Transit_Light_Curves_Data_Equations.ipynb

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      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Transit Light Curves\n",
        "\n",
        "* Transit Light Curves are graphs that show how the brightness of a star changes over time due to the transit of a planet or another body across its face. This method is widely used in the detection and study of exoplanets. When a planet transits in front of a star, it blocks a portion of the star's light, causing a temporary dip in the star's observed brightness. The characteristics of this dip can provide valuable information about the planet, such as its size and orbital period.\n",
        "* The amount of light blocked, and hence the depth of the transit in the light curve, depends on the ratio of the areas of the planet and the star. The formula for the fractional decrease in a star's brightness during a planetary transit, considering the areas of the star and planet as spheres, is given by the ratio of their surface areas.\n",
        "  > $\\Delta F = \\left( \\frac{R_p}{R_s} \\right)^2$\n",
        "\n",
        "  or\n",
        "\n",
        "  > $\\frac{\\Delta F}{F} = \\left( \\frac{4 \\pi R_{p}^{2}}{4 \\pi R_{s}^{2}} \\right)^2$\n",
        "\n",
        "  where:\n",
        "\n",
        "  $\\Delta F$: the fractional decrease in the star's brightness\n",
        "\n",
        "  $R_p$: the radius of the planet\n",
        "\n",
        "  $R_s$: the radius of the star"
      ],
      "metadata": {
        "id": "mQ-Bk7gxQUA7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!pip install astroquery"
      ],
      "metadata": {
        "id": "EwohLAPjgyHm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "EeYrvtP-QNu6"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.animation as animation\n",
        "from astropy.modeling.models import BlackBody\n",
        "from astropy.visualization import quantity_support\n",
        "from astropy import units as unit\n",
        "plt.style.use(\"dark_background\")\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def CalculateDeltaF(radiusPlanet:int or float,radiusStar:int or float)->int or float:\n",
        "  \"\"\"\n",
        "    Calculate the fractional decrease in brightness of a star due to a transiting planet.\n",
        "\n",
        "    :param radiusPlanet(R_p): Radius of the planet (in kilometers or any consistent unit)\n",
        "    :param radiusStar(R_s): Radius of the star (in the same unit as R_p)\n",
        "    :return: Fractional decrease in brightness (Delta F)\n",
        "  \"\"\"\n",
        "  return (radiusPlanet/radiusStar)**2"
      ],
      "metadata": {
        "id": "4sLJicqVSCQ1"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "radiusStar = 695700  # Radius of the Sun in kilometers\n",
        "radiusPlanet = 69911  # Radius of Jupiter in kilometers"
      ],
      "metadata": {
        "id": "-KL9x03ASWlB"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "deltaF = CalculateDeltaF(radiusPlanet,radiusStar)\n",
        "print(f\"The fractional decrease in brightness (Delta F) is: {deltaF}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IcWzpfm8SfDC",
        "outputId": "a59ac5be-b015-4f11-bdfb-035ab0972a5c"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The fractional decrease in brightness (Delta F) is: 0.010098271011134767\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Rstar = 1*unit.R_sun # Radius of the star in solar radii\n",
        "Rplanet = 1*unit.R_jup # Radius of the planet in Jupiter radii"
      ],
      "metadata": {
        "id": "2tPWAC0uSlWU"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "timeInterval = np.linspace(-0.05,0.05,1000) # Time in days around the transit"
      ],
      "metadata": {
        "id": "26yCe93uS77p"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# For simplicity, we assume a uniform disk (no limb darkening) and a central transit\n",
        "fractionalRadius = (Rplanet/Rstar).decompose() # Fractional size of the planet compared to the star\n",
        "print(f\"Fractional Radius: {fractionalRadius}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aSdxr3NvTBTv",
        "outputId": "29c179a0-9f5e-4b41-b8f6-14a7bc9e3faa"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fractional Radius: 0.10276268506540175\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lightCurve = 1 - (fractionalRadius**2*(np.abs(timeInterval)<0.01)) # Simple box model for transit"
      ],
      "metadata": {
        "id": "sXMHsSv2TM1y"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,8))\n",
        "plt.plot(timeInterval,lightCurve,color=\"red\",lw=2,label=\"Light Curve\")\n",
        "plt.xlabel(\"Time (days)\")\n",
        "plt.ylabel(\"Normalized Flux\")\n",
        "plt.title(\"Simulated Transit Light Curve\")\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 769
        },
        "id": "Gq0nshnkTXPc",
        "outputId": "36e11def-9181-4299-eb77-d6b90ab64cec"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "starRadius = 1.0 # Star radius\n",
        "planetRadius = 0.1 # Planet radius (relative to star)\n",
        "orbitRadius = 1.5 # Orbit radius (relative to star)\n",
        "transitDuration = 100 # Number of frames for the transit"
      ],
      "metadata": {
        "id": "Tede1b8vZXiD"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "figure,(axs1,axs2) = plt.subplots(2,1,figsize=(5,5))\n",
        "axs1.axis(\"off\")\n",
        "axs1.set_xlim(-2,2)\n",
        "axs1.set_ylim(-2,2)\n",
        "star = plt.Circle((0,0),starRadius,color=\"yellow\",ec=\"none\")\n",
        "axs1.add_artist(star)\n",
        "planet = plt.Circle((-orbitRadius,0),planetRadius,color=\"blue\",ec=\"none\")\n",
        "axs1.add_artist(planet)\n",
        "axs2.set_xlim(-0.05,0.05)\n",
        "axs2.set_ylim(0.9,1.01)\n",
        "axs2.set_xlabel(\"Time (days)\")\n",
        "axs2.set_ylabel(\"Normalized Flux\")\n",
        "fluxLine, = axs2.plot([],[],lw=2)\n",
        "timeArray = np.linspace(-0.05,0.05,transitDuration)\n",
        "fluxArray = np.ones(transitDuration)\n",
        "def Update(frame)->tuple:\n",
        "  x = -orbitRadius+(2*orbitRadius*frame/transitDuration)\n",
        "  planet.center = (x,0)\n",
        "  if -starRadius <= x <= starRadius:\n",
        "    star.set_alpha(0.5) # Dim the star during transit\n",
        "  else:\n",
        "    star.set_alpha(1.0) # Star is fully bright outside of transit\n",
        "  if np.abs(x) < starRadius:\n",
        "    fluxArray[frame] = 1 - (planetRadius/starRadius)**2\n",
        "  else:\n",
        "    fluxArray[frame] = 1\n",
        "  fluxLine.set_data(timeArray[:frame],fluxArray[:frame])\n",
        "  return star,planet,fluxLine\n",
        "anim = animation.FuncAnimation(figure,Update,frames=transitDuration,blit=True,repeat=False)\n",
        "anim.save('transit_animation.mp4', writer=\"ffmpeg\",fps=20)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "t5k6y7ZRZij5",
        "outputId": "938bed6e-6b32-44e1-ff57-6d6595bbf06c"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Process"
      ],
      "metadata": {
        "id": "Pu5VNOfwY0OV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/driveFile\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jb3x0yYlTmWj",
        "outputId": "9e353fdc-704c-4e52-dbdb-b1215a26e0e0"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/driveFile\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataPath = \"/content/driveFile/MyDrive/Astronomy/Project Data/dat_file/light_curve_data/tres2_data.dat\""
      ],
      "metadata": {
        "id": "SFavIrd2Y-Lp"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = np.loadtxt(dataPath)"
      ],
      "metadata": {
        "id": "TYBrrzrmZO5Q"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ESvnunx-dPYb",
        "outputId": "921c67fa-2ce0-4548-c4b4-353368d7fdcb"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(247, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "MJD = data[:,0] # The Modified Julian Date (MJD)\n",
        "FLUX = data[:,1]\n",
        "ERR = data[:,2]"
      ],
      "metadata": {
        "id": "XgmLARYwdY9_"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,8))\n",
        "plt.errorbar(MJD,FLUX,yerr=ERR,ecolor=\"steelblue\",linestyle=\"none\",marker=\"o\",color=\"red\")\n",
        "plt.xlabel(\"MJD\")\n",
        "plt.ylabel(\"Flux / ADU\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 767
        },
        "id": "YPNjn8Imd3x6",
        "outputId": "6488f78d-dd41-4737-c32e-194162154b8a"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to calculate a normalization factor from all flux values outside of the transit\n",
        "T1 = 5.645e4 + 0.445\n",
        "T4 = 5.645e4 + 0.520\n",
        "norm1 = np.mean(FLUX[MJD<T1])\n",
        "norm2 = np.mean(FLUX[MJD>T4])\n",
        "norm = 0.5*(norm1+norm2)\n",
        "print(f\"Normalization Factor: {norm}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rn4EkNkleJ4B",
        "outputId": "6b45d5d3-fbcb-44e4-b219-77b5074e5865"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Normalization Factor: 1.5094473684210525\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "FLUX /= norm\n",
        "ERR /= norm"
      ],
      "metadata": {
        "id": "QgAYfLjsekzG"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "offset = 7\n",
        "width = 2*offset+1\n",
        "fluxSmoothed = np.ones(FLUX.size-width+1)\n",
        "for index,value in enumerate(fluxSmoothed):\n",
        "  fluxSmoothed[index] = np.sum(FLUX[index:index+width])/width\n",
        "fluxMin = np.min(fluxSmoothed)\n",
        "print(f\"Minimum Flux: {fluxMin}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dT15trr0eom1",
        "outputId": "e51504a5-8b70-41c7-aa1f-6bac0608071b"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Minimum Flux: 0.9849520854036276\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "plt.errorbar(MJD,FLUX,yerr=ERR,ecolor=\"steelblue\",linestyle=\"none\",marker=\"o\",color=\"red\",zorder=1)\n",
        "plt.xlim(np.min(MJD),np.max(MJD))\n",
        "plt.xlabel(\"MJD\")\n",
        "plt.ylabel(\"rel. flux\")\n",
        "plt.plot(MJD[offset:-offset],fluxSmoothed,lw=2,color=\"white\",zorder=2)\n",
        "plt.axvline(T1,color=\"crimson\",lw=1,linestyle=\":\")\n",
        "plt.axvline(T4,color=\"crimson\",lw=1,linestyle=\":\")\n",
        "plt.axhline(fluxMin,lw=1,linestyle=\"--\",color=\"orange\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 958
        },
        "id": "6CDIexnhe_Jd",
        "outputId": "5fbe6358-ebdc-4931-e9f6-0c9defa1e6a8"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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iImKT8riIfWp0i0hEmcB+JWoREZG4pDwuYp9uLxeRiGpnJDMjvTvtDPV5ICIiEm+Ux0XsU6NbRCLKiYNMRyJOHNEORURERGxSHhexT7eXi0hEbTKLuDL/+2iHISIiIkFQHhexT1e6RURERERERMJEjW4RiaiezjSymw+gpzMt2qGIiIiITcrjIvap0S0iEbXdLGbagQ1sN4ujHYqIiIjYpDwuYp+e6RaRiNrjLuPlom3RDkNERESCoDwuYp+udItIRDV2ODk1sRmNHc5ohyIiIiI2KY+L2KdGt4hEVEcjheea9KKjkRLtUERERMQm5XER+3R7uYhE1E+uQvrv/ow8d1m0QxERERGblMdF7FOjW0Qiqgw3u9yl0Q5DREREguCbx52JyQyZOB+AJVMvxFWqDtZEfOn2chGJqMOMZKY17sphRnK0QxERERGblMdF7FOjW0QiKtlh0M2ZSrJDXz8iIiLxRnlcxD7dXi4iEbXBdZCL9q2NdhgiIiISBOVxEfv0E5WIiIiIiIhImKjRLSIR1cOZxpeZJ9LDmRbtUERERMQm5XER+9ToFpGI2mWW8NTBrewyS/wudyYmM3TKQoZOWYgzUZ20iIiIxJLa8riIVKdnukV8GG43vTesJbMgjzLTZBlgRjmm+jQcx253Kc8dzIl2GCIiIhIE5XER+9ToFqlkODCrrIT2cyYAMB7YAowFFkQxrvokzeHkqITGfF+2nwNuV7TDERERERuUx0Xs0+3lIuWGA/OALJ/5WeXzh0c8ovqpk5HCK02OppOREu1QRERExCblcRH71OgWwfNBmFVp2ncZwEw/y+qbSDxPvd5VyJA9X7LeVRiW9YuIiEj4KI+L2Fff2xAilgwC2hP4A2EAHcrLSd2U4GaTWUQJ7miHIiIiIjYpj4vYp0a3CNA2xOUksLZGEhPTDqetkRTtUERERMQm5XER+9ToFgG2hbicBJbmcHJiYhPSHM5ohyIiIiI2KY+L2Kfey0WAFXh6Kc/C/y9RJrC1vJzUzS+ug5y99+tohyEiIiJBUB4XsU+NbhE8jeqxeHopN6na8K4Yo3sc0R+vW0RERETscZUW88G9Z0c7DGnAdHu5SLkFwAggx2f+1vL5Gqc7NI5wpvJJs+M5wpka7VBERETEJuVxEft0pVukkgXAwoQkxl4+icyCPBYvmMkyV6mucIdQnlnKa8U7yDNLox2KiIiI2KQ8LmKfGt1ShTMxmSET5wOwZOqFuEqLoxxR5JkOB2s79wZg+VuPYbqiHFA9s8tdymOFm6MdhoiIiARBeVzEPt1eLiIR1QiDoxMa00hfPyIiInFHeVzEPn1aRCSiDnc2YkHTvhzubBTtUERERMQm5XER+3R7uYhE1C+ug5yV9xUbXUXRDkVERERsCmUeN9xuem9YS2ZBHmWmyTI0Uky06BHT8FKjW0QiqhiTn1yF0Q5DREREghCqPD4cmFVWQvs5EwAYD2zBM4SrRoyR+ka3l4tIRLU2krgjtSOtjaRohyIiIiI2hSKPDwfmAVk+87PK5w8PPjyRmKRGt4hEVIYjgTOTW5Dh0I02IiIi8aauedwAZlWa9l0GMNPPMpF4plqviETUelchQ/LWRDsMERERCUJd8/ggoH0Nyw2gQ3m55UG/i0hs0Y9IIiIiIiISEW1DXE4kHqjRLSIR1c2ZygdNj6WbMzXaoYiIiIhNdc3j20JcTiQeqNEtIhFV4C5jSckeCtxl0Q5FREREbKprHl+Bp5fyQEODmcDm8nIi9YUa3SISUdvNEh4u3Mh2syTaoYiIiIhNdc3jJp5hwSqmfZcBjPOzTCSeqdEtIhGVjEE3ZyrJ+voRERGJO6HI4wuAEUCOz/yt5fM1TrfUN6r1ikhEdXU24n/NjqWrs1G0QxERERGbQpXHFwBdEpK48+ppPDjiToY4E+mMGtxSP6nRLVILZ2IyQ6csZOiUhTgTk6MdTtzb4DrIRXu/ZYPrYLRDEREREZtCmcdNh4O1nXuzrPdglhuGbimXekvjdItIRBVi8nVZQbTDEBERkSAoj4vYpyvdIhJRLR2J3NioPS0didEORURERGxSHhexT1e6Q8xwu+m9YS2ZBXmUmSbLUO+LIpVlGolc1qgtS0p2s8tVGu1wRERExAbfPK66r0jt1OgOoeHArLIS2s+ZAMB4POMQjkWdQohU+MlVyIA9n0c7DBEREQlC5Tyuuq+INbq9PESGA/OALJ/5WeXzh0c8IhEJRJ3jiYiI1I3qviLWqdEdAgYwq9K07zKAmX6WiTREXZyNeKtpX7poyDAREZG4U5HHZ5bncdV9RWqnz0IIDALaE3hnGkCH8nIiDV2h2+Tr0nwK3XriS0REJN4Uuk12lObT1m2q7itikRrdIdA2xOVE6rNtZjGTDvzGNrM42qGIiIiITdvMYj468BuJFvJ4vNd99TiahIoa3SGwLcTlROqzJBxkGckk4Yh2KCIiImJTEg5KjGTcFvK46r4iHmp0h8AKPD01BrpZ1gQ2l5cTaei6OVNZnnk83Zyp0Q5FREREbOrmTOXvmcez2Zmquq+IRWp0h4CJZ2iEimnfZQDj/CwTaYg2mUVcse87NplF0Q5FREREbKrI438rz+Oq+0okhPJW/2g8NqBGd4gsAEYAOT7zt5bP11iFIh773S5Wle5jv9sV7VBERETEpoo8PtftUt1XxKKEaAdQnywAFiYkMfbySWQW5LF4wUyWuUr1K59IJc0diZyX3JK3inex210a7XBERETEhsp5fIG7VHVfEQt0pTvETIeDtZ17s6z3YJYbhr50RHy0MpL4S2oHWhlJ0Q5FREREbPLN46r7itROV7pFJKJ+cB2g755Pox2GiIiIBEF5XMQ+XekWERERERERCRM1ukXigOF203vDWk5du5zBphm2D24k3qezsxFzmxxNZ2ejqMUgIiIiwaktj4tIdbq9XCTGDQdmlZXQfs4EAMbjGRd+LKHtGTSU7+NMTGbIxPkALJl6Ia7SYu+yErfJJlcRJe7qT31FaltFREQkODXlcRHxTxeRRGLYcGAekOUzP6t8/vA4ex+AHLOYv+5fT45ZXGV+JGMQERGR4ATK4yISmBrdIjHKAGZVmvZdBjDTz7JYfZ8KCTjIdCSQgCNqMYiIiEhw/OVxEamZ6rAiMWoQ0J7AH1ID6FBeLh7ep0J3ZyqfNz+J7s7UqMUgIiIiwfGXx0WkZmp0i8SotiEuF+33qbDZLOL6/Gw2m0VRi0FERESC4y+Pi0jN1JGaSIzaFuJy0X6fCgVuF0tK9kQ1BhEREQmOvzwuIjXTlW6RGLUCT8/dgfoGNYHN5eXi4X0qZDoSGJXShkzHod/8Ih2DiIiIBMdfHheRmqnRLRKjTDxDZVVM+y4DGOdnWay+T4W2RjKT0rrQ1kiOWgwiIiISHH95XERqpka3SAxbAIwAcnzmby2fH6qxqyP1PgDrXAfosXsl61wHohaDiIiIBCdQHheRwHRfiEiMWwAsTEhi7OWTyCzIY/GCmSxzlYb8qm+k3ifWYxARERERCSVd6RaJA6bDwdrOvVnWezDLDSNsjdBIvE9HI4U5Gb3oaKRELQYREREJTm15XESqU6Nbws6ZmMzQKQsZOmUhzkQ9/9PQmbjZ73Zh4o52KCIiImKT8riIfbq9XEQscSYmM2TifACWTL0QV2lxUOvZYhZzS8GPoQxNREREIkR5XMQ+NbqlCsPtpveGtWQW5FFmmixDPUaHS6gasfHGAJIxKMbUuSUiIhJnlMfrJ7UBwiuqt5cPGjSIt99+m5ycHNxuN+edd16trxk8eDBr1qyhqKiI9evXc+WVV1Yrc+ONN7JhwwYOHjzIp59+yvHHH19l+dKlS3G73VX+zZ49O2TbFa+GA7+WlfCPORMYP+8fLHGVsrF8vkioHOlM47sWAzjSmRbtUERERMQm5fH6R22A8ItqozstLY1vv/2Wm266yVL5Tp068e6777J06VL69u3LzJkzee655xg6dKi3zMiRI5k+fTqTJ0/m2GOP5dtvv2XRokW0bNmyyrqeeeYZ2rRp4/131113hXTb4s1wYB6Q5TM/q3x+Q/rQuUqL+eDes/ng3rMbzNXnSNpqFjMu/0e2mtq3IiIi8UZ5PPRq6v8o3H0jqQ0QGVG9vfz999/n/ffft1x+zJgxbNiwgTvuuAOAH3/8kZNPPplbb72VDz74AIDbbruNZ599lhdffNH7mrPOOotrrrmGhx9+2LuuwsJCduzYEbqNiWMGMKvStO8yE5gJvIVuM5G62+cuY2FJbrTDEBERkSAoj9cfagNETlz1Xt6/f38WL15cZd6iRYvo378/AImJiRx33HFVyrjdbhYvXuwtU+HSSy9l165dfPfdd0ybNo1GjRrV+N5JSUmkp6dX+VdfDALaE/hkMIAO5eVE6qqJI4HzklvSxKEuJUREROKN8nj9oTZA5MTVp6VNmzbVrk7v2LGDJk2akJKSQrNmzUhISPBbpkePHt6///Of/7Bp0yZ+//13evfuzcMPP0z37t258MILA773+PHjmTRpUki3J1a0DXE5kZq0M5J5NL075+V9zT5XWbTDERERERtCmccrHumT6FAboHah6vg4rhrdofLss896p7///nu2bdvGRx99xOGHH85vv/3m9zUPPvgg06dP9/6dnp5OTk5O2GONhG0hLidSk2zXAXrmrqQ0wuN7Vv7SXPrQJRF9bxERkVCJ9ugn0crjEnpqA0ROXN1evn37dlq3bl1lXuvWrdm3bx9FRUXk5uZSVlbmt8z27dsDrvezzz4DoGvXrgHLlJSUUFBQUOVffbEC2ELgZzVMYHN5OZG6cgMluJWqRURE4pDyeP2hNkDkxFWje/Xq1QwZMqTKvNNPP53Vq1cDUFpaypo1a6qUcTgcDBkyxFvGn759+wKwbVvD/B3HBMZWmvZdBjDOzzKRYLQ3knkyvQftjdD3wCkiIiLhpTxef6gNEDlRHzKsT58+9OnTB4DOnTvTp08f2rdvD8C0adN46aWXvOWfeuopDj/8cO8z2DfccAMjR45kxowZ3jLTp0/nz3/+M1dccQU9evRg9uzZpKWlMWfOHAAOP/xw7rnnHo499lg6duzIOeecw7/+9S+WL1/Od999F8Gtjy0LgBGA7w3zW8vnL4h4RFJfGThIchgYOKIdioiIiNikPF6/qA0QGVF9prtfv34sW7bM+3dF4/nFF1/k6quvpm3btnTo0MG7fOPGjZx11lnMmDGDsWPHsnXrVq677jrvcGEAr732Gi1btmTKlCm0adOGb775hjPOOIOdO3cCntvE//jHPzJu3DjS0tLYsmUL8+fP5/7774/MRsewBcDChCTGXj6JzII8Fi+YyTJXqX7dkpDaZBbx5/zsaIchIiIiQVAer3/UBgi/qDa6ly9fjsMR+Feyq6++2u9rjj322BrX++STT/Lkk0/6XbZ161ZOPfVUW3E2JKbDwdrOvQFY/tZjmK5Dy6LdcYeIiIiIiIReTW0Aqbu4eqZbROJfL2cav7Q4mV7OtGiHIiIiIjYpj4vY1yCHDBOxw3C76b1hLZkFeZSZJss41KGErv7b97tZzISC9fxual+JiIjEm4aUx2uqAzZkqv/ap0a3SA2GA7PKSmg/ZwIA4/EMrTAWdSwRrDx3Ga8V74h2GCIiIhKEhpLHo1UHNHwatHWhxnHs0O3lIgEMB+YBWT7zs8rnD494RPVDhsPJ0KTmZDic0Q5FREREbGoIeVx1QAk1NbpF/DCAWZWmfZcBzMRz25HY095I4Z8ZR9LeSIl2KCIiImJTfc/jluuAkQrIBmdiMkOnLGTolIU4EzWOeizR7eUifgwC2tew3AA6AIPU6LbtR9cBjtv9KfvdZdEORURERGzyzeOu0mI+uPfsKEcVPN9bsE8uLbZWBwSWRyA+qR9i8Ucakahra7FcG9TotssF7HOXoZEoREREYofVq6R1yePxcCXWah3QajkRUKNbxK9tFsttJ/A48+JfOyOZRxsfQTsjNpOtiIiIBFbf87jVOqDVciKgRreIXyvw9FAZaFgIE9gMrHCo0W1XAg7aOJNJ0A8WIiIiEROqq8z1PY9brgNGLCKpD9ToFvHDxDMkRMW07zKAcYCpRrdtG80iLt33HRvNopCuNx5uWRMREYl34crjscJyHTBSAYklsV4PVKNbJIAFwAggx2f+1vL5GqdbREREpP5RHVBCTY1ukRosALokJHHn1dN4cMSdDHEm0hl92dbFkc401jbvz5HOtGiHIiIiIjY1lDyuOqCEkhrdIrUwHQ7Wdu7Nst6DWW4Yup2ojnaYJTxyYCM7zJJohyIiIiI2NaQ8rjpgdFW+ZdyIwVvG7dA43SISUXvcpfyrSH1+ioiIxCPlcRH7dKVbRCKqscPJoMSmNHY4ox2KiIiI2KQ8LmKfrnRL1DkTkxkycT4AS6ZeiKu0OMoRCYDhc1xsvdbtpveGtWQW5FFmmizjUC+fHY0U5jQ5ivPyvmad60BIYw5FfCIiIhJYLORxiS2RqMuHsu4WjXqgGt0iMSISX1iReI/hwKyyEtrPmQDAeDzjXY7F0/nIz65CTt7zObvN0pC/txUDs1fxXNH+gPGJiIhIYNHO49LwhLLuVls9NVx0e7mIhMxwYB6Q5TM/q3z+cKAUN9vNEkpxRzo8BmavYuLcaWS5q7535fhEREQksGjmcWl4Qll3s1JPDRc1ukUkJAxgVqVp32UAM4EsI5mpaV1oa0S2F0rD7eaG956pEo93Wfn/M/0sExERkUPaRimPS8MTyrqb1XpquOqBur08xFylxXxw79nRDkMk4gYB7WtYbgAdgJMdBr0SGtPIEdnm7SC3m5b5uQGXV8Q3CFgeqaBERETiTKMo5XFpeOzU3T6pbV1Yq6eGqx6oRreIhERbi+UcroNcsO/bsMbiTxuLt8FZ3Q4REZGG6DebebxyfzJLH7okqNepo92GKZR1N6v1u3DVA9XoFpGQsDpiZ7RG9tyOw1I5jTwqIiKxxLfxKdJQhLLuZrV+t9OZyND7PF2qhfLzpvtCRCQkVuDp/THQkAsmsBnY6Uzl88wT6e5MjVhsACscDnZltKg1vhURjElERCTedI9SHpeGJ5R1N6v11BUOaw19u9ToFhGvij4JPrj3bEybt3GZeIZbqJj2XQYwDthllvLCwZyIDzViOhzMHjYaRy3xabxuERGRwHZHKY9LwxPKupvVeqqpRnfD4kxMZuiUhQydshBnonqHlPiwABgB5PjM31o+fwGQ6y7lqYNbyXVHPlmv7DmAqaMmkOPzhVo5PhEREQksmnlcGp5Q1t2s1FPDRc90i0hILQAWJiQx9vJJZBbksXjBTJa5Sr2/IqZicGRCY34o209hFK4rr+w5gGkpjbll1Hi/8YmIiEhg0c7j0vCEsu5WWz01XHSlWyQOVL7tOx567zQdDtZ27s2y3oNZbhhVvsg6OxvxatPedHY2isn4REREJLBYyONSP9i5szeUdbdo1AN1pVtEImq9q5DT89aQ4yryu1xj3YuIiMSu2vK4iFSnRreIRFQJbja4DkY7DBEREQmC8njDo2Hr6k6Nbgk7w+2m94a1ZBbkUWaaLEM9REP1/RKuIQpCJVTHsY2RxDWNsnjhYA7bzZIQRykiIlK/RbtepTweepWPqctVhmG6MA1ntWWqR8cvNbolrIYDs8pKaD9nAgDj8YyRN5b47Sm68q99Sx+6JKh1BNovc7JXsbLngNAEWi4U8Z5vupjhKqse76gJrOw5gCVTL7T8rHljh5NBiU15rWh7ULGIiIg0VIHycSTrVcrjoeWvTrhr+rXMHjaa9Bg43hIa6khNwmY4MA/I8pmfVT5/eMQjig017ZeJc6cxMHtV5IOqwcDsVbzmKgtZvL+4DnLm3q/5RbemiYiIWFZTPo5kvUp5PHQC1Qmb5+cyce40Xo+B4y2HGHUY0lmNbgkLA5hVadp3GcBMP8vqu9r2ixsY894zGG53ROMKxDBd3PDeM55p32XEXrwiIiL1UW35GBpmvSqe+PbUbaWuXNOymX6WSezSsZKwGAS0J/AJZgAdyss1JFb2S6v8XAbFSCP2qE3raJmfG9J4uzlTWdasH92cqSGJUUREpL6zko8jVa9SHg+N2uqEjvJ//jTUenQ80zPdEhZtQ1yuvrC6vW2IjUZ3ZkGepXJ24t3nLuOt4p3sc5cFG5aIiEiDYjUf26lXBdsjtfJ4aISiDtzQ6tHxTI1uCYttIS5XV76JxWqnX6FmdXu3B/xtM7L2pDezVM5OvDvNEmYUbg42JBERkQbHaj6ORL1KeTw0QnGsIlWPlrrT7eUSFivw9K4YaEgDE9hcXq4hsbJfdma0iJnhw77v2ItdGS1CGm8KBr2caaTo60dERMQSK/k4UvUq5fHQqK1O6C7/509DrUfHM31aJCxMPMMZVEz7LgMY52dZfVfbfnEATw0bjRkjjW7TcDJ72GjPtO8ygou3i7MRbzU7hi7ORiGLU0REpD6rLR9D5OpVyuOhYaWuXNOycX6WSexSo1vCZgEwAsjxmb+1fH5DHV+wpv0ytXzc61iysucARjoTQhbvr66DnJf3Nb9qqBERERHLasrHkaxXKY+HTqA6YW5GC6aOmsBFMXC8I8m3h/f6RM90S1gtABYmJDH28klkFuSxeMFMlrlKG/wvc/72ywqHg9NirMFd4U3DydsOIyTxFmGyznUgDFGKiIjUb/7ycaTrVcrjoeVbJ1zyzpMk3vY8puFkSQSPt6u0mA/uPTsMaxbQlW6JANPhYG3n3izrPZjlhtHgG9wVqu2XGLmlPJBQxdvKSOLW1A60MpJCHKGIiEj9F+16lfJ46FU+ph87EzANp99lqkfHLzW6pd6oz7ek1CdNHAmcl9yKJg7daCMiIhJvlMdF7NOnRarQrSUSbutdhZya92W0wxAREZEgKI/HJ8Pm8LmVh9td+tAlYY+vvlOjWywz3G56b1hLZkEeZabJMtRrop194jtWeI3rjZFxxeuTysfK5SrDMF1Vbt8SERGpz3zrLL7DfUainheLdclYi6m242TntSshLHWfusQYb0JVf1SjWywZDswqK6H9nAkAjMcztuBY6mfviVYMzF7Fc0X7tU9s6upsxOPpPbil4Ed+iVDPp/7O313Tr2X2sNEsiUgEIiIi0ROoHjcnexUrew7gfNPFDFeZpTpNsHk8FutNsVa/DbSPKo5TTfxtSxmQEOK6T6BzxUqM8cbftga7D/VMt9RqODAPyPKZn1U+f3jEI4q+gdmrmDh3Gllud5X5DXWfGDaep9/vdrGidC/73a6IxBbo/G2en8vEudM434xMHCIiItFQUz1u4txpXLvoBV5zlVmu5wWTx2Ox3hRr9dua9tHEudMYmL0q4GsDbYvv9di61n0GZq8KeK7UFmO8CbStwe5DNbqlRgYwq9K07zKAmX6W1WeG280N7z3jmfZdVv7/TD/LxGO7WcK0AxvYbpaE/b1qO3/dwHRXmY6ViIjUS1by4IWr3gy4HKrXaezm8VisN1mu3/o0gH077Q1VJ7617SM3MOa9Z6rFU9u2+N70XZe6j2G6go4x3ljZVrv7UHVNqdEgoD2BTxQD6FBerqEY5HbTMj+32hdZhYa4T+xIwkFnZyOSAu7B0NH5KyIiDZmVPOh0m7bypN08Hov1Jsv1gwg1IK3so1b5uX7jqW1b/K0rmP191KZ1tMzPrXGfBYox3ljZVrv7UI1uqVHbEJerD9pg7cukIe0TO7o5U/mw2XF0c6aG/b10/oqISEOW5QxN902V86TdPB6L9Sar79XKVcoH957NB/eeHdZOba3uI3/lgt1vdl+XWZBnqZzVbYllVrfVzj5Uo1tqtC3E5eqD7RZ/2W1I+8SODa6DXLx3LRsi0Imazl8REWnIrNZZalM5T9rN47FYb4q1+oHVfeSvXLAx2n3dnvRmlsqF6pyLJqvbamcfqtEdJ0L1zIhdK/D0SBho6AQT2FxerqFY4XCwK6OF9kmQCjFZU5ZPYQQG5ND5KyIiDZmVOovLYdjKk3bzeCzWm2KtfmBlH+3MaOF3aK7atsXfuoLZtu879go6xnhjZVvt7kM1uqVGJp5hEyqmfZcBjPOzrD4zHQ5mDxuNA+2TYLRwJDKmUTtaOBLD/l61nb8O4DZngo6ViIjUS7XVWRzA/AHne//2t9w3T9rN47FYb4q1+q2V4/TUsNGYfhq0NW2L743edan7mIaT2cNG+32f2mKMN1a21e4+VKO7AajrVfIFwAggx2f+1vL5DXFM6pU9BzB11ARyfL5YGvI+saq5kcg1jbJoboS/0Q2Bz9/cjBZMHTWBNw3fATVCJ1p3qIiIiFSoqc4yddQEnv+/axjpTLCcJ4PJ47FYb4q1+m1tx6mmMbADbYvvoFZ1rfus7DnA77liJcZ4E2hbg92HoeldQeKGkZjMkInzAVgy9ULLnUIsABYmJDH28klkFuSxeMFMlrlKG/QVwpU9BzAtpTG3jBqvfWLDT65CTtjzWUTf0/f8XfLOkyTe9jym4YT5j0Y0FhERkUjzV2dZ4XBwWnkj6U3DydsOw1KeDDaPx2K9Kdbqt7Udp5r425aVwC1XTA5p3cf3XLETY7yx87moja50i2Wmw8Hazr1Z1nswy43Az/80JNon8aPysfrYmeD5whQREWkgqtVZfK6oRiJPxmK9KdZiqu042XltmWGE5ZjWJcZ4E6rPhRrdIhJRhzsb8UaTPhzubBTtUERERMQm5XER+3R7uUSd4XbTe8NaMgvyKDNNlhG44wpnkLfH14WrtJgP7j27SgxWVY536UOX1Fi28n5wucowTJelX9Ps7BM7+9q3bKh6ozzoNllXtp+D7tD+lmxn20REROJFuPJxsOzm8WDrN8rr8SNcx8rOuRPr54sa3RJVw4FZZSW0nzMBgPF4hj0YS8PqjOx808UMV1mV/bBr+rXMHjaaJSF6j4HZq3iuaL+lfR3ouMzJXlXnTjK2mcVMPPBrndbhS+eRiIjUR+HMx8Gyk8f9xW+lfqO8Hj/81WFDcazs1I3j4XzR7eUSNcOBeUCWz/ys8vnDIx5RdAzMXsVrrrJq+6F5fi4T507jfNO378ng3mPi3GlkuasOHuFvX9d0XCbOncbA7FV1iiURB22MJBIJzS/1Oo9ERKQ+Cnc+DpbVPB4o/trqN8rr8SNQHbaux8pO3Thezhc1uiUqDGBWpWnfZQAz/SyrbwzTxQ3vPeOZ9l2GZ3zF6a6yOu0Hw+2u8T3g0L6u7bi4gTHvPYPh03i34whnKp9knsARztSg11E5Jp1HIiJS30QiHwfLSh63Er+/+k1DyetGPRhStLY6LAR3rOzUjePpfImFGKQBGgS0J/AJaAAdysvVZ0dtWkfL/Nyw7odBbjct83MD/h5d+T2sHJdW+bkMqkOS32QWcfW+79lkFgW9jgo6j0REpD6KRD4OlpU8Hmx+Vl6PH+Gqw9pZbzydL3qmW6KibYjLxavMgjxL5eqyH9pgLSHbeQ+r6/Rnv9vFitK9Qb++slCdR9HooE9ERCQQq/mtLvk4WFbyeLD5WXk9foSrDhuO9cZCe0JXuiUqtoW4XLzak97MUrm67IftFp+d3mbjfayu059MRyJXpLQl05EY9Doq6DwSEZH6KBL5OFhW8niw+Vl5PX6Eqw5rZ73xdL6o0S1RsQJPr4KBuvI3gc3l5eqz7zv2YldGi7DuhxUOh+X3sHJcdma0qNNwJa2NJO5I60RrIynodVTQeSQiIvVRJPJxsKzk8WDzs/J6/AhXHdbOeuPpfFGjO8yc9aCjhHAw8XTjXzHtuwxgnJ9loRIrx8U0nMweNtoz7bsMcAC3ORPqtB9Mh4PZw0bjCPAecGhf13ZcHMBTw0Zj1iHJ/+A6QO/dq/nBdSDodVSOKZrnkYiISDhEIh8Hy0oetxK/v/qN8nr8qK0OC8EdKzt143g6X9TolqhZAIwAcnzmby2fHyvj6oXbyp4DGOlMqLYfcjNaMHXUBN40nCF5j6mjJpDjk5z97euajsvUURMsjQvqKi3mg3vP5oN7zw77c1Q6j0REpD4KRT6OpkDx11a/UV6PH4HqsHU9VnbqxvFyvqgjNYmqBcDChCTGXj6JzII8Fi+YyTJXaUz8IhVuhmFw/gmd2FdYwj2Gk7cdhnc/LHnnSRJvex7TcML8R0Pyfit7DmBaSmNuGTW+1n3t77iscDg4LQQJvpORwgPp3bi7YD0bQ9CDOTTs80hEROqvcObjYNnJ477xW63fKK/Hjzd96rChOla+663p3ImH80VXuiXqTIeDtZ17s6z3YJYbRkx9QMLpvnsncsP/9eJvw4/hrGFnVtkPHzsTPF8qIWZnX1crG6Jb2Mpws91VTFmIe1xtqOeRiIjUb+HKx8Gym8eDrd8or8ePcB0rO+dOrJ8vanRLSMTKM9Lxol27dtx26zjv33fcfnv0gomwrWYxt+//ma2mhu8QERGJN8rjIvap0S0SBVOnTKZRo0bevwcNOpnj+/WLYkSR4wSaOBII/XV8ERERCTflcRH79Ex3jDLcbnpvWEtmQR5lphmVISHEv8rHxuUqwzBd4HRiGAZuw/M7lmkGvqmla5sMLr/srGrzb711LM//GMI4y2NJcDjo9/sPHH1YE66aNJFj/tSf1JQkxnz6R/733rvBr9/Pfgh0248zMZkhE+cDsO2By1mQ3ovz8r5mXQh6MBcRERH7gq1r9nCm8VazY5THbbJTb4o14WqXxGJ7J1wxqdEdg4YDs8pKaD9nAgDj8YxBNyd7Vcz3VFnf+Ts2RT2KSb7oQhz3FHrLFRws4aMP32f58o852DqdDTsKvMtGn97TO/38kh+44MTDadY4mQsvuICFTy5nx76DQceXmprK6OtuZty4cbRr167GsgvemM+FF17Iu+/ab3ifb7qY4Sqrsh92Tb+W2cNGs6SW1241i7kx/we2hKgTNREREbHHXx63WtfcYhbFZB5//PHHufjii3nrrbeY9tDfI/a+iU6DUlfNTxD7qz9arTfZlWA46NGuGSdNGM8pgwZxwoCT+X1PIROzzyEfbPeoMzB7Fc8V7Q95uyQW2zvhjEmN7hgzHJjnZ34WMHHuNKaOmhDyD6dY4/fYnHMOKRdfVK1seqMkzjv3XM4791ygvBHe52XM1j3p06k5AOvX/8Ibn24g0WlwxandcTqdnH9CJ57+8AfbsSUnGJx1XEf+9dMPtG7d2tprkpN54403uOiii3j77bctv9fA7FXc4yqrNr95fi4T505jrTOB+TW8Ph8XH5Tstvx+IiIiEjqB8njluubSGl6f7469PH7VVVdx8803A3DddddxxRVXsGjtNv77yfqwvu+NN4xh5vgz+H7zHs57sT0bf/ulWplAdfvK9aZQDA8L0LZtW5788yA6tUoH+nvnd89K4rX/vsyv2/fx8sfrLbclBmavYuLcadUa6nVtlwTT3jHLh6MNBZefdVmJqabPRW30THcMMYBZlaZ9l7mBMe89g+EOba/PUruAx2bSJO9k8RdfsHLlStZt2cO+wpIqr/c0ws9h+ImdvfPG3303ZaabhWs2c/Cg5+r2Gcd0IC3Z3m9hqampzLh6ANcP7eltcJumSfHnn+P+5BP45BNYsQKefhouvhgOOwz3f/4DQFJSEvPmzeP888+39F7NUxO4ad371fcDh87R6a6yGr9YmjkSGJncmmYO/eYnIiISSYbp4ob3nvFM+y7DWl0zmnm8efPm1S4uHHbYYcyYMaPKvKSkJM7p15FnxwzmmL59wxJL+/btefihBzEcDnp3bM6Xn3/KsGHDqpSxUref7ioLWd3+wWkPlDe4Dyk4WOqd7tKmCfeN7Mej/6j9TgDD7a7zueJ3vcReeycSManRHUMGAe0JfFAMoFV+LoPU6I44v8fmnHPg2GM902vWkHzCCdw7+DRue3E1Fz/6Iccc149xt97Gyh+3U3CwaiN8xYpPePPNtwDYV1jCv/79MgCpyQmceWwHW7E9cP9UurRp4v371Vdf5Zqjjyb5xBNxDBoEgwbBKafAmDHw2muwbRuOK66Alz3vmZiYyOuvv87ttfSg3rVrF1665Q80X7sG4623Dm17JQbQAc/+CqStI4lp6d04zFAv9yIiInZVXKX74N6zcZXa60H8qE3raJmfW2td82TTFXAdhxnJUcnjAwYM4LfffmP79u089NBDJCR4Gv1PP/00TZs2BWDu3Lncf//9FBR4HutLS0nktVfnkpmZafl9rI7I88D9U6t0itu8eXPeffddXl35C0kpqYC1un0HCEnd/vjjj+eySy8BIL+whGuu/TNdj+jBiEc+4J7/fs6Xa77ylr355pvo0aNHjesb5HbTMj+XQE80B9suicX2jtWYavpc1EaN7hjS1mK5NiEe31hq5/fYVLrKXTFdcWzcwHfffc8TT/6TKa+v4aJHPuT4/ifz1AfZ/GfFei66eFSVVc167DFv52vnH98Jp9PabUannnoqt9x8EwBFpS5OPGkAo0aNojg7u+YXulxw5ZV8/NJLACQkJPDII4/w1ltv0axZM78vueRPfyIxKdHzx7nnwpo18OabcPPNcNNNnn9XXw0ZGTWey9lmIV1zP1HnKyIiIiFWW4MxsyDP0nra1tDgWec6EPE8npWVxfz588nIyADgr3/9K8uWLefZD7M5+2zPbcLbtm3jxhtvZOLEiXQ9ogc/bPVsa+fOnXjllVe8HcyGQo+splzyJ09dbl9hCat/2u5dNnJAF+68w3MhI5J1+5kzZ3qn/738Z/7173+zceNGAL74ZRcDTjmNVz723G7vdDq59957QxKT3dhjsb1jNSbfz4UzMZkhd79u6bVqdMeQbRbLbQ/4m5OES7Vjc+65h670fvklLFwIBD42buDbtd+x4LMNvLTsZ3Jzc6ss//nn9bz73nsAtGzSiDP/b2itMTVu3JgXXnjB+/cLS35kzVdf+Y/XH9Nk0jXX8MADD1TarHP56quvONbPVewzzzyj+jrOOw8efxyeeMLz74UX4J132BHCxCYiIiKhsSfd/w/rvrbFQC/SFZISDOa9/ipt2rSpMn/gwAFc98cjvX9ff/315OV5Gtq7d+/m/nlfsfeA506AM844o9ZGph3XDz3UKe6/l//MpNfWcPtdf8Msb5RddNEIIHJ1+4svvpgBAzwdfW3O3c+7X232W+7VVb9698nFF19Mz549/ZazE5Pd2GOxvWM1prp8LlQzjiEr8PSQF6j/QxPYmdEiJrrTb2iqHRufq9wmsBnqdGyeeeZZ7/S111xVbfmf//xn7r77bgYPHkxycjKPPPIInTt7nhH/duNu3v5iY+B4fVTEu9w0ueeeezjjjDO8PwR06tSJd955h6SkJG/5Vq1aeccRL1uXjXnjjbB1q/+Vn3IKvcs7M/Gng5HMsxk96WikBCwjIiIitbN6K3SF7zv2YldGi1rrmp/U0LFXRyMlonl87FlHe+sg2/IKufs/n7NpU9VG5Sv/+S/vvPNOlXm5BUVMm/81LpfnluD77ruv2jPXwTi112H0bOf58WJddjbvrvHE8viTs/ltRz4ARx91FOnp6ZbrY3WpP6akpPDwww97/376g2xcpv+rxMWlLl5b9SvgGVr2vvvuC7jeFQ6HpXPFbuyx2N6xGlNNn4vaqNEdQ0xgbKVp32UO4KlhozHV6I64KsfmvPPgmGM8f3zxBea77+IAbnMm1OnYLPrgQ3aWDxd2xtDTaZlxKJndcsstPPPMM9x///0sW7aMvLw8rr/+egD279/P9He+rXITjpVz6TZngnfZokWL6Nu3L19++SXg6ZTkrLMOJab/q3Tl/dP1O2H2bMyuXeHss+HSS+HSSzHHjfOWmfbgg3Tp0sXvdppuKHGbmFF8TMKsw/NwIiIi8co0nMweNtoz7bsMa3VNE3fE8vjwEzvzx96eIVD379/PpNe+5Mtfd9HvhBN5p/wuw5zdB7j1Nv/90ny7aTd33zPR+/c///nPOsWTkpLCtUMOPQt9111/9V7dBvhh617A06A94YQTLNfHSstKvPUS06deUtsz/HfddRcdO3YEYNGiD/jy1101bsPCLzexfbvndviRI0dy9NFH+y1nOhzMHjYaRw2xB9MuicX2TiRiUqM7ivz9OrkAGAHk+JTdCkwdNUHjdEfRAuAih4Myn6vcuRktmDpqgu3hHny/RE3T5P2vPb+WOp1OhvZtD0CLFi2YMmVKlddW7rjjrr/+je17q4/tHehcChRvTk4O48eP9/59+WWXeqfPPOP/vNNv7E1ipDOBnOJiePdd+M9/4D//IXfOK3y+aDXg6VH9+eefx+Hny2mru5ibCn5ki2m/sWv3F30RERGpamXPAZ487jPfal1zixl8HrejUaNGXHVad+/fV197HRt3ejpIy8vLY/gFI7j6yaXc8MzH7NmzJ+B6Hnl0OsuWLQOgY8eO9OnTJ+iYHps1k1ZNPHWw999fxKIPPqyyPHvroWfmK273tlIfO/7443n66aer1MNqk5SUxMyZM5k8eTIAZWVl3PnXv9b6uuIyk7//4xHv3zVd7V7ZcwBTR00gx6c+V9d2SSy2d8IdkxrdMWgB0CUhiTuvnsaDI+5kiDORrglJanDHAPO880gqH3ri918288cly7jytudDdmwWfbPVe0vQmX3bYzhg0n33envlXLRoEXPmzGHTpk2Ap5fOZ559LuD6fM+lPyan1hjvRx99xNby28bPPOMMmqQmYTgcDD39dMAz3vgPW/fypuH0u94Hvt7Hb79tAGDw4MHceOON1d7DASThUM8EIiIiUeKbx+3UNSOVx/sddxwpiZ4LBK/8578sWPBmtTK/7ymkuCzQTcGHvPbaa97pYG8xv/7667nm6qsAT+e1d9x1V7UyP1RqdPfvf2ic7ED1se1/OIM3F8zn888/Z/To0UybNo2LLx5ZayydOnVixYoVjB071jvv73//O9nZP1jalmeefY7ff/8dgAsvvJC+NQyrtrLnALqlNA55uyQW2zvhjEmN7hhlOhys7dybZb0Hs9wwdEt5DHA4HEyqdJX7n19s52NnAmYdnu/wlVtQxOe/7AQ8HaqNHNCFP193LQAFBQVcddVVXHPNNXTq1Im0tDT+9Kc/1brOyudSbfGapsnLlYYSG9zrMHq2a+pt9C98cz7vTzzLc2Xez3qLSl2Mvn6Md30zZ85kx44d/L51M/8ZN4Rbz+5Nv4yWZLcYSE9nWrC7SUREROoo2LpmT2daRPL4iSee4J1eunRZndb1v//9zzsdTKO7f//+PPbYY96/Zy5cy48//lSt3La8QvL2F3tfU/mOv9OGDOEv/7iFG16cyn+2bOS/d/wf//zzIM4+66wq6/hL+ag0FRISEvj3v//N9u3b2bFjBzt27OCHH37ghBM8+6eoqIjrr7+eu+++2/L2FBUV8eCDD3r/9neRpDI754ph467EWGzvhCsmNbol7OoynmQsOf/88723JP2Yk8cXv9T8zEyw/lepx8mr/9DDO3zY/fff730GB6CwsDAs7//vf//bO/3H3lkc362V9+/3yntYr8my5cu9z0wlJCTQqlUrWrVqRfP0FM44pj3/WrSQdy+6iG5/fbHWL2PdTi4iIhJbtprF3F7wE1vDfHv5SSed6J3+7PPP6rSujRs3kl0+nGr//v0DDo/qT9u2bZk/f763g9n5n/7G0u9/D1i+4hbzpk2bcuSRh3pXnz79Edo3b0zTtGRatWxJ07RD9ZotW7Z4h/c68cQT6X5YU++yG264gcsuu4zWrVt761QpKZ5+f3755Rf69+/PM888Y3l7Kjz//PPs378fgOHDh3vHPZfwUKNbxALfq9z/Xr4+bO/1xS+72JpT9YmSX375pcr4i+GUnZ3t7VCt+2FN+b8+nmfLTdNk0aJFltZx11138frrr/Prr7+W//uNopIyALod14frZ93EwBO6AWpYi4iIxJN97jLeKt7FPndZWN/nxPIrufuLSv1eVbar4sKB0+lk6NDah2atKPvaa6/Rtq1nJOdly5bz3OIfa3yNv1vM+/btS6/y4bkKi8v49bcN/L7nAL9s38fYcbfSrVu3Kv33DD+xEwDNmzf3PrMNeOtV69evZ/bs2Rx33HF88803lrbFV0mZyZpNnt7WW7RowamnnhrUeo479lgWvDGPd8afwbiz/XfKJmp0Sz1iuN303rCWU9cuZ7BpVjm5qy1z2+txc/jw4fTu3RuAzz77rNaeIe3GV5npdvPSS/+uMu+O226jpKTE9nsG61//+pd3ulljT0P4iy++YNcu/9vtu20HDxxg5MiRdO3ala5du9L9yJ6Mfvpj1m/bhysvn5J3lnLrKZ25e4L1DkNERETEnsr5+RRXGYbpsvy6Y7dmc8GuddXqLE0cCZyd1IImjvBdGe3YsaO3oftjzl7cNutt/lS+W8/qLea333YrJ598MgCbN2/mkj9dwlG/feut7ySYZrX9668ztcsvv9w779nFP3Dk0X25+sll3PTsJzz5z9kUFxfzn//8x1vPGnRkW5qnJzPpvnu9V+VffPFFb73qiCOO4MYbbyQ/P9/y9vurC3/8w6ERqi+66KKA5f2dO90Pa8KUUcfz2aerOOfss0lKcHLmMR04dfAplmOyEmMsqUt8uo9A6oXhwKyyEtrPmQDAeDzj7VV0L+Fv2ZzsVdY6DHE4qvTsOGXq/XDimBpeUd3A7FU8V7Tfb3wL/JS9etXLcPutkJwMH3zA4++8g+GnrD/OxGSGTJwPwJKpF9qKs8J///tfHn30URITE73zAt1abnXbduw9yK1zVnFJyXec+OQ7tFv8HJMn3ce67GwKKsfvdHLssceSnZ3tvTouIiIi9pxvupjhKquSn3dNv5bZw0azpIbXDcxexXOlB2l/xQAYOJDrW5ey5Z57vHm9nZHMzIwenJf3Nftc4cnTlTshq3zluC4++eQTCgoKSE9P58wzz8ThcHgb81mZaZSZVTtk69iyMfdd7RluzOVyMXvUKD7b/nuV/VkGJPjs32fPu5HSS48nMTGR/v3743Q6vX3wlJS5+Djb/63pxcXFPP3009xzzz0kOA1uPOMo+nc7DfD062OnZ3NfgepqLy98h6Lz+pKS6GT48OHceOONuFwuv/XqyufO8OHnM+PqgTiN6s873zP+rzywbJ/tGP2dr3bq6+EWaB++8tNnwP/V9FJAV7qlHhgOzAOyfOZnlc+fH2DZxLnTGJi9qsZ1JyUl8eSTT3qvcn/66afVhoeozcDsVUycO40sn1/DKuIb7qdsm82b4ayzYNIkuOgiv2XDKTc3l/+9/36Vef4a3Xa2DaDUZXLNPx7g4xcfJ+morgC8+MLzdGzZGIAOHTrwySef8Pnnn/Ptt9/aeuZKREREPAZmr+I1V1m1+k/z/Fwmzp3G+QGueHvz+qhRMHCgZ+Zdd5HVoYM3r//gOsDRuav4wXUgbPGfdNJJ3ulQNbpLS0v58ENPHa5ly5b069cPgEsu+RPP3jCYf93yBx64fypOpxOn4eDO8/qSnOy52+/tRx/lgdWrq+1P365pm+fnMv7fU9j49dcAHHnkkVx00UXeq/af/7KL/UWBf6j45z//SWlpKQAn92jj7dfngQceqNKvjx011dX+9tJkflvjeda9ZcuWnHrqqQHr1RXnzpUtmjP7ySe8De5NmzZz0823sCXX83z4KYNOpnfHTNsx+jtfrdbXw62mffjXeY/4f5EPNbolrhnArErTvsscNSxzA2PeeybgrSEdOnRgxYoV3HDDDd5599xzj7343G5ueO+ZgDEAzCyfrlZ2yRKYPBny86uVjYR/v/yKd3rnzp2sWbOmynI721aZCcz9KoePsj3Jo3Hjxkwa2Y/TjjqMLz//1Jtou3TpwswZ00OyLSIiIg2FYbpqzM9uYLqrrFr9x5vXDQOjcn0nMRGj/CrrzPJZBzGpfaAua/7whz9w9tlnV5lX+Ur3T7/vDdE7Vb/FvF+/fjzz1GxvA/Kvd93Jkg8/4M9/PJJubZsAsG7dOvrdey9QfX/6Xuet2L+tV670zvv73//unV6ydmuN8W3bto1589+oMu/XX38Nul+f2upqbiDrqVneeReNGFFjvdoNPP7YLFq0aAHAJz9so0fPXjz9zLO8suJQf0eXnXKE9RgtnK811dfDzco+tLSeUAYlEmmDgPYEPpEdVP9CrGAArfJzGeTnQzxkyBC++uqrKsMxXHPNNSxZUtMNWX7ic7tpmZ9bYwwd8GyHnbKR8O6777E9z9ND+iuv/Kfa81TBxtvOkcR17y3g3y8vZ81XXwFwWGYafxt+DJmZVX8ZvfSSPzGge+u6b4yIiEgDcdSmdbTMzw1YN/Lm50B5fdQo6N696ouuuQajfXs6AMONZJ5p0YcHb7mNxx57zPvvwQcf9N4ZaNWgQYP48MMPeeedd7j66qsBSElJ4ZhjjgFg066CGq8M21V56LCRI0eyYMECb0/gFU4+eSDDT+wMQFlZGY9ddRXti4stN5oMIGP1au/f7dt7OqTds2ePpZFvHnvs8Sp/33777RQXB9dTvJW6WpPX/0tR+Yg4Iy+4gPZOZ+BzZ8QIUkeMAGDvgWIee+9775X55et+Z8tuz9XuPp2ae692N23alJtuuqnKc+2VWTlfA9XXI8HKPrRCjW6Ja21DsI42Pr9RtWjRggULFtC8eXPA8wtj//79mTNnTp3XHUhbm2UjoaSkhHFzVjHhlc+YcM/EasuDjdcJpJSUUFbmYsRFF7Nz584qy9944w1uvvlm799jzzqaJqlJdsMXERGJG6EcySOzwNrt2L55vJWrFAwDJlbK+YsXe/5PSoLyq93d27TlDyf257ax47jlllu8//72t7/x7bff8uqrr1YZKqsy02cY2dGjR2MYnubIpEmTSExM5Nhjj/X2KfNjzl5L22J1bOjff//d29t3z549adeuHQDfb97DHS+tZvPmLVXK//0fj5BfPqKLLauq3w79+rz5lLpqvz/giy+/ZPXPOwBY8OabvPXWW/bfv5ylulphIWvLf4xo1qoVnBKgI7QWLaB8SFiAJ99fx77CQ538mm7474pfvH9feWp3Jk+6j40bN/LEE0/wr3/9i1tvvbXaaoM9X0Olts9eqN5XjW6Ja9tqL1Kr7T6/Xd16662kp6cDsGjRojoNx+C77kC22SwbKXkHilnzW673V8zKgo13k7uEJ86/mF1NM9myZQsjL/4TBQdLKSlzcdvtd3DhhRfy5JNPepNM07Rkbj6zV103RUREpEHYk26tPxTfPL4N4OKLoUcPz4yPP/b8XVDe3em110L//tz48WK6LJxNUpf2ftc7cuRIvv/+e5577jnv2Nb+pKWlMXz4od5fOnTowNVXXx2W57kr8+2jZsuWLQw5/kgevWoAxxzTl4XvvgvAtxt3M/X+B4Krd+XksHNL1Qb8f/7zX8svf2DeV9z07ApG/enSYN7dy2pdbdG8Sre0+/Ri7vXPf0LLlgB8PP8NPs6uvmeWfv8769d7Gt5Hdcjk7gnjadKkiXf5Aw88QHefuyiCPV8jJVTvq0a3xLUVeHoODPS7oZvAz1qYwM6MFqxwHPowNWvWzHuVtaSkhOuuu459++z3wOiNz+FgV0aLgPGZwGY822GnbCwIVbyfrFzJlY9/xEWPfMhjjz/hnX/99deze/duAE7peRgXXTQiFGGLiIjUa9937GUtPzuqNiZWGgalla9yT5oEe/bAY495/k5Kwr1iBVldugDw888/c/LJJ9OvXz/69evH2LFjvZ19GYbBtddey/333x8wzgsuuIC0tLQq8yZMmMDgwYO9f/9g8Uq3HZUb3QcPHmT48OHeu+727NnD+cMv5OLpH/LXf39KaWlprXVNXxX7d1mlq90bNmxgpZ+r34GUukx+2Z6Py2VtiLdArNTVdma0YPr//sfBgwcBcF1wAWb5xSevxx/3NsZdublc8pex+GO63Tz4939UmVdSUsLX5R3LNWrUiBdffNHbQRxYO1996+uRZGUfWqFGdwRZvfVFrDM5NCyY70lvcqjB7W+ZA3hq2GjMSh/iv/zlL2RkZAAwZ84ctm6tucOLWuNzOJg9bDSOADEAjCuftlM2FgQbb09HIx5//GHa7zzUC+eB4jKKSqsmlh07dnBzpS/1Bx94gIQEjXIoIlJZKG9LlvrBNJzMHjbaM+27rPz/cVCl/gMwYuRIEstvC3evWAFLl3oWTJ+Ou/xqt8PppPjbn/ilzWCuH/x/rFy5kjVr1rBmzRoee+wxunTpwp133ul9Bvn2229nQKVO0Sq77LLLvNO//OK5OtqxY0fOPfdcAPLz89m8q8Dva+ti1apVzJs3j127dnHppZdW6ygWYO+Bkip1yEB1Td8LOxX1y9ucCVUa2S+//HJIYq+Ny+f2/drqahV14YLCQu/z7s7WrXF89RXmsceCw+G5wl1+QcptmkwfcwPbdwV+Nv2/r77OJz9sY++BYmY/9TQ9eh7Ffe9vZ2v5894nnXQSd9xxx6E4ajlf/dXXI8nKPrQiqo3uQYMG8fbbb5OTk4Pb7ea8886r9TWDBw9mzZo1FBUVsX79eq688spqZW688UY2bNjAwYMH+fTTTzn++OOrLE9OTuaJJ54gNzeXgoIC5s2bR6tWrUK2XRJZC4ARQI7P/K3l8y8MsGzqqAlVxv1LT09n7FjP12pZWRkPPfRQSOJb2XMAU0dNIMfny6IivgVBlo0FwcS7zV3Cq6cOJa9xRq3rf/31eXzxi+fX506dOnLZpXW7zUpERKQhWNlzACOdCQHrRr75OSkpqcpV6V2TJh1auGcP+U8cuhNtc/F+HmUHa3dUvzBRWFjII488wsTyK+aGYfD8c8+SnFh1cK22bdsyZMgQwNN3zqV+8vsXX3yJGYbHeN1uNxdddBGtWrViwQJrNatAdU3f69C5GS2YOmoCbxpOXnrpJVavXs2qVauYNWsWVvk2nOvCVVrMfXOnWaoLT548mb179wLg6NoVx+rV8OGHUD6Kj9vl4s3Z8xn/9js1v6fLxdR5X3Hx9MXc8pexbN68meIyk0fe+tZ75X7y5Mn06nXo0cGazlff+no01FTffXjEHf5f5COqje60tDS+/fZbbrrpJkvlO3XqxLvvvsvSpUvp27cvM2fO5LnnnmPo0KHeMiNHjmT69OlMnjyZY489lm+//ZZFixbRsvwZBIAZM2ZwzjnncNFFFzF48GAOO+ww3njjDX9vGRf0K7fny7BLQhJ3Xj2NB0fcyRBnIp3L5/tb1jUhqdoH+Oabb/aOC/3vf/+bjRs31vq+vh2CBLKy5wC6pTT2G19dysYCu/Hm4eKTo49hf2qqpfW/8vGhISjG/+2vGFH6pbOCPm8iIhIP3jScAetGvm65+Sa6lN82/tFHH9Hu40+qvK7jgw8yf/583n33XU674Dye2vETe9yBexV/9NFHWVV+pbdbt65c84eqz/Fecskl3luMX375ZT7//PMqPYsDfPrZZ8FvfBj4q082diZ6//5jcipX3va8t365b98+BgwYwMCBA72Py8VS7L514bVr13LMMcfwWfl+dyQlQfkPIy6Xi7+/+Q1P7fHU3YL5YeCHnL3MKB/6LDk5uVoHxf7OV3/19WjxV9/tmpDEZ91PtPT6qDa633//fSZOnMibb75pqfyYMWPYsGEDd9xxBz/++CNPPvkk8+bNq9IT3m233cazzz7Liy++yA8//MCYMWMoLCzkmmuuASAjI4Nrr72W2267jaVLl/LVV19x9dVXM3DgQE480dpOk9hkOhys7dybZb0Hs9wwqtwCUm2ZT8MtLS2N2267DfB8sTz44IMRja8uZWOBnXjTMTj6t/U0Ki6ytO4fcvby9W+5AHTpcjinHXVYCCIWERGp/6zk5yapSdw9wdMzuWma3HrrrdVet6+ggBEjRnD22WeT//t2hiRlku5w+lkb3vVcddVVFJYPRXX+CZ3p07G5d3nlW8srbr2ePHlylXV8FmONbqi+P8sMw/v3x84ETCPwPom22urCABs3bmTQoEE88sgj3nku0+SKq6/jo+zt1crbdd+kKWRnZwNw/PHH07Nn1V7urcQYTXWJL66e6e7fvz+LK4YuKLdo0SL6lz8rkpiYyHHHHVeljNvtZvHixd4yxx13HElJSVXK/PTTT2zatMlbxp+kpCTS09Or/GtoQnm7S6y58cYbadGiBQCvvvoq69evr+UVEqz2jmRuWDifFvv2Wn7NyysOHY8/ndwVI7a+g0VEROLWlace4e3P5vnnn2ft2rU1lu9gpPB0Rk86GCk1llu/fj3jy4cZA7hv5HHcO/EeTj75ZPr27QvAp59+6n2e+7PPPvNe7T548CCrP429RndDUFpayp133smZw87mg2+2MOGVz3l9fmjuCC4uLua5557z/j040PBk9VBcNbrbtGnDjh07qszbsWMHTZo0ISUlhRYtWpCQkOC3TJs2bbzrKC4urtYjdeUy/owfP578/Hzvv5wc36cOJBwicStvq1atuPvuuwHPL7MPPPBAWN5HPH52H+Su6/5CTvOWtRcu9/3mPXz8sacf9PYtGjPoyEiNVi4iIlJ/dWqVzhnHdAA8HZfdc889tb7mJ1chJ+z+lJ9chbWWffzxx1m+/GMA0lISuXfiPaxYcWhcE98Oxq688kr+8Y9/cMEFF5CXF/rhwuqbcNaTP1y8mEffWcs3G0N7a/yyZcu804MHN5xGt7oCtujBBx9k+vTp3r/T09Mj2vA23G56b1hLZkEeZabJMmx0UV/pte6yUvr8+g3NDuyzvZ5YUJf9EMiUyZO8Ywg+//zz3tteQqFyvC5XGYbpCvmtR777JFpDKlhVBpaf567sgWkPcsopgwD406Cu3Bfj2wnhOV9FRERCZczpPXGW3z42bdo079BZNSnDXePz3JW53W4uvGgkLy/6gj/2ziLBeeh6X2lpKa+++mqV8rt27eKuu+4CqFMjMtbzb6zHF07ffvste/fupWnTppwyaBDPZn8R7ZAiIq4a3du3b6d169ZV5rVu3Zp9+/ZRVFREbm4uZWVlfstUjBu4fft2kpOTadKkSZWr3ZXL+FNSUkJJSUkIt8a6800XM1xltJ8zAYDxeMYLHEvtnWsNB2aVlVR5LS/dY3s9saAu+wGgV/tmHNUhk5/atfMOBda1TQbXXH0m4OnwwsovvHWJd9f0a5k9bDRLQvQe/o7vFmBO9ipLHU84E5MZMnE+AEumXhiRxwYOcyRy+YcLeffEk229bslHH/Hpp59y0kkn0blVBkNP/yPvvbswTFHWXaBjEy+fNxERqd+uvuoqjjnc82jdhg0bmVneyVVtsoxk/pLagccKN5Nj1l5v2Lt3LzMWruU/n6xnUKMNXHXlFSQmJvL666+Tm5tbl03wq671xXAbmL2K54r2x2x84WaaJitWrOCcc86hVatWdGjRmM25+6MdVtjF1e3lq1ev9g4vUOH0009n9erVgOcXszVr1lQp43A4GDJkiLfMmjVrKCkpqVLmiCOOoGPHjt4ysWRg9ipec5WR5TM/C5iHp2IfyPDyMr6vtbueWFCX/eB0Onng/qlMv2oA1/yhB199+TnnnHM2ADf8Xy8Mw/MxmDJliqVfeOsSb/P8XCbOncb5pu8gE/YFOr5ZwMS50xiYvar6i2JAEgYt9+4lwWV/HzwyfYZ3+vzzzw9hVKFV07GJh8+biIjUb8cffzxPPH5oGKs7//pX79jatUlyGHR0ppDksNeM2LH3IDfceBOHH3445513Htddd52t11tRl/piJAzMXsXEudPIclcdCy1W4ouUyreY967UwV59FtUr3WlpaXTt2tX7d+fOnenTpw979uxhy5YtTJs2jaysLO9Y3E899RQ333wzDz/8MC+88AJ/+MMfGDlyJGeddZZ3HdOnT+ell17iyy+/5PPPP2fcuHGkpaV5u6XPz8/n+eefZ/r06ezZs4f8/Hwef/xxVq1aFXO9JBqmixvee8Yz7bsMz20oM4GF7uqDGBrArErTAd+j0nreIjZvbbG6H95zOLhi8BG0btqILtuvY8niDykoKOC///0vgwcP9r4mMzOTBfPnsebXXRzVIROAn3/+mccffzxi8U53lbGA4Pd3Tce34j3GvPcM97vdMdfz40Z3MdMvuqz2gn4sWvQBRaUuUhKdnH3WMBwOB24/578/FR0BhpuVYzOT2P28iYhIbKlL/urS5XDuPK8PW3L382WTJuzJ3UnLli2ZP38+ycme27ff/mIjb775luV1bnAdZNS+74KKB2Dr1q3eOw5DyWp9MVL51/duQsPtDkl8lde79KFLQht0hCxfvtw73btjJgvXbIpiNJER1UZ3v379qvzSMWOG5yrWiy++yNVXX03btm3p0KGDd/nGjRs566yzmDFjBmPHjmXr1q1cd911fPDBB94yr732Gi1btmTKlCm0adOGb775hjPOOKPKFcxbb70V0zS9XziLFi3ixhtvDP8G23TUpnW0zA98240BdAAG+Wl0DALaW3wf73qA5bWUjQar++Hv14/m0lO6AfDHJ58APMN/VYwDWeYy+TFnr7ehfVyXQx153XrrrZSWlkY03rrs79qOrwG0ys9lkDOR5THW6K6LgwcP8vVvu+jfvQ1t27bl+OOP5/PPP492WFVYOTax/HkTEZH6Y9oD9/PH3u0AGL7+J2bNmsXgwYNp396Tqb7fvIenPwhdXzbRFIn6V10McrtjOr5I+uabb9i3bx9NmjTRle5IWL58OY4aGgRXX32139cce+yxNa73ySef5Mknnwy4vLi4mJtvvpmbb77ZerBRkFlgrdfGNrjx/QgH07dzrPYHbWk/JCVx2V13Vptd0eDesmUrj364iR9y9tJt5yIefeQf3l94339/Ee+9915k46Vu+9vqa9tg7SpwJPVwpPDo7OnMGHFpUK9f/fNO+nf3jDRw7rnnxlyj2+qxidXPm4iIRJcRov5WHA4Hp516qvfvpk2bct9993n/zsnJ4f55P1Bm2qsrHOlM479NjuZP+77jB9eBWsvbuVLve3W48utq61gtEvWvurBaJ2sI9QOXy8WKFSs4++yzadY4mfYtGkc7pIAqn7916twvVAE1VOHsqn9PejNL5bZT/YeLbUG8XzCviQRL++Gqq8hs5/kl9+sNufxt/AT+97//sWvXLubNm0e/E07kh5y9ADz19DMMOHkQP+Xs5bcd+dwydlzk46Vu+9vqa/2dG9G2y13GuyeezL604L5gP1u/A7P87o5zzzvP+/kzwjSknF1Wj02sft5ERCT8IjEkau/eR5OZ6bm7b3dBUZU7+kpKShh58Z/IO2C/Qb/TLOGxws3sNKPTwXAgkah/1YXVOllDqR9UvsW8T0fPedqjR3dmjx7Eo1f2p1WrVtEKLSzU6I5h33fsxa6MFgGf6zCBzeB3iKgVeHpCtPLMinc9QcYZbrXuh4QEyv72N+/fLyz5kUcenc6wYcNo1aoVF110Ebt3Vx1j8Ntv1/KXF1ZywzMr2LBhQ2Tjpe77u7bjawI7M1rE5PBhuynjo2NPoCA1LajX7z1Qwo9b9wJw9FFH0aZpoxBGV3dWjk0sf95ERKR+GHzKoTGQ5678lR49j2L27NmsWbOGiy++mM+CvFNst7uUF4p+Z7c7NI/lhUok6l91scLhiOn4Iq3yI8ZHd2xOeno68+e9zuGtMziqQyavvzqXxMTEoNefmJjIgAEDOOKII0IQbd2p0R3DTMPJ7GGjPdO+y8r/Hwd+O8oy8Qw94O+1AdcTXJhhV+t+uOwyEjp3BuDLX3by87Z9RFNt8TqA25wJddrfNR3fivd4atjomOtEDSANg+6bN5JisZdUf1b/vMM73f+I1jWUjLzajg3E9udNRETqh1NOGeSdXrtpN5s2beLGG2+kX79+vPnmm0Gvt7HDyYDEJjR2OEMQZehYrjdHMKYqMTgczB42GoefGGIhvkj7+uuvyc/PBzydqb3w/LN0r9RAHjhwALNmzgj0cr+6H9aEUQO78L93F5KXl8fKlSsZM2ZMSOMOlhrdMW5lzwGMdCaQ4zN/KzCCmsfzW1Bexve1dtcTCwLuB8Ng+4QJ3r9fWfFLZAMLIFC8uRktmDpqAm8a1RNVxTMjH9x7NqaF57cCHd+twNRRE/yO021Weo9IjMntT0dHMmPfnEvLfdaevfKncqP7pBhrdEPNxyYePm8iIhLfHMApgzyN7vzCEjbtLAjZujsaKfyrydF0NFJCts5QqUu9ORJW9hzA1FETyPG5KBIr8UWSy+Vi5UrP8LaZjVMYXj4U7P6iUkrKPMPKjv7zdZx1bAe/r6/8iEbL1m15Y/7rPHbtyVz9hx6cfvofSUvz3FF5aqV+DaIpqh2piTVvGk7edhiMvXwSmQV5LF4wk2WuUku/hC0AFiYkeV+79O0n6Punu2l2YJ+t9YST1Q42/O2HtiNH8HI3T4/lS5cuI3tr7R16RIpvvEveeZLE257HNJww/9GQvIfv8V28YCYrHA5O89PgtitcQ2ytdxdxz1VjyE8NvtOMLbn7Wb/+F7p168rRHTNJTwn+9qNw8XdsYuHzJiIi9V/n1une57nXbt4T0m5V17sKGbznC3bF2DPdFepSb46ElT0HMC2lMbeMGh+T8QUj2M7Gln/8MWeeeUaVeQ8t+JomqUnceV5fAG48oxebc/ezJMA6erVvxnNffE779u2qzN+6dStLly7lo48+shxPOOlKd5wwHQ7Wdu7Nst6DWW4Ytj6YlV+7LCGRb7v0DWo9saDytnzsdDL+7ru9yx6Y9mAUI/OvarwJngZ3GN9juWHE5C3llZXiZk9GU8oS6vab38KFCwFwGgb9uraspXR01OVzKyIiEqyjOxwahmntxt01lLSvBDc5ZjElMThCSoVYz7+xHl+kLF/+cZW/p0y9ny9+2cXitTnMnDkLgASnwcOXn8hzzz7D4Ycf7i3btWsXLh/cjX9ccZK3wb33QDGPv/cd3Y/sSfv27bniiit48cUXI7Y9NVGjW4JW+bYOOz1Hh6rHzuHDz6dXr14AfPLJJyxbXt9HNawf2joSuXjpBzQrqNuz92+/s9A7PaB77N1iXptI9FwrIiINU+9Ohxrd320ObaO7rZHMpLTDaWsod0ndfPX112zZvR+A9977H1Pvf8C77K/jJ/Dhh4sBzwWWq668gp9++olFixaxdetWfsxex2WnHIHT8DRnly//mBueWcHCNZv59dffIr8xtdDt5WFmuN303rCWzII8XK4yDNMVlqud0VZ5O8tMk2WEvyOICeMP9Vg+depUW68NV7y+643F3sOjofJ+aWm6OXz7VpJLj63TOletXs3u3btp3rw5/bq0onHj4G9Xj8b5KyIiUle++Wsl0GfjWo7LOhWAPXv2sGFH6J7nBkh1GByTmEFqUWQHt/K3reGoYwdbJ1Ad0D6Xy8Vd//qUw1tn8Mi1I3C73VWWnXvuOdx+++3cfvvtNGvWjISEBIYOHVplHabbzf33P8D9D0zjtLtfj/QmWKZGdxgNB2aVldB+jqejr/HArunXMnvY6IDPJcSjgdmreK5of5Xt3IKnB+dwdQhx0hGt6NunDwCff/45H3zwgeWrheebLma4ykIer7/jvQWYk73Kb6dmDYXvfgHYlfMxXbb3qNN6XS4Xbyx4kz9fdy2pyQlcefmlZIcgvkicvyIiInXlL3+VAQmf/wfSxwOQ8vHHDMj+IqT1kF9dBzlv7zchW58VAbc1xHXsYOsEgerCDb0OaMWe/cXs2b8Ll8tVbVlRUREPPPAAjz/+OGPHjuW2226jadOm7N+/n5UrV7E1oT2f/ryTOVPvj0Lk9uj28jAZDswDsnzmN8/PZeLcaZxvVj+x4tHA7FVMnDuNLHfV53qy8Gz/8DC97yWDunmn7VzlHpi9itdcZdWOS13jDXS8s4CJc6cxMHtVkGuOb+H+HDzx5JPe6VtuuhHD5o/KNR23cJ6/IiIidREofzkBKvXWnLJsWdzXQ2rc1krqWrcItk5QU1043vd9rMjPz2fq1KlkZWVxxBFH0KxZM84651xeW/Ubm3P3Rzs8S9ToDgMDmFVp2neZG5juKsNwx24HFFYYbjc3vPeMZ9p3Wfn/M/0sq6t+XVrS/bCmgGeMv4oOtWpjmK6wxGvleI9575m4P952Bdovxc5UNmeeQLEztc6fg3Xrslnz6y4ADu/ciZQ1z1keDq224wbhOX9FRETqoqb85YAqjW5j2bKQ10O6O1NZlXkC3Z2pIVlfTWrdVp+ywdaxLdcJfNZbW124odYBw6WwsJD169dTVlZWbZkrBobFrYnqk2EwCGhP4J1rAB2AQXH+ARzkdtMyP7fal14F73aG+H0vGdTVO33//dZvJzlq0zpa5ufWflxsxmPleLfKz+XkenJ3g1WB9ovTLKXJwW0kmKUh+Ry88dkG7/Stt95a5/gqhOv8FRERqQu/+SslBVJTIS0NBg/2zMvLg+++89ZDQlXv3GOW8vLBbewxS0OyvprUlqt9BVvHDrbubqUuHMp9L/FLje4waGuxXJsYHmrBCqvxW90fVpw6eDC92nvGnVyXnc2CBdafus0syLNUzm68Vsu3bWBfuIH2S4K7lGYHt5Dg9iTrun4Ovvx1F5t2eTqJOeWUUzj2WGsdtFk+bkHGJSIiEg5V8tLQobBqFRw8CAcOwP790KKFZ9nHH4N5qAsw33wb7JXBXe5S/nlwC7vc4W90B5uD7dYtgq27W32feK/zS92p0R0GVvty3B7wd7H4YDX+UPZtedFFI7zTDz389yq9HNZmT3ozS+Xsxmu1/LYG1otloP1iYlCUkI5Z/vUTis/BgiCudls+bkHEIyIiEi7bwHM1e8UKWLQI+vf3X3Dx4ip/hqremYrBMQnppEagGRFsDra7rcHW3a2+T7zX+aXu1OgOgxV4eiwMNLyACWyGuB9KYIXDwa6MFrVvZwjf85RTPDf7lrlM3nrrbVuv/b5jr7DEa+V478xowSf1cKi4mgTaL6XORuQ07UOxs1HIPgdLvsshNzcXgIsvvpjDDjss6PgqhOP8FRERqaue118Py5bByScfmvnTT55GdsW/J56AZ58FDtVDQlXv7OxsxOtN+9DZ2Sgk66tJbbnaV7B17GDr7lbqwhX7vvKdBWYMPnMs4aVGdxiYeIYWqJj2XeYAbnMmYMZ5o9t0OJg9bDQOyrezXTv44x89y8rLjCN04x23bt2aI3t4hpn6+fd9FBYW1ljemZjM0CkLGTplIUZiMqbhZPaw0VXi824LnuPy4qgJOCwOPVb5tbUd76eGjY77421XoP2S6DpIVt5XJLkOhuxzUFJm8syzz3nWn5jITTfdFHR8lf8e52eZiIjEt8r1A6vDjcaK1q1b89Df/+79252dDRddBEceCaefjvv00+H00+GWW6C4OCz1kF9cBzkz7yt+cR0MyfpqUlOu9r3XsS51bMt1Ap/1VqsL+4mnIdYBpTo1usNkATACyPGZn5vRgqmjJvBmPbnqubLnAKaOmsCOI4+E776DDz+EBx9kK57tD+U4xxVXuQHWbtod1DpW9hzASGdCteOyFZg6akLQYykGOt51XW+887dfDEzy01J5YNTfQvo5+OfspygpKQHg2muvJTExMaj4gLCcvyIiInU1bdo0MjIyAPhozhy2Hn00zJsH5Y/b+XbZGo56SDEm612FFEfoZ+lAudp3W+taxw62TlBRF87xaVg39DqgVJUQ7QDqswXAwoQkxl4+icyCPJa88ySJtz2PaThh/qNhe19nYjJDJs4HYMnUC8P2PhW+PuYUCiZfRdumTQEou/12/m/OHH78+eeQvs8pg+re6AZ403DytsPwHpfFC2aywuHgNItfihW3B/nyPd521xsugeIN5XprulLgu1/WLHyG00+8nHXtjwppPNu3b+fNN99k5MiRtG7dmvPPP5/XX3+91tf5O27LXKW6wi0iIjGlX79+XHPNNQDs3buXi++6i31GAmOvPJS/VgK3XDE5rPWQNkYSV6YcxktFv7PdLAnpugPxl6srb2uo6tjB1glW9hzAtJTG3DJqfEzVASV26Ep3mJkOB2s792ZZ78F87EzwfBkEaWifdpzbryOOEN6iUtdbrBzAXef35YgjunnnJSQm8vA//hF0TO3atWPKlCn06dOnyvzBg08BwGWarNtirSfyQCofl+WGEbLbfsK13nhXeb9kO5M4esOvNCoJ/fNMTz31lHf6+uuvDyq+5YYRdIM7nm9ZFBGR2DZr1izv9H333Udubm61/FVmGGGvh6Q7EhiSlEm6I7LX7mra1rrWsWt6H8vPk9fjOqAZ42NgxwM1uuPEmWf8H7ef24ebzjyKu+68I9rheF0yqCv9j2gNQMHBEnYXFAFw7rnn8sfy57vtWrBgARMnTmTx4sWkpaUB0DQtiZ5HHgnAT7/vo6i0YY15XZ/84i5m8hWj2da8ZcjXvXTpUn766ScAhgwZwhFHHBHy9xAREYm0Sy65hAEDPFdNf/jhB/75z39GLZb1rkKG7v2K9a6a+9YRkUPU6I4TZ5996Pbgv/31Llq1ahXxGDIzM5k7dy6rV69m9erVrPrkY644tTsALpeLaW98zfNLfvSWnz59Ok6nvV8dTzzxRPr16wdAixYtuOLyywA4ukOmt8x3dbi1XOq/Z555xjs9evToKEYiIiJSd6mpqTz88MPev8eNG0dZWVkUIxIRu/RMdxQZbje9N6wlsyCPMtNkGYF7Sh7yhz94p9PT05k8eTI33HBDJML0evjhh7n44ov9Lrt38lS+SjweB3BaVinHH388Rx99NOPHj2fnzp2cdtppHHPMMSQkJAAOGjVrzd7CEi6e14Pv137rXc+YMWOqrPeWW27mL3N/pnfH5t55azftCcfmxZXKz+0vfeiSKsvsnFfR0I0kHnzuMVb36kaZabISAsbruy2+Zf0NCfLSSy/xwAMPkJKSwlVXXcXdd99NcbFuhRKR+inWv/PFmsrH0eUqwzBd3tulzz77bNq1awfAO++8wwcffBDNUOnmTOWpjCMZk/9DzFzt9v0cxPuwvJEQru8OO8fCt9+heHs0z3C76bXpe+D/ai2rRneUnG+6mOEqo/2cCQCMxzM+4Fg8nThUPgk7depE165dqrz+z3/+M4899hg//PBDROLt1KkTV155ZbX5ZS6TRd9s4R+PzuC0v/0HN3D7HXfx8fKlAEydOjXgOg/LTOOF557lxBNPxO1206xZs2qN+iO6deOEbnvp08nT6C4rK2PdFjW6A6ntvIq2gdmruKq4gLTcn+i9aBk3maWUAQl+4gWYVVZSZVv8lZ2TvapKz6C7d+9m3rx5XHbZZTRv3pwRI0bwyiuvRGYDRUQiaDjVvydj6TtfrPGXu3dNv5bZw0azBOjZs6e37HPPPRedICvJd5fxv+Jc8t2xcbV9YPYqnivaX+1z4Fs/kEPCVV8M9J1UH4+Fdx++PBn+eXut5XV7eRQMzF7Fa64ysnzmZwHz8JywlQ0ZMsQ7vWOv5xdFp9PJP+rQWZldEyZM8A7BNHnyZBwOBwlJKZw17X889t73VcquWr2auXPnVlvHgQMH2LlzJzt37qS4/Jns448/nksu8VypveKKK2jUqBEA33zzjfd1V5x6BB1bpgPw1VdfcbBEz3P7Y/e8irSB2auYOHca7VzFNC/cREJ5j6e+DyBUxDu/fLoyf2Unzp3GwOxVVeZX7lDN9+4JEZH6YDie78pY/c4XawLl7ub5uUycO43zTVeV/kl+DvHIMMHYYZbwSOEmdkSo5/KaVNQtstxVR+0OVD+Q8NUXa/pOqm/HItA+rIka3RFmmC5ueM/zzKnvzq/4e6bPssodkj369lo2b94CwFlnnVWlQR6s1KQEUpMC3/TQsWNHrrrqKgD27dvHzJkza13nLbfcwvz58/nf//7H3/72N0444QSaNGlC69atOaxdBya9+qW37EMPPURqamqV3qYvvfRSb4dYXds08c5f/vHHNreuYQjmvIokw+32xgcGxc5UzPJofG86MirN843XX1k3MOa9ZzAqJdyVK1fy/feeH4NOPvlkRo4cWS2mxMREsrLsfF2KiMQGA5hVadp3GUT3O1+sqS13u4HprjK6dfOMEONyufjtt98iGqM/yRh0d6aSHOUzrHLdItD+860fNHThqi/W9p1Un45FTfuwxteFJxwJ5KhN62iZnxtwxxtAB6BiRGqHw8Efyp/nPlBUyneb93DPvfd6y0+fPr38OengHNO3L/+97Y+8cNOp9Ox5pN8y48eP917lnjVrFnv37q11vbm5uYwYMYJhw4bx8MMP88UXX+ByHbpC/dWGXD79eQfgGSJs3rx5HFneO/ny5cvJzs6uMjRGheXL1ej2x+55FWmD3G5a5ufiAEqdjdja7FhKnY0ClndQvYEdiAG0ys9lkM8X+ezZs73Tr776KrNmzSIpKYlGjRoxbtw4Nm3axNatW7nzzjttb4+ISDQNAtoTuBIX7e98scZq7j6y/Er3xo0bKSmJzNVlVw1DRHV1NuLdZsfSNUAej9TwmZXrFv4Eqh80ZOGqL1r5ToqXY1HTuQ+178NA1OiOsMwCa+NLty3//+ijj/b2VP7tpt2Ybjf//e9cvvjiCwB69+7NX+8KvtFwxx23kZLopFnjZJ575mkMo+op0aFDB6655hrA+lVuq55d/AOlpaUAnHnmmd75FbcGv/TSS+zZc+j5bZfLxcpVq0P2/vWJ3fMq0tpw6Es20XWQrL3fkOg6GLb3AHj66aerPMv9l7/8hc8//5wNGzYwY8YM2rb17I2K81tEJF5Y/S6P1ne+WGMpd7duTVpGBgDr16+3/R61NSCC8ZvrIMP3fsNvIc7jdvnm/bqWawjCVV+0Wr4+HAur+9CXGt0Rtie9maVy28r/r3xr+dcbPENlud1uxowZ4x0u4u4J4+ncKt12LE2aNOG8c8/1/n3CCScwduzYKmUqX+V+7LHHyMsL7kTzZ+vuA/xz9lNV5u3atYs33ngDgMLCQp57/nnvsq+++oqCgoKQvX99Yve8irTtlX6HNjBJKduPEeL+dbf7/Nbtcrm47LLLGDNmDEVFnvHj+/TpQ+vWrauU69GjB82aWdt/IiKxwOp3ebS+88UaS7m7/NZyiI3nuQEOYvJd2X4ORrmffN+8X9dyDUG46otWy9eHY2F1H/pSozvCvu/Yi10ZLQJ+TZnAZmBF+d9VG9253umvvvqKhx56CICkpCRuP7cPTsPeiXzhBReQkpJSZd79999Ply5dMAyDe++9lz//+c8A5OfnM2PGDFvrt+L+B6ZVuZo9Z86cKrdOPfHkbA4Ue66GL3jzrZC/f31h97yKtBUOhze+Mkcie1I7UOZIDFjeXf7PChPYmdEi4JAUTz/9NP379+eXX37xlDdNXn31Ve+POwAnnnC8xXcTEYm+FXh6BI7V73yxxkru3h1jnagBtHQk8pfUDrSsIY9HQuW6hT+11Q+C4XvnQOW/zRDdSRBO4aovWvlOCvWxiJba9mEganRHmGk4mT1stGfad1n5/+PKp5OSkjjllFMAyMnJYUvu/irlp06d6u0sqlvbJowcUHVYsdpcftml3umvf/M06FNTU5kzZw4ffvghkydPxun09Bf9j3/8I6RXuSvk5eV5n6nNy8ur8hwuwO+//87YF1Yx6bUvmT5jZsjfv76wc16FQ223r5kOB7OHjcYBlBmJ5Ce3xmV4krVv49qsNM83Xn9lHcBTw0Zj+nyRV36m7Lt1P3DMMccwatQoevbsyahRo3jttde8ZU866aSAr423MSNFpP4zOTS0YjS+8yU0asvdDmBFjx7eebHS6G5mJDIyuTXNjOg2uivXLQLtP3/1g4YsXPXF2r6T6tOxqGkf1kSN7ihY2XMAI50J5PjM3wrcP2oCB8or+ieddBJpaWkALPloabX1lJSUcPXVV3s7KLv0lG707dvHUgxtmjZi0KCTAdi0q4BJr33Jhg0bARg0aJC38zaXy8U999zDAw88YH