Skip to content
GitLab
Commit 1f8e6306 authored by BO ZHANG's avatar BO ZHANG 🏀
Browse files

reformated flux calibration module

parent fecd120c
Hide whitespace changes
Inline Side-by-side
csst/msc/calib_flux.py
View file @ 1f8e6306
#!/usr/bin/env python3.8
#!/usr/bin/python3.8
#Written by ZZM
#Edited on Jun. 17, 2016
#add color term
__author__ = 'ZZM'
import sys
import os import os
import time
from subprocess import Popen
import shutil
import numpy as np import numpy as np
from astropy import table
from astropy import units as u from astropy import units as u
from astropy.coordinates import SkyCoord from astropy.coordinates import SkyCoord
from astropy.io import fits
from astropy.stats import sigma_clip from astropy.stats import sigma_clip
from astropy.stats import sigma_clipped_stats from astropy.stats import sigma_clipped_stats
from astropy.io import fits
from astropy.wcs import WCS from astropy.wcs import WCS
from astropy import table from astropy.wcs.utils import proj_plane_pixel_scales
from multiprocessing import Pool
from subprocess import Popen, PIPE
from astropy.wcs.utils import proj_plane_pixel_scales
import time
from .. import PACKAGE_PATH from .. import PACKAGE_PATH
from ..core.processor import CsstProcessor from ..core.processor import CsstProcessor
#---------------------------------------------------------------------------
# Edited on Jun. 17, 2016
# add color term
__author__ = 'ZZM'
class CsstProcFluxCalibration(CsstProcessor): class CsstProcFluxCalibration(CsstProcessor):
def __init__(self): def __init__(self, **kwargs):
pass super().__init__(**kwargs)
def ps1_mags(self,cat,band,obs_region): def ps1_mags(self, cat, band, obs_region):
# get ps1 color-corrected magnitude for specific filter # get ps1 color-corrected magnitude for specific filter
#ref:https://desi.lbl.gov/trac/wiki/DecamLegacy/Reductions # ref:https://desi.lbl.gov/trac/wiki/DecamLegacy/Reductions
color_gi = cat['MEDIAN'][:,0] - cat['MEDIAN'][:,2] color_gi = cat['MEDIAN'][:, 0] - cat['MEDIAN'][:, 2]
coeff=np.array([0.0,0.0,0.0,0.0]) coeff = np.array([0.0, 0.0, 0.0, 0.0])
if band == 'g': if band == 'g':
ifilt = 0 ifilt = 0
magcut=21.5 magcut = 21.5
coeff=np.array([0.00225738,-0.02543153,0.10575001,0.01077462]) coeff = np.array([0.00225738, -0.02543153, 0.10575001, 0.01077462])
elif band == 'bokr' or band == 'r': elif band == 'bokr' or band == 'r':
ifilt = 1 ifilt = 1
magcut=20.0 magcut = 20.0
coeff=np.array([-0.00966711,0.02808938,-0.07650502,0.0024329]) coeff = np.array([-0.00966711, 0.02808938, -0.07650502, 0.0024329])
elif band == 'z': elif band == 'z':
magcut=20.0 magcut = 20.0
ifilt = 3 ifilt = 3
coeff=np.array([-0.0094723,0.03040996,-0.08774486,0.01029133]) coeff = np.array([-0.0094723, 0.03040996, -0.08774486, 0.01029133])
#coeff=coeff=np.array([0.0,0.0,0.0,0.0]) # coeff=coeff=np.array([0.0,0.0,0.0,0.0])
else: else:
print(('no filter:',band)) print(('no filter:', band))
return return
colorterm= coeff[0]*color_gi**3 + coeff[1]*color_gi**2 + coeff[2]*color_gi + coeff[3] colorterm = coeff[0] * color_gi ** 3 + coeff[1] * color_gi ** 2 + coeff[2] * color_gi + coeff[3]
mags=cat['MEDIAN'][:,ifilt] + colorterm mags = cat['MEDIAN'][:, ifilt] + colorterm
#select good phot_mag # select good phot_mag
goodid= (cat['NMAG_OK'][:,0] >1) & (cat['NMAG_OK'][:,2] >1) \ goodid = (cat['NMAG_OK'][:, 0] > 1) & (cat['NMAG_OK'][:, 2] > 1) \
& (cat['NMAG_OK'][:,ifilt] >1) & (cat['STDEV'][:,ifilt] <0.1)\ & (cat['NMAG_OK'][:, ifilt] > 1) & (cat['STDEV'][:, ifilt] < 0.1) \
& (color_gi <2.7) & (color_gi >0.4) \ & (color_gi < 2.7) & (color_gi > 0.4) \
& (cat['MEDIAN'][:,ifilt] <magcut) & (cat['MEDIAN'][:,ifilt] >16)\ & (cat['MEDIAN'][:, ifilt] < magcut) & (cat['MEDIAN'][:, ifilt] > 16) \
& (cat['RA']>obs_region[0]) & (cat['RA']<obs_region[1])\ & (cat['RA'] > obs_region[0]) & (cat['RA'] < obs_region[1]) \
& (cat['DEC']>obs_region[2]) & (cat['DEC']<obs_region[3]) #2016.06.03 & (cat['DEC'] > obs_region[2]) & (cat['DEC'] < obs_region[3]) # 2016.06.03
print('starPS1.sum()=',goodid.sum()) print('starPS1.sum()=', goodid.sum())
if goodid.sum() < 20: goodid=list(range(mags.size)) if goodid.sum() < 20: goodid = list(range(mags.size))
mags = mags[goodid] mags = mags[goodid]
magerr=cat['STDEV'][:,ifilt][goodid] magerr = cat['STDEV'][:, ifilt][goodid]
ra = cat['RA'][goodid] ra = cat['RA'][goodid]
dec = cat['DEC'][goodid] dec = cat['DEC'][goodid]
brmedian=np.median(color_gi[goodid]) brmedian = np.median(color_gi[goodid])
return mags,magerr,ra,dec,brmedian return mags, magerr, ra, dec, brmedian
#---------------------------------------------------------------------- def read_ps1cat(self, ra, dec, outcat, path='/line12/Pan-STARRS/chunks-qz-star-v2/', silent=True):
def read_ps1cat(self,ra,dec,outcat,path='/line12/Pan-STARRS/chunks-qz-star-v2/',silent=True): # Read a piece of the PS1 calibration catalog containing ra,dec
#Read a piece of the PS1 calibration catalog containing ra,dec
from healpy import ang2pix from healpy import ang2pix
c=SkyCoord(ra,dec, unit=(u.deg, u.deg)) c = SkyCoord(ra, dec, unit=(u.deg, u.deg))
if isinstance(ra[0],str): if isinstance(ra[0], str):
c=SkyCoord(ra,dec, unit=(u.hourangle, u.deg)) c = SkyCoord(ra, dec, unit=(u.hourangle, u.deg))
phi = c.ra.deg/(180./np.pi) phi = c.ra.deg / (180. / np.pi)
theta = (90.-c.dec.deg)/(180/np.pi) theta = (90. - c.dec.deg) / (180 / np.pi)
ipring=ang2pix(32,theta,phi) ipring = ang2pix(32, theta, phi)
pix=np.unique(ipring) pix = np.unique(ipring)
npix=pix.size npix = pix.size
if npix >8: if npix > 8:
print('too many healpix files:',npix,' check image wcs!') print('too many healpix files:', npix, ' check image wcs!')
return return
dt=np.dtype([('OBJ_ID', '>i8'), ('RA', '>f8'), ('DEC', '>f8'), ('NMAG_OK', '>i2', (5,)),\ dt = np.dtype([('OBJ_ID', '>i8'), ('RA', '>f8'), ('DEC', '>f8'), ('NMAG_OK', '>i2', (5,)), \
('STDEV', '>f4', (5,)), ('MEAN', '>f4', (5,)), ('MEDIAN', '>f4', (5,)), \ ('STDEV', '>f4', (5,)), ('MEAN', '>f4', (5,)), ('MEDIAN', '>f4', (5,)), \
('MEAN_AP', '>f4', (5,)), ('MEDIAN_AP', '>f4', (5,))]) ('MEAN_AP', '>f4', (5,)), ('MEDIAN_AP', '>f4', (5,))])
ps=table.Table(dtype=dt) ps = table.Table(dtype=dt)
for i in pix: for i in pix:
fname = path+'ps1-'+'%5.5d'%i+'.fits' fname = path + 'ps1-' + '%5.5d' % i + '.fits'
if not silent: print(('Reading ', fname)) if not silent: print(('Reading ', fname))
if os.path.isfile(fname): if os.path.isfile(fname):
d=table.Table.read(fname) d = table.Table.read(fname)
ps=[ps,d] ps = [ps, d]
ps=table.vstack(ps) ps = table.vstack(ps)
if outcat: ps.write(outcat,format='fits',overwrite=True) if outcat: ps.write(outcat, format='fits', overwrite=True)
return ps return ps
#-----------------------------------------------------------------------
def read_gaiacat(self,ra,dec,outcat,path='./',silent=True): def read_gaiacat(self, ra, dec, outcat, path='./', silent=True):
# function: Read a piece of the GAIA calibration catalog containing ra,dec # function: Read a piece of the GAIA calibration catalog containing ra,dec
from healpy import ang2pix from healpy import ang2pix
c=SkyCoord(ra,dec, unit=(u.deg, u.deg)) c = SkyCoord(ra, dec, unit=(u.deg, u.deg))
if isinstance(ra[0],str): if isinstance(ra[0], str):
c=SkyCoord(ra,dec, unit=(u.hourangle, u.deg)) c = SkyCoord(ra, dec, unit=(u.hourangle, u.deg))
phi = c.ra.deg/(180./np.pi) phi = c.ra.deg / (180. / np.pi)
theta = (90.-c.dec.deg)/(180/np.pi) theta = (90. - c.dec.deg) / (180 / np.pi)
ipring=ang2pix(32,theta,phi) ipring = ang2pix(32, theta, phi)
pix=np.unique(ipring) pix = np.unique(ipring)
npix=pix.size npix = pix.size
if npix >8: if npix > 8:
print('too many healpix files:',npix,' check image wcs!') print('too many healpix files:', npix, ' check image wcs!')
