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https://github.com/Richard-Sti/csiborgtools.git
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Add SFI groups (#121)
* Update LOS sampling * Bring SD back to speed * Update nb * Update script * Update requirements * Update flow chi2 calc * Update script * Add support for SFI groups * Update plot * Update script * Update script
This commit is contained in:
Richard Stiskalek
GitHub
parent
27c1f9249b
commit
29b37172fa
7 changed files with 253 additions and 135 deletions
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@ -254,7 +254,7 @@ class DataLoader:
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for key in f.keys():
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arr[key] = f[key][:]
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elif catalogue in ["LOSS", "Foundation", "SFI_gals", "2MTF",
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"Pantheon+"]:
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"Pantheon+", "SFI_gals_masked", "SFI_groups"]:
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with File(catalogue_fpath, 'r') as f:
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grp = f[catalogue]
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@ -746,10 +746,13 @@ class SD_PV_validation_model(BaseFlowValidationModel):
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self._r_xrange = r_xrange
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# Get the various vmapped functions
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self._f_ptilde_wo_bias = lambda mu, err: calculate_ptilde_wo_bias(r_xrange, mu, err, r2_xrange, True) # noqa
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self._f_simps = lambda y: simps(y, dr) # noqa
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self._f_zobs = lambda beta, Vr, vpec_rad: predict_zobs(r_xrange, beta, Vr, vpec_rad, Omega_m) # noqa
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self._vmap_ptilde_wo_bias = vmap(lambda mu, err: calculate_ptilde_wo_bias(r_xrange, mu, err, r2_xrange, True)) # noqa
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self._vmap_simps = vmap(lambda y: simps(y, dr))
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self._vmap_zobs = vmap(lambda beta, Vr, vpec_rad: predict_zobs(r_xrange, beta, Vr, vpec_rad, Omega_m), in_axes=(None, 0, 0)) # noqa
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self._vmap_ll_zobs = vmap(lambda zobs, zobs_pred, sigma_v: calculate_ll_zobs(zobs, zobs_pred, sigma_v), in_axes=(0, 0, None)) # noqa
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# Distribution of external velocity components
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self._Vext = dist.Uniform(-500, 500)
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@ -757,12 +760,15 @@ class SD_PV_validation_model(BaseFlowValidationModel):
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self._alpha = dist.LogNormal(*lognorm_mean_std_to_loc_scale(1.0, 0.5)) # noqa
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self._beta = dist.Normal(1., 0.5)
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# Distribution of velocity uncertainty sigma_v
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self._sv = dist.LogNormal(*lognorm_mean_std_to_loc_scale(150, 100))
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self._sigma_v = dist.LogNormal(*lognorm_mean_std_to_loc_scale(150, 100)) # noqa
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self._h = dist.LogNormal(*lognorm_mean_std_to_loc_scale(1., 0.25))
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self._Omega_m = Omega_m
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def predict_zobs_single(self, **kwargs):
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raise NotImplementedError("This method is not implemented yet.")
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def __call__(self, sample_alpha=False):
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def __call__(self, sample_alpha=True, sample_beta=True):
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"""
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The simple distance NumPyro PV validation model.
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@ -771,29 +777,39 @@ class SD_PV_validation_model(BaseFlowValidationModel):
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sample_alpha : bool, optional
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Whether to sample the density bias parameter `alpha`, otherwise
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it is fixed to 1.
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sample_beta : bool, optional
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Whether to sample the velocity bias parameter `beta`, otherwise
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it is fixed to 1.
