{
 "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",
    "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": 3,
   "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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "9715.0205"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fname = paths.flow_validation(\n",
    "    fdir, \"Carrick2015\", \"2MTF\", inference_method=\"mike\",\n",
    "    sample_alpha=True, sample_beta=None,\n",
    "    zcmb_max=0.05)\n",
    "\n",
    "\n",
    "get_gof(\"neg_lnZ_harmonic\", fname)"
   ]
  },
  {
   "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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Carrick2015_LOSS_mike_smooth_1_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 23/09/2024 11:16:49\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Lilow2024_LOSS_mike_smooth_1_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 23/09/2024 11:20:48\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_csiborg2_main_LOSS_mike_smooth_1_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 23/09/2024 11:25:46\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Carrick2015_Foundation_mike_smooth_1_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 23/09/2024 11:16:35\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Lilow2024_Foundation_mike_smooth_1_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 23/09/2024 11:22:21\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_csiborg2_main_Foundation_mike_smooth_1_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 23/09/2024 11:27:26\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: 23/09/2024 11:17:35\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Lilow2024_2MTF_mike_smooth_1_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 23/09/2024 11:22:41\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_csiborg2_main_2MTF_mike_smooth_1_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 23/09/2024 11:34:57\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: 23/09/2024 11:19:01\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Lilow2024_SFI_gals_mike_smooth_1_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 23/09/2024 11:23:28\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_csiborg2_main_SFI_gals_mike_smooth_1_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 23/09/2024 11:37:53\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: 23/09/2024 11:21:33\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Lilow2024_CF4_TFR_i_mike_smooth_1_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 23/09/2024 11:26:11\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: 23/09/2024 12:04:44\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Carrick2015_CF4_TFR_w1_mike_smooth_1_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 23/09/2024 11:21:09\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Lilow2024_CF4_TFR_w1_mike_smooth_1_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 23/09/2024 11:25:47\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] Unable to synchronously open file (unable to open file: name = '/mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_csiborg2_main_CF4_TFR_w1_mike_smooth_1_zcmb_max_0.05_sample_alpha.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[8], line 17\u001b[0m\n\u001b[1;32m     11\u001b[0m     fname \u001b[38;5;241m=\u001b[39m paths\u001b[38;5;241m.\u001b[39mflow_validation(\n\u001b[1;32m     12\u001b[0m         fdir, simname, catalogue, inference_method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmike\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     13\u001b[0m         sample_alpha\u001b[38;5;241m=\u001b[39msimname \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIndranilVoid_exp\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     14\u001b[0m         zcmb_max\u001b[38;5;241m=\u001b[39mzcmb_max, smooth\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m     16\u001b[0m     \u001b[38;5;66;03m# y_BIC[i, j] = get_gof(\"BIC\", fname)z\u001b[39;00m\n\u001b[0;32m---> 17\u001b[0m     y_lnZ[i, j] \u001b[38;5;241m=\u001b[39m \u001b[43mget_gof\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mneg_lnZ_harmonic\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     19\u001b[0m y_lnZ[i] \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m y_lnZ[i]\u001b[38;5;241m.\u001b[39mmin()\n",
      "File \u001b[0;32m~/csiborgtools/notebooks/flow/reconstruction_comparison.py:198\u001b[0m, in \u001b[0;36mget_gof\u001b[0;34m(kind, fname)\u001b[0m\n\u001b[1;32m    195\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kind \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBIC\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAIC\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mneg_lnZ_harmonic\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m    196\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`kind` must be one of \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBIC\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAIC\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mneg_lnZ_harmonic\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)  \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[0;32m--> 198\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    199\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m f[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgof/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkind\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m][()]\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_csiborg2_main_CF4_TFR_w1_mike_smooth_1_zcmb_max_0.05_sample_alpha.hdf5', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0)"
     ]
    }
   ],
   "source": [
    "zcmb_max = 0.05\n",
    "\n",
    "sims = [\"Carrick2015\", \"Lilow2024\", \"csiborg2_main\"]\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, smooth=1)\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": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 830x415 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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=90,\n",
    "            fontsize=\"small\")\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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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.xlim(0)\n",
    "    plt.ylim(0)\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": [
    "fname = paths.flow_validation(\n",
    "    fdir, \"CF4\", \"CF4_TFR_i\", inference_method=\"bayes\",\n",
    "    sample_alpha=True, zcmb_max=0.05)\n",
    "\n",
    "with File(fname, 'r') as f:\n",
    "    x = f[\"samples/Vext\"][...]"
   ]
  },
  {
   "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 * 1.25))\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": [
    "### What $\\sigma_v$ is preferred by the data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Carrick2015_LOSS_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 30/08/2024 16:24:46\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Carrick2015_Foundation_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 30/08/2024 16:24:56\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Carrick2015_2MTF_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 30/08/2024 16:26:54\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Carrick2015_SFI_gals_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 30/08/2024 16:28:43\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Carrick2015_CF4_TFR_i_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 12/09/2024 12:20:11\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Carrick2015_CF4_TFR_w1_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 12/09/2024 12:19:39\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Lilow2024_LOSS_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 30/08/2024 16:25:09\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Lilow2024_Foundation_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 30/08/2024 16:25:47\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Lilow2024_2MTF_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 30/08/2024 16:27:13\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Lilow2024_SFI_gals_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 30/08/2024 16:28:48\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Lilow2024_CF4_TFR_i_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 12/09/2024 12:19:59\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_Lilow2024_CF4_TFR_w1_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 12/09/2024 12:26:53\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_csiborg2_main_LOSS_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 30/08/2024 16:29:55\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_csiborg2_main_Foundation_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 30/08/2024 16:42:40\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_csiborg2_main_2MTF_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 30/08/2024 16:54:29\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_csiborg2_main_SFI_gals_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 30/08/2024 17:10:19\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_csiborg2_main_CF4_TFR_i_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 12/09/2024 13:27:52\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_csiborg2_main_CF4_TFR_w1_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 12/09/2024 12:58:15\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_csiborg2X_LOSS_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 30/08/2024 16:33:23\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_csiborg2X_Foundation_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 30/08/2024 16:39:06\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_csiborg2X_2MTF_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 30/08/2024 16:59:53\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_csiborg2X_SFI_gals_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 30/08/2024 17:16:15\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_csiborg2X_CF4_TFR_i_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 12/09/2024 13:39:34\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_csiborg2X_CF4_TFR_w1_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 12/09/2024 13:01:55\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_CF4_LOSS_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 30/08/2024 16:46:28\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_CF4_Foundation_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 30/08/2024 16:49:33\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_CF4_2MTF_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 30/08/2024 17:12:11\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_CF4_SFI_gals_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 12/09/2024 14:07:26\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_CF4_CF4_TFR_i_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 12/09/2024 13:23:47\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_CF4_CF4_TFR_w1_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 12/09/2024 13:19:35\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_CLONES_LOSS_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 18/09/2024 15:44:36\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_CLONES_Foundation_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 18/09/2024 15:48:10\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_CLONES_2MTF_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 12/09/2024 13:49:16\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_CLONES_SFI_gals_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 12/09/2024 13:50:20\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_CLONES_CF4_TFR_i_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 12/09/2024 13:52:12\n",
      "File:          /mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_CLONES_CF4_TFR_w1_bayes_zcmb_max_0.05_sample_alpha.hdf5\n",
      "Last modified: 12/09/2024 13:51:03\n"
     ]
    }
   ],
   "source": [
    "sims = [\"Carrick2015\", \"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)\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/sigma_v\"][...]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 350x477 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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(0.9, 0.85, catalogue_to_pretty(cat),\n",
    "            transform=ax.transAxes, fontsize=\"small\",\n",
    "            verticalalignment='center', horizontalalignment='right',\n",
    "            # bbox=dict(facecolor='white', alpha=0.5, edgecolor='k')\n",
    "            )\n",
    "\n",
    "        ax.set_ylabel(None)\n",
    "        ax.set_yticklabels([])\n",
    "\n",
    "        xmin = ax.get_xlim()[0]\n",
    "        if xmin < 0:\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, 1.075),\n",
    "               ncol=3)\n",
    "\n",
    "    # for i in range(3):\n",
    "    for j in range(2):\n",
    "        axs[-1, j].set_xlabel(r\"$\\sigma_v ~ [\\mathrm{km} / \\mathrm{s}]$\")\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/sigmav_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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jcwsz7pFmWVkZq1evprS0lD179sTSXyoqKuImg9fGvfa3fkgIAoLVycJsI0ePHh3bC9HR0dFhnEVTG4+qpSpogtifC8pA7ihDxmhjaY6RkydPEA6Hx+AqdHR0dK4zrtVzbRiYFi1q1e3+XFBG4o7SC9HCklkGwuEIp06dYsmSJdc/C7VD1D+qaxoUox0sKeN7Dp1xIxwOE41Gx/UcRqMRs9k85O01v4CCgoJYkvvq1avZu3cvpaWl41jS/ulpkD1aBjIy1q63urp6yphdj6toejwe9u7dG3NR2b59O9u3b6e1tbXP7ftb3x/NVztY/7kdsfeb1y1hc/E8XE4Ju83CkSNHrotmqB32fg2CLSO7mKFiTYfVXxqycA70hVi9enWcu7u2veberUXlQ3FY1xmccDjMgQMHCAaD43oeq9XKihUrhiScbrebdevWsW/fvrha2IYNG+IMO8aC4SSxV1dXj4lgav4FWt9FT7OOdevWoY29qaiooKqqasQ/EtqYc40pO/bc6XTGjaPWxstu2rSpTxeU4bqj5MxIZve3NsevVBQMRFk8dw6HDx/mve99r7o+6lcF02BTo8HxQDtH1D8k0RzsC1FUVERZWVmviGL79u2sXbuWffv2DfjQ6QyPaDRKMBjEaDRiNI7PV0M7RzQaHZJoas5FN9q1VVRUDNv/sic3TjehHXOowxeHOzqoP0HWjIy151UzMt6yZQuNjY2x7bRgYqRoY841RjP2fFzbNPv7JSoqKoqzFdNcUPpbPywEAYAlc2dy7NgxJEmK/9xoB3PS+CzDFOOBvhAD4XK5Yj8ugzms6wwfo9GIyWQal2U4Yuz1euMcqnricrniOktvNCO+0cS6rKyMgoICoG+j4rq6OjweDzt37oxluwz12No4cs3oeDhNagOZXWv6oZmCTJVhnOMqmjeK4J49e9i0aRPFxcVxriaaC0p/6/sjPMCw+aUF6QQCARoaGkZ5FePDUL8QN+J2u+PMZwdzWNeZvgzmOuRyuWJmxFrGSVFREeXl5b1MrHv+EGsmGZpR8erVq2NORZs2bYrVaoZ67MrKytgxRmPconUcb926Nbauvr4ej8dDWlraiI871ox7nuYTTzxBeXl5rP1FC8P7c0Hpb31fvBLt4JwcYrZo6fXZ3CwrFovarjl//vwxvaaxYChfCA3tYdL2c7lcfbpr3+iwrjO90Z6B/kyptVrGQGbEA9HTqLgvhnrssrIyVq1aFfu8tLR0TMyugVgNdMOGDVNm5tNxF03toiFeALVfuxvpb31fSCjc2XmY/7XP5X5TjzZE0YQp2sbChQs5fPgwDz300OguYhwYyhdCe3gcDkfcPamvr2fVqlU0NTXFPWB9PXQ60xdN0PozpR6J5d9Q8Hq9w3LldzqdNDU1UVdXFxO10tLSURkZa/Z2WlPTmjVrhp9+OE5M62GU8wN2CkUrD/iO8hl/E36lu/1SNEOwlSVLlnDkyBFkWZ7cgvZBzy9EXwz0hSgqKsLr9cb8NaH3Q6dzc1BdXU15eXmvDtKqqipKSkrYtGlTnEt6TzNih8MR22+oAuvxeOK8WPs7dk+2bduGw+GgpKSE6urquA6codCXkbHD4Yjz6tSa9qYC09rl6LjHQX14Oc8lnGdr8Bx/jrbzWtJS7EYbBK6xdNE7+OUvuzh79iz5M7onsRrPPM1hHru6urrPyawGS63QHmqtN7E/92ydkTGeeZrDPbY2/cONaWmaeA1mRl1ZWYnD4cDhcMRNV3KjUTHA1q1bqayspKCgINaM1texbzTITktLo6qqKvaD3bNNcjD6MzIuLi6mpqYmlk7ncrmmTEbItDYhdriW43rbp/jr5os0il3c3nmYMnMm3zFlQOAqoRVf4qEP/xOlpaWsf/N90yZPc+PGjXEPtpanqY3Hr62tpaKigqKiItxud6xXVKOiomLKPGDTiamYpzmW9JVmNFFMNSPj0ZgQT2vRfOO9t3PQ9Sm2FF2i/A4/3w5e5J+DZ3gxYRH3dV6AlZ/nnyt2kpaWxhe/+EV9RJDOoEzFEUE6Y88t69ye6JhB6coQ3zmQyUeXneNR20yejrTy0UAjhzCR4LvE0qVLee6551AUBcGSoguazoCYzWZd0HQGZFp3BIHA5x68DYMAj79iwIDCT+2FXJIjfFaMoHQ0sXTpUtra2jh//vxkF1ZHR+cmYFqLZnNzMx955F9ZO7Odn5yaTcOldgoNNrabCvkRIR5vq2fJ4kVYLBZeffXVyS6ujo7OJLNjxw7Wr18/qrHn01o0c3Jy2L17Nz/88j+SlSTy1T3ZoMi8KZxHaTiPL0Su8NSVP3Hbbbfx8ssvT3ZxdXR0JpnNmzeze/ducnJyRnyMaS2aGjazka3vXsSz53M5eTmAK6WLh6VC3hPO4OOnKkldMYvTp09z+fLlyS6qjo7ONOemEE2ATfctZYZd5n8OOTCKkJ8SYYt/DsvFLP7H/hfMZrMebero6Iyam0Y0LSYDpXel8it3Lq1+mTR7BJso8WmKOBI5T9aSOex98Xm4dml8l872IZXX7XZTVlaGIAh9juCpqalBEIQ4x5mxoqCgYMJHDdXU1LBq1SoKCgriRkG53W5WrVrFhg0bYmWqq6vr5ayjozNVmNYpRzfy0bffwTdf+AM/2S/y+btgacIZJGke95gXcnzGOT7zwgGCj7dhtVrHrxCp6VD2JUgaOLXJ5XJRVlaGx+OhsrKyl3mBx+PB4XCMemRPX0nFY2UgOxw03wHNJUdDuw89r7OioiLOuLqmpmZU7jk6OmPJTRNpAmQ409i80k7ViVzCEQnRbKfA0sQnbGtpyvCQQoRLbV5wpI3PYrVBWwsEh55AX1ZW1stMtr6+fliGqwMZN/fldjPaURnDMYruSUlJCR6PZ0C/xTGZJ0pHZxy5qUQT4JF3rOZywMbTJwxgTiFFvshKWxZ3JS4gnAhnrlyDhKTxWazDd4R3uVyxKY01tLG2N6IZvZaXl8e2r6urY9WqVbHqbE8R7stY9kYDWW07LaLrWR3uy4C2LwPbnttq2/dHaWlpnL1XXV1dnAnEmMwTpaMzjtxU1XOABa7ZFOeF+Z/DGbx3sQS+ZhZkwoeu3Icv6TcEL7Xi9/ux28dpyosRoBkrDGaJV1FRQWNjI8XFxaSmptLW1haz0vN6vZSWlsa8B0tKSuKMZbXo8kYDWbfbTXl5ecyDc9WqVZSWlvYyO9bEWjNT0Axs+7Lx6suEpOe1FhQUxITT7XbHlWe480Tp6Ew0vURz69atcS7J2tB0QRDoOUxde79r1644+6ipwMfvzea9P29h/5UWVlpkBM8hHshbzqspTtoud9F07gyLFyya7GLGKCkp4eGHHwZUcdJE8Ea0idP66sTRIlOn0zms6rPmkKShiedgBrQ9DWzLy8txOBxx0avb7e7Xlb6oqIiqqio2btwYF1XC8OeJ0tGZaHqJpsvlin2Bh8JUNLx98733kPP0Ln5y0MT37k2Hs7/Dbm5gSeIcnks+yqETR1g0fyFC93xCU4GNGzfG7LX6o7y8nLKyMoqKinqJzWD/D5qx7Hh0AHm9XtasWRMT38Ei5p4O3Dd2dBUVFcX9CI9onigdnXGkV5vmjYJ54MABDhw4AMCPfvQjtm7dSkdHR7/bTwWMZhsfvnMG1Wdm0SFmQnIhtJ1ghlnAkZOO1BXm0PEjk11M4HqnSllZWcz+rS+qqqrwer1xju89p8EYKDrraSx7I8XFxb0iRLfbPWQDWq3sPSdz047RH6WlpdTX1/dZ5uHOE6WjM9EM2hFUW1tLSkoKTzzxBNXV1Tz22GPs2rVrIso2Kj5UvIKgJLLrmAlMCSBHEUItrJ13O8EUOHLi2KS2n9XX17Nt2za2bdsWq8pqi9vtZtu2bXi93lhHi9aOWFdXR01NDVu2bGHbtm0YjUbq6urYuXNnzJvT4/HEOmO2bt1KdXU1dXV1sYm49u7dGzcx29atW2MdQdrUCj3NbWtqaqitraWyspK6urpYG6YmbtpUwzceYyC0ybr6QpsnSrsGPd1IZyoxqJ/mCy+8wBvf+Ebe/OY3U1ZWxoMPPsjzzz/P2rVrJ6qM/bJq1SpycnJ6zWmssfmrP6apNcKr77+McLkRdr8OuffwUksjLXubyJ9fwJzs2aT1E90Nm6AfggH47DbImDk2x9TR0RkzduzYwY4dO2hubh7xBISD9p673W5SU1PZs2dPLDppamoa0cnGGs2woz8+dk8W73myndcvtXJ7UgbYgK6r3JmczXP2U3iaTpNrtdLWoSaSC4xBG2dq+ohSj3R0dMYfLcBav379iI8xqGiuXbuWyspK9u3bh6IoPPbYY6Snp4/4hBPJG99wD/lP/x8/OmDh9rclw9oFkHY75gUf5sUX/4uLT7zO25a8kSULFnHJbGbhwjHoHLLaBx0NpKOjM33pJZrt7e088cQTlJSUkJeXR35+Po8//njs856vpzqiNZmPvsHJ154z8o0uLxnp+RA8BgYPpW//NJtf+BjR+n0sfMP9+GSZ/Rcus3LlyinVq66jozO16NURlJKSEps/e+vWrTz99NOTUa4x44Nvux9BEPj5AcDiACkIF+qYZ89hxtsW0nHJw7lz57BarciyzOHDhye5xDo6OlOZPnvPNeHctm0b+fn5PPbYY2zdujWWejSdSHOk8NDKVH5yOg/J3wIJs+Dafmhv4LNv+ADnliv85MmfkJGRgcFgIBKJEAqFJrvYOjo6U5RBU45WrlzJ448/zrZt22hsbOSxxx7jW9/6Vlyu5qQRHpq4Pfy22znns/OnBllNP4p0wZndrEtdjvKmmUStAt/97neZN28eACdOnBjPUuvo6ExjhjX2/KGHHuKhhx6ivb2dyspKPB4Pa9as4cEHHxyv8g3MEN2EVhemU5SXwo+alvG23OchKQ+uvI6Y/hc+N38Tjz3wbZRn63n11VdxOp1Eo1Gi0ShG4003NF9HR2eUjMjlqK2tjc9//vNs27aNlStXjnWZhk7AN+RNP168gNrzyTSGZ0OwBYwJcPopPmyfQ/6K+QSWJFBZWUlGRgYAp06dGq9S6+joTGOGJJpPP/00P/rRj2JLeXl57LP8/PxxK9ygdHWAJA1p05I75pBiN/GLK3er1fOEbAi3YzjxM36Q+yCv3d1FUI7w5JNPAhAIBJBleRwLr6OjMx0ZVDQ3btzIc889R0NDQ2xpbGyciLINTiQE1y4OaVOb2UjJHXP41SEFKWEOtJ+GhFzoaGT1qV/xsexlHF4b5ZVXXuHs2bMAU+c6dXR0pgyDNtpt2rSJhx56KG7d/v37x61Aw0KKwnk3ZOUOafMP3lvAj19o4HlxI29KrAbfJRBEkCJ83dNEzVwF2+oZ/PznP+eRRx6J2d/peZs6Ojoag0aaqampvXrKp8owyuauIOs/+Sg7duwY0vZF+U4WzUrhlweicNtXoegxSHZBtJPUpDz+VRL4/R2XSUpJ4NlnnyUajXLu3LlxvgodHZ2JYseOHaxfv57m5uYRH2NQ0WxrayMvL4+5c+cyd+5cCgsLp4wdXI4jmd0l97O55KHBN0Y1Tv7APS5+V3+B1qAJ0pfBkkcgKR86z/Bw0gJSTQLCm6M0NTXx4osv0trayiCeJjo6OtOEzZs3s3v3bnJyckZ8jEFFc8+ePezbt4+9e/fGlqkimh7JxulLMpGzQ49833tXPrKiUP3qGXVFUi4s+UdIdmHraOTzBgc70tp4x32Z/PnPf+bMmTNcunRpfC5AR0dn2jGoaK5bt478/HxSUlJISUnB4XDwhS98YSLKNihh2cDBznRO/PXIkKPBjGQrb12Rwy/+0sMkN8UFq74ArhI+IRtwCCKHl15mYW4S1bt2cubMmfG5AB0dnWnHoKLZ1NTEt771LZ5++mleeOEFnn766SkTaZoMkGiM0HT8Mm1X2oe834fuK+DQuTZeb2i5vtLigHnvI2He+/gXrPzEKPHBNwYJ+LvY/ezTXLt2bewvQEdHZ9oxoGi2t7fz+OOP09DQwHPPPceuXbt47rnnRmzeOR7YLEbCHZ20nr065H3etCybgsxEvv/HG4ZLCgLMeTuPzHuYREQqHVE+da/MgUNH+O2v/2+MS66jozMdGTDlKCUlhcrKyl4u7VMm5QgQrFaM/iDnXj9Klz/KotsLsdjNA+4jigL/+OYFfP4X+zjX4mN2ekKPAwok5b2dL4SvUH7mKf5pvpFVbju/3v1b7r5zDXMXrxrnK9LR0ZnKDFo97zmNq8aUyls0GLGKETqaLtB44Bwn9rj522/2EwlFBtzt/fe4SLGb+GHtyT4/f6TgA2Rb0vhSUipb3hjEahL4r+98GzkSGI+r0NHRmSYMKpo3tl82NTWNuE2zrKws9rquri42adf27dsHXT8QtkQ75q5WhEAXl5qucflMCy0XvQPuk2Ax8uH7C/nZi410BnoLrNVg5quFH6Y6fI1TebfzgbsSaTjfwo4f/SfoKUg6OrcsQ5qNUjMifuKJJ1i3bh2pqanDPtH27dvjpmatqKiIm5FQm3mwv/UDIVgsJOEnJXiNDk8X0UiUK2dbBt2vbN08/OEov/hL38MlP5RdzOKEOTwme3n7HXNYMCuB3XWvc76+/3mJdHR0bm4GFU2Px0NKSgpr1qyhvr4+1ik0HNxuNw6HI/a+rq4u7n1RURE7d+7sd/2QsCdibm0my2kiIdnG5TMtRMPRAXfJcdp58LbZ/OBPJ4lKvc05DIKBb8z9KC94j1A3624+vz4LXyDCL6t/TaipVo84dXRuQfoUzQMHDsQtaWlpbNy4kXXr1nHmzBk++clPDuskdXV1FBcXx97X19fj7DFtrtPppL6+vt/1Q8JqU02JL53FarcQ9Ifo8AxuHffoWxdytsXHr/ec7/Pzd2bcwZ0pi/jCxT+S/YZP8ODtDl4+dIkXn/8TSsMukAcWZh0dnZuLPnvPS0pKWLVqVa+E8T179vDUU08Nq/e8rq6OjRs34vF4YutaW1v73La/9UNDAFsCXDyHcZaLaESi09OFM2vgmSGX5zl5YHEW3/n9MR68fXavTi5BEPjG3I/wwN7P84zUzvs/+A+8cPT77P7bWXJSYIkchrmbQdQNi3V0bgX6/Kb3lWbUk+eff37IJ/B6vTgcjjjRTEtLw+v19tq2v/X9ca2zna3/99PY+7ULllOclY1w5QKCKRPvtU7mDOE4n337Qt61/c/85fgV7luU1evz+53LeVPaKv614UnedecP+dQHGvm3H/yGQ2d9ZJpfICPcDvM/BObkIZddR0dn4tixY0ecsc9oDDt6ieaZM2cGFEwg7vMzZ86Ql5fX53bbt2/H4XBQU1OD2+3G7XZTU1NDUVERe/bsiW3n8XgoKirqd31/ZCSlsO2hj8Sv7GqH5jOYC2dw9YKHgC+ELcEy4PU8sDiLZbNT+c7vjvcpmgBfL/wIa/7+KX5x6QX+4R2fZOFzB3j+4DXmZc8m2f0clnAXLPmEOrJIR0dnSrF582Y2b94ce79+/foRH6tXm2ZlZeWwDjDQ9lu2bIn1hBcXF+NyuWKve/ak19fXs2nTpn7XD4uEJOjqIDHkwXu1g8N/PTnouHRBEPjM2xZSd/gSh8+19bnN6pR5PDTjbr7S+HOCSoQPP/wpmq91ceyanaOB+SjNL8KR/4GQd3jl1dHRmVb0ijSLi4t57LHHhpTAPlSTDLfbTWVlZSzSLCkpoaKigu3bt+NyuQBiKUb9rR8ygggmM4bmJlIWZ3Kp6RptV9pxZjkG3O3B22fz1ZqD/OdvjvLkI3f3uc035n6UJX8r5Ztnqvny8g+wcuVK/nzgAgseup2TgSALLr4EKLDoYbBlDK/cOjo60wJBmcZmkXfNXdy7eg4gy9B2FWV2IdeS8phblMfyexcMeryf/rmBR3/6On/7j7eydHbfuahbT/+Y75x9huN3/YjQuXY+85nPsKHkQVZkSyyN/gEzAUgvgkUfA8e80V6ijo7OOLB+/Xp27x5ZvvWIZqOc8ogiJDoQms9gD3m52HiVUCA86G4fuMdFQWYiX/u/Q/1u88X895FmSuafT1Yyf/587rjjDv768t+QUpdy1PJ2dZbLa3vg0HfBe3osr0pHR2cKcHOKJoDFCqIB28VT+K+10dLcd1tlT0xGkS8+uIw/7G/m76f7toJLNNr4z/mlPHP1Ff7UspcPfehDXLlyhf2HjiKnFRGYvRFsWeA5Bke+D57jY31lOjo6IyAajdLS0sKJEycG33gAbl7RBEhyYPB3wuVzXHZfHtIuD90+h8W5Dr5ac7DfNtuNmfdxf+pyPn3i+2TPmcU999zDyy+/TFSSOR5eAMs+DanzoeUQHPx/cOEFffSQjs4kEI1GaW1t5cSJE+zZs4fDhw9z8eLQZrDtj5tbNAUBUpxY2q/SVn9oSB1Xoijw5ZJl/PX4VV48eqWfwwr8YOGnORO4wjeadvC+972PlpYWDh5UhbYzYQks+ww4F0N7AxyrgsZqUPR51HV0xptIJEJLSwsnT55kz549HDp0iEuXLiHLMunp6SxZsmRUx7/5h7EYTZisJoJnzhA6dgTr4qWD7vLWFTmsLkjj688c4v7FmX1mEixMnM3W/Peyrekp3nvn/dx99928/PLLrFixgtOnT6v5pUsfgWM/gtaD0FgDUgTy14M5aTyuVGeUyLJMJBLB5/Ph8/no6uoiGAzGPlcUJW4ZDYIgxC09MZvNmEymXn9NJhMGgwGDwYAgCLG/tzqKohAKhfB6vVy5cgWfz0coFALAYrGQnp5OZmYmDocDs3lgr92hcPOLJmBKsOFv9eH7026s8+aDaeAbJwgCX3jPUh781ov8+ehl3rhkZp/bbc1/L09dfpHSY9/hZ5s/xSP/+AhHjhxh+fLltLW1kZq6AFZ+Do49AZdfg9O/gtZDMHcTpK9UI2GdCUdRFHw+Hy0tLbS1tSHLMrI8eC1AEzhRFEclVj1FV5blXgIcjQ7sZ9CXYGvlEkW18mi1WrHZbLFFE97Rln2qoP24dXV1cenSJUKhEJFIBEEQsFgsZGVlkZ6eTkpKypgIZU96iWZ7eztVVVVDztMUBIHPfe5zY1qoscYgCEi2JPzu46QdeBXW3DfoPsVLZ6rR5tOHeWBxVp/3w2ow88NFj/LGvVt4MfsUd999Ny+99BJLliyhqalJtdCzZ8Hyf4bk38CFOmjZD/5LMOdtkP9uMFrH4Yp1ehIOh2lpaeHatWtEo9FekaIoihiNRoxGIwkJCSQmJpKQkIDVasVgMEx4eWVZRpIkJEkiEokQiUQIh8NEIhGi0SjRaDT2WpIkQP0uapEyqKLS2dkZO+aN16stBoMBq9UaE1mLxRK7F0ajMSbCk4ksywQCAXw+Hx0dHbS1tcXuh6IosWvIycnB4XCQnJw8rv9v0zpPc17WLDKSUli7cAXFi1bG1kdlCEZFrEYZY/f/+VW/kaVCE/Nnm+GTXwF74qDHrz10kQe/9SLPfv4B1i7tO9oEeO+hr/Oq9zi1OV/h0Uc+zfvf/34WLVrErFmzyMzMvL5h53lVOC/9FYIecC6CRaWQnDfSW6DTB1ovqdaO1TOK1MTC6XSSnp6O3W6/KSKvniiKEhPXUCiE3+8nEAjg9/uJRqOxe9JXlHvjcW4UWC1ytVgsmM3mWHNBz2UoQqv9cGk/Djf+QAQCATo7O4lEIrHPtP9Hg8GA2WwmMTGR1NRUkpOTSUhIGNL/ozYGvbm5ecRznU1r0byjYDHbN8Qnt4clgbaQSKJJoSsikm6NYhChNWAgyx7kzsAeeNeH4YHBx54qisLaf38OQRCo+9K6fv9TjnQ2sfTVMn68+J9p/skeGhsbeeSRRzAYDKxcubL3fu0NcKQS2o5D4iyY+17IvlcdzaQzbBRFob29nXPnzsW+XNo9F0URu91OVlYWycnJUyJymipo0akWuYbDYYLBIMFgkEAgQDgcjkW9msD2JRc3RrFAXFttz9c924S1Y/Z3bK3JwWAwYDKZyMjIiNUCLBbLqH7sRpPcPqI2zQMHDgCq7dtkVs0lJf6mKQp4QyK5SRGWzQjwanMCHWEDqVYJm1HGE7IQSMjA9vIfYdkdkDZjwONrbZvv+daLPHfoIm9entPndkuS8nnPjLv4hvsp/vC+f+XRT32aU6dOsWDBApqbm5k1a1b8DimFsOZLcOp/4eJf1DHr3hNQsAmsw3fFv1Xp7OykqakpVk0D9f/MarWSnZ1NamqqLpIDoHUmGQwGLJaBTW20qFCLBqPRKOFwmFAoFPe3Z/R6ozhqzXnaYjQaY8JoMplISEjAZrPFOr20Zao0E2gMWzQ/8YlPUFBQABDnSDQZKEAwKmA2KASiAl0RA3ajzFxnCLtJweUIU3/ZRkQGq1GhNSDiSZxFzuV6eOWPsP5Dg55j7dKZvGF+Bl+tPsi6pdmIYt+/bv/qeh+rXnuE1+1nuffee/nDH/5AYWEhV69eJScnp/evojkZFn8CUhfBqV+C+9fgOQHzPwAZq/ROon7w+/2cOXOGQCAQ+3KKokh6ejo5OTkYjbdE3+aEo4mc0WjEZrMNe/8bRXM6M+wnbMOGDTFruI6OjjEv0HAwCAr+qEhbUMBqVFiYFiQnKUKqVW0cn50c5lKXkUtdJjLs6rprQRM5Gdmw50VYeTfkugY8hyAI/NuGFbzpP2p5+vWzlNyR1+d2RclzeVv6bXzdvYM/fuDLfKLsE5w6dYpFixbR2NhIYWFhXweHnPtUc49jT4D3FBz8Dsx6IxSU6P6cPQiFQhw9ejTWriWKIg6Hg9zc3DHvHdUZe24GsdQYdsyr2bVt3bqVDRs2jEeZhkyyWeb2bB+LM4LckeNjcXowJpgABhHmOkMYRLWt02pUuOIzEUlOh852eOm3Qxqpc+e8DN6yIpuv1RwiEu0/NeVLrvdz3HeOWuEoxcXF/O53vyMSidDe3o7PN8DUG85FsObfYN4HwGAB9zOw56vqUMxbHEVRaGpq4siRI8iyjNVqZcmSJRQVFVFQUKALps6EM6KGgscff5zS0lJKS0vHujzDwmaSmZkYZUlGkMyEaJ812nSbRJotSmdEwGaU8UVE2kImyJ4Nh/8OJw8O6VxfKVlO07Uuft7PzJUAdzgW8pHsN/PoiR9w+7vvp6uri4aGBgBOnhzE19OSAvM2w6ovQtad4LsI+78JJ34BofYhlfFmo7Ozk/3799Pa2oogCCxcuJAlS5ZgteppWjqTx7BFs6CggPz8fPLz81m1atV4lGlMEQTITY4gySIGAWRF7UknMQWkKDz/rDoh2yAsmZ3KxjvzePzZI/hD/Scff2/BPzLHlsknL1ey7i1v4tlnnyU5ORlZljl+fAjmHSkuWFkOyx5VRw65n4Y9X4FLL0M0MIwrn77IssyRI0diPzQZGRmsXLmShISEyS6ajs7wRfMb3/gGa9asic1OOR1It0WxGmWCUQGTCFf8JrVWPssFDYfV9s0h8MUHl9HaGeIHz53sd5sEo42dy77IaX8zR1YHiUQivPzyy4iiSCAQoK1tcLclRIMaba7+EuQWQ6hNtZp77Qtw7jnwX74pDUAURaG5uZn9+/cTDAYRRZGlS5cyZ86cm6Y9TGf6M+yOoCeeeIKVK9VE8qampjEv0LCIRoa0WYJJxmmNctlnIsEk0xkS8UVEEs0WNeJ8cTcsWDloClL+jEQ+9sZCvv3bY3zk/kLSkvpO01ialM9/LfgkZcf+i8+tu4tnnnmGN7/5zVy6dImmpqahj1iwZcCSfwTvWrWd03sKjv4PWJxq2tKM1arRcWLutM7xlCQJt9tNR0dHrId1zpw5ZGTo7vc6U49hf9M0wQQm/9c/GBhSxCUIMCclgiiAJAuEZYH2ULdoZc6Cqxfhpd8M6Vhb3rUEWVH4z98eHXC7h3PexgdmrqXStQeLzcovfvEL5syZgyzLHDrUv8lxn4VPXQBFj8GaL0PWXYACV/6uRp9//zL87fNw4udqFHrpZbi6T7WlazmoRqVTdG72UCjEoUOH2L9/P+3t7QiCQE5ODkVFRbpg6kxZhhxpfutb3+Jzn/scb3rTm0hNTY31ak5qrqbRBJ5rg0aIANmJEQpTQ5xotSAIcMVnJCcporq8Z+XCnpfUhPfCxQMeJyPZyqNvXci3fnOUT6ybz+z0vtvZBEGgctFnONjpxn23l87f/Zl3v/vdmM1mwuEwly9fJiur75kv+zkgJOfDin+BwBXVFb7tGLQcAF8ztDdejzYFAQSD+iNgSgSjXV3sGZCUD0lz1JFI9qxJiVD9fj8nTpxAkqRYcrPL5cLhcEx4WXR0hsuwh1E2NTWRn5/f6/VksKognxwlyOZ772TzbcsG3b7Fb+Cv5xMxigqiAG+c04nN1H35jcdg7lL4WPmgLkidgQjLP/8b1i2bSWXpnQNue9rXzOpX/5G7fmlggTOPb37zmzHfzaVLl44+ZSbSBZ3nIOSBcIfaWRTuhHAbBFog6gNZAikA0RAoURCNIBjBNgNmv1lNebJljntCfTQa5ejRozFTCYPBwPz587Hb7eN6Xh0djbEYez7sNs39+/eTlpbGrl27WL169YhOOlbkzJvP7rsKIBwcfGPAYZVIMMuEogKBqMgVv4m8lO65g2blq+lH+/4Kdww873uSzcSW9Ysp/996/unti1iQk9LvtnMTcvjxkn/hE3f9B/L/HePVV19l+fLlnDp1iiNHjvQ9Nn04mBJV0RsIWVIFNdyudir5mqH5RdVt6cgPwJyiRp+ziiFjJRiHP+JjILRaicfjibnSLF68eNChezo6Y402//mYzns+GIqisHfvXhRFiZujfFIwmeHut0BH25A6hYwiZNojhCQRo6Bwrt2ErMXZFpvqfPTnX6tV/kH4yAOF5DhtfP3pwdsnS7Lu5d41d9FeYKTqiSrMZnMsDWlCOtNEgzqmPTlPFcW8d8CdFWobaf671YT6loPq1Byvll/vdJKG1tE2ENeuXaO+vp7W1lZEUWTevHkUFRXpgqkzbRm2aDocDiorK9m0adN4lGf43PYAZOZCS99TU9zIrOQIJlHBZFBoDRg55bEQkrojvaxcuHwefr8DBjGCtZgMPPbupTy75zwHz3gGPe9/L/gU7vtFrrZc46mnnqKwsBBRFPF4PLS0tAyp7GOKaFB74Od/AO58HFb8M6SvUKv3p36ljkj62+egYSd0XRj24YPBIPX19Zw5cwaAnJwcVq5cSUpK/1G5js50YNii6XQ62bhxI62trTQ29j86ZsKwJ8KKO6FjcOECcFolMhMihCQBgwCHr1k52dod9RgMau7m/pfh9RcGPdb77s6nMCuJfx9gyl+NbGsa/35HKQ1rZHbu2sW5c+dYtmwZoihy9uxZ/H7/kMo/LpiTYeZdsPpf4favw6KHISFHrc6f/F/Y8+/gfhYiQytjS0tLbJx4SkoKK1euJDs7e/KzLXR0xoABRVOLEgBeeOEFPvnJT9LU1MRDDz1Efn4+n//858e7fENjYRFY7eDvGnRTQYCsxCiyAikWiUSTzNl28/WqekKSujxXA+cH/lEwGkS++OBSnjt4kVdPDV6lf3jW28h50yICKQrf/M5/IooiCxYsAODEiRODTnMwIdhnwOw3qdX3274KrnernUcnfqaOTGo72W9qlqIoNDQ0xJ6bBQsWMG/evCll66WjM1r6fJr379+PwWCgoKCAL3zhC7zwwgsUFxdTWVnJhg0b+PGPfzzR5RyY3AJ1aRnaNL2p1ihmg2riYTcqyArUX7Fzpr27Jztzlhq5/vZ/ITBwdPXgbXNYOtvBl3buH3SyLVEQ2bXqy1x7awoNx0/xzB92Y7fb4/I3p4wntCConUMLP6qOh09fDp1noX4bnOw9Hl6SJPbv309bWxuiKLJ8+XISEwd3x9fRmW70KZrl5eWsXLmStWvX8sMf/pCKigoqKiqorq7m4x//OI8//vhEl3NgDAZYeRcE/TCECbKSzTKJZglfRFRzx60yBkHheIuVEy0WIrIAs+fCyQPw/DMDJr2LosDX31vE30+38Os95wc9d6Yllacf/CYtS4xU/aiKS61XSE9PJy0tDVmWOXhwaAYiE0pKgVp1X/QwGKzqePi/f1Gtsgc9+P1+Dhw4gCzLJCYmsnLlSt3XUuempd960969e3nuuefYtWtXrCr+0EMPUVlZOam5mf0ybzmkOKF98LZNQYDC1BCKAIGo2s6WbJaRFTjSYuVYixXFZIGMbNWs+NjA+VwPLMniTcuz+dLO/YQi0oDbAsxLmEXFo18hisxHvvEZglKI/Px8jEYj0Wg0rllkyiAaIXedKp45D0DEByd/ztWXHuf4356BaIDc3FwWLlyot13q3NT0KZou13Vj3uLi4phTe1+fTxnSM2H+CvBcHdLmuUkRChwhOsMGwpKAIECKRSbJLOP2WrjkM0Jquhq5/u5/B636f/29Kznf6qey7tSQzr929hre8/BmOOrlwR9/hpAcZvny5YiiSGtr6+T0qA+FxFmw7FG8C7ZQb3wXZ7tSoO04C7tqyOx6WU2219G5ielTNG+MFAZ7P2VYepv6NxIedFNBgMXpQWYnh/CGDLEauM2ogAAnW60Eo4Lam37xLPz6ZxDqP4l+QU4KH7m/kO2/PkJL59CS7T/1tn9gyX2riPzmDCXP/ythJcKyZcsQBIGzZ8/GTcE6FVAUhcuXL7Ovvp7TF7uQUxdjyS9mxeo7sZuBpmfhtS/Cmd/q4qlz09LnMMrVq1fH2b7V1dVRXFwc936y5weCPmaUC/jhO1sh4IOsWf3v2IPOsMhfzycgywLJFrU9VJKhJWAkNzlMUVYAc9QPZ0/D2gfhHe/vd7jhtY4gKz7/Gza9IY//9w9rhnT+QCDARz7xMc4JHhyPFPF/RV9BDkY5ceIEiqJgsVhYtGjRpMy/rSHLMufOnaO1tTU202NycjIul+t622XEBxeeV6coDraCNU21t8t7pzpqSUdnCjGa2Sj7FM3BUkQEQYhNUj+Z9Hnhf3gK/rQL5g0+Fl3jVKuFQ9espFklDN2XHpHAEzKQmxRhdkqYmZErCO2tsOmTsOqefo/1vT8c51+fOsDLX3sLS2cPbWbJkydP8tl/+ifO3AbZ71jC7hVfxSQbYlM8aO4/wzL4GAOi0SgNDQ10dXXF5sCeMWMGOTk5/T8jIS9cfAku/lUdD29KhOz71CGaFj2xXWdqMOaiuXHjRioqKvrcQVEUHnvsMXbt2jWiE44lq1atIicnJzaeFIAzp+CH/w7pWWri+xAISwJ/u5BAS8BIoknCZlQQBFU4vSEDogh5yWEKQg0kJRgRPvL5fidkC0cl7vziH5iRYuX3W9cOuSnjqaee4smf/Yzj7zaQu3IuNcu/RJbFSWtrK2fOnEGWZQwGAwsXLhzRbIDDIRwOc/LkSUKhUGyseG5uLunp6UNvmpEi0PwCnPsDBD1gSoIZq2DOO9RcUB2dSWAsDDv6FM3nn38+NuNkXwz2+UTR56+FJKmieb4RZvcxA2Q/dIZFDl214QkYiCoCTosUq4UHowLtYRGrQSHBd4X0rBSy3vd+MubP7lNE6g5d5D3fepGff+pu3nPb7CGdX5ZlvvGNb/D63j0cfp8Z/wyBXy4tZ13aKmRZpqGhITb7Z0JCAnPnzh3ztJ5AIBCzbNPEsqCgYHRDH6MBNfI89ycItnSL523geo8eeepMGmMeaU4X+r3wvz0Hu34IBYvVHM5hcMVnZM8lOyjE2jhBTdUMSQKhiEKoowtzegZz17+RuXfMw2jqfY6N336JI+fa2PP4O0iwDE3cgsEg//Iv/4K3o52Wj2dRGzpMef5G/r3gHzCJRoLBIMeOHYs1jYiiSGJiIjk5OSQkJAy7g06WZTo7O/F4PLS1tcWOazAYmDdv3tgmp0sh1Ry56VnVXcniVI1Dsu8Dgz6jpM7EoovmjbS3wQ/+DbraISdv2Md1e80cuGLDbpSxm/q4PZEwPq8PnyMb15vuYsW6Zb3a+NxXOrn9C7/n42vnsu19RUM+97Vr13j00UeZOXMmGZ9YzZfP/oKipEJ2LPsCLvtMANra2jh//jyRSCQ2D7ggCBgMBrKysrBYLCiKEhtdpP2VJIn29nb8fn8smtTQ9l+4cOH4zvYoheF8LZz7ozq23TZD7SyasQZMuq+mzsQwpqLZ3t5OVVXVkKIWbT6Xz33ucyM6+WgZ8ML/VgvVlTBnLpiHZ0MmK3C8xcpJjwWbQSHB3Mcoo0iYkLeDTudsljxUzNzb5/a6Z9/+3TH+bddBXvjKm1jlShvy+Y8fP86WLVtYvXo1xY88yAePf5OWcAf/b34ZH8l5EwbhemQbDAa5ePEiXq93yJ1zmlu6yWTC6XTidDqxWq0Tm0oWaofGarjyOkT9YHXCzLth1jrVxk5HZxzRI82+CIfgiW9A00lwLRj2sRUFTnksHGuxYjYoJJrk3plGkRBdXj/htFkse++byV+ZHyc8UUnmga/+iYik8JevvhmzcehNBa+//jpf+9rXKCoq4tHH/ol/aXyCn12spSipkP9a8I/cnbqk1z6yLNPe3k40Go2VQxCE2GtRFElISMBkMg37fowb/stw4QW48pra825KhLSlapunfWKzBXRuHUYjmjev/YzZAg+8S50DqKtj2LsLAsxzhliaEUBWoCVguG5YrGGykOiwY2xt5vCu57h4sjnuY6NB5Psfu4MTze18+7fHhnX+2267ja985SvU19fznW/8J1XzPsMrt30bURC5Z88/8476L/GaN34edVEUSU1NJSMjg/T09NiYdi2adDgcU0swQRXGee+DOx6HhR9RRfPqHnXCuL9/CdrdN+V0xTrTl5tXNAEWrFAnS7t4dkRfPEGAQmeYu2b5cNokWgLGPoUzOcWGcu0SJ3b9gVBXIO7jZXNS+ezbF1Lx66McPjeEOc97sHr1ar761a9y8OBBvvKVr7DMOIe/3/5d/nfpY7gDl7jz9c+wdu8Wnr7yMpEpOuPkkDHZ1THtd2yDpZ8GW7raYVS/TRXPlkPqtB06OpPMuFfPa2pq8Hg8NDY24vV6qaysBNRRRW63G6fTidvtZsuWLQOu74shhdgXmqDqP9SpMdIyR3wdnWGRvzcn0BYUsRkVLEYFi+H6rZODAVo6FRa/YR4LP7opbtRQKCJx/7/9CVlReOnf3oLVPLwe/YMHD/K1r32NlJQUvvzlL6tWcorM01de5ptnqnm94yQzzA7+IXsdH8l+MwsTh5bmNKVRFOhww4knIXBVnYbYmqYmyWfeBhbHZJdQZxozZavnbrcbt9tNaWkpFRUVeDwetm/fDkBFRQWlpaWUlJQAqrgOtH7EzMqHO4pVw41RjGJKMsusmunHlRrGalToCou0BFSzDwDRasNuEXH//QTtL8a7vltMBn70iTfQcLmTr9YM3/pt+fLlfPe738VkMvHZz36WV155BVEQKcm6l7/f8T0O3vlD3pt1Pz9u/iOL/vZx7vj7o3z37DM0+JsHP/hURRBUS7rbvwZrvgIzVqtDNU/9L7z6GNRvB89RPfrUmXDGVTS9Xi87d+6MvV+zZg21tbXU1dXFzXFdVFTEzp07+10/au56M2TNVuf/GQWpVolVWQHW5nWyJttPZkKUtpABqTvgTEiyElBMHHvmz0jH48Vxca6Dr2xYzn//8QQvHRuaWXJPsrOz+fa3v82qVav42te+xn//93/j8/kAWJbk4r8W/CMX79tBzfIvkW5K4fOnfsTclz/CvJc/wmdO/IA/tuwhIIVGdf2TRkI2LPkk3PF1tf3TlASeI6pwvrZVnVkz4pvsUurcIoyraBYVFcUNVWpsbKSoqIj6+nqcTmdsvdPppL6+vt/1oybFCQ+sV02KhzAlxmAIAsxKirAqy0+6LUprQHVJEgRIdZi56Ddz7pdPQfOZuP0+9eYF3LNwBh/9n7/R7Bn+nEA2m40vfvGLfPKTn+T555/n4Ycf5uWXX47lW1pEMw9l3sNvi75G6wM1/HrFV3nAuZxnrr7CW+u/iPPPD/HWfV/gv84+wwnfuanjEj9ULA51nvY7H4eVWyB1vjol8bEn1Ojz+E+6O44GN6LW0RkpE5Zy5PV6WbVqFfv27WPbtm1x7Zv19fVs2LCBkpKSPtf3N4GbNvZcI24M+o1Eo/DUD2DPn6FwybBHCvV7XUEDey7Z6QiLpFklRAG8QRFLVwv3LBCwf/Sz4MyIbX+1PcC9X/kTWQ4rf/zCumG3b2pcu3aNH/zgB7z66qusXr2aD37wg8yfP7/PbRVF4bjvHH9o2cOfWvfykucwYSVCjiWdNzpXsNa5grVpK5llzehz/ymN75IaaV75u5osLwhgSYPsuyHrDWCbhtekM+ZoY841xnzs+XiwYcMGKioqcLlcbN++ncbGxl7iWFZW1uf6/kRz2I25rVfhR9ug7dqwxqUPhjdoYO9lO+0hkXSrhAK0+EQKo26Wr8hC+MCjkJgc235/k4c3/Uct77ktl8rSO0eVVP7KK6/w5JNPcv78eYqKinjf+97H4sWLBzymLxrgL22Hed5zgOc9+znY6UZBYZ59Fg84l3Nf6jLuS11GtnXoCfmTjhSGthNw9vfQdV718zRYwDEPCjeo8x3p6HQzmo6gCZnIZfv27THBdLvdFBUVxflxejweioqK+l0/ZqTNgLdsgl99V50WI8U5+D5DwGGVWDHDz2sXE+iKiCSZZZIsCmfFPGYePkjm0z+GjZ8Aq+pOtDLfyX9/7DY+/sNXWTjLwT+9fdGIz33XXXdxxx138Morr/CrX/2Kz33uc8yZM4cHHniA+++/v087uQSjjbdm3MZbM1TT5pZwOy96DvK85wAvth2k8sLvACiwZXN36mLucSzhrtTFzLPPQhSmaJaawQzpy9Ql0qWOcz/7W2g5qIpp6nyY9361fVRHZxSMu2jW1NRQXFwcmyKjrq6O0tJSysvLY9vU19ezadMmiouL+1w/piy7Hdzr4MXdYE+CMUr2TrdLzE0NcfiaOmbdZlLwRQ0cty7Cue8VTDY7vPujsfNtekM+Jy928OWdB8hJtbPxDXkjPrfBYODee+/l7rvvZt++fbzwwgvs2LGDJ598koKCAhYtWsT8+fOZP38+mZmZmM3xBhnp5hRKsu6lJOteAK6E2vhL22Fe9h7hr21H+MXF55GRSTUmcXvKfFYkF7A80cWyJBeF9mzM4hRLmDclQs596lzuLQegoVrN82xvUMe4579Lr7brjJhxrZ673e5e8wtVVFSwZcsW6urqqK+vj0WfPfM0+1rfFyMOsbs64McVcK5BHWI5RmOuIxL8rTmR1oCBdJuEJENr0MiSxFYWBI5D8YPw1veqo5RQ2xrLql6j5rWzPPP5+7lv0dgNGwwGg7z66qvs27ePEydOcOHChdhnSUlJOJ1OkpKSMJvNmEwmTCYTBoMBo9GIwWDAZDJhsViwWCwIZpFrRh/nBA+nucJJ8TJnLG3IJjAKBgrt2cy3z2KuPYe5CTnkWjLItqYx0+wkzZwcN1Z+UpAluPq6Ootm4JpabU+cDXM3QXLBmP3/60wf9LHnI6HxGPz0m2rSe8bMMSvTNb+RV5vtmARIMMt0hUUkBe5IukiG/zy87X1w/ztjwhmJymz4fy+yp7GV3z62lpX5Y9NkcCOdnZ00NDTQ0tKCx+OhtbWVrq4uIpEIkUiEcDiMLMtIkoQkSYTDYUKhEKFQiEAggM/nizkqadiSErA47UhOM52pEpeT/DQlddDplFG66zACAk5TEmmmZFJNiTiMiaQYE0g22kky2kgxJsTWJRqs2A1WErqXRKOVRIMtthjFUYqvFIFre6Hp1+qYd0FUh3EWblLHu0/VpgedMUcXzZHywq/hN79Q7eOG6PI+GIoCJ1otHG2xkWqNYhSgNWggySxzp62JxEALPPBuWPdQrKreGYiwvuIFGq90srv8jazIGx/hHA2yLOPz+Whvb8fj8XD16lWuXr3KlStXuHjxIhcuXKCtTR0majAYSM+ZQUpuOvY5aRjykgimG2hX/LRFumiP+uiM+umQ/LRHfbRHfPjlwXNILaKph4haSTLaSTRYSTEmkGJSxddhTCDVlBT7m2pKJM2UTIY5BYcxUW2TVWRoO64myvsugiJBwiy1zdO5SBfPWwBdNEdKJKKaFb/+gmpYPEZO6FEZXm1O4JrfSLpN6jb8MJKdGOY223mMbZfhzmJ4xwfBpnpIen1h3v3NF3Bf6eI35W9k+RQUzsHw+XycPXsWt9tNY2MjDQ0NuN1uJEnCarWyYMECli1bxrJly5g3b15c22pEjuKTgvilED4piE8K0iUF1CUaoFMKxNZ1RtXXnZKfzmiA9qiPjqgfb7SLtoi6hJVIr/IZBJE0UzLpphTSzclkm9PIQWSm9zSZ4XYyFZHMpDnMyH836Rm3YzRMSD+pziSgi+ZoaG+Dn25X2zcLFo1Z+9alLiOvNSeQYJKxGhUiMrQFjSxIC7LYehXh0hlY8QZ4z8cgRfWP9PrCvGv7CzRd7WLXP9/HHXOnf2dFKBTi9OnTHD9+nCNHjnDkyBF8Ph9ms5mFCxeydOlSli5dyoIFC7BYhud72h+KohCQQ3gjPjyRDlojnbRE2rkWbqc10kFLuJ2rYS8XQx6aQy1cCnnokuKNVgQgw5hIji2THEs6WRYnmWYHWRYnMy1Osi1pZFvSmGlxTr2OMJ1B0UVztJw5BU9+Ux2bPnNszC4UBQ5csdHgtZBmjWIUwR8RCMsid+Z0kSF2wdlTMG85lDwMmWqSvtcXZtN3XqLe7eGn/3gX71g1tKmIpwuSJOF2uzl06BCHDx/myJEjdHV1YTQamTt3LosXL2bx4sUsWLCA1NSJMyP2S0GuBK5w9eJfuNr8AleCV7loMNFsTqLZlsYVJcqVcBtXQt5eUWymOZVZ1nRmWdLJtc5gtjWDObZM8myZuGwzSTMlT6zBs86g6KI5Fvz9BaipUp2QksfmyxqSBF6/aOeKz0S6LYoowLWAgRn2KG/I8WGQwqpJcmaO2qu+/E4QBIJhiYcr/8buvRf45gdX8fDa3q7wNwuyLHPmzBmOHDnC0aNHOXr0KC0tLQBkZWXFUqXmzZtHYWHh+E7FoRHugMuvwtk/gO8CGO1qcvy896OkzMMT7eJSqJXmUCvNoRYuBFu4ELzGhVAL54PXOBu4Sqd0fZhsstGOyzaTfFsW+bYsXLaZFNhnUmDLJs+WiUnUmwEmGl00xwJZhl8/CS/9FvLmD3uKjP7oDIu81qwmvadZJSKyOi3wqqwAeSlh9bzNTWqUe+c6ePMGsCciyTJf+NV+fvDcSTbflc93PrwG+xAnaJvOKIrC1atXOXnyJCdOnODEiRM0NDQQDocRRZHZs2fHBHTevHnk5+ePWbW+F9GAOh3H2d9197YLkJAD8z4Iqf2nqimKgjfaxZnAFdyBS7j9l2gMXKIpcJlG/yXOBq8QVbonsRNE5lgzYyLq6hbWAns2BfaZJBsTxufabnFuWdHsc97z0eD3wS+/A8fqoXBxLC1otFz2GXn9YgJGQSHRLNMWNGA3ydyd24XN2H372z1w5YI6Lv6dH1TnNgKeeqWJR3/6Oq7MJH72yF3Mz771pr2NRqOcPXuWU6dOcerUKU6fPs2ZM2eIRqOIokheXl5MROfOnUt+fn6vBP5RociqDd2pX0HXORCMqm3d/A9Bct7wr0eWuBC6RqP/Eo2BizT41UUT1Z5RaoYphQK7KqYu+0zyrJnMsWVSYJtJrnXG6NOwbjHGbd7z6cKYRpoaV5rhJ9uhvQVmzx2zw55stXDkmpUUi4xRVLgWMDLfGWRpRvB6wBIJw9nTkJQC975djTztiRy74OX93/0r51t9lL9rCZ9528JhzTd0MxIOh2lqaqKhoYHTp0/HhFSSJIxGI3l5ecydO5d58+Yxf/585syZg2G0Ji2KDNf2Q8NONVXJYFWHZ87dDIlj0/asKAqtkQ4a/RdpCFyk0X8Jd+ASjf5LnAlcpjnUioL6lTUJRvK6BVSLTgvt2cy15+CyzcSqT43cL7dspDkuoglwdC/873fBaof0sRmlI8lQf9nGmQ4LGbYooaiAPyqyZqafWck9OhYUBa5dAm+3aD+wHpbejl+Cx589wnf/cJz52clUvH8V9y/WJx7riSakPSPSc+fOIcsyFouFgoKCWPvo/PnzmTlz5sjaiuXo9eGZgSvqCKPUhbDgI2AZ35pAWI5wLnhVjVK7hbUpcBm3/zKNgYv4pCCgDiqYY53BvIRZzLPPYn7CLObZc1iQMJtca8ZN20Y+VHTRHGsUBV54Fn73K8ieM2aJ74GowKvNCbR1D7P0hgxYjTJvmOUj6cZpgqMR1Y9TisLCInjjuyFvHofOefnsk6+zp7GVexdm8qWSZTdFatJ4EQgEaGhoiAnpyZMnuXxZNYFOTExk7ty5saWwsJCsrKyhC4osgecwNOyCYCuIZkjMVYdnJs6e8OGZiqJwOezhtK+Z0/5mTvmbOek7z+nu6r/W659gsDLfPouFibNZlDCHhQm5LE7Mw2WbectU93XRHA8iEdj5P6r/ZsEiMI5NLp4nYOD1S3YCERGnVeJad9L7mpl+TH09r36f2lFks8OcebD8TpS5S/n92TD/8X+HOHLey8o8Jx9+oJANd8whyabnDA5Ge3t7rEqvRaRaj31iYiIFBQUUFhbG/ubk5AxctVcUNfJsrIagR11ndULeesgoUh2YJhlJkTgbuMoJ33mO+87FlmNd5/BGVWNus2BifsIsFnWL6ZLEPJYk5lFgnzn5/gFjjC6a40V7m5q/efb0mCa+X/UZef2SHUUBu0nGEzSQlxJm2Yxg3GRtMRRFNRlpvQKRECQ7Yd4y5HnL+OM1Mz891MFzx1sxGw3cvziTt62cRfHSmcxKs9/y1bCh0tbWFmsfbWxspLGxMRaRWiwWXC4XhYWFsXbS3Nzc3kKqKNB5Vo08fRdUj0+jTa26z36r2u45xf4/tOj0WNc5jnad4bjvPMd8ZznadZbWiDr1tUU0sSAhl8UJc1jcLaRLEvPIs2VOXavAQdBFczw5cwp+UqE6vWeMnRdjk9fMgas2bAYZowhtIQPZiRFWzAiQcGNVvSeyDO2t0HpNHTNtMILFxgWDg/+T8vh9RwqvtZuREci0KKxKgyWpIvOcJualmclzWklNsKidGpKkVjFlWf3Ci2L3YlD/Ct3vDQb1tcGgtvPaEq4vVtuUE4KxorOzE7fbTUNDQ2y5cOECiqJgs9liPfZaO2lmZub1H6lIF5z7E1x+DSIdgAjmJHDMhVnrIGn2lB7jrigKV8NejnSd4UjXGY75znKs6xxHus7EIlO7aGFxYh5Lk1QRXZwwhyWJ+cy0OKf8j7UumuPNC7+G3T9X04AsY5NcrShwtMXKiVYrKRYJg6Coxh4mmUJniDnJ4b6r6zcSiUAooC7BAEQjtEhG/h5ysC+iLsejyVxSbLFdUoQws0U/s0U/cwx+5hh85Il+8g1d5Bn82ISeoq2A9oQoijo+32hS3aFMZvV+pM6AjCxwpEOSAxxp6g9MsuOmE1Sfz9erjfTq1asAJCcnx6LRwsJCtY00cwZCRyOc+yN0nlPFFEWdHM4xD7LvU9OXpkmCu6IoXAy1cririaNdZznSdYbDXU0c6zpHoNt0Jc2UzLLEfJYm5bM0MV8V1MQ5JBntk1z66+iiOd6EQ/Dkt+DEftXYY4yEQJLhwFUbbq8Zp1XCKKjJ8EFJxGmLkm6LkmaTmGGPDE1AB6AjKnI6aOJsyMSZ7r/nQybOhoycC5kIKtejnlxzhEJbmPm2MAttYRbaQyy2hUk2SGp0Gg2rYh0Nq2lSoSCEgyArgAIGEyQkQWoG5M2DrFzInKUu9psvWdvr9XLy5Mm49KfW1lYAEhISKCgoUBeXC1emhdnKYYxdTRD2qpG+JUWd1yjnfjUStWVOux8bSZFoClzmcGcTh7uF9FBnEw3+i8ioP8IFtmyWJ7liy7LEfPJsw+h4G0N00ZwIzrvV+YUgNk58LIhIsPeSnQtdJpLNMjajgiRDR9hARFaNI1KsMnOSw1iMMum2KHbT2P6XyQpcjRhwB024g2YagiZOB82c8JtpDJqRUB/qfEuYpQkhViYEWZkQYmViEKexj6aESBh8neDvVDuyFBlMFnWepKxcdcRV5iw1Ik1MUSNS0+R3lowlHo8n1jaquT1dvHgRAJPJxJw5c3DNSqcwNUBBSgcuRxCbSQHBAOYUSHFB5p2Q7ALrxI3BH2sCUogTvvMc6nRzsMvNgY5GDna58UQ6AUgy2FmWlB8T0hVJBSxJzMNuGN/hsrpoThQv/Rae/SnkFsbm+xkLIhIca7HS6LVgFBVSzHIs0JBk6AwbCMvqCrtRZk5KmAVpQQwT0CQWkgVOBswc8Zs55LNyyG/hoM9Ch6SGvvmWMEWJQYoSQqxODLAiIYT9xs4sRVGjdV+H2qEV6nYUMlvAbFWXpBTVDDprtjqXU1omOGeo6V7TLOrqD5/PR1NTU6ydtLGxkbNnzxKNRhEEgewZDgrToWCGSGGGQkGGiZREC1hS1V74tKWqiBonYPz9OKJV8Q92ujnU5eZgp7qc9F1ARkZEZEFCLiuTC1iZVMjSxDyWJuWTZR67tlJdNCeKSBh+9v/g6B51uOMYfpkVBc52mDnRasEXNqAAJlHBapBjTYpWo4I/IuKPiuQ7Qsx3hkgcqNNonJAVaAya2O+zUt9lZZ/PykGfhYAsYkBhkT3Eqm4hXZUYZKEthOlGgdeENBxSq/ahoCqm0e5Ef7NVFcwUp+o8lZENqelqu2listohZbWP2RxPk0UkEuH8+fMxEdWWQED9YclINlEww0ThDIHCTCOFOQ7S0tIQcu5XRyMl5Eyb9tDBCEghjnad5UBnI/s7G9jf0cjBzsaYQXW6KSUmpFpkOs8+a0SGJ7poTiQXz8IT31C/3GNkI9cTf0SgJWDEHxG54jPRGRYRUYUqKAmkWSWisoA3LJJkklmQrnYaTXYwFpHheMDC3i4r+7qF9Lhf7cW3CDJL7SGWJIRY1v13iT1EUl/pVaAKaigIgS4I+CHoV5P8FcBsVqv6RpO6WG1q+2liiiqmCcnqOqtdfW9P6v48We3tn+wbNQRkWebSpUsxIW04dZKGhtN0dKlj0h12URXQTDNzc5KZ65pFxqK3IKTOB4tzWlzjUJEVWW0r7WriQEcj+7sF9XzwGqDmli5JnBM32d+ypHycpuQBj3vLiuaYG3YMldeeh2d+rH4JZ4xd++aNKN1CaRDAHxE5dNXKFb+JJJOEzajQHhaJygJ5KWFmJUVItUZH3WE0lvgkgUN+C/u7rBzwWTjit3A8YCGqqF9qlyXMYnuIRfYwC20hFtrDFFrDmPtrdlAUNdqPhNUfrV5LVO1YQVCFQ1HUSNRkUdtMrXa1HdWZcT1itSepEa22TNE0KkVRuHbtGg2nT9Nw4hCnTx6joeksbZ1hAFLsIvOyzBRmJzJvTgZzi4pJz1+jzro5xa5lLGiLdHK4s4mD3dX7A52NHOk6Q0hWayqzLOmsSCqIE9MC+0x2PbVLN+yY8EgT1C/jK3+C3/5STbnJnBij4EBE4ESrlfOdZsISOCwykqK2eYqCgt0kk2mPkmKVsBpljIIanEkKGAUwiup/dUgSiEgCCgKioGAzysiKQEQWkBW188kgKhgEMBsUTKKCKCpYDAriKL9/WhvpYb+Fwz4LxwJmjvstXI6oVSwDCgVWtee+5zLXFiaxv8i0P3qKbDikDgzQmgRk6XoqlSheT6EymdW21oRktZ01OVWNYrW8VE1Ye76exCaC1mtXOX3kdU4f38/pE0c51dyJ16fazjkTDczLtjEvN5V5K+9nflExSc7MSSvreBOVJU75L8TaSg90NHKg083lsDpKyyZaWJqYR1HyXJofq7s1I81JE01Qv5Cv1sFvfq5+0bJyJ+y0nqCB0x4LF7tMGAVItqjzEAWiIsGoAAgoKGj6pgCiQOy9HPc/LiAISmy90L0OFARB3c8gqK/NBoV0W5QUi0SyRSLFImMerpD1Q2tE5ETAwomAmRMBMye7l4vh64KUY44w1xpmni1CgTVMgVVNjZpjiWAcjZhL0nVxjUWxWlpVRO39pztyBVUkjZrImlTxTE69vsQS/+3qj6rJogqxxaoORpC7U7ekqPpXFK83NxhN6jEN3fmww3RmUmSZa80NnD78GqcO/51TZ69w+qKfrqDa9j3TaWFBYT4Llq5iwdLVuAoKMU3zduHBuBpq43DXGQ52qiK6v7OB/Mcv66I5KSiK6vi++2fqQz4ObZwDnfpsh5lTHgvtIQN2o6xGhN0RpSp7KtprTSwNwvUam6J0rxfU9T2Pr0WpsiwgAxFJICQLKIqAQVCwGBXSbFGcVolEs0SqVcJqHNvHqVMSONUtoKcDZk4HzZwKmHEHTYS6c0uNgkK+RRXQQmuYudYIc7tfZ5qksa2dKooqpDeKrPZakoj7tRIEVfgMRnURxesjsBT5uhBro7Bu/Gs0Xm9eMFvAYgNLd9aBJsiaeBu7t+m5mCwoso+LDS9z6sQ+Tlzs4OTFII1Xw0QkMBkF5s7OZOGiRSxctoaFi5eRlpY2hjdsanLLtmlOumiC+tDveRGefVKNDLImdk6fQFSgsc3ChU4TUUlAUgSisiqT3VoI3dVwQQARoDuyFLkeScYEtls8RUEV4P6q41EZQpIa2UqKup3FoOCwRmMRqMWg/rUbZWwmtZo/VgImK9AcNtIQNNMQMNEQVEW1oTtxX+6+8kRRptAWZq5VreJrglpgHUF1f0QFla9HlFJUfS+K6o0WRPXGKVwfzjro3x6vJQl6/C/H2nFR1NeisYdgd/8VZIh2EY600BiMcMIvcDxg5ESngatB9ThZSWYW5uWycP58Fi1bTf7CxRgSksbMlHsqMBrtuDlyFSYTQYDbHlCjjad/DB1tYzbH0FCwGRWWZASZ7wwSkVXB9EdFAhFV0BQEJBmCkkhUIpbvCRCRRHUf5fo6RYFgd9umrFz/SqopUGoalFFUMBsUEkwyCd01O0mBYFTkms/Epa7uWwMoCJhEVTBNBoUEs0SiSSbRLJNklrsFVR52W6koQK4lSq4lygM3WFiGZIGmkCkmpqqgmnixw861yPVHfqYpGotOC7ur+gXWMHmWKBZxjARVFFXLuImuAWuiKmuC3aM5wJCMGTsLxSgLDX7eY+4Ai4eWoI/jYRPHwhaOH2/nr4cbiNb8HqsgM98ms9CZwMJZmSwoLCQlKxccMyElXe1cS3GO2RQxUx1dNMeKO4pV1/cXf6NWmyxjl/w+FEwGMBnUKCPZMvTcTUkmTjRlmZj4an+jsoAvIuAJGOiKGAhJAl0REVmJF1KLQcZujO+sVRTteBCKCvjCJi5qNVKBmJimWCRVRE0yiSb1r9WoiqnSHTgJDK0j2CIqLLCFWWALA764z7xRMSaiapRqZm+XlZ0tyfhlNZISUJhljpJvjZBvjZBnUYV0tiXCHEuEDJM06g6xcUczXxmGWqcD98gR7ol0QcBDqL2Z021+jnep0eifLsJTF87Ba+eYKYaZbwqxwBxinl3BlZqENT1TzSbJyIH0XMiYA2mz1UyFm6jdVBfNsUIU1RklW6/AkdfVMeqjnV5hAjCIao/19RVg44YoKxpVE8+FAHI0QiAi0BE24A0ZaQnY6JIthDDgU0Tkbucei0HGYlQwi2A2iJhFA5iNcc4+UrdAR2SBK10mmm8QU6PYs0tLiQmn2aBg7naHMneLrtbLb+xetP2NooLVKMfacR1GmdWJQVYnBuMuUVHgUsSIO2iisXs4aVPQxEGfhV+3JuKVrv9fWgSZWZYoueYIud1immuJMtscYZYlSrZ5DCPViUY0qSOQLKlYHAUsmQNLZAmkAEq4iyudHRz3tHOyXeCkz8YrXYlEOgXEKwqzjVcoNJyh0BCgwBDGZZFIsBnV3NrkBHCmQPosVVAdGd0RarraLisa1YhcNHW/7v6rtf2iqK5eclT9C+qQU83nU5G6t9PWi+r+QrdbF6LqiSB2L6NAF82xxGqD93xUnSTtzClw9T9j4ZREUdRE8q4OdchjONzdNibGeoJFm52EVDsJtgRm2hPBYkWKSARDEoFgFF9Aoa1T4mq7TCCk0BFVj6vIMkbZj1WIYBIkjMgYjAYMBiNWzTnJqD7ksqJ2OkUVATkWBQuxzqmwJCArhlgnVnfftrpN99Zaj79BUDCIqogndEevVqPazmoxKnFtrzNNquDdnRzodWu8UZHzIWPM6ORc2MiFkIljfgt/9CbEVfsBZpii5JjV46l/I2SZJWaaomSZo8wwRXEah98sMSmIBhATEUyJZCVkkZUFD3R/FImEOONto8Hbxen2AKc7QrzkixJRgE7IMkXJM0bIuxIkX7jAHOE02YYQZqOgVo+MBkiygMMOCVYwGdX1ZpO6mAxgMYLFoP7CKzKg1aSE6z/CitJjfY82Y7V+cv29JqijQBfNsSZtBrznI+pwy0vn1OkypipSVDXW6GoHX7dlmcWm5icuXg05+WpbVUp3m1WKU216uOGHwAAkdC/pwBxAikr4vD58rR0EvZ0E2trxNHtob+kkGAghhbrdkUJBCEYwyRHMih8zEQwoWESwGLqFVOt5Ngztge8ppnJ3m60/ItIZMiApXI9cUb+Hxu6sA6tRxmqQsWiRa3dHkdidbuUUFTLMIW63htSONYi170oCXAwbOR8y0Rw2cjFs5ELYyMWwiVc6bVwMJdEmxdc8jIJChlEiwxQlwySRbpJIN0qkmSTSjBLO7iXVKJFqlHEYJRLGsDNtLDCZLMzNyGJuBry1e11UljnfFaSx3UdTh5+mDh+1HX5aQ1FAvZ8zrQZmWyDHKDErFGVWS5hZxggpQhiBKEgRtdPKIHRXhwSwmMBuAbtVzSCwGLtF1qRGkQZR3UeJqmJsMam1G4tBTVSmu5NMGXrzVV/oojkeuBbC298H//cjuHoRZoydefGIURTVb9OnRZEhVYASk9XJ49YshOw8tfd/Rs6ofUMNRgPJ6ckkp8cPZwv5w/g6AgR9IUKBMOFAGL+nA+/FFvyeDtr9QaRgGCESUpsEwmEIRVFkCUFSR7/EeqZipslaBCHE9UxfzwBQE/WNRhkRYvmn2m2RFFAUAV9YpFNRhbVnLqt2Oi2aFboFUzuOKKhNAnajjN2gsMQQYbU1hClBbSIQURNgo4pAqyzSFhVplYxci4q0Ro1cixpoiRg4GzRRH7XiiYq0RQ2xDICemAQFh0EVUodRxmGQSTZIpBjk7uW6wKaZJGaY1YjWYZT6H2k1xhhFkfxkO/nJ8f6ZneEo5zoDnOvyc7YzwPnOAH/1BbnqB6VbihKMBrITreQkWMm2mplpNZBtFsgyglOJIkQj0BZVm4wEUP+JqkIp0B1xCiBEwaSoTbomESxm9VlPTlE9X/nNyK9vxHvqDMya+1Vh+v2v4IJbjdomMkTQ7Nl8HRDwqQ+TxaaOdJm/QrVny8pVl9T0CSubxW7GYu9tA6coCqFAGH9HgFAgQjgQJugPE/IFUQIBLIQxS0EMYT+K34fs64IuL0ZfO2KwCyUaJRJRQIoSiSqEJIEwRsIGGyGjnZBoRcZAFEHN2lHUqrxW5VebAZQ4mYol+gtKLOlVQUHqkYGgZhmISAqgqNsKXE/limUDdS+ayNoEmIPCHCJABMEIglFBtF4/rk8R6JRFfIpAlyziU7pfKyJ+RcQXEWgNGzgnm+hSBHzd24T7EFsAm6AKq8MgkWKUSTVIOLuj2hlmNdJ1mtToNi0W5Y5dE0KS2cjitCQWpyXFrQ9JMhd9QZq7gjT7AjT7glzyBTnc2kFr8PpMrRaDyEy7hawEKzPtiWTaLcy0W8m0W8iyW7D2nNY65vvavQQC0NkO5xqvm8KMkGmdpzlpY8+Hw4FXVTu5rg7VkHc8ct2iEfB3XfewlOXrRsBpMyBvgZp4P0ZR5JQjElHbYrXF74NOL1y5AGdPwbVLKF0dRCNRZAxIqGP2ZQQkgxnZlohkSSBsSiDcnTAfkVSlEMXu3Fa0kVUKJlEdZoqiRo9hSSAYFegKi3RFDES682Vl5XruOlxPo7wu1vGpXXL3PihqNKsJr6g1yfUYrtDzldgj4hUFkFAIKCKdiki7JNIpdy+SSFcP4dVEWHsfUHo/myIKKQaZtG4hTTdJzDBFyTRHmWFSxXaGKUqmSSLTFCVhjHNfg1GJy/4Ql3xBLnb/veIPccmv/o30qBKkmI1k2S1kaotN/Tuj+7XVaGDH3sPs2HuEZlOiPvZ8StNwFGqq4GqzKmDGEQb4MfcfX7cDkE/tgjYar9uozZmrVrNn5Ny0U04MG1+nmg7W1X597Hm0u0310jl1ts+uDvWHB0XtlLL1MPEwdvfi9kxU1zqvbri3iqLmwoYlNVVLuR6kxuI/tfqvCqTU3eaqCm33ehkk1M/CUnzqlxQT4h65tcSLsbZdrFlBAJOg+Qko3U2EapOF1KMMQQnaZQMdkkCHIuKTVeHtVES6FCH2ulMW6VBE2mWxVxOCXZCZYeoWVGO3mJolZpoizDRLzLJEmGWJkGwcfdusrCh4ghEu+4Nc9oe44g9xJRDiavfrq4EwkhIvqpqg/r2lS09un9IULoYP/TPs+iE0HlU7Wpwz1B7p/tB6srUqdjgECGoCsS0B0rMh16VWrzOyVQNfR5oukH2RkKRmMvRHJKKmirVcgmuXVPu/5iY1Wr12URVL6J5krrtDKhpVhRdBjdztiZCQhGC1YzHQ96yiY0BPcexpVK1FrZIsEJQEQlGBUPfgBX9EoCNkIBhV34ej6oAHLUgTUHN8bQaFZLM6jv/GXFs1EiYWQcuKeox2WcArGfDKIh7ZQLss0q6ItEdEGkIW9ski3j6i2ARBJs0gMcOoZhTMNEfJMUXJsUSZZY6QbwuTZpExD9DxJQoC6TYz6TYzS/oY+SkpCp5gmCv+EJf9Ia4GuoXVHxrV/4EumhNF9hz4h3+BvS/BodfUqmM4pDroWG09DHlD18cm27o9IeevgNmFaoeNs9vV/CZyNJ90TN3DX3sOgZVlaGtRxdTXERvHHRvTHfSrn127pLZZX2lWX4cCgHDd4zMhaUzzdbVc1Z4BnmiAHrPf0d8sTD2jYLVJQcQfEekIi3iDqqgGwyKSlsOldGcXdEenRlHBKCjdfW3q+ZKBXKRe54mLfgG/JOKRRFpkA62SgRbJQKss0ioZOBKx8FKXjQ4l/j4lCDJOUSLDIJFplMg2RZhpipBtDJFjCJEtBkgRQhjlKAZFQkTCoEgYkDCgoBgMJIkiyaKBuTYjotWCmARiBN51LD5PdzjoojmRpKbDuofg/vVw7jScPgyH/q5WsxOSISdd9XpMcarCOCNHjSBvtjbI6YAodk+7MaP/bVwLr78Oh1ThvHweLp6BxmPQdk1tklFktRNOM0qepOGGgqBGwNej4OtipyiqZaA/IsbcsnwRka6wiD9qICwJBKKqoEqKoLaqCqidX90+Bz0HJahCq3QPKlBIESRSDBHytWaOmGFJd8iryEQkmVbZwNWokauKiauyhRbFREvExLGwiZex0HmDZNmRSRUkUkUJpyiRapBxGhRSRQkHERxKGAdRzKhTyAhGE4LFArww4vuoi+ZkYDJBwSJ1Wfsetao3BY1vdYaB2aI2l+S6gPtUYfBcgyvn1XZT9/Hr7afRqBp9JiZf9+qc5P97QVCnU7EaJegjcozKEIwKhMIKwWCUSERCiEZQolGkiERUUuiKGumULQRkdXbTqBLf5qkIAqIgqJ1WYnf6pSiqqZhGE0arkSyzmSzN19TYI0/XqNrlhTDQKom0RAVaowItIZmWkExrIMrloMSxUJQ2v6RmM/TAZhBINokkCyJJYQMpo7hXumhONtoDonNzIYqQnqkui1er63ydaiR66Rycb4QzJ6H1qlrVB/WH054EiUmq9dtEC6k2b1MocH1a5nAIIRpV0x1RSDIYu59ZCyRa1OaHJEe3l6gDbAlEBRNB2dDdrtrtY6CIapNAWCEUhVBIJhiSicoKYUVAisrIktzdcSWg9U8LgoAoCgiigCiIiAaRZItAqkFkvkHEYBAxGA0IPfKiZEWhMyzRFpLwBKN4Q1Js6QhLtIelvq5+yOiiqaMzUSQkXa9hgNoBdbVZXa5cgLOn1b+XL6iCBdcnmNMc4g3dxsSDCarS3c0ud7sbRaM9/kauGyxHwtc7ukAdJ27pnmMpYyakzlCblZJS1CakxGT1OrS5l/rIBDECid3LgEWUFcKhCOGgukRCUSKhCJFwlGhYIhqJquvDUaIRiXAgQjgUIdr9Xg5GkaISUrRHMwPdQisIJAmQBOSZBASziJBiRDSIiKLAD08MUrgBmJKiWVdXh9vtxul04na72bJly2QXSUdn7DGZICdPXTT8PrXHvuWy2pt/wa2KaLtHFTmpW/y08dQ9/TRBfa90/xW6zYwNhlj1NmZqnDpDjRBTUq93WGnO8ylOdd1IU+OGiCAKWGxmLLbh1bSkqEQ0LBGJRIkEI4SCEUL+MOGgKqihYJhoWEKRFRQUFFlBispEQlHCoQhS5CaMNCsqKqitrQVg+/bt1NTUUFJSMsml0tGZAOwJaq7tnLnX10XC4G1Vh8GGAterz5FuQ5VY1ClcNxuOTZ9hvO7ybrGpf82WaW0obDAaMBgNWDAzksZJWZb5wdPfHPH5p5xo1tXV4XA4Yu+LioqorKzURVPn1sVkVqvKOmOCOMofjCn3c1NfX4/T6Yy9dzqd1NfXT2KJdHR0dK4z5USztbV1sougo6Oj0y9TrnqelpaG1+sd0rbNzc2sX78+9n5KG3fo6OhMGjt27GDHjh2x983NzSM+1pQTzaKiIvbs2RN77/F4KCoq6nPbnJyc6WHYoaOjM6ncGFD1DLaGy5SrnhcXF+N2u2Pv6+vr2bRp0ySWaGrS81dTR0W/J32j35fejCbSnHKiCWrKkZZqBOg9532gfxF6o9+TvtHvS29uOtEsLi5my5YtlJSUjDixfaQPynTZb6RMZDn1ezI19hsp0+H6JuMHYUqK5lgwHf7DR7PfSJkOD/TNfE8mY7+RMh2ubzJEc1o7ty9evJiCgoI+P2tubiYnJ2fYx9T3G7v9pkMZ9f2m934jPVd9fT0XLlwY9n4wzUVTR0dHZ6K5aavnOjo6OuOBLpo6Ojo6w0AXTR0dHZ1hMOVGBOnEU19fj8PhwOl0snfvXlavXo3D4bhlPUe9Xm+cC1Z/9+FWuj833hP9mYGamho8Hg+NjY14vV4qKyuBMXpeFJ0pTUlJiQIoDodDqaioiK0vLi6Ova6oqFCqq6sno3gTRnV1tbJlyxalqKgobn1/9+FWuD/93ZNb/ZlpbGyMu+6SkpLY+7F4XvTq+RRn3bp1KIpCW1tb3K/ijZ6jO3funKQSTgwlJSWUlZXFrevvPtwq96evewL6M+P1euOubc2aNdTW1o7Z86KL5jTA7XbHeYrqnqMq/d0H/f7c2s9MUVER+/bti71vbGykqKhozJ4XXTSnAV6vF5fLxYYNG/B6vbrnaDf93Qf9/ujPjIbX66Wuro6tW7eO2fOidwRNcUpLS2Ov161bR3l5OQUFBUP2HL2Z6c97dTierDcj+jNznYcffpja2locDseYPS96pDmFqaurY926dXHrNH9Rj8fTa92tRn/34Va+P/ozc53t27dTUVGBy+XC7XaP2fOii+YUxuVyxTX079u3j02bNumeo930dx9u5fujPzMqNTU1FBcX43K5APXHZKyeF33s+RRHyzcDtX2mZ29ofX197Ff0Zs65A/V6a2trqaqqoqKiIvaF6O8+3Ar3p797cqs/M263u5eRT0VFBVu2bBmT50UXTR0dHZ1hoFfPdXR0dIaBLpo6Ojo6w0AXTR0dHZ1hoIumjo6OzjDQRVNHR0dnGOiiqaOjozMMdNHUmdLU1dVRXl4el3w8kQw2vK6qqory8vKJKYzOlEAXTZ0Jw+v1UlZWRmpqKlVVVWzfvp3y8nK2b9/e7z61tbVxQ+HKysoQBKFPEa2pqUEQBKqqqsasvNqxvF5vbBx3T0pLS0lLSxuT8+lMD3TDDp0Jw+FwUFBQwMaNG+NMJQoKCigqKqK4uHjA/bUhgh6Ph8rKSioqKuI+93g8OByOuGOPhl27dlFSUhIru8vl6jWuW+fWQ480dSaU2tpaNmzYEHvv9Xpxu92xMcJDoaysjJqamrh19fX1rF69ute2o3H2aWxsjCuXNn5Z59ZGjzR1JpS6ujqqq6sBVegqKyuprq4elmi6XK7YuHNNxDweTy/RrKurY8OGDZSWlrJmzRr27NlDQUFBLBIdaLxxfX09a9asib3XxFcT6z179vSKdHVuEcZuZg4dnYHZt2+f4nK5lNraWqW6ulopLi5W9u3bN+A+W7Zs6XWMxsZGpbq6WikpKYmtr62tVdra2hSHwxG3fUlJSdx8L0VFRbFj9Jxbp6ioSGlra+v3vFp5NVwuV+x1z/lodG5+9EhTZ8LQIkMtOnQ4HJSXl1NbWzvsY5WUlPDwww/HHbe/qnjPKLa4uJjKykoKCgriqto9p0foi9ra2lgn0I2zP+rcWuhtmjoTRm1tbVxHitvtjjN/HS4bN24cdk95WlraoIJXU1PTy0+xrq4uVv3X2zZvbXTR1JkwbhSbnh0tw5nkS4soy8rKqKioiJsUqy96pift3LkzZjxbV1cXt4223Z49e+Kcu7XzaWKrHWOsUpt0phe6aOqMO263m+3bt1NcXMyuXbti6zdt2hTLhRxKdbe+vp5t27axbdu22PQF2uJ2u9m2bVtcbqXGnj17qKurY/v27WzdujW2z9atW9m+fTs1NTWxDiGv19sr79LtdsdSj0CdEnbv3r199tbr3AJMdqOqjs5A3NghM1xKSkoG7WzqSUVFRVyH0FD30bl10CNNnZue4bSbtra26p08OgOii6bOlGekY8+1PMzq6uohJbm73e643MyhUFVVxc6dO4ddNp3piz5HkI6Ojs4w0CNNHR0dnWGgi6aOjo7OMNBFU0dHR2cY6KKpo6OjMwz+P0w6HVUZNxyKAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 350x262.5 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sims = [\"Carrick2015\", \"Lilow2024\", \"csiborg2_main\", \"csiborg2X\", \"manticore_2MPP_N128_DES_V1\", \"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\"$|\\mathbf{B}_{\\rm sim}| ~ [\\mathrm{km} / \\mathrm{s}]$\")\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, 1.15 * figwidth), sharex=True, )\n",
    "    fig.subplots_adjust(hspace=0, wspace=0)\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}\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, \"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",
    "    # Watkins numbers\n",
    "    # for n in range(len(sims)):\n",
    "    #     rx = 150\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], h_pad=0.01)\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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": []
  }
 ],
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