"""
Dumping ground for general utilities
"""
import os
import shutil
import glob
import inspect
from collections import OrderedDict
import warnings
import itertools
import logging
import astropy.io.fits as pyfits
import astropy.wcs as pywcs
import astropy.table
import numpy as np
import astropy.units as u
from sregion import SRegion, patch_from_polygon
from . import GRIZLI_PATH
KMS = u.km/u.s
FLAMBDA_CGS = u.erg/u.s/u.cm**2/u.angstrom
FNU_CGS = u.erg/u.s/u.cm**2/u.Hz
# character to skip clearing line on STDOUT printing
NO_NEWLINE = '\x1b[1A\x1b[1M'
# R_V for Galactic extinction
MW_RV = 3.1
MPL_COLORS = {'b': '#1f77b4', 'orange': '#ff7f0e', 'g': '#2ca02c', 'r': '#d62728', 'purple': '#9467bd', 'brown': '#8c564b', 'pink': '#e377c2', 'gray': '#7f7f7f', 'olive': '#bcbd22', 'cyan': '#17becf'}
# sns.color_palette("husl", 8)
SNS_HUSL = {'r': (0.9677975592919913, 0.44127456009157356, 0.5358103155058701),
'orange': (0.8087954113106306, 0.5634700050056693, 0.19502642696727285),
'olive': (0.5920891529639701, 0.6418467016378244, 0.1935069134991043),
'g': (0.19783576093349015, 0.6955516966063037, 0.3995301037444499),
'sea': (0.21044753832183283, 0.6773105080456748, 0.6433941168468681),
'b': (0.22335772267769388, 0.6565792317435265, 0.8171355503265633),
'purple': (0.6423044349219739, 0.5497680051256467, 0.9582651433656727),
'pink': (0.9603888539940703, 0.3814317878772117, 0.8683117650835491)}
GRISM_COLORS = {'G800L': (0.0, 0.4470588235294118, 0.6980392156862745),
'G102': (0.0, 0.6196078431372549, 0.45098039215686275),
'G141': (0.8352941176470589, 0.3686274509803922, 0.0),
'none': (0.8, 0.4745098039215686, 0.6549019607843137),
'G150': 'k',
'F277W': (0.0, 0.6196078431372549, 0.45098039215686275),
'F356W': (0.8352941176470589, 0.3686274509803922, 0.0),
'F444W': (0.8, 0.4745098039215686, 0.6549019607843137),
'F410M': (0.0, 0.4470588235294118, 0.6980392156862745),
'G280': 'purple',
'F090W': (0.0, 0.4470588235294118, 0.6980392156862745),
'F115W': (0.0, 0.6196078431372549, 0.45098039215686275),
'F150W': (0.8352941176470589, 0.3686274509803922, 0.0),
'F140M': (0.8352941176470589, 0.3686274509803922, 0.0),
'F158M': (0.8352941176470589, 0.3686274509803922, 0.0),
'F200W': (0.8, 0.4745098039215686, 0.6549019607843137),
'F140M': 'orange',
'BLUE': '#1f77b4', # Euclid
'RED': '#d62728',
'CLEARP': 'b'}
GRISM_MAJOR = {'G102': 0.1, 'G141': 0.1, # WFC3/IR
'G800L': 0.1, # ACS/WFC
'F090W': 0.1, 'F115W': 0.1, 'F150W': 0.1, # NIRISS
'F140M': 0.1, 'F158M': 0.1, 'F200W': 0.1,
'F277W': 0.2, 'F356W': 0.2, 'F444W': 0.2, # NIRCam
'F410M': 0.2,
'BLUE': 0.1, 'RED': 0.1, # Euclid
'GRISM':0.1, 'G150':0.1 # Roman
}
GRISM_LIMITS = {'G800L': [0.545, 1.02, 40.], # ACS/WFC
'G280': [0.2, 0.4, 14], # WFC3/UVIS
'G102': [0.77, 1.18, 23.], # WFC3/IR
'G141': [1.06, 1.73, 46.0],
'GRISM': [0.98, 1.98, 11.], # WFIRST/Roman
'G150': [0.98, 1.98, 11.],
'F090W': [0.76, 1.04, 45.0], # NIRISS
'F115W': [0.97, 1.32, 45.0],
'F140M': [1.28, 1.52, 45.0],
'F158M': [1.28, 1.72, 45.0],
'F150W': [1.28, 1.72, 45.0],
'F200W': [1.68, 2.30, 45.0],
'F140M': [1.20, 1.60, 45.0],
'CLEARP': [0.76, 2.3, 45.0],
'F277W': [2.5, 3.2, 20.], # NIRCAM
'F356W': [3.05, 4.1, 20.],
'F444W': [3.82, 5.08, 20],
'F410M': [3.8, 4.38, 20],
'BLUE': [0.8, 1.2, 10.], # Euclid
'RED': [1.1, 1.9, 14.]}
#DEFAULT_LINE_LIST = ['PaB', 'HeI-1083', 'SIII', 'OII-7325', 'ArIII-7138', 'SII', 'Ha+NII', 'OI-6302', 'HeI-5877', 'OIII', 'Hb', 'OIII-4363', 'Hg', 'Hd', 'H8','H9','NeIII-3867', 'OII', 'NeVI-3426', 'NeV-3346', 'MgII','CIV-1549', 'CIII-1908', 'OIII-1663', 'HeII-1640', 'NIII-1750', 'NIV-1487', 'NV-1240', 'Lya']
# Line species for determining individual line fluxes. See `load_templates`.
DEFAULT_LINE_LIST = ['BrA','BrB','BrG','PfG','PfD',
'PaA','PaB','PaG','PaD',
'HeI-1083', 'SIII', 'OII-7325', 'ArIII-7138',
'SII', 'Ha', 'OI-6302', 'HeI-5877', 'OIII', 'Hb',
'OIII-4363', 'Hg', 'Hd', 'H7', 'H8', 'H9', 'H10',
'NeIII-3867', 'OII', 'NeVI-3426', 'NeV-3346', 'MgII',
'CIV-1549', 'CIII-1906', 'CIII-1908', 'OIII-1663',
'HeII-1640', 'NIII-1750', 'NIV-1487', 'NV-1240', 'Lya']
LSTSQ_RCOND = None
[docs]def set_warnings(numpy_level='ignore', astropy_level='ignore'):
"""
Set global numpy and astropy warnings
Parameters
----------
numpy_level : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}
Numpy error level (see `~numpy.seterr`).
astropy_level : {'error', 'ignore', 'always', 'default', 'module', 'once'}
Astropy error level (see `~warnings.simplefilter`).
"""
from astropy.utils.exceptions import AstropyWarning
np.seterr(all=numpy_level)
warnings.simplefilter(astropy_level, category=AstropyWarning)
JWST_TRANSLATE = {'RA_TARG':'TARG_RA',
'DEC_TARG':'TARG_DEC',
'EXPTIME':'EFFEXPTM',
'PA_V3':'ROLL_REF'}
[docs]def get_flt_info(files=[], columns=['FILE', 'FILTER', 'PUPIL', 'INSTRUME', 'DETECTOR', 'TARGNAME', 'DATE-OBS', 'TIME-OBS', 'EXPSTART', 'EXPTIME', 'PA_V3', 'RA_TARG', 'DEC_TARG', 'POSTARG1', 'POSTARG2'], translate=JWST_TRANSLATE, defaults={'PUPIL':'---', 'TARGNAME':'indef','PA_V3':0.0}, jwst_detector=True):
"""Extract header information from a list of FLT files
Parameters
----------
files : list
List of exposure filenames.
Returns
-------
tab : `~astropy.table.Table`
Table containing header keywords
"""
import astropy.io.fits as pyfits
from astropy.table import Table
if not files:
files = glob.glob('*flt.fits')
N = len(files)
data = []
for c in columns[2:]:
if c not in translate:
translate[c] = 'xxxxxxxxxxxxxx'
for i in range(N):
line = [os.path.basename(files[i]).split('.gz')[0]]
if files[i].endswith('.gz'):
im = pyfits.open(files[i])
h = im[0].header.copy()
im.close()
else:
h = pyfits.Header().fromfile(files[i])
if os.path.basename(files[i]).startswith('jw0'):
with pyfits.open(files[i]) as _im:
h1 = _im['SCI'].header
if 'PA_V3' in h1:
h['PA_V3'] = h1['PA_V3']
filt = parse_filter_from_header(h, jwst_detector=jwst_detector)
line.append(filt)
has_columns = ['FILE', 'FILTER']
for key in columns[2:]:
has_columns.append(key)
if key in h:
line.append(h[key])
elif translate[key] in h:
line.append(h[translate[key]])
else:
if key in defaults:
line.append(defaults[key])
else:
line.append(np.nan)
continue
data.append(line)
tab = Table(rows=data, names=has_columns)
if 'TARGNAME' in tab.colnames:
miss = tab['TARGNAME'] == ''
targs = [t.replace(' ', '-') for t in tab['TARGNAME']]
if miss.sum() > 0:
for i in np.where(miss)[0]:
targs[i] = 'indef'
tab['TARGNAME'] = targs
return tab
[docs]def radec_to_targname(ra=0, dec=0, round_arcsec=(4, 60), precision=2, targstr='j{rah}{ram}{ras}{sign}{ded}{dem}', header=None):
"""Turn decimal degree coordinates into a string with rounding.
Parameters
----------
ra, dec : float
Sky coordinates in decimal degrees
round_arcsec : (scalar, scalar)
Round the coordinates to nearest value of `round`, in arcseconds.
precision : int
Sub-arcsecond precision, in `~astropy.coordinates.SkyCoord.to_string`.
targstr : string
Build `targname` with this parent string. Arguments
`rah, ram, ras, rass, sign, ded, dem, des, dess` are computed from the
(rounded) target coordinates (`ra`, `dec`) and passed to
`targstr.format`.
header : `~astropy.io.fits.Header`, None
Try to get `ra`, `dec` from header keywords, first `CRVAL` and then
`RA_TARG`, `DEC_TARG`.
Returns
-------
targname : str
Target string, see the example above.
Examples
--------
>>> # Test dec: -10d10m10.10s
>>> dec = -10. - 10./60. - 10.1/3600
>>> # Test ra: 02h02m02.20s
>>> cosd = np.cos(dec/180*np.pi)
>>> ra = 2*15 + 2./60*15 + 2.2/3600.*15
>>> # Round to nearest arcmin
>>> from grizli.utils import radec_to_targname
>>> print(radec_to_targname(ra=ra, dec=dec, round_arcsec=(4,60),
targstr='j{rah}{ram}{ras}{sign}{ded}{dem}'))
j020204m1010 # (rounded to 4 arcsec in RA)
>>> # Full precision
>>> targstr = 'j{rah}{ram}{ras}.{rass}{sign}{ded}{dem}{des}.{dess}'
>>> print(radec_to_targname(ra, dec,round_arcsec=(0.0001, 0.0001),
precision=3, targstr=targstr))
j020202.200m101010.100
"""
import astropy.coordinates
import astropy.units as u
import re
import numpy as np
if header is not None:
if 'CRVAL1' in header:
ra, dec = header['CRVAL1'], header['CRVAL2']
else:
if 'RA_TARG' in header:
ra, dec = header['RA_TARG'], header['DEC_TARG']
cosd = np.cos(dec/180*np.pi)
scl = np.array(round_arcsec)/3600*np.array([360/24, 1])
dec_scl = int(np.round(dec/scl[1]))*scl[1]
ra_scl = int(np.round(ra/scl[0]))*scl[0]
coo = astropy.coordinates.SkyCoord(ra=ra_scl*u.deg, dec=dec_scl*u.deg,
frame='icrs')
cstr = re.split('[hmsd.]', coo.to_string('hmsdms', precision=precision))
# targname = ('j{0}{1}'.format(''.join(cstr[0:3]), ''.join(cstr[4:7])))
# targname = targname.replace(' ', '').replace('+','p').replace('-','m')
rah, ram, ras, rass = cstr[0:4]
ded, dem, des, dess = cstr[4:8]
sign = 'p' if ded[1] == '+' else 'm'
targname = targstr.format(rah=rah, ram=ram, ras=ras, rass=rass,
ded=ded[2:], dem=dem, des=des, dess=dess,
sign=sign)
return targname
[docs]def blot_nearest_exact(in_data, in_wcs, out_wcs, verbose=True, stepsize=-1,
scale_by_pixel_area=False, wcs_mask=True,
fill_value=0):
"""
Own blot function for blotting exact pixels without rescaling for input
and output pixel size
test
Parameters
----------
in_data : `~numpy.ndarray`
Input data to blot.
in_wcs : `~astropy.wcs.WCS`
Input WCS. Must have _naxis1, _naxis2 or pixel_shape attributes.
out_wcs : `~astropy.wcs.WCS`
Output WCS. Must have _naxis1, _naxis2 or pixel_shape attributes.
scale_by_pixel_area : bool
If True, then scale the output image by the square of the image pixel
scales (out**2/in**2), i.e., the pixel areas.
wcs_mask : bool
Use fast WCS masking. If False, use ``regions``.
fill_value : int/float
Value in `out_data` not covered by `in_data`.
Returns
-------
out_data : `~numpy.ndarray`
Blotted data.
"""
from regions import Regions
from shapely.geometry import Polygon
import scipy.ndimage as nd
from drizzlepac import cdriz
try:
from .utils_c.interp import pixel_map_c
except:
from grizli.utils_c.interp import pixel_map_c
# Shapes, in numpy array convention (y, x)
if hasattr(in_wcs, 'pixel_shape'):
in_sh = in_wcs.pixel_shape[::-1]
elif hasattr(in_wcs, 'array_shape'):
in_sh = in_wcs.array_shape
else:
in_sh = (in_wcs._naxis2, in_wcs._naxis1)
if hasattr(out_wcs, 'pixel_shape'):
out_sh = out_wcs.pixel_shape[::-1]
elif hasattr(out_wcs, 'array_shape'):
out_sh = out_wcs.array_shape
else:
out_sh = (out_wcs._naxis2, out_wcs._naxis1)
in_px = in_wcs.calc_footprint()
in_poly = Polygon(in_px).buffer(5./3600.)
out_px = out_wcs.calc_footprint()
out_poly = Polygon(out_px).buffer(5./3600)
olap = in_poly.intersection(out_poly)
if olap.area == 0:
if verbose:
print('No overlap')
return np.zeros(out_sh)
# Region mask for speedup
if np.isclose(olap.area, out_poly.area, 0.01):
mask = np.ones(out_sh, dtype=bool)
elif wcs_mask:
# Use wcs / Path
from matplotlib.path import Path
out_xy = out_wcs.all_world2pix(np.array(in_poly.exterior.xy).T, 0)-0.5
out_xy_path = Path(out_xy)
yp, xp = np.indices(out_sh)
pts = np.array([xp.flatten(), yp.flatten()]).T
mask = out_xy_path.contains_points(pts).reshape(out_sh)
else:
olap_poly = np.array(olap.exterior.xy)
poly_reg = "fk5\npolygon("+','.join(['{0}'.format(p + 1) for p in olap_poly.T.flatten()])+')\n'
reg = Regions.parse(poly_reg, format='ds9')[0]
mask = reg.to_mask().to_image(shape=out_sh)
#yp, xp = np.indices(in_data.shape)
#xi, yi = xp[mask], yp[mask]
yo, xo = np.where(mask > 0)
if stepsize <= 1:
rd = out_wcs.all_pix2world(xo, yo, 0)
xf, yf = in_wcs.all_world2pix(rd[0], rd[1], 0)
else:
# Seems backwards and doesn't quite agree with above
blot_wcs = out_wcs
source_wcs = in_wcs
if hasattr(blot_wcs, 'pixel_shape'):
nx, ny = blot_wcs.pixel_shape
else:
nx, ny = int(blot_wcs._naxis1), int(blot_wcs._naxis2)
mapping = cdriz.DefaultWCSMapping(blot_wcs, source_wcs, nx, ny,
stepsize)
xf, yf = mapping(xo, yo)
xi, yi = np.cast[int](np.round(xf)), np.cast[int](np.round(yf))
m2 = (xi >= 0) & (yi >= 0) & (xi < in_sh[1]) & (yi < in_sh[0])
xi, yi, xf, yf, xo, yo = xi[m2], yi[m2], xf[m2], yf[m2], xo[m2], yo[m2]
out_data = np.ones(out_sh, dtype=np.float64)*fill_value
status = pixel_map_c(np.cast[np.float64](in_data), xi, yi, out_data, xo, yo)
# Fill empty
func = nd.maximum_filter
fill = out_data == 0
filtered = func(out_data, size=5)
out_data[fill] = filtered[fill]
if scale_by_pixel_area:
in_scale = get_wcs_pscale(in_wcs)
out_scale = get_wcs_pscale(out_wcs)
out_data *= out_scale**2/in_scale**2
return out_data.astype(in_data.dtype)
def _slice_ndfilter(data, filter_func, slices, args, size, footprint, kwargs):
"""
Helper function passing image slices to `scipy.ndimage` filters that is
pickleable for threading with `multiprocessing`
Parameters
----------
data, filter_func, args, size, footprint :
See `multiprocessing_ndfilter`
slices : (slice, slice, slice, slice)
Array slices for insert a cutout back into a larger parent array
Returns
-------
filtered : array-like
Filtered data
slices : tuple
`slices` as input
"""
filtered = filter_func(data, *args,
size=size, footprint=footprint,
**kwargs)
return filtered, slices
[docs]def multiprocessing_ndfilter(data, filter_func, filter_args=(), size=None, footprint=None, cutout_size=256, n_proc=4, timeout=90, mask=None, verbose=True, **kwargs):
"""
Cut up a large array and send slices to `scipy.ndimage` filters
Parameters
----------
data : array-like
Main image array
filter_func : function
Filtering function, e.g., `scipy.ndimage.median_filter`
filter_args : tuple
Arguments to pass to `filter_func`
size, footprint : int, array-like
Filter size or footprint, see, e.g., `scipy.ndimage.median_filter`
cutout_size : int
Size of subimage cutouts
n_proc : int
Number of `multiprocessing` processes to use
timeout : float
`multiprocessing` timeout (seconds)
mask : array-like
Array multiplied to `data` that can zero-out regions to ignore
verbose : bool
Print status messages
kwargs : dict
Keyword arguments passed through to `filter_func`
Returns
-------
filtered : array-like
Filtered version of `data`
Examples
--------
>>> import time
>>> import numpy as np
>>> import scipy.ndimage as nd
>>> from grizli.utils import multiprocessing_ndfilter
>>> rnd = np.random.normal(size=(512,512))
>>> t0 = time.time()
>>> f_serial = nd.median_filter(rnd, size=10)
>>> t1 = time.time()
>>> f_mp = multiprocessing_ndfilter(rnd, nd.median_filter, size=10,
>>> cutout_size=256, n_proc=4)
>>> t2 = time.time()
>>> np.allclose(f_serial, f_mp)
True
>>> print(f' serial: {(t1-t0)*1000:.1f} ms')
>>> print(f'parallel: {(t2-t1)*1000:.1f} ms')
serial: 573.9 ms
parallel: 214.8 ms
"""
import multiprocessing as mp
try:
from tqdm import tqdm
except ImportError:
verbose = False
sh = data.shape
msg = None
if cutout_size > np.max(sh):
msg = f'cutout_size={cutout_size} greater than image dimensions, run '
msg += f'`{filter_func}` directly'
elif n_proc == 0:
msg = f'n_proc = 0, run in a single command'
if msg is not None:
if verbose:
print(msg)
filtered = filter_func(data, *filter_args,
size=size, footprint=footprint)
return filtered
# Grid size
nx = data.shape[1]//cutout_size+1
ny = data.shape[0]//cutout_size+1
# Padding
if footprint is not None:
fpsh = footprint.shape
pad = np.max(fpsh)
elif size is not None:
pad = size
else:
raise ValueError('Either size or footprint must be specified')
if n_proc < 0:
n_proc = mp.cpu_count()
n_proc = np.minimum(n_proc, mp.cpu_count())
pool = mp.Pool(processes=n_proc)
jobs = []
if mask is not None:
data_mask = data*mask
else:
data_mask = data
# Make image slices
for i in range(nx):
xmi = np.maximum(0, i*cutout_size-pad)
xma = np.minimum(sh[1], (i+1)*cutout_size+pad)
#print(i, xmi, xma)
if i == 0:
slx = slice(0, cutout_size)
x0 = 0
elif i < nx-1:
slx = slice(pad, cutout_size + pad)
x0 = i*cutout_size
else:
slx = slice(pad, cutout_size + 1)
x0 = xmi+pad
nxs = slx.stop - slx.start
oslx = slice(x0, x0+nxs)
for j in range(ny):
ymi = np.maximum(0, j*cutout_size - pad)
yma = np.minimum(sh[0], (j+1)*cutout_size + pad)
if j == 0:
sly = slice(0, cutout_size)
y0 = 0
elif j < ny-1:
sly = slice(pad, cutout_size + pad)
y0 = j*cutout_size
else:
sly = slice(pad, cutout_size + 1)
y0 = ymi+pad
nys = sly.stop - sly.start
osly = slice(y0, y0+nys)
cut = data_mask[ymi:yma, xmi:xma]
if cut.max() == 0:
#print(f'Skip {xmi} {xma} {ymi} {yma}')
continue
# Make jobs for filtering the image slices
slices = (osly, oslx, sly, slx)
_args = (cut, filter_func, slices,
filter_args, size, footprint, kwargs)
jobs.append(pool.apply_async(_slice_ndfilter, _args))
# Collect results
pool.close()
filtered = np.zeros_like(data)
if verbose:
_iter = tqdm(jobs)
else:
_iter = jobs
for res in _iter:
filtered_i, slices = res.get(timeout=timeout)
filtered[slices[:2]] += filtered_i[slices[2:]]
return filtered
[docs]def parse_flt_files(files=[], info=None, uniquename=False, use_visit=False,
get_footprint=False,
translate={'AEGIS-': 'aegis-',
'COSMOS-': 'cosmos-',
'GNGRISM': 'goodsn-',
'GOODS-SOUTH-': 'goodss-',
'UDS-': 'uds-'},
visit_split_shift=1.5, max_dt=1e9,
path='../RAW'):
"""Read header information from a list of exposures and parse out groups based on filter/target/orientation.
Parameters
----------
files : list
List of exposure filenames. If not specified, will use ``*flt.fits``.
info : None or `~astropy.table.Table`
Output from `~grizli.utils.get_flt_info`.
uniquename : bool
If True, then split everything by program ID and visit name. If
False, then just group by targname/filter/pa_v3.
use_visit : bool
For parallel observations with ``targname='ANY'``, use the filename
up to the visit ID as the target name. For example:
>>> flc = 'jbhj64d8q_flc.fits'
>>> visit_targname = flc[:6]
>>> print(visit_targname)
jbhj64
If False, generate a targname for parallel observations based on the
pointing coordinates using `radec_to_targname`. Use this keyword
for dithered parallels like 3D-HST / GLASS but set to False for
undithered parallels like WISP. Should also generally be used with
``uniquename=False`` otherwise generates names that are a bit
redundant:
+--------------+---------------------------+
| `uniquename` | Output Targname |
+==============+===========================+
| True | jbhj45-bhj-45-180.0-F814W |
+--------------+---------------------------+
| False | jbhj45-180.0-F814W |
+--------------+---------------------------+
translate : dict
Translation dictionary to modify TARGNAME keywords to some other
value. Used like:
>>> targname = 'GOODS-SOUTH-10'
>>> translate = {'GOODS-SOUTH-': 'goodss-'}
>>> for k in translate:
>>> targname = targname.replace(k, translate[k])
>>> print(targname)
goodss-10
visit_split_shift : float
Separation in ``arcmin`` beyond which exposures in a group are split
into separate visits.
path : str
PATH to search for `flt` files if ``info`` not provided
Returns
-------
output_list : dict
Dictionary split by target/filter/pa_v3. Keys are derived visit
product names and values are lists of exposure filenames corresponding
to that set. Keys are generated with the formats like:
>>> targname = 'macs1149+2223'
>>> pa_v3 = 32.0
>>> filter = 'f140w'
>>> flt_filename = 'ica521naq_flt.fits'
>>> propstr = flt_filename[1:4]
>>> visit = flt_filename[4:6]
>>> # uniquename = False
>>> print('{0}-{1:05.1f}-{2}'.format(targname, pa_v3, filter))
macs1149.6+2223-032.0-f140w
>>> # uniquename = True
>>> print('{0}-{1:3s}-{2:2s}-{3:05.1f}-{4:s}'.format(targname, propstr, visit, pa_v3, filter))
macs1149.6+2223-ca5-21-032.0-f140w
filter_list : dict
Nested dictionary split by filter and then PA_V3. This shouldn't
be used if exposures from completely disjoint pointings are stored
in the same working directory.
