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odi_coords.py
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odi_coords.py
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import sys, os, glob, string
import numpy as np
import matplotlib.pyplot as plt
from pyraf import iraf
from tqdm import tqdm
import odi_config as odi
import pandas as pd
from astropy.wcs import WCS
from astropy.table import Table
from astropy.io import fits
from collections import OrderedDict
def get_sdss_coords(img, ota, inst,output='test.sdss'):
formats = ['csv','xml','html']
astro_url='http://skyserver.sdss3.org/public/en/tools/search/x_sql.aspx'
public_url='http://skyserver.sdss.org/dr12/SkyserverWS/ImagingQuery/Cone?'
default_url=public_url
default_fmt='csv'
hdulist = odi.fits.open(img.f)
hdu = odi.tan_header_fix(hdulist[ota])
xdim = hdu.header['NAXIS1']
ydim = hdu.header['NAXIS2']
if not os.path.isfile(output):
# and find the image center
xc = xdim/2.0
yc = ydim/2.0
# get the CD matrix keywords
cd11 = hdu.header['CD1_1']
cd22 = hdu.header['CD2_2']
# try to load cd12 and cd21, if they don't exist, set them to zero
try :
cd12 = hdu.header['CD1_2']
except:
cd12 = 0.0
try :
cd21 = hdu.header['CD2_1']
except:
cd21 = 0.0
# print xdim, ydim, cd12, cd21
# Parse the WCS keywords in the primary HDU
w = odi.WCS(hdu.header)
# Some pixel coordinates of interest.
pixcrd = np.array([[xc,yc]], np.float_)
# Convert pixel coordinates to world coordinates
# The second argument is "origin" -- in this case we're declaring we
# have 1-based (Fortran-like) coordinates.
world = w.wcs_pix2world(pixcrd, 1)
# print(world)
rac = world[0][0]
decc = world[0][1]
# print xc, yc, rac, decc
# get the biggest radius of the image in arcminutes
pixscal1 = 3600*abs(cd11)
pixscal2 = 3600*abs(cd22)
xas = pixscal1 * xdim
yas = pixscal2 * ydim
xam = xas/60
yam = yas/60
#print(xam,yam)
#radius for query: sqrt2 = 1.414
sizeam = 1.414*(xam+yam)/4
print(sizeam)
#qry = "limit=5000&format=csv&imgparams=ra,dec,u,err_u,g,err_g,r,err_r,i,err_i,z,err_z,probPSF&specparams=none&ra="+repr(rac)+"&dec="+repr(decc)+"&radius="+repr(sizeam)+"&magType=psf"
qry = "limit=5000&format=csv&imgparams=ra,dec,psfMag_u,psfMagErr_u,psfMag_g,psfMagErr_g,psfMag_r,psfMagErr_r,psfMag_i,psfMagErr_i,psfMag_z,psfMagErr_z,probPSF&specparams=none&ra="+repr(rac)+"&dec="+repr(decc)+"&radius="+repr(sizeam)+"&magType=psf"
#print 'with query\n-->', qry
print('fetching SDSS sources around',rac,decc,'with radius',sizeam,'arcmin')
url = default_url
fmt = default_fmt
writefirst = 1
verbose = 0
ofp = open(output,'w+')
if verbose:
odi.write_header(ofp,'#',url,qry)
file_ = odi.httpquery(qry,url,fmt)
# Output line by line (in case it's big)
line = file_.readline()
if line.startswith("ERROR"): # SQL Statement Error -> stderr
ofp = sys.stderr
if writefirst:
ofp.write(string.rstrip(line)+os.linesep)
line = file_.readline()
while line:
ofp.write(string.rstrip(line)+os.linesep)
line = file_.readline()
ofp.close()
else:
print('SDSS sources already fetched!')
return xdim, ydim
hdulist.close()
def refetch_sdss_coords(img, ota, gapmask, inst,gmaglim=19.,offline = False,source='sdss'):
image = odi.reprojpath+'reproj_'+ota+'.'+img.stem()
outcoords = odi.coordspath+'reproj_'+ota+'.'+img.base()+'.sdss'
hdulist = odi.fits.open(image)
hdu = odi.tan_header_fix(hdulist[0])
xdim = hdu.header['NAXIS1']
ydim = hdu.header['NAXIS2']
if offline == False:
formats = ['csv','xml','html']
astro_url='http://skyserver.sdss3.org/public/en/tools/search/x_sql.aspx'
public_url='http://skyserver.sdss.org/dr12/SkyserverWS/ImagingQuery/Cone?'
