csiborgtools/notebooks/flow/flow_calibration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calibrating the velocity field against observations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copyright (C) 2024 Richard Stiskalek\n",
    "# This program is free software; you can redistribute it and/or modify it\n",
    "# under the terms of the GNU General Public License as published by the\n",
    "# Free Software Foundation; either version 3 of the License, or (at your\n",
    "# option) any later version.\n",
    "#\n",
    "# This program is distributed in the hope that it will be useful, but\n",
    "# WITHOUT ANY WARRANTY; without even the implied warranty of\n",
    "# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General\n",
    "# Public License for more details.\n",
    "#\n",
    "# You should have received a copy of the GNU General Public License along\n",
    "# with this program; if not, write to the Free Software Foundation, Inc.,\n",
    "# 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import jax\n",
    "from numpyro.infer import MCMC, NUTS, init_to_median\n",
    "import corner\n",
    "from getdist import plots\n",
    "from scipy.stats import multivariate_normal\n",
    "\n",
    "import csiborgtools\n",
    "\n",
    "from flow_calibration import *\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "\n",
    "paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LOS density & radial velocity plots "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fpath = \"/mnt/extraspace/rstiskalek/catalogs/A2.h5\"\n",
    "fpath = \"/mnt/extraspace/rstiskalek/catalogs/PV_compilation_Supranta2019.hdf5\"\n",
    "\n",
    "loader_carrick = csiborgtools.flow.DataLoader(\"Carrick2015\", \"LOSS\", fpath, paths, ksmooth=0)\n",
    "loader_csiborg = csiborgtools.flow.DataLoader(\"csiborg1\", \"LOSS\", fpath, paths, ksmooth=0)\n",
    "loader_csiborg2 = csiborgtools.flow.DataLoader(\"csiborg2_main\", \"LOSS\", fpath, paths, ksmooth=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ks = [115,  53,  77, 105,  26,  61,  86,  29,  80,  21]\n",
    "ks = [19,  8, 15,  0, 16,  6, 48, 38, 26, 44]\n",
    "# ks = [19]\n",
    "# ks = np.random.choice(50, 10, replace=False)\n",
    "\n",
    "# k = 6\n",
    "for k in []:\n",
    "    fig, axs = plt.subplots(2, 1, figsize=(7, 7), sharex=True)\n",
    "    # Get rid of vertical spacing\n",
    "    fig.subplots_adjust(wspace=0)\n",
    "\n",
    "    # Plot CSiBORG\n",
    "    for i in range(loader_csiborg.los_density.shape[1]):\n",
    "        axs[0].plot(loader_csiborg.rdist, loader_csiborg.los_density[k, i, :], alpha=0.1, color=\"black\")\n",
    "        axs[1].plot(loader_csiborg.rdist, loader_csiborg.los_radial_velocity[k, i, :], alpha=0.1, color=\"black\")\n",
    "\n",
    "    # CSiBORG1\n",
    "    axs[0].plot(loader_csiborg.rdist, loader_csiborg.los_density[k, :, :].mean(axis=0), color=\"red\", label=\"CSiBORG1\")\n",
    "    axs[1].plot(loader_csiborg.rdist, loader_csiborg.los_radial_velocity[k, :, :].mean(axis=0), color=\"red\")\n",
    "\n",
    "    # CSiBORG2\n",
    "    axs[0].plot(loader_csiborg2.rdist, loader_csiborg2.los_density[k, :, :].mean(axis=0), color=\"violet\", label=\"CSiBORG2\")\n",
    "    axs[1].plot(loader_csiborg2.rdist, loader_csiborg2.los_radial_velocity[k, :, :].mean(axis=0), color=\"violet\")\n",
    "\n",
    "    # Plot Carrick+2015\n",
    "    axs[0].plot(loader_carrick.rdist, loader_carrick.los_density[k, 0, :], color=\"blue\", label=\"Carrick+2015\")\n",
