Source code for grizli.utils

"""
Dumping ground for general utilities
"""
import os
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

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 = ['PaB', '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':'---', '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 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) 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) 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 `pyregion`. fill_value : int/float Value in `out_data` not covered by `in_data`. Returns ------- out_data : `~numpy.ndarray` Blotted data. """ from shapely.geometry import Polygon import pyregion 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) for p in olap_poly.T.flatten()])+')\n' reg = pyregion.parse(poly_reg) mask = reg.get_mask(header=to_header(out_wcs), 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)) 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) 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 get_visit_footprints(visits): """ Add `~shapely.geometry.Polygon` 'footprint' attributes to visit dict. Parameters ---------- visits : list List of visit dictionaries. """ import os import astropy.io.fits as pyfits import astropy.wcs as pywcs from shapely.geometry import Polygon N = len(visits) for i in range(N): for j in range(len(visits[i]['files'])): flt_file = visits[i]['files'][j] if (not os.path.exists(flt_file)) & os.path.exists('../RAW/'+flt_file): flt_file = '../RAW/'+flt_file flt_j = pyfits.open(flt_file) h = flt_j[0].header if (h['INSTRUME'] == 'WFC3') & (h['DETECTOR'] == 'IR'): wcs_j = pywcs.WCS(flt_j['SCI', 1]) else: wcs_j = pywcs.WCS(flt_j['SCI', 1], fobj=flt_j) fp_j = Polygon(wcs_j.calc_footprint()) if j == 0: fp_i = fp_j else: fp_i = fp_i.union(fp_j) visits[i]['footprint'] = fp_i 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: im_i = pyfits.open(glob.glob(visits[i]['product']+'_dr?_sci.fits')[0]) wcs_i = pywcs.WCS(im_i[0]) fp_i = Polygon(wcs_i.calc_footprint()).buffer(buffer/3600.) 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: im_j = pyfits.open(glob.glob(visits[j]['product']+'_dr?_sci.fits')[0]) wcs_j = pywcs.WCS(im_j[0]) fp_j = Polygon(wcs_j.calc_footprint()).buffer(buffer/3600.) # im_j = pyfits.open(glob.glob(visits[j]['product']+'_dr?_sci.fits')[0]) # wcs_j = pywcs.WCS(im_j[0]) # fp_j = Polygon(wcs_j.calc_footprint()).buffer(buffer/3600.) 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 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
[docs]def parse_filter_from_header(header, filter_only=False, jwst_detector=False, **kwargs): """Get simple filter name out of an HST/JWST image header. ACS has two keywords for the two filter wheels, so just return the non-CLEAR filter. For example, >>> h = astropy.io.fits.Header() >>> h['INSTRUME'] = 'ACS' >>> h['FILTER1'] = 'CLEAR1L' >>> h['FILTER2'] = 'F814W' >>> from grizli.utils import parse_filter_from_header >>> print(parse_filter_from_header(h)) F814W >>> h['FILTER1'] = 'G800L' >>> h['FILTER2'] = 'CLEAR2L' >>> print(parse_filter_from_header(h)) G800L Parameters ----------- header : `~astropy.io.fits.Header` Image header with FILTER or FILTER1,FILTER2,...,FILTERN keywords filter_only : bool If true, don't do any special handling with JWST but just return the ``FILTER`` keyword itself. Otherwise, for JWST/NIRISS, return ``{PUPIL}-{FILTER}`` and for JWST/NIRCAM, return ``{FILTER}-{PUPIL}`` jwst_detector : bool If True, prepend ``DETECTOR`` to output for JWST NIRCam and NIRISS to distinguish NIRCam detectors and filter names common between these instruments. Returns -------- filter : str """ if 'INSTRUME' not in header: instrume = 'N/A' else: instrume = header['INSTRUME'] if instrume.strip() == 'ACS': for i in [1, 2]: filter_i = header['FILTER{0:d}'.format(i)] if 'CLEAR' in filter_i: continue else: filter = filter_i elif instrume == 'WFPC2': filter = header['FILTNAM1'] elif instrume == 'NIRISS': if filter_only: filter = header['FILTER'] else: filter = '{0}-{1}'.format(header['PUPIL'], header['FILTER']) if jwst_detector: filter = '{0}-{1}'.format(header['DETECTOR'], filter) elif instrume == 'NIRCAM': if filter_only: filter = header['FILTER'] else: filter = '{0}-{1}'.format(header['FILTER'], header['PUPIL']) if jwst_detector: filter = '{0}-{1}'.format(header['DETECTOR'], filter) filter = filter.replace('LONG','5') elif 'FILTER' in header: filter = header['FILTER'] else: msg = 'Failed to parse FILTER keyword for INSTRUMEnt {0}' raise KeyError(msg.format(instrume)) return filter.upper()