default_url=public_url
default_fmt='csv'
if not os.path.isfile(outcoords):
# and find the image center
xc = xdim/2.0
yc = ydim/2.0
# get the CD matrix keywords
cd11 = hdu.header['CD1_1']
cd22 = hdu.header['CD2_2']
# try to load cd12 and cd21, if they don't exist, set them to zero
try :
cd12 = hdu.header['CD1_2']
except:
cd12 = 0.0
try :
cd21 = hdu.header['CD2_1']
except:
cd21 = 0.0
w = odi.WCS(hdu.header)
# Some pixel coordinates of interest.
pixcrd = np.array([[xc,yc]], np.float_)
# Convert pixel coordinates to world coordinates
# The second argument is "origin" -- in this case we're declaring we
# have 1-based (Fortran-like) coordinates.
world = w.wcs_pix2world(pixcrd, 1)
# print(world)
rac = world[0][0]
decc = world[0][1]
# print xc, yc, rac, decc
# get the biggest radius of the image in arcminutes
pixscal1 = 3600*abs(cd11)
pixscal2 = 3600*abs(cd22)
xas = pixscal1 * xdim
yas = pixscal2 * ydim
xam = xas/60
yam = yas/60
#print(xam,yam)
#radius for query: sqrt2 = 1.414
sizeam = 1.414*(xam+yam)/4
print(sizeam)
#qry = "limit=5000&format=csv&imgparams=ra,dec,u,err_u,g,err_g,r,err_r,i,err_i,z,err_z,probPSF&specparams=none&ra="+repr(rac)+"&dec="+repr(decc)+"&radius="+repr(sizeam)+"&magType=psf"
qry = "limit=5000&format=csv&imgparams=ra,dec,psfMag_u,psfMagErr_u,psfMag_g,psfMagErr_g,psfMag_r,psfMagErr_r,psfMag_i,psfMagErr_i,psfMag_z,psfMagErr_z,probPSF&specparams=none&ra="+repr(rac)+"&dec="+repr(decc)+"&radius="+repr(sizeam)+"&magType=psf"
#print 'with query\n-->', qry
print('fetching SDSS sources around',rac,decc,'with radius',sizeam,'arcmin')
url = default_url
fmt = default_fmt
writefirst = 1
verbose = 0
ofp = open(outcoords,'w+')
if verbose:
odi.write_header(ofp,'#',url,qry)
file_ = odi.httpquery(qry,url,fmt)
# Output line by line (in case it's big)
line = file_.readline()
if line.startswith("ERROR"): # SQL Statement Error -> stderr
ofp = sys.stderr
if writefirst:
ofp.write(string.rstrip(line)+os.linesep)
line = file_.readline()
while line:
ofp.write(string.rstrip(line)+os.linesep)
line = file_.readline()
ofp.close()
else:
print('SDSS sources already fetched!')
ras,decs,psfMag_u,psfMagErr_u,psfMag_g,psfMagErr_g,psfMag_r,psfMagErr_r,psfMag_i,psfMagErr_i,psfMag_z,psfMagErr_z = np.loadtxt(outcoords,usecols=(0,1,2,3,4,5,6,7,8,9,10,11), unpack=True, delimiter=',', skiprows=2)
probPSF = np.loadtxt(outcoords, usecols=(12,), dtype=int, unpack=True, delimiter=',', skiprows=2)
w = odi.WCS(hdu.header)
with open(odi.coordspath+'reproj_'+ota+'.'+img.base()+'.sdssxy', 'w+') as fxy:
j=0
# k=0
for i,c in enumerate(ras):
coords2 = [[ras[i],decs[i]]]
pixcrd2 = w.wcs_world2pix(coords2, 1)
if psfMag_g[i]<gmaglim and probPSF[i]==1:
if 100.0 <= pixcrd2[0][0] < xdim-100.0 and 100.0 <= pixcrd2[0][1] < ydim-100.0:
# make an image cutout of the gap mask
x, y = int(round(pixcrd2[0][0])), int(round(pixcrd2[0][1]))
cutout = gapmask[y-30:y+30,x-30:x+30]
# print cutout.flatten()
# k+=1
# print k,cutout.astype(bool).any()
if not (cutout.astype(bool)).any():
print(pixcrd2[0][0], pixcrd2[0][1], ras[i],decs[i],psfMag_u[i],psfMagErr_u[i],psfMag_g[i],psfMagErr_g[i],psfMag_r[i],psfMagErr_r[i],psfMag_i[i],psfMagErr_i[i],psfMag_z[i],psfMagErr_z[i], file=fxy)
if offline == True:
if source == 'sdss':
outcoords = odi.sdsspath+'offline_'+ota+'.'+img.base()+'.sdss'
ras,decs,psfMag_u,psfMagErr_u,psfMag_g,psfMagErr_g,psfMag_r,psfMagErr_r,psfMag_i,psfMagErr_i,psfMag_z,psfMagErr_z = np.loadtxt(outcoords,usecols=(0,1,2,3,4,5,6,7,8,9,10,11), unpack=True, delimiter=',', skiprows=1)
if source == 'twomass':
outcoords = odi.twomasspath+'offline_'+ota+'.'+img.base()+'.mass'