"""
if info is None:
if not files:
files = glob.glob(os.path.join(path), '*flt.fits')
if len(files) == 0:
return False
info = get_flt_info(files)
else:
info = info.copy()
for c in info.colnames:
if not c.islower():
info.rename_column(c, c.lower())
if 'expstart' not in info.colnames:
info['expstart'] = info['exptime']*0.
so = np.argsort(info['expstart'])
info = info[so]
#pa_v3 = np.round(info['pa_v3']*10)/10 % 360.
pa_v3 = np.round(np.round(info['pa_v3'], decimals=1)) % 360.
target_list = []
for i in range(len(info)):
# Replace ANY targets with JRhRmRs-DdDmDs
if info['targname'][i] == 'ANY':
if use_visit:
new_targname = info['file'][i][:6]
else:
new_targname = 'par-'+radec_to_targname(ra=info['ra_targ'][i],
dec=info['dec_targ'][i])
target_list.append(new_targname.lower())
else:
target_list.append(info['targname'][i])
target_list = np.array(target_list)
_prog_ids = []
visits = []
for file in info['file']:
bfile = os.path.basename(file)
if bfile.startswith('jw'):
_prog_ids.append(bfile[2:7])
visits.append(bfile[7:10])
else:
_prog_ids.append(bfile[1:4])
visits.append(bfile[4:6])
visits = np.array(visits)
info['progIDs'] = _prog_ids
progIDs = np.unique(info['progIDs'])
dates = np.array([''.join(date.split('-')[1:])
for date in info['date-obs']])
targets = np.unique(target_list)
output_list = [] # OrderedDict()
filter_list = OrderedDict()
for filter in np.unique(info['filter']):
filter_list[filter] = OrderedDict()
angles = np.unique(pa_v3[(info['filter'] == filter)])
for angle in angles:
filter_list[filter][angle] = []
for target in targets:
# 3D-HST targname translations
target_use = target
for key in translate.keys():
target_use = target_use.replace(key, translate[key])
# pad i < 10 with zero
for key in translate.keys():
if translate[key] in target_use:
spl = target_use.split('-')
try:
if (int(spl[-1]) < 10) & (len(spl[-1]) == 1):
spl[-1] = '{0:02d}'.format(int(spl[-1]))
target_use = '-'.join(spl)
except:
pass
for filter in np.unique(info['filter'][(target_list == target)]):
angles = np.unique(pa_v3[(info['filter'] == filter) &
(target_list == target)])
for angle in angles:
exposure_list = []
exposure_start = []
product = '{0}-{1:05.1f}-{2}'.format(target_use, angle, filter)
visit_match = np.unique(visits[(target_list == target) &
(info['filter'] == filter)])
this_progs = []
this_visits = []
for visit in visit_match:
ix = (visits == visit) & (target_list == target)
ix &= (info['filter'] == filter)
# this_progs.append(info['progIDs'][ix][0])
# print visit, ix.sum(), np.unique(info['progIDs'][ix])
new_progs = list(np.unique(info['progIDs'][ix]))
this_visits.extend([visit]*len(new_progs))
this_progs.extend(new_progs)
for visit, prog in zip(this_visits, this_progs):
visit_list = []
visit_start = []
_vstr = '{0}-{1}-{2}-{3:05.1f}-{4}'
visit_product = _vstr.format(target_use, prog, visit,
angle, filter)
use = (target_list == target)
use &= (info['filter'] == filter)
use &= (visits == visit)
use &= (pa_v3 == angle)
use &= (info['progIDs'] == prog)
if use.sum() == 0:
continue
for tstart, file in zip(info['expstart'][use],
info['file'][use]):
f = file.split('.gz')[0]
if f not in exposure_list:
visit_list.append(str(f))
visit_start.append(tstart)
exposure_list = np.append(exposure_list, visit_list)
exposure_start.extend(visit_start)
filter_list[filter][angle].extend(visit_list)
if uniquename:
print(visit_product, len(visit_list))
so = np.argsort(visit_start)
exposure_list = np.array(visit_list)[so]
#output_list[visit_product.lower()] = visit_list
d = OrderedDict(product=str(visit_product.lower()),
files=list(np.array(visit_list)[so]))
output_list.append(d)
if not uniquename:
print(product, len(exposure_list))
so = np.argsort(exposure_start)
exposure_list = np.array(exposure_list)[so]
#output_list[product.lower()] = exposure_list
d = OrderedDict(product=str(product.lower()),
files=list(np.array(exposure_list)[so]))
output_list.append(d)
# Split large shifts
if visit_split_shift > 0:
split_list = []
for o in output_list:
_spl = split_visit(o, path=path,
max_dt=max_dt,
visit_split_shift=visit_split_shift)
split_list.extend(_spl)
output_list = split_list
# Get visit footprint from FLT WCS
if get_footprint:
from shapely.geometry import Polygon
N = len(output_list)
for i in range(N):
for j in range(len(output_list[i]['files'])):
flt_file = output_list[i]['files'][j]
if (not os.path.exists(flt_file)):
for gzext in ['', '.gz']:
_flt_file = os.path.join(path, flt_file + gzext)
if os.path.exists(_flt_file):
flt_file = _flt_file
break
flt_j = pyfits.open(flt_file)
h = flt_j[0].header
_ext = 0
if (h['INSTRUME'] == 'WFC3'):
_ext = 1
if (h['DETECTOR'] == 'IR'):
wcs_j = pywcs.WCS(flt_j['SCI', 1])
else:
wcs_j = pywcs.WCS(flt_j['SCI', 1], fobj=flt_j)
elif (h['INSTRUME'] == 'WFPC2'):
_ext = 1
wcs_j = pywcs.WCS(flt_j['SCI', 1])
else:
_ext = 1
wcs_j = pywcs.WCS(flt_j['SCI', 1], fobj=flt_j)
if ((wcs_j.pixel_shape is None) &
('NPIX1' in flt_j['SCI',1].header)):
_h = flt_j['SCI',1].header
wcs_j.pixel_shape = (_h['NPIX1'], _h['NPIX2'])
fp_j = Polygon(wcs_j.calc_footprint())
if j == 0:
fp_i = fp_j.buffer(1./3600)
else:
fp_i = fp_i.union(fp_j.buffer(1./3600))
flt_j.close()
output_list[i]['footprint'] = fp_i
return output_list, filter_list
[docs]def split_visit(visit, visit_split_shift=1.5, max_dt=6./24, path='../RAW'):
"""
Check if files in a visit have large shifts and split them otherwise
visit : visit dictionary
visit_split_shift : split if shifts larger than `visit_split_shift` arcmin
"""
ims = []
for file in visit['files']:
for gzext in ['', '.gz']:
_file = os.path.join(path, file) + gzext
if os.path.exists(_file):
ims.append(pyfits.open(_file))
break
#ims = [pyfits.open(os.path.join(path, file)) for file in visit['files']]
crval1 = np.array([im[1].header['CRVAL1'] for im in ims])
crval2 = np.array([im[1].header['CRVAL2'] for im in ims])
expstart = np.array([im[0].header['EXPSTART'] for im in ims])
dt = np.cast[int]((expstart-expstart[0])/max_dt)
for im in ims:
im.close()
dx = (crval1 - crval1[0])*60*np.cos(crval2[0]/180*np.pi)
dy = (crval2 - crval2[0])*60
dxi = np.cast[int](np.round(dx/visit_split_shift))
dyi = np.cast[int](np.round(dy/visit_split_shift))
keys = dxi*100+dyi+1000*dt
un = np.unique(keys)
if len(un) == 1:
return [visit]
else:
spl = visit['product'].split('-')
isJWST = spl[-1].lower().startswith('clear')
isJWST |= spl[-1].lower() in ['gr150r','gr150c','grismr','grismc']
if isJWST:
spl.insert(-2, '')
else:
spl.insert(-1, '')
visits = []
for i in range(len(un)):
ix = keys == un[i]
if isJWST:
spl[-3] = 'abcdefghijklmnopqrsuvwxyz'[i]
else:
spl[-2] = 'abcdefghijklmnopqrsuvwxyz'[i]
new_visit = {'files': list(np.array(visit['files'])[ix]),
'product': '-'.join(spl)}
if 'footprints' in visit:
new_visit['footprints'] = list(np.array(visit['footprints'])[ix])
visits.append(new_visit)
return visits
[docs]def parse_visit_overlaps(visits, buffer=15.):
"""Find overlapping visits/filters to make combined mosaics
Parameters
----------
visits : list
Output list of visit information from `~grizli.utils.parse_flt_files`.
The script looks for files like `visits[i]['product']+'_dr?_sci.fits'`
to compute the WCS footprint of a visit. These are produced, e.g., by
`~grizli.prep.process_direct_grism_visit`.
buffer : float
Buffer, in `~astropy.units.arcsec`, to add around visit footprints to
look for overlaps.
Returns
-------
exposure_groups : list
List of overlapping visits, with similar format as input `visits`.
"""
import copy
from shapely.geometry import Polygon
N = len(visits)
exposure_groups = []
used = np.arange(len(visits)) < 0
for i in range(N):
f_i = visits[i]['product'].split('-')[-1]
if used[i]:
continue
if 'footprint' in visits[i]:
fp_i = visits[i]['footprint'].buffer(buffer/3600.)
else:
_products = visits[i]['product']+'_dr?_sci.fits'
im_i = pyfits.open(glob.glob(_products)[0])
wcs_i = pywcs.WCS(im_i[0])
fp_i = Polygon(wcs_i.calc_footprint()).buffer(buffer/3600.)
im_i.close()
exposure_groups.append(copy.deepcopy(visits[i]))
for j in range(i+1, N):
f_j = visits[j]['product'].split('-')[-1]
if (f_j != f_i) | (used[j]):
continue
#
if 'footprint' in visits[j]:
fp_j = visits[j]['footprint'].buffer(buffer/3600.)
else:
_products = visits[j]['product']+'_dr?_sci.fits'
im_j = pyfits.open(glob.glob(_products)[0])
wcs_j = pywcs.WCS(im_j[0])
fp_j = Polygon(wcs_j.calc_footprint()).buffer(buffer/3600.)
im_j.close()
olap = fp_i.intersection(fp_j)
if olap.area > 0:
used[j] = True
fp_i = fp_i.union(fp_j)
exposure_groups[-1]['footprint'] = fp_i
exposure_groups[-1]['files'].extend(visits[j]['files'])
for i in range(len(exposure_groups)):
flt_i = pyfits.open(exposure_groups[i]['files'][0])
product = flt_i[0].header['TARGNAME'].lower()
if product == 'any':
product = 'par-'+radec_to_targname(header=flt_i['SCI', 1].header)
f_i = exposure_groups[i]['product'].split('-')[-1]
product += '-'+f_i
exposure_groups[i]['product'] = product
flt_i.close()
return exposure_groups
DIRECT_ORDER = {'G102': ['F105W', 'F110W', 'F098M', 'F125W', 'F140W', 'F160W', 'F127M', 'F139M', 'F153M', 'F132N', 'F130N', 'F128N', 'F126N', 'F164N', 'F167N'],
'G141': ['F140W', 'F160W', 'F125W', 'F105W', 'F110W', 'F098M', 'F127M', 'F139M', 'F153M', 'F132N', 'F130N', 'F128N', 'F126N', 'F164N', 'F167N'],
'G800L': ['F814W', 'F606W', 'F850LP', 'F775W', 'F435W', 'F105W', 'F110W', 'F098M', 'F125W', 'F140W', 'F160W', 'F127M', 'F139M', 'F153M', 'F132N', 'F130N', 'F128N', 'F126N', 'F164N', 'F167N'],
'GR150C': ['F115W', 'F150W', 'F200W'],
'GR150R': ['F115W', 'F150W', 'F200W']}
[docs]def parse_grism_associations(exposure_groups, info,
best_direct=DIRECT_ORDER,
get_max_overlap=True):
"""Get associated lists of grism and direct exposures
Parameters
----------
exposure_grups : list
Output list of overlapping visits from
`~grizli.utils.parse_visit_overlaps`.
best_direct : dict
Dictionary of the preferred direct imaging filters to use with a
particular grism.
Returns
-------
grism_groups : list
List of dictionaries with associated 'direct' and 'grism' entries.
"""
N = len(exposure_groups)
grism_groups = []
for i in range(N):
_espi = exposure_groups[i]['product'].split('-')
if _espi[-2][0] in 'fg':
pupil_i = _espi[-2]
f_i = _espi[-1]
root_i = '-'.join(_espi[:-2])
else:
pupil_i = None
f_i = _espi[-1]
root_i = '-'.join(_espi[:-1])
if f_i.startswith('g'):
group = OrderedDict(grism=exposure_groups[i],
direct=None)
else:
continue
fp_i = exposure_groups[i]['footprint']
olap_i = 0.
d_i = f_i
d_idx = 10
for j in range(N):
_espj = exposure_groups[j]['product'].split('-')
if _espj[-2][0] in 'fg':
pupil_j = _espj[-2]
f_j = _espj[-1]
root_j = '-'.join(_espj[:-2])
else:
f_j = _espj[-1]
root_j = '-'.join(_espj[:-1])
pupil_j = None
if f_j.startswith('g'):
continue
fp_j = exposure_groups[j]['footprint']
olap = fp_i.intersection(fp_j)
if (root_j == root_i):
if pupil_i is not None:
if pupil_j == pupil_i:
group['direct'] = exposure_groups[j]
else:
continue
else:
if f_j.upper() not in best_direct[f_i.upper()]:
continue
if best_direct[f_i.upper()].index(f_j.upper()) < d_idx:
d_idx = best_direct[f_i.upper()].index(f_j.upper())
group['direct'] = exposure_groups[j]
olap_i = olap.area
d_i = f_j
grism_groups.append(group)
return grism_groups
[docs]def get_hst_filter(header, **kwargs):
"""
Deprecated: use `grizli.utils.parse_filter_from_header`
"""
result = parse_filter_from_header(header, **kwargs)
return result
EE_RADII = [0.1, 0.15, 0.2, 0.25, 0.3, 0.4, 0.5, 0.8, 1., 1.5, 2.]
[docs]def get_filter_obsmode(filter='f160w', acs_chip='wfc1', uvis_chip='uvis2', aper=np.inf, case=str.lower):
"""
Derive `~pysynphot` obsmode keyword from a filter name, where UVIS filters
end in 'u'
"""
if filter.lower()[:2] in ['f0', 'f1', 'g1']:
inst = 'wfc3,ir'
else:
if filter.lower().endswith('u'):
inst = f'wfc3,{uvis_chip}'
else:
inst = f'acs,{acs_chip}'
obsmode = inst + ',' + filter.strip('u').lower()
if np.isfinite(aper):
obsmode += f',aper#{aper:4.2f}'
return case(obsmode)
[docs]def tabulate_encircled_energy(aper_radii=EE_RADII, norm_radius=4.0):
import pysynphot as S
from .pipeline import default_params
# Default spectrum
sp = S.FlatSpectrum(25, fluxunits='ABMag')
tab = GTable()
tab['radius'] = aper_radii*u.arcsec
tab.meta['RNORM'] = norm_radius, 'Normalization radius, arcsec'
# IR
for f in default_params.IR_M_FILTERS+default_params.IR_W_FILTERS:
obsmode = 'wfc3,ir,'+f.lower()
print(obsmode)
tab[obsmode] = synphot_encircled_energy(obsmode=obsmode, sp=sp, aper_radii=aper_radii, norm_radius=norm_radius)
tab.meta['ZP_{0}'.format(obsmode)] = synphot_zeropoint(obsmode=obsmode, radius=norm_radius)
# Optical. Wrap in try/except to catch missing filters
for inst in ['acs,wfc1,', 'wfc3,uvis2,']:
for f in (default_params.OPT_M_FILTERS +
default_params.OPT_W_FILTERS +
default_params.UV_M_FILTERS +
default_params.UV_W_FILTERS):
obsmode = inst+f.lower()
try:
tab[obsmode] = synphot_encircled_energy(obsmode=obsmode, sp=sp, aper_radii=aper_radii, norm_radius=norm_radius)
print(obsmode)
tab.meta['ZP_{0}'.format(obsmode)] = synphot_zeropoint(obsmode=obsmode, radius=norm_radius)
except:
# Failed because obsmode not available in synphot
continue
tab.meta['PSYNVER'] = S.__version__, 'Pysynphot version'
tab.write('hst_encircled_energy.fits', overwrite=True)
[docs]def synphot_zeropoint(obsmode='wfc3,ir,f160w', radius=4.0):
"""
Compute synphot for a specific aperture
"""
import pysynphot as S
sp = S.FlatSpectrum(25, fluxunits='ABMag')
if np.isfinite(radius):
bp = S.ObsBandpass(obsmode+',aper#{0:.2f}'.format(radius))
else:
bp = S.ObsBandpass(obsmode)
# bp = S.ObsBandpass(obsmode+',aper#{0:.2f}'.format(radius))
obs = S.Observation(sp, bp)
ZP = 25 + 2.5*np.log10(obs.countrate())
return ZP
[docs]def synphot_encircled_energy(obsmode='wfc3,ir,f160w', sp='default', aper_radii=EE_RADII, norm_radius=4.0):
"""
Compute encircled energy curves with pysynphot
"""
import pysynphot as S
if sp == 'default':
sp = S.FlatSpectrum(25, fluxunits='ABMag')
# Normalization
if np.isfinite(norm_radius):
bp = S.ObsBandpass(obsmode+',aper#{0:.2f}'.format(norm_radius))
else:
bp = S.ObsBandpass(obsmode)
obs = S.Observation(sp, bp)
norm_counts = obs.countrate()
counts = np.ones_like(aper_radii)
for i, r_aper in enumerate(aper_radii):
#print(obsmode, r_aper)
bp = S.ObsBandpass(obsmode+',aper#{0:.2f}'.format(r_aper))
obs = S.Observation(sp, bp)
counts[i] = obs.countrate()
return counts / norm_counts
[docs]def photfnu_from_photflam(photflam, photplam):
"""
Compute PHOTFNU from PHOTFLAM+PHOTPLAM, e.g., for ACS/WFC
"""
ZP = -2.5*np.log10(photflam) - 21.10 - 5*np.log10(photplam) + 18.6921
photfnu = 10**(-0.4*(ZP-23.9))*1.e-6
return photfnu
DEFAULT_PRIMARY_KEYS = ['FILENAME', 'INSTRUME', 'INSTRUME', 'DETECTOR', 'FILTER', 'FILTER1', 'FILTER2', 'EXPSTART', 'DATE-OBS', 'EXPTIME', 'IDCTAB', 'NPOLFILE', 'D2IMFILE', 'PA_V3', 'FGSLOCK', 'GYROMODE', 'PROPOSID']
# For grism
DEFAULT_EXT_KEYS = ['EXTNAME', 'EXTVER', 'MDRIZSKY', 'CRPIX1', 'CRPIX2', 'CRVAL1', 'CRVAL2', 'CD1_1', 'CD1_2', 'CD2_1', 'CD2_2', 'PC1_1', 'PC1_2', 'PC2_1', 'PC2_2', 'CDELT1', 'CDELT2', 'CUNIT1', 'CUNIT2', 'CTYPE1', 'CTYPE2', 'RADESYS', 'LONPOLE', 'LATPOLE', 'IDCTAB', 'D2IMEXT', 'WCSNAME', 'PHOTMODE', 'ORIENTAT', 'CCDCHIP']
[docs]def flt_to_dict(fobj, primary_keys=DEFAULT_PRIMARY_KEYS, extensions=[('SCI', i+1) for i in range(2)], ext_keys=DEFAULT_EXT_KEYS):
"""
Parse basic elements from a FLT/FLC header to a dictionary
TBD
Parameters
----------
fobj : `~astropy.io.fits.HDUList`
FITS object
primary_keys : list
Keywords to extract from the primary extension (0).
extensions : list
List of additional extension names / indices.
ext_keys : list
Keywords to extract from the extension headers.
Returns
-------
flt_dict : dict
"""
import astropy.time
flt_dict = OrderedDict()
flt_dict['timestamp'] = astropy.time.Time.now().iso
h0 = fobj[0].header
# Primary keywords
for k in primary_keys:
if k in h0:
flt_dict[k] = h0[k]
# Grism keys
for k in h0:
if k.startswith('GSKY'):
flt_dict[k] = h0[k]
# WCS, etc. keywords from SCI extensions
flt_dict['extensions'] = OrderedDict()
count = 0
for ext in extensions:
if ext in fobj:
d_i = OrderedDict()
h_i = fobj[ext].header
for k in ext_keys:
if k in h_i:
d_i[k] = h_i[k]
# Grism keys
for k in h_i:
if k.startswith('GSKY'):
d_i[k] = h_i[k]
count += 1
flt_dict['extensions'][count] = d_i
return flt_dict
[docs]def get_set_bits(value):
"""
Compute which binary bits are set for an integer
"""
if hasattr(value, '__iter__'):
values = value
single = False
else:
values = [value]
single = True
result = []
for v in values:
try:
bitstr = np.binary_repr(v)[::-1]
except:
result.append([])
nset = bitstr.count('1')
setbits = []
j = -1
for i in range(nset):
j = bitstr.index('1', j+1)
setbits.append(j)
result.append(setbits)
if single:
return result[0]
else:
return result
[docs]def unset_dq_bits(value, okbits=32+64+512, verbose=False):
"""
Unset bit flags from a DQ array
For WFC3/IR, the following DQ bits can usually be unset:
32, 64: these pixels usually seem OK
512: blobs not relevant for grism exposures
Parameters
----------
value : int, `~numpy.ndarray`
Input DQ value
okbits : int
Bits to unset
verbose : bool
Print some information
Returns
-------
new_value : int, `~numpy.ndarray`
"""
bin_bits = np.binary_repr(okbits)
n = len(bin_bits)
for i in range(n):
if bin_bits[-(i+1)] == '1':
if verbose:
print(2**i)
value -= (value & 2**i)
return value
[docs]def detect_with_photutils(sci, err=None, dq=None, seg=None, detect_thresh=2.,
npixels=8, grow_seg=5, gauss_fwhm=2., gsize=3,
wcs=None, save_detection=False, root='mycat',
background=None, gain=None, AB_zeropoint=0.,
rename_columns={'xcentroid': 'x_flt',
'ycentroid': 'y_flt',
'ra_icrs_centroid': 'ra',
'dec_icrs_centroid': 'dec'},
overwrite=True, verbose=True):
"""
Use `~photutils` to detect objects and make segmentation map
.. note::
Deprecated in favor of sep catalogs in `~grizli.prep`.
Parameters
----------
sci : `~numpy.ndarray`
TBD
err, dq, seg : TBD
detect_thresh : float
Detection threshold, in :math:`\sigma`
grow_seg : int
Number of pixels to grow around the perimeter of detected objects
witha maximum filter
gauss_fwhm : float
FWHM of Gaussian convolution kernel that smoothes the detection
image.
verbose : bool
Print logging information to the terminal
save_detection : bool
Save the detection images and catalogs
wcs : `~astropy.wcs.WCS`
WCS object passed to `photutils.source_properties` used to compute
sky coordinates of detected objects.
Returns
-------
catalog : `~astropy.table.Table`
Object catalog with the default parameters.
"""
import scipy.ndimage as nd
from photutils import detect_threshold, detect_sources, SegmentationImage
from photutils import source_properties
import astropy.io.fits as pyfits
from astropy.table import Column
from astropy.stats import sigma_clipped_stats, gaussian_fwhm_to_sigma
from astropy.convolution import Gaussian2DKernel
# DQ masks
mask = (sci == 0)
if dq is not None:
mask |= dq > 0
# Detection threshold
if err is None:
threshold = detect_threshold(sci, snr=detect_thresh, mask=mask)
else:
threshold = (detect_thresh * err)*(~mask)
threshold[mask] = np.median(threshold[~mask])
if seg is None:
# Run the source detection and create the segmentation image
# Gaussian kernel
sigma = gauss_fwhm * gaussian_fwhm_to_sigma # FWHM = 2.
kernel = Gaussian2DKernel(sigma, x_size=gsize, y_size=gsize)
kernel.normalize()
if verbose:
print('{0}: photutils.detect_sources (detect_thresh={1:.1f}, grow_seg={2:d}, gauss_fwhm={3:.1f}, ZP={4:.1f})'.format(root, detect_thresh, grow_seg, gauss_fwhm, AB_zeropoint))
# Detect sources
segm = detect_sources(sci*(~mask), threshold, npixels=npixels,
filter_kernel=kernel)
grow = nd.maximum_filter(segm.data, grow_seg)
seg = np.cast[np.float32](grow)
else:
# Use the supplied segmentation image
segm = SegmentationImage(seg)
# Source properties catalog
if verbose:
print('{0}: photutils.source_properties'.format(root))
props = source_properties(sci, segm, error=threshold/detect_thresh,
mask=mask, background=background, wcs=wcs)
catalog = props.to_table()
# Mag columns
mag = AB_zeropoint - 2.5*np.log10(catalog['source_sum'])
mag._name = 'mag'
catalog.add_column(mag)
try:
logscale = 2.5/np.log(10)
mag_err = logscale*catalog['source_sum_err']/catalog['source_sum']
except:
mag_err = np.zeros_like(mag)-99
mag_err._name = 'mag_err'
catalog.add_column(mag_err)
# Rename some catalog columns
for key in rename_columns.keys():
if key not in catalog.colnames:
continue
catalog.rename_column(key, rename_columns[key])
if verbose:
print('Rename column: {0} -> {1}'.format(key, rename_columns[key]))
# Done!
if verbose:
print(NO_NEWLINE + ('{0}: photutils.source_properties - {1:d} objects'.format(root, len(catalog))))
# Save outputs?
if save_detection:
seg_file = root + '.detect_seg.fits'
seg_cat = root + '.detect.cat'
if verbose:
print('{0}: save {1}, {2}'.format(root, seg_file, seg_cat))
if wcs is not None:
header = wcs.to_header(relax=True)
else:
header = None
pyfits.writeto(seg_file, data=seg, header=header, overwrite=overwrite)
if os.path.exists(seg_cat) & overwrite:
os.remove(seg_cat)
catalog.write(seg_cat, format='ascii.commented_header')
return catalog, seg
[docs]def safe_invert(arr):
"""
version-safe matrix inversion using np.linalg or np.matrix.I
"""
try:
from numpy.linalg import inv
_inv = inv(arr)
except:
_inv = np.matrix(arr).I.A
return _inv
[docs]def nmad(data):
"""Normalized NMAD=1.4826022 * `~.astropy.stats.median_absolute_deviation`
"""
import astropy.stats
return 1.4826022*astropy.stats.median_absolute_deviation(data)
[docs]def get_line_wavelengths():
"""Get a dictionary of common emission line wavelengths and line ratios
Returns
-------
line_wavelengths, line_ratios : dict
Keys are common to both dictionaries and are simple names for lines
and line complexes. Values are lists of line wavelengths and line
ratios.
