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"b/0003\350\234\202\347\252\235\345\244\232\350\276\271\345\275\242/ToolData/ToolData.gdb/timestamps" differ diff --git "a/011GDALPython\345\274\200\345\217\221/.ipynb_checkpoints/4.1 GDAL(1)\357\274\232\351\251\261\345\212\250\343\200\201\346\225\260\346\215\256\346\272\220\344\270\216\346\217\217\350\277\260-checkpoint.ipynb" "b/011GDALPython\345\274\200\345\217\221/.ipynb_checkpoints/4.1 GDAL(1)\357\274\232\351\251\261\345\212\250\343\200\201\346\225\260\346\215\256\346\272\220\344\270\216\346\217\217\350\277\260-checkpoint.ipynb" new file mode 100644 index 0000000..dda7b0c --- /dev/null +++ "b/011GDALPython\345\274\200\345\217\221/.ipynb_checkpoints/4.1 GDAL(1)\357\274\232\351\251\261\345\212\250\343\200\201\346\225\260\346\215\256\346\272\220\344\270\216\346\217\217\350\277\260-checkpoint.ipynb" @@ -0,0 +1,359 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "import ogr" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "netCDF\n", + "PCIDSK\n", + "JP2OpenJPEG\n", + "PDF\n", + "DB2ODBC\n", + "ESRI Shapefile\n", + "MapInfo File\n", + "UK .NTF\n", + "OGR_SDTS\n", + "S57\n", + "DGN\n", + "OGR_VRT\n", + "REC\n", + "Memory\n", + "BNA\n", + "CSV\n", + "NAS\n", + "GML\n", + "GPX\n", + "KML\n", + "GeoJSON\n", + "OGR_GMT\n", + "GPKG\n", + "SQLite\n", + "ODBC\n", + "WAsP\n", + "PGeo\n", + "MSSQLSpatial\n", + "PostgreSQL\n", + "OpenFileGDB\n", + "XPlane\n", + "DXF\n", + "CAD\n", + "Geoconcept\n", + "GeoRSS\n", + "GPSTrackMaker\n", + "VFK\n", + "PGDUMP\n", + "OSM\n", + "GPSBabel\n", + "SUA\n", + "OpenAir\n", + "OGR_PDS\n", + "WFS\n", + "HTF\n", + "AeronavFAA\n", + "Geomedia\n", + "EDIGEO\n", + "GFT\n", + "SVG\n", + "CouchDB\n", + "Cloudant\n", + "Idrisi\n", + "ARCGEN\n", + "SEGUKOOA\n", + "SEGY\n", + "XLS\n", + "ODS\n", + "XLSX\n", + "ElasticSearch\n", + "Walk\n", + "Carto\n", + "AmigoCloud\n", + "SXF\n", + "Selafin\n", + "JML\n", + "PLSCENES\n", + "CSW\n", + "VDV\n", + "GMLAS\n", + "TIGER\n", + "AVCBin\n", + "AVCE00\n", + "HTTP\n" + ] + } + ], + "source": [ + "cnt = ogr.GetDriverCount()\n", + "for i in range(cnt):\n", + " driver = ogr.GetDriver(i)\n", + " driverName = driver.GetName()\n", + " print(driverName)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ARCGEN\n", + "AVCBin\n", + "AVCE00\n", + "AeronavFAA\n", + "AmigoCloud\n", + "BNA\n", + "CAD\n", + "CSV\n", + "CSW\n", + "Carto\n", + "Cloudant\n", + "CouchDB\n", + "DB2ODBC\n", + "DGN\n", + "DXF\n", + "EDIGEO\n", + "ESRI Shapefile\n", + "ElasticSearch\n", + "GFT\n", + "GML\n", + "GMLAS\n", + "GPKG\n", + "GPSBabel\n", + "GPSTrackMaker\n", + "GPX\n", + "GeoJSON\n", + "GeoRSS\n", + "Geoconcept\n", + "Geomedia\n", + "HTF\n", + "HTTP\n", + "Idrisi\n", + "JML\n", + "JP2OpenJPEG\n", + "KML\n", + "MSSQLSpatial\n", + "MapInfo File\n", + "Memory\n", + "NAS\n", + "ODBC\n", + "ODS\n", + "OGR_GMT\n", + "OGR_PDS\n", + "OGR_SDTS\n", + "OGR_VRT\n", + "OSM\n", + "OpenAir\n", + "OpenFileGDB\n", + "PCIDSK\n", + "PDF\n", + "PGDUMP\n", + "PGeo\n", + "PLSCENES\n", + "PostgreSQL\n", + "REC\n", + "S57\n", + "SEGUKOOA\n", + "SEGY\n", + "SQLite\n", + "SUA\n", + "SVG\n", + "SXF\n", + "Selafin\n", + "TIGER\n", + "UK .NTF\n", + "VDV\n", + "VFK\n", + "WAsP\n", + "WFS\n", + "Walk\n", + "XLS\n", + "XLSX\n", + "XPlane\n", + "netCDF\n" + ] + } + ], + "source": [ + "drvName = []\n", + "cnt = ogr.GetDriverCount()\n", + "for i in range(cnt):\n", + " driver = ogr.GetDriver(i)\n", + " driverName = driver.GetName()\n", + " drvName.append(driverName)\n", + "drvName.sort()\n", + "for d in drvName:\n", + " print(d)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "path = os.getcwd()+\"/shp/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'D:\\\\workspace\\\\DevWork\\\\PyExample\\\\exam4/shp/'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "path" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "pnt = path + \"北京_point.shp\"" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "driver = ogr.GetDriverByName('ESRI Shapefile')\n", + "dataSource = driver.Open(pnt, 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "layer = dataSource.GetLayerByIndex(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "图层描述 :北京_point\n", + "图层范围 :(115.37294, 117.36857, 39.41652, 41.07743)\n", + "要素数量 :128554\n", + "元数据描述 :{'DBF_DATE_LAST_UPDATE': '2009-05-19'}\n", + "空间参考 :GEOGCS[\"Geographic Coordinate System\",\n", + " DATUM[\"WGS84\",\n", + " SPHEROID[\"WGS84\",6378137.0,298.257223560493]],\n", + " PRIMEM[\"Greenwich\",0.0],\n", + " UNIT[\"degree\",0.0174532925199433],\n", + " AUTHORITY[\"EPSG\",\"4326\"]]\n" + ] + } + ], + "source": [ + "print(\"图层描述 :{0}\".format(layer.GetDescription()))\n", + "print(\"图层范围 :{0}\".format(layer.GetExtent()))\n", + "print(\"要素数量 :{0}\".format(layer.GetFeatureCount()))\n", + "print(\"元数据描述 :{0}\".format(layer.GetMetadata()))\n", + "print(\"空间参考 :{0}\".format(layer.GetSpatialRef()))" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "字段名：NAME 字段类型：4 字段长度：String 字段精度：65\n", + "字段名：LAYER 字段类型：4 字段长度：String 字段精度：21\n", + "字段名：MARINE 字段类型：4 字段长度：String 字段精度：1\n", + "字段名：RegionName 字段类型：4 字段长度：String 字段精度：6\n", + "字段名：DataLevel 字段类型：0 字段长度：Integer 字段精度：1\n", + "字段名：MP_TYPE 字段类型：4 字段长度：String 字段精度：6\n", + "字段名：Phone 字段类型：4 字段长度：String 字段精度：12\n", + "字段名：StreetDesc 字段类型：4 字段长度：String 字段精度：46\n", + "字段名：HighwayIdx 字段类型：0 字段长度：Integer 字段精度：2\n", + "字段名：ZipIdx 字段类型：0 字段长度：Integer 字段精度：1\n", + "字段名：City 字段类型：4 字段长度：String 字段精度：1\n" + ] + } + ], + "source": [ + "layerDefinition = layer.GetLayerDefn()\n", + "for i in range(layerDefinition.GetFieldCount()):\n", + " fieldName = layerDefinition.GetFieldDefn(i).GetName()\n", + " fieldTypeCode = layerDefinition.GetFieldDefn(i).GetType()\n", + " fieldType = layerDefinition.GetFieldDefn(i).GetFieldTypeName(fieldTypeCode)\n", + " fieldWidth = layerDefinition.GetFieldDefn(i).GetWidth()\n", + " GetPrecision = layerDefinition.GetFieldDefn(i).GetPrecision()\n", + " print(\"字段名：{0} 字段类型：{1} 字段长度：{2} \\\n", + " 字段精度：{3}\".format(fieldName,fieldTypeCode,\n", + " fieldType,fieldWidth,GetPrecision))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git "a/011GDALPython\345\274\200\345\217\221/.ipynb_checkpoints/4.2 GDAL(2)\357\274\232\346\225\260\346\215\256\351\201\215\345\216\206-checkpoint.ipynb" "b/011GDALPython\345\274\200\345\217\221/.ipynb_checkpoints/4.2 GDAL(2)\357\274\232\346\225\260\346\215\256\351\201\215\345\216\206-checkpoint.ipynb" new file mode 100644 index 0000000..ed83769 --- /dev/null +++ "b/011GDALPython\345\274\200\345\217\221/.ipynb_checkpoints/4.2 GDAL(2)\357\274\232\346\225\260\346\215\256\351\201\215\345\216\206-checkpoint.ipynb" @@ -0,0 +1,492 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os,ogr\n", + "path = os.getcwd()+\"/shp/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "xzqh = path + \"行政区划.shp\"" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "driver = ogr.GetDriverByName('ESRI Shapefile')\n", + "dataSource = driver.Open(xzqh, 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "layer = dataSource.GetLayerByIndex(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "lyd = layer.GetLayerDefn()\n", + "fields = [lyd.GetFieldDefn(i).GetName() for i in range(lyd.GetFieldCount())]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fields" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NAME= 北京市首都国际机场 \tShape_Leng= 0.25127223204 \tShape_Area= 0.00298354465 \t面积= 28.2665 \t\n", + "POLYGON ((116.61254 40.08221 0,116.61293 40.07839 0,116.61562 40.06989 0,116.6179 40.06092 0,116.62017 40.05941 0,116.62715 40.05691 0,116.62758 40.05678 0,116.63263 40.05502 0,116.63649 40.04647 0,116.63529 40.04355 0,116.63276 40.04262 0,116.63229 40.04245 0,116.63148 40.04215 0,116.61876 40.03993 0,116.60766 40.0384 0,116.60328 40.03711 0,116.60037 40.03548 0,116.59891 40.03243 0,116.59733 40.02694 0,116.59613 40.02531 0,116.59376 40.02432 0,116.58871 40.02492 0,116.58171 40.02677 0,116.57982 40.0269 0,116.5781 40.02707 0,116.57294 40.02753 0,116.56947 40.02865 0,116.56594 40.02972 0,116.56591 40.02976 0,116.56552 40.02989 0,116.56529 40.02994 0,116.56186 40.03104 0,116.56147 40.03104 0,116.56075 40.03113 0,116.56006 40.03126 0,116.5595 40.03138 0,116.55924 40.03147 0,116.55941 40.03194 0,116.55954 40.03237 0,116.55967 40.03267 0,116.55975 40.03276 0,116.56096 40.03366 0,116.56225 40.03443 0,116.56315 40.03499 0,116.56336 40.03512 0,116.56349 40.03533 0,116.56805 40.04015 0,116.56947 40.05071 0,116.57088 40.05525 0,116.57238 40.07032 0,116.57119 40.08341 0,116.57384 40.08483 0,116.57586 40.08495 0,116.58981 40.08603 0,116.58904 40.09685 0,116.59466 40.09741 0,116.60391 40.09062 0,116.61241 40.08328 0,116.61254 40.08221 0))\n", + "NAME= 昌平区 \tShape_Leng= 2.0781475265 \tShape_Area= 0.14133271005 \t面积= 1336.1 \t\n", + "POLYGON ((116.32244 40.38358 0,116.33902 40.37214 0,116.36117 40.36605 0,116.36871 40.35738 0,116.37415 40.35171 0,116.38174 40.34738 0,116.38526 40.33776 0,116.38861 40.33472 0,116.40105 40.33398 0,116.40973 40.32944 0,116.42756 40.32753 0,116.43177 40.32308 0,116.44051 40.32171 0,116.44899 40.31182 0,116.43763 40.30103 0,116.44245 40.29816 0,116.44843 40.28758 0,116.45811 40.28395 0,116.46402 40.27937 0,116.47297 40.27871 0,116.47823 40.27268 0,116.50002 40.26111 0,116.50408 40.25769 0,116.50107 40.25739 0,116.48 40.24402 0,116.47957 40.22767 0,116.47513 40.2232 0,116.47001 40.21713 0,116.46781 40.21417 0,116.46734 40.21172 0,116.46764 40.20936 0,116.46845 40.20284 0,116.47173 40.20053 0,116.48258 40.19078 0,116.48177 40.18193 0,116.47579 40.17376 0,116.47617 40.16665 0,116.47582 40.1657 0,116.48048 40.16198 0,116.48836 40.15801 0,116.48785 40.15355 0,116.48096 40.1413 0,116.48018 40.11928 0,116.48436 40.11565 0,116.48639 40.10698 0,116.4832 40.10062 0,116.45999 40.09339 0,116.45978 40.08417 0,116.45961 40.08331 0,116.45939 40.08279 0,116.45927 40.08211 0,116.45939 40.08181 0,116.45986 40.08112 0,116.45999 40.08082 0,116.46214 40.08036 0,116.45874 40.07979 0,116.45762 40.07948 0,116.45707 40.07927 0,116.45555 40.07845 0,116.45469 40.07772 0,116.45439 40.07729 0,116.45375 40.07626 0,116.45354 40.07562 0,116.45332 40.07471 0,116.4531 40.06715 0,116.45285 40.06698 0,116.45276 40.06595 0,116.45259 40.06509 0,116.4522 40.0641 0,116.45199 40.06368 0,116.45173 40.06325 0,116.45103 40.06239 0,116.44966 40.06114 0,116.44915 40.0608 0,116.44833 40.06032 0,116.4479 40.06015 0,116.44652 40.05968 0,116.44566 40.05951 0,116.4288 40.05943 0,116.4288 40.06012 0,116.42768 40.06012 0,116.42613 40.05999 0,116.42433 40.05981 0,116.42368 40.05968 0,116.42287 40.05951 0,116.42132 40.05903 0,116.42033 40.05865 0,116.41869 40.05779 0,116.41784 40.05728 0,116.41689 40.05659 0,116.41578 40.05555 0,116.4144 40.05379 0,116.41179 40.05006 0,116.41093 40.04898 0,116.41006 40.04765 0,116.40984 40.04752 0,116.40838 40.04684 0,116.40697 40.04628 0,116.40615 40.04594 0,116.40577 40.04559 0,116.40529 40.04546 0,116.40478 40.04512 0,116.40409 40.04456 0,116.4031 40.04306 0,116.40156 40.04139 0,116.40079 40.04006 0,116.40006 40.0392 0,116.39946 40.0386 0,116.39808 40.03761 0,116.39032 40.03263 0,116.3892 40.03203 0,116.38852 40.03177 0,116.38607 40.03113 0,116.38564 40.03143 0,116.38388 40.03379 0,116.38144 40.03675 0,116.38042 40.03825 0,116.3793 40.04006 0,116.37895 40.0407 0,116.3787 40.04207 0,116.37836 40.04285 0,116.37805 40.04336 0,116.37758 40.04401 0,116.37711 40.04443 0,116.37664 40.04469 0,116.3762 40.04487 0,116.37561 40.04496 0,116.37522 40.04509 0,116.37479 40.04526 0,116.37437 40.04552 0,116.37234 40.04702 0,116.37128 40.04788 0,116.37076 40.04835 0,116.3702 40.04899 0,116.3696 40.04994 0,116.36875 40.05153 0,116.36849 40.05182 0,116.36849 40.05324 0,116.36934 40.0553 0,116.37157 40.05715 0,116.37071 40.06041 0,116.37106 40.06114 0,116.37144 40.06157 0,116.37244 40.06183 0,116.37402 40.06191 0,116.37501 40.06178 0,116.37578 40.06182 0,116.37604 40.06198 0,116.37612 40.06233 0,116.37582 40.0625 0,116.37501 40.0628 0,116.37411 40.06315 0,116.36313 40.06818 0,116.36215 40.06806 0,116.36052 40.06818 0,116.35541 40.06914 0,116.34702 40.06568 0,116.33962 40.06234 0,116.33735 40.06085 0,116.33478 40.05818 0,116.33448 40.0581 0,116.33414 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0))\n" + ] + } + ], + "source": [ + "for i in range(layer.GetFeatureCount()):\n", + " feat = layer.GetFeature(i)\n", + " s = \"\"\n", + " for field in fields:\n", + " s +=\"{0}= {1} \\t\".format(field, feat.GetField(field))\n", + " print(s)\n", + " geo = feat.GetGeometryRef()\n", + " print(geo)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "layer.ResetReading()\n", + "feDict = []\n", + "for feature in layer:\n", + " f = {}\n", + " for field in fields:\n", + " f[field] = feature.GetField(field)\n", + " geo = feature.GetGeometryRef()\n", + " f[\"Shape\"] = geo.ExportToWkt()\n", + " feDict.append(f)\n", + "layer.ResetReading()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'NAME': '北京市首都国际机场',\n", + " 'Shape': 'POLYGON ((116.61254 40.08221 0,116.61293 40.07839 0,116.61562 40.06989 0,116.6179 40.06092 0,116.62017 40.05941 0,116.62715 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NAMEShapeShape_AreaShape_Leng面积
0北京市首都国际机场POLYGON ((116.61254 40.08221 0,116.61293 40.07...0.0029840.25127228.2665
1昌平区POLYGON ((116.32244 40.38358 0,116.33902 40.37...0.1413332.0781481336.1000
2朝阳区MULTIPOLYGON (((116.48084 40.07908 0,116.48118...0.0492931.418457467.7140
3大兴区POLYGON ((116.43726 39.81707 0,116.43795 39.81...0.1054082.2331111004.4800
4东城区POLYGON ((116.40311 39.97191 0,116.40423 39.97...0.0044140.45451341.9047
5房山区POLYGON ((115.76348 39.92405 0,115.76959 39.91...0.2033412.4707041936.1600
6丰台区POLYGON ((116.24717 39.8955 0,116.26279 39.895...0.0316001.402951300.3640
7怀柔区POLYGON ((116.66525 41.04418 0,116.67757 41.03...0.2138063.4994652009.1700
8门头沟区POLYGON ((115.78883 40.16542 0,115.79283 40.15...0.1460562.2935221385.3800
9密云县POLYGON ((116.86994 40.79687 0,116.86994 40.74...0.2161302.6649162034.0600
10平谷区POLYGON ((117.3458 40.16933 0,117.34653 40.167...0.0899501.477878850.3080
11石景山区POLYGON ((116.14933 39.98845 0,116.14946 39.98...0.0090140.51000585.5634
12顺义区POLYGON ((116.86575 40.28756 0,116.86893 40.27...0.1067681.8355641010.1700
13通州区POLYGON ((116.61392 40.02513 0,116.61602 40.02...0.0955621.834911908.6750
14西城区POLYGON ((116.382 39.9671 0,116.382 39.96461 0...0.0052340.45012849.6903
15延庆县POLYGON ((116.49608 40.75031 0,116.4957 40.745...0.1944972.6174441830.1600
16海淀区POLYGON ((116.23744 40.1259 0,116.23769 40.117...0.0454101.259671430.4560
\n", + "
" + ], + "text/plain": [ + " NAME Shape Shape_Area \\\n", + "0 北京市首都国际机场 POLYGON ((116.61254 40.08221 0,116.61293 40.07... 0.002984 \n", + "1 昌平区 POLYGON ((116.32244 40.38358 0,116.33902 40.37... 0.141333 \n", + "2 朝阳区 MULTIPOLYGON (((116.48084 40.07908 0,116.48118... 0.049293 \n", + "3 大兴区 POLYGON ((116.43726 39.81707 0,116.43795 39.81... 0.105408 \n", + "4 东城区 POLYGON ((116.40311 39.97191 0,116.40423 39.97... 0.004414 \n", + "5 房山区 POLYGON ((115.76348 39.92405 0,115.76959 39.91... 0.203341 \n", + "6 丰台区 POLYGON ((116.24717 39.8955 0,116.26279 39.895... 0.031600 \n", + "7 怀柔区 POLYGON ((116.66525 41.04418 0,116.67757 41.03... 0.213806 \n", + "8 门头沟区 POLYGON ((115.78883 40.16542 0,115.79283 40.15... 0.146056 \n", + "9 密云县 POLYGON ((116.86994 40.79687 0,116.86994 40.74... 0.216130 \n", + "10 平谷区 POLYGON ((117.3458 40.16933 0,117.34653 40.167... 0.089950 \n", + "11 石景山区 POLYGON ((116.14933 39.98845 0,116.14946 39.98... 0.009014 \n", + "12 顺义区 POLYGON ((116.86575 40.28756 0,116.86893 40.27... 0.106768 \n", + "13 通州区 POLYGON ((116.61392 40.02513 0,116.61602 40.02... 0.095562 \n", + "14 西城区 POLYGON ((116.382 39.9671 0,116.382 39.96461 0... 0.005234 \n", + "15 延庆县 POLYGON ((116.49608 40.75031 0,116.4957 40.745... 0.194497 \n", + "16 海淀区 POLYGON ((116.23744 40.1259 0,116.23769 40.117... 0.045410 \n", + "\n", + " Shape_Leng 面积 \n", + "0 0.251272 28.2665 \n", + "1 2.078148 1336.1000 \n", + "2 1.418457 467.7140 \n", + "3 2.233111 1004.4800 \n", + "4 0.454513 41.9047 \n", + "5 2.470704 1936.1600 \n", + "6 1.402951 300.3640 \n", + "7 3.499465 2009.1700 \n", + "8 2.293522 1385.3800 \n", + "9 2.664916 2034.0600 \n", + "10 1.477878 850.3080 \n", + "11 0.510005 85.5634 \n", + "12 1.835564 1010.1700 \n", + "13 1.834911 908.6750 \n", + "14 0.450128 49.6903 \n", + "15 2.617444 1830.1600 \n", + "16 1.259671 430.4560 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "pd.DataFrame(feDict)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git "a/011GDALPython\345\274\200\345\217\221/.ipynb_checkpoints/4.5 GDAL\357\274\2105\357\274\211\357\274\232\346\225\260\346\215\256\346\240\274\345\274\217\350\275\254\346\215\242 -checkpoint.ipynb" "b/011GDALPython\345\274\200\345\217\221/.ipynb_checkpoints/4.5 GDAL\357\274\2105\357\274\211\357\274\232\346\225\260\346\215\256\346\240\274\345\274\217\350\275\254\346\215\242 -checkpoint.ipynb" new file mode 100644 index 0000000..a2b0588 --- /dev/null +++ "b/011GDALPython\345\274\200\345\217\221/.ipynb_checkpoints/4.5 GDAL\357\274\2105\357\274\211\357\274\232\346\225\260\346\215\256\346\240\274\345\274\217\350\275\254\346\215\242 -checkpoint.ipynb" @@ -0,0 +1,108 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import ogr\n", + "import os\n", + "path = os.getcwd()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "xzqhshp = path +\"/shp/\"+ \"行政区划.shp\"\n", + "outfile = path + \"/xzqh.json\"" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "xzqh = ogr.GetDriverByName(\"ESRI Shapefile\").Open(xzqhshp)\n", + "ogr.GetDriverByName(\"GeoJSON\").CopyDataSource(xzqh, outfile)\n", + "xzqh = None" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " >" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "outfile = path + \"/xzqh.kml\"\n", + "xzqh = ogr.GetDriverByName(\"ESRI Shapefile\").Open(xzqhshp)\n", + "ogr.GetDriverByName(\"KML\").CopyDataSource(xzqh, outfile)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " >" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "outfile = path + \"/xzqh.csv\"\n", + "xzqh = ogr.GetDriverByName(\"ESRI Shapefile\").Open(xzqhshp)\n", + "ogr.GetDriverByName(\"CSV\").CopyDataSource(xzqh, outfile)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git "a/011GDALPython\345\274\200\345\217\221/4.1 GDAL(1)\357\274\232\351\251\261\345\212\250\343\200\201\346\225\260\346\215\256\346\272\220\344\270\216\346\217\217\350\277\260.ipynb" "b/011GDALPython\345\274\200\345\217\221/4.1 GDAL(1)\357\274\232\351\251\261\345\212\250\343\200\201\346\225\260\346\215\256\346\272\220\344\270\216\346\217\217\350\277\260.ipynb" index ec8613f..6ee6db2 100644 --- "a/011GDALPython\345\274\200\345\217\221/4.1 GDAL(1)\357\274\232\351\251\261\345\212\250\343\200\201\346\225\260\346\215\256\346\272\220\344\270\216\346\217\217\350\277\260.ipynb" +++ "b/011GDALPython\345\274\200\345\217\221/4.1 GDAL(1)\357\274\232\351\251\261\345\212\250\343\200\201\346\225\260\346\215\256\346\272\220\344\270\216\346\217\217\350\277\260.ipynb" @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "metadata": { "scrolled": false }, @@ -13,17 +13,19 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "netCDF\n", "PCIDSK\n", - "JP2OpenJPEG\n", + "netCDF\n", + "JP2KAK\n", "PDF\n", + "MBTiles\n", + "EEDA\n", "DB2ODBC\n", "ESRI Shapefile\n", "MapInfo File\n", @@ -41,6 +43,8 @@ "GPX\n", "KML\n", "GeoJSON\n", + "ESRIJSON\n", + "TopoJSON\n", "OGR_GMT\n", "GPKG\n", "SQLite\n", @@ -48,7 +52,6 @@ "WAsP\n", "PGeo\n", "MSSQLSpatial\n", - "PostgreSQL\n", "OpenFileGDB\n", "XPlane\n", "DXF\n", @@ -64,6 +67,7 @@ "OpenAir\n", "OGR_PDS\n", "WFS\n", + "WFS3\n", "HTF\n", "AeronavFAA\n", "Geomedia\n", @@ -90,6 +94,7 @@ "CSW\n", "VDV\n", "GMLAS\n", + "MVT\n", "TIGER\n", "AVCBin\n", "AVCE00\n", @@ -107,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -130,7 +135,9 @@ "DGN\n", "DXF\n", "EDIGEO\n", + "EEDA\n", "ESRI Shapefile\n", + "ESRIJSON\n", "ElasticSearch\n", "GFT\n", "GML\n", @@ -147,9 +154,11 @@ "HTTP\n", "Idrisi\n", "JML\n", - "JP2OpenJPEG\n", + "JP2KAK\n", "KML\n", + "MBTiles\n", "MSSQLSpatial\n", + "MVT\n", "MapInfo File\n", "Memory\n", "NAS\n", @@ -167,7 +176,6 @@ "PGDUMP\n", "PGeo\n", "PLSCENES\n", - "PostgreSQL\n", "REC\n", "S57\n", "SEGUKOOA\n", @@ -178,11 +186,13 @@ "SXF\n", "Selafin\n", "TIGER\n", + "TopoJSON\n", "UK .NTF\n", "VDV\n", "VFK\n", "WAsP\n", "WFS\n", + "WFS3\n", "Walk\n", "XLS\n", "XLSX\n", @@ -351,7 +361,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.7.9" } }, "nbformat": 4, diff --git "a/011GDALPython\345\274\200\345\217\221/4.2 GDAL(2)\357\274\232\346\225\260\346\215\256\351\201\215\345\216\206.ipynb" "b/011GDALPython\345\274\200\345\217\221/4.2 GDAL(2)\357\274\232\346\225\260\346\215\256\351\201\215\345\216\206.ipynb" index cae338d..ed83769 100644 --- "a/011GDALPython\345\274\200\345\217\221/4.2 GDAL(2)\357\274\232\346\225\260\346\215\256\351\201\215\345\216\206.ipynb" +++ "b/011GDALPython\345\274\200\345\217\221/4.2 GDAL(2)\357\274\232\346\225\260\346\215\256\351\201\215\345\216\206.ipynb" @@ -484,7 +484,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.6.10" } }, "nbformat": 4, diff --git "a/011GDALPython\345\274\200\345\217\221/4.5 GDAL\357\274\2105\357\274\211\357\274\232\346\225\260\346\215\256\346\240\274\345\274\217\350\275\254\346\215\242 .ipynb" "b/011GDALPython\345\274\200\345\217\221/4.5 GDAL\357\274\2105\357\274\211\357\274\232\346\225\260\346\215\256\346\240\274\345\274\217\350\275\254\346\215\242 .ipynb" index 2789a52..a2b0588 100644 --- "a/011GDALPython\345\274\200\345\217\221/4.5 GDAL\357\274\2105\357\274\211\357\274\232\346\225\260\346\215\256\346\240\274\345\274\217\350\275\254\346\215\242 .ipynb" +++ 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"a/016GIS\347\256\227\346\263\225\350\256\276\350\256\241\345\222\214\345\272\224\347\224\250\345\274\200\345\217\221\357\274\232Python\347\257\207Demo/work.gdb/timestamps" "b/016GIS\347\256\227\346\263\225\350\256\276\350\256\241\345\222\214\345\272\224\347\224\250\345\274\200\345\217\221\357\274\232Python\347\257\207Demo/work.gdb/timestamps" index bb6fbaf..cdca914 100644 Binary files "a/016GIS\347\256\227\346\263\225\350\256\276\350\256\241\345\222\214\345\272\224\347\224\250\345\274\200\345\217\221\357\274\232Python\347\257\207Demo/work.gdb/timestamps" and "b/016GIS\347\256\227\346\263\225\350\256\276\350\256\241\345\222\214\345\272\224\347\224\250\345\274\200\345\217\221\357\274\232Python\347\257\207Demo/work.gdb/timestamps" differ diff --git "a/020numpy100\351\242\230/numpy-100-master/.ipynb_checkpoints/100_Numpy_exercises-checkpoint.ipynb" "b/020numpy100\351\242\230/numpy-100-master/.ipynb_checkpoints/100_Numpy_exercises-checkpoint.ipynb" new file mode 100644 index 0000000..dc72a04 --- /dev/null +++ "b/020numpy100\351\242\230/numpy-100-master/.ipynb_checkpoints/100_Numpy_exercises-checkpoint.ipynb" @@ -0,0 +1,2176 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 100 numpy exercises\n", + "\n", + "This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow and in the numpy documentation. The goal of this collection is to offer a quick reference for both old and new users but also to provide a set of exercises for those who teach.\n", + "\n", + "\n", + "If you find an error or think you've a better way to solve some of them, feel free to open an issue at " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1. Import the numpy package under the name `np` (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2. Print the numpy version and the configuration (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(np.__version__)\n", + "np.show_config()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3. Create a null vector of size 10 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.zeros(10)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4. How to find the memory size of any array (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.zeros((10,10))\n", + "print(\"%d bytes\" % (Z.size * Z.itemsize))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5. How to get the documentation of the numpy add function from the command line? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%run `python -c \"import numpy; numpy.info(numpy.add)\"`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6. Create a null vector of size 10 but the fifth value which is 1 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.zeros(10)\n", + "Z[4] = 1\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 7. Create a vector with values ranging from 10 to 49 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.arange(10,50)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 8. Reverse a vector (first element becomes last) (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.arange(50)\n", + "Z = Z[::-1]\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 9. Create a 3x3 matrix with values ranging from 0 to 8 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.arange(9).reshape(3,3)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 10. Find indices of non-zero elements from \\[1,2,0,0,4,0\\] (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nz = np.nonzero([1,2,0,0,4,0])\n", + "print(nz)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 11. Create a 3x3 identity matrix (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.eye(3)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 12. Create a 3x3x3 array with random values (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.random.random((3,3,3))\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 13. Create a 10x10 array with random values and find the minimum and maximum values (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.random.random((10,10))\n", + "Zmin, Zmax = Z.min(), Z.max()\n", + "print(Zmin, Zmax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 14. Create a random vector of size 30 and find the mean value (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.random.random(30)\n", + "m = Z.mean()\n", + "print(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 15. Create a 2d array with 1 on the border and 0 inside (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.ones((10,10))\n", + "Z[1:-1,1:-1] = 0\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 16. How to add a border (filled with 0's) around an existing array? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.ones((5,5))\n", + "Z = np.pad(Z, pad_width=1, mode='constant', constant_values=0)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 17. What is the result of the following expression? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(0 * np.nan)\n", + "print(np.nan == np.nan)\n", + "print(np.inf > np.nan)\n", + "print(np.nan - np.nan)\n", + "print(np.nan in set([np.nan]))\n", + "print(0.3 == 3 * 0.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 18. Create a 5x5 matrix with values 1,2,3,4 just below the diagonal (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.diag(1+np.arange(4),k=-1)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 19. Create a 8x8 matrix and fill it with a checkerboard pattern (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.zeros((8,8),dtype=int)\n", + "Z[1::2,::2] = 1\n", + "Z[::2,1::2] = 1\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 20. Consider a (6,7,8) shape array, what is the index (x,y,z) of the 100th element?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(np.unravel_index(99,(6,7,8)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 21. Create a checkerboard 8x8 matrix using the tile function (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.tile( np.array([[0,1],[1,0]]), (4,4))\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 22. Normalize a 5x5 random matrix (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.random.random((5,5))\n", + "Z = (Z - np.mean (Z)) / (np.std (Z))\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 23. Create a custom dtype that describes a color as four unsigned bytes (RGBA) (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "color = np.dtype([(\"r\", np.ubyte, 1),\n", + " (\"g\", np.ubyte, 1),\n", + " (\"b\", np.ubyte, 1),\n", + " (\"a\", np.ubyte, 1)])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 24. Multiply a 5x3 matrix by a 3x2 matrix (real matrix product) (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.dot(np.ones((5,3)), np.ones((3,2)))\n", + "print(Z)\n", + "\n", + "# Alternative solution, in Python 3.5 and above\n", + "Z = np.ones((5,3)) @ np.ones((3,2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 25. Given a 1D array, negate all elements which are between 3 and 8, in place. (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Evgeni Burovski\n", + "\n", + "Z = np.arange(11)\n", + "Z[(3 < Z) & (Z <= 8)] *= -1\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 26. What is the output of the following script? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Jake VanderPlas\n", + "\n", + "print(sum(range(5),-1))\n", + "from numpy import *\n", + "print(sum(range(5),-1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 27. Consider an integer vector Z, which of these expressions are legal? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z**Z\n", + "2 << Z >> 2\n", + "Z <- Z\n", + "1j*Z\n", + "Z/1/1\n", + "ZZ" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 28. What are the result of the following expressions?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(np.array(0) / np.array(0))\n", + "print(np.array(0) // np.array(0))\n", + "print(np.array([np.nan]).astype(int).astype(float))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 29. How to round away from zero a float array ? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Charles R Harris\n", + "\n", + "Z = np.random.uniform(-10,+10,10)\n", + "print (np.copysign(np.ceil(np.abs(Z)), Z))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 30. How to find common values between two arrays? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z1 = np.random.randint(0,10,10)\n", + "Z2 = np.random.randint(0,10,10)\n", + "print(np.intersect1d(Z1,Z2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 31. How to ignore all numpy warnings (not recommended)? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Suicide mode on\n", + "defaults = np.seterr(all=\"ignore\")\n", + "Z = np.ones(1) / 0\n", + "\n", + "# Back to sanity\n", + "_ = np.seterr(**defaults)\n", + "\n", + "An equivalent way, with a context manager:\n", + "\n", + "with np.errstate(divide='ignore'):\n", + " Z = np.ones(1) / 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 32. Is the following expressions true? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.sqrt(-1) == np.emath.sqrt(-1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 33. How to get the dates of yesterday, today and tomorrow? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "yesterday = np.datetime64('today', 'D') - np.timedelta64(1, 'D')\n", + "today = np.datetime64('today', 'D')\n", + "tomorrow = np.datetime64('today', 'D') + np.timedelta64(1, 'D')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 34. How to get all the dates corresponding to the month of July 2016? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.arange('2016-07', '2016-08', dtype='datetime64[D]')\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 35. How to compute ((A+B)\\*(-A/2)) in place (without copy)? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "A = np.ones(3)*1\n", + "B = np.ones(3)*2\n", + "C = np.ones(3)*3\n", + "np.add(A,B,out=B)\n", + "np.divide(A,2,out=A)\n", + "np.negative(A,out=A)\n", + "np.multiply(A,B,out=A)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 36. Extract the integer part of a random array using 5 different methods (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.random.uniform(0,10,10)\n", + "\n", + "print (Z - Z%1)\n", + "print (np.floor(Z))\n", + "print (np.ceil(Z)-1)\n", + "print (Z.astype(int))\n", + "print (np.trunc(Z))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 37. Create a 5x5 matrix with row values ranging from 0 to 4 (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.zeros((5,5))\n", + "Z += np.arange(5)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 38. Consider a generator function that generates 10 integers and use it to build an array (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def generate():\n", + " for x in range(10):\n", + " yield x\n", + "Z = np.fromiter(generate(),dtype=float,count=-1)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 39. Create a vector of size 10 with values ranging from 0 to 1, both excluded (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.linspace(0,1,11,endpoint=False)[1:]\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 40. Create a random vector of size 10 and sort it (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.random.random(10)\n", + "Z.sort()\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 41. How to sum a small array faster than np.sum? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Evgeni Burovski\n", + "\n", + "Z = np.arange(10)\n", + "np.add.reduce(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 42. Consider two random array A and B, check if they are equal (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "A = np.random.randint(0,2,5)\n", + "B = np.random.randint(0,2,5)\n", + "\n", + "# Assuming identical shape of the arrays and a tolerance for the comparison of values\n", + "equal = np.allclose(A,B)\n", + "print(equal)\n", + "\n", + "# Checking both the shape and the element values, no tolerance (values have to be exactly equal)\n", + "equal = np.array_equal(A,B)\n", + "print(equal)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 43. Make an array immutable (read-only) (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.zeros(10)\n", + "Z.flags.writeable = False\n", + "Z[0] = 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 44. Consider a random 10x2 matrix representing cartesian coordinates, convert them to polar coordinates (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.random.random((10,2))\n", + "X,Y = Z[:,0], Z[:,1]\n", + "R = np.sqrt(X**2+Y**2)\n", + "T = np.arctan2(Y,X)\n", + "print(R)\n", + "print(T)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 45. Create random vector of size 10 and replace the maximum value by 0 (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.random.random(10)\n", + "Z[Z.argmax()] = 0\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 46. Create a structured array with `x` and `y` coordinates covering the \\[0,1\\]x\\[0,1\\] area (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.zeros((5,5), [('x',float),('y',float)])\n", + "Z['x'], Z['y'] = np.meshgrid(np.linspace(0,1,5),\n", + " np.linspace(0,1,5))\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 47. Given two arrays, X and Y, construct the Cauchy matrix C (Cij =1/(xi - yj))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Evgeni Burovski\n", + "\n", + "X = np.arange(8)\n", + "Y = X + 0.5\n", + "C = 1.0 / np.subtract.outer(X, Y)\n", + "print(np.linalg.det(C))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 48. Print the minimum and maximum representable value for each numpy scalar type (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for dtype in [np.int8, np.int32, np.int64]:\n", + " print(np.iinfo(dtype).min)\n", + " print(np.iinfo(dtype).max)\n", + "for dtype in [np.float32, np.float64]:\n", + " print(np.finfo(dtype).min)\n", + " print(np.finfo(dtype).max)\n", + " print(np.finfo(dtype).eps)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 49. How to print all the values of an array? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.set_printoptions(threshold=np.nan)\n", + "Z = np.zeros((16,16))\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 50. How to find the closest value (to a given scalar) in a vector? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.arange(100)\n", + "v = np.random.uniform(0,100)\n", + "index = (np.abs(Z-v)).argmin()\n", + "print(Z[index])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 51. Create a structured array representing a position (x,y) and a color (r,g,b) (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.zeros(10, [ ('position', [ ('x', float, 1),\n", + " ('y', float, 1)]),\n", + " ('color', [ ('r', float, 1),\n", + " ('g', float, 1),\n", + " ('b', float, 1)])])\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 52. Consider a random vector with shape (100,2) representing coordinates, find point by point distances (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.random.random((10,2))\n", + "X,Y = np.atleast_2d(Z[:,0], Z[:,1])\n", + "D = np.sqrt( (X-X.T)**2 + (Y-Y.T)**2)\n", + "print(D)\n", + "\n", + "# Much faster with scipy\n", + "import scipy\n", + "# Thanks Gavin Heverly-Coulson (#issue 1)\n", + "import scipy.spatial\n", + "\n", + "Z = np.random.random((10,2))\n", + "D = scipy.spatial.distance.cdist(Z,Z)\n", + "print(D)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 53. How to convert a float (32 bits) array into an integer (32 bits) in place?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Thanks Vikas (https://stackoverflow.com/a/10622758/5989906) \n", + "# & unutbu (https://stackoverflow.com/a/4396247/5989906)\n", + "Z = (np.random.rand(10)*100).astype(np.float32)\n", + "Y = Z.view(np.int32)\n", + "Y[:] = Z\n", + "print(Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 54. How to read the following file? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from io import StringIO\n", + "\n", + "# Fake file \n", + "s = StringIO(\"\"\"1, 2, 3, 4, 5\\n\n", + " 6, , , 7, 8\\n\n", + " , , 9,10,11\\n\"\"\")\n", + "Z = np.genfromtxt(s, delimiter=\",\", dtype=np.int)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 55. What is the equivalent of enumerate for numpy arrays? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.arange(9).reshape(3,3)\n", + "for index, value in np.ndenumerate(Z):\n", + " print(index, value)\n", + "for index in np.ndindex(Z.shape):\n", + " print(index, Z[index])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 56. Generate a generic 2D Gaussian-like array (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X, Y = np.meshgrid(np.linspace(-1,1,10), np.linspace(-1,1,10))\n", + "D = np.sqrt(X*X+Y*Y)\n", + "sigma, mu = 1.0, 0.0\n", + "G = np.exp(-( (D-mu)**2 / ( 2.0 * sigma**2 ) ) )\n", + "print(G)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 57. How to randomly place p elements in a 2D array? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Divakar\n", + "\n", + "n = 10\n", + "p = 3\n", + "Z = np.zeros((n,n))\n", + "np.put(Z, np.random.choice(range(n*n), p, replace=False),1)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 58. Subtract the mean of each row of a matrix (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Warren Weckesser\n", + "\n", + "X = np.random.rand(5, 10)\n", + "\n", + "# Recent versions of numpy\n", + "Y = X - X.mean(axis=1, keepdims=True)\n", + "\n", + "# Older versions of numpy\n", + "Y = X - X.mean(axis=1).reshape(-1, 1)\n", + "\n", + "print(Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 59. How to sort an array by the nth column? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Steve Tjoa\n", + "\n", + "Z = np.random.randint(0,10,(3,3))\n", + "print(Z)\n", + "print(Z[Z[:,1].argsort()])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 60. How to tell if a given 2D array has null columns? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Warren Weckesser\n", + "\n", + "Z = np.random.randint(0,3,(3,10))\n", + "print((~Z.any(axis=0)).any())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 61. Find the nearest value from a given value in an array (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.random.uniform(0,1,10)\n", + "z = 0.5\n", + "m = Z.flat[np.abs(Z - z).argmin()]\n", + "print(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 62. Considering two arrays with shape (1,3) and (3,1), how to compute their sum using an iterator? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "A = np.arange(3).reshape(3,1)\n", + "B = np.arange(3).reshape(1,3)\n", + "it = np.nditer([A,B,None])\n", + "for x,y,z in it: z[...] = x + y\n", + "print(it.operands[2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 63. Create an array class that has a name attribute (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class NamedArray(np.ndarray):\n", + " def __new__(cls, array, name=\"no name\"):\n", + " obj = np.asarray(array).view(cls)\n", + " obj.name = name\n", + " return obj\n", + " def __array_finalize__(self, obj):\n", + " if obj is None: return\n", + " self.info = getattr(obj, 'name', \"no name\")\n", + "\n", + "Z = NamedArray(np.arange(10), \"range_10\")\n", + "print (Z.name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 64. Consider a given vector, how to add 1 to each element indexed by a second vector (be careful with repeated indices)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Brett Olsen\n", + "\n", + "Z = np.ones(10)\n", + "I = np.random.randint(0,len(Z),20)\n", + "Z += np.bincount(I, minlength=len(Z))\n", + "print(Z)\n", + "\n", + "# Another solution\n", + "# Author: Bartosz Telenczuk\n", + "np.add.at(Z, I, 1)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 65. How to accumulate elements of a vector (X) to an array (F) based on an index list (I)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Alan G Isaac\n", + "\n", + "X = [1,2,3,4,5,6]\n", + "I = [1,3,9,3,4,1]\n", + "F = np.bincount(I,X)\n", + "print(F)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 66. Considering a (w,h,3) image of (dtype=ubyte), compute the number of unique colors (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Nadav Horesh\n", + "\n", + "w,h = 16,16\n", + "I = np.random.randint(0,2,(h,w,3)).astype(np.ubyte)\n", + "#Note that we should compute 256*256 first. \n", + "#Otherwise numpy will only promote F.dtype to 'uint16' and overfolw will occur\n", + "F = I[...,0]*(256*256) + I[...,1]*256 +I[...,2]\n", + "n = len(np.unique(F))\n", + "print(n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 67. Considering a four dimensions array, how to get sum over the last two axis at once? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "A = np.random.randint(0,10,(3,4,3,4))\n", + "# solution by passing a tuple of axes (introduced in numpy 1.7.0)\n", + "sum = A.sum(axis=(-2,-1))\n", + "print(sum)\n", + "# solution by flattening the last two dimensions into one\n", + "# (useful for functions that don't accept tuples for axis argument)\n", + "sum = A.reshape(A.shape[:-2] + (-1,)).sum(axis=-1)\n", + "print(sum)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 68. Considering a one-dimensional vector D, how to compute means of subsets of D using a vector S of same size describing subset indices? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Jaime Fernández del Río\n", + "\n", + "D = np.random.uniform(0,1,100)\n", + "S = np.random.randint(0,10,100)\n", + "D_sums = np.bincount(S, weights=D)\n", + "D_counts = np.bincount(S)\n", + "D_means = D_sums / D_counts\n", + "print(D_means)\n", + "\n", + "# Pandas solution as a reference due to more intuitive code\n", + "import pandas as pd\n", + "print(pd.Series(D).groupby(S).mean())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 69. How to get the diagonal of a dot product? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Mathieu Blondel\n", + "\n", + "A = np.random.uniform(0,1,(5,5))\n", + "B = np.random.uniform(0,1,(5,5))\n", + "\n", + "# Slow version \n", + "np.diag(np.dot(A, B))\n", + "\n", + "# Fast version\n", + "np.sum(A * B.T, axis=1)\n", + "\n", + "# Faster version\n", + "np.einsum(\"ij,ji->i\", A, B)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 70. Consider the vector \\[1, 2, 3, 4, 5\\], how to build a new vector with 3 consecutive zeros interleaved between each value? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Warren Weckesser\n", + "\n", + "Z = np.array([1,2,3,4,5])\n", + "nz = 3\n", + "Z0 = np.zeros(len(Z) + (len(Z)-1)*(nz))\n", + "Z0[::nz+1] = Z\n", + "print(Z0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 71. Consider an array of dimension (5,5,3), how to mulitply it by an array with dimensions (5,5)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "A = np.ones((5,5,3))\n", + "B = 2*np.ones((5,5))\n", + "print(A * B[:,:,None])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 72. How to swap two rows of an array? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Eelco Hoogendoorn\n", + "\n", + "A = np.arange(25).reshape(5,5)\n", + "A[[0,1]] = A[[1,0]]\n", + "print(A)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 73. Consider a set of 10 triplets describing 10 triangles (with shared vertices), find the set of unique line segments composing all the triangles (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Nicolas P. Rougier\n", + "\n", + "faces = np.random.randint(0,100,(10,3))\n", + "F = np.roll(faces.repeat(2,axis=1),-1,axis=1)\n", + "F = F.reshape(len(F)*3,2)\n", + "F = np.sort(F,axis=1)\n", + "G = F.view( dtype=[('p0',F.dtype),('p1',F.dtype)] )\n", + "G = np.unique(G)\n", + "print(G)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 74. Given an array C that is a bincount, how to produce an array A such that np.bincount(A) == C? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Jaime Fernández del Río\n", + "\n", + "C = np.bincount([1,1,2,3,4,4,6])\n", + "A = np.repeat(np.arange(len(C)), C)\n", + "print(A)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 75. How to compute averages using a sliding window over an array? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Jaime Fernández del Río\n", + "\n", + "def moving_average(a, n=3) :\n", + " ret = np.cumsum(a, dtype=float)\n", + " ret[n:] = ret[n:] - ret[:-n]\n", + " return ret[n - 1:] / n\n", + "Z = np.arange(20)\n", + "print(moving_average(Z, n=3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 76. Consider a one-dimensional array Z, build a two-dimensional array whose first row is (Z\\[0\\],Z\\[1\\],Z\\[2\\]) and each subsequent row is shifted by 1 (last row should be (Z\\[-3\\],Z\\[-2\\],Z\\[-1\\]) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Joe Kington / Erik Rigtorp\n", + "from numpy.lib import stride_tricks\n", + "\n", + "def rolling(a, window):\n", + " shape = (a.size - window + 1, window)\n", + " strides = (a.itemsize, a.itemsize)\n", + " return stride_tricks.as_strided(a, shape=shape, strides=strides)\n", + "Z = rolling(np.arange(10), 3)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 77. How to negate a boolean, or to change the sign of a float inplace? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Nathaniel J. Smith\n", + "\n", + "Z = np.random.randint(0,2,100)\n", + "np.logical_not(Z, out=Z)\n", + "\n", + "Z = np.random.uniform(-1.0,1.0,100)\n", + "np.negative(Z, out=Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 78. Consider 2 sets of points P0,P1 describing lines (2d) and a point p, how to compute distance from p to each line i (P0\\[i\\],P1\\[i\\])? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def distance(P0, P1, p):\n", + " T = P1 - P0\n", + " L = (T**2).sum(axis=1)\n", + " U = -((P0[:,0]-p[...,0])*T[:,0] + (P0[:,1]-p[...,1])*T[:,1]) / L\n", + " U = U.reshape(len(U),1)\n", + " D = P0 + U*T - p\n", + " return np.sqrt((D**2).sum(axis=1))\n", + "\n", + "P0 = np.random.uniform(-10,10,(10,2))\n", + "P1 = np.random.uniform(-10,10,(10,2))\n", + "p = np.random.uniform(-10,10,( 1,2))\n", + "print(distance(P0, P1, p))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 79. Consider 2 sets of points P0,P1 describing lines (2d) and a set of points P, how to compute distance from each point j (P\\[j\\]) to each line i (P0\\[i\\],P1\\[i\\])? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Italmassov Kuanysh\n", + "\n", + "# based on distance function from previous question\n", + "P0 = np.random.uniform(-10, 10, (10,2))\n", + "P1 = np.random.uniform(-10,10,(10,2))\n", + "p = np.random.uniform(-10, 10, (10,2))\n", + "print(np.array([distance(P0,P1,p_i) for p_i in p]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 80. Consider an arbitrary array, write a function that extract a subpart with a fixed shape and centered on a given element (pad with a `fill` value when necessary) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Nicolas Rougier\n", + "\n", + "Z = np.random.randint(0,10,(10,10))\n", + "shape = (5,5)\n", + "fill = 0\n", + "position = (1,1)\n", + "\n", + "R = np.ones(shape, dtype=Z.dtype)*fill\n", + "P = np.array(list(position)).astype(int)\n", + "Rs = np.array(list(R.shape)).astype(int)\n", + "Zs = np.array(list(Z.shape)).astype(int)\n", + "\n", + "R_start = np.zeros((len(shape),)).astype(int)\n", + "R_stop = np.array(list(shape)).astype(int)\n", + "Z_start = (P-Rs//2)\n", + "Z_stop = (P+Rs//2)+Rs%2\n", + "\n", + "R_start = (R_start - np.minimum(Z_start,0)).tolist()\n", + "Z_start = (np.maximum(Z_start,0)).tolist()\n", + "R_stop = np.maximum(R_start, (R_stop - np.maximum(Z_stop-Zs,0))).tolist()\n", + "Z_stop = (np.minimum(Z_stop,Zs)).tolist()\n", + "\n", + "r = [slice(start,stop) for start,stop in zip(R_start,R_stop)]\n", + "z = [slice(start,stop) for start,stop in zip(Z_start,Z_stop)]\n", + "R[r] = Z[z]\n", + "print(Z)\n", + "print(R)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 81. Consider an array Z = \\[1,2,3,4,5,6,7,8,9,10,11,12,13,14\\], how to generate an array R = \\[\\[1,2,3,4\\], \\[2,3,4,5\\], \\[3,4,5,6\\], ..., \\[11,12,13,14\\]\\]? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Stefan van der Walt\n", + "\n", + "Z = np.arange(1,15,dtype=np.uint32)\n", + "R = stride_tricks.as_strided(Z,(11,4),(4,4))\n", + "print(R)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 82. Compute a matrix rank (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Stefan van der Walt\n", + "\n", + "Z = np.random.uniform(0,1,(10,10))\n", + "U, S, V = np.linalg.svd(Z) # Singular Value Decomposition\n", + "rank = np.sum(S > 1e-10)\n", + "print(rank)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 83. How to find the most frequent value in an array?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.random.randint(0,10,50)\n", + "print(np.bincount(Z).argmax())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 84. Extract all the contiguous 3x3 blocks from a random 10x10 matrix (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Chris Barker\n", + "\n", + "Z = np.random.randint(0,5,(10,10))\n", + "n = 3\n", + "i = 1 + (Z.shape[0]-3)\n", + "j = 1 + (Z.shape[1]-3)\n", + "C = stride_tricks.as_strided(Z, shape=(i, j, n, n), strides=Z.strides + Z.strides)\n", + "print(C)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 85. Create a 2D array subclass such that Z\\[i,j\\] == Z\\[j,i\\] (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Eric O. Lebigot\n", + "# Note: only works for 2d array and value setting using indices\n", + "\n", + "class Symetric(np.ndarray):\n", + " def __setitem__(self, index, value):\n", + " i,j = index\n", + " super(Symetric, self).__setitem__((i,j), value)\n", + " super(Symetric, self).__setitem__((j,i), value)\n", + "\n", + "def symetric(Z):\n", + " return np.asarray(Z + Z.T - np.diag(Z.diagonal())).view(Symetric)\n", + "\n", + "S = symetric(np.random.randint(0,10,(5,5)))\n", + "S[2,3] = 42\n", + "print(S)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 86. Consider a set of p matrices wich shape (n,n) and a set of p vectors with shape (n,1). How to compute the sum of of the p matrix products at once? (result has shape (n,1)) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Stefan van der Walt\n", + "\n", + "p, n = 10, 20\n", + "M = np.ones((p,n,n))\n", + "V = np.ones((p,n,1))\n", + "S = np.tensordot(M, V, axes=[[0, 2], [0, 1]])\n", + "print(S)\n", + "\n", + "# It works, because:\n", + "# M is (p,n,n)\n", + "# V is (p,n,1)\n", + "# Thus, summing over the paired axes 0 and 0 (of M and V independently),\n", + "# and 2 and 1, to remain with a (n,1) vector." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 87. Consider a 16x16 array, how to get the block-sum (block size is 4x4)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Robert Kern\n", + "\n", + "Z = np.ones((16,16))\n", + "k = 4\n", + "S = np.add.reduceat(np.add.reduceat(Z, np.arange(0, Z.shape[0], k), axis=0),\n", + " np.arange(0, Z.shape[1], k), axis=1)\n", + "print(S)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 88. How to implement the Game of Life using numpy arrays? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Nicolas Rougier\n", + "\n", + "def iterate(Z):\n", + " # Count neighbours\n", + " N = (Z[0:-2,0:-2] + Z[0:-2,1:-1] + Z[0:-2,2:] +\n", + " Z[1:-1,0:-2] + Z[1:-1,2:] +\n", + " Z[2: ,0:-2] + Z[2: ,1:-1] + Z[2: ,2:])\n", + "\n", + " # Apply rules\n", + " birth = (N==3) & (Z[1:-1,1:-1]==0)\n", + " survive = ((N==2) | (N==3)) & (Z[1:-1,1:-1]==1)\n", + " Z[...] = 0\n", + " Z[1:-1,1:-1][birth | survive] = 1\n", + " return Z\n", + "\n", + "Z = np.random.randint(0,2,(50,50))\n", + "for i in range(100): Z = iterate(Z)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 89. How to get the n largest values of an array (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.arange(10000)\n", + "np.random.shuffle(Z)\n", + "n = 5\n", + "\n", + "# Slow\n", + "print (Z[np.argsort(Z)[-n:]])\n", + "\n", + "# Fast\n", + "print (Z[np.argpartition(-Z,n)[:n]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 90. Given an arbitrary number of vectors, build the cartesian product (every combinations of every item) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Author: Stefan Van der Walt\n", + "\n", + "def cartesian(arrays):\n", + " arrays = [np.asarray(a) for a in arrays]\n", + " shape = (len(x) for x in arrays)\n", + "\n", + " ix = np.indices(shape, dtype=int)\n", + " ix = ix.reshape(len(arrays), -1).T\n", + "\n", + " for n, arr in enumerate(arrays):\n", + " ix[:, n] = arrays[n][ix[:, n]]\n", + "\n", + " return ix\n", + "\n", + "print (cartesian(([1, 2, 3], [4, 5], [6, 7])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 91. How to create a record array from a regular array? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.array([(\"Hello\", 2.5, 3),\n", + " (\"World\", 3.6, 2)])\n", + "R = np.core.records.fromarrays(Z.T, \n", + " names='col1, col2, col3',\n", + " formats = 'S8, f8, i8')\n", + "print(R)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 92. Consider a large vector Z, compute Z to the power of 3 using 3 different methods (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Ryan G.\n", + "\n", + "x = np.random.rand(int(5e7))\n", + "\n", + "%timeit np.power(x,3)\n", + "%timeit x*x*x\n", + "%timeit np.einsum('i,i,i->i',x,x,x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 93. Consider two arrays A and B of shape (8,3) and (2,2). How to find rows of A that contain elements of each row of B regardless of the order of the elements in B? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Gabe Schwartz\n", + "\n", + "A = np.random.randint(0,5,(8,3))\n", + "B = np.random.randint(0,5,(2,2))\n", + "\n", + "C = (A[..., np.newaxis, np.newaxis] == B)\n", + "rows = np.where(C.any((3,1)).all(1))[0]\n", + "print(rows)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 94. Considering a 10x3 matrix, extract rows with unequal values (e.g. \\[2,2,3\\]) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Robert Kern\n", + "\n", + "Z = np.random.randint(0,5,(10,3))\n", + "print(Z)\n", + "# solution for arrays of all dtypes (including string arrays and record arrays)\n", + "E = np.all(Z[:,1:] == Z[:,:-1], axis=1)\n", + "U = Z[~E]\n", + "print(U)\n", + "# soluiton for numerical arrays only, will work for any number of columns in Z\n", + "U = Z[Z.max(axis=1) != Z.min(axis=1),:]\n", + "print(U)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 95. Convert a vector of ints into a matrix binary representation (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Warren Weckesser\n", + "\n", + "I = np.array([0, 1, 2, 3, 15, 16, 32, 64, 128])\n", + "B = ((I.reshape(-1,1) & (2**np.arange(8))) != 0).astype(int)\n", + "print(B[:,::-1])\n", + "\n", + "# Author: Daniel T. McDonald\n", + "\n", + "I = np.array([0, 1, 2, 3, 15, 16, 32, 64, 128], dtype=np.uint8)\n", + "print(np.unpackbits(I[:, np.newaxis], axis=1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 96. Given a two dimensional array, how to extract unique rows? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Jaime Fernández del Río\n", + "\n", + "Z = np.random.randint(0,2,(6,3))\n", + "T = np.ascontiguousarray(Z).view(np.dtype((np.void, Z.dtype.itemsize * Z.shape[1])))\n", + "_, idx = np.unique(T, return_index=True)\n", + "uZ = Z[idx]\n", + "print(uZ)\n", + "\n", + "# Author: Andreas Kouzelis\n", + "# NumPy >= 1.13\n", + "uZ = np.unique(Z, axis=0)\n", + "print(uZ)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 97. Considering 2 vectors A & B, write the einsum equivalent of inner, outer, sum, and mul function (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Alex Riley\n", + "# Make sure to read: http://ajcr.net/Basic-guide-to-einsum/\n", + "\n", + "A = np.random.uniform(0,1,10)\n", + "B = np.random.uniform(0,1,10)\n", + "\n", + "np.einsum('i->', A) # np.sum(A)\n", + "np.einsum('i,i->i', A, B) # A * B\n", + "np.einsum('i,i', A, B) # np.inner(A, B)\n", + "np.einsum('i,j->ij', A, B) # np.outer(A, B)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 98. Considering a path described by two vectors (X,Y), how to sample it using equidistant samples (★★★)?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Bas Swinckels\n", + "\n", + "phi = np.arange(0, 10*np.pi, 0.1)\n", + "a = 1\n", + "x = a*phi*np.cos(phi)\n", + "y = a*phi*np.sin(phi)\n", + "\n", + "dr = (np.diff(x)**2 + np.diff(y)**2)**.5 # segment lengths\n", + "r = np.zeros_like(x)\n", + "r[1:] = np.cumsum(dr) # integrate path\n", + "r_int = np.linspace(0, r.max(), 200) # regular spaced path\n", + "x_int = np.interp(r_int, r, x) # integrate path\n", + "y_int = np.interp(r_int, r, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 99. Given an integer n and a 2D array X, select from X the rows which can be interpreted as draws from a multinomial distribution with n degrees, i.e., the rows which only contain integers and which sum to n. (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Evgeni Burovski\n", + "\n", + "X = np.asarray([[1.0, 0.0, 3.0, 8.0],\n", + " [2.0, 0.0, 1.0, 1.0],\n", + " [1.5, 2.5, 1.0, 0.0]])\n", + "n = 4\n", + "M = np.logical_and.reduce(np.mod(X, 1) == 0, axis=-1)\n", + "M &= (X.sum(axis=-1) == n)\n", + "print(X[M])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 100. Compute bootstrapped 95% confidence intervals for the mean of a 1D array X (i.e., resample the elements of an array with replacement N times, compute the mean of each sample, and then compute percentiles over the means). (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Jessica B. Hamrick\n", + "\n", + "X = np.random.randn(100) # random 1D array\n", + "N = 1000 # number of bootstrap samples\n", + "idx = np.random.randint(0, X.size, (N, X.size))\n", + "means = X[idx].mean(axis=1)\n", + "confint = np.percentile(means, [2.5, 97.5])\n", + "print(confint)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git "a/020numpy100\351\242\230/numpy-100-master/.ipynb_checkpoints/100_Numpy_exercises_no_solution-checkpoint.ipynb" "b/020numpy100\351\242\230/numpy-100-master/.ipynb_checkpoints/100_Numpy_exercises_no_solution-checkpoint.ipynb" new file mode 100644 index 0000000..6be33bb --- /dev/null +++ "b/020numpy100\351\242\230/numpy-100-master/.ipynb_checkpoints/100_Numpy_exercises_no_solution-checkpoint.ipynb" @@ -0,0 +1,1525 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 100 numpy exercises\n", + "\n", + "This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow and in the numpy documentation. The goal of this collection is to offer a quick reference for both old and new users but also to provide a set of exercises for those who teach.\n", + "\n", + "\n", + "If you find an error or think you've a better way to solve some of them, feel free to open an issue at " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1. Import the numpy package under the name `np` (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2. Print the numpy version and the configuration (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3. Create a null vector of size 10 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4. How to find the memory size of any array (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5. How to get the documentation of the numpy add function from the command line? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6. Create a null vector of size 10 but the fifth value which is 1 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 7. Create a vector with values ranging from 10 to 49 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 8. Reverse a vector (first element becomes last) (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 9. Create a 3x3 matrix with values ranging from 0 to 8 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 10. Find indices of non-zero elements from \\[1,2,0,0,4,0\\] (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 11. Create a 3x3 identity matrix (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 12. Create a 3x3x3 array with random values (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 13. Create a 10x10 array with random values and find the minimum and maximum values (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 14. Create a random vector of size 30 and find the mean value (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 15. Create a 2d array with 1 on the border and 0 inside (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 16. How to add a border (filled with 0's) around an existing array? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 17. What is the result of the following expression? (★☆☆)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "0 * np.nan\n", + "np.nan == np.nan\n", + "np.inf > np.nan\n", + "np.nan - np.nan\n", + "np.nan in set([np.nan])\n", + "0.3 == 3 * 0.1\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 18. Create a 5x5 matrix with values 1,2,3,4 just below the diagonal (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 19. Create a 8x8 matrix and fill it with a checkerboard pattern (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 20. Consider a (6,7,8) shape array, what is the index (x,y,z) of the 100th element?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 21. Create a checkerboard 8x8 matrix using the tile function (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 22. Normalize a 5x5 random matrix (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 23. Create a custom dtype that describes a color as four unsigned bytes (RGBA) (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 24. Multiply a 5x3 matrix by a 3x2 matrix (real matrix product) (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 25. Given a 1D array, negate all elements which are between 3 and 8, in place. (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 26. What is the output of the following script? (★☆☆)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "# Author: Jake VanderPlas\n", + "\n", + "print(sum(range(5),-1))\n", + "from numpy import *\n", + "print(sum(range(5),-1))\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 27. Consider an integer vector Z, which of these expressions are legal? (★☆☆)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "Z**Z\n", + "2 << Z >> 2\n", + "Z <- Z\n", + "1j*Z\n", + "Z/1/1\n", + "ZZ\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 28. What are the result of the following expressions?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "np.array(0) / np.array(0)\n", + "np.array(0) // np.array(0)\n", + "np.array([np.nan]).astype(int).astype(float)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 29. How to round away from zero a float array ? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 30. How to find common values between two arrays? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 31. How to ignore all numpy warnings (not recommended)? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 32. Is the following expressions true? (★☆☆)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "np.sqrt(-1) == np.emath.sqrt(-1)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 33. How to get the dates of yesterday, today and tomorrow? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 34. How to get all the dates corresponding to the month of July 2016? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 35. How to compute ((A+B)\\*(-A/2)) in place (without copy)? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 36. Extract the integer part of a random array using 5 different methods (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 37. Create a 5x5 matrix with row values ranging from 0 to 4 (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 38. Consider a generator function that generates 10 integers and use it to build an array (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 39. Create a vector of size 10 with values ranging from 0 to 1, both excluded (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 40. Create a random vector of size 10 and sort it (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 41. How to sum a small array faster than np.sum? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 42. Consider two random array A and B, check if they are equal (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 43. Make an array immutable (read-only) (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 44. Consider a random 10x2 matrix representing cartesian coordinates, convert them to polar coordinates (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 45. Create random vector of size 10 and replace the maximum value by 0 (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 46. Create a structured array with `x` and `y` coordinates covering the \\[0,1\\]x\\[0,1\\] area (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 47. Given two arrays, X and Y, construct the Cauchy matrix C (Cij =1/(xi - yj))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 48. Print the minimum and maximum representable value for each numpy scalar type (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 49. How to print all the values of an array? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 50. How to find the closest value (to a given scalar) in a vector? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 51. Create a structured array representing a position (x,y) and a color (r,g,b) (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 52. Consider a random vector with shape (100,2) representing coordinates, find point by point distances (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 53. How to convert a float (32 bits) array into an integer (32 bits) in place?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 54. How to read the following file? (★★☆)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```\n", + "1, 2, 3, 4, 5\n", + "6, , , 7, 8\n", + " , , 9,10,11\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 55. What is the equivalent of enumerate for numpy arrays? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 56. Generate a generic 2D Gaussian-like array (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 57. How to randomly place p elements in a 2D array? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 58. Subtract the mean of each row of a matrix (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 59. How to sort an array by the nth column? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 60. How to tell if a given 2D array has null columns? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 61. Find the nearest value from a given value in an array (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 62. Considering two arrays with shape (1,3) and (3,1), how to compute their sum using an iterator? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 63. Create an array class that has a name attribute (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 64. Consider a given vector, how to add 1 to each element indexed by a second vector (be careful with repeated indices)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 65. How to accumulate elements of a vector (X) to an array (F) based on an index list (I)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 66. Considering a (w,h,3) image of (dtype=ubyte), compute the number of unique colors (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 67. Considering a four dimensions array, how to get sum over the last two axis at once? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 68. Considering a one-dimensional vector D, how to compute means of subsets of D using a vector S of same size describing subset indices? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 69. How to get the diagonal of a dot product? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 70. Consider the vector \\[1, 2, 3, 4, 5\\], how to build a new vector with 3 consecutive zeros interleaved between each value? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 71. Consider an array of dimension (5,5,3), how to mulitply it by an array with dimensions (5,5)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 72. How to swap two rows of an array? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 73. Consider a set of 10 triplets describing 10 triangles (with shared vertices), find the set of unique line segments composing all the triangles (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 74. Given an array C that is a bincount, how to produce an array A such that np.bincount(A) == C? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 75. How to compute averages using a sliding window over an array? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 76. Consider a one-dimensional array Z, build a two-dimensional array whose first row is (Z\\[0\\],Z\\[1\\],Z\\[2\\]) and each subsequent row is shifted by 1 (last row should be (Z\\[-3\\],Z\\[-2\\],Z\\[-1\\]) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 77. How to negate a boolean, or to change the sign of a float inplace? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 78. Consider 2 sets of points P0,P1 describing lines (2d) and a point p, how to compute distance from p to each line i (P0\\[i\\],P1\\[i\\])? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 79. Consider 2 sets of points P0,P1 describing lines (2d) and a set of points P, how to compute distance from each point j (P\\[j\\]) to each line i (P0\\[i\\],P1\\[i\\])? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 80. Consider an arbitrary array, write a function that extract a subpart with a fixed shape and centered on a given element (pad with a `fill` value when necessary) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 81. Consider an array Z = \\[1,2,3,4,5,6,7,8,9,10,11,12,13,14\\], how to generate an array R = \\[\\[1,2,3,4\\], \\[2,3,4,5\\], \\[3,4,5,6\\], ..., \\[11,12,13,14\\]\\]? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 82. Compute a matrix rank (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 83. How to find the most frequent value in an array?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 84. Extract all the contiguous 3x3 blocks from a random 10x10 matrix (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 85. Create a 2D array subclass such that Z\\[i,j\\] == Z\\[j,i\\] (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 86. Consider a set of p matrices wich shape (n,n) and a set of p vectors with shape (n,1). How to compute the sum of of the p matrix products at once? (result has shape (n,1)) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 87. Consider a 16x16 array, how to get the block-sum (block size is 4x4)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 88. How to implement the Game of Life using numpy arrays? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 89. How to get the n largest values of an array (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 90. Given an arbitrary number of vectors, build the cartesian product (every combinations of every item) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 91. How to create a record array from a regular array? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 92. Consider a large vector Z, compute Z to the power of 3 using 3 different methods (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 93. Consider two arrays A and B of shape (8,3) and (2,2). How to find rows of A that contain elements of each row of B regardless of the order of the elements in B? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 94. Considering a 10x3 matrix, extract rows with unequal values (e.g. \\[2,2,3\\]) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 95. Convert a vector of ints into a matrix binary representation (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 96. Given a two dimensional array, how to extract unique rows? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 97. Considering 2 vectors A & B, write the einsum equivalent of inner, outer, sum, and mul function (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 98. Considering a path described by two vectors (X,Y), how to sample it using equidistant samples (★★★)?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 99. Given an integer n and a 2D array X, select from X the rows which can be interpreted as draws from a multinomial distribution with n degrees, i.e., the rows which only contain integers and which sum to n. (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 100. Compute bootstrapped 95% confidence intervals for the mean of a 1D array X (i.e., resample the elements of an array with replacement N times, compute the mean of each sample, and then compute percentiles over the means). (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git "a/020numpy100\351\242\230/numpy-100-master/100_Numpy_exercises.ipynb" "b/020numpy100\351\242\230/numpy-100-master/100_Numpy_exercises.ipynb" new file mode 100644 index 0000000..b86bf71 --- /dev/null +++ "b/020numpy100\351\242\230/numpy-100-master/100_Numpy_exercises.ipynb" @@ -0,0 +1,2181 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 100 numpy exercises\n", + "\n", + "This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow and in the numpy documentation. The goal of this collection is to offer a quick reference for both old and new users but also to provide a set of exercises for those who teach.\n", + "\n", + "\n", + "If you find an error or think you've a better way to solve some of them, feel free to open an issue at " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1. Import the numpy package under the name `np` (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2. Print the numpy version and the configuration (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(np.__version__)\n", + "np.show_config()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3. Create a null vector of size 10 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.zeros(10)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4. How to find the memory size of any array (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.zeros((10,10))\n", + "print(\"%d bytes\" % (Z.size * Z.itemsize))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5. How to get the documentation of the numpy add function from the command line? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%run `python -c \"import numpy; numpy.info(numpy.add)\"`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6. Create a null vector of size 10 but the fifth value which is 1 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.zeros(10)\n", + "Z[4] = 1\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 7. Create a vector with values ranging from 10 to 49 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.arange(10,50)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 8. Reverse a vector (first element becomes last) (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.arange(50)\n", + "Z = Z[::-1]\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 9. Create a 3x3 matrix with values ranging from 0 to 8 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.arange(9).reshape(3,3)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 10. Find indices of non-zero elements from \\[1,2,0,0,4,0\\] (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nz = np.nonzero([1,2,0,0,4,0])\n", + "print(nz)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 11. Create a 3x3 identity matrix (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.eye(3)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 12. Create a 3x3x3 array with random values (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.random.random((3,3,3))\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 13. Create a 10x10 array with random values and find the minimum and maximum values (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.random.random((10,10))\n", + "Zmin, Zmax = Z.min(), Z.max()\n", + "print(Zmin, Zmax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 14. Create a random vector of size 30 and find the mean value (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.random.random(30)\n", + "m = Z.mean()\n", + "print(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 15. Create a 2d array with 1 on the border and 0 inside (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.ones((10,10))\n", + "Z[1:-1,1:-1] = 0\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 16. How to add a border (filled with 0's) around an existing array? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.ones((5,5))\n", + "Z = np.pad(Z, pad_width=1, mode='constant', constant_values=0)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 17. What is the result of the following expression? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(0 * np.nan)\n", + "print(np.nan == np.nan)\n", + "print(np.inf > np.nan)\n", + "print(np.nan - np.nan)\n", + "print(np.nan in set([np.nan]))\n", + "print(0.3 == 3 * 0.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 18. Create a 5x5 matrix with values 1,2,3,4 just below the diagonal (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.diag(1+np.arange(4),k=-1)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 19. Create a 8x8 matrix and fill it with a checkerboard pattern (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.zeros((8,8),dtype=int)\n", + "Z[1::2,::2] = 1\n", + "Z[::2,1::2] = 1\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 20. Consider a (6,7,8) shape array, what is the index (x,y,z) of the 100th element?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(np.unravel_index(99,(6,7,8)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 21. Create a checkerboard 8x8 matrix using the tile function (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.tile( np.array([[0,1],[1,0]]), (4,4))\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 22. Normalize a 5x5 random matrix (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.random.random((5,5))\n", + "Z = (Z - np.mean (Z)) / (np.std (Z))\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 23. Create a custom dtype that describes a color as four unsigned bytes (RGBA) (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "color = np.dtype([(\"r\", np.ubyte, 1),\n", + " (\"g\", np.ubyte, 1),\n", + " (\"b\", np.ubyte, 1),\n", + " (\"a\", np.ubyte, 1)])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 24. Multiply a 5x3 matrix by a 3x2 matrix (real matrix product) (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.dot(np.ones((5,3)), np.ones((3,2)))\n", + "print(Z)\n", + "\n", + "# Alternative solution, in Python 3.5 and above\n", + "Z = np.ones((5,3)) @ np.ones((3,2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 25. Given a 1D array, negate all elements which are between 3 and 8, in place. (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Evgeni Burovski\n", + "\n", + "Z = np.arange(11)\n", + "Z[(3 < Z) & (Z <= 8)] *= -1\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 26. What is the output of the following script? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Jake VanderPlas\n", + "\n", + "print(sum(range(5),-1))\n", + "from numpy import *\n", + "print(sum(range(5),-1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 27. Consider an integer vector Z, which of these expressions are legal? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z**Z\n", + "2 << Z >> 2\n", + "Z <- Z\n", + "1j*Z\n", + "Z/1/1\n", + "ZZ" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 28. What are the result of the following expressions?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(np.array(0) / np.array(0))\n", + "print(np.array(0) // np.array(0))\n", + "print(np.array([np.nan]).astype(int).astype(float))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 29. How to round away from zero a float array ? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Charles R Harris\n", + "\n", + "Z = np.random.uniform(-10,+10,10)\n", + "print (np.copysign(np.ceil(np.abs(Z)), Z))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 30. How to find common values between two arrays? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z1 = np.random.randint(0,10,10)\n", + "Z2 = np.random.randint(0,10,10)\n", + "print(np.intersect1d(Z1,Z2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 31. How to ignore all numpy warnings (not recommended)? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Suicide mode on\n", + "defaults = np.seterr(all=\"ignore\")\n", + "Z = np.ones(1) / 0\n", + "\n", + "# Back to sanity\n", + "_ = np.seterr(**defaults)\n", + "\n", + "An equivalent way, with a context manager:\n", + "\n", + "with np.errstate(divide='ignore'):\n", + " Z = np.ones(1) / 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 32. Is the following expressions true? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.sqrt(-1) == np.emath.sqrt(-1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 33. How to get the dates of yesterday, today and tomorrow? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "yesterday = np.datetime64('today', 'D') - np.timedelta64(1, 'D')\n", + "today = np.datetime64('today', 'D')\n", + "tomorrow = np.datetime64('today', 'D') + np.timedelta64(1, 'D')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 34. How to get all the dates corresponding to the month of July 2016? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.arange('2016-07', '2016-08', dtype='datetime64[D]')\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 35. How to compute ((A+B)\\*(-A/2)) in place (without copy)? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "A = np.ones(3)*1\n", + "B = np.ones(3)*2\n", + "C = np.ones(3)*3\n", + "np.add(A,B,out=B)\n", + "np.divide(A,2,out=A)\n", + "np.negative(A,out=A)\n", + "np.multiply(A,B,out=A)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 36. Extract the integer part of a random array using 5 different methods (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.random.uniform(0,10,10)\n", + "\n", + "print (Z - Z%1)\n", + "print (np.floor(Z))\n", + "print (np.ceil(Z)-1)\n", + "print (Z.astype(int))\n", + "print (np.trunc(Z))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 37. Create a 5x5 matrix with row values ranging from 0 to 4 (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.zeros((5,5))\n", + "Z += np.arange(5)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 38. Consider a generator function that generates 10 integers and use it to build an array (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def generate():\n", + " for x in range(10):\n", + " yield x\n", + "Z = np.fromiter(generate(),dtype=float,count=-1)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 39. Create a vector of size 10 with values ranging from 0 to 1, both excluded (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.linspace(0,1,11,endpoint=False)[1:]\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 40. Create a random vector of size 10 and sort it (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.random.random(10)\n", + "Z.sort()\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 41. How to sum a small array faster than np.sum? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Evgeni Burovski\n", + "\n", + "Z = np.arange(10)\n", + "np.add.reduce(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 42. Consider two random array A and B, check if they are equal (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "A = np.random.randint(0,2,5)\n", + "B = np.random.randint(0,2,5)\n", + "\n", + "# Assuming identical shape of the arrays and a tolerance for the comparison of values\n", + "equal = np.allclose(A,B)\n", + "print(equal)\n", + "\n", + "# Checking both the shape and the element values, no tolerance (values have to be exactly equal)\n", + "equal = np.array_equal(A,B)\n", + "print(equal)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 43. Make an array immutable (read-only) (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.zeros(10)\n", + "Z.flags.writeable = False\n", + "Z[0] = 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 44. Consider a random 10x2 matrix representing cartesian coordinates, convert them to polar coordinates (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.random.random((10,2))\n", + "X,Y = Z[:,0], Z[:,1]\n", + "R = np.sqrt(X**2+Y**2)\n", + "T = np.arctan2(Y,X)\n", + "print(R)\n", + "print(T)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 45. Create random vector of size 10 and replace the maximum value by 0 (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.random.random(10)\n", + "Z[Z.argmax()] = 0\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 46. Create a structured array with `x` and `y` coordinates covering the \\[0,1\\]x\\[0,1\\] area (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.zeros((5,5), [('x',float),('y',float)])\n", + "Z['x'], Z['y'] = np.meshgrid(np.linspace(0,1,5),\n", + " np.linspace(0,1,5))\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 47. Given two arrays, X and Y, construct the Cauchy matrix C (Cij =1/(xi - yj))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Evgeni Burovski\n", + "\n", + "X = np.arange(8)\n", + "Y = X + 0.5\n", + "C = 1.0 / np.subtract.outer(X, Y)\n", + "print(np.linalg.det(C))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 48. Print the minimum and maximum representable value for each numpy scalar type (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for dtype in [np.int8, np.int32, np.int64]:\n", + " print(np.iinfo(dtype).min)\n", + " print(np.iinfo(dtype).max)\n", + "for dtype in [np.float32, np.float64]:\n", + " print(np.finfo(dtype).min)\n", + " print(np.finfo(dtype).max)\n", + " print(np.finfo(dtype).eps)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 49. How to print all the values of an array? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.set_printoptions(threshold=np.nan)\n", + "Z = np.zeros((16,16))\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 50. How to find the closest value (to a given scalar) in a vector? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.arange(100)\n", + "v = np.random.uniform(0,100)\n", + "index = (np.abs(Z-v)).argmin()\n", + "print(Z[index])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 51. Create a structured array representing a position (x,y) and a color (r,g,b) (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.zeros(10, [ ('position', [ ('x', float, 1),\n", + " ('y', float, 1)]),\n", + " ('color', [ ('r', float, 1),\n", + " ('g', float, 1),\n", + " ('b', float, 1)])])\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 52. Consider a random vector with shape (100,2) representing coordinates, find point by point distances (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.random.random((10,2))\n", + "X,Y = np.atleast_2d(Z[:,0], Z[:,1])\n", + "D = np.sqrt( (X-X.T)**2 + (Y-Y.T)**2)\n", + "print(D)\n", + "\n", + "# Much faster with scipy\n", + "import scipy\n", + "# Thanks Gavin Heverly-Coulson (#issue 1)\n", + "import scipy.spatial\n", + "\n", + "Z = np.random.random((10,2))\n", + "D = scipy.spatial.distance.cdist(Z,Z)\n", + "print(D)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 53. How to convert a float (32 bits) array into an integer (32 bits) in place?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Thanks Vikas (https://stackoverflow.com/a/10622758/5989906) \n", + "# & unutbu (https://stackoverflow.com/a/4396247/5989906)\n", + "Z = (np.random.rand(10)*100).astype(np.float32)\n", + "Y = Z.view(np.int32)\n", + "Y[:] = Z\n", + "print(Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 54. How to read the following file? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from io import StringIO\n", + "\n", + "# Fake file \n", + "s = StringIO(\"\"\"1, 2, 3, 4, 5\\n\n", + " 6, , , 7, 8\\n\n", + " , , 9,10,11\\n\"\"\")\n", + "Z = np.genfromtxt(s, delimiter=\",\", dtype=np.int)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 55. What is the equivalent of enumerate for numpy arrays? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.arange(9).reshape(3,3)\n", + "for index, value in np.ndenumerate(Z):\n", + " print(index, value)\n", + "for index in np.ndindex(Z.shape):\n", + " print(index, Z[index])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 56. Generate a generic 2D Gaussian-like array (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X, Y = np.meshgrid(np.linspace(-1,1,10), np.linspace(-1,1,10))\n", + "D = np.sqrt(X*X+Y*Y)\n", + "sigma, mu = 1.0, 0.0\n", + "G = np.exp(-( (D-mu)**2 / ( 2.0 * sigma**2 ) ) )\n", + "print(G)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 57. How to randomly place p elements in a 2D array? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Divakar\n", + "\n", + "n = 10\n", + "p = 3\n", + "Z = np.zeros((n,n))\n", + "np.put(Z, np.random.choice(range(n*n), p, replace=False),1)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 58. Subtract the mean of each row of a matrix (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Warren Weckesser\n", + "\n", + "X = np.random.rand(5, 10)\n", + "\n", + "# Recent versions of numpy\n", + "Y = X - X.mean(axis=1, keepdims=True)\n", + "\n", + "# Older versions of numpy\n", + "Y = X - X.mean(axis=1).reshape(-1, 1)\n", + "\n", + "print(Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 59. How to sort an array by the nth column? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Steve Tjoa\n", + "\n", + "Z = np.random.randint(0,10,(3,3))\n", + "print(Z)\n", + "print(Z[Z[:,1].argsort()])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 60. How to tell if a given 2D array has null columns? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Warren Weckesser\n", + "\n", + "Z = np.random.randint(0,3,(3,10))\n", + "print((~Z.any(axis=0)).any())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 61. Find the nearest value from a given value in an array (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.random.uniform(0,1,10)\n", + "z = 0.5\n", + "m = Z.flat[np.abs(Z - z).argmin()]\n", + "print(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 62. Considering two arrays with shape (1,3) and (3,1), how to compute their sum using an iterator? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "A = np.arange(3).reshape(3,1)\n", + "B = np.arange(3).reshape(1,3)\n", + "it = np.nditer([A,B,None])\n", + "for x,y,z in it: z[...] = x + y\n", + "print(it.operands[2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 63. Create an array class that has a name attribute (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class NamedArray(np.ndarray):\n", + " def __new__(cls, array, name=\"no name\"):\n", + " obj = np.asarray(array).view(cls)\n", + " obj.name = name\n", + " return obj\n", + " def __array_finalize__(self, obj):\n", + " if obj is None: return\n", + " self.info = getattr(obj, 'name', \"no name\")\n", + "\n", + "Z = NamedArray(np.arange(10), \"range_10\")\n", + "print (Z.name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 64. Consider a given vector, how to add 1 to each element indexed by a second vector (be careful with repeated indices)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Brett Olsen\n", + "\n", + "Z = np.ones(10)\n", + "I = np.random.randint(0,len(Z),20)\n", + "Z += np.bincount(I, minlength=len(Z))\n", + "print(Z)\n", + "\n", + "# Another solution\n", + "# Author: Bartosz Telenczuk\n", + "np.add.at(Z, I, 1)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 65. How to accumulate elements of a vector (X) to an array (F) based on an index list (I)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Alan G Isaac\n", + "\n", + "X = [1,2,3,4,5,6]\n", + "I = [1,3,9,3,4,1]\n", + "F = np.bincount(I,X)\n", + "print(F)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 66. Considering a (w,h,3) image of (dtype=ubyte), compute the number of unique colors (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Nadav Horesh\n", + "\n", + "w,h = 16,16\n", + "I = np.random.randint(0,2,(h,w,3)).astype(np.ubyte)\n", + "#Note that we should compute 256*256 first. \n", + "#Otherwise numpy will only promote F.dtype to 'uint16' and overfolw will occur\n", + "F = I[...,0]*(256*256) + I[...,1]*256 +I[...,2]\n", + "n = len(np.unique(F))\n", + "print(n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 67. Considering a four dimensions array, how to get sum over the last two axis at once? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "A = np.random.randint(0,10,(3,4,3,4))\n", + "# solution by passing a tuple of axes (introduced in numpy 1.7.0)\n", + "sum = A.sum(axis=(-2,-1))\n", + "print(sum)\n", + "# solution by flattening the last two dimensions into one\n", + "# (useful for functions that don't accept tuples for axis argument)\n", + "sum = A.reshape(A.shape[:-2] + (-1,)).sum(axis=-1)\n", + "print(sum)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 68. Considering a one-dimensional vector D, how to compute means of subsets of D using a vector S of same size describing subset indices? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Jaime Fernández del Río\n", + "\n", + "D = np.random.uniform(0,1,100)\n", + "S = np.random.randint(0,10,100)\n", + "D_sums = np.bincount(S, weights=D)\n", + "D_counts = np.bincount(S)\n", + "D_means = D_sums / D_counts\n", + "print(D_means)\n", + "\n", + "# Pandas solution as a reference due to more intuitive code\n", + "import pandas as pd\n", + "print(pd.Series(D).groupby(S).mean())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 69. How to get the diagonal of a dot product? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Mathieu Blondel\n", + "\n", + "A = np.random.uniform(0,1,(5,5))\n", + "B = np.random.uniform(0,1,(5,5))\n", + "\n", + "# Slow version \n", + "np.diag(np.dot(A, B))\n", + "\n", + "# Fast version\n", + "np.sum(A * B.T, axis=1)\n", + "\n", + "# Faster version\n", + "np.einsum(\"ij,ji->i\", A, B)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 70. Consider the vector \\[1, 2, 3, 4, 5\\], how to build a new vector with 3 consecutive zeros interleaved between each value? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Warren Weckesser\n", + "\n", + "Z = np.array([1,2,3,4,5])\n", + "nz = 3\n", + "Z0 = np.zeros(len(Z) + (len(Z)-1)*(nz))\n", + "Z0[::nz+1] = Z\n", + "print(Z0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 71. Consider an array of dimension (5,5,3), how to mulitply it by an array with dimensions (5,5)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "A = np.ones((5,5,3))\n", + "B = 2*np.ones((5,5))\n", + "print(A * B[:,:,None])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 72. How to swap two rows of an array? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Eelco Hoogendoorn\n", + "\n", + "A = np.arange(25).reshape(5,5)\n", + "A[[0,1]] = A[[1,0]]\n", + "print(A)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 73. Consider a set of 10 triplets describing 10 triangles (with shared vertices), find the set of unique line segments composing all the triangles (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Nicolas P. Rougier\n", + "\n", + "faces = np.random.randint(0,100,(10,3))\n", + "F = np.roll(faces.repeat(2,axis=1),-1,axis=1)\n", + "F = F.reshape(len(F)*3,2)\n", + "F = np.sort(F,axis=1)\n", + "G = F.view( dtype=[('p0',F.dtype),('p1',F.dtype)] )\n", + "G = np.unique(G)\n", + "print(G)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 74. Given an array C that is a bincount, how to produce an array A such that np.bincount(A) == C? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Jaime Fernández del Río\n", + "\n", + "C = np.bincount([1,1,2,3,4,4,6])\n", + "A = np.repeat(np.arange(len(C)), C)\n", + "print(A)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 75. How to compute averages using a sliding window over an array? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Jaime Fernández del Río\n", + "\n", + "def moving_average(a, n=3) :\n", + " ret = np.cumsum(a, dtype=float)\n", + " ret[n:] = ret[n:] - ret[:-n]\n", + " return ret[n - 1:] / n\n", + "Z = np.arange(20)\n", + "print(moving_average(Z, n=3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 76. Consider a one-dimensional array Z, build a two-dimensional array whose first row is (Z\\[0\\],Z\\[1\\],Z\\[2\\]) and each subsequent row is shifted by 1 (last row should be (Z\\[-3\\],Z\\[-2\\],Z\\[-1\\]) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Joe Kington / Erik Rigtorp\n", + "from numpy.lib import stride_tricks\n", + "\n", + "def rolling(a, window):\n", + " shape = (a.size - window + 1, window)\n", + " strides = (a.itemsize, a.itemsize)\n", + " return stride_tricks.as_strided(a, shape=shape, strides=strides)\n", + "Z = rolling(np.arange(10), 3)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 77. How to negate a boolean, or to change the sign of a float inplace? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Nathaniel J. Smith\n", + "\n", + "Z = np.random.randint(0,2,100)\n", + "np.logical_not(Z, out=Z)\n", + "\n", + "Z = np.random.uniform(-1.0,1.0,100)\n", + "np.negative(Z, out=Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 78. Consider 2 sets of points P0,P1 describing lines (2d) and a point p, how to compute distance from p to each line i (P0\\[i\\],P1\\[i\\])? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def distance(P0, P1, p):\n", + " T = P1 - P0\n", + " L = (T**2).sum(axis=1)\n", + " U = -((P0[:,0]-p[...,0])*T[:,0] + (P0[:,1]-p[...,1])*T[:,1]) / L\n", + " U = U.reshape(len(U),1)\n", + " D = P0 + U*T - p\n", + " return np.sqrt((D**2).sum(axis=1))\n", + "\n", + "P0 = np.random.uniform(-10,10,(10,2))\n", + "P1 = np.random.uniform(-10,10,(10,2))\n", + "p = np.random.uniform(-10,10,( 1,2))\n", + "print(distance(P0, P1, p))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 79. Consider 2 sets of points P0,P1 describing lines (2d) and a set of points P, how to compute distance from each point j (P\\[j\\]) to each line i (P0\\[i\\],P1\\[i\\])? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Italmassov Kuanysh\n", + "\n", + "# based on distance function from previous question\n", + "P0 = np.random.uniform(-10, 10, (10,2))\n", + "P1 = np.random.uniform(-10,10,(10,2))\n", + "p = np.random.uniform(-10, 10, (10,2))\n", + "print(np.array([distance(P0,P1,p_i) for p_i in p]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 80. Consider an arbitrary array, write a function that extract a subpart with a fixed shape and centered on a given element (pad with a `fill` value when necessary) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Nicolas Rougier\n", + "\n", + "Z = np.random.randint(0,10,(10,10))\n", + "shape = (5,5)\n", + "fill = 0\n", + "position = (1,1)\n", + "\n", + "R = np.ones(shape, dtype=Z.dtype)*fill\n", + "P = np.array(list(position)).astype(int)\n", + "Rs = np.array(list(R.shape)).astype(int)\n", + "Zs = np.array(list(Z.shape)).astype(int)\n", + "\n", + "R_start = np.zeros((len(shape),)).astype(int)\n", + "R_stop = np.array(list(shape)).astype(int)\n", + "Z_start = (P-Rs//2)\n", + "Z_stop = (P+Rs//2)+Rs%2\n", + "\n", + "R_start = (R_start - np.minimum(Z_start,0)).tolist()\n", + "Z_start = (np.maximum(Z_start,0)).tolist()\n", + "R_stop = np.maximum(R_start, (R_stop - np.maximum(Z_stop-Zs,0))).tolist()\n", + "Z_stop = (np.minimum(Z_stop,Zs)).tolist()\n", + "\n", + "r = [slice(start,stop) for start,stop in zip(R_start,R_stop)]\n", + "z = [slice(start,stop) for start,stop in zip(Z_start,Z_stop)]\n", + "R[r] = Z[z]\n", + "print(Z)\n", + "print(R)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 81. Consider an array Z = \\[1,2,3,4,5,6,7,8,9,10,11,12,13,14\\], how to generate an array R = \\[\\[1,2,3,4\\], \\[2,3,4,5\\], \\[3,4,5,6\\], ..., \\[11,12,13,14\\]\\]? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Stefan van der Walt\n", + "\n", + "Z = np.arange(1,15,dtype=np.uint32)\n", + "R = stride_tricks.as_strided(Z,(11,4),(4,4))\n", + "print(R)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 82. Compute a matrix rank (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Stefan van der Walt\n", + "\n", + "Z = np.random.uniform(0,1,(10,10))\n", + "U, S, V = np.linalg.svd(Z) # Singular Value Decomposition\n", + "rank = np.sum(S > 1e-10)\n", + "print(rank)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 83. How to find the most frequent value in an array?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.random.randint(0,10,50)\n", + "print(np.bincount(Z).argmax())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 84. Extract all the contiguous 3x3 blocks from a random 10x10 matrix (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Chris Barker\n", + "\n", + "Z = np.random.randint(0,5,(10,10))\n", + "n = 3\n", + "i = 1 + (Z.shape[0]-3)\n", + "j = 1 + (Z.shape[1]-3)\n", + "C = stride_tricks.as_strided(Z, shape=(i, j, n, n), strides=Z.strides + Z.strides)\n", + "print(C)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 85. Create a 2D array subclass such that Z\\[i,j\\] == Z\\[j,i\\] (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Eric O. Lebigot\n", + "# Note: only works for 2d array and value setting using indices\n", + "\n", + "class Symetric(np.ndarray):\n", + " def __setitem__(self, index, value):\n", + " i,j = index\n", + " super(Symetric, self).__setitem__((i,j), value)\n", + " super(Symetric, self).__setitem__((j,i), value)\n", + "\n", + "def symetric(Z):\n", + " return np.asarray(Z + Z.T - np.diag(Z.diagonal())).view(Symetric)\n", + "\n", + "S = symetric(np.random.randint(0,10,(5,5)))\n", + "S[2,3] = 42\n", + "print(S)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 86. Consider a set of p matrices wich shape (n,n) and a set of p vectors with shape (n,1). How to compute the sum of of the p matrix products at once? (result has shape (n,1)) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Stefan van der Walt\n", + "\n", + "p, n = 10, 20\n", + "M = np.ones((p,n,n))\n", + "V = np.ones((p,n,1))\n", + "S = np.tensordot(M, V, axes=[[0, 2], [0, 1]])\n", + "print(S)\n", + "\n", + "# It works, because:\n", + "# M is (p,n,n)\n", + "# V is (p,n,1)\n", + "# Thus, summing over the paired axes 0 and 0 (of M and V independently),\n", + "# and 2 and 1, to remain with a (n,1) vector." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 87. Consider a 16x16 array, how to get the block-sum (block size is 4x4)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Robert Kern\n", + "\n", + "Z = np.ones((16,16))\n", + "k = 4\n", + "S = np.add.reduceat(np.add.reduceat(Z, np.arange(0, Z.shape[0], k), axis=0),\n", + " np.arange(0, Z.shape[1], k), axis=1)\n", + "print(S)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 88. How to implement the Game of Life using numpy arrays? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Nicolas Rougier\n", + "\n", + "def iterate(Z):\n", + " # Count neighbours\n", + " N = (Z[0:-2,0:-2] + Z[0:-2,1:-1] + Z[0:-2,2:] +\n", + " Z[1:-1,0:-2] + Z[1:-1,2:] +\n", + " Z[2: ,0:-2] + Z[2: ,1:-1] + Z[2: ,2:])\n", + "\n", + " # Apply rules\n", + " birth = (N==3) & (Z[1:-1,1:-1]==0)\n", + " survive = ((N==2) | (N==3)) & (Z[1:-1,1:-1]==1)\n", + " Z[...] = 0\n", + " Z[1:-1,1:-1][birth | survive] = 1\n", + " return Z\n", + "\n", + "Z = np.random.randint(0,2,(50,50))\n", + "for i in range(100): Z = iterate(Z)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 89. How to get the n largest values of an array (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.arange(10000)\n", + "np.random.shuffle(Z)\n", + "n = 5\n", + "\n", + "# Slow\n", + "print (Z[np.argsort(Z)[-n:]])\n", + "\n", + "# Fast\n", + "print (Z[np.argpartition(-Z,n)[:n]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 90. Given an arbitrary number of vectors, build the cartesian product (every combinations of every item) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Author: Stefan Van der Walt\n", + "\n", + "def cartesian(arrays):\n", + " arrays = [np.asarray(a) for a in arrays]\n", + " shape = (len(x) for x in arrays)\n", + "\n", + " ix = np.indices(shape, dtype=int)\n", + " ix = ix.reshape(len(arrays), -1).T\n", + "\n", + " for n, arr in enumerate(arrays):\n", + " ix[:, n] = arrays[n][ix[:, n]]\n", + "\n", + " return ix\n", + "\n", + "print (cartesian(([1, 2, 3], [4, 5], [6, 7])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 91. How to create a record array from a regular array? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Z = np.array([(\"Hello\", 2.5, 3),\n", + " (\"World\", 3.6, 2)])\n", + "R = np.core.records.fromarrays(Z.T, \n", + " names='col1, col2, col3',\n", + " formats = 'S8, f8, i8')\n", + "print(R)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 92. Consider a large vector Z, compute Z to the power of 3 using 3 different methods (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Ryan G.\n", + "\n", + "x = np.random.rand(int(5e7))\n", + "\n", + "%timeit np.power(x,3)\n", + "%timeit x*x*x\n", + "%timeit np.einsum('i,i,i->i',x,x,x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 93. Consider two arrays A and B of shape (8,3) and (2,2). How to find rows of A that contain elements of each row of B regardless of the order of the elements in B? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Gabe Schwartz\n", + "\n", + "A = np.random.randint(0,5,(8,3))\n", + "B = np.random.randint(0,5,(2,2))\n", + "\n", + "C = (A[..., np.newaxis, np.newaxis] == B)\n", + "rows = np.where(C.any((3,1)).all(1))[0]\n", + "print(rows)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 94. Considering a 10x3 matrix, extract rows with unequal values (e.g. \\[2,2,3\\]) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Robert Kern\n", + "\n", + "Z = np.random.randint(0,5,(10,3))\n", + "print(Z)\n", + "# solution for arrays of all dtypes (including string arrays and record arrays)\n", + "E = np.all(Z[:,1:] == Z[:,:-1], axis=1)\n", + "U = Z[~E]\n", + "print(U)\n", + "# soluiton for numerical arrays only, will work for any number of columns in Z\n", + "U = Z[Z.max(axis=1) != Z.min(axis=1),:]\n", + "print(U)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 95. Convert a vector of ints into a matrix binary representation (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Warren Weckesser\n", + "\n", + "I = np.array([0, 1, 2, 3, 15, 16, 32, 64, 128])\n", + "B = ((I.reshape(-1,1) & (2**np.arange(8))) != 0).astype(int)\n", + "print(B[:,::-1])\n", + "\n", + "# Author: Daniel T. McDonald\n", + "\n", + "I = np.array([0, 1, 2, 3, 15, 16, 32, 64, 128], dtype=np.uint8)\n", + "print(np.unpackbits(I[:, np.newaxis], axis=1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 96. Given a two dimensional array, how to extract unique rows? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Jaime Fernández del Río\n", + "\n", + "Z = np.random.randint(0,2,(6,3))\n", + "T = np.ascontiguousarray(Z).view(np.dtype((np.void, Z.dtype.itemsize * Z.shape[1])))\n", + "_, idx = np.unique(T, return_index=True)\n", + "uZ = Z[idx]\n", + "print(uZ)\n", + "\n", + "# Author: Andreas Kouzelis\n", + "# NumPy >= 1.13\n", + "uZ = np.unique(Z, axis=0)\n", + "print(uZ)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 97. Considering 2 vectors A & B, write the einsum equivalent of inner, outer, sum, and mul function (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Alex Riley\n", + "# Make sure to read: http://ajcr.net/Basic-guide-to-einsum/\n", + "\n", + "A = np.random.uniform(0,1,10)\n", + "B = np.random.uniform(0,1,10)\n", + "\n", + "np.einsum('i->', A) # np.sum(A)\n", + "np.einsum('i,i->i', A, B) # A * B\n", + "np.einsum('i,i', A, B) # np.inner(A, B)\n", + "np.einsum('i,j->ij', A, B) # np.outer(A, B)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 98. Considering a path described by two vectors (X,Y), how to sample it using equidistant samples (★★★)?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Bas Swinckels\n", + "\n", + "phi = np.arange(0, 10*np.pi, 0.1)\n", + "a = 1\n", + "x = a*phi*np.cos(phi)\n", + "y = a*phi*np.sin(phi)\n", + "\n", + "dr = (np.diff(x)**2 + np.diff(y)**2)**.5 # segment lengths\n", + "r = np.zeros_like(x)\n", + "r[1:] = np.cumsum(dr) # integrate path\n", + "r_int = np.linspace(0, r.max(), 200) # regular spaced path\n", + "x_int = np.interp(r_int, r, x) # integrate path\n", + "y_int = np.interp(r_int, r, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 99. Given an integer n and a 2D array X, select from X the rows which can be interpreted as draws from a multinomial distribution with n degrees, i.e., the rows which only contain integers and which sum to n. (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Evgeni Burovski\n", + "\n", + "X = np.asarray([[1.0, 0.0, 3.0, 8.0],\n", + " [2.0, 0.0, 1.0, 1.0],\n", + " [1.5, 2.5, 1.0, 0.0]])\n", + "n = 4\n", + "M = np.logical_and.reduce(np.mod(X, 1) == 0, axis=-1)\n", + "M &= (X.sum(axis=-1) == n)\n", + "print(X[M])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 100. Compute bootstrapped 95% confidence intervals for the mean of a 1D array X (i.e., resample the elements of an array with replacement N times, compute the mean of each sample, and then compute percentiles over the means). (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Jessica B. Hamrick\n", + "\n", + "X = np.random.randn(100) # random 1D array\n", + "N = 1000 # number of bootstrap samples\n", + "idx = np.random.randint(0, X.size, (N, X.size))\n", + "means = X[idx].mean(axis=1)\n", + "confint = np.percentile(means, [2.5, 97.5])\n", + "print(confint)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git "a/020numpy100\351\242\230/numpy-100-master/100_Numpy_exercises.md" "b/020numpy100\351\242\230/numpy-100-master/100_Numpy_exercises.md" new file mode 100644 index 0000000..22dc4b7 --- /dev/null +++ "b/020numpy100\351\242\230/numpy-100-master/100_Numpy_exercises.md" @@ -0,0 +1,1231 @@ + +# 100 numpy exercises + +This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow and in the numpy documentation. The goal of this collection is to offer a quick reference for both old and new users but also to provide a set of exercises for those who teach. + + +If you find an error or think you've a better way to solve some of them, feel free to open an issue at + +#### 1. Import the numpy package under the name `np` (★☆☆) + + +```python +import numpy as np +``` + +#### 2. Print the numpy version and the configuration (★☆☆) + + +```python +print(np.__version__) +np.show_config() +``` + +#### 3. Create a null vector of size 10 (★☆☆) + + +```python +Z = np.zeros(10) +print(Z) +``` + +#### 4. How to find the memory size of any array (★☆☆) + + +```python +Z = np.zeros((10,10)) +print("%d bytes" % (Z.size * Z.itemsize)) +``` + +#### 5. How to get the documentation of the numpy add function from the command line? (★☆☆) + + +```python +%run `python -c "import numpy; numpy.info(numpy.add)"` +``` + +#### 6. Create a null vector of size 10 but the fifth value which is 1 (★☆☆) + + +```python +Z = np.zeros(10) +Z[4] = 1 +print(Z) +``` + +#### 7. Create a vector with values ranging from 10 to 49 (★☆☆) + + +```python +Z = np.arange(10,50) +print(Z) +``` + +#### 8. Reverse a vector (first element becomes last) (★☆☆) + + +```python +Z = np.arange(50) +Z = Z[::-1] +print(Z) +``` + +#### 9. Create a 3x3 matrix with values ranging from 0 to 8 (★☆☆) + + +```python +Z = np.arange(9).reshape(3,3) +print(Z) +``` + +#### 10. Find indices of non-zero elements from \[1,2,0,0,4,0\] (★☆☆) + + +```python +nz = np.nonzero([1,2,0,0,4,0]) +print(nz) +``` + +#### 11. Create a 3x3 identity matrix (★☆☆) + + +```python +Z = np.eye(3) +print(Z) +``` + +#### 12. Create a 3x3x3 array with random values (★☆☆) + + +```python +Z = np.random.random((3,3,3)) +print(Z) +``` + +#### 13. Create a 10x10 array with random values and find the minimum and maximum values (★☆☆) + + +```python +Z = np.random.random((10,10)) +Zmin, Zmax = Z.min(), Z.max() +print(Zmin, Zmax) +``` + +#### 14. Create a random vector of size 30 and find the mean value (★☆☆) + + +```python +Z = np.random.random(30) +m = Z.mean() +print(m) +``` + +#### 15. Create a 2d array with 1 on the border and 0 inside (★☆☆) + + +```python +Z = np.ones((10,10)) +Z[1:-1,1:-1] = 0 +print(Z) +``` + +#### 16. How to add a border (filled with 0's) around an existing array? (★☆☆) + + +```python +Z = np.ones((5,5)) +Z = np.pad(Z, pad_width=1, mode='constant', constant_values=0) +print(Z) +``` + +#### 17. What is the result of the following expression? (★☆☆) + + +```python +print(0 * np.nan) +print(np.nan == np.nan) +print(np.inf > np.nan) +print(np.nan - np.nan) +print(np.nan in set([np.nan])) +print(0.3 == 3 * 0.1) +``` + +#### 18. Create a 5x5 matrix with values 1,2,3,4 just below the diagonal (★☆☆) + + +```python +Z = np.diag(1+np.arange(4),k=-1) +print(Z) +``` + +#### 19. Create a 8x8 matrix and fill it with a checkerboard pattern (★☆☆) + + +```python +Z = np.zeros((8,8),dtype=int) +Z[1::2,::2] = 1 +Z[::2,1::2] = 1 +print(Z) +``` + +#### 20. Consider a (6,7,8) shape array, what is the index (x,y,z) of the 100th element? + + +```python +print(np.unravel_index(99,(6,7,8))) +``` + +#### 21. Create a checkerboard 8x8 matrix using the tile function (★☆☆) + + +```python +Z = np.tile( np.array([[0,1],[1,0]]), (4,4)) +print(Z) +``` + +#### 22. Normalize a 5x5 random matrix (★☆☆) + + +```python +Z = np.random.random((5,5)) +Z = (Z - np.mean (Z)) / (np.std (Z)) +print(Z) +``` + +#### 23. Create a custom dtype that describes a color as four unsigned bytes (RGBA) (★☆☆) + + +```python +color = np.dtype([("r", np.ubyte, 1), + ("g", np.ubyte, 1), + ("b", np.ubyte, 1), + ("a", np.ubyte, 1)]) +``` + +#### 24. Multiply a 5x3 matrix by a 3x2 matrix (real matrix product) (★☆☆) + + +```python +Z = np.dot(np.ones((5,3)), np.ones((3,2))) +print(Z) + +# Alternative solution, in Python 3.5 and above +Z = np.ones((5,3)) @ np.ones((3,2)) +print(Z) +``` + +#### 25. Given a 1D array, negate all elements which are between 3 and 8, in place. (★☆☆) + + +```python +# Author: Evgeni Burovski + +Z = np.arange(11) +Z[(3 < Z) & (Z <= 8)] *= -1 +print(Z) +``` + +#### 26. What is the output of the following script? (★☆☆) + + +```python +# Author: Jake VanderPlas + +print(sum(range(5),-1)) +from numpy import * +print(sum(range(5),-1)) +``` + +#### 27. Consider an integer vector Z, which of these expressions are legal? (★☆☆) + + +```python +Z**Z +2 << Z >> 2 +Z <- Z +1j*Z +Z/1/1 +ZZ +``` + +#### 28. What are the result of the following expressions? + + +```python +print(np.array(0) / np.array(0)) +print(np.array(0) // np.array(0)) +print(np.array([np.nan]).astype(int).astype(float)) +``` + +#### 29. How to round away from zero a float array ? (★☆☆) + + +```python +# Author: Charles R Harris + +Z = np.random.uniform(-10,+10,10) +print (np.copysign(np.ceil(np.abs(Z)), Z)) +``` + +#### 30. How to find common values between two arrays? (★☆☆) + + +```python +Z1 = np.random.randint(0,10,10) +Z2 = np.random.randint(0,10,10) +print(np.intersect1d(Z1,Z2)) +``` + +#### 31. How to ignore all numpy warnings (not recommended)? (★☆☆) + + +```python +# Suicide mode on +defaults = np.seterr(all="ignore") +Z = np.ones(1) / 0 + +# Back to sanity +_ = np.seterr(**defaults) +``` + +An equivalent way, with a context manager: + +```python +with np.errstate(divide='ignore'): + Z = np.ones(1) / 0 +``` + +#### 32. Is the following expressions true? (★☆☆) + + +```python +np.sqrt(-1) == np.emath.sqrt(-1) +``` + +#### 33. How to get the dates of yesterday, today and tomorrow? (★☆☆) + + +```python +yesterday = np.datetime64('today', 'D') - np.timedelta64(1, 'D') +today = np.datetime64('today', 'D') +tomorrow = np.datetime64('today', 'D') + np.timedelta64(1, 'D') +``` + +#### 34. How to get all the dates corresponding to the month of July 2016? (★★☆) + + +```python +Z = np.arange('2016-07', '2016-08', dtype='datetime64[D]') +print(Z) +``` + +#### 35. How to compute ((A+B)\*(-A/2)) in place (without copy)? (★★☆) + + +```python +A = np.ones(3)*1 +B = np.ones(3)*2 +C = np.ones(3)*3 +np.add(A,B,out=B) +np.divide(A,2,out=A) +np.negative(A,out=A) +np.multiply(A,B,out=A) +``` + +#### 36. Extract the integer part of a random array using 5 different methods (★★☆) + + +```python +Z = np.random.uniform(0,10,10) + +print (Z - Z%1) +print (np.floor(Z)) +print (np.ceil(Z)-1) +print (Z.astype(int)) +print (np.trunc(Z)) +``` + +#### 37. Create a 5x5 matrix with row values ranging from 0 to 4 (★★☆) + + +```python +Z = np.zeros((5,5)) +Z += np.arange(5) +print(Z) +``` + +#### 38. Consider a generator function that generates 10 integers and use it to build an array (★☆☆) + + +```python +def generate(): + for x in range(10): + yield x +Z = np.fromiter(generate(),dtype=float,count=-1) +print(Z) +``` + +#### 39. Create a vector of size 10 with values ranging from 0 to 1, both excluded (★★☆) + + +```python +Z = np.linspace(0,1,11,endpoint=False)[1:] +print(Z) +``` + +#### 40. Create a random vector of size 10 and sort it (★★☆) + + +```python +Z = np.random.random(10) +Z.sort() +print(Z) +``` + +#### 41. How to sum a small array faster than np.sum? (★★☆) + + +```python +# Author: Evgeni Burovski + +Z = np.arange(10) +np.add.reduce(Z) +``` + +#### 42. Consider two random array A and B, check if they are equal (★★☆) + + +```python +A = np.random.randint(0,2,5) +B = np.random.randint(0,2,5) + +# Assuming identical shape of the arrays and a tolerance for the comparison of values +equal = np.allclose(A,B) +print(equal) + +# Checking both the shape and the element values, no tolerance (values have to be exactly equal) +equal = np.array_equal(A,B) +print(equal) +``` + +#### 43. Make an array immutable (read-only) (★★☆) + + +```python +Z = np.zeros(10) +Z.flags.writeable = False +Z[0] = 1 +``` + +#### 44. Consider a random 10x2 matrix representing cartesian coordinates, convert them to polar coordinates (★★☆) + + +```python +Z = np.random.random((10,2)) +X,Y = Z[:,0], Z[:,1] +R = np.sqrt(X**2+Y**2) +T = np.arctan2(Y,X) +print(R) +print(T) +``` + +#### 45. Create random vector of size 10 and replace the maximum value by 0 (★★☆) + + +```python +Z = np.random.random(10) +Z[Z.argmax()] = 0 +print(Z) +``` + +#### 46. Create a structured array with `x` and `y` coordinates covering the \[0,1\]x\[0,1\] area (★★☆) + + +```python +Z = np.zeros((5,5), [('x',float),('y',float)]) +Z['x'], Z['y'] = np.meshgrid(np.linspace(0,1,5), + np.linspace(0,1,5)) +print(Z) +``` + +#### 47. Given two arrays, X and Y, construct the Cauchy matrix C (Cij =1/(xi - yj)) + + +```python +# Author: Evgeni Burovski + +X = np.arange(8) +Y = X + 0.5 +C = 1.0 / np.subtract.outer(X, Y) +print(np.linalg.det(C)) +``` + +#### 48. Print the minimum and maximum representable value for each numpy scalar type (★★☆) + + +```python +for dtype in [np.int8, np.int32, np.int64]: + print(np.iinfo(dtype).min) + print(np.iinfo(dtype).max) +for dtype in [np.float32, np.float64]: + print(np.finfo(dtype).min) + print(np.finfo(dtype).max) + print(np.finfo(dtype).eps) +``` + +#### 49. How to print all the values of an array? (★★☆) + + +```python +np.set_printoptions(threshold=np.nan) +Z = np.zeros((16,16)) +print(Z) +``` + +#### 50. How to find the closest value (to a given scalar) in a vector? (★★☆) + + +```python +Z = np.arange(100) +v = np.random.uniform(0,100) +index = (np.abs(Z-v)).argmin() +print(Z[index]) +``` + +#### 51. Create a structured array representing a position (x,y) and a color (r,g,b) (★★☆) + + +```python +Z = np.zeros(10, [ ('position', [ ('x', float, 