def find_nearest_tile(ra, dec):
from astropy.coordinates import SkyCoord
from astropy.wcs import WCS
from astropy.io import fits
from astropy import units as u
import numpy as np
sc = SkyCoord(ra=ra * u.degree, dec=dec * u.degree, frame='icrs')
line = None
with fits.open('/home/rt2122/Data/fulldepth_neo4_index.fits') as hdul:
sc1 = SkyCoord(ra=hdul[1].data['RA'] * u.degree,
dec=hdul[1].data['DEC'] * u.degree)
idx = np.argmin(sc.separation(sc1).degree)
line = hdul[1].data[idx]
w = WCS(naxis=2)
w.wcs.cd = line['CD']
w.wcs.cdelt = line['CDELT']
w.wcs.crpix = line['CRPIX']
w.wcs.crval = line['CRVAL']
w.wcs.ctype = ['RA---TAN', 'DEC--TAN']
w.wcs.lonpole = line['LONGPOLE']
w.wcs.latpole = line['LATPOLE']
w.wcs.set_pv([(0, 0, 0)])
w.array_shape = (2048, 2048)
return w
def from_header(cls, header, hdu_bands=None, format=None):
"""Create a WCS geometry object from a FITS header.
Parameters
----------
header : `~astropy.io.fits.Header`
The FITS header
hdu_bands : `~astropy.io.fits.BinTableHDU`
The BANDS table HDU.
format : {'gadf', 'fgst-ccube','fgst-template'}
FITS format convention.
Returns
-------
wcs : `~WcsGeom`
WCS geometry object.
"""
wcs = WCS(header, naxis=2)
# TODO: see https://github.com/astropy/astropy/issues/9259
wcs._naxis = wcs._naxis[:2]
axes = MapAxes.from_table_hdu(hdu_bands, format=format)
shape = axes.shape
if hdu_bands is not None and "NPIX" in hdu_bands.columns.names:
npix = hdu_bands.data.field("NPIX").reshape(shape + (2, ))
npix = (npix[..., 0], npix[..., 1])
cdelt = hdu_bands.data.field("CDELT").reshape(shape + (2, ))
cdelt = (cdelt[..., 0], cdelt[..., 1])
elif "WCSSHAPE" in header:
wcs_shape = eval(header["WCSSHAPE"])
npix = (wcs_shape[0], wcs_shape[1])
cdelt = None
wcs.array_shape = npix
else:
npix = (header["NAXIS1"], header["NAXIS2"])
cdelt = None
if "PSLICE1" in header:
cutout_info = {}
cutout_info["parent-slices"] = (
str_to_slice(header["PSLICE2"]),
str_to_slice(header["PSLICE1"]),
)
cutout_info["cutout-slices"] = (
str_to_slice(header["CSLICE2"]),
str_to_slice(header["CSLICE1"]),
)
else:
cutout_info = None
return cls(wcs, npix, cdelt=cdelt, axes=axes, cutout_info=cutout_info)
def custom_wcs(ra, dec):
from astropy.io import fits
from astropy.wcs import WCS
cutout_url = 'https://www.legacysurvey.org/viewer/cutout.fits?ra={:.4f}&dec={:.4f}&layer=sdss2&pixscale=4.00'
w = None
with fits.open(cutout_url.format(ra, dec)) as hdul:
w = WCS(hdul[0].header)
w1 = WCS(naxis=2)
w1.wcs.cd = w.wcs.cd[:2, :2]
w1.wcs.cdelt = w.wcs.cdelt[:2]
w1.wcs.crpix = [1024.5, 1024.5]
w1.wcs.crval = w.wcs.crval[:2]
w1.wcs.ctype = ['RA---TAN', 'DEC--TAN']
w1.wcs.lonpole = w.wcs.lonpole
w1.wcs.latpole = w.wcs.latpole
w1.wcs.set_pv([(0, 0, 0)])
w1.array_shape = [2048, 2048]
return w1
def brickwcs(ra, dec, npix=3600, step=0.262):
""" Create the WCS and header for a brick."""
# This creates a brick WCS given the brick structure
# Make the tiling file
#---------------------
# Lines with the tiling scheme first
nx = npix
ny = npix
step = step / npix
xref = nx // 2
yref = ny // 2
w = WCS()
w.wcs.crpix = [xref + 1, yref + 1]
w.wcs.cdelt = np.array([step, step])
w.wcs.crval = [ra, dec]
w.wcs.ctype = ["RA---TAN", "DEC--TAN"]
w.array_shape = (npix, npix)
# Make the header as well
hdu = fits.PrimaryHDU()
head = hdu.header
head['NAXIS'] = 2
head['NAXIS1'] = nx
head['CDELT1'] = step
head['CRPIX1'] = xref + 1
head['CRVAL1'] = ra
head['CTYPE1'] = 'RA---TAN'
head['NAXIS2'] = ny
head['CDELT2'] = step
head['CRPIX2'] = yref + 1
head['CRVAL2'] = dec
head['CTYPE2'] = 'DEC--TAN'
#head['BRAMIN'] = brickstr.ra1,'RA min of unique brick area'
#head['BRAMAX'] = brickstr.ra2,'RA max of unique brick area'
#head['BDECMIN'] = brickstr.dec1,'DEC min of unique brick area'
#head['BDECMAX'] = brickstr.dec2,'DEC max of unique brick area'
return w, head
def meascutout(meas, obj, size=10, outdir='./', domask=True):
""" Input the measurements and create cutouts. """
expstr = fits.getdata(
'/net/dl2/dnidever/nsc/instcal/v3/lists/nsc_v3_exposures.fits.gz', 1)
#expstr = fits.getdata('/net/dl2/dnidever/nsc/instcal/v3/lists/nsc_v3_exposure.fits.gz',1)
decam = Table.read('/home/dnidever/projects/delvered/data/decam.txt',
format='ascii')
objid = obj['id'][0]
# Sort by MJD
si = np.argsort(meas['mjd'])
meas = meas[si]
# Make cut on FWHM
# maybe only use values for 0.5*fwhm_chip to 1.5*fwhm_chip
sql = "select chip.* from nsc_dr2.chip as chip join nsc_dr2.meas as meas on chip.exposure=meas.exposure and chip.ccdnum=meas.ccdnum"
sql += " where meas.objectid='" + objid + "'"
chip = qc.query(sql=sql, fmt='table')
ind3, ind4 = dln.match(chip['exposure'], meas['exposure'])
si = np.argsort(ind4) # sort by input meas catalog
ind3 = ind3[si]
ind4 = ind4[si]
chip = chip[ind3]
meas = meas[ind4]
gdfwhm, = np.where((meas['fwhm'] > 0.2 * chip['fwhm'])
& (meas['fwhm'] < 2.0 * chip['fwhm']))
if len(gdfwhm) == 0:
print('All measurements have bad FWHM values')
return
if len(gdfwhm) < len(meas):
print('Removing ' + str(len(meas) - len(gdfwhm)) +
' measurements with bad FWHM values')
meas = meas[gdfwhm]
ind1, ind2 = dln.match(expstr['base'], meas['exposure'])
nind = len(ind1)
if nind == 0:
print('No matches')
return
# Sort by input meas catalog
si = np.argsort(ind2)
ind1 = ind1[si]
ind2 = ind2[si]
# Create the reference WCS
wref = WCS(naxis=2)
pixscale = 0.26 # DECam, "/pix
npix = round(size / pixscale)
if npix % 2 == 0: # must be odd
npix += 1
hpix = npix // 2 # center of image
wref.wcs.ctype = ['RA---TAN', 'DEC--TAN']
wref.wcs.crval = [obj['ra'][0], obj['dec'][0]]
wref.wcs.crpix = [npix // 2, npix // 2]
wref.wcs.cd = np.array([[pixscale / 3600.0, 0.0], [0.0, pixscale / 3600]])
wref.array_shape = (npix, npix)
refheader = wref.to_header()
refheader['NAXIS'] = 2
refheader['NAXIS1'] = npix
refheader['NAXIS2'] = npix
# Load the data
instrument = expstr['instrument'][ind1]
plver = expstr['plver'][ind1]
fluxfile = expstr['file'][ind1]
fluxfile = fluxfile.replace('/net/mss1/', '/mss1/') # for thing/hulk
maskfile = expstr['maskfile'][ind1]
maskfile = maskfile.replace('/net/mss1/', '/mss1/') # for thing/hulk
ccdnum = meas['ccdnum'][ind2]
figfiles = []
xmeas = []
ymeas = []
cutimarr = np.zeros((npix, npix, nind), float)
for i in range(nind):
#for i in range(3):
instcode = instrument[i]
plver1 = plver[i]
try:
if instrument[i] == 'c4d':
dind, = np.where(decam['CCDNUM'] == ccdnum[i])
extname = decam['NAME'][dind[0]]
im, head = getfitsext(fluxfile[i], extname, header=True)
mim, mhead = getfitsext(maskfile[i], extname, header=True)
#im,head = fits.getdata(fluxfile[i],header=True,extname=extname)
#mim,mhead = fits.getdata(maskfile[i],header=True,extname=extname)
else:
im, head = fits.getdata(fluxfile[i], ccdnum[i], header=True)
mim, mhead = fits.getdata(maskfile[i], ccdnum[i], header=True)
except:
print('error')
import pdb
pdb.set_trace()
# Turn the mask from integer to bitmask
if ((instcode == 'c4d') &
(plver1 >= 'V3.5.0')) | (instcode == 'k4m') | (instcode == 'ksb'):
omim = mim.copy()
mim *= 0
nonzero = (omim > 0)
mim[nonzero] = 2**((omim - 1)[nonzero]) # This takes about 1 sec
# Fix the DECam Pre-V3.5.0 masks
if (instcode == 'c4d') & (plver1 < 'V3.5.0'):
omim = mim.copy()
mim *= 0 # re-initialize
mim += (np.bitwise_and(omim, 1) == 1) * 1 # bad pixels
mim += (np.bitwise_and(omim, 2) == 2) * 4 # saturated
mim += (np.bitwise_and(omim, 4) == 4) * 32 # interpolated
mim += (np.bitwise_and(omim, 16) == 16) * 16 # cosmic ray
mim += (np.bitwise_and(omim, 64) == 64) * 8 # bleed trail
# Get chip-level information
exposure = os.path.basename(fluxfile[i])[0:-8] # remove fits.fz
chres = qc.query(sql="select * from nsc_dr2.chip where exposure='" +
exposure + "' and ccdnum=" + str(ccdnum[i]),
fmt='table')
w = WCS(head)
# RA/DEC correction for the object
lon = obj['ra'][0] - chres['ra'][0]
lat = obj['dec'][0] - chres['dec'][0]
racorr = chres['ra_coef1'][0] + chres['ra_coef2'][0] * lon + chres[
'ra_coef3'][0] * lon * lat + chres['ra_coef4'][0] * lat
deccorr = chres['dec_coef1'][0] + chres['dec_coef2'][0] * lon + chres[
'dec_coef3'][0] * lon * lat + chres['dec_coef4'][0] * lat
# apply these offsets to the header WCS CRVAL
#w.wcs.crval += [racorr,deccorr]
#head['CRVAL1'] += racorr
#head['CRVAL2'] += deccorr
print(racorr, deccorr)
# Object X/Y position
xobj, yobj = w.all_world2pix(obj['ra'], obj['dec'], 0)
# Get the cutout
xcen = meas['x'][ind2[i]] - 1 # convert to 0-indexes
ycen = meas['y'][ind2[i]] - 1
smim = dln.gsmooth(im, 2)
# use the object coords for centering
#cutim,xr,yr = cutout(smim,xobj,yobj,size)
# Mask the bad pixels
if domask == True:
badmask = (mim > 0)
im[badmask] = np.nanmedian(im[~badmask])
else:
badmask = (im < 0)
# Create a common TAN WCS that each image gets interpoled onto!!!
#hdu1 = fits.open(fluxfile[i],extname=extname)
smim1 = dln.gsmooth(im, 1.5)
hdu = fits.PrimaryHDU(smim1, head)
cutim, footprint = reproject_interp(hdu, refheader,
order='bicubic') # biquadratic
cutim[footprint == 0] = np.nanmedian(
im[~badmask]) # set out-of-bounds to background
#xr = [0,npix-1]
#yr = [0,npix-1]
xr = [-hpix * pixscale, hpix * pixscale]
yr = [-hpix * pixscale, hpix * pixscale]
# exposure_ccdnum, filter, MJD, delta_MJD, mag
print(
str(i + 1) + ' ' + meas['exposure'][ind2[i]] + ' ' +
str(ccdnum[i]) + ' ' + str(meas['x'][ind2[i]]) + ' ' +
str(meas['y'][ind2[i]]) + ' ' + str(meas['mag_auto'][ind2[i]]))
#figdir = '/net/dl2/dnidever/nsc/instcal/v3/hpm2/cutouts/'
figfile = outdir
figfile += '%s_%04d_%s_%02d.jpg' % (str(
obj['id'][0]), i + 1, meas['exposure'][ind2[i]], ccdnum[i])
figfiles.append(figfile)
matplotlib.use('Agg')
plt.rc('font', size=15)
plt.rc('axes', titlesize=20)
plt.rc('axes', labelsize=20)
plt.rc('xtick', labelsize=20)
plt.rc('ytick', labelsize=20)
#plt.rcParams.update({'font.size': 15})
#plt.rcParams.update({'axes.size': 20})
#plt.rcParams.update({'xtick.size': 20})
#plt.rcParams.update({'ytick.size': 20})
if os.path.exists(figfile): os.remove(figfile)
fig = plt.gcf() # get current graphics window
fig.clf() # clear
gskw = dict(width_ratios=[30, 1])
fig, ax = plt.subplots(ncols=2, nrows=1, gridspec_kw=gskw)
figsize = 8.0 #6.0
figheight = 8.0
figwidth = 9.0
#ax = fig.subplots() # projection=wcs
#fig.set_figheight(figsize*0.8)
fig.set_figheight(figheight)
fig.set_figwidth(figwidth)
med = np.nanmedian(smim)
