DA2_chapter2.ipynb

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      "name": "DA2-chapter2.ipynb",
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    "kernelspec": {
      "name": "python3",
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  "cells": [
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      "cell_type": "markdown",
      "source": [
        "# **Chapter 02:**\n",
        "Merging DataFrames with Pandas\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "uK6UGGJon1_O"
      }
    },
    {
      "cell_type": "markdown",
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Se6WFO4GK+4R8di07v+TF8iLbs2zVw8SxD74XHY4c8WGv2fGHc3Hh8L+KCv2nFfcMdF/o4U7hf7J73XENIm3IjVk2auDEahtC981sn99PdcZqqV952xtfIvfrtJ5S98k+1wx63v8Sd9QfStKZKdexEo0Owo7kttRbjR8bPvq7sr3uKRKTikhJC5711vcxD7u97hiE7JbU2aHBZd/A7HE3wwz8GetKb5MjDB7n1qJTlpFbSzRd2LjxlR06TyomRWpTT9dMhrhXIacQ6j3o/ZMV9wx0X+jhTuE/tIxR4qj8Jg/uVftIxJ4zDvjdrieJtNTYtnMTOiKKJYqJcWwSORNCm5hDglgmZxjkzxlLMZSzGUsxlLMZSzGUsxlLMZK0GStRlLMSs7HUVfaLRRwHoi/8AR8U9dnpPdJlRJPmIDyiUbPSe6WFO4GK+4R8dq07v+RG8iLbs2lpSnmIEQ984XHY4c8WGv2fGHc3Hh8L+KDn7Pig977HRf6OFO4X+ye1akoTNuUJDEGfYrg1kaJ7Lzzak7tk/vn7F96Ph8v7hbeRVYPqiUfk73yTrXHh2t7/EnexbB+MzH6+Jvnhk90ZSiW4j42ffV3ZXvcRZjsdtNpJM7nveXx0kPu73uKLtCIjNSdwsu/gdjib4YZ+DHXxE1wTMNddxSmpTlg5JYou7Fx4yo6dJ5XbNro8kOxJ1euFcoUEKStPtR+yYr7hjov8ARwp3Cf2kYo8VR+Ewf3Kv2kYk8Zh3xu1RklJ3zO+3mpmuItm0odtELGoIC5qVHm0gp6CLUEDV2xq7Y1dsau2NXbGrtjV2wV01u1toau2LGUmUtu8aS3W2Dc0/9HxT12ek90mPz9A/+Wek90sKdwMV9wj4+xad3/HjeRFt2bPw+gi9+LjscOeLDX7PjDubjw+F/FB39nxN12Oi/wBHCncL/ZNpmRFNuGmw2xYWi4FTGje6882FJ3CRAivuaTCEWKxGEmFGkq0mEIsOPGORXxX3UxmkxWqyI25KjMyU6TCDFfEYclRGJRxYzUZBVsQlp3bnYMd1xtCW0SIrL6tOiDToofhsPLQw2hhECMhciKy+qOy2wjToux+riLdbSTbcqKzKKLFZikmqhJVKisyiiw48ZUiviPuIrIaDjMMspFx4yo6dJ5X2zauPICmp9YuFcNOAjIy2o/ZMV9wx0X+jhTuE/tIxR4qj8Jg/uVftIxJ4zDvjdsjoYQiR5UnR6wPVNaS9Krg9WQCPTYIZrIBnpVcGamtNej1g0esGj1g0esGj1g0esGj1g0esGi1Q0WqB01Xvk1FamNhbr/6Pinrs9J7pMpJR8tAeSSTZ6T3Swp3AxX3CPj7Vp3f8aN5EW3ZtISpPLQIhbpwuOxw54sNfs+MO5uPD4X8UHf2fE3XY6L/Rwp3C/wBk2TbVhgFqFquDTR2PsXnm9ik7vvke4Ee/7qk7vsEe4Ee8XHjKjp0nlffNqGHh/X1aoVqw/tR+yYr7hjov9HCncJ/aRijxVH4TB/cq/aRiTxmHfG7ZHQwL3YkfMP8A5DH5Ef5/aP8AMvtMLdf/AEfFPXZ6T3SaIzPhWHSMjZ6T3Swp3AxX3CPj7lp3f8WN5EW3Ztkrh4ViH3wuOxw54sNfs+MO5uPD4X8UHf2fE3XY6L/Rwp3C/wBkm2ceOFO2FmuDRsthJEkvsXnm9qk7vvke4Ee/7qk7vsEe4Wx76uo6dJ5X7BkRlNp2nAh+fWrhWceQEfsmK+4Y6L/Rwp3Cf2kYo8VR+Ewf3Kv2kYk8Zh3xu2R0MC92JHzD/wCQx+RH+f2j/MvtMLdf/R8U9dnpPdJozI+JYdMzNnpPdLCncDFfcI+PvWnd/wASN5EW3Ztmrh4liH3wuOxw54sTcwxbzn5s5du2s6qHPsIjOsWohZh+3xG24pxNhYpSqwsVJw224h2wjSXbWBRJSG0IbR9q980hW7ZM7uj7TFCdyKryL/QFP43EnfYb7648aGOha+Rwz8MSdjA74cRodolEuvvutUd/M7QQ+0t+/oetc9kj5CZ3dH2mKU7kVXkX+gKfxuJO+w33wtPHVHTpPK/aWlK0zKRCxWxZTdtihpxTqbCySlVhZKThdpxLtgUli51i2EydYy2aVpxNPBemwV15SX7kYk8Zh3xu2R0MC92JHzD/AOQx+RH+f2j/ADL7TC3X/wBHxT12ek90mVEk+YgPKJRs9J7pYU7gYr7hHx+wtO7/AIcbyItuzaWlKeYgRD3zhcdjhzxf2y+gSe/7l75oIVuCoMVamGW2EyYzMlKK2Iy4ZEZaXADLaGm5MKNIXGhRo63m0Ot6XABERE7Xw3XI0ZiMUmO1IQithIWDq4JnGabjIdjsSAzDjtLWklJ0+GEJJKXocd1bEZlg3mkPI0+JsXBiqUwy2wmTGZkpTWxGXDIjLS4AZbQ03JhRpC40KNHWLXx1R06Tyv2y+gSe/wC5iTxmHfG7ZHQwL3YkfMP/AJDH5Ef5/aP8y+0wt1/9HxT12ek90mPz9A/+Wek90sKdwMV9wj4/ZWnd/wAKN5EW3Zs/D6CL34uOxw54sOy4zSynww4tDaNQhDUIQalxnVvvssDUIQ1CEGH2XxmGOcX0CT3/AG73zWxCt3sUnd98j3Aj3/dUnd7rXx1R06Tyv3CkM892UwyWpQRqUER5DEgPTYrK9SgjUoIacQ42djBI2ZsV5YxJ4zDvjdsjoYes0Vj3qyOHMTsKV6lYDmImVDX2Q3iJlI9SsBvE7CVerI49WRx6sjj1ZHHqyOPVkcerI49WRx6ujj1dHB4sj73sUsLZwt1/9HxT12ek90mUko+WgPJJJs9J7pYU7gYr7hHx+0tO7/gxvIi27NpCVJ5aBELdOFx2OHPFh5kpF7dwEQHbjw9RTtzYfpxkMslHvcTddulaU25StJbwz157anbZifMhLhT48kJPf9q980JTvIj646G75zfq6xqzg1ZYet3mz1x0a46NcdGuOjXHRrjo1x0a46NcdGuOjXHRrjo1x0a46CvXSDd0padWcGrODVnBqzg1Zwas4NWcGrODVnBqzg1Zwas4Hrd5s9cdGuOhm6Wp0Wvjqjp0nlftTJ0eKT8+ZNXAbU1bYm67dK0ptylaS3hTuJLJSL/04yLenbhQ6PwlJARPdjMlHvxiTxmHfG+w6+EZ4hYZYeahxTbkRIxDLMB1hklcloJjMcOWYGSijJRRkooyUUZKKMlFGSigoEPdkIYyUUXLTbTjNfCNliOyx/pGKeuz0nuk0RmfCsOkZGz0nulhTuBivuEfH7a07v8AgRvIi27NslcPCsQ++Fx2OHPFhr9nxh3Nx4fC/ig7+z4m67HRf6OGev8A+ifZafRMplpOLayoq4U2PLT9i980LXx1cRGiY8kgw7whtJuG00lCTQgysVE3sbQpxTLCG0cKRMeSWxtCnFMsIbRwpEx5JbG0KcUywhtHCkSJJNm2slpQk1qaaShPCkSnUlsQk1qaaShPCkYdSlVtiNKU224gaUmTrRJPhSHO9Fr46o6dJ5X7EqWxGKTaSZKodMtRsMtMI/8ARYm67HRf6OFO4T+0jFHiqPwmD+5V+0jEnjMO+N2vGZM10afYuKw7bKCqK2SNEtAqmskjR7EJpbFQ0OzCaG1Ufp+4Hp+4Hp+4Hp+4Hp+4Hp+4Hp+4Hp+5Hp26Hpy5CsOWxhdFcIbwy64p3/R8U9dnpPdJozI+JYdMzNnpPdLCncDFfcI+P3Fp3ffjeRFt2bZq4eJYh98LjscOeLDX7PjDubjw+F/FB39nxN12Oi/0cM9f/wBFskxmZCZVS+yqFdvNHGksSUe6980LXxzbykNBtCnFQyJhIlP7goiMjjr5jDSWkiZJ3bG0KcUw0lpImSd2xtCnFMNJaSJkndsj8ZuRkpSgSn92xCTWplsm0iU/uGGPK4l8vsMt5Oo4Td70Wvjqjp0nlfdIkMx0y7d104tS88caMzHTs/8ARYm67HRf6OFO4T+0jFHiqPwmD+5V+0jEnjMO+N2yOhgXuxI+Yf8AyGPyI/z+0f5l9phbr/6Pinrs9J7pMqJJ8xAeUSjZ6T3Swp3AxX3CPj91ad33o3kRbdm0tKU8xAiHvnC47HDniw1+z4w7m48PhfxQd/Z8Tddjov8ARwz1/wD0XslxGJRSK2VEXBvFJDDzT6PZe+aFr44NoU4phpLSQ7N4DCEmtTbSUIcRwnMk7tjaFOKYaS0kTJO7Y2hTimGktJEyTu2NoU4phpLSUqNJvSiMghJrUy2TaRKf3bMMeVxL5faZbylNmicLXx1R06TyvsedbZRMuVKEeslSlRIjEUvZ/wCixN12Oi/0cMfR9v64oGKPFUfhMH9yr9pGJPGYd8btkdDAvdiR8w/+Qx+RH+f2j/MvtMLdf/R8U9dnpPdJj8/QP/lnpPdLCncDFfcI+P3lp3fdjeRFt2bPw+gi9+LjscOeLDX7PjDubjw+F/FB39nxN12Oi/0cM9f/ANF7pkCPKDsKbAXAvSMNOIdRsvPNrSLXxzaFOKYaS0kTJO7ZDUo1stpbSJbxbnmzQbaFOKYaS0kTJO7Y2hTimGktJEyTu2NoU4phpLSRMk7ghZoVH/nBlsm0iU/u2ISa1YZ+ltiXy/sdLfKUncLXx1R06TyuxxaG0zboiDMGbOXDgR4vv/8ARYm67HRf6OGeu1+zjFHiqPwmD+5V+0jEnjMO+N2yOhgXuxI+Yf8AyGPyI/z+0f5l9phbr/6Pinrs9J7pMpJR8tAeSSTZ6T3Swp3AxX3CPj99ad33I3kRbdm0hKk8tAiFunC47HDniw1+z4w7m48PhfxQd/Z8Tddjov8ARwz1/wD0X2JtbHkg1v10mnmqmxxeebFunfX1vwEyTu2NoU4phpLSWnOESX9hN80IjFHITJO7Y2hTimGktJEyTu2NoU4phpLSRMk7tjaFOKioKOSFEopT+7YhJrUy2TacN+YxL5f2Od2LdO6vqOnSeVFtLVDYSp6wkwq2PG+z/wCixN12Oi/0cM9dr9nGKPFUfhMH9yr9pGJPGYcks5fbI6GBe7Ej5h/8hj8iP8/tH+ZfaYW6/wDo+Keuz0nuk0RmfCsOkZGz0nulhTuBivuEfH/gLTu+3G8iLbs2yVw8KxD74XHY4c8WGv2fGHc3Hh8L+KDv7PibrsdF/o4Z6/8A6L3zbCPGDkqdYrgUaEhCEoSLzzYseyit/wAMyTu2NoU4phpLSRMk7gw7whtJuGy2TaTLeVi6bextCnFMNJaSJkndsbQpxTDSWkiZJ3bG0KcUw0lpIkyzbNtZLShJrUy2TaRhvzGJfL+xzuxceMqOnSeVC0pWmbTIUGpc6vXCsI8n7H/osTddjov9HDPXa/ZxijxVH4TB/cq/aRiTxht8MeksswWyR0MC92JHzD/5DH5Ef5/aP8y+0wt1/wDR8U9dnpPdJozI+JYdMzNnpPdLCncDFfcI+P8AwVp3fajeRFt2bZq4eJYh98LjscOeLDX7PjDubjw+F/FB39nxN12Oi/0cM9f/ANF7Zk2PFJ+wmTFwqUMtoZSk9+2882k94seyQ6aG5DO8NoU4thpLSRMk7tjaFOKhkTCRKf3BREZHHXzGGktJEyTu2NoU4phpLSRMk7tjaFOKYaS0kTJO7ZH4zcjJSlAlP7hhjyuJfL+xzuxceMqOnSeV2uttuom0oYsJkNcObHlF7f8A0WJuux0X+jhnrtfs4xR4qm8Pg/uVftIxJ4ypcablTjbRMr5JSogcTxNpqbFs5iZ0RRRLFRLi2CRyJoU3MIcEsEzOMcmeMpZjKWYylmMpZjKWYylmMpZjJWgyVqMpZiVnY6ir7RaKOA9EX/o+Keuz0nukyoknzEB5RKNnpPdLCncDFfcI+P8Awlp3fZjeRFt2bS0pTzECIe+cLjscOeLDX7PjDubjw+F/FB39nxN12Oi/0cM9f/0W2VKYjJk2siQqHTuLNhhphGwvoEnv2XvmiPcJ574OzibbTnkh6YakbGJDTSc8kah9NjbraEOySbN6YakbGJDTSc8kPTDUjYxIaaTnkh6YakbGJDTSU2BJM5vNbFTLTCmWstMybmCGYIZghmCHFxyBceMqOnSeV9r7DT6JlO4g41rIjqiymJKdv/osTddjov8ARwz12v2cYo8VT+Hwf3Kv2kYk8Yo0copbOQqLHJFrzQ15oa80Leama4i2bSh20QsaggLmpUebSCnoItQQNXbGrtjV2xq7Y1dsau2NXbBXTW7W2hq7YsZSZS27xpLevNDXmhrzQ15oQpKJUf8A0PFPXZ6T3SY/P0D/AOWek90sKdwMV9wj4/8ADWnd9iN5EW3Zs/D6CL34uOxw54sNfs+MO5uPD4X8UHf2fE3XY6L/AEcM9f8A9EJD7TCJdw64cWpffVGjMRk+0voEnvF75rYUSKYycUZOKF10Mxp8MafDGnwxp8MafDGnww3EipGTijJxQcGIZKroZDT4Y0+GNPhjT4Y0+GNPhjT4Y0+GNPhjT4Y0+GNPhjT4Y0+GEwoiTKJFMZOKMnFGTijJxRk4oycUJiR0qFx4yo6dJ5X3yYzElMqpfYVEuHWzjvtPoH/osTddjov9HDPXa/ZxijxVP4fB/cufs6T3jEnjMM18KRV6RWjSK0aRWjSK0aRWjSK0aRWjSK0aRWjSK0aRWjSK0aRWjSK0aRWjSK0aRWjSK0aRWjSK0aRWjSK0aRWjSK0aRWjSK0aRWjSK0Ymr4Uerw743/Q8U9dnpPdJlJKPloDySSbPSe6WFO4GK+4R8f+ItO73xvIi27NpCVJ5aBELdOFx2OHPFhr9nxh3Nx4fC/ig7+z4m67HRf6OGeu4tLd9MuTMR62XLXEhx4pfYL6C7Pfc7CPcEnv2rT70K3exSd33yPcCPf9i48ZUdOk8r9mXDjyikVsuIuHcmQbWly+xN12Oi/wBHDPXa/ZxijxVP4fB/cu/s5fQYk8XhHw3/ADMXeGw743/Q8U9dnpPdJojM+FYdIyNnpPdLCncDFfcI+P8AxVp3e6N5EW3Ztkrh4ViH3wuOxw54sTqyUuaqqnrOwirfgJrLFJadZiDWSkTbqC9KVplkNMshSwXoqpdS8/Ph18eL9y2rpEiWVXZmNKsxpVmNKtBpVoNKtBpVoNKtBpVmNKsxpVmNKsxpVmNKsxpVoNKtBpVoNKtBpVmNKsxpVmNKsxpVmNKsxpVmNKsxpVmNKsxpVmNKsxpVmNKsxpVmNKtBpVoNKtBpVoNKtBpVoNKtBpVoFVVnuKpnEKmukR5X25lfHlCJUvMT7qC9KVplkNMshSwXoqp1ZKXN06zCqyxUVfFWxATVT0HBrJSJoxH4ve+0yTU/Lwm50s9OtRp1qNOtRp1qNOtRp1qNOtRp1qNOtRp1qNOtRp1qNOtRp1qNOtRp1qNOtRp1qNOtRp1qNOtRp1qNOtRp1qNOtRp1qF1dmsqmOuLD/wBDxT12ek90mjMj4lh0zM2ek90sKdwMV9wj4/8AGWnd7Y3kRbdm2auHiWIffC47HDni/wDkl9Ak9/8AiVp/5WI/F18Y5L1hxRxSxctC/wBfxT12ek90mVEk+YgPKJRs9J3pU85MFz1AyLicmc4UpJFm0jNpGbSM2kZtIzaRm0jNpGbSM2kZtIzaRm0jNpGbSM2kZtIzaRm0jNpGbSM2kZtIOQkZghmCGYIRD4pwtuzaWlKeYgRD3zhcdjhzxf3ZslEVnXGhrjQ1xoa40NcaGuNDXGhrjQ1xoa40NcaGuNAr1sh6gaHqBkeoGR6gZHqBkeoGR6gZEGSiXH+5ZWTUJXqBkeoGR6gZHqBkeoGR6gZHqBkeoGR6gZHqBkeoGR6gZHqBkeoGQmW0cH1AyPUDI9QMj1AyPUDIO9ZGuNDXGhrjQ1xoa40NcaGuNDXGhX2Tct2batRntcaGuNDXGhrjQ1xoa40NcaGuNDXGhrjQ1xoa40NcaBXjIxCZKqUOJZZpICn3f9gxT12ek90mPz9A/wDlnpDJxRk4oycUZOKMnFGTijJxRk4oycUHEikMpGGUjDKRhlIwykYZSMMpGGUjDKRhlIwykYZSMMpGGUjDKRhlIwykYZSMMpGGUjDKRgiOyhQtuzZ+H0EXvxcdjhzxYdcbaSzIYe+04skFcqNUUJH0G4huIbiG4huIbiH8I/hH8I/hG4huIbiH0H8I/hCi+mG/GfZcmxG1kZGUh3gF91pTTaYtRGYdh5OEMnCGThDJwhkYgyMQZGIMjEGThDJwhk4Quo7LLCDM6OoZadTVMtO3GnQBp1eNOrxp1eNOgDTYI02CNNgjToA06vGnV402AKk+GdZeQloSko7DBx8vGGXjDLxhl4wy0cZWOMrHGWjjLxhl4wy8YWbaG13nhKStRISRERf7Binrs9J7pMpJR8tAeSSTZ6X2TPd/ybbs2kJUnloEQt04XHY4c8WJxKm3VxCOnn646NcdGuOjXHRrjo1x0a46NcdGuOh23cWcqap9oIB/Ij3+5RexJ+wy3bFfGvtnIcf1A8PUDw9QPD1A8PUDw9QPD1A8PUDwkXj7jNbTlLqsLPLMn+rfdWb2dH2Ci3exKt3sWnbiHtmvCUXxpfOqL2ke/af1B/TYj8Vnf2PkZv4jfWKf09iVbvYpOxPyt+peeEw743/YcU9dnpPdJojM+FYdIyNnpOHubhsy5i5cSXFb5jg5jg5jghxJspOlWQ0qyGlWId5jbnMWOYsIN5a9KsRpNiNJsRLhy4rfMWOYscxYhxJspOk2I0mxGk2Id5jbnMWOYsIN5a9KsRpNiNJsRH5zNiLbs2yVw8KxD74XHY4c8WLWsOS5FqHlPcpocpocpocpocpocpocpocpocpoSm2+K1SkogQD/II9/uUXsSfsMtwV8cOttqreSyOSyOSyOSyOSyOSyOSyOSyJERh5ldJNSqqgpgsv9W+6s3s6PsD+oUW72JVu9i07MQ9s14Si+NL50KL2ke/af1B/QI/FZ39j5Gb+InbGW8H9NjslpsKnKBWDxBuxINOtukFJCflb9S88Jh3xv+w4p67PSe6TRmR8Sw6ZmbPSe6VLKbiCZJdlPbKmvVMW2hLaNl1ab9qEqWuorkxEbJ0pqIzMkuyntlTXqmLbQltGy6tN+1CVLXUVyYiNk6U1EZZdN+zFt2bZq4eJYh98LjscOeLFrZnGci27yXua0Oa0Oa0Oa0Oa0Oa0Oa0Oa0Oa0JTjfFaqScQIB/nYR79jSDccbiMNkRJLYoiMOxmlk82bSwk/Yr8YdcbTW85kc5kc5kc5kc5kc5kc5kc5kSJbDLK7uapVVOTOZf6t91ZvZ0fYA/qFFuDzzbRLtGSGqpDFnHM0qJSdikjEPbNeEovjS+d2KL2ke/af1CRWd/Y+Rm/iJ2wMt4n8wmWo7jgTBQMk0FwVBCH23tnD/Fb9S88Jh3xv+w4p67PSe6TKiSfMQHlEo2ek90ttTXqmLbQltGy6tN+1CVLXUVyYiNk6U1EZmSXZT2ypr1TFtoS2jZdWm/ahKlrqK5MRGydKaiMzJLsp6B3gtuzaWlKeYgRD3zhcdjhzxYnGqFdXE07ifobo0N0aG6NDdGhujQ3Robo0N0aG6HahxByoSmGggH+dpHvFX3Fpxb67i5W2xL+XsSe1X4r6lyZH9PvD0+8PT7w9PvD0+8PT7w9PvD0+8JFG+2zW3BRKrCzKyJ/q33Vm9nR9hsnrW3EQh+S43VLMHUkHq19Ar3X2JW3EPbNeEovjS+d2qL2ke/2Vnf2PkZv4idtsMt4P6exKt3st+peeEw743/YcU9dnpPdJj8/QP/lnpPdLZU16pi20JbRsurTftQlS11FcmIjZOlNRGZkl2U9sqa9UxbaEto2XVpv2oSpa6iuTERsnSmojMyS7KeEDvBbdmz8PoIvfi47HDnixKy/KhZHf9mX8rbswgH+fZXOJS6chkc9kJcQvbP6O1J7FfjDfjPsyNI56OHhf6t91ZvZ0fYbeEtyi3bdxb0q3bcQ9s14Si+NL53bNmx4aZN66o1Ws8w3czknDu2XDSZGRHv21nf2PkZv4idttMt4P6exKt2236l54TDvjf9hxT12ek90mUko+WgPJJJs9J7pCpr1TFtoS2jZdWm/ahKlrqK5MRGydKaiMzJLsp7ZU16pi20JbRsurTftQlS11FcmIjZOlNRGZkl2U9sgd4Lbs2kJUnloEQt04XHY4c8WJxKm3VxCOnn646NcdGuOjXHRrjo1x0a46NcdGuOh23cWcqap9oIB/nZFry4ctHIchgclocpoEhCdqyI16ZXiXR176J0VyHKCTCvxX2zkOP6geHqB4eoHh6geHqB4eoHh6geHqB4SLx9xmtpyl1WFnlmT/AFb7qzezo+w9h/UKLd7Eq3bMQ9s14Si+NL53bZVc1+xi0MdBaZBInamCsS6R5AqGHI0IEe/ZWd/Y+Rm/iJ22wzIiSZKIy3g/ps4k7wlW7Zb9S88Jh3xv+w4p67PSe6TRGZ8Kw6RkbPSe6VTDzkhtCW0bLq037UJUtdRXJiI2TpTURmZJdlPbKmvVMW2hLaNl1ab9qEqWuorkxEbJ0pqIzMkuyntlTXqmLcQlu7Ft2bZK4eFYh98LjscOeLFrWHJci1DynuU0OU0OU0OU0OU0OU0OU0OU0OU0JTbfFapSUQIB/kVjfHJ+x+XtmM2N7exXxoEIOAltkxyWRyWRyWRyWRyWRyWRyWRIiMPMrpJqVVUFMFl/q33Vm9nR9hsMyImnWnSB/UKLcOa3zNiT3DEPbNeEovjS+d9yi9pHvFZ39j5Gb+InbbLBpbrDbjrKkWLhBVhvJyW6oMtuOr2IMW/UvPCYd8b/ALDinrs9J7pNGZHxLDpmZs9J7pYX6+y6tN+1CVLXUVyYiNk6U1EZmSXZT2ypr1TFtoS2jZdWm/ahKlrqK5MRGydKaiMzJLsp7ZU16pi20JbRI88Lbs2zVw8SxD74XHY4c8WLWzOM5Ft3kvc1oc1oc1oc1oc1oc1oc1oc1oc1oSnG+K1Uk4gQFFspy+nvMyIqCGqTL2YkRzKfYr40LiE15PNEEvsmOcyOcyOcyOcyOcyOcyJEthlld3NUqqnJnMv9W+6s3s6PsCPfsmtG/FcRIiutWspBHcPh+fKeKBFecc24h7ZrwlF8aXzv/wDvuUXsR+Kvv7HyM38RfpF2ustOhcOPx6aQajROPh4dqflb9S88Jh3xv+w4p67PSe6TKiSfMQHlEo2ek90sL9cXVpv2oSpa6iuTERsnSmojMyS7Ke2VNeqYttCW0bLq037UJUtdRXJiI2TpTURmZJdlPbKmvVMW2hLaBI88Lbs2lpSnmIEQ984XHY4c8WJxqhXVxNO4n6G6NDdGhujQ3Robo0N0aG6NDdGhuh2ocQcqEphoI2KIVHT2Puk0iHRSJCPTUEemoQ9OQgxQwG1ERJLZdn/alFsV8YNYuUxobo0N0JoXTHp94en3h6feHp94en3hIo322a24KJVYWZWRP9W+6s3s6Tx6T3Aj37FpStMiBBJMeJWPORo0BKt30UW7biHtmvCUXxpfOr/KT2SrmOxLjSWJCQZkRTLiIwIchEuPsR+KnyFn5Kb+I/aJPdtky0MKdXxvaigR9+YMt4P6bE/K36l54TDvjf8AYcU9dnpPdJj8/QP/AJZ6T57msL9e6tN+1CVLXUVyYiNk6U1EZmSXZT2ypr1TFtoS2jZdWm/ahKlrqK5MRGydKaiMzJLsp7ZU16pi20JbRsdUSrwW3Zs/D6CL34uOxw54sSsvyoWR3/Zl/K27MIG/cYrT3O7KZpMi9+xih7grgogr44f8dsL6BJ7/AHSNI56OHhf6t91ZvZ0njwk9wI9+y5dU7NiMrRYwFKZsAf1Ci3bMQ9s14Si+NL51f5CTFtTlJcfiyoyimSyJS331QqaW+cKM3EjqLYj8VPkLTyU38R+0CT3bJcVL5utmh7TRGhoZUDLeD+gT8rfqXnhMO+N/2HFPXZ6T3SZSSj5aA8kkm2e5l7psvuMo2ISpa6iuTERsnSmojMyS7Ke2VNeqYttCW0bLq037UJUtdRXJiI2TpTURmZJdlPbKmvVMW2hLaNl1abxA7wW3ZtISpPLQIhbpwuOxw54sTiVNuriEdPP1x0a46NcdGuOjXHRrjo1x0a46NcdDtu4s5U1T7QQD/JHuDS+BaVEtIw1533mL6YUubsV+INmuKxrjo1x0a46CvXSHqB4eoHh6geHqB4SLx9xmtpyl1WFnlmT/AFb7qzezpPH7EnuBHvF0wpuXmXczVsqfm7D+oMtx4h7ZrwlF8aXzq/zsSYWpKEO3FcS03sRIivoksbFEEfip8haeSm/iP2mxJ7tkia4l7Pvhic6p3YZbwX0Vb9S88Jh3xv8AsOKeuz0nuk0RmfCsOkZG301FvTp0gadJGnSQmBKSeVnjKzxlZ4VBlqGnSRp0kadJBQ5qSys8ZWeMpOGnSRp0kadJCYEpJ5WeMrPGVnhUGWoadJGnSRp0kFDmpLKzxlZ4yk4adJESC8iQLbs2yVw8KxD74XHY4c8WLWsOS5FqHlPcpocpocpocpocpocpocpocpocpoSm2+K1SkogQD/II9wQtSQqQ+MIKNdtssUS1s5e+GXvhZuXUBgrqxMSLGbIRtV+KFtCq/lNDlNDlNDlNDlNBLbJjksjksiREYeZXSTUqqoKYLL/AFb7qzezpPH7UnuEmVDSDKm3sza5lDD7L5bDGIe2a8JRfGl86v8AO3EqlabWVrk4jw84IEYokX2VPkLTyU38R+02pPcJiuOVp7XBFPly/Zb9S88Jh3xv+w4p67PSe6TRmR8Sw6ZmbXS/wVt2bZq4eJYh98LjscOeLFrZnGci27yXua0Oa0Oa0Oa0Oa0Oa0Oa0Oa0Oa0JTjfFaqScQICi2ke7ZhAv7ntnSExYtZeqkzMXeOBHv9ivxQuITX81oc1oc1oc1oc1oE80QS+yY5zIkS2GWV3c1Sqqcmcy/wBW+6s3s6Tx/stiIpia6UplltTriFOxpDSyca2Yh7ZrwlF8aXzq/wA7b9zl18SXIinrVgKmQuVA9lT5C08lN/EftPZNbNDxSpBIgtG5I9lv1LzwmHfG/wCw4p67PSe6TKiSfMQHlEo2ukJE9ppcWU3IDzqGUJs2TURkZSJ7TS4spuQHnUMoTZsmojIykT2mlxZTcj2S5TccMOoeb2yJ7TS4spuQHnUMoTZsmojIykT2mlxZTcgPOoZQmzZNRGRlIntNLiym5Gy27NpaUp5iBEPfOFx2OHPFicaoV1cTTuJ+hujQ3Robo0N0aG6NDdGhujQ3RobodqHEHKhKYaCNii2ke4VcvJzGH232tkplEhiBSMw5GJLBEt7YR79qvxBrFymNDdGhujQ3Robo0N0aG6NDdCaF0xIo322a24KJVYWZWRP9W+6s3s6Tx/stUmma3bcLUFKnJ9orjnwkmmHsxD2zXhKL40vnV/nZJnxI7khUdUb+yD+wiNyuR7KnyFp5Kb+I/aex+Q0SsvG3RJTRr9lv1LzwmHfG/wCw4p67PSfPc0x+foH/AMtdJW/hb4eKB313v3r5fBXceQb4eKB313v3r5fBXceQb4eKB32ydKTHTFjLlL/nQJDDqHmwrfwt8PFA7673718vgruPIN8PFA7673718vgruPIN8PFA74W3Zs/D6CL34uOxw54sSsvyoWR3/Zl/K27MIG/6hRbSPcI0l+OprEEtBepQ7iR8xMsJcr2ke/Yr8Yf8d7y+gSe8SNI56OHhf6t91ZvZ0nj/AGWD0RStK3iFGbihUSK67txD2zXhKL40vnV/nZbV0pc6XAeXT6TYDSbAU7K48D2VPkLTyU38R+09j/XJxrlN9b2W/UvPCYd8b/sOKeu39GXumyklHy0B5JJNrpCRAadXFitxw80h5CaxklEREUiA06uLFbjh5pDyE1jJKIiIpEBp1cWK3H2TpSY6YcZcpwiIifaQ83/OgSGHUPNiRAadXFitxw80h5CaxklEREUiA06uLFbjh5pDyE1jJKIiIpEBp1cWK3H2W3ZtISpPLQIhbpwuOxw54sTiVNuriEdPP1x0a46NcdGuOjXHRrjo1x0a46NcdDtu4s5U1T7QQD/KT2KLaR7tii+wR7wr8QbNcVjXHRrjo1x0a46NcdGuOjXHRrjoeun1tVtOUuqws8syf6t91ZvZ0nj/AGWxIKXHs+BpixbddSe4Ee/biHtmvCUXxpfOr/O2S8iOxIuJji4lzJbW2tLiEn7KnyFp5Kb+I/aeyZGNZ8twjhxlEpJ7vZb9S88Jh3xv+w4p66Og70miMz4Vh0jI2ukZ7iekPSHK2S4T1pIUyknJCBFe5sZ6Q9IcrZLhPWkhTKSckIER3nMbJ0pMdMOMuU4RERbH2kPN/wA6BIYdQ83stJCmUk5IQIr3NjPSHpDlbJcJ60kKZSTkhAivc2M9IekOVslwnhbdm2SuHhWIffC47HDnixa1hyXItQ8p7lNDlNDlNDlNDlNDlNDlNDlNDlNCU23xWqUlECAZHv4TBb9hpHCY4TBby2GQ4THCY4THCY4THCY4TCvxQtoVX8pocpocpocpocpocpocpocpoPxWXWlU8xJ1EJENl/q33Vm9nR9gtPsl16Xniq0mI9aTbwSe4Ee/ZiHtmvCUXxpfOmW/2Yl36fh5iM8rEEeOw/h8zOsCT21PkLTyU38RO1UW72SZXLVnXQzMWpwJPdtt+peeEw743/YcU9dHQd6TRmR8Sw6ZmbXSMt5PR3o7lbGcN60jqeSTchYis8qM9HejuVsZw3rSOp5JNyFiI1yWBOlJjphxlynCIiL2PtIeb/nQJDDqHmxaR1PJJuQsRWeVGejvR3K2M4b1pHU8km5CxFZ5UZ6O9HcrYzhvC27Ns1cPEsQ++Fx2OHPF/cl/K0SpUTlrBIcIcLo4XRwujhdHC6OF0cLo4XRwujhdHC6OF0cLo4XRwujhdHC6OW8KNCkV/wBovoHT3u33Vm9nR9gFp228hRuwicRMgvuImbEnuBHvGIe2a8JRfGl86DLftmsJkxpMSTGXFhyZTkVlMePsSeyp8haeSm/iJ2wUW7a+R5goTe5uI0hexJ7tlv1LzwmHfG/7Dinro6DvSZUST5iA8olG10hIntNLiym5AedQyhNmyaiMjKRPaaXFlNyPZOlJjphxlynCIiL3PtIeb/nQJDDqHm9sie00uLKbkB51DKE2bJqIyMpE9ppcWU3I2W3ZtLSlPMQIh75wuOxw54v7kv5f8Nhrh+891bw97s3s6Tx6FbFp2WaFNzcz/Wwkqdm7UnuPEPbNeEovjS+dM9xkDLeLO3djy3rLgq9edGvOgr5zfClNy2NifxU+QtPJTfxH7RKtii3bJalG+TjhEw67ztqD+tv1LzwmHfG/7Dinro6DvSY/P0D/AOWukrfwt8PFA7673718vgruPIN8PFA77ZOlJjphxlynCIiL7D7SHm/50CQw6h5sK38LfDxQO+u9+9fL4K7jyDfDxQO+Ft2bPw+gYUlExpxDqLjscOeL2uLQ2jUIQ1CENQhDUIQ1CENQhDUIQ1CEFTYKhm64ZuuGbrhm64ZuuGbrhm64ZuuGbrhm64ZuuGbrhm64ZuuGbrhm64ZuuGbrgUyvI9QhDUIQ1CENQhDUIQ1CENQhDUIQ1CENQhDUIQ1CENQhDUIQdmRjctXW3XJTraotTJYahNOtuhCti0iSy082cBjihtR2S9mIe2a8JRfGl86v8ke4ELCmRKkrr2TglRRhoUQW9YzEjYXNXO2I/FT5C08lN/EftAlWxRbg8bSCzMfezyV+xPyt+peeEw743/YcU9dHQd6TKSUfLQHkkk2ukJEBp1cWK3HDzSHkJrGSURERSIDTq4sVuPsnSkx0w4y5ThERF9p9pDzf86BIYdQ82JEBp1cWK3HDzSHkJrGSURERSIDTq4jUdlYtuzLh3Q4EyYdpXP1zkJomY9x2OHPF7cQeOiQ0vM6cgIjkqdozY0ZsaM2NGbGjNjRmxozY0ZsaM2NGbGjNjRmxozY0ZsaM2NGbGjNjRmxozY0ZsaM2NGbGjNjRmxozY0ZsaM2NGbGjNjRmxozY0ZsaM2NGbGjNjRmxozY0ZsaM2NGbGjNjRmwuobSij6oQrZcvKXLKNIMoUaQUv2Yh7ZrwlF8aXzq/yCPcCBlv2vtNvNxozEZOxH4qfIWnkpv4j9psSrZLVxyck5wxVm2/tT8rfqXnhMO+N+6880z/AKJKtojAcvnAV7KDN8gRZceSWxakoJdjBQDuYJDWoQRbQFBqQw7slWkSOHL5YnzXZq2rT+FMhp5pojM+FYdIyNrpGe4npD0hytkuE9aSFMpJyQgRHecxsnSkx0w4y5ThERF9x9pDzf8AOgSGHUPN7LSQplJOSECK9zY0iU68usURTJdglAgU9laHXYcr4oSRJLFtU5OYiTjaFm807Bw34xad23EHjqztEBrzTnsQr/gKIy+0hX2FkPrtV29H1diFC9bQmUVq7uj2XMe9mIe2a8JRfGl86vaR7gQMt4u58pmZVSFyYW1H4qfIWnkpv4jdptSoTUG3KzyuGORuSNqflb9S88Jh3xv3cSmpU+HYyK9bFjDdLnMjMxwlxtX+ddcQ03Y2L01bUZBBPCkcYU0w4HIrjRxrx1tkpdtONrD1i+cbCsYwjC1YQ9MVQcwnAMScJSUidHt4bUfk8SeSkuMLQ2sOxzSG1cJ8Sw6ZmbXSMt5PR3o7lbGcN60jqeSTchYiNclgPqUlpZOrWUuWRZ2YK2U4+r7Uqa+mTnJgefkvIhG+0/stI6nkk3IWH3cvHw9h8ijJacN6iw6zFL2WdPBsBKwlISDRcVAh3jLgm275StXnCVYSZDVZ2iA15p38z3lMRinzDGemjPTQVhPGozxqM8ajPGozxqM8ajPGozxqM8ajPGozxqM8ajPGozxqM8HYzjGemjPTRnpoz00Z6aM9NGemjPTRnpoz00ajPGozxqM8ajPGozxqM8ajPGozwdlL36jKIIPiSrt6Et7u20JRTW6iMaG6xhlz2Yh7ZrwlF8aXzoMt20j3AhMgxpRttpaRtR+KnyFp5Kb+InbKT7HzjuA65Aj5ZKtqflb9S88Jh3xv3cSpUia5Gg28ORhFoz9IyR6QcC8IzAqkvYwOfaw1RrxhQZdaeT/mL+Wb8llsm0mZES5RDMOhEoNubylME4nCVgbiNjauJO38i1w7ClifCm1bpSlhp9C9ktoIdLheUSja6QkT2mlxZTcgPOoZQmzZNRGRlIntNLiym5AedQyhNmyalmRs0vV+1/3DzqGUJs2TURkZSJ7TS258dSTmRyS9ZkaMI0/MMN19exP5yQTqDBfX2/kWuHYUsrGvl1rxOpDq0qTWKTlkBrzTv5uOyqUqOHwLHAsElZAkrMcCxwLHAscCxwLHAscCxwLHAscCxwLHAsG2swbayHAscCxwLHAscCxwLHAscCwklmOBY4FjgWOBY4FjgWFNLMcCxwLDpf3OyLcprpK7fD3WUnfttOacsuMhW8/OrT7MQ9s14Si+NL53YZbtpHuBAy3+xH4qfIWnkpv4i9qFJ2vnvfTuNpo+F9Rbtqflb9S88Jh3xv2bW1yrh37YbvY5iNLjyRbRM3Ewvc5FRGSi9iySabGkrZQl01hAVAuw9ZwmxrkPexawnTL6l/kpjvIixfq4FccqRDrI7CVMJEqE2sPNuQ3kK3lEXl7nZG+PtkMNSGb+ldrHFMNSo8RziJX4L+B5/wDLXSVv4W+Higd9d796+XwV3HkG+Higd9d796+XwQOLTqXq/a/7i73718vgbkSOWiE4oFBQDgh2M62KLESXiUpSvYlRpNB8SfbJYaks4ipHK5cZhp5KoJgnJMU4a+ZaO/m47Kh8ftU6llLTiHWxzW+aFqShKFJWkc1vm+xyQw2pUuKYVNipPPwxn4Yz8MZ+GM/DGfhjPwxn4YbsIihnIwzkYZyMM5GGcjDORh/C6kSPNW3UQneyrt6A9zxfUKTv9iFbFp24h7ZrwlF8aXzqvyR79hlu2ke4EDLftR+KnyFp5Kb+I/aJVu2KTsnMGS+YvhgMm48FFu2J+Vv1LzwmHfG+8z3FaWjkhZo4F22GpUY/wfxOptzM7OBHmCFZ2VO5BxRAeDa0OI2SFfUTpTMOPYSF2UxMRJDkMhcVIhzZMByK+3JZ/wAjiE91ZG6a18LeGmi37FJJRXaCTHh9GKjn3AIt5oTwp9zraHW7qA7S2HGnOKPeJHUeSSTa6QkQGnVxYrccPNIeQmsZJRERFIgNOrixW44eaQ8hNYySlkRM0vV+1/3E/lFGjsm6pHC2lS9wOS2CkNhK95S2xh2acyv2kW80Fwp9zraHW7+sXVTWnSWW/wCkhjgFfMzKLjsqHx+2+8fh890Aj3j/ALoXHZU/ZD/uvYbJSLn04yHMMx1l6Tjj0nHHpOOMQViKx5nCzC2fSccek449Jxx6UYBYcZ3enGR6cZHpxkenGRbU7cKHT9ktO8P+bt+oz0XujR9VJ7gX1Ck7/YhWxadmIe2a8JRfGl86v8gj37DLdtI9wIGW/Yj8VPkLTyU38R+0CVbtik7OU3sSrYotwT8rfqXnhMO+N9+JJRtsxW+FM3dx0EsptVLgw5YkYVr1hzCCgrCMsJwjKDeEBAjIhxNi/mZkRWsxy0n7247ZyHFq4J25qTvN1JLRRyTjTf8AI36eKsjfB36t4aWXDtvJJPyTPkMYPiGt9KTUaEEkvsWcNufCeZXGmB/6uukZG10jPcT0h6Q5WyXCetJCmUk5IQIr3NjPSHpDlbJcJ60kKZSTkhAZd50Ol6v2v+4s3DdlISSUPuE2mNGfmKRVtETlawYdbdhunuW1gpZ5nYlJqNCCSX2LOG3PhSWXI0mM9xbDM40q1MlQKHx+2+8fQ+PSe4EZHci47Kn7If8AdeyL+xe3HXdxO0+zifxVP2Qledt+oz0XujQlvd4DCSUWxSd44DHAY4DCd+xSfriHtmvCUXxpfOqLeOExwmC2cI4THCYIjLYZbwn6Cp8haeSm/iN2nCY4TCd5bFJ3jhMcJjhMFv2cP8Vv1LzwmHfGrUlCV28FKpN3HQG74+KO82+0G7eGp64cS9auyEpFHRyLI4EqXh2zhymJbH2HC3LxG4bVNBLc2rikya+G22gXkJKmmFmpuR9Fx18xjbY27UZxq++usQRGmRpH+KkNk8w1vbdEZ5UOWw6h5oXFiSCjJJIYadsZsGMhhhCSSX2scwQ2re2W9TjpmZtdIy3k9HejuVsZw3rSOp5JNyFiKzyoz0d6O5WxnDetI6nkk3IWGGeVEYVIjqzkwZyYM5MGcmDOTBnJgzkwZyYM5MGcmCPzVTYv8b4bbOXPbQltAUkjF+ySY0ftsFl/WhJGo0JJJfaxzBEgiNCD4kTS3tqVxUtD48WNjLYl6vMEmbKkMR5kpprVZqQ3KfTMTYTVFJkyXWo0mS01npo5ruZz00Z6aIS3HI4eccatdWthq1sNWtgdtb7lXV6k7aZMmON3V0lvXLwa5eDXLwa5eDXLwa5eBm7t1DVrYatbCfPnyI9P2Qledt+oz0XujDfeYVqM8ajPGozxqM8ajPGozxqM8ajPGozxqFgJb8yQhptekR3ZMcR3ZLEnV7MavZjV7MavZjV7MavZjV7MavZjV7MavZjV7MaraCnQ5m7VDmec5ywiRLQjNTBmpgzUwZqYM1MGamDNTBmpgzUwZqYMzND2afVfEaaartGYkSXJfnvlFRuRHQk320m1hZf8sSIySt/SD++uwzBjKIiIrSujWLMirt6Z6Di1RFDkNyo3ukJF8wciphKEJZMTS3EQm7snFEoV5boGxZ8KIxcx1bSFEUVAdZNsQ7zhZhy2JaP8RiKIbbzS+NK0komXZMRT1lNfS21uB8b7tFWpgR20cJBa0oROxTBZM8Xv742LkGcCfEnI22VrCryfxf8AWbiVcyISj4WlJSl5RKNrpCRPaaXFlNyA86hlCbNk1EZGUie00uLKbkB51DKE2bJq4i4dTbGqIGqIGqIGqIGqIGqIGqIGqIGqIGqIGfQ6UI9xij3FabcSr/kkfDDwU3uZDaOEhOnRIKJGLo6TRi/6wMRVso/Y4tDaJuKK9k7DExTInEfLj9GWZclPh6Hx4WZypq47ZkTLxhptLZTEb0Vqv7gk9wtz3wafsh/3Xsi/sXtx13cTtFrQ2nUIG9JkovdifxVP2Qledt+oz0XujS9X3Ee/YRGZsNEgpnw+6w1x7D/P3EJNSmmybSMSeMZbSpLSSSew/qUGW7Bei3qFKxFF5rWFbdM2MLyQ+nF2yXV18s2Gm2GvcZbyWnhViKrVDfPdIKus3IwYkMPpvZyDbYTwo4DkS0kSUiwsGYZSbp51qEn+HYv8cpAhvKhTf8Q6hLrc9jJTG3SVsUokgubJcoahEBEdOyS83HYuLWTbPtx0JHCkKabUNz0R3DdwVkyMUXeRJDS3lJbbSDQkw8km3/oH/wAtdJW/hb4eKB313v3r5fBXceQb4eKB313v3r5fBA4tOpGWnncjEGRiDIxBkYgyMQZGIMjEGRiDIxBkYgNCU26C5Ugj3DjUxKjPIkM7Ll3MWEk9ycNs8iojp2YkuU1zZk9LdSy2kG22YcjJMUF29XuoUlaBOlMw41rYy7aQiOhI5aBITwvF+JJ/R5HBTUPjxHLhsD+h7ZaCIV9iycW0msKi1EqOmHnIw5iNVzkYZyMEKStIaWhq+1SANUgDVIA1SANUgDFTseY7OvY8OvlypEtwQJ8qC5Av4L8bWKwaxWDWKwazWBNvWqPEE2I/XU/ZCV5236jPRe6NE4w07m64ZuuGbrhm64ZuuGbrhm68FNrQmdXJGpQQqwr1FnKwZysGcrBnKwZysGcrBnKwZysGcrBnKwZysGcrBnKwajAGpQQc2tMZysGcrBnKwZysGcrBnKwZysGcrBnKwZysGcrBnKwJn16QzNivLGJPGR+mj5eyY0kmsPqNyssKp1l2FiiZHFvPztq3d3LJ1OKGn1fn2XN3FrRJxHbSFFb3SDr8VvJVFkMymXE8ROtkZXNCtk+MlDkhDJEHnOEsNxNsr+fbcBJ9i/iJRCqc5ld/iOApWJrDDSVGdHbJONhuW4qur4sFBfU0luIY5mmbrDfLQJUsmTjTScWIT511rIdSzGJS5cwzIicsP4oz6X0zi/iS2g0vJJJtdISIDTq4sVuOHmkPITWMkoiIikQGnVxYrccPNIeQmsZJSyImcPdb7S/N27PC42riStJKKNIkQnGbqOZTblHLYQGmjlTm0klKS3FJdSxHW45PnbJUsmTjTScXJb40YHnG6wMcylLnNpS025YfxRn0vpnF/ER/wRmzlSrfsqHx8h1LLMDeuQr87D+hPfVuvZYkO2NaiLGqYJPMFVtmMuWe0psaU2IzKWGgbJSLn04yPTjI9OMj04yPTjIuoCYDrqCWS0mhWyE2h6X6Tjj0nHHpOOPSccelGBa07cKHT9kJXnbfqM9F7oso4zy5DLkMuQy5DLkMuQy5DLkMuQy5DLkMuQy5DLkMuQy5DLkMuQy5DLkMuQy5DLkMuQy5DLkMuQy5DLkMuQy5DLkMuQy5DLkMuQy5B1okJhwkvspqkqOE1yLwYk8ZH6afltUZNtx2JNi9EYRGjibEZltyo5xZwfYJRYNtVEvZiWz02E0hT6yIiIOtJcKmsXqma2tLjbqOIhaUsWaJNHZRzKHZqNdFJagYZc4omy6rFqciPmszLcexz8CR08PHvrP8RSfxYn9jBb3NlwvnYh2LM1KDKuNqZ9XbVCjpIRkQsF/0+4xAUaJMw0mhG/hd/LbrXL5zQfkvPuV0taXbGXwIJ19Bx5TbjD8l59yulrS7Yy+BBOvoNiQh6MUZ1J8mQOTIGTmjJzRk5oyc0ZOaMnNGTmjJzRFhPpk273A0XE2aFkogbSDBNIIOr4U4PhfVgt7gxUZlQQensWZqUGVcbWFl8nEQxP8AsNkrhjivVwyp3wRxunEilFauOyofH3co3nmkEyztWew/pJvvH0Pj0nuBGR3Psi/sXtxd3AcQS0rSaFCKRqle3GHhqBfHAErztv1Gei90Yvy/wsnp05GqK02SCT+yjEnjI/TL2T/rGw8aTrNt55iZLNtUSYa1v8TMmG8UiKMUPnLvZT5R0NTl8eyWjibwRKN6tD5bl7ZDfNj4YVul7ZO5Vy58tjmyT08PFurP8RQ/svsjbbhHJxDsWRpUG/5TGGoip9wouJNtVyaxUTDC34p4TcIvTKx6ZWPT6x6fWCw0sy9MrCcKOKT6TcHpNwek3B6TcHpNwek3B6TcBYRcMekHAeEHN3pNwXlSqrCJUwkZyaM5NGcmjOTRnJozk0ZyaIs19Um0jqeSTchwO17yEk8pI56Qp8VFQ/PcabQ03G2T2ClQ2uKPI2LI0qDKeBrDSebiMY4hm3Pl/wA2EK9PFJm9Ks79383HZMS8tUQGgo957FHuIGe4mzc50uZMeZiTJjLOo2IRJfTNTYTVFnpohLccjh5xxq11a2GrWw1a2GrWw1a2FjKkyl7HEEtK0mhUPntva5eDXLwa5eDXLwa5eCys7OVFoPHoVvErzlqe9yMr+U90Yvy9j6zQSc0ouGWN0sf1Q/qh/VD+pH9SP6of1Q3ShwyxwyxwyxwyxwyxwyxwyxwyxwyxwyxwzBwzBwzBwzBwyxwyw04o1+yT08PEWQCf2UYk8ZH6YL8bFJ5jGGX+F7beeYePe6R7jnfDAbitRDpmq2sj3yRCPfFCvqnAKv60SfbU/wAF9smvZeJXJ3uH+di/yJXxp08NZ/iK3+Xir2RvxsxzCMnWHOYgSohPHHhpaMiemyKSubrYRuJI/wD+gCo8Sr4+xHxDXT97f5C/iMehi2bbY1lsay2NZbGstjWWxrLY1lsNuc60edQyhNmyaiMjKsQ0rECqSrUbFTXMq2RvxsxXSHLDL/DslRCeONCJtUhzgRg3LRnRYxGp0SQw5AnOV/8AFGYSwmb0qzv3fzcdnFaN5xR+wz3mHDNxdbFKLHvvH0Pjx/6BJ7gR79sX9i9h/QYhmNy5bDTj7sqknMjIzRFw8/JESMzEY9uMfC0PjxK81afNrpL7eL8vZK+OHvCiR8Nj/wAtiPiEfP7RfnYv8bFd/wCyUW5jD/jgn9lGJPGR+mG/xsQf1Uo4lghRLRsvPMTmjbeiNG69OV9cBp/uYuW8rf2bR70JUtTSOW2Hj4WsAsnskfD2V31xFsxPI+jCeVG2n+RJ+UdHLY/xFieVv/ZG2yWW5DFxVSap9ElBhUlBCHEm2j1HUMVjQhW0KbIxuKjxKvj7EfENdP3t/kL+Ix6Gchyf7aP7aP7aP7aP7aP7aP7aGMlzLvfvXy+CsJeQhu8ib7I3su6GNYCbX2NYpMoKlCmp5Vo9iRkod6xiCqdS9iGpbLEFiVnOKUncqUYkR5aWazv5CiQUl5c19CUst7Vnsfc3CvUlubn4YuZUd2FTSo7ULPwxzW9az8MJsYhBpxDrYaWhq+1SANUgDVIA1SALy2OSYp11cJjVIA1SANUgDU68Ktq1J6xWDWKwaxWDWKwYosYUmqofHiT5m0+bXSV28X5eyV8cPeFEj4bH/lsR8Qj5/aL87F/jYrv9qS3Cb0MP+OCf2UYk8ZH6YR+ds1HMYw3K42Nl55gyIyUaGkK4ljAsU24QxtXG61Gd40pSlO19RvOUcLT60SfjtUZJTh3e5Zh1aWmyNUye4f12L/GyMjnWX+JxQz/DRSM1V7WD3ObVpStMnDlU8qPhuqaU02hpGy5wy5zrVFmgVHiVfH2I+Ia6fvb/ACF/EY9DFS24xozY0ZsaM2NGbGjNjRmxozYbbJm0eaQ8hurZ4iIklcxkpFFJzVXtYPc57JE6Ew6usqpQZp6xpX4E6HGmtO4ShGbWE4KRErYMVpdHVLVGroMY8fufWHubn2EpUx5ltLCNqz3bHV8Ca2vOWnKp1LRGhY1qIsaurUSo2iNDKp1LRGhojQgspiMke8GyUi59OMj04yDw6wRS0sIfjsuSHreBkFNYeZW16cZHpxkenGR6cZDmGY6y9Jxx6Tjj0nHHpOOLuhar4ND48SfM2nza6Su3i/L2Svjh7wokfDY/8tiPiEfP7RfnYv8AGxXf7Elu2Tehh/xwT+yjEnjIp7UnvLY2Y3rgzY7qH2RenutlShWVE6zUkkrl16omWBkSivcOOtLKQtB5pIbzMxzDlCUHbJ/O28e5FThZv+AYimbzjN8lnas95gz3Fhprjm/4mcwUmLhOXl5m0voZHvLYw7YqsYmKpbQj4orHA3cVbgKfBMjnwSJdvWIGMbCHOOo8Sr4+xHxDXT97f5C/iMehuLMU3k5oyc0ZOaMnNGTmjJzRk5oiwn0ybSQplBOSGxEf50WRIelOYRmcmVtL6GR7y24wb3X6sM2rCuRimOM9ilsaviQaviQapiZYfmYlS1Ta9PfGLncxfP8A8yTHaJhG1R7thnuKKyubKaQltv8A9AL7x9D48f8AoNqT3CGZHiEKMkpvLU5So7Lkh6qr24LOLu4jdv8AZxj4Wh8eJPmbT5tdJXbxfl7JXxw94USPhsf+WxHxCPn9ovzsX+Niu/CS3bZvQw/44J/ZRiTxjSuFexB7j2uoJ9qpmqgvpMlFYpS5iGPVVzBuSGG12lNAtDdwjMJWG6OXWzNk5mudDqMKsHEv6hD+10969uM5P0qmMvAuLBMRuEyalLPeexR7i2SFbkYfY5UD/FYgiqaepJ6Z8PbHVtkOPUuIU39TNLIYXlD0tEdB4QXvLCCx6Zhti/hV8MVUuImrVNh8ObijNxRmY4zMcJlxeHNxQ1Mh8vOwxnYYzsMZ2GM7DGdhjOwwibD356EFTYW7Owxjd5l4NypiW85NGcmjOTRnJozk0ZyaM5NESa+uVbRVPIJqQ4IjHJivxn4zptSG00k9M+Htjq9mPIpmiLa2zSUYntWwjF0kJxgoesAeMFiyxJImw8CJUVZKeRHjuKclSY7KWEme/aZ7gZ79j695wpMlhGo2I5z2c1GxEuZMeZiTJjLOo2I5z2c1GxGo2Ir3HXYofXJiXBXlkaZlpMlNERmIMuVDGs2gnSJc1SLezSnWbQazaDWbQazaDWbQazaA7m03LvbpJ6/cifaWU2PSIUiAJPmbT5tdJXbxfl7JXxw94USPhsf+WxHxCPn9ovzsX+Nh+QSW72Tehh/xwT+yjEnjFxt9SyriRsQe1J7jkMpfTXWLsFcl5ty+GLKbedTSyrNj07coGg3o0G9Hpu4cDeEZhhOFYrRRo7B4n2Oq4U7X3UMsxeK1ubO2bYDLK5Dij+m0z3nsYaOXNSRJT/inW0ut/wBRSWMGWzNj7CPcbauIhiyoVOaqjrFhWE2HUrwlMHpa0QPSdiEYRlD0f/Ba1+QsIMbNO6OoHXGRnXmMoYTCNR6eYbqzWNHUNHUNHUNHUNHUNHUCplGeiKCqVRDR1CBGOU9oyg0jlte8/OvvIYbRasGojJRSbFllbEhiahpx+onwZbM2PsI9xtq4i2TozcuJJZfrJqFJWXCkctAmcJCjqWI1YhhlGzHFhwtxUk0yf12me4Ge/Y+vcRw+XW0Pjx/6AX3j6Hx4/wDQexRklNhZqdcjJOQqHGRGb+2ot4Mt3sk+ZtPm10ldvF+XslfHD3hRI+Gx/wCWxHxCPn9ovzsX+NheS9k3oYf8cE/soxJ4yjbS7UPNqiS9qT37SPcHG230vsrYVWSSmQLyO5Kqo0bEUBK7LEzKSxHcGDxBdmFXN2sLl2zoeac3YFiGuWD+gcVxK24rsuatp94mY8QkhSvYo9+19fCnDkTls/4zE75cNbOfgPsYjgLEd9mQgJM0mhZK2XNDFsAddfVK2MVT2jbxeyY9Wwg7i9Il4ltHiedcechyFRnNXeB2Lhnn3Bm1hMxZHqDgbs3UDV3hq7w1d4au8NXeGrvArh8hrT4VcvmNXeEKSqK7rD4RPiqRnogz0QZ6IM9EGeiDPRBnogJSV3V9xb18vgq+PTm+Hjrd+oXyv5lbOfgPwreDKbL67EmaTQslbbSujWLM7DVhGU7nY55tQS9uk67ezBhrU8pcWLNdE4nJkvapW4HsdXwFWoZU9cSo7sKmlR2oWfhjmt61n4YuZUd2FTSo7ULPwxzW9az8MZ+GG1ocQoySm1sDkqjMuSHYbLNcw04h1sc1vmhakoShSVpHNb5oWpKEoUlaRzW+aot4Mt22T5m0+bXSV28X5eyV8aS0r2arWKwP29aadVrhqtcHbOAatSgjUoITaV/DqtcE2tdxaxWDWKwaxWDWKwaxWDWKwaxWDWKwaxWDWKwFc1e/Wqoa1VBdzV7tYrBrFYEGSp/sm9DD/jgn9lGJPGYd8bewsyywvaX0BHv9j5c1rBlmTDuxaUPNSGVQLTY46hAix5NpLrojcGGf0Dq+L2YjtyjJYYW8baG2UqUZ+xStqlEkquIqdKIiIv8AF20vJxUJN5SWWtyo7ZhGYhuUVyidtS8ZAnUGONIebjPE5U1CxotMG6+qaGOzRuqUI0pTbfDwIHAgcCBwICEI4eBAabb5fLbHLbHLbHLbHLbHLbHLbCG29/LbCm293LbGO0pSGa5C2tLQNLQNLQNLQCqkGNJbGktiPXNsvPsofbRVMEoiJJSa5l5brrcMyZddPKpBxCEeRPrjprlmdtS8ZAnUGONI4kjiQFNxTMkxUg3iFrbMwWpkmRZyo5m29sNW1aiSVdDcnvnTMmizq0xI9dWolRtEaGVTqWiNCxrURY1dWolRtEaGVTqWiNDRGhGabhxrSeqUqMy5IdgxG4rV94/D57oBHvH/AHQuOyp+yH/dC47Kn7If90FFvBlu2SfM2nza6Su3i/L2OIJZLZIk10JMpGkNh2qbQnT0DT0A4aSPKJCISVHp6Bp6AmpbNOkNjSGxpDY0hsaQ2NIbGkNjSGxpDYKla3aI0FUrZFpDYlViGo8dknSZYS2r2Tehh/xwT+yjEnjMO+NF7XcJsucXsSrf7Jbe4YbxAl1NxEVOrjobyIcuuunXchchuiunhBwke+FEjw2VrJIWs1ey8viQI0c1g1e1St+0zIiYadmyIcduKx/jMSuGqaRbiR+dkls2lQ8TPJEe9rXg0606XsccbbKTd1zAvrQrJdR4lXx9iPiGun72/wAhfxGPQ3ElqRk5gycwZOYMnMGTmjJzRk5ohm83YW0pTCCdkNiI/wA6LJlvPripIErakxJjm2cHEryExr2ueDTrTpexxxtspN3XMCdiR9wcp15xKSSU1Abd4kbVqJJQozs+RGZbjsi7SSoFMg0QR/6AX3j6Hx4/9AFqShNpPVKVFYckOworcVoX3j6Hx6T3AjI7kXHZU/ZD/uhcdlT9kP8Autii3gy3CT5m0+bXSV28X5eySZkmLRWMmOnDtskKobdJaJaDQ7QKprJI0ewGj2AKjsxodoCorQz9P3A9P3A9P3A9P3A9P3A9P3A9P3A9P3A9P3A9P3A9P3I9O3Q9O3QPDtyDw9bmRYbtSEfjTJ9k3oYf8cE/soxJ4zDvjdlxVGg2nd/sSrbMXxLXG/gqMQS4Ar7aDOLeW03EkFPH7LC0hwitbqTOENklAz3+wz3Az37VqJJRmH5z0CI1DZ/xijJKZb5yZvsIiUjh4XeURjkqIJenoGetAc20MKdnLBRnFBMVIloSgVHiVfH2I+Ia6fvb/IX8Rj0Re196fM20VT6CakOCIxyYsqK7HVG+AQe1KtwkxeIKSaTINvzUjPWgObZmFOzlgozigmKkIQlG155vcwrcvY44SRBhuzXI7SGGkK37LZKlQ6tBlCUW4cl3XxdoUuBSIUiAOS7r61JbRZzlSlxWHJDsKK3FanzZzUrUbES5kx5mJMmMs6jYhEl9M1NhNUUmTJdajSZLTWemjmu5nPTRJkyXWo0mS01npo5ruZz00Z6aIS3HI6i3iV5q1LctstzSu3i/L2Svjh7wokfDY/8ALYj4hHz+0X52L/GwvJeyb0MP+OCf2UYk8Zh3xu20qEPg+bHWlRK2ke4LcJKIieJYWhKyXGURxriziCPihAbxBWqBXFaYO4rSDmIKxIkYoSJVxYygTYd+gQXCjaavY48RCvrH5Zx2G47X+NxFI5UT8HsT+Ag9x2KNzjG5ZcJDgIcJDhL2ThUeJV8fYj4hrp+9v8hfxGPRF7X3p8y+8hhtFqwaiMlFbTEcuOrcvYk9+0jMgfAslRGVBUJYNmSgc11AKUM0kZpsZlsZpsLeW6G4pJTKaSgJdTwcxbhuw3Go1D4/YhW/aZbwot3tcWltFnOVKXEjuSXYcZuK1tvvH0Pjx/6BJ7hbnvg0/ZD/ALoXHZU/ZD/uvZK87b9Rr6sOFuYi/L2Svjh7wokfDY/8tiPiEfP7RfnYv8bC8l7JvQw/44J/ZRiTxmHfG+yXFYlInVMiOaHgRkeyQe9TSeBvaZEYUykGwkcghyCHJQOWkiG4SE7jbVxo3g1+xbqUhhmTMXX07LH+Qtns1ZSS+qT3pDZ7VJJ1pClR3SMjL3ThUeJV8fYj4hrp+9v8hfxGPRF7X3p8zfcW9fL4Kvj05JGpchO4NK4k7Eq3+3eOIxxgyaMcmOYy8YZaOMtGCeWgj+om7+Bjl82IqIabjsqHx4dlxml5+GG7GIoZyMM5GBy4phcyKgZ+GM/DGfhiynKlrbTxrhvV8VptaXEB2XGaXn4YuZUd2FTSo7ULPwxzW9az8MWk1hUWolR0w85GHMRqucjC0kMuRauQy3FzkYcxGq5yMM5GCFJWkSvO2/UZ6L3Ri/L2Svjh7wokfDY/8tiPiEfP7RfnYv8AGwvJeyb0MP8Ajgn9lGJPGYd8b7p1bGlCVVy4wJ4yEVPG779xDhIcJDhIGW4wtPElC1MqSpKthqIgp5JBluTKVCo0kG0IbR/j7F/LwoxfWQW9uOe9GwvqWx5pL6SNyOttaVl7ZwjYllsRjxVMMvUkoepJQ1+SNfkgsSSiL1JKCcUzEl6rmD1XMHquYPVcweq5g9VzB6rmAsWTCHq2aDxbNMeq5guLV6zEb6RvenzL7KH20VTBKluExFjF9TLeRb2nCPeWwlAj3/eUy2oNGcebcdlQ+PCo5S7/ANJxx6UjgsOM7rWEmHN9OMheGo6k+k44xDUN1iDFXAfsJPpSOLuhar4ND48Sqpp9/RGhY1qIsaurUSo2iNDKp1LRGhY1qIsapgk8wVW2Yy5Z7SmxNgJjsQoCZDGlNjLlntKbGlNiMylhoSvO2/UZ6L3Ri/L2Svjh7wokfDY/8tiPiEfP7RfnYv8AGwvJeyb0MP8Ajgn9lGJPGYd8b9jE5tEcZPC19tzatJKBsGOU4CYMOtElFA/za/8AyOKHtzbRbmz+pNHwO7EHu9iiQ6l5lyOpl8l+0zIhxNilNCZXMjhS2eI1s7uJsNqb4+NkR3GBzI45kccyOOZHHMjjmRwhyNv5sUOOxhzI4qTSUjmMfZlQn1ScnNDzEllBqUYaLc2HEcZIUbaiPeW0lGCUX3FqJKYDapEy47Kh8eIf7VtxKstan37LSjvp5nFxEvfiGwdnyauA/YSa2ExAjDGPhaHx+2+8fQ+PH/oBfePofHpPcCMjuRcdlT9kP+69krztv1Gei90Yvy9kr44e8KJHw2P/AC2I+IR8/tF+di/xsLyXsm9DD/jgn9lGJPGYd8b9ie5mrT7h/UvcZbyw69yp3+Ru18612SEhlfEnYg/YSg/EJQbfU2aVJUWycImGFvxTwm4RemVj0ysen1j0+sFhpZl6ZWE4UcUn0m4PSbg9JuD0m4PSbg9JuD0m4Cwi4Y9IOA8IObvSbgvKlVWIvbfYfdQy3/OnyJSEplbXEEsv42jQ4lXu4jHGOMhxEOIhvIbyG8hvIcRCQsuVRr/juOyofHiQ66xda5eDXLwa5eCylyZcph3i2rSSkxJ9nBZ1y8GuXgsrOzlRaHx4nzZzUrUbES5kx5mJMmMs6jYjnPZzUbES5kx5mJMmMs6jYhEl9M1NhNUUmTJdajSZLTWemjmu5nPTRnpohLccjiV5236jPRe6MX5eyV8cPeFEj4bH/lsR8Qj5/aL87F/jYXkvZN6GH/HBP7KMSeMw74332DvIhQU/X7qvz7uI2JaTJSf8gSubL2qI2loUSi2JV7CPcHENvE5HdZNuSEqJRThUeJV8fYj4hrp+9v8AIX8Rj0Re1977qGW/50+Qw0hltB8cj2GW8LYHE62CfBOIP7i1cKf4311/8uwuOyw+nfWCH+1bbRw3bIMO8W2kVxVWzGPhaHx+2+8fQ+PH/oBfePofHj/0CT3C3PfBp+yH/deyV5236jPRe6MX5eyV8a3EbMSB6sjhzFLCi9SsD1KwHMRMqPX2Rr7ILEjBF6lYCcTMEfqyOPVkcerI49WRx6sjj1ZHHqyOPVkcerI49WRx6sjj1dHHq6ODxbHMerI49WRwyrmTfZN6GH/HBP7KMSeMw74334mc4YkUtzP3XPfJL+Gmc5tb/j5yuCFF2GpJDmIBrbUXTU2sllsSr2pUZBxlp4LjvMm64pwIxJLaiJxFaoVVzm58faj4hrp+9v8AIX8Rj0Re19z7qGW/50+Qw0hluSfDHi7DUkhzEDmIHEnYj5KZbUFRUg4yyHLfIb3yHNcIc9Q545456QTyBzEDmIDrnEGS5bPGRTrxP9Fhzxqk7w04hjE2sVg1isGsVgs+XqOxh3i2VU6CzXanXhVtXJGKLGFJqqHx4dlxml5+GLmVHdhU0qO1Cz8Mc1vWs/DFzKjuwqaVHahZ+GOa3rWfhi0msKi1EqOmHnIw5iNVzkYZyMEKStIledt+oz0XujF+XsXw7l8rgrMnwf2oO6Zw/wBAP6AHlN/9KEZPf/QD+gCdL4f7UP7UP7UP7UP7UP7UP7UP7UP7UC0fd/ZwrSN39qEvTstG5W5rk7/ZN6GH/HBP7KMSeMw74334nXvlpLcn7qvx7ni3t4XXvjf4+7Phq0OcCYtZYzAzhd0wnDEYHhiMHMLqE6lnQkNuke1KtwI9/tJRg+Wob0kHdxowbxc/aj4hrp+9v8hfxGPRF7X2vuoZb/nT5DDSGWxYnuhIc4ExayxmBnC7phOGIwPDEYOYXUHsP2TQdRMimmUQS82r27iHLQOU2OQ0DjNjKpCkspDCXXXIWGLCQMR1iKt+5PircOeND0REy49KRx6Tjj0nHF7ATXzjIyPY2l3Lw6Jp+J6cZC8NR1JvKFqvg0PjxKqmn39EaFjWoixq6tRKjaI0MqnUtEaFjWoixq6tRKjaI0MqnUtEaFjWoixqmCTzBVbZjLlntKbGlNiMylhoSvO2/UZ6L3Ri/L2SvjW4cZlwPSccOYWYSXppgemmA5h1lJ6AyNAZBYbYMvTTAThlgz9Jxx6Tjj0nHHpOOPSccek449Jxx6Tjj0nHHpOOPSccekY49IxweEo5D0nHHpOOGU8E32Tehh/xwT+yjEnjMO+N994vfaolJMJUlX3jWkgbyBzxzxzwTyDB7lJwur+o/wAfiE/7ZhOFHVD91vQNSA8iRDeQolbSV75pq4MJ8goG1HxDXT97f5C/iMeiL2vsfdQy3/OnyGGkMti0kKZSvm8OE4UdUP3I+UulrJIl4RQYk0dvEHPdbUmUgE80Y40DjQDebIKktkMw4s4tHbTBAwrFbEeOxGQMfl/OsvrT4c8aIv7Ftxf52xqIM41YTZ4oWHK+OrHP0ZpD31GzFSSVUUyDRB233j6Hx4/9AL7x9D48f+gF94+h8ek9wIyO59krztv1Gei90Yvy9kr44e8KJHw2P/LYj4hHz+0X52L/ABsLyXsm9DD/AI4J/ZRiTxmHfG++uSl/Fs3D1ZJE7C81gLVJjOIkNmCMj96lEkKktkOe64qPR28oo+Fwzh+tbCK2AgZSIHK6A4JOHYDgnUU2IMNnusf8fiLxuEvD++TFjWDFthuVECHtwIyPYRmQJRe1REokm9CehWMd+JmY4zMcJlxeHNxQ1Mh8vOwxnYYzsMZ2GM7DGdhjOwwibD356EFTYW7Owxjd5l4QZSo6iMjLY+6hlv8AnT5DDSGW9lpHU8lfN4cJeH97K+JOx9hl9MjDlU8HcIxzB4QWCwesN4RjkGMNVTQjxY0YvZ//AEA/rP8ACYfPdASe8SDfYs9ZtAu4tdx31yRzpEybJ1+5Gv3I1+5FlOn2BR7q1jsMX1ooazaCZYT5bNWgyhKLdtu0KXApEKRAHJd18XaFLgUiFIgDku6+LtClwKRCkQAhDyb8j3+yV5236jPRe6MX5eyV8cPeFEj4bH/lsR8Qj5/aL87F/jYXkvZN6GH/ABwT+yjEnjMO+N9+Hv4sWbJDDMhE7C0F0ScM2bBus2MYFKUM2M2My4YZgW0kRcKTXBEwxWsiPHYjpfVuR7636Yh/x9+W+rwcrfV+8j3G2slFbUkKwFnTz600PEe0jMgSi2n9BJd5gKvVuOvMZQwmEaj08w3VmsaOoaOoaOoaOoaOoaOoFTKM9EUFUqiGjqEKPmXdIUJUFDrESQuK4RkZPuoZb/nT5DDSGWxInNMriy2pAedQyiZOQ+xg5W+r95HuNtZKL7uPlf1Vkf8AaqHx5HuCT37VJ3/YQrftMt4UW7/gJPcCPftledt+oz0XujF+XslfHD3hRI+Gx/5bEfEI+f2i/Oxf42F5L2Tehh/xwT+yjEnjMO+N9+Hv4cW+5yOw4DrK4wmsrkhpppr2GZES1cSvfWfxYg/x9mjjr8FOfwbJMhmM1NxN9VXlssIvbVAhYmQo2Hmn2yPcbayVstcOQpYsK6dWLbdJW0j3AliW4ShFkZdR2Lhnn3Bm1hMxZHqDgbs3UDV3hq7w1d4au8NXeGrvArh8hrT4VcvmNXeFXxZ7ZPiJkoiyVxF/zp8hhpDLexG7mQu+u9+9fL4MFOfwbJMhmM1NxN9VXlssIvbVBwsTJM2Hmn2yPcbayV93GS+dfW/ZU0qO1Cz8MFYRCBWUIajBGowQqfBMZ+GM/DGfhjPwxn4Yz8MZ+GE2UIajBGowQdhBMHOhjPwxn4Yz8MZ+GM/DGfhjPwxn4Yz8MZ+GM/DGfhjPwxn4Yz8MJsYhDUYI1GCJDrZ29m4hxbPRe6MX5eyV8cPeFEj4bH/lsR8Qj5/aL87F/jYXkvZN6GH/ABwT+yjEnjMO+N98M+Ti77hmRE4vjMSJMeODuqsjYsIL57TMiLDv8dl/jzLeWG15S8FlMagxZcmRYyENpTtW2lQr5sitkQpLUyMGnOLYtCXEXWF94Xzo7iXUGHXCJMOtspyKLDbcRfLbHLbHLbFm8UmwaSXLlwW3QtL0dSXUGDUgibS++cWrUZv1rDiosNmOTzDTyEVTBKJCCKTXMvLiRGo21RiXFbkBhpDLe2RBaeXFiNRw80h5CaxklYZcy10LKY1Biy5MixkIbSnattKhXzZFbIhSWpkYNOcX3FOZ6+kspfa0psJqWjGjNjRmxozYVToIaU2NKbGlNjSmxpTY0psaU2NKbGjtDRmxozY0ZsHUoIaU2NKbGlNjSmxpTY0psaU2NKbGlNjSmxpTY0psaU2NKbGlNjSmxpTYKqbGjNDRmgRbie6MX5eyV8cPeFEj4bH/AJbEfEI+f2i/Oxf42F5L2Tehh/xwT+yjEnjMO+N993vj3CFEtH2lqJJLUajdcQ03aYgffVylrPkJBsCBazYCq2exPZFy7yKvCzf0/wAhetqjWUR9EmNiGWc2ySRJL2LSSk4XmHGngvoG1cSdkyHFmIlYSirONhFBOMtoZa24omZOoDXSCkpUTtcwoJqd5xmG47f2VH9uxJUebEfTJjYhlqm2SSJJexaSUnC8w41gC+gbVxJ+zieZk6mjYPl8BjgMcCgnf7FIHAY4DHAY4DHAY4DHAYSSi9ii3jgMcBjgMcBjgMcBjgMcBjgMcBjgMcBjgMcBjgMcBjgMcBhHEW17oxfl7JXxw94USPhsf+WxHxCPn9ovzsX+NheS9k3oYf8AHBP7KMSeMw74334nZ4o+FJWapvsqPhJajUYxLYqlym0EgtpkRlEkO18uK+iTHxlJ4Y1Izya7/IWsXNw6O0OHGjF9fc9vQ6w5zWBHP+P7H4GJ7LUJ579zXSUe5KZ0tZnMmpLU5Q1OUNTlBNhMUM7YDO2AztgFWMtJ6nKGpyhqcoZucM1PGang5k1JajJGoyRqMkJnS1DNTxmp4zU8SZLzpUVrlIsYvr7nt6HWHOawI5/x/Y/AxPZahPZnS0NajPGozxqM8ajPGozxqM8ajPGozxqM8ajPGozxqM8ajPGozxqM8ajPGozxqM8ajPGozxqM8ajPGozxqM8ajPGozxqM8ajPGozxqM8ajPGozxqM8ajPGozxqM8ajPGozxqM8HZzSGpzAqymGmKR7/ZJIzTFvbGNHTiK2UFX1uotbtBrloFXNkoaxYDWLAFeWY1y0BXtoR+oLgeoLgeoLgeoLgeoLgeoLgeoLgeoLgeoLgeoLgeoLkeoroeoroHiK5B4htyIsSWpiPxqk+yb0MP+OCf2UYk8Zh3xvvktJfYwpNyFp9l9W9QupBxayMXueTxIwfK3MOrVcXf+SxFCJBxvh7pQq/GBv6L+xiy7Iy5G5g1maGuk70sNrS25bWCpi9kOM7KegxWojOy3sUxELUpa9lLV79ri0totrBUxeyHGdlPQYrURnZb2KYiFqUtYjfD3ShV+MDf0X9jFd2RlyN0emc3tf8JJ/bUX2r5BocT8fe90qDoiT8NivkGfnsb6f2S/Ac+IsuxgfD2zehh/xwT+yjEnjMO+N9817kRSSpScLW5TmPsK+pjGSt1ax0vcla2xSQsrH/yWJ1boLHS90jebjSeBvYXukvsxmr3EbkoR2OHYotymuksv5W892yHGdlPQYrURnZb2KYiFqUteylq9+1xaW0W1gqYvZDjOynoMVqIzst7FMRC1KWvYx0vdJ+rjSeW1sL3SX2YzV7iNyUI7HDsrFcuaFSGEqL6ha0oJt5pzYqQwlRfULWlBNvNObFSGEqL6ha0oJt5pzYqQwlRfULWlBMPtO7FSY6VF9ScWhtLTzTuxUmOlRfUnFobS0806FEFSGEqL6ha0oJt5pzYqQwlRfUMNcIxN1ycRu5iBzEDmIHMQOYgcxA5iA4tBt0z7LTedih+ZGNGZYGZYCn2uLnNBp9klZlgZlgImReDOxRnYozsUZ2KM7FGdijOxRnYozsUFPh7s/DC50TdnYonS464kNaUp5zQ5zQ5zQ5zQ5zQlOIU1Sy4zMHUIQS81r2pQRezIr0CjmRmYGowRqMEajBGowRqMEajBGowRfTmXYkdbaWuM2H6bEkZ9nWqoa1VDWqoa1VDWqoa1VBV1V8OrVo1atGJ5sOVXMrSTfMQOYgcaPZX7itv8nik/4G+n7UlvNtPMtdrB70bZltXRRYYsEl6XOdYbSgtkstzzBfy3elshxnZT0GK1EZ2W9imIhalLXspavftcWltFtYKmL2Q4zsp6DFaiM7LexTEQtSlr2PxnGWW+n7UlvNtPMtdrB70bZltXRRYYsEl6XOcYbSgtj5m3JnOmmElG9NK4oxYrU5M/iZcnOmmElG9NK4oxYrU5M/iZcnOmmElG9NK4oxYrU5M/iZcnOmmElG9NK4oxYrU5M/iZcnPqTBSjemicUYtFqdmkamXLB5SICUb00TijFotTs0jUy5YOqRASjemlcUYsVqcmfxMuTnTTCSjenDSzM3Fkgr1RqeXDSlhmElbUeIlwZROakREth6ElDTMJK2o8RLgyic1IiJbD0JKGmYSVtR4iXBlE5qRES2HoSUNMwkrajxEuDKJzUiIlsPQkoaZhJW1HiJcGUTmpERLYehJQ0zCStpiIToOMvmmk0LSmIocqMOVGHKjDlRhyow5UYcqMOVGHKjDlRhyow5UYcqMOVGHKjDlRhyow5UYcqMOVGHKjDlRhyow5UYcqMOVGHKjDlRhyow5UYcqMOVGHKjDlRhyow5UYcqMOVGHKjDlRgplkcDhGl4yMjIyQfBP/AMjfLWiuhV9jLYsosmI4n4+1H4bfy9inFILFDY9UIHqpRGvFswP390tDsmdLImAlCU7G9s4vrHqbF9jQ7Uen7Een7EJorRI0W2Gi2w0W2B0FkZ+n7Een7Een7EaLbDRbYaLbBVHaqL0/Yj0/Yj0/YibFlV6kUdwtGgXI0C5B4ctDP01Zj01Zj01aC0hyoTqfj7Ufht/L2KcUgsUNj1QgeqlEa8WzA9iK4cS7JnSyJgJQlOxvbOL6zFcVW306Xqy+/e/Nj45vp0vVl9+9+bHxzfTperL79782Pjm+nS9WX3735sfHN9Oh6s3yD35s/GN9Oh6s3yD35s/GN9Ol6kvv3vzY+OZ6WHlkh1ajUd11f5m7+YCJwgfGQLjWbjUhLbbUhTZ8aDLjMGThj+YP5gInCG5zeZOGP5g/mAicIbnN5k4Y/mD+YCJwhuc3nxjiUYSzJNLCXlmlx+Mtk25T7nFEkx50t8udYDnWA51gOdYDnWA51gOdYDnWA51gOdYDnWA51gOdYDnWA51gOdYDnWA51gOdYDnWA51gOdYDnWA51gOdYDnWA51gOdYDnWA51gOdYDnWA51gOdYDnWA51gOdYDnWA51gOdYDnWAltS5JKQ9FceP+Mj3l/kMReNwr4jFyuKy99EhLl0qDCMHXQDGnQQmDCSENNIF41zamKf02o/OyaX8rDLnFUbxvG8bxvG8Fv27xvG8bwW/bvG8Yx7mIZ5XeYI9hjeY3mMaK4rX34dbS7fKr4CgdXXGRVdcMYx2I8iySkq2M2a6SKf02o/OyaX8oz30zfTpOrL79782JHpzfTpOrL79782JHpzfTpOrL79782JHpzfTpOrL79782JHpzfToetN8i9+bQj0xvp0Bb3ZvkXvzaEemN9OhLe7N+lg9+bNvhqGejS9UXXVL8BR7iL6qjpaJuy7at7aQlo2z+iknvL7u8go94ryZ5bnTquooiMpPLJ6CtnlMPHGklKmGWYmjMTRmJozE0ZiaMxNGYmjMTRmJozE0ZiaMxNGYmjMTRmJozE0ZiaMxNGYmjMTRmJozE0ZiaMxNGYmjMTRmJozE0ZiaMxNGYmjMTRmJozE0ZiaMxNGYmjMTRmJozE0ZiaJmaktKI0qinvjf5DEXjae6bgwrWWU6d7S/IqpaYc31IyPUjILErA9SsD1KwHMRx1txvntL87JJb2aW2RAjepGR6kZHqRkepGR6kZHqRgepWB6lYB4lZHqRkepGR6kZHqRgepWB6lYB4lZHqRkepGRd2CLB1nEzCGvVLA9UsD1WwPVccHiqOPVLAuJqbCf7S/IppqIM/1SwPVLALFLAxBZos3rPxmHkkuoY3pd2l+dkkt7LKt9Ez0cM9ew8rJ/Nx4dno4Z69h5WT+bjw7PRwz17Dysn83Hh2ejhnr2HlZP5uPDs9HDPXsPLSfzeeEZ6OFO4s/LyfzeeEZ6OFO4s/Lyfzcn/aGejS9UXXV3DcG0GtxyM2plLj0dTVfJeDtfJZCnHpCm4zaWXEGhzcNw3DcNw3DcNw3DcNw3DcDEWOlMZ1K4j7UeVMC6p9JOOyEJgxibTMa5MiebK2YFg03G1OKNTijU4o1OKNTijU4o1OKNTijU4o1OKNTijU4o1OKNTijU4o1OKNTijU4o1OKNTijU4o1OKNTijU4o1OKNTijU4o1OKNTijU4o1OKNTijU4o1OKNTijU4o1OKNTijU4o1OKNTijU4osnGXZFae+v/wAg62hxGnQRZttt2ntT+Rh9hp+Vp0EadBGnQRp0EadBGnQQ+gmrH2F+A59W65CFqXHZ4Khpt1zJxhYZRhO2ug8RZOMMnGC40RCZTja3Nkdlb7jcGOlGTjDJxhYZRhO2ug8RZOMMnGC40RCXVNrle1P5FdyMwbLUt+REZVJktNotmYTAueyw34yUnl2nsL8Bz6tsK/oWejhnr2HlZP5uPDs9HDPXsPKyfzceHZ6OGevYeVk/m48Oz0cM9ew8rJ/Nx4dno4Z69h5WT+bzwjPRwp3Fn5eT+bzwjPRwp3Fn5eT+bXedez0aXqi66vKHKFepCX3JTJIpmScetpD7AZsmlruWSbeblMmiwUhT/KHKHKHKHKHKHKHKHKHKHKHKHKCCInsywEJzdjOUtmHHtCJm1bS9DgyEExYuNONRkNKg0Ctx8SRxJHEkcSRxJHEkcSRxJHEkcSRxJHEkcSRxJHEkcSRxJHEkcSRxJHEkcSRxJHEkcSRxJHEkcSRxJHEkcSRxJHEkcSRxJHEkcSRxJHEkcSRxJHEkXnCqJQK4qv8AyVn9bf2p+S/hhYv53uuS4Lf2I+Oyt+khzp0fVsJhR0qUalbK2FtWpKEz5ZyFbI7K33IzCI7eywmFHSpRqVsrYW1akoTPlnIU11Pan5L+DLS3RXzsuKQ235kj64gM9wuexw34u5+lv7EfHYk+FLPRwsW9+x+ltJ/Nx4dno4Z69h5WT+bjw7PRwz17Dysn83Hh2ejhnr2HlZP5uPDs9HDPXsPKyfzcn/ZWejhTuLPy8n83nhGejhTuLPy8n82fjGejS9UXXVJad3GkRGSeccgJJFE4W+XAkOPUjaVLvXC3twEmiWyTLnGkcxI5iRzEjmJHMSOYkcxI5iRzEjmJHMSOYkERLf09AgmUexsoj8hyLH4bG1WluDEiE83LipYbYjtHDr2UOnko4yUcZKOMlHGSjjJRxko4yUcZKOMlHGSjjJRxko4yUcZKOMlHGSjjJRxko4yUcZKOMlHGSjjJRxko4yUcZKOMlHGSjjJRxko4yUcZKOMlHGSjjJRxko4yUcZKOMlHGSjjJRxOjtNM4eL+2f5Kb9bX2o+TvSwr+fdiQuGx9jf42Q/pOc6cSQccKUalbK2FtWpKEz5ZyFbI7K33IzCI7eywmFHSpRqVsrYW1akoTPlnIUGer7UfJ3pRXzYXEaYmqyClyYyVNWQuOxw54vERbrD2N/jY51GejhTuLPy8n83Hh2ejhnr2HlZP5uPDs9HDPXsPKyfzceHZ6OGevYeVk/m48Oz0cM9ew8rJ/Nx4dno4U7iz8vJ/N54Rno4aWTbs4+K0k/mz8Yz0aXqC66v0H8Ijucp9S0pRxml9i1bMn7Vsi4zU+laVIkOc1/8AhH8I/hH8I/hH8I/hH8I/hH8I/hH8I/h2R3CdZnLQ4/EszQldpHIpchySuEttTNivmPSmcuxCaf5fLljlyxy5Y5cscuWOXLHLljlyxy5Y5cscuWOXLHLljlyxy5Y5cscuWOXLHLljlyxy5Y5cscuWOXLHLljlyxy5Y5cscuWOXLHLljlyxy5Y5cscuWOXLHLljlyxy5Y5cscuWOXLE43SVh4/7Z/kn/rZ+1Hyf6OFun7sUF/PT+Nre1r6WC/qjgWOBY4VBHGhefmDPzBn5gkPyXy4VDhUOFQjyJDCM/MGfmDPzAonFK4VDhUEcaF5+YM/MGfmCQ/JfLhUOBQY6vtR8n+ini3ctwMOy2Ti85yeLjscOeLxMX89Px2t7SZN6aqPLaVh+C5FbvYD5SokGVMesIvOhqYlNnRQ1x27qE8UmLDky3p8bMQlMSmzooa47d1CeKTFhyZb0+NmISmJTZ0UNcdu6hPFJiw5Mt6fGzEJTEps6KGuO3dQnikxYcmW9PjZiEpiU2eHoioyL2A+UqJBlTHrCLmYK2JTKqmKphFpEdJ9iM/Jdlsc6KbUhsVMVTKFELrq8BDgSFI+nGtZR2UNN2DDSWa9hpTMhlDrfGtBJR9OBI4EjgSOBI4EjgSOBI4EjgSOBI4EjgSOBI4CBLWhNewlKJDDS017SHFm2jlukqLIhxucl03HXG1yEI50kc6SOdJHOkjnSRzpI50kc6SOdJHOkjnSRzpI50kc6SOdJHOkjnSRzpI50kc6SOdJHOkjnSRzpI50kc6SOdJHOkjnSRzpI50kc6SOdJHOkjnSRzpI50kc6SOdJHOkjnSQuQ+hLilOrw347/JOKLO81xQQ8W7muGEvFw81wyjrJYkdHC/be7FRBv4A/oXNWo2XyGYcUbckjTxLN2LIJ4gZ/wDII/6jmuKCHi3c1wwl4uHmuGUdZLEjo0iN7Znu2y5aI52576/Dx8NTbzW5gb+AP6FzVqNl8hmHFG3JSaW3XCkVNiicjaZbwZbv+MhWx1wm0rUale+9Lc7zBzBzAoyMMyXWhIl85mPL5LL0l10JMiHMHMHMHMHMHMHMHMHMHMHMHMHMHMHMClEYadW0efUaIj/INcp94RGEm8+skORUusnz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3xz3wqU6kpUlT4I08nDHYf4XeQ40DmIHNbHObHPbHPQMwgZhAzCRmUjNIGbQHT3u4ek1kKFeJilY08qpiQbNDCJ8KVTMQV8pme+6g2qF/kxs6M6M6M6YzpjOmL17mtNrSSOYgPrI0YZkV0KJiHKHY0EmshQbpMZNjUyqiJBf5DFrmGBz2Bz2Bz2Bz2Bz2Bz2Bz2Bz2Bz2Bz2Bz2Bz2Bz2Bz2Bz2Bz2Bz2Bz2Bz2Bz2Bz2Bz2BmGBmWAv6qw9JrIUK8KKVjTyqmJAs0MInwpVMxBXymZ77iDap32WmM3EGbiDNxA49XuC0kMORKKZFZr7s4HKbWkkcxAfWRowy/XQomIsodjh+TWQoN0mMmxqZVTEgpcYh3upQRqUEalBGpQRqMAxqFeNQrxqFeNQrxqFeNQrxqFeNQrxqFeNQrxqFeNQrxqFeNQrxqFeNQrxqFeNQrxqFeNQrxqFeNQrwqzgklc+OpWcijORRnIozkUZyKM5FGcijORRcOtuuOZTLs5PlRsqP6XNycqHsnymcnyo2VH9Lm5OVD2T5TOT5UbKj+lzcnKh7J8pnJ8qNlR/S5uTlQ9k+Uzk+VGyo/pc3JyoeyfKZyfKjHFIJkoZfedU840nlq57w57w57w57w57w57w57w57w57w57w57w57w57w57w57w57w57w57w57w57w57w57w57w57w57w57w57w57w57w57w57w57w57w57w57w57w57w57w57w57wOS6Qzr4ddcdPDr1XEr7VMdM6hkcqNnRnSGdIZ0hnCGcSM4kZtIzaBmkDMpGYSMwgZhA56Bz2xzmxzWxzEDjQN5f8AAUklDkNjLoGXSMuMsMuYy6hl1A4pmMkMkMkYyRhVJvVoY0QaINEGijRRowYgKZRlljLLGXWMuoZdY5ChIhG8jShpQ0oaUNKGlDShpQ0oaUNKGljSxpY0saWNLGljSxpY0saYNMGmDTBpg0waYNMGmDTBpg0waYNNGmjTRpo00aaNNGmjTRpo00aaNNGmjTRpo00aaNNGnDThpw04acNOGnDThpw04acNOGnDThpw04acNOGnDThpw04acNOGnDThpw04acNOGnDThpw04acNOGnDThpw04acNOGnDThpw04acNOGnDThpw04acNOGnDThpw04acNOGmjTRpo00aaNNGmjTRpo00aaNNGmjTRpo00aaNNGmjTBpg0waYNMGmDTBpg0waYNMGmDTBpY0saWNLGljSxpY0saWNLGlDShpQ0oaUNKGlDShpQ0oaUNJGkjSBpA0gaONHEevUyjKrGUWMosZRYyihk1DJqGSUMkYyRjJDJAopkMuoZdQy5jLDLjLpGXQOQ2EpJP+S3ENxDcQ4UjhSOFI4UjgQOBA4EDgQOWgctA5aBy0DlNjlNjlNjlNjlNjlNjlNjlNjktjktjktjktjktjktjkNjkNjkNjkNjkNjkNjkNjkNjkNjkNjkNjkNjkNjkIHIQOQgchA5CByEDkIHIQOQgchAy6Bl0DLoGXQMugZdAy6Bl0DLoGXQMugZdAy6Bl0DLoGXQMugZdAy6Bl0DLoGXQMugZdAy6Bl0DLoGXQMugZdAy6Bl0DLoGXQMugZdAy6Bl0DLoGXQMugZdAy6Bl0DLoGXQMugZdAy6Bl0DLoGXQOQgchA5CByEDkIHIQOQgchA5CByEDkNjkNjkNjkNjkNjkNjkNjkNjkNjkNjkNjkNjkNjktjktjktjktjktjktjlNjlNjlNjlNjlNjlNjlNjlNjloHLQOWgctA4EDgQOBA4EDhSOFI4UjhIbiG4huL/wDUip9lJkZKL/7ZY2TUUKfsLBbNG6ZabIhprZqZjP3JtswwZ2NjJU0m8SIM83HNkp4o8eFMZlo/+rXU/Loqq1UoNNoaQHOnRuG3Y++wsZEa0bVxt7LeyN1VdT7ybbQ2kYlTwivezEMXPjMNd9K+kWgkupl//U1qJCITSrGytF8itoZzz7gtneVX0LfMsffceSidqL+UbMeggkSbea+xZRHTfjDE6/4qDxoufGYa76X2lH5T/wCpzyNULDBFy8QH/bsNF/WDEcnjew6zy41lbOLXFjWXJq5JyollYNwyh3LbrlnNyTcR9Eli18ix0BfEblolJJTdfWzri3QPwLJ/MzYDXIhi58ZhrvpfaUflPsPWERl9CkrT/wDSmoSo0rEfYYYL+dZy0xI8ZpyXLvHCjV9JEKTKtXeTApP5NU3xT7K4gtrhypSpDVEndWWXkGvo2LeEt847hut2vkYhbol/N4EUMTnPh9S0NTrY3o9ZLKG+9dJcao/KTpKYkdi5dJYv5ilPpjyYdbVSVSociU9GtZjxR4yGVPRsMrM2f/peJT/oqiW3DRIeelyKiDlGcTK/qcPN8ECyeOwm2+5ipw8W+xs3SZgisLdX2XkE/RO2y7+fYpiMQozs6Sw0hhq2Xy67Dsh037pCDgYeQhc2VHYKLR+UuXVS7G+bS1Ijq3Q2yOTMsyLTsM9pZ+QxC+bj9k0mNR4X/wDpdjZLiO2Fg7MSxHeeTRuJasBiQv66LGnvwK+C1DRfS+fIw2f9ffOqflvo5T8H6Qp/fbHnUMtuW8JJSVk7Ig1b8g5hlXV1XOKajEjnDEw95G58ZhrvpfaQXsvJw6zzJWJD/r3FbqSiRx2V4vhrcOp4a+28jSoVKssQeOwv/wDSzIjLETbaIuGe3sWDiza6QUmJiRg1sYdd44N1NyzMav4amvkZWTQMqek2ZbrCJ2k36WWxaUrRpMHfIQlE4Yg8dhj54id45uHvI3PjMNd9L7QUrPJr8R+Qd+tBhvdn8TO/y65vlQbbyOHmuXBxB47C/wD9MxN2mGe0v4vOjUUvLyVJJSdOlRXolW6qSJFIlS4rKY8e5LdZwj3w7hBt2LUxhSdsjyovi/tmHnEtJZQchOHvIzmTkRKKM+xPldrGb50gvoWI/IQyS5W8LtXYoJVpai2I9SjN8qPiDx2F/wD6ZMjNSmayHk2hcQTiu01ilxBuIJz2YhQabCsVxV86CzMEGvjxD2yqpp2