csiborgtools/notebooks/flow/reconstruction_comparison.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "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",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from corner import corner\n",
    "from getdist import plots\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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $\\log Z$ and BIC for simulations/catalogues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sims = [\"Carrick2015\", \"Lilow2024\", \"CF4\", \"CF4gp\", \"csiborg2_main\", \"csiborg2X\"]\n",
    "catalogues = [\"LOSS\", \"Foundation\", \"Pantheon+\", \"2MTF\", \"SFI_gals\"]\n",
    "\n",
    "for catalogue in catalogues:\n",
    "    y_BIC = np.asarray([get_gof(\"BIC\", sim, catalogue) for sim in sims])\n",
    "    y_BIC -= y_BIC.min()\n",
    "    y_lnZ = np.full_like(y_BIC, np.nan)\n",
    "    # y_lnZ = np.asarray([get_gof(\"lnZ\", sim, catalogue) for sim in sims])\n",
    "    # y_lnZ -= y_lnZ.min()\n",
    "\n",
    "    fig, ax_left = plt.subplots()\n",
    "    fig.suptitle(f\"{catalogue}\")\n",
    "    ax_right = ax_left.twinx()\n",
    "\n",
    "\n",
    "    ax_left.plot(np.arange(len(sims)), y_lnZ, 'bo')\n",
    "    ax_right.plot(np.arange(len(sims)), y_BIC, 'rx')\n",
    "\n",
    "    # y-ticks\n",
    "    ax_left.set_ylabel(r\"$-\\Delta \\log \\mathcal{Z}$\", color=\"blue\")\n",
    "    ax_left.tick_params(axis='y', labelcolor=\"blue\")\n",
    "    ax_right.set_ylabel(r\"$\\Delta \\mathrm{BIC}$\", color=\"red\")\n",
    "    ax_right.tick_params(axis='y', labelcolor=\"red\")\n",
    "\n",
    "    ax_left.set_xticks(np.arange(len(sims)), simname_to_pretty(sims), rotation=35)\n",
    "    fig.tight_layout()\n",
    "    fig.savefig(f\"../../plots/GOF_{catalogue}.png\", dpi=450)\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting $\\beta = 1$?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sims = [\"Lilow2024\", \"CF4\", \"CF4gp\", \"csiborg1\", \"csiborg2_main\", \"csiborg2X\"]\n",
    "catalogues = [\"LOSS\", \"Foundation\", \"Pantheon+\", \"2MTF\", \"SFI_gals\"]\n",
    "\n",
    "for catalogue in catalogues:\n",
    "    y_lnZ = [get_gof(\"lnZ\", sim, catalogue, sample_beta=True) - get_gof(\"lnZ\", sim, catalogue, sample_beta=False) for sim in sims]\n",
    "    y_BIC = [get_gof(\"BIC\", sim, catalogue, sample_beta=True) - get_gof(\"BIC\", sim, catalogue, sample_beta=False) for sim in sims]\n",
    "\n",
    "    fig, ax_left = plt.subplots()\n",
    "    fig.suptitle(rf\"{catalogue} (higher signifies preference for $\\beta = 1$)\")\n",
    "    ax_right = ax_left.twinx()\n",
    "\n",
    "    ax_left.plot(np.arange(len(sims)), y_lnZ, 'bo')\n",
    "    ax_right.plot(np.arange(len(sims)), y_BIC, 'rx')\n",
    "\n",
    "    # y-ticks\n",
    "    ax_left.set_ylabel(r\"$\\log \\mathcal{Z}_{\\beta} - \\log \\mathcal{Z}_{\\beta = 1}$\", color=\"blue\")\n",
    "    ax_left.tick_params(axis='y', labelcolor=\"blue\")\n",
    "    ax_right.set_ylabel(r\"$\\mathrm{BIC}_{\\beta} - \\mathrm{BIC}_{\\beta = 1}$\", color=\"red\")\n",
