mirror of
https://github.com/Richard-Sti/csiborgtools_public.git
synced 2025-05-13 22:21:12 +00:00
More flow preparation & Olympics (#143)
* Add more comments * Add flow paths * Simplify paths * Update default arguemnts * Update paths * Update param names * Update some of scipts for reading files * Add the Mike method option * Update plotting * Update fnames * Simplify things * Make more default options * Add print * Update * Downsample CF4 * Update numpyro selection * Add selection fitting nb * Add coeffs * Update script * Add nb * Add label * Increase number of steps * Update default params * Add more labels * Improve file name * Update nb * Fix little bug * Remove import * Update scales * Update labels * Add script * Update script * Add more * Add more labels * Add script * Add submit * Update spacing * Update submit scrips * Update script * Update defaults * Update defaults * Update nb * Update test * Update imports * Add script * Add support for Indranil void * Add a dipole * Update nb * Update submit * Update Om0 * Add final * Update default params * Fix bug * Add option to fix to LG frame * Add Vext label * Add Vext label * Update script * Rm fixed LG * rm LG stuff * Update script * Update bulk flow plotting * Update nb * Add no field option * Update defaults * Update nb * Update script * Update nb * Update nb * Add names to plots * Update nb * Update plot * Add more latex names * Update default * Update nb * Update np * Add plane slicing * Add nb with slices * Update nb * Update script * Upddate nb * Update nb
This commit is contained in:
Richard Stiskalek
GitHub
parent
3d1e1c0ae3
commit
2b938c112c
20 changed files with 3106 additions and 379 deletions
588
notebooks/flow/PV_data.ipynb
Normal file
588
notebooks/flow/PV_data.ipynb
Normal file
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
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@ -13,8 +13,7 @@
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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
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from copy import copy, deepcopy
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import numpy as np
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from jax import numpy as jnp
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@ -41,25 +40,6 @@ def cartesian_to_radec(x, y, z):
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return d, ra, dec
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###############################################################################
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# Get the filename of the samples #
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###############################################################################
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def get_fname(simname, catalogue, ksmooth=0, nsim=None, sample_beta=True):
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"""Get the filename of the HDF5 file containing the posterior samples."""
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FDIR = "/mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/" # noqa
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fname = join(FDIR, f"samples_{simname}_{catalogue}_ksmooth{ksmooth}.hdf5")
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if nsim is not None:
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fname = fname.replace(".hdf5", f"_nsim{nsim}.hdf5")
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if sample_beta:
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fname = fname.replace(".hdf5", "_sample_beta.hdf5")
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return fname
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###############################################################################
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# Convert names to LaTeX #
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###############################################################################
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@ -69,30 +49,96 @@ def names_to_latex(names, for_corner=False):
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"""Convert the names of the parameters to LaTeX."""
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ltx = {"alpha": "\\alpha",
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"beta": "\\beta",
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"Vmag": "V_{\\rm ext}",
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"sigma_v": "\\sigma_v",
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"Vmag": "V_{\\rm ext} ~ [\\mathrm{km} / \\mathrm{s}]",
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"Vx": "V_x ~ [\\mathrm{km} / \\mathrm{s}]",
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"Vy": "V_y ~ [\\mathrm{km} / \\mathrm{s}]",
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"Vz": "V_z ~ [\\mathrm{km} / \\mathrm{s}]",
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"sigma_v": "\\sigma_v ~ [\\mathrm{km} / \\mathrm{s}]",
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"alpha_cal": "\\mathcal{A}",
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"beta_cal": "\\mathcal{B}",
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"mag_cal": "\\mathcal{M}",
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"e_mu": "\\sigma_\\mu",
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"aTF": "a_{\\rm TF}",
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"bTF": "b_{\\rm TF}",
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"l": "\\ell ~ [\\mathrm{deg}]",
