dr2-lmc-cluster/notebooks/Kinesis-model.ipynb

Kinesis-model.ipynb

{
  "cells": [
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from os import path\n\nimport astropy.coordinates as coord\nfrom astropy.table import Table, join\nimport astropy.units as u\nfrom astropy.io import fits\n\nimport numpy as np\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\n%matplotlib inline\nimport h5py\n\nfrom pyia import GaiaData\nimport gala.dynamics as gd\nimport gala.coordinates as gc\nimport gala.integrate as gi\nimport gala.potential as gp\nfrom gala.units import galactic\nfrom gala.mpl_style import center_emph, center_deemph\n\nimport arviz as az\nimport kinesis as kn\nimport gapipes as gp",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "vsun = coord.CartesianDifferential([11.1, 251.24, 7.25]*u.km/u.s)\ngalcen_frame = coord.Galactocentric(galcen_distance=8.1*u.kpc, galcen_v_sun=vsun)",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "mike_data = Table.read('../data/mike_results.fits')\nmike_data['pw1_id'] = [int(x.strip()[-2:]) if 'pw' in x else 9999 for x in mike_data['NAME']]\n\ndata = Table.read('../output/pw1-region-data-joined.fits')\ndata['pw1_id'] = [int(x.strip()[-2:]) if 'PW' in x else 9999 for x in data['pw1_name']]",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "all_tbl = join(data, mike_data, keys='pw1_id')\nmike_tbl = all_tbl[all_tbl['pw1_name'] != '']\n\ndec_mask = np.abs(mike_tbl['dec'] - -29.5) < 0.5\ntbl = mike_tbl[dec_mask]",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "fig, ax = plt.subplots(figsize=(6, 6))\n\nax.scatter(mike_tbl['ra'], mike_tbl['dec'], label='MIKE observed', \n           linewidth=0.5, edgecolor='k')\nax.scatter(tbl['ra'], tbl['dec'], label='sub-selected',\n           linewidth=0.5, edgecolor='k')\n\nax.set_xlim(183., 176)\nax.set_ylim(-34, -27.)\n\nax.set_xlabel('RA [deg]')\nax.set_ylabel('RA [deg]')\n\nax.legend(loc='lower left', fontsize=14)\n\nax.set_aspect('equal')\nfig.tight_layout()\nfig.set_facecolor('w')\n\nfig.savefig('../plots/MIKE-cluster-radec.png', dpi=250)",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x432 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "image/png": {
              "width": 434,
              "height": 404
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "fitter = kn.Fitter(include_T=True)",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": "INFO:kinesis.models:Reading model from disk\n",
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "def get_df(tbl):\n    tbl['radial_velocity'] = tbl['VHELIO']\n    tbl['radial_velocity_error'] = tbl['VERR']\n\n    # Set parallaxes to distance we get from isochrone fitting with big uncertainty\n    plx = coord.Distance(np.random.normal(28.7, 0.5, size=1000)*u.kpc).parallax\n    tbl['parallax'] = np.mean(plx.to_value(u.mas))\n    tbl['parallax_error'] = np.std(plx.to_value(u.mas))\n    for colname in tbl.colnames:\n        if 'parallax' in colname and 'corr' in colname:\n            tbl[colname] = 0.\n\n    df = tbl.to_pandas()\n    return df",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Use the same reference point for both runs:\ndf = get_df(tbl)\nb0 = df.g.icrs.cartesian.without_differentials().mean().xyz\n\nstanfits = []\nfor t in [tbl, mike_tbl]:\n    # fit = fitter.fit(df, sample=False, b0=b0)\n    # print(f\"v0, sigv = {fit['v0']}, {fit['sigv']:.4f}\")\n    df = get_df(t)\n    stanfits.append(fitter.fit(df, sample=True, b0=b0, iter=4000, control=dict(adapt_delta=0.95)))",
      "execution_count": 10,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "azfit = az.from_pystan(stanfits[0], coords={'x':['v1','v2','v3']}, dims={'v0':['x']})\nazfit.posterior",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 11,
          "data": {
            "text/plain": "<xarray.Dataset>\nDimensions:         (T_param_dim_0: 3, T_param_dim_1: 3, a_model_dim_0: 20, a_model_dim_1: 3, chain: 4, draw: 2000, rv_model_dim_0: 20, x: 3)\nCoordinates:\n  * chain           (chain) int64 0 1 2 3\n  * draw            (draw) int64 0 1 2 3 4 5 6 ... 1994 1995 1996 1997 1998 1999\n  * x               (x) <U2 'v1' 'v2' 'v3'\n  * a_model_dim_0   (a_model_dim_0) int64 0 1 2 3 4 5 6 ... 13 14 15 16 17 18 19\n  * a_model_dim_1   (a_model_dim_1) int64 0 1 2\n  * rv_model_dim_0  (rv_model_dim_0) int64 0 1 2 3 4 5 6 ... 14 15 16 17 18 19\n  * T_param_dim_0   (T_param_dim_0) int64 0 1 2\n  * T_param_dim_1   (T_param_dim_1) int64 0 1 2\nData variables:\n    v0              (chain, draw, x) float64 -260.9 82.24 -76.28 ... 77.63 -82.6\n    sigv            (chain, draw) float64 5.075 5.563 8.694 ... 2.062 0.8357\n    a_model         (chain, draw, a_model_dim_0, a_model_dim_1) float64 0.03496 ... 0.4448\n    rv_model        (chain, draw, rv_model_dim_0) float64 260.1 259.8 ... 262.9\n    T_param         (chain, draw, T_param_dim_0, T_param_dim_1) float64 62.26 ... 51.18\nAttributes:\n    created_at:                 2019-07-25T17:14:10.535013\n    inference_library:          pystan\n    inference_library_version:  2.19.0.1.dev0"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "azfit.sample_stats",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 12,
          "data": {
            "text/plain": "<xarray.Dataset>\nDimensions:      (chain: 4, draw: 2000)\nCoordinates:\n  * chain        (chain) int64 0 1 2 3\n  * draw         (draw) int64 0 1 2 3 4 5 6 ... 1994 1995 1996 1997 1998 1999\nData variables:\n    accept_stat  (chain, draw) float64 0.9981 0.9694 0.8603 ... 0.9681 0.9766\n    stepsize     (chain, draw) float64 0.03968 0.03968 ... 0.04029 0.04029\n    treedepth    (chain, draw) int64 6 7 6 6 6 6 6 6 6 6 ... 6 6 7 6 6 6 6 7 7 7\n    n_leapfrog   (chain, draw) int64 63 255 63 127 127 63 ... 63 63 127 127 191\n    diverging    (chain, draw) bool False False False ... False False False\n    energy       (chain, draw) float64 -346.6 -344.2 -340.3 ... -348.5 -348.1\n    lp           (chain, draw) float64 360.4 359.7 357.0 ... 362.1 364.4 364.4\nAttributes:\n    created_at:                 2019-07-25T17:14:10.541931\n    inference_library:          pystan\n    inference_library_version:  2.19.0.1.dev0"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "np.mean(stanfits[0]['v0'], axis=0), np.std(stanfits[0]['v0'], axis=0)",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 20,
