csiborgtools/notebooks/flow_calibration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calibrating the velocity field against observations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "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": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10:32:21: reading the catalogue.\n",
      "10:32:21: reading the interpolated field.\n",
      "10:32:22: calculating the radial velocity.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/users/rstiskalek/csiborgtools/csiborgtools/flow/flow_model.py:112: 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_Supranta2019.hdf5\"\n",
    "# fpath_data = \"/mnt/extraspace/rstiskalek/catalogs/A2.h5\"\n",
    "\n",
    "simname = \"Carrick2015\"\n",
    "catalogue = \"SFI_groups\"\n",
    "loader = csiborgtools.flow.DataLoader(simname, 0, catalogue, fpath_data, paths, ksmooth=0)\n",
    "get_model_kwargs = {\"zcmb_max\": 0.07}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Running HMC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected 587/587 galaxies.\n"
     ]
    }
   ],
   "source": [
    "model = csiborgtools.flow.get_model(loader, **get_model_kwargs)\n",
    "model_kwargs = {\"sample_alpha\": False, \"sample_beta\": True}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sample: 100%|██████████| 750/750 [01:11<00:00, 10.45it/s, 7 steps of size 6.90e-01. acc. prob=0.87]  \n"
     ]
    }
   ],
   "source": [
    "mcmc.run(rng_key, **model_kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                mean       std    median      5.0%     95.0%     n_eff     r_hat\n",
      "    Vext_x   -108.60     18.78   -109.17   -139.36    -78.67    591.09      1.00\n",
      "    Vext_y    -47.68     30.46    -47.88   -102.24      0.37    471.22      1.00\n",
      "    Vext_z   -108.83     31.09   -108.27   -156.70    -57.68    491.18      1.00\n",
      "      beta      0.39      0.03      0.39      0.34      0.44    479.47      1.00\n",
      "         h      0.98      0.01      0.98      0.96      0.99    702.15      1.00\n",
      "   sigma_v    139.35     15.08    138.02    118.31    165.93    582.94      1.00\n",
      "\n",
      "Number of divergences: 0\n",
      "{'BIC': 8967.5947265625, 'AIC': 8941.3447265625, 'logZ': -4454.95263671875}\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": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BIC': 8972.8076171875, 'AIC': 8950.9326171875, 'logZ': -4456.90234375}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{'BIC': 8972.8076171875, 'AIC': 8950.9326171875, 'logZ': -4456.90234375}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|V|  = 165.80032348632812 +- 25.755414962768555\n",
      "l    = 311.4824201173159 +- 11.923668185663816\n",
      "b    = 19.871077149704966 +- 8.699190278394884\n",
      "beta = 0.3904797434806824 +- 0.029465198516845703\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": 50,
