Source code for piff.input
# Copyright (c) 2016 by Mike Jarvis and the other collaborators on GitHub at
# https://github.com/rmjarvis/Piff All rights reserved.
#
# Piff is free software: Redistribution and use in source and binary forms
# with or without modification, are permitted provided that the following
# conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the disclaimer given in the accompanying LICENSE
# file.
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the disclaimer given in the documentation
# and/or other materials provided with the distribution.
"""
.. module:: input
"""
import numpy as np
import scipy
import glob
import os
import galsim
from .util import run_multi, calculateSNR
from .star import Star, StarData
[docs]class Input(object):
"""The base class for handling inputs for building a Piff model.
This is essentially an abstract base class intended to define the methods that should be
implemented by any derived class.
"""
nproc = 1 # Sub-classes can overwrite this as an instance attribute.
[docs] @classmethod
def process(cls, config_input, logger=None):
"""Parse the input field of the config dict.
:param config_input: The configuration dict.
:param logger: A logger object for logging debug info. [default: None]
:returns: stars, wcs, pointing
stars is a list of Star instances with the initial data.
wcs is a dict of WCS solutions indexed by chipnum.
pointing is either a galsim.CelestialCoord or None.
"""
import piff
# Get the class to use for handling the input data
# Default type is 'Files'
input_handler_class = getattr(piff, 'Input' + config_input.get('type','Files'))
# Build handler object
input_handler = input_handler_class(config_input, logger)
# Creat a lit of StarData objects
stars = input_handler.makeStars(logger)
if len(stars) == 0:
raise RuntimeError("No objects read in from input catalog(s).")
# Get the wcs for all the input chips
wcs = input_handler.getWCS(logger)
# Get the pointing (the coordinate center of the field of view)
pointing = input_handler.getPointing(logger)
return stars, wcs, pointing
[docs] def makeStars(self, logger=None):
"""Process the input images and star data, cutting out stamps for each star along with
other relevant information.
The base class implementation expects the derived class to have appropriately set the
following attributes:
:stamp_size: The size of the postage stamp to use for the cutouts
:x_col: The name of the column in the catalogs to use for the x position.
:y_col: The name of the column in the catalogs to use for the y position.
:param logger: A logger object for logging debug info. [default: None]
:returns: a list of Star instances
"""
logger = galsim.config.LoggerWrapper(logger)
if self.nimages == 1:
logger.debug("Making star list")
else:
logger.debug("Making star list from %d catalogs", self.nimages)
args = [(self.image_kwargs[k], self.cat_kwargs[k], self.wcs_list[k], self.chipnums[k])
for k in range(self.nimages)]
kwargs = dict(stamp_size=self.stamp_size,
pointing=self.pointing, use_partial=self.use_partial,
invert_weight=self.invert_weight,
remove_signal_from_weight=self.remove_signal_from_weight)
all_stars = run_multi(self._makeStarsFromImage, self.nproc, raise_except=True,
args=args, logger=logger, kwargs=kwargs)
# Concatenate the star lists into a single list
stars = [s for slist in all_stars if slist is not None for s in slist if slist]
logger.warning("Read a total of %d objects from %d image%s",len(stars),self.nimages,
"s" if self.nimages > 1 else "")
return stars
[docs] def getWCS(self, logger=None):
"""Get the WCS solutions for all the chips in the field of view.
:param logger: A logger object for logging debug info. [default: None]
:returns: a dict of WCS solutions (galsim.BaseWCS instances) indexed by chipnum
"""
return { chipnum : w for w, chipnum in zip(self.wcs_list, self.chipnums) }
[docs] def getPointing(self, logger=None):
"""Get the pointing coordinate of the (noinal) center of the field of view.
:param logger: A logger object for logging debug info. [default: None]
:returns: a galsim.CelestialCoord of the pointing direction.
"""
return self.pointing
[docs]class InputFiles(Input):
"""An Input handler that just takes a list of image files and catalog files.
Parse the input config dict (Normally the 'input' field in the overall configuration dict).
The two required fields in the input dict are:
:image_file_name: The file name(s) of the input image(s).
:cat_file_name: The file name(s) of the input catalog(s).
There are a number of ways to specify these file names.
1. A string giving a single file name. e.g.::
image_file_name: image.fits
cat_file_name: input_cat.fits
2. A list of several file names. e.g.::
image_file_name: [image_00.fits, image_01.fits, image_02.fits]
cat_file_name: [input_cat_00.fits, input_cat_01.fits, input_cat_02.fits]
3. A string that glob can recognize to list several file names. e.g.::
image_file_name: image_*.fits
cat_file_name: input_cat_*.fits
4. A dict parseable as a string value according to the GalSim configuration parsing types.
In this case, you also must specify nimages to say how many file names to generate
in this way. e.g.::
nimages: 20
image_file_name:
type: FormattedStr
format: image_%03d_%02d.fits.fz
items:
- { type : Sequence, first: 0, repeat: 4 } # Exposure number
- { type : Sequence, first: 1, last: 4 } # Chip number
cat_file_name:
type: Eval
str: "image_file_name.replace('image','input_cat')"
simage_file_name: '@input.image_file_name'
See the description of the GalSim config parser for more details about the various
types that are valid here.
