csiborgtools/notebooks/flow/reconstruction_comparison.ipynb

{
 "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "catalogue = \"CF4_TFR_i\"\n",
    "simname = \"Carrick2015\"\n",
    "zcmb_max=0.05\n",
    "sample_beta = None\n",
    "sample_alpha = True\n",
    "\n",
    "fname_bayes = paths.flow_validation(\n",
    "    fdir, simname, catalogue, inference_method=\"bayes\",\n",
    "    sample_alpha=sample_alpha, sample_beta=sample_beta,\n",
    "    zcmb_max=zcmb_max)\n",
    "\n",
    "fname_mike = paths.flow_validation(\n",
    "    fdir, simname, catalogue, inference_method=\"mike\",\n",
    "    sample_alpha=sample_alpha, sample_beta=sample_beta,\n",
    "    zcmb_max=zcmb_max)\n",
    "\n",
    "\n",
    "X = []\n",
    "labels = [\"Full posterior\", \"Delta posterior\"]\n",
    "for i, fname in enumerate([fname_bayes, fname_mike]):\n",
    "    samples = get_samples(fname)\n",
    "    if i == 1:\n",
    "        print(samples.keys())\n",
    "\n",
    "    X.append(samples_to_getdist(samples, labels[i]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = [f\"a_{catalogue}\", f\"b_{catalogue}\", f\"c_{catalogue}\", f\"e_mu_{catalogue}\",\n",
    "          \"Vmag\", \"l\", \"b\", \"sigma_v\", \"beta\", f\"alpha_{catalogue}\"]\n",
    "# params = [\"beta\", f\"a_{catalogue}\", f\"b_{catalogue}\", f\"e_mu_{catalogue}\"]\n",
    "# params = [\"Vmag\", \"l\", \"b\", \"sigma_v\", \"beta\", f\"mag_cal_{catalogue}\", f\"alpha_cal_{catalogue}\", f\"beta_cal_{catalogue}\", f\"e_mu_{catalogue}\"]\n",
    "\n",
    "\n",
    "g = plots.get_subplot_plotter()\n",
    "g.settings.figure_legend_frame = False\n",
    "g.settings.alpha_filled_add = 0.75\n",
    "\n",
    "g.triangle_plot(X, params=params, filled=True, legend_loc='upper right')\n",
    "plt.gcf().suptitle(catalogue_to_pretty(catalogue), y=1.025)\n",
    "plt.gcf().tight_layout()\n",
    "# plt.gcf().savefig(f\"../../plots/method_comparison_{simname}_{catalogue}.png\", dpi=500, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# catalogue = [\"LOSS\", \"Foundation\"]\n",
    "catalogue = \"CF4_TFR_i\"\n",
    "simname = \"IndranilVoid_exp\"\n",
    "zcmb_max = 0.05\n",
    "sample_alpha = False\n",
    "\n",
    "fname = paths.flow_validation(\n",
    "    fdir, simname, catalogue, inference_method=\"mike\",\n",
    "    sample_mag_dipole=True,\n",
    "    sample_beta=False,\n",
    "    sample_alpha=sample_alpha, zcmb_max=zcmb_max)\n",
    "\n",
    "\n",
    "samples = get_samples(fname, convert_Vext_to_galactic=True)\n",
    "\n",
    "samples, labels, keys = samples_for_corner(samples)\n",
    "fig = corner(samples, labels=labels, show_titles=True,\n",
    "             title_kwargs={\"fontsize\": 12}, smooth=1)\n",
    "# fig.savefig(\"../../plots/test.png\", dpi=250)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Paper plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0. LOS velocity example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpath = \"/mnt/extraspace/rstiskalek/catalogs/PV/CF4/CF4_TF-distances.hdf5\"\n",
    "\n",
    "loader_carrick = csiborgtools.flow.DataLoader(\"Carrick2015\", [0], \"CF4_TFR_i\", fpath, paths, ksmooth=0, )\n",
    "loader_lilow = csiborgtools.flow.DataLoader(\"Lilow2024\", [0], \"CF4_TFR_i\", fpath, paths, ksmooth=0, )\n",
    "loader_cb2 = csiborgtools.flow.DataLoader(\"csiborg2_main\", [i for i in range(20)], \"CF4_TFR_i\", fpath, paths, ksmooth=0, )\n",
    "loader_cb2X = csiborgtools.flow.DataLoader(\"csiborg2X\", [i for i in range(20)], \"CF4_TFR_i\", fpath, paths, ksmooth=0, )\n",