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"""
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Vx = numpyro.sample("Vext_x", self._Vext)
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Vy = numpyro.sample("Vext_y", self._Vext)
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Vz = numpyro.sample("Vext_z", self._Vext)
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alpha = numpyro.sample("alpha", self._alpha) if sample_alpha else 1.0
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beta = numpyro.sample("beta", self._beta)
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sigma_v = numpyro.sample("sigma_v", self._sv)
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beta = numpyro.sample("beta", self._beta) if sample_beta else 1.0
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sigma_v = numpyro.sample("sigma_v", self._sigma_v)
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h = numpyro.sample("h", self._h)
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Vext_rad = project_Vext(Vx, Vy, Vz, self._RA, self._dec)
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def scan_body(ll, i):
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# Calculate p(r) and multiply it by the galaxy bias
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ptilde = self._vmap_ptilde_wo_bias(self._r_hMpc, self._e2_rhMpc)
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ptilde *= self._los_density**alpha
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ptilde = self._f_ptilde_wo_bias(
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h * self._r_hMpc[i], h**2 * self._e2_rhMpc[i])
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ptilde *= self._los_density[i]**alpha
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# Normalization of p(r)
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pnorm = self._vmap_simps(ptilde)
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pnorm = self._f_simps(ptilde)
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# Calculate p(z_obs) and multiply it by p(r)
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zobs_pred = self._vmap_zobs(beta, Vext_rad, self._los_velocity)
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ptilde *= self._vmap_ll_zobs(self._z_obs, zobs_pred, sigma_v)
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zobs_pred = self._f_zobs(beta, Vext_rad[i], self._los_velocity[i])
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ptilde *= calculate_ll_zobs(self._z_obs[i], zobs_pred, sigma_v)
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ll = jnp.sum(jnp.log(self._vmap_simps(ptilde) / pnorm))
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return ll + jnp.log(self._f_simps(ptilde) / pnorm), None
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ll = 0.
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ll, __ = scan(scan_body, ll, jnp.arange(self.ndata))
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numpyro.factor("ll", ll)
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@ -1258,7 +1274,7 @@ def get_model(loader, zcmb_max=None, verbose=True):
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los_overdensity[mask], los_velocity[mask], RA[mask], dec[mask],
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zCMB[mask], mB[mask], x1[mask], c[mask], e_mB[mask], e_x1[mask],
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e_c[mask], loader.rdist, loader._Omega_m)
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elif kind in ["SFI_gals", "2MTF"]:
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elif kind in ["SFI_gals", "2MTF", "SFI_gals_masked"]:
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keys = ["RA", "DEC", "z_CMB", "mag", "eta", "e_mag", "e_eta"]
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RA, dec, zCMB, mag, eta, e_mag, e_eta = (loader.cat[k] for k in keys)
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@ -1271,6 +1287,15 @@ def get_model(loader, zcmb_max=None, verbose=True):
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los_overdensity[mask], los_velocity[mask], RA[mask], dec[mask],
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zCMB[mask], mag[mask], eta[mask], e_mag[mask], e_eta[mask],
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loader.rdist, loader._Omega_m)
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elif kind == "SFI_groups":
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keys = ["RA", "DEC", "z_CMB", "r_hMpc", "e_r_hMpc"]
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RA, dec, zCMB, r_hMpc, e_r_hMpc = (loader.cat[k] for k in keys)
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mask = (zCMB < zcmb_max)
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model = SD_PV_validation_model(
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los_overdensity[mask], los_velocity[mask], RA[mask], dec[mask],
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zCMB[mask], r_hMpc[mask], e_r_hMpc[mask], loader.rdist,
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loader._Omega_m)
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else:
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raise ValueError(f"Catalogue `{kind}` not recognized.")
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File diff suppressed because one or more lines are too long
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@ -35,8 +35,10 @@ def read_samples(catalogue, simname, ksmooth, include_calibration=False,
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if catalogue in ["LOSS", "Foundation", "Pantheon+"]:
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alpha_cal, beta_cal, mag_cal, e_mu_intrinsic = [], [], [], []
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elif catalogue in ["2MTF", "SFI_gals"]:
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elif catalogue in ["2MTF", "SFI_gals", "SFI_gals_masked"]:
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a, b, e_mu_intrinsic = [], [], []
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elif catalogue == "SFI_groups":
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h = []
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else:
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raise ValueError(f"Catalogue {catalogue} not recognized.")
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@ -86,6 +88,8 @@ def read_samples(catalogue, simname, ksmooth, include_calibration=False,
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a.append(f[f"sim_{nsim}/a"][:])
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b.append(f[f"sim_{nsim}/b"][:])
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e_mu_intrinsic.append(f[f"sim_{nsim}/e_mu_intrinsic"][:])
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elif catalogue == "SFI_groups":
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h.append(f[f"sim_{nsim}/h"][:])
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else:
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raise ValueError(f"Catalogue {catalogue} not recognized.")
