tutorials/cosmocnc_so_benchmark_class_sz.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f3ac3efd-e16c-4877-ba47-6964dfadc788",
   "metadata": {},
   "source": [
    "# Import packages\n",
    "\n",
    "\n",
    "To download an install $\\texttt{cosmocnc}$, see [github.com/inigozubeldia/cosmocnc](https://github.com/inigozubeldia/cosmocnc/tree/main).\n",
    "\n",
    "To download and install $\\texttt{class_sz}$, see [github.com/CLASS-SZ/class_sz](https://github.com/CLASS-SZ/class_sz)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ad27bc85-fffa-4ef3-8e6b-fefd8836b5b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ce3057d8-0fd7-483e-95e4-f240ca5baf76",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<frozen importlib._bootstrap>:228: RuntimeWarning: scipy._lib.messagestream.MessageStream size changed, may indicate binary incompatibility. Expected 56 from C header, got 64 from PyObject\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: PATH_TO_COSMOCNC is not set.\n",
      "defaulting to  /home/iz221/cosmocnc/cosmocnc/../\n"
     ]
    }
   ],
   "source": [
    "import cosmocnc as cosmocnc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "30f24892-7980-4403-8a54-d5ef04f22bf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/iz221/cosmocnc/cosmocnc/__init__.py'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosmocnc.__file__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fa306f21-04ef-4ce1-bb35-c590f977bdc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-01-09 20:22:49.080967: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:268] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'/home/iz221/anaconda3/lib/python3.9/site-packages/classy_sz.cpython-39-x86_64-linux-gnu.so'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import classy_sz\n",
    "from classy_sz import Class\n",
    "classy_sz.__file__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee280978-d1f8-4c64-b0ac-9b3b9ba93353",
   "metadata": {},
   "source": [
    "# Number counts calculations for a Simons Observatory like experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dfd99a1-64f8-4a54-8970-96ce17fa4d8f",
   "metadata": {},
   "source": [
    "## cosmocnc with internal halo mass function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "59062e5b-4758-4e18-a12d-5fa9d0642c38",
   "metadata": {},
   "outputs": [],
   "source": [
    "number_counts = cosmocnc.cluster_number_counts()\n",
    "number_counts.cnc_params.update(\n",
    "{\n",
    "# \"path_to_cosmopower_organization\" : \"/Users/boris/Work/CLASS-SZ/SO-SZ/cosmopower-organization/\",\n",
    "\"compute_abundance_matrix\" : True,\n",
    "\"cluster_catalogue\" : \"SO_sim_\" + str(0),\n",
    "\"observables\" : [[\"q_so_sim\"]],\n",
    "\"obs_select\" : \"q_so_sim\",\n",
    "\"data_lik_from_abundance\" : True,\n",
    "\"number_cores_hmf\" : 1,\n",
    "\"number_cores_abundance\" : 8,\n",
    "\"number_cores_data\" : 16,\n",
    "\"obs_select_min\" : 5.,\n",
    "\"obs_select_max\" : 200.,\n",
    "\n",
    "\"parallelise_type\" : \"redshift\",\n",
    "\"scalrel_type_deriv\" : \"numerical\",\n",
    "\n",
    "\n",
    "\n",
    "\"z_max\" : 3.,\n",
    "\"z_min\" : 0.01,\n",
    "\"n_z\" : 5000,\n",
    "\n",
    "\"M_min\" : 1e13,\n",
    "\"M_max\" : 1e16,\n",
    "\"n_points\" : 1024*32, #64*4#2**13, ##number of points in which the mass function at each redshift (and all the convolutions) is evaluated\n",
    "\n",
    "\"apply_obs_cutoff\" : False,\n",
    "    \n",
    "'cosmology_tool' : \"classy_sz\",\n",
    "\"interp_tinker\": \"log\",\n",
    "    \n",
    "    \n",
    "\"class_sz_ndim_redshifts\" : 500,\n",
    "\"class_sz_ndim_masses\" : 100,  # when using cosmopower this is automatically fixed. \n",
    "\"class_sz_concentration_parameter\" : \"B13\",\n",
    "    \n",
    "\"hmf_calc\": \"cnc\"\n",
    "})\n",
    "number_counts.scal_rel_params.update(\n",
    "{\n",
    "\"dof\" : 0.,\n",
    "\"bias_sz\" : 0.8,\n",
    "}\n",
    ")\n",
    "\n",
    "number_counts.cosmo_params.update(\n",
    "{\n",
    "'h':0.7,\n",
    "}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7fda0f6e-eab9-4b88-8cf7-4a48062714dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/iz221/anaconda3/lib/python3.9/site-packages/mcfit/mcfit.py:130: UserWarning: use backend='jax' if desired\n",
      "  warnings.warn(\"use backend='jax' if desired\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min 8s, sys: 5.09 s, total: 2min 13s\n",
      "Wall time: 2min 18s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "number_counts.initialise()\n",
    "number_counts.get_number_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f98259e2-2aad-43cd-bd25-4390e3036dc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "dndz = number_counts.n_z\n",
    "dndq = number_counts.n_obs\n",
    "cnc_zs = number_counts.redshift_vec\n",
    "cnc_qs = number_counts.obs_select_vec"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea0b9e8e-1721-4165-b4bc-16a8ba48e688",
   "metadata": {},
   "source": [
    "## cosmocnc with class_sz halo mass function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bcbb2bc0-ac57-40fa-b3d0-7fafeeee7d6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "number_counts = cosmocnc.cluster_number_counts()\n",
    "number_counts.cnc_params.update(\n",
    "{\n",
    "# \"path_to_cosmopower_organization\" : \"/Users/boris/Work/CLASS-SZ/SO-SZ/cosmopower-organization/\",\n",
    "\"compute_abundance_matrix\" : True,\n",
    "\"cluster_catalogue\" : \"SO_sim_\" + str(0),\n",
    "\"observables\" : [[\"q_so_sim\"]],\n",
    "\"obs_select\" : \"q_so_sim\",\n",
    "\"data_lik_from_abundance\" : True,\n",
    "\"number_cores_hmf\" : 1,\n",
    "\"number_cores_abundance\" : 8,\n",
    "\"number_cores_data\" : 16,\n",
    "\"obs_select_min\" : 5.,\n",
    "\"obs_select_max\" : 200.,\n",
    "\n",
    "\"parallelise_type\" : \"redshift\",\n",
    "\"scalrel_type_deriv\" : \"numerical\",\n",
    "\n",
    "\"z_max\" : 3.,\n",
    "\"z_min\" : 0.01,\n",
    "\"n_z\" : 5000,\n",
    "\n",
    "\"M_min\" : 1e13,\n",
    "\"M_max\" : 1e16,\n",
    "\"n_points\" : 1024*32, #64*4#2**13, ##number of points in which the mass function at each redshift (and all the convolutions) is evaluated\n",
    "\n",
    "\"apply_obs_cutoff\" : False,\n",
    "    \n",
    "'cosmology_tool' : \"classy_sz\",\n",
    "    \n",
    "    \n",
    "\"class_sz_ndim_redshifts\" : 500,\n",
    "\"class_sz_ndim_masses\" : 100,  # when using cosmopower this is automatically fixed. \n",
    "\"class_sz_concentration_parameter\" : \"B13\",\n",
    "\"class_sz_hmf\": \"T08M500c\", \n",
    "\"hmf_calc\": \"classy_sz\"\n",
    "})\n",
    "number_counts.scal_rel_params.update(\n",
    "{\n",
    "\"dof\" : 0.,\n",
    "\"bias_sz\" : 0.8,\n",
    "}\n",
    ")\n",
    "\n",
    "number_counts.cosmo_params.update(\n",
    "{\n",
    "'h':0.7,\n",
    "}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "81665418-1914-41c4-bfbd-41a5b89cf27c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 42s, sys: 4.37 s, total: 1min 46s\n",
      "Wall time: 1min 51s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "number_counts.initialise()\n",
    "number_counts.get_number_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e49fc5e1-f6d8-4ee3-8107-ffe377a047f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "dndz_class_sz_hmf = number_counts.n_z\n",
    "dndq_class_sz_hmf = number_counts.n_obs\n",
    "cnc_zs_class_sz_hmf = number_counts.redshift_vec\n",
    "cnc_qs_class_sz_hmf = number_counts.obs_select_vec"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ff71532-49ff-4608-83ed-d15a914a45e8",
   "metadata": {},
   "source": [
    "## class_sz calculation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f12d4949-dc97-40b6-80a0-d936c9dad8e6",
   "metadata": {},
   "source": [
    "### reformatting of the noise file for class_sz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cfbc7d85-6ef3-4ef4-9509-678fe7f715ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "theta_500_vec,sigma_sz_vec = np.load(\"../data/so_sim_sz_mf_noise.npy\")\n",
    "theta_500_vec = theta_500_vec*180.*60./np.pi\n",
    "x = np.log(theta_500_vec)\n",
    "y = np.log(sigma_sz_vec)\n",
    "sigma_sz_poly = np.polyfit(x,y,deg=3)\n",
    "sigma_sz_polyder = np.polyder(sigma_sz_poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fa4c9caa-f87e-4e11-b810-6ed38208ae63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.00000000e-03, 1.00230570e-03, 1.00461672e-03, ...,\n",
       "        9.95404500e+01, 9.97699604e+01, 1.00000000e+02]),\n",
       " array([2.06988018e+21, 1.98430884e+21, 1.90231220e+21, ...,\n",
       "        2.39807414e-06, 2.39448283e-06, 2.39088924e-06]))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta_min = 0.001\n",
    "theta_max = 100.\n",
    "ntheta = 5000\n",
    "theta_new = np.geomspace(theta_min,theta_max,ntheta)\n",
    "sigma_new = np.exp(np.polyval(sigma_sz_poly,np.log(theta_new)))\n",
