tutorial.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MPA Kavli Program 2023\n",
    "--\n",
    "\n",
    "---\n",
    "\n",
    "# Asteroseismology with GYRE\n",
    "\n",
    "\n",
    "by [Earl Patrick Bellinger](https://earlbellinger.com)$^{1,2,3}$\n",
    "\n",
    "$^1$_Max Planck Institute for Astrophysics, Garching, Germany_ \\\n",
    "$^2$_Yale University, New Haven, CT, USA_ \\\n",
    "$^2$_Stellar Astrophysics Centre, Aarhus, Denmark_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial is about [MESA](https://docs.mesastar.org/) and [GYRE](https://gyre.readthedocs.io). Let's install them! An installation script can be found [here](https://gist.github.com/earlbellinger/5c79b175bcb6b4ea9e934d039c701990).\n",
    "\n",
    "\n",
    "Once the installation script is finished, you should see: \n",
    "\n",
    "\n",
    "```\n",
    "************************************************\n",
    "************************************************\n",
    "************************************************\n",
    "\n",
    "MESA installation was successful\n",
    "\n",
    "************************************************\n",
    "************************************************\n",
    "************************************************\n",
    "```\n",
    "\n",
    "Hooray! You are ready to get started. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Your first MESA run "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's copy a fresh work directory over: \n",
    "\n",
    "```language:bash\n",
    "cp -R $MESA_DIR/star/work .\n",
    "cp -R $MESA_DIR/star/defaults/*.list work\n",
    "cd work\n",
    "./mk\n",
    "./rn\n",
    "```\n",
    "\n",
    "That should have worked. If so, we're ready to compute some models for asteroseismology. \n",
    "\n",
    "For a slower pace of the installation and the following tutorial, see the recorded tutorial [here](https://github.com/earlbellinger/mesa-gyre-tutorial-2022).\n",
    "\n",
    "Let's change the mass, initial chemical composition, and mixing length parameter so that we roughly get a solar-like model. You can copy over the values from my inlist given in the `src/` directory. I have also instructed MESA to output [FGONG](https://phys.au.dk/~jcd/ASTEC_results/file-format.pdf) whenever a profile file is generated. \n",
    "\n",
    "Let's also uncomment `delta_nu` and `nu_max` from `history_columns.list`, and `brunt_N` and `lamb_S` from `profile_columns.list`. \n",
    "\n",
    "Now when we run this we should get some output in the LOGS directory. Let's analyze that with python now. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import python packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll be making use of the following packages, most of which come with anaconda:\n",
    "\n",
    "- numpy\n",
    "- scipy\n",
    "- pandas\n",
    "- matplotlib\n",
    "- seaborn\n",
    "- ipywidgets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-27T21:36:28.513913Z",
     "start_time": "2023-06-27T21:36:28.069791Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from ipywidgets import interact, IntSlider\n",
    "\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-27T21:36:28.517695Z",
     "start_time": "2023-06-27T21:36:28.514798Z"
    }
   },
   "outputs": [],
   "source": [
    "# Set plotting style \n",
    "sns.set(style=\"ticks\", palette=\"muted\", \n",
    "        rc={\"xtick.bottom\" : True, \"ytick.left\" : True})\n",
    "plt.style.use('src/matplotlibrc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in MESA data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-27T21:36:28.525495Z",
     "start_time": "2023-06-27T21:36:28.518328Z"
    }
   },
   "outputs": [],
   "source": [
    "logs_dir = os.path.join('work', 'LOGS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-27T21:36:28.544960Z",
     "start_time": "2023-06-27T21:36:28.526298Z"
    },
    "scrolled": true
   },
   "outputs": [
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      "text/plain": [
       "     model_number  num_zones      star_age    log_dt  star_mass  log_xmstar  \\\n",
       "0               1        815  1.000000e+05  5.000000        1.0   33.298506   \n",
       "1               2        810  2.200000e+05  5.079181        1.0   33.298506   \n",
       "2               3        810  3.640000e+05  5.158362        1.0   33.298506   \n",
       "3               4        810  5.368000e+05  5.237544        1.0   33.298506   \n",
       "4               5        810  7.441600e+05  5.316725        1.0   33.298506   \n",
       "..            ...        ...           ...       ...        ...         ...   \n",
       "205           206        935  1.082755e+10  7.874877        1.0   33.298506   \n",
       "206           207        951  1.089027e+10  7.797447        1.0   33.298506   \n",
       "207           208        970  1.094450e+10  7.734252        1.0   33.298506   \n",
       "208           209        981  1.099234e+10  7.679774        1.0   33.298506   \n",
       "209           210        996  1.100000e+10  6.884091        1.0   33.298506   \n",
       "\n",
       "     log_abs_mdot  mass_conv_core  conv_mx1_top  conv_mx1_bot  ...  \\\n",
       "0           -99.0        0.000917      0.083352  1.690271e-02  ...   \n",
       "1           -99.0        0.073110      0.070339  7.812969e-08  ...   \n",
       "2           -99.0        0.070699      0.070339  7.812969e-08  ...   \n",
       "3           -99.0        0.068882      0.067512  7.812969e-08  ...   \n",
       "4           -99.0        0.067442      0.064710  7.812969e-08  ...   \n",
       "..            ...             ...           ...           ...  ...   \n",
       "205         -99.0        0.000000      1.000000  9.396474e-01  ...   \n",
       "206         -99.0        0.000000      1.000000  9.323121e-01  ...   \n",
       "207         -99.0        0.000000      1.000000  9.241092e-01  ...   \n",
       "208         -99.0        0.000000      1.000000  9.148368e-01  ...   \n",
       "209         -99.0        0.000000      1.000000  9.042932e-01  ...   \n",
       "\n",
       "     center_c12  center_o16  surface_c12  surface_o16  total_mass_h1  \\\n",
       "0      0.002783    0.008767     0.003221     0.008767       0.712299   \n",
       "1      0.002791    0.008767     0.003221     0.008767       0.712298   \n",
       "2      0.002783    0.008767     0.003221     0.008767       0.712297   \n",
       "3      0.002774    0.008767     0.003221     0.008767       0.712296   \n",
       "4      0.002762    0.008767     0.003221     0.008767       0.712294   \n",
       "..          ...         ...          ...          ...            ...   \n",
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       "206    0.000055    0.003506     0.002923     0.007954       0.587595   \n",
       "207    0.000055    0.003506     0.002939     0.007997       0.586424   \n",
       "208    0.000055    0.003507     0.002953     0.008037       0.585383   \n",
       "209    0.000055    0.003507     0.002968     0.008076       0.585217   \n",
       "\n",
       "     total_mass_he4    delta_nu       nu_max  num_retries  num_iters  \n",
       "0          0.268930  167.192184  3925.286859            0          4  \n",
       "1          0.268930  168.376842  3966.235681            0          3  \n",
       "2          0.268931  169.377006  4000.795292            0          3  \n",
       "3          0.268931  170.226016  4030.063141            0          3  \n",
       "4          0.268932  170.949370  4054.896776            0          2  \n",
       "..              ...         ...          ...          ...        ...  \n",
       "205        0.391321   74.921566  1303.381292            0         10  \n",
       "206        0.392656   73.278888  1267.727619            0         10  \n",
       "207        0.393821   71.742060  1235.057962            0         10  \n",
       "208        0.394856   70.265507  1204.309740            0         10  \n",
       "209        0.395021   70.032206  1199.513860            0          5  \n",
       "\n",
       "[210 rows x 59 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def load_history_file(logs_dir=logs_dir):\n",
    "    return pd.read_table(os.path.join(logs_dir, 'history.data'), \n",
    "        skiprows=5, sep='\\s+')\n",
    "\n",
    "DF = load_history_file()\n",
    "DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-27T21:36:28.551104Z",
     "start_time": "2023-06-27T21:36:28.545647Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model_number</th>\n",
       "      <th>priority</th>\n",
       "      <th>profile_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>120</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>180</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>210</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    model_number  priority  profile_number\n",
       "0             20         1               1\n",
       "1             40         1               2\n",
       "2             60         1               3\n",
       "3             80         1               4\n",
       "4            100         1               5\n",
       "5            120         1               6\n",
       "6            140         1               7\n",
       "7            160         1               8\n",
       "8            180         1               9\n",
       "9            200         1              10\n",
       "10           210         3              11"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_index(logs_dir=logs_dir):\n",
    "    return pd.read_table(os.path.join(logs_dir, 'profiles.index'), \n",
    "        names=['model_number', 'priority', 'profile_number'],\n",
    "        skiprows=1, sep='\\s+')\n",
    "\n",
    "index = get_index()\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-27T21:36:28.560552Z",
     "start_time": "2023-06-27T21:36:28.551715Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model_number</th>\n",
       "      <th>num_zones</th>\n",
       "      <th>star_age</th>\n",
       "      <th>log_dt</th>\n",
       "      <th>star_mass</th>\n",
       "      <th>log_xmstar</th>\n",
       "      <th>log_abs_mdot</th>\n",
       "      <th>mass_conv_core</th>\n",
       "      <th>conv_mx1_top</th>\n",
       "      <th>conv_mx1_bot</th>\n",
       "      <th>...</th>\n",
       "      <th>center_c12</th>\n",
       "      <th>center_o16</th>\n",
       "      <th>surface_c12</th>\n",
       "      <th>surface_o16</th>\n",
       "      <th>total_mass_h1</th>\n",
       "      <th>total_mass_he4</th>\n",
       "      <th>delta_nu</th>\n",
       "      <th>nu_max</th>\n",
       "      <th>num_retries</th>\n",
       "      <th>num_iters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>100</td>\n",
       "      <td>797</td>\n",
       "      <td>7.583086e+09</td>\n",
       "      <td>7.840028</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.298506</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.976877</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.00427</td>\n",
       "      <td>0.002801</td>\n",
       "      <td>0.007623</td>\n",
       "      <td>0.641283</td>\n",
       "      <td>0.339041</td>\n",
       "      <td>119.017305</td>\n",
       "      <td>2430.831361</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    model_number  num_zones      star_age    log_dt  star_mass  log_xmstar  \\\n",
       "99           100        797  7.583086e+09  7.840028        1.0   33.298506   \n",
       "\n",
       "    log_abs_mdot  mass_conv_core  conv_mx1_top  conv_mx1_bot  ...  center_c12  \\\n",
       "99         -99.0             0.0           1.0      0.976877  ...    0.000048   \n",
       "\n",
       "    center_o16  surface_c12  surface_o16  total_mass_h1  total_mass_he4  \\\n",
       "99     0.00427     0.002801     0.007623       0.641283        0.339041   \n",
       "\n",
       "      delta_nu       nu_max  num_retries  num_iters  \n",
       "99  119.017305  2430.831361            0          5  \n",
       "\n",
       "[1 rows x 59 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_history(profile_number, DF=DF, index=index):\n",
    "    model_number = index[index.profile_number == profile_number].model_number.values[0]\n",
    "    return DF[DF.model_number == model_number]\n",
    "\n",
    "get_history(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hertzsprung-Russell Diagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-27T21:36:28.917460Z",
     "start_time": "2023-06-27T21:36:28.561090Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_HR(profile_number=-1):\n",
    "    plt.plot(10**DF['log_Teff'][1:], \n",
    "             10**DF['log_L'][1:], \n",
    "             lw=3, c='k', label='evolutionary track')\n",
    "    \n",
    "    first = 1\n",
    "    for prof_num in index.profile_number:\n",
    "        hist = get_history(prof_num)\n",
    "        plt.plot(10**hist['log_Teff'], 10**hist['log_L'], '.',\n",
    "                 c='r' if prof_num == profile_number else 'b', \n",
    "                 label=r'%0.2f Gyr' % (hist.star_age.values[0]/1e9)\n",
    "                       if prof_num == profile_number else 'profile files' if first else '',\n",
    "                 ms=20)\n",
    "        if not prof_num == profile_number:\n",
    "            first = 0\n",
    "    \n",
    "    plt.gca().invert_xaxis()\n",
    "    plt.xlabel(r'effective temperature $T_{\\rm{eff}}/\\rm{K}$')\n",
    "    plt.ylabel(r'luminosity $L/\\rm{L}_\\odot$')\n",
    "    \n",
    "    plt.legend()\n",
    "    plt.title('HR Diagram', size=24)\n",
    "\n",
    "plot_HR()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot stellar structure "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-27T21:36:28.932239Z",
     "start_time": "2023-06-27T21:36:28.918263Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zone</th>\n",
       "      <th>mass</th>\n",
       "      <th>logR</th>\n",
       "      <th>logT</th>\n",
       "      <th>logRho</th>\n",
       "      <th>logP</th>\n",
       "      <th>x_mass_fraction_H</th>\n",
       "      <th>y_mass_fraction_He</th>\n",
       "      <th>z_mass_fraction_metals</th>\n",
       "      <th>pp</th>\n",
       "      <th>cno</th>\n",
       "      <th>tri_alpha</th>\n",
       "      <th>brunt_N</th>\n",
       "      <th>lamb_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.052312</td>\n",
       "      <td>3.763819</td>\n",
       "      <td>-6.888019</td>\n",
       "      <td>4.703911</td>\n",
       "      <td>0.758086</td>\n",
       "      <td>0.225912</td>\n",
       "      <td>0.016002</td>\n",
       "      <td>2.227183e-26</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.017715</td>\n",
       "      <td>0.000014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.052311</td>\n",
       "      <td>3.764341</td>\n",
       "      <td>-6.884401</td>\n",
       "      <td>4.708052</td>\n",
       "      <td>0.758086</td>\n",
       "      <td>0.225912</td>\n",
       "      <td>0.016002</td>\n",
       "      <td>2.245820e-26</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.017687</td>\n",
       "      <td>0.000014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.052310</td>\n",
       "      <td>3.765098</td>\n",
       "      <td>-6.879312</td>\n",
       "      <td>4.713900</td>\n",
       "      <td>0.758086</td>\n",
       "      <td>0.225912</td>\n",
       "      <td>0.016002</td>\n",
       "      <td>2.272301e-26</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.017545</td>\n",
       "      <td>0.000014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.052308</td>\n",
       "      <td>3.766250</td>\n",
       "      <td>-6.871840</td>\n",
       "      <td>4.722527</td>\n",
       "      <td>0.758086</td>\n",
       "      <td>0.225912</td>\n",
       "      <td>0.016002</td>\n",
       "      <td>2.311749e-26</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.017400</td>\n",
       "      <td>0.000014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.052306</td>\n",
       "      <td>3.767784</td>\n",
       "      <td>-6.862482</td>\n",
       "      <td>4.733422</td>\n",
       "      <td>0.758086</td>\n",
       "      <td>0.225912</td>\n",
       "      <td>0.016002</td>\n",
       "      <td>2.362122e-26</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.017095</td>\n",