SHm0utKpZBHOYRDpMOlvsNrddHbmDESFFOrfHmRHt3FssI2ajUcR6Kf8A9OcQlxD9Gg1V0BuH7XG23UtoQ2j7ECQUljErpFHwy37nW0OoSRJT/wDZpsGW1J0+xlvRWERmP/xi/wD/xABCEQACAQICBwUGBQMCBQQDAAAAAQIDEQQSEBMUITEyUQUVIjNBIDRCUmGBBjBQcfAjQJEkQ2BygqGxFkSAwWKQ4f/aAAgBAwEBPwH/AOPai2ZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZGZH+pYZXQqatvHTijKjKjKjKjKjKjKjKjKjKjKjKjKjKjKjKjKjKjKhx3bkRitYRju3oyR6GSPQyR6GSPQyR6GSPQyR6GSPQyR6GSPQyR6GSPQyR6GSPQyR6GSPQyR6GSPQyR6CowZqIGogaiBqIGogaiBqIGogaiBqIGogaiBqImRdDJHoZI9DJHoZI9DJHoZI9DJHoZI9DJHoZI9DUQKtOye4lx/UcIYp8BNnoQpZo3MvisZDIZDIZDIZDIZDIZDIZC1mifKy7MMroh5p2heFO6MDVlUoOTK3aFaFRxRRk5Ye533ic9txWqOnh8/QwPblKvufEjNS4ezi67pzNsfU2x9TbH1NsfU2t9Tan1NqfU2p9Tan1NqfU2t9TbH1NsfU2x9TbH1NrfU2p9Tan1NqfU2x9TCzdb1MMr0/ZxGNpUI3kzC9rLGV8kOCO28dVwslk9TsTHVcVJ5/Q7QxlSjUtE7LryrRzSKNebxWT0RieQlx0R3JmsZnZnZnZrGaxmsZrGaxmsZPj+iYQxLuI9DD8hLnf50uKJ8rGYXlIeadqeSzsz3YxXnS/cw/u3+D/cMX7m/vowna9fD7nvRgu2aOI3X3kZKXDT2n5hh6OtdieFiouUWRwkcqbZsq1mS4sLzb+BDDpwUmyWHtNRubLHqQw8ZLibPG9rjw8F8Q8P4Mw8Ks6jc2OPoyGFhNcR4aOZJMjh05OPQ1PgzGyx9WVqeSVjsswfISjfRWxVOkryZjvxDfw0d5Wr1KzzVHc/D3vD/nqfibjH+dD8M8Zfzqdr+cdieWUPfX9iryMlx0Llf5c+P6JheBiBHoYfkH5jGvzZcUTXhejC8pDzTtTyWdle6v8AYxXnS/cw/u3+D/cMX7m/vp4GD7brUN096/7mD7Vo4lbmJ3O0/MMDzM10oxcEVeFP7H/uPsTW5sqe7opVXUqK5TT1kmYXnZR84rUOM7lNXpofnIpRanNmB5mUPPKXmz+xXjamyqpNxylXNm8R2YYPkK2JhRXiO0PxFHlpb/8AwYjFVcQ71Hp/D3vD/nqfibjH+dD8M8Zfzqdr+cdieWUPfX9iryMlx0Llf5c+P6JheBiBHoYfkH5j0NfmPiiryPRheUh5p2p5LOyvdX+xivOl+5h1/pf8H+4Yv3N/fTxMH2JWr757l/3MH2VRwy3ISsdp85geZmqyUpXJQc4wsf8AuPsKWaEvuat1KCSJJKtGxCbc5R6GF52UfOKlCcpNkZZYRJeaiE25SRguZlDzyHmTKk82HuSjJuLRiec7MMHyFbDQrLxGP/Di5qW7/wAGIwtXDu1Rafw97w/56n4m4x/nQ/DPGX86na/nHYnllD31/Yq8jJcdEb+g81jxG88RefQvPoXn0Lz6Hj6HisSvff8AomF4GIEehh+QfmPS1+W+KKvIxmF5SHmnanks7K91f7GK86X7mG90/wAH+4Yv3N/fRhOyK+I3vcjBdjUcPvtvIxUeGntHzC7jvQ6k3ubFVmuDNZK97mskvUVScdyZrJXvcVSSd7kZtcBSadzWz6md8DPK97meXUUmuBdp3NZLqZ5WtcVWa9TNm4nZhg+TTXwdKsrNGP8Aw5l8VH/+FahUovLUVj8Pe8P+ep+JuMf50Pwzxl/Op2v5x2J5ZQ99f2KvIyXHQuVl2XZdl2XZdl2XZdl2T4/omF4GIEehh+QfmP2GvynxRV5HowvKQ807Ri5UrI7PpSp4dxfQrdnVp1HJFGDjhbHceJz33Fai54bL1MD2HSob3xIwUeHs9peYUqWsZqL1MhWo6uSVyvhtUr3Nm/p57lDDa1XuUaOsla46P9XV3K9HVPiVcPq43uKheGe5SoaxcSnSzyyjpWnkK1HVlTD5YZrkMLmp57lDD62+8hRzVMlytT1crHZbMHyeziez6VeNmjDdkrB4jPHgztvA1cVJZPQ7EwNXCyef1O0MHUrVLxOy6EqMcsilQksS6noVeRkuOhcr/Lnx/RMLwMQI9DD8g/MfstfkviiryMZheUh5o2lxFJM1kOpmVrmsh1MyJRt7XafmEZuPAzO9ybb3sc5S4szyta5Gco8GKTjwZmd7kpOXEc5PixVJWtcUmuDFJrgZne45OXEc5PizNK1rilKPBik07jk5cTsswfJ7UkhyS4ikmZ0KSZmRV5GS46Iq8WZGZWZWZGZGZGZGZGZJGRk+P6JheBiBHoYfkH5j9pr8h8UVeR6MLykPNO0najdHZs3PDuUuhiMXWjVkkyg74S7+h3nis9s5iJOGEzL0uYD8Rxfhq7iFaFTfH2e0/MOzsJHEOzK2DpU5ZVvKOAjV9CfZlGEbyFhabdkhdiRtvKuCo05ZVvMP2ZGt6bir2TSpRzSYsLTbskR7FjbeVsJSpyst5RwEavoVOzadNXYsPB+guyo23mpp3tY1FPoail0MXTjC2U7LMHyew2lvZje2KOGW9mA7VljsTa24/EWJq0JrVux2BiateT1judpYipSq2izsmpKpG8ijUk8W4ehV5GS46Ioe9cS31Pufct9S31LfUt9S31PTiSVn+iYXgYgR6GH5B+Y/ba9t8UVeR6MLykPNO1PJZ2V7q/2MV50v3MN7p/g/3DF+4v76MLj62G5HuMD2/Tq+Ge5/z1IVIz4ae0/MOz5OMW0YbDOs/oVslGFypUlVkYOgqe98TGYz4IGGwzrP6E5ww8CrVnXmYXCqks0uJjMZ8EDDYZ1n9CrloR+hUqSqyMPh9XvfExOJ+GJ8WnG+h2WYPk0XsYvtSjh43bMd+IKtbdT3EpSm7yZ+HfeX/PU/FHNH+dD8MK8pfzqdr+cdieWUPfX9iryMlx0Llf5c+P6JheBiBHoYfkH5j/Ia9p8UVeRjMLykPNO1PJZ2V7q/2MV50v3MN7p/g/3DF+4v7+xhO06+G4O6Oz+2YYnd6kZZldHaXOdlUtY8rJzhh4FWrOvMoYPVrM+JicTbwwMLh9dLfwJzhh4FWrOvMwuFVJZpcTGYz4IGGwzrP6E5ww8CrVnXmUcJqlmfExOJ+GJQoOq/oPn04v0OyzB8hicXGgt5j/xFOpupFSpOo803fT+HfeX/AD1PxRzR/nQ/C3PL+dTtjzjsTyyh76/sVeRkuOhcr/Lnx/RMLwMQI9DD8g/Mf5LXsviiryPRheUh5p2p5LOyvdX+xivOl+5hvdP8H+4Yv3F/fTCnKo8sVdmB/D06u+qUOzqeHjaK0do+Ydl1NU8xUqzrzMLhVSWaXExmM+CBh8LKtv8AQlKNGJUqzry3mFwqpLNLiYzGfBAw2GdZ/QnOGHgVas68zC4VUlmlxMZi/ggUMM6m/wBCUo0Ynx6cX6HZhg+QnSjPiY/8P063ihxMX2bXw3Mt2n8O+8v+ep+KOaP86H4W55fzqdsecdhq8H9yh76/sVeRj37tEeFjJ9TKupl+pkXUyrqZV1Mq6mVdTKupk+pPj+iYXgYgR6GH5B+Y/wApr2HxRV5GMwvKQ807U8lnZXur/YxXnS/cw3un+D/cMX7i/uRi5OyMD2BVrO9TcYPsqjhlZIStolGx2n5hh8QoKzKfaGrd4ku2ptWNuh0I9tSirIqdpa13kQx+rd4ku2ZtWNuh0I9tSirIqdp613kU+01Td4ofbU2rG3Q6Ee2ZRVkT7Q1juzaodDao9Dao9CvVVTgdmGD5NNSjCpxMf+HYT8VPc/56GJwNbDPxo/DvvL/nqfijmj/Oh+FueX86nbHnHYPI/uUPfX9irysc0OUWXiOUWZomeJmieA8B4DwHgM0S8ROLHx/QsLwMQI9DD8g/Mf5bWl8UVeR6MLykPNMfSlUp5YmCw06NFwZV7Hqzm5f/AEUqE44fJY/9N1s17/8AYq4eVTD6uxgux6OGXAUVFWXs43BTqzujuyod3VDu2Z3ZUO7Kh3ZUF2bUO7pndkzuyod2VDuyod2VDuyod2VDuyod3VDu6od3VDu6oYXD1KPpcwsXGG/2a2Gp1VvRR7Ijhq+spo7Y7LqY2e7dY7H7LqYKe/fcx3ZtTEVMyOzMHPD+FlLBVI4jWWMTyDhvMn1Mi6mRdTIupkXUyLqZF1Mi6mRdTIupkXUyLqZF1FFJ8R8f0LC8DECPQo1IxjZmeOds1kTWRNZE1kTWRNZE1kTWRNZE1kTWRHKJmRdNoq8j0YXlIeb7baSuzaaXU2ml1NppdTaaXU2ml1NppdR1qfozXQ6muh1NdDqJ39qUlFXZrodTXQ6muh1NdDqa6HU10OprodR1Iria6HUWIgvU2ml1NppdTaaXU2ml1I16cnZMeIpp2ubTS6m00uptNLqbTS6m00uptNLqVqkZweVj6j3/AKhheBiBCMqMqMqMqIwTMkehkj0Mkehkj0Mkehkj0Mkehkj0Mkehkj0MiKvIxmF5SHmletqo3KWNjJeI2ukbXSNrpG10itiIyTS/LhXgopG0QNogbRA2iBSxOeViu9xXqzWDvf4jbK/zP/Jtlb5n/kjiqr+J/wCTaKvzP/I8XXXxP/J2biKs8RFORU9BFi2mxT50WWWZSpQcVuNRDoOhDoaqHQVKn0NRDoVFac7Env8A1HC8DECPQu76JTsZ2Rci46j9DWSE2N2NYxTkzeOVjWMpSbkVeR6MLykPNK+TJ4zD6q3hPCeE8J4Svaz9huyuyXaEVwR3l/8AiUcVCruXsU7ZEbjcbh5bFLVZvCVuBX9x/wColG4oSk8qRHsvGPeoFSjVpeZGw1c7LVsVEn6C9qHOj4JlPlROpli5GsxVbfHchwxcN/Ew9Z1FvRcq89Qlx/UcLwMQI9NEpaIx66JSvojGw3YbuJXErDlbRFXIcyKvIxmF5SHmmIpayNijg4KO/ebJSNkpGyUjZKRVoQinb2MSm6TsOc3R1eUaa4mF82PsQoQcUzZ4GzwNngbPApYdRle5W4Ff3H/q0YTH7LB5Y736ku2MZfm/8C7bqSpuFWOYjK52d7zAn6C0XMxx0U+dHwTKfKtCd9DWirz1CXH9RwvAxAj0JStojHrolK+iMbDdhu4lcSsOVtEVfRB+JFXkejC8pDzTFQlOFomHoVkuNjVVvnNVW+c1Vb5zVVvnKsKiTvLTjcTKDyRNZPqayfUbb4mG82JYas9EaVTImpGrqfMaup8xq6nzDp1LcxQpTU7srcCv7j/1aMrtclG4oSfBHA7MlfEwJ+gtFtFtFPnR8EynyonJxi2iGOh8W4ePpJGGrusm2rDRV56hLj+o4XgYgR6EuJGPXRKV9EY2G7DdxK4lYcraIq+iUijzlXkejC8pDzTFTlCF4mHr1muFzW1vkNbW+Q1tb5DW1vkKs6jTvHTjPOZGLk7Ij2bJrezux/MYbAqk8zEirx0U6lXIrRHKr8prKnymsqfKOpUtylCrNzsytwK/uL/5iMrmC7QeGTi1eLFVwNRZ9U/8FftOMIOnQhbR2V7zEn6C9hPRDnR8EymrwWjFVKSfC7JToQayK4mMq89Qlx/UcLwMQIS3GXffRKV9EY2G7DdxK4lYcraIq+iUtFJeJFXkYzC8pDzTEVdXG5RxkHHfuNrpG10ja6RtdIq14STtpx9Nqebqdnr+roSEixUd5aKWJpxgkza6Q8RT9DaIG0QKWIUpWsVuBiPcX/zaIyuYdZ8MlS4Zf+5221rY9bEo3Oyveok/Qjot7EeZC8uZR5ENGLpU+Z7iE8Mpblovcq89Qlx/UcLwMQIXA2aBs0DZoGzQNmgbNA2aBs0DZoGzQNmgbNA2aBs0DZoCw8UVeR6MLykPNK+TJ4zD6q3hPCeE8J4Svaz0tJ8SnThF7kJGU3ZbozPTRtq0eE8JKKQ8tilqs3hK3AxHuL/5tOGw1KnRjN1XHN9bFbsenK+WTzfUas7M7O96gT9COh+xHmQvLmUeRaO0YvImhzhOUdWtNXnqEuP6jheBiPQQuBGvJyKlaSlZFSq0lb1FWerzFGq5OzI15OWjFYuNCN2UK8a0brRGvJyKlaSlZFSq0lb1FWerzFGq5OzI15ORV5GMwvKQ80xFLWRsUcHBR37zZKRslI2SkbJSKtCEU7ezGqvU1kCU1ay9ilhqcoJs2SkbJSNkpEsLBcClh1GV7lbgYj3F/wDNpjWw88PTWLja3DjvO+KO0Z0t1rE5ZpOR2d7zAn6EfajzIXlzKPItGNrSpRWUrzVKUXC2/TV56hLj+o4QxXoIXAjUhm4FWcM29FScbK6FOGS5RlF7krGrje9jF4uNCN2YnEyxErsweMlh5fQoV41o3Rq43vYqzhm3oqTjZXQpwyXKMo8ErEakM3Aq8j0YXlIeaYqEpwtEw9Cslxsaqt85qq3zmqrfOaqt85VhUSd5fl06dVwVpGqrfOaqt85qq3zjpVrcxRo1IzuytwK/uP8A1DVtEcTgK2GhCs+H7/8A0Y7ZlNbNwIyudne8wJ+gtD9iPMheXMovwrRidXk/qFFYeM07jWirz1CXH9Rwhi/QQuBGnDNxKsIZt7KkI2V2RpxcLIp0VDeYvFxoRuzE4mWIld6cHjJYeX0KFeNaN0ToKTuVIRtvFCGSxRjHincjThm4lXkejC8pDzfbqrNdGy/U2X6my/U2X6my/U2X6my/U2X6my/UWB+pCOWNvaxCsmV/cf8AqN0kNWMPT/0ihSW5x/7nb0YqrG3G2jsuV8TAn6C0XNwnojzIXlzKfIhM7RtkRDEULrwaGirz1CXH9Rwhi/QQuBGhJSKlGTldFSk2lb0KMHBbzF4uNCN2YnEyxErv2cHjJYeX0KFeNaN0VqblwFRerylGk4u7I0ZKRV5GWMK/Qh5uipLLFsl2uouzKnbEadsx39T/AJc7+p/y537SO/aR37SO/aR37SO/aR37SO/aR37SO/aR39T/AJc7+p/y539T/lzv6n/Lnf1P+XO/qf8ALnf1P+XJ9sUZqxLZZ0tVmfG5jsOsLXdOLN0kYLB1aNFN1st/56nadFU6vPmvo7K95iT9COmxbRHmQvLmUeRDRia+rgQr1ITWtXHQyrz1CXH9IUbl4ozR6GW/AydTwngMqfBjtEhXycCdbWoQuBHPnK2bORvbeVqjjuRicJia8rsq0pUpZZezQw8q7tEwdDE4eX0KU86Kt8u4pTy73wI1byuVcTKQ3cpVnTKeIjnzM2qCRUxcJRaRiPNZ2l8P7GHwNWvHNE7pxB3XXO6653XXO6653XXO6653XXH2VXZ3TiDunEHdOIO6cQLsrELod11zuuud11zuqud1V/oU+Y7aX+rf2E7EnhlhqW0b9x2g8M5rZ1ZaOyveYk/QXtR5kLy5lJ+FaMZRzw4ipVZziqz0Mq89Qlx9u6si0epaPUydBpr+9nu3FHD5yWFJRdNkuo9Kn1FTjNbjfFnqLgRrycipWkpWRUqtJW9RVnq8xSquW5naPnv2ex/OKlaSlZE8XZbjbSrUlNaLexGbRu4lXC55Zos7TVnFfQ/DyvA1ZKkjIZUZUZUZDIJxXqeDqeDqeDqeDqSUOp4Op4OpePUjFSK3va+4laodr2eKkv2JRsUe3NVTUNXw+pjcWsZJStYasdle8xJ+gvajzIXlzKa8CEztCDlBNFSrtEoqKExlXnqEuPsJXLRMiOG5jg9KkySiuJ4Bxt/cx4j4lHk0Yl3dkS6ezCWVlTjcQuBGpDNwKs4Zt6Kk42V0KcMlylKLukrHaPnv2ex/OMVV37iFNzNlHF02SVn7UZWJQtvRiKKxFPL6+h+HeX+fTRX8tlKN6aMP8X7n+/8AYxHw/vprNqbM7M7HJ2RnZnZmZmYpO5RdoIre9HxnbN9rdvocVvHBoV0WzI7LVsVEn6C030x5kLy5lHkQxMWVcBlyrz1CXEyscWiw07WQ47rF2jWSNZLQhblco0M+9lTCq24ivhenKyz/ALaW/eUa+XcyriFbcPdvZcSbMj0qLYou289RcCNOGbiVYQzb2VIRsrsUIZLFKEeKZPCYebvI2HC/T/BsOF+n+DYcL9P8Gw4X6f4KeGoU3eBU5jDJKOjFcxPjoUWxwa0pNkOFmLdI7IVqk/3ZjarhHcbTKS3sjip3smUKUqibUjVy1trlWnJW8RqZfMJWRWdpszLoZl0HJW4GZdDMuhmXQzLoJq/Ao8iK3vSPjO7qNfxzW87nw3yr/B3PhvlX+B9kYdfCv8HdWG+Vf4I9l0IeJLeSpXNR9TUfU1H1NR9TUfU1H1NRld7jp/U/qfMf1PmP6nzH9T5j+p8wlUfxDp5YybY2kZmQ3qxHwslKXAasZk+Psx3qxQqpbmTklG58WhRbJvxCm7lk+H9rAsmbokmRV2OXojeLx7iK9WOTkU+NhC4EaElIqUZOV0VKTaVvQVF6vKUqTjvZipyVTczWT6msn1NZPqayfUwEm6m8rwyyKFbLuZrY24kpZpXGyK9WOTYm0PxK5FXY5eiKb3l1BOT9DsCee8urZ2k1lS04SrKD3D1rq8N5WdTdmRmrdBXtvK1s7ueE8IkmtEoJloFoCUblHkRW96R8ZSryjGyRtM+htM+htM+hrp9DXT6Gun0NdPoa6fQ10+hrp9DXT6Gun0NdPoa6fQ10+hrp9DXT6Gun0FVm3axnqR4oq8jJcdL3Kxm6knYaTV1ojH1Zn+hZS4aFLqfcfLu0S3q60x4j4/2kfX2IeumHUhvVjwr1PDe9zMjbBYizvcniMzuSxOZWFirRykMRlM9MjqvU/oH9A/oENTfcV03LeTpSiWkPcraFvjbTD1KfESsLmO1qklaC4H4fmoU23/OBiKzqS0IpcTaI67MYitGdrG0xE7q5WjebMjMjI7lp1bNWxQdynH+mmVvekLmIcNDdjWI1iNYjWI1iNYjWI1iNYjWI1iNYjWIU09NBXqmKVlH9yryMlx0J2ZLjofoR3SJKzJdBoTsyfEWiPB6I8G9CI8R8f7SHsRdmOHqhRfqSl6IgrSHx/Khh41o2kd20up3bS6ndtLqUcFCnK8WVK0lKyMRLNFF3pTsWUuBlkJfCWZJPiRg73Z2tvmjCU5YejlfHSlcwKhHezPHaL39DEzi8tn6kpQfror87LMUWx+FGaQ22WZZkU7lDy0V/fF9yPmEOGiXAfH82hx0JGH80xXw/uVeRkuI9HGN9EvQhvdyfEe9XG7kVdkndi0R4PRPcraER/tYuzJKz9jI1wGpepYg2Pj+UqdScfAzZcT8xsuJ+Y2XE/MUKFaMvE7lWcb8Co04klZ+xFXZb6mRlpdTK+pDiOEcyqS4nHQkYPDZvFLgYejCWa69TUw1+W26xiKMI5bL1NmpdBwVtxXbU2JyZKVhydjMzMzOzOyMncoeWiv74vuR8whw0S4D4/m0OIlooecYr4f3KvIya3j0RlYkrDtZXFUGl1FKx4X6En6exwiR3b2N308I/2y8St7CtJXL/AFL/AFFJdTIuJaPUsjKuplj1LR6lo9S0epaPUtG17jxqpLwHedQ7zqHedQw2NnVlYnGN+JOMbFWluuvYi7MUIyNSicYwHJWsiPhVxu+hIpU3LeUqEnBNSKFKUr2kaqWuy5ivSlG15Gzz+cirKxisNCPibKNBzNij1Nij1Nhj1Nhj1Nhj1FgY9Tu6PUhHLFIr++L7kfMIcNEuA+P5tDm00POMV8P7lXkZx3aGtEZejJ8ERluPEWmZZlTjpSuSsuJKV9CQlcm/T+2ivUcoscfVaFdGe/FHgI5b8B1PSxmj0MyMy6GaPQzR6GaPQzR6GaPQzRtawsFGsvCjuo7qO6yhgtVK5Om2ydN2ViU1GNhuLHHTGTRr2Od/QUb72VOOhIpwcmRVSFNxsUpV8isihKrvyozVddw3leVXdmRmxHylSvq43lxKdOWIlmkcKmrSKs8licssbkJZoplOee5n/qZSpPJYjK2iv74vuR8whw0S3onTS3s8BaJZFojUTwHgPAeA8B4C0ClSs76aHnGK+H9yryMbtIe/etDWiPRjVnvIeF8TWonW6aUrjll3LSlo5V/by3JDIysxqJlXUyGVdSOW+4fH8rFYmdCCcTvSud6VzvSudn4ypXqWkVL5ipmyonx0J2EoyMhkMq6l4ocmxK8dFOk5uyKeG1SKvIzDv+mkYZc37lv9R9jErl/cr11SRSpSxEs0jaacPDY2iOuzGIrRnaxUxEXFop4iKikUq0Y3NctbmKtaMrG0xKc9xX98X3I+YQ4aJcB1GZ2Z2Z2Z2axmsZrGaxmsZrGaxmsZRm3LTQ84xXw/uVeRkuInYtfhosQ3bzP1MifAyMymVmTqSdlu02Erl1H+3gt5LfvHoXij7EOYfH8rtLy4+x2R5rJ1GmVKnhJb9/sZ5Gfqi8TwF4rgRk8xiq+zq+W52HXlWvKQ2lxJSh6M1kepGtF+prIdSriYwW4px10s02RSS3Gsj1M8dovf0MTOLy2fqV5QcHvKU45FvKEkrmZa65XknYzx66K3vSPjIcNEuA+P5uH5tNDzjFfD+5V5GS46c1+Jl6E+mlTZrGZy5bMjKJFkuI59P7hboi3rTwN0xq2iLsxWe+wmuhu6G7oXXQuuhddC66F10JzUYOVuBi8Vrkllt7HZtWNOo3Jm20Jsr1MxFjVtFvbnJKnJy4H4c5P59DH+WZ2RbbPGRzX3k52Iydyh5aHh6bd7Gphr8tt1jEUYRy2XqVcPTjBtIp4anKmnYo0oO90aqGttYrUoRtZGz0+glbcVvekfGQ4aJcB8fzcPzaaHnGK+H9yryMlx9iHG49/tLRnZmZJb/wC5kJ2JL19hT9GZOhDmM7M7M7M7NZI1kjWSNZI1kjWSO03enH2EnJ2R2b2bk8UuJVd3oUiyfAyvRYsWLGUxVO9CUT8Ou0P59DH+WZl0INPS5LoKSvwKHlodCbd85qpa7LmK9KUbXkVaM1BvOUqM3BPOUqEpXtI1ctba5VpyVvEamXzCVkVvekfGQ4aJcBwdzIzKzKzIzIzIzIzIzIzIzJIyMoJqWmh5xivh/cq8jJcfY4R9texLhf8AuFxJLeeETiOPT2VU6mZdCSs/ye0vLjpScnZHZvZuTxS4kvDEkt54TwmXoeOJrGZ/oZ49DNEvESQkmdhK0DGPwWLQIW9NDPCRy3KHlocq99yM1XXcN5XlV3ZkVZV8juilKvkVkUJVd+VD1rq8N5WdTdmRmrdBXtvK3vSPjIcNEuBOombupu6l0XXU3dTd1N3U3dTd1N3U8PU3dSlUT3aaHnGK+H9yryMlx9iSbS/IszKzKz4f7iPElx0p2LqQ1YtpTsyS9fycZh5VaSy+mhJydkdm9m5PFLiJWK9828lx9hSaM/VHgLR6ngM9uCG2ynzHYivf7mMTymqkQpyTFTqIVOd7sdKQqcrmHf8ATS0W/wBR9jErl/cr+Wyh5aMMub9y3+o+xio3y266a3vSPjIcNEuA+P5uH5tNDzjFfD+5V5GS4+xN2M/UtF8DVsySNWy0UZ+iMz0XZe6/uFxJcdGRmR6FNo3PgNFhRuyzW5HiPEeM/qH9Q/qH9Qp3v4jVxO0Ozr+OHE7N7NyeKXE4CrycitUd3Elx0ZGZH+RDidgT8Lb+pKUH6mePUjWivU1kOprIdTWQ6kpQ9GZ49SNaL9TWQ6msh1NZDqayHU1kOprIdTWQ6kpQfqZ49Sqv9SmfGQ4aJcB8fzcPzaaHnGK+H9yryMlx9ip7F37FtMeD/uZdTlWl+JaVPqXiOeikjKpLeVMJ0IYdp7xU42sZVwFFLgZFe+jKmrMSto1cb3HTjLiV45ZHKtL8S9vET1dCTKWIrUlaDFjsR8xttf5h43EekjbsT8xt2J+Y27E/MLHYj5jba/zDxuI+Y27E/MbdifmNuxPzG3Yn5jbsT8xt2J+Y27E/MbdifmNuxPzG3V/mI8SHDRLgPj+bh+bTQ84xXw/uVeRkuPsS3xv7aVuJmZm6klY4R/uYv0ZLjphx9pK5G3oehrZ34meXUdWS9TXSFUkzPLqOvJcGbRMVWfU1s+ptE+pr5kvFxJcdMOPtJXIUadbwyO66HQ7rodDuuh0O66HQ7rodDuuh0O66HQ7rodDuuh0O7aPQ7uodDu6h0O7qHQ7uodBdm0Oh3XQ6HddDod10Oh3XQ6FTs2jCN7Gw0flEraGrlSKgZ10Lrobi66DkuhnXQzroZ10M66GddDOuhmXQp0lx00POMV8P7lXkZLj7EOA1b2VxJcdMd6sSf9zDiPTDjoelQ6jl6IpcRvdolK2iMb6JS0RjobvojG2h6YcdD0qHUcvRGHllkVa2V2Q63gzEK107+hTr5nZksRZlWtldkOt4MxCtdO/oU6+Z2ZOvv4FSeV2HU8NyNS6dyFW7HVsydZx3IdbwZiFa6d/QpVs8rMxVlTsa41xrjXE5KZkiWRuLIyxMkTJEyRMkTVoyIyIVayNea8hiHGWYqYt1LXRLFNq1hxTMqMqMqLK1hpMyIyIyIyoSSLIyocSP9zHiKXit7EPUvEvHoJroNtaGQ5h1JI1jNYzOzWM1jM7M7NYyM22axmsZrGQm2/Yh6maPQjlbNQr2G2tDIcxN3aH5SKfCRS50S9Stzj8pFPhIpc6JepW5z4ER+IhzIfqT5i/9NFNbpFLdOJiOQuIzbzNvGXLly5cuXG7Izbhbxq/9qtxHf/criRab9iHBnhPCXiT46GLianNvHGIoI1aLRLRMiMkS0RJGrQ4RRaJFL2KfBlolOjfeiHmFRWloYuInwH5Ef3KXLMoeZEnxZiPM+w/Ij+5S5ZlDzIk+LMR5n2H5Ef3IcJFPmiS9Srzn+2inKymU+aJiOQ2uiTvfcN77IT6kL33m10Da6BtdA2ugbXQNroCr057okb+pd+g96PqSsmbjcbjcbjcbjcbjcbjcbjcbjcbjcbjcQauPj/cw9iPK/YfBaHop8pbfob0JaG76Ehu2hK56exT4DszCrwkPNMUrVHoeiB/tIpcsyh5kSfFmI8z7D8iP7lLlmUPMiT4sxHmfYfkR/cpLwzKfNEl6lXnP9tEfiKfNExHIbBDqTumO/FCW+5C7ZsEOp3fDqd3w6nd8Op3fDqd3w6kMKqW9MTb3M8S3G+KPQlxPufc+59z7n3Pufc+59z7n3Pufc+59z7n3Pufcjx4kuP8Acw9TKzKyUWkLl9j4VocWZGUnusao1H1NnRs6NR9TUGzo1CNR9TUI2dGoRUjlRlZlZKLSKEc24WGuQiqUSHmmLalO60OLMjKMFLdfeOinHKUqagmupSw0YSzXJYSLlcq4dVHceHi4ZCnh4wTXUp4VQlmuSwkXK5Vw6qO48PFwyFPDxgmupDCRi73JYZZirTUjVrLYjTSRGlZ3Kz8BvEZXcyu4zebzebzebzeNXQk7C3cT6/2yViXH+5g7GZCkjNE3JWXsJq28WUzIzIU0jaTaTaTaTaTaTaTaTaTaTaTaSVZSMyFJGaJTmqfA2s2oWJtLMVKkZsWUzIzIjNRdzbTbTbmbczbmbczbmbczbmbczbmbczbmbczazajajaiWIzKxdGZF0XRmRddTMi6LozIuupmRdF0ZomaJeJeJeJeJeJeJeJeJeJeJeJeJeJeJeJeJeJeJeJeJmiZkSd3/APpQbsrlpT4mrSKcsy9hST0ObbtE1b9WRe+zHwISurkZZl/xVPfJIb8aJuyItQjvIzuZ1exdXsU+ZlR2RNZY7hcCG+bZLgU+Qo8PYbt/xM1ck/6h5kvoc0y++5BJxbZS3yuUuLGrlbgTl8KIrIhtyjdEFLLuIStEg3dpkfFdkm8qZLxP9iny/wDErv6GW87MpP4S3jJfKiLuspS4spcXoqZvUy5VuHL+mQ5CnyFNXYuMiO6mT3RQlaBS5f8AibK89yorPMjdPeRikKKRT5mRi09FSLY1dGV23lLlIxsrFOGUhzs1ZOOYkt1imrR/4oUEt/5Em7WIKyt/8c//xABDEQABAwEEBwUFBgUDBAMBAAABAAIDEQQFEhMQITEzQVFxFBUyNFIgImGBoSMwQlBTwRZAYnLRJDVEQ2CRsQaA8KL/2gAIAQIBAT8B/wDr3ULEFiCxBYgsQWILEFiCxBYgsQWILEFiCxBYgsQWILEFiCxBYgsQWILEFiCxBYgsQWILEFiCxBYgsQWILEFiCxBYgsQWILEFiCxBYgsQWILEFiCxBYgsQWILEFiCxBYgsQWILEFiCxBYgsQWILEFiCxBYh+ZXi7CW66J1odi906kJ5DxWa/ms1/NZr+azX81mv5rNfzWa/ms1/NZr+azX81mv5rNfzWa/ms1/NZr+azX81mv5rNfzWa/mo5CXe+40U0juyVrxUsxDvs3miz5fUVny+orPl9RWfL6is+X1FZ8vqKz5fUVny+orPl9RWfL6is+X1FZ8vqKz5fUVny+orPl9RWfL6is+X1FZ8vqKz5fUUbXMPxLtk3qXbJvUu2Tepdsm9S7ZN6l2yb1Ltk3qXbJvUu2Tepdsm9S7ZN6l2yb1LtkvqKFokP4is+X1FZ8vqKz5fUVny+orPl9RWfL6is+X1FZ8vqKz5fUUbRNwcV2yb1FWSfE+OjzWutDZ+Y3tsCusCjkAvxK020xTGMBNlJja6m1Zx5LOPJZx5LOPJZx5LOPJZx5LOPJZx5LOPJZx5LOPJZx5LHja6vJWfetVFeZo8KfyXzVxkPlNeSt7G50WrihE3kpWjtzNXBdkjooo2utz2kcFbLjHij1KaCSE0ePZjkEcDOi7UPgu1D4LtQ+C7UPghaR8F2gfBdoHwXaB8F2gfBdoHwXaB8F2kfBdpHwXaR8F2kfBC0t+C7QPgu0D4LtA+C7SPgjMMovor3OG0fJA19iy3TNN4tQT7ujs1ndQcFdELH2cVV7wsZZzRWWNpibq4K9mNEQ6q0xtyHauBV3eYb1Q2aJTQVWc5ZpWYVmuWc5ZzlnOWc5ZzlnOTdn5JeuwK7RRrkF+JXp5kqA/wCmZX75nhd0Vm3rUVem8HRT+S+auDfO6K8N/F1Tdil8+zouCs/+4O6D9tE1ljlFCFbLjLdcSkifEaPFNMm5i6KzQZxopLG0MLmuTbE3CCXbV2MZuCqFj8VTsUdmBYHOKdZqPDa7V2NvAplla4bV2ZmKlUbKwfiTrMMvEnWMCQMBXYW8HKOxsePEjZWYw0O2ptmBe5tdiyPsy5CyN4lTxYHUQ8s7qr68wOgTXUQNVZrtmn+AVjuiODXxTWhuxXjuCrk8uFfflyrJuW9Ar43Q6hWjcO+asY/1DOqGzRP4UPuQm7PyS9OCu7wuQX4lenmSo/KxprvvWeF3RWc/bNRV6bwdFP5L5q4N87ory8xD1/wm7FL59nRcFZ/9wd0H7exaLFHMKEK13I5muNOaWGjlJuYuiu/xFZ7mtLApfDH8l/yfknjU4qTyrVDO6WUVUbSJHFWPxlQ79T2c631UQrGAnb5qhaRI8q7/ABlWfzCi30nyVobSMqZriW4VNixe+v8AjOV9eYHQKyXdNavDsVhuSODWdZTWBuzTeO4KuTy4V9+XKsm5b0CvjdDqFaNw75qx+YZ1Q2aJ/Ch9yE3Z+SXpwV3eFyC/Er08yVH5WPQ133jfC7orLvm9UVem8HRT+S+auDfO6K8vMQ9f8JuxTH/Xs6LgrP8A7g7oP29i0W2OEVJVrvtz9Uac4vNXKTcR9Fd/iKEOXC7FtToy9rKcKL/k/JB+JjvmssyWdoCe0CZgHxTJC6RzeSsfjKh36ls8jnEhNdhYxO3zVHIS5w5K7/GVZ/MKPeyfJSPx2bEnNcS0tVr3i/4zlfXmB0Csl4zWXw7FYb8im912oprw7ZpvHcFXJ5cK+/LlWTct6BXxuh1CtG4d81Y/MM6obNBpxT6/hXvL3l7yq/kqv5Kr+Sq/kqv5JnxQ/JL04K7vC5BfiV6eZKj8rHpa77tvhd0Vl3zUVem8HRT+S+auDfO6K8vMQ9f8JuxTf7izof3XBWf/AHB3QftomtUcQqSrZfhdqiUkr5TV5rpduI+iBLDVqdK9woShK8CgKzH1rVCR44psr2igKzH1rVCR4NapjyNiDiDVZr+axupSqzHVrVB7hxQcW+EoEg1Wa/msbqYa6kJpBxWLFtX/ABnK+vMDoNNkvaez6toVivmG0auKa4O2K8dwVcnlwr78uVZNy3oFfG6HUK0bh3zVj8wzqhs0T+FBxVSqlYisRWIrEViKxFYim7PyS9OCu7wuQX4lenmSo/Kx+w133TfC7orLvm6L03g6KfyXzVxOAmdXkrxe3Ph18f8ACErOame3vFmvh/ldrjptUMzBb3mvBWy/B4Y9amnkmNXn2Xn7GPooYc00QgrLl1U8GU4CqtFlyW1quy/ZZlVZ7LnNrVQQZriKowUly6q0QZJGtTWbKbiqhZ6x46qGDNG1RRY3YU6GkmBTQ5VNals2FmKqZZMUeOqs9mzq61HBjkwVU0WU/Cgf9M5X15gdB7NjvmaDU7WPqn3lFarO6h4K6JmMs4qr3mY+zmissjRE3XwV7PaYh1Vokbku18FY/MM6obNE/hQ+5Cbs/JL04K7vC5BfiV6eZKj8rH7LXfct8Luisu+air03g6KfyXzTWOeaNFU6J7dRC7NN6T/4RieHYaa12ab0n/wst9aUTH19qTcxdEx5bsWI1qnlx1kp0jnbSsx9KVTZHN2FNc5uwrEa1qnPc7aUZHnaUJHUpVBzm7Cg4jWFiNaouLtpRe4ihKxupQFNe5mwoOcDUFFxdrKHlXdVfXmB0HtR4uCbE94q0J0UjRUhCGQ6w0p0b2+IIwyAVwlWPzDOqGzRMKhCNywOWArA5ZblluWW5ZblluWW5N2fkl6cFd3hcgvxK9PMlR+Vj9prvuG+F3RWXfNRV6bwdFP5L5q4N87ory8xD1/wgBRTf7izof3WSymxQCt5PHwH7K3XFHL7zNRU9ims/jHz9mTcxdFYYGStOII2WIbAhZYzwXY4eS7JF6V2GLkjZIRwTbFEeCNihHBdki9K7DFyTrLEOCFljPBGyxjguzx8l2SPksiKtKLs8XJdnh9KtkTYyMIQ8q7qr68wOg9hjHPOFoqrFcD5NcqnscdlsrgwcD/6VyMDrK2qvhgbZzRWUVib0V7ikQ6hWjcu6FWPzDOqGzQ5wCeQ7YVT4r5r5qn9Sp/Uqf1Kn9Sp/UmPaAh+SXpwV3eFyC/Er08yVH5WP22u9tvhd0Vl3zUVem8HRT+S+auDfO6K8vMQ9f8ACbsU3+4s6H91wVn/ANzf0H7aHxNftVsuNrvej1KeyywH3xpk3MfRXf4CmtqnUCJqmBOcmtqtiJqmtonOTW1R91E1TW0TnL8em3/hQ8q7qr68wOg0AFxoFY7ilm1yagrJdsNnFGhAUV5+Xd0P/pXD5Vqvw0sxVk3LegV8bodQrRuHfNWPzDOqGzRP4UPuQm7PyS9OCu7wuQX4lenmSo/Kx/cNd7TfC7orLvmoq9N4Oin8l81cG+d0V5eYh6/4Tdim/wBxZ0P7rgrP/ub+g/b2JLOyQa1bbjb4o9SkjdG4sdtTtxH0V3D3SFsRNUGJzk0VWxE1TW0TnJrarYiaoMonOQFV+PTbRsQ8s7qr68wOgVgup9q96tArHdMNmGoIADZpvPy7uh/9K4fKtV/eVcrHuW9Ar43Q6hWjcO+asfmGdUNmifwofchN2fkl6cFd3hcgvxK9PMlR+Vj+5a72W+F3RWXfN0XpvB0U/kvmrg3zuivLzEPX/CbsU3+4s6H91wVn/wBzf0H7aSQNqtl6w2YayrRfUs5o3VoduY+isBo0rFiTW0TnIMqtixVTW0TnJrarYiapraJzuCDVsX49Ns4L/jOV9eYHQKz2qWzmsZVh/wDkDT7sur/0o52SDVpvPy7uh/8ASuHyrVf3lXKx7lvQK+TSIdQrRuHfNWPzDOqa6hpokbiFFlYdpWAc1g+KwDmssc1ljmssc1ljmssc0Ia7E3Z+SXpwV3eFyC/Er08yVH5WP7prvYb4XdFZd81FXpvB0U/kvmrg3zuivLzEPX/CbsU3+4s6H91wVn/3N/QfsiaK1XlDZxVxVtv2WXVHqTnFxqdDH1Um5i6KzWkRihQtzRwRvIcl3gzku8m8kbwYeCFtaOCN4jku8Gcl3k3kjeLTwQvFg4LvNvJd4M5LvJvJdvaeC7WytaLtbOS7Wzkp5hJSi/4zlfXmB0GmzW6azH3CrDf8cnuyaimStfsV5+Xd0P8A6Vw+Var+8q5WPct6BX3uR1CtO4d0KsRrOzqnSDYu0LtCdI121YmLG1Y2qrOSqzkqs5KrOSqzkmzBuxdoQnqUPyK9OCu7wuQX4lenmSo/Kx/dtdpb4XdFZd81FXpvB0U/kvmrmmjilJeaalb7XC+aItdsKF42b1hS2uE25j8Wqi74svqH/lRWyFlufJi1U/wrbf75NUSfI6Q4nGvsxyQywxgyAEBZcP6oWGH9QLDB+oFlw/qBZcP6oWXD+qEGQ/qBYYP1AsuD9QLLh/VCy4f1QsuH9ULLh/VCy4f1QsuH9ULLh/VCww/qBYYf1AsMP6gWGH9QIus+WY8wK9pWST1Ya6vZst4zWbwnUjfUVps7muNDRXTeEEFnaHO1q9rwgns7g12tWW32dsTQXjYr3tkMsQDHV1q0W+zmFwDxsKu7zDeqdHrrVZBWQVkFZBWQVkFZBWQVkFZBWQVkFZBQhIKH5FenBXd4XIL8SvCxzSzlzW6kyCQWdjaawuzycl2eTkuzycl2eTkuzycl2eTkuzycl2eTkuzycl2eTkuzyck2KTiFlP5LAWsdXkrLvmoq9N4Oin8l8/bYwvdhbtXddq9C7rtXoXddq9C7rtXoXddq9C7stfoTbvtXFi7vtPpXd9p9K7vtPpTmlpofajjdI7CwLu+0+ld32n0ru+0+ld32n0ru+0+ld32n0ru+0+lR2SaSuFuxd32n0o3baD+Fd12r0Luu1ehd12r0Luu1ehSXfaY24nN1Jl3Wl7cQau67V6F3XavQu67V6F3XavQu67V6F3XavQrLZZYLQzMFNaAJP5jenBXd4XIJ21Zr+azX81mv5rNfzTp3jis6T1LOk9SzpPUs6T1LOk9SzpPUs6T1LOk9SzpPUs6T1IyvPFWXfNRV6bwdFP5L5qwWTtUmGtAp7lkB+x1hdz2rl9V3PauX1Xc9q5fVdz2rl9VYrsmie1zh93abunfK5wC7rtPJd12nku67TyQuq0V2K1Xa2KMva7YrtaGyhcVRYVRUVE4IktikpzV5yyNkGE8F2qX1LtUvqK7RJ6is+X1Fdqm9RVjkdJDLiPBWXcN6IBYVRUVFhVv38XVN2fmN6cFd3hcgvxKmtYQnEBYk0c1hCLhwVfgg0IhoWL4Ia+CwhOwhYk/XG7UrLvm6L03g6KfyXzVgz877Darx7XjGd9F9ovtF9ovtFYMeNlfZEJWSnMLfYtePPf1Xvr3176bm11K29syvtdn/AO2q796Fx04hpcn7mTr+6vTejonN0A00ObVXfuZeisu4b0CCqquKo5A10Xhv4uqGz8xvTgru8LkF+LQ99NDGcToe+uhraImic6qAqgKJzqImqa2qm3Z6Ky75qKvTeDop/JfNXfajZpa0rVWq+JQ7DGMPVd8Wrn9F3xauf0XfFq5/Rd8Wrn9FZLwnkc0OO32GeJUGKtdD9nsWm8Z2SuaCu9LTzXelp5rvS080L1tFdqtlvzIsIbSqu/ehcdFFhCwaCn7mTr+6vTejpoisE0+tgRuWem0KWCWzmjxosO5l6Ky7hvQIe1eG/i6obPzG9OCu7wuQX4k91NDGcToe+uhraImic6qAqgKJzqImqa2uiY+44fBWXfNRV6bwdFP5L5q6p44ZqyK3W6yFwBbjXa7F+j9V2uxfo/VdrsX6P1Xa7F+j9VZJ7O5zcMdPnpjZXWVQKg0TeAoWqZpqHFWG0dohD9E9qsomc10evqu02T9L6rtNk/S+q7TZP0vqm2qyA7pW+1wvgIBrVXfvQuPtFP3MnX91em9HTQ68JiwMBou0TDWHFTW+WePLegaKwbmXorLuG9AhoxrGE010Xhv4uqGz8xvTgru8LkF+JP2pjOJ0PfXQ1tETROdVAVQFE51ETVNbXQ99E7wO6Ky75qKvTeDop/JfNXVBHNNSRW6w2QOBLsC7JYv1vouyWL9b6Lsli/W+i7JYv1vorJBZ2ubhkr8tMfhRNFJfVmY6lV37ZleF+NkjLIeKLlcDqwO66LVZrKZnF0tDXkhBY/1vouzWT9X6Ls1k/V+ibZbITvVb7JCyAkClFd+9C46KLWgNDk/cydf3V7Gkg6BA1T7PIxuJw1aJLLI1uMjVou7cy9FZty3ohocQtWm8N/F1Q2fmN6cFd3hcgnOo5YdddD310NbRE0TnVQFUBROdRE1TW10PfTQ9v2buisu+air03g6KfyXzV32U2mWlaUVqueUuxRnF1Xc9q5fVdz2rl9V3PauX1Xc9q5fVWS7543NLhs0xHVRXm4tsr6KqLkXIlXTZjZ7MAdp16LVddpkmc5o1Erue18vqm3XauIXddp5IXVaK7FbLBlxYg6tFd+9C4+wNDk/cydf3V77wdAgaKzXnJGMJ1hCeXa2FWu1yzHC/V8E5qu3dSKzbkIaHALVpvDfxdUNn5jenBXd4XII+Jd5zLvOZd5zfBd5zfBd5zLvOb4LvOb4LvOb4LvOZd5zfBd5zfBd5zfBd5zLvOb4LvOb4J14zOFFZd83Rem8HRT+S+asGfnfYbVePa8YzvovtF9ovtF9orBjxsr7F4uPZnouUNpeKMa0H5AqMSstTIZ426/gE2x2dpqGD/wAabbj7Q+nNfaL7T4pjnlNza6lbe2ZX2uz/APbVd+9C46Sq6Cn7mTr+6vfeDoNFiJ7QynNTMthf9m4UUuLMOLbou/dSqzbkIaHrppvDfxdUNn5jenBXd4XII+JSWCJsZptCstijfGHO4qzWNjnvDuCfYmdoEY2FWyyRxsDmKSwRNjNNo0WGwGc4nbFbLG6zu+GiSwRNjNNoVlsUb4w53FWaxsc94dwT7EztAjGwq2WSONgcxSWCJsZptCsu+air03g6KfyXzV32o2aWtK1VqviUOwxjD1XfFq5/Rd8Wrn9F3xauf0XfFq5/RWS8J5HNDjt9iRgkaWu2FWm4pmn7I1CZc1uDq4afMKyXfIJc+0uxO9i1XpaY5nNadQK74tXP6Lvi1c/ou+LVz+ijvi0cSrZb8yLCG0qrv3oXHT0WHQU/cydf3V77wdBoNms7RGQ/btQggH/I/wD6UlMZoa6Lv3cqs25CGh5onatmm8N/F1Q2fmN67Arr8LkE7apLPaBDrf8AJWWCcx1a6lVZYJsZoaU2qSCbPDa6+atsUrQHPdUI2iUtwE6lYbCZjidsUcYYKBTQtlbQq2WN1nd8EbRKW4CdSssE5jq11KqywTYzQ0ptUkE2eG1181bYZgAXOqFJZ7QIdb/krLvmoq9N4Oin8l81dU8cM1ZFbrdZC4Atxrtdi/R+q7XYv0fqu12L9H6rtdi/R+qsk9nc5uGOnz+7tVpsjZnB0VT1Xa7F+j9V2uxfo/VdrsX6P1TLZYg4HJ+qt9tgkhLQ6pKu/ehcdNDVCvHQU/cydf3V7Csg6aLNEJZWsPFUspl7NlqVmVI5vJA1V37uVWbchDQ6lNaGHTeG/i6obPzG9tgV1eFyCdtUlotBh1s+ass84jo1taKyzzYzQVrtUtplE2MihCtFsdMMNKBWGwmY4nbFHGGCg0zQtlbQq2WN1nd8FBbnRMw0qrLPLjOEVqpJ5s8Opr5K2zTEAObQKS0Wgw62fNWXfNRV6bwdFP5L5+3ZX4MLl3qfSu9T6V3qfSu9T6V3qfSu9T6V3qfSu9T6V3qfSnX8QdTFNLmyF/P2rsdWRq4rZo4pmgp+5k6/ur1NJB0CIqhY7QzA8DbsWbbsO7FeqtNjni9+QIGiu/dSqzbkIaJECOWgq8N/F1Q2fmN7bArr8LkEfEpLfE6M02lWW2xsjDXcFZrYxr3l3FWydsz6tVhsJmOJ2xRxhgoPZmhbK2hVssbrO74KxWhkNQ7in21naBINgVstccjA1ikt0b4zTbRWXfNRCvNrKB3FT+S+eizxZsrWHih/8djP4im//HGO2E/Rfwy3mfov4ZbzP0X8NgfiK/hz+or+HP6iv4c/qK/hz+or+HP6iv4c/qK/hz+or+HP6iv4bHqK/hlvM/Rfwy3mfov4ZbzP0X8Mt5n6L+GW8z9F/DLeZ+i/hlvM/RWe58hwcFkyIawtiJTdDk/cydf3V77wdAmuonWmZzWjFs2LvC0+tG8cdmMT9btF27qRWbchDQXUVSNum8N/F1Q2flBNEbRyQtCbIHIyAI2gLtATZQU+ailwTanhWazthrhO1BHxKbJyDspwVhysnh8VLhxnDsVhszZTidsCjkiYKBNdiFfZc4N2qZ0UraFWuziF9AdSsmDNGPYrVC2ajRt/ZZbCwxt1KOOODwD5oknanBkjcLwprHjgyozxXdNoc/DSis10TxTNeaalH4FZeKktDIzQo2yIrtUa7UxdqYu1MXamLtTF2piFrYF2yNdsjXbI12yNG1xFdqYu1MXamIWti7XGn+FN2aNabocn7mTr+6vUVkHTQ11FZHxRyYpBUKUhzyWig0XbupFZtyENDgqEnXpvDfxdUNntyPwFCdqz2oSAqv8AOzOqaIlsY95NmY7gvDrCfzR0tkptRDFrY5cUfEpLBE2M02hWWxRvjDncVZrGxz3h3BPsTO0CMbCrXZY42h7Oai8Ps23dFWWxRvjDncVHdrWn7Qo2CCu0psLIm/Z6Kew2QhVbSqjtLaUVk4q9jRZiEhWMrGVjKxlY1jXvcl7/ACXv8l7/ACXv8l7/ACXvcl73Je9yReQoj9iiasQ0YENWhyfuZOv7q9TSQdAiMWhrqIiui7d1IrNuQhofsRNdN4b+Lqhs9gmizEHp7Q8J0Thpa8hGXDtQtAQdX+ZKd4laN4dDQcsBScvZY6ieEOCdtUlntAh1v+SssE5jq11KqywTYzQ0ptUkE2eG1181a4pW0c91QovD7Nt3RVkYYYfiUG11lYo14TUJ4ofaY7CnR01hWS0FjtavfRDvApT9oVPwX/R+ag46S4gBZjlmOTnnCCsxyzHLG5Y3JrjVS+MqLcrgm7NFdLk/cydf3V7bwdAhUIiqoU2oTmq7t1IrNuQh7V4b+LqhsVQga6JqnUEW1GEIOIWa5ZrtATdQxJxEYq7amzNeaEKP3TTTiCr/AC0jaFSR5vVMgw63I6tZRKAJ2LKdpDCUGnDQrinbVJaLQYdbPmrLPOI6NbWiss82M0Fa7VJPNnh1NfJWyaY0Dm0QtVqH/T+q7Xa/R9V2u1+j6rtdr9H1Xa7X6PqpLTaXto5ik2q0nWBoj1xhSeLQGk7EWOGkAnYmA01pupyvE/ZtVlYCSSmiPaAvcc7YrQ9rCAQswZdaKKQGupZrfSjrKrQBYxyWMckXDCNSxjksY5LGOSxjkmuFdil8ZUW5XBWi8DDJgou9neld7O9KF6k/hXeZ5Lvkl1MKZasNQW1qn2qN5q6MLOh/SCzof0gs6H9ILOh/SCdaIGipjCZbWua5rGUqo7wLGhtF3m7ku9Hcl3o7ku9Hcl3o7kn3wW8F242qeOopQp0nBY3KBydrCc92wojDQrGD4h7LdYwqaMv1jao2EuohrdoLlIfeQkdVNkr/ACr9i90qrWpxqmtxGic/gFrXjFDtTANpRcXKI66IbUfEpLfE6M02lWW2xsjDXcFZrYxr3l3FPtrO0CQbArXao5GhjOabsVFRUVFaN2Ux+bGHqSPMHxWQ+tKJoA1ck4pja6ynPJQLgnDEMSY3EUXcAoycSiYXOor1bhACsuxx0vYHs97gqRZW3UohHroVhh5o0rqWqgqvcXuINBA0OYCqM5qjOaAbXUpfGVFuVwVrsMU0pc5xC7rg9ZXdcHrK7rg9ZXd0PrK7ts/qK7vh9ZXd8PrK7vh9ZXd8PrK7vh9ZXd8PrK7ugP4ihd0A/EV3fD6yu74fWV3fD6yu74fWV3fD6yn2GBjcReaLslmkDsDzUCqsfmGdU7bpiaQNadGHKQ4dSLQRVuhrRSpWMclQO2aA/mtZ4qKmiZp26Am7UP5SXZpKj46Y9WtR6xRe6OKGEGtViCN3Rk+JPihczAoYoom4VDZ4YnFwTrNC6XMU8EU3wKxO9X0T8+lYyF/rfgv9b8F/rfgpe2YDWisDmCHV80xzJNbDVUcnHCKIputhGlnFRbUNSHiVjYNqvVpcQAjRowDQAmrIdlYVDA5tars7kRQ0WGoCyysspooEDoyisopsZBUvjKi3KpQKfx6GjEaLIcshyyHLIcshyyHLIcshyyHLIcshyyHLIcjC4Cum8H4LJX4q6iSZSfSVY/MM6p23Q00Kbs0S7U3U5PbhNE/gEQmmhUg97TBt0TGg0BN2ofykg1ew12E1TmcQgw8U53AJgo5Hb90Z8lgNKrvA+grvA+grvA+gqW34mEYCrLYo3xhzuKsEOB7wsR0tNCqB+xYHIAeFYXJzXHWmsNalWPYrZOC7VoCAqrVi1NCwuyKU4qztcMWrggx3LQRqCoUGko+6FjciXFYSsJTAaqbeFReXTvCp/Hoi8YTtugaT9w3wnQ51FePk/mrp2yf2lWPzDOqdtR0QuqNE21RipqpDVyOsYkTVMFSnmpQ0QbdE7q6tDVGNf8qU8UOg6Mtw2JwdxVExzuKO37psrI2DGV2uD1Bdrg9QXa4PUFNaoTGaOVkgmy6h1AVY43iUnltTxQ6DoY3EVh5OWWeao/msB21UWtyfNgbhCJroAU8uWKDap5ntpQrOfk4qqCZ7q1K7RJzWIk1KLiAE0uKc7Ci40CxuWNyzHLMcmvcSpt4VD5dO8Kn8eiLxhO26BpP3DfCU51NF4eS+aunbJ/aVY/MM6p41o6I3YSgaqUtB1oS80Wt5pri1VaeCc4jVSnsQBSPwhE10wj+WlZX2BRwqqj1LF/Ug8c1ljbVYW81hbzWBvNYG81hZzWFnNYWc1hZzTYgdhXZ2uFHCq7FD6V2KH0rsUPpU1khDD7qs0soZRra0VnllxmgrVRzGQljxQ+w04TVOpyXuckcAFaIvFKAJnuNqia6AEKN1lSTNDzVqmlaKVas1uVXCoZWmtGrPZ6ETUqGQyaqKSQRrtPwXafgu0fBdo+C7R8F2j+ldr/pT3YnVUPl07wqfx6IvGE7boGk/cDwu03h5L5q6dsn9pVj8wzqntqNBGiOTCptetMccK99e8tfNSnXTS1uIovDAnOLtACAqmCg/lnuoi5p2ot4jQKjYsddoVWck3CTSiMvCixN5LEOSxjksbeSxt5LG3ksbeSxt5ISgbAn2tsTQ567zg5rvODmu8Yealt0TmEVVntcbIw13BQWloc7FxQbikEp2JxY5FnEaWvLVmDkjIDwQbXWVLtVEAhTaVije8GqkbDjNSpmxasRVIsrbqULYteErDBzUcOY6g2KSRsDcLURVmYSo2Y6pgxOATxhdRSMw0WD3MSjZiqiNEPl07wqfx6IzR1VRh1oBiwtWFqwtRaxYWc1hZzWFnNYWc1RnNUZzWFnNSPYwFum8PJfNXTtk/tKsfmGdVwUjKaCNDDX3Si2hoUwAcVh+KxNbsW3QBVFwZqGkDRGz+XlOtOTXYSi1qwDmsv4rAOaZhrqR2/dWaMPbrXZ2Ls7F2ditUTWxlWTLyuHxVkysx30T9Z0NdhQa1yMfxWX8VgHNVYE55cgCW69GoCrlLNmGnBReMKbxlWj8PRf9D5qz/i6KGEyFSSNgbhauzvf71VkOysKhgc2tUyBwcCnwOLiVLEXUWScvCooi2q7O5ObQ0UW4TvCp/Hoi8YRlKzCswrGVmFGUrNKzSs0rNKzSs0rNKc7Ex1dN4eS+aunbJ/aVY/MM6obERVPj5aCEwU95B/ByywfCVluWErA5CPmnnC3VoogEBVMj/l3GgT9YqjoHvNoqquiPxI7furHs9i2boqz2SN8Yc7irLZ25hr+FO160RpEjgszmFVnJe4sTBsTXkuUFnEh2q9IxGKBAE7EGO5LA7kst3JZbuSis7nlPdlNowIkk61gdyWF2RSnFWdrhi1cFExweNSkY7GdSmaTRYTkqFpFVgdy0RblfhU/j0ReMJ23QNJ+4HgdpvDyXzV07ZP7SrH5hnVDZpdGCjGQpD+HSHlZhWNVRGMLCgCmxVQYB/MTFDWKaCEDRan9UQRt0NNDVDCddECOS1clq5KreSq3kqt5KreSq30qNrXmlFFGGbPYtDC9lAm2CeMUDlDBkNNTtTSiKaMPt2XEX6lfCsm0rMcmOJKGNNx11p78Ka9xKm3hQnkGqqzn5OKqgme6tSop5C8AlSTvDyKqWRwpRZrsqtVFI41qs5/NE1UW5XBT+PRF4wnbdA0n7geB2m8PJfNXTtk/tKsfmGdUNnsPNAia+03RjKxlNOr+ZkOtA0Thx9gP4ORj9Kj8SzHLMcsZWY5ZrlmuWa5ZrlmuWa5WU1Hs3heGX7rdq1gAHQHUWEHYsJ0UWFYVhWFWX7N4V7qyeIrGOSjIJ2IjQXiuxNeK7FNvChM0DwrNblVwqGVprRqjmaXAYVJM0OIwqaRop7qzBl1oopAa6lmt9KOsqLcrgp/Hoi8YRjdVYHLA5YCsDkY3LLcstyy3LLcstyy3LLci0hjq6bw8l81dO2T+0qx+YZ1Q2exOdXtj2Ijq/mCnN1r3UHNCLeI0U0A0Ql5rE3knih+5sez2LwvDL91u1R1fKK8SnN1r3V7qw12L32rNKzPgsbeSxNVWIAJlA7ar0Vl8RVGc1HhB1aCjhTQyqm3hQbDTWVSLK26lC2LXhKjbDiFCpGw4zUqZsWrEVSLK26lEI9dCsMPNGldSi3K4Kfx6GCrqKrQKVXu81VvNVCxN5olvNVbzVW81VvNVbzVW81VvNVbzUrQ4FwOm8PJfNXTtk/tKsfmGdUNnsTAn7ihWArCVD/MHYn7dIJCxByIoiNLTQp44jRRUVFRUVFRWZ2Ea9N4Xhl+63aiS41KsGDLGHbxT9vsB7gsyu0L3CqM5r3AsymwIuJ2qLxK8/CFZtpWBMbQoB44poOKpKMZQZQqbxnR/0PmrP+LoofGFN4yrR+Hov+h81Z/xdNMW5XBT+PRF4wnbdA0n7geB2m8PJfNXTtk/tKsfmGdUNnsTOIdqWZ6lRh2FZRWW5ZZ4qjAsymwIvcqrEQonVP8wU/wAWjLKyyiE15CFHbEQqJrKlUI1Be8veX2i+0X2i+0X2ieXhhKNrlJBJ2KxW0SihV4Xhl+63aiS41Kfd8YjPMKxWVmFsh2p/i0ZZWWUR7cIq5Xq3gEGO5LA7kjG7kst3JZbuSy3ckGO5LA7kst3JZbuSy3cllu5LLdyWW7kst3JZbuSDHclgdyUe5XBT+PRF4wnbdA0n7geB2m8PJfNXTtk/tKsfmGdUNnsT+L2Kn2KHTBt/mZhQoe6NJ94aRJzVWJ0hOiEcU2V8b6tKjvFjt4KJ9tja33NZTrXK52KqMjy7FXWnzPk8RRnkLcJOpE0TZHNdiaiS41Og2iQtwV1Jk8kYo0oPzGB6HujSfeHt2JmJ6dHG81KMEfJZLOSbDHxCyIuSyIuSyIuSMEXJZLOSEMfJZEXJZEXJZEXJZEXJZEXJZEXJZEXJZEXJZEXJZDOWifx6IvGE7boGk/cDwO03h5L5q6dsn9pVj8wzqhs9i0Dj7YAbrKxlYuac2igH8y9tU7bpZtRR9hrcRTacF+Ndmir4AuzRelOhhH4VlxelNgjP4V2aL0p0MPpWTD6AhZYfSF2WH0hZMPoCEMR/AE2NrW4QE7bpZt0H2GtxFS2gwR4mLvqTl9V31Jy+q76k5fVd9Scvqu+pOX1XfUnL6rvqTl9V31Jy+q76k5fVC+JCu95F3vIu95F3vIjfEo4fVd9Scvqu+pOX1XfUnL6rvqTl9VZ72fNIGLtr092I10NdhNUHBzcVFjHJVHJVHJVHJFw5LGOSxjksY5LGOSxjksY5IOHJTTYasA03h5L5q6dsn9pVj8wzqhs9iUAiiIofYCaKlOOvS0VFE0UH8y7Yjt0s26DoohEeKLqagoTrTW+/oc6mhjK6Hv4DQxlNDnVQFU1tNB26WbdB0UQiPFF1NQWHG1zOastiErcTim2Gs5jJ2KexYHtDTtVosAjjxNOxR3aHRgk6yrLYhK3E4pthrOYydinsWB7Q07VaLAI48TTsUV31YDi1lWayiVuJxTbJWUsJ2KayYHNDTtU9jDGYmnYmWAOZt1qz2ESjE40TbDWcxk7FPYsD2hp2q12IQxGRprRXSHOtQeVlMWSxZLFlMQa0CiwtVGrUqNWFpWBqwNWBqwNWUFlhYAnwNe6q7KxdlYprGyaLKJVmu2Oz4qE6xRRXZFG8PBOpZizFmLMTzjRAI1rLastqy2rLCDQFQLCEWKPb/MnYg8l1PYj4lVZyVWckC3knOI1aCo/Ei6hWaVmFZhWaVmlZhWYVmlMeSVmlZpWa5MeXH2I+JWJnJNwONKITtwYqcaJziNWgqPxKztwscPim+bf0Vo8cXVWvcvUWxvRWLcnqm+bf0Vo8cXVWvcvUWxvRWLcnqm+Zf0U3ij6q07p6j/D0Vl3XzTR/qn9FaXAPh6q2DFBICruFJwqoLHrWPWisSxLEsSxLEqomgQdqQOJEV/lRqTdev+ZKMeHX7EIrVZAWQFkBSbdDkNqklY11CEWtCDGlZbVhaqNWW1ZbVRqbhrqWU1GNoWFiaG8PYi2FYWKSaOFTn/R1+KriAdochtRbhxdf2TPPydP8K17yDqrf5aVWfws6K7fLH+5M8/J0/wAK17yDqrf5aVWfws6K7fLH+5M8/J0Vp8cPVWzcSKHYzorDuD1TfOSdFaGYnwnkVatzIru34XzTq1ROugQJrrTa1XzXzXzXzXzXzQTa8VV3BHWNS4Ap9AVq5LVyWrktXJauS1clq5LVyWrktXJauS1clq5LVyWrktXJauS1clq5LVyTCKofzMmzSVZ/Yl26HaLTvFh16HO0NboJroAoiaImqAqtg0lRbCjQq8jRw6KfyXzVmdis7D8NDtEvhTR/rZOite8g6q3+WlVn8LOiu3yx/uTPPydP8K17yDqrf5aVWfws6K7fLH+5M8/J0/wrWftIOqtm4kUOxnRWHcHqmg9rkPwUm2JWncyK7t+NDiQiCDUIAk1TST7YTSTqK94alraFwTjr2rXzWvmtfNa+a181r5rXzWvmtfNa+a181r5rXzWvmtfNa+a181r5rXzWvmm1rtQ2fzMmxYSsJTmkBWf2JtBYaLLKtTDqcu204Lt55Ltv9K7d/Su3nku3H0rtv9K7d/Su3nku3f0rtv8ASu3H0qKcy11LCVhKc0gIy5UZcnXp/SppXWt4whT+S+asbHx2ZrX7dBYaLLKtc8sAxBtQmW+Rsxl5q1W18zmuGrDsVqvWSePLpTmo74lZFgpr5qx3k+zNLaVCZeUrZzNzVpvGSd7XbMOxWq9Xzx4KUUd8SsiwU181Y7yfZmltKhMvKVs5m5q03jJO9rtmHYrRe0kzMFKKK9niLDTXzVhtT4gRSoTbU8SZiltTpHA7KKa2OkbhorC2k4WtCqwmqwmqNVrWta1rWta1rRBIQBogKbV8f5WiAohs/k9S1LUtS1L3U9tdibZy12KqZZy01TbMW1+KiiwKgVAqBSRYihAQm2Yh2JNsxa7EhZSK/Fd3Bd3Bd3Bd3Bd3Bd3Bd3Bd3Bd3Bd3Bd3Bd3BRWTL2JtmLXYkyzFpqm2Utr8U+x42YF3Q3mhdQGsFPuwOiyqqKyGNmCtULOQm2Yh2JNsxa7EuxnWDxXcLOZXcLOZXcEfNdwR813BHzXcEfNdwR813BHzXcEfNdwR813BHzXcEfNdwR80LhjHFd0N5rukc13SOa7pHNQ3dlOxBZDlkuWQ5ZDlkuWQ5ZLlkOWQ5ZLlkOWS5ZDlkuWSVklZBWQVkFZBWQVkFZBWQVkFZBWQVkFZBWQVkFZBWQVkFZJWSVlJtnIdiTQKKgVAqBUavdWpalqWpavyaqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq/8A3rJotblhCaa+wDXRirsWFA8EUDUIGv8A3UdqPiTjQIHCEDVYgqpu1O2I6hobrKOxN2Jmz/us+JeIra5fFAakzambSiKp+xE8EBQLaE2tEDQJpQ160TqR1puz/uUqmtM5KnvI8ghsombUzQ6vFUoFX3U3wpuxNCG0oeFO2L8KZs/7mprThTWtRQFFQBN2oAg6HCqKoUzYgKJoohtKwpwqjsTdn/dGEfcEnYgKD/65/wD/xABOEAABAgIECgUJBgUCBgIDAQEBAAIDERIhMXIEEBMyMzRBUXGRICJhgZIjMEJSgpOhorEUYnODo8FAUGDR4UOyBSRTY8LwcPE1lKAV4v/aAAgBAQAGPwL/APq2aHMc4u3LQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5oRmAgHYf6ig3UHxIbAJWlUmw4ZC0LFoWLQsWhatC1aFq0LVoWrQtWhatC1aFq0LVoWrQtWhatC1aFq0LVoWrQtWhatC1aFq0LVoWrQtWhatC1aFq0LVoWrQtWhatC1aFq0LVoWrQtWhatC1aFqGUZDbPeqUOGAZ2hCJFgtc6kayjkYcF8rZLVmLVmLVmLVmLVmLVmLVmLVmLVmLVmLVmLVmLVmLVmLVmLVmLVmLVmLVmLVmLVmLVmLVmLVmLVmLVmLVmLVmLVmLVmLVmLVmLVmLVmLVmLVmLVmLVmLVmLV4a1eGtXhrV4a1eGtXhrV4a1eGtXhrV4a1eGtXhrV4a1eGtXhrV4a1eGtXhrV4a1eGtXhrV4a1eGtXhrV4a1eGtXhrVoa1Zi1Zi1Zi1Zi1Zi1Zi1Zi1Zi1Zi1Zi1Zi1Zi1Zi1Zi1Zi1Zi1Zi1Zi1Zi1Zi1Zi1aGtXhrV4aGVZBZOyaMSFBa11IVhC8f6ig3U2G4kVBZNsz/K2lziJblIbCFRNhJT3Me5xdv/AJRMfzRjnvc0t3KiLAWoXj/UUG6m8AnHsWe7ms881pHc00ncnEbk7JvcZVmblnfOgIjzXucs8+JZ58Szz4lnnxLPPiWefEs8+JZ58Szz4lnnxLPPiWefEs8+JZ58Szz4lnnxLPPiWefEs8+JZ58Szz4lnnxLPPiWefEs8+JZ58Szz4lnnxKG0vdbI14iWkivYph7uazz4lDBe62uvF3hC8cT4EOI6ZdJopJojRXAmsSeg9ryHGjMhZaDENGyuIs/9RMgRIjph0nCkoLWvc0SJqKDhEqP/cRcYlQ/7ijNc9zhIGsqKYUV4LXSAQZhbaJ9dtipw3BzTtHm8m2I4CiJAFaQ+NaQ+NaQ+NaQ+NaQ+NaQ+NaQ+NaQ+NaQ+NaQ+NaQ+NaQ+NaQ+NaQ+NaQ+NaQ+NZ58azz41nnxrPPjWefGs8+NZ58azz41nnxrPPjWefGs8+NZ58azz41nnxrSHxrPPjWefGs8+NZ58azz41nnxrPPjWefGs8+NFzohkPvrqxHeJUDEdKRmJ+bpxHBrRtKLMEbSPrusUIxIryXGRUFrXuaJE1FBwiVH/uIuMSof8AcUZrnucJA1lPgQ4jpl0miks/9RZaNENGyqIi9zyXClIlOEKI4kVmb0yBEiOmHSd1sRvBC8eg8i0NKe2BEJc2s0nrOHvVIuHvVn/qKt/6iz/1FU/9RZ/6ikHD3qzh71Zw96s4e9WcPerOHvVnD3qzh71Zw96s4e9WcPerOHvUXlwkBPSqK1z3OEp1n+iIN1N4BO4KoTWYqxJMuhPulRrqpNsUO6h/GMvYjeCqbNZihn72LvCF44h+KoFwpvsoXzi/NUG6mXQn3So11H8VFwGTf6zVThk0N7c3vVHCRknb/RVJpBHZ5kC6rWq1qtarWq1qtarWq1qtarWrOYrWK1itYrWK1itYrWK1itYrWK1itYrWq1qtarWq1qtarWq1qtas5itYrWK1itYrWK1itarWqK4ltQTz2o8HeZpOIA7VRwYZV2/0VTiE0N7s3uQcRlH+s5D8VQbqZdCfdKjXV+biN8J3tKPcCP4uI3ghePQiXSsJuD69AYj56NcP0Ua7/REG6m8AncCjNWoSTLoT7pUa7ihS2tQ/jGXsRvBSKtTD97F3hC8cQ/FUC4U32UL5xfmqDdTLoT7pUa6j+LjpQ/JP7LFVOhzaVRjeRf22eYb7OOUSLDYfvOktZg+MKcN7XjsM15SIxl4yWswfGF5OIx90zVF8aG07i5ZQPaWetOpANwiESdgeFSc4NHaVp4XjCkyKxx7HKbiAO1aeF4wqLYsNx3Byouiw2ncXLTwvGFp4XjCFKKwTrE3LTwvGFp4XiCovisadxcqbXtLN80GtwiESdzwqT3taN5K1mD4wqMONDcdwcqL48Jp3FwWswfGFrMHxhBzorGh1k3WqcKIx90zWnheIKb3taO0rqRGO4FSfEY09pWnheMKbXBw3g4o11P4o8HeYowfLP7LFXOhyaFSieVf22Yx+KoN1MuhPulRrq/NxG+E72lHuBfm4jeCF49CJdKwm4Pr0BiPno1w/RRrv9EQbqZwCfdKOIJl0J90qNdxQbqB/jGXsRvDGy9i7wheOIfiqBcKb7KF84vzVBupl0J90qNdR/F6EiJhUoByTt2xUXTobnVtQa45KJud0m+zjjzM+uVlHwnBm9Udjm1qLM2GQWWMJ2TtmoYBtnPkn8B9FhcE+ixxHJQL4UMA1UJy71lIcMlqY4W0lCGytThwy4LcQnPbCJBl9EWuEiEHNhOIKwdpqIhBUocMuCHkXI3QsIgn1SVA3ZRv1UJk6qM1ShQnOFlSY5pkQVFeyC5zTtRa4SItQc2A4g1hYI02gfsFH4hTCgP3mtRLqiONtIqm+GQ1PbsoYo91P4o8HdItacrE3NVFs6G5tTVSjnKu3bFICQ6A/FUG6mXQn3So11fm4jfCd7Sj3AvzcRvBC8ehEulYTcH16AxHz0a4foo13+iIN1MuhPulGasQkmXQn3So13FBuofxjL2I8QplWJg+9i7wheOIfiqBcKb7KF84vzVBupl0J90qNdR/F6VF7Q4bii7BnUT6psWSiglnqu/YoBrqET1HdBvs44/4jvqhDhCbiGrLRCyywKNeX5bVC7/oU/gPoom58JzfgoF8Jn4f7lOl6xTXuos7CVC70+8n3kFFUK6mXU6/+wxG6EdxaWlQb4+qhnZQT/wAT9gm8cUe+VA/Db9FA4lR+IUwqE7HzUS6n3kGQxM1IxIhbW2Uhij3U/ijwd0JF1KJ6rVkoQIZ6rf3KDsJdSPqixUWNDRuHSH4qg3Uy6E+6VGur83Eb4TvaUe4F+biN4IXj0Il0rCbg+vQGI+ejXD9FGu/0RBupl0J90qoyWeqzNMuhPulRruKDdQ/jGXsR4hVOks9Mn62LvCF44h+KoFwpvsoXzi/NUG6mXQn3So11H8XzFCI0Ob2qngrvYKyWENL2jY63mspCnuIOzE32ccf8R31TLoxRry/LaoXf9Cn8B9MUC+Ez8P8Aco3zihd6c2I4gl25OI3oKKoV1MuqTXuaOwqTnuIo7SjdGKDfH1TAfUTmRXGt8xUm8cUe+VA/Db9FA4lR+IxTCiEeqn3lk2NaZAWosc2i4V1Yo91P4o8HYqcWfYBtWSwdpaDsbaqeFO9gKhDaGt7PMD8VQbqZdCfdKjXV+biN8J3tKPcC/NxG8ELx6ES6VhNwfXoDEfPRrh+ijXf6Ig3Uy6E+6VUJrMVYkmXQn3So13FBuofxjL2I8QqmzWYmT9bF3hC8cQ/FUC4U32UL5xfmqDdTLoT7pUa6j+L5inEcGt7VQwVvtlZXCHFjTtdbyWThT3knbib7OOP+I76rKQnUXSbWhlIr3dU2lRry/LaoXf8AQp/AfRYPE9dp5gqBfCZ+H+5Tj94plOO/OG2SgEbiqcKUhUZlEHYgoqhXUy6nTaD193YqmgdyN0KFE9af1UG+PqmXUXMALZyrKbunij3yoH4bfooHEqPxCZeCiQvVdJRbqeR6xRwibqRlwTrn7jFHup/FHg7FQiz7CNiyuDuLgNrbVQwpvthU4bg5vZ5gfiqDdTLoT7pUa6vzcRvhO9pR7gX5uI3ghePQc3eJI5NwF18k1saM+Z3PQOVdX/3FXFPvFpHeNVxHeNaV3jWld41pXeNaZ3vFpne8Wmd7xaZ3vFpne8Wmd7xaZ3vFpne9Wmd71aZ3vEBEjxK9z0DlTIj/AKiiPjUesJCR/oiDdTLoT7pRmrUJJl0J90qNdxQbqH8Y29iPEKRVqYfvYu8IXjiH4qgXCm+yhfOI/iqDdKZdCfdKjXUfxelSe4NG8otwZtI+sbFlYpIZ6zv2CBa2nE9d3Qb7OOP+I76rIPoUarAvZKjXl9lNChKVihd/0T+A+igv2scT8SoF8Jn4f7lGFDoUSdoUO8FA4FRL/wCyfL1kFFUK6mXVQh0ZTnWEKofJG6EDtY6ag3x9Uy6nX/2CAOKPfKgfht+igcSo/EJl4IRNj2/FRbqiXjMKgaNHgnXP3xR7qfxR4O6Ey2i/1mrKQnEt9Zv7hBuEton1hYqTHBw3jpD8VQbqZdCfdKjXV+biN8J3tKPcC/NxG8ELx6BcbAqoETmmOawtoiVaDck6ob0PJOWjKnQKzCpZMrRlaF3NaF3NaF3NaF3NaF3NaF3NaF3NaF/NaF/NaF3NNLWlshtTW5B9QlanBrHNLd/9EQbqZdCfdKOIJl0J90qNdxQbqH8Y29iPEY2XsXeELxxD8VQLhTfZQvnEfxVCupl0J90qNdR/F6EyZBUYAyrt+xUnTob3VNQc4ZWJvd0m+zjykSH1t4K0Z8RRyLJTtVKLDm7fNaM+IomFDkTtRixWEuPavswb5OUpJsRjDSaZjrICMylKxaM+IrKMh9YWTKaYzSaNlaLYIkCZ2ovoGZ+8qkXvaaR7UGNsCDojSSO1Zh8SzD4lTiNJPFZFo6iDwwzBmK0HRGkkdqoQxITmsw+LE6I5hJcZ5yaxtjRIJojNnRsrThBbKlbWgRDMx94oCM2crK0XQWkE9qyj4fWO4qYYfEV5Jsp24o91P4o8HdIuAyb97VSaTQ3itqoxxknb9imDMdAfiqDdTLoT7pUa6vzcRvhO9pR7gX5uI3ghePQiXSo7cIhNiAMEprUoSqwSGtUhoSwWGtWYjPBYa1SGq8EhrUoS1KEtShLUoS1KEtShLUoS1KEtRhLUYS1KEojhgcMEMKjXf6Ig3Uy6E+6UZqxCSZdCfdKjXcUG6h/GNvYjxCmVYmD72LvCF44h+KoFwpvsoXziP4qhXUy6E+6VGuo/i46MPyr+yxVTocmhUo3ln9tnmG+z/Ko91P4o8HeYpQvJP7LF9zm0qjE8k/tsxj8VQbqZdCfdKjXV+biN8J3tKPcC/NxG8ELx6ES6VhNwfXoDEfPRrh+ijXf6Ig3Uy6E+6VUZLPVZmmXQn3So13FBuofxjb2I8QqnSWemT9bF3hC8cQ/FUC4U32UL5xH8VQrqZdCfdKjXUfxUWg5R/qtVCGDQ3Nze9UsJOVdu9FUWgAdnmW+z/Ko91P4o8HeZkQCFSwc5N27YqDwaG51ncg0nJv8AVch+KoN1MuhPulRrq/NxG+E72lHuBfm4jeCF49CJdKwm4Pr0BiPno1w/RRrv9EQbqZdCfdKqE1mKsSTLoT7pUa7ig3UP4xt7EeIVTZrMTJ+ti7wheOJ8eHDdMOpNNFNMaGSRUJMVFrSSKNQWRgs6ttbFmD3aZHiQ3TLqTjRUJzWOIlKoIAMqH/bRBZUf+2or3Mc0S2hRRChPJcZhB+FupH1G2KhDaGtGwebb7OONfP1Tr/7BQOJUG8n3Tig8P3TPw/3Kf+H+4Ubh++Jl0KNeUfiEz8T9ioH4jfriJG9Aj1iod1N71GuH6YoNwfRO7lEuo3ghijXz9U6/+wUHdMqDeT7pxQeH7pn4f7lP/D/cYo91P4o8HebovaHDcUX4I6ifUdYoQiwngtdMlQXNY5wkRUEGiHUP+2i0w6j/ANtRnOY5okBWE+PDhumHUmmisz9NZGNDNG2qGixzSHGlIFOMGE4E1GbEyPEhumXUnGjiN4IXj0Il0rCbg+vQGI+ejXD9FGu/0RBupl0J90ozVqEky6E+6VGu4oN1D+MbexHiFIq1MP3sXeELx/iW+zjL3Qpk15xVGE2iLUGxmUgO1CIyDIiw0iiDYVoPmKEOGJNFipxodJ0pWlU4MOi6UrSjDiCbTatB8xQAsCMR8Gbjb1iiILKM7a1QjNpNnO1Ne2DJzTMdY4p5D5iqEFtFu6aDojKRHaqcOHJ3EotdYaitF8xQa2wVBU4kObuJRMJlGfaqEQTatF8xxFzoVZrPWKowm0Rag2MykB2oRIcGRFhpFEGwrQfMUIcMSaLFTjQ6TpStKpwYdF0pWnFGup/FHg7+JN4IXj0Il0rCbg+vQGI+ejXD9FGu/wBEQbqZdCfdKOIJl0J90qNdxQbqH8Y29iPEY2XsXeELxxUIkZjXblrDFTe4NaNpWsMWsMVCHGY525DKxGsnZNawxawxHJRGvlbJZHKtynqz8632f5VGup/FHg7zuSEVuU9WaBjRWsnvWssWssRyMVr5WyVCJHY125ayxayxCJDcHNO0LWYaoQ47HO3YjeCF49CJdKivfDc+m2VS1SLzCn9lic1q0TmtXic1q7+a1eJzWrROan9lic1qkXmFqkXmFqkXmFqkXmFqkXmFqkXmFqkXmFqkXmFqkXxBapF8QWqRfEE9n2WJ1mkWqNd/oiDdTLoT7pRmrEJJl0J90qNdxQbqH8Y29iPEKZViYPvYu8IXjidAJID4kpqGxkQvpCdab7KEd0ZzTOUgFrMTkmwASQyJKahXU12WfWJ2Jzss+oTsUW6okNmcX1LJR2lwGx1vNeTfJ3qm3zbfZxPiynRE5LQM5qToLZcVom81om81oW81oGS4rQM5rQM5rQM5rQM5rQM5rQM5rQM5rQM5rQM5rQM5rQM5rQM5rQM5rQM5rQM5qYhN5rRN5rRN5rRN5rRN5rRN5rRN5rRN5rRN5rRN5rRN5rRN5rQt5rQMlxWgZzWgZzTWugNkTKo4o11P4o8Heb67pu9UWrJQGloOxtvNQ4b84PrUK6muyz6xOxOdln1CdijXU6ASQHxJTWsxOSMdsZzjOUiE72lEY+IWUROpNgAkhkSU8RvBC8ejq0NQhBhhgLdiaTBbYhKC1aIKqGFmBDyYWiC0DVoGrQNWgatA1aBq0DVq7Fq7FoGqGIbA2Y2JhODsnRRyMNrJ2y/oiDdTLoT7pVRks9VmaZdCfdKjXcUG6h/GNvYjxCqdJZ6ZP1sXeELxxD8VQLhTfZQvnEfxVCupl0J90qLdX5ioRWBw7VTwV0/um1ZPCWl8t+cFOE+va3b5lvs4o11OmBasnDAntKkc1SapSmpFoVCGBxxUWqUgTvWaOSycMCe04qLVKQJ3rNHJZOGBPacVFqlIE71mjkqMICe1TCotUpTWaFQYBPacVFqlKazQpOaCKLrVJrQBRFisCkWhWCSsCZxGKNdT+KPB3mZxXyOwbVksGaWA7s4qnhTpfdFqoQmBo7F+YoV1MuhPulRrq/NxG+E72lHuBfm4jeCF49B5FoaU9sCIS5tZpPXWoHjEUpt94rR7xV0fGvR8aqo+Nej41IUfeK1vvFa33itb7xWt94rW+8VrfeK1vvFnN96rW+9VrferrZM8YiLi4SaJ6VRWue5wlOs/0RBupl0J90qoTWYqxJMuhPulRruKDdQ/jG3sR4hVNmsxMn62LvCF44h+KoFwpvsoXziP4qhXUy6E+6VFur8zHRisDllMFeXS7nBZPC2F4G30gqUGIHft02+zijXU5jdu3FRaqFvbioMt2lSKotrmpC3acWThmvacVFqkLdpxZOGa9pxUWqQt2nFk4Zr2nEBDtXVt24qDLdpxUWqQtxUGW7ShdK9kY5FdiZxGKNdT+KPB3TpRXhqyeCtLZ7fSKymFOLJ97iqMJgbj/MUK6mXQn3So11fm4jfCd7Sj3AvzcRvBC8ehEulYTcH16AxHz0a4foo13+iIN1MuhPulGatQkmXQn3So13FBuofxjb2I8QpFWph+9i7wheOIfiqBcKb7KF84j+KoV1MuhPulRbq/M6PlWV+sLVlcFe5wG1ucqGGMn98f2VOE8Pb2dFvs4o13FRapC3acWSaeJ3YqLVR5rsWThmvacVFqkLdpxZOGa9pxUWqQt2nFk4Zr2nFRapC3aVMKjDNe3sxUWqQtxUGW7TiF0r2R0JFM3TGKNdT+KPB3RpxXho7VQwVsvvFZXCXFgPrZy8kyv1jb0fzFCupl0J90qLdQ/FxG+E72lHuBfm4jeCF49CJdKwm4Pr0BiPno1w/RRrv9EQbqZdCfdKOIJl0J90qNdxQbqH8Y29iPEY2XsXeELxxD8VQLhTfZQvnEfxVCupl0J90qLdX5nT67ZO9YWrK4O4kD0m/uEGYW2X32qnDcHNO0Y2+yphRrqotUhbtOLJwzXtOLJ2j6KrniMNveuxUWqQt2nFk4Zr2nFRapC3acWThmvacVFqkLdpxZOGa9pUwuopC3FQZbtOKi1AfdK9kdFoO8Yo11P4o8HY6URwa3eUW4K2f3ysrHcWtPpO/YLqNm71jb0/zFCupl0J90qLdQ/FxG+E72lHuBfm4jeCF49CJdKwm4Pr0BiPno1w/RRrv9EQbqZdCfdKM1YhJMuhPulRruKDdQ/jG3sR4hTKsTB97F3hC8cQ/FUC4U32UL5xH8VQrqZdCfdKi3V+Z5kulQf6wRbBwgG6akXvYGuaZVWHE32cUaXqp/HFk4Zr2nFRapC3aVI2Kgw8TioSmpCvtxZOGa9pxUWqQt2nFk4Zr2nFRapC3acWThmvacVFq6tu3tUwqDLdpxUWqQtXsuXsjot4jFGup/FHg7EHMZSLjKuwINi4QBeNSDpU3+sfM/mKFdTLoT7pUW6h+LiN8J3tKPcC/NxG8F9mLpRJkyO3oRLpWE3B9egMR89GuH6KNd/oiDdTLoT7pVRks9VmaZdCfdKjXcUG6h/GNvYjxCqdJZ6ZP1sXeELxxD8VQLhTfZQvnEfxVCupl0J90qLdX5nmJF1J/qtWSgtIb6rf3KD8LdTPqixBrGhrRsGJvs4ot1Pc3esnDNe04qLVIW7TiycM17SqJsUmqQtUiqEPniotUhbtOLJwzXtOKi1SFu04snDNe04qLVIW7TiowjXtUwqLVIW4vZcvZHRbxGKPdT+KPB2Ki9ocNxRdgzqJ9U2LJxWkt9V37FSDqL/Vd5j8xQrqZdCfdKi3UPxcRvhP8AaUe4F+biN4JkYP6xdKW1fZ458qLD62OJdKwm4Pr0BiPno1w/RRrv9EQbqZdCfdKqE1mKsSTLoT7pUa7ig3UP4xt7EeIVTZrMTJ+ti7wheOIfiqBcKb7KF84j+KoV1MuhPulRbq/M6XlH9b1Raslg7S0HY21U8Kd7AQbCaGjs6DfZxRbqLRtVJtqotFakLdpxZOGa9pxUWqhb24qDLdpUiqLa5qQt2nFk4Zr2nFRapC3acWThmvacVFqkLdpxZOGa9pxAQ7V1bduKgy3aULpXsjot4jFHup/FHg7oUIjA5vaqeCu9grJYQ0uA2OtXk39b1Tb0vzFCupl0J90qLdQ/FxG+E72lHuBfm4jeCGVFtTTuWUwV9ldWwpkXb6XHE5u8SRybgLr5JrY0Z8zuegcq6v8A7irin3i0jvGq4jvGtK7xrSu8a0rvGtM73i0zveLTO94tM73i0zveLTO94tM73i0zverTO96tM73iAiR4le56BypkR/1FEfGo9YSEj/REG6mXQn3SjNWoSTLoT7pUa7ig3UP4xt7EeIUirUw/exd4QvHEPxVAuFN9lC+cR/FUK6mXQn3Sot1fmdCcV4HZtWSwVpZPdnFU8LdR+6LVRhMDR0m+zii3cc2QzS29qzCpMBb245BhntKzCqABA346NA9qzDJSYC3txyDDPaVmFSYC3txyDDPaVmFSYC3txyDDPaVMMK8mJb8QjuaXCREgsu1haJCorNKzSs0rNKae0Yo91P4o8HdKjFYHBU8EdS+6bVksKaXy35wU4Twezb0PzFCupl0J90qLdQ/FxG+E72lHuBfm4jeCYACHidLtX2cwZGVo3709rml7XbloH81oH81oH80xzWFtESrQbknVDeh5Jy0ZU6BWYVLJlaMrQu5rQu5rQu5rQu5rQu5rQu5rQu5rQv5rQv5rQu5ppa0tkNqa3IPqErVoH81oH81oH81oH80IzAQDsP8AQkG6mXQn3SjiCZdCfdKjXcUG6h/GNvYjxGNl7F3hC8cQ/FUC4U32UL5xH8VQrqZdCfdKi3V+ZipRXhoWTwVpb27VlMKcWT31uKlCYB27fMN9nFI1haFq0LVoWqf2di1di1di1di1di1di1dilkGyWhatC1SMBi1di1di1di1di1di1di1di1di1di1di1di1di1di1di1dimIDFoWrQtWhatC1aFq0LVoWoObBbMYo91P4o8HeYlFYD27VlMFcXy3VOCyeFNLu3aqUJ4cMX5ihXUy6E+6VFuofi4jfCd7Sj3Aj+LiN4IRI+Dse+kaytThrU4a1OGtThrU4a1OGtThrU4a1OGtThrU4a1OGtThrU4a1OGtThrU4a1OGtThrU4a1OGtThrU4a1OGtThrU4a1OGtThoxIGDsY+kKwheP9CQbqZdCfdKM1YhJMuhPulRruKDdQ/jG3sR4hTKsTB97F3hC8cQ/FUC4U32UL5xH8VQrqZdCfdKi3UXvMmiJWqGCN9orK4S4sB2utXk2V+sbfNN9npzH8jj3U/ijwd5ryjK/WFqyuDOLwNrbVQwtvtBB7DNpiVKFdTLoT7pUW6h+LiN8J3tKPcCP4uI3gm33fxrr7ULx/oSDdTLoT7pVRks9VmaZdCfdKjXcUG6h/GNvYjxCqdJZ6ZP1sXeELxxPjQS2RMxXJdcg8XrIMIpCVqk10h2PWk/UTI0YtkDM1zUN0KjUJGazv1FnfqKI6LRrEhJPfTaIbjOam1tJ/rO85lYVGUt6z/1Fn/qLP8A1Fn/AKiz/wBRZ/6iz/1Fn/qLP/UWf+os/wDUWf8AqLP/AFFn/qLP/UWf+os/9RZ/6iz/ANRZ/wCos/8AUWf+os/9RZ/6iz/1Fn/qLP8A1Fn/AKiz/wBRZ/6iz/1Fn/qLP/UWf+os/wDUWf8AqLP/AFFn/qLP/UWf+os/9RVumPxFVRHtLKxaMpb/ADk3Nov9ZqZEptMNpnNQ3QqNQkZrO/UWd+oojotGsSEk+NBLZEzFclpP1FJzpjtesg8ikZ2LqEDg9MjRi2QMzXPEbwTXCKWtdOQDll8s8MlOt6cIMV/Vtm9aU+9WlPvVpT71aU+9WlPvVpT71aU+9WlPvVpT71aU+9WlPvVpT71aU+9WlPvVpT71aU+9WlPvVpT71aU+9WlPvVpT71aU+9WlPvVpT71aU+9WlPvVJz6Q7YiEKJKlOdX9CQbqZdCfdKqE1mKsSTLoT7pUa7ig3UP4xt7EeIVTZrMTJ+ti7wheP9CTH8UbwVE5jc5HBWxy+HuOxCkOu/rO/qCDdTLoT7pRmrUJJl0J/Ap7nMLqQlUtXfzTHNYW0RKtSolZpWaVmlZpWaVmlZpWaVmlZpWaVmlZpWaVmlZpWaVmlZpWaVmlZpWYVmlZpWaVmlMP3sR4hSKtTD97F3hC8fPZV8z2BaB/NaB/NaB/NaB/NaB/NaB/NaB/NaB/NaB/NaB/NaB/NaB/NaB/Nau/mtXfzWrv5rV381q7+a1d/Nau/mhGYCAdh861rmue47lq7+a1d/Nau/mtXfzWrv5rV381q7+a1d/Nau/mtXfzWrv5rV381q7+a1d/Nfa6wyjPtWrxOa1d/Nau/mtXfzWrv5qqA/mtA/mtA/mtA/mtA/mtA/mtA/mtA/mtA/mjDDHNdKdayVBzyLZLQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQP5rQv5qkLCWqHEhPIjzr4L7XhE6M5tn6R/qGDdTLoT7pRxBMujFoWrQtWhatC1aFq0LVoWrQtWhatC1aFq0LVoWrQtWhatC1aFq0LVoWrQtWhatC1aFq0LVoWrQtWhatC1aFq0LVoWqkyG0HEeIxsvYu8IXjipRHho7V5KK13A+amUSd/8ACi8fNUH4RDDuKmDMKi3OUPgg5rQDUqcSE1xmVoWLQsWhYtCxaBi0DFoGLQMWhYtCxaFiYYUMNNLYpboZUTKMDpSWSiMDmTdUtWhrVoa1aGtWhrVoa1aGtWhrVoa1aGtWhrVoa1aGn0auqUe5NoiSY50MTktG1aNq0bVo2rRNWiatE1aJq0bVo2rRtTaDQKk32V9ojGbZ1NUhZ/UMG6mXQn3SjNWISTLo/kZ4hTKsTB97F3hC8cQwV0QMbSDBPYoYgx6ZlSG8LQM5rQM5rQM5rQM5rQM5rQM5rQM5rQM5rQM5rQt5rJlgH8LkWwmuE51lauzmtXZzWrs5rV2c1q7Oa1dnNauzmtXZzTobYbWE1UgbFGw04SGFk5DhvUWATNrax2Jyh3UO5C8fMzGOHfXsFRe5d7/Ou4Fck1Q7vmZjGzgm+yheP9RQbqZdCfdKqMlnqszTLoTiNycITySKzNypxogA2de1Z7uaz3c1nu5ouhE0RtLlnDxrOHjVo8aLDFmRucs93NZ7uaDGF5cbACrR41aPGrR41TjRABs69qz3c1nu5rPdzRdCnRG0uVo8atHjVo8aLDFmRucs93NZ7uaDGF5cbACrR41aPGrR40IT3mYdI14jxCqdJZ6ZP1sXeELxxZaC4B+0HasphbxLdOZK0bOS0bOS0bOS0bOS0bOS0bOS0bOS0bOS0bOSHk28kZNArGzzc/NAuY0mkbQtEzwrRM8K0TPCtEzwrRM8K0TPCtEzwrRM8KdCdDaA7cEWQ4jCw9skROk92cU5Q7qHchePmpjFDvr2Covcu9/nXcCuSaod3oSnSPYuqwd6zWLykOXBTY4HFMYmcE32ULx/qKDdTLoT7pVQmsxViSZdCfdKjxInq1DejFingN2Ok6bYItO9BjGhrRYMZwbBnVem8fTGGMBLjYAspEkYx+XHlIncN6MWKeA3Y6Tptgi070GMaGtFgxnBsGdV6bx9MYYwEuNgCykSRjH5ceUidw3oRXSBc+eI8QqmzWYmT9bF3hC8cWRgtBftJ2LJ4WwS3ykQtIzmtIzmtIzmtIzmtIzmtIzmtIzmtIzmtIzmh5RvNGTgaxt8wGNtK6wpHeV1QBwxVgFZoaexUT0wHPaDSNpWlZ4lpWeJaVniWlZ4lpWeJaVniWlZ4lpWeJOiuiNIbuKL4cNgYOyaJlRe3OCcod1DuQvHoTiPDV1WPctCeak4lnFUmkEdmOYUO+vYKi9y73+ddwK5Jqh3cfVnbXJWSG8rrPJ4K1y6jp8UAGuD8c0zgm+yheP9RQbqZdCfdKM1ahJMuhPunoUnTbBFp3oMY0NaLBjODYM6r03j6YwxgJcbAFlIkjGPy48pE7hvRixTwG7HSdNsEWnegxjQ1osGM4NgzqvTePpjDGAlxsAWUiSMY/LjykTuG9GLFPAblD44jxCkVamH72LvCF44hhToYe2kHie1QzBgUDKiN5WnZyWnZyWnZyWnZyWnZyWnZyWnZyWnZyWnZyWmbyWULwfMG6m+qjOzZ0Gnt6eWbFa0TlWFrDOS1hnJawzktYZyWsM5LWGclrDOS1hnJOiNiNeRXRAtUbAjgweXzkeO9RY5EmuqHanKHdQ7kLxxxHwxNwCqDnneuvFDeAmqo58Km2Tx2JrG0hN0nN6EO+vYKi9y73+ddwK5Jqh3fOM4JvsoXj/UUG6mXQn3SjiCZdCfdOOk6bYItO9BjGhrRYMZwbBnVem8fTGGMBLjYAspEkYx+XHlIncN6MWKeA3Y6Tptgi070GMaGtFgxnBsGdV6bx9MYYwEuNgCykSRjH5ceUidw3oxYp4Ddih8cR4jGy9i7wheOL/maFD7yP2TJT20bfNBHiPMEuMuqtIFpAuq4HGL3TF4+a8t9nym2aFCVHZJOUO6h3IXj0JACXQnKvoQ769gqL3Lvf0Jxn17Gi0ryEJrB21lafk0LrOa/i1UY7ckd9oQc0gjeOg7gVyTVDu+cZwTfZQvH+ooN1MuhPulGasQkmXQn3TipOm2CLTvQYxoa0WDGcGwZ1XpvH0xhjAS42ALKRJGMflx5SJ3DejFingN2Ok6bYItO9BjGhrRYMZwbBnVem8fTGGMBLjYAspEkYx+XHlIncN6MWKeA3Y4fHEeIUyrEwfexd4QvHEMFdEDG0gwT2KGIMemZUhvC0DOa0DOa0DOa0DOa0DOa0DOa0DOa0DOa0DOa0LeayZYB0qUe31VomrRN5LRt5LRt5LqtAxwgbMoFqcDwIhsHJO2OYnQItrfj0Mi2E1wnOsrV2c1q7Oa1dnNauzmtXZzWrs5rV2c1q7OadDbDawmqkDYo2GnCQwsnIcN6iwCZtbWOxOUO6h3IXj5yHfXsFRe5d7+g97QHMec4usU473RTuFQVWDMWiLD90qeDuEUbjUU2HFzpzlu6DuBXJNUO7jmSAFMEEdnQo0hPd0GcE32ULx/qKDdTLoT7pVRks9VmaZdCfdKoudJja3IMY0NaLBjODYM6r03j6YwxgJcbAFlIkjGPy48pE7hvRixTwG7HSdNsEWnegxjQ1osGM4NgzqvTePpjDGAlxsAWUiSMY/LjykTuG9GLFPAbsdJ02wRad6LGCTQ+oYjxCqdJZ6ZP1sXeELxxZaC4B+0HasphbxLdOZK0bOS0bOS0bOS0bOS0bOS0bOS0bOS0bOS0bOSHk28kZNArGzogmxtfmYA25QY4OEgVg0D0ASxpNI2haJnhWiZ4VomeFaJnhWiZ4VomeFaJnhWiZ4U6E6G0B24IshxGFh7ZIidJ7s4pyh3UO5C8ccyZBThva7gehk8o2lun0Id9ewVF7l3v867gVyTVDu45MtBnJdUlp3LrMBWh+ZWhvBdWfHHJM4JvsoXj/UUG6mXQn3SqhNZirEky6E+6VGu4zg2DOq9N4+mMMYCXGwBZSJIxj8uPKRO4b0YsU8Bux0nTbBFp3oMY0NaLBjODYM6r03j6YwxgJcbAFlIkjGPy48pE7hvRixTwG7HSdNsEWnegxjQ1osCd+JiPEKps1mJk/Wxd4QvHFkYLQX7SdiyeFsEt8pELSM5rSM5rSM5rSM5rSM5rSM5rSM5rSM5rSM5oeUbzRk4Gsbcc8UR3DzEyU3C3tlBh5k/SOONvbJ3x6ADntBpG0rSs5rSs8S0rPEtKzxLSs8S0rPEtKzxLSs8SdFdEaQ3cUXw4bAwdk0TKi9ucE5Q7qHcheON8IGRKrpQ3bwpOov4hVQ4ak6JIbm1JkSVFgM5noQ769gqL3Lvf51/ArkmqHd6HXYCqIjUXbiVpfgqNOm7ipAS6DOCb7KF4/1FBupl0J90ozVqEky6E+6VGu4jg2DOq9N4+mMMYCXGwBZSJIxj8uPKRO4b0YsU8Bux0nTbBFp3oMY0NaLBjODYM6r03j6YwxgJcbAFlIkjGPy48pE7hvRixTwG7HSdNsEWnegxjQ1osGJ34mI8QpFWph+9i7wheOIYU6GHtpB4ntUMwYFAyojeVp2clp2clp2clp2clp2clp2clp2clp2clp2clpm8llC8Hov4452nYN6ETDI5gz/02WrTYV4h/ZabCvEP7LTYT4h/ZUiIkW+5UWgADYMeEXehlWxWtE5VrTs5LTs5LWGclrDOS1hnJawzktYZyWsM5J0RsRryK6IFqjYEcGDy+cjx3qLHIk11Q7U5Q7qHcu89Ci9ocO1F7xkxvDlRhx4jju/9ClCEN7h2zPRh317BUXuXe/oGAWPNGpzgqUGK1+KZqCk05Z33bOaEeHMbwdnQfwK5JqZd6AaWklGJvM1oymS9bos4JvsoXj/UUG6mXQn3SjiCZdCdwUa6jg2DOq9N4+mMMYCXGwBZSJIxj8uPKRO4b0YsU8Bux0nTbBFp3oMY0NaLBjODYM6r03j6YwxgJcbAFlIkjGPy48pE7hvRixTwG7HSdNsEWnegxjQ1osGNxaQRlMR4jGy9i7wheOL/AJmhQ+8j9kyU9tG3zQR4joubvGNtOsQWU5dv/p8zk9sR0ugLx815b7PlNs0KEqOyScod1DuXeeiYWxlQCEFxk6sVcFDv0T0Yd9ewVF7l3v6BjwHBsQ2g2FeUhPZLb/lSGFRx7ZUnPiRT2manEbkWb3W8kIMOcu3b0H8CuSamXegHUqJRh2lab5VSnSd0WcE32ULx/qKDdTLoT7pRmrEJJl0J3BRGwzKmJHGGMBLjYAspEkYx+XHlIncN6MWKeA3Y6Tptgi070GMaGtFgxnBsGdV6bx9MYYwEuNgCykSRjH5ceUidw3oxYp4DdjpOm2CLTvQYxoa0WDGcGwZ1XpvH0UPjiPEKZViYPvYu8IXjiGCuiBjaQYJ7FDEGPTMqQ3haBnNaBnNaBnNaBnNaBnNaBnNaBnNaBnNaBnNaFvNZMsA6IeFSbZijfgfu3zPUM4cOpvQyTYTXCc61oGc1oGc1oGc1oGc1q7Oa1dnNauzmtXZzTobYbWE1UgbFGw04SGFk5DhvUWATNrax2Jyh3UO5d56OWGa/b2r7RMZTemu2NNJx6MO+vYKi9y739EveQGi0lVYOX9tALq4PEHABNjQ813RfwK5JqZd6Lmta3qmVa9DkmtcGyJl0WcE32ULx/qKDdTLoT7pVRks9VmabwRG9ejzVjeasbzU2yB7HLSHxrSHxrSHxrrGfFysbzVjeasbzUg6QvrSHxrSHxrPPjVjeasbzVjeam2QPY5aQ+NaQ+NaQ+NdYz4uVjeasbzVjeakHSF9aQ+NaQ+NZ58asbzTXvoyHbiPEKp0lnpk/Wxd4QvHFloLgH7QdqymFvEt05krRs5LRs5LRs5LRs5LRs5LRs5LRs5LRs5LRs5IeTbyRk0CsbOl1XELP+CiucZnIn6jGBgcZsKJSrLhOpf8A5GD4B/Zf/kYPgH9kIsTDobgXUamD+y1j5G/2VCLHcW7hV0QXMaTSNoWjZyWjZyWjZyWjZyWjZ4VomeFaJnhWiZ4U6E6G0B24IshxGFh7ZIidJ7s4pyh3UO5d56OSjvFeyU1OmR4lQhvDRdKnCiB3Qh317BUXuXe/oyG14mnOERrGtqVWEt8KZADqUtvRfwK5JqZd6L5ACuSlN1Lem0gDXLos4JvsoXj/AFFBupl0J90qoTWYqxJN4fyM8QqmzWYmT9bF3hC8cWRgtBftJ2LJ4WwS3ykQtIzmtIzmtIzmtIzmtIzmtIzmtIzmtIzmtIzmh5RvNGTgaxtxz6MT8E/UdB+EPBIZsTYEaE1ofU0hQ/xR9D0wHPaDSNpWkZzWkZzWkZzWkZzWkZzWlZzWlZ4lpWeJOiuiNIbuKL4cNgYOyaJlRe3OCcod1DuXeei6Tpzt7FlAwcJ1oQ2ypHepibXtKbEFjhPHDvr2Covcu9/RPUDqZo17ETAiFs7VpR4QocaJKkZz6L+BXJNTLvRLtjlQyhkm7hWeizgm+yheP9RQbqZdCfdKM1ahJN4YqFbztkurMHcVTeZBSLXAb1MVhUK3nbJdWYO4qm8yCkWuA3qYrCoVvO2S6swdx6AnW47AqbD0KFbztkurMHcVTeZBSLXAb1MVhUK3nbJdWYO4qm8yCkWuA3qYrCoVvO2S6swdxxHiFIq1MP3sXeELxxDCnQw9tIPE9qhmDAoGVEbytOzktOzktOzktOzktOzktOzktOzktOzktOzktM3ksoXg+YZHlNtjh2IRITw9p3Y3wYma4SX2h8Yvo1tmJSTYUEzhw/S3np5VsVrROVa07OS07OS07OS07OS07OS07OS07OS1hnJOiNiNeRXRAtUbAjgweXzkeO9RY5EmuqHanKHdQ7l3novntrC0fXl3KFvp0iopG+XJQgbaAxw769gqL3Lvf0MnFiydwVKMWGCd9i/0V1sl3TTchRycurR6L+BXJNTLvRybm0t6pllVtqyTYdAbOizgm+yheP8AUUG6mXQncEcQTeCMrV5RMoWTUP1UKNqG+uS8omULJqH6qFG1DfXJeUTKFk8chW82BGLFJo7963j/AHKmw4jK1eUTKFk1D9VCjahvrkvKJlCyah+qhRtQ31yXlEyhZPEeIxsvYu8IXji/5mhQ+8j9kyU9tG3zQR4jzNKBFcw9i8pDhxPgVqX6v+F5LB4bOJmpRoxLfVFQ8wLx815b7PlNs0KEqOyScod1DuXeejkY1KY2jYptj9W6ptrcbSVlnQgXdCHfXsFRe5d7+g+JChl7X11KFgzSDEZIynatB84Wg+cKHBiZwn0X8CuSamXei+frKdIUU2XrdFnBN9lC8f6ig3U3gncEZqxCSbwxU62HbJdWZO8qg8TCmXOI3KQqCp1sO2S6syd5VB4mFMucRuUhUFTrYdsl1Zk7zikK3mwLLRiaP1UgJBUHhbx/uVNhxU62HbJdWZO8qg8TCmXOI3KQqCp1sO2S6syd5VB4mFMucRuUhUFTrYdsl1Zk7ziPEKZViYPvYu8IXjiGCuiBjaQYJ7FDEGPTMqQ3haBnNaBnNaBnNaBnNaBnNaBnNaBnNaBnNaBnNaFvNZMsA/hck2E1wnOtaBnNaBnNaBnNaBnNaBnNaBnNaBnNaBnNOY1gYTtBUbDThIYWTkOG9RYBM2trHYnKHdQ7l3nomiazaNyDHwySKpgoQ6Dmz6MO+vYKi9y739F0aJmtU2OEJu4BDLnKs21VoPYZtImOi/gVyTUy70abLdoUqDuSESIJSsHRZwTfZQvH+ooN1N4BO4KoyWeqzNN4KaMnEDYAVkYhJB37EGMqc7asplHc0Ipq3oycQNgBWRiEkHfsQYypztqymUdzTYm3bjkK3mwLLRiaP1UgJDHQeFvH+5U2HGGMqc7asplHc0Ipq3oycQNgBWRiEkHfsQYypztqymUdzQimrejJxA2AFZGISQd+zEeIVTpLPTJ+ti7wheOLLQXAP2g7VlMLeJbpzJWjZyWjZyWjZyWjZyWjZyWjZyWjZyWjZyWjZyQ8m3kjJoFY2dOro1eZBcxpNI2haNnJaNnJaNnJaNnJaNnJaNnJaNnJaNnJOhmG0A7giyHEaWHtkiJ0ojs4pyh3UO5C8VMdAxBEoztqVWEg+z/lNiGLSo1yl0Yd9ewVF7l3v6LZf9QT+KiGMGveLGlMyEmkjrNGxMnsJl0X8CuSaod3o0WiZWaxNa5rZGqros4JvsoXj/UUG6m8AncFUJrMVYkm8FJGTSRsICy0QEAb9qD2VubsWTybuSEI170ZNJGwgLLRAQBv2oPZW5uxZPJu5JsPbtxSFbzYFloxNH6qQEh0aDwt4/3Kmw4g9lbm7Fk8m7khCNe9GTSRsICy0QEAb9qD2VubsWTybuSEI170ZNJGwgLLRAQBv24jxCqbNZiZP1sXeELx86EaIJrWY7ksx3JZjuSzHclmO5LMdyWY7ksx3JZjuSzHclmO5LMdyWY7ksx3JZjuSzHclmO5LMdyWY7ktG/wpoe0tMya/OOUO6h3IXjimMZgNMmi3tTW1tfXbwTaTnGkaJn0Yd9ewVF7l3v6L4LtvwUnw3XhYpMY7tcbAmQWWNHRfwK5Jqh3ei4H1lnOQd1jLf0WcE32ULx/qKDdTeATuCM1ahJN4YqFbztkurMHcVTeZBSLXAb1MVhUK3nbJdWYO49CQrebAstGJo/VSAkOnQeFvH+5U2HoUK3nbJdWYO4qm8yCkWuA3qYrCoVvO2S6swdxxHiFIq1MP3sXeELx86P4SZt885Q+CHcu8qRxTGIu31hfaaPd3Jl6kejDvr2Covcu9/QdBhQ2GjaXKHhghzL6pdv/AKFoGc1oGc1Xg7JcVlYfeN3QfwK5JqZdUj0HUthkpB7h3pvXca9p6LOCb7KF4/1FBupvAJ3BHEE3gjK1eUTKFk1D9VCjahvrkvKJlCyeOQrebAstGJo/VSAkPM0Hhbx/uVNhxGVq8omULJqH6qFG1DfXJeUTKFk8R4jG1zjIAqlDdMLvCF49AviODWjaVrDFrDFrDFrDFrDFrDFrDFrDFXHhlaaGtNDWmhrTQ1poa00NaaGtNDWmhrTQ1poa00NaaGtNDWmhrTQ1poa00NTEaGtYYtYYtYYtYYtYYtYYtYYtYYtYYtYYtYYtYYtYYtYYiRGamGG4OqQa1wJqVCJFa10yvJvDpblI4phUYoq37lrbZdyownBztpnX0Yd9ewVF7l3v6BjNjGGXZwozTcDM6DbDtVcWN8FpI3Mf2WVhxHnrSk5Rh6NEdB/Arkmpl3FI46UQDktD8AqUMN5dFnBN9lC8f6ig3U3gE7gjNWISTeGKnWw7ZLqzJ3lUHiYUy5xG5SFQVOth2yXVmTvOKQrebAstGJo/VSAkPN0Hhbx/uVNhxU62HbJdWZO8qg8TCmXOI3KQqCp1sO2SLIbg6Jtrrxd4Vla/5bBnv7dnNMh4RQpPbSk02IN22ld4QvHoG8FTLyFpHIYPSqpSmtM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/ktM/knOyzqhuUThikcToc+o3YpiC+XBQzk3tkZkkdGHfXsFRe5d7/MGHFaHNKLYEMMB6D+BXJNTLuORxO7DIKcxPchyPRZwTfZQvHzwysRrJ2TP9CSDjFd9xeTgMHEzVcKDyP915aAR2tM15GICd23HN7g0dpVeEs7q1nuPsq1/hWnlxaV5ONDfwdilTyjtzF5PBwLxTXRWsFGrqoNfCs2gp1B1crFUZLPVZmm8FNGTiBsAKyMQkg79iDGVOdtWUyjuabE27cchW82BZaMTR+qkBIedoPC3j/cqbDjDGVOdtWUyjuaEU1b1OkQ3YAmzVGD1jv2IRX+Theu/9gg6I37RE3vs5KTRIJkfBxSjQvR9YLJYQ0ybV2heTiB1YQvHoG8F34hfxWq1WqR/gbVarVarVarVarVI+YqxWq1P4FROGORVNrhNwrbuVcNs02G6GBSMpz6MO+vYKi9y739PIwn0A0brUyLEzrD29F/Arkmpl3oSKd21hZgnvTeMz0WcE32ULx89Dh7mVL7NhUNxDapHOaqo7R2OqWlZ4lp4XjC6r2ngf56YkRwa0WlZKFNsLdv4qb+sV1WNHcqwFWwBU4LjVzVGLDyjhYZy5qWDtfL/tt/dUsIiMZedSK8thkQn7rZKvLu4vWbF8a6kfCGHiCp4PhUOJ2OFFGHhLY4hb5zbzXlZrqNarAutDb3KlDJVizFWJJvBSRk0kbCAstEBAG/ag9lbm7Fk8m7kmw9u3E5zBNwFSpOa4ngpBkhcWb8qc2JKrzbobJSHYs35VQeyYuoUGOrtEsYeytzdiyeTdyQwSGZu9Mo4ThjfKvb1GH0O09qyLWExKVGj2psfDAIsf1fRZ/fozjQqMT/qMqcp4NhMOJ2OFFdeE9sPhSYqOEtyR3isJ7YFDJiyqa9HwrJxZUeC78Qv4i9kpqoA+ys35Vm/KrPkWb8izfkWb8izfkWb8izfkWb8izfkWb8izfkWb8izfkWb8izfkWaPAs35Vm/Ks35Vm/Ks35Vm/Ks35Vm/Is35Fm/Is35Fm/Is35Fm/Is35FZ8is+RWfIq6M7qro8kHb0/gVFu9B9LbYgcrEd2ghCI0vJG89GHfXsFRe5d7+mDGhzI2zQhsaGtFg6L+BXJNUO6pjoZOM4T2LSmXBUITpnos4JvsoXj56HFG1v0UKNGgteHtm07Qv+Xwx7Ox7ZrW4XhK15vu/wDK6mEwDxmF5Kbh/wBuKpYTDd+aySlHhuh9orCpQnteOz+c/ZYZ6jLe0rt2qZXUE1OQ5LrN5KbTMKmzP+q+wxTWwTh8N2OfRL4Q+zxd7RVyVGM3qmxwzXKsBSsOLKN711kJJvDFQredsl1Zg7iqbzIKRa4DepisKhW87ZLqzB3FU3mQUi1wG9OIrFFROHm/bVN5kFItcBvUxWFQredslMuodhU8qCiIbCHbCUP+I4U2q2E07fvYnYayEBHdtmtuKrpF0EfZou9oqPcpYRDq9FwzXYpBUaQnOyeIX8R4oSabSs08lmnks08lmu5LMdyWY7ksx3JZjuSzHclmO5LMdyWY7ksx3JZjuSzHclmO5LMdyWY7ks08lmnks08lmnks08lmnks08lmnks08lmO5LMdyWY7ksx3JZjuSzHclmO5LMdyWaeSI+8mcE3gn8Cot3oOpTl6KqpKHRp218FMdCHfXsFRe5d7/ADr+BXJNUO7imMb5+sqBNUpJsvW6LOCb7KF4+abDghkQ+lXYqsHfzXXhRG/FeRihx3bUWjPbW1fYcMJbCn1XH0CpgzHRIeAW7ZolkHIO9aHV8FlcGcYjRth28lk8MHtj91XHDrtazI3If3UsrRP3qlMfzOJF9VqL3YhBhV11LrNER+0ldWpSewA+sFvGztQcFBiNq8oORtxnpOgx4Yex1oK+0QfKYNO0+j2FGPgwoRGZ8L9wqLrRikgm8EZWryiZQsmofqoUbUN9cl5RMoWTUP1UKNqr3GSicPN+2ofqoUbUIEMmXZapvcGqt5VUT4KcpjsTMFwhjIcSxjhmn+yrPQqU+k6DHYHsdaCsrCm/BnGo+r2FZ5Dtq6rx3qucu2xQ3ylN2I8ULx6Be9wa0WzQfDcHNO0YslTFPdipPIAVJhBGLJUxT3dGi+K0FaZqkY7VrDFrDFrDFrDFrDFrDFrDFrDFLLsmtM1aZq0zVpmrTNWmaqbCDwxOvpnBMI9UJ/AqJw6UjimMcO+vYKi9y73+dfwK5JqZdxzGIxGio2qjTMt00HHNHRZwTfZQvHzEzYjBwYkQ94tcgH96MTA54RC3ekP7qjEhyI7lSY4goQMLPB/91lIcRjYu+dR4rIzpQ/8ApurHcpR6WDu7axzQfDcHNdWCNuOjiMaO6TfqjEbAay6PqvKOJO4LM+K6pkqOdD2tNiEWEZg/zJ/aRidwUWMbR1RjkUQbQ6rFBhj/AKgxSCl03Q4jQ5jhIgpsWDoXHqH/AMSqcOppNm7GJJvDFTrYdsl1Zk7yqDxMKZc4jcpCoKnWw7ZLqzJ3lUHiYUy5xG5OAqFFROHm/bRywmNnFbmi0qjDElNzlaSrSFNrllG1b0KZnEh9V3b29CQUum6HEaHMdUQVShzMB9bD+yqNakawsrBnV8FRfpG/FHihePQN4IXji9vEeIQ4nF7fRyBJAe+U1rD+SkcIiclrcXwha3F8IWtxfCFCYyI59Ns60x/2qJ1mzsWtxfCFrcXwha3F8IWtxeQWsxOS1h/Jaw/ktYfyWsP5Ix2xnOMwJEIcSu1OvpnBMup90qJw6cjimMUO+vYKi9y73+dfwK5JqZd6Exink28sUj0GcE32ULx8w3B2muJncFSNpQ3qDFn1g2i/iF/zODw4naRXzU4T40LsBmF5PDh3w/8AKqwqCearwqCOAK8ph3KH/lQ8GhklrBKvGVM1BdUnJNqYOzepCob96lDaqWTiyuqUTmpFZJx6kSo8f5lE7JH44iosLbU7oCFDrDKuJXan4a8VM6reKkPNPwaL6QqPqnenQIok9jpHFJVmabwU0ZOIGwArIxCSDv2IMZU521ZTKO5oRTVvRk4gbACsjEJIO/YgxlTnbVlMo7msptkZqJw837ayTbG1d6DQu3YqXo711mxHLq0mlV1g/FcQsIh7CwH/AN545DzT8GielYdx3p8GIKL2OkVRdbiD2cVSFhkULx6BvBC8cVXr4jxCHE4vb6LPxOlg9w/VQbg+nmjeCHE4n/iJnBMup91RLvmK8VSh317BUXuXe/zr+BXJNUO70auhXjZwTfZQvFUnuDWjaVLKl3Bq8i0xTyC8pg4o/dKEWEZtOIwi4sl6TrCj1gWVCakysrLRSYUD15Vu4J8GOycM57fWHrBCNg8QPYfMlRy20ijzRdtKENvBVCr64jhMNsntzpbQqJ2IOChxPWaD0DCY3KPFtdQXlcHq+6UOu7wryMUOO7b/ACp8I+k2SdDdUbMQiNs+oQiQ3TacRweA7r+k4bFTemwIW34DemYPBHVaFIebh/8AEGD7kT9iplUpTVYkm8FJGTSRsICy0QEAb9qD2VubsWTybuSEI170ZNJGwgLLRAQBv2oPZW5uxZPJu5LJWmSNBhn2tWb8qzflWb8qzflWb8qzflWb8qzflWb8qzflQivYd5qReeOJsLYTLuQYwANGKsJrh62KMf8At/vikFIebh/8QYPuRP2KhYQyqlU68EDvU9yHY6SF44nQ2hrWiyYtVrPCqMSVDfRWRgukLagpFwn2tWX/ANQmcpKYb8ioxR1bqowh1bqzfkWVl5Sc7Fm/Is35EHRWydidEhZ7XVVLR/pLR/pLR/pLM/SUjD/RTHYY2RaJDqyTWth1ASHklo/0Vo/0Vo/0Vo/0Vo/0Vo/0VIwpH8JaP9JaP9JZPCGyZP1JIcTif+ImcEy6n3UTBtNtU1m/Is35Fm/Is35Fm/Is35Fm/Is35Fm/Is35EGxYZkDPMWTomlQNSIhsNe9q+0Q2GnX6KzR7tZo92s0e7WaPdrNHu1mj3azR7tZo92s0e7WaPdrNHu1mfppz3MIFHci4MOyVS6zHckGhhkPurN+VZvyrN+VZvyrN+VZvyrN+VZvyrN+VZvyrMPgQpQnk9jUAbRRQhPZEc6c6l1jJuxuwKskqvrcUeqKgo7J2EHEcHiPybXRM6VgK1yHK6g+MThLx61TeSkKgsnhDaxmvFrUY2Cue5nrw/wBwqOG4PS+9D/smYRCM2PEx06Sjw250qQ7q0Wd669liqxRp2UD9E5NUAf8AbH0xl24IufXtVbQqyVThuNXNBuEsc5w9Ju1UoLp7xtH8p+1MHVdndhXapFThOI+ioUqI+6FScmwYQLiTIAbVIyMd+ef2xl73BrRaSVRgNfhB3ioKrA4crylhOCFo3sdNUsGjNfvG0d3Q/wCYjCn6ja3KUDA6t73/ALKLg0bA2UXtlU6ztRG9dqEk3hioVvO2S6swdxVN5kFItcBvUxWFQredsl1Zg7iqbzIKRa4DeqU6rVo3LROWictE5aJy0TlonLROWictE5aJydDoOBLSnYq9x6EKHvdP/wB5ruWERt7g3l/94u3FSwmM1m4bT3LyGCRIna51FdfAOUT/AAgwvMB+6J/fol8RwY0WklUYIiYQd7agouDOwABj2y0vxsVDZOaaiE78VC8cT3PO34KqoqhXR+CkFT2hQS4+ltxd4Q4nF7fRZ+J0sHuH6qDcH0VJ7g1u8lS+3YN70Kk0gjs6ZvBDicT/AMRM4Jl1PuqJw8zIL7yHHz0zm+fkFIYjeCmVIY5J5Y0GdRBVHCIVD7zU3DIVch1pbt6GDRnf8zDG30xvxQmtjPAa6GAAd+OcfBYbnb7DzCbChNDGNEgOnJSRwrBx5BxnV6BUxVEHxWSjAvh/FqpQ4jXL7NCcCTnkfRcU2E3aaKDRYMUnTc82NCfDEJjQ4SRdv6Fia+fV29o/lJhvE2m1OhtfSlWFuOKsoQYLC4mwDaspFk7CDt9XsCpYnx4zqLGCZKoibIAzYf7lV9YrNCraEI8CI5pbY4bEWRJNwhmcN434vsuDH/mHCs+ojFjOJJrrtKqaFmhdmIJvBGVq8omULJqH6qFG1DfXJeUTKFk1D9VCjaq9xkogisDpDatAxaBi0DFoGLQMWgYtAxaBi0DFoGKg0SbTsRYeGJsZuwzQiwzMHGWtNTOqgFBna/rnvVLFkoMnYS+weqN6MaPEc4u2nas2fFZg5LqVIYPhJc/BrJbWcEHsIc0iYIxPwiO6TG/FdY0YQzYewKvrFZjeSI2KSATRvM0LxxPb2kdGk3vTMvFAiCozUoUVrjMVIUooBmtM1ZSkKFK1aZq0zVSYQRiESI4NaIlZWtQ1rUNa1DWtQ1rUNQslED5NtGxQ24O5sWPRAA2DisphEV0Q9uzFTweKW727Cg+NFbAiek0rXYS12EtdhLXYSkMMhow4Udr3UhUEOJxP/ETOCZdT7qiGO4NEqprTQ1poa00NaaGtNDWmhrTQ1poamI8NayxSOEQytNDWmhrTQ1poa00NaaGtNDWmhrTQ1poa00NaaGtNDWsw1rLFp4a00NaaGtNDWmhrTQ1poa00NaaGtNDWmhrTQ1poaqwiGFQhx2OduxG8OnTlIoNfWAS3uX2jAZ1V0Ra3gsnhcER5bc1yOGQWuhnqynaCFM4Q5w++0FCFhzBBcfTGb/jo0D5WP/0xs4o5Jwgt3Map/aoveAUGYdCD2+syo8kI0CIHsO0YjDiNBBqIO1HCMBBcy0s2t4KUQd66rgVN1akLV9sfwZ/fHFEQ+mRyUqIHSBUB33Zcv5SGOE25WRHD/wCkX4FFofcfZzUhBmOyIFPCIjIQ8RUoDOttebTilih4Aw1AU38dilt24qLRScqDxRJsxQo7TU11d3aokd2axpcVEjxqy40ipmxdRlXapio7QmlAyQkm8MVOth2yXVmTvKoPEwplziNykKgqdbDtkurMneVQeJhTLnEbk4CoUVFu+bN9CO3bbxU1IqbDUbRsK8qx7D2VotwYOpH0jsVMqHAb6TqKaxoqFQUlEjvzWNLiomERjMuMz/bHRApOVB4ok2Ke0KJgMQzMLrM4YoeBg9SG2kR2lfUrqMq7VMVHaE0oOO5Aejt4LvCF4p0V1jQnRD0XLJRYjmOOasq2I51e1ZbKOFcpLSu5L7PSqpSmtK7ktK7khDbM4sgSQHvlNaw/ktYfyWsP5LWH8lrD+Shta8vpCdakVI44UKI4ta9waSNi1uL4Qtbi+ELW4vhC1uL4Qtbi8gssIznmcqwhxOJ/4iZwTLqfdRrWcVnFZxWcVnFZxWcVnFZxWcVnFZxWcVnFZxWcVnFZxWcVnFZxWcVnFZxWcVnFZxWcVnFZxWcVnFZxWcVnFZxWcVOayheQpCI5Q4U50XyxG8OkYjl1c0WnY1Ngssb8cVGK2vY4WhZEupSIrxTbU5f/AObhDpg6InZ2Y+pp4lUPs7UY0ZxdMzr2qQxV271XMwXaRu8b02Iwza4TBXbiLx5KN6zdvFdRmVbvhn9lR+yYR7shRcKjuDXNbMMFafC9R31xnCsGEya3N/sslEt2Hps7Cf5TM+vEP18xhJP/AFSOVX7Yy42nE128IAWyWEwxnZA/ROmQFJpnM7FYhsBqKEiDWtJJZ003yjLN60rOaPXLRsE1kor5tO07EGwXiZ2jYqYiunxQe9zWnaJo9ctGwTWSivm07TsQbBeJnaNipiK6fFTc9rXESNa6saGOERawz3q1hvvVn/Os/wCdZ/zrP+dZ/wA6z/nWf86z/nTYkSVVdqEIempqrFYrF2p2HRB92H+5x4VR3D/cEeOMuNpxNdvChNFjqTTyxYTP1h9ApesZYm9tSB7U2EwV2Kja85xR4oXivs0PNaa+0qjt6TaNswjeCF44qvX6LPxOlAunFIqRxQmttLxLpG+1Cdszif8AiJnBMup91H+TgDeV2r83Ebw6Q4ptG0Ez6B9lUIcp7VQiSrsKZGhmTgZg9qhRxZEYHYojJ9WH5MfugGivYvKAFvZjntCdAcZmA6Q4H/04uPQiQvXaQosPeyfQfQsynTZ2k/yk3n/v0TjwkH/qk86/3xlptGJtL0WpryPJwjlH/sERvTBhFCT50S02yUKP9saMowOlQsmtdb7ta23wLW2+BayPAtZHgU/tbfAtbb4FP7a33a11vu/8rXW+7/ytdb7v/K11vu/8rXW+7/ytdb7v/K11vu/8rXm+7/yteb7v/K15vu/8rXW+7/yoNKMIuUnY2UpS/umhragKuqsz5FmfIsz5FmfIsz5FmfIsz5E2HElXVYg9lbm7EIeTdyQcw0t8lJwVhXVHNCJFmyBtdv4JsOG0Na0SARxRsHP+o0hPgRRRIMj2HGWm0Ymt3BQSLA5zvhibhYHUiiRP3gqW1prxN7K13qDexHig1p8o8mXZ2rLO7ulMrKMbSIM7FQjN6t2SoQW9W7NZnyLLy8pOySmG/Is35EHRWydidEhZ7XVVLR/pLR/pLR/pLR/pLR/pJpwkSIFXVljkVIpmEQIRcWOmOrMTWj/RWj/RWj/RWj/RWj/RWSwpkoc56OSF4rtT76ZwTAdwT7qPRqU2wnkdjFoIngWhieBaJ/gWif4Fon+FaJ3hWid4Von+BaJ/gWhf4FoIngWgieBaCJ4FoIngWgieBaCJ4FoIngWgieBaCJ4FoIngWgieBaCJ4CtBE8BWgieArQRPAtBE8Cou6U9tI4vzcRvDpOYomDn0usOgfZTyd6mEOKjwpmiYVKXeP74o7nW5R/1XAYmcMRWEs3wwfjiHRezteMcSN6oqToh6QUAfdn/KfzX/ABn0Tjh4ewVEUH8dint24qQNFyykRwMk3B8HYXFxqCEEVvNcR28qU1gPt/8AisE/AZ/tR6IxDzmB+3/4pkPJO6rQLVoX81oX81oX81oX81oX81oX81oX80IgEqTlTeZBSLXAb1MVhNZFY17C9wkbNqmcFHc4hTh4Kyf3ut9cZx/bMEb5YDrt9f8AyqEXFSaaLlTeaR2LtNiOEYTGZDiRepBB27zifg0bNdt3HeomCxxZUe0b11H1dqkKztK71BvYvaCl6ItVEWdIQ2V/ug30zW4o3gheOL8zos/E6Myhkq2wxKlvTYUJpc51gU2tEUfcWqR/dlD7T5FnzJsCAyiwdJ19qF44n30zgm8E+6UeiFgtzF34+7GMQ4/wMW+7pC8cX5uI3h02xm76SD2mYImMZ9lE+i6sIDYLU1qjP3QZfEYsIabMpS7jWhFA7Cg1omU1m7E4rCo+ypgxd/RiEeu/GzBm3nIDaekAocP1WgfylsfZSa/onG+DGbSY8SIVITfAObE/YrrAhVTKoQIcxtPotXV68Z2fExOgwXmkLKQlS4LBPb/8Vgn4DP8Aaj0RiHnMD9v/AMUynk6VGtf6a/01/pr/AE1/pr/TX+mvJZOmofqoUbUO+ShRj6DwT0T0DFb5HCPXHpcUcrCND1xW0rrN5Lqt5oRIk2YPtfv4JlKF/wAsAyg0eqLQp/acmdzwQtZpnc1pKyzYdBrW0RO0qtpXVbzTcIjwntY4yaXVTUG8qTjIBUW5uwfuqDehLFRChPeZNBWsMRZDitc6YsQZEita6ZtWsMWWpjJ05zWsMWsMQfDcHNO0YhEiODWiJWVrUNa1DWtQ1rUNGBg5Ig7T6/8AjFP7TDMZw6x/Za1DWtQ1rUNa1DUjhkNa7CWuwlrsJa7CRhQMJY99MVBC8cTr6ZwTeCfwKPRCwW5i78fdjGIcf4GLfd0heOL83Ebw6dIWtRwZx6zM3hjPsqRE1sA7EYh3qLhThpXSbwGJuHwmzdDEol3eqJzl1WgcMbYMIFxJlVtKh4P6ec/jiHQLjYE+IfVJxOiPMmtEynRn75/46cNn3x/KoUcbOqVBf6QFF3EeYLXtDmm0FUsiYR+46SmYLot9yDIbGsaLA0SxnCP+Gkb8mTKXAqEz/iIi1ToU/isE/AZ/tR6IxDzmB+3/AOKZEyrus0GxaZ/JaZ/JaZ/JaZ/JaZ/JaZ/JaZ/JCEDOi5UHiYU3OcRuUgJAIR2CUz1lBfPrAUXcR5nIx8JhMefRc5U/ssB/ayr6KkzAoU+0T+uLJYTCERuzsXk48dnIrykeO/vARhwcGYGuzpic1M4FD7qlODgkJh30a1gkG84/BQ6RkBasnCzBZ2qVrjt6XanRHuLW7O1fZaRlSlNaZ/JZVsRzq9qyrojm17Fpn8l9lpGVKU1pn8lpn8kITCSO3FkCSA98prWH8lrD+SmcJeBwRbg73PYPSO1NhQm0nFQm5SmXNmU132iJWJ2LWH8lrD+S1h/Jaw/kpHCInJa3F8IWtxfCFrcXwha3F8IRwhkd7zSAkQheOJ19M4JvBP4FHohYLcxd+PuxjEOP8DFvu6QvHF+biN4IjpSTYsOy0f2TYsMza7E43V1W81lKJZB9d37JkHCXfZ4TXUXdXM/ymQ8DiQ3Q2iQounikRMI4R/w5pfDtyYtbwVGI2sc1mlZHB4TnE7GoYThMnYRsGxmMDoYQ/bRojvqUaLvNEYvskM3/AOyrzjb0pp0U+g34n/0/yqJBPpCrin4FFqESyexw6E1PHGi4I+MY0y51C0qhhmDtiy2jquXlDFg3mz+iqw6D7TpfVTGGYOfzApnDMHH5gVeHQO581gwwWNlMnSpVEblgn4DP9qPRGIecwP2//FNLXVEVdZZ/zrP+dZ/zrP8AnWf86z/nWf8AOmxIkqq7U1rKnO2oRBEfzQimrejWaOxqdgkQ9WLm3uhNT6DiahEa0z+H7Klg8aG66+iVUcKP5lJVswr/APXn+y0cX/8AX/wtHF//AF/8Lqw4/dg3+EYsU4TDYKyTDo/smRoOFRjDY+Ti6LVy24jDnVDAZ+/7p2TrmalveelMoMFm07ghDYJNAqX5mI3gheOL8zosl/1MRc4gAIwIBIgD502FCbScVIdaIc5ygXSod0eadfaheOJ19M4JvBP4FHohYLcxd+PuxjEOP8DFvu6QvHF+biN4IHpy2owos8kbeztUwZgpjHibXPYCqULA4QO8iaoRI0NjtxdJZSdCL/1IZt4ryWEwHD70wnR48eHItlRYTXj/AOcZg5O98pqbvsx4OL0zBcFguY17pTbDAb0D0IWCNP33fsobCOtKZ4qgwzjOs7O1ZeJXunt6ct6DznROt/KxhsKqZrlsO9B9WVbVEHb0KOOM+G0Ta4yDrC0qX/EcBk71qNL/ACvI4WIP5kv9y/5f/iXwDlVh7fd/5VeHtA/D/wAry3/FW8gP3UEYDhf2gupU+uDKyViwVrsJggiCyYLxuR/5uB7wLWYPjC1mD4wtYheMLWIXjCH/ADMGz1wtZg+MIf8ANQPeBa3A94FrcD3gWtwPeBa3A94FrcD3gWtwPeBa3A94FrcD3gWt4P7wLW4HvAtbge8CwTJRYcSVOdF09yaGtqAq6qzPkWZ8izPkWZ8izPkWZ8izPkTIcQCRMrE18MTc3YhDyb+SEI170ZNJbsICGEFrm12oPqyraog7ehR6EDDGjN6jv2XkcLjEDf1vquuIL7zP7Lr4JBPAkKvAAfzf8L/8d+t/hVYAPe/4T8GODwmNfaZlRSWkB0WrtqCiR4maxtIp8Q1viOLipmt/ToixHIMqNpozWZ8iy8vKznYsz5FQjN6t2SoQW9W7NZnyLLy8rOdizPkWZ8ia+M2i7E7CITHTDpiqpUpCX4ayUR4o7gJTVQJRyEIAm0llasHu010dtbahJqDQBIVaNWD3asHu1YPdqwe7Vg92rB7tWD3akWt90s1vulkI7epOdUNND2kGZtxOvpnBN4J/Ao9ELBbmLvx92MYhx/gYt93SF44vzcRvBQ8JaM1xa7gu3HLoTGcslFBdD3buCgx2OBZTYZ4sI/4o2PsBLCO61OiwIkFoa6iaZP8AZTbFZ3RStL+stL+suvFh+1FK8phMBvCZVLCv+IGXYA36qFg+COL4LYwkZzmBb03RYhk1omU6PEzZ0jw2BGHg8nxd+xqy0ckzrr2qiOmyELCfgg1tQH8rdDeJtdag9lbP97UI0F0xtG7HPGMIwcTjwxZ6wX2b/iUJ0I7IzDKXFCJguHmgbJtpT711MJgHjMLqxsH7nn+y02C+I/2XXwuCOAJWv9b8L/K+yPjB1hpyRhh1GTZrTjwqWVHJaUcln/BSynwWlHJaYclpx4Vpx4Vpx4Vpx4Vpx4Vpx4Vpx4VrA8K1geFaceFGGH0ZNmtOPCmwx6Il5j8xU4hkFIse0b1MGYKoSLyLZJzObShEZW3/AHBCNBdMbRuxz6ETBoua8SToEdtnxG8KbTNWBZjeSDWtA7lCZHweE+Kes+kydZXUhMbwbib/AMPhmt3WicNgX3jb06ItRwmJnOIojsQvHF+ZiN4IXji/M6Jc4yAVCFoR8yDYVZKkK3G0/wAC6+mcE3gn8Cj0QsFuYu/H3YxiHH+Bi3ndIXji/NxG8Fk35riQU6E/Z5jrWrs2FQcJHptr47VhGDwRN7m1BFuDwcIhgmZDQCi6IMIaxtpdg4lzkqowP5YVUWX5Q/stZidzAP2XWwvCPeSRiRXz4lRMMcOrDFFvE/8AvxxT6P2GC7qNPlDvO5fZ4PVpZ0rXKlFrO5SHTltKOEvHWiZvD+Ww8HEp5xWUgmr0mmxylEEWFxEwqcCK2I3sOKYXbiMRvkY/rgW8Qi7BsoW74RpA9yo4Rg8KJL2SvKYHEbwfNathHwXksCPtPXUcyA37jf7oxIsRz3Ha4zRe0AkiVa0cNToMWY1ZrVOi1ZjFVDYtHDWjhrRw1o4a0cNaOGtFDWihrRQ1o4aMRgBJEq1ooaa4xWtJFm5adi07Fp2LTsWnYtOxadipNMwXqF6taFG1DfXRRyiZQsn8FDZOwTWUgmo5zdhQOWbDf6jzJTGKYXbjyeENrGa4WtRdgxGEM+7U7kpR4L2X2SWaE2OWh1FwNE2KWDsl+FCn/dP/AP8ASnSpdSlbJGNErd6DPWKfhEc0iTM9PtWVwqIGsGw+kUWQ4rXGYqCDIkVrXTNq1hiy1MZOnOa1hiLIcVrnTFiDIkVrXTNq1hiy1MZOnOa1hi1hiD2ODmnaEXOIACycOqEPmQhwxMqcwPXcdqD4bg5p2jFkqYp7sVJ5ACpMIIxZKmKe7FSeQAqTCCMWSpinu6Lr6ZwTeCfwKPRCweFFwqG17WVgrXYSqwyHatbhrW4aqwqGtZYtZYh/zcNa3DQ/5uGtdhLXYS12EtdhLXYS12EtdhLXYS12EtdhLXYS16EtehLXYS12EtdhKI4Vgud0heOL83EbwQvFZWGPKs+IVA+YLTbsR/4fHMmRD5MnY7djcx0nMcJHtUTBnei6XEbMdZTYEBv9mjeUzBoVjdu871WpCzoHBcGd5c5xHof5U7G71Jo8xMqbtG2t39lIWfywvGeamoxIriZ/FZqsIWWgRC0jaFkY0mR/g7HXWtyzgpRmQog+8AVXgkDuqWqs8RXUwOB4JrAgxsgKf/isE6jdAzZ2I9RvJZjeSzG8lmjks0ckOo2zcsxvJDqN5LMbyWY3ksxvJZjeSzG8lmN5LMbyWY3ktG3ktG3ksxvJYHRAGfZ7KY/KO6zZrSuWlctK5aVy0ruS0ruS0ruSESm4yVCIJhTL3uG5SAkAqcywm2SdDwWt9joh/ZU4jj3rOKqfzU4UQhu61qyb5Qo/q7HcMdda3LOCzhzWcFMw4R9lTEKH3NVQVKK7reiwWlZWKatg2NVA8MdWOZW6GM5yo5R0uCyoiOdWsq6I5texaZ/JfZaRlSlNaZ/JZVsRzq9qyrojm17Fpn8l9lpGVKU1pn8lpn8lRpdVtZJVBkxCHxQhwxMqi2t3pO3o3gheOL28R4hDicXt4jxCHE4vb6Lr6ZwTeCfwKPSJmU5znlsjsWmdyU8q7ktIVpCs8rPKlTK0hWkKByruS0zuS0zuS0zuS0zuS0zuS0zuS0zuS0zuS0zuS0z+S0z+S0z+S0zuSfEERxohEkyVKZPSF44vzcRvBC8cRwqAKvTA2dqkbfMZUVJuCYc+USxkQ+lx7VFwVsTJl+1f8s+f4Uaj/ZZTCMGwiI/1rVqmEeBVwHNH3ngIHDcJF2F/dZLBoQY36qvonB8BdN3pRN3BZWNZbxUm2eYmUIcMf4QhQ7B8f5ayHsa34lS6AjwSW1zq2FUcKgiJ95tRWmyZ3PElOFFY8fddPoziRGsH3jJafKHdDrUOjCLGw5ymbZ//AEsE/AZ/tR6IxDzmB+3/AOKa5rqiKuss75lnfMs75lnfMs751nfOs750yG55tka01kOpztqETKP5oRTVvRdSIbsAVM17lXjkbFlYMxKurYgzCoQi/ebUVpsmdzxJThRWPH3XT6M4kRrB2mS0+UO6HWqGCQxCHrGsoxI7iSbSbSpNCEQd67ehMqiKm7TuQhQmyaMRB3hS+8cX5mI3gheOL8zEXOIAG0qgyYhD4oQ4Yr+iost2u34jeCF44qvXxHiEOJxe3iPEIcTi9vouvpnBN4J/Ao9EJkeGGUHiYm9dWgOERTJb7xWj3i9H3iro+Nej416PjU+r416PvFLq+8VrfeK1vvFa33itb7xWt94rW+8VrfeK1vvFa33itb7xZzfeq1vvVa33qtb71SNH3iqEPxow3Gyo9IXji/NxG8ELxxnCMFHVtLBsUndCvGIY2IUc5CDhAMeCNhzm8F5CMKXqOqKtx2rq1dDysSb/AFG1lGG3yUH1Rt4rKPzR8fM1qhDFW07AqEO3a7f/AC0ucZAVlOjHa6rh0Sw2IsdwVS6rl1MIjDhEK1qP41rUf3i62ERjxiFTcQus4lNoiSwT8Bn+1HojEPOYH7f/AIqFcHmPbTXw63N2IQ8m/khCNe9EFpLPWxyx1qnC5KTgQVU6S8nhUUXYq1qP41rUf3i62ERjxiFTcQus4ldVoGMstUt+PtVI1Q9rkIcMSAxmiCa0A8EV4p5N0qc5y2YnBrSaxYmhzSKzbink3SpznLYi95AaLSqLZiELBvQhwxX9FQZbtO9OZDh9UWdWazPkVCM3q3ZKhBb1bs1mfIsvLyk7JKYb8ioxR1bqowh1bqzfkWVl5Sc7Fm/IqMUdW6qMIdW6s35FlZeUnOxZvyLN+RB0VsnYn30zgmXQn8Cj0QsFuYu/H3YxiHH+Bi3ndIXji/NxG8ELx6Bi4PJkTaNhWTitII2FVdAlGIdmKTgpw3KiYlNu6JWvL4KR2sK60R7LzP7LW2LW2KqI991h/deQwUnteVLKljd0OpdYoAIN3dCroSbWspFmyFvNp4IQ4TaLf5dkRnRfoh0hEG1UvMM71gn4DP8Aaj0RiHnMD9v/AMVCuDzHtqnEMgpFj2jepgzBTsHhmbvSUt/Tk9oK6ri1dV4KsPcus3mFmLNKsctq9JUWBTiVu3IObUhM1qjDB7llohkdyF4+cL3uDWi0lUWzEIWDesnDHE7lQhjid/QN4IXji/Mxd4Q4nF7eI8QhxOL2+i/8RM4Jl1P4FHohYLcxd+PuxjEOP8DFvO6QvHF+biN4IXj0aMZk9x2hU4M4rOy0KT1ViohBvRzQtoVpVpW1VNGMFB2KrobyqMJhP0CD40osT4D+YOlmt6oQQOKWMsci11imOmzvWCfgM/2o9EYh5zA/b/8AFQrg8x7aherWhRtQ310VJUgu3zVirht5LMWb8SrPirPiuo0DEJBNys6G2S/5WiN+9HiheOKhEjNa7ctYYpZdk1pmrTNWmaq47VrDFrDFrDVIVQhYEGlwb2lZOHHZ2neg9jg5p2jFQiRmtduWsMRZDitc6YsQZEita6ZtWsMWWpjJ05zWsMUoUVrjMVIUooBmtM1ZSkKFK1aZqosiBxmqL4gaZrTNWUpChStWmatM1UmEEYn/AIiZwTLqfdR6IWC3MXfj7sYxDj/AxbzukLxxfm4jeCF49OZbQf6zVSYMo3e3+yk4KmdnmLMdmOSkbFUcVZCqrVGCwu4KlhTqR9VtioMaGtGwfzCJF2yq4ouXBS3dLc5UXCpTaek1Q4DYEEiGwNE57Fq8D4rQQfitBB+K0ML4rQwvipZCD8VoIPxUvs8D4rV4HxWrwPitXgfFavA+K1eB8Vq8D4rV4HxWrwPitWwf4rVoHxWrwPioWVhw2ZOcqPb/APShj7g8x7aoRBMKZe9w3JxbVISCLlI4pjHX5+tqbRO1HiheOL7O5xaHxJTC1uL4Qtbi8gq8Jick2A15cCAZlaw/kpHCInJa3F8IUFzIromUJtCqQgwRedsaFrcTwhHCGR3vNICRCF44nRco5tJaZ/JZVsRzq9qyrojm17Fpn8l9lpGVKU1pn8llWxHOr2rLZRwrlJaV3JfZ6VVKU1pXcllA8mtZQvIrWldyX2elVSlNaV3JaV3JCG2ZxP8AxEzgmXU+6j0QsFuYu/H3YxiHH+Bi3ndIXji/NxG8ELx8zDYGNyh6xdKuSG81+cnjrVRX+VWQphBu2H1f5lCgDb1igpKR6dGIFSaat6kandGuStanUy0ChtWkhcwj14du9ZzOazmrOYs6HzRm+HzWkhcwtJC5haSFzC0kLmFpIXMLSQuYWkhcwtJB5haSFzC0kLmE6mQBR2rPh8/MuiQ5SJ3rO+dU3xJC8q3E96GORsUx/AlxVM2NrKPFC8cUP8XoMabaDfqizBmZU+t6K/0h7KlhMEEb2ITFGC3RhCDBF52xoQgQBxO1xxOvtQvHoG8ELxxfmYjeCF44qvXxHiEOJxe30X/iJnBMup91HohYLcxd+PuxjEOP8DFvO6QvHF+biN4IXj5l59GchwH8JJGCbIgl3/zJzdjZNx0gu3HLoSNipQeSoxAVNpnjZ3qFH+2NGUYHSoWTWut92tbb4FrbfAtZHgWsjwKf2tvgWtt8Cn9tb7ta633f+Vrrfd/5Wut93/la633f+Vrrfd/5Wut93/la633f+Vrzfd/5WvN93/la833f+Vrrfd/5UGlGEXKTsbKUpf3UK4PM03lbh/tWSZY2rpdvTsx2q1Wq1Wq1FRIe8TR4oXjidFgCcRr5tqmtH+itH+itH+isrhNUQCVklRdnY5FCDg0EZO2YhWrR/orR/orJYUyUOc9HJC8cTmQ4fVFnVmsz5FQjN6t2SoQW9W7NZnyLLy8rOdizPkVCM3q3ZKhBb1bs1mfIsvLyk7JKYb8ioxR1bqowh1bqzfkWVl5Sc7Fm/Is35EHRWydif+ImcEy6n3UeiFgtzF34+7GMQ4/wMW87pC8cX5uI3ghePmIsXaG1cUXd38KyKNhpIOFh/mL4m8l3QmLFMY5HoyeK96pMrHYpRB3hTaZpnesE/AZ/tR6IxDzmB+3/AOKhXB5im8rcP9qoMsTn9s+jWuqq1WFnecnia32UeKF44of4vQwmIdsR31xUXZ2PBz92WN19qF49A3gheOL8zEbwQvHF+Zi7whxOL2+i/wDETOCZdT7qPRCg4O7BojixspgrVIvMKX2WJzWrROa1aJzU/s8TmtXfzWrv5qX2aJzWrROaB+zROa1SLzC1SLzC1SLzC1SLzC1SLzC1SLzC1SLzC1SLzC1SLzC1SLzC1SL4gtUi+ILVIviC1SL4gtUi8wtUi8wnP9Yk9IXji/NxG8ELx8wyH67voh2/woKgnaBR5fzCM7cwonFWQs5SJXVcD0K+lZJ3YqTKx2IUtihYPAgwm5NgZSNdinELHN3FkllWdUipzd3QGIecwP2//FQrg6dN5W4f7VQYFEP3SicVZCzlnLOGPNC6riqiFt5qx3JVhWBZvxWarDizlnKQQpEBMiNNVIFE/eCF44hEiuDGti1krXYS12EtdhLCDCeHsMQlpG7HRdnYoMN2EsDg2sLWoarwuGjCgYSx76YqCF44qESM1rty1hiLIcVrnTFiDIkVrXTNq1hiy1MZOnOa1hiLIcVrnTFiDIkVrXTNq1hiy1MZOnOa1hilCitcZipClFAM1pmrKUhQpWrTNWmaqTCCMT/xEzgmXU+6j0etJGUpp/2mhOdU1/pLq5Oa9BegvRXorrUF6C9BCeSmv9Jf6S/0l/pL/SX+kv8ASX+kv9Jf6K/0VVkV/pJ+SydOVUkcpLvXk6M+kLxxfm4jeCF4+Yhw/VYgP4UqLD9V0+f/ANfzCMewfVVBTZBcGb3dULy2FMbdbNdbCYp4SVWERfgvJ4YDeYsqQ17RaYZsUnVHzXWYD3LqMATqSwj1aIn0BiHnMD9v/wAVCuDpU3lbh/tVBgxRFUFNkFwZvd1QvLYUxt1s11sJinhJVYRF+C8nhgN5i6gZEuP/ALry0GIy81dZvJVOHRsCzG8lmBZi2hZ5WeXcFQwaC5z/ALomVSwlzcHHb1nKFDhxHvpMmSd6nvIQvHFkXEtpPlMLW4vhC1uL4Qtbi+EIYPDe6JNoNaIIIIxsixGFrX1NJ9JQoxjvBe0Gxaw/kpHCInJHCGR3vNICRCF44nRco5tJaZ/JZVsRzq9qyrojm17Fpn8l9lpGVKU1pn8llWxHOr2rKuiObXsWmfyX2WkZUpTWmfyWVbEc6vastlHCuUlpXcl9npVUpTWldyWldyQhtmcT/wARM4Jl1Puo9EKDhDsJiNL2zkAtbi8gp/aonJazE5LWYnJS+0ROS1h/Jaw/kp/aYnJazE5ID7TE5LW4vILW4vILW4vILW4vILW4vILW4vILW4vILW4vILW4vILW4vILW4vhC1uL4Qtbi+ELW4vhC1uLyC1uLyCcz1SR0heOL83EbwQvHzET7svousCF1SD56twW1ZqzVmraEZKMze2f8wf2kL7U+E10WkQCdnTMXBJQovq+if7IwcIhuY4bCqsdfTAFhtTsm6cUu8p2bugMQ85gft/+KhXB0abytw/2qgwYgxlTnbVNznSPavtT4TXRaZAJ2dOteUwRgO9nV+ingmFEfdiD91VBdEbvhGl8FRiMr7RIqsELPCzhzWe3ms8KqZVGGys96mYRhN3xavgqWFxHRzuHVaqECEyG3c0SxYIfuu/ZM4NQvHEz8ToQ7jfqqUaFKJ67KiurhkQDtaqTw6O779nJYJKqRd+ywUn/AKQxuB9YKX3j0DeCF44vzMRvBC8cX5mI3gheOKr1+i/8RM4Jl1Puo9ELBbmLvx92MYhx/gYt53SF44vzcRvBC8fMBrgHDKuq4AqYg5F2+HV8FTwSI2O3dmuWTwiG5rtzhIqvq8VV06yAqplUYTK9wEypmCYbd8Q0fgv+YwruYFXDfEvOXVwOB3smtVgeALrYHA7mSXkqcE9hmPiqcLy7N7LeSlvYR/MDeCbfPmMjhUMPlZvHBGLgZMeFuGcP7qTwqvMSNiEaA8tOwqHFfFhMc4VtLrCtYheMLWIXjCH/ADMGz1wtZg+MIf8ANQPeBa3A94FrcD3gWtwPeBa3A94FrcD3gWtwPeBa3A94FrcD3gWt4P7wLW4HvAtbge8CwTJRYcSVOdF09yoRJ5M/BTBmMdN5W4f7VQYMYeytzdik5rgBvCbfPmJbRjoxoTIg+82a0BhH7jpLyWFxW3mgqrD2+7/yq8PHuv8AK8phcV11oCrhPi33qUCBDh3Wy6OBC/8A+KhXWIXjiMaGwzDpipWD3a6tGf4akWj3SGER2GmBKpizW+6Wa33SzW+6TBhDJ0LJMkmQYbRRYJDyak5on+GrB7tZKK3q21MQDxKs9Bwa0msWJoc0is24p5N0qc5y2YnBrSaxYmhzSKzbink3SpznLZicGtJrFiaHNIrNuKYY6jSnZVLov/ETOCZdT7qPRCwW5i78fdjGIcf4GLed0heOL83EbwQvHzFL78Q/A46EeEyI3c4TRdg734O7xNU4NCMPuOkfivLYPGbeYq2hZnxWZ8VJrQvJ4LGlwohTwiPDg8OsVOLlI5+8ZD4KjAgshj7okuPmIgFlN/8AMInZL6pw3RT9B5iYxFzm5ON/1G/vvReW5SF/1GWd+5darp1oMZWq3gHdJaUcln/BSynwWlHJaYclpx4Vpx4Vpx4Vpx4Vpx4Vpx4Vpx4VrA8K1geFaceFFlKjVNaceFNa2p7GyBWRjA0fopgzCpvK3D/aqDBioVudtkpNqduKpvMgnQwxwTxuin6DzEx5/BmbmE/FNbuooXj/ACV/4iZwTLqfdR6IWC3MXfj7sYxDj/AxbzukLxxfm4jeCF4+Yo/fiD4Hp+Ugw33mzVeAYN7oKrAMG92F5OGxl0S6Eyp+YiOHrPP8wjt+4SsJhdodjMWPEDG9qo4JAn96J/ZVRaI+7DCm6IHXoYVHC4NH7zP7IRILw9p2hTC7cRfB/wCXi722HuXlmdTY8VtK3HoVoNaqYhNc7edinQYsxqzWqdFqzGKqGxaOGtHDWjhrRw1o4a0cNaKGtFDWihrRw1Do45iqILCjCig0d25bh/tVBgxnKfFMob1D9VCjasJhdodjMWPEDG9qo4JAn96J/ZVRaI+7DCm6IHXoYVHC4NH7zP7IRILw9p2hTC7fOiEPQY1n7/uu8IMiRWtdM2rWGLWGLWGLWWLWWLWWLWGLWGLWGLWGLWGLWGLWGKvCGLWWLWWLWWLWGLWGLWGLWGLWGLWGLWGLWGLWGLWGLWGLWGLWGLWGLWGLWGLWGLWWLWWJ0UOFCnOabQcDUmXU+6j0QsFuYu/H3YxiHH+Bi3ndIXji/NxG8ELx8wztjfX/AO/OzK7MXl48OHecpfah4SpQsKhE7pyPQJNgT4h9Qn4/zCRsRgO9KcP/AN5YjGieyPWKysZ3AbGqzHuKpsM2HObscmx4J6p+GKRtxFj2hzTaCjG/4dV/2ifojCjw3NcLQ4Vq2XFVETQdAgvcw+lYEMIwstixRY0Zrf7rRt5LRt5LMbyUeMLHPMuGxNqFipM6j/gpPaq6lOYRENpdwVLCDIeqFNs4fBdUTO8qg9okpl73DcpBolwVMTYdsl1Jlx2noClU4bQqDB0KdbXbZKba3byqDxMKZc4jcsk705s/95YjGieyPWKysZ3AbGqzHuKpsM2nObscmx4J6p+GKRt85Ej+iXlw4bP2RhumFpXclpnclpn8lpn8lpn8lVFdyWldyWldyWldyWldyWldyWldyWldyWldyWmdyWmfyWmfyWmfyWldyWldyWldyWldyWldyWldyWldyWldyWldyWldyWldyWldyWldyWldyWldyWldyWldyWldyVcV3JaZ/JaZ6AGxPuo9ELBbmLvx92MYhx/gYt53SF44vzcRvBC8fMMwhtvVeO7/AOkHNMwax5uZVadEiODWNrJKMLApwofrekf7Kk91Z3q0qooCkXw/UdZ3LKQjWM5ptbiwiJ9yQ76lGi8Gj+YswqHVSk4Xgocdma8TRY0+Th9Vv7lSHRkV9mefJxauDtmOeOhhMFsQdtoU8HwmJC7HCkgcIwsvb6rWymmwoTQ1jRIAdCIQfKRPJs78TeGKTgCO1dWbFXGquqhDH+f4VmEQ6jMOHEKHHZY8TRY0+Thmi39ypDoyK+zvPk41XtbMc/NRCD5SJ1Gd6dG31DoV9Cr+Prxvuo9ELBbmLvx92MYhx/gYt53SF44vzcRvBC8fMMjD0DI96hTPWheTPdZ8PNTOP7LCPkYZleK7ehIpsaEf/wDobkyPCPVeJqFgoNbzSPAf+/BQwbXdY/zFzBnitvFYRg0Qy6rnQp7HbkT02vbUbVDij02h2KW/zXkz5CF1Wdvagm8ETuXVAPBqmWyFxWt5K1vJWt5LqgHg1Zh8CzD4FmHwKTpA9rVa3krW8la3ksz5Foz4Foz4FMtkLitbyVreStbyXVE+DVoz4Foz4Foz4Fk4oFXYsIwd59AuhXtyLum17ajaocUem0OxS3+a8mfIQuqzt7U1kNooizqrN+RZvyLN+RZvyLN+RZvyLN+RZvyLN+RZvyLN+RZvyLN+RZvyLN+RZvyLN+RZvyLN+RZvyLN+RZvyLN+RZvyLN+RZvyLN+RZvyLN+RZvyLN+RZvyLN+RZvyLN+RZvyLN+RZvyLN+RVyHsr0fCiDKv7qNXRCZAhllBgkJsXVoHhDUiG+7Vg92vR92q6PgXo+Bej4FLq+Bej7tT6vu1Y33asb7tWN92rG+7Vjfdqxvu1Y33asb7tWN92rG+7Wa33Ssb7pWN90rG+6UzR92qjD8CMRwtmT0heOL83EbwQvHzD4TrHCSdgsYyZFNA9jtnmpbsUaKM6Um8Si7px4ER0mw/KCewbVOvJz5MH/vx/mf2uHY49cdu/wAw1YL+Cz6Yh5l3/D8Dd2RXj6IuOdamt9VN4J3BR3vcGtDKyqLZtgiwb8YhQhxO5ZOH3nfjycORjH5UXvJLjaTjGE4S2r0GH64y97g1otKotm2CLBvxiFCHE7lk4fed+PJw5GMflRe8kuNpPmWrBfwWfTEPMu/4fgbuyK8fREnOtTofqn+UQp20UPMFRL2Lvxnojh5odCLdTumLxxfm4jeCF4+YiRfVanP3Wr7PGd/zMMeMb/Mk4obd8UfQodN7WOIpiie0Kk8eVfb2dn8zY3fEQ6YCaweiJeZMWPEbDYNpRwbAaTIRqL/Sd/ZUn24iE3gncCpTqOMQoQ4ncsnD7zvx5OHIxj8qL3klxtJxjCcJbV6DD9cZe9wa0WlUWzbBFg34xChDidyycPvO/Hk4cjGPyoveSXG0nGOmAmsHoiXmTFjxGw2DaUcGwGkyEai/0nf2VJ9uKhvqxUXRWg8VMKk9waO1dR4diouitB4qYVJ7g0dq6jw7FRdFaDxUwqT3Bo7V1Hh2Ki6K0HiphUnuDR2qTHh2Ki6MwHiphUnuDR2rycRruGKi6MwHiphUnuDR2rycRrsVF0VoPFTCpPcGjtXUeHYqLorQeKqrUzaoV1ZwWcFnBZwWcFnBZwWcEQHBPESIGzO1adqqjNWlC0oR64WeFXEC0oWlCHlmrTtWnatO1adq07Vp2rTtWnatO1awxawxadi07VEa2K0khGk6Szws8LPCzws8KTXAoMixWtdSNRWsMWWpjJ5SdJayxFkKM1zqQqCDIsZrXUjUVrLFrLFrLFrLFrLFrLFrLEIUCKHzdXJSLhNNjYNFouBmJbFRw5wgRm7fRctehLXoS16EtehLXoS16Ej/AM7CWtw1rcNBkHCGPeIgdIKRKzgs4LOHQhTHp/zSA3tJTenBZviNHQ4dDy2FQ5+q2sotwGB7cT+yymExXPPaqrcZ7U3gn3TjEKEOJ3LJw+878eThyMY/Ki95JcbScYwnCW1egw/XGXvcGtFpVFs2wRYN+MQoQ4ncsnD7zvx5OHIxj8qL3klxtJxwokQSyk5BN6cFm+I0dDh0PLYVDn6rayi3AYHtxP7LKYTFc8/e2KYtx0222ovbUTJTT4ZsFYRaTU2oIOaawi9tRMlNPhmwVhFpNTagg5prCL21EyU0+GbBWEWk1NqCDmmsIvbUTJTT4ZsFYRaTU2oIOaawi5tRMlNPhGwVhOaTU2oIPYawi9tRdJTT4RsFYTmk1NqCD2GsIvbUXKafDNgrCLSam1BBzTWEXtqJkpqJDNcqx2KZTCdyMSmc2aa+mawnzcRRdJZKkZUZpknE0nSTn0zUE19M1hPm4ii6SyVIyozTJOJpOknPpmoJr6ZrCfNxFF0lkqRlRmmScTSdJOfTNQTX0zWE+biKLpLJUjKjNMk4mk6Sc+magmvpmsJ83EUXSWSpGVGaZJxNJ0k59M1BNfTNYT+uRRdJPhs61FSiNIVX2k8GhWYV4ArMK8AVmFeAKzCvAFZhXgCswrwBWYV4ArMK8AVmFeAKzCvAFZhXgCswrwBWYV4ArMK8AVmFeAKzCvAFZhXgCswrwBWYV4ArMK8AVmFeAKzCvAFZhXgCswrwBWYV4ArMK8AVmFeAKzCvAFZhXgCswrwBWYV4ArMK8AVmFeAKzCvAFZhXgCswrwBWYV4ArMK8AVmFeAKzCvAF1ftPfD/yuq2JyUnhTChu3Paf5k4scWmYFSy0F/VnKuImswozcRMdaaHTbHo0qD5yXWwLlE/wq8Dd41qbvGupgcuMT/C6mDQBxmVSpZJm9sJeWwmLEH3n1LrFVDotKZFhuFBwmOurR7xZsPxrNh+NdWiOERZ/6iz/ANRZ/wCopkMJvrNh+NZsPxrNh+NZ/wCos/8AUWf+opOII7YizYfjWbD8azYfjQbENEvr6rkHhwkRPSrOHvVnD3qmRDJvrNheNZsLxrNheNNh4UZuImOtNDptj0aVB85LrYFyif4VeBu8a1N3jXUwOXGJ/hdTBoA4zKmIght+7DXlsJixB959S6xVQ6LSmO4YonBPvIId2KJwT7yCHdiicE+8gh3YonBPvIId2KJdUS8gm+ziiXVEvIJvs4onBPvIId2KMfuqZUPgs481nHmqieamXGfFSFJxVJ8w3iqTJlvFSNJpUw4z4qsnms481nHmqieanMz4qsnms481nHmqieanMz4qsnms481nHmqieanMz4qt3xUqRUwHAcUckTPiiXNzraW1OMXdJrVOFErCOSwdjpW9Zam3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xham3xhdbAmh3rB4VGI0hBwU/5ibwQvlMG6EPqfMND2hzesZET2KvA4HuwtUg+FanB8KqwSB7sLqQ2N4BYQ37tLlWiOnPtUMeqSPj/BwLhUK4Pp04Y3Qh9T5hge0Ob1jIiYsKrwHBvdhalA8K1KB4Fg7YEGHCBYZ0WyQk0DNUQS3lEdOfah2OliicE+8ghVuxROCfeQQq3YonBPvIIVbsUTgn3kEKt2KJwUS8gm1eriiXVEvIJtXq4ol1RLyCDjb1cUThih8MVuIUpyQyUqK713o5WVFGjOWK1Wq1Wq1Wq1Wq1Wq1Wq3HNg6+2adwT+Ck4AheRnJPbElPaTtVOF1m/UKYwKo/fWpfOtS+dal861L51qXzrUvnWpfOtS+dal861L51qXzrUvnWpfOtS+dal861L51qXzrUvnWpfOtS+dal861L51qXzrUvnWpfOtS+dal861L51qXzrUvnWpfOtS+dal861L51qXzrUvnWpfOtS+dal861L51qXzqg7ApbjSsRa4SIUI72D+Ym8EIDoL3GZMwVlmtLRICR8xl3tLqiJBatE5rVonNatE5rVonNatE5pzDg0SThK3zDk6E+E583UqitWic1q0TmtWic1q0TmtWic1q0TmtWic1q0TmtWic1q0TmtWic1q0TmtWic1q0TmtWic1q0TmtWic1q0Tmob2QyyiJVpjPs0TqtAtWqxOa1WJzWqxOYWqReYWqxOYWqxOay7WFokBI+Y+0PYXiREgtVic1qsTmtVicwoT2Q3MoNlWm+yqJsJcEWnpuUdm6MDii3VFvoJvs4ot1Rb6Cb7OKLdUW+gm+zii3VFvoJvs4ot1Rb6Cb7OKNdUa+gm+zijXVGvoJgu4onDFD4K0K0IMG1CHZKwp0NrlTiGjetVOGaV21NhucjDtnaUWHYrQrQrQrQrQrQrQrQrQrQrQrQrRiovFbrVNjuCpejvdYpse1xRgRCRxWUdW4/BTA6prCbQc2YsATYcUmbezYrXcla7krXcla7krXcla7krXcla7krXcla7krXcla7krXcla7krXcla7krXcla7krXcla7krXcla7krXcla7krXcla7krXcla7krXcla7krXcla7krXcla7krXcla7krXcla7krXcla7krXcla7kspBnWK6tqwf8ADH8xLIjQ5p2FasxPhwmhrQRUOHmHiKwPAZYVqzFqzFqzFqzFqzFqzFEhgSAeZdNw7E4PbNHyYsTxEaHVLQtVFsFhiH4dDKxxVsatC1aFqLnQmABeShhjBjoM/wDpBphhx3laFq0LVRbBYYh+HQyscVbGrQtWhai50JgAU4TaLJ1DzEsIALSJVrJ4NDDITc6JvUDBILAHOM3HsToIb1A+UlSMJqPEIXiozf8AuHpuHYsJZvonFFuqLfQTfZxRbqi30E32cUW6ot9BN9nFFuqLfQTfZxRbqi30E32cUa6o19BN9nFGuqNfQTSfu4onDFD4K1ZypPcBUiWxATKpOjPro/VQ8kZA21JkOi6bqu9NjMqpW8UC6IAZVqkxwNSzlnLOWcs5ZyzlnLOWcs5Zyzk0ONU61pQpejP4JzoMgW/BeWBdEnsQwhtoAPcqMR4BCFF4JBRNFtKRrUVpO5ZwWcFnBZwWcFnBZwWcFnBZwWcFnBZwWcFnBZwWcFnBZwWcFnBZwWcFnBZwWcFnBZwWcFnBZwWcFnBZwWcFnBZwWcFnBZwWcFnBAgiYcofZMfH+Zxb3TPBRj90dN/aQfh5gjsTuCicFRbXEPwRc4zJxiNGF1uMucZAKi2qGLBjoM/8ApUGd5346La4h+CLnGZOMRowutxlzjIBUW1QxYEOmeCNAWLIxmmhPYKwsIwukJnqMbOuSd+Ji7wheKed8j8PMRG7x++KNdCi30E32cUW6ot9BN9nFFuqLfQTfZxRbqi30E32cUW6ot9BNF3FGuqNfQTfZxRrqjX0E32cUThih8FarUWl0qkSHkkBRIW20KJEpAisiv4J7nQ5lth3KHC22lAl5BIQaHTqVqtVqtVqtVqtVqtVqtVqAnUStIUGusnRTaDhRlYShAisp/wDtqc3f1QFTc4itUg8mtZV4rkTanUxMBZvxWb8Vm/FZvxWb8Vm/FZvxWb8Vm/FZvxWb8Vm/FZvxWb8Vm/FZvxWb8Vm/FZvxWb8Vm/FZvxWb8Vm/FZvxWb8Vm/FZvxWb8Vm/FZvxWb8Vm/FZvxWb8Vm/FZvxWb8Vm/FZvxWb8Vm/FUmiue9NvH+ZxvxD03cFhBu/v0wd7AfMS4p3BPLR1iJBFzjMnGI0YXW4y5xkAqLaoYsGOgz/AOlQZ3nfjotriH4IucZk4xGjC63GXOMgFRbVDFgxDpu4Kcpg2qK+K7rE9VoNYUWFBeDk9pTWPzg7F3hC8UDvYD5h3HFGuqNfQTfZxRbqi30E32cUW6ot9BN9nFFuqLfQTfZxRbqi30E32cUa6o19BN9nFGJ9VRCfWQTfZxROGKHwW3E1+xUyeqjFgzbIzC8swg9i8iwk9qEWNN0zMqmD1U5+zz1SD+a8nXs4qhHaXS9IWrqh7iqb7BYNyAZVLYhCbXL6rqxXV1SVOHEDAexawPCtYHhWsDwrWB4VrA8K1geFawPCtYHhWsDwrWB4VrA8K1geFawPCtYHhWsDwrWB4VrA8K1geFawPCtYHhWsDwrWB4VrA8K1geFawPCtYHhWsDwrWB4VrA8K1geFawPCtYHhWsDwrWB4VrA8K1geFawPCtYHhWsDwrWB4VrA8K1geFCHEiB+2xNvH+Zxj/3HdN3BR+I6cF33UOmLyI7FmO5LNPJZp5IOoTlvCs+VWfKrPlVF85dgWaeSzTyWaeSow2geyrPlVnyqz5UXODiSs08lmnkg6hOW8Kz5VZ8qs+VUXzl2BZp5LNPJDpu4IybPuWY7kiYdMTt6qbEe0znM1Yu8IXioTvuodPItIBc6qaMIwYk7qfEjCT37NwRwqCwva6sy2FAFjmt2uIlJOgCr1UYRgxJ3U6JFEnv2bgjhMJpc11stiALHNbtcRYnQG1eqjCMGJO6nRIok9+zcEcJhNLmutlsQBY5rdriLE6A2r1UYRgxJ3U6JFEnv2bgjhMJpc11stiALHNbtcRYnQG1eqjCMGJO6nRIok9+zcEcJhNLmutlsQBY5rdriLE6A2r1UYRgxJ3U+JFEnv2bgjhUFhe11ZlsKALHNbtcRKSdg7aj6KMJ0J4PBOfEqc7YjHhNLgd2xCbSG7SQjCFW5GHk38k58QSc7Zih8MdSbDLqhZNSFc7TvVNrADPYqbmAme1SNUrDuToYdUbZKvz7mAyBtQizDnH4IuLBPenUxOSydEUdy8m9GI9xG5CHSykqgg0YNUPvLVvmWrfMtW+Zat8y1b5lq3zLVvmWrfMtW+Zat8y1b5lq3zLVvmWrfMtW+Zat8y1b5lq3zLVvmWrfMtW+Zat8y1b5lq3zLVvmWrfMtW+Zat8y1b5lq3zLVvmWrfMtW+Zat8y1b5lq3zLVvmWrfMtW+Zat8y1b5lSdg8heTohXtn+ZxXfecjQbYutaiWtqFqmbUXBvVHYu1OUU/f6eDm9+ybwxTUobUadSNBtQ7EaVRCMZrbK1ucNn8U53aUaDbF1rUS1tQtUzai4N6o7F2pye42T6DQ5pM9ymN4VI2AuUOgwtoztTeGKalDajTqRoNqHYjSqIX2lja2GfYiCKMVtrf4uRxTKmfMQ+CsVisVikDMbiqBZI8VQDJnipEyG4KxWKxWKxWKxWKxWKxWKxWKxWKxTY6SIcwdycaNKaowxK6nCNnNrop7MHcaDrQi77M5x4rVT4lqp8S1U+JaqfEtVPiWqnxLVT4lqp8S1U+JaqfEtVPiWqnxLVT4lqp8S1U+JaqfEtVPiWqnxLVT4lqp8S1U+JaqfEtVPiWqnxLVT4lqp8S1U+JaqfEtVPiWqnxLVT4lqp8S1U+JaqfEtVPiWqnxLVT4lqp8S1U+JaqfEtVPiVJ2DEC8rKLdyLdqf8Ain6D+TWhZw5rOCzlnK1bVtVhVhVis+K2c087yU0OwmEIjxN/9k84G9roTusKOxCAcJg1t6/adqitwV4dBpdUhfZPtEEssM/ST2wn0oVIgO3hOAdWniqt62cls5L/AArfgv8ACt+Ch9hQm5ZwUgVSiYRDbGfW6f0RiYFEa6G8UjR2FNa7CYQiOE3z3qJ9je10F3WFHZ2IYP8AaYNY6/3jtT/s8QOgUuqRuK0gWkatI1aRq0jVpGrSNWkatI1aRq0jVpGrSNWkatI1aRq0jVpGrSNWkatI1aRq0jVpAtIESmh2EwhEeJv/ALJ7sDe10J3WFHYhAOEwZkdftO1RW4K8Ogz6pC+yfaIJZYZ+knthPpQg4gO3hOAcJpwiRA00lpmrTMWmYhTdDdLeqMOICZ7EIcWM1rqRqKZ9jyc6XWooTcs4KTSqUTCIbYz63T2diMTAnsdDeJmjsKa12EwhEcJv4qJ9je10F1Yo7OxDB/tMGsdf7x2qlg8QGBTt+6VrLFrLFrLFrLFrMNaxDWsQ1rENaxDWsQ1rENaxDWsQ1rENaxDWsQ1rENaxDWsQ1rENaxDWsQ1rENaxDWsQ1rENaxDVWEMKmY7VpmrTNWmatM1aZq0zVpmrTNTDDeHSGxGjQp0U2nQpSrT8pRzql6OTopmTo51adQoUpVJtOhSlWn5SjnVL0cnRTMnRzq06hQpSqTadClKtPylHOqXo5OimZOjnVp1ChSlUm06FKVaflKOdUvRydFMydHOrTqFClKpNp0KUq0/KUc6pejk6KZk6OdWnUKFKVSbToUpVp5iUc6qYUQwxNjrNimZDYqQjQJ9q1iAtYgLWIC1iAtYgLWIC1iAtYgLWIC1iAtYgLWIC1iAtYgLWIC1iAtYgLWIC1iAtYgLWIC1iAtYgLWIC1iAtYgLWIC1iAtYgLWIC1iAtYgLWIC1iAtYgLWIC1iAtYgLWIC1iAtPB5FWt5Kb3TXlY8IR4mfP6J/2V4dCNYlsT21Z69Hkti2LYti2c1s5qweJbOas+KsVhVhW1bVas5Zyzgs4c1aP4DrLarSs4rOWf8FnLOCtCrkrBzVnxX+Vt5okOcOSz3fBZ71nv5LPfyWe/wrPf4Vnv8KoilyW3ktvJf46FEmS0i0i0i0i0i0i0iz1nrPWes4rOKzis4rOKzis4rOKzis4rOcs5yznLOcs5yznK1ytcrXK1ytcrXK1ytfyVr+StfyVr+StfyVr+StfyVr+StfyVr+StfyVr+StfyVr+StfyVr+StfyVr+StfyVr+Szn8lnP5LOfyWc/ks5/JZz+Szn8lnP5LOfyWc/ks5/JZz+Szn8lnP5LOfyWc/ks5/JZz+Szn8lnP5LOfyWc/ks5/JZz+Szn8lnP5LOfyWc/ks5/JZz+Szn8lnP5LOfyWc/ks5/JZz+Szn8lnP5LOfyWc/ks5/JZz+Szn8lnP5LOfyWc/ks5/JZz+Szn8lnP5LOfyWc/ks5/JZz+Szn8lnP5LOfyWc/krX8la/krX8la/krX8la/krX8la/krX8la/krX8la/krX8la/krX8la/krX8la/krX8la/krXK1ytcrXK1ytcrXLOcs5yznLOcs5yznLOKzis4rOKzis4rOKzis4rOKz1nrPWetItItItItItItItIeS0h5LSHktI7ktI7ks93hWe/wAKoike5el4Vt5LbyW1bVtVhVh5rbzX+VZ8VYOaqkrQs4LOWf8ABZyzirStq6v8ysVisVgWaFmhZoWY3ksxvJZjeSzQs0LNCzQs0LNCzQs0LNCzVmrNWas1ZqzVmrNWarFYrFYrFYtvT2ratq2ratq2ratq2rarXK1ytcrXK1ytcrXK1ytcrXK1ytcrXK1ytcrXK1ytcrXK1ytcrXK1ytcrXK1ytcrXK1ytcrXK1ytcrXK1ytcrXK1ytcrXK1ytcrXK1ytcrXK1ytcrXK1y2ratq2ratq2ratq2rb09qsVisVisVizVmrNWas1ZqzVmrNWas0LNCzQs0LNCzQs0LNCzQsxvJZjeSzG8lmhZoWaFYFYrFZ//AFIydFhg9rlMEEf/ADbQHlIu7cqLaZHqtqAU4sVrOAmjGwXCrKy0i1TseM5vnSyH5V/ZYpQpjshtVIUzeIX2fCYeRj7t+N8ZwJDQpwjWLWm0f/K+RhHyrtvqrLR5iH8XKhDaGt3DE7gmAWP6p8w9rTOGJdU8E149ITxnB8HPUsJHpIRMLnc/uqMNgaOwYoEYVOBlNQ4ptIrxRuH7p/4f7hRSPUKbg9ImG6dR2Vf/ACoXmwCaLolmc7huUQw+qQJCWxGBFNOTZh23FFO0iiO9MOxgpeYjcVCuD6YhCYetE+iGFxRWcwfuvJRCA1o6uxQ4pEi4TxQYXFyZxOKNw/dP/DP1Ci3D9FB7/of/AJUjBttAqM7bMI9rgoh/7f74m4O01MrdxTsIdVT+gRh4M6gz1tpRwpsZzKqQBdnJsRw61hQEqcQ+igyMzJT2zqTXUKZcZSmmxmWFR7yZdGKHD3tA+KDRYKlG7vooH4YxPiCyxvBQoRtDa+OKNw/dP/DP1Ci3D9FB7/ofM5GJEk7bVYg5jg5psI/+SzEwZwEN+fDd+yH4gUY/dCpemc0IMtc8zJTMHh1Uur3Kk8eTh1ntUV33ZDvWUfUK3IU/9R1fBUoTA10IVS3KCx3+m2X/AL8FD7Zn4qPfKaOzEzCIGlh7N6pGG+GdocFHvKCPuD6L7LDPWdn9gWXePJwzzOIuhw8o4ejOSiYO7BjDJqzrEYpZTm2Vqez7ORSaRnKD3/QoxXV7AN5TftEJuTdYW4jgzHEMbndpQwuFHc02luyRQiPzgZFRnw3nPrGwp8Y+iFhGGRZ1fFxKjQ/VcDz/APr/AOTGD/ufsVHe+ZcZUW71Tf1nGoAKb9K63s7FCZuZNU/XdNMwOAZsBrP7pzGVCQYFPcwqK8+rIccUAfcCj3ygOhHvlNhQyHRqI9lSmd73lNhQxJoUZ33Zc0YDnkw6MwDsUV5Y2lK2Xanh7Q4ZM2jtCikQYeYfRUHv+hTcGh2NNEcVBhNsbBA+JUN5/wCmD8EAbYj6+9RhsoKJf/ZR75TMDh17+KyLezvM1hHs/v8A/JdD7MXDY6lUUGua1rQZ1JzobCQwTKZSA63V4YmnexQ2NwiGyARstXV6zja4rIsPUh/VO7YZ/ZQ8Dh1y/wBxT4c50XFqgD/tt+ij/iO+uMxIjg1o2qp5fwaokQVBzi5Uos4bN5tK/wCWaBI7UZtovbamQ/Xd9ELpUbh+6f8Ahn6hRbh+iEb1QZck/CHV0PqU38MfuqX/AGP2TPuzKifekFP1nkqPeTsIiV0et3o3gsI9n9//AJLkQCFDoMa3r7Aot5PYKha3gmRdtjuKZHaMyo8Fk9sMrJsPlX/DtUZ7x5WI2Y7BahFlOop+GRa5Grio98qFcH0Uaf8A1T9cZY9oc07CtEfEVEhtHVEQgDvxG8FH4BCHsht+KF0qNw/dP/DP1Ci3D9MTN7+sULgX5A+id+Gf2UKDvNIqFD+7Wo95U9sQzRvBYR7P7/8AyZDv/sol/wDZZZo68P6LJvPk4nwKLXCYNqMTAIokfRcvtGGvDjOct+IugxaAPokJsFljQo3H9lAP/bb9FF7TNQhlBTiCpot6ET8Y/XFE7vqsJiPsa0ErC8LibB8ShdKiQQZFwUTKwy2TJT2WhRbhUOF6zpKQQuBQWmwwgDyQc5pIB8QWUonIj6Yowl6Shw/VaAjeCwj2f3/+TMnFFWwjYnMp05unZipsHknWdnYhAjulEFhPpIQy4UjYOiXSqe0KAfuSQykw4WEKkybn+s7oCOx2TdSm7txOhvzXCRRbCjsybrZqJCZ2TO8zXBh6AwmGC0j0RZipkGiW1FQLgVeOcsToVKibQVHEZoE5Srt/+Tyx7Q5p2FTgxiwbiJomZfENrj0ZRGNeO0KhDaGt3DzNL0gZOHamQdrnT7lFjeyOlQiNDm7ig1okBUP/AJndhGAOPXtaCqUcEb3OKbCZYP8A+Mb/xAAtEAACAQEHAwUBAAMBAQEAAAAAAREhEDFBUWHw8XGhsSCBkcHRMFBg4UBwoP/aAAgBAQABPyH/APVsnczaWBHHjjxx448ceOPHHjjxx448ceOPHHjjxx448ceOPHHjjxx448ceOPHHjjxx448ceOPHHjjxx448ceOPHHjjxx448ceOPHHjjxx448ceOPHHjjxx448ceOPHHjjxx448ceOPHHjjxx448ceOPHHjjxx448ceOPHHjjxx448ceOPHHjjxx448ceOPHHjjxx448ceOPHHjjxx448ceOPHHjjxx448ceOPHHjjxx448ceOPHHjjxx448ceOPHHjjxx448ceOPHHjjxx448ceOPHHjjxx448ceOPHHjjxx448ceOPHHjjxx4cqRp3if+xd18iiMWQX3nc16EROL/AMPd3d3d3d3d3d3d3d3d3d3d3d3d6MNmHqLoQAsMI/1WwcOcOcOcOcOcOcOcOcOcOcOcOcOcOcOcOcOcOcOcOcOcOcOcOcOcOcOcOcOcOcOcOcOcOcOcOcOcONP5nBHBHBHBHBHBHBHBHBHBHBHBHBHBHBHBHBHBHBHBHBHBHBHBHBHBHBFxM9jhzhzhzhzhzhzhzhzhzhzhzhzhzhzhzhzhzhzhzhzhxrdUexwRwRH/AJ6FAFjI/wBj93XyIoDZKwEBiE5beLtxX/iH/OnUhWHIIw0GxJTYu0X+hozaUJXX/wAqVEbQ7EoMi7VGCQl/sfu6+TZshmko0zQyQ541nKxYlLwhxzZLat/A5shp2vgnOFlZZyv1pKkYmpNfOOXHLjlxy45ccuOXHLjlxy45ccuOXHLjlxy45ccuOXHLjlxy45ccuOXHLjlxgoUqVj3a0UtFCgC6yP8ArDoqYpUmKQvLKKV1LzTsJKgj49XZWIaENzeLLB26hOUbDl5p2GnQdnriglyMAQSqhgCCXUKz1RSQ5IDME5URdFx0II7lgJVxxsp/zpB8gpSzn5z85+c/OfnPzn5z85+Jn7znZzs52c7OdnOzmhzQ5oc0OaHNBO/5hzQ5oc0OaHNDmhzQ5oJv6DmhzQ5oc0OaHNBpcP5xzQ5oQZJLqKubWLwrNaokqCV1/k1XnGwkdCCOxYkFuK3Cai6Lh2euKCXIwBBKqGAIJdQrPVFJDkvNOwkqGw48oFaqG5ZWIaEtRcL/AL1ZFzAtJp09efMShgn7CQeV1WOrwhpi0Xkm440fkmmNeF/M5znOc533tbg05sm6BG8qikhz/pHdfJs2R3PwOmGNtiphjaMjaMitenySWjWsju3k7D/3vheMKb5NtjpCh4Bttyy+sss2lDSdqGwZm30t7fb3Hc/JvmRvmR2PybTQqYeBf1Q11tLrN0ETpXVvwTWEpTaU/wCKQ2KHJs5NnJs5NnJs5NnJs5NnJsRcr52KJXyP8OWf4cs/w5Z/hyz/AA5Z/hyz/Dln+HLP8OWf4cs/w5Z/g33/ADP8Ml+r/Dk2cmzk2cmzk2cmzk2MvvYglfI/w5Z/hyz/AA5Z/hyz/Dln+CL7n+DSH8r/AA5NjeBYLYqgrCzDNpQldfW2oJS20JEzrXRP0S62lUk6Chh4F3RG00O5+TfMjfMjsfkut7re7fQ2DMSDtQRtD9WfbVlbJ4bO2s7azw/zv+pueY7L5/0juvk2bIr73IYDRQ3EMBpobRkbRkdj8jUqDBKxxlU7D/3viUlZNxCk1zsX1tndaGwZm30t7fb3Hc/JvmRvmR2PybTSxpNZkt7Nq6oaYyO9rDsyO96V++HuJppNOV6+ztrTYUpIZ/Jt77NR2KTsR9duhl8m3vsUNrVfDD4HRf3ppopJxuCui+o+t0Jg2a0eisY0/kpsc11e2hKxhJyYDY/Jm9BNWsMSCHUBSs1YwrpFPWSiTHNyCo0vcYQyEmG2aITSRt77NEHtsaFTeiaNvfZt77L+XxSXQMqKr4YfAnOEwl5pOE1IdNOVfG4NPNLTsY66cQxodh9j8W4ZtKErr6G0k23CJ73pT74+w0xmV7WPdkN7No6ISSWVm00O5+TfMjfMjsfkut7re7fQ2DMvt7hK+oPtqytk8NnbWdtZ4f53/U3PMdl8/wCkd18ipzbkbRkRrZHsI0sjaMjaMjsfmzufkoWuhf8AvfEb8Xnsty+ts7rQ2DMTm7fb7e47n5N8yN8yOx+TaaehyQZeniSfyk/4JymnSsdHgVZLGo+j9XZ22w5o/ZOEXNqrurcP6OphmVPvYFBAYhowM0JKqZqT+jfchLavSTb87m1ZjmMVyKtPgrDaG1gxzsInknT4kfA9qRw2hV6l/wCGIHfBq50D29+TwEl2lNYixWBtZMckI4lCzHf2YXW9CtIh/Rju8T9gzdGhrP8Awc1m5PMOwJWmsKjU8KExoh65+EeAp5aZmhIsQWsgZG2yLj/+FW5fyEmmdi8jEZbyGrHmJTKpNe6/RKjkGOx+PRhm0oSutlGSxqLqxuUk60jq8SD+El/RSQRclh6Npodz8m+ZG+ZHY/Jdb3W92+hsGZfb3ev/ALasrZPDZ21nbWeH+d/1NzzHZfP+kd18m0ZG0ZCASaG4xAJFDaMjaMjsfmzufk7VF4V3/ueUJKybjFJrlYvrbO60NgzN9pb2+3uO5+TfMjfMjsfk2mnqbHDeiUzure6PAQk9042aFaU0H7Z+js7ezZimdwpSyEFMpKaXZVxuqrLt9yEJbqP3aO8G1ZnYQZLw7vOiEGxtNyH2O3+hv9Ed08nZo75eFY2/1Z3O0xPpj3QX6IkmpX1iNWp7s7iHbLNqzNuyG86G86mHjFZiIsqdZSnO9TsXk7p5JKu6JgqOBMl3r8sVNjwHY/HpxKwbWfvkNTevu42aHZW91eImKG5EJeraaHc/JvmRvmR2PyXW91vdvobBmX293r/7asrZPDZ21nbWeH+d/wBTc8x2Xz/pHdfJtGRtGQ6YY20KmGNoyNoyOx+bO5+Ts1ZeFd/63UgzbifnCm6DbRJLaXisvrbO60NgzN9pb2+3uO5+TfMjfMjsfk2mn8H5vYJJe+6fD/Ra02arQMrYJwENrOzt7NmN0ytq43VVl2+5LNqzOwhtWSHNKEMrL6EuoCqdIEXSbNfJ2aO+XhWNv9WVk5MMQ9S9o14q3Gx5BCEp/ZiqaeJ3SF+HbLNqzNuyG86G862YeMVmPrwe1Tunki95TlkQ8paDVp9j8W4UnVOEJbHWX7ZasufdPl/gvJ7BI/htNDufk3zI3zI7H5Lre63u30NgzL7e71/9tWVsnhs7aztrPD/O/wCpueY7L5/0juvk2jI2jIdMMbbFTDG0ZG0ZHY/Nnc/J2atvCu/9LQ4NtuXZJeMKb5Ntkm9IeCy+ts7rQ2DM32lvb7e47n5N8yN8yOx+Taafwfk9i0F77J8L9FrXZutAypg3IS2s7O3s2YS4CpQTi4ycka18WVcbqqy7fchBKpN6leINqzOwg7QXrxoX7iciUDrkjXrVncO63ZBBDGX6aGdmjvl4Vjb/AFY33075YB3L7NQsxB6oiPqv+jY8hv8AVlbjGgPa90h2bVmbdkN50N51FTS1KcnyNzQTVYD4+XyMRnsVEBe6SbQ7naH2PxbhSVQ5YhsdJftFqi59k+V+C83sWn+G00O5+TfMjfMjsfkut7re7fQ2DMvt7vX/ANUmJpGWRzVC+NMqVk1ZJKqIG+6htvd7xrHygk3J9wgTXzxTUX7zmZzM5mczOZnMzmYnqe4OZHLyC41lSsVBs6r1TINECJP9I7r5NoyNoyGA0UNxDAaaG0ZG0ZHY/Nnc/J2a9F4V3/rOUpKybiFJrnYvrbO60NgzN9pb12nraDfa83zI3zI7H5Npp6mxQ3shI7K3uixEJvdONmhSlNR+2Xo2eo59LMbuYi9RTEoOatKic9BGltR/wIKKPOhCNx7zN9yEErshqf0bVmJXwiCimYbqOojakpb8xvOg5tbwHw2KVlU7NHfLwrG3+rG5TtiFdoQTqPP+2YotVHe0zY8hv9WdzBTEptDn0NqzNuyG86G86m6ZkDrFdFPEHavJghCvcEfBrqlAny/C0+x+PTiTnek/fMelv0YDure6rATHDcyU/VtNDufk3zI3zI7H5Lre63u30NgzL7e71/8AUQESyUkLCgTP2tKZqTC4FcFyJEao5lEVJpmcwLNxWYlOe6jjRxo40caONHGjjQki7Q4IcaKQ0qrx+2NdxkJ3Mm1iX+kd18m0ZG0ZEa2R7CNLI2jI2jI7H5s7n5OzXpvCu/8AUco34vPZbl9bZ3WhsGZvtLe7rQ7l5N8yN8yOx+TaaehyQRe3gSfwkv6JzknSkdFiUZLCoui9W31GpUMc+g/SXvYU9bHk6S7xttv3FmMKIMm17WPQ9tDZtuPcUgJS5odrxVHi619xHHIJXlXIVTDXvY8sZsMcDwKjoZDsswlnUQScl1VHZcg6avXNC34XCQnkEKpHNBJaapaih6YmSHStJpqZvvIZTVN6E8ghVIammyVmoktNUtQ0moZODgcrxMcKKySHwFZ0Mh8BWVTMQEslBFZvMGUC1Kw5djRmxsUnWT4w4LPky3afY/HrxUy8KjeqHyuK1TqiD+Un/BSQZc1j6Npodz8m+ZG+ZHY/Jdb3W92+hsGZfb3ev/tqyHY1EwuTjGRpNRkzgCCBTI4ckgUyOAI02oyZxjOMZxjOMZxjOMZxjOMZxLOJZMeBj5UCcOjg7L5/0juvk2jI2jIQCTQ3GIBIobRkbRkdj82dz8nZr1XhXf8ApOUJKybjFJrlYvrbO60NgzN9pb3daHcvJvmRvmR2PybTSxtJZEt7to6sSIjK9rHuyO9qU+2PuJJJJKF69vrY1Khjn0/u5pQpJX9Gk1DHPp/BzyISVYfY/H8MOGmmpRJ+zK/YnGpntYdmQ3u2roxNNZ2bTQ7n5N8yN8yOx+S63ut7t9DYMy+3u9f/AG1ZWyeGztrO2s8P87/qbnmOy+f9I7r5NoyNoyHTDG2hUwxtGRtGR2PzZ3PydmvXeFd/6Dk/OFN0G2iSW0vFZfW2d1obBmb7S3u60O5eTfMjfMjsfk2mhQw8S7qxrraHSTqInSuifomoJQkkJfx2etrUqGOfT+7mlCklf0aTUMc+n8HPIphHsfj+WHt4XpqUyU1lq/4ITbBWfqKmHiX9GbTQ7n5N8yN8yOx+S63ut7t9DYMy+3u9f/bVlbJ4bO2s7azw/wA7/qbnmOy+f9I7r5NoyNoyHTDG2xUwxtGRtGR2PzZ3Pydmv4XhXf8AnOS8YU3ybbJN6Q8Fl9bZx4FBp0EHbqyE+tciXS8VWUrdS3LszjwKCSoTvnZyQ5KMQhVFGIQ6iLQKTglyScSiVIi+bjqQR3PESrjiYS/n2ZuDus2PMImt5wdlL34srdMrO+eR2EO4h2zws3TKyt51Owhs2SzXelZnTGMjuXk7XwNjzWbHkO18DsXmzHdqzY8wkrecGM7MWVumVnfPI7CHcbB2kHsfj+mH5