          "data": {
            "text/plain": "(array([-261.23671739,   77.03374495,  -77.56363585]),\n array([3.69072904, 6.00879271, 3.46568956]))"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "scrolled": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "for stanfit, name in zip(stanfits, ['subselect', 'all-mike']):\n    fig, axes = plt.subplots(3, 3, figsize=(12, 12), sharex=True, sharey=True)\n    ax = axes.ravel()\n    bins = np.arange(-500, 500+1e-3, 20)\n    for cax, cT in zip(ax, stanfit[\"T_param\"].reshape((-1, 9)).T):\n        cax.hist(cT, bins=bins, density=True, histtype=\"step\")\n        cax.axvline(0, c=\"gray\", lw=0.5)\n        cax.set_xticks(np.arange(-500, 500+1e-3, 250))\n    axes[2, 1].set_xlabel(\"{:latex_inline}\".format(u.m/u.s/u.pc))\n    axes[1, 0].set_ylabel(\"prob. density\")\n    fig.tight_layout()\n    fig.set_facecolor('w')\n    fig.savefig('../plots/velocity-tensor-{}.png'.format(name), dpi=250)\n        \n    # ---\n    # Velocity dispersion:\n    fig, ax = plt.subplots(figsize=(6, 6))\n    ax.hist(stanfit['sigv'], bins=np.arange(0, 25, 1), density=True);\n    ax.set_xlabel('$\\sigma_v$ [{:latex_inline}]'.format(u.km/u.s))\n    ax.set_ylabel(\"prob. density\")\n    fig.tight_layout()\n    fig.set_facecolor('w')\n    fig.savefig('../plots/velocity-dispersion-{}.png'.format(name), dpi=250)",
      "execution_count": 120,
      "outputs": [
        {
          "data": {
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            "text/plain": "<Figure size 864x864 with 9 Axes>"
          },
          "metadata": {
            "image/png": {
              "height": 852,
              "width": 856
            }
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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wwOY1GVWZ7gUAAACoiAhTP0lOTtbNmzct5zVr1rTquoLt0tLSdOXKFZvXVlaV6V4AAACAiopNe3+SnJxc6Nzd3d2q6+5ul5SUpLp169qsLiMceS+HDh1SUFBQ2Qos4MSJE4avBQAAQOVQnu+TPj4+hRZTcyRmpn5y935Obm5uVl13d7vy7AtlK5XpXgAAAICKipmpn1SrVvifIn8lvHu5u93d/TiDI++lTZs2WrFihfXFAQAAAHcpz9NK4eHhNqykbJiZ+kmNGjUKnd++fduq67Kzs0vtxxkq070AAAAAFZXzp1EqiMDAQJlMJpnNZklSRkaGVdcVfBTOZDIVWl7cWSrTveD+1CRyvd3HOD2vv93HAAAAKA0zUz/x9PRUkyZNLOcpKSlWXVewXVBQkDw8PGxdWplVpnsBAAAAKirCVAHBwcGWY2uXBb98+XKx1ztbZboXAAAAoCIiTBXQq1cvy/GlS5eUlZVVavvMzEwlJSVZznv37m232sqqMt0LAAAAUBERpgro1q2b/P39Jd1Z2e7kyZOltj9x4oTlvaSAgACFhITYvUZrVaZ7AQAAACoiwlQBbm5uGj16tOV83759pbYv+PmYMWPk6upapM3atWvVpUsXPfHEE1q3bp3tir0He9wLAAAAgP9HmLpLRESEZQfmhISEUtvmf968eXMNHz68yOepqamKjIxUUlKSkpOTFRkZqdTUVNsXXQJb3gsAAACAwghTd3F3d1d0dLS8vLwUFxen8+fPF9vu3Llzio+Pl7e3txYuXCg3N7cibc6cOVPoXaXMzEydPXvWbrXfzZb3AgAAAKAw9pkqRqtWrbRkyRK99NJLmjhxov7yl7+oevXqls/T0tI0YcIEeXl56U9/+pNatmxZbD+NGzeWh4eHJVB5enqqUaNGVtdx9erVIucNGjRwyr1UZeyZBAAAgOIQpkrQtWtXxcbGasaMGerZs6cGDRqkwMBAXbhwQbGxsWrevLnWrFmjpk2bltiHr6+v5s2bp7lz50qSoqKi5OvrW2L7OXPmaOPGjZbz5OTkQp//5je/Ud26dS3nffv2VVRUlEPuBQAAAEBhJnP+Em4o0bFjx7Rnzx6lpqaqVq1aCg4OVrNmzZxdliG2vJfw8HBJ0ooVK2xZotUcMWPkKJVtZorZPAAA4CjO/E7KzJQVWrRooRYtWji7DJuoTPcCAAAAOBNhCqhCKtNsHgAAgLOxmh8AAAAAGECYAgAAAAADCFMAAAAAYADvTAG4Lznq/S9WDQQAACVhZgoAAAAADCBMAQAAAIABhCkAAAAAMIAwBQAAAAAGEKYAAAAAwABW8wMqAEetTAcAAADbYWYKAAAAAAwgTAEAAACAAYQpAAAAADCAMAUAAAAABhCmAAAAAMAAwhQAAAAAGECYAgAAAAADCFMAAAAAYABhCgAAAAAMIEwBAAAAgAGEKQAAAAAwgDAFAAAAAAYQpgAAAADAAMIUAAAAABhAmAIAAAAAAwhTAAAAAGAAYQoAAAAADCBMAQAAAIABhCkAAAAAMIAwBQAAAAAGEKYAAAAAwADCFAAAAAAYQJgCAAAAAAMIUwAAAABgAGEKAAAAAAwgTAEAAACAAYQpAAAAADCAMAUAAAAABhCmAAAAAMAAwhQAAAAAGECYAgAAAAADCFMAAAAAYABhCgAAAAAMIEwBAAAAgAGEKQAAAAAwgDAFAAAAAAYQpgAAAADAAMIUAAAAABhAmAIAAAAAAwhTAAAAAGAAYQoAAAAADCBMAQAAAIABhCkAAAAAMIAwBQAAAAAGEKYAAAAAwADCFAAAAAAYQJgCAAAAAAMIUwAAAABgAGEKAAAAAAwgTAEAAACAAYQpAAAAADCAMAUAAAAABhCmAAAAAMAAwhQAAAAAGECYAgAAAAADCFMAAAAAYABhCgAAAAAMIEwBAAAAgAGEKQAAAAAwgDAFAAAAAAYQpgAAAADAAMIUAAAAABhAmAIAAAAAAwhTAAAAAGAAYQoAAAAADCBMAQAAAIABhCkAAAAAMIAwBQAAAAAGEKYAAAAAwIBqzi4AACqyJpHrHTLO6Xn9HTIOAACwHWamAAAAAMAAwhQAAAAAGECYAgAAAAADCFMAAAAAYABhCgAAAAAMIEwBAAAAgAGEKQAAAAAwgDAFAAAAAAYQpgAAAADAgGrOLqCi2717t1asWKG9e/fKZDJJkjp27KgRI0aoU6dO9824586d09q1axUXF6fU1FRlZ2fLbDbrwQcf1JNPPqmnnnpKDRs2tPVtAAAAAJUWM1MlyMrK0tSpUzVy5Ei1bdtWW7du1c6dO7Vlyxa1bt1a4eHhmjZtmm7fvl2hx83MzNR7772nX/7yl7p+/bree+89bd26VQkJCdq6dauee+45/fOf/1SvXr20aNEi5ebm2vR+AAAAgMrKITNTCQkJCgkJkaurqyOGK7ecnByNHz9e27dv18yZMxUREWH5zNvbW2PHjpWHh4dmz56tlJQULVq0yCb3ZutxMzMz9fzzz+vAgQP65JNP9NhjjxX63MPDQ/3799eTTz6p3/72t4qOjtbVq1c1c+bMct8LAAAAUNk5ZGaqR48eSk1NdcRQNhEdHa3t27crICBAw4cPL7ZNRESE/P39tWnTJi1atKhCjjtlyhT95z//0bhx44oEqYIeeOABLVq0SDVq1NDy5csVHx9frvsAAAAAqgKHhCmz2ayTJ086YqhyO3r0qJYuXSpJCgsLk4tL8f9Erq6uCgsLkyQtXrxY//vf/yrUuAcPHtT69eslSQMGDLjn+HXq1NGQIUMkSX/5y1/KXD8AAABQ1TjsnalZs2YpLy/PUcMZtmDBAst7Q8HBwaW2zV8IIjc3V9HR0RVq3O3bt1uO/fz8rKqhffv2kqRDhw5Z1R4AAACoyhwWpv79738rNDRUO3bscNSQZXbhwgVt27bNct66detS27dp08ZyvGXLFiUmJlaYcS9fvmw5TkpKsqqO6tWrS5LS09Otag8AAABUZQ4LU2azWQkJCQoNDVVgYKDGjx+vuLi4CjVbtWHDBks91atXv+eMjr+/v7y9vSXdmSXauHFjhRm3YB+rV6+2qo780NWsWTOr2gMAAABVmcPC1NNPP60jR45o9+7dmjhxor7//nv17t1bDz74oMaMGaMNGzbYfJnxskpISLAc169f36prAgMDLcdGF26wx7gtW7a0HP/xj3/UqlWr7tnnV199JUklLn4BAAAA4P85LEzNnz9fDz30kIKDgzVlyhTFx8fr0qVLeu+993Tt2jU988wzqlevnp599lmtWrVKGRkZjirN4uDBg5bj2rVrW3VNrVq1LMcHDhyoMOP+4he/kL+/vyQpOztbU6ZM0dChQ7V161bl5OQUaX/kyBFt27ZN/fv319NPP13WWwAAAACqHIfsM/Xzn/9cvr6+RX5fp04djRo1SqNGjVJWVpY2b96stWvXavz48bpx44Z69+6twYMHKywsTDVr1rRrjcnJybp586bl3NrxCrZLS0vTlStXVLduXaeP6+bmprfeeksvvviiJTzt2bNHe/bsUd26dfXUU09p8ODBatmypX788Ue9/PLLGjZsmN544w2ZTCar6z906JCCgoKsbn+3EydOGL4WAAAAlUN5vk/6+PgUWlPAkRwyMxUXFycfH59S23h4eCgsLEwxMTG6ePGitmzZopYtW+rtt9+Wn5+f+vbtq5iYmEILK9hScnJyoXN3d3errru7nbWLPThi3B49eujDDz9UnTp1Cv3+ypUr+vOf/6x+/fopLCxMQ4cO1YwZMzRjxowSl2QHAAAAUJhDZqbKymQyqUuXLurUqZM6d+6st956S5s3b9aWLVs0btw4de3aVYMGDdLAgQPVoEEDm4x569atQudubm5WXXd3u7v7cfa4ffr0UZcuXfTaa68VWi4935EjRyRJL7/8soYOHaoJEyYUO4tYkjZt2mjFihVWtwcAAADuVp6nlcLDw21YSdlUuGkIs9msrVu3asyYMfL399eQIUN04MABmUwmmc1m5ebmKj4+XpMnT1bjxo3VuXNnvfvuuzp+/Hi5xq1WrXCutHaVwbvb3d2Ps8c9fvy4XnjhBSUmJurvf/+7Fi9erF69ehWZ2crMzNRf/vIX9evXzxKwAAAAAJTMIWEqNDRUaWlppbbJX+UvICBAffr00bJly5Samiqz2Wz5qVu3rl566SV99dVXSk9P19atW9W5c2d99NFHatmypfr166fvvvvOUI01atQodG7tyoLZ2dml9uPMcb/55hsNGjRI165d0xdffKFOnTqpT58+WrJkib755hvNmjXLslFvvqSkJD333HO6du1ame4DAAAAqGocEqZ27NhR5Mu/JB0+fFjTpk1TUFCQunTpoo8++khJSUmW8CRJDzzwgJ599ln961//UmJioj7++GOFhITI1dXV8k7Q2bNnLavUdenSRV9++WWZawwMDCy08IK1qwkWfLzOZDIVWrLcmeMmJyfr5ZdfVnp6uubMmVPknbVatWopPDxcsbGx+utf/1pob6krV65o2bJlZboPAAAAoKpx2GN+O3fulCSdOnVK8+bNU/v27dWuXTvNnTtXp06dKhSg3Nzc1L9/f3322WdKSkrSihUr1K9fP7m6upbYf48ePbR582bNnDlTERER+vbbb8tUn6enp5o0aWI5T0lJseq6gu2CgoLk4eFRIcZdsWKFbty4IX9/fz366KOl9hUSEqKVK1eqQ4cOlt/l/70AAAAAFM9hC1A8/fTTqlWrVqHV+PLDk3RndiUkJETPPvusnnnmGav3W7rb5MmTFR8fr9mzZ2vdunVlujY4OFinTp2SdGd2xhoF7yc4OLhM49lz3N27d0sqvHlvaXx8fDR9+nQNHDhQkvWhDgAAAKiqHDYzlZOTo+Tk5ELvQElS27ZtNXfuXJ0+fVoJCQl68cUXDQepfE8++aShmZVevXpZji9duqSsrKxS22dmZhZakrx3795lHtNe4+aHsrKszNe2bVt5e3tLKvu7XwAAAEBV45TV/Bo3bqzIyEh9//33+u677zR16lQ1bNjQcH+///3vVbt2bbVo0UJvv/22Vq9eXeYlyiWpW7du8vf3l3RntbyTJ0+W2v7EiROWUBgQEKCQkJCyF2+ncT09PSVZv6CFdGd2sHr16pKkhx56yOrrAAAAgKrIYWHKbDbrV7/6lXbs2KFTp05pzpw5at26dbn73b9/v9555x2lpqbq+PHjmj59unbt2qV27dqVuS83NzeNHj3acr5v375S2xf8fMyYMcW+07V27Vp16dJFTzzxRImPHdpj3FatWkmSzp07V2pfBWVlZVke7+vTp4/V1wEAAABVkcPC1OzZs7V27Vp1797dpv3mr4RnMpkse1GZzWZNmzbNUH8REREKCgqSJCUkJJTaNv/z5s2ba/jw4UU+T01NVWRkpJKSkpScnKzIyEilpqbafVxJGjx4sKQ7KyZevHix1P7ybd68WTk5OWrTpo2efPJJq64BAAAAqiqHhCmTyaSIiAi79P3II49o7NixlhBVu3ZtrVy5UmFhYYb6c3d3V3R0tLy8vBQXF6fz588X2+7cuXOKj4+Xt7e3Fi5cKDc3tyJtzpw5U+j9p8zMTJ09e9bu40rS448/rqFDhyovL0+zZ8++52bAaWlpmj9/vnx8fPThhx8WWq4dAAAAQFEOCVNms9nyLo49/PGPf9SZM2f07bff6sKFCxo0aFC5+mvVqpWWLFkiDw8PTZw4UTdv3iz0eVpamiZMmCAvLy/FxMSUuGJe48aNCy1Z7unpqUaNGtl93HyzZ8/Wr3/9a23evFnjxo0rtAJgQefOnbPMcC1btkyNGzcutV8AAAAADloaPS4uTjVr1rTrGA0bNizXIhZ369q1q2JjYzVjxgz17NlTgwYNUmBgoC5cuKDY2Fg1b95ca9asUdOmTUvsw9fXV/PmzdPcuXMlSVFRUfdcXc8W4+ZzdXXV+++/r1/+8pf68MMP1b17d4WGhqp9+/by8fHRlStXdPjwYe3evVsDBw7Uq6++WmRzXwAAAADFM5kLbvaEYh07dkx79uxRamqqatWqpeDgYDVr1uy+G/fcuXM6cOCAkpOTlZWVJV9fXwUFBemRRx4p82bDkhQeHi7pzgbBzlrB++4AACAASURBVNAkcr1TxgXs4fS8/s4uAQCA+5Izv5M6bNPesvriiy908+ZN1a1bV23btnVIeClJixYt1KJFi/t+XFvP3gEAAABVmUPemQoNDVVaWlqZrmnZsqX8/Px08uRJjRs3TkFBQfrDH/6gjIwMO1UJAAAAANZzyMzUjh07yrR5rCQ9+uijevTRRyVJr7/+ujZt2qSnnnpKe/bs0aZNm+xRJgAAAABYzWH7TJVXnz59NGzYMG3dulW7d+92djkAAAAAqrj7JkxJsiwrvmfPHidXAgAAAKCqu2/ClNls1o4dOySpyP5LAAAAAOBo5X5nKiEhwap233zzjWrUqFHm/nNycnTu3Dn99a9/1ddffy2TyeTUlf0AAAAAQLJBmHryyScLnZtMpmLbPfXUU+UdSpIUGBio/v3ZjwUAAACAc5U7TOXl5UmSMjIylJiYqP3792vDhg36xz/+ofT0dEs7W+wNXKdOHa1atUqenp7l7gsAAAAAysNmS6N7e3srKChIQUFBGjJkiBYsWKCpU6dq6dKlMplMatCggVxcyv6Klpubm/z9/RUaGqpx48bJz8/PViUDAAAAgGF222eqZs2aWrJkidzd3fXxxx9r//79ql27tr2GAwAAAACHsvtqflOmTLH3EAAAAADgcHYPUw0aNLDJ+1IAAAAAUJE4ZJ+pU6dO8YgfAAAAgErFIWGqcePGjhgGAAAAABzGIWHKVnJycqzeJBgAAAAA7Om+ClPXr19Xjx49nF0GAAAAANhvaXR7yM3NZTELAJVSk8j1dh/j9Lz+dh8DAICqxHCYOnz4sJ5//nkdOnRIHTp00JIlS9SqVati27755puGCyzo+++/l8lksklfAAAAAFAehsPUiBEjtH//fknS119/rZEjR+q///1vsW1nzpxpkxBkNpsJUwAAAAAqBMNh6sSJE5ZgYzabdfz48XtewyN6AAAAACoLw2Fq8ODB+vTTTy3nzzzzTKntg4OD9fDDDxsdTpL0ww8/lDj7BQAAAACOZDhMLV26VI899pi+//57dejQQaNGjSqxrclk0urVq1W/fn2jw0mSLly4oEaNGpWrDwAAAACwBcNhytXVVWPHjrWqrdlsloeHh9GhLDw9PXlUEAAAAECF4JB9pmbMmCFvb+9y9+Pt7a0ZM2bYoCIAAAAAKB+H7DNlqwDk5eVFmAIAAABQIThkZgoAAAAAKhvCFAAAAAAYUOHC1KVLlzR+/Hg1a9ZMXl5e8vX1VceOHfX222/r5s2bzi4PAAAAACQ56J2p7du3l/hZaGio5fjo0aN68sknlZycbFm1LysrS/v379f+/fv1xRdfKC4uTvXq1bN7zQAAAABQGoeEqVWrVmnDhg06e/as5Xcmk0m9e/e2hKmcnBwNHjxYSUlJku4sgx4dHa1hw4YpJSVF69ev17Rp0zRhwgR98cUXjigbAAAAAErkkMf8Pv74Y73xxhsym83y9PTU7373O505c0YbNmywtFm6dKmOHDki6U7QWrp0qV544QXVqFFDTZo00csvv6zNmzdr1apVunjxoiPKBgAAAIASOSRM5eTkKDo6Wo888ogOHz6sd955R4GBgYXafPDBBzKZTDKZTHr00UcVHh5epJ/g4GA99thjSkhIcETZAAAAAFAihzzm98477+jWrVvau3evatSoUeTzjRs36uTJk5LuzEq98sorJfb18MMP69y5c3arFQAAAACsYfcwdevWLX3wwQf6/PPPiw1SkrR48WLLsa+vr4YOHWrvsgAAAACgXOz+mN+OHTtUu3Zt9enTp9jPz549qw0bNlge8QsPD5eHh0eJ/f3www/y9/e3V7kAAAAAYBW7h6kff/xRXbp0KfHzJUuWKC8vz7IU+gsvvFBi21OnTum///1voeXUAQAAAMAZ7P6YX3Z2tlxcis9st27dUkxMjEwmkyTpiSeeUOvWrUvsa9KkSRo6dGiRxSsAAAAAwNHsHqYaNGig2NjYYj9bsmSJrl69KunOwhOTJ08usZ/XXntNe/bs0ffff2+XOgEAAACgLOz+mF/37t21d+9effPNN4V+f/78ec2cOdPyrlS7du301FNPFbk+OTlZAwYM0MKFC/Xhhx+qdu3a9i4ZAAAAAO7J7mGqYcOGGjRokIYMGaIvvvhCx48f18aNG9W7d2+lpaVJktzd3bV06dJC1yUnJ2vatGlq2bKl/vWvf0mSOnbsaO9yAQAAAMAqDtln6qOPPlLXrl2L3YhXkhYtWqTg4GBJdxaZmDNnjj777DNlZWVZFqYwm80aMGCAJk+erF69erGiHwAAAACnckiY8vPz07fffqt58+ZpzZo1OnPmjNzd3dW5c2dFRkYWWp3vwIEDyszM1ODBg4vta/PmzfLx8dGvf/1rR5QOAAAAAMVySJiS7mzGO2/ePM2bN6/UdgMHDtTAgQMdVBUAAAAAGGP3d6YAAAAAoDIiTAEAAACAAfdVmMrOztZf//pXZ5cBAAAAAPdXmEpLS9Nzzz3n7DIAAAAA4P4KU9nZ2c4uAQAAAAAkOXA1v3y7d+/WV199pbNnzyo9Pd2yj5Q1jh49asfKAAAAAMB6DgtTcXFxmjhxog4fPmy4D7PZLJPJZMOqAAAAAMAYh4Sp2NhYDRs2TLm5uWWaiQIAAACAisruYSorK0svvfSScnJy5ObmptDQUAUFBalevXpl7uvAgQNau3atHaoEAAAAgLKxe5iKi4vT5cuXFR4ervfff99QiMqXlJREmAIAAABQIdg9TB07dkzNmjXTsmXL5OJSvsUDq1WrxmOCAAAAACoEuy+NnpGRoe7du5c7SEmSj4+PPv30UxtUBQAAAADlY/cw1bhxY2VlZdmkL3d3d40cOdImfQEAAABAedj9Mb++fftq+vTpysnJUbVqDt/WCgDwkyaR6x0yzul5/R0yDgAAzmb3malatWppxIgRioqKKndft27d0ptvvmmDqgAAAACgfOwepiRp2rRpSkxM1PPPP6+MjAzD/WRkZGjWrFk2rAwAAAAAjHFImDKZTFq+fLk6duyoRx99VG+++ab27dtX5n7S09PtUB0AAAAAlJ1DXmIKDQ21HJtMJs2aNUuzZs2Sj4+P6tevr7p168rNze2e/Zw+fdqOVQIAAACA9RwSpnbs2CGTyVTod2azWTdu3FBaWpqOHTtmVT9ms7lIPwAAAADgDA5bXq+kzXbZhBcAAADA/chhYWr8+PHq2LFjufrYs2ePFi9ebKOKAAAAAMA4h4Qpk8mk6dOnq27duuXqp1+/foQpAAAAABWCQ1bzM5vNcnEp/1AuLi48FggAAACgQnBImIqLi1PNmjXL3U/NmjUVFxdng4oAAAAAoHwc8pjfz3/+c5v0U61aNZv1BQAAAADl4ZCZKQAAAACobBy2ml9JkpKSdPnyZbm4uKhVq1aW38fFxcnLy0vBwcFydXV1YoUAAAAAUJTDZ6Zyc3O1fPlyDR48WPXq1VNAQIDat2+v9u3b6/r165Z28fHxGjJkiPz8/DR58mRdu3bN0aUCAAAAQIkcGqZWr16tFi1aaNSoUVqzZo2uXr0qs9kss9msvLw83bhxw9J25syZOn/+vNauXavDhw+rXbt22rdvnyPLBQAAAIASOSxMvf/++3r66ad1+vRpSSp2ifPifhcSEqINGzZo/Pjx6tOnj06dOmXvUgEAAADgnhzyztTy5cs1ZcoUS1jy9PRUu3bt9LOf/Uz+/v7y8vLSggULSu1j6tSpOnbsmMaPH6/169c7omwAAAAAKJHdw9Tt27cVGRkps9msAQMGaNy4cerRo4fc3NwKtVu8ePE9+5o7d66aNGmiM2fOqHHjxvYqGQAAAADuye6P+X399ddKTEzUJ598ojVr1qh3795FgpS1/Pz81L17dyUkJNi4SgAAAAAoG7uHqUOHDikkJESjRo2ySX+NGzdWYmKiTfoCAAAAAKPsHqZu3rypli1b2qy/q1evysPDw2b9AQAAAIARdg9Tfn5+OnLkiE36ys7OVkJCgvz9/W3SHwAAAAAYZfcwFRISol27dik2Nrbcfc2YMUNXr15VSEiIDSoDAAAAAOPsHqYeeughhYSE6Nlnn9Xbb7+tzMzMMveRnJysMWPG6N1331VwcLAaNGhgh0oBAAAAwHoO2Wfqww8/1BNPPKHp06dr/vz5GjRokEJDQ9W2bVs1btxYDzzwgCQpLy9PGRkZSklJ0alTp7R//35t375dGzdu1O3btyVJb7/9tiNKBgAAAIBSOSRMdejQQf/4xz80ZMgQ3bhxQ8uWLdOyZcuKtAsKCir2+vzNfidNmqRf/OIX9iwVAAAAAKxi98f88oWFhSk2NlZ+fn6WcGQ2my0/d58X/J0kTZgwQR988IGjygUAAACAUjksTElS//79dfToUU2YMKHY5c1NJlOhc7PZrEceeUTr1q1TdHS0o8oEAAAAgHtyyGN+BdWoUUPR0dGaNWuW1q5dq/j4eP3444+6cuWKJKlWrVry9/fXY489ptDQUHXu3NnRJQIAAADAPTk8TOWrWbOmIiIiFBER4awSAAAAAMAwhz7mBwAAAACVBWEKAAAAAAxw2mN+RmRkZGjatGkOXdVv9+7dWrFihfbu3WtZIKNjx44aMWKEOnXqdN+Ne+XKFf3973/Xpk2bdP36deXm5qp27drq06ePRo4cqerVq9vqFgAAAIBK7b6ambp165YWLlzokLGysrI0depUjRw5Um3bttXWrVu1c+dObdmyRa1bt1Z4eLimTZtm2Uz4fhh306ZN6tOnjy5cuKCPPvpI8fHx+vrrrxUTE6Mff/xRv/71r3Xx4kWb3g8AAABQWd1XM1P5e07ZW05OjsaPH6/t27dr5syZhRbJ8Pb21tixY+Xh4aHZs2crJSVFixYtkqura4Ued+XKlYqMjNTkyZM1duzYQp89+OCDevHFFzVgwADNnDlTS5cuLfe9AAAAAJWdoTD173//2+YzMtb473//65BxoqOjtX37dgUEBGj48OHFtomIiFBMTIw2bdqkRYsWadKkSRV23F27dikqKkqPPvqoXnjhhSKfX7lyRW+88YYk6auvvlJOTo6qVbuvcjYAAADgcIa+MT/77LO6ceOGrWupEI4ePWqZmQkLC5OLS/FPQrq6uiosLEwxMTFavHix+vfvr+bNm1e4cbOzszV9+nTl5ubq5ZdfLrIxsiRNnjxZ3333nSTp9u3bunXrlnx8fAzfCwAAAFAVlOudKbPZXK6f0voo6TN7W7BggXJzcyVJwcHBpbbNXwgiNzdX0dHRFXLczz77TCdOnFCtWrXUrVu3YtukpaVZjnv16kWQAgAAAKxg+FkuDw8PPfjgg2W+7uLFi5ZHBF1cXFS7dm15e3sXapORkaGUlBRLuPD29la9evWMlmq1CxcuaNu2bZbz1q1bl9q+TZs2luMtW7YoMTFR9evXr1Djfvnll5LurARY0mzXwoUL9dlnn8nPz0/Dhg0ra/kAAABAlWQ4TCUkJJRpie6MjAzNnDlT69at0/PPP6/evXurTZs2xT52Jt2ZlTp8+LA2bdqkFStWaOTIkZowYUKJgcAWNmzYoLy8PElS9erV5efnV2p7f39/eXt7KyMjQ7m5udq4caOee+65CjPukSNHdOzYMUnSww8/XGJ/gYGBmjJlSpnrBgAAAKoyw8nkXl/4C0pPT1ffvn3l5+enQ4cO6fXXX1fbtm1LDFKSZDKZ1KZNG73++uvau3evcnNzNWDAAGVnZxst+Z4SEhIsx9bOMAUGBlqO4+PjK9S4Bftt2LChodoAAAAAFM/QzFRpIag4I0aM0LBhwzRu3Dgjw8nFxUWvv/66qlevrvDwcMuja7Z28OBBy3Ht2rWtuqZWrVqW4wMHDlSocQ8dOlSk31OnTumTTz7R7t27lZ6errp16+rxxx9XeHg4gQsAAAAoA0MzU2VZCOLAgQM6duyY4SBV0NixY/XDDz9YVp6zpeTkZN28edNyXrNmTauuK9guLS1NV65cqTDjHj582HLs5eWl2NhYDRo0SA0bNtTKlSuVkJCg119/XbGxserTp49iY2PLVDsAAABQlRmambp27ZrVbcv6btW9dOrUSTt27FD79u1t1qd0J9QU5O7ubtV1d7dLSkpS3bp1K8S4iYmJluOvvvpKMTExmj9/vgYMGGD5fffu3fXBBx9o1KhRmjp1qry9vdW3b1+r6z906JCCgoKsbn+3EydOGL4WAAAAlUN5vk/6+PgUWqDNkey3msNPMjMzC32pL6/ExERlZWXZrL98t27dKnTu5uZm1XV3t7u7H2eNm5GRUWhj5SVLlqhLly6FglS+bt266aGHHlJeXp5mzZql9PR0a8sHAAAAqizDq/lZq2XLlvrDH/6gb7/9Vo899li5+tq1a5e2bdumF1980UbV/b9q1Qr/U+Svrncvd7e7ux9njVvcpsojR44ssb8ePXroxx9/VHJystasWaPhw4dbVUebNm20YsUKq9oCAAAAxSnP00rh4eE2rKRs7D4z1bNnT9WoUUN9+/bVsmXLDG28azab9emnn6pfv37y8fFR7969bV5njRo1Cp0XnNUpzd2rC97dj7PG9fDwKHRerVo1de7cucT+Cu5ttXnzZqtqAAAAAKoyu89MPfDAA5oxY4YmTZqkMWPGKCoqSgMHDtTjjz+uhx56SA0aNJCvr688PT0l3XksMDU1VefPn9cPP/ygXbt2ac2aNUpKSpIkvf/++0U2+bWFwMBAmUwmS9jLyMiw6rqCj9eZTKZCS5Y7c9yaNWvKxcXFMoPVuHHjUv/dCl5/5MgRq+sHAAAAqiq7hylJmjBhgr799lt9/vnnSkpK0pIlS7RkyRKrr88PGsOGDdOkSZPsUqOnp6eaNGmiU6dOSZJSUlKsuq5gu6CgoCIzQs4a18XFRT4+Prp+/bokydfXt9T+qlevbjm+du2acnJyyvzIIgAAAFCV2P0xv3zLly/X66+/Lun/w5HZbC71J7+NyWTSa6+9Zvd3c4KDgy3H1i5xfvny5WKvrwjjNmnSxHKcP/NnDTc3N4IUAAAAcA8OC1Mmk0nvvfeedu7cqdDQ0ELvTplMpkI/+cxms3r06KGvvvpK8+fPl4uLfcvt1auX5fjSpUv3XDUwMzPT8vihJMPvctlr3IILfmRmZpbaZ8EQV79+/VLbAgAAAHDQY34FPf7449q6datOnjypdevWaffu3Tp+/LiuX78us9ksX19fBQUFqXPnzvrVr36lZs2aOay2bt26yd/fX5cuXVJeXp5Onjyphx9+uMT2J06csITCgIAAhYSEVKhxO3furJiYGEmFZ7KKUzBMPf7442W9BQAAAKDKcdqzXD/72c80ceJEZw1fLDc3N40ePVpz5syRJO3bt6/UULNv3z7L8ZgxY+Tq6lqkzdq1azVv3jyZzWZFRUUpLCzMIeNKUpcuXVS7dm2lpKTo4sWLunXrlry8vIpt+91331mOf/nLX5Y4NgAAAIA7HPaY3/0iIiLCsgNzQkJCqW3zP2/evHmx+zKlpqYqMjJSSUlJSk5OVmRkpFJTU+0+bj4PDw8999xzkqScnBzt3bu3xLbbtm2TJHXo0EFdu3YtdXwAAAAAhKki3N3dFR0dLS8vL8XFxen8+fPFtjt37pzi4+Pl7e2thQsXys3NrUibM2fOFHr/KTMzU2fPnrX7uAWNGTNGLVq0kCStWrWq2DYHDx7UqVOn5ObmpunTpxd6bw0AAABA8QhTxWjVqpWWLFkiDw8PTZw4UTdv3iz0eVpamiZMmCAvLy/FxMSoZcuWxfbTuHHjQkuWe3p6qlGjRnYftyAPDw/96U9/UkBAgNatW6cdO3YU6XPatGmqVq2aFi5cqHbt2t2zTwAAAABOfGeqouvatatiY2M1Y8YM9ezZU4MGDVJgYKAuXLig2NhYNW/eXGvWrFHTpk1L7MPX11fz5s3T3LlzJUlRUVH33O/JFuPerVGjRlq9erVmzZqll156SQMGDFC7du2UkpKilStXqlq1avrkk08ML6ABAAAAVEUmc8E1ylGsY8eOac+ePUpNTVWtWrUUHBzskFUG7THuhQsX9J///EfJycny8vJS69at1alTJ0PLzoeHh0uS3ff/KkmTyPVOGRdA6U7P6+/sEgAAVYgzv5MyM2WFFi1aWN47ut/HDQwM1JAhQ2zaJwAAAFAV8c4UAAAAABhAmAIAAAAAAwhTAAAAAGAAYQoAAAAADCBMAQAAAIABhCkAAAAAMIAwBQAAAAAGEKYAAAAAwADCFAAAAAAYQJgCAAAAAAPuizB17do17d27VykpKc4uBQAAAAAk3QdhatasWfL399djjz0mf39/zZgxw9klAQAAAEDFDlNxcXGaNWuWbt++LbPZrJycHL311luKi4tzdmkAAAAAqrgKHab2798vSTKZTDKZTEV+DwAAAADOUqHDVIcOHYr9/SOPPOLgSgAAAACgsGrOLqA0PXr0UFRUlN555x3l5ubK1dVVv/vd7xQaGurs0gAAJWgSud4h45ye198h4wAAUJIKHaYk6a233tKrr76qkydPqmnTpqpbt66zSwIAAACAih+mJKlOnTqqU6eOs8sAAAAAAIsK/c4UAAAAAFRUhCkAAAAAMIAwBQAAAAAGOP2dqezsbJ06dUrXrl2TyWRSzZo11bRpU3l4eDi7NAAAAAAokVPC1IULF/TJJ59o7dq1OnDggPLy8gp97uLiovbt22vAgAF67rnn1LBhQ2eUCQAAAAAlcuhjfhkZGXr11VfVrFkzzZw5U/v27VNubq7MZnOhn9zcXO3fv1+zZs1S8+bNNXHiRKWnpzuyVAAAAAAolcPC1JkzZ9SpUyd9+OGHysrKsvq67OxsffTRR+rYsaNOnTplxwoBAAAAwHoOecwvJSVFPXv21MmTJ2U2myVJbm5uatKkiRo2bKiaNWvKy8tLZrNZmZmZun79us6dO6fTp08rOztbZrNZx44dU8+ePfXtt9+y5xQAAAAAp3NImJo0aZJOnDihGjVqaPTo0RoyZIg6deokd3f3Uq/Lzs7Wnj179I9//EOffvqpTp8+rUmTJmn58uWOKBsAAAAASmT3x/zOnj2rzz//XN27d9exY8f0wQcfqEuXLvcMUpLk7u6uLl26aMGCBTp69KhCQkL0t7/9TWfPnrV32QAAAABQKruHqe3bt8vX11fr1q2Tn5+f4X4efPBBrVu3TjVq1NC2bdtsWCEAAAAAlJ3dw1RiYqJ69+4tHx+fcvdVs2ZN9e7dW0lJSTaoDAAAAACMs3uYevDBB5Wbm2uz/vLy8so1wwUAAAAAtmD3MNW3b1/95z//sck+UTdv3tTOnTvVp08fG1QGAAAAAMbZPUwFBATot7/9rV566SXLsuhGvfLKKxo6dKgCAwNtVB0AAAAAGOOQTXvfeOMN1alTR/369dPx48fLfP3x48fVr18/ZWZmav78+XaoEAAAAADKxtA+U+fPn1deXl6Zrnn11Vf173//W8HBwerWrZv69Omj9u3bq169evLy8irU9tatW7p8+bIOHjyoTZs2KS4uTlOmTNHvf/97mUwmIyUDAAAAgE0ZClNt27bVjRs3DA1oNpu1fv16rV+/3uprhg0bpvHjx8vV1dXQmAAAAABga+V6zM9sNpfpR1KRmaXS2ub729/+pqZNm2r69Om6du1aeUoGAAAAAJswHKaMLCZxr9BUWtvr16/rrbfeUpMmTYyWDAAA/o+9Ow/Lqsz/OP5BRAR3TUNQUwg30MkSf+aeS9lCpZUtippmtjg5U+O4ZLlNjKaNmuWkVlZqlhk6WpYpKCjTmGmN+5KGIimIgoqyCef3h/EMIMvD8dmA9+u6uK5zeO5z398TdDwfzjn3AQDYjKnb/CTpm2++YYpyAAAAAJWWqStTbm5uatOmja1rAQAAAIByw1SYutH3RQEAAABAeWcqTH311Vfy8fGxdS0AAAAAUG6Yemaqa9eutq4DAAAAAMqVG5oaHQAAAAAqK9Oz+dnCb7/9pq1bt+rgwYNKTk6Wm5ub6tevrzZt2qhXr17y8/NzZnkAAAAAUCynhKlNmzYpPDxcMTExJbbr2rWrJk2apP79+zuoMgAAAACwjkNv8zt//ryGDh2q/v37KyYmptgX9+Z9PzY2Vvfff78GDx6s5ORkR5YKAAAAACVy2JWpc+fOqXv37jp8+PB1Aaq4qdbzvv/ZZ59p165d2r59u2666Sa71woAAAAApXFImEpPT9d9992nQ4cOSZKaNGmirl27Kjg4WM2aNVO9evXk5eUlwzCUkZGhlJQUnTx5Uvv27VNsbKxOnTqlI0eO6N5771V0dLS8vb0dUTYAAAAAFMshYeqNN97Qzp07ddddd2natGnq1q1bmbbftm2bpk6dqi1btuhvf/ubwsPD7VQpAAAAAFjH7s9MXblyRe+8847GjBmjyMjIMgcpSerevbsiIyP14osv6t1339WVK1fsUCkAAAAAWM/uYSo6OlrVq1fXW2+9dcN9vfXWW/L29tbWrVtvvDAAAAAAuAF2D1OHDh1Sz5495eHhccN9VatWTT179tThw4dtUBkAAAAAmGf3MJWZmamaNWvarL8aNWooKyvLZv0BAAAAgBl2D1N+fn7au3evzfrbu3evfH19bdYfAAAAAJhh9zDVvXt37dq1S5s2bbrhvr777jvt2rVL3bt3t0FlAAAAAGCe3cNU8+bN1bdvXz322GP6/PPPTffz2Wef6fHHH1efPn3UvHlz2xUIAAAAACbYPUxJ0pw5c5SVlaWnnnpKbdu21bRp07R582adOXOm2G3OnDmjzZs3a9q0aWrbtq0GDx6szMxMzZkzxxElAwAAAECJHPLS3nbt2unzzz/XI488osOHD2v69On/K6BqVdWpU0fVq1eXm5ub0tPTdfHiRWVnZ1vaGIYhd3d3rVy5Uu3bt3dEyQAAAABQIodcmZKk0NBQrVixQnXq1JFhGJav7OxsJScn/8uNBAAAIABJREFU69SpU4qPj1dycrKysrIKtKldu7aWLVumhx56yFHlAgAAAECJHBamJOmxxx7TgQMH9Mgjj8jNzc3yfTc3twJf+T388MM6cOCAnnjiCUeWCgAAAAAlcshtfvn5+Pjoiy++0NGjR7Vy5UpFR0fr4MGDOnfunCSpQYMGat26tXr27KnHH39crVu3dnSJAAAAAFAqh4epPIGBgXr99dedNTwAAAAA3BCH3uYHAAAAABUFYQoAAAAATHBImDp79qyeeuop1a9fX40aNdLYsWOVmZnpiKEBAAAAwC4c8sxUaGiodu7cKcMwJEnvvPOOMjIytGjRIkcMDwAAAAA2Z/crU99995127typTp06qUqVKnJzc5NhGNq2bZu9hwYAAAAAu7F7mNq/f78eeughff/993rnnXcs3+/Vq5e9hwYAAAAAu7H7bX7e3t6qXbu2JOm5555Tly5ddPLkSd177732HhoAAAAA7MbuV6buv/9+RUVF6cqVK5Kk9u3b64EHHpC7u7u9hwYAAAAAu7F7mGrSpIlGjRql4cOHKycn54b6unr1qmJiYmxUGQAAAACY55Cp0V977TUFBQWpe/fuOnHihOl+Lly4oLvuusuGlQEAAACAOQ6ZGl2SpkyZok6dOmngwIFq166dXnjhBYWEhMjNzc3qPnJycizTqwMAAACAMzkkTE2fPt2y/MADD2jZsmVatmyZ6tatqy5dusjPz08NGzaUh4dHif3s3bu3TOELAAAAAOzFIWFq6tSp14UgwzCUkpKiDRs2WN2PYRiEKQAAAAAuwWG3+Ukq8hY9btsDAAAAUB45LEyFhISoTZs2N9THwYMHtXPnThtVBAAAAADmOSRMubm5ac2aNWrcuPEN9ZOQkKBmzZrZqCoAAAAAMM8hU6MbhiFPT88b7qd69ercFggAAADAJTgkTE2ZMkXe3t433I+3t7emTJlig4oAAAAA4MY45DY/WwUgLy8vwhQAAAAAl+DQ2fwAALCV5hO+tvsYcTPvt/sYAIDyyyG3+QEAAABAReO0K1Pp6emKiorS999/r2PHjiklJUVubm6qU6eObr31VnXu3Fm9e/e2ybNWAAAAAGBrDg9TCQkJCg8P1/Lly5WWllZi2xo1amjIkCGaNGmSmjRp4qAKAQAAAKB0Dr3N74MPPlCbNm303nvv6dKlSyVOc24YhtLS0rRo0SK1adNGS5YscWClAAAAAFAyh4Wp6dOn69lnn9Xly5cLhCjDMIr8yv/55cuX9dxzz+n11193VLkAAAAAUCKH3Oa3Zs0aTZ069dqAVauqV69e6tKli1q3bq2mTZuqTp068vLykmEYysjI0IULFxQfH6+DBw/q+++/19atW3X16lW98cYb+sMf/qBHHnnEEWUDAAAAQLHsHqZycnL0pz/9SVWrVtXLL7+scePGqUGDBmXq49y5c3rzzTc1d+5cvfLKK3r44Yfl7u5up4oBAAAAoHR2D1NbtmxRfHy8Vq1apUcffdRUHw0aNNCsWbPUsWNHPf7444qKilK/fv1sXGnRduzYoeXLl2vXrl1yc3OTJN1xxx0aOnSoOnbsWO7HPXr0qB588EE999xzGjt2rM36BQAAACo6uz8ztWfPHnXp0sV0kMrvscce05133qm9e/faoLKSZWZmavz48Ro2bJjatWunzZs3KzY2Vps2bVJQUJCGDBmiyZMnKzs7u9yOm5OTowkTJigrK8sGlQMAAACVi0Nu82vdurXN+mvdurVyc3Nt1l9Rrl69qjFjxigqKkpTp05VWFiY5TNvb2+NHj1anp6emjFjhs6fP68FCxbY5LZDR4/74Ycf6ueff77hugEAAIDKyO5Xpu644w4dOnTIZv0dOXJEHTp0sFl/RZk3b56ioqLk6+urwYMHF9kmLCxMPj4+2rhxoxYsWFDuxo2Li9O8efNMbw8AAABUdnYPU71791Z6erq2bNlyw31t2bJFFy5cUJ8+fUpsl5qaKn9/f1NjHD58WIsXL5YkhYaGqkqVov8Tubu7KzQ0VJK0cOFCHT161NR4zhjXMAxNnDhRGRkZ5gsGAAAAKjmHvGdq9erVGjdunLZt22a6j23btmnMmDFatWpVqW1zcnJ04sQJU+PMnTtXOTk5kqSQkJAS2+ZNBJGTk3PDV3kcOe6yZcv0448/ytPTs+yFAgAAAJDkoDDVokULrV+/XvPnz9eQIUO0c+dOq7fduXOnwsLCNHnyZK1du9aq56/MTqiQkJCgyMhIy3pQUFCJ7YODgy3LmzZt0unTp11+3ISEBM2ZM0fDhg3TTTfdVPZiAQAAAEhy0Et7hw4dKkny8vLSd999p5UrV6pevXoKDg5Ww4YN5eXlVaB9enq6zp49q3379iklJUVeXl4aMGCAZsyYYdV4R44cMVXnhg0bLJNb1KxZU40aNSqxvY+Pj7y9vXXlyhXl5OTo22+/1dNPP+3S406aNEkNGjTQK6+8ou+++67MtQIAAAC4xiFhavny5ZZ3JUnXntk5f/58qbf9GYYh6Vq4+vTTT60ezzCMAuNZKyYmxrLcuHFjq7bx8/OzPLcUHR1tKkw5atxVq1YpNjZWy5Ytuy7AAgAAACgbh4Qp6X/BqLTvWbutPezZs8eyXL9+fau2qVevnmXZ7DTjjhg3MTFR4eHheuKJJ3TnnXeWvUgAAAAABTgsTD344INq166dQ8bas2ePvvrqqzJtk5SUpLS0NMt6nTp1rNouf7tLly4pOTm5TM8iOWrc1157TTVr1tSECROsrs0a+/btU0BAgOntjx07ZsNqAAAAUB7dyPlkrVq1Cswp4EgOC1NLlixRw4YNHTJWYmKifH19y7RNUlJSgfVq1apZtV3hdomJiWUOU/Ye91//+pciIyP1wQcfqGbNmlbXBgAAAKB4DgtT7u7ujhpKVauWfbfS09MLrHt4eFi1XeF2hftx9rjJycmaMWOGBgwYoF69epWpNmsEBwdr+fLlNu8XAAAAlceN3K00ZMgQG1ZSNg6ZGr1nz55WhwRb8PDwUI8ePcq0TeEAlje7XmkKtytrkLP3uNOmTVPVqlX12muvlakuAAAAACVzyJWpLVu2OGIYi9q1a5d5zNq1axdYz87Otmq7wu+0KtyPM8fduHGjNmzYoIULF1r9LBYAAAAA6zjkylR54OfnV2A69StXrli1Xf7b69zc3OTn5+cS4164cEFTpkzRfffdp3vuuadMNQEAAAAoHWHqd9WrV1fz5s0t6+fPn7dqu/ztAgIC5Onp6RLjTp8+XVevXtWUKVPKVA8AAAAA6zhsAoryICQkRL/++qukaxM3WOPs2bMFtneVcaOiolS1alUNHDiw2D7OnDljWV66dKm+/PJLy7qPj49WrVplVS0AAABAZUSYyqdfv36WAHHmzBllZmaWeKUpIyNDiYmJlvW7777bZcb96aefSh23R48eSkhIkCQ9/fTTGjt2bFlLBwAAACotbvPLp3v37vLx8ZF0bba848ePl9j+2LFjMgxDkuTr66uuXbuWq3EBAAAAmEeYysfDw0MjRoywrO/evbvE9vk/HzlyZJHv0lq3bp26dOmiO++8U+vXr3fYuAAAAADsizBVSFhYmAICAiRJMTExJbbN+zwwMFCDBw++7vPU1FRNmDBBiYmJSkpK0oQJE5Sammr3cQEAAADYH2GqkGrVqmnevHny8vLSli1bdOrUqSLbxcfHKzo6Wt7e3po/f36RLyU+ceKEMjMzLesZGRk6efKk3ccFAAAAYH+EqSK0bdtW7733njw9PTV27FilpaUV+PzSpUt66aWX5OXlpSVLlqhVq1ZF9nPLLbcUmEiievXqatasmd3HtUZmZqYuXbpkWT937pzpvgAAAIDKiNn8itGtWzdFRERoypQp6tu3rwYOHCg/Pz8lJCQoIiJCgYGBWrt2rVq0aFFsH3Xr1tXMmTP197//XZI0adIk1a1b1+7jFuWbb76x1CFdC2YXL160rK9YsUKRkZGW56+YGh0AAAAomZuRNy0cinXkyBH9+OOPSk1NVb169RQSEqJbb721wo5rrSFDhkiSli9f7pTxm0/42injAqg84mbe7+wSAAClcOY5KVemrNCyZUu1bNmy0owLAAAAoHQ8MwUAAAAAJhCmAAAAAMAEwhQAAAAAmECYAgAAAAATCFMAAAAAYAJhCgAAAABMIEwBAAAAgAmEKQAAAAAwgTAFAAAAACYQpgAAAADABMIUAAAAAJhAmAIAAAAAEwhTAAAAAGACYQoAAAAATCBMAQAAAIAJhCkAAAAAMIEwBQAAAAAmEKYAAAAAwATCFAAAAACYQJgCAAAAABMIUwAAAABgAmEKAAAAAEwgTAEAAACACYQpAAAAADCBMAUAAAAAJhCmAAAAAMAEwhQAAAAAmECYAgAAAAATCFMAAAAAYAJhCgAAAABMIEwBAAAAgAmEKQAAAAAwgTAFAAAAACYQpgAAAADABMIUAAAAAJhAmAIAAAAAEwhTAAAAAGACYQoAAAAATKjq7AIAAHBVzSd87ZBx4mbe75BxAAC2xZUpAAAAADCBMAUAAAAAJhCmAAAAAMAEwhQAAAAAmECYAgAAAAATCFMAAAAAYAJhCgAAAABMIEwBAAAAgAmEKQAAAAAwgTAFAAAAACYQpgAAAADABMIUAAAAAJhAmAIAAAAAEwhTAAAAAGACYQoAAAAATCBMAQAAAIAJhCkAAAAAMIEwBQAAAAAmEKYAAAAAwATCFAAAAACYQJgCAAAAABMIUwAAAABgAmEKAAAAAEwgTAEAAACACYQpAAAAADCBMAUAAAAAJhCmAAAAAMAEwhQAAAAAmECYAgAAAAATCFMAAAAAYAJhCgAAAABMIEwBAAAAgAmEKQAAAAAwgTAFAAAAACYQpgAAAADABMIUAAAAAJhAmAIAAAAAEwhTAAAAAGACYQoAAAAATCBMAQAAAIAJhCkAAAAAMIEwBQAAAAAmEKYAAAAAwATCFAAAAACYQJgCAAAAABMIUwAAAABgAmEKAAAAAEwgTAEAAACACYQpAAAAADCBMAUAAAAAJlR1dgGubseOHVq+fLl27dolNzc3SdIdd9yhoUOHqmPHjuVi3NzcXG3atElfffWVTp8+rVOnTik7O1sNGzZUhw4d1L9/f/Xo0cMyDgAAAIDScWWqGJmZmRo/fryGDRumdu3aafPmzYqNjdWmTZsUFBSkIUOGaPLkycrOznbpcePi4jR8+HDFxcVp+vTpWr16tf7zn/9o7dq16tGjh1avXq0RI0Zo0KBBOnr0qE33BQAAAKjIuDJVhKtXr2rMmDGKiorS1KlTFRYWZvnM29tbo0ePlqenp2bMmKHz589rwYIFcnd3d7lxk5KS9MILL2j27NkKCgoq8FnTpk01adIkBQUF6eWXX9bu3bs1aNAgrVq1SoGBgTe8LwAAAEBFx5WpIsybN09RUVHy9fXV4MGDi2wTFhYmHx8fbdy4UQsWLHDJcWfMmKFHH330uiCV30MPPaS+fftKki5evKhRo0YpMzPT/E4AAAAAlQRhqpDDhw9r8eLFkqTQ0FBVqVL0fyJ3d3eFhoZKkhYuXHjDt8jZetykpCRt3LhRXbt2LXXsxx57zLIcHx+vL7/8sqzlAwAAAJUOYaqQuXPnKicnR5IUEhJSYtu8iSBycnI0b948lxr38OHDysnJ0W+//Vbq2O3atSuwvnXrVisqBgAAACo3wlQ+CQkJioyMtKyXdHucJAUHB1uWN23apNOnT7vMuGlpaZKkcePG6c0339SePXuK7a9evXoF1k+cOGFV3QAAAEBlRpjKZ8OGDcrNzZUk1axZU40aNSqxvY+Pj7y9vSVdu0r07bffusy4zZo1kySlpKRo0aJFGjhwoFasWFFkf5cvXy6wzhTpAAAAQOkIU/nExMRYlhs3bmzVNn5+fpbl6Oholxm3bdu2Ba5wGYahTz/9tMi+EhMTC6wHBARYVQMAAABQmRGm8sl/K1z9+vWt2ib/LXI///yzy4zr5uammTNnqmHDhpbv5b89ML/Y2NgC6wMGDLCqBgAAAKAy4z1Tv0tKSrI8ZyRJderUsWq7/O0uXbqk5ORk3XTTTS4xbtu2bRUVFaUdO3aoSpUq6tat23X9GIahiIgIy3rv3r0tU6UDAAAAKB5h6ndJSUkF1qtVq2bVdoXbJSYmljlM2XNcb29v3XXXXcX2s3LlSh06dEjStVkC586da9X4efbt23dDtwUeO3bM9LYAAACoGG7kfLJWrVrF3oFlb9zm97v09PQC6x4eHlZtV7hd4X5cdVxJOnDggGbNmiVJ6ty5s5YuXaqaNWuWuR8AAACgMuLK1O+qVi34nyJvdr3SFG5XuB9XHffAgQMaOnSo0tLSNGzYME2cONHqIJdfcHCwli9fXubtAAAAgDw3crfSkCFDbFhJ2RCmfle7du0C69nZ2VZtl5WVVWI/rjju9u3b9eKLL8owDM2fP18PPPCA1dsCAAAAuIbb/H7n5+dX4P1KV65csWq7/LfXubm5FZiy3BXHXbVqlUaOHClfX1+tXbuWIAUAAACYRJj6XfXq1dW8eXPL+vnz563aLn+7gIAAeXp6uuy4c+bM0cSJExUaGqqIiAj5+/uXqVYAAAAA/0OYyickJMSynJycbNU2Z8+eLXJ7Vxo3JydH48eP19KlSxUeHq45c+bIy8uryLZHjhzRli1brKoBAAAAqMwIU/n069fPsnzmzBllZmaW2D4jI0OJiYmW9bvvvtslx3311Ve1ZcsWrV69Wo8//niJbaOjoy1TpQMAAAAoHmEqn+7du8vHx0fStdnyjh8/XmL7Y8eOyTAMSZKvr6+6du3qcuMuW7ZMa9as0dKlS9WmTZtSa9m/f7/VLw4GAAAAKjPCVD4eHh4aMWKEZX337t0lts//+ciRI+Xu7n5dm3Xr1qlLly668847tX79eoeNK0kpKSmaPXu2+vbtq6CgoBL7lK5d8YqNjSVMAQAAAFYgTBUSFhZmeQNzTExMiW3zPg8MDNTgwYOv+zw1NVUTJkxQYmKikpKSNGHCBKWmptp93Dzr1q3T5cuXZRiGrl69Wmy73NxcxcfHa9y4cTp//rzq1q1b4vgAAAAAeM/UdapVq6Z58+Zp0KBB2rJli06dOqUmTZpc1y4+Pl7R0dHy9vbW/Pnzi3zh7YkTJwo8/5SRkaGTJ08WGVZsOW6erVu3SpI2btyo1q1bq0qVorOzYRgFXgJc1ndlAQAAAJURV6aK0LZtW7333nvy9PTU2LFjlZaWVuDzS5cu6aWXXpKXl5eWLFmiVq1aFdnPLbfcUmDK8urVq6tZs2Z2HzfPwYMHLcuGYSgnJ6fIr/xBShJXpgAAAAArcGWqGN26dVNERISmTJmivn37auDAgfLz81NCQoIiIiIUGBiotWvXqkWLFsX2UbduXc2cOVN///vfJUmTJk0qNajYYlzp2q171r6zqjCemQIAAABK52bkTQuHYh05ckQ//vijUlNTVa9ePYWEhOjWW2+tsONaa8iQIZKk5cuXO2X85hO+dsq4AGBrcTPvd3YJAFBuOfOclCtTVmjZsqVatmxZacYFAAAAUDqemQIAAAAAEwhTAAAAAGACYQoAAAAATOCZKQAAnMwRE+owyQUA2B5XpgAAAADABMIUAAAAAJhAmAIAAAAAEwhTAAAAAGACYQoAAAAATCBMAQAAAIAJhCkAAAAAMIEwBQAAAAAmEKYAAAAAwATCFAAAAACYQJgCAAAAABMIUwAAAABgAmEKAAAAAEwgTAEAAACACYQpAAAAADCBMAUAAAAAJhCmAAAAAMAEwhQAAAAAmECYAgAAAAATCFMAAAAAYAJhCgAAAABMIEwBAAAAgAmEKQAAAAAwgTAFAAAAACYQpgAAAADABMIUAAAAAJhAmAIAAAAAE6o6uwAAAGB/zSd87ZBx4mbe75BxAMAVcGUKAAAAAEwgTAEAAACACYQpAAAAADCBMAUAAAAAJhCmAAAAAMAEwhQAAAAAmECYAgAAAAATCFMAAAAAYAJhCgAAAABMIEwBAAAAgAmEKQAAAAAwgTAFAAAAACYQpgAAAADABMIUAAAAAJhAmAIAAAAAEwhTAAAAAGACYQoAAAAATCBMAQAAAIAJhCkAAAAAMIEwBQAAAAAmEKYAAAAAwATCFAAAAACYQJgCAAAAABMIUwAAAABgAmEKAAAAAEwgTAEAAACACYQpAAAAADCBMAUAAAAAJhCmAAAAAMAEwhQAAAAAmECYAgAAAAATCFMAAAAAYAJhCgAAAABMIEwBAAAAgAlVnV0AAACoOJpP+Noh48TNvN8h4wBASbgyBQAAAAAmEKYAAAAAwATCFAAAAACYQJgCAAAAABMIUwAAAABgAmEKAAAAAEwgTAEAAACACYQpAAAAADCBl/YCAIByxxEvB+bFwABKw5UpAAAAADCBMAUAAAAAJhCmAAAAAMAEwhQAAAAAmECYAgAAAAATCFMAAAAAYAJhCgAAAABMIEwBAAAAgAmEKQAAAAAwgTCFcikgIEDuX7zk7DLgJO5fvMTPv5Ljd6By4+dfuQUEBCggIMDZZcBJXO3nT5gCAAAAABMIUwAAAABgAmEKAAAAAEwgTAEAAACACVWdXYCr27Fjh5YvX65du3bJzc1NknTHHXdo6NCh6tixY7ka11n7AgAAAFREXJkqRmZmpsaPH69hw4apXbt22rx5s2JjY7Vp0yYFBQVpyJAhmjx5srKzs11+XGftCwAAAFCRcWWqCFevXtWYMWMUFRWlqVOnKiwszPKZt7e3Ro8eLU9PT82YMUPnz5/XggUL5O7u7pLjOmtfAAAo75pP+Noh48TNvN8h4wCwPa5MFWHevHmKioqSr6+vBg8eXGSbsLAw+fj4aOPGjVqwYIHLjuusfQEAAAAqOsJUIYcPH9bixYslSaGhoapSpej/RO7u7goNDZUkLVy4UEePHnW5cZ21LwAAAEBlQJgqZO7cucrJyZEkhYSElNg2b9KGnJwczZs3z+XGdda+AAAAAJUBYSqfhIQERUZGWtaDgoJKbB8cHGxZ3rRpk06fPu0y4zprXwAAAIDKgjCVz4YNG5SbmytJqlmzpho1alRiex8fH3l7e0u6dkXn22+/dZlxnbUvAAAAQGXBbH75xMTEWJYbN25s1TZ+fn6WZ4yio6P19NNPu8S4ztoXAABQNswaCJRfhKl89uzZY1muX7++VdvUq1fPsvzzzz+7zLjO2hcAAOCaHBHaCGyobAhTv0tKSlJaWpplvU6dOlZtl7/dpUuXlJycrJtuusmp4zpqX3777TdduHBBt912m1X9FyX/s1plUatWrWsLO94zPTbKMX7+4HegcuPn77KGDFlp9zHyzgGGDBli97HgOPv27bOqXd7PP//5p7u7u9Xnu7ZGmPpdUlJSgfVq1apZtV3hdomJiWUOU7Ye11H7UqNGDaWmplrVt62ZDWGoIPycc8CEC+F3oHLj51+pcQ6AwgzDUI0aNZwyNmHqd+np6QXWPTw8rNqucLvC/ThjXEfty/r1663qFwAAAKiImM3vd1WrFsyVeTPhlaZwu8L9OGNcZ+0LAAAAUJkQpn5Xu3btAuvZ2dlWbZeVlVViP84Y11n7AgAAAFQmhKnf+fn5yc3NzbJ+5coVq7bLfyucm5ub/Pz8nD6us/YFAAAAqEwIU7+rXr26mjdvblk/f/68VdvlbxcQECBPT0+nj+usfQEAAAAqE8JUPiEhIZbl5ORkq7Y5e/Zskds7e1xn7QsAAABQWRCm8unXr59l+cyZM8rMzCyxfUZGhhITEy3rd999t8uM66x9AWztzJkzio6OtqptbGysTp06ZeeKALgajhNA5Xb06FHt3r271HaGYWjjxo26cOGCzcZmurZ8unfvLh8fH505c0a5ubk6fvy42rRpU2z7Y8eOyTAMSZKvr6+6du3qMuM6a18AW5s7d65Wr16tkJAQPfjggwoJCZGfn588PDx06dIlnThxQrt379bXX3+tc+fOadWqVc4uGYBJ//3vfzVw4EBFR0erSZMmVm/HcQIof3Jzc7Vp0yZ99dVXOn36tE6dOqXs7Gw1bNhQHTp0UP/+/dWjR48C8wAUZ+rUqfrhhx/Uo0cP3X///erQoYN8fX0lSZcuXdKvv/6qHTt26KuvvpKnp6dNz3MJU/l4eHhoxIgRCg8PlyTt3r27xACSPwGPHDlS7u7u17VZt26dZs6cKcMwNGnSJIWGhjpkXHv06Qp27Nih5cuXa9euXZb/ue644w4NHTpUHTt2dHJ1sIe8Kft37typnTt3FtuuWbNm+uijj3TzzTc7qjTYgdmT6fw4TpRfa9asMbUdx4nyyZYn0/l99913WrVqlfbt2yd3d3e5u7vrzjvv1IgRI9SqVSs77Q3KIi4uTq+//rq6du2q6dOnq169epKk+Ph4LVu2TEuXLtWqVat0++23Kzw8XIGBgSX2ZxiGcnNztXXrVm3durXYdsHBwfrwww9Vs2ZNm+0Lt/kVEhYWpoCAAElSTExMiW3zPg8MDNTgwYOv+zw1NVUTJkxQYmKikpKSNGHCBKWmptp9XHv26SyZmZkaP368hg0bpnbt2mnz5s2KjY3Vpk2bFBQUpCFDhmjy5MlWTwOPisPNzU2PPvqo1q9frxYtWji7HNwgsyfTEseJ8i42NlYrVqywS98cJ1xPXFychg8frri4OE2fPl2rV6/Wf/7zH61du1Y9evTQ6tWrNWLECA0aNEhHjx61qs+LFy/qmWee0bhx43T33XcrJiZGsbGxWrdunerWrasHHnhAb7/9tp33DKVJSkrSCy+8oPHjx2v06NGWICVJTZs21aRJkzRnzhxJ1/7YX5bfgeJ4eHjo2Wef1erVq9WgQYMb6qswNyPv3i5YHDhwQIMGDVJWVpaioqKK/OtofHy8+vTpI09PT61evbrIv3Tk/YU1vzVr1qh9+/Z2HdfefTra1atX9fzzzysqKkpaOJVaAAAgAElEQVRTp05VWFjYdW0++ugjzZgxQ/fcc48WLFjgslfWUHbjxo