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 calc_header_zeropoint(im, ext=0): """ Determine AB zeropoint from image header Parameters ---------- im : `~astropy.io.fits.HDUList` or Image object or header. Returns ------- ZP : float AB zeropoint """ from . import model scale_exptime = 1. if isinstance(im, pyfits.Header): header = im else: if '_dr' in im.filename(): ext = 0 elif '_fl' in im.filename(): if 'DETECTOR' in im[0].header: if im[0].header['DETECTOR'] == 'IR': ext = 0 bunit = im[1].header['BUNIT'] else: # ACS / UVIS if ext == 0: ext = 1 bunit = im[1].header['BUNIT'] if bunit == 'ELECTRONS': scale_exptime = im[0].header['EXPTIME'] header = im[ext].header try: fi = parse_filter_from_header(im[0].header).upper() except: fi = None # Get AB zeropoint if 'APZP' in header: ZP = header['ABZP'] elif 'PHOTFNU' in header: ZP = -2.5*np.log10(header['PHOTFNU'])+8.90 ZP += 2.5*np.log10(scale_exptime) elif 'PHOTFLAM' in header: ZP = (-2.5*np.log10(header['PHOTFLAM']) - 21.10 - 5*np.log10(header['PHOTPLAM']) + 18.6921) ZP += 2.5*np.log10(scale_exptime) elif (fi is not None): if fi in model.photflam_list: ZP = (-2.5*np.log10(model.photflam_list[fi]) - 21.10 - 5*np.log10(model.photplam_list[fi]) + 18.6921) else: print('Couldn\'t find PHOTFNU or PHOTPLAM/PHOTFLAM keywords, use ZP=25') ZP = 25 else: print('Couldn\'t find FILTER, PHOTFNU or PHOTPLAM/PHOTFLAM keywords, use ZP=25') ZP = 25 # If zeropoint infinite (e.g., PHOTFLAM = 0), then calculate from synphot if not np.isfinite(ZP): try: import pysynphot as S bp = S.ObsBandpass(im[0].header['PHOTMODE'].replace(' ', ',')) spec = S.FlatSpectrum(0, fluxunits='ABMag') obs = S.Observation(spec, bp) ZP = 2.5*np.log10(obs.countrate()) except: pass return ZP
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 line_wavelengths['PaA'] = [18751.0] line_ratios['PaA'] = [1.] line_wavelengths['PaB'] = [12821.6] line_ratios['PaB'] = [1.] line_wavelengths['PaG'] = [10941.1] line_ratios['PaG'] = [1.] line_wavelengths['PaD'] = [10049.0] line_ratios['PaD'] = [1.] line_wavelengths['Ha'] = [6564.61] line_ratios['Ha'] = [1.] line_wavelengths['Hb'] = [4862.71] line_ratios['Hb'] = [1.] line_wavelengths['Hg'] = [4341.692] line_ratios['Hg'] = [1.] line_wavelengths['Hd'] = [4102.892] line_ratios['Hd'] = [1.] line_wavelengths['H7'] = [3971.198] line_ratios['H7'] = [1.] line_wavelengths['H8'] = [3890.166] line_ratios['H8'] = [1.] line_wavelengths['H9'] = [3836.485] line_ratios['H9'] = [1.] line_wavelengths['H10'] = [3798.987] line_ratios['H10'] = [1.] line_wavelengths['H11'] = [3771.70] line_ratios['H11'] = [1.] line_wavelengths['H12'] = [3751.22] 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.] # 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]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]) 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, :]) 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 transform_wcs(in_wcs, translation=[0., 0.], rotation=0., scale=1.): """Update WCS with shift, rotation, & scale Parameters ---------- in_wcs: `~astropy.wcs.WCS` Input WCS translation: [float, float] xshift & yshift in pixels rotation: float CCW rotation (towards East), radians scale: float Pixel scale factor Returns ------- out_wcs: `~astropy.wcs.WCS` Modified WCS """ out_wcs = in_wcs.deepcopy() #out_wcs.wcs.crpix += np.array(translation) # Compute shift for crval, not crpix crval = in_wcs.all_pix2world([in_wcs.wcs.crpix-np.array(translation)], 1).flatten() # Compute shift at image center if hasattr(in_wcs, '_naxis1'): refpix = np.array([in_wcs._naxis1/2., in_wcs._naxis2/2.]) else: refpix = np.array(in_wcs._naxis)/2. c0 = in_wcs.all_pix2world([refpix], 1).flatten() c1 = in_wcs.all_pix2world([refpix-np.array(translation)], 1).flatten() out_wcs.wcs.crval += c1-c0 theta = -rotation _mat = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]) try: out_wcs.wcs.cd[:2,:2] = np.dot(out_wcs.wcs.cd[:2,:2], _mat)/scale except: out_wcs.wcs.pc = np.dot(out_wcs.wcs.pc, _mat)/scale out_wcs.pscale = get_wcs_pscale(out_wcs) #out_wcs.wcs.crpix *= scale if hasattr(out_wcs, 'pixel_shape'): _naxis1 = int(np.round(out_wcs.pixel_shape[0]*scale)) _naxis2 = int(np.round(out_wcs.pixel_shape[1]*scale)) out_wcs._naxis = [_naxis1, _naxis2] elif hasattr(out_wcs, '_naxis1'): out_wcs._naxis1 = int(np.round(out_wcs._naxis1*scale)) out_wcs._naxis2 = int(np.round(out_wcs._naxis2*scale)) return out_wcs
[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_wcs_slice_header(wcs, slx, sly): """TBD """ #slx, sly = slice(1279, 1445), slice(2665,2813) h = wcs.slice((sly, slx)).to_header(relax=True) h['NAXIS'] = 2 h['NAXIS1'] = slx.stop-slx.start h['NAXIS2'] = sly.stop-sly.start for k in h: if k.startswith('PC'): h.rename_keyword(k, k.replace('PC', 'CD')) return h
[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]class WCSFootprint(object): """ Helper functions for dealing with WCS footprints """ def __init__(self, wcs, ext=1, label=None): if isinstance(wcs, pywcs.WCS): self.wcs = wcs.deepcopy() if not hasattr(self.wcs, 'pixel_shape'): self.wcs.pixel_shape = None if self.wcs.pixel_shape is None: self.wcs.pixel_shape = [int(p*2) for p in self.wcs.wcs.crpix] elif isinstance(wcs, str): hdu = pyfits.open(wcs) if len(hdu) == 1: ext = 0 self.add_naxis(hdu[ext].header) the_wcs = pywcs.WCS(hdu[ext].header, fobj=hdu) self.wcs = the_wcs elif isinstance(wcs, pyfits.HDUList): if len(wcs) == 1: ext = 0 self.add_naxis(wcs[ext].header) the_wcs = pywcs.WCS(wcs[ext].header, fobj=wcs) self.wcs = the_wcs else: print('WCS class not recognized: {0}'.format(wcs.__class__)) raise ValueError self.fp = self.wcs.calc_footprint() self.cosdec = np.cos(self.fp[0, 1]/180*np.pi) self.label = label self.pixel_scale = get_wcs_pscale(self.wcs) @property def centroid(self): return np.mean(self.fp, axis=0) @property def path(self): """ `~matplotlib.path.Path` object """ import matplotlib.path return matplotlib.path.Path(self.fp) @property def polygon(self): """ `~shapely.geometry.Polygon` object. """ from shapely.geometry import Polygon return Polygon(self.fp)
