{ "cells": [ { "cell_type": "markdown", "id": "95292550-750c-4547-9ef5-2c95f09f6c00", "metadata": {}, "source": [ "## Initialize" ] }, { "cell_type": "markdown", "id": "813e13fd-0fd0-46e5-94a7-8a59dd77cb13", "metadata": {}, "source": [ "> **Note**: This calculation does not support `jax`. *[jan25]*" ] }, { "cell_type": "code", "execution_count": 1, "id": "11d748cc-caa3-43b4-bc35-10c4a78745ff", "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "plt.rcParams.update({\n", " \"text.usetex\": True,\n", " \"font.family\": \"sans-serif\",\n", " \"font.sans-serif\": \"Helvetica\",\n", "})\n", "import numpy as np\n", "import os\n", "from classy_sz import Class as Class_sz" ] }, { "cell_type": "markdown", "id": "3a14b764-6f15-42f2-a7fa-4c5568659950", "metadata": {}, "source": [ "**Cosmological parameters**" ] }, { "cell_type": "code", "execution_count": 2, "id": "faaaa94e-5704-4a42-935f-8186b71c16e1", "metadata": {}, "outputs": [], "source": [ "cosmo_params= {\n", " 'omega_b': 0.02,\n", " 'omega_cdm': 0.12,\n", " 'H0': 80., \n", " 'tau_reio': 0.0561,\n", " 'ln10^{10}A_s': 3.047,\n", " 'n_s': 0.9665, \n", " \"cosmo_model\": 0, # 1: use mnu-lcdm emulators; 0: use lcdm with fixed neutrino mass, currently vrms2 does not support emulators\n", "\n", "}" ] }, { "cell_type": "markdown", "id": "71bf2eba-54d0-409d-b655-f79a44ba6203", "metadata": {}, "source": [ "**Precision parameters**" ] }, { "cell_type": "code", "execution_count": 3, "id": "5cef9c33-b101-4ea1-a483-b583c0cb2379", "metadata": {}, "outputs": [], "source": [ "precision_params = {\n", "## check k_min_for_pk_in_vrms2,... if needed\n", "}" ] }, { "cell_type": "markdown", "id": "19483c9d-590f-4e71-979a-447daf72df15", "metadata": {}, "source": [ "## Linear velocity dispersion" ] }, { "cell_type": "code", "execution_count": 23, "id": "b965332c-1d92-4fc9-8c82-6b9d8951cfa5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3.85 s, sys: 3.16 s, total: 7.01 s\n", "Wall time: 843 ms\n" ] } ], "source": [ "%%time\n", "\n", "zmin = 0.\n", "zmax = 5.\n", "\n", "classy_sz = Class_sz()\n", "classy_sz.set(cosmo_params)\n", "classy_sz.set(precision_params)\n", "classy_sz.set({\n", "'output': 'vrms2,mPk',\n", "'z_min' : zmin,\n", "'z_max' : zmax,\n", "'ndim_redshifts':100,\n", "})\n", "classy_sz.initialize_classy_szfast() \n", "# classy_sz.compute() ## comment has_pk in input.c if you need the faster implemntation." ] }, { "cell_type": "code", "execution_count": 24, "id": "3d1f00f8-e5a1-4b7f-9089-fd045ded1f8c", "metadata": {}, "outputs": [], "source": [ "# z = 0.3\n", "# pks,ks = classy_sz.get_pkl_at_z(z,params_values_dict = cosmo_params)\n", "# plt.loglog(ks,pks)" ] }, { "cell_type": "code", "execution_count": 25, "id": "34df3f89-d913-4cb8-b554-abaed1480a96", "metadata": {}, "outputs": [], "source": [ "nzs = 100\n", "zs = np.linspace(zmin,zmax,nzs)\n", "vrms2 = classy_sz.get_vrms2_at_z(zs)" ] }, { "cell_type": "code", "execution_count": 26, "id": "05379308-aacb-4393-a81b-008df0607e23", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "8.654435155101622e-07" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "classy_sz.get_vrms2_at_z(-0.)" ] }, { "cell_type": "code", "execution_count": 27, "id": "8b739dea-3d5d-4e03-bf3e-8de99ab16706", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "9.000370843751452e-07" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "classy_sz.get_vrms2_at_z(0.1)" ] }, { "cell_type": "code", "execution_count": 28, "id": "a04e2982-013f-4fb0-b095-0ab46cb485e7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "label_size = 17\n", "title_size = 18\n", "legend_size = 13\n", "handle_length = 1.5\n", "fig, (ax1) = plt.subplots(1,1,figsize=(5,5))\n", "ax = ax1\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=label_size)\n", "plt.setp(ax.get_xticklabels(), fontsize=label_size)\n", "ax.grid( visible=True, which=\"both\", alpha=0.2, linestyle='--')\n", "ax.set_xlabel(r\"zs\",size=title_size)\n", "ax.set_ylabel(r\"1/3*vrms2/c2\",size=title_size) ## 1/3*vrms2 in units of c2\n", "\n", "ax.plot(zs,vrms2,label='class_sz') ## \n", "ax.legend()" ] }, { "cell_type": "markdown", "id": "65b62933-dd7f-4e03-8cff-5ff117b68d4a", "metadata": {}, "source": [ "## Halo velocity dispersion" ] }, { "cell_type": "code", "execution_count": 29, "id": "e608db11-f4a7-4d24-a898-7ef1e8262873", "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "plt.rcParams.update({\n", " \"text.usetex\": True,\n", " \"font.family\": \"sans-serif\",\n", " \"font.sans-serif\": \"Helvetica\",\n", "})\n", "import numpy as np\n", "import os\n", "from classy_sz import Class as Class_sz" ] }, { "cell_type": "markdown", "id": "160f8954-2273-4608-8f64-04f1f7d53905", "metadata": {}, "source": [ "**Cosmological parameters**" ] }, { "cell_type": "code", "execution_count": 30, "id": "ab37cfc7-40de-451d-8f56-2367621dfb4b", "metadata": {}, "outputs": [], "source": [ "cosmo_params= {\n", " 'omega_b': 0.02,\n", " 'omega_cdm': 0.12,\n", " 'H0': 80., \n", " 'tau_reio': 0.0561,\n", " 'ln10^{10}A_s': 3.047,\n", " 'n_s': 0.9665, \n", " \"cosmo_model\": 0, # 1: use mnu-lcdm emulators; 0: use lcdm with fixed neutrino mass, currently vrms2 does not support emulators\n", "\n", "}" ] }, { "cell_type": "markdown", "id": "f1c273a9-17bf-41cd-9c73-db7e8daed3c5", "metadata": {}, "source": [ "**Precision parameters**" ] }, { "cell_type": "code", "execution_count": 31, "id": "4c3fb4b4-3608-4648-bb77-a1ad0390ff02", "metadata": {}, "outputs": [], "source": [ "precision_params = {\n", "## check k_min_for_pk_in_vrms2,... if needed\n", "}" ] }, { "cell_type": "code", "execution_count": 52, "id": "acf5299e-ed91-4702-9caa-c9175adb95c4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3.1 s, sys: 2.22 s, total: 5.32 s\n", "Wall time: 631 ms\n" ] } ], "source": [ "%%time\n", "\n", "zmin = 0.\n", "zmax = 5.