{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validation of image simulation in SLSim\n",
    "\n",
    "Most of the functions in the image simulation take Lens class as an input. So, we need to\n",
    "\n",
    "supply all the dc2 galaxy information in the form of Lens class. So, here we mimic Lens class only with necessary Lens class parts which are necessary for image simulation.\n",
    "\n",
    "Before runing this notebook, please download all the data needed from the following link:\n",
    "\n",
    "https://github.com/LSST-strong-lensing/data_public/tree/main/dp0_images_data_for_slsim_image_validation\n",
    "\n",
    "Then, please change data path to your storage path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "from astropy.table import Table\n",
    "from slsim.image_simulation import (\n",
    "    image_data_class,\n",
    "    lens_image,\n",
    "    rgb_image_from_image_list,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## lens_class for validation\n",
    "class Lens1(object):\n",
    "    def __init__(\n",
    "        self, kwargs_model, kwargs_lens, kwargs_source, kwargs_lens_light, kwargs_ps\n",
    "    ):\n",
    "        self.kwargs_model = kwargs_model\n",
    "        self.kwargs_lens = kwargs_lens\n",
    "        self.kwargs_source = kwargs_source\n",
    "        self.kwargs_lens_light = kwargs_lens_light\n",
    "        self.kwargs_ps = kwargs_ps\n",
    "\n",
    "    def lenstronomy_kwargs(self, band=None):\n",
    "        self.band = band\n",
    "        kwargs_params = {\n",
    "            \"kwargs_lens\": self.kwargs_lens,\n",
    "            \"kwargs_source\": self.kwargs_source,\n",
    "            \"kwargs_lens_light\": self.kwargs_lens_light,\n",
    "            \"kwargs_ps\": self.kwargs_ps,\n",
    "        }\n",
    "        return self.kwargs_model, kwargs_params\n",
    "\n",
    "\n",
    "class Lens2(object):\n",
    "    def __init__(self, kwargs_model):\n",
    "        self.kwargs_model = kwargs_model\n",
    "\n",
    "    def lenstronomy_kwargs(self, band=None):\n",
    "        return self.kwargs_model, None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's get dc2 galaxy center in slsim image grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "kwargs_model = {\n",
    "    \"lens_light_model_list\": [\"SERSIC_ELLIPSE\", \"SERSIC_ELLIPSE\"],\n",
    "    \"lens_model_list\": [\"EPL\", \"SHEAR\", \"CONVERGENCE\"],\n",
    "    \"source_light_model_list\": [\"SERSIC_ELLIPSE\", \"SERSIC_ELLIPSE\"],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "kwarg_model = Lens2(kwargs_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_class = image_data_class(\n",
    "    kwarg_model,\n",
    "    band=\"i\",\n",
    "    mag_zero_point=27,\n",
    "    delta_pix=0.2,\n",
    "    num_pix=35,\n",
    "    transform_pix2angle=np.array([[0.2, 0], [0, 0.2]]),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "galaxy_center_pix_x_1 = 17.43411518\n",
    "galaxy_center_pix_y_1 = 16.92526535\n",
    "galaxy_center_pix_x_2 = 16.84335831\n",
    "galaxy_center_pix_y_2 = 16.52869664\n",
    "center_cood_x_1, center_coord_y_1 = data_class.map_pix2coord(\n",
    "    galaxy_center_pix_x_1, galaxy_center_pix_y_1\n",
    ")\n",
    "center_cood_x_2, center_coord_y_2 = data_class.map_pix2coord(\n",
    "    galaxy_center_pix_x_2, galaxy_center_pix_y_2\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load dc2 galaxies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dp0_data_path = os.path.abspath(\"/Users/sanchez/Devel/DESC/data_public\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\n",
    "    os.path.join(\n",
    "        dp0_data_path,\n",
    "        \"dp0_images_data_for_slsim_image_validation/galaxy_sample_dc2_new_all_angle.txt\",\n",
    "    ),\n",
    "    \"rb\",\n",
    ") as file:\n",
    "    # Use pickle.load() to load the data from the file\n",
    "    dc2_galaxies = pickle.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dc2_galaxies = Table(dc2_galaxies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61.99284796403353, -36.9900209621661)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc2_galaxies[2][\"ra\"], dc2_galaxies[2][\"dec\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SLSim coordinate system\n",
    "\n",
    "SLsim follows the same coordinate convention as DP0. The ra aligned with the East towards left and\n",
    "\n",
    "North twords up. The position angle goes from North to East."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The slsim coordinate system and dp0 coordinate system are the same.\n",
    "rotation_angle_1 = np.pi * (dc2_galaxies[2][\"position_angle_true_dc2\"]) / 180\n",
    "rotation_angle_2 = np.pi * (dc2_galaxies[7][\"position_angle_true_dc2\"]) / 180"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "## axis ratios of disk dominated galaxy\n",
    "q_disk_1 = dc2_galaxies[2][\"size_minor_disk_true\"] / dc2_galaxies[2][\"size_disk_true\"]\n",
    "q_bulge_1 = (\n",
    "    dc2_galaxies[2][\"size_minor_bulge_true\"] / dc2_galaxies[2][\"size_bulge_true\"]\n",
    ")\n",
    "q_1 = dc2_galaxies[2][\"size_minor_true\"] / dc2_galaxies[2][\"size_true\"]\n",
    "\n",
    "## axis ratios of bulge dominated galaxy\n",
    "q_disk_2 = dc2_galaxies[7][\"size_minor_disk_true\"] / dc2_galaxies[7][\"size_disk_true\"]\n",
    "q_bulge_2 = (\n",
    "    dc2_galaxies[7][\"size_minor_bulge_true\"] / dc2_galaxies[7][\"size_bulge_true\"]\n",
    ")\n",
    "q_2 = dc2_galaxies[7][\"size_minor_true\"] / dc2_galaxies[7][\"size_true\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Disk dominated galaxy\n",
    "epsilon_disk_1 = (1 - q_disk_1) / (1 + q_disk_1)\n",
    "epsilon_bulge_1 = (1 - q_bulge_1) / (1 + q_bulge_1)\n",
    "epsilon_1 = (1 - q_1) / (1 + q_1)\n",
    "\n",
    "## Bulge dominated galaxy\n",
    "epsilon_disk_2 = (1 - q_disk_2) / (1 + q_disk_2)\n",
    "epsilon_bulge_2 = (1 - q_bulge_2) / (1 + q_bulge_2)\n",
    "epsilon_2 = (1 - q_2) / (1 + q_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "## disk dominated\n",
    "e1_disk_1 = epsilon_disk_1 * np.cos(2 * rotation_angle_1)\n",
    "e2_disk_1 = epsilon_disk_1 * np.sin(2 * rotation_angle_1)\n",
    "e1_bulge_1 = epsilon_bulge_1 * np.cos(2 * rotation_angle_1)\n",
    "e2_bulge_1 = epsilon_bulge_1 * np.sin(2 * rotation_angle_1)\n",
    "e1_1 = epsilon_1 * np.cos(2 * rotation_angle_1)\n",
    "e2_1 = epsilon_1 * np.sin(2 * rotation_angle_1)\n",
    "\n",
    "## Bulge dominated\n",
    "e1_disk_2 = epsilon_disk_2 * np.cos(2 * rotation_angle_2)\n",
    "e2_disk_2 = epsilon_disk_2 * np.sin(2 * rotation_angle_2)\n",
    "e1_bulge_2 = epsilon_bulge_2 * np.cos(2 * rotation_angle_2)\n",
    "e2_bulge_2 = epsilon_bulge_2 * np.sin(2 * rotation_angle_2)\n",
    "e1_2 = epsilon_2 * np.cos(2 * rotation_angle_2)\n",
    "e2_2 = epsilon_2 * np.sin(2 * rotation_angle_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(62.00273461841879, -37.00827463174009)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc2_galaxies[7][\"ra\"], dc2_galaxies[7][\"dec\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare lenstronomy kwargs from a dc2 galaxy\n",
    "We select galaxies centered at (ra, dec) =  (62.00273461841879, -37.00827463174009) \n",
    "\n",
    "and (61.99284796403353, -36.9900209621661) degree. One can find image of this galaxy in DP0 \n",
    "\n",
    "data. We download image of this galaxy from DP0 and compare it with image of the same galaxy \n",
    "\n",
    "silulated using slsim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "## lenstronomy kwargs for disk dominated galaxy\n",
    "kwargs_lens_1 = [\n",
    "    {\n",
    "        \"theta_E\": 1,\n",
    "        \"gamma\": 2,\n",
    "        \"e1\": -e1_1,\n",
    "        \"e2\": e2_1,\n",
    "        \"center_x\": center_cood_x_1,\n",
    "        \"center_y\": center_coord_y_1,\n",
    "    },\n",
    "    {\n",
    "        \"gamma1\": dc2_galaxies[2][\"shear_1\"],\n",
    "        \"gamma2\": dc2_galaxies[2][\"shear_2\"],\n",
    "        \"ra_0\": 0,\n",
    "        \"dec_0\": 0,\n",
    "    },\n",
    "    {\"kappa\": dc2_galaxies[2][\"convergence\"], \"ra_0\": 0, \"dec_0\": 0},\n",
    "]\n",
    "kwargs_source_1 = None\n",
    "flux_i_1 = 10 ** (-dc2_galaxies[2][\"mag_true_i_lsst\"] / 2.5)\n",
    "mag_bulge_i_1 = -2.5 * np.log10(dc2_galaxies[2][\"bulge_to_total_ratio_i\"] * flux_i_1)\n",
    "mag_disk_i_1 = -2.5 * np.log10(\n",
    "    (1 - dc2_galaxies[2][\"bulge_to_total_ratio_i\"]) * flux_i_1\n",
    ")\n",
    "kwargs_lens_light_i_1 = [\n",
    "    {\n",
    "        \"magnitude\": mag_bulge_i_1,\n",
    "        \"R_sersic\": dc2_galaxies[2][\"size_bulge_true\"],\n",
    "        \"n_sersic\": 4.0,\n",
    "        \"e1\": -e1_bulge_1,\n",
    "        \"e2\": e2_bulge_1,\n",
    "        \"center_x\": center_cood_x_1,\n",
    "        \"center_y\": center_coord_y_1,\n",
    "    },\n",
    "    {\n",
    "        \"magnitude\": mag_disk_i_1,\n",
    "        \"R_sersic\": dc2_galaxies[2][\"size_disk_true\"],\n",
    "        \"n_sersic\": 1.0,\n",
    "        \"e1\": -e1_disk_1,\n",
    "        \"e2\": e2_disk_1,\n",
    "        \"center_x\": center_cood_x_1,\n",
    "        \"center_y\": center_coord_y_1,\n",
    "    },\n",
    "]\n",
    "flux_r_1 = 10 ** (-dc2_galaxies[2][\"mag_true_r_lsst\"] / 2.5)\n",
    "mag_bulge_r_1 = -2.5 * np.log10(dc2_galaxies[2][\"bulge_to_total_ratio_i\"] * flux_r_1)\n",
    "mag_disk_r_1 = -2.5 * np.log10(\n",
    "    (1 - dc2_galaxies[2][\"bulge_to_total_ratio_i\"]) * flux_r_1\n",
    ")\n",
    "kwargs_lens_light_r_1 = [\n",
    "    {\n",
    "        \"magnitude\": mag_bulge_r_1,\n",
    "        \"R_sersic\": dc2_galaxies[2][\"size_bulge_true\"],\n",
    "        \"n_sersic\": 4.0,\n",
    "        \"e1\": -e1_bulge_1,\n",
    "        \"e2\": e2_bulge_1,\n",
    "        \"center_x\": center_cood_x_1,\n",
    "        \"center_y\": center_coord_y_1,\n",
    "    },\n",
    "    {\n",
    "        \"magnitude\": mag_disk_r_1,\n",
    "        \"R_sersic\": dc2_galaxies[2][\"size_disk_true\"],\n",
    "        \"n_sersic\": 1.0,\n",
    "        \"e1\": -e1_disk_1,\n",
    "        \"e2\": e2_disk_1,\n",
    "        \"center_x\": center_cood_x_1,\n",
    "        \"center_y\": center_coord_y_1,\n",
    "    },\n",
    "]\n",
    "flux_g_1 = 10 ** (-dc2_galaxies[2][\"mag_true_g_lsst\"] / 2.5)\n",
    "mag_bulge_g_1 = -2.5 * np.log10(dc2_galaxies[2][\"bulge_to_total_ratio_i\"] * flux_g_1)\n",
    "mag_disk_g_1 = -2.5 * np.log10(\n",
    "    (1 - dc2_galaxies[2][\"bulge_to_total_ratio_i\"]) * flux_g_1\n",
    ")\n",
    "kwargs_lens_light_g_1 = [\n",
    "    {\n",
    "        \"magnitude\": mag_bulge_g_1,\n",
    "        \"R_sersic\": dc2_galaxies[2][\"size_bulge_true\"],\n",
    "        \"n_sersic\": 4.0,\n",
    "        \"e1\": -e1_bulge_1,\n",
    "        \"e2\": e2_bulge_1,\n",
    "        \"center_x\": center_cood_x_1,\n",
    "        \"center_y\": center_coord_y_1,\n",
    "    },\n",
    "    {\n",
    "        \"magnitude\": mag_disk_g_1,\n",
    "        \"R_sersic\": dc2_galaxies[2][\"size_disk_true\"],\n",
    "        \"n_sersic\": 1.0,\n",
    "        \"e1\": -e1_disk_1,\n",
    "        \"e2\": e2_disk_1,\n",
    "        \"center_x\": center_cood_x_1,\n",
    "        \"center_y\": center_coord_y_1,\n",
    "    },\n",
    "]\n",
    "kwargs_ps_1 = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "## lenstronomy kwargs for bulge dominated galaxy\n",
    "kwargs_lens_2 = [\n",
    "    {\n",
    "        \"theta_E\": 1,\n",
    "        \"gamma\": 2,\n",
    "        \"e1\": -e1_2,\n",
    "        \"e2\": e2_2,\n",
    "        \"center_x\": center_cood_x_2,\n",
    "        \"center_y\": center_coord_y_2,\n",
    "    },\n",
    "    {\n",
    "        \"gamma1\": dc2_galaxies[7][\"shear_1\"],\n",
    "        \"gamma2\": dc2_galaxies[7][\"shear_2\"],\n",
    "        \"ra_0\": 0,\n",
    "        \"dec_0\": 0,\n",
    "    },\n",
    "    {\"kappa\": dc2_galaxies[7][\"convergence\"], \"ra_0\": 0, \"dec_0\": 0},\n",
    "]\n",
    "kwargs_source_2 = None\n",
    "flux_i_2 = 10 ** (-dc2_galaxies[7][\"mag_true_i_lsst\"] / 2.5)\n",
    "mag_bulge_i_2 = -2.5 * np.log10(dc2_galaxies[7][\"bulge_to_total_ratio_i\"] * flux_i_2)\n",
    "mag_disk_i_2 = -2.5 * np.log10(\n",
    "    (1 - dc2_galaxies[7][\"bulge_to_total_ratio_i\"]) * flux_i_2\n",
    ")\n",
    "kwargs_lens_light_i_2 = [\n",
    "    {\n",
    "        \"magnitude\": mag_bulge_i_2,\n",
    "        \"R_sersic\": dc2_galaxies[7][\"size_bulge_true\"],\n",
    "        \"n_sersic\": 4.0,\n",
    "        \"e1\": -e1_bulge_2,\n",
    "        \"e2\": e2_bulge_2,\n",
    "        \"center_x\": center_cood_x_2,\n",
    "        \"center_y\": center_coord_y_2,\n",
    "    },\n",
    "    {\n",
    "        \"magnitude\": mag_disk_i_2,\n",
    "        \"R_sersic\": dc2_galaxies[7][\"size_disk_true\"],\n",
    "        \"n_sersic\": 1.0,\n",
    "        \"e1\": -e1_disk_2,\n",
    "        \"e2\": e2_disk_2,\n",
    "        \"center_x\": center_cood_x_2,\n",
    "        \"center_y\": center_coord_y_2,\n",
    "    },\n",
    "]\n",
    "flux_r_2 = 10 ** (-dc2_galaxies[7][\"mag_true_r_lsst\"] / 2.5)\n",
    "mag_bulge_r_2 = -2.5 * np.log10(dc2_galaxies[7][\"bulge_to_total_ratio_i\"] * flux_r_2)\n",
    "mag_disk_r_2 = -2.5 * np.log10(\n",
    "    (1 - dc2_galaxies[7][\"bulge_to_total_ratio_i\"]) * flux_r_2\n",
    ")\n",
    "kwargs_lens_light_r_2 = [\n",
    "    {\n",
    "        \"magnitude\": mag_bulge_r_2,\n",
    "        \"R_sersic\": dc2_galaxies[7][\"size_bulge_true\"],\n",
    "        \"n_sersic\": 4.0,\n",
    "        \"e1\": -e1_bulge_2,\n",
    "        \"e2\": e2_bulge_2,\n",
    "        \"center_x\": center_cood_x_2,\n",
    "        \"center_y\": center_coord_y_2,\n",
    "    },\n",
    "    {\n",
    "        \"magnitude\": mag_disk_r_2,\n",
    "        \"R_sersic\": dc2_galaxies[7][\"size_disk_true\"],\n",
    "        \"n_sersic\": 1.0,\n",
    "        \"e1\": -e1_disk_2,\n",
    "        \"e2\": e2_disk_2,\n",
    "        \"center_x\": center_cood_x_2,\n",
    "        \"center_y\": center_coord_y_2,\n",
    "    },\n",
    "]\n",
    "flux_g_2 = 10 ** (-dc2_galaxies[7][\"mag_true_g_lsst\"] / 2.5)\n",
    "mag_bulge_g_2 = -2.5 * np.log10(dc2_galaxies[7][\"bulge_to_total_ratio_i\"] * flux_g_2)\n",
    "mag_disk_g_2 = -2.5 * np.log10(\n",
    "    (1 - dc2_galaxies[7][\"bulge_to_total_ratio_i\"]) * flux_g_2\n",
    ")\n",
    "kwargs_lens_light_g_2 = [\n",
    "    {\n",
    "        \"magnitude\": mag_bulge_g_2,\n",
    "        \"R_sersic\": dc2_galaxies[7][\"size_bulge_true\"],\n",
    "        \"n_sersic\": 4.0,\n",
    "        \"e1\": -e1_bulge_2,\n",
    "        \"e2\": e2_bulge_2,\n",
    "        \"center_x\": center_cood_x_2,\n",
    "        \"center_y\": center_coord_y_2,\n",
    "    },\n",
    "    {\n",
    "        \"magnitude\": mag_disk_g_2,\n",
    "        \"R_sersic\": dc2_galaxies[7][\"size_disk_true\"],\n",
    "        \"n_sersic\": 1.0,\n",
    "        \"e1\": -e1_disk_2,\n",
    "        \"e2\": e2_disk_2,\n",
    "        \"center_x\": center_cood_x_2,\n",
    "        \"center_y\": center_coord_y_2,\n",
    "    },\n",
    "]\n",
    "kwargs_ps_2 = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initiate Lens class for each band"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "## disk dominated galaxy\n",
    "lens_class_i_1 = Lens1(\n",
    "    kwargs_model, kwargs_lens_1, kwargs_source_1, kwargs_lens_light_i_1, kwargs_ps_1\n",
    ")\n",
    "lens_class_r_1 = Lens1(\n",
    "    kwargs_model, kwargs_lens_1, kwargs_source_1, kwargs_lens_light_r_1, kwargs_ps_1\n",
    ")\n",
    "lens_class_g_1 = Lens1(\n",
    "    kwargs_model, kwargs_lens_1, kwargs_source_1, kwargs_lens_light_g_1, kwargs_ps_1\n",
    ")\n",
    "\n",
    "## Bulge dominated galaxy\n",
    "lens_class_i_2 = Lens1(\n",
    "    kwargs_model, kwargs_lens_2, kwargs_source_2, kwargs_lens_light_i_2, kwargs_ps_2\n",
    ")\n",
    "lens_class_r_2 = Lens1(\n",
    "    kwargs_model, kwargs_lens_2, kwargs_source_2, kwargs_lens_light_r_2, kwargs_ps_2\n",
    ")\n",
    "lens_class_g_2 = Lens1(\n",
    "    kwargs_model, kwargs_lens_2, kwargs_source_2, kwargs_lens_light_g_2, kwargs_ps_2\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load psf and variance map for a dc2 galaxy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "## disk dominated galaxy\n",
    "psf_kernel_i_1 = np.load(\n",
    "    os.path.join(\n",
    "        dp0_data_path,\n",
    "        \"dp0_images_data_for_slsim_image_validation/dc2_galaxy_2_kernel_i.npy\",\n",
    "    )\n",
    ")\n",
    "psf_kernel_r_1 = np.load(\n",
    "    os.path.join(\n",
    "        dp0_data_path,\n",
    "        \"dp0_images_data_for_slsim_image_validation/dc2_galaxy_2_kernel_r.npy\",\n",
    "    )\n",
    ")\n",
    "psf_kernel_g_1 = np.load(\n",
    "    os.path.join(\n",
    "        dp0_data_path,\n",
    "        \"dp0_images_data_for_slsim_image_validation/dc2_galaxy_2_kernel_g.npy\",\n",
    "    )\n",
    ")\n",
    "\n",
    "## Bulge dominated galaxy\n",
    "psf_kernel_i_2 = np.load(\n",
    "    os.path.join(\n",
    "        dp0_data_path,\n",
    "        \"dp0_images_data_for_slsim_image_validation/dc2_galaxy_kernel_i.npy\",\n",
    "    )\n",
    ")\n",
    "psf_kernel_r_2 = np.load(\n",
    "    os.path.join(\n",
    "        dp0_data_path,\n",
    "        \"dp0_images_data_for_slsim_image_validation/dc2_galaxy_kernel_r.npy\",\n",
    "    )\n",
    ")\n",
    "psf_kernel_g_2 = np.load(\n",
    "    os.path.join(\n",
    "        dp0_data_path,\n",
    "        \"dp0_images_data_for_slsim_image_validation/dc2_galaxy_kernel_g.npy\",\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "## disk dominated galaxy\n",
    "dc2_galaxy_variance_i_1 = np.sqrt(\n",
    "    np.load(\n",
    "        os.path.join(\n",
    "            dp0_data_path,\n",
    "            \"dp0_images_data_for_slsim_image_validation/dc2_galaxy_2_variance_i.npy\",\n",
    "        )\n",
    "    )\n",
    ")\n",
    "dc2_galaxy_variance_g_1 = np.sqrt(\n",
    "    np.load(\n",
    "        os.path.join(\n",
    "            dp0_data_path,\n",
    "            \"dp0_images_data_for_slsim_image_validation/dc2_galaxy_2_variance_g.npy\",\n",
    "        )\n",
    "    )\n",
    ")\n",
    "dc2_galaxy_variance_r_1 = np.sqrt(\n",
    "    np.load(\n",
    "        os.path.join(\n",
    "            dp0_data_path,\n",
    "            \"dp0_images_data_for_slsim_image_validation/dc2_galaxy_2_variance_r.npy\",\n",
    "        )\n",
    "    )\n",
    ")\n",
    "\n",
    "## Bulge dominated galaxy\n",
    "dc2_galaxy_variance_i_2 = np.sqrt(\n",
    "    np.load(\n",
    "        os.path.join(\n",
    "            dp0_data_path,\n",
    "            \"dp0_images_data_for_slsim_image_validation/dc2_galaxy_variance_i.npy\",\n",
    "        )\n",
    "    )\n",
    ")\n",
    "dc2_galaxy_variance_g_2 = np.sqrt(\n",
    "    np.load(\n",
    "        os.path.join(\n",
    "            dp0_data_path,\n",
    "            \"dp0_images_data_for_slsim_image_validation/dc2_galaxy_variance_g.npy\",\n",
    "        )\n",
    "    )\n",
    ")\n",
    "dc2_galaxy_variance_r_2 = np.sqrt(\n",
    "    np.load(\n",
    "        os.path.join(\n",
    "            dp0_data_path,\n",
    "            \"dp0_images_data_for_slsim_image_validation/dc2_galaxy_variance_r.npy\",\n",
    "        )\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulate image of a dc2 galaxy using slsim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sanchez/.virtualenvs/slsim/lib/python3.12/site-packages/lenstronomy/Data/psf.py:80: UserWarning: Input PSF model has at least one negative element, which is unphysical except for a PSF of an interferometric array.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "## disk dominated galaxy images\n",
    "dc2_galaxy_image_from_slsim_i_1 = lens_image(\n",
    "    lens_class_i_1,\n",
    "    band=\"i\",\n",
    "    mag_zero_point=27,\n",
    "    num_pix=35,\n",
    "    psf_kernel=psf_kernel_i_1,\n",
    "    transform_pix2angle=np.array([[0.2, 0], [0, 0.2]]),\n",
    "    exposure_time=None,\n",
    "    t_obs=None,\n",
    "    std_gaussian_noise=dc2_galaxy_variance_i_1,\n",
    "    with_source=False,\n",
    "    with_deflector=True,\n",
    ")\n",
    "dc2_galaxy_image_from_slsim_g_1 = lens_image(\n",
    "    lens_class_g_1,\n",
    "    band=\"g\",\n",
    "    mag_zero_point=27,\n",
    "    num_pix=35,\n",
    "    psf_kernel=psf_kernel_g_1,\n",
    "    transform_pix2angle=np.array([[0.2, 0], [0, 0.2]]),\n",
    "    exposure_time=None,\n",
    "    t_obs=None,\n",
    "    std_gaussian_noise=dc2_galaxy_variance_g_1,\n",
    "    with_source=False,\n",
    "    with_deflector=True,\n",
    ")\n",
    "dc2_galaxy_image_from_slsim_r_1 = lens_image(\n",
    "    lens_class_r_1,\n",
    "    band=\"r\",\n",
    "    mag_zero_point=27,\n",
    "    num_pix=35,\n",
    "    psf_kernel=psf_kernel_r_1,\n",
    "    transform_pix2angle=np.array([[0.2, 0], [0, 0.2]]),\n",
    "    exposure_time=None,\n",
    "    t_obs=None,\n",
    "    std_gaussian_noise=dc2_galaxy_variance_r_1,\n",
    "    with_source=False,\n",
    "    with_deflector=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "## disk dominated galaxy images\n",
    "dc2_galaxy_image_from_slsim_i_1_test = lens_image(\n",
    "    lens_class_i_1,\n",
    "    band=\"i\",\n",
    "    mag_zero_point=27,\n",
    "    num_pix=35,\n",
    "    psf_kernel=psf_kernel_i_1,\n",
    "    transform_pix2angle=np.array([[0.2, 0], [0, 0.2]]),\n",
    "    exposure_time=None,\n",
    "    t_obs=None,\n",
    "    std_gaussian_noise=dc2_galaxy_variance_i_1,\n",
    "    with_source=False,\n",
    "    with_deflector=True,\n",
    ")\n",
    "dc2_galaxy_image_from_slsim_g_1_test = lens_image(\n",
    "    lens_class_g_1,\n",
    "    band=\"g\",\n",
    "    mag_zero_point=27,\n",
    "    num_pix=35,\n",
    "    psf_kernel=psf_kernel_g_1,\n",
    "    transform_pix2angle=np.array([[0.2, 0], [0, 0.2]]),\n",
    "    exposure_time=None,\n",
    "    t_obs=None,\n",
    "    std_gaussian_noise=dc2_galaxy_variance_g_1,\n",
    "    with_source=False,\n",
    "    with_deflector=True,\n",
    ")\n",
    "dc2_galaxy_image_from_slsim_r_1_test = lens_image(\n",
    "    lens_class_r_1,\n",
    "    band=\"r\",\n",
    "    mag_zero_point=27,\n",
    "    num_pix=35,\n",
    "    psf_kernel=psf_kernel_r_1,\n",
    "    transform_pix2angle=np.array([[0.2, 0], [0, 0.2]]),\n",
    "    exposure_time=None,\n",
    "    t_obs=None,\n",
    "    std_gaussian_noise=dc2_galaxy_variance_r_1,\n",
    "    with_source=False,\n",
    "    with_deflector=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Bulge dominated galaxy images\n",
    "dc2_galaxy_image_from_slsim_i_2 = lens_image(\n",
    "    lens_class_i_2,\n",
    "    band=\"i\",\n",
    "    mag_zero_point=27,\n",
    "    num_pix=35,\n",
    "    psf_kernel=psf_kernel_i_2,\n",
    "    transform_pix2angle=np.array([[0.2, 0], [0, 0.2]]),\n",
    "    exposure_time=None,\n",
    "    t_obs=None,\n",
    "    std_gaussian_noise=dc2_galaxy_variance_i_2,\n",
    "    with_source=False,\n",
    "    with_deflector=True,\n",
    ")\n",
    "dc2_galaxy_image_from_slsim_g_2 = lens_image(\n",
    "    lens_class_g_2,\n",
    "    band=\"g\",\n",
    "    mag_zero_point=27,\n",
    "    num_pix=35,\n",
    "    psf_kernel=psf_kernel_g_2,\n",
    "    transform_pix2angle=np.array([[0.2, 0], [0, 0.2]]),\n",
    "    exposure_time=None,\n",
    "    t_obs=None,\n",
    "    std_gaussian_noise=dc2_galaxy_variance_g_2,\n",
    "    with_source=False,\n",
    "    with_deflector=True,\n",
    ")\n",
    "dc2_galaxy_image_from_slsim_r_2 = lens_image(\n",
    "    lens_class_r_2,\n",
    "    band=\"r\",\n",
    "    mag_zero_point=27,\n",
    "    num_pix=35,\n",
    "    psf_kernel=psf_kernel_r_2,\n",
    "    transform_pix2angle=np.array([[0.2, 0], [0, 0.2]]),\n",
    "    exposure_time=None,\n",
    "    t_obs=None,\n",
    "    std_gaussian_noise=dc2_galaxy_variance_r_2,\n",
    "    with_source=False,\n",
    "    with_deflector=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Bulge dominated galaxy images for noise testing\n",
    "dc2_galaxy_image_from_slsim_i_2_test = lens_image(\n",
    "    lens_class_i_2,\n",
    "    band=\"i\",\n",
    "    mag_zero_point=27,\n",
    "    num_pix=35,\n",
    "    psf_kernel=psf_kernel_i_2,\n",
    "    transform_pix2angle=np.array([[0.2, 0], [0, 0.2]]),\n",
    "    exposure_time=None,\n",
    "    t_obs=None,\n",
    "    std_gaussian_noise=dc2_galaxy_variance_i_2,\n",
    "    with_source=False,\n",
    "    with_deflector=True,\n",
    ")\n",
    "dc2_galaxy_image_from_slsim_g_2_test = lens_image(\n",
    "    lens_class_g_2,\n",
    "    band=\"g\",\n",
    "    mag_zero_point=27,\n",
    "    num_pix=35,\n",
    "    psf_kernel=psf_kernel_g_2,\n",
    "    transform_pix2angle=np.array([[0.2, 0], [0, 0.2]]),\n",
    "    exposure_time=None,\n",
    "    t_obs=None,\n",
    "    std_gaussian_noise=dc2_galaxy_variance_g_2,\n",
    "    with_source=False,\n",
    "    with_deflector=True,\n",
    ")\n",
    "dc2_galaxy_image_from_slsim_r_2_test = lens_image(\n",
    "    lens_class_r_2,\n",
    "    band=\"r\",\n",
    "    mag_zero_point=27,\n",
    "    num_pix=35,\n",
    "    psf_kernel=psf_kernel_r_2,\n",
    "    transform_pix2angle=np.array([[0.2, 0], [0, 0.2]]),\n",
    "    exposure_time=None,\n",
    "    t_obs=None,\n",
    "    std_gaussian_noise=dc2_galaxy_variance_r_2,\n",
    "    with_source=False,\n",
    "    with_deflector=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load dc2 galaxy image from DP0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "## disk dominated galaxy\n",
    "dc2_galaxy_image_from_dp0_i_1 = np.load(\n",
    "    os.path.join(\n",
    "        dp0_data_path, \"dp0_images_data_for_slsim_image_validation/dc2_galaxy_2_i.npy\"\n",
    "    )\n",
    ")\n",
    "dc2_galaxy_image_from_dp0_r_1 = np.load(\n",
    "    os.path.join(\n",
    "        dp0_data_path, \"dp0_images_data_for_slsim_image_validation/dc2_galaxy_2_r.npy\"\n",
    "    )\n",
    ")\n",
    "dc2_galaxy_image_from_dp0_g_1 = np.load(\n",
    "    os.path.join(\n",
    "        dp0_data_path, \"dp0_images_data_for_slsim_image_validation/dc2_galaxy_2_g.npy\"\n",
    "    )\n",
    ")\n",
    "\n",
    "## Bulge dominated galaxy\n",
    "dc2_galaxy_image_from_dp0_i_2 = np.load(\n",
    "    os.path.join(\n",
    "        dp0_data_path, \"dp0_images_data_for_slsim_image_validation/dc2_galaxy_i.npy\"\n",
    "    )\n",
    ")\n",
    "dc2_galaxy_image_from_dp0_r_2 = np.load(\n",
    "    os.path.join(\n",
    "        dp0_data_path, \"dp0_images_data_for_slsim_image_validation/dc2_galaxy_r.npy\"\n",
    "    )\n",
    ")\n",
    "dc2_galaxy_image_from_dp0_g_2 = np.load(\n",
    "    os.path.join(\n",
    "        dp0_data_path, \"dp0_images_data_for_slsim_image_validation/dc2_galaxy_g.npy\"\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute the difference between two different realization of SLSim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "##disk dominated galaxy\n",
    "diff_i_1_test = dc2_galaxy_image_from_slsim_i_1 - dc2_galaxy_image_from_slsim_i_1_test\n",
    "diff_r_1_test = dc2_galaxy_image_from_slsim_r_1 - dc2_galaxy_image_from_slsim_r_1_test\n",
    "diff_g_1_test = dc2_galaxy_image_from_slsim_g_1 - dc2_galaxy_image_from_slsim_g_1_test\n",
    "diff_list_1_test = [diff_i_1_test, diff_r_1_test, diff_g_1_test]\n",
    "slsim_slsim_diff_rgb_1 = rgb_image_from_image_list(\n",
    "    image_list=diff_list_1_test, stretch=0.5\n",
    ")\n",
    "\n",
    "##Bulge dominated galaxy\n",
    "diff_i_2_test = dc2_galaxy_image_from_slsim_i_2 - dc2_galaxy_image_from_slsim_i_2_test\n",
    "diff_r_2_test = dc2_galaxy_image_from_slsim_r_2 - dc2_galaxy_image_from_slsim_r_2_test\n",
    "diff_g_2_test = dc2_galaxy_image_from_slsim_g_2 - dc2_galaxy_image_from_slsim_g_2_test\n",
    "diff_list_2_test = [diff_i_2_test, diff_r_2_test, diff_g_2_test]\n",
    "slsim_slsim_diff_rgb_2 = rgb_image_from_image_list(\n",
    "    image_list=diff_list_2_test, stretch=0.5\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute the difference between slsim and DP0 \n",
    "## images of the same galaxy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "##disk dominated galaxy\n",
    "diff_i_1 = dc2_galaxy_image_from_slsim_i_1 - dc2_galaxy_image_from_dp0_i_1\n",
    "diff_r_1 = dc2_galaxy_image_from_slsim_r_1 - dc2_galaxy_image_from_dp0_r_1\n",
    "diff_g_1 = dc2_galaxy_image_from_slsim_g_1 - dc2_galaxy_image_from_dp0_g_1\n",
    "\n",
    "##Bulge dominated galaxy\n",
    "diff_i_2 = dc2_galaxy_image_from_slsim_i_2 - dc2_galaxy_image_from_dp0_i_2\n",
    "diff_r_2 = dc2_galaxy_image_from_slsim_r_2 - dc2_galaxy_image_from_dp0_r_2\n",
    "diff_g_2 = dc2_galaxy_image_from_slsim_g_2 - dc2_galaxy_image_from_dp0_g_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot images and residual in i-band"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 34.5, -0.5, 34.5)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Disk dominated\n",
    "# This is a comparision of an image of a galaxy from DP0 at\n",
    "# (ra, dec)= (61.99284796403353, -36.9900209621661) degree. This comparision is in\n",
    "# i-band.\n",
    "global_min = min(\n",
    "    dc2_galaxy_image_from_slsim_i_1.min(),\n",
    "    dc2_galaxy_image_from_dp0_i_1.min(),\n",
    "    diff_i_1.min(),\n",
    ")\n",
    "global_max = max(\n",
    "    dc2_galaxy_image_from_slsim_i_1.max(),\n",
    "    dc2_galaxy_image_from_dp0_i_1.max(),\n",
    "    diff_i_1.max(),\n",
    ")\n",
    "# Plotting the images\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.imshow(\n",
    "    dc2_galaxy_image_from_slsim_i_1, origin=\"lower\", vmin=global_min, vmax=global_max\n",
    ")\n",
    "plt.title(\"Galaxy image from slsim\")\n",
    "plt.axis(\"off\")  # Turn off axis labels\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.imshow(\n",
    "    dc2_galaxy_image_from_dp0_i_1, origin=\"lower\", vmin=global_min, vmax=global_max\n",
    ")\n",
    "plt.title(\"Galaxy image from DP0\")\n",
    "plt.axis(\"off\")\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.imshow(diff_i_2, origin=\"lower\", vmin=global_min, vmax=global_max)\n",
    "plt.title(\"Residual\")\n",
    "plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7YAAAE7CAYAAADpSx23AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/TGe4hAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA8rElEQVR4nO3deZQW1Z3/8c/Te0OLCLYSxbCqiBJzjooGF3AZFVGjI+AWER2VjAsu436SiEsGNYlLEkDFUeKWCWIS0XgiKHqiJo4ax5gwQZHF4BLDvkM33ff3h+n+0TbP/T7y7aK53e/XOZyjT/WtulV161Z9e6lPLoQQBAAAAABAoopauwMAAAAAAHhQ2AIAAAAAkkZhCwAAAABIGoUtAAAAACBpFLYAAAAAgKRR2AIAAAAAkkZhCwAAAABIGoUtAAAAACBpFLYAAAAAgKRR2G5mypQpyuVyWrhwYWt3ZYtGjx6tnj17tnY3Mvfoo4+qX79+Ki0tVefOnVu7O249e/bU6NGjv1SbcePGKZfLZdMhoIUwZ24f2tqcCaBt+zLPOLlcTuPGjcu0P0OGDNGQIUMy3Qa2jWQL2wULFujSSy/VXnvtpQ4dOqhDhw7q37+/LrnkEr377rut3T1spTlz5mj06NHq06ePJk+erAceeKC1uwS0CcyZbVNqc+bo0aOVy+Ua/1VVVal3794aPny4nnrqKdXX1zdrM2TIkCZtunTpooMOOkgPPfRQs6//+OOPNXLkSHXu3FmdOnXSN7/5Tc2fP39b7R6QnIZvUDb8Kykp0e67767Ro0fr448/bu3uAV9KSWt3YGs8++yzOv3001VSUqKzzz5b+++/v4qKijRnzhz98pe/1KRJk7RgwQL16NGjtbvaoiZPnrzFm35b8vLLL6u+vl733nuv+vbt29rdaTXf+c53dP3117d2N9BGMGe2XSnOmeXl5XrwwQclSevXr9eHH36oZ555RsOHD9eQIUP09NNPq1OnTk3adO/eXePHj5ckLV68WI888oj+7d/+Te+//75uv/12SdKaNWt05JFHauXKlbrxxhtVWlqqu+++W4MHD9Y777yjrl27btsdBRJyyy23qFevXtqwYYNef/11TZkyRa+++qr+8pe/qKKiokW3xTMOspJcYTtv3jydccYZ6tGjh1588UV95StfabL8jjvu0MSJE1VUlOwPo/MqLS1t7S5k7h//+Ickmb9OF0LQhg0bVFlZuQ16te2VlJSopCS5yxPbIebMti3FObOkpETf+ta3mnx222236fbbb9cNN9ygCy+8UL/4xS+aLN9xxx2btBkzZoz23ntv/fSnP9Wtt96q0tJSTZw4UXPnztUbb7yhgw46SJI0dOhQ7bfffvrRj36k//zP/8x+54BEDR06VAceeKAk6YILLtDOO++sO+64Q9OnT9fIkSNbdFs84yAryT3J3HnnnVq7dq0efvjhZg9o0ucXy9ixY7XHHns0fvbuu+9q9OjR6t27tyoqKtStWzedf/75Wrp0qbm9p59+WsOGDdNuu+2m8vJy9enTR7feeqvq6uoav+avf/2rKisrNWrUqCZtX331VRUXF+u6666TJJ177rnaeeedVVtb22w7xx57rPbee+9oX77492ILFy5ULpfTD3/4Q02YMEG9e/dWhw4ddOyxx2rRokUKIejWW29V9+7dVVlZqW9+85tatmzZl96/Bg3bqKys1MCBA/XKK69s8e8SNm7cqJtuukl9+/ZVeXm59thjD1177bXauHFjdP969uypm266SZJUXV3d5O8qevbsqRNPPFHPP/+8DjzwQFVWVur++++XJM2fP18jRoxQly5d1KFDBx1yyCH6zW9+02TdL7/8snK5nKZOnaqbb75Zu+++u3bYYQcNHz5cK1eu1MaNG3XFFVdol112UVVVlc477zyzv5I0d+5cnXbaaerWrZsqKirUvXt3nXHGGVq5cmXeNrW1tbr55pu15557qqKiQl27dtVhhx2mmTNnNn7Nlv7+JJfL6dJLL9WTTz6p/v37q7KyUt/4xjf05z//WZJ0//33q2/fvqqoqNCQIUO22797xLbFnNmz8f+ZM1t/zoy5/vrrdeyxx+rJJ5/U+++/H/3ahn6vXbtWixcvliRNmzZNBx10UGNRK0n9+vXT0UcfralTp7r6BrQ3hx9+uKTPvznaYM6cORo+fLi6dOmiiooKHXjggZo+fXqTdlv7jLNx40ZdeeWVqq6u1g477KCTTz5ZH330UbN+5Xt3wpbW+fDDD+uoo47SLrvsovLycvXv31+TJk360scC6Uju2yXPPvus+vbtq4MPPrjgNjNnztT8+fN13nnnqVu3bpo9e7YeeOABzZ49W6+//nr0D9inTJmiqqoqXXXVVaqqqtKsWbP0ve99T6tWrdIPfvADSdI+++yjW2+9Vddcc42GDx+uk08+WWvXrtXo0aPVr18/3XLLLZKkc845R4888oief/55nXjiiY3b+Pvf/65Zs2Y1PqB8WY8//rhqamp02WWXadmyZbrzzjs1cuRIHXXUUXr55Zd13XXX6YMPPtBPfvITXX311XrooYe+1P5J0qRJk3TppZfq8MMP15VXXqmFCxfqlFNO0U477aTu3bs3fl19fb1OPvlkvfrqq7rooou0zz776M9//rPuvvtuvf/++/r1r3+ddz/uuecePfLII/rVr36lSZMmqaqqSl/72tcal7/33ns688wzNWbMGF144YXae++99dlnn2nQoEFat26dxo4dq65du+pnP/uZTj75ZE2bNk2nnnpqk22MHz9elZWVuv766xuPSWlpqYqKirR8+XKNGzeu8VdwevXqpe9973t5+1tTU6PjjjtOGzdu1GWXXaZu3brp448/1rPPPqsVK1Zoxx133GK7cePGafz48brgggs0cOBArVq1Sm+99Zbefvtt/cu//Eve7UnSK6+8ounTp+uSSy5p3J8TTzxR1157rSZOnKiLL75Yy5cv15133qnzzz9fs2bNiq4PbR9zZnPMma0zZxbinHPO0YwZMzRz5kzttdde0a+dP3++iouL1blzZ9XX1+vdd9/V+eef3+zrBg4cqBkzZmj16tXaYYcdXP0D2ouGb47vtNNOkqTZs2fr0EMP1e67767rr79eHTt21NSpU3XKKafoqaeeapw7tvYZ54ILLtBjjz2ms846S4MGDdKsWbM0bNgw1z5MmjRJ++67r04++WSVlJTomWee0cUXX6z6+vrG5yi0MSEhK1euDJLCKaec0mzZ8uXLw+LFixv/rVu3rnHZ5v/d4Oc//3mQFH73u981fvbwww8HSWHBggXRtmPGjAkdOnQIGzZsaPysrq4uHHbYYWHXXXcNS5YsCZdcckkoKSkJb775ZpOv6d69ezj99NObrO+uu+4KuVwuzJ8/P7r/5557bujRo0fj/y9YsCBICtXV1WHFihWNn99www1BUth///1DbW1t4+dnnnlmKCsra9LvQvZv48aNoWvXruGggw5qsr4pU6YESWHw4MGNnz366KOhqKgovPLKK03Wed999wVJ4bXXXovu40033RQkhcWLFzf5vEePHkFS+O1vf9vk8yuuuCJIarK91atXh169eoWePXuGurq6EEIIL730UpAU9ttvv1BTU9PkmORyuTB06NAm6/3GN77R5Fhvyf/+7/8GSeHJJ5+Mfl2PHj3Cueee2/j/+++/fxg2bFi0TcNx2JykUF5e3mR83n///UFS6NatW1i1alXj5w1jYPOvRfvDnMmcuT3NmSF8fk46duyYd3nDvHrllVc2fjZ48ODQr1+/xrH617/+NYwdOzZICieddFIIIYTFixcHSeGWW25pts4JEyYESWHOnDlm/4D2pmEef+GFF8LixYvDokWLwrRp00J1dXUoLy8PixYtCiGEcPTRR4cBAwY0mQ/r6+vDoEGDwp577tn42dY847zzzjtBUrj44oubfN1ZZ50VJIWbbrqp8bMvzuv51hnClufr4447LvTu3bvJZ4MHD24yLyNdSf0q8qpVqyRJVVVVzZYNGTJE1dXVjf8mTJjQuGzzvynasGGDlixZokMOOUSS9Pbbb0e3uXnb1atXa8mSJTr88MO1bt06zZkzp3FZUVGRpkyZojVr1mjo0KGaOHGibrjhhsa/V2j4mrPPPlvTp0/X6tWrGz9//PHHNWjQIPXq1avQQ9HEiBEjmvx0sOEnM9/61rea/A3DwQcfrJqamiZvuStk/9566y0tXbpUF154YZP1nX322Y3fyWvw5JNPap999lG/fv20ZMmSxn9HHXWUJOmll17aqn2UpF69eum4445r8tlzzz2ngQMH6rDDDmv8rKqqShdddJEWLlyo//u//2vy9aNGjWryd3cHH3ywQgjNvst/8MEHa9GiRdq0aVPe/jQc8+eff17r1q0reD86d+6s2bNna+7cuQW3aXD00Uc3+RWchnN92mmnNflJRMPnvA20fWPO3DLmzNaZMwvRMFY3P9/S578C2TBW99lnH/3kJz/RsGHDGn+avn79ekmfv5jqixpefNPwNQCaO+aYY1RdXa099thDw4cPV8eOHTV9+nR1795dy5Yt06xZszRy5MjGeW/JkiVaunSpjjvuOM2dO7dxntyaZ5znnntOkjR27Ngmn19xxRWufdp8vl65cqWWLFmiwYMHa/78+dE/GUO6kipsGx7c16xZ02zZ/fffr5kzZ+qxxx5rtmzZsmW6/PLLteuuu6qyslLV1dWND0TWwJ49e7ZOPfVU7bjjjurUqZOqq6sbX2DxxbZ9+vTRuHHj9Oabb2rffffVd7/73WbrGzVqlNavX69f/epXkj7/VbE//vGPOueccwo4Alv21a9+tcn/Nzywbf43c5t/vnz58i+1fx9++KEkNXvjZklJSbO/c5g7d65mz57d5IG5urq68VfKGl50sjW29BD74YcfbvHv7PbZZ58mfW/wZY5VfX19dHz06tVLV111lR588EHtvPPOOu644zRhwgRzTN1yyy1asWKF9tprLw0YMEDXXHNNwXErnnON9oc5c8uYM1tnzixEw1j94q8M9+zZUzNnztQLL7ygV199VX//+9/17LPPauedd5b0/x9gt/R3vhs2bGjyNQCamzBhgmbOnKlp06bphBNO0JIlSxq/UfTBBx8ohKDvfve7zeaqhj8JaZirtuYZ58MPP1RRUZH69OnT5HPrPQqW1157Tcccc4w6duyozp07q7q6WjfeeKMk+16GNCX1N7Y77rijvvKVr+gvf/lLs2UN33Hf0gtzRo4cqd///ve65ppr9PWvf11VVVWqr6/X8ccfH42CWLFihQYPHqxOnTrplltuUZ8+fVRRUaG3335b11133RbbzpgxQ5L0ySefaOnSperWrVuT5f3799cBBxygxx57TKNGjdJjjz2msrIy1xvniouLv9TnIYSt3j9LfX29BgwYoLvuumuLy7/4MPRltMRDydYeq3x+9KMfafTo0Xr66ac1Y8YMjR07VuPHj9frr7/e5O/oNnfEEUdo3rx5jW0efPBB3X333brvvvt0wQUXbNP+o21jztwy5szCbes5p2GsfvGbAh07dtQxxxyTt12XLl1UXl6uTz/9tNmyhs922203V9+AtmzgwIGNvzFzyimn6LDDDtNZZ52l9957r3Fuu/rqq5v9FkiDhmvW84xTiHzvePjiC/zmzZuno48+Wv369dNdd92lPfbYQ2VlZXruued09913t/kouPYqqcJWkoYNG6YHH3xQb7zxhgYOHGh+/fLly/Xiiy/q5ptvbvJSi0J+ReLll1/W0qVL9ctf/lJHHHFE4+cLFizY4tffd999mjlzpr7//e9r/PjxGjNmjJ5++ulmXzdq1ChdddVV+vTTT/XEE09o2LBhzX49bVsodP8asi0/+OADHXnkkY2fb9q0SQsXLmzyspI+ffroT3/6k44++ujoC2ZaSo8ePfTee+81+7zhVwK3RS7ngAEDNGDAAH3nO9/R73//ex166KG67777dNttt+Vt06VLF5133nk677zztGbNGh1xxBEaN25ci0z6wOaYM1sOc2b2Hn30UeVyOfNFel9UVFSkAQMG6K233mq27H/+53/Uu3dvXhwFFKi4uFjjx4/XkUceqZ/+9KeNf3pQWloa/QZTgy/7jNOjRw/V19dr3rx5TX5Ku6W5aqeddtKKFSuaff7F3zZ55plntHHjRk2fPr3Jb554/rwD27+kfhVZkq699lp16NBB559/vj777LNmy7/43eKG7yp/8fN77rnH3NaW2tbU1GjixInNvnbBggW65pprdNppp+nGG2/UD3/4Q02fPl2PPPJIs68988wzlcvldPnll2v+/PnN8vy2lUL378ADD1TXrl01efLkJn8/9fjjjzf7VdeRI0fq448/1uTJk5ttb/369Vq7dm1L7oJOOOEEvfHGG/rDH/7Q+NnatWv1wAMPqGfPnurfv3+Lbm9zq1atavb3ZAMGDFBRUVE09uKLkSlVVVXq27evOyoD2BLmzJbDnJmt22+/XTNmzNDpp5+uPffc80u3Hz58uN58880mxe17772nWbNmacSIES3ZVaDNGzJkiAYOHKh77rlHnTp10pAhQ3T//fdv8bciGiK3pK17xhk6dKgk6cc//nGTz7d03+nTp49WrlzZ5NebP/3008Y/V2mwpfl65cqVevjhh/P2A+lL7ie2e+65p5544gmdeeaZ2nvvvXX22Wdr//33VwhBCxYs0BNPPKGioqLGXwPt1KmTjjjiCN15552qra3V7rvvrhkzZuT9CcLmBg0apJ122knnnnuuxo4dq1wup0cffbTZA1/DizQqKysb87HGjBmjp556SpdffrmOOeaYJr8CVV1dreOPP15PPvmkOnfu7H6d+dYqdP/Kyso0btw4XXbZZTrqqKM0cuRILVy4UFOmTFGfPn2a/JThnHPO0dSpU/Xtb39bL730kg499FDV1dVpzpw5mjp1amOmYku5/vrr9fOf/1xDhw7V2LFj1aVLF/3sZz/TggUL9NRTT6moKLvv3cyaNUuXXnqpRowYob322kubNm3So48+quLiYp122ml52/Xv319DhgzRAQccoC5duuitt97StGnTdOmll2bWV7RfzJkthzmzZWzatKnxb7s3bNigDz/8UNOnT9e7776rI488Ug888MBWrffiiy/W5MmTNWzYMF199dUqLS3VXXfdpV133VX/8R//0ZK7ALQL11xzjUaMGKEpU6ZowoQJOuywwzRgwABdeOGF6t27tz777DP94Q9/0EcffaQ//elPkrbuGefrX/+6zjzzTE2cOFErV67UoEGD9OKLL+qDDz5o9rVnnHGGrrvuOp166qkaO3as1q1bp0mTJmmvvfZq8nLDY489VmVlZTrppJM0ZswYrVmzRpMnT9Yuu+yyxeIcbcQ2e/9yC/vggw/Cv//7v4e+ffuGioqKUFlZGfr16xe+/e1vh3feeafJ13700Ufh1FNPDZ07dw477rhjGDFiRPjkk0+avUJ8S9EVr732WjjkkENCZWVl2G233cK1114bnn/++SApvPTSSyGEEO69994gKTz11FNNtvu3v/0tdOrUKZxwwgnN+j916tQgKVx00UUF73O+6Iof/OAHTb6uIabhizE0Dfu3eZxGIfvX4Mc//nHo0aNHKC8vDwMHDgyvvfZaOOCAA8Lxxx/f5OtqamrCHXfcEfbdd99QXl4edtppp3DAAQeEm2++OaxcuTK6j7Hoinyvj583b14YPnx46Ny5c6ioqAgDBw4Mzz777FYfk1g/Njd//vxw/vnnhz59+oSKiorQpUuXcOSRR4YXXnihWd83j/u57bbbwsCBA0Pnzp0bx+33v//9JpEa+eJ+LrnkkiaffdkxgPaLOZM5s0FrzZkhfH5OJDX+69ChQ+jZs2c47bTTwrRp0xrjhjY3ePDgsO+++0bX22DRokVh+PDhoVOnTqGqqiqceOKJYe7cuQW1BdqjfNd0CJ9HrvXp0yf06dMnbNq0KcybNy+MGjUqdOvWLZSWlobdd989nHjiiWHatGmNbbb2GWf9+vVh7NixoWvXrqFjx47hpJNOCosWLWp23wkhhBkzZoT99tsvlJWVhb333js89thjW1zn9OnTw9e+9rVQUVERevbsGe64447w0EMPNbtvEffTduRC4O0yreHpp5/WKaecot/97nc6/PDDW7s7W6W+vl7V1dX613/91y3+Gh0AtBTmTAAAEJPc39i2FZMnT1bv3r2bZAluzzZs2NDs1+0eeeQRLVu2TEOGDGmdTgFoN5gzAQBATHJ/Y5u6//7v/9a7776r3/zmN7r33nu3yVswW8Lrr7+uK6+8UiNGjFDXrl319ttv67/+67+033778VIOAJlhzgQAAIXgV5G3sVwup6qqKp1++um67777VFKSxvcWFi5cqLFjx+qNN97QsmXL1KVLF51wwgm6/fbbtcsuu7R29wC0UcyZAACgEBS2AAAAAICk8Te2AAAAAICkUdgCAAAAAJJW8B8rHbfD6PgXWL/R7HnhhxUYX18fX15X51t/THFxfLnVN+++efpuac1tS9GXxJi/QZ/1cbfE+pfxmLFeruP66wPncXl+9RRX++3VcTuen93KvWMx4+vU07+ccS0Ea+7Okve4WcfFug6NY5Mrzt+/UOccMwZzjvGct4znZnPMee4tzr632fmx6tzW27h1v7XGapbzr9W32tr4cqu9MZZzxvsKotdCK85vkhRqN/m2H9248wWFWdYcFmtMWLIc79ZxMfr+/MqHCutCQV8FAAAAAMB2isIWAAAAAJA0ClsAAAAAQNIobAEAAAAASaOwBQAAAAAkjcIWAAAAAJA0ClsAAAAAQNIKzrE1WblPntwmZ/ZRq2a9em3Pfc84KzZE2ps5ivEtZ5/RG1t/xmOmVbM/PdlxKfNm+sXGc9Z5qhZr+7Hl1jXuzftLOefbeV6iOY4ZZxebWa+efd+ec2otWT8PpMq4X7vOmXXMrSzY0tL4cm/uvKetN5M0y5xwzz1Nsu8N3u3H9t17nTozejPlfRbJcvvWmGihZ1d+YgsAAAAASBqFLQAAAAAgaRS2AAAAAICkUdgCAAAAAJJGYQsAAAAASBqFLQAAAAAgaRS2AAAAAICktVyObZaZfVYGmpVp6syti2WsxbJWW4Sjb5KRU2b03TquFvO4WyL7bq3ZOi6WLLMOXedMBYx355iMrT9simRntmfe+c+T0+jJmVULzJ+x8eqcQ7Ie61nmjprzZ5YZk1lnF2c8Jj289x3PvcOcH53XQ7KMc5JptrB3rDmzNXNl+XNyQ53zOrX6Zi33ZPQabc2529t3T06uMd5yJfHSyJ2/7hlT1jnL8pwngp/YAgAAAACSRmELAAAAAEgahS0AAAAAIGkUtgAAAACApFHYAgAAAACSRmELAAAAAEgahS0AAAAAIGmF59hmmNNoZkZ5M9CcPOt3Z0Qax93b3sPatjvH0ZHt6c4yzDA/05uB5s5Qs45dvHWcMz84Wd5cz1j7jLNaM83idq7bO8e45kdnhrjFO3d7c8ZjWvO+4ubMYXRdD9Y52Z6PW5as41JbG18eO25Z53Jafc8yb9oaL1bfsswHNrhzakvz5/8WJLZ+655o5VF7n3M880SW+b7WtgtpH5Nxrdagnc6yAAAAAIC2gsIWAAAAAJA0ClsAAAAAQNIobAEAAAAASaOwBQAAAAAkjcIWAAAAAJC0wuN+MnxFdNbxJSZHrILVd/Pl1hlHOsT6lyst/PRvsb2Z8GREdRQb57Uu0j7h2AQrJsQ8btZ5qzfiseKtXREoWcYHJM0Rh9bazDnIiGuLMed+i3Et5GTEAdVlGLPUmooc+60WiFHycD5reOdX97MOvjwrPsVzT7J452ZrvMWuNe/8l2EMp8Udiejsu3mdx9ce5414yjD20IxHzTqqyCPDiLrNMUMDAAAAAJJGYQsAAAAASBqFLQAAAAAgaRS2AAAAAICkUdgCAAAAAJJGYQsAAAAASBqFLQAAAAAgab4g08158rK8uUrOLFgr1y6atWhm6sWXW3mC3izYnCLH1sg7tbIQLWb+pbV9z7qdssxp9OY2h1ojp8ybsxjrn3WtenP52ipPZp8zp9Gb65lpZmnGOd6eOcbNmD+tjF1L7N5hZYyb67buOxnm3Lb2eDW375njyPneMuuc1dbmX1Za6tu2dU6se57V3nE/NceilVlqcTx7u+8LzmcFc99jc5QnN7kQ1r457muu/Zb8fXPkgOfKyuKr9o7nf+IntgAAAACApFHYAgAAAACSRmELAAAAAEgahS0AAAAAIGkUtgAAAACApFHYAgAAAACSRmELAAAAAEhay+XYWjLMHXVn6hk5ZLEsMSvTLlgZZ84cXJMzi7bV1i0ji9Hadsb5lZ58TXfPPJmohfBcq95M6lQ555joOXXOIe68Qc/86D0uZoakL4vbmyUbX7lxnQZvPnFszLReDm0hXPe1ulbMJvby5mOmynvPirQ3x7IzN959P43NYdbzo/c6tO7lnn3bnp9DpPg92ZlNbOYLx9duH7vYuPDeMzPMqZUU3TdzPLdQndhOZ1kAAAAAQFtBYQsAAAAASBqFLQAAAAAgaRS2AAAAAICkUdgCAAAAAJJGYQsAAAAASBqFLQAAAAAgadsux9aT32bkJgUrC9bKRio1DkMsEzDrHNosWTmLXt6s2dJIezObzrdtc8x41u/MfzMz1JxZibF9zzrfMlW5kvgckulxs64FY7yYOZCO8WTOf9Zybx611T52bKxr3Druzpxa1/xsxUlbfbOyYq18YGP1oS7/F1jj0ZMhLrXAtdhes2g9amvjy61j6rknZXy+zPtxlpmkWefGx/ruvO9Y3PnEsfaettoGz0GxY2fNf97zYj77GjVP5FnIPG4tdFyZoQEAAAAASaOwBQAAAAAkjcIWAAAAAJA0ClsAAAAAQNIobAEAAAAASaOwBQAAAAAkjcIWAAAAAJC0wnNsvdmbGeaQmXlX1vqNzL5YFqOV1+dmZRl68lStDElr21YWonlefXmEUWZWl5Wn5TuxsTFn5jBmnU1ncGW0Wbl8bZSZqeeYw9w53U6Zrt+bU2tdK57r2Hudeo+bdR16jp2VbWy1NzN249dDq2ZlG3OU53oz+95O50eVlsaXW/NnjHVMrXNiXadGe9fc38rjxczgjS13PPNL9nEzr6WM73tZcmf0xljXmpc1f27alH/hNnq25Se2AAAAAICkUdgCAAAAAJJGYQsAAAAASBqFLQAAAAAgaRS2AAAAAICkUdgCAAAAAJJWeNyP87Xjmb/C38EVZ2FF1pixCE5mJEWG+1Zv7Ju1bSuuwhMHZK3bijqy3rZuxIh4xpR1rVjrzvRV8hYrFqytcu63Kx7Kse6WWH+mrDnAuo6D0b7IET/gnd+8649dx977rTdmTsZxdcxB7vFaGn/sCXUZRqy01/kxy3uOl3VOamvjyz0RJta93Irjsa5zo+9WtJXnvJnPGVbfreNqrD9Xlj/2JtQY59R5z7Rm32gkzucbyL8sw3PWIjy1Ygs9i7TTWRYAAAAA0FZQ2AIAAAAAkkZhCwAAAABIGoUtAAAAACBpFLYAAAAAgKRR2AIAAAAAkkZhCwAAAABIWsE5tu48rdi6rUwoY91mTqOVdejJG/Tm1Fp9c+YJRo+tlRnlzdyz8qxKrPMSaV+aP6NMKiCLsN7I+rJyGq2osNj1knHOmH09ZHctIw/rWohca97z4c799ORJe9dtzX+eDEk5j02Jcfu0zpszGz42f5vzn5HDbSYxujPI85+3nNU3bz6w0d41Joxzas29bZa1395rIcY6n9b92HjWMPseW248Y3kzyM2cWqvvWeapWufF6pv1HFObPys2Z8zd7nuutX4rxzbG+/yYdW59bFw4s4kL7kKLrAUAAAAAgFZCYQsAAAAASBqFLQAAAAAgaRS2AAAAAICkUdgCAAAAAJJGYQsAAAAASBqFLQAAAAAgaQXn2GaZpRiM7KLMs9+8WYoxzpzaXImx71aWWDRTyuqbN2eszNc+lsVo5QXG16xg5jgazFy+7HKd7YxdI/fZygqLjRlHHmub1orHxRwvVt6fkamXs64mz/zszNQz991aHssb9M6P1nLrnrpp6zP9ciXWuuPnPOScObhWjm6G3NmfGeZ4m3MvtsxzT3JyZ8XGzrkz69XctvfZOXZsPRm4kjs/2DwvsWWeZyA5c2ilbHOdjeNi1VPmsbHmR889vYVqvXb6FAoAAAAAaCsobAEAAAAASaOwBQAAAAAkjcIWAAAAAJA0ClsAAAAAQNIobAEAAAAASaOwBQAAAAAkreAcW69YNpyZq+TMpTMzRa1cUGt5jJVTWxbP6jIZ7UMsB9fK6io1hofz2yK5jUYWWCRPK2dlPHozJM0cWitnLNLeGk/Wtq0xlTPaO6+3qIwzBZPlOS7eDFwjczRn5bV6eHNqYzmzkp17Z8xh0fnRmlvNDPH4YmsKyVl5grX5509zfrTuqfHWCsF3XqPbr7eyOY11ezMmW/NabaucOd+xZzzzOrS2bSw3M0s9edbWvdZxXD5fvTOTObb92tp4W2tudmaWus9La8ryOckcU1a+cHyxVbOEWIb5NsrxZhYGAAAAACSNwhYAAAAAkDQKWwAAAABA0ihsAQAAAABJo7AFAAAAACSNwhYAAAAAkDQKWwAAAABA0grPsXXmkMW4c2otnhxai5GzmLOOi7VvVg6jlSlVWZZ3WV1lvG19uS9nLGed15r4mCpeV5N/YU08wyy3IdJWdk6jlYUYrDiu2KGz2hYZ15qRS+rOyY2u25kZ2FZ58ytjx83KWTRWnbOCP73zY2wOszKXrQxdK8c2lkMrKZTnn/8k3/wYyowccOO45ozr0DU/rnfOf9Z9ycqwtHjmIIP3eSHnyNd054a2V9YcF5lfzfNt5ba35j3Nm+Vq5YJmuW/ufPWMrxXHmDGvY2vMZZlnbZ0zo+/RnFnJ7HuI5Keb27eOWwuNCX5iCwAAAABIGoUtAAAAACBpFLYAAAAAgKRR2AIAAAAAkkZhCwAAAABIGoUtAAAAACBpFLYAAAAAgKQVnmNrZBt5suPMLC4ry9XIZcrlfFlhKsrf3p2xa+UwGjm19VXl0eW1O1bkXVbTOX5ca6ri57wu3jUVx6MUVbEift7LVuXffsnKjfGVWxmRVhZYnZVTZiyP7ZqV3Wnl3FrMbDsrXzP/MjPfLcv8tjbMk51p8uYaG/NvNKvW2ra1386c2kznx47xsV5vHLbimvhxL1sTn6PKV+TffrFxHRZZGePW8jpfPntQJAvRm2UYjG1bOY7WvSHLfMz2ypG3aj8/Gg8qVntrjrLax8aEM5PUzAVtzVx5Z83gvlZiY8aa34z8dLNnWZ7XjHOX3bnQsX2z+tZCz0E8hQIAAAAAkkZhCwAAAABIGoUtAAAAACBpFLYAAAAAgKRR2AIAAAAAkkZhCwAAAABIWuFxPwbz9dneWJzYuo1Xc5us+BUP67XhVpxFRfxV9Zt2iMdZrNs1f/s1e8T3e311/JzWV8Zf3V2yOr7+yr/Hj02nRfnHTEVdvG8l1uvUjdeO5zbFX+FvxWGEWATKpkjURQGC0d681qx4F4/WjBdoTY6IECnbGJCcnHOvNZ5C/n3P5Yy52TouWc+P3SLzY3fn/Fjhmx87fBo/djsUb/38mLMiSqz7uTE/WnNUNCIqGH1zzl/eOItMt91eOefPGDNWxjjfZvssz6k59zrjgCyx9VvrNuYY67hbzEgexz3VrGesyBtvhFTsevDES0lSbW10sfe8xFdunBOjb4XiJ7YAAAAAgKRR2AIAAAAAkkZhCwAAAABIGoUtAAAAACBpFLYAAAAAgKRR2AIAAAAAkkZhCwAAAABIWovl2FpiuVBWJpSZW5dh7JIkqSiSC2X1PdZWkkrjp6CuY1l0eU3nePt13fJ/72LNnvHMqP33+lt0eZ+qxdHlf1z61ejyv/21W3R5yfr8fS9ZF8+vLF4b37eclYtn5GfauXuxMWNs28xQM9rHMiIlOwfNw5E32JaZWYixc25lLhtziDkeipzZw7E5MOv5sYNzftwlMj/uXRNt+41+86LLe3ZYGl3+hyW9osv/Nvsr0eUlG/L3vXRNfL+L1xvZndZ5M+bHnDFmQ11kfjZzGn1zjJ1RaWQr18Vymx3XeVtm3Rescx4bT1YWtjc33roWPOfUmzNrtM80b9XKQ7WOu5UfbPXdkbdqXqc18bnfnfVq3dMd6/fOMdbzRKg1zmvserSem1soQ5enUAAAAABA0ihsAQAAAABJo7AFAAAAACSNwhYAAAAAkDQKWwAAAABA0ihsAQAAAABJo7AFAAAAACSt5XJsM8xlcrOyEjPdtpUhaWSwGX3fVB5vX1uVf9muuy+Ptr2s+wvR5QeXr40u/0WHT6LL71h2bHR57d/yd76+1DinJa38PZtY9rKVwxicOY5GNp2Z4xjJeDPz37LMyN2eWdexJ1vOmw0cjOw4czwafY+dc6vvOSMzz8wyNObHivj2N+6Uf1mPPZZE216722+jy79eXh5d/khlfH68bcmw6PKav3XMu2xTZXy/S8rix71IG6PLzWx5S2zMmePRl7tszWHWvkVzbq223tzS9ioyj5j3JGvd3mdTK4810j93rrHzXm/N7WGDMQ/E2hp9s64E68iY11JkzFh9M5+R4lu273tmznf+/lm1lJnDHV1aQE6t53nAYuXcFoif2AIAAAAAkkZhCwAAAABIGoUtAAAAACBpFLYAAAAAgKRR2AIAAAAAkkZhCwAAAABIGoUtAAAAACBpLZZja2UrxXKh7DwqY7mVa1dv1O9GFqIrs8+ZI2bJWat3RLRtCKXG8nhe1er6iujyeuO85CKrD1bUYdZxgWY+cWRM1tQabZ3j3eDJUjRz8byZq+1ULHsu1Dmz3axcUCP3zhSb+80c2/hYzLVQrt3WqDcmkRrj+8K1xvxYG+K3X2v7MTnjsBVtMr7AvM5bMY/VmaHryfE2t28dF2/+b6qs69jKko1kY1pZ19Yc48rdLGD9mZ5xc6xaz8bx5bnS/HOUN+80WJnPMs5LiVG+xM6rlQVbVhZfd9b3JUcGb64oflzM3Gfrnm1dL7H1e6+1AvEUCgAAAABIGoUtAAAAACBpFLYAAAAAgKRR2AIAAAAAkkZhCwAAAABIGoUtAAAAACBpFLYAAAAAgKS1WI6tmQ3nyUq0st+8mXpWzmOWrONWF19evDGep1W2PP++fbZop2jbn1QcHV3+644rosv/tGS36PLcR5XR5aVr8u97yXojf63WyBnz5mlZ7aMZalZOmDMjzXs9xK43K7+NHNut05r5ltZ4246zOa081tK1xvy4Mv94XbSoa7TtLeUnR5f37LgsuvyPS7pHl1vzY9nK/Me9qMY4p94cW2PMBGueiGVxmznevvnNyvi1RJ91ao2MSStzta2y7guOnFszl9MayxYra9s4p2FTJO/VyqH19t0ab8Z5CTU1kbbxvhdVVMTX7d03S2zfvTm0zixsMys20j9zvFn5wl5ZzmEtNCZ4CgUAAAAAJI3CFgAAAACQNApbAAAAAEDSKGwBAAAAAEmjsAUAAAAAJI3CFgAAAACQNApbAAAAAEDSWizH1uTJOjTysoKRqZcrM3KXYpl6UrZ5WJviuXdFG+KZVGWr4vvW4R/5v3cRikujbecv/2p0+dzyPaLLS1fHz9sOn0QXq3Jp/mNbsqY22raoJn7cclZ2p3FezAy2WDZyLNdOssejlbvszOiN7Vu7zWHMWIicM/OYW3NrqZGVaOXeFRm3idj8bI3FTcZYds6PpWviy6s+iW2/LNr2vSU9o8v/Wt4jutycHz+NLlaHJfmPTenq+PyYs/JWvVnarcmbe2+0j2VUmvmU7ZXzOSkXyX63nv+s3M1oVqukokojj9XafizT1BgvZh6q815vyZVF5kAzq9qX9Wqy1h8bM97nduu8WM941r5HxoW5bkOuJH4/N9dvjTnPefWOiX9iFgYAAAAAJI3CFgAAAACQNApbAAAAAEDSKGwBAAAAAEmjsAUAAAAAJI3CFgAAAACQtJaL+7Fenx155boVnZJT/BXQsdd6S7JfT228/joazxJ7HbpkxlVYrzwv2hB/FX3Jmvi+d/gs//pLNsb3u8M/4n2rM2JESjbEz2v5qvixKVuRP7KieG38uKgmHndhjlfrteNWpIQV2ZMlZzxWvK1xLVkxSG1Ua8cyRFnb9sYJxZYXW9eREdVmXcfG3F+yJr5vlZHzVmzMj5WL49sOxnVYsjF+XMtWG/Pj8vxzoDU/5jZsjC43I6Cs69wT72e1teLOioz5zVi/GeUWYUaYWFFDbZUZj+e431r3citSx4o/MePQHM+fRltzLFrb9sbaRNZvxdBZsTFme++zhCtiNOOf+VnnJbbculaM42YeV/N5wDGmjGcR9zn/J35iCwAAAABIGoUtAAAAACBpFLYAAAAAgKRR2AIAAAAAkkZhCwAAAABIGoUtAAAAACBpFLYAAAAAgKQVnmPrzAqL5RPZGZBG1qE3O87MCotk8Fo5tWZmlJGxtj6eN2h9Z6KsNn//StbFT39dhS/muKg2ft6Kaoxjtz5/hmVuo5HTaGXPGefNzP60cmpj591qa+WvOXMazeslxsqea8281lZkZnFb2XCedXszl+XMuY2M5xCMDPL4mu3r1LjOi9bG58/STfn7Xrw+Pv9VlBj3PCPD15wfNxo5kBvyL7fyf8350bjfWxmVmc6PVo6tJ79SzucJT+ZzW+bN3oyNN2t+8uZ4W6xn49LS/MtqjZxuK0/VmyVrZPjG9s28L1n5wN5nBavmiK3fnZHrq4dMsfaeDFwp25xaKX5srUxp35b//2ZaaD0AAAAAALQKClsAAAAAQNIobAEAAAAASaOwBQAAAAAkjcIWAAAAAJA0ClsAAAAAQNIobAEAAAAASfMFlW4jOSPr1Z0NZ+VpxdZv9C0Yq7byWGVkgeU2GjlokRzI4pr4uotXO1OlrDwsI5841t7cb28OozWmvDloMVbucsZZiNFj48nAbcuc2XGenG9zvGQtlplabFyHVuaylfO9IbrYzMEtiiwv2mjk/Vn568ZyM+PcyJqNZtFa+b9mDq3RN/f8GRkXVk6tkXPrzX222qsustyaB7K8b7RlsXNmHXMz59uZ22mIjreysmhbM+vV6LuVJWvlq4eayPNpLJ9XBfQ94yzZ2L65M3St9laOrZXnGhkz5vzkzWW2WPsWOzbms0zL/KyVn9gCAAAAAJJGYQsAAAAASBqFLQAAAAAgaRS2AAAAAICkUdgCAAAAAJJGYQsAAAAASBqFLQAAAAAgadtFjq2ZO+eNGbMy9awcyNhyK1PKCLINxrbNrEMjBzcXy4WK5U9KduaUtdzMA9z6HFuLmVPrzVjLMkvWO16N9ub1Fsm+MzPU2ikrD9DDytzLaeszcqUCcsLNzL7IeDTHqnN+dF7HudgcZB0Xb06txZpfYzm2WWdIWoys2SjPeFMBuc+taXvuW5a8uZ+ettZzRNbtI9x5qs7cT3P7sfuaN7PZumca6zef8WLbt7ZtHBe7ZvGNmej6ncfNzZEbbeYme6+Hf+IntgAAAACApFHYAgAAAACSRmELAAAAAEgahS0AAAAAIGkUtgAAAACApFHYAgAAAACSRmELAAAAAEjatsuxjWUfeTKd1AK5dVZuaC6y3MpptFh5Wc5MU9exsfKy3Dm2vqywKOu4WRm6Xp48rqxzao3zFs0S82b+tVFZ5vua2W/ebaecyWzk4KooPh6j18KmVs4cta61yHUarPnNypnNen70cM5/XtExk/W1miprLHueJax7rZU3bd0P42u381Zj++69n3pzP63jHuufJwNXBTybOrNmXbx9t3gygD3nbFuI3Ze20fzHT2wBAAAAAEmjsAUAAAAAJI3CFgAAAACQNApbAAAAAEDSKGwBAAAAAEmjsAUAAAAAJI3CFgAAAACQtMJzbL3ZSLH2RqZTrtTopjeH0cpSjGX+1RvHxVy31Xcrx8xYfSyPK+usV2dGpZm1GJNzjlfPtr2snEYjv83MPXW0N3PIWjtDLVWxOdDK8zOYOY3enPDoeLUmqIyzYo31B+88EZErNvLZrfnVyOCNHncr3zfLDMhCeO7Z1n2lzpnjbVwPvnz17TgfOEvezPtYe2v+ct4Ps7ynmWNxe86Nd2a5hk2bfOvP8ryUxGsOs+8W67x5jm3W2caW0tL8y7bReOUpFAAAAACQNApbAAAAAEDSKGwBAAAAAEmjsAUAAAAAJI3CFgAAAACQNApbAAAAAEDSCo/78Yq95tl4bbcZi2CwYhdMkWiCEOKv/bZeG26z4lWMV3PHXktuRS5Y67ZYUR7eOKEo57qzPDZGW2u8W/EFFlf8AXE+W+SNEMmq7bZoH9137xxiRRdY16l1rRR5YmeM68iKhLDOS5bzZ8ZzvzmHRcaMOV6NOB+39hrJkyVPnI930874EzPuzLF9c6w7I2/c96XYtWC1ra2NL7eeJazl1nmN7bt1jXsjc6xIRStOKHJszXPqja+y4lezfH5sobmXp1QAAAAAQNIobAEAAAAASaOwBQAAAAAkjcIWAAAAAJA0ClsAAAAAQNIobAEAAAAASaOwBQAAAAAkrfCQVW++UCy/yLluO1dp6zP1JF+mlDsPy8oTrHXkOFrrzjpD0lp/rH3wjRlvNrInSzHn3LQrV1QF5JzF1m9dq+0059aVByhFj1umWYTGtgvZfnQeyTorO0vWdWLmKBrXgnMOix5bM7/Xl6VtsbLjo+tv7Tkmy6zFdjo/msfMk83und+sTFFvLqgnR9zMdI73LVjH1cqajbX3nDPJzku1zovVvqwsf1sjY9ybfWzlD4eamnj7LI+7J/9XBeSzx2yjub2dzrIAAAAAgLaCwhYAAAAAkDQKWwAAAABA0ihsAQAAAABJo7AFAAAAACSNwhYAAAAAkDQKWwAAAABA0grPsbXytIxspVgWopXyZebUWrmenkw9g5V3lSs1DrGV82gtt0SyEkONkQNmHDfPtgtqHsnLcmVrqgX2zTgvsTFp5o6a244f1+DNAosd2/aaw9iK3Jl6znNmjtfaSP+sbRt50GaWYYhn6uWsOch5aKO8ObVWDm5RZP3OMWPOrwbPPdW6Z4ZaR46i7OcNc8zGlpvZ784x0VZleF8xnx+tZzhn++izsZXFam3bkykq+Y67JwO3gG1784OjWbHOvrk5srLNDNzS0q1et6QC7tlbf2/x1nKF4ikVAAAAAJA0ClsAAAAAQNIobAEAAAAASaOwBQAAAAAkjcIWAAAAAJA0ClsAAAAAQNIobAEAAAAASSs8x9bKffJka1p5VFa2kbVtZ25TNIPXzKMy+m7krWbZ3sxyzbrvDmZ2sXHKs86SjfJmpGXdPrZvVoaZM/8yWdZ4aKF8tq1atzM708qei90b3NeZmTFpjGUrC9axbXMOcmZte/ICWyoPMK8M81jdObMWb99j7dvr/GexsjUtsWOeceaomaea4fxrXsfW3OzNQI+1955Tz7Yl+1qLjYvtfX6M9c+bU+vlmOPM3OUWmj/5iS0AAAAAIGkUtgAAAACApFHYAgAAAACSRmELAAAAAEgahS0AAAAAIGkUtgAAAACApFHYAgAAAACSVniOrcHKOozmcVl5VVZWl5UnWBLfTSunLLp2Z4ZaqItnTplZiJ6s2QxzZgvhzoF0rNtibtsz3p3bNrPAjL6Z6884F7BNyvKYeXKHC2hvjgdPTmNrj6WQYYakt31ddvOfp63UAjm4rX3eW4u137W126Yf2xvnHBVlHNNg5X56nz8z5H4W8IqdlyzPqWQfd2v7nvPmzYL13rNj59173L3Zxo7nCavWsurIQrXTuw8AAAAAoK2gsAUAAAAAJI3CFgAAAACQNApbAAAAAEDSKGwBAAAAAEmjsAUAAAAAJI3CFgAAAACQtMJzbI3sJFf2pTe7yMoUdWYnxdq7MyANrgxJSTnl3zd3VmGtkYeVdX6mZ91WdrHV92IrKyx/3622ZraxN5fZOq6x68HKUPOOqbbKkz3nzdTLcu6WMs06NMeqcw7yyDor1j0/eziv80wzyL25zc7nhagsszVTlmEusnk+rW1nnXMba+8di9Z15s009TwLeJ8VvPsW482CzTrnNlZzWM9/nuc7yc7atvpu9C+mpXKZ+YktAAAAACBpFLYAAAAAgKRR2AIAAAAAkkZhCwAAAABIGoUtAAAAACBpFLYAAAAAgKQV/l7mDKMH3LEvWUdKZNRWkvm6devV3ubr4jOMzPFuO8s4C3fEk9V3I5InNiaD81Xx7qNmnVdP/9prnEXGkTxRGUbaFMTTd2/sgnffY9u37hvOOcYb++Xpu8kZd+Ga270RJwYzQsURU2fGVTj7jq2QdeyMtX7PObfmoJqa+KbLyuLrz/J+7Y0a8sRuFbL9GO99KUPm3Oo9btaYsJ6NY2PSitZqIfzEFgAAAACQNApbAAAAAEDSKGwBAAAAAEmjsAUAAAAAJI3CFgAAAACQNApbAAAAAEDSKGwBAAAAAEkrPMc2w+zL4MzMs5g5uZ5cO29+pTNHzNV3o22wchizzuTLMKcx6/zhKOuce8eUV2vnoqbIm9nn4Z0frUxnT98zntvdWnOs1zvz2VMW2zfrvpJxrr3FzKrFl2Y+o2WYeZ/5/djxDGZdC7kS4xE+yznEe06s6zzLjHKLM+vaVFu71es3rxXvePbmOkeyajOvGf6JJ1gAAAAAQNIobAEAAAAASaOwBQAAAAAkjcIWAAAAAJA0ClsAAAAAQNIobAEAAAAASaOwBQAAAAAkLRcyDQgDAAAAACBb/MQWAAAAAJA0ClsAAAAAQNIobAEAAAAASaOwBQAAAAAkjcIWAAAAAJA0ClsAAAAAQNIobAEAAAAASaOwBQAAAAAkjcIWAAAAAJC0/wdMmJRxlbzLzwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This is a comparision of an image of a galaxy from DP0 at\n",
    "# (ra, dec)= (62.00273461841879, -37.00827463174009) degree. This comparision is in\n",
    "# i-band.\n",
    "global_min = min(\n",
    "    dc2_galaxy_image_from_slsim_i_2.min(),\n",
    "    dc2_galaxy_image_from_dp0_i_2.min(),\n",
    "    diff_i_2.min(),\n",
    ")\n",
    "global_max = max(\n",
    "    dc2_galaxy_image_from_slsim_i_2.max(),\n",
    "    dc2_galaxy_image_from_dp0_i_2.max(),\n",
    "    diff_i_2.max(),\n",
    ")\n",
    "# Plotting the images\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.imshow(\n",
    "    dc2_galaxy_image_from_slsim_i_2, origin=\"lower\", vmin=global_min, vmax=global_max\n",
    ")\n",
    "plt.title(\"Galaxy image from slsim\")\n",
    "plt.axis(\"off\")  # Turn off axis labels\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.imshow(\n",
    "    dc2_galaxy_image_from_dp0_i_2, origin=\"lower\", vmin=global_min, vmax=global_max\n",
    ")\n",
    "plt.title(\"Galaxy image from DP0\")\n",
    "plt.axis(\"off\")\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.imshow(diff_i_2, origin=\"lower\", vmin=global_min, vmax=global_max)\n",
    "plt.title(\"Residual\")\n",
    "plt.axis(\"off\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize these images in RGB color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "## disk dominated\n",
    "slsim_image_list_1 = [\n",
    "    dc2_galaxy_image_from_slsim_i_1,\n",
    "    dc2_galaxy_image_from_slsim_r_1,\n",
    "    dc2_galaxy_image_from_slsim_g_1,\n",
    "]\n",
    "## Bulge dominated\n",
    "slsim_image_list_2 = [\n",
    "    dc2_galaxy_image_from_slsim_i_2,\n",
    "    dc2_galaxy_image_from_slsim_r_2,\n",
    "    dc2_galaxy_image_from_slsim_g_2,\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "## disk dominated\n",
    "slsim_rgb_image_1 = rgb_image_from_image_list(\n",
    "    image_list=slsim_image_list_1, stretch=0.5\n",
    ")\n",
    "\n",
    "## Bulge dominated\n",
    "slsim_rgb_image_2 = rgb_image_from_image_list(\n",
    "    image_list=slsim_image_list_2, stretch=0.5\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "## disk dominated\n",
    "dp0_image_list_1 = [\n",
    "    dc2_galaxy_image_from_dp0_i_1,\n",
    "    dc2_galaxy_image_from_dp0_r_1,\n",
    "    dc2_galaxy_image_from_dp0_g_1,\n",
    "]\n",
    "\n",
    "## Bulge dominated\n",
    "dp0_image_list_2 = [\n",
    "    dc2_galaxy_image_from_dp0_i_2,\n",
    "    dc2_galaxy_image_from_dp0_r_2,\n",
    "    dc2_galaxy_image_from_dp0_g_2,\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "## disk dominated\n",
    "dp0_rgb_image_1 = rgb_image_from_image_list(image_list=dp0_image_list_1, stretch=0.5)\n",
    "\n",
    "## Bulge dominated\n",
    "dp0_rgb_image_2 = rgb_image_from_image_list(image_list=dp0_image_list_2, stretch=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "## disk dominated\n",
    "diff_list_1 = [diff_i_1, diff_r_1, diff_g_1]\n",
    "## Bulge dominated\n",
    "diff_list_2 = [diff_i_2, diff_r_2, diff_g_2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "## disk dominated\n",
    "rgb_diff_1 = rgb_image_from_image_list(image_list=diff_list_1, stretch=0.5)\n",
    "## bulge dominated\n",
    "rgb_diff_2 = rgb_image_from_image_list(image_list=diff_list_2, stretch=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## disk dominated\n",
    "# This is a comparision of an image of galaxy from DP0 at\n",
    "# (ra, dec)= (61.99284796403353, -36.9900209621661) degree.\n",
    "global_min = min(slsim_rgb_image_1.min(), dp0_rgb_image_1.min(), rgb_diff_1.min())\n",
    "global_max = max(slsim_rgb_image_1.max(), dp0_rgb_image_1.max(), rgb_diff_1.max())\n",
    "# Plotting the images\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.imshow(slsim_rgb_image_1, origin=\"lower\", vmin=global_min, vmax=global_max)\n",
    "plt.title(\"Galaxy image from slsim\")\n",
    "plt.axis(\"off\")  # Turn off axis labels\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.imshow(dp0_rgb_image_1, origin=\"lower\", vmin=global_min, vmax=global_max)\n",
    "plt.title(\"Galaxy image from DP0\")\n",
    "plt.axis(\"off\")\n",
    "\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.imshow(rgb_diff_1, origin=\"lower\", vmin=global_min, vmax=global_max)\n",
    "plt.title(\"Residual\")\n",
    "plt.axis(\"off\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Bulge dominated\n",
    "# This is a comparision of an image of galaxy from DP0 at\n",
    "# (ra, dec)= (62.00273461841879, -37.00827463174009) degree.\n",
    "global_min = min(slsim_rgb_image_2.min(), dp0_rgb_image_2.min(), rgb_diff_2.min())\n",
    "global_max = max(slsim_rgb_image_2.max(), dp0_rgb_image_2.max(), rgb_diff_2.max())\n",
    "# Plotting the images\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.imshow(slsim_rgb_image_2, origin=\"lower\", vmin=global_min, vmax=global_max)\n",
    "plt.title(\"Galaxy image from slsim\")\n",
    "plt.axis(\"off\")  # Turn off axis labels\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.imshow(dp0_rgb_image_2, origin=\"lower\", vmin=global_min, vmax=global_max)\n",
    "plt.title(\"Galaxy image from DP0\")\n",
    "plt.axis(\"off\")\n",
    "\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.imshow(rgb_diff_2, origin=\"lower\", vmin=global_min, vmax=global_max)\n",
    "plt.title(\"Residual\")\n",
    "plt.axis(\"off\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quantitative comparision of SLSim and DP0 images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "pixel_slsim_disk = slsim_rgb_image_1.flatten()\n",
    "pixel_slsim_bulge = slsim_rgb_image_2.flatten()\n",
    "\n",
    "pixel_dp0_disk = dp0_rgb_image_1.flatten()\n",
    "pixel_dp0_bulge = dp0_rgb_image_2.flatten()\n",
    "pixel_diff_disk = rgb_diff_1.flatten()\n",
    "pixel_diff_bulge = rgb_diff_2.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14ab57b90>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkQAAAHHCAYAAABeLEexAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/TGe4hAAAACXBIWXMAAA9hAAAPYQGoP6dpAACJX0lEQVR4nOzdeVxU1fsH8M/MMAvrsK8iuGIK7kvkmpq4pmm5pmIulVq5pGalopaalmsumWtlafZVKzVzX0qsNMmdRFFMBZR9kRlm5vz+4MfkFRSwwWH5vF+vecHcc+a5z70Mw8O9554rE0IIEBEREVVicmsnQERERGRtLIiIiIio0mNBRERERJUeCyIiIiKq9FgQERERUaXHgoiIiIgqPRZEREREVOmxICIiIqJKjwURERERVXosiCqJ8PBwBAYGllr8DRs2QCaT4dq1a6W2jsJERERAJpM90XUSEBgYiPDwcGun8Z/t2bMHDRs2hEajgUwmQ2pq6hNb97Vr1yCTybBhwwbzssd5Px8+fBgymQzfffedxXLLj3n48GGLxSwP67YEa30WAhXn99JaWBCVc/m/fPkPjUaD2rVrY+zYsUhISLB2ekRlVlJSEvr27QtbW1ssX74cX375Jezt7a2dFj0B2dnZiIiIKLdFF5UOG2snQJYxa9YsVKtWDTk5Ofjll1+wcuVK7N69G+fOnYOdnR0+//xzmEwma6dJFUR0dDTk8vL9/9Qff/yBjIwMzJ49Gx07drR2OgCA999/H++8846107CqNm3a4N69e1CpVKW2juzsbMycORMA0K5du1JbD5UvLIgqiC5duqBp06YAgBEjRsDNzQ0LFy7E999/jwEDBkCpVFo5QyrvhBDIycmBra0t1Gq1tdP5zxITEwEAzs7O1k3kPjY2NrCxqdwfy3K5HBqNxtppUCVUvv/Fo4dq3749ACA2NhZAwTFEM2bMgFwux4EDBySvGzVqFFQqFf766y/zst9++w2dO3eGVquFnZ0d2rZti19//bXEOX388ceQyWS4fv16gbapU6dCpVIhJSUFAHDs2DG89NJLqFq1KtRqNfz9/TF+/Hjcu3fvkesobFxGPplMhoiICMmymzdv4pVXXoGXlxfUajXq1auHdevWFXubvvrqKzRv3hx2dnZwcXFBmzZtsHfvXkmfFStWoF69elCr1fD19cWYMWMKjFVp164dgoODcebMGbRt2xZ2dnaoWbOmeVzIkSNH0KJFC9ja2iIoKAj79++XvD5/7MmlS5fQt29fODk5wc3NDW+99RZycnIkfdevX4/27dvD09MTarUadevWxcqVKwtsW2BgILp3746ff/4ZTZs2ha2tLT777DNz2/1jFXJzczFz5kzUqlULGo0Gbm5uaNWqFfbt2yeJefDgQbRu3Rr29vZwdnZGz549cfHixUK3JSYmBuHh4XB2doZWq8WwYcOQnZ1d9A8FwNatW9GkSRPY2trC3d0dL7/8Mm7evCnZ30OHDgUANGvWDDKZ7JFjL65fv47Ro0cjKCgItra2cHNzw0svvVTscSKpqakIDw+HVquFs7Mzhg4dWuh4pcLGEO3btw+tWrWCs7MzHBwcEBQUhHffffeR69PpdOjevTu0Wi2OHz/+yL7//PMPevXqBXt7e3h6emL8+PHQ6XSF9i1qvwJ5nzUODg6Ii4tD9+7d4eDgAD8/PyxfvhwAcPbsWbRv3x729vYICAjA119/LXl9YWOI8n8/Lly4gGeffRZ2dnbw8/PD/PnzJa/V6/WYPn06mjRpAq1WC3t7e7Ru3RqHDh0y97l27Ro8PDwAADNnzjQPN7j/s+HSpUt48cUX4erqCo1Gg6ZNm+KHH34osD/Onz+P9u3bw9bWFlWqVMEHH3xQoiPxW7duRd26daHRaBAcHIzt27cXOt7z448/xjPPPAM3NzfY2tqiSZMmxRozlpycjLfffhshISFwcHCAk5MTunTpIvl8B4ChQ4dCo9EU+F0MCwuDi4sLbt26hatXr0Imk2HRokUF1nP8+HHIZDJ88803xd72MklQubZ+/XoBQPzxxx+S5UuWLBEAxKpVq4QQQgwdOlQEBASY2/V6vWjUqJEICAgQ6enpQggh9uzZIwCI2bNnm/sdOHBAqFQqERoaKj755BOxaNEiUb9+faFSqcRvv/1WII/Y2NiH5nr9+nUhk8nE/PnzC7RVr15ddOvWzfz8jTfeEF27dhVz5swRn332mRg+fLhQKBTixRdflLxuxowZ4v63cWxsrAAg1q9fX2AdAMSMGTPMz+Pj40WVKlWEv7+/mDVrlli5cqV4/vnnBQCxaNGih25HvoiICAFAPPPMM2LBggViyZIlYuDAgWLKlCkF8uvYsaNYtmyZGDt2rFAoFKJZs2ZCr9eb+7Vt21b4+voKf39/MWnSJLFs2TJRt25doVAoxObNm4W3t7eIiIgQixcvFn5+fkKr1Zp/bvevJyQkRPTo0UN8+umn4uWXXxYAxODBgyV5N2vWTISHh4tFixaJZcuWiU6dOgkA4tNPP5X0CwgIEDVr1hQuLi7inXfeEatWrRKHDh0ytw0dOtTc99133xUymUyMHDlSfP755+KTTz4RAwYMEPPmzTP32bdvn7CxsRG1a9cW8+fPFzNnzhTu7u7CxcVF8r7J35ZGjRqJ3r17ixUrVogRI0YIAGLy5MlF/lzy34vNmjUTixYtEu+8846wtbUVgYGBIiUlRQghxN69e8WoUaMEADFr1izx5ZdfiuPHjz805tatW0WDBg3E9OnTxerVq8W7774rXFxcREBAgMjKynpkPiaTSbRp00bI5XIxevRosWzZMtG+fXtRv379Au/VB9/P586dEyqVSjRt2lQsWbJErFq1Srz99tuiTZs25j6HDh0SAMTWrVuFEEJkZ2eL5557Tri4uIjff//9kbllZ2eL2rVrC41GIyZPniwWL14smjRpYs4t/+dd3P0qRN5njUajEXXr1hWvvfaaWL58uXjmmWfM2+rr62t+j9erV08oFApx9erVAttz/7rv//146623xIoVK0T79u0FALF7925zvzt37ggfHx8xYcIEsXLlSjF//nwRFBQklEqlOH36tBBCiMzMTLFy5UoBQLzwwgviyy+/FF9++aX466+/zPtcq9WKunXrio8++kh8+umnok2bNkImk4lt27aZ13X79m3h4eEhXFxcREREhFiwYIGoVauWed896rNQCCF27twpZDKZqF+/vli4cKGYNm2acHFxEcHBwZLPaiGEqFKlihg9erT49NNPxcKFC0Xz5s0FALFz505Jvwd/L//44w9Ro0YN8c4774jPPvtMzJo1y/z5cfPmTXO/lJQUUaVKFdGsWTNhMBiEEEKsWrVKABBffvmluV/Lli1FkyZNCmzL6NGjhaOjY5G/C2UdC6JyLv9Dav/+/eLOnTvixo0bYvPmzcLNzU3Y2tqKf/75RwhRsCASQoizZ88KlUolRowYIVJSUoSfn59o2rSpyM3NFULkfZDXqlVLhIWFCZPJZH5ddna2qFatmnjuuecK5FHUh0BoaGiBX6jff/9dABBffPGFZB0Pmjt3rpDJZOL69evmZf+lIBo+fLjw8fERd+/elfTr37+/0Gq1heaQ7/Lly0Iul4sXXnhBGI1GSVv+vkpMTBQqlUp06tRJ0ufTTz8VAMS6devMy9q2bSsAiK+//tq87NKlSwKAkMvl4sSJE+blP//880P/kD7//POSXEaPHi0AmD/shSh834aFhYnq1atLlgUEBAgAYs+ePQX6P/jB26BBA0lBW5iGDRsKT09PkZSUZF72119/CblcLoYMGVJgW1555RXJ61944QXh5ub2yHXo9Xrh6ekpgoODxb1798zLd+7cKQCI6dOnm5c97J+JwhS2zyIjIwu8bwuzY8cOAUDyj4DBYBCtW7cusiBatGiRACDu3Lnz0Pj3F0QZGRmibdu2wt3d3VwAPMrixYsFAPHtt9+al2VlZYmaNWtKipKS7NehQ4cKAGLOnDnmZSkpKcLW1lbIZDKxefNm8/L89/j9v5cPK4ge3Nc6nU54e3uLPn36mJcZDAah0+kk25iSkiK8vLwk76c7d+4UWG++Dh06iJCQEJGTk2NeZjKZxDPPPCNq1aplXjZu3DgBQPKPYWJiotBqtcX6LAwJCRFVqlQRGRkZ5mWHDx8WAAp8Vj/4/tPr9SI4OFi0b99esvzB38ucnJwCn0+xsbFCrVaLWbNmSZbnf6588MEH4urVq8LBwUH06tVL0uezzz4TAMTFixclubi7u0vWW17xlFkF0bFjR3h4eMDf3x/9+/eHg4MDtm/fDj8/v4e+Jjg4GDNnzsSaNWsQFhaGu3fvYuPGjeYxDFFRUbh8+TIGDhyIpKQk3L17F3fv3kVWVhY6dOiAo0ePlnigdr9+/XDq1ClcuXLFvGzLli1Qq9Xo2bOneZmtra35+6ysLNy9exfPPPMMhBA4ffp0idZZGCEE/ve//6FHjx4QQpi37e7duwgLC0NaWhr+/PPPh75+x44dMJlMmD59eoHBxfmnPPbv3w+9Xo9x48ZJ+owcORJOTk7YtWuX5HUODg7o37+/+XlQUBCcnZ3x1FNPoUWLFubl+d9fvXq1QF5jxoyRPH/jjTcAALt37zYvu3/fpqWl4e7du2jbti2uXr2KtLQ0yeurVauGsLCwh+6HfM7Ozjh//jwuX75caPvt27cRFRWF8PBwuLq6mpfXr18fzz33nCS/fK+99prkeevWrZGUlIT09PSH5nHy5EkkJiZi9OjRknEo3bp1Q506dQrs8+K6f5/l5uYiKSkJNWvWhLOz8yPfJ0DevrexscHrr79uXqZQKMw/m0fJH9/0/fffF/m7lpaWhk6dOuHSpUs4fPgwGjZsWGT83bt3w8fHBy+++KJ5mZ2dHUaNGiXp9zj7dcSIEZLtCAoKgr29Pfr27Wtenv8eL+y9/CAHBwe8/PLL5ucqlQrNmzeXvFahUJgHY5tMJiQnJ8NgMKBp06ZF/pyAvFNMBw8eRN++fZGRkWH+TEhKSkJYWBguX75sPkW4e/duPP3002jevLn59R4eHhg0aFCR67l16xbOnj2LIUOGwMHBwby8bdu2CAkJKdD//vdfSkoK0tLS0Lp16yK3Sa1Wmz97jEYjkpKSzKddH3xtp06d8Oqrr2LWrFno3bs3NBqN+RR5vr59+0Kj0WDTpk3mZT///DPu3r0r+dmUVyyIKojly5dj3759OHToEC5cuICrV68W6w/ZpEmT0KBBA/z++++YMWMG6tata27L/+M2dOhQeHh4SB5r1qyBTqcr8Ae0KC+99BLkcjm2bNkCIK8w2bp1K7p06QInJydzv7i4OPMfTwcHB3h4eKBt27YAUOJ1FubOnTtITU3F6tWrC2zbsGHDAPw76LYwV65cgVwul+yvB+WPlQoKCpIsV6lUqF69eoGxVFWqVCkwfkSr1cLf37/AMgDm8Vb3q1WrluR5jRo1IJfLJWNdfv31V3Ts2NE8jsfDw8M8JqWwgqg4Zs2ahdTUVNSuXRshISGYNGkSzpw5Y25/2L4AgKeeespcaN+vatWqkucuLi4ACt/u4qynTp06hY5fK4579+5h+vTp8Pf3h1qthru7Ozw8PJCamlrk+/H69evw8fGR/OF7WI4P6tevH1q2bIkRI0bAy8sL/fv3x7fffltocTRu3Dj88ccf2L9/P+rVq1es7bp+/Tpq1qxZ4H33YG4l3a8ajcY8TiefVqt96Hv8UT/TfIW91sXFpcBrN27ciPr165vHsnl4eGDXrl3F+tyIiYmBEALTpk0r8LkwY8YMAP9+Lly/fr3A7xtQvJ9r/v6qWbNmgbbClu3cuRNPP/00NBoNXF1d4eHhgZUrVxa5TSaTCYsWLUKtWrUk79szZ84U+tqPP/4Yrq6uiIqKwtKlS+Hp6Slpd3Z2Ro8ePSTjvjZt2gQ/Pz/zuNXyrHJfzlCBNG/e3HyVWUlcvXrVXPicPXtW0pb/obtgwYKH/rf54Id8UXx9fdG6dWt8++23ePfdd3HixAnExcXho48+MvcxGo147rnnkJycjClTpqBOnTqwt7fHzZs3ER4e/sj/lB82qZ3RaCx0215++WXz4NoH1a9fv0Tb9l8pFIoSLRdCFBnzwf1x5coVdOjQAXXq1MHChQvh7+8PlUqF3bt3Y9GiRQX27f3/mT5KmzZtcOXKFXz//ffYu3cv1qxZg0WLFmHVqlWSIwUl8V+229LeeOMNrF+/HuPGjUNoaCi0Wi1kMhn69+9fqtNZ2Nra4ujRozh06BB27dqFPXv2YMuWLWjfvj327t0r2Uc9e/bE5s2bMW/ePHzxxRdWnRahNN7LxXntV199hfDwcPTq1QuTJk2Cp6cnFAoF5s6dKzkq/TD5P8u33377of9QFlawlKZjx47h+eefR5s2bbBixQr4+PhAqVRi/fr1BQakP2jOnDmYNm0aXnnlFcyePRuurq6Qy+UYN25coe/b06dPmwu+s2fPYsCAAQX6DBkyBFu3bsXx48cREhKCH374AaNHjy7303AALIgqNZPJhPDwcDg5OWHcuHGYM2cOXnzxRfTu3RtA3tEFAHBycrLoPC39+vXD6NGjER0djS1btsDOzg49evQwt589exZ///03Nm7ciCFDhpiXP3jFUmHyjyI8eAXPg//Benh4wNHREUaj8bG2rUaNGjCZTLhw4cJDi8WAgAAAeXP2VK9e3bxcr9cjNja2VOa+uXz5suSoTkxMDEwmk/mqlR9//BE6nQ4//PCD5AjM/VfhPC5XV1cMGzYMw4YNQ2ZmJtq0aYOIiAiMGDFCsi8edOnSJbi7u1tkUsT71/Pgf6zR0dHm9pL67rvvMHToUHzyySfmZTk5OcWa2TogIAAHDhxAZmam5B+IwvZFYeRyOTp06IAOHTpg4cKFmDNnDt577z0cOnRI8h7q1asXOnXqhPDwcDg6OhZ65WBhuZ07dw5CCEnx/GBupbVfLe27775D9erVsW3bNsn25B/dyfewf5zyf0+VSmWRv58BAQGFniIuzs81f3/FxMQUaHtw2f/+9z9oNBr8/PPPkuku1q9fX+R6vvvuOzz77LNYu3atZHlqairc3d0ly7KysjBs2DDUrVsXzzzzDObPn48XXngBzZo1k/Tr3LkzPDw8sGnTJrRo0QLZ2dkYPHhwkbmUB+W/pKPHtnDhQhw/fhyrV6/G7Nmz8cwzz+D111/H3bt3AQBNmjRBjRo18PHHHyMzM7PA6+/cufNY6+3Tpw8UCgW++eYbbN26Fd27d5f8Mcz/T/D+//yEEFiyZEmRsZ2cnODu7o6jR49Klq9YsULyXKFQoE+fPvjf//6Hc+fOFYhT1Lb16tULcrkcs2bNKvCfVn7eHTt2hEqlwtKlSyXbsnbtWqSlpaFbt25Fbk9J5V/anG/ZsmUA8uapAgrft2lpacX6cH2UpKQkyXMHBwfUrFnTfPm2j48PGjZsiI0bN0qKiHPnzmHv3r3o2rXrf1p/vqZNm8LT0xOrVq2SXDr+008/4eLFi4+9zxUKRYGjGMuWLStw5LEwXbt2hcFgkBQoRqPR/LN5lOTk5ALL8gvwwi6NHzJkCJYuXYpVq1ZhypQpxcrt1q1bkku4s7OzsXr1akm/0tqvllbY+/u3335DZGSkpJ+dnR2Agv84eXp6ol27dvjss89w+/btAvHv/1zo2rUrTpw4gd9//13Sfv/4mofx9fVFcHAwvvjiC8ln65EjRwocqVcoFJDJZJL32rVr17Bjx44i11PY+3br1q0FpkoAgClTpiAuLg4bN27EwoULERgYiKFDhxZ4n9nY2GDAgAH49ttvsWHDBoSEhDzxo+mlhUeIKqmLFy9i2rRpCA8PNx+d2bBhAxo2bIjRo0fj22+/hVwux5o1a9ClSxfUq1cPw4YNg5+fH27evIlDhw7ByckJP/74Y4nX7enpiWeffRYLFy5ERkYG+vXrJ2mvU6cOatSogbfffhs3b96Ek5MT/ve//xVrnAGQN5hz3rx5GDFiBJo2bYqjR4/i77//LtBv3rx5OHToEFq0aIGRI0eibt26SE5Oxp9//on9+/cX+scoX82aNfHee+9h9uzZaN26NXr37g21Wo0//vgDvr6+mDt3Ljw8PDB16lTMnDkTnTt3xvPPP4/o6GisWLECzZo1K5VBiLGxsXj++efRuXNnREZG4quvvsLAgQPRoEEDAHkDJ1UqFXr06IFXX30VmZmZ+Pzzz+Hp6VnoH4Diqlu3Ltq1a4cmTZrA1dUVJ0+exHfffYexY8ea+yxYsABdunRBaGgohg8fjnv37mHZsmXQarUF5od6XEqlEh999BGGDRuGtm3bYsCAAUhISMCSJUsQGBiI8ePHP1bc7t2748svv4RWq0XdunURGRmJ/fv3w83NrcjX9ujRAy1btsQ777yDa9euoW7duti2bVuxxrTMmjULR48eRbdu3RAQEIDExESsWLECVapUQatWrQp9zdixY5Geno733nsPWq32kXMWjRw5Ep9++imGDBmCU6dOwcfHB19++aW5YMhXWvvV0rp3745t27bhhRdeQLdu3RAbG4tVq1ahbt26ksLD1tYWdevWxZYtW1C7dm24uroiODgYwcHBWL58OVq1aoWQkBCMHDkS1atXR0JCAiIjI/HPP/+Y5/CZPHkyvvzyS3Tu3BlvvfUW7O3tsXr1agQEBEjGzz3MnDlz0LNnT7Rs2RLDhg1DSkoKPv30UwQHB0ty7datGxYuXIjOnTtj4MCBSExMxPLly1GzZs0i19O9e3fMmjULw4YNwzPPPIOzZ89i06ZNkiPWQN78YCtWrMCMGTPQuHFjAHlHoNq1a4dp06YVmO8pv/A+dOiQZLhDufekL2sjyyrupcP3X3ZvMBhEs2bNRJUqVURqaqqkX/78RVu2bDEvO336tOjdu7dwc3MTarVaBAQEiL59+4oDBw4UyKOoS03zff755wKAcHR0lFzGm+/ChQuiY8eOwsHBQbi7u4uRI0eKv/76q8jLlIXIu0R1+PDhQqvVCkdHR9G3b1+RmJhY6GW2CQkJYsyYMcLf318olUrh7e0tOnToIFavXl2s7Vi3bp1o1KiRUKvVwsXFRbRt21bs27dP0ufTTz8VderUEUqlUnh5eYnXX39dMm+LEHmXFderV69A/ICAgEIvZwcgxowZU2A/XLhwQbz44ovC0dFRuLi4iLFjxxbYvz/88IOoX7++0Gg0IjAwUHz00Udi3bp1BX5+D1t3ftv9l9l+8MEHonnz5sLZ2VnY2tqKOnXqiA8//FAy15IQQuzfv1+0bNlS2NraCicnJ9GjRw9x4cIFSZ/8bXnwUvOSvMe2bNli/rm4urqKQYMGmaegeDBecS67T0lJEcOGDRPu7u7CwcFBhIWFiUuXLhXYDw+TlJQkBg8eLJycnIRWqxWDBw8Wp0+fLvL9fODAAdGzZ0/h6+srVCqV8PX1FQMGDBB///23uc+D8xDlmzx5cqHzSz3o+vXr4vnnnxd2dnbC3d1dvPXWW+Y5ye6/9F2I4u3XoUOHCnt7+wLrKe57/GGX3Rf22genEzGZTGLOnDkiICBAqNVq0ahRI7Fz585Cpx05fvy4aNKkiVCpVAU+G65cuSKGDBkivL29hVKpFH5+fqJ79+7iu+++k8Q4c+aMaNu2rdBoNMLPz0/Mnj1brF27ttjv082bN4s6deoItVotgoODxQ8//CD69Okj6tSpI+m3du1aUatWLaFWq0WdOnXE+vXrC/3sK+yy+4kTJwofHx9ha2srWrZsKSIjI0Xbtm1F27ZthRBCpKeni4CAANG4cWPzlCv5xo8fL+RyuYiMjCyQe7169YRcLi/w8y/PZEJYYYQiEVlUREQEZs6ciTt37hQYG0BE5UfDhg3h4eFRrDGT1tSoUSO4uroWuNtBecYxRERERE9Ybm4uDAaDZNnhw4fx119/lfkbzp48eRJRUVGSi14qAo4hIiIiesJu3ryJjh074uWXX4avry8uXbqEVatWwdvbu8CkpGXFuXPncOrUKXzyySfw8fEpMP6zvGNBRERE9IS5uLigSZMmWLNmDe7cuQN7e3t069YN8+bNK9ZgfWv47rvvMGvWLAQFBeGbb76RzFpeEXAMEREREVV6HENERERElR4LIiIiIqr0OIaoGEwmE27dugVHR8eHTvlOREREZYsQAhkZGfD19S3yfmssiIrh1q1bBe44TkREROXDjRs3UKVKlUf2YUFUDI6OjgDydqiTk5OVsyEiIqLiSE9Ph7+/v/nv+KOwICqG/NNkTk5OLIiIiIjKmeIMd+GgaiIiIqr0WBARERFRpceCiIiIiCo9jiEiIqrkjEYjcnNzrZ0G0WNRqVRFXlJfHCyIiIgqKSEE4uPjkZqaau1UiB6bXC5HtWrVoFKp/lMcFkRERJVUfjHk6ekJOzs7TjxL5U7+xMm3b99G1apV/9N7mAUREVElZDQazcVQWb27OlFxeHh44NatWzAYDFAqlY8dh4OqiYgqofwxQ3Z2dlbOhOi/yT9VZjQa/1McFkRERJUYT5NReWep9zALIiIiIqr0OIaIiIgkbqbeQ0qW/omsy8VeBT9n2yeyruKKiIjAjh07EBUV9cTXHRgYiHHjxmHcuHEWiRceHo7U1FTs2LHDIvEKY839ZUksiIiIyOxm6j10/OQI7uX+t/EYxWWrVGD/xLbFLoru3LmD6dOnY9euXUhISICLiwsaNGiA6dOno2XLlgCKLiq2b9+Ojz76CBcvXoTJZELVqlXx3HPPYfHixQCAt99+G2+88YYlNq/E/vjjD9jb21tl3ZUdCyIiIjJLydLjXq4Ri/s1RE1Ph1JdV0xiJsZtiUJKlr7YBVGfPn2g1+uxceNGVK9eHQkJCThw4ACSkpKK9foDBw6gX79++PDDD/H8889DJpPhwoUL2Ldvn7mPg4MDHBxKd9sfxsPDwyrrJY4hIiKiQtT0dECwn7ZUHyUtuFJTU3Hs2DF89NFHePbZZxEQEIDmzZtj6tSpeP7554sV48cff0TLli0xadIkBAUFoXbt2ujVqxeWL19u7hMREYGGDRuan4eHh6NXr16YM2cOvLy84OzsjFmzZsFgMGDSpElwdXVFlSpVsH79+keuu127dhg7dizGjh0LrVYLd3d3TJs2DUIIc5/AwEDzkarDhw9DpVLh2LFj5vb58+fD09MTCQkJAIAbN26gb9++cHZ2hqurK3r27Ilr164Va1+kp6fD1tYWP/30k2T59u3b4ejoiOzsbADAlClTULt2bdjZ2aF69eqYNm3aI2c2b9euXYGjc7169UJ4eLj5uU6nw9tvvw0/Pz/Y29ujRYsWOHz4sLn9+vXr6NGjB1xcXGBvb4969eph9+7dxdqux8WCqKzIvQfcipI+bv/1b/ud6ILt91Ly2jITC7YlX31yuRMRPQH5R2527NgBnU73WDG8vb1x/vx5nDt3rkSvO3jwIG7duoWjR49i4cKFmDFjBrp37w4XFxf89ttveO211/Dqq6/in3/+eWScjRs3wsbGBr///juWLFmChQsXYs2aNYX2zS8sBg8ejLS0NJw+fRrTpk3DmjVr4OXlhdzcXISFhcHR0RHHjh3Dr7/+CgcHB3Tu3Bl6fdFjwJycnNC9e3d8/fXXkuWbNm1Cr169zFMyODo6YsOGDbhw4QKWLFmCzz//HIsWLSrmnivc2LFjERkZic2bN+PMmTN46aWX0LlzZ1y+fBkAMGbMGOh0Ohw9ehRnz57FRx99VOpH7XjKrKxIuQasbitdplAB0+7kff+/EUD8GWn7SxuAei8AZ74F9r4nbavdBRi4ubSyJSJ64mxsbLBhwwaMHDkSq1atQuPGjdG2bVv0798f9evXL1aMN954A8eOHUNISAgCAgLw9NNPo1OnThg0aBDUavVDX+fq6oqlS5dCLpcjKCgI8+fPR3Z2Nt59910AwNSpUzFv3jz88ssv6N+//0Pj+Pv7Y9GiRZDJZAgKCsLZs2exaNEijBw5stD+H3zwAfbt24dRo0bh3LlzGDp0qPlo2JYtW2AymbBmzRrzpefr16+Hs7MzDh8+jE6dOhW5PwYNGoTBgwcjOzsbdnZ2SE9Px65du7B9+3Zzn/fff9/8fWBgIN5++21s3rwZkydPLjJ+YeLi4rB+/XrExcXB19cXQN64rT179mD9+vWYM2cO4uLi0KdPH4SEhAAAqlev/ljrKgkWRNaWGgccmQ+0fAsYdUTadv/cCn3W5B1Fup9LQN7X+n2BwFbSNpU9kHUX0GgBxePP3ElEVJb06dMH3bp1w7Fjx3DixAn89NNPmD9/PtasWSM5JfMw9vb22LVrF65cuYJDhw7hxIkTmDhxIpYsWYLIyMiHTlRZr149yQ1Evby8EBwcbH6uUCjg5uaGxMTER67/6aeflsybExoaik8++QRGoxEKhaJAf5VKhU2bNqF+/foICAiQHJn566+/EBMTA0dHR8lrcnJycOXKlUfviP/XtWtXKJVK/PDDD+jfvz/+97//wcnJCR07djT32bJlC5YuXYorV64gMzMTBoMBTk5OxYpfmLNnz8JoNKJ27dqS5Tqdzjxr+ptvvonXX38de/fuRceOHdGnT59iF72PiwWRtWUnA6e/BJqNAHwbPryfR9DD2xw88x73uxUFfNo0r8h6VFwionJGo9Hgueeew3PPPYdp06ZhxIgRmDFjRrEKonw1atRAjRo1MGLECLz33nuoXbs2tmzZgmHDhhXa/8FbQshkskKXmUymEm9PUY4fPw4ASE5ORnJysvkqtMzMTDRp0gSbNm0q8JriDs5WqVR48cUX8fXXX6N///74+uuv0a9fP9jY5JUHkZGRGDRoEGbOnImwsDBotVps3rwZn3zyyUNjyuVyybgoAJIxR5mZmVAoFDh16lSBIjD/tNiIESMQFhaGXbt2Ye/evZg7dy4++eSTUr36j2OIiIioXKtbty6ysrIe+/WBgYGws7P7TzGK67fffpM8P3HiBGrVqlXo0SEAuHLlCsaPH4/PP/8cLVq0wNChQ81FV+PGjXH58mV4enqiZs2akodWqy12ToMGDcKePXtw/vx5HDx4EIMGDTK3HT9+HAEBAXjvvffQtGlT1KpVC9evX39kPA8PD9y+fdv83Gg0SsZsNWrUCEajEYmJiQXy9vb2Nvfz9/fHa6+9hm3btmHixIn4/PPPi71Nj4MFERERlQtJSUlo3749vvrqK5w5cwaxsbHYunUr5s+fj549e0r63rx5E1FRUZJHSkoKIiIiMHnyZBw+fBixsbE4ffo0XnnlFeTm5uK5554r9W2Ii4vDhAkTEB0djW+++QbLli3DW2+9VWhfo9GIl19+GWFhYRg2bBjWr1+PM2fOmI/ODBo0CO7u7ujZsyeOHTuG2NhYHD58GG+++WaRg7vv16ZNG3h7e2PQoEGoVq0aWrRoYW6rVasW4uLisHnzZly5cgVLly6VjC8qTPv27bFr1y7s2rULly5dwuuvv47U1FRze+3atTFo0CAMGTIE27ZtQ2xsLH7//XfMnTsXu3btAgCMGzcOP//8M2JjY/Hnn3/i0KFDeOqpp4q9TY+Dp8yIiKiAmMTMMrcOBwcHtGjRAosWLcKVK1eQm5sLf39/jBw50jy4Od/HH3+Mjz/+WLLsyy+/RNu2bbF8+XIMGTLEPLFjo0aNsHfvXgQFPWJogoUMGTIE9+7dQ/PmzaFQKPDWW29h1KhRhfb98MMPcf36dezcuRMA4OPjg9WrV2PAgAHo1KkTGjRogKNHj2LKlCno3bs3MjIy4Ofnhw4dOpRojI9MJsOAAQMwf/58TJ8+XdL2/PPPY/z48Rg7dix0Oh26deuGadOmISIi4qHxXnnlFfz1118YMmQIbGxsMH78eDz77LOSPuvXr8cHH3yAiRMn4ubNm3B3d8fTTz+N7t27A8grBseMGYN//vkHTk5O6Ny583++sq0oMvHgiT4qID09HVqtFmlpaf9pIFnhwW8Bv68Gmo8CnHwtF/dWVN5VaxxDRESFyMnJQWxsLKpVqwaNRmNeXtZnqi7P2rVrh4YNG5rnGSLLeNh7GSjZ328eIbI2J1+gY4Tl43qHAO/cyLvajIiomPycbbF/YttKfS8zqpxYEFmbLiPvaI5vQ0DtWFTv4pMrAI2Fj2YRUaXg52zLIoUqHQ6qtrakK8DG7nlfLR33yxcsH5eIiB7L4cOHebqsDGNBVFHpMoArB/O+EhER0SOxICIiIqJKjwURERERVXosiKxNoQQcfXm/MSIiIiviVWbW5lUPmHjR8nG1VYCuH+d9JSIiokdiQVRR2bsDzUdaOwsiIqJygQWRtSWcB756EXj5u7yjRZaSnQxc3gfUeg6wc7VcXCKq+FJvANlJ0mV2roBzVSD3HnAnWtomkwE+DfK+vxOd1+d+LgGArQuQmZg3O78krhvg7G/Z/K0sPDwcqamp2LFjh0XibdiwAePGjZPcD8zSDh8+jGeffRYpKSlwdnYutfWUZSyIrM2YC2TcyvtqSalxwPZRebfuYEFERMVl0AEn1wK/LgGE6d/ljQYDPT8FUq7l3RbofgoVMO1O3vf/GwHEn5G2v7QBqPcCcOZbYO970rbaXYDenwGa4t2dPTw8HBs3bgQA2NjYwNXVFfXr18eAAQMQHh4OufzfobGBgYHmO7Pb2dkhKCgIU6dOxUsvvWTus3XrVkybNg3Xrl1DrVq18NFHH6Fr167FyuVhlixZAt4Vq/yx6qDquXPnolmzZnB0dISnpyd69eqF6Gjpfx45OTkYM2YM3Nzc4ODggD59+iAhIUHSJy4uDt26dYOdnR08PT0xadIkGAwGSZ/Dhw+jcePGUKvVqFmzJjZs2FDam0dEVP7YqIHQscDIQ3n/UOU/2k7Oa3cJlC4fdQQYsf/f1/dZU7C9eru8tvp9C7b1/LTYxVC+zp074/bt27h27Rp++uknPPvss3jrrbfQvXv3Ap/9s2bNwu3bt3H69Gk0a9YM/fr1w/HjxwEAx48fx4ABAzB8+HCcPn0avXr1Qq9evXDu3LnH3Hl5tFptpT3KUp5ZtSA6cuQIxowZgxMnTmDfvn3Izc1Fp06dkJWVZe4zfvx4/Pjjj9i6dSuOHDmCW7duoXfv3uZ2o9GIbt26Qa/X4/jx49i4cSM2bNgguWNvbGwsunXrhmeffRZRUVEYN24cRowYgZ9//vmJbi8RUZmnywASLwJuNfJuKZT/cK6a1660lS73bfjv6TIA8Agq2G7rktfm4Cldnj9MoIRHyNVqNby9veHn54fGjRvj3Xffxffff4+ffvqpwD+7jo6O8Pb2Ru3atbF8+XLY2trixx9/BJB3JKdz586YNGkSnnrqKcyePRuNGzfGp59++tB1R0REoGHDhvjss8/g7+8POzs79O3bF2lpaeY+4eHh6NWrFwDgzp078Pb2xpw5c8ztx48fh0qlwoEDBwAAOp0Ob7/9Nvz8/GBvb48WLVrg8OHDxd4fzzzzDKZMmSJZdufOHSiVShw9ehQA8OWXX6Jp06bm/TFw4EAkJiYWuZ33W7x4MQIDAyXL1qxZg6eeegoajQZ16tTBihUrzG16vR5jx46Fj48PNBoNAgICMHfu3GJv15Nm1YJoz549CA8PR7169dCgQQNs2LABcXFxOHXqFAAgLS0Na9euxcKFC9G+fXs0adIE69evx/Hjx3HixAkAwN69e3HhwgV89dVXaNiwIbp06YLZs2dj+fLl0Ovzbk64atUqVKtWDZ988gmeeuopjB07Fi+++CIWLVpktW0nIiqTSut2QoVJOA8sqJH39T9q3749GjRogG3btj20j42NDZRKpflvQ2RkJDp27CjpExYWhsjIyEeuKyYmBt9++y1+/PFH7NmzB6dPn8bo0aML7evh4YF169YhIiICJ0+eREZGBgYPHoyxY8eiQ4cOAICxY8ciMjISmzdvxpkzZ/DSSy+hc+fOuHz5crG2fdCgQdi8ebPkNN2WLVvg6+uL1q1bAwByc3Mxe/Zs/PXXX9ixYweuXbuG8PDwYsV/mE2bNmH69On48MMPcfHiRcyZMwfTpk0zn9JcunQpfvjhB3z77beIjo7Gpk2bChRUZUmZmocov8J2dc0b83Lq1Cnk5uZK3rB16tRB1apVzW/YyMhIhISEwMvLy9wnLCwM6enpOH/+vLlPSd70Op0O6enpkkepcasBDN2Z99WSVPZAlWa82z0RVRp16tTBtWvXCm3T6/WYO3cu0tLS0L59ewBAfHy85G8HAHh5eSE+Pv6R68nJycEXX3yBhg0bok2bNli2bBk2b9780Nd17doVI0eOxKBBg/Daa6/B3t7efKQkLi4O69evx9atW9G6dWvUqFEDb7/9Nlq1aoX169cXa7v79u2LW7du4ZdffjEv+/rrrzFgwADIZDIAwCuvvIIuXbqgevXqePrpp7F06VL89NNPyMzMLNY6CjNjxgx88skn6N27N6pVq4bevXtj/Pjx+Oyzz8zbVqtWLbRq1QoBAQFo1aoVBgwY8NjrK21lpiAymUwYN24cWrZsieDgYAB5b1aVSlXgXOz9b9iHvaHz2x7VJz09HffuPXA1BPLGNmm1WvPD378Ur4BQOwLVWlv2TvcA4F4r77y+ey3LxiUiKqOEEOYCIN+UKVPg4OAAOzs7fPTRR5g3bx66dev2n9ZTtWpV+Pn5mZ+HhobCZDIVGAN7v48//hgGgwFbt27Fpk2boFarAQBnz56F0WhE7dq14eDgYH4cOXIEV64U7yidh4cHOnXqhE2bNgHIGyYSGRmJQYMGmfucOnUKPXr0QNWqVeHo6Ii2bfMGxsfFxZV4+wEgKysLV65cwfDhwyV5f/DBB+a8w8PDERUVhaCgILz55pvYu3fvY63rSSkzV5mNGTMG586dk1S41jJ16lRMmDDB/Dw9Pb30iqL0W8Dvq4HmowAn39JZBxFRJXDx4kVUq1ZNsmzSpEkIDw+Hg4MDvLy8JAWTt7d3gYt0EhIS4O3tbfHcrly5glu3bsFkMuHatWsICQkBAGRmZkKhUODUqVNQKBSS1zg4OBQ7/qBBg/Dmm29i2bJl+PrrrxESEmJeR1ZWFsLCwhAWFoZNmzbBw8MDcXFxCAsLM58+fJBcLi9wpVxu7r9jvfKPLH3++edo0aKFpF/+djRu3BixsbH46aefsH//fvTt2xcdO3bEd999V+ztepLKREE0duxY7Ny5E0ePHkWVKv/OrOzt7Q29Xo/U1FTJUaL737De3t74/fffJfHy3+D39ynsTe/k5ARbW9sC+ajVanP1XuoyE4FfFiHGoyNy3C13ektz9yxqbu+WdxWHb0OLxSWiCq6c3k7o4MGDOHv2LMaPHy9Z7u7ujpo1axb6mtDQUBw4cADjxo0zL9u3bx9CQ0Mfua64uDjcunULvr55/8SeOHECcrkcQUFBhfbX6/V4+eWX0a9fPwQFBWHEiBE4e/YsPD090ahRIxiNRiQmJprH+zyOnj17YtSoUdizZw++/vprDBkyxNx26dIlJCUlYd68eeZ/7k+ePPnIeB4eHoiPj5ccdYuKijK3e3l5wdfXF1evXpUciXqQk5MT+vXrh379+uHFF19E586dkZycbB4aU5ZYtSASQuCNN97A9u3bcfjw4QKVfZMmTaBUKnHgwAH06dMHABAdHY24uDjzGzY0NBQffvghEhMT4enpCSDvDe3k5IS6deua++zevVsSuzhv+ichMVMHTwBvbY7CeZFWZP/iqieLxS71v/GJiIqltG4nVBjvEOCdGyUe66jT6RAfHw+j0YiEhATs2bMHc+fORffu3SWFQFHeeusttG3bFp988gm6deuGzZs34+TJk1i9evUjX6fRaDB06FB8/PHHSE9Px5tvvom+ffs+9MjSe++9h7S0NCxduhQODg7YvXs3XnnlFezcuRO1a9fGoEGDMGTIEHzyySdo1KgR7ty5gwMHDqB+/frFPr1nb2+PXr16Ydq0abh48aJkrE7VqlWhUqmwbNkyvPbaazh37hxmz579yHjt2rXDnTt3MH/+fLz44ovYs2cPfvrpJzg5OZn7zJw5E2+++Sa0Wi06d+4MnU6HkydPIiUlBRMmTMDChQvh4+ODRo0aQS6XY+vWrfD29i67UxIIK3r99deFVqsVhw8fFrdv3zY/srOzzX1ee+01UbVqVXHw4EFx8uRJERoaKkJDQ83tBoNBBAcHi06dOomoqCixZ88e4eHhIaZOnWruc/XqVWFnZycmTZokLl68KJYvXy4UCoXYs2dPsfJMS0sTAERaWprlNv7/XY46JsQMJ3Hw4F5x9p9Uiz0OHtwrxAynvPhERA+4d++euHDhgrh3717BxvTbQtw8LX0kx+a16e8VbLt5+t/X3vm7YFtWUl5b5p2CbZl3SpT30KFDBQABQNjY2AgPDw/RsWNHsW7dOmE0GiV9AwICxKJFix4Z79tvvxW1a9cWKpVK1KtXT+zateuR/WfMmCEaNGggVqxYIXx9fYVGoxEvvviiSE5OluTYs2dPIYQQhw4dEjY2NuLYsX8/i2NjY4WTk5NYsWKFEEIIvV4vpk+fLgIDA4VSqRQ+Pj7ihRdeEGfOnBFCCLF+/Xqh1WqL3De7d+8WAESbNm0KtH399dciMDBQqNVqERoaKn744QcBQJw+fdqcJwCRkpJifs3KlSuFv7+/sLe3F0OGDBEffvihCAgIkMTdtGmTaNiwoVCpVMLFxUW0adNGbNu2TQghxOrVq0XDhg2Fvb29cHJyEh06dBB//vlnkdtRUo96L5fk77dMCOtNp/ng4Ld869evN18OmJOTg4kTJ+Kbb76BTqdDWFgYVqxYIanEr1+/jtdffx2HDx+Gvb09hg4dinnz5sHG5t8DYIcPH8b48eNx4cIFVKlSBdOmTSv2JYfp6enQarVIS0uTVMeWEPPXL6i5vRtiXtiFmg1alfm4RFQx5OTkIDY2FtWqVYNGo/m3ISsJWNUqbwb9+4X0Bfp8nnc5/rLGBQNG/P8R7jUdgX/+kLa9sBpo0A/4/XNg99vSthrtgRfX/TtXURkXERGBHTt2SE4fkXU99L2Mkv39tvops6JoNBosX74cy5cvf2ifgICAAqfEHtSuXTucPn26xDmWNqPaBZsN7dBIXT4+DIiogrN3A0YdAjIeuITc1jnvq5Nf3tjEh+m1EtBnSZflT+pY74W86UDuZ+dWboohqtjKxKDqyizXsQreMYzCTscqRXcuAZ1zLbTVLcQKZ152T0Ql5Oid9yiMUvPoCzUeNdWHvXveg6gMKjPzEFVWMkMOasn+gcyQY9G4wkaD68IbwkZTdGciIiqWiIgIni6roFgQWZk69TL2qSdDnVq8KdqLS5keh0XK5VCmP96kW0RERJUJC6IKSqFPwwuKX6HQW+5SfiKqeKx4XQ2RRVjqPcyCiIioElIq8yZezM7OtnImRP9N/mzbD870XVIcVE1EVAkpFAo4OzsjMTERAGBnZ/fQqVCIyiqTyYQ7d+7Azs5OMtXO42BBZHUy6IQNAH4QEdGTlT+fW35RRFQeyeVyVK1a9T8X9CyIrCzHPRhBui+w0z3YonENdp5YbOiNzna8cQcRFU4mk8HHxweenp6SG3cSlScqlQpy+X8fAcSCqIIy2HlhseFFdLTzsnYqRFTGKRSK/zz+gqi846BqK1OnXMZO1btQp1j2snu5PgNt5H9Brs+waFwiIqKKiAWRlcmMOQiWX4PMaNmJGVXp1/CF6iOo0q9ZNC4REVFFxIKIiIiIKj0WRERERFTpsSAiIiKiSo8FkZXpHf0xWv8m9I7+Fo0r5CpcM3lByFUWjUtERFQRsSCyMpPaGbtNT8OkdrZoXJ1rENrpF0HnGmTRuERERBURCyIrU2TfwXDFLiiy71g7FSIiokqLBZGVKbPjMU25CcrseIvGVSddxCn1q1AnXbRoXCIiooqIBVEFJRMGuMkyIBMGa6dCRERU5rEgIiIiokqPBRERERFVeiyIrMykcsQ+Y2OYVI7WToWIiKjSYkFkZXqnQIzMfRt6p0DLxtVWR29dBPTa6haNS0REVBGxILI2Uy5ckQ6Yci0bVmmPP0VtmJT2Fo1LRERUEbEgsjJN8iX8qXkNmuRLFo1rk3kb79t8CZvM2xaNS0REVBGxIKqgbHLuYoTNT7DJuWvtVIiIiMo8FkRERERU6bEgIiIiokqPBRERERFVeiyIrCzHtS6Cc9Ygx7WuReMaNa74wvAcjBpXi8YlIiKqiKxaEB09ehQ9evSAr68vZDIZduzYIWmXyWSFPhYsWGDuExgYWKB93rx5kjhnzpxB69atodFo4O/vj/nz5z+JzSseuQKZsAPkCouGzXXww3TDMOQ6+Fk0LhERUUVk1YIoKysLDRo0wPLlywttv337tuSxbt06yGQy9OnTR9Jv1qxZkn5vvPGGuS09PR2dOnVCQEAATp06hQULFiAiIgKrV68u1W0rLlVaLL5QzoUqLdaicWWGe6gni4XMcM+icYmIiCoiG2uuvEuXLujSpctD2729vSXPv//+ezz77LOoXl06+7Kjo2OBvvk2bdoEvV6PdevWQaVSoV69eoiKisLChQsxatSo/74R/5E8NxNtFGcRk5tp0bjq1BjsUr+HmNSGQEDh+4aIiIjylJsxRAkJCdi1axeGDx9eoG3evHlwc3NDo0aNsGDBAhgMBnNbZGQk2rRpA5VKZV4WFhaG6OhopKSkFLounU6H9PR0yYOIiIgqLqseISqJjRs3wtHREb1795Ysf/PNN9G4cWO4urri+PHjmDp1Km7fvo2FCxcCAOLj41GtWjXJa7y8vMxtLi4uBdY1d+5czJw5s5S2hIiIiMqaclMQrVu3DoMGDYJGo5EsnzBhgvn7+vXrQ6VS4dVXX8XcuXOhVqsfa11Tp06VxE1PT4e/v//jJU5ERERlXrk4ZXbs2DFER0djxIgRRfZt0aIFDAYDrl27BiBvHFJCQoKkT/7zh407UqvVcHJykjxKS669L6blhiPX3teygWVyZAhbQFYufsRERERWVS7+Wq5duxZNmjRBgwYNiuwbFRUFuVwOT09PAEBoaCiOHj2K3Nx/7ya/b98+BAUFFXq67Ekz2rrhS2MnGG3dLBo3x60eQnRrkeNWz6JxiYiIKiKrFkSZmZmIiopCVFQUACA2NhZRUVGIi4sz90lPT8fWrVsLPToUGRmJxYsX46+//sLVq1exadMmjB8/Hi+//LK52Bk4cCBUKhWGDx+O8+fPY8uWLViyZInklJg1KXJS0Uv+CxQ5qdZOhYiIqNKy6hiikydP4tlnnzU/zy9Shg4dig0bNgAANm/eDCEEBgwYUOD1arUamzdvRkREBHQ6HapVq4bx48dLih2tVou9e/dizJgxaNKkCdzd3TF9+vQycck9ACgzb2CxagViMrsBCLBYXHXK39irmgRZykbAr5nF4hIREVVEVi2I2rVrByHEI/uMGjXqocVL48aNceLEiSLXU79+fRw7duyxciyvZEYdaslvIsaos3YqREREZV65GENEREREVJpYEBEREVGlx4LIykw2dvjTVBMmGztrp0JERFRpsSCyMr1zDfTWz4LeuYZl4zpWxQj9ROgdq1o0LhERUUXEgqiCMqm12G9qApNaa+1UiIiIyjwWRFamuXsW1zQDobl71qJxbbITMVrxPWyyEy0al4iIqCJiQVRB2WQnYLJyC2yyE4ruTEREVMmxICIiIqJKjwURERERVXosiIiIiKjSY0FkZTrnWmirWwidcy2LxjWqnLDL2BxGlZNF4xIREVVELIisTNhocF14Q9hoLBo31ykAY3LHIdfJcjeMJSIiqqhYEFmZMj0Oi5TLoUyPs2hcmVEPbyRBZtRbNC4REVFFxILIyhT6NLyg+BUKfZpF46pTonFC8wbUKdEWjUtERFQRsSAiIiKiSo8FEREREVV6LIiIiIio0mNBZGUGO08sNvSGwc7T2qkQERFVWiyIrMxg54XFhhdhsPOyaNwct3qonbMROW71LBqXiIioImJBZGVyfQbayP+CXJ9h2cAyOfRQAjL+iImIiIrCv5ZWpkq/hi9UH0GVfs2ycVOvYrNqNlSpVy0al4iIqCJiQVRByQ1ZeFp+EXJDlrVTISIiKvNYEBEREVGlx4KIiIiIKj0WRFYm5CpcM3lByFXWToWIiKjSYkFkZTrXILTTL4LONciicXMd/DAldyRyHfwsGpeIiKgiYkFUQRk1rthifBZGjau1UyEiIirzWBBZmTrpIk6pX4U66aJF4ypyktFPcQiKnGSLxiUiIqqIWBBZmUwY4CbLgEwYLBpXmXkTHyk/hzLzpkXjEhERVUQsiIiIiKjSs2pBdPToUfTo0QO+vr6QyWTYsWOHpD08PBwymUzy6Ny5s6RPcnIyBg0aBCcnJzg7O2P48OHIzMyU9Dlz5gxat24NjUYDf39/zJ8/v7Q3jYiIiMoRqxZEWVlZaNCgAZYvX/7QPp07d8bt27fNj2+++UbSPmjQIJw/fx779u3Dzp07cfToUYwaNcrcnp6ejk6dOiEgIACnTp3CggULEBERgdWrV5fadhEREVH5YmPNlXfp0gVdunR5ZB+1Wg1vb+9C2y5evIg9e/bgjz/+QNOmTQEAy5YtQ9euXfHxxx/D19cXmzZtgl6vx7p166BSqVCvXj1ERUVh4cKFksLJWvTa6uiti8AH2uoWjWuysccJ01NwtbG3aFwiIqKKqMyPITp8+DA8PT0RFBSE119/HUlJSea2yMhIODs7m4shAOjYsSPkcjl+++03c582bdpApfp34sOwsDBER0cjJSWl0HXqdDqkp6dLHqXFpLTHn6I2TErLFi565+ror58GvbNlCy0iIqKKqEwXRJ07d8YXX3yBAwcO4KOPPsKRI0fQpUsXGI1GAEB8fDw8PT0lr7GxsYGrqyvi4+PNfby8vCR98p/n93nQ3LlzodVqzQ9/f39Lb9q/+Wbexvs2X8Im87ZlAwsTVMgFhMmycYmIiCqgMl0Q9e/fH88//zxCQkLQq1cv7Ny5E3/88QcOHz5cquudOnUq0tLSzI8bN26U2rpscu5ihM1PsMm5a9G4mqTz+FszFJqk8xaNS0REVBGV6YLoQdWrV4e7uztiYmIAAN7e3khMTJT0MRgMSE5ONo878vb2RkJCgqRP/vOHjU1Sq9VwcnKSPIiIiKjiKlcF0T///IOkpCT4+PgAAEJDQ5GamopTp06Z+xw8eBAmkwktWrQw9zl69Chyc3PNffbt24egoCC4uLg82Q0gIiKiMsmqBVFmZiaioqIQFRUFAIiNjUVUVBTi4uKQmZmJSZMm4cSJE7h27RoOHDiAnj17ombNmggLCwMAPPXUU+jcuTNGjhyJ33//Hb/++ivGjh2L/v37w9fXFwAwcOBAqFQqDB8+HOfPn8eWLVuwZMkSTJgwwVqbTURERGWMVQuikydPolGjRmjUqBEAYMKECWjUqBGmT58OhUKBM2fO4Pnnn0ft2rUxfPhwNGnSBMeOHYNarTbH2LRpE+rUqYMOHTqga9euaNWqlWSOIa1Wi7179yI2NhZNmjTBxIkTMX369DJxyT2QdxPWLwzP8SasREREViQTQghrJ1HWpaenQ6vVIi0tzeLjic7dTEP3Zb9g5xutEOyntVjc83F3MHzFT1g7ugvqVfWwWFwiIqLyoiR/v8vVGKKKSGa4h3qyWMgM9ywaVyhUiIcbhEJVdGciIqJKjgWRlalTY7BL/R7UqTEWjatMv47lysVQpl+3aFwiIqKKiAVRBaXQp6Ob4nco9KU3yzYREVFFwYKIiIiIKj0WRERERFTpsSCyNpkcGcIWkPFHQUREZC38K2xlOW71EKJbixy3ehaNa7DzwvzcfjDYeRXdmYiIqJJjQVRBGew8scLYEwY7T2unQkREVOaxILIydcrf2KuaBHXK3xaNK9eloaP8FOS6NIvGJSIiqohYEFmZzKhDbflNyIw6i8ZVZcRhjeoTqDLiLBqXiIioImJBRERERJUeCyIiIiKq9FgQERERUaXHgsjK9I5VMUI/EXrHqhaNKxRq/G3yg1CoLRqXiIioImJBZGUmtRb7TU1gUmstGlfnUhud9Augc6lt0bhEREQVEQsiK7PJTsRoxfewyU60dipERESVFgsiK7PJTsBk5RbYZCdYNK4m6TzOqodDk3TeonGJiIgqIpvHeVFubi7i4+ORnZ0NDw8PuLq6Wjov+q+ECY6ye0gQJmtnQkREVOYV+whRRkYGVq5cibZt28LJyQmBgYF46qmn4OHhgYCAAIwcORJ//PFHaeZKREREVCqKVRAtXLgQgYGBWL9+PTp27IgdO3YgKioKf//9NyIjIzFjxgwYDAZ06tQJnTt3xuXLl0s7byIiIiKLKdYpsz/++ANHjx5FvXqF35G9efPmeOWVV7Bq1SqsX78ex44dQ61atSyaaEVlVDlhl7E5aqqcrJ0KERFRpVWsguibb74pVjC1Wo3XXnvtPyVU2eQ6BWBM7jjsdAqwaFydc010032I+c41LRqXiIioIuJVZlYmM+rhjSTIjHqLxhU2tjgvqkHY2Fo0LhERUUVU7KvMXnnllSL7yGQyrF279j8lVNmoU6JxQvMGYlJ2AVU9LBZXmXkTs2zWQ5lZDYBlJ30kIiKqaIpdEKWkpDy0zWg0Yv/+/dDpdCyIyghFTjKG2OxDTM44a6dCRERU5hW7INq+fXuhy7///nu8++67UKvVmD59usUSIyIiInpSHnsM0a+//orWrVtj4MCB6N69O65evYp33nnHkrkRERERPRElLoguXLiAHj16oF27dqhduzaio6Px0UcfwcXFpTTyIyIiIip1xS6Ibty4gWHDhqFBgwawsbHBmTNnsHbtWlSpUqU086vwctzqoXbORuS4FT7H0+MyaNyxxtAFBo27ReMSERFVRMUeQxQUFASZTIYJEyagZcuWuHz5cqEzUj///PMWTbDCk8mhhxKQWXYGBIODDz4wDMZOBx+LxiUiIqqIiv1XOCcnB/fu3cOCBQvQq1evQh8vvPBCiVZ+9OhR9OjRA76+vpDJZNixY4e5LTc3F1OmTEFISAjs7e3h6+uLIUOG4NatW5IYgYGBkMlkkse8efMkfc6cOYPWrVtDo9HA398f8+fPL1GepUmVehWbVbOhSr1q0bjy3Cw0lv0NeW6WReMSERFVRMUuiEwmU5EPo9FYopVnZWWhQYMGWL58eYG27Oxs/Pnnn5g2bRr+/PNPbNu2DdHR0YUegZo1axZu375tfrzxxhvmtvT0dHTq1AkBAQE4deoUFixYgIiICKxevbpEuZYWuSELT8svQm6wbOGiSruKbeoIqNIsW2gRERFVRMU+ZVYaunTpgi5duhTaptVqsW/fPsmyTz/9FM2bN0dcXByqVq1qXu7o6Ahvb+9C42zatAl6vR7r1q2DSqVCvXr1EBUVhYULF2LUqFGW2xgiIiIqt4p1hOjEiRPFDpidnY3z588/dkKPkpaWBplMBmdnZ8nyefPmwc3NDY0aNcKCBQtgMBjMbZGRkWjTpg1UKpV5WVhYGKKjox852SQRERFVHsUqiAYPHoywsDBs3boVWVmFn9q5cOEC3n33XdSoUQOnTp2yaJJA3himKVOmYMCAAXBy+vfO8G+++SY2b96MQ4cO4dVXX8WcOXMwefJkc3t8fDy8vLwksfKfx8fHF7ounU6H9PR0yYOIiIgqrmKdMrtw4QJWrlyJ999/HwMHDkTt2rXh6+sLjUaDlJQUXLp0CZmZmXjhhRewd+9ehISEWDTJ3Nxc9O3bF0IIrFy5UtI2YcIE8/f169eHSqXCq6++irlz50KtVj/W+ubOnYuZM2f+p5yLK9fBD1NyRyLcwc+icYXMBknCEUJm1bOiRERE5UKxjhAplUq8+eabiI6ORmRkJEaOHIng4GD4+fmhXbt2+Oyzz3Dr1i188803pVYMXb9+Hfv27ZMcHSpMixYtYDAYcO3aNQCAt7c3EhISJH3ynz9s3NHUqVORlpZmfty4ceO/b8hDGDWu2GJ8FkaNq0Xj6tyeQhPdZ9C5PWXRuERERBVRiQ8fNG3aFE2bNi2NXArIL4YuX76MQ4cOwc3NrcjXREVFQS6Xw9PTEwAQGhqK9957D7m5uVAqlQCAffv2ISgo6KGza6vV6sc+ulRSipxk9FMcgiKnLnhXeiIiIuuw7GyAJZSZmYmoqChERUUBAGJjYxEVFYW4uDjk5ubixRdfxMmTJ7Fp0yYYjUbEx8cjPj4eer0eQN6A6cWLF+Ovv/7C1atXsWnTJowfPx4vv/yyudgZOHAgVCoVhg8fjvPnz2PLli1YsmSJ5FSbNSkzb+Ij5edQZt60aFx1cjQOq8ZDnRxt0bhEREQVkVUHmJw8eRLPPvus+Xl+kTJ06FBERETghx9+AAA0bNhQ8rpDhw6hXbt2UKvV2Lx5MyIiIqDT6VCtWjWMHz9eUuxotVrs3bsXY8aMQZMmTeDu7o7p06dX+EvuZSY9AuUJiDHprZ0KERFRmWfVgqhdu3YQQjy0/VFtANC4ceNiTQlQv359HDt2rMT5ERERUeVg1VNmRERERGVBiQqi3NxcdOjQodCbutLjMdnY44TpKZhs7K2dChERUaVVooJIqVTizJkzpZVLpaR3ro7++mnQO1e3bFynQAzRT4HeKdCicYmIiCqiEp8ye/nll7F27drSyKVyEiaokAsIk0XDmlSOOGpqAJPK0aJxiYiIKqISD6o2GAxYt24d9u/fjyZNmsDeXnqqZ+HChRZLrjLQJJ3H35qhiEnaBVRpZbG4NtkJGGfzHWyya4HzGxERET1aiQuic+fOoXHjxgCAv//+W9Imk8kskxX9ZzbZiRhnsw0x2cMB1LZ2OkRERGVaiQuiQ4cOlUYeRERERFbz2Jfdx8TE4Oeff8a9e/cAFD1nEBEREVFZVeKCKCkpCR06dEDt2rXRtWtX3L59GwAwfPhwTJw40eIJEhEREZW2EhdE48ePh1KpRFxcHOzs7MzL+/Xrhz179lg0ucpA5xKEp3OWQecSZNG4RpUW240tYVRxQDUREVFRSjyGaO/evfj5559RpUoVyfJatWrh+vXrFkusshAKFeLhBqFQWTRurlNVjM8dg51OVS0al4iIqCIq8RGirKwsyZGhfMnJyVCr1RZJqjJRpl/HcuViKNMtW0zKDDkIkMVDZsixaFwiIqKKqMQFUevWrfHFF1+Yn8tkMphMJsyfP19y53oqHoU+Hd0Uv0OhT7doXHXqZRxRT4A6lbdZISIiKkqJT5nNnz8fHTp0wMmTJ6HX6zF58mScP38eycnJ+PXXX0sjRyIiIqJSVeIjRMHBwfj777/RqlUr9OzZE1lZWejduzdOnz6NGjVqlEaORERERKWqxEeIAECr1eK9996zdC5EREREVvFYBVFKSgrWrl2LixcvAgDq1q2LYcOGwdXV1aLJVQYGOy/Mz+2H5+28rJ0KERFRpVXiU2ZHjx5FYGAgli5dipSUFKSkpGDp0qWoVq0ajh49Who5VmgGO0+sMPaEwc7TonFz3EMQmPM1ctxDLBqXiIioIirxEaIxY8agX79+WLlyJRQKBQDAaDRi9OjRGDNmDM6ePWvxJCsyuS4NHeWnINeFgHelJyIiso4SHyGKiYnBxIkTzcUQACgUCkyYMAExMTEWTa4yUGXEYY3qE6gy4iwbN/UKtqmmQ5V6xaJxiYiIKqISF0SNGzc2jx2638WLF9GgQQOLJEX/ndyQjcbyGMgN2dZOhYiIqMwr1imzM2fOmL9/88038dZbbyEmJgZPP/00AODEiRNYvnw55s2bVzpZEhEREZWiYhVEDRs2hEwmgxDCvGzy5MkF+g0cOBD9+vWzXHZERERET0CxCqLY2NjSzqPSEgo1/jb5QabgfeCIiIispVgFUUBAQGnnUWnpXGqju34BdrrUtmjcXAd/jNOPxqsO/haNS0REVBE91sSMt27dwi+//ILExESYTCZJ25tvvmmRxOi/MWqcscPUCiM0ztZOhYiIqMwrcUG0YcMGvPrqq1CpVHBzc4NMJjO3yWQyFkQlpEk6j7Pq4UhI2gb4PWOxuIp7SRis2AvFvafA+Y2IiIgercQF0bRp0zB9+nRMnToVcnmJr9qnBwkTHGX3kCBMRfctAWXWLcxWbkBM1ksAqls0NhERUUVT4oomOzsb/fv3ZzFEREREFUaJq5rhw4dj69atpZELERERkVWUuCCaO3cujhw5gnbt2uGNN97AhAkTJI+SOHr0KHr06AFfX1/IZDLs2LFD0i6EwPTp0+Hj4wNbW1t07NgRly9flvRJTk7GoEGD4OTkBGdnZwwfPhyZmZmSPmfOnEHr1q2h0Wjg7++P+fPnl3SziYiIqAJ7rILo559/RkJCAs6ePYvTp0+bH1FRUSWKlZWVhQYNGmD58uWFts+fPx9Lly7FqlWr8Ntvv8He3h5hYWHIyckx9xk0aBDOnz+Pffv2YefOnTh69ChGjRplbk9PT0enTp0QEBCAU6dOYcGCBYiIiMDq1atLuumlQudcE910H0LnXNOicU1KBxw1hsCkdLBoXCIiogpJlJCzs7NYv359SV9WJABi+/bt5ucmk0l4e3uLBQsWmJelpqYKtVotvvnmGyGEEBcuXBAAxB9//GHu89NPPwmZTCZu3rwphBBixYoVwsXFReh0OnOfKVOmiKCgoGLnlpaWJgCItLS0x928hzr7T6oImLJTnP0ntVzEJSIiKi9K8ve7xEeI1Go1WrZsaem6rIDY2FjEx8ejY8eO5mVarRYtWrRAZGQkACAyMhLOzs5o2rSpuU/Hjh0hl8vx22+/mfu0adMGKpXK3CcsLAzR0dFISUkp9e0oijLzJmbZrIcy86ZlA5uMcEA2YDJaNi4REVEFVOKC6K233sKyZctKIxeJ+Ph4AICXl5dkuZeXl7ktPj4enp6eknYbGxu4urpK+hQW4/51PEin0yE9PV3yKC2KnGQMsdkHRU6yReNqki/gnGYENMkXLBqXiIioIirxPES///47Dh48iJ07d6JevXpQKpWS9m3btlksOWuZO3cuZs6cae00iIiI6AkpcUHk7OyM3r17l0YuEt7e3gCAhIQE+Pj4mJcnJCSgYcOG5j6JiYmS1xkMBiQnJ5tf7+3tjYSEBEmf/Of5fR40depUyRVz6enp8PfnPcGIiIgqqhIXROvXry+NPAqoVq0avL29ceDAAXMBlJ6ejt9++w2vv/46ACA0NBSpqak4deoUmjRpAgA4ePAgTCYTWrRoYe7z3nvvITc313w0a9++fQgKCoKLi0uh61ar1VCrefd5IiKiysKq001nZmYiKirKfLl+bGwsoqKiEBcXB5lMhnHjxuGDDz7ADz/8gLNnz2LIkCHw9fVFr169AABPPfUUOnfujJEjR+L333/Hr7/+irFjx6J///7w9fUFAAwcOBAqlQrDhw/H+fPnsWXLFixZsqTEcyaVFoPGHWsMXWDQuFs7FSIiokqrxEeIqlWrJrmh64OuXr1a7FgnT57Es88+a36eX6QMHToUGzZswOTJk5GVlYVRo0YhNTUVrVq1wp49e6DRaMyv2bRpE8aOHYsOHTpALpejT58+WLp0qbldq9Vi7969GDNmDJo0aQJ3d3dMnz5dMleRNRkcfPCBYTB2OvgU3bkEclzroHHOKnzhWseicYmIiCqiEhdE48aNkzzPzc3F6dOnsWfPHkyaNKlEsdq1awchxEPbZTIZZs2ahVmzZj20j6urK77++utHrqd+/fo4duxYiXJ7UuS5WWgs+xvy3Eaw6F3p5UokwwmQK4vuS0REVMmVuCB66623Cl2+fPlynDx58j8nVNmo0q5imzoCMWnNAPhaLm76NXyu/Biq9CqAXwOLxSUiIqqILDaGqEuXLvjf//5nqXD0H8n1GXhO8Sfk+gxrp0JERFTmWawg+u677+Dq6mqpcERERERPTIlPmTVq1EgyqFoIgfj4eNy5cwcrVqywaHJERERET0KJC6L8S97zyeVyeHh4oF27dqhTh1c0lZSQ2SBJOELISvyjICIiIgsp8V/hGTNmlEYelZbO7Sk00X2GnW5PWTRurp03ZucOwot2hc/GTURERP+y6sSMVHqMdh5Ya+wGo52HtVMhIiIq84pdEMnlcigUikc+bGx42qek1MnROKwaD3VytEXjynWp6Co/Abku1aJxiYiIKqJiVzDbt29/aFtkZCSWLl0Kk8lkkaQqE5lJj0B5AmJMeovGVWXcwArVUsRkhAEIsGhsIiKiiqbYBVHPnj0LLIuOjsY777yDH3/8EYMGDXrkjNJEREREZdVjjSG6desWRo4ciZCQEBgMBkRFRWHjxo0ICOCRCCIiIip/SlQQpaWlYcqUKahZsybOnz+PAwcO4Mcff0RwcHBp5UdERERU6opdEM2fPx/Vq1fHzp078c033+D48eNo3bp1aeZWKeidAjFEPwV6p0CLxhUKDc6ZAiEUGovGJSIiqoiKPYbonXfega2tLWrWrImNGzdi48aNhfbbtm2bxZKrDEwqRxw1NcBklaNF4+pcaqG7fg52utSyaFwiIqKKqNgF0ZAhQyS37CDLsMlOwDib72CTXQuA1trpEBERVUrFLog2bNhQimlUXjbZiRhnsw0x2cMB1LZYXM3dc4hWD8GNuz8Afi0tFpeIiKgi4kzVFZaAWmYAIKydCBERUZnHgoiIiIgqPRZEREREVOmxILIyo0qL7caWMKo4oJqIiMhaWBBZWa5TVYzPHYNcp6oWjatzroXndPOhc+Zl90REREVhQWRlMkMOAmTxkBlyLBpX2GhwWVSBsOHEjEREREVhQWRl6tTLOKKeAHXqZYvGVWb8g3k2q6HM+MeicYmIiCoiFkQVlEKXgv42h6HQpVg7FSIiojKPBRERERFVeiyIiIiIqNJjQURERESVHgsiK8txD0FgztfIcQ+xaFyDrQdWGJ6HwdbDonGJiIgqIhZEFZTB3hvzDf1hsPe2dipERERlHgsiK1OlXsE21XSoUq9YNK5cn4mn5Rcg12daNC4REVFFxILIyuSGbDSWx0BuyLZoXFV6LDarPoAqPdaicYmIiCqiMl8QBQYGQiaTFXiMGTMGANCuXbsCba+99pokRlxcHLp16wY7Ozt4enpi0qRJMBgM1tgcIiIiKoNsrJ1AUf744w8YjUbz83PnzuG5557DSy+9ZF42cuRIzJo1y/zczs7O/L3RaES3bt3g7e2N48eP4/bt2xgyZAiUSiXmzJnzZDaCiIiIyrQyXxB5eEivkpo3bx5q1KiBtm3bmpfZ2dnB27vwwcN79+7FhQsXsH//fnh5eaFhw4aYPXs2pkyZgoiICKhUqlLNn4iIiMq+Mn/K7H56vR5fffUVXnnlFchkMvPyTZs2wd3dHcHBwZg6dSqys/8djxMZGYmQkBB4eXmZl4WFhSE9PR3nz58vdD06nQ7p6emSR2nJdfDHOP1o5Dr4WzSukCtxW7hCyJUWjUtERFQRlfkjRPfbsWMHUlNTER4ebl42cOBABAQEwNfXF2fOnMGUKVMQHR2Nbdu2AQDi4+MlxRAA8/P4+PhC1zN37lzMnDmzdDbiAUaNM3aYWmGExtmicXWudfCc7lPsdK1j0bhEREQVUbkqiNauXYsuXbrA19fXvGzUqFHm70NCQuDj44MOHTrgypUrqFGjxmOtZ+rUqZgwYYL5eXp6Ovz9LXsEJ5/iXhIGK/ZCce8pANpSWQcRERE9Wrk5ZXb9+nXs378fI0aMeGS/Fi1aAABiYmIAAN7e3khISJD0yX/+sHFHarUaTk5OkkdpUWbdwmzlBiizblk0rjr5EiLVY6FOvmTRuERERBVRuSmI1q9fD09PT3Tr1u2R/aKiogAAPj4+AIDQ0FCcPXsWiYmJ5j779u2Dk5MT6tatW2r5WpvMlAsfWTJkplxrp0JERFTmlYtTZiaTCevXr8fQoUNhY/NvyleuXMHXX3+Nrl27ws3NDWfOnMH48ePRpk0b1K9fHwDQqVMn1K1bF4MHD8b8+fMRHx+P999/H2PGjIFarbbWJhEREVEZUi4Kov379yMuLg6vvPKKZLlKpcL+/fuxePFiZGVlwd/fH3369MH7779v7qNQKLBz5068/vrrCA0Nhb29PYYOHSqZt4iIiIgqt3JREHXq1AlCiALL/f39ceTIkSJfHxAQgN27d5dGav+ZSemAo8YQeCsdrJ0KERFRpVVuxhBVVHptNQzJnQq9tppl4zpVQ3/9+9A7WTYuERFRRcSCyNpMRjggGzAZi+5bkrAqB5ww1YVJxSNPRERERWFBZGWa5As4pxkBTfIFi8a1yYrHZJvNsMkqfPJJIiIi+hcLogrK5t4djLb5ATb37lg7FSIiojKPBRERERFVeiyIiIiIqNJjQURERESVHgsiK8txrYPGOauQY+G70hvVLthsaAej2sWicYmIiCoiFkTWJlciGU6AXGnRsLmOVfCOYRRyHatYNC4REVFFxILIylTp1/C58mOo0q9ZNK7MkINasn8gM+RYNC4REVFFxILIyuT6DDyn+BNyfYZF46pTL2OfejLUqZctGpeIiKgiYkFERERElR4LIiIiIqr0WBARERFRpceCyMpy7bwxO3cQcu28LRxZBp2wASCzcFwiIqKKhwWRlRntPLDW2A1GOw+Lxs1xD0aQ7gvkuAdbNC4REVFFxILIyuS6VHSVn4Bcl2rtVIiIiCotFkRWpsq4gRWqpVBl3LBoXHXKZexUvQt1Ci+7JyIiKgoLogpKZsxBsPwaZEZOzEhERFQUFkRERERU6bEgIiIiokqPBZGVCYUG50yBEAqNtVMhIiKqtFgQWZnOpRa66+dA51LLonH1jv4YrX8Tekd/i8YlIiKqiFgQVVAmtTN2m56GSe1s7VSIiIjKPBZEVqa5ew7R6iHQ3D1n0biK7DsYrtgFRfYdi8YlIiKqiFgQWZ2AWmYAICwaVZkdj2nKTVBmx1s0LhERUUXEgoiIiIgqPRZEREREVOmxICIiIqJKjwWRlemca+E53XzonC172b1J5Yh9xsYwqRwtGpeIiKgiKtMFUUREBGQymeRRp04dc3tOTg7GjBkDNzc3ODg4oE+fPkhISJDEiIuLQ7du3WBnZwdPT09MmjQJBoPhSW/KQwkbDS6LKhA2lp2YUe8UiJG5b0PvFGjRuERERBWRjbUTKEq9evWwf/9+83Mbm39THj9+PHbt2oWtW7dCq9Vi7Nix6N27N3799VcAgNFoRLdu3eDt7Y3jx4/j9u3bGDJkCJRKJebMmfPEt6Uwyox/MM9mNZQZgQC0lgtsyoUr0gFTruViEhERVVBl+ggRkFcAeXt7mx/u7u4AgLS0NKxduxYLFy5E+/bt0aRJE6xfvx7Hjx/HiRMnAAB79+7FhQsX8NVXX6Fhw4bo0qULZs+ejeXLl0Ov11tzs8wUuhT0tzkMhS7FonE1yZfwp+Y1aJIvWTQuERFRRVTmC6LLly/D19cX1atXx6BBgxAXFwcAOHXqFHJzc9GxY0dz3zp16qBq1aqIjIwEAERGRiIkJAReXl7mPmFhYUhPT8f58+cfuk6dTof09HTJg4iIiCquMl0QtWjRAhs2bMCePXuwcuVKxMbGonXr1sjIyEB8fDxUKhWcnZ0lr/Hy8kJ8fN5khPHx8ZJiKL89v+1h5s6dC61Wa374+/N+YERERBVZmR5D1KVLF/P39evXR4sWLRAQEIBvv/0Wtra2pbbeqVOnYsKECebn6enpLIqIiIgqsDJ9hOhBzs7OqF27NmJiYuDt7Q29Xo/U1FRJn4SEBHh7ewMAvL29C1x1lv88v09h1Go1nJycJI/SYrD1wArD8zDYepTaOoiIiOjRyvQRogdlZmbiypUrGDx4MJo0aQKlUokDBw6gT58+AIDo6GjExcUhNDQUABAaGooPP/wQiYmJ8PT0BADs27cPTk5OqFu3rtW2434Ge2/MN/RHG/uHF2iPI8e1LoJz1uADY1Xk3EyzaGwAcLFXwc+59I7SERERPUlluiB6++230aNHDwQEBODWrVuYMWMGFAoFBgwYAK1Wi+HDh2PChAlwdXWFk5MT3njjDYSGhuLpp58GAHTq1Al169bF4MGDMX/+fMTHx+P999/HmDFjoFarrbx1eeT6TDwtvwC5viEsedm9i6MtjEpHjPv2rMVi3s9WqcD+iW1ZFBERUYVQpguif/75BwMGDEBSUhI8PDzQqlUrnDhxAh4eeaeXFi1aBLlcjj59+kCn0yEsLAwrVqwwv16hUGDnzp14/fXXERoaCnt7ewwdOhSzZs2y1iYVoEqPxWbVB4hJDwXgZ7G4fsZbOF1tJeKengm9tprF4gJATGImxm2JQkqWngURERFVCGW6INq8efMj2zUaDZYvX47ly5c/tE9AQAB2795t6dTKPl0GNHGHUbszAF8LTvhIRERUAZWrQdVEREREpYEFEREREVV6LIisTMiVuC1cIeRKa6dCRERUabEgsjKdax2E6j6FzrWOZQNrqwBdP877SkRERI9UpgdV039g7w40H2ntLIiIiMoFHiGyMnXyJUSqx0Jt6bvSZycDf23J+0pERESPxCNEViYz5cJHlowYU65lA6fGAdtHAb0/B9xrAyp7wL1WXtutqIL9PeoASg2Qcg24lyptc/TOe+SkA8lXobmbCV/ctWy+REREVsSCqKKycwOUdsC2/z9tVqUZMGJ/3ver2xbs/8afgFsN4OCHwNlvpW1t3wGenQr88zvwVR/UBLBfrcaNzGaw5OzaRERE1sKCqKJy9gfG/A5kJ+U9V9n/2zbqSMH+Tv8/S3b794DQMdI2x/+/z1qV5sCoI7hxOQr+h96CIoen44iIqGJgQVSROfvnPR7k2/Dhr3EJBFwe0qZxAnwbIidJj2smLwi5ygJJEhERWR8HVVuZ3qka+uvfh97JsvcbK0061yC00y+CzjXI2qkQERFZBAsiKzOpHHDCVBcmlYO1UyEiIqq0WBBZmU1WPCbbbIZNVry1Uyk2ddJFnFK/CnXSRWunQkREZBEsiKzM5t4djLb5ATb37lg7lWKTCQPcZBmQCYO1UyEiIrIIFkRERERU6bEgIiIiokqPBRERERFVeiyIrMyodsFmQzsY1Q+b/Kfs0Wuro7cuAnptdWunQkREZBEsiKws17EK3jGMQq5jFWunUmwmpT3+FLVhUtoX3ZmIiKgc4EzVViYz5KCW7B/IDDkoL/cFs8m8jfdtvsQ/170B1LR4fBd7FfycbS0el4iI6GFYEFmZOvUy9qknIya1HhDgZe10isVVloYRNj+h24+tcF5Yfv4kW6UC+ye2ZVFERERPDAsiKjFPBzUAYEn/hshxD7Fo7JjETIzbEoWULD0LIiIiemJYENFjq+nhAPiWj9N8REREj8JB1URERFTpsSCyOhl0wgaAzNqJFJ+dG9BsRN5XIiKiCoCnzKwsxz0YQbovsNM92NqpFJ+zP/DcbODu30B20r/LZXLAp37e94mXAEOO9HUugYCtM5CRAGTclrZptIBrNciMevjibmlmT0REVAALIno8d/8GVreVLlM5Au/+k/f91qHAnUvS9v7fAHW6AlFfAQdmSdvq9gT6fgF1yt84rnkT0ZnNUF6mISAiovKPBZGVqVMuY6fqXahT1gJ+Ta2dTvG51wZGHZEuk913BvaljYUfIQKAhi8DNTpI2zR5xY/MqAcAKHKSLZgsERHRo7EgsjKZMQfB8muIMeYU3bksUdkBvg0f3u5Z5+Ftjl55j0IIhfK/5UVERPQYOKiaiIiIKr0yXRDNnTsXzZo1g6OjIzw9PdGrVy9ER0dL+rRr1w4ymUzyeO211yR94uLi0K1bN9jZ2cHT0xOTJk2CwWB4kptCREREZViZLoiOHDmCMWPG4MSJE9i3bx9yc3PRqVMnZGVlSfqNHDkSt2/fNj/mz59vbjMajejWrRv0ej2OHz+OjRs3YsOGDZg+ffqT3hwqhhy3eqidsxE5bvWsnQoREVUiZXoM0Z49eyTPN2zYAE9PT5w6dQpt2rQxL7ezs4O3t3ehMfbu3YsLFy5g//798PLyQsOGDTF79mxMmTIFERERUKlUpboNRdE7+mO0/k2MdfS3ah5lhkwOPZTSAdpERESlrFz91UlLSwMAuLq6SpZv2rQJ7u7uCA4OxtSpU5GdnW1ui4yMREhICLy8/h3EGxYWhvT0dJw/f77Q9eh0OqSnp0sepcWkdsZu09MwqZ1LbR3liSr1KjarZkOVetXaqRARUSVSpo8Q3c9kMmHcuHFo2bIlgoP/ncRw4MCBCAgIgK+vL86cOYMpU6YgOjoa27ZtAwDEx8dLiiEA5ufx8YXfqX3u3LmYOXNmKW2JlCL7DoYrdkGRHQTOuwPIDVl4Wn4RMYasojsTERFZSLkpiMaMGYNz587hl19+kSwfNWqU+fuQkBD4+PigQ4cOuHLlCmrUqPFY65o6dSomTJhgfp6eng5//9I5paXMjsc05SbEZA8EULNU1kFERESPVi5OmY0dOxY7d+7EoUOHUKVKlUf2bdGiBQAgJiYGAODt7Y2EhARJn/znDxt3pFar4eTkJHkQERFRxVWmCyIhBMaOHYvt27fj4MGDqFatWpGviYqKAgD4+PgAAEJDQ3H27FkkJiaa++zbtw9OTk6oW7duqeRNRERE5UuZPmU2ZswYfP311/j+++/h6OhoHvOj1Wpha2uLK1eu4Ouvv0bXrl3h5uaGM2fOYPz48WjTpg3q18+7yWinTp1Qt25dDB48GPPnz0d8fDzef/99jBkzBmq12pqbR4XIdfDDlNyRCHfws3YqRERUiZTpI0QrV65EWloa2rVrBx8fH/Njy5YtAACVSoX9+/ejU6dOqFOnDiZOnIg+ffrgxx9/NMdQKBTYuXMnFAoFQkND8fLLL2PIkCGYNWvWw1b7RJlUjthnbAyTytHaqZQJRo0rthifhVHjWnRnIiIiCynTR4iEEI9s9/f3x5EjRx7ZBwACAgKwe/duS6VlUXqnQIzMfRs7nQKtnUqZoMhJRj/FIShy6oJX3RER0ZNSpguiSsGUC1ekA6Zca2dSJigzb+Ij5ec4FBdm8aNELvYq+DnbWjQmERFVDCyIrEyTfAl/al5DTPIuwL+VtdOxOifbvLvdf7w3Gud/1ls0tq1Sgf0T27IoIiKiAlgQUZni6ZA30H1FDy9kBLQChAmapIIziutcgiAUKijTr0Ohl84kbrDzgsHOE3JdGlQZcQCAG8n38N7Pt5CSpWdBREREBbAgorLFzg1Q2iEgegPwzEuAQQes6Vaw3/gLgNYD+PVj4ML30rYO04HWE4FLvwLbBwDIm/Jyv1qNG5nNwLFJRET0IBZEVLY4+wNjfs8rhABArgRGFTJw3t4j72vHmUCrCdI2x7w5qBDwjPm1cVcvIWfvLMhzeUsQIiIqiAURlT3O990mRS4HfBs+vK/rIybrtHUGbPNemy6qobveETtdalsiQyIiqmDK9DxElUGOa10E56xBjitnzSYiIrIWHiGyNrkCmbAD5AprZ1KhaZLO46x6OE5e/grn0Mzi8XlJPxFR+caCyMpUabH4QjkXqrRPAb+G1k6nwnLSKOAou4ePf76E83t0Fo/PS/qJiMo3FkRWJs/NRBvFWcTkZlo7lQot/3L+Jf0bIsc9xKKxYxIzMW5LFC/pJyIqx1gQUaVS01kB+GmB1BtAdpK00d4D0PoBukwgKUbaJrcBvIPzvk+8+O9VcAA08kw4ILuUMyciotLEgogqBzu3vK/5l93/uhj4Y420z9NjgM5zgMQLwNrnCr5+8tW8778ZAKTEmptqAmgsnwKgU2lkTkRETwALIqocnP2Bcef+LYxajgMaDZb2yZ/byLNuwbmP5Pf9qgz4RnKEKOZOJv785rblcyYioieGBZGV5dr7YlpuOAba+1o7lYrv/vmNnP2lz++ndnj03EeeT0meGrL+xgibtbDJbgDOgk1EVD5xHiIrM9q64UtjJxht3aydCj0mm+xEjLPZBpvsRGunQkREj4kFkZUpclLRS/4LFDmp1k6FiIio0uIpMytTZt7AYtUKxGR2AxBg7XToP7iRfA85N9MsGpMTPhIRPRksiIj+IydbJQDg473RiPk5EzVlNwv0OS/y7rlWXXYLtpBODPmP8EAaHOCKdPjIpFMB3LNxxpcT+7AoIiIqZSyIiP4jT09fZNfpg4+bdoVCn4Ha34YX6HNuZBwAoPr3vWCX+Kek7Ua7xUir1Rmu5zfC9/g0SdtRYwjSklvDz5lHD4mIShMLIqL/ytkfdi8sxVNqByA3p+Al+wCCff//6rO+qwF9lqTN37kq/O20gPNAILiNeXnMnUyM++YGvlA7l2b2REQEFkRWZ7Kxw5+mmnCwsbN2KvRfqB3yvio1j75k373Ww9vs3fMe/y/HmAw9bgMmo2VyJCKih+JVZlamd66B3vpZ0DvXsHYqVMZoki/gnGYENMkXrJ0KEVGFx4KIiIiIKj0WRFamuXsW1zQDobl71tqpEBERVVosiIiIiKjS46BqovIg+SqQky5d5uQLOHgC91KAlOvSNqUt4BGU9/3tvwAhpO0eQbiZBaRk6Usv51LAiSqJqLSwICIqo3Jc66BxzirMMFSB546JcIrbL2m/3WIakuqPhNPV3ah6YLSk7Z5bMK703g0AqLu2I+QmaeHzR9fdGPJjBu7lWv4KNl/chYssAwCQKhxwEx5QQ1/IhJUynBeBAIAaspvQQJrjDeGBdDjAHWnwkiXnbRcnqjS7mXqv1ApaFp5UGbEgIiqjXBztcU/pgre2nkdVWTc44llJe8IRV9w98gucYAN/2YeStpxbKlxZ9gsAoJ4sAoD0CNG97RcwW7kLtTsPhp2LFwDAYOsBg7035PpMqNJjJf2FXAmdax0AgDr5EmSmXEm73qkaTCoHaJIuoPoPwyE33AMAJAf1x60286FOjkat/4VLXmOSq3BheAwAoMa2rrBNOidpj+uwAunVW8HtzOfw+W02ACBbqHEjoTHgXLeo3fd4bkUVXOZRJ286hZRrwL1UaZujd94jJz3vKN79bNSA51OlkubN1HsY/Mn/YGtIxQ3hiXTYwwOp8JSlSPqlww43hBeUMKC27EaBOBdEAATkqCa7DTvkmJez8KTKiAWRlemca6GtbiFWOD9ifhqqlPycbbF/YlsLHAVoVWCJMvMmam2dBvnhQ/d1Gw90jABizwDbu0tf4OgLTLyY9/3mcCDjlrR96E7ArzVw8WdAJgNe/h9g5w5XO1e4OmsBz5ACE1bKZTIE+/z/hJX91wG59yTtVV0CAFstoB0CNOiIG5ej4H/oLdy6dRO5dp7QJF8qsF05rnUBuQKqtFjIczMlbbn2vjDaukGRkwpl5o1C24I/b1sg5t99j0KvDUSVQxFwjtkuaUtsPA6JTSbA4cYRBO4ZLGnTOQXgcr9jBeJZws1rf2OnfALs1Dpc77QGGQGt4B71Kbz/mC/pl1atK250XAWbzNuo882QAnHOv3IZQqFGtZ19YX/7hKQtujQLT6IySCbEg4ML6EHp6enQarVIS0uDk5OTRWOfu5mG7st+wc43WiHYT2vR2ESPlHoDyL7v3mkOnnnjknQZQNIVaV+FEvCql/d9wnnAKD1CBLcagNoRSL+VN5Gks7/F042Pu4wLa1/F27qRAIA/Na8V6BOcswaZsMMXyrloo5BeuTktNxxfGjuhl/wXLFatkLT9aaqJV/ST4Ce7WyBmjPCDDipUkSVCC+ks44nCGXfgAgdkI0CWIGnTQwkFTNik+hAzcsMRK7wBALHCB9nQwBtJcJNJx4WlCEfcgjs00KGGTFp0Cshw4f9PMYbJf8dnqsVI7rwcrg26A7bOQEYCkHFbmrxGC7hWAwx6ILGQ+ay86wNyOXA3BtDnFZDXbtzAtz/8iB6vTMVTNaoVfA1VavFxl5GZIn2vG9UuyHWsApkhB+rUyw+8QoYc92AAgDrlMmTGHEmr3tEfJrUzFNl34KIW8K5q2YMDJfn7XakKouXLl2PBggWIj49HgwYNsGzZMjRv3rzI15VmQRR98SwufD0FdQd+hKCnQiwam6iiuZWQgGSDBjDlWvQIkVHjilwHP4vnm3ckrr35FCIAXHl+O+55NYF35Cy4n1sj6Z9Udwhut/wAmrtnUXN7N2mOSgdcDM8rampu7QBV5j+Qj/3D4sXnuZtpGLxsN7YO8EdND4d/G1QOgHtNwGQC4s8UfKFnXcBGBSTHAjlp0jZHH8DRK++Uoy6jVApmKn0376Zi19I3MVz2AxSyf0uHzYZ2eMcwCrVk/2CferLkNTphgyDdFwCAnap3ESy/JmkfrX8Tu01PY7LNZoy2+QHxr5y0aFFUkr/fleaU2ZYtWzBhwgSsWrUKLVq0wOLFixEWFobo6Gh4enpaLS+FPg0vKH5FjD6t6M5ElZyvlxd885/4FzwVaObX8BFRtACe1M1ytcDYPyRH4mq41cy71ctzE4BnpKfZ3Ozc4OasBTwaAx7SU4wKmfzfU4wDvwJU9qVWWHRSnELN7dIjcFk+TyO2+7eQGXWot67gqcVLA36DwcEH/vvfhTZ2t6Qtvtlk3G04Fi6XtsDv2GRED4i0eAHKgeClL0Unwyp9F9QKGwx/13/3dSO1C3b+/xGimNR6D7xKhp3mI0RrEfPAEaKxjv4YrXZG+tl04MQPeUefLHyUqLgqTUG0cOFCjBw5EsOGDQMArFq1Crt27cK6devwzjvvWDk7IqqwnP0LL1y0fnmPwqjsHn1PPM86FkmtMC72KhyVN0c3XaBkefY1DWKX/QIZTKj7wCB+APh7fTRycQX+sufghJaStsRjLrhz7BeEytPwjQpYsfFLnBeBiBFVAAB1Zdcge2Dg/xXhixyoJVct5ksSToiHG+yQg2qyvNOEGhs5Vg5uAk8ne8A7+P9XfBEw6KSJulYHNE5ARnze4362zoBLYN5Nmu/8ewQyMVOH9Hu5yHHPO4qvSr0CuSFb8tJcB38YNc5Q3EuCMkt6utOkdIBeWw0wGQu9FU+Oax1AroQq/Rrkeum25tp5w2jnAbkuFaoM6ZFNodBA55JXPGjunsODF0/onGtB2GigzPgHCp10wP3jXESRkngXT8svwCvwNdSsVth7VwsEeBWy/P/5NX1oU8xd6x81rBQFkV6vx6lTpzB16lTzMrlcjo4dOyIyMtKKmRERlS1+zrb4bmKPUrmkX5lZDaatC7EEK5DjXAsxLx0AADy1YRQUD5zijHlhF3LcQ+Dz6/twu/CFpO1u8AjEh06HbcIp1PjhlX8bvgYMGldcGhwFAKi1pR/U6dI5uq51/hKZ/m3heWolPP9cLGlLrfkC/nl2CVRp11D723+Pgnn+/yMw52sAwDbVdDSWx0heO04/GjtMrTBYsRezlRskbUeNIRiSOxUOyMY5zYgC+6VxziokwwmfKz/Gc4o/JW2zcwdhrbEbuspPYIVqqaTtnCkQ3fVzAADR6iFQywyS9ud083FZVME8m9Xob3NY0rbC8DzmG/rjafkFbFZ9IGm7LVzxnO5TAECkeix8/n/ai5oAWquAeEXBAfoVQaUYQ3Tr1i34+fnh+PHjCA0NNS+fPHkyjhw5gt9++03SX6fTQaf797+KtLQ0VK1aFTdu3LD4GKIrZyNRY+dLuNJ9K2qEhBb9AiKi8iz1H+BeMmCjATxq5y2LPwcIk7SfW828I2X5/e9n5w5ofQFdJpB8FXey9Bi/+TRyDCaYoMDfIu9oQw3ZTSghvQDghvBEFuzgjlS4y1Ilbemwxy3hARX0qH7foHaNjRwTw4Kg9msAAFClxUJmkF4VabD3g1GjheJeMmyypUeehNIeeqcAwGSEOiW6wC7RudQG5DZQZcRBppcWhgY7Txht3SHXpUGZ+cBcXgo1dP9/Y3B10kU8eIRIr60BYaOGMvMW5Drpthpt3WGw84RcnwVlxgMTu8qV5iNP6pTLwH3TbDg4e8LL3/I3I4+JPou4re+g6kvzUDPIcuNp09PT4e/vj9TUVGi1j75wqVIcISqpuXPnYubMmQWW+/uX4iG9eZ1LLzYRUSVUcOal4rUBwAPXWeLHBf8xGSqeBY8YG/gfZGRksCACAHd3dygUCiQkSC8VTEhIgLe3d4H+U6dOxYQJE8zPTSYTkpOT4ebmBplMZtHc8qvX0jj6RNy/TwL3ceni/i193Mely5r7VwiBjIwM+Pr6Ftm3UhREKpUKTZo0wYEDB9CrVy8AeUXOgQMHMHbs2AL91Wo11Gq1ZJmzs3Op5ujk5MRfxFLE/Vv6uI9LF/dv6eM+Ll3W2r9FHRnKVykKIgCYMGEChg4diqZNm6J58+ZYvHgxsrKyzFedERERUeVVaQqifv364c6dO5g+fTri4+PRsGFD7NmzB15ej7hEkIiIiCqFSlMQAcDYsWMLPUVmTWq1GjNmzChwio4sg/u39HEfly7u39LHfVy6ysv+rRSX3RMRERE9itzaCRARERFZGwsiIiIiqvRYEBEREVGlx4KIiIiIKj0WRFa0fPlyBAYGQqPRoEWLFvj999+tnVK5FRERAZlMJnnUqfPvHcFzcnIwZswYuLm5wcHBAX369Ckwczn96+jRo+jRowd8fX0hk8mwY8cOSbsQAtOnT4ePjw9sbW3RsWNHXL58WdInOTkZgwYNgpOTE5ydnTF8+HBkZkrv01SZFbWPw8PDC7ynO3eW3uKH+7hwc+fORbNmzeDo6AhPT0/06tUL0dHSe4gV5zMhLi4O3bp1g52dHTw9PTFp0iQYDNIbqFZWxdnH7dq1K/Aefu211yR9ytI+ZkFkJVu2bMGECRMwY8YM/Pnnn2jQoAHCwsKQmJho7dTKrXr16uH27dvmxy+//GJuGz9+PH788Uds3boVR44cwa1bt9C7d28rZlu2ZWVloUGDBli+fHmh7fPnz8fSpUuxatUq/Pbbb7C3t0dYWBhycnLMfQYNGoTz589j37592LlzJ44ePYpRo0Y9qU0o84raxwDQuXNnyXv6m2++kbRzHxfuyJEjGDNmDE6cOIF9+/YhNzcXnTp1QlZWlrlPUZ8JRqMR3bp1g16vx/Hjx7Fx40Zs2LAB06dPt8YmlTnF2ccAMHLkSMl7eP78+ea2MrePBVlF8+bNxZgxY8zPjUaj8PX1FXPnzrViVuXXjBkzRIMGDQptS01NFUqlUmzdutW87OLFiwKAiIyMfEIZll8AxPbt283PTSaT8Pb2FgsWLDAvS01NFWq1WnzzzTdCCCEuXLggAIg//vjD3Oenn34SMplM3Lx584nlXl48uI+FEGLo0KGiZ8+eD30N93HxJSYmCgDiyJEjQojifSbs3r1byOVyER8fb+6zcuVK4eTkJHQ63ZPdgHLgwX0shBBt27YVb7311kNfU9b2MY8QWYFer8epU6fQsWNH8zK5XI6OHTsiMjLSipmVb5cvX4avry+qV6+OQYMGIS4uDgBw6tQp5ObmSvZ3nTp1ULVqVe7vxxAbG4v4+HjJ/tRqtWjRooV5f0ZGRsLZ2RlNmzY19+nYsSPkcjl+++23J55zeXX48GF4enoiKCgIr7/+OpKSksxt3MfFl5aWBgBwdXUFULzPhMjISISEhEjuZhAWFob09HScP3/+CWZfPjy4j/Nt2rQJ7u7uCA4OxtSpU5GdnW1uK2v7uFLNVF1W3L17F0ajscBtQ7y8vHDp0iUrZVW+tWjRAhs2bEBQUBBu376NmTNnonXr1jh37hzi4+OhUqkK3KDXy8sL8fHx1km4HMvfZ4W9f/Pb4uPj4enpKWm3sbGBq6sr93kxde7cGb1790a1atVw5coVvPvuu+jSpQsiIyOhUCi4j4vJZDJh3LhxaNmyJYKDgwGgWJ8J8fHxhb7H89voX4XtYwAYOHAgAgIC4OvrizNnzmDKlCmIjo7Gtm3bAJS9fcyCiCqELl26mL+vX78+WrRogYCAAHz77bewtbW1YmZEj6d///7m70NCQlC/fn3UqFEDhw8fRocOHayYWfkyZswYnDt3TjKmkCzrYfv4/vFsISEh8PHxQYcOHXDlyhXUqFHjSadZJJ4yswJ3d3coFIoCVzQkJCTA29vbSllVLM7OzqhduzZiYmLg7e0NvV6P1NRUSR/u78eTv88e9f719vYucIGAwWBAcnIy9/ljql69Otzd3RETEwOA+7g4xo4di507d+LQoUOoUqWKeXlxPhO8vb0LfY/nt1Geh+3jwrRo0QIAJO/hsrSPWRBZgUqlQpMmTXDgwAHzMpPJhAMHDiA0NNSKmVUcmZmZuHLlCnx8fNCkSRMolUrJ/o6OjkZcXBz392OoVq0avL29JfszPT0dv/32m3l/hoaGIjU1FadOnTL3OXjwIEwmk/lDkUrmn3/+QVJSEnx8fABwHz+KEAJjx47F9u3bcfDgQVSrVk3SXpzPhNDQUJw9e1ZSdO7btw9OTk6oW7fuk9mQMqyofVyYqKgoAJC8h8vUPn7iw7hJCCHE5s2bhVqtFhs2bBAXLlwQo0aNEs7OzpLR9lR8EydOFIcPHxaxsbHi119/FR07dhTu7u4iMTFRCCHEa6+9JqpWrSoOHjwoTp48KUJDQ0VoaKiVsy67MjIyxOnTp8Xp06cFALFw4UJx+vRpcf36dSGEEPPmzRPOzs7i+++/F2fOnBE9e/YU1apVE/fu3TPH6Ny5s2jUqJH47bffxC+//CJq1aolBgwYYK1NKnMetY8zMjLE22+/LSIjI0VsbKzYv3+/aNy4sahVq5bIyckxx+A+Ltzrr78utFqtOHz4sLh9+7b5kZ2dbe5T1GeCwWAQwcHBolOnTiIqKkrs2bNHeHh4iKlTp1pjk8qcovZxTEyMmDVrljh58qSIjY0V33//vahevbpo06aNOUZZ28csiKxo2bJlomrVqkKlUonmzZuLEydOWDulcqtfv37Cx8dHqFQq4efnJ/r16ydiYmLM7ffu3ROjR48WLi4uws7OTrzwwgvi9u3bVsy4bDt06JAAUOAxdOhQIUTepffTpk0TXl5eQq1Wiw4dOojo6GhJjKSkJDFgwADh4OAgnJycxLBhw0RGRoYVtqZsetQ+zs7OFp06dRIeHh5CqVSKgIAAMXLkyAL/MHEfF66w/QpArF+/3tynOJ8J165dE126dBG2trbC3d1dTJw4UeTm5j7hrSmbitrHcXFxok2bNsLV1VWo1WpRs2ZNMWnSJJGWliaJU5b2sUwIIZ7c8SgiIiKisodjiIiIiKjSY0FERERElR4LIiIiIqr0WBARERFRpceCiIiIiCo9FkRERERU6bEgIiIiokqPBRERlTnh4eHo1auXxeJt2LChwJ3NLe3w4cOQyWQF7o9FROUDCyIieuLCw8Mhk8kgk8mgUqlQs2ZNzJo1CwaDAQCwZMkSbNiwwbpJElGlYmPtBIiocurcuTPWr18PnU6H3bt3Y8yYMVAqlZg6dSq0Wq210yOiSoZHiIjIKtRqNby9vREQEIDXX38dHTt2xA8//ABAesrszp078Pb2xpw5c8yvPX78OFQqlflu5TqdDm+//Tb8/Pxgb2+PFi1a4PDhw8XO5ZlnnsGUKVMky+7cuQOlUomjR48CAL788ks0bdoUjo6O8Pb2xsCBAyV36X5QREQEGjZsKFm2ePFiBAYGSpatWbMGTz31FDQaDerUqYMVK1YUO28ishwWRERUJtja2kKv1xdY7uHhgXXr1iEiIgInT55ERkYGBg8ejLFjx6JDhw4AgLFjxyIyMhKbN2/GmTNn8NJLL6Fz5864fPlysdY9aNAgbN68Gfff2nHLli3w9fVF69atAQC5ubn4v/btJ6TpP47j+NMZ67Bhh1jBIEEJJXGFYHSS8uLEDolEYt+YYi66OKK8hWWIJqEgSokobDG2ixcPUusgBAmCbuAwWhdLdghRQoR5sHL7HeI3WJTo/P0w+L4ex33e38+fHcaLz3vf3t5e4vE409PTrK6u0tbWdqgzh0IhHj16RF9fH4lEgv7+frq7u3n58uWh5hWRg1PLTESOVCaTYXZ2ljdv3tDZ2fnbmoaGBrxeL4ZhUF1djc1m4+nTpwAkk0n8fj/JZBKn0wlAV1cXkUgEv9+fc7P0Jzdu3ODevXvMzc1lA1A4HKalpYWCggIA2tvbs/WlpaWMjIxw8eJFUqkUdrs9r7M/fvyYoaEhmpqaACgpKeHDhw+Mj4/T2tqa15wikh8FIhE5EjMzM9jtdr5//046nebmzZv09PT8sX5wcJDKykqmpqaIxWIcP34cgOXlZXZ3dykrK8up39nZ4eTJk/vai8PhoK6ujlAoRE1NDZ8/f2Z+fp7x8fFsTSwWo6enh3g8zubmJul0GvgZyCoqKg54etje3mZlZYXbt2/j9Xqzn//48UP/oRI5AgpEInIkamtrGRsbw2q14nQ6OXZs75+jlZUVvnz5QjqdZnV1FZfLBUAqlaKwsJBYLEZhYWHOMwe5uTEMA5/Px+joKOFwGJfLlV1je3sbt9uN2+0mFArhcDhIJpO43e7ftvkALBZLTgsOfrbd/pVKpQCYmJjg0qVLOXW/nkNE/n8KRCJyJGw2G2fPnt1X7bdv37h16xbNzc2Ul5fT0dHB8vIyp06doqqqit3dXdbX17Ptrnxcu3aNO3fuEIlECIfDeDye7NjHjx/5+vUrAwMDnDlzBoBoNLrnfA6Hg7W1NTKZTLbttrS0lB0/ffo0TqeTT58+YRhG3vsWkf+GApGI/PUePnzI1tYWIyMj2O12Xr16RXt7OzMzM5SVlWEYBh6Ph6GhIaqqqtjY2GB2dpbz589z9erVfa1hs9lobGyku7ubRCJBS0tLdqy4uBir1cro6Ch3797l/fv39Pb27jnflStX2NjY4NmzZ1y/fp1IJMLr168pKirK1jx58gSfz8eJEyeor69nZ2eHaDTK5uYm9+/fz+/LEpG86C0zEfmrvX37luHhYYLBIEVFRVgsFoLBIO/evWNsbAwAv9+Px+PhwYMHlJeX09jYyOLiIsXFxQdayzAM4vE4NTU1Oc86HA4CgQBTU1NUVFQwMDDA4ODgnnOdO3eOFy9e8Pz5cy5cuMDCwgJdXV05NR0dHUxOTuL3+3G5XFy+fJlAIEBJScmB9i0ih1eQ+bXJLSIiImIyuiESERER01MgEhEREdNTIBIRERHTUyASERER01MgEhEREdNTIBIRERHTUyASERER01MgEhEREdNTIBIRERHTUyASERER01MgEhEREdNTIBIRERHT+wdJ/atIj4yj+wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Comparision of pixel values of a disk dominated galaxy.\n",
    "plt.title(\"Pixel value comparison of a disk dominated galaxy\")\n",
    "plt.hist(pixel_slsim_disk, bins=20, histtype=\"step\", label=\"SLSim pixel values\")\n",
    "plt.hist(pixel_dp0_disk, bins=20, ls=\"--\", histtype=\"step\", label=\"DP0 pixel values\")\n",
    "plt.xlabel(\"Pixel value\")\n",
    "plt.ylabel(\"Number (N)\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14ad7c950>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Comparision of pixel values of bulge dominated galaxy.\n",
    "plt.title(\"Pixel value comparison of a bulge dominated galaxy.\")\n",
    "plt.hist(\n",
    "    pixel_slsim_bulge,\n",
    "    bins=20,\n",
    "    histtype=\"step\",\n",
    "    label=\"SLSim pixel values\",\n",
    "    color=\"magenta\",\n",
    ")\n",
    "plt.hist(\n",
    "    pixel_dp0_bulge,\n",
    "    ls=\"--\",\n",
    "    bins=20,\n",
    "    histtype=\"step\",\n",
    "    label=\"DP0 pixel values\",\n",
    "    color=\"green\",\n",
    ")\n",
    "plt.xlabel(\"Pixel value\")\n",
    "plt.ylabel(\"Number (N)\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14ae06d50>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Histogram of pixel difference. \"SLSim-DP0 diff. for a disk dominated galaxy.\" is a\n",
    "# histogram of pixel difference between SLSim and DP0 galaxy image for a disk dominated\n",
    "# galaxy. \"SLSim-DP0 diff. for a bulge dominated galaxy.\" is a\n",
    "# histogram of pixel difference between SLSim and DP0 galaxy image for a bulge dominated\n",
    "# galaxy. \"SLSim-SLSim diff. for a disk dominated galaxy.\" is a\n",
    "# histogram of pixel difference between two realization of a disk dominated galaxy noise\n",
    "# using SLSim. \"SLSim-SLSim diff. for a bulge dominated galaxy.\" is a\n",
    "# histogram of pixel difference between two realization of a bulge dominated galaxy\n",
    "# noise using SLSim.\n",
    "# plt.title(\"Pixel difference\")\n",
    "plt.hist(\n",
    "    pixel_diff_disk,\n",
    "    bins=20,\n",
    "    histtype=\"step\",\n",
    "    label=\"SLSim-DP0 diff. for a disk dominated galaxy.\",\n",
    ")\n",
    "plt.hist(\n",
    "    pixel_diff_bulge,\n",
    "    ls=\"--\",\n",
    "    bins=20,\n",
    "    histtype=\"step\",\n",
    "    label=\"SLSim-DP0 diff. for a bulge dominated galaxy.\",\n",
    ")\n",
    "plt.hist(\n",
    "    slsim_slsim_diff_rgb_1.flatten(),\n",
    "    bins=20,\n",
    "    histtype=\"step\",\n",
    "    label=\"SLSim-SLSim diff. for a disk dominated galaxy.\",\n",
    ")\n",
    "plt.hist(\n",
    "    slsim_slsim_diff_rgb_2.flatten(),\n",
    "    ls=\"--\",\n",
    "    bins=20,\n",
    "    histtype=\"step\",\n",
    "    label=\"SLSim-SLSim diff. for a bulge dominated galaxy.\",\n",
    ")\n",
    "plt.xlabel(\"Difference in pixel value\")\n",
    "plt.ylabel(\"Number (N)\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's simulate an image of a DC2 galaxy using DP0\n",
    "## cutout for background noise and exposure map for \n",
    "## poisson noise.\n",
    "\n",
    "In above method, we directly used variance map to simulate DP0 background noise. Now,\n",
    "\n",
    "we simulate an image of the same galaxy with the different approach. Here, we take a\n",
    "\n",
    "DP0 cutout close to the galaxy image and we use this cutout as a background noise for\n",
    "\n",
    "our simulation. We take the exposure map of the DP0 galaxy image with specified pixel\n",
    "\n",
    "size and use this exposure map to generate poisson noise for our simulated image.\n",
    "\n",
    "Finally, our simulated image have both background noise from DC2 data and poisson noise.\n",
    "\n",
    "This is the approach we use in lens injection in DP0 field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Disk dominated galaxy\n",
    "## These are DP0 cutouts close to the dp0 galaxy which we want to simulate using slsim.\n",
    "dc2_galaxy_background_i_1 = np.load(\n",
    "    os.path.join(\n",
    "        dp0_data_path,\n",
    "        \"dp0_images_data_for_slsim_image_validation/dc2_2_background_i.npy\",\n",
    "    )\n",
    ")\n",
    "dc2_galaxy_background_g_1 = np.load(\n",
    "    os.path.join(\n",
    "        dp0_data_path,\n",
    "        \"dp0_images_data_for_slsim_image_validation/dc2_2_background_g.npy\",\n",
    "    )\n",
    ")\n",
    "dc2_galaxy_background_r_1 = np.load(\n",
    "    os.path.join(\n",
    "        dp0_data_path,\n",
    "        \"dp0_images_data_for_slsim_image_validation/dc2_2_background_r.npy\",\n",
    "    )\n",
    ")\n",
    "\n",
    "## These are exposure map of the dp0 galaxy with in (35, 35) pixel size.\n",
    "dc2_galaxy_exposure_i_1 = np.load(\n",
    "    os.path.join(\n",
    "        dp0_data_path,\n",
    "        \"dp0_images_data_for_slsim_image_validation/dc2_2_exposure_time_i.npy\",\n",
    "    )\n",
    ")\n",
    "dc2_galaxy_exposure_g_1 = np.load(\n",
    "    os.path.join(\n",
    "        dp0_data_path,\n",
    "        \"dp0_images_data_for_slsim_image_validation/dc2_2_exposure_time_g.npy\",\n",
    "    )\n",
    ")\n",
    "dc2_galaxy_exposure_r_1 = np.load(\n",
    "    os.path.join(\n",
    "        dp0_data_path,\n",
    "        \"dp0_images_data_for_slsim_image_validation/dc2_2_exposure_time_r.npy\",\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Bulge dominated galaxy\n",
    "## These are DP0 cutouts close to the dp0 galaxy which we want to simulate using slsim.\n",
    "dc2_galaxy_background_i_2 = np.load(\n",
    "    os.path.join(\n",
    "        dp0_data_path, \"dp0_images_data_for_slsim_image_validation/dc2_background_i.npy\"\n",
    "    )\n",
    ")\n",
    "dc2_galaxy_background_g_2 = np.load(\n",
    "    os.path.join(\n",
    "        dp0_data_path, \"dp0_images_data_for_slsim_image_validation/dc2_background_g.npy\"\n",
    "    )\n",
    ")\n",
    "dc2_galaxy_background_r_2 = np.load(\n",
    "    os.path.join(\n",
    "        dp0_data_path, \"dp0_images_data_for_slsim_image_validation/dc2_background_r.npy\"\n",
    "    )\n",
    ")\n",
    "\n",
    "## These are exposure map of the dp0 galaxy with in (35, 35) pixel size.\n",
    "dc2_galaxy_exposure_i_2 = np.load(\n",
    "    os.path.join(\n",
    "        dp0_data_path,\n",
    "        \"dp0_images_data_for_slsim_image_validation/dc2_exposure_time_i.npy\",\n",
    "    )\n",
    ")\n",
    "dc2_galaxy_exposure_g_2 = np.load(\n",
    "    os.path.join(\n",
    "        dp0_data_path,\n",
    "        \"dp0_images_data_for_slsim_image_validation/dc2_exposure_time_g.npy\",\n",
    "    )\n",
    ")\n",
    "dc2_galaxy_exposure_r_2 = np.load(\n",
    "    os.path.join(\n",
    "        dp0_data_path,\n",
    "        \"dp0_images_data_for_slsim_image_validation/dc2_exposure_time_r.npy\",\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Here we simulate disk dominated galaxy image using slsim and this includes\n",
    "# poisson noise.\n",
    "dc2_galaxy_image_from_slsim_i_21 = lens_image(\n",
    "    lens_class_i_1,\n",
    "    band=\"i\",\n",
    "    mag_zero_point=27,\n",
    "    num_pix=35,\n",
    "    psf_kernel=psf_kernel_i_1,\n",
    "    transform_pix2angle=np.array([[0.2, 0], [0, 0.2]]),\n",
    "    exposure_time=dc2_galaxy_exposure_i_1,\n",
    "    t_obs=None,\n",
    "    std_gaussian_noise=None,\n",
    "    with_source=False,\n",
    "    with_deflector=True,\n",
    ")\n",
    "dc2_galaxy_image_from_slsim_g_21 = lens_image(\n",
    "    lens_class_g_1,\n",
    "    band=\"g\",\n",
    "    mag_zero_point=27,\n",
    "    num_pix=35,\n",
    "    psf_kernel=psf_kernel_g_1,\n",
    "    transform_pix2angle=np.array([[0.2, 0], [0, 0.2]]),\n",
    "    exposure_time=dc2_galaxy_exposure_g_1,\n",
    "    t_obs=None,\n",
    "    std_gaussian_noise=None,\n",
    "    with_source=False,\n",
    "    with_deflector=True,\n",
    ")\n",
    "dc2_galaxy_image_from_slsim_r_21 = lens_image(\n",
    "    lens_class_r_1,\n",
    "    band=\"r\",\n",
    "    mag_zero_point=27,\n",
    "    num_pix=35,\n",
    "    psf_kernel=psf_kernel_r_1,\n",
    "    transform_pix2angle=np.array([[0.2, 0], [0, 0.2]]),\n",
    "    exposure_time=dc2_galaxy_exposure_r_1,\n",
    "    t_obs=None,\n",
    "    std_gaussian_noise=None,\n",
    "    with_source=False,\n",
    "    with_deflector=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Here we simulate bulge dominated galaxy image using slsim and this includes\n",
    "# poisson noise.\n",
    "dc2_galaxy_image_from_slsim_i_22 = lens_image(\n",
    "    lens_class_i_2,\n",
    "    band=\"i\",\n",
    "    mag_zero_point=27,\n",
    "    num_pix=35,\n",
    "    psf_kernel=psf_kernel_i_2,\n",
    "    transform_pix2angle=np.array([[0.2, 0], [0, 0.2]]),\n",
    "    exposure_time=dc2_galaxy_exposure_i_2,\n",
    "    t_obs=None,\n",
    "    std_gaussian_noise=None,\n",
    "    with_source=False,\n",
    "    with_deflector=True,\n",
    ")\n",
    "dc2_galaxy_image_from_slsim_g_22 = lens_image(\n",
    "    lens_class_g_2,\n",
    "    band=\"g\",\n",
    "    mag_zero_point=27,\n",
    "    num_pix=35,\n",
    "    psf_kernel=psf_kernel_g_2,\n",
    "    transform_pix2angle=np.array([[0.2, 0], [0, 0.2]]),\n",
    "    exposure_time=dc2_galaxy_exposure_g_2,\n",
    "    t_obs=None,\n",
    "    std_gaussian_noise=None,\n",
    "    with_source=False,\n",
    "    with_deflector=True,\n",
    ")\n",
    "dc2_galaxy_image_from_slsim_r_22 = lens_image(\n",
    "    lens_class_r_2,\n",
    "    band=\"r\",\n",
    "    mag_zero_point=27,\n",
    "    num_pix=35,\n",
    "    psf_kernel=psf_kernel_r_2,\n",
    "    transform_pix2angle=np.array([[0.2, 0], [0, 0.2]]),\n",
    "    exposure_time=dc2_galaxy_exposure_r_2,\n",
    "    t_obs=None,\n",
    "    std_gaussian_noise=None,\n",
    "    with_source=False,\n",
    "    with_deflector=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's add dp0 background/cutouts to the simulated disk galaxy image\n",
    "dc2_galaxy_image_from_slsim_with_dp0_background_cutout_i_1 = (\n",
    "    dc2_galaxy_image_from_slsim_i_21 + dc2_galaxy_background_i_1\n",
    ")\n",
    "dc2_galaxy_image_from_slsim_with_dp0_background_cutout_g_1 = (\n",
    "    dc2_galaxy_image_from_slsim_g_21 + dc2_galaxy_background_g_1\n",
    ")\n",
    "dc2_galaxy_image_from_slsim_with_dp0_background_cutout_r_1 = (\n",
    "    dc2_galaxy_image_from_slsim_r_21 + dc2_galaxy_background_r_1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's add dp0 background/cutouts to the simulated bulge galaxy image\n",
    "dc2_galaxy_image_from_slsim_with_dp0_background_cutout_i_2 = (\n",
    "    dc2_galaxy_image_from_slsim_i_22 + dc2_galaxy_background_i_2\n",
    ")\n",
    "dc2_galaxy_image_from_slsim_with_dp0_background_cutout_g_2 = (\n",
    "    dc2_galaxy_image_from_slsim_g_22 + dc2_galaxy_background_g_2\n",
    ")\n",
    "dc2_galaxy_image_from_slsim_with_dp0_background_cutout_r_2 = (\n",
    "    dc2_galaxy_image_from_slsim_r_22 + dc2_galaxy_background_r_2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "##disk dominated\n",
    "## compute the difference between dp0 image and slsim image in each band\n",
    "diff_i_n_1 = (\n",
    "    dc2_galaxy_image_from_slsim_with_dp0_background_cutout_i_1\n",
    "    - dc2_galaxy_image_from_dp0_i_1\n",
    ")\n",
    "diff_r_n_1 = (\n",
    "    dc2_galaxy_image_from_slsim_with_dp0_background_cutout_r_1\n",
    "    - dc2_galaxy_image_from_dp0_r_1\n",
    ")\n",
    "diff_g_n_1 = (\n",
    "    dc2_galaxy_image_from_slsim_with_dp0_background_cutout_g_1\n",
    "    - dc2_galaxy_image_from_dp0_g_1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "##bulge dominated\n",
    "## compute the difference between dp0 image and slsim image in each band\n",
    "diff_i_n_2 = (\n",
    "    dc2_galaxy_image_from_slsim_with_dp0_background_cutout_i_2\n",
    "    - dc2_galaxy_image_from_dp0_i_2\n",
    ")\n",
    "diff_r_n_2 = (\n",
    "    dc2_galaxy_image_from_slsim_with_dp0_background_cutout_r_2\n",
    "    - dc2_galaxy_image_from_dp0_r_2\n",
    ")\n",
    "diff_g_n_2 = (\n",
    "    dc2_galaxy_image_from_slsim_with_dp0_background_cutout_g_2\n",
    "    - dc2_galaxy_image_from_dp0_g_2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7YAAAE7CAYAAADpSx23AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/TGe4hAAAACXBIWXMAAA9hAAAPYQGoP6dpAABDIUlEQVR4nO3deZRV1Zn//+fcmouS0VJUbEYVUWLWUtE4gUM7oUZbwCkq2ippBxzaeSURh7RDEockgIqtxClpxKRF468FRVfUxFbbNiZ0nBhsNGozzzVQd//+MFVfyqKez4VdV9zF+7WWayW165x7zj77PGfvKup8shBCMAAAAAAAEpXb0gcAAAAAAEAMFrYAAAAAgKSxsAUAAAAAJI2FLQAAAAAgaSxsAQAAAABJY2ELAAAAAEgaC1sAAAAAQNJY2AIAAAAAksbCFgAAAACQNBa2G5g6daplWWYLFizY0oeyUWPHjrV+/fpt6cMoukceecQGDx5sZWVl1r179y19ONH69etnY8eO3aRtJkyYYFmWFeeAgA5Czfx66Gw1E0DntilznCzLbMKECUU9nhEjRtiIESOK+hn4aiS7sJ0/f75dfPHFtuuuu1p1dbVVV1fbkCFD7KKLLrJ33nlnSx8eNtO7775rY8eOtYEDB9qUKVPs/vvv39KHBHQK1MzOKbWaOXbsWMuyrOW/mpoaGzBggI0aNcqefPJJy+fzbbYZMWJEq2169uxp++67rz344INtvv+TTz6xMWPGWPfu3a1r16727W9/2+bNm/dVnR6QnOYfUDb/V1paajvttJONHTvWPvnkky19eMAmKd3SB7A5nnnmGTvllFOstLTUzjjjDNtrr70sl8vZu+++a7/+9a9t8uTJNn/+fOvbt++WPtQONWXKlI0+9DuTl156yfL5vN1zzz02aNCgLX04W8z3vvc9u/baa7f0YaCToGZ2XinWzIqKCnvggQfMzGzdunX20Ucf2dNPP22jRo2yESNG2FNPPWVdu3ZttU2fPn3s1ltvNTOzRYsW2cMPP2z/+I//aO+//77ddtttZma2evVqO/TQQ23FihV2/fXXW1lZmd111102fPhwe/vtt61Xr15f7YkCCbnpppusf//+VldXZ6+99ppNnTrVXnnlFfvzn/9slZWVHfpZzHFQLMktbOfOnWunnnqq9e3b11544QXbYYcdWrXffvvtNmnSJMvlkv1ldLvKysq29CEU3f/93/+Zmcl/ThdCsLq6OquqqvoKjuqrV1paaqWlyd2e+BqiZnZuKdbM0tJS+853vtPqa7fccovddtttdt1119n5559v//Zv/9aqvVu3bq22GTdunO22227285//3G6++WYrKyuzSZMm2QcffGCvv/667bvvvmZmdswxx9iee+5pP/nJT+xf/uVfin9yQKKOOeYY22effczM7LzzzrNtt93Wbr/9dpsxY4aNGTOmQz+LOQ6KJbmZzB133GFr1qyxhx56qM0EzeyLm2X8+PG28847t3ztnXfesbFjx9qAAQOssrLSevfubeeee64tWbJEft5TTz1lI0eOtB133NEqKips4MCBdvPNN1tTU1PL9/zlL3+xqqoqO+uss1pt+8orr1hJSYldc801ZmZ29tln27bbbmuNjY1tPufII4+03XbbzT2WL/+92IIFCyzLMvvxj39sEydOtAEDBlh1dbUdeeSRtnDhQgsh2M0332x9+vSxqqoq+/a3v21Lly7d5PNr1vwZVVVVNmzYMHv55Zc3+ncJ9fX1dsMNN9igQYOsoqLCdt55Z7v66qutvr7ePb9+/frZDTfcYGZmtbW1rf6uol+/fnbcccfZc889Z/vss49VVVXZfffdZ2Zm8+bNs9GjR1vPnj2turra9t9/f/vtb3/bat8vvfSSZVlm06ZNsxtvvNF22mkn22abbWzUqFG2YsUKq6+vt8suu8y22247q6mpsXPOOUcer5nZBx98YCeffLL17t3bKisrrU+fPnbqqafaihUr2t2msbHRbrzxRttll12ssrLSevXqZQcddJDNmjWr5Xs29vcnWZbZxRdfbE888YQNGTLEqqqq7Fvf+pb96U9/MjOz++67zwYNGmSVlZU2YsSIr+3fPeKrRc3s1/L/qZlbvmZ6rr32WjvyyCPtiSeesPfff9/93ubjXrNmjS1atMjMzKZPn2777rtvy6LWzGzw4MF2+OGH27Rp06KODdjaHHzwwWb2xQ9Hm7377rs2atQo69mzp1VWVto+++xjM2bMaLXd5s5x6uvr7fLLL7fa2lrbZptt7IQTTrCPP/64zXG19+6Eje3zoYcessMOO8y22247q6iosCFDhtjkyZM3uS+QjuR+XPLMM8/YoEGDbL/99it4m1mzZtm8efPsnHPOsd69e9ucOXPs/vvvtzlz5thrr73m/gH71KlTraamxq644gqrqamx2bNn2w9+8ANbuXKl/ehHPzIzs913391uvvlmu+qqq2zUqFF2wgkn2Jo1a2zs2LE2ePBgu+mmm8zM7Mwzz7SHH37YnnvuOTvuuONaPuOzzz6z2bNnt0xQNtVjjz1mDQ0Ndskll9jSpUvtjjvusDFjxthhhx1mL730kl1zzTX24Ycf2s9+9jO78sor7cEHH9yk8zMzmzx5sl188cV28MEH2+WXX24LFiywE0880Xr06GF9+vRp+b58Pm8nnHCCvfLKK3bBBRfY7rvvbn/605/srrvusvfff9/+/d//vd3zuPvuu+3hhx+23/zmNzZ58mSrqamxb3zjGy3t7733np122mk2btw4O//882233Xazzz//3A444ABbu3atjR8/3nr16mW/+MUv7IQTTrDp06fbSSed1Oozbr31VquqqrJrr722pU/Kysosl8vZsmXLbMKECS3/BKd///72gx/8oN3jbWhosKOOOsrq6+vtkksusd69e9snn3xizzzzjC1fvty6deu20e0mTJhgt956q5133nk2bNgwW7lypb355pv21ltv2d///d+3+3lmZi+//LLNmDHDLrroopbzOe644+zqq6+2SZMm2YUXXmjLli2zO+64w84991ybPXu2uz90ftTMtqiZW6ZmFuLMM8+0mTNn2qxZs2zXXXd1v3fevHlWUlJi3bt3t3w+b++8846de+65bb5v2LBhNnPmTFu1apVts802UccHbC2afzjeo0cPMzObM2eOHXjggbbTTjvZtddea126dLFp06bZiSeeaE8++WRL7djcOc55551njz76qJ1++ul2wAEH2OzZs23kyJFR5zB58mTbY4897IQTTrDS0lJ7+umn7cILL7R8Pt8yj0InExKyYsWKYGbhxBNPbNO2bNmysGjRopb/1q5d29K24f9u9stf/jKYWfjd737X8rWHHnoomFmYP3++u+24ceNCdXV1qKura/laU1NTOOigg8L2228fFi9eHC666KJQWloa3njjjVbf06dPn3DKKae02t+dd94ZsiwL8+bNc8//7LPPDn379m35//Pnzw9mFmpra8Py5ctbvn7dddcFMwt77bVXaGxsbPn6aaedFsrLy1sddyHnV19fH3r16hX23XffVvubOnVqMLMwfPjwlq898sgjIZfLhZdffrnVPu+9995gZuHVV191z/GGG24IZhYWLVrU6ut9+/YNZhb+4z/+o9XXL7vssmBmrT5v1apVoX///qFfv36hqakphBDCiy++GMws7LnnnqGhoaFVn2RZFo455phW+/3Wt77Vqq835r//+7+DmYUnnnjC/b6+ffuGs88+u+X/77XXXmHkyJHuNs39sCEzCxUVFa3G53333RfMLPTu3TusXLmy5evNY2DD78XWh5pJzfw61cwQvrgmXbp0abe9ua5efvnlLV8bPnx4GDx4cMtY/ctf/hLGjx8fzCwcf/zxIYQQFi1aFMws3HTTTW32OXHixGBm4d1335XHB2xtmuv4888/HxYtWhQWLlwYpk+fHmpra0NFRUVYuHBhCCGEww8/PAwdOrRVPczn8+GAAw4Iu+yyS8vXNmeO8/bbbwczCxdeeGGr7zv99NODmYUbbrih5Wtfruvt7TOEjdfro446KgwYMKDV14YPH96qLiNdSf1T5JUrV5qZWU1NTZu2ESNGWG1tbct/EydObGnb8G+K6urqbPHixbb//vubmdlbb73lfuaG265atcoWL15sBx98sK1du9befffdlrZcLmdTp0611atX2zHHHGOTJk2y6667ruXvFZq/54wzzrAZM2bYqlWrWr7+2GOP2QEHHGD9+/cvtCtaGT16dKvfDjb/ZuY73/lOq79h2G+//ayhoaHVW+4KOb8333zTlixZYueff36r/Z1xxhktP8lr9sQTT9juu+9ugwcPtsWLF7f8d9hhh5mZ2YsvvrhZ52hm1r9/fzvqqKNafe3ZZ5+1YcOG2UEHHdTytZqaGrvgggtswYIF9j//8z+tvv+ss85q9Xd3++23n4UQ2vyUf7/99rOFCxfa+vXr2z2e5j5/7rnnbO3atQWfR/fu3W3OnDn2wQcfFLxNs8MPP7zVP8FpvtYnn3xyq99ENH+dt4Fu3aiZG0fN3DI1sxDNY3XD6232xT+BbB6ru+++u/3sZz+zkSNHtvw2fd26dWb2xYupvqz5xTfN3wOgrSOOOMJqa2tt5513tlGjRlmXLl1sxowZ1qdPH1u6dKnNnj3bxowZ01L3Fi9ebEuWLLGjjjrKPvjgg5Y6uTlznGeffdbMzMaPH9/q65dddlnUOW1Yr1esWGGLFy+24cOH27x589w/GUO6klrYNk/cV69e3abtvvvus1mzZtmjjz7apm3p0qV26aWX2vbbb29VVVVWW1vbMiFSA3vOnDl20kknWbdu3axr165WW1vb8gKLL287cOBAmzBhgr3xxhu2xx572Pe///02+zvrrLNs3bp19pvf/MbMvvinYv/1X/9lZ555ZgE9sHF/93d/1+r/N0/YNvybuQ2/vmzZsk06v48++sjMrM0bN0tLS9v8ncMHH3xgc+bMaTVhrq2tbfknZc0vOtkcG5vEfvTRRxv9O7vdd9+91bE325S+yufz7vjo37+/XXHFFfbAAw/Ytttua0cddZRNnDhRjqmbbrrJli9fbrvuuqsNHTrUrrrqqoLjVmKuNbY+1MyNo2ZumZpZiOax+uV/MtyvXz+bNWuWPf/88/bKK6/YZ599Zs8884xtu+22Zvb/JrAb+zvfurq6Vt8DoK2JEyfarFmzbPr06Xbsscfa4sWLW35Q9OGHH1oIwb7//e+3qVXNfxLSXKs2Z47z0UcfWS6Xs4EDB7b6unqPgvLqq6/aEUccYV26dLHu3btbbW2tXX/99Wamn2VIU1J/Y9utWzfbYYcd7M9//nObtuafuG/shTljxoyx3//+93bVVVfZN7/5TaupqbF8Pm9HH320GwWxfPlyGz58uHXt2tVuuukmGzhwoFVWVtpbb71l11xzzUa3nTlzppmZ/fWvf7UlS5ZY7969W7UPGTLE9t57b3v00UftrLPOskcffdTKy8uj3jhXUlKySV8PIWz2+Sn5fN6GDh1qd95550bbvzwZ2hQdMSnZ3L5qz09+8hMbO3asPfXUUzZz5kwbP3683Xrrrfbaa6+1+ju6DR1yyCE2d+7clm0eeOABu+uuu+zee++188477ys9fnRu1MyNo2YW7quuOc1j9cs/FOjSpYsdccQR7W7Xs2dPq6iosE8//bRNW/PXdtxxx6hjAzqzYcOGtfyLmRNPPNEOOuggO/300+29995rqW1XXnllm38F0qz5no2Z4xSivXc8fPkFfnPnzrXDDz/cBg8ebHfeeaftvPPOVl5ebs8++6zdddddnT4KbmuV1MLWzGzkyJH2wAMP2Ouvv27Dhg2T379s2TJ74YUX7MYbb2z1UotC/onESy+9ZEuWLLFf//rXdsghh7R8ff78+Rv9/nvvvddmzZplP/zhD+3WW2+1cePG2VNPPdXm+8466yy74oor7NNPP7XHH3/cRo4c2eafp30VCj2/5mzLDz/80A499NCWr69fv94WLFjQ6mUlAwcOtD/+8Y92+OGHuy+Y6Sh9+/a19957r83Xm/9J4FeRyzl06FAbOnSofe9737Pf//73duCBB9q9995rt9xyS7vb9OzZ08455xw755xzbPXq1XbIIYfYhAkTOqToAxuiZnYcambxPfLII5ZlmXyR3pflcjkbOnSovfnmm23a/vM//9MGDBjAi6OAApWUlNitt95qhx56qP385z9v+dODsrIy9wdMzTZ1jtO3b1/L5/M2d+7cVr+l3Vit6tGjhy1fvrzN17/8r02efvppq6+vtxkzZrT6lycxf96Br7+k/imymdnVV19t1dXVdu6559rnn3/epv3LPy1u/qnyl79+9913y8/a2LYNDQ02adKkNt87f/58u+qqq+zkk0+266+/3n784x/bjBkz7OGHH27zvaeddpplWWaXXnqpzZs3r02e31el0PPbZ599rFevXjZlypRWfz/12GOPtfmnrmPGjLFPPvnEpkyZ0ubz1q1bZ2vWrOnIU7Bjjz3WXn/9dfvDH/7Q8rU1a9bY/fffb/369bMhQ4Z06OdtaOXKlW3+nmzo0KGWy+Xc2IsvR6bU1NTYoEGDoqMygI2hZnYcamZx3XbbbTZz5kw75ZRTbJdddtnk7UeNGmVvvPFGq8Xte++9Z7Nnz7bRo0d35KECnd6IESNs2LBhdvfdd1vXrl1txIgRdt999230X0U0R26Zbd4c55hjjjEzs5/+9Ketvr6x587AgQNtxYoVrf5586efftry5yrNNlavV6xYYQ899FC7x4H0Jfcb21122cUef/xxO+2002y33XazM844w/baay8LIdj8+fPt8ccft1wu1/LPQLt27WqHHHKI3XHHHdbY2Gg77bSTzZw5s93fIGzogAMOsB49etjZZ59t48ePtyzL7JFHHmkz4Wt+kUZVVVVLPta4cePsySeftEsvvdSOOOKIVv8Eqra21o4++mh74oknrHv37tGvM99chZ5feXm5TZgwwS655BI77LDDbMyYMbZgwQKbOnWqDRw4sNVvGc4880ybNm2affe737UXX3zRDjzwQGtqarJ3333Xpk2b1pKp2FGuvfZa++Uvf2nHHHOMjR8/3nr27Gm/+MUvbP78+fbkk09aLle8n93Mnj3bLr74Yhs9erTtuuuutn79envkkUespKTETj755Ha3GzJkiI0YMcL23ntv69mzp7355ps2ffp0u/jii4t2rNh6UTM7DjWzY6xfv77lb7vr6urso48+shkzZtg777xjhx56qN1///2btd8LL7zQpkyZYiNHjrQrr7zSysrK7M4777Ttt9/e/vmf/7kjTwHYKlx11VU2evRomzp1qk2cONEOOuggGzp0qJ1//vk2YMAA+/zzz+0Pf/iDffzxx/bHP/7RzDZvjvPNb37TTjvtNJs0aZKtWLHCDjjgAHvhhRfsww8/bPO9p556ql1zzTV20kkn2fjx423t2rU2efJk23XXXVu93PDII4+08vJyO/74423cuHG2evVqmzJlim233XYbXZyjk/jK3r/cwT788MPwT//0T2HQoEGhsrIyVFVVhcGDB4fvfve74e233271vR9//HE46aSTQvfu3UO3bt3C6NGjw1//+tc2rxDfWHTFq6++Gvbff/9QVVUVdtxxx3D11VeH5557LphZePHFF0MIIdxzzz3BzMKTTz7Z6nP/93//N3Tt2jUce+yxbY5/2rRpwczCBRdcUPA5txdd8aMf/ajV9zXHNHw5hqb5/DaM0yjk/Jr99Kc/DX379g0VFRVh2LBh4dVXXw177713OProo1t9X0NDQ7j99tvDHnvsESoqKkKPHj3C3nvvHW688cawYsUK9xy96Ir2Xh8/d+7cMGrUqNC9e/dQWVkZhg0bFp555pnN7hPvODY0b968cO6554aBAweGysrK0LNnz3DooYeG559/vs2xbxj3c8stt4Rhw4aF7t27t4zbH/7wh60iNdqL+7noootafW1TxwC2XtRMamazLVUzQ/jimphZy3/V1dWhX79+4eSTTw7Tp09viRva0PDhw8Mee+zh7rfZwoULw6hRo0LXrl1DTU1NOO6448IHH3xQ0LbA1qi9ezqELyLXBg4cGAYOHBjWr18f5s6dG84666zQu3fvUFZWFnbaaadw3HHHhenTp7dss7lznHXr1oXx48eHXr16hS5duoTjjz8+LFy4sM1zJ4QQZs6cGfbcc89QXl4edtttt/Doo49udJ8zZswI3/jGN0JlZWXo169fuP3228ODDz7Y5rlF3E/nkYXA22W2hKeeespOPPFE+93vfmcHH3zwlj6czZLP5622ttb+4R/+YaP/jA4AOgo1EwAAeJL7G9vOYsqUKTZgwIBWWYJfZ3V1dW3+ud3DDz9sS5cutREjRmyZgwKw1aBmAgAAT3J/Y5u6X/3qV/bOO+/Yb3/7W7vnnnu+krdgdoTXXnvNLr/8chs9erT16tXL3nrrLfvXf/1X23PPPXkpB4CioWYCAIBC8E+Rv2JZlllNTY2dcsopdu+991ppaRo/W1iwYIGNHz/eXn/9dVu6dKn17NnTjj32WLvttttsu+2229KHB6CTomYCAIBCsLAFAAAAACSNv7EFAAAAACSNhS0AAAAAIGkF/7HS0b0uKN5RhLzfrv6mqqHRby8vE58v/jV23mnPiReZNK7320tK/PamJr9dfX6MTPzcQ322esnLetE37r7FsW3JfjPT1zWGNx7N9LmpvvGOXYxn9ZcNz6140P/sRB1dO87/BtXnFRWbv62i7rNijlVV/2LvwyL+vW2ob4jaPisRNSq2vua8+1Qcu6ohYkwEMabkuceMOXXsSmx9jJkPiPP+j0X3+dsnSs4fRb+FunpnW3+sZWr+F/M8LGR7T1m5365qd0y/mcm+82SVlf43qHl9LDHvD851ySqd561ZfI1R9a+piH2jalCx62fEcymI+cJzq6b6n918CAV9FwAAAAAAX1MsbAEAAAAASWNhCwAAAABIGgtbAAAAAEDSWNgCAAAAAJLGwhYAAAAAkDQWtgAAAACApHVcCGAxs5NUzqzMGROZUTFZiiqnMVaZuEQqK9Y799hrpvLbVE6jum5O34YgchSLmG9ZkNjsUY+8l0S/qusSkdOYZUXMRP06i8m+NPPzCtW2aqyr+yw21y7ms1XWYWzGrsqBdO6F6PxLWf9Eu3ruefei6rdMPVNFNqiaOqi+866LeqaVRDzzCqGumznXXd1Lqt87q5jsSzPL1DzIE5ulHZn57KpXObNqrPvNst/Us8O7l2Q+emSWq6qv6twa2s9MVfm+MudWHZtoDs6xmZllzpgK6rkgZKq+yvtB5bM711XcK5msvYXhN7YAAAAAgKSxsAUAAAAAJI2FLQAAAAAgaSxsAQAAAABJY2ELAAAAAEgaC1sAAAAAQNJY2AIAAAAAklZ4OJjKG1TZmd72KrsoNhM0NvfTO3aV+RSbIanaQ0TfqDwqIYgcsqy0eNc1UxlpKp9NZbDFZBubRfVtEMcmz13dq+p+8/qu0c9f21rJe0FdM3fn4nrmIzN0i5k3KPMnI+ujqH/6unifHflzX3Xd1vvt8ti9RtFvMkcxNv9S5XN6xydz6SPnAzmV3Sn2H/NsiH2upEplZ8oa54wXNVbz4lkvyHulvNzfgVdfiz1/VPuPzESNEptTK8aUe11Ev6hrqsaEOresqtLf3stXV3NXdS/Fzo3V9l7fNjT628p1ZmH4jS0AAAAAIGksbAEAAAAASWNhCwAAAABIGgtbAAAAAEDSWNgCAAAAAJLGwhYAAAAAkDQWtgAAAACApBWeY1vsrNmYz1ZZXZnKQozITorNGYvNHFXc/DfRbyqLS2XTqXxMkeMYRWVxxWqMyMEVY0JmnlZU+O2xWWFeLqrMzSviNf0ayypElqHiZZaq3DiVMxubU6t4eYOxOYuKqL+5Sj8vMNQ7eYQx2Zpm+j4V+cOZzF93tlXjMfbcYutredlmbypzvtX9osj5hHM/xfZrZyWumcxs9q6pmnuqOZq4ZjKnVl3zhoicb3VukfPyUOfnTbvnHlnfgppfqhohxkzwMnrrRA6tGjNq7hv7zG5qfw4n+01l5KpzU7VdtXv1s9jrneZD6JC9AAAAAACwhbCwBQAAAAAkjYUtAAAAACBpLGwBAAAAAEljYQsAAAAASBoLWwAAAABA0gp/L76MjNj8g4h+fb969bZcvovXX3tU7Iv86LhX0cvtI05N7rss8rXh6tXe5U57bESJ6tdS8Sp7JSZmSUVCqDgfda8Gdb8417VcbOu9Yr8zK+Z4FLFYMioj+ONBbq/GU6M31kXtLnIcUKir87/Bq3FZZGRDo4iUUOeu+s4hz1vVXvXcEPVTjynn3MXzXM4H1JhRESjy2P3dYyPEXCKLiU2UsVtFjn+KiXCq9+N2JBWvp6IFa7r423v3gqpv6ppXV/vbK0GsG7y4IBUlJGM6ReSOiJlTKy+vfme52CghNfcV+1f3asy+Oyimk9/YAgAAAACSxsIWAAAAAJA0FrYAAAAAgKSxsAUAAAAAJI2FLQAAAAAgaSxsAQAAAABJY2ELAAAAAEha4QFfsfmUTu6dzKVTuXOxObiRuXeuiCxCM/MzIs103qB37EXMUZSfbRaXhxVLnbvK01Ln5u6/yOet7lV17F6OpMrQjcwdTZbKRVa5oV67uF6yvoljkzm1Rc6adcmxGvmzWe/c1K5js91VBqWXJ23m5khGZ7Gq2pwr99ubRI6ud26q9kaOd7V9ZhH568XMPE2ZyCbW2ZvOzaQySWOzYtU8SN2nzngO4lntZrEWIh8xls3crFhZY/w9R9W3gj7fu8/l81jkdNeLfl27zm8X1zU486ysosLfVq2H1L2o6mfEWjDU+fdiFrvm+Bt+YwsAAAAASBoLWwAAAABA0ljYAgAAAACSxsIWAAAAAJA0FrYAAAAAgKSxsAUAAAAAJI2FLQAAAAAgaQWHBnm5SmaxWYhxeadZbI5iVFZiRCZoIWJznbwsr9hs4rw4d5XBpvIKvZ+7xGbkqn5VeYNy/067yglTVOafOnbV7o0Ldd4qYxIb541nNVYbxX0UWx9j7xWPzBAX28fmB3vbZyovNS4vWmZYqgjLsvazZLMK0a+q9qqMSZWNXCFybmOoa67qoyLGc2hqP18zKy/ieacsNv/cee6Eej/vVI5FdR+re0Hl5DrHLjNFFXVs5SpXWRRYr8ZkooZEznOyqip/9ytX+Tvw8oNVnmqlnxUbfd1U1qyXVSvWWpla1hU5azsrcx5cat8dNH/kN7YAAAAAgKSxsAUAAAAAJI2FLQAAAAAgaSxsAQAAAABJY2ELAAAAAEgaC1sAAAAAQNJY2AIAAAAAklZwaJDMbVL5Q162nMq7KnLuksw6dD9f5f1FZorGZlB6VBasuqYyxzEyYzImj1D1mxrP6kc+Kju0xOkb1e+x11z1q8qi9Y5PbSuziTsp2edb8GeIMVmuZgWMdWdMxNa32DxpxclplBmP6thE/m+mtlf3knfdvPMy08eeiTER+1xrErmmMWJzwsWYzHLOdVX3eWx2fGelapS3qXqWy3mGemaJOlAuAqe9eYyqX7H3aazG9jOCg5ifyfxgUd+CynqNyYzORc6RssjnmpoDenVEzYtlzrffHLWWM7PQ6NR2dd6xtftv+I0tAAAAACBpLGwBAAAAAEljYQsAAAAASBoLWwAAAABA0ljYAgAAAACSxsIWAAAAAJA0FrYAAAAAgKQVnGMrc51i8tlUzlhs5qjKTlIanXOTmaOi36IzSyO2V1mHsRmTsVmInpKI7Dgzs5LIc1PZdZ7YrC5xv4T6en/zqkrxAc6Yjc0+7qxis4m9drHvIO4jWSFkBq/YPib3OCfavTzoQsga5hy7zNAtYl60WQE1zMsPjhyPsXmrqm+cOhHWrnU3leNZ1eaGyAxdL59Y9FuI/exUxWS9monMZvHMiam9ZvHzR6++qrlAbA54LKfGZGr+qM5NtavnUsS5yzlQsfPXZbay0zdqjqVqv5pP1LefXWxmllWKvvPyh1VGuNp3gfiNLQAAAAAgaSxsAQAAAABJY2ELAAAAAEgaC1sAAAAAQNJY2AIAAAAAksbCFgAAAACQtMKzOWJfb+29/l++6j02ekDELqhjL3POXcXteK++NtOv7o49d2/72DfFq9eKy5imiCgk0a9Zhf8q+iCjNPxmyet3dc1VJESFv30We6961L2k2jsrFTkRUx9Fn2axsQjRkRLO8ck4i8gojSKem6whjZHRLeq6mYrD8J5LYt+xkWOifob1ft94fZuVi35XtV89c4sdkeLIYuL5UhY7j/HGs7rH1XiIjbCLGW85EZkjovvkHEvOcyJiZxrFsVVUuM1qDpapqYTqO6/GyTmWf+zyuSX6NRNjKtTVbf5nCzJSR40Z9flev6v5YQfVR35jCwAAAABIGgtbAAAAAEDSWNgCAAAAAJLGwhYAAAAAkDQWtgAAAACApLGwBQAAAAAkjYUtAAAAACBphefYqnyhoLKVvJxGlbkncspicxhlu5O9FJtTq/IGm8T+SzY/9ylT/a7IvEF/TMR8fhAZuDJjUo1ndV1Ulpd3fHLbyGzk2PHu9U3svdhZqUxmi8xrjaGuicyaFfeCV+PUWFVkzrfKadz8+iizrsU1V5mlQZW/8jK/3bmusraW+Pt2cxTNZL/Lz1d9G7Pv2KxEUeOCk4Epc2pV3nVnFVuDipmPLvOkBXVsXk6uzDdX+xb9pjJ6Fe+6qPokZGp7dexqHhWRiRpWrfJ33aXa34G4biqL28qcubW6V1T9UrU98n7Iqqra/2yxJojOhv8bfmMLAAAAAEgaC1sAAAAAQNJY2AIAAAAAksbCFgAAAACQNBa2AAAAAICksbAFAAAAACSNhS0AAAAAIGmFh6qpvKyYvC2Vq6morMPYLMWmhvbbVOZTbEaa6hvR75nqG3dj9dn+uWVqTJSKdifzKisRWV3r48aUzKBUGWqNzphR/eJlmJnFj2fFy0FTuXv5Ih/b15W6JsXMaVT7VjVI1QhV++vr229TuZ0q/1c+G+LyLd37XNY/8dmqPopMP/n5Ts5jyIucWdVeJjImRbvMaYyZD6hoztgxJY496pkamyvaWcXM4VR9U7nHah7i1TezAuqA067Ggzo3UfuD2L+co8U8l2LnKeqzZXa8o6LCbZazR5mxG5nb7F13ta04Ny+H+wvi2SDu1VAn7hdP7FqweTcdshcAAAAAALYQFrYAAAAAgKSxsAUAAAAAJI2FLQAAAAAgaSxsAQAAAABJY2ELAAAAAEgaC1sAAAAAQNIKD2RT2UkqT9DLJ4rNhBKZpjLHTGSFZc72oVFlQsXJykWmqcp/885dZbWqDErV7uWhFsLLsRVZXFku8mc24thl2paT8xhULqi81wR1P8VkBsq86q00p1Gdt+o3N09V1S8xGtX2KjtObR9zzcVYzzJV3+JybK3cyWMVNUDWR3VdKkW7ypptbP8+VTncat+msg5VbS9mPqY8NzUexWfHZFDKTNXI8ZoqVWPUeHKuSQgiLzUyTlXeS2r7ysp228Ladf7GkZnOcv6o5sYxcwF17DI/XZyc2L933UJdXdS+5bFH5g/L7HiPmhurHNqGBn//MfNH1a/OM21T8BtbAAAAAEDSWNgCAAAAAJLGwhYAAAAAkDQWtgAAAACApLGwBQAAAAAkjYUtAAAAACBpLGwBAAAAAEkrPMc2Nr9S5Tp5VPaRojLSmvzspOB1U87vFy8D18wseJlPZmbl7WegmZmFygp/+9L2jy9f6V/+fKWT8WhmQVyXfJnIBxZ5gbnG9rO8Stb4WVtZoxiP9WJ7J0PXzHTGpZclFptTK7PnIjMqvXs9MtOv0xKZpu54MLMsONc0JjeuEKq2q8/3zl2Nl9i8U6e+FbJ9vsbJmCzzt1XtTaK+KlmTqI/17V93r83MLFsn6p/bajJnMZPP3PbraxC1WedfivshE/VRzTdyTjaouhe31vqpsoHVM82rMZG5nfKzxViPyv2UGeJ+exC5nzrHVtRPr28zcU1Vv68TWbIVYm6r1hze58c+8xQ13lVOrXduXvZ6IftW4z0n1iwqA9iRiWMP6rlRIH5jCwAAAABIGgtbAAAAAEDSWNgCAAAAAJLGwhYAAAAAkDQWtgAAAACApLGwBQAAAAAkjYUtAAAAACBphYc1qcwomVNW8CdtOpXLpLKRVJaYkzuqM9JEzm2FnzMWqv0c23wXP+ursVv77Q3d/GOv7+r3a5OIGcuX+v1a0uBfl7I17bdXLPPzsMpX+tl2JavENRc5uJnKcXSua2gUObLqXorNdVa8c4vJo+7MRH3MVH6ld81lNqaqfyLXTmbuidrviR0vqj5W+u35ar99/Tbtt9f38GtMY7V/TVW7UlLvt5etbf+6Vqzwr1npKr9fcg3+mMut8rMMM5VFG0ONZ9UeMZy/2N45N5WPiY2LyN6UczBF1aicXx8zkWFueWfA5SLHi8gclfeCerZ4uaOx82rRb5ma12ei77zjU/0unnlBZPBmVf68XV4XNxte9Is4tej7RfH6/SvK8WaWCgAAAABIGgtbAAAAAEDSWNgCAAAAAJLGwhYAAAAAkDQWtgAAAACApLGwBQAAAAAkrfD3PqtXRDeICBPv1d/5yFeWR7wqvhDu67HV66vFK82D9zp1M2vqVuW2N3T3M3fW1rb/+Wt29I997U4iF6G7f81zJf7r4PPL/MiJys/bf2959af+sVdV+de8osxvL13hZ23k1kfEAakoDBnPIsZz7P3g3quxWRmdlKqP6pp411y93l/F8ag4ChV9oGIbnM/PSsW+RTxKEMfe1NWPVajv4dfHdV593EHUxx1F5FcvPxLCRFpGWOofe9Vn7fdd1WJ/zHT5zG8vX+635+pETIiqcc64UBF4iujW+Pq43tleRWep+6GzUjWkUYynmDmc+ux6kaulIpzUNa9wapiaN4vnSnR0SzGj3FSUkLjmQc2tZUyTs32jqE8Vfu1VcT7yuoj24NUocezutma6/sWKuVfzHXNs/MYWAAAAAJA0FrYAAAAAgKSxsAUAAAAAJI2FLQAAAAAgaSxsAQAAAABJY2ELAAAAAEgaC1sAAAAAQNIKD8EKIqtL5LFGZSepTCiVlyXW7zJzyssxy/n7Vjm1+S5+Htb6an/7uh5+xpqXVbtmFz8Pa9/B8932g3t86LYrry4f6La/Pref0+r3W0mDyKld6/db6Sq3WY/3tU6GpcrVUzm1isoRU/lvISLbTmX+dVYyazYi51uJ7XOV46hycL28QZVTW+3fx6FS5HxX+sdWL+rj6j7tH/vaXfx+2XvQR277kdv+j9temfn199kl33Db//P9/u22hRI/h7Gk3u+XknV+DSgR/Z5r8K9bJnLAo6j7ISb/Uu1fZZpurVROrcgF1XM8h8pDVdT8UMwFMufzg8xMFucdm2Mran9w5/0ix1v0S1AZuqv9+phVVfnbe32j+i0yXzjU+RnmWWXxxrs33szMLBQ3xzbUt3/s6tiyDpo/8htbAAAAAEDSWNgCAAAAAJLGwhYAAAAAkDQWtgAAAACApLGwBQAAAAAkjYUtAAAAACBpLGwBAAAAAEmLDMHagMqp9bLhVPabyNBVeVYhH5nb5GUvlarMPD+3KZT5P1toqvDbG7v4+6/v2X7f1e6wwt32qF5z3PZTtlngti/P+1lcTeYf+5+67NBu2/pKP6cxLy5LUFFfKl9O5To77ZnKPs6LfDeVQyaPTezfG7Nq3yqvsLNSuXMyu9jp1zKR/Vam8gLFeCmPyO00MZ5VBq6on/kKf/v1Xfzt67r791rddu33Td+dlrjbfqf3a277iV1Wu+0r8uvc9sXru7rtc7r3bretobLc3bZJxc5XigIqapjk5Nj62ZnxMlHbg7qXvRxcVZtj8lhTpuqAyjR1n0n+tl6uplkBmaLqmqrHtbutepaLdjVXEP2qxrqXNasySfMNIoc2NrNUPVO9jF712ZH9lqmcW3Xd3RoVmX0s1luq/uosWuf41FpPXdMC8RtbAAAAAEDSWNgCAAAAAJLGwhYAAAAAkDQWtgAAAACApLGwBQAAAAAkjYUtAAAAACBpLGwBAAAAAEnruBxblbdlTm6Uyi5SWbCNjWJ7sX4v2fzPl3mnKjNPtGci9ikTMWY5p2uWr6x2t/3v1X3d9m1K/BzG5U1d3PbfLdnFbV+7rP184urV/jUrW+uPx5IGvz3X4HdsVudntLl5ryIDMisROWPimtt6cWwqw827X1TMWE4dXCel6p/M7HP6TWQDB1EfZf1TVC5euZOZqjLEVWZeZP0rafB3UFLXft+trPOzst9c099t37n0v9z2z5p6uO2vr+jntq9e1n79rl7rj4nSOv+6lK0W13x9ZJa2Q+d8q1xRMShExqQ+dufzY++1zkrVqAYxhyt3gpfL/MzmLBNjuTHyeanGk5fdWS72LfPRxfYqN1RllDvt+dV+TnfmPRfMLL92rduuzi1X5o8ZN29VPK+9/F4zs6zanzvLMaXavToi10uiBpWL9tVr/Paq9uflZuYfn3qgk2MLAAAAAAALWwAAAABA4ljYAgAAAACSxsIWAAAAAJA0FrYAAAAAgKSxsAUAAAAAJI2FLQAAAAAgaYXn2Ko8rJjtVRasEpM7Z2Zyfe/sP6sX+Wui30rK/Kyupmr/ElWs9M+tclH7+19X6mdx/XbtULf9pR6D3Pb16/1+rV/s52FVf9z+udd87Pdr5RL/upSu8nPE5HUV2aJu/pzKaWxSGZGiPTZL0du/ytXbWnMcVX0U97m/vcjlVNdEZujG8XJFZSqdyrcUeak5kUddsdK/LlWftT9eV5b6ObO/WrWP2/7/dd3dbV/f5F+XlZ9u47ZXL2y/Pnb5RJz3En9Mla7x619O1MdsvRizzjM1qG1V1qGYD8j6qrJB3doelxu6tZK5ocGZB5WI8aByZuUzLTJb0xuv9fXupqHOb1dZsVbmn7ub9WrmHntO5JkGMUfKiSxYOSbUusGbi8h9q+e1qFFqHqSytr1nfnmlv6kYU3K9U+ZkRpvpY885Y05tG8ixBQAAAACAhS0AAAAAIG0sbAEAAAAASWNhCwAAAABIGgtbAAAAAEDSWNgCAAAAAJLGwhYAAAAAkLTCc2wVmePo5BOpXDmZVyWyj0SmVBA5Zu7eVeaTyPLK6iLzVpv8fs+c5sqlfr80dPUz0poq/PYKMSS2WS6yaJe3n3lVudTvlxKVw7hG5MOtrXPbzcnuNBNZjDmVqxxJ5fapHEgvayzmPu/MRF6g5GUHq+ulqGtWHplb5xGZpFmdfx+rn7yWrfDbc+LZkjW1X8Mqlosc7o/9HMeGcr9dxav3WhZRH1WO92rR76v8+pfV+9ubao/Jc40Zj2Y6g1Lx7sfIuUinpbJiRfaw26+xObSqvqr9x8xf1bO6TMwzRL9lKpO5XM0FnM8X97DMyBVyFRX+N6jnmpcFW+w8ae95bibnC2Fd+/U3i82tV2NGzOvl/p3jk+O1g6bGW2mVBQAAAAB0FixsAQAAAABJY2ELAAAAAEgaC1sAAAAAQNJY2AIAAAAAksbCFgAAAACQtMIzKmIjRLxXfwfx+n31dn71am117Gr3zivPM5GUIV+tLWIRSlb5/Zpb68c6lNQ5cRaVfr80Vca9Njy3XrTX+xe2ZF3755arE688F/2aiRgm9Qr/0OD3u/vZORU/JaIylCZxbireRb1OHm2JCBI1XjJ1TWKoWIRiXm8Ri2WqX8TuZZxPo39dqhvb375yqV//mj7z24P6sbFKzlrrX5cSp13FmckYJhXXI8ZUaBT10RsXKo5HxYjIGBAhJk5IRWGUbqW/SxDRLZmKdvFqlHqWy7mpipMU7SqO0o3M8beVzwV17upeUmPduZcydd5i3h0a/BojYzhj1iTq2NUzMV/c+9i77pmIGA11IqoyNg5IzV+d+hwbAVWorbTKAgAAAAA6Cxa2AAAAAICksbAFAAAAACSNhS0AAAAAIGksbAEAAAAASWNhCwAAAABIGgtbAAAAAEDSCg94VXlYKjuuJCK/SOaEie1FLpPWfg6ZyuKSuU2iPVu9zm8XGW1eHmGJyEjz8qgKkakMS8XJaMtEfqXKx5RjQuW7qexkZ//Byc7sEOrYVEabl9snItC2WiLrMMtia5BD1V45lsV9rnLvnP0H8dmywjSK+qZycOv9Y8/qnO1F/QtV7WeEm5nZ+rj7XJ2bV2NkTrfIsQ0qx1Zlg6ox4yl21qE6dnU/uPVRzFXUPKqzks+cIl5zlXeqyLlCEZ/nqnaXRZ6b4t0rZX79U3mqmTp29VxTGb8x10XmUYtjV7Vb1WeHrM2xxLHJdUEx+71A/MYWAAAAAJA0FrYAAAAAgKSxsAUAAAAAJI2FLQAAAAAgaSxsAQAAAABJY2ELAAAAAEgaC1sAAAAAQNIKD8FSuUux2XAelQmlPluFb6q8LG/5L3LGgsi3tFDkHFwvr1BlQObUsUdmc6q8LC/7TuWIiQzdoDJ2VX5cDNVvajwqKgssdv9bat9fZyq/UmULe/2m6ptsj8w6VDXIG2+qdleo3M/I+1Tl6HqZexEZ4QUpZh0QuaFB5NjKMSX7PSLLUN0rinrmin4N4tizcme8i23dZ1onFurq3XaZaRqRb6lyP+UcS312TG0Xz42g5t1qrKp5krpXvPtcjOVM5cyq+qa6XdVf59mRVVb42+bEh6vnmqLGe0yus1prqX6Xx6bmOs64iM0HLhC/sQUAAAAAJI2FLQAAAAAgaSxsAQAAAABJY2ELAAAAAEgaC1sAAAAAQNJY2AIAAAAAksbCFgAAAACQtMJDg1SuXVm53x6T36ayj1QWmMoxU3lbETKZU+bnYQWRpyX37+Vt1fvZcjJnUWROqRxcde7udVc5tWrf6txUjpjKMfOOXWaeiozIYufUeve6ynfbSnMao3I7zfwsxEyUadXnERmQZqbvFe/zxXOj6Lme6rp4ea4qn1L0q8rHVHmpkte30TncYsyoLENTfedsH5N5bybz0WU+cUx2fInYd654c42vs6xczA/VHCyiTsTO72QGr8pEjSi/at/u/M5M17AOyg3d6EeLY1NzVzm/3ITly6buW+eAiwxfdW6NEXM0lTOrar86d/W8j8kZV+vIxshs+OaP6ZC9AAAAAACwhbCwBQAAAAAkjYUtAAAAACBpLGwBAAAAAEljYQsAAAAASBoLWwAAAABA0ljYAgAAAACSVngQlMpCVPlDXu6d2rfKZVK5d2r7RrG9d3wqklTlqcrMUZVpKg4gImMymsyKjfi5SvS+RRaX2r/KEotRzEzoQnhjMh+X79ZpqRoTkyWrxqKiakxslmHeOXeVCRqbt6ruc+/YzPzji+x3eS+oHFtVn73nXuy+1ZhRWYaq3bssalt1bKo2qyxuNZfx+lY9j2MzxlOlaozKY/XGq7reqs9js7bX+ft3c3RV/RL3gsxcVueunkveucfM38z0mIjNX3dkOf/Yg+hXmVOrqGxl79mj6pfq13o/l1nlq8tcaG9ciIzxEDvX+Rt+YwsAAAAASBoLWwAAAABA0ljYAgAAAACSxsIWAAAAAJA0FrYAAAAAgKSxsAUAAAAAJI2FLQAAAAAgaYUHGIq8wSDyiTI3C1YchsrLEseWVVX628dkrKmcMJX5pPLbFJVjFpu35RHnHtaLc1PH5lxWmXZV7OxPlQMZm89ZTCofzhtTItMvqxAZvFsplYXo5uKVVvg7V2M1diyqXFFvPMVmHcpM04icWrO4OqGeifV+HqrMQswi8oVjM8pVlquqITEZlOq81TNTZntG3g/OdZf3uTq2zkr1eUTusczdjMzpztV08fdf5s/xQl1d+42qNsuMXpGxG5sF620vnjuq37MK/7nm9lsBn+/uO4j6pqgxFZth7o4LsW8xZuT9omqUai93Pr/Jf+a568RNwG9sAQAAAABJY2ELAAAAAEgaC1sAAAAAQNJY2AIAAAAAksbCFgAAAACQNBa2AAAAAICkFf4edPEaZvkS+9jYh5h9q1dzx0a7eGLjfITQEPHa8tgojBD5Cn8V61Bf336buibqmqsxo16jL6hXqnsyFRGVE69E9/rNzNzsBDO/b2T01uafd9JUfVT3gic2zif2Ffrq8717pVzcpyoWQR17bJRRTBSIqI8yzif2unjPFrXvyDigIJ5rOsrIa4+MalM1SJ17mYgsc+7lLPazt1bqueKMpywv5lixfS7nEhHjScVqxZKRiqJ+RkTqKEHNU2Kfa96xx+5bRiaKfhfzARUb5n+0mItUiPEqantY58cwuZE9qt8izntD/MYWAAAAAJA0FrYAAAAAgKSxsAUAAAAAJI2FLQAAAAAgaSxsAQAAAABJY2ELAAAAAEgaC1sAAAAAQNIiQvy+vCexKy/frVFkF8XmkKksWbV/L6dM5NapPKosNiNNZc16eV0qa0v1S2zGWb6I+ZsyTzAyu1jkoGXqunnE/RCCyJCMydUz87PEYvJYt2YxuXYq807lqaqxrHKRFffYY2tE8XIUzcwf67H56LG1W/Hqo8rCVtSxN4n8zZiMSVVj1L7V9vnIDHPnfpLPe5UrurVS/eLcK0E9i9W9IK6ZymyW91pEfZV50bIGqbEekQMu77PI/HUl2/z6GdSaQ7RnZeLYKyr89voi1gmVDxy5nsqqq/1v8LKZZa7yph/PxlBlAQAAAABJY2ELAAAAAEgaC1sAAAAAQNJY2AIAAAAAksbCFgAAAACQNBa2AAAAAICksbAFAAAAACSt43JsVW6ol1kVm1Orcu1UXpbKTvKywMSxyww1lfWlMtRi5NQ1i9x/TA6tEpuBpjaPPfbYMe3IVJSsGnNezpiZP95Vv8Tk96ZM5rNFZHOqbZXYe0Vxjz32Po3cPioLW3x2bN5qMa+Lem6ozxY1ROZnxmTTx2Q+m+l84Zi5iplZaP/Yo5/3nZXqU3HNvXzgLPZZLcZLlo+8l7x29dmxM3SR0RuVRVumMnQjc2xjs7id7GM5ZkS7zKtWWbJbMOc7NPjzv6yq0t9/zOer2ttB82Z+YwsAAAAASBoLWwAAAABA0ljYAgAAAACSxsIWAAAAAJA0FrYAAAAAgKSxsAUAAAAAJI2FLQAAAAAgaYWnZKn8NZVJ5WUbxWRpffHhm//ZZmY5lSkVkTcojl3mYal8zJg8LJUZFZk9V9TMvtjsOtWvipNlaGb+mFLjXWZ9iXZ1XRRvXKhrWlrE7OKvs9hszBInsy/428oaoahjU9fcuxfUWFTHrnIYY8d6bN95VJaronIiPTHPY7P4vEFVH2Pqdz4yM1rVX9XvMbXdttIc28j7NPPy0dVYUjVEbS/GQ1bm562qOZ5L5VEr6j6V88eIY1f3QmxObUy/qntcPPOyinJ/eydD94sdqOvitKvaqo5djWeRrRzWrvM/P0a5yNAtEL+xBQAAAAAkjYUtAAAAACBpLGwBAAAAAEljYQsAAAAASBoLWwAAAABA0ljYAgAAAACSxsIWAAAAAJC0iKC8r5DK4orNU80iMyg9IidMZUZJKoO3qaH9tkbRLzKrUGX2CercvSwwlQOmMs7UJVXnrsaUl/+m+k1mPIr2Rueam+n7xRvvMtM0ckykKvaaxuaxxny2kkVkmKuPVnl/Sux48+qA2HcQeakyXzi29seMGZWxq3IeVf2NObbY/HS1vartsm+ctmJmt6csss+DUyfEnuPHixDUveBR85SYLOtCqBoUMxcoE1mv4txV/q+cO8c8l1Ttjs3KVmPS2z52riCuS6gX80c1Jr17WdWB+nq/vUD8xhYAAAAAkDQWtgAAAACApLGwBQAAAAAkjYUtAAAAACBpLGwBAAAAAEljYQsAAAAASBoLWwAAAABA0goPyVJZXyrbyMu8UnlYsTmMisoS83KjYvNOYzP1FDfzSlzTmJzZQqgsMK9vGho3f9uC2lVWrMjb8u6HfOTPk1RunsxSFNfdu9/Uraj6tbOSObXqmjnXJDa3LrYGKd65x9bH2HOXGehOu4pTjX0sqfopr5vTHvu8jsnmLOTzg7P/6JzGiLmImekL7/UNOd8bE8TzWmU+Z05abXTeaex4U3MRbzyqzy72/FHdp+7nR84fxbHL66Z4+486b7FvswLygUUd8LaPmTeb+XMNswJynSMefGq8x17zv+E3tgAAAACApLGwBQAAAAAkjYUtAAAAACBpLGwBAAAAAEljYQsAAAAASBoLWwAAAABA0jrm3cpm+rXiboSIeH10bGSEol5B7b2iX0S3RL+KXvSN3L8XTaD6NTaOQhGv+Hc/PzpGJC7yIYi+ybz7ITYaRlFxFzERUupV8LmOKylJiY0P8K5ZbKRDeZnfruIDFO8+lnEW4thVPEpsFJxXP4sdQ6fGjNq/t31sjEiTuC6qxqh2jxrP6n5QaRWqb9T+vTFTVu5v29jgt3dSKs4nZg4m963mWPUiuk/dh2qOpuKAHO78zUw/b8VYDqL2u32r+kXV7mLH0GUxkTmiXd3nYl4eGkQdyLXf71mF+OzouDTRHhElF+r8e03eywXiN7YAAAAAgKSxsAUAAAAAJI2FLQAAAAAgaSxsAQAAAABJY2ELAAAAAEgaC1sAAAAAQNJY2AIAAAAAklZ42JzM8opYI8fmKKq8LJXrJPNUnTwtkbknc8gUkeUlc5+866by3VT+WmxOmeLtX47HuI+WWV0xYvODY3MeVcZkLqLzYq95qmLPOyaTVF1PNZ5kNrEar057KOJ91AG8HMfoTD21veoamX3s5Bnmxc6LmaFbCG9Mx+YDK7HzBe/cRX5l0c/t60rNNVS/ePeC6nOVKaoy6WPmWGbmVU+ZZxqTB20mx3KWiRpTUeHs2t+3yiwN4rroDN/Nz+iNzj5W9VXIYuqAep6rZ66aP0bmNmdlThCuzE+PXAv+Db+xBQAAAAAkjYUtAAAAACBpLGwBAAAAAEljYQsAAAAASBoLWwAAAABA0ljYAgAAAACSxsIWAAAAAJC0LKgQLwAAAAAAvsb4jS0AAAAAIGksbAEAAAAASWNhCwAAAABIGgtbAAAAAEDSWNgCAAAAAJLGwhYAAAAAkDQWtgAAAACApLGwBQAAAAAkjYUtAAAAACBp/z/7Y776a6YTBQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# disk dominated\n",
    "# This is a comparision of an image of galaxy from DP0 at\n",
    "# (ra, dec)= (61.99284796403353, -36.9900209621661) degree.\n",
    "global_min = min(\n",
    "    dc2_galaxy_image_from_slsim_with_dp0_background_cutout_i_1.min(),\n",
    "    dc2_galaxy_image_from_dp0_i_1.min(),\n",
    "    diff_i_n_1.min(),\n",
    ")\n",
    "global_max = max(\n",
    "    dc2_galaxy_image_from_slsim_with_dp0_background_cutout_i_1.max(),\n",
    "    dc2_galaxy_image_from_dp0_i_1.max(),\n",
    "    diff_i_n_1.max(),\n",
    ")\n",
    "# Plotting the images\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.imshow(\n",
    "    dc2_galaxy_image_from_slsim_with_dp0_background_cutout_i_1,\n",
    "    origin=\"lower\",\n",
    "    vmin=global_min,\n",
    "    vmax=global_max,\n",
    ")\n",
    "plt.title(\"Galaxy image from slsim\")\n",
    "plt.axis(\"off\")  # Turn off axis labels\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.imshow(\n",
    "    dc2_galaxy_image_from_dp0_i_1, origin=\"lower\", vmin=global_min, vmax=global_max\n",
    ")\n",
    "plt.title(\"Galaxy image from DP0\")\n",
    "plt.axis(\"off\")\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.imshow(diff_i_n_1, origin=\"lower\", vmin=global_min, vmax=global_max)\n",
    "plt.title(\"Residual\")\n",
    "plt.axis(\"off\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bulge dominated\n",
    "# This is a comparision of an image of galaxy from DP0 at\n",
    "# (ra, dec)= (62.00273461841879, -37.00827463174009) degree.\n",
    "global_min = min(\n",
    "    dc2_galaxy_image_from_slsim_with_dp0_background_cutout_i_2.min(),\n",
    "    dc2_galaxy_image_from_dp0_i_2.min(),\n",
    "    diff_i_n_2.min(),\n",
    ")\n",
    "global_max = max(\n",
    "    dc2_galaxy_image_from_slsim_with_dp0_background_cutout_i_2.max(),\n",
    "    dc2_galaxy_image_from_dp0_i_2.max(),\n",
    "    diff_i_n_2.max(),\n",
    ")\n",
    "# Plotting the images\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.imshow(\n",
    "    dc2_galaxy_image_from_slsim_with_dp0_background_cutout_i_2,\n",
    "    origin=\"lower\",\n",
    "    vmin=global_min,\n",
    "    vmax=global_max,\n",
    ")\n",
    "plt.title(\"Galaxy image from slsim\")\n",
    "plt.axis(\"off\")  # Turn off axis labels\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.imshow(\n",
    "    dc2_galaxy_image_from_dp0_i_2, origin=\"lower\", vmin=global_min, vmax=global_max\n",
    ")\n",
    "plt.title(\"Galaxy image from DP0\")\n",
    "plt.axis(\"off\")\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.imshow(diff_i_n_2, origin=\"lower\", vmin=global_min, vmax=global_max)\n",
    "plt.title(\"Residual\")\n",
    "plt.axis(\"off\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Disk dominated\n",
    "## Produce RGB color image for slsim\n",
    "slsim_image_list_n_1 = [\n",
    "    dc2_galaxy_image_from_slsim_with_dp0_background_cutout_i_1,\n",
    "    dc2_galaxy_image_from_slsim_with_dp0_background_cutout_r_1,\n",
    "    dc2_galaxy_image_from_slsim_with_dp0_background_cutout_g_1,\n",
    "]\n",
    "\n",
    "slsim_rgb_image_n_1 = rgb_image_from_image_list(\n",
    "    image_list=slsim_image_list_n_1, stretch=0.5\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Bulge dominated\n",
    "## Produce RGB color image for slsim\n",
    "slsim_image_list_n_2 = [\n",
    "    dc2_galaxy_image_from_slsim_with_dp0_background_cutout_i_2,\n",
    "    dc2_galaxy_image_from_slsim_with_dp0_background_cutout_r_2,\n",
    "    dc2_galaxy_image_from_slsim_with_dp0_background_cutout_g_2,\n",
    "]\n",
    "\n",
    "slsim_rgb_image_n_2 = rgb_image_from_image_list(\n",
    "    image_list=slsim_image_list_n_2, stretch=0.5\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Disk dominated\n",
    "diff_n_list_1 = [diff_i_n_1, diff_r_n_1, diff_g_n_1]\n",
    "\n",
    "## Bulge dominated\n",
    "diff_n_list_2 = [diff_i_n_2, diff_r_n_2, diff_g_n_2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Disk dominated\n",
    "rgb_diff_nn_1 = rgb_image_from_image_list(image_list=diff_n_list_1, stretch=0.5)\n",
    "\n",
    "## bulge dominated\n",
    "rgb_diff_nn_2 = rgb_image_from_image_list(image_list=diff_n_list_2, stretch=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This is a comparision of an image of galaxy from DP0 at\n",
    "# (ra, dec)= (61.99284796403353, -36.9900209621661) degree.\n",
    "global_min = min(slsim_rgb_image_n_1.min(), dp0_rgb_image_1.min(), rgb_diff_nn_1.min())\n",
    "global_max = max(slsim_rgb_image_n_1.max(), dp0_rgb_image_1.max(), rgb_diff_nn_1.max())\n",
    "# Plotting the images\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.imshow(slsim_rgb_image_n_1, origin=\"lower\", vmin=global_min, vmax=global_max)\n",
    "plt.title(\"Galaxy image from slsim\")\n",
    "plt.axis(\"off\")  # Turn off axis labels\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.imshow(dp0_rgb_image_1, origin=\"lower\", vmin=global_min, vmax=global_max)\n",
    "plt.title(\"Galaxy image from DP0\")\n",
    "plt.axis(\"off\")\n",
    "\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.imshow(rgb_diff_nn_1, origin=\"lower\", vmin=global_min, vmax=global_max)\n",
    "plt.title(\"Residual\")\n",
    "plt.axis(\"off\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This is a comparision of an image of galaxy from DP0 at\n",
    "# (ra, dec)= (62.00273461841879, -37.00827463174009) degree.\n",
    "global_min = min(slsim_rgb_image_n_2.min(), dp0_rgb_image_2.min(), rgb_diff_nn_2.min())\n",
    "global_max = max(slsim_rgb_image_n_2.max(), dp0_rgb_image_2.max(), rgb_diff_nn_2.max())\n",
    "# Plotting the images\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.imshow(slsim_rgb_image_n_2, origin=\"lower\", vmin=global_min, vmax=global_max)\n",
    "plt.title(\"Galaxy image from slsim\")\n",
    "plt.axis(\"off\")  # Turn off axis labels\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.imshow(dp0_rgb_image_2, origin=\"lower\", vmin=global_min, vmax=global_max)\n",
    "plt.title(\"Galaxy image from DP0\")\n",
    "plt.axis(\"off\")\n",
    "\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.imshow(rgb_diff_nn_2, origin=\"lower\", vmin=global_min, vmax=global_max)\n",
    "plt.title(\"Residual\")\n",
    "plt.axis(\"off\")\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}