    "    axs[1].plot(loader_carrick.rdist, loader_carrick.los_radial_velocity[k, 0, :] * 0.43, color=\"blue\")\n",
    "\n",
    "\n",
    "    # for i in range(2):\n",
    "    #     label = \"SN\"\n",
    "    #     rdist = loader_csiborg.cat[\"r_hMpc\"][k]\n",
    "    #     axs[i].axvline(rdist, color=\"violet\", linestyle=\"--\",\n",
    "    #                 zorder=0, label=label)\n",
    "\n",
    "    axs[1].set_xlabel(r\"$r ~ [\\mathrm{Mpc} / h]$\")\n",
    "    axs[0].set_ylabel(r\"$\\rho_{\\rm LOS} / \\langle \\rho_{\\rm matter} \\rangle$\")\n",
    "    axs[1].set_ylabel(r\"$v_{\\rm LOS} ~ [\\mathrm{km/s}]$\")\n",
    "\n",
    "    axs[0].set_yscale(\"log\")\n",
    "\n",
    "    axs[0].legend(loc=\"upper right\")\n",
    "    axs[0].set_xlim(0, 200)\n",
    "\n",
    "    fig.tight_layout(w_pad=0, h_pad=0)\n",
    "    fig.savefig(f\"../plots/LOSS_los_{k}.png\", dpi=500, bbox_inches=\"tight\")\n",
    "\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test running a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpath_data = \"/mnt/extraspace/rstiskalek/catalogs/PV_compilation.hdf5\"\n",
    "# fpath_data = \"/mnt/extraspace/rstiskalek/catalogs/A2.h5\"\n",
    "# fpath_data = \"/mnt/extraspace/rstiskalek/catalogs/PV_mock_CB2_17417_large.hdf5\"\n",
    "\n",
    "simname = \"Carrick2015\"\n",
    "catalogue = \"Pantheon+\"\n",
    "loader = csiborgtools.flow.DataLoader(simname, 0, catalogue, fpath_data, paths, ksmooth=0)\n",
    "get_model_kwargs = {\"zcmb_max\": 0.05}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Running HMC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = csiborgtools.flow.get_model(loader, **get_model_kwargs)\n",
    "model_kwargs = {\"sample_alpha\": False, \"sample_beta\": True}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "kernel = NUTS(model, init_strategy=init_to_median(num_samples=100))\n",
    "mcmc = MCMC(kernel, num_warmup=250, num_samples=500)\n",
    "\n",
    "rng_key = jax.random.PRNGKey(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mcmc.run(rng_key, **model_kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mcmc.print_summary()\n",
    "samples = mcmc.get_samples(group_by_chain=False)\n",
    "# print(csiborgtools.numpyro_gof(model, mcmc, model_kwargs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Vmag = np.sqrt(samples[\"Vext_x\"]**2 + samples[\"Vext_y\"]**2 + samples[\"Vext_z\"]**2)\n",
    "\n",
    "V = np.vstack([samples[\"Vext_x\"], samples[\"Vext_y\"], samples[\"Vext_z\"]]).T\n",
    "V = csiborgtools.cartesian_to_radec(V)\n",
    "\n",
    "l, b = csiborgtools.flow.radec_to_galactic(V[:, 1], V[:, 2])\n",
    "\n",
    "print(f\"|V|  = {np.mean(Vmag)} +- {np.std(Vmag)}\")\n",
    "print(f\"l    = {np.mean(l)} +- {np.std(l)}\")\n",
    "print(f\"b    = {np.mean(b)} +- {np.std(b)}\")\n",
    "if \"beta\" in samples:\n",
    "    print(f\"beta = {np.mean(samples['beta'])} +- {np.std(samples['beta'])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [l, b, Vmag]\n",
    "labels = [r\"$l$\", r\"$b$\", r\"$|\\bf{V}_{\\rm ext}|$\"]\n",
    "if \"alpha\" in samples:\n",
    "    data.append(samples[\"alpha\"])\n",
    "    labels.append(r\"$\\alpha$\")\n",
    "\n",
    "if \"beta\" in samples:\n",
    "    data.append(samples[\"beta\"])\n",
    "    labels.append(r\"$\\beta$\")\n",
    "\n",
    "if \"h\" in samples:\n",
    "    data.append(samples[\"h\"])\n",
    "    labels.append(r\"$h$\")\n",
    "\n",
    "data = np.vstack(data).T\n",
    "fig = corner.corner(data, labels=labels, show_titles=True, title_fmt=\".3f\", title_kwargs={\"fontsize\": 12}, smooth=1)\n",