    "    ax_right.tick_params(axis='y', labelcolor=\"red\")\n",
    "\n",
    "    ax_left.set_xticks(np.arange(len(sims)), simname_to_pretty(sims), rotation=35)\n",
    "    ax_left.axhline(0, color=\"black\", linestyle=\"--\", linewidth=1)\n",
    "    fig.tight_layout()\n",
    "    fig.savefig(f\"../../plots/GOF_beta_{catalogue}.png\", dpi=450)\n",
    "    fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What $\\beta$ is preferred by the data? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sims = [\"Lilow2024\", \"CF4\", \"CF4gp\", \"csiborg1\", \"csiborg2_main\", \"csiborg2X\"]\n",
    "sims = [\"csiborg2_main\", \"csiborg2X\"]\n",
    "catalogues = [\"LOSS\", \"Foundation\", \"Pantheon+\", \"2MTF\", \"SFI_gals\"]\n",
    "key = \"beta\"\n",
    "\n",
    "for sim in sims:\n",
    "    plt.figure()\n",
    "    plt.title(simname_to_pretty(sim))\n",
    "\n",
    "    for catalogue in catalogues:\n",
    "        beta = get_samples(sim, catalogue)[key]\n",
    "        plt.hist(beta, bins=\"auto\", histtype=\"step\", label=catalogue, density=1)\n",
    "\n",
    "\n",
    "    plt.xlabel(names_to_latex([key], True)[0])\n",
    "    plt.ylabel(\"Normalized PDF\")\n",
    "    # plt.xlim(0., 1.5)\n",
    "    plt.legend()\n",
    "    plt.savefig(f\"../../plots/what_beta_{sim}.png\", dpi=450)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n"
   ]
  },
  {
   "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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "simname = \"Carrick2015\"\n",
    "catalogue = \"Pantheon+\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Goodness-of-fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed no burn in\n",
      "Removed no burn in\n",
      "Removed no burn in\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3872503/2011775450.py:22: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.gcf().tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA90AAAQFCAYAAACsBKtKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3ydZ33//9d9ny2doz0t2bJlyTu2Y8cjzh4kBBICAcooEJcw2pIyfxQoFJKUQlsopS0NhQIJlC9QICTsJGTHGc7y3kO2lrW3zj73/fvjWLJlybYkSzpH0vv5eOhBzj0/tmU/eOu6rs9l2LZtIyIiIiIiIiITzkx1ASIiIiIiIiIzlUK3iIiIiIiIyCRR6BYRERERERGZJArdIiIiIiIiIpNEoVtERERERERkkih0i4iIiIiIiEwShW4RERERERGRSaLQLSIiIiIiIjJJFLpFREREREREJolCt4iISJoxDIM777wz1WWIiIjIBFDoFhGRWe/+++/HMIzBL6/Xy6JFi7jzzjtpbm6elHc+//zz3HXXXXR1dU3K80VERCQ9OFNdgIiISLq45557WLBgAeFwmC1btvDtb3+bP/zhD+zevZuMjIwJfdfzzz/P3XffzebNm8nJyZnQZ4uIiEj60Ei3iIjISTfddBPvec97+MAHPsD999/Pxz/+cWpqavj1r3+d6tKmhfnz53PXXXelugwREZG0otAtIiJyFtdeey0ANTU1fP3rX2fTpk3k5+fj8/lYu3Ytv/zlL4fdM7Ae+6GHHmLFihV4PB6WL1/Oww8/PHjNXXfdxac//WkAFixYMDit/dixY0Oeda5nDGhoaOD9738/xcXFg9f94Ac/GHZdS0sLd9xxB8XFxXi9XlatWsUPf/jDIdccO3YMwzD4+te/zne/+10WLlyIx+Nh3bp1vPzyy2P+/RMRERFNLxcRETmrI0eOAJCfn8+Xv/xl3vSmN/Hnf/7nRKNRfvazn/H2t7+d3/3ud7zxjW8cct+WLVv41a9+xV//9V8TCAT4j//4D9761rdSW1tLfn4+t912GwcPHuSnP/0p//Zv/0ZBQQEAhYWFo34GQHNzMxs3bhwM+oWFhfzxj3/kjjvuoKenh49//OMAhEIhrr76ag4fPsydd97JggUL+MUvfsHmzZvp6uriYx/72JD6f/KTn9Db