Add void interpolation (#150)

* Update imports

* Remove multiple ICs for void

* Update submit

* Add void support

* All the void support

* Little update

* Update nb

* Update script

csiborgtools/flow/__init__.py

@@ -18,3 +18,4 @@ from .flow_model import (PV_LogLikelihood, PV_validation_model, dist2redshift,
                          Observed2CosmologicalRedshift, predict_zobs,           # noqa
                          project_Vext, stack_pzosmo_over_realizations)          # noqa
 from .selection import ToyMagnitudeSelection                                    # noqa
+from .void_model import load_void_data, interpolate_void                        # noqa

csiborgtools/flow/flow_model.py

@@ -25,6 +25,7 @@ from abc import ABC, abstractmethod
 
 import numpy as np
 from astropy import units as u
+from astropy.coordinates import SkyCoord, angular_separation
 from astropy.cosmology import FlatLambdaCDM, z_at_value
 from jax import jit
 from jax import numpy as jnp
@@ -37,6 +38,7 @@ from tqdm import trange
 from ..params import SPEED_OF_LIGHT
 from ..utils import fprint
 from .selection import toy_log_magnitude_selection
+from .void_model import interpolate_void, load_void_data
 
 H0 = 100  # km / s / Mpc
 
@@ -193,6 +195,33 @@ class BaseFlowValidationModel(ABC):
         self.z_xrange = jnp.asarray(z_xrange)
         self.mu_xrange = jnp.asarray(mu_xrange)
 
+    def _set_void_data(self, RA, dec, kind, h, order):
+        """Create the void interpolator."""
+        # h is the MOND model value of local H0 to convert the radial grid to
+        # Mpc / h
+        rLG_grid, void_grid = load_void_data(kind)
+        void_grid = jnp.asarray(void_grid, dtype=jnp.float32)
+        rLG_grid = jnp.asarray(rLG_grid, dtype=jnp.float32)
+
+        rLG_grid *= h
+        rLG_min, rLG_max = rLG_grid.min(), rLG_grid.max()
+        rgrid_min, rgrid_max = 0, 250
+        fprint(f"setting radial grid from {rLG_min} to {rLG_max} Mpc.")
+        rgrid_max *= h
+
+        # Get angular separation (in degrees) of each object from the model
+        # axis.
+        model_axis = SkyCoord(l=117, b=4, frame='galactic', unit='deg').icrs
+        coords = SkyCoord(ra=RA, dec=dec, unit='deg').icrs
+
+        phi = angular_separation(coords.ra.rad, coords.dec.rad,
+                                 model_axis.ra.rad, model_axis.dec.rad)
+        phi = jnp.asarray(phi * 180 / np.pi, dtype=jnp.float32)
+
+        self.void_interpolator = lambda rLG: interpolate_void(
+            rLG, self.r_xrange, phi, void_grid, rgrid_min, rgrid_max,
+            rLG_min, rLG_max, order)
+
     @property
     def ndata(self):
         """Number of PV objects in the catalogue."""
@@ -201,7 +230,24 @@ class BaseFlowValidationModel(ABC):
     @property
     def num_sims(self):
         """Number of simulations."""
-        return len(self.log_los_density)
+        if self.is_void_data:
+            return 1.
+
+        return len(self.log_los_density())
+
+    def log_los_density(self, **kwargs):
+        if self.is_void_data:
+            # Currently we have no densities for the void.
+            return jnp.zeros((1, self.ndata, len(self.r_xrange)))
+
+        return self._log_los_density
+
+    def los_velocity(self, **kwargs):
+        if self.is_void_data:
+            # We want the shape to be `(1, n_objects, n_radial_steps)``.
+            return self.void_interpolator(kwargs["rLG"])[None, ...]
+
+        return self._los_velocity
 
     @abstractmethod
     def __call__(self, **kwargs):
@@ -331,7 +377,8 @@ def sample_simple(e_mu_min, e_mu_max, dmu_min, dmu_max, alpha_min, alpha_max,
 
 def sample_calibration(Vext_min, Vext_max, Vmono_min, Vmono_max, beta_min,
                        beta_max, sigma_v_min, sigma_v_max, h_min, h_max,
-                       no_Vext, sample_Vmono, sample_beta, sample_h):
+                       rLG_min, rLG_max, no_Vext, sample_Vmono, sample_beta,
+                       sample_h, sample_rLG):
     """Sample the flow calibration."""
     sigma_v = sample("sigma_v", Uniform(sigma_v_min, sigma_v_max))
 