\n", "\n", "classy_sz = Class_sz()\n", "classy_sz.set(cosmo_params)\n", "classy_sz.set(precision_params)\n", "classy_sz.set({\n", "'output': 'vrms2',\n", "'z_min' : zmin,\n", "'z_max' : zmax,\n", "# 'ndim_redshifts':100,\n", "# 'skip_class_sz':0,\n", "'skip_background_and_thermo': 0,\n", "'ndim_masses':100,\n", "'ndim_redshifts':100,\n", "})\n", "classy_sz.initialize_classy_szfast() \n", "# classy_sz.compute()" ] }, { "cell_type": "code", "execution_count": 53, "id": "ec8687f0-5fe8-4e43-9370-fa12e2166a0e", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy.integrate import simpson\n", "from scipy.interpolate import RegularGridInterpolator\n", "from mcfit import TophatVar\n", "\n", "# ----------------------------------------------------------------------\n", "# ASSUME you already have:\n", "# - classy_sz (CLASS-SZ python object)\n", "# - cosmo_params (dict)\n", "# ----------------------------------------------------------------------\n", "params_values_dict = cosmo_params\n", "rparams = classy_sz.get_all_relevant_params(params_values_dict=params_values_dict)\n", "h = rparams[\"h\"]\n", "\n", "# ----------------------------------------------------------------------\n", "# 1. k-grid and z-grid\n", "# ----------------------------------------------------------------------\n", "z_ref = 1.0\n", "_, ks = classy_sz.get_pkl_at_z(z_ref, params_values_dict=params_values_dict) # (Nk,)\n", "z_grid = classy_sz.z_grid() # (Nz,)\n", "Nz = z_grid.size\n", "\n", "# build P(k,z) on that grid\n", "pks_cols = []\n", "for z_i in z_grid:\n", " pk_i, _ = classy_sz.get_pkl_at_z(z_i, params_values_dict=params_values_dict)\n", " pks_cols.append(pk_i.flatten())\n", "P = np.stack(pks_cols, axis=1) # (Nk, Nz)\n", "\n", "# ----------------------------------------------------------------------\n", "# 2. use mcfit to get σ0², σ_{-1}², σ1² for every z\n", "# σ_j²(R,z) = 1/(2π²) ∫ dk k^{2j+2} P(k,z) W²(kR)\n", "# mcfit.TophatVar does j=0, so do j=±1 by pre-multiplying P by k^{2j}\n", "# ----------------------------------------------------------------------\n", "TH = TophatVar(ks, lowring=True)\n", "\n", "R = None\n", "sig0_sq_cols = []\n", "sigm1_sq_cols = []\n", "sig1_sq_cols = []\n", "\n", "for iz in range(Nz):\n", " Pk = P[:, iz] # P(k, z_i)\n", "\n", " # j = 0\n", " R_i, sig0_sq_i = TH(Pk, extrap=True)\n", "\n", " # j = -1 -> multiply P by k^{-2}\n", " _, sigm1_sq_i = TH(Pk * ks**(-2), extrap=True)\n", "\n", " # j = +1 -> multiply P by k^{+2}\n", " _, sig1_sq_i = TH(Pk * ks**(+2), extrap=True)\n", "\n", " if R is None:\n", " R = R_i\n", "\n", " sig0_sq_cols.append(sig0_sq_i)\n", " sigm1_sq_cols.append(sigm1_sq_i)\n", " sig1_sq_cols.append(sig1_sq_i)\n", "\n", "# (NR, Nz)\n", "sigma_0 = np.sqrt(np.stack(sig0_sq_cols, axis=1))\n", "sigma_m1 = np.sqrt(np.stack(sigm1_sq_cols, axis=1))\n", "sigma_1 = np.sqrt(np.stack(sig1_sq_cols, axis=1))\n", "NR = R.size\n", "\n", "# ----------------------------------------------------------------------\n", "# 3. map R -> M (same formula you had)\n", "# M = 4π/3 ρ_cb,0 (R h)^3\n", "# ----------------------------------------------------------------------\n", "Rh = R * h\n", "rho_cb_0 = rparams[\"Omega0_cb\"] * rparams[\"Rho_crit_0\"]\n", "M_grid = (4.0 * np.pi / 3.0) * rho_cb_0 * Rh**3 # (NR,)\n", "\n", "lnm_grid = np.log(M_grid) # (NR,)\n", "lnx_grid = np.log(1.0 + z_grid) # (Nz,)\n", "\n", "# ----------------------------------------------------------------------\n", "# 4. interpolators (ln(1+z), lnM) -> σ_j\n", "# ----------------------------------------------------------------------\n", "sigma_m1_interp = RegularGridInterpolator(\n", " (lnx_grid, lnm_grid),\n", " sigma_m1.T,\n", " bounds_error=False,\n", " fill_value=None,\n", ")\n", "sigma_0_interp = RegularGridInterpolator(\n", " (lnx_grid, lnm_grid),\n", " sigma_0.T,\n", " bounds_error=False,\n", " fill_value=None,\n", ")\n", "sigma_1_interp = RegularGridInterpolator(\n", " (lnx_grid, lnm_grid),\n", " sigma_1.T,\n", " bounds_error=False,\n", " fill_value=None,\n", ")\n", "\n", "def _sigmaj_Mz(M, z, interp):\n", " M = np.asarray(M, float)\n", " z = np.asarray(z, float)\n", " M, z = np.broadcast_arrays(M, z)\n", " pts = np.stack([np.log(1.0 + z).ravel(), np.log(M).ravel()], axis=-1)\n", " out = interp(pts)\n", " return out.reshape(M.shape)\n", "\n", "def sigma_Mz(M, z):\n", " return _sigmaj_Mz(M, z, sigma_0_interp)\n", "\n", "# ----------------------------------------------------------------------\n", "# 5. σ_v(M,z) and σ_vc(M,z)\n", "#\n", "# **IMPORTANT FIX**:\n", "# physical: σ_v^2 = (f a H)^2 σ_{-1}^2 ⇒ σ_v = (f a H) σ_{-1}\n", "# (your earlier run had (f a H)^2 σ_{-1}, which makes it rise with z)\n", "# ----------------------------------------------------------------------\n", "def sigma_v_Mz(M, z, classy_sz):\n", " sig_m1 = _sigmaj_Mz(M, z, sigma_m1_interp) # σ_{-1}(M,z)\n", "\n", " z = np.atleast_1d(np.asarray(z, float))\n", " f = np.array([classy_sz.scale_independent_growth_factor_f(zi) for zi in z])\n", " a = 1.0 / (1.0 + z)\n", " try:\n", " H = classy_sz.Hubble(z)\n", " except TypeError:\n", " H = np.array([classy_sz.Hubble(zi) for zi in z])\n", "\n", " pref = (f * a * H) # NOTE: **one** power, not squared\n", " pref = np.broadcast_to(pref, sig_m1.shape)\n", "\n", " return pref * sig_m1\n", "\n", "def sigma_vc_Mz(M, z, classy_sz):\n", " sig_v = sigma_v_Mz(M, z, classy_sz)\n", " sig_m1 = _sigmaj_Mz(M, z, sigma_m1_interp)\n", " sig_0 = _sigmaj_Mz(M, z, sigma_0_interp)\n", " sig_1 = _sigmaj_Mz(M, z, sigma_1_interp)\n", "\n", " corr = 1.0 - (sig_0**4) / (sig_1**2 * sig_m1**2)\n", " corr = np.where(corr < 0, 0.0, corr)\n", "\n", " return sig_v * np.sqrt(corr)\n", "\n", "# ----------------------------------------------------------------------\n", "# 6. unsmoothed v_rms^2(z) (your image)\n", "# v_rms^2(z) = 1/(2π²) ∫ dk (f a H)² P_lin(k,z)\n", "# ----------------------------------------------------------------------\n", "# def vrms2_of_z(classy_sz, z, k=None):\n", "# z = np.atleast_1d(np.asarray(z, float))\n", "\n", "# if k is None:\n", "# k = np.geomspace(1e-5, 1e1, 1000)\n", "# k = np.asarray(k, float)\n", "\n", "# P = np.array([classy_sz.pk_lin(k, zi) for zi in z])\n", "\n", "# f = np.array([classy_sz.scale_independent_growth_factor_f(zi) for zi in z])\n", "# a = 1.0 / (1.0 + z)\n", "# try:\n", "# H = classy_sz.Hubble(z)\n", "# except TypeError:\n", "# H = np.array([classy_sz.Hubble(zi) for zi in z])\n", "\n", "# pref = (f * a * H) ** 2\n", "# Ik = simpson(P, x=k, axis=1)\n", "\n", "# vrms2 = pref * Ik / (2.0 * np.pi**2)\n", "# return