{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The autoreload extension is already loaded. To reload it, use:\n", " %reload_ext autoreload\n" ] } ], "source": [ "# Copyright (C) 2024 Richard Stiskalek\n", "# This program is free software; you can redistribute it and/or modify it\n", "# under the terms of the GNU General Public License as published by the\n", "# Free Software Foundation; either version 3 of the License, or (at your\n", "# option) any later version.\n", "#\n", "# This program is distributed in the hope that it will be useful, but\n", "# WITHOUT ANY WARRANTY; without even the implied warranty of\n", "# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General\n", "# Public License for more details.\n", "#\n", "# You should have received a copy of the GNU General Public License along\n", "# with this program; if not, write to the Free Software Foundation, Inc.,\n", "# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.\n", "from os.path import exists\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from corner import corner\n", "from getdist import plots\n", "from astropy.coordinates import angular_separation\n", "import scienceplots\n", "from os.path import exists\n", "import seaborn as sns\n", "\n", "\n", "from reconstruction_comparison import *\n", "\n", "%load_ext autoreload\n", "%autoreload 2\n", "%matplotlib inline\n", "\n", "paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)\n", "fdir = \"/mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Quick checks" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "catalogue = \"CF4_TFR_i\"\n", "simname = \"Carrick2015\"\n", "zcmb_max=0.05\n", "sample_beta = None\n", "sample_alpha = True\n", "\n", "fname_bayes = paths.flow_validation(\n", " fdir, simname, catalogue, inference_method=\"bayes\",\n", " sample_alpha=sample_alpha, sample_beta=sample_beta,\n", " zcmb_max=zcmb_max)\n", "\n", "fname_mike = paths.flow_validation(\n", " fdir, simname, catalogue, inference_method=\"mike\",\n", " sample_alpha=sample_alpha, sample_beta=sample_beta,\n", " zcmb_max=zcmb_max)\n", "\n", "\n", "X = []\n", "labels = [\"Full posterior\", \"Delta posterior\"]\n", "for i, fname in enumerate([fname_bayes, fname_mike]):\n", " samples = get_samples(fname)\n", " if i == 1:\n", " print(samples.keys())\n", "\n", " X.append(samples_to_getdist(samples, labels[i]))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "params = [f\"a_{catalogue}\", f\"b_{catalogue}\", f\"c_{catalogue}\", f\"e_mu_{catalogue}\",\n", " \"Vmag\", \"l\", \"b\", \"sigma_v\", \"beta\", f\"alpha_{catalogue}\"]\n", "# params = [\"beta\", f\"a_{catalogue}\", f\"b_{catalogue}\", f\"e_mu_{catalogue}\"]\n", "# params = [\"Vmag\", \"l\", \"b\", \"sigma_v\", \"beta\", f\"mag_cal_{catalogue}\", f\"alpha_cal_{catalogue}\", f\"beta_cal_{catalogue}\", f\"e_mu_{catalogue}\"]\n", "\n", "\n", "g = plots.get_subplot_plotter()\n", "g.settings.figure_legend_frame = False\n", "g.settings.alpha_filled_add = 0.75\n", "\n", "g.triangle_plot(X, params=params, filled=True, legend_loc='upper right')\n", "plt.gcf().suptitle(catalogue_to_pretty(catalogue), y=1.025)\n", "plt.gcf().tight_layout()\n", "# plt.gcf().savefig(f\"../../plots/method_comparison_{simname}_{catalogue}.png\", dpi=500, bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# catalogue = [\"LOSS\", \"Foundation\"]\n", "catalogue = \"CF4_TFR_i\"\n", "simname = \"IndranilVoid_exp\"\n", "zcmb_max = 0.05\n", "sample_alpha = False\n", "\n", "fname = paths.flow_validation(\n", " fdir, simname, catalogue, inference_method=\"mike\",\n", " sample_mag_dipole=True,\n", " sample_beta=False,\n", " sample_alpha=sample_alpha, zcmb_max=zcmb_max)\n", "\n", "\n", "samples = get_samples(fname, convert_Vext_to_galactic=True)\n", "\n", "samples, labels, keys = samples_for_corner(samples)\n", "fig = corner(samples, labels=labels, show_titles=True,\n", " title_kwargs={\"fontsize\": 12}, smooth=1)\n", "# fig.savefig(\"../../plots/test.png\", dpi=250)\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Paper plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 0. LOS velocity example" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fpath = \"/mnt/extraspace/rstiskalek/catalogs/PV/CF4/CF4_TF-distances.hdf5\"\n", "\n", "loader_carrick = csiborgtools.flow.DataLoader(\"Carrick2015\", [0], \"CF4_TFR_i\", fpath, paths, ksmooth=0, )\n", "loader_lilow = csiborgtools.flow.DataLoader(\"Lilow2024\", [0], \"CF4_TFR_i\", fpath, paths, ksmooth=0, )\n", "loader_cb2 = csiborgtools.flow.DataLoader(\"csiborg2_main\", [i for i in range(20)], \"CF4_TFR_i\", fpath, paths, ksmooth=0, )\n", "loader_cb2X = csiborgtools.flow.DataLoader(\"csiborg2X\", [i for i in range(20)], \"CF4_TFR_i\", fpath, paths, ksmooth=0, )\n", "loader_CF4 = csiborgtools.flow.DataLoader(\"CF4\", [i for i in range(20)], \"CF4_TFR_i\", fpath, paths, ksmooth=0, )\n", "loader_CLONES = csiborgtools.flow.DataLoader(\"CLONES\", [0], \"CF4_TFR_i\", fpath, paths, ksmooth=0, )\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "angdist = angular_separation(\n", " np.deg2rad(loader_carrick.cat[\"RA\"]), np.deg2rad(loader_carrick.cat[\"DEC\"]),\n", " np.deg2rad(csiborgtools.clusters[\"Virgo\"].spherical_pos[1]),\n", " np.deg2rad(csiborgtools.clusters[\"Virgo\"].spherical_pos[2]))\n", "k = np.argmin(angdist)\n", "print([loader_carrick.cat[\"RA\"][k], loader_carrick.cat[\"DEC\"][k]])\n", "print(csiborgtools.clusters[\"Virgo\"].spherical_pos[1:])\n", "print(csiborgtools.clusters[\"Virgo\"].spherical_pos[0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "loaders = [loader_carrick, loader_lilow, loader_CF4, loader_cb2, loader_cb2X, loader_CLONES]\n", "simnames = [\"Carrick2015\", \"Lilow2024\", \"CF4\", \"csiborg2_main\", \"csiborg2X\", \"CLONES\"]\n", "\n", "\n", "with plt.style.context(\"science\"):\n", " plt.rcParams.update({'font.size': 9})\n", " plt.figure()\n", " cols = plt.rcParams['axes.prop_cycle'].by_key()['color']\n", "\n", " for i, (simname, loader) in enumerate(zip(simnames, loaders)):\n", " r = loader.rdist\n", " vrad = loader.los_radial_velocity[:, k, :]\n", "\n", " if simname == \"Carrick2015\":\n", " vrad *= 0.43\n", "\n", " if len(vrad) > 1:\n", " ylow, yhigh = np.percentile(vrad, [16, 84], axis=0)\n", " plt.fill_between(r, ylow, yhigh, alpha=0.66, color=cols[i],\n", " label=simname_to_pretty(simname))\n", " else:\n", " plt.plot(r, vrad[0], label=simname_to_pretty(simname), c=cols[i])\n", "\n", " plt.xlabel(r\"$r ~ [\\mathrm{Mpc} / h]$\")\n", " plt.ylabel(r\"$V_{\\rm rad} ~ [\\mathrm{km} / \\mathrm{s}]$\")\n", "\n", " plt.xlim(0, 90)\n", " plt.ylim(-1000, 1000)\n", " plt.legend(ncols=2, fontsize=\"small\")\n", " plt.axvline(12.045, zorder=0, c=\"k\", ls=\"--\", alpha=0.75)\n", "\n", " plt.tight_layout()\n", " plt.savefig(\"../../plots/LOS_example.pdf\", dpi=450, bbox_inches='tight')\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Evidence comparison" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "zcmb_max = 0.05\n", "\n", "sims = [\"Carrick2015\", \"Lilow2024\", \"csiborg2_main\", \"csiborg2X\", \"CLONES\", \"CF4\",]\n", "catalogues = [\"LOSS\", \"Foundation\", \"2MTF\", \"SFI_gals\", \"CF4_TFR_i\", \"CF4_TFR_w1\"]\n", "\n", "y_BIC = np.full((len(catalogues), len(sims)), np.nan)\n", "y_lnZ = np.full_like(y_BIC, np.nan)\n", "\n", "for i, catalogue in enumerate(catalogues):\n", " for j, simname in enumerate(sims):\n", " fname = paths.flow_validation(\n", " fdir, simname, catalogue, inference_method=\"mike\",\n", " sample_alpha=simname != \"IndranilVoid_exp\",\n", " zcmb_max=zcmb_max)\n", "\n", " # y_BIC[i, j] = get_gof(\"BIC\", fname)z\n", " y_lnZ[i, j] = get_gof(\"neg_lnZ_harmonic\", fname)\n", "\n", " y_lnZ[i] -= y_lnZ[i].min()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "with plt.style.context('science'):\n", " plt.rcParams.update({'font.size': 9})\n", " figwidth = 8.3\n", " fig, axs = plt.subplots(2, 3, figsize=(figwidth, 0.5 * figwidth))\n", " fig.subplots_adjust(hspace=0)\n", "\n", " x = np.arange(len(sims))\n", " y = y_lnZ\n", " for n in range(len(catalogues)):\n", " i, j = n // 3, n % 3\n", " ax = axs[i, j]\n", " ax.text(0.1, 0.875, catalogue_to_pretty(catalogues[n]),\n", " transform=ax.transAxes, #fontsize=\"small\",\n", " verticalalignment='center', horizontalalignment='left',\n", " bbox=dict(facecolor='white', alpha=0.5),\n", " )\n", " ax.scatter(x, y[n], c=\"k\", s=7.5)\n", "\n", " y_min, y_max = ax.get_ylim()\n", " y_offset = (y_max - y_min) * 0.075 # Adjust the fraction (0.05) as needed\n", "\n", " for k, txt in enumerate(y[n]):\n", " ax.text(x[k], y[n, k] + y_offset, f\"({y[n, k]:.1f})\",\n", " ha='center', fontsize=\"small\")\n", "\n", " ax.set_ylim(y_min, y_max + 2 * y_offset)\n", "\n", " for i in range(3):\n", " axs[1, i].set_xticks(\n", " np.arange(len(sims)),\n", " [simname_to_pretty(sim) for sim in sims], rotation=35)\n", " axs[0, i].set_xticks([], [])\n", "\n", " for i in range(2):\n", " for j in range(3):\n", " axs[i, j].set_xlim(-0.75, len(sims) - 0.25)\n", "\n", " axs[i, j].tick_params(axis='x', which='major', top=False)\n", " axs[i, j].tick_params(axis='x', which='minor', top=False, length=0)\n", " axs[i, j].tick_params(axis='y', which='minor', length=0)\n", "\n", " axs[i, 0].set_ylabel(r\"$-\\Delta \\ln \\mathcal{Z}$\")\n", "\n", " fig.tight_layout()\n", " fig.savefig(f\"../../plots/lnZ_comparison.pdf\", dpi=500, bbox_inches='tight')\n", " fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Dependence of the evidence on smoothing scale" ] }, { "cell_type": "code", "execution_count": 2, "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", "File: /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Carrick2015_2MTF_mike_smooth_1_zcmb_max_0.05_sample_alpha.hdf5\n", "Last modified: 13/09/2024 11:32:04\n", "File: /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Carrick2015_2MTF_mike_smooth_2_zcmb_max_0.05_sample_alpha.hdf5\n", "Last modified: 13/09/2024 11:35:10\n", "File: /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Carrick2015_2MTF_mike_smooth_3_zcmb_max_0.05_sample_alpha.hdf5\n", "Last modified: 13/09/2024 11:35:08\n", "File: /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Carrick2015_2MTF_mike_smooth_4_zcmb_max_0.05_sample_alpha.hdf5\n", "Last modified: 13/09/2024 11:34:31\n", "File: /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Carrick2015_SFI_gals_mike_zcmb_max_0.05_sample_alpha.hdf5\n", "Last modified: 30/08/2024 15:28:34\n", "File: /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Carrick2015_SFI_gals_mike_smooth_1_zcmb_max_0.05_sample_alpha.hdf5\n", "Last modified: 13/09/2024 11:37:11\n", "File: /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Carrick2015_SFI_gals_mike_smooth_2_zcmb_max_0.05_sample_alpha.hdf5\n", "Last modified: 13/09/2024 11:39:21\n", "File: /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Carrick2015_SFI_gals_mike_smooth_3_zcmb_max_0.05_sample_alpha.hdf5\n", "Last modified: 13/09/2024 11:41:15\n", "File: /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Carrick2015_SFI_gals_mike_smooth_4_zcmb_max_0.05_sample_alpha.hdf5\n", "Last modified: 13/09/2024 11:43:31\n", "File: /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Carrick2015_CF4_TFR_i_mike_zcmb_max_0.05_sample_alpha.hdf5\n", "Last modified: 12/09/2024 10:48:49\n", "File: /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Carrick2015_CF4_TFR_i_mike_smooth_1_zcmb_max_0.05_sample_alpha.hdf5\n", "Last modified: 13/09/2024 11:44:13\n", "File: /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Carrick2015_CF4_TFR_i_mike_smooth_2_zcmb_max_0.05_sample_alpha.hdf5\n", "Last modified: 13/09/2024 11:46:11\n", "File: /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Carrick2015_CF4_TFR_i_mike_smooth_3_zcmb_max_0.05_sample_alpha.hdf5\n", "Last modified: 13/09/2024 11:46:57\n", "File: 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/mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_csiborg2_main_SFI_gals_mike_smooth_4_zcmb_max_0.05_sample_alpha.hdf5\n", "Last modified: 13/09/2024 12:14:03\n", "File: /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_csiborg2_main_CF4_TFR_i_mike_zcmb_max_0.05_sample_alpha.hdf5\n", "Last