@@ -357,12 +404,18 @@ def sample_calibration(Vext_min, Vext_max, Vmono_min, Vmono_max, beta_min,
     else:
         h = 1.0
 
+    if sample_rLG:
+        rLG = sample("rLG", Uniform(rLG_min, rLG_max))
+    else:
+        rLG = None
+
     return {"Vext": Vext,
             "Vmono": Vmono,
             "sigma_v": sigma_v,
             "beta": beta,
             "h": h,
             "sample_h": sample_h,
+            "rLG": rLG,
             }
 
 
@@ -386,9 +439,9 @@ class PV_LogLikelihood(BaseFlowValidationModel):
     Parameters
     ----------
     los_density : 3-dimensional array of shape (n_sims, n_objects, n_steps)
-        LOS density field.
+        LOS density field. Set to `None` if the data is void data.
     los_velocity : 3-dimensional array of shape (n_sims, n_objects, n_steps)
-        LOS radial velocity field.
+        LOS radial velocity field. Set to `None` if the data is void data.
     RA, dec : 1-dimensional arrays of shape (n_objects)
         Right ascension and declination in degrees.
     z_obs : 1-dimensional array of shape (n_objects)
@@ -409,6 +462,8 @@ class PV_LogLikelihood(BaseFlowValidationModel):
         Catalogue kind, either "TFR", "SN", or "simple".
     name : str
         Name of the catalogue.
+    void_kwargs : dict, optional
+        Void data parameters. If `None` the data is not void data.
     with_num_dist_marginalisation : bool, optional
         Whether to use numerical distance marginalisation, in which case
         the tracers cannot be coupled by a covariance matrix. By default
@@ -417,19 +472,31 @@ class PV_LogLikelihood(BaseFlowValidationModel):
 
     def __init__(self, los_density, los_velocity, RA, dec, z_obs, e_zobs,
                  calibration_params, abs_calibration_params, mag_selection,
-                 r_xrange, Omega_m, kind, name, with_num_dist_marginalisation):
+                 r_xrange, Omega_m, kind, name, void_kwargs=None,
+                 with_num_dist_marginalisation=True):
         if e_zobs is not None:
             e2_cz_obs = jnp.asarray((SPEED_OF_LIGHT * e_zobs)**2)
         else:
             e2_cz_obs = jnp.zeros_like(z_obs)
 
+        self.is_void_data = void_kwargs is not None
+
+        # This must be done before we convert to radians.
+        if void_kwargs is not None:
+            self._set_void_data(RA=RA, dec=dec, **void_kwargs)
+
         # Convert RA/dec to radians.
         RA, dec = np.deg2rad(RA), np.deg2rad(dec)
 
-        names = ["log_los_density", "los_velocity", "RA", "dec", "z_obs",
+        names = ["RA", "dec", "z_obs", "e2_cz_obs"]
-                 "e2_cz_obs"]
+        values = [RA, dec, z_obs, e2_cz_obs]
-        values = [jnp.log(los_density), los_velocity, RA, dec, z_obs,
+
-                  e2_cz_obs]
+        if not self.is_void_data:
+            names += ["_log_los_density", "_los_velocity"]
+            values += [jnp.log(los_density), los_velocity]
+
+        # Set the void data
+
         self._setattr_as_jax(names, values)
         self._set_calibration_params(calibration_params)
         self._set_abs_calibration_params(abs_calibration_params)
@@ -660,17 +727,24 @@ class PV_LogLikelihood(BaseFlowValidationModel):
                 mu_xrange[None, :], mu[:, None], e2_mu[:, None],
                 self.log_r2_xrange[None, :])
 