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"b": "b ~ [\\mathrm{deg}]",
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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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"l": r"$\ell_{V_{\rm ext}}$",
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"b": r"$b_{V_{\rm ext}}$",
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"l": r"$\ell$",
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"b": r"$b$",
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"sigma_v": r"$\sigma_v$",
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"alpha_cal": r"$\mathcal{A}$",
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"beta_cal": r"$\mathcal{B}$",
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"mag_cal": r"$\mathcal{M}$",
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"e_mu": r"$\sigma_\mu$",
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"aTF": r"$a_{\rm TF}$",
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"bTF": r"$b_{\rm TF}$",
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}
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names = copy(names)
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for i, name in enumerate(names):
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if "SFI_gals" in name:
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names[i] = names[i].replace("SFI_gals", "SFI")
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if "CF4_GroupAll" in name:
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names[i] = names[i].replace("CF4_GroupAll", "CF4Group")
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if "CF4_TFR_i" in name:
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names[i] = names[i].replace("CF4_TFR_i", "CF4,TFR")
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for cat in ["2MTF", "SFI", "CF4,TFR"]:
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ltx[f"a_{cat}"] = f"a_{{\\rm TF}}^{{\\rm {cat}}}"
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ltx[f"b_{cat}"] = f"b_{{\\rm TF}}^{{\\rm {cat}}}"
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ltx[f"c_{cat}"] = f"c_{{\\rm TF}}^{{\\rm {cat}}}"
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ltx[f"corr_mag_eta_{cat}"] = f"\\rho_{{m,\\eta}}^{{\\rm {cat}}}"
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ltx[f"eta_mean_{cat}"] = f"\\widehat{{\\eta}}^{{\\rm {cat}}}"
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ltx[f"eta_std_{cat}"] = f"\\widehat{{\\sigma}}_\\eta^{{\\rm {cat}}}"
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ltx[f"mag_mean_{cat}"] = f"\\widehat{{m}}^{{\\rm {cat}}}"
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ltx[f"mag_std_{cat}"] = f"\\widehat{{\\sigma}}_m^{{\\rm {cat}}}"
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ltx_corner[f"a_{cat}"] = rf"$a_{{\rm TF}}^{{\rm {cat}}}$"
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ltx_corner[f"b_{cat}"] = rf"$b_{{\rm TF}}^{{\rm {cat}}}$"
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ltx_corner[f"c_{cat}"] = rf"$c_{{\rm TF}}^{{\rm {cat}}}$"
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ltx_corner[f"corr_mag_eta_{cat}"] = rf"$\rho_{{m,\eta}}^{{\rm {cat}}}$"
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ltx_corner[f"eta_mean_{cat}"] = rf"$\widehat{{\eta}}^{{\rm {cat}}}$"
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ltx_corner[f"eta_std_{cat}"] = rf"$\widehat{{\sigma}}_\eta^{{\rm {cat}}}$" # noqa
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ltx_corner[f"mag_mean_{cat}"] = rf"$\widehat{{m}}^{{\rm {cat}}}$"
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ltx_corner[f"mag_std_{cat}"] = rf"$\widehat{{\sigma}}_m^{{\rm {cat}}}$"
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for cat in ["2MTF", "SFI", "Foundation", "LOSS", "CF4Group", "CF4,TFR"]:
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ltx[f"alpha_{cat}"] = f"\\alpha^{{\\rm {cat}}}"
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ltx[f"e_mu_{cat}"] = f"\\sigma_{{\\mu}}^{{\\rm {cat}}}"
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ltx[f"a_dipole_mag_{cat}"] = f"\\epsilon_{{\\rm mag}}^{{\\rm {cat}}}"
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ltx[f"a_dipole_l_{cat}"] = f"\\epsilon_{{\\ell}}^{{\\rm {cat}}} ~ [\\mathrm{{deg}}]" # noqa
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ltx[f"a_dipole_b_{cat}"] = f"\\epsilon_{{b}}^{{\\rm {cat}}} ~ [\\mathrm{{deg}}]" # noqa
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ltx["a_dipole_mag"] = "\\epsilon_{{\\rm mag}}"
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ltx["a_dipole_l"] = "\\epsilon_{{\\ell}} ~ [\\mathrm{{deg}}]"
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ltx["a_dipole_b"] = "\\epsilon_{{b}} ~ [\\mathrm{{deg}}]"
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ltx_corner[f"alpha_{cat}"] = rf"$\alpha^{{\rm {cat}}}$"
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ltx_corner[f"e_mu_{cat}"] = rf"$\sigma_{{\mu}}^{{\rm {cat}}}$"
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ltx_corner[f"a_dipole_mag_{cat}"] = rf"$\epsilon_{{\rm mag}}^{{\rm {cat}}}$" # noqa
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ltx_corner[f"a_dipole_l_{cat}"] = rf"$\epsilon_{{\ell}}^{{\rm {cat}}}$"
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ltx_corner[f"a_dipole_b_{cat}"] = rf"$\epsilon_{{b}}^{{\rm {cat}}}$"
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for cat in ["Foundation", "LOSS"]:
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ltx[f"alpha_cal_{cat}"] = f"\\mathcal{{A}}^{{\\rm {cat}}}"
