mirror of
https://github.com/Richard-Sti/csiborgtools.git
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c4557cf35b
* Rename file * Add indents * Update imports * Add counting * Docs * Add nb * Rename nb * Update nb * Add PV processing * Update nb * Add Pantheon+groups * Update submission scripts * Add Pantheon+zSN * Update nb * Edit param * Matchin SFI * Update nb * Fix path bug * Add list of clusters * Update imports * Update imports * Add cartesian & mass of clusters * Add observation to halo matching * Add nb * Add inverse CDF * Add import * Update nb * Add comments
194 lines
7.8 KiB
Python
194 lines
7.8 KiB
Python
# Copyright (C) 2024 Richard Stiskalek
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# This program is free software; you can redistribute it and/or modify it
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# under the terms of the GNU General Public License as published by the
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# Free Software Foundation; either version 3 of the License, or (at your
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# option) any later version.
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#
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# This program is distributed in the hope that it will be useful, but
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# WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
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# Public License for more details.
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#
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# You should have received a copy of the GNU General Public License along
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# with this program; if not, write to the Free Software Foundation, Inc.,
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# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
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"""Script to help with plots in `flow_calibration.ipynb`."""
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from copy import copy
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from os.path import join, exists
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import numpy as np
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from getdist import MCSamples
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from h5py import File
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import csiborgtools
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def read_samples(catalogue, simname, ksmooth, include_calibration=False,
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return_MCsamples=False, subtract_LG_velocity=-1):
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print(f"\nReading {catalogue} fitted to {simname} with ksmooth = {ksmooth}.", flush=True) # noqa
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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nsims = paths.get_ics(simname)
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# The last simulation was used to draw the mocks.
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if catalogue in ["CB2_small", "CB2_large"]:
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nsims = nsims[:-5]
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FDIR_LG = "/mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/observer" # noqa
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Vx, Vy, Vz, beta, sigma_v, alpha = [], [], [], [], [], []
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BIC, AIC, logZ, chi2 = [], [], [], []
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if catalogue in ["LOSS", "Foundation"] or "Pantheon+" in catalogue:
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alpha_cal, beta_cal, mag_cal, e_mu_intrinsic = [], [], [], []
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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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elif catalogue in ["CB2_small", "CB2_large"]:
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pass
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else:
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raise ValueError(f"Catalogue {catalogue} not recognized.")
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fname = f"/mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/flow_samples_{catalogue}_{simname}_smooth_{ksmooth}.hdf5" # noqa
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with File(fname, 'r') as f:
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for i, nsim in enumerate(nsims):
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Vx.append(f[f"sim_{nsim}/Vext_x"][:])
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Vy.append(f[f"sim_{nsim}/Vext_y"][:])
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Vz.append(f[f"sim_{nsim}/Vext_z"][:])
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alpha.append(f[f"sim_{nsim}/alpha"][:])
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beta.append(f[f"sim_{nsim}/beta"][:])
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sigma_v.append(f[f"sim_{nsim}/sigma_v"][:])
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if subtract_LG_velocity >= 0:
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fname = join(FDIR_LG, f"{simname}_{nsim}_observer_velocity.npz") # noqa
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if not exists(fname):
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raise FileNotFoundError(f"File {fname} not found.")
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d = np.load(fname)
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R = d["smooth_scales"][subtract_LG_velocity]
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if i == 0:
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print(f"Subtracting LG velocity with kernel {R} Mpc / h.", flush=True) # noqa
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Vx_LG, Vy_LG, Vz_LG = d["vobs"][subtract_LG_velocity]
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if simname == "Carrick2015":
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Vx[-1] += beta[-1] * Vx_LG
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Vy[-1] += beta[-1] * Vy_LG
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Vz[-1] += beta[-1] * Vz_LG
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else:
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Vx[-1] += Vx_LG
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Vy[-1] += Vy_LG
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Vz[-1] += Vz_LG
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BIC.append(f[f"sim_{nsim}/BIC"][...])