+            if self.is_void_data:
+                rLG = field_calibration_params["rLG"]
+                log_los_density = self.log_los_density(rLG=rLG)
+                los_velocity = self.los_velocity(rLG=rLG)
+            else:
+                log_los_density = self.log_los_density()
+                los_velocity = self.los_velocity()
+
             # Inhomogeneous Malmquist bias. Shape: (nsims, ndata, nxrange)
             alpha = distmod_params["alpha"]
-            log_ptilde = log_ptilde[None, ...] + alpha * self.log_los_density
+            log_ptilde = log_ptilde[None, ...] + alpha * log_los_density
 
             ptilde = jnp.exp(log_ptilde)
-
             # Normalization of p(r). Shape: (nsims, ndata)
             pnorm = simpson(ptilde, x=self.r_xrange, axis=-1)
 
             # Calculate z_obs at each distance. Shape: (nsims, ndata, nxrange)
-            vrad = field_calibration_params["beta"] * self.los_velocity
+            vrad = field_calibration_params["beta"] * los_velocity
             vrad += (Vext_rad[None, :, None] + Vmono)
             zobs = 1 + self.z_xrange[None, None, :]
             zobs *= 1 + vrad / SPEED_OF_LIGHT

csiborgtools/flow/io.py

@@ -64,13 +64,16 @@ class DataLoader:
         self._field_rdist, self._los_density, self._los_velocity = self._read_field(  # noqa
             simname, ksim, catalogue, ksmooth, paths)
 
-        if len(self._cat) != self._los_density.shape[1]:
+        if "IndranilVoid" not in simname:
-            raise ValueError("The number of objects in the catalogue does not "
+            if len(self._cat) != self._los_density.shape[1]:
-                             "match the number of objects in the field.")
+                raise ValueError(
+                    "The number of objects in the catalogue does not match "
+                    "the number of objects in the field.")
 
-        fprint("calculating the radial velocity.", verbose)
+            fprint("calculating the radial velocity.", verbose)
-        nobject = self._los_density.shape[1]
+            nobject = self._los_density.shape[1]
-        dtype = self._los_density.dtype
+            dtype = self._los_density.dtype
+            num_sims = len(self._los_density)
 
         if simname in ["Carrick2015", "Lilow2024"]:
             # Carrick+2015 and Lilow+2024 are in galactic coordinates
@@ -81,9 +84,8 @@ class DataLoader:
         else:
             d1, d2 = self._cat["RA"], self._cat["DEC"]
 
-        num_sims = len(self._los_density)
         if "IndranilVoid" in simname:
-            self._los_radial_velocity = self._los_velocity
+            self._los_radial_velocity = None
             self._los_velocity = None
         else:
             radvel = np.empty(
@@ -165,6 +167,9 @@ class DataLoader:
         return self._los_radial_velocity[:, self._mask, ...]
 
     def _read_field(self, simname, ksims, catalogue, ksmooth, paths):
+        if "IndranilVoid" in simname:
+            return None, None, None
+
         nsims = paths.get_ics(simname)
         if isinstance(ksims, int):
             ksims = [ksims]
@@ -340,8 +345,17 @@ def read_absolute_calibration(kind, data_length, calibration_fpath):
     return out, with_calibration, length_calibration
 
 
+def mask_fields(density, velocity, mask, return_none):
+    """Shortcut to mask fields, unless they are `None`"""
+    if return_none:
+        return None, None
+
+    return density[:, mask], velocity[:, mask]
+
+
 def get_model(loader, zcmb_min=None, zcmb_max=None, mag_selection=None,
-              absolute_calibration=None, calibration_fpath=None):
+              absolute_calibration=None, calibration_fpath=None,
+              void_kwargs=None):
     """
     Get a model and extract the relevant data from the loader.
 
@@ -366,10 +380,23 @@ def get_model(loader, zcmb_min=None, zcmb_max=None, mag_selection=None,
     zcmb_min = 0.0 if zcmb_min is None else zcmb_min
     zcmb_max = np.infty if zcmb_max is None else zcmb_max
 
-    los_overdensity = loader.los_density
+    if void_kwargs is None:
-    los_velocity = loader.los_radial_velocity
+        los_overdensity = loader.los_density
+        los_velocity = loader.los_radial_velocity
+    else:
+        los_overdensity = None
+        los_velocity = None
+
     kind = loader._catname
 
