Sec 6.8 - Analysis 3D - Distribution - normal FoV - Comparison.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2530ec01",
   "metadata": {},
   "source": [
    "## Results from Section 6.8.\n",
    "\n",
    "Data is for 3D quantities using the normal FoV and the distribution method.  Comparison between the results using different DM mass profiles. \n",
    "\n",
    "Model1 - gNFW; Model4 - gNFW with scalse radius fixed at 10 Reff;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "65e247ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import json\n",
    "from astropy.io import fits"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88366f2a",
   "metadata": {},
   "source": [
    "## Results whitin  2.5R$_{\\text{Eff}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9c0b3e24",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Column 0 for Jam result, Column 1 for dyLens \n",
    "subhalos = [\"7\", \"8\", \"9\", \"11\", \"20\", \"56405\", \"56406\", \"83991\",\n",
    "            \"83996\", \"84010\", \"100675\", \"116278\", \"172209\", \"313415\", \n",
    "            \"341482\", \"344595\", \"396742\", \"414107\", \"451938\", \"485608\",\n",
    "            \"545285\"]\n",
    "Nsubhalos = len(subhalos)  # Number of subhalos\n",
    "\n",
    "Mstar  = np.empty([Nsubhalos]) # \"True\" stellar mass\n",
    "Mdm    = np.empty([Nsubhalos]) # \"True\" dm mass\n",
    "Mtotal = np.empty([Nsubhalos]) # \"True\" total mass\n",
    "Mfdm   = np.empty([Nsubhalos]) # \"True\" dark matter fraction\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a27ac477",
   "metadata": {},
   "source": [
    "### JAM models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "66c0735c",
   "metadata": {},
   "outputs": [],
   "source": [
    "    # Where to store the Fractional difference\n",
    "Jam_star_model1 = np.empty([Nsubhalos])       # JAM measurement of stellar mass \n",
    "Jam_star_model4 = np.empty([Nsubhalos])       # JAM measurement of stellar mass \n",
    "\n",
    "Jam_DM_model1 = np.empty([Nsubhalos])         # JAM measurement of DM mass \n",
    "Jam_DM_model4 = np.empty([Nsubhalos])         # JAM measurement of DM mass \n",
    "\n",
    "Jam_fdm_model1 = np.empty([Nsubhalos])        # JAM measurement of fDM mass \n",
    "Jam_fdm_model4 = np.empty([Nsubhalos])        # JAM measurement of fDM mass \n",
    "\n",
    "Jam_total_model1 = np.empty([Nsubhalos])      # JAM measurement of total mass \n",
    "Jam_total_model4 = np.empty([Nsubhalos])      # JAM measurement of total mass "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bdbfd6ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, idNum in enumerate(subhalos):\n",
    "\n",
    "    with open(\"./{}/model1/normal/JAM/Analysis_2.5Reff/quantities.json\".format(idNum)) as f:\n",
    "        model1 = json.load(f)\n",
    "        f.close()\n",
    "    \n",
    "    with open(\"./{}/model4/normal/JAM/Analysis_2.5Reff/quantities.json\".format(idNum)) as f:\n",
    "        model4 = json.load(f)\n",
    "        f.close()\n",
    "    \n",
    "\n",
    "    \n",
    "    Jam_star_model1[i] = model1[\"Dstar\"]\n",
    "    Jam_star_model4[i] = model4[\"Dstar\"]    \n",
    "    \n",
    "    Jam_DM_model1[i] = model1[\"Ddm\"]\n",
    "    Jam_DM_model4[i] = model4[\"Ddm\"]\n",
    "    \n",
    "    Jam_total_model1[i] = model1[\"Dtotal\"]\n",
    "    Jam_total_model4[i] = model4[\"Dtotal\"]\n",
    "\n",
    "    Jam_fdm_model1[i] = model1[\"Dfdm\"]\n",
    "    Jam_fdm_model4[i] = model4[\"Dfdm\"]\n",
    "    \n",
    "    Mstar[i]  = model1[\"Mstar\"]\n",
    "    Mdm[i]    = model1[\"Mdm\"]\n",
    "    Mtotal[i] = model1[\"Mtotal\"] \n",
    "    Mfdm[i]   = model1[\"fdm\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7aa800cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To make some plots\n",
    "\n",
    "def median_hi_lo(dataset):\n",
    "    # Return the median, hi and low percentiles\n",
    "    p = np.percentile(dataset, [16, 50, 84])\n",
    "    median = p[1]\n",
    "    hi, lo = np.diff(p)\n",
    "    \n",
    "    return median, hi, lo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4445c4db",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Bias as a function of the Triaxiality\n",