[docs] def get_patch(self, **kwargs): """ `~descartes.PolygonPatch` object """ from descartes import PolygonPatch return PolygonPatch(self.polygon, **kwargs)
@property def region(self): """ Polygon string in DS9 region format """ return 'polygon({0})'.format(','.join(['{0:.6f}'.format(c) for c in self.fp.flatten()]))
[docs] @staticmethod def add_naxis(header): """ If NAXIS keywords not found in an image header, assume the parent image dimensions are 2*CRPIX """ for i in [1, 2]: if 'NAXIS{0}'.format(i) not in header: header['NAXIS{0}'.format(i)] = int(header['CRPIX{0}'.format(i)]*2)
[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)
[docs]def full_spectrum_wcsheader(center_wave=1.4e4, dlam=40, NX=100, spatial_scale=1, NY=10): """Make a WCS header for a 2D spectrum Parameters ---------- center_wave : float Wavelength of the central pixel, in Anstroms dlam : float Delta-wavelength per (x) pixel NX, NY : int Number of x & y pixels. Output will have shape `(2*NY, 2*NX)`. spatial_scale : float Spatial scale of the output, in units of the input pixels Returns ------- header : `~astropy.io.fits.Header` Output WCS header wcs : `~astropy.wcs.WCS` Output WCS Examples -------- >>> from grizli.utils import make_spectrum_wcsheader >>> h, wcs = make_spectrum_wcsheader() >>> print(wcs) WCS Keywords Number of WCS axes: 2 CTYPE : 'WAVE' 'LINEAR' CRVAL : 14000.0 0.0 CRPIX : 101.0 11.0 CD1_1 CD1_2 : 40.0 0.0 CD2_1 CD2_2 : 0.0 1.0 NAXIS : 200 20 """ h = pyfits.ImageHDU(data=np.zeros((2*NY, 2*NX), dtype=np.float32)) refh = h.header refh['CRPIX1'] = NX+1 refh['CRPIX2'] = NY+1 refh['CRVAL1'] = center_wave/1.e4 refh['CD1_1'] = dlam/1.e4 refh['CD1_2'] = 0. refh['CRVAL2'] = 0. refh['CD2_2'] = spatial_scale refh['CD2_1'] = 0. refh['RADESYS'] = '' refh['CTYPE1'] = 'RA---TAN-SIP' refh['CUNIT1'] = 'mas' refh['CTYPE2'] = 'DEC--TAN-SIP' refh['CUNIT2'] = 'mas' ref_wcs = pywcs.WCS(refh) ref_wcs.pscale = get_wcs_pscale(ref_wcs) return refh, ref_wcs
[docs]def make_spectrum_wcsheader(center_wave=1.4e4, dlam=40, NX=100, spatial_scale=1, NY=10): """Make a WCS header for a 2D spectrum Parameters ---------- center_wave : float Wavelength of the central pixel, in Anstroms dlam : float Delta-wavelength per (x) pixel NX, NY : int Number of x & y pixels. Output will have shape `(2*NY, 2*NX)`. spatial_scale : float Spatial scale of the output, in units of the input pixels Returns ------- header : `~astropy.io.fits.Header` Output WCS header wcs : `~astropy.wcs.WCS` Output WCS Examples -------- >>> from grizli.utils import make_spectrum_wcsheader >>> h, wcs = make_spectrum_wcsheader() >>> print(wcs) WCS Keywords Number of WCS axes: 2 CTYPE : 'WAVE' 'LINEAR' CRVAL : 14000.0 0.0 CRPIX : 101.0 11.0 CD1_1 CD1_2 : 40.0 0.0 CD2_1 CD2_2 : 0.0 1.0 NAXIS : 200 20 """ h = pyfits.ImageHDU(data=np.zeros((2*NY, 2*NX), dtype=np.float32)) refh = h.header refh['CRPIX1'] = NX+1 refh['CRPIX2'] = NY+1 refh['CRVAL1'] = center_wave refh['CD1_1'] = dlam refh['CD1_2'] = 0. refh['CRVAL2'] = 0. refh['CD2_2'] = spatial_scale refh['CD2_1'] = 0. refh['RADESYS'] = '' refh['CTYPE1'] = 'WAVE' refh['CTYPE2'] = 'LINEAR' ref_wcs = pywcs.WCS(h.header) ref_wcs.pscale = np.sqrt(ref_wcs.wcs.cd[0, 0]**2 + ref_wcs.wcs.cd[1, 0]**2)*3600. return refh, ref_wcs
[docs]def read_gzipped_header(file='test.fits.gz', BLOCK=1024, NMAX=256, nspace=16, strip=False): """ Read primary header from a (potentially large) zipped FITS file The script proceeds by reading `NMAX` segments of size `BLOCK` bytes from the file and searching for a string `END + ' '*nspace` in the data indicating the end of the primary header. Parameters ---------- file : str Filename of gzipped FITS file BLOCK, NMAX, nspace : int Parameters for reading bytes from the input file strip : bool Send output through `strip_header_keys`. Returns ------- header : `~astropy.io.fits.Header` Header object """ import gzip import astropy.io.fits as pyfits f = gzip.GzipFile(fileobj=open(file, 'rb')) data = b'' end = b' END'+b' '*nspace for i in range(NMAX): data_i = f.read(BLOCK) if end in data_i: break data += data_i if (i == NMAX-1): print('Error: END+{3}*" " not found in first {0}x{1} bytes of {2})'.format(NMAX, BLOCK, file, nspace)) f.close() return {} ix = data_i.index(end) data += data_i[:ix]+end # data_i[:ix] f.close() data_str = data.decode('utf8') h = pyfits.Header.fromstring(data_str) if strip: return strip_header_keys(h, usewcs=True) else: return h
DRIZZLE_KEYS = ['GEOM', 'DATA', 'DEXP', 'OUDA', 'OUWE', 'OUCO', 'MASK', 'WTSC', 'KERN', 'PIXF', 'COEF', 'OUUN', 'FVAL', 'WKEY', 'SCAL', 'ISCL']