   "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",
    "data = np.vstack(data).T\n",
    "fig = corner.corner(data, labels=labels, show_titles=True, title_fmt=\".3f\", title_kwargs={\"fontsize\": 12}, smooth=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vizualize the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Functions to read in the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Reading SFI_groups fitted to Carrick2015 with ksmooth = 0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BIC  = 8896.874023 +- 0.000000\n",
      "AIC  = 8866.249023 +- 0.000000\n",
      "logZ = -4417.928711 +- 0.000000\n",
      "chi2 = 0.000000 +- 0.000000\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1390x1390 with 36 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data, names, gof = read_samples(\"SFI_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": [
    "### LOSS-only 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": [
    "### Foundation-only 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": [
    "### PantheonPlus-only comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Reading Pantheon+ fitted to csiborg2_main with ksmooth = 0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BIC  = 10056.015674 +- 27.407189\n",
      "AIC  = 10010.824268 +- 27.407189\n",
      "logZ = -5006.756958 +- 48.432771\n",
      "chi2 = 0.000000 +- 0.000000\n",
      "Removed no burn in\n",
      "\n",
      "Reading Pantheon+ fitted to csiborg2_main with ksmooth = 1.\n",
      "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"
     ]
    }
   ],
   "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",
    "# STILL RUNNING\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": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABKYAAASlCAYAAACSitFIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd5hdVb3/8c/ep00vmZqp6ZVUEgiEFhAiVQRBEK6AVAMiPxsCiihSRMQCKChgVKRcRKmKgPROGoT0PpNkkplMMn1O3fv3xzhzE0iZM3P2KTPv1/PMc8Ocvdf6npOduXc+d63vMmzbtgUAAAAAAADEmZnoAgAAAAAAADA4EUwBAAAAAAAgIQimAAAAAAAAkBAEUwAAAAAAAEgIgikAAAAAAAAkBMEUAAAAAAAAEoJgCgAAAAAAAAnhTnQB8WZZlrZu3ars7GwZhpHocgDH2Lat1tZWlZWVyTTJoAEAAAAAyWfQBVNbt25VZWVlossA4qa2tlYVFRWJLgMAAAAAgM8YdMFUdna2pK5f1nNychJcDeCclpYWVVZW9jzzAAAAAAAkm0EXTHVv38vJySGYwqDAllUAAAAAQLKi8QwAAAAAAAASgmAKAAAAAAAACUEwBQAAAAAAgIQgmAIAAAAAAEBCDLrm5wAAAACAzwpv26pI0y5H53Dl5ctdWuboHABSC8EUAAAAAAxy4W1bteXME6RgwNmJvD6VP/li0oZTw4YN069+9SudfvrpiS4lpfE5Ihps5QMAAACAQS7StMv5UEqSgoGoVmUdc8wx8vl8ysrK0pAhQ3TMMcdo4cKFMSll2LBheuqpp2IyVrJ56623dOKJJyo/P195eXmaMmWK7rjjDgWDQUl7fq7Z2dmaOHGinnjiCUlSIBDQpZdequHDhys7O1vjxo3TQw89lMi3s4clS5bo0ksv1QknnKBvfetb+tGPfqQ//OEPiS5LknThhRfK6/UqKyur5+vdd9+VJIVCIV111VXKz8/XkCFD9I1vfEPhcLjn3gO9LknPPPOMpk6dqszMTJWVlem+++7bZy1btmzR6aefroKCAhUWFurss89WQ0ODM2+8nwimAAAAAABJ62c/+5na2tq0detWTZs2TV/4whcSXZLjXnvtNR1zzDF9uve5557TiSeeqLlz52rNmjVqamrS448/ruXLl6uurq7nuu7PtaWlRXfccYfOO+88bdq0SeFwWEOHDtXLL7+slpYWzZ8/X9/+9rf14osvxujd9d2LL76obdu26Ze//KUuuugi1dXV6Y033tAFF1yQ6NJ6zJs3T21tbT1fhx12mCTppz/9qd566y0tX75cy5Yt05tvvqlbb721574Dvf7CCy9o3rx5+tWvfqWWlhYtW7Zsv8/IlVdeKUnatGmTNmzYIL/fr6uvvtqZN91PBFMAAAAAgKSXlpamiy++WFu2bFFjY6PuuusujR49WtnZ2Ro5cqTuueeePa4fNmyY7rjjDs2aNUvZ2dk6+uijVVtbK0k666yzVFNTo3PPPVdZWVm64ooreu5bvXr1Xu9pa2vTVVddpaqqKhUXF+urX/2qmpube+7bvn27zj77bBUVFamqqko33HDDHite9ldPrNi2rauvvlrXXnutrrnmGhUWFkqSxo0bp/nz56u6uvoz9xiGoZNPPll5eXlatWqVMjMz9ZOf/EQjR46UYRiaNWuW5syZo7feeqvPde3vs9m8ebOOP/545eTk6OCDD9att96qYcOG7XWcsWPH6vOf/7yysrJ0zjnnqLi4WE888YS8Xm+fa4uXhx56SD/4wQ80dOhQDR06VDfccIMefPDBXr/+wx/+UDfeeKOOOeYYuVwu5efna9y4cfucb/369Tr77LN7VsV9+ctf1tKlSx19j31FMAUAAAAASHodHR164IEHVF1drYKCAlVXV+uVV15RS0uLHnjgAX33u9/V22+/vcc9Dz/8sB599FE1NDQoMzNTP/zhDyVJTzzxhKqqqvToo4+qra1tjy1R+7rna1/7mnbu3KmPP/5YGzZs6Nl61e0rX/mKPB6PNmzYoDfffFNPPfWU7rjjjl7VEytr1qzRhg0bdO655/b6Hsuy9PTTT6uzs1NTp079zOt+v18ffPCBJk+e3Oe69vfZfOUrX1F1dbW2b9+uRx99dI8w5tN2D9Z+/vOf68ILL+wJ3z5t3rx5ysvL2+dXf4K2/fnzn/+sIUOGaOLEifrFL34hy7K0a9cubd68eY/Pd+rUqaqpqVFzc/MBX29vb9fChQu1ZcsWjRkzRqWlpTrrrLP2WAH3ad/61rf0xBNPqLm5WU1NTXr00Ud16qmnOvKe+4tgCgAAAACQtK677jrl5eVpxIgRWrlypZ555hlJ0plnnqnKykoZhqE5c+Zo7ty5eu211/a4d968eRo+fLjS0tJ03nnn9ao/1d7uaWho0JNPPql7771XeXl5PauKHn/8cUUiEW3ZskWvvPKK7rrrLmVlZam6ulo33HCD5s+f3+96otHdQ6i8vPyA13Z/rpmZmTrjjDP0gx/8QMXFxXtcY9u2LrnkEo0ePVpnnHFGn2ra32dTW1urN998U7fffrvS09M1ZsyYPVav7c1vfvMbTZ06VS+88IJ+9rOfKRQK7fW63/72t2pqatrn1xFHHLHfeU455RQZhrHPr40bN37mnquvvlqrVq1SQ0ODHnzwQf3617/Wr3/9a7W1tUmS8vLyeq7t/nNra+sBX9+1a5ds29ZTTz2ll156SWvXrpXP59P555+/z/pnz56t+vr6np5Vu3bt0nXXXbff95wonMoHAAAAAEhat912m6655prPfP+vf/2rfvGLX2jjxo2yLEsdHR0aPnz4HteUlpb2/DkzM1Otra0HnG9v93TP8enxTdPUtm3btHnzZqWlpamkpKTntREjRmjz5s29rmfevHl65JFHJEnhcFh+v3+PoOK55547YJjSvXpoy5YtGjly5H6v3f1zXbt2rU477TTl5eXp8ssvl9QVSs2bN0+rVq3Syy+/LNPs27qW/X02W7duVVpa2h6rnqqqqvY73pw5c9TQ0KCbb765T/X01iOPPNLTLH5vhgwZ8pnvTZ8+vefPs2bN0ve//339+c9/1oUXXihJam5u7nmv3dtAs7OzZVnWfl+3bVtSV/DVvWrsxz/+sUaPHq329nZlZmbuUYdlWTr++ON19tln66WXXpIk3XTTTTrhhBP03nvvRfdBxAErpgAAAAAAKaWmpkYXXHCB7rjjDtXX16upqUknnXRSzy/wvRFN0FJZWSnTNLV169Y9Vt34/X6Vl5eroqJCfr9f27dv77ln48aNqqio6PUcu6/w6Q6holnhI0ljxozRsGHD9Nhjj/V6XkkaNWqUTjrpJD333HOSukKpK6+8Uu+//75efPFF5ebmRjXe7vb32ZSVlcnv92vHjh09r9XU1OxzrNWrV+uhhx7Sj3/84wPOe8UVV+xxOt6nv95888393p+Tk6PCwsJ9fvXm+em+Jj8/XxUVFVqyZEnPa0uWLFFlZaVyc3MP+HpeXt4+A7u9PfM7d+7Upk2bdPXVVysjI0MZGRn6xje+offff3+PzzpZEEwlgeDaVWr9219l+f2JLiVlNbZb+u1bfj31cTCq/2UEAAAAIPW0tbXJtm0VFxfLNE3985//jPrUuJKSEq1bt65X15aWlur000/XVVdd1fOL/bZt2/SPf/xDUtfWuTlz5ug73/mO2tvbVVNTo1tuucWR0+JuuummfZ7GZhiG7r77bt1+++26++671djYKKkr0Ln44ou1adOmvd63ceNG/fOf/9SkSZMkSVdddZXefvttvfTSS8rPz4+qhk/b32dTWVmp2bNn6/rrr1dnZ6fWrFmj3//+93sdZ8WKFfrzn/+sO++8s1eh0H333bfH6Xif/jryyCN7VX80/vd//1ctLS2ybVsLFizQ7bffrjPPPFOSdNFFF+mWW27Rtm3btG3bNt1666265JJLeu490OuXXXaZ7r77bm3ZskWdnZ36yU9+ouOOO05ZWVmfqaOwsFCjRo3SvffeK7/fL7/fr3vvvVcVFRX77MmVSARTCRbeUa8d139ToY3rtfO
      "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": [
    "### 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": [
    "### SFI galaxies"
   ]
  },
  {