`https://github.com/GalSim-developers/GalSim/wiki/Config-Values`_
There are many other optional parameters, which help govern how the input files are
read or interpreted:
:chipnum: The id number of this chip used to reference this image [default:
image_num]
:image_hdu: The hdu to use in the image files. [default: None, which means use
either 0 or 1 as typical given the compression sceme of the file]
:weight_hdu: The hdu to use for weight images. [default: None, which means a weight
image with all 1's will be automatically created]
:badpix_hdu: The hdu to use for badpix images. Pixels with badpix != 0 will be given
weight == 0. [default: None]
:noise: Rather than a weight image, provide the noise variance in the image.
(Useful for simulations where this is a known value.) [default: None]
:cat_hdu: The hdu to use in the catalog files. [default: 1]
:x_col: The name of the X column in the input catalogs. [default: 'x']
:y_col: The name of the Y column in the input catalogs. [default: 'y']
:ra_col: (Alternative to x_col, y_col) The name of a right ascension column in
the input catalogs. Will use the WCS to find (x,y) [default: None]
:dec_col: (Alternative to x_col, y_col) The name of a declination column in
the input catalogs. Will use the WCS to find (x,y) [default: None]
:flag_col: The name of a flag column in the input catalogs. [default: None]
By default, this will skip any objects with flag != 0, but see
skip_flag and use_flag for other possible meanings for how the
flag column can be used to select stars.
:skip_flag: The flag indicating which items to not use. [default: -1]
Items with flag & skip_flag != 0 will be skipped.
:use_flag: The flag indicating which items to use. [default: None]
Items with flag & use_flag == 0 will be skipped.
:property_cols: A list of column names of star properties (e.g. star colors).
[default: None]
:sky_col: The name of a column with sky values. [default: None]
:gain_col: The name of a column with gain values. [default: None]
:sky: The sky level to subtract from the image values. [default: None]
Note: It is an error to specify both sky and sky_col. If both are None,
no sky level will be subtracted off.
:gain: The gain to use for adding Poisson noise to the weight map. [default:
None] It is an error for both gain and gain_col to be specified.
If both are None, then no additional noise will be added to account
for the Poisson noise from the galaxy flux.
:satur: The staturation level. If any pixels for a star exceed this, then
the star is skipped. [default: None]
:use_partial: Whether to use stars whose postage stamps are only partially on the
full image. [default: False]
:nstars: Stop reading the input file at this many stars. (This is applied
separately to each input catalog.) [default: None]
:nproc: How many multiprocessing processes to use for reading in data from
multiple files at once. [default: 1]
:wcs: Normally, the wcs is automatically read in when reading the image.
However, this parameter allows you to optionally provide a different
WCS. It should be defined using the same style as a wcs object
in GalSim config files. [defulat: None]
The above values are parsed separately for each input image/catalog. In addition, there
are a couple other parameters that are just parsed once:
:stamp_size: The size of the postage stamps to use for the cutouts. Note: some
stamps may be smaller than this if the star is near a chip boundary.
[default: 32]
:ra, dec: The RA, Dec of the telescope pointing. [default: None; See
`setPointing` for details about how this can be specified]
:param config: The configuration dict used to define the above parameters.
:param logger: A logger object for logging debug info. [default: None]
"""
def __init__(self, config, logger=None):
import copy
logger = galsim.config.LoggerWrapper(logger)
req = { 'image_file_name': str,
'cat_file_name': str,
}
opt = {
'dir' : str,
'chipnum' : int,
'x_col' : str,
'y_col' : str,
'ra_col' : str,
'dec_col' : str,
'ra_units' : str,
'dec_units' : str,
'property_cols' : list,
'sky_col' : str,
'gain_col' : str,
'flag_col' : str,
'skip_flag' : int,
'use_flag' : int,
'image_hdu' : int,
'weight_hdu' : int,
'badpix_hdu' : int,
'sky_file_name' : str,
'sky_hdu': int,
'cat_hdu' : int,
'invert_weight' : bool,
'remove_signal_from_weight' : bool,
'stamp_size' : int,
'gain' : str,
'satur' : str,
'use_partial' : bool,
'sky' : str,
'noise' : float,
'nstars' : int,
}
ignore = [ 'nproc', 'nimages', 'ra', 'dec', 'wcs' ] # These are parsed separately
# We're going to change the config dict a bit. Make a copy so we don't mess up the
# user's original dict (in case they care).
config = copy.deepcopy(config)