/3plMW5lu7GSGYJSoi+bheeuNjhyMAQQqhgCCHUPz1RsUuS8U6DTobDjywdqoTlFAjoQ63CPj1dl4p0ElT1/wDbVlbJ4bO2s7azw/zv+pueY7L5/wBI7r5NoyNoyGA0UNxDAaaG0ZG0ZHY/Nnc/J2a/jeFd/wCY5SkrJuIUmudi+/rZZtKErr/Ts7NwdxPYLZ3jfuMsxclLdfcwocUlHwS+nMxD+RTUohm5+5gveluPkR4NA4qez1GeTSOanu9DBe9LU/BufuKahEIlFeWgnuRi8bxz8iPFoFJV9upEzBl21djYyNU994s1hNR+RzBCFUo+BHiVzm+xH0raM0bv6CPoWkZIZ5le4vsmoEh1PyX1dMS14ElpqvZjZL8e2Xj+RlmLkpbr7mBDikvBP4cyEdxTUohm5+5gveluPkR4NA4qez1GeTSOanu9LT7H4/rhm0oSuv8A4f8Atqytk8NnbWdtZ4f53/U3PMdl8/6R3XybRkbRkRrZHsI0sjaMjaMjsfmzufk7NfyvCu/8pyjfi89luX1tl+RF7OqG2l3xpv5MockckLzIuR1YkbPBeckckJGzwXDRIn3AzaUJXX+fZ27g7rWpUMc+n93NKFJK/o0moY59PWfY/H9sVy/cGFwya85c5cSNncuGVc3s6o5c5cuMPOlMaE6Woivm5HV+v/toyER1Cp0orYFHiaZTgozTSvacSGbbd7TgpPjaZf5hBBBBBBBccNswFbW5GNJR2Tz/AKR3XybRkbRkIBJobjEAkUNoyNoyOx+bO5+Ts1/O8K7/AMhyhJWTcYpNcrF9bZlZoJeqCUE9mkRU32g2FngGqHGSFmg17ody8jUKguBKHSXB2LyI+mEkxWD2dqy0EB8H/wDQldf5dnZkP58OQCkDzSoJqlNXUc2G1Q/lF7B7nI5AcgOQHIDkByA5AcgOQHIDkByA5AcgH0r5B1MalQ5sc2ObHNjmxzY5sc2ObHNjmw2KH8ovQPc5HIDkAlQSaSVafY/H9MNiy2v/AMHu7VlqEfRCSZrB3LyNQqC4EodJcHY/JK7QS9UOMiYWWASqbfQSglo0makLtBr3T+H/ADY3U0GoINrFUaZtq38CWNsWEI0spxjuxTr+Du7u7sTkZD/5Fmr5LW81RykyNuL6CtMkF/8ApHdfJtGRtGQ6YY20KmGNoyNoyOx+bO5+Ts1/S8K7/wAZyfnCm6DbRJLaXisvrbO60NgzN9pb3daHcvJvmRvmR2Lyb/YaND13QkXjWWE6MgVr139CAtqqlE9v49nbOeRqRJ6GLiff+Ak1ZKPGxbV45M10GrdzfG7SxZXLfYUs5Mr7PSehi6xZXLfYUs5Mr7PSehi6xZXLfYUs5Mr7POucsdj5ILIXkS/BR42LavOAJDTBdYvIl+CjxsW1ecAYTiElDFdEpCOOHxgnoSpOwODghEkJRl9bT7H4/liLvAat7GB3Cr9zAkXnWWW6sSND1/U3+x3Lyb5kb5kdj8l1vdb3b6GwZl9vd6/+YlDBP2Eg8rqH6c9XVBjJVC33tDZJhmvYNmLXOYE/5r3ve97gIp7Q1aghbcaMCEbyqKSHP+kd18m0ZG0ZDphjbYqYY2jI2jI7H5s7n5OzX9bwrv8AxHJeMKb5Ntkm9IeCy+ts7rQ2DM32lvd1ody8m+ZG+ZHYvJv9rdDseK6MjW2lKfYZlx0DVR1FiRDeKx6l6+ztnQRb1yWLK5b7C/cO/UJzVFUzwBrTKZELhceXUv2N7llT4AsWVy32L9je5ZU+ALFlct9i/Y3uWVPgCxVROLKNTFrExmyqZ4Fi8iX4L3GJ52VTPAsvfWqGIlDc7Azb87T7H4/hiOvwWL6Ih7aFCegsCOsqan3GRqdjxfV27/Y7l5N8yN8yOx+S63ut7t9DYMy+3u9f/bVlbJ4bO2s7azw/zv8AqbnmOy+f9I7r5NoyNoyGA0UNxDAaaG0ZG0ZHY/Nnc/J2a/teFd/4TlKSsm4hSa52L62zutDYMzfaW93Wh3Lyb5kb5kdi8m/29MNQ8NBPcwZEop7YjRUikMNdQsvzFruv8XNZXLfYv2N7liqwQk01KqLyJfgp5M3sxmdgZU+ALFlct9i/Y3uWVPgCxZXLfYv2N7llT4AsWVy32L9je4biMdssfwsXkS/Be4xPOyqZ4Ft770KGIlDBveN+9p9j8erD8h8WvHjq1I6vojG5h1N7YENQsdRvf07/AGO5eTfMjfMhm2snkaTtS3u30NgzL7e71/8AbVlbJ4bO2s7azw/zv+pueY7L5/0juvk2jI2jIjWyPYRpZG0ZG0ZHY/Nnc/J2a/veFd/4DlG/F57LcvrbO60NgzN9pb3daHcvJvmRvmR2Lyb/AG9apv2f92Z7/vMbM0VI+VT3QiXHGyrdvqS5lhrK5b7F+xvcsqfAFiIikERrbvzWOVKaNkZre5iyuW+xfsb3LKnwBYsrlvsX7G9yyp8AWLK5b7F+xvcsqfABNLrqNSkl4rIvcYnnZVM8CxeRL8CaId96VCJK+wc+lh9j8ejDWrL2QipJyaeyFLrIdnQVJ+7/ALMvXv8AY7l5N8yN8yOxeTdaW92+hsGZfb3ev/tqytk8NnbWdtZ4f53/AFNzzHZfP+kd18m0ZG0ZCASaG4xAJFDaMjaMjsfmzufk7Nf+C8K7+5yhJWTcYpNcrF9bZ3WhsGZvtLe7rQ7l5N8yN8yOxeTf7fxvI/u9ViMJSvczaNE3sN5DCzb62Tta4MxVcVhZU+ALFlct9i/Y3uGwIFpOU8DCxzRqX6GJ1fnsqfAFiyuW+xfsb3LKnwBYsrlvsX7G9yyp8AWLK5b7CTfa8PkYsiqZ4Fi8iX4L3GJ52Rfel2TMaTUM0uOx+LcJuQh5DEYC3c4k0SLiP7PRYfx3+x3Lyb5kb5kdi8m60t7t9DYMy+3ut/CwdSh7PRtWVsnhs7aztrPD/O/6m55jsvn/AEjuvk2jI2jIdMMbaFTDG0ZG0ZHY/Nnc/J2a/wDDeFd/Y5PzhTdBtokltLxWX1tndaGwZm+0t7utDuXk3zI3zI7F5N/t/CXjes/fIf2Z0YDNwZ6ervZddsTCVm31tZLUVvLMqfAGliyuW+xfsb3LKnwAPe98RJqyXuMTzFMRKGLdM35dLFlct9i/Y3uWVPgCxZXLfYv2N7llT4AsWVy32L9je5Y06x+j5MLIXkS/Be4xPO2L70uyZ2v2PxbhvcN6JTO6t7o8BCWvRgIed6T9s/4b/Y7l5N8yN8yOxeTdaW9ZQY/k2DMvt7rfph9xNLFDEUB037btWVsnhs7aztrPD/O/6m55jsvn/SO6+TaMjaMh0wxtsVMMbRkbRkdj82dz8nZr/wAV4V39TkvGFN8m2yTekPBZfW2d1obBmb7S3u60O5eTfMjfMjsXk3+3qvQYK7HXX7dasuPdPl/ggr7kgldbadu8StjUJ21+RUr1LMWJngX7G9yyp8AWLK5b7C/cO/UJzVFUzwBrTKZELhceXUv2N7llT4AsWVy32L9je5ZU+ALFlct9i/Y3uWVPgCxVROLKNTFrExmyqZ4Fl770uyZ2n2Px6cPTWwSS890+H+nTX7VaMuQYqaerf7HcvJvmRvmR2LybrS3rtQ7QbBmX291v4AXCS6OY2RTu6TFJoyhMkvsqTE0jLI5qhfGmVKyasklVEDfdQ23u941j5QSbk+4QJr54pqL95zM5mczOZnMzmZzMT1PcHMjl5BcaypWKg2dV6pkGiBEn+kd18m0ZG0ZDAaKG4hgNNDaMjaMjsfmzufk7Nf8AjvCu/ocpSVk3EKTXOxfW2d1obBmb7S3u60O5eTfMjfMjsXk3+3okDXdfboiLGoV/8CVVHWSX6s6W+vfV42s2lCV1s7MsaUIdtSLRqly49PjvlbOzbEvOXE9ZV8rhNNSq2V4pvZl+NrnI9PjvlbOzbEvOXHp8d8rZ2bYl5y49PjvlbOzbEvO9RvEVx3idVYgGphDqURESVoc4c4c4c4KPqP2tPsfj1462+vXR4EqqKskL0ZFjUO7+hIGq+6nVejf7HcvJvmRvmR2LybrS3u+0NgzL7e637tRgYZCWmtUdcARBdNJoh78HHjjxx4TP2tKZqTC4FcFyJEao5lEVJpmcwLNxWYlOe6jjRxo40caONHGjjQki7Q4IcaKQ0qrx+2NdxkceOPHHjjw5UjTvE/8ARO6+TaMjaMiNbI9hGlkbRkbRkdj82dz8nZr/AMl4V38zlG/F57LcvrbO60NgzN9pb3daHcvJvmRvmR2Lyb/azJ303voiJNdE6l+iwIN6py/4DXHbzdX6mbShK6nZ2Wk00Jihp3NCulvdvGs2NLzhjhjhjhjhjhhN/wAK27mRLoP8LocMcMcMcMcMcMcMcMcMcMcMcMcMcMRphdK9fd3d2rBZKcXWn2Px/HGmO3E6Mg3qlL/oIk1UaqF6rEzd9F66qzf7HcvJvmRvmR2LybrS3u+0NgzHaetoErrZ+BauKpxrONZxrONZxrONZxrONZxrONZxrONZxrONZxrONZxrONZxrONZxrONZxrONZxrONZxrONZAtXFX/Rfd18m0ZG0ZCASaG4xAJFDaMjaMjsfmzufk7Nf+W8K7+RyhJWTcYpNcrF9bZ3WhsGZvtLe7rQ7l5N8yN8yOxeRV+abKg8cHCOvsjEViq/RHvi1G9/4s2lEh3raxpQlbZcz13B3WtSoY59P7uaUKSV/E+x+P549sWgnuYCsUV6oeKThHX3Qq/NNnQ7l5N8yN8yOxeTdaW932hsGZutBm0qwO5Z/+3cs/wDRfd18m0ZG0ZDphjbQqYY2jI2jI7H5s7n5OzX/AJrwrv4nJ+cKboNtEktpeKy+ts1thFZCf1eaDCKC4nBFvlQo1fzK2wisyRtMhoEhUiGxVmIG0SGkVHVpXsKl7sn7Zf0UHsyqsGmi6O4aYaYRf9Dbc23Nt7I0w0w0w0w0w0w23Ntzbc2nNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMMjum25tubbm25tubbm2486ViKo8XDSfR0kpNt/0VP2ZP3zHQyaW/YkbTIaBIVIhsVZiBtEhpK2wisjV/Mi3ypUYxQXkpG/q8UlbYRWduU4uZOrr3AxJiGzAzDgpNSOcHODnBzg5wc4OcHODnBzg5wc4OcHODnBzg5wc4OcHODnBzg5wc4OcHODoHDGhpjTNgr/AKJ3XybRkbRkOmGNtiphjaMjaMjsfmzufk7Nf+e8K7+ByXjCm+TbZJvSHgsvv/XZZtKErr/oiZU2RUacMkM9tEm77TI5WLrCyX+wd18m0ZG0ZDAaKG4hgNNDaMjeshF/SiaIqcCEX9rJpmos3JZnNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHIEiiPf0IiU3ibUpSVk3EKTXOxff3st6akK8bOMHGDjBxg4wcYOMHGDjBxg4wcYG0r4Bws4EcCOBHAjgRwIa+xp3ia/qnKMxhOBHAjgRwI4EcCOBHAjgRwI4EcCOBHAheetRjBgcAOBHAjgRwIdcp+w4wcYOMHGDjBxg4wcYEb9DQ00MscVMkkcYOMHGDjBxg4wcYOMHGDjBxg4wcYJSla6C/IyQqJ2grnqERTLA+R0/2Huvk2jI2jIjWyPYRpZG0ZWNjbfx/xu7u7u13/mxEREREREREREREVZF3NK62Ub8Xnsty+ts6p3eB607V6qL2/lfseCzHLy42Km3JE4oaBoGgaBoGgew9h7D2GgaBoDS3o9h7BFSX8/tqucNO71FJZilNOjIV54DtsdXPyYzUhNvBn6SIiTVKDgjgjhvQRE+rot5IYss6QV7sekghOF5iCE3UTga/wOAOAOAE/wDA444444a/yOAOAEz8hi2Ae0oRNDqvyiDGTY9RZW3/AAVVWABqU2iqnURoNC6oNvoLsbgnGM/wQEJIoSWH+w918m0ZG0ZCASaG4xAJFDaMv5KQbbcv/wBMoSVk3GKTXKxfW2ZrCu5K970KYEuiKlzRzg5wc4OcHODnBzg5wc4E5tMahsR5TlOy8yhmKT1Yq9ENMLWk1DHNaIKOaTMTkZyM5GcjORnIzkZyMTp2oT0Fxos0omrAeySXXXndndvNiVENfT0OfQTTUq2XMsV5sNGXm+rO6+xtOo519CeKPeWokEbWeQ3TMvOvijvGLC0CNofouju9EWRZdHc/Jt9P9j93XybRkbRkOmGNtCphjaMhzZDTtfAgONRQmOpwim20Rzc5uc3JO0iUk+noCHruY7Dbmvk5ccuHqVhhLFlltrURxOEUn0I5ccuOXDG5kUSb09DWtdzHYbc18nLjlw9SsMJYssttalB2dadsn5wpug20SS2l4rL62zON1lNU5ie0Vy06C3kcdOOnHTjpx046cdOOnHReFNMFG4yvlYvMvxNpyhSerBeiGmFrSahjmsLxTpLODHBjgxwY4McGODHBhEmIlKa1QzQ9XNH1RXDcx/CWh3Z3bzakESQxr6ehz6CaalWy5grzYaMvN9Wd19jadbMVehPFHvLUSCNjyG6Zl518Ud4zswpIYjaHY8fQAzc+qTGBdH+jNGannsfcUqyLILo7n5Nvp/sfu6+TaMjaMh0wxtsVMMbRkbRkOmpoS72m4rsLkXJkratx/MhF4YRhb0kMRo/bXiVi8NiUhPV4Jkv21rZW5d7ldhci5MlbVuP5kIvDCMLekhiNH7a8SsXhsSkJ6vBMl+2tbK3LvcTdIiVytkvGFN8m2yTekPBZfW2YxuspojMT0juGnUWsjjpx046cdOOnHTjpx046PwtpgonGV0rF5l/Ym05QpLL/AEPgkaMS5+BdRtA2dyBDp1ikfa+TzVkNMLWk1Ds7hTpKODHBjgxwY4McGODHBhEmJhLb0QzQ9VNF1ZXDcT/DWh3Z3bz6EgiSGNfQg4MJvfsPI1m5Cxzr0iy0+UKzbubSnbO8Q2GjLzfVndfY2nW3FXoTxR7y1EgjSc5m6Zl518Ud4zs1ikhlK+pFeXtgUVS9g2qifdClV6KCU0qbytoRLjufk2+n+x+7r5NoyNoyGA0UNxDAaaG0ZG0ZeircfzIReGEYW9JDEaP214lYvDYlIT1eCZL9ta2VuXe5XYXIuTJW1bj+ZCLwwjC3pIYjR+2vErF4bEpCerwTJftrWyty73K7C5FyZL0HKUlZNxCk1zsX1tmKwruQvW9CmBKpmpe2c4OcHODnBzg5wc4OcHOBOTbOobEeUoSsvMv7U2nKFIIpXg3lE7a0+0kNfX/2NjY2KeJUO3/LYaYejQWc0GYHIzkZyM5GcjORnIzkYnTtQnoLzRZpRFGI9kkumvO7O7efSkLoolNbxobne36Ip0oEC8a/9CJ9XIfwNNQplXrGgmmpVuw0Zeb6s7r7G06+jFXoTxR7z0bpmXnXxR3jOzWqSGI2h+i6O70dz8m30/2P3dfJtGRtGRGtkewjSyNoyNoytq3H8yEXhhGFvSQxGj9teJWLw2JSE9XgmS/bWtlbl3uV2FyLkyVtW4/mQi8MIwt6SGI0ftrxKxeGxKQnq8EyX7a1srcu9yuwuRcmS9Jyjfi89luX1tmo7PB4wH/X8u3tleZf+hNpyhH9ZKvVDOKMlo04Gxsm4Nofohph/X/U4vAv1/6aGtGNDuzu3n1JBJjGRIa+luANWI59BNNSrNhoy831Z3X2Np19FMhliPaG9Zn4g3vroPomSfJS8QIzrs/CPQhSmSme8t3TMvOvijvGdm9CkhiNofoujut7n5Nvp/sfu6+TaMjaMhAJNDcYgEihtGRtGVlW4/mQi8MIwt6SGI0ftrxKxeGxKQnq8EyX7a1srcu9yuwuRcmStq3H8yEXhhGFvSQxGj9teJWLw2JSE9XgmS/bWtlbl3uV2FyLkyXqOUJKybjFJrlYvrbM1hXcle96FMCXRFS5o5wc4OcHODnBzg5wc4OcCc2mNQ2I8pynZeZf2XkFM2wOI6iQhfHJFcJjmactsdENjYp6WS1mjhgqhpiPa4VNWo1cmDVkNMLNBRzSZicjORnIzkZyM5GcjORidO1CeguNFmlE1YD2SS6687s7t59aQRJDGvp6HPoJpqUbDRl5vqzuvsbTr6J3wEokmTV9BUxRZ/2H9wGLXGYNX/B++kIfTH131SasBPFHvLN0zLzr4o7xnZrXJIXtuBMaVzaUKSGI2h2NSsl7pVsujus7n5Nvp/sfu6+TaMjaMh0wxtoVMMbRkbRkLfAtC96IReGEYW9JDEaP214lYvDYlIT1eCZL9ta2VuXe5XYXIuTJW1bj+ZCLwwjC3pIYjR+2vErF4bEpCerwTJftrWyty73K7C5FyZK2rcfzIRBi0YKLZPzhTdBtokltLxWX1tmcbrKapzE9orlp0FvI46cdOOnHTjpx046cdOOi8KaYKNxlfKxeZf2KWJT/AMjY2NjY2NjYx0WrRr3GNiauB+jqvD+fQXj7CGJ8foODHBjgxwY4McGODCJMRKU1qhmh6uaPqiuG5j+EtDuzu3n0pBySQpbdyGhNV9aLESQxr6F2M99a9tDYaMvN9Wd19jadfXir0J4oiSbpmXnXxR3jOzWqFTqAYq+r+kII19OCnJE8+AjhIBAjKa5basjufk2+n+x+7r5NoyNoyHTDG2xUwxtGRtGR23zb0kMRo/bXiVi8NiUhPV4Jkv21rZW5d7ldhci5MlbVuP5kIvDCMLekhiNH7a8SsXhsSkJ6vBMl+2tbK3LvcrsLkXJkratx/MhF4YRgbbS2S8YU3ybbJN6Q8Fl9bZjG6ymiMxPSO4adRayOOnHTjpx046cdOOnHTjo/C2mCicZXSsXmYKxC1yeRsbGxsbGxkaSWoyI3MHu+34NjZdiYD2T6n0FxAwhDSV8ESXL6DgxwY4McGODHBhEmJhLb0QzQ9VNF1ZXDcT/DWh3Z3bzaiFJKshW2j95EqKtzInoxMZr1uxCo37/o8TfA5E5aG5TDwtV5sNGXm+rO6+xtOpNz14q9HkKtjVF518Ud4xpWgTTVqysZ4/JftMI2KSrtb5lC6VlIsRFclbdHc/Jt9P8AY/d18m0ZG0ZDAaKG4hgNNDaMjaMjtvmzpIYjR+2vErF4bEpCerwTJftrWyty73K7C5FyZK2rcfzIReGEYW9JDEaP214lYvDYlIT1eCZL9ta2VuXe5XYXIuTJW1bj+ZCLwwjCzbaWylJWTcQpNc7F9bZisK7kL1vQpgSqZqXtnODnBzg5wc4OcHODnBzgTk2zqGxHlKErLzsxUNR0DY2TCpHCL2EZpJSKOr5sLJLmywJHhNypdkhVQaEiEkNjYpSbyYqtE9yNBGc4OcH6rORnIzkZyM5GJ07UJ6C80WaURRiPZJLprzuzu3mxLomvKFJKsbGLeklEeSvgI1RHcBpvJzG/Bo4YDX0sV5sNGXm+rO6+xtOp4CWjvsSjWoFH0xNXISdV1V9jAxIvbH7fyXo63BXqG2rw1vkNzzQsN18Ud4ztw1tBNNWOKJTTAos0qI5tFRvw8ikhiNodl0dz8m30/wBj93XybRkbRkRrZHsI0sjaMhHV/B23ydJDEaP214lYvDYlIT1eCZL9ta2VuXe5XYXIuTJW1bj+ZCLwwjC3pIYjR+2vErF4bEpCerwTJftrWyty73K7C5FyZK2rcfzIReGEYWu6qhp0dso34vPZbl9bZqOzweMB/wBfy7e2V5lTkXirqrfHI2NimSXVrqkl4P2tbGxsbGxsTFdFDRVf182Yq9IRm0oSuvqqcXgX6/8ATQ1oxod2d282JdWTXlCklWLg24s88S9eY2ul/wBEJMPuXDsRJDGvoK82GjLzfVndfY2nU8FktHeVRa38+mVF1yNPZKECBkv2E7AVMcQMxI7L3glFI5bvNmYqs8hueaL3e5HeM7dY1tBNNErCVExMlfaHCpebeZMrqubUJe1ikhiNoZdHc/Jt9P8AY/d18m0ZG0ZCASaG4xAJFBGwUGbe8/gv4O1fGVrxKxeGxKQnq8EyX7a1srcu9yuwuRcmStq3H8yEXhhGFvSQxGj9teJWLw2JSE9XgmS/bWtlbl3uV2FyLkyVtW4/mQi8MIwt6SGI0fvoOUJKybjFJrlYvrbM1hXcle96FMCXRFS5o5wc4OcHODnBzg5wc4OcCc2mNQ2I8pynZeZfntB1eQ/kXGSw2d4CRsbGxsbGxklLcIiEamzeL3l6NPUjSZnODnBzgbSvkHIzkZyM5GJ07UJ6C40WaUTVgPZJLrrzuzu3mxLq2a8oUkodlOk0mG1TwIYxBFJtfoP+2okhj9I2GjLzfVndfY2nU8FstHeL0rLFEhoXS4n81F8LdB9jr20Um9Zp24qPIbnmi93uR3jO3WtbQTTQslTEaSzTBpus0KtqkhitKZ3PybfT/Y/d18m0ZG0ZDphjbQqYYrny+DC+DQ0uie2MaJqaBnMTmJzEi2qN1X0DGNUhlyUEcxOYjb+30DGNE1NAzmJzE5iRbVG6r6BjGqQy5KCOYnMRt/bYOLY+Xetk/OFN0G2iSW0vFZfW2ZxuspqnMT2iuWnQW8jjpx046cdOOnHTjpx046Lwppgo3GV8rF5l/Z7Qr5F4XYJ4RWNsbIP+NIjg6XPGB2bYqRjoT5hvwERDgyr0JH1i/wBPeQMIZx046cdOOiQ6fEE+P0HBjgwiTESlNaoZoermj6orhuY/hLQ7s7t5sS69E15RMNYVd/lA1FpkoeBEybX5jxCr0r17WomoZsNGXm+rO6+xtOp4PQmksJdMP7geqBM2pbfQxT9/6XxBmMS259Cg3PNF7vcjvGdu9DW0FOdwNRVKkx49BanZdzevQ0nDZ3PybfT/AGP3dfJtGRtGQ6YY22KmGOx+P8JJeMKb5Ntkm9IeCy+tsxjdZTRGYnpHcNOotZHHTjpx046cdOOnHTjpx0fhbTBROMrpWLz9E9oXik9BtjY2J8FMMXMJfLGgUk3R4JjqDEtNpyhSenuIGEI46cdOOnHTjo0lfBEly+g4MIkxMJbeiGaHqpourK4bif4a0O7O7ebEuvTP6QiaZoJliiVePYwXSpRLFq+cn4ZdKlPe3YaMvN9Wd19jadTwehq9CtCl5z2MHW0Jp+zsGQAkSCo4bU+nc80Xu9yO8Z270xgrSnqQlQRcp+SDFXTMvT3PybfT/Y/d18m0ZG0ZDAaKG4hgNNDsfixmRI9BCJp0b74cfLGfMXgpvTFKaxGZEj0EImnRvvhx8sZ8xeCm9MUprEZkSPQQiadG++9CVJ3S+6iVKTvWKeT9DMiR6CETTo33w4+WM+YvBTemKU1iMyJHoIRNOjffDj5Yz5i8FN6YpTWIzIkeghE06N99bKUlZNxCk1zsX1tmKwruQvW9CmBKpmpe2c4OcHODnBzg5wc4OcHOBOTbOobEeUoSsvOyGqt9oOWQqDFr96CtxUYbGxDbc5F61L71oQup59iQZno0tLE2nKFJ6NPcjQRnODnBzg5wc4OcHOD9VidO1CegvNFmlEUYj2SS6a87s7t5sS69M2EpFmagcpOaQvqIkrB7DliU6I7SPoUvCSLKluw0Zeb6s7r7G06ngtS85pM46wMiYpzfLA31Gp4mr6iKn4oj07nmi93uR3jO3elveTRREVQjca4TZ9em96+nufk2+n+x+7r5NoyEdX8Ea2R7CNLI7H4OlzgqfLMnfKnxB7OfzYT5k9ip8syd8qfEHs5/NhPmT2KnyzJ3yp8RbRIt0NWL02dcTaHVPwgtTk71ink7OlzgqfLMnfKnxB7OfzYT5k9ip8syd8qfEHs5/NhPmT2KnyzJ3yp8RbKN+Lz2W5fW2ajs8HjAf9fy7e2V5lLp3WQ1VvtCRxXyo+quYuJHnD7VO1kK2tQdHwI3t4UvQm05QpP55GbShK6lTi8C/X/poa0Y0O7O7ebEuvTQefD74qCLaqeT5JSVSL0yuAZbl1equ9Gw0Zeb6s7r7G06ngtaQeDZU0YykraCvUn37Gz+ps/qQIqY0nMS249O55ovd7kd4zt3pksRLyNQ0eLHr5UfImmvR3PybfT/AGP3dfI8vT4Gbe8/gQCTQ3GIBIodj8WMyNnoMRNqzffDj5Yj4i8FNaQoSWAzI2egxE2rN98OPliPiLwU1pChJYDMjZ6DETas331lEi3Q1YwMJ1eOlCmshQksBonJ3PFPNHVPwgtTk71ink7GZGz0GIm1Zvvhx8sR8ReCmtIUJLAZkbPQYibVm++HHyxHxF4Ka0hQksBmRs9BiJtWb762UJKybjFJrlYvrbM1hXcle96FMCXRFS5o5wc4OcHODnBzg5wc4OcCc2mNQ2I8pynZeZfkNHdZDVW+0LzFXrTacoUlmnqRpMznBzg5wc4OcHODnBzgUh2J0roXGizSiasB7JJdded2d282Jdemac6E4r0CE2KS8IlsHhNvEa8oUkq3YaMvN9Wd19jadTwehgkIl5vJDxLFFn8tiyqOAk6NEBwmZolo7/RueaL3e5HeM7d6f0XBzBbykfIQAa2gmmre5+Tb6f7H7uvk2bI7n4HTDG2hUwx2PwIY1yUsZ2R0SQm0qqUthtuIyIYpEvOv5I6Sh+9DOyOiSE2lVSlsNtxGRDFIl51/JPihqJrbRIt0NWMDCdXjpQprIUJLC1onJ3PFPNHVPwgtTk71ink7W24jIhikS86/kjpKH70M7I6JITaVVKWw23EZEMUiXnX8kdJQ/ehnZHRJCbSqpS2tk/OFN0G2iSW0vFZfW2ZxuspqnMT2iuWnQW8jjpx046cdOOnHTjpx046Lwppgo3GV8rF5jTOwio7rKloF6Fk9f4gADvIGEM46cdOOnHTjpx046cdEdhiUJrVDjF1c0fVCnzEx/CWh3Z3bzakPtF6GpXuK0OWoy+JBuOSFGuGNjXlCklWbDRl5vqzuvsbTqRBppw7XyD4ECPSOFjOMSCzE7tkfv9CbwjomyWjvt3PNF7vcjvGdiL8rvQh0fvbuRxD/AEkoBXljW0E01Z3PybfT/Y/d18mzZHc/A6YY22KmGOx+BDGuahiOzOiaE2tVQhsJtxGZDFI15U/JPSUP3sR2Z0TQm1qqENhNuIzIYpGvKn5J8cpVtbKJFuhqxgYTq8dKFNZChJYelonJ3PFPNHVPwgtTk71ink7E24jMhika8qfknpKH72I7M6JoTa1VCGwm3EZkMUjXlT8k9JQ/exHZnRNCbWqoQ2tkvGFN8m2yTekPBZff2s9uIdqkcJHLi4PknITkJyE5CchOQnITkJyE5CchOQnITkJyE5CSftGxQ/mD8AiJDif5s2lEoxk7t59CQ+0VreEOjELKoSRgcokVdEVpmbWvKFJKNhoy831Z3X2Np1siDTTh2NSiNHmwY0podEy3RiRFN0LXUZgQac83bLR32bnmi93uR3jOzF5fldbdtbavuXs15yhAWxpUla1tBNNHc/Jt9P8AY/d18mzZHc/AwGihuIYDTQ7H4sZkSPQQiadG++HHyxnzF4Kb0xSmsRmRI9BCJp0b770USLdDVjAwnV46UKayFCSw9bROTueKeaOqfhBanJ3rFPJ+hmRI9BCJp0b74cfLGfMXgpvTFKaxGZEj0EImnRvvrZSkrJuIUmudi+/tZ7X/AMaUuFUQt/4/2aIsyY6/NiXVsn2isifR0/epPNzNeNAxr32Ll+hlA2GjLzfVndfY2nUiXoMmpREEhimY5ekMTIBjOiqnyOXHLiGkYodCpIqw+9srWkbnmi93uR3jO3k2YXl+V1mY4ToQg2ScjNWiaY59DFDBnc/Jt9P9j93XybNkdz8Ea2R7CNLI7H4OlzgqfLMnfKnxB7OfzYT5k9ip8syd8qfEW0SLdDVjAwnV46UKayFCSw/i0Tk7ninmjqn4QWpyd6xTydnS5wVPlmTvlT4g9nP5sJ8yexU+WZO+VPiLZRvxeewqlFbyL9TiS+9Nm/QIyEjkjkjkjkjkjkjkjkiG/wDHAAAAAAAAAAAAEaJnJHJHJHJHJHJHJHJHJHJHJHJHJHJDJNNigImmMKmE1ITLgJiZvE0rrU+0QxUCqlD9xcy8jq8jo3MC29CvNhoy831Z3X2Np1PAMYZNSjLyIDed6MjuDoefd/Jix0afVixMeoPA5+ERHeY+s0+7fIbnmi93uR3jO3WTZheX5XHTizJs6QTqj3jCJr0XR3PybfT/AGP3dfJs2R3PwIBJobjEAkUOx+LGZGz0GIm1Zvvhx8sR8ReCmtIUJLAZkbPQYibVm++sokW6GrGBhOrx0oU1kKElh/NonJ3PFPNHVPwgtTk71ink7GZGz0GIm1Zvvhx8sR8ReCmtIUJLAZkbPQZHpF5HYviZa3fUQNDNxQvuoM0xQITHzQSurVc2X3qskQnOWoSOKRJUm1Rwg4QcIOEHCDhBwg4QcIOEHCDhBwg4QcIOEHCDhBwg4QcIOEHCDhBwg4QcIOEHCDhBwg4QcIOEHCDhBwg4QcIOEHCDhBPRkdw7X59CIMyhSwNxVikxa5zGAVWwJRj6FebDRl5vqzuvsbTqeCxjDJqURBppw7L1CyY5gEurbfu62+Q3PNF7vcjvGdutmzC8fiVBGjMYBpuD0XR3PybfT+/kzy+ISkWa/wBDfNQYVJe9w23C63hBLqFogPNLbtDNXOup7W6sMgL9vX9S6unf7scwiroPovmMlN/Fj5vB1o97idSs2vwOCKQg15YlP8Il+Ih/ctVHdkOmGNtCphjsfgQxrkpYzsjokhNpVUpbDbcRkQxSJedfyT4oaia20SLdDVjAwnV46UKayFCSw/q0Tk7ninmjqn4QWpyd6xTydrbcRkQxSJedfyR0lD96HWrqqEh3400nrA2alj4X6TKMupprueBExV77S78yJKiFCSUJETEui9uC1X6PVjoj2Gh3eBSdfix94V3j0EuOolLN3oPDUMl5iXmJeYhyP7tSoGN7gl5iXmJeYl5iXmJeYl5iXmJeYl/gNdWqTmZLzEvMb1kdr8+hENlOxtQgibhVy1I+4tC48PQrzYaMvN9Wd19jadS8naxhk1KIg9wsdEbZ9RTF9sShQd/o8hueaL3e5HeMRuPJ6JswwhTTdRUbZrU+DPiq9F0dz8m30/v5KKwQ1bf/AAZH7wfQnATm/wA2fcoTT6LHKD0cf+dUIOWYDLUdFv1fgSrokwi4DKqPIvQM1Rk528ocJ0GRamMNelIkuuD5JBF3ydmncWrMyV5yJ6umfSQ8qK19YIeBK8kNt9mPUfglPYV6q5Ze4qDeWaX2NnSgd2CQyuossUObEm6G2xUwx2PwIY1zUMR2Z0TQm1qqENhNuIzIYpGvKn5J8cpVtbJPcNM2Mj/cttqi+shQkrhDUZBME00o/mhWZCVTdmvndlE8lOlMhq1NuIzIYpGvKn5LmFo4N4ISrbaXpJbGHW5XYsCKtUQX7Ppq/o159MhvAa+5LakTdpF7SG6ynyK7sd+rP2hmCMjGayahu7qydIlx1F9m70LgUE00lOAklBmrcysoX9pERERERERNIEUP+YmZmZkGZQR6RERNKNKNKHIwZBMXwO+UGiCZtWRryHkdHDtfcrjbNQOdMWCPpQaP2UlRPx6FebDRl5vqzuvsbTqOp7S1jDJqUIzrQpE4yoI9DCMPR5Dc80Xu9yO8Z2YmzPRCLMaaoQlwA7U/vbVX6Lo7n5Nvp/f1MZRp6t/1C+CbrPhKr7D80GR71FlHPgoq7uXMXyim6G0Ky7CGy6Og1TDssTVNLzH+ZrwMhFtp+kYva8MDISxYo46plK59RVj3gtYJ2XCXoNEqYPHH7PFibTTVGROLH0NJGmpTE6N+u9fxBR9ZjOg/q8RdSNE3SeNiYaK4fsiE1UMBpodj8WMyJHoIRNOjffDj5Yz5i8FN6YpTWIzIkeghE06N98OPljPmLwc2mM01jQ7T5/ns9hx8sZ8xeCm9MUprEZkSPQRPk2T+DpSsFe/YYa1IFCt4rzRm+vmx8wG2jOG72lcmzT+JiEdUMklpXpaSNNSmJyt/VXPiBbrDxXQf1eMqXKZKSskM9ezC+zd6FwdhHAwsJHJjkw6+8JJXzDnBzg5wc4OcHODnBzg5wc4OcHOBJ9gZQ/kHJjkxyY5McmOTHJjkxRHP1HODnBzg5wc4OcFUUnURfoOTD2/RlSSzHa/BtWR2jyRdR0cOxUSOvsiMBFQifJ0O44zSfaK1Xmw0Zeb6s7r7G06kqYHU9paxhk1KIg004dvkNzzRe73I7xjpMeSybMtcxme1S/hepvUEzeYSNal+V1t0dz8m30/n5F5XnLopiKpTM5VDZLU1AXakuJ7OpjTp9chTHg/yJPSfhiisxSmnR+l/CKhJQw9jQ/Aac/klWvIYmoOEfh+F7ll+Q6STUiIk7BfK4ZIZKdz/AMnnuxrrh3JVU89XZCaqCZ6sV8tvwsB9YvYmm3dRYmKZb2CC3ujQ+gCl6kdjdsvc9SsHQkVS3BEt/BqQa1KrmttBrnTcPNDpPNRam6YI0sjsfg6XOCp8syd8qfEHs5/NhPmT2KnyzJ3yp8Qezn82E7NRU7T5/ns9j2c/mwl1aJuk+wAzFP2Fx5gmlGxqFtklSdCMXYvQ9EgYhM/qRB6ED3GcX2VKBS3KC9XtEgVV0NTGfJxlQuDsPppeFiNQu8qMs85tbH9V3ti+67mrPObX0tKjvUiOGUWVnMHMHMHMHMHMHMEX6Hl0v9fd3d1Kmu5tR2JKd6CpdX5MdHyUN6yPZvyMklEXUdHD9KfaKxXmw0Zeb6s7r7G06l30PeDqe0tYwyalEQaacOzyG55ovd7kd4ztxdHdZNmWSRK2gSY/hgsaHzObyLy/K6y6O5+Tb6fx8hjEkUtvAuw3dD8EXD1RwyMcQwuun0+Bw9xiHiXsSkJyqwa9yQxNPr/QhaTyNrMlCA735WHsK0vHx7b7SFvel5SaWsoXK+yHZUSxbJak77uVSM2xYvENOF83GL9snVClJyVVXTI7k1J5P/JNSWHfzP0JezYz4QYRTBo4v6tcUyi5JEyUergZUkrfROv3Y6IqKV68BRDSmh8bbTfhnsqhykVdamHtcNYVKeaEAkUOx+LGZGz0GIm1Zvvhx8sR8ReCmtIUJLAZkbPQYibVm++HHyxHxF4KbSGSSwodp8/z2ew7nICveCBiqYSgLPFiloFqxF9gh99wi+AuotUr1zyOeZT45Ph3T9DoipCZfWgukNKaEaQHk8318oRnHiU1RNslOvTJEiJpamutFd6VdGZ2H1Uo99AhJRt9vTKW329L1LSS9UOHnTZONPQAggqOsdqlFR20tEY0lehBBBJaaWyiJc84nDzh5w84eIhaIEq2SvCFKXvB3/ydq8G6ZHa/I19Bkkoi6jo4fpT7RCvNhoy831Z3X2Np1PBZ7wdT2lrGGTUoiDTThnkNzzRe73I7xnbrLo7rJsyxtcmvOIkkoVCbMLy/K4ujufk2+n8fQFq2j/t+Ba1UvhDvC5RV0EymorFKh/vuLYadA8hu2HZHXuYpaR9wh3pJ9DSrmoKs0cvvENKx33jNry3Uc3JClt4IcYDHglmav8HcKX4mJhtIUtkL0y91oiFJB99l5CdXUWWB/Xv/AJJrSrPsBpdZMSDoTs6Gjw/RjNo1s2xSVgULVlMhNub3v48kQP8AhSV+f8USUuOuAhRhX9HZ2JCphjsfgQxrkpYzsjokhNpVUpbDbcRkQxSJedfyR0lD96GdkdEkJtKqlLYbbiMiGKRLzr+SfZQyk1O0+f57PYSmS2CzxF0ku5K72uF6CzV4RVSZuV4EEe7T5FlNgGrhNtXGl9hKcWogf8KSvxf8acqSY64SDMzbraHZ952UNknBZrFCW+WofrpSjXknbT/z6ZS2+3p2Onrs3PJ6l67y2Gh3/wAnavB3rwMh5PNi10si6+hVVUWL0Nhoy831Z3X2Np1GNQ6FhKKjqNpodA6AylWRBGkM3PNF7vcjvGI2jRb60ndZfL3o/pQLxOkauO5+Tb6Wef79DISOtobmhOlFul81I3WHVXyQOfR2TOZCRHufpEqiOVIhTX3Ym9ywQx1HqqNH2MDPULsDbozETYsNGsH/ABieo3aEvYyT7CkV5HsJdVMNM2IXVPHrEoUKgvxWQe91swVuo8zkzVPo6CPTMpzG95qr2Y1qrm+uhQ/Zn4P/ABV2Y7poRJgbZZNWI+m418iE7lL8rJ3TU7StTDRxTQWLevwxmIVsZa5t/wBFkeJxFs0+COddeO4kkzAqYY7H4EMa5qGI7M6JoTa1VCGwm3EZkMUjXlT8k9JQ/exHZnRNCbWqoQ2E24jMhika8qfkfnnOrY8UWUOT+e7u7u7u3Flt9CVCdSuvudiCtqSeSXiIawkrEcKZdJRjSjHobwTHZNQ7PyxZ/oLI8SiDZp8EQTUCwvPlQ/dkJ4JIXGw9V1fm20iGBfYZ2EjjK5hDuJlVOhbFXamTJASmSxH0FGkDZFlOATJspxWN8GmbpazS/KcInWLFJzNWqvQ3mN5jeYYZKDzkPPcqkgjMnklRqVN0G6xusbrG6xusbrCmidVTeY3mGqlTnG62y2Gh3/ydq8HevAneURkjSDSDSDSDSDRegiInOFM0B0IZVLgDxRriKlGdZGQQJtVmq3/0hCEIQhCEIJ6pL3ldSJbgpbRONTTMpJ0IaVFrIhwhVmpOpOpOpOpOpOpOpOpOpKdRO9wkLiilEHQxH1JbotFR5JVAu+o/orUXwTzoBkwORNIY2aEK6z+WUDv3yco+4ryuV85+C5jRCpti2KSEhQksD5/S2n4KTvXlb80I3UuH8v0Zqqwofrul6MRpKRM234Fw2+YGCbNu8pxFRVyKWMjc7+Re9g3kOdX/AErZ+xHUXVm9OLIN1CoxWZI3VQSsEFUm6hRrqMNKvadRf4lIrm58vuIkwXo+NjyHVcd8Vbqi8oUdJv3Ne5ZDYgzAwlohIfDQVHi77FYtmGSWrGdd8m69hx4+BN2xIxM8c48lPY0ete9EEQhKqHth7ipy8MRv2L7L+jxqbYXMHUQRdLxVzqvoMBpodj8WMyJHoIRNOjffDj5Yz5i8FN6YpTWIzIkeghE06N98OPljPmLwueUalkOaj11RyiOURyiOURyiOURyiOURyiOURHqqJ31gSk8Ui8dBXmldfQtYt3xUBF9X7j4a6H0NvwEpaSqxWuvsrnup0EqyAoslLyPopZ3fsGk5cmE/D5gTTUq70La3MMl7jWqcDuv8HghTcmnh0HD9hTMZelwydfIxSdPJNS9HwraM2VZvRLkQtdYYTgjm/wCRfEeLeItCLM6Gg8pb4GvIhjVtLb7enY6euzc8g4r6+El7ifA15f8AUTWXc2lP0r03lsNDv/k7V4O9eDtPn1JtOUKSxQRLZInVr3/fp135CSShURedf6py6+C8g8Xnb+IsuRUTFqJjXND0mlZi8UqjiSl1V5QBTZmEQj4Gh8s/mxuhpKSUJUaza5Pq9VN8DIz5ngvWhjXMZM9hnWodh0y+C5RSqECsKL6NNCEsiqmq6oYHkqKJBtS+oV6dafbEgQiF0sVoWm9Rm8hu97uW2kxyzyF6Nyt49RZ2qhc2/egmmpTlP/EJlrwjxHQsgzGHgyh+xYimIkauEqigZ63kmypev7WL4ziHWYL6n2MTT9QS7k+ws+UqCaUUswxB2couyv3ZRU5l6lf2NozJtLM2xJ4UiKG/2IClEk0ewjSyOx+Dpc4KnyzJ3yp8Qezn82E+ZPYqfLMnfKnxB7OfzYTs1FRABS1kqcMcMcMcMcMcMcMcMcMcMM6qILJQZe9NllxmLqHrmiCQvdPJ210Il++/ggfMakkKfgdoMX7WLcGSy7OfSGxilvlhdRdVQgqJZLzydxLuFSuro0FqRNJTTxsiaX3bBLUapGJU6Obzeou7y4ox2AlRRqoZpZ0Sgjzrkl69D+7tpBN3lCyr0PkqMiEcnMw4xG26O4oSUm0d6s7rs5Yt7l913NWSiDMuSg5Y5Y5Y5Y5YdK9yeKkckdaXN/4H/B2VOhXKxUSpqV6yLrVm7nmnkc4znGc4zlGSw31JRVpK2y2Gh3/ydq8HevA0zF1XPrAAAAS3KBlVS/cbQ7OXIQw3bN2zds3bN2zds3bN2zds3bN2zduy0pQkLqcuNm3Kbtm7Zu2btm7Zu2btm7Zu2btm7Zu2T5SK+bkdXb+76lx6ITvRREppdSiIZOOzJYRaLYdt1F2Gsn81I7Ew0osoBfaM3SJA+xSscn19Zr3CaRNOU/ROTQUb3MPJoBxvu5ZFLYyD4aLtmGuHrcfYu/3+B5MTG78GI8PASkLrRzX7i7iTpn8hu6oBzJLLARMV9i9Rn439Pm13qhspJLsXF0C9VQ9sY5cuLdn1/iYVnIxc0tUEcJ5z9l7ydcaj92LmLMP/AI9y9ULG+99CQJXsQtMFZT3aHFrn37oQnEvWa1rWiEBO8E6MaTUMfGSjTNqJ8SVQIaySkcFO9VsYGQilvIYoqedrx1TB7BBmVBiALMQCRQ7H4sZkbPQYibVm++HHyxHxF4Ka0hQksBmRs9BiJtWb74cfLEfEXgptIZJLCh2jz/Pc6DUClOnALVixNrEOWeiodtrBh1LiGF0ajpxcC+7VLJYshDISslgIWmCOziglJ/yDD9GHsaKzWva0QgJ3gnRsWxVlakdEVb53r2cfNj0cJLH/AJR8svQkkpBiip52vHVEHsHUKgXMQqmQlvTpCpOlRWVGO3jroJXe9t6saXtaRk8QJF4Qkoeg2OiRQiE1prMqUMTSvjGLvcjixxYcwhVl42PUtJL1Q4ecPOHnDzh4jPPZpEVLnmTyHxf/AG29vNFJxPpQQQQSWmlsmFerBCmbZbDQ7/5O1eDvXgSo4Qv8MiIiIiIiIiIiIiIiIiIiIiIiIkJs6i6zy1CQms29EJS4uzpb+76lx6KvEXIgiQ6rukrCovxbF2Nm6AmRqTiRRKdj0vQwYksaiNj05W1V1VV3ez7Q2RTG0t8W2QASWSsoRGDEKrZglg5DVC+a5a5p3MRprrHKU2FToY+RzfXp+heRM59xBLaVUnWsu66ScnLlWiT9TtuPEd85i/T7wmFb5bPMQLJ9/wDEpLfL3EelEPJTa57mLLG4TZeuEsThyOb3pbMbMD3MNAxqimW4L2Lkuyk5TQ1/wMVzICZUWBkq0aGShKFZbVThyGtNv4IxFoKSEC4JNvdSezfumAofMPyRHbcEMRaCkhAuCTb3Uns37pgKHzD8iY1hDRQxy60hTaAWq+Rqvkar5Gq+Rqvkar5Gq+RqvkN0i0nU2NQE3ebVyHUjEr5Js1cUNJqGpKtR0HMqXU60uG3drJjsj5Ih5KbHReh9qnYhLjYbhNlVxksThyOb3pbGUIzopR3SsZyMM+E7huljchNhDKtjOCzE+V5zf+HYbKLTZjQ20FpKve9fRM4wVmV6CM5tpRryTtp/59Ox09W9Z2Y+MHkPi/8Atl3JPVK9TtPTh/cf92Vmw0O/+TtXg714O1/w3lFmS2Wc2vZdbXW/u+o0P0SbWCz8MRaKD65/I9Gy0RE5lvPAW1o7hmNCCFsEYj+EFZSpsTmT1ox+pFGG9EwSF0kF8KoTTSacp2Iaub7Hl/IS72JX3ehTi5r7qB7ineJ/99DO8ZdpeVWmuso9xKs33j6/xO05eklXtctxNljUpoouMhiUuBEk0QSUXQZhR9x9pFscEXEwBtcXE+UI0jV10E4vGB3Szfpy39OW/pu36bt+ixeFm/Tlv6KEWdX6c2HNhzYc2HNhzYc2Dj7Q50FJd0ObDLCfkAPatRNNyjVfI1XyNV8jVfI1XyNV8jVfIRpFpKhoTbiMyGCGtYU/InE8UUNPQdOQ1fNGLEX0EQJX0+WqGuX6uIihFghKvY3uFK5NqjEfNqt4FjUpoouMhiUuBze9KY9EloeiaPqylveWRHiPhjxLVB+FjYV02LnosC4OwlzleliI5+X5Klb1ixTrhDyPp1vJPAcok0yUpk2y3nH6o03jPoKNJYaX5ThE6xYpOZq1V6G8xvMbzG8xvMUp/ZS3Hxg8h8X/ANHVDzJoS43WN1jdY3WN1h6maM5V4VMCFwYeH70GMPMS6RfB3rwdr6UGmWyBdVzY0zlI13q9xH/cI/7g4Xo95P8A2yf+2R/3CP8AuHKjlJyk5ScpOUnKTlJyk5ScpOQ+i66LlJykd0doj0+UQWNVSy62ut/d9bHlHbnnFOuBMOEkOqv7ePRstEMvw38jl3icovYUqTtKkpSsUAHc+shjU8BKxjL0d7FhvFMasD8B/wBWJR/SkXd2rf5atle3qeHcfW8KJ1Y0s7XstR1N0Etv7/xLxJ0TRF1yHn0+LbT3aHBL317IQnAuWa17SjJG2pLBEbhisdXoPRW/bBElVDJyToWyZDtn6ezX8r4LLy1nCe3LhWEcIOEHCDhBwg4QcIFtxJYeA4+WM+YvBTemKU1iMefFy0qHcjBPRuzNJWcodxorPFtkplM3osVo7jmrJqku9dRNNSnKNa1pRiCmi4lRMm+kI8Bm6pPkUTo7EmUaJfhILxOWgy5BjmtTuS4dWye4NT02BcF6CQ2o+hSSopa6KpWLFxt24SWIoNfUT9FKb/Ya8iElW7HT0skbISvYva0yq8NB5FoQIKN1bVXs6ie43nQZNn7026LD3FsLqsW828/VvWdtGbZvQ7odr8DNybEdr6e9Oz+bLq1c2uzVnav53fW276275n6EpcIW/OVaS62ut/d9bGmFsUcxUlEqarFeSOUzM07dloiM9ST6EqVaXERcKse8E75flY9AhnTazH5Mo6OpO2LhSSsY62LYZExv2lW/r5suvSe8Cln5tqtf/wAl57Ep0qPq/Q0s7H9scCav8Thd8fSfDNV6HqvS1PWcQzrcUFSOwxJUflC6lvwLDiXk9R7ZgNKiv0WSslM54AzF1Q2TIds/T2a/lfBZeWtppV3zFf4KqtL0kXns5/NhLTqzH4jqMqrR1NV6Gqvo9jIU6H3eUyN07qw94YmPKExVnUKCnV1EMs/W4Z7JWmTUkh9U/kX4WKrovruPXzmdo7iZHzDEty/kjKoeURaiN04KmisBlWWlt4C6tpX0tQkX+Lz9E9Fl8646CSMy3kcwS41QJcaoHMH1wsQcwM6fIXeVGWSiDMuSg5Y5Y5Y5YUHOm7BShVPetGhyxyxyxzRCFfVnOM5xnOM5xjLGbNWLabfQ7odr8G1ZHa+nvTs/my6tXNrs1Z2r+d31tu+tu+Z2pS4QldS86q0l1tdb+762NHo0axv0zKmWP/L827LRESCZND04wCokiXdwnXIiMNWpLX5b+LKVAJY4PY/OgqToXyiUqeSLU8EIqngJCWUUSrF7/wA9rGoa+hoMIlvQ1Lfq2v2yWgDNBKlHKslgJI5WtHXbSuU0Por/APFMaq8/OvsSzc9nH/ff0QCzUehBQIQlMYEeXzJ8Oi9hCcldO18KEXFZkReythCy70Nuo7hDmA5yoeOBsmQ7Z+ns1/K+Cy8tZwltyoUlHCDhBwg4QcIOEHCB5hIZeI4+WIWX2vSKKyEJLAV3So3TgxDGXZ5/339EAs1HpSVemEqai+azwlL3FHIudL3CSRJKEiEmVle2ad6GZ6M2jwNk9CSD7CfsRVetu8dFb60+E4Fxa3Kj+TqJiO5IfBfZM0Ops7lSovKc1PmyFs0L6u2FCvsqGK4nz+GSq2J8HfU4INjokUIhMdGyhEcEPg76nBDgggwnLeJiElD1LSS9UOHnDx2XCltpCRSHNDEtNB8dui+2IvkzEJPQYNDXCOKOHnDzh5w86bJxp6QEEEFVZBCVbabfQ7odr8G1ZHa+nvTs/my6tXNrs1Z2r+d31tu+tu+Z2JS4QldbLzqrSXW11v8Ay9tQtqNrmYQjgwaxEfwStNLFNsE3ZFPvsJrQkxQmslx8Dvl8BtJrKvYaSHpVBKxxWYoaaoyf4Jc0Wl46PyQ5onUcdJyHwHcv3eQ7k9Uq9HN62v7SfRXmG95/0Gp1XHsVflWR85Jy6xeA9zHVl6OgKxTWwQxelX3seP8AFLwioywdxjbMo9i9/pehoEwZEJc7WovIW0TisVoPcIH3JXdkIqh0XkXZQos7NfuRZ2b/AHJqf0vA0VVRTTetGbJkO2fp7NfyvgsvLWaHSjuaGq+Rqvkar5Gq+Rqvkar5Gq+Q3SLSdTY3Op6EPkJ51/ItXlD6WrxSQTvKJFHipLBP1eF6GgS9MiEufol70jKKhpATf3P0VAiWSL5ZjIlVyqfNs465TVrii/tiLDIMgHaN4uZ4NLEoL/8ABYu/YMnUjdsSXPMP6G5cu1CDbbkQxkJEgavgUdVIb/b00pv9vQ15Gs0/82JgNLbcJIbgSrubfmg+O3RfbKWE+votDeszbsv5b1nbTb6HdDtfg2rI7X096dn82XVq5tdmrO1fzu+tt31t3zMSlwhK623nVWkutrrf5CXO28LrVRj49MDyZVrNOfIJKyEpp0aIzkfNOJE9G3Sa6NiM7FRdvgZI2q4fBidYbm/CTGiLs8vYylbVl7JJ7upLpiw7QmxD3izE6Kl/b0S7C70LvZP1/bsUDPk1f+ewwrJUZcw5yVTiNmUxXK3rFsRjEUIbLph++/8Ai53URP2e2I1LToXN0fooT9rWyrNR3iuz+UJa8EUFXvcHUtHcl4y74ObvyNF7RauhK6wRRqbP9Y9ggL+wvfwJE9ITTjqaQdlTb32be+7UENJgtvfYlKrZmbE+zYn2bE+zYn2bE+zYn2bE+xNsvbU2X9jz7/3NifZonBQ1wNDtRXtDVfI1XyNV8jVfI1XyNV8jVfIrFqKhorYitSGSHPKn5Fu8ofW3eJjNyJoTV0sXWngxqWnQubo/RRn7ei+YG/R1+3yIdwSTxXtIp3UNeSE8x2kiHbI2LmUq1kFLxhDGqp0+Bu4Ddq5gD2oe9hC7KYM25ZegscugxpdqkkY0uyvFF+ojZ25l7m858Y2fobzjlEmmSlMm2W858Y2fobzm85P/AOaRE6xY2SvFbUIuTim4Rtpy/lA/hxklIjzgqyymyzyrRcNCF6UnrWta1rMtCXmGv7hZB2kslwdBnBOhIdm30O6Ha/BtWR2vp707P5surVza7NWdq/nd9bbvrasrWxiV19F51VpLra639957pUfz5IjJR2yqV9rJi7Itz+mVp7V79f4G63CaNTZci1W1pQIxwhB3J0hszHNdSG05tOar0cj6IDaTJELXG+6szLAB9W0ro7blvdF6JFvP0EUOH5J/wXyLHrk736MdFB5O8/4LVxV3S1tJSx0luF2G8sTEgQISWH+LVTWhBN0N103YiepMOaMvfJ2sQl6Ez44qyg9Z48vqsOrG9UYeNoW73jqOJky6U9hoXndzJl8mPKhNq6l52nQdpcNrniCDQIdRST0GJG+YnFfpsX6NlwZTTYdKKRFYy+aSIOxfpsX6bF+mxfpsX6bF+kQj21N//SDq7amxfolRMnE4r9FNu9xTJKSrzhfwvtrh080fQ9goHAorISmsRlXooXuQ1dVnYGcz4YY6epMOaMvfJ2sQl6Ezq/FWoQme0yfs6jRlV6uwghIQbL3ew23v9hPeV7aRCpRzKZYFclC9ipdGFYi53jw/RX2WYoUx9AzaXapKjGl2VYqv0HrEaRZve2m/29NKb/b0pgLS27kVuJlV5MTp4nU84x/0SsMc0P0bfQ7odr8G1ZHa+nvTs/my6tXNrs1Z2r+d31tu+tuzZ+m86q0l1tdb93VKEMXdE5rB2qU6CE1tc9CIJRc8ULDbmVEZanG9GB8yK4JpnEtNOOxEmIBb7mB2nRaj2uvzEENbNGvZOyoJ6ld6mnxIjIGbG2Xq1+d98KwySThFWww9CailI2I89B9W0eX7x5f9IeD4iOm+idpbSP8AwKGCo0f9/n+NreT6eC3oV3GpdjX9I1lxcJ8V7GvxJo65W5F+WSxvVei8zreOQlz8pftEoxeo/f4IL2S8kjcfsUUPPOPwiFMFMb5l2LyFzrP3ZR759S/DbMZL09RvTXlNgyHO8bRktInqbZm2ZtmbZm2ZtmPJ8n6b1/pA/b+m2ZQg59S/DcMWTg27vZHLHLHLHLHLHLHLC0vOprGhnVHypYTmtH4H6DMxHU7CVa8shDmwT3Ov6Tj69Nk/sZImSnc7ci/LJb7eI+kYcYNourfUlSFrBDsaNiKjB6DxnZ/mEkPNQhdVmBiifSSv4Cqiv5vLp6EamM25diJb2uRLxMraQT83BLjVA5g+uFiDmCXGqBLjVA5g+uFiDmDmC7RAyjF4Glt3JDI9ve7ZvTQlLvwlmxlakwYu8qMs85tbH9V3ti+67mrPObWx/Vd7Yvuu5qzzm1ErDHNDt2+h3Q7X4NqyO19PekBlD1Uc4xPTL1M5A5AVGxTM5c5cXRgZnICz3HKxOcZzjOcZzjOcZzjOcZzjOcZzjIDMzZzLOZY3Rf5nOM5xjzZAnmp9N51VpLra70f82qE0XaJ83C1m0oUlqlOgqmr7alUjPuD3eetjaSl3C6GtV6R0Y0xw6bxL3xFqfHyV5MRzfuGF3L1xYzDJJaENe48/RKZTGCbexnwVfEvUMXizQVl6JqK63cxkRXNL4CAiEUJZf4xMIb49c/Yb1mlu9sXFPqy5mdGQu1G1kQC11T6ddLE4qilLAxx9Qv8AqGijvMErH6fqPfvkfTKzat3GFanpKEH2tjeAymkvKcQOIWmNo8YDiB9Rxwc4OcHODnBzg5wcnMvKcTJ3wzg5eFa4QGkU0QhUlHCI4RHCI4RGHK6DixxYnHa0oSHTzRdD2CgUiishCSwGVeih+x1O8WtAzyDxeWxR1aM36egT5txS+xArg4vkfVicVRSlgY4+oX/Us04q/KSRmbRjaEeiYtY4ozW30Wo/wEpgGSHuim5dcLG0r3B01vxsWY3y3hS7Rak05SigztkEk0oEx0bKERwQ+DvqcEGx0SKEQmOjZQiOCHwd9TghwQh+MzMcXtqLHWyUu/CWbL551Gso99AhJRt9vTKW329MpbfaxKwxzQ7Nvod0O1+DasjtfSoJuIENdCKUiqLzjQgI2sXDiUcShxVy8jiCak0yOJRxKHorrIcaONHGjjRxo40caONHGhpH2hwQknaHGhmUkhpVHkYMhJyI9N51VpLra70/8yv+uKZBKc+1NpyhGh+hFTKaiia6nTS3lj1vYASUYaNOHpQb3133BR5cSU6aoTqFcQ5YqPiZ7FGRiq/3fgvYzRe2bd7EdXsO60WVrcKXQVYZpu0ZnqNZHJ0N11MSlBCehtJVGUK61yZCPfaYJmxI2Y8Wzf8AjVJeT1OCLTBDQtio5IVglSHhNCFI2b2rn2IhN38td3NXBI8PTr4RKdxc6csr97u5mDvDoX5DZMh2z9PZr+V8Fl5a0BBR6P8ADd3aIpydRa0x0VXORDJCnnX8i1eUPpavIre6qEkViJJwK8UWri/YnFIoaupCFIlE0urVz7EGmz9E97u5q4JHh6dVCL8hc6csr97u4jbKp4dXLuJstyzIK8REL6AOW+hjbdXW34WITKN01/TuyI9XYpeUSk+g7N/t6aU3+1iS7S2UQ4vbUWOtkrZr3gmbF5Uv1m9FKNeSdtP/AD6ZS2+3plLb7WpWGOaGbfQ7odr8G1ZHa+mIG1UZyXBHAk8MKaKS33tGoDSGtc6Ekvo6gIiObYiu/k73ve973umUtBKIKomxjrJm4EZqW5ImVK9N51VpLra70/8APJkinWmtaFFTeefowflayofJl6aFXJlyeCbQ1ZaMV4Ua0H2/BPuR+9lxfa+w5ROpjbblutrEl4Vd9sPcvqePXqY9LhxBLKLMPb0LvDr1subosxBpXQdSEUtV17/42Po5MkUCLplg7WJ1trOsoKIzDDYhmidKJ+Ckr9N9ig++IY+Nl9nd5n2MILV1Yj+AKlTe97jZMh2z9PZr+V8Fl5a265L+FzvcKiq5zIZIc8qfkW7yh9bd5hWIio1qPWtbMdbdqkJlCm78L6GhQGhVKcrWgnhVqL7FB98Rw/bZfZ3eZ9jCC1dWI/gH/WC2fpmocYFeXWl8X5CrBT/5I7uvN5suTv8ANiXaJHCWA7atmk1DGvB4EpRfNjKSnNJLgZSW5JIcWeBKUXyLkrLMCvh/0MlbNe8EzYprluuxGcvs5aybzjlEmmSlMm2W84/VGm8Z9BRpA2RZTgEybKcVjfBpm6WM2RZTgEybKcVjfBpm6Ws0vynCJ1gSsMRpG9BnVhuwUNqyO19PenZ/Nl1aubXZqztX87vrbd9bdmz9N51VpLra71/8wxLufxY7t/y8XQXy82vDS3pUWo2uZfLsrQfReqJysH8kIwrD87+4xCzpN9n+i/5Ef2JuPfTX0X37ab+hbOmofQRlZVD7L9JtneFH5v7j25BUyUKCErki1tK+hh+hiCzwFtzXSNH2Kjo+W83/AI6NbDIei376iTfejteVsgR91KX1ISGFep1jqWaTgKmlu57GyZDtn6ezX8r4LLy1t1yX8Lne4dPNH0PYKBwKKyEprEqADhlclkVJdYuqhKa23Ax4DOkl+RlMoTr1KhcT1qkkR2ojvX2YsceDRp/EeAm9h3NSeCvYrVb2IUIwbhoSZNCqGlmncklsekpElffv6KXJ3+bUJDGvHpRCCcAV8P8AoYl1HsJmxcqPfb10pv8AYa8iGNW0tvt6ZS2+3p3Gh3/yKkHk8EB3Qdr6e9Oz+bLq1c2uzVnav53fW27627Nn6bzqrSXW138P+gM9h0GNT0cnzIwT3FMtK0sjq0eTPjHr6FEND2J2tN2eSxpe5KWBM7kMYYqBaTFV6jRXuDIDbd7m2kzoIgWcXd1GdCKa7WP+QrFLvbV/eSJquiDWRWVHayMqPIVHqX2hSZKdz9e57GyZDtn6ezX8r4LLy1t1yX8Lne4zqj5UsJzWj8BJeZbqSlTVz0Id+6xOHKEUOj9KZXNoS8ZJXNGXvvYPBV8khN5yCtfuKF7YM2liPONJyyCK09aCh8CoiPdNTsNtGBCXs6o5gZd8vt7kUMXUK5HMHMDUmxrUd6+rFpnbrdoVZD32LmEDJTsYEJezqjmCXGqBLjVA5g+uFiDmBtujuKElJtHerO67OWLOb490Qnz7o7O67OWLe5fddzVmw0O/+TtXg714O19PenZ/Nl1aubXZqztX87vrbd9bdmz9N51VpLra7+X/AEjOdJvrmP3E+v7i/GycqKr3/hNgNE07EhiixTn9h86xFVl82XWPcuS3NTGhCXVkHr6j3O8QLniIS/yEVOEj1qInZ6FUzSddLE4Y0DVicOURd0bmP/TMHqiqxmsV6lo8Ur9D1NFUaSMxoV+trze/o3v6Nj+jY/oQLgtrze/oSUobYm3/AEbf9G3/AEbf9G3/AEbf9G3/AEOqb/U3H7EEbfubf9HXUbWi+QrZKGpF7fwud7h080XQ9goFIt1U6sHciuOgp1wxq6q8oUhkp2vVKhFx/wBqyhPNUJnIinqnejsNtGSYLeKliCQ5SmSiJp5xqQVMhWrZw8ani6RgESODiURH6Qm9cQAFe/3D/LJFVZBCVbaShzS0kmpOCDY6JFCITHRsoRHBD4O+pwQbHRIoRCa01mVKGJpXxjF3uRxYdW+ChoVW+ShI4sYu9yOLHFhzCFWXjZsNDv8A5O1eDvXg7X096dn82XVq5tdmrO1fzu+tt31t2bP03nVWkutrv6f8lX+NBV3cVdmP6JdaTwopdvqaowK6EiG2nWRVLml0we8v8lIC+/YovL+CO6SImNcxjrq52yod1qbToVhMnkSjPKw6kf8AXP0qK0WpyaHnOtXSUyrNvHAJnLyHIoXSfkRxQVcT1T17bbbbbX7Y1TKUWNs3pJv6oUn1v4sUrCdDVhOnujLF8KsmI572KhdHgx+XqQpDJVqcXCV9RjGP6XDCQpqrTeEdhtpvdPRNqJXWoiTdGzhOmZOE05KkicfabXs7x9V79repAAV7/cP8IRKvf7h2b1n/AApSm/29FKNeSdtP/PplLb7enYaHf/J2rwd68Ha+nvTs/my6tXNrs1Z2r+d31tu+tuzZ+m86q0l1td/T/q1ztjd/9VkXrU9sUObYYhoqvv8AyVcphfvu3bPHvF9XL7cV7eiJXgmIk8cPsTSEs70RoJbuewjSNXXQTi8YHdLN+nLf05b+m7fpu36LF4Wb9OW/ooRZ1fpzYc2HNhzYc2HNhzYOPtDnQUl3Q5sMsJ+QBVNtX8WiMlcsW8kdE/CDIm6ni836IA6PBicPDszHYyP0ptXOBZ4sw0Ga9i0hoPk0HyaSxKrmW1CImMBfbk7DbSgMMxIyN1jdY3WIkx8VTV6p7i1uPK1mXKFc7UOt628TdY3WHqZozlXhW2kZy+zlrJvOOUSaZKUybZbznxjZ+hvOOUSaZKUybZbzj9Uabxn0FGkDZFlOATJspxWN8GmbpazS/KcInWLNhod/8navB3rwdr6e9Oz+bLq1c2uzVnav53fW27627Nn6bzqrSXW138/+rtDHuUXdlR9H9lh/W74a09mNBlEp6f5BtJNuiGuf3H/21pNQ6oX+gPnYsrZsz0MaUyihgS8ny8697qzBBAhuexsmQ7Z+ns1/K+Cy8tbdcl/BojJXLFvJHRPwgjohL3i9WOeYt6aEhJRiPGjLhKNaoXl9C7kXWgmnc5/mubUSQSULsh3OUbfVkdhEZ02mnDN7p6HsTTdLnYTacqjFrceVrHij6nH0Ozes/wCFKU3+3ppTf7DXkQxq2lt9vTsNDv8A5O1eDvXg7X096TxBUYdgS+i6zdOCnBRHhUynEjiQs3BZTgpcE3N3+YQQQQQQQQQSS5tDjhdFsBBJRqFBvCX6bzqrSXW138/+jdw5Xqk/Wj5+/slE/XNkHBOjmU90eP8AIR3e2vgStlcC9zTDoi0S5UGmJQlHircH5emiXor3VUMnA86/8CRUUzL8YMu4JTkKTSvXR+6qQ1bUt1f89HZr+V8Fl5a265L1tEZK5Yt5I6J+EFqMle8W82aMwfAlbK4F7mmNMJ9zPeypC+lPNUMEuqkvb9jJ+2Riy4Q90LGKZRzfIsQmb217GmGvALWOk/IpRze5FOAk74IELB0XUmNEuiUHOM5xnOMyhW02crzYm05VGLW48rKq5c6p3s5oi486sZYzZqxbRgQl7OqOYJcaoEuNUDmD64WIOYJcaoEuNUDmD64WIOYG26O4oSUm0d6s7rs5Yt7l913NWbDQ7/5O1eDvXg7X06e1NM1DSo6JvqUmOwm1a2KZpZ6Ui1dOFbzfU31N9TfU31N9TfU31N9SgmqNTdU1x7m+p9q1Jn/NBX4DC/03nVWkutrv5/8AQVVSe7f/ABGhKj+1b+vvM2ok/wAg6JHyiHMUjbvEScn/ADJv9hQm0xTu9wQ6loQdVP1TCyb0qHumMl9dHDNq8+dDB2sovQhKelLUfuYjF2ZqCZVKHJcX3kuPv0dmv5XwWXlrbrkvU0Rkrli3kjon4QWoyV7xbzdkx0S7ocxSNu8RJyf8yb/YUJtMU7vcEOpaEHVT9Uwsm9Kh7pkm1vQ+h7th6T9zHa1F0J5Onpb73ew8VnsJfzGy9Pkbu9pjvYEU+Vl+hAcFyabBM3P2F+ibjTtXncl7ERnNrsN11VeiBBBKQlUrVunYc2CGmoadibTlUZJcDo1F8D4qFJKScPGp4ukT1kEJVtpKHNLSSak4INjokUIhMdGyhEcEPg76nBBsdEihEJjo2UIjgh8HfU4INjokUIhNaazKlDE0r4xi73I4scWHMIVZeNmw0O/+TtXg714O19Pek8QVGFYEvo+sXTgpwUR5VMpxI4kLN4WU4KXhNxd/mEEEEEEEEEEmuLQ44XTbAQSVblSecP03nVWkutrv5/8ATO9Qr4H6QoWz/VHc7OCV0vYa4d5PkLP3l+e4hpImmiPIPwf/AH/IQKyfcmiVZ4KLlh62snVdwV7nBfdPFCeX9rE4coW8nrSvAqFiGYS55HSPL9HZr+V8Fl5a265elojJXLFvJHRPwgtRkr3i3m7G24jIhq73su56k0aqPBRcsPW6S3lcyRbPhS/XuTDPhKXw/CSaRkD8uw2kZL0xAz+2XR7lDgw8ZHsLy9ipcj2xqcAJJSY9TRxo/bsR+z4xfyjSPRlYhRatE+/6HlzM+EZRWWx0Hbt+YYqSvvlwfuOyyilv5FRbXTUH0fcipVYQkqRQMvo/DapOjNSfQfrpTf7emlN/t6KUa8k7af8An07DQ7/5O1eDvXg7X096dn82XVq5tdmrO1fzu+tt31t2bP03nVWkutrv5/8AVRpmVHqOgtcjx/8Al2HjQVVcPmj+R8Rl74UVpBBLJrT1op64ykydBJnW90oJBqpuby7Fw+p9t/giU7m/6gVL3SG+Wbi+hI75ihvlCtvJ/GCeoGBT1/EjH1Lyif1/kfbDn/DNpi7UbAeJNWDt/T4GNTNYiCWlWXYPaP0tK5YT5DoeGillRKdw77UENJgtvfYlKrZmbE+zYn2bE+zYn2bE+zYn2bE+xNsvbU2X9jz7/wBzYn2aJwUNcCxjDWWK0FNZilNY2tEZK5Yt5I6J+EFqMle8W83am3EZkRdCANhz/hWFpox9Sdyaazx/Y6dic036mCXULWImUq0kIzXPsZHCbxYs8KERB+lL0oqKpOFhD2IY2gSsoqLO606WWbeCYA2I2g+SUSRKjp6IQg78jNz73Aq6Xnm4Qhie9ZZxnO1cHKH7XbIahjXi1lJTmklwMpLckkOLPAlKL5sZSU5pJcDKS3JJDizwJSi+bGUlOaSXAyktySQ4sZptO7yF8iElejYaHf8Aydq8HevB2vp707P5surVza7NWdq/nd9bbvrbs2fpvOqtJdbXfz/5Y2vW09bdGxFCVIYLwOvcZG4J/KKH2TSSrFsfJi32FmJ5CbtSfux2q1c33jhDSNO9S/PuRiNvF/0affWJJY0fwwag+f8AISPM/YKxZT+A6YqjUWKFOTyYfsELBeZDQKYks8BNNSnNlyDV9LWSS0LNimSaT8sfTPVSBNNh0opEVjL5pIg7F+mxfpsX6bF+mxfpsX6RCPbU3/8ASDq7amxfo9Ssa8Tiv0WJsdR70M/jCuY/oFcNO981oKazFKaxGiMlcsW8kdE/CC1GSveLebsfIJXLiJCRF98OPljGQhtQ3GYrEf4QdMVRqLFf2n/7m/4IHb4LKOaUJWVbF1GmnD9dyd/m1CQxrx/4GvIhJVuw0O/+TtXg714O19PenZ/Nl1aubXZqztX87vrbd9bdmz9N51VpLra7+f8AzR3pnbcvXW+nPkL2qnJP0Y9JmW9+BJCzJPh6HJkIdM9v4ZEfe/yEWVcD2r9EzLuSXWU/CtUg+OLpmJTJJhjez9HDc5h9hDGG5ILskKHy+l7q+S4K82UOmIYti7IOpA+esFbX8QR6vcdysPeGUN9C1lxmQJ5TDlsVkjy/AZL09RvTXlNgyHO8bRktInqbZm2ZtmbZm2ZtmPJ8n6b1/pA/b+m2ZiK9z0ittMCnX0Y2fR1xtodE/CC1GSveLebscw4vNQ1nVqZnHjiezn82EzMu5JfKfhWqQfHF0zEpkkwxvZ+jhucw+wiFNyQXZIVvl9fKvdlwV5sodMQxbF2T+uIL3JhSyRS41QOYH0r5BlXXucycyL8XqcgcwcwcwcwcwcwPKifU5k5kTQ/lE3Cd7nMHMHMHMHMHMHMHMHMHMHMHMHMHMHMDOnyCi+w5kY/PBhiBlqVVB2rwd68Ha+nvTs/my6tXNrs1Z2r+d31tu+tuzZ+m86q0l1td/P8A6aulH4x/U9MoNyMCsTTkCQm+iFhp9GrwLDDuqns6+hzUIliu/kjq0/yCnqllDGN8RdOacrbOx684YuAh0kr2DJIuOTzduDaiEeUKrofupOIvU72xT1E2nKdRC3FlUKYTlNaoQ76a903v+mNh0lEsGMc7AOym0maf7sqG2WqZ1vHEziY03/HFnw+976Gh7hoSsPcdSiqsJqn0ZdidjDFCr4XCqhmEt9WVtGKuv2HCp312OLJsqNHsFApFBGihJXBkY/0X7C5tLwvrYaIStK6X0CVCSveLeb9D5Jq9cZISMvvhx8sR8QeD56Ej+t62zsevOGLgIdJK9gySLjk83bg2ohFlKq6H7qTiL1O9sU9RNpynUUtx/O6EL16FAcwh1lYM4IPIaX0HCDhBwgTS37DghwQ4IcEOCHBDggpPrCcpXxjhBwg4YPofwjghwQ4IcEOCHBDghwQ4IcEOCHBDghwQ4IcEOCD7hP2HBBSKWLohV2khHevB2vp707P5surVza7NWdq/nd9bbvrbs2fpvOqtJdbXfz/58/i1YQTGmLFP+gyVXMhXdMGRhETZ/RItuo20t2F/3zW7fMNWBSr1sbxsrtDm7Wh3ZU2L7hv6/wAjfbjGWxDm6FNM18kvt7XhydkhJuF6WLgKfDZr5H17qxm0p1IbFjbll5p8DvQwyPAKvDFZa693Nsw5KmS9CYqT7l5+ynsNNJPM7H4sc3VeklD2XfpVDv0R9y4Avbd/V/KGi/nfVAZbkO7pejNfNCV+8vDZ2SEm49LFwFLRk1lifXurGbSnU1Zj/JcdJ968/ZT2J7V/21fvQ6Z07Chm9EzkdM6Z0zpnTOmdMc6ehKwdM6Z0zpnTOmdM6Z0zpnTOmdM6Z0zpnTOmJQuW968Ha+nvTs/my6tXNrs1Z2r+d31tu+tuzZ+m86q0l1td/P8A6B1fZP8ApdxCcm5PZ/JLbgkb9lYxt4B7r6ZGeMT9EaJRddL1hiMTWIjTT6FfkXTuGJEd+u7R/kVIXh2H0TnqcZDy8kz+HrdlQQjyaFXQQe6myBsj+LaRtuEiTUskN2b3+Eh2kUKKHY/BS+YNjR02SB8jl7cF6BjHN0m+qclOSnJRs0NA/QMZ5ap/mcxOYjpDL24L0DGObSo31TmJzE5iRVJTFLRTTUu8IeW7yZ71uyoIR5NCroIPdTZA2R/FtI23CRNqWSG7N7/CRHycQmaQaQaQaQaQaQaQaQaQaQaQaQaQaQaQaQaQaT+hVVVVVVU0g0g0g0g0g0g0g0g0g0gpvvo2bRSpDCDWyUemIFNRHJeEcCTywpopLXekaANJS1zoSS6joAiIppia7+Tve973ve6RW0EowqibEKs2bwRmpTkIhS/TedVaS62u/n/16e+jUckb0aIdX17/AMtI2Hrx7dQXmfYnl6L1SKxVULvcA3Aon7jBGShbFfISSSSUJXL/ACVEygALW836/sFhLUP6tjS9f4yqU6p2vz8Ek36kOfdKOjOx+DufgVOE2YVKtx/c7a7C9lyZsS0Vvfe9rUxPRYJm/wAHiVm8N29ZDEavy1F4ZZgVbj+5212F7LkzYlore+97WpieiwTN/g8Ss3huxa3m/X9hNLdPy2NL1/jP5mr/ACv38FB/qRNzrIuj/wDHLR/zhqrv4pS4VTGMZxlU7D+HYnavFlza792eD+tdtZ5LS7j1XnVWkutrv5/8jH2NdcO5Uy5VYiznw9h5/wAW1R2PSZzogSPWv7uTzE47IZT1XSw/ybQC+EmJHrVQ1Vqgm7yV9lYhpSfqva5vFKmTDo5O5mbCshpNNO4c5wcHY/BO13fUJTKyvFnbXYXsuTNiWit773tamJ6LBM3+DxKzeG7eshiNX5ai8MswKtx/c7a7C9lyZsS0Vvfe9rUxPRYJm/weJWbw3aketRo1Ligu4yV9lZdUaUn6r2ubxSpkw6OTuZmwrIaTTTuHSsLfftZnoRYBkiZKdzNbgNBMp8r0nWzPQiwDJEyU7ma3AaCZT5XpOtmehFgGSJkp3M1uA0EynyvSdbM9CLAMkTJTuZrcBoJRmWTrZnuFYBkhiadzWJqhxoJZMFemrZnuFYBkhiadzWJqhxoJdMVek6kNVcZ6EWAZImSnczW4DQTKfK9J1sz0IsA9LvroxELf+J3LyRadx/DMzMzHiDge60tZqWarJlzaqssV/ZUoRpaqrJa3H8t3d3d3YnIzOYH4p1c7Ni/tSQvUSfqIiIn9glUJAOA5ISR/aCIOXJ2+AkyYDkTkTkTkTkTkTkR0yU8pL/oroGq0XmhL1Yhxir/KsnocyzmWcyzmWcyzmWUZNGbObZzbEUxDqujX2LCKatAE675BNOqc20LNRv1u/wApHmvhR+ix0PV0gyn+Xa9EMsaPQhdFxO0hrUPton77GRKHdXRXITWk4m7eikkr3dHwbxlbXYXsuTNiWit773tamJ6LBM3+DxKzeG7eshiNX5ai8MswKtx/c7a7C9lyZsS0Vvfe9rUxPRYJm/weJWbw3bedM++FFX8ix0PV0g5nNr0Qyxo9CF0XE7SGtQu+ifvsZd8bU6FchNROJu2bkI0pSxKTlJJTEDUxaGZWWEGQnA+omUsSk5SSUxA1MWhmVlhBkJwPqJlLEpOUklMQNTFoZlZYQZCcD6iZSxKTlJJTEDUxaGZWWEGQnA+omOahonlJJTEjkxaGZVqUeWZAZZKY4OMieUklMSOTFoZlWpR5ZkBlkplMMJTlJJTEDUxaGZWWEGQnA+omUsSk5SSUyg6VeUX7Hgsxz8ufkXBrVCNBultTiC+aF289xjBdNC7cJ09ucQN0tqcQXzQu3nuMYLpoXbhOntziBultTiC+aF289xjBdNC7cJ09ucQN0tqcQXzQu3nuMYLpoXbhOntziBultTiC+aF289xjBdNC7cJ09ucQN0tqcQLL5FpMjzlOql3CxnJXMvSzVfZub7NzfZub7NzfZub7NzfZub7NzfZub7NzfZub7NzfZub7NzfZub7NzfZub7NzfZub7NzfZub7NzfZub7NzfZub7NzfZub7NzfZub7NzfZub7NzfZub7NzfZub7NzfZub7NzfZub7NzfYyrjosV/qpo8W1UKTJRgn5Bf5J7IONocSKz7dqhOUaF0YUiQjT1JBwhZZxMCKUc2KXQro/Djf4Vqdne+wfW7mqG9c4gl8tMfJK7mP4H/NM+c7Go1b7TaHRuCdBso7UIUzOe+hJojojoi5NS9v0BCBojojojoA6T0CEKH/IYyIzZJ1Wuc/Ni9s8MOGHBBcuh1FSJCNPUkHCFlnEwIpRzYpdCuj8ON/hWp2f0h9buaoY8eGqPlyPkldzH8D/AJpnznY1GrfabRrYjee52nzZXaWHee52nzZXaWHee52nzZXaWHee52nzZXaWHee52bzYnaWRee52bzYnaWRee523zZXaWGlT6jkVohZ1GN8s7z5IaFDIJKoSEJCFIm9YXhlGYBVYxYcr9RCw5T6jOMxCqib1heICVAadQ2IJKoSEJCFI7hBASoDTqGxBJVCQhIQpHcIICVAadQ2IJKoSEJCFI7hAsZc4zGOM4TeR09YwF7FVcRHyJa48h7aTvAvFVZWGjGJopUfLNqfZtT7NqfZtT7NqfZtT7NqfZtT7NqfZtT7NqfZtT7NqfZtT7NqfZtT7NqfZtT7NqfZtT7NqfZtT7NqfZtT7NqfZtT7NqfZtT7NqfZtT7NqfZtT7NqfZtT7NqfZtT7NqfZtT7NqfZtT7NqfZoj8h5qKDa1MTKaFy+CaIhLmp/wAn6rPW4FRQTeM6AdRq5wQJIftIhXa+GRkNrGp4OwrooXLUWzQ9xT6Hi3ImQNnpfm32SJEiRIkM1rfAkSJDNYyZIadpU2vuWU9HZKKW0/IV7+tVFBJrzpwGrCaK+iKP2lRNS7YdyARUq4wNd1kQhjOMDKv0e4p9FNuRcgdT573/AOl97itwKafIsLY7QUfJG+9xW4FNPkWFsdoKPkjfe4rcCmnyLC2O0FHyRvvcVuBTT5FhbHaCj5I33uK3Cpp8itJY7QQc2S0F97jIabo8itJY7QQc2S0F97jk0po8iQogdoX9H+Bee52nzZ3nyMoVJWQTJeMmr5WL40GEsivWPUv+gu+oYSwK94dBk1fKTfGomS4lZCVkJWQlZCVkJWQlZCVkJWQlZCVkJWQlZB3kj30H0mJfvL/hTJmO0DPiAncSt01x0GU6NV24TqvFDoSvMRKcKm+hvob6G+hvob6G+hvob6G+hvob6G+hvob6G+hvob6G+hvob6G+hvob6G+hvob6G+hvob6G+hvob6G+hvob6G+hvob6G+hvoXoycx5YfEYhp4M1NPb/ACXER1ECVRMdyLJiPVcdbH9XqGLzjJxkQ/OcXOLkoMGpuY8Os16GhHb7DkojJNCiiX0cZOMnGTjJxk4iJX5zi4z+E4ycZOMnETipxcZ/CcZOMiAE9k0zUa17kI1hHHzh4o62dE7U7j4owvXy1HquOtjXEkalVnHzj4m5OVHWOnTmtkYgmC0EalPr6GhLfYcnKlL/AIXnudi82J2xvtC89zsXmxO2N9oXnudi82J2xvtC89zsXmxO2N9oXnudi8jtse0HbG30Lz3Ox+TaaHbG30Lz3Ox+TaaHbCE46uj4Lz3O0+bO8+SJRKoO2gW1UdZMgUOYcV+BPBnnbb2E8GOdpPYkUOYU0+R7VR1mygO2giUSiUSiUSiUSiUSiUSiSRjJDC0z9RbvFdSyY3m+qj2IgAmFUzNESvfIkQpUyClIt0WGqHz8CDLvllEtH+L/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A+V0L7ATTR+F/kbxqiJTOBInBNwqJ9Vz1G4TZPuKsyUcCcCcCcCcCcCROmpksPTUjsTUGE+KU1Iu1LaYQJEsT1sShYqLyfoSipKtjq7URNZpbeA74QQqvV2r6OrwQVgSrfO1EoWKi8n6EoqSrY6u1ETWaW3gMEkXoeq56jdGMiwTMCeZODNJfBGatUUt/38CTSiNEDDUJgovtV/CBUejbj01I7E1BiHMPib/S89zsXmxO2N9oXnudi82J2xvtC89zsXmxO2N9oXnudi82J2xvtC89zsXmxO2NvoXnudj8m00O2NvoXnudj8m00O2JETT2F57nafNnefIqF9RwX4GZZTxOYgxWhmxbGTOLYkyTMlBzpUh8xM4omOkwsExGGKkMmIyy1mMxSX4N0G6DdBug3QboN0G6DdBug3QboKdCsnlYKlO5fs77i0UiilychLWJiJSsxe9AuKtxecWFORgTNWRciJm0EphJplPucgcgcgcgcgcgcgcgcgcgcgcgcgcgcgcgcgcgcgcgcgcgcgcgcgcgcgcgcgcgcgcgcgcgcgcgcgcgcgcgcgcgIxBPHCGPQePuP8m+1wXquh4bqGaZHf1zvDsT01Ja8ti67o7gdr8lCaVF5MdgYlt42z3DezHV2oLLS28Cvx7qau1fR1eCCijyNbQmlReTHYGJbeNs9w3sx1dqCy0tvAr8e6mrO59V0UdUN66MupSw291CmJUokpILjHx3ESbqvyKQvLIKxyhN2k+mpLZwyOwvPcUi9zFhjtjfaF57nYvNidsb7QvPc7F5sTtjfaF57nYvNidsb7QvPc7F5sTtid683nudj8m00O2NvoXnudj8m00O2si89ztPmzvPkhl4Djf1HZoyUYjH0GlF4ow4aNbMb6/y06CaU1PiHEHKTpZCH0G1Fw7NOTnAUa+ptI2kbSNpG0jaRtI2kbSNpG0jaQ7ME1JwKHExJJ63DnWWDYSTzKCZNprC6gQiFTtaDDcEoV4qsDhDQ5MvGCuKDgUVg1PzNT8zU/M1PzNT8zU/M1PzNT8zU/M1PzNT8zU/M1PzNT8zU/M1PzNT8zU/M1PzNT8zU/M1PzNT8zU/M1PzNT8zU/M1PzNT8zU/M1PzNT8zU/M1PzNT8zU/M1PzNT8zU/M1PzNT8zU/MR206q8RNzb3/wAm0qZ9TcFDdfgRZpJPW3mja+vTeLW35JfJ3AWzGiFqOwMS28bZ7hvZjq7UFlpbeBX491NXavo6vBBRR5GtoTSovJjsDEtvG2e4b2Y6u1BZaW3gV+PdTV2d76rgobr8DgmDCNnGiUZ6kKxCaNcqDZJhusjbbll9ZZ2Wjf56bzrasIybyXnudj8m00O2N9oXnudi82J2xvtC89zsXmxO2N9oXnudi82J2xvtC89zsXmxO2N9oXnudj8m00O2NvoXnuVzHGFnUbeZntrIvPc7b5s7z5OsRmZEFx16DumoTIlJ9kBBWZ60xhWZ6UhqT7oCum4TJMF506EZmRmZGZkZmRmZGZkZmRmZGZkZmRmZGZkZmNpNN2Zo17HiTNZXmsWgkXILyETvDSiC4lSRcLl1h4kabXqxCNG+nR5jjtjjOYOHfhw78OHfhw78OHfhw78OHfhw78OHfhw78OHfhw78OHfhw78OHfhw78OHfhw78OHfhw78OHfhw78OHfhw78OHfhw78OHfhw78OHfhw78OHfhw78OHfhw78OHfhw78OHfhw78OHfhw78OHfhw78IJYriKCZOSe/wDk3SC5v7v13chXPvh+vrx18P8A6NLtPRi9AEaK9sNTiD3Wm8qDM0pSo0B0B0BW0wuYhT9HvehFG5bvM0B0B0RcgCW2nW33lQZmlKVGgOgOgK2mFzEKdvnI/m8eu7kKyobxqgSYa+cSqDzOU/IktabSrL6yzDmmXw/+jSzT0YraXBmuSLj5XJnPRlIp0saMyN1cGX0sicy7uJ+k85QpYSrhHBK5M56FHVEseoVSeQy26ZEog3Qk/RLBOF0NXCOCVyZz0KOqJY9Qqk8hlt0yJRBuhJ+iWCcLoauEcErkznoUdUSx6hVJ5DLbpkSiDdCT9EsE4XQ1cI4JXJnPQo6olj1CqTyGW3TIlEG6En6JYJwuhq4RwSuTOehRxdLKZ6kbq4MvpZE5l3cT9FtCiU3cmrjKx6uejFRUcZEViBuEthMZORJC3eULpauGTFPKv4KWIrQiGquO8+RPSOsJ1Che6JISL9YBiiNshCmNsi/WAqXuiSUxOodY6x1jrHWOsdY6x1jrHWOsdYeGNy3FmNggUi7/AKKEpbhRiujpKUjbQnwIYFLk1lkyEgxJ1nMlznbcyjfIuG2htobaG2htobaG2htobaG2htobaG2htobaG2htobaG2htobaG2htobaG2htobaG2htobaG2htobaG2htobaG2htobaG2htoNiSL3AxxXLGb6Iu3+TkGim7skVKSXSYWo09xO58FNMdRl0QmGZeiFXcXVj1u5FVFPTF0UF29dDomOyWMmNRIl2XCmilwTMAvkbRTJd6FqMpYFyxE4VzTUlLqP6ax34siov/ADJSxJJQhKeopCRUpJdJhajD3E7nwU0x1GXRCYZl6IVdxdWPHgiqinpi7qLudBSDbbl2MYdegRhTYhOKExq151NU4jwNPSWMmNRIl2XCmilwTMh8jaKZLvQtRgLAuWPQQ5aNabTKRARacJrNehSQxzQ//NJmWXkHgsxufXx/Cgs/kSQqiGYhmK2oM7y0Q28MnKDbwzcsdpaIrbkyGYhmIZiGYhmIZiGYhmIZiGYhmIZiGYhmIZhPVupJRmWDEwc2oloEXIIvgbTJ4JX5J21eGJcwQi6dB+GdzohG/fhv34b9+G/fhv34b9+G/fhv34b9+G/fhv34b9+G/fhv34b9+G/fhv34b9+G/fhv34b9+G/fhv34b9+G/fhv34b9+G/fhv34b9+G/fhv34b9+G/fhv34b9+G/fhv34b9+G/fhv34O6axf/AyScDNNSi0XutRpm/wwNd/yDUaMt1G8mah+xvI0ficSrO3mNQaHwkU0ldxJZr3fL+lxA8/AM718+SRwRXWZXhtHScok6x7XexdpMehVvqQUr+BIrg2w9zVYVTyR1fJZJZ2AeYNV8B82suGgu0JxYFWKXURE5eKVafCvuLEzRcxf33FxF45tg+lw2WZkpX/ACGEoNWIrw8NDKsr2nt/6LzMzMzMzMzMzMzMxLuszaGubYos17vl/T9ITD4BnevnyRKDrrMr3gZR03KJOse13sXaTDoVb6kFPxhI/ibAaokkn0Q3X2qJMsOZBpi5QVAWMgdx+cOgu0JxYFWCXUTE5eKqp8K+4imrVcxf33ExFz3tsPa4bEGYaV/yGWoNeIrxEa4pXTE+J7HLnLnLnLjvjPc5I5I5I5I5I5I5I5I5I5I5I5I5I5I5I5I5I5I5I5I5I5IemxwSYzTvj+N3d3d2v0tTyVPZAzmDTwqzPcuvI2WJk9i6cjXwozNPCrM9y68jZYmT2LpyNfCjM08Ksz3LryNliZPYunI18KMzTwqzPcuvI2WJk9i6cjXwozNPCrM9y68jZYmT2LpyNfCjM08KszMZ3yhSmyCuMj8+LlKakK8S6Tcdjcv8Ny/w3L/Dcv8ADcv8Ny/w3L/Dcv8ADcv8Ny/w3L/Dcv8ADcv8Ny/w3L/Dcv8ADcv8Ny/w3L/Dcv8ADcv8Ny/w3L/Dcv8ADcv8Ny/w3L/Dcv8ADcv8Ny/w3L/Dcv8ADcv8Ny/w3L/Dcv8ADcv8Ny/w3L/Dcv8AC+n9GfQ1OE7UTlZFghVaZeelh0QU9vw8r18mdSVU8kRz+Q3UzZTNuTfZpBmqz1ofCJwbzs7iUaPxN5GoXsbyZuonJYRk+75P/AvhJQ2YJ7jwPlMoI8SWAZS/FkZZ16X3ZsMSB5A0fgEoGbuwEkLcSNhDYWyp2S659Zbbm45o/JZN8DRwzXQgUZymiJiYnqHUOodY6vY6vY6vY6vY34N+Dfg34J8ZLhJcJLhJcJLhNyDcg3INyDcg3UjbSNtI20jbSNtI20jbX8szMzMxYFgWBYFgWBYFgWBYWXYAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIAgCAIWDYFgWBYFgWBYFgWBYfyq5mZmZm2jbSNtI20jbSNtI20jdSNyDcg3INyDcglwkuElwkuElwk+M34N+Dfg34Or2Or2Or2Or2OsdQ6h1CJiY28xZLVMzs2MVWcts6XyWPoFpfE0zp9hzqOBGj8AsgSDYYr0PuzPs4zl+CWIR4mcCxPlEjBurF8JC/wAfeQskQskNl6fBp/g0/wAEn5HGnGnGnEDiBxA484c4c4cn9MREWlNKaU0o+UzebN5s3mzebN5s1Pyan5NT8mp+TU/Jqfk0PkaD+TQfyaD+TQfyaD+TQfybzN9m+zfZvs32b7N9m+zfZq/M5BHII5BHII5BHII5BHII5BHII5BHII5BHII5BHII5BHII5BHII5BHII5BHII5BHII5BHII5BHII5BHII5BHII5BHII5BHII5BHII5BHII5BHII5BHII5BHII5BHII1fmb7N9m+zfZvs32b7N9m+zeZoP5NB/JoP5NB/JoP5NB/JofI1Pyan5NT8mp+TU/Jqfk3mzebN5s3mzebFymaU0ppTS+kiIoy4c4c4c484gcQOIHGnGnGkP5Gn+DT/AkXJ8ELJELJF3/wCo7QhSELDQuacr/wC2y/RLp1P6HLkSv+ito+b9EtDa0KkXvUdlSp5rten9VgxXyo9z5zl/6QMRk32bJCoKNRdNskAELEcHf/q5B10iuR+jSa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)"
      ],
      "metadata": {
        "id": "UW0QW_M9wzSK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **LECTURE 01:** *Importing Multiple Files*\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "9Pdt_YHlpU4a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yu59tXxOokK4",
        "outputId": "34d12da7-e968-4d97-dfe5-edbc8f176a58"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HsaJRYd2Il8B",
        "outputId": "f9cf5db8-4009-4b93-b30f-d7eff017bae7"