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ltx[f"beta_cal_{cat}"] = f"\\mathcal{{B}}^{{\\rm {cat}}}"
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ltx[f"mag_cal_{cat}"] = f"\\mathcal{{M}}^{{\\rm {cat}}}"
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ltx_corner[f"alpha_cal_{cat}"] = rf"$\mathcal{{A}}^{{\rm {cat}}}$"
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ltx_corner[f"beta_cal_{cat}"] = rf"$\mathcal{{B}}^{{\rm {cat}}}$"
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ltx_corner[f"mag_cal_{cat}"] = rf"$\mathcal{{M}}^{{\rm {cat}}}$"
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for cat in ["CF4Group"]:
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ltx[f"dmu_{cat}"] = f"\\Delta\\mu^{{\\rm {cat}}}"
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ltx[f"dmu_dipole_mag_{cat}"] = f"\\epsilon_\\mu_{{\\rm mag}}^{{\\rm {cat}}}" # noqa
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ltx[f"dmu_dipole_l_{cat}"] = f"\\epsilon_\\mu_{{\\ell}}^{{\\rm {cat}}} ~ [\\mathrm{{deg}}]" # noqa
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ltx[f"dmu_dipole_b_{cat}"] = f"\\epsilon_\\mu_{{b}}^{{\\rm {cat}}} ~ [\\mathrm{{deg}}]" # noqa
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ltx_corner[f"dmu_{cat}"] = rf"$\Delta\mu_{{0}}^{{\rm {cat}}}$"
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ltx_corner[f"dmu_dipole_mag_{cat}"] = rf"$\epsilon_{{\rm mag}}^{{\rm {cat}}}$" # noqa
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ltx_corner[f"dmu_dipole_l_{cat}"] = rf"$\epsilon_{{\ell}}^{{\rm {cat}}}$" # noqa
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ltx_corner[f"dmu_dipole_b_{cat}"] = rf"$\epsilon_{{b}}^{{\rm {cat}}}$" # noqa
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labels = copy(names)
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for i, label in enumerate(names):
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if for_corner:
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@ -113,21 +159,35 @@ def simname_to_pretty(simname):
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}
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if isinstance(simname, list):
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return [ltx[s] if s in ltx else s for s in simname]
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names = [ltx[s] if s in ltx else s for s in simname]
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return "".join([f"{n}, " for n in names]).rstrip(", ")
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return ltx[simname] if simname in ltx else simname
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def catalogue_to_pretty(catalogue):
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ltx = {"SFI_gals": "SFI",
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"CF4_TFR_not2MTForSFI_i": r"CF4 $i$-band",
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"CF4_TFR_i": r"CF4 TFR $i$",
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"CF4_TFR_w1": r"CF4 TFR W1",
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}
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if isinstance(catalogue, list):
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names = [ltx[s] if s in ltx else s for s in catalogue]
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return "".join([f"{n}, " for n in names]).rstrip(", ")
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return ltx[catalogue] if catalogue in ltx else catalogue
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###############################################################################
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# Read in goodness-of-fit #
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###############################################################################
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def get_gof(kind, simname, catalogue, ksmooth=0, nsim=None, sample_beta=True):
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def get_gof(kind, fname):
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"""Read in the goodness-of-fit statistics `kind`."""
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if kind not in ["BIC", "AIC", "lnZ"]:
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raise ValueError("`kind` must be one of 'BIC', 'AIC', 'lnZ'")
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if kind not in ["BIC", "AIC", "neg_lnZ_harmonic"]:
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raise ValueError("`kind` must be one of 'BIC', 'AIC', 'neg_lnZ_harmonic'.") # noqa
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fname = get_fname(simname, catalogue, ksmooth, nsim, sample_beta)
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with File(fname, 'r') as f:
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return f[f"gof/{kind}"][()]
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@ -136,29 +196,48 @@ def get_gof(kind, simname, catalogue, ksmooth=0, nsim=None, sample_beta=True):
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# Read in samples #
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###############################################################################
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def get_samples(simname, catalogue, ksmooth=0, nsim=None, sample_beta=True,
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convert_Vext_to_galactic=True):
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def get_samples(fname, convert_Vext_to_galactic=True):
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"""Read in the samples from the HDF5 file."""