# In GalSim, the base dict holds additional parameters that may be of use.
# Here, we just make a dict with a few values that could be relevant.
base = { 'input' : config,
'index_key' : 'image_num',
}
# Convert options 2 and 3 above into option 4. (1 is also parseable by GalSim's config.)
nimages = None
image_list = None
cat_list = None
dir = None
if 'nproc' in config:
self.nproc = galsim.config.ParseValue(config, 'nproc', base, int)[0]
if 'nimages' in config:
nimages = galsim.config.ParseValue(config, 'nimages', base, int)[0]
if nimages < 1:
raise ValueError('input.nimages must be >= 1')
# Deal with dir here, since sometimes we need to have it already attached for glob
# to work.
if 'dir' in config:
dir = galsim.config.ParseValue(config, 'dir', base, str)[0]
del config['dir']
if 'image_file_name' not in config:
raise TypeError('Parameter image_file_name is required')
elif isinstance(config['image_file_name'], list):
image_list = config['image_file_name']
if len(image_list) == 0:
raise ValueError("image_file_name may not be an empty list")
if dir is not None:
image_list = [os.path.join(dir, n) for n in image_list]
elif isinstance(config['image_file_name'], str):
image_file_name = config['image_file_name']
if dir is not None:
image_file_name = os.path.join(dir, image_file_name)
image_list = sorted(glob.glob(image_file_name))
if len(image_list) == 0:
raise ValueError("No files found corresponding to "+config['image_file_name'])
elif isinstance(config['image_file_name'], dict):
if nimages is None:
raise TypeError(
'input.nimages is required if not using a list or simple string for ' +
'file names')
else:
raise ValueError("image_file_name should be either a dict or a string")
if image_list is not None:
logger.debug('image_list = %s',image_list)
if nimages is not None and nimages != len(image_list):
raise ValueError("nimages = %s doesn't match length of image_file_name list (%d)"%(
config['nimages'], len(image_list)))
nimages = len(image_list)
logger.debug('nimages = %d',nimages)
config['image_file_name'] = {
'type' : 'List_str',
'items' : image_list
}
logger.debug('nimages = %d',nimages)
assert nimages is not None
# Set up sky_file_name if appropriate
if 'sky_file_name' not in config:
pass
elif isinstance(config['sky_file_name'], str):
sky_file_name = config['sky_file_name']
if dir is not None:
sky_file_name = os.path.join(dir, sky_file_name)
sky_list = sorted(glob.glob(sky_file_name))
if len(sky_list) == 0:
raise ValueError("No files found corresponding to "+config['sky_file_name'])
config['sky_file_name'] = {
'type' : 'List_str',
'items' : sky_list
}
elif not isinstance(config['sky_file_name'], dict):
raise ValueError("sky_file_name should be either a dict or a string")
if 'cat_file_name' not in config:
raise TypeError('Parameter cat_file_name is required')
elif isinstance(config['cat_file_name'], list):
cat_list = config['cat_file_name']
if len(cat_list) == 0:
raise ValueError("cat_file_name may not be an empty list")
if dir is not None:
cat_list = [os.path.join(dir, n) for n in cat_list]
elif isinstance(config['cat_file_name'], str):
cat_file_name = config['cat_file_name']
if dir is not None:
cat_file_name = os.path.join(dir, cat_file_name)
cat_list = sorted(glob.glob(cat_file_name))
if len(cat_list) == 0:
raise ValueError("No files found corresponding to "+config['cat_file_name'])
elif not isinstance(config['cat_file_name'], (dict, type(None))):
raise ValueError("cat_file_name should be either a dict or a string")
if cat_list is not None:
logger.debug('cat_list = %s',cat_list)
if len(cat_list) == 1 and nimages > 1:
logger.info("Using the same catlist for all image")
cat_list = cat_list * nimages
elif nimages != len(cat_list):
raise ValueError("nimages = %s doesn't match length of cat_file_name list (%d)"%(
nimages, len(cat_list)))
config['cat_file_name'] = {
'type' : 'List_str',
'items' : cat_list
}
self.nimages = nimages
self.chipnums = list(range(nimages))
self.stamp_size = int(config.get('stamp_size', 32))
self.image_file_name = []
self.cat_file_name = []
self.image_kwargs = []
self.cat_kwargs = []
self.remove_signal_from_weight = config.get('remove_signal_from_weight', False)
self.invert_weight = config.get('invert_weight', False)
logger.info("Reading in %d images",nimages)
for image_num in range(nimages):
# This changes for each input image.
base['image_num'] = image_num
logger.debug("config = %s", config)
params = galsim.config.GetAllParams(config, base, req=req, opt=opt, ignore=ignore)[0]
logger.debug("image_num = %d: params = %s", image_num, params)
# Update the chipnum if not just using image_num
if 'chipnum' in params:
self.chipnums[image_num] = params['chipnum']
# Read the image
image_file_name = params['image_file_name']
image_hdu = params.get('image_hdu', None)
weight_hdu = params.get('weight_hdu', None)
badpix_hdu = params.get('badpix_hdu', None)
sky_file_name = params.get('sky_file_name', None)
sky_hdu = params.get('sky_hdu', None)
noise = params.get('noise', None)
self.image_file_name.append(image_file_name)
self.image_kwargs.append({
'image_file_name' : image_file_name,
'image_hdu' : image_hdu,
'weight_hdu' : weight_hdu,
'badpix_hdu' : badpix_hdu,
'sky_file_name' : sky_file_name,
'sky_hdu' : sky_hdu,
'noise' : noise})
# Read the catalog
cat_file_name = params['cat_file_name']
cat_hdu = params.get('cat_hdu', None)
x_col = params.get('x_col', 'x')
y_col = params.get('y_col', 'y')
ra_col = params.get('ra_col', None)
dec_col = params.get('dec_col', None)
ra_units = params.get('ra_units', 'deg')
dec_units = params.get('dec_units', 'deg')
flag_col = params.get('flag_col', None)
skip_flag = params.get('skip_flag', -1)
use_flag = params.get('use_flag', None)
property_cols = params.get('property_cols', None)
sky_col = params.get('sky_col', None)
gain_col = params.get('gain_col', None)
sky = params.get('sky', None)
gain = params.get('gain', None)
satur = params.get('satur', None)
nstars = params.get('nstars', None)
if sky_col is not None and sky is not None:
raise ValueError("Cannot provide both sky_col and sky.")