    "loader_CF4 = csiborgtools.flow.DataLoader(\"CF4\", [i for i in range(20)], \"CF4_TFR_i\", fpath, paths, ksmooth=0, )\n",
    "loader_CLONES = csiborgtools.flow.DataLoader(\"CLONES\", [0], \"CF4_TFR_i\", fpath, paths, ksmooth=0, )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "angdist = angular_separation(\n",
    "    np.deg2rad(loader_carrick.cat[\"RA\"]), np.deg2rad(loader_carrick.cat[\"DEC\"]),\n",
    "    np.deg2rad(csiborgtools.clusters[\"Virgo\"].spherical_pos[1]),\n",
    "    np.deg2rad(csiborgtools.clusters[\"Virgo\"].spherical_pos[2]))\n",
    "k = np.argmin(angdist)\n",
    "print([loader_carrick.cat[\"RA\"][k], loader_carrick.cat[\"DEC\"][k]])\n",
    "print(csiborgtools.clusters[\"Virgo\"].spherical_pos[1:])\n",
    "print(csiborgtools.clusters[\"Virgo\"].spherical_pos[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loaders = [loader_carrick, loader_lilow, loader_CF4, loader_cb2, loader_cb2X, loader_CLONES]\n",
    "simnames = [\"Carrick2015\", \"Lilow2024\", \"CF4\", \"csiborg2_main\", \"csiborg2X\", \"CLONES\"]\n",
    "\n",
    "\n",
    "with plt.style.context(\"science\"):\n",
    "    plt.rcParams.update({'font.size': 9})\n",
    "    plt.figure()\n",
    "    cols = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
    "\n",
    "    for i, (simname, loader) in enumerate(zip(simnames, loaders)):\n",
    "        r = loader.rdist\n",
    "        vrad = loader.los_radial_velocity[:, k, :]\n",
    "\n",
    "        if simname == \"Carrick2015\":\n",
    "            vrad *= 0.43\n",
    "\n",
    "        if len(vrad) > 1:\n",
    "            ylow, yhigh = np.percentile(vrad, [16, 84], axis=0)\n",
    "            plt.fill_between(r, ylow, yhigh, alpha=0.66, color=cols[i],\n",
    "                             label=simname_to_pretty(simname))\n",
    "        else:\n",
    "            plt.plot(r, vrad[0], label=simname_to_pretty(simname), c=cols[i])\n",
    "\n",
    "    plt.xlabel(r\"$r ~ [\\mathrm{Mpc} / h]$\")\n",
    "    plt.ylabel(r\"$V_{\\rm rad} ~ [\\mathrm{km} / \\mathrm{s}]$\")\n",
    "\n",
    "    plt.xlim(0, 90)\n",
    "    plt.ylim(-1000, 1000)\n",
    "    plt.legend(ncols=2, fontsize=\"small\")\n",
    "    plt.axvline(12.045, zorder=0, c=\"k\", ls=\"--\", alpha=0.75)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(\"../../plots/LOS_example.pdf\", dpi=450, bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Evidence comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "zcmb_max = 0.05\n",
    "\n",
    "sims = [\"Carrick2015\", \"Lilow2024\", \"csiborg2_main\", \"csiborg2X\", \"CLONES\", \"CF4\",]\n",
    "catalogues = [\"LOSS\", \"Foundation\", \"2MTF\", \"SFI_gals\", \"CF4_TFR_i\", \"CF4_TFR_w1\"]\n",
    "\n",
    "y_BIC = np.full((len(catalogues), len(sims)), np.nan)\n",
    "y_lnZ = np.full_like(y_BIC, np.nan)\n",
    "\n",
    "for i, catalogue in enumerate(catalogues):\n",
    "    for j, simname in enumerate(sims):\n",
    "        fname = paths.flow_validation(\n",
    "            fdir, simname, catalogue, inference_method=\"mike\",\n",
    "            sample_alpha=simname != \"IndranilVoid_exp\",\n",
    "            zcmb_max=zcmb_max)\n",
    "\n",
    "        # y_BIC[i, j] = get_gof(\"BIC\", fname)z\n",
    "        y_lnZ[i, j] = get_gof(\"neg_lnZ_harmonic\", fname)\n",
    "\n",
    "    y_lnZ[i] -= y_lnZ[i].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with plt.style.context('science'):\n",
    "    plt.rcParams.update({'font.size': 9})\n",