+    if void_kwargs is not None:
+        rdist = void_kwargs.pop("rdist", None)
+        if rdist is None:
+            raise ValueError(
+                "The radial distances must be provided for the void.")
+
+        loader._field_rdist = rdist
+
     if absolute_calibration is not None and "CF4_TFR_" not in kind:
         raise ValueError("Absolute calibration supported only for the CF4 TFR sample.")  # noqa
 
@@ -384,11 +411,14 @@ def get_model(loader, zcmb_min=None, zcmb_max=None, mag_selection=None,
                               "e_mag": e_mag[mask], "e_x1": e_x1[mask],
                               "e_c": e_c[mask]}
 
+        los_overdensity, los_velocity = mask_fields(
+            los_overdensity, los_velocity, mask, void_kwargs is not None)
+
         model = PV_LogLikelihood(
-            los_overdensity[:, mask], los_velocity[:, mask],
+            los_overdensity, los_velocity,
             RA[mask], dec[mask], zCMB[mask], e_zCMB, calibration_params,
             None, mag_selection, loader.rdist, loader._Omega_m, "SN",
-            name=kind)
+            name=kind, void_kwargs=void_kwargs)
     elif "Pantheon+" in kind:
         keys = ["RA", "DEC", "zCMB", "mB", "x1", "c", "biasCor_m_b", "mBERR",
                 "x1ERR", "cERR", "biasCorErr_m_b", "zCMB_SN", "zCMB_Group",
@@ -413,11 +443,15 @@ def get_model(loader, zcmb_min=None, zcmb_max=None, mag_selection=None,
         calibration_params = {"mag": mB[mask], "x1": x1[mask], "c": c[mask],
                               "e_mag": e_mB[mask], "e_x1": e_x1[mask],
                               "e_c": e_c[mask]}
+
+        los_overdensity, los_velocity = mask_fields(
+            los_overdensity, los_velocity, mask, void_kwargs is not None)
+
         model = PV_LogLikelihood(
-            los_overdensity[:, mask], los_velocity[:, mask],
+            los_overdensity, los_velocity,
             RA[mask], dec[mask], zCMB[mask], e_zCMB[mask], calibration_params,
             None, mag_selection, loader.rdist, loader._Omega_m, "SN",
-            name=kind)
+            name=kind, void_kwargs=void_kwargs)
     elif kind in ["SFI_gals", "2MTF", "SFI_gals_masked"]:
         keys = ["RA", "DEC", "z_CMB", "mag", "eta", "e_mag", "e_eta"]
         RA, dec, zCMB, mag, eta, e_mag, e_eta = (loader.cat[k] for k in keys)
@@ -425,10 +459,15 @@ def get_model(loader, zcmb_min=None, zcmb_max=None, mag_selection=None,
         mask = (zCMB < zcmb_max) & (zCMB > zcmb_min)
         calibration_params = {"mag": mag[mask], "eta": eta[mask],
                               "e_mag": e_mag[mask], "e_eta": e_eta[mask]}
+
+        los_overdensity, los_velocity = mask_fields(
+            los_overdensity, los_velocity, mask, void_kwargs is not None)
+
         model = PV_LogLikelihood(
-            los_overdensity[:, mask], los_velocity[:, mask],
+            los_overdensity, los_velocity,
             RA[mask], dec[mask], zCMB[mask], None, calibration_params, None,
-            mag_selection, loader.rdist, loader._Omega_m, "TFR", name=kind)
+            mag_selection, loader.rdist, loader._Omega_m, "TFR", name=kind,
+            void_kwargs=void_kwargs)
     elif "CF4_TFR_" in kind:
         # The full name can be e.g. "CF4_TFR_not2MTForSFI_i" or "CF4_TFR_i".
         band = kind.split("_")[-1]
@@ -488,11 +527,15 @@ def get_model(loader, zcmb_min=None, zcmb_max=None, mag_selection=None,
 