if gain_col is not None and gain is not None:
raise ValueError("Cannot provide both gain_col and gain.")
self.cat_file_name.append(cat_file_name)
self.cat_kwargs.append({
'cat_file_name' : cat_file_name,
'cat_hdu' : cat_hdu,
'x_col' : x_col,
'y_col' : y_col,
'ra_col' : ra_col,
'dec_col' : dec_col,
'ra_units' : ra_units,
'dec_units' : dec_units,
'flag_col' : flag_col,
'skip_flag' : skip_flag,
'use_flag' : use_flag,
'property_cols': property_cols,
'sky_col' : sky_col,
'gain_col' : gain_col,
'sky' : sky,
'gain' : gain,
'satur' : satur,
'nstars' : nstars,
'image_file_name' : image_file_name,
'stamp_size' : self.stamp_size})
self.use_partial = config.get('use_partial', False)
# Read all the wcs's, since we'll need this for the pointing, which in turn we'll
# need for when we make the stars.
self.setWCS(config, logger)
# Finally, set the pointing coordinate.
ra = config.get('ra',None)
dec = config.get('dec',None)
self.setPointing(ra, dec, logger)
[docs] def load_images(self, stars, logger=None):
"""Load the image data into a list of Stars.
We don't store the image data for Stars when we write them to a file, since that
would take up a lot of space and is usually not desired. However, we do store the
bounds in the original image where the star was cutout, so if you want to load back in
the original data from the image file(s), you can do so with this function.
:param stars: A list of Star instances.
:param logger: A logger object for logging debug info. [default: None]
:returns: a new list of Stars with the images information loaded.
"""
logger = galsim.config.LoggerWrapper(logger)
images = {}
weight_images = {}
for image_num in range(self.nimages):
image, weight, _, _ = self.getRawImageData(image_num)
images[self.chipnums[image_num]] = image
weight_images[self.chipnums[image_num]] = weight
loaded_stars = []
for star in stars:
chipnum = star['chipnum']
image = images[chipnum][star.data.image.bounds].copy()
if 'sky' in star.data.properties:
image -= star['sky']
weight = weight_images[chipnum][star.data.weight.bounds].copy()
data = StarData(image=image,
image_pos=star.data.image_pos,
weight=weight,
pointing=star.data.pointing,
properties=star.data.properties,
property_types=star.data.property_types,
_xyuv_set=True)
loaded_stars.append(Star(data=data, fit=star.fit))
return loaded_stars
def getRawImageData(self, image_num, logger=None):
return self._getRawImageData(self.image_kwargs[image_num], self.cat_kwargs[image_num],
self.wcs_list[image_num], self.invert_weight,
self.remove_signal_from_weight, logger=logger)
@staticmethod
def _getRawImageData(image_kwargs, cat_kwargs, wcs,
invert_weight, remove_signal_from_weight,
logger=None):
logger = galsim.config.LoggerWrapper(logger)
image, weight = InputFiles.readImage(logger=logger, **image_kwargs)
if invert_weight:
weight.invertSelf()
# Update the wcs
image.wcs = wcs
image_pos, extra_props = InputFiles.readStarCatalog(
logger=logger, image=image, **cat_kwargs)
if remove_signal_from_weight:
# Subtract off the mean sky, since this isn't part of the "signal" we want to
# remove from the weights.
if 'sky' in extra_props:
signal = image - np.mean(extra_props['sky'])
else:
signal = image
if 'gain' in extra_props:
# If given, use the mean gain when removing the signal.
# This isn't quite right, but hopefully the gain won't vary too much for
# different objects, so it should be close.
gain = np.mean(extra_props['gain'])
weight, _ = InputFiles._removeSignalFromWeight(signal, weight, gain=gain)
else:
# If no gain provided, then we want to estimate the gain from the weight image.
weight, g = InputFiles._removeSignalFromWeight(signal, weight)
extra_props['gain'] = np.array([g] * len(image_pos), dtype=float)
logger.warning("Empirically determined gain = %f",g)
logger.info("Removed signal from weight image.")