    "\n",
    "plt.rc('xtick', labelsize=18)\n",
    "plt.rc('ytick', labelsize=18)\n",
    "\n",
    "# Criar a figura com subplots\n",
    "fig, axs = plt.subplots(2, 2, figsize=(15, 10))\n",
    "\n",
    "# Adicionar os dados aos subplots\n",
    "    # Stellar mass\n",
    "m, hi, lo = median_hi_lo(Jam_star_model1)  \n",
    "_, bins, _ = axs[0, 0].hist(Jam_star_model1, bins=5, density=False, fill=False,\n",
    "                 label=\"Model1: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo), edgecolor='black',linestyle='--',  linewidth=2)\n",
    "\n",
    "m, hi, lo = median_hi_lo(Jam_star_model4)\n",
    "axs[0, 0].hist(Jam_star_model4, bins=bins, density=False, fill=False, hatch=\"/\",\n",
    "                 label=\"Model4: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo), edgecolor='indigo',linestyle=':',  linewidth=2)\n",
    "               \n",
    "\n",
    "axs[0, 0].set_ylabel(\"#Number of subhalos\", size=20)\n",
    "axs[0, 0].set_xlabel(r\"${\\Delta \\overline{M}_{\\rm star}}$\", size=20)\n",
    "axs[0, 0].legend(fontsize=20)\n",
    "axs[0, 0].set_ylim(0,20)\n",
    "\n",
    "\n",
    "\n",
    "    # DM mass\n",
    "m, hi, lo = median_hi_lo(Jam_DM_model1)  \n",
    "_, bins, _ = axs[0, 1].hist(Jam_DM_model1, bins=5, density=False,  fill=False,\n",
    "                 label=\"Model1: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo), edgecolor='black',linestyle='--',  linewidth=2)\n",
    "\n",
    "m, hi, lo = median_hi_lo(Jam_DM_model4)  \n",
    "axs[0, 1].hist(Jam_DM_model4, bins=bins, density=False, hatch=\"/\", fill=False,\n",
    "                 label=\"Model4: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo), edgecolor='indigo',linestyle=':',  linewidth=2)\n",
    "\n",
    "axs[0, 1].set_ylabel(\"#Number of subhalos\", size=20)\n",
    "axs[0, 1].set_xlabel(r\"${\\Delta \\overline{M}_{\\rm DM}}$\", size=20)\n",
    "axs[0, 1].legend(fontsize=20)\n",
    "axs[0, 1].set_ylim(0,20)\n",
    "\n",
    "\n",
    "    # DM fraction\n",
    "m, hi, lo = median_hi_lo(Jam_fdm_model1)  \n",
    "_, bins, _ = axs[1, 0].hist(Jam_fdm_model1, bins=5, density=False,  fill=False,\n",
    "                 label=\"Model1: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo), edgecolor='black',linestyle='--',  linewidth=2)\n",
    "\n",
    "m, hi, lo = median_hi_lo(Jam_fdm_model4)  \n",
    "axs[1, 0].hist(Jam_fdm_model4, bins=bins, density=False, hatch=\"/\", fill=False,\n",
    "                 label=\"Model4: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo), edgecolor='indigo',linestyle=':',  linewidth=2)\n",
    "\n",
    "axs[1, 0].set_ylabel(\"#Number of subhalos\", size=20)\n",
    "axs[1, 0].set_xlabel(r\"${\\Delta \\overline{f}_{\\rm DM}}$\", size=20)\n",
    "axs[1, 0].legend(fontsize=20)\n",
    "axs[1, 0].set_ylim(0,20)\n",
    "\n",
    "\n",
    "\n",
    "    # Total mass\n",
    "m, hi, lo = median_hi_lo(Jam_total_model1)  \n",
    "_, bins, _ = axs[1, 1].hist(Jam_total_model1, bins=5, density=False,  fill=False,\n",
    "                 label=\"Model1: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo), edgecolor='black',linestyle='--',  linewidth=2)\n",
    "\n",
    "m, hi, lo = median_hi_lo(Jam_total_model4)  \n",
    "axs[1, 1].hist(Jam_total_model4, bins=bins, density=False,  hatch=\"/\", fill=False,\n",
    "                 label=\"Model4: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo), edgecolor='indigo',linestyle=':',  linewidth=2)\n",
    "\n",
    "axs[1, 1].set_ylabel(\"#Number of subhalos\", size=20)\n",
    "axs[1, 1].set_xlabel(r\"${\\Delta \\overline{M}_{\\rm total}}$\", size=20)\n",
    "axs[1, 1].legend(fontsize=20)\n",
    "axs[1, 1].set_ylim(0,20)\n",
    "\n",
    "# Adicionar uma única legenda para todos os subplots\n",
    "#fig.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), fancybox=True, shadow=True)\n",
    "\n",
    "# Ajustar o layout\n",
    "plt.tight_layout()\n",
    "plt.savefig('/home/carlosmelo/Desktop/plots/DM_comparison_dynamics_3D.png', dpi=600,  bbox_inches='tight') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbeaedf5",
   "metadata": {},
   "source": [
    "### Slopes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4a04e517",