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fname = get_fname(simname, catalogue, ksmooth, nsim, sample_beta)
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samples = {}
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with File(fname, 'r') as f:
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grp = f["samples"]
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for key in grp.keys():
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samples[key] = grp[key][...]
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# Rename TF parameters
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if "a" in samples:
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samples["aTF"] = samples.pop("a")
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if "b" in samples:
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samples["bTF"] = samples.pop("b")
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if convert_Vext_to_galactic:
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Vext = samples.pop("Vext")
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samples["Vmag"] = np.linalg.norm(Vext, axis=1)
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Vext = csiborgtools.cartesian_to_radec(Vext)
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samples["l"], samples["b"] = csiborgtools.radec_to_galactic(
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Vext[:, 1], Vext[:, 2])
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else:
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Vext = samples.pop("Vext")
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samples["Vx"] = Vext[:, 0]
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samples["Vy"] = Vext[:, 1]
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samples["Vz"] = Vext[:, 2]
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keys = list(samples.keys())
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for key in keys:
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if "dmu_dipole_" in key:
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dmu = samples.pop(key)
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dmu = csiborgtools.cartesian_to_radec(dmu)
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dmu_mag = dmu[:, 0]
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l, b = csiborgtools.radec_to_galactic(dmu[:, 1], dmu[:, 2])
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samples[key.replace("dmu_dipole_", "dmu_dipole_mag_")] = dmu_mag
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samples[key.replace("dmu_dipole_", "dmu_dipole_l_")] = l
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samples[key.replace("dmu_dipole_", "dmu_dipole_b_")] = b
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if "a_dipole" in key:
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adipole = samples.pop(key)
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adipole = csiborgtools.cartesian_to_radec(adipole)
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adipole_mag = adipole[:, 0]
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l, b = csiborgtools.radec_to_galactic(adipole[:, 1], adipole[:, 2])
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samples[key.replace("a_dipole", "a_dipole_mag")] = adipole_mag
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samples[key.replace("a_dipole", "a_dipole_l")] = l
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samples[key.replace("a_dipole", "a_dipole_b")] = b
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return samples
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@ -180,12 +259,20 @@ def get_bulkflow_simulation(simname, convert_to_galactic=True):
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return r, B
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def get_bulkflow(simname, catalogue, ksmooth=0, nsim=None, sample_beta=True,
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convert_to_galactic=True, weight_simulations=True,
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downsample=1, Rmax=125):
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def get_bulkflow(fname, simname, convert_to_galactic=True, downsample=1,
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Rmax=125):
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# Read in the samples
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with File(fname, "r") as f:
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Vext = f["samples/Vext"][...]
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try:
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beta = f["samples/beta"][...]
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except KeyError:
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beta = jnp.ones(len(Vext))
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# Read in the bulk flow
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f = np.load(f"/mnt/extraspace/rstiskalek/csiborg_postprocessing/field_shells/enclosed_mass_{simname}.npz") # noqa
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r = f["distances"]
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# Shape of B_i is (nsims, nradial)
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Bx, By, Bz = (f["cumulative_velocity"][..., i] for i in range(3))
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@ -197,38 +284,18 @@ def get_bulkflow(simname, catalogue, ksmooth=0, nsim=None, sample_beta=True,
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By = By[:, mask]
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Bz = Bz[:, mask]
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# Read in the samples
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fname_samples = get_fname(simname, catalogue, ksmooth, nsim, sample_beta)
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with File(fname_samples, 'r') as f:
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# Shape of Vext_i is (nsamples,)
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Vext_x, Vext_y, Vext_z = (f["samples/Vext"][...][::downsample, i] for i in range(3)) # noqa
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nsamples = len(Vext_x)
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Vext = Vext[::downsample]
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beta = beta[::downsample]
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if weight_simulations:
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simulation_weights = jnp.exp(f["simulation_weights"][...])[::downsample] # noqa
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else:
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nsims = len(Bx)
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simulation_weights = jnp.ones((nsamples, nsims)) / nsims
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if sample_beta:
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beta = f["samples/beta"][...][::downsample]
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else:
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beta = jnp.ones(nsamples)
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# Multiply the simulation velocities by beta
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# Multiply the simulation velocities by beta.