modified: 12/09/2024 11:13:55\n", "File: /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_csiborg2_main_CF4_TFR_i_mike_smooth_1_zcmb_max_0.05_sample_alpha.hdf5\n", "Last modified: 13/09/2024 12:20:28\n", "File: /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_csiborg2_main_CF4_TFR_i_mike_smooth_2_zcmb_max_0.05_sample_alpha.hdf5\n", "Last modified: 13/09/2024 12:27:13\n", "File: /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_csiborg2_main_CF4_TFR_i_mike_smooth_3_zcmb_max_0.05_sample_alpha.hdf5\n", "Last modified: 13/09/2024 12:32:47\n", "File: /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_csiborg2_main_CF4_TFR_i_mike_smooth_4_zcmb_max_0.05_sample_alpha.hdf5\n", "Last modified: 13/09/2024 12:36:56\n" ] } ], "source": [ "zcmb_max = 0.05\n", "\n", "ksmooth = [0, 1, 2, 3, 4]\n", "scales = [0, 2, 4, 6, 8]\n", "sims = [\"Carrick2015\", \"csiborg2_main\"]\n", "catalogues = [\"2MTF\", \"SFI_gals\", \"CF4_TFR_i\"]\n", "\n", "y = np.full((len(sims), len(catalogues), len(ksmooth)), np.nan)\n", "for i, simname in enumerate(sims):\n", " for j, catalogue in enumerate(catalogues):\n", " for n, k in enumerate(ksmooth):\n", " fname = paths.flow_validation(\n", " fdir, simname, catalogue, inference_method=\"mike\",\n", " sample_alpha=True, smooth=k,\n", " zcmb_max=zcmb_max)\n", " if not exists(fname):\n", " raise FileNotFoundError(fname)\n", "\n", " y[i, j, n] = get_gof(\"neg_lnZ_harmonic\", fname)\n", "\n", " y[i, j, :] -= y[i, j, :].min()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Carrick2015 2MTF 322.1943359375\n", "Carrick2015 SFI_gals 414.5947265625\n", "Carrick2015 CF4_TFR_i 835.421875\n", "csiborg2_main 2MTF 760.97265625\n", "csiborg2_main SFI_gals 800.328125\n", "csiborg2_main CF4_TFR_i 1914.80859375\n" ] } ], "source": [ "for i, simname in enumerate(sims):\n", " for j, catalogue in enumerate(catalogues):\n", " print(simname, catalogue, y[i, j, -1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "with plt.style.context('science'):\n", " plt.rcParams.update({'font.size': 9})\n", " cols = plt.rcParams['axes.prop_cycle'].by_key()['color']\n", " plt.figure()\n", "\n", " ls = [\"-\", \"--\", \"-.\", \":\"]\n", " for i, simname in enumerate(sims):\n", " for j, catalogue in enumerate(catalogues):\n", " plt.plot(scales, y[i, j], marker='o', ms=2.5, ls=ls[i],\n", " label=catalogue_to_pretty(catalogue) if i == 0 else None, c=cols[j],)\n", "\n", " plt.xlabel(r\"$R_{\\rm smooth} ~ [\\mathrm{Mpc} / h]$\")\n", " plt.ylabel(r\"$-\\Delta \\ln \\mathcal{Z}$\")\n", " plt.legend()\n", "\n", " plt.tight_layout()\n", " plt.savefig(\"../../plots/smoothing_comparison.pdf\", dpi=450)\n", " plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. External flow consistency" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sims = [\"Carrick2015\", \"Lilow2024\", \"csiborg2_main\", \"csiborg2X\", \"CF4\", \"CLONES\"]\n", "# sims = [\"Carrick2015\", \"Lilow2024\", \"CF4\", \"csiborg2_main\", \"csiborg2X\"]\n", "# cats = [[\"LOSS\", \"Foundation\"], \"2MTF\", \"SFI_gals\", \"CF4_TFR_i\", \"CF4_TFR_w1\"]\n", "cats = [\"2MTF\", \"SFI_gals\", \"CF4_TFR_i\", \"CF4_TFR_w1\"]\n", "# cats = [\"2MTF\", \"SFI_gals\", \"CF4_TFR_not2MTForSFI_i\"]\n", "\n", "X = {}\n", "\n", "for sim in sims:\n", " for cat in cats:\n", " fname = paths.flow_validation(\n", " fdir, sim, cat, inference_method=\"bayes\",\n", " sample_alpha=True, zcmb_max=0.05)\n", "\n", " if not exists(fname):\n", " raise FileNotFoundError(fname)\n", "\n", " with File(fname, 'r') as f:\n", " X[f\"{sim}_{cat}\"] = np.linalg.norm(f[f\"samples/Vext\"][...], axis=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "with plt.style.context('science'):\n", " plt.rcParams.update({'font.size': 9})\n", "\n", "\n", " fig, axs = plt.subplots(2, 2, figsize=(3.5, 2.65 * 1.1))\n", " fig.subplots_adjust(hspace=0, wspace=0)\n", "\n", " for k, cat in enumerate(cats):\n", " i, j = k // 2, k % 2\n", " ax = axs[i, j]\n", "\n", " for sim in sims:\n", " sns.kdeplot(X[f\"{sim}_{cat}\"], fill=True, bw_adjust=0.75, ax=ax,\n", " label=simname_to_pretty(sim) if i == 0 else None)\n", "\n", " ax.text(0.725, 0.85, catalogue_to_pretty(cat),\n", " transform=ax.transAxes, fontsize=\"small\",\n", " verticalalignment='center', horizontalalignment='center',\n", " bbox=dict(facecolor='white', alpha=0.5, edgecolor='none'))\n", "\n", " ax.set_ylabel(None)\n", " ax.set_yticklabels([])\n", " ax.set_xlim(0)\n", "\n", " handles, labels = axs[0, 0].get_legend_handles_labels()\n", " fig.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5, 1.1),\n", " ncol=3)\n", "\n", " for i in range(2):\n", " axs[-1, i].set_xlabel(r\"$|\\mathbf{V}_{\\rm ext}| ~ [\\mathrm{km} / \\mathrm{s}]$\")\n", " axs[i, 0].set_ylabel(\"Normalised PDF\")\n", "\n", " fig.tight_layout()\n", " fig.savefig(f\"../../plots/Vext_comparison.pdf\", dpi=450)\n", " fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4. What $\\beta$ is preferred by the data? " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sims = [\"Lilow2024\", \"csiborg2_main\", \"csiborg2X\", \"CF4\", \"CLONES\"]\n", "cats = [\"LOSS\", \"Foundation\", \"2MTF\", \"SFI_gals\", \"CF4_TFR_i\", \"CF4_TFR_w1\"]\n", "# cats = [\"2MTF\", \"SFI_gals\", \"CF4_TFR_not2MTForSFI_i\"]\n", "\n", "X = {}\n", "for sim in sims:\n", " for cat in cats:\n", " fname = paths.flow_validation(\n", " fdir, sim, cat, inference_method=\"bayes\",\n", " sample_alpha=True, zcmb_max=0.05, sample_beta=True)\n", "\n", " if not exists(fname):\n", " raise FileNotFoundError(fname)\n", "\n", " with File(fname, 'r') as f:\n", " X[f\"{sim}_{cat}\"] = f[f\"samples/beta\"][...]