       "      <td>0.000014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>793</td>\n",
       "      <td>1.250073e-06</td>\n",
       "      <td>-2.746079</td>\n",
       "      <td>7.269971</td>\n",
       "      <td>2.484251</td>\n",
       "      <td>17.584421</td>\n",
       "      <td>0.045724</td>\n",
       "      <td>0.934879</td>\n",
       "      <td>0.019396</td>\n",
       "      <td>1.461063e+00</td>\n",
       "      <td>20.426418</td>\n",
       "      <td>8.605005e-36</td>\n",
       "      <td>0.000261</td>\n",
       "      <td>0.518776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>793</th>\n",
       "      <td>794</td>\n",
       "      <td>6.250366e-07</td>\n",
       "      <td>-2.846446</td>\n",
       "      <td>7.269990</td>\n",
       "      <td>2.484349</td>\n",
       "      <td>17.584493</td>\n",
       "      <td>0.045656</td>\n",
       "      <td>0.934948</td>\n",
       "      <td>0.019396</td>\n",
       "      <td>1.457282e+00</td>\n",
       "      <td>20.419824</td>\n",
       "      <td>8.628249e-36</td>\n",
       "      <td>0.000208</td>\n",
       "      <td>0.653629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>794</th>\n",
       "      <td>795</td>\n",
       "      <td>3.125184e-07</td>\n",
       "      <td>-2.946805</td>\n",
       "      <td>7.270002</td>\n",
       "      <td>2.484411</td>\n",
       "      <td>17.584539</td>\n",
       "      <td>0.045613</td>\n",
       "      <td>0.934991</td>\n",
       "      <td>0.019396</td>\n",
       "      <td>1.454904e+00</td>\n",
       "      <td>20.415660</td>\n",
       "      <td>8.642918e-36</td>\n",
       "      <td>0.000167</td>\n",
       "      <td>0.823531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>796</td>\n",
       "      <td>1.562592e-07</td>\n",
       "      <td>-3.047158</td>\n",
       "      <td>7.270010</td>\n",
       "      <td>2.484450</td>\n",
       "      <td>17.584568</td>\n",
       "      <td>0.045586</td>\n",
       "      <td>0.935018</td>\n",
       "      <td>0.019396</td>\n",
       "      <td>1.453406e+00</td>\n",
       "      <td>20.413065</td>\n",
       "      <td>8.652171e-36</td>\n",
       "      <td>0.000137</td>\n",
       "      <td>1.037593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>797</td>\n",
       "      <td>7.812969e-08</td>\n",
       "      <td>-3.147507</td>\n",
       "      <td>7.270016</td>\n",
       "      <td>2.484490</td>\n",
       "      <td>17.584592</td>\n",
       "      <td>0.045553</td>\n",
       "      <td>0.935051</td>\n",
       "      <td>0.019395</td>\n",
       "      <td>1.451538e+00</td>\n",
       "      <td>20.406101</td>\n",
       "      <td>8.660505e-36</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>1.307288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>797 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     zone          mass      logR      logT    logRho       logP  \\\n",
       "0       1  1.000000e+00  0.052312  3.763819 -6.888019   4.703911   \n",
       "1       2  1.000000e+00  0.052311  3.764341 -6.884401   4.708052   \n",
       "2       3  1.000000e+00  0.052310  3.765098 -6.879312   4.713900   \n",
       "3       4  1.000000e+00  0.052308  3.766250 -6.871840   4.722527   \n",
       "4       5  1.000000e+00  0.052306  3.767784 -6.862482   4.733422   \n",
       "..    ...           ...       ...       ...       ...        ...   \n",
       "792   793  1.250073e-06 -2.746079  7.269971  2.484251  17.584421   \n",
       "793   794  6.250366e-07 -2.846446  7.269990  2.484349  17.584493   \n",
       "794   795  3.125184e-07 -2.946805  7.270002  2.484411  17.584539   \n",
       "795   796  1.562592e-07 -3.047158  7.270010  2.484450  17.584568   \n",
       "796   797  7.812969e-08 -3.147507  7.270016  2.484490  17.584592   \n",
       "\n",
       "     x_mass_fraction_H  y_mass_fraction_He  z_mass_fraction_metals  \\\n",
       "0             0.758086            0.225912                0.016002   \n",
       "1             0.758086            0.225912                0.016002   \n",
       "2             0.758086            0.225912                0.016002   \n",
       "3             0.758086            0.225912                0.016002   \n",
       "4             0.758086            0.225912                0.016002   \n",
       "..                 ...                 ...                     ...   \n",
       "792           0.045724            0.934879                0.019396   \n",
       "793           0.045656            0.934948                0.019396   \n",
       "794           0.045613            0.934991                0.019396   \n",
       "795           0.045586            0.935018                0.019396   \n",
       "796           0.045553            0.935051                0.019395   \n",
       "\n",
       "               pp        cno     tri_alpha   brunt_N    lamb_S  \n",
       "0    2.227183e-26   0.000000  0.000000e+00  0.017715  0.000014  \n",
       "1    2.245820e-26   0.000000  0.000000e+00  0.017687  0.000014  \n",
       "2    2.272301e-26   0.000000  0.000000e+00  0.017545  0.000014  \n",
       "3    2.311749e-26   0.000000  0.000000e+00  0.017400  0.000014  \n",
       "4    2.362122e-26   0.000000  0.000000e+00  0.017095  0.000014  \n",
       "..            ...        ...           ...       ...       ...  \n",
       "792  1.461063e+00  20.426418  8.605005e-36  0.000261  0.518776  \n",
       "793  1.457282e+00  20.419824  8.628249e-36  0.000208  0.653629  \n",
       "794  1.454904e+00  20.415660  8.642918e-36  0.000167  0.823531  \n",
       "795  1.453406e+00  20.413065  8.652171e-36  0.000137  1.037593  \n",
       "796  1.451538e+00  20.406101  8.660505e-36  0.000121  1.307288  \n",
       "\n",
       "[797 rows x 14 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def load_profile(profile_number, logs_dir=logs_dir):\n",
    "    prof = pd.read_table(\n",
    "        os.path.join(logs_dir, 'profile' + str(profile_number) + '.data'), \n",
    "        skiprows=5, sep='\\s+')\n",
    "    return prof\n",
    "\n",
    "load_profile(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-27T21:36:28.975244Z",
     "start_time": "2023-06-27T21:36:28.933090Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_profiles(index=index, logs_dir=logs_dir):\n",
    "    return [load_profile(profile_number, logs_dir) \n",
    "            for profile_number in index.profile_number]\n",
    "\n",
    "profs = get_profiles()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Composition profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-27T21:36:29.095203Z",
     "start_time": "2023-06-27T21:36:28.976673Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ZAMS_X = profs[0].x_mass_fraction_H.values[0]\n",
    "ZAMS_Y = profs[0].y_mass_fraction_He.values[0]\n",
    "Y_p = 0.2463\n",
    "\n",
    "def plot_composition(profile_number):\n",
    "    prof = profs[profile_number-1]\n",
    "    x = 10**prof.logR / np.max(10**prof.logR)\n",
    "    plt.plot(x, prof.x_mass_fraction_H, lw=5, label='hydrogen', c='k')\n",
    "    plt.plot(x, prof.y_mass_fraction_He, lw=5, label='helium', c='b')\n",
    "    plt.axhline(ZAMS_X, c='k', ls='--', zorder=-99)\n",
    "    plt.axhline(ZAMS_Y, c='k', ls='--', zorder=-99)\n",
    "    plt.axhline(Y_p, c='lightgray', ls='--', zorder=-99)\n",
    "    plt.xlabel(r'fractional radius $r/R$')\n",
    "    plt.ylabel(r'mass fraction')\n",
    "    plt.legend()\n",
    "    plt.title('Internal Composition', size=24)\n",
    "\n",
    "plot_composition(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Propagation diagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-27T21:36:29.240175Z",
     "start_time": "2023-06-27T21:36:29.095915Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_propagation(profile_number, ell=1):\n",
    "    hist = get_history(profile_number)\n",
    "    prof = profs[profile_number-1]\n",
    "    \n",
    "    x = 10**prof.logR / np.max(10**prof.logR)\n",
    "    brunt = prof.brunt_N.values/(2*np.pi)*1e6\n",
    "    lamb  = prof.lamb_S.values*1e6/np.sqrt(2)*np.sqrt(ell*(ell+1))\n",
    "    plt.plot(x, brunt, lw=3, label='Buoyancy')\n",
    "    plt.plot(x, lamb, lw=3, label='Lamb')\n",
    "    \n",
    "    gmodes = np.minimum(brunt, lamb)\n",
    "    pmodes = np.maximum(brunt, lamb)\n",
    "    plt.fill_between(x, \n",
    "                     np.zeros(len(gmodes)), \n",
    "                     gmodes, \n",
    "                     color='blue', alpha=0.1, zorder=-99)\n",
    "    plt.fill_between(x, \n",
    "                     1e99*np.ones(len(pmodes)), \n",
    "                     pmodes, \n",
    "                     color='orange', alpha=0.1, zorder=-99)\n",
    "    \n",
    "    nu_max   = hist.nu_max.values[0]\n",
    "    Delta_nu = hist.delta_nu.values[0]\n",
    "    plt.axhline(nu_max, ls='--', c='k', label=r'$\\nu_\\max$', zorder=100)\n",
    "    plt.fill_between([0, 1], \n",
    "                     nu_max-5*Delta_nu, \n",
    "                     nu_max+5*Delta_nu, \n",
    "                     color='#aaaaaa', zorder=-98)\n",
    "    \n",
    "    plt.semilogy()\n",
    "    plt.ylim([1, 1e7]) #500*nu_max])\n",
    "    plt.xlim([0,1])\n",
    "    plt.ylabel(r'frequency $\\nu/\\mu\\rm{Hz}$')\n",
    "    plt.xlabel(r'fractional radius $r/R$')\n",
    "    plt.legend()\n",
    "    plt.title('Propagation Diagram', size=24)\n",
    "\n",
    "plot_propagation(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute frequencies with GYRE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's calculate some frequencies. An example GYRE inlist awaits you in the `src/` directory. Copy that over to your `LOGS/` directory and run it with GYRE: \n",
    "\n",
    "```\n",
    "    cp ../src/gyre.in LOGS\n",
    "    cd LOGS\n",
    "    $GYRE_DIR/bin/gyre gyre.in\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-27T21:36:29.248763Z",
     "start_time": "2023-06-27T21:36:29.240915Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>E_norm</th>\n",
       "      <th>Re(freq)</th>\n",
       "      <th>Im(freq)</th>\n",
       "      <th>l</th>\n",
       "      <th>n_g</th>\n",
       "      <th>n_p</th>\n",
       "      <th>n_pg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.221518e-05</td>\n",
       "      <td>1008.891465</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.050384e-05</td>\n",
       "      <td>1193.463457</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.879054e-06</td>\n",
       "      <td>1374.107108</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.970259e-06</td>\n",
       "      <td>1554.899236</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.848356e-06</td>\n",
       "      <td>1733.817022</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>4.973277e-09</td>\n",
       "      <td>5833.606530</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>5.100117e-09</td>\n",
       "      <td>6003.226830</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>5.296962e-09</td>\n",
       "      <td>6172.589653</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>5.579146e-09</td>\n",
       "      <td>6341.487888</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>6.093483e-09</td>\n",
       "      <td>6509.110976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>132 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           E_norm     Re(freq)  Im(freq)  l  n_g  n_p  n_pg\n",
       "0    5.221518e-05  1008.891465       0.0  0    0    5     5\n",
       "1    2.050384e-05  1193.463457       0.0  0    0    6     6\n",
       "2    8.879054e-06  1374.107108       0.0  0    0    7     7\n",
       "3    3.970259e-06  1554.899236       0.0  0    0    8     8\n",
       "4    1.848356e-06  1733.817022       0.0  0    0    9     9\n",
       "..            ...          ...       ... ..  ...  ...   ...\n",
       "127  4.973277e-09  5833.606530       0.0  3    0   32    32\n",
       "128  5.100117e-09  6003.226830       0.0  3    0   33    33\n",
       "129  5.296962e-09  6172.589653       0.0  3    0   34    34\n",
       "130  5.579146e-09  6341.487888       0.0  3    0   35    35\n",
       "131  6.093483e-09  6509.110976       0.0  3    0   36    36\n",
       "\n",
       "[132 rows x 7 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_frequencies(profile_number, logs_dir=logs_dir):\n",
    "    path = os.path.join(logs_dir, 'profile' + str(profile_number) + '-freqs.dat')\n",
    "    return pd.read_table(path, skiprows=5, sep='\\s+')\n",
    "\n",
    "get_frequencies(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I've prepared a script in the `src/` directory that will allow us to efficiently obtain the frequencies of all our stellar models. Let's run that in a loop: \n",
    "\n",
    "```\n",
    "    for FGONG in *.FGONG; do\n",
    "        ../../src/gyre6freqs.sh -i $FGONG -f\n",
    "    done\n",
    "```\n",
    "\n",
    "or even better, using `xargs` in parallel: \n",
    "\n",
    "```\n",
    "find . -name \"*.FGONG\" -print0 | xargs -0 -P 8 -I{} ../../src/gyre6freqs.sh -i {} -f -t 1\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-27T21:36:29.260332Z",
     "start_time": "2023-06-27T21:36:29.249455Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_all_frequencies(index=index, logs_dir=logs_dir):\n",
    "    return [get_frequencies(profile_number, logs_dir=logs_dir) \n",
    "            for profile_number in index.profile_number]\n",
    "\n",
    "freqs = get_all_frequencies()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Echelle diagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-27T23:08:26.518040Z",
     "start_time": "2023-06-27T23:08:26.306763Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ell_label = {0: 'radial', 1: 'dipole', 2: 'quadrupole', 3: 'octupole'}\n",
    "\n",
    "def plot_echelle(profile_number, sph_deg=-1, rad_ord=-1):\n",
    "    hist = get_history(profile_number)\n",
    "    prof = profs[profile_number-1]\n",
    "    freq = freqs[profile_number-1]\n",
    "    \n",
    "    nu_max   = hist.nu_max.values[0]\n",
    "    freq = freq[freq['Re(freq)'] < 5/3*nu_max]\n",
    "    radial = freq[np.logical_and(freq.l == 0, freq.n_p > 10)]\n",
    "    Dnu = np.mean(np.diff(radial['Re(freq)'].values))\n",
    "    \n",
    "    colors = ('black', 'red', 'blue', 'purple')\n",
    "    for ell in np.unique(freq.l.values):\n",
    "        nus = freq[freq.l == ell]\n",
    "        plt.plot(nus['Re(freq)'] % Dnu,\n",
    "                 nus['Re(freq)'], '.', \n",
    "                 mfc=colors[ell], mec='white', alpha=0.85,\n",
    "                 ms=15, mew=1, \n",
    "                 label=str(ell) + ' (' + ell_label[ell] + ')')#r'$\\ell=' + str(ell) + r'$')\n",
    "    \n",
    "    if sph_deg >= 0 and rad_ord >= 0:\n",
    "        freq = freq[np.logical_and(freq.l == sph_deg, freq.n_pg == rad_ord)]\n",
    "        if len(freq) > 0:\n",
    "            plt.plot(freq['Re(freq)'] % Dnu, freq['Re(freq)'], 'o', zorder=-99, mec='k', ms=10, mfc='w')\n",
    "    \n",
    "    plt.legend(loc='lower right', title=r'spherical degree $\\ell$', fontsize=12)\n",
    "    \n",
    "    plt.axvline(Dnu, ls='--', c='darkgray', zorder=-99)\n",
    "    plt.axhline(nu_max, ls='--', c='darkgray', zorder=-99)\n",
    "    \n",
    "    plt.ylim([0, nu_max*5/3*1.2])\n",
    "    plt.xlim([-10, Dnu*1.4])\n",
    "    \n",
    "    plt.ylabel(r'frequency $\\nu/\\mu\\rm{Hz}$')\n",
    "    plt.xlabel(r'$\\nu\\; \\rm{mod}\\; \\Delta\\nu/\\mu\\rm{Hz}$')\n",
    "    plt.title('Echelle Diagram', size=24)\n",
    "\n",
    "plot_echelle(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evolution! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-27T23:08:30.457135Z",
     "start_time": "2023-06-27T23:08:29.888564Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2b8c8b19609e4767a3aef5ff0d77bb22",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=1, description='profile_number', max=11, min=1), Output()), _dom_classes…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_panels(profile_number):\n",
    "    fig = plt.figure(figsize=(12,10))\n",
    "    \n",
    "    plt.subplot(2,2,1)\n",
    "    plot_HR(profile_number)\n",
    "    \n",
    "    plt.subplot(2,2,2)\n",
    "    plot_composition(profile_number)\n",
    "    \n",
    "    plt.subplot(2,2,4)\n",
    "    plot_propagation(profile_number)\n",
