def segmentation_figure(label, cat, segfile):
"""
Make a figure showing a cutout of the segmentation file
"""
import matplotlib.pyplot as plt
import numpy as np
import astropy.io.fits as pyfits
import astropy.wcs as pywcs
from grizli import utils
plt.ioff()
seg = pyfits.open(segfile)
seg_data = seg[0].data
seg_wcs = pywcs.WCS(seg[0].header)
# Randomize seg to get dispersion between neighboring objects
np.random.seed(hash(label.split('_')[0]) % (10 ** 8))
rnd_ids = np.append([0], np.argsort(np.random.rand(len(cat)))+1)
# Make cutout
th = pyfits.open('{0}.thumb.fits'.format(label), mode='update')
th_wcs = pywcs.WCS(th[0].header)
blot_seg = utils.blot_nearest_exact(seg_data, seg_wcs, th_wcs,
stepsize=-1, scale_by_pixel_area=False)
rnd_seg = rnd_ids[np.cast[int](blot_seg)]*1.
th_ids = np.unique(blot_seg)
sh = th[0].data.shape
yp, xp = np.indices(sh)
thumb_height = 2.
fig = plt.figure(figsize=[thumb_height*sh[1]/sh[0], thumb_height])
ax = fig.add_subplot(111)
rnd_seg[rnd_seg == 0] = np.nan
ax.imshow(rnd_seg, aspect='equal', cmap='terrain_r',
vmin=-0.05*len(cat), vmax=1.05*len(cat))
ax.set_xticklabels([])
ax.set_yticklabels([])
ix = utils.column_values_in_list(cat['number'], th_ids)
xc, yc = th_wcs.all_world2pix(cat['ra'][ix], cat['dec'][ix], 0)
xc = np.clip(xc, 0.09*sh[1], 0.91*sh[1])
yc = np.clip(yc, 0.08*sh[0], 0.92*sh[0])
for th_id, x_i, y_i in zip(cat['number'][ix], xc, yc):
if th_id == 0:
continue
ax.text(x_i, y_i, '{0:.0f}'.format(th_id), ha='center', va='center', fontsize=8, color='w')
ax.text(x_i, y_i, '{0:.0f}'.format(th_id), ha='center', va='center', fontsize=8, color='k', alpha=0.95)
ax.set_xlim(0, sh[1]-1)
ax.set_ylim(0, sh[0]-1)
ax.set_axis_off()
fig.tight_layout(pad=0.01)
fig.savefig('{0}.seg.png'.format(label))
plt.close(fig)
# Append to thumbs file
seg_hdu = pyfits.ImageHDU(data=np.cast[int](blot_seg), name='SEG')
if 'SEG' in th:
th.pop('SEG')
th.append(seg_hdu)
th.writeto('{0}.thumb.fits'.format(label), overwrite=True,
output_verify='fix')
th.close()
def resample_array(img,
wht=None,
pixratio=2,
slice_if_int=True,
int_tol=1.e-3,
method='drizzle',
drizzle_kwargs=DRIZZLE_KWARGS,
rescale_kwargs=RESCALE_KWARGS,
scale_by_area=False,
verbose=False,
blot_stepsize=-1,
**kwargs):
"""
Resample an image to a new grid. If pixratio is an integer, just return a
slice of the input `img`. Otherwise resample with `~drizzlepac` or `~resample`.
"""
from grizli.utils import (make_wcsheader, drizzle_array_groups,
blot_nearest_exact)
from skimage.transform import rescale, resize, downscale_local_mean
is_int = np.isclose(pixratio, np.round(pixratio), atol=int_tol)
if is_int & (pixratio > 1):
# Integer scaling
step = int(np.round(pixratio))
if method.lower() == 'drizzle':
_, win = make_wcsheader(ra=90,
dec=0,
size=img.shape,
pixscale=1.,
get_hdu=False,
theta=0.)
_, wout = make_wcsheader(ra=90,
dec=0,
size=img.shape,
pixscale=pixratio,
get_hdu=False,
theta=0.)
if wht is None:
wht = np.ones_like(img)
_drz = drizzle_array_groups([img], [wht], [win],
outputwcs=wout,
**drizzle_kwargs)
res = _drz[0]
res_wht = _drz[1]
method_used = 'drizzle'
elif slice_if_int:
# Simple slice
res = img[step // 2::step, step // 2::step] * 1
res_wht = np.ones_like(res)
method_used = 'slice'
else:
# skimage downscale with averaging
res = downscale_local_mean(img, (step, step), cval=0, clip=True)
res_wht = np.ones_like(res)
method_used = 'downscale'
else:
if method.lower() == 'drizzle':
# Drizzle
_, win = make_wcsheader(ra=90,
dec=0,
size=img.shape,
pixscale=1.,
get_hdu=False,
theta=0.)