    "# fig.savefig(f\"../../plots/mock_{simname}_{catalogue}.png\", dpi=500, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vizualize the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data, names, gof = read_samples(\"Pantheon+_groups\", \"Carrick2015\", 0)\n",
    "\n",
    "fig = corner.corner(data, labels=names_to_latex(names, True), show_titles=True,\n",
    "                    title_fmt=\".3f\", title_kwargs={\"fontsize\": 12}, smooth=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $\\texttt{LOSS}$ comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "LOSS_Carrick_0 = read_samples(\"LOSS\", \"Carrick2015\", 0, return_MCsamples=True)\n",
    "LOSS_Carrick_1 = read_samples(\"LOSS\", \"Carrick2015\", 1, return_MCsamples=True)\n",
    "\n",
    "LOSS_CB1_0 = read_samples(\"LOSS\", \"csiborg1\", 0, return_MCsamples=True)\n",
    "LOSS_CB1_1 = read_samples(\"LOSS\", \"csiborg1\", 1, return_MCsamples=True)\n",
    "\n",
    "LOSS_CB2_0 = read_samples(\"LOSS\", \"csiborg2_main\", 0, return_MCsamples=True)\n",
    "LOSS_CB2_1 = read_samples(\"LOSS\", \"csiborg2_main\", 1, return_MCsamples=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = [\n",
    "     LOSS_Carrick_0,\n",
    "     # LOSS_Carrick_1,\n",
    "     # LOSS_CB1_0,\n",
    "     LOSS_CB1_1,\n",
    "     LOSS_CB2_0,\n",
    "     LOSS_CB2_1,\n",
    "     ]\n",
    "\n",
    "# params = [\"l\", \"b\", \"Vmag\", \"beta\"]\n",
    "params = None\n",
    "\n",
    "g = plots.get_subplot_plotter()\n",
    "g.settings.figure_legend_frame = False\n",
    "g.settings.alpha_filled_add = 0.75\n",
    "# g.settings.title_limit_fontsize = 14\n",
    "g.triangle_plot(X, params=params, filled=True, legend_loc='upper right', )\n",
    "g.export(f\"../plots/LOSS_comparison.png\", dpi=500,)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $\\texttt{Foundation}$ comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "FOUNDATION_Carrick_0 = read_samples(\"Foundation\", \"Carrick2015\", 0, return_MCsamples=True)\n",
    "FOUNDATION_Carrick_1 = read_samples(\"Foundation\", \"Carrick2015\", 1, return_MCsamples=True)\n",
    "\n",
    "FOUNDATION_CB1_0 = read_samples(\"Foundation\", \"csiborg1\", 0, return_MCsamples=True)\n",
    "FOUNDATION_CB1_1 = read_samples(\"Foundation\", \"csiborg1\", 1, return_MCsamples=True)\n",
    "\n",
    "FOUNDATION_CB2_0 = read_samples(\"Foundation\", \"csiborg2_main\", 0, return_MCsamples=True)\n",
    "FOUNDATION_CB2_1 = read_samples(\"Foundation\", \"csiborg2_main\", 1, return_MCsamples=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = [\n",
    "    FOUNDATION_Carrick_0,\n",
    "    # FOUNDATION_Carrick_1,\n",
    "    # FOUNDATION_CB1_0,\n",
    "    FOUNDATION_CB1_1,\n",
    "    FOUNDATION_CB2_0,\n",
    "    FOUNDATION_CB2_1,\n",
    "    ]\n",
    "\n",
    "g = plots.get_subplot_plotter()\n",
    "g.settings.figure_legend_frame = False\n",
    "g.settings.alpha_filled_add = 0.75\n",
    "# g.settings.title_limit_fontsize = 14\n",
    "g.triangle_plot(X, filled=True, legend_loc='upper right')\n",
    "g.export(f\"../plots/FOUNDATION_comparison.png\", dpi=500,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $\\texttt{Pantheon+}$ comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Reading Pantheon+ fitted to Carrick2015 with ksmooth = 0.\n",
      "BIC  = 9952.745117 +- 0.000000\n",
      "AIC  = 9907.553711 +- 0.000000\n",
      "logZ = -4945.429688 +- 0.000000\n",
      "chi2 = 1.147044 +- 0.069862\n",
      "Removed no burn in\n",
      "\n",
      "Reading Pantheon+ fitted to Carrick2015 with ksmooth = 1.\n",
