{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copyright (C) 2024 Richard Stiskalek\n",
    "# This program is free software; you can redistribute it and/or modify it\n",
    "# under the terms of the GNU General Public License as published by the\n",
    "# Free Software Foundation; either version 3 of the License, or (at your\n",
    "# option) any later version.\n",
    "#\n",
    "# This program is distributed in the hope that it will be useful, but\n",
    "# WITHOUT ANY WARRANTY; without even the implied warranty of\n",
    "# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General\n",
    "# Public License for more details.\n",
    "#\n",
    "# You should have received a copy of the GNU General Public License along\n",
    "# with this program; if not, write to the Free Software Foundation, Inc.,\n",
    "# 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.\n",
    "from os.path import exists\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from corner import corner\n",
    "from getdist import plots\n",
    "import scienceplots\n",
    "from os.path import exists\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "from reconstruction_comparison import *\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "\n",
    "paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)\n",
    "fdir = \"/mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quick checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "catalogue = \"CF4_TFR_i\"\n",
    "simname = \"IndranilVoid_exp\"\n",
    "zcmb_max=0.05\n",
    "sample_beta = None\n",
    "no_Vext = True\n",
    "\n",
    "fname = paths.flow_validation(\n",
    "    fdir, simname, catalogue, inference_method=\"bayes\",\n",
    "    sample_alpha=False, sample_beta=sample_beta,\n",
    "    no_Vext=no_Vext, zcmb_max=zcmb_max)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = samples_to_getdist(get_samples(fname, False), \"Test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = [\"rLG\", \"sigma_v\"]\n",
    "\n",
    "\n",
    "# params = [\"beta\", f\"a_{catalogue}\", f\"b_{catalogue}\", f\"e_mu_{catalogue}\"]\n",
    "# params = [\"Vmag\", \"l\", \"b\", \"sigma_v\", \"beta\", f\"mag_cal_{catalogue}\", f\"alpha_cal_{catalogue}\", f\"beta_cal_{catalogue}\", f\"e_mu_{catalogue}\"]\n",
    "\n",
    "with plt.style.context(\"science\"):\n",
    "    g = plots.get_subplot_plotter()\n",
    "    g.settings.figure_legend_frame = False\n",
    "    g.settings.alpha_filled_add = 0.75\n",
    "    \n",
    "    g.triangle_plot(X, params=params, filled=True, legend_loc='upper right')\n",
    "    plt.gcf().suptitle(catalogue_to_pretty(catalogue), y=1.025)\n",
    "    plt.gcf().tight_layout()\n",
    "    plt.gcf().show()\n",
    "    # plt.gcf().savefig(f\"../../plots/method_comparison_{simname}_{catalogue}.png\", dpi=500, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# catalogue = [\"LOSS\", \"Foundation\"]\n",
    "catalogue = \"CF4_TFR_i\"\n",
    "simname = \"IndranilVoid_exp\"\n",
    "zcmb_max = 0.05\n",
    "sample_alpha = False\n",
    "\n",
    "fname = paths.flow_validation(\n",
    "    fdir, simname, catalogue, inference_method=\"mike\",\n",
    "    sample_mag_dipole=True,\n",
    "    sample_beta=False,\n",
    "    sample_alpha=sample_alpha, zcmb_max=zcmb_max)\n",
    "\n",
    "\n",
    "samples = get_samples(fname, convert_Vext_to_galactic=True)\n",
    "\n",
    "samples, labels, keys = samples_for_corner(samples)\n",
    "fig = corner(samples, labels=labels, show_titles=True,\n",
    "             title_kwargs={\"fontsize\": 12}, smooth=1)\n",
    "# fig.savefig(\"../../plots/test.png\", dpi=250)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Paper plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. No $V_{\\rm ext}$ and no $\\beta$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for simname in [\"IndranilVoid_exp\", \"IndranilVoid_gauss\", \"IndranilVoid_mb\"]:\n",
    "    X = []\n",
    "    for catalogue in [\"2MTF\", \"SFI_gals\", \"CF4_TFR_i\", \"CF4_TFR_w1\"]:\n",
    "\n",
    "        fname = paths.flow_validation(\n",
    "            fdir, simname, catalogue, inference_method=\"bayes\",\n",
    "            sample_alpha=False, sample_beta=None,\n",
    "            no_Vext=True, zcmb_max=0.05)\n",
    "\n",
    "        X_i = samples_to_getdist(get_samples(fname, False), catalogue_to_pretty(catalogue))\n",
    "        X.append(X_i)\n",
    "\n",
    "    params = [\"rLG\", \"sigma_v\"]\n",
    "    with plt.style.context(\"science\"):\n",
    "        g = plots.get_subplot_plotter()\n",
    "        g.settings.figure_legend_frame = False\n",