   "metadata": {},
   "outputs": [],
   "source": [
    "TPL_slope1  = np.empty([Nsubhalos]) # \"True\" PL_slope1\n",
    "TAV_slope1  = np.empty([Nsubhalos]) # \"True\" AV_slope1\n",
    "TPL_slope2  = np.empty([Nsubhalos]) # \"True\" PL_slope2\n",
    "TAV_slope2  = np.empty([Nsubhalos]) # \"True\" AV_slope2\n",
    "\n",
    "MPL_slope1_model1  = np.empty([Nsubhalos]) # model PL_slope1\n",
    "MAV_slope1_model1  = np.empty([Nsubhalos]) # model AV_slope1\n",
    "MPL_slope2_model1  = np.empty([Nsubhalos]) # model PL_slope2\n",
    "MAV_slope2_model1  = np.empty([Nsubhalos]) # model AV_slope2\n",
    "\n",
    "MPL_slope1_model4  = np.empty([Nsubhalos]) # model PL_slope1\n",
    "MAV_slope1_model4  = np.empty([Nsubhalos]) # model AV_slope1\n",
    "MPL_slope2_model4  = np.empty([Nsubhalos]) # model PL_slope2\n",
    "MAV_slope2_model4  = np.empty([Nsubhalos]) # model AV_slope2\n",
    "\n",
    "for i, s in enumerate(subhalos):\n",
    "    with open(\"./{}/model1/normal/JAM/Analysis_2.5Reff/quantities.json\".format(s)) as f:\n",
    "        model1 = json.load(f)\n",
    "        f.close()\n",
    "        \n",
    "    with open(\"./{}/model4/normal/JAM/Analysis_2.5Reff/quantities.json\".format(s)) as f:\n",
    "        model4 = json.load(f)\n",
    "        f.close()\n",
    "        \n",
    "    # True data\n",
    "    log_img = fits.open(\"/home/carlosmelo/Documents/GitHub/Illustris/my_illustris/TNG50-1-snap84-subhalo{}/imgs/log_img.fits\".format(s))\n",
    "    \n",
    "    TPL_slope1[i] = -log_img[1].data[\"PL_slope1\"] \n",
    "    TAV_slope1[i] = log_img[1].data[\"AV_slope1\"] \n",
    "    TPL_slope2[i] = -log_img[1].data[\"PL_slope2\"] \n",
    "    TAV_slope2[i] = log_img[1].data[\"AV_slope2\"]\n",
    "    ############################################\n",
    "        #Models\n",
    "        \n",
    "    #Model1\n",
    "    MPL_slope1_model1[i]  = -model1[\"PL_slope1\"]\n",
    "    MAV_slope1_model1[i]  = model1[\"AV_slope1\"]\n",
    "    MPL_slope2_model1[i]  = -model1[\"PL_slope2\"]\n",
    "    MAV_slope2_model1[i]  = model1[\"AV_slope2\"]\n",
    "    \n",
    "    #Model3\n",
    "    MPL_slope1_model4[i]  = -model4[\"PL_slope1\"]\n",
    "    MAV_slope1_model4[i]  = model4[\"AV_slope1\"]\n",
    "    MPL_slope2_model4[i]  = -model4[\"PL_slope2\"]\n",
    "    MAV_slope2_model4[i]  = model4[\"AV_slope2\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a4cb324e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "\n",
    "\n",
    "plt.subplot(221) # PL slope 1\n",
    "    # Model1\n",
    "diff1 = (MPL_slope1_model1 - TPL_slope1)/TPL_slope1\n",
    "m, hi, lo = median_hi_lo( diff1 )\n",
    "_ = plt.hist(diff1, bins=5, density=False,  color=\"dodgerblue\", alpha=0.8,\n",
    "                 label=\"Model1: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo))\n",
    "    \n",
    "    # Model4\n",
    "diff4 = (MPL_slope1_model4 - TPL_slope1)/TPL_slope1\n",
    "m, hi, lo = median_hi_lo( diff4 )\n",
    "_ = plt.hist(diff4, bins=5, density=False, alpha=0.8,  color=\"palevioletred\",\n",
    "             label=\"Model4: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo))\n",
    "\n",
    "plt.legend(fontsize=13)\n",
    "plt.ylabel(\"#Number of subhalos\", size=14)\n",
    "plt.xlabel(r\"$\\Delta \\overline{\\gamma}_{\\rm PL}(R_{min},1.5R_{eff})$\", size=14)\n",
    "plt.xticks(size=12)\n",
    "plt.yticks(size=12)\n",
    "\n",
    "\n",
    "plt.subplot(222) # AV slope 1\n",
    "       # Model1\n",
    "diff1 = (MAV_slope1_model1 - TAV_slope1)/TAV_slope1\n",
    "m, hi, lo = median_hi_lo( diff1 )\n",
    "_ = plt.hist(diff1, bins=5, density=False,  color=\"dodgerblue\", alpha=0.8,\n",
    "                 label=\"Model1: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo))\n",
    "\n",
    "    # Model4\n",
    "diff4 = (MAV_slope1_model4 - TAV_slope1)/TAV_slope1\n",
    "m, hi, lo = median_hi_lo( diff4 )\n",
    "_ = plt.hist(diff4, bins=5, density=False, alpha=0.8, color=\"palevioletred\",\n",
    "             label=\"Model4: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo))\n",
    "\n",
    "plt.legend(fontsize=13)\n",
    "plt.xlabel(r\"$\\Delta \\overline{\\gamma}_{\\rm AV}(R_{min},1.5R_{eff})$\", size=14)\n",