[docs]def strip_header_keys(header, comment=True, history=True, drizzle_keys=DRIZZLE_KEYS, usewcs=False, keep_with_wcs=['EXPTIME', 'FILTER', 'TELESCOP', 'INSTRUME', 'DATE-OBS', 'EXPSTART', 'EXPEND']): """ Strip header keywords Parameters ---------- comment, history : bool Strip 'COMMENT' and 'HISTORY' keywords, respectively. drizzle_keys : list Strip keys produced by `~drizzlepac.astrodrizzle`. usewcs : bool Alternatively, just generate a simple WCS-only header from the input header. keep_with_wcs : list Additional keys to try to add to the `usewcs` header. Returns ------- header : `~astropy.io.fits.Header` Header object. """ import copy import astropy.wcs as pywcs # Parse WCS and build header if usewcs: wcs = pywcs.WCS(header) h = to_header(wcs) for k in keep_with_wcs: if k in header: if k in header.comments: h[k] = header[k], header.comments[k] else: h[k] = header[k] if 'FILTER' in keep_with_wcs: try: h['FILTER'] = (parse_filter_from_header(header), 'element selected from filter wheel') except: pass return h h = copy.deepcopy(header) keys = list(h.keys()) strip_keys = [] if comment: strip_keys.append('COMMENT') if history: strip_keys.append('HISTORY') for k in keys: if k in strip_keys: h.remove(k) if drizzle_keys: if k.startswith('D'): if (k[-4:] in drizzle_keys) | k.endswith('VER'): h.remove(k) return h
[docs]def wcs_from_header(header, relax=True, **kwargs): """ Initialize `~astropy.wcs.WCS` from a `~astropy.io.fits.Header` Parameters ---------- header : `~astropy.io.fits.Header` FITS header with optional ``SIPCRPX1`` and ``SIPCRPX2`` keywords that define a separate reference pixel for a SIP header relax, kwargs : bool, dict Keywords passed to `astropy.wcs.WCS` Returns ------- wcs : `~astropy.wcs.WCS` WCS object """ wcs = pywcs.WCS(header, relax=relax) if ('SIPCRPX1' in header) & hasattr(wcs, 'sip'): wcs.sip.crpix[0] = header['SIPCRPX1'] wcs.sip.crpix[1] = header['SIPCRPX2'] elif ('SIAF_XREF_SCI' in header) & hasattr(wcs, 'sip'): wcs.sip.crpix[0] = header['SIAF_XREF_SCI'] wcs.sip.crpix[1] = header['SIAF_YREF_SCI'] return wcs
[docs]def to_header(wcs, add_naxis=True, relax=True, key=None): """Modify `astropy.wcs.WCS.to_header` to produce more keywords Parameters ---------- wcs : `~astropy.wcs.WCS` Input WCS. add_naxis : bool Add NAXIS keywords from WCS dimensions relax : bool Passed to `WCS.to_header(relax=)`. key : str See `~astropy.wcs.WCS.to_header`. Returns ------- header : `~astropy.io.fits.Header` Output header. """ header = wcs.to_header(relax=relax, key=key) if add_naxis: if hasattr(wcs, 'pixel_shape'): header['NAXIS'] = wcs.naxis if wcs.pixel_shape is not None: header['NAXIS1'] = wcs.pixel_shape[0] header['NAXIS2'] = wcs.pixel_shape[1] elif hasattr(wcs, '_naxis1'): header['NAXIS'] = wcs.naxis header['NAXIS1'] = wcs._naxis1 header['NAXIS2'] = wcs._naxis2 for k in header: if k.startswith('PC'): cd = k.replace('PC', 'CD') header.rename_keyword(k, cd) if hasattr(wcs, 'sip'): if hasattr(wcs.sip, 'crpix'): header['SIPCRPX1'], header['SIPCRPX2'] = wcs.sip.crpix return header
[docs]def make_wcsheader(ra=40.07293, dec=-1.6137748, size=2, pixscale=0.1, get_hdu=False, theta=0): """Make a celestial WCS header Parameters ---------- ra, dec : float Celestial coordinates in decimal degrees size, pixscale : float or 2-list Size of the thumbnail, in arcsec, and pixel scale, in arcsec/pixel. Output image will have dimensions `(npix,npix)`, where >>> npix = size/pixscale get_hdu : bool Return a `~astropy.io.fits.ImageHDU` rather than header/wcs. theta : float Position angle of the output thumbnail Returns ------- hdu : `~astropy.io.fits.ImageHDU` HDU with data filled with zeros if `get_hdu=True`. header, wcs : `~astropy.io.fits.Header`, `~astropy.wcs.WCS` Header and WCS object if `get_hdu=False`. Examples -------- >>> from grizli.utils import make_wcsheader >>> h, wcs = make_wcsheader() >>> print(wcs) WCS Keywords Number of WCS axes: 2 CTYPE : 'RA---TAN' 'DEC--TAN' CRVAL : 40.072929999999999 -1.6137748000000001 CRPIX : 10.0 10.0 CD1_1 CD1_2 : -2.7777777777777e-05 0.0 CD2_1 CD2_2 : 0.0 2.7777777777777701e-05 NAXIS : 20 20 >>> from grizli.utils import make_wcsheader >>> hdu = make_wcsheader(get_hdu=True) >>> print(hdu.data.shape) (20, 20) >>> print(hdu.header.tostring) XTENSION= 'IMAGE ' / Image extension BITPIX = -32 / array data type NAXIS = 2 / number of array dimensions PCOUNT = 0 / number of parameters GCOUNT = 1 / number of groups CRPIX1 = 10 CRPIX2 = 10 CRVAL1 = 40.07293 CRVAL2 = -1.6137748 CD1_1 = -2.7777777777777E-05 CD1_2 = 0.0 CD2_1 = 0.0 CD2_2 = 2.77777777777777E-05 NAXIS1 = 20 NAXIS2 = 20 CTYPE1 = 'RA---TAN' CTYPE2 = 'DEC--TAN' """ if np.isscalar(pixscale): cdelt = [pixscale/3600.]*2 else: cdelt = [pixscale[0]/3600., pixscale[1]/3600.] if np.isscalar(size): npix = np.cast[int](np.round([size/pixscale, size/pixscale])) else: npix = np.cast[int](np.round([size[0]/pixscale, size[1]/pixscale])) hout = pyfits.Header() hout['CRPIX1'] = npix[0]/2 hout['CRPIX2'] = npix[1]/2 hout['CRVAL1'] = ra hout['CRVAL2'] = dec hout['CD1_1'] = -cdelt[0] hout['CD1_2'] = hout['CD2_1'] = 0. hout['CD2_2'] = cdelt[1] hout['NAXIS1'] = npix[0] hout['NAXIS2'] = npix[1] hout['CTYPE1'] = 'RA---TAN' hout['CTYPE2'] = 'DEC--TAN' hout['RADESYS'] = 'ICRS' hout['EQUINOX'] = 2000 hout['LATPOLE'] = hout['CRVAL2'] hout['LONPOLE'] = 180 hout['PIXASEC'] = pixscale, 'Pixel scale in arcsec' wcs_out = pywcs.WCS(hout) theta_rad = np.deg2rad(theta) mat = np.array([[np.cos(theta_rad), -np.sin(theta_rad)], [np.sin(theta_rad), np.cos(theta_rad)]]) rot_cd = np.dot(mat, wcs_out.wcs.cd) for i in [0, 1]: for j in [0, 1]: hout['CD{0:d}_{1:d}'.format(i+1, j+1)] = rot_cd[i, j] wcs_out.wcs.cd[i, j] = rot_cd[i, j] cd = wcs_out.wcs.cd wcs_out.pscale = get_wcs_pscale(wcs_out) # np.sqrt((cd[0,:]**2).sum())*3600. if get_hdu: hdu = pyfits.ImageHDU(header=hout, data=np.zeros((npix[1], npix[0]), dtype=np.float32)) return hdu else: return hout, wcs_out