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AIC.append(f[f"sim_{nsim}/AIC"][...])
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logZ.append(f[f"sim_{nsim}/logZ"][...])
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try:
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chi2.append(f[f"sim_{nsim}/chi2"][...])
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except KeyError:
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chi2.append([0.])
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if catalogue in ["LOSS", "Foundation"] or "Pantheon+" in catalogue: # noqa
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alpha_cal.append(f[f"sim_{nsim}/alpha_cal"][:])
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beta_cal.append(f[f"sim_{nsim}/beta_cal"][:])
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mag_cal.append(f[f"sim_{nsim}/mag_cal"][:])
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e_mu_intrinsic.append(f[f"sim_{nsim}/e_mu_intrinsic"][:])
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elif catalogue in ["2MTF", "SFI_gals"]:
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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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elif catalogue in ["CB2_small", "CB2_large"]:
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pass
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else:
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raise ValueError(f"Catalogue {catalogue} not recognized.")
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Vx, Vy, Vz, alpha, beta, sigma_v = np.hstack(Vx), np.hstack(Vy), np.hstack(Vz), np.hstack(alpha), np.hstack(beta), np.hstack(sigma_v) # noqa
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gof = np.hstack(BIC), np.hstack(AIC), np.hstack(logZ), np.hstack(chi2)
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if catalogue in ["LOSS", "Foundation"] or "Pantheon+" in catalogue:
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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", "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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elif catalogue in ["CB2_small", "CB2_large"]:
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pass
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else:
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raise ValueError(f"Catalogue {catalogue} not recognized.")
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# Calculate magnitude of V_ext
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Vmag = np.sqrt(Vx**2 + Vy**2 + Vz**2)
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# Calculate direction in galactic coordinates of V_ext
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V = np.vstack([Vx, Vy, Vz]).T
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V = csiborgtools.cartesian_to_radec(V)
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l, b = csiborgtools.radec_to_galactic(V[:, 1], V[:, 2])
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data = [alpha, beta, Vmag, l, b, sigma_v]
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names = ["alpha", "beta", "Vmag", "l", "b", "sigma_v"]
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if include_calibration:
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if catalogue in ["LOSS", "Foundation"] or "Pantheon+" in catalogue:
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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", "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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print("BIC = {:4f} +- {:4f}".format(np.mean(gof[0]), np.std(gof[0])))
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print("AIC = {:4f} +- {:4f}".format(np.mean(gof[1]), np.std(gof[1])))
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print("logZ = {:4f} +- {:4f}".format(np.mean(gof[2]), np.std(gof[2])))
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print("chi2 = {:4f} +- {:4f}".format(np.mean(gof[3]), np.std(gof[3])))
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data = np.vstack(data).T
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if return_MCsamples:
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simname = simname_to_pretty(simname)
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if ksmooth == 1:
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simname = fr"{simname} (2)"
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if subtract_LG_velocity >= 0:
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simname += " (LG)"
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label = fr"{catalogue}, {simname}, $\log \mathcal{{Z}} = {np.mean(gof[2]):.1f}$" # noqa
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return MCSamples(samples=data, names=names,
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labels=names_to_latex(names), label=label)
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return data, names, gof
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def simname_to_pretty(simname):
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ltx = {"Carrick2015": "C+15",
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"csiborg1": "CB1",
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"csiborg2_main": "CB2",
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}
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return ltx[simname] if simname in ltx else simname
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def names_to_latex(names, for_corner=False):
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ltx = {"alpha": "\\alpha",
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"beta": "\\beta",
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"Vmag": "V_{\\rm ext} ~ [\\mathrm{km} / \\mathrm{s}]",
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"sigma_v": "\\sigma_v ~ [\\mathrm{km} / \\mathrm{s}]",
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}
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ltx_corner = {"alpha": r"$\alpha$",
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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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for i, label in enumerate(names):
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if label in ltx:
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labels[i] = ltx_corner[label] if for_corner else ltx[label]
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return labels
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