reorganize

fpfs/tasks.py

@@ -10,10 +10,6 @@
 import numpy as np
 import astropy.io.fits as pyfits
 from configparser import ConfigParser, ExtendedInterpolation
-from descwl_shear_sims.stars import StarCatalog
-from descwl_shear_sims.sim import make_sim
-from descwl_shear_sims.psfs import make_fixed_psf
-from descwl_shear_sims.galaxies import WLDeblendGalaxyCatalog
 
 logging.basicConfig(
     format="%(asctime)s %(message)s",
@@ -240,8 +236,8 @@ def __init__(self, config_name):
         self.sigma_as = cparser.getfloat("FPFS", "sigma_as")
         self.sigma_det = cparser.getfloat("FPFS", "sigma_det")
         self.rcut = cparser.getint("FPFS", "rcut", fallback=32)
-        psf_rcut = cparser.getint("FPFS", "psf_rcut", fallback=22)
-        self.psf_rcut = min(psf_rcut, self.rcut)
+        self.psf_rcut = cparser.getint("FPFS", "psf_rcut", fallback=22)
+        self.psf_rcut = min(self.psf_rcut, self.rcut)
         self.nnord = cparser.getint("FPFS", "nnord", fallback=4)
         if self.nnord not in [4, 6]:
             raise ValueError(
@@ -267,20 +263,6 @@ def __init__(self, config_name):
         # By default, we use uncorrelated noise
         # TODO: enable correlated noise here
         self.noise_pow = np.ones((ngrid, ngrid)) * self.nstd_f**2.0 * ngrid**2.0
-
-        self.rotate = cparser.getboolean("simulation", "rotate")
-        self.dither = cparser.getboolean("simulation", "dither")
-        # version of the PSF simulation: 0 -- fixed PSF; 1 -- variational PSF
-        self.coadd_dim = cparser.getint("simulation", "coadd_dim")
-        # buffer length to avoid galaxies hitting the boundary of the exposure
-        self.buff = cparser.getint("simulation", "buff")
-
-        self.stellar_density = cparser.getfloat(
-            "simulation",
-            "stellar_density",
-            fallback=0.0,
-        )
-        self.layout = cparser.get("simulation", "layout")
         return
 
     def get_image_fnames(self, ifield):
@@ -298,52 +280,6 @@ def get_image_fnames(self, ifield):
             assert os.path.isfile(rr), "file %s does not exist" % rr
         return refs
 
-    def prepare_systematics(self, fname):
-        """Prepare the systematic images"""
-        field_id = int(fname.split("image-")[-1].split("_")[0]) + 212
-        rng = np.random.RandomState(field_id)
-
-        star_catalog = StarCatalog(
-            rng=rng,
-            coadd_dim=self.coadd_dim,
-            buff=self.buff,
-            density=self.stellar_density,
-            layout=self.layout,
-        )
-        galaxy_catalog = WLDeblendGalaxyCatalog(
-            rng=rng,
-            coadd_dim=self.coadd_dim,
-            buff=self.buff,
-            layout=self.layout,
-        )
-        psf = make_fixed_psf(psf_type="moffat")  # .shear(e1=0.02, e2=-0.02)
-        star_image_array = (
-            make_sim(
-                rng=rng,
-                galaxy_catalog=galaxy_catalog,
-                star_catalog=star_catalog,
-                coadd_dim=self.coadd_dim,
-                psf=psf,
-                draw_gals=False,
-                draw_stars=True,
-                draw_bright=False,
-                dither=self.dither,
-                rotate=self.rotate,
-                bands=[self.band],
-                noise_factor=0.0,
-                cosmic_rays=False,
-                bad_columns=False,
-                star_bleeds=False,
-                draw_method="auto",
-                g1=0.0,
-                g2=0.0,
-            )["band_data"][self.band][0]
-            .getMaskedImage()
-            .getImage()
-            .getArray()
-        )
-        return star_image_array
-
     def prepare_noise_psf(self, fname):
         exposure = pyfits.getdata(fname)
         self.image_nx = exposure.shape[1]
@@ -373,8 +309,6 @@ def prepare_image(self, fname):
         gal_array = np.zeros((self.image_nx, self.image_nx))
         logging.info("processing %s band" % self.band)
         gal_array = pyfits.getdata(fname)
-        star_image_array = self.prepare_systematics(fname)
-        gal_array = gal_array + star_image_array
         if self.nstd_f > 1e-10:
             # noise
             seed = get_random_seed_from_fname(fname, self.band)