   "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": [
    "### SFI groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Reading SFI_groups fitted to Carrick2015 with ksmooth = 0.\n",
      "BIC  = 8896.874023 +- 0.000000\n",
      "AIC  = 8866.249023 +- 0.000000\n",
      "logZ = -4417.928711 +- 0.000000\n",
      "chi2 = 0.000000 +- 0.000000\n",
      "Removed no burn in\n",
      "\n",
      "Reading SFI_groups fitted to Carrick2015 with ksmooth = 1.\n",
      "BIC  = 8957.607422 +- 0.000000\n",
      "AIC  = 8926.982422 +- 0.000000\n",
      "logZ = -4447.800293 +- 0.000000\n",
      "chi2 = 0.000000 +- 0.000000\n",
      "Removed no burn in\n",
      "\n",
      "Reading SFI_groups fitted to csiborg2_main with ksmooth = 0.\n",
      "BIC  = 8782.843457 +- 17.080250\n",
      "AIC  = 8752.218457 +- 17.080250\n",
      "logZ = -4362.511084 +- 8.475959\n",
      "chi2 = 0.000000 +- 0.000000\n",
      "Removed no burn in\n",
      "\n",
      "Reading SFI_groups fitted to csiborg2_main with ksmooth = 1.\n",
      "BIC  = 8726.997656 +- 21.534941\n",
      "AIC  = 8696.372656 +- 21.534941\n",
      "logZ = -4333.264429 +- 10.550374\n",
      "chi2 = 0.000000 +- 0.000000\n",
      "Removed no burn in\n"
     ]
    }
   ],
   "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": 63,
   "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",
    "    SFIGROUP_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_comparison.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": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Reading SFI_gals fitted to Carrick2015 with ksmooth = 1.\n",
      "Subtracting LG velocity with kernel 0.0 Mpc / h.\n",
      "BIC  = 28930.990234 +- 0.000000\n",
      "AIC  = 28881.580078 +- 0.000000\n",
      "logZ = -14433.558594 +- 0.000000\n",
      "chi2 = 0.000000 +- 0.000000\n",
      "Removed no burn in\n",
      "\n",
      "Reading SFI_groups fitted to Carrick2015 with ksmooth = 1.\n",
      "Subtracting LG velocity with kernel 0.0 Mpc / h.\n",
      "BIC  = 8957.607422 +- 0.000000\n",
      "AIC  = 8926.982422 +- 0.000000\n",
      "logZ = -4447.800293 +- 0.000000\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",
      "\n",
      "Reading SFI_groups fitted to csiborg2_main with ksmooth = 1.\n",
      "Subtracting LG velocity with kernel 0.0 Mpc / h.\n",
      "BIC  = 8726.997656 +- 21.534941\n",
      "AIC  = 8696.372656 +- 21.534941\n",
      "logZ = -4333.264429 +- 10.550374\n",
      "chi2 = 0.000000 +- 0.000000\n",
      "Removed no burn in\n"
     ]
    }
   ],
   "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": 68,
   "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"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 600x600 with 6 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",
    "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": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv_csiborg",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}