return image, weight, image_pos, extra_props
@staticmethod
def _makeStarsFromImage(image_kwargs, cat_kwargs, wcs, chipnum,
stamp_size, pointing, use_partial,
invert_weight, remove_signal_from_weight,
logger):
"""Make "stars" from a single input image
"""
image, wt, image_pos, extra_props = InputFiles._getRawImageData(
image_kwargs, cat_kwargs, wcs, invert_weight, remove_signal_from_weight, logger)
logger.info("Processing catalog %s with %d objects",chipnum,len(image_pos))
objects = []
# Make a dict of the types to use for the extra_props, so we can preserve the type
# if we need to output them (e.g. in HSMCatalog).
prop_types = {key: extra_props[key].dtype.type for key in extra_props}
for k in range(len(image_pos)):
x = image_pos[k].x
y = image_pos[k].y
icen = int(x+0.5)
jcen = int(y+0.5)
half_size = stamp_size // 2
bounds = galsim.BoundsI(icen+half_size-stamp_size+1, icen+half_size,
jcen+half_size-stamp_size+1, jcen+half_size)
if not image.bounds.includes(bounds):
bounds = bounds & image.bounds
if not bounds.isDefined():
logger.warning("Object at position %f,%f is off the edge of the image.", x, y)
logger.warning("Skipping this object.")
continue
if use_partial:
logger.info("Object at position %f,%f overlaps the edge of the image. "
"Using smaller than the full stamp size: %s", x, y, bounds)
else:
logger.warning("Object at position %f,%f overlaps the edge of the image.", x, y)
logger.warning("Skipping this object.")
continue
stamp = image[bounds].copy()
wt_stamp = wt[bounds].copy()
props = { 'chipnum' : chipnum,
}
# if an object is totally masked, then don't add it!
if np.all(wt_stamp.array == 0):
logger.warning("Object at position %f,%f is completely masked.", x, y)
logger.warning("Skipping this object.")
continue
# Add this object's entry in each list of the extra_props dictionary
# to the StarData properties dictionary
for key in extra_props:
logger.debug("Assigning {} value = {}".format(key, extra_props[key][k]))
props[key] = extra_props[key][k]
# If any pixels are saturated, skip it.
if 'satur' in props:
max_val = np.max(stamp.array)
if max_val > props['satur']:
logger.warning("Object at position %f,%f has saturated pixels.", x, y)
logger.warning("Maximum value is %f.", max_val)
logger.warning("Skipping this object.")
continue
# Subtract the sky
if 'sky' in props:
sky = props['sky']
logger.debug("Subtracting off sky = %f", sky)
logger.debug("Median pixel value = %f", np.median(stamp.array))
stamp -= sky
pos = galsim.PositionD(x,y)
data = StarData(stamp, pos, weight=wt_stamp, pointing=pointing,
properties=props, property_types=prop_types)
star = Star(data, None)
objects.append(star)
return objects
def setWCS(self, config, logger):
self.wcs_list = []
self.center_list = []
for image_num, kwargs in enumerate(self.image_kwargs):
galsim.config.RemoveCurrent(config) # Makes any @ items work correctly
image_file_name = kwargs['image_file_name']
image_hdu = kwargs['image_hdu']
image = galsim.fits.read(image_file_name, hdu=image_hdu)
if 'wcs' in config:
logger.warning("Using custom wcs from config for %s",image_file_name)
base = { 'input' : config, 'index_key' : 'image_num', 'image_num' : image_num }
wcs = galsim.config.BuildWCS(config, 'wcs', base, logger)
else:
logger.warning("Getting wcs from image file %s",image_file_name)
wcs = image.wcs
self.wcs_list.append(wcs)
self.center_list.append(image.true_center)
@staticmethod
def _removeSignalFromWeight(image, weight, gain=None):
"""Remove the image signal from the weight map.
:param image: The image to use as the signal
:param weight: The weight image.
:param gain: Optionally, the gain to use as the proportionality relation.
If gain is None, then it will be estimated automatically and returned.
[default: None]
:returns: newweight, gain
"""
signal = image.array
variance = 1./weight.array
use = (weight.array != 0.) & np.isfinite(signal)
if gain is None:
fit = np.polyfit(signal[use].flatten(), variance[use].flatten(), deg=1)
gain = 1./fit[0] # fit is [ 1/gain, sky_var ]
variance[use] -= signal[use] / gain
newweight = weight.copy()
newweight.array[use] = 1. / variance[use]
return newweight, gain
[docs] @staticmethod
def readImage(image_file_name, image_hdu, weight_hdu, badpix_hdu,
sky_file_name, sky_hdu, noise, logger):
"""Read in the image and weight map (or make one if no weight information is given
:param image_file_name: The name of the file to read.
:param image_hdu: The hdu of the main image.
:param weight_hdu: The hdu of the weight image (if any).
:param badpix_hdu: The hdu of the bad pixel mask (if any).
:param sky_file_name: A file to use for a sky background to subtract from the image
(if any).
:param sky_hdu: The hdu to use in the sky_file_name (if any).
:param noise: A constant noise value to use in lieu of a weight map.
:param logger: A logger object for logging debug info.