    "theta_new,sigma_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ea55cf23-afac-406c-9f67-9b52266c4bec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(theta_new,sigma_new)\n",
    "plt.loglog()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "051551e7-fbc1-4a9d-a32c-fcba676bab66",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt(\"../data/so_sim_sz_mf_noise_skyfracs.txt\",np.c_[[0.4]])\n",
    "np.savetxt(\"../data/so_sim_sz_mf_noise_sigmas.txt\",np.c_[sigma_new])\n",
    "np.savetxt(\"../data/so_sim_sz_mf_noise_thetas.txt\",np.c_[theta_new])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "506eff8b-b19e-4f70-84ea-cf4608806f83",
   "metadata": {},
   "source": [
    "### compute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "889e6cb7-d95d-4b4b-8618-2edae0e7baeb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Absolute path to the data directory: /home/iz221/cosmocnc/data\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "# Define the relative path to the data directory\n",
    "relative_dir_path = \"../data/\"\n",
    "\n",
    "# Get the absolute path of the data directory\n",
    "absolute_dir_path = os.path.abspath(relative_dir_path)\n",
    "\n",
    "print(f\"Absolute path to the data directory: {absolute_dir_path}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "500d4fa4-7523-4717-977a-8ea823f004d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/iz221/anaconda3/lib/python3.9/site-packages/mcfit/mcfit.py:130: UserWarning: use backend='jax' if desired\n",
      "  warnings.warn(\"use backend='jax' if desired\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 3s, sys: 73.2 ms, total: 1min 3s\n",
      "Wall time: 1min 3s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "cnc_classy_sz = Class()\n",
    "\n",
    "\n",
    "szcounts_param ={\n",
    "'output': 'sz_cluster_counts_fft,m500c_to_m200c,m200c_to_m500',\n",
    "    \n",
    "\"concentration parameter\" :  number_counts.cnc_params[\"class_sz_concentration_parameter\"],    \n",
    "\n",
    "'mass function'  : number_counts.cnc_params[\"class_sz_hmf\"],\n",
    "    \n",
    "'omega_b': number_counts.cosmo_params['Ob0h2'],\n",
    "'omega_cdm':  number_counts.cosmo_params['Oc0h2'],\n",
    "'h': number_counts.cosmo_params['h'],\n",
    "'sigma8': number_counts.cosmo_params['sigma_8'],\n",
    "'n_s': number_counts.cosmo_params['n_s'],\n",
    "'tau_reio': 0.06,\n",
    "    \n",
    "    \n",
    "'B': 1./number_counts.scal_rel_params['bias_sz'],\n",
    "    \n",
    "'N_ncdm' : 1,\n",
    "'N_ur' : 2.0328,\n",
    "'m_ncdm' : 0.06,\n",
    "'T_ncdm' : 0.71611,\n",
    "# 'z_min': 0.,\n",
    "# 'z_max': 2.,\n",
    "'redshift_epsrel': 1e-6,\n",
    "'redshift_epsabs': 1e-100,    \n",
    "    \n",
    "\n",
    "    \n",
    "'M_min': number_counts.cnc_params['M_min']*number_counts.cosmo_params['h']*0.8,\n",
    "'M_max': number_counts.cnc_params['M_max']*number_counts.cosmo_params['h']*1.2,\n",
    "    \n",
    "    \n",
    "'mass_epsrel':1e-6,\n",
    "'mass_epsabs':1e-100,\n",
    "    \n",
    "'ndim_redshifts': 5000,#number_counts.cnc_params[\"class_sz_ndim_redshifts\"], \n",
    "'ndim_masses' : number_counts.cnc_params[\"class_sz_ndim_masses\"],\n",
    "'szcounts_fft_nz' : 585,\n",
    "'n_m_dndlnM' : 8000,\n",
    "\n",
    "\n",
    "    \n",
    "'has_selection_function' : 1,\n",
    "'experiment' : 1, # Planck: 0 / ACT or SO: 1\n",
    "'y_m_relation' : 1, # ycenter : 1, # y500 : 0\n",
    "'signal-to-noise_cut-off_for_survey_cluster_completeness' : number_counts.cnc_params['obs_select_min'],\n",
    "\n",
    "'sz_selection_function_thetas_file' : f'{absolute_dir_path}/so_sim_sz_mf_noise_thetas.txt',\n",
    "'sz_selection_function_skyfracs_file' : f'{absolute_dir_path}/so_sim_sz_mf_noise_skyfracs.txt',\n",
    "'sz_selection_function_ylims_file' : f'{absolute_dir_path}/so_sim_sz_mf_noise_sigmas.txt',    \n",
    "\n",
    "\n",
    "    \n",
    "    \n",
    "'A_ym'  : 10**number_counts.scal_rel_params['A_szifi'],\n",
    "'B_ym'  : number_counts.scal_rel_params['alpha_szifi']-1.,\n",
    "'C_ym' : 0.,\n",
    "'sigmaM_ym' : number_counts.scal_rel_params['sigma_lnq_szifi'],\n",
    "'m_pivot_ym_[Msun]' : 3e14,\n",
    "\n",
    "'use_m500c_in_ym_relation' : 1,\n",
    "'use_m200c_in_ym_relation' : 0,\n",
    "'use_skyaveraged_noise': 0, # this will speed-up everything\n",
    "    \n",
    "    \n",
    "# 'N_samp_fftw' : 2**12,\n",
    "'N_samp_fftw' : 1600*4,\n",
    "\n",
    "'szcounts_fft_nz' : 550,\n",
    "\n",
    "'z_min' : 0.8*number_counts.cnc_params['z_min'],\n",
    "'z_max' : 1.2*number_counts.cnc_params['z_max'],\n",
    "    \n",
    "'szcounts_fft_z_min' :0.8*number_counts.cnc_params['z_min'],\n",
    "'szcounts_fft_z_max' :1.2*number_counts.cnc_params['z_max'],\n",
    "    \n",
    "    \n",
    "    \n",
    "'tol_dlnm_dlnq':0.001,\n",
    "'ntab_dlnm_dlnq':1000, # fiducial 1000\n",
    "\n",
    "'szcounts_qmax_fft_padded':500.,\n",
    "    \n",
    "'sigma_derivative' : 0,\n",
    "'szcc_dof': 0.,\n",
    "'szcc_qtrunc':-1.,\n",
    "\n",
    "    \n",
    "'HMF_prescription_NCDM': 1,\n",
    "'no_spline_in_tinker': 1,\n",
    "    \n",
    "'cosmo_model': 0, # pick lcdm emulators for that. \n",
    "        }\n",
    "cnc_classy_sz.set(szcounts_param)\n",
    "        \n",
    "cnc_classy_sz.compute_class_szfast()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "40d0fd6b-c42f-4d40-9291-afdd85fd51da",
   "metadata": {},
   "outputs": [],
   "source": [
    "zmin = number_counts.cnc_params['z_min']\n",
    "zmax = number_counts.cnc_params['z_max']\n",
    "nz = szcounts_param['szcounts_fft_nz']\n",
    "z_arr = np.linspace(zmin,zmax,nz)\n",
    "\n",
    "q_threshold = number_counts.cnc_params['obs_select_min']\n",
    "q_max = 200.\n",
    "nq = 5000\n",
    "q_arr = np.geomspace(q_threshold, q_max,nq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a289ff70-0377-40d2-a636-9260c453b05a",
   "metadata": {},
   "outputs": [],
   "source": [
    "dq = (q_max-q_threshold)/(nq-1.)\n",
    "dz = (zmax - zmin)/(nz-1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cb603efa-318a-4296-8a9c-f94a24814dfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "get_dndzdq = np.vectorize(cnc_classy_sz.get_szcounts_dndzdq_at_z_q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c7516ffb-f20b-4842-85f5-876833cde4c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.integrate as integrate\n",
    "Nz = []\n",
    "for zp in z_arr:\n",
    "    Nz.append(integrate.simpson(get_dndzdq(zp,q_arr),x=q_arr,axis=0))\n",
    "Nz = np.asarray(Nz)\n",
    "\n",
    "Nq = []\n",
    "for qp in q_arr:\n",
    "    # Nq.append(np.trapz(get_dndzdq(z_arr,qp),x=z_arr))\n",
    "    Nq.append(integrate.simpson(get_dndzdq(z_arr,qp),x=z_arr,axis=0))\n",
    "Nq = np.asarray(Nq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ba9d980e-7e22-48a9-88d7-fd98f213c5eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15003.258116397177"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ntot = np.trapz(Nz,x=z_arr)\n",
    "Ntot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "caf53a58-62b6-48c6-b2d3-f3deee3c4787",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15003.258116397177"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ntot = np.trapz(Nz,x=z_arr)\n",
    "Ntot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "66fdf474-66bf-470b-b95c-f9cc6ec4fefe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'dndz')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+nQrcD5xsjFkBnBz8GWvtEmAq8CMwHbjGWusPbutq4BmqbnZYRdW1clBVEtONMSuBGwje6So1t2bTMpZ5FrG46Qh8MTGu4/zM4/VxTqsL8ZsAMz540HUcERGRsFNrp1OttV9S/TVrACP2s85kYHI10+dSdVPE3tNLgbOOIGa999KnD/Bq40Y82nXUwReuY2eNmkSX+/9Hi/KplBTdQUKDZNeRREREwkb9Ozkue5hbNJ8OZZbjj/6l6yj7MB4PMSf+iTR28MHrt7uOIyIiElZU4uqxpWu+Y1Wsn96+Lpgwvdi1+zGjubFxFx7yf8Sm7RtcxxEREQkb4fk3t9SJqXNfAuCk3hPdBjmIk3teRaHXw2Pv/951FBERqcaf/vSnaj/IV2qXSlw99mlRJb6Kpgztf5rrKAd04jHn07MsmWn+VWwtynMdR0REJCzU+of9SnjaWVrBxhWncf7gX4XtqdTd/XLwX7lr/hXc8clTPPmLm1zHEREJC5dMv2SfaaPajmJC1wmUVJbw649+vc/80zuezriO49hRuoMbPt3zsVn/Hv3vGr3ulClTeOihhzDG0Lt3bzp06PDzvKeffpqnnnqK8vJyOnbsyAsvvEBiYiKvvfYad911F16vl9TUVD7//HOWLFnCJZdcQnl5OYFAgDfeeINOnTrt83pFRUWcffbZZGVl4ff7ueOOO+jQoQOXXXYZUPXM1cWLF9e7xzWqxNVTyz56nte8/4R2L7mOUiPjew3m4e+6szL7HUpLriE+oYHrSCIi9dKSJUuYPHkyX331FRkZGeTm5vLoo4/+PP/MM8/k8ssvB+D222/n2Wef5brrruPuu+/mww8/pGXLluTl5QHwxBNP8Jvf/Ibzzz+f8vJy/H5/dS/J9OnTadGiBe+//z4A+fn5pKam/vyA+xtvvJHRo0fX3psOUypx9dRftr5P88ZeHu3S2XWUGnuw3S8Y9Nk1fDnzdYaPvfjgK4iIRLkDHTlL8CXsMX/vpws0im9U4yNvu/vkk08YP348GRkZAKSlpe0xf/Hixdx+++3k5eVRWFjIqFFVH2E1ZMgQJk6cyNlnn82ZZ54JwODBg5k8eTJZWVmceeaZ1R6FA+jVqxeTJk3i5ptvZsyYMQwdOvTneVOnTmX+/PnMmDHjkN9LpAv/82gScqUV5Sw361mddhweX+T0+MHDz+OGhv/g9mVNKalw+3xXEZH6ylrLAZ6DzsSJE3nsscdYtGgRd955J6WlpUDVUbd77rmHDRs20LdvX3JycjjvvPN45513SEhIYNSoUXzyySfVbrNz587MmzePXr16ceutt3L33XcDVUcF77zzTl555RW8Xm/o32yYU4mrh/63YBp4yjm+1dCDLxxGjMfDMcN6kZt+J/d9HhmngUVEos2IESOYOnUqOTk5AOTm7vnI8oKCApo3b05FRQUvvfT//69etWoVgwYN4u677yYjI4MNGzawevVq2rdvz/XXX8/YsWP54Ycfqn3NTZs2kZiYyAUXXMCkSZOYP38++fn5TJgwgSlTptC4cePae8NhLHIOw0jIzF/0ND6P5YLug11HOWRn9+nD0/Phu9V/x3/8RXi92oVFROpSjx49uO222xg+fDher5d+/frRtm3bn+f/+c9/ZtCgQbRp04ZevXpRUFAAVF23tmLFCqy1jBgxgj59+nD//ffz4osvEhMTQ7NmzfjjH/9Y7WsuWrSIG2+8EY/HQ0xMDI8//jhvvfUW69at+/n6O+Dna+TqC1Pf7uQYMGCAnTt3bq1tv6ysjLi4uFrbfiic9nRvkgOGV65c6DpKjew9pv945dc8VfYFv29+MRNHTnKYLDJFwj4aaTSmoacxrd7SpUvp1q3bYa279zVxcuRCPabV/X6NMfOstQOqW16/zXomZ+tGRhXlcUzsPo+ijRiXjJlMswo/76572XUUERERZ3Quqp5ZPWca1+fls3zopa6jHLakpEYM83RnqvcnPv/+PYb1G+M6koiIhEBOTg4jRozYZ/rHH39Menq6g0ThTSWuntmw+j1amwZ07DPMdZQjcvHIyYyecjyB/FdAJU5E6pGD3R0aydLT0+vddW27HM7lbTqdWo8E/H7+3mAFf2zWOqI+WqQ6rTO7YJNOpHfOh+zI2eY6johInYiPjycnJ6fePZkg2llrycnJIT4+/pDWi+y/yeWQzF7yITt8Hjom9HEdJSTST7iaBz79Cu/7v+f2i6a4jiMiUusyMzPJyspi27ZD/8drNB/BcyWUYxofH09mZuYhraMSV498tuR/AAztMd5xktDo0Os4fvwmldzy+dxcUU5MTKzrSCIitSomJoZ27dod1rq64zf0XI+pTqfWIz8VLKFpZYCBPU90HSVkTkgfyeYYw38+e9J1FBERkTqlEldP+AN+5vv8JHt64omiR5NceMof8VYm85+sr1xHERERqVMqcfXEupxiCtb/iqHtf+06Skg1SEymV8PT2GmW8M26H13HERERqTMqcfXEmrkzOLfiJ8Z27u06SsjdfOwFDM2LI2fGP1xHERERqTMqcfXEvDX/5riUt+nQNNV1lJDr2bwN51R2Zml2PGWVftdxRERE6oRKXD1greXF+Dz+3mwoJkqfm2fH/J0nbEeenP2x6ygiIiJ1Ijr/Rpc9LMhejfXm0zVjoOsotea4jukkZb7Gm8v+6jqKiIhInVCJqwc+n/McACdndnecpPb4vF7GxTRnu28TXy6c5jqOiIhIrVOJqwdWbv+WBoEAI7oc4zpKrbpw2C34rOWN7x51HUVERKTWqcTVA9v82+lQHktCfILrKLWqY9s+9C9LZI5nPQXFO13HERERqVUqcVGutLiQKZs28Cui+yjcLiNajcOLZebHT7mOIiIiUqtU4qLc2kVfE2v8pLUb4jpKnTjr5N/z8voS2i+Z4TqKiIhIrVKJi3Jv//Qit2Wkkdl7mOsodSImJo4NmePoUTKHNWuWuI4jIiJSa1Tiotzi0mV8H5dI42atXUepM81OuIQzWjXjn5/+wXUUERGRWqMSF8UCfj/rvUW0stH3lIYDaduhN+n+RL4LrKKsosx1HBERkVqhEhfFlqz+jlyfhzYNOruOUueGNBlFrs/w/Devuo4iIiJSK1Tiothnq+cA0KvNiY6T1L3zTrkD/A14ee0s11FERERqhUpcFFtWmkygtDnDBox3HaXOJcUn0rnBcLYHvmdV9mrXcUREREJOJS6K7dzWhTald5Aan+g6ihM39DyNB7bksOnjl1xHERERCTmVuCgV8Pv5W9YEfhv7lusozgzpMYx8M4ZXNrfEWus6joiISEipxEWpuesXM7p1GsubpriO4tTOIb9mplnI9OULXEcREREJKZW4KDVz3WLKvH5adzvVdRSnTunRnLi0T3nt2wdcRxEREQkplbgotST7Wwj4GNGhl+soTrVqmMHA8niW+3+gsLjAdRwREZGQUYmLUuUFX9Klwk+D2HjXUZw7PvMX5Hs9vDhTR+NERCR6qMRFoYrKctbFVNCKdNdRwsLZI24gozLAl1umu44iIiISMipxUWjt6sWcu7OAPqn9XEcJC3HxiQyxbWhWkcu2LRtcxxEREQkJlbgolL9yATfsyGNol3Guo4SNy469k4e2bWflx1NcRxEREQkJlbgotC3rW3biI7PrUa6jhI223Qex0tsB/9rX9ZlxIiISFVTiotBLdi4XtGxBXHwD11HCyoz2g7i6ZSXTZ7/iOoqIiMgRU4mLQlm+MprZVNcxws4vTriB2IBl2uLnXEcRERE5YipxUWbNhsXk+Dy0jG/jOkrYadWiEz0rGvKNZzvF5WWu44iIiBwRlbgoM/vHDwHo1FTXw1Wnf88bKfVW8uR377uOIiIickRU4qLM18XFAAzsMcZxkvB0+dGjwZ/EjOW6Lk5ERCKbz3UACa2ysv74cgN0zOzsOkpYSoyJY1JlF07Nfp8dOVtplN7EdSQREZHDoiNxUSYzawknJfR0HSOsHXfc9dxVdiXvLc1zHUVEROSwqcRFkeKSQgb6H+FkprmOEtY6dO/P4swmPL70766jiIiIHDaVuCjyxYZlTGqWxvy2fVxHCXs9W+RTEPsFX/3wsesoIiIih0UlLop8nbUEgAEdhztOEv6u7TUKYy3vzP2H6ygiIiKHRSUuimzKmokn4GFIm66uo4S9rh0G0rM8hvmBlVRW+l3HEREROWQqcVGkoHQp7SsCJMTEuo4SEYakDWNzjOGNz552HUVEROSQqcRFkU3eUprZFNcxIsa5J93MgJIyipd+6DqKiIjIIdPnxEWJnTu28p/Nm/khc6TrKBEjrWELflPam7Y7vqagsJDkpCTXkURERGpMR+KiRPby72lfUUnrzMGuo0SUBgMvwHhL+Oijp1xHEREROSQqcVFi7tqPeCU5iUbturuOElE6DDyVs1u04PUtL7uOIiIickhqrcQZY54zxmw1xizebdqfjDEbjTELgl+n7jbvVmPMSmPMT8aYUbtNP8oYsyg471FjjAlOjzPGvBqcPtsY07a23kskmFOwgIfTGtKqdTfXUSKKxxdLP08blsSVsGTN4oOvICIiEiZq80jcf4DR1Ux/2FrbN/j1AYAxpjswAegRXOdfxhhvcPnHgSuATsGvXdu8FNhhre0IPAz8pbbeSCTYGsihRYUXn8978IVlD2f0vxK/MTwx73XXUURERGqs1kqctfZzILeGi58OvGKtLbPWrgFWAgONMc2BFGvtN9ZaC0wBxu22zvPB718HRuw6SlcfZXvLaIruTD0cg/qeTnygNV/kzaFqNxMREQl/Lu5OvdYYcxEwF/i9tXYH0BL4drdlsoLTKoLf7z2d4J8bAKy1lcaYfCAd2L73CxpjrqDqaB6tW7emrKwspG9od+Xl5bW27f3ZlJNFjs9DU0+LWn1vrtTFmB7XbCQfbX2GaXM/ZkTvobX+ei652EejncY09DSmoacxDT3XY1rXJe5x4M+ADf75V+BXQHVH0OwBpnOQeXtOtPYp4CmAAQMG2Li4uENLfYhqe/t7m5uzGYD0jmfX+WvXldp+Xzf1O4Xrnv4za4reJe7ok2r1tcJBtO4nLmlMQ09jGnoa09BzOaZ1eneqtXaLtdZvrQ0ATwMDg7OygFa7LZoJbApOz6xm+h7rGGN8QCo1P30bVQKlrSj46U+M7nyi6ygRq3mzznze7Hb+vPVYSiv0GC4REQl/dVrigte47XIGsOt2wHeACcE7TttRdQPDHGttNlBgjDkmeL3bRcDbu61zcfD78cAntp5e0JS8aAp3+l6nQ+NGrqNEtIxjR7M5/U2enKMnOIiISPirzY8YeRn4BuhijMkyxlwKPBD8uJAfgBOA3wFYa5cAU4EfgenANdbaXYdDrgaeoepmh1XAtOD0Z4F0Y8xK4Abgltp6L+FuZumX7Gi0Eq+n3t7XERIndGpDQtJyvv3xUddRREREDqrWromz1p5bzeRnD7D8ZGByNdPnAj2rmV4KnHUkGaPFx/GFNE0+ihtcB4lwDWITGFqZxLe+LNZt20Sbxi1cRxIREdkvPbEhwmXvzMV682mT0t51lKhwWpdzKPEYXv7oQddRREREDkglLsLN+eE9AI6Oi3WcJDqMHHIlzSsCfL/zc9dRREREDkglLsKt2DQHgH4t9LitUDBeH6N9XRlWvI1lK1e5jiMiIrJfKnERrmRnFhmVfnp3GeY6StS49IQ7uSY/n3WfTXEdRUREZL9U4iLc2NxSntvoJT4uwXWUqJHapjcrYzuSu/0tyiv1mXEiIhKeVOIiXFrpenLi27iOEXWmd+zHvc0CvPXVq66jiIiIVEslLoKVlhZxU1M/XzdKch0l6kw44RZirOXTn/7jOoqIiEi1VOIi2ILlX7A4PpZASmPXUaJORuOO9Ag04VtvLjmFxa7jiIiI7EMlLoItXT8bgHbN+roNEqVG9Ps9Fb4yHpv9gesoIiIi+1CJi2Drc5cB0LeT7kytDef1OhlvIJHvV051HUVERGQfKnERbJW/lCS/oXXzjq6jRKVYXyx/q+jIS5tmsXbTVtdxRERE9qASF8GyAl0xgUEYowff15beo+/grIq7eWPxDtdRRERE9qASF8GKN5/A4JRrXceIahmtu+LvtJUX19xNIGBdxxEREfmZSlyEKtyZy6zy8zmtcobrKFGvb4afyvgfmDH3I9dRREREfqYSF6FmrZrH8a0zyUpLdB0l6l3dZwzGWmYsfMx1FBERkZ+pxEWo+XmbKfZV0qLjUNdRol7b1gPpWRHDYrOKotJy13FEREQAlbiItXb7j1hrGJjZyXWUeuH4xsPIjjFM/eRp11FEREQAlbiI5dnxKc0rA6TE68H3deHcE29h3M5iYpd94jqKiIgIoBIXsbabQpr441zHqDeSU5pznmcAY/K/Jjsnz3UcERERlbhIZAMBhhYV0ce0dB2lXmk0+EI2xFXywcx/uI4iIiKiEheJtmevY1JeDsNTh7iOUq807j2Sa5s2YdaOt7FWnxknIiJuqcRFoI1rF1IOJDTr7DpKveL1xXKsry1LEkr59sfvXccREZF6TiUuAs3cMJ2j27bCNm7sOkq9c/Yx11FpDE/+8IHrKCIiUs+pxEWgLcUb8Fno2nGA6yj1Tt8uo2lgWjOv4GvKKv2u44iISD2mEheBcipzaFoJcbHxrqPUSyMzR+H1ZTPz209dRxERkXpMJS4CbTfFpAf08SKuTOp7Ol9sWI+d+6brKCIiUo+pxEUYay0bfF48cbqpwZWUtDZM7f4Mk7aOJKewzHUcERGpp1TiIkxucQlF206mdbOzXUep1zoc1QVv20d45Ou3XUcREZF6SiUuwmzJ3kanHW04rnl/11HqtSFtOpHk28FPq/XBvyIi4oZKXITJXvYWTzW4gw6eTa6j1Gsx3hhOJJ3l8TuYv/JH13FERKQeUomLMK+WbuGUzJakt+niOkq9d1b/K6g0hje+esB1FBERqYdU4iLM+rId4G9Io5Q011HqvaP6jKdTOSwsm48/oMdwiYhI3VKJizDlpctpRLLrGAJgDGemDOPa/G3M/+4r12lERKSeUYmLMJVspmflTtcxJOis0XcwoqiMgtnPu44iIiL1jEpcBMnduZUdPg8ZsU1cR5GguNRmzGk4iKWVs8jZmec6joiI1CMqcRFke9YqHty6nf4N9czUcLKx+3CeSE/gpZm6wUFEROqOSlwEKdm0ltFFxXTNHOQ6iuzmzBN+S0ZlgNnbZ7iOIiIi9YhKXARZuWUe8+LiyGitjxcJJ76YeHrHD+SHuHIWbFrnOo6IiNQTKnER5NOS77muaWPSG7dwHUX2cv7wW8BYHp39iusoIiJST6jERZCcQB5NKz0Yj35t4WZgZhfS/K2p3DQTGwi4jiMiIvWA2kAE2e4pI90muo4h+/GXhiN5cutCFi9e4DqKiIjUAypxEaKsvJStPkjzpbuOIvvRa+SvGG6f5D/Ly11HERGRekAlLkIs2roevzHENTnBdRTZjwZJybTu+hMf7ryOrQV5ruOIiEiUU4mLECWlSRStvZpB7ce6jiIHMKFzL4y3lOffv9V1FBERiXI1KnHGmFXGmKv2mvZe7USS6pSv+4HzypfTM12nU8PZmf1OoU255audX7uOIiIiUa6mR+IqgBOMMf82xsQGp7WspUxSjax1rzM4dSrNG+rGhnDm8XoZEt+HVXEBZs59y3UcERGJYjUtccXW2nOApcAXxpg2gK29WLK3l2NjuC29HXHxKnHh7oITbyMuYHl7wT9dRxERkSjmq+FyBsBa+4AxZh7wIZBWa6lkH3kVW4kxevB9JGjVsjvXFDRleN4ySoqLSUhU8RYRkdCr6ZG4P+76xlr7MTAKeKxWEkm1fIG1tHEdQmrsmP7X0T6wk4Ufveg6ioiIRKkDljhjTH9jTH9g467vgz+nA7qxoY6UlZdS6C2jva1wHUVqqOuxv+CtxOa8tuFJ11FERCRKHex06l+Df8YDA4CFVJ1a7Q3MBo6rvWiyy0/rF+A3hsYJemZqpDAeL4ub9mUGS/j0+/c4vt8Y15FERCTKHPBInLX2BGvtCcA6oL+1doC19iigH7CyLgIK+LbvYOb6jQxpOsx1FDkE5514GzHW8sZ8XXkgIiKhV9Nr4rpaaxft+sFauxjoWyuJZB/FW9fRzO+nZWYP11HkELRv1YtWthufmhzySopcxxERkShT0xK31BjzjDHmeGPMcGPM01R93IjUge9z5/BsajKNW3Z0HUUO0dheV4C3lL9/87rrKCIiEmVqWuIuAZYAvwF+C/wYnCZ14PuKVUxJSSUxKdl1FDlEE/uNILOkKWk/vOQ6ioiIRJkafU6ctbYUeDj4JXUs1xaQ7ve6jiGHwev1cEfCMdiNn7B2cw5tm+mxaSIiEho1fXbqEGPMTGPMcmPM6l1ftR1OquzwlNPI6gNjI1XHcbdxceBG/v7NF66jiIhIFKnpExueBX4HzAP8tRdH9lZZWck2H/SmkesocpiapCbQpss0PtmxgLyC4TRM1u9SRESOXE2vicu31k6z1m611ubs+qrVZALAyh1bKMdDZfNTXUeRI3Bp64FYbwnPTb/bdRQREYkSNS1xs4wxDxpjBu/15AapZcUliRQuu4dRbce5jiJHYMLQiWRWWL7c8YnrKCIiEiVqWuIGUfXEhsnAQ1Q9yeGhA61gjHnOGLPVGLN4t2lpwWvrVgT/bLTbvFuNMSuNMT8ZY0btNv0oY8yi4LxHjTEmOD3OGPNqcPpsY0zbGr/rCFK6fBZ/j3mcDol65FYk83h9DI3ry4q4ANO+edV1HBERiQIHe3bqDcaYG6h6Tuq7wPvBr/c4+LNT/wOM3mvaLcDH1tpOwMfBnzHGdAcmAD2C6/zLGLPrdszHgSuATsGvXdu8FNhhre1I1V2zfzlInoj09dYvmdN4DU0apbiOIkdo4qi7iQ8E+GrhM66jiIhIFDjYkbjk4NdRwNVAc6AFcCXQ/UArWms/B3L3mnw68Hzw++eBcbtNf8VaW2atXUPVI70GGmOaAynW2m+stRaYstc6u7b1OjBi11G6aPKZCfBuUkNSG2a4jiJHqEWT9kwu7M4fsheSn7vNdRwREYlwB7w71Vp7F4AxZgZVz04tCP78J+C1w3i9ptba7OC2s40xTYLTWwLf7rZcVnBaRfD7vafvWmdDcFuVxph8IB3YvveLGmOuoOpoHq1bt6asrOwwotdMeXl5SLeXU7qZGJteq5nDXajH1KW2x15D4rsz+OK9xxh4zh+cZIim8QwXGtPQ05iGnsY09FyPaU0/YqQ1sHvScqBtCHNUdwTNHmD6gdbZd6K1TwFPAQwYMMDGxcUdTsYaC+X2E/3LaE1aSLcZiaLl/Xc+6nj+8mVHvix7gze9t+PzxTjJES3jGU40pqGnMQ09jWnouRzTmt7Y8AIwxxjzJ2PMncBs/v9U5qHYEjxFSvDPrcHpWUCr3ZbLBDYFp2dWM32PdYwxPiCVfU/fRrSA30+et5IWNe7aEgkaZJ7A2ljDKzP/6jqKiIhEsBqVOGvtZKqelboDyAMusdbedxiv9w5wcfD7i4G3d5s+IXjHaTuqbmCYEzz1WmCMOSZ4vdtFe62za1vjgU+C181Fjawtq2gY8NM4rrnrKBJCE0/5Iw39ljc3znQdRUREIliND/FYa+cD82u6vDHmZeB4IMMYkwXcCdwPTDXGXAqsB84KbnuJMWYq8CNQCVxjrd31ZIirqbrTNQGYFvyCqqdIvGCMWUnVEbgJNc0WKcq2bOajDZuYf+zJrqNICCUlptA6eRwLi95h3qZVHNWig+tIIiISgWrtPJ219tz9zBqxn+UnU/U5dHtPnwv0rGZ6KcESGK0KtlQ9nja1WXvHSSTUbjzuMi748B3+9sWTvHTOA67jiIhIBKrpNXHiwKdbP+O6JhmktmjnOoqEWN/mbfllSWduXfEyxUU7XccREZEIpBIXxtaWrWdefDzp6c1cR5FaMOGoq3mv5FTe+X6j6ygiIhKBVOLC2A6bT4bfQxR+hrEAXY8+kU9aDOVvi/+B3x9wHUdERCKMSlwYyzVlNArEu44htcQYw+AuZZQkfMELsx53HUdERCKMSlyYstayxWdo4GvqOorUopuOOZO0ygAzVv/bdRQREYkwKnFhamthAcWlrYhpNsZ1FKlFDZIaMcx0ZFFcGV8unO46joiIRBCVuDC1vcBSvO7XjGp9uusoUssmnnQPsQHLy3P0USMiIlJzKnFhqmzJB3wR+xs6mE0HX1giWofWvTipLINeBavJz93uOo6IiEQIlbgw9Vb+Yn6VmUJSeprrKFIHrjj2Xq7auYOl7z/qOoqIiEQIlbgwtdhfQXaMh+ZN27iOInWgQ+9jWRjbh03ZL1NSUug6joiIRACVuDCVW7qJmEAaHo9+RfXFgl6juLNpPM98cKfrKCIiEgHUEMJUcvlCevh3uI4hdej8U24is8Lycc5MAn6/6zgiIhLmVOLC1HZvgDST7DqG1CGfL4bjUkewKs7y/PezXMcREZEwpxIXhvLytnFsSQntY1u7jiJ17Joxk8HfgGcW6cN/RUTkwFTiwtCOTWv567YcjssY4jqK1LGGCUkManQKtnIFPyz6ynUcEREJYypxYSg/eyUASU3bO04iLtw3ZCIzstaw7vO3XEcREZEwphIXhj7YPIOhrVsS17iZ6yjiQOMmbXi2z2v8dtMQlm7Z7DqOiIiEKZW4MLS9JJsS46F1yy6uo4gjY4f3IaH9A9z+iR7FJSIi1VOJC0M7AjtoUglen891FHGkXXojept01pfOYH32ctdxREQkDKnEhaEdpoRGgVjXMcSxK/r8ilKP4YnpN7uOIiIiYUglLgxleX34Ytq6jiGOHX/UGfQrjedzu5ycPF0bJyIie1KJCzPF5RUU5B1DRpMzXEeRMHBml8vJ93p48d0/uo4iIiJhRiUuzGRvzyNl67Ec12K46ygSBsYdfwUPbG3AOas+prS01HUcEREJIypxYSZ32Ud8kXAtXSt+dB1FwkTbQZNoxnbmvPMv11FERCSMqMSFmTeL1nJ021b4m7Z0HUXCRNfjzuSRhu24P/85ikqLXMcREZEwoRIXZlaXFmGtly6turuOImHCeDw06vRLNsQaHn/7JtdxREQkTKjEhZnCohXE+VOI8XpdR5EwcsHoP9Ch3PB24WxKK8pdxxERkTCgEhdmYsuX0bmywHUMCTMer5fju95Bnq+Mez9/yXUcEREJAypxYSbHW0kjk+w6hoSh64f8kpjKTKat/TeVOhonIlLvqcSFkdKSIi7Ly6e/r4PrKBKGPB4PkzLH8Vr2EhZMe851HBERcUwlLoxs37iGCwsK6N3oKNdRJExNOOkypsVcyp+WtaTCH3AdR0REHFKJCyMbshaxzucjvnFr11EkTHm8XlqecgUrE1/k7lkvuo4jIiIOqcSFkS83f8aYVi0IZKS5jiJh7LSe7UhP2MzXa/9GUUmh6zgiIuKISlwY2V68EZ+1dGnX33UUCWNej5crmp3I1hg//3zz967jiIiIIypxYSS3MpeMSktCXKLrKBLmLhx9O13KPEwr+ZIdBTmu44iIiAMqcWFkB0Wk+WNcx5AI4PF6mdD+Urb7PDz25vWu44iIiAMqcWFkjS+eytiurmNIhBh/0vVclpfERWs/p3Bnrus4IiJSx1TiwkSlP0DB5jPp2Phc11Ekgowe+hfaBHay6PX7XEcREZE6phIXJjZv2cyJhUUMbtjGdRSJIF36D+ONhoP4V/k7rFj/o+s4IiJSh1TiwsTalZ9xScrjZFaucB1FIkzLE37HovgY/vHhb1xHERGROqQSFyaml+ZzWfOm+DM7uo4iEeaYPqcwpCKNL2Ky+Xr1EtdxRESkjqjEhYn1hduw1tCjVTfXUSQCXTv6n1TaGP7wxcOuo4iISB1RiQsT5fnfk+CPJyku3nUUiUDdWvWif8Nx5DCbN2e/7TqOiIjUAZW4MBGoWEMbf5nrGBLB/nryb7kkJ4bOMydjAwHXcUREpJapxIWJXE8FjWwD1zEkgmUkpTKw82+5t/hMPli82XUcERGpZSpxYcBfWclft2xjlEcf9CtHZvCpF5LVsgG3zb6K/NJi13FERKQWqcSFge2b19KnopRWDbu7jiIRzusxXDSwA/7YNTz06mWu44iISC1SiQsDy9Z8x9tJDahMa+o6ikSBS48+lX5lccwMLOTH1QtcxxERkVqiEhcGFm6eze2N0zHpGa6jSJS4bvA9lBnDIzOvdR1FRERqiUpcGNhStAGA7u0HOk4i0eLoXqM52d+ar+PzmfH5i67jiIhILVCJCwM7KraRVhmgYUq66ygSRW765TNM3lxC28/+ir+y0nUcEREJMZW4MJBrC8nwe13HkCiT0bAFLXveQmf/ar594yHXcUREJMRU4sLAT95UCuP6uI4hUaj/Kb/iX6ld+WPhFNZmLXMdR0REQkglzrFAwFK0/ioGpF3hOopEIePx0H3YLWz3enjogytdxxERkRBSiXMsZ3s2k/xvMyiuxHUUiVLHH3UGJ/lb8llcLlPmf+I6joiIhIhKnGOLVn9LRfrXJLPOdRSJYrdNmIrH34i/fX8fReV6Rq+ISDRQiXPsq9JinkhLxt92gOsoEsXSGqRyZY8b8Xu3MOXNu1zHERGREFCJc2xd/kYA+rTs6DiJRLtfD/oFf9nRhnNW/Y812dtdxxERkSOkEueYzfmSBL+Xxg2SXUeReuDo8x7nbPMAv31vJn5/wHUcERE5AipxjpVUbKJFpf4ylbrRuGkmpw6NZ3Xsvdz97r2u44iIyBFwUuKMMWuNMYuMMQuMMXOD09KMMTONMSuCfzbabflbjTErjTE/GWNG7Tb9qOB2VhpjHjXGGBfv50jke8poZONdx5B65LfHnkSH8iRm5r7MDyvnuY4jIiKHyeWRuBOstX2ttbuu6L8F+Nha2wn4OPgzxpjuwASgBzAa+JcxZtfjDR4HrgA6Bb9G12H+I2YDAV7J2spFgV6uo0g94vF4uP3Yuyg3hgc/vpKA3+86koiIHIZwOp16OvB88PvngXG7TX/FWltmrV0DrAQGGmOaAynW2m+stRaYsts6EWHH9mySTRkJDdu7jiL1zIAeIzkztj8L4st4eOp1ruOIiMhhcFXiLDDDGDPPGLPrUQVNrbXZAME/mwSntwQ27LZuVnBay+D3e0+PGAtWfsld6Y3Ib5jqOorUQzdPeJa+pT5abPyA7HU/uY4jIiKHyOfodYdYazcZY5oAM40xB3qoY3XXudkDTN93A1VF8QqA1q1bU1ZWex92Wl5eXuNlf8yey+spyQxObVyrmSLdoYypHNzu4/nn458n478jWffS5TT87XQ8Xu8B1pT90T4aehrT0NOYhp7rMXVS4qy1m4J/bjXGvAkMBLYYY5pba7ODp0q3BhfPAlrttnomsCk4PbOa6dW93lPAUwADBgywcXFxoXw7+6jp9rcVrgegV+chNV6nvtL4hNau8WzbpTff9rqZrWsfZNr/rmXSBc85Tha5tI+GnsY09DSmoedyTOv8dKoxpoExJnnX98BIYDHwDnBxcLGLgbeD378DTDDGxBlj2lF1A8Oc4CnXAmPMMcG7Ui/abZ2IkFO+lVR/gOYZmQdfWKSWDDzjel5Jac4rFXOYNe9d13FERKSGXFwT1xT40hizEJgDvG+tnQ7cD5xsjFkBnBz8GWvtEmAq8CMwHbjGWrvrdrqrgWeoutlhFTCtLt/IkdpCGWn+GNcxpJ7zeL3cNeoZ4q3l3vl3k19S4jqSiIjUQJ2fTrXWrgb6VDM9Bxixn3UmA5OrmT4X6BnqjHVlNS1IiW3oOoYIndr0Y2K73/LI+ke5+K07eevcB1xHEhGRgwinjxipV6y1FK2/lBPTfuc6iggAl51wOZ3iR7GqfBpvzXzGdRwRETkIlThHcrdt4iVzB4MCC1xHEfnZv0+/ixu2JTPwq/vZlLPTdRwRETkAlThHZq+fz4PNKimNL3QdReRnqfENGHH6E1we+AOXT51OSYU+kkBEJFypxDkyvzCfJQ38JHQ4xnUUkT207tSb8WN6si7hXn796vWu44iIyH6oxDmyNr/qYRN9m7VznERkX5cMHMjRth1z/V/x9Nt3uo4jIiLVUIlzJGbbTFL80DgpxXUUkWo9etazdCw3PJv7OrMXzXQdR0RE9qIS50heII+mldU9OUwkPCQnpXHnsH/htfDnb28gZ8cW15FERGQ3KnGOpFeW0dqf5DqGyAH17XIcv25xMSOL8ln+7FUE/AHXkUREJEglzgEbCPDQls2cQ2/XUUQO6vzRNzE4/UIGF37Cx1Nucx1HRESCVOIcyNm6kXhTgWnYxnUUkRoZcP7dvNdoCPcG3taNDiIiYUIlzoFvls/kjJbN2Nww3nUUkRoxHi9DfzWF1ICPp3NfZ+a3r7iOJCJS76nEObB224+sjI0lvUkH11FEaiw1OYO/jJxCfMDHTUseZcmWLNeRRETqNZU4BzYXrgOgW/uBjpOIHJoubfpy83HPUuEp48L3rmRr3jbXkURE6i2VOAdyy7eRVhkgo1Ez11FEDtlpXQZwdbc76Fi6jVVPTqC8vMJ1JBGRekklzoH1xpAU0MeLSOS65phx3NT0HGbt7MSk/80nENBHj4iI1DWVOAe2VvTCxI90HUPkiAwY/3vijr+aj/LvYOLUW13HERGpd1Ti6lhlpZ+CDScxtPH5rqOIHLHfjehFt8RMvi/7gLufv8B1HBGRekUlro5tWb+cRTEXM7T0M9dRRI6Yx+Phv+f8iwFlibzGQv7632tcRxIRqTdU4urYh5sWMqhNa7LTk11HEQmJuNh4HrvgQ3qUxTKl/DMen/p715FEROoFlbg69mNhHhXeAB06Hus6ikjINEhsyBPnTmdoSTxjl/2Hee895TqSiEjUU4mrY9l5yyDgpUeTVq6jiIRUw+TGPHjRLHb4utPru5t45a3JriOJiEQ1lbg6FpP/Na0qK4nxel1HEQm5hAbJtL/+XR5K78Bf8l7msdcmuY4kIhK1VOLqWI6nhIyAnpkq0SsxqSFXXPg2Hcp9PF00nT++fZ/rSCIiUUklrg7ZQICTC4voSwvXUURqVUajTJ499yNa+jN5M++/XP3uw64jiYhEHZW4OrRjezbX5+dwbPLRrqOI1LrU5Axeu+BtMszRfLV9Cm8/fQ0Bv57sICISKipxdWj92oUUGkN8k46uo4jUiQZxcXx43lPcXtqNnhs+5uZXv6G0otJ1LBGRqKASV4c+Xz+DwW1bUZye4jqKSJ2J9fk468oXmXXci7y1fjonvXghm7dnuY4lIhLxVOLqUHbBOjzW0quTPiNO6hfj8XDZyAH88qhMCsxirnjzFH5Y/o3rWCIiEU0lrg5tr9hG40pLclIj11FEnLj/5Cv5fbOL2OILcP0Xl/HuF1NcRxIRiVgqcXUohwIy/DGuY4g4ddHoG3mwzz14Mdyz8i/MfOMB15FERCKSSlwdsdayxufDF9fFdRQR54b1P4NnRk3lgp3JnLxoMrP/eSnlZaWuY4mIRBSVuDqSW1xK4dZT6dD8PNdRRMJCu8zuXP3rz/mm6bl4Ct7lov8cw+LV813HEhGJGCpxdSRrwwZ65zfi+Jb9XEcRCRu+mFgGX/0E8ztPZFVsBdfMupAXPn7GdSwRkYigEldHNi15jQeTJtM+bofrKCJh58ozH+Dv/e7HZ2P5y4Z/cO5rd1FSUe46lohIWFOJqyMvVG5nXItWNG+ja+JEqjOk7y949bxPaRU7hMXFr/PYY6NZu/wH17FERMKWSlwd2VC6FW+gGbGx8a6jiIStjMRUpp33L37X7Eouzf+JP0z5iL998g1+Pa5LRGQfKnF1pLJyFc2NntQgUhO/GnUtgWsX4uvUm+fW/oYznhvFwmVfuo4lIhJWVOLqQHFZEcXeQvrYItdRRCJGRkYG/77wBE5tOp5Nvmyu+OZK7nlhImXlZa6jiYiEBZW4OvD98i8IGEOLpA6uo4hEFK/HywOn3cTTQ56gQ0U8rwbmcf5/juaHuTNdRxMRcU4lrg54tmzjH5u3MSBzqOsoIhGpX+fjePHSOVyVMopuZWX0fPcs5vzjYrZv2+Q6moiIMypxdcBkr+D4khK6dx3mOopIxPJ4vVxzxkPcdOHXzG4yntT89znrnZO5/6XLKS0rcR1PRKTOqcTVgaX5c/k4LoPU9Kauo4hEvOSG6Qy+5hm2jHqKDH8ML1V+y2lTjueJ2R9irXUdT0SkzqjE1YE34jbwn7SGrmOIRJVhR5/Jq5fO49pGY8n1xvDPZZM46dlfsmj2x66jiYjUCZW4WlZSVkRWjKWFt4nrKCJRx+P1cuXYyXx23gyGZ1zCwMJNNP7gUi5+5kumL1vkOp6ISK3yuQ4Q7b79cSZ+Y2id0tV1FJGolRKfyGOn3UBp4a+Y/tkXLPxxAfO+fYqXP0virK6Xc9qwiRhjXMcUEQkpHYmrZQvXVn1Aac82QxwnEYl+8UkNGXfaL/jgygkMSTien2J3cuvav3HeU/154b178VdWuo4oIhIyKnG17JtKDwRiGNBrtOsoIvVGi5QMnjznUd4dN4OzvL3Y5KvguS0vsm5yL2ZPfYCCnXmuI4qIHDGdTq1lySXjaJjXnwaJSa6jiNQ7jRu14I8X/JdJJQXMmPEYlZv+x1E/TuacvH/TzrRm/NE3cEzfUa5jiogcFh2Jq2Unr3uMCck7XccQqdcSE5IZd/qtdLptNvNH/YcUUpgZs5ErFkxi8HPn8edZL5NbqP9ORSSyqMTVoqVZS/kp5Ttaxf3kOoqIAMbjYeDgM/j3FbN59YQXOS7xRIpYz9T19/LtY/144amH+GTZRir9AddRRUQOSqdTa9GH65fyWsN4OvX8hesoIrKXrm368q82j1BRWckrc16nyY43eGFTAvM/mExyyiJOpgHDulzAyMHn4vV5XccVEdmHSlwtmr95CdYaTup8lOsoIrIfMT4fFx47AY6dwL8r/Dz89et8sXIpH3izeG/1X3j4p/sYGGjFud2upsug0fhi41xHFhEBVOJqVUnONJqaeBo3SHYdRURqID7Gy63Dz+HW4eewbssK3vjsYeaWzSFQvoIeH1/Ezo8TuadpJ9o0HsSpx1xGu8wuriOLSD2mEldLKirKWe/bycDydNdRROQwtGnaiRvO/hcAxQX5zP/6XbYve4sv4pYxrWg6T300DZ+/I91Sj2ZC486MGHAKCQ10F7qI1B2VuFoy74ePiLcBOjXq6zqKiByhxORU+o+6AEZdwPHlpcyY8yIz1n/LN2XZLCx6hXGrC3nvoxd4vsVEUjIW84ukBEYcfS5NMjJdRxeRKKYSV1uWL+DT9RvZePGVrpOISAjFxsYz5rjLGMNlAKzL3cTGuR+xfGcyZblrWFT0GouK4cH3nqdDmaGlyeCURqNp3/V42nTpR2xsrON3ICLRQiWulsRmfcNmGpPZrrvrKCJSi9qktaDZ8HM4Ni6OicDWvPOYPucFFmz8gnWeLL7xbeWeH+8jacm9PJnSkE8bNKSVySC12Uh6dBjB0LbdSG/QwPXbEJEIpBJXCwqK87mz4UpGJXbmWtdhRKRONWnYjItG3shF3AhAZWU5W9f8yPKf5pCb/T7FrGNmzBYqd7wIc1/E920s72QV8liLByhsvJN28fn0T8mgZ5fhNElv7vjdiEg4U4mrBW9//iRrY30kNxvsOoqIOObzxdKiU19adOpLf64AoKy8jO82/cQ3G35kx6b5bE/6iWWFCawMPMHn8Vk8vwXM8nto7Lf0Ko3j/PKO+FPbsDo1nsZNOtKp9dG0zuyKx6vPaxepz1TiasH36z8kJtYydvjVrqOISBiKi43juLa9Oa5tb2ACAG8DO8uG8O3ST1mx7iuy85azNbCZFFtKq/x5NMmbwa2tW7C90Aurwfrj8AYaMbAihYvKkvi6663ke+bQ2RTSOimNzGbdaNWiCwlx8U7fq4jUHpW4ECsuKmAdG+lQmUqjlAzXcUQkgqTENWBk39MY2fe0feaVlhTzhx9nsGrTAjYXbmCxN53csq2klG4jdud2nv58OfGdH8IYW7XCcvBZy3l5ZZy1M44dvlSeSg+QZJJIjG9KWdMTSU9sSG/jpW1aW+KbdiAhDjISEnWETyRCRHyJM8aMBh4BvMAz1tr7XeZ5f/EKfoqN4azGw1zGEJEoE5+QyMlHjePko8ZVO3+p38+iLd1YtfprtuWuYEfhRnaWbadRnIe8hEpKK3P4yVdInreACrsZNi8E4Nwcg29HGpdxCUkdHyIuYEkOWBoEINF6+UVhIj396WyJjeejxAJiPYnYhOaUNexDclwDji0vJi29J+XpbSi1uWSU5ZLaIJ2k5AySGjQkLi4eY0wdjpRI/RHRJc4Y4wX+CZwMZAHfGWPesdb+6CrTsd2bM7joVM4ccKGrCCJSD8V4vfRv0Yn+LTrtd5lPAGst24pyydqZy+bCXJK3ric2kMKdsS35eusFJOV8RUmggBJbSjHleAIBGpZsIK+ymE9T4inzGKhYBdu+BKD/lp0s3jmIe+IGk9j6uT1ez2Mtj27OpX9JgM8TE3kkPYEYayglmXxvE3z4uCtnC2sTT+eL1Ex2BGbRseQnvMaLwYfX4+ME244UTyIbvEUsN3l4PT5KGrSlvEErEggwsriYkqbHkh3rpaBoBQ2L1uDzxOLzVn21T2yD1xdHfqCYwkApHo8PkprhiU8hJlBBpvVgUppTTAB/RR4xlcV4PT58Hh8xXh9JCal4jRdrAhiPh1hvLN7YBIzHw8/VVCVVHInoEgcMBFZaa1cDGGNeAU4HnJW4lsnNeeqUv7h6eRGRAzLG0CQpnSZJwafJdB4EwCBgIj33u147YC5QUVlGfvFOtpWWsKOkkKS8bbTzpvEwMSzeno5v63eUVhZR4S+hwl9KQXo6Sys9FNhc2gY2U4GfXG8KJZ44AoEyPIEK8otKWB3YSEmDReyIKaDSgB+oNHDVxtm0r6jk+ZRkXktvBAGg4HsoqMp19fqN3PfdlUxLKyWu8Uf75P567QaSreWvjRryn4Yp+8xfuGY955ffxvwmy4ltNHuPeQmBAHPWZQFwS+N03k/6/4+CsdZDfGUsc7OWM6L8QTY1/5S4pCXEUvlzuWtZ4WfKxjwscFPTFBbGx2CAQhIJ4KVJeRyvbV7FWPMYO5u+RELMKhIpq/o9Ad3K/PxpaylgmNQsnk2+qtPc20wGFg/dSn3cv30TlzT4J/mpj5BiNpBoS4OvbxlQYrlsR9Xp9d8381LigQCGjZ4WABxbbLk4389tiX8kJ/U+Gge2Exd8fYDhRYYzdhrKjOXmZlXbqcTHZk9TAMYUVDCkqBF/bfArcpL/QbPAZnxU/rz+mAIPJxZ52O613NfYD0AZcWz1NAFgYn4Rzco68nTSaHIS/01mYCOGwM/rn5fn5ahSD6tiLI+nVa1fbBLI8VZdqnRzznZ22uN4NakXefFTaenP2uP3d8UOH53KPSyIC/Df1KpcBZ5k8jyNMFju27qZRTHj+KBBGgUx79HCv3GP9X+XE0NTv4cvE/y8n1y1/g5PGjs9KcTYSq7LbYh3+L2cM7DtPvtVXYn0EtcS2LDbz1lU/b9oD8aYK6DqtrDWrVtTVla29yIhU15eXmvbrq80pqGl8Qy9+jamyXEpJMelQGpTaNYBgC7AqE7tgHOqXacPcMZ+tjcM+A0A1/08rby8/OcPRi71V3J6eTEnlhZQUVFKsYVSfJSVl1A40M/VcRmMqyxi286RmIINVPor8AfKqKws56fMdngDfjqWZXNtxTb8NkBBYktKYlLxlO9kTjJMyBhMn8pu7NzZhgZFa7E2QMD6McbyVYezsTZAZmAjYwJ5WCxbE9pR6kskpaKQr1oO5fQmPVnkr8BfnEDD0o1YLGBJMj4WN20J1