csiborgtools/notebooks/flow/flow_calibration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calibrating the velocity field against observations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10:16:51: reading the catalogue.\n",
      "10:16:51: reading the interpolated field.\n",
      "10:16:51: calculating the radial velocity.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/users/rstiskalek/csiborgtools/csiborgtools/flow/flow_model.py:94: UserWarning: The number of radial steps is even. Skipping the first step at 0.0 because Simpson's rule requires an odd number of steps.\n",
      "  warn(f\"The number of radial steps is even. Skipping the first \"\n"
     ]
    }
   ],
   "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 = \"csiborg2_main\"\n",
    "catalogue = \"Pantheon+_groups\"\n",
    "loader = csiborgtools.flow.DataLoader(simname, 10, catalogue, fpath_data, paths, ksmooth=1)\n",
    "get_model_kwargs = {\"zcmb_max\": 0.07}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Running HMC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected 188/741 galaxies.\n"
     ]
    }
   ],
   "source": [
    "model = csiborgtools.flow.get_model(loader, **get_model_kwargs)\n",
    "model_kwargs = {\"sample_alpha\": True, \"sample_beta\": True}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sample: 100%|██████████| 750/750 [00:32<00:00, 22.85it/s, 7 steps of size 4.86e-01. acc. prob=0.92]  \n"
     ]
    }
   ],
   "source": [
    "mcmc.run(rng_key, **model_kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                      mean       std    median      5.0%     95.0%     n_eff     r_hat\n",
      "          Vext_x     -7.07     28.15     -6.90    -51.51     40.12    431.62      1.00\n",
      "          Vext_y     11.58     27.69     12.30    -34.86     57.30    436.66      1.01\n",
      "          Vext_z   -112.96     33.42   -113.19   -159.74    -54.83    422.45      1.00\n",
      "           alpha      2.70      0.54      2.66      1.82      3.51    530.76      1.00\n",
      "       alpha_cal      0.13      0.02      0.13      0.09      0.16    436.28      1.01\n",
      "        beta_cal      2.71      0.30      2.69      2.19      3.18    544.84      1.00\n",
      "  e_mu_intrinsic      0.14      0.02      0.14      0.10      0.17    635.61      1.00\n",
      "         mag_cal    -18.50      0.03    -18.49    -18.55    -18.45    514.73      1.01\n",
      "         sigma_v    178.05     15.63    177.08    156.72    205.27    516.90      1.00\n",
      "\n",
      "Number of divergences: 0\n"
     ]
    }
   ],
   "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": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "#                      mean       std    median      5.0%     95.0%     n_eff     r_hat\n",
    "#           Vext_x     16.25     26.81     15.10    -23.17     65.77    523.48      1.00\n",
    "#           Vext_y     32.83     27.00     32.93    -13.96     75.47    322.45      1.01\n",
    "#           Vext_z    -97.87     35.48    -97.78   -159.36    -42.14    422.92      1.00\n",
    "#            alpha      0.96      0.12      0.95      0.78      1.18    470.16      1.00\n",
    "#        alpha_cal      0.15      0.02      0.16      0.12      0.19    407.33      1.00\n",
    "#         beta_cal      2.80      0.23      2.80      2.39      3.14    481.54      1.00\n",
    "#   e_mu_intrinsic      0.13      0.02      0.13      0.10      0.17    511.25      1.00\n",
    "#          mag_cal    -18.50      0.03    -18.50    -18.55    -18.45    390.57      1.01\n",
    "#          sigma_v    169.94     14.54    169.52    146.79    193.29    784.99      1.00\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|V|  = 120.92754364013672 +- 31.4892635345459\n",
      "l    = 294.0298299307121 +- 23.44887489204706\n",
      "b    = -23.89915683240672 +- 13.709079097191216\n"
     ]
    }
   ],