sig = dln.mad(smim)
bigim, xr2, yr2 = cutout(smim, xcen, ycen, 151, missing=med)
lmed = np.nanmedian(bigim)
# Get the flux of the object and scale each image to the same height
#meas.mag_aper1 = cat1.mag_aper[0] + 2.5*alog10(exptime) + chstr[i].zpterm
#cmag = mag_auto + 2.5*alog10(exptime) + zpterm
instmag = meas['mag_auto'][ind2[i]] - 2.5 * np.log10(
chres['exptime'][0]) - chres['zpterm'][0]
#mag = -2.5*log(flux)+25.0
instflux = 10**((25.0 - instmag) / 2.5)
print('flux = ' + str(instflux))
# Get height of object
# flux of 2D Gaussian is ~2*pi*height*sigma^2
pixscale1 = np.max(np.abs(w.wcs.cd)) * 3600
fwhm = chres['fwhm'][0] / pixscale1
instheight = instflux / (2 * 3.14 * (fwhm / 2.35)**2)
print('height = ' + str(instheight))
# Scale the images to the flux level of the first image
cutim -= lmed
if i == 0:
instflux0 = instflux.copy()
instheight0 = instheight.copy()
else:
scale = instflux0 / instflux
#scale = instheight0/instheight
cutim *= scale
print('scaling image by ' + str(scale))
#vmin = lmed-8*sig # 3*sig
#vmax = lmed+12*sig # 5*sig
if i == 0:
vmin = -8 * sig # 3*sig
#vmax = 12*sig # 5*sig
vmax = 0.5 * instheight # 0.5
vmin0 = vmin
vmax0 = vmax
else:
vmin = vmin0
vmax = vmax0
print('vmin = ' + str(vmin))
print('vmax = ' + str(vmax))
cutimarr[:, :, i] = cutim.copy()
ax[0].imshow(cutim,
origin='lower',
aspect='auto',
interpolation='none',
extent=(xr[0], xr[1], yr[0], yr[1]),
vmin=vmin,
vmax=vmax,
cmap='viridis') # viridis, Greys, jet
#plt.imshow(cutim,origin='lower',aspect='auto',interpolation='none',
# vmin=vmin,vmax=vmax,cmap='viridis') # viridis, Greys, jet
#plt.colorbar()
# show one vertical, one horizontal line pointing to the center but offset
# then a small dot on the meas position
# 13, 8
ax[0].plot(np.array([0, 0]),
np.array([-0.066 * npix, 0.066 * npix]) * pixscale,
c='white',
alpha=0.7)
ax[0].plot(np.array([-0.066 * npix, 0.066 * npix]) * pixscale,
np.array([0, 0]),
c='white',
alpha=0.7)
# Meas X/Y position
xmeas1, ymeas1 = wref.all_world2pix(meas['ra'][ind2[i]],
meas['dec'][ind2[i]], 0)
xmeas.append(xmeas1)
ymeas.append(ymeas1)
ax[0].scatter([(xmeas1 - hpix) * pixscale],
[(ymeas1 - hpix) * pixscale],
c='r',
marker='+',
s=20)
#plt.scatter([xmeas],[ymeas],c='r',marker='+',s=100)
#plt.scatter([xcen],[ycen],c='r',marker='+',s=100)
# Object X/Y position
#xobj,yobj = w.all_world2pix(obj['ra'],obj['dec'],0)
xobj, yobj = wref.all_world2pix(obj['ra'], obj['dec'], 0)
#plt.scatter(xobj,yobj,marker='o',s=200,facecolors='none',edgecolors='y',linewidth=3)
#plt.scatter(xobj,yobj,c='y',marker='+',s=100)
#leg = ax.legend(loc='upper left', frameon=False)
ax[0].set_xlabel(r'$\Delta$ RA (arcsec)')
ax[0].set_ylabel(r'$\Delta$ DEC (arcsec)')
ax[0].set_xlim((xr[1], xr[0])) # sky right
ax[0].set_ylim(yr)
#plt.xlabel('X')
#plt.ylabel('Y')
#plt.xlim(xr)
#plt.ylim(yr)
#ax.annotate(r'S/N=%5.1f',xy=(np.mean(xr), yr[0]+dln.valrange(yr)*0.05),ha='center')
co = 'white' #'lightgray' # blue
ax[0].annotate('%s %02d %s %6.1f ' %
(meas['exposure'][ind2[i]], ccdnum[i],
meas['filter'][ind2[i]], expstr['exptime'][ind1[i]]),
xy=(np.mean(xr), yr[0] + dln.valrange(yr) * 0.05),
ha='center',
color=co)
ax[0].annotate(
'%10.2f $\Delta$t=%7.2f ' %
(meas['mjd'][ind2[i]], meas['mjd'][ind2[i]] - np.min(meas['mjd'])),
xy=(xr[1] - dln.valrange(xr) * 0.05,
yr[1] - dln.valrange(yr) * 0.05),
ha='left',
color=co)
# xy=(xr[0]+dln.valrange(xr)*0.05, yr[1]-dln.valrange(yr)*0.05),ha='left',color=co)
ax[0].annotate('%s = %5.2f +/- %4.2f' %
(meas['filter'][ind2[i]], meas['mag_auto'][ind2[i]],
meas['magerr_auto'][ind2[i]]),
xy=(xr[0] + dln.valrange(xr) * 0.05,
yr[1] - dln.valrange(yr) * 0.05),
ha='right',
color=co)
# xy=(xr[1]-dln.valrange(xr)*0.05, yr[1]-dln.valrange(yr)*0.05),ha='right',color=co)
# Progress bar
frameratio = (i + 1) / float(nind)
timeratio = (meas['mjd'][ind2[i]] -
np.min(meas['mjd'])) / dln.valrange(meas['mjd'])
#ratio = frameratio
ratio = timeratio
print('ratio = ' + str(100 * ratio))
barim = np.zeros((100, 100), int)
ind = dln.limit(int(round(ratio * 100)), 1, 99)
barim[:, 0:ind] = 1
ax[1].imshow(barim.T, origin='lower', aspect='auto', cmap='Greys')
ax[1].set_xlabel('%7.1f \n days' %
(meas['mjd'][ind2[i]] - np.min(meas['mjd'])))
#ax[1].set_xlabel('%d/%d' % (i+1,nind))
ax[1].set_title('%d/%d' % (i + 1, nind))
ax[1].axes.xaxis.set_ticks([])
#ax[1].axes.xaxis.set_visible(False)
ax[1].axes.yaxis.set_visible(False)
#ax[1].axis('off')
right_side = ax[1].spines['right']
right_side.set_visible(False)
left_side = ax[1].spines['left']
left_side.set_visible(False)
top_side = ax[1].spines['top']
top_side.set_visible(False)
plt.savefig(figfile)
print('Cutout written to ' + figfile)
#import pdb; pdb.set_trace()
avgim = np.sum(cutimarr, axis=2) / nind
avgim *= instheight0 / np.max(avgim)
medim = np.median(cutimarr, axis=2)
# Make a single blank file at the end so you know it looped
figfile = outdir
figfile += '%s_%04d_%s.jpg' % (str(obj['id'][0]), i + 2, 'path')
figfiles.append(figfile)
matplotlib.use('Agg')
if os.path.exists(figfile): os.remove(figfile)
fig = plt.gcf() # get current graphics window
fig.clf() # clear
gskw = dict(width_ratios=[30, 1])