tLKl02svxQLbEzsQoWJo4W3hOXpbTmxWQt+rOxFXKklpWIbVXXJku6JY1XDxgA0IZvYQAUAJr4jATyk2XKykhvTvUkSKwKtaFheQoNAPjbYImO8cWxu0BCAVLaQEAhgjYciXzMAGsSUUZwYS8fGiVQEmtKovJSEwP9fD+nzxLMzPolyLGmBHAAqTQxFvqpM8THFBBIzyGyUSBkZpJbtJNaWB9MDnkQKYhMo9vhJDeRV/S49CRR50wDw+LzgaUjjpHhKTCNS/Nsx9v9LnN/XgMKYOMp9FSTbqtZuTCLFnqr3FPCW4/ElkxoXR6lJISmw5wdpV3iSKPLF4PeWk2gLq/KTQLFJxkOACm8ynph4EmLiKDMJxNuYPdYv9yZTgpeAp5Q4WwpALLHEmARiqMB6E/ESqNVOcTDGWuvsxY+UMeYsYJS19rLgzxcCA6211+1vnQEDBti5c+fWWqaysjLi4uJqbfv1kcY0tDSeoacxDT2NaehpTEOvLsbUGDPPWjugunmRfgtSFtBqt58zgU2OsoiIiIjUmUgvcd8BnYwx7YwxsVR94NI7jjOJiIiI1LqIvibOWltpjLkW+JCqjxh5zlq7xHEsERERkVoX0SUOwFr7AfCB6xwiIiIidSnST6eKiIiI1EsqcSIiIiIRSCVOREREJAKpxImIiIhEIJU4ERERkQikEiciIiISgVTiRERERCKQSpyIiIhIBFKJExEREYlAxlrrOkOdMsZsA9bV4ktkANtrcfv1kcY0tDSeoacxDT2NaehpTEOvLsa0jbW2cXUz6l2Jq23GmLnW2gGuc0QTjWloaTxDT2MaehrT0NOYhp7rMdXpVBEREZEIpBInIiIiEoFU4kLvKdcBopDGNLQ0nqGnMQ09jWnoaUxDz+mY6po4ERERkQikI3EiIiIiEUgl7jAYY0YbY34yxqw0xtxSzXxjjHk0OP8HY0x/FzkjSQ3G9HhjTL4xZkHw648uckYSY8xzxpitxpjF+5mv/fQQ1GA8tY8eImNMK2PMLGPMUmPMEmPMb6pZRvvpIajhmGpfrSFjTLwxZo4xZmFwPO+qZhln+6ivrl4oWhhjvMA/gZOBLOA7Y8w71tofd1vsFKBT8GsQ8HjwT6lGDccU4Atr7Zg6Dxi5/gM8BkzZz3ztp4fmPxx4PEH76KGqBH5vrZ1vjEkG5hljZur/p0ekJmMK2ldrqgw40VpbaIyJAb40xkyz1n672zLO9lEdiTt0A4GV1trV1tpy4BXg9L2WOR2YYqt8CzQ0xjSv66ARpCZjKofIWvs5kHuARbSfHoIajKccImtttrV2fvD7AmAp0HKvxbSfHoIajqnUUHC/Kwz+GBP82vtmAmf7qErcoWsJbNjt5yz2/Q+kJsvI/6vpeA0OHtKeZozpUTfRopr209DTPnqYjDFtgX7A7L1maT89TAcYU9C+WmPGGK8xZgGwFZhprQ2bfVSnUw+dqWba3q28JsvI/6vJeM2n6tEjhcaYU4G3qDp0LYdP+2loaR89TMaYJOAN4LfW2p17z65mFe2nB3GQMdW+egistX6grzGmIfCmMaantXb3a2Od7aM6EnfosoBWu/2cCWw6jGXk/x10vKy1O3cd0rbWfgDEGGMy6i5iVNJ+GkLaRw9P8DqjN4CXrLX/q2YR7aeH6GBjqn318Fhr84BPgdF7zXK2j6rEHbrvgE7GmHbGmFhgAvDOXsu8A1wUvGPlGCDfWptd10EjyEHH1BjTzBhjgt8PpGrfzanzpNFF+2kIaR89dMHxehZYaq39234W0356CGoyptpXa84Y0zh4BA5jTAJwErBsr8Wc7aM6nXqIrLWVxphrgQ8BL/CctXaJMeaq4PwngA+AU4GVQDFwiau8kaCGYzoeuNoYUwmUABOsPqn6gIwxLwPHAxnGmCzgTqouytV+ehhqMJ7aRw/dEOBCYFHwmiOAPwCtQfvpYarJmGpfrbnmwPPBT1HwAFOtte+Fy9/5emKDiIiISATS6VQRERGRCKQSJyIiIhKBVOJEREREIpBKnIiIiEgEUokTERERiUAqcSIiIiIRSCVOREREJAKpxImIHAFjzFXGmAXBrzXGmFmuM4lI/aAP+xURCYHg8yo/AR6w1r7rOo+IRD8diRMRCY1HgE9U4ESkrujZqSIiR8gYMxFoA1zrOIqI1CM6nSoicgSMMUcBzwNDrbU7XOcRkfpDp1NFRI7MtUAaMCt4c8MzrgOJSP2gI3EiIiIiEUhH4kREREQikEqciIiISARSiRMRERGJQCpxIiIiIhFIJU5EREQkAqnEiYiIiEQglTgRERGRCKQSJyIiIhKB/g8cNqqp433LTAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(10,6))\n",
    "\n",
    "ax.plot(cnc_zs,dndz,label=\"cnc\")\n",
    "ax.plot(cnc_zs_class_sz_hmf,dndz_class_sz_hmf,label=\"cnc class_sz hmf\",ls='-.')\n",
    "ax.plot(z_arr,Nz,label='class_sz',ls='--')\n",
    "\n",
    "plt.grid(which=\"both\",alpha=0.1)\n",
    "plt.legend()\n",
    "plt.xlabel(\"z\")\n",
    "plt.ylabel(\"dndz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7a267299-42ab-44ac-8773-261cae99270c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 2.0)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig,ax = plt.subplots(figsize=(10,6))\n",
    "\n",
    "class_sz_nz_interp = np.interp(cnc_zs,z_arr,Nz)\n",
    "ax.plot(cnc_zs,100.*(class_sz_nz_interp/dndz-1.))\n",
    "ax.plot(cnc_zs_class_sz_hmf,100.*(class_sz_nz_interp/dndz_class_sz_hmf-1.),ls='--')\n",
    "\n",
    "plt.grid(which='both')\n",
    "ax.set_ylim(-3.5,1.3)\n",
    "ax.set_xlim(0.,2.)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f31c5d12-10e0-456c-baa3-da8ba1dd4731",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14becc4aea00>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(10,6))\n",
    "ax.plot(cnc_qs,dndq,lw=3.,label=\"cosmocnc (internal HMF)\")\n",
    "ax.plot(cnc_qs_class_sz_hmf,dndq_class_sz_hmf,label=\"cosmocnc (class_sz HMF)\")\n",
    "ax.plot(q_arr,Nq,label='class_sz',ls='--')\n",
    "ax.set_yscale(\"log\")\n",
    "plt.grid(which=\"both\",alpha=0.1)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "63c81de0-9202-4d2d-861d-d67597828ff8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5.0, 100.0)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig,ax = plt.subplots(figsize=(10,6))\n",
    "\n",
    "class_sz_nq_interp = np.interp(cnc_qs,q_arr,Nq)\n",
    "ax.plot(cnc_qs,100.*(class_sz_nq_interp/dndq-1.))\n",
    "ax.plot(cnc_qs_class_sz_hmf,100.*(class_sz_nq_interp/dndq_class_sz_hmf-1.))\n",
    "\n",
    "plt.grid(which='both')\n",
    "ax.set_ylim(-0.25,0.25)\n",
    "ax.set_xlim(5.,100.)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48671c7f-daaa-4632-aa05-b606e6e04ec0",
   "metadata": {},
   "source": [
    "## Binned plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c59e45da-9149-4ef4-b7a4-66dfdbe2ca68",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/iz221/anaconda3/lib/python3.9/site-packages/mcfit/mcfit.py:130: UserWarning: use backend='jax' if desired\n",
      "  warnings.warn(\"use backend='jax' if desired\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3min 36s, sys: 177 ms, total: 3min 36s\n",
      "Wall time: 3min 36s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from classy_sz import Class\n",
    "\n",
    "cnc_classy_sz_binned = Class()\n",
    "        \n",
    "szcounts_param_binned = szcounts_param.copy()\n",
    "szcounts_param_binned.update({\n",
    "'output': 'sz_cluster_counts,m500c_to_m200c,m200c_to_m500',\n",
    "'bin_z_min_cluster_counts' : 0.01,\n",
    "'bin_z_max_cluster_counts' : 2.01,\n",
    "'bin_dz_cluster_counts' : 0.1,\n",
    "\n",
    "'bin_dlog10_snr' : 0.25,\n",
    "'log10_snr_min' : 0.6,\n",
    "'log10_snr_max' : 2.,\n",
    "\n",
    "\n",
    "'dlny' : 0.025,\n",
    "'lnymin' : -20., # planck  -11.5 so/act -11\n",
    "'lnymax' : 10.,  # planck 10. so/act -5\n",
    "'dlnM_cluster_count_completeness_grid' : 0.01,\n",
    "\n",
    "\n",
    "'cluster_count_completeness_grid_z_cutoff_low' : 0.4,\n",
    "'cluster_count_completeness_grid_z_cutoff_mid' : 1.,\n",
    "\n",
    "'dz_cluster_count_completeness_grid_low_z' : 1e-3,\n",
    "'dz_cluster_count_completeness_grid_mid_z' : 1e-3,\n",
    "'dz_cluster_count_completeness_grid_high_z' : 1e-2,\n",
    "})\n",
    "\n",
    "cnc_classy_sz_binned.set(szcounts_param_binned)\n",
    "cnc_classy_sz_binned.compute_class_szfast()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "246faefc-8717-47d0-b2b7-1e0dcf811586",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14983.433909230132"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dNdzdy_theoretical = cnc_classy_sz_binned.dndzdy_theoretical()['dndzdy']\n",
    "z_center = cnc_classy_sz_binned.dndzdy_theoretical()['z_center']\n",
    "z_edges = cnc_classy_sz_binned.dndzdy_theoretical()['z_edges']\n",
    "log10y_center = cnc_classy_sz_binned.dndzdy_theoretical()['log10y_center']\n",
    "log10y_edges = cnc_classy_sz_binned.dndzdy_theoretical()['log10y_edges']\n",
    "\n",
    "N_z,N_y = np.shape(dNdzdy_theoretical)\n",
    "N_clusters_z_theory = []\n",
    "for iz in range(N_z):\n",
    "    N_clusters_z_theory.append(np.sum(dNdzdy_theoretical[iz][0:]))\n",
    "N_clusters_y_theory = []\n",
    "\n",
    "for iy in range(N_y):\n",
    "    N_clusters_y_theory.append(np.sum(np.asarray(dNdzdy_theoretical)[:,iy]))\n",
    "    \n",
    "# print total number of clusters: \n",
    "np.sum(N_clusters_z_theory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0d1b3e4f-ea98-4d67-a092-0af28e3911be",
   "metadata": {},
   "outputs": [],
   "source": [
    "z_edges_low = z_edges[:-1]\n",
    "z_edges_high = z_edges[1:]\n",
    "Nz_binned = []\n",
    "for (zm,zp) in zip(z_edges_low,z_edges_high):\n",
    "    z_arr_bin = np.linspace(zm,zp,50)\n",
    "    Nz_in = []\n",
    "    for zpbin in z_arr_bin:\n",
    "        Nz_in.append(np.trapz(get_dndzdq(zpbin,q_arr)*q_arr,x=np.log(q_arr)))\n",
    "    Nz_binned.append(np.trapz(Nz_in,x=z_arr_bin))\n",
    "Nz_binned = np.asarray(Nz_binned)\n",
    "\n",
    "q_edges_low = log10y_edges[:-1]\n",
    "q_edges_low[0] = 0.6\n",
    "q_edges_high = log10y_edges[1:]\n",
    "Nq_binned = []\n",
    "for (qm,qp) in zip(q_edges_low,q_edges_high):\n",
    "    q_arr_bin = np.linspace(qm,qp,50)\n",
    "    Nq_in = []\n",
    "    for qpbin in q_arr_bin:\n",
    "        if 10.**qpbin<q_threshold:\n",
    "            Nq_in.append(0.)\n",
    "        else:\n",
    "            Nq_in.append(np.trapz(get_dndzdq(z_arr,10**qpbin),x=z_arr))\n",
    "    Nq_binned.append(np.trapz(Nq_in,x=10**q_arr_bin))\n",
    "Nq_binned = np.asarray(Nq_binned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f9435d71-b2cc-4bd7-8cf6-4102ec0a971f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import interpolate\n",
    "z_edges_low = z_edges[:-1]\n",
    "z_edges_high = z_edges[1:]\n",
    "Nz_binned_cnc = []\n",
    "for (zm,zp) in zip(z_edges_low,z_edges_high):\n",
    "    z_arr_bin = np.linspace(zm,zp,50)\n",
    "    dndz_inter = interpolate.interp1d(cnc_zs,dndz,bounds_error=False,fill_value=0.)\n",
    "    Nz_binned_cnc.append(np.trapz(dndz_inter(z_arr_bin),x=z_arr_bin))\n",
    "Nz_binned_cnc = np.asarray(Nz_binned_cnc)\n",
    "\n",
    "q_edges_low = log10y_edges[:-1]\n",
    "q_edges_low[0] = 0.6\n",
    "q_edges_high = log10y_edges[1:]\n",
    "Nq_binned_cnc = []\n",