1), + ('y', float, 1)]), + ('color', [ ('r', float, 1), + ('g', float, 1), + ('b', float, 1)])]) +print(Z) +``` + +#### 52. Consider a random vector with shape (100,2) representing coordinates, find point by point distances (★★☆) + + +```python +Z = np.random.random((10,2)) +X,Y = np.atleast_2d(Z[:,0], Z[:,1]) +D = np.sqrt( (X-X.T)**2 + (Y-Y.T)**2) +print(D) + +# Much faster with scipy +import scipy +# Thanks Gavin Heverly-Coulson (#issue 1) +import scipy.spatial + +Z = np.random.random((10,2)) +D = scipy.spatial.distance.cdist(Z,Z) +print(D) +``` + +#### 53. How to convert a float (32 bits) array into an integer (32 bits) in place? + + +```python +# Thanks Vikas (https://stackoverflow.com/a/10622758/5989906) +# & unutbu (https://stackoverflow.com/a/4396247/5989906) +Z = (np.random.rand(10)*100).astype(np.float32) +Y = Z.view(np.int32) +Y[:] = Z +print(Y) +``` + +#### 54. How to read the following file? (★★☆) + + +```python +from io import StringIO + +# Fake file +s = StringIO("""1, 2, 3, 4, 5\n + 6, , , 7, 8\n + , , 9,10,11\n""") +Z = np.genfromtxt(s, delimiter=",", dtype=np.int) +print(Z) +``` + +#### 55. What is the equivalent of enumerate for numpy arrays? (★★☆) + + +```python +Z = np.arange(9).reshape(3,3) +for index, value in np.ndenumerate(Z): + print(index, value) +for index in np.ndindex(Z.shape): + print(index, Z[index]) +``` + +#### 56. Generate a generic 2D Gaussian-like array (★★☆) + + +```python +X, Y = np.meshgrid(np.linspace(-1,1,10), np.linspace(-1,1,10)) +D = np.sqrt(X*X+Y*Y) +sigma, mu = 1.0, 0.0 +G = np.exp(-( (D-mu)**2 / ( 2.0 * sigma**2 ) ) ) +print(G) +``` + +#### 57. How to randomly place p elements in a 2D array? (★★☆) + + +```python +# Author: Divakar + +n = 10 +p = 3 +Z = np.zeros((n,n)) +np.put(Z, np.random.choice(range(n*n), p, replace=False),1) +print(Z) +``` + +#### 58. Subtract the mean of each row of a matrix (★★☆) + + +```python +# Author: Warren Weckesser + +X = np.random.rand(5, 10) + +# Recent versions of numpy +Y = X - X.mean(axis=1, keepdims=True) + +# Older versions of numpy +Y = X - X.mean(axis=1).reshape(-1, 1) + +print(Y) +``` + +#### 59. How to I sort an array by the nth column? (★★☆) + + +```python +# Author: Steve Tjoa + +Z = np.random.randint(0,10,(3,3)) +print(Z) +print(Z[Z[:,1].argsort()]) +``` + +#### 60. How to tell if a given 2D array has null columns? (★★☆) + + +```python +# Author: Warren Weckesser + +Z = np.random.randint(0,3,(3,10)) +print((~Z.any(axis=0)).any()) +``` + +#### 61. Find the nearest value from a given value in an array (★★☆) + + +```python +Z = np.random.uniform(0,1,10) +z = 0.5 +m = Z.flat[np.abs(Z - z).argmin()] +print(m) +``` + +#### 62. Considering two arrays with shape (1,3) and (3,1), how to compute their sum using an iterator? (★★☆) + + +```python +A = np.arange(3).reshape(3,1) +B = np.arange(3).reshape(1,3) +it = np.nditer([A,B,None]) +for x,y,z in it: z[...] = x + y +print(it.operands[2]) +``` + +#### 63. Create an array class that has a name attribute (★★☆) + + +```python +class NamedArray(np.ndarray): + def __new__(cls, array, name="no name"): + obj = np.asarray(array).view(cls) + obj.name = name + return obj + def __array_finalize__(self, obj): + if obj is None: return + self.info = getattr(obj, 'name', "no name") + +Z = NamedArray(np.arange(10), "range_10") +print (Z.name) +``` + +#### 64. Consider a given vector, how to add 1 to each element indexed by a second vector (be careful with repeated indices)? (★★★) + + +```python +# Author: Brett Olsen + +Z = np.ones(10) +I = np.random.randint(0,len(Z),20) +Z += np.bincount(I, minlength=len(Z)) +print(Z) + +# Another solution +# Author: Bartosz Telenczuk +np.add.at(Z, I, 1) +print(Z) +``` + +#### 65. How to accumulate elements of a vector (X) to an array (F) based on an index list (I)? (★★★) + + +```python +# Author: Alan G Isaac + +X = [1,2,3,4,5,6] +I = [1,3,9,3,4,1] +F = np.bincount(I,X) +print(F) +``` + +#### 66. Considering a (w,h,3) image of (dtype=ubyte), compute the number of unique colors (★★★) + + +```python +# Author: Nadav Horesh + +w,h = 16,16 +I = np.random.randint(0,2,(h,w,3)).astype(np.ubyte) +F = I[...,0]*256*256 + I[...,1]*256 +I[...,2] +n = len(np.unique(F)) +print(np.unique(I)) +``` + +#### 67. Considering a four dimensions array, how to get sum over the last two axis at once? (★★★) + + +```python +A = np.random.randint(0,10,(3,4,3,4)) +# solution by passing a tuple of axes (introduced in numpy 1.7.0) +sum = A.sum(axis=(-2,-1)) +print(sum) +# solution by flattening the last two dimensions into one +# (useful for functions that don't accept tuples for axis argument) +sum = A.reshape(A.shape[:-2] + (-1,)).sum(axis=-1) +print(sum) +``` + +#### 68. Considering a one-dimensional vector D, how to compute means of subsets of D using a vector S of same size describing subset indices? (★★★) + + +```python +# Author: Jaime Fernández del Río + +D = np.random.uniform(0,1,100) +S = np.random.randint(0,10,100) +D_sums = np.bincount(S, weights=D) +D_counts = np.bincount(S) +D_means = D_sums / D_counts +print(D_means) + +# Pandas solution as a reference due to more intuitive code +import pandas as pd +print(pd.Series(D).groupby(S).mean()) +``` + +#### 69. How to get the diagonal of a dot product? (★★★) + + +```python +# Author: Mathieu Blondel + +A = np.random.uniform(0,1,(5,5)) +B = np.random.uniform(0,1,(5,5)) + +# Slow version +np.diag(np.dot(A, B)) + +# Fast version +np.sum(A * B.T, axis=1) + +# Faster version +np.einsum("ij,ji->i", A, B) +``` + +#### 70. Consider the vector \[1, 2, 3, 4, 5\], how to build a new vector with 3 consecutive zeros interleaved between each value? (★★★) + + +```python +# Author: Warren Weckesser + +Z = np.array([1,2,3,4,5]) +nz = 3 +Z0 = np.zeros(len(Z) + (len(Z)-1)*(nz)) +Z0[::nz+1] = Z +print(Z0) +``` + +#### 71. Consider an array of dimension (5,5,3), how to mulitply it by an array with dimensions (5,5)? (★★★) + + +```python +A = np.ones((5,5,3)) +B = 2*np.ones((5,5)) +print(A * B[:,:,None]) +``` + +#### 72. How to swap two rows of an array? (★★★) + + +```python +# Author: Eelco Hoogendoorn + +A = np.arange(25).reshape(5,5) +A[[0,1]] = A[[1,0]] +print(A) +``` + +#### 73. Consider a set of 10 triplets describing 10 triangles (with shared vertices), find the set of unique line segments composing all the triangles (★★★) + + +```python +# Author: Nicolas P. Rougier + +faces = np.random.randint(0,100,(10,3)) +F = np.roll(faces.repeat(2,axis=1),-1,axis=1) +F = F.reshape(len(F)*3,2) +F = np.sort(F,axis=1) +G = F.view( dtype=[('p0',F.dtype),('p1',F.dtype)] ) +G = np.unique(G) +print(G) +``` + +#### 74. Given an array C that is a bincount, how to produce an array A such that np.bincount(A) == C? (★★★) + + +```python +# Author: Jaime Fernández del Río + +C = np.bincount([1,1,2,3,4,4,6]) +A = np.repeat(np.arange(len(C)), C) +print(A) +``` + +#### 75. How to compute averages using a sliding window over an array? (★★★) + + +```python +# Author: Jaime Fernández del Río + +def moving_average(a, n=3) : + ret = np.cumsum(a, dtype=float) + ret[n:] = ret[n:] - ret[:-n] + return ret[n - 1:] / n +Z = np.arange(20) +print(moving_average(Z, n=3)) +``` + +#### 76. Consider a one-dimensional array Z, build a two-dimensional array whose first row is (Z\[0\],Z\[1\],Z\[2\]) and each subsequent row is shifted by 1 (last row should be (Z\[-3\],Z\[-2\],Z\[-1\]) (★★★) + + +```python +# Author: Joe Kington / Erik Rigtorp +from numpy.lib import stride_tricks + +def rolling(a, window): + shape = (a.size - window + 1, window) + strides = (a.itemsize, a.itemsize) + return stride_tricks.as_strided(a, shape=shape, strides=strides) +Z = rolling(np.arange(10), 3) +print(Z) +``` + +#### 77. How to negate a boolean, or to change the sign of a float inplace? (★★★) + + +```python +# Author: Nathaniel J. Smith + +Z = np.random.randint(0,2,100) +np.logical_not(Z, out=Z) + +Z = np.random.uniform(-1.0,1.0,100) +np.negative(Z, out=Z) +``` + +#### 78. Consider 2 sets of points P0,P1 describing lines (2d) and a point p, how to compute distance from p to each line i (P0\[i\],P1\[i\])? (★★★) + + +```python +def distance(P0, P1, p): + T = P1 - P0 + L = (T**2).sum(axis=1) + U = -((P0[:,0]-p[...,0])*T[:,0] + (P0[:,1]-p[...,1])*T[:,1]) / L + U = U.reshape(len(U),1) + D = P0 + U*T - p + return np.sqrt((D**2).sum(axis=1)) + +P0 = np.random.uniform(-10,10,(10,2)) +P1 = np.random.uniform(-10,10,(10,2)) +p = np.random.uniform(-10,10,( 1,2)) +print(distance(P0, P1, p)) +``` + +#### 79. Consider 2 sets of points P0,P1 describing lines (2d) and a set of points P, how to compute distance from each point j (P\[j\]) to each line i (P0\[i\],P1\[i\])? (★★★) + + +```python +# Author: Italmassov Kuanysh + +# based on distance function from previous question +P0 = np.random.uniform(-10, 10, (10,2)) +P1 = np.random.uniform(-10,10,(10,2)) +p = np.random.uniform(-10, 10, (10,2)) +print(np.array([distance(P0,P1,p_i) for p_i in p])) +``` + +#### 80. Consider an arbitrary array, write a function that extract a subpart with a fixed shape and centered on a given element (pad with a `fill` value when necessary) (★★★) + + +```python +# Author: Nicolas Rougier + +Z = np.random.randint(0,10,(10,10)) +shape = (5,5) +fill = 0 +position = (1,1) + +R = np.ones(shape, dtype=Z.dtype)*fill +P = np.array(list(position)).astype(int) +Rs = np.array(list(R.shape)).astype(int) +Zs = np.array(list(Z.shape)).astype(int) + +R_start = np.zeros((len(shape),)).astype(int) +R_stop = np.array(list(shape)).astype(int) +Z_start = (P-Rs//2) +Z_stop = (P+Rs//2)+Rs%2 + +R_start = (R_start - np.minimum(Z_start,0)).tolist() +Z_start = (np.maximum(Z_start,0)).tolist() +R_stop = np.maximum(R_start, (R_stop - np.maximum(Z_stop-Zs,0))).tolist() +Z_stop = (np.minimum(Z_stop,Zs)).tolist() + +r = [slice(start,stop) for start,stop in zip(R_start,R_stop)] +z = [slice(start,stop) for start,stop in zip(Z_start,Z_stop)] +R[r] = Z[z] +print(Z) +print(R) +``` + +#### 81. Consider an array Z = \[1,2,3,4,5,6,7,8,9,10,11,12,13,14\], how to generate an array R = \[\[1,2,3,4\], \[2,3,4,5\], \[3,4,5,6\], ..., \[11,12,13,14\]\]? (★★★) + + +```python +# Author: Stefan van der Walt + +Z = np.arange(1,15,dtype=np.uint32) +R = stride_tricks.as_strided(Z,(11,4),(4,4)) +print(R) +``` + +#### 82. Compute a matrix rank (★★★) + + +```python +# Author: Stefan van der Walt + +Z = np.random.uniform(0,1,(10,10)) +U, S, V = np.linalg.svd(Z) # Singular Value Decomposition +rank = np.sum(S > 1e-10) +print(rank) +``` + +#### 83. How to find the most frequent value in an array? + + +```python +Z = np.random.randint(0,10,50) +print(np.bincount(Z).argmax()) +``` + +#### 84. Extract all the contiguous 3x3 blocks from a random 10x10 matrix (★★★) + + +```python +# Author: Chris Barker + +Z = np.random.randint(0,5,(10,10)) +n = 3 +i = 1 + (Z.shape[0]-3) +j = 1 + (Z.shape[1]-3) +C = stride_tricks.as_strided(Z, shape=(i, j, n, n), strides=Z.strides + Z.strides) +print(C) +``` + +#### 85. Create a 2D array subclass such that Z\[i,j\] == Z\[j,i\] (★★★) + + +```python +# Author: Eric O. Lebigot +# Note: only works for 2d array and value setting using indices + +class Symetric(np.ndarray): + def __setitem__(self, index, value): + i,j = index + super(Symetric, self).__setitem__((i,j), value) + super(Symetric, self).__setitem__((j,i), value) + +def symetric(Z): + return np.asarray(Z + Z.T - np.diag(Z.diagonal())).view(Symetric) + +S = symetric(np.random.randint(0,10,(5,5))) +S[2,3] = 42 +print(S) +``` + +#### 86. Consider a set of p matrices wich shape (n,n) and a set of p vectors with shape (n,1). How to compute the sum of of the p matrix products at once? (result has shape (n,1)) (★★★) + + +```python +# Author: Stefan van der Walt + +p, n = 10, 20 +M = np.ones((p,n,n)) +V = np.ones((p,n,1)) +S = np.tensordot(M, V, axes=[[0, 2], [0, 1]]) +print(S) + +# It works, because: +# M is (p,n,n) +# V is (p,n,1) +# Thus, summing over the paired axes 0 and 0 (of M and V independently), +# and 2 and 1, to remain with a (n,1) vector. +``` + +#### 87. Consider a 16x16 array, how to get the block-sum (block size is 4x4)? (★★★) + + +```python +# Author: Robert Kern + +Z = np.ones((16,16)) +k = 4 +S = np.add.reduceat(np.add.reduceat(Z, np.arange(0, Z.shape[0], k), axis=0), + np.arange(0, Z.shape[1], k), axis=1) +print(S) +``` + +#### 88. How to implement the Game of Life using numpy arrays? (★★★) + + +```python +# Author: Nicolas Rougier + +def iterate(Z): + # Count neighbours + N = (Z[0:-2,0:-2] + Z[0:-2,1:-1] + Z[0:-2,2:] + + Z[1:-1,0:-2] + Z[1:-1,2:] + + Z[2: ,0:-2] + Z[2: ,1:-1] + Z[2: ,2:]) + + # Apply rules + birth = (N==3) & (Z[1:-1,1:-1]==0) + survive = ((N==2) | (N==3)) & (Z[1:-1,1:-1]==1) + Z[...] = 0 + Z[1:-1,1:-1][birth | survive] = 1 + return Z + +Z = np.random.randint(0,2,(50,50)) +for i in range(100): Z = iterate(Z) +print(Z) +``` + +#### 89. How to get the n largest values of an array (★★★) + + +```python +Z = np.arange(10000) +np.random.shuffle(Z) +n = 5 + +# Slow +print (Z[np.argsort(Z)[-n:]]) + +# Fast +print (Z[np.argpartition(-Z,n)[:n]]) +``` + +#### 90. Given an arbitrary number of vectors, build the cartesian product (every combinations of every item) (★★★) + + +```python +# Author: Stefan Van der Walt + +def cartesian(arrays): + arrays = [np.asarray(a) for a in arrays] + shape = (len(x) for x in arrays) + + ix = np.indices(shape, dtype=int) + ix = ix.reshape(len(arrays), -1).T + + for n, arr in enumerate(arrays): + ix[:, n] = arrays[n][ix[:, n]] + + return ix + +print (cartesian(([1, 2, 3], [4, 5], [6, 7]))) +``` + +#### 91. How to create a record array from a regular array? (★★★) + + +```python +Z = np.array([("Hello", 2.5, 3), + ("World", 3.6, 2)]) +R = np.core.records.fromarrays(Z.T, + names='col1, col2, col3', + formats = 'S8, f8, i8') +print(R) +``` + +#### 92. Consider a large vector Z, compute Z to the power of 3 using 3 different methods (★★★) + + +```python +# Author: Ryan G. + +x = np.random.rand(int(5e7)) + +%timeit np.power(x,3) +%timeit x*x*x +%timeit np.einsum('i,i,i->i',x,x,x) +``` + +#### 93. Consider two arrays A and B of shape (8,3) and (2,2). How to find rows of A that contain elements of each row of B regardless of the order of the elements in B? (★★★) + + +```python +# Author: Gabe Schwartz + +A = np.random.randint(0,5,(8,3)) +B = np.random.randint(0,5,(2,2)) + +C = (A[..., np.newaxis, np.newaxis] == B) +rows = np.where(C.any((3,1)).all(1))[0] +print(rows) +``` + +#### 94. Considering a 10x3 matrix, extract rows with unequal values (e.g. \[2,2,3\]) (★★★) + + +```python +# Author: Robert Kern + +Z = np.random.randint(0,5,(10,3)) +print(Z) +# solution for arrays of all dtypes (including string arrays and record arrays) +E = np.all(Z[:,1:] == Z[:,:-1], axis=1) +U = Z[~E] +print(U) +# soluiton for numerical arrays only, will work for any number of columns in Z +U = Z[Z.max(axis=1) != Z.min(axis=1),:] +print(U) +``` + +#### 95. Convert a vector of ints into a matrix binary representation (★★★) + + +```python +# Author: Warren Weckesser + +I = np.array([0, 1, 2, 3, 15, 16, 32, 64, 128]) +B = ((I.reshape(-1,1) & (2**np.arange(8))) != 0).astype(int) +print(B[:,::-1]) + +# Author: Daniel T. McDonald + +I = np.array([0, 1, 2, 3, 15, 16, 32, 64, 128], dtype=np.uint8) +print(np.unpackbits(I[:, np.newaxis], axis=1)) +``` + +#### 96. Given a two dimensional array, how to extract unique rows? (★★★) + + +```python +# Author: Jaime Fernández del Río + +Z = np.random.randint(0,2,(6,3)) +T = np.ascontiguousarray(Z).view(np.dtype((np.void, Z.dtype.itemsize * Z.shape[1]))) +_, idx = np.unique(T, return_index=True) +uZ = Z[idx] +print(uZ) + +# Author: Andreas Kouzelis +# NumPy >= 1.13 +uZ = np.unique(Z, axis=0) +print(uZ) +``` + +#### 97. Considering 2 vectors A & B, write the einsum equivalent of inner, outer, sum, and mul function (★★★) + + +```python +# Author: Alex Riley +# Make sure to read: http://ajcr.net/Basic-guide-to-einsum/ + +A = np.random.uniform(0,1,10) +B = np.random.uniform(0,1,10) + +np.einsum('i->', A) # np.sum(A) +np.einsum('i,i->i', A, B) # A * B +np.einsum('i,i', A, B) # np.inner(A, B) +np.einsum('i,j->ij', A, B) # np.outer(A, B) +``` + +#### 98. Considering a path described by two vectors (X,Y), how to sample it using equidistant samples (★★★)? + + +```python +# Author: Bas Swinckels + +phi = np.arange(0, 10*np.pi, 0.1) +a = 1 +x = a*phi*np.cos(phi) +y = a*phi*np.sin(phi) + +dr = (np.diff(x)**2 + np.diff(y)**2)**.5 # segment lengths +r = np.zeros_like(x) +r[1:] = np.cumsum(dr) # integrate path +r_int = np.linspace(0, r.max(), 200) # regular spaced path +x_int = np.interp(r_int, r, x) # integrate path +y_int = np.interp(r_int, r, y) +``` + +#### 99. Given an integer n and a 2D array X, select from X the rows which can be interpreted as draws from a multinomial distribution with n degrees, i.e., the rows which only contain integers and which sum to n. (★★★) + + +```python +# Author: Evgeni Burovski + +X = np.asarray([[1.0, 0.0, 3.0, 8.0], + [2.0, 0.0, 1.0, 1.0], + [1.5, 2.5, 1.0, 0.0]]) +n = 4 +M = np.logical_and.reduce(np.mod(X, 1) == 0, axis=-1) +M &= (X.sum(axis=-1) == n) +print(X[M]) +``` + +#### 100. Compute bootstrapped 95% confidence intervals for the mean of a 1D array X (i.e., resample the elements of an array with replacement N times, compute the mean of each sample, and then compute percentiles over the means). (★★★) + + +```python +# Author: Jessica B. Hamrick + +X = np.random.randn(100) # random 1D array +N = 1000 # number of bootstrap samples +idx = np.random.randint(0, X.size, (N, X.size)) +means = X[idx].mean(axis=1) +confint = np.percentile(means, [2.5, 97.5]) +print(confint) +``` diff --git "a/020numpy100\351\242\230/numpy-100-master/100_Numpy_exercises_no_solution.ipynb" "b/020numpy100\351\242\230/numpy-100-master/100_Numpy_exercises_no_solution.ipynb" new file mode 100644 index 0000000..6be33bb --- /dev/null +++ "b/020numpy100\351\242\230/numpy-100-master/100_Numpy_exercises_no_solution.ipynb" @@ -0,0 +1,1525 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 100 numpy exercises\n", + "\n", + "This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow and in the numpy documentation. The goal of this collection is to offer a quick reference for both old and new users but also to provide a set of exercises for those who teach.\n", + "\n", + "\n", + "If you find an error or think you've a better way to solve some of them, feel free to open an issue at " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1. Import the numpy package under the name `np` (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2. Print the numpy version and the configuration (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3. Create a null vector of size 10 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4. How to find the memory size of any array (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5. How to get the documentation of the numpy add function from the command line? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6. Create a null vector of size 10 but the fifth value which is 1 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 7. Create a vector with values ranging from 10 to 49 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 8. Reverse a vector (first element becomes last) (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 9. Create a 3x3 matrix with values ranging from 0 to 8 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 10. Find indices of non-zero elements from \\[1,2,0,0,4,0\\] (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 11. Create a 3x3 identity matrix (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 12. Create a 3x3x3 array with random values (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 13. Create a 10x10 array with random values and find the minimum and maximum values (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 14. Create a random vector of size 30 and find the mean value (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 15. Create a 2d array with 1 on the border and 0 inside (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 16. How to add a border (filled with 0's) around an existing array? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 17. What is the result of the following expression? (★☆☆)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "0 * np.nan\n", + "np.nan == np.nan\n", + "np.inf > np.nan\n", + "np.nan - np.nan\n", + "np.nan in set([np.nan])\n", + "0.3 == 3 * 0.1\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 18. Create a 5x5 matrix with values 1,2,3,4 just below the diagonal (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 19. Create a 8x8 matrix and fill it with a checkerboard pattern (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 20. Consider a (6,7,8) shape array, what is the index (x,y,z) of the 100th element?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 21. Create a checkerboard 8x8 matrix using the tile function (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 22. Normalize a 5x5 random matrix (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 23. Create a custom dtype that describes a color as four unsigned bytes (RGBA) (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 24. Multiply a 5x3 matrix by a 3x2 matrix (real matrix product) (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 25. Given a 1D array, negate all elements which are between 3 and 8, in place. (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 26. What is the output of the following script? (★☆☆)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "# Author: Jake VanderPlas\n", + "\n", + "print(sum(range(5),-1))\n", + "from numpy import *\n", + "print(sum(range(5),-1))\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 27. Consider an integer vector Z, which of these expressions are legal? (★☆☆)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "Z**Z\n", + "2 << Z >> 2\n", + "Z <- Z\n", + "1j*Z\n", + "Z/1/1\n", + "ZZ\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 28. What are the result of the following expressions?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "np.array(0) / np.array(0)\n", + "np.array(0) // np.array(0)\n", + "np.array([np.nan]).astype(int).astype(float)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 29. How to round away from zero a float array ? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 30. How to find common values between two arrays? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 31. How to ignore all numpy warnings (not recommended)? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 32. Is the following expressions true? (★☆☆)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "np.sqrt(-1) == np.emath.sqrt(-1)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 33. How to get the dates of yesterday, today and tomorrow? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 34. How to get all the dates corresponding to the month of July 2016? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 35. How to compute ((A+B)\\*(-A/2)) in place (without copy)? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 36. Extract the integer part of a random array using 5 different methods (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 37. Create a 5x5 matrix with row values ranging from 0 to 4 (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 38. Consider a generator function that generates 10 integers and use it to build an array (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 39. Create a vector of size 10 with values ranging from 0 to 1, both excluded (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 40. Create a random vector of size 10 and sort it (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 41. How to sum a small array faster than np.sum? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 42. Consider two random array A and B, check if they are equal (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 43. Make an array immutable (read-only) (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 44. Consider a random 10x2 matrix representing cartesian coordinates, convert them to polar coordinates (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 45. Create random vector of size 10 and replace the maximum value by 0 (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 46. Create a structured array with `x` and `y` coordinates covering the \\[0,1\\]x\\[0,1\\] area (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 47. Given two arrays, X and Y, construct the Cauchy matrix C (Cij =1/(xi - yj))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 48. Print the minimum and maximum representable value for each numpy scalar type (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 49. How to print all the values of an array? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 50. How to find the closest value (to a given scalar) in a vector? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 51. Create a structured array representing a position (x,y) and a color (r,g,b) (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 52. Consider a random vector with shape (100,2) representing coordinates, find point by point distances (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 53. How to convert a float (32 bits) array into an integer (32 bits) in place?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 54. How to read the following file? (★★☆)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```\n", + "1, 2, 3, 4, 5\n", + "6, , , 7, 8\n", + " , , 9,10,11\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 55. What is the equivalent of enumerate for numpy arrays? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 56. Generate a generic 2D Gaussian-like array (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 57. How to randomly place p elements in a 2D array? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 58. Subtract the mean of each row of a matrix (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 59. How to sort an array by the nth column? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 60. How to tell if a given 2D array has null columns? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 61. Find the nearest value from a given value in an array (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 62. Considering two arrays with shape (1,3) and (3,1), how to compute their sum using an iterator? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 63. Create an array class that has a name attribute (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 64. Consider a given vector, how to add 1 to each element indexed by a second vector (be careful with repeated indices)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 65. How to accumulate elements of a vector (X) to an array (F) based on an index list (I)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 66. Considering a (w,h,3) image of (dtype=ubyte), compute the number of unique colors (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 67. Considering a four dimensions array, how to get sum over the last two axis at once? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 68. Considering a one-dimensional vector D, how to compute means of subsets of D using a vector S of same size describing subset indices? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 69. How to get the diagonal of a dot product? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 70. Consider the vector \\[1, 2, 3, 4, 5\\], how to build a new vector with 3 consecutive zeros interleaved between each value? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 71. Consider an array of dimension (5,5,3), how to mulitply it by an array with dimensions (5,5)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 72. How to swap two rows of an array? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 73. Consider a set of 10 triplets describing 10 triangles (with shared vertices), find the set of unique line segments composing all the triangles (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 74. Given an array C that is a bincount, how to produce an array A such that np.bincount(A) == C? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 75. How to compute averages using a sliding window over an array? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 76. Consider a one-dimensional array Z, build a two-dimensional array whose first row is (Z\\[0\\],Z\\[1\\],Z\\[2\\]) and each subsequent row is shifted by 1 (last row should be (Z\\[-3\\],Z\\[-2\\],Z\\[-1\\]) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 77. How to negate a boolean, or to change the sign of a float inplace? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 78. Consider 2 sets of points P0,P1 describing lines (2d) and a point p, how to compute distance from p to each line i (P0\\[i\\],P1\\[i\\])? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 79. Consider 2 sets of points P0,P1 describing lines (2d) and a set of points P, how to compute distance from each point j (P\\[j\\]) to each line i (P0\\[i\\],P1\\[i\\])? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 80. Consider an arbitrary array, write a function that extract a subpart with a fixed shape and centered on a given element (pad with a `fill` value when necessary) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 81. Consider an array Z = \\[1,2,3,4,5,6,7,8,9,10,11,12,13,14\\], how to generate an array R = \\[\\[1,2,3,4\\], \\[2,3,4,5\\], \\[3,4,5,6\\], ..., \\[11,12,13,14\\]\\]? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 82. Compute a matrix rank (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 83. How to find the most frequent value in an array?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 84. Extract all the contiguous 3x3 blocks from a random 10x10 matrix (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 85. Create a 2D array subclass such that Z\\[i,j\\] == Z\\[j,i\\] (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 86. Consider a set of p matrices wich shape (n,n) and a set of p vectors with shape (n,1). How to compute the sum of of the p matrix products at once? (result has shape (n,1)) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 87. Consider a 16x16 array, how to get the block-sum (block size is 4x4)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 88. How to implement the Game of Life using numpy arrays? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 89. How to get the n largest values of an array (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 90. Given an arbitrary number of vectors, build the cartesian product (every combinations of every item) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 91. How to create a record array from a regular array? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 92. Consider a large vector Z, compute Z to the power of 3 using 3 different methods (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 93. Consider two arrays A and B of shape (8,3) and (2,2). How to find rows of A that contain elements of each row of B regardless of the order of the elements in B? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 94. Considering a 10x3 matrix, extract rows with unequal values (e.g. \\[2,2,3\\]) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 95. Convert a vector of ints into a matrix binary representation (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 96. Given a two dimensional array, how to extract unique rows? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 97. Considering 2 vectors A & B, write the einsum equivalent of inner, outer, sum, and mul function (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 98. Considering a path described by two vectors (X,Y), how to sample it using equidistant samples (★★★)?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 99. Given an integer n and a 2D array X, select from X the rows which can be interpreted as draws from a multinomial distribution with n degrees, i.e., the rows which only contain integers and which sum to n. (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 100. Compute bootstrapped 95% confidence intervals for the mean of a 1D array X (i.e., resample the elements of an array with replacement N times, compute the mean of each sample, and then compute percentiles over the means). (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git "a/020numpy100\351\242\230/numpy-100-master/100_Numpy_exercises_no_solution.md" "b/020numpy100\351\242\230/numpy-100-master/100_Numpy_exercises_no_solution.md" new file mode 100644 index 0000000..44b3fbe --- /dev/null +++ "b/020numpy100\351\242\230/numpy-100-master/100_Numpy_exercises_no_solution.md" @@ -0,0 +1,446 @@ + +# 100 numpy exercises + +This is a collection of exercises that have been collected in the numpy mailing +list, on stack overflow and in the numpy documentation. I've also created some +to reach the 100 limit. The goal of this collection is to offer a quick +reference for both old and new users but also to provide a set of exercises for +those who teach. + +If you find an error or think you've a better way to solve some of them, feel +free to open an issue at + +#### 1. Import the numpy package under the name `np` (★☆☆) + + + +#### 2. Print the numpy version and the configuration (★☆☆) + + + +#### 3. Create a null vector of size 10 (★☆☆) + + + +#### 4. How to find the memory size of any array (★☆☆) + + + +#### 5. How to get the documentation of the numpy add function from the command line? (★☆☆) + + + +#### 6. Create a null vector of size 10 but the fifth value which is 1 (★☆☆) + + + +#### 7. Create a vector with values ranging from 10 to 49 (★☆☆) + + + +#### 8. Reverse a vector (first element becomes last) (★☆☆) + + + +#### 9. Create a 3x3 matrix with values ranging from 0 to 8 (★☆☆) + + + +#### 10. Find indices of non-zero elements from \[1,2,0,0,4,0\] (★☆☆) + + + +#### 11. Create a 3x3 identity matrix (★☆☆) + + + +#### 12. Create a 3x3x3 array with random values (★☆☆) + + + +#### 13. Create a 10x10 array with random values and find the minimum and maximum values (★☆☆) + + + +#### 14. Create a random vector of size 30 and find the mean value (★☆☆) + + + +#### 15. Create a 2d array with 1 on the border and 0 inside (★☆☆) + + + +#### 16. How to add a border (filled with 0's) around an existing array? (★☆☆) + + + +#### 17. What is the result of the following expression? (★☆☆) + + +```python +0 * np.nan +np.nan == np.nan +np.inf > np.nan +np.nan - np.nan +np.nan in set([np.nan]) +0.3 == 3 * 0.1 +``` + +#### 18. Create a 5x5 matrix with values 1,2,3,4 just below the diagonal (★☆☆) + + + +#### 19. Create a 8x8 matrix and fill it with a checkerboard pattern (★☆☆) + + + +#### 20. Consider a (6,7,8) shape array, what is the index (x,y,z) of the 100th element? + + + +#### 21. Create a checkerboard 8x8 matrix using the tile function (★☆☆) + + + +#### 22. Normalize a 5x5 random matrix (★☆☆) + + + +#### 23. Create a custom dtype that describes a color as four unsigned bytes (RGBA) (★☆☆) + + + +#### 24. Multiply a 5x3 matrix by a 3x2 matrix (real matrix product) (★☆☆) + + + +#### 25. Given a 1D array, negate all elements which are between 3 and 8, in place. (★☆☆) + + + +#### 26. What is the output of the following script? (★☆☆) + + +```python +# Author: Jake VanderPlas + +print(sum(range(5),-1)) +from numpy import * +print(sum(range(5),-1)) +``` + +#### 27. Consider an integer vector Z, which of these expressions are legal? (★☆☆) + + +```python +Z**Z +2 << Z >> 2 +Z <- Z +1j*Z +Z/1/1 +ZZ +``` + +#### 28. What are the result of the following expressions? + + +```python +np.array(0) / np.array(0) +np.array(0) // np.array(0) +np.array([np.nan]).astype(int).astype(float) +``` + +#### 29. How to round away from zero a float array ? (★☆☆) + + + +#### 30. How to find common values between two arrays? (★☆☆) + + + +#### 31. How to ignore all numpy warnings (not recommended)? (★☆☆) + + + +#### 32. Is the following expressions true? (★☆☆) + + +```python +np.sqrt(-1) == np.emath.sqrt(-1) +``` + +#### 33. How to get the dates of yesterday, today and tomorrow? (★☆☆) + + + +#### 34. How to get all the dates corresponding to the month of July 2016? (★★☆) + + + +#### 35. How to compute ((A+B)\*(-A/2)) in place (without copy)? (★★☆) + + + +#### 36. Extract the integer part of a random array using 5 different methods (★★☆) + + + +#### 37. Create a 5x5 matrix with row values ranging from 0 to 4 (★★☆) + + + +#### 38. Consider a generator function that generates 10 integers and use it to build an array (★☆☆) + + + +#### 39. Create a vector of size 10 with values ranging from 0 to 1, both excluded (★★☆) + + + +#### 40. Create a random vector of size 10 and sort it (★★☆) + + + +#### 41. How to sum a small array faster than np.sum? (★★☆) + + + +#### 42. Consider two random array A and B, check if they are equal (★★☆) + + + +#### 43. Make an array immutable (read-only) (★★☆) + + + +#### 44. Consider a random 10x2 matrix representing cartesian coordinates, convert them to polar coordinates (★★☆) + + + +#### 45. Create a random vector of size 10 and replace the maximum value by 0 (★★☆) + + + +#### 46. Create a structured array with `x` and `y` coordinates covering the \[0,1\]x\[0,1\] area (★★☆) + + + +#### 47. Given two arrays, X and Y, construct the Cauchy matrix C (Cij =1/(xi - yj)) + + + +#### 48. Print the minimum and maximum representable value for each numpy scalar type (★★☆) + + + +#### 49. How to print all the values of an array? (★★☆) + + + +#### 50. How to find the closest value (to a given scalar) in a vector? (★★☆) + + + +#### 51. Create a structured array representing a position (x,y) and a color (r,g,b) (★★☆) + + + +#### 52. Consider a random vector with shape (100,2) representing coordinates, find point by point distances (★★☆) + + + +#### 53. How to convert a float (32 bits) array into an integer (32 bits) in place? + + + +#### 54. How to read the following file? (★★☆) + + +``` +1, 2, 3, 4, 5 +6, , , 7, 8 + , , 9,10,11 +``` + +#### 55. What is the equivalent of enumerate for numpy arrays? (★★☆) + + + +#### 56. Generate a generic 2D Gaussian-like array (★★☆) + + + +#### 57. How to randomly place p elements in a 2D array? (★★☆) + + + +#### 58. Subtract the mean of each row of a matrix (★★☆) + + + +#### 59. How to sort an array by the nth column? (★★☆) + + + +#### 60. How to tell if a given 2D array has null columns? (★★☆) + + + +#### 61. Find the nearest value from a given value in an array (★★☆) + + + +#### 62. Considering two arrays with shape (1,3) and (3,1), how to compute their sum using an iterator? (★★☆) + + + +#### 63. Create an array class that has a name attribute (★★☆) + + + +#### 64. Consider a given vector, how to add 1 to each element indexed by a second vector (be careful with repeated indices)? (★★★) + + + +#### 65. How to accumulate elements of a vector (X) to an array (F) based on an index list (I)? (★★★) + + + +#### 66. Considering a (w,h,3) image of (dtype=ubyte), compute the number of unique colors (★★★) + + + +#### 67. Considering a four dimensions array, how to get sum over the last two axis at once? (★★★) + + + +#### 68. Considering a one-dimensional vector D, how to compute means of subsets of D using a vector S of same size describing subset indices? (★★★) + + + +#### 69. How to get the diagonal of a dot product? (★★★) + + + +#### 70. Consider the vector \[1, 2, 3, 4, 5\], how to build a new vector with 3 consecutive zeros interleaved between each value? (★★★) + + + +#### 71. Consider an array of dimension (5,5,3), how to mulitply it by an array with dimensions (5,5)? (★★★) + + + +#### 72. How to swap two rows of an array? (★★★) + + + +#### 73. Consider a set of 10 triplets describing 10 triangles (with shared vertices), find the set of unique line segments composing all the triangles (★★★) + + + +#### 74. Given an array C that is a bincount, how to produce an array A such that np.bincount(A) == C? (★★★) + + + +#### 75. How to compute averages using a sliding window over an array? (★★★) + + + +#### 76. Consider a one-dimensional array Z, build a two-dimensional array whose first row is (Z\[0\],Z\[1\],Z\[2\]) and each subsequent row is shifted by 1 (last row should be (Z\[-3\],Z\[-2\],Z\[-1\]) (★★★) + + + +#### 77. How to negate a boolean, or to change the sign of a float inplace? (★★★) + + + +#### 78. Consider 2 sets of points P0,P1 describing lines (2d) and a point p, how to compute distance from p to each line i (P0\[i\],P1\[i\])? (★★★) + + + +#### 79. Consider 2 sets of points P0,P1 describing lines (2d) and a set of points P, how to compute distance from each point j (P\[j\]) to each line i (P0\[i\],P1\[i\])? (★★★) + + + +#### 80. Consider an arbitrary array, write a function that extract a subpart with a fixed shape and centered on a given element (pad with a `fill` value when necessary) (★★★) + + + +#### 81. Consider an array Z = \[1,2,3,4,5,6,7,8,9,10,11,12,13,14\], how to generate an array R = \[\[1,2,3,4\], \[2,3,4,5\], \[3,4,5,6\], ..., \[11,12,13,14\]\]? (★★★) + + + +#### 82. Compute a matrix rank (★★★) + + + +#### 83. How to find the most frequent value in an array? + + + +#### 84. Extract all the contiguous 3x3 blocks from a random 10x10 matrix (★★★) + + + +#### 85. Create a 2D array subclass such that Z\[i,j\] == Z\[j,i\] (★★★) + + + +#### 86. Consider a set of p matrices wich shape (n,n) and a set of p vectors with shape (n,1). How to compute the sum of of the p matrix products at once? (result has shape (n,1)) (★★★) + + + +#### 87. Consider a 16x16 array, how to get the block-sum (block size is 4x4)? (★★★) + + + +#### 88. How to implement the Game of Life using numpy arrays? (★★★) + + + +#### 89. How to get the n largest values of an array (★★★) + + + +#### 90. Given an arbitrary number of vectors, build the cartesian product (every combinations of every item) (★★★) + + + +#### 91. How to create a record array from a regular array? (★★★) + + + +#### 92. Consider a large vector Z, compute Z to the power of 3 using 3 different methods (★★★) + + + +#### 93. Consider two arrays A and B of shape (8,3) and (2,2). How to find rows of A that contain elements of each row of B regardless of the order of the elements in B? (★★★) + + + +#### 94. Considering a 10x3 matrix, extract rows with unequal values (e.g. \[2,2,3\]) (★★★) + + + +#### 95. Convert a vector of ints into a matrix binary representation (★★★) + + + +#### 96. Given a two dimensional array, how to extract unique rows? (★★★) + + + +#### 97. Considering 2 vectors A & B, write the einsum equivalent of inner, outer, sum, and mul function (★★★) + + + +#### 98. Considering a path described by two vectors (X,Y), how to sample it using equidistant samples (★★★)? + + + +#### 99. Given an integer n and a 2D array X, select from X the rows which can be interpreted as draws from a multinomial distribution with n degrees, i.e., the rows which only contain integers and which sum to n. (★★★) + + + +#### 100. Compute bootstrapped 95% confidence intervals for the mean of a 1D array X (i.e., resample the elements of an array with replacement N times, compute the mean of each sample, and then compute percentiles over the means). (★★★) + diff --git "a/020numpy100\351\242\230/numpy-100-master/100_Numpy_exercises_with_hint.ipynb" "b/020numpy100\351\242\230/numpy-100-master/100_Numpy_exercises_with_hint.ipynb" new file mode 100644 index 0000000..5caf0b8 --- /dev/null +++ "b/020numpy100\351\242\230/numpy-100-master/100_Numpy_exercises_with_hint.ipynb" @@ -0,0 +1,1619 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 100 numpy exercises with hint\n", + "\n", + "This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow and in the numpy documentation. The goal of this collection is to offer a quick reference for both old and new users but also to provide a set of exercises for those who teach.