    "        g.settings.alpha_filled_add = 0.75\n",
    "\n",
    "        g.triangle_plot(X, params=params, filled=True, legend_loc='upper right')\n",
    "        plt.gcf().suptitle(simname_to_pretty(simname), y=1.025)\n",
    "        plt.gcf().tight_layout()\n",
    "        plt.gcf().show()\n",
    "        plt.gcf().savefig(f\"../../plots/void_{simname}_noVext_nobeta.png\", dpi=500, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. No  $V_{\\rm ext}$ but sampling $\\beta$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for simname in [\"IndranilVoid_exp\", \"IndranilVoid_gauss\", \"IndranilVoid_mb\"]:\n",
    "    X = []\n",
    "    for catalogue in [\"2MTF\", \"SFI_gals\", \"CF4_TFR_i\", \"CF4_TFR_w1\"]:\n",
    "\n",
    "        fname = paths.flow_validation(\n",
    "            fdir, simname, catalogue, inference_method=\"bayes\",\n",
    "            sample_alpha=False, sample_beta=True,\n",
    "            no_Vext=True, zcmb_max=0.05)\n",
    "\n",
    "        X_i = samples_to_getdist(get_samples(fname, False), catalogue_to_pretty(catalogue))\n",
    "        X.append(X_i)\n",
    "\n",
    "    params = [\"rLG\", \"sigma_v\", \"beta\"]\n",
    "    with plt.style.context(\"science\"):\n",
    "        g = plots.get_subplot_plotter()\n",
    "        g.settings.figure_legend_frame = False\n",
    "        g.settings.alpha_filled_add = 0.75\n",
    "\n",
    "        g.triangle_plot(X, params=params, filled=True, legend_loc='upper right')\n",
    "        plt.gcf().suptitle(simname_to_pretty(simname), y=1.025)\n",
    "        plt.gcf().tight_layout()\n",
    "        plt.gcf().show()\n",
    "        plt.gcf().savefig(f\"../../plots/void_{simname}_noVext_beta.png\", dpi=500, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Yes $V_{\\rm ext}$ and no $\\beta$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for simname in [\"IndranilVoid_exp\", \"IndranilVoid_gauss\", \"IndranilVoid_mb\"]:\n",
    "    X = []\n",
    "    for catalogue in [\"2MTF\", \"SFI_gals\", \"CF4_TFR_i\", \"CF4_TFR_w1\"]:\n",
    "\n",
    "        fname = paths.flow_validation(\n",
    "            fdir, simname, catalogue, inference_method=\"bayes\",\n",
    "            sample_alpha=False, sample_beta=False,\n",
    "            no_Vext=None, zcmb_max=0.05)\n",
    "\n",
    "        X_i = samples_to_getdist(get_samples(fname, False), catalogue_to_pretty(catalogue))\n",
    "        X.append(X_i)\n",
    "\n",
    "    params = [\"rLG\", \"sigma_v\", \"Vx\", \"Vy\", \"Vz\"]\n",
    "    with plt.style.context(\"science\"):\n",
    "        g = plots.get_subplot_plotter()\n",
    "        g.settings.figure_legend_frame = False\n",
    "        g.settings.alpha_filled_add = 0.75\n",
    "\n",
    "        g.triangle_plot(X, params=params, filled=True, legend_loc='upper right')\n",
    "        plt.gcf().suptitle(simname_to_pretty(simname), y=1.025)\n",
    "        plt.gcf().tight_layout()\n",
    "        plt.gcf().show()\n",
    "        plt.gcf().savefig(f\"../../plots/void_{simname}_Vext_nobeta.png\", dpi=500, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Yes $V_{\\rm ext}$ and yes $\\beta$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for simname in [\"IndranilVoid_exp\", \"IndranilVoid_gauss\", \"IndranilVoid_mb\"]:\n",
    "    X = []\n",
    "    for catalogue in [\"2MTF\", \"SFI_gals\", \"CF4_TFR_i\", \"CF4_TFR_w1\"]:\n",
    "\n",
    "        fname = paths.flow_validation(\n",
    "            fdir, simname, catalogue, inference_method=\"bayes\",\n",
    "            sample_alpha=False, sample_beta=True,\n",
    "            no_Vext=None, zcmb_max=0.05)\n",
    "\n",
    "        X_i = samples_to_getdist(get_samples(fname, False), catalogue_to_pretty(catalogue))\n",
    "        X.append(X_i)\n",
    "\n",
    "    params = [\"rLG\", \"sigma_v\", \"beta\", \"Vx\", \"Vy\", \"Vz\"]\n",
    "    with plt.style.context(\"science\"):\n",
    "        g = plots.get_subplot_plotter()\n",
    "        g.settings.figure_legend_frame = False\n",
    "        g.settings.alpha_filled_add = 0.75\n",
    "\n",
    "        g.triangle_plot(X, params=params, filled=True, legend_loc='upper right')\n",
    "        plt.gcf().suptitle(simname_to_pretty(simname), y=1.025)\n",
    "        plt.gcf().tight_layout()\n",
    "        plt.gcf().show()\n",
    "        plt.gcf().savefig(f\"../../plots/void_{simname}_Vext_beta.png\", dpi=500, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "markdown",
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   "source": []
  }
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