    "    \n",
    "    plt.subplot(2,2,3)\n",
    "    plot_echelle(profile_number)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "\n",
    "interact(plot_panels, profile_number=IntSlider(min=1, max=np.max(index.profile_number)));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Eigenfunctions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's go back into our `gyre.in` file and modify the `ad_output` block to the following in order to get eigenfunctions:\n",
    "\n",
    "```\n",
    "&ad_output\n",
    "    summary_file = 'profile1-freqs.dat'\n",
    "    summary_file_format = 'TXT'\n",
    "    summary_item_list = 'l,n_pg,n_p,n_g,freq,E_norm' \n",
    "    freq_units = 'UHZ' \n",
    "    detail_file_format = 'TXT'\n",
    "    detail_template = 'profile1-freqs/%L_%N'\n",
    "    detail_item_list = 'M_star,R_star,l,n_pg,n_p,n_g,freq,E,E_p,E_g,E_norm,M_r,x,xi_r,xi_h'\n",
    "/\n",
    "```\n",
    "\n",
    "Let's make a directory `profile1-freqs` to put the results in, and run GYRE:\n",
    "\n",
    "```\n",
    "    mkdir profile1-freqs\n",
    "    $GYRE_DIR/bin/gyre gyre.in\n",
    "```\n",
    "\n",
    "Alternatively to all of the above, just call the `../../src/gyre6freqs.sh` script with the `-e` flag (for eigenfunctions):\n",
    "\n",
    "    ../../src/gyre6freqs.sh -i profile1.data.FGONG -f -e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-27T22:20:39.943918Z",
     "start_time": "2023-06-27T22:20:39.703211Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Re(xi_r)</th>\n",
       "      <th>Re(xi_h)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.474572</td>\n",
       "      <td>0.474572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.474518</td>\n",
       "      <td>0.474560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.474489</td>\n",
       "      <td>0.474548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.474440</td>\n",
       "      <td>0.474531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.474361</td>\n",
       "      <td>0.474504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1077</th>\n",
       "      <td>1493.239058</td>\n",
       "      <td>5.498219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1078</th>\n",
       "      <td>1494.030036</td>\n",
       "      <td>5.501026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1079</th>\n",
       "      <td>1494.821727</td>\n",
       "      <td>5.503834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1080</th>\n",
       "      <td>1495.614133</td>\n",
       "      <td>5.506644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081</th>\n",
       "      <td>1496.320680</td>\n",
       "      <td>5.509149</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1082 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Re(xi_r)  Re(xi_h)\n",
       "0        0.474572  0.474572\n",
       "1        0.474518  0.474560\n",
       "2        0.474489  0.474548\n",
       "3        0.474440  0.474531\n",
       "4        0.474361  0.474504\n",
       "...           ...       ...\n",
       "1077  1493.239058  5.498219\n",
       "1078  1494.030036  5.501026\n",
       "1079  1494.821727  5.503834\n",
       "1080  1495.614133  5.506644\n",
       "1081  1496.320680  5.509149\n",
       "\n",
       "[1082 rows x 2 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_eigenfunctions(profile_number):\n",
    "    eig_dir = os.path.join(logs_dir, 'profile' + str(profile_number) + '-freqs')\n",
    "    eigs = {}\n",
    "    for fname in os.listdir(eig_dir):\n",
    "        if fname[:2] != '00':\n",
    "            continue\n",
    "        eig_i = pd.read_table(os.path.join(eig_dir, fname), sep='\\s+', skiprows=5)\n",
    "        sph_deg, rad_ord = fname.split('_')\n",
    "        eigs[(int(sph_deg), 'x')] = eig_i['x']\n",
    "        eigs[(int(sph_deg), int(rad_ord))] = eig_i[['Re(xi_r)', 'Re(xi_h)']]\n",
    "    return eigs\n",
    "\n",
    "eigs = get_eigenfunctions(1)\n",
    "eigs[(1,10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-27T23:23:49.067863Z",
     "start_time": "2023-06-27T23:23:48.955678Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_eigs(sph_deg, rad_ord):\n",
    "    x   = eigs[(sph_deg, 'x')]\n",
    "    eig = eigs[(sph_deg, rad_ord)]\n",
    "    #norm = lambda x: x/np.max(x)\n",
    "    #m = np.max([np.max(eig['Re(xi_r)']), np.max(eig['Re(xi_h)'])])\n",
    "    #norm = lambda x: x/m\n",
    "    plt.plot(x, eig['Re(xi_r)'], label='radial',     lw=3, zorder=11)\n",
    "    plt.plot(x, eig['Re(xi_h)'], label='horizontal', lw=3, zorder=10)\n",
    "    \n",
    "    plt.xlabel(r'fractional radius $r/R$')\n",
    "    plt.ylabel(r'normalized eigenfunction $\\xi$')\n",
    "    plt.axhline(0, ls='--', c='k', alpha=1, zorder=-10)\n",
    "    \n",
    "    plt.xlim([0,1])\n",
    "    plt.ylim([-20, 20])\n",
    "    #plt.ylim([-20, 20])\n",
    "    \n",
    "    plt.suptitle(r'$\\ell = {%d},\\; n = {%d}$' % (sph_deg, rad_ord))\n",
    "    plt.legend(loc='lower left')\n",
    "\n",
    "plot_eigs(1, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-27T23:23:53.536206Z",
     "start_time": "2023-06-27T23:23:53.410837Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4a51a7b8d5a243b88a48e50202715086",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=1, description='sph_deg', max=3), IntSlider(value=15, description='rad_o…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(plot_eigs, \n",
    "         sph_deg=IntSlider(min=0, max=3,  step=1, value=1), \n",
    "         rad_ord=IntSlider(min=5, max=36, step=1, value=15));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-27T22:20:40.219511Z",
     "start_time": "2023-06-27T22:20:40.196646Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[           E_norm     Re(freq)  Im(freq)  l  n_g  n_p  n_pg\n",
       " 0    5.221518e-05  1008.891465       0.0  0    0    5     5\n",
       " 1    2.050384e-05  1193.463457       0.0  0    0    6     6\n",
       " 2    8.879054e-06  1374.107108       0.0  0    0    7     7\n",
       " 3    3.970259e-06  1554.899236       0.0  0    0    8     8\n",
       " 4    1.848356e-06  1733.817022       0.0  0    0    9     9\n",
       " ..            ...          ...       ... ..  ...  ...   ...\n",
       " 127  4.973277e-09  5833.606530       0.0  3    0   32    32\n",
       " 128  5.100117e-09  6003.226830       0.0  3    0   33    33\n",
       " 129  5.296962e-09  6172.589653       0.0  3    0   34    34\n",
       " 130  5.579146e-09  6341.487888       0.0  3    0   35    35\n",
       " 131  6.093483e-09  6509.110976       0.0  3    0   36    36\n",
       " \n",
       " [132 rows x 7 columns],\n",
       "            E_norm     Re(freq)  Im(freq)  l  n_g  n_p  n_pg\n",
       " 0    1.854951e-05  1151.733714       0.0  0    0    6     6\n",
       " 1    7.992512e-06  1325.759809       0.0  0    0    7     7\n",
       " 2    3.550821e-06  1499.907412       0.0  0    0    8     8\n",
       " 3    1.636962e-06  1672.290272       0.0  0    0    9     9\n",
       " 4    7.487944e-07  1842.172704       0.0  0    0   10    10\n",
       " ..            ...          ...       ... ..  ...  ...   ...\n",
       " 123  5.092817e-09  5463.770788       0.0  3    0   31    31\n",
       " 124  5.215161e-09  5627.362910       0.0  3    0   32    32\n",
       " 125  5.400098e-09  5790.902137       0.0  3    0   33    33\n",
       " 126  5.680968e-09  5954.006309       0.0  3    0   34    34\n",
       " 127  6.143174e-09  6116.143658       0.0  3    0   35    35\n",
       " \n",
       " [128 rows x 7 columns],\n",
       "            E_norm     Re(freq)  Im(freq)  l  n_g  n_p  n_pg\n",
       " 0    1.423684e-05  1038.796498       0.0  0    0    6     6\n",
       " 1    6.038036e-06  1194.793084       0.0  0    0    7     7\n",
       " 2    2.621284e-06  1350.954769       0.0  0    0    8     8\n",
       " 3    1.172470e-06  1505.444173       0.0  0    0    9     9\n",
       " 4    5.231848e-07  1657.199654       0.0  0    0   10    10\n",
       " ..            ...          ...       ... ..  ...  ...   ...\n",
       " 113  5.624074e-09  4624.003209       0.0  3    0   29    29\n",
       " 114  5.759442e-09  4771.391876       0.0  3    0   30    30\n",
       " 115  5.975086e-09  4918.770182       0.0  3    0   31    31\n",
       " 116  6.303661e-09  5065.713601       0.0  3    0   32    32\n",
       " 117  6.845902e-09  5211.824076       0.0  3    0   33    33\n",
       " \n",
       " [118 rows x 7 columns],\n",
       "            E_norm     Re(freq)  Im(freq)  l  n_g  n_p  n_pg\n",
       " 0    4.561559e-06  1062.330847       0.0  0    0    7     7\n",
       " 1    1.923101e-06  1199.949532       0.0  0    0    8     8\n",
       " 2    8.306130e-07  1336.162898       0.0  0    0    9     9\n",
       " 3    3.644316e-07  1469.103484       0.0  0    0   10    10\n",
       " 4    1.743464e-07  1599.470514       0.0  0    0   11    11\n",
       " ..            ...          ...       ... ..  ...  ...   ...\n",
       " 101  6.431422e-09  3840.664788       0.0  3    0   27    27\n",
       " 102  6.586571e-09  3971.564221       0.0  3    0   28    28\n",
       " 103  6.833143e-09  4102.477474       0.0  3    0   29    29\n",
       " 104  7.217078e-09  4233.030487       0.0  3    0   30    30\n",
       " 105  7.852740e-09  4362.854660       0.0  3    0   31    31\n",
       " \n",
       " [106 rows x 7 columns],\n",
       "           E_norm     Re(freq)  Im(freq)  l  n_g  n_p  n_pg\n",
       " 0   1.496807e-06  1061.676004       0.0  0    0    8     8\n",
       " 1   6.216815e-07  1180.849197       0.0  0    0    9     9\n",
       " 2   2.734929e-07  1296.463794       0.0  0    0   10    10\n",
       " 3   1.363203e-07  1409.809174       0.0  0    0   11    11\n",
       " 4   7.943001e-08  1522.545805       0.0  0    0   12    12\n",
       " ..           ...          ...       ... ..  ...  ...   ...\n",
       " 89  7.680586e-09  3159.729138       0.0  3    0   25    25\n",
       " 90  7.836125e-09  3275.428419       0.0  3    0   26    26\n",
       " 91  8.095055e-09  3391.176695       0.0  3    0   27    27\n",
       " 92  8.508535e-09  3506.711993       0.0  3    0   28    28\n",
       " 93  9.162465e-09  3621.770970       0.0  3    0   29    29\n",
       " \n",
       " [94 rows x 7 columns],\n",
       "           E_norm     Re(freq)  Im(freq)  l  n_g  n_p  n_pg\n",
       " 0   5.361618e-07  1101.339292       0.0  0    0    9     9\n",
       " 1   2.386109e-07  1208.191549       0.0  0    0   10    10\n",
       " 2   1.230312e-07  1313.079607       0.0  0    0   11    11\n",
       " 3   7.336466e-08  1418.210608       0.0  0    0   12    12\n",
       " 4   4.643085e-08  1523.649260       0.0  0    0   13    13\n",
       " ..           ...          ...       ... ..  ...  ...   ...\n",
       " 85  8.729850e-09  2945.063347       0.0  3    0   25    25\n",
       " 86  9.014813e-09  3052.978010       0.0  3    0   26    26\n",
       " 87  9.436396e-09  3160.788544       0.0  3    0   27    27\n",
       " 88  1.013261e-08  3268.285874       0.0  3    0   28    28\n",
       " 89  1.169348e-08  3374.018873       0.0  3    0   29    29\n",
       " \n",
       " [90 rows x 7 columns],\n",
       "           E_norm     Re(freq)  Im(freq)  l  n_g  n_p  n_pg\n",
       " 0   4.777437e-07  1040.458655       0.0  0    0    9     9\n",
       " 1   2.158763e-07  1140.664265       0.0  0    0   10    10\n",
       " 2   1.147213e-07  1239.282708       0.0  0    0   11    11\n",
       " 3   6.937595e-08  1338.642811       0.0  0    0   12    12\n",
       " 4   4.403488e-08  1438.141079       0.0  0    0   13    13\n",
       " ..           ...          ...       ... ..  ...  ...   ...\n",
       " 79  9.530593e-09  2678.677122       0.0  3    0   24    24\n",
       " 80  9.759257e-09  2780.668967       0.0  3    0   25    25\n",
       " 81  1.018400e-08  2882.691187       0.0  3    0   26    26\n",
       " 82  1.083660e-08  2984.292402       0.0  3    0   27    27\n",
       " 83  1.199564e-08  3085.222507       0.0  3    0   28    28\n",
       " \n",
       " [84 rows x 7 columns],\n",
       "           E_norm     Re(freq)  Im(freq)  l  n_g  n_p  n_pg\n",
       " 0   2.008883e-07  1085.433374       0.0  0    0   10    10\n",
       " 1   1.096083e-07  1179.103009       0.0  0    0   11    11\n",
       " 2   6.682858e-08  1273.711014       0.0  0    0   12    12\n",
       " 3   4.243808e-08  1368.303499       0.0  0    0   13    13\n",
       " 4   2.832255e-08  1461.933308       0.0  0    0   14    14\n",
       " ..           ...          ...       ... ..  ...  ...   ...\n",
       " 74  1.027433e-08  2452.031361       0.0  3    0   23    23\n",
       " 75  1.050434e-08  2549.113813       0.0  3    0   24    24\n",
       " 76  1.084281e-08  2646.132579       0.0  3    0   25    25\n",
       " 77  1.140858e-08  2743.223475       0.0  3    0   26    26\n",
       " 78  1.237824e-08  2839.743026       0.0  3    0   27    27\n",
       " \n",
       " [79 rows x 7 columns],\n",
       "           E_norm     Re(freq)  Im(freq)  l  n_g  n_p  n_pg\n",
       " 0   1.915884e-07  1041.010723       0.0  0    0   10    10\n",
       " 1   1.068743e-07  1130.772531       0.0  0    0   11    11\n",
       " 2   6.533537e-08  1221.505942       0.0  0    0   12    12\n",
       " 3   4.150629e-08  1312.165025       0.0  0    0   13    13\n",
       " 4   2.820804e-08  1401.642645       0.0  0    0   14    14\n",
       " ..           ...          ...       ... ..  ...  ...   ...\n",
       " 73  1.116102e-08  2351.536162       0.0  3    0   23    23\n",
       " 74  1.145194e-08  2444.796899       0.0  3    0   24    24\n",
       " 75  1.194015e-08  2537.863263       0.0  3    0   25    25\n",
       " 76  1.266709e-08  2630.792821       0.0  3    0   26    26\n",
       " 77  1.404678e-08  2723.129374       0.0  3    0   27    27\n",
       " \n",
       " [78 rows x 7 columns],\n",
       "           E_norm     Re(freq)  Im(freq)  l  n_g  n_p  n_pg\n",
       " 0   1.045898e-07  1031.796484       0.0  0    0   11    11\n",
       " 1   6.403768e-08  1114.512824       0.0  0    0   12    12\n",
       " 2   4.091517e-08  1196.783808       0.0  0    0   13    13\n",
       " 3   2.913699e-08  1278.076614       0.0  0    0   14    14\n",
       " 4   2.328577e-08  1359.747858       0.0  0    0   15    15\n",
       " ..           ...          ...       ... ..  ...  ...   ...\n",
       " 66  1.329791e-08  2060.190540       0.0  3    0   22    22\n",
       " 67  1.362239e-08  2145.182448       0.0  3    0   23    23\n",
       " 68  1.411725e-08  2230.167011       0.0  3    0   24    24\n",
       " 69  1.494814e-08  2315.165958       0.0  3    0   25    25\n",
       " 70  1.645468e-08  2399.542872       0.0  3    0   26    26\n",
       " \n",
       " [71 rows x 7 columns],\n",
       "           E_norm     Re(freq)  Im(freq)  l  n_g  n_p  n_pg\n",
       " 0   4.166487e-08  1016.512678       0.0  0    0   14    14\n",
       " 1   3.451270e-08  1082.265142       0.0  0    0   15    15\n",
       " 2   2.933859e-08  1148.276908       0.0  0    0   16    16\n",
       " 3   2.609117e-08  1214.349715       0.0  0    0   17    17\n",
       " 4   2.436049e-08  1280.701130       0.0  0    0   18    18\n",
       " 5   2.340408e-08  1347.461985       0.0  0    0   19    19\n",
       " 6   2.285800e-08  1414.537720       0.0  0    0   20    20\n",
       " 7   2.266237e-08  1481.784580       0.0  0    0   21    21\n",
       " 8   2.276630e-08  1549.120174       0.0  0    0   22    22\n",
       " 9   2.319374e-08  1616.553326       0.0  0    0   23    23\n",
       " 10  2.401912e-08  1684.037513       0.0  0    0   24    24\n",
       " 11  2.539102e-08  1751.391559       0.0  0    0   25    25\n",
       " 12  2.781804e-08  1818.317793       0.0  0    0   26    26\n",
       " 13  3.982308e-08  1048.939673       0.0  1    2   14    13\n",
       " 14  3.281854e-08  1112.516935       0.0  1    2   15    14\n",
       " 15  2.843321e-08  1176.832243       0.0  1    2   16    15\n",
       " 16  2.626538e-08  1241.010986       0.0  1    1   16    16\n",
       " 17  2.818531e-08  1302.578764       0.0  1    1   17    17\n",
       " 18  5.622917e-08  1343.582827       0.0  1    1   18    18\n",
       " 19  2.729665e-08  1384.104460       0.0  1    1   19    19\n",
       " 20  2.356736e-08  1446.239652       0.0  1    1   20    20\n",