vrms2[0] if vrms2.size == 1 else vrms2\n", "\n", "def vrms2_of_z(classy_sz, z, k=None):\n", " \"\"\"\n", " v_rms^2(z) = 1/(2π^2) ∫ d ln k (f a H)^2 P_lin(k,z) k\n", " \"\"\"\n", " import numpy as np\n", " from scipy.integrate import simpson\n", "\n", " z = np.atleast_1d(np.asarray(z, float))\n", "\n", " if k is None:\n", " k = np.geomspace(1e-5, 1e1, 1000)\n", " k = np.asarray(k, float)\n", " lnk = np.log(k)\n", "\n", " # P(k,z): (Nz, Nk)\n", " P = np.array([classy_sz.pk_lin(k, zi) for zi in z])\n", "\n", " # growth stuff\n", " f = np.array([classy_sz.scale_independent_growth_factor_f(zi) for zi in z])\n", " a = 1.0 / (1.0 + z)\n", " try:\n", " H = classy_sz.Hubble(z)\n", " except TypeError:\n", " H = np.array([classy_sz.Hubble(zi) for zi in z])\n", "\n", " pref = (f * a * H) ** 2 # (Nz,)\n", "\n", " # integrand = P * k (because dk = k dlnk)\n", " Ik = simpson(P * k[None, :], x=lnk, axis=1)\n", "\n", " vrms2 = pref * Ik / (2.0 * np.pi**2)\n", "\n", " return vrms2[0] if vrms2.size == 1 else vrms2\n", "\n", "\n", "# ----------------------------------------------------------------------\n", "# 7. plot\n", "# ----------------------------------------------------------------------\n", "z_plot = np.linspace(0, 5, 100)\n", "vrms2 = vrms2_of_z(classy_sz, z_plot)\n", "vrms = np.sqrt(vrms2)\n", "\n", "Ms = [1e9,1e11, 1e13, 1e15] # in the SAME units as M_grid above\n", "sigma_vc_curves = {M: sigma_vc_Mz(M, z_plot, classy_sz) for M in Ms}\n", "\n", "fig, ax = plt.subplots(figsize=(6, 4))\n", "\n", "ax.plot(z_plot, vrms, label=r\"$v_{\\rm rms}(z)$\", lw=2, color=\"k\")\n", "for M, col in zip(Ms, [\"C0\",\"C1\", \"C2\", \"C3\"]):\n", " ax.plot(z_plot, sigma_vc_curves[M], lw=1.8, color=col,\n", " label=rf\"$\\sigma_{{vc}}(M={M:.0e})$\")\n", "\n", "ax.set_xlabel(r\"$z$\")\n", "ax.set_ylabel(r\"velocity (same units)\")\n", "ax.set_xlim(0, 5)\n", "ax.grid(True, alpha=0.3)\n", "ax.legend()\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 54, "id": "187d989b-fbef-4851-8c23-837e09f84016", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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0p2txjOcn/N31Inq9dwWweUV4M3RAH+Cvb8F1xXzc85ez4XLYtDmpp773EyJJFDbcRAxqYwx1ISoTNSYsA6olMMrXgPXZUvxF0rcUkCVGKzO8GCEDzs0+iyS7q+rw8uL1uNv1AuxwA8eMArr0Df+Bs3K0xx9ysvDgn49BH9smPPXhz5jz9cbwH5sQQmKQqDBhMU6pvRo1QRutGdlSjVWfitIMCdgyqmnrs6sEUwsPFS9+tR6n1H+BvvZSeFypwBn3RzwPFyZ8iXeS7sRtzmL8/fXvsGqzf5GAhBBCosiExWQlCEsMUPpzvR8ylte3VivNyRJA5U/frRFisr7N0XJsff3JSE1hKVGaRtTUN+D5T3/BOOfv03Xa/ngjkNYJEae+Bk5PHW5wvo5zPR/g+peWoaK6zjJdCLUxg7qYo0cTqxyEuzDA1s5Q6iL9uuHuhlQ+MEuGIukGbITvuF2ZXEPSm9V2xbTFqM0Qk5VJPHQjln1JM7S+AHQkaGlO19eXb0Ze1bvIdv0GT2pH2P54Ayyh3yVA2Trg4yn4p+tpXLCzJya+2g6PX9TP74CEUOoS71AbY6iLOXqkL7WxWJfAZwElVi1lKO/Peehdz7a7u/8+L/RXRR5LaWjweF4coeVl3d19PEeNn6MtgRguYm1ZulBCbeJDl2hcylCQayDLEnrPCy0U/29uaO85puU/VJ779++vvfZettZsPzr6d2U+aFkOUd7Lkoj6vuTZey0B789kfzJ3tLcu3vNah2vuaOWbo8l+vvt1D77dXod73VehLicP6H+5tRmSZdDOnwa074Getq2Y6irE/fO/xw9b2D9MSESprTR/1FUHkLbKv7RtaNEUZLIjmWRJWjb1JWQHDBigdfcJ8izz9kuLpKSVZ2ntlG0t7ccb2Yd0YeqzKcrn0pIpK/JJy6Z3etmfHF8+kzRz5sxpXFUpUijfHE3289a3W6SxBO7DzoHrknugBCmZwIjn4Hl6KM7E18hr+AK3zk7D6zeciESn2v1NhMQMk7qYf9bnDOASr0Vuph4M1O2DzcgAep4EXPHW/vf/PhrYJ+uR+/CPpjOKtYT06Upsj5imHsMjJimjXQoKCrTX+nKzEstjNjVwa/vxNmHvIF55773coff+5Xu6WUsa3ZAjCU04SpCWlrc1EwbOPvogKEXX/rCd+SAqy7dh8Rf9seO3Cjzy3mpMPPtwq3NGCLEYPUhWZvnS8Q52ys7O1sxQaqgtzc3f2n50fGN3vN/7BvKKAUvNV4xXDF6Mvl+/fogkNGEFkYAA36XXvv91N+6puBcrEg7DoN5/gHIMvAqpACZ33YrRzy9B0SdrMOiwTjg+OyusupDfoTZxrssdm80/s/m0SI37pfFlswAkWfTFm1u+DTprEiQrRihNyGaU/q/22dKoFn/2o6dr6b23gUuQrtwEyEyMMhHU5MmTNT0iubwj+4QVRJ9U3Xumn5Wfv4PBjuW4wTEPKT7zNavEkCM645L+nXCm7Sv8bc7KkA5bMtKF/A61iXNdElLNHy6fdYf/t93jSmny/HvaZP/2GwBicr7zOUhfrr487LJly7S+WGlaltqw2ZCg1vYTKFLrlZEvUvuWGrFeY25p5a1wQBNWFO/VWqQwHPjTf7XXv/U8F0gyX6nJcuqqcN+2GzAt4VH02fMF7n9zVdh0IU2hNsZQF3N8VxsKB2Ju8pC+W6npivmNHj26cfbB0aNHa8NCxQilT9Y30EqvHbe2n0CRpmkxfDF2eZZmaTF1eR0JXXRowlHAT7/8os2QJXQadB2UxpUMR85p2sspriKULFmFj1dvtzpXhBAL0YOppPlXaq5iuBLxPGXKFM309OhnqQ2LGeo1Xn3ZWUnX0n7aggRvyfclQlr6mfUIaXm88soriBRRuZShquhLGba2dFVr6Cu/6H1Zn8zMx8kbC1GadCRyJnwO5ZFhDoWnAjt+wlsNx2FS6kS8d+spSE0MLgTBVxeyH2oTH7roS+eJafiu2hMoLa2iFM94AtClpevhrx+wJqwo+sX3NNTh0E2/zxZWfpTF44L9RfqV/lwEj82BPzkW46g9H2HKuz+GZNf8szCH2hhDXYjK0IQVj+jc9NU8dPLsxC5POg47/VJEDV36wnbSLdrL+13PYt4X3+PrdbuC2mXcRLq2AWpjDHUxRzRxOp3UxmJdaMKKNofU1dVpzx9sdqG4/hR8nDUcqamBRSVazin5QFYfdLKVI985G+PnfoPquoaQ6EKaQm2MoS7mxE3kuOK60IQVpbKyEg1uD6b93A7j6q+B87RxiDpkaMR5T6AuOw9zkoZhzY5K/OeD/WMU26oLMYbaGENdzBGzIdbqQhNWmC/W7MSW3dVon+xC3uGdEZX0OAGuy17Bteedrr196qM1KN2+1+pcEUKIEtCEFWbd+0/jaNsanHvMQUhSeIIOfzjzqANx2qEdkdWwHXe99h2bwAgJAv5+YqfGzGkrFWVf5V4M2/wQ/pJYi596v41ox+auxxOpTyMhcQ7OWTMJb6zsjvP6dg14P7I2LDGG2sS+Li6XSwsY2r59Ozp27BhU8JDe98n1lgPXRdLU1tZq10HSJSQkoK3QhBVELvzaJW+ji60Wm20H4pBjFJwrOlAcLqS5KwBbAx5wPY3r52fjtEM7aU3tgegSzPjrWIbaxIcuMna1W7du2LRpE9atW2d1duKelJQU9OjRI6gbPZqwqk1Nq17XXm/rPhRdYuVO/swH4Sl9H8fV/YTTqkvw0IIuuP/8o/z+un73KXedvHNvCrWJH13S0tLQp08fLeo7FJHjeu2aBKaL3BCFYigTTVhB1m3ZgX7VX8vSwehx0sWIGTK6w3baBKDkbkx0voS8r3Ix6rjuOLKL/3NhV1VVBdX0E8tQm/jRRQxAHsGaTU1NjTbTE03YOl1ipIoVW3zzYTFSbDXY7jgQmX2OR0xxwnVApyOQaduLcY5ZuPeNVQwyIYTELTRhxZCxwSm/vKm93pN9tnRqIaZwuIA/PaK9vMj5AWrWL8b8b7ZYnStCCLEEmrBiLF6zA4fWr9ZedztxFGKSnn8AjhmFfa4OyLLtwaS3fsC+2nq/vip9MMQYamMMdTGH2livC01YMU7IOQC/Xf45Pj7xWST2PA4xy9BJsN+0DD9nnIjf9lTjPx+UtvoV6Z+RoBT2XzWH2hhDXcyhNmroQhNWDLnwA7M7YuBJZyKme0pTs5CUnok7zz5Ce1v0yRps2Lmvxa9I37EE2bAPuTnUxhjqYg61UUMXmrCiSHRePDD0iE64vcu3uNozD5Pe/qHV9PGiS1ugNsZQF3OojfW6sEOAWIrt16W4Yddk1DvtOGtVLr5c0wsnZGdZnS1CCIkIrAkTa+k+EDjsHDhtbtzlfAH3z/9eixAnhJB4gCasKLE2uUCLnHE/PI4EnOL4Fp23foRXlm0yTRpXugQItTGGuphDbazXhSasaHCWzEkaN1GLmdmwnXCt9vJO54v417vfobKm+ZCluNMlAKiNMdTFHGqjhi40YQWRqLx9+/bFV9TiybfDk9oROfYtOLvqTUz7sPmQpbjUxU+ojTHUxRxqo4YuNGFFkUnn44qkdrANukt7ebPzVbz0yXf4tbyqWbK40yUAqI0x1MUcamO9LjRhog79LoXn6BH4d9Y92FWfhIcX/GR1jgghJKzQhIk62B2wXTgd5//59+k6X13+K777dbfVuSKEkLARNeOEp0yZgp07d2LZsmXa+/HjxyMvL8/v7w8ZMgT9+/fH2LFjkZ2djTVr1mDu3LnaPgsKCpqlLyoqQnl5OTIyMlBaWqp9P5DjBUtiYiLilWO6ZeD8vl3wwYrVeOCtVXh59AmNQRLxrEtrUBtjqIs51EYBXTxRQH5+vqesrKzxfUlJifSYewoKCvzeR3Z2tvYd78ewYcMM044ZM8ZTWFjYZJukleO2xO7du7X9yjMJjvL3Cjx77+7ouXjiZM/CVb9ZnR1CCAkIf/1A+Zqw1Eil9io1Uh2pkUrtVa8NSw23NYYNG6bVZqUGnJmZqX1HasS+SE174cKFKCwsbLJdjpebm4uysjKEG4nKq6ysRGpqatwOH2hfvwuw1eDvzhdx01sDceohHeGw2+JeFzNYZoyhLuZQGzV0Ub5PWJqCjcxyzJgx2vPs2bP92k9WVpZm2PI9MWSjfQpivkbNznp6MehIUF/v39J+Mcup+fAktsfh9g3oW/YuXv56o7Y57nVpAWpjDHUxh9pYr4s9GvqCpSbsi9SM5aH3EYcKMVnvWrevEZeUlIT0eMSElEzYTs3XXt7unIOikpXYazCBByGERDPKm7DUSs1MUQ+cCgT5jhitNEsbIdtzcnIMP5Nm7FCbPmmB40bD06EXOtvKMaxmHmZ8YnzNCCEkWlG+T9is5qmbqPTz+oNEQUutWvqCBwwYgCVLlmh9ysXFxQHlZ9euXa2m2b17d5PZVqRfQd5LxJ131J2+3YikpKRmn5mlN9oeSFpltzsSgLx7geLLMdrxFs76ZAgu7PsntIuhcw3VvkNRZlQ7p1Bsl/fJyckxdU6h3K5KmVGp7AVaZmTZQ5ncw3f7nj17EBMmbIb03UotWO8b9gdJq9ecpYYtRi4mrhu91JJbQr5rVoP2pkePHobbxfQnTJigvRYzlgstAQDe/Q+yTT6Ti1pdXd24XYIEXC6XdmG9L3Z6ejrsdrtm/N60b98ebrcbFRUVTQqQbJfjyXF15Pvt2rXTjimLWes4nU6kpaVp+fBeX1MmN5e5VSWt98wyrZ2T5EXyFNA5dTkVaQf2g+u3b3FU3Q+Y9vFRGD+4R3SfU5iukxzP+4cfC+cUquskeY+1cwrFddLTx9I5uUN0nWSbP+c0depUTJo0CW3FJiHSiDKkSXjw4MFYtGiRX5HRLSEXaOnSpdp+xIQ7dOigGbyRuQ8fPlwzYUlvhBQUudgbNmzQLm4wNWHZlxQ07+i8uL1z37YKK3+rwfkvb4HDBrx788k4uHO6WnlUoCYcbJlR7ZxCVavZu3ev9qfqG+karecUyu2qlBmVyl6gZaalmrBUyORGwdsPYqImLGYozcjBGrBeu5UIa3/21VpNWUeMuCXRvfG9yIJcTHnIZ0aFwN/9BJJW6e2dj0TfzkDeiq+x8IdtmPreahRdNkCtPAaxPRT7CFWZCdV2lfSVmpGRLtF8TqHYrlqZUansBVJmpElfHv4eL+oCs4wMWMbshmr2Kn32LEFvqm7JbCU4i1jD+DMPw9H2tdjywxf4el3rffOEEKI6UWXC0qc6cuRIbZxvIMgkG/JdI3wNV2rEEsRlhN6HTKzh4C3zMT/hTkxyzcDkt743bZYlhJBoIWpMWGbOEgP0NWB/Js8Qox04cKBptLP3Z3rAlhGyPVLzR0tAAvHh4CFwJ6TjaPs6dP/1bbz73W9W50gpWGaMoS7mUBvrdYkKExajlWZjIwM0GrfrW7uVyT6Mas+yX0nr/ZnUtI2MXY4jzdWh6IduDelLkIhAf/sU4gVbWkfYT7pFez3ONQePvPMt6hr2R1LGMywzxlAXc6iNGroob8JifhKtLGYpqx55P2Tcr+9kHdL0LBHO3rVZMVnf5mjZn5izBHh5T2EpJjtixAit5u1NW8YUtxVpZvUda0z+p8sRl8KddiC62XbglN2v4eXFG6zOlhKwzBhDXcyhNmroovwQJTHUlgKlZIyvdw1ZjFVqsjKMyNugZR+TJ09ufC3N0BMnTjSt2YrBC4EsZagPUWotJN3fQiD74l2qgS6lr8M2/yaUe1JxvuNJzM8/B+lJLsQzLDPGUBdzqE14dfHXD5Q34WiCJhxeGnVJTwWeOgm27T/iqfr/w96T78LtQw9FPMMyYwx1MYfaqGHCyjdHE9IMuxO2vHtRk5iFNZ4DMfPTtdi2Z//sYoQQEi3QhBVFZrEhLehyyFAk/O0b/Nz1AlTVNeDRRT8j3mGZMYa6mENtrNeFJqwg0gQi85qyiagFXWQ2m8Q0TDjzMO2zWV9vxNod++eFjTdYZoyhLuZQGzV0oQkrCKMW/dfl+N6ZGN/te9xrn4GH3vsJ8QrLjDHUxRxqo4YuUTl3NCGNlG/ANbsKYHPW463vTsA3m7JxTLfA1pgmhBCrYE2YRDcdesI24Ert5QTny5jyziqrc0QIIX5DEybRzyn5cLtScax9DTLWvo1Pft5udY4IIcQvaMIKIgEBHLsXgC4yneWJN2svxzln4+F3voXbHV/9XCwzxlAXc6iNGrrQhBVEAgJkPUsGTASgyx+uhzu1E3rat+HYra/hzW+3IJ5gmTGGuphDbdTQhSasKBUVFVZnIbp0SUyD/bQJ2subnK/iyQUrUVsfX4s7sMwYQ13MoTbW60ITJrFD/8tQf/j5uMtxK37a5casr7m4AyFEbWjCJHZwuOAc+Rz+MORC7e1ji35GZU291bkihBBTaMKKwmCJtusy6rge6JWVgr17KzDjk7WIF1hmjKEu5lAb63WhCSsIoxaD08VlAwp7LMRXidej5OOPsWNvDWIdlhljqIs51MYYRkcTLSqvrq6OUYtt1cVuxyENpWhv24cbPS/hifd/QazDMmMMdTGH2qihC01YUSor43cxglDoYhtyLzw2O4Y6luCHxe9h/c7Y15NlxhjqYg61sV4XmjCJTToeClu/v2gv8+0vYuq7P1qdI0IIaQZNmMQup02E25GEXPvPqPt+PlZuLLc6R4QQ0gSasKLIepYkSF3aHQT7H2/QXuY7Z2Hq29/FdP8Xy4wx1MUcamO9LrwCCiJRee3atWPUYih0OfFmNCRnorttG6rXfYWPVsfm4g4sM8ZQF3OojRq60IQVRGprNTU1MV1ri5guSe3g+PN0TD9mFpZ4DsOD7/yIhhhc3IFlxhjqYg61UUMXmrCiVFVVWZ2F2NGlTx4uOes0tEty4sffKjBv+a+IRVhmjKEu5lAb63WhCZO4ICMlAdedfjCOtK3DMwu+QnVdg9VZIoQQmjCJH65yv4K3Eu/AqH0v4bnP11mdHUIIoQmritPptDoLMaeLq/eJ2vNFjvfx1gcfo3xfLWIJlhljqIs51MZ6XWjCCiJReWlpaYxaDLUuvU6E55Cz4LS5cV3Di/jPh6WIFVhmjKEu5lAbNXShCSuIROVJYACjFkOviz6d5ZmOr/Ht5+9iU9k+xAIsM8ZQF3OojRq60IQVRULkSRh06Xgo0O8y7eU4+3/xUAxNZ8kyYwx1MYfaWK8LTZjEHbbTJ6LBmYL+9l9Q8+1r+HbTbquzRAiJU2jCJP5IPxCOE29ChSMDTjRg0ts/sEmOEGIJNGFFSUhIsDoLsa3LiTdjz+jFWGA7CV+s2YkPf4r+6SxZZoyhLuZQG+t1iZr49ClTpmDnzp1YtmyZ9n78+PHIy8sLyz6GDBmC/v37Y+zYscjOzsaaNWswd+5c7bsFBQUINxKVl5KSEvbjRBsh1SUhBV0PTMFfT+yFoo/XYPI7P+DkPgfA6YjO+1KWGWOoiznURhFdPFFAfn6+p6ysrPF9SUmJtB16CgoKwrKP7Oxs7TPvx7Bhw1o9xu7du7W08hwMbrfbU1lZqT2T8OpSvrfGc/s993juu+M6z8tfrfdEKywzxlAXc6hNeHXx1w+Uv+0vKirSaqQZGRmN26T2KjVSqcnqtdpQ7mPYsGEoKSlBYWEhiouLUVpaqj1Hktra2JpIQlVd2m9fjKn4F/Kds/HSe5+isqYe0QrLjDHUxRxqY70uypuwGKA0CfsyZswY7Xn27Nkh30dWVpZm0vK5GLLRd0mM0PNEuHuejERbHa6o+S+mf7LG6hwRQuII5U1Y+nGlFuuL1Grl4U9NOBT7IDGKzQb70AfggQ0XOD7DZx+9h217qq3OFSEkTlDehKVG6t2M7E15ebnpZ6HYh3y2cOFCLTAr0iQmJkb8mNFAWHTp0hc4dqT28nbbC3jkvZ8QjbDMGENdzKE21uuifHS09M0aoRujRDKHeh8SBS21Z4mQHjBgAJYsWaL1HfvbL7x79+4m404l2k7ey4X1vrj6dl9ke1JSkvbaaD9G6X23B5I2WraHQhfT7af/He7v5uF4/Ihnls/DDyf2wmEHtovIOYVq32HTJsrLXnJysrY9mHyqdk7K/56ivOwlB1BmZHYt6UP23b5nzx7EhAmbIUFTUoPV+3VDvQ/ZpteQpSYthi1mbWbo3vTo0cNwuxj5hAkTtNdixnKhKysrUV+/PxhItskYta1bt2pp9EnEU1NT4XK5tAvrfbHT09Nht9s14/emffv2cLvdqKioaNwm+5Ltcjw5ro58v127dlpB8l7MWlYSkYnMq6urm0zjJvmTEH5J6x3A0NI5yWeSF8mTTqDnJHnU96HrErpzykLKH28EPnkI4x0v4a7XB+GJUceE/ZxCdZ3kuLt27dLS6NpYdZ1UKntiMvK+oaGhSd6j+Zyi4/cUvWXP4XBo35Fnf85p6tSpmDRpEtqKTUKkEWVIH+7gwYOxaNEirbYaqX3IhVm6dKlpeikocrE3bNigXdy21oT1pnDZl/dKHvF+5x4KXVrcXrsX+/57Ca5ZexI+rj8Sz14xEKce0jEq7tzDrk2Ulj15L79L+T36rooTrecUyu2qlBmVyl6gZaalmrBUyORGwdsPYqImPHz4cK1puK0G3NZ9SM1YIqlb+44U6pZE98b3IgtyMWW7/mgtvdn2QNJGw/ZQ6WK6PTEdKVe9gYPnr8LHn63VprM86WCZwKP58UJ53FDsI+zaxEDZCzafKp5TMNtVKzMqlj1/9i+tLXqzvj/7jbrALCPzlPG9gc6WFYp96LNnkdjm5sF9kJHiwoatOzF7yUars0MIiWGiyoSlT3XkyJHa2N1w7SM3N1dLY4Q03UQK6SMh1ujSPsmBmdkf4YvEG1G84CNUVNchGmCZMYa6mENtrNclakxYZr2SwChf85QhRKHchxjtwIEDDb8vgS9mn4USacbwDsoiEdbFbkc//IgOtr24tu45PPlBKVSHZcYY6mIOtVFDl6gwYTFJaQo2aj42mmjDqMbq7z5kUg+jWrJ8X/YbTC3cX/TAgCiMmYsZXexn/BNumwNDHUvw3WdvYuOufVAZlhljqIs51EYNXZQPzBKDlKFE0oQsKxl5I/2zvhNtSHOyfMd7qspA9iEmK83R3qslifmKOUsgV6SmsPQO0ycW6NLpMNhyrwCWzMAE+wsoeOdUPHFJ+FtBgoFlxhjqYg61sV4X5U1YhhGJCfqap47vuF2ZXEPSZ2ZmtmkfYrITJ05s7BeW70kzdLDR2CT6sJ0+EQ0rZ+OounVIXjUHS9blYECv/eWKEEKCJSrHCauKPk64tXFhrSGXRPbhO34v3rFEl88eBUruxjZPBm44YAZmXZ8Hu129a8IyYwx1MYfahFcXf/0gKvqE4xGZKYYooMvx16Ahoxc6oAIpWxZj3vJfoSosM8ZQF3OojfW6KN8cHY/I3ZdM1UYU0MWZCMefizD7u3348ON9+GHBjzjr6AORkqDWT4dlxhjqYg61UUMX1oQVbg5hT4EiuvQ4Hn8ecgq6ZyZj654aPPWRehO2sMwYQ13MoTZq6EITVhT+MNTSJcnlwB1nHY4jbWvx6ccL8Wv5/gnfVYFlxhjqYg61sV4XmjAhfnJmw4eYn/h33GsrRMHbq6zODiEkBqAJE+IntoPz4HGl4Wj7OiR+PwuL1+6yOkuEkCiHJqwosl4mUUyXtI5wnP77etD5zlmY+vpiNLjVac5jmTGGuphDbazXhSasaHSe9+LsRCFdjhuDhsyD0dG2B0N2PI85iqyypIQ2CkJdzKE2auhCE1YQRi0qrIszAY6zHtReXuF4F8Xvvo/dVdavsqSENgpCXcyhNsYwOpoQ1ekzBO4+Z8Bla8ANdc/gsUU/W50jQkiUQhMmpA3Yh05GTXJnLHTn4tnP12L11gqrs0QIiScTfvXVV3HttddqKxPNnDmzxbRStZe01113XVsPR4haHHAwEm/7DtsPvRgNbuAfb3zPZj1CSPgXcFi3bh2GDBmiLQEoyNelA7tDhw5YtGgRjj322GbfWbt2LXJycrR0DQ0NiFVCtYCDt65EbV1kneHBj3yE2voG/OeSXJx99EGW5UU1bVSBuphDbcKnS9gWcJDl/MSAZecXXnghxowZo72W5f5kLd/169cHlXHyewGQ9SxZs1Jfl+4dkvHwkeuxIGE8npr/CfbV1luSDxW1UQHqYg61UUOXgEx4woQJ2vq6suau1G7nzJmDp556SjNgMWTJ+PDhw8OX2ziiooJ9jFGhi8eDs/fOxaH2Tbiq6hlM+7DUsqwop40iUBdzqI31ugRkwgsXLtSq6IWFhVrt1xtZ9L53795YunQp5s2bF+p8EqImdjscf3oIHthwnuNzLP34LazfWWl1rgghUUJAJqz3Aw8aNMjwczFnqcKPHz8+NLkjJBro0hfof7n28i77M7jv9W/YxEcICb0JS1N0S+Tl5aFfv34oLS1tNWKatAyDJaJLF9vgu9GQmIHD7RvQtfRlLPxhW+TzoKg2VkNdzKE21usSkAlLX7DwwQcfmKaZPn16Y21YosNI2wqANPfzBxJFuqRmwZF3l/bydmcxHn3jM1TXRW4kgNLaWAh1MYfaqKFLQCYswVdisBJ8ZRYFLdHT48aN04K1RowYEap8xhWicV1dHZs0o02X3CvgPvBYtLPtQ7+KjyIapKW8NhZBXcyhNmroEpAJ33HHHY3DkaRWLBN1PPTQQ83SFRQUaEFaJSUlOOSQQ1BUVBTKPMcFlZUM7ok6XewO2M99FItPnI4XGs7AtI9KIxqkpbQ2FkJdzKE21usSkAmLAUv0sxis3CVIRPTkyZMN00q6vn374pdffsGUKVNClV9C1KZLPwzMG46TDj4AtfVu3Dt/FWsahJDQTdYhNWAJvBIDlok6Bg8ebJguIyNDM2IZSyxmTEi8IH1J/zj3SHRxlKNu9UK8t2qr1VkihMTKtJXBINN3+Y4vjiVCNW2lXBIZLC4LSzNoIkp12fo9aoryUFPvwcWJT2D2becjNdEZtsNFlTYRhLqYQ23Cq0vYpq00m0964sSJ2qIO3sg26UfWg7hi2YBDiVx4uWj8YUSxLh0Pg6vTYWhnq8LVVTPx2PvhXe4wqrSJINTFHGqjhi5Bm/Arr7yiLc4g/b76ZB460hz94IMPak3YHDcc2J1YTU0N+xKjWRcJ0vq/R7SZtM53fI5Vn87HT7+Fbyq8qNImglAXc6iNGroEZcIyf7QMV5LMSt+wTNbhjYwV1oc1Sf+x1JiJf1RVVVmdBSWJKl269INt4NXay384nsa985aF9YcdVdpEEOpiDrWxXpegTFifnlKe33vvvWYBWGLMEsAl44blz0cWgCAkrhj0dzSkdESOfQv6bfovipdusjpHhBCFCMqE9QUdpO+3JWTcsN48TUhckZwBx5mTtJc3Oufh+bc+xK7KWqtzRQiJBRPW55L2NxLYt8+YmON0hi+SNpqJSl2OHg539ul4M+lPWFOVgklv/xCWw0SlNhGAuphDbazXJSgTlikqBbMpLHUkRNs7PWkZaV1IS0tj1GKs6GKzwX7pK+h98b9RZUvC3KWb8OWanSE+RJRqE2aoiznURg1dgrL73NxcLFu2TOsTnjVrlmm60aNHayc0YMCANh9Loq937typHU+QY/oGgrWGTJ8ptXeZSEQmHBkyZIjpPgJJG2qk/7y6uhpJSUn8gcSKLnYHcnt2wEXH9cDLX63DP15ditdvGYxEpyMku49qbcIIdTGH2iiiiycIysvLPRkZGR673e4ZOnSo5/3332/y+fLlyz1nnHGGx2azaWnWrl3bpuPk5+d7ysrKGt+XlJRIiKmnoKDA732MGTPGU1hY2GTbsGHDtH0Fk9ab3bt3a/mS52Bwu93a+coziS1d9qz/1rP8nuM8z915oefRhatDtt9Y0CYcUBdzqE14dfHXD4IyYUGMSUxWN1rfh/7Z3Llz27R/McPS0tJm28WA5QSXLl3a6j4kTXZ2drPtsl+5iWhrWl9owuElJnQp/dDjuaedp+Hu9p4L73zMU7qtIiS7jQltwgB1MYfaqGHCQU/WIU20ZWVlWpOzzIj1P2NvfAwbNkxrzpXxwm1BvquvY+yNjDsWZs+e3eo+CgsLDZuS9f1KlHdb0hISMNmnwnPsKNhtHtxnn467Xl3OyRIIiWNCMm2lmK+YlyxxKIYsQ5Hk2e12aws4yKpLwfQFjx07ttl26auVh95H3BJinJLWCDFXWXKxLWnDSUJCQkSOE23Egi62M/6JhqQOOMK+HkdteBHFS0IzdjgWtAkH1MUcamO9Ln6bcGZmJq677jqsXLmyVUPu169fyOaJllqpmSnqgVP+DI2SqTXNzsvbyANJGy4kGCAlJYXBErGqS+oBjWOHb3XOxXNvfYDtFTVB7TJmtAkx1MUcaqOGLn5HR8tsV1Krfeqpp9ChQweMGDFCe5x++ulhzaBZzVMfcyxRy8EiNfhQppUhWd5NjHIx5X1iYqL28N1uxL59+5CcnNykIJilN9oeSNpo2h6sLspsP2YUPCtmIWndR5jY8BTue+NIPHZx/zbvWyVtlNDXj0jXaD2nUG5XpcyoVPYCLTMyz3RtbW2z7bKKUkhNOD8/X3uI+c2dO1czZGmCjqQheyPHllqw3jfc2oQiZsg+dEMPJG1L9OjRw3C7DKvSp+4UM5bCX1lZifr6+sY0sk2aQiQvcmH1QpCamgqXy6VdWO+LLctt2e32xrHYOtISId0BsiSXjuxLtsvx5Lg68n2ZcEWO5z1nqgxYl/FyUiCloOlI/uROUdLKd3RaOif5TPIiedIJ9Jwkj5IXb12i+Zzsp92HtBfOQIa7Ep9+uxpvLuuAk3My23ROcty9e/c20caq66RS2ZM/UklXV1fXJO/RfE78PSGs18nhcKChoUH7nj/nNHXqVEya9HvLVsTXExYBxAzFkKWZNlKGLMeSeakXLVrU6gQgYmaSL8mnkWHLAhRirNKPHUjaltaP3LBhQ5NZxNpSE5a8yL5YEw6tLqptt/26DA8sd2HGZxvRpX0S3r3lZKQnudpUE1ZFG5X0lffyuzRami5azymU21UpMyqVvUDLTEs1YamQtbaecFCTdcjF02vIciCJVJZashikmJn0515zzTUhN2QxQ1kYIhQzcLVW+21LWtHF36k8fS+yIBdTtuuP1tKbbQ8kbTRsD5UuSm3vlou/darHez/sxIZd+zB1wWrcf/5RAe9bNW2U0ddre7D5VPGcgtmuWplRsez5s39pbZGHv/sNS3S0bjxSe5TVlCQyevLkydqzGLJU70eOHIl58+aFxIBlQQh/Z6/SA7daMlAJuAo0bbjxrjGT2NYlJcGJgvMOwa3OYvy6eB4Wr/U/RiHWtQkF1MUcamO9LiEz4ZYMedq0aVpAk4wZDgbpUxUzD3Q/UmOWKS+NkOZl7+CuQNKGC7mD8g2WILGtyx+2F+Nm5zz80/U07pv7BarrGgL6fixrEwzUxRxqo4YuYTFhI0OWKOdAopCN5nIWA/Q1YH8mz5Bas1lAlWz3rlUHkjZcSDORBNkE0V0fk8S0LseNQUOH3jjItgsX7Z6JRxf9HNDXY1qbIKAu5lAbNXRpswm/+uqruPbaa7Wa6cyZM1tMK/3Fkra1dYfNEKOViTKMDNBo3K5vc7Lk0cis5bvSBO3dtxxI2nDiHTlI4kCXhBQ4zntCe3mJcxFWfvImvtnkf7xCTGsTJNTFHGpjvS4BR0evW7dOq5HqtUW9c18CsSRa+dhjj232nbVr12oTYEg6Cf0OBDE/6V8Wc/RF8uA7TElf2cl3ukuZdUs+804r52G0GlMgaY2io1uLhmsN0VT24Ru1GO/EhS5v3goseRrr3J1xc4cnUHxjHhKcrd8rx4U2bYC6mENtwquLv34QsAlLYJKeQTEkeS9DlKT2KeOuxPx69uwZMhMWc28pUEqaub2NUQxUarIyjMh3Ni2ZAlPwZ3nCQNLq0ITDS1zoUr0HDU8eD0fFZsyoPwt7Tr0PfxtySKtfiwtt2gB1MYfaRKEJy0QTYk5iqEuWLGkyNaVELb/yyivamsGLFy8OmQlHE6E0YRl3JoPD+eOIQ11+LgFeHIYKTzIG1/0bz9xwFo7s0vI0sHGjTYBQF3OoTXh18dcPAuoTlhqmZEoms/CdG1rG7cpCDVIDDcVQpHhGNJYQef4w4lSXPkPgOeMBFPScjm3udIwr/gZ1DftnEYprbQKEuphDbdTQJSAT1vuBBw0aZPi5mLPcRUjfKWk7+owtjFqMX11sf7wRNw8/AxkpLqzasgfTPixtMX08aRMI1MUcaqOGLgGZcGszRkmfqaygJH2orUVMk5bxnj+VxKcuHdMTce+5R+IE+yp8+MG7WLW55Qnh40mbQKAu5lAb63UJyIT1aOMPPvjANM306dMba8P+riJBCDHmXHyMWQkPYKr9SUyY/RVq6/mnSUgsEZAJX3jhhZrBShDW+vXrDdPIONpx48ZpE3PIQg6EkLZjO2QoGlI7I8e+Bf+382k8/n5gk3gQQmLIhO+44w4tIEsMVmrFMnb3oYceapZO5naWIC0ZPnTIIYdos12RwJAlvEhz4k6XlEw4zntce3mV4x0s/ugtrNxo3C0Ud9r4CXUxh9pYr0vA44T1+ZNl2JE+jtdormXpP5bFG5YvX964/BOHKBHSRl67HljxX6x3d8L17R/H3JuGIMnlsDpXhJBIDlESpAYsgVcyJElmlBKjNUImuZDhSjKRR9++fQM9TFyjDxZn1GJT4lqXMyfBnd4VPe3bMKJsBh5a8FOTj+NamxagLuZQGzV0CbgmHAz6LCSxCmfMCi9xr0vp+8ALF2gvz66dhLuuGoU/5GRp7+NeGxOoiznURo0Zs8K6itL777+vLfSgR0nHsgETEnZyBgEn3YrZ3e/CKndP3F68EhXVdVbnihASBEGbsBisrJA0atSoxm3i/H369NH6jiWSWvqNOYsWISEg7x/406W3oHtmCn4tr8J981dZnSNCiFUmLGYrUdAS/Sz9xDoyRljeS81XPpfqvawDLCswEf9IT0+3OgtKQl2AtEQnHhnRFx1sFVi7bCEWfP+btp3aGENdzKE21usSlAnLEoNlZWWa0criDjoSjCVt6bKk4C+//KKNGxYj1lcmIi0j2smKVOynaQp12c/A1B34JO0OzEh4GA+/8hG2762hNgawzJhDbdTQJSgTnjt3rpZReZaJPAQZkiTDk2QKy169ejWOLxZk3DBpHUYtGkNdvMjsjZSsbsiwVeLOuicwbs5K7XdHbZrCMmMOtVFDl6BMWF/QwXsIkqy0JEh/sI4ekKWnJ4QEicMF+4XT4XYk4lTHN+i15kXMXvZ7szQhJHoIyoRlLLAvUtuV2rHUhP1JTwhpIx0PhX3oP7WXdzhfwvwPPsHPWyuszhUhJFImLH3BwsqVK5vVhL1rx9JELWRmZgZzOEKILwOvhufgIUi01eEh++O4fdYS1NTH7qx0hMQaQZnw2LFjGxd0mDFjBoYOHarVgiUSWmfFihUYPXq0ae2YNEe04gD65lAXA2w22M57Eu7kLBxu34BTt7/YbDateIZlxhxqo4YuQc+YlZub22x+aBmeJEFZsn3AgAGN2yWSOpbnVA7ljFmyniUjF5tCXczx/Pg2tnz+MoauPhcVSMHzVx6HUw7piHiHZcYcahNeXSI2Y5bMDy1DkPr166fVdN97773GqGj9hGR5QxmqFMsGHGoqKti3ZwR1MeHQs5Dy58dx3gmHaW//NmclduytsTpXSsAyYw61sV4XZyh2IksXGiHGLHcUhJDIcOfZh+Or0p3osfNj5Be3w8y/DmQthxCFCevc0YSQyJLktOOVjtMxM+FhdPvlv3juc85SR4jK0IQVhbUXY6hLK9rYbGh3yMna+zudL+GVtxfg+827Ec+wzJhDbazXJWgTllWSBg4ciKysLDgcjhYfTmdIWr9jHkYtGkNd/NTm+LHw9DlDG7b0sOMx3P7Sl6isqUc8wjJjDrVRQ5egTPiVV17RhifJHNES+SxBWC092D/sH6JVXV0dp5Pzgbr4qY02bOk/cKd2wiH2X3FZ+TTc88b3iEdYZsyhNmro4gx2AQc9+lmCs7Kzs0OXszinsrKS6y8bQF381Catozatpef583GR8wN8vvxIzDs4Cxf064Z4g2XGHGpjvS5BmbDUgKXK/v7773P4ESGqkX0abKfcDnw8Ffe7nsGQeQNwbLcMZHdMszpnhJBQNEdLzVfmg6YBE6Iop06A+6jheKTj/dhem4DrX1qO6jpOa0lITJiwjAOW5dM44Dv0yGwtpDnUJUBtHE7Yh83A9ZddgqzUBPywZQ/ue3MV4gmWGXOojfW6BHWkiRMnan3CMjc0CR3SxC+tC4xabAp1abs2ndsl4d+j+uJQ+0b8+vV8vL7iV8QDLDPmUBs1dAmqT1gCsgoLC7WFHGT6Slm4IScnp8XvXH311YgUsn6xrNwUbUsoyo1NbW0tEhIS+APxgroEp83JKRvwZtLdqGqwY8SrXXBU12HIifH+YZYZc6iNGroEtYCDTEw9ePBgbaEGfZGG1mhoaGiTmcpQqOnTp2vG7y9FRUVa1LbcJIgRGy2lKP3a+j6HDBmivZb0sl2OO3fuXOzcudN0as5wLeAg++AYvqZQlyC1aaiD55k/wbbpK3zr7oU7OjyMOdefjuQEB2IVlhlzqE14dfHXD4KqCY8fP16LkBbEtEI9REnMcNeuXdp+9eMEgqzmJEYq+TSjuLi40YQlrayHPGXKlMbPpXYvaQiJehwu2IY/A/e0k3B09TqM2DkNd7/eEVOHH2t1zgiJW4Iy4Tlz5mh3CtIkHY5mZtmvIMFf3sboL/I9mUTErDlazNl77WN5LbVhvRlbzJljn0lM0b6rNn4YL16IvzgX4qblh2JOr0yMGNjd6pwREpcEZcJich06dIhoP28gSP+0mQGLqUtgmTcy9aYsx6gCnOLTGOoSAm365AGnjNPGDz/omoHhr/fCkV1H4sgusTlpA8uMOdTGel2Cio7u3bs3VGbMmDGG26XJWWq5qgZsSetCWloa+2l8oC4h1Oa0ifD0Pg0pthpcgndw3YvLsKe6DrEGy4w51EYNXezB9tlKbXjlypVQETOTLSkpabHGK+ckRi3N0lYFBlRVVXFOVx+oSwi1sTtgGzYTVafciWmp12H9zn24bc5KuN2xpS3LjDnURg1dgjLh/Px8LTp60KBByhqxP83QOhIFLZ8vWbIEAwYMaIzKDhSJhvN+SJScPFdXVzdZ0EIwWuhC8E3bWvpg0kbL9lDootr2UO47YG1SspB0+jg8fskAJDjsKFm1FdM+KlXqnEKxvaamJubOib8nT1ivUyBlRjTU/+N9H/4QVMP3yJEjtT5hCX7Sm3eNhgHpSPV+9erVsAoxVTHalpqhpQlb/1xqy/IdCdaS2rO/9OjRwzQQbMKECdrrxMREJCcnaxOF19fvX2ZOtsn4tH379mnv9SaR1NRUuFwu7WLrhUFIT0/XZnfxveASGi+rVnnPZqYv0SXHk+PqyPclhF7GxskdoHe/iDTLSCGTQqkj+UtJSdHSynd0Wjon+Uzy4r2SVqDnJHmU78t2XZdoP6dQXSc5rmz31sbfc+rVzoaJg7vC+f69eKvkBOR0GIETeu3vH47mspeUlKQ9S1688x7N58TfE8J6nWTZXUHOx59zmjp1KiZNmgRLxgkHOrWXiNGWccJ6AJhMCBLIOGGj5nN5BLoPybc/x9bHhW3YsKHJuDD5vsgshUwevtvNztl3nJpZeqPtgaSNpu3B6qLa9lDtO2htPpoC24eTsN3TDpfYpmDmTeeiW4cUS88pFNvlvfwujWZAitZzCuV2/p5sQZcZMWoxZd/tsg+pkIV1nHAgtUMVkCFV+rCnQJCa8ezZs/02bynU/k7WYdT5rxu2fGZUCPzdTyBpo2F7qHRRbXso9hG0Nn+8Ee5Vb6Djtu9Q4H4IN/63M2ZdeyqSXI6oL3v6zEfB5lOlcwrFdv6eQlNmpLVFb3FpKV1YTFj6g6MFmfmqpabyltBnz4oUcvGk2YM0hbqEUZuEFNhH/RfuwtPQr+YXjNj2OO54NRMPjzjW7z8TFWGZMYfaqKFL3CyhIbX2libeyM3NNZ1ZS5psIoncoUqfcBA9BTEJdQmzNpm9tRWXPLDhYuf7SPzmeTzz2TpEMywz5lAbNXRpswlLO7d0YB9yyCGIBmTIUUsBWWK0AwcONPxMps40+yxceHf8k/1QlzBr02cIbIP+rr281/ks3n3nNXxeugPRDMuMOdTGel3abMJ6v6fMz6wKLdVY9akozZCALe8pLL3NW/Zr9BkhMcnJt8FzxPmodyQjwVOD619cho27fo/WJ4Qo1BwtKwtJlX3mzJkIJ1ITbQ1pTpYI6rb23YrJ+jZHi/mKOcsCDpxDmsQNEpBy/n/guOYj7D7oJJTtq8OYF5ZiX+3+ISWEkNAQVGCWjKmVmrA8i2GNHj06qCX8fBFT1GevEmTiDH2mK98oZ5lcQ9Ka1XalKVqM2gwxWZnEQzdi2ZeYv/cqS5HEeygT2Q91iZA2CalI7HQwCv/SFec+8Sl2bFmP2+ck48lLcqMuUItlxhxqY70uQY0TvvbaaxuDntauXdtodma1RvnxLl68GLFKqNYTJkQlfvr8DXRecA3+U38u0gbdhpsG97E6S4QoT0TWEzYacyuzZ8nEFkZE2x20Vch9kczsIrPFULP9UBdrtDnU8Rtgq8QE5yxctagb3u18Nc486kBEAywz5lAbNXSJq8k6ognvKd3IfqiLBdocNxrYtgr2pc/gMdcTuHjOgeiZNQKHHxQdrT0sM+ZQG+t1iZvJOgghbURqA2dPhWfHaqSv/wyPe6bg2mcPwPM3nIWO6exTJMSy6OisrCzccccdWLcuugf0E0JaweGCbcQLcLfviV72rbin6kFc9/wXqK4LfC54QkiITFj6f2WYUk5ODs4880zMmzcvmN0RL2RVEdIc6mKhNqlZsF8yG+6EdJxg/wG5m19G/txvlJ9xiWXGHGpjvS5BmfBTTz2Ffv36aT/C9957TxtrK7Xj6667DitWrAhdLuMMCQbQJ1Yn+6EuCmjT6XDYRzyLrTnD8LznbLyxcjMeW/QLVIVlxhxqo4YuQQ1R0pHhSTKeVqKl9aFKcgJSQ5ZxtzK+Nx6G7IRqiJJcElkPU9bM5A9kP9RFLW1eXrwBE1/9Vnv96Ki+OK9vV6gGy4w51Ca8uvjrByFZwKF3797Iz8/XJu6Qx7hx47Rtv/zyizaRh8xkNWrUKLz//vuhOFxc4L2wNdkPdVFHm4uO64GxJ/fEeOfLeGHuq/h6Xesz21kBy4w51MZ6XUK+ipKYr/QTiwHLeGGZRUvuBmQt3zPOOCPUhyOEWMj49PdwrXM+pjmm4J7n3sa6HZVWZ4mQqCJsSxlKVVxMWJqnpTouqB7AQQgJDPvxo+HudBQ62vbg0YZJuPGZD1C+jyvzEGKJCYvxzpgxA0OHDtWaoGXxA5nQQ8xX5nyW2jDxD5mthTSHuiimTWI67JfMQUPagehj/xUT9kzCtc+pNXSJZcYcamO9LkFN1qEbr5irBGbpCy3oNV5Z+ECMeMSIEVqTNPEPCQZwuVxWZ0M5qIui2rTvCsclxXA/fSZOxPf4bfNU3D4nDY9d1B92u7UBPywz5lAbNXQJqiZsVOPV+4QlQGvJkiWNfcLEf0RHacJn831TqIvC2hx0DOwjnofH5sCFjk9w6A+PoWDBj0C866Iw1EYNXUIyd7SsnCRR0GLGYsIkePjDMIa6KKxNnzzY/u/faHjzdnzv7oV3P1qDbh1S8JcTesa3LgpDbazXJej1hMV4ZcIOQghB/8vgyBmMI5fsw7slq3HP69+hU3oihh4ZHasuERJpQjJjFiGENNK+K24YdDAuOq47DvJsx/SX52KJomOICbGaoAOzSHiQ2VpIc6hLdGgjwS33n5SEiu/vA+prcMWzKXjouuE4uFN6XOuiGtTGel3CNk6YBPcHZrfbOZWcD9QlurRxZnRF+8490cG2F096HsBtM9/Fb7urEe+6qAK1UUMXmrCCMGrRGOoSZdokpMJ+yVw0ZB6MrradKKi6F9fPfB+799XFty6KQG3U0IUmTAgJH6lZcFw2D/WpB+Iw+0ZMKL8H1z77Capq1ZnMgxAroQkTQsJLRg84L3sVDQntMdC+GldvuRc3v7gY9Q1cPIAQmjAhJPx0PhKOS4vR4EhCZ3s5Fv+0HhNe/RZuN5tCSXxDE1YQCQiQWcYYMNEU6hLl2vQ4Ho7LXsO2C15Bhb0d5i7dhAfe+iGsfW9RoYtFUBs1dKEJK4j8Kcl6lgyYaAp1iQFtev4Bp/c9GFMuPEZ7+/Xni/DYol/Cdrio0cUCqI0autCEFaWiosLqLCgJdYkNbS7M7YY5xyzF/MS/o+qDh/D0p2vDdqxo0iXSUBvrdaEJE0Is4bge7bTnCa5ZWPPOo5izZKPVWSIk4tCECSHWcNIt8Jx0m/byPuez+HLek5i/crPVuSIkotCEFYXBEsZQl9jSxjb4LngGjobd5sFU51N4b85TWLhqK+Jdl0hBbazXhSasIIxaNIa6xKA2NhtsZ02Bp++lcNg8eMT5BOa+VIRPft4e37pEAGpjDKOjiRaVV1dXx6hFH6hLjGoj8/Se+xjcRw2Dy9aAbM9GjH5+Cb5cszO+dQkz1EYNXWjCilJZWWl1FpSEusSoNnYH7BcUom74i/jh4KtRXefGlc9+ja9DsARiVOsSZqiN9bpEhQmvWbMGubm5WLZsWcDfHTJkCMaPH6/tQ9/XlClTtG1GFBUVaZ/Ls6RZuHBh0PknhPiBwwnXkedg2qW5OLnPAbDV7sXDT7+EpevLrM4ZIfG5nvDYsWOxa9cuZGdnt8mAddMVIxVj1Rk2bBiKi4sNjydmn5+f37ht+PDh2nNeXl6bjk8ICYwklwPTRx6C9Y/dgu41pbju6TrccvUV6Ns9w+qsERJfJlxYWKg9l5eXNzHRQBDDldqwmHFmZib69++vmbovYvJi1voxdQoKCjRjLiuL7N24rGdJmkNd4kObpMRkHNztQDjWrMKTnsm4dqYdt111OY5tgxHHki6hhtpYr4vSJhwKsrKy/KrFivkapdMNWww6UrVhicpr1+73iQzIfqhLHGnjSoLjopfQ8OIopK77ENM8/8R1Mz247eq/4phuGfGrSwihNmrowtug/yEmm5Fh/OMWIy4pKYlYXiQqr6amhlGLPlCXONPGlQzHJbNQ3+s0pNpq8B/PJPxrxjP4dtPu+NYlRFAbNXSJGxOWJm0xWj1AyxfZnpOTY/iZNGO3tU+6rVRVVUX0eNECdYkzbVzJcIoR9z5dM2Jpmn5kxtNYubE8vnUJEdTGel1i3oR37typ9ScvWbIEAwYM0MxWD7YKBAkQI4RYZMQXz0J99mDU2ZPwa00yLp3xFZau52+SRD8x3ycsjBkzprGpWfp1xYglWEtvYpZackvId81q0Ebs3r27SVOG9DHI+8TERO3hu90I2e77mVl6o+2BpI2m7cHqotr2UO1bJW3Csm9nIhwXvQjH9vXImF+G1Wt34S8zF2Pm5QNwQnaW6X7090qekwLbVSkzKpW9QMuMNF3X1tY2275nzx74Q8ybsEQ3G5myDEeSJmaJlvYHs/5iI3r06GG4XcYdT5gwQXstZpycnKwNCq+vr29MI9sSEhK0Cytmrk+dlpqaCpfLpV1Y74udnp6uRfJJWm9k2jVZE9N7SS59OjY5nvdgdPm+BCJIQfJuhnE6nUhLS0N1dbWWHx3JX0pKipZWvqPT0jnJZ5IXyZNOoOckedS367pE+zmF6jrJcWU/3tpE+zkZXqeMHnjuij646tmv4Fn3KV54djHK/vwXnH7YgYbnlJSUpO1L8uKdd6XOib8nqFT2HA6Hdl5yPv6c09SpUzFp0iS0FZsnCnrlpabaoUMHLF261G/TbA3Zn5ixmLS+f4mQlm2+SK1ZmqPl+C0hBUUu9oYNG5pE1wVaE1bljlC17Srlhedk7TnV/PodMGMw7O463N5wA84edS2GHnVQVJ9TLF6neDinmhZqwlIhkxuFlqKtY74mbIZEPOtNzHott6VmaQnO8hcxYn9D3PU7UG/kYsqdpdzF+35ulN5seyBpo2F7qHRRbXso9qGaNuE+ZuKBh6HhyHPh+G4OHnE8homzqlE97Gac17drM12k5mKki2rnxN8TlCh7gZYZSScPf48XV4FZMsmG2fSUvoYrNWwJ4jJC70OOJN7NIGQ/1MWcuNLG4YTjz0/B3e9ybfWlKc5CLC8uwEtfbYhvXQKE2livS0ybsBjtwIEDDT+T5mXvz/SALSNkO6etJETBRR/OfRSeE67X3v7D9Rw2vXE/pn/sfxAlIVYTUybsW7uV4CuZttIXGS8sab0/GzlypOFiDRK8Jc3VoeqLJoSEeD3iof+E59TfW7zyXXOw4t1n8NCCn0yjyAlRiagwYX/G6ErTswRXeddmxWR9m6PFfMWcZQEH7zmkxWRHjBihrZ7kjXzfaLGHcCOReKQ51MWcuNVGjPj0O4AzHsDarFOxwD0AT3zwC