return return
dt=np.dtype([('source_id', '>i8'), ('ra', '>f8'), ('dec', '>f8'), ('ra_error', '>f4'), ('dec_error', '>f4'), ('phot_g_mean_mag', '>f4'), ('phot_g_mean_flux_over_error', '>f4'), ('phot_g_n_obs', '>i2'), ('phot_bp_mean_mag', '>f4'), ('phot_bp_mean_flux_over_error', '>f4'), ('phot_bp_n_obs', '>i2'), ('phot_rp_mean_mag', '>f4'), ('phot_rp_mean_flux_over_error', '>f4'), ('phot_rp_n_obs', '>i2'), ('astrometric_weight_al', '>f4'), ('astrometric_n_obs_al', '>i2'), ('astrometric_n_good_obs_al', '>i2'), ('astrometric_excess_noise', '>f4'), ('astrometric_excess_noise_sig', '>f4'), ('duplicated_source', '?'), ('ref_epoch', '>f4'), ('parallax', '>f4'), ('parallax_error', '>f4'), ('pmra', '>f4'), ('pmra_error', '>f4'), ('pmdec', '>f4'), ('pmdec_error', '>f4'), ('phot_variable_flag', '?')]) dt = np.dtype([('source_id', '>i8'), ('ra', '>f8'), ('dec', '>f8'), ('ra_error', '>f4'), ('dec_error', '>f4'),
ps=table.Table(dtype=dt) ('phot_g_mean_mag', '>f4'), ('phot_g_mean_flux_over_error', '>f4'), ('phot_g_n_obs', '>i2'),
('phot_bp_mean_mag', '>f4'), ('phot_bp_mean_flux_over_error', '>f4'), ('phot_bp_n_obs', '>i2'),
('phot_rp_mean_mag', '>f4'), ('phot_rp_mean_flux_over_error', '>f4'), ('phot_rp_n_obs', '>i2'),
('astrometric_weight_al', '>f4'), ('astrometric_n_obs_al', '>i2'),
('astrometric_n_good_obs_al', '>i2'), ('astrometric_excess_noise', '>f4'),
('astrometric_excess_noise_sig', '>f4'), ('duplicated_source', '?'), ('ref_epoch', '>f4'),
('parallax', '>f4'), ('parallax_error', '>f4'), ('pmra', '>f4'), ('pmra_error', '>f4'),
('pmdec', '>f4'), ('pmdec_error', '>f4'), ('phot_variable_flag', '?')])
ps = table.Table(dtype=dt)
for i in pix: for i in pix:
fname = path+'chunk-'+'%5.5d'%i+'.fits' fname = path + 'chunk-' + '%5.5d' % i + '.fits'
if not silent: print(('Reading ', fname)) if not silent: print(('Reading ', fname))
if os.path.isfile(fname): if os.path.isfile(fname):
d=table.Table.read(fname) d = table.Table.read(fname)
ps=[ps,d] ps = [ps, d]
ps=table.vstack(ps) ps = table.vstack(ps)
if outcat: ps.write(outcat,format='fits',overwrite=True) if outcat: ps.write(outcat, format='fits', overwrite=True)
return ps return ps
##########################
def gaia_mags(self,cat,band,obs_region): def gaia_mags(self, cat, band, obs_region):
# get Gaia color-corrected magnitude for specific filter # get Gaia color-corrected magnitude for specific filter
#color transp. parameters # color transp. parameters
filterlist=['N','u','g','r','i','z','y'] filterlist = ['N', 'u', 'g', 'r', 'i', 'z', 'y']
#ffpar=np.array([[-0.12372707,0.87415984,2.68152378,0.46688053],\ # ffpar=np.array([[-0.12372707,0.87415984,2.68152378,0.46688053],\
# [ 0.0715824,0.42018862,1.58311364,0.30184685],\ # [ 0.0715824,0.42018862,1.58311364,0.30184685],\
# [-0.02193249,0.22376644,0.51403047,-0.04977904],\ # [-0.02193249,0.22376644,0.51403047,-0.04977904],\
# [ 0.0850583, 0.0045054, -0.17416451, 0.02696644],\ # [ 0.0850583, 0.0045054, -0.17416451, 0.02696644],\
# [ 0.03638692, 0.0513301, -0.58839035, 0.02328468],\ # [ 0.03638692, 0.0513301, -0.58839035, 0.02328468],\
# [-0.14492398, 0.26053385, -0.82220974, -0.0115362 ],\ # [-0.14492398, 0.26053385, -0.82220974, -0.0115362 ],\
# [-0.27416685, 0.36465315, -0.89341154, -0.02812699]]) # [-0.27416685, 0.36465315, -0.89341154, -0.02812699]])
ffpar=np.array([[-1.92165818e+00, 7.88689593e+00, -1.60028226e+00, 1.53590717e+00],\ ffpar = np.array([[-1.92165818e+00, 7.88689593e+00, -1.60028226e+00, 1.53590717e+00], \
[-2.00544954e+00, 7.17304891e+00, -4.92617803e+00, 3.05285639e+00],\ [-2.00544954e+00, 7.17304891e+00, -4.92617803e+00, 3.05285639e+00], \
[ 2.36560800e-02, 1.78153510e-01, 4.17186700e-01, 2.06523985e+00],\ [2.36560800e-02, 1.78153510e-01, 4.17186700e-01, 2.06523985e+00], \
[ 8.04794200e-02, -6.17626400e-02, -1.81644150e-01, 1.89622143e+00],\ [8.04794200e-02, -6.17626400e-02, -1.81644150e-01, 1.89622143e+00], \
[ 2.01861000e-03, 1.22630040e-01, -6.96895960e-01, 9.65536332e-01],\ [2.01861000e-03, 1.22630040e-01, -6.96895960e-01, 9.65536332e-01], \
[-4.75608000e-02, 1.86804250e-01, -9.25372450e-01, 2.67927720e+00],\ [-4.75608000e-02, 1.86804250e-01, -9.25372450e-01, 2.67927720e+00], \
[-7.93956600e-02, 2.28310630e-01, -1.02675552e+00, 1.76769826e+00]]) [-7.93956600e-02, 2.28310630e-01, -1.02675552e+00, 1.76769826e+00]])
coeff=ffpar[filterlist.index(band),:] coeff = ffpar[filterlist.index(band), :]
#print('filterlist.index(band):', filterlist.index(band)) # print('filterlist.index(band):', filterlist.index(band))
color_br = cat['phot_bp_mean_mag'] - cat['phot_rp_mean_mag'] ##- 0.3161466496 #VEGAMAG -> AB color_br = cat['phot_bp_mean_mag'] - cat['phot_rp_mean_mag'] ##- 0.3161466496 #VEGAMAG -> AB
colorterm= coeff[0]*color_br**3 + coeff[1]*color_br**2 + coeff[2]*color_br + coeff[3] colorterm = coeff[0] * color_br ** 3 + coeff[1] * color_br ** 2 + coeff[2] * color_br + coeff[3]
gaia_G=cat['phot_g_mean_mag'] ## +0.1001113078 #VEGAMAG -> AB gaia_G = cat['phot_g_mean_mag'] ## +0.1001113078 #VEGAMAG -> AB
mags=gaia_G + colorterm mags = gaia_G + colorterm
magcut=19.0 magcut = 19.0
#select good phot_mag # select good phot_mag
goodid= (cat['phot_g_n_obs'] >3) & (cat['phot_bp_n_obs'] >1) & (cat['phot_rp_n_obs'] >1)\ goodid = (cat['phot_g_n_obs'] > 3) & (cat['phot_bp_n_obs'] > 1) & (cat['phot_rp_n_obs'] > 1) \
& (color_br <2.5) & (color_br >0.5) \ & (color_br < 2.5) & (color_br > 0.5) \
& (gaia_G <magcut) & (gaia_G >16)\ & (gaia_G < magcut) & (gaia_G > 16) \
& (cat['ra']>obs_region[0]) & (cat['ra']<obs_region[1])\ & (cat['ra'] > obs_region[0]) & (cat['ra'] < obs_region[1]) \
& (cat['dec']>obs_region[2]) & (cat['dec']<obs_region[3]) & (cat['dec'] > obs_region[2]) & (cat['dec'] < obs_region[3])
print('starGAIA.sum()=',goodid.sum()) print('starGAIA.sum()=', goodid.sum())
if goodid.sum() < 20: goodid=list(range(mags.size)) if goodid.sum() < 20: goodid = list(range(mags.size))
mags = mags[goodid] mags = mags[goodid]
magerr= mags * 0.0 magerr = mags * 0.0
ra = cat['ra'][goodid] ra = cat['ra'][goodid]
dec = cat['dec'][goodid] dec = cat['dec'][goodid]
gimedian=np.median(color_br[goodid]) gimedian = np.median(color_br[goodid])
refc=SkyCoord(ra,dec, unit=(u.deg, u.deg),frame='fk5') refc = SkyCoord(ra, dec, unit=(u.deg, u.deg), frame='fk5')
return mags,magerr,gimedian,refc return mags, magerr, gimedian, refc
#########################################
def read_inputcat(self, image, outcat='refcat.fits', refdir='', silent=True):
def read_inputcat(self,image,outcat='refcat.fits',refdir='',silent=True): # imname=os.path.split(image)[-1]
#imname=os.path.split(image)[-1]
if refdir == '': if refdir == '':
refdir=os.path.split(image)[0] refdir = os.path.split(image)[0]
