{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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\", \"csiborg1\", \"csiborg2_main\", \"csiborg2X\"]\n",
    "catalogues = [\"LOSS\", \"Foundation\", \"Pantheon+\", \"2MTF\", \"SFI_gals\"]\n",
    "\n",
    "for catalogue in catalogues:\n",
    "    y_lnZ = np.asarray([get_gof(\"lnZ\", sim, catalogue) for sim in sims])\n",
    "    y_lnZ -= y_lnZ.min()\n",
    "    y_BIC = np.asarray([get_gof(\"BIC\", sim, catalogue) for sim in sims])\n",
    "    y_BIC -= y_BIC.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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sims = [\"Lilow2024\", \"CF4\", \"CF4gp\", \"csiborg1\", \"csiborg2_main\", \"csiborg2X\"]\n",
    "sims = [\"Lilow2024\"]\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",
    "\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')"
   ]
  },
  {
   "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\", \"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": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] Unable to synchronously open file (unable to open file: name = '/mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Lilow2024_LOSS_ksmooth0_nsim1_sample_beta.hdf5', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[28], line 5\u001b[0m\n\u001b[1;32m      2\u001b[0m catalogue \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLOSS\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m      3\u001b[0m key \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbeta\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 5\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[43m[\u001b[49m\u001b[43mget_samples\u001b[49m\u001b[43m(\u001b[49m\u001b[43msim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcatalogue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnsim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnsim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert_Vext_to_galactic\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mnsim\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m20\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m      6\u001b[0m Xmarg \u001b[38;5;241m=\u001b[39m get_samples(sim, catalogue, convert_Vext_to_galactic\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)[key]\n\u001b[1;32m      9\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure()\n",
      "Cell \u001b[0;32mIn[28], line 5\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      2\u001b[0m catalogue \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLOSS\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m      3\u001b[0m key \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbeta\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 5\u001b[0m X \u001b[38;5;241m=\u001b[39m [\u001b[43mget_samples\u001b[49m\u001b[43m(\u001b[49m\u001b[43msim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcatalogue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnsim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnsim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert_Vext_to_galactic\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m[key] \u001b[38;5;28;01mfor\u001b[39;00m nsim \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m20\u001b[39m)]\n\u001b[1;32m      6\u001b[0m Xmarg \u001b[38;5;241m=\u001b[39m get_samples(sim, catalogue, convert_Vext_to_galactic\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)[key]\n\u001b[1;32m      9\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure()\n",
      "File \u001b[0;32m~/csiborgtools/notebooks/flow/reconstruction_comparison.py:142\u001b[0m, in \u001b[0;36mget_samples\u001b[0;34m(simname, catalogue, ksmooth, nsim, sample_beta, convert_Vext_to_galactic)\u001b[0m\n\u001b[1;32m    140\u001b[0m fname \u001b[38;5;241m=\u001b[39m get_fname(simname, catalogue, ksmooth, nsim, sample_beta)\n\u001b[1;32m    141\u001b[0m samples \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m--> 142\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mFile\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m    143\u001b[0m     grp \u001b[38;5;241m=\u001b[39m f[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msamples\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m    144\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m grp\u001b[38;5;241m.\u001b[39mkeys():\n",
      "File \u001b[0;32m~/csiborgtools/venv_csiborg/lib/python3.11/site-packages/h5py/_hl/files.py:562\u001b[0m, in \u001b[0;36mFile.__init__\u001b[0;34m(self, name, mode, driver, libver, userblock_size, swmr, rdcc_nslots, rdcc_nbytes, rdcc_w0, track_order, fs_strategy, fs_persist, fs_threshold, fs_page_size, page_buf_size, min_meta_keep, min_raw_keep, locking, alignment_threshold, alignment_interval, meta_block_size, **kwds)\u001b[0m\n\u001b[1;32m    553\u001b[0m     fapl \u001b[38;5;241m=\u001b[39m make_fapl(driver, libver, rdcc_nslots, rdcc_nbytes, rdcc_w0,\n\u001b[1;32m    554\u001b[0m                      locking, page_buf_size, min_meta_keep, min_raw_keep,\n\u001b[1;32m    555\u001b[0m                      alignment_threshold\u001b[38;5;241m=\u001b[39malignment_threshold,\n\u001b[1;32m    556\u001b[0m                      alignment_interval\u001b[38;5;241m=\u001b[39malignment_interval,\n\u001b[1;32m    557\u001b[0m                      meta_block_size\u001b[38;5;241m=\u001b[39mmeta_block_size,\n\u001b[1;32m    558\u001b[0m                      \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m    559\u001b[0m     fcpl \u001b[38;5;241m=\u001b[39m make_fcpl(track_order\u001b[38;5;241m=\u001b[39mtrack_order, fs_strategy\u001b[38;5;241m=\u001b[39mfs_strategy,\n\u001b[1;32m    560\u001b[0m                      fs_persist\u001b[38;5;241m=\u001b[39mfs_persist, fs_threshold\u001b[38;5;241m=\u001b[39mfs_threshold,\n\u001b[1;32m    561\u001b[0m                      fs_page_size\u001b[38;5;241m=\u001b[39mfs_page_size)\n\u001b[0;32m--> 562\u001b[0m     fid \u001b[38;5;241m=\u001b[39m \u001b[43mmake_fid\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muserblock_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfapl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfcpl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mswmr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mswmr\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    564\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(libver, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m    565\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_libver \u001b[38;5;241m=\u001b[39m libver\n",
      "File \u001b[0;32m~/csiborgtools/venv_csiborg/lib/python3.11/site-packages/h5py/_hl/files.py:235\u001b[0m, in \u001b[0;36mmake_fid\u001b[0;34m(name, mode, userblock_size, fapl, fcpl, swmr)\u001b[0m\n\u001b[1;32m    233\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m swmr \u001b[38;5;129;01mand\u001b[39;00m swmr_support:\n\u001b[1;32m    234\u001b[0m         flags \u001b[38;5;241m|\u001b[39m\u001b[38;5;241m=\u001b[39m h5f\u001b[38;5;241m.\u001b[39mACC_SWMR_READ\n\u001b[0;32m--> 235\u001b[0m     fid \u001b[38;5;241m=\u001b[39m \u001b[43mh5f\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflags\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfapl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfapl\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    236\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr+\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m    237\u001b[0m     fid \u001b[38;5;241m=\u001b[39m h5f\u001b[38;5;241m.\u001b[39mopen(name, h5f\u001b[38;5;241m.\u001b[39mACC_RDWR, fapl\u001b[38;5;241m=\u001b[39mfapl)\n",
      "File \u001b[0;32mh5py/_objects.pyx:54\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mh5py/_objects.pyx:55\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mh5py/h5f.pyx:102\u001b[0m, in \u001b[0;36mh5py.h5f.open\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] Unable to synchronously open file (unable to open file: name = '/mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Lilow2024_LOSS_ksmooth0_nsim1_sample_beta.hdf5', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0)"
     ]
    }
   ],
   "source": [
    "sim = \"csiborg2_main\"\n",
    "catalogue = \"Pantheon+\"\n",
    "key = \"beta\"\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": [
    "### Bulk flow in the CMB rest frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = \"Lilow2024\"\n",
    "catalogue = \"LOSS\"\n",
    "\n",
    "r, B = get_bulkflow(sim, catalogue, convert_to_galactic=True, simulation_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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",
    "c = cols[0]\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=simname_to_pretty(sim))\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=0.25, 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=0.25, zorder=0.5, hatch=\"x\")\n",
    "axs[2].fill_between([r.min(), 10.], [30 - 3, 30 - 3], [30 + 3, 30 + 3], color='black', alpha=0.25, 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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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