fig, ax = plt.subplots(ncols=2, nrows=1, gridspec_kw=gskw)
fig.set_figheight(figheight)
fig.set_figwidth(figwidth)
ax[0].imshow(avgim,
origin='lower',
aspect='auto',
interpolation='none',
extent=(xr[0], xr[1], yr[0], yr[1]),
vmin=vmin,
vmax=vmax,
cmap='viridis') # viridis, Greys, jet
ax[0].plot(np.array([0, 0]),
np.array([-0.066 * npix, 0.066 * npix]) * pixscale,
c='white',
alpha=0.7,
zorder=1)
ax[0].plot(np.array([-0.066 * npix, 0.066 * npix]) * pixscale,
np.array([0, 0]),
c='white',
alpha=0.7,
zorder=1)
xmeas = np.array(xmeas)
ymeas = np.array(ymeas)
ax[0].plot((xmeas - hpix) * pixscale, (ymeas - hpix) * pixscale, c='r')
#plt.scatter((xmeas-hpix)*pixscale,(ymeas-hpix)*pixscale,c='r',marker='+',s=30)
ax[0].set_xlabel(r'$\Delta$ RA (arcsec)')
ax[0].set_ylabel(r'$\Delta$ DEC (arcsec)')
ax[0].set_xlim((xr[1], xr[0])) # sky -right
ax[0].set_ylim(yr)
ax[1].axis('off')
plt.savefig(figfile)
# Make four copies
for j in np.arange(2, 11):
#pathfile = figfile.replace('path1','path'+str(j))
pathfile = figfile.replace('%04d' % (i + 2), '%04d' % (i + 1 + j))
if os.path.exists(pathfile): os.remove(pathfile)
shutil.copyfile(figfile, pathfile)
figfiles.append(pathfile)
# Make the animated gif
animfile = outdir + str(objid) + '_cutouts.gif'
if os.path.exists(animfile): os.remove(animfile)
# put list of files in a separate file
listfile = outdir + str(objid) + '_cutouts.lst'
if os.path.exists(listfile): os.remove(listfile)
dln.writelines(listfile, figfiles)
delay = dln.scale(nind, [20, 1000], [20, 1])
delay = int(np.round(dln.limit(delay, 1, 20)))
print('delay = ' + str(delay))
print('Creating animated gif ' + animfile)
#ret = subprocess.run('convert -delay 100 '+figdir+str(objid)+'_*.jpg '+animfile,shell=True)
#ret = subprocess.run('convert -delay 20 '+' '.join(figfiles)+' '+animfile,shell=True)
ret = subprocess.run('convert @' + listfile + ' -delay ' + str(delay) +
' ' + animfile,
shell=True)
#import pdb; pdb.set_trace()
dln.remove(figfiles)
def meascutout(meas, obj, size=10, outdir='./'):
""" Input the measurements and create cutouts. """
expstr = fits.getdata(
'/net/dl2/dnidever/nsc/instcal/v3/lists/nsc_v3_exposures.fits.gz', 1)
#expstr = fits.getdata('/net/dl2/dnidever/nsc/instcal/v3/lists/nsc_v3_exposure.fits.gz',1)
decam = Table.read('/home/dnidever/projects/delvered/data/decam.txt',
format='ascii')
objid = obj['id'][0]
# Sort by MJD
si = np.argsort(meas['mjd'])
meas = meas[si]
ind1, ind2 = dln.match(expstr['base'], meas['exposure'])
nind = len(ind1)
if nind == 0:
print('No matches')
return
# Sort by input meas catalog
si = np.argsort(ind2)
ind1 = ind1[si]
ind2 = ind2[si]
# Create the reference WCS
wref = WCS(naxis=2)
pixscale = 0.26 # DECam, "/pix
npix = round(size / pixscale)
if npix % 2 == 0: # must be odd
npix += 1
hpix = npix // 2 # center of image
wref.wcs.ctype = ['RA---TAN', 'DEC--TAN']
wref.wcs.crval = [obj['ra'][0], obj['dec'][0]]
wref.wcs.crpix = [npix // 2, npix // 2]
wref.wcs.cd = np.array([[pixscale / 3600.0, 0.0], [0.0, pixscale / 3600]])
wref.array_shape = (npix, npix)
refheader = wref.to_header()
refheader['NAXIS'] = 2
refheader['NAXIS1'] = npix
refheader['NAXIS2'] = npix
# Load the data
instrument = expstr['instrument'][ind1]
fluxfile = expstr['file'][ind1]
fluxfile = fluxfile.replace('/net/mss1/', '/mss1/') # for thing/hulk
maskfile = expstr['maskfile'][ind1]
maskfile = maskfile.replace('/net/mss1/', '/mss1/') # for thing/hulk
ccdnum = meas['ccdnum'][ind2]
figfiles = []
for i in range(nind):
try:
if instrument[i] == 'c4d':
dind, = np.where(decam['CCDNUM'] == ccdnum[i])
extname = decam['NAME'][dind[0]]
im, head = getfitsext(fluxfile[i], extname, header=True)
mim, mhead = getfitsext(maskfile[i], extname, header=True)
#im,head = fits.getdata(fluxfile[i],header=True,extname=extname)
#mim,mhead = fits.getdata(maskfile[i],header=True,extname=extname)
else:
im, head = fits.getdata(fluxfile[i], ccdnum[i], header=True)
mim, mhead = fits.getdata(maskfile[i], ccdnum[i], header=True)
except:
print('error')
import pdb
pdb.set_trace()
# Get chip-level information
exposure = os.path.basename(fluxfile[i])[0:-8] # remove fits.fz
chres = qc.query(sql="select * from nsc_dr2.chip where exposure='" +
exposure + "' and ccdnum=" + str(ccdnum[i]),
fmt='table')
w = WCS(head)
# RA/DEC correction for the object
lon = obj['ra'][0] - chres['ra'][0]
lat = obj['dec'][0] - chres['dec'][0]
racorr = chres['ra_coef1'][0] + chres['ra_coef2'][0] * lon + chres[
'ra_coef3'][0] * lon * lat + chres['ra_coef4'][0] * lat
deccorr = chres['dec_coef1'][0] + chres['dec_coef2'][0] * lon + chres[
'dec_coef3'][0] * lon * lat + chres['dec_coef4'][0] * lat
# apply these offsets to the header WCS CRVAL
w.wcs.crval += [racorr, deccorr]
head['CRVAL1'] += racorr
head['CRVAL2'] += deccorr
# Object X/Y position
xobj, yobj = w.all_world2pix(obj['ra'], obj['dec'], 0)
# Get the cutout
xcen = meas['x'][ind2[i]] - 1 # convert to 0-indexes
ycen = meas['y'][ind2[i]] - 1
smim = dln.gsmooth(im, 2)
# use the object coords for centering
#cutim,xr,yr = cutout(smim,xobj,yobj,size)
# Mask the bad pixels
badmask = (mim > 0)
im[badmask] = np.nanmedian(im[~badmask])