\n", + "\n", + "\n", + "If you find an error or think you've a better way to solve some of them, feel free to open an issue at " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1. Import the numpy package under the name `np` (★☆☆) \n", + "(**hint**: import … as …)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2. Print the numpy version and the configuration (★☆☆) \n", + "(**hint**: np.\\_\\_version\\_\\_, np.show\\_config)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3. Create a null vector of size 10 (★☆☆) \n", + "(**hint**: np.zeros)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4. How to find the memory size of any array (★☆☆) \n", + "(**hint**: size, itemsize)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5. How to get the documentation of the numpy add function from the command line? (★☆☆) \n", + "(**hint**: np.info)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6. Create a null vector of size 10 but the fifth value which is 1 (★☆☆) \n", + "(**hint**: array\\[4\\])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 7. Create a vector with values ranging from 10 to 49 (★☆☆) \n", + "(**hint**: np.arange)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 8. Reverse a vector (first element becomes last) (★☆☆) \n", + "(**hint**: array\\[::-1\\])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 9. Create a 3x3 matrix with values ranging from 0 to 8 (★☆☆) \n", + "(**hint**: reshape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 10. Find indices of non-zero elements from \\[1,2,0,0,4,0\\] (★☆☆) \n", + "(**hint**: np.nonzero)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 11. Create a 3x3 identity matrix (★☆☆) \n", + "(**hint**: np.eye)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 12. Create a 3x3x3 array with random values (★☆☆) \n", + "(**hint**: np.random.random)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 13. Create a 10x10 array with random values and find the minimum and maximum values (★☆☆) \n", + "(**hint**: min, max)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 14. Create a random vector of size 30 and find the mean value (★☆☆) \n", + "(**hint**: mean)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 15. Create a 2d array with 1 on the border and 0 inside (★☆☆) \n", + "(**hint**: array\\[1:-1, 1:-1\\])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 16. How to add a border (filled with 0's) around an existing array? (★☆☆) \n", + "(**hint**: np.pad)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 17. What is the result of the following expression? (★☆☆) \n", + "(**hint**: NaN = not a number, inf = infinity)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "0 * np.nan\n", + "np.nan == np.nan\n", + "np.inf > np.nan\n", + "np.nan - np.nan\n", + "np.nan in set([np.nan])\n", + "0.3 == 3 * 0.1\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 18. Create a 5x5 matrix with values 1,2,3,4 just below the diagonal (★☆☆) \n", + "(**hint**: np.diag)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 19. Create a 8x8 matrix and fill it with a checkerboard pattern (★☆☆) \n", + "(**hint**: array\\[::2\\])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 20. Consider a (6,7,8) shape array, what is the index (x,y,z) of the 100th element? \n", + "(**hint**: np.unravel_index)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 21. Create a checkerboard 8x8 matrix using the tile function (★☆☆) \n", + "(**hint**: np.tile)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 22. Normalize a 5x5 random matrix (★☆☆) \n", + "(**hint**: (x - mean) / std)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 23. Create a custom dtype that describes a color as four unsigned bytes (RGBA) (★☆☆) \n", + "(**hint**: np.dtype)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 24. Multiply a 5x3 matrix by a 3x2 matrix (real matrix product) (★☆☆) \n", + "(**hint**: np.dot | @)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 25. Given a 1D array, negate all elements which are between 3 and 8, in place. (★☆☆) \n", + "(**hint**: >, <=)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 26. What is the output of the following script? (★☆☆) \n", + "(**hint**: np.sum)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "# Author: Jake VanderPlas\n", + "\n", + "print(sum(range(5),-1))\n", + "from numpy import *\n", + "print(sum(range(5),-1))\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 27. Consider an integer vector Z, which of these expressions are legal? (★☆☆)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "Z**Z\n", + "2 << Z >> 2\n", + "Z <- Z\n", + "1j*Z\n", + "Z/1/1\n", + "ZZ\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 28. What are the result of the following expressions?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "np.array(0) / np.array(0)\n", + "np.array(0) // np.array(0)\n", + "np.array([np.nan]).astype(int).astype(float)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 29. How to round away from zero a float array ? (★☆☆) \n", + "(**hint**: np.uniform, np.copysign, np.ceil, np.abs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 30. How to find common values between two arrays? (★☆☆) \n", + "(**hint**: np.intersect1d)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 31. How to ignore all numpy warnings (not recommended)? (★☆☆) \n", + "(**hint**: np.seterr, np.errstate)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 32. Is the following expressions true? (★☆☆) \n", + "(**hint**: imaginary number)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "np.sqrt(-1) == np.emath.sqrt(-1)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 33. How to get the dates of yesterday, today and tomorrow? (★☆☆) \n", + "(**hint**: np.datetime64, np.timedelta64)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 34. How to get all the dates corresponding to the month of July 2016? (★★☆) \n", + "(**hint**: np.arange(dtype=datetime64\\['D'\\]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 35. How to compute ((A+B)\\*(-A/2)) in place (without copy)? (★★☆) \n", + "(**hint**: np.add(out=), np.negative(out=), np.multiply(out=), np.divide(out=))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 36. Extract the integer part of a random array using 5 different methods (★★☆) \n", + "(**hint**: %, np.floor, np.ceil, astype, np.trunc)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 37. Create a 5x5 matrix with row values ranging from 0 to 4 (★★☆) \n", + "(**hint**: np.arange)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 38. Consider a generator function that generates 10 integers and use it to build an array (★☆☆) \n", + "(**hint**: np.fromiter)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 39. Create a vector of size 10 with values ranging from 0 to 1, both excluded (★★☆) \n", + "(**hint**: np.linspace)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 40. Create a random vector of size 10 and sort it (★★☆) \n", + "(**hint**: sort)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 41. How to sum a small array faster than np.sum? (★★☆) \n", + "(**hint**: np.add.reduce)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 42. Consider two random array A and B, check if they are equal (★★☆) \n", + "(**hint**: np.allclose, np.array\\_equal)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 43. Make an array immutable (read-only) (★★☆) \n", + "(**hint**: flags.writeable)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 44. Consider a random 10x2 matrix representing cartesian coordinates, convert them to polar coordinates (★★☆) \n", + "(**hint**: np.sqrt, np.arctan2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 45. Create random vector of size 10 and replace the maximum value by 0 (★★☆) \n", + "(**hint**: argmax)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 46. Create a structured array with `x` and `y` coordinates covering the \\[0,1\\]x\\[0,1\\] area (★★☆) \n", + "(**hint**: np.meshgrid)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 47. Given two arrays, X and Y, construct the Cauchy matrix C (Cij =1/(xi - yj)) \n", + "(**hint**: np.subtract.outer)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 48. Print the minimum and maximum representable value for each numpy scalar type (★★☆) \n", + "(**hint**: np.iinfo, np.finfo, eps)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 49. How to print all the values of an array? (★★☆) \n", + "(**hint**: np.set\\_printoptions)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 50. How to find the closest value (to a given scalar) in a vector? (★★☆) \n", + "(**hint**: argmin)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 51. Create a structured array representing a position (x,y) and a color (r,g,b) (★★☆) \n", + "(**hint**: dtype)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 52. Consider a random vector with shape (100,2) representing coordinates, find point by point distances (★★☆) \n", + "(**hint**: np.atleast\\_2d, T, np.sqrt)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 53. How to convert a float (32 bits) array into an integer (32 bits) in place? \n", + "(**hint**: view and [:] = )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 54. How to read the following file? (★★☆) \n", + "(**hint**: np.genfromtxt)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```\n", + "1, 2, 3, 4, 5\n", + "6, , , 7, 8\n", + " , , 9,10,11\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 55. What is the equivalent of enumerate for numpy arrays? (★★☆) \n", + "(**hint**: np.ndenumerate, np.ndindex)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 56. Generate a generic 2D Gaussian-like array (★★☆) \n", + "(**hint**: np.meshgrid, np.exp)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 57. How to randomly place p elements in a 2D array? (★★☆) \n", + "(**hint**: np.put, np.random.choice)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 58. Subtract the mean of each row of a matrix (★★☆) \n", + "(**hint**: mean(axis=,keepdims=))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 59. How to sort an array by the nth column? (★★☆) \n", + "(**hint**: argsort)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 60. How to tell if a given 2D array has null columns? (★★☆) \n", + "(**hint**: any, ~)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 61. Find the nearest value from a given value in an array (★★☆) \n", + "(**hint**: np.abs, argmin, flat)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 62. Considering two arrays with shape (1,3) and (3,1), how to compute their sum using an iterator? (★★☆) \n", + "(**hint**: np.nditer)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 63. Create an array class that has a name attribute (★★☆) \n", + "(**hint**: class method)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 64. Consider a given vector, how to add 1 to each element indexed by a second vector (be careful with repeated indices)? (★★★) \n", + "(**hint**: np.bincount | np.add.at)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 65. How to accumulate elements of a vector (X) to an array (F) based on an index list (I)? (★★★) \n", + "(**hint**: np.bincount)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 66. Considering a (w,h,3) image of (dtype=ubyte), compute the number of unique colors (★★★) \n", + "(**hint**: np.unique)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 67. Considering a four dimensions array, how to get sum over the last two axis at once? (★★★) \n", + "(**hint**: sum(axis=(-2,-1)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 68. Considering a one-dimensional vector D, how to compute means of subsets of D using a vector S of same size describing subset indices? (★★★) \n", + "(**hint**: np.bincount)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 69. How to get the diagonal of a dot product? (★★★) \n", + "(**hint**: np.diag)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 70. Consider the vector \\[1, 2, 3, 4, 5\\], how to build a new vector with 3 consecutive zeros interleaved between each value? (★★★) \n", + "(**hint**: array\\[::4\\])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 71. Consider an array of dimension (5,5,3), how to mulitply it by an array with dimensions (5,5)? (★★★) \n", + "(**hint**: array\\[:, :, None\\])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 72. How to swap two rows of an array? (★★★) \n", + "(**hint**: array\\[\\[\\]\\] = array\\[\\[\\]\\])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 73. Consider a set of 10 triplets describing 10 triangles (with shared vertices), find the set of unique line segments composing all the triangles (★★★) \n", + "(**hint**: repeat, np.roll, np.sort, view, np.unique)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 74. Given an array C that is a bincount, how to produce an array A such that np.bincount(A) == C? (★★★) \n", + "(**hint**: np.repeat)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 75. How to compute averages using a sliding window over an array? (★★★) \n", + "(**hint**: np.cumsum)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 76. Consider a one-dimensional array Z, build a two-dimensional array whose first row is (Z\\[0\\],Z\\[1\\],Z\\[2\\]) and each subsequent row is shifted by 1 (last row should be (Z\\[-3\\],Z\\[-2\\],Z\\[-1\\]) (★★★) \n", + "(**hint**: from numpy.lib import stride_tricks)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 77. How to negate a boolean, or to change the sign of a float inplace? (★★★) \n", + "(**hint**: np.logical_not, np.negative)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 78. Consider 2 sets of points P0,P1 describing lines (2d) and a point p, how to compute distance from p to each line i (P0\\[i\\],P1\\[i\\])? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 79. Consider 2 sets of points P0,P1 describing lines (2d) and a set of points P, how to compute distance from each point j (P\\[j\\]) to each line i (P0\\[i\\],P1\\[i\\])? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 80. Consider an arbitrary array, write a function that extract a subpart with a fixed shape and centered on a given element (pad with a `fill` value when necessary) (★★★) \n", + "(**hint**: minimum, maximum)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 81. Consider an array Z = \\[1,2,3,4,5,6,7,8,9,10,11,12,13,14\\], how to generate an array R = \\[\\[1,2,3,4\\], \\[2,3,4,5\\], \\[3,4,5,6\\], ..., \\[11,12,13,14\\]\\]? (★★★) \n", + "(**hint**: stride\\_tricks.as\\_strided)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 82. Compute a matrix rank (★★★) \n", + "(**hint**: np.linalg.svd) (suggestion: np.linalg.svd)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 83. How to find the most frequent value in an array? \n", + "(**hint**: np.bincount, argmax)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 84. Extract all the contiguous 3x3 blocks from a random 10x10 matrix (★★★) \n", + "(**hint**: stride\\_tricks.as\\_strided)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 85. Create a 2D array subclass such that Z\\[i,j\\] == Z\\[j,i\\] (★★★) \n", + "(**hint**: class method)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 86. Consider a set of p matrices wich shape (n,n) and a set of p vectors with shape (n,1). How to compute the sum of of the p matrix products at once? (result has shape (n,1)) (★★★) \n", + "(**hint**: np.tensordot)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 87. Consider a 16x16 array, how to get the block-sum (block size is 4x4)? (★★★) \n", + "(**hint**: np.add.reduceat)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 88. How to implement the Game of Life using numpy arrays? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 89. How to get the n largest values of an array (★★★) \n", + "(**hint**: np.argsort | np.argpartition)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 90. Given an arbitrary number of vectors, build the cartesian product (every combinations of every item) (★★★) \n", + "(**hint**: np.indices)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 91. How to create a record array from a regular array? (★★★) \n", + "(**hint**: np.core.records.fromarrays)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 92. Consider a large vector Z, compute Z to the power of 3 using 3 different methods (★★★) \n", + "(**hint**: np.power, \\*, np.einsum)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 93. Consider two arrays A and B of shape (8,3) and (2,2). How to find rows of A that contain elements of each row of B regardless of the order of the elements in B? (★★★) \n", + "(**hint**: np.where)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 94. Considering a 10x3 matrix, extract rows with unequal values (e.g. \\[2,2,3\\]) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 95. Convert a vector of ints into a matrix binary representation (★★★) \n", + "(**hint**: np.unpackbits)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 96. Given a two dimensional array, how to extract unique rows? (★★★) \n", + "(**hint**: np.ascontiguousarray | np.unique)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 97. Considering 2 vectors A & B, write the einsum equivalent of inner, outer, sum, and mul function (★★★) \n", + "(**hint**: np.einsum)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 98. Considering a path described by two vectors (X,Y), how to sample it using equidistant samples (★★★)? \n", + "(**hint**: np.cumsum, np.interp)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 99. Given an integer n and a 2D array X, select from X the rows which can be interpreted as draws from a multinomial distribution with n degrees, i.e., the rows which only contain integers and which sum to n. (★★★) \n", + "(**hint**: np.logical\\_and.reduce, np.mod)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 100. Compute bootstrapped 95% confidence intervals for the mean of a 1D array X (i.e., resample the elements of an array with replacement N times, compute the mean of each sample, and then compute percentiles over the means). (★★★) \n", + "(**hint**: np.percentile)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git "a/020numpy100\351\242\230/numpy-100-master/100_Numpy_exercises_with_hint.md" "b/020numpy100\351\242\230/numpy-100-master/100_Numpy_exercises_with_hint.md" new file mode 100644 index 0000000..e19970b --- /dev/null +++ "b/020numpy100\351\242\230/numpy-100-master/100_Numpy_exercises_with_hint.md" @@ -0,0 +1,634 @@ + +# 100 numpy exercises with hint + +This is a collection of exercises that have been collected in the numpy mailing +list, on stack overflow and in the numpy documentation. I've also created some +to reach the 100 limit. The goal of this collection is to offer a quick +reference for both old and new users but also to provide a set of exercises for +those who teach. + +If you find an error or think you've a better way to solve some of them, feel +free to open an issue at + +#### 1. Import the numpy package under the name `np` (★☆☆) + +(**hint**: import … as …) + + + +#### 2. Print the numpy version and the configuration (★☆☆) + +(**hint**: np.\_\_version\_\_, np.show\_config) + + + +#### 3. Create a null vector of size 10 (★☆☆) + +(**hint**: np.zeros) + + + +#### 4. How to find the memory size of any array (★☆☆) + +(**hint**: size, itemsize) + + + +#### 5. How to get the documentation of the numpy add function from the command line? (★☆☆) + +(**hint**: np.info) + + + +#### 6. Create a null vector of size 10 but the fifth value which is 1 (★☆☆) + +(**hint**: array\[4\]) + + + +#### 7. Create a vector with values ranging from 10 to 49 (★☆☆) + +(**hint**: np.arange) + + + +#### 8. Reverse a vector (first element becomes last) (★☆☆) + +(**hint**: array\[::-1\]) + + + +#### 9. Create a 3x3 matrix with values ranging from 0 to 8 (★☆☆) + +(**hint**: reshape) + + + +#### 10. Find indices of non-zero elements from \[1,2,0,0,4,0\] (★☆☆) + +(**hint**: np.nonzero) + + + +#### 11. Create a 3x3 identity matrix (★☆☆) + +(**hint**: np.eye) + + + +#### 12. Create a 3x3x3 array with random values (★☆☆) + +(**hint**: np.random.random) + + + +#### 13. Create a 10x10 array with random values and find the minimum and maximum values (★☆☆) + +(**hint**: min, max) + + + +#### 14. Create a random vector of size 30 and find the mean value (★☆☆) + +(**hint**: mean) + + + +#### 15. Create a 2d array with 1 on the border and 0 inside (★☆☆) + +(**hint**: array\[1:-1, 1:-1\]) + + + +#### 16. How to add a border (filled with 0's) around an existing array? (★☆☆) + +(**hint**: np.pad) + + + +#### 17. What is the result of the following expression? (★☆☆) + +(**hint**: NaN = not a number, inf = infinity) + + +```python +0 * np.nan +np.nan == np.nan +np.inf > np.nan +np.nan - np.nan +np.nan in set([np.nan]) +0.3 == 3 * 0.1 +``` + +#### 18. Create a 5x5 matrix with values 1,2,3,4 just below the diagonal (★☆☆) + +(**hint**: np.diag) + + + +#### 19. Create a 8x8 matrix and fill it with a checkerboard pattern (★☆☆) + +(**hint**: array\[::2\]) + + + +#### 20. Consider a (6,7,8) shape array, what is the index (x,y,z) of the 100th element? + +(**hint**: np.unravel\_index) + + + +#### 21. Create a checkerboard 8x8 matrix using the tile function (★☆☆) + +(**hint**: np.tile) + + + +#### 22. Normalize a 5x5 random matrix (★☆☆) + +(**hint**: (x - mean) / std) + + + +#### 23. Create a custom dtype that describes a color as four unsigned bytes (RGBA) (★☆☆) + +(**hint**: np.dtype) + + + +#### 24. Multiply a 5x3 matrix by a 3x2 matrix (real matrix product) (★☆☆) + +(**hint**: np.dot | @) + + + +#### 25. Given a 1D array, negate all elements which are between 3 and 8, in place. (★☆☆) + +(**hint**: >, <=) + + + +#### 26. What is the output of the following script? (★☆☆) + +(**hint**: np.sum) + + +```python +# Author: Jake VanderPlas + +print(sum(range(5),-1)) +from numpy import * +print(sum(range(5),-1)) +``` + +#### 27. Consider an integer vector Z, which of these expressions are legal? (★☆☆) + + +```python +Z**Z +2 << Z >> 2 +Z <- Z +1j*Z +Z/1/1 +ZZ +``` + +#### 28. What are the result of the following expressions? + + +```python +np.array(0) / np.array(0) +np.array(0) // np.array(0) +np.array([np.nan]).astype(int).astype(float) +``` + +#### 29. How to round away from zero a float array ? (★☆☆) + +(**hint**: np.uniform, np.copysign, np.ceil, np.abs) + + + +#### 30. How to find common values between two arrays? (★☆☆) + +(**hint**: np.intersect1d) + + + +#### 31. How to ignore all numpy warnings (not recommended)? (★☆☆) + +(**hint**: np.seterr, np.errstate) + + + +#### 32. Is the following expressions true? (★☆☆) + +(**hint**: imaginary number) + + +```python +np.sqrt(-1) == np.emath.sqrt(-1) +``` + +#### 33. How to get the dates of yesterday, today and tomorrow? (★☆☆) + +(**hint**: np.datetime64, np.timedelta64) + + + +#### 34. How to get all the dates corresponding to the month of July 2016? (★★☆) + +(**hint**: np.arange(dtype=datetime64\['D'\])) + + + +#### 35. How to compute ((A+B)\*(-A/2)) in place (without copy)? (★★☆) + +(**hint**: np.add(out=), np.negative(out=), np.multiply(out=), np.divide(out=)) + + + +#### 36. Extract the integer part of a random array using 5 different methods (★★☆) + +(**hint**: %, np.floor, np.ceil, astype, np.trunc) + + + +#### 37. Create a 5x5 matrix with row values ranging from 0 to 4 (★★☆) + +(**hint**: np.arange) + + + +#### 38. Consider a generator function that generates 10 integers and use it to build an array (★☆☆) + +(**hint**: np.fromiter) + + + +#### 39. Create a vector of size 10 with values ranging from 0 to 1, both excluded (★★☆) + +(**hint**: np.linspace) + + + +#### 40. Create a random vector of size 10 and sort it (★★☆) + +(**hint**: sort) + + + +#### 41. How to sum a small array faster than np.sum? (★★☆) + +(**hint**: np.add.reduce) + + + +#### 42. Consider two random array A and B, check if they are equal (★★☆) + +(**hint**: np.allclose, np.array\_equal) + + + +#### 43. Make an array immutable (read-only) (★★☆) + +(**hint**: flags.writeable) + + + +#### 44. Consider a random 10x2 matrix representing cartesian coordinates, convert them to polar coordinates (★★☆) + +(**hint**: np.sqrt, np.arctan2) + + + +#### 45. Create random vector of size 10 and replace the maximum value by 0 (★★☆) + +(**hint**: argmax) + + + +#### 46. Create a structured array with `x` and `y` coordinates covering the \[0,1\]x\[0,1\] area (★★☆) + +(**hint**: np.meshgrid) + + + +#### 47. Given two arrays, X and Y, construct the Cauchy matrix C (Cij =1/(xi - yj)) + +##### (hint: np.subtract.outer) + + + +#### 48. Print the minimum and maximum representable value for each numpy scalar type (★★☆) + +(**hint**: np.iinfo, np.finfo, eps) + + + +#### 49. How to print all the values of an array? (★★☆) + +(**hint**: np.set\_printoptions) + + + +#### 50. How to find the closest value (to a given scalar) in a vector? (★★☆) + +(**hint**: argmin) + + + +#### 51. Create a structured array representing a position (x,y) and a color (r,g,b) (★★☆) + +(**hint**: dtype) + + + +#### 52. Consider a random vector with shape (100,2) representing coordinates, find point by point distances (★★☆) + +(**hint**: np.atleast\_2d, T, np.sqrt) + + + +#### 53. How to convert a float (32 bits) array into an integer (32 bits) in place? + +(**hint**: view and [:] = ) + + + +#### 54. How to read the following file? (★★☆) + +(**hint**: np.genfromtxt) + + +``` +1, 2, 3, 4, 5 +6, , , 7, 8 + , , 9,10,11 +``` + +#### 55. What is the equivalent of enumerate for numpy arrays? (★★☆) + +(**hint**: np.ndenumerate, np.ndindex) + + + +#### 56. Generate a generic 2D Gaussian-like array (★★☆) + +(**hint**: np.meshgrid, np.exp) + + + +#### 57. How to randomly place p elements in a 2D array? (★★☆) + +(**hint**: np.put, np.random.choice) + + + +#### 58. Subtract the mean of each row of a matrix (★★☆) + +(**hint**: mean(axis=,keepdims=)) + + + +#### 59. How to sort an array by the nth column? (★★☆) + +(**hint**: argsort) + + + +#### 60. How to tell if a given 2D array has null columns? (★★☆) + +(**hint**: any, ~) + + + +#### 61. Find the nearest value from a given value in an array (★★☆) + +(**hint**: np.abs, argmin, flat) + + + +#### 62. Considering two arrays with shape (1,3) and (3,1), how to compute their sum using an iterator? (★★☆) + +(**hint**: np.nditer) + + + +#### 63. Create an array class that has a name attribute (★★☆) + +(**hint**: class method) + + + +#### 64. Consider a given vector, how to add 1 to each element indexed by a second vector (be careful with repeated indices)? (★★★) + +(**hint**: np.bincount | np.add.at) + + + +#### 65. How to accumulate elements of a vector (X) to an array (F) based on an index list (I)? (★★★) + +(**hint**: np.bincount) + + + +#### 66. Considering a (w,h,3) image of (dtype=ubyte), compute the number of unique colors (★★★) + +(**hint**: np.unique) + + + +#### 67. Considering a four dimensions array, how to get sum over the last two axis at once? (★★★) + +(**hint**: sum(axis=(-2,-1))) + + + +#### 68. Considering a one-dimensional vector D, how to compute means of subsets of D using a vector S of same size describing subset indices? (★★★) + +(**hint**: np.bincount) + + + +#### 69. How to get the diagonal of a dot product? (★★★) + +(**hint**: np.diag) + + + +#### 70. Consider the vector \[1, 2, 3, 4, 5\], how to build a new vector with 3 consecutive zeros interleaved between each value? (★★★) + +(**hint**: array\[::4\]) + + + +#### 71. Consider an array of dimension (5,5,3), how to mulitply it by an array with dimensions (5,5)? (★★★) + +(**hint**: array\[:, :, None\]) + + + +#### 72. How to swap two rows of an array? (★★★) + +(**hint**: array\[\[\]\] = array\[\[\]\]) + + + +#### 73. Consider a set of 10 triplets describing 10 triangles (with shared vertices), find the set of unique line segments composing all the triangles (★★★) + +(**hint**: repeat, np.roll, np.sort, view, np.unique) + + + +#### 74. Given an array C that is a bincount, how to produce an array A such that np.bincount(A) == C? (★★★) + +(**hint**: np.repeat) + + + +#### 75. How to compute averages using a sliding window over an array? (★★★) + +(**hint**: np.cumsum) + + + +#### 76. Consider a one-dimensional array Z, build a two-dimensional array whose first row is (Z\[0\],Z\[1\],Z\[2\]) and each subsequent row is shifted by 1 (last row should be (Z\[-3\],Z\[-2\],Z\[-1\]) (★★★) + +(**hint**: from numpy.lib import stride\_tricks) + + + +#### 77. How to negate a boolean, or to change the sign of a float inplace? (★★★) + +(**hint**: np.logical_not, np.negative) + + + +#### 78. Consider 2 sets of points P0,P1 describing lines (2d) and a point p, how to compute distance from p to each line i (P0\[i\],P1\[i\])? (★★★) + + + +#### 79. Consider 2 sets of points P0,P1 describing lines (2d) and a set of points P, how to compute distance from each point j (P\[j\]) to each line i (P0\[i\],P1\[i\])? (★★★) + + + +#### 80. Consider an arbitrary array, write a function that extract a subpart with a fixed shape and centered on a given element (pad with a `fill` value when necessary) (★★★) + +(**hint**: minimum, maximum) + + + +#### 81. Consider an array Z = \[1,2,3,4,5,6,7,8,9,10,11,12,13,14\], how to generate an array R = \[\[1,2,3,4\], \[2,3,4,5\], \[3,4,5,6\], ..., \[11,12,13,14\]\]? (★★★) + +(**hint**: stride\_tricks.as\_strided) + + + +#### 82. Compute a matrix rank (★★★) + +(**hint**: np.linalg.svd) + + + +#### 83. How to find the most frequent value in an array? + +(**hint**: np.bincount, argmax) + + + +#### 84. Extract all the contiguous 3x3 blocks from a random 10x10 matrix (★★★) + +(**hint**: stride\_tricks.as\_strided) + + + +#### 85. Create a 2D array subclass such that Z\[i,j\] == Z\[j,i\] (★★★) + +(**hint**: class method) + + + +#### 86. Consider a set of p matrices wich shape (n,n) and a set of p vectors with shape (n,1). How to compute the sum of of the p matrix products at once? (result has shape (n,1)) (★★★) + +(**hint**: np.tensordot) + + + +#### 87. Consider a 16x16 array, how to get the block-sum (block size is 4x4)? (★★★) + +(**hint**: np.add.reduceat) + + + +#### 88. How to implement the Game of Life using numpy arrays? (★★★) + + + +#### 89. How to get the n largest values of an array (★★★) + +(**hint**: np.argsort | np.argpartition) + + + +#### 90. Given an arbitrary number of vectors, build the cartesian product (every combinations of every item) (★★★) + +(**hint**: np.indices) + + + +#### 91. How to create a record array from a regular array? (★★★) + +(**hint**: np.core.records.fromarrays) + + + +#### 92. Consider a large vector Z, compute Z to the power of 3 using 3 different methods (★★★) + +(**hint**: np.power, \*, np.einsum) + + + +#### 93. Consider two arrays A and B of shape (8,3) and (2,2). How to find rows of A that contain elements of each row of B regardless of the order of the elements in B? (★★★) + +(**hint**: np.where) + + + +#### 94. Considering a 10x3 matrix, extract rows with unequal values (e.g. \[2,2,3\]) (★★★) + + + +#### 95. Convert a vector of ints into a matrix binary representation (★★★) + +(**hint**: np.unpackbits) + + + +#### 96. Given a two dimensional array, how to extract unique rows? (★★★) + +(**hint**: np.ascontiguousarray | np.unique) + + + +#### 97. Considering 2 vectors A & B, write the einsum equivalent of inner, outer, sum, and mul function (★★★) + +(**hint**: np.einsum) + + + +#### 98. Considering a path described by two vectors (X,Y), how to sample it using equidistant samples (★★★)? + +(**hint**: np.cumsum, np.interp) + + + +#### 99. Given an integer n and a 2D array X, select from X the rows which can be interpreted as draws from a multinomial distribution with n degrees, i.e., the rows which only contain integers and which sum to n. (★★★) + +(**hint**: np.logical\_and.reduce, np.mod) + + + +#### 100. Compute bootstrapped 95% confidence intervals for the mean of a 1D array X (i.e., resample the elements of an array with replacement N times, compute the mean of each sample, and then compute percentiles over the means). (★★★) + +(**hint**: np.percentile) + diff --git "a/021\351\253\230\347\250\213\347\273\237\350\256\241\345\217\212\345\217\257\350\247\206\345\214\226/.ipynb_checkpoints/021\351\253\230\347\250\213\347\273\237\350\256\241\345\217\212\345\217\257\350\247\206\345\214\226-checkpoint.ipynb" "b/021\351\253\230\347\250\213\347\273\237\350\256\241\345\217\212\345\217\257\350\247\206\345\214\226/.ipynb_checkpoints/021\351\253\230\347\250\213\347\273\237\350\256\241\345\217\212\345\217\257\350\247\206\345\214\226-checkpoint.ipynb" new file mode 100644 index 0000000..70b6640 --- /dev/null +++ "b/021\351\253\230\347\250\213\347\273\237\350\256\241\345\217\212\345\217\257\350\247\206\345\214\226/.ipynb_checkpoints/021\351\253\230\347\250\213\347\273\237\350\256\241\345\217\212\345\217\257\350\247\206\345\214\226-checkpoint.ipynb" @@ -0,0 +1,213 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import arcpy,numpy\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def getColorDist(colName, colValue):\n", + " cm = plt.cm.get_cmap(colName)\n", + " col = [cm(float(i)/(len(colValue))) for i in range(len(colValue))]\n", + " carr = numpy.ceil(numpy.array(col)*255).astype(\"int16\")\n", + " cols = []\n", + " for c in carr:\n", + " cols.append(\"#\"+\"\".join(['%02X'% c1 for c1 in c]))\n", + " return cols" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "#dem = arcpy.RasterToNumPyArray(\"data/dem/Himalaya100.tif\")\n", + "dem = arcpy.RasterToNumPyArray(\"data/dem/rkzNorth2.tif\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "dem2 = numpy.ceil(dem / 10)*10" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wall time: 114 ms\n" + ] + }, + { + "data": { + "text/plain": [ + "(array([3700., 3710., 3720., 3730., 3740., 3750., 3760., 3770., 3780.,\n", + " 3790., 3800., 3810., 3820., 3830., 3840., 3850., 3860., 3870.,\n", + " 3880., 3890., 3900., 3910., 3920., 3930., 3940., 3950., 3960.,\n", + " 3970., 3980., 3990., 4000., 4010., 4020., 4030., 4040., 4050.,\n", + " 4060., 4070., 4080., 4090., 4100., 4110., 4120., 4130., 4140.,\n", + " 4150., 4160., 4170., 4180., 4190., 4200., 4210., 4220., 4230.,\n", + " 4240., 4250., 4260., 4270., 4280., 4290., 4300., 4310., 4320.,\n", + " 4330., 4340., 4350., 4360., 4370., 4380., 4390., 4400., 4410.,\n", + " 4420., 4430., 4440., 4450., 4460., 4470., 4480., 4490., 4500.,\n", + " 4510., 4520., 4530., 4540., 4550., 4560., 4570., 4580., 4590.,\n", + " 4600., 4610., 4620., 4630., 4640., 4650., 4660., 4670., 4680.,\n", + " 4690., 4700., 4710., 4720., 4730., 4740., 4750., 4760., 4770.,\n", + " 4780., 4790., 4800., 4810., 4820., 4830., 4840., 4850., 4860.,\n", + " 4870., 4880., 4890., 4900., 4910., 4920., 4930., 4940., 4950.,\n", + " 4960., 4970., 4980., 4990., 5000., 5010., 5020., 5030., 5040.,\n", + " 5050., 5060., 5070., 5080., 5090., 5100., 5110., 5120., 5130.,\n", + " 5140., 5150., 5160., 5170., 5180., 5190., 5200., 5210., 5220.,\n", + " 5230., 5240., 5250., 5260., 5270., 5280., 5290., 5300., 5310.,\n", + " 5320., 5330., 5340., 5350., 5360., 5370., 5380., 5390., 5400.,\n", + " 5410., 5420., 5430., 5440., 5450., 5460., 5470., 5480., 5490.,\n", + " 5500., 5510., 5520., 5530., 5540., 5550., 5560., 5570., 5580.,\n", + " 5590., 5600., 5610., 5620., 5630., 5640., 5650., 5660., 5670.,\n", + " 5680., 5690., 5700., 5710., 5720., 5730., 5740.]),\n", + " array([ 17, 26, 40, 102, 350, 936, 2683, 8758, 26630,\n", + " 50875, 55944, 56509, 55378, 42059, 40445, 33884, 17016, 11877,\n", + " 9974, 9033, 8282, 7986, 7654, 7612, 7223, 7111, 7104,\n", + " 7061, 7068, 6776, 6723, 6843, 6743, 6852, 6769, 6670,\n", + " 6672, 6917, 6817, 6522, 6542, 6353, 6593, 6560, 6508,\n", + " 6544, 6415, 6585, 6439, 6507, 6464, 6424, 6367, 6373,\n", + " 6164, 6378, 6650, 6736, 7000, 6977, 7077, 7198, 7165,\n", + " 7271, 7353, 7430, 7922, 8301, 8236, 8390, 8214, 8103,\n", + " 8203, 8354, 8429, 8428, 8464, 8306, 8306, 8629, 8924,\n", + " 8582, 8427, 8312, 8238, 8278, 8344, 8163, 8275, 8400,\n", + " 8437, 8411, 8670, 8737, 8816, 8555, 8527, 8348, 8404,\n", + " 8654, 8479, 8510, 8326, 8360, 8638, 8306, 8249, 8174,\n", + " 7898, 7825, 7757, 7843, 7761, 7724, 7607, 7592, 7597,\n", + " 7540, 7848, 7580, 7709, 7702, 7497, 7726, 7555, 7411,\n", + " 7511, 7503, 7536, 7476, 7611, 7578, 7603, 7806, 7903,\n", + " 7937, 7857, 8027, 8086, 8427, 8591, 8730, 8918, 9323,\n", + " 10094, 10567, 10906, 10911, 11198, 11598, 12424, 12625, 12505,\n", + " 12610, 12460, 12935, 12839, 12719, 12817, 13312, 13702, 13789,\n", + " 13370, 13264, 13106, 13157, 13149, 13086, 12777, 12414, 12271,\n", + " 11738, 11515, 10999, 10116, 9698, 8869, 8177, 7163, 6591,\n", + " 5916, 5452, 4938, 4512, 