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Bx = Bx[..., None] * beta
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By = By[..., None] * beta
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Bz = Bz[..., None] * beta
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# Shape of B_i is (nsims, nradial, nsamples)
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Bx = Bx + Vext_x
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By = By + Vext_y
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Bz = Bz + Vext_z
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simulation_weights = simulation_weights.T[:, None, :]
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Bx = jnp.sum(Bx * simulation_weights, axis=0)
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By = jnp.sum(By * simulation_weights, axis=0)
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Bz = jnp.sum(Bz * simulation_weights, axis=0)
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# Add V_ext, shape of B_i is `(nsims, nradial, nsamples)``
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Bx = Bx + Vext[:, 0]
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By = By + Vext[:, 1]
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Bz = Bz + Vext[:, 2]
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if convert_to_galactic:
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Bmag, Bl, Bb = cartesian_to_radec(Bx, By, Bz)
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@ -237,6 +304,8 @@ def get_bulkflow(simname, catalogue, ksmooth=0, nsim=None, sample_beta=True,
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else:
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B = np.stack([Bx, By, Bz], axis=-1)
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# Stack over the simulations
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B = np.hstack([B[i] for i in range(len(B))])
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return r, B
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###############################################################################
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@ -245,25 +314,30 @@ def get_bulkflow(simname, catalogue, ksmooth=0, nsim=None, sample_beta=True,
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def samples_for_corner(samples):
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samples = deepcopy(samples)
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# Remove the true parameters of each galaxy.
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keys = list(samples.keys())
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for key in keys:
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# Generally don't want to plot the true latent parameters..
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if "x_TFR" in key or "_true_" in key:
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samples.pop(key)
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keys = list(samples.keys())
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if any(x.ndim > 1 for x in samples.values()):
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raise ValueError("All samples must be 1D arrays.")
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data = np.vstack([x for x in samples.values()]).T
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labels = names_to_latex(list(samples.keys()), for_corner=True)
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return data, labels
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return data, labels, keys
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def samples_to_getdist(samples, simname, catalogue=None):
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data, __ = samples_for_corner(samples)
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names = list(samples.keys())
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if catalogue is None:
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label = simname_to_pretty(simname)
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else:
|
||||
label = catalogue
|
||||
def samples_to_getdist(samples, label):