      "BIC  = 9995.297852 +- 0.000000\n",
      "AIC  = 9950.106445 +- 0.000000\n",
      "logZ = -4966.300293 +- 0.000000\n",
      "chi2 = 1.139592 +- 0.069120\n",
      "Removed no burn in\n",
      "\n",
      "Reading Pantheon+ fitted to csiborg2_main with ksmooth = 0.\n",
      "BIC  = 10055.604150 +- 27.237237\n",
      "AIC  = 10010.412744 +- 27.237237\n",
      "logZ = -5000.136133 +- 23.062465\n",
      "chi2 = 0.985968 +- 0.117400\n",
      "Removed no burn in\n",
      "\n",
      "Reading Pantheon+ fitted to csiborg2_main with ksmooth = 1.\n",
      "BIC  = 10023.778857 +- 13.951634\n",
      "AIC  = 9978.587451 +- 13.951634\n",
      "logZ = -4979.896411 +- 6.903517\n",
      "chi2 = 1.115029 +- 0.091582\n",
      "Removed no burn in\n"
     ]
    }
   ],
   "source": [
    "PANTHEONP_Carrick_0 = read_samples(\"Pantheon+\", \"Carrick2015\", 0, return_MCsamples=True)\n",
    "PANTHEONP_Carrick_1 = read_samples(\"Pantheon+\", \"Carrick2015\", 1, return_MCsamples=True)\n",
    "\n",
    "# PANTHEONP_CB1_0 = read_samples(\"Pantheon+\", \"csiborg1\", 0, return_MCsamples=True)\n",
    "# PANTHEONP_CB1_1 = read_samples(\"Pantheon+\", \"csiborg1\", 1, return_MCsamples=True)\n",
    "\n",
    "PANTHEONP_CB2_0 = read_samples(\"Pantheon+\", \"csiborg2_main\", 0, return_MCsamples=True)\n",
    "PANTHEONP_CB2_1 = read_samples(\"Pantheon+\", \"csiborg2_main\", 1, return_MCsamples=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1200x1200 with 21 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = [\n",
    "    PANTHEONP_Carrick_0,\n",
    "    # PANTHEONP_Carrick_1,\n",
    "    # PANTHEONP_CB1_0,\n",
    "    # PANTHEONP_CB1_1,\n",
    "    PANTHEONP_CB2_0,\n",
    "    PANTHEONP_CB2_1,\n",
    "    ]\n",
    "\n",
    "g = plots.get_subplot_plotter()\n",
    "g.settings.figure_legend_frame = False\n",
    "g.settings.alpha_filled_add = 0.75\n",
    "# g.settings.title_limit_fontsize = 14\n",
    "g.triangle_plot(X, filled=True, legend_loc='upper right')\n",
    "# g.export(f\"../plots/PANTHEONP_comparison.png\", dpi=500,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $\\texttt{Pantheon+}$ groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Reading Pantheon+ fitted to Carrick2015 with ksmooth = 0.\n",
      "BIC  = 9952.745117 +- 0.000000\n",
      "AIC  = 9907.553711 +- 0.000000\n",
      "logZ = -4945.429688 +- 0.000000\n",
      "chi2 = 1.147044 +- 0.069862\n",
      "Removed no burn in\n",
      "\n",
      "Reading Pantheon+_groups fitted to Carrick2015 with ksmooth = 0.\n",
      "BIC  = 2578.150635 +- 0.000000\n",
      "AIC  = 2545.786133 +- 0.000000\n",
      "logZ = -1259.616211 +- 0.000000\n",
      "chi2 = 1.185719 +- 0.318355\n",
      "Removed no burn in\n",
      "\n",
      "Reading Pantheon+_groups_zSN fitted to Carrick2015 with ksmooth = 0.\n",
      "BIC  = 2796.622070 +- 0.000000\n",
      "AIC  = 2764.257568 +- 0.000000\n",
      "logZ = -1364.911255 +- 0.000000\n",
      "chi2 = 1.072540 +- 0.119186\n",
      "Removed no burn in\n",
      "\n",
      "Reading Pantheon+_zSN fitted to Carrick2015 with ksmooth = 0.\n",
      "BIC  = 10115.830078 +- 0.000000\n",
      "AIC  = 10070.638672 +- 0.000000\n",
      "logZ = -5025.696777 +- 0.000000\n",
      "chi2 = 1.106855 +- 0.064999\n",
      "Removed no burn in\n"
     ]
    }
   ],
   "source": [
    "LG = -1\n",
    "\n",
    "PANTHEONP_Carrick = read_samples(\"Pantheon+\", \"Carrick2015\", 0, return_MCsamples=True, subtract_LG_velocity=LG, )\n",