       " 21  2.303798e-08  1511.944745       0.0  1    1   21    21\n",
       " 22  2.315746e-08  1578.460903       0.0  1    1   22    22\n",
       " 23  2.369828e-08  1645.262247       0.0  1    1   23    23\n",
       " 24  2.466885e-08  1712.196716       0.0  1    0   23    24\n",
       " 25  2.632119e-08  1779.074764       0.0  1    0   24    25\n",
       " 26  2.979038e-08  1845.228344       0.0  1    0   25    26\n",
       " 27  4.265033e-08  1010.294546       0.0  2    4   13     9\n",
       " 28  3.748032e-08  1075.152340       0.0  2    4   14    10\n",
       " 29  4.482965e-07  1091.210429       0.0  2    3   14    11\n",
       " 30  2.987574e-08  1142.480756       0.0  2    3   15    12\n",
       " 31  2.635632e-08  1208.364150       0.0  2    3   16    13\n",
       " 32  2.456489e-08  1274.532677       0.0  2    3   17    14\n",
       " 33  2.552983e-08  1340.088619       0.0  2    3   18    15\n",
       " 34  2.282562e-07  1358.296501       0.0  2    2   18    16\n",
       " 35  2.310556e-08  1409.150219       0.0  2    2   19    17\n",
       " 36  2.271974e-08  1476.090424       0.0  2    2   20    18\n",
       " 37  2.278121e-08  1543.301750       0.0  2    2   21    19\n",
       " 38  2.321202e-08  1610.588804       0.0  2    2   22    20\n",
       " 39  2.418395e-08  1677.674927       0.0  2    2   23    21\n",
       " 40  3.029986e-08  1741.973545       0.0  2    2   24    22\n",
       " 41  1.119730e-07  1764.225326       0.0  2    1   24    23\n",
       " 42  2.835032e-08  1814.417415       0.0  2    1   25    24\n",
       " 43  7.593310e-08  1033.823134       0.0  3    6   13     7\n",
       " 44  8.016739e-08  1036.722212       0.0  3    5   13     8\n",
       " 45  3.269238e-08  1101.467592       0.0  3    5   14     9\n",
       " 46  2.535464e-08  1234.208952       0.0  3    4   16    12\n",
       " 47  2.393130e-08  1300.935096       0.0  3    4   17    13\n",
       " 48  2.270165e-08  1435.449405       0.0  3    3   19    16\n",
       " 49  2.257895e-08  1502.956475       0.0  3    3   20    17\n",
       " 50  2.280435e-08  1570.539246       0.0  3    3   21    18\n",
       " 51  2.344632e-08  1638.080070       0.0  3    3   22    19\n",
       " 52  2.415299e-06  1671.524707       0.0  3    2   22    20\n",
       " 53  2.441011e-08  1706.000572       0.0  3    2   23    21\n",
       " 54  2.593608e-08  1773.416430       0.0  3    2   24    22\n",
       " 55  2.914365e-08  1840.192476       0.0  3    2   25    23]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freqs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-27T23:24:37.103276Z",
     "start_time": "2023-06-27T23:24:36.414228Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e9e9d7a3a3aa42da8c86dcfae78625a4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=1, description='profile_number', max=11, min=1), IntSlider(value=1, desc…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prev = None\n",
    "\n",
    "def plot_eigs2(profile_number, sph_deg, rad_ord, log_r=0):\n",
    "    plt.figure(figsize=(13,9))\n",
    "    \n",
    "    hist = get_history(profile_number)\n",
    "    prof = profs[profile_number-1]\n",
    "    freq = freqs[profile_number-1]\n",
    "    freq = freq[np.logical_and(freq.l == sph_deg, freq.n_pg == rad_ord)]\n",
    "    \n",
    "    eigs = get_eigenfunctions(profile_number)\n",
    "    \n",
    "    \n",
    "    plt.subplot(1,2,1)\n",
    "    plot_echelle(profile_number, sph_deg, rad_ord)\n",
    "    \n",
    "    plt.subplot(2,2,2)\n",
    "    plot_propagation(profile_number, ell=sph_deg)\n",
    "    if len(freq) > 0: \n",
    "        plt.axhline(freq['Re(freq)'].values[0], ls='--', c='r', lw=3, label=r'$\\nu$')\n",
    "    if log_r>0:\n",
    "        plt.xlim([1e-2, 1])\n",
    "        plt.semilogx()\n",
    "    \n",
    "    plt.subplot(2,2,4)\n",
    "    x   = eigs[(sph_deg, 'x')]\n",
    "    key = (sph_deg, rad_ord)\n",
    "    if key in eigs:\n",
    "        eig = eigs[key]\n",
    "        plt.plot(x, eig['Re(xi_r)'], label='radial',     lw=3, zorder=11)\n",
    "        plt.plot(x, eig['Re(xi_h)'], label='horizontal', lw=3, zorder=10)\n",
    "        plt.legend(loc='upper left')\n",
    "    \n",
    "    plt.xlabel(r'fractional radius $r/R$')\n",
    "    plt.ylabel(r'eigenfunction $\\xi$')\n",
    "    plt.axhline(0, ls='--', c='k', alpha=1, zorder=-10)\n",
    "    \n",
    "    plt.xlim([0,1])\n",
    "    plt.ylim([-20, 20])\n",
    "    \n",
    "    if log_r>0:\n",
    "        plt.xlim([1e-3, 1])\n",
    "        plt.semilogx()\n",
    "    \n",
    "    plt.suptitle(r'$\\ell = {%d},\\; n = {%d}$' % (sph_deg, rad_ord))\n",
    "    plt.tight_layout()\n",
    "\n",
    "interact(plot_eigs2, \n",
    "         profile_number=IntSlider(min=1, max=np.max(index.profile_number)),\n",
    "         sph_deg=IntSlider(min=0, max=3,  step=1, value=1), \n",
    "         rad_ord=IntSlider(min=5, max=36, step=1, value=15),\n",
    "         log_r=IntSlider(min=0, max=1, step=1, value=0));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A small grid "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now try to run a grid of tracks with different masses. We'll begin by copying our work over to a new directory and removing the logs files: \n",
    "\n",
    "```\n",
    "cp -R work grid\n",
    "cd grid\n",
    "rm -rf LOGS\n",
    "```\n",
    "\n",
    "Now let's edit `inlist_project` to stop the run at a central hydrogen fraction of 0.1, and also specify the `log_directory = '1'` in the `&controls` block. \n",
    "\n",
    "We want to run a grid of models, which means we need a way to automatically change parameters of the inlist in a loop. Here is a bash function that will do just that: \n",
    "\n",
    "```\n",
    "INLIST=inlist_project \n",
    "change() { \n",
    "    # Modifies a parameter in the current inlist. \n",
    "    # args: ($1) name of parameter \n",
    "    #       ($2) new value \n",
    "    #       ($3) filename of inlist where change should occur \n",
    "    # example command: change initial_mass 1.3 \n",
    "    # example command: change log_directory 'LOGS_MS' \n",
    "    # example command: change do_element_diffusion .true. \n",
    "    param=$1 \n",
    "    newval=$2 \n",
    "    filename=$3 \n",
    "    escapedParam=$(sed 's#[^^]#[&]#g; s#\\^#\\\\^#g' <<< \"$param\")\n",
    "    search=\"^\\s*\\!*\\s*$escapedParam\\s*=.+$\" \n",
    "    replace=\"    $param = $newval\" \n",
    "    if [ ! \"$filename\" == \"\" ]; then\n",
    "        sed -r -i.bak -e \"s#$search#$replace#g\" $filename \n",
    "    fi\n",
    "    if [ ! \"$filename\" == \"$INLIST\" ]; then \n",
    "        change $param $newval \"$INLIST\"\n",
    "    fi \n",
    "} \n",
    "```\n",
    "\n",
    "Now we are ready to calculate a small grid: \n",
    "\n",
    "```\n",
    "for M in 0.7 0.8 0.9 1.0 1.1 1.2; do\n",
    "    echo \"Running mass $M\"\n",
    "    change initial_mass $M\n",
    "    change log_directory \"'$M'\"\n",
    "    ./rn\n",
    "\n",
    "    cd $M\n",
    "    for FGONG in *.FGONG; do\n",
    "        ../../src/gyre6freqs.sh -i $FGONG -f -t $OMP_NUM_THREADS\n",
    "    done\n",
    "    cd -\n",
    "done \n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-27T22:20:41.626038Z",
     "start_time": "2023-06-27T22:20:41.209160Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ewubNm/HSSy/hvffe87oPMd0jEAgEAiH4CNpIjU6nw4EDB7Bw4UKf9xGLxXjppZdwzTXXYMeOHbBarUM4QgKBQCAQCMNJ0EZqfvnlF2RmZiI6Orrf+y5btgxbtmyBSqVCVFSUx3pvBnskekMgEAgEQmATtJGaHTt24PLLL/drX5lMBi6XC7lcPriDIhAIBAKBMGIEZaRGp9Nh//79WL16tV/7nzhxAnPnzgWfzx/kkREIBAKBMHrRarUwmUyw2+2w2+2gKAoAEBISgsjIyBEeXYCKGlfJtcPh8Lp+165dSE1NRUJCgse6devWYevWrfjss8+gUCiwevVqSCQS/OEPf4BcLkddXR2+/vprvPPOO0N6DgQCgUAgBDIus9uUlBSUl5dj7ty5uO6663rc/u9//zvWrFnjdd2yZcvwl7/8ZYhG6jsBJ2oOHjyITZs2AXBOMaWkpODSSy+FRCKht+mt6kmlUqGtrQ1msxmAMzn4u+++w/bt27F48WIoFAp8+OGHUCgUQ38yBAKBQCAEIMXFxVi5ciV++uknyOVy6PV6XHHFFRAKhT1eX1tbW/H2228jJCQEbDYbLBYLdrsdH3/8MVasWDHMZ+AdFuWKHRF6hXTpJhAIBMJo4b777kNMTAxeffVVetlbb72F4uJibN682es+f/vb3/Dkk08ylq1ZswbTp0/HzJkzh3S8Lvq6FgdtojCBQCAQCIT+Y7FYUFhYiNTUVMbytLQ0nDx5EhqNxut+9913H+P58ePHYTabh03Q+ELATT8FEqSMm0AgEAijjdraWthsNkZaBwBIJBLY7XZUVVUhKyvLYz/37W02Gz744AOsXbt2yMfbH0ikhkAgEAiEiwhXm6Du/RTFYjEAZ25qX/znP//B9OnTIRQKB3+AA4BEanrBfc6ORG0IBAKB4KK/lUOAs8jl2LFjCAkJgUqlQnp6Om677bbhGbAbXK7z0t+9ebSr8thut/e6v8PhwIYNGwKyipiIGgKBQCAQ+oE/lUN79+5FW1sb/u///g8AQFEUXnrpJXz55ZdYvnz5cA6frv7tbpui1+sBAKGhob3uf+jQIVRXV2P8+PFDM8ABQKafCAQCgUDoB2vXrsWCBQtoV3qxWIwlS5Zg/fr1Pe7z3//+F9nZ2fRzFouF2267Dbt37x7q4XoQFRUFoVCI9vZ2xnK1Wg0AGDt2bK/779+/H0Kh0GP6KhAgooZAIBAIBB/xt3KIx+PhtddeYwiJ0tJSZGRkDOl4vcHn8zF79mxUVFQwlp85cwYZGRmIiIjodf8zZ84EpKABiKghEAgEAsFnfKkc8sY999yDkpISXHHFFfjPf/6DI0eO4MCBA3j00UeHYdSeLFu2DDt27IBWqwUAKJVK7Ny5kzGevXv3YtasWdi/fz9j37a2NvB4vGEdr6+QnBoCgUAgEHzE38qhrKwsfPLJJ/jDH/6AF198EREREfj0009HrAdhQUEBVqxYgdWrVyM9PR2lpaV44YUXsHDhQsZ2VqsVNpuNsSw1NdVjWaBARE0vkIonAoFAGFz8qRrasmUL9u3bh7i4ODQ0NODyyy/HggULhmfA3fC3cqijowObN2/GG2+8gdOnT+Nf//oXbrrpJvz1r38dsXO58cYbceONN/a4vqCgAEePHvVY/t577w3lsAYEETUEAoFAGBb8qRr67LPPsGHDBmzduhUSiQQGgwGXXXYZFAoFcnJyhvkM/KscoigKjz32GB599FHMmjULCxcuxNVXX41Vq1bhj3/8I/Lz8wPO7yVYITk1vXDu3Dn6QSAQCISB0d+qIaPRiA8++ABz586lc1hEIhGmT5+ODRs2DNOomfhTOVRRUQGNRoNZs2bRy8aPH0+fQ3l5+VAN96KDiBoCgUAgDDn+VA2dP38eOp2Ojo64iImJwYEDB0Ykr8OfyiEWiwWTyeSxXCqVIjY2FtHR0UM23osNImoIBAKBMOT4UzXkSqKlKIqx3GazQavVorGxccjG2xv9rRxKSUnB2LFj8dFHHzGO8/PPP2P69OlE1AwiJKeGQCAQCEOOP1VDqampiImJQVtbG2O5a7pGpVIhMTFxKIbbK/5UDn3wwQf4xz/+geeeew5yuRxmsxnJycm0wzBhcPBL1DQ3N3soy8rKShw5cgSRkZGYN2/eoAyOQCAQCKMDf6qG2Gw2/vSnP+HPf/4zlEolFAoFiouLYbVavR5rOOlv5VBISAieeuqpYRjZxY1f74grrrgCmzZtYixLTk7GggUL8O677w7KwAgEAoEwevC339D8+fOxZs0a/Otf/8K6devQ0dGBqVOngsViISEhYWgHTQg6/BI1bDYbL7/8Mu6++240NDTQyxUKBfLz8wdtcAQCgXCxUVFRgfvuuw9Hjhzpc9vi4mI89thjyM3NRWZmJr744oset7VYLLjsssswYcIE3HDDDXjjjTcGc9h9MpB+Q1lZWVixYgUefPBBzJs3Dw0NDZg0aRJdRUXoG4PVhoP1ShTWq2DtJixHE36JmuXLl+ONN95ASUkJlixZgs2bN9PrArUfhD9MmDCBfhAIBMJQ8uuvv+L555/Hl19+iX379vVo4uZOTk4O1qxZgzvuuANz5szxiKC7s2fPHkycOBEA8PXXX+P5558ftLH7gr/9ht58800sW7aMfm6xWFBUVIQ777xzSMc7mrDYHdhfp0SjzowGnQkNWs9KrNGCX6KGxWLh2muvxbZt2zBr1iz8+c9/xj333MOI2hAIBALBd+bPn4833ngDd999d7/3ZbPZuPnmm1FRUeHVARYAWltbaeEwUrko/vQbUqvVSE9Pp9d/8MEHyMnJwZIlS4Z38EGKg6JQ1KCC1uIUyWwWEC4cmdYMw4Ff72yj0QgAiIyMxIcffoi33noLJSUluPrqq/H7778P6gBHEmK+RyAQgoX09HRMnDgR//3vfz3W1dbWjkiVUHfcq4b+8Y9/4KWXXuqzauipp56C3W7Hyy+/jJUrV4LD4eCvf/3riCYJBwsUReF4cwdaDBZ62bQYOST80Vv47NeZbdu2DWPHjsWyZcvA5XJxzTXXID8/H3/605+wa9euwR4jgUAgEHxg6dKleOONN/DCCy9AKpXSy3///Xfccsst2LNnz8gNrpP+Vg1FR0fjlVdeGY6hjTrOq/So6jDSzzPCJUgMHd3tGPySugcPHsRtt91Gl+gBQHh4ONasWYMPPvhg0AZHIBAIBN+5+uqrwWKxsGXLFnqZzWYDi8UikY2LjAatCadatfTzRKkA6eGSXvYYHfj0Lj948KDPB4yMjPR7MAQCgUDwH4lEgsWLFzOmoPbu3Yu5c+eO4KhGBzqLDR0mq4e7cSCiMllxuFFNPw8X8pATIweLxRq5QQ0TPomaf/3rX9DpdH1uZzAYsHHjxgEPikAgEAj+sXTpUpSUlODUqVMAgLq6un75uVgsFvz444/49ttv8e233+KXX34ZqqEGDTUdBvx8oRW7qtvQqDeP9HB6xWi142C9EvZO8SXmcTArLgwc9ugXNICPOTX79u3D9OnTfT7oe++95/eACAQCgeA/06ZNQ0pKCv773/8iIiICsbGxPu/b1taG7777Drfccgudk9Pc3IxPP/0Ut9xyy6iy7PCVFr0ZR5s64IrPGK19l9qPFDaHAwfqlTDZnD40PDYLs+MVCOFyRnhkw4fPicIikQgymYyxrK2tjeEtYDAY6P4eBAKBQBgZbrrpJqxZswaJiYm46667fN7v22+/xQMPPMCYpoiOjsby5cuxefNm3HPPPUMx3IClw2RFYYOKFjShfC7GBGiiLUVRONyoRofZWTXGAjAzLgzSkNFb6eQNn8528eLF+Otf/+oxH3fHHXfg888/ZywbTW0SiOkegUAYblw5G93bCfSGWq1GR0cHPc103XXX4b333oNKpaI7Xbsfm6Ioj+/zpqYmTJo0iV6+atUqZGVl4bbbboNQKIRCoYDVagWPxxvQ+QULBqsd++uVsDmcfzMBl43ZCQrwOIGZcH26VYtGXdfU2JRoGaLEISM4opHBp//Ogw8+6DXByNuy6667bsCDIhAIFzf9aRUwkH0CiSNHjuCpp57CfffdBwB4/vnn8cQTT+Dnn3/ucZ/i4mLce++92LRpE+6//36sXLkSgLNlzdVXX02XThcWFuLRRx/F999/D6Cr9NudhoYGhpdNWVkZqqqq6Ofh4eFeO2mPRqx2Bw7UdU3jcNks5McrIOIF5jTOBbUB5So9/Tw1TIxx8otvqhAAWNQAUrnvvPPOiyYx2BW1IUZ8BMLQ8euvv2Lnzp2QSCT4/PPPsXHjRsycOXPQ9yF40tjYiAsXLmD27Nle1//www+48sor+xWpMdnsKG7qgNVBYUasHMIAFQXuOCgK++uUaO00rGMByE9QBGzUo0Vvxv46JT1FFisJway4sFFb6dTXtdinSE1tba3X5d70UHNzs69jGzQqKyuH/TUJBMLg40+rgIG0FyB0ERsbi5MnT3r9XjeZTLBYLP0SNE57fjWa9Ga0Gy2o0Rj73mmEoSgKR5s6aEEDANNiAncaR2O24pBbzo88hIvpsRdH6XZP+CRqPvroI6/Lvf3hfvjhh4GNCM4E5LfffhsbNmzwWHfhwgVkZGQwmk32Fp51He///b//h3Xr1uGNN97An/70J7rdPYFAIBCc3HDDDdi4cSM0Gg29rKWlBZs3b8Y111zTr2OVtOnQZuwSB5GiwO83dLZNi1o38ZUZIcEYWWBO45htdhyoV8HqlvOTF68Ad4Ami1qtFq2trWhqakJ9fT3q6upQV1eH1tbWwRj2kONTovA333yDkydPIiwsjLG8tLSU0SnVbDbjzJkzePDBB/0e0N69e7Ft2zZs2bIFjz32mMf6Tz/9FC+88AJdWshms7FgwYIej6fT6XD33Xfj5ptvpsf6yiuv4Mknn8Q///lPv8dJIBAIo42oqCjcdttt2LFjBywWpyCRyWT9joI16Uw4p+zyNssIl0AxQk0UHQ4HTCYTjEYj4xEfH4/w8HB6u0q1HueUXTe79rYGnDpZiRIuFxwOBxwOB1wuF9zO5xkZGYiOjma8VkVFBex2O71tTz+5XC54PJ7fERW7g8LBehUMneXlHJazdLv79J7repqSkoLy8nLMnTu317zXv//971izZo3XdcuWLcNf/vIXv8Y7nPhc61VeXu51eVFREeP5QMNeBQUFGDduHMPm20VzczPUajVuv/12n4+3ceNGVFVVMXqNLF++HFdeeSV++eWXXgURgUAgXGxwudwBdcA2WO040qSmn0eJ+AO256coCjabjSFKpFIpQ5QAzh5XVVVVjO3MZu9meVdddRW9f6POhOPNXdEpTX01qvftBHpJOY2IiPAQNT/++KPPydR33XUXxo4dy1i2du1aOBwOhnjq/pPD5QLx40GFdp379Dg55ALm1GBxcTFWrlyJn376CXK5HHq9HldccQWEQiEWLVrkdUytra14++23ERISAjabDRaLBbvdjo8//hgrVqzw6bxGGp9EzdSpU/GnP/2J0SDNGzqdblB6P/XUo2TDhg3YuXMnCgoKkJ+fj9tvvx2ZmZm9HuvHH39EamoqxGIxvSwlJQWhoaHYsmULETUEAoEwSDjzaFSw2LumRHLdcjwoiqKjJmw2G3K5nLH/2bNnUV5e7hFVMRqNsNuZpnezZ8/26O7d0tLic46l0eicZlIaLShqUNPLDcpW1B7c1augAQAOxzPpufsY+7u/Uqnss5Q/amIOot0EzeRIKeIkAo/t1q5diwULFtB/Y7FYjCVLlmD9+vU9iprw8HCPacY1a9Zg1apVfV7/AwWfRM0999yD9PR0nw64fPnyAQ2oNyZMmID7778fRUVF+Oabb/DDDz/gz3/+M5YuXep1e4vFgsrKSsybN89jXWxsLG0j7u11CAQCgdA/DlY2QGlz3pRSFAX1qSJ8/msTjEYjLWZcZGZmenx3NzY24vjx4z69lvuxXAgEnhd3ABAKhRAIBBAKhfQjIiICeosNB+tVdEuBEDYwKVqKmTffDLvdDpvNRv90/91ut3vtc5icnAy9Xt/jPu7L3BtCu/5efQkaWVIKoifn0s/HyUQYHyb22M5isaCwsNAjupKWloYNGzZAo9EgNDTUYz+XnYCL48ePw2w2B1U1oU+i5vLLL+9zm7KyMlAUhTlz5gx4UD3hPhd46NAhrFy5Ev/v//0/TJ06FePHj/fYXq1Ww+FwMKI0LkQiES5cuDBkYyUQCITRgNVqhVqtZjw6OjogEolw5ZVX0ts1aE1otnVF2ZtOHEJb6ckej+tNlAiFPbv1cjgchihRKBQe2+Tm5iI9PZ0hYAQCgde0CLPdgb3VbTDbnUKCz2bhkqQISPn+O/Bee+21fu8LAE8++WSPQkhrB2pYErrSKULIQ3Z0qNdzq62thc1mg0TCnPaTSCSw2+2oqqpCVlaWx37u29tsNnzwwQdYu3btgM5puPHrv1dbW4szZ84gNzeXbpMQHR2NgwcPYseOHSgoKMCUKVMGc5wezJw5E59++imuvfZabN++HU888YTHNi4nTW//dDab3WN5orf6dxK9IRAIg42DosAOoPJblUqFI0eOoKOjgxYwPVWKuheO6C02HHXLo9HUV/coaEJCQiAUCr3ebKakpHhEVFwPLpfbZ85mdHS0R56LN+wOCgfrlNB1JtqyWUBevGJAgmagsFgsj+k4F3qLDbtr2kF1CjApn4tZ8Yoe3zuudkXde3W5/ua+5P385z//wfTp03sVmoGI36LmT3/6EzQaDcaMGYPp06cjNzcXOTk5KCgowD/+8Y8hFzWA8wMwbdo0tLW1eV0vl8vB4/FgMBg81hkMBp/e/AQCYfjprVXA3r17sWrVKrz77rvIz8/3aZ9ApE5jxLHmDoSGcDE3MXzIxI3VamWIFNdDq9Xi7rvvZggFo9GIAwcO+HTcjo4OOBwOUGDhUENXaTHbZsUECQc511/PiJa4fnrLJXHhqygZCK4eSUqTlV42PVaO8AAtObfYHThQr4KlU9CEcNiYHR8Gfi/tGlxTW93zU10VbX3l/jgcDmzYsAHvvPPOQIY+IvglambPno3CwkKUlpbiyJEjOHz4MN555x20tbWBzWZ72G8PJTKZzCMD3p3MzEw0NjZ6LG9sbGR8IRIIhJHnyJEj+OKLL3D27FkAzlYB2dnZuPrqqxlJoVarFTabrV/7BBJKowVHmtRwUEC70QqN2eZRveIPNpsNe/bsQUdHB1QqVa+RFgDQ6/WMKYfuth2A88Iok8kgl8vpn64HAJxq1UDt1kTxkuQYKIRjBnwuQ8XJVg0adCb6eVZkKOKlgRmNcCVeay3Ovy+bBcyKD4O4j4iSa2quu8B3vRe85dO4c+jQIVRXV3tN6wh0/I61sVgsZGRkICMjA3fccQcA4MyZM9i6dSuuvvrqQRtgb9hsNpw9e7ZXX5wrr7wS7733HoxGIx1Gq6qqglqtHvD8J4FAGFxyc3ORm5vb6zYFBQU4evRov/YJJEw2Ow41qNAZ2IAshAtZL52UbTYbQ6S4clrUajWuvvpqREVF0dtyOBwUFhb6XIWjVqsZokYgEGD+/PkIDQ2lhYtUKu2xIrVWY0SluisSnhUVOmJ+NL5QrtShQtU13vFhYoxXeE6DBQolbTq0MNyN5Qj34e8bFRUFoVCI9vZ2xnK1Wg0AHqXk3dm/fz+EQqHH9FUwMKgTiBMnTkRsbCy+/PJL3HrrrX4fxxUic1eZhw4dwsaNG7F06VJceumlAJxGQUuWLMGkSZPo7datW4etW7fis88+g0KhwE033YSNGzfiu+++o8f05ZdfYsqUKUOa1EwgEAjdcXROfRg7GyXy2CzM7Nan5+DBg2hoaKAFjE6n6+lwaG9vZ4gaV16G+8WMxWJ5RFhcj+4VPCwWC3PnzvXpXLQWG441ddDP46UCJAdwE8U6rRGnWrX08ziJAJMjA7dMuVlvZhgYpodLkBjqW0SJz+dj9uzZqKioYCw/c+YMMjIy6FzYnjhz5kxQChrAT1Hz3Xffgc/nIzc312P+U6FQQKvV9rBn3xw8eBCbNm0CAOzYsQMpKSm49NJLERoaivr6ejz22GMoKChAUlISZs6ciYKCAsb+KpUKbW1ttOGSRCLBF198gXfeeQdKpRImkwlarRaffPJJr3O7BAKBMFhQFAW1Wo1jDUqouSJ6WbhRBQk/hrFt9+7YveG683Zn7ty5oCgKcrkcYWFhvUZa/MXmoHCoXgVbZx6TmMdBTrQsYHsOKY0WHGlU088VAl5A90gyWu047DbeKBEfGf00MFy2bBlWrVqFZ599FlKpFEqlEjt37sRrr71Gb9NTflpbW1u/+nwFEn6Jmt27d+PXX3+F3W5HYmIicnNzMWXKFMTFxeH8+fMD6quUl5eHvLw8j+UZGRn4/vvv+9x/1apVWLVqFWNZXFwc3nvvPb/HRCAQCP3BarWioaEBtbW1dO8cjiIKSfldOT4tp49CGhoCZDENRLtXwHSPtLj/7s0rJTs7e0jOyZ0TzR3QuOV5zIwLA6+XxNWRxOpw4HCjmp7uk/A4yEtQgMMOTEHjoCgUNarpxGABh2lg6CsFBQVYsWIFVq9ejfT0dJSWluKFF17wyDNzz09zkZqa6rEsWGBR3lqy+oDJZEJxcTGdKHzy5EmYzWY6fHnVVVchPz+/zzBXIOOtjLunducEAoEAAL/99hv27t3LmD4PCZUjZeH14HTe/Wrqq1H9+09eDehcOX8u4RIaGjrokZaBUNVhQLHbtNPUaBnGBfC009EmNao7nJ44XDYLlyVF9JloO5KcbtWgzK0H1dxEBSJFgdklfCRwXZd7uhb7/Z8VCASYPXs2Zs+eDcCZB3Py5EkcPnwYR44cwZ///GeYTCbMmjUL69ev9/dlCAQCIaCw2WxobGxEXV0dbDabRw6KVCplCBo2l4ekOZfTgoZts2CyQohL77rLq4FcX0mcI0mH2YoTzV2CZkyoEGNlgVk5BDgNAV2CBgCmRMsCWtA06UwMQZMZISGCpp8M2n/XlWPjqkKw2+04ffo0ampqBuslhh13JUjM9wiEixOtVova2lp6KqmxsZGuLhIKhZgzZw5jaiAxMRGAs+FhfEICBOOzYOA67fs5LBYuHR8HWUjS8J/IALE6HDjUoEJnWydI+VxM6cHRNhAw2ewodhNgCVIBEqXe2ygEAt4agU5QDKwR6MXIkElWDoeD7OzsYZnfJRAIhMHCYrHg2LFjtIhxubN6w2g0or29nTHNHh4ejpUrV0IoFOJcuw5n2roKJ3JiZJCFBF8CJkVRONbUAZ3FKeY4LBZmxsnBDaBpMXcoisLRpo6uvBQuG1MCOJHZWRXHbAQayInMgUyfoubxxx+HTCbDNddcgxkzZgzHmAgEwkWEg6JgsTsQwmEP+5e4Xq8Hn89nVHqw2Wz8/PPPvXq9KBQKJCYmIiEhwaP0lcViQSgUollvZgia8WFin0tyA40LagPqtF2GdVOjQxEawOLsgtqAZr2Zfp4bI+/VgXekOdumRbuxy+F4RmwYQrikOtcf+hQ1f//731FVVYUffvgBf//73zFlyhRce+21Qek0SCAQAgubg8K+2nYoTVakh0uQGTF0viEOhwPNzc10NVJtbS1UKhWWLVuG9PR0ejsul4vY2FjU1dXRz+Pj45GQkEALGW99i9zRW2043NDVXydCyMekAPZE6Q2VyYqTrRr6+ViZCGNkgZsYrLXYcMptvOPDxIgSB25eSvc8mokRUkQEaMuGYMCn6aexY8fiySefBAAUFxfjiy++QHV1NS655BIsWbLEa1khgUAYGioqKvDaa6/h4Ycf7tNJ12q14vPPP0dTUxMaGhrQ0tKCO+64Y9hcv/uiTKmje/C0Gy19bN0/DAYDLV7q6upQX18Pq9XqsV1dXR1D1ADA9OnTMXnyZCQkJCA6Orpfnlb2Tg8Xi6NrKmFGnDygGlf6itnm8HA/zo7q3WJ/JHGZG7ryfkL5XEwcQqE8UAxWO8M/J1ocgrQAdjgOBvqdU5OTk4OcnBxYrVbs3bsXr732GkwmExYtWoTLL788aF0ICYRA59dff8XOnTshkUiwb9++XtuDuFizZg2uueYapKSk0Md4+OGHoVKpcOeddw71kHtFZ7GhzM0xNWEQ++/8/PPPPjVm5HA4tIO5O1lZWX69LkVRONbcweiFNDMuDIIgnEpw+qWoYOjsZM3tdD8OVH8XACht10HdKZLZLCA3Vh6w43X1dXIXv7kxJI9moPidKMzj8bBgwQIsWLAAWq0WO3bswJNPPgm5XI6rr74ac+bMCShvBQIh2Jk/fz7mz5+Puro6fP75531ub7FYsHHjRqhUKvzlL3+hjzFp0iSsWbMGt99++4h9RimKwokWDR0BCBPw+lUabDKZ6CgMi8WiW6e48FYqDTgb+bmmkBISEhAbGzuozuIXOgyo0XSVEGdFhfrUqycQOdWiQatb36HcWDkkAVwO3W60oLS9SyRnRkgHpUnoUHGmVUtHKVlw5dGQa+ZAGZR3qFQqxdKlS7F06VI0NjZiy5Yt+Oc//4kJEybgmmuuweTJkwfjZYYdUsZNCGZsNhvCwsI8egeNGTMGp0+fhlKpHDFzzEadmZHI2VtpMEVRaG9vZ5RVt7a20uvFYjEKCgoY+yckJIDNZiM2NpbOhUlMTOyzO/FAUBotONHclcsxJlQY0L2QeqNKbUCFW6PKzAgJ4iSBWw5tdTgY0zgRQj5SwwJ3GqdRZ0K5yt2PhuTRDBaDLrtjY2Px0EMP4aGHHsLZs2dpgfO3v/1tsF+KQCD0gkgkwq+//uqxvLq6mrbaHwlsDgonW7ou/uNkIoQJmF/oJpMJRUVFtIgxmUzdD0Oj1+uhVqsRFhZGL4uKisL//d//gcsdnsiCyWZHYYMKLnt2WQgXUwO4hLg32o0WHGtmNqoMdL+Uky0a6N2myXJjA/dvb7DacZTk0QwZfn3i//e//2HChAlITk7udbvMzExkZmb2uk0gQ8z3CKON0tJSnD17dlgv+N0pU+pgsDkvQHw2C7EcK6xWK6OsmsPhYM+ePeipiwuLxUJMTAwdhREKhR7rh+v8nLkRapjcOm/PCvDck54wWO0orGeKs2kxgSsQAO+uwSJeYE6Tdc+jEZI8mkHHr//8l19+icOHD2PevHn44x//iISEhMEeF4FAGGQcDgdeffVVXH755SOWJNxhNONcuxbOLAKgrvggjpaexK233orU1FR6Ox6Ph5iYGDQ2NgJwRp3cS6rj4uLA5wdGuP50qxZtbpVbM+LCAtqKvyfsDgqFDSqYOw3r+Bw2ZsWHBazBHhB8rsEkj2bo8euTt3HjRpSXl2Pz5s248cYb8Ze//AWLFi0a7LERCIRB5O2330ZiYiL+8pe/DOudoclkQnl5Oc6dOwdD5BhIYpxtBAztLWguPQkAqK2tZYgaAMjPz4fNZkNiYiLCwsIC8m62TmPEeRWzV090AHui9ARFUShuUtOVQ86qLTnEARrxAILPNbihWx7NxEgpwkkezaDj9zs2NTUVL774Iu644w6sXLkSFoslYLwvCAQCkw0bNkAkEmHVqlUAgPr6ekRGRg5ptKOkpASHDx9GdXU1HA4HpHFJGDvZKWgoikLDkX0AnM1xvU0zTZw4ccjGNhh0mK046tatOlYSEvC5Jz1RrtKj1s0xOCsqNOAbKQaTa7DBamPk0cSIQwI6kTmYGbAMHzt2LL744gs8++yzSEpK8tvfgUAgDA3btm0Di8XC448/Ti/bvHkznnjiiSF9XZVKhQsXLgAAWBwO4nLy6HXsjlYsnDsbCQkJiIiICNi7656w2B04VK+CvVOMSXicoM2NaNKbcLq1q53DWJko4Ku2gsk12EFRONSghtUtj2Ya6es0ZPgla7vfVfH5fLzzzjv44osvBmVQBAKhZ1yfP4fD4bFu7969mDVrFvbv3w8A2LdvHz777DOYzWasW7cO69atw8cff4ySkhJGYq4/2Gw2nD9/Hj/++CM2btzosd49uT55xlzwJc5yah6bhStzJ2Pq1KmIjIwMui93l2utzurW3DE+DLwAjRL0htZiw+EGNf08XMgL6M7bQPC5Bp9u1ULlnkcTF4aQIHyvBAt+RWpmzZqFtLQ0zJw5E7NmzUJ2djZCQkJwxx134Mcff8RVV1012OMkEC56jhw5gi+++AJnz54FADz//PPIzs7G1VdfjYULF9LbWa1W2Gw2qFQqPP744zAYDDh58iTjWJdddplfYzAajXR+zPnz5xluvO3t7QgPD6efh4eH4/rrr0dUwhgUthlpo71JkaFB+6VOURSON3cwpj2mBWnnbavdgcJ6JSOCMDMuLODbOQSTa3CD1sTIuZoUKQ1aM8ZgwS9RI5VKkZKSgn379uGjjz4Cl8vFlClTMGPGDCiVylEjakgZNyGQyM3N7bPXU0FBAY4ePUo/P3bs2IBfV6VS4dy5czh37hyqq6t7LLMuKytDXl4eY1n6xEnYW9Put3NwoHFOqUeVW/nwBIUYCUHYeZvqjHZoLc5oE5sFzIpXBHw7h2ByDdZbbTjapKafx4hDMJ7k0Qw5fomaGTNm4M9//jMAZ9O4w4cP49ChQ/jll19QXV2N1atXD+YYCQTCCLF582aGX1N3wsLCMGHCBEyYMAFjxoxhrHN5cmgtzj5IbBYCujqlL2o6DDjb1pV7khgqHNKu4kPJmTYtmhjRJjnCAlQcuAgm12CXd1FXFIyDXJJHMyz4JGooimL8M1577TX6d5FIhIKCAhQUFAAAzGazx/7BCjHfI1ws2Gw26PV6yGQyxnJvPZTi4+NpIdNTToxrmqbFrXdQThBcOHuiRW9mVDpFivgBb0rXE3UaI8qUXVMiaQoxEoMg2uTuGsxjswJaJHTPo5kZF7iVWaMNn0TN008/jVmzZuGWW25hLNdqtWCz2RCLu9RySEhgZqATCAQmRqMRZWVlKCsrw/nz5xEXF4e77rqLsc2ECRNQVFSE5ORkTJgwAWlpaZBK+45OlKuY0zQZ4RKMCYILpzc6zFZGC4RQPjcock+8oTZZGVMi0eKQgE6yddHdNTg7WgYRLzCnyuq95NEovOTR7N27F9u2bUNKSgrKy8sxd+5cXHfddb0e2263Y9OmTTh79iyioqLAZrNxww03IDExcbBPI2jxSdScPHkSDzzwAOP5q6++SicfxsTEYMmSJbjjjjsQFRU1NCMlEAgDRqlU0vkxNTU1jPyY6upqGI1GRsuBxMRErFy5sl9+NvVaZolwYqgQ6eHB6d9itNpxoE4JW+c0goDLxuwERVDedZtsdhysV9JVQxI+BzMCONrhojfXYIfDAYfDAbvdznhIpVJG93W73Y7q6mrY7Xav23d/zJ07l9HBXqPR4Ndff+1zX/AFCJ8xD2yuMyIZK/GeR1NcXIyVK1fip59+glwuh16vxxVXXAGhUNirke2LL74IiUSC1157DSwWC8uWLUNFRQU++OCDAf+dRws+iZqUlBTaCOuHH37Aiy++iKSkJLz66quYNGkSTCYTfvvtN9xxxx147733At40i0C4WKAoCvX19bSQce9u3R25XA61Ws0QNWw2u1+CRmm04Eijin4eLuQjJ0jzaKx2Bw7UK2Hs7OnEZbMwO14RsBGC3nB5pbifS168YlDL0B0OB2w2G6xWK/h8vodlwPnz52E0GmG1WhkP1z7uv1922WWIjIz0cA22GfXYs+1L/Gwywm6395i0/vjjjzOmTm02Gz7//HOfzyUvL4/xvrdYLDhx4kSv+7DYbCRfdg0taERcDqb14F20du1aLFiwgG4qKxaLsWTJEqxfv75HUfPTTz9h3759+PXXX+lj5ufnY/z48T6f18WAT6ImLS0NpaWl+Oqrr/Dbb7/hqquuwv3334+UlBR6m6ysLNxzzz1444038PLLLw/ZgAkEgu84HA78+9//7rHLdUJCAp0fM1ATPIPVjoP1KjoSIOZxMCs+OBs7OkWACh1mZ5KzMy8iLGArbfriRLMG7W79qZJ5NrTV16KxB3Exbtw4jymNn3/+GQ0NDT2KErvdTm97ww03YPLkyYz9//e//0GpVPo03pkzZyIyMtLDNbimcDeMOm0vezpxHwsARtTFF7rv7x716YmY7JkQhTtnKpx+NN7zaCwWCwoLC7FixQrG8rS0NGzYsAEajQahoaEe+/3zn/9EQUEBo1HrUBtoBiM+iZrHHnsMBw4cwOLFi7F69WqYzWY8++yzeOuttxjz62KxGJGRkUM2WAKB4B2DwYCysjIoFApGFRKHw0FqaipOnToFAOByuYz8GIlkcKaFrHYHDtQpu5ohslmYnaAISj8aiqJwrKl7krMs4Ho6URQFg8EAnU4HrVbL+JmUlITMzEwAQKVajwsdBnq/xuOHcKq096gDm832EDVNTU2oqqryaWw2m81jWX/MHq1Wq4drsLqiBPrmBo9tORyOx8PbNmPHjvW6LZvN7vMYYrEY11xzjdftOBwOOigOzpu79pkcFeo1jwZw9jmz2Wwenz2JRAK73Y6qqioPZ36VSoVTp04hPz8fmzZtglqtxoULFzBjxgwsXbq07z/oRYRPokYoFDLMukQiEZYvX47rrrsOd955J+bPn4+YmBiUlJSgurp6yAY73JCKJ8JI4qAoNOnNCAvhQehlykOpVKK0tBTnzp1DbW0tKIrC5MmTPUqrJ02aBA6HgwkTJiA5OXnQ+z05KApFjWpoLF1RjVnxYZAGYadqwGnuVq1hJjknyYavbYDD4YDFYoFAwOw2ffr0aZw6dQo6nY5+eHOVdh0jMzMTbQYzTjS7CYPq82jrQ9AATlHRHV9ECY/HA4/H8xrxS0lJQWRkJLhcLng8Hv2z+4PL5SImNtbDNfiyudPBLZjpIUh8gc1meyTB9wc+n4+pU6d6Xae32HCiug3oTCWPlYQgpZc2Ex0dzvwgkYi5javgRqVSeexTV1cHiqKwe/dufPrppwgPD4fdbsdVV10Fi8WC2267zZ/TGpX4/a1zySWX4LXXXsNLL72EN954A4Cz1PPjjz8etMERCBcrVocD+2qVUJmsCOGwsTg5CmyW88vNlR/T1tbmsV95eTnsdjvjTjMtLQ1paWlDMk6KonCiRcOYIsiJkSEiwJsh9kRVhwElbuZuSYOY5Gyz2aDVaj2iKt0jLQaDAampqbj11lsZ+6tUKpSVlfn0WjqdDgarDYca1HTVFsdqgr7sJKKioryKCvfnSUlJHscsKCjAzJkzvW7P4/HA4XB6nb50d73ui7NtWg/XYFkATv3ZHc5cJVciuYjXcx6NC9f0UXdB5nLn7j71BXS1Rhk3bhzt2s3hcDB79mx88MEHuPnmmwfc9mS0MKBbqZkzZ2L79u2orKyE2WzG+PHjR9UflvjUEEYCu4NCYb2K9rmw2h3Ytv1HlJeWQq/X97hfYmIiJkyY4CFqhpLzKj0uqLumNiYMc1RjMGnWm3HMzYsmSsTH1D68aCiKgtls9hAmFEUhPz+fsW1JSQm+/fZbn8ai1XrmjXgrpRcIBJBIJJBIJJBKpfTvkVHROFivoqcDQzhszEseA9GkR316fW/Exsb6vW9/CCbX4FOtGqjNbn2dYvv2o3ElMHePsrk+297yaVwJxd2jsKGhoVCr1aiuriYJw50MSnw4OTl5MA5DIFz0uBJUW93yOdLDhPhPcbFHpQeXy0VKSgomTJiA1NTUQcuP8ZUGrQmn3Eq3E6QCZAZp6bbaZMUhNy8aWYinF41Go8G+ffs8oive8kcEAoGHqPHF38eFt2mlsWPHYunSpQzx4u0m0tUCoUPrTA53JTkHQ9VWMLkG12mNqHQT9L3l0bgTFRUFoVCI9vZ2xnK1Wg3A+X/uTlxcHEJDQz2mBV3fCd6mCy9WgnPSexDQ6XTQ6/WIjo4e6aEQCACcX1BHGtUM+/qJEVJn9CMpCVVVVRCJREhLS0N6ejqSk5NHLDKqMllx2O3ioxDw+gy7BxIURUGj0aC1tRXN7So0S6IArvOCZDPqUbT1B8x5/DGA0zWNZrfbcfjwYZ+ObzKZYLPZGJUqUqkUoaGhHlEV1++un2Kx2GukTS6X03fsvVGm1KNO21XtNiVahghRcDRRDBbXYJ3FhmK3qF6cRNBrHo07fD4fs2fPRkVFBWP5mTNnkJGRgYiICI99uFwu8vPz0djYyFje1tYGuVzOqES+2AlIUdPW1ob169cjMjISd999N73carXik08+wffff4/W1laMGzcOjz/+OObNm9fnMZ977jls3bqVfi6TybBnz54hGD2B0H8oisKx5g7GxShNIcaEzsjHvHnzwGKxEB8f3+/y1MHGWbqthL3zLlHM4yAvCEq3bTYbtmzZgtbWVrS3t8NqtYLN4yPlsmsg6BQ0dosZlXu2w6J3RmPcHdJ7ioTxeDyvQqV7tCU8PBxPP/300J0ggEadCWfc+lMly0UY5+PFdqQJFtdgZx6NipFHk9PPlhnLli3DqlWr8Oyzz0IqlUKpVGLnzp2MFkR79+7FqlWr8O677yI/Px/3338/HnjgASiVSigUCnR0dGDv3r148sknBz35P5gJOFHjso7esmULHnvsMca6t99+G2w2G8899xzq6+vxz3/+E4888gg2btyI6dOn93jMuro6dHR04PXXX6eXJSUleWSfEwgjAUVRONWqZbQVSJaLGPb13efSRwqrw4GD9UqYOk3ceJ2GdCEj3N3ZaDSitbUVbW1t9MNVlemCy+XSBnCA0ywtac5CCOSdOQ52O6r3/Qxzh7P6RKfT0UmZgFO8LF68GCKRiBFd4fP5ARFN0JiZ0bMIIR9ZUZ75GYFIb67BgcapVg3tX8RmOaf2+uswXVBQgBUrVmD16tVIT09HaWkpXnjhBY9kapcPEOCsYnzjjTewevVqxMXFoaWlBc8++yyuv/76wTmxUULAiZqCggKMGzcOW7ZsYSxvbm6GQCDAM888Qy+bNm0ali5dim+++aZXUbN+/Xo88cQTHmZQBEIgUNquY/SKGRMqRHZUaEBcKN1xUBQON6g9DOmkIcP3NdLR0YGWlhaGeGlra4PBYPDYNiMjgyFqACAiIgK1tbUAgKS8+ZBEx9Pr4lkmzFyyuNd8lZkzZw7yGQ0OFrsDhfVu0QMuBzPj5EHRn6q7a7CAyw7Ybu51mm55NJGhfjdpvfHGG3HjjTf2uL6goABHjx71WOZqHk3wzqB/Gx05cgS5ubkDOoa38Hp7ezvuv/9+xrKsrCzI5XK67t8bSqUSX3/9Nb766itkZWVh8eLFuOWWW0i4jhAQnFfqGSXEcZKQfoeyh4tTLRpGvs/UGBmihsCQzmq1or29HTwejxEpAYAff/wR5eXlPh3HW8n7ZZddBhaLBSVPgkpN17lkRkiRHj481T2DDUVRKGpQQ9eZi8JhsTArPmzEo2e+0t01ODcmMDta6yw2RjQpXiJAcpBM7V1M+CVqjEYjvv32W5SXl8Ns7nozUhSFw4cPY9euXYM2QBcud8zumM1m5OTk9LifTqfDo48+ivLycuzevRtHjx7Fd999hw0bNkAmk3ndh5RvE4aDqg4DTro5pkaJ+JgeG5jdnytUelS43aGmKcQYO8DSbYPBgLa2No9pI1cVyLRp07BkyRLGPhEREV5FDZfLRUREBCIiIhAeHo6IiAjEx8d7bJeUlIQLagMq3S5OY2UiTFAEZoWNL5xu1aLF0PU9PC1WFrAl0N3p7ho8Pkw8JEJ5oHTPoxH7kUdDGB78EjVPPfUUfvvtN4SHhzMiHlar1evd0VBx/PhxCIXCXm2ix4wZgwcffBCA0wfgrbfewubNm/HSSy/hvffeG66hEggM6jRGRvVEuJAXsH2SGnUmnGjpuvDESwWMfJ/+UFZWhv3796O1tZXObekJb98lcXFxSEpKooVLZGQkIiIiIJP5doFp0plw3E3QRItDMCU68Kb6fKWmw4Byt6nLCeESJEiFvewRODg6S8/dXYP9fV8NNSdbmHk0M+LCBrUZKGHw8EvUFBUVYdOmTV5tozdt2jTgQfnKRx99hJdfftmnMkfAaUP90ksvwWAw4Mcff8Sbb77pdd7c3XTPBYneEAaLJp2JkdApC+FidrwC3BGuavKG2mRFUYOafh4m4CHXS+m2a8rIPeJCUZTHDYfNZkNNTU2vr8lisRAWFub1cz1p0iRMmjTJr3NRmawMh115CC9o8k68oTJZGNMhsZKQoPIJKmnXebgGB6Kor9UYGb2zBpJHQxh6/BI1U6dO7dF2/YYbbhjQgHzl3//+N/Ly8rBgwYJ+77ts2TJs2bIFKpUKUVFRQzA6AsE7bQYzCt1M3qR8DuYkKALyrs9os+OAW+m2iMfB9OhQ1NXW0NNGLiHjmjJyh8vlwuFwMHLk3D04eDweY8rIFXVRKBQMf5fBQG+14UCd27lwOZidEBaQQtIXjDZnR/TO2RBI+VyvYjNQaTdacC4IXIO1FhvDZTpeSvJoAh2/vjlWr16Nr776Cvfee6/Huk2bNnldPpj88ssv0Ol0eOihh/zaXyaTgcvl+hzhIRAGA53FxrgQiXgczEkID8iETp3BiN9q22GCc2xcNguz48PgMBuxYcMGn45hs9nQ0dGBsLAwell4eDhuv/12REREIDR0eKZ9LN06iPPYLMxOCIMgAP/uvmB3UDhUr2KU1efFB890SLC4BtsdFIoaVLC5+THlBGhVFqELv0TNI488grq6Orz99tte1w+lqNm3bx9KS0s9PGxKS0uRnp7u0zFOnDiBuXPnkgoowrBhsTtwoF4Ja6eiCeGwMSdB4bX79kjgcDjQ0NCAiooKVFZWgpWUjtD4sQC6SrdDQ3ig+Fzw+Xy6+Z4L15SRK/LienRvDcDhcIbV/dTVR0trcVYGsVlAXrwCoSGBFxXwBYqicLylA0pTly3+jLgwSIKoI3qwuAafaPH0owkW4Xgx49cnIS8vD3a73WPqxmq1Yvfu3QMelOsLs7sj55EjR7B27Vrcdddd2LFjB4BOJ9ZjxzBnzhykp6dj3bp12Lp1Kz777DMoFAqsXr0aEokEf/jDHyCXy1FXV4evv/4a77zzzoDHSSD4goNy3vHpGBfWkb8QqdVqWsRUVlbCZHK6GcdOmYWITkEDOG32ozsrUlgsFtLT00FRFEO8DMWU0UBx+p+o0WbsEmC5MfKgaRngjUq1geG6OzlSSv9vgoFgcQ2u1RhR5ZZHkxUVGpDTYwRP/PoWuvHGG5GZmenVT2bRokUDGtDBgwfpZOMdO3YgJSUFl156KWpra/Hggw9Cr9ejuLiYsU98fDyef/55AIBKpUJbWxtdai4Wi/Hdd99h+/btWLx4MRQKBT788EO6UyqBMNScatGgxa1B5bQYuU+N74aKffv24fjx4x4N9QBAMT4DEelZ9PMIWDxs9oPFwfR0q5bRdmJSpBQJocFRGeSNFr0ZJ92q0MaECjE+AKdteiJYXIO759EkSAUYF6Sd5y9GWFT31r/9oKioCKdOnQKXy8XMmTN9nv4JRlzVT94qowiEnrigNuCY2xd5ergEmcNUtkpRFAwGA8Ri5oVvx44dOHTokMf2UclpiJ5eAHROBcRJBJgZF5hTA31RodIzytCT5aKAdGn2Fb3Fht3VbbB0Tl+GCXi4JDE8IKuFvEFRFA7Uq2iTPSGXjcvGRgacyZ7dQWF3dRs0Fue0k4THwbykCDLtFED0dS32K1JjsVjwxBNPYO/evXTrcxaLhRtuuAGvvPJK0H5xdIeUcRMGQqvBzPBEiZMIkDHEJbcajQYVFRX0tFJMTAzuvPNOxjYpKSk4dOgQuFwuxo4di+TkZMSMTcYpHUWbi8kFvIDNdeiLhm6+OrGSkKAWNDaHAwfrVbSgEXDYAetp1BPdXYOnBahr8ImWDlrQED+a4MQvUbN27VqcPn0aK1euxOTJk6FQKNDU1IRvvvkGn3zyCW12RyBcrOgsNhyq7yrdloVwkRs7+JUTFosF1dXVtIhpbW1lrK+pqYHFYmEkxY8dOxZ33HEHxowZAy6XC6PNjj3VbbSgEXLZyIsPAzeILpoulEYLDjeo6OdhAh6mx4YFraChKApHGpkX2lnxYRAGUeVWsLgG12iMjKay2VHB48xM6MIvUXPo0CFs2bKFkZeSnJyM2bNn45VXXhm0wY007uEtErUh+IrVzryzDuGwkTeI5noWiwVFRUWorKxETU0N7HZ7j9sKBAIolUpGY0cej4fk5GQAXVEAY2d5MJfNwuwERVBdNF24SuZdDrViHidoxZmL0nYdGnRdeUFTo2Ujmo/VX4LFNVhr9syjGSsL3vyrixm/RE1ubm6PibYiEUmoIly8UBSFokY1tG531nnxYQOq8KAoihFp4HK52LdvH6PvmgsOh4OkpCSkpKQgJSUFUVFRPUYpnFEANe3qygIwI04OWRCWO5ttTC8aPoeF/ARF0HrRAM5KIfdmp+PDxEgKsoTVYHANtjsoFDWqaGNGCY+DqaSvU9Dil6jp6c6wtbXVozKJQLiYONWq9cgd6O+dtdVqRU1NDZ0bM3fuXEZrADabjXHjxqG0tBQAEBUVheTkZKSkpCApKclr6w9vnG7VokHXNdbsqFDEiAOvGqUv7A4KB+uVdJdqlxfNSJfMD4QOs5VhUBcp4mNSZOBFOHojWFyDS9q1nn40Qeo0TfBT1IhEIrzyyiu49tprIRKJ0NjYiKKiInz55Ze46667BnuMBEJQUKU24Lx7c0GFGIk+lBBTFIWWlhZaxFRXVzNuHCoqKjz6HU2fPh0TJkxAcnIyQkND+z3WC2pmI8TxYWIkB1F5sAuqc3rD3Yxueqwc4UE0RdMdc+f0pbuT7Yy4wOze3hPB4hrcZrCgTNn1OZgcGQrZAIWX1WoFm80Gh8OMEnbPbSMMDX6Jmj/84Q94/PHHsXTpUjpER1EUrrrqKjzyyCODOkACIRhoM5gZpduxkpBeS7cNBgPOnz+PyspKVFRUQKfT9bhtVVWVxzJXTow/NOuZVVmxkhBMDrIogItTrVpGzklWVCjig6RLtTdcRo0GV9QJFMbz7dAo28HhcMDlchkPb15hgUAwuAZbbHaG8IoS8b32ddq7dy+2bduGlJQUlJeXY+7cubjuuut6PO6xY8fw5JNPIi8vD5GRkVCr1SgpKcFbb701qm1PAgW/RA2Px8NHH32Ew4cP4+TJk2Cz2Zg2bRqysrL63plAGGXoLTYUunV/loVwMb2PL/HKykp89913Pa6PiIig82KSkpIGbawasxWH3Bpqyn0Ya6ByXqlnRMbGh4kDxozOZrNBr9fDYDBAr9fTD9dz9+X33XcfJBJnqf+pVg1a3YwaL/y+Eyfqq3t8HRaLhWeeeYbeHwB0Oh3+/e9/ewgglyjqLo4uvfRSxv/faDTiwoULXvf19ugekejJNZiiKNhsNthsNtjtdthsNnA4HI9WGu3t7WhtbaW36e1nXFwcJk+ezNi/sLAQ586d63Pf6JzZUCQ7RQaPzcI0Lw1Bi4uLsXLlSvz000+Qy+XQ6/W44oorIBQKezSapSgKHA4He/bsAY/HQ35+Pj744AOMHTu2x/8jYfAY0KTz9OnTMX36dMayioqKYe3tQiCMJHSlU2eCqqvSicNiobW1lZ5OWrp0KeOuunukRSgUIjk5mc6Nkclkgz5Wk82OA3UqZul2wuBVZQ0n9VojTrqVCcdJBEMabbLb7R7CxPX71KlTPQon3nzzTdhsNp+OrdfrIZFIUNVhQIWqy5q/+dQRaHoRNIDzAtq9PYXFYkFTU5NPr83hcDBv3jzGsra2Nvz3v//1aX+ZTIannnqKfm6y2XG4QQln2jmgrbuAL77ZA7vd7tH2BnB6Jt1+++2MZWfOnPG53U52draHqFEqlV6jm+5I45NoQQM4W4F468O2du1aLFiwgG5+LBaLsWTJEqxfv75X9/xnnnkGN9xwg0/nQBhcfBI1jY2NkEgkkEqloCgKjY2NXrezWq3YsGEDXn755UEd5EhByrgJveHK53B5iLAARJlV+Hl7ISoqKqDVault6+rqMGbMGPq5SCRCTk4OZDIZUlJSEBsbO6RTCc5kWhUMNueUAJfFQl58cJZutxstOOw2baAQ8PodbbLb7bQ4kUgkjEgHAPzvf/9DQ0MDvY23SjMXCQkJHqJGLBajo6Ojhz2Y6PV6KI0WxpSgXdkCkV6JhIQEOrrRPcphtTrziLpHSnor8e+Ot35dvoqx7vs7e211wN4paCwGHWoO7YXDau1pd6+v1f18esPb/n31IOOECJAw/RL6eYJU4DX3zWKxoLCwECtWrGAsT0tLw4YNG6DRaPzKZyMMLT6Jmuuvvx75+fl49913wWKxcOedd6K+vr7H7UeLqCEQeuNUiwZNbpVONYW7cbKq3Ou2FRUVDFEDAFdfffWQjs+Fq3Rb5Z5MGycPyEqUvtBabDhYr0RnsAkSHgd5CQqPMuHq6mo0NTX1OOXjat4JOPvVzZo1i7F/c3Mz6urqfBqTXq/3WCaVSmG32yEWiyESiRg/XQ/Xc75Ygt/qVPQ5hfK5uHTmZHDzsnt9XYqi4HA4PMSwXC7Hgw8+6CGG3AWR6+FNCAqFQqSnp3ts6+0Y7omv3V2D6w7tgcPK7ObefQqsewsPAAgPD0daWhq9Xfd93H9GRkZ67D9t2jSkpqZ63Z7D4eCEyoQWo1MMCbhsTIn2HhWtra2FzWbzELwSiQR2ux1VVVU9plyUlJTgr3/9K0QiEcrKyjB//nxcddVVXrclDC4+iZqHH36YMR941VVXwWq1IjU1lbGdxWLB9u3bB3WAIwkx3yP0RHWHAefVXVMFrSXHoe4maEJCQujppPHjxw/3EGnOtGlR75ZMmx0VilhJcJRuu/pXKZVKtChVqOXK4OA6L6QOqxkV+3aDOy4J8+fPZ+x3/PhxHD9+3KfXMBgMHsu6X2xZLBZDlLgLldjYWI/97733Xp8iR3YHhd9q27v8ddisTsPAvqN2LBbLa1SDx+N5HZOvxMTEYNmyZf3ap8NkZUwHJgg5WHDrMoaoYLPZPv1N0tPTB5RQGx4ejvDwcK/rqjoMtKABem/X4Iq0dfdec703VCqVxz6AU7hRFIWnn36a3m7hwoXg8/lYuHBh/06G0G98EjXdy7Svv/56hIaGejXg6y50CITRRrvBwqh00tRXo+nkYbBYLCQkJNBCJj4+fsSrU6o6DIyS1RS5CCkBkkzbG8ePH0dRURGUSiXMZjNYHC6S5y+BKLxT0NhsqNy9HUZlK6LDPO+0+zIBdYkUkUgEgcBT4M2ZMwczZsygxYtQKOzX9JYv21IUhWPNHXQEzWl+GAZxkPnr2BwOFDUyI03TEiICzmRPb7Uxupwny0WI7qVdg2saq/tn2GJxRp96mubLzc1Fbm4u/TwsLAyzZ8/G+++/T0TNMODXp+f8+fNYsGCBx/Ljx4/DaDR62YNAGB0YrDYUNnR9gVu1HQg3KjFz6VKMGzfO6wVypGjRmxnW7zHiEGRFjUwOgMPhgEajgVKppB8qlQpKpRJyuRzLly9nbG+xWLpy91gsjMmbD1F4FACAcjhQc2AXjEpnnytv0z9jxoyBxWLxOu0jFoshEAh6FZwDiXT4ynmVHjWaru/LrKjQgOyJ1BfHmzXQWpwXeA6LhZlxgddsk6IoHG3soJPkJTxOn2aGrpv27gnOrvdbf/JpRCIRzp8/D7VaTScdE4YGv0TN4cOHvYqazMxM3Hjjjdi6deuAB0YgBBquPklmt0qnRdnjIeYFnveE1mxjlG7LQriYHjd8pdtKpRKHDh2ihYtKpfJa/QLAaxKuexQ4IXcOQhPG0s/DzB2YkJcLsbgAYrHYI+cBcE4XB/KUcbPejFOtXYnkSTKhV4+UQKe6w8AQZlOiQyENCbxIU7lKjzajM8LCgrNdQ19TfFFRURAKhWhvb2csV6vVAOC1RFun0+Haa6/FokWLsHLlSsZyoO8kZsLA8fkvbDab8fLLL6OmpgY1NTUoKSlhrKcoCrW1tb1WCRAIwYqr0sllp86C005dzAu8LymzzY799UpYO+9KBVxnmflgWL9brVZaqLgeGo0Gy5cvZwgms9mMoqIin46p0Whgs9kYX/gJCQm455570MGToFzT9Z2SphBjUuTQR1GGEq3FhiK3TuIKAQ9TooKv15DWbMPx5q7pnDGhwoDsTdVhtuJsW5eAnBAu8al1CZ/Px+zZs1FRUcFYfubMGWRkZCAiIsJjHzabDbPZjISEBMbyqqoqZGdnexXghMHF52/kkJAQPP/883jiiSdAURQoimKsZ7FYyM7Oxq233jrogyQQRpozbVo0uvVJmhojQ4Qo8CzP6dJta9d0QF68ot8NNR0OB0pKSjymitzL1N3R6XQME7WwsDCPbcRiMRQKBRQKBcLCwujfFQqFxx2sQCAAWx6JcrfS7QSpICA7PPcHp68RU3DOig+86Zq+8GgCyedgSnTglTfbHc7KP9d0sVzAQ3q478Ji2bJlWLVqFZ599llIpVIolUrs3LkTr732Gr3N3r17sWrVKrz77rvIz8/H9ddfj7y8PHr9yZMnUVNTgy+++GLQzovQM/26zZRIJPjHP/6Bn3/+GUuWLBmqMQUMgRy+JgwfNd2SbVPDxBgbgHekTp+Qbn2Q4uQI81K6bTQaGdGW7Oxsxlw/i8XC999/77NniVKpZIgagUCARYsW0QUFYWFhCAnxPV+kzWDGkSY1/TxCyPfq+BpMuDq46yzujTfDgrKT+MkWDbMJZKxvFVvDTfdmldNj5P3qoVVQUIAVK1Zg9erVSE9PR2lpKV544QWPhF+r1Up/Vh5//HF89NFHaGtrA5/Ph1KpxObNm5GZmTl4J0boERbVPeQyAJRKJT777DO6lC3Y8SZq3Mu8CaMfpdGC32rb6Tu9GHEI8uLDAvLieqZNy+iKPEEWAqlF5xFtUSqVDJ8WAFi6dKnHl+6HH36I1tZWxjIWiwW5XO4RbRkzZgyEwsHpuaQxW7G3pp2OZkj5XBSMCe+x9DZYONWiYTQRnRErR4IPDU8DjTqNEUVuEbQp0aFIlgdeRV2bwfnZdZEVFRowbTQI/uO6Lvd0LfYrIcBoNGLjxo2oqalhJP+1tLTg9OnTo0bUEJ+aixuD1Y6D9cxS1UDsk6TX61Gts+CcukuojJOLcGTH933axbtQKpUeyyZNmgSDwcCYJpLJZP1yfO0vrlYOLkETwmEjPyEs6AVNdQezK/oEhSQoBY3OYkOxm51BvFSAcQEYtbQ6HIxIX6SIj5QgTMQm9B+/RM1LL72EX375BRKJBFarFSEhIaAoCmq1Grfccstgj5FAGHaclU7KLlM0jtMUjRcAF1eLxYKamhpUVlaisrISFnEYEmZ02b5Hi0OQHRWK2rCwHkUNj8dj5LckJiZ6bHPJJZd42XPosDkcOFCnpFs5cFgszE5QQBSAydj9od1o8dLBPfgSRu0OCkUNarosWszjICc6MBOcT7Vo6LyynppVEkYnfn1bcDgcHDp0CADwn//8h/aY+OWXXxi22QRCMOJqK+Be6TRrBE3RHA4HGhoaaBFTW1tLR0hlSSlInD6X3jZcyMPMOGfeQExMDOLi4jySchUKBcRicUB9yTsoCoca1FAzqsu85wMFEwarHYXdon25ARjt84UzbVqozV1GgdNj5QEh8rvTqDOhykuXcMLFgV/f0q6+GoCzoZhOp4NEIsGll16Km266adjv8AiEweRsmw4NbpVOU6JliBANvylaQ0MDfv/9d1y4cMGrVUJofBISZ86jL5DyEC5mx3d13Z4xYwZmzJgxrGP2B4qicLxZw+gbNCVahpggaeXQEzYHhUJv0b4ATKjtiwadCefdps8mR4b6VBY93JhtdhQ3MafHEqXB/T4i9A+/RE1ZWRn+9re/Yf78+bj88svx5JNP4vbbb8exY8d8nsMnEAKRWo0R55Rdybbjw8QYNwxz8Uaj0SPR1m63o7S01Ov2CemTEJY9C2A5L5ChfC7yE8MD8s65L84p9ajq6Oq/NEEhGZa/+VBCURSKm7pHnoKvBQLgjDYddUsMjpWEICUs8P4/rrYTLhEp4DibVQZjVIzgP359wm6//XY8+OCD4PP5ePjhhzF37lw8/PDDAIZ/Hp5AGCyURguOuiUXRotD+rRS95fueTHNzc147rnnGI0U4+PjERISArPZDKlUiuTkZCQnJ0MeNwbHlEbYO6c0xDwO5iQqEBKEgqamw8AwRhsTKgzKfJPunFPqUKd1ayIaHYrIEYj2DRQHRaGooStxW8hlB2x+So3GyIiw5sTIgvIzQRgYfomazMxM7Nu3j35+9913Izc3F01NTZg7d24vexIIgUn3Sicpn4sZsf3ztOgN97yYCxcuoLa21qMhXmVlJSZPnkw/Z7PZuOGGG6BQKBAeHg4WiwWl0YJ9dUpa0Ai5HMxNDA9Kr5MWvRlH3aYKIkV85MQE/511g9aEs21d0b5kuSggS5594WyblvY9YgGYERuYlWgGqw0n3JpVjpOLgn76kuAffomaG264AdOnT8f//d//0csmTZqESZMmDdrAAgFSxn1xYHM4mLkP7MGrdDp16hTOnj3bY16MCzabTfeUcSctLY3+vcNkxf46JV19EsJhY25i/92CAwG1yYpCt95UoXwuZsWFDZqIHCk6TFYcdpuqiRTxR6yJ6EBp1psZppOZEVKEB6CLtjOxn9mscvIQRVgJgY9fokan02HWrFle1zU1NSEmJmZAgyIQhgtX915G7kN8GCR+5D7Y7XYPD5cLFy70mBcTFRWFcePGITk5GUlJSb067motNuyr67LX53NYmJuo8GucI43BasMBN3Em5LIxO0ERlPlA7phtzmifq3WAmMfBjCAVakabHUfcxFmUiI80RWBGm867NasEfGtWSRi9+PWN+N577+Hs2bNe1+3cuRN33nnngAYVKBDzvdFPSbsO9Tr/ch8sFguqq6vpKSW5XO7h05ScnIxjx44BACMvZty4cYy2Ar2ht9jwe207HUnislnITwhHaEjwlTtb7A7sr1PC1HkuPLbLiyb4ok3uuErSXR473M5oXzDmdLgsDdy70QdqGXqH2YozfjSrJIxe/BI1a9aswenTp/HBBx8w7i7tdjtaWlpGjaghjG7qNEaUurUVSOkj96E3vxgAUKlUcDgcYLvdJSYnJ2Px4sVISUmh82L6g9Fqx746JUw25+twWCzkJyiC0r/F2WxTCa1b76NZ8WGQBaE4c8dVku4eLZgeKw9K0QkApe06tBqY5xKIOVsOqluzyhAuMvrRrJIwOvFL1GRkZKCjowNjx45lLLfb7SgqKhrwoNra2rB+/XpERkbi7rvvZqzbvn07Dh48iDFjxuDUqVNYunRpn8nJbW1t+OCDD5CQkAClUgmj0YiVK1cyKk0IFxcqk4Vhox4l4mOyl9wHi8WC48eP48KFC33mxdhsNrS2tiI6OppeJhKJMHPmTL/GaLbZsa+uHXorswFieBDeiVIUhcONarQbu5pt5sbIg7IiqDuVagOjJH1ihBSxQZqk2mowo8RN6KeHSxAlDsz/0dk2ZrPK3EFM7CcEL36JmiuvvBI33HCDV2v17du3D2hAe/fuxbZt27BlyxY89thjjHX//e9/sWbNGvzvf/+DSCRCU1MTrrrqKnz88cfIzc31ejydToe7774bN998Mx1BeuWVV/Dkk0/in//854DGSghOjN0qnSS95D6wWCzs3LnTo1LJRVRUFD2llJSUNGiO2ha7A/vquqIaLp+TQL3A9AZFUTjRokGD2zTf5EhpUPY+6k6L3oyTblU3iVJBwOae9IXZZmckOUcI+QEb+Wg3WhhJzJMiQoM2MkYYXPx2FPaGUqnEuXPncOWVV/o9oIKCAowbNw5btmxhLNfpdHjrrbewfPlyiERO46eYmBhccskleOWVV/D99997Pd7GjRtRVVWFG2+8kV62fPlyXHnllfjll1+wYMECv8dKCD5sDgoHG1T0dA6PzUK8XYvdvxzDhQsXMH/+fEbFEY/HQ1JSEiorKwE482JSUlLoBF+JZPC/9K2dPZBcd6GAcwogWO/+y5V6VKq7Ihnjw8RIVQTmxbI/6Cw2HHKr4AoT8JAToB4ufUFRFI40ddCfCz6HHZDNWwFnteKRbhVmgWgGSBgZArJLN9tL5vrvv/8OjUaD7OxsxvLs7Gxs374dZWVljIuRix9//BGpqamMqaaUlBSEhoZiy5YtRNRcRDgrnVRQd/puUJQD5b/uQHFTHb1NRUWFx/soNzcXEyZMQHJysl95Mf3B7qBwsE5Fe4MAwLQYWdBGNWo1Rpx2S+SMlwpGRbmt1e7AwfouUzoBh41Z8WHgsANPBPhCuUrPaFORGyODMECTt0+2aOkpWS5pVknoRtB06XaVxcbFxTGWx8fHA3D6gXS/GFksFlRWVmLevHkex4uNjcWpU6eGZKyEwKS0XYd6N8fRhqMHoHUTNAC8tvnIyMgY6qEBcCY+FjaoGAmn2VGhSJIF511oi97MuKOOEPKROwouQK78IK2lK59jVnwYhAGYTOsL7UYLzrR2Cc/UMHHAGtc5m1V2Rf2mRIUGfeUcYXAJmi7dKpUKADySe11TUe3t7R77qNVqOBwOrwnBIpEIFy5c8PpapHx79FGvNTISINvLz0B53mlL0D0vZiRwUBQON6gZd8uTIqRICQvO/Ayv5noBFsmwWCyw2Wyw2Wyw2+2w2+30791/jh07lu7NdaZNiya3/9N4EQt8uwUOB9drlDmQsdgdKGpQ0/8nhYCHiQEaSTPbHIxmlXESARKDNIJJGDqCpku3Syx1v8tzfYnweJ5JYj3t49rP2z6E0YfKZGVEDIxtTYi0GzDz+uuHLC+mPzibH3Yw/HLSwyVIC9Akzb7obq4n4LAxK1bmYa+vVquh1+t7FROun+PGjUNsbCxj/99++w1tbW0+7b9kyRKPXMC1a9dCo9HAF+677z4kJCSgpsPASFBtOXsMp04epp8LhUKIRCKIxWKIxWKIRCKIRCLMmDGD8T6jKAoOh8PDrHE4cTXdNHZ66/DYLEyPC8wKou7NKkM4bEwlzSoJXgiaLt3h4eEAAL1ez1jueu5eRutCLpeDx+PBYDB4rDMYDF73AZimey5I9CY4MdrsOFivZDR/vGpmFkK4U0d2YJ1QFIXjLRrUaIz0svFh4mGpOrFYLDAajbBarfTDZrMxnruWhYeHe3wGjh49itLSUsa2dhYLkdPngS+VAQDsFjNO7toC/qRMj/y1nTt3oqSkxKexLlq0yEPUnD9/HrW1tT7t760Uvz+CwmazQWm0oLi5K1Kgqa9G86kjjO2MRiOMRqNH5HjKlCmM51arFa+//joEAgEtglwCyPW7+8/o6OhBjwJVqg3dGkDKIeYFpkN1rcbIqJ6bFiNDCDe4omKE4SFounRPnDgRgLMNQ3p6Or28sbERABjL3MnMzKS3caexsRH5+flDMFJCoGB3UCisZ1Y6zY5XICRAch8oisLpVi0uuFUGJYh5GMN3QKlUMsQCm8328IVqbm7G6dOnGWKkJ1ESFxfHqAAEgKKiIuzatcunsU6ePNlD1LS1teH8+fP0cxabg3HzrqQFjcNuR/W+nTB3qGCz2dAdLtf3rx9vJfUD3V8oFMJoNILL5YLD4Xj96fqdHSJAoXvDUx4HNmU9xiQmwmAwQK/Xw2g0eryGC9c0uQvXzZjJZILJZIJSqex1/KtXr2Y8VyqV+OmnnxhCyJsY6ikarTJZcaq1K0qVIhchXhqYeTQGqx3H3ZtVykizSkLPDFqX7mnTpqG5uXnIunTPnj0bcrkcJ06cwKWXXkovP378OCZPnozk5GSv+1155ZV47733YDQa6TnxqqoqqNVqXHvttUMyVsLI4wqtq9w7DMeFQRoSOHeiu0+XQ83vyl9QV5Xj1KE9+B9FeWwbFhaGJ554grGstbWV8TnsDW95Zf2ZfrVarR7LGKKCxUJi3jyII7uiKXWFu6FvaQSLxQLVwznFxsZ6iAlvv3cvEACc3wnZ2dm9ihLXz+6iAgAeeOABn87d7qCwt7YdJrvzb8Bns5CXoIAkmSkSHQ4HjEYj9Ho99Ho9LXYMBoNHXy+TydTj36U7AoHAI6rU0dGBsrKyPvfl8XiIiYnBvffeSy+z2h0orGunBRrfYQXVVIVTLWyngGOz6QeHw4FYLEZUVBTjuAaDAWazmbFd9/0GY2qIoigcbVLTU5liHgeTozxzflz+ZikpKSgvL8fcuXNx3XXX+fw6J0+exJo1a7Bu3boBj5kwsvj9DW+1WrFnzx5UV1dDKpVi9uzZmDx58qAMymJxVn+4l4tzuVw88sgj+Oyzz/DQQw9BIBCgpaUFv/76K9577z16u3Xr1mHr1q347LPPoFAocNNNN2Hjxo347rvvcOuttwIAvvzyS0yZMgVz5swZlPESAo9zSj1qtV3h6qyoUESPkHEdRVHQaDSQyWT0snKljiFoOuouoPbQHqCHi5w3UTFQUSIUChEaGgoejwcejwcul0v/3v1594saAGRlZWHMmDHgcLhopPhotnVNB6TLBVhy+zJwuT0nz86bN89rZaKvjB8/3u99fcV1UVV3E8feGomy2Ww6YtIXsbGxePHFF2kR5C6A3EWRwWDw+n/uPg3fE65Infv5HGvugLFzPtZuteDUT9/iqK7n3KL09HQsW7aMsezgwYN9CmoWiwU2m43c3FwsXryYse6XX35BaWlpr6KIzWaDG50IbkLX/9lbs8ri4mKsXLkSP/30E+RyOfR6Pa644goIhUIsWrSo9z8QnFOGK1as8PoeJwQffufUPPLII6ivr6fvNDgcDu68806sWrVqQAM6ePAgNm3aBADYsWMHUlJScOmll0IikeCuu+4Cn8/Hiy++iLS0NJw+fRpvvfUW44tRpVKhra2NnkOXSCT44osv8M4770CpVMJkMkGr1eKTTz4Z0SQ9wtBRrzXhrJs3yjiZCMny4S2LttvtqKmpwblz51BWVgaNRoMVK1YgJCQElWo9TrmV0Goba1F7YBc4ncnr3kSFtwtlZGQk5s2b55Mo8dYBPCsrC1lZWX6fY2RkJCIjI1Gm1KHZ7XzGh4mR6aXlRDBSptSjrps4HixXZxaLRU8f9ZekpCTcfPPNDDHUXRgZDAbY7XbG8as6jIzzqT/8Oyy9CBrAu2+Y+w1nT1AUBbvd7jUapdVqvVasuhMSGobxU7oi/xMUYq8tQtauXYsFCxZALpcDcEYllyxZgvXr1/skav71r38hMTGx1xYohODBL1Hz4osvQq1W46mnnkJeXh4kEglqa2vxxRdfYN26dXjwwQf9HlBeXh7y8vJ6XL98+XK6hNwbq1at8hBWcXFxjGgOYfSi7lbpFCHkIzs6dFiqJIxGI8rLy1FWVobz5897fElWVFRAEj8Wx5u7LiJyPhtXzMpGyCW5/U4EVSgUQ5bD5iu1GiNOt44+cz3A6Ylypps4DpQSe6lU2qd/EkVRMJvNdD5Rh9mKEy1dic7sjjakRMjgUEyBw+GA3W6Hw+GgH67n3iIYAoEAcrncYz/33114u3nsSxSx2GwkzpoHNsd5iZKFcJER4fm+slgsKCwsxIoVKxjL09LSsGHDBmg0GoSG9iyw9+/fjwkTJqCurg719fW9jokQHPglakpKSvD+++9j/vz59LLk5GTMnTt3wG7CgQSpeAoutBYb9tcpYae65t9nxnvv6TRYKJVKlJaWoqysDDU1NT3mSPD5fLSY7Ch189kIE/AwJ0EBHic4qzhGq7ke4BQAhxvU9HOXOA4mWCwWBAJnQq3N4fSjceXRhPK5uDR3Erhs/1IG5s6d22v+JEVRdNm6NxYtWoSCgoIeRVGDjYMWqtOSA842Id4+x7W1tbDZbB62DBKJBHa7HVVVVT1GIzUaDYqKivD000/7nDBPCHz8EjXjx4/3aFcAOMOU3RP6amtrvTa+JBAGE4PVjn217bSPBY/NQl58GEKGWDDs2rULZ8+e9bpOJpMhLS0NEyZMgCAiBkVNXRGaUD4X+UEsaILBXM9fzJ0tEGydAlXE42BmDw1Pg4UTLRraAZnDAmbEycEdwv8Vi8Wic2q8IZFIevSHajdacLyma2pqUqS0x2aVHR3Om4TuU3iu6VqXaas31q1bh4ceeqjnkyAEJX6Jmj/+8Y/46aef6MRbFzqdziMh8aeffsL999/v/whHEHe/GhK1CVxMNqegMXaWbnNYLMxOUAxa116TyYSKigqYTCZMmzaNsS4tLY0hauLj42khExUVBRaLhVaDGfvrlLQAkPA4mJOo8DCjCxYMVjsO1LuZ63HZmJ0QvOfjjoOicKheBYOrtxCrUxwHsSdKTYcB1R1d5ebZ0bKA7WjdvVllhJCP8b1M+bkq8LqLJ1exibdSfsCZr5mfnw+pdHRMlRK68EvUvPvuuzh37hz++c9/MkLNWq0WPB4Pu3fvBuDMvG9rawtaUUMIfCx2B/bXKaHrvAi5+vB4SyjsDyqVCmVlZSgrK0NVVRUcDgdEIhGmTp3K+AJNTU3FhAkTkJaWhrS0NI+7T6XRggN1Xf4mIh4HcxLDIQgQr5z+4vp7u7x/uGwW8hMUo6b/zokWDaP3Vm6sHLIAFQC+oLXYcMwthysxVIikAG4tcKpbs8rc2N5dgxUKBQDPHB1XdZi3fJqWlhacP38ejz322GANmxBA+CVqsrKyQFEUxo0b1+t2NpsNhYWFfg2MQOgLm8OBA3VKdJidYXUWgBmxYX6VblMUhfr6erpaqaWlxWMbg8GAuro6jBkzhl4mEol6bOKqNlkZOT4CLhtzglgA2B0UDtarGI0c8+LDgvqi706lSs8wQsyMkCIuQA3pfMHuoFDUoKLffxIeB1OGKWneH5p0Jlxwa1aZHRUKUR8Ox1FRURAKhR6VVGq1GgA8DCsBp6dNZWUlnn/+eXpZYWEhrFYrnn/+edopnxCc+CVqrrnmGtx+++0+5cps377dn5cgEHrFdYFVmrqmO6fFyPp9EaIoClu3bkVZWVmv3h+xsbFIS0vrtZLCHY3ZKWisnSEaPscpaLz5mwQDFEXhSKMa7W5RjGkxckSKRsb7Z7BpNZhxws21NkEqwARFYFQ6+cupVg0t+Nksp78OL0AbbprtDhxlNKsMwRgfIkp8Ph+zZ89GRUUFY/mZM2eQkZGBiIgIj32WLl2KpUuXMpbdcccdAIA33njDn+ETAgi/3uETJ06ETCajcwksFgu+++47mEwmj22vvPLKgY2QQOiGg6JQ1KhCq6HrApsdFYoxsr79PrpXJ7FYLLS1tXkIGg6Hg9TUVFx11VV4+umn8eCDD+LSSy+lvTB6Q2+xYV+dkpG0PGcQc3yGG4qicLJFw2i4OTlSOmo6JOstNhyq70p6lofwkBPkVVz1WiMq3aJOkyNDIRcE5vuPoigcH0CzymXLlmHHjh3Qap3l90qlEjt37sSjjz5Kb7N3717MmjUL+/fv93oMV5d2QvDj121jcXExHnroIaSkpGDz5s3g8/mYMWMGnnrqKbz66qt080kCYbChKApHG9VodGvENzFC2qN/CEVRaGxspKeVxo8fj8suu4yxTVpaGmprayEWi+kcmeTkZLrLe38wWO343S3nhMNy5pwE6gXFF8pVelS4XSDHh4l7Td4MJqwOZ6WTpTOiFsJhY1Z82JBWBg01eosNxYyoh2DYzSf7Q63WhHo3Q8CcGFm/+rMVFBRgxYoVWL16NdLT01FaWooXXngBCxcuZGzX3V0ZcDZV3bx5M44fPw4AuPfee3HLLbeQ6acghkX50nykGzfffDPkcjmEQiH+9re/0cu///57HDp0CK+//vqgDjIQcFU/eevgTRgenHd0Gsa8e5pCjEmRzCkhq9WKCxcu4Ny5cygvL6fv4ADnHLyr+aoLrVaLjo4OxMfHD+ju3GSz47eadkbScn6CIqinaGo1Rhx2q0aJlwowIza4oxguKIpCYYOKFshsFnBJYjgUA0wyH0kcFIW9Ne10zzMRj4P5SREBW5lmsNqxq6qVnqYdKxMiJ0Y+soMiBDR9XYv9itSIxWKsW7cOr732GmP5mDFj8Pbbb/tzyICElHEHDhRF4UybliFoxslFmNjpMqrT6ehqpYqKCq9doQFnZ2mdTseoUpJKpQMu7exehcUCMCsuLKgFzWg21wOAM21aRsQvJ1oW1IIGAM60aplNXGPlAStoXH21rG7NKrNGSXsNwsjhl6jpqSP2/v37YTAYvK4jEAbCOaUeZcquvJfEUCGmRDkrOVQqFT744IMe9xUKhUhNTUVaWhrGjx/vtQ/SQLB2ChpXUibgNDeLkQRv5UxHN3M96Sgy1wOcESj391NqmNinnKxAplFnQrmq65wmRUoDWqRVqA2MvLjcGM9mlQRCf/FL1EgkEpSUlNB3bHq9Hv/+97/x8ccf92qdHWwQ873AoEKlZzSojBRwMS2mK5FQLpdDLpfTZZwAEB4eTvvHJCYm9ruvkq/YOquwVG5VWLkxMsRLgzeJ1mC1Y383c738UWKuBzi9g442qenn0eIQTAryflUGqx1H3aJqMeKQgM570pitON3aVW2WphAjXBS4AowQPPglah555BE8+eSTOHToELZv3w6lUgm73Y60tDS8+OKLgz1GwkXM+dYOnFR2Rf90TXWIF1JgJ0XSy1gsFjIyMtDQ0EALmeFIVrc7KBTWqxhmbVOifavCClS8muvFB6+3TneMNjsKG7rMEKV8btDnCDkoCocbu5KdBVw2pgXwNKGDonCksYP+H8hCuMj00qySQPAHv0RNSEgIPvroIxw/fpzOGk9OTsacOXOG7I6YcPFRrzXiZLsecEUE25pQvW8n2ElJmDljBmPbhQsXDuuXuKusvMXQlZMxOVKKZHng3h33RXdzPRY6zfWCuHLLHZcIdQk2V3+wYO2/5aKkTYd2Y1ekcEZsYLd1KG3XQW12jpfNcro2B3NfLUJgMSAnsClTpmDKlCkAnFM1SqXSq9kRgdBfmvVmFDWoaUFjVLWhau8OyKRSREdHe2w/nILGW1l5RrgEqQrvDfqCAW/mermxo8dcj6IoFDd3MJJoZ8aFBa0ZoosWvRnnlDr6eWaEBBEBPI2jNFpwrt19vNJR40hNCAz8+kT/3//9H6KionDbbbchKioKL7zwAr799lvweDz89a9/9fABIRD6Q7vBgsL6rgaQLKsZiXYdLn/gfkRERIxoWN1kMuFAVTPUrK4Lx1gJP6jdZymKwslWprnepFFkrgc4vXZqNV1NHbOiQhHlRzuNQMJkszPK7aNEfEwIYGHtalbp+lxHCPlIDeC8H0Jw4peoqa6uxp/+9CcIBALs3r0b33zzDZ566inccsst+Pvf/05EDcFv1CYr9tcrYe/85hNy2ShIToSIN3bExmQ0GnHu3DmUlJRAJ4lAeNokel17+VmcOroP21gsiMViXH/99R7VgWVlZQgJCYFEIoFEIgGfzw+ofIfzKj0qVF15SylholF1sWnUmXC6tSvRfKxMGNBmdL5AURQON6oZLry5AZ4bdLpVS1secNksTOujWSWB4A9+iZqrrroKAoEAVqsVb7zxBrKysvCHP/wBgLNrMYHgD1qzs72Azc3ddW5i+IgmqTocDnzwwQcwmUyIyZ6BSDdBo7pQhoaj+wA4LzI6nQ48nmco/euvv4bV2pXzwOPxaIEjkUggFovp3ydOnAiBYPhKwWs1Rpxyu+DHSwTIigzcpof9RWO2MqIZ4UI+pvTDgj9QOafUMcqhp8fKA7rze7PezGjbkBUVCnEfzSoJBH/w613V0tICnU6H999/HzU1Ndi0aRO97ujRoz12LQ42SBn38KG32vB7XTss3folDWfOQ0dHB+x2OxQKBb2MzWYjJSUFrSwBIjOm0MupjjZwm6sREREBnU5H9z1zN/UDnH3R3AUN4HQ8VqlUUKlUHmNITU1liBqDwYANGzZ4FUDuD5FI1O8LdXdzvXAhP+Dv9vuDxe5sgeASySIuB7Pigj8ptc1gwdm2rryUCQpJQE+lme0ORrl5rCQESaNoapMQWPh1xZg0aRLy8/Nht9vx4osvYurUqTh58iTWr1+PHTt2jCpXYcLQY7TZsa/Ws1/ScFTdtLe3o6SkBCUlJWhoaEB2djauu+46xjZRE3PAZnd9CcdKQjAzbRLYMybTy2w2G/R6vYczsc1mQ3p6OnQ6Hf3oye0YcLp1u6PT6dDa2orW1tZez4PFYkEmk+GJJ55giBKdTofq6mpIpVLG9Jc3c728UWSu56AoHGpQQd853cFhsTArPqxfPYUCEbPNgaLGLjEcLuQhIyJw82hczSpNbtNkOaMgUkYIXPwSNQsXLsThw4dhs9kgEjnnpuPj4/Hcc8/hueeeG9QBjiTEfG/oMdsd2F+rpC8+bJazjHionFApikJLSwstZFpaWhjrz507B7vdDg7HefErV+rQ5iZoosUhmBEb5nG3z+VyIZPJPF5PJBJh2bJljNe3WCzQarUMoaPT6WCxWOjXdaHT6bofssfzcjgcHheLhoYGfP3114xlwlA5xs5fAq7A+dmlLGZozh1BUX0Y5syZw9i2ra0Nra2t4PP54PF44PP5jAePxwvIC9TJFg3TrTZWHtRNRYGutgIu8c/nsDDdy3sxkKgbYLNKAqG/+B3bd32puSCduQn9xepw4ECdEho3X5QZcWGDHkqnKAoNDQ20kFEqlV63Y7PZiI+Ph8FggFQqRYVKz8g3iRTxMStuYNEMFouFkJAQhISE+GR/kJiYiD/84Q+08HGJIb1ezxBEJpPJY+oL8BRFbB4fCbMX0ILGbrGgctdWmDqU0I8b5yFqzp07h19++aXXMbq+CyZOnIjFixcz1p05cwbV1dUeYsjbQywWQygc+LTEBbWBkb+RES5BvDR4W1a4OK/So0nfZSMwLUYe0KaIBqsdx5u7uoUnyYSIDeLWIYTggGRqEUYEu4PCwbpu7QVi5Ygbgi89g8GAf/3rX/DWkJ7L5SIlJQUZGRlIS0ujL6oX1AacaOmycQ8X8kdkeobH4yE6OtqrN487NpsNZrPZY7lUKu2a/jIYoJiaD4HcmTPksNtRvW8nTB1Oked+k+LCYrF4LPO2jevRnQsXLuDo0aN9HgMA5syZ41E5+eOPP/YqityjR2PHjgVLHMq4kMZLBUgPD9zpGV9RGi2MCq7UMHFACwSKolDs1qxSRJpVEoYJImoIw44r36F7e4GB+qLY7XZUVVUhLCyMkewrFouRlJSEqqoqAM6Ld2pqKjIyMpCamupxMa/pMOCY24VRIeBhdkJYQDfb43K54HI9P86pqalITU0FRVEoalQzpgLGi1jIWXQZLUi8RXrCw8ORnp7OEC7uD/f8IG+iqHuSdG9421+lUvWZT+Ri8dXXojXUQecJyUK4uPD7zyhWtkMqlSI0NNTjIZVKIRAIAnIKzYXF7kCRm79LmICHiQHeq6pSbUBLt2aVvAD+/BBGD0TUEIYVl3Otexh90gDaC9hsNlRUVKCkpATnzp2DyWRCfn4+FixYwNguOzsbMpkMGRkZSElJ8SoAAKBOY8SRpi5BIw/hYnaCIui/kE+1ahmCZlKkFGk+GLVlZWUhKyurx/UOhwNWq9VrPhDg/LvHxcX1KIrcH92TpAHfIkUAwOZy0SoMp6vnQjhs5MUr8K+WZiiVSo/cKXd4PB5CQ0Nx9dVXIykpibGupaUFYrHYr+qywcAZ8eiAoTPnjMdmYUaAtxXQmm041a1ZZSC7HBNGF36JmjNnzmDixImDPRbCKIeiKBxr7kCd28V1gkLi08XVHYvFgvLycpSUlKC8vNzjwldSUoLLLruMcRFyb+nREw1aE8PTRBbCRX5ieNB3py5X6nBepaefp8gHz1yPzWbTOULeSE5O9jAj7A833XQTjEajhwByCSmLxQKzxQJDeAIsbOfXGZsFzIoPg5DLhkaj6eMVnNGk9vZ2D6FLURQ++eQT2Gw2cDgcr9Ee92VSqXTQhU+l2oAGHTPRVhzArR0cFIXDTWpGs8qM8MCOKhFGF359Oh544AEsX74c1113HRITEwd7TIRRCEVRONWqRVVHl1V9ilyETB/LUc1mM0pLS1FSUoKKiooey6JDQ0ORmpoKu93eYzTGG006Ew51K3HOT1AgJMgFTXdzvTiJAFlRwWOu5xIMvXG2TYtSt35CU6NlCBfyQVEUnnrqKWg0GsZDq9UynrumyLq/jtFopN9ndrsdarUaarW6x3E8++yzjCk8s9mMo0ePeoggbxEtb6hNVkbEI1kuQrw0sP1dStt1UJvcmlXGyEeNTQAhOPBL1KxYsQIxMTH4xz/+gfb2dlx22WW48sorvc7JBzOkjHvwKG1nRgvGhAr7dXHVaDT4/vvvva5TKBTIyMhARkYG4uLi/DKhc/dsEfM4mJOoCGiHVl9oNZhxtElNPw8X8jB9FJnrAc7pQndBMz5MjCSZs7KL1dm6QiwWIzY21uv+FEXBbDZDo9F4fH+ZzWZERUVBo9HQ5oo9wWazPabPlEolfv75Z49txWKxR8RHLpdj0qQut2qrw4GiBhUj4jE5MrATbT2aVYZLR02Hd0Lw4Jeouf766wEAeXl5sFgs2L17N1avXg0Wi4Vrr70Wl1xyCdhBnoNAGDzOq/Qocfuyi5MIkBPj3YBLo9GgpKQEGRkZjDvniIgIhIeHo729HQAQFRVFC5moqCi/L9RtBjMO1ivpi4eIx8HcxHAIg1zQdJitOFjfdVF0muspRtVds8pkZYi2KFEIJvUzgZbFYkEgEHhtTREWFoaHH34YgHPKs3vExz3qw2KxPN6DPU196fV66PV6NDY20svcRQ1FUTje1EH3SaLsdnScPoYdZyiEhIRAIBB4/JRKpQgLC+vXuQ8mNgfFaFYZLuQjNYibvBKClwFPzlosFtTV1eH48eNoampCXV0dPvnkE8yYMQN33323V0OyYIGY7w2cqg4DTrqVRkeJ+JjeLdFRqVTSHjL19fUAnAmoeXl59DYsFgt5eXkwGo3IyMgYFF+kdqMFB+pUjOaZcxMUAe394QsGqx373XpoCThs5Ccogj43yB2TzY5Ct8anEj4HM4awBQKfz0dERIRP3kIuZDIZZsyYwRA/Wq3W67buAr5aY0StW95ZXdFeqKvP9/paaWlpWL58OWNZYWEhiouLGQKoJ1EUHh7er3PrzulWTVezShYLuV5uWvbu3Ytt27YhJSUF5eXlmDt3rod7tzsWiwU//PADVCoV9Ho9SktLcckll+C2227ze5yE0Y9foubXX39FamoqNm7ciG+//RYmkwmLFi3C/fffj8zMTADON/Dy5cvphpeBiMvErC8PEIJ/1GmNKHarJAoX8jArPgxsFhiuvs3NzR77lpaWMkQNAEybNm1QxmWz2XD8XDlqWBKA4/wICDqbZwZyEqYvWOxOQ0OX6yyXzcLsUSDU3LE7KBTWq2C0dfUJy4sPPNEWExODK664grHMbrdDp9N55Pa4bv40ZitOuNkJKCtL+xQ0ALxGmjo6Onwuh58xY4bHWLdt24Zz5855FULuv9uFUjQLuiwUsqJDPT5HxcXFWLlyJX766SfI5XLo9XpcccUVEAqFWLRokdcxvfvuuzhy5Ai+/PJL8Pl8nD59GjfeeCMMBgMeeOABn86LcPHh1zf4U089BZvNBi6Xi+uuuw4PPPCAR8JwQUEBCgsLsXr1amzZsmVQBuvivvvuw759+zyWc7lcFBYWevTfcfHcc89h69at9HOZTIY9e/YM6tgITpr0JhxuUNPPZSFcZIrZ2Lt7N0pKSuhppO6wWCyMGzcOmZmZoChq0PM/jhw5gn1HjiFu9gJwQ5xvf5vJiAuH94CTGI/MzEwkJCQEZd6J62Lv7tA8Ky4s6NsDuOOqoFO6mTbOiAuDNEjEKIfDgUwm8xrBtjkoFDWo6eiTmMtCdvo42JLjYDKZYDab6Z+uh+u5t8hlX3lA7ngTRe6O1T2eDz8EqYtvgusd1lOzyrVr12LBggWQy+XOcxOLsWTJEqxfv75HUcNms6FSqWC3OyNAKSkpAIBDhw4RUUPoEb++CTgcDu68807cfffdvYYsS0tLGfPGg0F1dTXsdjvef/99xgdx7969qKmp6VHQ1NXVoaOjA6+//jq9LCkpie5dRRg82gxmHKp3ryTiYE6CAg21NV7FKIfDoV19J0yYMChW+T1B8QWIy7sM3BDne8dmNuHC7h9h6lCirb4WhYWFkEgkSE9Px+TJkzFmzJghG8tgQlEUjjSpGYaG02LlAd292R/KVXrUaLoq6LIiQxE9Ss7xZEsHLUjZLGBWQjhkIf4L0gULFmDWrFleBVH33yMjIz329+ZQ3Z24afngiZy5MyEcNqZ6aVZpsVhQWFiIFStWMJanpaVhw4YN0Gg0XivcVq1ahVWrVtHPKysrAQBTp07t++QJFy1+iZq3337bw9zMG48++mi/7hZ8obKyEh9++KGHGPnqq696VPwAsH79ejzxxBOYPHlyj9sQBk67wYx9te1wwPnFJuJykJ8QjhAuB0lJSRAKhTAajeDxeAxX3558TvylubkZZ86cwbx58+gvWa3FhhZRBLiuW2GHHdz6MlAmPWNfnU6HI0eOgMvlBo2o8TDXi5BizAAdmgONC2oDo1VAkkyIlLDRcVNSqzEy7A6yo2QDEjQA6Movf1m+fDlMJlOPokjLDoFBEU9vPzVa5rVisLa2FjabzaO6TCKR0C7gvqQorFu3Dpdeeikeeughv8+JMPrxS9QsWLAASqWSYUVfVlaGtLQ0xna5ubkDG50X5s2b57FMq9Xi4MGDePXVV73uo1Qq8fXXX+Orr75CVlYWFi9ejFtuucWrLTvBfzRmK/a7CRoO5cCcxEg6n4PNZmPhwoUQCoVISUkBjze40yIWiwWnT59GcXExnXCclJSElJQU6C02/F7bDnOnoOGyWJgzNgqKjATYL1+ACxcuoKSkBKWlpTAYnM0QMzIyPF7j999/h0KhwPjx4wddiPmLV3O9UVZ5UqsxMlpXhAv5mBLlvYIu2NBZbDjmlnuWIBVgrGzkBamrp5a3KIrRascvVa1wldclhQoR10PT0I4O57l1vxF1CS6VStXrONatW4eqqiqYTCa88cYb/fKfIlx8+PXu2LlzJ5566im8/vrruPbaawE4rcZfffVVrFy5ctAvVn2xa9cuZGVl9VgRo9Pp8Oijj6K8vBy7d+/G0aNH8d1332HDhg1e57ZJpVP/0Vts2FerhK1T0NjMJtirzkKSfhVju6EIHTc2NuLo0aM4deqUh7twcXExYseMxe+1XcmzHJYzeVYhdIpaDoeD8ePHY/z48bjqqqtQU1OD8vJyJCQkMI5lNBqxZ88eOByOYZ0y6wmKonBOqcPZNma5fDCZ6/lCg9aEI25Oz3IBD7NHoLnoUGB3OPug2TqbrYp5HK9TOIEERVE42o9mlS4R0t3mw/VZdeXM9MSDDz4IACgqKsLixYvx/vvvexQREAgu/BI1n376KW6//XZceuml9LJx48YhJSUFf/vb3/Dcc88N1vh8YseOHb1OPY0ZM4b+YOj1erz11lvYvHkzXnrpJbz33nvDNcxRi9Fqx+91Spg6++7A4YC4rRqTpwzdVJ/ZbMapU6dQXFzsNW+LzWYjIyMDWTnT8HttOww25xcnmwXkxYf12IuGzWZj7NixGDt2rMe6srIyOBzOc7Tb7SgrK0NZWRmd3Jyeno709PQe87oGE4qicLJFgwq1gV42Gs31mvVmFDV25WeFdjo98wKs0slfTrdq0GHuyqOZERcW8OdWptR3a1Yp63XMroi+67PjQq93Rhf7cox2MWPGDIwdOxZPPfUUdu/eTfIhCV7xS9TMnj0bTzzxhMfymJgYrFmzZlhFjU6nw4EDB/DSSy/5tL1YLMZLL70Eg8GAH3/8EW+++aZHZMndn8YFid54x2xzYF+dkm64x2YB+UkRiMyI72NP/ykrK8PXX3/ttQN0eHg4cnJykJ2dDU6IAL/XtkNv6RrbrPgwv5NnExMTcemll3qUoVMUhcrKSlRWVmL79u1ITExEVlbWkEy/As7+Okcb1Qwvk0gRH7NGSfTCRZvBgkI3Y0RJp9NzsLeucFGnMTJE6eTIUIQFeKVau9GCs21deU3OZpW9f56ioqIgFAo9Kh5dLSe83UC0trbihhtuwKWXXoqXX36ZXp6QkIDjx4/j/PnzAWsVQhhZ/BI1PfXd2b9/P62+h4tffvkFmZmZ/faaWbZsGbZs2QKVSoWoqKghGt3oxmp3YH+dEtpuJcSRfXzJDZSYmBjGe5DD4WDixInIycnBmDFjwGKxYLY58HttO7SdgoYF511