{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 向量数据的属性和索引\n", "\n", "每个 GeoSeries 都可以包含任何几何类型(例如点、线、多边形),并拥有 `GeoSeries.crs` 属性,它存储关于投影(projection)的信息(`crs` 代表坐标参考系(Coordinate Reference System))。因此,`GeoDataFrame` 中的每个 `GeoSeries` 可以在不同的投影中,例如,允许您在不同的 CRS 中拥有相同几何图形的多个版本。\n", "\n", "## 创建新属性\n", "\n", "最基本的运算之一是创建新属性。举个例子,想看看世界人口以百万为单位。可以从现有的数据列 `pop_est` 开始。从查看列名开始:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['pop_est', 'continent', 'name', 'iso_a3', 'gdp_md_est', 'geometry'], dtype='object')" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import geopandas\n", "world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))\n", "world.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "然后,可以根据列名进行基本运算。这里创建新列 `m_pop_est`:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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pop_estcontinentnameiso_a3gdp_md_estgeometrym_pop_est
0889953.0OceaniaFijiFJI5496MULTIPOLYGON (((180.00000 -16.06713, 180.00000...0.889953
158005463.0AfricaTanzaniaTZA63177POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...58.005463
\n", "
" ], "text/plain": [ " pop_est continent name iso_a3 gdp_md_est \\\n", "0 889953.0 Oceania Fiji FJI 5496 \n", "1 58005463.0 Africa Tanzania TZA 63177 \n", "\n", " geometry m_pop_est \n", "0 MULTIPOLYGON (((180.00000 -16.06713, 180.00000... 0.889953 \n", "1 POLYGON ((33.90371 -0.95000, 34.07262 -1.05982... 58.005463 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "world['m_pop_est'] = world['pop_est'] / 1e6\n", "world.head(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 索引和选择数据\n", "\n", "`GeoPandas` 继承了用于索引/选择数据的标准 pandas 方法。这包括使用 `.loc` 的基于标签的索引和使用 `.iloc` 的基于整数位置的索引,它们适用于 `GeoSeries` 和 `GeoDataFrame` 对象。\n", "\n", "### 按索引位置选择\n", "\n", "Pandas 提供了一套方法,以获得纯基于整数的索引。语义与 Python 和 NumPy 切片密切相关。这些是基于 `0` 的索引。切片时,包含起始边界,而不包含上界。" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "plt.style.use('bmh') # better for plotting geometries vs general plots.\n", "\n", "world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_cities'))\n", "northern_world = world.iloc[ 0:4 ] \n", "northern_world.plot(figsize=(10, 5)) \n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 不同的索引选择\n", "\n", "为了支持更明确的基于位置的索引,对象选择有许多用户要求的附加功能。\n", "\n", "从具有多轴选择的对象中获取值使用以下表示法(以 `.loc` 为例,但以下表示法也适用于 `.iloc`)。任何轴访问器都可以是空切片 `:`。规格中遗漏的轴被假设为 `:`,例如 `p.loc['a']` 等价于 `p.loc['a',:,:]`。\n", "\n", "### 按位置划分的子集点\n", "\n", "除了标准的 pandas 方法之外,GeoPandas 还使用 `cx` 索引器提供基于坐标的索引,`cx` 索引器使用边界框进行切片。`GeoSeries` 或 `GeoDataFrame` 中与边界框相交的几何图形将被返回。\n", "\n", "使用 world 数据集,可以使用这个功能,使用 `.cx` 的 `_CoordinateIndexer` 快速选择北半球和南半球的所有城市。`.cx` 允许您快速访问表的几何形状,其中索引反射(reflects) `[x,y]` 或 `[lon,lat]`。这里将查询纬度 $0$ 度以上和 $0$ 度以下的点:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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eD6V43bQtgAPvDOJY7yxe757Bsd65pA+CjDW2HRUuHG4eB/94zNnCeym+6y0NyV83sZDdsgtpyyakq7YgJ4YgCEJilDyRXuqT56WMIvEZW6zUrnhIXXivhgNDEARBJAcV9huIyspKtYdAyARpqx2kbOHLcVxcbScWfNj7RDOekfDkeakOggw5Q4cSjG1lp7LCdBvynClItcZ+NLFUeE92yy6kLZuQrtqCIjEGYnJyEk6nU+1hEDJA2mqHZFv4ro5ebHP5kJpbsCay4vYF8NU/tmLet9ZhSqYAXqookpCUutWpXYtLQdxxqEP1wnu5Yclu1UrJ0yosaUuchXTVFuTEGIjZWWVOoSaUh7TVFvHqPOJFEqKlcmVn+/BklFSuA8cHozowIcSkrQmJIiVasIp1hkwmk2YK7+VG73YrdSojS+hdWyI6pKu2ICfGQFgsxn6osAxpqy3EtvCNFr3wBqNHL5q6ZxKOQ2gBvFQHQUrhDPEtvNdzBEDPditl7RSL6FlbIjakq7YgJ8ZA1NTUqD0EQiZIW20hNpIQLXrx9JADQGT0gu/5KmIK4MVGkVYilTO08vNWwkoEQM92K2UHPhbRs7ZEbEhXbUGF/QaitbVV7SEQMkHaao9QJOHRPZvx++s249E9m3FLQ1nMRXYsx+SLpZ7w/18ZvVhcStwYQEwBfOO2EpRlOaI+HEwmwBcI8moYsKPCFfMBk0xxPt+GAXpAz3bLJ13QyOhZWyI2pKu2ICdmFXw6BukVlr+b0SFttQ2fiEOs6MXKn0RELxJ8pAkQVQAfiiL9bW0uLKv+RoADnm+b5OUsxHKGki3Ol6p7mhbQq91K2YGPVYz83VmGdNUW5MRgeWdvf1Mf9h5sxvWPn8Teg83Y39Snqx09PmRlZak9BEImSFs2iBa9OL2wHLlZGb3gOC5uG2IA4AB87Y+touYyp80Ci8WEYJTnNV9nIVrr5MJ0G3ZvzsNDnxVfL8FSBECvdit1uiCL6FVbIj6kq7YwfE2MkYoTMzIy1B4CIROkLRtEawgw6DFHjV6kWBLvQY0uLImey6Rotcy3OJ8vUnZP0wJ6tlspaqdYRs/aErEhXbWF4SMxLKUmJKKvr0/tIRAyQdqyQbToxc7CAHZvzsN9l6/HgeOD4YjxrNfP6zPFzGV8nYUgj+YCIaRwKliLAOjZbuVKF2QFPWtLxIZ01RaGj8RIdbAbQRCEFKyOXrS2tqJyQ0nUiDFfhM5lfJyFycUl3HCwWfHOYBQB0AZGOctHLfQSTSQINTG0E8NaakIiysvL1R4CIROkLZuYTCaUl5fHjBiHSLWa4fEH4zo4QueyeM4CAAQ5YMK9HA16pnkcL3dO4TdX1yI3zSbrnCn2DB4tone7lTpdkCXEaMtK63CW0bvNsoahnRjWUhMSMTs7i/T0dLWHQcgAacsus7OzcSPGAOByWJHBcRhdWIr5ntVzWaJFZyxnIRZz3gD2PtGMnNQUBDhOtgUYSxEAluyWleekVAjV1kj1uXqGJZtlAUM7MYCxUhOmp6dRUqKfXUqCP6Qtu0xNTfGKGH+80oU/t0xEnctMWJ7LhOz0RnMWJheXonYsC+EPIsKRkmMBxnEcMxEAslt2EaotHR6qD8hmtYXhnRiWUhMSodcHPZEY0pZdzGYzEnRThsVswv86rxTvDy5EjZyYTcAb3TP471OTmPdFvhrP0VjpLASDQdxwsDmcQsYHqRZgrKbZkN2yi1BtqT5XH5DNagvDdyeT6ywDLVJXV6f2EAiZIG3Zpa6uLur5MSFCEeOVc1l+WkrE+wMcMLawtMaBAfh3L1t2poQ/MpI9uyWUZnOoeRwj8z5MuP0YmffhcPM4r0M3tQzZLbsI0ZYOD9UPZLPawvBODHB2t/HRPZvx++s249E9m3FLQxlTDgwAtLe3qz0EQiZIW3Zpb28X1M6WAzDvC/CqYwnBx9HgOA47RKbXJrMAY7kNPtktuwjR1mj1uXqGbFZbGD6dbDUsTxKBgH53LIWg5xx5sRhFWyMSCAR4FbPHKgzm/XeidC9bncYFLKemxauLiUYyCzCW02zIbtlFqLZGqs/VM2Sz2oKcGAPhcrE7CbKaM88XlrU1OiFtExWzJ2rDnIjVjkayTlGIZBZgrLfBJ7tlF6HaGqk+V8+QzWoLcmIMRHZ2ttpDkAVqTcmutkR0baMt2BO1YY5HNEdDqFOUYjYhEOTWLsCyxC/AWE+zIbtlF6HastQ6nGXIZrUF1cQYiJ6eHrWHIAss58zzhVVtCX7a8olYxCLWTq9Qpygr1RpukJKTakVqihl2qxlz3iV8/U9t2N/UJ6gIP1RDw6epgV4hu2UXMdoapT5Xz5DNaguKxBC6h+WceYLgA5+IBbC86E+zW+CwmsFxiLnTGwwGBTtFHAfcvGMdbtoWxO3PtGNq0Q8OwKIfAAK8IqPR0kLPW5eB0kw7Bma8lGZDGAa9RhcJQknIiTEQ69atU3sISbM69531nHm+sKAtER2+2sYrDAaAVKsZu2pywg5LoiL+qUX+58EAyw4RsBwZ7ZvxCj60L1Za6HNtEyjNsuOK2hy80z/PVJoN2a38qDX3k7ZsQrpqC3JiDITb7dZlUVqion2Wc+b5oldticTw1TZWYTCw3FEs3W6JcA6kLuKf8/px/eMnwxGYaMSLjMZLCx2Y9mJbaQYe3bOZqQ0Jslt50EKjF9KWTUhXbUE1MQZicnJS7SEIhs9BdyznzPNFj9oS/OCr7crDLgvSU2BZsc4PfnjYZawDIpPtbAYA7qUgJtz+hO2XY50ZwyctFGArzcYodqvkIY1aORzVKNoaDdJVW1AkhtA0fIr2qTUlYRQSRSFChcEcgGeax9e8HiulK1ERv9kE5KSmwGQCMuyW5cM0g8CCL4BFv7DamZWR0dD3obRQ9lArGsLnmREtnZEgCP1BToyBqK+vV3sIguFXtF9m+NaUetSW4Eflhhrsb+oTtBg89mHUIhqrU7r4OBA5qSn4rz31MK9I3eQ4Dl9+ogWL8z5B38eRYsJDr/finf65iO9jSeCcsJgWyqrdqtn2XiuNXljV1uiQrtqCnBgD0dHRgU2bNin6N5PZORWyO5voIEDWUUNbQn7cvgD+8Oq7ONRr5b0YFBrV4HsWiznKe8S0de6Z8qJnyhvxs8PN43DazDADhjqxnFW7VSsaoqWIHqvaGh3SVVtQTYyB8PuFdRsSi9sXwP6mPuw92IzrHz+JvQebBZ8RAYg/6M5oDgygnLaEshw4PohgYG2hfLwzkMTYzY4KF2JZjQnRHQi+bZ35EASw4AsizW5Z81DSS1qomLoPVu2Wb32T1GjpcFRWtTU6pKu2ICfGQGRkZMj+N6QuqqSifX4ooS2hPMd6ZtG3GN0C4i0GhdrNVXV5MRedHIDddXmC/45QOAAOqzl8YGaeMwWF6Tbs3pyHhz4rX/pRMiS7YaNHu03krAmJhsiBVp4ZetSWSAzpqi0oncxA5OVFX4hIidRpBFS0zw8ltCWUJbQYbJ2LPU3HSo0Rajf/5+XuuGP515e78aur69b8PPR3eqY9ib4OL0IHZt7SYIqb8qOFlFEp6j70YrdCivTVjoZo5ZmhF20JYZCu2oIiMQaiq6tL9r8hdRrByraxYndnlWzvqRZKaEtIT7x7M7QY/HRB7ML5WItBoXbTMxXfCemO8brTZsGDn90Iu0WaBenK77P6e0mVpioVfDZs4sFxnC7sVkx0Xc1oiBTPDCnQg7aEcEhXbUGRGEIy5CqqFFO0r4XDzggiGkLuzR0VLpimozv+iRaDfO0mGAwioZ/PLb8vWnF/mt2KrNQUjAjsUraaeN9HzW5XsRDTBWu19lfmu/HKTJ+m5yUx0XW1oyFGb/RCEEYhqUjMfffdB5PJhDvvvDP8M4/Hg3379iE3Nxfp6em45pprMDIykuw4iSgIjTCUlMj74FAijYCvA6OFw86URG5tCWkQem82bivBmaW0pIvd49mN2WxGQrMyIaoDEyLZ2phE30dM1EPOCKyYuo9o2h8Zs2h+XhITXddKNARQr9GLludkI2QnyIWWdTUioiMxb7/9Nn7961/jnHPOifj5XXfdhWeffRZPPvkkMjMzceutt+Lqq6/GG2+8kfRgieQiDF6vN+7rUrCjwoXDzeOqtkk14mFnSmhLJI/Qe9Nps+Dr5xWgpGdJ1jOQKrId6JqMnVJWme2I+/uN20rwXv8cemeE3YcOqxmZDmvC78M36qFUBNZkMsGcYHFsMkUuoKNpn2kNogcWzc5LyUTXjR4N0dqcTNkJ0qA1XY2OqM2z+fl53HDDDXj44YeRnZ0d/vnMzAz+4z/+A/fffz8+9alPYdu2bThw4ACOHj2KY8eOSTZoo5JshGFiYkL2MTZuK0FZlkPVNqlqtfdUEyW0JZJHzL05Nz2JWxrK8Oiezfj9dZvx6J7NuKWhTNKFx492rUeKOfoiM8Vswg93rY/7+06bBT+/qgbOFH6PFDOWHafHeXwfvgvpBa9f0Qhsuj3+9c9Y9Xo07esylsek1XlJqui60RwYQFtzslLZCUaI8GhJV0KkE7Nv3z5ceeWV2LlzZ8TPjx8/jqWlpYif19bWory8HE1NTcmNlEi6kFQJ1EgjWDlxqt3ekzAuSrSelWsxmJtmw++urUd1jgNm0/KDwWwCqnMc+N219chNsyX8DKfNgk9vyon7UHFYTBHzQZo9cTIA34X0I+8OKTo/LnjjL/zmVywM9TwvaaVlMSEeOdcOWmu4QRgLwelkBw8exLvvvou33357zWvDw8Ow2WzIysqK+HlhYSGGh4djfubc3FxEvrXdbofdbhc6NOYRU0i6ktraWjmGtQYl0gjihca1ctiZkiilLRGJEq1nldI2N80WbqMcq4g/EYkKuh/8zEZejstq+KSpNiU5PwqB4zgEEjgcweDZ+S+W9v938OxzTqvzktpF+npFS3NysmuHWGix4YbcaElXQqAT09fXhzvuuAMvvvgiHI74OdJC2LJlC9xud/i/Gxsbcdttt6G4uBinT58GsOwIcRyH0dFRAMDGjRvR39+PxcVFOBwOlJWV4dSpUwCAgoICmM3msOO0fv16DA8PY2FhAXa7HZWVlWhvbwew3PPbZrNhcHB5J6KqqgpjY2OYn59HSkoKNmzYgNbWVgBATk4OUlNTMTAwAACorKzE5OQkZmdnYbFYUFNTg9bWVnAch6ysLGRkZKCvrw8AUF5ejtnZWUxPT8NkMqGurg7t7e0IBAJwuVzIzs5GT08PAGDdunVwu92YnJwEANTX16OjowOX5czhjM2E1jlruO3qsakUZFqDqMsIwGbxIhAIoKurCz6fD2lpaSgsLMSZM2cALD9QCwoKMDY2BgDYtGkTent74fF4kJqaitLSUnR2doavN4BwU4YNGzZgYGAgfL3Ly8vR0dEBAMjPz4fVasXQ0BAAoLq6GiMjI1hYWIDNZkN1dTXa2toAALm5ubDb7RHXe3x8HHNzc7Bardi0aRNaWlrC19vpdKK/vx8AUFFRgampKUxNz+C9oQUc6knBNSUeWEzAGbcFb3cuImdhENeus+KP3RyKHUGsTwsgCODJAQc+V+yBwwxkuFLgdrvR3d0NACgtLcXi4mL4etfV1aGzsxNLS0tIT09Hfn5+uK1iSUkJfD4fxsfHAQA1NTXo7u6G1+tFWloaioqKwvdsUVERgsFgxD3b19cXvt7r1q2LuGdNJlP4eq9fvx5DQ0Nwu92w2+2oqKiIe73b2tpgs9miXm+HwxH1nl19vbOzs5Genh5xz87MzGBmZgZmsxm1tbVoa2tDMBhEZmYmMjMz0dvbCwAoKyvD/Pw8pqamIu5Zv98Pl8uFnJyciOvt8XjCYfna2lqcOXMGPp8P6enpKCgoCN+zxcXF8Pv9EfdsT08PvF4vnE6nqnPEzOwc3h5043CvBV8sXc6Tbp+34MSZRTyzMIgtRelYX10VMUfsqHDBPt0PE4DTCxYMesz4RO4SAOB/xlNwRRnQ0tISMUfMzs6iuLiY1xzh9/uRkZGBvLy8iHvW6/VGvd6r54ho11vIHDEyMIDba4PonDHjz/0W/E32/PIi3pmJT23MQ8/pjvA9K2SOuKKYQ86CD5MeDs8M23Ft6XL9Tue8BSa7AxdlzCIjZw4vLKWgOi2AitQAfEHgqSEHvvDhHDG6FMTc3Fz4/i4rK8Pc3FzMOXn1Pbt6jvhU7iLM2QEMesw4OWvFZR/OyW9NpSDdyuFj2T60traG54g96zz467gf70xb8beFy+81g8OJ2RRsdflRlBHE0tJSUnNEe3s7TCZT1DlidHQU8/PzgueIdZXrsbvIjYkMHzrmLBj2mnFx3hLy0mzYsbkUU2PD6KY5Ys0c0d7eDpvNpvo6YmJiApflzOHReTt2F3mRauHQv2iOWEe0upftWOgc0TW5iJFZEz5d4EN2CodxnxnHJlPwmSIvTPDgT+904oqaPE2uI2Jdb5fLFXHPrp4j3n33XTidTl5zhN7WEWLnCKnXEaFryAcTJyB+/fTTT+Pzn/88LJaznnUgEFgucjSb8cILL2Dnzp2YmpqKiMZUVFTgzjvvxF133RXxeX6/H0eOHEF1dbWmIjFaLULce7A5bhvTwnQbHt2zOebrLS0tqK+vl2NoirK/qQ+Hmsej7iyZAVxRm4OTw+6YO4daPf07GVjRVk8kug93b85bU6gd2rkUcm/qWdtYc6mYImO3L4BHjg/GbHCQ7PwolP1NfXGjQ6v1j6b9taUePDngSGpekrNgO9ZOuxlAWZaDyZ12qdCS3cplG0rbnBbQkq6sEggE8P777+OSSy6B1Ro/1iIoEnPppZfigw8+iPhZY2Mjamtr8U//9E8oKytDSkoKXnrpJVxzzTUAgPb2dvT29qKhIXasMiMjI8IxUgM9dO5ItvNXWlqaLONSmkSh8Xf65/Hrq2vjLnhYgxVt9YSYFI1QzZiQe1PP2sZyYMSkoCRKU1W6M6LQNKto2s9zAezenCd6XpI7nceInR6lQkt2K4dt8KnzGlvwYf/RPjSex85zV0u6EgKdmIyMDGzZsiXiZ2lpacjNzQ3//Ctf+Qruvvtu5OTkwOVy4bbbbkNDQwN27Ngh3aglRi95ncnmJofSP/QM3wLZ1BSzodp7sqCtnlCy9Sxr2kqxMI52zZSu3RDjkK7W3uv1JpWaLbeTIVcthRHQkt3KYRt8avyCHHC4ZRwnBrWzjkoWLelKJHnYZTQeeOABfOYzn8E111yDiy++GEVFRfjTn/4k9Z+RFD10/QKS7/wVymnVM2KKo1l3YAA2tNUTSraejaetFN2slO6IJVcLdDU6I4acEjHtr00mU9J2K2c7eT13VNMCWpqT5bINPofcam0dlSxa0pVI4rDLEK+++mrEfzscDuzfvx/79+9P9qMVQ0+7TUY/QAzQxoGaBKHWfShF6qta6bPJRLD4oOb8qPRcLPe1lMpRJ7SBHLYRK8KzGq2towh2kDwSozf0vNskdBIqLi6WaSTKooUDNbUGK9rqCaXuw5XaSnFondjPkGIOVHJhrIfFdTJ2q8S1pDNixKPlOVkq2whFeD5bn4sYZ+WG0eo6Siha1tWIGN6JMdJuk9/vV3sIkqBG2ojWYUVbPaHUfbhSWylSX4V8hhwH2dHC+CwrtRWzwJP7WtKGkXiMMic7bRbs+3g58hMciKvUOkpuR8kouuqFpNPJWMAo6UljY2PIz89XexiSQGl1kbCkrZ5Q4j5cqa0Uqa98P0OuhidKFuBrfW4YHhnFHzo9otP65L6WYpoXEMsYbU5Wcx2lZHqs0XTVOuTEgE4k1jtaXqQQxkHu+1CKGgghnyFX5yu5F8Z6aJcPLI/z5PA8DnX7RDuJSjgZtGFE8EHudVQowrL6/tNLd1lCHsiJgXF2mzZt2qT2EAiZIG3ZJaStFKmvQj5DzoYnci2M9bSgOXB8EC/0piTtJCrpZJhMJnJkeGK0OVmOdZTbF8DDbw3g5dNT8PqXXSO7xYxPbsjC1y5YB6fNovhZRkbTVeuQE/MhYh4EepvMe3t7UV1drfYwCBkgbbWJFHPESm2lSNng8xlyd75aiZRzqJ4OZzzWM4uL83z4f6P2Na+JdRLleh7pJbqlJYw4J0vpULt9Adx2qB19096Iny/6g/hL2yQ+GFrAL66qUby7rBF11TLkxEQhnuHpeTL3eDxqD2ENenMEtYoWtTUqUs8RK7WVImWDz2foteEJnwXNzTvUn3NCTmJ2SuwiZKmcxGQRG93SwtjVxOhzcrLaHzg+uMaBWUnfjBcH3hlQbLMlhNF11RrkxAhAT6kK0UhNTVV7CAD07QhqFa1oa3TkmCNWaitFygbfz9BbwxM+0aOxBR+ue+wDpFgs2F6egf91Xqkkc47QRVLISRz3xXYUteIkColu0dx+FpqTk+MYj4Naj/XOKb7ZQrpqC3JiBKCnVIVolJaWqj0E3TuCWkUL2hLAb97qR8/02p26ZOaI1dpKkbLB5zP01vCET/QoyAGTiwEAARxqmcCzrRO4vCYXX71AuDOT7IJ9R4ULL7dF32nWkpOodjc7vUJzsnj4bEgAgD8QxEVVWTjcotxmC+mqLQx/TowQ+EzmWqazs1PtIUhyzgWxFi1oa3TcvgBeaJ+M+brYOSKetnIeCum0WfDgZzfq6jymeOemRCPAAc+2TfA+KDSEFIeONm4rwRfKljR9BovU3eyMBM3J4uGzIQEAVosZjecpe5YR6aotKBLDEyULXVlG6SI8glCKA+8MIpDgnDU9zBGxIgw3nVuMNLu2HxmxokeJEBolkyIq77RZsKUoHbvTXJrtiqmVbnaE8dhR4cIzzeNx39NQ4TJMd1kiOtp+ImkIvRa6rqSwsFDVv0+OoHyorS0BHOtNHGURM0coqa3eU4JWL2j8wSCmFv0IJnAuhS6ypVqwlxQX4ZYtuZo+g0Vr3ez0As3JydG4rQTvDszFLO4vy7KHoyxKthknXbUFpZMJIF6qgpZymLUKC44gQUSDbw73jvIMBUYjHhZSgkILmkf3bMa/X1MHu4XfYy60yE6EkAW7ELQ67zVuS5yuQ3M7ITVOmwW/2F2Dz9TmwplihtkEmE1AqtWMK2tz8IvdNVE3VOgeMxYUiRGA3gpdVzMyMoLc3FxVx6C3jkd6QQvaGhl+izig8TzhRaFKastSSpDbF8Cdh09h0c8vsYzvIlvKBbse7JbVbnZyowdt+aJWBM1ps+D2i8px+0Xl4U0BtZ0UlnRlAXJiBEC5l8mjd0eQIGIRbxEHAFdsytX0HMFaSlAoqsQXIYtsoy3YWexmR8RHa+2y9TDnEMpDToxAlMy9lJoNGzaoPQRyBGVCC9oanUSLuL+/QFxrTqW0ZS0lKF5UaTUWEwQtsqVasOvRbuN1s6O5/Sx8tNXqGkLvtXFyokebZRlyYpJAi5NPPAYGBlBVVaX2MHTtCGoVrWhrZORaxCmpLSsRBr41SiEyHVakpvAvEZVKa9bslub2s8TSVmsRjmjo/Uw8OWHNZvUOOTEGYnFxkfd7lXoAGfkhJyVCtCXkQ45FnJLa6jklaOX15nvORIgUi1mwVlJozbLdGn1uj6atXiIcLNXGSQ3LNqtHyIkxEA6HI+7retghIqKTSFtCeaRaxCmprd5SguLNWXzOmQCkiTDx0Tqao0N2yy7RtNVDhIO12jipIZvVFuTEGIjy8vKYr+llh4iITjxtCX2jtLZ6SQlKNGfdd8V6vNw5hTlvIO7nyBlhSrQxRHbLLtG01UOEg7XaOKkhm9UWdE6Mgejo6Ij5GgvnQxiZeNoS+kZNbbW8UEk0Zz3x1xH85upapNuiP+YsJuDK2hw89Fl5NmhCTtah5nGMzPsw4fZjZN6Hw83juONQB9y+ANktw6zWVq7zheSAzsSLDdmstiAnhgDAb4eIIAhCK/CZs3LTbPivPVvwuc15KEhLQZ4zBYXpNnyuPg9/3HsO7rioQrYIM20MJUYLC3al0FOEg88BpwShBSidzEDk5+dH/TnlwOqfWNoS+oe0XYuQOetselyZonMYHyfri5cWKDIWLWGU2stodquX7n96q41TEpqPtQU5MQbCao0ut552iIjoxNKW0D+k7VrEzllKzWF8nSyLxViLQblrL7W00RbNbvXU/U8vtXFKQ/OxtqB0MgMxNDQU8zXKgdU38bQl9A1pGx0tz1l8nazh4WGFRqQN5Eixc/sC2N/Uh70Hm3H94yex92Az9jf1we2L3dBBiTS2aHYbinDs3pyHwnRbOL1x9+Y82WqzpIAcmLPQfKwtyKUkAOhrh4gglIR2IbWJ1ucsfqlDcwqPSl2k7s4lJLKjlTQ2inAQhHSQE2MgqqurY75GObD6Jp62hHC0suABSNtYaH3O4uNkmYNLag1PceSoveR77ooaRwjwsVtyYPQHzcfagpwYAzEyMoKKioqYr9MOkX5JpC3BH62dmUTaxkbLcxYfJ6unpx/l5eWaGrdcyFF7yTeyo8Yhk2S3bEK6agtyYgzEwsIC7/ca4aHKEkK0JeIjZsEj5wKatOWHFuesWE5WqI4jc34Yz74xy2yHrtVI2Z1LSGRHjUMmyW7ZhHTVFuTEGASO42Cz2dQehmi0tsuqNfSsrdbgu+BRKuWMtGWDlQ5MKNJ3RSGHCbcfgHqRPiWRso6Jb2QHgCpHCJDdsgnpqi3IiWGY1Yssu9mE88f6dLPbp6W6BK1DebrSwHd3d8Hrx52HTymSckbassXKSN8Lo2cXRHxSm/S+mSN1HROfyI5aRwiQ3bIJ6aotyIlhlGh5/deWevBk85IudvuE1CXo/cEuBW1tbaivr1d7GLqH74LnkXeHFMuxJ23ZYmWk7wslXjwx4Ai/FgTwQsdkxIKetc0cKeuY+EZ21DhkkiW7pWfsWVjSlQXIiWEUNQoZpSTR+P/9rQFYLCZmHuyEduCz4GlSIcee0B+rF398In2LS0Hc/kw7fn5VDQBoqsmE1CS7MOYb2dF6O24twprzTLAJOTGMEi2vv3VueeLRwyIrUV3Cc+0TCHJg8sEuhtzcXLWHwAyJFjw3nluM/+majvsZUubYk7b6ItHib2WkLzQnr6Z/xouH3+pHy6gbPdOeNa/rZTNKCfhEdtRox61nu9Vah0YtoWddWYScGAaJtds34z/78JSjkFEqeNUlRPFwjPxgt9vtag+BGfgseJTMsSdt9QOfxd/KSN/KOXklQQDPt09GnedWvkfrm1FKE8/mojk7cj4D9Wy3es/kkBM968oi5MQwSKy8/h3ZS+hxL++eyFHIKBV86hJiYdQH++DgILKystQeBjMk2t1VMseetNUPfBZ/oUhfz7QnYk5eTTwHJvyeVZtRWt2Y0hJKpUnp2W7VaEmtF/SsK4uIWykSmmdHhSumuHIVMkoFx3Fxx5+I0IOdIKQg2qKwcVsJyrIca+5RyrE3NnwWf6FIX6o1+cevxWzC4lIQ+5v6sPdgM65//CT2HmzG/qY+uH2BpD+fNUKRskPN4xiZ92HC7cfIvA+Hm8dxx6EOumYQdv4OQagNRWIYJVpe/4ujNs0uslbvjplNJqTZLVjwBtbUJZhM8XcptRxlkouqqiq1h2AolMyxJ231gZDFn9NmwWU1OfjvDr/ov2cGcP66DKpdEICSaVJ6tVu1WlLrBb3qyirkxDBKtEXWtkwPUnPyZCtkFEusPHITgDSbGU6bBcEgwotEXyCI59smFW2XqXXGx8dRVmbMHGW1kLJVbDxIW30gdPHXuK0E/ukxPDeAtRs1ZiAQ3x9CWbYDQXBUu8ATjuMUTZPSgt2KnZfUaEmtF7SgK3EWcmIYZvUiq7W1FfX12jO+WLtjHAC3L4idG3Nw8451ESdeNw+7qV3mCubm5tQegqGRc1eStNUPQhZ/TpsFnyqzIyXLtSaatxTg8FzbRNTPAYDqHAfu/8wmfP1PbVS7EIfVEf6pxfiRLykb3qhlt1LU/FBL6tjQfKwtyIkxCCaTCVarNuXmtzt29qGiRrtMraNVbYnkIW31g9DFn92WEjWa5/YFcHJ4Yc3nmACUZy87MKkpZt7pa0ZM/YkV4Y+HlGlSatitVK2R6RkbG5qPtQWpYSA2bdqk9hDWICSPfOXDRalUHr2gRW0JaSBt9YPQxd9KbVfPb3w+h2oXYhMrwh8LqdOk1LBbKWt+6BkbHZqPtQU5MQaipaUF9fX1ag8jAimKCGly1aa2hDSQtvpCyOIvnrZ8PodqF2ITL8K/GjnSpMTYbbLOglw1P/SMPQvNx9qCnBhCdehBTBAEi0i1+Iv1OVS7EB0+EX6zCch2WGG1mFVNk5Lq3BqxWQ0EoWfIiTEQOTk5ag8hKvQgTh6takskD2nLLslqS7UL0eET4c9Ps+F319bLtqDno61UNSwAtUZWCpqPtQU5MQbC6XSqPYSo0IM4ebSqLZE8pC27SKEt1S5Eh0+EX8y14nuN+Wgr9bk1lNUgPzQfawtBRwb/+Mc/xvnnn4+MjAwUFBTgc5/7HNrb2yPe4/F4sG/fPuTm5iI9PR3XXHMNRkZGJB00IY7+/n4A0ORJu6EH8aN7NuP3123Go3s245aGMnJgeBLSlmAP0pZdpNaWHJizNG4rQVmWY80iR0yE3+0LYH9TH/YebMb1j5/E3oPN2N/UB7cvEPN3+GjLp4ZFCFJ+ZyI6NB9rC0GRmCNHjmDfvn04//zz4ff78e1vfxuXXXYZWlpakJaWBgC466678Oyzz+LJJ59EZmYmbr31Vlx99dV44403ZPkCBD/cvgC6Jhfx44PNSeXdKgE9iAmCIPSLFiJCUkX4pUz5WokcNSyU1UAYDROXxLb82NgYCgoKcOTIEVx88cWYmZlBfn4+HnvsMXzhC18AALS1taGurg5NTU3YsWNHxO/7/X4cOXIEW7duhcVCxiUXoUnYs+jGiPfsHo0ZQFmWQ/QkTGiHhYWF8EYCwRakLbuwpK1UBepyIdax2t/Uh0PN41EjJmYAuzfnRU354qPt3oPNGJn3xXy9MN2GR/dsFjjis2jBmWQNlmxWqwQCAbz//vu45JJLEp7LIyidbDUzMzMAzhY6HT9+HEtLS9i5c2f4PbW1tSgvL0dTU1PMz5mbm8Ps7Gz4n9frTWZYxCpCebdVaZGh75V5t0Yimt+uxRQ7IUxNTak9BEImSFt2YUXb0EbZoeZxjMz7MOH2Y2Teh8PN47jjUEfctCulELuYF5vyxUfbHRWumIswKWpYyIGRHlZslhVEF/YHg0HceeeduPDCC7FlyxYAwPDwMGw2G7KysiLeW1hYiOHh4ZiftWXLFrjd7vB/NzY24rbbbkNxcTFOnz4d/gyO4zA6OgoA2LhxI/r7+7G4uAiHw4GysjKcOnUKAFBQUACz2Rz+m+vXr8fw8DAWFhZgt9tRWVkZruXJy8uDzWbD4ODyQr6qqgpjY2OYn59HSkoKNmzYgNbWVgDLzlpqaioGBgYAAJWVlZicnMTs7CwsFgtqamrQ2toKjuOQlZWFjIwM9PX1AQDKy8sxOzuL6elpmEwm1NXVob29HYFAAC6XC9nZ2ejp6QEArFu3Dm63G5OTkwCA+vp6dHR0wO/3IyMjA3l5eejq6gIAlJSUwOv1YmJiAsCy03jmzBn4fD6kpaWhsLAQmfOD+FJpEE4Lh9klEz7i8gMAnh6y45I8H1zzg+jq8qO0tBSdnZ3h6w0gXM+0YcMGDAwMhK93eXk5Ojo6AAD5+fmwWq0YGhoCAFRXV2NkZAQLCwuw2Wyorq5GW1sbACA3Nxd2uz3ieo+Pj2Nubg5WqxWbNm1CS0tL+Ho7nc5wDmpFRQWmpqaiXu/MzExkZmait7cXAFBWVoa5ubnw9a5Yvwkvvf1XzLh9GPRY0Oe1YVeRD4AJR0atyLD4Ue30IzvVir85fysGe7uwtLSE9PR05OfnR1xvn8+H8fFxAEBNTQ26u7vh9XqRlpaGoqKi8D1bVFSEYDAYcc/29fXB4/EgNTUV69ati7hnTSZT+HqvX78eQ0NDcLvdsNvtqKioSHi9Z2dno15vh8MR9Z5dfb2zs7ORnp4ecc/OzMxgZmYGZrMZtbW1aGtrQzAYjHq95+fnwxP8ynvW5XIhJycH3d3dAIDS0lJ4PJ6o92x6ejoKCgpw5swZAEBxcTH8fj/GxsYALB801tPTA6/XC6fTKekcsfJ6a2mOmJ6eDusj1xwR73r39vaG71k9zxFZWVlwuVwx54jVc/Lqe3ZxcTF8vevq6tDZ2Zn0HOHz+eBwOBSZI0ZHRzE/Py/LHHGiaxjnOX3omXbgmhIPrCag221Bt9uMhrRpvPrWCXxi6ybdzREmkwmX5czBl8nhLyM2bMvyo9AexKzfhBdHbbimxAubxYuRkZE192xoTo43R1ycwaEr14p0zo2y1CC8QeDpIQe+VOpBms2Mj68zY25uThPrCJojlq/34OAgZmdnFZsjlFxHyDlHCFlHhK4hH0Snk91888147rnn8Prrr2PdunUAgMceewyNjY1rIikXXHABPvnJT+InP/lJxM9D6WTV1dUwr2gNaLfbYbfbxQyLWAXHcbj+8ZOYcPvx+WIPnhpyrHlPnjMFv79us+52bfimL8TKaY6GXlPs2tvbUVNTo/YwdIfWU2AA0pZlWNFW7rQoNRH73WJpuzrFy+0LUA2LjmDFZrWMkHQyUZGYW2+9FX/+85/x2muvhR0YYNlj9Pl8mJ6ejojGjIyMoKioKObnZWRkUE2MTKzsHR/NgQH02TteSLFlrDaW0RDb2hJQN/+YJlXhyFWwKzWkLbuwoC3rhyyKbVu8UttEmyXUIls/sGCzLCGoJobjONx666146qmn8PLLL6Oqqiri9W3btiElJQUvvfRS+Gft7e3o7e1FQ0ODNCMmBBPKu/1CiWfNa3L0jleivoRPf/0Q8XKaoyGktWW01pv/39FexXPAQ6kKBH+E3ENqQtqyCwvasn7Ioti2xSFthdQL6fUaGQkWbJYlBEVi9u3bh8ceewzPPPMMMjIywvnkmZmZSE1NRWZmJr7yla/g7rvvRk5ODlwuF2677TY0NDSs6UxGKEfjthKcGJiH1RTpxEjZO17ptBw+xZahna1Eu4TR4LNzGGsn/1DLBJ5tncDlNbn46gWliuzm670xgRrwvYfUhrRlF1a0ZfmQxdVti/2BIKwWc8KUr5C2Uh9oSagLKzbLCoIiMb/85S8xMzODv/mbv0FxcXH43xNPPBF+zwMPPIDPfOYzuOaaa3DxxRejqKgIf/rTnyQfOMGf0CSc5nKhMN2GPGcKCtNt2L05Dw99NvmUGaU70whNX0i0SxgNPjuH8dLUAhzwbNuEYp15VjfTIOIj5B5SG9KWXVjR1giHLIZnAtOq/45BSFupD7RUCi3MfVqEFZtlBUGRGD43tcPhwP79+7F//37RgyKkx2mz4MqtFbj2wnTJ826V3mkSmr4Qb5cwGnx3DvmkqSm10+Zy6XenUw30lAJD2rILK9qyfMii2No5l8ulu3ohPTQ6URtWbJYVkjonhtAXoTZ2Uk+Wauw0CemvH2uXMNbv8tk55JumptROW0hbgj9yn9EgFaStcPSyi8yStqEC9Uf3bMbvr9uMR/dsxi0NZbpf/Iqtnevt7dXVZokezvrRAizZLAuIPieGIAD1OtOE6nz6pj0REZZoTkisXcLz1qXDBBPe7p8TvHMoJE1NSzttxFmE3EOE9pFqF1mMrZJ9R8LStUi2dk4v9UJUu0PoEXJiDERZmfQTkFo7TULTFxK1sRSzCOGbprawFMDiUlDWHUk5tNULYheQekmBMbK2fEm2XbYYB0gKp4m01TbJbNKFtNXLZoleGp2oDdmstiAnxkDMzc0hIyND8s/dUe7C4Rbld5rE9teP9j4xi+DQw6lnem3r6pUsLgVxx6EOWc8dkUtbrSLFApLjOF2c0WA0bcWQzC6yGAdIqjOGSFttk8wmXUhbPWyW6K12R03IZrUFOTEGYnp6GiUl0uz6rFxELgUCMJmwpl1LaKfpxnOLJfmb8VBjYg09nB5+qx/Pt08iECcNX+5wvJTaap1kFpB6LFw1krZiSWYXWYwDJFXqDWmrfcSmg63UVuubJXycNZOJrTRBsZDNagsq7DcQUk1AqwsAJxcD4QW8xQTkpFpRkJ6CihwH3L4A/v6Prdh7sBn7m/qYKw502iy446IK/HHvOUi1xr6+chf4G+nhIrbQVq+Fq0bSVgzJtssW05hEqmYmpK32Eds+2mQyRb3ntKr5jgoX4o3M4w9qdo5UEq3qZ1TIiTEQdXV1knxOvPNROA5oKHch1WpB96QHYwtLulksJkNqijnhTv5SIChb1ySptNUDYheQYp0ftYmmrV66bylBMik/YhwgKc8YMpLd6pVQxH335jxe56y5fQHsb+rDj94P4vrHT+pmA69xWwnSbLHtaMEb0OwcqSRks9qCnBgD0d7eLsnnJFpE/qV9Ej06XCwmA5+F1IzHj8UlvqfVCEMqbbVOMgvIRPftCx2TmlxohLQNLY72HmzW1eJICcS2yxbjAEnZzMQodqt3+LaPXhntbciY1dUGntNmgTMl9kaclg/mVBKyWW1BToyBCASSn0D5LCLj7T+yPBHuSNDAIMAB1z12UpaFpxTa6gGxC0g+9+3iUhC3P9MuiTZSRkoCgYBuU+GUIpkT48U4QFKdMWQUu42H3qKK8ZzTldFe+4obRA8beBzHIZBAC74RRpYhm9UW5MQYCClOmhVyPkosWJ0I95xTmPA9i/6gLAtPI50iLGYByfe+7Z/xil5oSBUpWW0bLpdLt6lwSiE05WclYhygZJymlRjJblfCalRxZbS3dzHy7tD6Bp6eDuZUE6ParFah7mQGIicnR5LP4Xs+SixYnQgff3+Y1/vkODxMKm31gNhzF/jct2LPQ5DznJKcnBwce71LE2c4aLGzUgixHaDEtMCVqm2ukew2hFTtqbXG6mjvqfm1yyuttynWy8GcamJEm9Uy5MQYiO7ubtTX1yf9ObEWkXxJs5nh9gV0+aCKh5BdNqkXnlJpqwfELiD5nusjZqEh5zklt9UGVT3DQY9tqYVeBzEOUGqKOem2uUay2xCsngy/OpJxab4PTww4It6j9Q08vRzMqSZGtFktQ04MIZiVi8hDLeMICswM6570yH74o9IseP2YcC8J+h2t78ppGTGLztB9e91jJ7Hoj+0UTC4u4d+O9QtapMt5Tkn/dBBWsyPar4aRa3HE6q55POJdRz06dFqD5ZPhxUYytPIc0MPBnASxEnJiDERpaalknxVaRHIADjWPxy3mX43ed9yi8ci7Q4KdOSkXnlJqqzeEXEOnzYLLanLippUFOWGL9GRPu060qHtl1Kpamgeru+ZikMOhM5rdsn4y/MpIxtHJlPDPo0UytOoQa/1gTrUxms1qHSrsNxCLi4uSf2bjthKUxyhwtcSZ+7Re5CiUYwK/i9QLTzm0ZZVYRdkrEVIwL/c5JS6LHzedWyxJIblQpDrUkQXkaK5gNLtlvXh8ZYOJqgxzzAYTeuk2qFcd5MRoNqt1yIkxEJOTk5J/ZsyuQPV5yHTE303SS5eyRGPksxBdiRwLTzm0ZZWV96xZIkdbznNKqpx+pNmtortviUXKQx1ZQA6Hzoh2K1V7aq0SimRcvcER80wZ6jaoX4xos1qG0smIpIkVfm7qnQUQe0cptOOmxZC1kFC/yWSCJcH4zSYgJzWF8os1gtNmwc071uF/uqYx4fbHfB/f1JZkCmITpYrlpFrDY1YyzYP1XXMhsJ4GpSRGKh6PdS+wXBdEEEpCToyBqKurk/1vrJy04y3OTFjuUrb3YLOm8oEBcbnvaXYLsBC7sL8y24Fffr5WtgWOEtqyBp9FutnEL6UimYLYRIu6S87fGHXsSkAtV5eRy6Ezot0apXg8lrbkEOsbI9qsliEnxkB0dnZi48a1CyK5iLU4MwGwmk3omtRmxyMxxczz3vg5zHPegKwPJKW1ZYVEZ8fMev3Ye7CZl4Mt1zklAz1nVNPWSLvmiUh0r4hpHW9UuzVC8XgsbSnCqW+MarNahWpiDMTSkrAWwMkSq16mKscBf5ATnA+sVO690Nx3juMQTFg3I+/4ldaWFRIV+Xv8nKiCW7HnlDy6Z/OaPHoptBV778WseZOxFkdtYl2r0L0SS9lQ63ghRdlkt+wWj8fTlvW6IJYhm9UWFIkxEOnp6Yr/zWg7bnsPNvPOB1a6DaWYUL8WdtbU0JYFVkdBZhaX4AmsvTuVbCm8+j4Rq61UtmOEXXM+1yp0r9z95w6cmVx7YKqYe4Tsll3iaUsRTv1CNqstKBJjIPLz81X9+6Eifr5OghptKMU6JGrvrKmtrZ5ZGQXJTE2J+b4ggKM9M8oN7EPEaCuX7bDqwPC9Vk6bBQu+2POX0C5lZLfsEk9bI0Y4WYFsVluQE2Mgurq61B6CICdBrTaUYhySWGlJSu2saUFbvcPHwZ5YWMKCN3Y3MzkQoy21cOWPkGslddtpslt2SaRtvBRSQruQzWoLcmIIxeHrJKh10J4Yh4R21vQPHwc7wAG/fXdIoRGJhw6p5I+Qa6WF1FGCPeh+iY1RzqEixEE1MQaipEQbebZ88oHVbEMptgWomrUDWtFW7+yocOGZ5vG471H6DAeh2lILV/6IuVZStp0mu2UX0lY8StfCCoElXVl4BpATYyB8Pp/aQwDA30lQc8czWYck0fulnjy0oq3euencYhxuGUcwzuZfMg6AmN8Tqi1FC/gj5lpJWZRNdhubYDAIcwJttAxpKw4x57QpidS6Ku1IaNlBFAM5MQZifHwcBQUFag8DAD8nQSsH7Uk1wcg5eWhJWz2TZrci15mCsTgHl4YWtXwfPsnqHk3bUIpFrHFoxXb0gNBrJeVhjWS3kUws+PDtF06jZ8oDjgNMJqAi24Ef7VqP3DSb2sMTBGkrDjHntCmJFLqq5Uho3UEUAzkxhOrEWgiy1IaSxcmDVT5emRlzUWvC8qGGew8283r4SKm72xfAw28O4OUzU/AsBcF9OB6b1YRMuxUNlZnhcbBkO3Ij5loZoe200kws+PDlJ1qwtCIMynFA16QHX36iBb+7tl53jgwhHD41akqm80qNmmsBrTuIYtBvrJYQTE1NjdpDEARLxfJCOyAJRW/aaplYjR1MAKxmE7omPbzbFifbJYzjOJRXbcCDr/fgmkf/imfbJ7D4oQMDABwAr5/D6MJSxDhYsh25SfZaJePAkN2e5dsvnI5wYFayFOTwv184rfCIkoO0FY7U3f/kIFld1ewcyWLDF4rEGIju7m6sX79e7WEIgpUdz0STxxvdM+A+fJ+Y8LIetdUqsdKF0mxmdE0K28USs6u4MtVgKRDEDtcC/jJiTzju1eNgxXaUQK1rRXZ7lp6ptQeIrqQ7wetag7QVjh7q+ZLVVa1IE6sNXygSYyC8Xq/aQ0gKPRnWSvhMHpPupaQOJtS7tloj2hkOC76goF0sPrqPLfiw/2hfWOPVBy9OLvqRYeW/6xhrN02vtiMUKXZolbxWZLfLBINBJJSOW36fXtCStnpqU6z2wdGJSEZXNSNNenAQxUCRGAORlpam9hAURwu7CnzPH1mNkDxVI2qrFKHieaG7WHx0D3LA4ZZxnBhczoWOlmow4hW216TH3bRk0HO3HbLbZcxmM0wmxHdkTNBVtzK1tdWrXWi9ni8ZXdV2JFhs+EJOjIEoKipSewiKoMXJO97kEQ++4WWjaKsWYh8+fHRf6axGSzU4Pi1smlZ7N01JB0rvDTNYt1sh90JFtgNdk7FTxiqzHVINSxHU1FbPdiFl9z85SFZXNR0JrTuIYiAnxkCcPn0a9fX1ag9DVrQ6eceaPEwAzKbokZgQfHbWjaCt2oh5+MTSfTUhZzVatOdvC314YoDfAi7Zh6BYB0StjQO9d9th0W7F3gs/2rV+TXeyEClmE364S1/1JWpqq3e70HI9X7K6qulIaN1BFAM5MQRTaHXyjjd5HO2ewSiPc0kIdRHbiveh3Ztw4J0BHG6dSHiIpiUJnYU8BFcuDJJ1QNTcOGC9HaveSOZeyE2z4XfX1uN/v3B6uYj/wx7ildkO/FCH58SoCUt2wdqzT21HQssOohjIiTEQrKcuANqevGNNHhyQdHjZCNqqjdiHj9Nmwb6Pl+NY7xxG5mOf9mwxm6JGe95NkE7msJrgcljx8YrMuOOI5qxsW5eOD4YX0D/tFe2AqLVxwEK3HdbsNtl7ITfNhl9dXbf8O8GgrmpgVrNaW6XuQxbsQstIYbNacSRY0J+cGAOhp84uYtDT5L3y70sRXmZdW62QzMOHTzraTVHuBeuH6ziLCch0WJFiMWNHeQZu2laCNLuV1zhi7ZD/pW0y6vuFOCBqbRyoXSQrBazZrZT3gp4dGGBZWzXSLFmwCy0jtc2SDslBToyBGB0dRV5entrDkA29Tt5ShJdZ11aLCL2P+Dir0e6F83N8qCnPw03bSpCaYl7zd/mMI9YOeTz4LDrV3jjQe7cdluxW7XtBawwNj+DBV0fRNx3ZkveZ5nG8OzCHX+yukc2R0btdaBmWbJYFyIkhmEKvk7dWwsuEfPB1VlffC62traivTy4dK94OeTwSLTrV3jjQYrcdo9qv2veC1uiZ9qBvOvr16Jv24t/fGsDtF5XL8re1aBcEIQfkxBiIjRs3qj0E2WFh8hbzkDeCtiwg1Fk1mUxJa8tnhzwWfBadam4cqF0kG0Js2hBrdqvXTSQ5eLTLAsTZOnj59JRsToxW7IJFWLNZvUNOjIHo6+tDdXW12sOQFaNO3kbQljX4OqvJastnhzwafBedam8cqB3FTKYjF2t2q/a9oBU4jsP5mR78v1F7zPd4/EFZmxeobReswprNrkSP9wk5MQbC44l9kBhLGHHyNoq2RkQKbYUetipk0amljQM1bD2Zjlys2a2W7gW1ybHFT+AMcsANB5sVK/YnpIE1m9Xi4eBCICfGQKSmpqo9BMUxyuRtRG2NghTaxtshL82yY2tROt7unxO96DTixkGIZDpysWi3Rr4XQphMJkwvJY6wTLj9ANQ/jJngD0s2q9XDwYVAToyBWLdundpDIGSCtGWXZLXlOC7qDrnZBHy8MvJsGSkWnVpYtOrlTA7W7VYL94Ja2LLzgZEZXu9V+zBmgj8s2axWDwcXAjkxBuLUqVOor69XexiEDJC27CJG21gpAnvOKQQHhH/e9GEUIZQ6oOdFpx7P5CC7ZZdLshbxXqYdfTPexG+G+ocxE/xgyWa1fDg4X2Q7TWr//v2orKyEw+HA9u3b8dZbb8n1pwiCIIgPCaUIHGoex8i8DxNuP0bmfTjcPI4vP9GCZ6L8/I5DHXD7AmoPXTTxvrPc321HhSvmg9RoHbmIs1jMJvziqhp8bnMeCtNtyHFYYE6wRxCK2hGE3AiJImsZWZyYJ554AnfffTe++93v4t1338XWrVuxa9cujI6OyvHnCJ4UFBSoPQRCJkhbdhGqbbwUgaXg2gfSytQBvcInLUIuGreVoCzLseZhyqc5AtktuxQUFITrgx7dsxmP3/AR5KfZ4v6Okc7R0Sus2Cwr5zrJ4sTcf//9+OpXv4rGxkbU19fjV7/6FZxOJ/7zP/9Tjj9H8ETrNyMhHtKWXYRqK+Zgy1DqgF7hkxYhF6F6o90f7rjnOVNQmG7D7s15eOiz8QtjyW7ZZbW2JpOJonYMwJLNsnA/Sl4T4/P5cPz4cdxzzz3hn5nNZuzcuRNNTU1Rf2dubi6iV7rdbofdHru/OiGOkZER5Obmqj0MQgZIW3YRom0yB1vGK0DXMskW10uB2I5cZLfsEk1bOkdH/7Bksyzcj5I7MePj4wgEAigsLIz4eWFhIdra2qL+zpYtW+B2u8P/3djYiNtuuw3FxcU4ffp0+Pc5jgunpG3cuBH9/f1YXFyEw+FAWVkZTp06BWA53Gc2mzE8PAwAWL9+PYaHh7GwsAC73Y7Kykq0t7cDAPLy8mCz2TA4uJxuUFVVhbGxMczPzyMlJQUbNmxAa2srACAnJwepqakYGBgAAFRWVmJychKzs7OwWCyoqalBa2srOI5DVlYWMjIy0NfXBwAoLy/H7OwspqenYTKZUFdXh/b2dgQCAbhcLmRnZ6OnpwfAcvcLt9uNyclJAEB9fT06Ojrg9/uRkZGBvLw8dHV1AQBKSkrg9XoxMTEBAKitrcWZM2fg8/mQlpaGwsJCnDlzBsCygzk2NoaxsTEAwKZNm9Db2wuPx4PU1FSUlpais7MzfL2BZYMFgA0bNmBgYCB8vcvLy9HR0QEAyM/Ph9VqxdDQEACguroaIyMjWFhYgM1mQ3V1dVj73Nxc2O32iOs9Pj6Oubk5WK1WbNq0CS0tLeHr7XQ60d/fDwCoqKjA1NRUzOvtcrnQ29sLACgrK8Pc3FzM652Tk4Pu7m4AQGlpKRYXF8PXu66uDp2dnVhaWkJ6ejry8/MjrrfP58P4+DgAoKamBt3d3fB6vUhLS0NRUVH4ni0qKkIwGIy4Z/v6+sLXe926dRH3rMlkCl/v9evXY2hoCG63G3a7HRUVFXGv9+LiIlpaWqJeb4fDEfWeXX29s7OzkZ6eHnHPzszMYGZmBmazGbW1tWhra0MwGERmZiYyMzMjrvf8/DympqbW3LPRrrfH44l6z6anp6OgoCB8zxYXF8Pv90fcsz09PfB6vXA6nYaYI+bm5tDf3897jjgv04u3l0z4dIEPAHBsKgWZ1iDqMpbrQv7voB27CnzIsHIY9prx3rQVVxT6YLf6MD09veZ662GOuDLfDa8/iFfGbahOC6AiNQBfEHhqyIEvlHjgTPFhaGhIc3NE6H1KzBGjo6OYn5+nOUKhOSI0J6+eI77TkIWXe6ywLkyC4zi8NevExcVm1GYG0dfVqfl1RLTrrYc5Qqp1xPz8PFpaWphZR9xeG0TfdBCHh1KwMdWHAnsAGU47dm7bgO7O9vD1VnKOCF1DPpg4iat2BgcHUVpaiqNHj6Kh4Wxbg29961s4cuQI3nzzzfDP/H4/jhw5gurqaorEKIDX66XryiikLbsI1XZ/U5+ggy2B5Z233ZvzNN9OMxbxvrOWvxvZLbvw0VaPkU+jw7LNauV+DAQCeP/993HJJZfAao0fa5G8JiYvLw8WiyXsCYYYGRlBUVFR1N/JyMiAy+UK/2P1BlGb0A4HwR6kLbsI1TZeoXmK2YTVjyg9pQ7EIpniejUhu2UXPtpqYcFICINlm9Xj/Si5E2Oz2bBt2za89NJL4Z8Fg0G89NJLEZEZQlm8Xi9+8YtfwOvl17Oe0A+kLbuI0TZeofnvrq3HVSIK0LVOMsX1akF2yy6kLZuQrtpD8nQyYLnF8o033ohf//rXuOCCC/Dggw/iD3/4A9ra2iJqZULpZFu3boXFor2HDEvMzs6isrIS3d3dcLm033GC4A9pyy5SaBsrRUArqQNyoIfvRnbLLqQtm5CuyiAknUzywn4AuPbaazE2NoZ7770Xw8PD+OhHP4rnn39+TbE/QRAEIS+xFvNaX+QnA8vfjSAIglhGFicGAG699Vbceuutcn08QRAEQRAEQRAGRTYnhg+hTLZAIKDmMAxBMBiE0+lEMBik680YpC27kLbsQtqyC2nLJqSrMoSuLZ9qF1lqYvji8XjwxhtvqPXnCYIgCIIgCILQGBdeeCEcDkfc96jqxASDQfh8PlgsFsphJgiCIAiCIAgDw3EcAoEAbDZbxBmS0VDViSEIgiAIgiAIghCK5OfEEARBEARBEARByAk5MQRBEARBEARB6ApyYgiCIAiCIAiC0BXkxDDGD3/4Q3z84x+H0+lEVlZW1Pf09vbiyiuvhNPpREFBAb75zW/C7/dHvOfVV1/FueeeC7vdjg0bNuCRRx6Rf/CEYCorK2EymSL+3XfffRHv+etf/4pPfOITcDgcKCsrw09/+lOVRksIYf/+/aisrITD4cD27dvx1ltvqT0kQiDf+9731thnbW1t+HWPx4N9+/YhNzcX6enpuOaaazAyMqLiiIlYvPbaa