28uHP/xhDMPgX/7lX7jttts4evQoLpdrsn67RUREZiZbRERklrvvvvtswH7sscfs1tZWu66uzv7Zz35m5+fn2z6fz66vr7eDweCQe6LRqL1ixQr72muvHXIcsN1ut3348OHBYzt27LAB+z//8z8Hj33ta1+zAbumpmZYPaN9xh133GGXlpbabW1tQ+5/5zvfaWdnZw/W/M1vftMG7B//+MdD6r/00kttv99v9/T02LZt2zU1NTZg5+fn2x0dHYPX/vrXv7YB+7e//e05fx8rKirsL33pS+e8RkREZLbR9HIREZGTrr/+egoLC5k7dy7vfOc78fv9PPjgg5SVleHz+Qav6+zspLu7myuuuILXXnttxOcsXLhw8PPKlSvJysri6NGjY6rlXM+wbZsHHniAW265Bdu2aWtrG/y68cYb6e7uHqztD3/4AyUlJbzrXe8afJ7L5eKjH/0ofX19PP3000Pe/Y53vIPc3NzBz1dccQXAkPojkciQd7a1tWFZFsFgcNhxERGR2UzTy0VERE76r//6LxYtWoTT6aS4uJjFixdjmsmfT//ud7/jy1/+Mtu3bycSiQzeYxjGsOfMmzdv2LHc3Fw6OztHXcv5ntHa2kpXVxff/e53+e53vzviM1paWgA4fvw41dXVg7+WAUuXLh08f653DwTw0+v/6U9/yl/8xV8Me+fXvvY1vva1rw05Ztv2iPWJiIjMBgrdIiIiJ61fv55LLrlk2PFnn32WN73pTVx55ZXce++9lJaW4nK5uO+++/jJT34y7HqHwzHi88cSPs/3DMuyAHjPe97D7bffPuK1K1euHPX7xvJugBtvvJE//elPQ86/5z3v4YYbbuB973vfuN4rIiIyEyl0i4iInMcDDzyA1+vlkUcewePxDB6/7777xv3MkUbIx6KwsJBAIEAikeD6668/57UVFRXs3LkTy7KGjHbv379/8PxYlZaWUlpaOuSY1+ulsrLyvPWIiIjMJlrTLSIich4OhwPDMEgkEoPHjh07xkMPPTTuZ2ZmZgLQ1dU17pre+ta38sADD7B79+5h51tbWwf/+w1veANNTU383//93+CxeDzOf/7nf+L3+7nqqqvGVYOIiIicn0a6RUREzuONb3wj3/jGN3j961/Pu9/9blpaWviv//ovqqqq2Llz57ieuXbtWgA+//nP8853vhOXy8Utt9wyGMZH45/+6Z948skn2bBhAx/84AdZtmwZHR0dvPbaazz22GN0dHQA8KEPfYjvfOc7bN68mVdffZX58+fzy1/+kueee45vfvObBAKBcf0aRERE5PwUukVERM7j2muv5fvf/z7/9E//xMc//nEWLFjAP//zP3Ps2LFxh+5169bxD//wD/z3f/83Dz/8MJZlUVNTM6bQXVxczEsvvcQ999zDr371K+69917y8/NZvnw5//zP/zx4nc/n46mnnuKzn/0sP/zhD+np6WHx4sXcd999bN68eVz1i4iIyOgYtlqKioiIiIiIiEwKrekWERERERERmSQK3SIiIiIiIiKTRKFbREREREREZJIodIuIiIiIiIhMEoVuERERERERkUmi0C0iIiIiIiIySRS6RURERERERCaJQreIiIiIiIjIJFHoFhEREREREZkkCt0iIiIiIiIik0ShW0RERERERGSSKHSLiIiIiIiITBKFbhEREREREZFJotAtIiIiIiIiMkkUukVEREREREQmiUK3iIiIiIiIyCRR6BYRERERERGZJArdIiIiIiIiIpNEoVtERERERERkkih0i4iIiIiIiEwShW4RERERERGRSaLQLSIiIiIiIjJJFLpFREREREREJokz1QVMNcuyaGxsJBAIYBhGqssRmVC2bdPb28ucOXMwTf1MTUREREQk1WZd6G5sbGTu3LmpLkNkUtXV1VFeXp7qMkREREREZr1ZF7oDgQCQDCVZWVkprkZkYvX09DB37tzB73MREREREUmtWRe6B6aUZ2VlKXTLjKWlEyIiIiIi6UGLPkVEREREREQmiUK3iIiIiIiIyCSZddPLRUREREREpqvu7m6CweCkviMjI4Ps7OxJfcdsotAtIiIiIiIyDXR3d/Otb32LeDw+qe9xOp3ceeed0yp4b968mZycHL75zW+mupRhNL1cRERERERkGggGg5MeuAHi8fikj6anq1gsxp133klubi55eXn8zd/8zQX/nit0i4iIiIiIiABf/vKX2bJlC3v37mXPnj08++yzfOUrX7mgZyp0i4iIiIiIyLh94xvfYN68eQQCAebPn8/3vvc9amtred3rXkdhYSG5ubm88Y1v5NixY4P3bN68mTvuuIO3ve1t+P1+li9fzu7du/nOd75DeXk5hYWF3HvvvYPX33XXXdx8883ccccdZGVlUV1dzYMPPnjWmo4cOcItt9xCYWEhFRUVfPnLX8ayLABqamq4/vrryc7OJi8vj8suu2xwZP8HP/gBX/jCFygtLaW0tJTPf/7zfP/737+g3x+FbhERERERERmXgwcP8oUvfIFHH32U3t5etm7dyvr167Esi09+8pPU1dVx/PhxMjIy+OAHPzjk3l/84hd84hOfoKuri3Xr1nHrrbdy5MgRjh49ys9+9jM+8YlP0NzcPHj9ww8/zPr16+no6OAb3/gG73rXuzhy5MiwmoLBINdddx3XXXcdDQ0NPPvss/zsZz/jvvvuA+Dzn/88VVVVtLW10dzczNe+9jWcTiednZ3U19ezevXqwWetXr2a2tpauru7x/17pNAtIiIiIiIi4+JwOLBtmz179hAKhSguLmblypXMnz+fm266Ca/XS1ZWFp///Od59tlnB0ebAd74xjdy2WWX4XQ6+bM/+zOOHTvG3Xffjdvt5rrrriM7O5tdu3YNXr9o0SI+/OEP43Q
      "text/plain": [
       "<Figure size 1000x1000 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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
}