# Create a common TAN WCS that each image gets interpoled onto!!!
#hdu1 = fits.open(fluxfile[i],extname=extname)
smim1 = dln.gsmooth(im, 1.5)
hdu = fits.PrimaryHDU(smim1, head)
cutim, footprint = reproject_interp(hdu, refheader,
order='bicubic') # biquadratic
cutim[footprint == 0] = np.nanmedian(
im[~badmask]) # set out-of-bounds to background
#xr = [0,npix-1]
#yr = [0,npix-1]
xr = [-hpix * pixscale, hpix * pixscale]
yr = [-hpix * pixscale, hpix * pixscale]
# exposure_ccdnum, filter, MJD, delta_MJD, mag
print(
str(i + 1) + ' ' + meas['exposure'][ind2[i]] + ' ' +
str(ccdnum[i]) + ' ' + str(meas['x'][ind2[i]]) + ' ' +
str(meas['y'][ind2[i]]) + ' ' + str(meas['mag_auto'][ind2[i]]))
#figdir = '/net/dl2/dnidever/nsc/instcal/v3/hpm2/cutouts/'
figfile = outdir
figfile += '%s_%04d_%s_%02d.jpg' % (str(
obj['id'][0]), i + 1, meas['exposure'][ind2[i]], ccdnum[i])
figfiles.append(figfile)
matplotlib.use('Agg')
plt.rcParams.update({'font.size': 11})
if os.path.exists(figfile): os.remove(figfile)
fig = plt.gcf() # get current graphics window
fig.clf() # clear
figsize = 8.0 #6.0
ax = fig.subplots() # projection=wcs
#fig.set_figheight(figsize*0.8)
fig.set_figheight(figsize)
fig.set_figwidth(figsize)
med = np.nanmedian(smim)
sig = dln.mad(smim)
bigim, xr2, yr2 = cutout(smim, xcen, ycen, 151, missing=med)
lmed = np.nanmedian(bigim)
# Get the flux of the object and scale each image to the same height
#meas.mag_aper1 = cat1.mag_aper[0] + 2.5*alog10(exptime) + chstr[i].zpterm
#cmag = mag_auto + 2.5*alog10(exptime) + zpterm
instmag = meas['mag_auto'][ind2[i]] - 2.5 * np.log10(
chres['exptime'][0]) - chres['zpterm'][0]
#mag = -2.5*log(flux)+25.0
instflux = 10**((25.0 - instmag) / 2.5)
print('flux = ' + str(instflux))
# Get height of object
# flux of 2D Gaussian is ~2*pi*height*sigma^2
pixscale1 = np.max(np.abs(w.wcs.cd)) * 3600
fwhm = chres['fwhm'][0] / pixscale1
instheight = instflux / (2 * 3.14 * (fwhm / 2.35)**2)
print('height = ' + str(instheight))
# Scale the images to the flux level of the first image
cutim -= lmed
if i == 0:
instflux0 = instflux.copy()
instheight0 = instheight.copy()
else:
scale = instflux0 / instflux
#scale = instheight0/instheight
cutim *= scale
print('scaling image by ' + str(scale))
#vmin = lmed-8*sig # 3*sig
#vmax = lmed+12*sig # 5*sig
if i == 0:
vmin = -8 * sig # 3*sig
#vmax = 12*sig # 5*sig
vmax = 0.5 * instheight # 0.5
vmin0 = vmin
vmax0 = vmax
else:
vmin = vmin0
vmax = vmax0
print('vmin = ' + str(vmin))
print('vmax = ' + str(vmax))
plt.imshow(cutim,
origin='lower',
aspect='auto',
interpolation='none',
extent=(xr[0], xr[1], yr[0], yr[1]),
vmin=vmin,
vmax=vmax,
cmap='viridis') # viridis, Greys, jet
#plt.imshow(cutim,origin='lower',aspect='auto',interpolation='none',
# vmin=vmin,vmax=vmax,cmap='viridis') # viridis, Greys, jet
#plt.colorbar()
# show one vertical, one horizontal line pointing to the center but offset
# then a small dot on the meas position
# 13, 8
plt.plot(np.array([0, 0]),
np.array([-0.066 * npix, 0.066 * npix]) * pixscale,
c='white',
alpha=0.7)
plt.plot(np.array([-0.066 * npix, 0.066 * npix]) * pixscale,
np.array([0, 0]),
c='white',
alpha=0.7)
# Meas X/Y position
xmeas, ymeas = wref.all_world2pix(meas['ra'][ind2[i]],
meas['dec'][ind2[i]], 0)
plt.scatter([(xmeas - hpix) * pixscale], [(ymeas - hpix) * pixscale],
c='r',
marker='+',
s=20)
#plt.scatter([xmeas],[ymeas],c='r',marker='+',s=100)
#plt.scatter([xcen],[ycen],c='r',marker='+',s=100)
# Object X/Y position
#xobj,yobj = w.all_world2pix(obj['ra'],obj['dec'],0)
xobj, yobj = wref.all_world2pix(obj['ra'], obj['dec'], 0)
#plt.scatter(xobj,yobj,marker='o',s=200,facecolors='none',edgecolors='y',linewidth=3)
#plt.scatter(xobj,yobj,c='y',marker='+',s=100)
#leg = ax.legend(loc='upper left', frameon=False)
plt.xlabel(r'$\Delta$ RA (arcsec)')
plt.ylabel(r'$\Delta$ DEC (arcsec)')
#plt.xlabel('X')
#plt.ylabel('Y')
#plt.xlim(xr)
#plt.ylim(yr)
#ax.annotate(r'S/N=%5.1f',xy=(np.mean(xr), yr[0]+dln.valrange(yr)*0.05),ha='center')
co = 'white' #'lightgray' # blue
ax.annotate('%s %02d %s %6.1f ' %
(meas['exposure'][ind2[i]], ccdnum[i],
meas['filter'][ind2[i]], expstr['exptime'][ind1[i]]),
xy=(np.mean(xr), yr[0] + dln.valrange(yr) * 0.05),
ha='center',
color=co)
ax.annotate(
'%10.2f %10.2f ' %
(meas['mjd'][ind2[i]], meas['mjd'][ind2[i]] - np.min(meas['mjd'])),
xy=(xr[0] + dln.valrange(xr) * 0.05,
yr[1] - dln.valrange(yr) * 0.05),
ha='left',
color=co)
ax.annotate('%s = %5.2f +/- %4.2f' %
(meas['filter'][ind2[i]], meas['mag_auto'][ind2[i]],
meas['magerr_auto'][ind2[i]]),
xy=(xr[1] - dln.valrange(xr) * 0.05,
yr[1] - dln.valrange(yr) * 0.05),
ha='right',
color=co)
plt.savefig(figfile)
print('Cutout written to ' + figfile)
#import pdb; pdb.set_trace()
# Make a single blank file at the end so you know it looped
figfile = outdir
figfile += '%s_%04d_%s.jpg' % (str(obj['id'][0]), i + 2, 'blank')
figfiles.append(figfile)
matplotlib.use('Agg')
if os.path.exists(figfile): os.remove(figfile)
fig = plt.gcf() # get current graphics window
fig.clf() # clear
figsize = 8.0 #6.0
fig.set_figheight(figsize)
fig.set_figwidth(figsize)
plt.savefig(figfile)
print(figfile)
# Make the animated gif
animfile = outdir + str(objid) + '_cutouts.gif'
print('Creating animated gif ' + animfile)
if os.path.exists(animfile): os.remove(animfile)
#ret = subprocess.run('convert -delay 100 '+figdir+str(objid)+'_*.jpg '+animfile,shell=True)
ret = subprocess.run('convert -delay 20 ' + ' '.join(figfiles) + ' ' +
animfile,
shell=True)
#import pdb; pdb.set_trace()
dln.remove(figfiles)
def main():
parser = argparse.ArgumentParser(
description="RGB predictions for Gaia EDR3 stars")
parser.add_argument("ra_center",
help="right Ascension (decimal degrees)",
type=float)
parser.add_argument("dec_center",
help="declination (decimal degrees)",
type=float)
parser.add_argument("search_radius",
help="search radius (decimal degrees)",
type=float)
parser.add_argument("g_limit",
help="limiting Gaia G magnitude",
type=float)
parser.add_argument("--basename",
help="file basename for output files",
type=str,
default="rgbsearch")
parser.add_argument(
"--brightlimit",
help=
"stars brighter than this Gaia G limit are displayed with star symbols (default=8.0)",
type=float,
default=8.0)
parser.add_argument(
"--symbsize",
help="multiplying factor for symbol size (default=1.0)",
type=float,
default=1.0)
parser.add_argument("--nonumbers",
help="do not display star numbers in PDF chart",
action="store_true")
parser.add_argument("--noplot",
help="skip PDF chart generation",
action="store_true")
parser.add_argument("--nocolor",
help="do not use colors in PDF chart",
action="store_true")
parser.add_argument("--starhorse_block",
help="number of stars/query (default=0, no query)",
default=0,
type=int)
parser.add_argument("--verbose",
help="increase program verbosity",
action="store_true")
parser.add_argument("--debug", help="debug flag", action="store_true")
args = parser.parse_args()
if len(sys.argv) == 1:
parser.print_usage()
raise SystemExit()
if args.ra_center < 0 or args.ra_center > 360:
raise SystemExit('ERROR: right ascension out of valid range')
if args.dec_center < -90 or args.dec_center > 90:
raise SystemExit('ERROR: declination out of valid range')
if args.search_radius < 0:
raise SystemExit('ERROR: search radius must be > 0 degrees')
if args.search_radius > MAX_SEARCH_RADIUS:
raise SystemExit(
f'ERROR: search radius must be <= {MAX_SEARCH_RADIUS} degrees')
# check whether the auxiliary FITS binary table exists
if args.debug:
auxbintable = RGB_FROM_GAIA_ALLSKY
else:
auxbintable = EDR3_SOURCE_ID_15M_ALLSKY
if os.path.isfile(auxbintable):
pass
else:
urldir = f'http://nartex.fis.ucm.es/~ncl/rgbphot/gaia/{auxbintable}'
sys.stdout.write(f'Downloading {urldir}... (please wait)')
sys.stdout.flush()
urllib.request.urlretrieve(urldir, auxbintable)
print(' ...OK!')