:returns: image, weight
"""
# Read in the image
logger.warning("Reading image file %s",image_file_name)
image = galsim.fits.read(image_file_name, hdu=image_hdu)
# If requested, subtract a sky image
if sky_file_name is not None:
hdu_str = " from hdu %s"%(sky_hdu) if sky_hdu is not None else ""
logger.info("Reading sky image %s.", sky_file_name + hdu_str)
sky = galsim.fits.read(sky_file_name, hdu=sky_hdu)
image -= sky
# Either read in the weight image, or build a dummy one
if weight_hdu is not None:
logger.info("Reading weight image from hdu %d.", weight_hdu)
weight = galsim.fits.read(image_file_name, hdu=weight_hdu)
if np.all(weight.array == 0):
logger.error("According to the weight mask in %s, all pixels have zero weight!",
image_file_name)
if np.any(weight.array < 0):
logger.error("Warning: weight map has invalid negative-valued pixels. "+
"Taking them to be 0.0")
weight.array[weight.array < 0] = 0.
elif noise is not None:
logger.debug("Making uniform weight image based on noise variance = %f", noise)
weight = galsim.ImageF(image.bounds, init_value=1./noise)
else:
logger.debug("Making trivial (wt==1) weight image")
weight = galsim.ImageF(image.bounds, init_value=1)
# If requested, set wt=0 for any bad pixels
if badpix_hdu is not None:
logger.info("Reading badpix image from hdu %d.", badpix_hdu)
badpix = galsim.fits.read(image_file_name, hdu=badpix_hdu)
# The badpix image may be offset by 32768 from the true value.
# If so, subtract it off.
if np.any(badpix.array > 32767): # pragma: no cover
logger.debug('min(badpix) = %s',np.min(badpix.array))
logger.debug('max(badpix) = %s',np.max(badpix.array))
logger.debug("subtracting 32768 from all values in badpix image")
badpix -= 32768
if np.any(badpix.array < -32767): # pragma: no cover
logger.debug('min(badpix) = %s',np.min(badpix.array))
logger.debug('max(badpix) = %s',np.max(badpix.array))
logger.debug("adding 32768 to all values in badpix image")
badpix += 32768
if np.all(badpix.array != 0): # pragma: no cover
logger.error("According to the bad pixel array in %s, all pixels are masked!",
image_file_name)
weight.array[badpix.array != 0] = 0
return image, weight
@staticmethod
def _flag_select(col, flag):
if len(col.shape) == 1:
# Then just treat this as a straightforward bitmask.
return col & flag
else:
# Then treat this as an array of bools rather than a bitmask
mask = np.zeros(col.shape[0], dtype=bool)
for bit in range(col.shape[1]): # pragma: no branch
if flag % 2 == 1:
mask |= col[:,bit]
flag = flag // 2
if flag == 0: break
return mask
[docs] @staticmethod
def readStarCatalog(cat_file_name, cat_hdu, x_col, y_col,
ra_col, dec_col, ra_units, dec_units, image,
flag_col, skip_flag, use_flag, property_cols, sky_col, gain_col,
sky, gain, satur, nstars, image_file_name, stamp_size, logger):
"""Read in the star catalogs and return lists of positions for each star in each image.
:param cat_file_name: The name of the catalog file to read in.
:param cat_hdu: The hdu to use.
:param x_col: The name of the column with x values.
:param y_col: The name of the column with y values.
:param ra_col: The name of a column with RA values.
:param dec_col: The name of a column with Dec values.
:param ra_units: The units of the ra column.
:param dec_units: The units of the dec column.
:param image: The image that was already read in (mostly for the wcs).
:param flag_col: The name of a column with flag values.
:param skip_flag: The flag indicating which items to not use. [default: -1]
Items with flag & skip_flag != 0 will be skipped.
:param use_flag: The flag indicating which items to use. [default: None]
Items with flag & use_flag == 0 will be skipped.
:param property_cols: A list of column names with star properties (e.g. star colors).
:param sky_col: A column with sky (background) levels.
:param gain_col: A column with gain values.
:param sky: Either a float value for the sky to use for all objects or a str
keyword to read a value from the FITS header.
:param gain: Either a float value for the gain to use for all objects or a str
keyword to read a value from the FITS header.
:param satur: Either a float value for the saturation level to use or a str
keyword to read a value from the FITS header.
:param nstars: Optionally a maximum number of stars to use.
:param image_file_name: The image file name in case needed for header values.
:param stamp_size: The stamp size being used for the star stamps.
:param logger: A logger object for logging debug info. [default: None]
:returns: lists image_pos, extra_props
"""
import fitsio
if cat_file_name is None:
# This is possible e.g. when loading images into an existing list of star instances.
logger.warning("Not reading input catalog.")