    "plt.xticks(size=12)\n",
    "plt.yticks([])\n",
    "\n",
    "plt.subplot(223) # PL slope 2\n",
    "    # Model1\n",
    "diff1 = (MPL_slope2_model1 - TPL_slope2)/TPL_slope2\n",
    "m, hi, lo = median_hi_lo( diff1 )\n",
    "_ = plt.hist(diff1, bins=5, density=False,  color=\"dodgerblue\", alpha=0.8,\n",
    "                 label=\"Model1: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo))\n",
    "\n",
    "    # Model4\n",
    "diff4 = (MPL_slope2_model4 - TPL_slope2)/TPL_slope2\n",
    "m, hi, lo = median_hi_lo( diff4 )\n",
    "_ = plt.hist(diff4, bins=5, density=False, alpha=0.8, color=\"palevioletred\",\n",
    "             label=\"Model4: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo))\n",
    "\n",
    "plt.legend(fontsize=13)\n",
    "plt.ylabel(\"#Number of subhalos\", size=14)\n",
    "plt.xlabel(r\"$\\Delta \\overline{\\gamma}_{\\rm PL}(1.5R_{eff},2.5R_{eff})$\", size=15)\n",
    "\n",
    "plt.xticks(size=12)\n",
    "plt.yticks(size=12)\n",
    "\n",
    "plt.subplot(224) # AV slope 2\n",
    "       # Model1\n",
    "diff1 = (MAV_slope2_model1 - TAV_slope2)/TAV_slope2\n",
    "m, hi, lo = median_hi_lo( diff1 )\n",
    "_ = plt.hist(diff1, bins=5, density=False, color=\"dodgerblue\", alpha=0.8,\n",
    "                 label=\"Model1: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo))\n",
    "\n",
    "    # Model4\n",
    "diff4 = (MAV_slope2_model4 - TAV_slope2)/TAV_slope2\n",
    "m, hi, lo = median_hi_lo( diff4 )\n",
    "_ = plt.hist(diff4, bins=5, density=False, alpha=0.8, color=\"palevioletred\",\n",
    "             label=\"Model4: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo))\n",
    "\n",
    "plt.legend(fontsize=13)\n",
    "plt.xlabel(r\"$\\Delta \\overline{\\gamma}_{\\rm AV}(1.5R_{eff},2.5R_{eff})$\", size=14)\n",
    "plt.xticks(size=12)\n",
    "plt.yticks([])\n",
    "\n",
    "\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7effb02",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33c844b3",
   "metadata": {},
   "source": [
    "### dyLens models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "82b56cc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Where to store the Fractional difference\n",
    "dyLens_star_model1 = np.empty([Nsubhalos])       # dyLens measurement of stellar mass \n",
    "dyLens_star_model4 = np.empty([Nsubhalos])       # dyLens measurement of stellar mass \n",
    "\n",
    "\n",
    "dyLens_DM_model1 = np.empty([Nsubhalos])         # dyLens measurement of DM mass \n",
    "dyLens_DM_model4 = np.empty([Nsubhalos])         # dyLens measurement of DM mass \n",
    "\n",
    "dyLens_fdm_model1 = np.empty([Nsubhalos])        # dyLens measurement of fDM mass \n",
    "dyLens_fdm_model4 = np.empty([Nsubhalos])        # dyLens measurement of fDM mass \n",
    "\n",
    "dyLens_total_model1 = np.empty([Nsubhalos])      # JAM measurement of total mass \n",
    "dyLens_total_model4 = np.empty([Nsubhalos])      # JAM measurement of total mass "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eebbb77c",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, idNum in enumerate(subhalos):\n",
    "\n",
    "    with open(\"./{}/model1/normal/shear/dyLens/phase5/Analysis_2.5Reff/quantities.json\".format(idNum)) as f:\n",
    "        model1 = json.load(f)\n",
    "        f.close()\n",
    "    \n",
    "    with open(\"./{}/model4/normal/shear/dyLens/phase5/Analysis_2.5Reff/quantities.json\".format(idNum)) as f:\n",
    "        model4 = json.load(f)\n",
    "        f.close()\n",
    "    \n",
    "\n",
    "    \n",
    "\n",
    "    \n",
    "    dyLens_star_model1[i] = model1[\"Dstar\"]\n",
    "    dyLens_star_model4[i] = model4[\"Dstar\"]\n",
    "    \n",
    "    dyLens_DM_model1[i] = model1[\"Ddm\"]\n",
    "    dyLens_DM_model4[i] = model4[\"Ddm\"]\n",
    "    \n",
    "    dyLens_total_model1[i] = model1[\"Dtotal\"]\n",
    "    dyLens_total_model4[i] = model4[\"Dtotal\"]\n",
    "\n",
    "    dyLens_fdm_model1[i] = model1[\"Dfdm\"]\n",
    "    dyLens_fdm_model4[i] = model4[\"Dfdm\"]\n",
    "    \n",
    "    Mstar[i]  = model1[\"Mstar\"]\n",
    "    Mdm[i]    = model1[\"Mdm\"]\n",
    "    Mtotal[i] = model1[\"Mtotal\"] \n",