# read the previous file
try:
with fits.open(auxbintable) as hdul_table:
edr3_source_id_15M_allsky = hdul_table[1].data.source_id
if args.debug:
edr3_b_rgb_15M_allsky = hdul_table[1].data.B_rgb
edr3_g_rgb_15M_allsky = hdul_table[1].data.G_rgb
edr3_r_rgb_15M_allsky = hdul_table[1].data.R_rgb
edr3_g_br_rgb_15M_allsky = hdul_table[1].data.G_BR_rgb
edr3_g_gaia_15M_allsky = hdul_table[1].data.G_gaia
edr3_bp_gaia_15M_allsky = hdul_table[1].data.BP_gaia
edr3_rp_gaia_15M_allsky = hdul_table[1].data.RP_gaia
edr3_av50_15M_allsky = hdul_table[1].data.av50
edr3_met50_15M_allsky = hdul_table[1].data.met50
edr3_dist50_15M_allsky = hdul_table[1].data.dist50
except FileNotFoundError:
raise SystemExit(
f'ERROR: unexpected problem while reading {EDR3_SOURCE_ID_15M_ALLSKY}'
)
# define WCS
naxis1 = 1024
naxis2 = naxis1
pixscale = 2 * args.search_radius / naxis1
wcs_image = WCS(naxis=2)
wcs_image.wcs.crpix = [naxis1 / 2, naxis2 / 2]
wcs_image.wcs.crval = [args.ra_center, args.dec_center]
wcs_image.wcs.cunit = ["deg", "deg"]
wcs_image.wcs.ctype = ["RA---TAN", "DEC--TAN"]
wcs_image.wcs.cdelt = [-pixscale, pixscale]
wcs_image.array_shape = [naxis1, naxis2]
if args.verbose:
print(wcs_image)
# ---
# EDR3 query
query = f"""
SELECT source_id, ra, dec,
phot_g_mean_mag, phot_bp_mean_mag, phot_rp_mean_mag
FROM gaiaedr3.gaia_source
WHERE 1=CONTAINS(
POINT('ICRS', {args.ra_center}, {args.dec_center}),
CIRCLE('ICRS',ra, dec, {args.search_radius}))
AND phot_g_mean_mag IS NOT NULL
AND phot_bp_mean_mag IS NOT NULL
AND phot_rp_mean_mag IS NOT NULL
AND phot_g_mean_mag < {args.g_limit}
ORDER BY ra
"""
sys.stdout.write(
'<STEP1> Starting cone search in Gaia EDR3... (please wait)\n ')
sys.stdout.flush()
job = Gaia.launch_job_async(query)
r_edr3 = job.get_results()
# compute G_BP - G_RP colour
r_edr3.add_column(
Column(r_edr3['phot_bp_mean_mag'] - r_edr3['phot_rp_mean_mag'],
name='bp_rp',
unit=u.mag))
# colour cut in BP-RP
mask_colour = np.logical_or((r_edr3['bp_rp'] <= -0.5),
(r_edr3['bp_rp'] >= 2.0))
r_edr3_colorcut = r_edr3[mask_colour]
nstars = len(r_edr3)
print(f' --> {nstars} stars found')
nstars_colorcut = len(r_edr3_colorcut)
print(
f' --> {nstars_colorcut} stars outside -0.5 < G_BP-G_RP < 2.0')
if nstars == 0:
raise SystemExit('ERROR: no stars found. Change search parameters!')
if args.verbose:
r_edr3.pprint(max_width=1000)
# ---
# intersection with StarHorse star sample
if args.starhorse_block > 0:
param_starhorse = [
'dr3_source_id', 'sh_gaiaflag', 'sh_outflag', 'dist05', 'dist16',
'dist50', 'dist84', 'dist95', 'av05', 'av16', 'av50', 'av84',
'av95', 'teff16', 'teff50', 'teff84', 'logg16', 'logg50', 'logg84',
'met16', 'met50', 'met84', 'mass16', 'mass50', 'mass84', 'xgal',
'ygal', 'zgal', 'rgal', 'ruwe', 'angular_distance',
'magnitude_difference', 'proper_motion_propagation',
'dup_max_number'
]
print(
'<STEP2> Retrieving StarHorse data from Gaia@AIP... (please wait)')
print(f' pyvo version {pyvo.__version__}')
print(f' TAP service GAIA@AIP')
nstars_per_block = args.starhorse_block
nblocks = int(nstars / nstars_per_block)
r_starhorse = None
if nstars - nblocks * nstars_per_block > 0:
nblocks += 1
for iblock in range(nblocks):
irow1 = iblock * nstars_per_block
irow2 = min(irow1 + nstars_per_block, nstars)
print(f' Starting query #{iblock+1} of {nblocks}...')
dumstr = ','.join(
[str(item) for item in r_edr3[irow1:irow2]['source_id']])
query = f"""
SELECT {','.join(param_starhorse)}
FROM gaiadr2_contrib.starhorse
WHERE dr3_source_id IN ({dumstr})
"""
tap_session = requests.Session()
tap_session.headers['Authorization'] = "kkk"
tap_service = pyvo.dal.TAPService('https://gaia.aip.de/tap',
session=tap_session)
tap_result = tap_service.run_sync(query)
if args.debug:
print(tap_result.to_table())
if iblock == 0:
r_starhorse = tap_result.to_table()
else:
r_starhorse = vstack(
[r_starhorse, tap_result.to_table()],
join_type='exact',
metadata_conflicts='silent')
nstars_starhorse = len(r_starhorse)
if args.verbose:
if nstars_starhorse > 0:
r_starhorse.pprint(max_width=1000)
# join tables
print(f' --> {nstars_starhorse} stars found in StarHorse')
print(' Joining EDR3 and StarHorse queries...')
r_starhorse.rename_column('dr3_source_id', 'source_id')
r_edr3 = join(r_edr3, r_starhorse, keys='source_id', join_type='outer')
r_edr3.sort('ra')
if args.verbose:
r_edr3.pprint(max_width=1000)
else:
print('<STEP2> Retrieving StarHorse data from Gaia@AIP... (skipped!)')