return None, None
# Read in the star catalog
logger.warning("Reading input catalog %s.",cat_file_name)
cat = fitsio.read(cat_file_name, cat_hdu)
if flag_col is not None:
if flag_col not in cat.dtype.names:
raise ValueError("flag_col = %s is not a column in %s"%(flag_col,cat_file_name))
col = cat[flag_col]
if len(col.shape) == 2:
logger.warning("Flag col (%s) is multidimensional. Treating as an array of bool",
flag_col)
if use_flag is not None:
# Remove any objects with flag & use_flag == 0
mask = InputFiles._flag_select(col, use_flag) == 0
logger.info("Removing objects with flag (col %s) & %d == 0",flag_col,use_flag)
if skip_flag != -1:
mask |= InputFiles._flag_select(col, skip_flag) != 0
logger.info("Removing objects with flag (col %s) & %d != 0",flag_col,skip_flag)
else:
# Remove any objects with flag & skip_flag != 0
mask = InputFiles._flag_select(col, skip_flag) != 0
if skip_flag == -1:
logger.info("Removing objects with flag (col %s) != 0",flag_col)
else:
logger.info("Removing objects with flag (col %s) & %d != 0",flag_col,skip_flag)
cat = cat[mask == 0]
# Limit to nstars objects
if nstars is not None and nstars < len(cat):
logger.info("Limiting to %d objects for %s",nstars,cat_file_name)
cat = cat[:nstars]
# Make the list of positions:
if ra_col is not None or dec_col is not None:
if ra_col is None or dec_col is None:
raise ValueError("ra_col and dec_col are both required if one is provided.")
if ra_col not in cat.dtype.names:
raise ValueError("ra_col = %s is not a column in %s"%(ra_col,cat_file_name))
if dec_col not in cat.dtype.names:
raise ValueError("dec_col = %s is not a column in %s"%(dec_col,cat_file_name))
logger.debug("Starting to make a list of positions from ra, dec")
ra_values = cat[ra_col]
dec_values = cat[dec_col]
ra_units = galsim.AngleUnit.from_name(ra_units)
dec_units = galsim.AngleUnit.from_name(dec_units)
ra = ra_values * ra_units
dec = dec_values * dec_units
logger.debug("Initially %d positions",len(ra))
# First limit to only those that could possibly be on the image by checking the
# min/max ra and dec from the image corners.
cen = image.wcs.toWorld(image.center)
logger.debug("Center at %s",cen)
x_corners = [image.xmin, image.xmin, image.xmax, image.xmax]
y_corners = [image.ymin, image.ymax, image.ymax, image.ymin]
corners = [image.wcs.toWorld(galsim.PositionD(x,y))
for (x,y) in zip(x_corners, y_corners)]
logger.debug("Corners at %s",corners)
min_ra = np.min([c.ra.wrap(cen.ra) for c in corners])
max_ra = np.max([c.ra.wrap(cen.ra) for c in corners])
min_dec = np.min([c.dec.wrap(cen.dec) for c in corners])
max_dec = np.max([c.dec.wrap(cen.dec) for c in corners])
logger.debug("RA range = %s .. %s",min_ra,max_ra)
logger.debug("Dec range = %s .. %s",min_dec,max_dec)
use = [(r.wrap(cen.ra) > min_ra) & (r.wrap(cen.ra) < max_ra) &
(d.wrap(cen.dec) > min_dec) & (d.wrap(cen.dec) < max_dec)
for r,d in zip(ra,dec)]
ra = ra[use]
dec = dec[use]
logger.debug("After limiting to image ra,dec range, len = %s",len(ra))
# Now convert to x,y
def safe_to_image(wcs, ra, dec):
try:
return wcs.toImage(galsim.CelestialCoord(ra, dec))
except galsim.GalSimError: # pragma: no cover
# If the ra,dec is way off the image, this might fail to converge.
# In this case return None, which we can get rid of simply.
return None
image_pos = [ safe_to_image(image.wcs,r,d) for r,d in zip(ra, dec) ]
image_pos = [ pos for pos in image_pos if pos is not None ]
logger.debug("Resulting image_pos list has %s positions",len(image_pos))
else:
if x_col not in cat.dtype.names:
raise ValueError("x_col = %s is not a column in %s"%(x_col,cat_file_name))
if y_col not in cat.dtype.names:
raise ValueError("y_col = %s is not a column in %s"%(y_col,cat_file_name))
x_values = cat[x_col]
y_values = cat[y_col]
logger.debug("Initially %d positions",len(x_values))
image_pos = [ galsim.PositionD(x,y) for x,y in zip(x_values, y_values) ]
# Check for objects well off the edge. We won't use them.
big_bounds = image.bounds.expand(stamp_size)
image_pos = [ pos for pos in image_pos if big_bounds.includes(pos) ]
logger.debug("After remove those that are off the image, len = %s",len(image_pos))
# Make a dictionary of the star properties:
extra_props = {}
if property_cols is not None:
logger.debug('property_cols = %s'%property_cols)
for col_name in property_cols:
if col_name not in cat.dtype.names:
raise ValueError("Entry in property_cols = " +
"%s is not a column in %s"%(col_name,cat_file_name))
extra_props[col_name] = cat[col_name]