9DLXrhhRc45phj6Nu3Lxs3bqy2fEvuflb/tMN7O328Mm08VxXMM1g1nVcjiP6YrSt7DmDqqAnsMg/SKe8Lkl2e4e98B4CriPdCqLYt/spOHTXB0tiP+/fv59VXX/X2ir969aH+AU484QQbWyIiEn0L8HxXxup3vlgTKHfnZrRg6qgJuCvdXh7MM93h8LOrkJPzvuDn8jweTRV1ixyfxpyd+kFDE676Yk3fSfXtWATahzVRoztK3jScdElI4s6rp/HgiDsZ4kyka0JSlROy8nBgSz76yO96vvzySx55dDoAiU6DZR8t4aKLRniXJyUl8efrrmPSxf04tddhh9bdu513evHaHIpKXdxw401V1r1161ZOO+00HnjgAdxh7m3wm2++YfPmzWF9j4bA33nVmdipfK3sOYBuKY2rxNfYmeg33gVQbVt8y/p+ZuzYvHkz27Z5nkI64YTj68FTRiLS0Pj7noyl73yxxjd3/zE5lStve96TM8sb3cXFxaonBeCvblGX+kFDEK76or/vpPp6LCr24T2X3WepvBrdUWQ6HKzt3JtlvQez3DCq3XJR+Xnuj/xc6a4wZer9/PT7XgAaN27Mf195mRv+rydnH9eRH7PXMfufT9D/iNaMv+AYxoy+zrPuoz3jE5umyUffeX6nWbxkCZMnT6a4uJj58+fTt29fVqzQE2Hxptp5Fe2AfHQ2Ujhn1Rf8mNWL5YZBmWEEjNd3W6qVreNtShVXu5s0aUKHlo3rtC4RkWiI9e98sabycfzYmYBpODEc0LWL59HBX375BdOMjaPb1dmIhU2PCThkWDTUVqeW6ndR1vbdEWzP9w3pWJgOB+s6HmWpbPADPEtYZWRkcEL5La/r1q1j27Zt9AxQtri4mDtfWs0tw47m9D6eK9jnn9DZb9nHZjzK/77azGGZntvWP/poKbkFRd7lkyZNYurUqVXG1A6GMzGZIRPnA7Bk6oW2PqyVX7v0oUssv67iy6HyeiT2HMDk53YdKE5KinYorF69mgsuuACAI7OasWnX/lpeISIiEhktMxp5O7yNlee5AQ64XXxWuo8D7rrVFUUaEl3pjlEtW7Zg2bJlFBUVsXjx4lrLF5eZPPL2t1w/5kbv8EgVFr77Lm9/sdH795nHdvBO/7vSOMYV6trgFqnJdncprw8+nbz0jGiHwqeffuqdPrKdhg0TEZHYkVV+gQRi53lugG1mCVMP/MY2s6T2wiIC6Ep3RBluN703rCWzII8y06yx2/xff/2N008/nZSUFG9nalbWu3jBc5zy1Rpm/3ch+QdLuOuaC/n6m28YMnE+hcVljDq5q/d1+/fvZ8GCN+l/+4Uh28ZQs7PPxD7f/buM8Pd2m4SDlnvzyGtsf2z5UFuzZg2lpaUkJiZyZLumll4TjX0mIiL1S031m4plp7aNvZ7LwZPH2xrJbDOLKbE8wKc19SnHhmtbKq/XXVZKn1+/odmBfTG7v2L9mEYqPjW6I2Rg9iqeK9pP+zkTABiPZzy7OdmrauxcoKioiKKiooC3Sg8HZpWVVF3vF58z556ZrOw5gK+/+cZbds7Sn9jy2TvcefutALw+bz6FhdHveTKQYPeZWOP33MEz5EM4O+Hp6khh8r+e5sFRV4XxXaw5ePAg3377Lf369aNjy3TSkmv+SozWPhMRkfojUC6Zk70K4FDdp89M72vaxFCju5szlbeaHcN5eV+zznUgZOutTzn2fNPFDFdZyLfF3z7ipXtC+h6hZHc/+D4qGm6RPOd0e3kEDMxexcS508jy6QE8C5g4dxoDy79k7RoOzCtfj9X13n3vJB59+1ve+nwjd/31b0G9bySEa5+JR03nzrzy5eGy0V3MjAv+xM6msXE7d+Whw7pnNQ1YLpr7TERE6ofa6m5V6j6Vhgubsn59zOSZjWYRl+77jo1mUe2FLapPOXZg9ipec5WFfFsC7aNQvkcohWs/hEqkzzk1usPMcLu54b1nPNO+ywA3MOa9ZzBsDsllALMqTdtZ7wffbuWfi9aRl5dn6z0jJVz7TDxqO3cAZvpZFiqFmKxv15HipNjo6K5yozvQc92W95nOSRERCcBKLqkyfcQRnv8LCmD79rDmZjtC3ZFafcqxhumqsQ4LwW1LTfso4HvYeofQsrwfIhiTbwyRrgvHwme3XhvkdtMyPzfgGMAG0Co/l0E2P3yDgPYEPoDBrjcWhGOfmUEOe1AfWTl3OpSXC4fmJPDHNZ+RXhi6W9LqonJnaj0DXOm2vM/i8PMmIiKRUVsucZT/AyAxETqXj0Tz889hz812NHckcl2jLJo7EkOyvvqUY4/atI6W+bkh35ba9pHf97D1DqFleT9EMKbKolEX1jPdYdbGYgcTVstVaBvi948l4dpn4mH13LFazq4WjgT+78vV/NChU5jewZ4NGzawY8cOWrduTfesZjgcDtw+ydDO5y039CGKiEgc8h0+ta2dH/07dwan0zNd6XnucOVmO1oaSYxp1B7XdVPIadna9tCwvsKRYyP9bHCFzAJrd5HarS8Ec9yjea5Y3Q/RijEadWFd6a4jo5axoLcHvF4bXLkK2yyWs7veWBCufSYeVs8dq+Xs+sldxJ3XjyOnZeswvYN9n33+OQDpjRLpd9xx1ZbX58+biIgEx5mYzNApCxk6ZWHADm8rGInJtLt6mvWVV9xaDlBpuLBw5WY7fnQd4MSCr0OWx+tTjt2Tbq2/mnDV++v6mlCxuh+iFWM06sJqdIfZCoeDXRktAnY9bwI7M1rYHgprBZ7e9UK93lgQrn0mHlbOnc3l5RqKTz/9zDv98fKlPDX7n7Ru2sg7z/I+0zkpIiIBfN+xV431G3f5P6Bqo/vnn+t1bq5POba2YxzsttS2j/y+h613CC3L+yGCMVUWjbqwGt1hZjoczB42GgfVD6yJ59mdp4aNxrT54TPxdGdfMR2q9caCcO0z8ajt3AEY52dZqHRyJHPHa/+iVd7uML2Dfa/Pm8++whIAEhMTue7aa5hz06lcPLALYGOf6ZwUEZEATMPJ7GGjPdO+y3ynKzW6zfLby8f5eV00dHY2Ym7akSHL4/Upx1o5xuOA0rISW30N1bSPAr1HNM8Vq/shWjEGWxd21aGPKDW6I2BlzwFMHTWBHJ8vi63A1FETgh5zegEwAsjxmV/X9caCcO0z8ajp3BlBeMd3LMFkW2YLypyx06XEhg0buPbJZbzy8Xr27dsHgNMwuOYPPejZ80gguvtMRETqh5U9BzDSmRCw7uat+1QaLixn/fqYyjPFbpNfzIMhzeP1KcfWdIzrsi2B9lEo3yOUwrUfQiXS51zs1HrruZU9BzAtpTG3jBpPZkEeixfMZIXDwWl1bDwuABYmJDH28kkhXW8s8LfPlrlKMefaeCYqyiLRkYfp8x61PVdWwd+5s8xVGvZfHX93l/LKH4eF+V3sKygq5V/Lf2bsiEH8/eGHuObqqwAYceGFfPftN0Bk95lvBzxWf1EN9nUiIhIZbxpO3nYYAetu01Ias71vP5oCe3ftotPevTFxhbvC72Yx9xzcyJCMJiFdb7TqJeHg7xiHYlt899HSt5+g75/uptmBfTG5v8K1H0IlkuecrnRHkOlwsLZzb5b1HsxywwjZbTLhWm8sqLZt0Q6ononG/k0AMg7sx3CFZnzPUNu7dy+TJk/x/n3BBcOrLNc5KSIidVVT3e2wdu1ompkBwLqf18dcnknAQUtHYljyeH3KseHalsrrXZaQyLdd+sb0/or1Yxqp+NTolrhlp9dQiR1HOBrx0PNPkLV7V7RDCej333/n+817ADiqVy969OgR5YiiQ58xEZHIO/usM73THy5eHMVI/OvuTOWTjL4xncdFYo0a3Q2MWYcOAERCYbO7mH+eM4JdTZpGO5QaffLDoYEiLrrooihGIiIiDcm55xx6ZOytt9+OYiT+bTKLGH3g55jP4yKxRM9015HhdtN7w1oyC/IoM02WERs9S0ZC5W13ucowTBem4bT1uoa2zwT2Y/J9564hWVc4z6UVP2xnzP/1AjyN7qlTp4ZozR76HIiIREcsf/82Tklg8KCTAdiWV8h3330f5Yiq2+92sbxsH0OSU6IdijRgsfw59keN7jr6umg/7eZMAGA8njHfxhL9HvkqC0dnXsOBWWUltK+07bumX8vsYaNZYvN1sbjPJHya4eSUb9fwVbcj67Se800XM1xlYTuXcguKyN6SR8/2zTj66KPp3r07P/30k611BPrs6XMgIhIdsf79e3zXViQmJgKw6qftUY7Gv0xHAmclteJAYSH7U1OjHY40QLH+OfZHt5fX0WFud5W/s4B5eE6GUKrLuHChNhzPNmb5zG+en8vEudM43/TfsUag14Vrn0lsauNI5MIVH9F0f37Q6xiYvYrXXGVhP5c+DsMt5vociIhERzx8/w7o3sY7veqnHVGMJLA2RjLjU9rXKY+LBCsePsf+qNFdR747sOLvmX6W1QcGMKvStO8yNzDdVYbh82NEba+D+rvPpKof3EWMvflOtrZqU3thPwzTxQ3vPeOZ9l1W/v9MqHYOBiPUz3XrcyAiEh3x8P2b6DTo16UlAPsKS8jeklfra6JxUSbbdYCj89cEncejTZ2Exq94+BwHEosxxT0D6AAMinYgYTAIaE/gE8e77T4NHsuvC0mUUp8dtWkdLfNzbZ+DwdiVX8Snn34KQO/evTniiCPqtD59DkREoiMevn/7dm5OarLnyc/Pft6BGYI8JlKfxMPnOBA1usOobbQDCAOr29SGqonC6uvq4z6Tqjo6kvjLgv/Scu+eoF6fWVD7L/9Q/RwM1rz5b3inr7rqqjqtS58DEZHoiIfv31DcWh6JK98djRTmpB0RdB4Plu+2Vf7b1Ig8DUKwn+NYuLtBHamF0bbai8Qdq9u0HUeVk8vq6+rjPpOqynBT0CgV0xHcb3570ptZKud7DgZr/hsLeOQffwdg/PjxNMtszrt7DErK7PeRqc+BiEh0xPr3r2EY9D+iNQCFhYV89VvsjoHtws0esyzoPB7rgu2A2Pd1unU99GL9c1yT+vlpiTIT2AysiHYgYbACT++AgZob3m13OIJ7XUiilFiW4y5lzhnnsTvI8T2/79iLXRktbJ+DwdqyZQvTp0/3/j3m+tHMumYg7Zun2V6XPgciItER69+/J55wAs0aexppHy5eQnEQP+xGylazmNsP/hZ0HhcJVqx/jmuiRncd+R70ir/H+VlWH5h4uuOvmPZd5gBucyZg+jR4ansd1N99JlUZQEpxMQ4zuKNtGk5mDxvtmfZdVv7/OKh2DtbF7bffzrXXXkthYSEAh7fO4PHrTqZXr5621qPPgYhIdMT69++55x66Qvr2u+9FKQprDCANI+g8LhKsWP8c10SN7jr63adivxUYQeyOERcKC/BsY47P/NyMFkwdNYE3Daet19WnfRZLQ7vFqh6ORkx/egbtcncGvY6VPQcw0pkQ0XPphRde4Pjjj2dddjYAjZISuOP2222vpyF8DkREYlEsf/+efvrpAJhuN+/97/0oRlK7I51pfNXkuDrlcWm46vp8tZXPcSw8w+1Lz3TX0TEpjbnqwr+RWZDH4gUzWeYqjclfV0JtAbAwIYmxl08isyCPJe88SeJtz2MaTpj/qOXXNaR91pBVfs6pWWIqz15wPrkZTeu0zjcNJ287jIieS9nZ2fQfcDI527aT3iiJC4afz42NG7N//35b69HnQEQkOmLx+7dZs2b0PvpoAH7dns/u3ZHtoMyuLWYRYw/8QkId87hIsGLxc1wbXemuI9PhYG3n3izrPZjlhhHTBzvUKm/7x84ET4Pb5usa2j4TyMfF1916cDAlpc7risa5VFhYyPJ1ni460tLSuOCCC4Jajz4HIiLREWvfvyefPBDD8FTJ127aHeVoapfvdvF+WV5I8rhIsGLtc1wbNbolJEzdVi0WNXU4OSl7LWkHD0Y7lKB9uHard/qKK64I+/sZMXiblIiIhMapp5zinY6HRndTRwLDE5vHdR4XiTTdXl4PGW43vTesJbMgjzLTZBmBOxSwUzbe1Of9EG/xVnaYI5krFr/Hg6OuinYoQfsxZy9bd++nXfPGnHbaabRv354tW7bE9XEREZHoOGWwp9Ftut18vzm2by0HyDKSeSj1cB4s2MeBRo2qLVcuFKlOje565nzTxQxXGe3nTABgPJ6u9cdSvYOQ4cCsshJLZeONnW2zs89iQbwft2yzkJtvviukvYtHw+K1OVx1WncMw+Cyyy7jxwcfjOvjIiIikdc4JYE+vXsD8NuOfPYXlUU5otqtcx2g574vOLXlX6sti/c6iki46PbyemRg9ipec5WR5TM/C5iH54uwwvDyeVbKxhs722Znn8WC+nLcTMOAOG90f/TdoX4zb7ziinpxXEREJLKO6pB56HnujbF/a3kFF1TL4/WljiISDmp0h1nlIaTMMD7rbJgubnjvGc+077Ly/2eWTxvALJ9lgcrGG6vb5i4tZvG9Z3P13Gm1lo2V/WD5uLndkQopKO2NZMa8M48We/OiHUqd7Nh3kOXLPwagXY8ecMIJcXEeiUj9oqEq41vvDs2902s3xf6t5QAdjBRmp3atksfrc91SJBR07tcTR21aR8v83IAH1AA6AIPK/7Un8MGvXDbe2Nm2eNsPluON8UZ3ffLvl1/xThsBOlSLtfNIRERiR+9Onka3aZpx8Tx3IPFWpxKJND3TXU9kFli7atjWxjrtlI0VVmOOx/1gNY42uMkNayR1s8Us5qlzRkQ7jCoqjyMOWO4hfP4bb/DkrBkkp6bCn/4E//gHbNrkt2ysnEciIhIb0pIT6NImA4C1a9cy/67/A6znoGjZbBZxQ+EvDGnazDsvHPUvO3zzuEis0ZXuemJPerPaCwHbyv9ZLRtv7GxbvO0Hq3FsJ/aflTZME+rBFfmCggI+XlDeNUxmJnz9NZxzjt+ysXIeiYhIbDiqQyZG+XPRy5cvj3I09jihSh6PtzqVSKSp0V1PfN+xF7syWgQcksEENgMryv9tIfDwDZXLxhs72xZv+8FyvDHeQVlPI5Unnvg77XftiHYoITF6wgTKfv3V80ezZvD2254r3gmeG4li7TwSEQk3Z2IyQ6csZOiUhRG7ahuN96yr3h0PPc+9bNmy6AViUy9nGtlNjq+Sx+OtTiUSaWp01xOm4WT2sNGead9l5f+PK5828QzdYKVsvLGzbfG2HyzHG+ON7t/dxfzrj8PYk94k2qGExMbNm7nq2GNxz59/aOYdd8Ajj8TkeSQiIrHh6I6Z3umPP/44ipHYk2MW87fC36rk8XirU4lEmhrd9cjKngMY6Uwgx2f+VmAEVcdHXFA+z0rZeGNn2+JtP8RbvP7sdbv4tGdvDjRqFO1QQuaV/HwuHDGCvL/8BUpLPTMvuyyujouIiERORkYGXdt4Gq1rv/uOPXvipxO1ve4yFpTurpbH60MdRSRc1JFaPfOm4eRth8HYyyeRWZDH4gUzWeYq9fvL4gJgYUKSpbLxxs62xdt+qC3eWO9MJAMnx6z/kR/bd4p2KCG1AFg4+2m+Gz2O7kcdDs2bM6BtW3K26Qk2ERGpatDJA3EaFc9zx89VboAMh5NBCc1IKCriYEpKlWXxVqcSiRRd6a6HTIeDtZ17s6z3YJYbRo1fdHbKxpv6vB/iLd7K2hnJ/Pl/b9Iify9Qv8aYNR0Ovis49Hevo4+OXjAiIhKzzjzzTO/00jh6nhugvZHCrLSu3jzuK57rKA1Jfap/xQNd6RaRiFpXnEffktUUPvFJrYk42KG8omnDznzv9NFHH80HH3wQxWhERCQWnXP2WQCUlLlYvHhJlKOx5wfXAY7dt4b+Le6KdigicUONbhGJKBPY73ZFO4yw2bjz0KXuo3WlW0REfBx37LFkZWUB8PVvuRQWFkY5IntM4AAmbkM3zIpYpUZ3DDHcbnpvWEtmQR5lpsky4reXx7psS+XXulxlGKYL03CGMVqJpHZGMrendeLRAxvZaobvdqZQnYN2X7s5dz8u043TcFRrdDsTkxky0dPL+dKHLrG4xuoqr2fJ1At1W5iISBTUlisqL3eXldLn129odmAf7cqvcgOs/jn+hs9sZyRzZ6PD2bvuUxJdxXFfZ5XQise7FCNBje4oqnxSDgdmAe3nTABgPJ7xDscSf709DgdmlZUEtS3+Xrtr+rXMHjaa+Lr5SgJx4iDTkYgT+0ObWf0iD/U5aOezWFJm8vueA7Rv0ZiePXvidDpxuervlX0RkYaotlzhbzkv3eN58d1fe9fz2fqdEYs5VM7Ewdk4aff6IySZRXFdZxWJFN0XEgOGA/OALJ/5WeXzh0c8ouDVZVsCvbZ5fi4T507jfFMNl/pgk1nElfnfs8ksCsv6w3EO2v0sbii/xTwlJYVu3bpZfJWIiMSD2nLFQwGWA9CxI/TtC4D7s8848vOlYYszHIYDT5lFdMr/nqRKeTwe66wikaRGd5QZeK5wV0z7LgOY6WdZLLK8LW637de6gemusrjYDxI9dfk81eX89eXbmZqIiNQPVnLF7QGWA3DOOd5J99tvM+a9ZyzllVgQyjwp0tCoDRNlg4D2BD4QBtChvFyss7wtfr6M69N+kJr1dKaR3XwAPZ1pIV93Xc6jupy/vjaoMzURkXrJSq5IqGE55557qOxbb9EqP5cBZSWhDDFsKra91JnGr80HUOyTx+3kSZGGRs90R1nbEJeLJqsxtsFNbpCvjYf9IDXbbhYz7cAGtoehE7W6nEd1OX99xVIP5up0TUQkdOpUD2nSBE491TP922+wbp3fdfr2XxIrKuJMMItpcWADCQHyuJU8GUisbruIP67SYpY8cBH8Nb/WsrrSHWXbQlwumqzGuN1PB1r1aT9Izfa4y3i5aBt73GUhX3ddzqO6nL/VyuQVsn//fiD6jW4REQmdOtVDzjgDEhM902+/HZp1RlBFnE53GU2KtuEMkMet5EmRhkaN7ihbgafHx0DDLJjA5vJysc7ytjiqfxnXp/0gNWvscHJqYjMaO0I/DFxdzqO6nL++3EB2djYAXbp0IS0t9LfSi4hI5FnJFWWBlle6tZy33oq7uk3Ftpc5nBxIbIbLJ4/byZMNQcVV+w/uPTukd5lVXq+pu9fihhrdUWbiGWKhYtp3GcA4P8tikeVt8fNlXNtrHcBtzoS42A9Ss45GCs816UVHIyXk667L56ku568/332/zjvdq1cvS68REZHYZiVXPOpv+ejRcOGFnum8PMxPPgHip44Hh7a91Ehhe5NelFXK48HkSZGGRI3uGLAAGAHk+MzfWj4/nsY8rMu2BHptbkYLpo6awJtG6K+MNnTh+hW2Jj/9P3v3HR5FtT5w/Luzm0JCAiS0EFpoUjRiA2kXkKJiA/SnWNBrQ/ReBVFQUFSKiKgUFcUKtisCGkEEQaoIKliQEkBK6AkkEEgvu7O/PzZZdje7yU6yNXk/z8PDZObMnHfPlLNnZ+YcUx7dzvzGPlOeV7bvjWOwMufirl27rNPyiLkQQlQfFdUVz9our10bvvgC3nsPwsIsCRcs4LjRGHTf8cAS73BTHiFnfiPUph4Pxu+sQviSdKQWIJKA5YZQRg1/iZjsTNYkzWaDqThofv20VZXP4rju2u/mEjLmI1RFD1+/UeH6IvAZMZNuLvZqHp48Bit7Lu6yudMtjW4hhKheKqorkoDdbS9i00+badg41rrethU/M/G5ifxI8NzhdvQ1ZpbpdYwa/nLQf2cVwlek0R1AVJ2OHQmJAGxc+iaqyc8BVUFVPovtuhuWvonppVu8EaLwkyZKGP+NaMbbecc46YUezEt56his7Lm4U+50CyFEtVZRXfH23LetDe7z58/z5uoD/Lz3PGuNxqBuoDZRwng8oiV/x7ZgR0Ji0H9nDWbS23vwkMfLhRA+FaZTaKuPIExXvS8/GRkZpKWlAZCYmOjnaIQQQvhSYmIiAwb0B+D0+Xy69ujNz3vT/ByVZ4TpFNootTCYPD8KiRDVVfX+1iuECDgppnz+7/wOUkz5/g7F63bu3AlA/fr1ady4sZ+jEUII4StjxoyxTi/ecpBDKSl+jMazUkz5DMvdw+l6sRUnFkIA8nh5lfUwGSlWTZZ3jv3E8dESfUiY32LxJcVsJjFlBzHZmRhVlQ0E7/tRInhoeZRr586dDBgwALA8Yr5uw09u52N7fJtMRhQ3rzOO50VVhm6Rc0wIIbSLi4vjzjvvBCA7v4hVfx/3c0TCltRtwh+k0V1FywrzSJ/5IO8OGsFafwdTgwwB5hiLaDZ/AgDjsYwdOQrpOTPQtddH8nmdi7nn/C72mnL9HY5Xld7pBm2NbmfHtzvXGVfnxfzkLWzu2F1T7HKOCSFE5Tz++OOEhoYCsPyPoxQWV68XntvrI/k86hLmpp/iRING/g5HE6nbhL9Io9sDYrMymLhwGjv0Br4uJ510duAZQ4AlTubHl8yXISsCW7paxLz846SrRf4Oxev++usv6/S//vUv5rw1t8J1XB3fttcZZ8PnlXdeTFw4jSnDJrj9w6CcY0KI6syb38ciIyMZOXIkAIWFhSzbdtgr+fhTulrE+4WpZEXW9ncomkjd5h819YlcR35/p/uxxx4jJSWF/Px8fv31V6666iqXaQ0GAxMnTuTAgQPk5+ezfft2rr32Wrs0tWvXZtasWRw+fJi8vDw2b97MlVdeaZdm/vz5mM1mu38rV66s9GdQADMw02T0f4FWcwowx2bacRnAbCfLROA4Yy7mw/wTnPHysGGBYMeOHaSmpgIwYMAAwsPDy01f0fFtvc6YzZrXG7ni/TLrVSYGkHNMCCFcuf/++6lXrx4AXy78irM53hulw1/OmIv5qCiN7IhIf4fiNqnbhL/59di6/fbbmTlzJpMmTeLyyy/n77//ZtWqVTRo0MBp+qlTp/LII4/w+OOP07FjR+bNm0dSUhKdO3e2pvnwww8ZMGAAw4cP55JLLmH16tWsWbOGJk2a2G1r5cqVNG7c2Pqv9N2bylKA5kCvKm1FVKQX0AzXB67sh8AXqdPTNaQOkTr/9YPgK2azme+//x6AiIgIrunbp9z0bh/fDo1nd9ZrmJVRZr0qxVDhloQQomZRFIUnn3zS+vfsOXPKSR28InV6uuijCCsKnh8UpG4T/ubXRveYMWP44IMPWLBgAXv27GHkyJHk5eXxwAMPOE0/fPhwpk2bxsqVK0lJSWHevHmsWLGCp556CoDw8HBuvfVWxo0bx6ZNmzh48CCTJk3iwIEDPProo3bbKiws5NSpU9Z/586dKzfW0NBQoqKi7P45E6e9GIQG7pav7IfA1VIJ54s6l9BSKf+ub3WxfPly6/QNN9xQblp3j9vG2DeeK7teVWKQc0wIIewNGjSIVq1aAbBq1Sp27drt54i8o6USzme129PwXKa/Q3Gb1G3CldLH31e/cCOmYu/9kOS3RndISAhXXHEFa9assc4zm82sWbOGbt26OV0nLCyMgoICu3n5+fn07NkTsDx+bjAYyk1Tqk+fPpw6dYq9e/fyzjvvEBMTU26848ePJysry/rvxIkTTtOllrsVUVXulq/sh8C135RHv7O/s9+U5+9QfOLHH3+0XpNuGHR9uWndPW7TsO+RvLLrVSUGOceEEIFMHxLGwMnLGTh5OSER0dZpb75POmjQIOv03LkV9+ERrPab8hiQvYPUmPr+DsVtUrcJf/Nbo7t+/foYDAZOnTplN//UqVMux7NdtWoVY8aMoU2bNuh0Ovr378/QoUOJi7P8LpWTk8OWLVuYOHEicXFxKIrC3XffTbdu3axpAH744Qfuvfde+vXrxzPPPEPv3r1ZuXIliuK6OF555RWio6Ot/+Lj4+2Wq8BRYFPlikO4aROWXiZdDe0g+yHwFWHmiFpAkRt3XauDvLw81q1bB0DTpk1p3TjaZVq3j2+HYcDcWe90dH23hg+Tc0wIISqnX79+ABQXF1uv+9VREWaOqoUYDcHTH7PUbcLfgqq/gFGjRrF//3727t1LUVERb7/9NvPnz0dVL5xCw4cPR6fTcfLkSQoLC3niiSf48ssv7dJ89dVXfPfdd+zatYulS5dy44030qVLF/r06eMy76KiIrKzs+3+lVIBHTBGb5Bx/rxMxTKsQ+m04zKA0U6WicARp4QyMbIVcUqov0Pxme+++846fXXbhi7TVXR8W68zDo1nd9abN2hEmfUqEwPIOSaEEI6aN29Ou3btAPjll1/Iza2+Q2LGKaE8F96cetlZ/g7FbVK3CX/zW6M7IyMDo9FIo0b24/s1atSItLQ0l+sMGTKEyMhIWrRoQfv27cnJyeHQoUPWNIcOHaJPnz5ERkbSrFkzunbtSkhIiF0aRykpKaSnp9OmTZvKfZbo+kwZNsHpMD7C85KwDOvg+ID/cWS4h2BQkzpSK2X7XnfXduWPaerq+K7oOlPeeTFl2ARN43TLOSaEENpc07evddr21cnqKFKnp6shirCi4Br6U+o24U9+a3QXFxfzxx9/WB/FAdDpdPTr149ffvml3HULCws5efIkBoOBW2+9laVLl5ZJk5eXR1paGnXr1uXaa691mqZUfHw8sbGx1qF9tLg5LIL7xnyk6QutqLokoLUhlLH3T+OV28bSTx9CAnLBDAYHTPnceO4vDpjy/R2Kzxw/ftw6ZvdFTeoSU7v8dwodj+/+bl5nnJ0XbQyhlbo+yTkmhBDu69fvGut0dW90HzDlc3PObtJig+ed7lJStwl/8evj5TNnzuThhx/m3nvvpX379rz77rtERkYyf/58AD755BOmTZtmTd+lSxeGDBlCQkICPXv25IcffkBRFGbMmGFNM3DgQK699lpatmxJ//79Wb9+PXv37rVuMzIykhkzZtC1a1datGjBNddcw9KlSzlw4ACrVq3S/Bk26w2o1fQOt20nJIE4kL2q07EjIZENib3ZqCjySJDwC9vzRCnnPLF9xLxrOY+Yl7I9vn/ScJ0pc1648Ui529uq9JaEEKL60gH9rrHc6c7KymLbtm3+DUiUS+o24Q9+7QFh0aJFNGjQgMmTJ9O4cWO2b9/Oddddx+nTpwHL+zG272KHh4czdepUWrVqRU5ODitWrGD48OGcP3/emqZOnTq88sorNG3alLNnz/L111/z3HPPYTQaATCZTCQmJnLfffdRt25dTp48yerVq5k4cSJFQfaYjL+VdrHvq/VE9dBOH8HH0Z14IGs3/9SQHszB0uh+4YUXAMsj5moAnAf6kDD6TfwagLVTbgUo87cQQojytWwYRcOGlh9TN2zYYP3OWV2100fwcdTFfJhxmpP1K/4RWQjh50Y3WIZUcDWsQl+b92MAfvrpJzp16lTu9hYvXszixYtdLi8oKOC6667THqgQwiMy1WIWFZ4iUy32dyg+9ccff5CamkpcXByXJdQnIiKC7PPeGw9SCCGEb1yWcOEx6+r+aDlY6vHFRenk1IrwdyhCBA2/N7qFEDVLurmYN/OO+jsMnzObzSz/fgUPP/Qg4SF6ht9zN+/MfdutdRWzmcSUHcRkZ2JUVbeG/hJCCH9xvGZtwPu9QvvzOlnTGt3p5mLeLjxJv8ja/g5FBCFfXB8c89gMPr8mOZJGtxDCp2qh0MYQwQFjHvk17E2qee+9x8MPPQjA6FGjmPfuO3av0DjTI3kLHxbk0Gz+BADGYxlrdH7yFunAUQgRcIYAc4xFZa5Zo/BeZ1Wu8vTFddKg6LikRQwAJ0+eZM+ePV7NLxDUQuEifSShxUUUhdSc4T9F1Xny+uDqdVVneRgBgw+vSc4E1TjdQojg10pfi6S6nWmlr+XvUHzu77938FdKBgBt27bhpptuKjd9j+QtTFw4jXiz2W5+PDBx4TR6JG/xVqhCCKHZEGAJlmuUrfiS+UN8nKcvrpPt4+tSK9RyD2vt2rVezStQtNLXYkntjjTKPOvvUEQQ8cX1wVUejl3RevOa5Io0uquotDOk1S/ciKlY3s8UoiIHTPnckPmnR4YMMwXh+bfkl0PW6aeeesplOsVs5tEV71umHZcBZmDkivdRHBrkQgjhDwowx2bacRnAbCfLvJmnL66Tl7WqWY+WQ8mQYdm7SIuJ9XcoIkj44vpQXh6OL5t465pUHml0CyF8qhCVfaY8CmvYo+Wlfj+YzpH0bAB69epFly5dnKbrZTbTICujTEVRSgEaZmXQSxrdQogA0AtohusvlgrQvCSdL/NsmJVBT9XkwVzt2b7PXVPudBeisk/Np9gQ4u9QRJDwxfWhojy8kacW0ugWQvhUIyWUpyNa0Eipue+Bff1rinXa1d3uxrjXmHY3nRBCeFOch9N5NE8v/TgZGxtL+/i6AOzZu5cTJ054JZ9A00gJZUxYU+rkZPs7FBEkfHF9qOy6nrwmlUca3UIIn4rWGbg+rD7Ruprbj+O6nSdIS0sD4NZbb6Vly5Zl0qS5vMdduXRCCOFNqR5O54w+JIyBk5czcPJy9CFh7udZhZ7MHfO0NeyO29Erlq/S33//faXzCDbROgPXhdQjorDA6fJgfPVLeJcvrg+VXbcqeWohjW4hhE/tN+XRL/MP9pvy/B2K3xSbVN55dx4Aer2e0aNHl0mzSacjPbq+y4fwVeB0dH0ZPkwIERA2YekRuLxr1tGSdL7M83R0fX5WHLtR8ox77rnbOv3Z5194JY9AtN+Ux8CcnaTGNvB3KCJI+OL6UFEe3shTC2l0CyGEH8x7733y8iw/PDz88MM0aGD/5UXV6Xh30Ah0lK1AVCydgswbNAJVGt1CiBLl3ZX1NhXLEDyl047LdMAYvcGjvXm4k6e3rpMdOnTgqiuvBGB/6nl27072eB5CVBcVnasAo50s81Qeji+YeCpPLaTRLYTwqbb6CFbXvZy2+gh/h+JXZ8+e5f33Lb2TR0REMHbs2DJpNnfszpRhEzjh8IXxODBl2AQZp1sIEVCSgNsAxzebM6LrM2XYBL71wh1nV3l6+zo5fPhw6/SPfx/3Sh6Bqq0+gh9qX0zcmXR/hyKCSHnn6m14ZsxsV3k4dqXoyTzdVXNfqhQiiJW+LxWMss1G1hadJdts9HcofjdjxgxGjhxJeHg4jz32GK+99hrp6fZfYjZ37M608No8Pmw8MdmZrEmazSadjr7S4BZCBKAkYLkhlFHDXyImO5O1380lZMxHqIoevn7DJ3l6+zqpKAr33HMPAEaTyobdJ72ST6DKNhtZZzxHfqhvn6YQwc/ZubrBVOzRu83O8tgMPH7vJK/l6Q650y2E8Kk0tYhX8w6Tphb5OxS/S01Ntd7tjoyMdNmTuarTsSMhkQ2JvdmoKPJIuRAioNles37SGywNbh/m6e3rZN++fWnWrBkA2w6mcz6vZtVnaWoRMwqOcy4q2t+hiCBU5lz1QR5GRfF6nhWRRrcQwqfCUGirjyAsSC4/ipffkXz11VcpLLT07vqf//yH2NhYj+chhBDCc+69917r9JodNevRcrDU422UcEKMxf4ORYigERzfeoXXyLAOwtfa6Guxst7ltNHX8ncoHqdW4nw6efIkH3zwAQC1a9fmqaeeIiQkhO7dunHzVS25qEldL0YshBBCi8jISG699VYAMjMz+e2f036OyPfa6GvxfdQlND57xt+hCBE05J3uaiiY3/cV1V+KKZ//O/c3KaZ8f4fic4rZTGLKDmKyMzGqKhuw9Jo5ffp0Hn74YcLCwhgzZgxPPPEEkZGRABQZTayZ08qt7QghhPCuoUOHWq/PXy1aTLEp3s8R+V6KKZ87cpJpUjfG36EIETTkTrcQwqfyUPnLmE1eDWsmDgEOGot4bf4Exi95jbWmYg6XzD9x4gQffvghAGFhYdYvdAChBj3PjL3wrvdg1eRyO0IIIbzL9tHyz2vQ2Ny28lDZbsqlMDTU36EIETSk0S2qLS2Pzstj9r7TQBfCY7Wa0UAX4u9QfGYIsARwvB8SXzJ/CDBt2jROnLAMcnH8+HE+/+J/ZOdb3pe7+85hNKpbix7JW1hkMpa7HSGEEN4RFxfHNddcA8CBAwf49bff/ByRfzTQhfBoWBzRuTn+DkWIoCGPlwshfCpGCeGeWnGsLTpDuqn6d8KiAHNsph2XqcBsIOHkSdq1a0fdunU5efIk+pAw5je9kuG922EwGBjWvRVXPX+HZb3oaJg5E+rUgQceQMnOtm5nudnsk88lhBA1zR23/x+KYrmSf/HFF5pe51Or0at/MUoId4U25IP8PLIia/s7HCGCgjS6hRA+tc+UR/ezW/0dhs/0ApqVs1wBmpek25iXR15ennXZt1tTGHp1ApFhIQy8tBmG6HDQ1YFVq6BrV0uiv/6CadMubEca3UII4RV33jnMOv3FFzXz0XKw1OO9sv+mX/2G/g5FiKAhjW4hhPCiuCqkyykw8t22Iwzr2QaDQQ9Tp0KnTnDllRcSXXMNTJtm/bMxZjKqFLEQQtQs+pAw+k38GoC1U251+ppZs9hIrrj8cgC2bdvG/v37K9xude7Ytjp/NiG8Qd7pFkL4VGt9LZbW7UzrajhkmDOpVUz3zW8p5ObmWv647z77BjdA9+5g05lNGjrNMQohAos+JIyBk5czcPJy9CFh/g6nRlFclH3fSy70plGT73JDzavHhfAEaXQLIXwqz6zyV3EWeeaa0Xv5JuAYrof0UoGjJemcOZ9XxHsffmw/My0N1q+3TNeqBVdddWE7Oml0CyGqh0D68aFvpyYAmEwmvvrqK7/G4m81rR4XwhOk0S2E8KlUtZCXcg+RqtaMXuJVYJTNtOMygNFOltmaNedNCoqMAJhPnkTt0wc+/fTCdvr0ubAdaXQLIYRHdYivS5MYy1CO69atIy0tzc8R+VdNq8eF8ARpdAshfCoUHfFKGKE16DHoJOA24ITD/OMl85MqWP/UqdM89+U2Fm4+wGO9/sWJfftgwwbr8qI+fdzajhBCCO2ukUfL7dTEelyIqpJGtxDCp9rqI9gYcxVt9RH+DsWnkoDWhlDG3j+NV24bSz99CAm431DedfQs89ft44Njxy3b6TuC8xmZAKjdu7M8pOaMey6EEL5iMBj4V0dLV5f5+fl88803fo7I/2pqPS5EVUjv5cIrqtKrpfSIWb0dUQu49/xOjqgF/g7F51Sdjh0JiQBsXPomqqlq29l2Mpf+9esRERHBVVddxZYtWzwYrRBCiOuuu5a6kZb3yZd//z3Z2dl+jsj/anI9LkRlyZ1uIYRP5ZhNbCk+T465ki1OYfX34TPW6T4l73ULIYTwnFGPP26d/vxzebQcpB4XojKk0S2E8KlYXQgPhDchViePQ1fVzqNnrdO9e/d2mkYxm0lM2UGfHRvprapy0RdC1Ai2174+xmIuPbhd83Wwc+dL6du3DwDHzuSwYuUPXoo2uEg9LoR28nh5AJHHqkVN0FAJ5YmI5vxSfJ4zpmJ/hxPUUjPzOHbsOM2aNaVHjx6EhITY9YI+WDUxy2Sk2fwJAIzHMnzZKKTTNSFE9eXs2scnz1un3b0Ojh41yjqd9GsKZrPZC9EGH6nHhdBObnoIIXxqjymXzmd/ZY8p19+hVAs/bbKM8B0ZGcmVV15pnd8jeQuLTEbiHdLHA0uAIT6LUAghfMfVtc+WO9fB2Kgw7rj9/wDIyitizY7jHowyuEk9LoR20ugWQlRLpU+OrH7hRkzF2sYStV1X1biur23c+JN1uvQRc0U18eiK9y3TDulL/56N5fFLIYSoLsq79tmlK/l/djnpbr6qJSElo0Is/+MIhUbVRUohhKiYNLqFED6VoK/FwjqXkKCv5e9QqoWfNl1odJd2pnbxkd00yMpweYFXgOZAL2l0CyGqkYqufbas10Eny8JD9NxweQsAioqK+O73Ix6MMvhJPS6EdtLoFkL4VJFZ5YipgCKz3DXwhAMHDnLixAkAevbsicFgICY70611GyONbiGEb+lDwhg4eTkDJy9HHxLm0W27e+2zFedk3oBLmxJVy3KX+8uFX3E2J7CfePI1qceF0E4a3UIInzqhFvJMzn5OqPIlxlM2bNgAWN7r7tqlC2ej6rm1Xho6L0YlhBC+5e61z1aqw9+KojCkS4L17zlvvlnFqKofqceF0E4a3UIInzKgI0ZnwCANPo9Zs2aNdbp//37satGJ9Oj6uLoHoQJHgU062QdCiOqjomufLet10GH+jYOuJz42EoC1a9exY8dOD0cZ/IK9Hq9Kny9CVJY0uoUQPnWRPoKtsVdzkT7C36FUGz/++KN1un+/a1AVPe8OGgFQ5stn6d+jAVUa3UKIaqS8a59dupL/RztJN+rx/1inZ82e7bngqhGpx4XQThrdQcqb70QJ4U1H1QIeyUrmqFrg71CqjRMnTrBnzx4ArrrqKiLCDGzu2J3b9QZOOKQ9DkwdNoHcyctR5NohhKhmXF37bB0HbqPsON3t4urQq2cPAI6kZ7Nq9Y+OqwqkHheiMgz+DkAIUbNkm02sLTrr7zCqndWrV9OhQwcMBgOXtozll32n+FbRs0ynMGr4S8RkZ7ImaTabdDr6duzu73CFEMJrHK9965e9Tec7n6Ne7nnWJM1mg6nY6Z3woVdfeJc76bcUzDLCg1NSjwuhndzpFkL4VIzOwLDwxsTo5Dc/W1V9esX2EfPLE+pbp1Wdjh0JiWxI7M1GRZFHyoUQNYLttW+DIYS/W3e+cB10kr5BdDj/6mjpy/xcbiFrd5Z3r7xmk3pcCO3kbBFBq7QjDBFc4pQwXopszc7ibM6ajP4Op9rYuHEjxcXFhISEcHmr+hWv4AY5x4QQNcUtV7VEr1juRS3/4whFRhkOyxWpx4XQTu50CyF8arcpl/ZnNrPblOvvUKqVnJwcfvnlFwCaxtamUZ1afo5ICCGCQ+3atRl0eXMACgsL+e73I36OKLBJPS6EdnKnW7ikmM0kpuwgJjsTo6qygfJ7AxWiJvLWeWJ7l9ndx81//PFH/vWvfwEQtvNLTMWF5a7rGPtmkHNeiCAWiPW2Y0xVGarQW5/vgfv/TWR4CAD/W7iIc7kxXs9TCFGzaL7T/eKLL6JzcsGMjo7mf//7n0eCEv43BDhoLOK1+RMYv+Q11pqKOVwyX4iqaKGEMz+6Ey2UcH+HUmWBdp7Yvtc9YMCActP2SN7C/oIcu9hzTMUB81mEENoE2vUInF9nDhqL6JG8RfO2Bqsmr3y+sLAwnhrzpPXvOW+97fU8g111qseF8BXNje4HH3yQn3/+mYSECz089u7dm507d9K6dWuPBif8YwiwBIh3mB9fMr+mVzaialTM5JhNqAR3r7CBeJ78/vvvZGZmAtCvXz8UxfklvkfyFiYunEa8Q8+8eod0cs4LERwC8Xrk6joTD0xcOE1Tw7tH8hYWmYxe+XwPP/ww8fGWLf/yzymS9+z1ep7BrrrU40L4kuZGd2JiIsePH2f79u089NBDzJgxg9WrV/PZZ5/RvbsMQxPsFGCOzbTjMoDZTpYJ4a5jaiGPZ+/lmFro71AqLVDPE5PJxLp16wCIjY3lsssuK5NGMZt5dMX7lmmHZY7PMMk5L0TgC8TrUXnXGQUwAyNXvI/ixpBcimoqd1tQ8vkqMbxXeHg4EyZMsP792cZ/tOWpOcfqoTrU40L4mubrxblz57jjjjt4++23ee+99xg1ahTXX389zz//PCaTyRsxBjRTcfW64PQCmuH6wFCA5iXphKgMBaiFEtRfVgL5PKnoEfNeZjMNsjLKNLBdkXNeiMAWiNej7saicq8zCtAwK4NebjSULz6ymwZZGRV+vu7GIla/cCOrX7jR7e9mjzzyCHFxlmHCft6bxsG0LE151qTrou2wliEhYUFfjwvha5U6X/773/8yatQovvzySw4dOsSbb75JYmKip2MTfhDn4XRCOOqgj2Rn/e500Ef6O5RKC+TzxLbRPXjw4DLLG1fycUA554UITIF4PXI3L8frkW3DrrQTyJjsTI/mWapWrVo8++yz1r8/L7nL7c08q4v2SkTQ1+NC+JrmRvfKlSt58cUXue+++7jnnnu47LLL+Omnn/j1118ZO3asN2IUPpTq4XRCODquFjI6ay/Hg/ixtEA+Tw4dOsSuXbsA6Nq1K7fccrPd8jS373Hbk3NeiMDkreuRswawp2Ny53p0Nqqe64WdOsGYMRAbq/nzPfroozRu3BiAxUuWkHI62708bdTU6+IJc/DX40L4muZGt16vJzExka+//hqAgoICHnvsMW677TaefPLJCtYWgW4TcAzXw2GowNGSdEJUxnmzkeVFGZw3G/0dSqUF+nny3HPPWadnTJ9OiP7CpX6TTkd6dH23h7zx92cRQpQvEK9H7sR0Orq+W8OH7WrRyfk1KzERtm6FN94g9+23NX2+iIgInnnmGUssqsqUqS+7l6dN/DX5unjebAr6elwIX9Pc6B44cCCpqWV/21uxYgWXXHKJR4IS/qMCo2ymHZcBjHayTAh31dEZuCWsAXV0Bn+HUmmBfp4sW7bM2qFa69atuOWqltZlqk7Hu4NGoKNsfI4PngfCZxEiWFXlTrEWgXg9qigmHTBv0AhUNxrdqqLn3UEj7LcVEwNJSRARAYD+ppswhIa6Hd+9995Lw4YNAfjqq69ITt5TcZ4On2e0k2U1RR2dPujrcSF8zaN9IJw5c8aTmxN+kgTcBpxwmH+8ZH6SzyMS1UlTJYw3oi6iqeK9L6G+EOjnyZNPPomqWr4S3tWrDXUiLnwh3dyxO1OGTeCEwxdex64wA+WzCCHKF4jXo/JimjJsAps7uj/izeaO3bldb7BsS1Hgf/+DVq2sy8MjI+nWrZvb2xs2bJh1esaMGRXn6RB/Tb8uxuuqRz0uhC9pbnSbTCaMRqPLf6J6SAJaG0IZe/80XrltLP30ISRQsysZ4RnJplw6Zmwm2ZTr71CqLJDPkx07dvDRRx8BEBkewr2929kt39yxO23Da9vFXlsfEpCfRQhRsUC8HjmLqY0hVFODu9S3ip7WhlDWLdsE114LgGrT+7mz0RqciYuLo1cvS7/je/fuZfv27RXmGUhlGgj2qHnVph4Xwlc0N7qHDBnC0KFDrf/uuOMOpk+fTmpqKiNGjPBGjMJPVJ2OHQmJbEjszUZFqbGPUQnPMgNFmCvZh3bgCeTz5PnnnycryzIEzvWXN6dTp452yx1jNypKwH4WIUTFAvF6VCYmNx4pd+WGm2/imhssDXaj0cj0b/6yLnO30X3r0CEoiuXr71dffVVh+kAsU3+rbvW4EL6gudG9bNkyu39ff/01zz//POPGjePmm2+ueANCiBqtmRLG3Kj2NJPH0txmKi7UPP4swOnTp5n+quXRSb2i44F//9tLEQohhHfpdDpemXahw7Nx459jY3IqKactPyxeeeWV1KtXca/j/3fbbdbpRYsWeT7QGqCpTupxIbTy2Dvdv/76K/369fPU5oQQ1ZSCjlCdglLJoauENu+9/wGmkne7r73uWj9HI4QQlTPo+utof9FFAOw6epa335kHwJ8HMwBQFIVrrrmm3G3UjwqnRw/LnfLdu3eTnJzsxYirL0WH1ONCaOSRbgfDw8N54oknOHHCsbsJIYSwd0Qt4OGs4Pmio5jNJKbsICY7E6OqsoHg6rH2/PnzJB/L5JIWsbS/6CJa2XQ+JIQITMF+3fEU23K4fvQo6/zFvxy0Tv+ZksGt3SzXtQEDBliHtHWmV8c467Tto+W2+ZiNxVx6cDv1cs9jVFW3hjWraY6qhUFVjwsRCDQ3us+ePYvZpuMKnU5HVFQUeXl53HPPPR4NTggh/GkIMMdYRLP5EwAYj2Xs2VEEV0c6Ww+kc0mLWACuv+5a9vo5HiGEa9XlulNVg1UTs0xGSzlcdhn0sbwqk3Eynd/+OW1Nt/PIGQoLCwkLC7N7r1un03Fpy1gOn862zvuXTaN78eLFgPPy5pPnrdPHgPnJWyrV+ZsQoqzSV+Z8mYc3h210l+ZG95NPPmnX6FZVlfT0dH777TfOnTvnydiEENVQJ30kS+tdxi2Zf7E7gHs+HQIscTI/vmR+MA0Zs+3AaR7s1x6A66+7jr3bZKQJIQJRdbruVEWP5C08b7K5Tj31lHUydvJEup+PZV3J34VGlS1bfqFv3z60atWKVq1akZKSwqKvvmTI4KvJLSxG+fMWQurUomNTyzvfO3bsYO/evS7L21Y8MHHhNKYMm8Baz33EoNZRiSCp/lUBX48LEUg0N7o/+eQTb8QhhKghTqqFTMjez0nV/Q7BfE0B5thMOy5TgdnAUh/GVBUpp7NJz8qnQXQt+vTpzTt/rafQWLWHVfUhYfSbaHmMc+2UWzV18BZMeQrhK1quO9X5UXNFNfHoivct0wDNmsEdd1gWZmRg/vRTRoZEMs3mBtCatWvp27cPYHnEPCYmhiGDBwMQGRbCwi8+5UDaeWv6r776qtzytosHS3mPXPE+U81mTFX9gNVAqrko4OtxIQKNW43uSy65xO0N7ty5s9LBCCGqv0yzkUWFp/wdRrl6Ac3KWa4AzUvS/eyTiKpu24F0Bl3enPDwcC5tWZ+tB05XvJIQwme0XHc2+iQi/7j4yG4aZGVcmPHEE2Ao+bo6dy5Kfj4N8/PpGRZhTbJmzVpenjoFsDyR2bZt2zLbbdO4jnV60aJFFZa3LQVomJVBL32I9Q57TZZpNrKoKLDrcSECjVuN7u3bt2M2m9FV0JmE2WzGYPBI32xCiGoqWqfn6pC6/Fp8jixzYN4ziKs4iaZ0gWDrgdMMurw5AFe1aSCNbiECTHW87lRGTHbmhT+iouDhhy3TBQUwd651UZzZTOlV7K/t2zlz5gyxsbFcVNLDOcCnG/ZxPq+YR/q3IzQ0FIA///qLAwcOcGUlYmssI1MDEI2eq0JjA7oeF8KWL94jr4hbLeSEhARvxyGEqCGaKeG8E90hoN8FS/VwukCwPSWDoqIiQkND6dKmIXPZ7e+QhAg63nzFoTpedyrjbJTNWNuPPQZ1Su5Qf/YZpKdbF6XqdOhLplVVZc2aNdxR+hg68P33K/jfn2bMwJfTH+frpd/TqG4tXn9jpmX9SsSWJkNkAdBUCeOdqMCux4UING6N052UlERWVhZHjx7lvvvuIz09naNHjzr9J4QQ5dlryuWKM7+yN4Ar6k1Yeqx19d6kChwtSRcs8otMbNpkeRi+cb0ImtWv7eeIhBC2quN1pzJ2tehEenR91KgoGDvWMtNkgtdfByzlcDq6Pj8rerv1fvzxR+v0wYMHue/+B6z3pX//408emLue21//kUWLLL2WV1TetkrzlOHDLPapeQFfjwsRaNxqdHfo0IHIyEgAXnzxRWrXli9rQojKMQHnzcaA7oxGxTI8T+m04zKA0U6WBbqVP/xgne7SpoEfIxEicOlDwhg4eTkDJy/36TAz3rju+OuzVIWq6Hl30Ah4/HGItQx1yP/+B//8gwrogHmDRqA6NIC//PJLNmzYwK5duxg8eHCZEXVUM2QXFF/4G9flbbdeOXnWVMFQjwsRaNx+p3v+/Pn8/PPP6HQ6nn76aXJycpymnTJlikcDFEJUL02VMJ6MaMGsvCMcD+CeT5OwDM8zB/vOdo5j+eIbjMP2rPzhB15/zTLW7VVtGvo5GiGEo+p43amMvy77F7kPXUUUgNEIkycDlnJYMGyCZczsZW/brZOXl0ffvn2tf7vzI4Or8rZll+fXb2j/MNVQU10oT9QO/HpciEDiVqP73//+N5MmTeLGG2/EbDZz/fXXYzSWHefVbDZLo1sIUS4DOhrrwzAEwbtxScByQyijhr9ETHYma5Jms8FUXOk73J7qyKOy29m37x8OHUqhVasELmkeQ506dSpeSQjhU56+7gSjIV1aEhUTA8C2X5NZ03kIa1Jms0mno2/H7h7Ny7G81y97m853Pke93POsSfJOnsHOoAueelyIQOFWo/uff/7hzjvvBMBkMtGvXz/SbTqzEEIIdx1WC7j7fPAMLajqdOxISARg49I3UYP8ebrvV6zg8f/+B4NeYdxTT7Imz98RCSEcVbfrjhaRYQaGXt0KAKPRyNztZ0hN7M3GpW96LU/b8t6w4j10rTsDeDXPYHZYLQyqelyIQODWO9229Hq9NLiFECJIzXnzLYqMlm/wT/z3MRrXreXniIQQ4oIhXROoHR4CwCeffkZqpvwyKIQIfpob3UIIURUd9JHsiO1GB32kv0OpkQ4fPsw3v6UAEBYWxoP9Ovg5IiGEsIiKimJoV8swtcXFxUx7ZbqfIxLOtFdqST0uhEbS6BZC+NQptYjXcw9zSi3ydyg11lc/HyQzx9L5zb86xnFx8xg/RySEqCkUs5nElB302bGR3qpq90V02B13EGlzl/vIkSP+CVKU67S5WOpxITSSRrcQwqfOmov5tCCVs+biihMLr8grMvLJhn3Wv0cO7IhOhsIRQnjZYNXEQWMRr82fwPglr7HWVMxhYEjJ8oceesCa9t158/wRonDDWbNR6nEhNJJGtxDCp2rr9PQKqUttnd7fodRoq7YfY8dOS0c4bePqMPyee/wckXaOYxAH45jEQtQUPZK3sMhkJN5hfjywBHjq8su54vLLAdh34hx//73D1yEKN0WiSD0uhEbS6BZC+FQLJZz5dS6mhRLu71BqNNUMTz8zwfr300+PqdL2gq3BG2zxChHMFNXEoyvet0w7Liv5/8URI6zzVv51tMp5lg6tuPqFGynOy7JOm4plXOmqknpcCO081uhesGABa9eu9dTmhBDV1D+mPHqe3co/JumR1t82bPyJ5OOZAHTs0IH4eMd7UEIIUXUXH9lNg6wMl186lchIou66C4C8QiMbdp/0XXBCs/1qvtTjQmjksUb3iRMnpMMLIUSFijGTphZRjNnfoQjgz0MXhoC85pq+foxECFFdxWRnlp9g2DCIigJg/a4T5BfVoIHJg5DU40Jo57FG93PPPccDDzxQcUIhRI0Wp4QxJbI1cYo80hsI/jqUYZ3ud801foxEBJpAeAQ/EGIQFatoP52Nqlf+BmweLV/xZ9UfLfeXmnK8xulCpR4XQiN5p1sI4VO1dAqdDLWppZPLTyDYe+Ic+UVGAK7p28evsYgLasqXd1Ez7GrRifTo+qjOFl56KXTpAsCff/7JgbQsn8YmtJN6XAjtDO4keuONN9ze4FNPPVXpYIQQ1d8hUz5Dz//t7zBECaNqZseRs3Rt25AmTZrQvP5+jmbk+DssIUQ1oip63h00gucXTkPF/o6P+ZFHKB2w8MOPPobG1/shQqHFIbVA6nEhNHKr0X3ZZZe5tTGzWd7tEEKIYPNXSgZd2zYE4LKE+tLoFkJ43OaO3bldb2CWyUiz0plNmsD99wOQk5PDlwu/ostoaXQLIaoftxrd18h7fkIID7lIH8FndS5h+Pmd7JOeTwPCXykX3uu+rFV9lm477L9ghBDV1reKnmU6hVHDXyImO5P2vVswJNwy7NTcuXPJzs72c4TCHRcptVgQ01nqcSE0qPTLGK1bt2bgwIGEh8sYfUII951Ri/k4/wRn1GJ/hyJKHD6dzalTpwBIbBGDXtFVsIYQQlSOqtOxIyGRnT0GMujhhwDLXe7XX3/dz5EJd50xSz0uhFaaG90xMTGsWbOGf/75hxUrVhAXFwfARx99JBdMIUSFMszFzMs/ToZZKutAsm79BgAiw0K4qEldv8YihKj+hvVoQ1iYpZPAt99+m4yMjArWEIEiw2yUelwIjdx6vNzWrFmzKC4upnnz5uzZs8c6/6uvvmLmzJk8/fTTHg1QOKeYzSSm7CAmOxOjqrIBnPcKKkSAiUChg6E2e4w55MlRa+Wrc9o2H5PJiKKaUBU9a9eu485hdwBwWUKsF3IWomYJ9nram/HXjwrnusssb3bb3uV2zHOTzvVTN8FevsHGtrz1KmQYotkt9bgQbtPc6B44cCDXXnstJ06csJu/f/9+WrRo4bHAhGtDgDnGIprNnwDAeOAYMApI8mNcQrgjQV+Lr+omckvmX+w25fo7nIDgq3N6sGqydGJkk0/6zAd5d9AI5m9dZ03XOaG+B3MVouYJ9nra2/Hf0aM1oQY9AG+99RZnzpxxmef85C1s7tjdbv0eyVv4sCAnaMs32DjumzH6SI7XTWR25l+8LfW4EG7R/Hh5ZGQkeXllO02IiYmhsLDQI0EJ14YAS4B4h/nxJfOH+DwiIbTZb8pjQOYf7JfOVwDfndM9krewyGQsk09sVgYTF07jiiOH+Wf/fgA6NK1HZGSkh3IWomYJ9nra2/E3bdrUepc7OzubN954o9w8Jy6cRo/kLdZ5PZK3MHHhNOIdRswJlvINNs72TYgpj/jMP5hlypPyFsJNmhvdmzZt4t5777X+bTab0el0jBs3jvXr13s0OGFPAebYTDsuA5jtZJkQgaQIMymmfIqQIQZ9dU4rqolHV7zvMh8zMNNkZN1ay93uEL1Cr549qpirEDWPv+tpU3Ehq1+4kdUv3IipWPuNEG/E7xjTuLFPW+9yz33nXTLPnCk3TzMwcsX7KGYzitlc7rWsMvEJ11wdDwpmwk35KJilvIVwk+bzZNy4cYwYMYIVK1YQGhrKjBkz2LVrF//617945plnvBGjKNELaIbrnaYAzUvSCRGoGiuhTIhMoLES6u9Q/M5X5/TFR3bTICujwnxS16y1zhs6dGgVcxWi5gn2etrb8Tdt2pQHH7CMy51XaGTmrNlu5dkwK4NeZjPdjUU0yMrA1Zve3ijfqv6QEcxc7RujEkpGZAKqEhrQx7MQgURzo3v37t20a9eOn3/+maVLlxIZGck333zDZZddxqFDh7wRoygR5+F0QvhDbZ2eXiF1qa3T+zsUv/PFOW0qLuR4yXt4FTm2ehW5BZbeaG//v9uoXbt2FXKuHvQhYQycvJyBk5ejDwnzdzgiwAV7Pe3t+MePH2/tsXzptsOcPXvW7W01xhz05RtsXJWjqtOTF1IXtaQel/IWomKaOlIzGAz88MMPjBw5kmnTpnkrJuFCqofTCeEPB0z5XH/uL3+HERB8dU67u/6RvHzW7zrJjVe2oHbt2txxxx189NFHVcxdiJoj2Otpb8bfrFkzHnrIMi53bmExX/96SNO20tBhcjNtoJZvsHFVjqGmfJrb1ONS3kJUTNOdbqPRSGJiordiERXYhKV3TleDM6jA0ZJ0QojA56tz2u18dDpW/nXUOr/0C7KoGeSuftW5e65tMYT6pawr2sfevCaNHz+e0FDLa0VLtx4mO7/Y7TxPR9dnk04n34N8TMpbCM/R/Hj5559/zoMPPuiNWEQFVCzDYZROOy4DGO1kmRCBpK0+gg31rqStPsLfofidr87pivLRAWP0BlSdjgNpWRxIOw/A1VdfzcUXX1zF3IU/SUPat9w+p8sZf9qfvHVNat68ufW7Y1ZWFt/8muJ2njpg3qARqDqdfA/yMVflXaiP4Ei9KynSR0h5C+EmzY1ug8HAo48+yrZt25g3bx5vvPGG3T/hXUnAbcAJh/nHS+bL+JQi0J03G1laeJrzZqO/QwkIvjqnXeWTEV2fKcMm8K1y4R37H/46Zp2WH1mF0CbY62lX8Z+oU4f3H3mEHa1ba96m7V3ut96eS3ZJ3xEV5XkcmDJsgt043cFevsHGWXnrzUbUwtOMNBulvIVwk6Z3ugEuvvhi/vzzTwDatWtnt8xsliGAfCEJWG4IZdTwl4jJzmRN0mw2mIrll0YRFE6rRczKO1pxwhrEV+e0Yz5rv5tLyJiPUBU9fH3hR9P1u07wYJ/W1KpVi+HDh/Pss89SWFizeu0VoiqCvZ52jH/zjx8y/eefGHnxxVx/5Aht27aluLi4wu0AxMfH88ADDwBw/vx5Zs95k8v/+2GFea5Jms0mnY6+Ng3u8tIGU/kGG6flnXdUylsIDTTf6b7mmmtc/uvXr5/mAB577DFSUlLIz8/n119/5aqrrnKZ1mAwMHHiRA4cOEB+fj7bt2/n2muvtUtTu3ZtZs2axeHDh8nLy2Pz5s1ceeWVZbY1adIkTp48SV5eHj/++CNt2rTRHLs/qTodOxIS2ZDYm42KIhc+ETTCUeikjyRcRva046tz2jafn/QGS4PbQU6Bka+/sdy/iI2NZfDgwV6KRojqK9jr6dL4Nyb25qFP5ltfNWnRogW33HKL29t54oknrHe53377bTIzMyvM01pm5TyGH+zlG2xsy/s3xUAHqceF0MSvZ8vtt9/OzJkzmTRpEpdffjl///03q1atokGDBk7TT506lUceeYTHH3+cjh07Mm/ePJKSkujcubM1zYcffsiAAQMYPnw4l1xyCatXr2bNmjU0adLEmmbcuHE88cQTjBw5kq5du5Kbm8uqVausw1gIIbyntb4WS+tdRmt9LX+HUuOpDuPP2o5H+8H771nTSYdqQtRcd/2rLYMdGtkjRoxwa93atWtb0xYWFvLmm296PD7he62UcKnHhdDIr43uMWPG8MEHH7BgwQL27NnDyJEjycvLsz6G5Gj48OFMmzaNlStXkpKSwrx581ixYgVPPfUUAOHh4dx6662MGzeOTZs2cfDgQSZNmsSBAwd49NFHrdsZPXo0U6dOZdmyZezcuZN7772XJk2alHs3JzQ0lKioKLt/QgjtDpryuSXzLw6a8v0diijHTz/9xP79+wHo37+/3Q+XQoiaodtFjbi3t+VVQpPJREZGBgADBgwgISGhwvUffPBB6tatC8Bnn33G6dOnvRar8J1DaoHU40Jo5LdGd0hICFdccQVr1qyxzjObzaxZs4Zu3bo5XScsLIyCggK7efn5+fTs2ROwPH5uMBjKTZOQkEBcXJxdvllZWfz2228u8wVLJyBZWVnWfydOOHbhUf2YHO6CCeEJBajsNuVSUE0eBqzO58nnn39unb755pvtlilmM4kpO+izYyO9VRXFB316+CNPUfOUOc7cXBaIKnvOGAwG/n3ffYy7pbN13rPPPsvrr79u/buiJ2D0ej2jR4+2/j1z5kxNsYvAVd3qcSF8wW/1Rf369TEYDJw6dcpu/qlTp2jcuLHTdVatWsWYMWNo06YNOp2O/v37M3ToUOLi4gDIyclhy5YtTJw4kbi4OBRF4e6776Zbt27WNKXb1pIvwCuvvEJ0dLT1X3x8fKU/uxA1WUMllCcjmtNQCfV3KKICS5cutU7bPgk0BDhoLOK1+RMYv+Q11pqKOWgsokfyFq/F4o88Rc3j7Dg7XDK/vGWByJ1z5tFHH+XkyZNs3bqV1157jRtuuIF///vf7N27lw8/eI+IMEt/u18u/IrXX3+dBQsWWDtQe+CBBzAYXPfHO3ToUFq2bAnAihUr2LNnj9c+q/CthroQqceF0CjQf6S1M2rUKPbv38/evXspKiri7bffZv78+ajqhV/ahg8fjk6n4+TJkxQWFvLEE0/w5Zdf2qWpjKKiIrKzs+3+CSG0q6MzcEtYQ+roNA+eIHzs77//5vDhw4ClE806deowBFgCOP7sGA9MXDjNK41gf+Qpap7yjrMlwNflLAu0hrc750ytWrV47bXXiIuL46qrruLpp59m+fLlzJ8/n9Y2w4Jt2ZfGiEdGApYbFKU/xjVu3JibbrrJZQxPP/20dbqyQ8pW5yeJgpnU40Jo57dGd0ZGBkajkUaNGtnNb9SoEWlpaS7XGTJkCJGRkbRo0YL27duTk5PDoUOHrGkOHTpEnz59iIyMpFmzZnTt2pWQkBBrmtJta8lXCOE5+0159Mn8nf2mPH+HItzw7bffApZXgm64/nrmlMx3rDwUwAyMXPG+Rx/7VsDneYqap6LjTFfOMoDZTpb5i7vnzPUDBxAZGelyOz/+uIYn529h0qI/yM+/8O7u+++/b5121aFaz5496dKlCwDbt29n3bp12j+ICFj71Xypx4XQyG91RHFxMX/88YfdMGM6nY5+/frxyy+/lLtuYWEhJ0+exGAwcOutt9o9AlkqLy+PtLQ06taty7XXXmtNk5KSQmpqql2+UVFRdO3atcJ8hRCipiltdAM8eMstNMN1xaEADbMy+JfewMDJyxk4eTn6kPJHhdCHhJWbthe4lWevKjS6lQpiENVfRceZjgsNb0cK0LxkG55SlTu87p4zI2+91TrvrrvuYujQocyZM4d58+bRs2dPrr/hRpKPlx3ea82aNaSkpAAwcOBAhg8fzuDBt9CrQ2P+3fciftqw3q6RXdm73P7iOKqDEEJ4gl+fC5k5cyaffPIJv//+O1u3bmX06NFERkYyf/58AD755BNOnDjBhAkTAOjSpQvx8fFs376d+Ph4XnrpJRRFYcaMGdZtDhw4EJ1Ox759+2jTpg2vvfYae/futW4TYPbs2Tz//PPs37+flJQUpkyZwsmTJ+2+XAohvKONvhZvRbXn8ey9HJCeTwPezz//zJkzZ4iNjeXqQYMgNBSKispdJ85sxlN9FMe5ma4xcqdbVJ67x5m3t+EJbsURHk73G28A4MyZMyxevBij0UhSUpI1iasfoMxmMx988AHTpk1DURQ+/fRThxRtrFNHjx7lq6++0vgJfK/0Rw7hntZKOHPqdpR6XAgN/Po01KJFi3j66aeZPHky27dvp3Pnzlx33XXWISWaN29u7QANLEOCTZ06leTkZJKSkjhx4gQ9e/bk/Pnz1jR16tRh7ty57N27l08//ZSff/6Za6+9FqPRaE0zY8YM3nrrLd5//322bdtG7dq1ue666ygslF80hfC2HLOJTcXnyDGb/B2KcIPJZGL58uUARERHQ9++Fa6TqnN1T1C7VDfTpbm8DylExdw9zry9DU9wK47rriO8dm3A8jSL7Xckd8yfP7/cvm327dvHvHnzuO6666wdr4nqQ+pxIbTzew8Ic+fOZe7cuU6X9XX4cvfTTz/RqVOncre3ePFiFi9eXGG+L774Ii+++KL7gQohPCJNLWJaboq/wxAafPvtt9x3330A5AweTMSqVU5/sVWBjOj6/FxcSG8P5b0JOIalA6jy8tyUl+WhHAOfPiSMfhO/BmDtlFvlEVgntJZRRcdZ6XMUzn7aUYHjJdsIBO6cM0V33UN4yd9LlizRnEdaWho9e/ZkwIABKIqCojfQpt9w0rMKeOfJoRw/etjtbdnuq/XT79Ici/C9U+ZiqceF0ChQ+v0QQtQQoehI0NciVO5MBo3Vq1dbO1IqvuUW0OnKjM6qYmmQzBs0AtWDd7pVYJTNtC/yFMHJtn8AReO7+RUdZ+ZylumABcMmoAuQ/gAqPGfCwtDdaHmUOjMzE6Xn4wycvJyQiGhNfRvs2LGDN954g9dee43X35jJ4l8OsWH3SVJTA+Wev/AWqceF0E4a3UIIn2qrj+DHelfQVh/h71CEm/Ly8vjxxx8BqBcXx/irruKEQ5rjwJRhE9jcsbvH808CbgOf5ilqnvKOs9uAW10sC8RjsLzP8tXUDwmrZWlUL132HUZV+kMQ2rRRakk9LoRGfn+8XAhRs6SY8rnj3A5SpPOVoPLtt99y8803AzDg5ZcZ9+FHXNToasJOnmBN0mw26XT09WLDIwlYbghl1PCXiMnO9EmeouZxdpxtMBVb7xgH0zHo6pxZee2FV/e+/vpruMr5sF9CuHJYLZB6XAiNpNEthPCpPFT+MNac92+ri++++w6TyYRer6d///70798fgD3HM3ln62JUHzxSqup07EhIBGDj0je9np+omRyPM9Xkelmgc4w3LCyMq9s1BCyPlq9Zu45/SaNbaCT1uBDayePlQgifqq8LYWStptTXhfg7FLdUZbxcX6lorGtPyMjI4OWXXy7Ty3GHpvWYMnmSV/IUQnjWgAH9iQyzXHuXfbdcehYXlVJfZwiqelyIQCCNbiGET8UqITxQK55YRSrrYPPiiy9Sr149rrvuOl57/XVyCyxf2O+5+y7atGnt5+iEEBV5+MEHrdNff/21HyMRwSxWJ/W4EFrJ4+VCCJ/aZ8qjy9nf/B2GqKScnBxWrVrFmnUbaHDFTfy770UYDAaenzCev9ftICY7E5PJiKKaUBW9V2NRzGYSUyx5GlWVzWD39wbK9t7sbF1fxauF42fbgOvPIjEId7RvfxE33DAIgFPn8li1+kePbVv2k0VNKYd9ar7U40JoJI1uIYQQlbJ062GGdk0gOiKUu++8k3umdIR//mE8kD7zQd4dNIK1Xsp7sGpilslIs/kTABgPGAGDzd/HsAydlOSw7hBgjrHIbl1vx6uFs/hcfRZvcVa+vo4hEMqhOhk9apR1+tuthzGZTOWkdp/sJwspByFEeeTxciGET7XS1+KbOpfSSl/L36GIKsorMrJt2XoAdHo9vPCCdVlsVgYTF05jsOqZL/a2eiRvYZHJSLzDfMf71PHAEixfhksNKZnnuK4349XCVXzOPou3uCpfX8YQCOXgbfUiw7iydQNq166ted327dvzwAMPWP/df//9tG3b1mX6upGh3HP3XQDkFhTzw1/HKh23rZqwn9xR08qhlRIu9bgQGkmjWwjhU/lmld3GHPLN1fGhu5pFUU1c+tQjmNPTLTPuvBPat7csA8zATJPRoxWNopp4dMX71jxs6RzTlvw/u2RaAeY4LLNN6414tagoPij5LGbvjatcXvk6lqfXYsDNcvBiDN5Wt25d3nqoBy/f1YWD+/cxfvx4oqKiADAYDHTt2pX//Oc/dO7cucy6I0eOZM+ePXz00UfWfx9//DF//PEHLVq0cJrfTVe2IDw8HIAVfx4lr8joNJ0WNWE/uaMmloPU40JoV52uAUKIIJCqFjIx9yCpamD2BC7cd/GR3dQ/eQTdjBmWGYoCL75oXa4AzYFeHs6zQVaG25WXbQy9gGa4rvhK0/bRh1h7g1e81Bu8M+7G18uLje6Kytcb+9SR2+XgxRi8bcKzz9Ag2nKXMDY2lmnTpnH48GFWr17N2bNn+fXXX3n77bfZtm0bzz77LDqd5SelRx55hHfffdfpNqOiopg+fXqZ+WEGhRuvsDTGi4uLWbrtsEc+Q03YT+6oieWQai6SelwIjaTRLYTwqRB0NFZCCSlzX1IEm5jsTMvEO+/AqVOW6dtvh27d7NLFeSNPjeI0xNEY7zVqyxMI8blbvp7cp5Xdtjdj8KaEhAT+85/HADCaVOu71TExMQwYMMB6xxssd71feeUVfvjhB55++mnmzZtnXTZv3jweeughHn74YU6fPg3AsGHD6OZw/vVPbErdSMuPR4uXfE16VoFHPkdl91MwDMOoRXU/Xp2RelwI7aTRLYTwqXb6CH6O6UI7fYS/QxFVdDaqnmUiLw9eftkyrSjw0UcQduEOcao38tQoVUMcaT74IulsbPXKxufJcdrdLV9P7tPKbtubMdiyLd+qPPlQup353/1MWMn5seSXQ3S6JJH58+djNFoe+U5NTeV///sfc+fORVUtj+8OHDiQ1157zbqt6dOn8+ijj/LRRx/x4YcfMnHiROuy2bNnW++Mh4WFMeTqBOuyWbNmVzp+R4G2n/ylJpZDW6WW1ONCaCSNbiGETx1RC7j//C6OqJ652yL8Z1eLTqRH17cMifPOO7B1q2VBhw7w/POowFFgk7fydINtDJuw9Cbsal1rWp1/7t4EQnwVla839qkjt8vBizF4S4f4uvTu1ASAzJxCvtp8kAMHDvLAAw/QqFEjEhISaNKkCXfffTf//e9/6d+/P6mp9s21GTNmMH78eLt5H330ETt37gSgS5cu3HXXXbRr147NP/9Es1hLR23r12/gr+3bPfZZqvN+0qImloPU40JoJ41uIYRP5ZhNbCo+R47Zv71Ei6pTFT3vDhphmTaZ4IEHoKgIAPOzz6K79FLG6A0eHafWLk+HZY4PXatYOlcrjUHFMnyPs3Xt0vqp0V1RfACjwavxlVe+djF4KD9nd+ndLocq5uMPIwZ0tE5/tvEfuw7Nzp49y+HDh+3Sr1+/ns6dO/Ptt9+Sm5vLpEmTmPD8C2U+i8lkYsyYMdb1Zs2axR9//EHnSy8FoLDYxPMTX8CTvLGfglFNLIdcVKnHhdBIGt1CCJ+K0YVwb3gcMboQf4ciPGBzx+7crjdwAmD3bpg2DQCdwUBa0gqWh3q+gWOXpw3Hr38Z0fWZMmwC3yoXBhNLAm6DMus6S+sPruI7XjLfF+P9uipfX8YQCOXgaUOHDqFjM8vj+3v27GWlm8N2nT59miFDhhAdHc1LL73kMt2aNWv47rvvAGjQ4MJQZEfSs3ni4838VvokigdVx/1UGTWtHGJ0BqnHhdBIGt1CCJ9qpITydGRLGimh/g5FeMi3ip7WhlDG3j+NV/cVkrJrNwBxCU348n+f065dO6/m+cptY+mnD6G2PsT6d/+wCO4b8xGbO3Yvs24S2K1bXlp/cIyvnz6EBHz7xd1Z+fo6hkAoB08aOWKEdfrZ5yaiauyFvvT97vI8/fTTFBcXW//+eP4CHv9oM4dPZ2vKS4vqtp8qqyaVQ0NdiNTjQmgkjW4hhE/tMeWSeOYX9phy/R2K8CBVp2NHQiLrOvbk7sf+i0m1NCiGDB5McnIyn376KW3atPFKnhsSe7NRUTAqivXvn/QG1HLuWtuuW1Faf3D8bP54NFVi8Jy6devyr39ZBo06eTaXlatWeyWff/75hwcffJC1a9dy5513MuKRkRQWe/8R4Oqyn6qqppTDXjVf6nEhNJJGtxBCCI/6/Y8/eX3Z35zPs7zfrdfrGT58OH/99RedO3f2b3BC+MGgQddjMBgA+GXfKa/m9dlnn9G/f38WLlzo1XyEEEK4TxrdQgifaqmE80WdS2iphPs7FOFF63ae4N431zHhuec5c+YMALVr1+bl0qHFhKhBbrn5Juv0ln1pfoxEiKprqYRJPS6ERgZ/ByCEqFmMmEkzFWIs09e0qG4Kik3MeO113npzDjt27CAhIYFBgwZxxRVX8Mcff/g7PLcoZjOJKTuIyc7EqKpsILh6IdYSv2Nafw2dVt2EGhSuHTgQgHO5hSQfz/TbcSX7WHiC0Sz1uBBaSaNbCOFTx9VCnsr5x99hCB/Kyclh+vTpvPfeewA8//zzDBkyxM9RVaxH8hY+LMih2fwJAIzHMh7vKIKjc6QhwBxjkVvxu0o7P3lLwHQwF6wuS6hv7Un8t/2n6bZ7C+/74bgarJqYZTLKPhZVdtxcJPW4EBrJ4+VCCJ/SA3V0BgKr2yrhbQsWLOD48eMADB48mEsuucTPEZWvR/IWJi6cRrxDD9PxwBIsjdRANgRLnPEO853FX17aiQun0SN5i7fCrNZMxYWsfuFGmmVvt87LXLPBL8dVj+QtLDIZZR8Lj5B6XAjtpNEthPCp9vpI/oi9mvb6SH+HInyoqKiIGTNmWP9+/vnn3VpPHxLGwMnLGTh5OUpI5cf8tt2OvoLtKGYzj6543zLtuKzk/9lOlgUKBZhjM+24DC7EX1FaMzByxfsoGoe3EhaKonDzzTcDkJubS7/XLHeZfXlcKaqp3OPZ1T4u/dFg9Qs3Yiou9HBUVRfo8VVnFykRUo8LoVGgfmcQQlRTx9QCHsvawzG1wN+hCB/74IMPSEuzdCJ122230b59ez9H5Fwvs5kGWRm4ettVAZoDvXwYkxa9gGa4ruBt43cnbcOsDHpJo7tSrr76aho1agTAn6t/pMHp4z4/ri4+spsGWRkV7uOeqveHFhPVw3G1UOpxITSSRrcQwqeyzCZWF50hyyxf8GqagoICXn/9dcByB/C5557zc0TONXazc6A4L8dRWe7GFachrbtlIuwNHjzYOr1r6VK31vH0cRWTnelevgHww4oqd6+DQhZSjwuhlTS6hRA+VU9n4PawRtTTST+ONdG8efPIyMgAYNiwYTRv3tzPEZWV5vJepL1UL8dRWe7GlaohrbtlUlVaXgMINIqT2Esb3UajkdUrVrq1HU8fV2ej6rmXr/RkLsph+zh/tNEk9bgQGkmjWwjhU02UMKZFtaWJElxfqIVn5Obm8uabbwJgMBh4/D+P+TmisjbpdKRH13c5hJMKHAU2+TAmLTZh6ZXanfjdSXs6un6ZoaWCuXHsKx06dKBt27YAbNq0ie8zMyt1XFW1rHe16FRhvqej6/OzUn63WLLPRSmpx4XQThrdQgif2m3KpU3Gz+w25fo7FOEn7777LgUFlncBH3zwASJCA+tuiarT8e6gEego2xgt/Xu0k2WBQsUy/FTptOMyuBB/RWl1wLxBI1DlLqhmA0vG5gZYtmyZ344rVdHz7qARdvnY5iv7WGgl9bgQ2kmjWwghhE9lZGTw6aefAhAdHc21lzXzc0Rlbe7YnSnDJnDCoSFyHLiNwB+nOwlLnCcc5juLv7y0U4ZNkDGcK6lHjx7W6fXr1wP+O642d+zO7XqD7GMhhPATaXQLIXyqhRLOB9EdaaGE+zsU4UezZs2yTg/u0hIlAO+ybe7YnbbhtRl7/zReuW0s/fQhJBD4De5SSUBrQ6hb8TtL28YQKo2xKuje3VJ2WVlZ7Ny50zrfX8fVt4pe9rHwCKnHhdBOGt1CCJ9SMVNkVlGlN+Qabe/evXz//fcANK4bQY/2jfwckXOqTseOhEQ2JPZmo6IE7CPlrmiJv0zaAPwhJFi0aNGC+Ph4AH799VdU1b7k/XVcyT4WniD1uBDaSaNbCOFTx9RC/pO9l2OqDAdT082cOdM6fevVrfwYiRCe1b17N+v0li1b/BiJEJ4n9bgQ2gVW7zVCiGpPB4Sgoxiz/EZuo3Q4lupAMZtJTNlBTHYmRlVlA847h1q3bh07duwgMTGRDk3r8WjXLrzz8yaf3012jNexp+5AE2zx+optuZiNxVx6cDv1cs97tYxs8zSZjCiqCVXR073bhUb35s2bvZK3J5RXZhvwTWeB7l4vROCQelwI7aTRHaSq0xd0UbN01EeytN5l3JL5l/R86gfevnb0SN7ChwU5NJs/AYDxWIakGkXZd1aHAPFvvAGffALAW6OeYNzPm5ym9XW885O3BOS7rkOAOcaioInXV5yVC588b532RhkNVk3MMhnt8kyf+SDvDhpBt2dvB8BkMvHrr796LE9Pcha/Y5l5+1x0dTz78hogtJN6XAjt5PFyIYRPHVcLeSp7H8flsbRqp0fyFiYunEa82f7eRzywBMsX7FJDSubVW7gQUlNLZg4hPiGhTFp/xDtx4TR6JAfWY8GlZRbvMD9Q4/UVV+Viy9Nl1CN5C4tMxjJ5xmZlMPH7t7jk4osB2LFjBzk5OR7J05NcxW/L2XnrSeUdz766BojKkXpcCO2k0S2E8KnzZiNLC9M5bzb6OxShgT4kjIGTlzNw8nL0IWFllitmM4+ueN8y7bis5P/ZJdMKMKd0WVERvP12SSZ6lFGj7NJ6S0XxmoGRK95HMQfGw5N2ZeZkWaDF6yvllYtjOnfLqPRpkNUv3IipuGyjQlFN5R87V1+NXq8H/P8+t7PPUl78thzPW0+q6Hj2Vr7CM6QeF0I7uZ4JIXyqjs7AjaH1qaOTt1uqk15mMw2yMnD15qwCNAd6lfxrhk0FNG8e5OVZph98EKVOHWtaf8bbMCuDnqrJi1G4r0yZOSiNt1cNa3RXVC62PFFGanEh+S/dQoOsDNf7wmZ87kB8n/viI7vLjd+W7XnrSe4cz96+BojKk3pcCO2k0S2E8KmmShizo9vTVCl7t1QEr8ZudqcTrzcQ5zjz7FlYsMAyXbs2PPwwgDVdiN7zVZW78cY5NNAquuPvLWXKzAV3P1d14W652KpqGVWYZ4A3umOyMzWv4/iZlSqeB+7ut8rsX+F9Uo8LoZ00uoUQPrXHlMslGVvYI52vVCtpLu8Zl02X6mzB7NkXpp94AgwGzoSFMWvmG3wzbiCv3tOVunXreiDSC3G4IzVAegZ3WmZOuPu5qgt3y8VWVcvoTFgYNG7sfKFeD127ApB+/DhHjx6tUl7ecDaqnuZ1KlPOntiep/MVniH1uBDaSaNbCOFTKpCPKkPCBDnHd0U36XSkR9d3uV9V4HR0fTbpdGzC0kOxXdr9+2HZMst0s2acGzeO6Vu28Ph//0OoQU/nhPp8v/Rrfnvjbpfv2mrhbrw/K/oq5eMJSkgYtV5a6nb51iROjyUXPFFGkZGRvLt7N6SmYl66FC691D7BJZdAVBQA6wPwLjfArhadyj2WbKnAUSzl7EkV7Tdv5Ss8Q+pxIbSTRrcQwqeaKWG8FdWeZvJYWrWi6nS8O2gEOsp+kVaxjOs6b9AIVJ0OFcuQQDimnTnTOln35Ze5/PLL7bZz1ZVXsnr1aurUqePTeAOBquh5d9AIy7TjMioXr78elfckl8eSk3Se2Ke33XorrVu3BkB3882wfTssWQLduoHBgGr7aLmfO1FzpbxjyS5dyf+jK0hXqRhwvd+8ma/wDKnHhdBOGt1CCJ9S0FFbp0epYY/B1gSbO3ZnyrAJnHBo1BwHpgybYDdGchJwG3DCNuHGjRT9+afdunv37WPqkj84l2u5s92lSxdWrVrFpZdeiq6KDWIt8QaCzR27c7veYF9mBG68vuL0WHLgqTK65+67ys689VbYsgXOnsU88QXr7EB8n7uUq2PJ1nEs5eqt8bJd7Tdv5yuqTupxIbSTbgeFED51RC3g/qzd/g5DeMnmjt2ZFl6bx4eNJyY7kzVJs9mk09HXSWMnCVhuCGXU8JesaWNefpnFX38NwIIFCxj15FNcPeZTjmbk8PKtHWnQoAFdu3Zl+/btnDlzhj3pxfyyL411Oh3mSvRKrSXeQPCtomeZTrErs0CO11ccj6X1y96m853PUS/3vMfKqEF0OL17/wuA/fv38+abbzJhwgTi4kq6+4qKQl/yaHlubi5///13lfLzNsdjybHMNpiKvX6n2dk1wBf5iqqRelwI7eROtxBCCI9SdTp2JCSyIbE3GxWl3Md5HdMu+eYbBgwYQJ8+fbj//vvJzbV01HMkPYcB115Henq6dd3Y2Fh6tm/M2Fs68+03X1OvnvYOorTGGwiCLV5fsS2XDYYQ/m7d2aNl1PfiJiiK5WvTZ599xttvv0279h15Y9nfrN91gtS0NGvarxYtxmgM/DGMyy0zP8Tgy3yFEMKXpNEthPCpTvpI9sb2oJM+0t+hiAC1Zs0aNm7cWGb+rl276dSpE6NGjSIpKYmzZ89al91wwyC2/voLbeOq/r63EM70u6Spdfrzzz8HID8/n9V/H2d60nZatL6IB9/ZwFOf/MITo0b7KUohvE/qcSG0k0a3EMKnUtVCXso9SKpatd6nRc2Unp7Om2++ydChQ2kUF88LC7dxPq8IgISElsz8dzd6dXAxnJMQldSqUTQtG1oeHd+8eTMpKSlO0x0/k8uuo2cpKCjwZXhC+JTU40JoJ+90CyF86qzZyMKCtIoTCrcpZjOJKTuIyc7EqKpsoGb0+ms2m/lt/2n+88Em/ts1kquvvppQg54nBl3CNJ3Orky0DBNlW54mkxFFNaF6eOgwT+6zYN7/jrF7a8izqpSRYjYzrEWo9e8vPvusyjFU5bgK5v0tqgepx4XQThrdQgifitLp6RJSh63F58k2m/wdTtAbAswxFtFs/gQAxmMZ/3YUNaf33/SsAvr2G8zK75fTp09voiNCOdiyKXEOZTI/eUuFvVcPVk3MMhntyjN95oO8O2gEaz0Uryf3mbN4g2X/uyoHd/aTFlUpoyHAHNVIs8TmlhlFRTy3aBFpbqxbZjsOn7Uyx1Uw729RfUg9LoR28ni5EMKnmivhvBfdkeZKuL9DCXpDgCVAvMP8+JL5Q3wekf8UFxfzw6pV1r8bdelitzwemLhwGj2SXY+d3CN5C4tMxjLlGZuVwcSF0xisVv3Lpbv7zFRcyOoXbmT1CzeiFjt/hNNVvMGw/8srh4r2kxblllHz5rx5330sWLCAw4cPc+7cOVavXs1zzz1Hjx49uL9RI5Y0bEj84MFQ2kP5998Tl5mpqXxdfVatx1Uw729RvUg9LoR2cqdbCOFT+0x5dDnzK1ny63iVKMAcm2nHZSowG1hKzXn0dNtvW63TSpcuMH/+hb+xlMPIFe8z1Wwu05u1opp4dMX71rR2y0rWnWkyskxX+d+qPbnP3Il3NrC8EsOoeZs75eBqP2nKp7wymj4dnnmGxx3mDxgwgAEDBrje6GefaSpfdz7rTJORJMrf5+7u75p0vgv/kXpcCO2k0S2E8CkjZs6aA38onUDXC2hWznIFaF6Srmw/4IFLHxJGv4mWcbrXTrlV07rhf/wBJhPo9dC1a5nlCtAwK4Ne+hA2OjTmLj6ymwZZGS63bS3PKjRiPbnPfBFv6d12T3OnHEr30zpjUaVjcFlGDz8MzzxjNys/P5/MzEyaNGnieoMZGfD999YY3SlfT+1zt/d3BdvxFm8dK8GmppSD1ONCaCeNbiGET8UrYTwR0Zw3845yQno+rbQ4D6fzJduG9frpd3lsu3Vzc2D3bkhMhEsugVq1ID+/TLrGlG0oxWRnupVHY8y4bvqUz5P7zBfxeou75eBsP2nhtIyuvhrefvvC36+/zktLl/LK1q0UFRWRkJBAnz59uLdnT/rExFxIV1AA774LRUVlYiyvfD21z93d34F4vovqR+pxIbSTRrcQwqdCdQot9OGEVuExXQGpHk5XHaShg61bLY1ugwEuvxw2b3aezsHZqHpu51HZitOT+8wX8XqLu+XgbD9pUaaMGjWCJUsgtKQn8tmzYexYNgClTemUlBRSUlI4NH8+G9yMsbzy9dQ+d3d/16TzXfiP1ONCaCdnixDCp1JM+Qw7v5MUU9k7kMJ9m7D0Wuzq/U0VOFqSzhfc6fjL2zbpdGTv2H1hhsMj5ipwOro+m3S6MvHuatGJ9Oj6FZdnFd4x9uQ+80W8nqIPCWPg5OUMnLwcfUiYW+VQup+qwq6MQkJg8WKIL+mGbMMG1LFjOQpsMYTaxQca9lUFMXpqn7u9vyvYjhCeIPW4ENpJo1sIIYKQimWYoNJpx2UAo50sq05sG86m4kKKjUW8sMFmACabHsxVQAfMGzTCaedcqqLn3UEjrGntlpWsO0ZvqFLHXp7cZxXFa92Whxrdjg3nqqioHMrbT5rysS2jceOgVy/LgmPHUO+4A4xGl2Xk9r6qIEZ3PusYvaHCfe72/q5gO0IIIfxDGt1CCJ/qoI9ke8zVdNBH+juUoJcE3AaccJh/vGR+TRy3963kZApycy1/2NzpPg5MGTah3PGfN3fszu16Q5nyzIiuz5RhE/hW0Vc5Pk/uM1fxBsP+L68cKtpPWmzu2J0R8U3h2WctM4xGuPVWjp8+XWEZeWpfudqO1uMqmPe3qF6kHhdCu0B71UsIUc2dVot4M+8op9WiihOLCiUByw2hjBr+EjHZmaxJms0GU3GNveNlMpn47fff6d27N7RsyZwHJ7FswVQ26XT0daMh962iZ5lOsZbn2u/mEjLmI1RFD1+/4ZEYPbnPHOMNpv3vrBzc3U9aXD1pEkrt2gD8sWYr4/7czgbcuyvsqX3luJ3KHlfBvL9F9SH1uBDayZ1uIYRPnTEX83HBSc6Yi/0dSrWh6nTsSEhkQ2JvNipKjf8CvnXrhfG6z/S/3lImGh5Vti3Pn/QGS8PIwzy5z4J5/5eJ3cPvoLdsGMX9/74PgNyCYl7dnau5jDxVvp46roJ5f4vqQepxIbSTRrcQwqdq6/R0D6lDbZ3nGzJCAPz222/W6fbxdf0XiPC7h/u1R6+3XGsWbj7A+Ty5MydEVUk9LoR20ugWQvhUCyWcT+tcQgsl3N+hiGrK9k73RU3q+i8Q4VdXtKrPlW0aAnD48BGSfjvs34CEqCakHhdCO3mnWwjhU/tNefQ+u410eRdMuEExm0lM2UFMdiZGVXXrXdxjx46RmppKXFwcF8XXRafToVNVzdsRrlVmv/hyu3Xq1OGRgR2tfz83cSLF7e7wQISu+apM/DUMnG0cJpMRRTV55dULEfikHhdCO2l0CyF8qggzJ1T/jOMsgstg1cQsk5Fm8ycAMB7LmMejqLin5m2//87NN91E7fAQHm3dirF7kiu1HVFWVfaLL7YbFR7CD8uX0qJBFADbtm1j0aLFXPO89xrdPZK38GFBjsfLZAgwx1hUZrvzk7d4rIf3ysaRPvNB3h00grXlrimqI6nHhdBOHi8XQvhUnBLGS5GtiFOqNtavqN56JG9hkclIvMP8eGAJlkZAebZu3Wadnn3lFZXeTjCqW7cuN998M0OHDuWWm26g9vaPSU+aiKm46l+Sq7pfvL3dqFohTB/elSsuvwyAzJxC7n/wIcxmcyUjq1iP5C1MXDiNeIc8qlomQ0rWd1YmExdOo0fylkpu2TNxxGZlMHHhNAarJp/EIQKH1ONCaCd3uoUQPhWhU7gsJJqIglR/hyIClKKaeHTF+5Zpx2VYHtmdDSzF9eO7to1u3ZQp6PR6+OwzMJnKbGe5Fxtkvla3bl22/PwT7dq1K7Ps/vvvZ8GCBZXetif2ize3Gxsby6v3dKV14zoAnM0p4JnPfmPv3n0aotFGMZvdjl3TdoE5NtPOtjtyxftMNZvxZpPXnThmmowkIa9r1CRSjwuhnTS6hRA+ddCUzy3ntvs7DBHALj6ymwZZGS6XK0BzoBew0UWardu2UZBfSHitMHQtWsD8+fDcc/Dss/D11/bbqSaNbkUHn3/6idMGN8C8efPYs2ePXe/utkzFhax+4UaX2/fEfvHmdj/84D1rg/tkaiovJO3j2JlcDZFo18tsdjv2n23mqxWUdS+gWTn5KkDDrAx66UNYpyFex32sDyn/TqU7cVRmn4vgJvW4ENrJ4+VCCCECSkx2plvp4spZlpOTw/9eeBd+/PHCzDZtYMkSuOEGu7SNcd3oLm0crX7hRo88nu1Nw3u347rrrgUgPT2dsWPH8vTTT7N48WIAwsLC+Oabb4iLK6/kXPPEfvHWdi+66CJuutHSmMzMKWTAdTd4vcEN5R87trSWibvp3c2/styNo3JHVHDSh4QxcPJyBk5eXuGPFkIIUUrudAshfOoifQTz61zM/ed3sc+U5+9wRAA6G1XPrXQVPdi450w+DBwIPXrApEnQr59lwaxZlsZ4kaXn3TR0PqkM9SFh9Jtoucu+fvpdLpetnXKr5gZ+94sacVevtgAYjUZuv/12NmzYAIDBYKB+/fr07duXJk2akJSURL9+/bi08+Xc3r01MbXDOPy/Vvyzd0+5ebi7X07rQ8BU7HbsWrY78EVLt2Rrp9wKYC2zwQ2OW9Mt2nKQ/QcO0tTtCCovDfd6Etf6EK676d3Nv7LcjUMeMq5ZpB4XQju50y2E8KmzajGf56dyVnX/S7moWXa16ER6dH2X74iqwFFgk7vb2bwZ+veHkkYobdvCE09c2I6fhmDylGsHDmDsLZ2tf48bN87a4IYLjfDDhw8D0LVrV86dO8fmn3/iwX7tGdI1gS8+/6zCfNzZL6ej62suT7f3t4vt1g43cO/wewDILzKyavsxTflXxSadziPHapntYuml3NNlrZU7cVTm84ngJvW4ENpJo1sI4VPp5mLeyT9Gulkqa+Gcquh5d9AIy7TjspL/RztZVuF2Ro+2dqRmnjgRGjWybCdIG92Xt6rPpo3r+X75d0SEWe7Vf7nwK2bNmlUmbUZGBoMHDyYvz3JXymCwv7d/5RVXcPHFF5ebX0X7RQfMGzRCc3m6vb9dbPe6zs2JjIwE4Me/j5NbaNSUf1WoOh3vDhqBjqodq46PLKtYhhuz3Y7tditb1lq5E8cYvUE6UathpB4XQjtpdAshfCoChcsMUUTI5UeUY3PH7tyuN3DCYf5x4DbcH/vYbjt//w0ffACALjqadS+/HJTjdIeFhfHK3V145e6udOvWzTp/19GzPDLyUZfr/f3339xz733kFhRzJruAhYsWs3bnhRK+5557Ksy7vP0yZdiESo8dXdn9reh03HRVC+vfS7cdrlT+VbG5Y3emDJvACYcGsNZj1VFSyfqeLmtPxZERXZ8pwybwraL3SRwicEg9LoR2crYIIXwqQV+LxXUvJUFfy9+hiAD3raKntSGUsfdP45XbxtJPH0IC2hsxttuZve0UOefOAXDN/fdzxRVXeDpsrxswoD+Xt2pg/XvX7t1MXfIHT3/yi/VOtivLln3H0NdWc9fstdx7/0O8tzoZo8lyn/Luu+9G58adU2f7pY0hVFMjUK8v21CrzP7udlEjGteNAGDlyh847oPO05zZ3LE7bcNrV/lYdZQEVS5rT3CMo39YBPeN+cjncYjAIPW4ENpJo1sI4VMHTPlcn/knB0z5/g5FBAFVp2NHQiIbEnuzUVEq/Rhr6XZWtrySF6ZMBUBRFFauXMmC+R8z8NKmXNGqPpNeepGN69exfML1zH24J3Xr1vXYZ/GUizt1sk6/+NIkLrv8SjbtSatUP9bn84r4/WA6AE2bNqVPnz5urVdmv7j5mHNISAgzZ84kLy+PX375hetLelt3ud0Ktje4S0vr9Ftvv+1WDN7iqWO1wu366XUI2zh+0htQ5Q53jSX1uBDaSe/lQgifKkRlv/R2KvzonXfn8fBDD9KhQwcaNGjAPXfb9iTe1TrVpnEdnhw9iuefm+D7IMvRsWNH6/TSZcswV3Gc8XU7T3B1u0aA5RHz9evXa95GSEgIzerX5vQ51+d2g+hwNqxbQ9euljK++uqr+W7ZUvaeyOSzjftZqzHPawcOILFFLAB79u5l9Y9r6He168frhRCeIfW4ENrJnW4hhE81VkJ5JqIljZVQf4ciaiij0cigQYNYtmwZubnlP4783/88Rp06dXwUmXs6dGgPgElV+eef/VXe3i//nCIrKwuA2267jVq13HtktE3jaO7q1YbVP6zkTPopPny0N7Mf6EFERESZtJe3qs/ch3tZG9y22sfX4+W7uvDYoyPdjnlA//4sWbzI+vdbb/n3LrcQNYnU40JoJ41uIYRPRekM9AuNIUonD9oEE8VsJjFlB312bKS3qmqqPMqsW8U7s55w+PBhbrnlFurVq8e/evdlwfp9JP2WwvD7/k3zlq1Y+edRAOrUqcPjjz9uXa8q5VBZtnn2BdpfdBEAJ8/mUVQy1nhVttv9z/Vs/vobAKKjo7n55psBiIqK4rbbbqN169Z264WGhjJr5hvMfbgX9/W5iGuu6WttaLdqFM2UyZPs0t8w6Hqm3tmFOhGWL+gpKSlceeWVDB06lB07d1rTTXrpRaKjoyuM+8rWDUj6Zon1x4Et+9L46OP5lSwFURn+OA9E4JB6XAjt5GwRQvjUflMeA8/96e8whAZDgDnGIprNtzxmPR7L2L2jqLijqMGqiVkmY5l15ydvCYhOmIqLi9nyyy/U+vkAAGu/XAjAws0HGNi5KXpFYfTo0cyePZsBOTmVLofKKlP2rVtDSWPzSHp2pbfbI3kLHxbkWLdLRh+4/98APPjgg7Rs2ZKxY8cSGxuLyWTif18uZN35CEyqmY3r13LVVVfZbe/IkaM0bhJPWIiex//7HxYv+orNmzdz0UUX8dmnn6BXLO8hL//+e4bfcw/nzp3jjz/+4LvvV7Jy2376JzalXr16PP7f//BrOXFf1aYBL/zfFYQaLO8T/7wnlWnf/IWpZCg44X2Ox44vzgMRWKQeF0I7aXQLIYRwaQiwxMn8+JL55Q2J1CN5C8+byo6ZHA9MXDiNKcMmoP3tYd9IO5fPup0nGXBpU2JjY3nnsce4e8aMMuncKYfKclr2Nu9zq3v2Vmq7PZK3MHHhNPuO1zZuxHzsGLpmzRgwYAADBgywLtLr9Qy/527uUlUKi1XrmOBFRhOfrP+H6Y8N5siRI8xd/iePDOyIoih8/PHH9O7dm6VLl1rvXv+UfJIhQ2/DWFRg3bbZbObzn/bT9+Im6BWFUU88zo4PtpFXVPa4uaxzZybedqHBvXjJEubvs/wQAGAqLmT1CzdaYg4Jq1TZaOWPPD3JNn7AbtoZp8cO3j0PhBCiOpAngoQQPtVWH8HaelfQVl/2vU8RWBRgjs204zKA2U6WASiqiUdXvO9yXTMwcsX7AfGouSsLNx9AVS19UN/11FMQUXZU2orKobJclr1Nz+UdF72HubhQ23bNZuf7xWxG98UXdmlNJhNLly7lzJkzAOgVxdrg3r//AKPnb2HJr4c4cuQIAN9uTSH5WCYA7dq1Y9euXVxU8ij8oVNZvL5sh9NO31Iz81i7wzIKdExMDLfY9Eheqn79+ixZvIiwEEuDe9Hixdwz/D5rg9tRaWNy9Qs3YtJYRsI5l8cOVTsPZF8FH6nHhdBOGt1CCJ/KMhtZWZhBlrnsnSwRWHoBzXBdUShA85J0ji4+spsGWRnlrtswK4OeauA+Fnz8TC6LFlvuNesbNkTZuBF++MHyb+5cqF0bKL8cKstl2dvc6a6fnKw5z15mMw2yMnA66NR770FJh2rrFi6kU6dODB48mJYtW/L8xBfIzre8P754yRK6XN2Ng2lZdqurZpj53d8UFFjuZMfGWnoWz8jI4KVFv1NY7Hpff/nzAYxGyzXh1qsTiAi98CCeXq9n4cKFtGjRHIDk45n8+/4H5ZFyHyv32ME754EITFKPC6GdPF4uhPCpU2oRr+cd8XcYwg1xVUgXk53p3rpmM6fdjsj3Xpk+nWF33G7548or7Rc2bQqDB0PJ3dt4vYGBL34LwNopt1bprp3Lsi+9020ywT//uL2PSjUubzTvw4chIQHCwvggNZV9JbNzcnKY/uoMdkR0o17tML546h6Xmzh2JpcXX5rEq9NfASw9xd951z0ovR53uQ7Aycw8vvjfl9x373CiaoVy81Ut+K5k2fTp0+nXrx8AZ7ILmLL4jyp1ICcqp9xjx4bWY1IEH6nHhdBO7nQLIXwqDIWL9BGEyeUn4KVWMp2puJBVS15za92TJqPfHy0t7/HW3buT+er116HkMXM7N98Mky701J3m8h6gdk7LXlGgQwfL9IEDUFhYJp0SEsbAycsZOHm503eMHPeaQAAAVtBJREFUK4zx7FlITXWaf6FRJe1cfoWxz5o9h6SkJPLz83nkkUdYv2FDheuA5QeO0rvXd/+rLSkHD3Ds2DGefvppwNLp3ctf/8nZHHkE2R/cPb7dvW6I4CX1uBDaydkihPCpNvpafF/vctro3RsLWPjPJiy9EjtpbkLJ/KMl6Sqz7uno+mzSea6h6i13jR3L8chI1OhoiI6GQYMsd5oBJk5EvfVWSzmUfJYG0eHcddedfPDBB+zbt8/u3WZ3OS2/li2tPZebk5Ndln2529XpSI+uX6l96i5VVRk6dCgRERF8/PHHbq934MBB/lfSe3yoQU+zZk1p2rSpdfmTY55i9zH3nqAQnueLY0cEB6nHhdBOGt1CCJ86ZMpnyLntHDJVfMdM+JeKZRig0mnHZQCjnSxzZ10dMG/QCNQgaHSrwBMFBZCdjZqdDStXwtix1uW6Tz5h9X//y7z332P+f/rw+ah+fLpgPg899BDt2rWjU6dOvPnmm5rzLFN+Nu9zs3u3y7Ivd7s6He8OGoHOyboV7VN3VXS3vTwTnnuenUfOkJ6Vz/Hjxzl+/DhHjhxh6tSpzHvv/SpEZU9vE6MShL2O+4Mvjh0RHKQeF0I7aXQLIXwqH5Wdxhzy5atZUEjCMgzQCYf5x6l4eKDy1p0ybEJAjNPtrjKfZdYs+OwzAHSRkTz01ls8cP+/aRIT6XT9gQMH0rNHj6rladNz+ezk5EoPzbS5Y3emDJvACYcfPNzZp96WmprK05/+yj1z1tGyVRuaNWtGy5YtmThxoh+jEqUC+dgRviP1uBDaSaNbCOFTDXQhPBHRnAa6EH+HItyUBLQ2hDL2/mm8cttY+ulDSMC9L9jO1m1jCA2qBncpx89y/aP/Ydu2bXZpiowmdhw5w5SpL3PNNdcwcuRI67IXX9DecLTNc+f1/2edv2D37sp+DMDSeGobXrtS+1TUbHLsCKnHhdBOei8XQvhUPSWE28Ma8UNhBummYn+HI9yk6nTsSEgEYOPSN9Ey0pfjusHM9rOsXfomvw8axLhx48jNzWXTz1uI6PckRUaVtVOmYCou5KeffmLMmDG0a9eOvn37sOLYL+w4crZSeYa1bQ1Yxs/+559/PPpZtO5TUbPJsVOzST0uhHZyp1sI4VP/mPLombmNf0x5/g5FiCrLyMhg3LhxTJo0iY0//USR0f5xS5PJxOTJk61/D+/drlL56IDm9S3jgh86dMg6FrYQQvia1ONCaCeNbiGEEMKLvvzyS/bu3QtAYotYOreMJa5xY/pe3IS7erWhefPmFW6jYd1ahIdaHk7bXcVHy4UQQgjhW/J4uRDCp9roazE7qj2js/dyQHo+rZEUs5nElB3EZGdiVFU2cKHHY8dlwTCkWEVUVWXSpEl8+eWXAEwadhWvDt9nXT5w00b6DRhY7jZaNoiyTicnJ3sn0HJUZb+Ut78DQaDHJ0SgkXpcCO2k0S2E8Klcs4nfis+Ta5aXAGuiwaqJWSYjzeZPAGA8lvGoS4fHmmMsKrNsfvKWoOx4zdaiRYuY+MILdOzQgfAQvd2yuLg41qxexfNJ/3DibK7T9UsfLQff3+keQuX3S3n7OxA63uqRvIUPC3ICNj4hApHU40JoJ41uIYRPpapFTMk95O8whB/0SN7C8yZjmfnxwBIs7y2bnSybuHAaU4ZNYK33Q/QaVVV58smn+G75dyg6HVu3buWwMZYubRvSpnEdmjRpwozhMYz97Ben67fw053uIVj2jSN39ktF+/s2IKm4kNUv3GhdpnVcb3eZnOTTI3kLExdOc3rMWePzSjRV4/hZhPA1qceF0E4a3UIInwpFR5wSRqpaSFGZr7uiulJUE4+ueN8y7biMC41tZ8tUYOSK95lqNhPM91XWrlvHba//CMCKFwZjKi4kJiaGtWvX0rlzZ+pHh/Pa8G78Mrc1+/bYN6xbNrQ0ulVVtb4fXsq2EebJRqsCzLGZdlxmu19Uh8fNK9rfKjAbWIp/HuVWzGa34/NUI1cay6K6kHpcCO383pHaY489RkpKCvn5+fz6669cddVVLtMaDAYmTpzIgQMHyM/PZ/v27Vx77bV2aRRFYfLkyRw6dIi8vDwOHDjA888/b5dm/vz5mM1mu38rV670yucTQthrq49gbcyVtNVH+DsU4UMXH9lNg6wMl5WOruSfMwrQMCuDXubg/3JXWGyisPjCTwdnz56lf//+/L1jBwD1o8NZs3oVrVu3tqaZPOkl2sbVAWD/gQM+67m8F9AM118UytsvFe1vBWhekoc/9DKbaZCVUe4x58/4hAhkUo8LoZ1fG9233347M2fOZNKkSVx++eX8/fffrFq1igYNGjhNP3XqVB555BEef/xxOnbsyLx580hKSqJz587WNM888wyPPvoo//3vf+nQoQPPPPMM48aN4/HHH7fb1sqVK2ncuLH135133unNjyqEKHFYLeDu8zs5rMqQRzVJTHZmlbfROMjuqOhDwhg4eTkDJy8v9w70mTNnGHjt9Rw6lQVA06ZNWb9+Pa1atWLatGlMGP+sNe30V2d4Pe5ScW6mc7Zf3N3f7ubhae4eS/6KT4hAJvW4ENr59fHyMWPG8MEHH7BgwQIARo4cyQ033MADDzzAq6++Wib98OHDefnll613pefNm0f//v156qmnGD58OADdu3dn6dKlrFixAoAjR45w55130qVLF7ttFRYWcurUKbdjDQ0NJSzswpemqKioclILIVwp7YBF1Cxno+pVeRtpLu9LluXsHd7KrusLZ86c4dnPf+PV4V1JaBhNs2bN+Pvvv6ld+0IHanNX7uKzzz73WUypbqZztl/c3d/u5uFp7h5L/opPiEAm9bgQ2vmt0R0SEsIVV1zBK6+8Yp1nNptZs2YN3bp1c7pOWFhYmcfq8vPz6dmzp/XvLVu2MGLECNq2bcv+/ftJTEykZ8+ejBkzxm69Pn36cOrUKTIzM1m3bh3PP/88Z8+edRnv+PHjeemll8rMv7QF5OZc+DszFw6nQ1gIdIwvu52/Dlv+bxcHkQ7fAQ+nW9avHwXNYu2XZefDgVOg6Cx5Otp5DIwmaNUQ6jg87XPiLJzOgroRkNDQfll+Eew9aZnu3AIcR4HZcwIKiqF5fYitbb/s1Hk4mQm1w6FtY/tlxSbYdcwyfXEzcOisl/1pkFMATepBozr2y87kwNEMCA+BDg5laDbD9iOW6fZNoFao/fKU03AuDxpGQ3yM/bLzeXDoNBj0cEkzyvj7CKhmaNMIomrZLzt2BjKyoV4ktHR4ECO3EP4p+WZ2Wcuy200+AYXFlvXqRdovSz0Haecs+bVpZL+ssNiyLljiNTiU4T+plrzjYyyf11ZGtiXmWqGWcrKlmi2fFSzlGx5iv/zQaUtZNapj2T+2zuVCSrplf17spAy3H7Hso7aNLceFraMZln3bNsrA8Cb1WF+UyTmzpZOlnALLMaHTWY5DR7uOWY6phAZQ16EMT2ZajsU6EZbj31ZBseUYBst5ozgc33tPWs6BZrGW887W6SzLuRMZZjlfbRlNlnMOLOd5mEMZHjhlOWcb14W4uvbLvHGN0BtUmoSc4GRxPIrOTGLLstv1xDWidehBdJg520KltH8s964RZvLbhZPZqS51cs9ZHrEyYekmGqAZmEuOb+suSgMKgHqg1oFzkXXJLsjhMlPlrhF6g0qb0AOkGS0Xq8peIwCahJwgQpdvVw7HzkBmAUQp2TQynOJsC8ubym1CD1BgvnAytAk9AGC3bvIJMALhRUeZ9+V+Hu7egDatW1MbID8OasUx7/tfSN61lcts1nN2jSj9nAC/hpnJKra/RpQuP69aZoTpCmgWctwuntJrxCYgNR4ahTg8FncayCvZL01L9ovOst0c1XIQ7G3Rzn5/lzoCmEFtDKfCIQe4rGTR0Qw4VwjRynkaGtLtYsopgENnQIdK69BDduUL8JPejKnY/WtEtt5MZnhd6pw7h1JShrTAegCqwCng95OAm9cI27I/3VRle4olnTvXCNt1D9Y3czD1wjXCdtnZFiq/WyZ9/j3iTD7UVnJobEgjs7nJGtOJOJXdRy3pasL3iCOZoMdIQuhhu2MUas73iGizgbanG5FUcJqGTYp9/j0itrbleLIl3yMukLaGha+uEZEOMbvirLNYn4iLi+PkyZN069aNX3/91Tr/1VdfpXfv3lx99dVl1vniiy+49NJLGTx4MAcPHqRfv34sXboUvV5PeLjlzNTpdEybNo1x48ZhMpnQ6/U899xzTJ8+3bqdO+64g7y8PFJSUmjdujXTpk0jJyeHbt26oarOu3Rxdqf7xIkTsCgajNnW+Z//DMPfhdaN4MDMstvR3W35f8tL0K2t/bJ73oEvNsNjA2Duv+2XrdoB171quahmfVh2uw1GWi6QS8fAzVfYLxvzOcxaCbd1gcWj7Jf9mQJXlLzyXrCg7EnfaZzlgv3BQ/BQX/tlryyDCV9B7w6wwf61eY6fhWYlT/QfewuaOlRcfabCxj0w7Q4Yf7P9sg/Xw8MfWi4kux2epCwshvB/W6b/mAqXJ9gv/785sGQrPHk9zLzHftmyP+CWmZYLTfo8yoh+yHLB+eEZuDbRftl/FsA7P8LdPeDzx+yX/bIfur9kmTZ/UXa7bcbAwVPw2aNwT0/7ZS99DZO+gYGXwKpn7ZcdSIO2T1mmT78LDRwqxG4vwq8H4I27Ycwg+2Vzf4T/LrBU3n++bL8sKx/qPGSZ3vUqdGpqv/zmN+C7P+HZm+GVO+yXLf4Nbn/TcpE5/lbZzxp2HxQZYf1z0Kej/bKHPoCPNsDz14Qy5cEiu2UbkqHvyxBqgMJPym636eOWC/qiJ+D/utovG/8VTF8GN10Oy56yX7b7OFz8jGX6/IcQ7fAl6PLnLJXT2/+G/wywXzZzBTz1BVzdBn6ZZL8sPQsaPmqZ3v8GtHGoCK6dDqt3wotD4aVb7Zd56xrxe95lPJc6ha2vDSVznn35QtWvEfqQMPLmmwjV2fdG7c414ppLQlj7bLH9wrNAyTXC/BboHK4RTAX2gPkO0HnwGjE17VleenYWT/QvrNQ1Is8Yxq/vdOTKiL/slv1nAby3IYyXX/0vzzR6w25ZcsFFJI48DIBxQWGZ7bYZA4fPhrHqrW70i9pgv/DiFxn5bjoxrRsxrcmLdosqukb0nBLC5r3FTq8Ry87fwG1Pb+LhFyYzt9lou2W214gjr0Jzh2sEb4D6p2W/6ByuET/l9KD/kzv5v+fe5IuW/y7zWbkPVCPongOdk2vEgs1hvDZjBE82tL/AbEiG/jPCuG7iQpa3HlJmsy2eDOXo6SLN1wjzcTA/U/KjwodAAFwjvtiicM9cNeC+R7y5JoyXpj/J842n2y3787COK56zfJWsCd8jhr4VxtAXP2ZRwt04qinfI3LyYd+4rtxzfhdLpuX6/HvEg33gw4ftl8n3iAukrWHhs2uEIQpuzyI6Oprs7GxcCapGd/369fnggw+46aabMJvNHDx4kDVr1vDAAw8QEWH5yeWOO+7gtddeY+zYsezevZvOnTsze/ZsxowZw6effuo0loSEBA4dOkS/fv1Yt26dW/FHRUWRlZVFr0uiyc25UMDy65NFoPz6ZEvudFv4+063/ELtjTvdIVz84NucLI5n/dShXNykbKO7qtcIfUgYI6bMQIeZrR+OxWS0NKLduUbUiQrl7ucstVrE+0/xlGqisc2d7tRm8EbJ8f000Bisd7pP1IPvb3+AvxMSrflW7k53CF0eeo00Y2OWTrqP2FqFlbpG6AxhDJ/8JhG6fLtysNzpDmPwiwtoZDjF1g/HAtDlodcoMIfzyQuWbyGPTLG8OmW7ruVOdxh3TZpLlJLD1g/HElkrnJtvvplNvx/gu3XbuemFz2gSctJuPed3ui2fE+DzqePIyilyuNNtWX5ejWbR1NFcN/5jmoUct9uu4zViUIjNfgE4DUfzYPF9T3CoQyu7z5qj1ubLqWMY8OwntAw9cmF/l657BI6a4dXG8IuTa8S5wjCGvPgxDQ3pdjFZ7nSH0X/iYlqHHrLLE+Djl8ZRUFDk9jWitBw6HdjNTR99SDOz2XqnOxV4A1iHtmuEbdlveW8s21Mssbt3p/vCuqvmjONgapHNne4Ly7Z+OJbfD1i26/s73WHc8uInNDaksW3Bc1z1b0tLbOM7Y9l91BJTTfgecSQzjIETvyIh9LDdMQryPaKUfI+wCKbvESBtjVLa73RHsWlnxY1uvz1enpGRgdFopFEj+ytEo0aNSEtLc7nOkCFDCAsLIzY2lpMnTzJ9+nQOHbowVuBrr73G9OnT+eqrrwDYtWsXLVq0YPz48S4b3SkpKaSnp9OmTRu3G92l/j4Czsq3sPjCQe9M6cXVmYxsyz9nVHP52z102vWyc3nlr1t6gDlzNMPyz5mcgvK3W3pCOHMy0/LPmYIKyrD0BHbmdJblnzNGU/nbPVDOq/6ZuZZ/rpS33cPpln/OZOeXv+7OcsrwxFnLP2fyi8rfbmlF4syp85Z/zhRXUIb7nZ/CgOVCdybH+TJzBcd3SjrgogzPV3B8/13O8X3sjOWfM7mF5W83uZwyTDtn+eeMJ68R+hCFmGJLraGadV67RhwssvSo/dcRBZPDjevyrxE6DhS1AWDtMQNzzHpGDX+JmOxM1iTNZsOxYuuwUW8aQu2WbcrW0Td+KBSVzVfLNUIfohBTEgNU/hqhB06WlLVjPPoQyFajyC6K4q8jloeqbfMErOXgbN1Txsacsi7LZuPOL0qWhZFnjuBAURunZQ8XrhG2nzO30PLNxvYa4VgOhebwcre75wTswcl+Mejo23qgdb84flYjIRwoalN2fx+bzQZTMaqLa4Q+BLLUOmQV1XFaRmYUa7y2eRabLJ/V3WtEaTkcaN6GMeELeXzY+AvHo6m4zDBm7lwjbMt25/ELD9W7c42wXfdwhuWzlF4jbJeVfm7w/fcIfQjkqLU5UNSG7Uf11CuJaW/qhZhqwvcIfQiYMJR73sj3CAv5HmERDN8jbElb4wJ3rhHudvPlt97Li4uL+eOPP+jXr591nk6no1+/fvzyyy/lrltYWMjJkycxGAzceuutLF261LosIiKizCPiJpMJRXH9UePj44mNjSU1VbpMEcLbEvS1WFwnkQR9rYoTC7eUdvy1+oUbMRWXfXw50Kg6HTsSEtmQ2JuNimLXwCmzzPEnceEXVdkv5e3vQBDo8QkRaKQeF0I7v/ZePnPmTD755BN+//13tm7dyujRo4mMjGT+/PkAfPLJJ5w4cYIJEyYA0KVLF+Lj49m+fTvx8fG89NJLKIrCjBkXHsb/7rvveO655zh69Ci7d+/msssuY8yYMXz88ccAREZG8uKLL/L111+TlpZG69atmTFjBgcOHGDVqlW+LwQhaphCs8p+Ux6FZvlqK4QQQgQbqceF0M6vje5FixbRoEEDJk+eTOPGjdm+fTvXXXcdp09bnlto3ry53V3r8PBwpk6dSqtWrcjJyWHFihUMHz6c8+cvPLvy+OOPM2XKFN555x0aNmzIyZMnee+995g8eTJgueudmJjIfffdR926dTl58iSrV69m4sSJFBWVfQ9SCOFZJ9VCJuQc8HcYQvicYjaTmLKDmOxMjKrKBrDeVS1vmbdiMJmMKKoJVdFXvKKftusqD6Oqshl8Uma+4Kv9L6pO9pXU40JUhl8b3QBz585l7ty5Tpf17Wvfhd1PP/1Ep06dyt1eTk4OTz75JE8++aTT5QUFBVx33XWVC1YIUWUGdNTTGcg0GzH6px9HIXxusGpilslIs/mWJ7fGY+lHrrST1znGIqfLkjwYwxAn+aTPfJB3B41gbRW26+yzeWK7tpzFbgQMXi4zX3D22YL1s1R3sq8spB4XQju/vdMthKiZLtJH8EtsVy7SR1ScWIhqoEfyFhaZjDh2MhsPLAG+Lpl2tqzs4FiVM6Rke475xGZlMHHhNAarpkpt19Vnq+p2bbmK3fE+uqfLzBdcfbZg/CzVneyrC6QeF0I7v9/pFkLULEfUAh46v5sjaoG/QxHC6xTVxKMr3rdMOy7jwpidzpapwGxgublqd5IUYE4F+cw0GUnC/cdkTcWFrHnhRkqHci1vu8t0lf99v7zYHbtysy2zpQT+I7/u7JfZwNKSjhL9zWQThz4krILU1Yvb+4rAP+48QepxIbSTRrcQwqdyzCY2FLsYu0GIaubiI7tpkOViDBTKNhxtKUBzoFcVG929ACfD4ZbNB9joje1qiN/k0MDsXUEeLvNE22fxBMfYK+Kt/SI8T/aVPanHhdBOHi8XQvhUjM7APeFxxOjkNz9R/cVkV/2LaeMqvjMZ5+F0WtNXJX6tMVV1PV/y1n4Rnif7yp7U40JoJ41uIYRPNVbCmBCZQGOlZj2eKGqms1H1qryNtHLvh1cs1cPptKavSvxaY6rqer7krf0iPE/2lT2px4XQTn6iEkL4VLIpl45ntvg7DBEktD6y6+3taLWrRSfSo+sTm5Xh9Ffu0nvAzpqlKnAc2KTT0dfJcndtwtLDcjzOf2m35uOt7VYh/orycJlnJfPzJW/tF+F5sq/sST0uhHZyp1sIIYTwElXR8+6gEZZpx2VcaHQ7WwYwGlB1VbvTrXJhaDJn+eiAMXqD5g6g3N5uFeIvLw/Hh9btyqzSOfpOReUHwfNZqjvZV0KIqpJGtxDCp1oo4XwSfTEtlHB/hyKET2zu2J3b9QZOOMw/DtwG3Aoul3lq7N+kku055pMRXZ8pwybwreI4AJd/t+tOHo6DkXm6zHzB1WcLxs9S3cm+ukDqcSG0k8fLhRA+ZcLMWXMxpip2DiVEMPlW0bNMpzBq+EvEZGeyJmk2G0zF1jtjyw2hLpd5SpJDPmu/m0vImI9QFT18/UbAbbe8PNYkzWYz8Pi9k7xaZr7g7LMF62ep7mRfWUg9LoR2cqdbCOFTx9VCnszex3G10N+hCOFTqk7HjoRENiT2ZqOi2H1RL2+Zt2L4SW+wNIwDeLuu8tioKBgVxSdl5gu+2v+i6mRfST0uRGVIo1sI4VMKUFunl4uPEEIIEYSkHhdCOzlfhBA+1UEfyfbYbnTQR/o7FCGEEEJoJPW4ENrJO91CCJ86phbweNYejqkF/g5FCI9SzGYSU3YQk52JUVXZVMVexz0RwwZ836NyIJRDRWxjNJmMKKrJK4/EC1EdST0uhHbS6BZC+FSW2cTKojP+DkMIjxoCzDEW0Wz+BADGYxnXd37yFjZ37O7XGEbhu56VeyRv4cOCHL+WQ0WclVP6zAd5d9AI1vo1MiGCg9TjQmgnj5cLIXyqrs7A0LCG1NXJb36iehgCLAHiHebHAxMXTqNH8ha/xrCkZLkjtbiQ1S/cyOoXbsRUXPUOkXokb2HiwmnEm+17NPZlOVTEVTnFZmUwceE0BquOA5EJIRxJPS6EdtLoFkL4VLwSxoyodsQrYf4ORYgqU4A5NtOOy8zAyBXvo5i9N7RORTEAzHayzKMxmM08uuJ9lzH4ohwq4s6+mmkyyhcjISog9bgQ2slPVEIIn9ptyuWijJ+R+0miOugFNCtnuQI0zMqglz6EjZV8t9lUcke6KjE0L0n3c6UiqFgvs5kGWRnlxlBaDuu8FENFtJTTRp9EJERwknpcCO2k0S2E8DmpqGsWx0ajPiR47444fpY4N9drjPfu8Lobg7vpKsPdz+fNcqhIIJRTdVHRD0Gi+pN6XAht5CkqIYRPNVfCeS+qA82VcH+HIkSVpbqZLg3v9eDtbgzupqsMdz9fGjr0IWEMnLycgZOX+/QHmEAoJyGqA6nHhdBOGt1CCCFEJW3C0ju3q2G5VOB0dH2vDpvlTgxHS9J5LQadjvTo+n4th4oEQjkJIYSomaTRLYTwqaNqAY9k7+GojO8ZVEwe7um6ulCxDMlVOu24TAfMGzQC1YuNzYpiABjtZJlHY9DpeHfQCHQuYvBFOVTEnX01Rm/w+bjmQgQbqceF0E4a3UIIn9P7OwAhPCgJuA044TD/ODBl2ASfjE9dXgy34Ztxujd37M6UYRM44dCw9mU5VMRVOWVE12fKsAl8q8jVSQh3yJkihDbS6BZC+FQnfST76vekkz7S36EI4TFJQGtDKGPvn8Yrt42lnz6ENoZQnzY0ncWQgG8a3KU2d+xO2/Dafi2HijiWU/+wCO4b81FAxShEIJN6XAjtpNEthPCpE2oh47L/4YQqjyiL6kXV6diRkMiGxN5sVBS/PEpdJgafRxAY5VAR2xh/0htQ5Q63EG6TelwI7WTIMCGET50zG/mm8LS/wxBCCCFEJUg9LoR2cqdbCOFT0To914fGEq2TO0tCCCFEsJF6XAjtpNEthPCpZko4b0V3oJmM7ymqOcVsJjFlB312bKS3qqKYzf4OSQghqkzqcSG0k8fLhRA+tceUS+czv5BnNvk7FCG8ZrBqYpbJSLP5EwAYj2WM6PnJW6TDLiFEUJN6XAjtpNEthPApFciRilpUYz2St/C8yVhmfjwwceE0pgybwFrfh1UlpeO0CyGE1ONCaCeNbiGETzVVwngqsiVv5B7muPR8KqoZRTXx6Ir3LdOOy7B8WR254n2mms3UlK+sjg12fUiYH6MpS5UfFITQROpxIbSTd7qFED6lR0eMLgQ9gTeMkBBVdfGR3TTIynBZuSpAw6wMesn73UKIICX1uBDayZ1uIYRPHVELuC9rl7/DEH5UnR9VjsnOdCtdY6TRLYQITlKPC6GdNLqFEEIEjGBvkJ+NqudWujS5QySEEELUGPJ4uRDCpzrqI0mO7U5HfaS/QxHC43a16ER6dH1UF8tV4HR0fTbppNEthAhOUo8LoZ00uoUQPpWmFjItN4U06XxFVEOqoufdQSMs047LAB0wb9AIVGl0CyGClNTjQmgnjW4hhE+dNRv5vCCVs+ayQyoJUR1s7tid2/UGTjjMPw5MGTZBxukWQgQ1qceF0E4a3UIIn6qt09MnpB61dXp/hyKE13yr6GltCGXs/dN45bax9NOH0MYQKg1uIUTQk3pcCO2k0S2E8KkWSjgf1ulECyXc36EI4VWqTseOhEQ2JPZmo6LII+VCiGpB6nEhtJPey4UQPrXPlEe3M7+RKY+lCSGEEEFH6nEhtJNGtxDCp4yYSTcX+zsMIYQQQlSC1ONCaCePlwshfKqJEsa02m1oooT5OxQhhBBCaCT1uBDaSaNbCOFTYTqFtvoIwnRy+RFCCCGCjdTjQmgnj5cLIXwqxZTP/53f4e8whKiRTMWFrH7hRn+HIRzIfhHBROpxIbSTRrcQQgjhA+U1rIK90WUbvz7EO4+cOiujYC4zIYQQNYc8FyKE8Kn2+kh+j+lKe32kv0MRQgghhEZSjwuhndzpFkL4VLpaxLz846SrRf4ORQghhNAk2J9K8QSpx4XQThrdQgifOmMu5sP8E/4OQwghhBCVIPW4ENrJ4+VCCJ+K1OnpGlKHSJ3e36EIIYQQQiOpx4XQThrdQgifaqmE80WdS2iphPs7FCGEEEJoJPW4ENrJ4+VCCJ/ab8qj39nfSVUL/R2KEEIIITSSelwI7aTRLYTwqSLMHFEL/B2GEEIIISpB6nEhtJPHy4UQPhWnhDIxshVxSqi/QxFCCCGERlKPC6GdNLqFED4lHbAIIYQQwUvqcSG0k8fLhRA+dcCUz43n/vJ3GEIIP1HMZhJTdhCTnYlRVdkAqP4OSgjhNqnHhdBOGt1CCCGE8IkhwBxjEc3mTwBgPHAMGAUk+TEuIYQQwpvk8XIhhE+100fwc72raKeP8HcoQvx/e/ceHVV97n/8s2cmmZAwAREIFyEGuQQvFEVAlKptvNZaRSq6rEXBuqS4LP7USlHxoPaAl1+NFU6rxUMarfZ3rDbSalGMh3rDoCIg5aJcwi2BcAmQhExuM9/fH9bRQMB8h8zejHm/1nrWyuz57tnPzvpmnnmy954NF42R9JKk3gct7/3v5WNczwhAPKjjgD2OdANw1d5oo16sr9DeaKPXqQBIgEhjvRbe/8Nmy3ySfvu1nw9+LirpCUnz5c6p5i3lCKB1qOOAPZpuAK7aZRr1ZO0Wr9MAvpWO1Wbyu5L6HOF5n6S+/x73tisZAYgXdRywR9MNwFUd5FP/QLrWN9UqzNcn4Vvk4IbXnxL0MJtjS882HgfAO9RxwB7XdANwVT9/BxV1Hqp+/g5epwLAJdvbeBwA71DHAXs03QBctT4S1mV7P9H6SNjrVAC45F198S3lhzsmFpW05d/jABzbqOOAPZpuAK6qV1SfRWpVzylpQLsR1Re3Bfvy54Ofk6TbW3gOwLGHOg7Yo+kG4KosX6ruSs9Wli/V61QAuKhI0o8llR20fNu/l3OfbiA5UMcBezTdAFyV6QR0abCrMh2+xxFob4oknRRI1S8nzNSsH/9Sef4U5YiGG0gm1HHAHn8tAFy1LlKrvL1LvU4DgEeijqNPc4ZIkt6e/6SiEY8TAmCFOg7Y40g3AAAAAAAJQtMNwFUD/Ola2PkMDfCne50KAACwRB0H7NF0A3BVtWnSWw2VqjZNXqcCAAAsUccBe1zTDcBVO6INeqR2k9dpAACAOFDHAXsc6QbgqqB8GuBPV5C3HwAAkg51HLDHXwsAV/X3d9CC485Qf38Hr1MBAACWqOOAPZpuAK4qjYR19b4VKo2EvU4FAABYoo4D9rimG4CrahXVsqZqr9MAEi7SWK+F9//Q6zQAoE1RxwF7HOkG4KpuToomd+ijbk6K16kAAABL1HHAHk03AFd18aXo+g491cVHsQYAINlQxwF7nF4OwFWfRWp1duWHXqcBAADiQB0H7HGkGwAAAACABKHpBuCqk/wdNL/zUJ3ErUYAAEg61HHAHk03AFfVmqiWNVap1kS9TgUAAFiijgP2uKYbgKu2R+s148BGr9MAAABxoI4D9jjSDcBVqXLU2xdUqhyvUwEAAJao44A9mm4ArhrgT9fbXYZrgD/d61QAAIAl6jhgj6YbgKs2R+s0fv9KbY7WeZ0KAACwRB0H7HFNNwBX1ZiIFjfu9zoNAAAQB+o4YI8j3QBcdbyToolpvXS8k+J1KgAAwBJ1HLBH0w3AVd19qfpFel9196V6nQoAALBEHQfscXo5AFetiRzQ0MoSr9MAAABxoI4D9jjSDQAAAABAgtB0A3BVjr+D/l+n05Tj7+B1KgAAwBJ1HLBH0w3AVQ0mqs2ROjWYqNepAAAAS9RxwB7XdANwVVm0XlNr1nmdBgAAiAN1HLDHkW4ArgrIURcnoIAcr1MBAACWqOOAPZpuAK4a5E/Xh8efpUH+dK9TAQAAlqjjgD3Pm+7JkyertLRU4XBYJSUlGj58+GHHBgIBTZ8+XevXr1c4HNby5ct18cUXNxvj8/n04IMPauPGjaqtrdX69et13333HfJaDzzwgMrLy1VbW6s333xT/fv3b/N9A3CoLdE63VK1WluidV6nAgAALFHHgfgYr2LcuHGmrq7O3HjjjWbw4MHm6aefNpWVlaZbt24tjn/44YfNtm3bzKWXXmpycnLMpEmTTG1trRk6dGhszLRp08yuXbvMD37wA5OdnW3Gjh1rqqqqzG233RYbc/fdd5u9e/eaH/3oR+a0004zr7zyitmwYYMJBoOtzj0UChljjAmFQp79/giCIAgi2cKfEjQXPfiquejBV40/pfV1lyAIgiCOtbDoCb1LsqSkxMyePTv22HEcs23bNjN16tQWx5eVlZnJkyc3W/bSSy+Z5557Lvb473//u3nmmWeOOKa8vNzceeedsceZmZkmHA6ba6655rC5pqammlAoFItevXrRdBNEHNHFCZhr03qYLk7A81wIgnA/aLoJIrmDOk4QX0Vrm27PTi9PSUnRsGHDVFxcHFtmjFFxcbFGjRrV4jrBYFB1dc1PZQmHwxo9enTs8eLFi5WXl6cBAwZIkoYMGaLRo0drwYIFkqScnBz17Nmz2Xarqqq0ZMmSw25XkqZNm6aqqqpYlJWV2e80APX0BTUj4yT19AW9TgUAAFiijgP2PLtlWNeuXRUIBFRRUdFseUVFhXJzc1tc54033tAdd9yhd955Rxs2bFBeXp6uuuoq+f3+2JiHH35YmZmZWrt2rSKRiPx+v+6991698MILkqQePXrEtnPwdr98riWzZs3S448/HnscCoVovIE4rIocUO6e971OAwAAxIE6DthLqvt0T5kyRXPnztXatWtljNGGDRtUUFCgiRMnxsaMGzdOP/nJT3Tddddp1apVGjp0qJ544gmVl5fr2WefjXvbDQ0NamhoaIvdAACg3Yo01mvh/T/0Og0AAFzj2enlu3fvVlNTk7Kyspotz8rK0o4dOw67zpgxY5SRkaHs7Gzl5uaqpqZGGzdujI157LHH9PDDD+t//ud/9K9//Ut/+tOflJ+fr2nTpklS7LVttgug7WT70lSQeYqyfWlepwIAACxRxwF7njXdjY2NWrp0qfLy8mLLHMdRXl6ePvjggyOuW19fr/LycgUCAY0dO1bz58+PPZeenq5oNNpsfCQSkc/3xa6WlpZq+/btzbYbCoU0cuTIb9wugKMXlVGNiSgq43UqAADAEnUciI9n3/Y2btw4Ew6Hzfjx401ubq556qmnTGVlpenevbuRZAoLC83MmTNj40eMGGHGjBljcnJyzOjRo01xcbHZsGGD6dSpU2xMQUGB2bp1a+yWYVdeeaXZuXOnefjhh2Nj7r77blNZWWkuv/xyc+qpp5qioiJuGUYQBEEQBEEQBEG0OpLilmGSzK233mo2bdpk6urqTElJiRkxYkTsuUWLFpmCgoLY43PPPdesWrXKhMNhs2vXLlNYWGh69uzZ7PU6duxo8vPzzaZNm0xtba1Zv369eeihh0xKSkqzcQ888IDZvn27CYfD5s033zQDBgxI1C+YIIivhU8yHeQzvmMgF4IgCIIg7II6ThBfRWt7QuffP8BSKBRSVVWVMjMzVV1d7XU6QNI4xZ+h+cedriv2LtOqyAGv0wEAABao48BXWtsTenZNN4D2aVu0XrdXrdW2aL3XqQAAAEvUccBeUt0yDEDy22+a9GrDbq/TAAAAcaCOA/Y40g3AVZ2cgK4IdlMnh//5AQCQbKjjgD2abgCuOsEX1G9Cg3SCL+h1KgAAwBJ1HLDHF6nFiS9SA+LjSEqRo0YZ3nwAAEgy1HHgK63tCTkvBICrjKQGyjQAAEmJOg7Y4/RyAK7q4wvqv0K56sNpaQAAJB3qOGCPphuAq3xylOr45JPjdSoAAMASdRywxzXdceKabgAAAABov1rbE3KkGwAAAACABKHpBuCqU/wZWt91tE7xZ3idCgAAsEQdB+zRdANwVXm0XvdUr1N5tN7rVAAAgCXqOGCPW4YBcNVe06QX6yu8TgMAAMSBOg7Y40g3AFdlOn5dlHq8Mh2/16kAAABL1HHAHk03AFf18aXpd5mD1ceX5nUqAADAEnUcsMfp5QBctTZyQMP2lKjGNHmdCgAAsEQdB+zRdANwVUTSfgo1AABJiToO2OP0cgCuOsEX1G86DtQJvqDXqQAAAEvUccAeTTcAVwXkqIc/qIAcr1MBAACWqOOAPUeS8TqJZBQKhVRVVaXMzExVV1d7nQ4AAAAAwEWt7Qk50g0AAAAAQILQdANw1WB/hj49fpQG+zO8TgUAAFiijgP2aLoBuKoi2qD/e2CTKqINXqcCAAAsUccBe9wyDICrKk2jnq3b7nUaAAAgDtRxwB5HugG4qqPj13dTOquj4/c6FQAAYIk6Dtij6Qbgqmxfmgo6napsX5rXqQAAAEvUccAep5cDcNXnkVqNrvxQe6KNXqcCAAAsUccBezTdAFzVKKMdfPkKAABJiToO2OP0cgCu6ukL6qGMk9TTF/Q6FQAAYIk6Dtij6Qbgqg6OT6cEOqqDw9sPAADJhjoO2HMkGa+TSEahUEhVVVXKzMxUdXW11+kAAAAAAFzU2p6Qf1EBAAAAAJAgNN0AXDXIn64Pu4zUIH+616kAAABL1HHAHk03AFftiTZqXriMW40AAJCEqOOAPa7pjhPXdAMAAABA+8U13QCOSenyaVggU+m8/QAAkHSo44A9/loAuCrH30H/03mIcvwdvE4FAABYoo4D9gJeJwCgfVkXqdWFe5eqLFLndSoAAMASdRywR9MNwFUNMiqNhL1OAwAAxIE6Dtjj9HIArurhS9U9GTnq4Uv1OhUAAGCJOg7Yo+kG4KqOjl/fTemsjo7f61QAAIAl6jhgj1uGxYlbhgEAAABA+8UtwwAAAAAA8BhNNwBXDfCn65/HnakB/nSvUwEAAJao44A9mm4ArtpvmjS/fqf2myavUwEAAJao44A9rumOE9d0AwAAAED7xTXdAI5JafLpFH+G0nj7AQAg6VDHAXv8tQBw1Un+Dpp/3Ok6yd/B61QAAIAl6jhgj6YbgKs2RMK6Yu8ybYiEvU4FAABYoo4D9gJeJwCgfalTVKsiB7xOAwAAxIE6DtjjSDcAV3X3per/pPdVd1+q16kAAABL1HHAHk03AFd1cgK6IthdnRxOtAEAINlQxwF73DIsTtwyDAAAAADaL24ZBgAAAACAx2i6Abiqv7+DFnQ+Xf251QgAAEmHOg7Yo+kG4KoaE9G7jftUYyJepwIAACxRxwF7XNMdJ67pBgAAAID2i2u6ARyTUuUox99BqXK8TgUAAFiijgP2aLoBuGqAP11vHjdMA/zpXqcCAAAsUccBezTdAFxVGgnrmn2fqjQS9joVAABgiToO2OOu9gBcVauoljZVeZ0GAACIA3UcsMeRbgCu6uqkaFKHE9TVSfE6FQAAYIk6Dtij6QbgquN9KZrYobeO91GsAQBINtRxwB63DIsTtwwDAAAAgPaLW4YBAAAAAOAxmm4Arurn76C/dvqO+vk7eJ0KAACwRB0H7NF0A3BV2ES1qqlGYRP1OhUAAGCJOg7Y45ruOHFNNwAAAAC0X1zTDeCYlCJHPXypSpHjdSoAAMASdRywR9MNwFUD/el6r8sIDfSne50KAACwRB0H7NF0A3DV5midJuz/lzZH67xOBQAAWKKOA/YCXicAoH2pMRG927jP6zQAAEAcqOOAPY50A3BVFydF49N6qouT4nUqAADAEnUcsEfTDcBVWb5U3ZVxorJ8qV6nAgAALFHHAXvcMixO3DIMAAAAANovbhkGAAAAAIDHaLoBuOpEX5qe73SaTvSleZ0KAACwRB0H7NF0A3BVk4x2ROrVxJUtAAAkHeo4YI9ruuPENd0AAAAA0H5xTTeAY5JfUicnIL/XiQAAAGvUccAeTTcAV+X6M7T0+LOU68/wOhUAAGCJOg7Yo+kG4Kqt0TpNrlqjrdE6r1MBAACWqOOAvYDXCQBoX6pMRAsb9nidBgAAiAN1HLDHkW4ArjrOCWhcMEvHOfzPDwCAZEMdB+zRdANwVS9fUDNDA9TLF/Q6FQAAYIk6DtjjlmFx4pZhAAAAANB+ccswAAAAAAA8RtMNwFXZvjTNzTxZ2b40r1MBAACWqOOAPZpuAK6KyqjBRBXlyhYAAJIOdRywxzXdceKabgAAAABov7imG8AxyZGUKkeO14kAAABr1HHAHk03AFed7M/Q6q7n6GR/htepAAAAS9RxwN4x0XRPnjxZpaWlCofDKikp0fDhww87NhAIaPr06Vq/fr3C4bCWL1+uiy++uNmY0tJSGWMOiTlz5sTGLFq06JDnf//73ydsHwF8YVu0XndWf6Zt0XqvUwGOmj8lqIsefFUXPfiq/CncsxbAtx91HLAX8DqBcePG6fHHH9ekSZO0ZMkS3X777XrjjTc0aNAg7dq165Dxv/71r3X99dfr5ptv1tq1a3XxxRerqKhIZ599tpYvXy5JGj58uPx+f2ydU089VcXFxfrLX/7S7LX+8Ic/6P777489rq2tTcxOAojZb5o0v/7Qv20AAHDso44D8TFeRklJiZk9e3bsseM4Ztu2bWbq1Kktji8rKzOTJ09utuyll14yzz333GG3kZ+fb9atW9ds2aJFi0x+fn7ceYdCIWOMMaFQyNPfH0EkW3RyAuaHqV1NJyfgeS4EcbThTwmaix581Vz04KvGnxL0PB+CIIhEB3WcIL6K1vaEnp5enpKSomHDhqm4uDi2zBij4uJijRo1qsV1gsGg6urqmi0Lh8MaPXr0Ybdx/fXXa968eYc895Of/ES7du3SypUrNXPmTHXo0OGwuaampioUCjULAPZO8AX1RGauTvBxKi4AAMmGOg7Y8/T08q5duyoQCKiioqLZ8oqKCuXm5ra4zhtvvKE77rhD77zzjjZs2KC8vDxdddVVzU4n/7orr7xSnTt31h//+Mdmy1944QVt3rxZ5eXlGjJkiB555BENGjRIY8eObfF1pk2bphkzZljvI4Dm1kQO6LTdi1WvqNepAAAAS9RxwJ7n13TbmjJliubOnau1a9fKGKMNGzaooKBAEydObHH8TTfdpAULFmj79u3Nls+dOzf287/+9S9t375d//u//6t+/fpp48aNh7zOrFmz9Pjjj8ceh0IhlZWVtdFeAe1HVFKYQg0AQFKijgP2PD29fPfu3WpqalJWVlaz5VlZWdqxY8dh1xkzZowyMjKUnZ2t3Nxc1dTUtNgo9+3bVxdccIGeeeaZb8xlyZIlkqT+/fu3+HxDQ4Oqq6ubBQB7fXxBzQ7lqg+npQEAkHSo44A9T5vuxsZGLV26VHl5ebFljuMoLy9PH3zwwRHXra+vV3l5uQKBgMaOHav58+cfMmbChAnauXOnXnvttW/MZejQoZJ0yBFxAG3LJ0cdHb98crxOBQAAWKKOA/Y8P7388ccfV2FhoT7++GN9+OGHuv3225WRkaGCggJJUmFhocrKynTPPfdIkkaMGKHevXtr+fLl6t27t2bMmCGfz6dHH3202es6jqMJEyaosLBQkUik2XP9+vXTddddp3/84x/as2ePhgwZovz8fL399ttauXKlOzsOtFObo3WaULXK6zQAAEAcqOOAPc+b7hdffFHdunXTgw8+qB49emj58uW65JJLtHPnTklfnCIejX513UhaWpp+/etfq1+/fqqpqdE//vEP/fSnP9X+/fubve4FF1yg7OzsFr+1vKGhQRdccEGswd+6datefvll/frXv07szgIAAAAA2hVHX9w7DJZCoZCqqqqUmZnJ9d2AhVP8GXq581CN3bdcqyIHvE4HOCr+lKDypr8sSXrrobGKNNZ7nBEAJBZ1HPhKa3tCT6/pBtD+bI/Wa8aBDdoepTkBACDZUMcBe56fXg6gfak0Tfp/dS3fnQAAABzbqOOAPY50A3BVyPErL7WLQo7f61QAAIAl6jhgj6YbgKv6+tL0dObJ6utL8zoVAABgiToO2OP0cgCu+ixSqxF7SlRlIt88GAAAHFOo44A9mm4ArmqSUaVp8joNAAAQB+o4YI/TywG4qrcvqEc6DlBvX9DrVAAAgCXqOGCPphuAq1Idn7L9aUp1ePsBACDZUMcBe5xeDsBVpZGwrt2/0us0AABAHKjjgD3+RQUAAAAAQILQdANw1WB/hpZ3OUuD/RlepwIAACxRxwF7NN0AXLUz2qAna7doZ7TB61QAAIAl6jhgj2u6Abhqj2nUvLpyr9MAAABxoI4D9jjSDcBVHR2/zk7ppI6O3+tUAACAJeo4YI+mG4Crsn1perbTacr2pXmdCgAAsEQdB+xxejkAV62L1Oq8yo+0i2vBAABIOtRxwB5NNwBXNcioLFrvdRoAACAO1HHAHqeXA3BVT19QMzL6qacv6HUqAADAEnUcsEfTDcBV6Y5Pp6dkKt3h7QcAgGRDHQfscXo5AFdtiIR1xb7lXqcBAADiQB0H7PEvKgAAAAAAEoSmG4CrBvnTtbjLCA3yp3udCgAAsEQdB+zRdANwVWW0UX8Kb1dltNHrVAAAgCXqOGCPa7oBuGqXadTvwlu9TgMAAMSBOg7Y40g3AFely6fTAyGl8/YDAEDSoY4D9vhrAeCqHH8H/aXzd5Tj7+B1KsBR8xmjIaWf6vxP39Z50ShFFcC3HnUcsOdIMl4nkYxCoZCqqqqUmZmp6upqr9MBkkZQPvX1p2lLpE71inqdDhC3MZJ+K6nP15ZtlTRFUpEnGQFA4lHHga+0tifkn/IAXFWvqNZFainUSGpjJL0kqfdBy3v/e/kY1zMCAHdQxwF7NN0AXNXDl6qp6Seqhy/V61SAuPj0xRHuL38++DlJeqKF5wDg24A6DtjjMwEAV4WcgPJSuyjkcPMEJKfv6otTyg9XQH2S+v57HAB821DHAXv8tQBw1bpIrS7a94nXaQBx69nG4wAgmVDHAXsc6QYAwML2Nh4HAAC+3Wi6AbhqgD9dbx03TAP86V6nAsTlXX3xLeWH+wqhqKQt/x4HAN821HHAHk03AFdVmSYtqN+tKtPkdSpAXKL64rZgX/588HOSdHsLzwHAtwF1HLDHfbrjxH26AaB9a+k+3Vv0RcPNfboBAPj2a21PyBepAXBVUD6d6E/Tpkgd9/hEUiuS9GogVVN+OkNdqvequOgJ/TPSyKwG8K1GHQfscXo5AFf193fQa8edof7+Dl6nAhy1qOPo05wh+ueQ8/S2z8fHTwDfetRxwB5NNwBXbYyENWbfcm2MhL1OBQAAWKKOA/Y4vRyAq8KKamVTjddpAACAOFDHAXsc6Qbgqm5Oin6R3lfdnBSvUwEAAJao44A9mm4ArjrOl6JxwSwd56NYAwCQbKjjgD1OLwfgqs8jtRq99yOv0wAAAHGgjgP2ONINAAAAAECC0HQDcFV/fwe92vl0bjUCAEASoo4D9mi6AbjqgIloSeN+HTARr1MBAACWqOOAPa7pBuCq7dEGPXRgo9dpAACAOFDHAXsc6QbgqlQ5yvalKVWO16kAAABL1HHAHk03AFcN8KfrrS5naoA/3etUAACAJeo4YI+mG4CrNkXr9JP9K7UpWud1KgAAwBJ1HLDHNd0AXPXlF7AAAIDkQx0H7HGkG4CrjndS9LMOvXW8k+J1KgAAwBJ1HLBH0w3AVd18qZrU4QR186V6nQoAALBEHQfscXo5AFetjRzQmZVLvE4DAADEgToO2ONINwAAAAAACULTDcBVOf4O+kunIcrxd/A6FQAAYIk6Dtij6QbgqnoT1bpIrepN1OtUAACAJeo4YI9rugG4qjxar3tq1nudBgAAiAN1HLDHkW4ArgrIUTcnRQE5XqcCAAAsUccBezTdAFw1yJ+uD44fqUH+dK9TAQAAlqjjgD2abgCu2hyt08/2r9LmaJ3XqQAAAEvUccAe13QDcFWNieifjXu9TgMAAMSBOg7Y40g3AFd1cQK6Pq2nujj8zw8AgGRDHQfs0XQDcFUPX1D3ZOSohy/odSoAAMASdRywx7+oALhqdeSATt6z2Os0AABAHKjjgD2OdAMAAAAAkCA03QBcle1LU2Hmqcr2pXmdCgAAsEQdB+zRdANwVURGlaZRERmvUwEAAJao44A9rukG4Kpt0Xr9n+rPvE4DAADEgToO2ONINwBX+SR1dPy8+QAAkISo44A9/l4AuGqwP0PLjx+lwf4Mr1MBAACWqOOAPZpuAK7aGq3TbVVrtDVa53UqAADAEnUcsMc13QBcVWUiWtCwx+s0AABAHKjjgD2abgCu6uwE9P3ULvrfhkrtM01epwMclUhjvRbe/0Ov0wAA11DHAXucXg7AVb19QT0aGqjevqDXqQAAAEvUccCeI3GTvXiEQiFVVVUpMzNT1dXVXqcDJBW/pIjXSQAAgLhQx4EvtLYn5Eg3ANdRqAEASF7UccAOTTcAV/X1penp0GD19aV5nQoAALBEHQfs0XQDAAAAAJAgXNMdJ67pBgAAAID2i2u6ARyz/F4nAAAA4kYdB+zQdANw1Sn+DH3WdbRO8Wd4nQoAALBEHQfs0XQDcFVZtF53V3+usmi916kAAABL1HHAXsDrBAC0L/tMk/5av9PrNAAAQByo44A9jnQDcFWm49elqccr0+GKMAAAkg11HLBH0w3AVX18aZqdOVh9uL8nAABJhzoO2OP0cgCuWhM5oKF7PlCtiXidCgAAsEQdB+zRdANwVVRSDYUaAICkRB0H7HF6OQBXneALKj80SCf4gl6nAgAALFHHAXs03QBc5ZejLk6K/HK8TgUAAFiijgP2HEnG6ySSUSgUUlVVlTIzM1VdXe11OgAAAAAAF7W2J+RINwAAAAAACULTDcBVJ/sztPr4s3WyP8PrVAAAgCXqOGCPphuAq3ZE6zXzQKl2ROu9TgUAAFiijgP2jomme/LkySotLVU4HFZJSYmGDx9+2LGBQEDTp0/X+vXrFQ6HtXz5cl188cXNxpSWlsoYc0jMmTMnNiYYDGrOnDnavXu3qqur9dJLL6l79+4J20cAX6g0TfpT3XZVmiavUwEAAJao40B8jJcxbtw4U1dXZ2688UYzePBg8/TTT5vKykrTrVu3Fsc//PDDZtu2bebSSy81OTk5ZtKkSaa2ttYMHTo0NqZr164mKysrFnl5ecYYY84777zYmN/97ndm8+bN5nvf+54544wzzOLFi817773X6rxDoZAxxphQKOTp748gki06On5zfspxpqPj9zwXgiAIgiDsgjpOEF+FRU/obaIlJSVm9uzZsceO45ht27aZqVOntji+rKzMTJ48udmyl156yTz33HOH3UZ+fr5Zt25d7HFmZqapr683Y8eOjS0bNGiQMcaYkSNHtvUvmCCIr8Up/gyzvutoc4o/w/NcCIIgCIKwC+o4QXwVre0JPT29PCUlRcOGDVNxcXFsmTFGxcXFGjVqVIvrBINB1dXVNVsWDoc1evTow27j+uuv17x582LLhg0bptTU1Gbb/eyzz7R58+bDbjc1NVWhUKhZALD3WaRWo/Ys0WeRWq9TAQAAlqjjgD1Pm+6uXbsqEAiooqKi2fKKigr16NGjxXXeeOMN3XHHHerfv78cx9EFF1ygq666Sj179mxx/JVXXqnOnTvrj3/8Y2xZjx49VF9fr/3797d6u9OmTVNVVVUsysrKLPYUwJeaZLTLNKpJxutUAACAJeo4YO+Y+CI1G1OmTNG6deu0du1aNTQ0aM6cOSooKFA0Gm1x/E033aQFCxZo+/btR7XdWbNmKTMzMxa9e/c+qtcD2qtevqBmduyvXr6g16kAAABL1HHAnqdN9+7du9XU1KSsrKxmy7OysrRjx47DrjNmzBhlZGQoOztbubm5qqmp0caNGw8Z27dvX11wwQV65plnmi3fsWOHgsGgOnXq1OrtNjQ0qLq6ulkAsBd0fBrgT1fQSbr/+QEA0O5RxwF7nv61NDY2aunSpcrLy4stcxxHeXl5+uCDD464bn19vcrLyxUIBDR27FjNnz//kDETJkzQzp079dprrzVbvnTpUjU0NDTb7sCBA5Wdnf2N2wVwdEojYV29/1OVRsJepwIAACxRx4H4ePqNb+PGjTPhcNiMHz/e5ObmmqeeespUVlaa7t27G0mmsLDQzJw5MzZ+xIgRZsyYMSYnJ8eMHj3aFBcXmw0bNphOnTo1e13HccymTZvMrFmzWtzu7373O7Np0yZz/vnnmzPOOMO8//775v3332/zb6ojCIIgCIIgCIIgvn2RNLcMk2RuvfVWs2nTJlNXV2dKSkrMiBEjYs8tWrTIFBQUxB6fe+65ZtWqVSYcDptdu3aZwsJC07Nnz0Ne88ILLzTGGDNgwIAWtxkMBs2cOXPMnj17TE1NjXn55ZdNVlZWIn7BBEF8LXL9GebjLiNNLrcaIQiCIIikC+o4QXwVre0JnX//AEuhUEhVVVXKzMzk+m7AwvFOisakdVdR3U7tMY1epwMAACxQx4GvtLYnDLiYEwBoj2nUM2FuuQcAQDKijgP2+NpBAK7KcPwamdJJGY7f61QAAIAl6jhgj6YbgKtO9KXp+U6n6URfmtepAAAAS9RxwB6nlwNw1bpIrfIqP9b2aL3XqQAAAEvUccAeTTcAVzXIaHO0zus0AABAHKjjgD1OLwfgqp6+VE3P6KeevlSvUwEAAJao44A9mm4AruILWAAASF7UccAe9+mOE/fpBgAAAID2q7U9IUe6AQAAAABIEL5I7SiFQiGvUwCSSn9/un6XOViTq9ZofaTW63QAAIAF6jjwldb2gjTdcfryF1xWVuZxJkBy+sTrBAAAQNyo48BXQqHQEU8v55ruo9CrV69j/nruUCiksrIy9e7d+5jPFYnHfMDBmBM4GHMCX8d8wMGYEzhYe58ToVBI5eXlRxzDke6j8E2/3GNJdXV1u/wjQMuYDzgYcwIHY07g65gPOBhzAgdrr3OiNfvMF6kBAAAAAJAgNN0AAAAAACQITfe3XH19vWbMmKH6+nqvU8ExgPmAgzEncDDmBL6O+YCDMSdwMObEN+OL1AAAAAAASBCOdAMAAAAAkCA03QAAAAAAJAhNNwAAAAAACULTDQAAAABAgtB0J5nJkyertLRU4XBYJSUlGj58eKvWu+aaa2SMUVFRUWxZIBDQww8/rE8//VQ1NTUqKytTYWGhevbsmaj0kQBtOScO9vvf/17GGE2ZMqWt0kWCJWI+5Obmav78+dq3b59qamr04Ycfqk+fPm2dOhKkredERkaGZs+era1bt6q2tlarVq3SLbfckojUkSA2c+KGG26QMaZZhMPhQ8Y98MADKi8vV21trd588031798/kbuANtaWc4LPl98OiXif+FJ7/XxpiOSIcePGmbq6OnPjjTeawYMHm6efftpUVlaabt26HXG97Oxss3XrVvP222+boqKi2PLMzEyzcOFCc/XVV5uBAweakSNHmpKSEvPRRx95vq+EN3Pi63HllVeaZcuWmW3btpkpU6Z4vq+EN/OhX79+Zvfu3eaRRx4xQ4cONf369TOXX375N74mcWxEIubE008/bdatW2fOO+88k52dbW6++WbT2NhoLr/8cs/3l2j7OXHDDTeYffv2maysrFh079692Zi7777b7N271/zoRz8yp512mnnllVfMhg0bTDAY9Hx/CffnBJ8vkz8S8T7xZbTjz5eeJ0C0MkpKSszs2bNjjx3HMdu2bTNTp0497Do+n8+89957ZuLEiaagoOCwDdaXceaZZxpjjOnTp4/n+0t4Nyd69epltm7dak4++WRTWlra3t4UkzYSMR/+/Oc/m2effdbzfSOOnTmxcuVKc9999zVb9vHHH5uHHnrI8/0l2n5O3HDDDWbv3r1HfM3y8nJz5513xh5nZmaacDhsrrnmGs/3l/BmThwcfL5MrkjUnGjPny85vTxJpKSkaNiwYSouLo4tM8aouLhYo0aNOux6999/v3bu3Kl58+a1ajudOnVSNBrVvn37jjZlJFii5oTjOHruuef02GOPafXq1W2eNxIjEfPBcRxddtll+vzzz/X666+roqJCJSUluuKKKxKyD2hbiXqPWLx4sX70ox+pV69ekqTzzz9fAwcO1MKFC9t2B9Dm4p0THTt21KZNm7Rlyxa98sorOvnkk2PP5eTkqGfPns1es6qqSkuWLDnia+LYkIg50RI+XyaPRM2J9v75kqY7SXTt2lWBQEAVFRXNlldUVKhHjx4trnPOOefopptu0s0339yqbQSDQT3yyCP685//rOrq6qPOGYmVqDkxdepUNTU16cknn2zTfJFYiZgP3bt3VygU0q9+9Su9/vrruuiii1RUVKS//vWvOvfcc9t8H9C2EvUecdttt2n16tUqKytTQ0ODXn/9dd16661699132zR/tL145sRnn32miRMn6oorrtD1118vn8+nxYsXq3fv3pIUW8/mNXHsSMScOBifL5NLouZEe/98GfA6ASRGx44d9dxzz+nmm2/Wnj17vnF8IBDQiy++KMdx9POf/9yFDOG21syJM844Q1OmTNEZZ5zhcnZwW2vmg8/3xf9l58+fryeeeEKStGLFCp199tmaNGmS3nnnHbfShQtaWzduu+02nXXWWbr88su1efNmnXvuufqv//ovlZeX66233nIxY7ihpKREJSUlsceLFy/WmjVrdMstt+j+++/3MDN4xWZO8PmyffimOcHnS5rupLF79241NTUpKyur2fKsrCzt2LHjkPEnnXSScnJy9Pe//z227MsP0I2NjRo0aJA2btwo6as3xOzsbH3/+9/nv5BJIhFz4rvf/a66d++uLVu2xMYEAgH95je/0e23366cnJwE7Q2OViLmw9atW9XY2HjIaWBr1qzR6NGjE7AXaEuJmBPl5eWaOXOmxowZo3/84x+SpJUrV2ro0KG66667aLqPcbZzoiVNTU1atmxZ7NvJv1zv4NfIysrS8uXL2yZxJEwi5sSX+HyZnBIxJ/h8yenlSaOxsVFLly5VXl5ebJnjOMrLy9MHH3xwyPi1a9fq1FNP1dChQ2Pxt7/9TYsWLdLQoUO1detWSV+9IQ4YMEAXXHCBKisrXdsnHJ1EzInnnntOQ4YMaTamrKxMjz32mC6++GI3dw+WEjEfGhsb9dFHH2nQoEHN1h04cKA2b96c8H3C0UnEnEhJSVFqaqqi0WizdSORSKxBx7HLdk60xOfz6bTTTtP27dslSaWlpdq+fXuz1wyFQho5cmSrXxPeScSckPh8mcwSMSf4fPkFz7/NjWhdjBs3zoTDYTN+/HiTm5trnnrqKVNZWRn7Sv7CwkIzc+bMw65/8LfQBgIB88orr5gtW7aYIUOGNPua/5SUFM/3l3B/TrQU7e3bJZM5EjEfrrzySlNfX29+9rOfmZNOOsnceuutprGx0Zxzzjme7y/hzZxYtGiRWblypTnvvPPMiSeeaG644QZTW1trJk2a5Pn+Em0/J6ZPn24uvPBCk5OTY04//XTzwgsvmNraWjN48ODYmLvvvttUVlaayy+/3Jx66qmmqKiIW4YlUbT1nODzZfJHIt4nDo729vmS08uTyIsvvqhu3brpwQcfVI8ePbR8+XJdcskl2rlzpySpb9++hxx9OJLevXvHvoV4xYoVzZ47//zz9fbbb7dd8kiItp4TSG6JmA+vvPKKJk2apGnTpunJJ5/UZ599prFjx+r9999PxC6gjSViTlx77bWaNWuWnn/+eXXp0kWbN2/Wvffeq6eeeioRu4A2ZjsnjjvuOM2dO1c9evTQ3r17tXTpUp199tlas2ZNbMyjjz6qjIwM/eEPf1Dnzp313nvv6ZJLLlF9fb3r+wd7bT0n+HyZ/BLxPtHeOfqi+wYAAAAAAG2MC7AAAAAAAEgQmm4AAAAAABKEphsAAAAAgASh6QYAAAAAIEFougEAAAAASBCabgAAAAAAEoSmGwAAAACABKHpBgAAAAAgQWi6AQAAAAAJUVpaKmNMs5g6deo3rnfWWWfprbfeUk1Njfbv36+3335baWlph4xLTU3VsmXLZIzRd77zndjy7OzsQ7ZrjNHIkSPj2o/Dbac1aLoBAIAkqaCgQMYY/f73vz/kuTlz5sgYo4KCgtjYoqKiQ9Y1xqihoUE7duzQwoULNWHCBDmO49o+AADct2jRIt1www2HfX769Onq0aNHLGbPnn3E1zvrrLP0+uuva+HChRoxYoSGDx+uOXPmKBqNHjL20UcfVXl5+WFfKy8vr9m2ly5d2vods9jOkdB0AwCAmC1btujaa69tdjQhGAzquuuu0+bNm4+47oIFC9SjRw+deOKJuvTSS7Vo0SL99re/1auvviq/35/o1AEAx6jq6mpVVFTEora29ojj8/Pz9eSTT+qRRx7R6tWr9fnnn+svf/mLGhoamo275JJLdNFFF+muu+467Gvt2bOn2babmpqaPX/TTTdp9erVCofDWrNmjX7+858f8hqt2c6R0HQDAICYTz75RFu3btVVV10VW3bVVVdpy5YtWrZs2RHXra+vV0VFhcrLy7Vs2TLNmjVLV1xxhX7wgx/oxhtvTHDmAIBj1a9+9Svt3r1bn3zyie66664j/iO2W7duOuuss7Rz5069//772rFjh/75z3/qnHPOaTaue/fumjt3rn76058esYn/29/+poqKCr377ru6/PLLmz133XXX6cEHH9S9996rwYMH65577tFDDz2k8ePHW2/nSGi6AQBAM/PmzdOECRNijydOnBg7rdzWokWLtHz58mZNPACg/XjyySd17bXX6nvf+56efvpp3XPPPXr00UcPO75fv36SpBkzZmju3Lm65JJL9Mknn+itt95S//79Y+P++Mc/6qmnnjrs6eI1NTW64447dPXVV+uyyy7Te++9p1deeaVZ4/3AAw/ozjvvVFFRkTZt2qSioiLl5+frlltuafV2WssQBEEQBEEUFBSYoqIi07VrVxMOh03fvn1N3759TW1trTn++ONNUVGRKSgoaDb24HVbet0///nPZtWqVZ7vH0EQBNE2MW3aNFNdXR2LpqYmEw6Hmy3r06dPi+tOmDDBNDQ0mNTU1BafHzVqlDHGmP/8z/9stnzFihVm5syZRpK57bbbzLvvvmt8Pp+RZLKzs40xxnznO985Yt6FhYXmnXfeMZJMenq6McaYAwcONMs7HA6bHTt2HNV2Do6AAAAAvmb37t167bXXdOONN8pxHL322mvas2dP3K/nOI6MMW2YIQDAS0899ZRefPHF2OPnn39eL7/8sv7617/Glh3uS8eWLFmilJQUnXjiifr8888PeX779u2SpNWrVzdbvmbNGvXt21eS9P3vf1+jRo1SfX19szEff/yxnn/++cNe0rRkyRJdeOGFkqSOHTtKkm6++WYtWbKk2bhIJHJU2zkYTTcAADjEvHnzNGfOHEnSrbfeelSvNXjwYJWWlrZFWgCAY8DevXu1d+/e2ONwOKydO3dqw4YN37ju0KFDFYlEtHPnzhaf37Rpk8rKyjRo0KBmywcOHKgFCxZIkn7xi1/ovvvuiz3Xq1cvLVy4UNdcc80hDfTB2/6yqd+5c6fKysrUr18/vfDCCy2Oj3c7B6PpBgAAh3j99deVmpoqY4zeeOONuF/ne9/7noYMGaL8/Pw2zA4AkAzOOussjRw5UosWLVJ1dbVGjRql/Px8/elPf9K+ffskfdHIvvXWWxo/frw++ugjSdJjjz2mBx54QCtWrNDy5ct1ww03KDc3Vz/+8Y8lSVu3bm22nZqaGknShg0bVFZWJkkaP368GhoaYl8CetVVV2nixIn62c9+FlvvP/7jP/Tkk09q//79ev311xUMBnXmmWfquOOOU35+fqu20xo03QAA4BDRaFSDBw+O/dwawWBQWVlZ8vv9ysrK0iWXXKJp06bp73//u5599tlEpgsAOAbV19fr2muv1YwZMxQMBlVaWqr8/Hw9/vjjsTEpKSnKzc1Venp6bNlvf/tbpaWlKT8/X126dNGKFSt04YUXauPGjVbbnz59urKzs9XU1KS1a9fqmmuu0csvvxx7/r//+79VW1urX/7yl3rsscd04MABrVy5Uk888cRR7/vXOfri4m4AANDOFRQUqHPnzhozZkyLzxcVFWnfvn2aMGGCCgsLlZ6erquvvjq27pfXtjU2Nmrv3r1asWKFXnjhBRUWFnJNNwCg3aLpBgAA1hYsWKD169frtttu8zoVAACOadynGwAAtFrnzp112WWX6fzzz1dxcbHX6QAAcMzjmm4AANBq8+bN0/Dhw/Wb3/xG8+fP9zodAACOeZxeDgAAAABAgnB6OQAAAAAACULTDQAAAABAgtB0AwAAAACQIDTdAAAAAAAkCE03AAAAAAAJQtMNAAAAAECC0HQDAAAAAJAgNN0AAAAAACTI/wd4V2DRsKZmdgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Example - Exoplanet"
      ],
      "metadata": {
        "id": "SBIk5s-Jg45E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from astroquery.ipac.nexsci.nasa_exoplanet_archive import NasaExoplanetArchive\n",
        "from astropy.table import Table"
      ],
      "metadata": {
        "id": "sxkPQf7Lfmgq"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "# Example exoplanet data\n",
        "exoplanetName = \"Kepler-10 b\"\n",
        "dataTable = NasaExoplanetArchive.query_object(exoplanetName,table=\"ps\")"
      ],
      "metadata": {
        "id": "EM9ijBSohC_Q"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = Table(dataTable).to_pandas()"
      ],
      "metadata": {
        "id": "cvnx8ioShVWy"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Ogp7JDmuhe3r",
        "outputId": "0f5a283c-6b90-4dd4-dcc8-6e19f52e3b8d"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        pl_name pl_letter   hostname hd_name hip_name         tic_id  \\\n",
              "0   Kepler-10 b         b  Kepler-10                   TIC 377780790   \n",
              "1   Kepler-10 b         b  Kepler-10                   TIC 377780790   \n",
              "2   Kepler-10 b         b  Kepler-10                   TIC 377780790   \n",
              "3   Kepler-10 b         b  Kepler-10                   TIC 377780790   \n",
              "4   Kepler-10 b         b  Kepler-10                   TIC 377780790   \n",
              "5   Kepler-10 b         b  Kepler-10                   TIC 377780790   \n",
              "6   Kepler-10 b         b  Kepler-10                   TIC 377780790   \n",
              "7   Kepler-10 b         b  Kepler-10                   TIC 377780790   \n",
              "8   Kepler-10 b         b  Kepler-10                   TIC 377780790   \n",
              "9   Kepler-10 b         b  Kepler-10                   TIC 377780790   \n",
              "10  Kepler-10 b         b  Kepler-10                   TIC 377780790   \n",
              "11  Kepler-10 b         b  Kepler-10                   TIC 377780790   \n",
              "12  Kepler-10 b         b  Kepler-10                   TIC 377780790   \n",
              "13  Kepler-10 b         b  Kepler-10                   TIC 377780790   \n",
              "14  Kepler-10 b         b  Kepler-10                   TIC 377780790   \n",
              "15  Kepler-10 b         b  Kepler-10                   TIC 377780790   \n",
              "16  Kepler-10 b         b  Kepler-10                   TIC 377780790   \n",
              "17  Kepler-10 b         b  Kepler-10                   TIC 377780790   \n",
              "18  Kepler-10 b         b  Kepler-10                   TIC 377780790   \n",
              "19  Kepler-10 b         b  Kepler-10                   TIC 377780790   \n",
              "20  Kepler-10 b         b  Kepler-10                   TIC 377780790   \n",
              "\n",
              "                         gaia_id  default_flag  \\\n",
              "0   Gaia DR2 2132155017099178624             0   \n",
              "1   Gaia DR2 2132155017099178624             0   \n",
              "2   Gaia DR2 2132155017099178624             0   \n",
              "3   Gaia DR2 2132155017099178624             0   \n",
              "4   Gaia DR2 2132155017099178624             0   \n",
              "5   Gaia DR2 2132155017099178624             0   \n",
              "6   Gaia DR2 2132155017099178624             0   \n",
              "7   Gaia DR2 2132155017099178624             0   \n",
              "8   Gaia DR2 2132155017099178624             1   \n",
              "9   Gaia DR2 2132155017099178624             0   \n",
              "10  Gaia DR2 2132155017099178624             0   \n",
              "11  Gaia DR2 2132155017099178624             0   \n",
              "12  Gaia DR2 2132155017099178624             0   \n",
              "13  Gaia DR2 2132155017099178624             0   \n",
              "14  Gaia DR2 2132155017099178624             0   \n",
              "15  Gaia DR2 2132155017099178624             0   \n",
              "16  Gaia DR2 2132155017099178624             0   \n",
              "17  Gaia DR2 2132155017099178624             0   \n",
              "18  Gaia DR2 2132155017099178624             0   \n",
              "19  Gaia DR2 2132155017099178624             0   \n",
              "20  Gaia DR2 2132155017099178624             0   \n",
              "\n",
              "                                           pl_refname  \\\n",
              "0   <a refstr=FOGTMANN_SCHULZ_ET_AL__2014 href=htt...   \n",
              "1   <a refstr=MORTON_ET_AL__2016 href=https://ui.a...   \n",
              "2   <a refstr=RAJPAUL_ET_AL__2017 href=https://ui....   \n",
              "3   <a refstr=HOLCZER_ET_AL__2016 href=https://ui....   \n",
              "4   <a refstr=Q1_Q12_KOI_TABLE href=https://exopla...   \n",
              "5   <a refstr=Q1_Q17_DR25_SUPPLEMENTAL_KOI_TABLE h...   \n",
              "6   <a refstr=Q1_Q17_DR25_KOI_TABLE href=https://e...   \n",
              "7   <a refstr=EXOFOP_TESS_TOI href=https://exofop....   \n",
              "8   <a refstr=BONOMO_ET_AL__2023 href=https://ui.a...   \n",
              "9   <a refstr=DAI_ET_AL__2019 href=https://ui.adsa...   \n",
              "10  <a refstr=Q1_Q8_KOI_TABLE href=https://exoplan...   \n",
              "11  <a refstr=DUMUSQUE_ET_AL__2014 href=https://ui...   \n",
              "12  <a refstr=HU_ET_AL__2015 href=https://ui.adsab...   \n",
              "13  <a refstr=VAN_EYLEN___ALBRECHT_2015 href=https...   \n",
              "14  <a refstr=WEISS_ET_AL__2016 href=https://ui.ad...   \n",
              "15  <a refstr=BERGER_ET_AL__2018 href=https://ui.a...   \n",
              "16  <a refstr=GAJDO_SCARON__ET_AL__2019 href=https...   \n",
              "17  <a refstr=Q1_Q17_DR24_KOI_TABLE href=https://e...   \n",
              "18  <a refstr=BATAHLA_ET_AL__2011 href=https://ui....   \n",
              "19  <a refstr=Q1_Q16_KOI_TABLE href=https://exopla...   \n",
              "20  <a refstr=ESTEVES_ET_AL__2015 href=https://ui....   \n",
              "\n",
              "                                           sy_refname  ...  \\\n",
              "0   <a refstr=STASSUN_ET_AL__2019 href=https://ui....  ...   \n",
              "1   <a refstr=STASSUN_ET_AL__2019 href=https://ui....  ...   \n",
              "2   <a refstr=STASSUN_ET_AL__2019 href=https://ui....  ...   \n",
              "3   <a refstr=STASSUN_ET_AL__2019 href=https://ui....  ...   \n",
              "4   <a refstr=STASSUN_ET_AL__2019 href=https://ui....  ...   \n",
              "5   <a refstr=STASSUN_ET_AL__2019 href=https://ui....  ...   \n",
              "6   <a refstr=STASSUN_ET_AL__2019 href=https://ui....  ...   \n",
              "7   <a refstr=STASSUN_ET_AL__2019 href=https://ui....  ...   \n",
              "8   <a refstr=STASSUN_ET_AL__2019 href=https://ui....  ...   \n",
              "9   <a refstr=STASSUN_ET_AL__2019 href=https://ui....  ...   \n",
              "10  <a refstr=STASSUN_ET_AL__2019 href=https://ui....  ...   \n",
              "11  <a refstr=STASSUN_ET_AL__2019 href=https://ui....  ...   \n",
              "12  <a refstr=STASSUN_ET_AL__2019 href=https://ui....  ...   \n",
              "13  <a refstr=STASSUN_ET_AL__2019 href=https://ui....  ...   \n",
              "14  <a refstr=STASSUN_ET_AL__2019 href=https://ui....  ...   \n",
              "15  <a refstr=STASSUN_ET_AL__2019 href=https://ui....  ...   \n",
              "16  <a refstr=STASSUN_ET_AL__2019 href=https://ui....  ...   \n",
              "17  <a refstr=STASSUN_ET_AL__2019 href=https://ui....  ...   \n",
              "18  <a refstr=STASSUN_ET_AL__2019 href=https://ui....  ...   \n",
              "19  <a refstr=STASSUN_ET_AL__2019 href=https://ui....  ...   \n",
              "20  <a refstr=STASSUN_ET_AL__2019 href=https://ui....  ...   \n",
              "\n",
              "            sy_jmagstr  sy_hmag sy_hmagerr1 sy_hmagerr2          sy_hmagstr  \\\n",
              "0   9.889&plusmn;0.022    9.563       0.025      -0.025  9.563&plusmn;0.025   \n",
              "1   9.889&plusmn;0.022    9.563       0.025      -0.025  9.563&plusmn;0.025   \n",
              "2   9.889&plusmn;0.022    9.563       0.025      -0.025  9.563&plusmn;0.025   \n",
              "3   9.889&plusmn;0.022    9.563       0.025      -0.025  9.563&plusmn;0.025   \n",
              "4   9.889&plusmn;0.022    9.563       0.025      -0.025  9.563&plusmn;0.025   \n",
              "5   9.889&plusmn;0.022    9.563       0.025      -0.025  9.563&plusmn;0.025   \n",
              "6   9.889&plusmn;0.022    9.563       0.025      -0.025  9.563&plusmn;0.025   \n",
              "7   9.889&plusmn;0.022    9.563       0.025      -0.025  9.563&plusmn;0.025   \n",
              "8   9.889&plusmn;0.022    9.563       0.025      -0.025  9.563&plusmn;0.025   \n",
              "9   9.889&plusmn;0.022    9.563       0.025      -0.025  9.563&plusmn;0.025   \n",
              "10  9.889&plusmn;0.022    9.563       0.025      -0.025  9.563&plusmn;0.025   \n",
              "11  9.889&plusmn;0.022    9.563       0.025      -0.025  9.563&plusmn;0.025   \n",
              "12  9.889&plusmn;0.022    9.563       0.025      -0.025  9.563&plusmn;0.025   \n",
              "13  9.889&plusmn;0.022    9.563       0.025      -0.025  9.563&plusmn;0.025   \n",
              "14  9.889&plusmn;0.022    9.563       0.025      -0.025  9.563&plusmn;0.025   \n",
              "15  9.889&plusmn;0.022    9.563       0.025      -0.025  9.563&plusmn;0.025   \n",
              "16  9.889&plusmn;0.022    9.563       0.025      -0.025  9.563&plusmn;0.025   \n",
              "17  9.889&plusmn;0.022    9.563       0.025      -0.025  9.563&plusmn;0.025   \n",
              "18  9.889&plusmn;0.022    9.563       0.025      -0.025  9.563&plusmn;0.025   \n",
              "19  9.889&plusmn;0.022    9.563       0.025      -0.025  9.563&plusmn;0.025   \n",
              "20  9.889&plusmn;0.022    9.563       0.025      -0.025  9.563&plusmn;0.025   \n",
              "\n",
              "   sy_kmag sy_kmagerr1 sy_kmagerr2  sky_coord.ra sky_coord.dec  \n",
              "0    9.496       0.021      -0.021    285.679298     50.241484  \n",
              "1    9.496       0.021      -0.021    285.679298     50.241484  \n",
              "2    9.496       0.021      -0.021    285.679298     50.241484  \n",
              "3    9.496       0.021      -0.021    285.679298     50.241484  \n",
              "4    9.496       0.021      -0.021    285.679298     50.241484  \n",
              "5    9.496       0.021      -0.021    285.679298     50.241484  \n",
              "6    9.496       0.021      -0.021    285.679298     50.241484  \n",
              "7    9.496       0.021      -0.021    285.679298     50.241484  \n",
              "8    9.496       0.021      -0.021    285.679298     50.241484  \n",
              "9    9.496       0.021      -0.021    285.679298     50.241484  \n",
              "10   9.496       0.021      -0.021    285.679298     50.241484  \n",
              "11   9.496       0.021      -0.021    285.679298     50.241484  \n",
              "12   9.496       0.021      -0.021    285.679298     50.241484  \n",
              "13   9.496       0.021      -0.021    285.679298     50.241484  \n",
              "14   9.496       0.021      -0.021    285.679298     50.241484  \n",
              "15   9.496       0.021      -0.021    285.679298     50.241484  \n",
              "16   9.496       0.021      -0.021    285.679298     50.241484  \n",
              "17   9.496       0.021      -0.021    285.679298     50.241484  \n",
              "18   9.496       0.021      -0.021    285.679298     50.241484  \n",
              "19   9.496       0.021      -0.021    285.679298     50.241484  \n",
              "20   9.496       0.021      -0.021    285.679298     50.241484  \n",
              "\n",
              "[21 rows x 355 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-93e202e5-295c-4b73-8b51-8c9d00a090e7\" class=\"colab-df-container\">\n",
              "    <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>pl_name</th>\n",
              "      <th>pl_letter</th>\n",
              "      <th>hostname</th>\n",
              "      <th>hd_name</th>\n",
              "      <th>hip_name</th>\n",
              "      <th>tic_id</th>\n",
              "      <th>gaia_id</th>\n",
              "      <th>default_flag</th>\n",
              "      <th>pl_refname</th>\n",
              "      <th>sy_refname</th>\n",
              "      <th>...</th>\n",
              "      <th>sy_jmagstr</th>\n",
              "      <th>sy_hmag</th>\n",
              "      <th>sy_hmagerr1</th>\n",
              "      <th>sy_hmagerr2</th>\n",
              "      <th>sy_hmagstr</th>\n",
              "      <th>sy_kmag</th>\n",
              "      <th>sy_kmagerr1</th>\n",
              "      <th>sy_kmagerr2</th>\n",
              "      <th>sky_coord.ra</th>\n",
              "      <th>sky_coord.dec</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Kepler-10 b</td>\n",
              "      <td>b</td>\n",
              "      <td>Kepler-10</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>TIC 377780790</td>\n",
              "      <td>Gaia DR2 2132155017099178624</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;a refstr=FOGTMANN_SCHULZ_ET_AL__2014 href=htt...</td>\n",
              "      <td>&lt;a refstr=STASSUN_ET_AL__2019 href=https://ui....</td>\n",
              "      <td>...</td>\n",
              "      <td>9.889&amp;plusmn;0.022</td>\n",
              "      <td>9.563</td>\n",
              "      <td>0.025</td>\n",
              "      <td>-0.025</td>\n",
              "      <td>9.563&amp;plusmn;0.025</td>\n",
              "      <td>9.496</td>\n",
              "      <td>0.021</td>\n",
              "      <td>-0.021</td>\n",
              "      <td>285.679298</td>\n",
              "      <td>50.241484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Kepler-10 b</td>\n",
              "      <td>b</td>\n",
              "      <td>Kepler-10</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>TIC 377780790</td>\n",
              "      <td>Gaia DR2 2132155017099178624</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;a refstr=MORTON_ET_AL__2016 href=https://ui.a...</td>\n",
              "      <td>&lt;a refstr=STASSUN_ET_AL__2019 href=https://ui....</td>\n",
              "      <td>...</td>\n",
              "      <td>9.889&amp;plusmn;0.022</td>\n",
              "      <td>9.563</td>\n",
              "      <td>0.025</td>\n",
              "      <td>-0.025</td>\n",
              "      <td>9.563&amp;plusmn;0.025</td>\n",
              "      <td>9.496</td>\n",
              "      <td>0.021</td>\n",
              "      <td>-0.021</td>\n",
              "      <td>285.679298</td>\n",
              "      <td>50.241484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Kepler-10 b</td>\n",
              "      <td>b</td>\n",
              "      <td>Kepler-10</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>TIC 377780790</td>\n",
              "      <td>Gaia DR2 2132155017099178624</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;a refstr=RAJPAUL_ET_AL__2017 href=https://ui....</td>\n",
              "      <td>&lt;a refstr=STASSUN_ET_AL__2019 href=https://ui....</td>\n",
              "      <td>...</td>\n",
              "      <td>9.889&amp;plusmn;0.022</td>\n",
              "      <td>9.563</td>\n",
              "      <td>0.025</td>\n",
              "      <td>-0.025</td>\n",
              "      <td>9.563&amp;plusmn;0.025</td>\n",
              "      <td>9.496</td>\n",
              "      <td>0.021</td>\n",
              "      <td>-0.021</td>\n",
              "      <td>285.679298</td>\n",
              "      <td>50.241484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Kepler-10 b</td>\n",
              "      <td>b</td>\n",
              "      <td>Kepler-10</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>TIC 377780790</td>\n",
              "      <td>Gaia DR2 2132155017099178624</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;a refstr=HOLCZER_ET_AL__2016 href=https://ui....</td>\n",
              "      <td>&lt;a refstr=STASSUN_ET_AL__2019 href=https://ui....</td>\n",
              "      <td>...</td>\n",
              "      <td>9.889&amp;plusmn;0.022</td>\n",
              "      <td>9.563</td>\n",
              "      <td>0.025</td>\n",
              "      <td>-0.025</td>\n",
              "      <td>9.563&amp;plusmn;0.025</td>\n",
              "      <td>9.496</td>\n",
              "      <td>0.021</td>\n",
              "      <td>-0.021</td>\n",
              "      <td>285.679298</td>\n",