|
||||
data, __, keys = samples_for_corner(samples)
|
||||
|
||||
return MCSamples(
|
||||
samples=data, names=names,
|
||||
labels=names_to_latex(names, for_corner=False),
|
||||
samples=data, names=keys,
|
||||
labels=names_to_latex(keys, for_corner=False),
|
||||
label=label)
|
||||
|
|
|
|||
506
notebooks/flow/reconstruction_slice.ipynb
Normal file
506
notebooks/flow/reconstruction_slice.ipynb
Normal file
File diff suppressed because one or more lines are too long
586
notebooks/flow/selection.ipynb
Normal file
586
notebooks/flow/selection.ipynb
Normal file
|
|
@ -0,0 +1,586 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Selection fitting "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"from tqdm import trange\n",
|
||||
"from h5py import File\n",
|
||||
"from jax.random import PRNGKey\n",
|
||||
"from numpyro.infer import MCMC, NUTS, init_to_median\n",
|
||||
"from astropy.cosmology import FlatLambdaCDM \n",
|
||||
"from corner import corner\n",
|
||||
"\n",
|
||||
"import csiborgtools\n",
|
||||
"\n",
|
||||
"%matplotlib inline\n",
|
||||
"%load_ext autoreload\n",
|
||||
"%autoreload 2\n",
|
||||
"\n",
|
||||
"Om0 = 0.3\n",
|
||||
"H0 = 100\n",
|
||||
"cosmo = FlatLambdaCDM(H0=H0, Om0=Om0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Fit parameters of the toy selection model\n",
|
||||
"\n",
|
||||
"Choose either CF4 TFR or SFI."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# with File(\"/mnt/extraspace/rstiskalek/catalogs/PV_compilation.hdf5\", 'r') as f:\n",
|
||||
"# grp = f[\"SFI_gals\"]\n",
|
||||
"# # # print(grp.keys())\n",
|
||||
"# mag = grp[\"mag\"][...]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# with File(\"/mnt/extraspace/rstiskalek/catalogs/PV/CF4/CF4_TF-distances.hdf5\", 'r') as f:\n",
|
||||
" # mag = f[\"w1\"][...]\n",
|
||||
"# mag = mag[mag > 3]\n",
|
||||
"\n",
|
||||
"model = csiborgtools.flow.ToyMagnitudeSelection()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"nuts_kernel = NUTS(model, init_strategy=init_to_median(num_samples=5000))\n",
|
||||
"mcmc = MCMC(nuts_kernel, num_warmup=15_000, num_samples=15_000)\n",
|
||||
"mcmc.run(PRNGKey(42), extra_fields=(\"potential_energy\",), mag=mag)\n",
|
||||
"samples = mcmc.get_samples()\n",
|
||||
"\n",
|
||||
"mcmc.print_summary()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"keys = [\"alpha\", \"a\", \"m1\", \"m2\"]\n",
|
||||
"data = np.vstack([samples[key] for key in keys]).T\n",
|
||||
"labels = [r\"$\\alpha$\", r\"$a$\", r\"$m_1$\", r\"$m_2$\"]\n",
|
||||
"\n",
|
||||
"fig = corner(data, labels=labels, show_titles=True, smooth=True)\n",
|
||||
"# fig.savefig(\"../../plots/selection_corner_CF4.png\", dpi=450)\n",
|
||||
"\n",
|
||||
"fig.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"for key in keys:\n",
|
||||
" print(f\"{key}: {np.mean(samples[key]):.3f} +/- {np.std(samples[key]):.3f}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"mrange = np.linspace(mag.min(), mag.max(), 1000)\n",
|
||||
"nsamples = len(samples[\"m1\"])\n",
|
||||
"\n",
|
||||
"indx = np.random.choice(nsamples, 500)\n",
|
||||
"\n",
|
||||
"y = [model.log_observed_pdf(mrange, samples[\"alpha\"][i], samples[\"m1\"][i], samples[\"m2\"][i], samples[\"a\"][i]) for i in indx]\n",
|
||||
"y = np.asarray(y)\n",
|
||||
"y = 10**y"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.figure()\n",
|
||||
"plt.hist(mag, bins=\"auto\", density=True, histtype=\"step\", color=\"blue\",\n",
|
||||
" label=\"Data\", zorder=1)\n",
|
||||
"\n",
|
||||
"for i in range(100):\n",
|
||||
" plt.plot(mrange, y[i], color=\"black\", alpha=0.25, lw=0.25)\n",
|
||||
"\n",
|
||||
"plt.xlabel(r\"$m$\")\n",
|
||||
"plt.ylabel(r\"$p(m)$\")\n",
|
||||
"plt.tight_layout()\n",
|
||||
"\n",
|
||||
"plt.savefig(\"../../plots/CF4_selection.png\", dpi=450)\n",
|
||||
"\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Hubble \n",
|
||||
"\n",
|
||||
"$p(m) \\propto 10^{0.6 m}$ ?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from scipy.integrate import quad\n",
|
||||
"from scipy.interpolate import interp1d\n",
|
||||
"\n",
|
||||
"zmin=0.00001\n",
|
||||
"zmax=5\n",
|
||||
"z_range = np.linspace(zmin, zmax, 100000)\n",
|
