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 jax import numpy as jnp\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": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-06-26 08:43:14.168393:   reading the catalogue,\n",
      "2024-06-26 08:43:14.224388:   reading the interpolated field,\n",
      "2024-06-26 08:43:14.298118:   calculating the radial velocity.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/users/rstiskalek/csiborgtools/csiborgtools/flow/flow_model.py:91: 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 = \"/mnt/extraspace/rstiskalek/catalogs/PV_compilation.hdf5\"\n",
    "\n",
    "loader_carrick = csiborgtools.flow.DataLoader(\"Carrick2015\", 0, \"LOSS\", fpath, paths, ksmooth=0, )\n",
    "loaders_csiborg2X = [csiborgtools.flow.DataLoader(\"csiborg2X\", i, \"LOSS\", fpath, paths, ksmooth=1, verbose=False) for i in range(20)]\n",
    "loaders_csiborg2 = [csiborgtools.flow.DataLoader(\"csiborg2_main\", i, \"LOSS\", fpath, paths, ksmooth=1, verbose=False) for i in range(20)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'loaders_csiborg2X' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[2], line 8\u001b[0m\n\u001b[1;32m      6\u001b[0m cols \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maxes.prop_cycle\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mby_key()[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcolor\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m# CSiBORG2\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[43mloaders_csiborg2X\u001b[49m[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mrdist\n\u001b[1;32m      9\u001b[0m y \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray([loaders_csiborg2[i]\u001b[38;5;241m.\u001b[39mlos_density[k, :] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(loaders_csiborg2X))])\n\u001b[1;32m     10\u001b[0m ylow, ymed, yhigh \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mpercentile(y, [\u001b[38;5;241m16\u001b[39m, \u001b[38;5;241m50\u001b[39m, \u001b[38;5;241m84\u001b[39m], axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'loaders_csiborg2X' is not defined"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 700x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ks = [i for i in range(50)]\n",
    "\n",
    "for k in ks:\n",
    "    fig, axs = plt.subplots(2, 1, figsize=(7, 7), sharex=True)\n",
    "    fig.subplots_adjust(wspace=0)\n",
    "    cols = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
    "    # CSiBORG2\n",
    "    x = loaders_csiborg2X[0].rdist\n",
    "    y = np.asarray([loaders_csiborg2[i].los_density[k, :] for i in range(len(loaders_csiborg2X))])\n",
    "    ylow, ymed, yhigh = np.percentile(y, [16, 50, 84], axis=0)\n",
    "    axs[0].fill_between(x, ylow, yhigh, color=cols[0], alpha=0.25)\n",
    "    axs[0].plot(x, ymed, color=cols[0], label=\"CSiBORG2\")\n",
    "\n",
    "    y = np.asarray([loaders_csiborg2[i].los_radial_velocity[k, :] for i in range(len(loaders_csiborg2X))])\n",
    "    ylow, ymed, yhigh = np.percentile(y, [16, 50, 84], axis=0)\n",
    "    axs[1].fill_between(x, ylow, yhigh, color=cols[0], alpha=0.25)\n",
    "    axs[1].plot(x, ymed, color=cols[0], label=\"CSiBORG2\")\n",
    "\n",
    "    # CSiBORG2X\n",
    "    x = loaders_csiborg2X[0].rdist\n",
    "    y = np.asarray([loaders_csiborg2X[i].los_density[k, :] for i in range(len(loaders_csiborg2X))])\n",
    "    ylow, ymed, yhigh = np.percentile(y, [16, 50, 84], axis=0)\n",
    "    axs[0].fill_between(x, ylow, yhigh, color=cols[1], alpha=0.25)\n",
    "    axs[0].plot(x, ymed, color=cols[1], label=\"CSiBORG2X\")\n",
    "\n",
    "    y = np.asarray([loaders_csiborg2X[i].los_radial_velocity[k, :] for i in range(len(loaders_csiborg2X))])\n",
    "    ylow, ymed, yhigh = np.percentile(y, [16, 50, 84], axis=0)\n",
    "    axs[1].fill_between(x, ylow, yhigh, color=cols[1], alpha=0.25)\n",
    "    axs[1].plot(x, ymed, color=cols[1], label=\"CSiBORG2X\")\n",
    "\n",
    "    # Plot Carrick+2015\n",
    "    axs[0].plot(loader_carrick.rdist, loader_carrick.los_density[k, :], color=\"red\", label=\"Carrick+2015\")\n",
    "    axs[1].plot(loader_carrick.rdist, loader_carrick.los_radial_velocity[k, :] * 0.43, color=\"red\")\n",
