def checkShiftfile(asn_direct):
"""
checkShiftfile(asn_direct)
Make sure that there is a line in the shiftfile for each exposure
in the ASN table. Also check that no scales are zero.
"""
from threedhst.utils import ASNFile
asn = ASNFile(asn_direct)
sf_file = asn_direct.split("_asn.fits")[0] + "_shifts.txt"
sf = ShiftFile(sf_file)
flag = False
for exp in asn.exposures:
if exp + "_flt.fits" not in sf.images:
flag = True
print "Exposure, %s, not in %s" % (exp, sf_file)
# print sf.nrows
sf.append(exp + "_flt.fits")
# print sf.nrows
#### Check if scales are zero in the shiftfile
if 0.0 in sf.scale:
flag = True
print "Found scale=0 in the shiftfile, setting to default no shift/rotation\n"
for i in range(len(sf.scale)):
sf.xshift[i], sf.yshift[i], sf.rotate[i], sf.scale[i] = 0.0, 0.0, 0.0, 1.0
if flag:
sf.write(sf_file)
else:
# print "\n3DHST.shifts.checkShiftfile: %s looks OK.\n" %sf_file
threedhst.showMessage("Shiftfile, %s, looks OK" % sf_file)
def write_fits(self):
"""
Save the ascii catalog data into a FITS bintable.
The modification date of the ascii catalog is saved in the 'MODTIME'
keyword of the FITS file
"""
import time
formats = {}
formats['bool'] = 'L'
formats['int16'] = 'I'
formats['int32'] = 'J'
formats['int64'] = 'K'
formats['float32'] = 'E'
formats['float64'] = 'D'
formats['>i8'] = 'K'
formats['>f8'] = 'D'
#### Make the table columns, translating numpy data types to "TFORM"
coldefs = []
for column in self.columns:
dtype = str(self.__getitem__(column).dtype)
#print column, dtype
if dtype in list(formats.keys()):
TFORM = formats[dtype]
else:
if 'S' not in dtype:
threedhst.showMessage('Unrecognized data type in %s: %s' %
(self.filename, dtype),
warn=True)
return False
#
TFORM = 'A' + dtype.split('S')[1]
#
data = self.__getitem__(column)
if '>' in dtype:
cast_types = {'>i8': np.int64, '>f8': np.float64}
data = np.cast[cast_types[dtype]](data)
#
coldefs.append(pyfits.Column(name=column, array=data,
format=TFORM))
#### Done, now make the binary table
tbhdu = pyfits.new_table(coldefs)
#### Primary HDU
hdu = pyfits.PrimaryHDU()
thdulist = pyfits.HDUList([hdu, tbhdu])
#### Add modification time of "infile" to FITS header
infile_mod_time = time.strftime(
"%m/%d/%Y %I:%M:%S %p",
time.localtime(os.path.getmtime(self.filename)))
thdulist[1].header.update('MODTIME', infile_mod_time)
thdulist.writeto(self.filename + '.FITS', clobber=True)
return True
def run_lacosmic(flc='j9cv13pcq_flc.fits', split=1024, pssl=0.0, sigclip=4.0, objlim=3.8, sigfrac=0.3):
"""
drizzlepac.astrodrizzle.AstroDrizzle('ESO550-IG02-13-083-F814W_asn.fits', skysub=True, clean=True, final_wcs=True, final_scale=0.05, final_pixfrac=0.8, context=False, resetbits=0, final_bits=576, preserve=False)
"""
import cosmics
import threedhst
im = pyfits.open(flc, mode='update')
h = im[0].header
nx, ny = 4096/split, 2048/split
for ext in [1,2]:
if flc.startswith('i'):
dq = np.zeros((2051, 4096), dtype=int)
else:
dq = np.zeros((2048, 4096), dtype=int)
for i in range(nx):
for j in range(ny):
threedhst.showMessage('ext: %d, (i,j)=%d,%d' %(ext, i,j))
subim = im['sci',ext].data[j*split:(j+1)*split, i*split:(i+1)*split]
c = cosmics.cosmicsimage(subim, pssl=pssl, gain=1, readnoise=h['READNSEA'], sigclip=objlim, sigfrac=sigfrac, objlim=objlim, satlevel=84700.0, verbose=True)
c.run(maxiter=4)
dq[j*split:(j+1)*split, i*split:(i+1)*split] += c.mask*1
im['dq',ext].data |= dq*4096
pyfits.writeto(im.filename().replace('.fits', '_lac_%d.fits' %(ext)), data=dq*4096, header=im['dq', ext].header, clobber=True)
im.flush()
def scatter_annotate(x,
y,
labels,
xtol=None,
ytol=None,
ax=None,
*args,
**kwargs):
if ax is None:
axi = plt
else:
axi = ax
if len(labels) != len(x):
threedhst.showMessage(
'`x`, `y`, and `labels` inputs must be same size', warn=True)
return False
if not isinstance(labels, list):
labels = list(labels)
plt.scatter(x, y, *args, **kwargs)
af = AnnoteFinder(x, y, labels, xtol=xtol, ytol=ytol, axis=ax)
plt.connect('button_press_event', af)
return af
def checkShiftfile(asn_direct):
"""
checkShiftfile(asn_direct)
Make sure that there is a line in the shiftfile for each exposure
in the ASN table. Also check that no scales are zero.
"""
from threedhst.utils import ASNFile
asn = ASNFile(asn_direct)
sf_file = asn_direct.split('_asn.fits')[0] + '_shifts.txt'
sf = ShiftFile(sf_file)
flag = False
for exp in asn.exposures:
if exp + '_flt.fits' not in sf.images:
flag = True
print 'Exposure, %s, not in %s' % (exp, sf_file)
#print sf.nrows
sf.append(exp + '_flt.fits')
#print sf.nrows
#### Check if scales are zero in the shiftfile
if 0.0 in sf.scale:
flag = True
print 'Found scale=0 in the shiftfile, setting to default no shift/rotation\n'
for i in range(len(sf.scale)):
sf.xshift[i], sf.yshift[i], sf.rotate[i], sf.scale[
i] = 0.0, 0.0, 0.0, 1.0
if flag:
sf.write(sf_file)
else:
#print "\n3DHST.shifts.checkShiftfile: %s looks OK.\n" %sf_file
threedhst.showMessage('Shiftfile, %s, looks OK' % sf_file)
def copy_adriz_headerlets(direct_asn='GOODS-S-15-F140W_asn.fits', grism_asn='GOODS-S-15-G141_asn.fits', order=None, force=False, ACS=False):
"""
Copy Tweaked WCS solution in direct image to the paired grism exposures.
If same number of grism as direct exposures, match the WCS headers
directly. If not, just get the overall shift from the first direct
exposure and apply that to the grism exposures.
"""
import stwcs
from stwcs import updatewcs
import drizzlepac
direct = threedhst.utils.ASNFile(direct_asn)
grism = threedhst.utils.ASNFile(grism_asn)
Nd = len(direct.exposures)
Ng = len(grism.exposures)
if ACS:
NCHIP=2
sci_ext = [1,4]
ext = 'flc'
else:
NCHIP=1
sci_ext = [1]
ext = 'flt'
if Nd == Ng:
if order is None:
order = range(Nd)
for i in range(Nd):
imd = pyfits.open('%s_%s.fits' %(direct.exposures[i], ext))
#img = pyfits.open('%s_%s.fits' %(grism.exposures[i]))
#
for sci in sci_ext:
#sci_ext=1
direct_WCS = stwcs.wcsutil.HSTWCS(imd, ext=sci)
#
drizzlepac.updatehdr.update_wcs('%s_%s.fits' %(grism.exposures[order[i]], ext), sci, direct_WCS, verbose=True)
else:
#### Get overall shift from a shift-file and apply it to the
#### grism exposures
sf = threedhst.shifts.ShiftFile(direct_asn.replace('_asn.fits', '_shifts.txt'))
imd = pyfits.open(direct_asn.replace('asn','wcs'))
print imd.filename()
direct_WCS = stwcs.wcsutil.HSTWCS(imd, ext='wcs')
#
for i in range(Ng):
img = pyfits.open('%s_%s.fits' %(grism.exposures[i], ext))
if 'WCSNAME' in img[1].header:
if img[1].header['WCSNAME'] == 'TWEAK':
if force is False:
threedhst.showMessage('"TWEAK" WCS already found in %s_flt.fits.\nRun copy_adriz_headerlets with force=True to force update the shifts' %(grism.exposures[i]), warn=True)
continue
#
updatewcs.updatewcs('%s_%s.fits' %(grism.exposures[i], ext))
drizzlepac.updatehdr.updatewcs_with_shift('%s_%s.fits' %(grism.exposures[i], ext), direct_WCS, rot=sf.rotate[0], scale=sf.scale[0], xsh=sf.xshift[0], ysh=sf.yshift[0], wcsname='TWEAK')
def write_fits(self):
"""
Save the ascii catalog data into a FITS bintable.
The modification date of the ascii catalog is saved in the 'MODTIME'
keyword of the FITS file
"""
import time
formats = {}
formats['bool'] = 'L'
formats['int16'] = 'I'
formats['int32'] = 'J'
formats['int64'] = 'K'
formats['float32'] = 'E'
formats['float64'] = 'D'
formats['>i8'] = 'K'
formats['>f8'] = 'D'
#### Make the table columns, translating numpy data types to "TFORM"
coldefs = []
for column in self.columns:
dtype = str(self.__getitem__(column).dtype)
#print column, dtype
if dtype in formats.keys():
TFORM=formats[dtype]
else:
if 'S' not in dtype:
threedhst.showMessage('Unrecognized data type in %s: %s' %(self.filename, dtype), warn=True)
return False
#
TFORM = 'A'+dtype.split('S')[1]
#
data = self.__getitem__(column)
if '>' in dtype:
cast_types = {'>i8':np.int64, '>f8':np.float64}
data = np.cast[cast_types[dtype]](data)
#
coldefs.append(pyfits.Column(name=column, array=data, format=TFORM))
#### Done, now make the binary table
tbhdu = pyfits.new_table(coldefs)
#### Primary HDU
hdu = pyfits.PrimaryHDU()
thdulist = pyfits.HDUList([hdu,tbhdu])
#### Add modification time of "infile" to FITS header
infile_mod_time = time.strftime("%m/%d/%Y %I:%M:%S %p",
time.localtime(os.path.getmtime(self.filename)))
thdulist[1].header.update('MODTIME',infile_mod_time)
thdulist.writeto(self.filename+'.FITS', clobber=True)
return True
def asn_region(asn_file, path_to_flt="./"):
"""
asn_region(asn_file)
Create a DS9 region file for the exposures defined in an ASN file.
"""
##### Output file
output_file = asn_file.split(".fits")[0] + ".pointing.reg"
fp = open(output_file, "w")
fp.write("fk5\n") ### WCS coordinates
##### Read ASN file
asn = threedhst.utils.ASNFile(asn_file)
NEXP = len(asn.exposures)
RAcenters = np.zeros(NEXP)
DECcenters = np.zeros(NEXP)
##### Loop through exposures and get footprints
for i, exp_root in enumerate(asn.exposures):
flt_file = threedhst.utils.find_fits_gz(path_to_flt + "/" + exp_root.lower() + "_flt.fits", hard_break=True)
# head = pyfits.getheader(exp_root.lower()+'_flt.fits')
head = pyfits.getheader(flt_file)
if head.get("INSTRUME") == "ACS":
extensions = [1, 4]
else:
extensions = [1]
for ext in extensions:
regX, regY = wcs_polygon(flt_file, extension=ext)
line = "polygon(%10.6f,%10.6f,%10.6f,%10.6f,%10.6f,%10.6f,%10.6f,%10.6f)" % (
regX[0],
regY[0],
regX[1],
regY[1],
regX[2],
regY[2],
regX[3],
regY[3],
)
RAcenters[i] += np.mean(regX) / len(extensions)
DECcenters[i] += np.mean(regY) / len(extensions)
fp.write(line + " # color=magenta\n")
##### Text label with ASN filename
fp.write(
'# text(%10.6f,%10.6f) text={%s} font="Helvetica 14 normal" color=magenta\n'
% (np.mean(RAcenters), np.mean(DECcenters), asn_file.split("_asn.fits")[0])
)
fp.close()
# print '3D-HST / ASN_REGION: %s\n' %(output_file)
threedhst.showMessage("Create region file, %s." % output_file)
def run_lacosmic(flc='j9cv13pcq_flc.fits',
split=1024,
pssl=0.0,
sigclip=4.0,
objlim=3.8,
sigfrac=0.3):
"""
drizzlepac.astrodrizzle.AstroDrizzle('ESO550-IG02-13-083-F814W_asn.fits', skysub=True, clean=True, final_wcs=True, final_scale=0.05, final_pixfrac=0.8, context=False, resetbits=0, final_bits=576, preserve=False)
"""
import cosmics
import threedhst
im = pyfits.open(flc, mode='update')
h = im[0].header
nx, ny = 4096 / split, 2048 / split
for ext in [1, 2]:
if flc.startswith('i'):
dq = np.zeros((2051, 4096), dtype=int)
else:
dq = np.zeros((2048, 4096), dtype=int)
for i in range(nx):
for j in range(ny):
threedhst.showMessage('ext: %d, (i,j)=%d,%d' % (ext, i, j))
subim = im['sci', ext].data[j * split:(j + 1) * split,
i * split:(i + 1) * split]
c = cosmics.cosmicsimage(subim,
pssl=pssl,
gain=1,
readnoise=h['READNSEA'],
sigclip=objlim,
sigfrac=sigfrac,
objlim=objlim,
satlevel=84700.0,
verbose=True)
c.run(maxiter=4)
dq[j * split:(j + 1) * split,
i * split:(i + 1) * split] += c.mask * 1
im['dq', ext].data |= dq * 4096
pyfits.writeto(im.filename().replace('.fits', '_lac_%d.fits' % (ext)),
data=dq * 4096,
header=im['dq', ext].header,
clobber=True)
im.flush()
def read_bd_templates(self):
import glob
temps = glob.glob(self.template_path + '/spex*txt')
if len(temps) == 0:
threedhst.showMessage('No BD templates found in %s' %
(self.template_path),
warn=True)
list = []
for temp in temps:
list.append(BD_template(temp))
self.templates = list
self.NTEMP = len(self.templates)
def fits_regions(fits_list, output_file="list.reg", force_extension=None):
##### Output file
fp = open(output_file, "w")
fp.write("fk5\n") ### WCS coordinates
NEXP = len(fits_list)
##### Loop through exposures and get footprints
for i, exp_root in enumerate(fits_list):
flt_file = threedhst.utils.find_fits_gz(exp_root, hard_break=True)
# head = pyfits.getheader(exp_root.lower()+'_flt.fits')
head = pyfits.getheader(flt_file)
if (head.get("INSTRUME") == "ACS") | ("UVIS" in head.get("APERTURE")):
extensions = [1, 4]
else:
extensions = [1]
if force_extension is not None:
extensions = force_extension
RAcenters, DECcenters = 0.0, 0.0
for ext in extensions:
regX, regY = wcs_polygon(flt_file, extension=ext)
line = "polygon(%10.6f,%10.6f,%10.6f,%10.6f,%10.6f,%10.6f,%10.6f,%10.6f)" % (
regX[0],
regY[0],
regX[1],
regY[1],
regX[2],
regY[2],
regX[3],
regY[3],
)
fp.write(line + " # color=magenta\n")
RAcenters += np.mean(regX) / len(extensions)
DECcenters += np.mean(regY) / len(extensions)
##### Text label with ASN filename
fp.write(
'# text(%10.6f,%10.6f) text={%s} font="Helvetica 9 normal" color=magenta\n'
% (RAcenters, DECcenters, exp_root.split(".fits")[0])
)
fp.close()
threedhst.showMessage("Create region file, %s." % output_file)
def asn_region(asn_file, path_to_flt='./'):
"""
asn_region(asn_file)
Create a DS9 region file for the exposures defined in an ASN file.
"""
##### Output file
output_file = asn_file.split('.fits')[0] + '.pointing.reg'
fp = open(output_file, 'w')
fp.write('fk5\n') ### WCS coordinates
##### Read ASN file
asn = threedhst.utils.ASNFile(asn_file)
NEXP = len(asn.exposures)
RAcenters = np.zeros(NEXP)
DECcenters = np.zeros(NEXP)
##### Loop through exposures and get footprints
for i, exp_root in enumerate(asn.exposures):
flt_file = threedhst.utils.find_fits_gz(path_to_flt + '/' +
exp_root.lower() + '_flt.fits',
hard_break=True)
#head = pyfits.getheader(exp_root.lower()+'_flt.fits')
head = pyfits.getheader(flt_file)
if head.get('INSTRUME') == 'ACS':
extensions = [1, 4]
else:
extensions = [1]
for ext in extensions:
regX, regY = wcs_polygon(flt_file, extension=ext)
line = "polygon(%10.6f,%10.6f,%10.6f,%10.6f,%10.6f,%10.6f,%10.6f,%10.6f)" % (
regX[0], regY[0], regX[1], regY[1], regX[2], regY[2], regX[3],
regY[3])
RAcenters[i] += np.mean(regX) / len(extensions)
DECcenters[i] += np.mean(regY) / len(extensions)
fp.write(line + ' # color=magenta\n')
##### Text label with ASN filename
fp.write('# text(%10.6f,%10.6f) text={%s} font="Helvetica 14 normal" color=magenta\n' \
%(np.mean(RAcenters),np.mean(DECcenters),
asn_file.split('_asn.fits')[0]))
fp.close()
#print '3D-HST / ASN_REGION: %s\n' %(output_file)
threedhst.showMessage('Create region file, %s.' % output_file)
def make_grism_shiftfile(asn_direct, asn_grism):
"""
make_grism_shiftfile(asn_direct, grism_direct)
Make a shiftfile for grism exposures to match
corresponding direct images
"""
from threedhst.utils import ASNFile
ROOT_DIRECT = asn_direct.split("_asn.fits")[0] # .lower()
ROOT_GRISM = asn_grism.split("_asn.fits")[0] # .lower()
#### Read shiftfile and ASN table
sf = ShiftFile(ROOT_DIRECT + "_shifts.txt")
asn = ASNFile(asn_grism)
if sf.nrows == len(asn.exposures):
#### Assume one direct image for each grism images, so just
#### change the image names in the shiftfile to the grism exposures
for i, exp in enumerate(asn.exposures):
sf.images[i] = exp + "_flt.fits"
else:
#### Have different number of grism and direct images. Just use the
#### shifts/rotations for the first direct image
xs = sf.xshift[0]
ys = sf.yshift[0]
rot = sf.rotate[0]
scl = sf.scale[0]
sf.images = []
sf.xshift = []
sf.yshift = []
sf.rotate = []
sf.scale = []
for i, exp in enumerate(asn.exposures):
sf.images.append(exp + "_flt.fits")
sf.xshift.append(xs)
sf.yshift.append(ys)
sf.rotate.append(rot)
sf.scale.append(scl)
sf.nrows = len(asn.exposures)
#### Write the new shiftfile
sf.write(ROOT_GRISM + "_shifts.txt")
# print "\n3DHST.shifts.make_grism_shiftfile: %s_shifts.txt\n" %ROOT_GRISM
threedhst.showMessage("Making grism shiftfile, %s_shifts.txt" % ROOT_GRISM)
def __add__(self, newcat, prepend='x_'):
"""
Append columns of 'newcat' gTable object to the table. Add the
"prepend" string to column names that already exist in the table.
"""
if len(self) != len(newcat):
threedhst.showMessage('Number of elements in %s and %s don\'t match.' %(self.filename, newcat.filename))
cnew = self.copy()
for column in newcat.columns:
col = newcat[column]
if column in cnew.columns:
col.name = prepend + col.name
cnew.add_column(col)
return cnew
def make_grism_shiftfile(asn_direct, asn_grism):
"""
make_grism_shiftfile(asn_direct, grism_direct)
Make a shiftfile for grism exposures to match
corresponding direct images
"""
from threedhst.utils import ASNFile
ROOT_DIRECT = asn_direct.split('_asn.fits')[0] #.lower()
ROOT_GRISM = asn_grism.split('_asn.fits')[0] #.lower()
#### Read shiftfile and ASN table
sf = ShiftFile(ROOT_DIRECT + '_shifts.txt')
asn = ASNFile(asn_grism)
if sf.nrows == len(asn.exposures):
#### Assume one direct image for each grism images, so just
#### change the image names in the shiftfile to the grism exposures
for i, exp in enumerate(asn.exposures):
sf.images[i] = exp + '_flt.fits'
else:
#### Have different number of grism and direct images. Just use the
#### shifts/rotations for the first direct image
xs = sf.xshift[0]
ys = sf.yshift[0]
rot = sf.rotate[0]
scl = sf.scale[0]
sf.images = []
sf.xshift = []
sf.yshift = []
sf.rotate = []
sf.scale = []
for i, exp in enumerate(asn.exposures):
sf.images.append(exp + '_flt.fits')
sf.xshift.append(xs)
sf.yshift.append(ys)
sf.rotate.append(rot)
sf.scale.append(scl)
sf.nrows = len(asn.exposures)
#### Write the new shiftfile
sf.write(ROOT_GRISM + '_shifts.txt')
#print "\n3DHST.shifts.make_grism_shiftfile: %s_shifts.txt\n" %ROOT_GRISM
threedhst.showMessage('Making grism shiftfile, %s_shifts.txt' % ROOT_GRISM)
def scatter_annotate(x, y, labels, xtol=None, ytol=None, ax=None,*args, **kwargs):
if ax is None:
axi = plt
else:
axi = ax
if len(labels) != len(x):
threedhst.showMessage('`x`, `y`, and `labels` inputs must be same size', warn=True)
return False
if not isinstance(labels, list):
labels = list(labels)
plt.scatter(x, y, *args, **kwargs)
af = AnnoteFinder(x, y, labels, xtol=xtol, ytol=ytol, axis=ax)
plt.connect('button_press_event', af)
return af
def fits_regions(fits_list, output_file='list.reg', force_extension=None):
##### Output file
fp = open(output_file, 'w')
fp.write('fk5\n') ### WCS coordinates
NEXP = len(fits_list)
##### Loop through exposures and get footprints
for i, exp_root in enumerate(fits_list):
flt_file = threedhst.utils.find_fits_gz(exp_root, hard_break=True)
#head = pyfits.getheader(exp_root.lower()+'_flt.fits')
head = pyfits.getheader(flt_file)
if (head.get('INSTRUME') == 'ACS') | ('UVIS' in head.get('APERTURE')):
extensions = [1, 4]
else:
extensions = [1]
if force_extension is not None:
extensions = force_extension
RAcenters, DECcenters = 0., 0.
for ext in extensions:
regX, regY = wcs_polygon(flt_file, extension=ext)
line = "polygon(%10.6f,%10.6f,%10.6f,%10.6f,%10.6f,%10.6f,%10.6f,%10.6f)" % (
regX[0], regY[0], regX[1], regY[1], regX[2], regY[2], regX[3],
regY[3])
fp.write(line + ' # color=magenta\n')
RAcenters += np.mean(regX) / len(extensions)
DECcenters += np.mean(regY) / len(extensions)
##### Text label with ASN filename
fp.write('# text(%10.6f,%10.6f) text={%s} font="Helvetica 9 normal" color=magenta\n' \
%(RAcenters,DECcenters, exp_root.split('.fits')[0]))
fp.close()
threedhst.showMessage('Create region file, %s.' % output_file)
def remove_visit_sky(asn_file='GDN12-G102_asn.fits',
list=['zodi_G102_clean.fits', 'excess_G102_clean.fits'],
add_constant=False,
column_average=True,
mask_grow=18,
flat_correct=True):
"""
Require that all exposures in a visit have the same zodi component.
"""
from scipy.linalg import lstsq
import scipy.optimize
import scipy.ndimage as nd
import astropy.io.fits as pyfits
import copy
import threedhst.grism_sky as bg
asn = threedhst.utils.ASNFile(asn_file)
flt = pyfits.open('%s_flt.fits' % (asn.exposures[0]))
bg.set_grism_flat(grism=flt[0].header['FILTER'], verbose=True)
if flat_correct:
flat = bg.flat * 1.
else:
flat = bg.flat * 0. + 1
data = []
whts = []
masks = []
for exp in asn.exposures:
flt = pyfits.open('%s_flt.fits' % (exp))
segfile = '%s_flt.seg.fits' % (exp)
seg = pyfits.open(segfile)[0].data
seg_mask = nd.maximum_filter((seg > 0), size=18) == 0
dq_ok = (flt[3].data & (4 + 32 + 16 + 512 + 2048 + 4096)) == 0
#
flat_corr = flt[1].data * flat
mask = seg_mask & dq_ok
mask &= (flat_corr < np.percentile(flat_corr[mask], 98)) & (
flt[2].data > 0) & (flat_corr > np.percentile(flat_corr[mask], 1))
#
data.append(flat_corr.flatten())
whts.append(1 / flt[2].data.flatten()**2)
masks.append(mask.flatten())
data = np.array(data)
whts = np.array(whts)
masks = np.array(masks)
#### Read in the master skies
ims = []
skies = copy.deepcopy(list)
for sky in skies:
ims.append(
pyfits.open(os.getenv('THREEDHST') + '/CONF/' +
sky)[0].data.flatten())
if add_constant:
ims.append(flt[1].data.flatten() * 0. + 1)
skies.append('Constant')
ims = np.array(ims)
#### Do the fit
tol = 1.49e-8 # not sure what this controls
p0 = np.ones((ims.shape[0] - 1) * len(asn.exposures) + 1)
popt = scipy.optimize.leastsq(bg.obj_lstsq_visit,
p0,
args=(data, ims, whts, masks),
full_output=True,
ftol=tol / 1000.,
xtol=tol / 1000.)
xcoeff = popt[0]
sh_temp = ims.shape
logstr = 'Master grism sky: %s\n\n FLT %s\n' % (asn_file,
' '.join(skies))
for i in range(len(asn.exposures)):
coeff = np.zeros(sh_temp[0])
coeff[0] = xcoeff[0]
coeff[1:] = xcoeff[1 + i * (sh_temp[0] - 1):1 + (i + 1) *
(sh_temp[0] - 1)]
bg_model = np.dot(coeff, ims).reshape((1014, 1014))
logstr += '%s %s\n' % (asn.exposures[i], ''.join(
[' %9.4f' % (c) for c in coeff]))
flt = pyfits.open('%s_flt.fits' % (asn.exposures[i]), mode='update')
flt[1].data = flt[1].data * flat - bg_model
for j in range(sh_temp[0]):
if 'GSKY%02d' % (j) in flt[0].header:
flt[0].header['GSKY%02d' % (j)] += coeff[j]
else:
flt[0].header['GSKY%02d' % (j)] = (coeff[j], 'Master sky: %s' %
(skies[j]))
#
flt[1].header['MDRIZSKY'] = 0.
if 'SKYFLAT' in flt[0].header.keys():
flt[0].header['SKYFLAT'] = (flat_correct
| flt[0].header['SKYFLAT'],
'Direct image flat applied')
else:
flt[0].header['SKYFLAT'] = (flat_correct,
'Direct image flat applied')
flt.flush()
threedhst.showMessage(logstr)
if column_average:
#for iter in range(2):
grism_sky_column_average(asn_file=asn_file, mask_grow=mask_grow)
def process_acs_pair(asn_direct_file='ib3706050_asn.fits',
asn_grism_file='ib3706060_asn.fits',
field = 'COSMOS',
ALIGN_IMAGE='../ACS/h_nz_sect*img.fits',
ALIGN_EXTENSION=0,
SKIP_GRISM=False,
adjust_targname=True,
align_geometry='shift',
PATH_TO_RAW='../RAW',
get_shift=True,
TWEAKSHIFTS_ONLY=False,
FLC=True):
"""
Does the basic processing for ACS F814W and G800L pointings: background subtraction, allignment and drizzlign.
"""
import threedhst
import threedhst.prep_flt_files
from threedhst.prep_flt_files import make_targname_asn
#### Copy corrected FLT files to .
asn = threedhst.utils.ASNFile(asn_direct_file)
for exp in asn.exposures:
print exp
os.system('rm %s_flt.fits' %(exp))
if FLC:
os.system('cp ../RAW/%s_flc.fits %s_flt.fits' %(exp, exp))
else:
os.system('cp ../FIXED/%s_flt.fits . ' %(exp))
#
asn = threedhst.utils.ASNFile(asn_grism_file)
for exp in asn.exposures:
print exp
os.system('rm %s_flt.fits' %(exp))
if FLC:
os.system('cp ../RAW/%s_flc.fits %s_flt.fits' %(exp, exp))
else:
os.system('cp ../FIXED/%s_flt.fits . ' %(exp))
#DIRECT REDUCTION
ROOT_DIRECT = asn_direct_file.split('_asn.fits')[0]
from threedhst.prep_flt_files import make_targname_asn
#this makes new asn.fits files but with ACS the names start with ANY
#must add an optional tag to replace ANY with the field name
if (asn_direct_file is not None) & adjust_targname:
asn_direct_file = make_targname_asn(asn_direct_file,field=field, ext='flc')
if (asn_grism_file is not None) & adjust_targname:
asn_grism_file = make_targname_asn(asn_grism_file,field=field, ext='flc')
#run = threedhst.prep_flt_files.MultidrizzleRun((asn_direct_file.split('_asn.fits')[0]).upper())
threedhst.shifts.run_tweakshifts(asn_direct_file, verbose=True)
threedhst.prep_flt_files.startMultidrizzle(asn_direct_file, use_shiftfile=True,
skysub=True,
final_scale=0.05, pixfrac=1, driz_cr=True,
updatewcs=True, clean=True, median=True)
for i,exp in enumerate(asn.exposures):
asn_mask = asn.exposures[i]+'_flt.fits.mask.reg'
print asn_mask
if os.path.exists(asn_mask):
threedhst.showMessage("Apply ASN mask: %s" %(asn_mask))
threedhst.regions.apply_dq_mask(asn.exposures[i]+'_flt.fits', extension=3,
mask_file = asn_mask)
threedhst.shifts.refine_shifts(ROOT_DIRECT=asn_direct_file.split('_as')[0].upper(),
ALIGN_IMAGE=ALIGN_IMAGE,
ALIGN_EXTENSION = ALIGN_EXTENSION,
fitgeometry=align_geometry, clean=True)
unicorn.go_acs.testing_f814w_background(asn_direct_file)
SCALE = 0.06
PIXFRAC=1.0
threedhst.prep_flt_files.startMultidrizzle(asn_direct_file, use_shiftfile=True,
skysub=True,
final_scale=SCALE, pixfrac=PIXFRAC, driz_cr=False,
updatewcs=True, clean=True, median=False)
#GRISM REDUCTION
threedhst.shifts.make_grism_shiftfile(asn_direct_file, asn_grism_file)
threedhst.prep_flt_files.startMultidrizzle(asn_grism_file, use_shiftfile=True,
skysub=True,
final_scale=SCALE, pixfrac=PIXFRAC, driz_cr=True,
updatewcs=True, clean=False, median=True)
unicorn.go_acs.testing_g800l_background(asn_grism_file)
threedhst.prep_flt_files.startMultidrizzle(asn_grism_file, use_shiftfile=True,
skysub=True,
final_scale=SCALE, pixfrac=PIXFRAC, driz_cr=True,
updatewcs=True, clean=False, median=True)
def prep_direct_grism_pair(direct_asn='goodss-34-F140W_asn.fits', grism_asn='goodss-34-G141_asn.fits', radec=None, raw_path='../RAW/', mask_grow=18, scattered_light=False, final_scale=None, skip_direct=False, ACS=False, jump=False, order=2, get_shift=True, align_threshold=20, column_average=True, sky_iter=3):
"""
Process both the direct and grism observations of a given visit
"""
import threedhst.prep_flt_astrodrizzle as prep
import drizzlepac
from stwcs import updatewcs
import time
t0 = time.time()
#direct_asn='goodss-34-F140W_asn.fits'; grism_asn='goodss-34-G141_asn.fits'; radec=None; raw_path='../RAW/'
#radec = os.getenv('THREEDHST') + '/ASTRODRIZZLE_FLT/Catalog/goodss_radec.dat'
################################
#### Direct image processing
################################
#### xx add astroquery 2MASS/SDSS workaround for radec=None
if not skip_direct:
#### Get fresh FLTS from ../RAW/
asn = threedhst.utils.ASNFile(direct_asn)
if ACS:
for exp in asn.exposures:
print 'cp %s/%s_flc.fits.gz .' %(raw_path, exp)
os.system('cp %s/%s_flc.fits.gz .' %(raw_path, exp))
os.system('gunzip %s_flc.fits.gz' %(exp))
else:
threedhst.process_grism.fresh_flt_files(direct_asn, from_path=raw_path)
if (not ACS):
#### Subtract WFC3/IR direct backgrounds
prep.subtract_flt_background(root=direct_asn.split('_asn')[0], scattered_light=scattered_light, order=order)
#### Flag IR CRs again within runTweakReg
#### Run TweakReg
if (radec is None) & (not ACS):
drizzlepac.astrodrizzle.AstroDrizzle(direct_asn, clean=True, final_scale=None, final_pixfrac=0.8, context=False, final_bits=576, preserve=False, driz_cr_snr='5.0 4.0', driz_cr_scale = '2.5 0.7') # ,
else:
if get_shift:
prep.runTweakReg(asn_file=direct_asn, master_catalog=radec, final_scale=None, ACS=ACS, threshold=align_threshold)
#### Subtract background of direct ACS images
if ACS:
for exp in asn.exposures:
flc = pyfits.open('%s_flc.fits' %(exp), mode='update')
for ext in [1,4]:
threedhst.showMessage('Subtract background from %s_flc.fits[%d] : %.4f' %(exp, ext, flc[ext].header['MDRIZSKY']))
flc[ext].data -= flc[ext].header['MDRIZSKY']
flc[ext].header['MDRIZSK0'] = flc[ext].header['MDRIZSKY']
flc[ext].header['MDRIZSKY'] = 0.
#
flc.flush()
else:
pass
#### Do this later, gives segfaults here???
#prep.subtract_flt_background(root=direct_asn.split('_asn')[0], scattered_light=scattered_light)
#### Flag CRs again on BG-subtracted image
#drizzlepac.astrodrizzle.AstroDrizzle(direct_asn, clean=True, final_scale=None, final_pixfrac=0.8, context=False, final_bits=576, preserve=False, driz_cr_snr='5.0 4.0', driz_cr_scale = '2.5 0.7') # ,
################################
#### Grism image processing
################################
if grism_asn:
asn = threedhst.utils.ASNFile(grism_asn)
if ACS:
for exp in asn.exposures:
print 'cp %s/%s_flc.fits.gz .' %(raw_path, exp)
os.system('cp %s/%s_flc.fits.gz .' %(raw_path, exp))
os.system('gunzip %s_flc.fits.gz' %(exp))
updatewcs.updatewcs('%s_flc.fits' %(exp))
prep.copy_adriz_headerlets(direct_asn=direct_asn, grism_asn=grism_asn, ACS=True)
prep.subtract_acs_grism_background(asn_file=grism_asn, final_scale=None)
else:
#### Remove the sky and flag CRs
## with mask from rough zodi-only subtraction
prep.subtract_grism_background(asn_file=grism_asn, PATH_TO_RAW='../RAW/', final_scale=None, visit_sky=True, column_average=False, mask_grow=mask_grow, first_run=True)
## Redo making mask from better combined image
prep.subtract_grism_background(asn_file=grism_asn, PATH_TO_RAW='../RAW/', final_scale=final_scale, visit_sky=True, column_average=column_average, mask_grow=mask_grow, first_run=False, sky_iter=sky_iter)
#### Copy headers from direct images
if radec is not None:
prep.copy_adriz_headerlets(direct_asn=direct_asn, grism_asn=grism_asn, ACS=False)
#### Run CR rejection with final shifts
drizzlepac.astrodrizzle.AstroDrizzle(grism_asn, clean=True, skysub=False, final_wcs=True, final_scale=final_scale, final_pixfrac=0.8, context=False, final_bits=576, driz_sep_bits=576, preserve=False, driz_cr_snr='8.0 5.0', driz_cr_scale='2.5 0.7') # driz_cr_snr='5.0 4.0', driz_cr_scale = '2.5 0.7')
if not grism_asn:
t1 = time.time()
threedhst.showMessage('direct: %s\n\nDone (%d s).' %(direct_asn, int(t1-t0)))
else:
t1 = time.time()
threedhst.showMessage('direct: %s\ngrism: %s\n\nDone (%d s).' %(direct_asn, grism_asn, int(t1-t0)))
def refine_shifts(
ROOT_DIRECT="f160w",
ALIGN_IMAGE="../../ACS/h_sz*drz_img.fits",
fitgeometry="shift",
clean=True,
ALIGN_EXTENSION=0,
shift_params=None,
toler=3,
maxtoler=5,
align_sdss_ds9=False,
verbose=False,
):
"""
refine_shifts(ROOT_DIRECT='f160w',
ALIGN_IMAGE='../../ACS/h_sz*drz_img.fits',
fitgeometry='shift', clean=True)
Refine shifts by catalog matching an input multidrizzle image,
ROOT_DIRECT+'_drz.fits' to one or more alignment images
"""
run = threedhst.prep_flt_files.MultidrizzleRun(ROOT_DIRECT.upper())
## radius for match is 2**toler. Make it larger if fit comes out bad
# toler, maxtoler = 3, 5
iter, MAXIT = 0, 5
xrms, yrms = 100, 100
if shift_params is not None:
xshift, yshift, rot, scale = shift_params
threedhst.showMessage("Using specified DRZ-frame shifts: %f %f %f %f" % (xshift, yshift, rot, scale))
else:
threedhst.showMessage("Aligning WCS to %s (%s)" % (threedhst.options["ALIGN_IMAGE"], fitgeometry))
while ((xrms > 1) | (yrms > 1)) & (toler <= maxtoler) & (iter < MAXIT):
iter = iter + 1
xshift, yshift, rot, scale, xrms, yrms = threedhst.shifts.align_to_reference(
ROOT_DIRECT,
ALIGN_IMAGE,
fitgeometry=fitgeometry,
clean=clean,
ALIGN_EXTENSION=ALIGN_EXTENSION,
toler=toler,
skip_swarp=(toler > 3),
align_sdss_ds9=align_sdss_ds9,
verbose=verbose,
)
toler += 1
#### shifts measured in DRZ frame. Translate to FLT frame
drz = pyfits.open(ROOT_DIRECT + "_drz.fits")
# alpha = (180.-drz[1].header['PA_APER'])/360.*2*np.pi
#### Get reference angle from first image in the ASN file
asn = threedhst.utils.ASNFile(ROOT_DIRECT + "_asn.fits")
alpha = (180.0 - pyfits.getheader(asn.exposures[0] + "_flt.fits", 1)["PA_APER"]) / 360.0 * 2 * np.pi
xsh = (xshift * np.cos(alpha) - yshift * np.sin(alpha)) * np.float(run.scl)
ysh = (xshift * np.sin(alpha) + yshift * np.cos(alpha)) * np.float(run.scl)
print "Final shift:", xsh, ysh, drz[1].header["PA_APER"]
fp = open(ROOT_DIRECT + "_align.info", "w")
fp.write("%s %8.3f %8.3f %8.3f\n" % (ALIGN_IMAGE, xsh, ysh, rot))
fp.close()
#### Read the shiftfile
shiftF = threedhst.shifts.ShiftFile(ROOT_DIRECT + "_shifts.txt")
#### Apply the alignment shifts to the shiftfile
shiftF.xshift = list(np.array(shiftF.xshift) - xsh)
shiftF.yshift = list(np.array(shiftF.yshift) - ysh)
shiftF.rotate = list((np.array(shiftF.rotate) + rot) % 360)
shiftF.scale = list(np.array(shiftF.scale) * scale)
shiftF.write(ROOT_DIRECT + "_shifts.txt")
def helper_read(self,
filename,
format=None,
check_FITS=True,
save_FITS=False,
*args,
**kwargs):
"""
Wrapper around `astropy.table.Table.read() for better
auto-sensing of ascii table formats
"""
if not os.path.exists(filename):
threedhst.showMessage('File %s not found.' % (filename), warn=True)
return False
if format is not None:
data = table_base.read(filename, format=format, *args, **kwargs)
data.input_format = {'format': format}
data.filename = filename
return data
if filename.lower().endswith('.fits'):
format = 'fits'
else:
### Try to read the ".FITS" version first
if check_FITS:
#threedhst.showMessage('read', warn=True)
status = self.load_FITS(filename)
if status:
return status
if format is None:
try:
t = pyfits.open(filename)
format = 'fits'
except:
print('Try ascii:')
line = open(filename).readline()
if line.strip().startswith('#'):
if line.split()[1].isdigit():
format = 'ascii.sextractor'
else:
format = 'ascii.commented_header'
else:
format = 'ascii.basic'
data = self.read(filename, format=format, *args, **kwargs)
data.input_format = {'format': format}
data.filename = filename
#### Split "APER" columns
if format == 'ascii.sextractor':
combine_columns = [
'FLUX_APER', 'FLUXERR_APER', 'MAG_APER',
'MAGERR_APER', 'FLUX_RADIUS'
]
for base_col in combine_columns:
if '%s2' % (base_col) in data.colnames:
continue
ncol = 0
for col in data.colnames:
if base_col in col:
ncol += 1
if ncol == 0:
continue
dtype = data[base_col].dtype
out = np.zeros((len(data), ncol), dtype=dtype)
for i in range(ncol):
if i == 0:
incol = base_col
else:
incol = '%s_%d' % (base_col, i)
out[:, i] = data[incol]
data.remove_column(incol)
#data.remove_column(base_col)
data.add_column(Column(name=base_col, data=out))
if save_FITS:
#threedhst.showMessage('write', warn=True)
data.write_FITS()
if format == 'fits':
t = pyfits.open(filename)
if t[0].header['EXTEND']:
if 'EXTNAME' in t[1].header:
if t[1].header['EXTNAME'] == 'LDAC_IMHEAD':
hdu = 2
else:
hdu = 1
else:
hdu = 1
else:
hdu = 0
data = self.read(filename, format='fits', hdu=hdu, *args, **kwargs)
data.input_format = {'format': 'fits', 'HDU': hdu}
data.filename = filename
return data
def test():
import interp_c
import time
import scipy
import threedhst
import numpy as np
N = int(1.e6)
xfull = np.arange(0,N+1,1)*1.
#yfull = np.sin(xfull/(N/1239.)*2*np.pi)+1
yfull = np.sin(xfull/np.pi/2/20)+0.2
# coeffs = np.random.random(size=12)*5
# yfull = scipy.polyval(coeffs, xfull)
xint = np.arange(0,N+1,N/100)*1.
tstart = time.time()
denom = np.trapz(yfull,xfull)
tstart = time.time()
yint_0 = np.interp(xint, xfull, yfull)
t0 = time.time()
print 'Linear : %.3f (%.4e)' %(t0-tstart, np.trapz(yint_0, xint)/denom-1)
yint_x = interp_c.interp_c(xint, xfull, yfull)
tx = time.time()
print 'Linear(c) : %.3f (%.4e)' %(tx-t0, np.trapz(yint_x, xint)/denom-1)
xreverse = xint[::-1]
yint_y = interp_c.interp_c(xreverse, xfull, yfull, assume_sorted=0)
ty = time.time()
print 'Linear(c) rev : %.3f (%.4e)' %(ty-tx, np.trapz(yint_y, xint)/denom-1)
yint_1 = threedhst.utils.interp_conserve(xint, xfull, yfull)
t1 = time.time()
print 'Conserve : %.3f (%.4e)' %(t1-ty, np.trapz(yint_1, xint)/denom-1)
yint_2 = interp_c.interp_conserve(xint, xfull, yfull)
t2 = time.time()
print 'Conserve (Cython): %.3f (%.4e)' %(t2-t1, np.trapz(yint_2, xint)/denom-1)
yint_3 = interp_c.interp_conserve_c(xint, xfull, yfull)
t3 = time.time()
print 'Conserve (more c): %.3f (%.4e)' %(t3-t2, np.trapz(yint_3, xint)/denom-1)
yint_4 = threedhst.utils.interp_conserve_c(xint, xfull, yfull)
t4 = time.time()
print 'Inline c : %.3f (%.4e)' %(t4-t3, np.trapz(yint_4, xint)/denom-1)
#### Test interpolation
threedhst.showMessage('Interpolation')
#### Faster while n(int)/n(full) < 1./50
xint = xfull[1000:-1000:40]
tstart = time.time()
yint = np.interp(xint, xfull, yfull, left=0., right=0.)
t0 = time.time()
print 'Python : %.4f' %(t0-tstart)
yint1 = interp_c.interp_c(xint, xfull, yfull, extrapolate=0.)
t1 = time.time()
print 'Cython rewrite : %.4f (%.2e)' %(t1-t0, np.sum((yint1-yint)**2))
#### Test midpoint definition --- slices work better than by hand
threedhst.showMessage('Midpoint')
xmid = xfull
tstart = time.time()
midpoint = (xmid[1:]+xmid[:-1])/2.
midpoint = np.append(midpoint, np.array([xmid[0],xmid[-1]]))
midpoint = midpoint[np.argsort(midpoint)]
t0 = time.time()
print 'Python : %.3f %.2e' %(t0-tstart, np.sum((midpoint-midpoint)**2))
midpoint_c1 = interp_c.midpoint(xmid)
t1 = time.time()
print 'Cython : %.3f %.2e' %(t1-t0, np.sum((midpoint_c1-midpoint)**2))
midpoint_c2 = interp_c.midpoint_c(xmid, N+1)
t2 = time.time()
print 'Cython (opt): %.3f %.2e' %(t2-t1, np.sum((midpoint_c2-midpoint)**2))
def grism_sky_column_average_GP(asn_file='GDN12-G102_asn.fits', mask_grow=8):
"""
Remove column-averaged residuals from grism exposures, smooth with Gaussian Processes
"""
import scipy.ndimage as nd
import astropy.io.fits as pyfits
from sklearn.gaussian_process import GaussianProcess
asn = threedhst.utils.ASNFile(asn_file)
for k in range(len(asn.exposures)):
#### 1D column averages
flt = pyfits.open('%s_flt.fits' %(asn.exposures[k]), mode='update')
segfile = '%s_flt.seg.fits' %(asn.exposures[k])
seg = pyfits.open(segfile)[0].data
seg_mask = nd.maximum_filter((seg > 0), size=mask_grow) == 0
dq_ok = (flt[3].data & (4+32+16+512+2048+4096)) == 0
mask = seg_mask & dq_ok & (flt[2].data > 0)
threedhst.showMessage('Remove column average (GP): %s' %(asn.exposures[k]))
#### Iterative clips on percentile
#mask &= (flt[1].data < np.percentile(flt[1].data[mask], 98)) & (flt[2].data > 0) & (flt[1].data > np.percentile(flt[1].data[mask], 2))
#mask &= (flt[1].data < np.percentile(flt[1].data[mask], 84)) & (flt[2].data > 0) & (flt[1].data > np.percentile(flt[1].data[mask], 16))
xmsk = np.arange(1014)
masked = flt[1].data*1
masked[~mask] = np.nan
yres = np.zeros(1014)
yrms = yres*0.
for i in range(1014):
# ymsk = mask[:,i]
# yres[i] = np.median(flt[1].data[ymsk,i])
ymsk = masked[:,i]
#ymsk = masked[:,np.maximum(i-10,0):i+10]
#yres[i] = np.median(ymsk[np.isfinite(ymsk)])
ok = np.isfinite(ymsk)
ymsk[(ymsk > np.percentile(ymsk[ok], 84)) | (ymsk < np.percentile(ymsk[ok], 16))] = np.nan
msk = np.isfinite(ymsk)
yres[i] = np.mean(ymsk[msk])
yrms[i] = np.std(ymsk[msk])/np.sqrt(msk.sum())
#
yok = np.isfinite(yres)
if 'GSKY00' in flt[0].header.keys():
bg_sky = flt[0].header['GSKY00']
else:
bg_sky = 1
gp = GaussianProcess(regr='constant', corr='squared_exponential', theta0=8,
thetaL=7, thetaU=12,
nugget=(yrms/bg_sky)[yok][::1]**2,
random_start=10, verbose=True, normalize=True) #, optimizer='Welch')
#
gp.fit(np.atleast_2d(xmsk[yok][::1]).T, yres[yok][::1]+bg_sky)
y_pred, MSE = gp.predict(np.atleast_2d(xmsk).T, eval_MSE=True)
gp_sigma = np.sqrt(MSE)
resid = threedhst.utils.medfilt(yres, 41)
flt[1].data -= y_pred-bg_sky
flt.flush()
#flt.writeto(flt.filename(), clobber=True)
#plt.plot(yres_sm)
### Make figure
from matplotlib.figure import Figure
from matplotlib.backends.backend_agg import FigureCanvasAgg
fig = Figure(figsize=[6,4], dpi=100)
fig.subplots_adjust(wspace=0.25,hspace=0.02,left=0.15,
bottom=0.08,right=0.97,top=0.92)
ax = fig.add_subplot(111)
ax.set_title(flt.filename())
ax.plot(yres, color='black', alpha=0.3)
ax.plot(y_pred-bg_sky, color='red', linewidth=2, alpha=0.7)
ax.fill_between(xmsk, y_pred-bg_sky + gp_sigma, y_pred-bg_sky - gp_sigma, color='red', alpha=0.3)
ax.set_xlim(0,1014)
ax.set_xlabel('x pix'); ax.set_ylabel('BG residual (e/s)')
fig.tight_layout(pad=0.2)
canvas = FigureCanvasAgg(fig)
canvas.print_figure(flt.filename().split('.fits')[0] + '.column.png', dpi=100, transparent=False)
def grism_sky_column_average_GP(asn_file='GDN12-G102_asn.fits', mask_grow=8):
"""
Remove column-averaged residuals from grism exposures, smooth with Gaussian Processes
"""
import scipy.ndimage as nd
import astropy.io.fits as pyfits
from sklearn.gaussian_process import GaussianProcess
asn = threedhst.utils.ASNFile(asn_file)
for k in range(len(asn.exposures)):
#### 1D column averages
flt = pyfits.open('%s_flt.fits' %(asn.exposures[k]), mode='update')
segfile = '%s_flt.seg.fits' %(asn.exposures[k])
seg = pyfits.open(segfile)[0].data
seg_mask = nd.maximum_filter((seg > 0), size=mask_grow) == 0
dq_ok = (flt[3].data & (4+32+16+512+2048+4096)) == 0
mask = seg_mask & dq_ok & (flt[2].data > 0)
threedhst.showMessage('Remove column average (GP): %s' %(asn.exposures[k]))
#### Iterative clips on percentile
#mask &= (flt[1].data < np.percentile(flt[1].data[mask], 98)) & (flt[2].data > 0) & (flt[1].data > np.percentile(flt[1].data[mask], 2))
#mask &= (flt[1].data < np.percentile(flt[1].data[mask], 84)) & (flt[2].data > 0) & (flt[1].data > np.percentile(flt[1].data[mask], 16))
xmsk = np.arange(1014)
masked = flt[1].data*1
masked[~mask] = np.nan
yres = np.zeros(1014)
yrms = yres*0.
for i in range(1014):
# ymsk = mask[:,i]
# yres[i] = np.median(flt[1].data[ymsk,i])
ymsk = masked[:,i]
#ymsk = masked[:,np.maximum(i-10,0):i+10]
#yres[i] = np.median(ymsk[np.isfinite(ymsk)])
ok = np.isfinite(ymsk)
ymsk[(ymsk > np.percentile(ymsk[ok], 84)) | (ymsk < np.percentile(ymsk[ok], 16))] = np.nan
msk = np.isfinite(ymsk)
yres[i] = np.mean(ymsk[msk])
yrms[i] = np.std(ymsk[msk])/np.sqrt(msk.sum())
#
yok = np.isfinite(yres)
if 'GSKY00' in list(flt[0].header.keys()):
bg_sky = flt[0].header['GSKY00']
else:
bg_sky = 1
gp = GaussianProcess(regr='constant', corr='squared_exponential', theta0=8,
thetaL=7, thetaU=12,
nugget=(yrms/bg_sky)[yok][::1]**2,
random_start=10, verbose=True, normalize=True) #, optimizer='Welch')
#
gp.fit(np.atleast_2d(xmsk[yok][::1]).T, yres[yok][::1]+bg_sky)
y_pred, MSE = gp.predict(np.atleast_2d(xmsk).T, eval_MSE=True)
gp_sigma = np.sqrt(MSE)
resid = threedhst.utils.medfilt(yres, 41)
flt[1].data -= y_pred-bg_sky
flt.flush()
#flt.writeto(flt.filename(), clobber=True)
#plt.plot(yres_sm)
### Make figure
from matplotlib.figure import Figure
from matplotlib.backends.backend_agg import FigureCanvasAgg
fig = Figure(figsize=[6,4], dpi=100)
fig.subplots_adjust(wspace=0.25,hspace=0.02,left=0.15,
bottom=0.08,right=0.97,top=0.92)
ax = fig.add_subplot(111)
ax.set_title(flt.filename())
ax.plot(yres, color='black', alpha=0.3)
ax.plot(y_pred-bg_sky, color='red', linewidth=2, alpha=0.7)
ax.fill_between(xmsk, y_pred-bg_sky + gp_sigma, y_pred-bg_sky - gp_sigma, color='red', alpha=0.3)
ax.set_xlim(0,1014)
ax.set_xlabel('x pix'); ax.set_ylabel('BG residual (e/s)')
fig.tight_layout(pad=0.2)
canvas = FigureCanvasAgg(fig)
canvas.print_figure(flt.filename().split('.fits')[0] + '.column.png', dpi=100, transparent=False)
def helper_read(self, filename, format=None, check_FITS=True, save_FITS=False, *args, **kwargs):
"""
Wrapper around `astropy.table.Table.read() for better
auto-sensing of ascii table formats
"""
if not os.path.exists(filename):
threedhst.showMessage('File %s not found.' %(filename), warn=True)
return False
if format is not None:
data = table_base.read(filename, format=format, *args, **kwargs)
data.input_format = {'format':format}
data.filename = filename
return data
if filename.lower().endswith('.fits'):
format = 'fits'
else:
### Try to read the ".FITS" version first
if check_FITS:
#threedhst.showMessage('read', warn=True)
status = self.load_FITS(filename)
if status:
return status
if format is None:
try:
t = pyfits.open(filename)
format = 'fits'
except:
print 'Try ascii:'
line = open(filename).readline()
if line.strip().startswith('#'):
if line.split()[1].isdigit():
format='ascii.sextractor'
else:
format='ascii.commented_header'
else:
format='ascii.basic'
data = self.read(filename, format=format, *args, **kwargs)
data.input_format = {'format':format}
data.filename = filename
if save_FITS:
#threedhst.showMessage('write', warn=True)
data.write_FITS()
if format == 'fits':
t = pyfits.open(filename)
if t[0].header['EXTEND']:
if 'EXTNAME' in t[1].header:
if t[1].header['EXTNAME'] == 'LDAC_IMHEAD':
hdu = 2
else:
hdu = 1
else:
hdu = 1
else:
hdu = 0
data = self.read(filename, format='fits', hdu=hdu, *args, **kwargs)
data.input_format = {'format':'fits','HDU':hdu}
data.filename = filename
return data
def prep_direct_grism_pair(direct_asn='goodss-34-F140W_asn.fits', grism_asn='goodss-34-G141_asn.fits', radec=None, raw_path='../RAW/', mask_grow=18, scattered_light=False, final_scale=None, skip_direct=False, ACS=False, jump=False, order=2, get_shift=True, align_threshold=20, column_average=True, sky_iter=3, run_acs_lacosmic=False):
"""
Process both the direct and grism observations of a given visit
"""
import threedhst.prep_flt_astrodrizzle as prep
import drizzlepac
from stwcs import updatewcs
import time
t0 = time.time()
#direct_asn='goodss-34-F140W_asn.fits'; grism_asn='goodss-34-G141_asn.fits'; radec=None; raw_path='../RAW/'
#radec = os.getenv('THREEDHST') + '/ASTRODRIZZLE_FLT/Catalog/goodss_radec.dat'
################################
#### Direct image processing
################################
#### xx add astroquery 2MASS/SDSS workaround for radec=None
if not skip_direct:
#### Get fresh FLTS from ../RAW/
asn = threedhst.utils.ASNFile(direct_asn)
if ACS:
for exp in asn.exposures:
print 'cp %s/%s_flc.fits.gz .' %(raw_path, exp)
os.system('cp %s/%s_flc.fits.gz .' %(raw_path, exp))
os.system('gunzip -f %s_flc.fits.gz' %(exp))
if run_acs_lacosmic:
try:
import lacosmicx
status = True
except:
print 'import lacosmicx failed!'
status = False
if status:
im = pyfits.open('%s_flc.fits' %(exp), mode='update')
for ext in [1,2]:
indata = im['SCI',ext].data
#inmask = im['DQ',ext].data > 0
if im['SCI',ext].header['BUNIT'] == 'ELECTRONS':
gain = 1
else:
gain = 1./im[0].header['EXPTIME']
if 'MDRIZSK0' in im['SCI',ext].header:
pssl = im['SCI',ext].header['MDRIZSK0']
else:
pssl = 0.
if 'FLASHLVL' in im[0].header:
pssl += im[0].header['FLASHLVL']
sig_scale = 1.8
else:
sig_scale = 1.
out = lacosmicx.lacosmicx(indata, inmask=None,
sigclip=3.5*sig_scale, sigfrac=0.2,
objlim=7.0, gain=gain,
readnoise=im[0].header['READNSEA'],
satlevel=np.inf, pssl=pssl, niter=5,
sepmed=True, cleantype='meanmask',
fsmode='median', psfmodel='gauss',
psffwhm=2.5,psfsize=7, psfk=None,
psfbeta=4.765, verbose=True)
crmask, cleanarr = out
im['DQ',ext].data |= 16*crmask
### Low pixels
if im[0].header['INSTRUME'] == 'WFC3':
bad = im['SCI',ext].data < -4*im['ERR',ext].data
im['DQ',ext].data |= 16*bad
im.flush()
else:
threedhst.process_grism.fresh_flt_files(direct_asn, from_path=raw_path)
if (not ACS):
#### Subtract WFC3/IR direct backgrounds
prep.subtract_flt_background(root=direct_asn.split('_asn')[0], scattered_light=scattered_light, order=order)
#### Flag IR CRs again within runTweakReg
#### Run TweakReg
if (radec is None) & (not ACS):
print len(asn.exposures)
if len(asn.exposures) > 1:
drizzlepac.astrodrizzle.AstroDrizzle(direct_asn, clean=True, final_scale=None, final_pixfrac=0.8, context=False, final_bits=576, preserve=False, driz_cr_snr='5.0 4.0', driz_cr_scale = '2.5 0.7')
else:
drizzlepac.astrodrizzle.AstroDrizzle(direct_asn, clean=True, final_scale=None, final_pixfrac=1, context=False, final_bits=576, preserve=False, driz_separate=False, driz_sep_wcs=False, median=False, blot=False, driz_cr=False, driz_cr_corr=False, driz_combine=True)
else:
if get_shift:
prep.runTweakReg(asn_file=direct_asn, master_catalog=radec, final_scale=None, ACS=ACS, threshold=align_threshold)
#### Subtract background of direct ACS images
if ACS:
for exp in asn.exposures:
flc = pyfits.open('%s_flc.fits' %(exp), mode='update')
if 'SUB' in flc[0].header['APERTURE']:
extensions = [1]
else:
extensions = [1,4]
for ext in extensions:
threedhst.showMessage('Subtract background from %s_flc.fits[%d] : %.4f' %(exp, ext, flc[ext].header['MDRIZSKY']))
flc[ext].data -= flc[ext].header['MDRIZSKY']
flc[ext].header['MDRIZSK0'] = flc[ext].header['MDRIZSKY']
flc[ext].header['MDRIZSKY'] = 0.
#
flc.flush()
else:
pass
#### Do this later, gives segfaults here???
#prep.subtract_flt_background(root=direct_asn.split('_asn')[0], scattered_light=scattered_light)
#### Flag CRs again on BG-subtracted image
#drizzlepac.astrodrizzle.AstroDrizzle(direct_asn, clean=True, final_scale=None, final_pixfrac=0.8, context=False, final_bits=576, preserve=False, driz_cr_snr='5.0 4.0', driz_cr_scale = '2.5 0.7') # ,
################################
#### Grism image processing
################################
if grism_asn:
asn = threedhst.utils.ASNFile(grism_asn)
if ACS:
for exp in asn.exposures:
print 'cp %s/%s_flc.fits.gz .' %(raw_path, exp)
os.system('cp %s/%s_flc.fits.gz .' %(raw_path, exp))
os.system('gunzip -f %s_flc.fits.gz' %(exp))
updatewcs.updatewcs('%s_flc.fits' %(exp))
prep.copy_adriz_headerlets(direct_asn=direct_asn, grism_asn=grism_asn, ACS=True)
prep.subtract_acs_grism_background(asn_file=grism_asn, final_scale=None)
else:
#### Remove the sky and flag CRs
## with mask from rough zodi-only subtraction
prep.subtract_grism_background(asn_file=grism_asn, PATH_TO_RAW='../RAW/', final_scale=None, visit_sky=True, column_average=False, mask_grow=mask_grow, first_run=True)
## Redo making mask from better combined image
prep.subtract_grism_background(asn_file=grism_asn, PATH_TO_RAW='../RAW/', final_scale=final_scale, visit_sky=True, column_average=column_average, mask_grow=mask_grow, first_run=False, sky_iter=sky_iter)
#### Copy headers from direct images
if radec is not None:
prep.copy_adriz_headerlets(direct_asn=direct_asn, grism_asn=grism_asn, ACS=False)
#### Run CR rejection with final shifts
drizzlepac.astrodrizzle.AstroDrizzle(grism_asn, clean=True, skysub=False, final_wcs=True, final_scale=final_scale, final_pixfrac=0.8, context=False, final_bits=576, driz_sep_bits=576, preserve=False, driz_cr_snr='8.0 5.0', driz_cr_scale='2.5 0.7') # driz_cr_snr='5.0 4.0', driz_cr_scale = '2.5 0.7')
if not grism_asn:
t1 = time.time()
threedhst.showMessage('direct: %s\n\nDone (%d s).' %(direct_asn, int(t1-t0)))
else:
t1 = time.time()
threedhst.showMessage('direct: %s\ngrism: %s\n\nDone (%d s).' %(direct_asn, grism_asn, int(t1-t0)))
def helper_read(self, filename, format=None, check_FITS=True, save_FITS=False, *args, **kwargs):
"""
Wrapper around `astropy.table.Table.read() for better
auto-sensing of ascii table formats
"""
if not os.path.exists(filename):
threedhst.showMessage('File %s not found.' %(filename), warn=True)
return False
if format is not None:
data = table_base.read(filename, format=format, *args, **kwargs)
data.input_format = {'format':format}
data.filename = filename
return data
if filename.lower().endswith('.fits'):
format = 'fits'
else:
### Try to read the ".FITS" version first
if check_FITS:
#threedhst.showMessage('read', warn=True)
status = self.load_FITS(filename)
if status:
return status
if format is None:
try:
t = pyfits.open(filename)
format = 'fits'
except:
print('Try ascii:')
line = open(filename).readline()
if line.strip().startswith('#'):
if line.split()[1].isdigit():
format='ascii.sextractor'
else:
format='ascii.commented_header'
else:
format='ascii.basic'
data = self.read(filename, format=format, *args, **kwargs)
data.input_format = {'format':format}
data.filename = filename
#### Split "APER" columns
if format == 'ascii.sextractor':
combine_columns = ['FLUX_APER', 'FLUXERR_APER', 'MAG_APER', 'MAGERR_APER', 'FLUX_RADIUS']
for base_col in combine_columns:
if '%s2' %(base_col) in data.colnames:
continue
ncol = 0
for col in data.colnames:
if base_col in col:
ncol += 1
if ncol == 0:
continue
dtype = data[base_col].dtype
out = np.zeros((len(data), ncol), dtype=dtype)
for i in range(ncol):
if i == 0:
incol = base_col
else:
incol = '%s_%d' %(base_col, i)
out[:,i] = data[incol]
data.remove_column(incol)
#data.remove_column(base_col)
data.add_column(Column(name=base_col, data=out))
if save_FITS:
#threedhst.showMessage('write', warn=True)
data.write_FITS()
if format == 'fits':
t = pyfits.open(filename)
if t[0].header['EXTEND']:
if 'EXTNAME' in t[1].header:
if t[1].header['EXTNAME'] == 'LDAC_IMHEAD':
hdu = 2
else:
hdu = 1
else:
hdu = 1
else:
hdu = 0
data = self.read(filename, format='fits', hdu=hdu, *args, **kwargs)
data.input_format = {'format':'fits','HDU':hdu}
data.filename = filename
return data
def grism_sky_column_average(asn_file='GDN12-G102_asn.fits',
iter=2,
mask_grow=8):
"""
Remove column-averaged residuals from grism exposures
"""
import scipy.ndimage as nd
import astropy.io.fits as pyfits
asn = threedhst.utils.ASNFile(asn_file)
for k in range(len(asn.exposures)):
#### 1D column averages
flt = pyfits.open('%s_flt.fits' % (asn.exposures[k]), mode='update')
segfile = '%s_flt.seg.fits' % (asn.exposures[k])
seg = pyfits.open(segfile)[0].data
seg_mask = nd.maximum_filter((seg > 0), size=mask_grow) == 0
dq_ok = (flt[3].data & (4 + 32 + 16 + 512 + 2048 + 4096)) == 0
mask = seg_mask & dq_ok & (flt[2].data > 0)
#### Iterative clips on percentile
#mask &= (flt[1].data < np.percentile(flt[1].data[mask], 98)) & (flt[2].data > 0) & (flt[1].data > np.percentile(flt[1].data[mask], 2))
#mask &= (flt[1].data < np.percentile(flt[1].data[mask], 84)) & (flt[2].data > 0) & (flt[1].data > np.percentile(flt[1].data[mask], 16))
residuals = []
for j in range(iter):
masked = flt[1].data * 1
masked[~mask] = np.nan
yres = np.zeros(1014)
for i in range(1014):
# ymsk = mask[:,i]
# yres[i] = np.median(flt[1].data[ymsk,i])
ymsk = masked[:, i]
#ymsk = masked[:,np.maximum(i-10,0):i+10]
#yres[i] = np.median(ymsk[np.isfinite(ymsk)])
ok = np.isfinite(ymsk)
ymsk[(ymsk > np.percentile(ymsk[ok], 84)) |
(ymsk < np.percentile(ymsk[ok], 16))] = np.nan
yres[i] = np.mean(ymsk[np.isfinite(ymsk)])
#
resid = threedhst.utils.medfilt(yres, 41)
#
#resid = np.dot(np.ones((1014,1)), yres_sm.reshape(1,1014))
flt[1].data -= resid
residuals.append(resid * 1)
threedhst.showMessage('Remove column average: %s' % (asn.exposures[k]))
flt.flush()
#flt.writeto(flt.filename(), clobber=True)
#plt.plot(yres_sm)
### Make figure
from matplotlib.figure import Figure
from matplotlib.backends.backend_agg import FigureCanvasAgg
fig = Figure(figsize=[6, 4], dpi=100)
fig.subplots_adjust(wspace=0.25,
hspace=0.02,
left=0.15,
bottom=0.08,
right=0.97,
top=0.92)
ax = fig.add_subplot(111)
ax.set_xlim(0, 1014)
ax.set_title(flt.filename())
ax.plot(yres, color='black')
for resid in residuals:
ax.plot(resid, color='red', linewidth=2, alpha=0.7)
ax.set_xlim(0, 1014)
canvas = FigureCanvasAgg(fig)
canvas.print_figure(flt.filename().split('.fits')[0] + '.column.png',
dpi=100,
transparent=False)
def process_acs_pair(asn_direct_file='ib3706050_asn.fits',
asn_grism_file='ib3706060_asn.fits',
field='COSMOS',
ALIGN_IMAGE='../ACS/h_nz_sect*img.fits',
ALIGN_EXTENSION=0,
SKIP_GRISM=False,
adjust_targname=True,
align_geometry='shift',
PATH_TO_RAW='../RAW',
get_shift=True,
TWEAKSHIFTS_ONLY=False,
FLC=True):
"""
Does the basic processing for ACS F814W and G800L pointings: background subtraction, allignment and drizzlign.
"""
import threedhst
import threedhst.prep_flt_files
from threedhst.prep_flt_files import make_targname_asn
#### Copy corrected FLT files to .
asn = threedhst.utils.ASNFile(asn_direct_file)
for exp in asn.exposures:
print exp
os.system('rm %s_flt.fits' % (exp))
if FLC:
os.system('cp ../RAW/%s_flc.fits %s_flt.fits' % (exp, exp))
else:
os.system('cp ../FIXED/%s_flt.fits . ' % (exp))
#
asn = threedhst.utils.ASNFile(asn_grism_file)
for exp in asn.exposures:
print exp
os.system('rm %s_flt.fits' % (exp))
if FLC:
os.system('cp ../RAW/%s_flc.fits %s_flt.fits' % (exp, exp))
else:
os.system('cp ../FIXED/%s_flt.fits . ' % (exp))
#DIRECT REDUCTION
ROOT_DIRECT = asn_direct_file.split('_asn.fits')[0]
from threedhst.prep_flt_files import make_targname_asn
#this makes new asn.fits files but with ACS the names start with ANY
#must add an optional tag to replace ANY with the field name
if (asn_direct_file is not None) & adjust_targname:
asn_direct_file = make_targname_asn(asn_direct_file,
field=field,
ext='flc')
if (asn_grism_file is not None) & adjust_targname:
asn_grism_file = make_targname_asn(asn_grism_file,
field=field,
ext='flc')
#run = threedhst.prep_flt_files.MultidrizzleRun((asn_direct_file.split('_asn.fits')[0]).upper())
threedhst.shifts.run_tweakshifts(asn_direct_file, verbose=True)
threedhst.prep_flt_files.startMultidrizzle(asn_direct_file,
use_shiftfile=True,
skysub=True,
final_scale=0.05,
pixfrac=1,
driz_cr=True,
updatewcs=True,
clean=True,
median=True)
for i, exp in enumerate(asn.exposures):
asn_mask = asn.exposures[i] + '_flt.fits.mask.reg'
print asn_mask
if os.path.exists(asn_mask):
threedhst.showMessage("Apply ASN mask: %s" % (asn_mask))
threedhst.regions.apply_dq_mask(asn.exposures[i] + '_flt.fits',
extension=3,
mask_file=asn_mask)
threedhst.shifts.refine_shifts(
ROOT_DIRECT=asn_direct_file.split('_as')[0].upper(),
ALIGN_IMAGE=ALIGN_IMAGE,
ALIGN_EXTENSION=ALIGN_EXTENSION,
fitgeometry=align_geometry,
clean=True)
unicorn.go_acs.testing_f814w_background(asn_direct_file)
SCALE = 0.06
PIXFRAC = 1.0
threedhst.prep_flt_files.startMultidrizzle(asn_direct_file,
use_shiftfile=True,
skysub=True,
final_scale=SCALE,
pixfrac=PIXFRAC,
driz_cr=False,
updatewcs=True,
clean=True,
median=False)
#GRISM REDUCTION
threedhst.shifts.make_grism_shiftfile(asn_direct_file, asn_grism_file)
threedhst.prep_flt_files.startMultidrizzle(asn_grism_file,
use_shiftfile=True,
skysub=True,
final_scale=SCALE,
pixfrac=PIXFRAC,
driz_cr=True,
updatewcs=True,
clean=False,
median=True)
unicorn.go_acs.testing_g800l_background(asn_grism_file)
threedhst.prep_flt_files.startMultidrizzle(asn_grism_file,
use_shiftfile=True,
skysub=True,
final_scale=SCALE,
pixfrac=PIXFRAC,
driz_cr=True,
updatewcs=True,
clean=False,
median=True)
def subtract_flt_background(root='GOODN-N1-VBA-F105W', scattered_light=False, sex_background=False, order=2):
"""
Subtract polynomial background
"""
import scipy.optimize
import astropy.units as u
from astropy.table import Table as table
import stwcs
from stwcs import updatewcs
import drizzlepac
from drizzlepac import astrodrizzle, tweakreg, tweakback
import threedhst
asn = threedhst.utils.ASNFile(root+'_asn.fits')
for exp in asn.exposures:
updatewcs.updatewcs('%s_%s.fits' %(exp, 'flt'))
if not os.path.exists('%s_drz_sci.fits' %(root)):
if len(asn.exposures) == 1:
drizzlepac.astrodrizzle.AstroDrizzle(root+'_asn.fits', clean=False, context=False, preserve=False, skysub=True, driz_separate=False, driz_sep_wcs=False, median=False, blot=False, driz_cr=False, driz_cr_corr=False, driz_combine=True)
else:
drizzlepac.astrodrizzle.AstroDrizzle(root+'_asn.fits', clean=False, context=False, preserve=False, skysub=True, driz_separate=True, driz_sep_wcs=True, median=True, blot=True, driz_cr=True, driz_cr_corr=True, driz_combine=True)
se = threedhst.sex.SExtractor()
se.options['WEIGHT_IMAGE'] = '%s_drz_wht.fits' %(root)
se.options['WEIGHT_TYPE'] = 'MAP_WEIGHT'
se.options['CHECKIMAGE_TYPE'] = 'SEGMENTATION,BACKGROUND'
se.options['CHECKIMAGE_NAME'] = '%s_drz_seg.fits,%s_drz_bkg.fits' %(root, root)
se.options['BACK_TYPE'] = 'AUTO'
se.options['BACK_SIZE'] = '256'
#
se.params['X_IMAGE'] = True; se.params['Y_IMAGE'] = True
se.params['MAG_AUTO'] = True
#
se.options['CATALOG_NAME'] = '%s_drz_sci.cat' %(root)
se.options['FILTER'] = 'Y'
se.copyConvFile()
se.options['FILTER_NAME'] = 'gauss_4.0_7x7.conv'
se.options['DETECT_THRESH'] = '0.8'
se.options['ANALYSIS_THRESH'] = '0.8'
#
se.options['MEMORY_OBJSTACK'] = '30000'
se.options['MEMORY_PIXSTACK'] = '3000000'
se.options['MEMORY_BUFSIZE'] = '2048'
se.sextractImage('%s_drz_sci.fits' %(root))
#threedhst.sex.sexcatRegions('%s_flt.cat' %(exp), '%s_flt.reg' %(exp), format=1)
#### Blot segmentation map to FLT images for object mask
asn = threedhst.utils.ASNFile('%s_asn.fits' %(root))
#print 'Read files...'
ref = pyfits.open('%s_drz_sci.fits' %(root))
ref_wcs = stwcs.wcsutil.HSTWCS(ref, ext=0)
seg = pyfits.open('%s_drz_seg.fits' %(root))
#### Fill ref[0].data with zeros for seg mask
#seg_data = ref[0].data
#seg_data[seg[0].data == 0] = 0
seg_data = np.cast[np.float32](seg[0].data)
bkg_data = pyfits.open('%s_drz_bkg.fits' %(root))[0].data
yi, xi = np.indices((1014,1014))
if scattered_light:
bg_components = np.ones((4,1014,1014))
bg_components[1,:,:] = xi/1014.*2
bg_components[2,:,:] = yi/1014.*2
bg_components[3,:,:] = pyfits.open(os.getenv('THREEDHST') + '/CONF/G141_scattered_light.fits')[0].data
#### Use flat-field itself for images affected by full-field
#### persistence from the tungsten lamp
if scattered_light == 2:
bg_components[3,:,:] = pyfits.open(os.getenv('iref') + 'flat_UDF_F140W_v0.fits')[1].data[5:-5,5:-5]
NCOMP=4
else:
# bg_components = np.ones((3,1014,1014))
# bg_components[1,:,:] = xi/1014.*2
# bg_components[2,:,:] = yi/1014.*2
# NCOMP=3
#
if order == 2:
NCOMP=6
bg_components = np.ones((NCOMP,1014,1014))
bg_components[1,:,:] = (xi-507)/507.
bg_components[2,:,:] = (yi-507)/507.
bg_components[3,:,:] = ((xi-507)/507.)**2
bg_components[4,:,:] = ((yi-507)/507.)**2
bg_components[5,:,:] = (xi-507)*(yi-507)/507.**2
else:
NCOMP=3
bg_components = np.ones((NCOMP,1014,1014))
bg_components[1,:,:] = (xi-507)/507.
bg_components[2,:,:] = (yi-507)/507.
bg_flat = bg_components.reshape((NCOMP,1014**2))
#### Loop through FLTs, blotting reference and segmentation
models = []
for exp in asn.exposures:
flt = pyfits.open('%s_flt.fits' %(exp)) #, mode='update')
flt_wcs = stwcs.wcsutil.HSTWCS(flt, ext=1)
### segmentation
print 'Segmentation image: %s_blot.fits' %(exp)
blotted_seg = astrodrizzle.ablot.do_blot(seg_data, ref_wcs, flt_wcs, 1, coeffs=True, interp='nearest', sinscl=1.0, stepsize=10, wcsmap=None)
blotted_bkg = 0.
if sex_background:
blotted_bkg = astrodrizzle.ablot.do_blot(bkg_data, ref_wcs, flt_wcs, 1, coeffs=True, interp='nearest', sinscl=1.0, stepsize=10, wcsmap=None)
flt[1].data -= blotted_bkg
mask = (blotted_seg == 0) & (flt['DQ'].data == 0) & (flt[1].data > -1) & (xi > 10) & (yi > 10) & (xi < 1004) & (yi < 1004)
mask &= (flt[1].data < 5*np.median(flt[1].data[mask]))
data_range = np.percentile(flt[1].data[mask], [2.5, 97.5])
mask &= (flt[1].data >= data_range[0]) & (flt[1].data <= data_range[1])
data_range = np.percentile(flt[2].data[mask], [0.5, 99.5])
mask &= (flt[2].data >= data_range[0]) & (flt[2].data <= data_range[1])
### Least-sq fit for component normalizations
data = flt[1].data[mask].flatten()
wht = (1./flt[2].data[mask].flatten())**2
templates = bg_flat[:, mask.flatten()]
p0 = np.zeros(NCOMP)
p0[0] = np.median(data)
obj_fun = threedhst.grism_sky.obj_lstsq
popt = scipy.optimize.leastsq(obj_fun, p0, args=(data, templates, wht), full_output=True, ftol=1.49e-8/1000., xtol=1.49e-8/1000.)
xcoeff = popt[0]
model = np.dot(xcoeff, bg_flat).reshape((1014,1014))
models.append(model)
# add header keywords of the fit components
flt = pyfits.open('%s_flt.fits' %(exp), mode='update')
flt[1].data -= model+blotted_bkg
for i in range(NCOMP):
if 'BGCOMP%d' %(i+1) in flt[0].header:
flt[0].header['BGCOMP%d' %(i+1)] += xcoeff[i]
else:
flt[0].header['BGCOMP%d' %(i+1)] = xcoeff[i]
flt.flush()
coeff_str = ' '.join(['%.4f' %c for c in xcoeff])
threedhst.showMessage('Background subtraction, %s_flt.fits:\n\n %s' %(exp, coeff_str))
return models
def swarpImage(self,inputImage, mode='direct', verbose=True):
"""
swarpImage(self,inputImage,mode='waiterror', verbose=True)
Writes configuration files and runs swarp on the input image
mode can be:
* 'waiterror': waits for swarp to finish, and raises an
SExtractorError if it does not complete sucessfully. stdout
and sterr are saved to self.lastout and self.lasterr (returns 0)
* 'wait': waits for swarp to finish and returns the return code
stdout and sterr are saved to self.lastout and self.lasterr
* 'proc': stars the processes but does not wait - returns the Popen
instance of the processes
"""
from subprocess import Popen, PIPE
from os.path import exists
self.swarpInputImage = inputImage
fnbase = self.name
if not self.overwrite:
fnbase = fnbase.replace('.swarp','')
if exists(fnbase+'.swarp'):
fns = fnbase.split('-')
try:
i = int(fns[-1])
i+=1
except ValueError:
i = 2
if len(fns)<2:
fns.append(str(i))
else:
fns[-1] = str(i)
fnbase = '-'.join(fns)
self.name = fnbase
self._saveFiles(fnbase)
if isinstance(inputImage,list):
imgList = ' '.join(inputImage)
else:
imgList = inputImage
clstr = 'swarp %s -c %s' %(imgList,self.name+'.swarp')
#print "\n3DHST.sex.swarp.swarpImage:\n\n %s\n" %clstr
if verbose:
threedhst.showMessage('Running swarp: %s' %clstr)
if mode == 'waiterror' or mode =='wait':
# proc = Popen(clstr.split(),
# executable='swarp',stdout=PIPE,stderr=PIPE)
#### Send STDERR output to temporary file because
#### swarp seems to spawn additional processed for large files
#### and proc.wait() never finishes
fp = open('sex_stderr','w')
proc = Popen(clstr.split(),executable='swarp', stdout=PIPE,
stderr=fp)
res = proc.wait()
fp.close()
if verbose:
print 'Done.\n'
sout, serr = proc.communicate()
## Read stderr output
fp = open('sex_stderr','r')
serr = ' '.join(fp.readlines())
fp.close()
self.lastout = sout
self.lasterr = serr
if res!=0 and mode == 'waiterror' :
raise SError(serr,sout)
return res
elif mode == 'proc':
return proc
elif mode == 'direct':
proc = Popen(clstr.split()) #,executable='swarp' #,stdout=PIPE,stderr=PIPE)
res = proc.wait()
else:
raise ValueError('unrecognized mode argument '+str(mode))
def drzTweakReg(sci='goodss-34-F140W_drz_sci.fits', master_catalog='goodss_radec.dat', threshold=20, apply=True):
import drizzlepac
from drizzlepac import tweakback
from stwcs import updatewcs
from threedhst import catIO
se = threedhst.sex.SExtractor()
se.options['WEIGHT_IMAGE'] = sci.replace('sci','wht')
se.options['WEIGHT_TYPE'] = 'MAP_WEIGHT'
#
se.params['X_IMAGE'] = True; se.params['Y_IMAGE'] = True
se.params['X_WORLD'] = True; se.params['Y_WORLD'] = True
se.params['MAG_AUTO'] = True
#
se.options['CATALOG_NAME'] = sci+'.align.cat'
se.options['FILTER'] = 'N'
se.options['DETECT_THRESH'] = '%f' %(threshold)
se.options['ANALYSIS_THRESH'] = '%f' %(threshold)
#
se.sextractImage(sci)
threedhst.sex.sexcatRegions(sci+'.align.cat', sci+'.align.reg', format=2)
c = catIO.Table(sci+'.align.cat', format='ascii.sextractor')
#c.ra = c['X_WORLD']
#c.dec = c['Y_WORLD']
m = catIO.CoordinateMatcher(c, ra_column='X_WORLD', dec_column='Y_WORLD')
r0, d0 = np.loadtxt(master_catalog, unpack=True)
### clip list to nearby objects
rmed, dmed = np.median(c['X_WORLD']), np.median(c['Y_WORLD'])
delta = np.sqrt((r0-rmed)**2/np.cos(dmed/180*np.pi)**2+(d0-dmed)**2)*60.
nearby = delta < 8 # arcmin
r0, d0 = r0[nearby], d0[nearby]
dr, idx = m.match_list(r0, d0)
dx = (c['X_WORLD'][idx]-r0)*np.cos(d0/180*np.pi)*3600
dy = (c['Y_WORLD'][idx]-d0)*3600
x0 = (c['X_WORLD'][idx]-np.median(c['X_WORLD']))*np.cos(d0/180*np.pi)*3600
y0 = (c['Y_WORLD'][idx]-np.median(c['Y_WORLD']))*3600
ok = dr < 1.5
if ok.sum() == 0:
threedhst.showMessage('No matches found within 1.5".')
return False
# plt.scatter(x0[ok], y0[ok], color='black')
# for i in np.arange(len(ok))[ok]:
# plt.plot(x0[i]+np.array([0, dx[i]*20]), y0[i]+np.array([0, dy[i]*20]), color='black')
dra = (c['X_WORLD'][idx]-r0)
dde = (c['Y_WORLD'][idx]-d0)
rshift, dshift = np.median(dra[ok]), np.median(dde[ok])
fp = open(sci.split('.fits')[0]+'.align.dat','w')
lines = ['# dx dy xrms yrms N\n# %s %s\n' %(sci, master_catalog), '%f %f %f %f %d\n' %(np.median(dx[ok]), np.median(dy[ok]), np.std(dx[ok]), np.std(dy[ok]), ok.sum())]
fp.writelines(lines)
fp.close()
threedhst.showMessage(''.join(lines))
if not apply:
print 'Not applying shift. Re-run with apply=true to apply them.'
return rshift, dshift
for fits in [sci.replace('sci','wht'), sci]:
print 'Update WCS: %s' %(fits)
im = pyfits.open(fits, mode='update')
im[0].header['CRVAL1'] -= rshift
im[0].header['CRVAL2'] -= dshift
im.flush()
im = pyfits.open(sci)
for i in range(im[0].header['NDRIZIM']):
flt_im = im[0].header['D%03dDATA' %(i+1)].split('[')[0]
print 'Update WCS: %s' %(flt_im)
flt = pyfits.open(flt_im, mode='update')
for ext in [1,2]:
flt[ext].header['CRVAL1'] -= rshift
flt[ext].header['CRVAL2'] -= dshift
flt.flush()
def subtract_flt_background(root='GOODN-N1-VBA-F105W',
scattered_light=False,
sex_background=False,
order=2):
"""
Subtract polynomial background
"""
import scipy.optimize
import astropy.units as u
from astropy.table import Table as table
import stwcs
from stwcs import updatewcs
import drizzlepac
from drizzlepac import astrodrizzle, tweakreg, tweakback
import threedhst
asn = threedhst.utils.ASNFile(root + '_asn.fits')
for exp in asn.exposures:
updatewcs.updatewcs('%s_%s.fits' % (exp, 'flt'))
if not os.path.exists('%s_drz_sci.fits' % (root)):
if len(asn.exposures) == 1:
drizzlepac.astrodrizzle.AstroDrizzle(root + '_asn.fits',
clean=False,
context=False,
preserve=False,
skysub=True,
driz_separate=False,
driz_sep_wcs=False,
median=False,
blot=False,
driz_cr=False,
driz_cr_corr=False,
driz_combine=True)
else:
drizzlepac.astrodrizzle.AstroDrizzle(root + '_asn.fits',
clean=False,
context=False,
preserve=False,
skysub=True,
driz_separate=True,
driz_sep_wcs=True,
median=True,
blot=True,
driz_cr=True,
driz_cr_corr=True,
driz_combine=True)
se = threedhst.sex.SExtractor()
se.options['WEIGHT_IMAGE'] = '%s_drz_wht.fits' % (root)
se.options['WEIGHT_TYPE'] = 'MAP_WEIGHT'
se.options['CHECKIMAGE_TYPE'] = 'SEGMENTATION,BACKGROUND'
se.options['CHECKIMAGE_NAME'] = '%s_drz_seg.fits,%s_drz_bkg.fits' % (root,
root)
se.options['BACK_TYPE'] = 'AUTO'
se.options['BACK_SIZE'] = '256'
#
se.params['X_IMAGE'] = True
se.params['Y_IMAGE'] = True
se.params['MAG_AUTO'] = True
#
se.options['CATALOG_NAME'] = '%s_drz_sci.cat' % (root)
se.options['FILTER'] = 'Y'
se.copyConvFile()
se.options['FILTER_NAME'] = 'gauss_4.0_7x7.conv'
se.options['DETECT_THRESH'] = '0.8'
se.options['ANALYSIS_THRESH'] = '0.8'
#
se.options['MEMORY_OBJSTACK'] = '30000'
se.options['MEMORY_PIXSTACK'] = '3000000'
se.options['MEMORY_BUFSIZE'] = '2048'
se.sextractImage('%s_drz_sci.fits' % (root))
#threedhst.sex.sexcatRegions('%s_flt.cat' %(exp), '%s_flt.reg' %(exp), format=1)
#### Blot segmentation map to FLT images for object mask
asn = threedhst.utils.ASNFile('%s_asn.fits' % (root))
#print 'Read files...'
ref = pyfits.open('%s_drz_sci.fits' % (root))
ref_wcs = stwcs.wcsutil.HSTWCS(ref, ext=0)
seg = pyfits.open('%s_drz_seg.fits' % (root))
#### Fill ref[0].data with zeros for seg mask
#seg_data = ref[0].data
#seg_data[seg[0].data == 0] = 0
seg_data = np.cast[np.float32](seg[0].data)
bkg_data = pyfits.open('%s_drz_bkg.fits' % (root))[0].data
yi, xi = np.indices((1014, 1014))
if scattered_light:
bg_components = np.ones((4, 1014, 1014))
bg_components[1, :, :] = xi / 1014. * 2
bg_components[2, :, :] = yi / 1014. * 2
bg_components[3, :, :] = pyfits.open(
os.getenv('THREEDHST') + '/CONF/G141_scattered_light.fits')[0].data
#### Use flat-field itself for images affected by full-field
#### persistence from the tungsten lamp
if scattered_light == 2:
bg_components[3, :, :] = pyfits.open(
os.getenv('iref') + 'flat_UDF_F140W_v0.fits')[1].data[5:-5,
5:-5]
NCOMP = 4
else:
# bg_components = np.ones((3,1014,1014))
# bg_components[1,:,:] = xi/1014.*2
# bg_components[2,:,:] = yi/1014.*2
# NCOMP=3
#
if order == 2:
NCOMP = 6
bg_components = np.ones((NCOMP, 1014, 1014))
bg_components[1, :, :] = (xi - 507) / 507.
bg_components[2, :, :] = (yi - 507) / 507.
bg_components[3, :, :] = ((xi - 507) / 507.)**2
bg_components[4, :, :] = ((yi - 507) / 507.)**2
bg_components[5, :, :] = (xi - 507) * (yi - 507) / 507.**2
else:
NCOMP = 3
bg_components = np.ones((NCOMP, 1014, 1014))
bg_components[1, :, :] = (xi - 507) / 507.
bg_components[2, :, :] = (yi - 507) / 507.
bg_flat = bg_components.reshape((NCOMP, 1014**2))
#### Loop through FLTs, blotting reference and segmentation
models = []
for exp in asn.exposures:
flt = pyfits.open('%s_flt.fits' % (exp)) #, mode='update')
flt_wcs = stwcs.wcsutil.HSTWCS(flt, ext=1)
### segmentation
print('Segmentation image: %s_blot.fits' % (exp))
blotted_seg = astrodrizzle.ablot.do_blot(seg_data + 0,
ref_wcs,
flt_wcs,
1,
coeffs=True,
interp='nearest',
sinscl=1.0,
stepsize=10,
wcsmap=None)
blotted_bkg = 0.
if sex_background:
blotted_bkg = astrodrizzle.ablot.do_blot(bkg_data + 0,
ref_wcs,
flt_wcs,
1,
coeffs=True,
interp='nearest',
sinscl=1.0,
stepsize=10,
wcsmap=None)
flt[1].data -= blotted_bkg
mask = (blotted_seg == 0) & (flt['DQ'].data
== 0) & (flt[1].data > -1) & (xi > 10) & (
yi > 10) & (xi < 1004) & (yi < 1004)
mask &= np.isfinite(flt[1].data) & np.isfinite(flt[2].data)
mask &= (flt[1].data < 5 * np.median(flt[1].data[mask]))
data_range = np.percentile(flt[1].data[mask], [2.5, 97.5])
mask &= (flt[1].data >= data_range[0]) & (flt[1].data <= data_range[1])
data_range = np.percentile(flt[2].data[mask], [0.5, 99.5])
mask &= (flt[2].data >= data_range[0]) & (flt[2].data <= data_range[1])
### Least-sq fit for component normalizations
data = flt[1].data[mask].flatten()
wht = (1. / flt[2].data[mask].flatten())**2
templates = bg_flat[:, mask.flatten()]
p0 = np.zeros(NCOMP)
p0[0] = np.median(data)
obj_fun = threedhst.grism_sky.obj_lstsq
print('XXX: %d' % (mask.sum()))
popt = scipy.optimize.leastsq(obj_fun,
p0,
args=(data, templates, wht),
full_output=True,
ftol=1.49e-8 / 1000.,
xtol=1.49e-8 / 1000.)
xcoeff = popt[0]
model = np.dot(xcoeff, bg_flat).reshape((1014, 1014))
models.append(model)
# add header keywords of the fit components
flt = pyfits.open('%s_flt.fits' % (exp), mode='update')
flt[1].data -= model + blotted_bkg
for i in range(NCOMP):
if 'BGCOMP%d' % (i + 1) in flt[0].header:
flt[0].header['BGCOMP%d' % (i + 1)] += xcoeff[i]
else:
flt[0].header['BGCOMP%d' % (i + 1)] = xcoeff[i]
flt.flush()
coeff_str = ' '.join(['%.4f' % c for c in xcoeff])
threedhst.showMessage('Background subtraction, %s_flt.fits:\n\n %s' %
(exp, coeff_str))
return models
def subtract_grism_background(asn_file='GDN1-G102_asn.fits', PATH_TO_RAW='../RAW/', final_scale=0.06, visit_sky=True, column_average=True, mask_grow=18, first_run=True, sky_iter=1):
"""
Subtract master grism sky from FLTs
"""
import os
import scipy.ndimage as nd
import pyregion
from drizzlepac import astrodrizzle
import drizzlepac
from stwcs import updatewcs
import stwcs
import threedhst.grism_sky as bg
asn = threedhst.utils.ASNFile(asn_file)
root = asn_file.split('_asn')[0]
sky_images = {'G141':['zodi_G141_clean.fits', 'excess_lo_G141_clean.fits', 'G141_scattered_light.fits'],
'G102':['zodi_G102_clean.fits', 'excess_G102_clean.fits']}
#
# sky_images = {'G141':['zodi_G141_clean.fits', 'excess_lo_G141_clean.fits', 'G141_scattered_light_v2.fits'],
# 'G102':['zodi_G102_clean.fits', 'excess_G102_clean.fits']}
# ### Don't use scattered light
# sky_images = {'G141':['zodi_G141_clean.fits', 'excess_lo_G141_clean.fits'],
# 'G102':['zodi_G102_clean.fits', 'excess_G102_clean.fits']}
#
# ## Use aXe images
# sky_images = {'G141':['WFC3.IR.G141.sky.V1.0.flat.fits', 'WFC3.IR.G141.sky.V1.0.flat.fits'],
# 'G102':['zodi_G102_clean.fits', 'excess_G102_clean.fits']}
if first_run:
### Rough background subtraction
threedhst.process_grism.fresh_flt_files(asn_file, from_path=PATH_TO_RAW, preserve_dq=False)
flt = pyfits.open('%s_flt.fits' %(asn.exposures[0]))
GRISM = flt[0].header['FILTER']
bg.set_grism_flat(grism=GRISM, verbose=True)
zodi = pyfits.open(os.getenv('THREEDHST')+'/CONF/%s' %(sky_images[GRISM][0]))[0].data
for exp in asn.exposures:
updatewcs.updatewcs('%s_flt.fits' %(exp))
flt = pyfits.open('%s_flt.fits' %(exp), mode='update')
#flt = pyfits.open('%s_flt.fits' %(exp))
flt[1].data *= bg.flat
#
mask = (flt['DQ'].data == 0)
data_range = np.percentile(flt[1].data[mask], [20, 80])
mask &= (flt[1].data >= data_range[0]) & (flt[1].data <= data_range[1]) & (flt[2].data != 0) & np.isfinite(flt[1].data) & np.isfinite(flt[2].data)
### Least-sq fit for component normalizations
data = flt[1].data[mask].flatten()
wht = (1./flt[2].data[mask].flatten())**2
zodi_mask = zodi[mask].flatten()
coeff_zodi = np.sum(data*zodi_mask*wht)/np.sum(zodi_mask**2*wht)
flt[1].data -= zodi*coeff_zodi
flt.flush()
threedhst.showMessage('Rough background for %s (zodi): %0.4f' %(exp, coeff_zodi))
#templates = bg_flat[:, mask.flatten()]
### Run astrodrizzle to make DRZ mosaic, grism-SExtractor mask
drizzlepac.astrodrizzle.AstroDrizzle(asn_file, clean=True, context=False, preserve=False, skysub=True, driz_separate=True, driz_sep_wcs=True, median=True, blot=True, driz_cr=True, driz_combine=True, final_wcs=False, resetbits=4096, final_bits=576, driz_sep_bits=576, driz_cr_snr='8.0 5.0', driz_cr_scale = '2.5 0.7')
else:
flt = pyfits.open('%s_flt.fits' %(asn.exposures[0]))
GRISM = flt[0].header['FILTER']
bg.set_grism_flat(grism=GRISM, verbose=True)
se = threedhst.sex.SExtractor()
se.options['WEIGHT_IMAGE'] = '%s_drz_wht.fits' %(root)
se.options['WEIGHT_TYPE'] = 'MAP_WEIGHT'
se.options['CHECKIMAGE_TYPE'] = 'SEGMENTATION'
se.options['CHECKIMAGE_NAME'] = '%s_drz_seg.fits' %(root)
#
se.params['X_IMAGE'] = True; se.params['Y_IMAGE'] = True
se.params['MAG_AUTO'] = True
#
se.options['CATALOG_NAME'] = '%s_drz_sci.cat' %(root)
se.options['FILTER'] = 'Y'
se.copyConvFile(grism=True)
se.options['FILTER_NAME'] = 'grism.conv'
se.options['DETECT_THRESH'] = '0.7'
se.options['ANALYSIS_THRESH'] = '0.7'
#
se.sextractImage('%s_drz_sci.fits' %(root))
#### Blot segmentation map to FLT images for object mask
ref = pyfits.open('%s_drz_sci.fits' %(root))
ref_wcs = stwcs.wcsutil.HSTWCS(ref, ext=0)
seg = pyfits.open('%s_drz_seg.fits' %(root))
seg_data = np.cast[np.float32](seg[0].data)
#### Loop through FLTs, blotting reference and segmentation
threedhst.showMessage('%s: Blotting grism segmentation masks.' %(root))
for exp in asn.exposures:
flt = pyfits.open('%s_flt.fits' %(exp))
flt_wcs = stwcs.wcsutil.HSTWCS(flt, ext=1)
### segmentation
#print 'Segmentation image: %s_blot.fits' %(exp)
blotted_seg = astrodrizzle.ablot.do_blot(seg_data, ref_wcs, flt_wcs, 1, coeffs=True, interp='nearest', sinscl=1.0, stepsize=10, wcsmap=None)
seg_grow = nd.maximum_filter((blotted_seg > 0)*1, size=8)
pyfits.writeto('%s_flt.seg.fits' %(exp), header=flt[1].header, data=seg_grow, clobber=True)
if first_run:
### Run background subtraction scripts
threedhst.process_grism.fresh_flt_files(asn_file, from_path=PATH_TO_RAW, preserve_dq=False)
for exp in asn.exposures:
updatewcs.updatewcs('%s_flt.fits' %(exp))
#threedhst.grism_sky.remove_grism_sky(flt=exp+'_flt.fits', list=sky_images[GRISM], path_to_sky=os.getenv('THREEDHST')+'/CONF/', verbose=True, second_pass=True, overall=True)
if visit_sky:
threedhst.grism_sky.remove_visit_sky(asn_file=asn_file, list=sky_images[GRISM], add_constant=False, column_average=(column_average) & (sky_iter == 1), mask_grow=mask_grow, flat_correct=first_run)
if (sky_iter > 1) & (~first_run):
for i in range(1, sky_iter):
threedhst.grism_sky.remove_visit_sky(asn_file=asn_file, list=sky_images[GRISM], add_constant=False, column_average=column_average & (i == (sky_iter-1)), mask_grow=mask_grow, flat_correct=False)
else:
for exp in asn.exposures:
threedhst.grism_sky.remove_grism_sky(flt='%s_flt.fits' %(exp), list=sky_images[GRISM], path_to_sky = os.getenv('THREEDHST')+'/CONF/', out_path='./', verbose=False, plot=False, flat_correct=first_run, sky_subtract=True, second_pass=column_average, overall=True, combine_skies=False, sky_components=True, add_constant=False)
### Astrodrizzle again to reflag CRs and make cleaned mosaic
drizzlepac.astrodrizzle.AstroDrizzle(asn_file, clean=True, skysub=False, skyuser='******', final_wcs=True, final_scale=final_scale, final_pixfrac=0.8, context=False, resetbits=4096, final_bits=576, driz_sep_bits=576, preserve=False, driz_cr_snr='8.0 5.0', driz_cr_scale='2.5 0.7') # , final_wcs=True, final_rot=0)
def get_vizier_cat(image='RXJ2248-IR_sci.fits', ext=0, catalog="II/246"):
"""
Get a list of RA/Dec coords from a Vizier catalog that can be used
for WCS alignment.
`catalog` is any catalog ID recognized by Vizier, e.g.:
"II/328/allwise": WISE
"II/246": 2MASS
"V/139": SDSS DR9
"""
import threedhst.dq
import astropy.wcs as pywcs
from astropy.table import Table as table
import astropy.io.fits as pyfits
import astroquery
from astroquery.vizier import Vizier
import astropy.coordinates as coord
import astropy.units as u
im = pyfits.open(image)
wcs = pywcs.WCS(im[ext].header)
#wcs = pywcs.WCS(pyfits.getheader('Q0821+3107-F140W_drz.fits', 1))
Vizier.ROW_LIMIT = -1
r0, d0 = wcs.wcs_pix2world([[im[ext].header['NAXIS1']/2., im[ext].header['NAXIS2']/2.]], 1)[0]
foot = wcs.calc_footprint()
corner_radius = np.sqrt((foot[:,0]-r0)**2/np.cos(d0/360.*2*np.pi)**2 + (foot[:,1]-d0)**2).max()*60*1.1
try:
c = coord.ICRS(ra=r0, dec=d0, unit=(u.deg, u.deg))
except:
c = coord.ICRSCoordinates(ra=r0, dec=d0, unit=(u.deg, u.deg))
#### something with astropy.coordinates
# c.icrs.ra.degree = c.icrs.ra.degrees
# c.icrs.dec.degree = c.icrs.dec.degrees
#
vt = Vizier.query_region(c, radius=u.Quantity(corner_radius, u.arcminute), catalog=[catalog])
if not vt:
threedhst.showMessage('No matches found in Vizier %s @ (%.6f, %.6f).\n\nhttp://vizier.u-strasbg.fr/viz-bin/VizieR?-c=%.6f+%.6f&-c.rs=8' %(catalog, r0, d0, r0, d0), warn=True)
return False
vt = vt[0]
#### Make a region file
ra_list, dec_list = vt['RAJ2000'], vt['DEJ2000']
print 'Vizier, found %d objects in %s.' %(len(ra_list), catalog)
fp = open('%s.vizier.radec' %(image.split('.fits')[0]), 'w')
fpr = open('%s.vizier.reg' %(image.split('.fits')[0]), 'w')
fp.write('# %s, r=%.1f\'\n' %(catalog, corner_radius))
fpr.write('# %s, r=%.1f\'\nfk5\n' %(catalog, corner_radius))
for ra, dec in zip(ra_list, dec_list):
fp.write('%.7f %.7f\n' %(ra, dec))
fpr.write('circle(%.6f, %.6f, 0.5")\n' %(ra, dec))
fpr.close()
fp.close()
return True
def refine_shifts(ROOT_DIRECT='f160w',
ALIGN_IMAGE='../../ACS/h_sz*drz_img.fits',
fitgeometry='shift',
clean=True,
ALIGN_EXTENSION=0,
shift_params=None,
toler=3,
maxtoler=5,
align_sdss_ds9=False,
verbose=False):
"""
refine_shifts(ROOT_DIRECT='f160w',
ALIGN_IMAGE='../../ACS/h_sz*drz_img.fits',
fitgeometry='shift', clean=True)
Refine shifts by catalog matching an input multidrizzle image,
ROOT_DIRECT+'_drz.fits' to one or more alignment images
"""
run = threedhst.prep_flt_files.MultidrizzleRun(ROOT_DIRECT.upper())
## radius for match is 2**toler. Make it larger if fit comes out bad
#toler, maxtoler = 3, 5
iter, MAXIT = 0, 5
xrms, yrms = 100, 100
if shift_params is not None:
xshift, yshift, rot, scale = shift_params
threedhst.showMessage('Using specified DRZ-frame shifts: %f %f %f %f' %
(xshift, yshift, rot, scale))
else:
threedhst.showMessage('Aligning WCS to %s (%s)' %
(threedhst.options['ALIGN_IMAGE'], fitgeometry))
while ((xrms > 1) | (yrms > 1)) & (toler <= maxtoler) & (iter < MAXIT):
iter = iter + 1
xshift, yshift, rot, scale, xrms, yrms = threedhst.shifts.align_to_reference(
ROOT_DIRECT,
ALIGN_IMAGE,
fitgeometry=fitgeometry,
clean=clean,
ALIGN_EXTENSION=ALIGN_EXTENSION,
toler=toler,
skip_swarp=(toler > 3),
align_sdss_ds9=align_sdss_ds9,
verbose=verbose)
toler += 1
#### shifts measured in DRZ frame. Translate to FLT frame
drz = pyfits.open(ROOT_DIRECT + '_drz.fits')
#alpha = (180.-drz[1].header['PA_APER'])/360.*2*np.pi
#### Get reference angle from first image in the ASN file
asn = threedhst.utils.ASNFile(ROOT_DIRECT + '_asn.fits')
alpha = (180. - pyfits.getheader(asn.exposures[0] + '_flt.fits',
1)['PA_APER']) / 360. * 2 * np.pi
xsh = (xshift * np.cos(alpha) - yshift * np.sin(alpha)) * np.float(run.scl)
ysh = (xshift * np.sin(alpha) + yshift * np.cos(alpha)) * np.float(run.scl)
print 'Final shift:', xsh, ysh, drz[1].header['PA_APER']
fp = open(ROOT_DIRECT + '_align.info', 'w')
fp.write('%s %8.3f %8.3f %8.3f\n' % (ALIGN_IMAGE, xsh, ysh, rot))
fp.close()
#### Read the shiftfile
shiftF = threedhst.shifts.ShiftFile(ROOT_DIRECT + '_shifts.txt')
#### Apply the alignment shifts to the shiftfile
shiftF.xshift = list(np.array(shiftF.xshift) - xsh)
shiftF.yshift = list(np.array(shiftF.yshift) - ysh)
shiftF.rotate = list((np.array(shiftF.rotate) + rot) % 360)
shiftF.scale = list(np.array(shiftF.scale) * scale)
shiftF.write(ROOT_DIRECT + '_shifts.txt')
def __init__(self, infile='files.info', force_lowercase = True,
comment_char='#', verbose=False, save_fits = True):
self.filename = infile
self.verbose = verbose
#### Load the FITS version of the catalog, if it exists
status = self.load_fits()
if status:
return None
self.comment_char = comment_char
#### read the lines of the file
fp = open(infile,'r')
lines = fp.readlines()
fp.close()
if len(lines) < 2:
threedhst.showMessage('Only %d lines in %s.' %(len(lines), infile), warn=True)
self.status = None
return None
if not lines[0].startswith(comment_char):
threedhst.showMessage('First line of %s doesn\'t start with \'%s\':\n%s' %(infile,
comment_char, lines[0]), warn=True)
self.status = None
return None
#### get the column names from the first line
header = lines[0]
columns = header.replace(comment_char,'').split()
NCOLUMNS = len(columns)
#### parse column names, fixing characters.
dict = {}
for i in range(NCOLUMNS):
if verbose > 1:
print columns[i]
col = columns[i].replace('-','_').replace('.','p')
if force_lowercase:
col = col.lower()
for str in '()[]':
col = col.replace(str,'')
#
if col[0].isdigit():
col = '_'+col
#
columns[i] = col
dict[col] = []
#### skip header lines
ix=0
line = lines[ix]
while line.startswith(comment_char) & (ix < len(lines)-1):
ix+=1
line = lines[ix]
if ix == len(lines):
self.status = None
return None
#### Parse the lines into the data columns
N=0
for line in lines[ix:]:
spl = line.split()
if len(spl) == NCOLUMNS:
N+=1
for i in range(NCOLUMNS):
dict[columns[i]].append(spl[i])
if verbose > 3:
print dict
#### Convert data to numpy arrays and change data types from
#### strings to int and float as necessary
for col in columns:
dict[col] = np.array(dict[col])
item = dict[col][0]
try:
fl = float(item)
isNumber = True
except:
isNumber = False
if isNumber:
try:
dict[col] = np.cast[float](dict[col])
except:
pass
#int
#print 'x '+item+' x'
if item.isdigit():
try:
dict[col] = np.cast[int](dict[col])
except:
pass
str = 'self.%s = dict[col]' %(col)
#print str
exec(str)
self.NCOLUMNS = NCOLUMNS
self.columns = columns
self.N = N
self.status = True
if save_fits:
self.write_fits()
def swarpImage(self, inputImage, mode='direct', verbose=True):
"""
swarpImage(self,inputImage,mode='waiterror', verbose=True)
Writes configuration files and runs swarp on the input image
mode can be:
* 'waiterror': waits for swarp to finish, and raises an
SExtractorError if it does not complete sucessfully. stdout
and sterr are saved to self.lastout and self.lasterr (returns 0)
* 'wait': waits for swarp to finish and returns the return code
stdout and sterr are saved to self.lastout and self.lasterr
* 'proc': stars the processes but does not wait - returns the Popen
instance of the processes
"""
from subprocess import Popen, PIPE
from os.path import exists
self.swarpInputImage = inputImage
fnbase = self.name
if not self.overwrite:
fnbase = fnbase.replace('.swarp', '')
if exists(fnbase + '.swarp'):
fns = fnbase.split('-')
try:
i = int(fns[-1])
i += 1
except ValueError:
i = 2
if len(fns) < 2:
fns.append(str(i))
else:
fns[-1] = str(i)
fnbase = '-'.join(fns)
self.name = fnbase
self._saveFiles(fnbase)
if isinstance(inputImage, list):
imgList = ' '.join(inputImage)
else:
imgList = inputImage
clstr = 'swarp %s -c %s' % (imgList, self.name + '.swarp')
#print "\n3DHST.sex.swarp.swarpImage:\n\n %s\n" %clstr
if verbose:
threedhst.showMessage('Running swarp: %s' % clstr)
if mode == 'waiterror' or mode == 'wait':
# proc = Popen(clstr.split(),
# executable='swarp',stdout=PIPE,stderr=PIPE)
#### Send STDERR output to temporary file because
#### swarp seems to spawn additional processed for large files
#### and proc.wait() never finishes
fp = open('sex_stderr', 'w')
proc = Popen(clstr.split(),
executable='swarp',
stdout=PIPE,
stderr=fp)
res = proc.wait()
fp.close()
if verbose:
print('Done.\n')
sout, serr = proc.communicate()
## Read stderr output
fp = open('sex_stderr', 'r')
serr = ' '.join(fp.readlines())
fp.close()
self.lastout = sout
self.lasterr = serr
if res != 0 and mode == 'waiterror':
raise SError(serr, sout)
return res
elif mode == 'proc':
return proc
elif mode == 'direct':
proc = Popen(
clstr.split()) #,executable='swarp' #,stdout=PIPE,stderr=PIPE)
res = proc.wait()
else:
raise ValueError('unrecognized mode argument ' + str(mode))
def remove_visit_sky(asn_file='GDN12-G102_asn.fits', list=['zodi_G102_clean.fits', 'excess_G102_clean.fits'], add_constant=False, column_average=True, mask_grow=18, flat_correct=True):
"""
Require that all exposures in a visit have the same zodi component.
"""
from scipy.linalg import lstsq
import scipy.optimize
import scipy.ndimage as nd
import astropy.io.fits as pyfits
import copy
import threedhst.grism_sky as bg
asn = threedhst.utils.ASNFile(asn_file)
flt = pyfits.open('%s_flt.fits' %(asn.exposures[0]))
bg.set_grism_flat(grism=flt[0].header['FILTER'], verbose=True)
if flat_correct:
flat = bg.flat*1.
else:
flat = bg.flat*0.+1
data = []
whts = []
masks = []
for exp in asn.exposures:
flt = pyfits.open('%s_flt.fits' %(exp))
segfile = '%s_flt.seg.fits' %(exp)
seg = pyfits.open(segfile)[0].data
seg_mask = nd.maximum_filter((seg > 0), size=18) == 0
dq_ok = (flt[3].data & (4+32+16+512+2048+4096)) == 0
#
flat_corr = flt[1].data*flat
mask = seg_mask & dq_ok
mask &= (flat_corr < np.percentile(flat_corr[mask], 98)) & (flt[2].data > 0) & (flat_corr > np.percentile(flat_corr[mask], 1))
#
data.append(flat_corr.flatten())
whts.append(1/flt[2].data.flatten()**2)
masks.append(mask.flatten())
data = np.array(data)
whts = np.array(whts)
masks = np.array(masks)
#### Read in the master skies
ims = []
skies = copy.deepcopy(list)
for sky in skies:
ims.append(pyfits.open(os.getenv('THREEDHST') + '/CONF/' + sky)[0].data.flatten())
if add_constant:
ims.append(flt[1].data.flatten()*0.+1)
skies.append('Constant')
ims = np.array(ims)
#### Do the fit
tol=1.49e-8 # not sure what this controls
p0 = np.ones((ims.shape[0]-1)*len(asn.exposures)+1)
popt = scipy.optimize.leastsq(bg.obj_lstsq_visit, p0, args=(data, ims, whts, masks), full_output=True, ftol=tol/1000., xtol=tol/1000.)
xcoeff = popt[0]
sh_temp = ims.shape
logstr = 'Master grism sky: %s\n\n FLT %s\n' %(asn_file, ' '.join(skies))
for i in range(len(asn.exposures)):
coeff = np.zeros(sh_temp[0])
coeff[0] = xcoeff[0]
coeff[1:] = xcoeff[1+i*(sh_temp[0]-1):1+(i+1)*(sh_temp[0]-1)]
bg_model = np.dot(coeff, ims).reshape((1014,1014))
logstr += '%s %s\n' %(asn.exposures[i], ''.join([' %9.4f' %(c) for c in coeff]))
flt = pyfits.open('%s_flt.fits' %(asn.exposures[i]), mode='update')
flt[1].data = flt[1].data*flat - bg_model
for j in range(sh_temp[0]):
if 'GSKY%02d' %(j) in flt[0].header:
flt[0].header['GSKY%02d' %(j)] += coeff[j]
else:
flt[0].header['GSKY%02d' %(j)] = (coeff[j], 'Master sky: %s' %(skies[j]))
#
flt[1].header['MDRIZSKY'] = 0.
if 'SKYFLAT' in flt[0].header.keys():
flt[0].header['SKYFLAT'] = (flat_correct | flt[0].header['SKYFLAT'], 'Direct image flat applied')
else:
flt[0].header['SKYFLAT'] = (flat_correct, 'Direct image flat applied')
flt.flush()
threedhst.showMessage(logstr)
if column_average:
#for iter in range(2):
#grism_sky_column_average(asn_file=asn_file, mask_grow=mask_grow)
grism_sky_column_average_GP(asn_file=asn_file, mask_grow=mask_grow)
def runTweakReg(asn_file='GOODS-S-15-F140W_asn.fits', master_catalog='goodss_radec.dat', final_scale=0.06, ACS=False, threshold=5):
"""
Wrapper around tweakreg, generating source catalogs separately from
`findpars`.
"""
import glob
import shutil
import drizzlepac
from drizzlepac import tweakreg
from stwcs import updatewcs
import threedhst.prep_flt_astrodrizzle
asn = threedhst.utils.ASNFile(asn_file)
if ACS:
NCHIP=2
sci_ext = [1,4]
wht_ext = [2,5]
ext = 'flc'
dext = 'crclean'
else:
NCHIP=1
sci_ext = [1]
wht_ext = [2]
ext = 'flt'
dext = 'flt'
### Generate CRCLEAN images
for exp in asn.exposures:
updatewcs.updatewcs('%s_%s.fits' %(exp, ext))
has_crclean = True
for exp in asn.exposures:
has_crclean &= os.path.exists('%s_crclean.fits' %(exp))
threedhst.showMessage('# exposures: %d' %(len(asn.exposures)))
if not has_crclean:
if len(asn.exposures) == 1:
drizzlepac.astrodrizzle.AstroDrizzle(asn_file, clean=False, context=False, preserve=False, skysub=True, driz_separate=False, driz_sep_wcs=False, median=False, blot=False, driz_cr=False, driz_cr_corr=False, driz_combine=True)
shutil.copy('%s_%s.fits' %(asn.exposures[0], ext), '%s_crclean.fits' %(asn.exposures[0]))
else:
drizzlepac.astrodrizzle.AstroDrizzle(asn_file, clean=False, context=False, preserve=False, skysub=True, driz_separate=True, driz_sep_wcs=True, median=True, blot=True, driz_cr=True, driz_cr_corr=True, driz_combine=True)
#### Make SExtractor source catalogs in *each* flt
for exp in asn.exposures:
#updatewcs.updatewcs('%s_%s.fits' %(exp, ext))
for i in range(NCHIP):
se = threedhst.sex.SExtractor()
se.options['WEIGHT_IMAGE'] = '%s_%s.fits[%d]' %(exp, dext, wht_ext[i]-1)
se.options['WEIGHT_TYPE'] = 'MAP_RMS'
#
se.params['X_IMAGE'] = True; se.params['Y_IMAGE'] = True
se.params['MAG_AUTO'] = True
#
se.options['CATALOG_NAME'] = '%s_%s_%d.cat' %(exp, ext, sci_ext[i])
se.options['FILTER'] = 'N'
se.options['DETECT_THRESH'] = '%f' %(threshold)
se.options['ANALYSIS_THRESH'] = '%f' %(threshold)
#
se.sextractImage('%s_%s.fits[%d]' %(exp, dext, sci_ext[i]-1))
threedhst.sex.sexcatRegions('%s_%s_%d.cat' %(exp, ext, sci_ext[i]), '%s_%s_%d.reg' %(exp, ext, sci_ext[i]), format=1)
#### TweakReg catfile
asn_root = asn_file.split('_asn')[0]
catfile = '%s.catfile' %(asn_root)
fp = open(catfile,'w')
for exp in asn.exposures:
line = '%s_%s.fits' %(exp, ext)
for i in range(NCHIP):
line += ' %s_%s_%d.cat' %(exp, ext, sci_ext[i])
fp.write(line + '\n')
fp.close()
#### First run AstroDrizzle mosaic
#drizzlepac.astrodrizzle.AstroDrizzle(asn_file, clean=True, context=False, preserve=False, skysub=True, driz_separate=False, driz_sep_wcs=False, median=False, blot=False, driz_cr=False, driz_combine=True)
#### Make room for TWEAK wcsname
for exp in asn.exposures:
threedhst.prep_flt_astrodrizzle.clean_wcsname(flt='%s_%s.fits' %(exp, ext), wcsname='TWEAK', ACS=ACS)
#### Main run of TweakReg
if ACS:
refimage = '%s_drc_sci.fits' %(asn_root)
else:
refimage = '%s_drz_sci.fits' %(asn_root)
tweakreg.TweakReg(asn_file, refimage=refimage, updatehdr=True, updatewcs=True, catfile=catfile, xcol=2, ycol=3, xyunits='pixels', refcat=master_catalog, refxcol=1, refycol=2, refxyunits='degrees', shiftfile=True, outshifts='%s_shifts.txt' %(asn_root), outwcs='%s_wcs.fits' %(asn_root), searchrad=5, tolerance=12, wcsname='TWEAK', interactive=False, residplot='No plot', see2dplot=False, clean=True, headerlet=True, clobber=True)
#### Run AstroDrizzle again
if ACS:
drizzlepac.astrodrizzle.AstroDrizzle(asn_file, clean=True, final_scale=final_scale, final_pixfrac=0.8, context=False, resetbits=4096, final_bits=576, preserve=False)
else:
if len(asn.exposures) == 1:
drizzlepac.astrodrizzle.AstroDrizzle(asn_file, clean=True, final_scale=final_scale, final_pixfrac=0.8, context=False, resetbits=4096, final_bits=576, driz_sep_bits=576, preserve=False, driz_cr_snr='8.0 5.0', driz_cr_scale = '2.5 0.7', driz_separate=False, driz_sep_wcs=False, median=False, blot=False, driz_cr=False, driz_cr_corr=False) # , final_wcs=True, final_rot=0)
else:
drizzlepac.astrodrizzle.AstroDrizzle(asn_file, clean=True, final_scale=final_scale, final_pixfrac=0.8, context=False, resetbits=4096, final_bits=576, driz_sep_bits=576, preserve=False, driz_cr_snr='8.0 5.0', driz_cr_scale = '2.5 0.7') # , final_wcs=True, final_rot=0)
for exp in asn.exposures:
files=glob.glob('%s*coo' %(exp))
files.extend(glob.glob('%s*crclean.fits' %(exp)))
for file in files:
os.remove(file)
def grism_sky_column_average(asn_file='GDN12-G102_asn.fits', iter=2, mask_grow=8):
"""
Remove column-averaged residuals from grism exposures
"""
import scipy.ndimage as nd
import astropy.io.fits as pyfits
asn = threedhst.utils.ASNFile(asn_file)
for k in range(len(asn.exposures)):
#### 1D column averages
flt = pyfits.open('%s_flt.fits' %(asn.exposures[k]), mode='update')
segfile = '%s_flt.seg.fits' %(asn.exposures[k])
seg = pyfits.open(segfile)[0].data
seg_mask = nd.maximum_filter((seg > 0), size=mask_grow) == 0
dq_ok = (flt[3].data & (4+32+16+512+2048+4096)) == 0
mask = seg_mask & dq_ok & (flt[2].data > 0)
#### Iterative clips on percentile
#mask &= (flt[1].data < np.percentile(flt[1].data[mask], 98)) & (flt[2].data > 0) & (flt[1].data > np.percentile(flt[1].data[mask], 2))
#mask &= (flt[1].data < np.percentile(flt[1].data[mask], 84)) & (flt[2].data > 0) & (flt[1].data > np.percentile(flt[1].data[mask], 16))
residuals = []
for j in range(iter):
masked = flt[1].data*1
masked[~mask] = np.nan
yres = np.zeros(1014)
for i in range(1014):
# ymsk = mask[:,i]
# yres[i] = np.median(flt[1].data[ymsk,i])
ymsk = masked[:,i]
#ymsk = masked[:,np.maximum(i-10,0):i+10]
#yres[i] = np.median(ymsk[np.isfinite(ymsk)])
ok = np.isfinite(ymsk)
ymsk[(ymsk > np.percentile(ymsk[ok], 84)) | (ymsk < np.percentile(ymsk[ok], 16))] = np.nan
yres[i] = np.mean(ymsk[np.isfinite(ymsk)])
#
resid = threedhst.utils.medfilt(yres, 41)
#
#resid = np.dot(np.ones((1014,1)), yres_sm.reshape(1,1014))
flt[1].data -= resid
residuals.append(resid*1)
threedhst.showMessage('Remove column average: %s' %(asn.exposures[k]))
flt.flush()
#flt.writeto(flt.filename(), clobber=True)
#plt.plot(yres_sm)
### Make figure
from matplotlib.figure import Figure
from matplotlib.backends.backend_agg import FigureCanvasAgg
fig = Figure(figsize=[6,4], dpi=100)
fig.subplots_adjust(wspace=0.25,hspace=0.02,left=0.15,
bottom=0.08,right=0.97,top=0.92)
ax = fig.add_subplot(111)
ax.set_xlim(0,1014)
ax.set_title(flt.filename())
ax.plot(yres, color='black')
for resid in residuals:
ax.plot(resid, color='red', linewidth=2, alpha=0.7)
ax.set_xlim(0,1014)
canvas = FigureCanvasAgg(fig)
canvas.print_figure(flt.filename().split('.fits')[0] + '.column.png', dpi=100, transparent=False)
def test():
import interp_c
import time
import scipy
import threedhst
import numpy as np
N = int(1.e6)
xfull = np.arange(0, N + 1, 1) * 1.
#yfull = np.sin(xfull/(N/1239.)*2*np.pi)+1
yfull = np.sin(xfull / np.pi / 2 / 20) + 0.2
# coeffs = np.random.random(size=12)*5
# yfull = scipy.polyval(coeffs, xfull)
xint = np.arange(0, N + 1, N / 100) * 1.
tstart = time.time()
denom = np.trapz(yfull, xfull)
tstart = time.time()
yint_0 = np.interp(xint, xfull, yfull)
t0 = time.time()
print 'Linear : %.3f (%.4e)' % (
t0 - tstart, np.trapz(yint_0, xint) / denom - 1)
yint_x = interp_c.interp_c(xint, xfull, yfull)
tx = time.time()
print 'Linear(c) : %.3f (%.4e)' % (
tx - t0, np.trapz(yint_x, xint) / denom - 1)
xreverse = xint[::-1]
yint_y = interp_c.interp_c(xreverse, xfull, yfull, assume_sorted=0)
ty = time.time()
print 'Linear(c) rev : %.3f (%.4e)' % (
ty - tx, np.trapz(yint_y, xint) / denom - 1)
yint_1 = threedhst.utils.interp_conserve(xint, xfull, yfull)
t1 = time.time()
print 'Conserve : %.3f (%.4e)' % (
t1 - ty, np.trapz(yint_1, xint) / denom - 1)
yint_2 = interp_c.interp_conserve(xint, xfull, yfull)
t2 = time.time()
print 'Conserve (Cython): %.3f (%.4e)' % (
t2 - t1, np.trapz(yint_2, xint) / denom - 1)
yint_3 = interp_c.interp_conserve_c(xint, xfull, yfull)
t3 = time.time()
print 'Conserve (more c): %.3f (%.4e)' % (
t3 - t2, np.trapz(yint_3, xint) / denom - 1)
yint_4 = threedhst.utils.interp_conserve_c(xint, xfull, yfull)
t4 = time.time()
print 'Inline c : %.3f (%.4e)' % (
t4 - t3, np.trapz(yint_4, xint) / denom - 1)
#### Test interpolation
threedhst.showMessage('Interpolation')
#### Faster while n(int)/n(full) < 1./50
xint = xfull[1000:-1000:40]
tstart = time.time()
yint = np.interp(xint, xfull, yfull, left=0., right=0.)
t0 = time.time()
print 'Python : %.4f' % (t0 - tstart)
yint1 = interp_c.interp_c(xint, xfull, yfull, extrapolate=0.)
t1 = time.time()
print 'Cython rewrite : %.4f (%.2e)' % (t1 - t0, np.sum(
(yint1 - yint)**2))
#### Test midpoint definition --- slices work better than by hand
threedhst.showMessage('Midpoint')
xmid = xfull
tstart = time.time()
midpoint = (xmid[1:] + xmid[:-1]) / 2.
midpoint = np.append(midpoint, np.array([xmid[0], xmid[-1]]))
midpoint = midpoint[np.argsort(midpoint)]
t0 = time.time()
print 'Python : %.3f %.2e' % (t0 - tstart,
np.sum((midpoint - midpoint)**2))
midpoint_c1 = interp_c.midpoint(xmid)
t1 = time.time()
print 'Cython : %.3f %.2e' % (t1 - t0,
np.sum((midpoint_c1 - midpoint)**2))
midpoint_c2 = interp_c.midpoint_c(xmid, N + 1)
t2 = time.time()
print 'Cython (opt): %.3f %.2e' % (t2 - t1,
np.sum((midpoint_c2 - midpoint)**2))