              "      <td>50.241484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Kepler-10 b</td>\n",
              "      <td>b</td>\n",
              "      <td>Kepler-10</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>TIC 377780790</td>\n",
              "      <td>Gaia DR2 2132155017099178624</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;a refstr=Q1_Q12_KOI_TABLE href=https://exopla...</td>\n",
              "      <td>&lt;a refstr=STASSUN_ET_AL__2019 href=https://ui....</td>\n",
              "      <td>...</td>\n",
              "      <td>9.889&amp;plusmn;0.022</td>\n",
              "      <td>9.563</td>\n",
              "      <td>0.025</td>\n",
              "      <td>-0.025</td>\n",
              "      <td>9.563&amp;plusmn;0.025</td>\n",
              "      <td>9.496</td>\n",
              "      <td>0.021</td>\n",
              "      <td>-0.021</td>\n",
              "      <td>285.679298</td>\n",
              "      <td>50.241484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Kepler-10 b</td>\n",
              "      <td>b</td>\n",
              "      <td>Kepler-10</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>TIC 377780790</td>\n",
              "      <td>Gaia DR2 2132155017099178624</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;a refstr=Q1_Q17_DR25_SUPPLEMENTAL_KOI_TABLE h...</td>\n",
              "      <td>&lt;a refstr=STASSUN_ET_AL__2019 href=https://ui....</td>\n",
              "      <td>...</td>\n",
              "      <td>9.889&amp;plusmn;0.022</td>\n",
              "      <td>9.563</td>\n",
              "      <td>0.025</td>\n",
              "      <td>-0.025</td>\n",
              "      <td>9.563&amp;plusmn;0.025</td>\n",
              "      <td>9.496</td>\n",
              "      <td>0.021</td>\n",
              "      <td>-0.021</td>\n",
              "      <td>285.679298</td>\n",
              "      <td>50.241484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Kepler-10 b</td>\n",
              "      <td>b</td>\n",
              "      <td>Kepler-10</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>TIC 377780790</td>\n",
              "      <td>Gaia DR2 2132155017099178624</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;a refstr=Q1_Q17_DR25_KOI_TABLE href=https://e...</td>\n",
              "      <td>&lt;a refstr=STASSUN_ET_AL__2019 href=https://ui....</td>\n",
              "      <td>...</td>\n",
              "      <td>9.889&amp;plusmn;0.022</td>\n",
              "      <td>9.563</td>\n",
              "      <td>0.025</td>\n",
              "      <td>-0.025</td>\n",
              "      <td>9.563&amp;plusmn;0.025</td>\n",
              "      <td>9.496</td>\n",
              "      <td>0.021</td>\n",
              "      <td>-0.021</td>\n",
              "      <td>285.679298</td>\n",
              "      <td>50.241484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Kepler-10 b</td>\n",
              "      <td>b</td>\n",
              "      <td>Kepler-10</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>TIC 377780790</td>\n",
              "      <td>Gaia DR2 2132155017099178624</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;a refstr=EXOFOP_TESS_TOI href=https://exofop....</td>\n",
              "      <td>&lt;a refstr=STASSUN_ET_AL__2019 href=https://ui....</td>\n",
              "      <td>...</td>\n",
              "      <td>9.889&amp;plusmn;0.022</td>\n",
              "      <td>9.563</td>\n",
              "      <td>0.025</td>\n",
              "      <td>-0.025</td>\n",
              "      <td>9.563&amp;plusmn;0.025</td>\n",
              "      <td>9.496</td>\n",
              "      <td>0.021</td>\n",
              "      <td>-0.021</td>\n",
              "      <td>285.679298</td>\n",
              "      <td>50.241484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Kepler-10 b</td>\n",
              "      <td>b</td>\n",
              "      <td>Kepler-10</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>TIC 377780790</td>\n",
              "      <td>Gaia DR2 2132155017099178624</td>\n",
              "      <td>1</td>\n",
              "      <td>&lt;a refstr=BONOMO_ET_AL__2023 href=https://ui.a...</td>\n",
              "      <td>&lt;a refstr=STASSUN_ET_AL__2019 href=https://ui....</td>\n",
              "      <td>...</td>\n",
              "      <td>9.889&amp;plusmn;0.022</td>\n",
              "      <td>9.563</td>\n",
              "      <td>0.025</td>\n",
              "      <td>-0.025</td>\n",
              "      <td>9.563&amp;plusmn;0.025</td>\n",
              "      <td>9.496</td>\n",
              "      <td>0.021</td>\n",
              "      <td>-0.021</td>\n",
              "      <td>285.679298</td>\n",
              "      <td>50.241484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Kepler-10 b</td>\n",
              "      <td>b</td>\n",
              "      <td>Kepler-10</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>TIC 377780790</td>\n",
              "      <td>Gaia DR2 2132155017099178624</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;a refstr=DAI_ET_AL__2019 href=https://ui.adsa...</td>\n",
              "      <td>&lt;a refstr=STASSUN_ET_AL__2019 href=https://ui....</td>\n",
              "      <td>...</td>\n",
              "      <td>9.889&amp;plusmn;0.022</td>\n",
              "      <td>9.563</td>\n",
              "      <td>0.025</td>\n",
              "      <td>-0.025</td>\n",
              "      <td>9.563&amp;plusmn;0.025</td>\n",
              "      <td>9.496</td>\n",
              "      <td>0.021</td>\n",
              "      <td>-0.021</td>\n",
              "      <td>285.679298</td>\n",
              "      <td>50.241484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Kepler-10 b</td>\n",
              "      <td>b</td>\n",
              "      <td>Kepler-10</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>TIC 377780790</td>\n",
              "      <td>Gaia DR2 2132155017099178624</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;a refstr=Q1_Q8_KOI_TABLE href=https://exoplan...</td>\n",
              "      <td>&lt;a refstr=STASSUN_ET_AL__2019 href=https://ui....</td>\n",
              "      <td>...</td>\n",
              "      <td>9.889&amp;plusmn;0.022</td>\n",
              "      <td>9.563</td>\n",
              "      <td>0.025</td>\n",
              "      <td>-0.025</td>\n",
              "      <td>9.563&amp;plusmn;0.025</td>\n",
              "      <td>9.496</td>\n",
              "      <td>0.021</td>\n",
              "      <td>-0.021</td>\n",
              "      <td>285.679298</td>\n",
              "      <td>50.241484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Kepler-10 b</td>\n",
              "      <td>b</td>\n",
              "      <td>Kepler-10</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>TIC 377780790</td>\n",
              "      <td>Gaia DR2 2132155017099178624</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;a refstr=DUMUSQUE_ET_AL__2014 href=https://ui...</td>\n",
              "      <td>&lt;a refstr=STASSUN_ET_AL__2019 href=https://ui....</td>\n",
              "      <td>...</td>\n",
              "      <td>9.889&amp;plusmn;0.022</td>\n",
              "      <td>9.563</td>\n",
              "      <td>0.025</td>\n",
              "      <td>-0.025</td>\n",
              "      <td>9.563&amp;plusmn;0.025</td>\n",
              "      <td>9.496</td>\n",
              "      <td>0.021</td>\n",
              "      <td>-0.021</td>\n",
              "      <td>285.679298</td>\n",
              "      <td>50.241484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Kepler-10 b</td>\n",
              "      <td>b</td>\n",
              "      <td>Kepler-10</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>TIC 377780790</td>\n",
              "      <td>Gaia DR2 2132155017099178624</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;a refstr=HU_ET_AL__2015 href=https://ui.adsab...</td>\n",
              "      <td>&lt;a refstr=STASSUN_ET_AL__2019 href=https://ui....</td>\n",
              "      <td>...</td>\n",
              "      <td>9.889&amp;plusmn;0.022</td>\n",
              "      <td>9.563</td>\n",
              "      <td>0.025</td>\n",
              "      <td>-0.025</td>\n",
              "      <td>9.563&amp;plusmn;0.025</td>\n",
              "      <td>9.496</td>\n",
              "      <td>0.021</td>\n",
              "      <td>-0.021</td>\n",
              "      <td>285.679298</td>\n",
              "      <td>50.241484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Kepler-10 b</td>\n",
              "      <td>b</td>\n",
              "      <td>Kepler-10</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>TIC 377780790</td>\n",
              "      <td>Gaia DR2 2132155017099178624</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;a refstr=VAN_EYLEN___ALBRECHT_2015 href=https...</td>\n",
              "      <td>&lt;a refstr=STASSUN_ET_AL__2019 href=https://ui....</td>\n",
              "      <td>...</td>\n",
              "      <td>9.889&amp;plusmn;0.022</td>\n",
              "      <td>9.563</td>\n",
              "      <td>0.025</td>\n",
              "      <td>-0.025</td>\n",
              "      <td>9.563&amp;plusmn;0.025</td>\n",
              "      <td>9.496</td>\n",
              "      <td>0.021</td>\n",
              "      <td>-0.021</td>\n",
              "      <td>285.679298</td>\n",
              "      <td>50.241484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Kepler-10 b</td>\n",
              "      <td>b</td>\n",
              "      <td>Kepler-10</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>TIC 377780790</td>\n",
              "      <td>Gaia DR2 2132155017099178624</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;a refstr=WEISS_ET_AL__2016 href=https://ui.ad...</td>\n",
              "      <td>&lt;a refstr=STASSUN_ET_AL__2019 href=https://ui....</td>\n",
              "      <td>...</td>\n",
              "      <td>9.889&amp;plusmn;0.022</td>\n",
              "      <td>9.563</td>\n",
              "      <td>0.025</td>\n",
              "      <td>-0.025</td>\n",
              "      <td>9.563&amp;plusmn;0.025</td>\n",
              "      <td>9.496</td>\n",
              "      <td>0.021</td>\n",
              "      <td>-0.021</td>\n",
              "      <td>285.679298</td>\n",
              "      <td>50.241484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Kepler-10 b</td>\n",
              "      <td>b</td>\n",
              "      <td>Kepler-10</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>TIC 377780790</td>\n",
              "      <td>Gaia DR2 2132155017099178624</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;a refstr=BERGER_ET_AL__2018 href=https://ui.a...</td>\n",
              "      <td>&lt;a refstr=STASSUN_ET_AL__2019 href=https://ui....</td>\n",
              "      <td>...</td>\n",
              "      <td>9.889&amp;plusmn;0.022</td>\n",
              "      <td>9.563</td>\n",
              "      <td>0.025</td>\n",
              "      <td>-0.025</td>\n",
              "      <td>9.563&amp;plusmn;0.025</td>\n",
              "      <td>9.496</td>\n",
              "      <td>0.021</td>\n",
              "      <td>-0.021</td>\n",
              "      <td>285.679298</td>\n",
              "      <td>50.241484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Kepler-10 b</td>\n",
              "      <td>b</td>\n",
              "      <td>Kepler-10</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>TIC 377780790</td>\n",
              "      <td>Gaia DR2 2132155017099178624</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;a refstr=GAJDO_SCARON__ET_AL__2019 href=https...</td>\n",
              "      <td>&lt;a refstr=STASSUN_ET_AL__2019 href=https://ui....</td>\n",
              "      <td>...</td>\n",
              "      <td>9.889&amp;plusmn;0.022</td>\n",
              "      <td>9.563</td>\n",
              "      <td>0.025</td>\n",
              "      <td>-0.025</td>\n",
              "      <td>9.563&amp;plusmn;0.025</td>\n",
              "      <td>9.496</td>\n",
              "      <td>0.021</td>\n",
              "      <td>-0.021</td>\n",
              "      <td>285.679298</td>\n",
              "      <td>50.241484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Kepler-10 b</td>\n",
              "      <td>b</td>\n",
              "      <td>Kepler-10</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>TIC 377780790</td>\n",
              "      <td>Gaia DR2 2132155017099178624</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;a refstr=Q1_Q17_DR24_KOI_TABLE href=https://e...</td>\n",
              "      <td>&lt;a refstr=STASSUN_ET_AL__2019 href=https://ui....</td>\n",
              "      <td>...</td>\n",
              "      <td>9.889&amp;plusmn;0.022</td>\n",
              "      <td>9.563</td>\n",
              "      <td>0.025</td>\n",
              "      <td>-0.025</td>\n",
              "      <td>9.563&amp;plusmn;0.025</td>\n",
              "      <td>9.496</td>\n",
              "      <td>0.021</td>\n",
              "      <td>-0.021</td>\n",
              "      <td>285.679298</td>\n",
              "      <td>50.241484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Kepler-10 b</td>\n",
              "      <td>b</td>\n",
              "      <td>Kepler-10</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>TIC 377780790</td>\n",
              "      <td>Gaia DR2 2132155017099178624</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;a refstr=BATAHLA_ET_AL__2011 href=https://ui....</td>\n",
              "      <td>&lt;a refstr=STASSUN_ET_AL__2019 href=https://ui....</td>\n",
              "      <td>...</td>\n",
              "      <td>9.889&amp;plusmn;0.022</td>\n",
              "      <td>9.563</td>\n",
              "      <td>0.025</td>\n",
              "      <td>-0.025</td>\n",
              "      <td>9.563&amp;plusmn;0.025</td>\n",
              "      <td>9.496</td>\n",
              "      <td>0.021</td>\n",
              "      <td>-0.021</td>\n",
              "      <td>285.679298</td>\n",
              "      <td>50.241484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Kepler-10 b</td>\n",
              "      <td>b</td>\n",
              "      <td>Kepler-10</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>TIC 377780790</td>\n",
              "      <td>Gaia DR2 2132155017099178624</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;a refstr=Q1_Q16_KOI_TABLE href=https://exopla...</td>\n",
              "      <td>&lt;a refstr=STASSUN_ET_AL__2019 href=https://ui....</td>\n",
              "      <td>...</td>\n",
              "      <td>9.889&amp;plusmn;0.022</td>\n",
              "      <td>9.563</td>\n",
              "      <td>0.025</td>\n",
              "      <td>-0.025</td>\n",
              "      <td>9.563&amp;plusmn;0.025</td>\n",
              "      <td>9.496</td>\n",
              "      <td>0.021</td>\n",
              "      <td>-0.021</td>\n",
              "      <td>285.679298</td>\n",
              "      <td>50.241484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Kepler-10 b</td>\n",
              "      <td>b</td>\n",
              "      <td>Kepler-10</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>TIC 377780790</td>\n",
              "      <td>Gaia DR2 2132155017099178624</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;a refstr=ESTEVES_ET_AL__2015 href=https://ui....</td>\n",
              "      <td>&lt;a refstr=STASSUN_ET_AL__2019 href=https://ui....</td>\n",
              "      <td>...</td>\n",
              "      <td>9.889&amp;plusmn;0.022</td>\n",
              "      <td>9.563</td>\n",
              "      <td>0.025</td>\n",
              "      <td>-0.025</td>\n",
              "      <td>9.563&amp;plusmn;0.025</td>\n",
              "      <td>9.496</td>\n",
              "      <td>0.021</td>\n",
              "      <td>-0.021</td>\n",
              "      <td>285.679298</td>\n",
              "      <td>50.241484</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>21 rows × 355 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-93e202e5-295c-4b73-8b51-8c9d00a090e7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-93e202e5-295c-4b73-8b51-8c9d00a090e7 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-93e202e5-295c-4b73-8b51-8c9d00a090e7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8c9a2c8a-c8c6-4a95-a404-e8b0a83d8802\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8c9a2c8a-c8c6-4a95-a404-e8b0a83d8802')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8c9a2c8a-c8c6-4a95-a404-e8b0a83d8802 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.to_csv(\"kepler_10b.csv\")"
      ],
      "metadata": {
        "id": "Iq8fsWerhfgB"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Additional"
      ],
      "metadata": {
        "id": "bO9NoahriINw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!pip install lightkurve"
      ],
      "metadata": {
        "id": "WzW1m9g2hkmc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import lightkurve as lk"
      ],
      "metadata": {
        "id": "M3NV_LX8iOJS"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "# Search and download light curve data for Kepler-10 b\n",
        "searchResult = lk.search_lightcurve(\"Kepler-10 b\",mission=\"Kepler\",cadence=\"long\")\n",
        "lightCurve = searchResult.download()"
      ],
      "metadata": {
        "id": "znlttzmfiGa6"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lightCurve"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 877
        },
        "id": "SYKutRI7iZZ2",
        "outputId": "5037afc8-b1f6-4445-e2c5-2f020a5bfef6"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<KeplerLightCurve length=473 LABEL=\"KIC 11904151\" QUARTER=0 AUTHOR=Kepler FLUX_ORIGIN=pdcsap_flux>\n",
              "       time             flux      ...   pos_corr1      pos_corr2   \n",
              "                    electron / s  ...      pix            pix      \n",
              "       Time           float32     ...    float32        float32    \n",
              "------------------ -------------- ... -------------- --------------\n",
              " 120.5391465105713  5.0146684e+05 ...  1.7792054e-03  3.3286733e-03\n",
              "120.55958073025249  5.0141212e+05 ...  1.9566093e-03  3.5631030e-03\n",
              "120.58001484981651  5.0140281e+05 ...  2.0698714e-03  3.4072644e-03\n",
              "120.60044916937477  5.0135972e+05 ...  2.4259784e-03  3.0491522e-03\n",
              "120.62088338893955  5.0144759e+05 ...  2.7262936e-03  3.2327282e-03\n",
              "120.64131750838715  5.0142422e+05 ...  2.8024365e-03  2.5497559e-03\n",
              "120.66175172782823  5.0134612e+05 ...  2.9968296e-03  2.9109719e-03\n",
              "120.68218604727736  5.0136928e+05 ...  2.9321318e-03  2.7981971e-03\n",
              "120.70262016672496  5.0140212e+05 ...  2.9108874e-03  2.8240951e-03\n",
              "               ...            ... ...            ...            ...\n",
              "130.06148738515185  5.0152938e+05 ... -4.6389936e-03 -7.1408441e-03\n",
              "130.08192158433667  5.0157003e+05 ... -4.6909074e-03 -6.7579346e-03\n",
              "130.10235588352953  5.0152728e+05 ... -5.0965576e-03 -7.4954131e-03\n",
              "130.12278998272086  5.0156572e+05 ... -5.2787364e-03 -7.5620078e-03\n",
              "130.14322418178926  5.0152403e+05 ... -5.2255061e-03 -7.7070645e-03\n",
              " 130.1636584807493  5.0150003e+05 ... -5.3084558e-03 -8.0727953e-03\n",
              "130.18409267970856  5.0151734e+05 ... -5.4392498e-03 -7.3902770e-03\n",
              "130.20452677865978  5.0150722e+05 ... -5.3689722e-03 -8.1970813e-03\n",
              "130.22496097750263  5.0154591e+05 ... -5.2869776e-03 -7.5565036e-03\n",
              "130.24539527622983  5.0159747e+05 ... -5.1991190e-03 -7.1574575e-03"
            ],
            "text/html": [
              "<div><i>KeplerLightCurve length=473 LABEL=&quot;KIC 11904151&quot; QUARTER=0 AUTHOR=Kepler FLUX_ORIGIN=pdcsap_flux</i>\n",
              "<table id=\"table134302462886480\" class=\"table-striped table-bordered table-condensed\">\n",
              "<thead><tr><th>time</th><th>flux</th><th>flux_err</th><th>quality</th><th>timecorr</th><th>centroid_col</th><th>centroid_row</th><th>cadenceno</th><th>sap_flux</th><th>sap_flux_err</th><th>sap_bkg</th><th>sap_bkg_err</th><th>pdcsap_flux</th><th>pdcsap_flux_err</th><th>sap_quality</th><th>psf_centr1</th><th>psf_centr1_err</th><th>psf_centr2</th><th>psf_centr2_err</th><th>mom_centr1</th><th>mom_centr1_err</th><th>mom_centr2</th><th>mom_centr2_err</th><th>pos_corr1</th><th>pos_corr2</th></tr></thead>\n",
              "<thead><tr><th></th><th>electron / s</th><th>electron / s</th><th></th><th>d</th><th>pix</th><th>pix</th><th></th><th>electron / s</th><th>electron / s</th><th>electron / s</th><th>electron / s</th><th>electron / s</th><th>electron / s</th><th></th><th>pix</th><th>pix</th><th>pix</th><th>pix</th><th>pix</th><th>pix</th><th>pix</th><th>pix</th><th>pix</th><th>pix</th></tr></thead>\n",
              "<thead><tr><th>Time</th><th>float32</th><th>float32</th><th>int32</th><th>float32</th><th>float64</th><th>float64</th><th>int32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int32</th><th>float64</th><th>float32</th><th>float64</th><th>float32</th><th>float64</th><th>float32</th><th>float64</th><th>float32</th><th>float32</th><th>float32</th></tr></thead>\n",
              "<tr><td>120.5391465105713</td><td>5.0146684e+05</td><td>2.0574972e+01</td><td>0</td><td>9.940906e-04</td><td>659.36896</td><td>250.31948</td><td>568</td><td>5.3272462e+05</td><td>2.0045162e+01</td><td>2.4877234e+03</td><td>1.3237703e+00</td><td>5.0146684e+05</td><td>2.0574972e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36896</td><td>3.6308160e-05</td><td>250.31948</td><td>5.3553398e-05</td><td>1.7792054e-03</td><td>3.3286733e-03</td></tr>\n",
              "<tr><td>120.55958073025249</td><td>5.0141212e+05</td><td>2.0597826e+01</td><td>0</td><td>9.947103e-04</td><td>659.36919</td><td>250.31978</td><td>569</td><td>5.3274544e+05</td><td>2.0045479e+01</td><td>2.4910432e+03</td><td>1.3229831e+00</td><td>5.0141212e+05</td><td>2.0597826e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36919</td><td>3.6305861e-05</td><td>250.31978</td><td>5.3553318e-05</td><td>1.9566093e-03</td><td>3.5631030e-03</td></tr>\n",
              "<tr><td>120.58001484981651</td><td>5.0140281e+05</td><td>2.0634075e+01</td><td>0</td><td>9.953298e-04</td><td>659.36918</td><td>250.31934</td><td>570</td><td>5.3274400e+05</td><td>2.0045910e+01</td><td>2.4830063e+03</td><td>1.3249981e+00</td><td>5.0140281e+05</td><td>2.0634075e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36918</td><td>3.6309517e-05</td><td>250.31934</td><td>5.3547948e-05</td><td>2.0698714e-03</td><td>3.4072644e-03</td></tr>\n",