>>> from grizli.utils import get_line_wavelengths
>>> line_wavelengths, line_ratios = get_line_wavelengths()
>>> print(line_wavelengths['Ha'], line_ratios['Ha'])
[6564.61] [1.0]
>>> print(line_wavelengths['OIII'], line_ratios['OIII'])
[5008.24, 4960.295] [2.98, 1]
Includes some additional combined line complexes useful for redshift
fits:
>>> from grizli.utils import get_line_wavelengths
>>> line_wavelengths, line_ratios = get_line_wavelengths()
>>> key = 'Ha+SII+SIII+He'
>>> print(line_wavelengths[key], '\\n', line_ratios[key])
[6564.61, 6718.29, 6732.67, 9068.6, 9530.6, 10830.0]
[1.0, 0.1, 0.1, 0.05, 0.122, 0.04]
"""
line_wavelengths = OrderedDict()
line_ratios = OrderedDict()
# Paschen: https://www.gemini.edu/sciops/instruments/nearir-resources/astronomical-lines/h-lines
# Rh = 0.0010967757
# k = Rh * (1/n0**2 - 1/n**2)
# wave = 1./k # Angstroms
# Pfund n0=5
line_wavelengths['PfA'] = [74598.8]
line_ratios['PfA'] = [1.]
line_wavelengths['PfB'] = [46537.9]
line_ratios['PfB'] = [1.]
line_wavelengths['PfG'] = [37405.7]
line_ratios['PfG'] = [1.]
line_wavelengths['PfD'] = [32970.0]
line_ratios['PfD'] = [1.]
line_wavelengths['PfE'] = [30392.1]
line_ratios['PfE'] = [1.]
# Brackett n0=4
line_wavelengths['BrA'] = [40522.8]
line_ratios['BrA'] = [1.]
line_wavelengths['BrB'] = [26258.8]
line_ratios['BrB'] = [1.]
line_wavelengths['BrG'] = [21661.3]
line_ratios['BrG'] = [1.]
line_wavelengths['BrD'] = [19451.0]
line_ratios['BrD'] = [1.]
line_wavelengths['BrE'] = [18179.2]
line_ratios['BrE'] = [1.]
# Paschen n0=3
line_wavelengths['PaA'] = [18756.3]
line_ratios['PaA'] = [1.]
line_wavelengths['PaB'] = [12821.7]
line_ratios['PaB'] = [1.]
line_wavelengths['PaG'] = [10941.2]
line_ratios['PaG'] = [1.]
line_wavelengths['PaD'] = [10052.2]
line_ratios['PaD'] = [1.]
line_wavelengths['Pa8'] = [9548.65]
line_ratios['Pa8'] = [1.]
line_wavelengths['Pa9'] = [9231.60]
line_ratios['Pa9'] = [1.]
line_wavelengths['Pa10'] = [9017.44]
line_ratios['Pa10'] = [1.]
# Balmer n0=2
line_wavelengths['Ha'] = [6564.697]
line_ratios['Ha'] = [1.]
line_wavelengths['Hb'] = [4862.738]
line_ratios['Hb'] = [1.]
line_wavelengths['Hg'] = [4341.731]
line_ratios['Hg'] = [1.]
line_wavelengths['Hd'] = [4102.936]
line_ratios['Hd'] = [1.]
line_wavelengths['H7'] = [3971.236]
line_ratios['H7'] = [1.]
line_wavelengths['H8'] = [3890.191]
line_ratios['H8'] = [1.]
line_wavelengths['H9'] = [3836.511]
line_ratios['H9'] = [1.]
line_wavelengths['H10'] = [3799.014]
line_ratios['H10'] = [1.]
line_wavelengths['H11'] = [3771.739]
line_ratios['H11'] = [1.]
line_wavelengths['H12'] = [3751.255]
line_ratios['H12'] = [1.]
# Groves et al. 2011, Table 1
# Osterbrock table 4.4 for H7 to H10
# line_wavelengths['Balmer 10kK'] = [6564.61, 4862.68, 4341.68, 4101.73]
# line_ratios['Balmer 10kK'] = [2.86, 1.0, 0.468, 0.259]
line_wavelengths['Balmer 10kK'] = [6564.61, 4862.68, 4341.68, 4101.73, 3971.198, 3890.166, 3836.485, 3798.987]
line_ratios['Balmer 10kK'] = [2.86, 1.0, 0.468, 0.259, 0.159, 0.105, 0.0731, 0.0530]
# Paschen from Osterbrock, e.g., Pa-beta relative to H-gamma
line_wavelengths['Balmer 10kK'] += line_wavelengths['PaA'] + line_wavelengths['PaB'] + line_wavelengths['PaG'] + line_wavelengths['PaD']
line_ratios['Balmer 10kK'] += [0.348 * line_ratios['Balmer 10kK'][i] for i in [1, 2, 3, 4]]
# Osterbrock table 4.4 for H7 to H10
line_wavelengths['Balmer 10kK + MgII'] = line_wavelengths['Balmer 10kK'] + [2799.117]
line_ratios['Balmer 10kK + MgII'] = line_ratios['Balmer 10kK'] + [3.]
# # Paschen from Osterbrock, e.g., Pa-beta relative to H-gamma
# line_wavelengths['Balmer 10kK + MgII'] += line_wavelengths['PaA'] + line_wavelengths['PaB'] + line_wavelengths['PaG']
# line_ratios['Balmer 10kK + MgII'] += [0.348 * line_ratios['Balmer 10kK + MgII'][i] for i in [1,2,3]]
# With Paschen lines & He 10830 from Glikman 2006
# https://iopscience.iop.org/article/10.1086/500098/pdf
#line_wavelengths['Balmer 10kK + MgII'] = [6564.61, 4862.68, 4341.68, 4101.73, 3971.198, 2799.117, 12821.6, 10941.1]
#line_ratios['Balmer 10kK + MgII'] = [2.86, 1.0, 0.468, 0.259, 0.16, 3., 2.86*4.8/100, 2.86*1.95/100]
# Redden with Calzetti00
if False:
from extinction import calzetti00
Av = 1.0
Rv = 3.1
waves = line_wavelengths['Balmer 10kK + MgII']
ratios = line_ratios['Balmer 10kK + MgII']
for Av in [0.5, 1.0, 2.0]:
mred = calzetti00(np.array(waves), Av, Rv)
fred = 10**(-0.4*mred)
key = 'Balmer 10kK + MgII Av={0:.1f}'.format(Av)
line_wavelengths[key] = [w for w in waves]
line_ratios[key] = [ratios[i]*fred[i] for i in range(len(waves))]
line_wavelengths['Balmer 10kK + MgII Av=0.5'] = [6564.61, 4862.68, 4341.68, 4101.73, 3971.198, 2799.117, 12821.6, 10941.1]
line_ratios['Balmer 10kK + MgII Av=0.5'] = [2.009811938798515, 0.5817566641521459, 0.25176970824566913, 0.1338409369665902, 0.08079209880749984, 1.1739297839690317, 0.13092553990513178, 0.05033866127477651]
line_wavelengths['Balmer 10kK + MgII Av=1.0'] = [6564.61, 4862.68, 4341.68, 4101.73, 3971.198, 2799.117, 12821.6, 10941.1]
line_ratios['Balmer 10kK + MgII Av=1.0'] = [1.4123580522157504, 0.33844081628543266, 0.13544441450878067, 0.0691636926953466, 0.04079602018575511, 0.4593703792298591, 0.12486521707058751, 0.045436270735820045]
line_wavelengths['Balmer 10kK + MgII Av=2.0'] = [6564.61, 4862.68, 4341.68, 4101.73, 3971.198, 2799.117, 12821.6, 10941.1]
line_ratios['Balmer 10kK + MgII Av=2.0'] = [0.6974668768037302, 0.11454218612794999, 0.03919912269578289, 0.018469561340758073, 0.010401970393728362, 0.0703403817712615, 0.11357315292894044, 0.03701729780130422]
###########
# Reddened with Kriek & Conroy dust, tau_V=0.5
line_wavelengths['Balmer 10kK t0.5'] = [6564.61, 4862.68, 4341.68, 4101.73]
line_ratios['Balmer 10kK t0.5'] = [2.86*0.68, 1.0*0.55, 0.468*0.51, 0.259*0.48]
# Reddened with Kriek & Conroy dust, tau_V=1
line_wavelengths['Balmer 10kK t1'] = [6564.61, 4862.68, 4341.68, 4101.73]
line_ratios['Balmer 10kK t1'] = [2.86*0.46, 1.0*0.31, 0.468*0.256, 0.259*0.232]
line_wavelengths['OIII-4363'] = [4364.436]
line_ratios['OIII-4363'] = [1.]
line_wavelengths['OIII'] = [5008.240, 4960.295]
line_ratios['OIII'] = [2.98, 1]
# Split doublet, if needed
line_wavelengths['OIII-4959'] = [4960.295]
line_ratios['OIII-4959'] = [1]
line_wavelengths['OIII-5007'] = [5008.240]
line_ratios['OIII-5007'] = [1]
line_wavelengths['OII'] = [3727.092, 3729.875]
line_ratios['OII'] = [1, 1.]
line_wavelengths['OI-6302'] = [6302.046, 6365.535]
line_ratios['OI-6302'] = [1, 0.33]
line_wavelengths['OI-5578'] = [5578.89]
line_ratios['OI-5578'] = [1]
# Auroral OII
# lines roughly taken from https://arxiv.org/pdf/1610.06939.pdf
line_wavelengths['OII-7325'] = [7322.0, 7332.]
line_ratios['OII-7325'] = [1.2, 1.]
# Weak Ar III in SF galaxies
line_wavelengths['ArIII-7138'] = [7137.77]
line_ratios['ArIII-7138'] = [1.]
line_wavelengths['NeIII-3867'] = [3869.87]
line_ratios['NeIII-3867'] = [1.]
line_wavelengths['NeIII-3968'] = [3968.59]
line_ratios['NeIII-3968'] = [1.]
line_wavelengths['NeV-3346'] = [3343.5]
line_ratios['NeV-3346'] = [1.]
line_wavelengths['NeVI-3426'] = [3426.85]
line_ratios['NeVI-3426'] = [1.]
line_wavelengths['SIII'] = [9071.1, 9533.2][::-1]
line_ratios['SIII'] = [1, 2.44][::-1]
# Split doublet, if needed
line_wavelengths['SIII-9068'] = [9071.1]
line_ratios['SIII-9068'] = [1]
line_wavelengths['SIII-9531'] = [9533.2]
line_ratios['SIII-9531'] = [1]
line_wavelengths['SII'] = [6718.29, 6732.67]
line_ratios['SII'] = [1., 1.]
line_wavelengths['SII-6717'] = [6718.29]
line_ratios['SII-6717'] = [1.]
line_wavelengths['SII-6731'] = [6732.67]
line_ratios['SII-6731'] = [1.]
line_wavelengths['SII-4075'] = [4069.75, 4077.5]
line_ratios['SII-4075'] = [1., 1.]
line_wavelengths['SII-4070'] = [4069.75]
line_ratios['SII-4075'] = [1.]
line_wavelengths['SII-4078'] = [4077.5]
line_ratios['SII-4078'] = [1.]
line_wavelengths['HeII-4687'] = [4687.5]
line_ratios['HeII-4687'] = [1.]
line_wavelengths['HeII-5412'] = [5412.5]
line_ratios['HeII-5412'] = [1.]
line_wavelengths['HeI-5877'] = [5877.249]
line_ratios['HeI-5877'] = [1.]
line_wavelengths['HeI-3889'] = [3889.75]
line_ratios['HeI-3889'] = [1.]
line_wavelengths['HeI-1083'] = [10832.057, 10833.306]
line_ratios['HeI-1083'] = [1., 1.]
line_wavelengths['HeI-6678'] = [6678.10]
line_ratios['HeI-6678'] = [1.]
line_wavelengths['HeI-7065'] = [7067.1]
line_ratios['HeI-7065'] = [1.]
line_wavelengths['HeI-8446'] = [8446.7]
line_ratios['HeI-8446'] = [1.]
# Osterbrock Table 4.5
# -> N=4
line_wavelengths['HeI-series'] = [4472.7, 5877.2, 4027.3, 3820.7, 7067.1, 10833.2, 3889.7, 3188.7]
line_ratios['HeI-series'] = [1., 2.75, 0.474, 0.264, 0.330, 4.42, 2.26, 0.916]
line_wavelengths['MgII'] = [2799.117]
line_ratios['MgII'] = [1.]
line_wavelengths['CIV-1549'] = [1549.480]
line_ratios['CIV-1549'] = [1.]
line_wavelengths['CIII-1906'] = [1906.683]
line_ratios['CIII-1906'] = [1.]
line_wavelengths['CIII-1908'] = [1908.734]
line_ratios['CIII-1908'] = [1.]
line_wavelengths['OIII-1663'] = [1665.85]
line_ratios['OIII-1663'] = [1.]
line_wavelengths['HeII-1640'] = [1640.4]
line_ratios['HeII-1640'] = [1.]
line_wavelengths['SiIV+OIV-1398'] = [1398.]
line_ratios['SiIV+OIV-1398'] = [1.]
# Weak line in LEGA-C spectra
line_wavelengths['NI-5199'] = [5199.4, 5201.76]
line_ratios['NI-5199'] = [1., 1.]
line_wavelengths['NII'] = [6549.86, 6585.27][::-1]
line_ratios['NII'] = [1.0, 3.0][::-1]
line_wavelengths['NII-6549'] = [6549.86]
line_ratios['NII-6549'] = [1.]
line_wavelengths['NII-6584'] = [6585.27]
line_ratios['NII-6584'] = [1.]
line_wavelengths['NIII-1750'] = [1750.]
line_ratios['NIII-1750'] = [1.]
line_wavelengths['NIV-1487'] = [1487.]
line_ratios['NIV-1487'] = [1.]
line_wavelengths['NV-1240'] = [1240.81]
line_ratios['NV-1240'] = [1.]
line_wavelengths['Lya'] = [1215.4]
line_ratios['Lya'] = [1.]
line_wavelengths['QSO-UV-lines'] = [line_wavelengths[k][0] for k in ['Lya', 'CIV-1549', 'CIII-1906', 'CIII-1908', 'OIII-1663', 'HeII-1640', 'SiIV+OIV-1398', 'NV-1240', 'NIII-1750']]
line_ratios['QSO-UV-lines'] = [1., 0.5, 0.1, 0.1, 0.008, 0.09, 0.1, 0.3, 0.05]
line_wavelengths['QSO-Narrow-lines'] = [line_wavelengths[k][0] for k in ['OII', 'OIII-5007', 'OIII-4959', 'SII-6717', 'SII-6731', 'OI-6302', 'NeIII-3867', 'NeVI-3426', 'NeV-3346']]
line_ratios['QSO-Narrow-lines'] = [0.2, 1.6, 1.6/2.98, 0.1, 0.1, 0.01, 0.5, 0.2, 0.02]
# redder lines
line_wavelengths['QSO-Narrow-lines'] += line_wavelengths['SIII']
line_ratios['QSO-Narrow-lines'] += [lr*0.05 for lr in line_ratios['SIII']]
line_wavelengths['QSO-Narrow-lines'] += line_wavelengths['HeI-1083']
line_ratios['QSO-Narrow-lines'] += [0.2]
line_wavelengths['Lya+CIV'] = [1215.4, 1549.49]
line_ratios['Lya+CIV'] = [1., 0.1]
line_wavelengths['Gal-UV-lines'] = [line_wavelengths[k][0] for k in ['Lya', 'CIV-1549', 'CIII-1906', 'CIII-1908', 'OIII-1663', 'HeII-1640', 'SiIV+OIV-1398', 'NV-1240', 'NIII-1750', 'MgII']]
line_ratios['Gal-UV-lines'] = [1., 0.15, 0.1, 0.1, 0.008, 0.09, 0.1, 0.05, 0.05, 0.1]
line_wavelengths['Ha+SII'] = [6564.61, 6718.29, 6732.67]
line_ratios['Ha+SII'] = [1., 1./10, 1./10]
line_wavelengths['Ha+SII+SIII+He'] = [6564.61, 6718.29, 6732.67, 9068.6, 9530.6, 10830.]
line_ratios['Ha+SII+SIII+He'] = [1., 1./10, 1./10, 1./20, 2.44/20, 1./25.]
line_wavelengths['Ha+NII+SII+SIII+He'] = [6564.61, 6549.86, 6585.27, 6718.29, 6732.67, 9068.6, 9530.6, 10830.]
line_ratios['Ha+NII+SII+SIII+He'] = [1., 1./(4.*4), 3./(4*4), 1./10, 1./10, 1./20, 2.44/20, 1./25.]
line_wavelengths['Ha+NII+SII+SIII+He+PaB'] = [6564.61, 6549.86, 6585.27, 6718.29, 6732.67, 9068.6, 9530.6, 10830., 12821]
line_ratios['Ha+NII+SII+SIII+He+PaB'] = [1., 1./(4.*4), 3./(4*4), 1./10, 1./10, 1./20, 2.44/20, 1./25., 1./10]
line_wavelengths['Ha+NII+SII+SIII+He+PaB+PaG'] = [6564.61, 6549.86, 6585.27, 6718.29, 6732.67, 9068.6, 9530.6, 10830., 12821, 10941.1]
line_ratios['Ha+NII+SII+SIII+He+PaB+PaG'] = [1., 1./(4.*4), 3./(4*4), 1./10, 1./10, 1./20, 2.44/20, 1./25., 1./10, 1./10/2.86]
line_wavelengths['Ha+NII'] = [6564.61, 6549.86, 6585.27]
n2ha = 1./3 # log NII/Ha ~ -0.6, Kewley 2013
line_ratios['Ha+NII'] = [1., 1./4.*n2ha, 3./4.*n2ha]
line_wavelengths['OIII+Hb'] = [5008.240, 4960.295, 4862.68]
line_ratios['OIII+Hb'] = [2.98, 1, 3.98/6.]
# Include more balmer lines
line_wavelengths['OIII+Hb+Hg+Hd'] = line_wavelengths['OIII'] + line_wavelengths['Balmer 10kK'][1:]
line_ratios['OIII+Hb+Hg+Hd'] = line_ratios['OIII'] + line_ratios['Balmer 10kK'][1:]
# o3hb = 1./6
# for i in range(2, len(line_ratios['Balmer 10kK'])-1):
# line_ratios['OIII+Hb+Hg+Hd'][i] *= 3.98*o3hb
# Compute as O3/Hb
o3hb = 6
for i in range(2):
line_ratios['OIII+Hb+Hg+Hd'][i] *= 1./3.98*o3hb
line_wavelengths['OIII+Hb+Ha'] = [5008.240, 4960.295, 4862.68, 6564.61]
line_ratios['OIII+Hb+Ha'] = [2.98, 1, 3.98/10., 3.98/10.*2.86]
line_wavelengths['OIII+Hb+Ha+SII'] = [5008.240, 4960.295, 4862.68, 6564.61, 6718.29, 6732.67]
line_ratios['OIII+Hb+Ha+SII'] = [2.98, 1, 3.98/10., 3.98/10.*2.86*4, 3.98/10.*2.86/10.*4, 3.98/10.*2.86/10.*4]
line_wavelengths['OIII+OII'] = [5008.240, 4960.295, 3729.875]
line_ratios['OIII+OII'] = [2.98, 1, 3.98/4.]
line_wavelengths['OII+Ne'] = [3729.875, 3869]
line_ratios['OII+Ne'] = [1, 1./5]
# Groups of all lines
line_wavelengths['full'] = [w for w in line_wavelengths['Balmer 10kK']]
line_ratios['full'] = [w for w in line_ratios['Balmer 10kK']]
line_wavelengths['full'] += line_wavelengths['NII']
line_ratios['full'] += [1./5/3.*line_ratios['Balmer 10kK'][1]*r for r in line_ratios['NII']]
line_wavelengths['full'] += line_wavelengths['SII']
line_ratios['full'] += [1./3.8/2*line_ratios['Balmer 10kK'][1]*r for r in line_ratios['SII']]
# Lines from Hagele 2006, low-Z HII galaxies
# SDSS J002101.03+005248.1
line_wavelengths['full'] += line_wavelengths['SIII']
line_ratios['full'] += [401./1000/2.44*line_ratios['Balmer 10kK'][1]*r for r in line_ratios['SIII']]
# HeI
line_wavelengths['full'] += line_wavelengths['HeI-series']
he5877_hb = 127./1000/line_ratios['HeI-series'][1]
line_ratios['full'] += [he5877_hb*r for r in line_ratios['HeI-series']]
# NeIII
line_wavelengths['full'] += line_wavelengths['NeIII-3867']
line_ratios['full'] += [388./1000 for r in line_ratios['NeIII-3867']]
line_wavelengths['full'] += line_wavelengths['NeIII-3968']
line_ratios['full'] += [290./1000 for r in line_ratios['NeIII-3968']]
# Add UV lines: MgII/Hb = 3
line_wavelengths['full'] += line_wavelengths['Gal-UV-lines']
line_ratios['full'] += [r*3/line_ratios['Gal-UV-lines'][-1] for r in line_ratios['Gal-UV-lines']]
# High O32 - low metallicity
o32, r23 = 4, 8
o3_hb = r23/(1+1/o32)
line_wavelengths['highO32'] = [w for w in line_wavelengths['full']]
line_ratios['highO32'] = [r for r in line_ratios['full']]
line_wavelengths['highO32'] += line_wavelengths['OIII']
line_ratios['highO32'] += [r*o3_hb/3.98 for r in line_ratios['OIII']]
line_wavelengths['highO32'] += line_wavelengths['OII']
line_ratios['highO32'] += [r*o3_hb/2/o32 for r in line_ratios['OII']]
# Low O32 - low metallicity
o32, r23 = 0.3, 4
o3_hb = r23/(1+1/o32)
line_wavelengths['lowO32'] = [w for w in line_wavelengths['full']]
line_ratios['lowO32'] = [r for r in line_ratios['full']]
line_wavelengths['lowO32'] += line_wavelengths['OIII']
line_ratios['lowO32'] += [r*o3_hb/3.98 for r in line_ratios['OIII']]
line_wavelengths['lowO32'] += line_wavelengths['OII']
line_ratios['lowO32'] += [r*o3_hb/2/o32 for r in line_ratios['OII']]
return line_wavelengths, line_ratios
[docs]def emission_line_templates():
"""
Testing FSPS line templates
"""
import numpy as np
import matplotlib.pyplot as plt
from grizli import utils
import fsps
sp = fsps.StellarPopulation(imf_type=1, zcontinuous=1)
sp_params = {}
sp_params['starburst'] = {'sfh': 4, 'tau': 0.3, 'tage': 0.1,
'logzsol': -1, 'gas_logz': -1,
'gas_logu': -2.5}
sp_params['mature'] = {'sfh': 4, 'tau': 0.2, 'tage': 0.9,
'logzsol': -0.2, 'gas_logz': -0.2,
'gas_logu': -2.5}
line_templates = {}
for t in sp_params:
pset = sp_params[t]
header = 'wave flux\n\n'
for p in pset:
header += '{0} = {1}\n'.format(p, pset[p])
if p == 'tage':
continue
print(p, pset[p])
sp.params[p] = pset[p]
spec = {}
for neb in [True, False]:
sp.params['add_neb_emission'] = neb
sp.params['add_neb_continuum'] = neb
wave, spec[neb] = sp.get_spectrum(tage=pset['tage'], peraa=True)
#plt.plot(wave, spec[neb], alpha=0.5)
neb_only = spec[True] - spec[False]
neb_only = neb_only / neb_only.max()
neb_only = spec[True] / spec[True].max()
plt.plot(wave, neb_only, label=t, alpha=0.5)
neb_only[neb_only < 1.e-4] = 0
np.savetxt('fsps_{0}_lines.txt'.format(t), np.array([wave, neb_only]).T, fmt='%.5e', header=header)
line_templates[t] = utils.SpectrumTemplate(wave=wave, flux=neb_only, name='fsps_{0}_lines'.format(t))
[docs]def pah33(wave_grid):
"""
Set of 3.3 um PAH lines from Li et al. 2020
Returns
-------
pah_templates : list
List of `~grizli.utils.SpectrumTemplate` templates for three components
around 3.3 um
"""
pah_templates = {}
for lc, lw in zip([3.29, 3.40, 3.47], [0.043, 0.031, 0.100]):
ti = pah_line_template(wave_grid, center_um=lc, fwhm=lw)
pah_templates[ti.name] = ti
return pah_templates
[docs]def pah_line_template(wave_grid, center_um=3.29, fwhm=0.043):
"""
Make a template for a broad PAH line with a Drude profile
Default parameters in Lai et al. 2020
https://iopscience.iop.org/article/10.3847/1538-4357/abc002/pdf
from Tokunaga et al. 1991
Drude equation and normalization from Yamada et al. 2013
Parameters
----------
wave_grid : array-like
Wavelength grid in angstroms
center_um : float
Central wavelength in microns
fwhm : float
Drude profile FWHM in microns
Returns
-------
pah_templ : `~grizli.utils.SpectrumTemplate`
Template with the PAH feature
"""
br = 1.
gamma_width = fwhm/center_um
Iv = br*gamma_width**2
Iv /= ((wave_grid/1.e4/center_um - center_um*1.e4/wave_grid)**2
+ gamma_width**2)
Inorm = np.pi*2.99e14/2.*br*gamma_width/center_um
Iv *= 1 / Inorm
# Flambda
Ilam = Iv * 2.99e18 / (wave_grid)**2
pah_templ = SpectrumTemplate(wave=wave_grid,
flux=Ilam,
name=f'line PAH-{center_um:.2f}')
return pah_templ
[docs]class SpectrumTemplate(object):
def __init__(self, wave=None, flux=None, central_wave=None, fwhm=None, velocity=False, fluxunits=FLAMBDA_CGS, waveunits=u.angstrom, name='template', lorentz=False, err=None):
"""Container for template spectra.