wjNj7vL8vKBQKFBQUoKCggDYMLC0tRV1dHWO72tpayGSyIRE1NocDhxrUaHbrch4vESA3dnT12FEaLTjgZq4n5HIwJzE86FtXuFAaLTji5oYcJwlBsjywIw8WuwNFDV2uwQoBD5kRfUcl+Xw+Zs+ejYqKCsbyM2fOICMjw2sFbXt7O1paWjx6bbkaj8bHD91NEyG48euWJyEhAR9//DE9J2oymfDJJ5/g3//+N+bOnTuoA+yLHTt24PLLL+/3fjKZDFwul/ZNIPQPm4PCgXoV1OauaMn0WDliJP6LBncoikJ1dTW+++47tLW1MdaFhoYiLS0NUVFRWLx4MZ599llcf/31SEpKAovFgsXuwL66dro8FgBmxMkRN0hjA5wCJz8/H/fddx+efvppXHHFFRg3bhw99eMtyfjXX3/Frl270NjYCIqiPNb3hcXuwL5aJUPQjJOJMCNudAmaDrMVB9wckUM4bMxNHD0GggarjdHCQsLnICcmsKcNXXk0xs5pXB6b1S8H52XLlmHHjh20o7JSqcTOnTvx6KOP0tvs3bsXs2bNwv79+zFhwgTMmTOHsb6+vh5HjhzB3XffPSiO4oTRiV+RmptvvhmvvPIKcnJyIJfLoVQq4XA4MGnSJLz44ouDPcYe0el02L9/P1avXt3vfU+cOIG5c+eSCig/cFDO5MZ2N0+UqdEyJAxCCbHBYMDx48dx7NgxWsyIxWIP4Xr99deDz+d7XAhc0SNXngIA5MbKh7S7cWhoKGbMmIEZM2bAYDCgtLQU48ePZ2xjt9tx+PBhmEwm7Nu3DzKZjO5dlZiY2OcFzdX6QOsm1NLDJcgIlwT0xbC/aDsTzi2dV3w+h4W5iQpIgsRcry+sDgcO1Klg7sw/47NZmB2AbsjdqVQb0KjrEtPTYuQQ8Xz/nxQUFGDFihVYvXo10tPTUVpaihdeeAELFy5kbGe1WmGz2cBisfC3v/0NH330ETZv3gwWi4Xa2lq8+OKLuPnmmwftvAijDxblzy1jJ2VlZSgqKoLVasX48eMxZ86cYf2C/eGHH/DZZ5/h22+/ZSxft24dtm7dis8++wwKhQKrV6+GRCLBH/7wB8jlctTV1eG5557DO++841Hh0hOunBpv+TYXEw6KwuEGNep1XfkckyOlSFX436GdoihcuHABxcXFKC0t9aiGEIlEeOaZZ8Dh9H6nbnM4BU27sSt6lBMjw1jZyIf1Kyoq8MUXX3hdJ5FIMGHCBGRkZGDs2LEe5+m60LvukgEgOyoUKWGjq2xbb7Xht5p2uv0Bl83C3MTwgM8z8RWKonCwXoWmzkgbC8CcRMWQT9cOFLXJij01bXRkKVkuwpTo4O3pRwhu+roWD+j2Jy0tzcOb5siRI0OWINmdnqqeVCoV2traaEdMsViM7777Dtu3b8fixYuhUCjw4YcfMnx2CH1DURSONXUwBE16uMRvQaPT6XD8+HEUFxd79arg8/mYPHkycnJyfBA0zguGu6CZEh0aEIIGcFbgLVu2DCUlJTh37hzDrVWn0+Ho0aM4evQoBAIB0tPTcc0114DFYkFlsmB/nQqWzjt7FpyRp8RRZqxntNk7hVtX2X1+vGLUCBrAaZDY5DZ1mBMjC3hBY3U4UNTQNVUmC+FicqRv1UoEwkjgV6TGaDTi22+/RXl5OePLmaIoHD58GLt27RrUQQYCF3ukhqIonGzVoELVVa0xPkyMyZFSv6JzSqUSa9eu9SjzBID4+Hjk5ORg0qRJPk0P2h0UDtYrGWWmWZGhGB+gBnQuF9WzZ8/i3LlzHsn1Y8eOxV133YUWvRmF9V0eJhwWCzPj5QNKdg5EzDYHfqttp6fW2CxgdrxiVLV4qFTrcby5q1t9mkKMSQEuDiiKwhG3Kjsui4V5YyOCps8WYXQyJJGap556Cr/99hvCw8MZFx2r1eqR1BnMkDLuLkradQxBkyQT+i1oACAsLAyRkZF0abRAIMDkyZMxbdq0flWyufJ73AXNxAhpwAoaoKvXVUpKCq666irU1tbSlVQdHR3IyMhAndaII41q+g4Zdhsu/P4TdBIhEhMTkZiYiPj4+KDPCXMldbtX0M2M87/sPhBp0Ztxwk3QxElCMNGHqqGRplpjZNgGTImREUFDCHj8eocWFRVh06ZNXt1hN23aNOBBEQKLcqUOpe1drrXxUgFyfHA9dTgcKC8vR3FxMcaPH4/p06fT61gsFqZNm4bTp08jJycHmZmZ/XKidubhVOFoswYcRVdJfka4BBPC/c/vGW7YbDaSkpKQlJSERYsWobGxERquEEVuHc6FXDaqD/yKjqZ6dAA4f/48AOffMCYmhhY5Y8aM8dnILBCwORw40C2pe3qsHLGDWKU20mjNNhxq6DIPlIdwkRsEBokas5UhxJJChaOulxhhdOKXqJk6dapHLo2LG264YUADCiTcw1sXa9TmgtqAU25NBKPFIX261qrVahQXF+P48eN0CadarUZubi5jv9zcXIbQ8QWHw4GzZ8/iwIEDYMUlIzx1Ir0uTSFGehAJGm908KUocROQEj4HM6OlOKv0jIBSFIXGxkY0NjaiqKgIgLMS6+abbw54Hw97Zw6U0sRM6h6MCrpAwWxz4EC9km4nIOCykZegAJcd2JVOdgeFogY17J3TnlI+F9nRwSOWCRc3foma1atX46uvvsK9997rsW7Tpk1elxOCD29NBGfGhXn1prDb7Th37hyKi4s9TLYAoKWlBW1tbYiMjKSX9edu1WKx4Pjx4zh48KDTkIvFwsT8K+j1KXIRJkZ0TYcZDAbw+fygaX5HURROtGhQ6eYwKxfwkB8fhhAuB8888wxUKhVqa2vpR0tLi8dxNBqNRz8zm82G33//HYmJiUhISIBAMLKRENeUYat7DlRU4CR1DwZ2B4XCBhX0nU7bHBYLefEKCIPAPPBki4b2eHK2bpAHvBAjEFz49Y3/yCOPoK6uDm+//bbX9UTUBD+Num5NBEOcTQS53UzelEolHZXx5iYtFosxZcoU5OTk+FVtptfrUVRUhMOHD8NoNHatoCioKksRnpKBMRKeRxPHbdu2obGxEZdddhkmTpwY0OF+R2dCZp1b/kKUiI+Z8WHgdV5MWCwWFAoFFAoFsrOzAThNL+vq6lBTU4Pa2lrU19dDKpVCImFGqxobG/Hbb791HTsqijFlJZcP33SIK/nUvQpoYoQU40dReTpFUTjW3MHwcZoeKw+KSq46jREXOrqEdVZUKGQhgT9uAsGFX6ImLy8Pdrvdo72A1WrF7t27B2VghJGj1WBm5AFIe2gi6HA48Omnn3oVMykpKcjJycGECRP6LMfuiT179mD//v0ebTmEQiGmT5+OGTNmQCQSeVyQ6+rqUFJSAgD45ptvcODAASxcuBDjxo3zaxxDic3hQGG9Gi0Gt7YHUgFyY/p2CRYIBHR3ccD5/9BoNB7b1dbWMp63tLSgpaUFR48eBeD0yXGJnKSkJMTFxQ30tLxCURSKmzoY4i1NIQ6qHChfKFPqUaPpEuCTIqSIkwZ+npDeYkOxW2Q2XirAuFEUPSNcHPglam688UZkZmZ6tJIH0Gu3bELgozRacLCuy5dC7GoiyGXDZrMxpnPYbDays7Nx4MABAIBUKsXUqVMxderUQWk/wePxGIJGLpcjLy8PU6ZM6bXqR6PRQCgU0pGdxsZGbNy4EePHj8eCBQv63SdsqDDbnYmyKre8knFyEaZ0izr5CpvN9vp3T0hIwPTp01FXV4empiaPFg06nQ4lJSUoKSnB+PHjcdtttzHWOxwOr5/1/uDqKl7tdrF3TRmOJuq1Rpxxa/iYJBMiNYAr8Vw4KAqHGtV0awoxj+NTMQCBEGj4JWomTZoEjUaDuro6ZGZmwmKx4Mcff8QVV1yBzMzMwR4jYZjoMFuxv05J+6IIuGzMiglF2ZnTKC4uhkAgwPLlyxn75OTkoK2tDTk5OUhNTfXr4kdRFOrr6z3cnadNm0ZbB8yePbtHId2dzMxMJCcnY9++fTh06BAtjM6fP4/z588jOzsb8+bN88g9GU6Gs+3BmDFjMGbMGADO3KS6ujo6L6euro7hNZWYmOix//bt21FdXY2EhASMGTMGiYmJCA8P79c4z7RpGR2pk2RCjynDYEdlsjCmbCOEfEwNEmFwulULdae4ZsE5XdY9MksgBAN+me8VFxfjoYceQkpKCjZv3gzA2Wzs5Zdfxquvvjoqm42NdvM9ncWGvTXtdE8aLgv4/+3dd3hTZfsH8G/SvfceQEsXHZRS2trBKFMEURTEMsRXRLEg/lABfZ2viuAARUVEX8Ci4qvsbdmjtGW3pXQxOuigbdKVjszz+6P0tCHpIDRtk96f6/KSPDkneZKenNx5zvPcN7cgC5lXmusVAc3zOt54441uWzYslUqRkZGB5ORklJeX45VXXoGjo6PcNlVVVY8056O2thYnT55EWlqa3AiFjo4OwsPDER0dDSOjnl1xUyeU4Nzd1nIAQO+VPZDJZKioqGCDnBEjRigElxs2bEBFRYVcm5FRa74cNzc3ODs7t7skP5snwI02oxeuZoadrqDTNA1iKU4VVKLp/ufHVE8HowbYwkADAoNSQROSi1szej9q2RNC1Kmz72KVgpqZM2fC0tISRkZG+Pbbb9n2PXv2IDU1FZ9//rmK3e1blC3j1sagpkEsxZlCHhru1xaSScS4dXw/mqrklxFzuVw8/fTTCAgIeKTnEwqFuHz5MlJSUtgl3wAQGBiotpQA5eXlOHbsGPLy8uTa582b16NzbfiNIpwv5kMkbf7Y9fWyByKRCF988YVCPa4HcblcODk5YerUqXKX925W1SO9vHWej6OJASJclK+g01QSmQynC3lsvh09LgejB2hG5t0GsRQnCirY49HRxACPuVhpVcBJtItaMgqbmJhg06ZNWLVqlVy7u7t7uyuiSN8klMhwKr8cTfcHDWQSCe6cPiwX0FhZWSEkJATBwcEKK2seRl1dHVJSUnD58mW5Sx5A8/wZMzMzMAyjlhOqvb094uLikJ+fj6NHj6KkpAReXl49GtDcu1/2QNqm7EGEixUc+nD2XH19faxYsQIlJSVyy8nlVqKhecSnuLgYxsatE0vzqxvkAho74/ZTAmgqhmFwsbSaDWhaMiJrQkAju9/3loDGUJeL4Y7aNYJG+h+VPnkeHh5K25OSktDQ0KD0Pk2k7cn3ZAyDC6VVrQGNVIqCc4loqCiDjo4OfH19MXz4cAwcOPCRTnQVFRU4f/480tPTFWo9mZiYIDw8HKGhoT1yGWjgwIFYsGABbty4IZczp0VOTg50dXXh6enZrc97t7YRF0ur2RVl+lwOIl2tYW3U98sc6OnpsVmPgeYvch6Ph6KiInY5OY/Hg4WFBczMmif+FtU2yq2kqa8oAy/nKsSDBsLLywsuLi6PPPm4L7heUYdSQWuAHuxgoTElHrJ5Arll52FOVjDQ1fy/CenfVApqTE1NkZWVxX7R1dfX4/fff8dPP/2EmJiYbu0gUZ/rFXVyCdCKUk7AQNyIyAkTMHToULlf3ari8/nYsGGDQnvL5N+goKAeT5DH4XDg7++v0C4Wi3Ho0CHU1tbCw8MD48aNg5OT0yM/3+2qelxrM2JhpMtFlKs1zDU0/weHw4GtrS1sbW3ZUikNDQ2oqWkOYkoeyHHUyK9A/pkjkIlFKLlbhLNnz8LQ0BCenp4YPHgwPD092WBIk9ypbkBeVWs6Ay8rEwyy1Iwl0OX1QrnSJ342prA17vsBNiGdUWlOjVAoxNKlS5GamgpTU1Pw+XxIpVJ4e3tj48aNastz0Zu0baJw0f2RgxZ6VWUIcrKGu7t7tw8/JyQk4M6dOwCaV9dERkbCx8enzw1zJyUl4dixY3JtgYGBGDNmDKysrB768RiGQTZPoFD2INrVBsZ6fT+zrCrK64U4X8xnUwLoyySovHQahXduKywlb8vR0RHDhw9HaGhoD/X00VQ0CHGuiM+OvDmZGiDCWTPmojRJpDieX8kuCrAz1ke0q7VG9J0QtcypMTAwwMaNG3H16lWkpaUBaL4kFR0drRVDytquukmMK2XV7G0nUwNEeAc/0klNLBYjLS0Nenp6bMbbFlFRUdDX10dkZCS7tLgvCgoKAp/Px9WrV9kv4IyMDNy4cQOhoaEYOXJkl0evlJU9sDLUQ6SLtdYO8Vc2iJBcLJ/jaKS7PYz85qCpqQm3b99ml9W3nSAOAGVlZUoTB4rF4ocqdNoT6kQSpBS3Jqe0MNDVmNVczRmda9iAxkCHqxEFNgnpKpVGatrDMAxycnLg6+vbXQ/ZZ2jLSI1QIsPJgkp2pZOZvg5Gu9uqnJOioaEBFy9exIULF9DQ0ABzc3O8/vrrKmcR7gsqKytx/PhxZGdny7UbGBggKioKERERHX7Rtlf2IMLFSmtr6FQ1iXG2iMcmbzPS5WKUuw2M9RR/NzEMg/LycjbAKSwshEwmw4svvqgQ9P7yyy8Qi8UYPHgwvLy84Obm1qvHlkgqw6mCSgju13Qy1OFi9ABbjRl5y+UJcL3N8vooV+s+PVGdkAepZaTm+++/V9peUlICW1tbrQxqtEHLxOCWgEaXy0GEi2L5g66orq5GcnIyrl69CrG4NSNubW0tMjMzERQU1G397mm2trZ47rnnUFhYiKNHj+Lu3bsAmi+7njhxAhcvXsTMmTMV8rkALWUPqlDeZq6Sq5khhneh7IGmqhGKkdQmoDHQ4SLaTXlAAzTPyXFwcICDgwOioqIgFApx584dhfezvr4excXFAJqX5J8/fx76+vrw8PBgy0P0ZAJFGcMgpbiKDWh0OMBjrlYaE9DwGkVy2Y69rU0ooCFap1uDGiMjI/j5+T1Sh4j6ZD4wMXiEk+VDLz0tLS3F+fPnkZmZqTBHwsLCAhEREVqzUszd3R3/+te/kJ2djePHj4PH4wFoviSiLMGkUCLD+WL5sgcelsYYqmWZc9sSiCQ4V8SH6H5Ao8/lINrN+qGOKwMDA6U/hO7duwcdHR25HDkikQjZ2dnsKJqdnR0b4AwaNEht73NLkcrKNquFQp0sYWWoGZNrRVIZLpS0rr6zNtTDEC0rUUEIoOLlp61bt2L+/PlybSKRCFu2bMG4ceO6fTlsb9Gm5HsPTgz2szGF30Oc1PLz83HmzBl2wm9bDg4OiIyMhL+/v0ZfduqITCbDlStXcPr0aURERCAqKkrufoFQjOSSKtSJWr+A/WxM4auGsgd9RYNYitOFPDS2GfmL7uZl6iKRCHfu3GEvVVVXVyvdztTUFMuWLVPbe53LF+B6ResoxxBbM/hqSCFOhmGQWlKFkvtLz/W4HIwdaNvuSBohfZlaLj9NmTJFoU1fXx8zZ87EkiVL8Ntvv6nysERNlE0MftgTcmFhoUJAM2jQIERFRcHDw0Nrv7hbcLlchIaGIigoSGEyfK1QjGN5xYBe61B+b5U96CmNEinOFrUGNDocINKl+/Pu6Ovrw8fHBz4+Pmx+nJYAJz8/nx3FGTx4sMIxmJWVhYKCAnh5eWHAgAEqpw4oqWuSC2jczY3gowFFKlvcrm5gAxoACHG0oICGaC2VjmxbW1ul7ffu3cP169cfqUN9iTYk3xNKZUgpqcL9pKEw1ddBaCdZQx+sxg0AI0aMwLlz5yCRSODv74/IyMhuyeGiaR6sDs5vFOFcEY8NaBiZFJaCSgwcbN8b3esRQqkMSUV81N+fW8LlABEu1mrPc9I2P05ERATEYjHy8/ORl5cHLy8vhe0zMjKQlZWF1NRU6OrqYtCgQeylKmtr6y49Z3WTWG6E08ZIT2OKVALN/c+oaF1V5mFpDBezvlmSg5DuoFJQM2/ePIU2mUyGrKwsmlPTh8gYBhdKqtAgbr088Jhz+xODBQIBUlNTcenSJfzrX/+Sy7hrZGSEp59+Go6OjirlbNFGVU0inC3iswGjVCxGYVIiBGXFyL6QhCeeeAIDBw7s1T52N/H9gKZW1FoWIMypd0o96OnpwcvLS2lAI5VKcfv2bfa2RCJBXl4eW/vL2tqaDXAGDhyodDVbo0SK88V8tqyFiZ4OIpytNWbCt1gmw4WS1iX2Fga6CLTrnmK0hPRVKs2pCQsLg7e3t9yvFQ6HAzc3N8THx1PyvT4io7xWLuNphIsVnE0NFbarrKxkyxi0DOcHBwdj2rRpPdZXTXSptBqFtc01kHTAoPziaZTcypXbJjAwEBMmTHikmll9hUQmQ9JdPniNrROhR/TRYpwymQx37txBXl4ebt68yU7yVkZHRwfz58+XW30lkTE4U8hDtbD5tepxORjlbqMxWaAZhsGlshoUtRyfHA5iB2pGkU1COqKWOTVffvklRo0apXqviNoV1TbKBTS+NqYKAU1hYSHOnz+v9OAoLi6GVCrV2om/3cHRxAB36xphoqeLcGdLmAyeiQsXLuDUqVMQiZpXyWRkZCA3NxdjxozBiBEjNDY5pVTWvJy5bUAzzMGiTwY0QPMcKE9PT3bRQlVVFTsX586dO3JpCIDmgqctGIbBhWI+G9BwAIQ5W2lMQAMAhbWNbEADAMMczCmgIf1CtybfU+bEiROIjY1V51P0CE0aqaluEuN0YSV7WcTRxACPuTSncG9JkHj+/HkUFRUp7Ovi4oKoqCj4+Pho7BdwT5LIZNDhcORGLWtra5GYmIjMzEy5bR0dHTFnzhyYmGjOJFOg+TJmakmVXOHGIDtzDNagybJtSSQSFBYWsqM45ubmmDt3Lnv/9Ypa5PJbfxBUZV6GNUcMV1dXuLq6wsHBoU9/NmqFYpws4LGXzdzNjRDqZNm7nSKkm6hlpOazzz6DQCDodDuGYZCSkqIVQY2mUDYxuCWFe2NjI/773/8qHYr39vZmyxhoyiTIvkBZhmBzc3M8++yzGDZsGA4dOgQ+nw+geZJxdxQJ7UnM/ezIbQOaIbamGhvQAICuri48PDzg4eGBiRMnyo3aFNQ0yAU0lTkZKM24jLsA0tPTATTP5XFxcYGrqyvc3Nzg6uraZ/6uUhmDCyXVbEBjpq+DYAeaR0P6D5WCGmNjY/z1118KCciqqqrkJpEKhUL2hK6JNG3Fk4xhcLGDicFGRkYwMTFhgxoul4ugoCBERkbKTQom3cPT0xOLFi3C+fPnkZSUhMmTJ2tUwMgwDK7cq5Er9+BtbQIfa82fH9RWyyThygYhrpTVsO3iqnKUp19U2L5l1VV+fj6AvjX/LL28lp3EzeU0XzbT1tIchCijUlBTX1+P5ORkhV8n+/btg7Ozs1yl3f/85z+P1kPSZZkVdXLp+QfoimFmIP8njoyMxL179xAaGorw8HCYmVFWUXXS1dXFyJEjER4eDgMD+RVCUqkU+/fvR3h4eJ9bHs8wDNIralFQ0zovw8PSGP62ZhoVmHWV4IEileb6uhgVFgjuiACUlZWhqKgId+/eRVFRkULhTWXlMnbv3g2BQMCO5Li6usLQUHGSfne6W9eIOzWtBVSD7M1hoUHzgAjpDioFNZaWlkqHWyMiIvDGG2/gjz/+YNvi4uJU710v06Q8NQ9ODC7PvIKiopsIXLJE7vq/t7c3/u///k/hC5aol7L3OzU1FWlpaUhPT0doaChiY2PV/sXXVTd4Atyqav2CdDc30tpyDyJpc3kLUZvaVZGuVtC7/7lxcXGBi4sLu31tba1ckPNgEU6GYXDz5k00NDTILSu3s7NjL1m5ubnBxsam297PepFEbpTJxcwQgyz6xiUxQnqSSkFNYWGh0vZbt24pTI4cPHiwKk9BHkJ1kwiXSqvQvE4DqC0uwL2MSwCas6r6+/uz23I4HApo+gCxWIxz584BaP4SvHjxIm7cuIEJEyYgMDCwV4OHHJ4AObzWOXMuZoYIcdSchHMPoyWXk0DUmkjwMRerDjPumpubw9/fX+5z1VZ1dTUaGhoU2isqKlBRUYGrV68CAAwNDeHq6oqpU6fC3Fz1eS/NhWqr2YKixno6CNGgBIGEdCeVLra6u7tj+fLluHnzJkQiEWpqanDo0CGsXLkSjo6O3d3HLmv7q6i/EEplOFtQAeZ+QCOsrUZRygkAwIABA+jyUh+lp6eHl156Sa5OWn19PXbv3o1ff/0V5eXlvdKvW1X1cpWcHU0MMMLJElwt/IJkGAZp92rlLtkOd7R85FIPlpaWWLp0KaZPn44RI0bAyclJaYDR1NSE27dvw8jISKE9LS0NfD5foWisMpkVdWwR1eZkiJbtJtgkRNuptKRbKpVi+fLlOHjwIPthZRgGhoaG+Pbbb3skh82dO3cwefJkyGQytm3ZsmV45ZVXlG5fWVmJ9evXw9XVFXw+H42NjVi+fHmXl9f2xSXdDMMg6S6fPSlLxSLcProXHq5OiIyMlBsyJ30TwzDIysrCkSNHUFfXGkxwuVyEh4dj9OjRCqUZ1KWgpgGX21zCsDPWR6SL5mTQfVg3+fVIb1NC4GGLvD4MkUiEkpISuctWjY2NcHZ2xssvvyy3bW5uLrZv3w6geVFGy7wcNzc3ODs7y2U/LhU0Ibm4ir0daGcGLy2byE1IW519Fz9SnprU1FScPXuWnRA3depUuSRW6vT+++/Dx8eHndvD5XIxbtw4pZlbBQIBZs2ahZkzZ7IlHj799FPk5+fjl19+6dLz9cWg5sF8GrqltxAdNKTLdW1I3yEUCnH69GmkpqbKBerm5uaYNGmS2suP3K1txIU2NY6sDfUQ7WattStnHgwGXM0M2dQHPYFhGPD5fDQ1NSn8+Dh+/Dh7afJBXC4Xjo6OcHV1haPbANzVt4JYST6qtk6fPo0DBw7A09MTeXl5iImJwVNPPdVu38RiMbZt24aysjKUlJSgvLwcc+fOxdSpUx/pNRPSHdSSpwZonixnZmaGt956CyKRCAcOHHik68IP4969e6iursacOXO6tH1CQgLy8/PxzDPPsG3PP/88Jk+ejGPHjmHcuHHq6qraVDeJ5QIaXxtTDPGJ7sUekUdhYGCACRMmIDg4GIcOHUJBQQGA5s9ZWVmZWoOaUkGTXNFGCwNdRLpqb0BT0yTGxZJq9ra1oR6Gd1LktbtxOByFlBgt7OzsMHjwYBQVFUEoFMrdJ5PJUFJSgpLSUgwytYepfXMKDUNdrtLXcOXKFSxfvhz//PMPLC0tUV9fj8cffxxGRkaYOHGi0uf//vvv8eSTT7KXRk+cOIFFixahqqpKad0/QvoSlc5aV65cwdixY9nl2vr6+ggPD8cbb7zRYY2V7rJ161YkJiZi1KhRePfdd3Hjxo0Otz948CC8vLzkLjV5enrC3Nwc+/btU3d31aJJImX/7WhiAD8bGnLWBvb29njhhRfw9NNPw8TEBJaWloiOVl+wWl4vRGpJ61JmM31dRLtaQ19L52Q03S9SKWFaJ9VGuFj1qUtsQUFBmD17NlasWIHXXnsNU6dORXBwMGxtbdlt7P1DYGrfWmMvzMkSBrqKf7MffvgB48aNg6WlJQDAxMQEU6ZMwZYtW5Q+t0gkQkJCAn799Ve2LTY2FgEBAfj+++/lRhEJ6YtUGqlZvXo1hg0bJjfBzcXFBZMmTcJXX32Fzz//vNs6qIyPjw8WLFiACxcuYOfOndi7dy8++ugjzJgxQ2FbkUiE27dvY8yYMQr3OTk5ISMjQ+nj93UOJgYYam8OsYzBYCtjWumgRTgcDoKCguDt7Y2amhqFCtJVVVWoqKiAt7f3Iz0Pr1GE5OLWKs4mejqIdrWGga521vuSyhgkF1ehUdL8xazL5eAxFysY9tHXy+FwYGdnBzs7O4SEhAAAGhsbkV1Uijuy1qX/fjamsDVWXNEoEomQkpKCt99+W67d29sbW7duRW1trcLoukQigZWVlULGeHd3d1y/fh18Pl8uuCKkr1EpqDExMcGmTZuwatUquXZ3d3d8+eWX3dKxjrS9Hpyamorly5fjgw8+wLBhwxSWkFdXV0MmkymdEGxsbIw7d+6ou7tqweFw4GmluanqSecMDQ0V8tYwDIPDhw8jLy8PPj4+mDRpEvsr/GFUNYlx/i6fTadvqMtFtJs1jPT65hf8o2quWl3NrhICmkc3NC05HUdPH6VcE4BpDszsjPXh284obVFRESQSicI8Q1NTU0ilUuTn5yMoKEjuPmNjY5w4cULhsQoKCmBhYaHSsUZIT1JpjNnDw0Npe1JSktL8DOoUHh6OzZs3Q0dHB4cOHVK4v2XliLKRDC6Xq/ArGGiegPTgf4T0BTk5OcjLy2P//cMPP+Ds2bOQSCRdfoxaoRhJd3kQt0k2F+NmA5MOcrNouiyeAMVtyj0MtTeHo2nfSHTYVQzD4HJZDZqkzQGNgQ4XoR1Mbq6paV7J9mCi1JYfeFVVVQr7KJOdnY0bN24gPj4eurrae4wQ7aBSUGNqaoqsrCz2w1RfX49Nmzbhp59+QkRERLd2sCs8PT0xfPhwVFZWKtxnaWkJPT09pcFWQ0MDHBwceqKLhHQLd3d3DBs2jL0tkUhw4sQJbNy4sUt5mgQiCc4V8SG6X/FUj8tBtKs1zPS198uqsLYR2W2SCXpYGmvkKGdeVT3u1bdOHB7uZAGjDi6dtQQgD1YUF4nup4CQShX2eZBMJsNnn32GCRMm0CRhohFUOpO99tprWLp0KVJTU9kqxFKpFN7e3nj//fe7u49dYmFh0e5qgiFDhqC0tFShvbS0FFFRUeruGiHdxtjYGE8++SRbAbysrAwAwOPxsG3bNvj7+2PChAlKVyI2iKU4V8Rnf+nrcjiIcrWGhaFmXYJ5GLwGEa6UVbO37Y31EWSveVWr+Y0iZFa05jHytjaBo0nHI00tqR0enNxbX9+8arIrq1W//PJLuLm54T//+Q/N2yMaQaWgxsDAABs3bsS1a9dw7do1AM2XpKKjoxV+FfQEiUSCGzduYOHChUrvnzx5MtauXYvGxkZ2cnN+fj6qq6v7THVdQh6Gm5sbXn75ZVy8eBEnT55kl/5mZmYiLy8Po0ePRlhYGHR0mn/JN0mkOFfEQ4OkTTkAV6tHzp7bl9WLJEgpaZ0Ibaavi3BnK43LjiySynChpJpdoWZtqIchXUgSaG9vDyMjI4UVqdXV1QCAgQMHdrj/1q1bYWxsjBUrVgAAiouLYWdn12PJIAlRhUoRyPTp0/H5558jODgY8+fPx/z58zFy5MgeCWhSU1MRHx+PU6dOsW3fffcdpkyZgoCAAADApk2bMHXqVPD5fADAs88+C1tbW+zevZvdZ/v27QgODlbrcllC1Kkl63B8fDwCAwPZdpFIhMTERBQXFzfflspwrogPgbg5oOEAiHCxgp2SFTPaQiyV4XxxFYT3R6X0dbiIdLHSuPIBDMPgSlkNG4zqcTkY4dy1shX6+vqIjIzErVu35NozMzPh5+fX4SqmAwcOgMPhYMmSJWzbn3/+SaM1pM9TaaRGIBC0O3emrKxMrfWfzM3NUVxcjMWLF2PUqFEYMGAAwsPD5UozVFVVobKykv31ampqit9++w1fffUVm8Wzrq4OP//8M/tLlhBNZWZmhunTpyMkJASHDh1CRUUFAgMD4e7uDrFUhqS7fNSKmicScwCEOVt2eulCk8kYBqkl1ai7/5q5HOAxZyuYaOC8odvVDSgRtE5wDnG0eKgJ3c899xxWrFiBN998E2ZmZuDz+UhMTJRbuXr69GmsWLECX3/9NaKionDu3Dn8+uuvGD9+PDZt2gSgtZyHsoUVhPQlKpVJuH79Om7cuIGZM2cq3JeQkKCVE8r6YpkEQh4klUqRmpqKoKAgGBqbIOkuH7zG1oKNw+zNMMhKuxM1XrtXg9vVrQsDQh0t4G5h3MEefVN1kxinCivZy2celsYIdrB46MfZuXMnzpw5A19fX2RnZ2PMmDFyaTFOnz6NZcuWYe3atQgKCkJsbKzShRVjx47Fhg0bVH05hHQLtdR+evXVV3H9+nUAzfNrWkilUpSXl3ea4VdTKEvCR0EN0RS5PAGut6m4XZGWisaSfIwdOxYBAQFaeSnhVlU90spbi1T62JjCX01FKtVJIpPhRH4le8nQwkAXo91t+1TmY0J6g1pqP/n5+aGmpkZhoplUKsWFCxdUeUhCSDeTofX3ikFVKcqy0gAAu3btQmpqKiZMmAB3d/fe6l63K6tvkgtoXMwMMURDy4dcu1fLBjQ6HA7CnPtWKQdC+qouBTVff/01hEIh3n33XQDNq4mmT58ONzc3hW2VJcDTVG0jQU0onUBIW4OtTGGoqwMTPR0UCkpgbGzMXlYoLi7Gli1b4Ofnh3Hjxml8ZfcaoRgX2hSptDLUQ2gPF6nsLgU1DSisbWRvD3Mw1+o8QoR0py4tBdixYwdGjBjB3r59+7bSgAYAJk2a1D09I4Q8El0uBwMtjGFnbIDhw4djyZIliIyMlJscn5WVhR9++AH//PMPGhsbO3i0vqtJIkVycRUk9yefGOly8VgfK1LZVXVCCa7dax1tcjc30sj5QIT0li4FNXFxcRg/fjx7+8qVK+1ue/z48UfvFSGk2xkaGmL8+PFYvHgxm/4AaE7OlpKSgvXr1yMlJaVLmWb7CqmMQUpxFRruX6rR5XAQ6WLdZ4tUdkQqY3ChtIqtx2Wmr4NgB81LFEhIb+rSmKZYLEZCQgKbgfLmzZvYs2ePwnYikQh79uyRC4AIIX2LpaUlnnnmGYSHhyMxMRFFRUUAgKamJiQlJSEkJEQjUh0053CpBr9NkcoRzpYamyE5vaIWNcLWZehhTlbQ7YVkpoRosi4FNQsXLsQXX3yBvXv3srlfkpKSlG6ridewCemPXF1d8eKLLyIrKwvHjh1DVVUVxowZozEZY7N5AhS1KVIZaGcGJw0rUtnibl0j7rRZhh5kb66xwRkhvemhlnSLxWLw+Xx8++23iI+PV7hfKBRiy5Yt+OSTT7q1k30B5akh2kwikSA9PR3BwcEKmcFTU1Ph6+sLC4uHz5GiLndrG3GhtJq9PdDCGMMczDXyR1W9SILjBZXsnCAXM0OEdVB9m5D+rFuXdOvp6cHBwQEjR46Ei4uL0m3i4uIesot9F614Iv2Frq4uQkJCFNoLCgpw5MgRHD16FBEREYiJiZHLTdUb+I0iXGpTpNLOWB/BGhrQyBgGF0qr2YDGWE8HIQ4WGvlaCOkLVLpg29EKJz8/P5U7QwjpOxiGQWJiIoDmHFRJSUlYv349Ll26pFD5uac0iCVILm4tUmmqr6ORRSpbZFbUoer+nCAOgDAnS42rT0VIX0Kfng7k5OSw/xHS33A4HEyYMAHOzs5sW0NDAw4ePIiNGzciLy8PKiQkV5lYKsP5u22KVHKbVzrpa2gQUCZoQl5VPXs7wM5Mq6umE9ITNPNsQAjpEQMGDMCCBQvw9NNPs6sfAaCiogJ//PEHfvvtN5SVlam9H8z9yzRtC3NGuFjBVEOT0jWKpXKX0BxMDDDYyqT3OkSIlqCghhDSIQ6Hg6CgICxevBixsbFyq6Nu376Nn376CXv37lVrfpuMijrcqxeyt0McLWBr3Ltze1TFMAwullZDJG0e5TLU5Wps9mNC+ppuD2roUg0h2klPTw8xMTFYsmQJhg8fLvcl3NjYqLbcNrer63GzzWUab2sTDNDgLLtZPAEq21ROH+FkCQNd+n1JSHfo0tjtpUuXunTtXCqVYufOnfjyyy8fuWOEkL7J1NQUU6ZMQVhYGI4ePYrbt2+rLeHmvXoh0tqUDXA2NdTIqtstKhqEyOYJ2Nt+Nqaw09ARJ0L6oi7lqZk+fTqysrI6fTCGYcDhcLq0raahPDWEKMfj8WBjYyPXJhKJsGPHDkRERMDDw0Olx60VinG6kAfx/aVOlgZ6GOlurbFZdoUSKY7nV6Lp/kRnO2N9RLta02UnQh5Ct+SpiY6ORlxcHNzd3TvcTiwWY9u2bQ/ZRUKIJnswoAGA5ORk5OXlIS8vD97e3hg/fjxsbW27/JhCiQzJxVVsQGOoy8VjrppbNoBhGFwqq2EDGgMdLkIpwR4h3a5LQc24ceMQFBTUpQe0tLR8lP70KZR8j5CHJ5FIcPHiRfZ2bm4u8vLyMHz4cIwePRomJh2v8pHKGKSU8FF/v0ilDoeDx1ysYaSBRSpb5FXVy010Hu5kodGvh5C+6qHKJPQ3yoIauvxESOdqa2tx8uRJXLt2Ta7dwMAA0dHRiIiIgK6u4m8qhmFwuawGhbWNbFuEsxWczTSzphPQnAH5dCEPLSdab2sTBNhR9W1CVNHZ5aduD2pycnK0coSD5tQQ8vBKS0uRmJiI/Px8uXYLCwuMHTsWAQEBcpdgcngCZFbWsbcD7MzgbW3aU93tdiKpDCcKKtFwf9TJ2lAPI91tNDYDMiG9rVuCGlr9REENIapiGAa5ubk4evQoeDye3H1eXl5svbjiukakllSz9w2wMNLoOkgMwyC1pBolguZK4npcDmIH2sJETzMTBhLSF3TLROFVq1Y91OonbQxqCCGq4XA48PHxweDBg3H58mWcPn0aDQ0NAMCujKpqEuFSm6rbtkb6GKbBAQ0A3KluYAMaoDlhIAU0hKgXrX4ihPQIHR0dhIWFISgoCGfPnsXNmzcxYsQINIilOH+3CvcT7EKPkSLUwUyjL9FUN4mRXtGaX8fD0hguZka92CNC+oduXf