ras,decs = np.loadtxt(outcoords,usecols=(2,3), unpack=True, delimiter=',', skiprows=1)
# Just creating dummy variables so that the file formats remain the same for other functions
psfMag_u = np.ones(len(ras))
psfMagErr_u = np.ones(len(ras))
psfMag_g = np.ones(len(ras))
psfMagErr_g = np.ones(len(ras))
psfMag_r = np.ones(len(ras))
psfMagErr_r = np.ones(len(ras))
psfMag_i = np.ones(len(ras))
psfMagErr_i = np.ones(len(ras))
psfMag_z = np.ones(len(ras))
psfMagErr_z = np.ones(len(ras))
if source == 'gaia':
outcoords = odi.gaiapath+'offline_'+ota+'.'+img.base()+'.gaia'
ras,decs = np.loadtxt(outcoords,usecols=(0,1), unpack=True, delimiter=',', skiprows=1)
# Just creating dummy variables so that the file formats remain the same
# for other functions
psfMag_u = np.ones(len(ras))
psfMagErr_u = np.ones(len(ras))
psfMag_g = np.ones(len(ras))
psfMagErr_g = np.ones(len(ras))
psfMag_r = np.ones(len(ras))
psfMagErr_r = np.ones(len(ras))
psfMag_i = np.ones(len(ras))
psfMagErr_i = np.ones(len(ras))
psfMag_z = np.ones(len(ras))
psfMagErr_z = np.ones(len(ras))
tqdm.write('Using Ra and Dec from {:s} for reproject'.format(outcoords))
w = odi.WCS(hdu.header)
with open(odi.coordspath+'reproj_'+ota+'.'+img.base()+'.sdssxy', 'w+') as fxy:
for i,c in enumerate(ras):
coords2 = [[ras[i],decs[i]]]
pixcrd2 = w.wcs_world2pix(coords2, 1)
if psfMag_g[i]<gmaglim:
if 100.0 <= pixcrd2[0][0] < xdim-100.0 and 100.0 <= pixcrd2[0][1] < ydim-100.0:
# make an image cutout of the gap mask
x, y = int(round(pixcrd2[0][0])), int(round(pixcrd2[0][1]))
cutout = gapmask[y-30:y+30,x-30:x+30]
if not (cutout.astype(bool)).any():
print(pixcrd2[0][0], pixcrd2[0][1], ras[i],decs[i],psfMag_u[i],psfMagErr_u[i],psfMag_g[i],psfMagErr_g[i],psfMag_r[i],psfMagErr_r[i],psfMag_i[i],psfMagErr_i[i],psfMag_z[i],psfMagErr_z[i], file=fxy)
hdulist.close()
def repoxy_offline(img, ota, gapmask, inst,gmaglim=19.,source='sdss'):
image = odi.reprojpath+'reproj_'+ota+'.'+img.stem()
hdulist = odi.fits.open(image)
hdu = odi.tan_header_fix(hdulist[0])
xdim = hdu.header['NAXIS1']
ydim = hdu.header['NAXIS2']
if source == 'sdss':
outcoords = odi.sdsspath+'offline_'+ota+'.'+img.base()+'.sdss'
ras,decs,psfMag_u,psfMagErr_u,psfMag_g,psfMagErr_g,psfMag_r,psfMagErr_r,psfMag_i,psfMagErr_i,psfMag_z,psfMagErr_z = np.loadtxt(outcoords,usecols=(0,1,2,3,4,5,6,7,8,9,10,11), unpack=True, delimiter=',', skiprows=1)
outputxy = odi.coordspath+'reproj_'+ota+'.'+img.base()+'.sdssxy'
if source == 'twomass':
outcoords = odi.twomasspath+'offline_'+ota+'.'+img.base()+'.mass'
outputxy = odi.coordspath+'reproj_'+ota+'.'+img.base()+'.massxy'
ras,decs = np.loadtxt(outcoords,usecols=(2,3), unpack=True, delimiter=',', skiprows=1)
# Just creating dummy variables so that the file formats remain the same for other functions
psfMag_u = np.ones(len(ras))
psfMagErr_u = np.ones(len(ras))
psfMag_g = np.ones(len(ras))
psfMagErr_g = np.ones(len(ras))
psfMag_r = np.ones(len(ras))
psfMagErr_r = np.ones(len(ras))
psfMag_i = np.ones(len(ras))
psfMagErr_i = np.ones(len(ras))
psfMag_z = np.ones(len(ras))
psfMagErr_z = np.ones(len(ras))
if source == 'gaia':
outcoords = odi.gaiapath+'offline_'+ota+'.'+img.base()+'.gaia'
outputxy = odi.coordspath+'reproj_'+ota+'.'+img.base()+'.gaiaxy'
ras,decs = np.loadtxt(outcoords,usecols=(0,1), unpack=True, delimiter=',', skiprows=1)
# Just creating dummy variables so that the file formats remain the same
# for other functions
psfMag_u = np.ones(len(ras))
psfMagErr_u = np.ones(len(ras))
psfMag_g = np.ones(len(ras))
psfMagErr_g = np.ones(len(ras))
psfMag_r = np.ones(len(ras))
psfMagErr_r = np.ones(len(ras))
psfMag_i = np.ones(len(ras))
psfMagErr_i = np.ones(len(ras))
psfMag_z = np.ones(len(ras))
psfMagErr_z = np.ones(len(ras))
tqdm.write('Using Ra and Dec from {:s} for reproject'.format(outcoords))
w = odi.WCS(hdu.header)
fxy = open(outputxy, 'w+')
for i,c in enumerate(ras):
coords2 = [[ras[i],decs[i]]]
pixcrd2 = w.wcs_world2pix(coords2, 1)
if psfMag_g[i]<gmaglim:
if 100.0 <= pixcrd2[0][0] < xdim-100.0 and 100.0 <= pixcrd2[0][1] < ydim-100.0:
# make an image cutout of the gap mask
x, y = int(round(pixcrd2[0][0])), int(round(pixcrd2[0][1]))
cutout = gapmask[y-30:y+30,x-30:x+30]
if not (cutout.astype(bool)).any():
print(pixcrd2[0][0], pixcrd2[0][1], ras[i],decs[i],psfMag_u[i],psfMagErr_u[i],psfMag_g[i],psfMagErr_g[i],psfMag_r[i],psfMagErr_r[i],psfMag_i[i],psfMagErr_i[i],psfMag_z[i],psfMagErr_z[i], file=fxy)
fxy.close()
hdulist.close()
def sdss_coords_full(img, inst,gmaglim=19.):
formats = ['csv','xml','html']
astro_url='http://skyserver.sdss3.org/public/en/tools/search/x_sql.aspx'
#public_url='http://skyserver.sdss.org/SkyserverWS/dr12/ImagingQuery/Cone?'
public_url='http://skyserver.sdss.org/dr12/SkyserverWS/ImagingQuery/Cone?'
default_url=public_url
default_fmt='csv'
image = img
outcoords = img.nofits()+'.sdss'
hdulist = odi.fits.open(image)
hdu = odi.tan_header_fix(hdulist[0])
data = hdu.data
# hdu = hdulist[ota]
xdim = hdu.header['NAXIS1']
ydim = hdu.header['NAXIS2']
if not os.path.isfile(outcoords):
# and find the image center
xc = xdim/2.0
yc = ydim/2.0
# get the CD matrix keywords
cd11 = hdu.header['CD1_1']
cd22 = hdu.header['CD2_2']
# try to load cd12 and cd21, if they don't exist, set them to zero
try :
cd12 = hdu.header['CD1_2']
except:
cd12 = 0.0
try :
cd21 = hdu.header['CD2_1']
except:
cd21 = 0.0
# print xdim, ydim, cd12, cd21
# Parse the WCS keywords in the primary HDU
w = odi.WCS(hdu.header)
# Some pixel coordinates of interest.
pixcrd = np.array([[xc,yc]], np.float_)
# Convert pixel coordinates to world coordinates
# The second argument is "origin" -- in this case we're declaring we
# have 1-based (Fortran-like) coordinates.
world = w.wcs_pix2world(pixcrd, 1)
# print(world)
rac = world[0][0]
decc = world[0][1]
# print xc, yc, rac, decc
# get the biggest radius of the image in arcminutes
pixscal1 = 3600*abs(cd11)
pixscal2 = 3600*abs(cd22)
xas = pixscal1 * xdim
yas = pixscal2 * ydim
xam = xas/60
yam = yas/60
#print(xam,yam)
#radius for query: sqrt2 = 1.414
sizeam = 1.414*(xam+yam)/4
print(sizeam)
#qry = "limit=5000&format=csv&imgparams=ra,dec,u,err_u,g,err_g,r,err_r,i,err_i,z,err_z,probPSF&specparams=none&ra="+repr(rac)+"&dec="+repr(decc)+"&radius="+repr(sizeam)+"&magType=psf"
qry = "limit=10000&format=csv&imgparams=ra,dec,psfMag_u,psfMagErr_u,psfMag_g,psfMagErr_g,psfMag_r,psfMagErr_r,psfMag_i,psfMagErr_i,psfMag_z,psfMagErr_z,probPSF&specparams=none&ra="+repr(rac)+"&dec="+repr(decc)+"&radius="+repr(sizeam)+"&magType=psf"
#print 'with query\n-->', qry
print('fetching SDSS sources around',rac,decc,'with radius',sizeam,'arcmin')
url = default_url
fmt = default_fmt
writefirst = 1
verbose = 0
ofp = open(outcoords,'w+')
if verbose:
odi.write_header(ofp,'#',url,qry)
file_ = odi.httpquery(qry,url,fmt)
# Output line by line (in case it's big)
line = file_.readline()
if line.startswith("ERROR"): # SQL Statement Error -> stderr
ofp = sys.stderr
if writefirst:
ofp.write(string.rstrip(line)+os.linesep)
line = file_.readline()
while line:
ofp.write(string.rstrip(line)+os.linesep)
line = file_.readline()
ofp.close()
else:
print('SDSS sources already fetched!')
ras,decs,psfMag_u,psfMagErr_u,psfMag_g,psfMagErr_g,psfMag_r,psfMagErr_r,psfMag_i,psfMagErr_i,psfMag_z,psfMagErr_z = np.loadtxt(outcoords,usecols=(0,1,2,3,4,5,6,7,8,9,10,11), unpack=True, delimiter=',', skiprows=2)
probPSF = np.loadtxt(outcoords, usecols=(12,), dtype=int, unpack=True, delimiter=',', skiprows=2)
# print ras, decs
w = odi.WCS(hdu.header)
with open(img.nofits()+'.wcs.coo','w+') as f:
with open(img.nofits()+'.sdssxy', 'w+') as fxy:
for i,c in enumerate(ras):
coords2 = [[ras[i],decs[i]]]
pixcrd2 = w.wcs_world2pix(coords2, 1)
if psfMag_g[i]<gmaglim and probPSF[i]==1 and 100.0 <= pixcrd2[0][0] < xdim-100.0 and 100.0 <= pixcrd2[0][1] < ydim-100.0:
r, d = odi.deg_to_sex(ras[i], decs[i])
x, y = int(round(pixcrd2[0][0])), int(round(pixcrd2[0][1]))
cutout = data[y-30:y+30,x-30:x+30]
# print cutout.flatten()
# k+=1
# print i,(cutout<-900).any()
if not (cutout<-900).any():
print(r, d, psfMag_g[i], file=f)
print(pixcrd2[0][0], pixcrd2[0][1], ras[i],decs[i],psfMag_u[i],psfMagErr_u[i],psfMag_g[i],psfMagErr_g[i],psfMag_r[i],psfMagErr_r[i],psfMag_i[i],psfMagErr_i[i],psfMag_z[i],psfMagErr_z[i], file=fxy)
hdulist.close()
# def get_sdss_coords_offline(img, ota, inst,output='test.sdss'):
# hdulist = odi.fits.open(img.f)
#
# hdu = odi.tan_header_fix(hdulist[ota])
# xdim = hdu.header['NAXIS1']
# ydim = hdu.header['NAXIS2']
#
# sdss_cat_img = hdulist['CAT.PHOTCALIB']
# sdss_cat_img_df = pd.DataFrame.from_dict(sdss_cat_img.data)
# hdulist.close()
#
# ota = float(ota.strip('OTA.SCI'))
# ota_matches_df = sdss_cat_img_df.iloc[np.where(sdss_cat_img_df['ODI_OTA'] == ota)]
#
# needed_columns = ['SDSS_RA','SDSS_DEC','SDSS_MAG_U',
# 'SDSS_ERR_U', u'SDSS_MAG_G', u'SDSS_ERR_G', u'SDSS_MAG_R',
# 'SDSS_ERR_R', u'SDSS_MAG_I', u'SDSS_ERR_I', u'SDSS_MAG_Z',
# 'SDSS_ERR_Z','ODI_OTA','ODI_OTA']
#
# output_df = ota_matches_df[needed_columns]
# output_df.to_csv(output,index=False)
# return xdim, ydim
def refetch_sdss_coords_offline(img, ota, gapmask, inst,gmaglim=19.):
image = odi.reprojpath+'reproj_'+ota+'.'+img.stem()
outcoords = odi.coordspath+'reproj_'+ota+'.'+img.base()+'.sdss'
hdulist = odi.fits.open(image)
hdu = odi.tan_header_fix(hdulist[0])
xdim = hdu.header['NAXIS1']
ydim = hdu.header['NAXIS2']
if not os.path.isfile(outcoords):
xc = xdim/2.0
yc = ydim/2.0
# get the CD matrix keywords
cd11 = hdu.header['CD1_1']
cd22 = hdu.header['CD2_2']
# try to load cd12 and cd21, if they don't exist, set them to zero
try :
cd12 = hdu.header['CD1_2']
except:
cd12 = 0.0
try :
cd21 = hdu.header['CD2_1']
except:
cd21 = 0.0
# print xdim, ydim, cd12, cd21
# Parse the WCS keywords in the primary HDU
w = odi.WCS(hdu.header)
# Some pixel coordinates of interest.
pixcrd = np.array([[xc,yc]], np.float_)