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "with plt.style.context('science'):\n", " plt.rcParams.update({'font.size': 9})\n", "\n", "\n", " fig, axs = plt.subplots(3, 2, figsize=(3.5, 2.65 * 1.8))\n", " fig.subplots_adjust(hspace=0, wspace=0)\n", "\n", " for k, cat in enumerate(cats):\n", " i, j = k // 2, k % 2\n", " ax = axs[i, j]\n", "\n", " for sim in sims:\n", " sns.kdeplot(X[f\"{sim}_{cat}\"], fill=True, bw_adjust=0.75, ax=ax,\n", " label=simname_to_pretty(sim) if i == 0 else None)\n", "\n", " ax.text(0.1, 0.85, catalogue_to_pretty(cat),\n", " transform=ax.transAxes, fontsize=\"small\",\n", " verticalalignment='center', horizontalalignment='left',\n", " bbox=dict(facecolor='white', alpha=0.5, edgecolor='k')\n", " )\n", "\n", " ax.axvline(1, c=\"k\", ls=\"--\", alpha=0.75)\n", " ax.set_ylabel(None)\n", " ax.set_yticklabels([])\n", "\n", " handles, labels = axs[0, 0].get_legend_handles_labels()\n", " fig.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5, 1.075),\n", " ncol=3)\n", "\n", " # for i in range(3):\n", " for j in range(2):\n", " axs[-1, j].set_xlabel(r\"$\\beta$\")\n", "\n", " for i in range(3):\n", " axs[i, 0].set_ylabel(\"Normalised PDF\")\n", "\n", " fig.tight_layout()\n", " fig.savefig(f\"../../plots/beta_comparison.pdf\", dpi=450)\n", " fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5. Bulk flow in the simulation rest frame " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sims = [\"Carrick2015\", \"Lilow2024\", \"csiborg2_main\", \"csiborg2X\", \"CLONES\", \"CF4\"]\n", "\n", "\n", "with plt.style.context('science'):\n", " plt.rcParams.update({'font.size': 9})\n", " cols = plt.rcParams['axes.prop_cycle'].by_key()['color']\n", "\n", " plt.figure()\n", " for i, sim in enumerate(sims):\n", " r, B = get_bulkflow_simulation(sim, convert_to_galactic=True)\n", " B = B[..., 0]\n", "\n", " if sim == \"Carrick2015\":\n", " B *= 0.43\n", "\n", " if sim in [\"Carrick2015\", \"Lilow2024\", \"CLONES\"]:\n", " plt.plot(r, B[0], label=simname_to_pretty(sim), color=cols[i])\n", " else:\n", " ylow, yhigh = np.percentile(B, [16, 84], axis=0)\n", " plt.fill_between(r, ylow, yhigh, alpha=0.5,\n", " label=simname_to_pretty(sim), color=cols[i])\n", "\n", " plt.xlabel(r\"$R ~ [\\mathrm{Mpc} / h]$\")\n", " plt.ylabel(r\"$|\\mathbf{B}| ~ [\\mathrm{km} / \\mathrm{s}]$\")\n", " plt.xlim(5, 200)\n", " plt.legend(ncols=2)\n", "\n", " plt.tight_layout()\n", " plt.savefig(\"../../plots/bulkflow_simulations_restframe.pdf\", dpi=450)\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6. Bulk flow in the CMB frame" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sims = [\"Carrick2015\", \"Lilow2024\", \"csiborg2_main\", \"csiborg2X\", \"CLONES\", \"CF4\"]\n", "# cats = [[\"LOSS\", \"Foundation\"], \"2MTF\", \"SFI_gals\", \"CF4_TFR_i\"]\n", "cats = [\"2MTF\", \"SFI_gals\", \"CF4_TFR_i\", \"CF4_TFR_w1\"]\n", "\n", "\n", "data = {}\n", "for sim in sims:\n", " for cat in cats:\n", " fname = paths.flow_validation(\n", " fdir, sim, cat, inference_method=\"bayes\",\n", " sample_alpha=True, zcmb_max=0.05)\n", " data[f\"{sim}_{cat}\"] = get_bulkflow(fname, sim)\n", "\n", "def get_ax_centre(ax):\n", " # Get the bounding box of the specific axis in relative figure coordinates\n", " bbox = ax.get_position()\n", "\n", " # Extract the position and size of the axis\n", " x0, y0, width, height = bbox.x0, bbox.y0, bbox.width, bbox.height\n", "\n", " # Calculate the center of the axis\n", " center_x = x0 + width / 2\n", " center_y = y0 + height / 2\n", " return center_x, center_y" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "with plt.style.context('science'):\n", " plt.rcParams.update({'font.size': 9})\n", " nrows = len(sims)\n", " ncols = 3\n", "\n", " figwidth = 8.3\n", " fig, axs = plt.subplots(nrows, ncols, figsize=(figwidth, 1.25 * figwidth), sharex=True, )\n", " cols = plt.rcParams['axes.prop_cycle'].by_key()['color']\n", " # fig.suptitle(f\"Calibrated against {catalogue}\")\n", "\n", " for i, sim in enumerate(sims):\n", " for j, catalogue in enumerate(cats):\n", " r, B = data[f\"{sim}_{catalogue}\"]\n", " c = cols[j]\n", " for n in range(3):\n", " ylow, ymed, yhigh = np.percentile(B[..., n], [16, 50, 84], axis=-1)\n", " axs[i, n].fill_between(\n", " r, ylow, yhigh, alpha=0.5, color=c, edgecolor=c,\n", " label=catalogue_to_pretty(catalogue) if i == 1 else None)\n", "\n", "\n", " # CMB-LG velocity\n", " kwargs = {\"color\": \"mediumblue\", \"alpha\": 0.5, \"zorder\": 10, \"hatch\": \"x\"}\n", " for n in range(len(sims)):\n", " axs[n, 0].fill_between([r.min(), 15.], [627 - 22, 627 - 22], [627 + 22, 627 + 22], label=\"CMB-LG\" if n == 0 else None, **kwargs)\n", " axs[n, 1].fill_between([r.min(), 15.], [276 - 3, 276 - 3], [276 + 3, 276 + 3], **kwargs)\n", " axs[n, 2].fill_between([r.min(), 15.], [30 - 3, 30 - 3], [30 + 3, 30 + 3], **kwargs)\n", "\n", " # LCDM expectation\n", " Rs,mean,std,mode,p05,p16,p84,p95 = np.load(\"/mnt/users/rstiskalek/csiborgtools/data/BulkFlowPlot.npy\")\n", " m = Rs < 175\n", " kwargs = {\"color\": \"black\", \"zorder\": 0, \"hatch\": \"//\", \"alpha\": 0.25}\n", " for n in range(len(sims)):\n", " axs[n, 0].fill_between(\n", " Rs[m], p16[m], p84[m],\n", " label=r\"$\\Lambda\\mathrm{CDM}$\" if n == 0 else None, **kwargs)\n", "\n", " for n in range(3):\n", " axs[-1, n].set_xlabel(r\"$R ~ [\\mathrm{Mpc} / h]$\")\n", "\n", " for n in range(len(sims)):\n", " axs[n, 0].set_ylabel(r\"$|\\mathbf{B}| ~ [\\mathrm{km} / \\mathrm{s}]$\")\n", " axs[n, 1].set_ylabel(r\"$\\ell ~ [\\mathrm{deg}]$\")\n", " axs[n, 2].set_ylabel(r\"$b ~ [\\mathrm{deg}]$\")\n", "\n", " for i, sim in enumerate(sims):\n", " ax = axs[i, -1].twinx()\n", " ax.set_ylabel(simname_to_pretty(sim), rotation=270, labelpad=7.5)\n", " ax.set_yticklabels([])\n", "\n", " axs[0, 0].set_xlim(r.min(), r.max())\n", "\n", " axs[0, 0].legend()\n", " handles, labels = axs[1, 0].get_legend_handles_labels() # get the labels from the first axis\n", " fig.