[docs]def get_flt_footprint(flt_file, extensions=[1, 2, 3, 4], patch_args=None): """ Compute footprint of all SCI extensions of an HST exposure Parameters ---------- extensions : list List of extensions to retrieve (can have extras). patch_args : dict or None If a `dict`, then generate a patch for the footprint passing `**patch_args` arguments (e.g., `{'fc':'blue', 'alpha':0.1}`). Returns ------- fp / patch : `~shapely.geometry` object or `~descartes.PolygonPatch` The footprint or footprint patch. """ from shapely.geometry import Polygon from descartes import PolygonPatch im = pyfits.open(flt_file, mode='update') fp = None for ext in extensions: if ('SCI', ext) not in im: continue wcs = pywcs.WCS(im['SCI', ext].header, fobj=im) p_i = Polygon(wcs.calc_footprint()) if fp is None: fp = p_i else: fp = fp.union(p_i) if patch_args is not None: patch = PolygonPatch(fp, **patch_args) return patch else: return fp
[docs]def make_maximal_wcs(files, pixel_scale=0.1, 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`. 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 #from descartes import PolygonPatch 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: #im = pyfits.open(file) # if im[0].header['INSTRUME'] == 'ACS': # chips = 2 # elif im[0].header['INSTRUME'] == 'WFPC2': # chips = 4 # elif im[0].header['INSTRUME'] == 'WFC3': # if im[0].header['INSTRUME'] == 'IR': # chips = 1 # else: # chips = 2 # else: # chips = 1 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)) 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: print('{0:>3d}/{1:>3d}: {2}[SCI,{3}] {4:>6.2f}'.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: print('\n Mosaic WCS: ({0:.5f},{1:.5f}) {2:.1f}\'x{3:.1f}\' {4:.3f}"/pix\n'.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', 'CD2_2']: 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 header_keys_from_filelist(fits_files, keywords=[], ext=0, colname_case=str.lower): """Dump header keywords to a `~astropy.table.Table` Parameters ---------- fits_files : list List of FITS filenames keywords : list or None List of header keywords to retrieve. If `None`, then generate a list of *all* keywords from the first file in the list. ext : int, tuple FITS extension from which to pull the header. Can be integer or tuple, e.g., ('SCI',1) for HST ACS/WFC3 FLT files. colname_case : func Function to set the case of the output colnames, e.g., `str.lower`, `str.upper`, `str.title`. Returns ------- tab : `~astropy.table.Table` Output table. """ import numpy as np import astropy.io.fits as pyfits from astropy.table import Table # If keywords=None, get full list from first FITS file if keywords is None: h = pyfits.getheader(fits_files[0], ext) keywords = list(np.unique(list(h.keys()))) keywords.pop(keywords.index('')) keywords.pop(keywords.index('HISTORY')) # Loop through files lines = [] for file in fits_files: line = [file] h = pyfits.getheader(file, ext) for key in keywords: if key in h: line.append(h[key]) else: line.append(None) lines.append(line) # Column names table_header = [colname_case(key) for key in ['file']+keywords] # Output table tab = Table(data=np.array(lines), names=table_header) return tab
[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 drizzle_from_visit(visit, output, pixfrac=1., kernel='point', clean=True, include_saturated=True, keep_bits=None, dryrun=False, skip=None, extra_wfc3ir_badpix=True): """ Make drizzle mosaic from exposures in a visit dictionary """ from shapely.geometry import Polygon import boto3 from botocore.exceptions import ClientError 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, pyfits.PrimaryHDU) | isinstance(output, pyfits.ImageHDU): outputwcs = pywcs.WCS(output.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'])): olap = visit['footprints'][i].intersection(output_poly) if olap.area > 0: 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] print('\n({0:4d}/{1:4d}) Add exposure {2}\n'.format(count+1, NTOTAL, file)) 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 = [], [], [] if 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=True) else: bits = 64+32 bpdata = 0 if include_saturated: bits |= 256 if keep_bits is not None: bits |= keep_bits keys = OrderedDict() for k in ['EXPTIME', 'FILTER', 'FILTER1', 'FILTER2', 'DETECTOR', 'INSTRUME', 'PHOTFLAM', 'PHOTPLAM', 'PHOTFNU', 'PHOTZPT', 'PHOTBW', 'PHOTMODE', 'EXPSTART', 'EXPEND', 'DATE-OBS', 'TIME-OBS']: if k in flt[0].header: keys[k] = flt[0].header[k] if 'PHOTFLAM' in keys: print(' 0 PHOTFLAM={0:.2e}, scale={1:.1f}'.format(keys['PHOTFLAM'], 1.)) 