#catname='MSC_'+imname[7:]+'.cat' # catname='MSC_'+imname[7:]+'.cat'
catid=image[image.rfind('_')-10:image.rfind('_')] catid = image[image.rfind('_') - 10:image.rfind('_')]
catname='MSC_210525120000_'+catid+'.cat' catname = 'MSC_210525120000_' + catid + '.cat'
cat=os.path.join(refdir,catname) cat = os.path.join(refdir, catname)
data=table.Table.read(cat,format='ascii') data = table.Table.read(cat, format='ascii')
data.write(outcat,format='fits',overwrite=True) data.write(outcat, format='fits', overwrite=True)
return data return data
###########################
def input_mags(self, cat, usepixcood=False):
def input_mags(self,cat,usepixcood=False): goodid = (cat['mag'] < 20) & (cat['mag'] > 16)
goodid=(cat['mag']<20) & (cat['mag']>16) n = 0
n=0 while (n < 4):
while(n<4): goodid = (cat['mag'] < 20 + n) & (cat['mag'] > 16)
goodid=(cat['mag']<20+n) & (cat['mag']>16) n = n + 0.5
n=n+0.5 if goodid.sum() > 20:
if goodid.sum()>20: n = n + 5
n=n+5 mags = cat['mag'][goodid]
mags=cat['mag'][goodid] magerr = mags * 0.0
magerr= mags * 0.0
if usepixcood: if usepixcood:
x=cat['xImage'][goodid] x = cat['xImage'][goodid]
y=cat['yImage'][goodid] y = cat['yImage'][goodid]
z=np.zeros_like(x) z = np.zeros_like(x)
refc = SkyCoord(x=x,y=y,z=z,unit=u.pix,representation_type='cartesian') refc = SkyCoord(x=x, y=y, z=z, unit=u.pix, representation_type='cartesian')
else: else:
ra = cat['ra'][goodid] ra = cat['ra'][goodid]
dec = cat['dec'][goodid] dec = cat['dec'][goodid]
refc=SkyCoord(ra,dec, unit=(u.deg, u.deg),frame='fk5') refc = SkyCoord(ra, dec, unit=(u.deg, u.deg), frame='fk5')
brmedian=0.0 brmedian = 0.0
return mags,magerr,brmedian,refc return mags, magerr, brmedian, refc
#########################################
def rewrite_sex_cat(self, cat, workdir=''):
def rewrite_sex_cat(self,cat,workdir=''): data = fits.open(cat)
data=fits.open(cat)
if workdir == '': if workdir == '':
workdir=os.path.split(cat)[0] workdir = os.path.split(cat)[0]
index=[] index = []
if (np.size(data)%3 == 0) & (np.size(data) !=0): if (np.size(data) % 3 == 0) & (np.size(data) != 0):
for i in range(int(np.size(data)/3)): for i in range(int(np.size(data) / 3)):
#print(i) # print(i)
hdr=data[3*i+1].data hdr = data[3 * i + 1].data
filename=str(hdr)[str(hdr).rfind('FITSFILE')+11: filename = str(hdr)[str(hdr).rfind('FITSFILE') + 11:
str(hdr).rfind(".fits' / File name of the analyse")] str(hdr).rfind(".fits' / File name of the analyse")]
subfile=os.path.join(workdir,filename+'.acat') subfile = os.path.join(workdir, filename + '.acat')
if not os.path.exists(subfile): if not os.path.exists(subfile):
hdu=fits.HDUList([data[0],data[3*i+1],data[3*i+2]]) hdu = fits.HDUList([data[0], data[3 * i + 1], data[3 * i + 2]])
hdu.writeto(subfile,overwrite=True) hdu.writeto(subfile, overwrite=True)
index.append(filename) index.append(filename)
return index return index
##########################
def split_wcs_head(self,wcshead,im_index=[],workdir=''): def split_wcs_head(self, wcshead, im_index=[], workdir=''):
if len(im_index) == 0: if len(im_index) == 0:
return return
head=fits.open(wcshead,ignore_missing_simple=True) head = fits.open(wcshead, ignore_missing_simple=True)
if np.size(head) != np.size(im_index): if np.size(head) != np.size(im_index):
return return
for i,filename in enumerate(im_index): for i, filename in enumerate(im_index):
pathsplit=os.path.split(filename) pathsplit = os.path.split(filename)
if workdir == '': if workdir == '':
workdir = pathsplit[0] workdir = pathsplit[0]
headname=os.path.join(workdir,pathsplit[1] + '.whead.fits') headname = os.path.join(workdir, pathsplit[1] + '.whead.fits')
wheader=head[i].header wheader = head[i].header
prihdu = fits.PrimaryHDU(header=wheader) prihdu = fits.PrimaryHDU(header=wheader)
prihdu.writeto(headname,overwrite=True) prihdu.writeto(headname, overwrite=True)
##########################
def run_sextractor(self, image, cat):
def run_sextractor(self,image,cat): image0 = image
image0=image header0 = fits.getheader(image, 0)
header0=fits.getheader(image, 0) header = fits.getheader(image, 1)
header=fits.getheader(image, 1)
CONFIG_PATH = PACKAGE_PATH + '/msc/flux_calib_config/' CONFIG_PATH = PACKAGE_PATH + '/msc/flux_calib_config/'
#sexfile='/home/zhouzm/data/csst/code/cali_default.sex' # sexfile='/home/zhouzm/data/csst/code/cali_default.sex'
sexfile = CONFIG_PATH + 'cali_default.sex' sexfile = CONFIG_PATH + 'cali_default.sex'
sex_comd3 = ' -PARAMETERS_NAME ' + CONFIG_PATH + 'cali_default.param' + ' -FILTER_NAME ' + CONFIG_PATH + 'cali_default.conv' + ' -STARNNW_NAME ' + CONFIG_PATH + 'cali_default.nnw' sex_comd3 = ' -PARAMETERS_NAME ' + CONFIG_PATH + 'cali_default.param' + ' -FILTER_NAME ' + CONFIG_PATH + 'cali_default.conv' + ' -STARNNW_NAME ' + CONFIG_PATH + 'cali_default.nnw'
exptime=header0['EXPTIME'] exptime = header0['EXPTIME']
gain=header['GAIN1']*exptime gain = header['GAIN1'] * exptime
#saturate=header['SATURATE']/exptime # saturate=header['SATURATE']/exptime
sex_zp='0.0' sex_zp = '0.0'
imwht=image.replace('_img','_wht') imwht = image.replace('_img', '_wht')
#apture='4,9,15,21,27,36,39,44' # apture='4,9,15,21,27,36,39,44'
apture='10' apture = '10'
sex='sex '+ image+' -c '+ sexfile+' -CATALOG_NAME '+cat+\ sex = 'sex ' + image + ' -c ' + sexfile + ' -CATALOG_NAME ' + cat + \
' -PHOT_APERTURES '+apture+' -MAG_ZEROPOINT '+sex_zp+\ ' -PHOT_APERTURES ' + apture + ' -MAG_ZEROPOINT ' + sex_zp + \
' -WEIGHT_IMAGE '+imwht +' -GAIN '+str(gain) +\ ' -WEIGHT_IMAGE ' + imwht + ' -GAIN ' + str(gain) + \
' -GAIN_KEY '+ 'abcdefg' + sex_comd3 ' -GAIN_KEY ' + 'abcdefg' + sex_comd3
#sex WFST_000001_r_210103150723_9.fits -c cali_default.sex -CATALOG_NAME ./WFST_000001_r_210103150723_9_calibphot.fits -PHOT_APERTURES 36 -MAG_ZEROPOINT 0.0 -WEIGHT_IMAGE WFST_000001_r_210103150723_9.wht.fits -GAIN 2.047026888573 -GAIN_KEY abcdefg # sex WFST_000001_r_210103150723_9.fits -c cali_default.sex -CATALOG_NAME ./WFST_000001_r_210103150723_9_calibphot.fits -PHOT_APERTURES 36 -MAG_ZEROPOINT 0.0 -WEIGHT_IMAGE WFST_000001_r_210103150723_9.wht.fits -GAIN 2.047026888573 -GAIN_KEY abcdefg
print(sex) print(sex)
p=Popen(sex,shell=True) p = Popen(sex, shell=True)
p.wait() p.wait()
return cat return cat
##########################
def prepare(self,image,wcsdir,workdir,usewcsresult=False,newcat=False): def prepare(self, image, wcsdir, workdir, usewcsresult=False, newcat=False):
#make calibration files: Obs. cat (SExtractor), PSF model (PSFex) # make calibration files: Obs. cat (SExtractor), PSF model (PSFex)
#sex_zp=str(25.5+2.5*np.log10(exptime)) # sex_zp=str(25.5+2.5*np.log10(exptime))
if not os.path.exists(workdir): if not os.path.exists(workdir):
os.mkdir(workdir) os.mkdir(workdir)