Parameters
----------
wave : array-like
Wavelength
In `astropy.units.Angstrom`.
flux : float array-like
If float, then the integrated flux of a Gaussian line. If
array, then f-lambda flux density.
central_wave, fwhm : float
Initialize the template with a Gaussian at this wavelength (in
`astropy.units.Angstrom`.) that has an integrated flux of `flux`
and `fwhm` in `astropy.units.Angstrom` or `km/s` for
`velocity=True`.
velocity : bool
`fwhm` is a velocity in `km/s`.
Attributes
----------
wave, flux : array-like
Passed from the input parameters or generated/modified later.
Methods
-------
__add__, __mul__ : Addition and multiplication of templates.
Examples
--------
.. plot::
:include-source:
import matplotlib.pyplot as plt
from grizli.utils import SpectrumTemplate
ha = SpectrumTemplate(central_wave=6563., fwhm=10)
plt.plot(ha.wave, ha.flux)
ha_z = ha.zscale(0.1)
plt.plot(ha_z.wave, ha_z.flux, label='z=0.1')
plt.legend()
plt.xlabel(r'$\lambda$')
plt.xlim(6000, 7500)
plt.show()
"""
self.wave = wave
if wave is not None:
self.wave = np.cast[np.float64](wave)
self.flux = flux
if flux is not None:
self.flux = np.cast[np.float64](flux)
if err is not None:
self.err = np.cast[np.float64](err)
else:
self.err = None
self.fwhm = None
self.velocity = None
self.fluxunits = fluxunits
self.waveunits = waveunits
self.name = name
if (central_wave is not None) & (fwhm is not None):
self.fwhm = fwhm
self.velocity = velocity
self.wave, self.flux = self.make_gaussian(central_wave, fwhm,
wave_grid=wave,
velocity=velocity,
max_sigma=50,
lorentz=lorentz)
self.fnu_units = FNU_CGS
self.to_fnu()
[docs] @staticmethod
def make_gaussian(central_wave, fwhm, max_sigma=5, step=0.1,
wave_grid=None, velocity=False, clip=1.e-6,
lorentz=False):
"""Make Gaussian template
Parameters
----------
central_wave, fwhm : None or float or array-like
Central wavelength and FWHM of the desired Gaussian
velocity : bool
`fwhm` is a velocity.
max_sigma, step : float
Generated wavelength array is
>>> rms = fwhm/2.35
>>> xgauss = np.arange(-max_sigma, max_sigma, step)*rms+central_wave
clip : float
Clip values where the value of the gaussian function is less than
`clip` times its maximum (i.e., `1/sqrt(2*pi*sigma**2)`).
lorentz : bool
Make a Lorentzian line instead of a Gaussian.
Returns
-------
wave, flux : array-like
Wavelength and flux of a Gaussian line
"""
import astropy.constants as const
from astropy.modeling.models import Lorentz1D
if hasattr(fwhm, 'unit'):
rms = fwhm.value/2.35
velocity = u.physical.get_physical_type(fwhm.unit) == 'speed'
if velocity:
rms = central_wave*(fwhm/const.c.to(KMS)).value/2.35
else:
rms = fwhm.value/2.35
else:
if velocity:
rms = central_wave*(fwhm/const.c.to(KMS).value)/2.35
else:
rms = fwhm/2.35
if wave_grid is None:
#print('xxx line', central_wave, max_sigma, rms)
wave_grid = np.arange(-max_sigma, max_sigma, step)*rms
wave_grid += central_wave
wave_grid = np.hstack([91., wave_grid, 1.e8])
if lorentz:
if velocity:
use_fwhm = central_wave*(fwhm/const.c.to(KMS).value)
else:
use_fwhm = fwhm
lmodel = Lorentz1D(amplitude=1, x_0=central_wave, fwhm=use_fwhm)
line = lmodel(wave_grid)
line[0:2] = 0
line[-2:] = 0
line /= np.trapz(line, wave_grid)
peak = line.max()
else:
# Gaussian
line = np.exp(-(wave_grid-central_wave)**2/2/rms**2)
peak = np.sqrt(2*np.pi*rms**2)
line *= 1./peak # np.sqrt(2*np.pi*rms**2)
line[line < 1./peak*clip] = 0
return wave_grid, line
#self.wave = xgauss
#self.flux = gaussian
[docs] def zscale(self, z, scalar=1, apply_igm=True):
"""Redshift the template and multiply by a scalar.
Parameters
----------
z : float
Redshift to use.
scalar : float
Multiplicative factor. Additional factor of 1./(1+z) is implicit.
Returns
-------
new_spectrum : `~grizli.utils.SpectrumTemplate`
Redshifted and scaled spectrum.
"""
if apply_igm:
try:
import eazy.igm
igm = eazy.igm.Inoue14()
igmz = igm.full_IGM(z, self.wave*(1+z))
except:
igmz = 1.
else:
igmz = 1.
return SpectrumTemplate(wave=self.wave*(1+z),
flux=self.flux*scalar/(1+z)*igmz)
def __add__(self, spectrum):
"""Add two templates together
The new wavelength array is the union of both input spectra and each
input spectrum is linearly interpolated to the final grid.
Parameters
----------
spectrum : `~grizli.utils.SpectrumTemplate`
Returns
-------
new_spectrum : `~grizli.utils.SpectrumTemplate`
"""
new_wave = np.unique(np.append(self.wave, spectrum.wave))
new_wave.sort()
new_flux = np.interp(new_wave, self.wave, self.flux)
new_flux += np.interp(new_wave, spectrum.wave, spectrum.flux)
out = SpectrumTemplate(wave=new_wave, flux=new_flux)
out.fwhm = spectrum.fwhm
return out
def __mul__(self, scalar):
"""Multiply spectrum by a scalar value
Parameters
----------
scalar : float
Factor to multipy to `self.flux`.
Returns
-------
new_spectrum : `~grizli.utils.SpectrumTemplate`
"""
out = SpectrumTemplate(wave=self.wave, flux=self.flux*scalar)
out.fwhm = self.fwhm
return out
[docs] def to_fnu(self, fnu_units=FNU_CGS):
"""Make fnu version of the template.
Sets the `flux_fnu` attribute, assuming that the wavelength is given
in Angstrom and the flux is given in flambda:
>>> flux_fnu = self.flux * self.wave**2 / 3.e18
"""
#import astropy.constants as const
#flux_fnu = self.flux * self.wave**2 / 3.e18
# flux_fnu = (self.flux*self.fluxunits*(self.wave*self.waveunits)**2/const.c).to(FNU_CGS) #,
if ((FNU_CGS.__str__() == 'erg / (cm2 Hz s)') &
(self.fluxunits.__str__() == 'erg / (Angstrom cm2 s)')):
# Faster
flux_fnu = self.flux*self.wave**2/2.99792458e18*fnu_units
if self.err is not None:
err_fnu = self.err*self.wave**2/2.99792458e18*fnu_units
else:
# Use astropy conversion
flux_fnu = (self.flux*self.fluxunits).to(fnu_units, equivalencies=u.spectral_density(self.wave*self.waveunits))
if self.err is not None:
err_fnu = (self.err*self.fluxunits).to(fnu_units, equivalencies=u.spectral_density(self.wave*self.waveunits))
self.fnu_units = fnu_units
self.flux_fnu = flux_fnu.value
if self.err is not None:
self.err_fnu = err_fnu.value
else:
self.err_fnu = None
[docs] def integrate_filter(self, filter, abmag=False, use_wave='filter'):
"""Integrate the template through an `~eazy.FilterDefinition` filter
object.
Parameters
----------
filter : `~pysynphot.ObsBandpass`
Or any object that has `wave` and `throughput` attributes, with
the former in the same units as the input spectrum.
abmag : bool
Return AB magnitude rather than fnu flux
Returns
-------
temp_flux : float
Examples
--------
Compute the WFC3/IR F140W AB magnitude of a pure emission line at the
5-sigma 3D-HST line detection limit (5e-17 erg/s/cm2):
>>> import numpy as np
>>> from grizli.utils import SpectrumTemplate
>>> from eazy.filters import FilterDefinition
>>> import pysynphot as S
>>> line = SpectrumTemplate(central_wave=1.4e4, fwhm=150.,
velocity=True)*5.e-17
>>> filter = FilterDefinition(bp=S.ObsBandpass('wfc3,ir,f140w'))
>>> fnu = line.integrate_filter(filter)
>>> print('AB mag = {0:.3f}'.format(-2.5*np.log10(fnu)-48.6))
AB mag = 26.619
"""
INTEGRATOR = np.trapz
try:
#import grizli.utils_c
#interp = grizli.utils_c.interp.interp_conserve_c
from .utils_c.interp import interp_conserve_c
interp = interp_conserve_c
except ImportError:
interp = np.interp
#wz = self.wave*(1+z)
nonzero = filter.throughput > 0
if (filter.wave[nonzero].min() > self.wave.max()) | (filter.wave[nonzero].max() < self.wave.min()) | (filter.wave[nonzero].min() < self.wave.min()):
if self.err is None:
return 0.
else:
return 0., 0.
if use_wave == 'filter':
# Interpolate to filter wavelengths
integrate_wave = filter.wave
integrate_templ = interp(filter.wave.astype(np.float64),
self.wave, self.flux_fnu, left=0, right=0)
if self.err is not None:
templ_ivar = 1./interp(filter.wave.astype(np.float64),
self.wave, self.err_fnu)**2
templ_ivar[~np.isfinite(templ_ivar)] = 0
integrate_weight = filter.throughput/filter.wave*templ_ivar/filter.norm
else:
integrate_weight = filter.throughput/filter.wave
else:
# Interpolate to spectrum wavelengths
integrate_wave = self.wave
integrate_templ = self.flux_fnu
# test = nonzero
test = np.isfinite(filter.throughput)
interp_thru = interp(integrate_wave, filter.wave[test],
filter.throughput[test],
left=0, right=0)
if self.err is not None:
templ_ivar = 1/self.err_fnu**2
templ_ivar[~np.isfinite(templ_ivar)] = 0
integrate_weight = interp_thru/integrate_wave*templ_ivar/filter.norm
else:
integrate_weight = interp_thru/integrate_wave # /templ_err**2
if hasattr(filter, 'norm') & (self.err is None):
filter_norm = filter.norm
else:
# e.g., pysynphot bandpass
filter_norm = INTEGRATOR(integrate_weight, integrate_wave)
# f_nu/lam dlam == f_nu d (ln nu)
temp_flux = INTEGRATOR(integrate_templ*integrate_weight, integrate_wave) / filter_norm
if self.err is not None:
temp_err = 1/np.sqrt(filter_norm)
if abmag:
temp_mag = -2.5*np.log10(temp_flux)-48.6
return temp_mag
else:
if self.err is not None:
return temp_flux, temp_err
else:
return temp_flux
[docs]def load_templates(fwhm=400, line_complexes=True, stars=False,
full_line_list=DEFAULT_LINE_LIST, continuum_list=None,
fsps_templates=False, alf_template=False, lorentz=False):
"""Generate a list of templates for fitting to the grism spectra
The different sets of continuum templates are stored in
>>> temp_dir = os.path.join(GRIZLI_PATH, 'templates')
Parameters
----------
fwhm : float
FWHM of a Gaussian, in km/s, that is convolved with the emission
line templates. If too narrow, then can see pixel effects in the
fits as a function of redshift.
line_complexes : bool
Generate line complex templates with fixed flux ratios rather than
individual lines. This is useful for the redshift fits where there
would be redshift degeneracies if the line fluxes for individual
lines were allowed to vary completely freely. See the list of
available lines and line groups in
`~grizli.utils.get_line_wavelengths`. Currently,
`line_complexes=True` generates the following groups:
Ha+NII+SII+SIII+He
OIII+Hb
OII+Ne
stars : bool
Get stellar templates rather than galaxies + lines
full_line_list : None or list
Full set of lines to try. The default is set in the global variable
`~grizli.utils.DEFAULT_LINE_LIST`.
The full list of implemented lines is in `~grizli.utils.get_line_wavelengths`.
continuum_list : None or list
Override the default continuum templates if None.
fsps_templates : bool
If True, get the FSPS NMF templates.
Returns
-------
temp_list : dictionary of `~grizli.utils.SpectrumTemplate` objects
Output template list
"""
if stars:
# templates = glob.glob('%s/templates/Pickles_stars/ext/*dat' %(GRIZLI_PATH))
# templates = []
# for t in 'obafgkmrw':
# templates.extend( glob.glob('%s/templates/Pickles_stars/ext/uk%s*dat' %(os.getenv('THREEDHST'), t)))
# templates.extend(glob.glob('%s/templates/SPEX/spex-prism-M*txt' %(os.getenv('THREEDHST'))))
# templates.extend(glob.glob('%s/templates/SPEX/spex-prism-[LT]*txt' %(os.getenv('THREEDHST'))))
#
# #templates = glob.glob('/Users/brammer/Downloads/templates/spex*txt')
# templates = glob.glob('bpgs/*ascii')
# info = catIO.Table('bpgs/bpgs.info')
# type = np.array([t[:2] for t in info['type']])
# templates = []
# for t in 'OBAFGKM':
# test = type == '-%s' %(t)
# so = np.argsort(info['type'][test])
# templates.extend(info['file'][test][so])
#
# temp_list = OrderedDict()
# for temp in templates:
# #data = np.loadtxt('bpgs/'+temp, unpack=True)
# data = np.loadtxt(temp, unpack=True)
# #data[0] *= 1.e4 # spex
# scl = np.interp(5500., data[0], data[1])
# name = os.path.basename(temp)
# #ix = info['file'] == temp
# #name='%5s %s' %(info['type'][ix][0][1:], temp.split('.as')[0])
# print(name)
# temp_list[name] = utils.SpectrumTemplate(wave=data[0],
# flux=data[1]/scl)
# np.save('stars_bpgs.npy', [temp_list])
# tall = np.load(os.path.join(GRIZLI_PATH,
# 'templates/stars.npy'))[0]
#
# return tall
#
# temp_list = OrderedDict()
# for k in tall:
# if k.startswith('uk'):
# temp_list[k] = tall[k]
#
# return temp_list
#
# for t in 'MLT':
# for k in tall:
# if k.startswith('spex-prism-'+t):
# temp_list[k] = tall[k]
#
# return temp_list
# return temp_list
templates = ['M6.5.txt', 'M8.0.txt', 'L1.0.txt', 'L3.5.txt', 'L6.0.txt', 'T2.0.txt', 'T6.0.txt', 'T7.5.txt']
templates = ['stars/'+t for t in templates]
else:
# Intermediate and very old
# templates = ['templates/EAZY_v1.0_lines/eazy_v1.0_sed3_nolines.dat',
# 'templates/cvd12_t11_solar_Chabrier.extend.skip10.dat']
templates = ['eazy_intermediate.dat',
'cvd12_t11_solar_Chabrier.dat']
# Post starburst
# templates.append('templates/UltraVISTA/eazy_v1.1_sed9.dat')
templates.append('post_starburst.dat')
# Very blue continuum
# templates.append('templates/YoungSB/erb2010_continuum.dat')
templates.append('erb2010_continuum.dat')
# Test new templates
# templates = ['templates/erb2010_continuum.dat',
# 'templates/fsps/tweak_fsps_temp_kc13_12_006.dat',
# 'templates/fsps/tweak_fsps_temp_kc13_12_008.dat']
if fsps_templates:
#templates = ['templates/fsps/tweak_fsps_temp_kc13_12_0{0:02d}.dat'.format(i+1) for i in range(12)]
templates = ['fsps/fsps_QSF_12_v3_nolines_0{0:02d}.dat'.format(i+1) for i in range(12)]
#templates = ['fsps/fsps_QSF_7_v3_nolines_0{0:02d}.dat'.format(i+1) for i in range(7)]
if alf_template:
templates.append('alf_SSP.dat')
if continuum_list is not None:
templates = continuum_list
temp_list = OrderedDict()
for temp in templates:
data = np.loadtxt(os.path.join(GRIZLI_PATH, 'templates', temp), unpack=True)
#scl = np.interp(5500., data[0], data[1])
scl = 1.
name = temp # os.path.basename(temp)
temp_list[name] = SpectrumTemplate(wave=data[0], flux=data[1]/scl,
name=name)
temp_list[name].name = name
if stars:
return temp_list
# Emission lines:
line_wavelengths, line_ratios = get_line_wavelengths()
if line_complexes:
#line_list = ['Ha+SII', 'OIII+Hb+Ha', 'OII']
#line_list = ['Ha+SII', 'OIII+Hb', 'OII']
#line_list = ['Ha+NII+SII+SIII+He+PaB', 'OIII+Hb', 'OII+Ne', 'Lya+CIV']
#line_list = ['Ha+NII+SII+SIII+He+PaB', 'OIII+Hb+Hg+Hd', 'OII+Ne', 'Lya+CIV']
line_list = ['Ha+NII+SII+SIII+He+PaB', 'OIII+Hb+Hg+Hd', 'OII+Ne', 'Gal-UV-lines']
else:
if full_line_list is None:
line_list = DEFAULT_LINE_LIST
else:
line_list = full_line_list
#line_list = ['Ha', 'SII']
# Use FSPS grid for lines
wave_grid = None
# if fsps_templates:
# wave_grid = data[0]
# else:
# wave_grid = None
for li in line_list:
scl = line_ratios[li]/np.sum(line_ratios[li])
for i in range(len(scl)):
if ('O32' in li) & (np.abs(line_wavelengths[li][i]-2799) < 2):
fwhm_i = 2500
lorentz_i = True
else:
fwhm_i = fwhm
lorentz_i = lorentz
line_i = SpectrumTemplate(wave=wave_grid,
central_wave=line_wavelengths[li][i],
flux=None, fwhm=fwhm_i, velocity=True,
lorentz=lorentz_i)
if i == 0:
line_temp = line_i*scl[i]
else:
line_temp = line_temp + line_i*scl[i]
name = 'line {0}'.format(li)
line_temp.name = name
temp_list[name] = line_temp
return temp_list
[docs]def load_beta_templates(wave=np.arange(400, 2.5e4), betas=[-2, -1, 0]):
"""
Step-function templates with f_lambda ~ (wave/1216.)**beta
"""
cont_wave = np.arange(400, 2.5e4)
t0 = {}
for beta in betas:
key = 'beta {0}'.format(beta)
t0[key] = SpectrumTemplate(wave=cont_wave, flux=(cont_wave/1216.)**beta)
return t0
[docs]def load_quasar_templates(broad_fwhm=2500, narrow_fwhm=1200, broad_lines=['HeI-5877', 'MgII', 'Lya', 'CIV-1549', 'CIII-1906', 'CIII-1908', 'OIII-1663', 'HeII-1640', 'SiIV+OIV-1398', 'NIV-1487', 'NV-1240', 'PaB', 'PaG'], narrow_lines=['NIII-1750', 'OII', 'OIII', 'SII', 'OI-6302', 'OIII-4363', 'NeIII-3867', 'NeVI-3426', 'NeV-3346', 'OII-7325', 'ArIII-7138', 'SIII', 'HeI-1083'], include_feii=True, slopes=[-2.8, 0, 2.8], uv_line_complex=True, fixed_narrow_lines=False, t1_only=False, nspline=13, Rspline=30, betas=None, include_reddened_balmer_lines=False):
"""
Make templates suitable for fitting broad-line quasars
"""
from collections import OrderedDict
import scipy.ndimage as nd
t0 = OrderedDict()
t1 = OrderedDict()
broad1 = load_templates(fwhm=broad_fwhm, line_complexes=False, stars=False, full_line_list=['Ha', 'Hb', 'Hg', 'Hd', 'H7', 'H8', 'H9', 'H10'] + broad_lines, continuum_list=[], fsps_templates=False, alf_template=False, lorentz=True)
narrow1 = load_templates(fwhm=400, line_complexes=False, stars=False, full_line_list=narrow_lines, continuum_list=[], fsps_templates=False, alf_template=False)
if fixed_narrow_lines:
if t1_only:
narrow0 = narrow1
else:
narrow0 = load_templates(fwhm=narrow_fwhm, line_complexes=False, stars=False, full_line_list=['QSO-Narrow-lines'], continuum_list=[], fsps_templates=False, alf_template=False)
else:
narrow0 = load_templates(fwhm=narrow_fwhm, line_complexes=False, stars=False, full_line_list=narrow_lines, continuum_list=[], fsps_templates=False, alf_template=False)
if t1_only:
broad0 = broad1
else:
if uv_line_complex:
full_line_list = ['Balmer 10kK + MgII Av=0.5', 'QSO-UV-lines']
#broad0 = load_templates(fwhm=broad_fwhm, line_complexes=False, stars=False, full_line_list=['Balmer 10kK + MgII', 'QSO-UV-lines'], continuum_list=[], fsps_templates=False, alf_template=False, lorentz=True)
else:
full_line_list = ['Balmer 10kK + MgII Av=0.5']
#broad0 = load_templates(fwhm=broad_fwhm, line_complexes=False, stars=False, full_line_list=['Balmer 10kK'] + broad_lines, continuum_list=[], fsps_templates=False, alf_template=False, lorentz=True)
if include_reddened_balmer_lines:
line_wavelengths, line_ratios = get_line_wavelengths()
if 'Balmer 10kK + MgII Av=1.0' in line_wavelengths:
full_line_list += ['Balmer 10kK + MgII']
full_line_list += ['Balmer 10kK + MgII Av=1.0']
full_line_list += ['Balmer 10kK + MgII Av=2.0']
broad0 = load_templates(fwhm=broad_fwhm, line_complexes=False, stars=False, full_line_list=full_line_list, continuum_list=[], fsps_templates=False, alf_template=False, lorentz=True)
for k in broad0:
t0[k] = broad0[k]
for k in broad1:
t1[k] = broad1[k]
for k in narrow0:
t0[k] = narrow0[k]
for k in narrow1:
t1[k] = narrow1[k]
# Fe II
if include_feii:
feii_wave, feii_flux = np.loadtxt(os.path.dirname(__file__) + '/data/templates/FeII_VeronCetty2004.txt', unpack=True)
# smoothing, in units of input velocity resolution
feii_kern = broad_fwhm/2.3548/75.
feii_sm = nd.gaussian_filter(feii_flux, feii_kern)
t0['FeII-VC2004'] = t1['FeII-VC2004'] = SpectrumTemplate(wave=feii_wave, flux=feii_sm, name='FeII-VC2004')
# Linear continua
# cont_wave = np.arange(400, 2.5e4)
# for slope in slopes:
# key = 'slope {0}'.format(slope)
# t0[key] = t1[key] = SpectrumTemplate(wave=cont_wave, flux=(cont_wave/6563.)**slope)
if Rspline is not None:
wspline = np.arange(4200, 2.5e4, 10)
df_spl = log_zgrid(zr=[wspline[0], wspline[-1]], dz=1./Rspline)
bsplines = bspline_templates(wspline, df=len(df_spl)+2, log=True,
clip=0.0001)
for key in bsplines:
t0[key] = t1[key] = bsplines[key]
elif nspline > 0:
# Spline continua
cont_wave = np.arange(5000, 2.4e4)
bsplines = bspline_templates(cont_wave, df=nspline, log=True)
for key in bsplines:
t0[key] = t1[key] = bsplines[key]
elif betas is not None:
btemp = load_beta_templates(wave=np.arange(400, 2.5e4), betas=betas)
for key in btemp:
t0[key] = t1[key] = btemp[key]
else:
# Observed frame steps
onedR = -nspline
wlim = [5000, 18000.0]
bin_steps, step_templ = step_templates(wlim=wlim, R=onedR,
round=10)
for key in step_templ:
t0[key] = t1[key] = step_templ[key]
# t0['blue'] = t1['blue'] = SpectrumTemplate(wave=cont_wave, flux=(cont_wave/6563.)**-2.8)
# t0['mid'] = t1['mid'] = SpectrumTemplate(wave=cont_wave, flux=(cont_wave/6563.)**0)
# t0['red'] = t1['mid'] = SpectrumTemplate(wave=cont_wave, flux=(cont_wave/6563.)**2.8)
return t0, t1
PHOENIX_LOGG_FULL = [3.0, 3.5, 4.0, 4.5, 5.0, 5.5]
PHOENIX_LOGG = [4.0, 4.5, 5.0, 5.5]
PHOENIX_TEFF_FULL = [400.0, 420.0, 450.0, 500.0, 550.0, 600.0, 650.0, 700.0, 750.0, 800.0, 850.0, 900.0, 950.0, 1000.0, 1050.0, 1100.0, 1150.0, 1200.0, 1250.0, 1300.0, 1350.0, 1400.0, 1450.0, 1500.0, 1550.0, 1600.0, 1650.0, 1700.0, 1750.0, 1800.0, 1850.0, 1900.0, 1950.0, 2000.0, 2100.0, 2200.0, 2300.0, 2400.0, 2500.0, 2600.0, 2700.0, 2800.0, 2900.0, 3000.0, 3100.0, 3200.0, 3300.0, 3400.0, 3500.0, 3600.0, 3700.0, 3800.0, 3900.0, 4000.0, 4100.0, 4200.0, 4300.0, 4400.0, 4500.0, 4600.0, 4700.0, 4800.0, 4900.0, 5000.0]
PHOENIX_TEFF = [400., 420., 450., 500., 550., 600., 650., 700., 750.,
800., 850., 900., 950., 1000., 1050., 1100., 1150., 1200.,
1300., 1400., 1500., 1600., 1700., 1800., 1900., 2000., 2100.,
2200., 2300., 2400., 2500., 2600., 2700., 2800., 2900., 3000.,
3100., 3200., 3300., 3400., 3500., 3600., 3700., 3800., 3900., 4000.,
4200., 4400., 4600., 4800., 5000., 5500., 5500, 6000., 6500., 7000.]