      },
      "source": [
        "cd /content/drive/MyDrive/DataAnalysis-with-Python"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/DataAnalysis-with-Python\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "fGHR_waHo1-d"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zLzeJbMq3rRb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "9a621f2d-04ce-4a94-a7c2-8c8b309f2193"
      },
      "source": [
        "silver = pd.read_csv('Silver.csv')\n",
        "gold = pd.read_csv('Gold.csv')\n",
        "bronze = pd.read_csv('Bronze.csv')\n",
        "bronze.head()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NOC</th>\n",
              "      <th>Country</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>USA</td>\n",
              "      <td>United States</td>\n",
              "      <td>1052.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>URS</td>\n",
              "      <td>Soviet Union</td>\n",
              "      <td>584.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GBR</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>505.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>FRA</td>\n",
              "      <td>France</td>\n",
              "      <td>475.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GER</td>\n",
              "      <td>Germany</td>\n",
              "      <td>454.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   NOC         Country   Total\n",
              "0  USA   United States  1052.0\n",
              "1  URS    Soviet Union   584.0\n",
              "2  GBR  United Kingdom   505.0\n",
              "3  FRA          France   475.0\n",
              "4  GER         Germany   454.0"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "silver.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "nMbdlxLWqvZH",
        "outputId": "b20f59db-6bb4-4d9b-9bde-8ae2d7df3cbd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NOC</th>\n",
              "      <th>Country</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>USA</td>\n",
              "      <td>United States</td>\n",
              "      <td>1195.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>URS</td>\n",
              "      <td>Soviet Union</td>\n",
              "      <td>627.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GBR</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>591.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>FRA</td>\n",
              "      <td>France</td>\n",
              "      <td>461.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GER</td>\n",
              "      <td>Germany</td>\n",
              "      <td>350.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   NOC         Country   Total\n",
              "0  USA   United States  1195.0\n",
              "1  URS    Soviet Union   627.0\n",
              "2  GBR  United Kingdom   591.0\n",
              "3  FRA          France   461.0\n",
              "4  GER         Germany   350.0"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "silver.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CgVitYIfqkJy",
        "outputId": "08944461-d09d-4add-ddf2-ae5a57e830ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(138, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PnJGKKW4-bmE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "62d9aad3-4b68-4f4b-8c1f-b999874c49fd"
      },
      "source": [
        "# Create the list of file names: filenames\n",
        "filenames = ['Gold.csv', 'Silver.csv', 'Bronze.csv']\n",
        "\n",
        "# Create the list of three DataFrames: dataframes\n",
        "dataframes = []\n",
        "for filename in filenames :\n",
        "    dataframes.append(pd.read_csv(filename))\n",
        "\n",
        "# Print top 5 rows of 1st DataFrame in dataframes\n",
        "print(dataframes[2].head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   NOC         Country   Total\n",
            "0  USA   United States  1052.0\n",
            "1  URS    Soviet Union   584.0\n",
            "2  GBR  United Kingdom   505.0\n",
            "3  FRA          France   475.0\n",
            "4  GER         Germany   454.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make a copy of gold: medals\n",
        "medals = gold.copy()\n",
        "\n",
        "# Create list of new column labels: new_labels\n",
        "new_labels = ['NOC', 'Country', 'Gold']\n",
        "\n",
        "# Rename the columns of medals using new_labels\n",
        "medals.columns = new_labels\n",
        "medals.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "bC88giQRsXJL",
        "outputId": "057ef8b4-9338-4b56-8ef5-a7684e5ce550"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NOC</th>\n",
              "      <th>Country</th>\n",
              "      <th>Gold</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>USA</td>\n",
              "      <td>United States</td>\n",
              "      <td>2088.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>URS</td>\n",
              "      <td>Soviet Union</td>\n",
              "      <td>838.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GBR</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>498.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>FRA</td>\n",
              "      <td>France</td>\n",
              "      <td>378.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GER</td>\n",
              "      <td>Germany</td>\n",
              "      <td>407.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   NOC         Country    Gold\n",
              "0  USA   United States  2088.0\n",
              "1  URS    Soviet Union   838.0\n",
              "2  GBR  United Kingdom   498.0\n",
              "3  FRA          France   378.0\n",
              "4  GER         Germany   407.0"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "medals.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4uwfMHjENnlj",
        "outputId": "d47ce1d4-d454-4c00-8355-93cecffcbbd7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['NOC', 'Country', 'Gold'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "medals.rename({\"Gold\": \"Golden\"}, axis=1, inplace=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "DKVjUV9qs7CE",
        "outputId": "9b3b2ba0-2bbf-419e-8820-adc240612360"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NOC</th>\n",
              "      <th>Country</th>\n",
              "      <th>Golden</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>USA</td>\n",
              "      <td>United States</td>\n",
              "      <td>2088.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>URS</td>\n",
              "      <td>Soviet Union</td>\n",
              "      <td>838.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GBR</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>498.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>FRA</td>\n",
              "      <td>France</td>\n",
              "      <td>378.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GER</td>\n",
              "      <td>Germany</td>\n",
              "      <td>407.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>SEN</td>\n",
              "      <td>Senegal</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>SUD</td>\n",
              "      <td>Sudan</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>TGA</td>\n",
              "      <td>Tonga</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>BDI</td>\n",
              "      <td>Burundi</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>UAE</td>\n",
              "      <td>United Arab Emirates</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>138 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     NOC               Country  Golden\n",
              "0    USA         United States  2088.0\n",
              "1    URS          Soviet Union   838.0\n",
              "2    GBR        United Kingdom   498.0\n",
              "3    FRA                France   378.0\n",
              "4    GER               Germany   407.0\n",
              "..   ...                   ...     ...\n",
              "133  SEN               Senegal     NaN\n",
              "134  SUD                 Sudan     NaN\n",
              "135  TGA                 Tonga     NaN\n",
              "136  BDI               Burundi     1.0\n",
              "137  UAE  United Arab Emirates     1.0\n",
              "\n",
              "[138 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "medals.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "kyo5iC8ltC_N",
        "outputId": "c9d0a07e-5eb1-404b-b55e-01d646354a5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NOC</th>\n",
              "      <th>Country</th>\n",
              "      <th>Gold</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>USA</td>\n",
              "      <td>United States</td>\n",
              "      <td>2088.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>URS</td>\n",
              "      <td>Soviet Union</td>\n",
              "      <td>838.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GBR</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>498.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>FRA</td>\n",
              "      <td>France</td>\n",
              "      <td>378.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GER</td>\n",
              "      <td>Germany</td>\n",
              "      <td>407.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   NOC         Country    Gold\n",
              "0  USA   United States  2088.0\n",
              "1  URS    Soviet Union   838.0\n",
              "2  GBR  United Kingdom   498.0\n",
              "3  FRA          France   378.0\n",
              "4  GER         Germany   407.0"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Add columns 'Silver' & 'Bronze' to medals\n",
        "medals['Silver'] = silver['Total']\n",
        "medals['Bronze'] = bronze['Total']\n",
        "\n",
        "medals.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "rp4aHyFhsrJv",
        "outputId": "d76a2e50-3719-4739-eb7c-557c1edce369"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NOC</th>\n",
              "      <th>Country</th>\n",
              "      <th>Gold</th>\n",
              "      <th>Silver</th>\n",
              "      <th>Bronze</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>USA</td>\n",
              "      <td>United States</td>\n",
              "      <td>2088.0</td>\n",
              "      <td>1195.0</td>\n",
              "      <td>1052.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>URS</td>\n",
              "      <td>Soviet Union</td>\n",
              "      <td>838.0</td>\n",
              "      <td>627.0</td>\n",
              "      <td>584.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GBR</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>498.0</td>\n",
              "      <td>591.0</td>\n",
              "      <td>505.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>FRA</td>\n",
              "      <td>France</td>\n",
              "      <td>378.0</td>\n",
              "      <td>461.0</td>\n",
              "      <td>475.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GER</td>\n",
              "      <td>Germany</td>\n",
              "      <td>407.0</td>\n",
              "      <td>350.0</td>\n",
              "      <td>454.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   NOC         Country    Gold  Silver  Bronze\n",
              "0  USA   United States  2088.0  1195.0  1052.0\n",
              "1  URS    Soviet Union   838.0   627.0   584.0\n",
              "2  GBR  United Kingdom   498.0   591.0   505.0\n",
              "3  FRA          France   378.0   461.0   475.0\n",
              "4  GER         Germany   407.0   350.0   454.0"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **LECTURE 02:** *Indexing and Reindexing*"
      ],
      "metadata": {
        "id": "-gyFAs_NtqnQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "max_weather = pd.read_csv('monthly_max_temp.csv', index_col='Month')\n",
        "mean_weather = pd.read_csv('monthly_mean_temp.csv', index_col='Month')\n",
        "\n",
        "weather1 = max_weather.copy()\n",
        "weather1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "id": "s_g8oyv46dTr",
        "outputId": "a60b4fc8-f88e-4e43-e5dc-15fac493acd3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Max TemperatureF</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Jan</th>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Feb</th>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mar</th>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Apr</th>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>May</th>\n",
              "      <td>88</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Max TemperatureF\n",
              "Month                  \n",
              "Jan                  68\n",
              "Feb                  60\n",
              "Mar                  68\n",
              "Apr                  84\n",
              "May                  88"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weather1.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9vdegIxR7chS",
        "outputId": "32f33494-f187-48aa-a699-e8371d2b0176"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct',\n",
              "       'Nov', 'Dec'],\n",
              "      dtype='object', name='Month')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Sorting Index"
      ],
      "metadata": {
        "id": "njty9yU9_Y_G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Sort the index of weather1 in alphabetical order: weather2\n",
        "weather2 = weather1.sort_index()\n",
        "\n",
        "# Print the head of weather2\n",
        "print(weather2.head())\n",
        "\n",
        "# Sort the index of weather1 in reverse alphabetical order: weather3\n",
        "weather3 = weather1.sort_index(ascending=False)\n",
        "\n",
        "# Print the head of weather3\n",
        "print(weather3.head())\n",
        "\n",
        "# Sort weather1 numerically using the values of 'Max TemperatureF': weather4\n",
        "weather4 = weather1.sort_values('Max TemperatureF')\n",
        "\n",
        "# Print the head of weather4\n",
        "print(weather4.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fNqMDPZy6h2h",
        "outputId": "edfc03d6-e62b-4cbd-84e0-77bbb13ef405"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       Max TemperatureF\n",
            "Month                  \n",
            "Apr                  84\n",
            "Aug                  86\n",
            "Dec                  68\n",
            "Feb                  60\n",
            "Jan                  68\n",
            "       Max TemperatureF\n",
            "Month                  \n",
            "Sep                  90\n",
            "Oct                  84\n",
            "Nov                  72\n",
            "May                  88\n",
            "Mar                  68\n",
            "       Max TemperatureF\n",
            "Month                  \n",
            "Feb                  60\n",
            "Jan                  68\n",
            "Mar                  68\n",
            "Dec                  68\n",
            "Nov                  72\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weather1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "id": "tgMfFu8i8OEC",
        "outputId": "7169cddc-aea8-4b07-91ec-fb3eac905d0b"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>Max TemperatureF</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Jan</th>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Feb</th>\n",
              "      <td>60</td>\n",
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              "    <tr>\n",
              "      <th>Mar</th>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Apr</th>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>May</th>\n",
              "      <td>88</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            "text/plain": [
              "       Max TemperatureF\n",
              "Month                  \n",
              "Jan                  68\n",
              "Feb                  60\n",
              "Mar                  68\n",
              "Apr                  84\n",
              "May                  88"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "### Reindexing"
      ],
      "metadata": {
        "id": "jxzsFyob_dLS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ordered = ['Jan', 'Apr', 'Jul', 'Oct']\n",
        "weather1.reindex(ordered)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "Ti1kQIKr7z_F",
        "outputId": "68b6da44-411d-4f08-dac7-569f9796adcc"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>Max TemperatureF</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Jan</th>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Apr</th>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jul</th>\n",
              "      <td>91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oct</th>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Max TemperatureF\n",
              "Month                  \n",
              "Jan                  68\n",
              "Apr                  84\n",
              "Jul                  91\n",
              "Oct                  84"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Reindex from a DataFrame Index"
      ],
      "metadata": {
        "id": "xsBZ9y6j93XW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mean_weather.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GNBI_hNH9sYE",
        "outputId": "5ffa5ac6-daaf-4ff0-84b8-42d22fb04e43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Apr', 'Aug', 'Dec', 'Feb', 'Jan', 'Jul', 'Jun', 'Mar', 'May', 'Nov',\n",
              "       'Oct', 'Sep'],\n",
              "      dtype='object', name='Month')"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weather1.reindex(mean_weather.index)"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "kCUuYgaK9Q53",
        "outputId": "c296be5a-d91a-4474-a214-9c5ce56510ee"
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      "execution_count": null,
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              "      <th>Max TemperatureF</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Apr</th>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Aug</th>\n",
              "      <td>86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dec</th>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Feb</th>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jan</th>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jul</th>\n",
              "      <td>91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jun</th>\n",
              "      <td>89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mar</th>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>May</th>\n",
              "      <td>88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nov</th>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oct</th>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sep</th>\n",
              "      <td>90</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Max TemperatureF\n",
              "Month                  \n",
              "Apr                  84\n",
              "Aug                  86\n",
              "Dec                  68\n",
              "Feb                  60\n",
              "Jan                  68\n",
              "Jul                  91\n",
              "Jun                  89\n",
              "Mar                  68\n",
              "May                  88\n",
              "Nov                  72\n",
              "Oct                  84\n",
              "Sep                  90"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Reindexing with missing labels"
      ],
      "metadata": {
        "id": "PX60x46u-AqZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "weather1.reindex(['Apr','May','XYZ'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        },
        "id": "TZJhTNY19_ta",
        "outputId": "8ed792bf-5600-4c9f-cef2-33bb038a1f6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Max TemperatureF</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Apr</th>\n",
              "      <td>84.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>May</th>\n",
              "      <td>88.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XYZ</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Max TemperatureF\n",
              "Month                  \n",
              "Apr                84.0\n",
              "May                88.0\n",
              "XYZ                 NaN"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "weather1.reindex(['Apr','May','XYZ']).dropna()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "4PngjZuk-YSh",
        "outputId": "56d169cb-6db6-428d-9879-496867beee3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Max TemperatureF</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Apr</th>\n",
              "      <td>84.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>May</th>\n",
              "      <td>88.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Max TemperatureF\n",
              "Month                  \n",
              "Apr                84.0\n",
              "May                88.0"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weather1.reindex(['Apr','May','XYZ']).bfill()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        },
        "id": "0EqT6XYv-eT1",
        "outputId": "42d15a95-b66f-448d-a40c-c54a3b1d6896"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "    <tr>\n",
              "      <th>Apr</th>\n",
              "      <td>84.0</td>\n",
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              "    <tr>\n",
              "      <th>May</th>\n",
              "      <td>88.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XYZ</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "       Max TemperatureF\n",
              "Month                  \n",
              "Apr                84.0\n",
              "May                88.0\n",
              "XYZ                 NaN"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
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    },
    {
      "cell_type": "markdown",
      "source": [
        "### Resetting Index"
      ],
      "metadata": {
        "id": "wPCN_0FI_kOB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "weather2 = weather1.reset_index(inplace=False)\n",
        "weather2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "8wgPeusN_KT5",
        "outputId": "c0144113-41b1-4867-c9ef-c44642fe81d9"
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      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
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              "      <th>Max TemperatureF</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>68</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Feb</td>\n",
              "      <td>60</td>\n",
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              "      <th>2</th>\n",
              "      <td>Mar</td>\n",
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              "      <td>88</td>\n",
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            "text/plain": [
              "  Month  Max TemperatureF\n",
              "0   Jan                68\n",
              "1   Feb                60\n",
              "2   Mar                68\n",
              "3   Apr                84\n",
              "4   May                88"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "weather1.reset_index(inplace=True)"
      ],
      "metadata": {
        "id": "1S-u7XDs_2sN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "weather1.head()"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "-7u0uNTj_52G",
        "outputId": "b8ebfd2a-ecbc-41a5-dc7b-e2f1ce33e8c8"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>Month</th>\n",
              "      <th>Max TemperatureF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>68</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Feb</td>\n",
              "      <td>60</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mar</td>\n",
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              "      <td>Apr</td>\n",
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              "      <td>May</td>\n",
              "      <td>88</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Month  Max TemperatureF\n",
              "0   Jan                68\n",
              "1   Feb                60\n",
              "2   Mar                68\n",
              "3   Apr                84\n",
              "4   May                88"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weather1.set_index('Month')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "EZoWRVwgAoaI",
        "outputId": "b59d3294-11b2-4e39-858d-2ad0f4bc9065"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Max TemperatureF</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Jan</th>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Feb</th>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mar</th>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Apr</th>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>May</th>\n",
              "      <td>88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jun</th>\n",
              "      <td>89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jul</th>\n",
              "      <td>91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Aug</th>\n",
              "      <td>86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sep</th>\n",
              "      <td>90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oct</th>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nov</th>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dec</th>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Max TemperatureF\n",
              "Month                  \n",
              "Jan                  68\n",
              "Feb                  60\n",
              "Mar                  68\n",
              "Apr                  84\n",
              "May                  88\n",
              "Jun                  89\n",
              "Jul                  91\n",
              "Aug                  86\n",
              "Sep                  90\n",
              "Oct                  84\n",
              "Nov                  72\n",
              "Dec                  68"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_weather.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "id": "u-mwbLRMTC01",
        "outputId": "d7b2dda2-2b2a-4d5b-9453-e611a03d190a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Max TemperatureF</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Jan</th>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Feb</th>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mar</th>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Apr</th>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>May</th>\n",
              "      <td>88</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Max TemperatureF\n",
              "Month                  \n",
              "Jan                  68\n",
              "Feb                  60\n",
              "Mar                  68\n",
              "Apr                  84\n",
              "May                  88"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.merge(max_weather, mean_weather, left_index=True, right_index=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "h-k6vuCuBM3f",
        "outputId": "940860f5-aa72-4abc-a09b-28c34f375492"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Max TemperatureF</th>\n",
              "      <th>Mean TemperatureF</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Jan</th>\n",
              "      <td>68</td>\n",
              "      <td>32.354839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Feb</th>\n",
              "      <td>60</td>\n",
              "      <td>28.714286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mar</th>\n",
              "      <td>68</td>\n",
              "      <td>35.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Apr</th>\n",
              "      <td>84</td>\n",
              "      <td>53.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>May</th>\n",
              "      <td>88</td>\n",
              "      <td>62.612903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jun</th>\n",
              "      <td>89</td>\n",
              "      <td>70.133333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jul</th>\n",
              "      <td>91</td>\n",
              "      <td>72.870968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Aug</th>\n",
              "      <td>86</td>\n",
              "      <td>70.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sep</th>\n",
              "      <td>90</td>\n",
              "      <td>63.766667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oct</th>\n",
              "      <td>84</td>\n",
              "      <td>55.451613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nov</th>\n",
              "      <td>72</td>\n",
              "      <td>39.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dec</th>\n",
              "      <td>68</td>\n",
              "      <td>34.935484</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Max TemperatureF  Mean TemperatureF\n",
              "Month                                     \n",
              "Jan                  68          32.354839\n",
              "Feb                  60          28.714286\n",
              "Mar                  68          35.000000\n",
              "Apr                  84          53.100000\n",
              "May                  88          62.612903\n",
              "Jun                  89          70.133333\n",
              "Jul                  91          72.870968\n",
              "Aug                  86          70.000000\n",
              "Sep                  90          63.766667\n",
              "Oct                  84          55.451613\n",
              "Nov                  72          39.800000\n",
              "Dec                  68          34.935484"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **LECTURE 03:** *Concatinating and Appending Data*"
      ],
      "metadata": {
        "id": "Q5LpHlqSxV-D"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Append:** Stack one row below another  \n",
        "**Concat:** Can stack row-wise or column-wise"
      ],
      "metadata": {
        "id": "iUsKcSH1R4SW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pop1 = pd.read_csv('population_01.csv')\n",
        "pop2 = pd.read_csv('population_02.csv')"
      ],
      "metadata": {
        "id": "KqmEpi4KxY7F"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pop1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "JB2CdkF3RTKc",
        "outputId": "c41f513f-0b48-4d92-cb88-fdd17a59c738"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "        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>Zip Code ZCTA</th>\n",
              "      <th>2010 Census Population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>66407</td>\n",
              "      <td>479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>72732</td>\n",
              "      <td>4716</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>50579</td>\n",
              "      <td>2405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>46421</td>\n",
              "      <td>30670</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Zip Code ZCTA  2010 Census Population\n",
              "0          66407                     479\n",
              "1          72732                    4716\n",
              "2          50579                    2405\n",
              "3          46421                   30670"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pop2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "I7zKE-KeRT8u",
        "outputId": "0c37afc8-e684-46fb-ae8f-98a638488b8b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>Zip Code ZCTA</th>\n",
              "      <th>2010 Census Population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12776</td>\n",
              "      <td>2180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>76092</td>\n",
              "      <td>26669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>98360</td>\n",
              "      <td>12221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>49464</td>\n",
              "      <td>27481</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Zip Code ZCTA  2010 Census Population\n",
              "0          12776                    2180\n",
              "1          76092                   26669\n",
              "2          98360                   12221\n",
              "3          49464                   27481"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Appending population dataframe"
      ],
      "metadata": {
        "id": "v-0GunDRRdkf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pop3 = pop1.append(pop2)"
      ],
      "metadata": {
        "id": "H2rtp-GXRhDJ"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pop3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "c3mE6E-nYOKf",
        "outputId": "c4739f44-55d5-4213-9b10-86e377659ac1"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th></th>\n",
              "      <th>Zip Code ZCTA</th>\n",
              "      <th>2010 Census Population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <th>1</th>\n",
              "      <td>72732</td>\n",
              "      <td>4716</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>50579</td>\n",
              "      <td>2405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>46421</td>\n",
              "      <td>30670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12776</td>\n",
              "      <td>2180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>76092</td>\n",
              "      <td>26669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>98360</td>\n",
              "      <td>12221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>49464</td>\n",
              "      <td>27481</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Zip Code ZCTA  2010 Census Population\n",
              "0          66407                     479\n",
              "1          72732                    4716\n",
              "2          50579                    2405\n",
              "3          46421                   30670\n",
              "0          12776                    2180\n",
              "1          76092                   26669\n",
              "2          98360                   12221\n",
              "3          49464                   27481"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pop3.loc[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "rL0RrCEOX0mm",
        "outputId": "1c7fc513-78dc-436a-a7f6-8cef2c9deb27"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Zip Code ZCTA</th>\n",
              "      <th>2010 Census Population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>66407</td>\n",
              "      <td>479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12776</td>\n",
              "      <td>2180</td>\n",
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              "</table>\n",
              "</div>"
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            "text/plain": [
              "   Zip Code ZCTA  2010 Census Population\n",
              "0          66407                     479\n",
              "0          12776                    2180"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pop3.reset_index(drop=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "TZMblpPDgLOY",
        "outputId": "3f10e04a-31d9-4774-8b44-6a8bd4d262d4"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "      <th></th>\n",
              "      <th>Zip Code ZCTA</th>\n",
              "      <th>2010 Census Population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>72732</td>\n",
              "      <td>4716</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>50579</td>\n",
              "      <td>2405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>46421</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12776</td>\n",
              "      <td>2180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>76092</td>\n",
              "      <td>26669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>98360</td>\n",
              "      <td>12221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>49464</td>\n",
              "      <td>27481</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Zip Code ZCTA  2010 Census Population\n",
              "0          66407                     479\n",
              "1          72732                    4716\n",
              "2          50579                    2405\n",
              "3          46421                   30670\n",
              "4          12776                    2180\n",
              "5          76092                   26669\n",
              "6          98360                   12221\n",
              "7          49464                   27481"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pop3.reset_index(drop=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "oTHkTRcFqvS-",
        "outputId": "5a77a90e-2382-40a4-884c-a5c247bfb586"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>index</th>\n",
              "      <th>Zip Code ZCTA</th>\n",
              "      <th>2010 Census Population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>66407</td>\n",
              "      <td>479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>72732</td>\n",
              "      <td>4716</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>50579</td>\n",
              "      <td>2405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>46421</td>\n",
              "      <td>30670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>12776</td>\n",
              "      <td>2180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>76092</td>\n",
              "      <td>26669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2</td>\n",
              "      <td>98360</td>\n",
              "      <td>12221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>3</td>\n",
              "      <td>49464</td>\n",
              "      <td>27481</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   index  Zip Code ZCTA  2010 Census Population\n",
              "0      0          66407                     479\n",
              "1      1          72732                    4716\n",
              "2      2          50579                    2405\n",
              "3      3          46421                   30670\n",
              "4      0          12776                    2180\n",
              "5      1          76092                   26669\n",
              "6      2          98360                   12221\n",
              "7      3          49464                   27481"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Concatinating"
      ],
      "metadata": {
        "id": "ox4WVYC7Xg_4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.concat([pop1,pop2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "A8RU_YHhSKtu",
        "outputId": "5daaa47f-05e7-4984-9d64-5c3d1ed3bfca"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Zip Code ZCTA</th>\n",
              "      <th>2010 Census Population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>66407</td>\n",
              "      <td>479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>72732</td>\n",
              "      <td>4716</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>50579</td>\n",
              "      <td>2405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>46421</td>\n",
              "      <td>30670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12776</td>\n",
              "      <td>2180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>76092</td>\n",
              "      <td>26669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>98360</td>\n",
              "      <td>12221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>49464</td>\n",
              "      <td>27481</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Zip Code ZCTA  2010 Census Population\n",
              "0          66407                     479\n",
              "1          72732                    4716\n",
              "2          50579                    2405\n",
              "3          46421                   30670\n",
              "0          12776                    2180\n",
              "1          76092                   26669\n",
              "2          98360                   12221\n",
              "3          49464                   27481"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.concat([pop1,pop2], ignore_index=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "qOwSz-stXWAR",
        "outputId": "26ea20e7-fd2d-44ae-fd87-c107343e593e"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Zip Code ZCTA</th>\n",
              "      <th>2010 Census Population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>66407</td>\n",
              "      <td>479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>72732</td>\n",
              "      <td>4716</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>50579</td>\n",
              "      <td>2405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>46421</td>\n",
              "      <td>30670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12776</td>\n",
              "      <td>2180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>76092</td>\n",
              "      <td>26669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>98360</td>\n",
              "      <td>12221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>49464</td>\n",
              "      <td>27481</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Zip Code ZCTA  2010 Census Population\n",
              "0          66407                     479\n",
              "1          72732                    4716\n",
              "2          50579                    2405\n",
              "3          46421                   30670\n",
              "4          12776                    2180\n",
              "5          76092                   26669\n",
              "6          98360                   12221\n",
              "7          49464                   27481"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Concat and append two different dataframes"
      ],
      "metadata": {
        "id": "JhFDDUflwY2d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.read_csv('population_00.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "UaGBsoRjzKj2",
        "outputId": "c5b01c37-05c6-41ed-f1a8-fa0d8d42c521"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Zip Code ZCTA</th>\n",
              "      <th>2010 Census Population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>57538</td>\n",