3951, 3608, 3111, 2803, 2502,\n", + " 2113, 1849, 1504, 1182, 833, 604, 435, 316, 229,\n", + " 180, 126, 78, 66, 84, 50, 4], dtype=int64))" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "h = numpy.unique(dem2,return_counts=True)\n", + "h" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "cls = getColorDist(plt.cm.gist_earth,h[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.figure(figsize=(7,7))\n", + "plt.imshow(dem2,cmap=plt.cm.gist_earth)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(18,7))\n", + "plt.bar(range(len(h[0])),h[1],tick_label=h[0],color=cls)\n", + "plt.xticks(rotation=90)\n", + "pass" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git "a/021\351\253\230\347\250\213\347\273\237\350\256\241\345\217\212\345\217\257\350\247\206\345\214\226/021\351\253\230\347\250\213\347\273\237\350\256\241\345\217\212\345\217\257\350\247\206\345\214\226.ipynb" "b/021\351\253\230\347\250\213\347\273\237\350\256\241\345\217\212\345\217\257\350\247\206\345\214\226/021\351\253\230\347\250\213\347\273\237\350\256\241\345\217\212\345\217\257\350\247\206\345\214\226.ipynb" new file mode 100644 index 0000000..70b6640 --- /dev/null +++ "b/021\351\253\230\347\250\213\347\273\237\350\256\241\345\217\212\345\217\257\350\247\206\345\214\226/021\351\253\230\347\250\213\347\273\237\350\256\241\345\217\212\345\217\257\350\247\206\345\214\226.ipynb" @@ -0,0 +1,213 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import arcpy,numpy\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def getColorDist(colName, colValue):\n", + " cm = plt.cm.get_cmap(colName)\n", + " col = [cm(float(i)/(len(colValue))) for i in range(len(colValue))]\n", + " carr = numpy.ceil(numpy.array(col)*255).astype(\"int16\")\n", + " cols = []\n", + " for c in carr:\n", + " cols.append(\"#\"+\"\".join(['%02X'% c1 for c1 in c]))\n", + " return cols" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "#dem = arcpy.RasterToNumPyArray(\"data/dem/Himalaya100.tif\")\n", + "dem = arcpy.RasterToNumPyArray(\"data/dem/rkzNorth2.tif\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "dem2 = numpy.ceil(dem / 10)*10" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wall time: 114 ms\n" + ] + }, + { + "data": { + "text/plain": [ + "(array([3700., 3710., 3720., 3730., 3740., 3750., 3760., 3770., 3780.,\n", + " 3790., 3800., 3810., 3820., 3830., 3840., 3850., 3860., 3870.,\n", + " 3880., 3890., 3900., 3910., 3920., 3930., 3940., 3950., 3960.,\n", + " 3970., 3980., 3990., 4000., 4010., 4020., 4030., 4040., 4050.,\n", + " 4060., 4070., 4080., 4090., 4100., 4110., 4120., 4130., 4140.,\n", + " 4150., 4160., 4170., 4180., 4190., 4200., 4210., 4220., 4230.,\n", + " 4240., 4250., 4260., 4270., 4280., 4290., 4300., 4310., 4320.,\n", + " 4330., 4340., 4350., 4360., 4370., 4380., 4390., 4400., 4410.,\n", + " 4420., 4430., 4440., 4450., 4460., 4470., 4480., 4490., 4500.,\n", + " 4510., 4520., 4530., 4540., 4550., 4560., 4570., 4580., 4590.,\n", + " 4600., 4610., 4620., 4630., 4640., 4650., 4660., 4670., 4680.,\n", + " 4690., 4700., 4710., 4720., 4730., 4740., 4750., 4760., 4770.,\n", + " 4780., 4790., 4800., 4810., 4820., 4830., 4840., 4850., 4860.,\n", + " 4870., 4880., 4890., 4900., 4910., 4920., 4930., 4940., 4950.,\n", + " 4960., 4970., 4980., 4990., 5000., 5010., 5020., 5030., 5040.,\n", + " 5050., 5060., 5070., 5080., 5090., 5100., 5110., 5120., 5130.,\n", + " 5140., 5150., 5160., 5170., 5180., 5190., 5200., 5210., 5220.,\n", + " 5230., 5240., 5250., 5260., 5270., 5280., 5290., 5300., 5310.,\n", + " 5320., 5330., 5340., 5350., 5360., 5370., 5380., 5390., 5400.,\n", + " 5410., 5420., 5430., 5440., 5450., 5460., 5470., 5480., 5490.,\n", + " 5500., 5510., 5520., 5530., 5540., 5550., 5560., 5570., 5580.,\n", + " 5590., 5600., 5610., 5620., 5630., 5640., 5650., 5660., 5670.,\n", + " 5680., 5690., 5700., 5710., 5720., 5730., 5740.]),\n", + " array([ 17, 26, 40, 102, 350, 936, 2683, 8758, 26630,\n", + " 50875, 55944, 56509, 55378, 42059, 40445, 33884, 17016, 11877,\n", + " 9974, 9033, 8282, 7986, 7654, 7612, 7223, 7111, 7104,\n", + " 7061, 7068, 6776, 6723, 6843, 6743, 6852, 6769, 6670,\n", + " 6672, 6917, 6817, 6522, 6542, 6353, 6593, 6560, 6508,\n", + " 6544, 6415, 6585, 6439, 6507, 6464, 6424, 6367, 6373,\n", + " 6164, 6378, 6650, 6736, 7000, 6977, 7077, 7198, 7165,\n", + " 7271, 7353, 7430, 7922, 8301, 8236, 8390, 8214, 8103,\n", + " 8203, 8354, 8429, 8428, 8464, 8306, 8306, 8629, 8924,\n", + " 8582, 8427, 8312, 8238, 8278, 8344, 8163, 8275, 8400,\n", + " 8437, 8411, 8670, 8737, 8816, 8555, 8527, 8348, 8404,\n", + " 8654, 8479, 8510, 8326, 8360, 8638, 8306, 8249, 8174,\n", + " 7898, 7825, 7757, 7843, 7761, 7724, 7607, 7592, 7597,\n", + " 7540, 7848, 7580, 7709, 7702, 7497, 7726, 7555, 7411,\n", + " 7511, 7503, 7536, 7476, 7611, 7578, 7603, 7806, 7903,\n", + " 7937, 7857, 8027, 8086, 8427, 8591, 8730, 8918, 9323,\n", + " 10094, 10567, 10906, 10911, 11198, 11598, 12424, 12625, 12505,\n", + " 12610, 12460, 12935, 12839, 12719, 12817, 13312, 13702, 13789,\n", + " 13370, 13264, 13106, 13157, 13149, 13086, 12777, 12414, 12271,\n", + " 11738, 11515, 10999, 10116, 9698, 8869, 8177, 7163, 6591,\n", + " 5916, 5452, 4938, 4512, 3951, 3608, 3111, 2803, 2502,\n", + " 2113, 1849, 1504, 1182, 833, 604, 435, 316, 229,\n", + " 180, 126, 78, 66, 84, 50, 4], dtype=int64))" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "h = numpy.unique(dem2,return_counts=True)\n", + "h" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "cls = getColorDist(plt.cm.gist_earth,h[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.figure(figsize=(7,7))\n", + "plt.imshow(dem2,cmap=plt.cm.gist_earth)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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"b/021\351\253\230\347\250\213\347\273\237\350\256\241\345\217\212\345\217\257\350\247\206\345\214\226/data/dem/rkzNorth2.tif.vat.dbf" differ diff --git "a/021\351\253\230\347\250\213\347\273\237\350\256\241\345\217\212\345\217\257\350\247\206\345\214\226/data/dem/rkzNorth2.tif.xml" "b/021\351\253\230\347\250\213\347\273\237\350\256\241\345\217\212\345\217\257\350\247\206\345\214\226/data/dem/rkzNorth2.tif.xml" new file mode 100644 index 0000000..807e10c --- /dev/null +++ "b/021\351\253\230\347\250\213\347\273\237\350\256\241\345\217\212\345\217\257\350\247\206\345\214\226/data/dem/rkzNorth2.tif.xml" @@ -0,0 +1,2 @@ + +20180312101317001.0TRUEExtractByMask ASTGTM_N29E089E.img Converted_Graphics E:\GIS_data\data\dem\日喀则\rkzNorth2.tif diff --git "a/022\347\275\256\344\277\241\345\272\246/.ipynb_checkpoints/\346\212\275\346\240\267-checkpoint.ipynb" "b/022\347\275\256\344\277\241\345\272\246/.ipynb_checkpoints/\346\212\275\346\240\267-checkpoint.ipynb" new file mode 100644 index 0000000..960c6b7 --- /dev/null +++ "b/022\347\275\256\344\277\241\345\272\246/.ipynb_checkpoints/\346\212\275\346\240\267-checkpoint.ipynb" @@ -0,0 +1,177 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy\n", + "import seaborn as sns " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 创建10000个男生变量，72%设置为喜欢打篮球记为1，剩下的设置不喜欢打篮球，记为0" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "play_bb = 0.72\n", + "boy = 10000\n", + "love = int(play_bb * boy)\n", + "dont_love = int(boy * (1 - play_bb))\n", + "loveb = numpy.ones(love)\n", + "nt_love = numpy.zeros(dont_love)\n", + "all_boy = numpy.hstack([loveb, nt_love])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 进行10次抽样，随机找100个男生，统计有多少人喜欢打篮球" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.72, 0.68, 0.75, 0.77, 0.67, 0.73, 0.68, 0.72, 0.77, 0.72]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "mn = []\n", + "for i in range(10):\n", + " sample = numpy.random.choice(all_boy, size=100)\n", + " mn.append(numpy.mean(sample))\n", + "sns.distplot(mn,bins=10,\n", + " kde_kws={\"color\":\"r\", \"lw\":3 }, \n", + " hist_kws={ \"color\": \"b\" }) \n", + "print(mn)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 进行1万次抽样" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "mn = []\n", + "for i in range(10000):\n", + " sample = numpy.random.choice(all_boy, size=100)\n", + " mn.append(numpy.mean(sample))\n", + "sns.distplot(mn,bins=10,\n", + " kde_kws={\"color\":\"r\", \"lw\":3 }, \n", + " hist_kws={ \"color\": \"b\" }) \n", + "pass" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.distplot(mn,bins=30,\n", + " kde_kws={\"color\":\"r\", \"lw\":3 }, \n", + " hist_kws={ \"color\": \"b\" }) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git "a/022\347\275\256\344\277\241\345\272\246/\346\212\275\346\240\267.ipynb" "b/022\347\275\256\344\277\241\345\272\246/\346\212\275\346\240\267.ipynb" new file mode 100644 index 0000000..960c6b7 --- /dev/null +++ "b/022\347\275\256\344\277\241\345\272\246/\346\212\275\346\240\267.ipynb" @@ -0,0 +1,177 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy\n", + "import seaborn as sns " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 创建10000个男生变量，72%设置为喜欢打篮球记为1，剩下的设置不喜欢打篮球，记为0" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "play_bb = 0.72\n", + "boy = 10000\n", + "love = int(play_bb * boy)\n", + "dont_love = int(boy * (1 - play_bb))\n", + "loveb = numpy.ones(love)\n", + "nt_love = numpy.zeros(dont_love)\n", + "all_boy = numpy.hstack([loveb, nt_love])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 进行10次抽样，随机找100个男生，统计有多少人喜欢打篮球" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.72, 0.68, 0.75, 0.77, 0.67, 0.73, 0.68, 0.72, 0.77, 0.72]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "mn = []\n", + "for i in range(10):\n", + " sample = numpy.random.choice(all_boy, size=100)\n", + " mn.append(numpy.mean(sample))\n", + "sns.distplot(mn,bins=10,\n", + " kde_kws={\"color\":\"r\", \"lw\":3 }, \n", + " hist_kws={ \"color\": \"b\" }) \n", + "print(mn)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 进行1万次抽样" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Wzh5e2FLFyvl5urrOAOKiPdy2sICfvHOI6qb+J+AKxlWdJDJoZC5h5bcfVNLe7eXuJUVOR4kIdy4qxBLY/glxB5W5hA1rLU9vrGDe+BTmjE9xOk5EKEhP5LJp2fy6tAKvT5eWG81U5hI2tlQ0se9YK3cs0maAobhzcSH1rV3sqdF8LaOZylzCxq83VpAY6+HaeXlOR4kol0zLJi8lno1ljU5HEQepzCUstHX18vL2aq6bm0dSnPbLD4UnynDbwkIO1rXR0NbldBxxiMpcwsIr26pp7/Zy26ICp6NEpNsWFhBlYFOZTvUYrVTmEhae2VTB1Jwkzhvlc5YPV25KPNNyx/JB+Ql6fT6n44gDVObiuNrmTrZWNHHbwsJReUm4YFlUlM7Jrl52V2tH6GikMhfHbTraSKwnihvPy3c6SkSbkpNEamIMG49oR+hopDIXR3X2eNl89MSfTk2X4YsyhkVF6Rw+fpJjLZ1Ox5EQU5mLozaXn6Cr18d9FxY7HcUVFhalEx1lWH9Yk2+NNipzcYzPWt4/1EBheiLzteMzKMbERTN3fCpbypvo7PE6HUdCSGUujtlX20rjyW4unJzpdBRXWToxg26vjw+O6jDF0URlLo557+BxUhJimDlurNNRXCU/LYHC9ETWH27AZzVfy2ihMhdHVDd1cPj4SZZOzMCjq+QE3ZKJGTSc7OZgXZvTUSREVObiiPcOHifWE8XConSno7jS7PyxJMdFs05XIRo1NAmGhFxda99JQhdMyiAh1uN0HFeKjopi0cR03thTx7GWTnLGxg/4NbqQRWTTyFxC7g+7jxETHcXyadlOR3G1pcUZxHqiWLu/3ukoEgIBl7kxxmOM2WKMeSUYgcTdKk+0s7O6hWWTMzU74ghLjItmYVEa2yqbONHe7XQcGWHBGJl/DtgThO8jo8CaXcdIjPWwTIcjhsSyKVkYDH88cNzpKDLCAipzY8x44M+Ax4MTR9zsYF0bB+vbuHRaNvEx2lYeCikJMcwvTKW0rJG2rl6n48gICnRk/jDwj4Dm3JRz8lnL73fVkpIQw6JiHcESShdPycLrs7x/SKNzNxv2RktjzLVAnbX2A2PMJed43oPAgwCFhdoz7gbDOephU1kjVU0dfLJkPDEe7XcPpazkOGbmjWX94QZt3nKxQH6rLgSuN8aUAc8Alxljfnnmk6y1j1lrS6y1JVlZWQG8nESqk129rNl1jOLMMcwbrzlYnHD59By6eny8oyNbXGvYZW6t/ZK1dry1tgi4HXjTWntX0JKJa6zZXUtXr5fr5uXp4hMOyU2JZ35BKusONVDd1OF0HBkBer8rI6qisZ3SshNcMCmT3EGcuCIj54qZOVjg4T/sdzqKjICglLm19m1r7bXB+F7iHj5reWlbNUnx0Vw2XScIOS0tMZYlxek890ElB461Oh1Hgkwjcxkxp3Z6XjN7nA5FDBOXTMtmTGw031m9z+koEmQqcxkRDW1drNl1jImZY5g7PsXpOOI3Ji6av1g+kT/sOcZ7B3WoopuozGVEfGf1Xu30DFMPXDSRwvREvvbSLrp7dYqIW6jMJeg+ONrIs6WVLJucOajZ+iS04mM8fO26mRysa+N/3jvidBwJEpW5BFWv18dXXtjFuJR4LtVOz7B1+YwcrpiRzX+9cYCaZh2q6AYqcwmqVRvL2V3TwleunUlctHZ6hrOvXTcLr8/yzd9pnjw3UJlL0DS0dfH93+9j2eRMrp6d63QcGUBBeiJ/dckkXtlewx8P6MzQSKcyl6D57up9tHd7+fr1M7XTM0L85fJJTMwcw0PP79TO0AinMpeg2FJ+gl+XVnD/smImZyc7HUcGKT7Gw7/fNIfyxnbe3HvM6TgSAJW5BMxnLV97aRc5Y+P47OVTnI4jQ7RkYga3Lyzg3YPHNW9LBFOZS8A2HGlke2UzX75mhi4FF6G+dPUMEmOjeX5LFT5rnY4jw6DfPDmr/uYtP1NLRw9rdtVy0ZRMrp+XF4JUMhJSEmO4du44ntlUwbpDDVyoec8jjkbmEpBXtlfj9Vn+7YbZ2ukZ4ebkpzA1J4nX9xyjSReAjjgamcuw7a1pYWd1C1fOzGFCxhin40iAjDGsnJfPw2/s5+Vt1dy1ZEK/zzvbO7aBrjYlI0sjcxmWrl4vL22vJjs5jmVT9JbcLdLGxHL59Bz21Layu6bF6TgyBCpzGZZXd9TS3N7DjeflEx2lHyM3uXByJuNS4nl5WzWtnT1Ox5FB0m+hDNne2hY2lTWybEqmNq+4kCfKcMP8fFo7e/nBGl2VKFKozGVITnb18tvNVeSOjecTM3KcjiMjpCA9kcUTM3hyXRnbKpqcjiODoDKXQbPW8sLWKjq6vdxaMp5oj3583OzKmTlkJcXx5ed30OvVqf7hTr+NMmgbyxrZVd3CFTOyGZeS4HQcGWF9857PYld1C0+uO+p0HBmAylwGpbyxnVe21TA1J4mLpmY5HUdC5Jo5uVw6LYsfrNmnU/3D3LDL3BhTYIx5yxiz2xizyxjzuWAGk/DR2tnDqg1HSUmM4ZMlBUTp5KBRwxjDN1bOxlp46PkdWJ3qH7YCGZn3An9vrZ0JLAH+2hgzMzixJFx4fZanN5bT0ePlzxcXkhir88xGm4L0RL5w1TTe2lfPC1urnI4jZzHs30xrbQ1Q47/daozZA+QDu4OUTUZIf2fw9Xf2ns9afrO5krKGdj5ZMl7byUexey8o4pXt1fzLy7v5q+WTSI6PcTqSnCEo28yNMUXAecCGfh570BhTaowpra/X1UwihbWW13bUsLWiiStm5DC/IM3pSOIgT5Thu7fMpb3Ly8vbqp2OI/0IuMyNMUnAb4DPW2s/dv6vtfYxa22JtbYkK0s7ziLFo+8c5r1DDSydmMGl0/T/JjA5O5nPXTGFndUt7KhqdjqOnCGgMjfGxNBX5L+y1v42OJHEaU++X8Z3Vu9l7vgU/mzuOM2GKH/y4MUTGZ+WwPNbKjmhmRXDSiBHsxjgZ8Aea+1/BC+SOOnHbx/iay/t4ooZOdxy/ngduSIfEeOJ4raSAqyFZzdV4PXp6JZwEcjI/ELgbuAyY8xW/8c1QcolIWat5Qdr9vGd1Xu5fl4eP75rgSbQkn5lJMWxcn4+RxvbeXNvndNxxC+Qo1neBTRscwGvz/LQCztZtaGc20oK+Peb5uCJ0n+tnN38glQO1rXx9r46JmaNYVJW0qCPkpKRoaHXKNfR7eWJ94+wakM5f7l8Et9SkcsgXTdvHJnJcazaUE59a5fTcUY9nQHiEsMZFR1v7eIX68s4cbKH790yl1tLCkYqnrhQXLSHe5cW8eO3D/LkujL+cvkkXdDbQRqZj1LbK5v40dsHae/28ullxSpyGZb0MbHcvbSIlo4efrn+KD2aXdExKvNRpqvXy0vbqnlmUwU5Y+P5zKWTKc7UBSZk+ArTE7m1pIDyxnZWbSinu1eF7gS9JxpF9tW28re/3srumhaWTc7kqlm52j4uQTEnP4WO+fm8uLWK/3n/CPcsKSIh1nPW52tnafBpZD4K+HyWx/94mOseeZe61k7uXjKBa+aMU5FLUC0qTue2hQVUNnbw+LuHdf3QEFOZu9yR4ye546fr+bff7eHiKVms/vzFzBg31ulY4lJzx6dy99IJHG/r4kdvHWTdoQanI40aKnOX8vosj75ziBUPr2V3TQvfvXkuP73nfDKT4pyOJi43NSeZv7h4ErHRUdz5+Hp+sGafLjsXAtpm7kLlje28uLWKmuZOrpqVwzdWziZnbLzTsWQUyUtN4K8vncyu6hZ++OZB1h44zrdunMPMPL0rHCkq8wjU384jgPauXlbvqqX06AnGxkfz4z9fwNVzxoU4nUifuGgP3791HsunZvH1l3Zx3SPv8sCyYj53xRSno7mSytwFvD7L+sMNvLm3jq5eL8smZ3L59GwVuYSF6+blcdGUTL716l5+svYwr2yv4bLp2dp3E2Qq8whmrWVvbSuv7azheFs3k7OSuGbuOHK1SUXCTGpiLN+5ZS43Lcjnn1/YyVPrjzJj3FiunTuOtMRYp+O5gso8AllrOVR/ktd311JxooPMpDjuWTqBaTnJmntcwtriiRm8+rmL+OyqLbyx9xgP/6GVy6fncOHkTKejRTyVeQSx1rL2wHF++scjlDWcJCUhhhvPy2dBYZqOGZeIEeOJ4uKpWcwZn8Ir26pZvauWrRVNzMxL5vwJ6U7Hi1gq8zBxrjPierw+XttZy6NvH2J3TQtj46O5du44FhWlE+3R0aUSmdIS++Z12V3dzMvba7j5x+tYOT+Pf1oxnbxUXTx8qFTmYex4WxdPbyjnlxuOcqyli4mZY/juzXPp7PXqwhHiGjPzUpiUncQ7++v53fYaXt1Rw7LJmVw4KZMHLp7odLyIoTIPMz5rOVx/kk1ljXz9pV10e31cNCWTb94wh0unZ+OJMmc9NFEkUsVFe7hyZi4Li9JZvbOWt/bV8+7B41Q1d3D/smLGpyU6HTHsqczDgLWWmuYOtlc2s72yiRPtPSTEeLhzcSF3LSlkcnay0xFFQiItMZY7FhVyaUsn7x6o56l1R3ni/TKWFGdw3bw8rp6dS9oYHf3SH5W5Qzp7vGw80sja/fW8ta+OQ/UniTIwKSuJK2flMnPcWO69oMjpmCKOyB0bzy3nF/DInQt4ZlMFr2yr5svP7+ArL+5kdn4KKfExTMwaw/jUBBL9F8QY7bMuqsxHmNdnOdbSSXljO4frT/LCliqqmzuobe6k12fxRBmKMhJZOT+PWXkpulKLyGnyUhP4u09M5W+vmMKu6hZ+v6uWdYcaePdgPWsP1AOQmhhDXkoCtc0dTMlJZkpOEkUZY4iPOfsUvG4UUHMYY1YA/wV4gMettd8OSqoI0trZQ3VTJ6s2lNPU0U1zew9NHT00d/Tg9VlqmzvpPm2SofiYKMalJLBkYgaTspIozhxDbLR2ZoqcizGG2fkpzM5PAeCJ98oob2ynuqmDqqYOqps6+OGbB7Gnnk9fyc/OT6E4cwwTMsYwIT2RwoxE8lMTGOPCQdOw/0XGGA/wI+ATQCWwyRjzkrV2d7DCDZe1Fq/P4vV/7vVZfP7PXp+lx+vzf7b0+nz0evuW9fosPb0+urw+unp8dPZ46ezx0tbVS0tnLy0dPZxo76aupYv6ti6ONXfS2tX7kdeOMjA2IYaUhBjmF6SSNyeBwvRECtMTmZCRyNr99TqxRyRAsdFRTM5OYnJ20p+W9Xh9HG/roq6li+NtfR9N7T08v7nqY7+naYkx5KYkkJ0cR87YODKS4khNiCEtMZaxCdGMiYsmMTaaxFgP8TEe4qKjiIuOItoTRYzHEB0VhSfKEGUIm9/nQP48LQIOWmsPAxhjngFWAkEv8399ZTdPb/zwCA5rwfr/Blv74X2f7dusMRKMgeS4aKI9USTHRZMUH82s/BRSE2JITewr79TEWJLjo4ny/+f2tw0vXP7jRdwmxtP3rndcyofHqN+5uBBrLSfaeyhrOEnliQ4qT7Tz9t56Wjp7OFjXxubyE5zs6mW41XF6qRv6uqLvVt/tU35y9/lcNCUrgH/huRlrh/cvMMbcAqyw1j7gv383sNha+5kznvcg8KD/7jRg3/DjBkUmcNzhDIOhnMGlnMGlnME1UM4J1tpz/iUY8Q1H1trHgMdG+nUGyxhTaq0tcTrHQJQzuJQzuJQzuIKRM5A9b1VAwWn3x/uXiYhIiAVS5puAKcaYYmNMLHA78FJwYomIyFAMezOLtbbXGPMZ4Pf0HZr4c2vtrqAlGzlhs8lnAMoZXMoZXMoZXAHnHPYOUBERCR86W0VExAVU5iIiLuCqMjfGrDDG7DPGHDTGfLGfxz9ljKk3xmz1fzxw2mPe05aP6I7cgXL6n/NJY8xuY8wuY8yq05bfa4w54P+4N4xzhs36NMb852lZ9htjmk57LCTrM8CM4bQuC40xbxljthhjthtjrjntsS/5v26fMeaqcMxpjCkyxnSctj4fdTjnBGPMG/6Mbxtjxp/22NB+Nq21rvigbyfsIWAiEAtsA2ae8ZxPAY+c5evbwijnFGALkOa/n+3/nA4c9n9O899OC7ec4bY+z3j+Z+nbWR+y9RlIxnBbl/TtqPsr/+2ZQNlpt7cBcUCx/2/WKOoAAAMpSURBVPt4wjBnEbAzjNbn/wL3+m9fBjw13J9NN43M/zS9gLW2Gzg1vUC4GUzO/wf8yFp7AsBaW+dffhXwurW20f/Y68CKMMwZSkP9f78DeNp/O1TrM5CMoTSYnBYY67+dAlT7b68EnrHWdllrjwAH/d8v3HKG0mByzgTe9N9+67THh/yz6aYyzwcqTrtf6V92ppv9b2meM8acftJTvDGm1Biz3hhzg8M5pwJTjTHv+fOsGMLXhkNOCK/1CfS9paVv1HjqlydU6zOQjBBe6/LrwF3GmErgVfreRQz2a4MlkJwAxf7NL+8YYy4aoYyDzbkNuMl/+0Yg2RiTMciv/Qg3lflgvAwUWWvn0veX7snTHptg+06nvRN42BgzyYmAftH0bcK4hL5R2k+NMakO5jmbc+UMp/V5yu3Ac9Zar9NBzqG/jOG0Lu8AnrDWjgeuAZ4yxoRjj5wtZw1QaK09D/g7YJUxZuw5vs9I+wdguTFmC7CcvrPoh/XzGY7/CcM14PQC1toGa22X/+7jwPmnPVbl/3wYeBs4z6mc9P0Vfsla2+N/y7qfvtIM5RQKgeQMt/V5yu18dPNFqNZnIBnDbV3eDzzrz7MOiKdvkqhw+9nsN6d/M1CDf/kH9G3TnupUTmtttbX2Jv8fl4f8y5oG87UfE4odASHa2RBN306CYj7c2TDrjOeMO+32jcB6/+00IM5/OxM4wDl2UIUg5wrgydPyVAAZ9O0MOeLPm+a/nR6GOcNqffqfNx0ow3+inH9ZSNZngBnDal0CrwGf8t+eQd+2aAPM4qM7QA8zcjtAA8mZdSoXfTsmqxz+HcoEovy3vwl8Y7g/m0H/Bzj5Qd/bqf30/bV9yL/sG8D1/tvfAnb5V+pbwHT/8guAHf7lO4D7Hc5pgP+gb274HcDtp33tp+nbuXQQuC8cc4bb+vTf/zrw7X6+NiTrc7gZw21d0rfD7j1/nq3Alad97UP+r9sHXB2OOYGb/R2wFdgMXOdwzlvo+wO9n76tBXHD/dnU6fwiIi7gpm3mIiKjlspcRMQFVOYiIi6gMhcRcQGVuYiIC6jMRURcQGUuIuIC/wfT/kK40eLQPAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "mn = []\n", + "for i in range(10000):\n", + " sample = numpy.random.choice(all_boy, size=100)\n", + " mn.append(numpy.mean(sample))\n", + "sns.distplot(mn,bins=10,\n", + " kde_kws={\"color\":\"r\", \"lw\":3 }, \n", + " hist_kws={ \"color\": \"b\" }) \n", + "pass" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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/Lsqvv8Lnn/s7UQcMCDuVhKiOXEwrkfLSS/D11wBsaNqKBQeeVs0L6ohWreCSS+LtG2/UUXgdpwIumWfMmNLFzw85l00NmoQYJsMMHQotWvjl+fPhscfCzSOhUgGXzPLxx35CX2BTVn3mHTYk5EAZpmXL8ndnDh/uu1SkTlIfuGSWMqPufdVtAOu2bhNimPBs7gal7K0v5a9bP0DTlT/Ajz/6ySxu0QVgdZGOwCVzfPcdPPtsaXPOkUM3s3HdVdSoGR/3LXOD05gxpecMpG5RAZfMceed8ZNyhx/Oz+32CzdPBvuy+ynQvbtvFBTAFVeEG0hCoQIumWHZsvKTFA8bFl6WKKhXD8aOjbefew7efTe0OBIOFXDJDGPH+pEHAfbfH3r3DjdPFBx4IJx8crw9ZIifdk7qDBVwCd/KlXDvvfH28OGbnaRYyrjttvgt9p99pqnX6hgVcAnfvffC6tV+uVMn6Ns33DxR0ratv6GnxMiR8O234eWRtFIBl3CtXVv+qPHqq+vObDtBuegi6NzZL69bBxdfHG4eSRv9S5FwjRtXftAqje2x5bKz/dC7JV56CV59Nbw8kjYq4BKeH34ofwPKVVdB/frh5Ymynj1h0KB4++KLYcOG8PJIWqiAS3iuuio+Se8++5QvQLLlRo+Oz9yzaBHccUe4eaTGqYBLOKZPh8cfj7fHjvVdAZK81q1h1Kh4+5ZbdEKzllMBl/QrLi5/oq1vX+jVK7w8tcngwbBf7A7W9ev9GOJSa6V8yGNmWUAesMQ5d1zqkaTWe/RRP+ogQIMG+qqfpMoHvMpiu6Puoc+nB/vm88/D1Klw1FHpjCZpEsQR+MXA5wG8j9QFy5bB5ZfH25deCrvsEl6eWuin3XqyoMff4iuGDdMkyLVUSgXczNoCfwQeDCaO1HqXXAK//OKX27f3kxVL4D46YTSFJRNhzJoFkyaFG0hqRKpdKGOBK4DmAWSR2u7118sXkvHjoWnT8PLUYutb7MCcXkPZ//XYSc1rr4V+/XyXVQWbG3u8rMGDAwwogUj6CNzMjgOWOedmVrPdYDPLM7O8/Pz8ZD9Oom7NGjj33Hj7lFPg6KPDy1MHzDp6mJ/NHvx44Q88EG4gCVwqXSg9gT+b2TfA08ARZvZExY2ccxOcc7nOudycnJwUPk4ibeRIP2ED+KKiQZdqXGHjFn5gsBI33aTp12qZpLtQnHNXA1cDmNlhwOXOuYEB5ZLaZMGC8mNXjxnDhBf0n3lanH++3/fffw/5+f4/zuuuCzuVBETXgUvNGzoUCgv9cs+ecOqp4eapSxo18t9+SowZ44fvlVohkALunHtX14BLpV57zT/Aj/H9979rrO90GzgQ9tjDL69a5f8OpFbQvctSczZu9EffJQYN8rPtSNr4K0yy2b3nNRy+wF8bXnDbXTzV4mLfRy6Rpi4UqTn33OP7vwFatCg/Toek1cJuA1i17W4ANFy3kn3+fXfIiSQIKuBSM1avLl+wr78ett02vDx1nMvK5n/Hxm+a2vetMdRfvzrERBIEFXAJ3IQJMPOUMaV3XK5u3YEHGw5hwgRKH5J+C7ufwqqcXQFotG4F+7yjo/CoUwGXwDVcs5x9p95Z2p75pxspzv7tHYCSXi4rm0+OGVHa7jz1DhqsXRFiIkmVCrgEbr83bqNBwRoAftlhLxZ2PznkRFLiyx4Dy/WFd3nz9pATSSpUwCVYS5aw97v3lDbz+tyMq5cVYiApy2XVZ8afby5t7/v2OBqvWhpiIkmFCrgE66abyC70czEu2zmXb/Y7PuRAUtGiA/qxvJ2f9CG7cD37//OmkBNJslTAJTjz58OD8ZGFZxw/SjftZKJ69fj4+FtLm3u+N5Hm+V+FGEiSpQIuwRk+HDZtAmBxp14s2VOzwGSqxXsfzQ+7HwJAveIiur84oppXSCZSAZdgfPghTJlS2vz4hNt09J3JzJjRN34UvmveM7Sd+0aIgSQZKuCSOufgyitLmwu79Wf5zgeEGEgS8dOuB/FlmSuEfv/UuWRvWBNiItlSKuCSuldfhffe88v16zOjz82b314yxocnjWVDUz/pQ/OfvyX3ZQ01GyUq4JKadev8xMQlzj2XX2N3+0nm29A8hw/7jSlt7/PvceR8/XGIiWRLqIBLam64ARYu9MstWmiS4gj6ssepLI6dcK7nijn00TPILlgbcipJhAq4JC8vD+6M3zLPnXdqwKooMuO9gQ+UzmLfaulnHPzU+f7chmQ0FXBJzsaNcOaZUFzs20cc4dsSSb+27sB/+8cHt9pj+mN0/OChEBNJIlTAJTm33AJz5vjlxo1h4kRdNhhx83ueyfwDTy9t95x0Adt890l4gaRaSc/IY2btgMeA7QAHTHDOjQsqmAQrkSFcBw9O8M0mT4Ybb4y3R42CXXZJKpdklvdPvpfW381kmyVzyC4q4KgH/sKUETPZ2GTrsKNJJVI5Ai8CLnPO7QX0AIaY2V7BxJKM9cEH8Le/xduHHgoXXRReHgnUpgZNeGvws2xs2AyArZYv4rBHTot3lUlGSfoI3Dm3FFgaW/7VzD4HdgQ+CyibZJoFC9hw9J9pVFAAwMrtOvJSnykUPKTRBmuTVdt35D+nPcxRE04CoP2sl+ny5v/BuVdW80pJt0D6wM2sPdAV+KiS5wabWZ6Z5eXn5wfxcRKGadPgkENotNbPsrO+eQ6vX/gaBU1bhRxMasLXB/Rjdq/4hNTdXhwO77wTYiKpTMoF3MyaAc8DlzjnfjPJnnNugnMu1zmXm5OTk+rHSbo5B3//O/TqBT/9BEBR/ca8MeRVfs1Rv3dt9tGJt/Hjrj0Bf304/fuX/g5IZkipgJtZfXzxftI5N6W67SViFi2CE06Aiy+GoiIA1jdrzesXvkZ+h+4hh5Oa5rLq89bgyaxrHru2f9kyOOMMXR+eQZIu4GZmwEPA5865MdVtLxGyapUfnGrPPeHFF+Pru3Vjyoj/sbTjYaFFk/Rat3Ub3jnzifiK11+HuzUZcqZI5Qi8J3AqcISZfRp7HBtQLglDYSHcdx/sthvcfru/WafEOefAtGmsbdUuvHwSiiV7HcWsoy6Lr7jiCpg9O7xAUiqVq1DeB3TnRi3Rbu7rsO9QP6tOWT16wF13+Z9SZ83oM4ou+W/Dp59CQQEMGAAzZkCTJmFHq9N0J2Ydl7VxPT2fGsIxdx9bvnjvtBM8+ST8978q3kJx/YYwaZK/6xbgs8/Kj0IpoUj6CFyir+WSOfSa2J9WS+OX7m9stBWfHDuCuUdcxKY1jWBiiAEls3TqBOPGxW/ZfeABf3VSv37h5qrDVMDrqJ1mv8qRE04iu3B96bqvu57Ae6eMZ0NzXe4pVTjrLHjrLT+cAsDZZ0O3btC+faix6ip1odRBHT94mD/cf3xp8S5s0IRpAycw9ZznVLxl88z8wDolBXvVKt8fHrs7V9JLBbwucY6u/7yZQx8bRL1iP3v86tYdeGF4Hl/8/myNJiiJadHC94dnx77AT59efmhhSRsV8LrCObpPuYpuL19bump5u668dMV/WbnDniEGk0jq0QNGj463n3pKszGFQAW8LnCOAycPZb83by9dtbhTL1657F3Wt9g+xGASaZde6u8PKHHrrf7EpqSNCnhtV1wMF1zAvv+OD9X+TZc+vHHBPylsvFWIwSTyzOCee+C44+Lrzj/fX6mi2+3TQgW8Nisq8mNX3Hdf6apF+/+Fqec866/rFUlVdjY8/TTk5vp2cTFccokv5IWF4WarA1TAa6sNG/z1uY89Vrrqy+4n8/ZZk3BZ9UMMJrVO06bw2mvlb/gaPx6OOQa+/Ta8XHWAuTR+1cnNzXV5eXlp+7w6a9UqOPFEePvt0lWfH3w2759yP66eJl+QmpG1cT2DPjjTH5GXaNwYrr4ahg2DRo3CCxdxZjbTOZdbcb2OwGub2bP919kyxZvLL+e9gQ+oeEuN2tSgsb8a5YYb4ivXr4frrvN3cY4d6w8uJDAq4LWFc/Doo/5r7MKF8fU33+xHFtQ13pIOZnD99X7u1K5d4+u//RaGDoUdd/T949On60RnAFTAa4P33vOTC59+uj/iAWjWDJ55BkaMUPGW9DvoID9a4X33Qasy0+6tXQv33w8HHuiHLb72Wj8wliRFfeBR9f338K9/+SL91lvln9tzT3j+ef8zZsKENOcTickuWMtuHz3JPu/cTasf5la+UZcucPLJ/tG2bXoDRkBVfeAq4JmosBDy8/38g8uX++Vly/zX0EWL/LCvFcftBn9J1znn+DvkmjUr95QKuITOOXb4chp/Wvk4PPdc5f3hZnD44XDqqX46v610rwKogGee4mJfjD/9FObM8f3WCxf6dcuXb9l71asHAwf6vsddKp9oWAVcMkm9wgJ2mvsau86YxM6zXyG7cMNvN2rYEI491k+m3Lt3nS7mKuBhWrHCHzHPm+cL9iefwKxZsGZN8u/ZoAEccggccwyT1vfh15xdg8srkkb116+m/acvsPtHT7DjF29jldWk7Gw4+GB/bfnBB/sTpCWTS9QBNVLAzaw3MA7IAh50zo3e3PaRK+DOwaZNfqjMio8NG/wJw7Vr/WP1al+oV6zwXR5LlvjHN9/47o8t+VgzNjRrzfrm27G++bZsaNaaDc1as6ZVO35tvQurW+/Cyh32pKhh05r5c4uEpMmKJez28VP0WPSUP9ipSnY27LuvP8+z++6w667Qpg1stx1su60fMbFBg1pzAj/wAm5mWcAC4ChgMTADGOCcq/KUclIF/P33y496Br6wluR2zndHlPzctCn+s+xycXH8UbZdVBTftrDQP4qK/IS+BQU1eqnT+uY5/Nx2P35u24WV23di9ba7sTpnV9ZttT0uS3NtSN3W4scv2G3G0+w86yVaf7+ZYl6V7Gxo3tzfQNSwoX/Urw9ZWf65erGL8ErqScX6UVJXSpj515Y86tf371PyXiXr69Xz25Y8Sl4LMGWK/49lC1VVwFOpEt2Bhc65RbEPeBroAwR7TdDSpfDPfwb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"b/022\347\275\256\344\277\241\345\272\246/\347\254\254\345\205\255\346\254\241\344\272\272\345\217\243\346\231\256\346\237\245\345\271\264\351\276\204\347\273\223\346\236\204.xlsx" new file mode 100644 index 0000000..822a542 Binary files /dev/null and "b/022\347\275\256\344\277\241\345\272\246/\347\254\254\345\205\255\346\254\241\344\272\272\345\217\243\346\231\256\346\237\245\345\271\264\351\276\204\347\273\223\346\236\204.xlsx" differ diff --git "a/023\344\270\255\345\233\2752000-2018GDP/cngdp2000_2018.CPG" "b/023\344\270\255\345\233\2752000-2018GDP/cngdp2000_2018.CPG" new file mode 100644 index 0000000..3ad133c --- /dev/null +++ "b/023\344\270\255\345\233\2752000-2018GDP/cngdp2000_2018.CPG" @@ -0,0 +1 @@ +UTF-8 \ No newline at end of file diff --git "a/023\344\270\255\345\233\2752000-2018GDP/cngdp2000_2018.dbf" "b/023\344\270\255\345\233\2752000-2018GDP/cngdp2000_2018.dbf" new file mode 100644 index 0000000..6bf5f18 Binary files /dev/null and "b/023\344\270\255\345\233\2752000-2018GDP/cngdp2000_2018.dbf" differ diff --git "a/023\344\270\255\345\233\2752000-2018GDP/cngdp2000_2018.prj" "b/023\344\270\255\345\233\2752000-2018GDP/cngdp2000_2018.prj" new file mode 100644 index 0000000..f45cbad --- /dev/null +++ "b/023\344\270\255\345\233\2752000-2018GDP/cngdp2000_2018.prj" @@ -0,0 +1 @@ +GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]] \ No newline at end of file diff --git "a/023\344\270\255\345\233\2752000-2018GDP/cngdp2000_2018.sbn" "b/023\344\270\255\345\233\2752000-2018GDP/cngdp2000_2018.sbn" new file mode 100644 index 0000000..d03f1d7 Binary files /dev/null and "b/023\344\270\255\345\233\2752000-2018GDP/cngdp2000_2018.sbn" differ diff --git "a/023\344\270\255\345\233\2752000-2018GDP/cngdp2000_2018.sbx" "b/023\344\270\255\345\233\2752000-2018GDP/cngdp2000_2018.sbx" new file mode 100644 index 0000000..7e954e9 Binary files /dev/null and "b/023\344\270\255\345\233\2752000-2018GDP/cngdp2000_2018.sbx" differ diff --git "a/023\344\270\255\345\233\2752000-2018GDP/cngdp2000_2018.shp" "b/023\344\270\255\345\233\2752000-2018GDP/cngdp2000_2018.shp" new file mode 100644 index 0000000..35412cd Binary files /dev/null and "b/023\344\270\255\345\233\2752000-2018GDP/cngdp2000_2018.shp" differ diff --git "a/023\344\270\255\345\233\2752000-2018GDP/cngdp2000_2018.shp.xml" "b/023\344\270\255\345\233\2752000-2018GDP/cngdp2000_2018.shp.xml" new file mode 100644 index 0000000..f8bcffb --- /dev/null +++ "b/023\344\270\255\345\233\2752000-2018GDP/cngdp2000_2018.shp.xml" @@ -0,0 +1,2 @@ + +20180622204144001.0TRUECopyFeatures CNPG_S E:\GIS_data\Pro\blogMapProject\blogMapProject.gdb\CNGDP # # # #AlterField CNGDP gdp_csv_GDP2016 GDP2016 # DOUBLE 8 NULLABLE falseAlterField CNGDP gdp_csv_GDP2015 GDP2015 # DOUBLE 8 NULLABLE falseAlterField CNGDP CNPG_S_FIRST_NAME NAME # TEXT 60 NULLABLE falseAlterField CNGDP gdp_csv_GDP2000 GDP2000 # DOUBLE 8 NULLABLE falseAlterField CNGDP gdp_csv_GDP2001 GDP2001 # DOUBLE 8 NULLABLE falseAlterField CNGDP gdp_csv_GDP2002 GDP2002 # DOUBLE 8 NULLABLE falseAlterField CNGDP gdp_csv_GDP2003 GDP2003 # DOUBLE 8 NULLABLE falseAlterField CNGDP gdp_csv_GDP2004 GDP2004 # DOUBLE 8 NULLABLE falseAlterField CNGDP gdp_csv_GDP2005 GDP2005 # DOUBLE 8 NULLABLE falseAlterField CNGDP gdp_csv_GDP2006 GDP2006 # DOUBLE 8 NULLABLE falseAlterField CNGDP gdp_csv_GDP2007 GDP2007 # DOUBLE 8 NULLABLE falseAlterField CNGDP gdp_csv_GDP2008 GDP2008 # DOUBLE 8 NULLABLE falseAlterField CNGDP gdp_csv_GDP2009 GDP2009 # DOUBLE 8 NULLABLE falseAlterField CNGDP gdp_csv_GDP2010 GDP2010 # DOUBLE 8 NULLABLE falseAlterField CNGDP gdp_csv_GDP2011 GDP2011 # DOUBLE 8 NULLABLE falseAlterField CNGDP gdp_csv_GDP2012 GDP2012 # DOUBLE 8 NULLABLE falseAlterField CNGDP gdp_csv_GDP2013 GDP2013 # DOUBLE 8 NULLABLE falseAlterField CNGDP gdp_csv_GDP2014 GDP2014 # DOUBLE 8 NULLABLE false diff --git "a/023\344\270\255\345\233\2752000-2018GDP/cngdp2000_2018.shx" "b/023\344\270\255\345\233\2752000-2018GDP/cngdp2000_2018.shx" new file mode 100644 index 0000000..9561c87 Binary files /dev/null and "b/023\344\270\255\345\233\2752000-2018GDP/cngdp2000_2018.shx" differ diff --git "a/024\345\214\227\344\272\254\345\214\273\351\231\242\346\225\260\346\215\256/\345\214\227\344\272\254\344\270\211\347\272\247\345\214\273\351\231\242.csv" "b/024\345\214\227\344\272\254\345\214\273\351\231\242\346\225\260\346\215\256/\345\214\227\344\272\254\344\270\211\347\272\247\345\214\273\351\231\242.csv" new file mode 100644 index 0000000..1f8ceaa --- /dev/null +++ "b/024\345\214\227\344\272\254\345\214\273\351\231\242\346\225\260\346\215\256/\345\214\227\344\272\254\344\270\211\347\272\247\345\214\273\351\231\242.csv" @@ -0,0 +1,68 @@ +医院名称,等级,分类,科室,医生,主任医师,副主任医师,主治医师,医院地址1,医院地址2,床位数,日门诊量,建院时间,医院简介,徽标,Y,X +北京协和医院,三级甲等,中国医科院所属医院,51,1409,401,288,720,北京市东城区东单帅府园1号,北京市西城区大木仓胡同41号,2000,12000,1921,北京协和医院是集医疗、教学、科研于一体的大型三级甲等综合医院，是北京协和医学院的临床学院、中国医学科学院的临床医学研究所，是卫生部指定的全国疑难重症诊治指导中心，也是最早承担干部保健和外宾医疗的医院之一。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024519461.jpg,39.9121437070005,116.414176941 +中国人民解放军总医院,三级甲等,驻京部队医院,53,1263,379,455,429,北京市海淀区复兴路28号,,3400,9315,1953,国人民解放军总医院(301医院)创建于1953年，是集医疗、保健、教学、科研于一体的大型现代化综合性医院。,http://pimg.39.net//PictureLib/A/f4/c3/20130608/m/71068.jpg,39.9069480900005,116.278099060001 +北京大学第三医院,三级甲等,北京大学附属医院,50,681,269,254,158,北京市海淀区花园北路49号 ,,1463,9589,1958,北京大学第三医院(简称“北医三院”)始建于1958年，是卫生部部管的集医疗、教学、科研和预防保健为一体的现代化综合性三级甲等医院。现有在职职工2447人，开放床位1463张。医院设有34个临床科室、11个医技科室。拥有28个博士点、1个临床博士后流动站。在岗博士生导师55人，中科院院士1人、国家自然科学基金杰出青年基金获得者1人、科技部“973”首席科学家1人、教育部 “长江学者特聘教授”2人，1人入选国家级“新世纪百千万人才工程”。,http://images.guahao114.com/img/142.jpg,39.9823951720004,116.359344482001 +首都医科大学附属北京同仁医院,三级甲等,北京市卫生局直属医院,48,625,254,184,187,北京市东城区东交民巷1号,北京市东城区崇文门内大街8号,860,4250,1886,首都医科大学附属北京同仁医院创建于1886年(清光绪12年)，是一所以眼科、耳鼻咽喉科和心血管疾病诊疗为重点的大型综合性医院。“同仁”字号和图徽是国家商标局认定的国内医疗服务业首家驰名商标。,http://images.guahao114.com/img/105.jpg,39.9031715390005,116.417816162001 +首都医科大学附属北京儿童医院,三级甲等,北京市卫生局直属医院,28,443,161,140,142,北京市西城区南礼士路56号,,970,6301,1942,首都医科大学附属北京儿童医院的前身是我国现代儿科医学奠基人诸福棠院士于1942年创建的北平私立儿童医院，至今已有71年历史。北京儿童医院是集医疗、科研、教学、保健于一体的儿科医学基地，是我国目前规模最大的三级甲等综合性儿科医院。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024512195.jpg,39.9121284480005,116.355026245001 +阜外心血管病医院,三级甲等,中国医科院所属医院,28,397,164,138,95,北京市西城区北礼士路167号,,967,342,1956,阜外心血管病医院始建于1956年，是国家级三级甲等心血管专科医院，还是国内唯一一家集医疗、科研、预防和人才培养于一体的国家级心血管病的医疗诊治、医学教育和医学研究中心。,http://images.guahao114.com/img/4.jpg,39.9246368410004,116.352005005001 +中日友好医院,三级甲等,卫生部直属医院,63,720,283,198,239,北京市朝阳区樱花园东街2号,,1500,6100,1984,"中日友好医院于1984年10月23日开院，位于北京市朝阳区樱花园东街2号，建筑面积20余万平方米(含在建面积)，现编制床位1500张，设有68个临床、医技科室，附设中日友好临床医学研究所及培训中心。医院集医疗、教学、科研、康复和预防保健等多项功能为一体，同时承担中央保健医疗康复任务、涉外医疗任务,以及国家卫生应急队伍基地医院中央本级单位建设的重任。",http://pimg.39.net//PictureLib/A/f4/c3//org/13417024443616.jpg,39.9744071960005,116.425788879001 +北京大学人民医院,三级甲等,北京大学附属医院,46,613,245,249,119,北京市西城区西直门南大街11号,北京市西城区阜内大街133号,1700,6669,1918,北京大学人民医院创建于1918年，是中国人自行筹资建设和管理的第一家综合性西医医院，最初命名为“北京中央医院”，中国现代医学先驱伍连德博士任首任院长。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024508601.jpg,39.9366188050005,116.353889465001 +中国中医科学研究院广安门医院 ,三级甲等,中国中医科学院,29,409,165,145,99,北京市西城区广安门内北线阁5号,西城区白纸坊东街27号,649,7200,1955,中国中医科学院广安门医院(暨中国中医科学院第二临床医药研究所)始建于1955年，是国家中医药管理局直属的集医疗、教学、科研和预防保健为一体的三级甲等中医医院，是中央干部保健基地，全国“示范中医医院”， 2008北京奥运会和残奥会定点医院，北京市医疗保险A类定点医院，首都文明单位标兵，中央国家机关文明单位标兵，首都卫生系统文明单位标兵，中央国家机关平安先进单位，ISO9001质量管理认证单位，还是世界卫生组织传统医学合作中心组成单位，国家食品药品监督管理局国家药物临床实验机构，卫生部西医学习中医教学基地，国家中医药管理局批准的全国中医肿瘤医疗中心和全国中医糖尿病专病及中医肛肠病专病中心以及中医急诊基地建设单位，国家中医药管理局中医药重点学科(中医肿瘤病学、中医内分泌病学、中医肛肠病学、中医心病学、中医心理学、中医痹病学)及肿瘤扶正培本重点研究室建设单位，国家中医药管理局和北京市中医管理局指定为中医药治疗艾滋病定点医院以及北京市艾滋病抗病毒治疗承担单位。承担四所高等中医药大学的临床带教任务。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024513539.jpg,39.9122123720005,116.365676880001 +首都医科大学附属北京中医医院,三级甲等,北京市卫生局直属医院,32,368,180,125,63,北京市东城区美术馆后街23号,,597,6000,1956,首都医科大学附属北京中医医院始建于1956年，是北京市唯一的一所市属综合性、现代化三级甲等中医医院。承担着北京市中医医疗、教学、科研、预防等任务。医院下设北京市中医研究所、北京市国际针灸培训中心。,http://pimg.39.net//PictureLib/A/f4/c3/20130618/m/71340.jpg,39.9317436220004,116.408226013001 +首都医科大学附属北京天坛医院,三级甲等,北京市卫生局直属医院,35,676,183,225,268,北京市东城区天坛西里6号,,950,1500,1956,首都医科大学附属北京天坛医院始建于 1956年8月23日，座落在世界著名的天坛公园西南侧，是一所以神经科为重点的三级甲等大型综合医院。1997年由中国医学科学院与北京市卫生局共建成为中国医学科学院北京天坛医院和中国医学科学院神经科学研究所。北京市神经外科研究所、北京天坛医学影像中心、北京市伽玛刀治疗研究中心、北京神经外科学院、首都医科大学第五临床医学院、北京市脑血管病抢救治疗中心和全国脑血管病防治研究办公室、北京市脑防办同设在院内。北京天坛医院和北京市神经外科研究所是世界三大神经外科研究中心之一、亚洲最大的神经外科临床、科研、教学基地和WHO在中国的神经科学培训合作中心。,http://images.guahao114.com/img/100.jpg,39.8769340520005,116.402328491001 +北京大学第一医院 ,三级甲等,北京大学附属医院,42,794,252,235,307,北京市西城区西什库大街8号,,1500,7000,1915,北京大学第一医院(简称“北大医院”)位于北京老皇城内，是距离中南海最近的医院，是一所融医疗、教学、科研、预防为一体的大型综合性三级甲等医院。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024507335.jpg,39.9306030270005,116.379959106001 +首都医科大学附属北京安贞医院,三级甲等,北京市卫生局直属医院,42,417,157,185,75,北京市朝阳区安贞路2号,,1062,3964,1984,北京安贞医院成立于1984年4月，北京市心肺血管疾病研究所成立于1981年9月，二者为一个医疗科研联合体，集医疗、教学、科研、预防、国际交流五位一体，是以治疗心肺血管疾病为重点的大型三级甲等综合性医院。北京安贞医院是首都医科大学第六临床医学院，全院职工在以吴英恺、孙衍庆、张兆光、魏永祥为院长的四届院领导带领下，始终把握世界先进医学脉搏，不断改革、创新，实现了跨越式发展。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024444475.jpg,39.9729690550005,116.404579163001 +北京积水潭医院,三级甲等,北京市卫生局直属医院,44,413,129,153,131,北京市西城区新街口东街31号,,1500,3000,1956,北京积水潭医院是一家王府花园式医院。这里曾是元代大运河终点上樯帆林立的码头——“积水潭港”，也是清朝延续了二百年的“棍贝子府”。,http://images.guahao114.com/img/103.jpg,39.9444007870005,116.376396179001 +中国中医科学院西苑医院,三级甲等,中国中医科学院,34,342,152,122,68,北京市海淀区西苑操场1号,,569,4000,1955,中国中医科学院西苑医院(简称“西苑医院”)位于北京市著名风景文化游览区海淀区，与世界最大的皇家园林—颐和园比邻而建，占地面积6万多平米，建筑面积10万多平米。医院拥有3个国家临床重点专科，分别为血液病科、心血管病科、脾胃病科等以及国家中医药管理局重点专科14个，包括心血管病科、脾胃病科、老年病科、血液病科、肺病科、肿瘤科、脑病科、肾病科、肝病科、皮肤病科、重症医学科、耳鼻喉科、预防保健科、护理学等。是中国中西医结合学会神经内科、血液学专业委员会、中国中西医结合学会养生学与康复医学专业委员会专业分会的主任委员单位和《中国中西医结合杂志》的挂靠单位，是卫生部“西学中班”培训基地。,http://pimg.39.net//PictureLib/A/f4/c3/20130608/m/71070.jpg,40.0269203190004,116.233215332001 +首都医科大学附属北京妇产医院 ,三级甲等,北京市卫生局直属医院,21,268,98,66,104,北京市朝阳区姚家园路251号,北京市东城区骑河楼17号,660,2795,1959,首都医科大学附属北京妇产医院 北京妇幼保健院创建于1959年6月，其前身是直属中央卫生部的北京妇幼保健实验院。经过50年的建设和发展，目前已发展成为集医疗、保健、教学、科研为一体，以诊治妇产科常见病、多发病和疑难病症为重点的国内知名的三级甲等妇产专科医院。我国著名妇产科专家林巧稚是首任院长。1984年被世界卫生组织(WHO)批准为围产保健研究和培训合作中心，1999年更名为WHO母婴和妇女保健研究及培训中心，1992年成为我国首批符合国际标准的爱婴医院之一，1994年被首都医科大学批准为首都医科大学附属北京妇产医院，同年建立了妇产科学硕士研究生培养点，2002年成为首都医科大学妇产科博士生培养点，2005年首批通过技术准入，成立遗传诊断研究中心、北京市产前诊断中心，2008年成为北京市中医药管理局中西医结合妇科重点学科，2008年成为北京地区专科医师妇产科专业临床技能培训考核中心，2010年产科、妇科系列(妇科、妇科微创中心、妇科肿瘤科、计划生育科、生殖医学科、妇科内分泌科、妇科中医科、乳腺科)成为卫生部临床重点专科建设单位。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024445991.jpg,39.9313468930004,116.471122742001 +首都医科大学附属北京朝阳医院,三级甲等,北京市卫生局直属医院,46,521,197,229,95,北京市朝阳区工人体育场南路8号,北京市石景山区京原路5号,1910,6849,1958,首都医科大学附属北京朝阳医院创建于1958年2月24日，是北京市卫生局直属医院，是集医疗、教学、科研、预防为一体的三级甲等医院，是首都医科大学第三临床医学院，也是北京市医疗保险A类定点医疗机构。2004年年底，中铁建总医院正式划转北京市并入我院，正式命名为北京朝阳医院京西院区。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024444084.jpg,39.9258079530005,116.453399658001 +北京大学口腔医院 ,三级甲等,北京大学附属医院,15,326,121,85,120,北京市海淀区中关村南大街22号,,157,3205,1941,北京大学口腔医学院始建于1941年，是集北京大学口腔医学院、口腔医院和口腔医学研究所为一体的医疗机构，长期以来承担着向社会提供口腔医疗保健服务和口腔教学、医学研究的重任。拥有诸多国内外著名的口腔医学专家，为我国口腔界培养了一批批高素质、高层次专业人才，成为我国重要的口腔医学研究基地之一，是我国口腔医学对外交流的重要窗口。,http://images.guahao114.com/img/133.jpg,39.9522361760005,116.324943542001 +北京中医药大学东直门医院,三级甲等,北京中医药大学,31,358,161,106,91,北京市东城区海运仓5号,北京市通州区翠屏西路116号,862,3992,1958,北京中医药大学东直门医院创建于1958年，是一所集医疗、教学、科研为一体的大型综合性中医院。是全国唯一一所进入国家“211工程”建设的高等中医药院校——北京中医药大学的第一临床医学院，并率先成为全国示范中医医院、三级甲等中医院和国家食品药品监督管理局认定的国家药物临床试验机构。是北京市医疗保险定点医疗机构，民政部“明天计划”脑瘫患儿手术康复和“点燃心希望”先心病治疗定点医院。2010年，医院被确立为“国家中医药发展综合改革试验区”建设示范基地和战略合作签约单位。2011年8月6日，原通州区中医医院与我院整合，并正式命名为北京中医药大学东直门医院东区。医院开创性地于1991年最早在欧洲建立了分院——德国魁茨汀医院，作为重要的中医药国际交流窗口，载入中德外交史册。,http://pimg.39.net//PictureLib/A/f4/c3/20131121/m/173977.jpg,39.9340629580005,116.427818298001 +首都医科大学附属宣武医院,三级甲等,北京市卫生局直属医院,36,491,174,180,137,北京市西城区长椿街45号,,1380,5000,1958,首都医科大学宣武医院坐落于古都北京宣武区长椿街、明代万历帝敕建皇家寺院——长椿寺旁，1958年9月1日创建，是一所以神经科学和老年医学的临床和研究为重点，以治疗心脑血管疾患为特色，承担医疗、教育、科研、预防、保健和康复任务的大型三级甲等综合医院。,http://pimg.39.net//PictureLib/A/f4/c3/20131128/m/176133.jpg,39.8920745850005,116.362144470001 +中国医学科学院肿瘤医院 ,三级甲等,中国医科院所属医院,28,294,158,110,26, 北京市朝阳区潘家园南里17号,,1198,1644,1958,中国医学科学院肿瘤医院肿瘤研究所，始建于1958年，原名日坛医院，1963年增设肿瘤研究所，此后又相继建立了河南林州、江苏启东等肿瘤高发防治现场。1983年迁至北京市东南龙潭湖畔，正式更名为中国医学科学院肿瘤医院肿瘤研究所，1996年通过三级甲等医院评审。院所是建国以来第一个肿瘤专科医院，是亚洲地区最大的肿瘤防治研究中心，也是国家药品监督管理局国家药品临床研究基地。院所集肿瘤医疗、科研、教学为一体，全方面进行肿瘤的预防、诊断及治疗的研究。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024445663.jpg,39.8726425170005,116.447593689001 +北京大学第一医院,三级甲等,北京大学附属医院,18,159,69,40,50,北京市西城区西什库大街8号,,1500,7000,1915,北大医院创建于1915年，北大医院前身为民国教育部批准北京医科专门学校设立附属诊察所;1946年随北京医学院与北京大学合并，由此得名“北大医院”;2000年北京大学与北京医科大学两校再次合并，医院随之更名为“北京大学第一医院”。,http://images.guahao114.com/img/14.jpg,39.9306030270005,116.379959106001 +中国人民解放军总医院第一附属医院,三级甲等,驻京部队医院,35,308,63,135,110,北京市海淀区阜城路51号,,1100,135,1954,"解放军总医院第一附属医院(原304医院)始建于1954年3月，是一所以医教研相结合、创(烧)伤外科、骨科、急救医学和危重症救治为主要特色的三级甲等综合性医院。医院始终坚持“适度规模，内涵发展，突出特色，精字建院”的发展思路,努力建设一流的现代化医院 ",http://pimg.39.net//PictureLib/A/f4/c3/20130608/m/71071.jpg,39.9244041440004,116.304824829001 +北京医院 ,三级甲等,卫生部直属医院,35,522,225,132,165,北京市东城区东单大华路1号,,1100,132,1905,北京医院的前身是德国医院，始建于1905年。现为三级甲等医院，是中央的干部保健基地。北京医院是一所以高干医疗保健为中心、老年医学研究为重点 、向社会全面开放的融医疗、教学、科研、预防为一体的现代化大型综合性医院。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024521820.jpg,39.9031562810004,116.415985107001 +北京大学肿瘤医院,三级甲等,北京市卫生局直属医院,44,269,75,89,105,北京市海淀区阜成路52号,,700,767,1976,北京大学肿瘤医院(北京肿瘤医院、北京大学临床肿瘤学院、北京市肿瘤防治研究所)始建于1976年，是集医、教、研于一体，预防、治疗、康复相结合的肿瘤防治研究中心。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024459866.jpg,39.9224853520005,116.28842926 +中国人民解放军空军总医院,三级甲等,驻京部队医院,51,337,113,154,70,北京市海淀区阜成路30号,,1059,3562,1956,空军总医院创建于1956年10月，1994年被总后卫生部评定为“三级甲等医院”，1996年、1998年先后被国家卫生部、总后卫生部确定为全国、全军临床药理基地。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024456913.jpg,39.9235115050005,116.302650452001 +首都医科大学附属北京世纪坛医院,三级甲等,北京市卫生局直属医院,49,336,96,163,77,北京市海淀区羊坊店铁医院路10号,,1008,3000,1915,首都医科大学附属北京世纪坛医院、首都医科大学肿瘤医学院，前身为创建于1915年4月的“京汉铁路医院”，已有96年院史。解放后相继名为“北京铁道医学院附属医院”、“铁道部北京铁路总医院”。2004年由铁道部移交北京市政府，更名为“北京世纪坛医院”。2011年3月25日正式挂牌为“首都医科大学附属北京世纪坛医院”和“首都医科大学肿瘤医学院”，标志着医院进入了一个新的历史发展阶段。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024454678.jpg,40.0269203190004,116.233215332001 +首都医科大学附属北京友谊医院,三级甲等,北京市卫生局直属医院,33,405,189,176,40,北京市西城区永安路95号,,1256,8000,1952,首都医科大学附属北京友谊医院原名为北京苏联红十字医院，始建于1952年。是新中国成立后，在苏联政府和苏联红十字会援助下，由中国政府建立的第一所大医院。1954年，医院从甘水桥旧址迁入现址。毛泽东主席、刘少奇副主席、周恩来总理、朱德委员长特为医院亲笔题词。1957年3月，苏联政府将医院正式移交我国，周总理亲自来院参加了移交仪式。1970年，周总理亲自为医院定名为“北京友谊医院”。,http://pimg.39.net//PictureLib/A/f4/c3/20131130/m/176678.jpg,39.8859367370005,116.392356873001 +北京军区总医院,三级甲等,驻京部队医院,46,400,113,151,136,北京市东城区东四十条南门仓5号,北京市东三环麦子店,1600,4000,1913,北京军区总医院成立于1913年，是一所历史悠久、设备精良、技术领先，集预防、保健、医疗、科研、教学、康复为一体的大型三级甲等综合医院。,http://images.guahao114.com/img/152.jpg,39.9336318970004,116.425270081001 +中国人民解放军第三〇二医院,三级甲等,驻京部队医院,19,166,53,58,55,北京市丰台区西四环中路100号,,1100,58,1954,医院前身是解放战争时期的“中共中央直属机关医院”，1954年7月正式命名为“中国人民解放军第三〇二医院”。现展开床位1100余张，编设31个临床、医技科室。主要承担驻京部队的传染病收治、全军疑难重症传染病会诊、转诊以及反恐和应对突发公共卫生事件的应急处置等任务，是全军传染病肝病保健专科医院。拥有8个省部级重点学(专)科，是传染病学硕士、博士学位授权点和博士后流动站，是北京大学等军地十余所院校的教学实习医院和全军传染病防治技术临床培训基地。,http://images.guahao114.com/img/158.jpg,39.8849029540005,116.275802612001 +首都儿科研究所附属儿童医院 ,三级甲等,北京市卫生局直属医院,24,186,104,60,22,北京市朝阳区雅宝路2号,北京市月坛南街1号,400,5886,1958,首都儿科研究所的前身是中国医学科学院儿科研究所，成立于1958年，是新中国第一家以医学基础研究、儿科疾病发病机理研究、儿童保健为重点，承担有医疗、教学和预防任务的应用医学研究机构。研究所于1983年隶属北京市，1986年建立附属儿童医院。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024446819.jpg,39.9151191710005,116.438049316001 +武警总医院,三级甲等,驻京武警医院,47,348,99,128,121,北京市海淀区永定路69号,,1360,4500,1937,武警总医院目前已发展成为集医疗、保健、教学、科研、救援于一体的大型现代化综合性三级甲等医院，展开床位1360张，设置专业科室82个。拥有各类专家 450余名，博士、硕士生导师74名，博士和博士后高学历人才120余名;先后有115名专家获得国务院政府特殊津贴和军队优秀人才岗位津贴，4人被授予 “有突出贡献的中青年专家”，2人当选为武警部队第十三届“中国武警十大忠诚卫士”，2人被授予“全军专业技术重大贡献奖”，2人为中央保健委员会成员，1人荣获第四十三届南丁格尔奖。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024454007.jpg,39.9086227420005,116.264930725001 +中国人民解放军第三〇六医院,三级甲等,驻京部队医院,38,380,49,84,247,北京市朝阳区德外安翔北路9号,,1008,1800,1971,医院始建于1971年，前身是国防科工委第514医院，1997年1月经中央军委批准为国防科工委总医院， 1999年3月更名为中国人民解放军第三○六医院。现拥有功能齐全的病房楼3栋，日门诊量1800人次，年收容病人量1万人次，平均床位使用率90%。是解放军第四军医大学、北京大学医学部临床教学医院，第三军医大学、首都医科大学、军事医学科学院、航天医学工程研究所等研究生联合培养点。,http://images.guahao114.com/img/157.jpg,39.9941749570005,116.377449036001 +首都医科大学附属北京口腔医院,三级甲等,北京市卫生局直属医院,22,247,52,85,110,北京市东城区天坛西里4号,东城区锡拉胡同11号,100,2000,1945,首都医科大学附属北京口腔医院创建于1945年，是集医疗、教学、科研、预防为一体的三级甲等口腔专科医院。医院分为天坛部和王府井部，天坛部位于古老的天坛公园南侧，王府井部位于北京市王府井中心商业区，环境优美，设施齐全。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024524742.jpg,39.8780250550005,116.402038574001 +北京中医药大学东方医院,三级甲等,北京中医药大学,39,253,103,91,59,北京市丰台区方庄芳星园一区6号,,1200,91,1986,东方医院筹建于1986年并于1999年12月12日正式开业，是北京中医药大学第二临床医学院，国家中医药管理局托管单位，是一所特色明显、功能齐全、设备先进，集医疗、教学、科研、预防和健康咨询为一体的三级甲等中医医院。教育部211工程建设的中医药大学附属医院，北京市医疗保险定点医院。2011年6月，北京中医药大学东方医院正式接收北京二七机车厂医院，命名为北京中医药大学东方医院二七院区。医院现为一院两区(即东方医院方庄院区和东方医院二七院区)。总占地面积4.62万平方米，总建筑面积9.96万平方米。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024530039.jpg,39.8641014100004,116.431587219001 +中国人民解放军海军总医院,三级甲等,驻京部队医院,40,269,82,117,70,北京市海淀区阜成路6号,,1300,2100,1954,医院现有专业科室56个，展开床位1300余张，拥有耳鼻咽喉科、神经外科、高压氧科、优生优育指导中心和航海航空医学中心5个全军医学专科中心;拥有腰椎间盘疾病诊治中心、结节病中心和视光学疾病中心3个全军专病中心;拥有海军神经疾病研究所和核医学科、骨科、呼吸内科、心血管内科、眼科5个海军医学专科中心;医院作为博士后科研工作站，是第二军医大学、南方医科大学、安徽医科大学三所高等学府的临床医学院;是全军神经疾病护理示范基地;是国家药物临床试验机构。拥有全国优秀共产党员、白求恩奖章获得者冯理达，海军首位国际南丁格尔奖获得者、第三届全国道德模范王文珍等一批先进典型;拥有硕士以上学历医务人员400余人。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024459085.jpg,39.9227027890005,116.317840576001 +首都医科大学附属北京安定医院,三级甲等,北京市卫生局直属医院,6,127,39,35,53,北京市西城区德胜门外安康胡同5号,,800,425,1914,首都医科大学附属北京安定医院是一所集医教研防和对外交流于一体的三级甲等精神专科医院。医院创建于1914年，1990年成为全国精神科新药临床药理研究基地;1999年成为国家药品临床研究基地;2000年成为首都医科大学精神卫生学院，也是全国第一所精神卫生学院，是精神病和精神卫生专业的硕士点、博士点以及应用心理学硕士点;2006年成为北京地区精神病学专业住院/专科医师培训基地;2007年建立首都医科大学精神病学系和临床心理学系。医院有职工878人，医护技人员631人，具有高级职称70人，病床800张。(占地约26800平方米),http://pimg.39.net//PictureLib/A/f4/c3//org/13417024516789.jpg,39.9528465270005,116.376777649001 +中国人民解放军第二炮兵总医院,三级甲等,驻京部队医院,34,293,66,82,145,北京市西城区新外大街16号,,700,1095,1956,二炮总医院的前身是北京军区第262医院，始建于1954年5月;1999年9月，挂牌成立二炮总医院。,http://images.guahao114.com/img/145.jpg,39.9122123720005,116.365676880001 +中国人民解放军第三〇九医院,三级甲等,驻京部队医院,34,234,60,104,70,北京市海淀区黑山扈路甲17号,,1000,104,1958,解放军第309医院(总参总医院)位于北京市海淀区黑山扈，南接皇家园林颐和园，北靠百望山森林公园，西临西山风景区，东衔京密运水河。