    "    Mfdm[i]   = model1[\"fdm\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e16777b6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89a3595c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "## Bias as a function of the Triaxiality\n",
    "\n",
    "plt.rc('xtick', labelsize=18)\n",
    "plt.rc('ytick', labelsize=18)\n",
    "\n",
    "# Criar a figura com subplots\n",
    "fig, axs = plt.subplots(2, 2, figsize=(15, 10))\n",
    "\n",
    "# Adicionar os dados aos subplots\n",
    "    # Stellar mass\n",
    "m, hi, lo = median_hi_lo(dyLens_star_model1)  \n",
    "_, bins, _ = axs[0, 0].hist(dyLens_star_model1, bins=5, density=False,  fill=False,\n",
    "                 label=\"Model1: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo), edgecolor='black',linestyle='--',  linewidth=2)\n",
    "\n",
    "m, hi, lo = median_hi_lo(dyLens_star_model4)\n",
    "axs[0, 0].hist(dyLens_star_model4, bins=bins, density=False, hatch=\"/\", fill=False,\n",
    "                 label=\"Model4: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo), edgecolor='indigo',linestyle=':',  linewidth=2)\n",
    "\n",
    "\n",
    "axs[0, 0].set_ylabel(\"#Number of subhalos\", size=20)\n",
    "axs[0, 0].set_xlabel(r\"${\\Delta \\overline{M}_{\\rm star}}$\", size=20)\n",
    "axs[0, 0].legend(fontsize=20)\n",
    "axs[0, 0].set_ylim(0,20)\n",
    "\n",
    "\n",
    "    # DM mass\n",
    "m, hi, lo = median_hi_lo(dyLens_DM_model1)  \n",
    "_, bins, _ = axs[0, 1].hist(dyLens_DM_model1, bins=5, density=False, fill=False,\n",
    "                 label=\"Model1: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo), edgecolor='black',linestyle='--',  linewidth=2)\n",
    "\n",
    "m, hi, lo = median_hi_lo(dyLens_DM_model4)  \n",
    "axs[0, 1].hist(dyLens_DM_model4, bins=bins, density=False, hatch=\"/\", fill=False,\n",
    "                 label=\"Model4: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo), edgecolor='indigo',linestyle=':',  linewidth=2)\n",
    "\n",
    "\n",
    "axs[0, 1].set_ylabel(\"#Number of subhalos\", size=20)\n",
    "axs[0, 1].set_xlabel(r\"${\\Delta \\overline{M}_{\\rm DM}}$\", size=20)\n",
    "axs[0, 1].legend(fontsize=20)\n",
    "axs[0, 1].set_ylim(0,20)\n",
    "\n",
    "    # DM fraction\n",
    "m, hi, lo = median_hi_lo(dyLens_fdm_model1)  \n",
    "_, bins, _ = axs[1, 0].hist(dyLens_fdm_model1, bins=5, density=False,  fill=False,\n",
    "                 label=\"Model1: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo), edgecolor='black',linestyle='--',  linewidth=2)\n",
    "\n",
    "m, hi, lo = median_hi_lo(dyLens_fdm_model4)  \n",
    "axs[1, 0].hist(dyLens_fdm_model4, bins=bins, density=False,  hatch=\"/\", fill=False,\n",
    "                 label=\"Model4: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo), edgecolor='indigo',linestyle=':',  linewidth=2)\n",
    "\n",
    "\n",
    "axs[1, 0].set_ylabel(\"#Number of subhalos\", size=20)\n",
    "axs[1, 0].set_xlabel(r\"${\\Delta \\overline{f}_{\\rm DM}}$\", size=20)\n",
    "axs[1, 0].legend(fontsize=20)\n",
    "axs[1, 0].set_ylim(0,20)\n",
    "\n",
    "\n",
    "\n",
    "    # Total mass\n",
    "m, hi, lo = median_hi_lo(dyLens_total_model1)  \n",
    "_, bins, _ = axs[1, 1].hist(dyLens_total_model1, bins=5, density=False,  fill=False,\n",
    "                 label=\"Model1: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo), edgecolor='black',linestyle='--',  linewidth=2)\n",
    "\n",
    "m, hi, lo = median_hi_lo(dyLens_total_model4)  \n",
    "axs[1, 1].hist(dyLens_total_model4, bins=bins, density=False, hatch=\"/\", fill=False,\n",
    "                 label=\"Model4: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo), edgecolor='indigo',linestyle=':',  linewidth=2)\n",
    "\n",
    "\n",
    "axs[1, 1].set_ylabel(\"#Number of subhalos\", size=20)\n",
    "axs[1, 1].set_xlabel(r\"${\\Delta \\overline{M}_{\\rm total}}$\", size=20)\n",
    "axs[1, 1].legend(fontsize=20)\n",
    "axs[1, 1].set_ylim(0,20)\n",
    "\n",
    "\n",
    "# Adicionar uma única legenda para todos os subplots\n",
    "#fig.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), fancybox=True, shadow=True)\n",
    "\n",
    "# Ajustar o layout\n",
    "plt.tight_layout()\n",