    "\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",
    "    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": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-06-26 08:49:16.604687:   reading the catalogue,\n",
      "2024-06-26 08:49:16.612221:   reading the interpolated field,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-06-26 08:49:16.629812:   calculating the radial velocity.\n",
      "2024-06-26 08:49:16.637461:   reading the catalogue,\n",
      "2024-06-26 08:49:16.645362:   reading the interpolated field,\n",
      "2024-06-26 08:49:16.690361:   calculating the radial velocity.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/users/rstiskalek/csiborgtools/csiborgtools/flow/flow_model.py:91: 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 = \"csiborg2X\"\n",
    "catalogue = \"LOSS\"\n",
    "loader = csiborgtools.flow.DataLoader(simname, [0], catalogue, fpath_data, paths, ksmooth=0, )\n",
    "loader_mult = csiborgtools.flow.DataLoader(simname, [0, 0, 0, 0, 0], catalogue, fpath_data, paths, ksmooth=0, )\n",
    "\n",
    "\n",
    "SN_hyperparams = {\"e_mu_mean\": 0.1, \"e_mu_std\": 0.05, \n",
    "                  \"mag_cal_mean\": -18.25, \"mag_cal_std\": 0.5,\n",
    "                  \"alpha_cal_mean\": 0.148, \"alpha_cal_std\": 0.05,\n",
    "                  \"beta_cal_mean\": 3.112, \"beta_cal_std\": 1.0,\n",
    "                  }\n",
    "calibration_hyperparams = {\"Vext_std\": 250,\n",
    "                           \"alpha_mean\": 1.0, \"alpha_std\": 0.5,\n",
    "                           \"beta_mean\": 1.0, \"beta_std\": 0.5,\n",
    "                           \"sigma_v_mean\": 150., \"sigma_v_std\": 100.,\n",
    "                           \"sample_alpha\": True, \"sample_beta\": True,\n",
    "                           }\n",
    "get_model_kwargs = {\"zcmb_max\": 0.05}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Running HMC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected 50/50 galaxies.\n",
      "Selected 50/50 galaxies.\n"
     ]
    }
   ],
   "source": [
    "model = csiborgtools.flow.get_model(loader, **get_model_kwargs)\n",
    "model_mult = csiborgtools.flow.get_model(loader_mult, **get_model_kwargs)\n",
    "model_kwargs = {\"distmod_hyperparams\": SN_hyperparams, \"calibration_hyperparams\": calibration_hyperparams,}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sample: 100%|██████████| 1000/1000 [01:12<00:00, 13.84it/s, 7 steps of size 3.69e-01. acc. prob=0.90]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                 mean       std    median      5.0%     95.0%     n_eff     r_hat\n",
      "    Vext[0]     52.11     78.33     51.46    -79.62    181.68    411.62      1.00\n",
      "    Vext[1]    118.56    115.94    119.97    -59.27    312.47    324.37      1.00\n",
      "    Vext[2]     -4.41    159.70    -12.18   -261.86    252.69    255.23      1.02\n",
      "      alpha      0.50      0.35      0.49     -0.02      1.07    185.82      1.00\n",
      "  alpha_cal      0.13      0.02      0.13      0.10      0.17    551.23      1.01\n",
      "       beta      1.11      0.36      1.15      0.52      1.66    197.49      1.00\n",
      "   beta_cal      3.56      0.23      3.56      3.16      3.90    262.57      1.00\n",
      "       e_mu      0.08      0.03      0.08      0.04      0.12    248.50      1.00\n",
      "    mag_cal    -18.18      0.04    -18.19    -18.25    -18.12    313.96      1.04\n",
      "    sigma_v    216.41     75.53    212.88     94.54    333.95    187.59      1.00\n",
      "\n",
      "Number of divergences: 0\n"
     ]
    }
   ],
   "source": [
    "kernel = NUTS(model, init_strategy=init_to_median(num_samples=100))\n",
    "mcmc = MCMC(kernel, num_warmup=500, num_samples=500)\n",
    "\n",
    "rng_key = jax.random.PRNGKey(5)\n",
    "mcmc.run(rng_key, extra_fields=(\"potential_energy\",), **model_kwargs)\n",
    "mcmc.print_summary()\n",
    "samples = mcmc.get_samples()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpyro.infer import log_likelihood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "ll_single = log_likelihood(model, samples, **model_kwargs)[\"ll\"]\n",