    "PANTHEONP_Carrick_Groups = read_samples(\"Pantheon+_groups\", \"Carrick2015\", 0, return_MCsamples=True, subtract_LG_velocity=LG)\n",
    "PANTHEONP_Carrick_Groups_zSN = read_samples(\"Pantheon+_groups_zSN\", \"Carrick2015\", 0, return_MCsamples=True, subtract_LG_velocity=LG)\n",
    "PANTHEONP_Carrick_zSN = read_samples(\"Pantheon+_zSN\", \"Carrick2015\", 0, return_MCsamples=True, subtract_LG_velocity=LG)\n",
    "\n",
    "# ksmooth = 1\n",
    "# PANTHEONP_CB2 = read_samples(\"Pantheon+\", \"csiborg2_main\", ksmooth, return_MCsamples=True, subtract_LG_velocity=LG)\n",
    "# PANTHEONP_CB2_Groups = read_samples(\"Pantheon+_groups\", \"csiborg2_main\", ksmooth, return_MCsamples=True, subtract_LG_velocity=LG)\n",
    "# PANTHEONP_CB2_Groups_zSN = read_samples(\"Pantheon+_groups_zSN\", \"csiborg2_main\", ksmooth, return_MCsamples=True, subtract_LG_velocity=LG)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed no burn in\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1200x1200 with 21 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = [\"Vmag\", \"l\", \"b\"]\n",
    "CMB = MCSamples(samples=multivariate_normal([627, 276, 30], [22, 3, 3]).rvs(size=20000),\n",
    "                names=params, labels=names_to_latex(params, True), label=\"CMB\")\n",
    "\n",
    "\n",
    "X = [\n",
    "    PANTHEONP_Carrick,\n",
    "    # PANTHEONP_Carrick_Groups,\n",
    "    # PANTHEONP_Carrick_Groups_zSN,\n",
    "    PANTHEONP_Carrick_zSN,\n",
    "    # PANTHEONP_CB2,\n",
    "    # PANTHEONP_CB2_Groups,\n",
    "    # PANTHEONP_CB2_Groups_zSN,\n",
    "    # CMB,\n",
    "    ]\n",
    "\n",
    "g = plots.get_subplot_plotter()\n",
    "g.settings.figure_legend_frame = False\n",
    "g.settings.alpha_filled_add = 0.75\n",
    "# g.settings.title_limit_fontsize = 14\n",
    "g.triangle_plot(X, filled=True, legend_loc='upper right')\n",
    "g.export(f\"../../plots/PANTHEON_GROUPS_Carrick_comparison_LG.png\", dpi=500,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $\\texttt{2MTF}$ comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "TWOMTF_Carrick_0 = read_samples(\"2MTF\", \"Carrick2015\", 0, return_MCsamples=True)\n",
    "TWOMTF_Carrick_1 = read_samples(\"2MTF\", \"Carrick2015\", 1, return_MCsamples=True)\n",
    "\n",
    "TWOMTF_CB1_0 = read_samples(\"2MTF\", \"csiborg1\", 0, return_MCsamples=True)\n",
    "TWOMTF_CB1_1 = read_samples(\"2MTF\", \"csiborg1\", 1, return_MCsamples=True)\n",
    "\n",
    "TWOMTF_CB2_0 = read_samples(\"2MTF\", \"csiborg2_main\", 0, return_MCsamples=True)\n",
    "TWOMTF_CB2_1 = read_samples(\"2MTF\", \"csiborg2_main\", 1, return_MCsamples=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = [\n",
    "    TWOMTF_Carrick_0,\n",
    "    # TWOMTF_Carrick_1,\n",
    "    # TWOMTF_CB1_0,\n",
    "    TWOMTF_CB1_1,\n",
    "    TWOMTF_CB2_0,\n",
    "    TWOMTF_CB2_1,\n",
    "    ]\n",
    "\n",
    "g = plots.get_subplot_plotter()\n",
    "g.settings.figure_legend_frame = False\n",
    "g.settings.alpha_filled_add = 0.75\n",
    "# g.settings.title_limit_fontsize = 14\n",
    "g.triangle_plot(X, filled=True, legend_loc='upper right')\n",
    "g.export(f\"../plots/2MTF_comparison.png\", dpi=500,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $\\texttt{SFI++ galaxies}$ comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "SFIGAL_Carrick_0 = read_samples(\"SFI_gals\", \"Carrick2015\", 0, return_MCsamples=True)\n",
    "SFIGAL_Carrick_1 = read_samples(\"SFI_gals\", \"Carrick2015\", 1, return_MCsamples=True)\n",
    "\n",
    "# SFIGAL_CB1_0 = read_samples(\"SFI_gals\", \"csiborg1\", 0, return_MCsamples=True)\n",