医院于1958年组建，先后隶属解放军总医院、总后勤部和总参谋部。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024500366.jpg,40.0227355960005,116.265602112001 +首都医科大学附属北京佑安医院,三级甲等,北京市卫生局直属医院,33,207,77,61,69,北京市丰台区右安门外西头条8号,,750,682,1957,明嘉靖四十三年，在北京城西南取“安定、安宁”之祥意建右安门，以护城市的安康。建国初期，彭真市长亲临右安门外，规划始建北京第二传染病医院，其建筑规模及学科设置均为亚洲之首。1989年更名为北京佑安院。 2003年成为首都医科大学附属北京佑安医院和首都医科大学第九临床医学院，同时承担着“国家生命科学与技术人才培养基地”和“国家感染与传染病专科医师进修基地”的教学和培训任务。同时医院还设有“北京市肝病研究所”“首都医科大学肝病与肝癌临床研究所”“北京市性病艾滋病临床诊疗中心”及“国家药物临床试验机构”等临床科研机构。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024530383.jpg,39.8672256470004,116.355789185001 +首都医科大学附属北京地坛医院,三级甲等,北京市卫生局直属医院,22,163,49,50,64, 北京市朝阳区京顺东街8号,,600,2600,1946,首都医科大学附属医院北京地坛医院 (原名北京第一传染病医院 )始建于 1946年，是北京市卫生局直属三级甲等医院，也是北京大学医学部、北京中医药大学教学医院。国家肝病、艾滋病临床药物验证基地、国家中医药管理局中西医结合传染病临床基地设在这里。医院主要承担除结核病以外的 39种法定传染病的诊治、研究和培训任务。是一家集医疗、教学、科研、预防为一体的公共卫生临床医疗机构。建设现代化、数字化、人文化、花园式，以传染病为特色的三级甲等综合性医院是医院新的追求目标。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024448381.jpg,39.9711418150005,116.425109863001 +首都医科大学附属复兴医院,三级,北京区县属医院,43,307,90,118,99,北京市西城区复兴门外大街甲20号,北京市西城区月坛北街4号,816,118,1950,首都医科大学附属复兴医院建于1950年，经过60多年的发展，现已成为集医疗、教学、科研、预防和社区卫生服务为一体的、拥有妇科内镜、心血管防治、危重症医学(ICU)、月坛社区服务等特色学科的三级综合医院。,http://images.guahao114.com/img/154.jpg,39.9060020450005,116.339553833001 +中国医学科学院整形外科医院,三级甲等,中国医科院所属医院,25,123,38,33,52,北京市石景山区八大处路33号,,320,33,1957,中国医学科学院整形外科医院的前身为中国人民解放军总后勤部和平医院，位于北京市东交民巷39号，1957年中国人民解放军总后勤部决定，将总后勤部和平医院与北京协和医院整形外科合并，朱德总司令为医院亲笔题名 “中国人民解放军整形外科医院”。1958年国务院决定将“中国人民解放军整形外科医院”等7个部队院所移交中央卫生部，归属地方领导，隶属中国医学科学院。,http://images.guahao114.com/img/146.jpg,39.9426651000004,116.200119019001 +北京大学第六医院,三级甲等,北京大学附属医院,4,108,57,30,21,北京市海淀区海淀花园北路51号,,240,30,1942,北京大学精神卫生研究所(北京大学第六医院、北京大学精神卫生学院)是北京大学精神病学与精神卫生学的临床医疗、人才培训与科学研究基地，是世界卫生组织(WHO)北京精神卫生研究和培训协作中心，也是中国疾病预防控制中心的精神卫生中心。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024502163.jpg,39.9820594790005,116.357398987001 +首都医科大学附属北京同仁医院亦庄院区,三级甲等,北京市卫生局直属医院,19,118,41,49,28,北京经济技术开发区西环南路2号,,600,49,2004,亦庄同仁医院始建于1886年(清光绪12年)，具有118年悠久历史。“同仁”名称源自“圣经”，取“一视同仁”之意。“同仁”的金字品牌在海、内外享有盛誉，是国家商标局认定的国内医疗服务业首家驰名商标。,http://images.guahao114.com/img/105.jpg,39.7746238710005,116.519340515001 +中国中医科学院望京医院,三级甲等,中国中医科学院,28,180,62,80,38,北京市朝阳区花家地街,,1100,3500,1997,中国中医科学院望京医院建院于1997年1月，由原中国中医研究院骨伤科研究所、北京针灸骨伤学院附属医院和骨伤系合并组建。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024447334.jpg,39.9805336000004,116.477424622001 +中国人民解放军三〇七医院,三级甲等,驻京部队医院,33,168,41,52,75,北京市丰台区东大街8号,,1100,4000,1957,解放军307医院是军事医学科学院附属医院，创建于1957年，是一所医疗、教学、科研相结合的三级甲等综合医院，是北京市首批医疗保险定点医疗机构，现设置专业科室38个，展开床位1100余张，为北京西南地区一所规模最大的现代化医院，医院致力于以精湛技术为军民提供优质服务。,http://images.guahao114.com/img/149.jpg,39.8625793460005,116.295349121001 +首都医科大学附属北京朝阳医院西院区,三级甲等,北京市卫生局直属医院,48,118,40,53,25,北京市石景山区京原路5号,,550,1300,2004,首都医科大学附属北京朝阳医院创建于1958年2月24日，是北京市卫生局直属医院，是集医疗、教学、科研、预防为一体的三级甲等医院，是首都医科大学第三临床医学院，也是北京市医疗保险A类定点医疗机构。2004年年底，中铁建总医院正式划转北京市并入我院，正式命名为北京朝阳医院京西院区。我院是2008年第二十九届北京奥运会定点医院。,http://images.guahao114.com/img/121.jpg,39.9004898070005,116.210426331001 +航天中心医院,三级,部属厂矿高校医院,36,323,95,104,124,北京市海淀区玉泉路15号,,900,1682,1958,航天中心医院是集医疗、教学、科研于一体的大型三级综合性医院，是北京大学的临床医学院、北京急救中心西区分中心、航天体检中心、国家临床药理试验机构、北京市职业健康检查机构、北京市工伤定点医疗机构，是全国百姓放心百佳示范医院。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024458600.jpg,39.9165191650005,116.251770020001 +北京回龙观医院,三级甲等,北京市卫生局直属医院,17,109,37,45,27,北京市昌平区回龙观,,1369,45,1986,北京回龙观医院是一所大型公立精神卫生专科医院、是首批获得国家精神病临床重点专科的单位。医院位于北京市德胜门外，占地面积14.7万平方米，设置病床1369张，现有职工1200余人，是北京市卫生局直属三级甲等医院、北京大学教学医院、中法友好合作医院、中国科学院心理研究所临床心理学教学医院、北京市心理危机研究与干预中心、北京市心理援助热线、世界卫生组织心理危机预防研究与培训合作中心、北京市专科医师培训基地、国家药物临床试验机构。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024547681.jpg,40.2160758970004,116.340583801001 +北京华信医院,三级,部属厂矿高校医院,36,186,51,109,26, 北京市朝阳区酒仙桥一街坊6号,,760,2329,1959,医院前身为国家信息产业部(前为四机部、机电部、电子工业部)的直属医院，创办于1959年2月，先后称为电子总医院、401医院、北京酒仙桥医院。1994年被北京市卫生局评定为三级医院和爱婴医院，1995年起被北京市列为大病统筹定点医院、公费医疗定点医院和首批医保定点医院。2003年4月划归国家教育部成为清华大学附属医院。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024447991.jpg,39.9510116580005,116.513648987001 +煤炭总医院,三级,部属厂矿高校医院,38,221,84,88,49,北京市朝阳区西坝河南里29号,,515,1300,1993,在抗击"非典"的战斗中，煤炭总医院作为首批"非典"医院在收治180例SARS病人的严重形势下，以高治愈率，低死亡率的医疗质量位居北京医疗机构前列，以病人之间零交叉感染、全员职工零感染的辉煌业绩被国内外广泛报道而受到党和政府的表彰。中德心脏中心的成立，揭开了国际合作，走向世界的新篇章。 技术上一流，服务无止境，煤炭总医院全体职工正以饱满的热情，坚定的信心，在"内抓管理，外树形象，创建人民满意医院"的大路上阔步前进!,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024446256.jpg,39.9716911320005,116.533203125001 +民航总医院,三级,部属厂矿高校医院,35,158,27,49,82,北京市朝外高井甲1号,,500,2400,1974,民航总医院位于北京市朝阳区东部、朝阳路旁，是一所设备先进、科室齐全、服务优良、集医教研防于一体的三级综合性医院。,http://images.guahao114.com/img/141.jpg,39.9450340270005,116.136352539001 +中国康复研究中心北京博爱医院,三级甲等,北京市卫生局直属医院,29,116,50,51,15,北京市丰台区角门北路10号,,1100,489,1989,中国康复研究中心是集康复医疗、康复科学技术研究、康复人才培养、康复信息服务、康复工程研究以及社会服务指导于一体的综合性康复机构和技术资源中心。她由职能处室、业务科室、康复信息研究所、康复工程研究所、康复医学研究所、康复医学院、社会服务指导中心等部门组成。“中心”占地面积近300亩，建筑面积15万平方米，员工1373多人。,http://images.guahao114.com/img/153.jpg,39.8500556950005,116.379295349001 +北京大学首钢医院,三级,北京大学附属医院,32,144,43,75,26,北京市石景山区晋元庄路9号,,1006,2742,1949,北京大学首钢医院是一所集医疗、教学、科研、预防保健为一体的三级综合医院，始建于1949年10月。2002年，首钢总公司与北京大学签订联合办院协议，医院更名为北京大学首钢医院，成为北京大学附属医院，北京大学教学医院、北京大学临床学院。,http://images.guahao114.com/img/150.jpg,39.9284439090005,116.203269958001 +首都医科大学附属北京胸科医院,三级甲等,北京市卫生局直属医院,17,105,43,52,10,北京市通州区马厂97号,,900,463,1955,北京市结核病胸部肿瘤研究所、首都医科大学附属北京胸科医院是以胸科疾病和结核病患者群体为主要服务对象，集医疗、科研、教学、预防为一体的三级甲等专科医院。创建于1955年，原名“中央结核病研究所、中央直属结核病医院”，兼设“WHO结核病研究培训合作中心”、“国家药物临床试验机构”、“中国CDC结核病防治临床中心”、“北京肺癌诊疗中心”、“北京骨关节结核诊疗中心”，是国家首批博士和硕士学位授予单位。,http://images.guahao114.com/img/107.jpg,39.9238128660004,116.656997681001 +北京三博脑科医院,三级,北京市卫生局直属医院,6,33,19,9,5,北京市海淀区香山一棵松50号,,300,9,2004,北京三博脑科医院是一家以博医、博教、博研为宗旨创建的学院型医院。始建于2004年，前身是北京三博复兴脑科医院。2006年成为首批卫生部神经外科专科医师培训基地，2007年与天坛、宣武医院组建首都医科大学神经外科学院，成为学院三系。2010年成为首都医科大学第十一临床医学院、首医博士点、硕士点。同时还是北京市首批三级专科民营医院、三级专科医保定点和新农合定点医疗机构，也是中国抗癫痫协会临床实践与培训基地、中国医师协会神经调控专业委员会会长单位。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024506913.jpg,40.0269203190004,116.233215332001 +中国中医科学院眼科医院,三级,中国中医科学院,12,75,17,22,36,北京市石景山区鲁谷路9号,,204,800,1986,"中国中医科学院眼科医院于1986年经卫生部批准兴建,成立于1994年，是一所中医、中西医结合的眼病专科医院，是北京市基本医疗保险定点医院，三级中医医院。",http://pimg.39.net//PictureLib/A/f4/c3//org/13417024541149.jpg,39.9040489200005,116.250007629001 +北京中医药大学第三附属医院,三级,北京中医药大学,25,130,30,54,46, 北京市朝阳区安定门外小关街51号,,431,1800,1964,"北京中医药大学第三附属医院(原北京冶金医院),始建于1964年，2006年7月划转至北京中医药大学，2007年7月成为北京中医药大学第三临床医学院。是北京市医保定点医院，现为三级中西医结合医院，设有床位520张。骨伤科、脑病科是国家中医药管理局重点专科建设单位，中医全科医学、中医骨伤科学是国家中医药管理局重点学科。",http://pimg.39.net//PictureLib/A/f4/c3//org/13417024451038.jpg,39.9778900150005,116.408477783001 +中国人民解放军第三〇五医院,三级,驻京部队医院,30,79,32,28,19,北京市西城区文津街甲13号,,500,1000,1969,经过40年的建设和发展，医院已成为一所集保健、医疗、教学、科研和预防于一体的三级甲等综合医院，是“全军老年病中心”，“全军冠心病诊治中心”，“解放军第三军医大学临床教学医院”，“南方医科大学临床教学医院”，北京市首批医疗保险定点医院。,http://images.guahao114.com/img/144.jpg,39.9247016910004,116.384727478001 +北京航天总医院,三级,部属厂矿高校医院,36,169,42,105,22,北京市丰台区东高地万源北路7号,,550,1700,1958,北京航天总医院始建于1958年，隶属于中国航天科技集团公司，是北京南郊地区规模最大的一所三级综合性医院。承担着航天科技集团公司数万名员工的职业病防治、检工作，肩负着大型飞行试验的医疗保障任务和广大职工家属以及周边约50万居民的医疗、护理、健康管理、预防保健工作。本院与解放军总医院第一附属医院(304医院)都是大型综合性医院，304医院在政府的和医疗卫生部门的领导下，借鉴国际前沿的医疗模式，我们在继承和挖掘科学技术的同时，又培养了新型现代肿瘤生物免疫治疗后起之秀;始终秉承一切为任命健康服务的宗旨，以精准的诊疗方法最大限度的减少患者的精神、肉体痛苦和财务损失;在医护人员中间深化“以病人为先”的服务理念。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024531696.jpg,39.8040618900004,116.418838501001 +北京燕化工公司职工医院,三级,部属厂矿高校医院,26,115,31,42,42,北京市房山区燕山迎风街15号,,670,1000,2005,北京燕化医院位于北京市房山区，占地7.4万平方米，原为国家特大型企业燕山石化公司的职工医院，2005年1月医院改制为股份制医院，是北京西南地区一家大型三级非营利性综合医院，北京市医疗保险定点医院。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024546446.jpg,39.7299613950005,115.955627441001 +北京市宣武区中医院,三级,北京区县属医院,5,55,16,36,3,北京市西城区万明路甲8号,,300,1200,1906,北京市宣武中医医院隶属宣武卫生局，是一所集医疗、科研、教学、预防、保健、康复为一体的综合性“三级乙等中医医院”，是北京中医药大学和首都医科大学中医药学院的临床教学医院。在医疗上具有鲜明的专科特色。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024518179.jpg,39.8875312810005,116.389518738001 +北京电力医院,三级,部属厂矿高校医院,38,124,57,54,13,北京市丰台区太平桥西里甲1号,,1000,1600,1989,北京电力医院成立于1989年，位处北京西客站南侧，北临古莲花池，西接京港澳高速公路入口。是一所学科齐全、设备先进、技术精良，集医疗、教学、科研、预防等为一体的三级综合医院。目前编制床位518张(改扩建期间开放370 张，2013年6月改扩建完成后预计开放病床1000张)。现有各类员工1000余人，博士、硕士近百名，具有高级技术职称的达到150人，有30余人任北京市各临床专业委员会委员和首都医科大学各临床学系委员，其中主任、副主任委员3人。首都医科大学研究生导师3人，已有2名硕士进站。同时建立了由 150名有影响力知名专家组成的专家会诊库，为系统内服务和医疗质量提供了保障。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024534367.jpg,39.8863525390005,116.315895081001 +北京老年医院,三级,北京市卫生局直属医院,33,100,14,44,42,北京市海淀区温泉路118号,,600,978,1949,北京老年医院是直属北京市卫生局的三级综合医院，是市基本医疗保险及工伤定点医院。作为北京中医药大学附属医院、中科院生物所合作伙伴和首都医科大学第六临床医学院教学基地，医院承担着多项科研项目和临床教学工作。医院编制床位六百张，职工近千人。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024503788.jpg,40.0537986760005,116.134391785001 +北京京煤集团总医院,三级,部属厂矿高校医院,25,202,15,50,137,北京市门头沟区黑山大街18号,,1486,548,1956,北京京煤集团总医院(原北京矿务局总医院)，坐落在环境幽雅、依山傍水的永定河畔，地处门头沟区中心，是京西地区唯一一家集医疗、教学、科研、预防和康复医疗服务为一体，面向全社会开放的三级综合性医院。2002年成为北京大学教学医院及北大医院医疗集团成员之一.,http://images.guahao114.com/img/155.jpg,39.9396972660005,116.095855713001 +中国人民解放军二六一医院,三级,驻京部队医院,24,100,20,30,50,北京市海淀区上庄镇皂甲屯村116号,,600,1700,1949,中国人民解放军第二六一医院位于北京市海淀区上庄镇，是一所以生物诊疗中心、综合治疗科、精神病科、神经内外科、消化呼吸内科、心血管内科、骨科、普通外科、妇产科、眼科为特色的现代化综合性三级乙等医院，全面实现了微机网络化管理，是北京市首批“基本医疗保险定点医院”，北京市工伤保险定点医疗机构，北京市中小学生及婴幼儿住院互助金定点医疗机构，北京市农民医疗保险定点医疗机构。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417030115044.jpg,40.1144371030005,116.220367432001 diff --git "a/024\345\214\227\344\272\254\345\214\273\351\231\242\346\225\260\346\215\256/\345\214\227\344\272\254\344\270\211\347\272\247\345\214\273\351\231\242_utf8.csv" "b/024\345\214\227\344\272\254\345\214\273\351\231\242\346\225\260\346\215\256/\345\214\227\344\272\254\344\270\211\347\272\247\345\214\273\351\231\242_utf8.csv" new file mode 100644 index 0000000..d7ad8bd --- /dev/null +++ "b/024\345\214\227\344\272\254\345\214\273\351\231\242\346\225\260\346\215\256/\345\214\227\344\272\254\344\270\211\347\272\247\345\214\273\351\231\242_utf8.csv" @@ -0,0 +1,68 @@ +医院名称,等级,分类,科室,医生,主任医师,副主任医师,主治医师,医院地址1,医院地址2,床位数,日门诊量,建院时间,医院简介,徽标,Y,X +北京协和医院,三级甲等,中国医科院所属医院,51,1409,401,288,720,北京市东城区东单帅府园1号,北京市西城区大木仓胡同41号,2000,12000,1921,北京协和医院是集医疗、教学、科研于一体的大型三级甲等综合医院，是北京协和医学院的临床学院、中国医学科学院的临床医学研究所，是卫生部指定的全国疑难重症诊治指导中心，也是最早承担干部保健和外宾医疗的医院之一。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024519461.jpg,39.9121437070005,116.414176941 +中国人民解放军总医院,三级甲等,驻京部队医院,53,1263,379,455,429,北京市海淀区复兴路28号,,3400,9315,1953,国人民解放军总医院(301医院)创建于1953年，是集医疗、保健、教学、科研于一体的大型现代化综合性医院。,http://pimg.39.net//PictureLib/A/f4/c3/20130608/m/71068.jpg,39.9069480900005,116.278099060001 +北京大学第三医院,三级甲等,北京大学附属医院,50,681,269,254,158,北京市海淀区花园北路49号 ,,1463,9589,1958,北京大学第三医院(简称“北医三院”)始建于1958年，是卫生部部管的集医疗、教学、科研和预防保健为一体的现代化综合性三级甲等医院。现有在职职工2447人，开放床位1463张。医院设有34个临床科室、11个医技科室。拥有28个博士点、1个临床博士后流动站。在岗博士生导师55人，中科院院士1人、国家自然科学基金杰出青年基金获得者1人、科技部“973”首席科学家1人、教育部 “长江学者特聘教授”2人，1人入选国家级“新世纪百千万人才工程”。,http://images.guahao114.com/img/142.jpg,39.9823951720004,116.359344482001 +首都医科大学附属北京同仁医院,三级甲等,北京市卫生局直属医院,48,625,254,184,187,北京市东城区东交民巷1号,北京市东城区崇文门内大街8号,860,4250,1886,首都医科大学附属北京同仁医院创建于1886年(清光绪12年)，是一所以眼科、耳鼻咽喉科和心血管疾病诊疗为重点的大型综合性医院。“同仁”字号和图徽是国家商标局认定的国内医疗服务业首家驰名商标。,http://images.guahao114.com/img/105.jpg,39.9031715390005,116.417816162001 +首都医科大学附属北京儿童医院,三级甲等,北京市卫生局直属医院,28,443,161,140,142,北京市西城区南礼士路56号,,970,6301,1942,首都医科大学附属北京儿童医院的前身是我国现代儿科医学奠基人诸福棠院士于1942年创建的北平私立儿童医院，至今已有71年历史。北京儿童医院是集医疗、科研、教学、保健于一体的儿科医学基地，是我国目前规模最大的三级甲等综合性儿科医院。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024512195.jpg,39.9121284480005,116.355026245001 +阜外心血管病医院,三级甲等,中国医科院所属医院,28,397,164,138,95,北京市西城区北礼士路167号,,967,342,1956,阜外心血管病医院始建于1956年，是国家级三级甲等心血管专科医院，还是国内唯一一家集医疗、科研、预防和人才培养于一体的国家级心血管病的医疗诊治、医学教育和医学研究中心。,http://images.guahao114.com/img/4.jpg,39.9246368410004,116.352005005001 +中日友好医院,三级甲等,卫生部直属医院,63,720,283,198,239,北京市朝阳区樱花园东街2号,,1500,6100,1984,"中日友好医院于1984年10月23日开院，位于北京市朝阳区樱花园东街2号，建筑面积20余万平方米(含在建面积)，现编制床位1500张，设有68个临床、医技科室，附设中日友好临床医学研究所及培训中心。医院集医疗、教学、科研、康复和预防保健等多项功能为一体，同时承担中央保健医疗康复任务、涉外医疗任务,以及国家卫生应急队伍基地医院中央本级单位建设的重任。",http://pimg.39.net//PictureLib/A/f4/c3//org/13417024443616.jpg,39.9744071960005,116.425788879001 +北京大学人民医院,三级甲等,北京大学附属医院,46,613,245,249,119,北京市西城区西直门南大街11号,北京市西城区阜内大街133号,1700,6669,1918,北京大学人民医院创建于1918年，是中国人自行筹资建设和管理的第一家综合性西医医院，最初命名为“北京中央医院”，中国现代医学先驱伍连德博士任首任院长。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024508601.jpg,39.9366188050005,116.353889465001 +中国中医科学研究院广安门医院 ,三级甲等,中国中医科学院,29,409,165,145,99,北京市西城区广安门内北线阁5号,西城区白纸坊东街27号,649,7200,1955,中国中医科学院广安门医院(暨中国中医科学院第二临床医药研究所)始建于1955年，是国家中医药管理局直属的集医疗、教学、科研和预防保健为一体的三级甲等中医医院，是中央干部保健基地，全国“示范中医医院”， 2008北京奥运会和残奥会定点医院，北京市医疗保险A类定点医院，首都文明单位标兵，中央国家机关文明单位标兵，首都卫生系统文明单位标兵，中央国家机关平安先进单位，ISO9001质量管理认证单位，还是世界卫生组织传统医学合作中心组成单位，国家食品药品监督管理局国家药物临床实验机构，卫生部西医学习中医教学基地，国家中医药管理局批准的全国中医肿瘤医疗中心和全国中医糖尿病专病及中医肛肠病专病中心以及中医急诊基地建设单位，国家中医药管理局中医药重点学科(中医肿瘤病学、中医内分泌病学、中医肛肠病学、中医心病学、中医心理学、中医痹病学)及肿瘤扶正培本重点研究室建设单位，国家中医药管理局和北京市中医管理局指定为中医药治疗艾滋病定点医院以及北京市艾滋病抗病毒治疗承担单位。承担四所高等中医药大学的临床带教任务。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024513539.jpg,39.9122123720005,116.365676880001 +首都医科大学附属北京中医医院,三级甲等,北京市卫生局直属医院,32,368,180,125,63,北京市东城区美术馆后街23号,,597,6000,1956,首都医科大学附属北京中医医院始建于1956年，是北京市唯一的一所市属综合性、现代化三级甲等中医医院。承担着北京市中医医疗、教学、科研、预防等任务。医院下设北京市中医研究所、北京市国际针灸培训中心。,http://pimg.39.net//PictureLib/A/f4/c3/20130618/m/71340.jpg,39.9317436220004,116.408226013001 +首都医科大学附属北京天坛医院,三级甲等,北京市卫生局直属医院,35,676,183,225,268,北京市东城区天坛西里6号,,950,1500,1956,首都医科大学附属北京天坛医院始建于 1956年8月23日，座落在世界著名的天坛公园西南侧，是一所以神经科为重点的三级甲等大型综合医院。1997年由中国医学科学院与北京市卫生局共建成为中国医学科学院北京天坛医院和中国医学科学院神经科学研究所。北京市神经外科研究所、北京天坛医学影像中心、北京市伽玛刀治疗研究中心、北京神经外科学院、首都医科大学第五临床医学院、北京市脑血管病抢救治疗中心和全国脑血管病防治研究办公室、北京市脑防办同设在院内。北京天坛医院和北京市神经外科研究所是世界三大神经外科研究中心之一、亚洲最大的神经外科临床、科研、教学基地和WHO在中国的神经科学培训合作中心。,http://images.guahao114.com/img/100.jpg,39.8769340520005,116.402328491001 +北京大学第一医院 ,三级甲等,北京大学附属医院,42,794,252,235,307,北京市西城区西什库大街8号,,1500,7000,1915,北京大学第一医院(简称“北大医院”)位于北京老皇城内，是距离中南海最近的医院，是一所融医疗、教学、科研、预防为一体的大型综合性三级甲等医院。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024507335.jpg,39.9306030270005,116.379959106001 +首都医科大学附属北京安贞医院,三级甲等,北京市卫生局直属医院,42,417,157,185,75,北京市朝阳区安贞路2号,,1062,3964,1984,北京安贞医院成立于1984年4月，北京市心肺血管疾病研究所成立于1981年9月，二者为一个医疗科研联合体，集医疗、教学、科研、预防、国际交流五位一体，是以治疗心肺血管疾病为重点的大型三级甲等综合性医院。北京安贞医院是首都医科大学第六临床医学院，全院职工在以吴英恺、孙衍庆、张兆光、魏永祥为院长的四届院领导带领下，始终把握世界先进医学脉搏，不断改革、创新，实现了跨越式发展。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024444475.jpg,39.9729690550005,116.404579163001 +北京积水潭医院,三级甲等,北京市卫生局直属医院,44,413,129,153,131,北京市西城区新街口东街31号,,1500,3000,1956,北京积水潭医院是一家王府花园式医院。这里曾是元代大运河终点上樯帆林立的码头——“积水潭港”，也是清朝延续了二百年的“棍贝子府”。,http://images.guahao114.com/img/103.jpg,39.9444007870005,116.376396179001 +中国中医科学院西苑医院,三级甲等,中国中医科学院,34,342,152,122,68,北京市海淀区西苑操场1号,,569,4000,1955,中国中医科学院西苑医院(简称“西苑医院”)位于北京市著名风景文化游览区海淀区，与世界最大的皇家园林—颐和园比邻而建，占地面积6万多平米，建筑面积10万多平米。医院拥有3个国家临床重点专科，分别为血液病科、心血管病科、脾胃病科等以及国家中医药管理局重点专科14个，包括心血管病科、脾胃病科、老年病科、血液病科、肺病科、肿瘤科、脑病科、肾病科、肝病科、皮肤病科、重症医学科、耳鼻喉科、预防保健科、护理学等。是中国中西医结合学会神经内科、血液学专业委员会、中国中西医结合学会养生学与康复医学专业委员会专业分会的主任委员单位和《中国中西医结合杂志》的挂靠单位，是卫生部“西学中班”培训基地。,http://pimg.39.net//PictureLib/A/f4/c3/20130608/m/71070.jpg,40.0269203190004,116.233215332001 +首都医科大学附属北京妇产医院 ,三级甲等,北京市卫生局直属医院,21,268,98,66,104,北京市朝阳区姚家园路251号,北京市东城区骑河楼17号,660,2795,1959,首都医科大学附属北京妇产医院 北京妇幼保健院创建于1959年6月，其前身是直属中央卫生部的北京妇幼保健实验院。经过50年的建设和发展，目前已发展成为集医疗、保健、教学、科研为一体，以诊治妇产科常见病、多发病和疑难病症为重点的国内知名的三级甲等妇产专科医院。我国著名妇产科专家林巧稚是首任院长。1984年被世界卫生组织(WHO)批准为围产保健研究和培训合作中心，1999年更名为WHO母婴和妇女保健研究及培训中心，1992年成为我国首批符合国际标准的爱婴医院之一，1994年被首都医科大学批准为首都医科大学附属北京妇产医院，同年建立了妇产科学硕士研究生培养点，2002年成为首都医科大学妇产科博士生培养点，2005年首批通过技术准入，成立遗传诊断研究中心、北京市产前诊断中心，2008年成为北京市中医药管理局中西医结合妇科重点学科，2008年成为北京地区专科医师妇产科专业临床技能培训考核中心，2010年产科、妇科系列(妇科、妇科微创中心、妇科肿瘤科、计划生育科、生殖医学科、妇科内分泌科、妇科中医科、乳腺科)成为卫生部临床重点专科建设单位。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024445991.jpg,39.9313468930004,116.471122742001 +首都医科大学附属北京朝阳医院,三级甲等,北京市卫生局直属医院,46,521,197,229,95,北京市朝阳区工人体育场南路8号,北京市石景山区京原路5号,1910,6849,1958,首都医科大学附属北京朝阳医院创建于1958年2月24日，是北京市卫生局直属医院，是集医疗、教学、科研、预防为一体的三级甲等医院，是首都医科大学第三临床医学院，也是北京市医疗保险A类定点医疗机构。2004年年底，中铁建总医院正式划转北京市并入我院，正式命名为北京朝阳医院京西院区。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024444084.jpg,39.9258079530005,116.453399658001 +北京大学口腔医院 ,三级甲等,北京大学附属医院,15,326,121,85,120,北京市海淀区中关村南大街22号,,157,3205,1941,北京大学口腔医学院始建于1941年，是集北京大学口腔医学院、口腔医院和口腔医学研究所为一体的医疗机构，长期以来承担着向社会提供口腔医疗保健服务和口腔教学、医学研究的重任。拥有诸多国内外著名的口腔医学专家，为我国口腔界培养了一批批高素质、高层次专业人才，成为我国重要的口腔医学研究基地之一，是我国口腔医学对外交流的重要窗口。,http://images.guahao114.com/img/133.jpg,39.9522361760005,116.324943542001 +北京中医药大学东直门医院,三级甲等,北京中医药大学,31,358,161,106,91,北京市东城区海运仓5号,北京市通州区翠屏西路116号,862,3992,1958,北京中医药大学东直门医院创建于1958年，是一所集医疗、教学、科研为一体的大型综合性中医院。是全国唯一一所进入国家“211工程”建设的高等中医药院校——北京中医药大学的第一临床医学院，并率先成为全国示范中医医院、三级甲等中医院和国家食品药品监督管理局认定的国家药物临床试验机构。是北京市医疗保险定点医疗机构，民政部“明天计划”脑瘫患儿手术康复和“点燃心希望”先心病治疗定点医院。2010年，医院被确立为“国家中医药发展综合改革试验区”建设示范基地和战略合作签约单位。2011年8月6日，原通州区中医医院与我院整合，并正式命名为北京中医药大学东直门医院东区。医院开创性地于1991年最早在欧洲建立了分院——德国魁茨汀医院，作为重要的中医药国际交流窗口，载入中德外交史册。,http://pimg.39.net//PictureLib/A/f4/c3/20131121/m/173977.jpg,39.9340629580005,116.427818298001 +首都医科大学附属宣武医院,三级甲等,北京市卫生局直属医院,36,491,174,180,137,北京市西城区长椿街45号,,1380,5000,1958,首都医科大学宣武医院坐落于古都北京宣武区长椿街、明代万历帝敕建皇家寺院——长椿寺旁，1958年9月1日创建，是一所以神经科学和老年医学的临床和研究为重点，以治疗心脑血管疾患为特色，承担医疗、教育、科研、预防、保健和康复任务的大型三级甲等综合医院。,http://pimg.39.net//PictureLib/A/f4/c3/20131128/m/176133.jpg,39.8920745850005,116.362144470001 +中国医学科学院肿瘤医院 ,三级甲等,中国医科院所属医院,28,294,158,110,26, 北京市朝阳区潘家园南里17号,,1198,1644,1958,中国医学科学院肿瘤医院肿瘤研究所，始建于1958年，原名日坛医院，1963年增设肿瘤研究所，此后又相继建立了河南林州、江苏启东等肿瘤高发防治现场。1983年迁至北京市东南龙潭湖畔，正式更名为中国医学科学院肿瘤医院肿瘤研究所，1996年通过三级甲等医院评审。院所是建国以来第一个肿瘤专科医院，是亚洲地区最大的肿瘤防治研究中心，也是国家药品监督管理局国家药品临床研究基地。院所集肿瘤医疗、科研、教学为一体，全方面进行肿瘤的预防、诊断及治疗的研究。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024445663.jpg,39.8726425170005,116.447593689001 +北京大学第一医院,三级甲等,北京大学附属医院,18,159,69,40,50,北京市西城区西什库大街8号,,1500,7000,1915,北大医院创建于1915年，北大医院前身为民国教育部批准北京医科专门学校设立附属诊察所;1946年随北京医学院与北京大学合并，由此得名“北大医院”;2000年北京大学与北京医科大学两校再次合并，医院随之更名为“北京大学第一医院”。,http://images.guahao114.com/img/14.jpg,39.9306030270005,116.379959106001 +中国人民解放军总医院第一附属医院,三级甲等,驻京部队医院,35,308,63,135,110,北京市海淀区阜城路51号,,1100,135,1954,"解放军总医院第一附属医院(原304医院)始建于1954年3月，是一所以医教研相结合、创(烧)伤外科、骨科、急救医学和危重症救治为主要特色的三级甲等综合性医院。医院始终坚持“适度规模，内涵发展，突出特色，精字建院”的发展思路,努力建设一流的现代化医院 ",http://pimg.39.net//PictureLib/A/f4/c3/20130608/m/71071.jpg,39.9244041440004,116.304824829001 +北京医院 ,三级甲等,卫生部直属医院,35,522,225,132,165,北京市东城区东单大华路1号,,1100,132,1905,北京医院的前身是德国医院，始建于1905年。现为三级甲等医院，是中央的干部保健基地。北京医院是一所以高干医疗保健为中心、老年医学研究为重点 、向社会全面开放的融医疗、教学、科研、预防为一体的现代化大型综合性医院。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024521820.jpg,39.9031562810004,116.415985107001 +北京大学肿瘤医院,三级甲等,北京市卫生局直属医院,44,269,75,89,105,北京市海淀区阜成路52号,,700,767,1976,北京大学肿瘤医院(北京肿瘤医院、北京大学临床肿瘤学院、北京市肿瘤防治研究所)始建于1976年，是集医、教、研于一体，预防、治疗、康复相结合的肿瘤防治研究中心。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024459866.jpg,39.9224853520005,116.28842926 +中国人民解放军空军总医院,三级甲等,驻京部队医院,51,337,113,154,70,北京市海淀区阜成路30号,,1059,3562,1956,空军总医院创建于1956年10月，1994年被总后卫生部评定为“三级甲等医院”，1996年、1998年先后被国家卫生部、总后卫生部确定为全国、全军临床药理基地。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024456913.jpg,39.9235115050005,116.302650452001 +首都医科大学附属北京世纪坛医院,三级甲等,北京市卫生局直属医院,49,336,96,163,77,北京市海淀区羊坊店铁医院路10号,,1008,3000,1915,首都医科大学附属北京世纪坛医院、首都医科大学肿瘤医学院，前身为创建于1915年4月的“京汉铁路医院”，已有96年院史。解放后相继名为“北京铁道医学院附属医院”、“铁道部北京铁路总医院”。2004年由铁道部移交北京市政府，更名为“北京世纪坛医院”。2011年3月25日正式挂牌为“首都医科大学附属北京世纪坛医院”和“首都医科大学肿瘤医学院”，标志着医院进入了一个新的历史发展阶段。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024454678.jpg,40.0269203190004,116.233215332001 +首都医科大学附属北京友谊医院,三级甲等,北京市卫生局直属医院,33,405,189,176,40,北京市西城区永安路95号,,1256,8000,1952,首都医科大学附属北京友谊医院原名为北京苏联红十字医院，始建于1952年。是新中国成立后，在苏联政府和苏联红十字会援助下，由中国政府建立的第一所大医院。1954年，医院从甘水桥旧址迁入现址。毛泽东主席、刘少奇副主席、周恩来总理、朱德委员长特为医院亲笔题词。1957年3月，苏联政府将医院正式移交我国，周总理亲自来院参加了移交仪式。1970年，周总理亲自为医院定名为“北京友谊医院”。,http://pimg.39.net//PictureLib/A/f4/c3/20131130/m/176678.jpg,39.8859367370005,116.392356873001 +北京军区总医院,三级甲等,驻京部队医院,46,400,113,151,136,北京市东城区东四十条南门仓5号,北京市东三环麦子店,1600,4000,1913,北京军区总医院成立于1913年，是一所历史悠久、设备精良、技术领先，集预防、保健、医疗、科研、教学、康复为一体的大型三级甲等综合医院。,http://images.guahao114.com/img/152.jpg,39.9336318970004,116.425270081001 +中国人民解放军第三〇二医院,三级甲等,驻京部队医院,19,166,53,58,55,北京市丰台区西四环中路100号,,1100,58,1954,医院前身是解放战争时期的“中共中央直属机关医院”，1954年7月正式命名为“中国人民解放军第三〇二医院”。现展开床位1100余张，编设31个临床、医技科室。主要承担驻京部队的传染病收治、全军疑难重症传染病会诊、转诊以及反恐和应对突发公共卫生事件的应急处置等任务，是全军传染病肝病保健专科医院。拥有8个省部级重点学(专)科，是传染病学硕士、博士学位授权点和博士后流动站，是北京大学等军地十余所院校的教学实习医院和全军传染病防治技术临床培训基地。,http://images.guahao114.com/img/158.jpg,39.8849029540005,116.275802612001 +首都儿科研究所附属儿童医院 ,三级甲等,北京市卫生局直属医院,24,186,104,60,22,北京市朝阳区雅宝路2号,北京市月坛南街1号,400,5886,1958,首都儿科研究所的前身是中国医学科学院儿科研究所，成立于1958年，是新中国第一家以医学基础研究、儿科疾病发病机理研究、儿童保健为重点，承担有医疗、教学和预防任务的应用医学研究机构。研究所于1983年隶属北京市，1986年建立附属儿童医院。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024446819.jpg,39.9151191710005,116.438049316001 +武警总医院,三级甲等,驻京武警医院,47,348,99,128,121,北京市海淀区永定路69号,,1360,4500,1937,武警总医院目前已发展成为集医疗、保健、教学、科研、救援于一体的大型现代化综合性三级甲等医院，展开床位1360张，设置专业科室82个。拥有各类专家 450余名，博士、硕士生导师74名，博士和博士后高学历人才120余名;先后有115名专家获得国务院政府特殊津贴和军队优秀人才岗位津贴，4人被授予 “有突出贡献的中青年专家”，2人当选为武警部队第十三届“中国武警十大忠诚卫士”，2人被授予“全军专业技术重大贡献奖”，2人为中央保健委员会成员，1人荣获第四十三届南丁格尔奖。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024454007.jpg,39.9086227420005,116.264930725001 +中国人民解放军第三〇六医院,三级甲等,驻京部队医院,38,380,49,84,247,北京市朝阳区德外安翔北路9号,,1008,1800,1971,医院始建于1971年，前身是国防科工委第514医院，1997年1月经中央军委批准为国防科工委总医院， 1999年3月更名为中国人民解放军第三○六医院。现拥有功能齐全的病房楼3栋，日门诊量1800人次，年收容病人量1万人次，平均床位使用率90%。是解放军第四军医大学、北京大学医学部临床教学医院，第三军医大学、首都医科大学、军事医学科学院、航天医学工程研究所等研究生联合培养点。,http://images.guahao114.com/img/157.jpg,39.9941749570005,116.377449036001 +首都医科大学附属北京口腔医院,三级甲等,北京市卫生局直属医院,22,247,52,85,110,北京市东城区天坛西里4号,东城区锡拉胡同11号,100,2000,1945,首都医科大学附属北京口腔医院创建于1945年，是集医疗、教学、科研、预防为一体的三级甲等口腔专科医院。医院分为天坛部和王府井部，天坛部位于古老的天坛公园南侧，王府井部位于北京市王府井中心商业区，环境优美，设施齐全。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024524742.jpg,39.8780250550005,116.402038574001 +北京中医药大学东方医院,三级甲等,北京中医药大学,39,253,103,91,59,北京市丰台区方庄芳星园一区6号,,1200,91,1986,东方医院筹建于1986年并于1999年12月12日正式开业，是北京中医药大学第二临床医学院，国家中医药管理局托管单位，是一所特色明显、功能齐全、设备先进，集医疗、教学、科研、预防和健康咨询为一体的三级甲等中医医院。教育部211工程建设的中医药大学附属医院，北京市医疗保险定点医院。2011年6月，北京中医药大学东方医院正式接收北京二七机车厂医院，命名为北京中医药大学东方医院二七院区。医院现为一院两区(即东方医院方庄院区和东方医院二七院区)。总占地面积4.62万平方米，总建筑面积9.96万平方米。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024530039.jpg,39.8641014100004,116.431587219001 +中国人民解放军海军总医院,三级甲等,驻京部队医院,40,269,82,117,70,北京市海淀区阜成路6号,,1300,2100,1954,医院现有专业科室56个，展开床位1300余张，拥有耳鼻咽喉科、神经外科、高压氧科、优生优育指导中心和航海航空医学中心5个全军医学专科中心;拥有腰椎间盘疾病诊治中心、结节病中心和视光学疾病中心3个全军专病中心;拥有海军神经疾病研究所和核医学科、骨科、呼吸内科、心血管内科、眼科5个海军医学专科中心;医院作为博士后科研工作站，是第二军医大学、南方医科大学、安徽医科大学三所高等学府的临床医学院;是全军神经疾病护理示范基地;是国家药物临床试验机构。拥有全国优秀共产党员、白求恩奖章获得者冯理达，海军首位国际南丁格尔奖获得者、第三届全国道德模范王文珍等一批先进典型;拥有硕士以上学历医务人员400余人。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024459085.jpg,39.9227027890005,116.317840576001 +首都医科大学附属北京安定医院,三级甲等,北京市卫生局直属医院,6,127,39,35,53,北京市西城区德胜门外安康胡同5号,,800,425,1914,首都医科大学附属北京安定医院是一所集医教研防和对外交流于一体的三级甲等精神专科医院。医院创建于1914年，1990年成为全国精神科新药临床药理研究基地;1999年成为国家药品临床研究基地;2000年成为首都医科大学精神卫生学院，也是全国第一所精神卫生学院，是精神病和精神卫生专业的硕士点、博士点以及应用心理学硕士点;2006年成为北京地区精神病学专业住院/专科医师培训基地;2007年建立首都医科大学精神病学系和临床心理学系。医院有职工878人，医护技人员631人，具有高级职称70人，病床800张。(占地约26800平方米),http://pimg.39.net//PictureLib/A/f4/c3//org/13417024516789.jpg,39.9528465270005,116.376777649001 +中国人民解放军第二炮兵总医院,三级甲等,驻京部队医院,34,293,66,82,145,北京市西城区新外大街16号,,700,1095,1956,二炮总医院的前身是北京军区第262医院，始建于1954年5月;1999年9月，挂牌成立二炮总医院。,http://images.guahao114.com/img/145.jpg,39.9122123720005,116.365676880001 +中国人民解放军第三〇九医院,三级甲等,驻京部队医院,34,234,60,104,70,北京市海淀区黑山扈路甲17号,,1000,104,1958,解放军第309医院(总参总医院)位于北京市海淀区黑山扈，南接皇家园林颐和园，北靠百望山森林公园，西临西山风景区，东衔京密运水河。医院于1958年组建，先后隶属解放军总医院、总后勤部和总参谋部。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024500366.jpg,40.0227355960005,116.265602112001 +首都医科大学附属北京佑安医院,三级甲等,北京市卫生局直属医院,33,207,77,61,69,北京市丰台区右安门外西头条8号,,750,682,1957,明嘉靖四十三年，在北京城西南取“安定、安宁”之祥意建右安门，以护城市的安康。建国初期，彭真市长亲临右安门外，规划始建北京第二传染病医院，其建筑规模及学科设置均为亚洲之首。1989年更名为北京佑安院。 2003年成为首都医科大学附属北京佑安医院和首都医科大学第九临床医学院，同时承担着“国家生命科学与技术人才培养基地”和“国家感染与传染病专科医师进修基地”的教学和培训任务。同时医院还设有“北京市肝病研究所”“首都医科大学肝病与肝癌临床研究所”“北京市性病艾滋病临床诊疗中心”及“国家药物临床试验机构”等临床科研机构。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024530383.jpg,39.8672256470004,116.355789185001 +首都医科大学附属北京地坛医院,三级甲等,北京市卫生局直属医院,22,163,49,50,64, 北京市朝阳区京顺东街8号,,600,2600,1946,首都医科大学附属医院北京地坛医院 (原名北京第一传染病医院 )始建于 1946年，是北京市卫生局直属三级甲等医院，也是北京大学医学部、北京中医药大学教学医院。国家肝病、艾滋病临床药物验证基地、国家中医药管理局中西医结合传染病临床基地设在这里。医院主要承担除结核病以外的 39种法定传染病的诊治、研究和培训任务。是一家集医疗、教学、科研、预防为一体的公共卫生临床医疗机构。建设现代化、数字化、人文化、花园式，以传染病为特色的三级甲等综合性医院是医院新的追求目标。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024448381.jpg,39.9711418150005,116.425109863001 +首都医科大学附属复兴医院,三级,北京区县属医院,43,307,90,118,99,北京市西城区复兴门外大街甲20号,北京市西城区月坛北街4号,816,118,1950,首都医科大学附属复兴医院建于1950年，经过60多年的发展，现已成为集医疗、教学、科研、预防和社区卫生服务为一体的、拥有妇科内镜、心血管防治、危重症医学(ICU)、月坛社区服务等特色学科的三级综合医院。,http://images.guahao114.com/img/154.jpg,39.9060020450005,116.339553833001 +中国医学科学院整形外科医院,三级甲等,中国医科院所属医院,25,123,38,33,52,北京市石景山区八大处路33号,,320,33,1957,中国医学科学院整形外科医院的前身为中国人民解放军总后勤部和平医院，位于北京市东交民巷39号，1957年中国人民解放军总后勤部决定，将总后勤部和平医院与北京协和医院整形外科合并，朱德总司令为医院亲笔题名 “中国人民解放军整形外科医院”。1958年国务院决定将“中国人民解放军整形外科医院”等7个部队院所移交中央卫生部，归属地方领导，隶属中国医学科学院。,http://images.guahao114.com/img/146.jpg,39.9426651000004,116.200119019001 +北京大学第六医院,三级甲等,北京大学附属医院,4,108,57,30,21,北京市海淀区海淀花园北路51号,,240,30,1942,北京大学精神卫生研究所(北京大学第六医院、北京大学精神卫生学院)是北京大学精神病学与精神卫生学的临床医疗、人才培训与科学研究基地，是世界卫生组织(WHO)北京精神卫生研究和培训协作中心，也是中国疾病预防控制中心的精神卫生中心。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024502163.jpg,39.9820594790005,116.357398987001 +首都医科大学附属北京同仁医院亦庄院区,三级甲等,北京市卫生局直属医院,19,118,41,49,28,北京经济技术开发区西环南路2号,,600,49,2004,亦庄同仁医院始建于1886年(清光绪12年)，具有118年悠久历史。“同仁”名称源自“圣经”，取“一视同仁”之意。“同仁”的金字品牌在海、内外享有盛誉，是国家商标局认定的国内医疗服务业首家驰名商标。,http://images.guahao114.com/img/105.jpg,39.7746238710005,116.519340515001 +中国中医科学院望京医院,三级甲等,中国中医科学院,28,180,62,80,38,北京市朝阳区花家地街,,1100,3500,1997,中国中医科学院望京医院建院于1997年1月，由原中国中医研究院骨伤科研究所、北京针灸骨伤学院附属医院和骨伤系合并组建。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024447334.jpg,39.9805336000004,116.477424622001 +中国人民解放军三〇七医院,三级甲等,驻京部队医院,33,168,41,52,75,北京市丰台区东大街8号,,1100,4000,1957,解放军307医院是军事医学科学院附属医院，创建于1957年，是一所医疗、教学、科研相结合的三级甲等综合医院，是北京市首批医疗保险定点医疗机构，现设置专业科室38个，展开床位1100余张，为北京西南地区一所规模最大的现代化医院，医院致力于以精湛技术为军民提供优质服务。,http://images.guahao114.com/img/149.jpg,39.8625793460005,116.295349121001 +首都医科大学附属北京朝阳医院西院区,三级甲等,北京市卫生局直属医院,48,118,40,53,25,北京市石景山区京原路5号,,550,1300,2004,首都医科大学附属北京朝阳医院创建于1958年2月24日，是北京市卫生局直属医院，是集医疗、教学、科研、预防为一体的三级甲等医院，是首都医科大学第三临床医学院，也是北京市医疗保险A类定点医疗机构。2004年年底，中铁建总医院正式划转北京市并入我院，正式命名为北京朝阳医院京西院区。我院是2008年第二十九届北京奥运会定点医院。,http://images.guahao114.com/img/121.jpg,39.9004898070005,116.210426331001 +航天中心医院,三级,部属厂矿高校医院,36,323,95,104,124,北京市海淀区玉泉路15号,,900,1682,1958,航天中心医院是集医疗、教学、科研于一体的大型三级综合性医院，是北京大学的临床医学院、北京急救中心西区分中心、航天体检中心、国家临床药理试验机构、北京市职业健康检查机构、北京市工伤定点医疗机构，是全国百姓放心百佳示范医院。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024458600.jpg,39.9165191650005,116.251770020001 +北京回龙观医院,三级甲等,北京市卫生局直属医院,17,109,37,45,27,北京市昌平区回龙观,,1369,45,1986,北京回龙观医院是一所大型公立精神卫生专科医院、是首批获得国家精神病临床重点专科的单位。医院位于北京市德胜门外，占地面积14.7万平方米，设置病床1369张，现有职工1200余人，是北京市卫生局直属三级甲等医院、北京大学教学医院、中法友好合作医院、中国科学院心理研究所临床心理学教学医院、北京市心理危机研究与干预中心、北京市心理援助热线、世界卫生组织心理危机预防研究与培训合作中心、北京市专科医师培训基地、国家药物临床试验机构。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024547681.jpg,40.2160758970004,116.340583801001 +北京华信医院,三级,部属厂矿高校医院,36,186,51,109,26, 北京市朝阳区酒仙桥一街坊6号,,760,2329,1959,医院前身为国家信息产业部(前为四机部、机电部、电子工业部)的直属医院，创办于1959年2月，先后称为电子总医院、401医院、北京酒仙桥医院。1994年被北京市卫生局评定为三级医院和爱婴医院，1995年起被北京市列为大病统筹定点医院、公费医疗定点医院和首批医保定点医院。2003年4月划归国家教育部成为清华大学附属医院。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024447991.jpg,39.9510116580005,116.513648987001 +煤炭总医院,三级,部属厂矿高校医院,38,221,84,88,49,北京市朝阳区西坝河南里29号,,515,1300,1993,在抗击"非典"的战斗中，煤炭总医院作为首批"非典"医院在收治180例SARS病人的严重形势下，以高治愈率，低死亡率的医疗质量位居北京医疗机构前列，以病人之间零交叉感染、全员职工零感染的辉煌业绩被国内外广泛报道而受到党和政府的表彰。中德心脏中心的成立，揭开了国际合作，走向世界的新篇章。 技术上一流，服务无止境，煤炭总医院全体职工正以饱满的热情，坚定的信心，在"内抓管理，外树形象，创建人民满意医院"的大路上阔步前进!,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024446256.jpg,39.9716911320005,116.533203125001 +民航总医院,三级,部属厂矿高校医院,35,158,27,49,82,北京市朝外高井甲1号,,500,2400,1974,民航总医院位于北京市朝阳区东部、朝阳路旁，是一所设备先进、科室齐全、服务优良、集医教研防于一体的三级综合性医院。,http://images.guahao114.com/img/141.jpg,39.9450340270005,116.136352539001 +中国康复研究中心北京博爱医院,三级甲等,北京市卫生局直属医院,29,116,50,51,15,北京市丰台区角门北路10号,,1100,489,1989,中国康复研究中心是集康复医疗、康复科学技术研究、康复人才培养、康复信息服务、康复工程研究以及社会服务指导于一体的综合性康复机构和技术资源中心。她由职能处室、业务科室、康复信息研究所、康复工程研究所、康复医学研究所、康复医学院、社会服务指导中心等部门组成。“中心”占地面积近300亩，建筑面积15万平方米，员工1373多人。,http://images.guahao114.com/img/153.jpg,39.8500556950005,116.379295349001 +北京大学首钢医院,三级,北京大学附属医院,32,144,43,75,26,北京市石景山区晋元庄路9号,,1006,2742,1949,北京大学首钢医院是一所集医疗、教学、科研、预防保健为一体的三级综合医院，始建于1949年10月。2002年，首钢总公司与北京大学签订联合办院协议，医院更名为北京大学首钢医院，成为北京大学附属医院，北京大学教学医院、北京大学临床学院。,http://images.guahao114.com/img/150.jpg,39.9284439090005,116.203269958001 +首都医科大学附属北京胸科医院,三级甲等,北京市卫生局直属医院,17,105,43,52,10,北京市通州区马厂97号,,900,463,1955,北京市结核病胸部肿瘤研究所、首都医科大学附属北京胸科医院是以胸科疾病和结核病患者群体为主要服务对象，集医疗、科研、教学、预防为一体的三级甲等专科医院。创建于1955年，原名“中央结核病研究所、中央直属结核病医院”，兼设“WHO结核病研究培训合作中心”、“国家药物临床试验机构”、“中国CDC结核病防治临床中心”、“北京肺癌诊疗中心”、“北京骨关节结核诊疗中心”，是国家首批博士和硕士学位授予单位。,http://images.guahao114.com/img/107.jpg,39.9238128660004,116.656997681001 +北京三博脑科医院,三级,北京市卫生局直属医院,6,33,19,9,5,北京市海淀区香山一棵松50号,,300,9,2004,北京三博脑科医院是一家以博医、博教、博研为宗旨创建的学院型医院。始建于2004年，前身是北京三博复兴脑科医院。2006年成为首批卫生部神经外科专科医师培训基地，2007年与天坛、宣武医院组建首都医科大学神经外科学院，成为学院三系。2010年成为首都医科大学第十一临床医学院、首医博士点、硕士点。同时还是北京市首批三级专科民营医院、三级专科医保定点和新农合定点医疗机构，也是中国抗癫痫协会临床实践与培训基地、中国医师协会神经调控专业委员会会长单位。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024506913.jpg,40.0269203190004,116.233215332001 +中国中医科学院眼科医院,三级,中国中医科学院,12,75,17,22,36,北京市石景山区鲁谷路9号,,204,800,1986,"中国中医科学院眼科医院于1986年经卫生部批准兴建,成立于1994年，是一所中医、中西医结合的眼病专科医院，是北京市基本医疗保险定点医院，三级中医医院。",http://pimg.39.net//PictureLib/A/f4/c3//org/13417024541149.jpg,39.9040489200005,116.250007629001 +北京中医药大学第三附属医院,三级,北京中医药大学,25,130,30,54,46, 北京市朝阳区安定门外小关街51号,,431,1800,1964,"北京中医药大学第三附属医院(原北京冶金医院),始建于1964年，2006年7月划转至北京中医药大学，2007年7月成为北京中医药大学第三临床医学院。是北京市医保定点医院，现为三级中西医结合医院，设有床位520张。骨伤科、脑病科是国家中医药管理局重点专科建设单位，中医全科医学、中医骨伤科学是国家中医药管理局重点学科。",http://pimg.39.net//PictureLib/A/f4/c3//org/13417024451038.jpg,39.9778900150005,116.408477783001 +中国人民解放军第三〇五医院,三级,驻京部队医院,30,79,32,28,19,北京市西城区文津街甲13号,,500,1000,1969,经过40年的建设和发展，医院已成为一所集保健、医疗、教学、科研和预防于一体的三级甲等综合医院，是“全军老年病中心”，“全军冠心病诊治中心”，“解放军第三军医大学临床教学医院”，“南方医科大学临床教学医院”，北京市首批医疗保险定点医院。,http://images.guahao114.com/img/144.jpg,39.9247016910004,116.384727478001 +北京航天总医院,三级,部属厂矿高校医院,36,169,42,105,22,北京市丰台区东高地万源北路7号,,550,1700,1958,北京航天总医院始建于1958年，隶属于中国航天科技集团公司，是北京南郊地区规模最大的一所三级综合性医院。承担着航天科技集团公司数万名员工的职业病防治、检工作，肩负着大型飞行试验的医疗保障任务和广大职工家属以及周边约50万居民的医疗、护理、健康管理、预防保健工作。本院与解放军总医院第一附属医院(304医院)都是大型综合性医院，304医院在政府的和医疗卫生部门的领导下，借鉴国际前沿的医疗模式，我们在继承和挖掘科学技术的同时，又培养了新型现代肿瘤生物免疫治疗后起之秀;始终秉承一切为任命健康服务的宗旨，以精准的诊疗方法最大限度的减少患者的精神、肉体痛苦和财务损失;在医护人员中间深化“以病人为先”的服务理念。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024531696.jpg,39.8040618900004,116.418838501001 +北京燕化工公司职工医院,三级,部属厂矿高校医院,26,115,31,42,42,北京市房山区燕山迎风街15号,,670,1000,2005,北京燕化医院位于北京市房山区，占地7.4万平方米，原为国家特大型企业燕山石化公司的职工医院，2005年1月医院改制为股份制医院，是北京西南地区一家大型三级非营利性综合医院，北京市医疗保险定点医院。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024546446.jpg,39.7299613950005,115.955627441001 +北京市宣武区中医院,三级,北京区县属医院,5,55,16,36,3,北京市西城区万明路甲8号,,300,1200,1906,北京市宣武中医医院隶属宣武卫生局，是一所集医疗、科研、教学、预防、保健、康复为一体的综合性“三级乙等中医医院”，是北京中医药大学和首都医科大学中医药学院的临床教学医院。在医疗上具有鲜明的专科特色。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024518179.jpg,39.8875312810005,116.389518738001 +北京电力医院,三级,部属厂矿高校医院,38,124,57,54,13,北京市丰台区太平桥西里甲1号,,1000,1600,1989,北京电力医院成立于1989年，位处北京西客站南侧，北临古莲花池，西接京港澳高速公路入口。是一所学科齐全、设备先进、技术精良，集医疗、教学、科研、预防等为一体的三级综合医院。目前编制床位518张(改扩建期间开放370 张，2013年6月改扩建完成后预计开放病床1000张)。现有各类员工1000余人，博士、硕士近百名，具有高级技术职称的达到150人，有30余人任北京市各临床专业委员会委员和首都医科大学各临床学系委员，其中主任、副主任委员3人。首都医科大学研究生导师3人，已有2名硕士进站。同时建立了由 150名有影响力知名专家组成的专家会诊库，为系统内服务和医疗质量提供了保障。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024534367.jpg,39.8863525390005,116.315895081001 +北京老年医院,三级,北京市卫生局直属医院,33,100,14,44,42,北京市海淀区温泉路118号,,600,978,1949,北京老年医院是直属北京市卫生局的三级综合医院，是市基本医疗保险及工伤定点医院。作为北京中医药大学附属医院、中科院生物所合作伙伴和首都医科大学第六临床医学院教学基地，医院承担着多项科研项目和临床教学工作。医院编制床位六百张，职工近千人。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417024503788.jpg,40.0537986760005,116.134391785001 +北京京煤集团总医院,三级,部属厂矿高校医院,25,202,15,50,137,北京市门头沟区黑山大街18号,,1486,548,1956,北京京煤集团总医院(原北京矿务局总医院)，坐落在环境幽雅、依山傍水的永定河畔，地处门头沟区中心，是京西地区唯一一家集医疗、教学、科研、预防和康复医疗服务为一体，面向全社会开放的三级综合性医院。2002年成为北京大学教学医院及北大医院医疗集团成员之一.,http://images.guahao114.com/img/155.jpg,39.9396972660005,116.095855713001 +中国人民解放军二六一医院,三级,驻京部队医院,24,100,20,30,50,北京市海淀区上庄镇皂甲屯村116号,,600,1700,1949,中国人民解放军第二六一医院位于北京市海淀区上庄镇，是一所以生物诊疗中心、综合治疗科、精神病科、神经内外科、消化呼吸内科、心血管内科、骨科、普通外科、妇产科、眼科为特色的现代化综合性三级乙等医院，全面实现了微机网络化管理，是北京市首批“基本医疗保险定点医院”，北京市工伤保险定点医疗机构，北京市中小学生及婴幼儿住院互助金定点医疗机构，北京市农民医疗保险定点医疗机构。,http://pimg.39.net//PictureLib/A/f4/c3//org/13417030115044.jpg,40.1144371030005,116.220367432001 diff --git a/025pygeoda/data/Guerry.dbf b/025pygeoda/data/Guerry.dbf new file mode 100644 index 0000000..10b2f9d Binary files /dev/null and b/025pygeoda/data/Guerry.dbf differ diff --git a/025pygeoda/data/Guerry.prj b/025pygeoda/data/Guerry.prj new file mode 100644 index 0000000..017c7f0 --- /dev/null +++ b/025pygeoda/data/Guerry.prj @@ -0,0 +1 @@ +PROJCS["NTF_Paris_Lambert_zone_II",GEOGCS["GCS_NTF_Paris",DATUM["D_NTF",SPHEROID["Clarke_1880_IGN",6378249.2,293.46602]],PRIMEM["Paris",2.33722917],UNIT["grad",0.01570796326794897]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["latitude_of_origin",52],PARAMETER["central_meridian",0],PARAMETER["scale_factor",0.99987742],PARAMETER["false_easting",600000],PARAMETER["false_northing",2200000],UNIT["Meter",1],PARAMETER["standard_parallel_1",52]] \ No newline at end of file diff --git a/025pygeoda/data/Guerry.shp b/025pygeoda/data/Guerry.shp new file mode 100644 index 0000000..c6576b3 Binary files /dev/null and b/025pygeoda/data/Guerry.shp differ diff --git a/025pygeoda/data/Guerry.shx b/025pygeoda/data/Guerry.shx new file mode 100644 index 0000000..e48cfcd Binary files /dev/null and b/025pygeoda/data/Guerry.shx differ diff --git a/025pygeoda/pygeoda.ipynb b/025pygeoda/pygeoda.ipynb new file mode 100644 index 0000000..660a2a1 --- /dev/null +++ b/025pygeoda/pygeoda.ipynb @@ -0,0 +1,196 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### geopandas包在windows上安装比较麻烦，需要GDAL等一系列东西，如果是windows，建议使用conda来进行安装" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 如果是macos或者Linux，则相对简单" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import geopandas " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "gdf = geopandas.read_file(\"./data/Guerry.shp\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import pygeoda " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 目前把geopandas的数据结构导入到geoda里面" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "guerry = pygeoda.geopandas_to_geoda(gdf)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 定义空间权重矩阵为queen(共点共边即相邻）" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "queen_w = pygeoda.weights.queen(guerry) # create spatial weights" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 获得需要分析的数据列（用pandas的方法）" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "crm_prp = gdf.Crm_prs.to_list() # using data in geopandas df directly" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### LISA分析" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "crm_lisa = pygeoda.local_moran(queen_w, crm_prp) # local moran" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 