# ---
# intersection with 15M star sample
sys.stdout.write(
'<STEP3> Cross-matching EDR3 with 15M subsample... (please wait)')
sys.stdout.flush()
set1 = set(np.array(r_edr3['source_id']))
set2 = set(edr3_source_id_15M_allsky)
intersection = set2.intersection(set1)
print(f'\n --> {len(intersection)} stars in common with 15M sample')
if args.verbose:
print(len(set1), len(set2), len(intersection))
# ---
# DR2 query to identify variable stars
query = f"""
SELECT source_id, ra, dec, phot_g_mean_mag, phot_variable_flag
FROM gaiadr2.gaia_source
WHERE 1=CONTAINS(
POINT('ICRS', {args.ra_center}, {args.dec_center}),
CIRCLE('ICRS',ra, dec, {args.search_radius}))
AND phot_g_mean_mag < {args.g_limit}
"""
sys.stdout.write(
'<STEP4> Looking for variable stars in Gaia DR2... (please wait)\n ')
sys.stdout.flush()
job = Gaia.launch_job_async(query)
r_dr2 = job.get_results()
nstars_dr2 = len(r_dr2)
if nstars_dr2 == 0:
nvariables = 0
mask_var = None
else:
if isinstance(r_dr2['phot_variable_flag'][0], bytes):
mask_var = r_dr2['phot_variable_flag'] == b'VARIABLE'
elif isinstance(r_dr2['phot_variable_flag'][0], str):
mask_var = r_dr2['phot_variable_flag'] == 'VARIABLE'
else:
raise SystemExit(
'Unexpected type of data in column phot_variable_flag')
nvariables = sum(mask_var)
print(
f' --> {nstars_dr2} stars in DR2, ({nvariables} initial variables)'
)
if nvariables > 0:
if args.verbose:
r_dr2[mask_var].pprint(max_width=1000)
# ---
# cross-match between DR2 and EDR3 to identify the variable stars
dumstr = '('
if nvariables > 0:
# generate sequence of source_id of variable stars
dumstr = ','.join([str(item) for item in r_dr2[mask_var]['source_id']])
# cross-match
query = f"""
SELECT *
FROM gaiaedr3.dr2_neighbourhood
WHERE dr2_source_id IN ({dumstr})
ORDER BY angular_distance
"""
sys.stdout.write(
'<STEP5> Cross-matching variables in DR2 with stars in EDR3... (please wait)\n '
)
sys.stdout.flush()
job = Gaia.launch_job_async(query)
r_cross_var = job.get_results()
if args.verbose:
r_cross_var.pprint(max_width=1000)
nvariables = len(r_cross_var)
if nvariables > 0:
# check that the variables pass the same selection as the EDR3 stars
# (this includes de colour cut)
mask_var = []
for item in r_cross_var['dr3_source_id']:
if item in r_edr3['source_id']:
mask_var.append(True)
else:
mask_var.append(False)
r_cross_var = r_cross_var[mask_var]
nvariables = len(r_cross_var)
if args.verbose:
r_cross_var.pprint(max_width=1000)
else:
r_cross_var = None
else:
r_cross_var = None # Avoid PyCharm warning
print(f' --> {nvariables} variable(s) in selected EDR3 star sample')
# ---
sys.stdout.write('<STEP6> Computing RGB magnitudes...')
sys.stdout.flush()
# predict RGB magnitudes
coef_B = np.array([
-0.13748689, 0.44265552, 0.37878846, -0.14923841, 0.09172474,
-0.02594726
])
coef_G = np.array([
-0.02330159, 0.12884074, 0.22149167, -0.1455048, 0.10635149, -0.0236399
])
coef_R = np.array([
0.10979647, -0.14579334, 0.10747392, -0.1063592, 0.08494556,
-0.01368962
])
coef_X = np.array([
-0.01252185, 0.13983574, 0.23688188, -0.10175532, 0.07401939,
-0.0182115
])
poly_B = Polynomial(coef_B)
poly_G = Polynomial(coef_G)
poly_R = Polynomial(coef_R)
poly_X = Polynomial(coef_X)
r_edr3.add_column(Column(np.round(
r_edr3['phot_g_mean_mag'] + poly_B(r_edr3['bp_rp']), 2),
name='b_rgb',
unit=u.mag,
format='.2f'),
index=3)
r_edr3.add_column(Column(np.round(
r_edr3['phot_g_mean_mag'] + poly_G(r_edr3['bp_rp']), 2),
name='g_rgb',
unit=u.mag,
format='.2f'),
index=4)
r_edr3.add_column(Column(np.round(
r_edr3['phot_g_mean_mag'] + poly_R(r_edr3['bp_rp']), 2),
name='r_rgb',
unit=u.mag,
format='.2f'),
index=5)
r_edr3.add_column(Column(np.round(
r_edr3['phot_g_mean_mag'] + poly_X(r_edr3['bp_rp']), 2),
name='g_br_rgb',
unit=u.mag,
format='.2f'),
index=6)
print('OK')
if args.verbose:
r_edr3.pprint(max_width=1000)
# ---
sys.stdout.write('<STEP7> Saving output CSV files...')
sys.stdout.flush()
outtypes = ['edr3', '15m', 'var']
outtypes_color = {'edr3': 'black', '15m': 'red', 'var': 'blue'}
r_edr3.add_column(
Column(np.zeros(len(r_edr3)), name='number_csv', dtype=int))
for item in outtypes:
r_edr3.add_column(
Column(np.zeros(len(r_edr3)), name=f'number_{item}', dtype=int))
outlist = [f'./{args.basename}_{ftype}.csv' for ftype in outtypes]
filelist = glob.glob('./*.csv')
# remove previous versions of the output files (if present)
for file in outlist:
if file in filelist:
try:
os.remove(file)
except:
print(f'ERROR: while deleting existing file {file}')
# columns to be saved (use a list to guarantee the same order)
outcolumns_list = [
'source_id', 'ra', 'dec', 'b_rgb', 'g_rgb', 'r_rgb', 'g_br_rgb',
'phot_g_mean_mag', 'phot_bp_mean_mag', 'phot_rp_mean_mag'
]
# define column format with a dictionary
outcolumns = {
'source_id': '19d',
'ra': '14.9f',
'dec': '14.9f',
'b_rgb': '6.2f',
'g_rgb': '6.2f',
'r_rgb': '6.2f',
'g_br_rgb': '6.2f',
'phot_g_mean_mag': '8.4f',
'phot_bp_mean_mag': '8.4f',
'phot_rp_mean_mag': '8.4f'
}
if set(outcolumns_list) != set(outcolumns.keys()):
raise SystemExit('ERROR: check outcolumns_list and outcolumns')
csv_header_ini = 'number,' + ','.join(outcolumns_list)
flist = []
for ftype in outtypes:
f = open(f'{args.basename}_{ftype}.csv', 'wt')
flist.append(f)
if (args.starhorse_block > 0) and (ftype in ['edr3', '15m']):
if args.debug and (ftype == '15m'):
csv_header = csv_header_ini + \
',av50,met50,dist50,b_rgb_bis,g_rgb_bis,r_rgb_bis,g_br_rgb_bis,' \
'phot_g_mean_mag_bis,phot_bp_mean_mag_bis,phot_rp_mean_mag_bis,' \
'av50_bis,met50_bis,dist50_bis'
else:
csv_header = csv_header_ini + ',av50,met50,dist50'
else:
csv_header = csv_header_ini
f.write(csv_header + '\n')
# save each star in its corresponding output file
krow = np.ones(len(outtypes), dtype=int)
for irow, row in enumerate(r_edr3):
cout = []
for item in outcolumns_list:
cout.append(eval("f'{row[item]:" + f'{outcolumns[item]}' + "}'"))
iout = 0
if nvariables > 0:
if row['source_id'] in r_cross_var['dr3_source_id']:
iout = 2
if iout == 0:
if args.starhorse_block > 0:
for item in ['av50', 'met50', 'dist50']:
value = row[item]
if isinstance(value, float):
pass
else:
value = 99.999
cout.append(f'{value:7.3f}')
if row['source_id'] in intersection:
iout = 1
if args.debug:
iloc = np.argwhere(
edr3_source_id_15M_allsky == row['source_id'])[0][0]
cout.append(f"{edr3_b_rgb_15M_allsky[iloc]:6.2f}")
cout.append(f"{edr3_g_rgb_15M_allsky[iloc]:6.2f}")
cout.append(f"{edr3_r_rgb_15M_allsky[iloc]:6.2f}")
cout.append(f"{edr3_g_br_rgb_15M_allsky[iloc]:6.2f}")
cout.append(f"{edr3_g_gaia_15M_allsky[iloc]:8.4f}")
cout.append(f"{edr3_bp_gaia_15M_allsky[iloc]:8.4f}")
cout.append(f"{edr3_rp_gaia_15M_allsky[iloc]:8.4f}")
cout.append(f"{edr3_av50_15M_allsky[iloc]:7.3f}")
cout.append(f"{edr3_met50_15M_allsky[iloc]:7.3f}")
cout.append(f"{edr3_dist50_15M_allsky[iloc]:7.3f}")
flist[iout].write(f'{krow[iout]:6d}, ' + ','.join(cout) + '\n')
r_edr3[irow]['number_csv'] = iout
r_edr3[irow][f'number_{outtypes[iout]}'] = krow[iout]
krow[iout] += 1
for f in flist:
f.close()
print('OK')
if args.verbose:
print(r_edr3)
if args.noplot:
raise SystemExit()
# ---
sys.stdout.write('<STEP8> Generating PDF plot...')