imag_file_ename=os.path.split(image)[-1] imag_file_ename = os.path.split(image)[-1]
imname=imag_file_ename[0:imag_file_ename.find('.fits')] imname = imag_file_ename[0:imag_file_ename.find('.fits')]
imname0=imag_file_ename[0:imag_file_ename.find('_img')-3] imname0 = imag_file_ename[0:imag_file_ename.find('_img') - 3]
wcscat0=os.path.join(wcsdir,imname0+'.acat.fits') wcscat0 = os.path.join(wcsdir, imname0 + '.acat.fits')
wcscat1=os.path.join(wcsdir,imname+'.acat') wcscat1 = os.path.join(wcsdir, imname + '.acat')
wcshead0=os.path.join(wcsdir,imname0+'.acat.head.fits') wcshead0 = os.path.join(wcsdir, imname0 + '.acat.head.fits')
wcshead0txt=wcshead0[:wcshead0.rfind('.fits')] wcshead0txt = wcshead0[:wcshead0.rfind('.fits')]
wcshead1=os.path.join(wcsdir,imname+'.acat.head.fits') wcshead1 = os.path.join(wcsdir, imname + '.acat.head.fits')
cat=os.path.join(workdir,imname+'.acat') cat = os.path.join(workdir, imname + '.acat')
ref=os.path.join(workdir,imname+'.rcat') ref = os.path.join(workdir, imname + '.rcat')
wcshead2=os.path.join(workdir,imname+'.whead.fits') wcshead2 = os.path.join(workdir, imname + '.whead.fits')
#cali_ref='GAIA' #calibration reference data # cali_ref='GAIA' #calibration reference data
#run SExtractor # run SExtractor
im_index=[] im_index = []
if (os.path.isfile(cat)) & (newcat): if (os.path.isfile(cat)) & (newcat):
os.remove(cat) os.remove(cat)
if (not os.path.isfile(wcscat1)) or ((os.path.isfile(wcscat0)) & (usewcsresult)): if (not os.path.isfile(wcscat1)) or ((os.path.isfile(wcscat0)) & (usewcsresult)):
im_index=self.rewrite_sex_cat(wcscat0,workdir) im_index = self.rewrite_sex_cat(wcscat0, workdir)
if os.path.isfile(wcshead0): if os.path.isfile(wcshead0):
self.split_wcs_head(wcshead0,im_index,workdir) self.split_wcs_head(wcshead0, im_index, workdir)
elif os.path.isfile(wcshead0txt): elif os.path.isfile(wcshead0txt):
wcshead0=self.rewrite_wcs_head(wcshead0txt) wcshead0 = self.rewrite_wcs_head(wcshead0txt)
self.split_wcs_head(wcshead0,im_index,workdir) self.split_wcs_head(wcshead0, im_index, workdir)
if (not os.path.isfile(wcscat1)): if (not os.path.isfile(wcscat1)):
self.run_sextractor(image,wcscat1) self.run_sextractor(image, wcscat1)
header = self.combine_head(image, wcshead1, wcshead2, prime=False)
header=self.combine_head(image,wcshead1,wcshead2,prime=False) return wcscat1, cat, ref, header
return wcscat1,cat,ref,header
#-----------------------------------------------------------------------
#def getebv(image): # def getebv(image):
# ebv=0.0 # ebv=0.0
# header=fits.getheader(image, 0) # header=fits.getheader(image, 0)
# if image[-3:]=='.fz': # if image[-3:]=='.fz':
...@@ -325,518 +319,514 @@ class CsstProcFluxCalibration(CsstProcessor): ...@@ -325,518 +319,514 @@ class CsstProcFluxCalibration(CsstProcessor):
# if tid.sum()==1: ebv=tiles['EBV'][tid].data[0] # if tid.sum()==1: ebv=tiles['EBV'][tid].data[0]
# #header.set('ebv',round(ebv,4),'E(B-V) from SFD1998') # #header.set('ebv',round(ebv,4),'E(B-V) from SFD1998')
# return ebv # return ebv
#########################
def getmlim(self,fwhm=0.15,avsky=7.0,rdnoise=5.0,zpt=25.8,ebv=0.0,filter='g'): def getmlim(self, fwhm=0.15, avsky=7.0, rdnoise=5.0, zpt=25.8, ebv=0.0, filter='g'):
# E(B-V): 3.995, 3.214, 2.165, 1.592, 1.211, 1.064 for ugrizY decals # E(B-V): 3.995, 3.214, 2.165, 1.592, 1.211, 1.064 for ugrizY decals
#https://github.com/dstndstn/tractor/blob/master/tractor/sfd.py#L10 # https://github.com/dstndstn/tractor/blob/master/tractor/sfd.py#L10
#if filter=='g':k=3.214; pixsize=0.33 # if filter=='g':k=3.214; pixsize=0.33
k=0.0 k = 0.0
pixsize=0.075 pixsize = 0.075
snr=5.0 snr = 5.0
#nea=(((fwhm/2.35)**2*4*np.pi)**(1/1.15)+(8.91*(0.45/pixsize)**2)**(1/1.15))**1.15 # nea=(((fwhm/2.35)**2*4*np.pi)**(1/1.15)+(8.91*(0.45/pixsize)**2)**(1/1.15))**1.15
#noise=np.abs(avsky)*nea*exptime+7.3**2*nea # noise=np.abs(avsky)*nea*exptime+7.3**2*nea
#flim=0.5/exptime*(snr**2+np.sqrt(snr**4+4*snr**2*noise)) # flim=0.5/exptime*(snr**2+np.sqrt(snr**4+4*snr**2*noise))
noise=np.sqrt(avsky*np.pi*(fwhm/2.35)**2+rdnoise**2) noise = np.sqrt(avsky * np.pi * (fwhm / 2.35) ** 2 + rdnoise ** 2)
flim=noise*snr flim = noise * snr
mlim=-2.5*np.log10(flim)+zpt-k*ebv mlim = -2.5 * np.log10(flim) + zpt - k * ebv
return mlim return mlim
#########################
def getfwhm(self,fwhmsex,ellip,obsme,obsflags): def getfwhm(self, fwhmsex, ellip, obsme, obsflags):
nanid=np.isnan(fwhmsex) nanid = np.isnan(fwhmsex)
if nanid.sum() > 0: if nanid.sum() > 0:
fwhmsex[np.isnan(fwhmsex)]=0.0 fwhmsex[np.isnan(fwhmsex)] = 0.0
starid1= (ellip <=0.15) & (obsme <=0.15) &(obsme >0)& (obsflags<1) &(fwhmsex>0) starid1 = (ellip <= 0.15) & (obsme <= 0.15) & (obsme > 0) & (obsflags < 1) & (fwhmsex > 0)
starid2= fwhmsex[starid1] < 5.0 #temp. value, 20220314 starid2 = fwhmsex[starid1] < 5.0 # temp. value, 20220314
starid=starid1.nonzero()[0][starid2] starid = starid1.nonzero()[0][starid2]
if starid2.sum()<10: if starid2.sum() < 10:
starid=list(range(fwhmsex.size)) starid = list(range(fwhmsex.size))
fwhmsub=fwhmsex[starid] fwhmsub = fwhmsex[starid]
fwhmid=sigma_clip(fwhmsub,sigma=2.5) fwhmid = sigma_clip(fwhmsub, sigma=2.5)
f1,f2=np.histogram(fwhmid.data[~fwhmid.mask]) f1, f2 = np.histogram(fwhmid.data[~fwhmid.mask])
fbin=np.where(f1==f1.max()) fbin = np.where(f1 == f1.max())
f2l=f2[np.max([fbin[0][0]-1,0])] f2l = f2[np.max([fbin[0][0] - 1, 0])]
f2h=f2[np.min([fbin[0][0]+1,10])] f2h = f2[np.min([fbin[0][0] + 1, 10])]
id=np.where((fwhmid.data[~fwhmid.mask]>f2l) & (fwhmid.data[~fwhmid.mask]<f2h)) id = np.where((fwhmid.data[~fwhmid.mask] > f2l) & (fwhmid.data[~fwhmid.mask] < f2h))
fwhm=np.median(fwhmid.data[~fwhmid.mask][id]) fwhm = np.median(fwhmid.data[~fwhmid.mask][id])
if np.isnan(fwhm): if np.isnan(fwhm):
fwhm=np.mean(fwhmsex) fwhm = np.mean(fwhmsex)
#print 'fwhm=',fwhm,f1,f2,fbin, f2l,f2h # print 'fwhm=',fwhm,f1,f2,fbin, f2l,f2h
#print id,fwhmid.data[~fwhmid.mask][id] # print id,fwhmid.data[~fwhmid.mask][id]
#print fwhmid, fwhmsex # print fwhmid, fwhmsex
return fwhm return fwhm
#########################
def makedatedir(self, path):
def makedatedir(self,path): index = path.rfind('/')
index=path.rfind('/') rootpath = path[0:path.rfind('/', 0, index)]
rootpath=path[0:path.rfind('/',0,index)] walk = os.walk(rootpath)
walk=os.walk(rootpath) datedir = walk.next()[1]
datedir=walk.next()[1]
for i in datedir: for i in datedir:
if not os.path.exists(i): if not os.path.exists(i):
os.mkdir(i) os.mkdir(i)
print(('makedir ', i)) print(('makedir ', i))
########################## ##########################
def rewrite_wcs_head(self,head): def rewrite_wcs_head(self, head):
#rewrite the WCS head from Scamp to the standard fits header. # rewrite the WCS head from Scamp to the standard fits header.