||||
"r_range = cosmo.comoving_distance(z_range).value\n",
|
||||
"distmod_range = cosmo.distmod(z_range).value\n",
|
||||
"r2mu = interp1d(r_range, distmod_range, kind=\"cubic\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def schechter_LF(M, M0=-20.83, alpha=-1):\n",
|
||||
" return 10**(0.4 * (M0 - M) * (alpha + 1)) * np.exp(-10**(0.4 * (M0 - M)))\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def sample_schechter_LF(M0=-20.83, alpha=-1, Mfaint=-16, Mbright=-30, npoints=1):\n",
|
||||
" norm = quad(schechter_LF, Mbright, Mfaint, args=(M0, alpha))[0]\n",
|
||||
"\n",
|
||||
" samples = np.full(npoints, np.nan)\n",
|
||||
" for i in trange(npoints):\n",
|
||||
" while np.isnan(samples[i]):\n",
|
||||
" M = np.random.uniform(Mbright, Mfaint)\n",
|
||||
" if np.random.uniform(0, 1) < schechter_LF(M, M0, alpha) / norm:\n",
|
||||
" samples[i] = M\n",
|
||||
"\n",
|
||||
" return samples\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def sample_radial_distance(rmax, npoints):\n",
|
||||
" return rmax * np.random.rand(npoints)**(1/3)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# z = np.linspace(0.001, 0.15, 100000)\n",
|
||||
"# r = cosmo.comoving_distance(z).value\n",
|
||||
"# mu = cosmo.distmod(z).value\n",
|
||||
"# \n",
|
||||
"# \n",
|
||||
"# drdmu = np.gradient(r, mu)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"rmax = 300\n",
|
||||
"npoints = 5000\n",
|
||||
"\n",
|
||||
"r_150 = sample_radial_distance(100, npoints)\n",
|
||||
"r_300 = sample_radial_distance(300, npoints)\n",
|
||||
"r_1000 = sample_radial_distance(5000, npoints)\n",
|
||||
"\n",
|
||||
"mu_150 = r2mu(r_150)\n",
|
||||
"mu_300 = r2mu(r_300)\n",
|
||||
"mu_1000 = r2mu(r_1000)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def p_hubble(m, a, b):\n",
|
||||
" norm = np.log10(- 5 / np.log(1000) * (10**(3 / 5 * a) - 10**(3 / 5 * b)))\n",
|
||||
" return 10**(0.6 * m - norm)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"M_LF = sample_schechter_LF(npoints=npoints)\n",
|
||||
"\n",
|
||||
"M_LF2 = sample_schechter_LF(npoints=npoints, M0=-20.83, alpha=-1.5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.figure()\n",
|
||||
"M = -20.3\n",
|
||||
"\n",
|
||||
"# m = mu + M\n",
|
||||
"# x = np.linspace(11, m.max(), 1000)\n",
|
||||
"# plt.plot(x, p_hubble(x, m.min(), m.max()) * 5.5, color=\"black\")\n",
|
||||
"\n",
|
||||
"# plt.hist(m, bins=\"auto\", density=True, histtype=\"step\", color=\"blue\",)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"cols = [\"red\", \"green\", \"blue\"]\n",
|
||||
"rmax = [150, 300, 1000]\n",
|
||||
"# for i, mu in enumerate([mu_150, mu_300, mu_1000]):\n",
|
||||
"for i, mu in enumerate([mu_150, mu_300, mu_1000]):\n",
|
||||
" plt.hist(mu + M_LF, bins=\"auto\", density=True,\n",
|
||||
" histtype=\"step\", color=cols[i], label=rmax[i])\n",
|
||||
"\n",
|
||||
" plt.hist(mu + M_LF2, bins=\"auto\", density=True,\n",
|
||||
" histtype=\"step\", color=cols[i], label=rmax[i], ls=\"--\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"plt.hist(mag, bins=\"auto\", density=True, histtype=\"step\", color=\"black\", label=\"Data\")\n",
|
||||
"\n",
|
||||
"plt.yscale(\"log\")\n",
|
||||
"# plt.axvline(r2mu(rmax) + M, c=\"red\")\n",
|
||||
"plt.legend()\n",
|
||||
"\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"M = sample_schechter_LF(npoints=10000)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.figure()\n",
|
||||
"plt.hist(x, bins=\"auto\", density=True, histtype=\"step\", color=\"blue\",)\n",
|
||||
"# plt.yscale(\"log\")\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"\n",
|
||||
"yeuclidean = 10**(0.6 * mu)\n",
|
||||
"ycomoving = r**2 * drdmu\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"k = np.argmin(np.abs(mu - 35)) \n",
|
||||
"\n",
|
||||
"yeuclidean /= yeuclidean[k]\n",
|
||||
"ycomoving /= ycomoving[k]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"plt.figure()\n",
|
||||
"plt.plot(z, yeuclidean, label=\"Euclidean\")\n",
|
||||
"plt.plot(z, ycomoving, label=\"Comoving\")\n",
|
||||
"\n",
|
||||
"# plt.yscale('log')\n",
|
||||
"plt.xlabel(r\"$z$\")\n",
|
||||
"plt.ylabel(r\"$p(\\mu)$\")\n",
|
||||
"\n",
|
||||
"plt.legend()\n",