sys.stdout.flush()
# generate plot
r_edr3.sort('phot_g_mean_mag')
if args.verbose:
print('')
r_edr3.pprint(max_width=1000)
symbol_size = args.symbsize * (50 /
np.array(r_edr3['phot_g_mean_mag']))**2.5
ra_array = np.array(r_edr3['ra'])
dec_array = np.array(r_edr3['dec'])
c = SkyCoord(ra=ra_array * u.degree,
dec=dec_array * u.degree,
frame='icrs')
x_pix, y_pix = wcs_image.world_to_pixel(c)
fig = plt.figure(figsize=(13, 10))
ax = plt.subplot(projection=wcs_image)
iok = r_edr3['phot_g_mean_mag'] < args.brightlimit
if args.nocolor:
sc = ax.scatter(x_pix[iok],
y_pix[iok],
marker='*',
color='grey',
edgecolors='black',
linewidth=0.2,
s=symbol_size[iok])
ax.scatter(x_pix[~iok],
y_pix[~iok],
marker='.',
color='grey',
edgecolors='black',
linewidth=0.2,
s=symbol_size[~iok])
else:
cmap = plt.cm.get_cmap('jet')
sc = ax.scatter(x_pix[iok],
y_pix[iok],
marker='*',
edgecolors='black',
linewidth=0.2,
s=symbol_size[iok],
cmap=cmap,
c=r_edr3[iok]['bp_rp'],
vmin=-0.5,
vmax=2.0)
ax.scatter(x_pix[~iok],
y_pix[~iok],
marker='.',
edgecolors='black',
linewidth=0.2,
s=symbol_size[~iok],
cmap=cmap,
c=r_edr3[~iok]['bp_rp'],
vmin=-0.5,
vmax=2.0)
# display numbers if requested
if not args.nonumbers:
for irow in range(len(r_edr3)):
number_csv = r_edr3[irow]['number_csv']
text = r_edr3[irow][f'number_{outtypes[number_csv]}']
ax.text(x_pix[irow],
y_pix[irow],
text,
color=outtypes_color[outtypes[number_csv]],
fontsize='5',
horizontalalignment='left',
verticalalignment='bottom')
# stars outside the -0.5 < G_BP - G_RP < 2.0 colour cut
if nstars_colorcut > 0:
mask_colour = np.logical_or((r_edr3['bp_rp'] <= -0.5),
(r_edr3['bp_rp'] >= 2.0))
iok = np.argwhere(mask_colour)
ax.scatter(x_pix[iok],
y_pix[iok],
s=240,
marker='D',
facecolors='none',
edgecolors='grey',
linewidth=0.5)
# variable stars
if nvariables > 0:
sorter = np.argsort(r_edr3['source_id'])
iok = np.array(sorter[np.searchsorted(r_edr3['source_id'],
r_cross_var['dr3_source_id'],
sorter=sorter)])
ax.scatter(x_pix[iok],
y_pix[iok],
s=240,
marker='s',
facecolors='none',
edgecolors='blue',
linewidth=0.5)
# stars in 15M sample
if len(intersection) > 0:
sorter = np.argsort(r_edr3['source_id'])
iok = np.array(sorter[np.searchsorted(r_edr3['source_id'],
np.array(list(intersection)),
sorter=sorter)])
ax.scatter(x_pix[iok],
y_pix[iok],
s=240,
marker='o',
facecolors='none',
edgecolors=outtypes_color['15m'],
linewidth=0.5)
ax.scatter(0.03,
0.96,
s=240,
marker='o',
facecolors='white',
edgecolors=outtypes_color['15m'],
linewidth=0.5,
transform=ax.transAxes)
ax.text(0.06,
0.96,
'star in 15M sample',
fontsize=12,
backgroundcolor='white',
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
ax.scatter(0.03,
0.92,
s=240,
marker='s',
facecolors='white',
edgecolors=outtypes_color['var'],
linewidth=0.5,
transform=ax.transAxes)
ax.text(0.06,
0.92,
'variable in Gaia DR2',
fontsize=12,
backgroundcolor='white',
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
ax.scatter(0.03,
0.88,
s=240,
marker='D',
facecolors='white',
edgecolors='grey',
linewidth=0.5,
transform=ax.transAxes)
ax.text(0.06,
0.88,
'outside colour range',
fontsize=12,
backgroundcolor='white',
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
ax.set_xlabel('ra')
ax.set_ylabel('dec')
ax.set_aspect('equal')
if not args.nocolor:
cbaxes = fig.add_axes([0.683, 0.81, 0.15, 0.02])
cbar = plt.colorbar(sc,
cax=cbaxes,
orientation='horizontal',
format='%1.0f')
cbar.ax.tick_params(labelsize=12)
cbar.set_label(label=r'$G_{\rm BP}-G_{\rm RP}$',
size=12,
backgroundcolor='white')
ax.text(0.98,
0.96,
f'Field radius: {args.search_radius:.4f} degree',
fontsize=12,
backgroundcolor='white',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax.text(0.02,
0.06,
r'$\alpha_{\rm center}$:',
fontsize=12,
backgroundcolor='white',
horizontalalignment='left',
verticalalignment='bottom',
transform=ax.transAxes)
ax.text(0.25,
0.06,
f'{args.ra_center:.4f} degree',
fontsize=12,
backgroundcolor='white',
horizontalalignment='right',
verticalalignment='bottom',
transform=ax.transAxes)
ax.text(0.02,
0.02,
r'$\delta_{\rm center}$:',
fontsize=12,
backgroundcolor='white',
horizontalalignment='left',
verticalalignment='bottom',
transform=ax.transAxes)
ax.text(0.25,
0.02,
f'{args.dec_center:+.4f} degree',
fontsize=12,
backgroundcolor='white',
horizontalalignment='right',
verticalalignment='bottom',
transform=ax.transAxes)
ax.text(0.98,
0.02,
f'RGBfromGaiaEDR3, version {VERSION}',
fontsize=12,
backgroundcolor='white',
horizontalalignment='right',
verticalalignment='bottom',
transform=ax.transAxes)
f = np.pi / 180
xp = naxis1 / 2 + args.search_radius / pixscale * np.cos(
np.arange(361) * f)
yp = naxis2 / 2 + args.search_radius / pixscale * np.sin(
np.arange(361) * f)
ax.plot(xp, yp, '-', color='orange', linewidth=0.5, alpha=0.5)
ax.set_xlim([-naxis1 * 0.12, naxis1 * 1.12])
ax.set_ylim([-naxis2 * 0.05, naxis2 * 1.05])
ax.set_axisbelow(True)
overlay = ax.get_coords_overlay('icrs')
overlay.grid(color='black', ls='dotted')
plt.savefig(f'{args.basename}.pdf')
plt.close(fig)
if args.verbose:
pass
else:
print('OK')