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 = h['MDRIZSKY'] else: sky = 0 print(' ext (SCI,{0}), sky={1:.3f}'.format(ext, sky)) if h['BUNIT'] == 'ELECTRONS': to_per_sec = 1./keys['EXPTIME'] else: to_per_sec = 1. phot_scale = to_per_sec if 'PHOTFLAM' in h: if ref_photflam is None: ref_photflam = h['PHOTFLAM'] phot_scale = h['PHOTFLAM']/ref_photflam print(' PHOTFLAM={0:.2e}, scale={1:.1f}'.format(h['PHOTFLAM'], phot_scale)) keys['PHOTFLAM'] = h['PHOTFLAM'] for k in ['PHOTFLAM', 'PHOTPLAM', 'PHOTFNU', 'PHOTZPT', 'PHOTBW', 'PHOTMODE']: 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=False, verbose=True, 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") 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=False, verbose=True, 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 #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) 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 = 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 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: pyfits.open(iref_file) 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) 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_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'] 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 = 'http://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(object): 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/ Attributes ---------- Methods ------- """ 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 data = pyfits.open(file)[0].data.T 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: data = pyfits.open(file, ignore_missing_end=True)[0].data.T 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: data = pyfits.open(file, ignore_missing_end=True)[0].data.T data[data < 0] = 0 filt = '_'.join(file.strip('.fits').split('_')[2:]) self.epsf[filt] = 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) data = pyfits.open(file)[0].data # .T 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'): """Evaluate ePSF at detector coordinates TBD """ epsf = self.epsf[filter] if filter in ['F098M', 'F110W', 'F105W', 'F125W', 'F140W', 'F160W', 'G102','G141','F128N','F130N','F132N']: isir = True else: isir = False if isir: # 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 else: sh = epsf.shape if sh[2] == 90: # ACS WFC iX, iY = 9, 10 # 9, 10 else: # UVIS iX, iY = 7, 8 rx = 1+(np.clip(x, 1, 4095)-0)/(4096/(iX-1)) ry = 1+(np.clip(y, 1, 4095)-0)/(4096/(iY-1)) # zero index rx -= 1 ry -= 1 nx = np.clip(np.cast[int](rx), 0, iX-1) ny = np.clip(np.cast[int](ry), 0, iY-1) # print x, y, rx, ry, nx, ny fx = rx-nx fy = ry-ny psf_xy = (1-fx)*(1-fy)*epsf[:, :, nx+ny*iX] psf_xy += fx*(1-fy)*epsf[:, :, (nx+1)+ny*iX] psf_xy += (1-fx)*fy*epsf[:, :, nx+(ny+1)*iX] psf_xy += fx*fy*epsf[:, :, (nx+1)+(ny+1)*iX] self.eval_filter = filter return psf_xy
[docs] def eval_ePSF(self, psf_xy, dx, dy, extended_data=None): """Evaluate PSF at dx,dy coordinates TBD """ # So much faster than scipy.interpolate.griddata! from scipy.ndimage.interpolation import map_coordinates # ePSF only defined to 12.5 pixels ok = (np.abs(dx) <= 12.5) & (np.abs(dy) <= 12.5) coords = np.array([50+4*dx[ok], 50+4*dy[ok]]) # Do the interpolation interp_map = map_coordinates(psf_xy, coords, order=3) # Fill output data out = np.zeros_like(dx, dtype=np.float32) out[ok] = interp_map # Extended PSF if extended_data is not None: ok = (np.abs(dx) < self.extended_N) & (np.abs(dy) < self.extended_N) x0 = self.extended_N coords = np.array([x0+dy[ok]+0, x0+dx[ok]]) interp_map = map_coordinates(extended_data, coords, order=0) out[ok] += interp_map return out
[docs] @staticmethod def objective_epsf_center(params, self, psf_xy, sci, ivar, xp, yp, extended_data, ret, ds9): """Objective function for fitting ePSFs TBD params = [normalization, xc, yc, background] """ from numpy.linalg import lstsq sh = sci.shape y0, x0 = np.array(sh)/2.-1 dx = xp-params[0] dy = yp-params[1] ddx = xp # -x0 ddy = yp # -y0 ddx = ddx/ddx.max() ddy = ddy/ddy.max() # bkg = params[3] + params[4]*ddx + params[5]*ddy #+ params[6]*ddx*ddy psf_offset = self.eval_ePSF(psf_xy, dx, dy, extended_data=extended_data) # *params[0] A = (np.array([psf_offset, np.ones_like(sci), ddx, ddy])*np.sqrt(ivar)).reshape((4, -1)) scif = (sci*np.sqrt(ivar)).flatten() mask = (scif != 0) coeffs, _resid, _rank, _s = lstsq(A[:, mask].T, scif[mask], rcond=LSTSQ_RCOND) resid = (scif - np.dot(coeffs, A)) if ds9: Ax = (np.array([psf_offset, np.ones_like(sci), ddx, ddy])).reshape((4, -1)) psf_model = np.dot(coeffs[:1], Ax[:1, :]).reshape(sci.shape) bkg = np.dot(coeffs[1:], Ax[1:, :]).reshape(sci.shape) ds9.view((sci-psf_model-bkg)*mask.reshape(sci.shape)) if ret == 'resid': return resid elif ret == 'lm': # masked residuals for LM optimization if False: print(params, (resid**2).sum(), coeffs[0]) return resid[resid != 0] elif ret == 'model': Ax = (np.array([psf_offset, np.ones_like(sci), ddx, ddy])).reshape((4, -1)) psf_model = np.dot(coeffs[:1], Ax[:1, :]).reshape(sci.shape) bkg = np.dot(coeffs[1:], Ax[1:, :]).reshape(sci.shape) return psf_model, bkg, Ax, coeffs else: chi2 = (resid**2).sum() #print(params, chi2, coeffs[0]) return chi2
[docs] @staticmethod def objective_epsf(params, self, psf_xy, sci, ivar, xp, yp, extended_data, ret, ds9): """Objective function for fitting ePSFs TBD params = [normalization, xc, yc, background] """ dx = xp-params[1] dy = yp-params[2] ddx = xp-xp.min() ddy = yp-yp.min() ddx = ddx/ddx.max() ddy = ddy/ddy.max() bkg = params[3] + params[4]*ddx + params[5]*ddy # + params[6]*ddx*ddy psf_offset = self.eval_ePSF(psf_xy, dx, dy, extended_data=extended_data)*params[0] resid = (sci-psf_offset-bkg)*np.sqrt(ivar) if ds9: ds9.view(sci-psf_offset-bkg) if ret == 'resid': return resid elif ret == 'lm': # masked residuals for LM optimization if False: print(params, (resid**2).sum()) return resid[resid != 0] elif ret == 'model': return psf_offset, bkg, None, None else: chi2 = (resid**2).sum() #print(params, chi2) return chi2