/jsZz+LkpISmEwmPP300xGvcxyHe++9F8XFxUhNTcXOnTtx6tSpiPdMTk7ihhtugMvlQlZWFr7yla9gfn5ewW9BRCORtjfddNMaO7788ssj3kPaqgM5MYzh8/nwxS9+ETfffHPU1wOBAK688kr4fD4cPXoUv/3tb/HII4/g3nvvDb+nq6sLV155JT75yU/ixIkTuPPOO/H3f//3eOGFF5T6GoQAfvCDH2BoaCj877bbbgu/Njs7i8suuwwVFRU4fvw4fvazn+F73/sefvOb36g4YiIRTzzxBO6++25897vfxbvvvoutW7di165dGB0dVXtohEA2b94cYZ+vv/56+LW77roLhw8fxpNPPokjR45gcHAQV199tYqjJWKxsLCArVu3Yv/+/VFf/+lPf4qf//zn+NWvfoU333wTaWlp2LVrFzweT/g9N9xwA5qbm/Hiiy/iz3/+M1577TV87WtfU+orEDFIpC0AXH755RF2/Pjjj0e8TtqqBEcwyYEDB7jMzMw1P//LX/7Cmc1mbnh4OPyzX/7yl5zL5eK8Xi/HcRz3rW99i9u8eXPE71177bXcrl27ZB0zIZyKigrugQceiPn6v/3bv3HZ2dlhbTmO4/7pn/6Jq6mpUWB0hFguuOACbt++feH/DgQCXElJCffjH/9YxVERQvnud7/Lbd26Nepr09PTXEpKCvfkk0+Gf9ba2soB4JqamhQaISEGANxTTz0V/u9gMMgVFRVxP/vZz8I/m56e5ux2O/f4449zHMdxLS0tHADu7bffDr/nueee40wmEzcwMKDY2In4rNaW4zjuxhtv5K666qqYv0PaqgdFYgxGU1MTPvKRj6CwsDD8s127dmF2dhbNzc3h9+zcuTPi93bt2oWmpiZFx0rw47777kNubi4+9rGP4Wc/+1lEamBTUxMuvvhi2Gy28M927dqF9vZ2TE1NqTFcIgE+nw/Hjx+PsEGz2YydO3eSDeqQU6dOoaSkBNXV1bjhhhvQ29sLADh+/DiWlpYidK6trUV5eTnprDO6urowPDwcoWVmZia2b98e1rKpqQlZWVk477zzwu/ZuXMnzGYz3nzzTcXHTAjj1VdfRUFBAWpqanDzzTdjYmIi/Bppqx5WtQdAKMvw8HCEAwMg/N/Dw8Nx3zM7O4vFxUWkpqYqM1giIbfffjvOPfdc5OTk4OjRo7jnnnswNDSE+++/H8CyllVVVRG/s1Lv7OxsxcdMxGd8fByBQCCqDba1tak0KkIM27dvxyOPPIKamhoMDQ3h+9//Pj7xiU/g5MmTGB4ehs1mW1O7WFhYGJ6LCX0Q0iuaza58rhYUFES8brVakZOTQ3prnMsvvxxXX301qqqqcPr0aXz729/GFVdcgaamJlgsFtJWRciJ0QH//M//jJ/85Cdx39Pa2hpRMEroFyF633333eGfnXPOObDZbPj617+OH//4x7Db7XIPlSCIOFxxxRXh/3/OOedg+/btqKiowB/+8AfaDCIInbBnz57w///IRz6Cc845B+vXr8err76KSy+9VMWREeTE6IBvfOMbuOmmm+K+p7q6mtdnFRUVrelyFOqGU1RUFP7f1R1yRkZG4HK56MGrAMnovX37dvj9fnR3d6OmpiamlsBZvQltkZeXB4vFElU30kzfZGVlYdOmTejs7MSnP/1p+Hw+TE9PR0RjSGf9EdJrZGQExcXF4Z+PjIzgox/9aPg9qxtz+P1+TE5Okt46o7q6Gnl5eejs7MSll15K2qoI1cTogPz8fNTW1sb9t7LmIR4NDQ344IMPIgzuxRdfhMvlQn19ffg9L730UsTvvfjii2hoaJDuSxExSUbvEydOwGw2h0PbDQ0NeO2117C0tBR+z4svvoiamhpKJdMoNpsN27Zti7DBYDCIl156iWxQ58zPz+P06dMoLi7Gtm3bkJKSEqFze3s7ent7SWedUVVVhaKioggtZ2dn8eabb4a1bGhowPT0NI4fPx5+z8svv4xgMIjt27crPmZCPP39/ZiYmAg7rKStiqjdWYCQlp6eHu69997jvv/973Pp6ence++9x7333nvc3Nwcx3Ec5/f7uS1btnCXXXYZd+LECe7555/n8vPzuXvuuSf8GWfOnOGcTif3zW9+k2ttbeX279/PWSwW7vnnn1fraxFROHr0KPfAAw9wJ06c4E6fPs3913/9F5efn899+ctfDr9nenqaKyws5Pbu3cudPHmSO3jwIOd0Orlf//rXKo6cSMTBgwc5u93OPfLII1xLSwv3ta99jcvKyoroKkhon2984xvcq6++ynV1dXFvvPEGt3PnTi4vL48bHR3lOI7j/uEf/oErLy/nXn75Ze6dd97hGhoauIaGBpVHTURjbm4u/DwFwN1///3ce++9x/X09HAcx3H33Xcfl5WVxT3zzDPcX//6V+6qq67iqqqquMXFxfBnXH755dzHPvYx7s033+Ref/11buPGjdx1112n1lciPiSetnNzc9w//uM/ck1NTVxXVxf33//939y5557Lbdy4kfN4POHPIG3VgZwYxrjxxhs5AGv+vfLKK+H3dHd3c1dccQWXmprK5eXlcd/4xje4paWliM955ZVXuI9+9KOczWbjqquruQMHDij7RYiEHD9+nNu+fTuXmZnJORwOrq6ujvvRj34UMbFyHMe9//773EUXXcTZ7XautLSUu++++1QaMSGEX/ziF1x5eTlns9m4Cy64gDt27JjaQyIEcu2113LFxcWczWbjSktLuWuvvZbr7OwMv764uMjdcsstXHZ2Nud0OrnPf/7z3NDQkIojJmLxyiuvRH223njjjRzHLbdZ/s53vsMVFhZydrudu/TSS7n29vaIz5iYmOCuu+46Lj09nXO5XFxjY2N4g5FQj3jaut1u7rLLLuPy8/O5lJQUrqKigvvqV7+6ZkOJtFUHE8dxnOLhH4IgCIIgCIIgCJFQTQxBEARBEARBELqCnBiCIAiCIAiCIHQFOTEEQRAEQRAEQegKcmIIgiAIgiAIgtAV5MQQBEEQBEEQBKEryIkhCIIgCIIgCEJXkBNDEARBEARBEISuICeGIAiCIAiCIAhdQU4MQRAEQRAEQRC6gpwYgiAIgiAIgiB0BTkxBEEQBEEQBEHoCnJiCIIgCIIgCILQFf8/vyUPp9iPy7sAAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_cities'))\n", "northern_world = world.cx[ : , 0: ] # subsets all rows above 0 with a slice\n", "northern_world.plot(figsize=(10, 5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_cities'))\n", "southern_world = world.cx[ : , :0 ] # subsets all rows below 0 with a slice\n", "southern_world.plot(figsize=(10, 5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3.10.4 ('tvmx': conda)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.4" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "e579259ee6098e2b9319de590d145b4b096774fe457bdf04260e3ba5c171e887" } } }, "nbformat": 4, "nbformat_minor": 2 }