              "<tr><td>120.60044916937477</td><td>5.0135972e+05</td><td>2.0607729e+01</td><td>0</td><td>9.959494e-04</td><td>659.36967</td><td>250.31932</td><td>571</td><td>5.3266388e+05</td><td>2.0044411e+01</td><td>2.4886853e+03</td><td>1.3235550e+00</td><td>5.0135972e+05</td><td>2.0607729e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36967</td><td>3.6312224e-05</td><td>250.31932</td><td>5.3564647e-05</td><td>2.4259784e-03</td><td>3.0491522e-03</td></tr>\n",
              "<tr><td>120.62088338893955</td><td>5.0144759e+05</td><td>2.0585588e+01</td><td>0</td><td>9.965689e-04</td><td>659.36989</td><td>250.31906</td><td>572</td><td>5.3276494e+05</td><td>2.0046177e+01</td><td>2.4869612e+03</td><td>1.3239022e+00</td><td>5.0144759e+05</td><td>2.0585588e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36989</td><td>3.6307927e-05</td><td>250.31906</td><td>5.3543699e-05</td><td>2.7262936e-03</td><td>3.2327282e-03</td></tr>\n",
              "<tr><td>120.64131750838715</td><td>5.0142422e+05</td><td>2.0622503e+01</td><td>0</td><td>9.971884e-04</td><td>659.37001</td><td>250.31846</td><td>573</td><td>5.3270619e+05</td><td>2.0045256e+01</td><td>2.4852412e+03</td><td>1.3248761e+00</td><td>5.0142422e+05</td><td>2.0622503e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.37001</td><td>3.6310295e-05</td><td>250.31846</td><td>5.3552700e-05</td><td>2.8024365e-03</td><td>2.5497559e-03</td></tr>\n",
              "<tr><td>120.66175172782823</td><td>5.0134612e+05</td><td>2.0596804e+01</td><td>0</td><td>9.978078e-04</td><td>659.37024</td><td>250.31868</td><td>574</td><td>5.3262088e+05</td><td>2.0043943e+01</td><td>2.4874382e+03</td><td>1.3227766e+00</td><td>5.0134612e+05</td><td>2.0596804e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.37024</td><td>3.6316073e-05</td><td>250.31868</td><td>5.3557891e-05</td><td>2.9968296e-03</td><td>2.9109719e-03</td></tr>\n",
              "<tr><td>120.68218604727736</td><td>5.0136928e+05</td><td>2.0572752e+01</td><td>0</td><td>9.984273e-04</td><td>659.37011</td><td>250.31860</td><td>575</td><td>5.3262475e+05</td><td>2.0044262e+01</td><td>2.4853333e+03</td><td>1.3250123e+00</td><td>5.0136928e+05</td><td>2.0572752e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.37011</td><td>3.6316418e-05</td><td>250.31860</td><td>5.3558793e-05</td><td>2.9321318e-03</td><td>2.7981971e-03</td></tr>\n",
              "<tr><td>120.70262016672496</td><td>5.0140212e+05</td><td>2.0617153e+01</td><td>0</td><td>9.990467e-04</td><td>659.37004</td><td>250.31870</td><td>576</td><td>5.3265825e+05</td><td>2.0044573e+01</td><td>2.4833210e+03</td><td>1.3247201e+00</td><td>5.0140212e+05</td><td>2.0617153e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.37004</td><td>3.6314828e-05</td><td>250.31870</td><td>5.3555530e-05</td><td>2.9108874e-03</td><td>2.8240951e-03</td></tr>\n",
              "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
              "<tr><td>130.06148738515185</td><td>5.0152938e+05</td><td>2.0701473e+01</td><td>0</td><td>1.278665e-03</td><td>659.36246</td><td>250.30569</td><td>1034</td><td>5.3243350e+05</td><td>2.0039118e+01</td><td>2.4280154e+03</td><td>1.3248780e+00</td><td>5.0152938e+05</td><td>2.0701473e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36246</td><td>3.6351150e-05</td><td>250.30569</td><td>5.3674110e-05</td><td>-4.6389936e-03</td><td>-7.1408441e-03</td></tr>\n",
              "<tr><td>130.08192158433667</td><td>5.0157003e+05</td><td>2.0702055e+01</td><td>0</td><td>1.279264e-03</td><td>659.36245</td><td>250.30597</td><td>1035</td><td>5.3248006e+05</td><td>2.0039707e+01</td><td>2.4223347e+03</td><td>1.3238698e+00</td><td>5.0157003e+05</td><td>2.0702055e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36245</td><td>3.6348833e-05</td><td>250.30597</td><td>5.3667514e-05</td><td>-4.6909074e-03</td><td>-6.7579346e-03</td></tr>\n",
              "<tr><td>130.10235588352953</td><td>5.0152728e+05</td><td>2.0686260e+01</td><td>0</td><td>1.279864e-03</td><td>659.36209</td><td>250.30544</td><td>1036</td><td>5.3241306e+05</td><td>2.0039089e+01</td><td>2.4190269e+03</td><td>1.3250171e+00</td><td>5.0152728e+05</td><td>2.0686260e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36209</td><td>3.6353140e-05</td><td>250.30544</td><td>5.3684296e-05</td><td>-5.0965576e-03</td><td>-7.4954131e-03</td></tr>\n",
              "<tr><td>130.12278998272086</td><td>5.0156572e+05</td><td>2.0702715e+01</td><td>0</td><td>1.280463e-03</td><td>659.36180</td><td>250.30505</td><td>1037</td><td>5.3247606e+05</td><td>2.0039822e+01</td><td>2.4248203e+03</td><td>1.3230473e+00</td><td>5.0156572e+05</td><td>2.0702715e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36180</td><td>3.6350415e-05</td><td>250.30505</td><td>5.3667653e-05</td><td>-5.2787364e-03</td><td>-7.5620078e-03</td></tr>\n",
              "<tr><td>130.14322418178926</td><td>5.0152403e+05</td><td>2.0685968e+01</td><td>0</td><td>1.281062e-03</td><td>659.36180</td><td>250.30512</td><td>1038</td><td>5.3242694e+05</td><td>2.0039162e+01</td><td>2.4237664e+03</td><td>1.3226272e+00</td><td>5.0152403e+05</td><td>2.0685968e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36180</td><td>3.6353420e-05</td><td>250.30512</td><td>5.3678523e-05</td><td>-5.2255061e-03</td><td>-7.7070645e-03</td></tr>\n",
              "<tr><td>130.1636584807493</td><td>5.0150003e+05</td><td>2.0679955e+01</td><td>0</td><td>1.281661e-03</td><td>659.36179</td><td>250.30474</td><td>1039</td><td>5.3239231e+05</td><td>2.0038630e+01</td><td>2.4218579e+03</td><td>1.3242674e+00</td><td>5.0150003e+05</td><td>2.0679955e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36179</td><td>3.6356709e-05</td><td>250.30474</td><td>5.3684431e-05</td><td>-5.3084558e-03</td><td>-8.0727953e-03</td></tr>\n",
              "<tr><td>130.18409267970856</td><td>5.0151734e+05</td><td>2.0703188e+01</td><td>0</td><td>1.282260e-03</td><td>659.36159</td><td>250.30516</td><td>1040</td><td>5.3244000e+05</td><td>2.0039101e+01</td><td>2.4224158e+03</td><td>1.3207772e+00</td><td>5.0151734e+05</td><td>2.0703188e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36159</td><td>3.6353227e-05</td><td>250.30516</td><td>5.3670177e-05</td><td>-5.4392498e-03</td><td>-7.3902770e-03</td></tr>\n",
              "<tr><td>130.20452677865978</td><td>5.0150722e+05</td><td>2.0690376e+01</td><td>0</td><td>1.282859e-03</td><td>659.36174</td><td>250.30453</td><td>1041</td><td>5.3240738e+05</td><td>2.0038670e+01</td><td>2.4242563e+03</td><td>1.3214228e+00</td><td>5.0150722e+05</td><td>2.0690376e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36174</td><td>3.6355188e-05</td><td>250.30453</td><td>5.3682859e-05</td><td>-5.3689722e-03</td><td>-8.1970813e-03</td></tr>\n",
              "<tr><td>130.22496097750263</td><td>5.0154591e+05</td><td>2.0696840e+01</td><td>0</td><td>1.283458e-03</td><td>659.36189</td><td>250.30524</td><td>1042</td><td>5.3245538e+05</td><td>2.0039616e+01</td><td>2.4196887e+03</td><td>1.3241822e+00</td><td>5.0154591e+05</td><td>2.0696840e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36189</td><td>3.6353529e-05</td><td>250.30524</td><td>5.3676045e-05</td><td>-5.2869776e-03</td><td>-7.5565036e-03</td></tr>\n",
              "<tr><td>130.24539527622983</td><td>5.0159747e+05</td><td>2.0697966e+01</td><td>0</td><td>1.284056e-03</td><td>659.36180</td><td>250.30545</td><td>1043</td><td>5.3249700e+05</td><td>2.0040234e+01</td><td>2.4261963e+03</td><td>1.3226049e+00</td><td>5.0159747e+05</td><td>2.0697966e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36180</td><td>3.6352234e-05</td><td>250.30545</td><td>5.3671167e-05</td><td>-5.1991190e-03</td><td>-7.1574575e-03</td></tr>\n",
              "</table></div>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lightCurve.to_csv(\"lightcurve_kepler.csv\")"
      ],
      "metadata": {
        "id": "ySoy3gZIiel7"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "flatLightCurve = lightCurve.remove_nans().flatten()"
      ],
      "metadata": {
        "id": "haZM5RbKilAc"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "planetPeriod = 0.837491  # period in days\n",
        "planetEpoch = 2454964.575  # time of first transit in Julian Day"
      ],
      "metadata": {
        "id": "IfBFBcieiqGu"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "# Fold the light curve with the known period and epoch of Kepler-10 b\n",
        "foldedLightCurve = flatLightCurve.fold(period=planetPeriod,epoch_time=planetEpoch)"
      ],
      "metadata": {
        "id": "LtegqKaUiu-H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "foldedLightCurve"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 877
        },
        "id": "G1Ixdnsoi5NS",
        "outputId": "e39966ae-4d51-401f-c673-be3005ad472f"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<FoldedLightCurve length=469 LABEL=\"KIC 11904151\" QUARTER=0 AUTHOR=Kepler FLUX_ORIGIN=pdcsap_flux>\n",
              "        time              flux      ...   pos_corr1      pos_corr2   \n",
              "                                    ...      pix            pix      \n",
              "     TimeDelta          float64     ...    float32        float32    \n",
              "-------------------- -------------- ... -------------- --------------\n",
              "-0.40524283067235634  9.9985028e-01 ...  2.4259784e-03  3.0491522e-03\n",
              "-0.40493105355861847  1.0000221e+00 ...  2.9130119e-03  4.3751081e-03\n",
              "-0.40461922840795206  1.0000625e+00 ...  1.9143780e-03  4.3429015e-03\n",
              " -0.4043073598121223  1.0000017e+00 ...  1.2651097e-03  3.9867782e-03\n",
              " -0.4039957525936978  9.9998461e-01 ...  2.5723226e-04  1.7068184e-03\n",
              " -0.4036841114668532  1.0001339e+00 ...  3.0875951e-04  7.5705140e-04\n",
              " -0.4033725409825505  9.9999163e-01 ... -1.3266093e-04 -4.4393452e-04\n",
              "  -0.403061045732555  9.9995414e-01 ... -1.5559904e-03 -1.5541808e-03\n",
              "-0.40274963066184466  9.9994150e-01 ... -2.7408421e-03 -3.1907735e-03\n",
              "                 ...            ... ...            ...            ...\n",
              "  0.4124376544514546  9.9999580e-01 ...  2.2084960e-03  4.3496452e-03\n",
              " 0.41274932454174146  9.9999657e-01 ...  1.2955223e-03  3.9730887e-03\n",
              " 0.41306103348542633  9.9993663e-01 ...  3.1813729e-04  1.8558974e-03\n",
              "  0.4133727763375314  9.9996803e-01 ...  3.1814261e-04  1.1845760e-03\n",
              " 0.41368424862870073  1.0000788e+00 ... -1.9127612e-04 -2.7923766e-04\n",
              " 0.41399574564475977  9.9996489e-01 ... -1.6015661e-03 -1.7701549e-03\n",
              "  0.4143072627939433  1.0000198e+00 ... -2.8932779e-03 -2.6051088e-03\n",
              "  0.4146184956068954  9.9996005e-01 ... -2.5552646e-03 -4.2674006e-03\n",
              " 0.41492973936944155  9.9993163e-01 ... -3.1474836e-03 -5.9353639e-03\n",
              " 0.41524098948981647  1.0001049e+00 ... -4.1886452e-03 -6.7668976e-03"
            ],
            "text/html": [
              "<div><i>FoldedLightCurve length=469 LABEL=&quot;KIC 11904151&quot; QUARTER=0 AUTHOR=Kepler FLUX_ORIGIN=pdcsap_flux</i>\n",
              "<table id=\"table134302318752816\" class=\"table-striped table-bordered table-condensed\">\n",
              "<thead><tr><th>time</th><th>flux</th><th>flux_err</th><th>time_original</th><th>quality</th><th>timecorr</th><th>centroid_col</th><th>centroid_row</th><th>cadenceno</th><th>sap_flux</th><th>sap_flux_err</th><th>sap_bkg</th><th>sap_bkg_err</th><th>pdcsap_flux</th><th>pdcsap_flux_err</th><th>sap_quality</th><th>psf_centr1</th><th>psf_centr1_err</th><th>psf_centr2</th><th>psf_centr2_err</th><th>mom_centr1</th><th>mom_centr1_err</th><th>mom_centr2</th><th>mom_centr2_err</th><th>pos_corr1</th><th>pos_corr2</th></tr></thead>\n",
              "<thead><tr><th></th><th></th><th></th><th></th><th></th><th>d</th><th>pix</th><th>pix</th><th></th><th>electron / s</th><th>electron / s</th><th>electron / s</th><th>electron / s</th><th>electron / s</th><th>electron / s</th><th></th><th>pix</th><th>pix</th><th>pix</th><th>pix</th><th>pix</th><th>pix</th><th>pix</th><th>pix</th><th>pix</th><th>pix</th></tr></thead>\n",
              "<thead><tr><th>TimeDelta</th><th>float64</th><th>float64</th><th>Time</th><th>int32</th><th>float32</th><th>float64</th><th>float64</th><th>int32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int32</th><th>float64</th><th>float32</th><th>float64</th><th>float32</th><th>float64</th><th>float32</th><th>float64</th><th>float32</th><th>float32</th><th>float32</th></tr></thead>\n",
              "<tr><td>-0.40524283067235634</td><td>9.9985028e-01</td><td>4.1097525e-05</td><td>120.60044916937477</td><td>0</td><td>9.959494e-04</td><td>659.36967</td><td>250.31932</td><td>571</td><td>5.3266388e+05</td><td>2.0044411e+01</td><td>2.4886853e+03</td><td>1.3235550e+00</td><td>5.0135972e+05</td><td>2.0607729e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36967</td><td>3.6312224e-05</td><td>250.31932</td><td>5.3564647e-05</td><td>2.4259784e-03</td><td>3.0491522e-03</td></tr>\n",
              "<tr><td>-0.40493105355861847</td><td>1.0000221e+00</td><td>4.1273377e-05</td><td>121.43825194652163</td><td>0</td><td>1.021327e-03</td><td>659.36980</td><td>250.31987</td><td>612</td><td>5.3283662e+05</td><td>2.0046791e+01</td><td>2.4872632e+03</td><td>1.3241435e+00</td><td>5.0151619e+05</td><td>2.0698809e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36980</td><td>3.6300436e-05</td><td>250.31987</td><td>5.3515596e-05</td><td>2.9130119e-03</td><td>4.3751081e-03</td></tr>\n",
              "<tr><td>-0.40461922840795206</td><td>1.0000625e+00</td><td>4.1321462e-05</td><td>122.27605477174075</td><td>0</td><td>1.046652e-03</td><td>659.36870</td><td>250.31970</td><td>653</td><td>5.3289669e+05</td><td>2.0047987e+01</td><td>2.4715176e+03</td><td>1.3247509e+00</td><td>5.0155856e+05</td><td>2.0723837e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36870</td><td>3.6295576e-05</td><td>250.31970</td><td>5.3507480e-05</td><td>1.9143780e-03</td><td>4.3429015e-03</td></tr>\n",
              "<tr><td>-0.4043073598121223</td><td>1.0000017e+00</td><td>4.1368673e-05</td><td>123.11385764026636</td><td>0</td><td>1.071920e-03</td><td>659.36804</td><td>250.31922</td><td>694</td><td>5.3288262e+05</td><td>2.0047342e+01</td><td>2.4566765e+03</td><td>1.3251861e+00</td><td>5.0157950e+05</td><td>2.0749643e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36804</td><td>3.6296791e-05</td><td>250.31922</td><td>5.3516567e-05</td><td>1.2651097e-03</td><td>3.9867782e-03</td></tr>\n",
              "<tr><td>-0.4039957525936978</td><td>9.9998461e-01</td><td>4.1354898e-05</td><td>123.95166024756327</td><td>10000000000000</td><td>1.097128e-03</td><td>659.36707</td><td>250.31647</td><td>735</td><td>5.3281988e+05</td><td>2.0045723e+01</td><td>2.4594421e+03</td><td>1.3235071e+00</td><td>5.0159088e+05</td><td>2.0743559e+01</td><td>10000000000000</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36707</td><td>3.6305544e-05</td><td>250.31647</td><td>5.3548734e-05</td><td>2.5723226e-04</td><td>1.7068184e-03</td></tr>\n",
              "<tr><td>-0.4036841114668532</td><td>1.0001339e+00</td><td>4.1368496e-05</td><td>124.78946288872976</td><td>0</td><td>1.122269e-03</td><td>659.36715</td><td>250.31520</td><td>776</td><td>5.3290869e+05</td><td>2.0046877e+01</td><td>2.4499531e+03</td><td>1.3227571e+00</td><td>5.0171209e+05</td><td>2.0752296e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36715</td><td>3.6302074e-05</td><td>250.31520</td><td>5.3547450e-05</td><td>3.0875951e-04</td><td>7.5705140e-04</td></tr>\n",
              "<tr><td>-0.4033725409825505</td><td>9.9999163e-01</td><td>4.1351958e-05</td><td>125.62726545923942</td><td>0</td><td>1.147339e-03</td><td>659.36661</td><td>250.31367</td><td>817</td><td>5.3284806e+05</td><td>2.0045933e+01</td><td>2.4422432e+03</td><td>1.3223270e+00</td><td>5.0168772e+05</td><td>2.0745943e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36661</td><td>3.6328347e-05</td><td>250.31367</td><td>5.3576168e-05</td><td>-1.3266093e-04</td><td>-4.4393452e-04</td></tr>\n",
              "<tr><td>-0.403061045732555</td><td>9.9995414e-01</td><td>4.1392008e-05</td><td>126.4650679544211</td><td>0</td><td>1.172334e-03</td><td>659.36537</td><td>250.31235</td><td>858</td><td>5.3274650e+05</td><td>2.0044693e+01</td><td>2.4445286e+03</td><td>1.3235205e+00</td><td>5.0165594e+05</td><td>2.0765499e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36537</td><td>3.6318488e-05</td><td>250.31235</td><td>5.3594820e-05</td><td>-1.5559904e-03</td><td>-1.5541808e-03</td></tr>\n",
              "<tr><td>-0.40274963066184466</td><td>9.9994150e-01</td><td>4.1367549e-05</td><td>127.30287036962545</td><td>0</td><td>1.197250e-03</td><td>659.36427</td><td>250.31053</td><td>899</td><td>5.3267050e+05</td><td>2.0042185e+01</td><td>2.4391863e+03</td><td>1.3252444e+00</td><td>5.0164897e+05</td><td>2.0753202e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36427</td><td>3.6332360e-05</td><td>250.31053</td><td>5.3621414e-05</td><td>-2.7408421e-03</td><td>-3.1907735e-03</td></tr>\n",
              "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
              "<tr><td>0.4124376544514546</td><td>9.9999580e-01</td><td>4.1319076e-05</td><td>122.25562065462145</td><td>0</td><td>1.046035e-03</td><td>659.36885</td><td>250.31963</td><td>652</td><td>5.3287006e+05</td><td>2.0047695e+01</td><td>2.4697522e+03</td><td>1.3255842e+00</td><td>5.0152469e+05</td><td>2.0722624e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36885</td><td>3.6298141e-05</td><td>250.31963</td><td>5.3503623e-05</td><td>2.2084960e-03</td><td>4.3496452e-03</td></tr>\n",
              "<tr><td>0.41274932454174146</td><td>9.9999657e-01</td><td>4.1361302e-05</td><td>123.09342332466622</td><td>0</td><td>1.071305e-03</td><td>659.36808</td><td>250.31909</td><td>693</td><td>5.3288369e+05</td><td>2.0047407e+01</td><td>2.4573660e+03</td><td>1.3249918e+00</td><td>5.0157672e+05</td><td>2.0745937e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36808</td><td>3.6296165e-05</td><td>250.31909</td><td>5.3514275e-05</td><td>1.2955223e-03</td><td>3.9730887e-03</td></tr>\n",
              "<tr><td>0.41306103348542633</td><td>9.9993663e-01</td><td>4.1329750e-05</td><td>123.93122603347001</td><td>0</td><td>1.096513e-03</td><td>659.36709</td><td>250.31654</td><td>734</td><td>5.3280200e+05</td><td>2.0045288e+01</td><td>2.4519150e+03</td><td>1.3229163e+00</td><td>5.0156656e+05</td><td>2.0730934e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36709</td><td>3.6306163e-05</td><td>250.31654</td><td>5.3544689e-05</td><td>3.1813729e-04</td><td>1.8558974e-03</td></tr>\n",
              "<tr><td>0.4133727763375314</td><td>9.9996803e-01</td><td>4.1338874e-05</td><td>124.7690287763835</td><td>0</td><td>1.121656e-03</td><td>659.36716</td><td>250.31560</td><td>775</td><td>5.3285075e+05</td><td>2.0046026e+01</td><td>2.4533945e+03</td><td>1.3230351e+00</td><td>5.0162681e+05</td><td>2.0737350e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36716</td><td>3.6304795e-05</td><td>250.31560</td><td>5.3544081e-05</td><td>3.1814261e-04</td><td>1.1845760e-03</td></tr>\n",
              "<tr><td>0.41368424862870073</td><td>1.0000788e+00</td><td>4.1303273e-05</td><td>125.60683124863863</td><td>0</td><td>1.146729e-03</td><td>659.36666</td><td>250.31386</td><td>816</td><td>5.3289894e+05</td><td>2.0046629e+01</td><td>2.4413618e+03</td><td>1.3229259e+00</td><td>5.0173166e+05</td><td>2.0721527e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36666</td><td>3.6303332e-05</td><td>250.31386</td><td>5.3555013e-05</td><td>-1.9127612e-04</td><td>-2.7923766e-04</td></tr>\n",
              "<tr><td>0.41399574564475977</td><td>9.9996489e-01</td><td>4.1363015e-05</td><td>126.44463374568295</td><td>0</td><td>1.171726e-03</td><td>659.36530</td><td>250.31210</td><td>857</td><td>5.3275538e+05</td><td>2.0044533e+01</td><td>2.4384763e+03</td><td>1.3216738e+00</td><td>5.0166175e+05</td><td>2.0750971e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36530</td><td>3.6318204e-05</td><td>250.31210</td><td>5.3593514e-05</td><td>-1.6015661e-03</td><td>-1.7701549e-03</td></tr>\n",
              "<tr><td>0.4143072627939433</td><td>1.0000198e+00</td><td>4.1377596e-05</td><td>127.2824362628744</td><td>0</td><td>1.196643e-03</td><td>659.36404</td><td>250.31103</td><td>898</td><td>5.3272681e+05</td><td>2.0044369e+01</td><td>2.4385225e+03</td><td>1.3240824e+00</td><td>5.0168906e+05</td><td>2.0758276e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36404</td><td>3.6324829e-05</td><td>250.31103</td><td>5.3616131e-05</td><td>-2.8932779e-03</td><td>-2.6051088e-03</td></tr>\n",
              "<tr><td>0.4146184956068954</td><td>9.9996005e-01</td><td>4.1348298e-05</td><td>128.12023849565594</td><td>0</td><td>1.221476e-03</td><td>659.36454</td><td>250.30932</td><td>939</td><td>5.3262588e+05</td><td>2.0041386e+01</td><td>2.4307776e+03</td><td>1.3250725e+00</td><td>5.0163597e+05</td><td>2.0742622e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36454</td><td>3.6334448e-05</td><td>250.30932</td><td>5.3637086e-05</td><td>-2.5552646e-03</td><td>-4.2674006e-03</td></tr>\n",
              "<tr><td>0.41492973936944155</td><td>9.9993163e-01</td><td>4.1306273e-05</td><td>128.95804073938052</td><td>0</td><td>1.246219e-03</td><td>659.36392</td><td>250.30715</td><td>980</td><td>5.3249681e+05</td><td>2.0039207e+01</td><td>2.4259446e+03</td><td>1.3237810e+00</td><td>5.0155519e+05</td><td>2.0718792e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36392</td><td>3.6345860e-05</td><td>250.30715</td><td>5.3661504e-05</td><td>-3.1474836e-03</td><td>-5.9353639e-03</td></tr>\n",
              "<tr><td>0.41524098948981647</td><td>1.0001049e+00</td><td>4.1259908e-05</td><td>129.79584298951522</td><td>0</td><td>1.270870e-03</td><td>659.36300</td><td>250.30623</td><td>1021</td><td>5.3249038e+05</td><td>2.0040110e+01</td><td>2.4226838e+03</td><td>1.3260700e+00</td><td>5.0158856e+05</td><td>2.0693327e+01</td><td>0</td><td>———</td><td>———</td><td>———</td><td>———</td><td>659.36300</td><td>3.6347436e-05</td><td>250.30623</td><td>5.3668813e-05</td><td>-4.1886452e-03</td><td>-6.7668976e-03</td></tr>\n",
              "</table></div>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**The \"fold\" process in the context of analyzing light curves, especially for exoplanet transits, is a crucial technique. When astronomers have a time series of data showing the brightness of a star (its light curve), folding the data helps in identifying and analyzing periodic events like transits.**"
      ],
      "metadata": {
        "id": "4ucDarFljkdL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "timeInterval = foldedLightCurve[\"time\"]\n",
        "flux = foldedLightCurve[\"flux\"]"
      ],
      "metadata": {
        "id": "yHzca60Ti7Ve"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "plt.plot(timeInterval.value,flux,marker=\".\",linestyle=\"none\",color=\"red\")\n",
        "plt.xlabel(\"Phase (days)\")\n",
        "plt.ylabel(\"Normalized Flux\")\n",
        "plt.title(\"Folded Light Curve of Kepler-10 b\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 957
        },
        "id": "B_ahpa8sjCxb",
        "outputId": "fb7fcccf-9011-49fc-8222-cbfc42b5c459"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zmfsWUVYjP6h"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}