    "for (qm,qp) in zip(q_edges_low,q_edges_high):\n",
    "    q_arr_bin = np.linspace(qm,qp,50)\n",
    "    dndq_inter = interpolate.interp1d(cnc_qs,dndq,bounds_error=False,fill_value=0.)\n",
    "    Nq_binned_cnc.append(np.trapz(dndq_inter(10**q_arr_bin),x=10**q_arr_bin))\n",
    "Nq_binned_cnc = np.asarray(Nq_binned_cnc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "430fc6cd-d0a4-49fe-a238-c529db90718d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bXFxsx4wZY8ePH28LCgpsQUGBHTdunB03bly1bQYMGGC3bNliS0pK7MSJE+2FF15o7733XltUVGS///572717d/uXv/ylzjE3JuBcdNFFdtOmTbasrMw+9NBDdsCAAdW279mzp12/fr09c+aMnT9/vu3WrZtdvnx5VZ+aAaeh/dXXbq03RN5444320KFD9syZM3bWrFl24MCBtqSkxFpr7bXXXmtvvfVWW1hYaA8ePGhTUlJaPOAE8hycGLyHyI76fu4PJAAfVHaw1p4CPsU7EwOQDHSo0Wcf8J1fn+FAEeB/7e8a4KRfHwlBehyEtAtffw0nT1Zfd/IkbNjQKi935MgRALp169bsfRw4cIAVK1Zw9913A3D33XeTkZHBqVOnqvpkZmbidrt5+umnufzyy4mPj2fevHlVh6Aaaq9PY7d96623mD9/Pu+88w79+vVrVq333nsvV1xxBcYYjh49yqpVq1iwYAFdunShS5cuLFiwgJUrV1aduwQwdepUBg0aRIcOHZgwYQK7du3iscceIyoqir59++J2u/nnP//ZrPFUuu+++xg8eDAul4spU6awc+dOjh07VtV+zz33kJycTEREBI888ggdO3Zk3bp1zd5ffe15eXksWbKEhQsXEh8fT0REBLNnz+bgwYN88cUX5OTk8PHHH/PUU08RGxtLQkICs2fPPqf6axPIp4k/g/fQUuVvOMH39XCNfoeBXn59yoG8Wvok+PU5Yv3+ZFtrrTEm169PNcaYqcBUgPj4+DqfMl1UVBSyT6B2Qu1ut7vqSeI16T2vLlTrhsDX3jU8nEHnn0+4XzgoO+88vnO5yG+Fce3bt6/qa311FxYWsnv37lr7vPbaa8TExBATE0NmZiaJiYmcPHmSOXPmcMMNN1T1Gz16NKNHj+b06dNkZmYyZ84cTpw4wbhx4xrVXp+Gtt22bRsPPfQQDz/8MGfOnKmqo673+9ChQ7hcrmptp0+f5tSpU1XrtmzZAsDevXvJyckBoNx3vtQ777zDoEGDOH36NIWFhVXbbN++nbCwMDZv3ly13+PHj3Py5Mk6f/+nT5/mu+++q2o/dOgQAOvWraN79+5nvUZl+wcffFDVXvl7qRQbG0tOTk7VOv/3tzH7q69969atAFx22WXV6igrK2PVqlX07NkTgD179lT9+cvNzQXg888/b7krqWqb1mntBViA9zyaC/3WjcB7GKlPjb6vAO/7vp8AlAGmRp9PgOd9388E/qeW19wN/O+GxqZDVLUL1dpVd+gJeO0BOgdn7Nix9fap6xBVeXm57du3r42IiLDx8fFVS3h4uB0+fHi9+/zxj3981qGsprQ3dt/79++3F1xwgU1LSzurX1MPUfkfKsrJybFAtXNLKg/5HThwoNZtPvnkE+tyuRp8LX+DBw+2zz//fNXPa9asOesQVUOHsPxrr6iosH369LF/+MMfqtbVPETV0P7qaz98+LAFbG5ubq317Nu3zwJ2586dVes++OCD9n+Iyhjz/wF3ANdaa3f5NR3yfa05y9KDH2Z1DgEuoOZcas0+PYwxxu81DdCds2eHRESCi8sFq1bBkiUwd67366pV3vWtZOHChXz44YfMnDmTAwcOAN7/Uc+fP5+lS5dW9SsrK6uaDahc3n//ffbv38/atWvZsGFD1bJixQrWrVvHxo0bOXr0KL///e/ZtGkTpaWllJWVsWzZMjZt2sTIkSMbbK9PQ9sWFxdzyy23MHz4cObOnduiv7cLLriA0aNHM336dAoLCzl69CjTp09n7NixVbMULWHYsGEsWbKEoqIijhw5wrx585q8j5dffpmvvvqK0tJSnnzySYqLi/mXf/mXFhujvx49ejBhwgSmTZtWNbNVWFjI22+/TVFREb1798btdjNjxgyOHz/O4cOHm1VTQ9o04BhjnsE7C3OttXZrjebdeMPJKL/+5wMj+eF8mmygtEaf3nhPLK7ssw7vCczD/fY9HIii+nk5IiLByeWC8eMhLc37tRXDDcCoUaN47rnn2LJlC0lJScTExJCSkkJubi6pqalV/ebMmUPHjh2rLc888wy33norycnJJCQkVC2jR49m+PDhvPDCC0RERJCbm8vPf/5z4uLi6N69O+np6Tz33HPcdtttDbbXp6Ftly1bRnZ2NitXriQmJuas++GsXr36nH53r7/+OjExMVx66aVceumldO7cudb7AJ2L9PR0XC4XPXv2xO12c/vttzd5H1OnTuXBBx+kS5cuLF26lBUrVhAdHd2i4/S3aNEiBg4ciNvtJiYmhqSkJN566y0q5x7eeOMNzpw5Q58+fRg5ciSTJk1q+UHUNq3TGgvw38Bx4Fq8szSVS7Rfn0d8fX6O9zLxN6n9MvEc4HrgCryHpzZw9mXiG4F/wRtuNqLLxM9JqNauukNPqNauukOPU2qnjkNUbXmS8TTf149qrJ8DeHzfP4H30vD/BroAXwCjrbUn/Pr/J97zcJb6+n4ETLLW+t8JayLwLD9cbfV34LctUoWIiIgEvTY7RGWtNXUsHr8+1lrrsdb2tNaeb61NtdZuqrGf09baB6y1Xa21kdbam6z3UnH/PgXW2l9Zazv5ll9ZawvbplLn8Xg8XHPNNVWXVevyahFpK3v37q31UQvR0dHcf//9gR6eBLFAXiYu7YTH48HtdlcFmlC9fFhE2l7fvn0pKioK9DCkHdLDNkVERMRxFHBERETEcRRwpFHKS0oYvHs3N3/zDV/OmkV5SUmghyQiIlInBRxpUHlJCbH/+q/8ce9exhTGc1n6Y2zs1UshR0REgpYCjjQoOz2dS44V8RsWM4StTLOvcFF+Idnp6YEemoiISK0UcKRBxWvWUEwcGXjvNJnBJIptZ4rX6sbQIiISnBRwpEGRKSnEmDzCKQUgnFJiwgqIHDEiwCMTERGpnQKONCg5LY3vOnUil0hKCCPPRLM9Lo7ktLRAD01ERKRWCjjSIFdEBMf++lfS+l7As51j2Zb2CEk5ObgiIgI9NBERkVop4EijuCIi2Ny/P+8OHcqVc+cq3IhIiykoKGDMmDHExsaSnJwc6OGck8TERF5//fU628eOHcsTTzzRhiOqbv/+/Rhj2LNnT8DG0FYUcEREhG3btnHrrbfSvXt3OnXqxCWXXMJDDz3EwYMHAXC73Rhj+PTTT6ttN2DAAF599dWqn9evX1/vfmrz/PPPU1RURH5+PtnZ2a1SX7B47733mDFjRqCHERIUcEREgpC1sHWr92trW716NQ888AADBw5kw4YNHD9+nKysLLp27UpWVlZVv65du/K73/0OW8egVq9ezdVXX93gfmratWsXgwYNIjy89scjlpaWnluBEpIUcKRBlU8Tz8rKIisrS08TF2ll5eUweTIMGuT9Wl7euq83bdo0rrvuOv7rv/6LXr16AdCzZ08effRRbr/99qp+9957L/v372fJkiV17mfChAkN7sffTTfdxOLFi1m8eDHR0dHMnj2bxMRE5s6dyzXXXENUVBTLli0DID8/n0mTJtGzZ08SEhKYPHkyBQUFgPfQUHp6Otdccw3R0dEkJSXx7bffsmTJEgYMGEBsbCxTpkyhrKyszt9DzcNLe/bswRjD/v37q/V5/PHHue6664iOjmbIkCGsrXHLjF27dnH11VcTHR3NsGHD+Oc//1nV5na7Sfe7h1hD+2uovbi4mN/97nf079+fuLg4brjhBnbu3FnVfujQIW6++WZiY2O55JJLeP/99+us32kUcKRBHo+HTz75BGtttUUBR6R15OdDRob3+4wM8P0b3iq2b9/Ozp07uf766xvsGxUVxdy5c5k5cyZnzpypdT8TJkxo0uu/++67TJw4kcmTJ1NUVMScOXMAWLRoEQsWLKCoqIhbbrkFgIkTJ3L06FG2bNnCd999R15eHnfeeWfVvhYvXszChQs5evQoQ4cO5Wc/+xmffPIJ33zzDRs3buTvf/87f/3rX5s0vtq8/PLLPPvssxw7doxRo0YxefLkau3PP/88zzzzDAUFBfzyl79k3LhxHD9+vNn7q699ypQpbN26lc8//5xDhw5x1VVXMX78+KpZr4kTJ+Jyudi7dy+ffvpptcOJTqeAIyISZHr0gMqjNeHh0L17673WkSNHAOjWrVuj+v/6178mJiaGZ555ptb9VM7cnKt7772XK664AmMMHTt25MCBA6xatYoFCxbQpUsXunTpwoIFC1i5cmXV+T1Tp05l0KBBdOjQgQkTJrBr1y4ee+wxoqKi6Nu3L263u9psSnPdd999DB48GJfLxZQpU9i5cyfHjh2rar/nnntITk4mIiKCRx55hI4dO7J8+fJm76+u9ry8PJYsWcLChQuJj48nIiKC2bNnc/DgQb744gtycnL4+OOPeeqpp4iNjSUhIYHZs2efc/3thQKOSAvyeDxVh/D8F812SVMVF3sPTRUXt+7rdPelp7y8vEb1d7lcPPHEEzz++OPk5+eftZ+cnJwWGVdiYmK1n/ft2wdA//79q9ZddNFF1dp69uxZ1RYZGYnL5aoaV+W6EydOnPPY/F8nKioKoNp+/cdujKFv377VDnM1dX91te/evRuAH/3oR3Tu3JnOnTsTFxdHaWkp+/btq3rNfv36VW3v//tzOgUckRbk8Xiw1pKamkpqaqoO50mzdegAYWHer63pkksuYcCAAXz00UeN3mbs2LFceeWVzJ0796z91HV+TlOFhVX/56lPnz4A1S5v3rVrV7W2cxUdHc3Jkyerfj5w4ECz9uM/Rmste/fupXfv3uc6vLNUBpcdO3ZQWFhYtRQXF3PHHXdUzaZ9//33VdtUhqJQoIAjIhLiFi5cyIcffsjMmTOr/lHPzc1l/vz5LF26tNZtnnzySV588cWqQ1OV+/nLX/7SpP001gUXXMDo0aOZPn06hYWFHD16lOnTpzN27NhqMxznYtiwYSxZsoSioiKOHDnCvHnzmrWfl19+ma+++orS0lKefPJJiouLufHGG1tkjP569OjBhAkTmDZtWtXMWWFhIW+//TZFRUX07t0bt9vNjBkzOH78OIcPH252Te2RAo5ICysvKWHw7t3c/M03fDlrFuUlJYEekki9Ro0axXPPPceWLVtISkoiJiaGlJQUcnNzSU1NrXWboUOHcvvtt1c7eXbUqFH84x//aNJ+muL1118nJiaGSy+9lEsvvZTOnTvz2muvnfN+K6Wnp+NyuejZsydut7vOK78aMnXqVB588EG6dOnC0qVLWbFiBbGxsS02Tn+LFi1i4MCBuN1uYmJiSEpK4q233sIYA8Abb7zBmTNn6NOnDyNHjmTSpEmtMo5gZOq6n0GoGjZsmF2/fn2tbZmZmbjd7rYdUJAI1dqbWnd5SQkbe/Xiorw89jKQfmYHO7vGtbtHW4Tq+w2hW7vqDj1Oqd0Yk22tHVZzvWZwRFpQdno6F+YX8hsWM4StTLOvcFF+Idl+970QEZHWp4Aj0oKK16yh2HYhA+80cAaTKLadKa5xIzAREWldCjgiLSgyJYWYsHzC8d5kK5xSYsIKiBwxIsAjExEJLQo4Ii0oOS2NHXFx5BJJCWHkmWi2x8WRnJYW6KGJiIQUBRyRFuSKiCApJ4e0vhfwbOdYtqU90u5OMBYRcYLaH90qIs3miohgc//+bAam+90ITURE2o5mcERERMRxFHBERETEcRRwRFpQ5cM2s7KyyMrK0sM2RUQCROfgiLQgj8ejMCMiEgQ0gyMiIiKOo4AjIiIijqOAIyIiIo6jgCMiIiKOo4AjIiIijqOAIyIiIo6jgCMiIiKOo4AjIiIijqOAIyIiIo6jgCMiIiKOo4AjIiIijqOAIyIiIo6jgCMiIiKOo4AjIiIijqOAIyIiIo6jgCMiIiKOo4AjIiIijqOAIyIiIo6jgCMiIiKOo4AjIiIijqOAIyIiIo6jgCMiIiKOo4AjIiIijqOAIyIiIo6jgCMiIiKOo4AjIiIijqOAIyIiIo6jgCMiIiKOo4AjIiIijqOAIyIiIo6jgCMiIiKOo4AjIiIijqOAIyIiIo6jgCMiIiKOo4AjIiIijqOAIyIiIo7TpgHHGPNTY8zfjTE5xhhrjLmrRvurvvX+y+c1+pxnjHnOGJNnjDnp21/vGn26GGMyjDHHfEuGMaZz61co0vo8Hg/GmLMWj8cT6KGJiASNtp7BiQY2Af8BnKqjz4dAT79lXI32p4FfAHcAI4FOwHJjjMuvzxvAj4GxwA2+7zNapAKRAPN4PFhrSU1NJTU1FWst1loFHBERP+Ft+WLW2pXASvDO1tTR7Yy19lBtDcaYWOAe4NfW2tW+dXcC3wPXA6uMMYPwhpqrrbVrfX3uAz4zxgy01m5rwZJEAqO8nOH5+QwoKoLly2HsWHC5Gt5ORCREBOM5OFcbY3KNMduNMYuMMT382pKBDsAHlSustfuA74ARvlXDgSJgrd92a4CTfn1E2q/ycuzo0aRt3sxde/ZQdttt2NGjobw80CMTEQkabTqD0wjvA/8X2A0kAunAx8aYZGvtGSABKAfyamx32NeG7+sRa62tbLTWWmNMrl+faowxU4GpAPHx8WRmZtY6uKKiojrbnC5Uaw/GujuvWcMlmZlEWss2BjLw9DZOZWay/Y9/pDAlpUVeIxjrbiuhWrvqDj1Orz2oAo619k2/HzcaY7LxHn66EW/wqYsBrN/PthF9/F/3ReBFgGHDhlm3213ri2RmZlJXm9OFau3BWPe2l16iQwVMZjEZTOJOXuOlil9z/tatuP/whxZ5jWCsu62Eau2qO/Q4vfZgPERVxVp7ANgPXOxbdQhwAd1qdO2Bdxansk8PY4ypbPR9392vj0i79TVwKDyBDCYBkMEkcsPj2RDQUYmIBJegDjjGmG5AL+Cgb1U2UAqM8uvTGxjED+fcrMN7tdZwv10NB6Kofl6OSLsUfdttHOh4gnBKAQinlJzzTxD9y18GeGQiIsGjTQ9RGWOigQG+H8OAvsaYy4EC3+IBluENNInAfCAXeBvAWnvMGPNn4EnfOTX5wALgW7yXl2Ot/c4Y8z7wgjHmXryHpl4AlusKKnGCsePH84vUVG5eHceVES72dYtn/mA3y8aPD/TQRESCRlvP4AzDO8P+NdARmOP7fi7ek4eTgL8B24HFwDZguLX2hN8+/hPv+ThL8V4dVQTcZK31v4RkIvAN3qutVvm+v7PVqhJpQy6Xi2XvvMP2ixN5vmsXbnj2/7DsnXdw6TJxEZEqbX0fnEy8Myp1GdOIfZwGHvAtdfUpAH7V1PGJtBcul4uuXbvStWtXxmvmRkTkLEF9Do6IiIhIcyjgiLQzlc+iysrKIisrS8+iEhGpRVDdB0dEGubxeBRmREQaoBkcERERcRwFHBEREXEcBRwRERFxHAUcERERcRwFHBEREXEcBRwRERFxHAUcERERcRwFHBEREXEcBRwRERFxHAUcERERcRwFHBEREXEcBRwRERFxHAUcERERcRwFHBEREXEcBRwRERFxHAWcds7j8WCMOWvxeDyBHpqIiEjAhAd6AHJuPB4PHo8Ht9sNQGZmZkDHIyIiEgw0gyMiIiKOo4DjAOUlJQzevZubv/mGL2fNorykJNBDEhERCSgFnHauvKSEjb168ce9exlTGM9l6Y+xsVcvhRwREQlpCjjtXHZ6OhfmF/IbFjOErUyzr3BRfiHZ6emBHpqIiEjAKOC0c8Vr1lBsu5DBJAAymESx7Uzx2rUBHpmIiEjgKOC0c5EpKcSE5RNOKQDhlBITVkDkiBEBHpmIiEjgKOC0c8lpaeyIiyOXSEoII89Esz0ujuS0tEAPTUREJGAUcNo5V0QESTk5pPW9gGc7x7It7RGScnJwRUQEemgiIiIBoxv9OYArIoLN/fuzGZg+d26ghyMiIhJwmsFp5yof1ZCVlUVWVpYe1SAiIoJmcNq9ykc1iIiIyA80gyMiIiKOo4AjIiIijqOAIyIiIo6jgCMiIiKOo4AjIiIijqOAIyIiIo6jgCMiIiKOo4AjIiIijqOAIyIiIo6jgCMiIiKOo4AjImd59dVXq55r5r/osSAi0l7oWVQicpa77rqLV199FbfbDUBmZmZAxyMi0lSawRGRWpWXlDB4925u/uYbvpw1i/KSkkAPSUSk0RRwROQs5SUlbOzViz/u3cuYwnguS3+Mjb16KeSISLuhgCMiZyn4y1+4ML+Q37CYIWxlmn2Fi/ILyU5PD/TQREQaRQFHRM4SvnEjxbYLGUwCIINJFNvOFK9dG+CRiYg0jgKOiJylLCmJmLB8wikFIJxSYsIKiBwxIsAjExFpHAUcETlL3MSJ7IiLI5dISggjz0SzPS6O5LS0QA9NRKRRFHBE5CyuiAiScnJI63sBz3aOZVvaIyTl5OCKiAj00EREGkX3wRGRWrkiItjcvz+bgelz5wZ6OCIiTaIZHBE5S+WdjLOyssjKytKdjEWk3dEMjoicpfJOxiIi7VW9MzjGGFdbDURERESkpTQ0g1NkjPkWyPZbNllry1p9ZCIiIiLN1FDAuQe4AkgG7gBigTPGmE38EHi+stZmt+ooRURERJqg3oBjrX0DeKPyZ2PMxXjDTmXo+TcgpqH9iIiIiLSlJgUTa+0OY8xBvOfuXAKcB+S2xsBEREREmqtRl4kbYzoZY+40xvwNOALMB74HRgO9WnF8IiIiIk1W7wyOMWYycBtwPZADLAMet9Z+0QZjExEREWmWhg5RvYI32PwH8Iq1tqT1hyQiIiJybho6RJUJRAF/Ak4YY74yxiwyxtxvjPmJMUYPphEREZGg09BVVNcCGGMuBIYBP8Z79dTPgDig1Biz2Vr749YeqIiIiEhjNeoqKmvtLmAX8NfKdcaYRH4IPSIiIiJBo9n3r7HW7gH2AP9/Sw1GREREpCXoaeIiIiLiOAo4IiIi4jhtGnCMMT81xvzdGJNjjLHGmLtqtBtjjMcYc8AYc8oYk2mMGVyjz3nGmOeMMXnGmJO+/fWu0aeLMSbDGHPMt2QYYzq3foUiIiISDNp6Bica2IT3vjqnammfAUwHHgB+gvcxEKuNMTF+fZ4GfoH34Z8jgU7AcmOMy6/PG3hPfh4L3OD7PqMlCxEREZHg1aYPybTWrgRWAhhjXvVvM8YY4CHgj9baZb51k/GGnAnAC8aYWLxPOP+1tXa1r8+deB8bcT2wyhgzCG+oudpau9bX5z7gM2PMQGvtttauU0RERAIrmM7B6Q8kAB9UrrDWngI+BUb4ViUDHWr02Qd859dnOFAErPXb9xrgpF8fERERcbA2ncFpQILv6+Ea6w/zwwM9E4ByIK+WPgl+fY5Ya21lo7XWGmNy/fpUY4yZCkwFiI+PJzMzs9YBFhUV1dnmdKFau+oOPaFau+oOPU6vPZgCTiVb42dTy7qaavaprX+d+7HWvgi8CDBs2DDrdrtrfZHMzEzqanO6UK1ddYeeUK1ddYcep9ceTIeoDvm+1pxl6cEPszqHABfQrYE+PXzn9ABV5/d05+zZIREREXGgYAo4u/GGk1GVK4wx5+O9UqryfJpsoLRGn97AIL8+6/BerTXcb9/D8T401P+8HBEREXGoNj1EZYyJBgb4fgwD+hpjLgcKrLV7jTFPA38wxmwFtgNpeE8YfgPAWnvMGPNn4EnfOTX5wALgW+BDX5/vjDHv473q6l68h6ZeAJbrCioREZHQ0Nbn4AwDPvH7eY5vWQzcBTwBdAT+G+gCfAGMttae8NvmP4EyYKmv70fAJGttuV+ficCz/HC11d+B37ZwLSIiIhKk2vo+OJl4Z1TqareAx7fU1ec03hsBPlBPnwLgV80cpoiIiLRzwXQOjoiIiEiLUMARERERx1HAEREREcdRwBERERHHUcARERERx1HAEREREcdRwBERERHHUcARERERx1HAEREREcdRwBGRgPB4PBhjzlo8Hk+ghyYiDtDWz6ISEQG8Acfj8eB2uwHIzMwM6HhExFk0gyMiAVNeUsLg3bu5+Ztv+HLWLMpLSgI9JBFxCAUcEQmI8pISNvbqxR/37mVMYTyXpT/Gxl69FHJEpEUo4IhIQGSnp3NhfiG/YTFD2Mo0+woX5ReSnZ4e6KGJiAMo4IhIQBSvWUOx7UIGkwDIYBLFtjPFa9cGeGQi4gQKOCISEJEpKcSE5RNOKQDhlBITVkDkiBEBHpmIOIECjogERHJaGjvi4sglkhLCyDPRbI+LIzktLdBDExEHUMARkYBwRUSQlJNDWt8LeLZzLNvSHiEpJwdXRESghyYiDqCAIyIB4fF4CD/vPBbu3cvvCgu5at48ws87Tzf6E5EWoRv9iUhAVN7oT0SkNWgGR0RERBxHAUdEREQcRwFHREREHEcBR0RERBxHAUdEREQcRwFHREREHEcBR0RERBxHAUdEREQcRwFHREREHEcBR0RERBxHAUdEREQcRwFHREREHEcBR0RERBxHAUdEREQcRwFHREREHEcBR0RERBxHAUdEREQcRwFHREREHEcBR0RERBxHAUdEREQcRwFHREREHEcBR0RERBxHAUdEREQcRwFHREREHEcBR0RERBxHAUdEREQcRwFHREREHEcBR0RERBxHAUdEHMvj8eAyhvHG8Kjvq8sYPB5PoIcmIq1MAUdEHOvRmTPJ7taNJcDtDORNE0Z2t248OnNmoIcmIq1MAUdEHCs7PZ0L8wv5DYsZwlam2Ve4KL+Q7PT0QA9NRFqZAo6IOFbxmjUU2y5kMAmADCZRbDtTvHZtgEcmIq1NAUdEHCsyJYWYsHzCKQUgnFJiwgqIHDEiwCMTkdamgCMijpWclsaOuDhyiaSEMPJMNNvj4khOSwv00ESklSngiIhjuSIiSMrJIa3vBTzbOZZtaY+QlJODKyIi0EMTkVamgCMijuXxeAg/7zwW7t3L7woLuWrePMLPO0+XiYuEgPBAD0BEpLV4PB6FGZEQpRkcERERcRwFHBEREXEcBRwRERFxHAUcERERcRwFHBEREXEcBRwRERFxHAUcERERcRwFHBEREXEcBRwRERFxHAUcERERcZygCjjGGI8xxtZYDvm1G1+fA8aYU8aYTGPM4Br7OM8Y85wxJs8Yc9IY83djTO+2r0ZEREQCJagCjs82oKffkuTXNgOYDjwA/ATIBVYbY2L8+jwN/AK4AxgJdAKWG2NcrT5yERERCQrB+LDNMmvtoZorjTEGeAj4o7V2mW/dZLwhZwLwgjEmFrgH+LW1drWvz53A98D1wKo2qUBEREQCKhhncC40xuQYY3YbY940xlzoW98fSAA+qOxorT0FfAqM8K1KBjrU6LMP+M6vj4iIiDicsdYGegxVjDFjgRhgK9ADSAMuBQYDA4E1QD9r7V6/bV4GellrxxhjJgCvAR2sX2HGmI+BHdba++p43anAVID4+PjkN998s9bxFRUVER0dfc51tkehWrvqDj2hWrvqDj1Oqf2aa67JttYOq7k+qA5RWWvf8//ZGPM5sAuYDHxe2a3GZqaWdTXV28da+yLwIsCwYcOs2+2utV9mZiZ1tTldqNauukNPqNauukOP02sPxkNUVay1RcBm4GKg8rychBrdegCHfd8fAlxAt3r6iIiIiMMFdcAxxpyP9xDVQWA33gAzqkb7SGCtb1U2UFqjT29gkF8fERERcbigOkRljHkKeBfYi3fW5VEgClhsrbXGmKeBPxhjtgLb8Z6jUwS8AWCtPWaM+TPwpDEmF8gHFgDfAh+2cTkiIiISIEEVcIDewBK8h5iO4D3v5l+std/72p8AOgL/DXQBvgBGW2tP+O3jP4EyYKmv70fAJGtteZtUICIiIgEXVAHHWnt7A+0W8PiWuvqcxnsjwAdacmwiIiLSfgT1OTgiIsHO4/HgMobxxvCo76vLGDweT6CHJhLSFHBERM7BozNnkt2tG0uA2xnImyaM7G7deHTmzEAPTSSkKeCIiJyD7PR0Lswv5DcsZghbmWZf4aL8QrLT0wM9NJGQpoAjInIOitesodh2IYNJAGQwiWLbmeK1ujOFSCAp4IiInIPIlBRiwvIJpxSAcEqJCSsgcoQefycSSAo4IiLnIDktjR1xceQSSQlh5JlotsfFkZyWFuihiYQ0BRwRkXMw7/HHSc7L407KmINloi0hOS+PeY8/HuihiYS0oLoPjohIe+PxeHRJuEgQ0gyOiIiIOI4CTivSDcBEREQCQwGnFekGYCIiIoGhgNOKdAMwERGRwFDAaUW6AZiIiEhgKOC0It0ATEREJDAUcFqRbgAmIiISGAo4rUg3ABMREQkM3eivFekGYCIiIoGhGRwRERFxHAUcERERcRwFHBEREXEcBRwRERFxHAUcERERcRwFHBGRIFf54N4bjeHfzaXcqAf3ijRIAUdEJMg9OnMmX3btQSyLeZ6txPIaX3btoQf3itRDAUdEJMhlp6cTV2BY4nuu3RLupFsBenCvSD0UcEREglzxmjX0s4erPdeuj83Vg3tF6qGAIyIS5CJTUjgVFkYxkZTj/Xo6LEwP7hWphwKOiEiQW1FRwbqKCk5ThsVymjLWVlSwoqIi0EMTCVp6FpWISJCbk55O+axZZKenU7x2LZEjRnBNWhrXR0QEemgiQUsBR0SkHXBFRHDl3LmBHoZIu6FDVCIiIuI4CjgiIiLiOAo4IiIi4jgKOCIiIuI4CjgiIiLiOAo4IiIi4jgKOCIiIuI4CjgiIkJ5SQlfzppF5nXX8eWsWZSXlAR6SCLnRAFHRCTEzU5Lw44Zw6Xz5tH94xwunTePT847j9lpaYEemkizKeCIiIS4G8PCuBIXv2UxQ9jKb1nMVSacG8P0T4S0X/rTKyIS4orXrKGYODKYBEAGkyi2nSleuzbAIxNpPgUcEZEQF5mSQozJI5xSAMIpJSasgMgRIwI8MpHm08M2RURC3IqKCo5by2EiiaGcE7hYW1HBZxUVXBnowYk0kwKOiEiIm5Oezkc//Sk7//EPiteuJXLECK5JS+P6iIhAD02k2RRwREQEV0QEV86dG+hhiLQYnYMjIiIijqOAIyIiIo6jgCMiIiKOo4AjIiJtQo+DkLakgCMiIq1udloan5x3nh4HIW1GAUdERFrdjWFhXGnC9TgIaTP6kyUiIq2ueM0aim0XPQ5C2owCjoiItLrIlBRiwvL1OAhpM7rRn4iItLoVFRUcr6jQ4yCkzSjgiIhIq5uTnk75rFlkp6frcRDSJhRwRESkTbTF4yDKS0q8IWrNGiJTUkhOS8OlEBWSFHBERMQRyktK2NirF5flF7DXXkzfTx5j45/+RFJOjkJOCNJJxiIi4ggLRo0iMa+AafYVBrOVafYVEvMKWDBqVKCHJgGggCMiIo7wk/BwTtO12qXoZ4jjJx06BHhkEggKOCIi4gi6FF38KeCIiIgjrKioYJ3vUvQSwjhMJGsrKlhRUdGir6NnarUPOslYREQcoS0uRdeJzO2HZnBERMQxKi9Fd3/4IVfOndvioUMnMrcfCjgiIiKN1FYnMusw2LlTwBEREWmkf5aVEcWRaicyR5LHP0tLW+w1qg6DpT9Gj49zuCz9MTb26qWQ00QKOCIiIo308OrV/E+3buSZaEoJI89E8z/duvHw6tUt9hptdRjM6bNECjgiIiKN5IqIICknh21pj/CP665lW9ojLX6CcVscBisvKSH2X/+11WeJAhmiFHBERESaoLVPZG6Lw2ALRo1iwLETrTpLFOhDbY4OOMaYacaY3caY08aYbGPMyECPSUREpD5tcRisLWaJstPTuTC/sFqIuii/kOz09BZ7jfo4NuAYY/4NeAZ4HLgCWAu8Z4zpG9CBiYiI1KMtDoO1xSzRpowMTtou1ULUSduZTa+/3mKvUR8n3+jvYeBVa+0i388PGGNuAP4d+H3ghiUiIlK/ysNgreXh1av5ukcP8o5HE2VLOWk68D9dW3aWKL9vX6L3fEo4pZTRgXBKiSKP/D6Xtdhr1MdYa9vkhdqSMSYCKAbusNa+5bf+v4Eh1trUGv2nAlMB4uPjk998881a91tUVER0dHSrjTuYhWrtqjv0hGrtqjv0HCsooORvfyN80ybKhgwhbuLEFp0lqjyRecCxYqIpo4hwdsZGcuyvf23R17nmmmuyrbXDaq536gxON8AFHK6x/jBwfc3O1toXgRcBhg0bZt1ud607zczMpK42pwvV2lV36AnV2lV36MnMzMS9eHGrvkZ5bm61R2ckp6W12SMtnBpwKtWcnjK1rBMREZFW0NqH2urj1JOM84ByIKHG+h6cPasjIiIiDuPIgGOtLQGygZoX9I/CezWViIiIOJiTD1EtADKMMV8Ca4D7gQuA5wM6KhEREWl1jg041tqlxpiuQBrQE9gEjLPWfh/YkYmIiEhrc2zAAbDWLgQWBnocIiIi0rYceQ6OiIiIhDYFHBEREXEcBRwRERFxHAUcERERcRwFHBEREXEcBRwRERFxHAUcERERcRwFHBEREXEcY60eru3PGHMEqOtux93wPsgzFIVq7ao79IRq7ao79Dil9n7W2u41VyrgNIExZr21dligxxEIoVq76g49oVq76g49Tq9dh6hERETEcRRwRERExHEUcJrmxUAPIIBCtXbVHXpCtXbVHXocXbvOwRERERHH0QyOiIiIOI4CjoiIiDiOAo6IiIg4TsgGHGPMNGPMbmPMaWNMtjFmZAP9k4wxWcaYU8aYHGPMLGOMqdEn1bev08aYXcaY+1u3iuZpSu3GGLcx5m/GmIPGmGJjzLfGmLtr6WNrWS5t/Woar4l1J9ZR0w01+jnxPffUUbs1xvTw9Qn699wY81NjzN99n1drjLmrEdu0+895U+t2yme8GXU75jPejNod8RlvSEgGHGPMvwHPAI8DVwBrgfeMMX3r6N8JWA0cBn4CPAj8L+Bhvz79gZW+fV0BzAeeM8b8ovUqabqm1g6MADYCvwSGAH8CXjTGTKil72Cgp9+yo2VH33zNqLvSDVSv6WO/fTr1PX+K6jX3BLKATGttbo2+QfueA9HAJuA/gFMNdXbQ57xJdeOQzzhNr7tSu/+M0/TanfIZr5+1NuQW4AtgUY11O4D5dfT/d+A40NFvXRqQww9Xov0XsKPGdi8B6wJd77nUXsc+/gos8/vZDVigW6Dra8H3PNFX07B69hkS7znQBygHJrSn97xGDUXAXQ30ccznvCl117Fdu/uMN+P9dsxn/Fzfcyd8xmtbQm4GxxgTASQDH9Ro+gDv/2RqMxz4zFrrn4xXARfg/ZBU9qm5z1XAMGNMh3MZc0tpZu216QQcrWX9et8090fGmGuaOcwWd451/19jTK4xZo0x5pc12kLlPb8HKASW1dIWlO95Mznic95C2tVn/By16894C3HkZzzkAg7eh4u58E5D+zsMJNSxTUId/Svb6usT7nvNYNCc2qsxxowHrqP6DaIO4v3f7y+AnwPbgI+MMT891wG3kObUXQT8DvhXYBzwEbDUGPMrvz6Of8+NMWHA3cBr1tozfk3B/p43h1M+5+eknX7Gm8Mpn/Fz4uTPeHigBxBANe9waGpZ11D/musb0ycYNLV2bydjUoA3gAettV9W7czabXj/8FdaZ4xJxPuXx6fnPNqW0+i6rbV5wP/xW7XeGNMNmAG83sA+a1sfaM16z4GxeKevX6q2s/bznjeVkz7nTeaAz3ijOfAz3lyO/YyH4gxOHt5jjTX/99qDs5N6pUN19Mdvm7r6lAH5zRppy2tO7QAYY64G3gNmWWv/1IjX+gK4uDmDbAXNrruGmjU5+j33mQqstdZubkTfYHrPm8Mpn/Nmaeef8ZbSHj/j58qxn/GQCzjW2hIgGxhVo2kU3jPla7MOGGmMOb9G/wPAHr8+19eyz/XW2tJzGXNLaWbt+KYk3wPmWGufbuTLXY53ijPgmlt3LS6nek2Ofc8BjDEXADcCixr5cpcTJO95Mznic94c7f0z3oIup519xs+F4z/jgT7LORAL8G9ACTAFGIT3EtoioJ+vfT7wkV//WLxJ/k28l1H+HO/VFtP9+vQHTgJP+/Y5xfcavwh0vedYu9tX15N4/ydTuXT36/MQcCveZD/Ytw8L/DzQ9Z5D3ZOBCb6+A/FOy5YA/+n099xvuzTgGBBZS1t7eM+j8f6FfDlQDMzyfd+3jvfcEZ/zZtTtlM94U+t20me8SbX7bdeuP+MN/l4CPYAA/oGYhvd/ZWfw/g/3p35trwJ7avRPwnvc8TTeBDsb36Wjfn1Sga98+9wN3B/oOs+1dt/PtpbFv88MYCfe+y8UAJ8B4wJd5znWPRnY4vvL7TiwHvhVLft03HvuW2d89SysY39B/57zw2WuNZdX66m73X/Om1q3Uz7jzajbMZ/xZv5Zb/ef8YYWPU1cREREHCfkzsERERER51PAEREREcdRwBERERHHUcARERERx1HAEREREcdRwBERERHHUcARERERx1HAEREREcdRwBERERHHUcAREccyxswwxthalrmBHpuItC49qkFEHMsYEwNE+a36HTARGGmt3RmYUYlIW1DAEZGQYIx5BHgQuNZauy3Q4xGR1hUe6AGIiLQ2Y8zvgd8C11hrtwd6PCLS+hRwRMTRjDF/AO4HUnVYSiR0KOCIiGMZYx4F7gXc1tr/CfR4RKTtKOCIiCP5Zm7+A7gZOGmMSfA1FVprTwduZCLSFnSSsYg4jjHGAIVAp1qar7fWftS2IxKRtqaAIyIiIo6jG/2JiIiI4yjgiIiIiOMo4IiIiIjjKOCIiIiI4yjgiIiIiOMo4IiIiIjjKOCIiIiI4yjgiIiIiOP8PzRxbl0oR3LTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "\n",
    "plt.errorbar(z_center,N_clusters_z_theory,yerr=np.sqrt(N_clusters_z_theory),\n",
    "            marker='o',\n",
    "            markersize=5.,\n",
    "            fillstyle='none',\n",
    "            ls='none',\n",
    "            label=r'$\\mathrm{CLASS\\_SZ\\,\\,binned}$',capsize=3,c='k')\n",
    "plt.errorbar(np.asarray(z_center),Nz_binned,#yerr=np.sqrt(Nz_binned),\n",
    "             marker='o',markersize=5.,ls='none',\n",
    "            label=r'$\\mathrm{CLASS\\_SZ\\,\\,from\\,\\,unbinned}$',capsize=5,c='red')\n",
    "\n",
    "\n",
    "plt.errorbar(np.asarray(z_center),Nz_binned_cnc,#yerr=np.sqrt(Nz_binned),\n",
    "             marker='*',markersize=3.,ls='none',\n",
    "            label=r'$\\mathrm{CNC\\,\\,from\\,\\,unbinned}$',capsize=5,c='blue')\n",
    "\n",
    "plt.xlabel('$z$', fontsize=14)\n",
    "plt.ylabel('$N$', fontsize=14)\n",
    "\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.legend(fontsize=13,frameon=False)\n",