wcshead=head+'.fits' wcshead = head + '.fits'
f = open(head, 'r') f = open(head, 'r')
f1 = open(wcshead, 'w') f1 = open(wcshead, 'w')
a='' a = ''
i=0 i = 0
for v in f.readlines(): for v in f.readlines():
sp='' sp = ''
asp='' asp = ''
i+=1 i += 1
if len(v)<=81: if len(v) <= 81:
sp=' '*(81-len(v)) sp = ' ' * (81 - len(v))
if 'END' in v: if 'END' in v:
asp=' '*80*(36-i%36) asp = ' ' * 80 * (36 - i % 36)
i=i+(36-i%36) i = i + (36 - i % 36)
#print(i) # print(i)
a=a+v+sp+asp a = a + v + sp + asp
f1.write(a.replace('\n','')) f1.write(a.replace('\n', ''))
f1.close() f1.close()
f.close() f.close()
return wcshead return wcshead
##########################
def combine_head(self,image,wcshead1='',wcshead2='',prime=False): def combine_head(self, image, wcshead1='', wcshead2='', prime=False):
#combine image head and wcs head keywords # combine image head and wcs head keywords
inst_head_file=image[0:image.find('.fits')]+'.head' inst_head_file = image[0:image.find('.fits')] + '.head'
if os.path.isfile(inst_head_file): if os.path.isfile(inst_head_file):
h0=fits.getheader(inst_head_file,ignore_missing_simple=True) h0 = fits.getheader(inst_head_file, ignore_missing_simple=True)
else: else:
h0=fits.getheader(image,1,ignore_missing_simple=True) h0 = fits.getheader(image, 1, ignore_missing_simple=True)
if os.path.isfile(wcshead1): if os.path.isfile(wcshead1):
hw=fits.getheader(wcshead1,ignore_missing_simple=True) hw = fits.getheader(wcshead1, ignore_missing_simple=True)
elif os.path.isfile(wcshead2): elif os.path.isfile(wcshead2):
hw=fits.getheader(wcshead2,ignore_missing_simple=True) hw = fits.getheader(wcshead2, ignore_missing_simple=True)
if prime: if prime:
hprime=fits.getheader(image,0,ignore_missing_simple=True) hprime = fits.getheader(image, 0, ignore_missing_simple=True)
h0.extend(hprime,unique=True, update=True) h0.extend(hprime, unique=True, update=True)
try: try:
h0.extend(hw,unique=True, update=True) h0.extend(hw, unique=True, update=True)
except: except:
print(image, ': no wcs header file') print(image, ': no wcs header file')
#update the keywords CTYPE1 & CTYPE2 in header # update the keywords CTYPE1 & CTYPE2 in header
if ('CTYPE1' in list(h0.keys())) & ('PV1_0' in list(h0.keys())): if ('CTYPE1' in list(h0.keys())) & ('PV1_0' in list(h0.keys())):
h0['CTYPE1'] = 'RA---TPV' h0['CTYPE1'] = 'RA---TPV'
h0['CTYPE2'] = 'DEC--TPV' h0['CTYPE2'] = 'DEC--TPV'
return h0 return h0
##########################
def match_calib(self,obsc,refc,obsm,refm,obsme,refme,obsflags,fwhmsex=np.array([])): def match_calib(self, obsc, refc, obsm, refm, obsme, refme, obsflags, fwhmsex=np.array([])):
# matching obs and ref catalogs # matching obs and ref catalogs
''' '''
coeff0=self.match_calib(obsc,refc,obsm,refm) coeff0=self.match_calib(obsc,refc,obsm,refm)
obsc,refc: obs and ref coordinates obsc,refc: obs and ref coordinates
obsm,refm: obs and ref magnitudes obsm,refm: obs and ref magnitudes
''' '''
coeff0=cstd=cobsm=crefm=-1 coeff0 = cstd = cobsm = crefm = -1
ccdraoff=ccddecoff=-1 ccdraoff = ccddecoff = -1
csize=0 csize = 0
fwhm=-1 fwhm = -1
if obsm.size >0 and refm.size>0: if obsm.size > 0 and refm.size > 0:
obsid=(obsme < 0.2) #0.2->0.1 #20170518 obsid = (obsme < 0.2) # 0.2->0.1 #20170518
obsc=obsc[obsid] obsc = obsc[obsid]
obsm=obsm[obsid] obsm = obsm[obsid]
obsme=obsme[obsid] obsme = obsme[obsid]
obsflags=obsflags[obsid] obsflags = obsflags[obsid]
idx, d2d,d3d=obsc.match_to_catalog_sky(refc) idx, d2d, d3d = obsc.match_to_catalog_sky(refc)
ref_uid=np.unique(idx) ref_uid = np.unique(idx)
obs_uid=np.full_like(ref_uid,-1) obs_uid = np.full_like(ref_uid, -1)
tmpj=-1 tmpj = -1
for i in ref_uid: for i in ref_uid:
tmpj=tmpj+1 tmpj = tmpj + 1
iid=(idx == i) iid = (idx == i)
iiid = (d2d.deg[iid] == d2d.deg[iid].min()) iiid = (d2d.deg[iid] == d2d.deg[iid].min())
obs_uid[tmpj]=iid.nonzero()[0][iiid.nonzero()[0]][0] obs_uid[tmpj] = iid.nonzero()[0][iiid.nonzero()[0]][0]
uidlim=d2d[obs_uid].arcsecond <0.5 # set match radius=0.5 arcsec uidlim = d2d[obs_uid].arcsecond < 0.5 # set match radius=0.5 arcsec
if uidlim.sum()>0: if uidlim.sum() > 0:
#csize=uidlim.sum() # csize=uidlim.sum()
obs_uidlim=obs_uid[uidlim] obs_uidlim = obs_uid[uidlim]
ref_uidlim=ref_uid[uidlim] ref_uidlim = ref_uid[uidlim]
ccdraoff=np.median((obsc[obs_uidlim].ra- refc[ref_uidlim].ra).arcsec*np.cos(obsc[obs_uidlim].dec.deg*np.pi/180)) ccdraoff = np.median(
ccddecoff=np.median((obsc[obs_uidlim].dec- refc[ref_uidlim].dec).arcsec) (obsc[obs_uidlim].ra - refc[ref_uidlim].ra).arcsec * np.cos(obsc[obs_uidlim].dec.deg * np.pi / 180))
#if fwhmsex.size >1: ccddecoff = np.median((obsc[obs_uidlim].dec - refc[ref_uidlim].dec).arcsec)
# if fwhmsex.size >1:
# fwhm=np.median(fwhmsex[obs_uidlim]) # fwhm=np.median(fwhmsex[obs_uidlim])
#matched magnitude # matched magnitude
#select good photometric value # select good photometric value
goodphot= (obsme[obs_uidlim] < 0.1) & (obsflags[obs_uidlim] < 2 ) goodphot = (obsme[obs_uidlim] < 0.1) & (obsflags[obs_uidlim] < 2)
csize=goodphot.sum() csize = goodphot.sum()
if goodphot.sum()<=5: if goodphot.sum() <= 5:
goodphot= (obsme[obs_uidlim] < 0.2) & (obsflags[obs_uidlim] < 5 ) goodphot = (obsme[obs_uidlim] < 0.2) & (obsflags[obs_uidlim] < 5)
if goodphot.sum()>=1: if goodphot.sum() >= 1:
obsm=obsm[obs_uidlim][goodphot] obsm = obsm[obs_uidlim][goodphot]
refm=refm[ref_uidlim][goodphot] refm = refm[ref_uidlim][goodphot]
obsme=obsme[obs_uidlim][goodphot] obsme = obsme[obs_uidlim][goodphot]
refme=refme[ref_uidlim][goodphot] refme = refme[ref_uidlim][goodphot]
combme=np.sqrt(obsme**2+refme**2) combme = np.sqrt(obsme ** 2 + refme ** 2)
clip=sigma_clip(refm-obsm,sigma=2.5) #sigma=2.5 clip = sigma_clip(refm - obsm, sigma=2.5) # sigma=2.5
coeff0=np.median(clip.data[~clip.mask]) coeff0 = np.median(clip.data[~clip.mask])
#wht=1./combme # wht=1./combme
#coeff0=np.average(refm-obsm,weights=wht) # coeff0=np.average(refm-obsm,weights=wht)
cstd=np.std(clip.data[~clip.mask]) cstd = np.std(clip.data[~clip.mask])
cobsm=obsm[~clip.mask] cobsm = obsm[~clip.mask]
crefm=refm[~clip.mask] crefm = refm[~clip.mask]
#if fwhmsex.size >1: # if fwhmsex.size >1:
# fwhm=np.median(fwhmsex[obsid][obs_uidlim][goodphot]) # fwhm=np.median(fwhmsex[obsid][obs_uidlim][goodphot])
#print 'fwhm=',fwhm*0.26 # print 'fwhm=',fwhm*0.26
#print(coeff0,cstd,csize,cobsm,crefm,ccdraoff,ccddecoff) # print(coeff0,cstd,csize,cobsm,crefm,ccdraoff,ccddecoff)
#print('crefm.min,max=',refm.min(),refm.max()) # print('crefm.min,max=',refm.min(),refm.max())
return coeff0,cstd,csize,cobsm,crefm,ccdraoff,ccddecoff,fwhm return coeff0, cstd, csize, cobsm, crefm, ccdraoff, ccddecoff, fwhm
########################
def calib(self, image, imgdata, whtdata, flgdata, wcsdir='./', L1dir='./', workdir='./', refdir='', addhead=False,
def calib(self,image,imgdata,whtdata,flgdata,wcsdir='./',L1dir='./',workdir='./',refdir='',addhead=False,morehead=True,plot=False,nodel=True,update=False,upcat=True): morehead=True, plot=False, nodel=True, update=False, upcat=True):
print('calibration for:',image) print('calibration for:', image)
if not os.path.exists(L1dir): if not os.path.exists(L1dir):
os.mkdir(L1dir) os.mkdir(L1dir)
if not os.path.exists(workdir): if not os.path.exists(workdir):
os.mkdir(workdir) os.mkdir(workdir)
imag_file_ename=os.path.split(image)[-1] imag_file_ename = os.path.split(image)[-1]
imname=imag_file_ename[0:imag_file_ename.rfind('.')] imname = imag_file_ename[0:imag_file_ename.rfind('.')]
image_head_file=os.path.join(workdir,imname+'.head') image_head_file = os.path.join(workdir, imname + '.head')
#if os.path.exists(image_head_file) and (not update): # if os.path.exists(image_head_file) and (not update):
# print((image+' has been calibrated.')) # print((image+' has been calibrated.'))
# return # return
psname =os.path.join(workdir,imname+'_calib.png') psname = os.path.join(workdir, imname + '_calib.png')
if plot and os.path.isfile(psname) and (not update): if plot and os.path.isfile(psname) and (not update):
print((psname,' is existed, pass')) print((psname, ' is existed, pass'))
return return
ckf=os.path.join(workdir,'checkwcs.ls') ckf = os.path.join(workdir, 'checkwcs.ls')
ckim=os.path.join(workdir,'checkim.ls') ckim = os.path.join(workdir, 'checkim.ls')
#get astrometry head # get astrometry head
#header=self.combine_head(image) # header=self.combine_head(image)
cali_ref='GAIA' #calibration reference data cali_ref = 'GAIA' # calibration reference data
#Get the photometric catalog, astrometry head # Get the photometric catalog, astrometry head
cat,newcat,ref,header=self.prepare(image,wcsdir,workdir,newcat=False) cat, newcat, ref, header = self.prepare(image, wcsdir, workdir, newcat=False)
#get obs. Information from header # get obs. Information from header
#k=list(header.keys()) # k=list(header.keys())
w=WCS(header) w = WCS(header)
band=header['FILTER'][0] #['CCDLABEL'] band = header['FILTER'][0] # ['CCDLABEL']
#!#airmass=header['AIRMASS'] # !#airmass=header['AIRMASS']
#exptime=header['EXPTIME'] # exptime=header['EXPTIME']
exptime=150.0 exptime = 150.0
naxis1=9216 #header['NAXIS1'] naxis1 = 9216 # header['NAXIS1']
naxis2=9232 #header['NAXIS2'] naxis2 = 9232 # header['NAXIS2']
#gain=header['GAIN1'] # gain=header['GAIN1']
pixsize=np.mean(proj_plane_pixel_scales(w))*3600.0 #pixsize=0.33 pixsize = np.mean(proj_plane_pixel_scales(w)) * 3600.0 # pixsize=0.33
imr0,imd0=w.all_pix2world(naxis1/2.,naxis2/2.,0) imr0, imd0 = w.all_pix2world(naxis1 / 2., naxis2 / 2., 0)
#obs catalog: coord & magnitude # obs catalog: coord & magnitude
catdata=fits.open(cat) catdata = fits.open(cat)
sexcat=catdata[2].data sexcat = catdata[2].data
#sexcat=table.Table.read(cat,2) # sexcat=table.Table.read(cat,2)
if upcat: if upcat:
x=sexcat['XWIN_IMAGE'] x = sexcat['XWIN_IMAGE']
y=sexcat['YWIN_IMAGE'] y = sexcat['YWIN_IMAGE']
r,d=w.all_pix2world(x,y,0) r, d = w.all_pix2world(x, y, 0)
sexcat['ALPHAWIN_J2000']=r sexcat['ALPHAWIN_J2000'] = r
sexcat['DELTAWIN_J2000']=d sexcat['DELTAWIN_J2000'] = d
catdata.writeto(newcat,overwrite=True) catdata.writeto(newcat, overwrite=True)
catdata.close() catdata.close()
select_stars=(sexcat['MAG_APER']-sexcat['MAG_AUTO']<0.05) select_stars = (sexcat['MAG_APER'] - sexcat['MAG_AUTO'] < 0.05)
if select_stars.sum()>0: if select_stars.sum() > 0:
sexcat=sexcat[select_stars] sexcat = sexcat[select_stars]
r=sexcat['ALPHAWIN_J2000'] #ALPHA_J2000->ALPHAWIN_J2000 r = sexcat['ALPHAWIN_J2000'] # ALPHA_J2000->ALPHAWIN_J2000
d=sexcat['DELTAWIN_J2000'] d = sexcat['DELTAWIN_J2000']
#back=sexcat['BACKGROUND'] # back=sexcat['BACKGROUND']
fwhmsex=sexcat['FWHM_IMAGE'] fwhmsex = sexcat['FWHM_IMAGE']
obsflags=sexcat['FLAGS'] obsflags = sexcat['FLAGS']
cstar=sexcat['CLASS_STAR'] cstar = sexcat['CLASS_STAR']
ellip=sexcat['ELLIPTICITY'] ellip = sexcat['ELLIPTICITY']
obsm=sexcat['MAG_APER'] #aperture radii=13pix obsm = sexcat['MAG_APER'] # aperture radii=13pix
obsme=sexcat['MAGERR_APER'] obsme = sexcat['MAGERR_APER']
ccdnstar=obsm.size #ccdnstar; total number of stars detected on a CCD ccdnstar = obsm.size # ccdnstar; total number of stars detected on a CCD
if cstar.size <= 0: if cstar.size <= 0:
#ckim=image[image.rfind('/')+1:image.rfind('.')][0:5]+'checkim.ls' # ckim=image[image.rfind('/')+1:image.rfind('.')][0:5]+'checkim.ls'
imcheck= open(ckim,"a") imcheck = open(ckim, "a")
print('line501: image needs update. Pass.') print('line501: image needs update. Pass.')