# Convert pixel coordinates to world coordinates
# The second argument is "origin" -- in this case we're declaring we
# have 1-based (Fortran-like) coordinates.
world = w.wcs_pix2world(pixcrd, 1)
# print(world)
rac = world[0][0]
decc = world[0][1]
# print xc, yc, rac, decc
# get the biggest radius of the image in arcminutes
pixscal1 = 3600*abs(cd11)
pixscal2 = 3600*abs(cd22)
xas = pixscal1 * xdim
yas = pixscal2 * ydim
xam = xas/60
yam = yas/60
#print(xam,yam)
#radius for query: sqrt2 = 1.414
sizeam = 1.414*(xam+yam)/4
print(sizeam)
ras,decs,psfMag_u,psfMagErr_u,psfMag_g,psfMagErr_g,psfMag_r,psfMagErr_r,psfMag_i,psfMagErr_i,psfMag_z,psfMagErr_z = np.loadtxt(outcoords,usecols=(0,1,2,3,4,5,6,7,8,9,10,11), unpack=True, delimiter=',', skiprows=2)
probPSF = np.loadtxt(outcoords, usecols=(12,), dtype=int, unpack=True, delimiter=',', skiprows=2)
# print ras, decs
w = odi.WCS(hdu.header)
with open(odi.coordspath+'reproj_'+ota+'.'+img.base()+'.sdssxy', 'w+') as fxy:
j=0
# k=0
for i,c in enumerate(ras):
coords2 = [[ras[i],decs[i]]]
pixcrd2 = w.wcs_world2pix(coords2, 1)
if psfMag_g[i]<gmaglim and probPSF[i]==1:
if 100.0 <= pixcrd2[0][0] < xdim-100.0 and 100.0 <= pixcrd2[0][1] < ydim-100.0:
# make an image cutout of the gap mask
x, y = int(round(pixcrd2[0][0])), int(round(pixcrd2[0][1]))
cutout = gapmask[y-30:y+30,x-30:x+30]
# print cutout.flatten()
# k+=1
# print k,cutout.astype(bool).any()
if not (cutout.astype(bool)).any():
# j+=1
# print pixcrd2[0][0], pixcrd2[0][1],x-30,x+30,y-30,y+30
# plt.imshow(cutout)
# plt.show()
print(pixcrd2[0][0], pixcrd2[0][1], ras[i],decs[i],psfMag_u[i],psfMagErr_u[i],psfMag_g[i],psfMagErr_g[i],psfMag_r[i],psfMagErr_r[i],psfMag_i[i],psfMagErr_i[i],psfMag_z[i],psfMagErr_z[i], file=fxy)
# print j
hdulist.close()
def get_sdss_coords_offline(img, ota, inst,output='test.sdss'):
"""
Pull out and parse the ``CAT.PHOTCALIB`` table from ``img`` header. This
function will separate the SDSS stars in ``CAT.PHOTCALIB`` based on which
``ota`` they fall on.
Parameters
----------
img : str
Name of image
ota : str
Name of OTA
int : str
Version of ODI used, ``podi`` or ``5odi``
Returns
-------
xdim : int
Size of OTA in the x direction ``NAXIS1``
ydim : int
Size of OTA in the y direction ``NAXIS2``
Note
----
If the images being processed do not fall in the SDSS footprint,
the QuickReduce pipeline will use PanSTARRS. This function will still
pull out these stars and treat them as SDSS stars. There will be no
``u`` magnitudes available, however.
"""
ota_id = ota
hdulist = odi.fits.open(img.f)
hdu = odi.tan_header_fix(hdulist[ota])
xdim = hdu.header['NAXIS1']
ydim = hdu.header['NAXIS2']
try:
sdss_cat_img = hdulist[u"CAT.PHOTCALIB"]
cat_img_data = Table.read(sdss_cat_img, format='fits')
# print(cat_img_data.colnames)
# force little-endian byte order to make FITS play nice with pandas
sdss_cat_img_df = cat_img_data.to_pandas()
# sdss_cat_img_df = pd.DataFrame.from_dict(cat_img_dict)
# print sdss_cat_img_df.keys()
ota = float(ota.strip('OTA.SCI'))
# print('catalog source:', hdulist[u"CAT.PHOTCALIB"].header['PHOTMCAT'])
source = hdulist[u"CAT.PHOTCALIB"].header['PHOTMCAT']
# if 'sdss_dr' in hdulist[0].header['PHOTMCAT']:
# try:
# # print sdss_cat_img_df.columns
ota_matches_df = sdss_cat_img_df.iloc[np.where(sdss_cat_img_df['ODI_OTA'] == ota)]
needed_columns = ['REF_RA','REF_DEC', 'REF_G', 'REF_ERR_G', 'REF_R',
'REF_ERR_R', 'REF_I', 'REF_ERR_I', 'REF_Z',
'REF_ERR_Z', 'ODI_OTA']
output_df = ota_matches_df[needed_columns]
output_df.to_csv(output,index=False)