              "      <td>322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>59916</td>\n",
              "      <td>130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37660</td>\n",
              "      <td>40038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2860</td>\n",
              "      <td>45199</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Zip Code ZCTA  2010 Census Population\n",
              "0          57538                     322\n",
              "1          59916                     130\n",
              "2          37660                   40038\n",
              "3           2860                   45199"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "population = pd.read_csv('population_00.csv',index_col=0)\n",
        "population"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "goXBo8zcgYnS",
        "outputId": "3199bc9f-8c60-4067-98a1-cbd24405ad65"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>2010 Census Population</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zip Code ZCTA</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>57538</th>\n",
              "      <td>322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59916</th>\n",
              "      <td>130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37660</th>\n",
              "      <td>40038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2860</th>\n",
              "      <td>45199</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               2010 Census Population\n",
              "Zip Code ZCTA                        \n",
              "57538                             322\n",
              "59916                             130\n",
              "37660                           40038\n",
              "2860                            45199"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unemployment = pd.read_csv('unemployment_00.csv',index_col=0)\n",
        "unemployment"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "SueWulXRgw6z",
        "outputId": "237fa0e7-7fba-4253-d082-f7cfbee9de0c"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>unemployment</th>\n",
              "      <th>participants\\t</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zip</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2860</th>\n",
              "      <td>0.11</td>\n",
              "      <td>34447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46167</th>\n",
              "      <td>0.02</td>\n",
              "      <td>4800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1097</th>\n",
              "      <td>0.33</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80808</th>\n",
              "      <td>0.07</td>\n",
              "      <td>4310</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       unemployment  participants\\t\n",
              "Zip                                \n",
              "2860           0.11           34447\n",
              "46167          0.02            4800\n",
              "1097           0.33              42\n",
              "80808          0.07            4310"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "population.append(unemployment)\n",
        "\n",
        "## notice we have repeated index <2860>"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "U8sm2g3Jwcpt",
        "outputId": "e4b519b9-d222-4135-f44f-5d8627df0159"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>2010 Census Population</th>\n",
              "      <th>unemployment</th>\n",
              "      <th>participants\\t</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>57538</th>\n",
              "      <td>322.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59916</th>\n",
              "      <td>130.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37660</th>\n",
              "      <td>40038.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2860</th>\n",
              "      <td>45199.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2860</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.11</td>\n",
              "      <td>34447.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46167</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.02</td>\n",
              "      <td>4800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1097</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.33</td>\n",
              "      <td>42.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80808</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.07</td>\n",
              "      <td>4310.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       2010 Census Population  unemployment  participants\\t\n",
              "57538                   322.0           NaN             NaN\n",
              "59916                   130.0           NaN             NaN\n",
              "37660                 40038.0           NaN             NaN\n",
              "2860                  45199.0           NaN             NaN\n",
              "2860                      NaN          0.11         34447.0\n",
              "46167                     NaN          0.02          4800.0\n",
              "1097                      NaN          0.33            42.0\n",
              "80808                     NaN          0.07          4310.0"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Concatenating rows"
      ],
      "metadata": {
        "id": "VuwdKMmcxS5c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.concat([population, unemployment], axis=0)\n",
        "\n",
        "## still that repeated index <2860>"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "k3m9ZiCzwvZp",
        "outputId": "b2cfeda0-88dd-4e51-84eb-9848add91404"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\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>2010 Census Population</th>\n",
              "      <th>unemployment</th>\n",
              "      <th>participants\\t</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>57538</th>\n",
              "      <td>322.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59916</th>\n",
              "      <td>130.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37660</th>\n",
              "      <td>40038.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2860</th>\n",
              "      <td>45199.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2860</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.11</td>\n",
              "      <td>34447.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46167</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.02</td>\n",
              "      <td>4800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1097</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.33</td>\n",
              "      <td>42.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80808</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.07</td>\n",
              "      <td>4310.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       2010 Census Population  unemployment  participants\\t\n",
              "57538                   322.0           NaN             NaN\n",
              "59916                   130.0           NaN             NaN\n",
              "37660                 40038.0           NaN             NaN\n",
              "2860                  45199.0           NaN             NaN\n",
              "2860                      NaN          0.11         34447.0\n",
              "46167                     NaN          0.02          4800.0\n",
              "1097                      NaN          0.33            42.0\n",
              "80808                     NaN          0.07          4310.0"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Concatenating columns"
      ],
      "metadata": {
        "id": "xoM0vMi0xUWF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.concat([population, unemployment], axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "9mVa97xGw3mO",
        "outputId": "008191b8-4f6c-43e7-fe3e-e763f326e089"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>2010 Census Population</th>\n",
              "      <th>unemployment</th>\n",
              "      <th>participants\\t</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1097</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.33</td>\n",
              "      <td>42.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2860</th>\n",
              "      <td>45199.0</td>\n",
              "      <td>0.11</td>\n",
              "      <td>34447.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37660</th>\n",
              "      <td>40038.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46167</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.02</td>\n",
              "      <td>4800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57538</th>\n",
              "      <td>322.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59916</th>\n",
              "      <td>130.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80808</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.07</td>\n",
              "      <td>4310.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       2010 Census Population  unemployment  participants\\t\n",
              "1097                      NaN          0.33            42.0\n",
              "2860                  45199.0          0.11         34447.0\n",
              "37660                 40038.0           NaN             NaN\n",
              "46167                     NaN          0.02          4800.0\n",
              "57538                   322.0           NaN             NaN\n",
              "59916                   130.0           NaN             NaN\n",
              "80808                     NaN          0.07          4310.0"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### keys and MultiIndexes"
      ],
      "metadata": {
        "id": "cRiIcs-xyRiP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('q1_rainfall_2013.csv')\n",
        "data = data.set_index('Month')\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "QWNOiVn-17vU",
        "outputId": "97bbe86b-3057-4506-a13f-758671258e52"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Precipitation</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Jan</th>\n",
              "      <td>0.096129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Feb</th>\n",
              "      <td>0.067143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mar</th>\n",
              "      <td>0.061613</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Precipitation\n",
              "Month               \n",
              "Jan         0.096129\n",
              "Feb         0.067143\n",
              "Mar         0.061613"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rain2013 = pd.read_csv('q1_rainfall_2013.csv', index_col='Month')\n",
        "rain2013"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "POoCx7-TygEd",
        "outputId": "88d00a73-68fb-4fa1-a7de-8da32d395330"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Precipitation</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Jan</th>\n",
              "      <td>0.096129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Feb</th>\n",
              "      <td>0.067143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mar</th>\n",
              "      <td>0.061613</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Precipitation\n",
              "Month               \n",
              "Jan         0.096129\n",
              "Feb         0.067143\n",
              "Mar         0.061613"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rain2014 = pd.read_csv('q1_rainfall_2014.csv', index_col='Month')\n",
        "rain2014"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "uvxHn4Sfzwqh",
        "outputId": "4c7d6322-53cc-43cf-f9d7-771d00dec1a0"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>Precipitation</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Jan</th>\n",
              "      <td>0.050323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Feb</th>\n",
              "      <td>0.082143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mar</th>\n",
              "      <td>0.070968</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Precipitation\n",
              "Month               \n",
              "Jan         0.050323\n",
              "Feb         0.082143\n",
              "Mar         0.070968"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.concat([rain2013, rain2014], axis=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "lyJsW8fyBOOl",
        "outputId": "06db1f8e-db4e-462f-ddcf-a982604d04fe"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Precipitation</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Jan</th>\n",
              "      <td>0.096129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Feb</th>\n",
              "      <td>0.067143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mar</th>\n",
              "      <td>0.061613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jan</th>\n",
              "      <td>0.050323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Feb</th>\n",
              "      <td>0.082143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mar</th>\n",
              "      <td>0.070968</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Precipitation\n",
              "Month               \n",
              "Jan         0.096129\n",
              "Feb         0.067143\n",
              "Mar         0.061613\n",
              "Jan         0.050323\n",
              "Feb         0.082143\n",
              "Mar         0.070968"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Using multi-index on rows"
      ],
      "metadata": {
        "id": "--HniZ3EC0PI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rain1314 = pd.concat([rain2013, rain2014], keys=[2013, 2014], axis=0)\n",
        "rain1314"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "_-BKqd_ZBTH6",
        "outputId": "64004999-7874-4053-ad0d-4cc4b4a9e1e8"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Precipitation</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">2013</th>\n",
              "      <th>Jan</th>\n",
              "      <td>0.096129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Feb</th>\n",
              "      <td>0.067143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mar</th>\n",
              "      <td>0.061613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">2014</th>\n",
              "      <th>Jan</th>\n",
              "      <td>0.050323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Feb</th>\n",
              "      <td>0.082143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mar</th>\n",
              "      <td>0.070968</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Precipitation\n",
              "     Month               \n",
              "2013 Jan         0.096129\n",
              "     Feb         0.067143\n",
              "     Mar         0.061613\n",
              "2014 Jan         0.050323\n",
              "     Feb         0.082143\n",
              "     Mar         0.070968"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rain1314.loc[2014]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "aBMWtRITDdi2",
        "outputId": "2528ff51-1d33-4175-a76b-c9f691079276"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\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>Precipitation</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Jan</th>\n",
              "      <td>0.050323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Feb</th>\n",
              "      <td>0.082143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mar</th>\n",
              "      <td>0.070968</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Precipitation\n",
              "Month               \n",
              "Jan         0.050323\n",
              "Feb         0.082143\n",
              "Mar         0.070968"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rain1314 = pd.concat([rain2013, rain2014], axis=1)\n",
        "rain1314"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "RzF6GJ1HEp-M",
        "outputId": "96c51835-47e0-423b-cdfc-97eb64d2e602"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Precipitation</th>\n",
              "      <th>Precipitation</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Jan</th>\n",
              "      <td>0.096129</td>\n",
              "      <td>0.050323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Feb</th>\n",
              "      <td>0.067143</td>\n",
              "      <td>0.082143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mar</th>\n",
              "      <td>0.061613</td>\n",
              "      <td>0.070968</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Precipitation  Precipitation\n",
              "Month                              \n",
              "Jan         0.096129       0.050323\n",
              "Feb         0.067143       0.082143\n",
              "Mar         0.061613       0.070968"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Using multi-index on columns"
      ],
      "metadata": {
        "id": "y8Ksfe56FZ3V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rain1314 = pd.concat([rain2013, rain2014], keys=[2013, 2014], axis='columns')\n",
        "rain1314"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "XD0ccgrAE2Mz",
        "outputId": "f93a4908-7b3a-493f-f05b-b134e2d17cde"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>2013</th>\n",
              "      <th>2014</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>Precipitation</th>\n",
              "      <th>Precipitation</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Jan</th>\n",
              "      <td>0.096129</td>\n",
              "      <td>0.050323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Feb</th>\n",
              "      <td>0.067143</td>\n",
              "      <td>0.082143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mar</th>\n",
              "      <td>0.061613</td>\n",
              "      <td>0.070968</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               2013          2014\n",
              "      Precipitation Precipitation\n",
              "Month                            \n",
              "Jan        0.096129      0.050323\n",
              "Feb        0.067143      0.082143\n",
              "Mar        0.061613      0.070968"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rain1314[2013]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "CemcvTRnE5gI",
        "outputId": "a13ac09d-942b-4dd0-afc5-d637c710733e"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Precipitation</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Jan</th>\n",
              "      <td>0.096129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Feb</th>\n",
              "      <td>0.067143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mar</th>\n",
              "      <td>0.061613</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Precipitation\n",
              "Month               \n",
              "Jan         0.096129\n",
              "Feb         0.067143\n",
              "Mar         0.061613"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Conact with dict"
      ],
      "metadata": {
        "id": "6U2prUB4FjOt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rain_dict = {\n",
        "    2013: rain2013, \n",
        "    2014: rain2014\n",
        "    }"
      ],
      "metadata": {
        "id": "AextLXrSFhG6"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rain1314 = pd.concat(rain_dict, axis='columns')\n",
        "rain1314"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "99IfOx9CFd6N",
        "outputId": "12029738-2f9d-4718-82c5-4fbf13a015a4"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>2013</th>\n",
              "      <th>2014</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>Precipitation</th>\n",
              "      <th>Precipitation</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Jan</th>\n",
              "      <td>0.096129</td>\n",
              "      <td>0.050323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Feb</th>\n",
              "      <td>0.067143</td>\n",
              "      <td>0.082143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mar</th>\n",
              "      <td>0.061613</td>\n",
              "      <td>0.070968</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               2013          2014\n",
              "      Precipitation Precipitation\n",
              "Month                            \n",
              "Jan        0.096129      0.050323\n",
              "Feb        0.067143      0.082143\n",
              "Mar        0.061613      0.070968"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **LECTURE 04:** *Joining Tables*"
      ],
      "metadata": {
        "id": "KkDJPHtnGDzC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "lHzWnAi4eclb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Joins with concatination"
      ],
      "metadata": {
        "id": "AyczLcq6eiZt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.concat([population, unemployment], axis=1, join='inner')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "6-ZfAisCGRlq",
        "outputId": "64f64af5-57f7-47b9-96a5-267444f3f4ee"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>2010 Census Population</th>\n",
              "      <th>unemployment</th>\n",
              "      <th>participants\\t</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2860</th>\n",
              "      <td>45199</td>\n",
              "      <td>0.11</td>\n",
              "      <td>34447</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      2010 Census Population  unemployment  participants\\t\n",
              "2860                   45199          0.11           34447"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.concat([population, unemployment], axis=1, join='outer')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Q6VGdVM_eS1-",
        "outputId": "dd23cdc3-aac2-4f13-af47-81954ee7fb86"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>2010 Census Population</th>\n",
              "      <th>unemployment</th>\n",
              "      <th>participants\\t</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1097</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.33</td>\n",
              "      <td>42.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2860</th>\n",
              "      <td>45199.0</td>\n",
              "      <td>0.11</td>\n",
              "      <td>34447.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37660</th>\n",
              "      <td>40038.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46167</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.02</td>\n",
              "      <td>4800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57538</th>\n",
              "      <td>322.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59916</th>\n",
              "      <td>130.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80808</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.07</td>\n",
              "      <td>4310.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       2010 Census Population  unemployment  participants\\t\n",
              "1097                      NaN          0.33            42.0\n",
              "2860                  45199.0          0.11         34447.0\n",
              "37660                 40038.0           NaN             NaN\n",
              "46167                     NaN          0.02          4800.0\n",
              "57538                   322.0           NaN             NaN\n",
              "59916                   130.0           NaN             NaN\n",
              "80808                     NaN          0.07          4310.0"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.concat([population, unemployment], join='inner', axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "OQgoLwb6esSe",
        "outputId": "7f71257e-a6a2-483b-bb30-7b4a662a6408"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>57538</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59916</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37660</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2860</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2860</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46167</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1097</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80808</th>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: []\n",
              "Index: [57538, 59916, 37660, 2860, 2860, 46167, 1097, 80808]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "population"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "GZlbzj9rfuBm",
        "outputId": "8dfc5029-26d5-4815-e3dc-495e89373fae"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>2010 Census Population</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zip Code ZCTA</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>57538</th>\n",
              "      <td>322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59916</th>\n",
              "      <td>130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37660</th>\n",
              "      <td>40038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2860</th>\n",
              "      <td>45199</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               2010 Census Population\n",
              "Zip Code ZCTA                        \n",
              "57538                             322\n",
              "59916                             130\n",
              "37660                           40038\n",
              "2860                            45199"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "population.join(unemployment, on='Zip Code ZCTA', how='inner')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "RlsBcDPnfixB",
        "outputId": "af03040b-3d02-48f5-8b7a-3a5eb60b70cc"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>2010 Census Population</th>\n",
              "      <th>unemployment</th>\n",
              "      <th>participants\\t</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zip Code ZCTA</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2860</th>\n",
              "      <td>45199</td>\n",
              "      <td>0.11</td>\n",
              "      <td>34447</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               2010 Census Population  unemployment  participants\\t\n",
              "Zip Code ZCTA                                                      \n",
              "2860                            45199          0.11           34447"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **LECTURE 05:** *Merging Dataframes*"
      ],
      "metadata": {
        "id": "UlPxDBL_einT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
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Ws2bdpEKkVnzpyJ0dVyU1BQQCtFXbFRLkv0FT+VOq3zsX7umiENgiBMnDgR/kZu7rvvvtGjR5Pa0b/+9a+YOwqAN3a7febMmaRSVIyOuMrjCzSZGlzzE6kUveYQV1ffTlWDPdU9mzZt2rhxI6wnKzqdLjU1lZya8vPzF7z/Ps7SQPGCEBnC5smWLVuWLVtGNqG/8cYbUVFRsImslJWVvZGWWh81SDUhHdVgs9lemTIFNpSbkSNHDhs2jIjwGTNmoFgXAILD4UhNTfVMkelgqLq3B2wiN8Fb9pApMrxa5Uzue80hrqLZSDXhkiVL8vLyYENZCQsLS0tLI/2E2dnZS5cuhSYEyhaEeAc3Q/Ly8hYtWkQ8zbRp06KjEeuVF7vdPmnSJDJTtP7BdUmndSb3pbsoZs+eDUvKCs/z48aNI3NHbTbb66+9jmJdAFyiOGfOnIsbJtA3KD/qnYfphgnHA7H1XOkhmo10xszsd+ZilY7chIeHz5w5k8wdXb9+/Zo1a2AToGBBiAxhc6OgoODdd98l18//+bm4uDjYRFYcDsdLL73kdDo9I9qvJ7gutQl1PBBLrnNzczFYnIEmnPLKK2Q/odVqTU9Px8AA0JyRJGnB+55JS2TDBNSg3KgKrervDpDrqsEx17Xu32WJJvsJJZc4ffr0iooK2FNWoqKiZsyYQfYTLlu2bNu2bbAJUKogRIawWVFWVjZ79mwiTsaOHTtw4EDYRO7j1LRp00jjDakCvd7jlLujgRYCrV+/fsuWLbCqrKhVqhkzZpCBAYWFhXPmzMFDEjRbli9fTnaxeBexAxlPb0crg7cWeNRdbBTdMFF/qu7tQfrPnU7nSy+9hJCW3JjN5smTJ5Prv//fB5hTDZQqCJEhbD44HI5Zs2aRKrjhNcAmcvPee++RUitPH0iDjlPehUCLFi0qLS2FYWWlzsAAFAKB5kleXl5GRkYjH1/gesScqP1il+ex3+DJPcGqKq/dRdOmTYNd5SY+Pv6FF14gidl3330X/edAkYIQwe/mw+LFi8lUgLi4uLFjxyIWIDdbtmyhi87r3wdyWWghEMdx06dPR2+b3ISFhdFCoOWrVhYWFsImoFlRUVFBpyI38vEF6qkGNavySKv59TYX1D3X/W//+cKFC2FduRkwYADZZysIwjtz38FgcKA8QQhV0EzIzs7OzMwkKwcnT56M+y43Vqv1oyWLPXLu7m7X1Qdy+dPCvT3cHQzE37z++uuwsNwYjcaJEyeSoO/cuXPtdjtsApoJLlGcP38+XTnY+McXuCbB3+yjSyYa36vp3X+emZmJoaNyw/P8w488QvrPCwsLV61cCZsAhQlCZAibA2VlZSRGyKtVU6dOJUOxgHxIkvTaa6+RWK8rNsrVt1NTnBdUVUNiSCFQSUkJKeUCsmKxWJKSksjQ0fexaQo0Gz77179IVtzdwYCVgywObUcryVjRJqzO9e4/f/fdd1FXIjdqleqVV18h56uMjAw0EwKFCUJkigIeh8Px7rvvkkEyT4wegyUTDHjvvffIIBl3m1BXvy5NpjN1Wurgly1bhmZCBowbN44MmMnPz1+/fj0MAgIe+lavfuAMiYFBZMerddB5b/cmrM4VzUbSa+B0OlFXwgCD3kAHzKCZEChMECLmHfAsXryYzDWJi4vDIBk2xynSOsirVdXHqSad0u7uaKADZtLS0tCoIDdarXby5MmkmfCfyz4rKiqCTUAAU2mrpHuJqgbHYJAMAy62DnYwNGCs6NVxJUajroQl9KCFZkKgMEGIDGFg4906+OKLL+J2y40gCHSDfFXCLXJMYnD160KmittstnnvvAOby01kZCRtJpwzZw6aCUGg4hLFd+a+g9ZBlqh3Hr7YOihDPhZ1JewZ8/jjaCYEyhOEyBAGMHa7/aOPPqKtg2FhYbCJ3KSnp3t20HcwNE3r4KXUNBOSy9zcXDQqMMC7mfCzzz6DQUBAsnHDBrQOsoQXHHQHvevubjLlY+vUleDUJ7vIV6lefvll2kx46NAh2AQoQBAiZRTAfPLJJyTW+9jDj6B1kAF5eXnkOOUpFpUNqU2o6+5u5Prdd99FUQoDxo0bRzYTZmZmYgsFCDwqbZUrVqxg8PgClOC1O8mFGB3R5MWi3rj6dSFq02azLV26FJaXm/DwcLKZkOO4hQsXQoQDBQhCvE0DEkmSCgsLSSeb0WgckZwMm8gNGd5Drp33dpe798bVtxPdQrHg/fdhf7nRarXPPvssuf74448hwkGAsfjjxaS6oSrhFrQOMuB/ikUbsXWwftJTRQtH169fTzYSA1m544474uLiSPfm1q1bYRDg74IQGcKARHS76U7hZ599Vq1SwSZyM2fOHFosKmusl1J1TzRZPZydnV1cXIxbIDcxMTEJCQnEwW/csAEGAQFDfn5+bm6uZzCyTLXu4H8e3yItFq2WasGy+2jvwtH09HTcAbnheX7ChAlkINlnn32G5nPg74IQGcKAZOOGDSQEaLFYYmJQ/CM7paWlpJeP5aB2qU2o897u5Hr+/Pm4CwwY98w40hmyYsUKjBQHgYHD4ViyZAm5RusgGzS1eyZYDu+hA8msVuu2bdtwF+QmPDx81KhRpJAHzefA3wUhMoSBB20F0el0T4x9AgZhwJw5czweV7bBAJdFNBvh4Fli0BseeeQRstdr8ceLYRAQAGyoDSCK0RGYLMoA/sSZoGOVJIDYhItqr02wigr+Tz75BPkABoxITkbzOVCGIMQTIfCgrSCPPPKIQQ/vLjvZ2dnkOOVuE8qmWNQb6uAXLVqExjYGDBk6lKyqz83NLSgogEGAorFaratXr/aIk0SkB5nosi2e54YYG8WgWNQbd0cDbT5fvBghLdlRBQWh+RwoQxAiQxhg0FYQk8k0ZOhQGERuJEkiuz2qvbsvem+og3c6nYs+/BB3RG7UKtUzzzxDrj/88EM4eKBolixZQgKIjKsbmq9CKLTSWTKuXh3Zv4Cqezyyf/NXW9HYJjc8z3s3n2/ZvBk2AX4qCJEhDDBxsmzZMnI9fvx4zJJhwPLly8luD2azZK7i4L/ZnkVeDJAVs9lM1hJardbMr7+GQYBC/UVhYSFpfvbh46u5oflmP7lw3d2NcXrQc9/bhIrREdUXLvF9TKhmwrhnxpHpMitXrnQ4HDAI8EdBiAxhILFr166SkhKO4xISErB4kM2J6osvvvB499tNPnsZXg6epiuBrIwZM4Y4+NWrVyNJCJQIz/MZGRk+f3w1K9Q7D0su0Vf9BRRXomdCdX5+PoZjMcCgN9DpMpuRJAT+KQiRIQwkcUK9+8MPPwyDMGD58uV01YRvhzFQB5/zw/dIEjIgLCyMJAltNlv2t9/CIEBxFBcX0/QgZsmwITj3oOeJ7dNprpJOWxXTgVx/sPAD3BcGDB06lEyo3rBhA5KEwB8FITKEAcPevXvJDKuEhISoqCgYRHYNJoo0PUiLNn3u4CWX+Pe//x13hwEPjnyQiPA1a9YgsgYUx8qVKz2PMqQHmXAxPegHClzs24m0jObn51dUVODuyI1OpxtaM9bBZrNlZWXBIMDvBCHOMYGBJEnr16/3nFMffBAGYcCnn3xC0oNidIRUs/vB5w6e6JPc3FyMCmCAQW+4t/8A0kmYk5MDgwAFQVenIj3IDJoe9HkAkcQQxdo99Vhjy4b77rsPjQbAfwUhMoSBwYEDB4h3j4uL69KlCwzCQIFnZmZWf4LUKj+Z1S7ptFUJt5DrTz75BPeIAcnJyeRi/fr1CK4BBbF27VpygfQgG2h60E8CiNW3vldHkiQsLCxEDJEBYWFhNEmIRgPgd4IQh5jAgCyS4jguJSUF1mDAl19+6ZnVfku4/8xqd/XqSDsJEYBkgNFotFgsZJ74rl27YBCgCKxWa3Z2NhltgvQgG1S7Sj1P6dv8RoEHq2iSEDFENtz/wP200QA+GviXIESGMACgxT/mGmAQBlyMr9/mT/H1YJXrlnDSSbiqtkEIyAqZHcdxXEZGBuJrQFmPL5+sTm2OZ7KjlbzgIAW6fpIe9LwBzEbEEFni3Wjww/ffwyDAjwQhTjCB5N2RHmRDQUGBzWYj8XW/8u7eAhWzrdkQGRlJlg4XFhYWFRXBIMDPqbRVfrM9y+ebD5oV6mzPk0Hs3t6vXpik0yKGyJhRo0YREU7nPgDgF4IQGUKlIwhCbm4ux3Emkyk2NhYGYcCKFSs83t3/4utSm1B3jUYVBKGgoAA3iwF0jNO2bdtgDeDn5GTneJrZkB5kQ5UYdOKMZ47LLe387dWJ3SPx+GJJeHh44p13kUaD0tJSGAT4iyBEhlDp5OXlkWa2AQMGQN7LLrckySWKRcW/kHEyfujdvc95VLgCWbnllluMRiPHcTt27MCCKeDvgrBmIq7fPr4CD/WOYs+TuVsEF6zyt5fn7mggMUSbzQZ9wgbSec5x3Pbt22EN4C+CEBJC6dBZl/3u7AdryA3P8+vWriXx9aqYDn7o3auPHbe0IxUpRcW/oC2EzbtiwIABJCubl5cHgwC/pbS0tKSkhEzD8s/HV+ChOlDueTL7Wb3oRU1YWzn8r3/9C/eLAb1699br9SQ6Ax8N/EUQIkOoaCoqKsgy+thevQ16DItjAe3N81vvzgWr6JL6dbX9pUBWaMSXDG8EwD+hGQlaKAjkPY356zgZb+h46l17duNMyAC1SpWYmEiysnt274ZBgF8IQmQIFQ09fdLzKJCV0tJSzzgZP/bu3mIVo2XYEB4eTgb85ufnV9oqYRDgj+d+UST1opJOi20TjI7+/ymp80z2R7xiiFu3bsVdY0D//v3rnOIA8LEgRDRIuUiSlJWVxXGcTqeLj4+HQRiwatUqBXh3Mlqmg4EEIK1WK24cA5KSkshFTnYOrAH8kD27d5N4ltgtAtZgdBo7VukZJ+PfA12pR9uwYQPuGgOioqJMJhPHcbm5uYIgwCDA94IQGULlcvDgQXLW79evn1arhUEYQOZ2KmIeA3Xwa1E1yoT4+HiNRkOHdgDgb9BchJ/HswIGVaEnGOf/CpyOp7ZarZiMxQbSee50OtF5DvxCECJDqFxoN8jAgQNhDQYcOnSIRPIUMY+BloTl5+fj3jFAp9ORhYQYJg78ELqgyA+3pwasINx52PM07tTW/18tdRloNGADHQRIRgMC4GNBiAyhQpEkiXh3vV4fHR0NgzDgiy++8DjOKAV4d0mnpcPE7XY7bh8DaCvv999/D2sAv2Lnzp1kQZEby+iZHcVq1g/yapU74kYFCMJa1YoaBzYY9Ia4uDiO4woLC+Gjge8FITKECuXIkSOkGyQhIQGqng179+71OE6FzGOgr/Prr7/G7WNAr969dTodx3H79u2DNYB/Pr5ECEI257CjnuFSStnw4e5oILNGDx89gpMhG2677TZysRuzRoHPBSG0hNK9O4kwAblxOBye+aJtQiWdMjo2acR3x44duIMMUKtUJF1fVPwL5gQAv2L//v2kcEApjy+lo9pf5nkOK6Gi5KJ2rZk1euDAAdxBBvTq1avOiQ4AnwlCxIEU7d05jkO9KBvIQFcFpQe9I75lZWW4g2zo06cPOVEVFRXBGsBPqKioIBPI3B30sAajc9gxm/JcRq12RRshG4xGI9lQT090APhMECJDqEQkSSLjLk0mEylRA3JDB/QpYjwAhUR8nU7noUOHcBMZ0LNnT3JBPqEA+AMXy92j2sIabPDso1dORYm3dkXCihmkyMtqtWKBLfCxIESGUIkcPHiQFKShXpQZR48eVdB4gIsOvvb8t2XLFtxEBtx8880kRgNBCPyHPXv21DnxA1mhCyeUZXDvUWQuUcR9ZIDZbCYXu39GGyHwqSBEhlCJ0JEV9FECZKXSVqmghRP/IwhrjyOoSGEDz/MxMTFk+QQGxwG/OOVLEkn4KCtbpWxBuP9XzxO4k8JSstRlZH/7Le4jA3r36U0uaNQGAN8IQmQIlQjx7rxa1aNHD1iDAVs2e9Jriiu4ohFfq9WKDzsb6JyAwsJCWAP4nF9//dUzEAvpQVbwtrNKrCjxVrA//PAD7iMDDHqD0Wgk5zr4aOBLQYgMoeJwiSIRhNFdumq1CPeygKZk3W2Vt9CZngIx5oQNaCMEfgXtB5PCb4A1WFAlkgZCMeJGZVWUVPuLWgV7+PBh3Ek2dO/enZTplpeXwxrAZ4IQAQnFcbC4mOwXRnqQGaSBsPrzEhaiuBdPT4F5eXm4lQxo3749GRyHDCHwB9BAyPoEVn7KY3AFBhC5YBVtI8T5kA00hgiXAXwpCJEhVBy//uppTkADISNBJUmkgbDaTSot3EuqRskFVkuxgef5Tp06kf3OGMwAfM7vv/+OBkKmJ7DDxz3PXmWmZKU2rcgFElZsoGc5GnoGwAeCEBEgxUEfGREREbAGA44fP17HTSoLWgJEzoWAAR06dCDbCCtgc+Dbw70kedaQttTAGoxOYMfPeIyvTAUutfXoWIw5YUPbtm01muqP57Fjx2AN4DNBiAyh4iCPDF6tCm/XDtZgQH5+fh03qTCCVeRccvbsWdxNNnTs2JFcIMQOfMvx48dJi4EiyxeVCZko4x2MU5ggrNWx2F7L6A3D85GRkfAXwMeCEBlCxUEeGRHh7dQqFazBAOoUlVtwJelbkfX0qGBkA5kahxIg4HNozgETZdidwBwuj78IVqSPprEDCEJmtKuJ71utVvho4DNBiAyhsnCJYnnF72T/NazBWBAqN8ROX/l/sY2QrSCkHb8A+ASr1bMhHQ2EzJBcIg3DKfL1twnl1dVS9tSpU7ibbCAZQo7jfkOSEPhKECJDqCx+Ky8nzoY+PoDcEKfIq1VSG6UKQpocwCIENoSFhel0Oo7jPO1bAPiII0eOeB4ChlawBovj19FKcqHoGl3xxhCO406etuOGsoGOhIDLAD4ThMgQKgv6sMBEGWYQp0gcpEKhR5OSkhLcUDaQNkIM8gH+4DJ4tQoZQkbHL4WPGPW8+JoJapJLtNuhCdn5C8yVAb4UhMgQKgvac9y+fXtYgwF2u91T/9NGwfF1uj4RPevMIDl8m81GdpYA4BNISELR8SyFHb9qR4wqOkNIJ6jt3r0b95QBtMsAghD4TBAiQ6gsaEsSmWsP5ObgwYN1HKQiqR00inAvM9rVDgGmTVwAMEYQBJvNpvR4lsI47/S4jDAFi3CaT0ZRCRt0Op1er+c4rqKiAtYAvhGEyBAqC+LddTXAGgwImNO8VLOF7JzjAu4pG8LDw8lFZWUlrAF8wsmTJz0f/7YYMcoIvlYQKnTEaB1BiKJ3ZpAYIgwOfCYIkSFUFufOneM4TqtFNwgjaEpN8R04NYKQlL8CBrRu3ZpcoGQU+Irz58/DCL6RhWplL4WSQjTkgiyxBAwICQnhOM7hcMAUwDeCEBlCZUEeFi1atIApWCpwAK4XjUaDtxDwE0GIiTLsjl81SwjdWjU8IGiAyxAEAcdy4BtBiAyhsiArEMLCwmAKNpw9ezYwTlSSzqNPsPeWDTRqc+ECynQBTvPNC1Kir2BqXz8SVsxo1crT6IusLPCNIEQoQlmQpzMpLQBMBWGIJjD+RTa0tDGBfkhxogK+gpYrI0PITgqSsnyFC0JJ48lwIp7FDFpUApsD3whCZAiVBRkKQh8cQHaDB0qIXQrxnAgxaJQNtNEXWRrgK5BqAA2kdiIOxAkzaAwRbefAN4IQGULFHOglySWKJPoIQciMi+kdpZcA1aYIMPSSkb1r639okhkAxgRegYNi/LVOo/x/ghYFDkxdRu3oeMyCAr4RhMgQKgWe54Va705rzYHc0PgoLaFROog+skGtUpFJg8jSAF+B0zxrNy04ahV4gNToVlVV4baygbadw2UA3whCZAiV6N3RQ8heECp6qZR3ExFZZQkYEKJtAe8OfMjFcuWWyBAy4XzgfNjJXJwqN4aQsfIXtec6dBkA3whCZAiV6N2xh5CxCA+kkQyoYGQG+ZzCuwNfQYMRAVPg4Ofw52oFYQC4DAQRfOEv4KOBzwQhMoQAAAAAAAAA0EwFITKECgKD7NlDgna0MyQAQAMqM7AkBvgWOn6Md7pgDQZcHN4TAC6jpvw1OEiF28rSX8BHA58JQmQIFSdOUITGEtrnzVUpu5WCalq9Xo/bygYsiQG+5WIw4jwaWZlQW2bJn1O8IOTxnmHsL2rPdYghAt8IQmQIFQQG2ftQEAZMiJ3OtgaygiUxAB/25kYgdZsTgoODcVvZQCfYwWUA3whCZAgVBAbZs+fi/B6lh0trM4QGgwG3lYW9sSQG+JqL8axzcBlM4QWn8v8JDkywY+oyajdCtWzZEtYAPhCEyBAqCwyyZ23wQCneoCVMegNKRlmAJTHA5yDVABpIbYvExaYJIDO0ZBSJfeAbQYgMobLAIHvG0PROwITYw8JuxG1l6d0RYge+gp4sA2kslp9DqniUXlFCWyQgCJlBA/2wOfCNIESGUFnceGP1ad5ut8MUrAWhwk9UtIRJrcLUOBacP38e3h34FmSnffa8VXqLQe3rRzyLGXQ2BBL7wDeCEBlCZUGezrT5GOBEBfwTjIwDPoc2IyFDyAy3Vl19CHO44AHBdUEyhDqdDnka4BtBiHeeEp/O2EPIjLCwsAA5UdVEfD3lTEB+aLgXDSHA54IQMIZMGFYutEUC2SpmkBgiUrLAZ4IQGUJlQZbICYKAqlE2GI3GwPiHkBImMpQIMKCiooJcYKwr8BVt27b1fPyPn4Y1GEnB2lWEil5dSwOg7dq1wz1lw++//w6DA18KQmQIlUX79u3JRXl5OazBgOjo6EA4UVWJxMHThCeQm2PHjgVYTAEoDq1WS2KI/AmsrmUF3U1vPxcAgtBkMuGWMkAQBJvNxnFcZGQkrAF8IwiRIVQWHTt2JBe//vorrMEAnU5HyiwVfaKiR5OIiAjcUzaQDKFer0fJKPAhJOGgOoXB1Ixwtw31nMOOn1Gwy6gNgPbt2xf3lAFWq9X7AwuADwQhMoTKgh7okSFkRusbwpR+oqJHE4R72SBJ0tGjR+Hdgc8hH3nJJWKuDCNB2Km2TLdCwUUlJADKq1WIZ7GB+AuO4zp06ABrAN8IQmQIlUV4u3YkYVVWVgZrsIGs+qg+UZ1QasSXHk1iYmJwQxlw+vRpQRBQ/wN8Do0h8pWoGmUiCDt6eoYVnSEkAVASDAUMoCF+uAzgM0GIDKGyUKtUEeHtOI47cuQIrMGGzp07K93B01d+a/fuuKEMoPU/tOkXAJ9AW1iRIWSGp8vAplQFzp84Q6akkmAoYAAJ8fNq1U1o6wC+EoTIECoOEvEtr/jdJYqwhtxIkmQ2m5V+oiJHE41Gg630bN4zVBDSpl8AfAKtQFN0BaOyIKsIq/2FMgeN0gAiDYYCuSEjRiPC28FHA58JQmQIFergJZdYUfMEAfJKKZ7v2bOn51qhg0ZrR4y2atUKN5TNe4Y2hGCKD/Atbdu2JavkFF3BqCwkvedJG1R+SpFPsNrQJwQhozeMJJEMIfwF8KUgRIZQcdCcA+bKMDtRedykMgeN0kMJBpwwg+yc4NWqcNgc+BrSlaTcCkbFQQeNKrSohIY+4+LicDcZcPz4cafTiYkywMeCEBlC5QrCwsJCWIOFd+R5Mmkt6MQZJZYA0UNJt27dcDcZIEnS4cOHOY7r1PFm1P8Anz++yCmTFxxoI2QkCBU+aJSGPmkwFMgKPcuhxQD4UhAiQ6g4OplMpARo3759sAZjEa7EXcP0UJKQkIBbyYBff/2VrBim3acA+BD6Pgw6WglrsBCEEZ5ZLIos060Sg2rmaev1eiQM2LB37946H1UAfCAI8YFXHGqVinS1FRX/4nAg4suCHj16kAtV6QnlPQ5qT4HIEDL27ljyAfyBXr16edw95sqwIVgl6bTV/qL8lOKKSmiLQadOnXAn2bB//36iwNFDCHwpCJEhVCJEEEouEUlCNgwZOsTz0VKaIOQFBwn3Go1GRH/YsGfPHu/PKQC+JSIiQq/XI0PIEjJXRnKJipsrE3T4OLmwWCy4jwyoqKggU6nj4uLgo4EvBSHef0qEZh4KCgpgDQYY9AbSRqi4iC89AnbHBkI2B0FJIp9Kk8lE3jMA+BY6KjnoxBm0EbJB7N6+jr5SnMu48667cB8ZgHpR4C+CEBlCJULPmhCEzCBthIqL+AaVeo4jf/zjH3ETGVBSUiIIAupFgV9Bq0aRJGQkCM1GJRqcVpTo9XoMxGIDrSjp3ac3rAF8KQiRIVQiPM+T4yY9fQK5SUpK8ny6FBXxVR+sICvpo6KicBMZQGM0EITAf6DVyzRCBOSGtBEqKytL5SsWTjB6k0gSyRAajUaD3gCDAF8KQmQIFQqtAKT1BkBW7qqtn1FQxDfoaKXkEukiMsAA8nnk1aro6GhYA/gJ4eHhRqOx+plwzAZrsMHdQa88l1EbL/jDH/6AO8iA8vJyMpIaPR3A94IQGUKFQiO+qBplg1ar9QxmUE7ElyYz+/XrhzvIAJcoFhUVcRwX3aUrGgiBX0FOnNhGyAyxe2QdleX/kIoSXq265ZZbcAcZQOtF+/btC2sAHwtCZAgVys0330z0SW5uLm4iYxGuKrQq40FQG5keNGgQbh8L7757NynhpntKAMDjq3ni7mjg1SqPylLCKDJaUdKp481IFbDhp59+IheYKAN8LwjxsVcoPM+TPeM2mw1Vo2xITk72fMCUsHzCezxAWFgYbh8DsrOzycVdGNAH/Iy+fftqNJrqxxcEISvEmg31ShlFRitKEhMTce8YUGmrzM/PJx2b8NHA94IQySXl0r9/f3KRk5MDazAgKipKQcsnMB6AMYIg5ObmkiHAGOED/A2dTkdiiEEnzvA1oSIguyBU1PIJ6jLuu/9+3DsG5GR7Tm533nknrAF8LwiRIVQut9xyC5kTsGPHDocDbSEsIKMjJZeoOvi7n79U1f5fyQVNbAJZycvLczqdHMcNGDAA1gB+CF01Th8OQF5BWLt8QnWg3M9fKn/iDKkoMRqNWDjBSBDWhPI1Gk18fDysAXwvCJEhVC48z5OjpyAIeXl5MAgDRo8erYgTVbV3P1ZJ6kVJ1ADITWZmJpnHgIIr4J/ExsaSznP/1ycBg7uDgRTw+3nrJvVo9yM9yITS0tKSkhKO4xISEjCBDPiFIESGUNHQiC9tXgKyEhkZ6Zk1eqzSn8uuqHcfOnQo7hoDKioqCgsLq8/cvXqjGwT4JzzPk2gFLziwoZ4NVfdE13km++WrFIMLjpFs1eDBg3HXGLB9+3Zyce+998IawC8EITKEiiY8PJwMp8rPz6+0wcGzgEos/3Xwtd6dV6tGoF6UCTQiQ2M0APghtPNctb8M1mCA1CbUs6Hej2OI6j1HyXzRnj17IknAAJcoknpRvV5Px/8C4GNBiA+/0klKSiIXtEEZyMqI5GQyTLxadPnlaBnVwd+Jd4/u0hXdICzOfJKUlZVF5nagGwT4M1FRUSaTSUG7EAIAsVtErQj30xgiHTz75JNP4n4xYM/u3WQffWJiIg7hwF8EITKESic+Pp4ME8/JycHdZIBapYru0tWfR8uodh4mF0888QTuFwMOHDhgtVafqPr166fVamEQ4M+QznNFTMYKDFz9upAL/4whBh2tpAuKIiMjcb8YQBrOvTP2APheECI4oXToMPGSkpJdu3bBIAx49NFH60gv/4EOi9PpdNh1y4b169eTi4EDB8IawM9JTEwkNQ5++PgKTIJV7jahfivCafEwGs7ZYLVasaAI+KMgRE4pAKB7BTIyMnBDGRATE+MZLXPijL/NZlD/VEIuRowYgTvFgNLSUuLdzWZzdHQ0DAL8nLCwsHv7DyCPLxWW1DPBZakdLeNnIpwXHKqicjScs2Tt2rXkYvjw4bAG8CNBiAxhABAVFUWShIWFhXv37oVBGDBq1CiPg/en2Qy84FAfrCDD4kaOHInbxNK7p6Sk4HEKFAGNISJJyAZ3R4NntMyJM341WoZGBBLvvAsN5wyotFV+sz2L7HvEgiLgX4IQCaXA4MEHHyQXtHoNyMrgwYNJ66b6YAUvOPzHu5NxMgkJCRAnDLBarWS+qMlkio2NhUGAIjAajWQcrh/WOAQqrru7kQtaxOF7qkTVrlJy+dRTT+EeMeDzNZ8THz1y5Ej4aOBfghDvyMCgS5cucXFxZP9EcXExDCI3PM+TjgvJJapzivziJQmO4NyD8O4soenBhx56CM9SoCBoklCdXQRrMEA0Gz2tm0XlfpIkVO85SqKZZrMZ21MZYLfbyTgZvV5vueceGAT4lyBEhjAwkCQpJSWFXK9cuRIGYcCYxx8nSUI/cfCqnYdpehDenQEVFRW0+OeOO+6AQYCCoI0GSBIyoyrhFnIR/K3vRbh3APGVV1/B3WHAxo0bnU4naTlBgS7wO0GIqHZgwPO8uQaSJCwtLYVN5EatUj3wwAN+4uB5waGuKf7h1arnn38ed4cB69evR/EPUC40hqj+TwmswQBX304kSRh0rNLnIpwGEOPi4gx6A+6O3AiC8MUXX5D0IFn9AoB/CUJkCAPSwa9atQrWYMBjjz1GkoQ+d/C0bDXxzrt0Oh1ujdxU2ipJ8Y/RaETxD1AinTt3Jo0G/qBPmgk0SejbSl1ecAQXHCPXz018DveFAevWrSPpweTkZKyrBf4oCBHYDiRiY2NNJhPHcbm5uYWFhTCI7G6V5+m4UR86eP7EGTo6/Lnn4N1Z8M+l/yTe/f7770fxD1AoF5OE6CRkgqtvJzpu1Ic7P9Q5RUgPsqTSVknSgzqdbtCgQTAI8EdBiAxhgOmTP/3pT+T6448/dokibCIrkiSNHDmSZOR86OBpwerQPwxG6JHBTS8sLCTDRY1GYxK8O1AsZrPZkyTETkJmmpCOG/3uAFflAx/tHUB88cUXcUcYsPjjxSSA+Mgjj8BHAz8VhMgQBhgxMTFknnhJScnGDRtgELkVOM/z48ePJ9/6ZKlX0NHKoGOVJPT4JIaLyo/odi9YsIBcP/vss0gPAkUzYcIEzwad7w74zwadQH6AmI3uNqGexu89R9m/ABpAfOCP92H8GAPy8/Nzc3PJdqIhNcPJAfBHQYgMYeDxxNgnSM5qxYoVlTZ0hsiOxWIxGo0kyq5mrglprddTTz0FccKAjRs2WK1Wct9jYmJgEKBowsPDSd17tT7JQeEoC6qGeJ4bql2ljJOE3gHEsWPH4l7IjcPhWLJkCbkeP348fDTwX0GIDGHgYdAbHnnkEY7jnE7n4o8XwyAMeO2116iDZxllV+88HFSz8cJoNA4cOBA3Qm4qbZUrVqwgx6knxj4Bg4AAYERyMglpqYrKMV2GAVKbUHcHg0eE72C4N7hKpAHE8ePH4/jHgA21AcSkpKTo6GgYBPivIESGMCAZMnQonS6Tn58Pg8hNZGQkWerFC47gLQVs/lL+xBm6SGrKlCm4CwzwbgXBMAYQGKiCgp599llyjekybHA+EEtWUKh3lTIT4eodxTSASFpLgKxYrdbVq1eTAOKYMWNgEODXghAhooBErVLRxrYlS5ZgugwDprzyCl1BwWY8Q/CWAjIpzmKxdO7cGbdAbtAKAgISnudjYmLonnq1L3qhmx3Bqot76rcWMKgrCTpaSXbVchyXlpaGOyA3kiQtWbKEBBCfeuoptGsCfxeEyBAGKtHR0UlJSSRGtWrlShiEgQifPHkyudZ8s5+vCcTK+NfVFovqdLpJkybB/nKDVhAQ2Ix7ZhxpPg/OPYjpMgxw9e10cbqM3N2bVWLwVk/pSkpKSnh4OOwvtxr88ccfSX2W2WzGJnqgAEGIDGEAM2bMGOLgMzIysJaQAfHx8WazudoZuEQ6yU0O+BNn1N8dINfTpk3Dp5gBixcvRisICGBo83n144tV3Xszpyq5L7lQFZXLWlcS/M0+IvL1ev1jjz0Gy8vN8ePH6TDqCRMmwEcDBQhCZAgDmLCwMFo4OnfuXLvdDpvIzYwZM2jhqFyVV1UXj2sJCQkYdMmAbdu2ZWZmkuMUXfUJQIAxZOhQEtKqfnyhmVB+JJ3Wey2hTInZoKOVZPEgx3Hp6ekQJ3LjEsW3335bEASSj42KioJNgAIEIR4NgY3FYhk2bBjHcTab7f3334f+lxudTjdt2rSLDl6GwlE6GECv10955RXYXG7KysoWLVpE9jhPnz6dZN0BCDzUKtUrr75C3uEsh500a/HgVTgqR2K2+o+tLRYdPXp0ZGQkbC43n/3rXyUlJaRYFPlYoBhBCIUQ8DzxxBNk4mh+fv6aNWtgELmJi4sj3Ztk7kvTrpnyHgyQnp6OTja5cTgcs2bN8gwGGPunLl26wCYggDHoDbQXms2wE+B8OJ5MHJVjIJk6p4jcRJPJlJKSAmvLTV5e3vr160nE9pVXX0HSBShGEOLNGvBotdpXagdgLl+1Es2EDHjuuef0er1nZF/TrZlCrJc9tHUwLi7uj3/8IwwCAp64uDiiHFgu0WnWBKscD8SSy6YdSKYqtJJiUY1G8+abb8LSclNRUUFbBydNmoTVREBJghAZwuaA0WicOHEimRaAZkIG8Dw/a9YsumaqaZoJq8TgtTsR62WJd+vg5MmTET4DzYSHH3kEzYQscXc0iNERxEdrap/zjT0IHq2kAcRp06ah1l1uXKI4f/582jqI9n6gMEGII04zwWKxkDpGNBMyE+HP//k5cq3+7kDju3GCv9lH90zMnj0bFpYb79bBqVOn4jgFmg9oJmRP1ZBeF5sJ1+5sZK8Bf+KM9otd5HrYsGFxcXGwsNx89q9/kQoss9n8cM3AXgCUJAghDJoP48aNo82Eixcvxq2Xm4EDB9JmQu0XuxpTCKTOLiKVP7xa9dZbb2m1WphXVux2+4wZM0jr4BOjx2DPBGhueDcTNvLxBerJxWbCE2c0tXKuIWpQcGjW7pRcIikneeaZZ2BbudmyZYt36yDa+4HyBCEyhM0H0kxIgr6bNm3CgBkGPPfcc0SEN6YQSFVopYNknv/zc5hhLTeCIKSmptpsNrLYY/jw4bAJaIbQZkLP4wuaUG6CVRceifcc4xpcrOvVXIByEjbk5eXRchK0DgKlCkKkiZoVRqORLspbtmzZtm3bYBO5eWfePDJgpmGFQN59ICkpKQMHDoRJZcXhcKSnp9Oh4ZMmTULUDDRbRo8ebbFYaNIJQ0flRmoTWjXY03vWsP5z2lyg0Wj+vvDvKCeRm4KCgtnvzCXXz//5ObQOAqUKQpx1mhtms3natGmkLmXBggV5eXmwiXzwPK9Wqf72t7+RxOz1FgJ594FYLJbRo0fDpLLiEsX33nuP9IGYTKYZM2bgOAWaOS+8+CLpQGuS3jZwTUSz0Xtb/XU1cHo3F7zzzjtIVcnNoUOHZs+eTapzx44di4gtULAgRIawGRIXF0fnnbz77rtYRCE3YWFh8+bNo5um6lkI5N0HYjabX375ZVhSViRJWvThh7m5uaQP5PUZr2OQDABqlWrq1KmeoaMkpAVNKDOuvp1csZ7WgPo3cHo3F8yY/hqaC+TGarW++eabZKzo8OHDR4wYAZsABQtCZAibJwMHDiTpJqfTmZ6eXlpaCpvIitFo/OvMVM8Ba1fpNTUhCcaTAi2j0fj222/DhnKzfPlysmRCp9PNmjULwXUAPJpEq50xY4bRaCQhreBv9sEmsmtCS7S7g6H+DZyqQittLpgwYQLGisqN3W5PT08nreYWi2Xs2LGwCVC2IESGsNkycuRIMi1DEIS0tLSysjLYRFZiYmJeeOEFqgmDt+y5oho8cUazfAddMjFv3jwEbuRm3bp1GRkZpPEmLS2NnH0BAASdTvfmrDdJO7SqqPwqjy/QVDhH3kYXUbRYmXeV2lH1zsNUDQ4fPnzIkCGwntxqMDU11Wq1kpKrF158ET4aKF4Q4k3cbOF5fuzYsWRggM1me/XVV4uLi2EWWRk4cODTTz9Nrq90qKpWg14z4j788EMULsqKJEnLli1bunQpabyZNm1aly5dYBYA6mDQG9LT08njyPP4Qu2o3JpwzJ1EE0ouUfvFrstqQnV2kfq7A+Q6KSnpT3/6E+wmKxUVFdOmTaODx6ZOnYolEyAQBCEyhM1cE77w4osJCQkkTzh9+vT8/HyYRVbuu+8+b02oWfOT96Gqjhr8+8K/h4WFwWjy4RLFDz74gOQGq9XgK6+i1AqAKxEZGZmWlkY1IfoJWWjCh+OpJtR8/pOq0FpXDdb2DSYlJU2cOBEWk5XS0tKpU6eS3CAGj4GAEoTIEDZzyMAAsj/d6XSmvzUrOzsbZpFbE9La0aBjlfRQFXS0ssXKPKIG9Xr94sWL0cYmKw6H461Zs0jfoEaj+evM1Pj4eJgFgKvQpUuXuXPnktrR6sfXqjzsopCXYBXNE1Z/t7WAasLgLXuoGkxJSYEalJvCwsLp06eTvsG4uLg333wT9TsgcAQhMoSA5/mJEyfSBcTz58/fuHEjzCIrAwcOfOONN+jcUc2qPFWhVfvFLjJT1Gg0Llq0CJ5GVgRBmDlzJkmJ6/X62bNnY38UAPUhMjJy3rvzPDNmTpwJxn5C+anWhB0MVBOqdx4O3rKHbJggCw+wlEhu8vPz33jjDTJT1GKxTH/tNfhoEFCCEBlCQBg9evSECRPI9ZIlS5YtW4ZggazExcW9M3uORxOeOBO8tYCoQZPJ9H//93+oQpGVSlvl66+/Thau6PX6WbNmde7cGWYBoJ4Y9IZ58+Zd3EWxfEc9VyOAhmvCkbeJ0RHkWv3dAaoGX3jhBSw8kJtt27alvzXL6XRyHDds2LBJkyahbxAEmiDEoR9QhgwZ8vLLLxOJkpGR8cEHHzgciPvKSJcuXRa+v4AYnGA2m+fPn48wjayUlpZOmTyFjAQwmUw01wEAqD86nS4tLY3urL/6GEzQJFQN6UU1IeEvf/kLlqHLiiRJGRkZCxYsIBHb0aNHjxs3Dj4aBKAgxNsaeGOxWP46M1Wj0XAcl5mZOW3aNNI8DeTAbrd/+umnxM0Qiop/+fLLLxGmkc+1b9u27ZVXXiFNIGaz+c0330SjJgANQ6vVTn/tNTKqmow8ueaGVdCos93RyqBjNu+f/Pvf/8bKKFl9dHp6+rJly8i3L7zwQkpKCo7NIDAFIY6eoA4xMTGzZ88mMwNKSkqmTJmCMTNyUFhY+NJLL5EeNo1GQ4qvJJe4ZMmSOXPmkEYF0IQ4HI733ntvwYIFpOwnISGBzksEADQMtUo1adIk0oJONqxq1vyElsKmp0pUZxdpPvfY1mQyUR89adIk+GgGPhrJWKBEVKmpqfX8VZ7n3ZL0ds4y+pNX7n5UowrmOO7kyZOi6MldtG7dWq1Ww7LNBL1e379//6NHj5aXl1dVVeXm5lZWVvbq1QvvgSZBkqQ1a9bMnz///PnzxLWnp6cPHz48PDz8559/FkWxrKwsNze3W7duBgOSV01DaWnpjBkz9u3bR9ZLPP3kU08++STezwA0Hp7nY2JiOnXq9PPPP1dVVfGnz6uKyqW2N0hhITBO01hYcGgy/qM6VEG+TUlJeXny5IEDBhAfLYoifLQcPvq9Be+fF84RH52WlnbrrbfCMsBX7LL+srX4R3J9a3hU8q0WEuYWBMHtdpN6jRtuuKFRglCqAYIQ1KFFixaJiYlqtbqgoICEIfPz82NiYkJDQ2GcxmC32+fOnfvVV1+Rb4cNGzZlypTWrVvzPG8ymW6//faCgoIzNWRmbdOFhHTt2hUFKo1ky5Ytc+fOPXXqFJngOnPGGwkJCbAqAE1IZGTk3XffffDgwRMnTvBVoqrQyjld7qg2sEwjCTpa2WJVHn/2AonVTp8+PSkpKSgoiPjoFi1a7Nm3l3NLxEf36NHjsodC0BAf7ZbIdsepU6eSfCwAgSwIkSEEV3lvdO/evXfv3rt27Tp//vzJkyczMzPbtWsXFRUF4zSMgoKC1NRUMs5Ep9M9//zzDz74oPcn68Ybb0xKSqqoqDhy5Ajnlnbt2nXkyJE+ffqQrk5wvQiCsHDhws8//5w8yhISEmbMmHHTTTfBMgA0OTqd7p7+/Z0OR1FRUbWSKT8V9OtJ981tOA0ODw2iSlR//0tw1n+JMjGbzWl/TesU1cnbR5vN5l4xMdRHf/PNNwaDwWQywXgNo7CwcMaMGcRHazSaF198cdSoUTj9gmYhCJEhBFenbdu2tHyUlKYcOXLEfKs5pCXKga5PmSxduvSjjz6iZaJpaWk9e/a89DfVanV8fLx3+ej27dtbt2598803I6lVfyRJ+vHHH9PT08nZlFerJjwzfuzYsZDWAMhHUFBQnz59unbtunPnTlI+GrznGKcKchtbwzjXZ8mjlcGbdnuXiU6aNCkkJOSaPvrHH388cuRI165d0SDdAB/94YcferdyXNZHAxCYghAZQnBNaPno3v/u59xSWVnZli1bgoODu3TtGhQUBPtcU5lkZWXNnj2bFN96l4le5VPpXT56/vz53NzcPXv2REdHoxyoPlit1r/97W+rV68mrt1oNKbNTL399tuhqAFggNFopOWjnFsKOloZdPB3d/vWXAjWq9bjVCY4grftD/7uAH/O6V0mepXHV53y0bKysq+++orn+W7R0fDR9fHROTk5b731FvXRpEz0Kj4aAKUIQr7+s0MlSRLd7hve/AP9ScVfNrbStOQ47tChQ2QcHwmWYFM2KC4u/r//+z9STUG8/rPPPhsTEwPLXInS0tJFixaRBejEtU+cOJFs7qoPDodjxYoVX3y5kaym4NWqkSOSU1JS8GG8isU2bNiwevVq+uwaNmzYY489hmA5AIxxieK6tWu9P4yu2CixbydJh8fXFVHvPByce5DuIrJYLE899VRYWBh8tEyUlZUtXLjQ20ePHz8+Pj4elgF+xeKdG1/c9D65frC75bORM0i/a0VFhcvl4jguNDQ0MjKyUYKQPLUhCEH9IwhffvnlypUr6V4Ei8XyxNgnsMytDoIgrF69ukm03KWqEh7rsuTn5y9ZsoRuzjSZTBMnTuzcuTMsA4CvqKio+PTTT3Nzcz0eRKd13d1NNBthmbrnthNngrcUBJ0400gtJ0nS1q1bP/vsM/joq4N4K4AgrPvsQIYQXC+Vtsp/Lv0n3X2k0+keeeSRIUOHqlUqGIfUnyxdupRsP+c4Li4u7sknn7zsZ7X+f2ZWVtayZcu8/8ynn37aaMS56jKHTp1ON2bMmMGDB6NGFAB/ID8/f+HChfTx5e5gqLonWmqDmdUcqRFV7Tys3lVKvtVoNKNGjRqRnNwYf2q32z/55BP46Hr6aLPZPHHixMb4aAAULwiRIQQNpqCg4MMPP6QJGb1en5ycPGjQoGb7VnGJ4g/ff79q1Spvm4wdOzYxMbFJlIkgCMuXL9+0aRP9icViSU5Obs5zX8vKylavXp3zw/e0yCopKWnMmDH1L7ICADDA4XBkZGSsWbeWflTdHQyu203ujoZmLgWDC45RmyQkJDz55JPh4eFN8ucXFhYuWLAAPpqZjwZAwYIQGULQyMfrxg0bVqxYQd8qer1+aA3NqmvLJYqZX3+9YcMG6mZ4teqBP943atSoJrfDoUOHFi5cSLtEyBniwQcf7NKlS7N675WWlq5atYpmBcljavz48dHR0fhgAuCfWK3WJUuW5Ofn0580T1nICw51TpGqqJz+xGg0Pv300/XvMG+wj9bpdCNGjGiGPjr722/XrFlDfTQ6zAEE4WU+JxCEoDFU2io3fLFh8+bN3i5n6NCh9913X8AnahwOx9dff7127VpafMKrVff2H5CcnCxfPSfZqfDvf//bWxbGxcWlpKSYzeaAf78VFxevXLnS+0xpNBoffvjhO++6CwVRAPg5kiTt2rUrIyOD9kU3K1nInzij/qnEWwrq9fpRo0YNGDBAvlMWfHQdH514512jRo1CjSiAIPyfRzMyhKBJsNvtGzdu3Lx5M+1l12g0Q4cOHTZsWFMVwPjbv/frr7/evHkzdTMajSYpKWn48OFs/r2XPVeZzeaUlJRevXsHnjQi/95NmzZ5S0GTyfTQQw/dcccdKPgBQFkUFhZmZGT8T7awTajYt5N4SzsuOAAjO0FHK1X7y+pkBUeOHGm55x42j2tBENatW3epj/7DH/4QkO3odrs9Kytrw4YNdcK1o0aNCsgzCYAgbJQgRIYQNLnL2bx587p166jLISolKSkpPj4+AGozHA5HXl5ednb2rj27aeMHcav3P3A/+0lukiTt3bt3xYoV3rJQr9cnJib2798/MNbZl5aWbt++PScnh/p1Kn1jY2MhBQFQLsXFxZ9//rl37TevVlX9P3t3Ax5ldSf8fyYzmYmZhMgkJOQNQgJNUkiCBAsBNxEUV3BVsEK1amt9qfiUbbfVVldlrVJ3sdW17eIjVOoragtWEFf0McIaVgRfgpDgP6GYEMKQSEgCgQwwb5n/lZzMzc0khAnJ3HPfM9/Ptddek2mIyZmZc87v/H7nnMLMrnGjwiNhqLc7DDVNUTVN0gmiYk41f/78kGxdE2O0fClTdKdlZWWlpaXhMUZ/+eWXH3zwgUrGaEAbASEZQgSpR/Yr0hA9cnFx8Zw5c7SYv/J6vbW1tZs3b/7kk0/ksa56Cm9qa2vXrl0rX24Xn9zS0tLSslItjoIdHR1bt27dsmWLvDJWFMfOnz+/oKCAUBAIDw0NDW+99ZZ0KqbQlRTflZ/myU/T4tWFerujJyV4KOpgm/x5laxkMUYDBIT+yBAiSNwezxeff+63UCflr0pLS7Ozs1U+p/d6vYcOHfroo4+2bdsm34yu2rSnzWYrLy/3S6aJIKqsrGzq1KnqXwDuNwcrT3tG8qmqQBhraWmpqKjYsmWLX2fblZnomZjeNSZRA5GhyxPVfMzwlc34dYu8+7JYLDNmzLjyyitzc3PVM+oNMEaXlJTMmjVr/Pjx6h+jm5ubt23b1vdtE05pTxAQkiGEhnm9XjGWnCvVY7FYCgsLi4qKCgoK0tPT1TPwtLW37fpy1+7du6urq/0iq7S0tNmzZ5eVlal5E4KoI/3www+3b98ufYpFLVbehG9NmjSpsLAwNzdXPR9qh8Oxd+/eqqqqPXv21O77u3xqYjKZSkpKysrKwnJjJIC+3dfevXs//PBDv1RPb85wTGLXuFFdqRerap9hVGNbVMvxqIZWQ/MxefelNxqmFE2+6qqrLrnkEhVOosJ4jJ45cyb39IKAkAwhVKqhoeHjjz/evHmzXw8uliQLCgrEwBOSWKujo2PXrl3V1dVfffWV30KjGBcvv/zy0tJSVa3vBhJoffzxx9u2bfMrJZWCw+Li4kmTJk341reUj7XcHs/X+/ZVV1dXVlbKextJOG09BXABXUS/+SuVBIf61hOGhta+QaA0a5o9e3Zpaam2KhVtNttHH32k0TFahTlYQDMBIRlCKE/kryorK6uqqvzWI6WBZ9y4cZmZmaNHj05PT0/PSLeOtA5vF2+32w8ePHjo0KHm5mabzXbgwIG+A4xYa5w4ceLUqVOnXnqpptNTbe1tn2z75Msvv6ytrfVbdBdZuJycnIyMjJSUlMzMzIyMjNGpqcP797o9nm96mrq5ufnQoUM2m63fINBkMhX0mDlzJgfBARE+Ukj5q08++WT37t1VVVV9uy8RHHqT4ryjRngt5q5R8d6E2GEPEfWtJ/QnnVEtx3V2R9SRE/0GgaL7ys/PLykp0fSVBozRQMQFhGQIEVodHR01NTVVVVVffvllv929YLFYRKySmZlptVotFkt0dHRsbOxFF10UGxtrNptjYmJMJpMYkNwej+P0abvdfvLkydM9D1wul91uP378uAhFDh8+3Hf5Uz7UFRcX5+fnT75kcvidSOb2ePbX1+/ataumpqa6urpvSNbbiRgNqckpqampYsgXjSwavFvsRTHmmNjYWNHgXq+3u6kdp0+dPOVwOESDd3Z2njx58ptvvjl48GBzc3Nzy+G+8ye/WVRBQcH4CRMY1AGcS0NDQ3V19bnWts6ENBaz1xrXNSpeZzF7e/6v+8lYU/f/dpHJazKeFTG6PHqnW3equzPUn3SKI2G6/3/Lcf1Jh76103Ds5Lm6L9XW4atnjDaZTBaLhTEaUHVASIYQ6tHW3raneo8oCBkgfjgXvdEQa45xuVznCnIG+IepySkTJkwoKirKz89PTU2NkIITt8ez7+9/37NnT2Vl5cAD8LmYTN0TrME2uJg9jBkzJoxnUQCC3X1Ja1v79++/gO5Lb+yOCQc70EjdV25ubmFh4aRJkyKn+5LG6Lq6OpvNdgFDbaw55qTj9AUM7qnJKWPHji3qETljNDDEgNA4iI8ZHyqoRqI1sayHdHRYc3NzU1PTgQMHzrtkKMZ1u9se4FiekZGRnp6empoajPJIrTAaDPk9Fi5cKEp0mpqaGhsbpRKd84blgUwIpLE8IyMjNTU1PT09MzOTbYEAhth9TeghvrTb7d98843NZjt48KAYL84bsQQSlvjVSmRlZaWlpY0YMSIy505+Y/SRI0cOHjzY1NTU3NxcX1/PGA2orp8M/FsHVVwKKEav16f1KC4ulp4UQ76oM5GqTRwOx8mTJzs7O509TD3i4uKkUtLY2FhRvjJixIi0tDRCkQGGYfkESwrLW1tbpRJQeXmP0+ns7OwUecK4uDjRyKI0KDY2Ni4uLjo6OikpKTklhbEcQLC7r5we8u6r/Wh7y+EWMUC43W7Rg3V2dp63+xIV8larlVBkgDE6ucfwjtHJyclcGAiEICAkQwjNDfm0g/JhOU0BQHPdV6I1kT1mjNFAxIoK/FvJEAIAAABAhAaEZAgBAAAAIEIDQjKEAAAAABChASEZQgAAAACI0ICQDCEAAAAARGhASIYQAAAAACI0ICRDCAAAAAARGhCSIQQAAACACA0IyRACAAAAQDgxBv6tZAgB9Mvt8dg7O+12uznGnJBwsdFgoE0AAADCLSAkQ4ihczgcTz75pPRlQUHBggUL+r7T3nzzzZqaGvmTd9xxR0ZGRiDv0mXLlklf3nzzzRMmTPD7Hrvd/vTTT8ufsVqtP/nJTwJZ8qiqqtqwYYN4bDKZHnzwwb7fU1lZ+e6775pMJqfTGcj/t1qtS5Ys8fshK1asaG9v7/vDTSaT1WodNWqU1WodOXJkTk6OxWIZ4ouybt06eWs/9PDDgUR0HR0dX3zxxe7du/ft29fa2ir/n+Li4nJzcydOnDht2rTk5OQL+B2WLl3a9+WQN75w6aWXzp07N5Cfv379+urq6gHedQAAAASE50GGEEPndrulebmIHPp9p9XU1Mi/TafTvfDCC/0GCX48XV3yf3jNNdec93cQ8vPzr7jiivP+/kePHpX+bb+/vE6na2pq6vvzB5CUlNT3ya+++sovyupXdHT0nDlzrrnmmgDjrn7V1dUN6he22+2bNm1av369y+Xq9xs6Ozsre7zyyitz5sz57o3fTbQmDup38HR19Q1K5Y0v1NbWFhUVpaWlnfd33r9//3lfOAAAgAjEHkJoQ3V19datW4P389esWdPR0aG5ZnG5XJs2bfrJT36yZs0aZT6hDQ0N999//9q1a88VDfopLy+//777q6qqgvTnr169mq4JAABAiYCQDCFC66WXXgpezNbZ2fnqq69qt3HefvvtLVu2BPu/UlVV9dhjj50rdXmuzFtnZ+fy5csrKiqCtFLw6aef8ukAAAC4MOwhhGZ0dnb+9a9//fGPfxykn19RUVFaWlpYWDi8P/b73/9+Tk7OAN9gMpkG/glJSUn33nuveHzy5MmjR4+2tbVVVlbabDb5t/35z3/OycnJysoKUvu0tbc988wznZ2d8iczMjJmz549tkdCQoLD4WjssW3bNnl5p8vlWrVq1dixY4Px661ataqgoGDoeykBDKC2tnbfvn3SlzNmzjhvKbifqqqqAwcOBP79o0aNmj59uvwZt8fz3qZNg/qPit+zoqLi+PHjFz5VMhoD3K4MzZG/NwJ/ob1eb3Nz8+7du2tqajo7O48ePXr69OmYmJiRI0fGxcXl5+cXFRWlpqYGmEoJ5HfYt29fbW2t/JmCgoIAh1T5zx87duywz3MQWQEhGUKEXHl5eWlpaV5eXpB+/osvvvi7p54a3kMyc3Jyhtj5xsTE9P0Jt9xyS3V1tTxCc7lc//3f/933fJph4fV6n13xrF80WFJS8pOf/MRsNkvPmM3mCT1mz569YcOG119/XR4T/uEPf1i+fLn8+4drpeC1114L3koBAJGNX7t2rfRlXl7eYAPCHTt2lJeXB/79BQUFfgGhx+1+5ZVXBvUfnTBhQqI18b333qurq7vgvz0uLo6AMFzJ3xuBvNBer3fnzp3r1q3r9x0lFmq3b98uRv+FCxdOmTLlvPPnQH6HPXv2yIdUsSAb4IyloqJCWqKdN28eASH6Yg8h1K6kpET+5apVq9wez3D98KSkJHn6zmazrX/rLU00i16vLyws/PnPf953KArSXNDvQJcf/OAHP//5z88V3en1+gULFixdujQ6Olr+6w1XXWtxcbH8J5eXl8tzFwAADPvMtqOjY9myZcuXLw9kfaGurm758uXLli2z2+3B+OVtNts7Gzcye4fSASEZQoREWVmZX8wWSA8YoNOnTy9evFj+zPr165uamrTSOIWFhfKde42NjcMYLcttOrtMKyMj45/+6Z/O2ycUFhbOmDFD/swHH3wwLINTXFzcwoUL5c/8+c9/DtLfDiAkzltOr5iYmBhejrA0qJltU1PTgw8+OKhDucVy6iOPPNLS0hKM33/dunXn/cl6vf7kyZO81hgYewihdi6X684773zooYfkPeDMmTOHctGCxOFwZGVlzZs3Twp4xMGVgVxxoRLjxo2TxieXy/VNc3MgFzYOit1ur6yslD/zve99L8D2ueGGG+THydhstq+//rrv5ZCD1dnZee11123dulVKitbV1b23adO1117LRwbQhDlz5sTGxg7wDampqQP/hIyMjOLi4oG/x2q16nS6adOmZWdn9/1f29vb5Z1bTk5Ov9828O8J7Qp8Zmu325ctW9bvpbs5OTmZmZmjR49ua2urr6+vq6vbu3evfIeFzWb7j//4j2DsmHC5XC+88MIDDzww8IjMGxjDGRCSIURIOByOCRMmzJkzR9p8EmAPGGBn6vV6Fy1a9Nlnn0kdvTi40m/vimr5rfzFx8cP+3/CL2WalJQ0bdq0AP9tWlpaSUmJ2FAhNDY2Dj0gdDqdRoPhxz/+8b/9279JT77xxhsXcNAFgJCYP3/+ENf1cnJybr311kC+c8GCBf0+L25Mlb4sKytjr2BECXAW4fV6V61a5RcN5uTk/OIXv5C/h7OyssQKRVt72+9++zt5WanNZnv55ZeDsde9srJSQzMWqBZ7CKGNgOeWW26R10aKHnBYfr7T6bRYLD/84Q/lT65atSpIRf/Dy+v1Hj58WPoyOjp6xIgRwQ4Iv/Od7wwqFC8qKpJ/2dzcPFzvivz8/LKyMnl4//JLL/ORAQAM48y2trZWvqwp1g4ee+yxc61oJFoTf/PEE3PmzJE/WV5ePpSTjfxiUfmXL7/88gAzFmbvGOaAkAwhQsLtdut0OovFcvvttwfeAwbu9OnToqBIXnokDq5Uf+O8//778rqUwsLCYHxO/bYoJCUlDeqfjxo1aoCfNkS33XabfKVg+/btftWtAAAMZWb73nvvyb/MyMjwO2G7L6PBcPfdd/vt4PD7ORds0qRJBQUF0petra3yE4D7/o3sIcR5sYcQaicCNp1OV1paKj86WfSAP/rRj4b48x0Oh+gx77jjjqqqKpfLJZ4vLy+fNWvW0Isbn3nmmYEPJBhglXGgONnj2fbxx36X6fudszJc5EnIvgHeefkFkMMSEIpXTafTJSQk3HrrrStXrpT+p9WrV0+aNGnYt2oAAMJMIDPbtvY2v/Tg/PnzA4kk9Xr9/PnzV6xYIT3zySef3HbbbQkJCUP8tV0u11133XX//fdLM5ZNmzbNmjXrXNcSsocQ50WGEGonTf31ev1dd90lv2xg06ZNDQ0NQ/z50spZcnJyMA6u7OzsbB2QSIEOHBJX+lRUVKxfv/7FF1/85yVLVqxYIQ0G4nKhgW/Av2B+t1kkJg5uk57f9x8+fHjoq0vSMoFOp5s9e7bfWum6dev44AAAhj6zPWQ7JP8yKSlp5mWXBfjzZ152mXxJ1OVyDcsx5na7PS0tzW9n7MqVK8ncQImAkPcZQkIe8wSjB/TIQr5rr7tOXuAhDq4MeQu0trYu91mxYsXrr7++adMmv93t8+bN8yupHUYdHR3yLwe7umk2m+VVnZ2dnUMvX5EHhHq9/gc/+MGwrxQAAMJbIPOHQ4fOCghnzpwZyF3wgtFgmDlzpvyZYQkInU6nTqdbcMMNfjOW999/n9k7gh4QkiGEGiy44Qb5eptfD2iIihrKDxcHV8qfeeONN9ra29TcIBkZGffdd9/tt98evE+oXwQ42HDO7fHINzpGR0cPe/mKuDtEvojAWikAYOgz27a2s+YA570NxY/f9w/LsWoiIDQaDH67ZtauXdt3xsLsHQFNgAP/VmZXUMVb1mC49957ly1bJu8BZ8yYIYKWoXd84uBK6eo8cXDlL37xiwv+gffdd5+8oLGvoURHGRkZv3vqqcBXKy9McnKy/Gy0tra2c21U6FdHxzH5lykpKcEYnxYuXLht2zYpcVpXV7dly5YrrrhCfKmeG64BCGvWrBn4g7lkyRJaCUEVyMzWb9PEoHbRe73eYByrJgJCcZKcfMbS2dnZd8bC7B3DHBCyxgCVKCwslF9t19nZ+ec//1nqAaOjo+VVphfgtttuq6yslJJa4uBKcQbpBRxVEhsba7FYhvL7xMXFlZSUSF9+88030sk6Npvti88/D/YFRH5n3gx2PDvScsQvIAzGL2k2m++6667ly5fLp5tTp04d+vZ9AMHgd1DHBQSENptt/fr1A3xDfn5+Xl4eTY2hzGz9jlWzWq2D+vl+3z8sAaG8TqfvjKWqqqqwsFD+O3DKKIYzIGSNAepx5513VldX99sDms3mIQaEAxxcKd+oppiLL75YXshqt9uXLFki/e2rVq0qKCgYYsw5ML+KF7/6mfP2G8eOHVMgINTpdMXFxX4rBa+++qqYU5IhBMJPXY8BvmHRokUEhBjKzNbvst8LOGd75MiR8i8bGxu9Xu8wplgSEhIWLVr0wgsvSM8899xzv//97+Xr15wyivNiDyE0SfSA8meee+456TzSoZs9e7b8xE7p4MqQBIR+LBbLrbfeKn3Z2dn54osvBvW/mJaWJv+yvr5+UP3G119/LX9m9OjRwftVf3j7D+WvUUVFRU1NjUpeOACAqpx3ZqvX6/0qg857MPjAzGbzsE+nr776ar8Zy8aNG3lxMShkCKFVV199dUVFhbQ8LHrAhQsXxsTEyI8wueBBYvHixQ899JD8kp/LLrvsoosuUsPffsUVV5SXl0t/e0VFRWlpqbxEZHglp5xVMlpdXd3Q0BDgNkK73b558+azftrgL10MXKI18eabb37llVekZ/70pz/97qmn5MecAlCDO+64wy95AigskJltSkqKfFJx5MiRQZXkHDkS9E0TYsbyy1/+Unpm/fr1JSUlftfiA8MTEJIhhKqcqwcc+Bb4wImDK99++23xpTi4cvHixSr58+++++4HH3xQ+rJvicgwso605uTkyEuz3nrrrQAP2tmyZYt8KI2Li5s0aVJQW2buvHnbtm2TflubzfbOxo1Go5GPDKAqxcXFQ1weysjIELu7zyU3N5d2xhBntkM8Vs1vk0WQlkTFjGWT76Isl8v1wgsvLF26VPyBbJrAcAaEZAihNv32gMMYFPU9uPLjjz9Wyd+ek5Mj/9tFUau8lHR4h8y5c+euWLFCemb79u2BJAntdvumsy9yvOKKK4IUtZ7p1AyGO++886GHHpKeWbdu3Zw5c/i8AGEmJycnSJ0eIkQgM1u/EM5vV/x5+Z0ik5mZGaS/ZdGiRZ999pk0Y6murt66dWtZWRmvMgLBHkJo26JFi+TXElZXVzc2Ng7XDxcHV8qf2aSCe+rlf7u8EvLtt98O3m3sMy+7TN7OOp3uscceq6ysHOCf2Gy2Rx55RH6BfnR09Nx5cxVomQkTJsgjQJfLVV5ezocFADDYma3fsWqDnWMokyEU5wv88Ic/lD/z0ksv2e12MoQY5oCQDCFUqG8POMQjRv0U9wjSDx/63+63Or5y5Uq3xxOM/5YhKuqnP/2p/JnOzs7ly5evWbOmrb3Nr3MQ+wZ/9atf+V3fdM899yRaE5VpnFtuuUUeLavqhQMAqEEgM1u/Y9W2bt0a+Al2DofDbxe9308bXtOmTZPPWDo7O1977TUCQgSCPYTQPNEDDpytGoq7f3x31ZKqC44onnnmmYG3NSYkJMhvzxuU2bNny0+Xqaure2/TpmuvvXbYG0Gv1+fn5y9atGjt2rXy59/uERcXN27cuMzMzMOHDx84cECeFZSUlZWVlpYqGS3ffvvt8jJXAAAGO7MdM2aM/H7jzs7OLVu2zJ0bULVL3130Y8aMCeqfc8cdd1RVnZmxlJeXl5aWBvViKoQHMoQIhw79jjvuCN69AuLgygv+552dna0D6ujoGMrf7nfOzRtvvDEs9972a+HChXfccUe/f2N1dfWmTZsqKyv7jQbnzZv3k5/8ROFFpdLS0oKCAj4gAIALntlaLJbLL79c/szGjRsDKcZxezx+1z+UlpYGexd9cnLywoUL5c+sWrWKi5cwnAEhGUKoVt8ecHjNnTdPfsmPqmRlZfntl1u5cmXwlm/mzp27dOnSwA+zTkpKWrJkyY9+9CPlOxC9Xn/XXXcxEAIAhjKznTdvnvzL1tbWf3/iiYEXc+12+1O/+53fLvprrrlGgT/q2uuuk4/RNpvNr2wVGFJASIYQaubXAw4vcXClav92v/1y4myxwP+50+mUj1iGqPN0C4WFhf/5n/+5ZMmS4uLic4Vb0dHRBQUFd9xxx3+tWBHCU87S0tIWLFjApwMAcMEz24yMjOuvv17+THV19YMPPrhv375+v7+uru7+++/328mycOHCoF7DK5+x/PjHP5Y/M/TLmRH22EMIRVkslpWrVsq6rf7fgT/72c/cHrd4HGOOCbAH/M1vfnPacVr+ZJyln+vIR4wYIf8dEhIuDuTnT5gwQf6vBjB79uwZM2cMsaF+88RvzttK8lb9/e9/L7VYIP9Errm5WXqckpISyCddr9eX9XB7PF/v23fs2LGjR492dnbGxcXFxsaOHDkyJydnUJsW7r33Xvnv329QOn369EkFg77DcMENN8y+YvbZbxXuJASG6o033pCvQ/WrrKxsgHsC16xZM/BZF2PHjg3GjmiE1vHjx7tH9pgYNZx0EvjM9qabb96zZ4/8QsLW1taHHnooIyMjJydn7NixaWlpTU1NBw4csNlsjY2NfucOFBQUzJ8/X7G/Kz8/v6ysrKKigvcbhj8gJEOIYRHIOZMJCQkXFm0GEoTo9foLO+sywH8V4K8xLP+tIbaYKGuR172kpKQMrgcxGPLy8ob+rgjk9zf3GHQfZzAodrQpEDmqq6sDmZUO8L9u37594H9eUFBAQBh+RowYoZ5fJvCZrdFgWLp06dNPP+33zrf1OO87+b777lM4rXLbbbdVVlaSG0SA2EMIRLRNmzbJFzIHGxACAKBRg5rZWiyWhx5+WL5pPxDz5s176OGHlT/nMyEhwe9iKmAAZAiBCNLS0uJ0OkenphoNho6Ojg8++MDvGonLLruMVgIARAj5pYID3xElbc+bN2/e2rVrv/jiiwHuo4qOjp46depNN90U1IsHBzZ79uxt27YFkswH2EMIRJBPP/30lVdeEbch9a0kKSkpmTBhAq0EoK9LL710sEdi+B3OPGvWrNzc3MD/+ciRI/2eMRiNS5Yskb4c+hEdWeOy5D9QtadJI3iOHTsmPQ5w80VGRsYvfvELh8Oxd+/eqqqqY8eOdXZ2Op1Ok8kUFxeXkpKSm5v77YkTjQZDgL/DwoULz1vbeckll1itVvH4vNt3pXn74sWLa2pq5E+GMEBFmASEZAiBsNF37ImOjqa8BMA5Y6ceQ/kJE3oMacpiMAzvqcWJ1sQQHoOMkLPb7fLRcFBnlZvN5sIeQ/81Bjh7aYgfwOQevNAYzoCQDCEQrnJycn76058ybAAAIscHH3wg/5Jd9CAgPD8yhIDWjRo1KicnRzoROykpaezYsUVFRVdffTUrPgCAMOb1euUjXWVl5caNG+XfMGnSJFoJBITnwXwR0LrpPbxeb/vR9jhL3AXc4gAAgBa99NJLn332WWpqalxc3MGDB/2uiyguLh74ohSAgFBHhhAIGxd8EyMAANrV2qPf/4ld9CAgDHQSSXsBAAAgbMTFxS1ZsmRQJ8oAkRsQkiEEAACAFkVHR/d9ZurUqXfeeWeAF04ABIRkCAEAAKBJt9xyy7XXXtvU1NTS0jJy5Mj0jHTrSCuTW4AMIQAAAMKfXq9P6JGfn+934igQ4aIG9UGivQAAAKD14JBGAC4kICRDCAAAAAARGhCymgIAAAAAERoQkiEEAAAAgAgNCMkQAgAAAECEBoRkCAEAAAAgQgNCMoQAAAAAEKEBIRlCAAAAAIjQgJAMIQAAAABEaEBIhhAAAAAAIjQgJEMIAAAAABEaEJIhBAAAAIAIDQjJEAIAAABAhAaEZAgBAAAAIEIDQjKEAAAAABChASEZQgAAAACI0ICQDCEAAAAARGhASIYQAAAAACI0ICRDCAAAAAARGhCSIQQAAACACA0IyRACAAAAQIQGhGQIAQAAACBCA0IyhAAAAAAQoQEhGUIAAAAAiNCAkAwhAAAAAERoQEiGEAAAAAAiNCAkQwgAAAAAERoQkiEEAAAAgAgNCMkQAgAAAECEBoRkCAEAAAAgQgNCMoQAAAAAEKEBIRlCAAAAAIjQgJAMIQAAAABEaEBIhhAAAAAAIjQgJEMIAAAAABEaEJIhBAAAAIAIDQjJEAIAAABAhAaEZAgBAAAAIEIDQjKEAAAAABChASEZQgAAAACI0ICQDCEAAAAARGhASIYQAAAAACI0ICRDCAAAAAARGhCSIQQAAACACA0IyRACAAAAQIQGhGQIAQAAACBCA0IyhAAAAAAQoQEhGUIAAAAAiNCAkAwhAAAAAERoQEiGEAAAAAAiNCAkQwgAAAAAERoQkiEEAAAAgAgNCMkQAgAAAECEBoRkCAEAAAAgQgNCMoQAAAAAEKEBIRlCAAAAAIjQgJAMIQAAAABEaEBIhhAAAAAAIjQgJEMIAAAAABEaEJIhBAAAAIAIDQjJEAIAAABAhAaEZAgBAAAAIEIDQjKEAAAAABChASEZQgAAAACI0ICQDCEAAAAARGhASIYQAAAAACI0ICRDCAAAAAARGhCSIQQAAACACA0IyRACAAAAQIQGhGQIAQAAACBCA0IyhAAAAAAQoQEhGUIAAAAAiNCAkAwhAAAAAERoQEiGEAAAAAAiNCAkQwgAAAAAERoQkiEEAAAAgAgNCMkQAgAAAECEBoRkCAEAAAAgQgNCMoQAAAAAEKEBIRlCAAAAAIjQgJAMIQAAAABEaEBIhhAAAAAAIjQgJEMIAAAAABEaEJIhBAAAAIAIDQjJEAIAAABAhAaEZAgBAAAAIEIDQjKEAAAAABChASEZQgAAAACI0ICQDCEAAAAARGhASIYQAAAAACI0ICRDCAAAAAARGhCSIQQAAACACA0IyRACAAAAQIQGhGQIAQAAACBCA0IyhAAAAAAQoQEhGUIAAAAAiNCAkAwhAAAAAERoQEiGEAAAAAAiNCAkQwgAAAAAERoQkiEEAAAAgAgNCMkQAgAAAECEBoRkCAEAAAAgQgNCMoQAAAAAEKEBIRlCAAAAAIjQgJAMIQAAAABEaEBIhhAAAAAAwokx8G8lQwht8Xq9Tqfz9OnTdrvd6XSeOnXq5MmTnZ2dbrf75MmTp0+fNhqNMTExsbGxcXFx0dHRFovloosuMseY4yxxJpOJN/wFcHs8DlmDizYXX3Z2dup0OtHgFoslNjbWZDJJX5pjYowGAw0IIFQcDocYL06dOuV0OsV4IQYLt9vtcDgsFovossxmc2xs7EVC7EUx5u4nGTIuYIwWA4RoZNHm0pcOh8NsNktjRHR0tGhz0f6WuDiGDCA0ASEZQqiZ3W5v6nHw4EGbzXb48GGbzeZ0Oi/sp+mNhtTklLFjx2ZkZKSmpqanp6empiYkJNDO8g6huUdTU9OBAwdEm7e3t1/wD7RarSkpKRkZGenp6WPGjElNTU1OSWHIBzDs3B5Py+HDzc3NjY2Nhw4dGnr3ZTKZMjIyMjMzk5OTMzMzM3qYzWaaWhovjh8/LsboxsbGgwcPdg8eLYe9bs9Qhoxx48aNHj06MzMzPT09PSPdOtJKWA4EPSDkYwZVaWlpqa6urqmpGfpY3s/o5faIoUv+pMViGTNmTEZGRk5OTkFBQXp6ekR9KBwOx969e6uqqmw224EDB1pbWy843u5Xe4+ampp+w/L8/PxJkyYxwQIwlO6rvr5+6KFIX06ns76HX8SSkpKSnZ2dm5s7efLkiFpS9Hq9hw4dEmN0S0tLY2Oj3W4f3v+EGDL6DcvHjRtXWFiYnZ3NxBUY/oCQDCFCrq29bU/1nurq6q+++sovWvOTlpaWmppqtVpNJpOoMImLizOZTKJY8aKLLoqOjna5XKKm0eFwiBoVu93ucrnsdnt7e3vfSYPdbq/pUV5eLgb7goKCoqKigoKC5OTksGxwt8ez7+9/37NnT2VlZe2+vw8whfJazN6LTN6kOG9sT8xmMYsnz/z/WFN3jHeyO4bU2x3S/9eJxycdertT397Z+2R/YbneaMib8K1JkyYVFhZ+e+JEkocAhq37ssZ5LSap++rbd/X2QufowaKOnPDrvuSLXO+++65Op8vOzs7Pzy8sLCwoKLBYLGHZ5mKhdvfu3dXV1QOv0nYlxZ93vDjPkNHaaTh2Uv6ySmF5RUWFWMMtLCycOHFiQUHB2LFjCQ6B4QkI+SwhJOx2e3V1dVVV1ZdfftlvECgSd6JQJzMzc/To0ampqcOSShq4rKi9vb2ihwgOi4uLCwoKwmAZ2Ov11tfXV1VVVffoNw3YlRSvu8jUNSremzyiey6VGCdG8fP/8PP2M3aHvq17XqVvOS6G/KjWE1J8KALydevWmUymgoKC/Pz8yZMnjx8/nt4JgNR97dy5s6amZoDuS4Qig+2+zt+DuTz6jpPdwaHdoT9y3C9iEbGKFBwW9giDwoeOjo5du3YNsFAr4u2uUfE6i7kreYQ31uRNih+eBvcNGVEtx3V2h19Ybrfbt/cQY3Rubm5RUVFxcXG4LuACQ6EPPO/n9Xo9XV0jfvOPZ5aC/vWdONNFOp2urq5O6nazs7Mp68KwBGO7d+0qLy+vrKzsO6hLAdikgkkKbxvo6OioqakZIEAtLi4uKyubPn265j4ITU1N27Zt27JlS9+/S280eFIv7spK8mQleRNiddGKZuf0rScMDa1RDa2G5mN9l/mtVusVV1xx+eWXZ2Rk8MEBIlNLS0tFRcX5u6+AQ5Fh7L6ijpyIajgSdbDdL4UoFjRnzJhx5ZVX5ubmamthy+3xfPH55x988MHO3bv6dstdSfFdYxK9ySO6/79F2aHQ5YlqPha1/0hUY5u0niiXn58/Z86c6dOnh2ueFpHs+S/e+dm7fxCPvzux7NUbl4qJa0tLi9vt1ul08fHx/U6W9IMqBHV7PASECCqxvrt58+bt27f7FZyosERzgBJWk8lUVlZWWlpaUFCg8mHebrdv3bq1oqJCvn+vd1DPTOzKSupKHtGVerHCQeBAI33L8X6Dw+zs7KuuumrGjBkc/wNECLvdvmPHjvLycg10X761Lf2R48avW/y6r7S0tNmzZ8+cOTMtLU3lY/TevXs//PDDTz75xG9boNdi7sq0dmWNCkEQeK4GF7W/I0wAAHR2SURBVPnD/oJDk8lUXFw8Z86cosmT2YAAAkIyhFCLtva2LZu39F3fzc7Onj179iWXXKLyQ1zE3olt27ZVVlb6xbHqzF+dKwerNxpchZld40apZxY1UHC4/4hhb7N83V1vNEy/9DuXX3751EsvZZgHwpLmuy+p8KGmyS9Qyc/PF4uJastfnauEpCsz0TMxXT1B4ADBYVRjm+GrQ1EH2/zG6JKSkiuuuIJDaEBAOIgumIAQw27fvn1/+9vfRKG/+uOo866btB9t7zeyzc/PX7hw4ZQpU0I+5Njt9k09/HKwnrzUrqxRnvEpKp9I9dUzzNv8Ft0tFsuCBQvmzZtHXRAQNhwOx6ZNmzZu3OjXfYmwRKPdV9T+I9FVB+Xdl8lkmjNnzvz589VQDiN2bvutdXYlxXflp3ny03rjQJdHKy2vtzsMNU19Q/Hs7Ozvfe9706ZNIywEAeF5ZrpkCBHsYUZDlZbn/byI2tePPvpIXlcT2iGno6PjnXfe2bRpk/xX8h/XNavfBWCLxTJv3rxrr72WOlJA08RK1vr168Oy+9LpdN1RSsMRQ23zmT7NaLhy1uwbbrghJHWkXq+3urr69ddf97sNqDcHOyZR6w2ubz1h+OqQX41JWlraTTfdNPOyyygwAQHhOZEhxHANMzt37ly3bp18mMnPz587d64Wz2I576dm965d7777rjzuzc7Onj9/fmlpqWJhYVt728a3N27atEn6qIpx3TMxXflTFpQf5sVy+3dv/G6iNZEPIKAtfVeywrn76slfGXY2yKOUkpKSm266KSsrS7Ex+tNPP/3rX/8qv1lRuznY8xI1JvJQPC0t7cYbbyy7/HLCQhAQ9tNBkCFEMIaZ4uLi+fPnaz0leF4NDQ1/+ctf5JWxygw5LS0tGzZsKC8vlz6kXovZMyXLXTQm/Mb1s7g8xt2N8nlVaJfbAQxW35Us0X2FR0pwYN1h4Rf75WWNxcXFN99884QJE4I6Rm/dunXDhg1+oaD7O9lhkBI8TyjeesL4eb08LLRarTfccMNVV13FtBYEhGchQ4ihqKmp+dOf/uQXCi5atCgvLy9yGsFms61du3brto+l7SJWq/Wee+4JRhGp3W5fu3bt2//9jvTfipRQcMCwUKfTzZkz50c/+hF7CwHVcjgc69ate3P9WxHdfZ0jLFy8eHEw9hZWVVU999xz8t3vnrxU96XZ4ZeGHSgstDsMX+yXb+m0Wq233nrr7Nmz2VsIAkIdGUIMRUdHx6uvvlpeXi49o3ABjNq0tLSsXbv2w//ZIg05xcXFd91113BlrsQq70svvSSdu+C1mN3/kBuW1T4XNq+yWCx33nknYzygNqKQZNWqVfLuKwJDQbmoxjbjZ/XS7miTybRo0aIFN9wwXNUlbe1tL7/0ckVFRSSHggOHhfn5+YsXL47YSQsICM9ChhAXMLS///77r776qrT3o6Sk5NZbb+UOcTEG/+3Nv7377rvyMf66664b4ifIZrOtWLFC2p/ZGwrmUyTZGxYa/3evlC1kjAdUpampafXq1dKOa1ay/MPCilppVSstLe3ee+8tLCwcys90ezzvbdr0xhtvSGN0hIeCfcNC486G3i+Nhuv/6drvf//7zHIR0QEhGUIMVl1d3YoVK6Qa0WEZvcJPQ0PDypUrpfgtLS3trrvuKi4uvoAf1bfIyj0lyz1jAnOps/QUkUZv/1q0khjjFy1aRAUpEEIOh2Pjxo1r166VphN0X/3yW9UqKSm5+8d3X9hxWbW1tatWrZLG6K6keHdZXtjvFRx0WNh6IvqjWik9G7xdHoA2AkIyhAic3W5/7bXX/HJfw1jfEma8Xu+WLVv+/Oc/y/Oogx3jd+zYIS+y6spMdF2exypv4GP87bffruS5rwAklZWVq1evlnavdSXFu+YW0n2ds/uyO4xba6XjT0wm0w9+8IO58+YFPsL67ePQGw2ukvHuqeNo2wDj8OHd5QFoKSAkQ4gA1dTU/Pa3v5Uik+DtgA8zfiO0xWK55557ysrKzvsPHQ7Hs88+K23/oEb0gsf4kpKSf/7nfyZVCCjG4XA8//zzUr9H9xU4vwrS/Pz8X/ziF4EMtVVVVc8884w0RnvyUt2leWF/auswcHmMn+yTKkhNJtPixYvZiI7wCAgNv/71rwP8b+j1+i6v9z+2rpGe+eU/fN9kiNbpdEePHvV4ekvURo4caTQaeUkik9frffPNN//zP//z1KlTIuvyy1/+8uabb2aGHYiYmJjvfOc7l1xySX19/dGjR10u1/bt29va2oqKigb4TDU0NCxdunTPnj3iS/eULNd1U7wpXMIe2Dt2VLyncIzO3RXVfExsv9y+fXtubm5iIkVTQNA1NTU9/vjj0o5BT16q69pLutJG0jIBdV8JsZ6eg3YMTcd0Xd7W1tYtW7akp6cPsEXf6/W+9tprK1asEGN0Tya2yDN1nM7EtC0AhqiurCTPhJSooyf1x095PJ5PP/20ubl5ypQpzHuhEjub/v7+vk/F428nZ93w7TKx7ma327u6unQ6ndlsHjFixJACQm8PAkKcS0dHx29/+9v/9//+n/jymmuueeSRRzIzM2mZQUlKSvrHf/xHo9FY/f99pevy1tfXV1ZWFhYWxsf3Uz313nvv/fa3vz127JhYWXfeNL3r2+k6QxTNeCFjfNMx/UnniRMnyrdstsTGfutb32LdFwieioqKxx9/vLW1VXRfruumEJlcgK60kZ5JGVEtJ/THT7lcro8//vj06dMFhYVRUf4DQVt72+OPPy7VkrinZLnmFnmtLNcOUqzZ8+10XbQhqrFNp9MdOHBg27ZtkyZNuvjii2kbRERASIYQA6iqqvr1r38t9qZbLJb777//+uuv551wYfR6/cSJE4sKC3fu3Hnq1KmjR4+Wl5enpKTID8O02+0rVqz429/+Jj56nrxU53cv1cXH0HpDGeP1x09FtXbqurw7d+48cODAJZdcYjKZaBtgeDkcjpUrV7722mui++rKTHTdMNU7ih2DF8pk7A5RnG5R6VBbW/vF559PmTJFXptTVVX1+GOPHzx4UOwYdC6Y6ikaw+rhkOLwCSmGuha9y3PixIktW7bEx8ePHz+eZUSEf0BIhhDnemO8+eabf/jDH0QJSnZ29mOPPZafn0/LDNGoUaNmzZrV2NjY3Nzs8Xjk5aMNDQ2PPvqoKBPVGw3umd9yl+UxtA+VIaprwmjvxbHGhlZdl9dms/3P//zPxIkTKR8FhpHNZlu2bJlUJur+h1zXlRNJDA5DiJKV1JU+MqqxTe/yHD16VCofdXs8r7/+urxM1Pn9EsLvYRBr9hSOEcuIHo/niy++aG5uLiwsZBkRYR4QkiFEX33LRB948MGEBDawDY+YmJjS0tKYmJjde6ql8tETJ078/ve/F2Wi3UP7guKu8Sm01bAtcIyK9+Qki/LRU6dOifLR3NxcWgYYuh07djz66KNnlYlyfswwdl8JsV15afLy0ba2tg0bNviViepiommr4dGzjCht4zxw4MD27dsnT57c74QbUHNAOIjIbVAXVCAStLW3PfLwI+KgcIvF8tOf/nT69Ok0y/DS6/ULFizIz89/8skn29vb63uI/8mTl+q6chKXdA3/pCop3nnT9OgP9xhqm71uz+rVq1tbW2+//XZqgYCheOedd1avXi0ed2UmuuYWcrLl8HdfFrPz+inG3Y3G/92r0+nkF0s4rp/CHYPB4J46zpOVFP1eVVTriaampl/96lf/9m//lpeXR8tAQ6IGNTGlvSBpaGi4/777RTSYnZ391FNPEQ0GT15e3lNPPyUvROmOBucWEQ0GS7TBNbfIdXWh3tjdwhs2bHjmmWfcvjoIAIOLUrzeNWvWSNGg+x9ynTdeSjQYvO7LPXWc6+pC+XNEg8F9h/csI3ryUsUO/6VLl0pF0UC4BYRkCCGpra196KGHxC1GxcXFv/nNb7ieNajsdvv9990v3fap0+kMtc3GilpaJqg8+WmO66eImLCiouLfn3jC4XDQLMCguD2eZ599dt26deJL19WFXIAe9LldY5vpw6/kz5jf3qn33ViIIMXhrisnuadk6XQ6p9O57N+f2Lx5M62CMAwIyRBCqKysXLp0qd1uFxd5P/Tww1wzGFRt7W133323CL+9FrNYg9TpdMadDdHv7aZ9gqprTOLpm6eLVEZlZeWjjz7a0dFBswABcjgc//7EE6JwUW80OL97KZsGFYgGzW/v9Lp7T3DtSorvHjvcHvOr28RNCQheTOguy3P/Q65o8D/+8Y/r168nm4JwCwh5T0On023evHnZvz8hUlXXXHPNAw88YDRQtRhENptt8T2LRfjttZidN0x1zS0S443IE5re/JxWCipvUrzzhqliUlVTU/Pggw+2tLTQLMB52e32Rx99VNTOeS3m0zdPp2ox2Aw1Taa/fS6iQfeULOeNl7pumNqV2dvspr99TkwYbPJ63Zd6MH9GWAWEZAixfv36P/7xj2KkufXWW++++27eFUG1b9++JT/7qQi/u5LinbfM8PaEJfLxJupgm+nVbbRVsGNCly8mbGpqeuCBBxoaGmgWYABt7W2PPPJITU2NtJglui8ENRqMfr+qNyyZkuUuy5OOmZHHhIaaJtoqqDz5aWxBR9gGhKxwRLh33nnnpZdeEo8XL168cOFCosGgqqur++WDD/SW/fQEJPIzGMR40/sxbj1BTBj0mNBidt40XUyq2tvbH3vsMXGiEoC+7Hb7Iw8/Io5Eli9mIXiMX+w/Ew3+Q66IBntFG5zXT5G2G0S/X0VMqEBMKN+CvvK555hFI0wCQmb/kayiokIcEKc3Gv71X/917ty5tElQNTQ03PerX54rGpTGG+d3LxXjDTGhEmSTqvb29mXLlrGfEOjL4XAsW7ZMrJh0ZSY6b5rOgaIKRIPinolzHtvTc3IyMaGS5FvQy8vLpSV1QNsBIWsbEauysvKZP/5BPP75T3/G9RLB1tHR8ctf/lI6EmCA6VTXmERpDTKq9QRnzCgQE7rmFok8YVNT069//WuxvROA4PZ4nnzySVEp2pUU77x+CrfjBJuhpkkeDQ5wbI90DKaICdlPGGxiC7pUO7p+/XraBJoPCMkQRqZ9+/YtX75cBCd33XVXWVkZbRJUDofjX/7lX3r3DWYmnnc6JY8JuYtCGdKGnPr6+mXLlrE5BOid/nq9K597TjpFxnXDVKLBoE/jZDdMDBwN6qRjMH0xofntnXo7V+kEPSaUxuiXXnqJuyig+YCQDGEEampqevTRR0VwsnDhwmuvvZY2CfZ06l/+5V/EDRNdSfGuuYWBTKe6xiQ6r5woHht3NlAIFHTRBtfcQunc0ad+9zu6R0DMd8UNE72nyFApGmR6uyP6/areM0X/ITfAKz3cZXliScvr9sS8sFXnYkkruORj9H/932e5sx7aDgjJEEaatva2hx9+WFTEzZkz55ZbbqFNgk3aeOO1mF1zCwOfTnny06S7KCgEUiJ078l+iBdo+/btzz//PDEhItz69es3bNjQe98gZ4oqwOWJfusLkeJzT8ka1HX/zuunnLmf8MWttGWwSefAed2e5cuX19ZSywPNBoRMdyKK3W7/zbLfiFRVSUnJ4nvvZUUg2FasWCEWDvVGg+vqwsFOp9xTx0mFQKa/fa5vPUGTBjsmlHIg77777ptvvkmbIGJVVFSIMzP0RoPj+ilEgwpEg6a3d0b19PNdmYlnnSkaiGiDtKSltzs4k0yZmFCs2zqdzscff9xms9Em0GRASDwQQTNdr/e//uu/xInh+fn5P//5z7l9Ptg2b94sSq26R4srJ17Y9c1SIZBOp4t5YweFQEH/pCTFS5dNrVmzhkIgRKaGhoYVK1YMsfvC4AK6D/dEHWw7c3LPBXRfPUtanEmmJPfUceKgV7vd/sQTTzgcbOCEBgNCMoSR4/3339++fbtOp0tLS1u6dKnZzD6QoE+n/uv/Pts7YAS8D6Rf8kIg01920LbBJg71EY+ffvrptnaKdRFZHA7Hk08+KbaaD7H7QoCMX+w31DYP/eQecd6JeGyobWb/uQLk51Q///zzNAi0FxCSIYwQdXV1q1Z3d1Imk+mBBx6wWCy0SbCnUw899JA4FcCTlzqofSD9kBUCseirWEwoinXtdvvvfvs7Dh1FRHn22WfFzudh6L4QyLytsU1cMtG7V3NoJ/d0d1+y/efsNVCA8/op0uWEFRUVNAg0FhCSIYwEdrv9qaeeEsHJHXfckZWVRZsE26OPPipO7unKTHRdOWnoP1BeCGSobeaAGQVIxbo1NTV/eeMNGgQRYvPmzWJG67WYh6X7wsDEsaLi8XDt1Txr//lbX9DIQRdtEAfMiLMD2EwIjQWEZAjDntg6KNZ6y8rKrr76atok2N577z1xg7M4VnS47uzyJsVLh1yb397JZkIFSKfCrlu3js2EiAQNDQ0rV64Uj51cOagI49Za6VjRYdyr6S7LE3sNugNO6kqCT0rMOp3Op59+ms2E0FJASIYw7Mm3Dt5zzz0sAQRbS0uLqM4Ve2+G984uT36adNMUmwkV4DUZ5Yu+bCZEePPbOsixogow1DSJrYNdSfHuGROG94e75hZSV6Ik99RxYoyur69nMyG0FBASHoQ3tg4qHT94vQ888IC0dTAYJzFIOauo1hPGL/bT5sEVbZA2E7a3t7OZEOHt+eefZ+ugkvR2h+nDr6S+fdjzsd6keFfJePGYuhJlSGM0mwmhpYCQDGEYc3s8bB1UfjolrnkM3t4br8UsnRZg/N+9otAIwf0oyTYTrn/rLRoEYWnHjh3impzuTqY0jwZRQPR7VWKMDl4+VspZUVeijO7R31dXsmrVKupKoI2AkAxhGFv/1ltirbekpIStgwqw2WzvvvuueNw9HgRt740nP03cetQ9n+C0AEVIlVdr165taWmhQRBmHA7HqlWrxOOhn3KJQBi/2N9762BmYlDzsVL3FdV6glsoFCBtJrTb7c//icJRaCEgJEMYrlpaWtauXavT6SwWy7333kvkr4ClS5eKB8N7MEC/3KV5FI4qyWsxi8orp9P5wgsv0CAIM6+//rqobnBPyWLroAL0rSeit38t7plwXZ4X7O5LOpDM9OFXFI4qwD11nDjRZ/v27VVVVTQI1B4QEieEqxdeeEEcDHDbbbclJCTQIMG2Zs0aMZ0KxsEA/ccnvqIUCkeVH+A5cRThxGazvf3f7/QWiwa/+4K8WNRVMl6BCFyqK/G6Paa3d9L+SgwZZb1x/nPPPcfmc6g9ICRDGJYqKyvFyaLZ2dkUiyrAbre/ub53a1kwDgbol3TYiZhb8CoooPvF7bF69WqOFEd48Hq9K1askHaycc+EAgw1TVE9l8UHu1j0rPhEqis52MaJo8qM0SIIb2pqYvM51B4QkiEMPw6HY8WKFeLx//k//4eXWAFPP/1073RK2Wor94wJ0gCv75leILhT56R4EYQ3NTWtW7eOBkEY2Lp1q7g3tSszMRgHI8Ofy2P83729xaK+NSYlui/ZgWTSPfhQJghn8znUHhCSIQw/69atE7WL11xzzYQJFP8EXUNDgygg1BsNHoUPao82SAO8idNlFOGZOk4M8G+u7z20CdAuu93+0ksv9cYpl3OyqBKMuxtFkb+rMFPhw3s8+WnSVfWcLqNkEO50OleuXEmDQL0BIemjMNPU1CRqF61W6y233EKDKODJJ5/snU6VjFf+aD4G+FAN8F63Z/Xq1aypQdNee+01zpJRkt7ukM6S8YTipkdpYxunyyg8RldWVu7YwbUfUGtAyGwmzLzyyiuidvHWW2/lGnoF7NixQ6SJuuOEojEh+A1cnrMGeCgzwPfc61VZWblzJ8czQKuamprETTmcJaMYwxf7z5wlE4q7PbrGJErXEho/2ccrosQo7SsMfvnll5l1Q6UBIRnCcNLQ0CDOksnPz589ezYNogDp5q6QHcYQbThrgOcKCmUGeF9x3bp16xjgoVFv+Q664CwZZejtjuiqg6FcQDy7+zLubCBJqABvUrx0uszWrVtpEKgxIGQqE5aj+8KFCwn1FVBRUSFdNRHawxjc38nuDQ97ipGg2ABfU1NTXV1Ng0BzWlpaPvyfLWroviKHcWutGk5zlbqv7iHjwz28LkqM0aW9QfiGDRuYeEONASFhQ9hoamqqqKgQV01MmTKFBlGAdBiDVLQZKtLx1iQJFeOZmCEN8LQGNKd7YtoTnIRkJ1sE0reeMNQ2qyQCl+KT7l+JJKECQbjFLMbo+vr6Tz/9lAaB6gJCFirChpQe/N73vkecr4DNmzf3pgczE7vGJIb893FfSpJQ6SBc2km4bx9bcaAlbe1t5eXlvfNU0oOKiP6otrevLstTQ3xy5hpbkoTKjtF//etfmXtDdQEhkUN4kIp/0tLSpk2bRoMoYM2aNeKBSs5ql6qASBIqN8D7KnXfeOMNWgMasvHtjU6ns7d2EcGnbz0RdbBNPQuIIjOsNxpIEio/RtfX13MaGVQXELJKER6k4p8bb7yRIF8BVVVVUnpQPWe1n6kC2tnAa6QAeZKwoYE2hzZ0dHRs2rSpNz04PoUGUYDhq0O9vbRvFSn08YnF7CrMFI85blShMdqXJFy3bh2tAXUFhAQPYUAq/klLSyu7/HIaRAEvvviieOCZmK6e38prMYv4hDsJlRvgfdO7v/zlL7QGNOGdd94R6UHPlCwOF1WCyyMOF+1KildJetBv/BK/HoI+RifFizG6pgcNAhUFhGQIw8B7m94To/t1111nNDC6B11TU1N9fb06t99I8YmBqlFFdI1JFJcOb9++XdxICaiZw+GQ0oMhvPkgohi+PixKeLpUNl5I8YnX7WENUeExmiQh1BUQkiHUfOfi8WzevFmn01mt1jlXXUWDKOCVV14RDzy+Tfkqik9SLxaXHUe1ntDbHbxYCpAOadyyZQutAZX7+OOP7XY76UFFA8Ke5Tm90aDC83ukJCFriAqN0bKNBm3tbTQI1BIQkiHUut27donNbKWlpaQHFeD1eisrK3uHUhWezhdtkMJU49ZaXi8lZlT5aSII37x5Mz0qVG7btm3q7b7CckLW2BbVekKn07nHJ4uOQp3dF2uIygfhWzazhgjVBIRkCLVO3D2o0+lmzZpFayhg69atvdtv8lJVOLrL53nGr1t4vRRq89xUnU7X3t7OJfVQs7b2NrGe1ZWZqM7uK/wYvrL5woAMlXZfvjVEkoQKNfj4FHG+65YtW1hDhFoCQt6Lmma327dv3y4uo8/KyqJBFCDdQq7a0V26AJdtIcoN8L4V361bt9IaUC0pI6Gq07DCmN7uEAtzajtO5qzuy7eGyNEyCok2uMcny88jAEIfEJIh1LQdO3aIbNXs2bNpDWUicNF9q3l0lwerrPgqFIQnxYujZSoqKhwOyq6gynep1yu2ueqNBm6bUIahpkmdx8mc9caQrSFGNbKrTdExWpwBAYQ+ICRDqGnitgm90VBaWkprKOC1114TD7rUvf1GOvoyqvUENw4r1OY9bwmn07ljxw5aAypUX18vDsJ1j0/mOBmFZmM9NRp6o0HlB7pK8YnxMxJWCo3RomZ7+/btbg9jNFQQEJIh1K6WlhZxj82UoskJCQk0iAJEga4mzmOQQlbj7kZeOCVmVL4Gl7b1Aqoi5SJUW+4eZvStJ6TjZFQegUvxSdRBMoRKDRm+nee7d+2iNRD6gJAMoXZJ886ysjJaQwFt7W3iQFdNnMfgyUrq7Q7YRqgIr8XMYeJQLbfHI9azut+oKi53DyeGhtbecCtrlFbiE1HmymunRIP79vGKUi8gxAEhGULtErtBLBbL9OnTaQ0FbHx7o290T9JAfJIULx0mTtWowgP8J9s+oTWgKtIFRdK8H0GfivkCQk3s2PQmj+gNCL86xGunzBgtdnZUVlay8xyhDwjJEGpUW3ub2A0ydepUs5nTw5Xw2Wef9Y7uWggIz1rx/fowL58SDe6b9n355Ze0BlSlqqqq913K+aLKcHkMzcdERYkmdmxKdyGIXxsKkHae79mzh9ZAiANCMoQatevL3qLzoqIiWkMBXq+3ueWwKLjy9qzqaeB3ZsVXYdEGUTVaXV3NOQFQYUCooe5L8/Ow5mO954tqZAFRF23wpF4szhrlhnqFgnDfe0NarwFCFhCSIdQocZyMTqebfMlkWkMBO3fu7B3dM62aGWx8CStWfBXTNSperPh+vW8frQGV6Ojo6L0vRzvdl+bnYfuP+E36NdB9+X5V7itShrSzg4AQoQ8IyRBqshPxeisrK3U6XVpaWqKV4wGUIG371sTxAL18CStWfJWbUY3rfXtUV1fTGlAJaQHRO2oEraEMw95mzaVkzxxFxm2Eig0ZPWs09fX1drud1kAoA0IyhFrU3NwsjgeYOHEiraGMvXv39nbfmjqg78yKLwfHKdPgqReLfThiyQZQgzMbCLWTrdI0fesJsQanrSN8zjqKDMq0uW+NhjVEhDggJEOoRbt37xYPCgoKaA0FOBwODV04IcflE0rz7cOpq6vj4DiohDjliA2EipEunJA2cmtmyPBFsCQJFR6jqRpFiANCMoRadGYD4WQ2ECpB3PChpeMBpA+4b8XXcOwkr6MyxJvE6XRKWWUghKQjqdlAqNwkTFMXTpzVffmK3g1f2XgdlRyjpXkdEJqAkAyh9roPr1eUFmRnZyckJNAgCvj88897B8tk7e3AEbNAr9ujpwpImQb3vUlY8YUa7KnuPdFeS/ufNU7f3qmhCyfO6r56Chy655EH23kdlRyj6+vrOzo6aA2ELCAkQ6g5hw4dEuWL+fn5tIYy9u/f7zdYamkFwbdFQapiQnBH9zGJYhshV0tBDaS9Sdra/6zhaNDuEBsIxZnDGhNtEAkrziFTbsjwrdTs2rWL1kDIAkIyhJpTV1cnHuTm5tIayjh6vEPswNHccq88YRVFQKgUaRshK25QyZDhtZi1tf9ZwwFhW2fvI202uNcaJ0W2vJpKjNG+hYODBw/SGghZQMh8RXOkLmPs2LG0hgLsdru4gVAaJjU2uif6fu1TTl5NJQd4p9PZfpSyK4T04+/1Hj58WLvdlyZnYC3He/uB5BGajk84V0ahD2lCrHhgs7FvE6ELCMkQak5LS4t4kJqaSmso4EzB1ShNHtAnZQY4V0Y5vszAIdshGgMh1H60XdxvptHuS5N8ibUzi3HaGjJ8cazeF9kiuKINXT3H/4q1GyA0ASEZQs0RGUKr1Wo2U/+jBOloEG+yVu90FskBkeeEAqTMwKFDBIQIpTNLEtSLKjYDO3JC0zW6ZzKERziHTCkXmUSGkDk5QhYQkiHU2Mze6xVFBePGjaM1lFFfX+83TGp3gOegUYU+p77MAHtCEOKA0Lck0aXZ9SzNMTQf03SNrlTBKM5KhWJjtNPpPHLkCK2B0ASErEZoS/vRdqfTqdPpRo8eTWsoQ6rikIZJ7fEtVHPQqEIzKotZHDT6zTff0BoIIekdqNHyRc3R2x2iFkPDNbq+CsYoh5sXVKEhw7dewxoiQhYQkiHUFqn+JzMzk9ZQRmdnz41SSfFaPGJUkJID+iPsCVGI5+JYnU7X3NxMUyCExPxSbzRwxKhCAaHGjxjtjU+SfLsMXGw0UKTBfe+WpqYmWgOhCQjJEGosIPTV/6Snp9MaCnA4HCIlKwZIrQ42vuSAvpUSIEVnVE1NTQ4HR7cjZMSShFiegBLTL40fMdrbfcX2xidRPeWvUGyMPnDgAK2B0ASEZAi1RSonSM8gIFTC3r17/QZITQ42vuMN9Nw8ofiMiiQhQsXt8YiEg6bXszRG40eM9v7y0u21+9nSptAYLXYZcPMEQhYQkiHUFrEhxGQyWUdaaQ0FnDklUuMFV96eQ8zYE6L8jIpthAiVFmn/cyz1ogrRn3RIU3xNxyd+fw6CTaTxuXkCIQsIyRBqy8mTJ3U6XVxcHC+cMsQGQq2P7meGeW6eUHxGJW6BA5R36tSp3kdsIFQsILQ7xabNcPpzoISeRVu2GCBkASEZQm0RnUVMTAxNoYyjR4+G02AD5Yk9qIDypMUITpRRWJfZqOnf3xvLeKF4m1u62/yk4zRNgdAEhCSatOX06dMEhEoKmwyhGGyg/IxKegsBCnO5XDSC0nr2aXu1vgAn/f5sO1d44HB73B4KeRCKgJAMoZZ6Cq9XBIRmM8u9SgeEYUNvpyJFUZQAIeTdFxlC5TrYsIigvCYjL6XSbe7b6GtnDREhCQjJEGpppNHrxQAfG8sZ4goRmzbDoITmzKkSrPgqw7fELr2FgFB1XwjVx1+rfJfucjC18lhDRGgCQjKEGuL2eMR+pLg4zhBXvGsOlz14+pMM8IpE4L4ldvYQIlRERQlbwhTtYHtKMMKgRD9szsXRDF8an3UchCYgJEOopeDEN7qbTIzuis+otF5CIx0jTsmoMnxL7OwhRKhwwq3SXOGz+0uci8NNRco7czgwoGRASIZQS8GJ7/gpi8VCaygcEErze43yEhAqTiyxkyFEyMIT6VAZDhlW5iPv7A2fwuDiRy/vmRCN0WQIEZqAkAyhhpw62btuRMloSGb2YYKAUCliiZ3RHSH7rEvXTnBGiEKDtG/1JwxO8ekJCLm6VnkUlSA0ASEZQg1xuyneUJrYQ6j1S6UQ8rcQAADnnI1zFDBCGxCSIdQQaesgU0zFiBs+wmofBaNOKN5CQAiHDKmUEcEllVmGQSFGT7aTo2UUI+3mYMhAaAJCMoRamsn7tg5ShKaYmJiY3rIZjZ8WwNbBEHTEPesIXBKDUOG9pzBKc8HHFloNCMkQai444ZiKkLR52CyxU5eiXFP3bL9hxy9C5UyqgdvklCFd33dS8wtw3ECodIP7Fm0vuugiWgMhCAjJEGqIVP/DnmNmVIPmG2wICBXiSylzSQxC5cx6FrePKjat76mx1Ns13+CiwIH988ojIERoAkIyhFoaafR6UTVKhlAx4Ve8wRXVCn1afSllLokB3VfkCJMIyuXhfFGlSRnCWAJChCIgJEOoLSJhxR5CxZw5lUHjS+xSCZM3gTmiInwpZUpGESrSYgRbiBXTe32fxitKzlyoyG2Eio/RMeYYWgMhCAjJEGqLKAHilFHFhM1s/kwJUzSnxoXgMwsojwxhCPREUJrfgCf9/gSEfGwRIQEhGUItTi5Pnz5NUyjj4osv9gVUBOEYTATuSykzuiNUzhQ40H0BKh8yehZtTSYTeRqEJiDknactI0eO1Ol0HR0dNIUywqfmiiPjlB7de98wRiOnMiA0yE4rz2sxhcF4Ia1niT8HimGLAUIWEJIh1NJI4/WKzsJut1M1qkyDp6amhkl8wi3DIQoIxSIOoLwz7z0yhMpzhcOhLN5YTqVWasho75QXJQFKB4RkCLXUX+j1mZmZ4nFzczMNokCDZ2dnh8OMyuUR8QlniCvH94ZJz0inMRASCQkJosYh6sgJWkPhCErfoeGz36Jajvf+OckjeE2VHKNTUlJoDIQmICRDqC3JycniwYEDB2gNJRtc0zMqaWritVKOolQv3POGsVgs1pFWWgOhMmbMGCn5ACUCQl8Epe0h40hvQNg1JpHXVMkxOiMjg9ZAaAJCMoTaMnbsWPGgpaWF1lCGWGI3NB/TenDSPbqPiucFVWiA75mCp6Sk0McihMT8Um93hEcFo/pJfazel2TTZPfV2ruC4LVQMqroGC1VgQFKB4RkCLVF2tJ28OBBWkMZooTD6/Zo95wAPfU/CvPV/zC6Qw3dl9YrGDVEuuhVulZOiwzHTrLnXNEx2je7IEOIkAWErF5ri9lstlqtBIShmVG1abXsSpqaeMazP0GRBvdNvgkIEVrSO5BthAqJNnQlxcuTbFoMTrxuT/d4cTFX5ijV5r4aXQJChCwgJEOoLV6vd9y4cTqdzmaz8dopIzc3t/dzpdkSoDNTE26lV6YLpv4H6iDNLzVdwaixYToprrsTaD2h0TJdaelT/CFQbIy2Wq1mMzW6CFFASIZQY72GXj969GidTud0OtuPttMgCrjkkkt6G/+INmdULk/31ITdIEp+Tn31P+LTCoTK6NRUUfin6QpGjQWEo3or8zVapnvmiNFRbDFQiKjRFcv9QGgCQrJMmiNtIzxkO0RrKEA6yEejJUBnjhi9iCuGlWpz39pB2NxjCY0yGgypySmarmDUXkDoW3rTaJmu1H15spJ4NZVocF+NLguICGVASIZQu/FJXV0draFEZ63Xm0wmaQ1Pe92Bb1JC/Y9y75meyXdaWhr1Pwg5sSqh3QpGzdH6QaNnjhhN4lRqRRrcV6PLAiJCGRCSIdScnJwcUQJUXV1NayhD7MPR6EGj0qSkK2sUL6USDW53iBrdCRMm0BoIufz8/N6ZgZbvztEQKY7SaIZQLH2yxUC5Kfv+I+LBt7/9bVoDIQsIyRBqjsViyZvwLZ1OV1tb6/aw4qvsjKqxTXvdgW9SwhGjCjW4701SVFREayDkJk+e7DfvRLD1HjTarr0yXX3rCVG+6LVSUaLokGGxWLKzs2kNhCwgJEOoRZMmTdLpdHa7fX99Pa2hgFmzZvV+tBq0NqNyeaIOtvUu93LEqDL9r+9NUlBQQGsg5MZlZ1ssFo2uZ2k1IByTKIoF9K0aSxIaGlp7/wQ2ECoTgfsqSgoLC8nQIJQBIe8/LSosLBQPdu3aRWsoYMKECWIbYdRBjZ3sKhWJdWVaeR0VavOeN0laWlpycjKtgZAzGgx5eXlsI1Q0IBw3yi++0kz35fuF3UVjeB2VCAh9GwipKEGIA0IyhFqUm5srthHW1NTQGsoQ2wg1t+Jr+MomHngmct2tIqO73SE2mk6cOJHWgEpId+ewjVChgDD1YjFGR2krIKSiRPn5+n4qSqCOgJAMoRaZzWaxjbC6uppthMqYMWNGb4ilqQFeSmmKEiYEvcF9VXmM7lAP6d3INkKFRBs8qRf39MBtGjqKzPD14d7xgooSZYcMq9Wanp5OayCUASEZQo0qLi4W19Pv+/vfaQ0FXHXVVb2fLu0EhPrWE2Iu0sXp4Yp1vr4NhJMKJtEaUImxY8darVa2ESpJ2oOnoTaXui8qShQao30bCAsKCkjPIMQBIW9BjRLnyuh0uj179tAaCkhISOg9mEE7K75njgcgPahY5+vbQJhopc2hmnmnXp+bmyu2EWrx7hwt8uSn+UVZWum+GDKUa3COpIZ6AkIyhBo14VvfEsecfPLJJ7SGMsTBDBpa8ZWSmZ6J1KIoMu32zbalLVuASkgzTpKEyvBazKI0QytHkUndFzcQKjhG9y4WSFdbASELCMkQapTRYBBVo/X19TabjQZRgKxqVAsrvr7jAfRGg5eSUUUYvjokHkyfPp3WgKp8Z9p3/N6lCDZtXT4hVZR4clN57ZQZo41ft+h0uuzs7LS0NNoDIQ4IyRBq15w5c8SDjz76iNZQwLRp03oPjtPCiq90nKA42wBKzKj2NovjAThRBmqTaE0Ua4jaOuZE2wGhpi6fOFNRMnUcr50S48XXh71uj06nKy0tpTUQ+oCQDKF2FU2eLHa1bd68mcBeAXq9Xhzuqrc71F92JV044f5ONq+dEt1uY+88u7S0lH4VKlRWVtbbOdQ00RpKBITS5ROqb/DuQc134QQlo0qN0b25+tIyAkKoICAkkNAuo8Fw+eWX63S69vb26upqGiTYvF7vwoUL/cIt1Y7uhtpmUS/K8QAKR+CzZs2iNaBC06dPFzvPowgIlRFtcI9PFmf5qHwN0fDFfvGAelGFI/Di4mJOIIMqAkJWsjXtiiuuEA+2bt1KawS9B9fri4uLRVbWUNus5rIrKQPgKszkhVOCbDdIVlYW7QEVMpvNIkkY1XpCE7vawoD70t4CDZWvIYpy9+5feMYEXjUlx2gpbw+EOCAkQ6hp0l7kiooKh4NtIUHn9XrP3FCv4lV2w84G8YDdIAo1uG83yOzZs2kNqJa0W4mjZRQaMpLixVmjal5DNNQ0nbmxNtrAq6bENL1n/mAymTiBDGoJCMkQapperxcTUKfTuWPHDhpEgQa/7bbb/IIu1X3+fZvZupLi2Q2i0IyqZ3qtNxo4HgBqVlBQIG6olzJCCDZpVU61a4jS6oC7LI/XS4mJROsJcR99WVmZ2cwYDXUEhGQItW72Fb0ZiYqKClpDAQkJCSIrq9qjZYyf1ftNRBDc0d23G2RK0eSEhAQaBOp9r+r1YqOBJk7GCpOAcHyKOFpGnWuI3cGJ74IiNpwrHIGzgAgVBYRkCLVOOky8srKyoaGBBlHAdddd5xd6qTA40RsNnnyuNlJkdPedx8BuEKifOIpMnd1XeIo2iL3c6gzCz6QHe86/gQJjdHTVQS4oguoCQjKEWuf1eq+55hrx+K233qJBFHD11Vf3Htanvhu9pOCE0V350X3mZZfRIFC5jIwM6UJCkoTK8ExM90VfKjtaxuUR3Vf3kFFKvagiY3RNk9hwft1115GSgYoCQt6Omp+P6vVTpkzJzs4WVaNNTZwnrkSbz5kzxy8AU8tg49sa5LpyEq+UMhG4GN0XLVpkNHAeAzRAuj6HJKEyvEnxXZmJKjxaRjoNqyszkQ3nykTgonLYYrHMmzeP9oCKAkIyhOHhe9/7nnhAklAZP/zhD3u3hext1rk8ahndpcPiMhM5LE6JpQFZepDzRaEV+T16axy4f0IRZ5KEalpDlH4Z1+WkB5Vg3N0oxugFCxZwnAzUFRCSIQyHWaleP23aNHHSyYf/s6WlpYU2CTaz2TylaLIICYy7G1XxO7k8xv/dy+iucAQuFf8wukNDvv/97/fOUD8nSahIQDg+RaTgoqsOqiRJaKhpEmdddiXFe3vuxkCwx2jSg1BvQEiGMGxiwhtvvLH7BXV7NmzYQIMo4Gc/+9n/z97dwDVZ7/0Dvy72RGwwGIgyUVEkWToqsQQtlhoexSw1ITvqUfOYWpa3D4XZIfVWO96V3dXp3D3510pPddR87KBHquM4JnoUS9CgTEWloWOggw3YE9f/JT+7urp42sY2YPu8e716sTnGuDZ+1/X5fX8P5AtRwU9d4QTPdj3i7I6zO0Db1Go1mWggKK1AkdAXRAL7/YPIOVqYX9olmi92wjl2m/DtOTojI0MqleKAQNcKhKgQ+g3NAw+QImFeXp7RaMQB8Ta5XJ6amkpO8J0/CuiXcHLzy/FJeHd8eXbH4B/odmiaZicaoEjoG46BPX/dpL6zQ7jw9GW2PIjdJnx5jhaLxRMnTsTxgC4XCFEh9J/LU4GAbIdgtVoxk9A3nnnmGTKTUHiqrHOLhMKj59jZgygP+vLsjvIgdFPDhw9HkdCnRAJ2b9hODuHoQPQ5wU/X2PIgtquFrhgIUSH0J+ljxyoUCoqicnNzMZPQB6RSaco99946wXfeKCB2aRPMHvRdAsfgH+jmaJqeNGlSl8gnAcOhUrLLjXbinh+YX+D7BE5m+IvF4ocfeRjHA7piIESF0K8uUgWCrKwsUiTcvHkzDogPsEXCTuxlZ3c+QHnQdwm84CfSI4DBP9B9paWlsUVC7EnoG/Z7B9w6X3fSnh9s84XyoO+uzX4ZwpORkRGpwABd6JKBEBVCPzNu3Dhygi8oKCgsLMQB8TapVPrIQ7cigehwJxQJaUOtEIN/fHx2zy8lCXzmzJkY/APdF03TTz755K1Ptba06+yg48ca+0aSImHQlSpBSSfsG4wOxM46RysUCtJlD9AVAyEqhP53gn/qqafI15s2bbJYLDgm3jZ79mwyaDDoSpXve9nZsV72oXHYWdgXzevlKkFpBUVRAwYMGDduHA4IdGsqlSo9Pf3mB9tQ21V20PF37MB+369Gxu1AtD4yFO+FD7A9xeylAkBXDISoEPqfhIQEcoLX6XQ7duzAAfFBCH/22WdvxTPf9rKz4YQWCrB0uE8u5Ryig0Xky6eeegrtJ/iBmTNnkuvULrKDjt9jokIdiTG3QrhvMyEbTuxD4yiRAO+FtwlKdEFXqkjPS1paGg4IdN1AiAqhX5ozZw45we/cvUun0+GAeFtKSgrZ8+PmCf7oOd+HE+uDg/Eu+AA7FSQ9PT0hIQEHBPyAXC6fOXMm2UFHdKAIB8QH7GmJZPL5zRDuq8nnbDhBB6LPztFkLRlaKFi0aBE6EKFLB0J8QP2SVCqdO3cuOcFv2rQJsd8H1qxZcysznCrzzcBR0Zdn2JXiHCol3gJvow21ZDVXhUIxZ84cHBDwG+zk804Z9x6AGKmE9OL5LITTZov4y7PkawsGi/oE24E4dfKU2NhYHBDo0oEQUcFfjR49WqVSURRVWFh4/PhxHBBvi46OzszMvBXVDhZ5e+AoO1iUoijblGE4/r5I4IdvrSUzY8YMTAUBf0LT9KJFi3zWfAF3CwrfDBwVHShi15LBTvS++JvirCXDXhsAdN1AiAqhH5/gFyxYQAalvPXWW1XV6PT1uunTp5N9IGmzxasDR2mzhR0sar9/ENaS8QHhyYvsVJDRo0fjgICfiY+PnzBhwq3m5cszOCA+YBufRFpv4b9/8OrAUbb5ooUCrCXjk7f218Lv/PnzJRKco6HLB0JUCP1YXFzc1MlTKIoym82vvvKq3YFOX6+H8FWrVt06AXtz4Kgwv5QdLGof1h9H3uvvrKGW3VYYU0HAX7FdWoLSik7ZESHQMFKJ/f5B5Oub+cE7hVnaUMtuPGh9cDDWkvEB0ZdngpoSfmpqakpKCg4IdINAiCsb/zbt8cfJwNGSkpLPPv0UB8QHIXzGjBm3TgkHi7yxZJ+gRMeuLIrBor5gc4h3nSRfLliwAFNBwF9JpdKVK1eScSXiL8/6bLGTQOZQKX9dcdQb40qaSlVksKgjMQazzX2APUcrlcpnnnkGBwS6RyBEhdC/CQWC555/jsx32rFjB7aq94HMzEyyPANttgjzPbxVPXdhAOuDgzFY1AfYxXs0Gg0Gi4J/S0hImDt7zq+LnWAyoffZ0xJvDRz1wrgS4dFzpFTFSCW28XfiaHsbbagl52haKFi+fDlmm0O3CYSoEPq9SEXksmXLyNcbN27EZEIfWLdunVgs9sbIK+7CAOjr9QHhyYtsX+/TTz+NBhP83kMPPZScnExqVphM6AM3o9q4pFstvEfHlQRdrvp1G3oMJ/EBTj127uw58fHxOCTQbQIhKoSBIDk5edKkSZhM6DNSqZQN4eIvz3qq01eoLSULAzBSiXXqPTjO3sbOvRGLxdnZ2VgYAALiY0/Ty5Ytw2RCX2rsG2kfGndrRR8PFWZvNl/ctceiQnGcvY07dfChhx7CAYHuFAjR4R0gZv7hD5hM6EspKSnp6em3Rl4dLOr4bBzhyYvo6/UpTl/vggUL4uLicEggQEil0uzsbEwm9CX7iITGpswWdKWq44VZEixvrT3WJxJrj/kAb+ogrq6hmwVCVAgDBG8yoVarxTHxtqeffpqdTMiem90+05BVLm/mlHFJ6Ov1AfHeU6SvF1MHIQAlJibOmjGTnUzojfWx4DdEAtuUYWQyoaC0QqjtwPzzps4sduoghpP44rL7chU7dXDp0qWYOgjdLxCiDyNwcCcT/u9bbxYVFeGYeBVN0xs2bCAnhiBDrdsDgdhJ6lgmznfXZgdOk9G5mDoIAWvSpEm/TibcdRILzHjbzfA2ZRgpzApPlbk9WFf05RnSfFEUZZ0+AgfW6+d6Q61k7yl26mBCQgKOCXS/QIgKYUBJTk7+4x//SDp9165de+7cORwTr5JIJK+99ho5wbs3EIg2W8S7TrILyWCZOB8QakvJyB+FQrFmzRpMHYQAvcyl6ezsbDLMIchQK957CpnQ65kwKtTyy8bxooNFbsw/Z9fBupkGH70HK1F7/c+Ec46eMGECpg5Cdw2E6PkONBMnTszMzLx5qrBaV61apdNhwQDvUiqVf163nnwtKK0QnrzowjfbHKJdJ8lgLYz88VEa/GWuplQqXbVqVXR0NI4JBCyJRLJ69WqlUumpuW3Qrsa+keyio5K9p1yawMmbXNDYNxLH09tpkD1HazSaefPm4aIaumsgRIUwAE2fPp2sd2I2m1988UVsROFtKpVq6dKlt/LGv39wfiAQO42NkUosc9JwJL2NvZwSi8U5OTlYSAZALpevWbOGXXS0Q3PbwDkOlZIsOsrYHeJf8kb7V36/TGOjKMo+NA6TC7yuqceWnKOTk5OfXbwYaRC6cSDExzcA0TS9YOHC1NRUiqKqq6vXrV1nNptxWLxKo9HMmDHjVsxzZiMKm4OdxkYLBdbpIyiRAIfRu+3m5Sp2lfZly5aRVXkBIDo6etWqVWQ6tPBUmWvDHMAtdk2iIzGGV4Nqu/lip7E5EmPsmkQcQ2+nQbbHdsCAAdnZ2UIBztHQnQMhKoSBSSgQLFmyhFzyXrhwYe3atRYLFpHzrszMTHYjCvHnJ9qqEzadadh5IA2Pp2AeiA/SoGTvKfL1s88+m5KSgmMCwIqLi8vJyRGLxa4OcwD3E8eDQ25tRNG0qE8bmZCbBjHV3DfYlXuUSuXq1asx1Ry6fSBEhTBgSSSSnJwcsmBASUnJqlWrUCf0tkWLFpFV+8iCAS13tJN+R3aNuEfvwSYTvkmD5HJqxowZY8aMwTEB4FGpVCtWrCBLZLXafIEnM4fANmUYmwnFfzvaYib8TRqMCsVUc+8ndYfowGnuwmNyuRxHBbp9IESFMJBJpdI/5fyJLBhQUlKyfPlyzCf0tpdeeonNhMJ//8C7qKLNFvFnx7hpEKsCeJugRCf+/AS5nJo0adLUqVNxTABalJyc/MxTT//afGE+oZcxUol1Wkpjn8hbZ4e/HeWtMcNtvm6mwZkjcdC8nQbZ8TtYeAz8KhCiQhjgIhWRGzZsIHVCnU63fNny8vJyHBZvZ0KNRtP8oorMFQn65XyPNOgDwpMX2XmDM2bMmD17NppEgDaMGTPmhRdeYPfKEx04jWPiXSKB9ZGhv2bCXSfZTCgo0bHNV2OfSKRBb+P22CoUildeeQULj4H/BEJUCCEsLGzDhg1kPmF1dfXzzz9fWop+X+9aunTppEmTbmWSU2VCbSk3DdJCgWXmSKRBr6dBbSm7RPuCBQsyMzORBgHalZKS8t+rVpM1ZgSlFeKdJ7A/oQ8yIbvGTPCnx4IuV3E7s26mQYwU9XYabBq1y64i89rG12JjY3FYwH8CIS6AgKZpiUSybv16su6o2WzOyckpLCzEkfGqOXPmsOuOCk+VBW/OZ9NgwxNpmDfo9UusA6fJfoO0UPDCCy+MHz8exwTASUlJSWvXriV7UQRdqRLvPeXk1gjgdia0jb+TZEKyLBnbmeVIjEEa9EUa/GVdH5VKtW7dukgFemzBvwIhKoRACIKCsrOzJ0yYQPasX/vy+q+++gqHxasyMzMXLFhw6y+xaRIII5XcTINYU9SrbA7xzhPsJJD/XrUaa4oCuCo+Pn79+vW/7lnv9HZ54H7TNf5Osj8hyz40DmuKev2S+nJV8KfHyMc7OTl5zZo1pDwO4FeBEBVCYD8JNE3PmzePlK0Yu+Ott956//33sR2F9zAMYzKZfnPPbWKq3ooj48XPuaGWOwlk7dq1SUlJOCwAblAqlewUdLIMZvs7rELHmi/eEabrLBiv61XCkxfZZXvS09NXvvgidpgA/wyEqBACLxZyy1b/+Mc/VqxYodNhyynPMxqNa9eu3bZtG7lJehyDDLXBnx7Deu5eIijRBX96jAzNVSqV69evj4+Px2EBcJtcLl+3bh1ZOZk2W8Sfn8DSoz5ovqRSKVnXR1BaIf7sGG/pUfDM5ZDZIt7569DczMzMp59+GrvPg98GQlQIobnx48ez80MuXLiwfPlyrVaLw+JBJSUl//Vf/0UmaorF4qVLl37wwQdk6VHG7hD++wfRgdPo9/Wkpp2jRAeLSEdvamrqa6+9Rka7AUBHSKXSlS++mJmZSW4KT5WJd57A8FHPNl9CbSnbfCUnJ7/zzjuvbvifW3M4m7oRBSXot/XoZfTlKvHfjpKxJFKp9IUXXpgxYwYumKHbEaxevdrJhzJN/py/jb3nuft/LxaIKIq6fv26w3HrkjQiIkIoFOLIBo6ePXuOGjXq8uXLFRUVNputoKCgtrZ2yJAh+Bh0EMMwO3fu/N+33qw315HFytauXatWq8VicUpKSnR09LfffutwOIIMJsHZnxv7RVEhGJ3SUbShVrynUNA01IoWCv74xNwnnnhCLBbjyAB45uo5KCgpKen2228/efKkzWaja+oFpRVMjzBGHoKD45nm67ye3JwxY8bChQuDg4MjIyPT09N1Ol15eTnVyAh+ukbfMDfG9aAEQThoHY3f314SHSyim/pkBwwYsGbNGrIMO0BnOaX78eC54+TrO6LjptyhoSjKYrGYzebGxkaKoiQSSVhYWIcCIU3TjQiE0JLg4OC0tDShUFhUVERR1Llz5woLC5OSkkJDsQCmm4xG4yuvvPLPf/6TamTIhITs7OyIiAj2j3HAgAH33ntvUVFRbW0tbXMIi65QIkGjMgKHzm2CEp1k37e0qYEME12V81Jqaio6egE8TqlUPjDqgR9//NFgMNA2h6BER1ntjb0jEFE80nwpFIqVK1eOHj2abb7EYvHIkSNlMtm3RaepRibIYAq6UHnzgKMb0e34bbaIv/hOUHyF3JwwYUL2ihVyuRxHBvw/EKJCCG33FwwePPiuu+46depUfX399evX8/LyQkNDBw4ciEtqlzAMc/z48fXr11+4cIEMQXnmmWeysrKa/1mFh4enp6fr9fpLly6RgStBP19v7BdFifEH6PKpXfTVWeGx8yR+p6am5uTk9OrVC0cGwEtCbgt5YNQoprHx+++/v9l8VdxARPFI85WcnLx69ep+/fo1P0cPGjQoeejQM2fO1NbW0nVW0fc6ShDU2CMMOdyN+C364jt2luby5csfeeSRoCAcRgiMQIgKIbSrR48e7PBRh8Nx8uTJ//znP/Hx8ZGR2IfHKTqd7o033ti+fXt9fT07BEWtVrf2eKFQyB0+StfUi05fwTneJcKTFyX7v6X1NWSY6IIn58+ePRvDRAG8jR0+WlxcXF9fT9dZhUVXKKud6RGKXi03mi/uMNHWHh8ZGTlq1Ch2+GjQ5aqgC5VMhBRDdp29EjbUinOLhKfK2GGi69evHzRoEI4MBFAgRIUQnEGGjwYHB3///fcOh+P69euHDh2qqqoaPHgwLrLbYLFYPvvss1df36j7+WeSTCY9/MiSJUvYYaKtnp9+GT76448/Xr9+Hed4F65HL1eJ9n0rKNWRnvUBAwbkvPine++9FzVtAJ9RKpX3p91fXl5eUVFBSoWC0goqRHIzFkLbyeTzk2zzpVQqX3zxRY1G027zRYaPhoeHl5SU2Gw2us4qKNHRN8yMMgI5vC02h/DIj+J/FtM19eQcnfno1KXLlmFqDPhHIKRd2kzC7nCErfsde1P/wn6Z+DaKos6fP2+13toSbcCAAdh6BW5+PPT6zZs3FxQUkJtSqXTu3LncWQ3AKiws3LRpE7tph0qlWrBgQVxcnEtPwjDMwYMHt27dajabyT2OxBh7WiI2r2+h4TNbhPmlZMd5coX0hz/8YXxGBhYKB+gsx44de++996qrq8nNxj6RtgcSmShcbbfQfAlOXhSeKmObr6ysrMlTprjafFVVV3304UfswuC0UGBLHWgf1h9HuDlBiU747x/YFXFVKtWiRYtiY2NxZKCr+eDk/sX/eJN8/ehgzdapOWRZCr1eb7fbKYoKDQ1t8aPrQiBkGMbR2IhACL6POojNzjMajVu3bs3Ly8M5vo1Tu/jLs2RZdoqiNBrNrNmzIhUY1QzQySwWy44dO3bu3sX+edqHxtlHJFAi9NS0nExSU1OfeOKJ6Ohot5+wpKTkrbfeYs/RjVGhdk1iY1+0h7/Gb9GBIrKrBFmwZ/bs2WlpaejahsANhKgQgqfO8ampqdOmTQvwWKjX6/fs2ZOXl8f+7aSnp8+ZM4fsO99BJSUl77//PlmWhqIoRiqx3z/IMbBngF9XCUp0gpMXg37ZmlmpVC5cuDApKQl/pABdR3l5+ebNm8nmq6T5cgyNs9/ZF80Xt/lSKBSLFi0iG/13kN3h2L9v3yeffMKejBr7RNrvHRDgsZA21ApPXGAHkpClRKdPn+6RczRANw6EqBBCR+h0uk2bNrHneLIY2uOPP56QkBCAh2LXrl1f/utrNiEPGDDgqaee8uyhaH6OD9zrKptD8NM1bre624OsAMAHGIbJz8//8MMP2RGktFBgS+rjGNY/4MbANzVf3ChICwVTJ0/JzMz07LUWb7hKIMfC5lFQpVLNmzcvPj4ef5uAQEihQggdP8cfP37873//O1u5IrFw0qRJarU6EAZglJWVffbZZ9wzrlKpnDp1qvemVlZVV32+83NuHTKwYqHNITx9WXCqjI2CtFDw4KjRWVlZHRlkBQA+YDabc3Nzd+/ezc6LDqxY2FLzlTbyvmnTpimVSi/9zKKiok8++aSkpCQwY2HzKOjtczRA9wuEqBCCp2LhqVOnduzYwT3lqFSqzMzMoUOH+mube+7cuU8//ZRbIFUqldOmTRt5330+KFJVVVft27svNzeX/TslcwsdKqXfXlc1u5YSi8Xp6emPTn0U0wUBulcsPHTo0L59+wKoWthKT9aUKVO8FwW5SkpKduzYwT1hNUaFOob1d6iU/voxC7pcJfzPBXauILmafeyxx4YPH44oCAiEfKgQggdjYXFx8Z49e3gZaeTIkaNHj/bNOc8Hqqqr8rX5+fn53KJoZ51mjEbj/v37c3Nzud3t9oHRjsGx/tT7G3S5SnC2XPiTnh2RKxaLMzIypkyZIpfL8acH0B1ZLJZDhw7t2rWLjYWkeOUY3NufZke31nw9/MjDvu/JOnfu3Oeff84d0sJIJY5BMY7Bvf1m9VfabBGU6IJKdOyIXF4PNcMwCISAQMi/gkeFELxxyuGVzkhzrNFo0tLSuunsbYvFcuzYMa1W2/z36vRCaPNRWP5xmqcNtYKzPwt+qGD71MmqrRkZGZMnT8YyAAB+wO5waA8f3rlzJ7sqpn90bLXRfE2cOLFze7LKysp27drF7k5xK4pHhTaqlN14jAmZmXn2Z25JkJyjf//73wfIHBZAIHQzEKJCCF495Rw6dOjw4cPclEILBSn33Juenn7nXXd1i8U/SNkzPz//6NGj3F9ELBanpqaOHz9epVJ1ncj69ddfHzp0iFu67I6n+Rb7d0lDNLoJoiCA/8XCb44cad7j1u06ttpovtLS0saOHdt1mi+dTvfPf/4zPz+fW6HtjkXa5jVYco7WaDQPPvhgYmIi/r4AgbD9i11UCMGracrR2HjyxInDhw8fO/EfbmMtlUqTkpLuvPNOtVrdu3fvrtZ1p9fri4uLT58+XVxczDtZqlSq9PT0lJSULhtLysvLDx8+/NVXX/FP81GhjX0jG/v3aIwJ73JnepsjqOJG0MXKoMpaXv+uQqFIS0sbNWoU9roE8Hstjslnmy8mOuzm/7ta31Z3br7sDsfp777TarUFBQXsVR/pvXXEhDfGRTniorpgGqcNtTePdlll0JVqbg2WLGun0WhSUlJw4QoIhKgQQpdjNBqPHj3avH5FTplqtZqEw05cKNJoNH733XfFxcVnz57lDl4ilErl6NGjNRpNd1nKkq1tarVa7mn+1tVVn8jGuKjG6LDODYdBl6uC9DVBZQZBxQ1ufwFbg9VoNH68LhEAtKasrOxf//pX8/pVV+nbIiGwvearu4yFIVMPjh07lpeXx10c7tbZRCpp7KNgeoR1bjikzZabp4yWQiBbg03TpGGNMUAgdDkQokIIvkfqV4WFhc2TIcldgwcP7t27d9++fWNiYqJ79vTS2ZRhmMrKyqtXr166dKmiouLbb79tHgLJ67n77rtHjRo1cODAbhpL2NmPpaWl3FGvt9oL0g3cI5SJDrv5f3mIFy+wbA7aWBdUWUvra4Iqa5tfRZELKbVaPXLkyK5cgwUA32DrV80Ha3DDISWVNEaHMZEyLxYPmxIgbbb4ffOl1+vJ2N3Scz82/x3ZcHjzCy+fMmhDLV1nDdLX0JU1tMHEG4XLXqAmJSVhCAkgEHYoEKJCCJ3IaDSWlJQUFRW1FsZIXImJ7tmvX7/Y2NiYmJjevXsrFAqJRCKVyZwMinaHw9LQYDabjUajTqe7cuVKeXn5tWvXysvLm9fNCIVCkZycrFarh6iH+FNfo93huHjhwnfffVdSUlJcXNzar89IJYxC1tgj9NY1VoiYuk3MiIUunPVtDtpqp+qt5FxOmS03Q2C1qXmfLvcqSqVSqdXqgQkJ2FkeAPjtEsNUVFScbvLDDz+0GA5v9XCFhzBRMjax3LzX+RaMtF0UxTZfN6OI2RqYzZfZbC4tLS1q0mIHrmdOGZzzBcnbdJ2ltfhHkI7jYcOGqVQqLDQNCIStBUKhSy0sDjR0FrlcntKEzBs5U3ym+XBNxu7QNWnxxB8iCZZIJOHh4RKJJCQkRCwWW63Wuro6i8XS0NBgNBptNltrsad5CCTDVlUqVUxMjF+OURQKBAlNSDg89+OPZ86cad4NfPOUbLbwZsKwx7xRImRuE5NrrKZLATFttlL1Nw8yXW8Nstib9yi3dt2WmHD7kCFDkpKSBg0ahC4nAGirxaBpZZPx48czDHPp0iVyvigqKvrN6sp2x80gYailOBuRt9GC3cp+TS2Ye83XHYMH+2sfllQqTW5CwmFxcXGLHbgunTLIAXf1fMF21KpUqrvuvguDQgGcuupzqYXF8YKuIFIRqWlCuj1ICLx8+fKVK1cqKioq9NdaGLhid5jtZrPZ3FpXcRuUSmVMTEyvXr369OnTu3fv3rG9FRGKgPpzEAoEqiaZmZl2h+NqRUV5efmVJnq9/vLly80Hl5JjTtsdrfWU33xA6xcWPXv27POLXr16xcTEIAQCgHvhMK7JxIkTeYP/r169evHixdZOCm23YG00X3379o2NjSWNWGxsbK+YmEAbyCCVStkOXLPZfPXqVXLKaHvEjXsHvPnIoJiYGFQCAbwYCFEhhC5I3oS7nQObWCoqKsxmc11dnclksjapq6szGo0NDQ0Wi6XO0kBqhsFNSPFQLBbLZDKRSCSTyRBFWguHsU3IyZ60DDU1NSSWV1RU2Gw2s9lstVpNJhNbgG1oaDCZTBRFyWQy9oCTOq1MJpNKpSKRKDIyMi4uTqlUhoWFofsJALwRDqObJCUlsXdaLJaKiopLly5VV1eTUwZpvtghJDdu3LBYbqYUcr6Qy+Wk7SLNV0hIiEQiUSgUiCKthcP4JtyLyerr1T+X//zzzz+bmrR4yrBYLNwTNO98IZfLSV+t99YOAEAgbLUlxfGCbpRYcCh8do3VPJYDAHQLEomElBCd/xaGYXBF1JFTRqQiMlIRyY3lANC5glxqAXG8AAAAIMAjDQ4CAARoIEQLCAAAAAAAEKCBEBVCAAAAAACAAA2EqBACAAAAAAAEaCBEhRAAAAAAACBAAyEqhAAAAAAAAAEaCFEhBAAAAAAACNBAiAohAAAAAABAgAZCVAgBAAAAAAACNBCiQggAAAAAABCggRAVQgAAAAAAAH8idP6hqBBCBxmNRoPBYDab6+rqKIqKjIxURCrk8nChQICDA/7BbDbrdLqamhqTyURRlEwmCwsLUyqVUqkUBwcAAAC6dyBEhbDbWbJkybVr15rfL5FIwsPDIyIiFApFz549Bw0apFar2w78ZrN53rx57M0RI0YsWrTIyY/N8ePHT548efbsWYPB0OJjoqKi7r333pSUlITbb28xHGq12vfee4+9+eyzz6akpLT7o3nfNX/+fI1GQ74uKirasGGD2wf2k08+wafLXxUWFm7cuJG9OXPmzPHjx7f7XVXVVV9/9XVhYeH58+dbfEB8fHxycvLYsWPlcrmrr2Hu3Lljxoxp/rA5c+ZYLBbu3/VrG1+LVES2+/y8z/8HH3yAvAoAAIBA2D5UCLudhoYGm83W/H6bzWYymcrLy7lXq5MmTRp2zz1tFOu4T2W1Wp2Jgvn5+Xv27OH+oBYZDIbcJjKZLDs7OzExkfcAu93e4i/Srja+y70nJOrq6kJCQvAB81cufTbMZvPu3btzc3Pb/q7zTXbv3p2RkZGZmSmRSJx/Da39uVksFu7DbDbbRx9+tHTpUo//jgDgEQzD1NTU1DahKCq0SVhYGK6vui+LxWI0Gmtra81ms1QqDQ0Nlcvl7bbwAN04EKJC6MfOnz+/cePG+Pj4devXe2QAp9ls3rhxY3Fxsavf2L9/fx+ckjv4DGVlZXfccYcb32i1WsViMT5vXflyzaXHV1VXrVu7rt0uD24M27t375kzZ1544QVnSoWuKigoKCoqSkpKwlsJ0HWYzeZjx46dPn26uLiYDCbnioqKGjx48LBhw9ruk2Vt27atsLCQvbl69WpnGpMDBw4cOnSIvbl48eK4uLgWn9ANGzZsYCPQ/v37v/76a+e/t0+fPs27sZYsWdLig8n4pp49e4aHhysUiiHqIc6MiWjb66+/fuXKFfb5nRw9VFZWduTIkTNnzrQ4KiQ+Pn7IkCH33Xcfe5Cdfw0REREvvfRS88d89dVXX3zxBfeesWPHOjOAhaKot99+m32daWlpkydPxl8luB8I0YMVCLFw8//7f08++WQHn0en061du7aNAaIxMTEURVVUVBiNRm6lIi0tzQf9ah3/JLsdCJEGuziXPht6vX7VqlUtfs5lMhnp2rh48WLz67/z58+vWLFi3fp1Hb+Uae6dd95544030D8N0BVYLJZDhw7t2rWreTvAMhgM2iaxsbGPPfbY8OHD226Ibty44XwnFIs3LIh75nXvCVtjNBpderYWGysnn0EkEo0YMWLSpEmxsbFuv2C9Xs/+OJlM1u7jy8vLt2/fXlBQ0PbV1Pnz5/fu3Zuamjpt2jSlUun8a7hx40aLj6mpqeEdlq1btyYnJ0dHR7f7mqurq9t9fkAgdBYqhN1abGzsq6+9Rr42m0zXr1+vqak5duxYQUEB90SVl5eXlJTkzAy91pjN5ldffZV3lSwSiTIyMpKTk5VKJbcvk2GYn3/+uaCg4OjRo+Xl5RMmTPDBoWAY5o7Bgzdt2tTiv65evZp7bnjjjTeaP0aAVXD8lPOtnN3heP3113mf86ioqFmzZiXcnqCIULCXdEaj8aefftq0aRP3wQaD4a9v/zUnJ8fjHW0Gg2Hfvn2ZmZl4NwE6l9Fo/POf/9zavOIWk8bGjRs1Gs2ChQux1pozbDYbydIZGRlz5szxaptPHDt27K233nJ+yH1BQcHJkyeXLVuWnJzsjV9/8+bNK1aswNU7+DQQokLY7d/sX04w8iYURSUlJU2fPv1Pf/oTt9vp8OHDbgdChmH+8pe/8DqxYmNjFy1aFB8f3+KHKjY2NjMzc+rUqRUVFc50dHUcTdNCgaC1MTa83kpvjOuDLsv5Vm73rl286zy1Wr148eLmHxi5XJ6cnJyYmPiXv/yFOy6ruLj44MGDTg74ccnu3btTU1M70mUOAB3UxggCclpsaGho8V+1Wm11dfXKF19EJnRebm7ugAED2HXjvHRle+DAgc2bN7f4TyKRqG/fvteuXWteCrbZbBs3buQua+dBhU3aTps0TZOl3QE8EwjRx+CXpFLpCy+88PTTT7P3XLp0ye1nKy4u5k1FcLKzk6bpdsdUAHibk62cxWLJzc3l3pOamrpkyZI2ri2kUml2dvZf//pXrVbL3rlv3770sWM7ftlHKvB79+5lrz82b97sjfIjADjZkjQfQSCTybKysgYOHNi3b1/S82g2m69cuVJSUrJjxw5u0am4uPizTz+dMWOGb17tlClTfve73zW/v6qqirvccWpq6sSJE1t8hjZmQyxbtqztzql2Z1LEx8ezq5rX19dXVVVdv35dq9XyuuTee++9fv36OTlnz40r23Pnzm3dupV3Z3Jy8gMPPNC3b9+YmBiaphmGqaysvHDhwtGjR7ljSm02m3svzxmbNm0aMmRI29MEsAweeDIQ4trCX0VHR8fGxrJlPbJVoBvL0DMMw9uPISoqat68eejmhO7CyVbuyJEjvG7gGTNmtPu9NE1nZWUdPXqUvfIzGAwnT5zoyAht9mpj4sSJ33zzDXsBWlxcnJ+f740OaQBo186dO3lxJT4+/rnnn+NNG5ZKpYlN7rnnno0bN3IH1+zduzepiQ9ebWu9sbzLgIiIiISEBFefvFevXh0crRASEsJ9BvIaxo8ff+7cuZdffpltim0225EjR1xNXE62+RaL5fXXX+eNFP39738/adIk7jPQNB3dZPjw4SqVauvWrey32Gy2N99889XXXvP4FZHBYNixY4fPug/AXwV5ox8Fup2ePXtyb1ZWVrrxJBcuXOCdAmfNmoXFLaAbcbKVO3HiBPemRqNxcrRzdHT0iBEjuPccPXrUUy/+j3/8I/fmhx9+aDQa8Z4C+JjZbG4+gmDd+vVtLCIVGxu7bt063sQKbHjbtoSEBN5+yBcuXPBSm3/kyBFevTcnJ2fy5Mmt5UmapseNG5eTk8O9s7y8/JsjRzzyu2s0GpFIxO0+KCsrw9U7+CgQokLox3irTkVERLjxJEVFRdybarV6+PDhOLbQjTjZyl27do17c9KkSc7/iIceeoh7k11qvIMsFktyE/Yek8n097//He8pgI/l5+dzRxCIRKJZs2e1WxeSSqWzZs3i3kNWqsTxbMOdd93FzUUXL150Nfw40+YzDMPb7yE1NbXd4i1N0yqVKjU1lXvngQMHPBLPwsPDeVtHvPvuu609M+YQgocDIfoY/JXd4bh8+TJ7UyaThYWFufE8vF0HR44ciU4E6F6caeUYhuEGQplM5tKAqH79+nGvYK5du+aRppWc7+c9OY/75Hl5eaWlpXhbAXyJO0+YoqgRI0Y4ucFMYmIir0iYn5+P49kGoUDQt29f9qbJZHI1+TjT/F66dIm3VN4jjzzi5PNPmTKFF/IrKio80uBPnjKFe+o5f/78wYMHW3s85hCCJwMhLu791daPP+aOjE9NTXXvvb548SL3Zu/evb30gisrK8udUF1djTcXXOLMJ7+yspL79xIeHu7qj+CO0LbZbNXXPfBBJS8pUhH5+OOPc+9/77337A4H3lkAn+GNIOBFgrYbB96GMbyngua4A+NFIpGryceZNv/q1avcm2q1usVV01sUFxenVqu593gkEFqtVqFAwNtpY/v27ZgmAG7DKqMBTa/X79mzJy8vj9ueujT+jfvx4C2zQXaf94aPm+DtA49zppXjXRzw5t86IyIigtvfrL+m7/gO9fX19eSL8RkZX3/9Nfv85eXl+/ft4w0uAgAvMZvN3FNhVFSUSwtoJyYmIhC6dLS5U/t69uzpane2M20+L8LxAl671Go1d/yUTqfr+J6EVquV7ByWmprKLmdqMpm2bt3Km1eJq3fwfCBEhbBbu3HjxrZt28jXdXV11dXVN27caD4/Ye7cue5tBsgbp9HGuNMNGzbcuHEjJCSkrq6u+f+HDBmCxbKgszhZIeTeVCgUrv4U3rfodDqVStXBV84WLYUCwfz587mLGezYsWPkyJG+2eQTIMDx2gdXO0alUqlMJmMj5bVr1+wOB1bqbs3f/vY37k03gpYbbb6r7ymvwfdUhZC9ZisuLmY/MFqtNi0tjTe/EVfv4OFAiD6Gbs1kMrHblLVIJBJ1ZOPUBksD92Z4eHhrbdAPP/zQfOdW7jfizYLO4kwrxxuK7MbcDN6HvKampuOvnDuKNTExMT09na38k20Js7OzcVkA4G28EQRudBiFh4dzt1Korqrq1r05zz33XNsP2LZtmxurkRuNxv379/PGN7W2TWIH23zee+rqVQrv7eM9WwcDoVwuz8rK4m6Xv2XLFt7mFrh6Bw8HQlxM+DGNRpOVldWRs45cHi4SidirUt6ypZ4lk8mCg4Pbz6gNDW0kTwD3Wjnen4kbH3Xet7hxydiu6dOnFxQUsJ//wsLC48ePd3zDQwBoG6/+40YXJ29I+dWrVwO8vH/9+vUDBw6wp/UbN25cu3atqKiItyvg3Llz5XK5N9p83nvaI7qHSz8iMjKyjWdzD3dM1rhx47jb9JeXl+/etYs7GRVX7+DhQIg+Bn8VFRW1YOHCDg5KEQoEcrmcHc1vMpnc293eGfPnz3fm0lar1b799tt4f8GzrRxv0qAbaxfxvsWlKUZOkkqls2fP5n7+P/roI7VaTf4kxWIx3msAb+B197gRUXg9RO5tC+xPysvLuRWwFi1YsGDMmDHeaPMZhuGt1CKXuxbyFb8NhEajkWEYD4Y0mqYXLFjArcTu3r175MiR7JkFV+/g4UCIPobunvqmTZvG3rxw4QK7c67BYOD1J7mnX79+3OndlZWVLQbCJUuW8O7ZsGEDr6sPoFM408rxJpBcv37d1Z/C6yH2RiCkKCotLU2r1bKLGRgMhu3bt5NV6bhbUwCAB/FKgm6s+sgb2NLdp1F4u7VJTk6eNm1aXFycl9p8mqa5nd1N7+kNl5YBMxr5fQQev5yOi4vLyMhgL+psNtumTZtycnLID8LVO3g4EKKPoVsLDg7mzg8ced99P/zwAzvGYPfu3ampqS5tp9Ycr3JSVVXVYhtNpjuzHyc0VdB1ONPKhYWFcUdHk40Enf8Y8/qbZTKZlwrpNE0/8cQTzz//PPtSc3NzR40aFRcXJ8AaFQDeweswcmNIOW9lUe+t1+0bL7/8sttprV1qtXrFihXebvNjYmK4gbC6qtqlQFipr/TBG5qVlfWf//yHfZ3FxcX5+flurwoBAQj7EAZqT4BAMHv2bPamzWZ7++23O5j5e/Xqxb1ZWFjY9seJwHsBXYczH0iaprlbIdtstp9++sn5H/HTTz9x6+Fu7FrhvNjYWN6GE++++y7DMBgyCuAlPXr8ZoKZG0PKeRky2ptNRLcQFRWVxcHd9aG4uLjtKw2PtPm8a5uqqqqOvKG8Z/MUqVQ6a9Ys7j0ffvih2WwmX6PNB08GQlQI/UxiYiK39+j8+fMHDx50+9kYhuHtRH/48GGdTofjDN2Ik60cb42Hzz//3PkfwXtwB8vy7Xr44YejoqJ4f+aSYAneawBv4F3uu7qCiMVi4W1j2O70frvD7uQzd9NDGhMTk8mxePFimUzG/uumTZvY2OOlNp8X8l2d1dnBXSucN3z4cO7GGyaTid2WA4EQPBkIUczxPzNnzuQ2rNu3b6+qrnLvqWiavmPwYO6lp81m++yzz3CQoRtxspXjjcMpLCwsKytz5hvLysp4/dlpaWle/Y0kEsnChQu592zfvt3SYMF7DeANZCNB9qbRaHTprHrhwoV2wwNvVqH+mt6ZZ+bVqbw0Ut0HyEYL7E2DwcDbjdDjbT7vXTh37pxLP4L3eF689Oz564knnuBO2szLy3P11QICYftQIfQ/vIbVZDJ98P4Hbr/RgqCgsWPHcu8pKCjo4HAOAF9y8sM/dOhQbt8HRVEff/xxux3wdoeD10USGxvLHf7kJUlJSampqdw/c/TUAHgPdxy4zWY7kHvA+e/lbRfcp0+f5o/h3anXOxUIuVtZNN8LoXsZN25cfHw8N/aUlpZ6r83nVX0LCgqcH/2k1+sLCgp4zb73jkx0dDRvgcD/+7//szsc3E4KgI4GQlQI/VL62LHchpXsV+beU9E0PXbsWN6F8oYNG7Zs2dJ9B6tAQHGylaNpmrtmL5nKsmrVqjauzPR6/Z9efJHXP/LYY4/5pl2dNXsW94KAd4ECAB40cuRI7s3c3FwnxzQ2H0HQ4gZL/fr1497kFRVbZLFYuGvVyGQyN/aC71IN9dy5c7n3vPfee3aHw0ttfr9+/Xgp7osvvnDy+ffs2cNLg7zJNR438eGHua+2vLz8QG4uVpYGTwZCVAj9Em91GdKwuj0iXyqV8sankdPhihUrtFptWVkZt8lmGKaqugrjGaDrcL6VS0tL45bdyPS87Ozs/fv3nzt3ju0BsVgs586dO3DgQHZ2NruoL5Genu6zneIjFZHcsQAA4D2jR4/mXn/bbLbt27e327Y0H0EQHx+fmJjY/JGxv60Q5uXltTsqdceOHdypidyZZt1UQkICd+g+2Y3dS20+6ezm3nP48GFexbVFOp3u8OHD3Hseeughb3cCCgWCJ598knvPp59+2pFplhAoccCz/SjQHZHVZbRaLblpMpm2bNmyaNEi954tKSnpD3/4w8cff8y9s7y8nOyRLRKJevbsKZFIjE2w/SB0Kc63cjRNz58/X6/Xc2OeyWRiP/mkj7a1i4b4+HjeinDeNm7cOK1WywulAOBxUqk0IyODO/iTFAnnzZvXWl3OaDS++eab7K6hxKRJk1pskYQCQXJyMltLtNlsH3340bOLF7e2/ExZWRm7Qx3xu9/9zg+O88yZMwsLC9mg697uWU62+Wlpafv27WM3dbDZbDk5OfPnz2+jU6+wsPDtt9/mXuRERUXdd999PjgyKpWKe1Fns9nYrwFagwohUM1Xl9FqtUVFRW4/28SJE5ctW9biEAWbzVZeXn7+/HmDwYA0CF2NS62cVCpds2ZNa33t5U1a/KfU1NR169f7eMgWTdPz5s3DWwzgA9Mef5yXTLRa7YoVK4qKirjbkJJhMseOHVuxYgUvDWo0mjbCxrwn5/EGgT+3fHlRURGvBbNYLNu2bVu5ciX3bKtWqwcOHOgHB5m3CIJ7u2c5+fjmo59MJtPGjRu3bNnCG/pkdzjKysq2bdu2YcMGblWWoqilS5f6rNnnXdQBtAsVQvi1Yd28eTN7zzvvvPPGG2+43XgNHz78lVde2b59u0uzlUQikbdX4Qdou5VzqZ9CIpEsf+457eHDO3fu5O5c3JqoqKhp06alpaV1SnMaHx+fkZHBqxUAgOcvrQSCZcuW5eTkcCNBeXn52rVrSTvQr18/q9V68eJFXmZg/1Tb7r6JVETOnj2bjLvhPjkZg9OnTx+TyVRRUdG8UZLJZE8vetqX7c9zzz3Xbru0YcMG956cN/CBbKszfvx4b1zZJiUlZWVlbd++nXtnbhORSNS3b9/o6Gi9Xn/58uUWTyJPPPFEQkKCLy/qZsyY8e677+KPETwfCFEh7I4xj/06IiKi7Qenjx174sQJ7qZJX3/9NduwCoVC7mox7fY80TQdGxu7dOnSsrKyEydOFBYWtjZWTSaT9e/fX61Wq1SqgQkJLQ56kUql3J/u5PRoiUTC/S4nw214eDj7XdwDCAGCtwBSu384QoFgzJgxmgce+ObIEbJLcvMrvKioqMGDB6vV6pH33dfurmLkE8796IaEhLT4sJ49ezY0NLT9GJ6srKyioiL2uwDAS2JjY//nf/5n1apVzVOZoUlr35icnLxkyZJ2T1hpaWnFxcW8oYBkDE5rYxNkMtnKlSsjFZH+dJznzp27cuVK9ub27dvvHX6v87+jS1e2mZmZMpmM23XOHvbzTVprz+fPn8/brMgHxowZk5eXh2kC4PlAiApht+NSr5tQIHjppZfaCFfvvPOOG68hrklmZqbd4TAab9TW1NbX14vF4tDQUKlUGhIS4sznKqWJqz/ave9asWIFPjmBjLdSqJN7RgkFAk0ThmHq6uqMRmNNTQ1FUWFhYXK53MnPOSspKcmZP7f//d//dfW3k0qlbnwXALghOjp6w4YNu3btysvLc2bogUwmmzJlyviMDGe6jWiaXrRoUVpa2pYtW5xZ4CQ5OXnhwoV+1stJ0zRZXYa7CMIH73+QnZ3t/JLRLv3E8ePHx8XFffTRR04GLbVa/fjjj/uyNsi1aNGi559/HtNzwMOBEBVC6NBHTSCIVET6Wd8k+J+SkhLuTd4OVM5cXkibKJVKHEyAACeXy+fMmTNhwoRDhw6dOXOmxRQhEokSExPvvvvu0aNHu7pffFJS0quvvXYgN/ebb765du1a8+EJMpksNTX1/vvvT0xM9Ndufd7qMmT3LOe7g7mzOnmb/rdIpVL9+c9/PnXqFNlpucVBv2RUSFpamlqt7sTDHhsby1vfCKDVqxeXYp7d4Qhb9+viVPoX9svEt5Fx21arldw5YMCAbr2/DQAErKKiIjLJh72W2rJlCw4LAHiE0WjU6/U1NTUkRUREREil0tjYWE9dNZHn1+l0EokkPDxcoVAoIiOdqTe2qPn1oTPZxo3iQfOn5T1Juz/XvZdKvnH69OlsDU2tVrcxVKrFb6+oqCCrppvNZqlUKm8SExPjUg505vd19Zi08XZgxJ8f++Dk/sX/eJN8/ehgzdapOWzLYLfbKYoKDQ1tcbUOVAgBIBAxDONobDyQm9ujR49evXrV19efOXNm9+7d3Mc8/PDDOFAA4CkkLXj7+T01QNG92OCRsOHqk7j9Q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      ],
      "metadata": {
        "id": "QlIY2Er0fXDf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "revenue = pd.read_csv('Book1.csv')\n",
        "managers = pd.read_csv('Book2.csv')\n",
        "print(revenue)\n",
        "print(managers)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a53d-IblflNW",
        "outputId": "791b9dda-5cf4-4776-f16b-fd23657f8a3e"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     city  revenue  branch_id\n",
            "0  austin      100         10\n",
            "1  denver       83         20\n",
            "2  spring        4         30\n",
            "3  mexico      200         47\n",
            "     city managers  branch_id\n",
            "0  austin      tom         10\n",
            "1  denver      jon         20\n",
            "2  mexico  charles         47\n",
            "3  spring    jassy         31\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Pandas Merge"
      ],
      "metadata": {
        "id": "YLlm_h_wpnPr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Merge revenue with managers on 'city': merge_by_city\n",
        "merge_by_city = pd.merge(revenue, managers, on='city')\n",
        "\n",
        "# Print merge_by_city\n",
        "merge_by_city"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "nHMuFVjTLLSr",
        "outputId": "41534bdd-705b-4a85-99e2-2df6fadf81b2"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>city</th>\n",
              "      <th>revenue</th>\n",
              "      <th>branch_id_x</th>\n",
              "      <th>managers</th>\n",
              "      <th>branch_id_y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>austin</td>\n",
              "      <td>100</td>\n",
              "      <td>10</td>\n",
              "      <td>tom</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>denver</td>\n",
              "      <td>83</td>\n",
              "      <td>20</td>\n",
              "      <td>jon</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spring</td>\n",
              "      <td>4</td>\n",
              "      <td>30</td>\n",
              "      <td>jassy</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>mexico</td>\n",
              "      <td>200</td>\n",
              "      <td>47</td>\n",
              "      <td>charles</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     city  revenue  branch_id_x managers  branch_id_y\n",
              "0  austin      100           10      tom           10\n",
              "1  denver       83           20      jon           20\n",
              "2  spring        4           30    jassy           31\n",
              "3  mexico      200           47  charles           47"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Merge revenue with managers on 'branch_id': merge_by_id\n",
        "merge_by_id = pd.merge(revenue, managers, on='branch_id')\n",
        "\n",
        "merge_by_id"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "iwm5-2GVfvav",
        "outputId": "b100a864-5029-406f-99a4-3f65f9f831b4"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>city_x</th>\n",
              "      <th>revenue</th>\n",
              "      <th>branch_id</th>\n",
              "      <th>city_y</th>\n",
              "      <th>managers</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>austin</td>\n",
              "      <td>100</td>\n",
              "      <td>10</td>\n",
              "      <td>austin</td>\n",
              "      <td>tom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>denver</td>\n",
              "      <td>83</td>\n",
              "      <td>20</td>\n",
              "      <td>denver</td>\n",
              "      <td>jon</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>mexico</td>\n",
              "      <td>200</td>\n",
              "      <td>47</td>\n",
              "      <td>mexico</td>\n",
              "      <td>charles</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   city_x  revenue  branch_id  city_y managers\n",
              "0  austin      100         10  austin      tom\n",
              "1  denver       83         20  denver      jon\n",
              "2  mexico      200         47  mexico  charles"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "managers['branch']=managers['city']\n",
        "managers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "egNyGGA0frIz",
        "outputId": "bcb29859-586f-4602-d25a-11cfd3c94870"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>city</th>\n",
              "      <th>managers</th>\n",
              "      <th>branch_id</th>\n",
              "      <th>branch</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>austin</td>\n",
              "      <td>tom</td>\n",
              "      <td>10</td>\n",
              "      <td>austin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>denver</td>\n",
              "      <td>jon</td>\n",
              "      <td>20</td>\n",
              "      <td>denver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>mexico</td>\n",
              "      <td>charles</td>\n",
              "      <td>47</td>\n",
              "      <td>mexico</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>spring</td>\n",
              "      <td>jassy</td>\n",
              "      <td>31</td>\n",
              "      <td>spring</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     city managers  branch_id  branch\n",
              "0  austin      tom         10  austin\n",
              "1  denver      jon         20  denver\n",
              "2  mexico  charles         47  mexico\n",
              "3  spring    jassy         31  spring"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.merge(revenue, managers, left_on='city', right_on='branch')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "e_HhA4PU-IV_",
        "outputId": "dc9bb69b-1e83-4bc7-9c49-c0126129b7b9"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>city_x</th>\n",
              "      <th>revenue</th>\n",
              "      <th>branch_id_x</th>\n",
              "      <th>city_y</th>\n",
              "      <th>managers</th>\n",
              "      <th>branch_id_y</th>\n",
              "      <th>branch</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>austin</td>\n",
              "      <td>100</td>\n",
              "      <td>10</td>\n",
              "      <td>austin</td>\n",
              "      <td>tom</td>\n",
              "      <td>10</td>\n",
              "      <td>austin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>denver</td>\n",
              "      <td>83</td>\n",
              "      <td>20</td>\n",
              "      <td>denver</td>\n",
              "      <td>jon</td>\n",
              "      <td>20</td>\n",
              "      <td>denver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spring</td>\n",
              "      <td>4</td>\n",
              "      <td>30</td>\n",
              "      <td>spring</td>\n",
              "      <td>jassy</td>\n",
              "      <td>31</td>\n",
              "      <td>spring</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>mexico</td>\n",
              "      <td>200</td>\n",
              "      <td>47</td>\n",
              "      <td>mexico</td>\n",
              "      <td>charles</td>\n",
              "      <td>47</td>\n",
              "      <td>mexico</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   city_x  revenue  branch_id_x  city_y managers  branch_id_y  branch\n",
              "0  austin      100           10  austin      tom           10  austin\n",
              "1  denver       83           20  denver      jon           20  denver\n",
              "2  spring        4           30  spring    jassy           31  spring\n",
              "3  mexico      200           47  mexico  charles           47  mexico"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vlF_u-c5XEEE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "a2298bda-8673-499e-da7a-723216d328c1"
      },
      "source": [
        "# Merge revenue & managers on 'city' & 'branch': combined\n",
        "combined = pd.merge(revenue, managers, left_on='city', right_on='branch', how='outer')\n",
        "combined"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>city_x</th>\n",
              "      <th>revenue</th>\n",
              "      <th>branch_id_x</th>\n",
              "      <th>city_y</th>\n",
              "      <th>managers</th>\n",
              "      <th>branch_id_y</th>\n",
              "      <th>branch</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>austin</td>\n",
              "      <td>100</td>\n",
              "      <td>10</td>\n",
              "      <td>austin</td>\n",
              "      <td>tom</td>\n",
              "      <td>10</td>\n",
              "      <td>austin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>denver</td>\n",
              "      <td>83</td>\n",
              "      <td>20</td>\n",
              "      <td>denver</td>\n",
              "      <td>jon</td>\n",
              "      <td>20</td>\n",
              "      <td>denver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spring</td>\n",
              "      <td>4</td>\n",
              "      <td>30</td>\n",
              "      <td>spring</td>\n",
              "      <td>jassy</td>\n",
              "      <td>31</td>\n",
              "      <td>spring</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>mexico</td>\n",
              "      <td>200</td>\n",
              "      <td>47</td>\n",
              "      <td>mexico</td>\n",
              "      <td>charles</td>\n",
              "      <td>47</td>\n",
              "      <td>mexico</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   city_x  revenue  branch_id_x  city_y managers  branch_id_y  branch\n",
              "0  austin      100           10  austin      tom           10  austin\n",
              "1  denver       83           20  denver      jon           20  denver\n",
              "2  spring        4           30  spring    jassy           31  spring\n",
              "3  mexico      200           47  mexico  charles           47  mexico"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tSOdopg_cL0M",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "edbd91f4-3554-40a3-fc44-f09d192bb22e"
      },
      "source": [
        "# Add 'state' column to revenue: revenue['state']\n",
        "revenue['state'] = ['TX','CO','IL','CA']\n",
        "\n",
        "# Add 'state' column to managers: managers['state']\n",
        "managers['state'] = ['TX','CO','CA','MO']\n",
        "\n",
        "print(revenue)\n",
        "print(managers)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     city  revenue  branch_id state\n",
            "0  austin      100         10    TX\n",
            "1  denver       83         20    CO\n",
            "2  spring        4         30    IL\n",
            "3  mexico      200         47    CA\n",
            "     city managers  branch_id  branch state\n",
            "0  austin      tom         10  austin    TX\n",
            "1  denver      jon         20  denver    CO\n",
            "2  mexico  charles         47  mexico    CA\n",
            "3  spring    jassy         31  spring    MO\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Merge revenue & managers on 'branch_id', 'city', & 'state': combined\n",
        "combined = pd.merge(revenue, managers, on=['branch_id', 'city', 'state'])\n",
        "combined"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "qj-BHaZTO6qk",
        "outputId": "6dd2046e-301b-4b70-a7e9-833afc25551e"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>city</th>\n",
              "      <th>revenue</th>\n",
              "      <th>branch_id</th>\n",
              "      <th>state</th>\n",
              "      <th>managers</th>\n",
              "      <th>branch</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>austin</td>\n",
              "      <td>100</td>\n",
              "      <td>10</td>\n",
              "      <td>TX</td>\n",
              "      <td>tom</td>\n",
              "      <td>austin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>denver</td>\n",
              "      <td>83</td>\n",
              "      <td>20</td>\n",
              "      <td>CO</td>\n",
              "      <td>jon</td>\n",
              "      <td>denver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>mexico</td>\n",
              "      <td>200</td>\n",
              "      <td>47</td>\n",
              "      <td>CA</td>\n",
              "      <td>charles</td>\n",
              "      <td>mexico</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     city  revenue  branch_id state managers  branch\n",
              "0  austin      100         10    TX      tom  austin\n",
              "1  denver       83         20    CO      jon  denver\n",
              "2  mexico      200         47    CA  charles  mexico"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(revenue)\n",
        "print(managers)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7W_X-ctxBDml",
        "outputId": "1502de03-1608-4077-8914-265e02fb88cb"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     city  revenue  branch_id state\n",
            "0  austin      100         10    TX\n",
            "1  denver       83         20    CO\n",
            "2  spring        4         30    IL\n",
            "3  mexico      200         47    CA\n",
            "     city managers  branch_id  branch state\n",
            "0  austin      tom         10  austin    TX\n",
            "1  denver      jon         20  denver    CO\n",
            "2  mexico  charles         47  mexico    CA\n",
            "3  spring    jassy         31  spring    MO\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "revenue.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pKa6-IdJCx30",
        "outputId": "d30ea16d-4ac1-48c5-afde-c983c810dd2c"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4 entries, 0 to 3\n",
            "Data columns (total 4 columns):\n",
            " #   Column     Non-Null Count  Dtype \n",
            "---  ------     --------------  ----- \n",
            " 0   city       4 non-null      object\n",
            " 1   revenue    4 non-null      int64 \n",
            " 2   branch_id  4 non-null      int64 \n",
            " 3   state      4 non-null      object\n",
            "dtypes: int64(2), object(2)\n",
            "memory usage: 256.0+ bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RQe3O5iwlz_O",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "4a5c59ea-4c50-4bd1-9d9c-cd301379bb7c"
      },
      "source": [
        "# Merge revenue and sales: revenue_and_sales\n",
        "revenue_and_sales = pd.merge(revenue, managers, how='right', on=['city', 'state'])\n",
        "revenue_and_sales"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>city</th>\n",
              "      <th>revenue</th>\n",
              "      <th>branch_id_x</th>\n",
              "      <th>state</th>\n",
              "      <th>managers</th>\n",
              "      <th>branch_id_y</th>\n",
              "      <th>branch</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>austin</td>\n",
              "      <td>100.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>TX</td>\n",
              "      <td>tom</td>\n",
              "      <td>10</td>\n",
              "      <td>austin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>denver</td>\n",
              "      <td>83.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>CO</td>\n",
              "      <td>jon</td>\n",
              "      <td>20</td>\n",
              "      <td>denver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>mexico</td>\n",
              "      <td>200.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>CA</td>\n",
              "      <td>charles</td>\n",
              "      <td>47</td>\n",
              "      <td>mexico</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>spring</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MO</td>\n",
              "      <td>jassy</td>\n",
              "      <td>31</td>\n",
              "      <td>spring</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     city  revenue  branch_id_x state managers  branch_id_y  branch\n",
              "0  austin    100.0         10.0    TX      tom           10  austin\n",
              "1  denver     83.0         20.0    CO      jon           20  denver\n",
              "2  mexico    200.0         47.0    CA  charles           47  mexico\n",
              "3  spring      NaN          NaN    MO    jassy           31  spring"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "revenue_and_sales2 = pd.merge(revenue, managers, how='left', on=['city', 'state'])\n",
        "revenue_and_sales2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "gpc8uD3PQO7w",
        "outputId": "2a511894-f0ce-4873-ee99-4429c2a3ac84"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>city</th>\n",
              "      <th>revenue</th>\n",
              "      <th>branch_id_x</th>\n",
              "      <th>state</th>\n",
              "      <th>managers</th>\n",
              "      <th>branch_id_y</th>\n",
              "      <th>branch</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>austin</td>\n",
              "      <td>100</td>\n",
              "      <td>10</td>\n",
              "      <td>TX</td>\n",
              "      <td>tom</td>\n",
              "      <td>10.0</td>\n",
              "      <td>austin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>denver</td>\n",
              "      <td>83</td>\n",
              "      <td>20</td>\n",
              "      <td>CO</td>\n",
              "      <td>jon</td>\n",
              "      <td>20.0</td>\n",
              "      <td>denver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spring</td>\n",
              "      <td>4</td>\n",
              "      <td>30</td>\n",
              "      <td>IL</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>mexico</td>\n",
              "      <td>200</td>\n",
              "      <td>47</td>\n",
              "      <td>CA</td>\n",
              "      <td>charles</td>\n",
              "      <td>47.0</td>\n",
              "      <td>mexico</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     city  revenue  branch_id_x state managers  branch_id_y  branch\n",
              "0  austin      100           10    TX      tom         10.0  austin\n",
              "1  denver       83           20    CO      jon         20.0  denver\n",
              "2  spring        4           30    IL      NaN          NaN     NaN\n",
              "3  mexico      200           47    CA  charles         47.0  mexico"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Merge sales and managers: sales_and_managers\n",
        "sales_and_managers = pd.merge(revenue, managers, how='left', left_on=['city'], right_on=['state'])\n",
        "sales_and_managers"
      ],
      "metadata": {
        "id": "qk3YlNrLQLMI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SYu29ij8maTz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "outputId": "63c4b2be-a230-4588-cdb6-cd9a26a025a0"
      },
      "source": [
        "combined_outer = pd.merge(revenue, managers, on=['city', 'state'], how='outer')\n",
        "combined_outer"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>city</th>\n",
              "      <th>revenue</th>\n",
              "      <th>branch_id_x</th>\n",
              "      <th>state</th>\n",
              "      <th>managers</th>\n",
              "      <th>branch_id_y</th>\n",
              "      <th>branch</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>austin</td>\n",
              "      <td>100.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>TX</td>\n",
              "      <td>tom</td>\n",
              "      <td>10.0</td>\n",
              "      <td>austin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>denver</td>\n",
              "      <td>83.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>CO</td>\n",
              "      <td>jon</td>\n",
              "      <td>20.0</td>\n",
              "      <td>denver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spring</td>\n",
              "      <td>4.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>IL</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>mexico</td>\n",
              "      <td>200.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>CA</td>\n",
              "      <td>charles</td>\n",
              "      <td>47.0</td>\n",
              "      <td>mexico</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>spring</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MO</td>\n",
              "      <td>jassy</td>\n",
              "      <td>31.0</td>\n",
              "      <td>spring</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     city  revenue  branch_id_x state managers  branch_id_y  branch\n",
              "0  austin    100.0         10.0    TX      tom         10.0  austin\n",
              "1  denver     83.0         20.0    CO      jon         20.0  denver\n",
              "2  spring      4.0         30.0    IL      NaN          NaN     NaN\n",
              "3  mexico    200.0         47.0    CA  charles         47.0  mexico\n",
              "4  spring      NaN          NaN    MO    jassy         31.0  spring"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kMpsew9V3nt1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4d679124-f186-4143-c610-c2fe9c62897b"
      },
      "source": [
        "print(revenue)\n",
        "print(managers)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     city  revenue  branch_id state\n",
            "0  austin      100         10    TX\n",
            "1  denver       83         20    CO\n",
            "2  spring        4         30    IL\n",
            "3  mexico      200         47    CA\n",
            "     city managers  branch_id  branch state\n",
            "0  austin      tom         10  austin    TX\n",
            "1  denver      jon         20  denver    CO\n",
            "2  mexico  charles         47  mexico    CA\n",
            "3  spring    jassy         31  spring    MO\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Merge asof"
      ],
      "metadata": {
        "id": "PUuWw35FgnDz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "left = pd.DataFrame({\"a\": [1, 5, 10], \"left_val\": [\"a\", \"b\", \"c\"], \"Tag\": [\"big\", \"small\", \"medium\"] })\n",
        "left"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "XpPBWC5rg8HF",
        "outputId": "2b913aae-ef72-4d6a-8d66-dc55caf72a74"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>a</th>\n",
              "      <th>left_val</th>\n",
              "      <th>Tag</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>a</td>\n",
              "      <td>big</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>b</td>\n",
              "      <td>small</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10</td>\n",
              "      <td>c</td>\n",
              "      <td>medium</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    a left_val     Tag\n",
              "0   1        a     big\n",
              "1   5        b   small\n",
              "2  10        c  medium"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "right = pd.DataFrame({\"a\": [1, 2, 3, 6, 7], \"right_val\": [1, 2, 3, 6, 7], \"Tag\": [\"big\", \"small\", \"medium\",\"small\",\"large\"]})\n",
        "right"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "id": "iP6cI4lIhmhx",
        "outputId": "3b588b5b-23f6-4e42-f14e-7f76a60c8eea"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>a</th>\n",
              "      <th>right_val</th>\n",
              "      <th>Tag</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>big</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>small</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>small</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>large</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   a  right_val     Tag\n",
              "0  1          1     big\n",
              "1  2          2   small\n",
              "2  3          3  medium\n",
              "3  6          6   small\n",
              "4  7          7   large"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.merge_asof(left, right, on=\"a\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "uH1vItD_i0TZ",
        "outputId": "f4da8005-911f-4fe7-fb9c-553dfcad83d1"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>a</th>\n",
              "      <th>left_val</th>\n",
              "      <th>Tag_x</th>\n",
              "      <th>right_val</th>\n",
              "      <th>Tag_y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>a</td>\n",
              "      <td>big</td>\n",
              "      <td>1</td>\n",
              "      <td>big</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>b</td>\n",
              "      <td>small</td>\n",
              "      <td>3</td>\n",
              "      <td>medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10</td>\n",
              "      <td>c</td>\n",
              "      <td>medium</td>\n",
              "      <td>7</td>\n",
              "      <td>large</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    a left_val   Tag_x  right_val   Tag_y\n",
              "0   1        a     big          1     big\n",
              "1   5        b   small          3  medium\n",
              "2  10        c  medium          7   large"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.merge_asof(left, right, on=\"a\",by=\"Tag\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "xGJHqNlWhqCm",
        "outputId": "725dc954-b67e-47dd-9dc2-8c5d0c2c2430"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>a</th>\n",
              "      <th>left_val</th>\n",
              "      <th>Tag</th>\n",
              "      <th>right_val</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>a</td>\n",
              "      <td>big</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>b</td>\n",
              "      <td>small</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10</td>\n",
              "      <td>c</td>\n",
              "      <td>medium</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    a left_val     Tag  right_val\n",
              "0   1        a     big          1\n",
              "1   5        b   small          2\n",
              "2  10        c  medium          3"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.merge_asof(left, right, on=\"a\", direction=\"forward\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "_HfBth27jMWh",
        "outputId": "7590b4fc-f5d5-462b-92f3-0ffffabb591b"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>a</th>\n",
              "      <th>left_val</th>\n",
              "      <th>Tag_x</th>\n",
              "      <th>right_val</th>\n",
              "      <th>Tag_y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>a</td>\n",
              "      <td>big</td>\n",
              "      <td>1.0</td>\n",
              "      <td>big</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>b</td>\n",
              "      <td>small</td>\n",
              "      <td>6.0</td>\n",
              "      <td>small</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10</td>\n",
              "      <td>c</td>\n",
              "      <td>medium</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    a left_val   Tag_x  right_val  Tag_y\n",
              "0   1        a     big        1.0    big\n",
              "1   5        b   small        6.0  small\n",
              "2  10        c  medium        NaN    NaN"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title merge revenue and managers using merge_asof\n",
        "managers = managers.sort_values('branch_id')\n",
        "pd.merge_asof(revenue, managers, on='branch_id')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "cellView": "form",
        "id": "kAjH6VBhkAdR",
        "outputId": "9e24c209-c132-435f-8ac1-d5b909f1a1f0"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>city_x</th>\n",
              "      <th>revenue</th>\n",
              "      <th>branch_id</th>\n",
              "      <th>state_x</th>\n",
              "      <th>city_y</th>\n",
              "      <th>managers</th>\n",
              "      <th>branch</th>\n",
              "      <th>state_y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>austin</td>\n",
              "      <td>100</td>\n",
              "      <td>10</td>\n",
              "      <td>TX</td>\n",
              "      <td>austin</td>\n",
              "      <td>tom</td>\n",
              "      <td>austin</td>\n",
              "      <td>TX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>denver</td>\n",
              "      <td>83</td>\n",
              "      <td>20</td>\n",
              "      <td>CO</td>\n",
              "      <td>denver</td>\n",
              "      <td>jon</td>\n",
              "      <td>denver</td>\n",
              "      <td>CO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spring</td>\n",
              "      <td>4</td>\n",
              "      <td>30</td>\n",
              "      <td>IL</td>\n",
              "      <td>denver</td>\n",
              "      <td>jon</td>\n",
              "      <td>denver</td>\n",
              "      <td>CO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>mexico</td>\n",
              "      <td>200</td>\n",
              "      <td>47</td>\n",
              "      <td>CA</td>\n",
              "      <td>mexico</td>\n",
              "      <td>charles</td>\n",
              "      <td>mexico</td>\n",
              "      <td>CA</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   city_x  revenue  branch_id state_x  city_y managers  branch state_y\n",
              "0  austin      100         10      TX  austin      tom  austin      TX\n",
              "1  denver       83         20      CO  denver      jon  denver      CO\n",
              "2  spring        4         30      IL  denver      jon  denver      CO\n",
              "3  mexico      200         47      CA  mexico  charles  mexico      CA"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Merge Ordered"
      ],
      "metadata": {
        "id": "1QvGrHOtpeFj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.merge_ordered(revenue, managers, fill_method=\"ffill\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "R8A5ZdcdoT-u",
        "outputId": "de3a6e11-74cf-403d-a4f7-a9aebffc4a04"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>city</th>\n",
              "      <th>revenue</th>\n",
              "      <th>branch_id</th>\n",
              "      <th>state</th>\n",
              "      <th>managers</th>\n",
              "      <th>branch</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>austin</td>\n",
              "      <td>100</td>\n",
              "      <td>10</td>\n",
              "      <td>TX</td>\n",
              "      <td>tom</td>\n",
              "      <td>austin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>denver</td>\n",
              "      <td>83</td>\n",
              "      <td>20</td>\n",
              "      <td>CO</td>\n",
              "      <td>jon</td>\n",
              "      <td>denver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>mexico</td>\n",
              "      <td>200</td>\n",
              "      <td>47</td>\n",
              "      <td>CA</td>\n",
              "      <td>charles</td>\n",
              "      <td>mexico</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>spring</td>\n",
              "      <td>4</td>\n",
              "      <td>30</td>\n",
              "      <td>IL</td>\n",
              "      <td>charles</td>\n",
              "      <td>mexico</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>spring</td>\n",
              "      <td>4</td>\n",
              "      <td>31</td>\n",
              "      <td>MO</td>\n",
              "      <td>jassy</td>\n",
              "      <td>spring</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     city  revenue  branch_id state managers  branch\n",
              "0  austin      100         10    TX      tom  austin\n",
              "1  denver       83         20    CO      jon  denver\n",
              "2  mexico      200         47    CA  charles  mexico\n",
              "3  spring        4         30    IL  charles  mexico\n",
              "4  spring        4         31    MO    jassy  spring"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NVHbNeZp2www",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d3195ee5-c4e9-4c15-d494-adcda4173860"
      },
      "source": [
        "pd.merge(revenue, managers)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     city  revenue  branch_id state managers  branch\n",
            "0  austin      100         10    TX      tom  austin\n",
            "1  denver       83         20    CO      jon  denver\n",
            "2  mexico      200         47    CA  charles  mexico\n",
            "3  spring        4         30    IL  charles  mexico\n",
            "4  spring        4         31    MO    jassy  spring\n",
            "     city  revenue  branch_id state managers  branch\n",
            "0  austin      100         10    TX      tom  austin\n",
            "1  denver       83         20    CO      jon  denver\n",
            "2  mexico      200         47    CA  charles  mexico\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}