print(image, file=imcheck) print(image, file=imcheck)
imcheck.close() imcheck.close()
return return
#obs coordinate # obs coordinate
obsc = SkyCoord(r,d, unit=(u.deg, u.deg),frame='fk5') obsc = SkyCoord(r, d, unit=(u.deg, u.deg), frame='fk5')
obsrmin=r.min() obsrmin = r.min()
obsrmax=r.max() obsrmax = r.max()
obsdmin=d.min() obsdmin = d.min()
obsdmax=d.max() obsdmax = d.max()
obs_region=[obsrmin,obsrmax,obsdmin,obsdmax] obs_region = [obsrmin, obsrmax, obsdmin, obsdmax]
#measure image seeing: # measure image seeing:
seeing=-1 seeing = -1
fwhm=-1 fwhm = -1
fwhm=self.getfwhm(fwhmsex,ellip,obsme,obsflags) fwhm = self.getfwhm(fwhmsex, ellip, obsme, obsflags)
seeing=fwhm*pixsize seeing = fwhm * pixsize
#get reference catalog # get reference catalog
#ps=self.read_gaiacat(r,d,outcat=ref,silent=True) # ps=self.read_gaiacat(r,d,outcat=ref,silent=True)
ps=self.read_inputcat(image,outcat=ref,refdir=refdir) ps = self.read_inputcat(image, outcat=ref, refdir=refdir)
#remove tmp file: cat & ref # remove tmp file: cat & ref
if np.size(ps) <1: if np.size(ps) < 1:
wcscheck= open(ckf,"a") wcscheck = open(ckf, "a")
print(image, file=wcscheck) print(image, file=wcscheck)
wcscheck.close() wcscheck.close()
return return
else: else:
#refm,refme,gimedian,refc=self.gaia_mags(ps,band,obs_region) # refm,refme,gimedian,refc=self.gaia_mags(ps,band,obs_region)
refm,refme,gimedian,refc=self.input_mags(ps,usepixcood=False) refm, refme, gimedian, refc = self.input_mags(ps, usepixcood=False)
#4: different doors # 4: different doors
im1= (obsc.ra.deg >= imr0) & (obsc.dec.deg <= imd0) im1 = (obsc.ra.deg >= imr0) & (obsc.dec.deg <= imd0)
im2= (obsc.ra.deg < imr0) & (obsc.dec.deg <= imd0) im2 = (obsc.ra.deg < imr0) & (obsc.dec.deg <= imd0)
im3= (obsc.ra.deg >= imr0) & (obsc.dec.deg > imd0) im3 = (obsc.ra.deg >= imr0) & (obsc.dec.deg > imd0)
im4= (obsc.ra.deg < imr0) & (obsc.dec.deg > imd0) im4 = (obsc.ra.deg < imr0) & (obsc.dec.deg > imd0)
ref1= (refc.ra.deg >= imr0) & (refc.dec.deg <= imd0) ref1 = (refc.ra.deg >= imr0) & (refc.dec.deg <= imd0)
ref2= (refc.ra.deg < imr0) & (refc.dec.deg <= imd0) ref2 = (refc.ra.deg < imr0) & (refc.dec.deg <= imd0)
ref3= (refc.ra.deg >= imr0) & (refc.dec.deg > imd0) ref3 = (refc.ra.deg >= imr0) & (refc.dec.deg > imd0)
ref4= (refc.ra.deg < imr0) & (refc.dec.deg > imd0) ref4 = (refc.ra.deg < imr0) & (refc.dec.deg > imd0)
#for all chips # for all chips
coeff0=self.match_calib(obsc,refc,obsm,refm,obsme,refme,obsflags,fwhmsex) coeff0 = self.match_calib(obsc, refc, obsm, refm, obsme, refme, obsflags, fwhmsex)
if coeff0[2] <= 0: if coeff0[2] <= 0:
imcheck= open(ckim,"a") imcheck = open(ckim, "a")
print('line560: image needs update. Pass.') print('line560: image needs update. Pass.')
print(image, file=imcheck) print(image, file=imcheck)
imcheck.close() imcheck.close()
#return # return
coeff1=self.match_calib(obsc[im1],refc[ref1],obsm[im1],refm[ref1],obsme[im1],refme[ref1],obsflags[im1]) coeff1 = self.match_calib(obsc[im1], refc[ref1], obsm[im1], refm[ref1], obsme[im1], refme[ref1], obsflags[im1])
coeff2=self.match_calib(obsc[im2],refc[ref2],obsm[im2],refm[ref2],obsme[im2],refme[ref2],obsflags[im2]) coeff2 = self.match_calib(obsc[im2], refc[ref2], obsm[im2], refm[ref2], obsme[im2], refme[ref2], obsflags[im2])
coeff3=self.match_calib(obsc[im3],refc[ref3],obsm[im3],refm[ref3],obsme[im3],refme[ref3],obsflags[im3]) coeff3 = self.match_calib(obsc[im3], refc[ref3], obsm[im3], refm[ref3], obsme[im3], refme[ref3], obsflags[im3])
coeff4=self.match_calib(obsc[im4],refc[ref4],obsm[im4],refm[ref4],obsme[im4],refme[ref4],obsflags[im4]) coeff4 = self.match_calib(obsc[im4], refc[ref4], obsm[im4], refm[ref4], obsme[im4], refme[ref4], obsflags[im4])
coeff=[coeff0[0],coeff1[0],coeff2[0],coeff3[0],coeff4[0]] coeff = [coeff0[0], coeff1[0], coeff2[0], coeff3[0], coeff4[0]]
std=[coeff0[1],coeff1[1],coeff2[1],coeff3[1],coeff4[1]] std = [coeff0[1], coeff1[1], coeff2[1], coeff3[1], coeff4[1]]
match=[coeff0[2],coeff1[2],coeff2[2],coeff3[2],coeff4[2]] match = [coeff0[2], coeff1[2], coeff2[2], coeff3[2], coeff4[2]]
ccdraoff=coeff0[5] ccdraoff = coeff0[5]
ccddecoff=coeff0[6] ccddecoff = coeff0[6]
colortpar='0.0,0.0,0.0,0.0' colortpar = '0.0,0.0,0.0,0.0'
aperture_radii=10 aperture_radii = 10
aper_r_com='(pixels) photo-aperture radius' aper_r_com = '(pixels) photo-aperture radius'
colortpar_com='par.in [br^3,br^2,br^1,br^0]' colortpar_com = 'par.in [br^3,br^2,br^1,br^0]'
ccdzp_com='zero point for CCD' ccdzp_com = 'zero point for CCD'
#ccdzpa_com='zero point for CCD ampA' # ccdzpa_com='zero point for CCD ampA'
#ccdzpb_com='zero point for CCD ampB' # ccdzpb_com='zero point for CCD ampB'
#ccdzpc_com='zero point for CCD ampC' # ccdzpc_com='zero point for CCD ampC'
#ccdzpd_com='zero point for CCD ampD' # ccdzpd_com='zero point for CCD ampD'
ccdphrms_com='zpt rms of the matched objects in CCD' ccdphrms_com = 'zpt rms of the matched objects in CCD'
#ccdphrms_coma='zpt rms of the matched objects in CCD ampA' # ccdphrms_coma='zpt rms of the matched objects in CCD ampA'
#ccdphrms_comb='zpt rms of the matched objects in CCD ampB' # ccdphrms_comb='zpt rms of the matched objects in CCD ampB'
#ccdphrms_comc='zpt rms of the matched objects in CCD ampC' # ccdphrms_comc='zpt rms of the matched objects in CCD ampC'
#ccdphrms_comd='zpt rms of the matched objects in CCD ampD' # ccdphrms_comd='zpt rms of the matched objects in CCD ampD'
fwhm_com='FWHM in pixel' fwhm_com = 'FWHM in pixel'
seeing_com='seeing in arcsec' seeing_com = 'seeing in arcsec'
ccdraoff_com='median positional offset from GAIA, in arcsec' ccdraoff_com = 'median positional offset from GAIA, in arcsec'
ccddecoff_com='median positional offset from GAIA, in arcsec' ccddecoff_com = 'median positional offset from GAIA, in arcsec'
#transparency_com='median transparency.'# # transparency_com='median transparency.'#
ccdnstar_com='total number of stars detected on a CCD' ccdnstar_com = 'total number of stars detected on a CCD'
ccdnmatch_com='total number of matched stars in 2 arcsec' ccdnmatch_com = 'total number of matched stars in 2 arcsec'
#ccdnmatcha_com='number of matched stars in CCD ampA' # ccdnmatcha_com='number of matched stars in CCD ampA'
#ccdnmatchb_com='number of matched stars in CCD ampB' # ccdnmatchb_com='number of matched stars in CCD ampB'
#ccdnmatchc_com='number of matched stars in CCD ampC' # ccdnmatchc_com='number of matched stars in CCD ampC'
#ccdnmatchd_com='number of matched stars in CCD ampD' # ccdnmatchd_com='number of matched stars in CCD ampD'
ccdmdncol_com='median (BP-RP)_GAIA of matched stars in CCD' ccdmdncol_com = 'median (BP-RP)_GAIA of matched stars in CCD'
cali_ref_com = 'the reference database for calibration' cali_ref_com = 'the reference database for calibration'
vernum='FluxCalib_v1.0' vernum = 'FluxCalib_v1.0'
vernum_com='version of calibration code' vernum_com = 'version of calibration code'
#keys=['cali_ref','ccdzp','ccdzpa','ccdzpb','ccdzpc','ccdzpd','ccdphoff','ccdphrms', 'phrmsA','phrmsB','phrmsC','phrmsD','aper_r','fwhm','seeing','raoff','decoff','trans','ccdnstar','nmatch','nmatcha','nmatchb','nmatchc','nmatchd', 'mdncol','colt_par','ebv','EXTNAME','cali_v'] # keys=['cali_ref','ccdzp','ccdzpa','ccdzpb','ccdzpc','ccdzpd','ccdphoff','ccdphrms', 'phrmsA','phrmsB',
# 'phrmsC','phrmsD','aper_r','fwhm','seeing','raoff','decoff','trans','ccdnstar','nmatch','nmatcha',
#tmpa=round(coeff0[0],4) # 'nmatchb','nmatchc','nmatchd', 'mdncol','colt_par','ebv','EXTNAME','cali_v']
######################### # tmpa=round(coeff0[0],4)
##set header keywords: flux calibration information##
header.set('cali_ref',cali_ref,cali_ref_com) # set header keywords: flux calibration information##