    "# SFIGAL_CB1_1 = read_samples(\"SFI_gals\", \"csiborg1\", 1, return_MCsamples=True)\n",
    "\n",
    "SFIGAL_CB2_0 = read_samples(\"SFI_gals\", \"csiborg2_main\", 0, return_MCsamples=True)\n",
    "SFIGAL_CB2_1 = read_samples(\"SFI_gals\", \"csiborg2_main\", 1, return_MCsamples=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = [\n",
    "    SFIGAL_Carrick_0,\n",
    "    # SFIGAL_Carrick_1,\n",
    "    # SFIGAL_CB1_0,\n",
    "    # SFIGAL_CB1_1,\n",
    "    # SFIGAL_CB2_0,\n",
    "    SFIGAL_CB2_1,\n",
    "    ]\n",
    "\n",
    "g = plots.get_subplot_plotter()\n",
    "g.settings.figure_legend_frame = False\n",
    "g.settings.alpha_filled_add = 0.75\n",
    "# g.settings.title_limit_fontsize = 14\n",
    "g.triangle_plot(X, filled=True, legend_loc='upper right')\n",
    "g.export(f\"../plots/SFI_gals_comparison.png\", dpi=500,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $\\texttt{SFI++ groups}$ comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "SFIGROUP_Carrick_0 = read_samples(\"SFI_groups\", \"Carrick2015\", 0, return_MCsamples=True)\n",
    "SFIGROUP_Carrick_1 = read_samples(\"SFI_groups\", \"Carrick2015\", 1, return_MCsamples=True)\n",
    "\n",
    "SFIGROUP_CB2_0 = read_samples(\"SFI_groups\", \"csiborg2_main\", 0, return_MCsamples=True)\n",
    "SFIGROUP_CB2_1 = read_samples(\"SFI_groups\", \"csiborg2_main\", 1, return_MCsamples=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = [\n",
    "    SFIGROUP_Carrick_0,\n",
    "    SFIGAL_Carrick_0,\n",
    "    # SFIGROUP_Carrick_1,\n",
    "    # SFIGROUP_CB2_0,\n",
    "    # SFIGROUP_CB2_1,\n",
    "    ]\n",
    "\n",
    "g = plots.get_subplot_plotter()\n",
    "g.settings.figure_legend_frame = False\n",
    "g.settings.alpha_filled_add = 0.75\n",
    "# g.settings.title_limit_fontsize = 14\n",
    "g.triangle_plot(X, filled=True, legend_loc='upper right')\n",
    "g.export(f\"../plots/SFI_gals_vs_groups_comparison.png\", dpi=500,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SN to TF comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "LG = 0\n",
    "\n",
    "# PANTHEONP_Carrick = read_samples(\"Pantheon+\", \"Carrick2015\", 0, return_MCsamples=True, subtract_LG_velocity=LG, )\n",
    "# PANTHEONP_Groups_Carrick = read_samples(\"Pantheon+_groups\", \"Carrick2015\", 0, return_MCsamples=True, subtract_LG_velocity=LG, )\n",
    "# TWOMTF_Carrick = read_samples(\"2MTF\", \"Carrick2015\", 0, return_MCsamples=True, subtract_LG_velocity=LG)\n",
    "# SFIGAL_Carrick = read_samples(\"SFI_gals\", \"Carrick2015\", 0, return_MCsamples=True, subtract_LG_velocity=LG)\n",
    "\n",
    "k = 1\n",
    "PANTHEONP_CB2 = read_samples(\"Pantheon+\", \"csiborg2_main\", k, return_MCsamples=True, subtract_LG_velocity=LG, )\n",
    "PANTHEONP_Groups_CB2 = read_samples(\"Pantheon+_groups\", \"csiborg2_main\", k, return_MCsamples=True, subtract_LG_velocity=LG, )\n",
    "TWOMTF_CB2 = read_samples(\"2MTF\", \"csiborg2_main\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
    "SFIGAL_CB2 = read_samples(\"SFI_gals\", \"csiborg2_main\", k, return_MCsamples=True, subtract_LG_velocity=LG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "params = [\"Vmag\", \"l\", \"b\"]\n",
    "CMB = MCSamples(samples=multivariate_normal([627, 276, 30], [22, 3, 3]).rvs(size=20000),\n",
    "                names=params, labels=names_to_latex(params, True), label=\"CMB\")\n",
    "\n",
    "\n",
    "X = [\n",
    "    # PANTHEONP_Carrick,\n",
    "    # PANTHEONP_Groups_Carrick,\n",
    "    # TWOMTF_Carrick,\n",
    "    # SFIGAL_Carrick,\n",