PHOENIX_ZMET_FULL = [-2.5, -2.0, -1.5, -1.0, -0.5, -0., 0.5]
PHOENIX_ZMET = [-1.0, -0.5, -0.]
[docs]def load_phoenix_stars(logg_list=PHOENIX_LOGG, teff_list=PHOENIX_TEFF, zmet_list=PHOENIX_ZMET, add_carbon_star=True, file='bt-settl_t400-7000_g4.5.fits'):
"""
Load Phoenix stellar templates
"""
from collections import OrderedDict
try:
from urllib.request import urlretrieve
except:
from urllib import urlretrieve
# file='bt-settl_t400-5000_g4.5.fits'
# file='bt-settl_t400-3500_z0.0.fits'
try:
hdu = pyfits.open(os.path.join(GRIZLI_PATH, 'templates/stars/', file))
except:
#url = 'https://s3.amazonaws.com/grizli/CONF'
#url = 'https://erda.ku.dk/vgrid/Gabriel%20Brammer/CONF'
url = ('https://raw.githubusercontent.com/gbrammer/' +
'grizli-config/master')
print('Fetch {0}/{1}'.format(url, file))
#os.system('wget -O /tmp/{1} {0}/{1}'.format(url, file))
res = urlretrieve('{0}/{1}'.format(url, file),
filename=os.path.join('/tmp', file))
hdu = pyfits.open(os.path.join('/tmp/', file))
tab = GTable.gread(hdu[1])
hdu.close()
tstars = OrderedDict()
N = tab['flux'].shape[1]
for i in range(N):
teff = tab.meta['TEFF{0:03d}'.format(i)]
logg = tab.meta['LOGG{0:03d}'.format(i)]
try:
met = tab.meta['ZMET{0:03d}'.format(i)]
except:
met = 0.
if (logg not in logg_list) | (teff not in teff_list) | (met not in zmet_list):
#print('Skip {0} {1}'.format(logg, teff))
continue
label = 'bt-settl_t{0:05.0f}_g{1:3.1f}_m{2:.1f}'.format(teff, logg, met)
tstars[label] = SpectrumTemplate(wave=tab['wave'],
flux=tab['flux'][:, i], name=label)
if add_carbon_star:
cfile = os.path.join(GRIZLI_PATH, 'templates/stars/carbon_star.txt')
sp = read_catalog(cfile)
if add_carbon_star > 1:
import scipy.ndimage as nd
cflux = nd.gaussian_filter(sp['flux'], add_carbon_star)
else:
cflux = sp['flux']
tstars['bt-settl_t05000_g0.0_m0.0'] = SpectrumTemplate(wave=sp['wave'], flux=cflux, name='carbon-lancon2002')
return tstars
[docs]def load_sdss_pca_templates(file='spEigenQSO-55732.fits', smooth=3000):
"""
Load SDSS eigen templates
"""
from collections import OrderedDict
import scipy.ndimage as nd
im = pyfits.open(os.path.join(GRIZLI_PATH, 'templates', file))
h = im[0].header
log_wave = np.arange(h['NAXIS1'])*h['COEFF1']+h['COEFF0']
wave = 10**log_wave
name = file.split('.fits')[0]
if smooth > 0:
dv_in = h['COEFF1']*3.e5
n = smooth / dv_in
data = nd.gaussian_filter1d(im[0].data, n, axis=1).astype(np.float64)
skip = int(n/2.5)
wave = wave[::skip]
data = data[:, ::skip]
else:
data = im[0].data.astype(np.float64)
N = h['NAXIS2']
temp_list = OrderedDict()
for i in range(N):
temp_list['{0} {1}'.format(name, i+1)] = SpectrumTemplate(wave=wave, flux=data[i, :])
im.close()
return temp_list
[docs]def cheb_templates(wave, order=6, get_matrix=False, log=False, clip=1.e-4, minmax=None):
"""
Chebyshev polynomial basis functions
"""
from numpy.polynomial.chebyshev import chebval, chebvander
if minmax is None:
mi = wave.min()
ma = wave.max()
else:
mi, ma = np.squeeze(minmax)*1.
if log:
xi = np.log(wave)
mi = np.log(mi)
ma = np.log(ma)
else:
xi = wave*1
x = (xi-mi)*2/(ma-mi)-1
n_bases = order+1
basis = chebvander(x, order)
# basis = np.empty((x.shape[0], n_bases), dtype=float)
#
# xr = np.arange(n_bases)
# for i in range(n_bases):
# _c = (xr == i)*1
# #print(_c, xr, i)
# basis[:,i] = chebval(x, _c)
#for i in range(n_bases):
out_of_range = (xi < mi) | (xi > ma)
basis[out_of_range,:] = 0
if get_matrix:
return basis
temp = OrderedDict()
for i in range(n_bases):
key = f'cheb o{i}'
temp[key] = SpectrumTemplate(wave, basis[:,i])
temp[key].name = key
return temp
[docs]def bspline_templates(wave, degree=3, df=6, get_matrix=False, log=False, clip=1.e-4, minmax=None):
"""
B-spline basis functions, modeled after `~patsy.splines`
"""
from scipy.interpolate import splev
order = degree+1
n_inner_knots = df - order
inner_knots = np.linspace(0, 1, n_inner_knots + 2)[1:-1]
norm_knots = np.concatenate(([0, 1] * order,
inner_knots))
norm_knots.sort()
if log:
xspl = np.log(wave)
else:
xspl = wave*1
if minmax is None:
mi = xspl.min()
ma = xspl.max()
else:
mi, ma = minmax
width = ma-mi
all_knots = norm_knots*width+mi
n_bases = len(all_knots) - (degree + 1)
basis = np.empty((xspl.shape[0], n_bases), dtype=float)
coefs = np.identity(n_bases)
basis = splev(xspl, (all_knots, coefs, degree))
for i in range(n_bases):
out_of_range = (xspl < mi) | (xspl > ma)
basis[i][out_of_range] = 0
wave_peak = np.round(wave[np.argmax(basis, axis=1)])
maxval = np.max(basis, axis=1)
for i in range(n_bases):
basis[i][basis[i] < clip*maxval[i]] = 0
if get_matrix:
return np.vstack(basis).T
temp = OrderedDict()
for i in range(n_bases):
key = 'bspl {0} {1:.0f}'.format(i, wave_peak[i])
temp[key] = SpectrumTemplate(wave, basis[i])
temp[key].name = key
temp[key].wave_peak = wave_peak[i]
temp.knots = all_knots
temp.degree = degree
temp.xspl = xspl
return temp
[docs]def eval_bspline_templates(wave, bspl, coefs):
from scipy.interpolate import splev
xspl = np.log(wave)
basis = splev(xspl, (bspl.knots, coefs, bspl.degree))
return np.array(basis)
[docs]def split_spline_template(templ, wavelength_range=[5000, 2.4e4], Rspline=10, log=True):
"""
Multiply a single template by spline bases to effectively generate a
spline multiplicative correction that can be fit with linear least
squares.
Parameters
----------
templ : `~grizli.utils.SpectrumTemplate`
Template to split.
wavelength_range : [float, float]
Limit the splines to this wavelength range
Rspline : float
Effective resolution, R=dlambda/lambda, of the spline correction
function.
log : bool
Log-spaced splines
Returns
-------
stemp : dict
Dictionary of spline-component templates, with additional attributes:
wspline = wavelength of the templates / spline correction
tspline = matrix of the spline corrections
knots = peak wavelenghts of each spline component
"""
from collections import OrderedDict
from grizli import utils
if False:
stars = utils.load_templates(stars=True)
templ = stars['stars/L1.0.txt']
wspline = templ.wave
clip = (wspline > wavelength_range[0]) & (wspline < wavelength_range[1])
df_spl = len(utils.log_zgrid(zr=wavelength_range, dz=1./Rspline))
tspline = utils.bspline_templates(wspline[clip], df=df_spl+2, log=log, clip=0.0001, get_matrix=True)
ix = np.argmax(tspline, axis=0)
knots = wspline[clip][ix]
N = tspline.shape[1]
stemp = OrderedDict()
for i in range(N):
name = '{0} {1:.2f}'.format(templ.name, knots[i]/1.e4)
stemp[name] = utils.SpectrumTemplate(wave=wspline[clip], flux=templ.flux[clip]*tspline[:, i], name=name)
stemp[name].knot = knots[i]
stemp.wspline = wspline[clip]
stemp.tspline = tspline
stemp.knots = knots
return stemp
[docs]def step_templates(wlim=[5000, 1.8e4], bin_steps=None, R=30, round=10, rest=False, special=None, order=0):
"""
Step-function templates for easy binning
"""
if special == 'Dn4000':
rest = True
bin_steps = np.hstack([np.arange(850, 3849, 100),
[3850, 3950, 4000, 4100],
np.arange(4200, 1.7e4, 100)])
elif special == 'D4000':
rest = True
bin_steps = np.hstack([np.arange(850, 3749, 200),
[3750, 3950, 4050, 4250],
np.arange(4450, 1.7e4, 200)])
elif special not in ['D4000', 'Dn4000', None]:
print('step_templates: {0} not recognized (options are \'D4000\', \'Dn4000\', and None)'.format(special))
return {}
if bin_steps is None:
bin_steps = np.round(log_zgrid(wlim, 1./R)/round)*round
else:
wlim = [bin_steps[0], bin_steps[-1]]
ds = np.diff(bin_steps)
xspec = np.arange(wlim[0]-ds[0], wlim[1]+ds[-1])
bin_mid = bin_steps[:-1]+ds/2.
step_templ = {}
for i in range(len(bin_steps)-1):
yspec = ((xspec >= bin_steps[i]) & (xspec < bin_steps[i+1]))*1
for o in range(order+1):
label = 'step {0:.0f}-{1:.0f} {2}'.format(bin_steps[i], bin_steps[i+1], o)
if rest:
label = 'r'+label
flux = ((xspec-bin_mid[i])/ds[i])**o * (yspec > 0)
step_templ[label] = SpectrumTemplate(wave=xspec, flux=flux,
name=label)
return bin_steps, step_templ
[docs]def polynomial_templates(wave, ref_wave=1.e4, order=0, line=False):
temp = OrderedDict()
if line:
for sign in [1, -1]:
key = 'poly {0}'.format(sign)
temp[key] = SpectrumTemplate(wave, sign*(wave/ref_wave-1)+1)
temp[key].name = key
return temp
for i in range(order+1):
key = 'poly {0}'.format(i)
temp[key] = SpectrumTemplate(wave, (wave/ref_wave-1)**i)
temp[key].name = key
temp[key].ref_wave = ref_wave
return temp
[docs]def split_poly_template(templ, ref_wave=1.e4, order=3):
"""
Multiply a single template by polynomial bases to effectively generate a
polynomial multiplicative correction that can be fit with linear least
squares.
Parameters
----------
templ : `~grizli.utils.SpectrumTemplate`
Template to split.
ref_wave : float
Wavelength where to normalize the polynomials.
Order : int
Polynomial order. Returns order+1 templates.
Returns
-------
ptemp : dict
Dictionary of polynomial-component templates, with additional
attributes:
ref_wave = wavelength where polynomials normalized
"""
from collections import OrderedDict
from grizli import utils
tspline = polynomial_templates(templ.wave, ref_wave=ref_wave,
order=order, line=False)
ptemp = OrderedDict()
for i, t in enumerate(tspline):
name = '{0} poly {1}'.format(templ.name, i)
ptemp[name] = utils.SpectrumTemplate(wave=templ.wave,
flux=templ.flux*tspline[t].flux,
name=name)
ptemp[name].ref_wave = ref_wave
ptemp.ref_wave = ref_wave
return ptemp
[docs]def dot_templates(coeffs, templates, z=0, max_R=5000, apply_igm=True):
"""Compute template sum analogous to `np.dot(coeffs, templates)`.
"""
if len(coeffs) != len(templates):
raise ValueError('shapes of coeffs ({0}) and templates ({1}) don\'t match'.format(len(coeffs), len(templates)))
# for i, te in enumerate(templates):
# if i == 0:
# tc = templates[te].zscale(z, scalar=coeffs[i])
# tl = templates[te].zscale(z, scalar=coeffs[i])
# else:
# if te.startswith('line'):
# tc += templates[te].zscale(z, scalar=0.)
# else:
# tc += templates[te].zscale(z, scalar=coeffs[i])
#
# tl += templates[te].zscale(z, scalar=coeffs[i])
wave, flux_arr, is_line = array_templates(templates, max_R=max_R, z=z,
apply_igm=apply_igm)
# # IGM
# if apply_igm:
# try:
# import eazy.igm
# IGM = eazy.igm.Inoue14()
#
# lylim = wave < 1250
# igmz = np.ones_like(wave)
# igmz[lylim] = IGM.full_IGM(z, wave[lylim]*(1+z))
# except:
# igmz = 1.
# else:
# igmz = 1.
#
# is_obsframe = np.array([t.split()[0] in ['bspl', 'step'] for t in templates])
#
# flux_arr[~is_obsframe,:] *= igmz
#
# # Multiply spline?
# for i, t in enumerate(templates):
# if 'spline' in t:
# for j, tj in enumerate(templates):
# if is_obsframe[j]:
# print('scale spline: {0} x {1}'.format(tj, t))
# flux_arr[j,:] *= flux_arr[i,:]
# Continuum
cont = np.dot(coeffs*(~is_line), flux_arr)
tc = SpectrumTemplate(wave=wave, flux=cont).zscale(z, apply_igm=False)
# Full template
line = np.dot(coeffs, flux_arr)
tl = SpectrumTemplate(wave=wave, flux=line).zscale(z, apply_igm=False)
return tc, tl
[docs]def array_templates(templates, wave=None, max_R=5000, z=0, apply_igm=False):
"""Return an array version of the templates that have all been interpolated to the same grid.
Parameters
----------
templates : dictionary of `~grizli.utils.SpectrumTemplate` objects
Output template list with `NTEMP` templates.
max_R : float
Maximum spectral resolution of the regridded templates.
z : float
Redshift where to evaluate the templates. But note that this is only
used to shift templates produced by `bspline_templates`, which are
defined in the observed frame.
Returns
-------
wave : `~numpy.ndarray`, dimensions `(NL,)`
Array containing unique wavelengths.
flux_arr : `~numpy.ndarray`, dimensions `(NTEMP, NL)`
Array containing the template fluxes interpolated at `wave`.
is_line : `~numpy.ndarray`
Boolean array indicating emission line templates (the key in the
template dictionary starts with "line ").
"""
from grizli.utils_c.interp import interp_conserve_c
if wave is None:
wstack = []
for t in templates:
if t.split()[0] in ['bspl', 'step', 'poly']:
wstack.append(templates[t].wave/(1+z))
else:
wstack.append(templates[t].wave)
wave = np.unique(np.hstack(wstack))
clipsum, iter = 1, 0
while (clipsum > 0) & (iter < 10):
clip = np.gradient(wave)/wave < 1/max_R
idx = np.arange(len(wave))[clip]
wave[idx[::2]] = np.nan
wave = wave[np.isfinite(wave)]
iter += 1
clipsum = clip.sum()
#print(iter, clipsum)
NTEMP = len(templates)
flux_arr = np.zeros((NTEMP, len(wave)))
for i, t in enumerate(templates):
if t.split()[0] in ['bspl', 'step', 'poly']:
flux_arr[i, :] = interp_conserve_c(wave, templates[t].wave/(1+z),
templates[t].flux*(1+z))
else:
if hasattr(templates[t], 'flux_flam'):
# Redshift-dependent eazy-py Template
flux_arr[i, :] = interp_conserve_c(wave, templates[t].wave,
templates[t].flux_flam(z=z))
else:
flux_arr[i, :] = interp_conserve_c(wave, templates[t].wave,
templates[t].flux)
is_line = np.array([t.startswith('line ') for t in templates])
# IGM
if apply_igm:
try:
import eazy.igm
IGM = eazy.igm.Inoue14()
lylim = wave < 1250
igmz = np.ones_like(wave)
igmz[lylim] = IGM.full_IGM(z, wave[lylim]*(1+z))
except:
igmz = 1.
else:
igmz = 1.
obsnames = ['bspl', 'step', 'poly']
is_obsframe = np.array([t.split()[0] in obsnames for t in templates])
flux_arr[~is_obsframe, :] *= igmz
# Multiply spline?
for i, t in enumerate(templates):
if 'spline' in t:
for j, tj in enumerate(templates):
if is_obsframe[j]:
ma = flux_arr[j, :].sum()
ma = ma if ma > 0 else 1
ma = 1
flux_arr[j, :] *= flux_arr[i, :]/ma
return wave, flux_arr, is_line
[docs]def compute_equivalent_widths(templates, coeffs, covar, max_R=5000, Ndraw=1000, seed=0, z=0, observed_frame=False):
"""Compute template-fit emission line equivalent widths
Parameters
----------
templates : dictionary of `~grizli.utils.SpectrumTemplate` objects
Output template list with `NTEMP` templates.
coeffs : `~numpy.ndarray`, dimensions (`NTEMP`)
Fit coefficients
covar : `~numpy.ndarray`, dimensions (`NTEMP`, `NTEMP`)
Covariance matrix
max_R : float
Maximum spectral resolution of the regridded templates.
Ndraw : int
Number of random draws to extract from the covariance matrix
seed : positive int
Random number seed to produce repeatible results. If `None`, then
use default state.
z : float
Redshift where the fit is evaluated
observed_framme : bool
If true, then computed EWs are observed frame, otherwise they are
rest frame at redshift `z`.
Returns
-------
EWdict : dict
Dictionary of [16, 50, 84th] percentiles of the line EW distributions.
"""
# Array versions of the templates
wave, flux_arr, is_line = array_templates(templates, max_R=max_R, z=z)
keys = np.array(list(templates.keys()))
EWdict = OrderedDict()
for key in keys[is_line]:
EWdict[key] = (0., 0., 0.)
# Only worry about templates with non-zero coefficients, which should
# be accounted for in the covariance array (with get_uncertainties=2)
clip = coeffs != 0
# No valid lines
if (is_line & clip).sum() == 0:
return EWdict
# Random draws from the covariance matrix
covar_clip = covar[clip, :][:, clip]
if seed is not None:
np.random.seed(seed)
draws = np.random.multivariate_normal(coeffs[clip], covar_clip, size=Ndraw)
# Evaluate the continuum fits from the draws
continuum = np.dot(draws*(~is_line[clip]), flux_arr[clip, :])
# Compute the emission line EWs
tidx = np.where(is_line[clip])[0]
for ix in tidx:
key = keys[clip][ix]
# Line template
line = np.dot(draws[:, ix][:, None], flux_arr[clip, :][ix, :][None, :])
# Where line template non-zero
mask = flux_arr[clip, :][ix, :] > 0
ew_i = np.trapz((line/continuum)[:, mask],
wave[mask]*(1+z*observed_frame), axis=1)
EWdict[key] = np.percentile(ew_i, [16., 50., 84.])