# If we used a flag column, keep it as a property.
if flag_col is not None:
extra_props[flag_col] = cat[flag_col]
# Make the list of sky values:
if sky_col is not None:
if sky_col not in cat.dtype.names:
raise ValueError("sky_col = %s is not a column in %s"%(sky_col,cat_file_name))
extra_props['sky'] = cat[sky_col]
elif sky is not None:
try:
sky = float(sky)
except ValueError:
fits = fitsio.FITS(image_file_name)
hdu = 1 if image_file_name.endswith('.fz') else 0
header = fits[hdu].read_header()
if sky not in header:
raise KeyError("Key %s not found in FITS header"%sky)
sky = float(header[sky])
extra_props['sky'] = np.array([sky]*len(cat), dtype=float)
# Make the list of gain values:
# TODO: SV and Y1 DES images have two gain values, GAINA, GAINB. It would be nice if we
# could get the right one properly. OTOH, Y3+ will be in electrons, so gain=1 will
# the right value for all images. So maybe not worth worrying about.
if gain_col is not None:
if gain_col not in cat.dtype.names:
raise ValueError("gain_col = %s is not a column in %s"%(gain_col,cat_file_name))
extra_props['gain'] = cat[gain_col]
elif gain is not None:
try:
gain = float(gain)
except ValueError:
fits = fitsio.FITS(image_file_name)
hdu = 1 if image_file_name.endswith('.fz') else 0
header = fits[hdu].read_header()
if gain not in header:
raise KeyError("Key %s not found in FITS header"%gain)
gain = float(header[gain])
extra_props['gain'] = np.array([gain]*len(cat), dtype=float)
# Get the saturation level
if satur is not None:
try:
satur = float(satur)
logger.debug("Using given saturation value: %s",satur)
except ValueError:
fits = fitsio.FITS(image_file_name)
hdu = 1 if image_file_name.endswith('.fz') else 0
header = fits[hdu].read_header()
if satur not in header:
raise KeyError("Key %s not found in FITS header"%satur)
satur = float(header[satur])
logger.debug("Using saturation from header: %s",satur)
extra_props['satur'] = np.array([satur]*len(cat), dtype=float)
return image_pos, extra_props
[docs] def setPointing(self, ra, dec, logger=None):
"""Set the pointing attribute based on the input ra, dec (given in the initializer)
There are a number of ways the pointing can be specified.
Even this is probably not sufficiently generic for all applications, but it's a start.
1. numerical values (in Hours, Degrees respective) for ra, dec
2. hh:mm:ss.ssss, dd:mm:ss.ssss strings giving hours/degrees, minutes, seconds for each
3. FITS header key words to read to get the ra, dec values
4. None, which will attempt to find the spatial center of all the input images using the
midpoint of the min/max ra and dec values of the image corners according to their
individual WCS functions. [Not implemented currently.]
"""
import fitsio
logger = galsim.config.LoggerWrapper(logger)
if (ra is None) != (dec is None):
raise ValueError("Only one of ra, dec was specified")
if ra is None:
if self.wcs_list[0].isCelestial():
if self.nimages == 1:
# Here we can just use the image center.
wcs = self.wcs_list[0]
center = self.center_list[0]
self.pointing = wcs.toWorld(center)
logger.info("Setting pointing to image center: %.3f h, %.3f d",
self.pointing.ra / galsim.hours,
self.pointing.dec / galsim.degrees)
else:
# Use the mean of all the image centers
plist = [wcs.toWorld(center)
for wcs,center in zip(self.wcs_list,self.center_list)]
# Do this in x,y,z coords, not ra, dec so we don't mess up near ra=0.
xlist, ylist, zlist = zip(*[p.get_xyz() for p in plist])
x = np.mean(xlist)
y = np.mean(ylist)
z = np.mean(zlist)
self.pointing = galsim.CelestialCoord.from_xyz(x,y,z)
logger.info("Setting pointing to mean of image centers: %.3f h, %.3f d",
self.pointing.ra / galsim.hours,
self.pointing.dec / galsim.degrees)
else:
self.pointing = None
elif type(ra) in [float, int]:
ra = float(ra) * galsim.hours
dec = float(dec) * galsim.degrees
self.pointing = galsim.CelestialCoord(ra,dec)
logger.info("Setting pointing to: %.3f h, %.3f d",
self.pointing.ra / galsim.hours,
self.pointing.dec / galsim.degrees)
elif str(ra) != ra:
raise ValueError("Unable to parse input ra: %s"%ra)
elif str(dec) != dec:
raise ValueError("Unable to parse input dec: %s"%dec)
elif ':' in ra and ':' in dec:
ra = galsim.Angle.from_hms(ra)
dec = galsim.Angle.from_dms(dec)
self.pointing = galsim.CelestialCoord(ra,dec)
logger.info("Setting pointing to: %.3f h, %.3f d",
self.pointing.ra / galsim.hours,
self.pointing.dec / galsim.degrees)
else:
file_name = self.image_file_name[0]
if len(self.chipnums) == 1:
logger.info("Setting pointing from keywords %s, %s", ra, dec)
else:
logger.info("Setting pointing from keywords %s, %s in %s", ra, dec, file_name)
fits = fitsio.FITS(file_name)
hdu = 1 if file_name.endswith('.fz') else 0
header = fits[hdu].read_header()
if ra not in header:
raise KeyError("Key %s not found in FITS header"%ra)
ra = header[ra]
if dec not in header:
raise KeyError("Key %s not found in FITS header"%dec)
dec = header[dec]
# Recurse to do further parsing.
self.setPointing(ra, dec, logger)