    "    PANTHEONP_CB2,\n",
    "    PANTHEONP_Groups_CB2,\n",
    "    TWOMTF_CB2,\n",
    "    SFIGAL_CB2,\n",
    "    CMB,\n",
    "    ]\n",
    "\n",
    "g = plots.get_subplot_plotter()\n",
    "g.settings.figure_legend_frame = False\n",
    "g.settings.alpha_filled_add = 0.75\n",
    "# g.settings.title_limit_fontsize = 14\n",
    "g.triangle_plot(X, filled=True, legend_loc='upper right')\n",
    "# g.export(f\"../../plots/SN_TF_CB2_consistency.png\", dpi=500,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mock $\\texttt{CB2}$ comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "SMALLMOCK_CB2_0 = read_samples(\"CB2_small\", \"csiborg2_main\", 0, return_MCsamples=True)\n",
    "SMALLMOCK_CB2_1 = read_samples(\"CB2_small\", \"csiborg2_main\", 1, return_MCsamples=True)\n",
    "\n",
    "LARGEMOCK_CB2_0 = read_samples(\"CB2_large\", \"csiborg2_main\", 0, return_MCsamples=True)\n",
    "LARGEMOCK_CB2_1 = read_samples(\"CB2_large\", \"csiborg2_main\", 1, return_MCsamples=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = [\n",
    "    # SMALLMOCK_CB2_0,\n",
    "    # SMALLMOCK_CB2_1,\n",
    "    LARGEMOCK_CB2_0,\n",
    "    LARGEMOCK_CB2_1,\n",
    "    ]\n",
    "\n",
    "g = plots.get_subplot_plotter()\n",
    "g.settings.figure_legend_frame = False\n",
    "g.settings.alpha_filled_add = 0.75\n",
    "# g.settings.title_limit_fontsize = 14\n",
    "g.triangle_plot(X, filled=True, legend_loc='upper right')\n",
    "g.export(f\"../plots/CB2_mocks_large.png\", dpi=500,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## External flow consistency"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Carrick2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = [\n",
    "    # LOSS_Carrick_0,\n",
    "    # FOUNDATION_Carrick_0,\n",
    "    PANTHEONP_Carrick_0,\n",
    "    TWOMTF_Carrick_0,\n",
    "    SFIGAL_Carrick_0,\n",
    "    ]\n",
    "\n",
    "params = [\"Vmag\", \"l\", \"b\", \"beta\"]\n",
    "g = plots.get_subplot_plotter()\n",
    "g.settings.figure_legend_frame = False\n",
    "g.settings.alpha_filled_add = 0.75\n",
    "# g.settings.title_limit_fontsize = 14\n",
    "g.triangle_plot(X, params=params, filled=True, legend_loc='upper right',)\n",
    "g.export(f\"../plots/Carrick2015_external_flow.png\", dpi=500,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CSiBORG1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = [\n",
    "    # LOSS_CB1_1,\n",
    "    # FOUNDATION_CB1_1,\n",
    "    PANTHEONP_CB1_1,\n",
    "    TWOMTF_CB1_1,\n",
    "    # SFIGAL_CB1_1,\n",
    "    ]\n",
    "\n",
    "params = [\"Vmag\", \"l\", \"b\", \"beta\"]\n",
    "g = plots.get_subplot_plotter()\n",
    "g.settings.figure_legend_frame = False\n",
    "g.settings.alpha_filled_add = 0.75\n",
    "# g.settings.title_limit_fontsize = 14\n",
    "g.triangle_plot(X, params=params, filled=True, legend_loc='upper right',)\n",
    "g.export(f\"../plots/CB1_external_flow.png\", dpi=500,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CSiBORG2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = [\n",
    "    # LOSS_CB2_1,\n",
    "    # FOUNDATION_CB2_1,\n",
    "    PANTHEONP_CB2_1,\n",
    "    TWOMTF_CB2_1,\n",
    "    SFIGAL_CB2_1,\n",
    "    ]\n",
    "\n",
    "params = [\"Vmag\", \"l\", \"b\", \"beta\"]\n",
    "g = plots.get_subplot_plotter()\n",
    "g.settings.figure_legend_frame = False\n",
    "g.settings.alpha_filled_add = 0.75\n",
    "# g.settings.title_limit_fontsize = 14\n",
    "g.triangle_plot(X, params=params, filled=True, legend_loc='upper right',)\n",