# except KeyError:
# oditable = hdulist['CAT.ODI'].data
# oditalbe_df = pd.DataFrame.from_dict(oditable)
# ODI_RA = np.squeeze(np.array(oditalbe_df['RA']))
# ODI_DEC = np.squeeze( np.array(oditalbe_df['DEC']))
# ODI_OTA = np.squeeze( np.array(oditalbe_df['OTA']))
# junkdict = OrderedDict([('ODI_RA',ODI_RA),
# ('ODI_DEC',ODI_DEC),
# ('ODI_OTA',ODI_OTA.astype(float))])
# junk_df = pd.DataFrame.from_dict(junkdict)
# matched_df = pd.merge(sdss_cat_img_df,junk_df ,on = ['ODI_RA','ODI_DEC'],how='inner')
# # print matched_df.columns
# needed_columns = np.insert(sdss_cat_img_df.columns.values,0,'ODI_OTA')
# full_df = matched_df[needed_columns]
# ota_matches_df = full_df.iloc[np.where(full_df['ODI_OTA'] == ota)]
# needed_columns = ['SDSS_RA','SDSS_DEC',
# 'SDSS_MAG_U','SDSS_ERR_U',
# 'SDSS_MAG_G', 'SDSS_ERR_G',
# 'SDSS_MAG_R','SDSS_ERR_R',
# 'SDSS_MAG_I', 'SDSS_ERR_I',
# 'SDSS_MAG_Z','SDSS_ERR_Z',
# 'ODI_OTA']
# output_df = ota_matches_df[needed_columns]
# output_df.to_csv(output,index=False)
# else:
# ota_matches_df = sdss_cat_img_df.iloc[np.where(sdss_cat_img_df['ODI_OTA'] == ota)]
# ota_matches_df = ota_matches_df.reset_index()
# junk_u = np.ones(len(ota_matches_df))
# junk_u_err = np.ones(len(ota_matches_df))
# ota_matches_df['IPP_MAG_U'] = junk_u
# ota_matches_df['IPP_ERR_U'] = junk_u_err
# needed_columns = ['IPP_RA', 'IPP_DEC',
# 'IPP_MAG_U', 'IPP_ERR_U',
# 'IPP_MAG_G', 'IPP_ERR_G',
# 'IPP_MAG_R', 'IPP_ERR_R',
# 'IPP_MAG_I', 'IPP_ERR_I',
# 'IPP_MAG_Z','IPP_ERR_Z',
# 'ODI_OTA']
# output_df = ota_matches_df[needed_columns]
# output_df.to_csv(output,index=False)
# if 'SDSS' in hdulist[0].header['PHOTMCAT']:
# try:
# # print sdss_cat_img_df.columns
# ota_matches_df = sdss_cat_img_df.iloc[np.where(sdss_cat_img_df['ODI_OTA'] == ota)]
# needed_columns = ['SDSS_RA','SDSS_DEC','SDSS_MAG_U',
# 'SDSS_ERR_U', 'SDSS_MAG_G', 'SDSS_ERR_G', 'SDSS_MAG_R',
# 'SDSS_ERR_R', 'SDSS_MAG_I', 'SDSS_ERR_I', 'SDSS_MAG_Z',
# 'SDSS_ERR_Z', 'ODI_OTA']
# output_df = ota_matches_df[needed_columns]
# output_df.to_csv(output,index=False)
# except KeyError:
# oditable = hdulist['CAT.ODI'].data
# oditalbe_df = pd.DataFrame.from_dict(oditable)
# ODI_RA = np.squeeze(np.array(oditalbe_df['RA']))
# ODI_DEC = np.squeeze( np.array(oditalbe_df['DEC']))
# ODI_OTA = np.squeeze( np.array(oditalbe_df['OTA']))
# junkdict = OrderedDict([('ODI_RA',ODI_RA),
# ('ODI_DEC',ODI_DEC),
# ('ODI_OTA',ODI_OTA.astype(float))])
# junk_df = pd.DataFrame.from_dict(junkdict)
# matched_df = pd.merge(sdss_cat_img_df,junk_df ,on = ['ODI_RA','ODI_DEC'],how='inner')
# # print matched_df.columns
# needed_columns = np.insert(sdss_cat_img_df.columns.values,0,'ODI_OTA')
# full_df = matched_df[needed_columns]
# ota_matches_df = full_df.iloc[np.where(full_df['ODI_OTA'] == ota)]
# needed_columns = ['SDSS_RA','SDSS_DEC',
# 'SDSS_MAG_U','SDSS_ERR_U',
# 'SDSS_MAG_G', 'SDSS_ERR_G',
# 'SDSS_MAG_R','SDSS_ERR_R',
# 'SDSS_MAG_I', 'SDSS_ERR_I',
# 'SDSS_MAG_Z','SDSS_ERR_Z',
# 'ODI_OTA']
# output_df = ota_matches_df[needed_columns]
# output_df.to_csv(output,index=False)
except KeyError:
tqdm.write(img.f+'['+ota_id+']: missing PHOTCALIB table, skipping SDSS')
hdulist.close()
return xdim, ydim
def get_gaia_coords(img,ota,inst,output='test.gaia',cluster=False,**kwargs):
"""
Query the online Gaia DR1 based on the central coordinates of the current
OTA. If the ``cluster`` flag is set to ``True``, the querey will avoid
a crowded region based on coordinates and a radius set by the user in
the configuration files.