_, wout = make_wcsheader(ra=90,
dec=0,
size=img.shape,
pixscale=pixratio,
get_hdu=False,
theta=0.)
if wht is None:
wht = np.ones_like(img)
_drz = drizzle_array_groups([img], [wht], [win],
outputwcs=wout,
**drizzle_kwargs)
res = _drz[0]
res_wht = _drz[1]
method_used = 'drizzle'
elif method.lower() == 'blot':
# Blot exact values
_, win = make_wcsheader(ra=90,
dec=0,
size=img.shape,
pixscale=1.,
get_hdu=False,
theta=0.)
_, wout = make_wcsheader(ra=90,
dec=0,
size=img.shape,
pixscale=pixratio,
get_hdu=False,
theta=0.)
# Ones for behaviour around zeros
res = blot_nearest_exact(img + 1,
win,
wout,
verbose=False,
stepsize=blot_stepsize,
scale_by_pixel_area=False,
wcs_mask=False,
fill_value=0) - 1
res_wht = np.ones_like(res)
method_used = 'blot'
elif method.lower() == 'rescale':
res = rescale(img, 1. / pixratio, **rescale_kwargs)
res_wht = np.ones_like(res)
method_used = 'rescale'
else:
raise ValueError("method must be 'drizzle', 'blot' or 'rescale'.")
if scale_by_area:
scale = 1. / pixratio**2
else:
scale = 1
if verbose:
msg = 'resample_array x {4:.1f}: {0} > {1}, method={2}, scale={3:.2f}'
print(msg.format(img.shape, res.shape, method_used, scale, pixratio))
if not np.isclose(scale, 1, 1.e-4):
res = res * scale
res_wht = res_wht / scale**2
#print(res_wht, res_wht.dtype, scale, res_wht.shape)
#res_wht /= scale**2
return res, res_wht
def run_root(root='j002532m1223', min_zoom=2, get_grism=True):
"""
Prepare images for fitsmap.convert
"""
from grizli.pipeline import auto_script
from grizli import utils
import eazy.utils
from fitsmap import convert
print('sync')
os.system(
f'aws s3 sync s3://grizli-v1/Pipeline/{root}/Prep/ {root}/ --exclude "*" --include "*sci.fits.gz" --include "*phot.fits" --include "*seg.fits.gz"'
)
os.system(
f'aws s3 sync s3://grizli-v1/Pipeline/{root}/IRAC/ {root}/ --exclude "*" --include "*sci.fits*" --include "*model.fits"'
)
os.system(
f'aws s3 sync s3://grizli-v1/Pipeline/{root}/Map/ {root}/ --exclude "*" --include "{root}.*png"'
)
os.chdir(root)
if not os.path.exists(f'{root}.rgb.png'):
_ = auto_script.field_rgb(root=root,
xsize=6,
full_dimensions=True,
HOME_PATH=None,
gzext='*',
suffix='.rgb',
output_format='png')
# IR
files = glob.glob(f'{root}-[if][r01]*sci.fits*')
files.sort()
filts = [file.split(f'{root}-')[1].split('_')[0] for file in files]
for filt in filts:
if os.path.exists(f'{root}.{filt}.png'):
continue
_ = auto_script.field_rgb(root=root,
xsize=6,
full_dimensions=True,
HOME_PATH=None,
gzext='*',
filters=[filt],
suffix=f'.{filt}',
output_format='png',
invert=True,
scl=2)
# Optical, 2X pix
files = glob.glob(f'{root}-[f][2-8]*sci.fits*')
files.sort()
filts = [file.split(f'{root}-')[1].split('_')[0] for file in files]
for filt in filts:
if os.path.exists(f'{root}.{filt}.png'):
continue
_ = auto_script.field_rgb(root=root,
xsize=6,
full_dimensions=2,
HOME_PATH=None,
gzext='*',
filters=[filt],
suffix=f'.{filt}',
output_format='png',
invert=True,
scl=2)
# Spitzer
if glob.glob(f'{root}-ch*fits*'):
import reproject
out_img = pyfits.open(f'{root}-ir_drz_sci.fits.gz')
repr_hdu = out_img[0]
# repr_hdu = utils.make_maximal_wcs([out_wcs], pixel_scale=0.2,
# verbose=False, pad=0, poly_buffer=0)
repr_wcs = pywcs.WCS(repr_hdu.header)
mosaics = glob.glob(f'{root}-ch[12]*sci.fits*')
mosaics.sort()
for mos in mosaics:
ch = mos.split(f'{root}-')[1].split('_')[0]