         calibration_params = {"mag": mag[mask], "eta": eta[mask],
                               "e_mag": e_mag[mask], "e_eta": e_eta[mask]}
+
+        los_overdensity, los_velocity = mask_fields(
+            los_overdensity, los_velocity, mask, void_kwargs is not None)
+
         model = PV_LogLikelihood(
-            los_overdensity[:, mask], los_velocity[:, mask],
+            los_overdensity, los_velocity,
             RA[mask], dec[mask], z_obs[mask], None, calibration_params,
             abs_calibration_params, mag_selection, loader.rdist,
-            loader._Omega_m, "TFR", name=kind)
+            loader._Omega_m, "TFR", name=kind, void_kwargs=void_kwargs)
     elif kind in ["CF4_GroupAll"]:
         # Note, this for some reason works terribly.
         keys = ["RA", "DE", "Vcmb", "DMzp", "eDM"]
@@ -505,11 +548,15 @@ def get_model(loader, zcmb_min=None, zcmb_max=None, mag_selection=None,
         mu += 5 * np.log10(0.75)
 
         calibration_params = {"mu": mu[mask], "e_mu": e_mu[mask]}
+
+        los_overdensity, los_velocity = mask_fields(
+            los_overdensity, los_velocity, mask, void_kwargs is not None)
+
         model = PV_LogLikelihood(
-            los_overdensity[:, mask], los_velocity[:, mask],
+            los_overdensity, los_velocity,
             RA[mask], dec[mask], zCMB[mask], None, calibration_params, None,
             mag_selection,  loader.rdist, loader._Omega_m, "simple",
-            name=kind)
+            name=kind, void_kwargs=void_kwargs)
     else:
         raise ValueError(f"Catalogue `{kind}` not recognized.")
 

csiborgtools/flow/void_model.py

@@ -56,7 +56,7 @@ def load_void_data(kind):
            for f in files]
     rLG = np.sort(rLG)
 
-    for i, ri in enumerate(tqdm(rLG, desc="Loading observer data")):
+    for i, ri in enumerate(tqdm(rLG, desc="Loading void observer data")):
         f = join(fdir, f"v_pec_{kind}profile_rLG_{ri}.dat")
         data_i = np.genfromtxt(f).T
 

csiborgtools/read/paths.py

@@ -135,23 +135,10 @@ class Paths:
             files = [search(r'realization(\d+)_delta\.fits', file).group(1)
                      for file in files if search(r'realization(\d+)_delta\.fits', file)]  # noqa
             files = [int(file) for file in files]
-            # Downsample to only 20 realisations
-            # files = files[::5]
         elif simname in ["Carrick2015", "Lilow2024", "no_field", "CLONES"]:
             files = [0]
         elif "IndranilVoid" in simname:
-            kind = simname.split("_")[-1]
+            files = [0]
-            if kind not in ["exp", "gauss", "mb"]:
-                raise ValueError(f"Unknown void kind `{simname}`.")
-
-            kind = kind.upper()
-            fdir = join(self.aux_cat_dir, "IndranilVoid", f"{kind}profile")
-            files = glob(join(fdir, "v_pec_*.dat"))
-
-            files = [
-                search(rf'v_pec_{kind}profile_rLG_(\d+)\.dat', file).group(1)
-                for file in files]
-            files = [int(file) for file in files]
         else:
             raise ValueError(f"Unknown simulation name `{simname}`.")
 

notebooks/flow/reconstruction_comparison.ipynb

@@ -48,6 +48,47 @@
     "## Quick checks"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Carrick2015_2MTF_mike_zcmb_max_0.05_sample_alpha.hdf5\n",
+      "Last modified: 30/08/2024 15:27:56\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "9715.0205"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fname = paths.flow_validation(\n",
+    "    fdir, \"Carrick2015\", \"2MTF\", inference_method=\"mike\",\n",
+    "    sample_alpha=True, sample_beta=None,\n",
+    "    zcmb_max=0.05)\n",
+    "\n",
+    "\n",
+    "get_gof(\"neg_lnZ_harmonic\", fname)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,

scripts/flow/flow_validation.py

@@ -74,8 +74,9 @@ import sys
 from os.path import join                                                        # noqa
 
 import csiborgtools                                                             # noqa
-from csiborgtools import fprint                                                 # noqa
 import jax                                                                      # noqa
+import numpy as np                                                              # noqa
+from csiborgtools import fprint                                                 # noqa
 from h5py import File                                                           # noqa
 from numpyro.infer import MCMC, NUTS, init_to_median                            # noqa
 
@@ -86,7 +87,8 @@ def print_variables(names, variables):
     print(flush=True)
 