header.set('COMMENT','='*66,before='cali_ref') header.set('cali_ref', cali_ref, cali_ref_com)
header.set('COMMENT','Flux calibration information',before='cali_ref') header.set('COMMENT', '=' * 66, before='cali_ref')
header.set('COMMENT','='*66,before='cali_ref') header.set('COMMENT', 'Flux calibration information', before='cali_ref')
header.set('ccdzp',round(coeff0[0],4),ccdzp_com) header.set('COMMENT', '=' * 66, before='cali_ref')
#header.set('ccdzpa',float('%.4f' % coeff1[0]),ccdzpa_com) header.set('ccdzp', round(coeff0[0], 4), ccdzp_com)
#header.set('ccdzpb',float('%.4f' % coeff2[0]),ccdzpb_com) # header.set('ccdzpa',float('%.4f' % coeff1[0]),ccdzpa_com)
#header.set('ccdzpc',float('%.4f' % coeff3[0]),ccdzpc_com) # header.set('ccdzpb',float('%.4f' % coeff2[0]),ccdzpb_com)
#header.set('ccdzpd',float('%.4f' % coeff4[0]),ccdzpd_com) # header.set('ccdzpc',float('%.4f' % coeff3[0]),ccdzpc_com)
header.set('ccdphrms',round(coeff0[1],4),ccdphrms_com) # header.set('ccdzpd',float('%.4f' % coeff4[0]),ccdzpd_com)
#header.set('phrmsA',float('%.4f' % coeff1[1]),ccdphrms_coma) header.set('ccdphrms', round(coeff0[1], 4), ccdphrms_com)
#header.set('phrmsB',float('%.4f' % coeff2[1]),ccdphrms_comb) # header.set('phrmsA',float('%.4f' % coeff1[1]),ccdphrms_coma)
#header.set('phrmsC',float('%.4f' % coeff3[1]),ccdphrms_comc) # header.set('phrmsB',float('%.4f' % coeff2[1]),ccdphrms_comb)
#header.set('phrmsD',float('%.4f' % coeff4[1]),ccdphrms_comd) # header.set('phrmsC',float('%.4f' % coeff3[1]),ccdphrms_comc)
# header.set('phrmsD',float('%.4f' % coeff4[1]),ccdphrms_comd)
header.set('aper_r',round(aperture_radii,4),aper_r_com)
header.set('fwhm',float('%.4f' % fwhm),fwhm_com) header.set('aper_r', round(aperture_radii, 4), aper_r_com)
#header.set('seeing',round(seeing,4),seeing_com) header.set('fwhm', float('%.4f' % fwhm), fwhm_com)
header.set('raoff',round(ccdraoff,4),ccdraoff_com) # header.set('seeing',round(seeing,4),seeing_com)
header.set('decoff',round(ccddecoff,4),ccddecoff_com) header.set('raoff', round(ccdraoff, 4), ccdraoff_com)
#header.set('trans',round(transparency,4),transparency_com) header.set('decoff', round(ccddecoff, 4), ccddecoff_com)
header.set('ccdnstar',round(ccdnstar),ccdnstar_com) # header.set('trans',round(transparency,4),transparency_com)
header.set('nmatch',round(coeff0[2]),ccdnmatch_com) header.set('ccdnstar', round(ccdnstar), ccdnstar_com)
#header.set('nmatcha',round(coeff1[2]),ccdnmatcha_com) header.set('nmatch', round(coeff0[2]), ccdnmatch_com)
#header.set('nmatchb',round(coeff2[2]),ccdnmatchb_com) # header.set('nmatcha',round(coeff1[2]),ccdnmatcha_com)
#header.set('nmatchc',round(coeff3[2]),ccdnmatchc_com) # header.set('nmatchb',round(coeff2[2]),ccdnmatchb_com)
#header.set('nmatchd',round(coeff4[2]),ccdnmatchd_com) # header.set('nmatchc',round(coeff3[2]),ccdnmatchc_com)
header.set('mdncol',round(gimedian,4),ccdmdncol_com) # header.set('nmatchd',round(coeff4[2]),ccdnmatchd_com)
#header.set('colt_par',colortpar,colortpar_com) header.set('mdncol', round(gimedian, 4), ccdmdncol_com)
#header.set('ebv',round(ebv,4),'E(B-V) from SFD1998') # header.set('colt_par',colortpar,colortpar_com)
# header.set('ebv',round(ebv,4),'E(B-V) from SFD1998')
###Calculate and set SKY & magnitude limiting#####################
if not('SKYRMS' in list(header.keys())): # Calculate and set SKY & magnitude limiting
#imdata=fits.getdata(image, 0) if not ('SKYRMS' in list(header.keys())):
imdata=imgdata[1].data # imdata=fits.getdata(image, 0)
skystat=sigma_clipped_stats(imdata[500:1500,500:1500], sigma=3.) imdata = imgdata[1].data
ccdskyrms=skystat[2]#rms/pixel of the sky in CCD skystat = sigma_clipped_stats(imdata[500:1500, 500:1500], sigma=3.)
ccdsky_com='(e-/s per pixel)' ccdskyrms = skystat[2] # rms/pixel of the sky in CCD
ccdskyrms_com='rms/pixel of the sky in unit of e-/s' ccdsky_com = '(e-/s per pixel)'
ccdsky=skystat[1] ccdskyrms_com = 'rms/pixel of the sky in unit of e-/s'
header.set('sky',float('%.4f' % ccdsky),ccdsky_com) ccdsky = skystat[1]
header.set('skyrms',float('%.4f' % ccdskyrms),ccdskyrms_com) header.set('sky', float('%.4f' % ccdsky), ccdsky_com)
header.set('skyrms', float('%.4f' % ccdskyrms), ccdskyrms_com)
avsky=header['SKYRMS']
#ebv=getebv(image) avsky = header['SKYRMS']
ebv=0.0 # ebv=getebv(image)
ccdzp=coeff0[0] ebv = 0.0
mlim=self.getmlim(fwhm=fwhm,avsky=avsky,rdnoise=5.0,zpt=ccdzp,ebv=ebv,filter=band) ccdzp = coeff0[0]
mlim_com='magnitude limiting of 5-sigma galaxy detection' mlim = self.getmlim(fwhm=fwhm, avsky=avsky, rdnoise=5.0, zpt=ccdzp, ebv=ebv, filter=band)
header.set('mlim',round(mlim,2),mlim_com) mlim_com = 'magnitude limiting of 5-sigma galaxy detection'
#######set signals of calibration progress################ header.set('mlim', round(mlim, 2), mlim_com)
opetime=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) # set signals of calibration progress
header.set('FLUX_S',0,'flux calibration status') opetime = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
header.set('FLUX_V','1.3',vernum_com) header.set('FLUX_S', 0, 'flux calibration status')
header.set('FLUX_TOL',opetime,'flux calibration operation time') header.set('FLUX_V', '1.3', vernum_com)
header.set('FLUX_TOL', opetime, 'flux calibration operation time')
if morehead: if morehead:
#catroot=cat[:cat.rfind('phot.fits')] # catroot=cat[:cat.rfind('phot.fits')]
prihdu = fits.PrimaryHDU(header=header) prihdu = fits.PrimaryHDU(header=header)
prihdu.writeto(image_head_file,overwrite=True) prihdu.writeto(image_head_file, overwrite=True)
#chead = fits.open(image_head_file,mode='update') # chead = fits.open(image_head_file,mode='update')
#imh=chead[0].header # imh=chead[0].header
#imh.extend(header,unique=True,update=True) # imh.extend(header,unique=True,update=True)
#chead.flush() # chead.flush()
#chead.close() # chead.close()
#crate and open QC1 list file to save the calibrated image names # crate and open QC1 list file to save the calibrated image names
qc1list=os.path.join(workdir,'file_list.tmp') #the name of QC1 list qc1list = os.path.join(workdir, 'file_list.tmp') # the name of QC1 list
qc1= open(qc1list,"a") #open the file qc1 = open(qc1list, "a") # open the file
if addhead: if addhead:
#print(('add head for ',image)) # print(('add head for ',image))
imwht=image.replace('_img','_wht') imwht = image.replace('_img', '_wht')
imflg=image.replace('_img','_flg') imflg = image.replace('_img', '_flg')
for img,im in zip([image,imwht,imflg],[imgdata,whtdata,flgdata]): for img, im in zip([image, imwht, imflg], [imgdata, whtdata, flgdata]):
newimage=os.path.join(L1dir,os.path.split(img)[-1]) newimage = os.path.join(L1dir, os.path.split(img)[-1])
newimage=newimage.replace('.fits','_L1.fits') newimage = newimage.replace('.fits', '_L1.fits')
#im = fits.open(img,mode='readonly') # im = fits.open(img,mode='readonly')
h0 = fits.PrimaryHDU(data=im[0].data,header=im[0].header) h0 = fits.PrimaryHDU(data=im[0].data, header=im[0].header)
h1 = fits.ImageHDU(data=im[1].data,header=header) h1 = fits.ImageHDU(data=im[1].data, header=header)
hdulist=fits.HDUList([h0,h1]) hdulist = fits.HDUList([h0, h1])
hdulist.writeto(newimage,overwrite=True) hdulist.writeto(newimage, overwrite=True)
#im.writeto(newimage, overwrite=True) # im.writeto(newimage, overwrite=True)
#print the calibrated image name to QC1 list file # print the calibrated image name to QC1 list file
print(newimage, file=qc1) print(newimage, file=qc1)
#close the QC1 list file # close the QC1 list file
qc1.close() qc1.close()
#################plot###################### # plot
if plot: if plot:
print('plot calibration chart ...') print('plot calibration chart ...')