if os.path.exists(f'{root}.{ch}.png'):
continue
print(f'Reproject {ch}')
in_img = pyfits.open(mos)
in_wcs = pywcs.WCS(in_img[0].header)
reproj = utils.blot_nearest_exact(in_img[0].data,
in_wcs,
repr_wcs,
scale_by_pixel_area=False)
pyfits.writeto(f'{root}-{ch}s_drz_sci.fits',
data=reproj,
header=repr_hdu.header,
overwrite=True)
ext = [ch + 's']
if os.path.exists(f'{root}-{ch}_model.fits'):
# resid
print(f' {ch} model')
in_img = pyfits.open(f'{root}-{ch}_model.fits')
reproj = utils.blot_nearest_exact(in_img[1].data,
in_wcs,
repr_wcs,
scale_by_pixel_area=False)
pyfits.writeto(f'{root}-{ch}m_drz_sci.fits',
data=reproj,
header=repr_hdu.header,
overwrite=True)
ext.append(ch + 'm')
for filt in ext:
_ = auto_script.field_rgb(root=root,
xsize=6,
full_dimensions=True,
HOME_PATH=None,
gzext='',
filters=[filt],
suffix=f'.{filt}',
output_format='png',
invert=True,
scl=2)
if not os.path.exists(f'{root}.seg.png'):
sfig = make_seg(f'{root}-ir_seg.fits.gz', outfile=f'{root}.seg.png')
filelist = []
for q in ['*f[2-8]', '*f[01]*', '*ir*', '*ch[12]', '*seg', '*rgb']:
l_i = glob.glob(q + '*png')
l_i.sort()
filelist.extend(l_i)
ph = utils.read_catalog(f'{root}_phot.fits')
ph['id'] = ph['number']
ph['ra'].format = '.6f'
ph['dec'].format = '.6f'
ph['mag'] = ph['mag_auto']
ph['mag'].format = '.2f'
ph['query'] = [
eazy.utils.query_html(r, d).split(') ')[1]
for r, d in zip(ph['ra'], ph['dec'])
]
ph['id', 'ra', 'dec', 'query', 'mag'].write('phot.cat',
format='ascii.csv',
overwrite=True)
filelist += ['phot.cat']
if get_grism:
from grizli.aws import db
engine = db.get_db_engine()
gr = db.from_sql(
f"select root, id, ra, dec, z_map from redshift_fit where root='{root}'",
engine)
print(f'grism.cat: {len(gr)} sources')
if len(gr) > 0:
gr['query'] = [
eazy.utils.query_html(r, d).split(') ')[1]
for r, d in zip(gr['ra'], gr['dec'])
]
gr['stack'] = [
f'<img src="https://s3.amazonaws.com/grizli-v1/Pipeline/{root}/Extractions/{root}_{id:05d}.stack.png" height="100px"/>'
for id in gr['id']
]
gr['full'] = [
f'<img src="https://s3.amazonaws.com/grizli-v1/Pipeline/{root}/Extractions/{root}_{id:05d}.full.png" height="100px"/>'
for id in gr['id']
]
gr['line'] = [
f'<img src="https://s3.amazonaws.com/grizli-v1/Pipeline/{root}/Extractions/{root}_{id:05d}.line.png" height="80px"/>'
for id in gr['id']
]
gr['ra'].format = '.6f'
gr['dec'].format = '.6f'
gr['z_map'].format = '.4f'
gr['id', 'ra', 'dec', 'query', 'z_map', 'stack', 'full',
'line'].write('grism.cat', format='ascii.csv', overwrite=True)
filelist += ['grism.cat']
convert.MPL_CMAP = 'gray_r'
convert.cartographer.MARKER_HTML_WIDTH = '650px'
convert.cartographer.MARKER_HTML_HEIGHT = '440px'
convert.POPUP_CSS = [
"span { text-decoration:underline; font-weight:bold; line-height:12pt; }",
"tr { line-height: 7pt; }",
"table { width: 100%; }",
"img { height: 100px; width: auto; }",
]
convert.dir_to_map("./",
filelist=filelist,
out_dir="output",
cat_wcs_fits_file=f"{root}-ir_drz_sci.fits.gz",
catalog_delim=',',
min_zoom=min_zoom,
task_procs=False,
image_engine='MPL')
plt.close('all')
if os.path.exists('output/index.html'):
os.system(
f'aws s3 sync output/ s3://grizli-v1/Pipeline/{root}/Map/ --acl public-read --quiet'
)
os.system(
f'aws s3 sync ./ s3://grizli-v1/Pipeline/{root}/Map/ --exclude "*" --include "{root}.*png" --acl public-read'
)