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@ -95,8 +99,10 @@ def read_samples(catalogue, simname, ksmooth, include_calibration=False,
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if catalogue in ["LOSS", "Foundation", "Pantheon+"]:
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alpha_cal, beta_cal, mag_cal, e_mu_intrinsic = np.hstack(alpha_cal), np.hstack(beta_cal), np.hstack(mag_cal), np.hstack(e_mu_intrinsic) # noqa
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elif catalogue in ["2MTF", "SFI_gals"]:
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elif catalogue in ["2MTF", "SFI_gals", "SFI_gals_masked"]:
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a, b, e_mu_intrinsic = np.hstack(a), np.hstack(b), np.hstack(e_mu_intrinsic) # noqa
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elif catalogue == "SFI_groups":
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h = np.hstack(h)
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else:
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raise ValueError(f"Catalogue {catalogue} not recognized.")
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@ -114,9 +120,12 @@ def read_samples(catalogue, simname, ksmooth, include_calibration=False,
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if catalogue in ["LOSS", "Foundation", "Pantheon+"]:
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data += [alpha_cal, beta_cal, mag_cal, e_mu_intrinsic]
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names += ["alpha_cal", "beta_cal", "mag_cal", "e_mu_intrinsic"]
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elif catalogue in ["2MTF", "SFI_gals"]:
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elif catalogue in ["2MTF", "SFI_gals", "SFI_gals_masked"]:
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data += [a, b, e_mu_intrinsic]
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names += ["a", "b", "e_mu_intrinsic"]
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elif catalogue == "SFI_groups":
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data += [h]
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names += ["h"]
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else:
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raise ValueError(f"Catalogue {catalogue} not recognized.")
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@ -162,6 +171,7 @@ def names_to_latex(names, for_corner=False):
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"beta": r"$\beta$",
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"Vmag": r"$V_{\rm ext}$",
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"sigma_v": r"$\sigma_v$",
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"h": r"$h$",
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}
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labels = copy(names)
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@ -58,7 +58,8 @@ def get_los(catalogue_name, simname, comm):
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if comm.Get_rank() == 0:
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folder = "/mnt/extraspace/rstiskalek/catalogs"
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if catalogue_name in ["LOSS", "Foundation", "SFI_gals", "2MTF",
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if catalogue_name in ["LOSS", "Foundation", "SFI_gals",
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"SFI_gals_masked", "SFI_groups", "2MTF",
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"Pantheon+"]:
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fpath = join(folder, "PV_compilation_Supranta2019.hdf5")
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with File(fpath, 'r') as f:
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@ -1,13 +1,13 @@
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nthreads=3
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nthreads=5
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memory=40
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on_login=${1}
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on_login=0
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queue="berg"
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env="/mnt/users/rstiskalek/csiborgtools/venv_csiborg/bin/python"
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file="field_los.py"
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simname=${1}
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catalogue=${2}
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nsims="-1"
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simname="csiborg2_main"
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MAS="SPH"
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grid=1024
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@ -52,7 +52,7 @@ def get_model(args, nsim_iterator, get_model_kwargs):
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if args.catalogue == "A2":
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fpath = join(folder, "A2.h5")
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elif args.catalogue in ["LOSS", "Foundation", "Pantheon+", "SFI_gals",
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"2MTF"]:
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"2MTF", "SFI_groups", "SFI_gals_masked"]:
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fpath = join(folder, "PV_compilation_Supranta2019.hdf5")
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else:
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raise ValueError(f"Unknown catalogue: `{args.catalogue}`.")
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@ -107,10 +107,13 @@ def run_model(model, nsteps, nburn, nchains, nsim, dump_folder,
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# Calculate the chi2
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keys = list(thinned_samples.keys())
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nsamples = len(thinned_samples[keys[0]])
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try:
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zobs_mean, zobs_std = model.predict_zobs(thinned_samples)
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nu = model.ndata - len(keys)
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chi2 = [np.sum((zobs_mean[:, i] - model._z_obs)**2 / zobs_std[:, i]**2) / nu # noqa
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for i in range(nsamples)]
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except NotImplementedError:
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chi2 = [0. for _ in range(nsamples)]
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gof = csiborgtools.numpyro_gof(model, mcmc, model_kwargs)
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@ -1,5 +1,5 @@
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memory=4
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on_login=1
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on_login=0
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nthreads=${1}
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ksmooth=${2}
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@ -7,8 +7,8 @@ queue="berg"
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env="/mnt/users/rstiskalek/csiborgtools/venv_csiborg/bin/python"
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file="flow_validation.py"
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catalogue="LOSS"
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simname="Carrick2015"
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catalogue="SFI_groups"
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simname="csiborg2_main"
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pythoncm="$env $file --catalogue $catalogue --simname $simname --ksmooth $ksmooth"
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