    "plt.savefig('/home/carlosmelo/Desktop/plots/DM_comparison_dyLens_3D.png', dpi=600,  bbox_inches='tight') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a830160c",
   "metadata": {},
   "source": [
    "## Slopes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "18608e31",
   "metadata": {},
   "outputs": [],
   "source": [
    "TPL_slope1  = np.empty([Nsubhalos]) # \"True\" PL_slope1\n",
    "TAV_slope1  = np.empty([Nsubhalos]) # \"True\" AV_slope1\n",
    "TPL_slope2  = np.empty([Nsubhalos]) # \"True\" PL_slope2\n",
    "TAV_slope2  = np.empty([Nsubhalos]) # \"True\" AV_slope2\n",
    "\n",
    "MPL_slope1_model1  = np.empty([Nsubhalos]) # model PL_slope1\n",
    "MAV_slope1_model1  = np.empty([Nsubhalos]) # model AV_slope1\n",
    "MPL_slope2_model1  = np.empty([Nsubhalos]) # model PL_slope2\n",
    "MAV_slope2_model1  = np.empty([Nsubhalos]) # model AV_slope2\n",
    "\n",
    "MPL_slope1_model4  = np.empty([Nsubhalos]) # model PL_slope1\n",
    "MAV_slope1_model4  = np.empty([Nsubhalos]) # model AV_slope1\n",
    "MPL_slope2_model4  = np.empty([Nsubhalos]) # model PL_slope2\n",
    "MAV_slope2_model4  = np.empty([Nsubhalos]) # model AV_slope2\n",
    "\n",
    "\n",
    "\n",
    "for i, s in enumerate(subhalos):\n",
    "    with open(\"./{}/model1/normal/shear/dyLens/phase5/Analysis_2.5Reff/quantities.json\".format(s)) as f:\n",
    "        model1 = json.load(f)\n",
    "        f.close()\n",
    "        \n",
    "    with open(\"./{}/model4/normal/shear/dyLens/phase5/Analysis_2.5Reff/quantities.json\".format(s)) as f:\n",
    "        model4 = json.load(f)\n",
    "        f.close()\n",
    "        \n",
    "    # True data\n",
    "    log_img = fits.open(\"/home/carlosmelo/Documents/GitHub/Illustris/my_illustris/TNG50-1-snap84-subhalo{}/imgs/log_img.fits\".format(s))\n",
    "    \n",
    "    TPL_slope1[i] = -log_img[1].data[\"PL_slope1\"] \n",
    "    TAV_slope1[i] = log_img[1].data[\"AV_slope1\"] \n",
    "    TPL_slope2[i] = -log_img[1].data[\"PL_slope2\"] \n",
    "    TAV_slope2[i] = log_img[1].data[\"AV_slope2\"]\n",
    "    ############################################\n",
    "        #Models\n",
    "        \n",
    "    #Model1\n",
    "    MPL_slope1_model1[i]  = -model1[\"PL_slope1\"]\n",
    "    MAV_slope1_model1[i]  = model1[\"AV_slope1\"]\n",
    "    MPL_slope2_model1[i]  = -model1[\"PL_slope2\"]\n",
    "    MAV_slope2_model1[i]  = model1[\"AV_slope2\"]\n",
    "    \n",
    "    \n",
    "    #Model3\n",
    "    MPL_slope1_model4[i]  = -model4[\"PL_slope1\"]\n",
    "    MAV_slope1_model4[i]  = model4[\"AV_slope1\"]\n",
    "    MPL_slope2_model4[i]  = -model4[\"PL_slope2\"]\n",
    "    MAV_slope2_model4[i]  = model4[\"AV_slope2\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cfe3d153",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "\n",
    "\n",
    "plt.subplot(221) # PL slope 1\n",
    "    # Model1\n",
    "diff1 = (MPL_slope1_model1 - TPL_slope1)/TPL_slope1\n",
    "m, hi, lo = median_hi_lo( diff1 )\n",
    "_ = plt.hist(diff1, bins=5, density=False,  color=\"dodgerblue\", alpha=0.8,\n",
    "                 label=\"Model1: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo))\n",
    "    \n",
    "    # Model4\n",
    "diff4 = (MPL_slope1_model4 - TPL_slope1)/TPL_slope1\n",
    "m, hi, lo = median_hi_lo( diff4 )\n",
    "_ = plt.hist(diff4, bins=5, density=False, alpha=0.8,  color=\"palevioletred\",\n",
    "             label=\"Model4: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo))\n",
    "\n",
    "plt.legend(fontsize=13)\n",
    "plt.ylabel(\"#Number of subhalos\", size=14)\n",
    "plt.xlabel(r\"$\\Delta \\overline{\\gamma}_{\\rm PL}(R_{min},1.5R_{eff})$\", size=14)\n",
    "plt.xticks(size=12)\n",
    "plt.yticks(size=12)\n",
    "\n",
    "\n",
    "plt.subplot(222) # AV slope 1\n",
    "       # Model1\n",
    "diff1 = (MAV_slope1_model1 - TAV_slope1)/TAV_slope1\n",
    "m, hi, lo = median_hi_lo( diff1 )\n",
    "_ = plt.hist(diff1, bins=5, density=False,  color=\"dodgerblue\", alpha=0.8,\n",
    "                 label=\"Model1: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo))\n",
    "\n",
    "    # Model4\n",
    "diff4 = (MAV_slope1_model4 - TAV_slope1)/TAV_slope1\n",