    "g.export(f\"../plots/CB2_external_flow.png\", dpi=500,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = 1\n",
    "LG = 0\n",
    "\n",
    "# Carrick\n",
    "# LOSS_Carrick_LG = read_samples(\"LOSS\", \"Carrick2015\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
    "# FOUNDATION_Carrick_LG = read_samples(\"Foundation\", \"Carrick2015\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
    "# PANTHEON_Carrick_LG = read_samples(\"Pantheon+\", \"Carrick2015\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
    "# TWOMTF_Carrick_LG = read_samples(\"2MTF\", \"Carrick2015\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
    "SFIGAL_Carrick_LG = read_samples(\"SFI_gals\", \"Carrick2015\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
    "SFIGROUPS_Carrick_LG = read_samples(\"SFI_groups\", \"Carrick2015\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
    "\n",
    "\n",
    "# # CSiBORG2\n",
    "# LOSS_CB2_LG = read_samples(\"LOSS\", \"csiborg2_main\", k, return_MCsamples=True,subtract_LG_velocity=LG)\n",
    "# FOUNDATION_CB2_LG = read_samples(\"Foundation\", \"csiborg2_main\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
    "# PANTHEON_CB2_LG = read_samples(\"Pantheon+\", \"csiborg2_main\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
    "# TWOMTF_CB2_LG = read_samples(\"2MTF\", \"csiborg2_main\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
    "SFIGAL_CB2_LG = read_samples(\"SFI_gals\", \"csiborg2_main\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
    "SFIGROUP_CB2_LG = read_samples(\"SFI_groups\", \"csiborg2_main\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
    "\n",
    "# # CSiBORG1\n",
    "# LOSS_CB1_LG = read_samples(\"LOSS\", \"csiborg1\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
    "# FOUNDATION_CB1_LG = read_samples(\"Foundation\", \"csiborg1\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
    "# PANTHEON_CB1_LG = read_samples(\"Pantheon+\", \"csiborg1\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
    "# TWOMTF_CB1_LG = read_samples(\"2MTF\", \"csiborg1\", k, return_MCsamples=True, subtract_LG_velocity=LG)\n",
    "# SFIGAL_CB1_LG = read_samples(\"SFI_gals\", \"csiborg1\", k, return_MCsamples=True, subtract_LG_velocity=LG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = [\"Vmag\", \"l\", \"b\"]\n",
    "CMB = MCSamples(samples=multivariate_normal([627, 276, 30], [22, 3, 3]).rvs(size=20000),\n",
    "                names=params, labels=names_to_latex(params, True), label=\"CMB\")\n",
    "\n",
    "X = [\n",
    "    # LOSS_Carrick_LG,\n",
    "    # FOUNDATION_Carrick_LG,\n",
    "    # PANTHEON_Carrick_LG,\n",
    "    # TWOMTF_Carrick_LG,\n",
    "    # SFIGAL_Carrick_LG,\n",
    "    # SFIGROUPS_Carrick_LG,\n",
    "    # LOSS_CB1_LG,\n",
    "    # FOUNDATION_CB1_LG,\n",
    "    # PANTHEON_CB1_LG,\n",
    "    # TWOMTF_CB1_LG,\n",
    "    # SFIGAL_CB1_LG,\n",
    "    # LOSS_CB2_LG,\n",
    "    # FOUNDATION_CB2_LG,\n",
    "    # PANTHEON_CB2_LG,\n",
    "    # TWOMTF_CB2_LG,\n",
    "    SFIGAL_CB2_LG,\n",
    "    SFIGROUP_CB2_LG,\n",
    "    CMB,\n",
    "    ]\n",
    "\n",
    "g = plots.get_subplot_plotter()\n",
    "g.settings.figure_legend_frame = False\n",
    "g.settings.alpha_filled_add = 0.75\n",
    "# g.settings.title_limit_fontsize = 14\n",
    "g.triangle_plot(X, params=params, filled=True, legend_loc='upper right', )\n",
    "# g.export(f\"../plots/ALL_dipole.png\", dpi=500,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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