return EWdict
#####################
# Photometry from Vizier tables
# CFHTLS
CFHTLS_W_VIZIER = 'II/317/cfhtls_w'
CFHTLS_W_BANDS = OrderedDict([('cfht_mega_u', ['umag', 'e_umag']),
('cfht_mega_g', ['gmag', 'e_gmag']),
('cfht_mega_r', ['rmag', 'e_rmag']),
('cfht_mega_i', ['imag', 'e_imag']),
('cfht_mega_z', ['zmag', 'e_zmag'])])
CFHTLS_D_VIZIER = 'II/317/cfhtls_d'
CFHTLS_D_BANDS = OrderedDict([('cfht_mega_u', ['umag', 'e_umag']),
('cfht_mega_g', ['gmag', 'e_gmag']),
('cfht_mega_r', ['rmag', 'e_rmag']),
('cfht_mega_i', ['imag', 'e_imag']),
('cfht_mega_z', ['zmag', 'e_zmag'])])
# SDSS DR12
SDSS_DR12_VIZIER = 'V/147/sdss12'
SDSS_DR12_BANDS = OrderedDict([('SDSS/u', ['umag', 'e_umag']),
('SDSS/g', ['gmag', 'e_gmag']),
('SDSS/r', ['rmag', 'e_rmag']),
('SDSS/i', ['imag', 'e_imag']),
('SDSS/z', ['zmag', 'e_zmag'])])
# PanStarrs
PS1_VIZIER = 'II/349/ps1'
PS1_BANDS = OrderedDict([('PS1.g', ['gKmag', 'e_gKmag']),
('PS1.r', ['rKmag', 'e_rKmag']),
('PS1.i', ['iKmag', 'e_iKmag']),
('PS1.z', ['zKmag', 'e_zKmag']),
('PS1.y', ['yKmag', 'e_yKmag'])])
# KIDS DR3
KIDS_DR3_VIZIER = 'II/347/kids_dr3'
KIDS_DR3_BANDS = OrderedDict([('OCam.sdss.u', ['umag', 'e_umag']),
('OCam.sdss.g', ['gmag', 'e_gmag']),
('OCam.sdss.r', ['rmag', 'e_rmag']),
('OCam.sdss.i', ['imag', 'e_imag'])])
# WISE all-sky
WISE_VIZIER = 'II/328/allwise'
WISE_BANDS = OrderedDict([('WISE/RSR-W1', ['W1mag', 'e_W1mag']),
('WISE/RSR-W2', ['W2mag', 'e_W2mag'])])
# ('WISE/RSR-W3', ['W3mag', 'e_W3mag']),
# ('WISE/RSR-W4', ['W4mag', 'e_W4mag'])])
# VIKING VISTA
VIKING_VIZIER = 'II/343/viking2'
VIKING_BANDS = OrderedDict([('SDSS/z', ['Zpmag', 'e_Zpmag']),
('VISTA/Y', ['Ypmag', 'e_Ypmag']),
('VISTA/J', ['Jpmag', 'e_Jpmag']),
('VISTA/H', ['Hpmag', 'e_Hpmag']),
('VISTA/Ks', ['Kspmag', 'e_Kspmag'])])
# UKIDSS wide surveys
UKIDSS_LAS_VIZIER = 'II/319/las9'
UKIDSS_LAS_BANDS = OrderedDict([('WFCAM_Y', ['Ymag', 'e_Ymag']),
('WFCAM_J', ['Jmag1', 'e_Jmag1']),
('WFCAM_J', ['Jmag2', 'e_Jmag2']),
('WFCAM_H', ['Hmag', 'e_Hmag']),
('WFCAM_K', ['Kmag', 'e_Kmag'])])
UKIDSS_DXS_VIZIER = 'II/319/dxs9'
UKIDSS_DXS_BANDS = OrderedDict([('WFCAM_J', ['Jmag', 'e_Jmag']),
('WFCAM_K', ['Kmag', 'e_Kmag'])])
# GALEX
GALEX_MIS_VIZIER = 'II/312/mis'
GALEX_MIS_BANDS = OrderedDict([('FUV', ['FUV', 'e_FUV']),
('NUV', ['NUV', 'e_NUV'])])
GALEX_AIS_VIZIER = 'II/312/ais'
GALEX_AIS_BANDS = OrderedDict([('FUV', ['FUV', 'e_FUV']),
('NUV', ['NUV', 'e_NUV'])])
# Combined Dict
VIZIER_BANDS = OrderedDict()
VIZIER_BANDS[CFHTLS_W_VIZIER] = CFHTLS_W_BANDS
VIZIER_BANDS[CFHTLS_D_VIZIER] = CFHTLS_D_BANDS
VIZIER_BANDS[SDSS_DR12_VIZIER] = SDSS_DR12_BANDS
VIZIER_BANDS[PS1_VIZIER] = PS1_BANDS
VIZIER_BANDS[KIDS_DR3_VIZIER] = KIDS_DR3_BANDS
VIZIER_BANDS[WISE_VIZIER] = WISE_BANDS
VIZIER_BANDS[VIKING_VIZIER] = VIKING_BANDS
VIZIER_BANDS[UKIDSS_LAS_VIZIER] = UKIDSS_LAS_BANDS
VIZIER_BANDS[UKIDSS_DXS_VIZIER] = UKIDSS_DXS_BANDS
VIZIER_BANDS[GALEX_MIS_VIZIER] = GALEX_MIS_BANDS
VIZIER_BANDS[GALEX_AIS_VIZIER] = GALEX_AIS_BANDS
VIZIER_VEGA = OrderedDict()
VIZIER_VEGA[CFHTLS_W_VIZIER] = False
VIZIER_VEGA[CFHTLS_D_VIZIER] = False
VIZIER_VEGA[SDSS_DR12_VIZIER] = False
VIZIER_VEGA[PS1_VIZIER] = False
VIZIER_VEGA[KIDS_DR3_VIZIER] = False
VIZIER_VEGA[WISE_VIZIER] = True
VIZIER_VEGA[VIKING_VIZIER] = True
VIZIER_VEGA[UKIDSS_LAS_VIZIER] = True
VIZIER_VEGA[UKIDSS_DXS_VIZIER] = True
VIZIER_VEGA[GALEX_MIS_VIZIER] = False
VIZIER_VEGA[GALEX_AIS_VIZIER] = False
[docs]def get_Vizier_photometry(ra, dec, templates=None, radius=2, vizier_catalog=PS1_VIZIER, bands=PS1_BANDS, filter_file='/usr/local/share/eazy-photoz/filters/FILTER.RES.latest', MW_EBV=0, convert_vega=False, raw_query=False, verbose=True, timeout=300, rowlimit=50000):
"""
Fetch photometry from a Vizier catalog
Requires eazypy/eazy
"""
from collections import OrderedDict
import astropy.units as u
from astroquery.vizier import Vizier
Vizier.ROW_LIMIT = rowlimit
Vizier.TIMEOUT = timeout
#print('xxx', Vizier.ROW_LIMIT, Vizier.TIMEOUT)
import astropy.coordinates as coord
import astropy.units as u
#import pysynphot as S
from eazy.templates import Template
from eazy.filters import FilterFile
from eazy.photoz import TemplateGrid
from eazy.filters import FilterDefinition
res = FilterFile(filter_file)
coo = coord.SkyCoord(ra=ra, dec=dec, unit=(u.deg, u.deg),
frame='icrs')
columns = ['*']
#columns = []
if isinstance(vizier_catalog, list):
for c in [VIKING_VIZIER]:
for b in VIZIER_BANDS[c]:
columns += VIZIER_BANDS[c][b]
columns = list(np.unique(columns))
#print("xxx columns", columns)
else:
for b in bands:
columns += bands[b]
if isinstance(vizier_catalog, list):
v = Vizier(catalog=VIKING_VIZIER, columns=['+_r']+columns)
else:
v = Vizier(catalog=vizier_catalog, columns=['+_r']+columns)
v.ROW_LIMIT = rowlimit
v.TIMEOUT = timeout
#query_catalog = vizier_catalog
try:
tabs = v.query_region(coo, radius="{0}s".format(radius),
catalog=vizier_catalog) # [0]
if raw_query:
return(tabs)
tab = tabs[0]
if False:
for t in tabs:
bands = VIZIER_BANDS[t.meta['name']]
for b in bands:
for c in bands[b]:
print(t.meta['name'], c, c in t.colnames) # c = bands[b][0]
ix = np.argmin(tab['_r'])
tab = tab[ix]
except:
tab = None
return None
viz_tables = ', '.join([t.meta['name'] for t in tabs])
if verbose:
print('Photometry from vizier catalogs: {0}'.format(viz_tables))
pivot = [] # OrderedDict()
flam = []
eflam = []
filters = []
for tab in tabs:
# Downweight PS1 if have SDSS ? For now, do nothing
if (tab.meta['name'] == PS1_VIZIER) & (SDSS_DR12_VIZIER in viz_tables):
# continue
err_scale = 1
else:
err_scale = 1
# Only use one CFHT catalog
if (tab.meta['name'] == CFHTLS_W_VIZIER) & (CFHTLS_D_VIZIER in viz_tables):
continue
if (tab.meta['name'] == UKIDSS_LAS_VIZIER):
flux_scale = 1.33
else:
flux_scale = 1.
convert_vega = VIZIER_VEGA[tab.meta['name']]
bands = VIZIER_BANDS[tab.meta['name']]
# if verbose:
# print(tab.colnames)
#filters += [res.filters[res.search(b, verbose=False)[0]] for b in bands]
to_flam = 10**(-0.4*(48.6))*3.e18 # / pivot(Ang)**2
for ib, b in enumerate(bands):
filt = res.filters[res.search(b, verbose=False)[0]]
filters.append(filt)
if convert_vega:
to_ab = filt.ABVega()
else:
to_ab = 0.
fcol, ecol = bands[b]
pivot.append(filt.pivot())
flam.append(10**(-0.4*(tab[fcol][0]+to_ab))*to_flam/pivot[-1]**2)
flam[-1] *= flux_scale
eflam.append(tab[ecol][0]*np.log(10)/2.5*flam[-1]*err_scale)
for i in range(len(filters))[::-1]:
if np.isscalar(flam[i]) & np.isscalar(eflam[i]):
continue
else:
flam.pop(i)
eflam.pop(i)
filters.pop(i)
pivot.pop(i)
lc = np.array(pivot) # [pivot[ib] for ib in range(len(bands))]
if templates is not None:
eazy_templates = [Template(arrays=(templates[k].wave, templates[k].flux), name=k) for k in templates]
zgrid = log_zgrid(zr=[0.01, 3.4], dz=0.005)
tempfilt = TemplateGrid(zgrid, eazy_templates, filters=filters, add_igm=True, galactic_ebv=MW_EBV, Eb=0, n_proc=0, verbose=False)
else:
tempfilt = None
phot = OrderedDict([('flam', np.array(flam)), ('eflam', np.array(eflam)), ('filters', filters), ('tempfilt', tempfilt), ('lc', np.array(lc)), ('source', 'Vizier '+viz_tables)])
return phot
[docs]def generate_tempfilt(templates, filters, zgrid=None, MW_EBV=0):
from eazy.templates import Template
from eazy.photoz import TemplateGrid
# twave, tflux, is_line = array_templates(templates, z=0)
# eazy_templates = []
# for i, t in enumerate(templates):
# eazy_templates.append(Template(arrays=[twave, np.maximum(twave, 1.e-30)], name=t))
eazy_templates = [Template(arrays=(templates[k].wave, templates[k].flux), name=k) for k in templates]
if zgrid is None:
zgrid = log_zgrid(zr=[0.01, 3.4], dz=0.005)
tempfilt = TemplateGrid(zgrid, eazy_templates, filters=filters, add_igm=True, galactic_ebv=MW_EBV, Eb=0, n_proc=0, verbose=False)
return tempfilt
[docs]def combine_phot_dict(phots, templates=None, MW_EBV=0):
"""
Combine photmetry dictionaries
"""
phot = {}
phot['flam'] = []
phot['eflam'] = []
phot['filters'] = []
for p in phots:
phot['flam'] = np.append(phot['flam'], p['flam'])
phot['eflam'] = np.append(phot['eflam'], p['eflam'])
phot['filters'].extend(p['filters'])
if templates is not None:
phot['tempfilt'] = generate_tempfilt(templates, phot['filters'], MW_EBV=MW_EBV)
return phot
[docs]def get_spectrum_AB_mags(spectrum, bandpasses=[]):
"""
Integrate a `~pysynphot` spectrum through filter bandpasses
Parameters
----------
spectrum : type
bandpasses : list
List of `pysynphot` bandpass objects, e.g.,
>>> import pysynphot as S
>>> bandpasses = [S.ObsBandpass('wfc3,ir,f140w')]
Returns
-------
ab_mags : dict
Dictionary with keys from `bandpasses` and the integrated magnitudes
"""
import pysynphot as S
flat = S.FlatSpectrum(0, fluxunits='ABMag')
ab_mags = OrderedDict()
for bp in bandpasses:
flat_obs = S.Observation(flat, bp)
spec_obs = S.Observation(spectrum, bp)
ab_mags[bp.name] = -2.5*np.log10(spec_obs.countrate()/flat_obs.countrate())
return ab_mags
[docs]def log_zgrid(zr=[0.7, 3.4], dz=0.01):
"""Make a logarithmically spaced redshift grid
Parameters
----------
zr : [float, float]
Minimum and maximum of the desired grid
dz : float
Step size, dz/(1+z)
Returns
-------
zgrid : array-like
Redshift grid
"""
zgrid = np.exp(np.arange(np.log(1+zr[0]), np.log(1+zr[1]), dz))-1
return zgrid
[docs]def trapz_dx(x):
"""
Return trapezoid rule coefficients, useful for numerical integration
using a dot product
Parameters
----------
x : array-like
Independent variable
Returns
-------
dx : array_like
Coefficients for trapezoidal rule integration.
"""
dx = np.zeros_like(x)
diff = np.diff(x)/2.
dx[:-1] += diff
dx[1:] += diff
return dx
[docs]def get_wcs_pscale(wcs, set_attribute=True):
"""Get correct pscale from a `~astropy.wcs.WCS` object
Parameters
----------
wcs : `~astropy.wcs.WCS` or `~astropy.io.fits.Header`
set_attribute : bool
Set the `pscale` attribute on `wcs`, along with returning the value.
Returns
-------
pscale : float
Pixel scale from `wcs.cd`
"""
from numpy import linalg
if isinstance(wcs, pyfits.Header):
wcs = pywcs.WCS(wcs, relax=True)
if hasattr(wcs.wcs, 'cd'):
det = linalg.det(wcs.wcs.cd)
else:
det = linalg.det(wcs.wcs.pc)
pscale = np.sqrt(np.abs(det))*3600.
with warnings.catch_warnings():
warnings.filterwarnings('ignore',
'cdelt will be ignored since cd is present', RuntimeWarning)
if hasattr(wcs.wcs, 'cdelt'):
pscale *= wcs.wcs.cdelt[0]
wcs.pscale = pscale
return pscale
[docs]def sip_rot90(input, rot, reverse=False, verbose=False, compare=False):
"""
Rotate a SIP WCS by increments of 90 degrees using direct transformations
between x / y coordinates
Parameters
----------
input : `~astropy.io.fits.Header` or `~astropy.wcs.WCS`
Header or WCS
rot : int
Number of times to rotate the WCS 90 degrees *clockwise*, analogous
to `numpy.rot90`
reverse : bool
If `input` is a header and includes a keyword ``ROT90``, then undo
the rotation and remove the keyword from the output header
Returns
-------
header : `~astropy.io.fits.Header`
Rotated WCS header
wcs : `~astropy.wcs.WCS`
Rotated WCS
desc : str
Description of the transform associated with ``rot``, e.g,
``x=nx-x, y=ny-y`` for ``rot=±2``.
"""
import copy
import astropy.io.fits
import astropy.wcs
import matplotlib.pyplot as plt
if isinstance(input, astropy.io.fits.Header):
orig = copy.deepcopy(input)
new = copy.deepcopy(input)
if ('ROT90' in input):
if reverse:
rot = -orig['ROT90']
new.remove('ROT90')
else:
new['ROT90'] = orig['ROT90'] + rot
else:
new['ROT90'] = rot
else:
orig = to_header(input)
new = to_header(input)
orig_wcs = pywcs.WCS(orig, relax=True)
### CD = [[dra/dx, dra/dy], [dde/dx, dde/dy]]
### x = a_i_j * u**i * v**j
### y = b_i_j * u**i * v**j
ix = 1
if compare:
xarr = np.arange(0,2048,64)
xp, yp = np.meshgrid(xarr, xarr)
rd = orig_wcs.all_pix2world(xp, yp, ix)
if rot % 4 == 1:
# CW 90 deg : x = y, y = (nx - x), u=v, v=-u
desc = 'x=y, y=nx-x'
new['CRPIX1'] = orig['CRPIX2']
new['CRPIX2'] = orig['NAXIS1'] - orig['CRPIX1'] + 1
new['CD1_1'] = orig['CD1_2']
new['CD1_2'] = -orig['CD1_1']
new['CD2_1'] = orig['CD2_2']
new['CD2_2'] = -orig['CD2_1']
for i in range(new['A_ORDER']+1):
for j in range(new['B_ORDER']+1):
Aij = f'A_{i}_{j}'
if Aij not in new:
continue
new[f'A_{i}_{j}'] = orig[f'B_{j}_{i}']*(-1)**j
new[f'B_{i}_{j}'] = orig[f'A_{j}_{i}']*(-1)**j*-1
new_wcs = astropy.wcs.WCS(new, relax=True)
if compare:
xr, yr = new_wcs.all_world2pix(*rd, ix)
xo = yp
yo = orig['NAXIS1'] - xp
elif rot % 4 == 3:
# CW 270 deg : y = x, x = (ny - u), u=-v, v=u
desc = 'x=ny-y, y=x'
new['CRPIX1'] = orig['NAXIS2'] - orig['CRPIX2'] + 1
new['CRPIX2'] = orig['CRPIX1']
new['CD1_1'] = -orig['CD1_2']
new['CD1_2'] = orig['CD1_1']
new['CD2_1'] = -orig['CD2_2']
new['CD2_2'] = orig['CD2_1']
for i in range(new['A_ORDER']+1):
for j in range(new['B_ORDER']+1):
Aij = f'A_{i}_{j}'
if Aij not in new:
continue
new[f'A_{i}_{j}'] = orig[f'B_{j}_{i}']*(-1)**i*-1
new[f'B_{i}_{j}'] = orig[f'A_{j}_{i}']*(-1)**i
new_wcs = astropy.wcs.WCS(new, relax=True)
if compare:
xr, yr = new_wcs.all_world2pix(*rd, ix)
xo = orig['NAXIS2'] - yp
yo = xp
elif rot % 4 == 2:
# CW 180 deg : x=nx-x, y=ny-y, u=-u, v=-v
desc = 'x=nx-x, y=ny-y'
new['CRPIX1'] = orig['NAXIS1'] - orig['CRPIX1'] + 1
new['CRPIX2'] = orig['NAXIS2'] - orig['CRPIX2'] + 1
new['CD1_1'] = -orig['CD1_1']
new['CD1_2'] = -orig['CD1_2']
new['CD2_1'] = -orig['CD2_1']
new['CD2_2'] = -orig['CD2_2']
for i in range(new['A_ORDER']+1):
for j in range(new['B_ORDER']+1):
Aij = f'A_{i}_{j}'
if Aij not in new:
continue
new[f'A_{i}_{j}'] = orig[f'A_{i}_{j}']*(-1)**j*(-1)**i*-1
new[f'B_{i}_{j}'] = orig[f'B_{i}_{j}']*(-1)**j*(-1)**i*-1
new_wcs = astropy.wcs.WCS(new, relax=True)
if compare:
xr, yr = new_wcs.all_world2pix(*rd, ix)
xo = orig['NAXIS1'] - xp
yo = orig['NAXIS2'] - yp
else:
# rot=0, do nothing
desc = 'x=x, y=y'
new_wcs = orig_wcs
if compare:
xo = xp
yo = yp
xr, yr = new_wcs.all_world2pix(*rd, ix)
if verbose:
if compare:
xrms = nmad(xr-xo)
yrms = nmad(yr-yo)
print(f'Rot90: {rot} rms={xrms:.2e} {yrms:.2e}')
if compare:
fig, axes = plt.subplots(1,2,figsize=(10,5), sharex=True, sharey=True)
axes[0].scatter(xp, xr-xo)
axes[0].set_xlabel('dx')
axes[1].scatter(yp, yr-yo)
axes[1].set_xlabel('dy')
for ax in axes:
ax.grid()
fig.tight_layout(pad=0.5)
return new, new_wcs, desc
[docs]def get_common_slices(a_origin, a_shape, b_origin, b_shape):
"""
Get slices of overlaps between two rectangular grids
"""
ll = np.min([a_origin, b_origin], axis=0)
ur = np.max([a_origin+a_shape, b_origin+b_shape], axis=0)
# other in self
lls = np.minimum(b_origin - ll, a_shape)
urs = np.clip(b_origin + b_shape - a_origin, [0, 0], a_shape)
# self in other
llo = np.minimum(a_origin - ll, b_shape)
uro = np.clip(a_origin + a_shape - b_origin, [0, 0], b_shape)
a_slice = (slice(lls[0], urs[0]), slice(lls[1], urs[1]))
b_slice = (slice(llo[0], uro[0]), slice(llo[1], uro[1]))
return a_slice, b_slice
[docs]def reproject_faster(input_hdu, output, pad=10, **kwargs):
"""Speed up `reproject` module with array slices of the input image
Parameters
----------
input_hdu : `~astropy.io.fits.ImageHDU`
Input image HDU to reproject.
output : `~astropy.wcs.WCS` or `~astropy.io.fits.Header`
Output frame definition.
pad : int
Pixel padding on slices cut from the `input_hdu`.
kwargs : dict
Arguments passed through to `~reproject.reproject_interp`. For
example, `order='nearest-neighbor'`.
Returns
-------
reprojected : `~numpy.ndarray`
Reprojected data from `input_hdu`.
footprint : `~numpy.ndarray`
Footprint of the input array in the output frame.
Notes
-----
`reproject' is an astropy-compatible module that can be installed with
`pip`. See https://reproject.readthedocs.io.
"""
import reproject
# Output WCS
if isinstance(output, pywcs.WCS):
out_wcs = output
else:
out_wcs = pywcs.WCS(output, relax=True)
if 'SIP' in out_wcs.wcs.ctype[0]:
print('Warning: `reproject` doesn\'t appear to support SIP projection')
# Compute pixel coordinates of the output frame corners in the input image
input_wcs = pywcs.WCS(input_hdu.header, relax=True)
out_fp = out_wcs.calc_footprint()
input_xy = input_wcs.all_world2pix(out_fp, 0)
slx = slice(int(input_xy[:, 0].min())-pad, int(input_xy[:, 0].max())+pad)
sly = slice(int(input_xy[:, 1].min())-pad, int(input_xy[:, 1].max())+pad)
# Make the cutout
sub_data = input_hdu.data[sly, slx]
sub_header = get_wcs_slice_header(input_wcs, slx, sly)
sub_hdu = pyfits.PrimaryHDU(data=sub_data, header=sub_header)
# Get the reprojection
seg_i, fp_i = reproject.reproject_interp(sub_hdu, output, **kwargs)
return seg_i.astype(sub_data.dtype), fp_i.astype(np.uint8)
DRIZZLE_KEYS = ['GEOM', 'DATA', 'DEXP', 'OUDA', 'OUWE', 'OUCO', 'MASK', 'WTSC', 'KERN', 'PIXF', 'COEF', 'OUUN', 'FVAL', 'WKEY', 'SCAL', 'ISCL']
[docs]def make_maximal_wcs(files, pixel_scale=None, get_hdu=True, pad=90, verbose=True, theta=0, poly_buffer=1./3600, nsci_extensions=4):
"""
Compute an ImageHDU with a footprint that contains all of `files`
Parameters
----------
files : list
List of HST FITS files (e.g., FLT.) or WCS objects.
pixel_scale : float
Pixel scale of output WCS, in `~astropy.units.arcsec`. If `None`,
get pixel scale of first file in `files`.
get_hdu : bool
See below.
pad : float
Padding to add to the total image size, in `~astropy.units.arcsec`.
theta : float
Position angle, degrees
nsci_extensions : int
Number of 'SCI' extensions to try in the exposure files.
Returns
-------
hdu : `~astropy.io.fits.ImageHDU`
If `get_hdu` is True.
-or-
header, wcs : `~astropy.io.fits.Header`, `~astropy.wcs.WCS`
If `get_hdu` is False.
"""
import numpy as np
from shapely.geometry import Polygon
import astropy.io.fits as pyfits
import astropy.wcs as pywcs
if isinstance(files[0], pywcs.WCS):
# Already wcs_list
wcs_list = [(wcs, 'WCS', -1) for wcs in files]
else:
wcs_list = []
for i, file in enumerate(files):
if not os.path.exists(file):
continue
with pyfits.open(file) as im:
for ext in range(nsci_extensions):
if ('SCI', ext+1) not in im:
continue
wcs = pywcs.WCS(im['SCI', ext+1].header, fobj=im)
wcs_list.append((wcs, file, ext))
if pixel_scale is None:
pixel_scale = get_wcs_pscale(wcs_list[0][0])
group_poly = None
for i, (wcs, file, chip) in enumerate(wcs_list):
p_i = Polygon(wcs.calc_footprint())
if group_poly is None:
if poly_buffer > 0:
group_poly = p_i.buffer(1./3600)
else:
group_poly = p_i
else:
if poly_buffer > 0:
group_poly = group_poly.union(p_i.buffer(1./3600))
else:
group_poly = group_poly.union(p_i)
x0, y0 = np.cast[float](group_poly.centroid.xy)[:, 0]
if verbose:
msg = '{0:>3d}/{1:>3d}: {2}[SCI,{3}] {4:>6.2f}'
print(msg.format(i, len(files), file, chip+1,
group_poly.area*3600*np.cos(y0/180*np.pi)
)
)
px = np.cast[float](group_poly.convex_hull.boundary.xy).T
#x0, y0 = np.cast[float](group_poly.centroid.xy)[:,0]
x0 = (px.max(axis=0)+px.min(axis=0))/2.
cosd = np.array([np.cos(x0[1]/180*np.pi), 1])
_mat = np.array([[np.cos(theta), -np.sin(theta)],
[np.sin(theta), np.cos(theta)]])
# Rotated
pr = ((px-x0)*cosd).dot(_mat)/cosd+x0
size_arcsec = (pr.max(axis=0)-pr.min(axis=0))*cosd*3600
sx, sy = size_arcsec
# sx = (px.max()-px.min())*cosd*3600 # arcsec
# sy = (py.max()-py.min())*3600 # arcsec
size = np.maximum(sx+pad, sy+pad)
if verbose:
msg = '\n Mosaic WCS: ({0:.5f},{1:.5f}) '
msg += '{2:.1f}\'x{3:.1f}\' {4:.3f}"/pix\n'
print(msg.format(x0[0], x0[1], (sx+pad)/60., (sy+pad)/60., pixel_scale))
out = make_wcsheader(ra=x0[0], dec=x0[1],
size=(sx+pad*2, sy+pad*2),
pixscale=pixel_scale,
get_hdu=get_hdu, theta=theta/np.pi*180)
return out
[docs]def half_pixel_scale(wcs):
"""
Create a new WCS with half the pixel scale of another that can be
block-averaged 2x2
Parameters
----------
wcs : `~astropy.wcs.WCS`
Input WCS
Returns
-------
half_wcs : `~astropy.wcs.WCS`
New WCS with smaller pixels
"""
h = to_header(wcs)
for k in ['NAXIS1', 'NAXIS2']: #, 'CRPIX1', 'CRPIX2']:
h[k] *= 2
for k in ['CRPIX1', 'CRPIX2']:
h[k] = h[k]*2 - 0.5
for k in ['CD1_1', 'CD1_2', 'CD2_1', 'CD2_2']:
if k in h:
h[k] /= 2
if 0:
# Test
new = pywcs.WCS(h)
sh = new.pixel_shape
wcorner = wcs.all_world2pix(new.all_pix2world([[-0.5, -0.5],
[sh[0]-0.5, sh[1]-0.5]], 0),0)
print('small > large')
print(', '.join([f'{w:.2f}' for w in wcorner[0]]))
print(', '.join([f'{w:.2f}' for w in wcorner[1]]), wcs.pixel_shape)
sh = wcs.pixel_shape
wcorner = new.all_world2pix(wcs.all_pix2world([[-0.5, -0.5],
[sh[0]-0.5, sh[1]-0.5]], 0),0)
print('large > small')
print(', '.join([f'{w:.2f}' for w in wcorner[0]]))
print(', '.join([f'{w:.2f}' for w in wcorner[1]]), new.pixel_shape)
new_wcs = pywcs.WCS(h, relax=True)
return new_wcs
[docs]def parse_s3_url(url='s3://bucket/path/to/file.txt'):
"""
Parse s3 path string
Parameters
----------
url : str
Full S3 path, e.g., ``[s3://]{bucket_name}/{s3_object}``
Returns
-------
bucket_name : str
Bucket name
s3_object : str
Full path of the S3 file object
filename : str
File name of the object, e.g. ``os.path.basename(s3_object)``
"""
surl = url.strip('s3://')
spl = surl.split('/')
if len(spl) < 2:
print(f"bucket / path not found in {url}")
return None, None, None
bucket_name = spl[0]
s3_object = '/'.join(spl[1:])
filename = os.path.basename(s3_object)
return bucket_name, s3_object, filename
[docs]def fetch_s3_url(url='s3://bucket/path/to/file.txt', file_func=lambda x : os.path.join('./',x), skip_existing=True, verbose=True):
"""
Fetch file from an S3 bucket
Parameters
----------
url : str
S3 url of a file to download
file_func : function
Function applied to the file name extracted from `url`, e.g., to
set output directory, rename files, set a prefix, etc.