3TxYsX1blzZ23dulXJycnKzc3VTTfdpNtuu00PPfSQbr31VmeXCRuIjY3V8OHDlZubW+YrUxwnyq9z585p7dq1mjt3ruUVHWX9+XOcKF+SkpI0fPhwzZ49W0FBQUW2+de//qWXX35Z0rX3Xq5atarEqxOXL1/WkCFDtGfPHi1ZskS9e/e+rs0bb7yhDz/8UMOHD9drr71mm51Bmf3xj39Uhw4dNGLEiBLbjR49Wps3b5Z0LWRt3Lix2Jmmn3rqKfn7+6tx48aKjY1VSkqK3NzcdPPNN6tjx44aMGCA5bY/mzNQpG3bthnBwcHGwIEDjUuXLhX47OLFi8bDDz9stG/f3vj++++L7SMlJcVo06aN4e/vb/j7+xtt27Y1UlJS7D6uI/p0pNmzZxv+/v5Gt27djJycnCLbXL161ejSpYvh7+9vzJ0718EVwp7+8pe/GH/5y1+cXQbsKDk52Xj//feNoKAgy/EyPj6+TH1wnChf3njjDaN79+5Gly5djNtuu80IDg62/OzN/Pw5TpQvY8aMMT744INS2z377LOW34mePXsaGRkZxbYdO3as4e/vbzz22GPFtrlw4YLRtm1bw9/f3/jiiy9M1Y4bk5iYaAQGBhqHDh0qte2mTZsKHBdWrFhRbNsnn3zSmDdvni1LtRq3+RWjW7duioiIkKenp/r27as333xTK1as0Jtvvql+/fqpZs2aWrt2rTp37lxsH3Xr1tXMmTPVqFEjNWrUSDNnzlTdunXtPq4j+nSUw4cPa/HixZKk0NBQValS9K+su7u75Xm0hQsX3vDlYAD2FR4erh49eqhr167q27ev5s2bV+DF4WXBcaL8mTRpkuUWrJ9++klLlixxdklwkKSkJG3cuNGqCQAee+wxy3J8fLy+/PLLIttt2bJF69evlyQ9/PDDxfZXu3ZtyxWrv/3tbzp37lxZSocNHD58WDk5Ofrtt99KbduuXbsC6yU9C+VMhKkSBAYG6tNPP9Unn3yiJk2a6MKFC2ratKmWL1+uZcuWWXXf9YMPPqjvv/9e33//fZGTT9hrXEf06Qhz585VTk6OpNLffZX3YHlOTo7mzZtn99oAmGfLk2mOE0D5YY+T6X/84x+W5dImmbnjjjskXZvhbdGiRaXWANtKS0uTdO3W3DfffFN79uwptm3+Z6kk6cSJE3atzSxm87NCy5Yt1bJlywoxrrP2xYyEhARFRkZa1ou7rzpPcHCwZXnTpk06ffq0GjdubLf6ADgfxwmgfMl/Mj1o0CD179+/2GfJrTmZ/vHHH3XgwAFJkqenZ6mzvuU/BnzxxRd6+eWXVb169TLtA8xr1qyZJCklJUWLFi3S4sWLNW3atCInP7t8+XKB9bLO6ugoXJmCy9qwYYNlutuaNWuqUaNGJbb38fGRt7e3pGt/df7222/tXiMcJzc3Vxs2bNDzzz+v0NBQdezYUR07dtRDDz2kWbNm6eTJk84uEU7AcQL5cZxwfYVPpgcOHFjsLI7WnEx/9dVXluVbbrml1BPu/HfiXLx4sdTZjmFbbdu2LfBHL8Mw9OmnnxbZNjExscB63gzVJcnKytLnn3+ukSNH6r777lOHDh3UqVMnDRo0SAsWLFBycvKN7UARCFNwWfkPcNb+5djPz8+yHB0dbfOa4BxnzpzRsGHDdPToUc2aNUvr16/Xjz/+qBUrVujmm2/W4sWLdffdd2v+/PmWl0+jcuA4gTwcJ8oHW59M5/9/OP//28Vp0KCBvLy8itwe9ufm5qaZM2eqYcOGlu/lv1qYX2xsbIH1AQMGlNj3L7/8oieeeEKZmZlasGCBNmzYoJ9++kmLFi1Sbm6u5s2bp169emnlypU3viP5EKbgsvLfR1u/fn2rtsl/S8DPP/9s85rgHP/+97913333aezYsapdu7bl+61atdJ7772nnj17Kjs7W2+//bZmzJjhxErhaBwnkIfjRPlgy5PplJSUAlcbOQaUD23btlVUVJTef/99ffjhhwoPD7+ujWEYioiIsKz37t1bffv2LbHfDRs26E9/+pOGDh1quQNBkjp06KAVK1aobdu2Sk9P1+TJk/Xhhx/abH8IU3BJSUlJlvuqJalOnTpWbZe/3aVLl+xyOReO16JFCz3xxBNFflalShW9+uqrlls7Pv744wLP0KDi4jiB/DhOlB+2Opn+9ddfC6xbewzIP7NyXFwcVyqdwNvbW3fddZd69uxZ5Dv/Vq5cqUOHDkm6NqnI3LlzS+2zW7du6tGjR5GfeXp6aty4cZb1WbNmaf/+/SarL4gwBZeUlJRUYL1atWpWbVe4XeFbBFD+VKtWTZ06dSrxPviAgAC1bdvWsj5//nxHlAYn4ziBPBwnyh9bnEyfPXu2wLqZY0BGRoYuXrxYltJhZwcOHNCsWbMkSZ07d9bSpUtVs2bNErepVq2a/u///q/ENt26dbNcvbx69areffddm9TLbH5wSYXfN+Ph4WHVdoXbmX1vDVzHM888Y9VMS7fddpvlr0z79+/XyZMnLQ86o2LiOIE8HCcqlsIn00uWLClw21aeK1euFFg3ewy4cuWK1Ve1YF8HDhzQ0KFDlZaWpmHDhmnixIlW/VzHjx9f6nOzVapUUbt27SzPyW3ZskUZGRk3PJsjV6bgkqpWLZjz82brKk3hdoX7QfnTokULqyYWaNq0aYH1n376yV4lwUVwnEAejhMVR+GT6Y8++qjIICVd//9u3vvmSlO4nbUhDPa1fft2Pfnkk8rKytL8+fP1+uuvW/2zadOmTYHbN4uT/48nWVlZ2rdvn+l68/AvCFxS/oeHJSk7O9uq7bKyskrsBxVXjRo1CqzzHEzFx3ECZcVxwrVt375dL774ogzD0Pz58/XAAw+U2N5Wx4BatWqVrVDY3KpVq/Taa6/J399f7777rvz9/e0yTuFjwLlz5264T65MwSX5+fkVuPe98KX84uS/XcfNzc2qaVLh2jIzMxUTE6PIyEhdvXq12HZVqhQ8nGVmZtq7NDgZxwnk4ThR/q1atUojR46Ur6+v1q5dW2qQkqQmTZoUWLf2lt2MjAzLcqNGjeTp6Vm2YmFTc+bM0cSJExUaGqqIiAhTQSotLU2bN2++bgbIwgo/V2mLYwBXpuCSqlevrubNm1tm6jl//rxV2+VvFxAQwAGynEtJSdHgwYN1+PBhSVKfPn20ePHiItsW/kc0/9S3qJg4TkDiOFERzJkzR//85z81YMAAzZgxo8B7oEpyyy23yMvLy/JzNXMMaNOmTdkLhk3k5ORo0qRJ+uqrrxQeHq7HH3+82LZHjhxRQkKC7rrrrus+i4uLU1hYmH777TdJ0tChQzVlypQi+7HHMYArU3BZISEhlmVrb8XIP7NP/u1RPi1cuNBygiRJkZGRunDhQpFtC8/I1qpVK7vWBtfAcQIcJ8qvnJwcjR8/XkuXLlV4eLjmzJlTbJA6cuSItmzZUuB7VatWVYcOHSzr1hwDsrOzlZqaalnv1KmTyepxo1599VVt2bJFq1evLjFISdderpw3u2Nhs2bNsgQpSQWm1C+s8CywtjgGEKbgsvr162dZPnPmTKmXYjMyMgr8Q3n33XfbrTY4xq5duwqs165du9h/aI8fP25Zrl+/vv7whz/YtTa4Bo4T4DhRftniZDr/MSAuLq7UMU+ePFlgEhqOAc6xbNkyrVmzRkuXLrXq6uD+/fuLnXGx8DHAx8en2H6OHTtmWQ4KClKjRo2srLh4hCm4rO7du1v+h8jNzS3wj2BRjh07Znnxnq+vr7p27Wr3GmFf+S/H169fX3Pnzi3yPSK5ubn68ccfLetPPfVUke8tQcXDcQIcJ8onW51Mh4aGWqa2TkpKKvWdUb/88otl+fbbb7fbRAcoXkpKimbPnq2+ffsqKCio1PYZGRmKjY0tNkzlfwauadOmmj17dpHtUlNTdfToUct6WFhYGSsvGmEKLsvDw0MjRoywrO/evbvE9vk/HzlyJP9IVgC33nqrpGsHx++++069evUqst22bdsst/X4+/tr9OjRjioRTsZxAhwnyh9bnkzXq1dPgwYNsqyX5RjA74BzrFu3TpcvX5ZhGCVOGJObm6v4+HiNGzdO58+fL3bq87xjQIcOHfTNN9+offv2RbbbsGGD5apkp06dNHDgwBvck2sIU3BpYWFhCggIkCTFxMSU2Dbv88DAQA0ePNjutcH+nnzySUlS3bp1i31I1DAMLVq0SNK1CQnefvvtYt9JgoqJ40TlxnGi/LH1yfRLL72k+vXrS7L+GNCtWzf17dvX5B7gRmzdulWStHHjRrVu3VotW7Ys8qtVq1bq1auXNmzYIKn411jkHQN8fHyKvcU3MzNTH3zwgaT/XcG21R/TCFNwadWqVdO8efPk5eWlLVu26NSpU0W2i4+PV3R0tLy9vTV//nxewFdBdOnSRaNGjdLBgwf1ww8/FNnmrbfe0o4dO+Tr66vPP/+cmZkqIY4T5V/hd72U5d0vHCfKH1ufTNerV09z5syRu7u71q5dq7S0tCLb/fDDDzpy5IgaNmxY7K1gsL+DBw9alg3DUE5OTpFfhV+wXlyYfvTRR3X//fdr27ZtBZ6JynP16lVNmjRJcXFxat26tb788ssSn6sqKzcj7+ZxwIVt375dzz//vFq2bKmPP/5YNWvWtHx26dIlDR06VMePH9eiRYvUuXNnJ1YKe4iIiNCCBQt0++23Kzg4WDVq1NDZs2e1ceNGHTt2TAMGDNDLL79s+cskyp+vv/5aL730kmU9IiKizJMDcJwoP8LDw/Xtt99a1pOSkgq8cNXT01M33XSTZb1///6aNGlSiX1ynCg/OnfuXGBWTWtt3bpVTZs2LfbziIgITZw4UXfddZfefvvtAs/OJSUladCgQcrMzNTHH3+sli1bmqodNyY3N1etW7dWTk5Ombf96aefig3UOTk5Wrp0qZYvX64uXbqoVatW8vT01JkzZ7R+/XqdO3dOgwcP1pgxY6yeet9ahCmUG0ePHtWUKVN0/PhxDRw4UH5+fkpISFBERIQCAwM1ffp0tWjRwtllwk4Mw9BPP/2ko0eP6vz586pWrZr8/f0VEhJS4KQZ5YM9TqYljhOVHccJ12evk+k8u3bt0tSpU3X58mWFhoaqYcOG+vXXX/Xll1+qW7dumjJliho2bGi2fLi4q1ev6ocfflBcXJxSU1Pl5eWlli1bqmPHjnZ7pyBhCuXOkSNH9OOPPyo1NVX16tVTSEiI5eFDAJA4TgCVmWEY2rt3r/773//q0qVLatSokTp37qwmTZo4uzRUQIQpAAAAADCBCSgAAAAAwATCFAAAAACYQJgCAAAAABMIUwAAAABgAmEKAAAAAEwgTAEAAACACYQpAAAAADCBMAUAAAAAJhCmAAAAAMAEwhQAAAAAmECYAgAAAAATCFMAAAAAYAJhCgAAAABMIEwBAAAAgAmEKQAAAAAwgTAFAAAqhM2bN6tBgwZKSEhwdikAKomqzi4AAADADMMwlJSUpO3bt+uTTz7RunXrJEnZ2dlOrgxAZUGYAgAA5U5UVJSeeeYZ1alTR8HBwQoICHB2SQAqITfDMAxnFwEAAHAjtm7dqrvuukuS9Ouvv6p58+bOLQhApcAzUwAAAABgAmEKAAAAAEwgTAEAKoxevXrJzc2t2K+33nrLqn5ycnLsXClcSbt27Ur8vfnoo4+cXSIAF8UEFACACqlWrVoaPHiw2rVrp+DgYAUHB6t+/foF2rz66qv65ZdfdO7cOctXcnKyBg0axAl0JbJnzx4dP35c+/bt0/79+7Vt2zZ9++23zi4LQDlAmAIAVEh//vOfNW3atBLbrFixQidOnHBQRXBVbm5uCggIUEBAgB566CFJUtOmTXXq1CknVwbA1XGbHwCgQmrRokWpbY4fP64LFy5ow4YNqlWrlgOqQnnBVOsArEGYAgBUWlWqVFHt2rV177336pFHHnF2OQCAcoYwBQCApCZNmji7BABAOUOYAgBAkru7u7NLAACUM0xAAQAAbG7btm0aNmyYcnNzb6ifqlWrKiIiQu3bt7dRZQBgO4QpAABgc927d9fx48edXQYA2BW3+QEAAACACYQpAAAAADCBMAUAAMrMMAxnlwAATsczUwBQycXHx2vFihWKiYlRQkKCMjMzrdrOzc1N/fr109tvv23nCl1HTk5OiSGiSpUqqlLlf3+nfO+997Rz504lJycX+Hr88ce1cOFCSVJaWpoWLFigzz77TIcOHVJubq58fX3VtWtXjRkzRl26dCkwhmEYWrFihT744APt2rVL6enpatiwoTp06KCwsDA98cQTN7SPx44d0/vvv6+tW7fq6NGjSklJUc2aNRUSEqJnn31WgwYN0rRp07Rjxw5t2LDhhsYCgPKOMAUAlVR6eromTZqkd999V9nZ2ab6SEhIqFRhqk+fPoqOji728ylTpmjq1KmW9c8++6zI9nkz3B08eFD9+/dXmzZt9Morr8jf31/nzp3TggULtHLlSq1cuVLh4eGaOHGiJOnixYsaMGCAzpw5o2effVavv/66cnNztWbNGi1cuFAbNmzQypUr9eWXX6pq1bL9E28YhiZOnKg5c+aoZcuWeuKJJ/TXv/5VN998sy5evKjt27fr+eef14wZM7Rv3z717NmzTP3bU2JiolatWmVZX7FihV588UXVrVvXiVUBqBQMAECls3fvXqN169aGJNNf1atXN+bMmePsXSmgZ8+elvqWLl1apm2nTJli2XbYsGFFttmxY4fRqlWrAv8d/Pz8jFdeecWIiIgwTp48WaB9QkKCsW/fPmPFihVG3bp1LduMHj3a2L9/v9G4cWPjk08+uW6crKwso2XLlpb2kZGRxuXLl43bb7/deP75542srKzrthk1apSl/euvv16mfTcMwxg7dqxl29zc3CLbJCQkGG3atDEkGT179izzGLYUHx9v+Pr6GrVq1Sr2d7Ru3bqGn5+fqf5v5HcJQOXBM1MAUMns27dPvXv31qFDhyRJvXr1UkREhBITE5WVlaVjx47ptddeK/AS2xUrVsgwjAJf6enpeuWVV5y1G07RqVMndevWTZLk5eWlWbNm6dixY5ozZ44GDBigpk2bFmjv6+uroKAgPfXUU3ruuecs3zcMQ08//bQmTJigsLCw68bx8PDQoEGDLOtvvfWWpk6dqubNm+vdd9+Vh4fHdds89dRTluV3331XV69etXq/jh07prfffluNGzfWlClT5ObmVmQ7X19fffbZZ8V+7khNmjRRQkKCLl68eN3vZt5XSkqKTp065exSAVRg3OYHAJVIQkKC+vTpo7Nnz8rNzU1z587V2LFjC7Tx9/fX9OnT5efnZwkAb775ZoGT9crqyJEj+uKLL9S8eXOtWbNGt912m9Xb+vv7W5YjIiLUokUL/fGPfyy2fV5ok6TIyEjFxsbq8OHDxQaZ/M9WnTt3Trt371anTp2sqi0yMlKGYah58+YFnvkqSvv27dW1a1er+gWAio4rUwBQSRiGoWHDhikpKUmSNHny5OuCVH6jRo2yXGn573//q/j4eIfU6ar279+vPn36KCAgQP/5z3/KFKQkFbialJycrD/96U8lXuFp1KiRZTkzM1ODBg3SzTffXGz7atWqqXbt2pb106dPW11bcnKypGv7eOHChVLb33HHHVb3DQAVGWEKACqJRYsWKTIyUpJ09913F5gooShVqlRRx44dLet79+61Z3kuLTY2Vt27d1dgYKCio6NLDDXWcHNz0z333FNim8KTJ/Tv37/UfvNvk5qaanU9TZo0kXRtgouHHnpIJ0+eLLF9jx49XGoCCgBwFsIUAFQCWVlZeuONNyzrc+bMKfV2Lqng1ZSynJxXJOvWrVO/fv3UqVMnffPNN6pVq9YN91mvXj01aNCgxDaFr1oFBgaW2m/+bYwyvAfq3nvvlZeXlyQpOjparVu31vDhw7V69Wr98ssv1/U1cOBATZs2zer+AaCiIkwBQCXwySefWB7E79+/v9q1a2fVdvlvFct/C1ll8cEHH2jgwIFKT0/XsWPHlJOTY5N+zQQyW4S44jRs2FBvvvmmZT09PV0ff/yxHnvsMQUGBqp27drq06eP/vnPfyo9Pd1udQBAeUOYAoBK4PPPP7csjxo1yurtEhISLMuNGze2aU2uLjw8XM8884w8PT0lSb/88osmTJjg5KrsZ8yYMVq8eLFq1Khx3WdpaWmKiorSCy+8oFtvvVVff/21EyoEANdDmAKACu7ixYuWF8dWqVJFvXr1smq73377TcePH5d0bRrwoKAge5XociIiIvTaa69p/vz5+vLLLy3ff+edd7R161bnFWZno0aNUlxcnGbMmKHbb7+9yAkyfvvtN4WGhuqjjz5yfIEA4GIIUwBQwR08eFDZ2dmSrk1rXb9+fau2++677yzLvXv3VvXq1e1SnyvKzs7WqlWr9NJLL6l///565plnJF17DmnEiBFKS0tzcoX2c9NNN2ny5MnatWuXLl68qJiYGM2bN0+9e/e2hCvDMPT888/rl19+cXK1AOBchCkAqODy36rXsmVLq7dbu3atZXnIkCE2rcnVvfzyy3rkkUcs6//4xz90yy23SJJ+/fVX/eUvf3FWaXaxc+dOrV69+rrv16xZU927d9fYsWMVGRmpH374wTIRRkZGht555x1HlwoALoUwBQCVSP53F5Xk8OHDWr9+vaRrAWzQoEGlbnPx4kWNGDFC7dq1U4sWLbRkyZLr2hw6dEgtW7ZUp06dlJGRUbbiHSj/LIbStckfPvzwQ8uVmUWLFmnTpk3OKM0uvv76a7344oultuvYsaO2b99umRp+x44d9i4NAFwaYQoAKri8F+9KKnJygaLMnj1bubm5kqRZs2ZZNY364MGD1b9/f+3du1ehoaEaPXq0vv/++wJtFi1apKNHj2rnzp3l7tmj3r1764UXXrCsjxw5UhcvXnRiRbaVlJRk1bvEGjVqZJnEJCUlxd5lAYBLI0wBQAV32223Wd5pFBcXV2r7qKgoLV26VNK1wPDwww+Xuk1ERIT8/PwsV7AuX74swzD073//u0C7UaNGWd5nVB5PxGfNmqWAgABJUnx8vP785z87uSLbWrZsmVXt8gK6j4+PPcsBAJdHmAKACs7Dw8PyzFNMTIwyMzOLbXv69Gk9+eSTys3NVffu3TV//nyrxpg9e7YmTpwo6drkBHm3wBV+Rqtt27b629/+Jsn6Ww5dSY0aNfT+++9b1j/88EOtW7fOiRXZ1nvvvaekpKRS2x09elTStat1AFCZEaYAoBKYPn26mjVrptOnT2vBggVFtomPj9c999yjpKQk9erVS998841VtwWeOHFC7u7ulgkaoqOjFR8fr7p166pv377XtX/iiSckyeWmWs9/pez8+fPFtuvVq5fat29vWR8+fLgOHDhQav/nzp2zLKemppb6AuD87YtaLywnJ0cXLlywun1RLl26pLCwMGVlZRXb5syZM/roo49Uu3btMr2zDAAqoqrOLgAAYH+1a9fWN998owceeEATJ05UZmamRowYofr16ysuLk5ffPGFZs+erczMTIWHh+uvf/2r3N3dreq7WbNm2rx5s2U97/1DTz75pOWWvsLatGnjEreI/X97dxMK2x/HcfyjruSyYMgs5GFslOcFsiAPiY2krIWVREbZ2czCwkYUCzbYKBtZsdSkKEwZRNkgWXhIGA9NUn7/xe1Of3/u5R5zzX94v+pszvnN+X3nzKnpc+bM96ysrOj09FTr6+uamJgIrJ+YmFBMTIwKCwtls9lUWVkpSfL5fJqbm3tyu+Tl5aVKS0vldDpVVFSk7OzsQLDc2NjQ0dGRtre3NTg4GHiNz+dTQ0ODGhsblZycrLy8PNntdh0eHmp3d1f7+/saHR19Umtzc7Pa29uVkZGhjIwMZWZmyufzyePx6Pj4WDMzM7q6ugqM7+/vl9/vV25urux2u0pKSl49Hna7XVtbW6qurtbw8LAKCgoC2x4eHjQ9PS2Xy6Xr62vNzMx8uQc5A8AzBgDwZZyfn5ve3l6TlJRkJAWW1NRU43Q6zd7e3rv2f3d3Z2JjY40ks7S09OKY+fl5093d/a55fqW8vDzwniYnJ18dn5aW9uQ4/Gr5ye12vzrW5XK9WM/vlp+1ulyuN41vbm5+cz2STFpa2m+Pg8vlMjExMcbj8ZijoyNTX19vJJn09HRTVlZmsrKyzPfv301ERISpqqoyXq/3Dz+Z8POn5xKArynCGGOCms4AAGHh4uJC5+fnSkhICDSoeK/Z2Vk1NjYqMTFRp6enL3YB7OjoUH19vWpra4My579VVFRocXFRkjQ5OamWlpagz/EZra2t6f7+XmVlZYF1BwcHcrvdOjk5UVRUlFJTU1VSUvKkO+RnxrkE4C24zQ8AviibzSabzRbUfS4vL0uSampqXgxS9/f3crvdb25sgY9RXFz8bJ3D4ZDD4QhBNQAQPmhAAQAImv39fUlSfn7+i9unpqbU0NCgb9+4lgcACH98mwEAgsbv90uS4uPjn227vb3VyMiIFhYWProsAAD+Cn6ZAgAETW5urqQf7dL/y+l0yul0Bu3/WQAAhBphCgAQNF1dXUpMTNT4+Lj29vYk/Xh2UVtbm+Li4tTa2hriCoG3eXx8DHUJAMIAt/kBAIImJSVFGxsb6uvrU11dnSIjIxUdHa3Ozk41NTV9aC0HBwcfOh8+j9vbW21tbYW6DABhgDAFAAiq5ORkjY2NhboMDQ0N6ezsTHl5ecrJyVF2dnbQuxfic1hdXdXm5qa2t7e1s7Mjr9crn88X6rIAhAHCFADgU7q5uXkW6gYGBtTT0xOiivB/1dLSot3d3VCXASAM8dBeAAAAALCABhQAAAAAYAFhCgAAAAAsIEwBAAAAgAWEKQAAAACwgDAFAAAAABYQpgAAAADAAkw30WEAAABSSURBVMIUAAAAAFhAmAIAAAAACwhTAAAAAGABYQoAAAAALCBMAQAAAIAFhCkAAAAAsIAwBQAAAAAWEKYAAAAAwALCFAAAAABYQJgCAAAAAAv+AfphLBG4jNHbAAAAAElFTkSuQmCC\n",
            "text/plain": "<Figure size 432x432 with 1 Axes>"
          },
          "metadata": {
            "image/png": {
              "height": 420,
              "width": 425
            }
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": "<Figure size 864x864 with 9 Axes>"
          },
          "metadata": {
            "image/png": {
              "height": 853,
              "width": 856
            }
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x432 with 1 Axes>"
          },
          "metadata": {
            "image/png": {
              "height": 421,
              "width": 425
            }
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "conda-root-py",
      "display_name": "Python [conda env:root] *",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.7.3",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "gist": {
      "id": "",
      "data": {
        "description": "dr2-lmc-cluster/notebooks/Kinesis-model.ipynb",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}