcs.get_crpix()

subwcs.set_crpix((cx - dx - corrx, cy - dy - corry))

# Now grab existing data from the LegacySurvey site

# What size of image are we going to request?

scale = 1.

if max(sh,sw) > 1024:

scale = 4.

elif max(sh,sw) > 512:

scale = 2.

rh,rw = int(np.ceil(sh/scale)),int(np.ceil(sw/scale))

# make it square

mx = max(rh, rw)

rw = rh = mx

fitsimgs = []

# We'll resample the new image into the existing-image WCS.

newimg = None

for layer in legacy_survey_layers:

url = ('http://legacysurvey.org/viewer-dev/fits-cutout/?ra=%.4f&dec=%.4f&pixscale=%.3f&width=%i&height=%i&layer=%s' %

(obj.ra, obj.dec, subwcs.pixel_scale() * scale, rw, rh, layer))

print('URL:', url)

r = requests.get(url)

ftmp = tempfile.NamedTemporaryFile()

ftmp.write(r.content)

ftmp.flush()

#fits,hdr = fitsio.read(ftmp.name, header=True)

try:

fitsfile = fitsio.FITS(ftmp.name)

except:

print('no coverage in layer', layer)

continue

ftmp.close()

print('FITS file:', len(fitsfile), 'extensions')

hdr = fitsfile[0].read_header()

fits = fitsfile[0].read()

#print('fits:', fits)

if fits is None:

print('no coverage in layer', layer)

continue

# If you need to keep a copy (debugging...)

# f,tmpfn = tempfile.mkstemp(suffix='.fits')

# os.write(f, r.content)

# os.close(f)

# fits,hdr = fitsio.read(tmpfn, header=True)

# print('Wrote FITS to', tmpfn)

if np.all(fits == 0):

continue

### HACK -- surface brightness correction...

if layer in ['sdssco', 'sdss']:

s = (subwcs.pixel_scale() / 0.396)

fits *= s**2

N,ww,hh = fits.shape

# pull out the image planes

imgs = [fits[n,:,:] for n in range(N)]

bands = hdr['BANDS'].strip()

fitsimgs.append((layer, bands, imgs))

# Resample the new image to this layer's WCS

if newimg is not None:

continue

thiswcs = Tan(hdr)

newimg = np.zeros((rh,rw), dtype=subimg.dtype)

try:

#Yo,Xo,Yi,Xi,rims = resample_with_wcs(thiswcs, subwcs)

# Laczos

Yo,Xo,Yi,Xi,rims = resample_with_wcs(thiswcs, subwcs, [subimg])

except:

continue

#newimg[Yo,Xo] = subimg[Yi,Xi]

newimg[Yo,Xo] = rims[0]

if len(fitsimgs) == 0:

# No overlap with existing surveys

continue

#print()

newband = primhdr['FILTER'][0]

#print('New image is', newband)

if False:

mn,mx = np.percentile(flatsub, [25,99])

plt.clf()

#plt.imshow(newflat, interpolation='nearest', origin='lower',

plt.imshow(flatsub, interpolation='nearest', origin='lower',

vmin=mn, vmax=mx)

#plt.colorbar()

plt.colorbar()

plt.savefig('ngcbot-flat.png')

med = np.median(newflat.ravel())

#mn,mx = np.percentile(newimg, [25,99])

#mn,mx = np.percentile(newimg, [25,90])

#mn,mx = np.percentile(rawsub, [25,95])

mn,mx = np.percentile(rawsub, [50,95])

plt.clf()

# plt.subplot(1,2,1)

#plt.imshow(newimg, interpolation='nearest', origin='lower',

# plt.imshow(subimg, interpolation='nearest', origin='lower',

plt.imshow(rawsub, interpolation='nearest', origin='lower',

vmin=mn, vmax=mx)

plt.colorbar()

plt.savefig('ngcbot-unflattened.png')

plt.clf()

#plt.subplot(1,2,2)

#plt.imshow(newimg / (newflat / med), interpolation='nearest', origin='lower',

#plt.imshow(subimg / (flatsub / med), interpolation='nearest', origin='lower',

plt.imshow(rawsub / (flatsub / med), interpolation='nearest', origin='lower',

vmin=mn, vmax=mx)

plt.colorbar()

plt.savefig('ngcbot-flattened.png')

mn,mx = np.percentile(zerosub, [5,95])

plt.clf()

plt.imshow(zerosub, interpolation='nearest', origin='lower',

vmin=mn, vmax=mx)

plt.colorbar()

plt.savefig('ngcbot-zero.png')

zp = M['zp']

zpscale = 10.**((zp - 22.5)/2.5)

exptime = primhdr['EXPTIME']

newimg /= (zpscale * exptime)

nh,nw = newimg.shape

coverage = np.sum(newimg != 0) / float(nw*nh)

print('Fraction', coverage, 'of new image has data')

def my_rgb(imgs, bands, **kwargs):

S = 1.5

return sdss_rgb(imgs, bands,

scales=dict(g=6.0*S, r=3.4*S, i=2.5*S, z=2.2*S),

m=-0.02, clip=False, **kwargs)

def grayscale(img, band):

rgb = my_rgb([img,img,img], [band,band,band])

index = 'zrg'.index(newband)

gray = rgb[:,:,index]

return gray

# plot title args

targs = dict(fontsize=8)

# DEBUG

if self.opt.ps:

ocx,ocy = subwcs.get_crpix()

subwcs.set_crpix((cx, cy))

newimgA = np.zeros((rh,rw), dtype=subimg.dtype)

Yo,Xo,Yi,Xi,rims = resample_with_wcs(thiswcs, subwcs, [subimg])

newimgA[Yo,Xo] = rims[0]

subwcs.set_crpix((cx-dx, cy-dy))

newimgB = np.zeros((rh,rw), dtype=subimg.dtype)

Yo,Xo,Yi,Xi,rims = resample_with_wcs(thiswcs, subwcs, [subimg])

newimgB[Yo,Xo] = rims[0]

subwcs.set_crpix((cx-dx-corrx, cy-dy-corry))

newimgC = np.zeros((rh,rw), dtype=subimg.dtype)

Yo,Xo,Yi,Xi,rims = resample_with_wcs(thiswcs, subwcs, [subimg])

newimgC[Yo,Xo] = rims[0]

#newgray = grayscale(newimg, newband)

#hi = np.percentile(newgray, 99.9)

grayargs = dict(interpolation='nearest', origin='lower',

cmap='gray', vmin=0.)