Returns
-------
local_file : str
Name of local file or `None` if failed to parse `url`
status : int
Bit flag of results: **1** == file found, **2** = download successful
"""
import traceback
import boto3
import botocore.exceptions
s3 = boto3.resource('s3')
bucket_name, s3_object, filename = parse_s3_url(url=url)
if bucket_name is None:
return url, os.path.exists(url)
bkt = s3.Bucket(bucket_name)
local_file = file_func(filename)
status = os.path.exists(local_file)*1
if (status > 0) & skip_existing:
print(f'{local_file} exists, skipping.')
else:
try:
bkt.download_file(s3_object, local_file,
ExtraArgs={"RequestPayer": "requester"})
status += 2
if verbose:
print(f'{url} > {local_file}')
except botocore.exceptions.ClientError:
trace = traceback.format_exc(limit=2)
msg = trace.split('\n')[-2].split('ClientError: ')[1]
if verbose:
print(f'Failed {url}: {msg}')
# Download failed due to a ClientError
# Forbidden probably means insufficient bucket access privileges
pass
return local_file, status
[docs]def niriss_ghost_mask(im, init_thresh=0.05, init_sigma=3, final_thresh=0.01, final_sigma=3, erosions=0, dilations=9, verbose=True, **kwargs):
"""
Make a mask for NIRISS imaging ghosts
See also Martel. JWST-STScI-004877 and
https://github.com/spacetelescope/niriss_ghost
Here
"""
import scipy.ndimage as nd
if im[0].header['PUPIL'] not in ['F115W','F150W','F200W']:
return False
if im[0].header['PUPIL'] == 'F115W':
xgap, ygap = 1156, 927
elif im[0].header['PUPIL'] == 'F115W':
xgap, ygap = 1162, 938
else:
xgap, ygap = 1156, 944-2
yp, xp = np.indices((2048, 2048))
yg = 2*(ygap-1) - yp
xg = 2*(xgap-1) - xp
dx = (xp - xgap)
dy = (yp - ygap)
in_img = (xg >= 0) & (xg < 2048)
in_img &= (yg >= 0) & (yg < 2048)
in_img &= np.abs(dx) < 400
in_img &= np.abs(dy) < 400
if 'MDRIZSKY' in im['SCI'].header:
bkg = im['SCI'].header['MDRIZSKY']
else:
bkg = np.nanmedian(im['SCI'].data[im['DQ'].data == 0])
thresh = (im['SCI'].data - bkg)*init_thresh > init_sigma*im['ERR'].data
thresh &= in_img
_reflected = np.zeros_like(im['SCI'].data)
for xpi, ypi, xgi, ygi in zip(xp[thresh], yp[thresh],
xg[thresh], yg[thresh]):
_reflected[ygi, xgi] = im['SCI'].data[ypi, xpi] - bkg
ghost_mask = _reflected*final_thresh > final_sigma*im['ERR'].data
if erosions > 0:
ghost_mask = nd.binary_erosion(ghost_mask, iterations=erosions)
ghost_mask = nd.binary_dilation(ghost_mask, iterations=dilations)
im[0].header['GHOSTMSK'] = True, 'NIRISS ghost mask applied'
im[0].header['GHOSTNPX'] = ghost_mask.sum(), 'Pixels in NIRISS ghost mask'
msg = 'NIRISS ghost mask {0} Npix: {1}\n'.format(im[0].header['PUPIL'],
ghost_mask.sum())
log_comment(LOGFILE, msg, verbose=verbose)
return ghost_mask
[docs]def get_photom_scale(header, verbose=True):
"""
Get tabulated scale factor
Parameters
----------
header : `~astropy.io.fits.Header`
Image header
Returns
-------
key : str
Detector + filter key
scale : float
Scale value. If `key` not found in the ``data/photom_correction.yml``
table or if the CTX is newer than that indicated in the correction
table then return 1.0.
"""
import yaml
if 'TELESCOP' in header:
if header['TELESCOP'] not in ['JWST']:
msg = f"get_photom_scale: TELESCOP={header['TELESCOP']} is not 'JWST'"
log_comment(LOGFILE, msg, verbose=verbose)
return header['TELESCOP'], 1.0
else:
return None, 1.0
corr_file = os.path.join(os.path.dirname(__file__),
'data/photom_correction.yml')
if not os.path.exists(corr_file):
msg = f'{corr_file} not found.'
log_comment(LOGFILE, msg, verbose=verbose)
return None, 1
with open(corr_file) as fp:
corr = yaml.load(fp, Loader=yaml.SafeLoader)
if 'CRDS_CTX' in header:
if header['CRDS_CTX'] > corr['CRDS_CTX_MAX']:
msg = f"get_photom_scale {corr_file}: {header['CRDS_CTX']} > {corr['CRDS_CTX_MAX']}"
log_comment(LOGFILE, msg, verbose=verbose)
return header['CRDS_CTX'], 1.0
key = '{0}-{1}'.format(header['DETECTOR'], header['FILTER'])
if 'PUPIL' in header:
key += '-{0}'.format(header['PUPIL'])
if key not in corr:
msg = f"get_photom_scale {corr_file}: {key} not found"
log_comment(LOGFILE, msg, verbose=verbose)
return key, 1.0
else:
msg = f"get_photom_scale {corr_file}: Scale {key} by {1./corr[key]:.3f}"
log_comment(LOGFILE, msg, verbose=verbose)
return key, 1./corr[key]
[docs]def drizzle_from_visit(visit, output=None, pixfrac=1., kernel='point',
clean=True, include_saturated=True, keep_bits=None,
dryrun=False, skip=None, extra_wfc3ir_badpix=True,
verbose=True,
scale_photom=True,
calc_wcsmap=False,
niriss_ghost_kwargs={}):
"""
Make drizzle mosaic from exposures in a visit dictionary
Parameters
----------
visit : dict
Visit dictionary with 'product' and 'files' keys
output : `~astropy.wcs.WCS`, `~astropy.io.fits.Header`, `~astropy.io.ImageHDU`
Output frame definition. Can be a WCS object, header, or FITS HDU. If
None, then generates a WCS with `grizli.utils.make_maximal_wcs`
pixfrac : float
Drizzle `pixfrac`
kernel : str
Drizzle `kernel` (e.g., 'point', 'square')
clean : bool
Remove exposure files after adding to the mosaic
include_saturated : bool
Include pixels with saturated DQ flag
keep_bits : int, None
Extra DQ bits to keep as valid
dryrun : bool
If True, don't actually produce the output
skip : int
Slice skip to drizzle a subset of exposures
extra_wfc3ir_badpix : bool
Apply extra WFC3/IR bad pix to DQ
verbose : bool
Some verbose message printing
scale_photom : bool
For JWST, apply photometry scale corrections from the
`grizli/data/photom_correction.yml` table
niriss_ghost_kwargs : dict
Keyword arguments for `~grizli.utils.niriss_ghost_mask`
Returns
-------
outsci : array-like
SCI array
outwht : array-like
Inverse variance WHT array
header : `~astropy.io.fits.Header`
Image header
flist : list
List of files that were drizzled to the mosaic
wcs_tab : `~astropy.table.Table`
Table of WCS parameters of individual exposures
"""
from shapely.geometry import Polygon
import boto3
from botocore.exceptions import ClientError
import scipy.ndimage as nd
from astropy.io.fits import PrimaryHDU, ImageHDU
from .version import __version__ as grizli__version
bucket_name = None
s3 = boto3.resource('s3')
s3_client = boto3.client('s3')
if isinstance(output, pywcs.WCS):
outputwcs = output
elif isinstance(output, pyfits.Header):
outputwcs = pywcs.WCS(output)
elif isinstance(output, PrimaryHDU) | isinstance(output, ImageHDU):
outputwcs = pywcs.WCS(output.header)
elif output is None:
_hdu = make_maximal_wcs(files=visit['files'],
pixel_scale=None,
get_hdu=True,
verbose=False,
pad=4)
outputwcs = pywcs.WCS(_hdu.header)
else:
return None
if not hasattr(outputwcs, '_naxis1'):
outputwcs._naxis1, outputwcs._naxis2 = outputwcs._naxis
outputwcs.pscale = get_wcs_pscale(outputwcs)
output_poly = Polygon(outputwcs.calc_footprint())
count = 0
ref_photflam = None
indices = []
for i in range(len(visit['files'])):
if 'footprints' in visit:
olap = visit['footprints'][i].intersection(output_poly)
if olap.area > 0:
indices.append(i)
else:
indices.append(i)
if skip is not None:
indices = indices[::skip]
NTOTAL = len(indices)
wcs_rows = []
wcs_colnames = None
wcs_keys = {}
bpdata = 0
for i in indices:
file = visit['files'][i]
msg = '\n({0:4d}/{1:4d}) Add exposure {2}\n'
msg = msg.format(count+1, NTOTAL, file)
log_comment(LOGFILE, msg, verbose=verbose)
if dryrun:
continue
if not os.path.exists(file):
bucket_i = visit['awspath'][i].split('/')[0]
if bucket_name != bucket_i:
bucket_name = bucket_i
bkt = s3.Bucket(bucket_name)
s3_path = '/'.join(visit['awspath'][i].split('/')[1:])
remote_file = os.path.join(s3_path, file)
print(' (fetch from s3://{0}/{1})'.format(bucket_i, remote_file))
try:
bkt.download_file(remote_file, file,
ExtraArgs={"RequestPayer": "requester"})
except ClientError:
print(' (failed s3://{0}/{1})'.format(bucket_i, remote_file))
continue
try:
flt = pyfits.open(file)
except OSError:
print(f'open({file}) failed!')
continue
sci_list, wht_list, wcs_list = [], [], []
keys = OrderedDict()
for k in ['EXPTIME', 'TELESCOP', 'FILTER','FILTER1', 'FILTER2',
'PUPIL', 'DETECTOR', 'INSTRUME', 'PHOTFLAM', 'PHOTPLAM',
'PHOTFNU', 'PHOTZPT', 'PHOTBW', 'PHOTMODE', 'EXPSTART',
'EXPEND', 'DATE-OBS', 'TIME-OBS',
'UPDA_CTX', 'CRDS_CTX', 'R_DISTOR', 'R_PHOTOM', 'R_FLAT']:
if k in flt[0].header:
keys[k] = flt[0].header[k]
if flt[0].header['TELESCOP'] in ['JWST']:
bits = 4
include_saturated = False
#bpdata = 0
_inst = flt[0].header['INSTRUME']
if (extra_wfc3ir_badpix) & (_inst in ['NIRCAM']):
_det = flt[0].header['DETECTOR']
bpfiles = [os.path.join(os.path.dirname(__file__),
f'data/nrc_badpix_230120_{_det}.fits.gz')]
bpfiles += [os.path.join(os.path.dirname(__file__),
f'data/nrc_lowpix_0916_{_det}.fits.gz')]
for bpfile in bpfiles:
if os.path.exists(bpfile):
bpdata = pyfits.open(bpfile)[0].data
bpdata = nd.binary_dilation(bpdata > 0)*1024
msg = f'Use extra badpix in {bpfile}'
log_comment(LOGFILE, msg, verbose=verbose)
break
else:
bpdata = np.zeros(flt['SCI'].data.shape, dtype=int)
# NIRISS ghost mask
if (_inst in ['NIRISS']) & (niriss_ghost_kwargs is not None):
if 'verbose' not in niriss_ghost_kwargs:
niriss_ghost_kwargs['verbose'] = verbose
_ghost = niriss_ghost_mask(flt, **niriss_ghost_kwargs)
bpdata |= _ghost*1024
# Negative
if 'MDRIZSKY' in flt['SCI'].header:
_low = ((flt['SCI'].data - flt['SCI'].header['MDRIZSKY']) <
-5*flt['ERR'].data)
msg = f'Extra -5 sigma low pixels: N= {_low.sum()} '
msg += f' ( {_low.sum()/_low.size*100:.1} %)'
log_comment(LOGFILE, msg, verbose=verbose)
bpdata |= _low*1024
elif flt[0].header['DETECTOR'] == 'IR':
bits = 576
if extra_wfc3ir_badpix:
if (i == indices[0]) | (not hasattr(bpdata, 'shape')):
bpfile = os.path.join(os.path.dirname(__file__),
'data/wfc3ir_badpix_spars200_22.03.31.fits.gz')
bpdata = pyfits.open(bpfile)[0].data
msg = f'Use extra badpix in {bpfile}'
log_comment(LOGFILE, msg, verbose=verbose)
else:
bits = 64+32
bpdata = 0
if include_saturated:
bits |= 256
if keep_bits is not None:
bits |= keep_bits
if scale_photom:
_key, _scale_photom = get_photom_scale(flt[0].header,
verbose=verbose)
else:
_scale_photom = 1.0
if 'PHOTFLAM' in keys:
msg = ' 0 PHOTFLAM={0:.2e}, scale={1:.3f}'
msg = msg.format(keys['PHOTFLAM'], _scale_photom)
log_comment(LOGFILE, msg, verbose=verbose)
if ref_photflam is None:
ref_photflam = keys['PHOTFLAM']
for ext in [1, 2, 3, 4]:
if ('SCI', ext) in flt:
h = flt[('SCI', ext)].header
if 'MDRIZSKY' in h:
sky_value = h['MDRIZSKY']
else:
sky_value = 0
if ('BKG',ext) in flt:
has_bkg = True
sky = flt['BKG',ext].data + sky_value
else:
has_bkg = False
sky = sky_value
msg = f' ext (SCI,{ext}), sky={sky_value:.3f}'
msg += f' has_bkg:{has_bkg}'
log_comment(LOGFILE, msg, verbose=verbose)
if h['BUNIT'] == 'ELECTRONS':
to_per_sec = 1./keys['EXPTIME']
else:
to_per_sec = 1.
phot_scale = to_per_sec * _scale_photom
if 'PHOTFLAM' in h:
if ref_photflam is None:
ref_photflam = h['PHOTFLAM']
phot_scale = h['PHOTFLAM'] / ref_photflam * _scale_photom
if 'PHOTFNU' not in h:
h['PHOTFNU'] = (photfnu_from_photflam(h['PHOTFLAM'],
h['PHOTPLAM']),
'Inverse sensitivity, Jy/DN')
msg = ' PHOTFLAM={0:.2e}, scale={1:.3f}'
msg = msg.format(h['PHOTFLAM'], phot_scale)
log_comment(LOGFILE, msg, verbose=verbose)
keys['PHOTFLAM'] = h['PHOTFLAM']
for k in ['PHOTFLAM', 'PHOTPLAM', 'PHOTFNU',
'PHOTZPT', 'PHOTBW', 'PHOTMODE',
'PHOTMJSR', 'PIXAR_SR']:
if k in h:
keys[k] = h[k]
phot_scale *= to_per_sec
try:
wcs_i = pywcs.WCS(header=flt[('SCI', ext)].header,
fobj=flt)
wcs_i.pscale = get_wcs_pscale(wcs_i)
except KeyError:
print(f'Failed to initialize WCS on {file}[SCI,{ext}]')
continue
wcsh = to_header(wcs_i)
row = [file, ext, keys['EXPTIME']]
if wcs_colnames is None:
wcs_colnames = ['file','ext','exptime']
for k in wcsh:
wcs_colnames.append(k.lower())
wcs_keys[k.lower()] = wcsh[k]
for k in wcs_colnames[3:]:
ku = k.upper()
if ku not in wcsh:
print(f'Keyword {ku} not found in WCS header')
row.append(wcs_keys[k]*0)
else:
row.append(wcsh[ku])
for k in wcsh:
if k.lower() not in wcs_colnames:
print(f'Extra keyword {ku} found in WCS header')
wcs_rows.append(row)
sci_list.append((flt[('SCI', ext)].data - sky)*phot_scale)
err = flt[('ERR', ext)].data*phot_scale
dq = unset_dq_bits(flt[('DQ', ext)].data, bits) | bpdata
wht = 1/err**2
wht[(err == 0) | (dq > 0)] = 0
wht_list.append(wht)
# wcs_i = HSTWCS(fobj=flt, ext=('SCI',ext), minerr=0.0,
# wcskey=' ')
if not hasattr(wcs_i, 'pixel_shape'):
wcs_i.pixel_shape = wcs_i._naxis1, wcs_i._naxis2
if not hasattr(wcs_i, '_naxis1'):
wcs_i._naxis1, wcs_i._naxis2 = wcs_i._naxis
wcs_list.append(wcs_i)
if count == 0:
res = drizzle_array_groups(sci_list, wht_list, wcs_list,
outputwcs=outputwcs,
scale=0.1, kernel=kernel,
pixfrac=pixfrac, calc_wcsmap=calc_wcsmap,
verbose=verbose, data=None)
outsci, outwht, outctx, header, xoutwcs = res
header['EXPTIME'] = flt[0].header['EXPTIME']
header['NDRIZIM'] = 1
header['PIXFRAC'] = pixfrac
header['KERNEL'] = kernel
header['OKBITS'] = (bits, "FLT bits treated as valid")
header['PHOTSCAL'] = _scale_photom, 'Scale factor applied'
header['GRIZLIV'] = grizli__version, 'Grizli code version'
for k in keys:
header[k] = keys[k]
else:
data = outsci, outwht, outctx
res = drizzle_array_groups(sci_list, wht_list, wcs_list,
outputwcs=outputwcs,
scale=0.1, kernel=kernel,
pixfrac=pixfrac, calc_wcsmap=calc_wcsmap,
verbose=verbose, data=data)
outsci, outwht, outctx = res[:3]
header['EXPTIME'] += flt[0].header['EXPTIME']
header['NDRIZIM'] += 1
count += 1
header['FLT{0:05d}'.format(count)] = file
flt.close()
#xfiles = glob.glob('*')
#print('Clean: ', clean, xfiles)
if clean:
os.remove(file)
if 'awspath' in visit:
awspath = visit['awspath']
else:
awspath = ['.' for f in visit['files']]
if len(awspath) == 1:
awspath = [awspath[0] for f in visit['files']]
elif isinstance(awspath, str):
_awspath = [awspath for f in visit['files']]
awspath = _awspath
flist = ['{0}/{1}'.format(awspath, visit['files'][i])
for i in indices]
if dryrun:
return flist
elif count == 0:
return None
else:
wcs_tab = GTable(names=wcs_colnames, rows=wcs_rows)
outwht *= (wcs_i.pscale/outputwcs.pscale)**4
return outsci, outwht, header, flist, wcs_tab
[docs]def drizzle_array_groups(sci_list, wht_list, wcs_list, outputwcs=None,
scale=0.1, kernel='point', pixfrac=1.,
calc_wcsmap=False, verbose=True, data=None):
"""Drizzle array data with associated wcs
Parameters
----------
sci_list, wht_list : list
List of science and weight `~numpy.ndarray` objects.
wcs_list : list
scale : float
Output pixel scale in arcsec.
kernel, pixfrac : str, float
Drizzle parameters
verbose : bool
Print status messages
Returns
-------
outsci, outwht, outctx : `~numpy.ndarray`
Output drizzled science, weight and context images
header, outputwcs : `~astropy.fits.io.Header`, `~astropy.wcs.WCS`
Drizzled image header and WCS.
"""
from drizzlepac import adrizzle
from drizzlepac import cdriz
#from stsci.tools import logutil
#log = logutil.create_logger(__name__)
# Output header / WCS
if outputwcs is None:
#header, outputwcs = compute_output_wcs(wcs_list, pixel_scale=scale)
header, outputwcs = make_maximal_wcs(wcs_list,
pixel_scale=scale,
verbose=False,
pad=0,
get_hdu=False)
else:
header = to_header(outputwcs)
header['DRIZKERN'] = kernel, "Drizzle kernel"
header['DRIZPIXF'] = pixfrac, "Drizzle pixfrac"
if not hasattr(outputwcs, '_naxis1'):
outputwcs._naxis1, outputwcs._naxis2 = outputwcs._naxis
# Try to fix deprecated WCS
for wcs_i in wcs_list:
if not hasattr(wcs_i, 'pixel_shape'):
wcs_i.pixel_shape = wcs_i._naxis1, wcs_i._naxis2
if not hasattr(wcs_i, '_naxis1'):
wcs_i._naxis1, wcs_i._naxis2 = wcs_i._naxis[:2]
# Output WCS requires full WCS map?
if calc_wcsmap < 2:
ctype = outputwcs.wcs.ctype
if '-SIP' in ctype[0]:
print('Output WCS ({0}) requires `calc_wcsmap=2`'.format(ctype))
calc_wcsmap = 2
else:
# Internal WCSMAP not required
calc_wcsmap = 0
shape = (header['NAXIS2'], header['NAXIS1'])
# Output arrays
if data is not None:
outsci, outwht, outctx = data
else:
outsci = np.zeros(shape, dtype=np.float32)
outwht = np.zeros(shape, dtype=np.float32)
outctx = np.zeros(shape, dtype=np.int32)
# Do drizzle
N = len(sci_list)
for i in range(N):
if verbose:
#log.info('Drizzle array {0}/{1}'.format(i+1, N))
msg = 'Drizzle array {0}/{1}'.format(i+1, N)
log_comment(LOGFILE, msg, verbose=verbose, show_date=True)
if calc_wcsmap > 1:
wcsmap = WCSMapAll # (wcs_list[i], outputwcs)
#wcsmap = cdriz.DefaultWCSMapping
else:
wcsmap = None
adrizzle.do_driz(sci_list[i].astype(np.float32, copy=False),
wcs_list[i],
wht_list[i].astype(np.float32, copy=False),
outputwcs, outsci, outwht, outctx, 1., 'cps', 1,
wcslin_pscale=wcs_list[i].pscale, uniqid=1,
pixfrac=pixfrac, kernel=kernel, fillval='0',
wcsmap=wcsmap)
return outsci, outwht, outctx, header, outputwcs
[docs]class WCSMapAll:
""" Sample class to demonstrate how to define a coordinate transformation
"""
def __init__(self, input, output, origin=0):
# Verify that we have valid WCS input objects
import copy
self.checkWCS(input, 'Input')
self.checkWCS(output, 'Output')
self.input = input
self.output = copy.deepcopy(output)
#self.output = output
self.origin = 1 #origin
self.shift = None
self.rot = None
self.scale = None
[docs] def checkWCS(self, obj, name):
try:
assert isinstance(obj, pywcs.WCS)
except AssertionError:
print(name + ' object needs to be an instance or subclass of a PyWCS object.')
raise
[docs] def forward(self, pixx, pixy):
""" Transform the input pixx,pixy positions in the input frame
to pixel positions in the output frame.
This method gets passed to the drizzle algorithm.
"""
# This matches WTRAXY results to better than 1e-4 pixels.
skyx, skyy = self.input.all_pix2world(pixx, pixy, self.origin)
result = self.output.all_world2pix(skyx, skyy, self.origin)
return result
[docs] def backward(self, pixx, pixy):
""" Transform pixx,pixy positions from the output frame back onto their
original positions in the input frame.
"""
skyx, skyy = self.output.all_pix2world(pixx, pixy, self.origin)
result = self.input.all_world2pix(skyx, skyy, self.origin)
return result
[docs] def get_pix_ratio(self):
""" Return the ratio of plate scales between the input and output WCS.
This is used to properly distribute the flux in each pixel in 'tdriz'.
"""
return self.output.pscale / self.input.pscale
[docs] def xy2rd(self, wcs, pixx, pixy):
""" Transform input pixel positions into sky positions in the WCS provided.
"""
return wcs.all_pix2world(pixx, pixy, 1)
[docs] def rd2xy(self, wcs, ra, dec):
""" Transform input sky positions into pixel positions in the WCS provided.
"""
return wcs.all_world2pix(ra, dec, 1)
[docs]def compute_output_wcs(wcs_list, pixel_scale=0.1, max_size=10000):
"""
Compute output WCS that contains the full list of input WCS
Parameters
----------
wcs_list : list
List of individual `~astropy.wcs.WCS` objects.
pixel_scale : type
Pixel scale of the output WCS
max_size : int
Maximum size out the output image dimensions
Returns
-------
header : `~astropy.io.fits.Header`
WCS header
outputwcs : `~astropy.wcs.WCS`
Output WCS
"""
from shapely.geometry import Polygon
footprint = Polygon(wcs_list[0].calc_footprint())
for i in range(1, len(wcs_list)):
fp_i = Polygon(wcs_list[i].calc_footprint())
footprint = footprint.union(fp_i)
x, y = footprint.convex_hull.boundary.xy
x, y = np.array(x), np.array(y)
# center
crval = np.array(footprint.centroid.xy).flatten()
# dimensions in arcsec
xsize = (x.max()-x.min())*np.cos(crval[1]/180*np.pi)*3600
ysize = (y.max()-y.min())*3600
xsize = np.minimum(xsize, max_size*pixel_scale)
ysize = np.minimum(ysize, max_size*pixel_scale)
header, outputwcs = make_wcsheader(ra=crval[0], dec=crval[1],
size=(xsize, ysize),
pixscale=pixel_scale,
get_hdu=False,
theta=0)
return header, outputwcs
[docs]def symlink_templates(force=False):
"""Symlink templates from module to $GRIZLI/templates as part of the initial setup
Parameters
----------
force : bool
Force link files even if they already exist.