    "    figwidth = 8.3\n",
    "    fig, axs = plt.subplots(2, 3, figsize=(figwidth, 0.5 * figwidth))\n",
    "    fig.subplots_adjust(hspace=0)\n",
    "\n",
    "    x = np.arange(len(sims))\n",
    "    y = y_lnZ\n",
    "    for n in range(len(catalogues)):\n",
    "        i, j = n // 3, n % 3\n",
    "        ax = axs[i, j]\n",
    "        ax.text(0.1, 0.875, catalogue_to_pretty(catalogues[n]),\n",
    "            transform=ax.transAxes, #fontsize=\"small\",\n",
    "            verticalalignment='center', horizontalalignment='left',\n",
    "            bbox=dict(facecolor='white', alpha=0.5),\n",
    "            )\n",
    "        ax.scatter(x, y[n], c=\"k\", s=7.5)\n",
    "\n",
    "        y_min, y_max = ax.get_ylim()\n",
    "        y_offset = (y_max - y_min) * 0.075  # Adjust the fraction (0.05) as needed\n",
    "\n",
    "        for k, txt in enumerate(y[n]):\n",
    "                ax.text(x[k], y[n, k] + y_offset, f\"({y[n, k]:.1f})\",\n",
    "                        ha='center', fontsize=\"small\")\n",
    "\n",
    "        ax.set_ylim(y_min, y_max + 2 * y_offset)\n",
    "\n",
    "    for i in range(3):\n",
    "        axs[1, i].set_xticks(\n",
    "            np.arange(len(sims)),\n",
    "            [simname_to_pretty(sim) for sim in sims], rotation=35)\n",
    "        axs[0, i].set_xticks([], [])\n",
    "\n",
    "    for i in range(2):\n",
    "        for j in range(3):\n",
    "            axs[i, j].set_xlim(-0.75, len(sims) - 0.25)\n",
    "\n",
    "            axs[i, j].tick_params(axis='x', which='major', top=False)\n",
    "            axs[i, j].tick_params(axis='x', which='minor', top=False, length=0)\n",
    "            axs[i, j].tick_params(axis='y', which='minor', length=0)\n",
    "\n",
    "        axs[i, 0].set_ylabel(r\"$-\\Delta \\ln \\mathcal{Z}$\")\n",
    "\n",
    "    fig.tight_layout()\n",
    "    fig.savefig(f\"../../plots/lnZ_comparison.pdf\", dpi=500, bbox_inches='tight')\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Dependence of the evidence on smoothing scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Bulk flow in the simulation rest frame "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sims = [\"Carrick2015\", \"Lilow2024\", \"csiborg2_main\", \"csiborg2X\", \"CLONES\", \"CF4\"]\n",
    "\n",
    "\n",
    "with plt.style.context('science'):\n",
    "    plt.rcParams.update({'font.size': 9})\n",
    "    cols = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
    "\n",
    "    plt.figure()\n",
    "    for i, sim in enumerate(sims):\n",
    "        r, B = get_bulkflow_simulation(sim, convert_to_galactic=True)\n",
    "        B = B[..., 0]\n",
    "\n",
    "        if sim == \"Carrick2015\":\n",
    "            B *= 0.43\n",
    "\n",
    "        if sim in [\"Carrick2015\", \"Lilow2024\", \"CLONES\"]:\n",
    "            plt.plot(r, B[0], label=simname_to_pretty(sim), color=cols[i])\n",
    "        else:\n",
    "            ylow, yhigh = np.percentile(B, [16, 84], axis=0)\n",
    "            plt.fill_between(r, ylow, yhigh, alpha=0.5,\n",
    "                             label=simname_to_pretty(sim), color=cols[i])\n",
    "\n",
    "    plt.xlabel(r\"$R ~ [\\mathrm{Mpc} / h]$\")\n",
    "    plt.ylabel(r\"$|\\mathbf{B}_{\\rm sim}| ~ [\\mathrm{km} / \\mathrm{s}]$\")\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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    "name": "ipython",
    "version": 3
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