#vmin=0., vmax=hi, cmap='gray')

(layer, bands, imgs) = fitsimgs[0]

nicelayer = nicelayernames.get(layer, layer)

for band,img in zip(bands, imgs):

newbands = ['g','r','z']

newindex = dict(g=0, r=1, i=2, z=2)

if newindex[band] == newindex[newband]:

oldgray = grayscale(img, band)

plt.clf()

plt.imshow(oldgray, **grayargs)

plt.title(nicelayer)

ps.savefig()

plt.clf()

plt.imshow(grayscale(newimgA, newband), **grayargs)

#plt.imshow(newimgA, **grayargs)

plt.title('No astromety correction')

ps.savefig()

plt.clf()

plt.imshow(grayscale(newimgB, newband), **grayargs)

#plt.imshow(newimgB, **grayargs)

plt.title('Astromety shift only correction')

ps.savefig()

plt.clf()

plt.imshow(grayscale(newimgC, newband), **grayargs)

#plt.imshow(newimgC, **grayargs)

plt.title('Astromety shift+affine correction')

ps.savefig()

subwcs.set_crpix((ocx,ocy))

plt.figure(1, figsize=(10,6))

plt.clf()

plt.subplots_adjust(left=0.03, right=0.97, bottom=0.03)

NC = 1 + len(fitsimgs)

NR = 2

# New Image plot

newgray = grayscale(newimg, newband)

hi = np.percentile(newgray, 99.9)

grayargs = dict(interpolation='nearest', origin='lower',

vmin=0., vmax=hi, cmap='gray')

plt.subplot(NR, NC, 1)

plt.imshow(newgray, **grayargs)

plt.xticks([]); plt.yticks([])

plt.title('New image (%s)' % newband, **targs)

# Select images & bands for the New RGB image.

zeroimg = np.zeros_like(newimg)

newimgs = [zeroimg, zeroimg, zeroimg]

# sdss_rgb reverses the order, so do like grz.

newbands = ['g','r','z']

newindex = dict(g=0, r=1, i=2, z=2)

# Start with the new image plane of the New RGB image

j = newindex[newband]

newimgs [j] = newimg

newbands[j] = newband

#print('Setting band', newband, '(index %i)'%j, 'to new image')

# We wait to choose the scaling until after the "New RGB" image

# has been built, so we store the RGB images along the way...

rgbs = []

# In the link to the legacysurvey viewer, we select the best

# imaging layer. The DECaLS layer is listed first, so if it

# has three bands, it wins.

bestdata = None

bestn = 0

for i,(layer, bands, imgs) in enumerate(fitsimgs):

nicelayer = nicelayernames.get(layer, layer)

# For DECaLS missing bands: "--z"

nicebands = ''

goodbands = []

goodimgs = []

for band,img in zip(bands, imgs):

if np.all(img == 0.):

print('Band', band, 'of', nicelayer, 'is all zero')

nicebands += '-'

continue

goodbands.append(band)

goodimgs.append(img)

nicebands += band

#print(' ', nicelayer, band)

j = newindex[band]

if newimgs[j] is zeroimg:

#print(' Setting band', band, '(index %i)'%j, 'to', nicelayer)

newimgs[j] = img

newbands[j] = band

elif band == newbands[j]:

# patch empty regions if same band

#print(' Patching index %i from' % j, nicelayer, 'band', band)

Z = (newimgs[j] == 0)

newimgs[j][Z] = img[Z]

# z -> i

if newindex[band] == newindex[newband]:

# Old image grayscale

oldgray = grayscale(img, band)

plt.subplot(NR, NC, 2+i)

plt.imshow(oldgray, **grayargs)

plt.xticks([]); plt.yticks([])

plt.title('%s (%s)' % (nicelayer, band), **targs)

if len(goodbands) == 1:

#rgb = grayscale(goodimgs[0], goodbands[0])

img,band = goodimgs[0], goodbands[0]

rgb = my_rgb([img,img,img], [band,band,band])

else:

rgb = my_rgb(imgs, bands)

if len(goodbands) > bestn:

bestn = len(goodbands)

bestdata = layer

# print('bands for', nicelayer, ':', bands, ', actually', nicebands)

rgbs.append((2+i+NC, rgb, '%s (%s)' % (nicelayer, nicebands)))

# list to string

newbands = ''.join(newbands)

#print('Newbands:', newbands)

# New RGB

plt.subplot(NR, NC, 1 + NC)

rgb = my_rgb(newimgs, newbands)

lo = 0.

hi = np.percentile(rgb.ravel(), 99.9)

rgb = np.clip((rgb - lo) / (hi - lo), 0., 1.)

plt.imshow(rgb, interpolation='nearest', origin='lower')

plt.xticks([]); plt.yticks([])

plt.title('New+Old (%s)' % newbands, **targs)

# Old RGBs

for sp, rgb, tt in rgbs:

plt.subplot(NR, NC, sp)