   "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": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 970x970 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Reading Pantheon+ fitted to Carrick2015 with ksmooth = 0.\n",
      "Subtracting LG velocity with kernel 0.0 Mpc / h.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BIC  = 9947.804688 +- 0.000000\n",
      "AIC  = 9902.613281 +- 0.000000\n",
      "logZ = -4942.983398 +- 0.000000\n",
      "chi2 = 0.000000 +- 0.000000\n",
      "Removed no burn in\n",
      "\n",
      "Reading Pantheon+_groups fitted to Carrick2015 with ksmooth = 0.\n",
      "Subtracting LG velocity with kernel 0.0 Mpc / h.\n",
      "BIC  = 2574.760986 +- 0.000000\n",
      "AIC  = 2542.396484 +- 0.000000\n",
      "logZ = -1258.320190 +- 0.000000\n",
      "chi2 = 1.096741 +- 0.262201\n",
      "Removed no burn in\n",
      "\n",
      "Reading Pantheon+_groups_zSN fitted to Carrick2015 with ksmooth = 0.\n",
      "Subtracting LG velocity with kernel 0.0 Mpc / h.\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",
      "Subtracting LG velocity with kernel 0.0 Mpc / h.\n",
      "BIC  = 10111.704102 +- 0.000000\n",
      "AIC  = 10066.512695 +- 0.000000\n",
      "logZ = -5023.536621 +- 0.000000\n",
      "chi2 = 1.089721 +- 0.062810\n",
      "Removed no burn in\n"
     ]
    }
   ],
   "source": [
    "LG = 0\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": 7,
   "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": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Reading Pantheon+ fitted to csiborg2_main with ksmooth = 1.\n",
      "Subtracting LG velocity with kernel 0.0 Mpc / h.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BIC  = 10027.133105 +- 13.850270\n",
      "AIC  = 9981.941699 +- 13.850270\n",
      "logZ = -4981.690186 +- 6.885272\n",
      "chi2 = 0.000000 +- 0.000000\n",
      "Removed no burn in\n",
      "\n",
      "Reading Pantheon+_groups fitted to csiborg2_main with ksmooth = 1.\n",
      "Subtracting LG velocity with kernel 0.0 Mpc / h.\n",
      "BIC  = 2651.293762 +- 12.943075\n",
      "AIC  = 2618.929260 +- 12.943075\n",
      "logZ = -1293.734613 +- 6.356175\n",
      "chi2 = 0.955844 +- 0.134530\n",
      "Removed no burn in\n",
      "\n",
      "Reading 2MTF fitted to csiborg2_main with ksmooth = 1.\n",
      "Subtracting LG velocity with kernel 0.0 Mpc / h.\n",
      "BIC  = 19167.596582 +- 20.190445\n",
      "AIC  = 19121.432520 +- 20.190445\n",
      "logZ = -9555.558252 +- 9.820362\n",
      "chi2 = 0.000000 +- 0.000000\n",
      "Removed no burn in\n",
      "\n",
      "Reading SFI_gals fitted to csiborg2_main with ksmooth = 1.\n",
      "Subtracting LG velocity with kernel 0.0 Mpc / h.\n",
      "BIC  = 28646.324902 +- 24.227278\n",
      "AIC  = 28596.914746 +- 24.227278\n",
      "logZ = -14288.365332 +- 12.050230\n",
      "chi2 = 0.000000 +- 0.000000\n",
      "Removed no burn in\n"
     ]
    }
   ],
   "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": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed no burn in\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:2D kernel density bandwidth optimizer failed for Vmag, l. Using fallback width: f(a) and f(b) must have different signs\n",
      "WARNING:root:2D kernel density bandwidth optimizer failed for l, b. Using fallback width: f(a) and f(b) must have different signs\n",
      "WARNING:root:2D kernel density bandwidth optimizer failed for l, b. Using fallback width: f(a) and f(b) must have different signs\n",
      "WARNING:root:2D kernel density bandwidth optimizer failed for l, sigma_v. Using fallback width: f(a) and f(b) must have different signs\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1200x1200 with 21 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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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