[docs] def fit_ePSF(self, sci, center=None, origin=[0, 0], ivar=1, N=7, filter='F140W', tol=1.e-4, guess=None, get_extended=False, method='lm', ds9=None, psf_params=None, only_centering=True): """Fit ePSF to input data TBD """ from scipy.optimize import minimize, least_squares sh = sci.shape if center is None: y0, x0 = np.array(sh)/2.-1 else: x0, y0 = center xd = x0+origin[1] yd = y0+origin[0] xc, yc = int(x0), int(y0) psf_xy = self.get_at_position(x=xd, y=yd, filter=filter) yp, xp = np.indices(sh) if guess is None: if np.isscalar(ivar): ix = np.argmax(sci.flatten()) else: ix = np.argmax((sci*(ivar > 0)).flatten()) xguess = xp.flatten()[ix] yguess = yp.flatten()[ix] else: xguess, yguess = guess med_bkg = np.median(sci) if only_centering: # Only fit for centering and compute normalizations guess = [xguess, yguess] _objfun = self.objective_epsf_center else: # Fit for centering and normalization guess = [(sci-med_bkg)[yc-N:yc+N, xc-N:xc+N].sum(), xguess, yguess, med_bkg, 0, 0] _objfun = self.objective_epsf sly = slice(yc-N, yc+N) slx = slice(xc-N, xc+N) sly = slice(yguess-N, yguess+N) slx = slice(xguess-N, xguess+N) ivar_mask = np.zeros_like(sci) ivar_mask[sly, slx] = 1 ivar_mask *= ivar if get_extended: if filter in self.extended_epsf: extended_data = self.extended_epsf[filter] else: extended_data = None else: extended_data = None # Get model if psf_params is not None: px = psf_params*1 if len(px) == 2: _objfun = self.objective_epsf_center px[0] += x0 px[1] += y0 else: _objfun = self.objective_epsf px[1] += x0 px[2] += y0 args = (self, psf_xy, sci, ivar_mask, xp, yp, extended_data, 'model', ds9) psf_model, bkg, A, coeffs = _objfun(px, *args) return psf_model, bkg, A, coeffs if method == 'lm': args = (self, psf_xy, sci, ivar_mask, xp, yp, extended_data, 'lm', ds9) x_scale = 'jac' #x_scale = [guess[0], 1, 1, 10, 10, 10] #out = least_squares(_objfun, guess, args=args, method='trf', x_scale=x_scale, loss='huber') out = least_squares(_objfun, guess, args=args, method='lm', x_scale=x_scale, loss='linear') psf_params = out.x*1 else: args = (self, psf_xy, sci, ivar_mask, xp, yp, extended_data, 'chi2', ds9) out = minimize(_objfun, guess, args=args, method=method, tol=tol) psf_params = out.x*1 if len(guess) == 2: psf_params[0] -= x0 psf_params[1] -= y0 else: psf_params[1] -= x0 psf_params[2] -= y0 # if False: # # psf_fit = epsf.get_ePSF(psf_params, origin=origin, # filter=filter, shape=sh, # get_extended=get_extended) # # xargs = (self, psf_xy, sci, ivar_mask, xp, yp, extended_data, 'lm', None) # lm = _objfun(out.x, *xargs) # cargs = (self, psf_xy, sci, ivar_mask, xp, yp, extended_data, 'chi2', None) # chi2 = _objfun(out.x, *cargs) return psf_params
# dx = xp-psf_params[1] # dy = yp-psf_params[2] # output_psf = self.eval_ePSF(psf_xy, dx, dy)*psf_params[0] # # return output_psf, psf_params
[docs] def get_ePSF(self, psf_params, sci=None, ivar=1, origin=[0, 0], shape=[20, 20], filter='F140W', get_extended=False, get_background=False): """ Evaluate an Effective PSF """ sh = shape y0, x0 = np.array(sh)/2.-1 xd = x0+origin[1] yd = y0+origin[0] xc, yc = int(x0), int(y0) psf_xy = self.get_at_position(x=xd, y=yd, filter=filter) yp, xp = np.indices(sh) if len(psf_params) == 2: _objfun = self.objective_epsf_center dx = xp-psf_params[0]-x0 dy = yp-psf_params[1]-y0 else: _objfun = self.objective_epsf dx = xp-psf_params[1]-x0 dy = yp-psf_params[2]-y0 if get_extended: if filter in self.extended_epsf: extended_data = self.extended_epsf[filter] else: extended_data = None else: extended_data = None if sci is not None: ivar_mask = np.ones_like(sci) ivar_mask *= ivar else: sci = np.ones(sh, dtype=float) ivar_mask = sci*1 args = (self, psf_xy, sci, ivar_mask, xp-x0, yp-y0, extended_data, 'model', None) output_psf, bkg, _a, _b = _objfun(psf_params, *args) #output_psf = self.eval_ePSF(psf_xy, dx, dy, extended_data=extended_data)*psf_params[0] if get_background: return output_psf, bkg else: return output_psf
[docs]def read_catalog(file, sextractor=False, format=None): """ Wrapper around `~grizli.utils.Gtable.gread`. Auto-detects formats 'csv' and 'fits' and defaults to 'ascii.commented_header'. """ return GTable.gread(file, sextractor=sextractor, format=format)
[docs]class GTable(astropy.table.Table): """ Extend `~astropy.table.Table` class with more automatic IO and other helper methods. """
[docs] @classmethod def gread(cls, file, sextractor=False, format=None): """Assume `ascii.commented_header` by default Parameters ---------- sextractor : bool Use `format='ascii.sextractor'`. format : None or str Override format passed to `~astropy.table.Table.read`. Returns ------- tab : `~astropy.table.Table` Table object """ import astropy.units as u if format is None: if sextractor: format = 'ascii.sextractor' elif isinstance(file, pyfits.BinTableHDU): format = 'fits' else: if file.endswith('.fits'): format = 'fits' elif file.endswith('.csv'): format = 'csv' elif file.endswith('.vot'): format = 'votable' else: format = 'ascii.commented_header' #print(file, format) tab = cls.read(file, format=format) return tab