    "m, hi, lo = median_hi_lo( diff4 )\n",
    "_ = plt.hist(diff4, bins=5, density=False, alpha=0.8, color=\"palevioletred\",\n",
    "             label=\"Model4: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo))\n",
    "\n",
    "plt.legend(fontsize=13)\n",
    "plt.xlabel(r\"$\\Delta \\overline{\\gamma}_{\\rm AV}(R_{min},1.5R_{eff})$\", size=14)\n",
    "plt.xticks(size=12)\n",
    "plt.yticks([])\n",
    "\n",
    "plt.subplot(223) # PL slope 2\n",
    "    # Model1\n",
    "diff1 = (MPL_slope2_model1 - TPL_slope2)/TPL_slope2\n",
    "m, hi, lo = median_hi_lo( diff1 )\n",
    "_ = plt.hist(diff1, bins=5, density=False,  color=\"dodgerblue\", alpha=0.8,\n",
    "                 label=\"Model1: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo))\n",
    "\n",
    "    # Model4\n",
    "diff4 = (MPL_slope2_model4 - TPL_slope2)/TPL_slope2\n",
    "m, hi, lo = median_hi_lo( diff4 )\n",
    "_ = plt.hist(diff4, bins=5, density=False, alpha=0.8, color=\"palevioletred\",\n",
    "             label=\"Model4: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo))\n",
    "\n",
    "plt.legend(fontsize=13)\n",
    "plt.ylabel(\"#Number of subhalos\", size=14)\n",
    "plt.xlabel(r\"$\\Delta \\overline{\\gamma}_{\\rm PL}(1.5R_{eff},2.5R_{eff})$\", size=15)\n",
    "\n",
    "plt.xticks(size=12)\n",
    "plt.yticks(size=12)\n",
    "\n",
    "plt.subplot(224) # AV slope 2\n",
    "       # Model1\n",
    "diff1 = (MAV_slope2_model1 - TAV_slope2)/TAV_slope2\n",
    "m, hi, lo = median_hi_lo( diff1 )\n",
    "_ = plt.hist(diff1, bins=5, density=False, color=\"dodgerblue\", alpha=0.8,\n",
    "                 label=\"Model1: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo))\n",
    "\n",
    "    # Model4\n",
    "diff4 = (MAV_slope2_model4 - TAV_slope2)/TAV_slope2\n",
    "m, hi, lo = median_hi_lo( diff4 )\n",
    "_ = plt.hist(diff4, bins=5, density=False, alpha=0.8, color=\"palevioletred\",\n",
    "             label=\"Model4: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo))\n",
    "\n",
    "plt.legend(fontsize=13)\n",
    "plt.xlabel(r\"$\\Delta \\overline{\\gamma}_{\\rm AV}(1.5R_{eff},2.5R_{eff})$\", size=14)\n",
    "plt.xticks(size=12)\n",
    "plt.yticks([])\n",
    "\n",
    "\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bee1160d",
   "metadata": {},
   "source": [
    "------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9676e0c3",
   "metadata": {},
   "source": [
    "## Results whitin  R$_{\\text{Eins}}$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5315cc6a",
   "metadata": {},
   "source": [
    "### dyLens models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "19e86186",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Where to store the Fractional difference\n",
    "dyLens_star_model1 = np.empty([Nsubhalos])       # dyLens measurement of stellar mass \n",
    "dyLens_star_model4 = np.empty([Nsubhalos])       # dyLens measurement of stellar mass \n",
    "\n",
    "\n",
    "dyLens_DM_model1 = np.empty([Nsubhalos])         # dyLens measurement of DM mass \n",
    "dyLens_DM_model4 = np.empty([Nsubhalos])         # dyLens measurement of DM mass \n",
    "\n",
    "dyLens_fdm_model1 = np.empty([Nsubhalos])        # dyLens measurement of fDM mass \n",
    "dyLens_fdm_model4 = np.empty([Nsubhalos])        # dyLens measurement of fDM mass \n",
    "\n",
    "dyLens_total_model1 = np.empty([Nsubhalos])      # JAM measurement of total mass \n",
    "dyLens_total_model4 = np.empty([Nsubhalos])      # JAM measurement of total mass "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "61a5029e",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, idNum in enumerate(subhalos):\n",
    "\n",
    "    with open(\"./{}/model1/normal/shear/dyLens/phase5/Analysis_Re/quantities.json\".format(idNum)) as f:\n",
    "        model1 = json.load(f)\n",
    "        f.close()\n",
    "    \n",
    "    with open(\"./{}/model4/normal/shear/dyLens/phase5/Analysis_Re/quantities.json\".format(idNum)) as f:\n",
    "        model4 = json.load(f)\n",
    "        f.close()\n",
    "\n",
    "    \n",
    "    dyLens_star_model1[i] = model1[\"Dstar\"]\n",
    "    dyLens_star_model4[i] = model4[\"Dstar\"]\n",
    "    \n",
    "    dyLens_DM_model1[i] = model1[\"Ddm\"]\n",