|
||||
"plt.tight_layout()\n",
|
||||
"plt.savefig(\"../../plots/pmu_comoving_vs_euclidean.png\")\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from scipy.interpolate import interp1d\n",
|
||||
"from scipy.integrate import quad\n",
|
||||
"from scipy.stats import norm\n",
|
||||
"\n",
|
||||
"z = np.linspace(0.001, 0.1, 100000)\n",
|
||||
"r = cosmo.comoving_distance(z).value\n",
|
||||
"mu = cosmo.distmod(z).value\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"drdmu = np.gradient(r, mu)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"mu2drdmu = interp1d(mu, drdmu, kind='cubic')\n",
|
||||
"mu2r = interp1d(mu, r, kind='cubic')\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def schechter_LF(M):\n",
|
||||
" M0 = -20.83\n",
|
||||
" alpha = -1\n",
|
||||
" return 10**(0.4 * (M0 - M) * (alpha + 1)) * np.exp(-10**(0.4 * (M0 - M)))\n",
|
||||
" \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def phi(M):\n",
|
||||
" # return 1\n",
|
||||
" # return schechter_LF(M)# * norm.pdf(M, loc=-22, scale=1)\n",
|
||||
" loc = -22\n",
|
||||
" std = 0.1\n",
|
||||
"\n",
|
||||
" return norm.pdf(M, loc=loc, scale=std)\n",
|
||||
"\n",
|
||||
" # if -22 < M < -21:\n",
|
||||
" # return 1\n",
|
||||
" # else:\n",
|
||||
" # return 0\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"xrange = np.linspace(-24, -18, 1000)\n",
|
||||
"\n",
|
||||
"plt.figure()\n",
|
||||
"plt.plot(xrange, schechter_LF(xrange))\n",
|
||||
"# plt.yscale(\"log\")\n",
|
||||
"plt.show()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"mu_min = mu.min()\n",
|
||||
"mu_max = mu.max()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"m = 12\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"m_range = np.linspace(10, 16, 100)\n",
|
||||
"y = np.full_like(m_range, np.nan)\n",
|
||||
"for i in trange(len(m_range)):\n",
|
||||
" m = m_range[i]\n",
|
||||
" # y[i] = quad(lambda x: mu2drdmu(x) * mu2r(x)**2 * phi(m - x), mu_min, mu_max)[0]\n",
|
||||
" y[i] = quad(lambda x: 10**(0.6 * x) * phi(m - x), mu_min, mu_max)[0]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"y_hubble = 10**(0.6 * m_range)\n",
|
||||
"ycomoving = r**2 * drdmu\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"k = np.argmin(np.abs(m_range - 12))\n",
|
||||
"\n",
|
||||
"y_hubble /= y_hubble[k]\n",
|
||||
"y /= y[k]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"mu_max - 18"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.figure()\n",
|
||||
"plt.plot(m_range, y, label=\"Numerical\")\n",
|
||||
"plt.plot(m_range, y_hubble, label=\"Hubble\")\n",
|
||||
"# plt.plot(mu, ycomoving, label=\"Comoving\")\n",
|
||||
"\n",
|
||||
"plt.xlabel(r\"$m$\")\n",
|
||||
"plt.ylabel(r\"$p(m)$\")\n",
|
||||
"plt.legend()\n",
|
||||
"\n",
|
||||
"# plt.yscale(\"log\")\n",
|
||||
"plt.tight_layout()\n",
|
||||
"# plt.xlim(10, 14)\n",
|
||||
"\n",
|
||||
"plt.savefig(\"../../plots/pm.png\", dpi=450)\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Simple simulation"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"npoints = 10000\n",
|
||||
"rmax = 30000\n",
|
||||
"\n",
|
||||
"# pos = np.random.uniform(-boxsize, boxsize, (npoints, 3))\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"r = rmax * np.random.rand(npoints)**(1/3)\n",
|
||||
"\n",
|
||||
"mu = 5 * np.log10(r) + 25\n",
|
||||
"\n",
|
||||
"# M = np.ones(npoints) * -22\n",
|
||||
"# M = np.random.normal(-22, 100, npoints)\n",
|
||||
"M = np.random.uniform(-24, -18, npoints)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"m = mu + M"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def f(m, a, b):\n",
|
||||
" norm = np.log10(- 5 / np.log(1000) * (10**(3 / 5 * a) - 10**(3 / 5 * b)))\n",
|
||||
" return 10**(0.6 * m - norm)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.figure()\n",
|
||||
"plt.hist(m, bins=\"auto\", density=True, histtype=\"step\")\n",
|
||||
"m_range = np.linspace(m.min(), m.max(), 100)\n",
|
||||
"# plt.plot(m_range, f(m_range, m.min(), m.max()))\n",
|
||||
"# plt.yscale(\"log\")\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "venv_csiborg",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Loading…
Add table
Add a link
Reference in a new issue