[docs] def gwrite(self, output, format='ascii.commented_header'): """Assume a format for the output table Parameters ---------- output : str Output filename format : str Format string passed to `~astropy.table.Table.write`. """ self.write(output, format=format)
[docs] @staticmethod def parse_radec_columns(self, rd_pairs=None): """Parse column names for RA/Dec and set to `~astropy.units.degree` units if not already set Parameters ---------- rd_pairs : `~collections.OrderedDict` or None Pairs of {ra:dec} names to search in the column list. If None, then uses the following by default. >>> rd_pairs = OrderedDict() >>> rd_pairs['ra'] = 'dec' >>> rd_pairs['ALPHA_J2000'] = 'DELTA_J2000' >>> rd_pairs['X_WORLD'] = 'Y_WORLD' NB: search is performed in order of ``rd_pairs.keys()`` and stops if/when a match is found. Returns ------- rd_pair : [str, str] Column names associated with RA/Dec. Returns False if no column pairs found based on `rd_pairs`. """ from collections import OrderedDict import astropy.units as u if rd_pairs is None: rd_pairs = OrderedDict() rd_pairs['RA'] = 'DEC' rd_pairs['ALPHA_J2000'] = 'DELTA_J2000' rd_pairs['X_WORLD'] = 'Y_WORLD' rd_pairs['ALPHA_SKY'] = 'DELTA_SKY' rd_pairs['_RAJ2000'] = '_DEJ2000' for k in list(rd_pairs.keys()): rd_pairs[k.lower()] = rd_pairs[k].lower() rd_pair = None for c in rd_pairs: if c in self.colnames: rd_pair = [c, rd_pairs[c]] break if rd_pair is None: #print('No RA/Dec. columns found in input table.') return False for c in rd_pair: if self[c].unit is None: self[c].unit = u.degree return rd_pair
[docs] def match_to_catalog_sky(self, other, self_radec=None, other_radec=None, nthneighbor=1, get_2d_offset=False): """Compute `~astropy.coordinates.SkyCoord` projected matches between two `GTable` tables. Parameters ---------- other : `~astropy.table.Table`, `GTable`, or `list`. Other table to match positions from. self_radec, other_radec : None or [str, str] Column names for RA and Dec. If None, then try the following pairs (in this order): >>> rd_pairs = OrderedDict() >>> rd_pairs['ra'] = 'dec' >>> rd_pairs['ALPHA_J2000'] = 'DELTA_J2000' >>> rd_pairs['X_WORLD'] = 'Y_WORLD' nthneighbor : int See `~astropy.coordinates.SkyCoord.coo.match_to_catalog_sky`. Returns ------- idx : int array Indices of the matches as in >>> matched = self[idx] >>> len(matched) == len(other) dr : float array Projected separation of closest match. Examples -------- >>> import astropy.units as u >>> ref = GTable.gread('input.cat') >>> gaia = GTable.gread('gaia.cat') >>> idx, dr = ref.match_to_catalog_sky(gaia) >>> close = dr < 1*u.arcsec >>> ref_match = ref[idx][close] >>> gaia_match = gaia[close] """ from astropy.coordinates import SkyCoord if self_radec is None: rd = self.parse_radec_columns(self) else: rd = self.parse_radec_columns(self, rd_pairs={self_radec[0]: self_radec[1]}) if rd is False: print('No RA/Dec. columns found in input table.') return False self_coo = SkyCoord(ra=self[rd[0]], dec=self[rd[1]]) if isinstance(other, list) | isinstance(other, tuple): rd = [slice(0, 1), slice(1, 2)] else: if other_radec is None: rd = self.parse_radec_columns(other) else: rd = self.parse_radec_columns(other, rd_pairs={other_radec[0]: other_radec[1]}) if rd is False: print('No RA/Dec. columns found in `other` table.') return False other_coo = SkyCoord(ra=other[rd[0]], dec=other[rd[1]]) try: idx, d2d, d3d = other_coo.match_to_catalog_sky(self_coo, nthneighbor=nthneighbor) except: print('Couldn\'t run SkyCoord.match_to_catalog_sky with' 'nthneighbor') idx, d2d, d3d = other_coo.match_to_catalog_sky(self_coo) if get_2d_offset: cosd = np.cos(self_coo.dec.deg/180*np.pi) dra = (other_coo.ra.deg - self_coo.ra.deg[idx])*cosd[idx] dde = (other_coo.dec.deg - self_coo.dec.deg[idx]) return idx, d2d.to(u.arcsec), dra*3600*u.arcsec, dde*3600*u.arcsec else: return idx, d2d.to(u.arcsec)
[docs] def match_triangles(self, other, self_wcs=None, x_column='X_IMAGE', y_column='Y_IMAGE', mag_column='MAG_AUTO', other_ra='X_WORLD', other_dec='Y_WORLD', pixel_index=1, match_kwargs={}, pad=100, show_diagnostic=False, auto_keep=3, maxKeep=10, auto_limit=3, ba_max=0.99, scale_density=10): """ x_column = 'X_IMAGE' y_column = 'Y_IMAGE' mag_column = 'MAG_AUTO' pixel_index=1 pad=100 auto_keep=3 maxKeep=10 auto_limit=3 ba_max = 0.99 """ from tristars import match if hasattr(other, 'shape'): other_radec = other*1. else: other_radec = np.array([other[other_ra], other[other_dec]]).T self_xy = np.array([self[x_column], self[y_column]]).T #xy_drz = np.array([cat['X_IMAGE'][ok], cat['Y_IMAGE'][ok]]).T if self_wcs is None: other_xy = other_radec cut = (other_xy[:, 0] > -pad) & (other_xy[:, 0] < self_xy[:, 0].max()+pad) & (other_xy[:, 1] > -pad) & (other_xy[:, 0] < self_xy[:, 1].max()+pad) other_xy = other_xy[cut, :] xy_center = np.zeros(2) else: other_xy = self_wcs.all_world2pix(other_radec, pixel_index) if hasattr(self_wcs, 'pixel_shape'): _naxis1, _naxis2 = self_wcs._naxis else: _naxis1, _naxis2 = self_wcs._naxis1, self_wcs