    "    dyLens_DM_model4[i] = model4[\"Ddm\"]\n",
    "    \n",
    "    dyLens_total_model1[i] = model1[\"Dtotal\"]\n",
    "    dyLens_total_model4[i] = model4[\"Dtotal\"]\n",
    "\n",
    "    dyLens_fdm_model1[i] = model1[\"Dfdm\"]\n",
    "    dyLens_fdm_model4[i] = model4[\"Dfdm\"]\n",
    "    \n",
    "    Mstar[i]  = model1[\"Mstar\"]\n",
    "    Mdm[i]    = model1[\"Mdm\"]\n",
    "    Mtotal[i] = model1[\"Mtotal\"] \n",
    "    Mfdm[i]   = model1[\"fdm\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e61d31a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Bias as a function of the Triaxiality\n",
    "\n",
    "plt.rc('xtick', labelsize=12)\n",
    "plt.rc('ytick', labelsize=12)\n",
    "\n",
    "# Criar a figura com subplots\n",
    "fig, axs = plt.subplots(2, 2, figsize=(12, 8))\n",
    "\n",
    "# Adicionar os dados aos subplots\n",
    "    # Stellar mass\n",
    "m, hi, lo = median_hi_lo(dyLens_star_model1)  \n",
    "axs[0, 0].hist(dyLens_star_model1, bins=5, density=False,  color=\"dodgerblue\", alpha=0.8,\n",
    "                 label=\"Model1: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo))\n",
    "\n",
    "m, hi, lo = median_hi_lo(dyLens_star_model4)\n",
    "axs[0, 0].hist(dyLens_star_model4, bins=5, density=False, alpha=0.8, color=\"palevioletred\",\n",
    "                 label=\"Model4: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo))\n",
    "\n",
    "axs[0, 0].set_ylabel(\"#Number of subhalos\", size=14)\n",
    "axs[0, 0].set_xlabel(r\"${\\Delta \\overline{M}_{\\rm star}}$\", size=14)\n",
    "axs[0, 0].legend(fontsize=12)\n",
    "\n",
    "\n",
    "    # DM mass\n",
    "m, hi, lo = median_hi_lo(dyLens_DM_model1)  \n",
    "axs[0, 1].hist(dyLens_DM_model1, bins=5, density=False,  color=\"dodgerblue\", alpha=0.8,\n",
    "                 label=\"Model1: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo))\n",
    "\n",
    "m, hi, lo = median_hi_lo(dyLens_DM_model4)  \n",
    "axs[0, 1].hist(dyLens_DM_model4, bins=5, density=False,  alpha=0.8, color=\"palevioletred\",\n",
    "                 label=\"Model4: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo))\n",
    "\n",
    "axs[0, 1].set_ylabel(\"#Number of subhalos\", size=14)\n",
    "axs[0, 1].set_xlabel(r\"${\\Delta \\overline{M}_{\\rm DM}}$\", size=14)\n",
    "axs[0, 1].legend(fontsize=12)\n",
    "\n",
    "    # DM fraction\n",
    "m, hi, lo = median_hi_lo(dyLens_fdm_model1)  \n",
    "axs[1, 0].hist(dyLens_fdm_model1, bins=5, density=False,  color=\"dodgerblue\", alpha=0.8,\n",
    "                 label=\"Model1: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo))\n",
    "\n",
    "m, hi, lo = median_hi_lo(dyLens_fdm_model4)  \n",
    "axs[1, 0].hist(dyLens_fdm_model4, bins=5, density=False,  alpha=0.8, color=\"palevioletred\",\n",
    "                 label=\"Model4: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo))\n",
    "\n",
    "axs[1, 0].set_ylabel(\"#Number of subhalos\", size=14)\n",
    "axs[1, 0].set_xlabel(r\"${\\Delta \\overline{f}_{\\rm DM}}$\", size=14)\n",
    "axs[1, 0].legend(fontsize=12)\n",
    "\n",
    "\n",
    "    # Total mass\n",
    "m, hi, lo = median_hi_lo(dyLens_total_model1)  \n",
    "axs[1, 1].hist(dyLens_total_model1, bins=5, density=False,  color=\"dodgerblue\", alpha=0.8,\n",
    "                 label=\"Model1: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo))\n",
    "\n",
    "m, hi, lo = median_hi_lo(dyLens_total_model4)  \n",
    "axs[1, 1].hist(dyLens_total_model4, bins=5, density=False,  alpha=0.8, color=\"palevioletred\",\n",
    "                 label=\"Model4: $%.2f^{+%.2f}_{-%.2f}$\"%(m, hi,lo))\n",
    "\n",
    "axs[1, 1].set_ylabel(\"#Number of subhalos\", size=14)\n",
    "axs[1, 1].set_xlabel(r\"${\\Delta \\overline{M}_{\\rm total}}$\", size=14)\n",
    "axs[1, 1].legend(fontsize=12)\n",
    "\n",
    "# Adicionar uma única legenda para todos os subplots\n",
    "#fig.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), fancybox=True, shadow=True)\n",
    "\n",
    "# Ajustar o layout\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5db167f",
   "metadata": {},
   "source": [
    "----"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}