3E5/N7rXovIUQzGBoaYvz48Rg7dixkAJKLeWyRSh0wuH5oBzJFTRgyZAiCgoIwYMAAjRqxkchkuFDSWkncwkAXgTQxmJAe0aWg5sGAprGxEQkJCSgsLJQLYsrLy3H9+vXu7SEhRCtxuVxcLqlCjbC1SKX4diZEdTUAgKtXr+Lq1aswNzdHYGAggoKCYG9v34s97ppr92ohaLMcPczZCjpczQnKCNFkKl3g/fjjj3Hs2DGYmppCLBbDwMAADMOguroas2bN6u4+EkK0kIxhUNxmzkmwgwWKK4xQYmWFqqoqtr22thZJSUlISkqCo6MjgoKCEBAQADOzvlcuoaCmQW45+jAHc5hpaCVxQjSRSp82HR0dpKamAgD++usvPP/88wCAY8eOyVXw1XTauDSdkL6Cy+FgsJUJCmoa4W1tgkGWxhgUHY2oqCgUFxcjLS0NmZmZaGxsDRLKyspQVlaGo0ePYsaMGfDz8+vFVyCvTiTBtTZ1qtzNjeCuwYU3CdFEKgU1Xl5ebEVeiUQCgUAAU1NTjB49Gs8++yxGjhzZrZ0khGinADtz+Nuayc2Z4XA4cHV1haurKyZNmoSbN28iIyMD2dnZkEql7HYPLlxgGAYMw4DbC6UUpDIGF0qqIL2f+sJUXwfBDjSPhpCeplJQk5ubi2+//RaxsbGYMGECli5dijlz5uDq1asKSbY0Wdt18DRqQ4h6dDQJWEdHBz4+PvDx8UFTUxOysrKQnp4OfX19hXILZWVl+P333xEQEICgoCA4OTn12ATj9Ipadm4QlwOEO2lunSpCNJlKQc2cOXOwcOFC6OvrY9GiRYiJicGiRYsAQKNGaQQCAerr6+Hg4NDbXSGEdMLQ0BDDhg3DsGHD5EZsWqSlpaG+vh6pqalITU2Fra0tAgMDERgYCCsrK7X1q7iuEXeqG9jbQfbmsDDUU9vzEULa121lEjIyMnDv3j3ExMTAwMCgOx6yXWKxGD///DP27NmDiooKDBo0CEuWLMGYMWM63O+tt97C/v372dsWFhY4deoUjI07v+5NGYUJ6ds2bdqE0tJSpfe5u7sjMDAQ/v7+MDLqvnwx9SIJThRUstXEXUwNEeZM1bcJUZcer/109uxZxMTEdOdDKli1ahW4XC5CQkJQXFyMX375BXw+HwkJCRgxYoTSfe7evYuPP/4Yjz/+ONs2YMAADB8+vEvPSUENIX2bTCZDfn4+0tPTkZWVBZFIpLCNjo4OvLy8MHr06EceoZUxDE4X8lDVJAYAGOvpIHaALfR16LITIerSLWUSHiQSiXDgwAHk5OSgrq5Ori7UlStX8M8//6jysF1y7949GBoaYtmyZWzb8OHDMWPGDOzcubPdoGbLli14/fXXERgYqLa+EUJ6D5fLhYeHBzw8PDB58mTk5OQgIyMDN2/eZM9RUqkU2dnZiI2NfeTny6yoYwMaDoAwJ0sKaAjpZSoFNUuXLsWpU6dgY2Mjt4RbJpOhsrKy2zqnDI/Hw4IFC+TagoKCYGlpiZqaGqX78Pl87NixA//73/8QFBSESZMmYdasWVq1/JwQ0kpfX5+dTyMQCJCZmYn09HSUlJTAyckJdnZ2ctvX1tbi4sWLGDp0KGxtbTt9/DJBE/Kq6tnbAXZmsDai8wkhvU2loCY1NRW7du1SmiPi77//fuROdWTIkCFK24VCIUJCQpTeJxAIEB8fj7y8PJw8eRKXL1/G7t27sXXrVlhYWKizu4SQXmZqaorw8HCEh4ejsrJSLu9Ni4yMDJw7dw7nzp2Dk5MTm+DP1NRUYdtGsRSXylp/QDmYGGCwlYnCdoSQnqdSUBMYGNjuEucnn3zykTqkimvXrsHIyAgzZsxQer+7uzsWLlwIAKivr8cXX3yBP//8Ex9//DHWrl2rsD0t3yZEO7U3CpOens7+u7S0FKWlpUhMTISnpycCAwPh6+sLfX19MAyDi6XVEN2vVWWoy0Woo/KJwTk5OVi3bh2Sk5PR1NQENzc3LFmyBNOmTYNMJsMLL7yACxcuwMzMDMOHD8eyZct69Nxz+vRpHDhwAJ6ensjLy0NMTAyeeuqpLu1769YtrFq1CosWLUJoaKh6O0rIQ1BpovCFCxfA5/MxadIkhfv+/vvvdoMLdXn11Vfx7LPPYty4cV3e5+2338bBgweRlpYGPT355ZcdnVhoojAh2oVhGOTk5CA9PR25ublKl4vr6enBz88P9gEhuCdrvcwU42YNO+OOV3tu374dH330EdasWSMXNJw+fRr79+/HRx99pHRESJ2uXLmCRYsW4Z9//oGlpSXq6+vx+OOP49///jcmTpzY7n4nTpxAYmIiTE1NsW3bNiQkJCA8PLwHe076O7WsfiosLER8fDwaGhrk2qVSKSoqKpCZmalCV1Xz+++/QyKR4IUXXnio/S5duoTZs2fj7NmzXSqSR6ufCNF+jY2NbIK/goICuftM7JwwaMwT4NxPqudnYwo/287rTzU2NmLkyJEYOHAge3k+MzMT+/btw8qVK3tl+fdLL70ER0dHfPbZZ2zbF198gStXruDPP//sdP+7d+9i7NixFNSQHqeW1U/x8fGor69HcHCwXE4amUyGa9euqfKQKjl27BgEAgFeeeWVh97XwsICurq6sLS07P6OEUI0kpGREUJCQhASEoLq6mpkZGQgPT0dlTwe3B4bwwY0tkb68LXp2uiKkZERnn32WWzevBnp6ekwNDTEzp078d577/VKQCMSiZCSkoK3335brt3b2xtbt25FbW0tzM2pxAPRTCoFNffu3Ws3aV1ycvIjd6orzp07h+zsbCxevFiuPTs7G76+vp3un5aWhpiYGFoBRQhRytLSEjExMYiOjkZ+SRmuCprb9XW4GPGQCfbi4uKwdetWrF27Fu7u7vjggw96pUYVABQVFUEikShc8jI1NYVUKkV+fj6CgoJ6pW+EPCqVgpqAgACV7usuly5dwg8//IAXXngBR44cAdB8Xfzq1auIjo7GmTNnsH//fvz666+wtrbGe++9B1NTU7z66quwtLTE3bt3sWPHDnz11Vdq7yshRLNxOBwMcnFCU2UdeI0i+NuZwUhX56Eew83NDaNHj8aJEyewatUq6OqqdOrtFi2pLx78UdpSS6uqqqrH+0RId1Hpk/XOO+9g9+7dmD17tsJ9Bw4cwPPPP//IHWtPVlYWFi5ciPr6ely5ckXuPhcXF6xcuRLJycmorKyEUCgE0Pxh3b17Nw4dOoRJkybB2toaGzZsgLW1tdr6SQjRLl2ZP9MeHo/HJgDcvXs34uPjVX6s5cuXg8fjdWlba2trfPnll3JtLQHVgyNFLRmYlU2UJkRTqDRR+Mknn0R+fj7EYrHS+7Oysh65Y30NTRQmhKiipqYGq1evxrvvvou4uDhUV1fjxIkTCqsue0rLJN9169Zh8uTJbPuhQ4fwf//3f/j99987XaZNE4VJb1HLROFhw4bB19cX7u7ucu0SiQRnzpxR5SH7JMpXQwh5FAKBAKtXr8by5cthZmaGuXPn4v3330diYiKeeOKJXumTvb09jIyMFEZ7qqurAQADBw7s+U4R0k1UCmqmTZuGgIAApZNs1V3MkhBCNEFTUxNWrVqF//u//4OVlRWA5lHur7/+Gtu2bVMIaoRCIX766Sc4OzuDx+Nh6tSpcHZ2VnjcR738pK+vj8jISNy6dUuuPTMzE35+fl0qE0FIn8V0k+zsbKaioqK7Hq7P8fb2Zry9vXu7G4QQDVBaWsrMnTuXKSoqUrhv9erVjLe3N5ORkSHX/tprrzHnz59n/3369Gm19e/UqVNMeHg4U1tbyzAMw/B4PCY0NJRJTExU2ObcuXMK+xcWFjLe3t5sfwnpKZ19F6s8Udje3h6zZ8+Gvb09/v3vf2Pnzp3Q19fHN9980y0VcAkhRNOcOnUKW7duxeXLlyESiZCQkIB3332Xvf/777/H7t27AQALFy7E8OHDsWrVKuTk5KC0tBSPPfYYAOC9996Do6Oj2vo5atQovP3223jvvffg6+uL7Oxs/Pvf/8b48ePlthOLxZBIJOztS5cu4bfffsONGzcAACtXrsTQoUMxdepUhX0J6Q0qTRSOi4vD5s2bYWhoiJMnT2LRokV44403MGvWLHz33Xd4//331dHXXkUThQkh6vLf//4XFRUVWLlyZW93hZA+TS0ThZ944gkYGhpCLBZj9erVCAoKwquvvgoA8PLyUrGrhBDSP7m4uKCuro69nZubC4ZhaLECIQ9JpZSW5eXlEAgEWLNmDQoLC/HOO++w912+fLnbOkcIIf3B+PHjIRQKsWvXLuzatQslJSUU0BCiApUzCkdFRUEqleL999/HsGHDkJ6eji1btuDIkSMKs+0JIYS0T0dHBytWrOjtbhCi8VSaUwM0Z5+USCRsqm0ej4empiYAzUOp2obm1BBCCCG9Sy1zaoDmXAdt89TY2Nio+lB9Fg3/EkIIIZqjd8rEEkIIIYR0s94rFasB2g5v0agNIYQQ0rfRSA0hhBBCtAIFNYQQQgjRChTUEEIIIUQrUFBDCCGEEK1AQQ0hhBBCtAIFNYQQQgjRCrSkuwO0jJsQQgjRHDRSQwghhBCtQCM1HaDke4QQQojmoJEaQgghhGgFCmoIIYQQohUoqCGEEEKIVqCghhBCCCFagYIaQgghhGgFCmoIIYQQohVoSXcHaBk3IYQQojlopIYQQgghWoFGajpAyfcIIYQQzUEjNYQQQgjRCho7UnPo0CEkJyfD3d0dGRkZmDFjBmJiYtrdvrKyEuvXr4erqyv4fD4aGxuxfPlymJiY9GCvCSGEEKIuGhnU/P333/j+++9x+PBhGBsbo6ysDE888QR++uknhIaGKmwvEAgwf/58zJw5E/PmzQMAfPrpp1i6dCl++eWXnu4+IYQQQtRA4y4/CQQCfPHFF5g2bRqMjY0BAI6Ojhg5ciQ+/fRTpfskJCQgPz8fzzzzDNv2/PPP4+zZszh27FiP9JsQQggh6qVxQc3Zs2dRW1uLoUOHyrUPHToUWVlZyM3NVdjn4MGD8PLykrvU5OnpCXNzc+zbt0/tfSaEEEKI+mnc5afs7GwAgLOzs1y7i4sLACAjIwPe3t5su0gkwu3btzFmzBiFx3JyckJGRoZCe0crnWgVFCGEENI3adxITVVVFQAoTPBtuRTF4/Hk2qurqyGTyZROCDY2NkZlZaWaekoIIYSQnqRxIzX6+voAAA6HI9fO5TbHZ3p6el3avmWfB7cH5PPTPKqWkR1VHlPVfbV9v954Tk3ZrzeeU1P2643n1Pb9euM5NWW/3nhOTdmvO56zPRo3UmNjYwMAqK+vl2tvue3g4CDXbmlpCT09PTQ0NCg8VkNDg8L2hBBCCNFMGhfU+Pv7AwDKysrk2ktLSwEAvr6+CvsMGTKEvf/Bffz8/NTQS0IIIYT0NA7DMExvd+JhSCQSREVFIS4uDkuXLmXb33zzTRQUFGDHjh0K+2zduhVr165FamoqjIyMAAD5+fmYOHEiNm3ahFGjRqmtv48yPKft6L1pH7037aP3pn303rSP3pv2adN7o3EjNbq6unjttdewd+9eNDU1AQDKy8tx4sQJxMfHAwA2bdqEqVOngs/nAwCeffZZ2NraYvfu3ezjbN++HcHBwYiOju75F0EIIYSQbqdxIzUttm/fjitXrsDb2xvXr1/HlClTMH78eADAmjVrsGfPHuzatQtOTk4AgJKSEnz11Vfw8PBAU1MT+Hw+Vq5cCXNz8958GYQQQgjpJhob1BBCCCGEtKVxl58IIYQQQpShoKaX3b59u7e7QLRAfz2O+uvr7i70/hFV9OXjRuOS7/VVL730Es6dO6fQrquri5SUFJiZmeHOnTuYPHkyZDIZe/+yZcvwyiuv9GRXe0RlZSW2bNkCOzs7zJ8/X+6+Q4cOITk5Ge7u7sjIyMCMGTMQExPz0NtoqvbeG7FYjJ9//hl79uxBRUUFBg0ahCVLliiU+NDm46ij46Yrr7s/HjddOfcA2nncdOUz01/PN529N9p6vqE5Nd2goKAAH374IZ577jkYGhqy7adPn0ZhYSE2b94MAHj//ffh4+PDlnTgcrkYN24cTE1Ne6Xf6nL69GkcOHAA+/btw+LFi7FkyRL2vr///hvff/89Dh8+DGNjY5SVleGJJ57ATz/9hNDQ0C5vo6k6em9WrVoFLpeLkJAQFBcX45dffgGfz0dCQgJGjBjBbqetx1FH7w3Q+evuj8dNV889gHYeN519Zvrz+aaz90ZrzzcMeWQnTpxg6uvrFdpfeeUV5s8//2QYhmHKysqYxYsX93TXek1BQQHj7e3NrF+/nm2rq6tjQkNDma+//lpu2zfeeIOZNm1al7fRdMrem7KyMoXXnJaWxnh7ezMrVqyQ206bjyNl7w3DdP66++tx05VzD8No53HT2WemP59vOntvtPl8Q3NqusGYMWPYKLZFXV0dkpOTMW7cOADNCQATExMxatQovPvuu7hx40ZvdLXHtNTiauvs2bOora3F0KFD5dqHDh2KrKws5ObmdmkbTafsveHxeFiwYIFcW1BQECwtLVFTU8O2aftxpOy9ATp/3f31uOnKuQfQzuOms89Mfz7fdPbeaPP5hoIaNTl+/DiCgoLYWlU+Pj5YsGAB7O3tsXPnTsyYMQN///13L/eyZ2VnZwMAnJ2d5dpdXFwAABkZGV3aRhsNGTJEac4koVCIkJAQ9nZ/PY46e9399bhR5sFzD6Cdx01nn5n+fL7p7L3R5vMNTRRWkyNHjmDixIns7aeeeor9d2pqKpYvX44PPvgAw4YNw+DBg3uhhz2vqqoKAGBiYiLX3vJLk8fjdWmb/uLatWswMjLCjBkz2Lb+ehx19rrpuGn14LkH6D/HTdvPzNq1awHQ+aaFsvNJZ/dr4nFDIzVqIBAIcP78eTbD8YPCw8OxefNm6Ojo4NChQz3cu96jr68PAOBwOHLtLcPqenp6Xdqmv9i4cSM++eQTWFpaKr2/vx5Hyl43HTfNOjv3ANp93LT9zND5Rl5n5xNtOd/QSI0aHDt2DEOGDIGDg0O723h6emL48OGorKzswZ71rpbh8Pr6ern2ltsODg5sPa+OtukPfv/9dzz22GNy8yKU6Y/HEaD4urtybPUHXTn3ANp53Dz4maHzTavOzifadL6hkRo1OHLkCCZMmNDpdhYWFnLXvbWdv78/AKCsrEyuvbS0FADg6+vbpW203bFjxyAQCPDCCy90afv+dhy1aPu66bhp1tVzD6Bdx42yzwydb5p1dj7RtvMNBTXdTCAQICkpqdMTi0QiwY0bNzB27Nge6lnvi4yMhKWlJdLS0uTar127hsDAQHh4eHRpG2127tw5ZGdnKyS2apnQ+KD+eBwBiq+7vx83QNfPPYB2HTftfWbs7e37/fmms/OJNp5vKKjpZsePH4eXlxdcXV3ZttTUVMTHx+PUqVNs23fffYcpU6YgICCgF3qpfiKRCADkslDq6uritddew969e9lh3/Lycpw4cQLx8fFd3kbTKXtvAODSpUv44YcfMHjwYBw5cgRHjhzB4cOHsWrVKpSXl/eL40jZe9OV192fj5sWys49gHaffzr7zPTn801n7422nm8oo3A3W7RoEYKDg+Ui36ysLLzzzju4efMmRo0ahQEDBiA8PByjRo3qxZ6qT3JyMv744w8kJibCw8MD8fHxGD16NJuBcvv27bhy5Qq8vb1x/fp1TJkyRWFiY1e20UTtvTdFRUWYPXu2wrV9oHl56bFjx5CTk6PVx1FH701XX3d/O27aZnVVdu4BtPf8k5WV1elnhsvl9svzTWfvzXfffYe5c+dq5fmGghpCCCGEaAW6/EQIIYQQrUBBDSGEEEK0AgU1hBBCCNEKFNQQQgghRCtQUEMIIYQQrUBBDSGEEEK0AgU1hBBCCNEKFNQQQgghRCtQUEMIIYQQrUBBDSGEEEK0AgU1hBBCeszixYt7uwtEi1FQQ/qN9PR0fP3113j66adx8eLF3u5OuwQCAb7++musX78eERER+PLLL3u7S6QHbN26FeHh4SguLu61PuTn5yMiIgJFRUVqefw7d+7A3Nz8off7559/MGvWLPj4+MDHxwfvvPMOCgsLAQCZmZlYunQpe99LL72EpKSk7u460RAU1JB+IyAgAO7u7rhx4wZkMllvd6dda9asgY2NDV5//XV8/PHHEAqFvd0logZNTU1ytwcNGoTIyEi5yts97ccff0RVVRW+/fZbtTz+3r178eSTTz70fhMnTsSHH34IAPD09MTnn38Od3d3AIC/vz/WrVsHExMTAMDatWsRFRXVfZ0mGoWCGtJvcLlc9kTYV4lEIuzdu5f9NTtx4kS89957vdwr0t3q6urwySefyLWNGjUK69atg4WFRa/0KTMzE25uboiNjcWBAweQnZ3d7c9x6dIlhIWFqbSvmZkZAMDGxkbhPi6XC0tLSwDotfeP9A0U1BDSh1RVVdHIjJZramrC0qVL2csnD+qtUcQtW7bgxRdfxLJly8DlcrF27dpuffzLly9j6NCh4HLpa4eoj25vd4CQ3nbjxg1s27YN3t7eyMvLg1QqxQcffIDi4mIcOnQI2dnZiI+Px7Jly2Bqaopdu3aBw+Fgx44duHXrFvh8Pvbs2QMdHR2MGzcOzz33HKKiotDU1IQff/wRtbW1uHPnDurr67Fy5UoMHz5caT8OHz6MM2fOAAAOHDiArKwshISEwNPTs91+CIXCDp9DIpFg48aNyM/Ph7u7O2pqamBiYoKGhga89957OHXqFN5++234+vpi27ZtqKiowIEDB7B69WosXrwYS5YsAYAOX0tGRgZ27tyJ5ORkbNiwAZ9++imuXr2KsLAwucsCDMPgr7/+QlFREfT09JCcnIwXX3wREydOxLFjx/DWW29BKpVi3bp1GDduHABg165d+PLLL7Fp0yYEBgYqvGeJiYm4fv06HBwccODAAQwYMACrV6/utM/Xr1/Hvn37kJ6ejjfffBOff/458vLy4Ofnh08++QQ+Pj4dHhsmJibIzc1t9++SlZXV7n5Hjx7FzZs3AQCfffYZwsLCMHjwYOzZswcHDhzAr7/+CldXVwDNI3cbN25EQ0MDdHR0cPnyZYSFhWHx4sXQ19fv8nvfmeTkZAwbNgwmJibw8vLCtGnTsGvXLly8eBEjRoxQ2P6dd97BhQsXUFdXh+TkZOjo6LD3zZ07F88//zwmT54st8++ffswe/Zspc+/ZMkSjB49Gs888wzblpSUBDc3t0caXY2IiMCUKVNgaGgIAPjzzz8hlUpx6NAhODk5qfy4pA9jCOlHUlJSGG9vbyYlJYVti4qKYtauXcswDMOIxWImJCSE2bBhA1NYWMg899xzTGRkJLN9+3bm2LFjzKeffsowDMMcP36cGTNmDCOTyRiGYZg333yT8fb2ZoqKitjHXbhwIXP9+nX29oIFC5iQkBCmqqqq3f4VFRUx3t7ezM6dO9m2jvrR2XMsW7aMefPNN9l+CgQCJjAwkFmxYgW7T1xcHDNnzhz2tkwmY7y9vZn169d36bVIJBLmvffeY4YMGcL8/PPPTF1dHXP58mXGx8eHSUhIYPf55JNPmA0bNrC3P/30U8bHx4eprKxkGIZhvvjiC8bHx4cpKChgt0lKSmK++eYbpe+VUChkAgMDmdraWoZhGKa+vp756quvutTngoIC5rnnnmOCg4OZ3377jSkvL2eOHj3KhISEMGPGjGGEQiHDMO0fG539XTraj2EYZs6cOXLveWVlJbN69WqFY+iVV15h/vOf/7C3eTweExkZybz88ssMwzBdfu87s2zZMkYkErG3S0pKmMDAQOa5555Tur1UKmU++OADZvbs2XLtubm5jLe3N3Pz5k25dqFQyMybN0/pYzU0NDD+/v7Mvn375B5/xIgRzMGDB9m2ls9G2/etrTFjxjDe3t5ybdu2bWP/vXv3bsbb21uujWgfGqkh/d7zzz+PmJgYAACHw4GpqSkKCwvh5uaGgQMHoqioCLNmzQIAjB07FgCwf/9+2NnZgcPhAACmTZuG/fv3g8/nw9XVFVevXsWFCxewZ88e7NmzBwBgZGQET09P3L17l73+3xXt9aOz58jJycGBAwewb98+tp8mJiawtbWVe/wHLwe0bNuis+cJCAiAvb09JBIJFixYAAAICQmBra0t8vLyAAC5ubnYvn07rly5wj7ua6+9Bn9/f1hbWwMA5s+fj4SEBCQkJLDziPbv349XX31V6fsiFoshk8nwzTffYMWKFTA2NsacOXO63OeBAwfi3r177OjBuHHj8PLLL2PdunU4efIkJk6c2O6x0dHfBWj/mGqPjY0NvLy85NouXryIkydP4u+//2bbrK2t8dJLL2HNmjVISUlBREREp+99Z44cOYLY2Fjo6emxbU5OTpg9ezY2b96MY8eOsSNnLbhcLnJychQm5P75558YM2YMPD095drPnDmDkSNHKn3+q1evQiwWy40I5eTkoKamBqGhoQrbZ2VlYe7cuQrtFRUVCm0zZ85k7/v8888RFhbW7mgR0Q4U1JB+Lz4+HmVlZUhISICuri7EYjEYhmHvNzAwUNhHLBajuLgYDMOAw+HA2dkZHA4Hbm5uAIBr167BxMQE//73v7utnw/2o7PnaFkKPnDgwEd6XlVfi76+PkQiEQAgNTUVFhYWcq/BysoKTz31FHvbzs4OTz/9NHbu3InXX38dBgYGqKysxIABA5Q+vomJCd555x188sknOHv2LFauXInY2NiH6vODAdxjjz2GdevWoaCgAEDnxwag/Pjoyn6dSUtLAwAYGxvLtQcFBQEAMjIyEBERoXTftu99R6RSKY4ePYqvvvpK4b5XX30VO3bswDfffIPY2Fi54Le+vh4ZGRlYtmwZ25afn499+/Zh586dCo914MABvPPOO0r7cPHiRbi5ucHR0VGubcCAAbC3t1fY3s/PD9u2bVNoj42NVVgOr6+vDwD48MMPIRKJsGrVKoW/OdEuNGOL9Htbt27Fu+++i6eeegpxcXHs9feOxMXFoaqqCgcPHgQAZGdnY/bs2bCysmK34fF4EAgECvt25cumqzp6jpb2hoYGtT5PV4jFYvD5/E77smDBAgiFQvzvf//DyZMn2ZGP9syePRu///47zM3NsWjRIrkvTlX63LKcumWljSrHxqPs11ZLEFFSUiLX3nKMtR1ZUdWuXbvwzDPPKP2it7CwwMsvv4y8vDzs3btX7r5Lly5BR0cHwcHBAJrf07fffhsff/yxwhyY2tpaNDU1wcHBQWkflM3buXTpktJRGlXs27cPx48fx1tvvcX+6CDai4Ia0q9lZGTg888/x+zZs9ll1F1ZfRIZGYnly5fj8OHD+PnnnyEWi+WWXnt4eEAmk+G3336T2+/u3bvYv39/t/S9s+doOYE/mIhM2YiBVCpl//3gl353vJbBgweDYRiFx7h9+7bc0mF3d3dMnDgRv/32Gw4dOqQw2bStyspKpKenY/jw4dixYweWLl2KXbt2ITs7W+U+3717F1wuF5GRkSofG13dr7PHavlSv3Dhglz7vXv3AEDpBN6HIRQKcfXqVURGRra7zbx58+Dg4IDvvvtO7rhISUlBcHAw9PX1IRQKsWzZMsyePVvp3+vIkSOYOHGi0scXiURIT0/H0KFD5dovX76M0NBQVFdXIycnR8VX2HyMfPbZZwgPD0dcXBzbfuvWLZUfk/RtdPmJ9CsSiQRA6xdKWVkZgObr+iNHjsSpU6dQXV2Ne/fugcfjQSaTsfu0tX37dqSkpGDy5MnsL+qkpCSEhYVBX18f0dHR8Pb2xvr16yESiRAVFYXCwkIcPnwY33zzTbv9a/nieDCwUNaPzp6jrq4O3333HZvMz8PDAykpKaiqqpJ7HDc3Nxw+fBjZ2dmwt7fHX3/9BWNjY5SUlEAqlXbptbT0reVyXMtraHmfo6OjERAQgLVr16K8vBxRUVEoKyvDrVu3FPLwLFiwANOnT0dwcHCH2WebmpqwefNmtg9xcXHYsGEDrK2t4eXl1aX3v7a2FnV1dTAzM4NMJsO2bdswd+5cDBgwAEePHgXQ/rFhY2Oj9O/S2TFlY2MDKysrXLt2DSKRCJmZmRg2bBjEYjEAsP8PCgrClClTsH37dsybN4+9FLNjxw5Mnz4d/v7+XXrv27Nt2zZwOBxs3bq1w+28vLxw7tw5/PHHH5g/fz6A5suJsbGxSE9Px7Zt2/Dyyy8rBCYtEhMT2z3m09LSIBQK4ezszLZlZmaisrISISEhSE5OxqBBgwA0X/ICFJMWtmgZBRQIBOyI20cffaRw2YnP5+PSpUsK836IdqCghvQb169fZ6/F79ixA1ZWVoiJiUFMTAwSEhJw5swZvPnmm5g1axZ27tyJOXPmoKGhAeXl5di6dSsmTJjAnnxtbW2Rm5uLrKwsNrcMwzDw9/fH//73P+jp6eHHH3/Ep59+is2bN2P79u0YOXIkPvvsM4U5Ei1u3LiBP/74AwCwZ88eWFlZYezYsUhMTERqaqpCP3R0dDp8DmNjY3z77bf47LPPsHDhQvj7+2PZsmXsxNwW8fHxuH37NmbOnImAgAB8/PHHOHXqFGxsbJCbmws/P78OnyczMxMnTpwAAPz3v/9FXFwcdu/ejYqKCly6dIm9vLBhwwZ88skn2LFjBw4fPoxp06Zh+fLlCu+Dv78/AgMDMW3atE7/pocPH4aOjg5CQ0ORl5eHb775hv3y78r7r6enh3Xr1kFfXx8lJSUIDQ3FwoULAaDDY+PXX3+Fr6+v0r9LZ/stW7YMcXFxSE1NxfTp07FixQpcvnyZvZT5yy+/YP78+fDy8sKaNWvw448/4o033kBISAiamprg4+PDTgru6nv/oIaGBvz888+orq7u9D1usXHjRnZC9J07d8Dn81FQUIA1a9a0m3umpKQEFhYW7WZJbilXcuvWLYwcORI8Ho+d2G5vb4/du3fj8ccfx9mzZ7F9+3YAzZ+TjRs34qmnnoKjoyNu3ryJPXv2sMH6e++9h3nz5qGyshJHjx6Fv78/O9m6sbER586dw5tvvtnl1000C4d52NlrhPRzDMPgww8/xLx58zB48GC2rba2Fr/88gsmTZrE/orui2JjYxEWFsbmc+mLnn/+eWzbtg26uur73bVy5UpcuHCBDQpI99u4cSP8/PwwatQopff/61//ApfLha+vL0xNTWFmZoZZs2Zh//79uHnzJmJjYxESEtLDvSaajEZqCHlIhw8fRlpaGhvQAM2raCwsLDBgwIB2J0SSrjl9+jTCwsLUGtCQnpGUlMSOKj1IIpHg6tWr+OCDD/D000/L3dd2VRwhD4POGoQ8JLFYjLy8PGzbtg2TJ0+GmZkZKioqcPLkSVhYWCjkgelrmpqa+lxBz4sXL+KHH36Ar68vTp06xV5qUCepVNqtK9GIvMzMTPj4+LQbnGZmZqKhoYGKT5JuRUENIQ9p2rRpqKqqwu+//441a9bAzMwMI0aMwIIFC9gcIn1RXl4e/vjjD/B4PKSkpChNqtZbmpqakJGRAR6Ph9WrV8stjVeHgwcPIjk5GZWVldi0aRMmTpzYbj4copp9+/Z1WJG7oKCATR5ISHehOTWEEEK63euvv47169f3djdIP0NBDSGEEEK0AiXfI4QQQohWoKCGEEIIIVqBghpCCCGEaAUKagghhBCiFSioIYQQQohWoKCGEEIIIVqBghpCCCGEaAUKagghhBCiFf4fwI+EqGl5Tm8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "X_cs = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]\n",
    "Dnus_, dnus_ = [], []\n",
    "for mass in (\"0.7\", \"0.8\", \"0.9\", \"1.0\", \"1.1\", \"1.2\"):\n",
    "    logs_dir_ = os.path.join(\"grid\", mass)\n",
    "    DF_    = load_history_file          (logs_dir_)\n",
    "    index_ = get_index                  (logs_dir_)\n",
    "    profs_ = get_profiles       (index_, logs_dir_)\n",
    "    freqs_ = get_all_frequencies(index_, logs_dir_)\n",
    "    \n",
    "    Dnus, dnus, X_c = [], [], []\n",
    "    for prof_num, freq_ in enumerate(freqs_):\n",
    "        hist = get_history(prof_num+1, DF=DF_, index=index_)\n",
    "        nu_max = hist.nu_max.values[0]\n",
    "        freq_ = freq_[freq_['Re(freq)'] < 5/3*nu_max]\n",
    "        \n",
    "        l0 = freq_[np.logical_and(freq_.l == 0, freq_.n_p > 10)].copy()\n",
    "        l2 = freq_[np.logical_and(freq_.l == 2, freq_.n_p > 10)].copy()\n",
    "        l2.n_p += 1 \n",
    "        freq_merge = pd.merge(l0, l2, on='n_p')\n",
    "        freq_merge['d02'] = freq_merge['Re(freq)_x'] - freq_merge['Re(freq)_y']\n",
    "        \n",
    "        Dnus += [np.mean(np.diff(l0['Re(freq)'].values))]\n",
    "        dnus += [np.mean(freq_merge['d02'])]\n",
    "        X_c  += [hist.center_h1.values[0]]\n",
    "    \n",
    "    plt.plot(Dnus, dnus, c='lightblue', lw=3, zorder=10)\n",
    "    plt.text(Dnus[0], dnus[0]+0.6, r'$'+mass+r'$' \n",
    "             if mass!='1.0' else r'$1~\\rm{M_\\odot}$')\n",
    "    \n",
    "    Dnus_ += [sp.interpolate.interp1d(X_c, Dnus)(X_cs)]\n",
    "    dnus_ += [sp.interpolate.interp1d(X_c, dnus)(X_cs)]\n",
    "\n",
    "for idx, X_c in enumerate(X_cs):\n",
    "    Dnus = np.array([Dnu[idx] for Dnu in Dnus_])\n",
    "    dnus = np.array([dnu[idx] for dnu in dnus_])\n",
    "    plt.plot(Dnus, dnus, c='gray', ls='--', lw=3)\n",
    "    plt.text(Dnus[0]+2, dnus[0]-0.5, r'$'+str(X_c)+r'$' if X_c!=0.1 else r'$X_c = 0.1$')\n",
    "\n",
    "plt.ylim([0, 18])\n",
    "plt.xlim([70, 240])\n",
    "plt.xlabel(r'large frequency separation $\\Delta\\nu/\\mu$Hz')\n",
    "plt.ylabel(r'small frequency separation $\\delta\\nu/\\mu$Hz')\n",
    "plt.title('CD Diagram', size=24)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additional useful resources\n",
    "---\n",
    "\n",
    "- [tomso](https://tomso.readthedocs.io/): Tools for Models of Stars and their Oscillations\n",
    "- [astero](https://cococubed.com/mesa_market/astero_module.html): Overview of the `astero` module \n",
    "- [tulips](https://astro-tulips.readthedocs.io/): Tools for Understanding the Lives, Interiors, and Physics of Stars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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