def clean(self):
""" Clean up bad input Gaussian sigma values."""
if self._sigma is not None:
smlen = np.round(self.npix // 50).astype(int)
if smlen == 0: smlen = 3
_sigma = self._sigma.reshape(self.npix, self.norder) # make 2D
for o in range(self.norder):
sig = _sigma[:, o]
smsig = dln.gsmooth(sig, smlen)
bd, nbd = dln.where(sig <= 0)
if nbd > 0:
sig[bd] = smsig[bd]
if self.ndim == 2:
self._sigma[:, o] = sig
else:
self._sigma = sig
def selectvariables(obj):
""" Select variables using photometric variability indices."""
nobj = len(obj)
fidmag = np.zeros(nobj, float) + np.nan # fiducial magnitude
# Loop over the objects
for i in range(nobj):
# Fiducial magnitude, used to select variables below
# order of priority: r,g,i,z,Y,VR,u
if obj['nphot'][i] > 0:
magarr = np.zeros(7, float)
for ii, nn in enumerate(
['rmag', 'gmag', 'imag', 'zmag', 'ymag', 'vrmag', 'umag']):
magarr[ii] = obj[nn][i]
gfid, ngfid = dln.where(magarr < 50)
if ngfid > 0: fidmag[i] = magarr[gfid[0]]
# Select Variables
# 1) Construct fiducial magnitude (done in loop above)
# 2) Construct median VAR and sigma VAR versus magnitude
# 3) Find objects that Nsigma above the median VAR line
si = np.argsort(fidmag) # NaNs are at end
varcol = 'madvar'
gdvar, ngdvar, bdvar, nbdvar = dln.where(np.isfinite(obj[varcol])
& np.isfinite(fidmag),
comp=True)
if ngdvar > 0:
nbins = np.ceil((np.max(fidmag[gdvar]) - np.min(fidmag[gdvar])) / 0.25)
nbins = int(np.max([2, nbins]))
fidmagmed, bin_edges1, binnumber1 = bindata.binned_statistic(
fidmag[gdvar], fidmag[gdvar], statistic='nanmedian', bins=nbins)
numhist, _, _ = bindata.binned_statistic(fidmag[gdvar],
fidmag[gdvar],
statistic='count',
bins=nbins)
# Fix NaNs in fidmagmed
bdfidmagmed, nbdfidmagmed = dln.where(np.isfinite(fidmagmed) == False)
if nbdfidmagmed > 0:
fidmagmed_bins = 0.5 * (bin_edges1[0:-1] + bin_edges1[1:])
fidmagmed[bdfidmagmed] = fidmagmed_bins[bdfidmagmed]
# Median metric
varmed, bin_edges2, binnumber2 = bindata.binned_statistic(
fidmag[gdvar],
obj[varcol][gdvar],
statistic='nanmedian',
bins=nbins)
# Smooth, it handles NaNs well
smlen = 5
smvarmed = dln.gsmooth(varmed, smlen)
bdsmvarmed, nbdsmvarmed = dln.where(np.isfinite(smvarmed) == False)
if nbdsmvarmed > 0:
smvarmed[bdsmvarmed] = np.nanmedian(smvarmed)
# Interpolate to all the objects
gv, ngv, bv, nbv = dln.where(np.isfinite(smvarmed), comp=True)
fvarmed = interp1d(fidmagmed[gv],
smvarmed[gv],
kind='linear',
bounds_error=False,
fill_value=(smvarmed[0], smvarmed[-1]),
assume_sorted=True)
objvarmed = np.zeros(nobj, float)
objvarmed[gdvar] = fvarmed(fidmag[gdvar])
objvarmed[gdvar] = np.maximum(np.min(smvarmed[gv]),
objvarmed[gdvar]) # lower limit
if nbdvar > 0:
objvarmed[bdvar] = smvarmed[
gv[-1]] # objects with bad fidmag, set to last value
# Scatter in metric around median
# calculate MAD ourselves so that it's around our computed median metric line
varsig, bin_edges3, binnumber3 = bindata.binned_statistic(
fidmag[gdvar],
np.abs(obj[varcol][gdvar] - objvarmed[gdvar]),
statistic='nanmedian',
bins=nbins)
varsig *= 1.4826 # scale MAD to stddev
# Fix values for bins with few points
bdhist, nbdhist, gdhist, ngdhist = dln.where(numhist < 3, comp=True)
if nbdhist > 0:
if ngdhist > 0:
varsig[bdhist] = np.nanmedian(varsig[gdhist])
else:
varsig[:] = 0.02
# Smooth
smvarsig = dln.gsmooth(varsig, smlen)
# Interpolate to all the objects
gv, ngv, bv, nbv = dln.where(np.isfinite(smvarsig), comp=True)
fvarsig = interp1d(fidmagmed[gv],
smvarsig[gv],
kind='linear',
bounds_error=False,
fill_value=(smvarsig[gv[0]], smvarsig[gv[-1]]),
assume_sorted=True)
objvarsig = np.zeros(nobj, float)
objvarsig[gdvar] = fvarsig(fidmag[gdvar])
objvarsig[gdvar] = np.maximum(np.min(smvarsig[gv]),
objvarsig[gdvar]) # lower limit
if nbdvar > 0:
objvarsig[bdvar] = smvarsig[
gv[-1]] # objects with bad fidmag, set to last value
# Detect positive outliers
nsigvarthresh = 10.0
nsigvar = (obj[varcol] - objvarmed) / objvarsig
obj['nsigvar'][gdvar] = nsigvar[gdvar]
isvar, nisvar = dln.where(nsigvar[gdvar] > nsigvarthresh)
print(str(nisvar) + ' variables detected')
if nisvar > 0:
obj['variable10sig'][gdvar[isvar]] = 1
return obj
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)