Parameters
----------
img : ODIImage or StackedImage object
Name of image
ota : str
Name of OTA
int : str
Version of ODI used, ``podi`` or ``5odi``
"""
from astropy import units as u
from astropy.coordinates import SkyCoord
try:
from astroquery.vizier import Vizier
from astropy import __version__ as astropyversion
except ImportError:
print("astroquery not installed")
print("try pip --user --no-deps install astroquery or contact admin")
hdulist = fits.open(img.f)
if ota=='None':
hdu_ota = hdulist[0]
else:
hdu_ota = odi.tan_header_fix(hdulist[ota])
w = WCS(hdu_ota.header)
naxis1 = hdu_ota.header['NAXIS1']
naxis2 = hdu_ota.header['NAXIS2']
ota_center_radec = w.wcs_pix2world([[naxis1/2.,naxis2/2.]],1)
# print ota_center_radec
corners = w.calc_footprint()
center_skycoord = SkyCoord(ota_center_radec[0][0]*u.deg,
ota_center_radec[0][1]*u.deg,frame='icrs')
corner_skycoord = SkyCoord(corners[0,0]*u.deg,
corners[0,1]*u.deg,frame='icrs')
cone_radius = center_skycoord.separation(corner_skycoord).value
# tqdm.write('{:4.0f} {:4.0f} {:6.4f}'.format(naxis1/2., naxis2/2., cone_radius))
# print '{:4.0f} {:4.0f} {:6.4f}'.format(naxis1/2., naxis2/2., cone_radius)
#Set up vizier query for Gaia DR2
vquery = Vizier(columns=['RA_ICRS', 'DE_ICRS', 'e_RA_ICRS', 'e_DE_ICRS', 'Gmag'],
column_filters={"Gmag":"<21.0"},
row_limit = -1, catalog='I/345/gaia2')
# vquery = Vizier(columns=['ra', 'dec','ra_error', 'dec_error','phot_g_mean_mag'],
# column_filters={"phot_g_mean_mag":"<21.0"},
# row_limit = -1)
# vquery.catalog = 'I/345/gaia2'
# print vquery.catalog
check = vquery.query_region_async(SkyCoord(ra=ota_center_radec[0][0], dec=ota_center_radec[0][1], unit=(u.deg, u.deg), frame='icrs'), radius=cone_radius*u.deg, catalog='I/345/gaia2')
# print check
try:
gaia_table = vquery.query_region(SkyCoord(ra=ota_center_radec[0][0], dec=ota_center_radec[0][1], unit=(u.deg, u.deg), frame='icrs'), radius=cone_radius*u.deg, catalog='I/345/gaia2')[0]
except:
print(vquery.response.content)
hdulist.close()
if cluster == True:
try:
racenter = kwargs['racenter']
deccenter = kwargs['deccenter']
min_radius = kwargs['min_radius']
G_lim = kwargs['G_lim']
except KeyError:
print('Must provide racenter, deccenter, and min_radius')
cluster_center = SkyCoord(racenter*u.degree
,deccenter*u.degree,
frame='icrs')
gaia_coords = SkyCoord(gaia_table['RA_ICRS'],
gaia_table['DE_ICRS'],frame='icrs')
dist_from_center = cluster_center.separation(gaia_coords).arcmin
gaia_table['dis'] = dist_from_center
# ota_gaia_df = ota_gaia_df[(ota_gaia_df.dis >= min_radius) &
# (ota_gaia_df.phot_g_mean_mag <= G_lim)]
gaia_table = gaia_table[gaia_table['dis'] > min_radius]
ra_min, ra_max = min(corners[:,0]), max(corners[:,0])
dec_min, dec_max = min(corners[:,1]), max(corners[:,1])
# print ra_min, ra_max, dec_min, dec_max
gaia_table_cut = gaia_table[(gaia_table['RA_ICRS'] > ra_min) &
(gaia_table['RA_ICRS'] < ra_max) &
(gaia_table['DE_ICRS'] > dec_min) &
(gaia_table['DE_ICRS'] < dec_max)]
gaia_table_cut['e_RA_ICRS'].convert_unit_to(u.deg)
gaia_table_cut['e_DE_ICRS'].convert_unit_to(u.deg)
ota_gaia_df = gaia_table_cut.to_pandas()
cols_needed = ['RA_ICRS','DE_ICRS','Gmag','e_RA_ICRS','e_DE_ICRS']
ota_gaia_df = ota_gaia_df[cols_needed]
ota_gaia_df.columns = ['ra', 'dec','phot_g_mean_mag','e_ra','e_dec']
gaia_catalog_out = output
ota_gaia_df.to_csv(gaia_catalog_out,
columns=['ra', 'dec','phot_g_mean_mag','e_ra','e_dec'],
index=False)
return ota_gaia_df
if __name__ == '__main__':
from odi_config import ODIImage
img = ODIImage("20161025T221120.1_HI0126+05_odi_g.7376.fits", 1, '5odi')
get_sdss_coords_offline(img, 'OTA33.SCI', '5odi', output='test.sdss')
# get_gaia_coords(img, list(img.otas.values())[0], img.inst, output='test.gaia', cluster=False)