    "plt.grid()\n",
    "plt.tight_layout()\n",
    "# plt.loglog()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5766aca6-8707-4064-8809-bb53e2206b9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "plt.errorbar(10**np.asarray(log10y_center),N_clusters_y_theory,yerr=np.sqrt(N_clusters_y_theory),\n",
    "            marker='o',\n",
    "            markersize=5.,\n",
    "            fillstyle='none',\n",
    "            ls='none',\n",
    "            label=r'$\\mathrm{CLASS\\_SZ\\,\\,binned}$',\n",
    "             capsize=3,c='k')\n",
    "plt.errorbar(10**(np.asarray(log10y_center)),Nq_binned,#yerr=np.sqrt(Nq_binned),\n",
    "             marker='o',markersize=5.,ls='none',\n",
    "            label=r'$\\mathrm{CLASS\\_SZ\\,\\,from\\,\\,unbinned}$',\n",
    "             capsize=5,c='red')\n",
    "\n",
    "plt.errorbar(10**(np.asarray(log10y_center)),Nq_binned_cnc,#yerr=np.sqrt(Nq_binned),\n",
    "             marker='o',markersize=3.,ls='none',\n",
    "            label=r'$\\mathrm{CNC\\,\\,from\\,\\,unbinned}$',\n",
    "             capsize=5,c='blue')\n",
    "\n",
    "\n",
    "plt.xlabel('$q$', fontsize=14)\n",
    "plt.ylabel('$N$', fontsize=14)\n",
    "\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.legend(fontsize=13,frameon=False)\n",
    "plt.grid()\n",
    "plt.tight_layout()\n",
    "plt.xscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61dc3795-3845-42b3-a6a5-2de050b63244",
   "metadata": {},
   "source": [
    "# Final Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cec8b1c5-394e-46fc-bdcb-8515a76e0b7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rc('text', usetex=True)\n",
    "plt.rc('font', family='serif')\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "gs = fig.add_gridspec(3,2)\n",
    "axs = gs.subplots()\n",
    "\n",
    "color_lines = \"k\"\n",
    "\n",
    "ax = axs[0,0] \n",
    "ax.tick_params(axis = 'x',which='both',length=5,direction='in', pad=10)\n",
    "ax.tick_params(axis = 'y',which='both',length=5,direction='in', pad=5)\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "plt.setp(ax.get_yticklabels(), rotation='horizontal', fontsize=12)\n",
    "plt.setp(ax.get_xticklabels(), fontsize=12)\n",
    "ax.plot(cnc_zs,dndz,label=r\"$\\mathrm{\\texttt{cosmocnc}\\,\\,(internal\\,\\,hmf)}$\")\n",
    "ax.plot(cnc_zs_class_sz_hmf,dndz_class_sz_hmf,label=r\"$\\mathrm{\\texttt{cosmocnc}\\,\\,(\\texttt{class\\_sz}\\,\\,hmf)}$\",ls='-.')\n",
    "ax.plot(z_arr,Nz,label=r'$\\texttt{class\\_sz}$',ls='--')\n",
    "ax.set_xlabel(r\"$z$\", fontsize=15)\n",
    "ax.set_ylabel(r\"$dN/dz$\", fontsize=15)\n",
    "ax.legend(fontsize=10,frameon=True)\n",
    "ax.set_xlim(0,3)\n",
    "ax.set_ylim(0,22000)\n",
    "\n",
    "ax = axs[0,1] \n",
    "ax.tick_params(axis = 'x',which='both',length=5,direction='in', pad=10)\n",
    "ax.tick_params(axis = 'y',which='both',length=5,direction='in', pad=5)\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "plt.setp(ax.get_yticklabels(), rotation='horizontal', fontsize=12)\n",
    "plt.setp(ax.get_xticklabels(), fontsize=12)\n",
    "ax.set_xlabel(r\"$q_\\mathrm{obs}$\", fontsize=15)\n",
    "ax.plot(cnc_qs,dndq,label=r\"$\\mathrm{\\texttt{cosmocnc}\\,\\,(internal\\,\\,hmf)}$\")\n",
    "ax.plot(cnc_qs_class_sz_hmf,dndq_class_sz_hmf,label=r\"$\\mathrm{\\texttt{cosmocnc}\\,\\,(\\texttt{class\\_sz}\\,\\,hmf)}$\",ls='-.')\n",
    "ax.plot(q_arr,Nq,label=r'$\\texttt{class\\_sz}$',ls='--')\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set_ylabel(r\"$dN/dq_\\mathrm{obs}$\", fontsize=15)\n",
    "ax.legend(fontsize=10,frameon=True)\n",
    "ax.set_xlim(0,200)\n",
    "\n",
    "ax = axs[1,0]\n",
    "ax.tick_params(axis = 'x',which='both',length=5,direction='in', pad=10)\n",
    "ax.tick_params(axis = 'y',which='both',length=5,direction='in', pad=5)\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "plt.setp(ax.get_yticklabels(), rotation='horizontal', fontsize=12)\n",
    "plt.setp(ax.get_xticklabels(), fontsize=12)\n",
    "ax.set_xlabel(r\"$z$\", fontsize=15)\n",
    "ax.set_ylabel(r\"$\\mathrm{Relative\\,\\,Difference\\,(\\%)}$\", fontsize=13)\n",
    "class_sz_nz_interp = np.interp(cnc_zs,z_arr,Nz)\n",
    "ax.plot(cnc_zs,100.*(class_sz_nz_interp/dndz-1.),label=r\"$\\mathrm{\\texttt{class\\_sz}\\,\\,vs.\\,\\texttt{cosmocnc}\\,\\,(internal\\,\\,hmf)}$\")\n",
    "ax.plot(cnc_zs_class_sz_hmf,100.*(class_sz_nz_interp/dndz_class_sz_hmf-1.),label=r\"$\\mathrm{\\texttt{class\\_sz}\\,\\,vs.\\,\\texttt{cosmocnc}\\,\\,(\\texttt{class\\_sz}\\,\\,hmf)}$\",ls='-.')\n",
    "ax.grid( visible=True, which=\"both\", alpha=0.2, linestyle='--')\n",
    "ax.set_ylim(-3.5,1.3)\n",
    "ax.set_xlim(0.,2.)\n",
    "ax.legend(fontsize=10,frameon=True)\n",
    "\n",
    "ax = axs[1,1]\n",
    "ax.tick_params(axis = 'x',which='both',length=5,direction='in', pad=10)\n",
    "ax.tick_params(axis = 'y',which='both',length=5,direction='in', pad=5)\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "plt.setp(ax.get_yticklabels(), rotation='horizontal', fontsize=12)\n",
    "plt.setp(ax.get_xticklabels(), fontsize=12)\n",
    "ax.set_xlabel(r\"$q_\\mathrm{obs}$\", fontsize=15)\n",
    "ax.set_ylabel(r\"$\\mathrm{Relative\\,\\,Difference\\,(\\%)}$\", fontsize=13)\n",
    "class_sz_nq_interp = np.interp(cnc_qs,q_arr,Nq)\n",
    "ax.plot(cnc_qs,100.*(class_sz_nq_interp/dndq-1.),label=r\"$\\mathrm{\\texttt{class\\_sz}\\,\\,vs.\\,\\texttt{cosmocnc}\\,\\,(internal\\,\\,hmf)}$\")\n",
    "ax.plot(cnc_qs_class_sz_hmf,100.*(class_sz_nq_interp/dndq_class_sz_hmf-1.),label=r\"$\\mathrm{\\texttt{class\\_sz}\\,\\,vs.\\,\\texttt{cosmocnc}\\,\\,(\\texttt{class\\_sz}\\,\\,hmf)}$\",ls='-.')\n",
    "ax.grid( visible=True, which=\"both\", alpha=0.2, linestyle='--')\n",
    "ax.set_ylim(-0.25,0.2)\n",
    "ax.set_xlim(5.,100.)\n",
    "ax.legend(fontsize=10,frameon=True)\n",
    "\n",
    "\n",
    "\n",
    "ax = axs[2,0]\n",
    "ax.tick_params(axis = 'x',which='both',length=5,direction='in', pad=10)\n",
    "ax.tick_params(axis = 'y',which='both',length=5,direction='in', pad=5)\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "plt.setp(ax.get_yticklabels(), rotation='horizontal', fontsize=12)\n",
    "plt.setp(ax.get_xticklabels(), fontsize=12)\n",
    "ax.set_xlabel(r\"$z$\", fontsize=15)\n",
    "ax.set_ylabel(r\"$N$\", fontsize=15)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax.errorbar(np.asarray(z_center),Nz_binned_cnc,yerr=np.sqrt(Nz_binned_cnc),\n",
    "             marker='o',markersize=5.,ls='none',\n",
    "            label=r'$\\mathrm{\\texttt{cosmocnc}}$',\n",
    "            capsize=5,c='red')\n",
    "\n",
    "\n",
    "ax.errorbar(np.asarray(z_center),Nz_binned,#yerr=np.sqrt(Nz_binned),\n",
    "             marker='o',markersize=3.,ls='none',\n",
    "            label=r'$\\mathrm{\\texttt{class\\_sz}\\,\\,unbinned}$',\n",
    "            capsize=5,c='blue')\n",
    "\n",
    "\n",
    "ax.errorbar(z_center,N_clusters_z_theory,#yerr=np.sqrt(N_clusters_z_theory),\n",
    "            marker='o',\n",
    "            markersize=5.,\n",
    "            fillstyle='none',\n",
    "            ls='none',\n",
    "            label=r'$\\mathrm{\\texttt{class\\_sz}\\,\\,binned}$',\n",
    "            capsize=3,\n",
    "            c='k')\n",
    "\n",
    "ax.legend(fontsize=12,frameon=True)\n",
    "ax.set_xlim(0,1.8)\n",
    "ax.set_ylim(0,2400)\n",
    "\n",
    "ax = axs[2,1]\n",
    "ax.tick_params(axis = 'x',which='both',length=5,direction='in', pad=10)\n",
    "ax.tick_params(axis = 'y',which='both',length=5,direction='in', pad=5)\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "plt.setp(ax.get_yticklabels(), rotation='horizontal', fontsize=12)\n",
    "plt.setp(ax.get_xticklabels(), fontsize=12)\n",
    "ax.set_xlabel(r\"$q_\\mathrm{obs}$\", fontsize=15)\n",
    "ax.set_ylabel(r\"$N$\", fontsize=15)\n",
    "\n",
    "ax.errorbar(10**(np.asarray(log10y_center)),Nq_binned_cnc,yerr=np.sqrt(Nq_binned_cnc),\n",
    "             marker='o',markersize=5.,ls='none',\n",
    "            label=r'$\\mathrm{\\texttt{cosmocnc}}$',\n",
    "             capsize=5,c='r')\n",
    "\n",
    "\n",
    "ax.errorbar(10**(np.asarray(log10y_center)),Nq_binned,#yerr=np.sqrt(Nq_binned),\n",
    "             marker='o',markersize=3.,ls='none',\n",
    "            label=r'$\\mathrm{\\texttt{class\\_sz}\\,\\,unbinned}$',\n",
    "             capsize=5,c='blue')\n",
    "\n",
    "\n",
    "ax.errorbar(10**np.asarray(log10y_center),N_clusters_y_theory,#yerr=np.sqrt(N_clusters_y_theory),\n",
    "            marker='o',\n",
    "            markersize=5.,\n",
    "            fillstyle='none',\n",
    "            ls='none',\n",
    "            label=r'$\\mathrm{\\texttt{class\\_sz}\\,\\,binned}$',\n",
    "             capsize=3,c='k')\n",
    "\n",
    "\n",
    "\n",
    "ax.set_xlim(0,60)\n",
    "ax.set_ylim(0,8500)\n",
    "\n",
    "ax.legend(fontsize=12,frameon=True)\n",
    "fig.tight_layout()\n",
    "# plt.savefig(\"figures/cosmocnc_benchmark_plot.pdf\")"
   ]
  }
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