 
-def get_models(ksim, get_model_kwargs, mag_selection, verbose=True):
+def get_models(ksim, get_model_kwargs, mag_selection, void_kwargs,
+               verbose=True):
     """Load the data and create the NumPyro models."""
     paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
     folder = "/mnt/extraspace/rstiskalek/catalogs/"
@@ -125,12 +127,21 @@ def get_models(ksim, get_model_kwargs, mag_selection, verbose=True):
                                               cat, fpath, paths,
                                               ksmooth=ARGS.ksmooth)
         models[i] = csiborgtools.flow.get_model(
-            loader, mag_selection=mag_selection[i], **get_model_kwargs)
+            loader, mag_selection=mag_selection[i], void_kwargs=void_kwargs,
+            **get_model_kwargs)
 
     fprint(f"num. radial steps is {len(loader.rdist)}")
     return models
 
 
+def select_void_h(kind):
+    hs = {"mb": 0.7615, "gauss": 0.7724, "exp": 0.7725}
+    try:
+        return hs[kind]
+    except KeyError:
+        raise ValueError(f"Unknown void kind: `{kind}`.")
+
+
 def get_harmonic_evidence(samples, log_posterior, nchains_harmonic, epoch_num):
     """Compute evidence using the `harmonic` package."""
     data, names = csiborgtools.dict_samples_to_array(samples)
@@ -339,8 +350,18 @@ if __name__ == "__main__":
     if mag_selection and inference_method != "bayes":
         raise ValueError("Magnitude selection is only supported with `bayes` inference.")   # noqa
 
-    if "IndranilVoid" in ARGS.simname and ARGS.ksim is None:
+    if "IndranilVoid" in ARGS.simname:
-        raise ValueError("`IndranilVoid` must be run only per specific realization.")       # noqa
+        if ARGS.ksim is not None:
+            raise ValueError(
+                "`IndranilVoid` does not have multiple realisations.")
+
+        kind = ARGS.simname.split("_")[-1]
+        h = select_void_h(kind)
+        rdist = np.arange(0, 165, 0.5)
+        void_kwargs = {"kind": kind, "h": h, "order": 1, "rdist": rdist}
+    else:
+        void_kwargs = None
+        h = 1.
 
     if inference_method != "bayes":
         mag_selection = [None] * len(ARGS.catalogue)
@@ -360,6 +381,8 @@ if __name__ == "__main__":
                                "sample_Vmono": sample_Vmono,
                                "sample_beta": sample_beta,
                                "sample_h": sample_h,
+                               "sample_rLG": "IndranilVoid" in ARGS.simname,
+                               "rLG_min": 0.0, "rLG_max": 500 * h,
                                }
     print_variables(
         calibration_hyperparams.keys(), calibration_hyperparams.values())
@@ -394,7 +417,7 @@ if __name__ == "__main__":
             print(f"{'Current simulation:':<20} {i + 1} ({ksim}) out of {len(ksim_iterator)}.")  # noqa
 
         fname_kwargs["nsim"] = ksim
-        models = get_models(ksim, get_model_kwargs, mag_selection)
+        models = get_models(ksim, get_model_kwargs, mag_selection, void_kwargs)
         model_kwargs = {
             "models": models,
             "field_calibration_hyperparams": calibration_hyperparams,

scripts/flow/flow_validation.sh

@@ -37,11 +37,9 @@ else
 fi
 
 
-# for simname in "IndranilVoid_exp" "IndranilVoid_gauss" "IndranilVoid_mb"; do
+for simname in "IndranilVoid_exp" "IndranilVoid_gauss" "IndranilVoid_mb"; do
-for simname in "Carrick2015"; do
 # for simname in "no_field"; do
-    # for catalogue in "LOSS" "Foundation" "2MTF" "SFI_gals" "CF4_TFR_i" "CF4_TFR_w1"; do
+    for catalogue in "LOSS" "Foundation" "2MTF" "SFI_gals" "CF4_TFR_i" "CF4_TFR_w1"; do
-    for catalogue in "LOSS"; do
     # for catalogue in "CF4_TFR_i" "CF4_TFR_w1"; do
     # for catalogue in "2MTF" "SFI_gals" "CF4_TFR_i" "CF4_TFR_w1"; do
         for ksim in "none"; do