    "ll_mult = log_likelihood(model_mult, samples, **model_kwargs, )[\"ll\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       0., 0., 0., 0., 0., 0., 0.], dtype=float32)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ll_single - ll_mult"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       -375.8968 , -373.49118, -373.32666, -374.5882 , -375.28967,\n",
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       "       -378.351  , -375.5417 , -378.686  , -376.46448, -377.3991 ,\n",
       "       -376.28412, -379.40198, -374.2234 , -372.92682, -376.41956],      dtype=float32)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       391.64557, 392.49634, 393.31058, 395.3479 , 390.1821 , 392.41928,\n",
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       "       393.62537, 392.98868, 395.08075, 394.6271 , 389.055  , 390.7642 ,\n",
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       "       390.31824, 391.90237, 399.44983, 393.19128, 393.52548, 392.53802,\n",
       "       390.68805, 391.63797, 396.24985, 394.57785, 392.13458, 390.36664,\n",
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       "       392.4267 , 393.0737 , 397.35675, 398.8355 , 396.29037, 391.05008,\n",
       "       389.4605 , 391.93326, 391.39075, 390.61874, 394.65973, 394.07922,\n",
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       "       389.75055, 391.72717, 392.82794, 390.9369 , 392.5645 , 391.68805,\n",
       "       393.5547 , 392.33005, 389.79407, 396.49945, 393.72034, 393.51117,\n",
       "       392.1612 , 390.8545 , 390.19092, 389.54028, 390.081  , 393.38885,\n",
       "       389.43814, 391.40814, 392.77875, 390.07477, 389.34094, 390.62073,\n",
       "       391.42688, 396.5186 , 397.25574, 388.39703, 391.31366, 398.72787,\n",
       "       391.05505, 392.2018 , 391.27768, 393.10678, 395.05096, 393.76315,\n",
       "       392.66342, 392.51956, 390.97275, 390.50626, 393.93262, 391.706  ,\n",
       "       390.8292 , 394.87894, 393.27704, 390.10495, 389.4763 , 388.9353 ,\n",
       "       388.51953, 388.58496, 389.6656 , 394.77295, 391.35245, 391.1977 ,\n",
       "       391.07584, 391.1299 , 390.0027 , 392.0007 , 392.77393, 392.88647,\n",
       "       388.6409 , 392.9662 , 390.44272, 391.4916 , 389.46524, 392.48782,\n",
       "       391.1928 , 391.6972 , 392.49786, 397.5695 , 391.7954 , 391.18668,\n",
       "       394.03104, 391.3363 , 393.5302 , 391.74683, 389.46597, 389.0724 ,\n",
       "       395.04776, 395.0067 , 394.68988, 392.55582, 391.55222, 391.8968 ,\n",
       "       392.51648, 389.999  , 390.82315, 391.85864, 397.0075 , 393.12473,\n",
       "       394.1504 , 388.86996, 390.43127, 393.28723, 394.8042 , 394.32358,\n",
       "       394.70056, 394.62448, 393.04788, 391.87747, 392.00067, 390.53983,\n",
       "       390.52933, 390.52118, 391.3734 , 389.67596, 391.80765, 391.9735 ,\n",
       "       388.86786, 389.31982, 389.36093, 390.76718, 393.16095, 392.73416,\n",
       "       392.00232, 391.61856, 394.8812 , 398.4053 , 398.8124 , 393.909  ,\n",
       "       399.0349 , 393.04034, 390.39853, 390.08875, 389.0157 , 388.95517,\n",
       "       392.23053, 390.5207 ], dtype=float32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mcmc.get_extra_fields()[\"potential_energy\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(774.734619140625, 755.6143798828125)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples = mcmc.get_samples()\n",
    "csiborgtools.numpyro_gof(model, mcmc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Reading LOSS fitted to Carrick2015 with ksmooth = 0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BIC  = 773.225037 +- 0.000000\n",
      "AIC  = 754.104797 +- 0.000000\n",
      "logZ = -356.240234 +- 0.000000\n",
      "chi2 = 1.207006 +- 0.228673\n"
     ]
    }
   ],
   "source": [
    "data, names, __ = read_samples(\"LOSS\", \"Carrick2015\", 0, return_MCsamples=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "key = \"beta\"\n",
    "\n",
    "plt.figure()\n",
    "plt.hist(data[:, names.index(key)], bins=\"auto\", density=1, histtype=\"step\")\n",