import matplotlib import matplotlib
matplotlib.use('Agg') matplotlib.use('Agg')
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
fig = plt.figure(figsize = (6,5), dpi=120) fig = plt.figure(figsize=(6, 5), dpi=120)
plt.subplots_adjust(left= 0.2,bottom=0.15) plt.subplots_adjust(left=0.2, bottom=0.15)
plt.ylim(coeff0[0]-5*coeff0[1]-0.1,coeff0[0]+5*coeff0[1]+0.1) plt.ylim(coeff0[0] - 5 * coeff0[1] - 0.1, coeff0[0] + 5 * coeff0[1] + 0.1)
plt.xlim(np.min(coeff0[4])-0.2,np.max(coeff0[4])+0.2) plt.xlim(np.min(coeff0[4]) - 0.2, np.max(coeff0[4]) + 0.2)
plt.xlabel(r'$\mathrm{MAG_{Gaia}}$') plt.xlabel(r'$\mathrm{MAG_{Gaia}}$')
plt.ylabel(r'$\mathrm{MAG_{Gaia}-MAG_{inst}}$') plt.ylabel(r'$\mathrm{MAG_{Gaia}-MAG_{inst}}$')
plt.plot(coeff1[4],-coeff1[3]+coeff1[4],'r.') plt.plot(coeff1[4], -coeff1[3] + coeff1[4], 'r.')
plt.plot(coeff2[4],-coeff2[3]+coeff2[4],'y.') plt.plot(coeff2[4], -coeff2[3] + coeff2[4], 'y.')
plt.plot(coeff3[4],-coeff3[3]+coeff3[4],'b.') plt.plot(coeff3[4], -coeff3[3] + coeff3[4], 'b.')
plt.plot(coeff4[4],-coeff4[3]+coeff4[4],'g.') plt.plot(coeff4[4], -coeff4[3] + coeff4[4], 'g.')
label0='zpt=%6.3f, rms:%6.3f, matched:%i' %(coeff0[0:3]) label0 = 'zpt=%6.3f, rms:%6.3f, matched:%i' % (coeff0[0:3])
label1='zpta=%6.3f, rms:%6.3f, matched:%i' %(coeff1[0:3]) label1 = 'zpta=%6.3f, rms:%6.3f, matched:%i' % (coeff1[0:3])
label2='zptb=%6.3f, rms:%6.3f, matched:%i' %(coeff2[0:3]) label2 = 'zptb=%6.3f, rms:%6.3f, matched:%i' % (coeff2[0:3])
label3='zptc=%6.3f, rms:%6.3f, matched:%i' %(coeff3[0:3]) label3 = 'zptc=%6.3f, rms:%6.3f, matched:%i' % (coeff3[0:3])
label4='zptd=%6.3f, rms:%6.3f, matched:%i' %(coeff4[0:3]) label4 = 'zptd=%6.3f, rms:%6.3f, matched:%i' % (coeff4[0:3])
xtmin=np.min(coeff0[4])-0.2 xtmin = np.min(coeff0[4]) - 0.2
xtmax=np.max(coeff0[4])+0.2 xtmax = np.max(coeff0[4]) + 0.2
l1,=plt.plot([xtmin,xtmax],np.array([0,0])+coeff0[0],'k-',lw=1,label=label0) l1, = plt.plot([xtmin, xtmax], np.array([0, 0]) + coeff0[0], 'k-', lw=1, label=label0)
l2,=plt.plot([xtmin,xtmax],np.array([0,0])+coeff1[0],'r-',lw=1,label=label1) l2, = plt.plot([xtmin, xtmax], np.array([0, 0]) + coeff1[0], 'r-', lw=1, label=label1)
l3,=plt.plot([xtmin,xtmax],np.array([0,0])+coeff2[0],'y-',lw=1,label=label2) l3, = plt.plot([xtmin, xtmax], np.array([0, 0]) + coeff2[0], 'y-', lw=1, label=label2)
l4,=plt.plot([xtmin,xtmax],np.array([0,0])+coeff3[0],'b-',lw=1,label=label3) l4, = plt.plot([xtmin, xtmax], np.array([0, 0]) + coeff3[0], 'b-', lw=1, label=label3)
l5,=plt.plot([xtmin,xtmax],np.array([0,0])+coeff4[0],'g-',lw=1,label=label4) l5, = plt.plot([xtmin, xtmax], np.array([0, 0]) + coeff4[0], 'g-', lw=1, label=label4)
plt.legend(handles=[l1,l2,l3,l4,l5],frameon=False,loc=2,prop={'size': 10}) plt.legend(handles=[l1, l2, l3, l4, l5], frameon=False, loc=2, prop={'size': 10})
plt.savefig(psname) plt.savefig(psname)
return coeff,std,match
#---------------------------------------------------------------------------
def run(self,fn_list,img_list=[], wht_list=[], flg_list=[],wcsdir='./',L1dir='./',workdir='./',refdir='',addhead=True,morehead=False,plot=False,nodel=True,update=False,upcat=True): return coeff, std, match
def run(self, fn_list, img_list=[], wht_list=[], flg_list=[], wcsdir='./', L1dir='./', workdir='./', refdir='',
addhead=True, morehead=False, plot=False, nodel=True, update=False, upcat=True):
if len(fn_list) == 0: if len(fn_list) == 0:
print('Flux calibration: No input images in img_list!') print('Flux calibration: No input images in img_list!')
return return
#time1=time.time() # time1=time.time()
print('\n\n############### run flux calibration ###############') print('\n\n############### run flux calibration ###############')
for i in range(len(fn_list)): for i in range(len(fn_list)):
image=workdir + fn_list[i] image = workdir + fn_list[i]
if len(img_list) == len(fn_list): if len(img_list) == len(fn_list):
imgdata=img_list[i] imgdata = img_list[i]
whtdata=wht_list[i] whtdata = wht_list[i]
flgdata=flg_list[i] flgdata = flg_list[i]
else: else:
print("Image data is not defined, will be read from files") print("Image data is not defined, will be read from files")
imgdata=fits.open(image,mode='readonly') imgdata = fits.open(image, mode='readonly')
whtdata=fits.open(image.replace('_img','_wht'),mode='readonly') whtdata = fits.open(image.replace('_img', '_wht'), mode='readonly')
flgdata=fits.open(image.replace('_img','_flg'),mode='readonly') flgdata = fits.open(image.replace('_img', '_flg'), mode='readonly')
#print(('flux calibration: '+image)) # print(('flux calibration: '+image))
if not os.path.isfile(image): if not os.path.isfile(image):
print(('cannot find the file:'+image)) print(('cannot find the file:' + image))
else: else:
self.calib(image,imgdata,whtdata,flgdata,wcsdir=wcsdir,L1dir=L1dir,workdir=workdir,refdir=refdir,addhead=addhead,morehead=morehead,plot=plot,nodel=nodel,update=update) self.calib(image, imgdata, whtdata, flgdata, wcsdir=wcsdir, L1dir=L1dir, workdir=workdir, refdir=refdir,
#time2=time.time() addhead=addhead, morehead=morehead, plot=plot, nodel=nodel, update=update)
# time2=time.time()
print('\n############### flux calibration done #############\n') print('\n############### flux calibration done #############\n')
######################################
def cleanup(self, fn_list, workdir, nodel=False): def cleanup(self, fn_list, workdir, nodel=False):
# clean up environment # clean up environment
for image in fn_list: for image in fn_list:
cat=os.path.join(workdir,image[:-5]+'.acat') cat = os.path.join(workdir, image[:-5] + '.acat')
ref=os.path.join(workdir,image[:-5]+'.rcat') ref = os.path.join(workdir, image[:-5] + '.rcat')
whead=os.path.join(workdir,image[:-5]+'.whead.fits') whead = os.path.join(workdir, image[:-5] + '.whead.fits')
#psname =os.path.join(workdir,image+'_calib.png') # psname =os.path.join(workdir,image+'_calib.png')
if not nodel: if not nodel:
try: try:
os.remove(cat) os.remove(cat)
except(FileNotFoundError): except FileNotFoundError:
print() print()
try: try:
os.remove(ref) os.remove(ref)
except(FileNotFoundError): except FileNotFoundError:
print() print()
try: try:
os.remove(whead) os.remove(whead)
except(FileNotFoundError): except FileNotFoundError:
print() print()
Supports Markdown
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment