Sec 6.8 - Analysis 2D - Distribution - normal FoV - Comparison.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2530ec01",
   "metadata": {},
   "source": [
    "## Results from Section 6.8.\n",
    "\n",
    "Data is for 2D 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": 8,
   "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": 9,
   "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": 10,
   "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": 11,
   "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[\"projDstar\"]\n",
    "    Jam_star_model4[i] = model4[\"projDstar\"]    \n",
    "    \n",
    "    Jam_DM_model1[i] = model1[\"projDdm\"]\n",
    "    Jam_DM_model4[i] = model4[\"projDdm\"]\n",
    "    \n",
    "    Jam_total_model1[i] = model1[\"projDtotal\"]\n",
    "    Jam_total_model4[i] = model4[\"projDtotal\"]\n",
    "\n",
    "    Jam_fdm_model1[i] = model1[\"projDfdm\"]\n",
    "    Jam_fdm_model4[i] = model4[\"projDfdm\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 13,
   "id": "4445c4db",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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(Jam_star_model1)  \n",
    "axs[0, 0].hist(Jam_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(Jam_star_model4)\n",
    "axs[0, 0].hist(Jam_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(Jam_DM_model1)  \n",
    "axs[0, 1].hist(Jam_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(Jam_DM_model4)  \n",
    "axs[0, 1].hist(Jam_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(Jam_fdm_model1)  \n",
    "axs[1, 0].hist(Jam_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(Jam_fdm_model4)  \n",
    "axs[1, 0].hist(Jam_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(Jam_total_model1)  \n",
    "axs[1, 1].hist(Jam_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(Jam_total_model4)  \n",
    "axs[1, 1].hist(Jam_total_model4, bins=5, density=False,  alpha=0.8, color=\"palevioletred\",\n",
    "                 label=\"Model3: $%.2f^{+%.2f}_{-%.2f}$\"%(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": "f7effb02",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33c844b3",
   "metadata": {},
   "source": [
    "### dyLens models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 15,
   "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[\"projDstar\"]\n",
    "    dyLens_star_model4[i] = model4[\"projDstar\"]\n",
    "    \n",
    "    dyLens_DM_model1[i] = model1[\"projDdm\"]\n",
    "    dyLens_DM_model4[i] = model4[\"projDdm\"]\n",
    "    \n",
    "    dyLens_total_model1[i] = model1[\"projDtotal\"]\n",
    "    dyLens_total_model4[i] = model4[\"projDtotal\"]\n",
    "\n",
    "    dyLens_fdm_model1[i] = model1[\"projDfdm\"]\n",
    "    dyLens_fdm_model4[i] = model4[\"projDfdm\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "89a3595c",
   "metadata": {
    "scrolled": false
   },
   "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": [
    "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": "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": 17,
   "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": 18,
   "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[\"projDstar\"]\n",
    "    dyLens_star_model4[i] = model4[\"projDstar\"]\n",
    "    \n",
    "    dyLens_DM_model1[i] = model1[\"projDdm\"]\n",
    "    dyLens_DM_model4[i] = model4[\"projDdm\"]\n",
    "    \n",
    "    dyLens_total_model1[i] = model1[\"projDtotal\"]\n",
    "    dyLens_total_model4[i] = model4[\"projDtotal\"]\n",
    "\n",
    "    dyLens_fdm_model1[i] = model1[\"projDfdm\"]\n",
    "    dyLens_fdm_model4[i] = model4[\"projDfdm\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": [
    "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",
    "_, bins, _ = 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=bins, 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",
    "_, bins, _ = 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=bins, 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",
    "_, bins, _ = 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=bins, 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",
    "_, bins, _ = 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=bins, 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
}