+5543u43Z741cUPqI31uigdHS0G6D3TlZim3izsu9CCGKukkwhm7+FE8v3Jkyc3vhZDnzhxomnNVl8oQvZRWlqq9QX72xStR0e3Fg1HCAkTbjdeWLwRd7/+HWweNy44tjMeHDEALkdU1DdIDOGvHyhtwtFGqExYj86TsXP+RtjFA9TFHGrTlNeXb0LVqzeiC7bjhe734t+Xn4LURJfV2VIKlpnw6uKvH/D2UFG8B4OT/VAXc6jNfs7rXo1hiV/iFMe3uGHT7biuqARlldTHF5YZ63WhCRNCYo8DDobzr2+iLjETx9rX4J7tt+KGaa9hczkXLCBqQRMmhMQm3XLhvHoBalK7INv+Gx6pGIf8J17Cj7/5N6cvIZGAJqwo3tNbkv1QF3OojQEH9IH78rdRm3U4OtvK8Z+6OzF12nR8XrrD6pwpAcuM9brQhBVEggEYLNEc6mIOtWlBl069kXD1u6jv/kdtTeKGumr89emv8cbKzYhnWGbU0IUmrGh03t69eznZgA/UxRxq04ouSe3hvGweGi5+BSlHnonaBjduenk5nvqoNG41Y5lRQxeasKJ4L/NF9kNdzKE2rejiSkLCIYPw+EX9ccWJvdDT9hs8JffgntdWoL5h//J58QTLjPW60IQJIXGFw27DPWf1wesdHsW1zvk4ZdmtuPG5T7GvloZEIg9NmBASfzgTkHHO/WiwJyLPsRzXrrsJY6a9ja17qq3OGYkzaMKKIoEBpDnUxRxqE6AuR5wHxxW/jyU+xr4WD+66FX977CV8v3k34gWWGet1oQkriETlSYg8oxabQl3MoTZt1KX7cXCNWYi6jBx0s+3AU3V34NGnpmHRD1sR67DMqKELTVhBJCpP5h1l1GJTqIs51CYIXbJyNCOu73Ei0m1VuArzMPr5rzHjkzUxrSfLjBq60IQVxe2Oz2jN1qAu5lCbIHRJyYTzstfQ8MebUXLUFLg9Njzw1g8Y/8o3qKlvQKzCMmO9LjRhQggRnAlwnHEf7hx+Cu4+5wjYbYBn+X8xpmgRdu6tsTp3JEahCRNCiBfSF3jlSb3x9qkbMdVVhLt/uwnXPjYXqzZzzmkSemjCipKammp1FpSEuphDbUKry2HH/gF1qQchx74FRTXj8NC0p/D2t1sQS7DMWK8LTVjRO3GXy8WoRR+oiznUJgy6HHQsXNd8iPqDcpFhq0SRfTIWz5qERxb8CLc7+oOZWGbU0IUmrCASlbd7925GLfpAXcyhNmHSJf1AOK98G+5jLoLT5sY/XM+j6yfjcd3zX6Ciug7RDMuMGrrQhBWFPwxjqIs51CZMuriSYL9gGjB0EjywY6TzQ1Ss/hjnP/kZSrfvRTTDMmO9LjRhQghpDWma/MP1sF06F5sHTEBp2kCUbq/E+U98hoWrYn9iDxI+aMKEEOIvBw9Gl3MmYv6NJ+G4XplIq9mK916ciocX/IiGGOgnJpHH5mF7RMiQWVbat2+v9Se0a9euzfuRSyKDxe12O4MmvKAu5lCbyOtSW12F7Y+dhq77fkRx/Sl4p+c4PHzxCeiQmoBogGUmvLr46wesCSuIXHj+MJpDXcyhNpHXJSExCV3/eJHWTzzc+TFu3Xgjrn70VazcWI5ogGVGDV1owgrCqEVjqIs51MYCXeRP+qRbYLtsHuqTMnG0fR1m1NyGxwqn4b9frlf+WrDMGMPoaEIIiSayT4Pzmo9Rf1A/dLDtxXRHAXa++Q/cNnsZ9tXWW507ojg0YUIICZaM7nBetQCe3Ctht3mQZ1+Kt1Zs0IYx/bKtwurcEYWhCRNCSChwJsL2f/8C/jwdtRc+i/S0dKzeuhfnPvEZ5i3fZHXuiKIwOlrB6GhBLgsDJppDXcyhNmrpsq2iGrfMWoH+62agBi7s6XsN/nHeUUhOcEAVWGbCp4u/fuAM6igkLHDogDHUxRxqo54undKT8MI5qbAXzoUNHpR88yMu3XAbHrz0VPTpnA6rYZlRQxc2RytKRQX7kYygLuZQG/V0cRx4FGznPAK3PQFDHMvw2J6bcPcTMzHn641KRCWzzFivC02YEELChdSkBlwJ++iFqO+Qja62nXjBfi/WvXYf/jZrGfbWMHo63okKE16zZg1yc3OxbNmyNn1/ypQpGD9+PIYMGaI9Fi5caJhOPpN0cjz9uPp3CSGkzRx0rDaMyXP0CG01pnzXHJy1ahz+9OjH+GZTdEzuQcKD0n3CY8eOxa5du5Cdnd1mAxYDnThxIjIyMrT3YsBitgUFBcjPz2+SVkxXPhfj1Rk2bBiKi4sRadhHYwx1MYfaKK5LYjpsfy4Csk9Fw1u3Y6nzBKzfVYULp32O/KGH4aqTesNut8WnNooRSV2iIjq6vLwcHTp0wNKlS9G/f3+/v1dUVIS8vDzNxL3Ra7e++9Nry2LGmZmZ2me+341UdDQhJIbZvQm7nZ0wYd63eOe739DbtgW9Dz4MDw4fgE7tkqzOHQkBnDsaQGlpqaGJjhkzRnuePXt2k+1ZWVmaacvnUgMOxIBDidwX1dXVKRG4oRLUxRxqE2W6tO+G9qkJ+M8l/THlTz3wYsIk3L7+Olz375dQEqGlEZXVxmIirUtMm7DUeKVJ2xdpmpZHW5u4I0FlZaXVWVAS6mIOtYk+XaTZc0TvWnRKBo6wr8d/G/Lx8YuTcOer36CqtiGutbGSSOoS0yYstVq9L9ioibulz6RvWA/QIoSQsNEtF87rv4A7ZzCSbHW43/UsBi+/EZc++gaDtuKAmDbhkpISLQDLF91cpf/Xm507d2q15yVLlmDAgAFauuHDh0csv4SQOCW9M+yXvgKcNVUbUzzIsQLT996AadMexZMf/IIGN5uMYxWlo6PDRWFhoVYL1vuGvZFteg1ZatJixGLWYuj+4rsMljQ5yfvExETt4bvdCKPPzNIHmzbat6uUF6vyHu79R+v1kPcy81FUnJO8OG40bL1OQv0ro5G57Tv8yf4ZblgwAO//uA0PDz8GPbNS4+r6WZH3QMtMTU0Namtrm22XwCzEe3S0EdIPPHjwYCxatMjvfYnw/hxbj4YzQ6KvJ0yYoL0WM05OTsbevXtRX79/wL5sk89kXzJ1mk5qaipcLlczg09PT9cKjGz3RvIh3/ee+UXOQ7ZL0IF3n4d8X6L3pDBVVVU1bnc6nUhLS9O2yWc6CQkJSElJwb59+7TCp8Nz4jnxnEJ0Tg21SFlehHdTz8fEdzahsrYBqS7g1sE5uPSEXkhKSoq+c4rF6wRg6tSpmDRpEsxoLTo67kw4JydHqwlLLddf5NhSQzZq2jYy4Q0bNjQRvS01Ybn4ctG9x6vFy91sS9uD1SWWa8KqaKOSvnqkq/yR+479jJZz2rirErfNWYm//noPtniysLj39bh/+EB0TE8Mev+qlBmVyl6gZaalmnCPHj24gIM30r8rRhqIAQsyVCmQIC0xYn/HCfteZEEuZnV1tWbYRoXA3/0EkjYatodKF9W2h2Ifqmmjkr5So/E1mmg6p+6ZqZh1thP2pxdr709dvxJ/e+QGDD//fJx7bJc2X2/VyoxKZS+QMiOtEvLw93hxFZjl2xQ8cuRIbfyvETItptn0lFITJ4QQq7D3OA64ZC7qUzrjYPtmPOe5E1vmjseNz3+JHXv3N5mS6CMuTFhmzpLgKl8D9p5DWox24MCBht+XqTPNPiOEkIjQZwicN3wJ99Ej4bB5cI1zPm4uvQq3PjITb36z2bSbgqhNTJmwUY1VjFaak42aoL0n65BJPYxqyfJ92a9ZDTpcSHAAaQ51MYfaxIEuKZmwX1gEjHoJdckd0cf+K+6r/zdufmkJrntxGbZX1MSvNiEkkrpExRWQmmhr6KsseU9VKe8lCEuaoefOndskvfTxek/WISYrzdHewVdivmLOsoBDJKewlL4Eic4jTaEu5lCbONPlsD/B1eMPaHg7H0s9p8G23KnNQf3lmp245/+OxHl9m/cVx402QRJpXZSOjhZT9J69yrtGK+bqjZilpJMIat1cJaq5pf5cGfvrXUOWtJMnT258LeYvKzD5G5EdqgUc9IAJ6ez3t3M/HqAu5lCb+Nbl+827cXvxN+i/7VUcYtuIL3vfgLsuPB5dMpIR79oESqh08dcPlDbhaCOUJiz7kH3xx7Ef6mIOtTEmnnSp27sTnn8dhYSGffjVk4UHMBp/PPMiXHJ8T8MlEuNJm0AIlS5cRYkQQuIIV1oWEi5+CXXteqCrbSem2R5E+tvX4qpp72D11v2TVxC1oAkTQkiskHM6XBJBfcL1cMOO8x2f45Fto/H04/fjkQU/orou/CszkcCgCSuKDBQnzaEu5lAbY+JOl4RU2M+cBPvohag74Eh0sO3FA47pePPDT3D2o5/g89Id8auNn0RSF/YJK9gnTAghIaGhDp4v/oOfN23FJaWDGocwDevXBXeccyQyU2nC4YJ9wlGM3BfJJOG8P2oKdTGH2hgT97o4XLCddDMOGTUJC/92Kv5yQk8cbt+AMd9fgglTH8fzn/6Chob9CyYQRLzM0IQVxXuVDrIf6mIOtTGGuvxO+2QX7j//KLzc5wMcYv8VRbgP7RbciDHT3sYPW/xbdi9eqI1gmaEJE0JIHJFx0XS4B45uDNz61/arUfzk3/HA/G9QUV1ndfbiDpowIYTEE0ntYf/TQ7CNeR9VBxyDdrYq3O18Dn/++lLcMrUQr6/4NX6b7y2AJqwo3usOk/1QF3OojTHUxYSD+gJXvgvPOf9GXUJ7HGFfj27Vq3HzrBUYVfRlXDdRJ0awzDA6OoQwOpoQEpVU7kT9l0+hEH/G4x+uQ3WdG71tv+GU43JxyxlHoAOjqAOG01ZG+bSVlZWVSE1N5XRyXlAXc6iNMdQlcG02le3DlDdX4rafL0MtXHjIcSVOPGMYLj6uB5yO2G889YSozHCIUpRTX19vdRaUhLqYQ22MoS6BadOtQwoey0tFl+R6banEQs/96Pz2VbjqX3Pw6c/7J/qIZeojWGZowoQQQppy0DFw3bIc7uOugdvmwFDHEhRVXI9vn7sF1z/zMdZs32t1DmMGmjAhhJDmJGfAfnYB7Nd+hrpepyHRVo9rnfNxz7q/4KJ/vYH75q9C+T6OwQ4WmrCiJCebrwMaz1AXc6iNMdQlSG06HQ7X5a8BF81Cbfte2JJyCLa62+Ppz9bi1KkfYuana1FbH1uzbiVHsMwwMCuEMDqaEBLT1NcC1bvxyRbgn2/9gB2/bcLdrucxO/VSXHT2IPzp6IMYAPc/GJgVxch9kVxA3h81hbqYQ22MoS4h1saZAKR1xMl9OuKtm07Gy33ex7mOL/Bs1U3YMedmXP7EO/hqzU5EM54IlxmasKK43bHVvBMqqIs51MYY6hIebRx2G/qcOw71Bw+Fy9aAvzrfwxM7rsSHM+/ANU9/itVbKxCtuCNYZmjChBBC2kbHQ+G8dA5w2Ruo63S0NgXmeNcs3L3+L5j22CTkz12JzeVVVudSaWjChBBCgiP7VLiu+Ri4oAh1aV3RxbYLR9nWYs6STTjtoQ/xwJursKuSkdRGMDBL0RmzZLC40+lkkIMX1MUcamMMdbFAm7pq4OsZWJF1NiZ/uBVfrd2FQ20bkJVQj+NPPhNXndwbaYlOxLouezhtZeRhdDQhhOxH7OXj1dvRvvhC9K3/Bu815GK661KcOeh0XHJ8DyS5HIhVGB0d5QVXLhzvj5pCXcyhNsZQF2u1kZrkqTntcezRx8IDO85wLMXshr8hY8GNuHjqHMxavAF1De64LjM0YUXhn4Yx1MUcamMMdbFYG2cibOc9Adv1X8J92Lmw2zy40PEJZtVcj9o3/oZRD83Da8t/RYPbE5dlhiZMCCEk/HQ8FPZRLwCjP0BD79OQYGvAZc4S9NnzOW6ZvQJnPfox3v1uS9zdNNGECSGERI6u/eG4/HXgr2+h/ohh6DnoarRLcmL11r148sW5GPXoO3j/x61xY8YMzFI0OloGi9vtdkZ0ekFdzKE2xlCX6NBmd1UdnvnoJwz74ny0w14803AWvuo8CmOH9scpfQ6IaP5CpQujo6M8Oloui9U/DBWhLuZQG2OoS5RoU7Ye9S9dBOf277W3ezwpmFl/FpZ1GYVrhvbHH3OyIpbXUOjC6OgohhGdxlAXc6iNMdQlirTp0BPOaz8Fhj+L+qzD0M62D7e6XsET2y7H4mfyceVTJRGZl5rR0YQQQuITux048gI4r//if2Z8KNr/z4x3b/geI4u+xCUzvsTS9bsQK6g7bQkhhJD4NuPDzwN+eB2Vqz/EEbY8fPv1Rnz2y04krnkSqb2Pw1VnHo++3TMQzdCECSGEKG3GqUdegAcAXHNqDp4t+Rq3ff8YbJs8eLlwEF7o9VdccdaJOKpre0QjURGYtWbNGgwfPhzTp09H//79A/5+UVERysvLkZGRgdLSUgwZMgR5eXlBp/WFgVnhh7qYQ22MoS4xps22H1DzyrVI3Lpce1vrceCVhlOwKvtKXHr26Tj0wPSoCsxSuiY8duxY7Nq1C9nZ2Vi2bFmb95Gbm4v8/PzGbWLogq+5BpI2XoYOqAR1MYfaGENdYlCbTocj8ZoPgDUfompRAZI3f4GLnB+gYf2HePvJ4/Fin1tw+VknI6djWlToEhU1YamZdujQAUuXLg2oJizGLSYqNVrfmrWYbVlZWZvSRmKcsOxD9hVVP44wQ13MoTbGUJc40GbDl6hcNAWp6xehxuPESTWPYqetAy7o1w03D+6DHlkplujCIUoACgsLDWuwUrMWFi5c2Ka0hBBCFKHHCUi94lVg7CfYceokHHv4YZBpqF9Ztgkl/74KLzzzBDaXVUJVYtqExTilb9cIMdeSkpI2pSWEEKIYBx2DroPGYsblA/Da9Sfi0p7luMrxNv6y/k5U/zsXbzw9CdvKyqEaMW3C0pSck5Nj+FlmZmaTfuZA0kaCqG4eCiPUxRxqYwx1iT9t+nbPwAOXDcXmY67HXlsasm1bcO6GAtj/fQw+mTEOe3b8powuSgdmhRsJ+gpHWt/ZVuSCyvvExETt4bvdF9mu9yEY7ccove/2QNJGy/ZQ6KLa9lDuWxVtVNJXkH452R5MPlU7J/6e0Pp1SsnCQRf8E56zxmPNwiKkLS9EJ/d2nLypCFVPPIvivoU4+6xzkZLgCKrM1NTUoLa2ttl26ROOaxOWYK6WkKZnqf0GmtYfevToYbh9/PjxmDBhgvZazDg5ORmVlZWor69vTCPbEhIStEAwueD6HVlqaipcLpd2Yb0vdnp6uhbFJ8bvW4gkwq+ioqJxm+xLtsvx5Lg68n35MUpBqqqqatzudDqRlpaG6upqraDpSP5SUlK0tPIdnZbOST6TvEiedAI9J+886rpE+zmF6jrJcffu3at9R9cm2s8pFNcpKSkJDocD+/bta5L3aD4n/p4Q8HXK/r9xqM27DksWPIt238xAu4Yy3PGlAwXffYCxJ/fCBb0a4GzXWRJr5UXKjeTPn3OaOnUqJk2ahLYSs9HR+nck4GrMmDHNPpdIaDFW2Wcgaf2JhtuwYUOTaLhAa8J6/n2j8+L9zj0Uuqi2PVT7VkkblfSV9/K7lN+jbxNjtJ5TKLerUmYiWfbcDW6ULF6ByZ/uwfpd+2CHG58k34aU9Aykn3oj7EdfiD37av0uMy3VhKVCFtXjhMNJa7XftqYVpFD7O0TJ9yILcjH1WrBRIfB3P4GkjYbtodJFte2h2Idq2qikr7492HyqeE7BbFetzESq7DmcDpz5x1wMPt6N4iWb8HbJAmTWlSF5z1Zg/g2oee8eOPP+CduAUX7tX2rN8vA3H3ETmKVHOrdkoBJwFWhaQggh0Y/LYcfFx/fAjPFXYs4pC/Bv2yXY7MlEYs1O3PnxPqzYGJlI6pg1YUGarnfuNF76SpqXZUrKtqSNBNLfQZpDXcyhNsZQF3OoDZDkcuDywf1x5fhHMfsPb+Kqhjvw+vYDUVu/v286nMR0c7RMvmEWUCXbvSfnCCRtuJFmjGDnno5FqIs51MYY6mIOtWlKuyQXbj3zSGw7MQcLvvsNx2dnIRLE1G2Qb3PyyJEjDWe6kjG/0gTtHeQVSNpwI3010tkfBTFzEYW6mENtjKEu5lAbYzqmJWJE/4MipktUmLA/Y3RlfmeJcPauzYpxjhgxQlsZyXeoUHFxcZNtgaSNBN7DAMh+qIs51MYY6mIOtbFeF6WHKIkBSu1WaqhirjJ9pN4sLMOJfFdAknQyjMh3+skpU6Zoz/4sTxhIWl+4gEN4oS7mUBtjqIs51Ca8uvjrB0qbcLRBEw4v1MUcamMMdTGH2qhhwlHRHB2PyEw0pDnUxRxqYwx1MYfaWK8Lr4CCyN2XTAVHmkJdzKE2xlAXc6iNGrqwJqxoc4gEBrCnoCnUxRxqYwx1MYfaqKELTVhRvCcOJ/uhLuZQG2OoiznUxnpdaMKEEEKIRdCEFb0Le/DBB3mX6gN1MYfaGENdzKE2aujCIUoKDlGS78s4ZX2ZMfI71MUcamMMdTGH2oRXFw5RimL0sWkcu9cU6mIOtTGGuphDbdTQhSasIHrjBBspmkJdzKE2xlAXc6iNGrrQhAkhhBCLoAkTQgghFsEZs0KI3nwhHfLBoH9fntlfsx/qYg61MYa6mENtwquLvp/WmrUZHR1CNm3ahO7du1udDUIIIYqwceNGdOvWzfRzmnAIcbvd2Lx5M9LT03lnSQghcYzH40FFRQW6dOkCu92855cmTAghhFgEA7MIIYQQi6AJE0IIIRZBEyaEEEIsgiZMCCGEWARNmBBCCLEImjAhhBBiETRhQgghxCJowoQQQohF0IQJIYQQi6AJE0IIIRZBEyaEEEIsgiZMCCGEWATXE1aMoqIilJeXIyMjA6WlpRgyZAjy8vIQz6xZswbDhw/H9OnT0b9/f6uzoxRTpkzBzp07sWzZMu39+PHj4768CPIbmjx5cpMyNHDgQOTn51uaLxWR31ZxcTHinSFDhmj/L2PHjkV2drZWZubOnav9vgoKCsJ3YFlFiajBmDFjPIWFhU22DRs2zFNSUuKJVz3k/PPz82WlL8/SpUutzpJSiC5lZWWN76WciE4FBQWeeEY0EW186d+/vycvL8+SPKmKlJXs7Gyrs6EEooP8frwf8v8TbmjCiiAGY/RjKC0t9WRkZHji/U+VJtwUuVmTsmH0pxrvWokGRmYrmsW7Nt5I+RGdaMK/IzduciMr5aS4uNjw9xUO2CesCIWFhYbNiNIsIixcuNCCXBFVka4KvWx4M2bMGO159uzZiFekK2fJkiVak7Q3mZmZ2vOuXbssyplaSFOrNEWT38nKytL+g+U3NGzYMMPfVzigCSuCmKz8eRghhaGkpCTieSJq9wVL35UvUobkofcRxyPyJ1pWVtbs9/T1119rz+wz//3/RoyGWA9NWBEkCCAnJ8fwM7mDj+c/VdIcMRKzmzY9sI80D3qUFify+/9NpGp60UZ5ebl2kyIaRQKacJTAJjTijbSMGEVs6n8cEulJmkbXi156c32834xQh+ZIFLS0MElXxoABAxrLTbjhECUF8O278kVqNZG6KyPRjdT0pLzwTxZNhpjI8CTW/FgDbg353eitSNLaJHrJDW04uwNpwlECmxdJa0iXhdRyFi1aZHVWlEDMxntcsPyZynjYeG6SlpsSjpU2xqhlSUxZYi/ktxWuOQrYHB0DNWVCvCdd4IQm5n+ycpMiRhSPyHmzhaRtFaBwjjagCStUy23JbPXhFYQYofd5MvLXHP3mJB5rwvLfInElbFELHH32rHDB5miF/iCk78oIKQBGw1EI0aeqHDlyJIec/A8ZZSC/F6NmV/lDlcCbeENaAGRsue//iGjh/f8iZSke+4xzc3O1G1ijJulwt0TShBVBDwIwQrazhkPM/lyN5heXIRbxWGbkt9JSrSVef0tm/cASDSwmE4+tA96IBhK8Z4S0IJh9FgrYHK0IUpMxmhVLAgKkCYn9fMQXKS9SazEylXgdVy56SL+nkenomnCWKOKLtAQYtSTJb0wMOpytTDRhRRCTHTFihFaz8Uaah+J9hROOkYahoUjtRf4gJODG+yG1m3ju+5M/VPnd+DJ69Gjtz5TBSfuRJmr+vqCVC98yI78tKUvy/xvOJnqbTCAdtr2TgJE/UIFLGf5+A+I9e413rS/em886dOjQYl+VjGuM13JjtpSh/JZowL8jN2tSRubMmdNY05Pgz3j+XZV7lRk9kG3ixIlhb4WkCRNCCCEWweZoQgghxCJowoQQQohF0IQJIYQQi6AJE0IIIRZBEyaEEEIsgiZMCCGEWARNmBBCCLEImjAhhBBiETRhQgghxCJowoQQQohF0IQJIYQQi6AJE0IIIRZBEyaEEEIsgiZMCCGEWARNmBDiF7Ier81ma/XhS1FREXJzc7U1kOXznJycxrWiCYl3uJ4wIcQvxo4di4ULFxp+Jgug66bq/Zci5rts2TLD72RkZGDt2rXaMyHxCmvChBC/KCwsRGlpabPH0qVLG9MUFBQ0vpbarhhwdna2lkbMWR7yWraJaUsaQuIZmjAhJCgGDx6sGeqwYcOQn5/fuF2vNYvR9u/fv3G7vBazlmepQRMSz7A5mhDSZoYPH465c+dqhupdI9b7kMWI8/LyUFJSYlkeCVEZmjAhpE1IDXfKlCmmfbtiwGLEOmLG8l6evWvGhMQzNGFCSMBI7VdqwYLUgM1Mdc2aNZpZS3pvxLBHjBihNUszMIvEMzRhQkhASLCVRD0LxcXFWl+wv9+T2rE0Tev9xRKgJcFdhMQrNGFCiN9IAFbv3r21ZwnC8o6G9kUfmmRUS5Yashi57EdMWZqoCYlHGB1NCPEb3Til9tuSAetpzcYJSw04MzNTe81JO0g8QxMmhPiFBFVJDVZqttIM3Rp6DXj06NFNjFgfHyz7ElgLJvEMm6MJIQH1A0sgldRkzZAashirmKxMUdkSgfQpExKLOK3OACEkupCarNlUlPrnghh1WVmZVuuVQCx9akvZLiYt21syc0LiAdaECSGEEItgnzAhhBBiETRhQgghxCJowoQQQohF0IQJIYQQi6AJE0IIIRZBEyaEEEIsgiZMCCGEWARNmBBCCLEImjAhhBBiETRhQgghxCJowoQQQohF0IQJIYQQi6AJE0IIIRZBEyaEEEJgDf8PmMX3bDPEIb0AAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nzs = 100\n", "\n", "zs = np.linspace(zmin,zmax,nzs)\n", "vrms2 = vrms2_of_z(classy_sz, zs)\n", "vrms2c = classy_sz.get_vrms2_at_z(zs)\n", "\n", "\n", "label_size = 17\n", "title_size = 18\n", "legend_size = 13\n", "handle_length = 1.5\n", "fig, (ax1) = plt.subplots(1,1,figsize=(5,5))\n", "ax = ax1\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=label_size)\n", "plt.setp(ax.get_xticklabels(), fontsize=label_size)\n", "ax.grid( visible=True, which=\"both\", alpha=0.2, linestyle='--')\n", "ax.set_xlabel(r\"zs\",size=title_size)\n", "ax.set_ylabel(r\"vrms2/c2\",size=title_size) ## 1/3*vrms2 in units of c2\n", "\n", "ax.plot(zs,3.*vrms2c,label='class_sz') ## \n", "ax.plot(zs,vrms2,label='external',ls='--') ## \n", "ax.legend()" ] }, { "cell_type": "code", "execution_count": null, "id": "82e974aa-a896-45d9-8286-22b4ba6a3d1f", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python (venv_class_sz)", "language": "python", "name": "venv_class_sz312" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.9" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": {}, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 5 }