"""
# if 'GRIZLI' not in os.environ:
# print('"GRIZLI" environment variable not set!')
# return False
module_path = os.path.dirname(__file__)
templates_path = os.path.join(module_path, 'data/templates')
out_path = os.path.join(GRIZLI_PATH, 'templates')
if (not os.path.exists(out_path)) | force:
if os.path.exists(out_path): # (force)
shutil.rmtree(out_path)
os.symlink(templates_path, out_path)
print('Symlink: {0} -> {1}'.format(templates_path, out_path))
else:
print('Templates directory exists: {0}'.format(out_path))
print('Use `force=True` to force a new symbolic link.')
[docs]def fetch_acs_wcs_files(beams_file, bucket_name='grizli-v1'):
"""
Fetch wcs files for a given beams.fits files
"""
from urllib import request
try:
import boto3
HAS_BOTO = True
except:
HAS_BOTO = False
im = pyfits.open(beams_file)
root = '_'.join(beams_file.split('_')[:-1])
for i in range(len(im)):
h = im[i].header
if 'EXTNAME' not in h:
continue
if 'FILTER' not in h:
continue
if (h['EXTNAME'] != 'SCI') | (h['FILTER'] not in ['G800L']):
continue
ext = {1: 2, 2: 1}[h['CCDCHIP']]
wcsfile = h['GPARENT'].replace('.fits', '.{0:02d}.wcs.fits'.format(ext))
# Download the file with S3 or HTTP
if not os.path.exists(wcsfile):
print('Fetch {0} from {1}/Pipeline/{2}'.format(wcsfile,
bucket_name, root))
if HAS_BOTO:
s3 = boto3.resource('s3')
s3_client = boto3.client('s3')
bkt = s3.Bucket(bucket_name)
s3_path = 'Pipeline/{0}/Extractions/{1}'.format(root, wcsfile)
bkt.download_file(s3_path, './{0}'.format(wcsfile),
ExtraArgs={"RequestPayer": "requester"})
else:
url = 'https://s3.amazonaws.com/{0}/'.format(bucket_name)
url += 'Pipeline/{0}/Extractions/{1}'.format(root, wcsfile)
print('Fetch WCS file: {0}'.format(url))
req = request.urlretrieve(url, wcsfile)
im.close()
[docs]def fetch_hst_calib(file='iref$uc72113oi_pfl.fits', ftpdir='https://hst-crds.stsci.edu/unchecked_get/references/hst/', verbose=True, ref_paths={}, remove_corrupt=True):
"""
TBD
"""
import os
ref_dir = file.split('$')[0]
cimg = file.split('{0}$'.format(ref_dir))[1]
if ref_dir in ref_paths:
ref_path = ref_paths[ref_dir]
else:
ref_path = os.getenv(ref_dir)
iref_file = os.path.join(ref_path, cimg)
if not os.path.exists(iref_file):
os.system('curl -o {0} {1}/{2}'.format(iref_file, ftpdir, cimg))
if 'fits' in iref_file:
try:
_im = pyfits.open(iref_file)
_im.close()
except:
msg = ('Downloaded file {0} appears to be corrupt.\n'
'Check that {1}/{2} exists and is a valid file')
print(msg.format(iref_file, ftpdir, cimg))
if remove_corrupt:
os.remove(iref_file)
return False
else:
if verbose:
print('{0} exists'.format(iref_file))
return iref_file
[docs]def fetch_hst_calibs(flt_file, ftpdir='https://hst-crds.stsci.edu/unchecked_get/references/hst/', calib_types=['BPIXTAB', 'CCDTAB', 'OSCNTAB', 'CRREJTAB', 'DARKFILE', 'NLINFILE', 'DFLTFILE','PFLTFILE', 'IMPHTTAB', 'IDCTAB', 'NPOLFILE'], verbose=True, ref_paths={}):
"""
TBD
Fetch necessary calibration files needed for running calwf3 from STScI FTP
Old FTP dir: ftp://ftp.stsci.edu/cdbs/iref/"""
import os
im = pyfits.open(flt_file)
if im[0].header['INSTRUME'] == 'ACS':
ref_dir = 'jref'
if im[0].header['INSTRUME'] == 'WFC3':
ref_dir = 'iref'
if im[0].header['INSTRUME'] == 'WFPC2':
ref_dir = 'uref'
if not os.getenv(ref_dir):
print('No ${0} set! Put it in ~/.bashrc or ~/.cshrc.'.format(ref_dir))
return False
calib_paths = []
for ctype in calib_types:
if ctype not in im[0].header:
continue
if verbose:
print('Calib: {0}={1}'.format(ctype, im[0].header[ctype]))
if im[0].header[ctype] == 'N/A':
continue
path = fetch_hst_calib(im[0].header[ctype], ftpdir=ftpdir,
verbose=verbose, ref_paths=ref_paths)
calib_paths.append(path)
im.close()
return calib_paths
[docs]def mast_query_from_file_list(files=[], os_open=True):
"""
Generate a MAST query on datasets in a list.
"""
if len(files) == 0:
files = glob.glob('*raw.fits')
if len(files) == 0:
print('No `files` specified.')
return False
datasets = np.unique([file[:6]+'*' for file in files]).tolist()
URL = "http://archive.stsci.edu/hst/search.php?action=Search&"
URL += "sci_data_set_name="+','.join(datasets)
if os_open:
os.system('open "{0}"'.format(URL))
return URL
[docs]def fetch_default_calibs(get_acs=False, **kwargs):
"""
Fetch a set of default HST calibration files
"""
paths = {}
for ref_dir in ['iref', 'jref']:
has_dir = True
if not os.getenv(ref_dir):
has_dir = False
# Do directories exist in GRIZLI_PATH?
if os.path.exists(os.path.join(GRIZLI_PATH, ref_dir)):
has_dir = True
paths[ref_dir] = os.path.join(GRIZLI_PATH, ref_dir)
else:
paths[ref_dir] = os.getenv(ref_dir)
if not has_dir:
print("""
No ${0} set! Make a directory and point to it in ~/.bashrc or ~/.cshrc.
For example,
$ mkdir $GRIZLI/{0}
$ export {0}="${GRIZLI}/{0}/" # put this in ~/.bashrc
""".format(ref_dir))
return False
# WFC3
files = ['iref$uc72113oi_pfl.fits', # F105W Flat
'iref$uc721143i_pfl.fits', # F140W flat
'iref$u4m1335li_pfl.fits', # G102 flat
'iref$u4m1335mi_pfl.fits', # G141 flat
'iref$w3m18525i_idc.fits', # IDCTAB distortion table}
]
if 'ACS' in kwargs:
get_acs = kwargs['ACS']
if get_acs:
files.extend(['jref$n6u12592j_pfl.fits', # F814 Flat
'jref$o841350mj_pfl.fits', # G800L flat])
'jref$v971826jj_npl.fits'])
for file in files:
fetch_hst_calib(file, ref_paths=paths)
badpix = os.path.join(paths['iref'], 'badpix_spars200_Nov9.fits')
print('Extra WFC3/IR bad pixels: {0}'.format(badpix))
if not os.path.exists(badpix):
os.system('curl -o {0}/badpix_spars200_Nov9.fits https://raw.githubusercontent.com/gbrammer/wfc3/master/data/badpix_spars200_Nov9.fits'.format(paths['iref']))
# Pixel area map
pam = os.path.join(paths['iref'], 'ir_wfc3_map.fits')
print('Pixel area map: {0}'.format(pam))
if not os.path.exists(pam):
os.system('curl -o {0} https://www.stsci.edu/files/live/sites/www/files/home/hst/instrumentation/wfc3/data-analysis/pixel-area-maps/_documents/ir_wfc3_map.fits'.format(pam))
[docs]def fetch_wfpc2_calib(file='g6q1912hu_r4f.fits', path=os.getenv('uref'), use_mast=False, verbose=True, overwrite=True, skip_existing=True):
"""
Fetch static WFPC2 calibration file and run `stsci.tools.convertwaiveredfits` on it.
path : str
Output path of the reference file (generally should be in $uref).
use_mast : bool
If True, try to fetch from "mast.stsci.edu//api/v0/download/file?uri",
otherwise, fetch from a static directory
"ssb.stsci.edu/cdbs_open/cdbs/uref_linux/".
"""
from stsci.tools import convertwaiveredfits
try: # Python 3.x
import http.client as httplib
except ImportError: # Python 2.x
import httplib
if file.endswith('h'):
# File like "g6q1912hu.r4h"
file = file[:-1].replace('.', '_')+'f.fits'
outPath = os.path.join(path, file)
if os.path.exists(outPath) & skip_existing:
print("# fetch_wfpc2_calib: {0} exists".format(outPath))
return True
if use_mast:
server = 'mast.stsci.edu'
uri = 'mast:HST/product/'+file
request_path = "/api/v0/download/file?uri="+uri
else:
server = 'ssb.stsci.edu'
request_path = '/cdbs_open/cdbs/uref_linux/'+file
if verbose:
print('# fetch_wfpc2_calib: "{0}" to {1}'.format(server+request_path, path))
conn = httplib.HTTPSConnection(server)
conn.request("GET", request_path)
resp = conn.getresponse()
fileContent = resp.read()
conn.close()
# check for file
if len(fileContent) < 4096:
print('ERROR: "{0}" failed to download. Try `use_mast={1}`.'.format(server+request_path, (use_mast is False)))
status = False
raise FileNotFoundError
else:
print("# fetch_wfpc2_calib: {0} (COMPLETE)".format(outPath))
status = True
# save to file
with open(outPath, 'wb') as FLE:
FLE.write(fileContent)
if status:
# Convert to standard FITS
try:
hdu = convertwaiveredfits.convertwaiveredfits(outPath)
while 'HISTORY' in hdu[0].header:
hdu[0].header.remove('HISTORY')
hdu.writeto(outPath.replace('.fits', '_c0h.fits'),
overwrite=overwrite, output_verify='fix')
except:
return True
[docs]def fetch_nircam_skyflats():
"""
Download skyflat files
"""
conf_path = os.path.join(GRIZLI_PATH, 'CONF', 'NircamSkyFlat')
os.system(f'aws s3 sync s3://grizli-v2/NircamSkyflats/ {conf_path} --exclude "*" --include "nrc*fits"')
_files = glob.glob(conf_path+'/*fits')
_files.sort()
return _files
[docs]def fetch_config_files(get_acs=False, get_sky=True, get_stars=True, get_epsf=True, get_jwst=False, get_wfc3=True, **kwargs):
"""
Config files needed for Grizli
"""
if 'ACS' in kwargs:
get_acs = kwargs['ACS']
cwd = os.getcwd()
print('Config directory: {0}/CONF'.format(GRIZLI_PATH))
os.chdir(os.path.join(GRIZLI_PATH, 'CONF'))
ftpdir = 'ftp://ftp.stsci.edu/cdbs/wfc3_aux/'
tarfiles = []
# Config files
# BASEURL = 'https://s3.amazonaws.com/grizli/CONF/'
# BASEURL = 'https://erda.ku.dk/vgrid/Gabriel%20Brammer/CONF/'
BASEURL = ('https://raw.githubusercontent.com/gbrammer/' +
'grizli-config/master')
if get_wfc3:
tarfiles = ['{0}/WFC3.IR.G102.cal.V4.32.tar.gz'.format(ftpdir),
'{0}/WFC3.IR.G141.cal.V4.32.tar.gz'.format(ftpdir)]
tarfiles += [f'{BASEURL}/WFC3.IR.G102.WD.V4.32.tar.gz',
f'{BASEURL}/WFC3.IR.G141.WD.V4.32.tar.gz']
if get_jwst:
tarfiles += [f'{BASEURL}/jwst-grism-conf.tar.gz',
f'{BASEURL}/niriss.conf.220725.tar.gz',
f'{BASEURL}/nircam-wisp-aug2022.tar.gz']
if get_sky:
ftpdir = BASEURL
tarfiles.append('{0}/grism_master_sky_v0.5.tar.gz'.format(ftpdir))
#gURL = 'http://www.stsci.edu/~brammer/Grizli/Files'
#gURL = 'https://s3.amazonaws.com/grizli/CONF'
gURL = BASEURL
tarfiles.append('{0}/WFC3IR_extended_PSF.v1.tar.gz'.format(gURL))
if get_acs:
tarfiles += [f'{BASEURL}/ACS.WFC.CHIP1.Stars.conf',
f'{BASEURL}/ACS.WFC.CHIP2.Stars.conf']
tarfiles.append('{0}/ACS.WFC.sky.tar.gz'.format(gURL))
tarfiles.append('{0}/ACS_CONFIG.tar.gz'.format(gURL))
for url in tarfiles:
file = os.path.basename(url)
if not os.path.exists(file):
print('Get {0}'.format(file))
os.system('curl -o {0} {1}'.format(file, url))
if '.tar' in file:
os.system('tar xzvf {0}'.format(file))
if get_epsf:
# ePSF files for fitting point sources
#psf_path = 'http://www.stsci.edu/hst/wfc3/analysis/PSF/psf_downloads/wfc3_ir/'
#psf_path = 'https://www.stsci.edu/~jayander/STDPSFs/WFC3IR/'
#psf_root = 'PSFSTD'
#psf_path = 'https://www.stsci.edu/~jayander/HST1PASS/'
psf_path = 'https://www.stsci.edu/~jayander/HST1PASS/LIB/'
psf_path += 'PSFs/STDPSFs/WFC3IR/'
psf_root = 'STDPSF'
ir_psf_filters = ['F105W', 'F125W', 'F140W', 'F160W']
# New PSFs
ir_psf_filters += ['F110W', 'F127M']
files = ['{0}/{1}_WFC3IR_{2}.fits'.format(psf_path, psf_root, filt)
for filt in ir_psf_filters]
for url in files:
file = os.path.basename(url).replace('STDPSF', 'PSFSTD')
if not os.path.exists(file):
print('Get {0}'.format(file))
os.system('curl -o {0} {1}'.format(file, url))
else:
print('File {0} exists'.format(file))
if get_stars:
# Stellar templates
print('Templates directory: {0}/templates'.format(GRIZLI_PATH))
os.chdir('{0}/templates'.format(GRIZLI_PATH))
url = 'https://www.stsci.edu/~brammer/Grizli/Files/'
files = [url+'stars_pickles.npy', url+'stars_bpgs.npy']
for url in files:
file = os.path.basename(url)
if not os.path.exists(file):
print('Get {0}'.format(file))
os.system('curl -o {0} {1}'.format(file, url))
else:
print('File {0} exists'.format(file))
print('ln -s stars_pickles.npy stars.npy')
os.system('ln -s stars_pickles.npy stars.npy')
os.chdir(cwd)
[docs]class MW_F99(object):
"""
Wrapper around the `specutils.extinction` / `extinction` modules, which are called differently
"""
def __init__(self, a_v, r_v=3.1):
self.a_v = a_v
self.r_v = r_v
self.IS_SPECUTILS = False
self.IS_EXTINCTION = False
try:
from specutils.extinction import ExtinctionF99
self.IS_SPECUTILS = True
self.F99 = ExtinctionF99(self.a_v, r_v=self.r_v)
except(ImportError):
try:
from extinction import Fitzpatrick99
self.IS_EXTINCTION = True
self.F99 = Fitzpatrick99(r_v=self.r_v)
except(ImportError):
print("""
Couldn\'t find extinction modules in
`specutils.extinction` or
`extinction.Fitzpatrick99`.
MW extinction not implemented.
""")
self.status = self.IS_SPECUTILS | self.IS_EXTINCTION
[docs] def __call__(self, wave_input):
import astropy.units as u
if isinstance(wave_input, list):
wave = np.array(wave_input)
else:
wave = wave_input
if self.status is False:
return np.zeros_like(wave)
if self.IS_SPECUTILS:
if hasattr(wave, 'unit'):
wave_aa = wave
else:
wave_aa = wave*u.AA
return self.F99(wave_aa)
if self.IS_EXTINCTION:
if hasattr(wave, 'unit'):
wave_aa = wave.to(u.AA)
else:
wave_aa = wave
return self.F99(wave_aa, self.a_v, unit='aa')
[docs]class EffectivePSF:
def __init__(self):
"""Tools for handling WFC3/IR Effective PSF
See documentation at http://www.stsci.edu/hst/wfc3/analysis/PSF.
PSF files stored in $GRIZLI/CONF/
"""
self.load_PSF_data()
[docs] def load_PSF_data(self):
"""Load data from PSFSTD files
Files should be located in ${GRIZLI}/CONF/ directory.
"""
self.epsf = OrderedDict()
for filter in ['F105W', 'F110W', 'F125W', 'F140W', 'F160W', 'F127M']:
file = os.path.join(GRIZLI_PATH, 'CONF',
'PSFSTD_WFC3IR_{0}.fits'.format(filter))
if not os.path.exists(file):
continue
with pyfits.open(file) as _im:
data = _im[0].data.T*1
data[data < 0] = 0
self.epsf[filter] = data
# UVIS
filter_files = glob.glob(os.path.join(GRIZLI_PATH, 'CONF',
'PSFSTD_WFC3UV*.fits'))
filter_files.sort()
for file in filter_files:
with pyfits.open(file, ignore_missing_end=True) as _im:
data = _im[0].data.T*1
data[data < 0] = 0
filt = '_'.join(file.strip('.fits').split('_')[2:])
self.epsf[filt+'U'] = data
# ACS
filter_files = glob.glob(os.path.join(GRIZLI_PATH, 'CONF',
'PSFSTD_ACSWFC*.fits'))
filter_files.sort()
for file in filter_files:
with pyfits.open(file, ignore_missing_end=True) as _im:
data = _im[0].data.T*1.
data[data < 0] = 0
filt = '_'.join(file.strip('.fits').split('_')[2:])
self.epsf[filt] = data
# JWST
filter_files = glob.glob(os.path.join(GRIZLI_PATH, 'CONF/JWSTePSF',
'nircam*.fits'))
filter_files += glob.glob(os.path.join(GRIZLI_PATH, 'CONF/JWSTePSF',
'niriss*.fits'))
filter_files += glob.glob(os.path.join(GRIZLI_PATH, 'CONF/JWSTePSF',
'miri*.fits'))
filter_files.sort()
for file in filter_files:
with pyfits.open(file, ignore_missing_end=True) as _im:
data = _im[0].data*1 # [::-1,:,:]#[:,::-1,:]
data[data < 0] = 0
key = '{0}-{1}'.format(_im[0].header['DETECTOR'].upper(),
_im[0].header['FILTER'])
if 'LABEL' in _im[0].header:
key += '-' + _im[0].header['LABEL']
self.epsf[key] = data
# Dummy, use F105W ePSF for F098M and F110W
self.epsf['F098M'] = self.epsf['F105W']
self.epsf['F128N'] = self.epsf['F125W']
self.epsf['F130N'] = self.epsf['F125W']
self.epsf['F132N'] = self.epsf['F125W']
# Dummy filters for IR grisms
self.epsf['G141'] = self.epsf['F140W']
self.epsf['G102'] = self.epsf['F105W']
# Extended
self.extended_epsf = {}
for filter in ['F105W', 'F125W', 'F140W', 'F160W']:
file = os.path.join(GRIZLI_PATH, 'CONF',
'extended_PSF_{0}.fits'.format(filter))
if not os.path.exists(file):
#BASEURL = 'https://erda.ku.dk/vgrid/Gabriel%20Brammer/CONF/'
BASEURL = ('https://raw.githubusercontent.com/gbrammer/' +
'grizli-config/master')
msg = 'Extended PSF file \'{0}\' not found.'.format(file)
msg += 'Get the archive from '
msg += f' {BASEURL}/WFC3IR_extended_PSF.v1.tar.gz'
msg += ' and unpack in ${GRIZLI}/CONF/'
raise FileNotFoundError(msg)
with pyfits.open(file) as _im:
data = _im[0].data*1
data[data < 0] = 0
# Mask center
NX = data.shape[0]/2-1
yp, xp = np.indices(data.shape)
R = np.sqrt((xp-NX)**2+(yp-NX)**2)
data[R <= 4] = 0.
self.extended_epsf[filter] = data
self.extended_N = int(NX)
self.extended_epsf['F098M'] = self.extended_epsf['F105W']
self.extended_epsf['F110W'] = self.extended_epsf['F105W']
self.extended_epsf['F128N'] = self.extended_epsf['F125W']
self.extended_epsf['F130N'] = self.extended_epsf['F125W']
self.extended_epsf['F132N'] = self.extended_epsf['F125W']
self.extended_epsf['G102'] = self.extended_epsf['F105W']
self.extended_epsf['G141'] = self.extended_epsf['F140W']
[docs] def get_at_position(self, x=507, y=507, filter='F140W', rot90=0):
"""Evaluate ePSF at detector coordinates
TBD
"""
epsf = self.epsf[filter]
psf_type = 'HST/Optical'
if filter in ['F098M', 'F110W', 'F105W', 'F125W', 'F140W', 'F160W',
'G102','G141','F128N','F130N','F132N']:
psf_type = 'WFC3/IR'
elif filter.startswith('NRC') | filter.startswith('NIS'):
# NIRISS, NIRCam 2K
psf_type = 'JWST/2K'
elif filter.startswith('MIRI'):
psf_type = 'JWST/MIRI'
self.eval_psf_type = psf_type
if psf_type == 'WFC3/IR':
# IR detector
rx = 1+(np.clip(x, 1, 1013)-0)/507.
ry = 1+(np.clip(y, 1, 1013)-0)/507.
# zero index
rx -= 1
ry -= 1
nx = np.clip(int(rx), 0, 2)
ny = np.clip(int(ry), 0, 2)
# print x, y, rx, ry, nx, ny
fx = rx-nx
fy = ry-ny
psf_xy = (1-fx)*(1-fy)*epsf[:, :, nx+ny*3]
psf_xy += fx*(1-fy)*epsf[:, :, (nx+1)+ny*3]
psf_xy += (1-fx)*fy*epsf[:, :, nx+(ny+1)*3]
psf_xy += fx*fy*epsf[:, :, (nx+1)+(ny+1)*3]
self.eval_filter = filter
if psf_type == 'JWST/MIRI':
# IR detector
NDET = int(np.sqrt(epsf.shape[2]))
rx = 1+(np.clip(x, 1, 1023)-0)/512.
ry = 1+(np.clip(y, 1, 1023)-0)/512.
# zero index
rx -= 1
ry -= 1
# nx = np.clip(int(rx), 0, 2)
# ny = np.clip(int(ry), 0, 2)
nx = np.clip(int(rx), 0, NDET-1)
ny = np.clip(int(ry), 0, NDET-1)
# print x, y, rx, ry, nx, ny
fx = rx-nx
fy = ry-ny
# psf_xy = (1-fx)*(1-fy)*epsf[:, :, nx+ny*3]
# psf_xy += fx*(1-fy)*epsf[:, :, (nx+1)+ny*3]
# psf_xy += (1-fx)*fy*epsf[:, :, nx+(ny+1)*3]
# psf_xy += fx*fy*epsf[:, :, (nx+1)+(ny+1)*3]
if NDET == 1:
psf_xy = epsf[:,:,0]
else:
psf_xy = (1-fx)*(1-fy)*epsf[:, :, nx+ny*NDET]
psf_xy += fx*(1-fy)*epsf[:, :, (nx+1)+ny*NDET]
psf_xy += (1-fx)*fy*epsf[:, :, nx+(ny+1)*NDET]
psf_xy += fx*fy*epsf[:, :, (nx+1)+(ny+1)*NDET]
# psf_xy = np.rot90(psf_xy.T, 2)
psf_xy = psf_xy.T
self.eval_filter = filter
if psf_type == 'JWST/2K':
NDET = int(np.sqrt(epsf.shape[2]))
# IR detector
rx = 1+(np.clip(x, 1, 2047)-0)/1024.
ry = 1+(np.clip(y, 1, 2047)-0)/1024.
# zero index
rx -= 1
ry -= 1
nx = np.clip(int(rx), 0, NDET-1)
ny = np.clip(int(ry), 0, NDET-1)
# print x, y, rx, ry, nx, ny
fx = rx-nx
fy = ry-ny
if NDET == 1:
psf_xy = epsf[:,:,0]
else:
psf_xy = (1-fx)*(1-fy)*epsf[:, :, nx+ny*NDET]
psf_xy += fx*(1-fy)*epsf[:, :, (nx+1)+ny*NDET]
psf_xy += (1-fx)*fy*epsf[:, :, nx+(ny+1)*NDET]
psf_xy += fx*fy*epsf[:, :, (nx+1)+(ny+1)*NDET]
psf_xy = psf_xy.T
# psf_xy = np.rot90(psf_xy.T, 2)
self.eval_filter = filter
el