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5, 0.975), ncol=len(cats) + 2)\n", "\n", " fig.tight_layout(rect=[0, 0, 0.95, 0.95])\n", " fig.savefig(f\"../../plots/bulkflow_CMB.pdf\", dpi=450)\n", " fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 8. Full vs Delta comparison" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "catalogue = \"CF4_TFR_i\"\n", "simname = \"csiborg2X\"\n", "zcmb_max=0.05\n", "sample_beta = True\n", "sample_alpha = True\n", "\n", "fname_bayes = paths.flow_validation(\n", " fdir, simname, catalogue, inference_method=\"bayes\",\n", " sample_alpha=sample_alpha, sample_beta=sample_beta,\n", " zcmb_max=zcmb_max)\n", "\n", "fname_mike = paths.flow_validation(\n", " fdir, simname, catalogue, inference_method=\"mike\",\n", " sample_alpha=sample_alpha, sample_beta=sample_beta,\n", " zcmb_max=zcmb_max)\n", "\n", "\n", "X = []\n", "labels = [\"Full posterior\", \"Delta posterior\"]\n", "for i, fname in enumerate([fname_bayes, fname_mike]):\n", " samples = get_samples(fname)\n", " if i == 1:\n", " print(samples.keys())\n", "\n", " X.append(samples_to_getdist(samples, labels[i]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "params = [f\"a_{catalogue}\", f\"b_{catalogue}\", f\"c_{catalogue}\", f\"e_mu_{catalogue}\",\n", " \"Vmag\", \"l\", \"b\", \"sigma_v\", \"beta\", f\"alpha_{catalogue}\"]\n", "# params = [\"beta\", f\"a_{catalogue}\", f\"b_{catalogue}\", f\"e_mu_{catalogue}\"]\n", "# params = [\"Vmag\", \"l\", \"b\", \"sigma_v\", \"beta\", f\"mag_cal_{catalogue}\", f\"alpha_cal_{catalogue}\", f\"beta_cal_{catalogue}\", f\"e_mu_{catalogue}\"]\n", "\n", "with plt.style.context('science'):\n", " plt.rcParams.update({'font.size': 11})\n", " g = plots.get_subplot_plotter()\n", " g.settings.figure_legend_frame = False\n", " g.settings.alpha_filled_add = 0.75\n", " g.settings.fontsize = 12\n", "\n", " g.triangle_plot(X, params=params, filled=True, legend_loc='upper right')\n", " # plt.gcf().suptitle(catalogue_to_pretty(catalogue), y=1.025)\n", " plt.gcf().tight_layout()\n", " plt.gcf().savefig(f\"../../plots/method_comparison_{simname}_{catalogue}.pdf\", dpi=300, bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Guilhem plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Manticore vs linear comparison" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "zcmb_max = 0.05\n", "\n", "sims = [\"Carrick2015\", \"csiborg2X\"]\n", "catalogues = [\"LOSS\", \"Foundation\", \"2MTF\", \"CF4_TFR_i\", \"CF4_TFR_w1\"]\n", "\n", "y_lnZ = np.full((len(catalogues), len(sims)), np.nan)\n", "\n", "for i, catalogue in enumerate(catalogues):\n", " for j, simname in enumerate(sims):\n", " fname = paths.flow_validation(\n", " fdir, simname, catalogue, inference_method=\"mike\",\n", " sample_alpha=simname != \"IndranilVoid_exp\",\n", " zcmb_max=zcmb_max)\n", "\n", " y_lnZ[i, j] = - get_gof(\"neg_lnZ_harmonic\", fname)\n", "\n", " # y_lnZ[i] -= y_lnZ[i].min()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "bayes_factor = y_lnZ[:, 1] - y_lnZ[:, 0]\n", "\n", "with plt.style.context('science'):\n", " plt.rcParams.update({'font.size': 9})\n", "\n", " plt.figure()\n", "\n", " sns.barplot(x=np.arange(len(catalogues)), y=bayes_factor / np.log(10), color=\"#21456D\")\n", " plt.xticks(\n", " np.arange(len(catalogues)),\n", " [catalogue_to_pretty(cat) for cat in catalogues],\n", " rotation=35, fontsize=\"small\", minor=False)\n", " plt.ylabel(r\"$\\log \\left(\\mathcal{Z}_{\\rm Manticore} / \\mathcal{Z}_{\\rm linear}\\right)$\")\n", " plt.tick_params(axis='x', which='both', bottom=False, top=False)\n", "\n", " plt.tight_layout()\n", " plt.savefig(\"../../plots/manticore_vs_carrick.png\", dpi=450)\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## All possible things" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dipole magnitude" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cats = [\"2MTF\", \"SFI_gals\", \"CF4_TFR_i\", \"CF4_TFR_w1\"]\n", "sim = \"IndranilVoid_gauss\"\n", "\n", "X = []\n", "for cat in cats:\n", " fname = paths.flow_validation(\n", " fdir, sim, cat, inference_method=\"mike\",\n", " sample_mag_dipole=False,\n", " sample_alpha=False, zcmb_max=0.05)\n", " \n", " if not exists(fname):\n", " raise FileNotFoundError(fname)\n", "\n", " samples = get_samples(fname, convert_Vext_to_galactic=False)\n", "\n", " # keys = list(samples.keys())\n", " # for key in keys:\n", " # if cat in key:\n", " # value = samples.pop(key)\n", " # samples[key.replace(f\"_{cat}\",'')] = value\n", " \n", " samples = samples_to_getdist(samples, catalogue_to_pretty(cat))\n", " X.append(samples)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# params = [\"Vmag\", \"l\", \"b\", \"a_dipole_mag\", \"a_dipole_l\", \"a_dipole_b\"]\n", "params = [\"Vx\", \"Vy\", \"Vz\"]\n", "# params = [\"Vmag\", \"l\", \"b\"]\n", "\n", "with plt.style.context('science'):\n", " g = plots.get_subplot_plotter()\n", " g.settings.figure_legend_frame = False\n", " g.settings.alpha_filled_add = 0.75\n", "\n", " g.triangle_plot(X, params=params, filled=True, legend_loc='upper right')\n", " # plt.gcf().suptitle(catalogue_to_pretty(cat), y=1.025)\n", " plt.gcf().tight_layout()\n", " plt.gcf().savefig(f\"../../plots/vext_{sim}.png\", dpi=500, bbox_inches='tight')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Flow | catalogue" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "catalogues = [\"LOSS\", \"Foundation\", \"Pantheon+\", \"2MTF\", \"SFI_gals\"]\n", "sims = [\"Carrick2015\", \"csiborg2_main\", \"csiborg2X\"]\n", "params = [\"Vmag\", \"beta\", \"sigma_v\"]\n", "\n", "for catalogue in catalogues:\n", " X = [samples_to_getdist(get_samples(sim, catalogue), sim)\n", " for sim in sims]\n", "\n", " g = plots.get_subplot_plotter()\n", " g.settings.figure_legend_frame = False\n", " g.settings.alpha_filled_add = 0.75\n", "\n", " g.triangle_plot(X, params=params, filled=True, legend_loc='upper right')\n", " plt.gcf().suptitle(f'{catalogue}', y=1.025)\n", " plt.gcf().tight_layout()\n", " plt.gcf().savefig(f\"../../plots/calibration_{catalogue}.png\", dpi=500, bbox_inches='tight')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Flow | simulation" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "catalogues = [\"Pantheon+\", \"2MTF\", \"SFI_gals\"]\n", "sims = [\"Carrick2015\", \"csiborg2_main\", \"csiborg2X\"]\n", "params = [\"Vmag\", \"l\", \"b\", \"beta\", \"sigma_v\"]\n", "\n", "for sim in sims:\n", " X = [samples_to_getdist(get_samples(sim, catalogue), sim, catalogue)\n", " for catalogue in catalogues]\n", "\n", " g = plots.get_subplot_plotter()\n", " g.settings.figure_legend_frame = False\n", " g.settings.alpha_filled_add = 0.75\n", "\n", " g.triangle_plot(X, params=params, filled=True, legend_loc='upper right')\n", " plt.gcf().suptitle(f'{sim}', y=1.025)\n", " plt.gcf().tight_layout()\n", " plt.gcf().savefig(f\"../../plots/calibration_{sim}.png\", dpi=500, bbox_inches='tight')\n", " plt.gcf().show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Stacking vs marginalising CB boxes\n", "\n", "#### $V_{\\rm ext}$" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sim = \"csiborg2X\"\n", "catalogue = \"2MTF\"\n", "key = \"Vext\"\n", "\n", "X = [get_samples(sim, catalogue, nsim=nsim, convert_Vext_to_galactic=False)[key] for nsim in range(20)]\n", "Xmarg = get_samples(sim, catalogue, convert_Vext_to_galactic=False)[key]\n", "\n", "\n", "fig, axs = plt.subplots(1, 3, figsize=(15, 5), sharey=True)\n", "fig.suptitle(f\"{simname_to_pretty(sim)}, {catalogue}\")\n", "fig.subplots_adjust(wspace=0.0, hspace=0)\n", "\n", "for i in range(3):\n", " for n in range(20):\n", " axs[i].hist(X[n][:, i], bins=\"auto\", alpha=0.25, histtype='step',\n", " color='black', linewidth=0.5, density=1, zorder=0,\n", " label=\"Individual box\" if (n == 0 and i == 0) else None)\n", "\n", "axs[i].hist(np.hstack([X[n][:, i] for n in range(20)]), bins=\"auto\",\n", " histtype='step', color='blue', density=1,\n", " label=\"Stacked individual boxes\" if i == 0 else None)\n", "axs[i].hist(Xmarg[:, i], bins=\"auto\", histtype='step', color='red',\n", " density=1, label=\"Marginalised boxes\" if i == 0 else None)\n", " \n", "axs[0].legend(fontsize=\"small\", loc='upper left', frameon=False)\n", "\n", "axs[0].set_xlabel(r\"$V_{\\mathrm{ext}, x} ~ [\\mathrm{km} / \\mathrm{s}]$\")\n", "axs[1].set_xlabel(r\"$V_{\\mathrm{ext}, y} ~ [\\mathrm{km} / \\mathrm{s}]$\")\n", "axs[2].set_xlabel(r\"$V_{\\mathrm{ext}, z} ~ [\\mathrm{km} / \\mathrm{s}]$\")\n", "axs[0].set_ylabel(\"Normalized PDF\")\n", "fig.tight_layout()\n", "fig.savefig(f\"../../plots/consistency_{sim}_{catalogue}_{key}.png\", dpi=450)\n", "fig.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### $\\beta$ and others" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sim = \"csiborg2_main\"\n", "catalogue = \"Pantheon+\"\n", "key = \"alpha\"\n", "\n", "X = [get_samples(sim, catalogue, nsim=nsim, convert_Vext_to_galactic=False)[key] for nsim in range(20)]\n", "Xmarg = get_samples(sim, catalogue, convert_Vext_to_galactic=False)[key]\n", "\n", "\n", "plt.figure()\n", "plt.title(f\"{simname_to_pretty(sim)}, {catalogue}\")\n", "for n in range(20):\n", " plt.hist(X[n], bins=\"auto\", alpha=0.25, histtype='step',\n", " color='black', linewidth=0.5, density=1, zorder=0,\n", " label=\"Individual box\" if n == 0 else None)\n", "\n", "plt.hist(np.hstack([X[n] for n in range(20)]), bins=\"auto\",\n", " histtype='step', color='blue', density=1,\n", " label=\"Stacked individual boxes\")\n", "plt.hist(Xmarg, bins=\"auto\", histtype='step', color='red',\n", " density=1, label=\"Marginalised boxes\")\n", "\n", "plt.legend(fontsize=\"small\", frameon=False, loc='upper left', ncols=3)\n", "plt.xlabel(names_to_latex([key], True)[0])\n", "plt.ylabel(\"Normalized PDF\")\n", "\n", "plt.tight_layout()\n", "plt.savefig(f\"../../plots/consistency_{sim}_{catalogue}_{key}.png\", dpi=450)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SN/TFR Calibration consistency" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# catalogues = [\"LOSS\", \"Foundation\", \"Pantheon+\", \"2MTF\", \"SFI_gals\"]\n", "catalogues = [\"Pantheon+\"]\n", "sims = [\"Carrick2015\", \"csiborg2_main\", \"csiborg2X\"]\n", "\n", "for catalogue in catalogues:\n", " X = [samples_to_getdist(get_samples(sim, catalogue), sim)\n", " for sim in sims]\n", "\n", " if \"Pantheon+\" in catalogue or catalogue in [\"Foundation\", \"LOSS\"]:\n", " params = [\"alpha_cal\", \"beta_cal\", \"mag_cal\", \"e_mu\"]\n", " else:\n", " params = [\"aTF\", \"bTF\", \"e_mu\"]\n", "\n", " g = plots.get_subplot_plotter()\n", " g.settings.figure_legend_frame = False\n", " g.settings.alpha_filled_add = 0.75\n", "\n", " g.triangle_plot(X, params=params, filled=True, legend_loc='upper right')\n", " plt.gcf().suptitle(f'{catalogue}', y=1.025)\n", " plt.gcf().tight_layout()\n", " # plt.gcf().savefig(f\"../../plots/calibration_{catalogue}.png\", dpi=500, bbox_inches='tight')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### $V_{\\rm ext}$ comparison" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "catalogues = [\"LOSS\"]\n", "# sims = [\"Carrick2015\", \"csiborg2_main\", \"csiborg2X\"]\n", "sims = [\"Carrick2015\"]\n", "params = [\"Vmag\", \"l\", \"b\"]\n", "\n", "for sim in sims:\n", " X = [samples_to_getdist(get_samples(sim, catalogue), sim, catalogue)\n", " for catalogue in catalogues]\n", "\n", " g = plots.get_subplot_plotter()\n", " g.settings.figure_legend_frame = False\n", " g.settings.alpha_filled_add = 0.75\n", "\n", " g.triangle_plot(X, params=params, filled=True, legend_loc='upper right')\n", " plt.gcf().suptitle(f'{simname_to_pretty(sim)}', y=1.025)\n", " plt.gcf().tight_layout()\n", " # plt.gcf().savefig(f\"../../plots/calibration_{sim}.png\", dpi=500, bbox_inches='tight')\n", " plt.gcf().show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bulk flow in the simulation rest frame" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sims = [\"Carrick2015\", \"csiborg1\", \"csiborg2_main\", \"csiborg2X\"]\n", "convert_to_galactic = False\n", "\n", "fig, axs = plt.subplots(1, 3, figsize=(15, 5))\n", "cols = plt.rcParams['axes.prop_cycle'].by_key()['color']\n", "\n", "for i, sim in enumerate(sims):\n", " r, B = get_bulkflow_simulation(sim, convert_to_galactic=convert_to_galactic)\n", " if sim == \"Carrick2015\":\n", " if convert_to_galactic:\n", " B[..., 0] *= 0.43\n", " else:\n", " B *= 0.43\n", "\n", " for n in range(3):\n", " ylow, ymed, yhigh = np.percentile(B[..., n], [16, 50, 84], axis=0)\n", " axs[n].fill_between(r, ylow, yhigh, color=cols[i], alpha=0.5, label=simname_to_pretty(sim) if n == 0 else None)\n", "\n", "axs[0].legend()\n", "if convert_to_galactic:\n", " axs[0].set_ylabel(r\"$B ~ [\\mathrm{km} / \\mathrm{s}]$\")\n", " axs[1].set_ylabel(r\"$\\ell_B ~ [\\degree]$\")\n", " axs[2].set_ylabel(r\"$b_B ~ [\\degree]$\")\n", "else:\n", " axs[0].set_ylabel(r\"$B_{x} ~ [\\mathrm{km} / \\mathrm{s}]$\")\n", " axs[1].set_ylabel(r\"$B_{y} ~ [\\mathrm{km} / \\mathrm{s}]$\")\n", " axs[2].set_ylabel(r\"$B_{z} ~ [\\mathrm{km} / \\mathrm{s}]$\")\n", "\n", "for n in range(3):\n", " axs[n].set_xlabel(r\"$R ~ [\\mathrm{Mpc}]$\")\n", "\n", "\n", "fig.tight_layout()\n", "fig.savefig(\"../../plots/bulkflow_simulations_restframe.png\", dpi=450)\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bulk flow in the CMB rest frame" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sim = \"csiborg2_main\"\n", "catalogues = [\"Pantheon+\", \"2MTF\", \"SFI_gals\"]\n", "\n", "\n", "fig, axs = plt.subplots(1, 3, figsize=(15, 5), sharex=True)\n", "cols = plt.rcParams['axes.prop_cycle'].by_key()['color']\n", "# fig.suptitle(f\"Calibrated against {catalogue}\")\n", "\n", "for i, catalogue in enumerate(catalogues):\n", " r, B = get_bulkflow(sim, catalogue, sample_beta=True, convert_to_galactic=True,\n", " weight_simulations=True, downsample=3)\n", " c = cols[i]\n", " for n in range(3):\n", " ylow, ymed, yhigh = np.percentile(B[..., n], [16, 50, 84], axis=-1)\n", " axs[n].plot(r, ymed, color=c)\n", " axs[n].fill_between(r, ylow, yhigh, alpha=0.5, color=c, label=catalogue)\n", "\n", "\n", "# CMB-LG velocity\n", "axs[0].fill_between([r.min(), 10.], [627 - 22, 627 - 22], [627 + 22, 627 + 22], color='black', alpha=0.5, zorder=0.5, label=\"CMB-LG\", hatch=\"x\")\n", "axs[1].fill_between([r.min(), 10.], [276 - 3, 276 - 3], [276 + 3, 276 + 3], color='black', alpha=0.5, zorder=0.5, hatch=\"x\")\n", "axs[2].fill_between([r.min(), 10.], [30 - 3, 30 - 3], [30 + 3, 30 + 3], color='black', alpha=0.5, zorder=0.5, hatch=\"x\")\n", "\n", "# LCDM expectation\n", "Rs,mean,std,mode,p05,p16,p84,p95 = np.load(\"/mnt/users/rstiskalek/csiborgtools/data/BulkFlowPlot.npy\")\n", "m = Rs < 175\n", "axs[0].plot(Rs[m], mode[m], color=\"violet\", zorder=0)\n", "axs[0].fill_between(Rs[m], p16[m], p84[m], alpha=0.25, color=\"violet\",\n", " zorder=0, hatch='//', label=r\"$\\Lambda\\mathrm{CDM}$\")\n", "\n", "for n in range(3):\n", " axs[n].set_xlabel(r\"$r ~ [\\mathrm{Mpc} / h]$\")\n", "\n", "axs[0].legend()\n", "axs[0].set_ylabel(r\"$B ~ [\\mathrm{km} / \\mathrm{s}]$\")\n", "axs[1].set_ylabel(r\"$\\ell_B ~ [\\mathrm{deg}]$\")\n", "axs[2].set_ylabel(r\"$b_B ~ [\\mathrm{deg}]$\")\n", "\n", "axs[0].set_xlim(r.min(), r.max())\n", "\n", "fig.tight_layout()\n", "fig.savefig(f\"../../plots/bulkflow_{sim}_{catalogue}.png\", dpi=450)\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Smoothing scale dependence" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "simname = \"Carrick2015\"\n", "catalogue = \"Pantheon+\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Goodness-of-fit" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "scales = [0, 4, 8, 16, 32]\n", "\n", "y = np.asarray([get_gof(\"BIC\", simname, catalogue, ksmooth=i)\n", " for i in range(len(scales))])\n", "ymin = y.min()\n", "\n", "y -= ymin\n", "y_CF4 = get_gof(\"BIC\", \"CF4\", catalogue) - ymin\n", "y_CF4gp = get_gof(\"BIC\", \"CF4gp\", catalogue) - ymin\n", "\n", "plt.figure()\n", "plt.axhline(y[0], color='blue', label=\"Carrick+2015, no smoothing\")\n", "plt.plot(scales[1:], y[1:], marker=\"o\", label=\"Carrick+2015, smoothed\")\n", "\n", "plt.axhline(y_CF4, color='red', label=\"CF4, no smoothing\")\n", "\n", "plt.xlabel(r\"$R_{\\rm smooth} ~ [\\mathrm{Mpc}]$\")\n", "plt.ylabel(r\"$\\Delta \\mathrm{BIC}$\")\n", "plt.legend(ncols=1)\n", "\n", "plt.tight_layout()\n", "plt.savefig(\"../../plots/test_smooth.png\", dpi=450)\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sim = \"Carrick2015\"\n", "catalogue = \"Pantheon+\"\n", "\n", "\n", "X = [samples_to_getdist(get_samples(sim, catalogue, ksmooth=ksmooth), ksmooth)\n", " for ksmooth in [0, 1, 2]]\n", "\n", "params = [\"Vmag\", \"l\", \"b\", \"sigma_v\", \"beta\"]\n", "# if \"Pantheon+\" in catalogue or catalogue in [\"Foundation\", \"LOSS\"]:\n", "# params += [\"alpha_cal\", \"beta_cal\", \"mag_cal\", \"e_mu\"]\n", "# else:\n", "# params += [\"aTF\", \"bTF\", \"e_mu\"]\n", "\n", "\n", "\n", "g = plots.get_subplot_plotter()\n", "g.settings.figure_legend_frame = False\n", "g.settings.alpha_filled_add = 0.75\n", "\n", "g.triangle_plot(X, params=params, filled=True, legend_loc='upper right')\n", "plt.gcf().suptitle(f'{catalogue}', y=1.025)\n", "plt.gcf().tight_layout()\n", "plt.gcf().savefig(f\"../../plots/calibration_{catalogue}.png\", dpi=500, bbox_inches='tight')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Void testing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Evidence comparison" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "zcmb_max = 0.05\n", "\n", "sims = [\"no_field\", \"IndranilVoid_exp\"]\n", "cats = [\"LOSS\", \"Foundation\", \"2MTF\", \"SFI_gals\", \"CF4_TFR_i\", \"CF4_TFR_w1\"]\n", "\n", "neglnZ = {}\n", "kfound = []\n", "for sim in sims:\n", " for cat in cats:\n", " sample_alpha = sim not in [\"IndranilVoid_exp\", \"no_field\"]\n", " fname = paths.flow_validation(\n", " fdir, sim, cat, inference_method=\"mike\",\n", " sample_alpha=sample_alpha, zcmb_max=zcmb_max)\n", " \n", "\n", " neglnZ[f\"{sim}_{cat}\"] = get_gof(\"neg_lnZ_harmonic\", fname)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "simA = sims[0]\n", "simB = sims[1]\n", "\n", "print(f\"lnZ_({simA}) - lnZ_({simB})\\n\")\n", "for cat in cats:\n", " lnZ_A = - neglnZ[f\"{simA}_{cat}\"]\n", " lnZ_B = - neglnZ[f\"{simB}_{cat}\"]\n", " print(f\"{cat:15s} {lnZ_A - lnZ_B:.1f}\")\n", "\n", "\n", "print(f\"\\n(Positive -> preference for {simA})\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Goodness-of-fit comparison" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "zcmb_max = 0.05\n", "no_Vext = True\n", "\n", "sims = [\"IndranilVoid_exp\", \"IndranilVoid_gauss\", \"IndranilVoid_mb\"]\n", "cats = [\"2MTF\", \"SFI_gals\", \"CF4_TFR_i\", \"CF4_TFR_w1\"]\n", "\n", "neglnZ = {}\n", "kfound = {}\n", "for sim in sims:\n", " for cat in cats:\n", " kfound[f\"{sim}_{cat}\"] = []\n", " for ksim in range(500):\n", " sample_alpha = False\n", " fname = paths.flow_validation(\n", " fdir, sim, cat, inference_method=\"mike\", nsim=ksim,\n", " sample_alpha=sample_alpha, zcmb_max=zcmb_max,\n", " sample_beta=True,\n", " no_Vext=no_Vext, verbose_print=False)\n", "\n", " if not exists(fname):\n", " continue\n", "\n", " kfound[f\"{sim}_{cat}\"].append(ksim)\n", " neglnZ[f\"{sim}_{cat}_{ksim}\"] = get_gof(\"neg_lnZ_harmonic\", fname)\n", "\n", "\n", "neglnZ_no_field = {}\n", "neglnZ_dipole = {}\n", "sim = \"no_field\"\n", "for cat in cats:\n", " sample_alpha = False\n", " fname = paths.flow_validation(\n", " fdir, sim, cat, inference_method=\"mike\",\n", " sample_alpha=sample_alpha, zcmb_max=zcmb_max,\n", " no_Vext=True, verbose_print=False)\n", "\n", " if not exists(fname):\n", " continue\n", "\n", " neglnZ_no_field[f\"{cat}\"] = get_gof(\"neg_lnZ_harmonic\", fname)\n", "\n", " fname = paths.flow_validation(\n", " fdir, sim, cat, inference_method=\"mike\",\n", " sample_alpha=sample_alpha, zcmb_max=zcmb_max,\n", " no_Vext=None, verbose_print=False)\n", "\n", " if not exists(fname):\n", " continue\n", "\n", " neglnZ_dipole[f\"{cat}\"] = get_gof(\"neg_lnZ_harmonic\", fname)\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving to `../../plots/void_goodness_of_fit_observer_no_Vext.png`.\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with plt.style.context('science'):\n", " plt.rcParams.update({'font.size': 9})\n", "\n", " figwidth = 8.3 \n", " fig, axs = plt.subplots(2, 2, figsize=(figwidth, 0.65 * figwidth))\n", "\n", " for n, cat in enumerate(cats):\n", " i, j = n // 2, n % 2\n", " ax = axs[i, j]\n", "\n", " for sim in sims:\n", " x = kfound[f\"{sim}_{cat}\"]\n", " y = [neglnZ[f\"{sim}_{cat}_{ksim}\"] / np.log(10) for ksim in x]\n", " x = np.array(x) * 0.674\n", " ax.plot(x, y, label=simname_to_pretty(sim))\n", " \n", " # if no_Vext is None:\n", " # y_no_field = neglnZ_no_field[cat] / np.log(10)\n", " # if cat != \"CF4_TFR_w1\":\n", " # ax.axhline(y_no_field, color=\"black\", ls=\"--\", label=\"No peculiar velocity\")\n", " y_no_field = neglnZ_no_field[cat] / np.log(10)\n", " ax.axhline(y_no_field, color=\"black\", ls=\"--\", label=\"No peculiar velocity\")\n", "\n", " y_dipole = neglnZ_dipole[cat] / np.log(10)\n", " ax.axhline(y_dipole, color=\"black\", ls=\":\", label=\"Constant dipole\")\n", "\n", " ax.text(0.5, 0.9, catalogue_to_pretty(cat),\n", " transform=ax.transAxes, #fontsize=\"small\",\n", " verticalalignment='center', horizontalalignment='center',\n", " bbox=dict(facecolor='white', alpha=0.5),\n", " )\n", "\n", " if n == 0:\n", " ax.legend(fontsize=\"small\", loc=\"upper left\")\n", "\n", " ax.set_ylabel(r\"$-\\Delta \\log \\mathcal{Z}$\")\n", " ax.set_xlabel(r\"$R_{\\rm offset} ~ [\\mathrm{Mpc} / h]$\")\n", " ax.set_xlim(0)\n", "\n", " fig.tight_layout()\n", " fname = f\"../../plots/void_goodness_of_fit_observer.png\"\n", " if no_Vext:\n", " fname = fname.replace(\".png\", \"_no_Vext.png\")\n", " print(f\"Saving to `{fname}`.\")\n", " fig.savefig(fname, dpi=450)\n", " fig.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Single parameter radial dependence" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "zcmb_max = 0.05\n", "key = \"beta\"\n", "# key_label = r\"$\\sigma_v ~ [\\mathrm{km} / \\mathrm{s}]$\"\n", "# key_label = r\"$|\\mathbf{V}_{\\rm ext}| ~ [\\mathrm{km} / \\mathrm{s}]$\"\n", "key_label = r\"$\\beta$\"\n", "no_Vext = True\n", "\n", "sims = [\"IndranilVoid_exp\", \"IndranilVoid_gauss\", \"IndranilVoid_mb\"]\n", "cats = [\"2MTF\", \"SFI_gals\", \"CF4_TFR_i\", \"CF4_TFR_w1\"]\n", "\n", "data_mean = {}\n", "data_std = {}\n", "kfound = {}\n", "for sim in sims:\n", " for cat in cats:\n", " kfound[f\"{sim}_{cat}\"] = []\n", " for ksim in range(500):\n", " sample_alpha = False\n", " fname = paths.flow_validation(\n", " fdir, sim, cat, inference_method=\"mike\", nsim=ksim,\n", " sample_alpha=sample_alpha, zcmb_max=zcmb_max,\n", " sample_beta=True,\n", " no_Vext=no_Vext, verbose_print=False)\n", "\n", " if not exists(fname):\n", " continue\n", "\n", " kfound[f\"{sim}_{cat}\"].append(ksim)\n", " with File(fname, 'r') as f:\n", " x = f[f\"samples/{key}\"][...]\n", " if key == \"Vext\":\n", " x = np.linalg.norm(x, axis=-1)\n", "\n", " data_mean[f\"{sim}_{cat}_{ksim}\"] = x.mean()\n", " data_std[f\"{sim}_{cat}_{ksim}\"] = x.std()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving to `../../plots/void_beta_per_observer_no_Vext.png`.\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with plt.style.context('science'):\n", " plt.rcParams.update({'font.size': 9})\n", "\n", " figwidth = 8.3\n", " fig, axs = plt.subplots(2, 2, figsize=(figwidth, 0.65 * figwidth))\n", "\n", " for n, cat in enumerate(cats):\n", " i, j = n // 2, n % 2\n", " ax = axs[i, j]\n", "\n", " for sim in sims:\n", " x = kfound[f\"{sim}_{cat}\"]\n", " y = [data_mean[f\"{sim}_{cat}_{ksim}\"] for ksim in x]\n", " yerr = [data_std[f\"{sim}_{cat}_{ksim}\"] for ksim in x]\n", " x = np.array(x) * 0.674\n", "\n", " ax.plot(x, y, label=simname_to_pretty(sim))\n", " ax.fill_between(x, np.array(y) - np.array(yerr), np.array(y) + np.array(yerr), alpha=0.5)\n", "\n", " ax.text(0.5, 0.9, catalogue_to_pretty(cat),\n", " transform=ax.transAxes, #fontsize=\"small\",\n", " verticalalignment='center', horizontalalignment='center',\n", " bbox=dict(facecolor='white', alpha=0.5),\n", " )\n", "\n", " if n == 0:\n", " ax.legend(fontsize=\"small\", loc='upper right')\n", "\n", " ax.set_ylabel(key_label)\n", " ax.set_xlabel(r\"$R_{\\rm offset} ~ [\\mathrm{Mpc} / h]$\")\n", " ax.set_xlim(0),\n", "\n", " fig.tight_layout()\n", " fname = f\"../../plots/void_{key}_per_observer.png\"\n", " if no_Vext:\n", " fname = fname.replace(\".png\", \"_no_Vext.png\")\n", " print(f\"Saving to `{fname}`.\")\n", " fig.savefig(fname, dpi=450)\n", " fig.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": "markdown", "metadata": {}, "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 }