    "plt.hist(samples[key], bins=\"auto\", density=1, histtype=\"step\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples = mcmc.get_samples()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array([-369.51086, -369.97043, -370.17526, ..., -372.54755, -376.03503,\n",
       "       -374.88458], dtype=float32)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples[\"ll_values\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nparam = 0\n",
    "for val in samples.values():\n",
    "    if val.ndim == 1:\n",
    "        nparam += 1\n",
    "    elif val.ndim == 2:\n",
    "        nparam += val.shape[-1]\n",
    "    else:\n",
    "        raise ValueError(\"Invalid dimensionality of samples to count the number of parameters.\")\n",
    "    \n",
    "\n",
    "nparam\n",
    "    \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|V|  = 197.8179931640625 +- 99.38513946533203\n",
      "l    = 213.2463176948003 +- 116.2995226818662\n",
      "b    = -5.31730133782022 +- 27.004291397137365\n",
      "beta = 0.4450029134750366 +- 0.10768470168113708\n"
     ]
    }
   ],
   "source": [
    "Vmag = np.sqrt(samples[\"Vext\"][:, 0]**2 + samples[\"Vext\"][:, 1]**2 + samples[\"Vext\"][:, 2]**2)\n",
    "\n",
    "V = np.vstack([samples[\"Vext\"][:, 0], samples[\"Vext\"][:, 1], samples[\"Vext\"][:, 2]]).T\n",
    "V = csiborgtools.cartesian_to_radec(V)\n",
    "\n",
    "l, b = csiborgtools.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": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1390x1390 with 36 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",
    "if \"sigma_v\" in samples:\n",
    "    data.append(samples[\"sigma_v\"])\n",
    "    labels.append(r\"$\\sigma_v$\")\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": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<getdist.mcsamples.MCSamples at 0x7f34904c5b90>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_samples(\"Pantheon+\", \"csiborg2_main\", 0, return_MCsamples=True)"
   ]
  },
  {
   "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": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Reading LOSS fitted to Carrick2015 with ksmooth = 0.\n",
      "BIC  = 773.225037 +- 0.000000\n",
      "AIC  = 754.104797 +- 0.000000\n",
      "logZ = -356.240234 +- 0.000000\n",
      "chi2 = 1.207006 +- 0.228673\n",
      "Removed no burn in\n"
     ]
    }
   ],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": 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",
    "\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": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Reading 2MTF fitted to Carrick2015 with ksmooth = 0.\n",
      "BIC  = 19517.031250 +- 0.000000\n",
      "AIC  = 19470.867188 +- 0.000000\n",
      "logZ = -9731.227539 +- 0.000000\n",
      "chi2 = 0.000000 +- 0.000000\n",
      "Removed no burn in\n",
      "\n",
      "Reading 2MTF fitted to Carrick2015 with ksmooth = 1.\n",
      "BIC  = 19632.685547 +- 0.000000\n",
      "AIC  = 19586.521484 +- 0.000000\n",
      "logZ = -9788.892578 +- 0.000000\n",
      "chi2 = 0.000000 +- 0.000000\n",
      "Removed no burn in\n",
      "\n",
      "Reading 2MTF fitted to csiborg1 with ksmooth = 0.\n",
      "BIC  = 19922.607596 +- 33.988735\n",
      "AIC  = 19876.443533 +- 33.988735\n",
      "logZ = -9934.180538 +- 17.010780\n",
      "chi2 = 0.000000 +- 0.000000\n",
      "Removed no burn in\n",
      "\n",
      "Reading 2MTF fitted to csiborg1 with ksmooth = 1.\n",
      "BIC  = 19840.144473 +- 31.749545\n",
      "AIC  = 19793.980411 +- 31.749545\n",
      "logZ = -9891.951984 +- 16.078607\n",
      "chi2 = 0.000000 +- 0.000000\n",
      "Removed no burn in\n",
      "\n",
      "Reading 2MTF fitted to csiborg2_main with ksmooth = 0.\n",
      "BIC  = 19248.799609 +- 38.583873\n",
      "AIC  = 19202.635547 +- 38.583873\n",
      "logZ = -9598.394336 +- 19.251815\n",
      "chi2 = 0.000000 +- 0.000000\n",
      "Removed no burn in\n",
      "\n",
      "Reading 2MTF fitted to csiborg2_main with ksmooth = 1.\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"
     ]
    }
   ],
   "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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