下面是可视化方法" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "plt.rcParams['font.sans-serif'] = ['SimHei']\n", + "plt.rcParams['axes.unicode_minus'] = False" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+kE/dDOCQECJZng6qdjdrIcRN+ns36xXy8apGZgiAkfzfAQDmEpH6B74PpGXOI+Q2ID/bCiI6AGmkpheA9yAFPFsgTfNcgjQi0kUI8aCiMSFES7V+NpHP2QAgpXIukRzATMbfu2vPVHv5NwCHhRAfEtEwIjIVQlyB2u7aRPQGgEio7a4tTydyYT7GNIyDGcZ0zxRAd3p4l+wY/P2bfxNIIx8VFKh+mumhonVE1APAIgDd1ZYD28ltt4eUK6J+fiikvJavhRAbK44LIQ4R0QwAh4hoEaScD63uZi2E+AnSNJV6/xZAqrvz2T8eXtoRPAvSUuwlkJZi34c0xTQK0ijMbiLaCWn0K1m+Tglp5GYhpB27dwH4iIh+BfC/FZtygnfXZqzR4H9UjOmYEOIA/rlL9gwAkwC8KoQ4VOkSI1Q/zRSn9r05pITX0UII9emnXvLxzVAbmSEiZ0h1Y14XQmyvop+biOgWpFGPNQDySbu7WVfFEGoBQqX+XZKDt2cBRAgh4oioPwAjIUQqgBFyDsxYALnySNFaAD0g5eYMFULckJubTUT9AHxCRLaQRnT2gXfXZqxR4I0mGWsA5CChWAhxTMf3JVGLNwFqYLtZ1xYRtQAQL4Sobvk5iMhbCBFfx/Z5d23G9ICDGcYYY4w1alxnhjHGGGONGgczjDHGGGvUOJhhjDHGWKPGwQxjjDHGGjUOZhhjjDHWqHEwwxhjjLFGjYMZxhhjjDVqHMwwxhhjrFHjYIYxxhhjjRoHM4wxxhhr1DiYYYwxxlijxsEMY4wxxho1DmYYY4wx1qhxMMMYY4yxRo2DGcYYY4w1ahzMMMYYY6xR42CGMcYYY40aBzOMMcYYa9Q4mGGMMcZYo8bBDGOMMcYaNQ5mGGOMMdaocTDDGGOMsUaNgxnGGGOMNWoczDDGGGOsUeNghjHGGGONGgczjLF/pSAiG333gTGmGRzMMMb+VQYQNfua6JPPgSvjiDrpuz+Msfoz0HcHGGNMG8KJWvYEBlwAtpwE0ocCw7oDQz4D+vgAlgAQCwwEcFLPXWWM1RMHM4yxRq8dUZA74KYECgcBIz2Abt8CrZwAw+vA3BJABAG2VOm6JoC3PvrLGNMsDmYYY40WESnfAt7yAP6cB6zyA2ysgYdiFn+g2twYGyBA+71kjGkbBzOMsUalNZHZcOCtRMDvTeDBfGDMWSC7/SOCluqYAk28iYzjhSjWRl8ZY7pBQgh994ExxqoURqRsDgxvB7SyBjyaAK0sANcQwCUJKNgAPHgTsK9r+wLANuDYx8DIY0Lc1WDXGWM6xCMzjLEGhYgoGGg+HZjxKtAqAuhe1RuVB2AeCNzLBeyt6novAMOB0OvAeACL695rxpg+8cgMY6xBmETULRx4zhnoEAAEOtegdIQAsAa48xzgWZ977wIODxCiZ33aYIzpD4/MMMb0qh2RwbPAF4uA8Y6AUW2uJQBWQEl97l8K4CZwpj5tMMb0i0dmGGN6Q0T0OfDtNGBsXSt4pgLFV4HiXkCdZptWA2cmAx2FEOV17AJjTM+4AjBjTG/eAeY8W49ABgBcAOM7QFpdr38aCG7J9WYYa9R4mokxplNEREGAy3hgxnRgprEG2vQELAsBmNXhWktAaVnHUR3GWMPAwQxjTKe+Bo70BUK8AU3EMQCA7oDLRiBjAuBQl+vN6hYHMcYaCJ5mYozplBcgvDUYyADSb2UK4H5drk0AMtrJezUxxhonDmYYYzqVApzWRru2gEldrmsJOIQCb2u6P4wx3eFghjGmU6eBE6lAmabb9QRsDwJZdbm2PdB2PdG6zUTHviP6dDJRG033jzGmPbw0mzGmU0Rk9Adwty57KT3OJSArFVD2BGxUqGXRGjWLgDffEGJhba5pQWQeAUwtBc58KsTROt6aMVYHHMwwxnSKiAz+ANLbA7baaD8eKNoN5LgDqmIg/y6gtAGUY2ux/DoWuPMdMPtdIbY+6rznibq7An1aAq1zgbLxQGQMcGUKEHJFiIL6PgtjrGZ4NRNjTKfGAB29tZhw6w2YvgCYVnx/CyjaAtytzdLtAMCzMzCnNdEvfwpRWPn1p4h8RgKj5wKve1UaYeoCtHgWmA/gjfo8B2Os5jhnhjGmU0FAhyY6+kVqPxCfBeA1wLu2a68jgK5TpKDkIS2JXL8Czr8CvF85kAGkLRZGAc9PJQqpY7cZY7XEwQxjTGemEfX0Bibo6n73AWUbtVGa2iAA3YHhk4nGzCGaTkQKABgODA8GrB61ttwNsDQFelRcwxjTLs6ZYYzpzBKi/3kVWKCr+5UB+Aq4NQFoalHHNlTyny+BDUeBtSOBOSOBAY+77j5Qfh3I2AcsmCfEijrenjFWA5wzwxjTGQfAXpf3MwBgB6AYEBbSYEutKeQ/M4CxoUB4qxpWGbYFlB0B59NA07rclzFWczwEyhjTmWvAZV3f0wMw2gkkHwRyanNdLoAjQNn3arVr2gAOtXnTvA0UxgMHa3NfxljtcTDDGNOZk8ClPB3fsxvgPhFwtwYK1gC3VDW8biWQ2AIw6A1YrajFderMAaMzQFIdLmWM1QJPMzHGdOaktLqo3BJQ6vre7QDXFgC+AuIMAOQBimDA5jKQGwxYhQK2q4BkE8BiAGDVFDBbDaTNBZyHAz6fAEmzAPfa3NMBMAgD+gG4oI1nYoxJOAGYMaYzRNTkFpDiAxjquy9FAOKABy0Bk+NAUiZQ0h5wcgbMPwfueAIlQ4Bml4DMKwD6Ak3s6nCf7cDBYUBfIURdBncYYzXAwQxjTGeIyOYakOpfx00hdSUdKC4ByiwA071A1qgaJv1WpQDAdCB8vRB7NdhFxpganmZijOmMOaBUNoJcPUfAGIDxUSDdpZ7vk+YA3HlFE2Na1eDfVBhjT44CIDcJuKPvftRUd8AxCUivbzskrRBnjGkJBzOMMZ0RQpTuBOZlAMX67ktN3AaKs9W2LKhr0osTv9cyplX8D4wxpjNEZGgLvPhZI6m94ggYZwKl64Gb3wM3PlerOVMbrYBBE4h6VD4+m6jbL0RXvyL6sf69ZezfixOAGWM604qo+WEg9jqQeQjAa0ATvS9rqqG7AA4ChaZAbgGQbQbYDAOca3p9ElC0CVh6Fdi4CrjxNjCjPTB0INDjG+DHiUBUC8DrshDxRESC35wZqzEOZhhjOtOPqMfPwGEjfXekjuKAEmsATQCjJKDwZyBlLNCsNvs+/QFcKQAU3YCAiszi74AjHoCnB+B2CTibBKSsBKLOCFGmhcdg7InDwQxjTGcmEUWuBTbrux+akgqUXQVKe9VxZ+7qlAGYBby+AljCIzSMPR7nzDDGdMYXcNJ3HzQpBsjoAph+AKRpsl0DAO8DHw6Vqgczxh6DgxnGmM44Am767oMmdQBsdgAJ/QHLw8ADTbZtBaAP0FuTbTL2pOJghjGmMy5AgL77oEmegKkLoDgEZHTXQlVjV8BV020y9iTiCsCMMZ0xe4KCmdNAfjKQaQKYztLS9JkJYK2Ndhl70vDIDGNMZ/YDqwr13QkN8QLMzQGncC3mAcUDN7XVNmNPEg5mGGM6s799+1/HEg352MjoQOXXClH3Crv64ABQIpCozXukA0nq3xMRORCZE5EFEVHl84nIIIqojzb7xFhDxNNMjDGdeOqpp/y//PLLc8XFxQ8MFQrDk2FhdzuWlztlAWKvnV180YsvWpYSKVVbt2b1v3TJzVvDy521oQfg9ilwZzTg4QD8I7ior6ZASwCwI7JcBmw5BrTMAIqcALM/gYPPEn20F7gyB5gPoNN+wM0JMH+LaM57QmzSdH8Ya6i4zgxjTCemTZs2ZuHChRsqvv+/N99MKT94sMhs2jTnwePHmxsY/P271Yndu/ONpk/P6Jef75MHqO4C+c2kBT4NTjmA1cDNqUAzTbd9HchYBjxdBvh8AfyorPT6AeCSMaDsCgSqR1LJQP4nQNQiIXZruk+MNUQczDDGdGLx4sVvTp48+f2ann98w4b0/HfeKchwc1MV+PgYv7ljh7s2+1cfV4H808CDcUATTbf9M3D8EFDyEdCrNtfFALdjgAOvAVO58B570nEwwxjTiVWrVn0eGRn5fG2vO7x9e77vc8+VtRbC5vFn68dC4M4swFPja7MBVLxD12UOKwcQg4EOwUC348AK3h6BPak4AZgxphMKhcKsLtd1GzjQ4qdnninZbm6eUKrpTtVTHPBgEZCcDpj/CRRr4x6EuifjmALUBxjdFuj5EXDuLaLhmuwbYw0FBzOMMZ24ePHinlOnTqXdvXu3VpVyDQwMMOOzzxy73r7ttWr8+MR1zs5xWoka6sAQULUEyt8G7F0B+gVIXADcaSjDH0YAooDn2gPtw4CWjkB3ffeJMW3gaSbGmM4QkWLt2rXrhgwZMq6ubRQWFGDJkCGpH5096/K438bKoPslm+eBjLNA4UTAq6H8tigALANW/wjMihEiX9/9YUzTGsq/NcbYv4AQQnXw4MGvUlNT61w7z8zcHD6mpkXVTb3cBnJuArm7FIqcRc7OidlA+dfu7nHfubjc+NnM7E5d71tT3oBDc8DxZ6DBBA0rgb0rgBc4kGFPKh6ZYYxp3YgRI4IGDhz4walTp/Z8/vnnX0yYMKG/paWl/8yZMxc3adKk1oMn+Xl52NWsWaaPEKpOZWX25w0N0/9s06YwLy9P2XT4cAMTJyfj2F27ipwiIuziv/ji3qxTpzwB4Pzhw3nZ48ffG5qf76XxojCyFcDtfoCPn5bar4vlwIKZQryl734wpi1cNI8xplUjRowImjlz5r6WLVu6+Pn5tXz55ZeNvL29jfbs2bP27Nmzo/r169e5tm1aWFoi+OBBWwdnZ+XSMWMShyxf7jy2eXND9XN6jB8vfTFpkkXFsZbdull+O2RIXtLGjSUeUkqJxhkDpLX9DeroPpCuzfYjIiJ6/PLLL0e0eQ/GHoVHZhhjWrV69eofhg8fPqry8YsXLyYXFhZmduzYsY0u+rFx6NA4lwsXFINzcnwMH396rZwB7mYBpcGA6z4gqZ+WKgLXRQmA+cALC4X4Qhvtz5gx45nhw4cvz87OvnD16tUr8+bNmy2EaEw7U7AnAAczjDGtCQoKsv76669vtWjRwk6f/Vg5cGD8+JgYR2egTsvDq5MMFO8H7nkAFn8CmfcBg5cAD41XzquHn4ADQ4G+2iict3Dhwg9HjRo1y9bW1hAALl68eGfx4sWjd+7c+Zum78XYo3ACMGNMa3r16hUZGBio10CmpKQET50+beQMmKkAHDYwSFtvZ3f7PiC+tbGJv0J0r65tbwVShgEu1wCDKMAzDDDfB9zXXO/rzw3w+AU4s50osS1pbgasWbNmdm3bth1XEcgAwOnTpzdwIMP0gYMZxpjWtGvXrlcVmzvr1PE9ewpDSkpcAeAzX99bPtevO/e5dMlnmZlZRs5LL9lZCfFQ4d4LQN6KDh1u3gUePG6uxBwo/wa4NRAweACUpQCKZwBbrT1MHbQH/MOBp8IB92ZAB020+fLLL4///PPPT7Zv39654tivv/56LDExscFvDsqeTJwAzBjTitmzZw979tlnB+i7HxampuXfBQXdCL982cPxpZfsrWykXRGmXrvmqFKpsP38+dukUiF49267ACHMY83MssM3bmx2cNu2rJwPP7w7PSvLq7q2o4FmRvj7t8K9QBKABrntgimAp4ExRLSrPlNOffr0abp48eJPvb29H9r4Mykp6bePP/74jXp3lLE64JEZxphGERHNnTv39cmTJ69zd3fX+wd7SL9+lqOPHfNb16dPVt+xY60rjpuZm8PC0hJjvvnGJ3r9eh+xb5/Z3uXLCy5YWChiT58uiJgyxd7G3LwYkIrOlVfRtgn+fhO9DeQ2iIzfRxgNjB4NDKpPG61atRpsa2trrn4sJibmzu+//76+fr1jrO44AZgxpjG9e/d2bdq06ccjR46MiI2Nvdu2bVvroKAgR333qy5+Xb06q9drr1mUA8qNXl5JgSkppgNKS6vNOdkEpEQArlbVnVAH+QD2ArdSAHQDrNoATeoSMBUDyACKVgPvvgMsEfXYcJKIDPfs2RPXsWNHj7/6mZ+Pjz76aOqyZctW1rVdxuqDp5kYYxoxa9asQfPmzVsWHBzc9M8//ywdNyXOvkwAACAASURBVG6c3/79++8EBQXpu2t1kr5sWe5tA4OSc6++avTc6697r5oyJSliyxZcBfJ/d3G5656ebn2rbdvsgubNzR3+/DM3LiHBtGdOTimAOq38jgeKfgKyLYD8XoCjJ2C9HoibBvgqAMQCpV8ByYZAoRKABWAYCrg4AcYAcA24dx3IHQR4V257LbBmM7B8vxB/vl2PnwkAhIaGtgkICHBXP5aUlJSckJBwoJ5NM1ZnHMwwxupt7ty5L0yZMmWhq6urJQC0bt3aEABSU1ORmJiY7+HhYfHoFhqeJnPm2Co7dDCLatnSCAAKL15UrfX2vmEwZIjp0Lfe8k1NShIhnp4Vq7BdAGBNdPSdIb/84uILGBKAM0DGRaC4NaBsC7jkA2IFkGgLlJgD1BXwcQEUMUBKMWA9U25nNZBQBuT4APYV01gBgGEA4Kbex4XAnQjA7DSQ6w7YZEllZR5yA8jZCSzYL8RtTfxciMjM2Nj4oQGiu3fv3tq2bdstTbTPWF3wNBNjrM6IiObPn//exIkT37Cxsanyl6MdO3akOTg4lHTu3NlT1/3TpC1LlmT0nTzZoSKBuColJSX47cCBkgtvvZXic+tW8VOARyvA7HsgrQgoKgEUU+QNKFUAYoCyu8ADL8CgvZSCUyv7gXw7gNoC5gDwMxA/sNLITD4glgBvvyPEe7VtvwJJS9IsXnzxxcjmzZv369mzZ6Sbm5sRAAghsGXLlk1TpkyJqmv7jNUXBzOMsToJCAgwHD9+/McTJ0580dT00Styd+7cmda2bVsrNzc3jRata6iWBwUlvZ6a6q7rh10HZOUDud6A0hYw7wrYlwFYDfw4HXhGCFFVHvMjRUdHPx0VFfWRs7OzZ9OmTc0NDB6OWU+fPp06d+7coDNnzjSo+jrs34WDGcZYrYWGhrpMnz59d0RERLBCUbNFkT///HOau7s7goODnR9/duOWEBenWtW/f8ZnGRl626YpFig9CcQrAKtAwOgekLgYGJbfsWNZ7969h9ra2tqZmJjYX7hw4duVK1f+UVUbH3744drBgwePcXJyqjYP6MaNG2l79+795OjRo/v27t17WntPxFj1OJhhjNVYUFCQWVRU1Bs9e/Z8rmXLlm6Pv+JhP/74Y2JERISbpaXlE10W4v96974x/dw5v4b0kALAAeDG76Ghihd++sm34nhcXNzdtLS0rPv3719KTEy8dO7cufPh4eFDzczMnLp06RJubW1do9zKjIyMskWLFo1evXr1Fq09BGPV4GCGMfZY48ePb9eqVauhrVu3HtWhQwf/+lT1Xb16ddyIESO8ra2tlRrsYoNxeu/e3JJJk3IGFBV5PP5s3boP4MC6dUW9Bw+udl6wsLAQZmZ1myC7ceNG6ieffDJ4w4YNPELDdIpXMzHGHjJixIigdu3aDc7MzMwoKioqbdu2bY85c+YM9vLystdE+88995zvzp077xYXF+f17du3qbW1dUMawKiXsrIyFE6alDukAQYyAFAGiPTLlwtVAweaVjc9WNdABgD8/Pxc3NzcOgLgYIbpFI/MMMYQGBjo0KdPn0HBwcFdg4ODBzdt2lQnGz9v3LjxxpgxY/x0cS9d2LNpU2H/6dPN9F72+BGSgcKNQ4bcm7J2rfvjz66d2NjYjJkzZ7Y+depUmqbbZuxReGSGsX8RIqKKfXn8/f1thw4dOi4kJCTq22+/be3r62uh600hW7Ro4XTy5Mk7nTp1atTLtiuUFRaW1Xp9tY65AWb21tbp2mj72LFj33Egw/SBgxnGnmBEpJwwYcLQTp06dbe3t/fftWuX/7Zt23KNjIxM1q9f7+7n56fzAEbdU089ZfXdd9+lderUSW990JTp3bvfH5KSkm0CaHJHA40rBVBqY6OVmCsnJydZG+0y9jgczDD2BOrevbtDZGTkO7/++uvQNm3auBoZGem7S9UiIvPHn9Xw9TMwyIi+d89f3/14nB8cHeMi583zffyZtde1a9eZRLRMCFGqjfYZqw4HM4w9YYYOHRry6quvfh0aGtpG332pCT8/vyZJSUll7u7ujfb96FJMTIHj7duNov/3w8NNKhe+0xQrKysDPz8/C0gLpxjTmSdmFQFj/3bNmzc3f//99z98//33DzWWQAYA2rZta3zz5s0MffejPtJiYwvLVKoGv9S8CICJr6/WRsL++OOPs+3atevu4ODg8OKLL761e/fua59//vmBgICAOm2+yVhN8WompjPR0dH+xcXF3lu3bt0n+C+exhCRYubMmZPCw8Nf6dixY6A+c2Dqau3atXGTJk3SytSHrmwaMyZu2p49DfoZigCsjoxMGLNypZc22hdCoKSkBCkpKSWurq5GxsbGUKlU2Llz565jx46tWbNmzXb5PJU27s/+vTiYYTrzxRdfLB00aNDs5cuXLzA1NS3Kyso6vWLFir367ldj9uabb77Url276NDQ0E4NOS/mcRITE1WJiYnZXbp0sdN3X+ri6rlzxVlDhqQNys/XSpCgKVeA3AO9e2cOXrmy6aM2zNSGoqIinD9/PuH8+fOb//Of/7yq05uzJ16jmONlT4bk5OREc3NzvPLKK/MMDAxw5MiRk66urkdTUlIe1LatJk2amAcGBrZRqVQiJibm93/bb3qenp7G48aNe/uFF16Ya2Fh0fiGYiqxtrbGxYsXDVJTU0tcXFwaXVTm6OFhfMHOrvxafn5ec8BS3/2pTgvAyurAAeXxHTtywsePt9blvU1NTdG5c2cvT0/PqePGjftx/fr1v+vy/uzJxiMzTGdeffXVuTNnzvyvhYWFApCGpC9cuJAeHx9/5PLly/uzs7PzVq9e/f2jpqBefPHFIcHBwZH+/v5P+/n5NVGpVLhw4cLVxMTE4/Hx8WcTEhKO/N///d9V3T2V7hARTZ06dVRYWNhkBwcH/6eeesqzpps8NnTbtm27WV5ebp+WlpbXs2dP86CgII1UG9YllUqFi05OmWHl5TopOFhXP3h7X+t39mxzffZhxYoVqy9cuHB48+bNG3nKmWkCBzNMp6Kiovp169ZtVHh4+DMODg4P1U0vLi7GgQMHTq9evXr2oUOHYgAI9Te6gQMHeowaNeqHQYMGda6u/ffee+/lpUuXfqrFR9C5Nm3amEZHR8/39fUN7dq1a1cTk4Zelq12Tp06lXrz5s3S6OhoTwD45JNPsgYNGmTm4+NT7f5BDdWWkJAbk2/ebLAVjX+wt79lsXChbZfISFt99qO8vBxnz57NnzRpUlcPD4/S33///XpUVFSfq1evHr1w4UKRPvvGGicOZphefPrpp5+OHTt2RlWvXb9+/X5ycnLB8uXLv50wYYKHUqlUCiHK/Pz8erdo0eKROzUfPXr0wuzZs0Nu3bpVop2e61ZQUJDZrFmztkZGRoY3xsTemtiyZcv1yMjIv+qzfPfdd2ndu3e3dXd3N759+3bOgQMH7jVr1swgOzvb0M7OziAgIMDC0dFR5xGdEAJnz54t8vX1NbaxsVEAf2/KeObMmYycnJyS1CtXbCbMn2/eEOeZLgJ5lz/+uDx8woQGsduCSqXCvXv3UFJSUpycnJzl6+vr8NNPP21ZvXr1c5cvXy7Ud/9Y48I5M0wvrl27dkYIgao+oP39/W39/f1tw8LC/lPbD/Bu3bq1CQkJGQ7gew11Va9Gjhw540kOZE6cOHHXxMTEbenSpQlz5szxAoDo6Gjn77//Pk2lUuXa2dlZjho1yicmJiZ36NChVoWFhdi1a1d8SEiIU0ZGxgN3d3fz/fv331EqlQoApQqFQmFiYmIYHh7ubWxsXOXfr7q6ceNG/u7du/OFEKpp06bZ7ty5MyUvL88iKirK6ujRo0UAjJv5+ir/UCju9VKpGkwicymAtWZmKa127WoS3qZNg8lHUigUaNKkCQAYu7q6ugLAxIkTRzs4OLiGhYUNPXz4MNeqYTXGwQzTi+PHj/+Rk5MDm0esqKjLB5FCoUDXrl3b4wkJZpo1a9btSQ1kAKBNmzaOBw8eTDMzMyOVSoWKHKDRo0c7A3CuOC8iIsIKkHZ09vPzc9+1a9e96Ohox4sXLxaNGjWqmfpKLpVKhR9++CE5PT1dFRERYd68efN6BxYqlQr79+/PzMnJMZs3b57z22+/nTV//nxfe3t7rFy5MvX555/3NDExwbXLl8vNVaoGVVPlAlAQcuKEq5dXg15oBUD6N29lZWVqaGj4ZCSDMZ3haSamF82bN/fftWvXNXt7zeZ57t+//8TFixd3vPvuux9qtGE9CAgIMFy2bNnljh07NtgcDE3YvXt3SkREhKs2kpk3bdoUr1QqlU5OTobl5eXo2rWrc22q3wohsGnTpiQiKnFzc3MNDAw0sbOzw40bN8q9vLyUlZfDrxoyJD78xAmL1ipVg0kCPqRUZrZMTW2iraq/mrZs2bJXCgoKHnh4ePhu3bp1w5EjR87pu0+s4Wscf7vZE8fLy8tZk4msKpUKFy9ezDp06NDKzz///BuNNaxH4eHhE9q1a/dEBzKFhYWIi4sjba3KGjVqlHd+fj4UCgUUCgXWr19/a8yYMU3z8/NVdnZ2j71pdnY2XFxcHLt37/5Q1OLn51d1td/iYtEQAplzRPdPd+6cYXnnDu77+yuDDQz03qea6tmz50sODg5ONjY2pqWlpSPmzJnz+tKlS3/Qd79Yw8bBDNO5rl27Ok6ZMmWuubnmqqofOXLk1OLFiz/PyMjYorFG9Sg4ONhg/vz50xrLb9N1ZWZmBnd391rXGaoNCwuLv77u0aOH57vvvpvu6uoqPD09iyIiIrwr/4x3796doFKpyMbGRtmlSxe3lJSUHAAONbpZWlqDmBM87uqaPfbnnxvsppfbtm276+bmZhYSEvKPXOk2bdp4f/XVV/E2NjalUVFRzWJjYz91dnbekZaWxqucWLWU77zzjr77wP5lDAwM9g0ZMqSnJts8ffr01i+++GLhyy+//ESsYkpOTn4tKipq/JNSR6Y6GRkZD27cuFHQsmVLnaywsbW1VYSFhZmHhIRYWFtbWy1ZsiS1uLg479ChQ5kBAQG2d+/ezS8pKTHp3bu3o7GxsfnOnTtTunTp0sTGxqZG+y7dSUrKc/njDyMLQK95M/55eZbfnjwZ32b0aL0uwa5KSUkJcnNzi/Pz8/OMjY2tf/vtt7yUlJQiLy8vEwDYu3dvVs+ePe07duzoaGRkRM7OzuYFBQWmffv2rVG1cCIyVSqViz7++OM7o0ePztTu07CG4sn+tY81SPfv308rKiqCqanmyogYGRk5aawxPXN1dTVZs2bNc0plg9+3sN6MjIwM4+LidLrCpiJAdHJyUowfP94+Ozu7qFOnTq7Xr1/Pu3Hjxt1Ro0Y1k19XRkdHu9em7aELFnjs2b79RnRKil6nB20BA/ezZxW5OTmwstZpod/HOnfuXE5RUVFxWFiYx6lTpzI7duzYJDMzM3fbtm0J9vb2ZmVlZQWOjo5/JdMpFAp4enoGVNfevHnzJrds2XJ0YmLi2fj4+GtTp051fuaZZ+b07t17wpQpU1ZfuHBh8alTpzioecJxMMN0bvbs2SPMzc03jhgxYrSm2mzbtm3/p59+OmjPnj2XNdWmvkyYMOH5kJCQJzpXpsKdO3cKCgsL9TY106xZM2MAxiqVCmfOnEl3cHCodyJXmqVlg4hCm+fnW187c6agQ69eWtsluy5u3bp195lnnvEHgC5dujQBABsbGyshRIlKpaLu3bt7V75m0KBBETNnzhy3fPny9QAwefLkbvfv3zc+d+7clQ0bNiwODAy0AdBb/Rp7e3v7OXPmzH7xxRe3A6hRMENE1LNnzx5EZNm1a9enAOQdPXp0HS8Tb/g4mGE6J4RQrVy5MkuTbXp4eFhERka+CyBSk+3qGhHR1q1bxz7Jy7HVtWrVysrQ0NA4ISEB+lw6rFAoMHnyZI3seG1valqqiXbq64StbW5LM7MGl/hbVFRUZbDn5+dXbV+TkpJyr1+/fr1Dhw7Nu3fv3mfKlClvuLm5eSxYsOB7Nze3aoeenJ2dDadPn744MjLyfRsbGysbGxurK1eu/LJnz547lc8NCwuzHT58+MRPP/10qYmJyV+lIaytrTt7eXlNTkhIyK3D4zId4aXZTC9eeumlqW+//fZXmpxKuXfvXtl///vfQWvWrPlFY43qWFhYWJf169efUE9afdJlZWWVbtu2LWny5Mk++u6LJvzcuvWNcUlJeh9ZSwdKNo0blz5m+fJaTZVp008//ZTo7+9vEhgYWLOEatnGjRuT0tLSMgcNGtTS39//r1/Cqyu8WVlZWRmWLFmS3L59e9jY2BjPmTMn5OLFi7fVz3nzzTffmz59+jwrK6uHrn3w4AFiYmL+2LVr17qUlJQYCwsL3379+k2MjY09lpGRcWz9+vW/1eZZmHbwyAzTi40bN373zDPPLA4MDLR6/Nk1Y2dnZzBs2LCFISEhJ0+dOpWtqXZ1KTc39+KOHTt+jI6OHqnvvuiKkZGR0szMjPLz89HYg7iN48bd7p2Sovek23wA33Tpkjzq3XcbVICoUCgMLSwsajWVV1JSAiGE6tatW25NmzZ96DOrpiOYBgYGmDt3rtu6detSe/fu3aRHjx5DACzz9PQ0fv7555f9/vvvt5cvX/6fyoEMAJiYmKBXr14devbs2eHq1av3AgMD7eT7DoyPj8/8+OOP12/evHnhiRMn0mvzXEyznuylEqzBunfvXv79+/dTNd1ut27d2kRFRX2k6XZ15ezZs3mnTp36Njs7u6wu11+6dCknMzOzUa3oOnXqVGpaWprp2bNn7+3evbtR5yYojIzItQFsZVAMoPns2W4NLfl30KBBzr/99ltaba4xMjLC2LFjPUNDQ+/Xt1SBoaGhQVFREQYMGPDS7t27b//nP//ZO3Xq1Onvvffeu1ZWVo/8PCQitGjRwk49gPL29m4yceLE2e+++27M888/3/sRlzMt42CG6Y1SqTR7/Fm1FxUVNXHu3LkvaKNtXfjmm29+XrZs2ZgtW7ZsvX37dklNpoJTUlKKvvnmm5vW1tYW+/fvz8zIyCjWQVc1onfv3m7PPvusIxEV+vr6Guu7P/XRff58z1OGhnf13Q9LAOnXrjXIuiytWrVy+/XXX5Nre11QUJDNL7/8klf5+P79+3OysrJqlC8hhFAWFRWpOnXq1LRz587eUVFR3RUKBdzd3ev19+6pp57yNTEx6VafNlj98DQT0xuVSlWujXbNzc0VQ4YM+Z8BAwbs3rVrV7w27qFty5cv30REP9rb2zuMHj36aR8fHy8HB4fm+fn5/oaGhv4jRoywysvLw+XLlzOSk5Oz7OzsHCdMmNAMAEaPHu26atWquEmTJvnqenl3eXk5fv/99/sdO3a0rc29i4uLH8THxz8IDQ3VSoCrKxd37sywUSrLy0pL9frmagRAuWJFpmr6dOuGVqsoMDDQLC0tLae219nY2Fjv27cvs127dhYlJSVi3759mfn5+cWhoaHWly9fTr5z545BdHS0c3XPq1KpYG1tnW9vb6/xkTMiQsuWLUNGjRoVumnTpmOabp89HgczTG9iY2NjsrOzlV26dHG1trbW6KduQECAa9u2bYcB+Fj9OBGRubm5bUFBQYkQIl+T99Q0IQ3JpBPR+pEjR4bb2treEEKYEFG/69evu7Zv3759ly5dHDp16vSPZMrx48f7rlu37tawYcOarlmzJv3555931GTF5apcu3at6MSJE2kqlcoiKSkp58GDB9mGhoYlbm5uFh4eHrZWVlYmZmZm/6gvJITA9u3bU6dNm9ZMqx3UgV5Tpzph6lTMHzAg6f0zZ/SWeFsI4GKrVlZ9CgpgYfmPIrt6VVZWhri4uOKePWteN/PBgwc4e/Zsemlpqcm+fftuuru7m06YMMFdbcrH6tKlSw9SU1Ph5uZWbTvXr1/X2jLB4cOH92/atGnnQYMGhezcufOmtu7DqsbBDNObV155ZcLAgQM7OTk5bWzbtq2nJtu+e/dufmFhYRwA9O7d22n06NGf2draOu7cudPZ2dnZq7S0tGzXrl13Tpw4se6DDz5YLBrgsr7+/fu3DQ0N7b9ly5YhXbp0aW9sLI2EHzp0KLlnz57Vv2NDyjOYNGlS06NHjxbb2tqKO3fupAcGBjoCQFFREa5cuZKdkJBg8PTTT1toonjh8ePHE2NiYgxmzJjhY2b21+CKNQDcvXsXJ0+evKdUKksTEhKyXnjhBW/1vIOjR4+mRUVFNa13JxqA75o3T3I1NCwenZmp1yXRZgD83dweNLRABgDWrVuXPnLkyFqtwzcyMoIQwhBAcXR0dJUrxZydnRV79uyJGzduXJVL7BUKBWxtbY3Ly8uhrRHL9PT0O4GBgREAPtPKDVi1OJhheiOEKANw/OWXX36jadOm621sbDT29zEvL6/QxcWle2Bg4B/z5s37oX///qGVTjEGEBgcHLzI09Oz7dChQ/+7ffv2i5q6f320b9/eJDo6+stPPvlkjL29/T9+Jjk5OQU1aUehUCAsLMw4LCzM6auvvrp95cqV2OzsbCNTU1OTHj16OOfn5xeePHnybs+ePetdPTknJ+fB66+/XuWHjJOTE4YMGWIHABkZGRbffvttnKurq3GnTp3cAND169eL2rZtW98uNAh+QMnAzEyN1KupjU992ya2Trtl06Mg+6/o5XymSWnSsu3XlAqgvNxQCJSjtDTT9M3Xn9VfQR8AEydOdDx06FBa3759nWt6jUKhwNChQ52zs7NV1Z3TpEkTIx8fH8vjx4/nduvWrcpVkmFhYXZff/11wrRp07y0Mf2Wl5eXEBsbu13jDbPH4jozrEF47733FqtUqsHTpk3zrxiB0IRNmzald+7c2dHDw+OR5yUnJxdevHjxUGpq6tkLFy5cOnLkyMH4+Hidl0AnIuWSJUvWTpo0aVx1y063bNlyLTIysrkm7hcTE5Nz69atvOTkZIwaNcrG0dHRorbTUdeuXcu6ceNG4cCBAx/9Q1Zz9+5d1fHjx+9fu3atDIBRVFSUsa+vb6POlwGAYy4uKYOLi111db9fLexy9vd/Id2nz7NuSZePZUR8Ntm1eXmpYTmAjR+fLfLwDHpo2O3+vdSiuJuHk1Xlmco3Xx/vo+uNTDdu3Hjb2tpaER4e7mVoqJ3tq/bu3ZvYr1+/av8url+//o6tra3zwIEDtbKNxtKlSxe89957b2mjbVY9DmZYg7Fly5bjvXr16qrJNhMSEsqys7MVbdq0qfGvYUII3L59+0FaWlpCaWnp/bKysrzs7OykW7dunTY1NTVxcnLyVqlUxampqSkHDhzYdfz48eua6GvTpk2NZsyY8V10dHTkowK6bdu2XRs2bJhGgpkK27dvzywpKTFs0aKFYcuWLWsVVOzcufPGoEGD6lwkrqysDN99913cuHHjfBtz5eMLhw/nWo4YUdJOpdLJFFMZgDcnf3y73dMv/FVLJjMjsTzlxqkUj2UTXMpWXFE4OHhW+fe+tLQY1y+vSpwze2KNA9D6Sk1NVV27di05LCxMq/dctWpV3KOqOW/dujUhODjYumnTplrZ3DQlJSXvm2++WbBjx46PY2NjG0Q16H8DDmZYg/HOO+/8Z+LEiQusra018olWWlqKVatW3Xn++ec1mo+jLi0trSQuLu52SUlJanZ2dvzBgwfXbNiwodarGUaNGtVi2LBhX4WHh3d73Af6jh07Mnr06NFEUz+n3NxcZGVlwc3NDfv370/p37//Y0cWYmJicgMCAqysra2xdu3ahMmTJ9dr6uLChQvC3Ny8tFmzZjrddFKTtk6cePO5HTu0msS80sYp+3qHgZmGgd2Uqt9/woPWPQ07RUz/R6Jx+t348iYOnspHTaXk5WaVnvrt65QPF76sk2mndevW3Z44caLWi/ht2bLl5u3bt03mzJnjXt3z79q1K/XixYvlc+fO1UqSdllZGTZv3vzzgQMHXtm8ebNGftlhj8Y5M6zB+N///d8PSkpKwl577bW+mmiPiGBvb6+xCsNVcXZ2NnJ2dm4OoDkAdOvW7ZlFixatWrFixSt37tx5bK2XwYMHtwoLCxv36quvjvfz86tR7kpOTk6BhYVFrcrBP8rWrVtvdurUyS0mJuael5fXI98TDhw4kHzx4sUyR0dHZXl5uUlSUlLRiBEj6h0stmrVin7++eekZs2aNapE4D/27MlRAYoH6elFQXv2aG2abLeVQ/a5wC6ZptHvOrZzD2gGAKL7M6iuuoGjk/djM1wtrewNg9qMsl26bNPN4DZuDrk5uRg69GmtVdmztLS000WV58jIyGaxsbFFj1rZNGDAAJe8vLw4bfXBwMAAI0eOHLh///4vAHAwowMczLAGQwghlixZ8gcAjQQzBgYGSExMzAWgleHkqjg4OBhPmTLlRX9//46zZ89evWfPnp2xsbEPFQjz9/e37dKly8C+fftGL1y4sIeLi0uNlxOpVCrExcUZaXI1hrOzs0tMTEzKoEGDfI4ePZoeFBRU5XkpKSmllpaW9rNmzVIvR6+RkRSFQoGysrJGNST/03//m9Jp6VITMyEMExUKZSeVylHT99hv43Rv74g3s306DXXztXV5aNSHiKBU1u8t3NnZ18rZ2dcqMTWuIDs7JT8t7a6Vs7OTVub6OnXqZLl9+/b4sWPHemuj/Qrp6enFx44dS50yZcojA2OVSmWTm5uLqrYw0ASFQoHIyMilISEhV06dOhWvlZuwv3AwwxqUkydPxowfPx4GBgY13nflUbp162Z07NixgtDQUO0WWVFDRAgLC2vfo0eP9uPHj//o22+//dXExMSMiGBoaGi3fv365n5+flZ1eT6FQgEvLy/Vnj174rOyskoHDhzobWNjU69MyoiICPP8/HzfRYsWpT777LOOp06dygoJCbEHpMrCBw8eTFIqldbl5eU0evRojY0IVdYQl8c/SqZKZeYshI0NAE+VSiujMruD+2V38ON8XwAAIABJREFUiJjeVNu5RM4uvuZOzk3NP/vi69tjnwlxDwjwNywsLERCwh388uvJ5HI0U6Qk/1m6dPELdR6FS0xMLL1/X/u7VRw5cuTOlClTHpvDFRERYf/TTz9Vu5S7vogI4eHhzQ8ePNiZiBLU/34TEUVFRY04f/789qFDhz5nYGBQvmDBgpXa6Me/BefMsAYlICDAcNasWX9s3rx5o5eX14APPvigR31XPezduzfZz8/P0cfHRzvLJ3TsyJEj9zw9PS19fHwMY2Ji7nXp0kWjFU0XLlyY/Morr7gZGhqioKBAbN26NX3cuHH1Xr79OD/++GNKQECAolWrVjVesqtPKpUK68aMSTA7ftwwsqDA3lFa7q9RZ5QGZQeXnCrx8AzS2UqvxMTLOXdTLmZZ23jb2Ni6WNs38VAqFAqkJF3N8HC9Q316d3tsgvO77y3JNjX3zOjQ1s2le/fOFl9++WXisGHDnBwcHIy0vYLqhx9+uDVy5MimNVl6/eWXXyZPnz79kTWb6uuPP/64fu7cudg33nhjSMWxiIiIdgsWLNifkZFxr1WrVj75+flFZ8+ePRMbG3tiw4YNn9y6datW+9YREXXv3r1VaGhonxMnTmw+dOjQHc0/ScPGwQxrcMLCwppHRkbOGz169FilUqmREZo9e/bEFxUVmQ4fPvyvD+VFixbdf+2112wbWrn3x0lMTCw7evRokqWlZVloaKinra2tRpNmT5w4UXL27NnUKVOmeO3atavgwYMHZWPGjNFaLsXhw4eLbt68mRUdHe2+ffv2G9UVRWuotq1bVzBwzhwjR0DjwfIDAP+Zs/5eSNdRet+8EgDOn9l47Y1Xhzbfu/fo/ZhT9wqMjUShnU2BvbGxEVJS8vPatfW2t7SyEtdvuajs7N1tfj/xxW1b63yaPn26l4WFhU6Wqp04cSLVzs6uSWBg4CP/fxw7dqzA0tKyMDg4WGujjRW+/vrrRW+88cZc9WMrV678YcSIEaPUj6lUKhw+fDhvxowZAampqSk1bf/999//ctSoUZPt7OyUa9as2e3h4WG2ZcuW/27atGm/pp6hoWtc7+LsX+Hw4cPXjIyM7mlqqgkAnn76ae9mzZoZbtq0KUelUuG3337L7tatW+k333yTqJEb6JClpaWBh4eH8eDBg5tpOpABgK5duxp1797dbteuXRndunUzHzlypNYCmTNnzuQ5OjqKZ5991v3LL7+8Y2Vl1ehWM1lZW0MJaKWkrAkARdrte9pouy6cXTs6fvDh1uu3k5zLOncd4962w1h/N++J9k7uE+zbdpzhff22Xc4Pm07kurj62xgbm6Fbj1k+eUXNLYSAztbcp6en5ymVSmRnZ1d7TllZGS5fvqyTQAYAbGxsfC0sLOz69u3bu127drYAoFKpXCqfp1Ao0Lp1a9OnnnrKGQD8/Pw86BFvgkREwcHBQW5ubv729vZKIsJzzz3Xv0+fPmHe3t4h2nuihoeDGdYgPXjwoNYb0T1O69at7QYNGmS9devWW2ZmZg+6du3qeO/ePWRlZWn6VlplY2ODkpISgz179iRnZ2eXaeMebdq0sYyMjPx/9s47rslr/+PfJzuEkJBAIEBIwoyCIAjKFlQcWLUuFBFX3dXW3u7e9tbb3vZ2qe1V21u3oiIoKu5VFFRAUBzsvTeEJGSPJ78/Wvy5ZSQBvL5fL/8QknMOT56c53u+4/O1trGxAQLBePZFfn6+JjMzUwgA8N577zlOnjx5QNVpe0N6QkJH8sKF5VYMBnofh2szxhwoAGBzL5Be+kITYct2sfT1j3Pj8X0eGgF4PBEwGAwgCAJu7sEOUdM/e1jujMFiISAo2urHn/bXmGqNfn5+tq2trcq8vLznKmVjMBggEomSoqIikwhjzpkzZ05qamrdwYMHr2zZsiVv27Zt2318fATPeq2VlRUuMjJynrW1tfWyZcuyV6xYsebR3yMIghMIBHgAgK+//vqb5OTk/OnTp0c88Rrg8Xih4eHhlsb7qwYXrxOAXzMokclkBjdmAADIZDLMmTPHSa/XQ0FBQfukSZOsmEymMaYyKhMmTLAGADh16lT59OnTh2SDxra2NpREIoknT57MT0hIkMTGxlqYWpG2P3T8+mvHmZISB3JOjvI7rdYoJTFdANDgNZ7oJhXpKeb0Iaco2NFe19VUd1r4/t/iTGakcjgci+Li4prIyMjnzonBYCA2Ntb5l19+aeTxeE81PzU0GAwGXF1dzQAARowYYT9ixIi1L3o9l8sNjIuL854wYQJibW3987Zt2wKtrKw879+/X3zhwoUABEFwW7duvR4ZGRnOYDwdgUQQBGJiYiZLpdKPAeCTp2d49Rg6O8dr/qcoKCgorampUXC5XKPsMsnJycLCwkL9P/7xD+PuYkaGSCQO2e/wuXPn2hctWsTfunVr3bJly3qsCnv9+vWO0tJSCYvFIk+bNm3AkoW1fn7Et4uK1J737zMOf/VVG3vHDpUfiuKcAMwN9aHQAGB70tfWy3NOdcz8PpOJMVKDRGOB6lFk1Cg3Oo1mVLmnpyAQCJiamhqUy+U+N/qwbdu22piYGCtjGzJ9wdPTcxSDwfjNxcXFBgAgNjZ2IQDAxIkTR3ZHnfz9/WNeNs60adPWfvHFF7KkpKRELpdrd/ny5bShVjXYU16HmV4zKBk9evQbvdFf6S1z5sxhBAUFKW7cuGH8WlEjgiDIkN2YMBhMF4IgsGrVKs7OnTtrd+7cWfXrr7/WPHjwQAfwZzJkN5mZmZ3nz58X7927t5zD4eDfeustvo2NjS45Oblqz5491Wq1Gurq6nRCoVAPAFBdXa3bsWNHpVQqNdr6Xd94wzIjMlL2d0/PDlcvLyztxAmLnX5+kgwEMahXkQYA39QWWjxI+ros5+z2KkOObWysrbnmmbeaW0w9r4uLCyslJUXyoteYmZkh9vb2g7IfmLW1tZm3t/dTHtfe5hDa2tpSN2zY8NWZM2dK9u7de/X777/f+tc4CIIgWARBEA8PD7N///vfm99///2V3fk5CIIQ1q1bN3nDhg3zDfIHmYDX1UyvGZScOXMmPzg4+NnqbQZCr9fDxo0bO9555x3mUAw1AQD89ttv1WFhYZYeHh5GS9I1FkePHi2eO3euAABALpc/dPVfuXJFpNFoOsvKyrCOjo5yMzMzMz6fb25lZWVpaWn5VD5kXl5e561bt+QBAQHmaWlpojVr1nD37t1bsXTpUue9e/dWxsbGOpFIxks7yTl1SqjDYLAYKpV0NSFBxr55UxzX0ODA6Gd1E/rXvzYAKCFSFM5qBXHzrI+qxiz4p8m7cveH2po80fixGKoT/+WqxIYiJyenTavVWlRWVkpHjhxJ9vD4/9L2tLQ0YWNjo0Iul+uDg4PpAoHAuJLEfSQvL0+Wk5PTbG5uToqOjjZI+fiBAwfqiETieYlEInF2dl5YU1Nz19raGjt16tSJSqUSzp07d6WgoODGpEmTVllZWSEMBoO5efPmrysrK0+dO3fuviHWYCxeGzOvGXQgCIK/dOlSvZ+fn8EVVR8lMTGxi8fjoWVlZV0LFy40So8WY9PS0oKeOHGiZvXq1UbveWNIlEolXL9+vT4yMtJg110sFuvPnj1baW1tTR41apQNg8HAVlRUSHJzcyUIgshRFNUPHz7c0dPT02gev2uXLmnuf/pp88dVVZz+TvKTg6BZvmo7iUiiYHB4IirtaJS7j5xgso7chiQna1fZ55/GmLTk/sGDBxIvLy+LR41mAICjR4+WzZ07d9CX/6enp4u5XC4pNTVVunTp0n6ftlAUhcOHD1csXLiw18ZwS0uL8v79++n5+fnJX3/99Y7+rsUYDNl4+2teXfR6vWbDhg1NxjZm5s2bRwUAKC0tVRhzHmNy9erVjhUrVgwpQwYAoLCwUO7i4mLQ7tI0Gg1ZsGDBYxu1s7OzhbOzswUAgFqthqSkpJYbN240R0dH85+VONkfWhob9RWrVrW+IxbbGMJaqgudLw8cHvL/OUGc4SZry2FoLJkjWKboy/QoXl5eFlqtFuRy+WMfh0gkoup0OjBkSxBjUFtb2zl69GgeBoNpBIB+GzOnT5+Wvvnmm33y6tnY2JAmTpw40c/PL3zLli0jbty4cT4tLS2tvb39uRVjpuZ1zsxrBiUjR440WeUGgUB4YWx9MNPc3Kweat5VtVoNpaWlzXw+36QlxwQCARYuXGizYsUKfkJCQv3OnTur4+PjH+agVFVV9flCoigKB778ssaJQJBnEwgdnQD9Lpnn/7GPKpOK0Je/cvDDYHDMCwqKTd57SyqVAoqi2qtXrz4UnZk+fTorJSXFaE0mDQWLxcKQSCQIDQ1l5+fnv7Rp7cuQSCTN/TUmGQwGYcmSJev++9//nj1+/Hh5SkpK9rFjx+689957p0aPHj2g3m3sxo0bB3L+17zmmRQVFU25d+9e9a+//tpsaWlJ4HA4RjnSlZeXd2q1WjKfzx+UiYAvQigUQkZGhgpBEDWPxxt8JRl/gaLoY4mLCQkJzdHR0ZyBUl7+q0eW3traGmNubq7r7OxELl261C4SiRRZWVmtXl5elt3rlUqlsG/fvmo8Hk+0sbF5mAMjlUrh8uXLMhqNhjt9+nTH/fv3YVhICC5i0yZ7hw8+oP7S3Czyvn+/X3Uyo2UiSiLbuYbt5DNkPTLdkM2omML8P5r8/TxNWtZEIpHAx8eHceXKlcZusTpzc3NEJBIhV69erW9paUGdnZ1N1retp6jVaqipqelydna2sLS0xBUUFLTl5eVJU1NTxaNGjbKQyWS91n+qq6sTubm5GUR3BoPBgI2NjTmXy7VXq9X606dPL09NTS0zxNh95XWY6TWDkrKysts3b960TkhIWJ+RkWE0VTscDodpamoSggHcuKbm6NGjNZ999hl3sHtmvvjiC6GjoyOKw+HUtra2KjqdThpIPRkEQcDHx+dhwvS2bduqYmJi7JlMJkGlUsHBgwerJRKJ+dq1a60++ugj8XfffcdraGjo+uWXX2rodDoQCAQMiqLakSNH2iUkJNS/8847jiqVCjQazUODOHzWLFLp4cONYzSaPue4/OAdWUkiUYectsyzEIlalPZ2VgbvXdUTKioqpC4uLo/FFAMDAy0DAwMti4uL1RcuXGiYPHmyUfsz9ZbExMS2BQsWPPR0REREsAEAFAoFHD16tJrH49Gam5vbzM3N6ePHj38sHH/jxg1paWlpV0hICMPNzY0I8GcLFAcHB6Pscbdu3UpOSUkZ8OTg18bMawYlFy5c+I9ard7Q1dUFwcHBRvkSqlQq+OOPPzrd3d2N3kTRGCgUCgRF0UEb+9+zZ0+1UqnEjR07VhsREcHrb8NQY4HFYrFMJpMAAEAkEsHb25tMpVKpBw8eLFu3bh3dwsICLCwsqE5OTlQ8Hg+PepQ8PDwcAf4UY3xUr8Q7NNTs4NSprX4nT/apz8F/eF4NzPcOOPCojCHX3uFJUJ0Ozp/6t9hd4Csmka8jEyNDDZor9TJSU1OFK1aseGa3b4FAQGhoaLASi8VAow2egkAUReXPKsMmk8kwb9483l//tdy1a1dVSUmJ5sGDB+1qtVpOIpH0ISEhjiEhIezU1NSq27dvkwMCApgZGRk1CxcuNLi4ZmFhYePp06e/27Bhg6GH7jWvjZnXDEoyMjLEv/32myWVSjXaHJmZmU2LFy/mDSXV2UdZvny549GjR1tUKpU0JibG2ZhtB/oCmUxWL1u2jDfQ63gRYrEYbW1tfezCeXt72wAAODk5PVbxQiT2zrGAw2JBDQB9CTWZ6XRQduNIvXlptlbEdsH6R38+pMqxH0WtUULU9M8YNDrLJu/+kaKJkWAyYwZFUcBgMLoXvaa1tVVVUFCg9fT0JFlYWAzoyQBFUdi1a1erm5sbuSdh2OXLl/Pv3LnTGRUVxaZQHo+WjRs3jg8AkJ2d3Txu3DijVMGlp6fv++OPP3rcENOYvC7Nfs2gZc+ePTtmzpy5whhjNzU1yTMzMztmzZrVY+XZwcr169c7eDyeGYfT72pgg3LlypVWZ2dnPJ/PH5T9YSoqKsTp6entixcvdjZ0/k5bWxtUDBvWPgVF+/3gTiWYKYu25mGYVg6Dy1rtJZUVtxvxSJF2+VuznuklMQZ6vR7S0tIaurq69NOmTXtmgqpWq4WWlhY4duxYDYPB0MyfP99lILyIKSkpUqFQqIyJibEypi6Soaiqqup65513PG7cuDEomvW+rmYaorDZbLvuZmOvKg0NDY+pwPaHpKSkqpSUlAapVAopKSmVOTk5olfBkAEAaGpqahtshgwAwIQJE1jZ2dkmaeTXF+rr61VlZWVEoVBo8Gad1tbW0PbJJ9ozNFplf8b5RRBUnbp+VyuDaT+kDRkAgObGEkQq15i0oglBEAgPD7dXKpXP9bjgcDiwt7eHd999lzt37lyXGzdudKSlpbVLpVLIz8+Xm2qtOByui8vl4oaCIQMAUFpaen2wGDIAr42ZQc/HH38c+/333x/r/v+yZcvG7dixY7ulpeWRkpISLQCAo6Mj0dHRcUCS64zJvn37qpubm/v9oLlx44bI29vbJiIiwv7y5cuSqVOnOk2fPn1IiY+hKAqFhYWPuVGTk5PbhEIh6HS6Qfk9zsrKUjk4ONjcv39/0JW+SyQStL29Xfvtt9863Llzp9YYc0z74ANbZmKibTKN1ucWBC1T38Z6Bs127K2M/WAkKDSWrVaTjdLl/XlkZ2c3HTx4sNrBwaFHFgKJRAKhUKi2tLS0SEtLk4lEIt2hQ4fK//vf/zYnJia2tra2qo211qlTp7Lb2trExmzBYUhaWloGPOn3UYZmssAQZ8mSJWOtrKzeo9FoypKSkp8OHjx4u/t3CIJg3n777blKpZKCIIjN0qVL1xYXF3fExsZ6hYaGbvn000+DraysiPX19fu++uorPQCAu7v7JY1GsxQA+nUKHEwgCIKEhITMt7Oz6/c9WlVVJWtoaGi9desWbtGiRU6GWJ8xKS8vV2VmZrbGxcVxUBSFxsZGuHTpUgWDwaDl5ubKPD09rdrb27W+vr7E33//vWHy5MmDJ3PxEUaPHk28cOGCPjs7u93Ly8vClA9kqVSqJxKJSEtLizI9Pb0hMDDQis/n0wAAcnJyWu7fv69evnw5p6ysTMdgMIwWBhOMHm0m3bXL+tSyZdXTu7p4PX1fFwD8EDirwtbJd0gZ3c8D1ekgMyOxY+5MN5N5Qy9cuFAFAMSFCxfyevO+2bNnswEAvLy8CAAAISEhVIA/pRASExOr1qxZYzSRSg8PD7tr166JJRJJS0hIiJ2jo+OgbLUAAFBRUVE+0Gt4lNfGjAlZvHixB4lEGhYeHj53+vTpMxAEgQcPHviuX7/+123btv3y97///fODBw9ODAsLC6ZQKIherwcMBgNardZaIpH8Pm/evACAP/vY1NfXt3WPa2lpeTApKemVMWQAAEaPHu35448/Cl7+ypcTFxdnL5VK9UePHh3UQlktLS3oxYsXq1xcXIh+fn7M9PR0XVVVVYOXl5fF2LFjGc7OzpYAYFVUVCR2dXXFc7lcMzs7u1YfHx+DVykYAgwGA1FRUaTW1ladKQ0ZpVIJu3btqmOxWAQCgYBdsGCB86VLl5ozMjLaEARBvby8OMuXLycDALi6umL37dsn9Pf3N5pB4zd+vPmRUaOapdeu6WsAJB5/9o58IblmFl3IhLcsWLZOgy582BvUaiXkZO2qsLYi4T79cCaXTDZdCKWjowM7d+5cgxmDFRUVXQKBwKjhPk9PT7ynpycNAGh79+5tp1Ao9WZmZoQ33njDCQBAKBTqLC0tsQPtqSstLW3Ly8u7OKCLeILXxowJqa2tdZg9e/ZKLy8vn+6bccSIEa75+flRiYmJk/z8/CZYWlo+/Ey6X3P27FnRypUrfbt/jsPhgEQiBSQmJl5DUVTV1tb2gan/FmMTEhIS6O7u3u+cIK1WCxgMBvbv31+3Zs2aQfnQ7+bSpUv1ixYtcgb4M3HR3NxcGxoa+lSIYdiwYTQAgPz8fMnYsWN5pl9pzzh06FAjk8lU6XQ6VK/X97rjb18oLS0VZmdny9atW+f4aJXaxIkTbZ/3nvDwcF5BQYHIw8PDaOJ0Iz/5xC45OLgNvXat0+PmTVoLgM4Gnl+1fSH2X63eIycM2Qqmbu7fPVX9+aexA1JpN3/+fMc9e/ZUrlixwiDeWBwOh1er1SbLUxQIBNT29naph4eHJQDAgQMHKjQaDcXT05Pi5eVFJfdLkrFv6PV6kMlk6JUrV369evVqg8kX8AIGZax9MDJv3rywr7/++mRsbKxXX8eIiIjwjo2NjeTz+Q8rHBAEgQULFkRGRkZOftSQ6ebBgwddXl5eKgbj//UmMjMzK2fOnMkfP378WCqVyrWyshryCqGPgiAI4ubmNra/42i1Wvjll19qf//996rFixc7DpTi7MvIzc2VHzlypNTd3f3h0x5BEOBwOLgXGQAsFgt/9erVaqVSaZJ19hadTiebPHky38PDg7Vp0yaj5KU8Sl5eXmt9fb1+4cKFnN6U22dnZ7c5OjoaVZlWMHq02fT332dZvPmm5TEisfGbkSPbnqVPv8/Rs/7LOZ8VWwmCTKqUayz0qEgzUJIBUqkUtFqtwbQd0tPT2ydMmGDUfnGP0tzcrJowYQKPz+fT5HK5XqVSYd566y3b1tZW7b/+9a+OX375xWhios/j0KFDicuWLRv397//faOp534Zr9sZ9JDo6OgGoVDYolQqrZctW1balzEOHDig0mg0XDc3tx57CGxsbIiurq4P3dKNjY3igwcP5sfGxvo0NjZKDx8+nBQfH7/zVfocy8vLN8bFxa02Nzfvs+aDQqGAHTt21Kxdu5YbEBBgaYwN9erVq+35+fn1mZmZnQqFAulLRdH27dsbKRQKYfr06Wx7e/te5b5QKBT8iBEjGN9//31Denq6tK2tTSoSichsNhuzb9++itzcXLmvr++APRSbm5vB3NwcJxAIzFQqlVaj0aiZTKbB4wwoikJSUlIFjUajBQcH9zpcdOXKFWFYWJhJDgR8X1+K44YNVLFCoR2WmkokADy0VusRRJ/+YaLKPSKOa0G3GXQS+32hpTFbFBribdiOnj2ESCRCQUFBy8iRIw0SQhSLxfITJ06IcTic0sHBweifD4vFIvz222+tGAxGefXqVdGyZcscEQQBd3d38vjx483y8vKEJSUlbcOGDWMY+6DW0dGhefDgQdX169d/TkpKumDUyfrIa52ZHoAgCLJu3brZTCZz1bBhwwQ//vjjG7dv3+5TJve6devWfP3117/2dS1FRUVirVYrHTFihD0AQHV1dWtWVlZiZ2dns1AoLPjpp59S+jr2YGLz5s2/LF269J2+vv+bb75p+PTTT+37+iXXarWwd+/eOiaTae7p6Wnu5uaGBwC4e/euuKysrLm2tpY8a9YshpOTkzkAwLFjxypUKhV9xIgRZkVFRU1isRhvZmaG8ff3Zz0rXIaiKOzdu7dy1qxZHDqdju9vCEan04FSqQSFQgF5eXnNfn5+Nnv27GmOjo5ms9nsfo3dV86dO9cVFBRkTqfTEalUCleuXKl+8803eYae59q1aw1+fn72fWmid+zYMVFgYKCZvb1pS59RFIXdfH7jsq4ultVf4f6fbJ3a7LcVWg90PoQhuX/3XP2iBQI7W1ubAXGLJicnl8yePdvdEGNptVr44YcfWigUii4uLo7FYDCMnqZx/vz5lvr6enlUVBTP3t7+qRujpaUFysvLm4KDg436Jd+9e/emDz74YFCnM/xP5swgCIJZv379IhRFG7Zv335F/4hFt3r16iAvL685LBbLCwDQ9PT0wrCwsMZZs2b9c+TIkaR79+51KBSKxxpqLV++PCg4OHhja2trdXt7+30sFqvPzc09denSpfpHX/evf/3rn0uWLHm/P2v/K1/i4Qmex+OxeDzeegCAc+fOnQOAV8KYkcvl1f15/+TJkwmJiYmlrq6uLD8/vz6dujEYDPrGG29Y7t69u+b+/fsqPB5PtLa2hujo6Kc2xzlz5jjHx8fX5+bmIvPnz3cikUig1+uhsLCwIyEhQUomkyE4ONhx37599Y6OjnKVSoWbP3++E4VCMciTC4vFAoVCAQqFAhEREbYAAIsWLWJt2bKl6auvvjKpNXPmzJkGqVQq8/T0ZNLpdAQAQKVS6ZuamvRNTU0yNptt0FNtS0uLtK/dgH18fGiZmZkNc+bMMWnHXwwGA2NOnrTd++9/19nn5ECEWMxtXfQ9zuEVMmQAAHB4IlRW1cpsbW2MJ+X9AlAUNdhpHYfDwWeffWaTkZEhl0qlWgaDgVOp/gwW9lYduqdMmTLlha1WbGxs4MiRI5rg4GCjzN8NHo9XGHUCA/A/ZcysWrVqiru7++ikpKRJY8eODWxpaZFbW1vvAID3AACWL18+dvXq1YnOzs4Pb6Bx48ZF/vjjj0IOh0MCAODz+Qw7O7sQALjU/Zrw8PCPp06dGqnT6QBBEMBgMHDp0qWYsLCwD5ycnIbl5uaefvDgQQefz3czZofWmpqaPoW/BiM6na5fm9CoUaOsR40aZR0fH1/WF2MmLy+vSyAQ4AgEAqxZs4bbk/csWLDA4e7du53dolcIgoCHhwfTw8ODWV9f33X8+PEqBwcH5rx580xSnkokErEsFsukT8czZ840u7u7k11dXR9r3MdkMpEVK1bwk5KSGhcsWGDQ74BQKOyzV8XZ2RnJyckxqfZJN14+PhivpCTuiW+/bfrt2rU6HJGAAMCgVEvuC3fvpNQsWTiCM1Bemfr6etXt27et5s6da9Bxg4KCzH7++ec6Foul1ul0+M7OTnT9+vW8gfCoKZVKGDNmjFFDyZWVle0ZGRnJ7777rjGn6TevrDHj5+fHnjJlygaVSnX3xx9/TFy7du2bfD5/Ph6Pj5gwYYI1AIBGo9GXlJSU79mzZw+BQDBTG94EAAAgAElEQVTncDg6iUTyWN4CgiDw4YcfMrpvVBqNhkydOnXm119/Hd3e3t6Rm5v7W3fZ3KMN/yZOnBgSFBSUVVpa2kqj0TwjIiK2eHp63o+KippvrL+Zz+eHu7u7U0pKSmTGmsNU0Ol0gzR/pFAomNOnT3c1NjYKJ0yY4Nje3q69e/du3bhx4xzc3NweewjK5XJISkqqsrCwUAsEAhsfH59eddLFYrHg5+f3zIeRg4MDddWqVVRThnXNzMwgPDwcHx8f3xgbG2tn7Lj6xYsXJf7+/nQbG5tn5sXgcDjQarUGz1am0+n9uqgKhWLAYu1KpRJmfvYZ+1rwWNmdu4h4oNZhDOQyIVhYUAcs676qqqotKirKKLlQGzZseHggqaur0x4+fFgkEok6/f39GbW1tcJx48Y5MhgMo/d5unTpUvvYsWON0ogX4M/Q2vHjxzcnJibeM9YchuKVy5lxcnIiuLi4sKdMmfL1kiVL4pqamroaGhq6nJycnopxKpVKqK6ubhcIBL3qn/KPf/yj7Msvv3TdvXv3zz///PNXhw4dqvPx8XnmaXP//v1FU6ZMcampqaknk8lYT09Po/Ul0ev1kJmZWZCSkrJix44dmcaaxxT8/vvvO6Ojo5cbYiyJRAJUKhVKS0sVRCIRy+VyCVlZWY0lJSWK6Oho52vXrnWVlJQgNBqtZcmSJQbv0zPQKJVKOHjwYEV0dLSzhYXxDnHZ2dlSFoul5/F4zwwpoCgKZ8+erZs2bZpBPVNXr16tam9vR+bOncvry/vPnz+vCA4OJhvz2jyPlau+bKJQ7fQjvKNo5lQGlkZjDQ0t+x6g1WqgojxHiGqKRBvemW9ysco9e/bULVu2zGQifRKJBLRaLdDpdNizZ09FdHQ0z9iNK0+ePFn65ptvuhlr/JSUlItLly6N0uv1hukrY0ReuWqmvLy8+XFxcb/IZDJ1SkoKJTw83JrH41HJZPJTTygcDgdWVlZmvZ2jqKgIO2bMGJKrq6u/hYWFbVlZmQ6Px9va2dk9deMWFRXJAwICGPb29pYsFsuoSq1/lfOySktLRatWrcpet27d4KzZ7QH//Oc/81EUZVpbW7tSKJR+aTsQiURAEASsrKzwdDod+9d1onp5eTFSU1Mb2tvbNUQisWv27NlcEon0alky8Od97u3tzcjJyWlOS0trHTFihGVvXeJlZWXaM2fOVJNIJIqVldUzN2g2m024cOFC7YgRI555UqytrQUGg0E29ImVz+dbyuVyVWpqaqu3t3evwzQNDQ06c3Nz/UB0TOZyrcl1dZ1tAo9JtiQS5ZXylGMwWLCy4pA1GjJaW50tFQiMF2J/kpSUlFqBQEBns9kma/NCJBKBTCYDgiDg6+vLOHDgQDWFQtEzGAyyMUJQycnJTb6+vjQ6nW4UwZmcnJyyQ4cOLSoqKhq0/dUeZchv3AiCYL/88svtmzdv3nvw4METFAplLZvNZi9YsGDau+++y960aVPDgwcPDOq+7ezslAAAWFhYYPV6fdSkSZOGPa/pIxaLfZachFFZtGjR+x9++OFpW1tbg93kkyZNCujL++YjiL8fgjCWIEjENwjyyXQECenJ+06fPl21fPnyuOzsbKOpTGIwGJg8ebK9XC5XCAQCykA8zEwFFouFsLAw27lz5zrt3bu3/uXveByFQoEdN24c7/Llyw3x8fHl6enp8vj4+EalUgmnT5+WSSQS2L17d2VkZORzpd7ZbDZUVFS0Pe/3/cHf399m0qRJ9ocOHep147v6+nqJTCbrMsa6XsbJM7Vtw73mDfoWG/3BzmEYs7XTmbB16+6Ozk6RSea0tLTEE4nEAW3Eu2LFCieZTIb9/fffG0tKSoSGHp/L5VKVSqXBQyt6vR4SEhKSf/rpp7HHjx8vMfT4xmJIe2a+/fbbTbNnz94SExMzZcyYMSPd3NwEfn5+nK+++qqitLRUp1arZUuXLrXH4/EkM7NeO2CeC4fDsbC0tEQAAEQiEc7Dw8OcQnn2oaO4uFg6fPhwk4raEYlEEAgEjgiC6MLCwq71ZywEQfBYLHZTTEzMxi1bthRNmTKlR/04JiIIX/fPf24aD/C3GICPZwMsmwQwKQsge8rGjXd6Ov+ZM2f4/v7+Ex79mU6nA0OGgnx9fS3FYjFeJBIpmUzmkO9O/CLweDwUFxe3PM978iharRa2b99elZmZKbl9+7Zy+vTp1ICAAEtvb29GXV2dNDw83Do1NbUhPDzc6v79++3Tp0+3Nzc3f+4Hg8ViITc3V9Le3i7m8XgG91JSKBQciqLytLS0RhKJRO1p6aynpyfl6NGjQn9/f5PGmSQSCaRezRbi8GYKS4bdgFT7mAoa3YZENHMzEwvvqPl8rtEPDVwul1pcXNyuUCgIVlZWA+bxsrOzI/n7+1O3bt0qsbe3xzGZTJxCoQA8vv92lp2dHSEnJ0dy5MgRcVhYmMF6n+Xl5bV8/vnnEdnZ2QY3wIzJkHVrfvjhh7PeeuutNTY2No95H8zNzeHXX3/1BgC4efOmqLq6WsPn8w1qofP5/Id3zfjx418Y4/bx8WHs3r27bsKECfZcLtdknjAEQcDOzq7XJbmff/75v729vf2ys7MvmJub0y5cuBDn6+vL6+rq0mi1WjyCIFi9Xq970RgOCEJ6F+CjJQDLHv35CYCL9wCO9GY9t27d2q9UKolTpkxZIRAI7Nvb2zW//fZb4xdffNGjCqOegMFgwNPTk5CcnNwMAGo3N7dXpqLkWTCZTJJQKAQG42ktMxRF4cyZMxIzMzPC5cuXRd988w0fh8PBk+0IQkJCLAEApk2b5gAAEBoa2qO8s+joaIcdO3ZUjR3bb4HnZzJy5Ei2t7c3+/33329zdHRUWVhYqMRiMfG99957rPS6rq4OOJw/0ymOHTsmXLBgwXPbHRgLCwsL+O6bZdwdu45X3b/bVO3tM41n6jWYEpm0U9lQVSaNiAjrVY5iX/Hx8WFfunRJKBAITBZqeh5hYWFUuVyO7Nu3r6G+vl6/evVqlpWVVb8PTq6urpY0Gq3VkGGsqqqqG0OxiGRIGjNOTk6En376aeOThsyTBAcHD7jMv5OTE9XJyYl65MiRGi6Xa7AHcE/A4/G92jQQBEH+85//cCdMmDABRdER48ePt8FisaBUKuHo0aO7qFQq/YsvvriLIIh3tzbPVARxGAbwBgkAxADyRoC07wB2xAJMfHRsPQBcB0hM1+t7FfJLSUlpAoB/vvHGG1lRUVEbrly5cunbb7/9pjdj9JTZs2c7ZmRkSLOzs5sWLlw4MEpzRubixYtV5ubmtEcNmcuXLzfV19fLAICq0+lks2bN4mMwGGT8+PG23ZukoTbLs2fPNs2YMcOohgOCIPDvf//burKyUjps2DDzEydOVB8+fLiysbGRoNfrMQKBQGZtbc2ura3FBwcHE7u6ujrNzc0HRKUWAGDl8ln8zT+fKjbFXOVlWS0KhUiOw5lRqytvKuzsnOQEvByLwTkT3YeFGTxZVqtRAw5PAJlMBI0NhcJFC6JM1gX83r17rdOnTzdZ+4EXMX78eDMAgJEjR9orlUo4cuRIDYFAwKrVatWSJUucAQDKy8vb8Xg8gcvl9thDmJeXp5w8ebLBNJJaWlqUFy9e3LRkyRJDDWkyBtyYQRAE8/HHH6+1sbGJrK2tvbply5afX/Yec3NzNx8fnxGmWJ+haGtrQ+7cuSMeNWqUUZOAH0UqlWICAwOZmZmZL+zhgSAIftKkSV6bNm1aPWvWrPkAABMnTnxYGl1SUtIhk8moS5cu3WlmZkbMyckZBwB/AADY0elzvhCJNlH/yr/aCXBODdD85KMPAQArgF5vLHZ2dqTGxkbl2bNnLzU0NFRxOJwp+/fvv7VixYpgS0tLg8fEHzx40Lp8+fJXNoeBy+Wy0tPTW4KDgx8+vOVyuWTp0qXupmgGKRKJ1C87hBgCIpEIw4YNMwcAmDlzJg/gYZM86BbY+89//lPN4/F4np6ezEdlFQYCuayZbIzrr1YrIfP6z7U0Ol8pl6tIS+L87Pj8ETYoigKKekN3D6uf/3OsFCCs3/MVF6bV6VA9xc09kFFafL1Br8nDNrcqVZHjPWztbZrxbW2MTjs7tkk8nyqVStebHl2mgkQiwZIlS7gAAMXFxYqkpKQStVpNIJPJ2Pb2dsno0aORjo4OqZeXF53FYr3wuyKXy1vIZLLBwqPl5eWVCQkJQ7ISdsA/6djY2HgXFxfe6NGjRx04cMBs5MiRB+7du/fCWF1eXl5hfHx8BhaL9Vi5ciVtMN6wT7J+/XrHtLS0mqysLGxAQEDf5Ep7yfz582fweLxR48aNG5+amvpMQb2ZM2c6JSYmnhg1apTH86pMvL29md7e3gu7/79w4cK/T548WUEgEIZtSE7+8NysWSUxYvEwAIBIgAmfAXw7G0BPe6TvDADAmwDrv0MQ9AzA4Rt6/WMdVxEEQYYD2EUAhPsAeNTOnOnjGROj9/Dw2E4mk+8sWLBgbExMzCdjxowZ2dHRIb9y5Ur73LlzDeo9QVEU3N3dKUPhfuorAoGAUlhYiGZnZ3eVlpbKLCwsJIGBgY4AhvO+vAgCgUAsKCiQeHh4mLwOGkEQeFQpeNWqVbxjx45VKxQKGDNmzIB5cRsbm1AUmDpjXP/GhmLp6pVvOjo6ckCr1T40XjAYzGN5ZziszAJF0X7lot3OTqyYOonrwONxiIeP7G9aOH+iHYsV+PCPOnnypJrNZptk7wMAGDFiBCM1NbVj3LhxRtNh6S8CgYAsEAgeUxQXCoVQX1+v6erqUr/ImEFRFLRarUEPdLa2tnwej2dVXV09JCqYHmXAd+2MjIylOp1u2/79+5ffuHGjTK/Xv1SNc8WKFWEsFstp3rx5JvNy9BedTgelpaW6efPmmezLDAAQEBDg8P777x//5JNP9iQlJW2rrKxUd/9u4sSJ7DVr1iSGh4f3qhN4WFhY+OXLlz8rKiq6RafTaYVBQUrV+fNABAAnAEIQwPCtAPsmA4T7ATysbhkOYD8c4IfZAJ+fQ5BcBEBOBLDCAzBuAFAcAJiOAAQEAK5euFADCxcyPv/88/EMBgNvZ2eHdG+0NjY2ZnPnzjVcRvdfYDAYqKur62ptbbV42YloKDNlyhSXhoaGLrVaLZ8+fbrRNCqehZmZGdXR0XFQNFHU6XSg0+l0DAZjQD/rhCPnmv0D1hnMG6jVqCE7a18FlUrRcOwIFo6OUeYAAC8y0sVdIO9vUj2D6cLMuo1ozl26I/L35RJYLOvHrDMsFosjkUhEpVIJOBzuhesxBCwWi1hTU9O8detWzdKlS2372vLC1DAYDIiMjGQkJSVVyeVy1YgRI57p0U5ISKibN28ez5Bz29vbk7lcrjsAvDZmestfD9eVvXmPl5fXynnz5pk8Ya+vKJVKiI+Pr1y8eLGTMbo3v4ygoCAPe3v7b9PT008BQDkAwKJFi3xWr169NTw83K+345mZmSGffvrpBBKJNNXCwgK48fFOO6dMKeeUlODCJRJHX4BR2wF2uAFEPGtwVwALV4DwF80RoVBw/3v8eM28rVtNmmeExWIpuFfZNQMAZDIZXFxcqKWlpS8MPxqDyMhIyuHDh8u9vLwsfH19BzSfobq6unPYsGEsf3//Aa0kWrsm1m77jtQWVKdWScRtBL8xs2zJ5L4vqbTkZssXn81z7s1tTKPiCP0Nczk5j6IDAPCdfc0L8+NLIyfAYx4RHA5njqIocvz48XInJyfLgIAAo3pMWltbVRgMRmNra4sbKoZMNyQSCRYtWsTfv39/paurK6u7RUo3+/fvr/bz8yM/+XNDzGttbW2SBG1DM2h1ZpDnfKsCAgK4wcHB0ww1j0xm/KTttLS0+rfeemtADJlumEwm0d7e3nPjxo2fr1y5cnVkZOTvERERfepOhsFggMViEbsVUzEYDMRevOgyLDubdwKHE+avXk3uGD1a4gqAVb9krBeSkoJDUdMJT6IoCiqVSs5gMAZUn8JUaLVaokQiMemcBAIBlixZ4pKdna0YSPVxvV4PFRUVcrwhamT7CYIAgDZPv2Kpt2NwgBW2uOByZV+vjVajhurKS73OFRF2agyih6XVqCE7c38ZAa94Kow4fvx4ypkzZ9qCg4O5NTU1nXq9HhQK4/QvfPDgQcfdu3dlixcvdpk7d26v2pIMJuLi4pzmzJkjS0tLUyUkJLTW1NTA8ePHGydOnGjr4eFhkJYvj9LR0aEBAKNoQRmbQaszk56e/v7ixYufSkT66aef5K6uruMFAgGvP+OjKArbt2+vU6vVeEOXbj+JXC5H8/PzdU5OTgNmzRAIBFCpVDorKyvm2rVrP3N1dbU3dIyeQqFAnYODdvqGDVYkGo2dce5c3Vk+32ySUEjty0wFtrYtgjVrTBbvPn/+vDQqKsp2II1OUyKRSLqys7PbPT09TV6KTqfTKUePHq2tqKiQ29nZkclksskzcC9dutQxZcoU64FO/sXhcBAUNMqcTCZBa2uDRCQs0pZVNGsdHIb3Khz34N6F+uLCK6J/f7Paobd/k1wuQvPySttsbF3pfd0XbmcnVWpUt2XvvTOHP9rf+ylXCBaLBS8vLwqdTseam5vTLl68KL569WpbYGCgwdMFCgoKROPHj2cNRPNHQ4IgCIhEohYcDod1cnIiYTAY/bBhw8xtbGwMXm7e3t6u3rFjx9927dp13NBjm4JB65lJTU3d9KyfFxcXa1pbW7N7O173Cb+hoUG0Y8eOnI0bNz6YOXMmc9y4cQbPvXgSLy8vOp1OF506darJ2HO9iNDQ0Fkoio59MvnPkETGxFgAABQXF+cS//EPbdyhQzYlAPK+jGXT2EiT98BzlnboUEfa0aPiO9nZfXbjCIVCqKqqajekO7rwzh2VWmVyAege4+/vb0ehUF6oGWQsXFxc8KtXr+ZzOBwLpVJp8q7VCILAjBkzHAsKCgZNz5mGhgbZvXv3sGvXrnViW7WQxeK2HrtnOjoa1OEhFOvvvl3i2BdjfMrkcKu3V43hZlzf2WevEOilujmzJjj0ZG/h8XjY+fPnW86ePdsuMTGxrqCgwKA5GoGBgXbJycnl8fHxdWp1v/zDAw6dTsfcvHlTk5iYKHV3dydaWVkZ5fDd0NAg++mnn3YYY2xTMOQaTYaEhHB0Ot1/N2/ePE4gELwwYNjZ2akrLCysVKvVHYcPHz5vbW3tQaPRHBctWuTHZrNNlhchFot1p0+frhw5cqSDp6fnK5tY2o1EItF9/PHHjb/++isn59w5sUtcHNYZoNdWghYAdrz5ZnXMnj28570GRVG4wWa3BGg0zCoA5YOwsCb+22/b+kVG9jjpoKioSF5bW9s5btw4e0NFHbRaLZwSCKo5UimxdNQoeezZs84GGdjAiEQi7dmzZyupVCp1+vTpJtfWSUhIaPD19bUkEAgYPp9v0iaLmzZtqnv//fdN1ojwRVRUVHTm5+fLZsyY4QAAoFKpYcvP++ukcpI2NHz1c1tEAACcOPZpo5uLnf5v7y3rdzhFLJbAll9OVXj5zONRzOlPuXdQnQ5Eoma9SiXXsO1cH+86LxNDY83B9rffXtKrnAsURSErK0ukVqu7wsPDDfp55OXldWg0GjNfX98ht++mpaUJxWKxKCwsjFtVVdVSXl6u7msz1Z5w586d5sjISIeXiaIOVoZcouPNmzfrt2/fzrG1tcXq9Xq4fPlytkAgcHN0dHxYWnn79u3K1tbWljNnznyakJCQhiAIsm7duhWrVq36yN7e3uSVFKmpqZULFy50NfW8A4VYLEZ/+uknDoIgIFMoEDOAPlkIOADg/PHHM61tFEVBLpPBgQULat/VaDjmAIgNgHlAerrr3Rs32m4jiLJk9GgJOmYMjefqioTGxDABAPJv3OjK++MPxcQ1a6yZLBZSWVmprqura580aZJBu5njcDhQW1rqJgqFbJ/MTNWBGTMqZx086GROHVyq9XQ6HRcbG+vW2dmp3bVrV8Xy5ctNanTFxMTY19TUwO3bt0UkEglls9lG95QCAKjValAoFIPmAadQKNBhw4Y9TIgmEgnwyccrOHv3Jb40f0EgGN61Yf0s95e9rifQaBaw8R8LnY+fON9RlCfrUKmpGJVKhXfk+ZqLRQ1SjL4UFxriZXfuQnaLWh3B7Opq70K1IhEgBKyZmQ1TKke6AKBXxgwGg4GgoCD69evX8fv3768aOXIk1dvb2yBJqM3NzdLIyEgmAEBDQwPU1tYqAwMDB3Vn8oKCAnlpaWlrQEAAm81mMwAAfHx87MrLy40qrNjZ2Zk/VA0ZgCFozOj1ev0PP/zQlJeXp5fJZMj3338/y9PT0yYoKOhDNpvNLS8vvxYfH39ArVZXFBcXawAAlixZEvXBBx/818LCAtFoNHDr1i2RVCrFsdlsnbW1tZmdnZ1Rc2akUunQDtz2Eg6H8/B61vzrXx3THinP7i2jurpsTo0aVaYKDSXM/fnnh5VN382c2Tzlxg3SJ3q945PHRx8UtQYAiMzMtN6fmVnuAWB9KDW1ZtzGjdyCDRta11RWOn97/XqNw6JFVvlnz7a9s2sXrzdryrx4UeYzdiwl+d13a/QEAtY2MtIqZOJEEolEgvhp0yqJcrlO6uZmNq+igg0AYA1AjLxxg35206bmeRs3DsoqPEtLSxwejyfK5XIwZB+znsDlcoHL5dK3bNlS895775mkei0pKan+s88+M5hyal/RarWAxWIBj8cjpaWlIjc3t8cqvPz9vKgnzxyrJRLJWgAMHkCnodEdGRyOBx3zV15MR4fE4PvXrJlTmAB/ViOhKAo7dp2o8vKws4wIj6MDAIwc6c3ZsWOXaOXSeTZUKtUG4M9eU/fuifp884SGhlJCQ0P5lZWV2v3791fK5XLw8PDAhYWF9fmg0dbWRigqKlJnZGTUOTs7k5ycnOgHDx4sd3V15Y4ZM8ao+75arYb4+PhqAoGgjYuLc+npe4qKiprnzJnzVKm+Wq026kG8trb2ljHHNzZDLsz0Mj755JOFbm5u/4yPj4+4evVq7V8/O8jhcDhubm4jCwoKTiclJaV5eXnNIRKJ8k8++eRNQ5e3PUlqamptc3OzOiIigs9ms1/ZzsxPcuPECZH5229LIpTKfns90rHYNvnx45ajAgNxmceOCZ3WrsV6Arw0cfA4QOksADcFAJQAyLwAKBj4s72CDAAoAPDr5MmVCw4ffmrzKC4o0As8PB4zRFN//73Z4Ysv9BK9HjtWp2PpAaAZABJDQ2tshg2DcTt2WNsDmBUDSIc/Elrb7OZWuTQra1ArC+t0Ojh48GDjjBkz7Oh002vInT59unTatGkm0b1JSUkpnTFjhkk1dp7F7t2760gkEtbZ2ZkSEBDQo0TY8vJK9Y2b2YrGZl2bHiyQyZFc1ihfL5O7/Hbt2lXx1ltvORszyTYrK0tiY2OD8Pn8Xv99KIrC+fPnqxkMBi0gIMCye50ZGRktbDbbms/nGzVndO/eveVxcXEuly9frpk0aRL3eblEKIqCSCQCBoMBO3bsqHzrrbecnkzgVigUcPLkyaaYmBijhIJlMhmsW7cu4uTJk9eMMb4pGHKemZfB5XJ9pVLpNZlMJgwPD7e8du1a5/fff79o9uzZ9g8ePAh78ODBBTs7O9b06dPXcTgct8uXLzdNmzbNqLkC48aNc2xublZXV1dr/peMGfE337RMUyoN4v4O0+msG2bMQHMJhMaRajXN8U875KV0AmABAMgAMPKR9yDw/5ZG0IULNqlnzqjNEEQVMHUqddeMGdVApWIsLl40R9LS6HwXF0zCN9+0S6qr1VPPncON0ukeOz1zAeDt69cd82/eFHIAzAAAhj+RIxReWmrd0tQENuy+3Wr9VWftCQiCgEKhUA6EIQPwp5fChHORJRIJdMsLDBRxcXGc5OTkhp4aMgAALi5OBBcXJwL0wJg3Jmw2GzV2tVBAQIDFuXPnWnNychpdXV2pPj4+L+zt9OWXXzaGh4dDY2OjVK/XI1FRUfwnu6c3NjZ2BQUFGbysuZuamhodk8nEslgsAg6Hg5CQEO63334rWrp0KZ3NZj/2PUZRFH799dc6LBarRRAEFi5cyH9WJVpdXZ06KCjIaM+pmpoaaUpKSo6xxjcFr4wx4+LiYu7i4sIJDAxswOPxVHt7+33t7e0ro6KiBGPGjCElJyfn6fX6QwAAdnZ2MiwW+4VIJDKPiYn5+86dO/ErVqwwqlBQXl5e+/jx403WZG0w0D5mDAUqKw02nj0Axl6t7vE1VAIA6YmWCs/CC4AiXbKko37YsFaYOnWYvqQE81FrqyMKAB/8+GOnd3Z2x7qmJhcMPL/8jwKAjEHR55aRuwNQj12+LJu+aFGvXMXHjx9v1Gg0UgRBiBQKBW1tbcXHxsY6GKt8XKfTDVhItKuri6LRaMAU0i9sNtt8oLURlUol/PHHH9U4HG7IaQHs27ev3dvb2yTNOaOiolgAwEpOTi5+ljGjVquhoKBAU1BQ0PHGG28g/v7+L3vo43bv3t0SEhJCdXd373FYrKGhAT1//ny1tbU1acaMGY+to7q6WqtSqeDmzZudzs7OqpqaGlxDQwMGAIBKpcKnn35Kv3LlSlNGRoYGRVENDofDBwQEON68ebNl8eLFDhQKBQGA5x5YWltbuxgMBg2M9MzmcDjmEyZMGAEAWcYY3xS8MmGmTz/99OPZs2dv0Gg0ejc3N7ZIJNLevXs3283NzZNGo1ls27btu99///37FStWzHZycvLmcDiRFy5caMrOzv7Yw8PjY0dHR4+//e1vAmOtr7CwUM5kMs1sbIx2IBh0JHzzTdviTZusB0q7vhUAmgDk3n95S15GPgbTVsBgSCLb23kMAKweAGoAVDyAfms66AHgkI1NhTI0FNG1tupUnZ2YuLS0ZybbarVa+O6771qYTCZ2wYIFljQaDfvo7xISEir8/f3tBAKBQRNY09PThZ6enoxHO2qbkjZHAxsAACAASURBVKamJqipqRF5enrSja3YeurUqVJTt3J4krt373bg8XizoVjheOrUqVo6nY4PCQlhG9tj2E1eXp7swYMHLfPmzXPC4XCQmZnZWVtb26bT6Sh+fn5MsVis9/f3f+G1LC0t7WhtbcWeOHFCvXTpUuqLrn1hYaGuqqpKPXXqVLJWq4VNmzY1fvTRR3aXL1+uaG1tJaAoqkIQhGBpaYl1dHQkoSiK8/b2pvXEW9Xe3q4uLi5WEYlEtb+/f4+0tPbs2VOxbNkyoyXo//bbb59/9tln3xhrfGPzyhgzs2fPHhEeHv4hnU439/DwCOXz+Y95WpRKJdTX1ys5HA6JSPzz2fT222+fnz17ts+4ceOMnpQpk8ngww8/bF+7dq2lp6fn/0SoKXH27PJVV6+6DNRRXwkAqQANUQCDTgH0iIND6aQHD556mBYUFMjv37/fQqfTMVOmTOE+b2M8fPhw2YIFCwxaIadSqWD//v21ixYtcjR2HtnzyMzMVOXm5ja//fbbXACA0tLSTgaDQbGysjKo9+LQoUOVsbGxA57DdODAgcpFixYN+Dr6glQqhZs3bzZOmjTJZB7n2tpaNDExsZXD4Yibm5vJGzZs6FU+3o0bN+pqa2sVCoXCnEKhkKOioiyfF2rcu3dvubOzM00ikYh1Op15VFSU7ZNew+vXrzdbWFhgvL29jdqaIz8/vx0AKMY0fC9cuHA8JiZmtrHGNzavTJgpOTk5DwAWvfvuu2/yeDxPALDq6OgAHA4HNBoNSCQSuLi4PLZDb9q0acpHH32UlZmZ2bVmzZqnYquGpL6+Xubt7a3/XzFkAAAQkUg/kGVcJABoANDqoQexJhOjs7F5SqxNKpVCdnZ249KlS19a+cBisaiGzqMhEong5eVllpWVJQkPDx+QZJLAwECii4sLJz4+vtLCwkKtUqmoKIp2zZ8/36Cl8wwGY8BLUFEUBb1eP2T3YHNzc8jPz9eFhYUBmWx851JxcbGusLCw9sMPP+QDQJ8OoCEhIRyAP9tZtLS06M6ePdsR85dsAwBATk6OpKqqShQdHe2Ix+P1YWFh1gBg/bzxQkNDTVKdqNPp4M6dO40CgcD5jz/+6FAqlYpuTSJDUFhY2FBYWHjWUOMNBEP2i/Q8mEymwNPT07W8vFwRHR091tHREY2Ojn4nODh4GpfLfUy2nUQiwQ8//BBQWVnZvG/fvnIMBmMmEonEdXV16ObNm71oNJrBnoHu7u6U6upqeUtLC/wvhJou79zZJlKpcCgMrMy0GgCrg8F3o2MUCqisrFTdu3evc9asWbZXr17twOFw6MyZM3t0SpfJZDJDu/f1ej3k5eWhK1asGNCsWGtra0xcXJwTwJ/5EJcuXao29Byenp62R44cqZ4/fz7P0GP3FJ1OByUlJeSmpiaUzWYPWjX2F/Hee+9xtm7d2uzv70/w8/NjGLMVyJkzZ5odHBxAoVD023hCEASoVCpGJpOpUBSFpKSkTplMJm1sbMQ6OTlhtm7dWj916tRBs1F7e3tbeXh4WN26davDx8fHPCsrq6ujowOYzP53eykpKWn97rvvwk+fPl1ugKUOGINtj+83lZWVVQAAFy5c+K2qqqo7O3vxnDlzPCIiIr6cO3fu3EddhSQSCYYPH247fPhwW4A/S+D279+vMKQhA/Dng6KkpEQ9adIkQw47aBGVlio/Lizss76MoaAAaAbTTX4HQEIAwNzGYCxwlZVN/v7+zPj4+AqRSIRbv359jzVWMBiMwePDd+/ebfTx8RlUqn5tbW3Q2NioP3fuXHVUVBTPEGPqdDqg0+nUxsbGruvXr6tDQ0NNnoCLoigcOnSoaebMmXSJRALsPla5DTQYDAbi4uJsLSws4NSpU1WzZs0y2nf+gw8+sD948GAFmUwGhUIBWCwW+mM8USgUhMvlqk6ePFk9Y8YMHh6Pt8ThcICiKEil0gGvdHsSHA4HwcHBTAAANzc3y8OHDzdwuVyFXq9HFQqF2fz58/vkqWlubq4Y6oYMwCtozJiZmdH++OOP7JMnT/7riy++ePjzY8eOFfj6+r517Nix33x9fcPXrl37MYPBeCqxk0wmw4IFCwzuM718+XLVzJkzDeoqH0yo1WpQKhRgQaOBWq0GjVA44G58AAACAOWvqqYBJ97GpmJ4crJDp1yOfO7n9zDPIC4urtcGhIWFhUEzZO/fvy9pb2/HTpw4cVAZM/b29rBy5Ur+zp07DVYWt2/fvgYWi0XbsGGDnTGTV0UiETxa5n7q1CmxjY0N1sPDw3z//v3S+fPn2545c6YjLi6OXFdXB2w2Gwa6wqovdCeMC4VCNCcnRzxq1Ciasa6rlZUV7sKFC5UymcxSo9GonJ2dsf7+/s8NA72MyMjIp4wvDAYz6AyZR9Hr9ZCZmSlev379w+dJWVlZ171799ReXl6E3l57hUIxoD0DDcXQ++a8BBqNpjt16tTaO3fudD75u9zc3C4AuAoAV48cOeI/adKkKc8awxg3cldXl4rD4byS+TJqtRqSXV3rgcFQOf74I0exYIHIAoMxrYzscyABIDIAHekvvZmB4i4O13SNRrN4Y/hwg3S7tbe3Z5SVleldXV377EHU6/XQ2dmpv3r1ar29vT1r4sSJg3YHFwgE7NraWqWjo2Of7dKuri70wIEDdWFhYTYjRowwuH0rFApBLBbrS0tLm0QikcLa2tqsqqpKQaPRNDqdDgkMDGTrdDpMYWGhKCIigsBkMpFZs2Yxt2zZUjtx4kTr/Pz8tq6uLjQ6Oppn6LWZguXLlztLpVKIj4+vcHJyMgsNDTW4u0koFOJHjx5t053/eODAgTI/Pz/rod4duzekp6cLnzwY29jYmO/evbsxOzubunLlyl59jzs7O2sMu8KB4ZWpZuote/bsOTdz5sxnGjPGoL6+vuvWrVvts2fPHvDQiyHQarWQlZ6uwePxSOWcOaKVGo0VDgDKAJTOAKTBkABwE0CMB1CNBjBqpUFPSBAIiidnZBis9D8jI6PD09OT2RfDu729XX3x4sVqiURCJJPJjCVLlgwqb8yzOHjwYM28efO4L9KgUavVLww7VFZWKmtqarQREREGr/u+ePGi0MLCgkCj0RChUCgNCQnpU75FQ0OD/O7du+I33nhjaMadAODYsWONYWFhDBaLZXSHqFAohJs3b1ZPmzaNZ+y5BgN6vR4OHDhQu3jx4qe8/OXl5Z25ubmtOp0OuFyufVBQUI/u86+//nrl5s2bdxp+tabllfPM9JS9e/eudXJyuu3t7d3/DKoX0NHRAYmJiXVCoRBja2tLvHLlitzV1RWPw+Fw9vb2PT5OpKeni8vLy7vUarXe3d2dHhERMWAPoJqKCl3h+PH1ERKJ4x0stmOWTkfrvpFcB0dEBwAAGgHa5gL0qCeKsXEvLbW+n5Wl9A4IMMj1CQgIYO7bt6+SwWBg33zzzV71M7p8+XJNbGzsgEv59wa9Xv9C+7impqYrMTFR4u3tjZk4cSIbQRDIzc1tYbPZVDabbXb27NmmkpIS9bJly4zS+6mhoUH6SLPSPksr2dvbm929e1dYU1MDXK5J2lQZHDabbdnc3KxnsYx/hmAwGODk5MTKzs5uqq2t1QQGBlrZ29sPCq+wMSgrK1OFh4c/M13BxcXF0sXFxRIAoKmpSbV3795yBoPBnDFjhuWzXg8AkJWVlX///v1TxlqvKcFu3LhxoNcwINy9e3dkUFBQLJ1ON4jb/0nS0tJEubm5dRgMBj9t2jSWm5ubGY1Gw3l4eJjV1NS0FhYWysViMaa7KaNcLn+u8mlHRwcIhULFjBkzbPz8/GgEAgEOHDjQNHr06AGRMz+/bVv70rQ0eyoA4qbXm5kPcAjnWWQBqNwAKJYAg0JdVa3Xo41RUSjH1dUg60EQBHx8fCxxOJzu5s2bDVlZWZ1isRh4PN4L872EQqHuxIkTMG7cOLOh5Jq/dOlSV0hIiMWja0ZRFBAEgVu3brWkp6eLN2zY4JiWlqZEEERZWVmpbGlpQfLy8trJZLJZVlZW16JFi9gMBsPg92pbW5teq9V28Xg8g3wfHRwcLM6fP1/D4XDopih5NjSOjo74Y8eOdY4aNYpiCkE9FouFv3Xrli4kJMT63r17nXq9XsNkMofehesBOTk5dX5+fs81TrqhUqk4Hx8fBpvNJh49erTKysrqKT0djUYDW7ZsWX/ixIlsoy3YhLzSnpmoqKgxer3easqUKZHE/2PvvOOauvf//z4hi0BCCAQSViAMEWWIIiIiiAtXFdwILtyjdtvb++1tva21Xlu7HbUW3BYriAtFcODCxRRQIUCAMAKEkISQec7vDws/6wIkC+vz8ejjUcPJ5/M5Sc45r897kkggEonaDx48uLm4uFgeEBCwgMPh6DxG4Jdffqny9fWleHt7W9nb23cVNWOz2Xg2m40HABg+fDi7vr5eQCAQYO/evdX29vaquro60rRp05yfzGq4efNmu5WVFfnBgwdSPz+/LqXj6OhI4nA4apVKBTgcDjQaDRiqyNmvcXE1bY2NiAp0UBZXj6AAQDIRIQMAkO3iUj910iSdFrkDAPDw8LDx8PCwaW9vh5SUFFF3x1tZWZl9+umntp9//nnjf//7X5NJPe0OGxsbbaeQ4fF4rXw+vz09PZ04atQocXl5ufn777/vBgDg4+NjWV9f3zpq1CiHv27etrW1tfDee++5pKamlkVHR+v8OwAA0Gq1OrMGUCgUiI+P5yQlJVUuWbKkX7qlw8PDqaWlpc2DBw/Wa5uYTmJiYugAAAMHDrTt6OiQGWJOY1BbW9srdWhlZYUbN26cW11dHero6Pi3916/fj331q1bqbpdofHot2Jm4MCBHn5+fpbW1ta+IpFIERwc7M9isQbJZDJwc3ML6ejoaP7xxx+9GAwGofMmiGEYCASCWV988cXl6dOnz9b1mo4cOdIQHx/vTKPRut39TZs2zbG4uFiSkJDgAgBQX18vr66ulh08eFDi4uIira6utoyKiqISiUTNqFGjLO3s7P72XU2ePJm7c+dOvo+PjzmJREIaGhpabG1tbcPDw/V683Dg89s3Fhfrre2DrhgJQDoJUPUWgKux1wIAII2O1qsVzcLCAphMpjgrK0s9duzYLpFSWFiodHFxIXVm1ZiZmYFCocBCQkJMPk7mSSZOnOi4bdu2SgcHBxKbzSZHRkY6RUZGAjwVDxUSEmIOj/uKduHk9DhjFYfD6aWTJZPJRB4+fCgfM2aMja4sEQiCgLe3N+n48eOV/THOrrGxUT148GCDdzLBMEybn5/feP/+/bbJkyc7d/Y8eh1IS0vjTZ8+vde/BYVCoaHT6X8z+7e1tWnPnz//3wcPHqh1t0LjYvJiZvDgwUvu37+f+ORrCIIQ9u3bdyEgIMDe2dnZHMMweI7J/JldJ4Ig8Nlnn3nh8Xi9xAsQCARFT4QMwOP0P19f3y7LEJvNprDZbBg+fLglgiCg1WoBh8M977wA4PFDad26dU861ZnXr1/np6SkmKlUKm1gYCDFy8ur291ifn6+4tGjR8q2trY2c3NzpYODA6W5uVk1Z86c5140NIHAZKwd3SEDMJ0LtaqqXd9TREVFcTMzM6sPHTrEJxKJ1hwOhyIUCsVSqRRFEITaGRBoYWGBiEQiMfSwZ5Up8PDhQ2l0dDTDw8PjlUVha2srUa1WAw6Hg+d1Ju4L48ePd66qqlJxuVydXR8hISEOZ8+erdXVeIZEqVRiJBLJ4PcKNzc3opubmxeKorBnz56KFStWcPuTO/VFYBgGYrGYwmQye62WnZycCDdu3Gi4cOGCfNWqVVyAx4Xydu3alab7lRoPU0g6eSkqleoGgiB/W+e8efO2ODg4UJycnJ4RMvX19Ypbt241t7Y+k5kNAKDXOg4YhtEOHDhQ05cxOs/FzMzshULmRYSGhnImTpxoPWfOHNvc3Nw6FP3/FfNRFIWammeX1tTUJAkLCyMuXbrUJS4uzjMyMtLRw8ODnJ+f/zdT7YXjx9v3TJ1aFSEW95uoRM1jb5NJwLh50yBrGTdunMuCBQs4MTExNAaDgbJYLEJoaCi7rKysofMYEokETk5OmEjUrVfKZFAoFG19ETIAAHFxce5Hjx5t2r59e3VVVZVSV2sDALh69SqPTqfr9Kkpk8lAIBCY/IbzefD5/I6Ojg6j1ZrC4XDg5+fnlpGR0dD90abPvXv3mqKiol6pdQKBQIDw8HDW0KFDqWlpaW05OTmavLy8G8jroPKewOQvlEePHj18+rUjR458MHDgwC8jIyPfolKptgMGDFgwceLEQHNzczhy5Ig0IiKCoFQqtWDgwNTZs2czLl++rNObZG+xsHhs2Z00aZJ7enp6e01NjZRGoyEdHR1yNptNJZPJtkzm/68x5efnx6ypqWlhs9ldpvnAwED2H3/88cjHx8fr5s2bwqsnT6reSUx08AKwtjLBYN8XgX/crNro1AK0Nw0ZYtCNg5mZGXh4eBABgAEAMGrUKOadO3ekQUFBVACA0aNHO+7fv79i0aJF/aLJoS4qHuPxeIiPj2dqNBpISUlptrS0xNna2r4417uHXL9+XRoVFeXAYDD6PNaTWFhYgEajUelyTEOxevVq1r59+8oWLVqklxil7ti3b19VZGSks52dnUF6J+kbtVqtys3NbZg0adIrp+wHBQUxFQoFSKVSOHXq1AXsNavLYvJi5kWUlpaKAWA/AACCID988cUXmzkcTmRTU1OetbX1XBaLZZSHrkgkwsrLy1s7U+SMhZWVFZKVldX++eef21MoFASHw0FJScnfXB0ymQzOnj1bsWTJkmfaynM4HMt9+/bVBgcH4z/assXp/JkzvIVCod7az+uDNgBLgyvaJ8AA4BYO11y8cmVH3ObNRo17cHd3t8rOzq709/en4nA42Lt3b/WoUaMM1u24r0gkEp091PF4PEyePNk2Pz+/ZdSoUX0qzXD8+PFqrVarDg0N1fm1UVpa2uzn52fwuBNdoFAowNXV1WhFGIlEovZ1KlI6YsQIx0OHDj0CgD7VHyKTyUAkEkGtVkt1tDSTod+KmSfBMEwLAB8jCIJgGIZNmDBh24oVK/ZHRkaGGNqSFhMT43DmzJkyY4sZAIAPPviATqPRuj6Ampqa5ieD8lJTU2sWL1783JvwiBEjHEaMGNH1b83779spNm40nSIyPWAJgNNtgLYQAIOnsAsA2v+MjGycsnMnN4b5ytXWdUp8fLzbsWPHymk0Gt7Ozg4BE3LDvYzbt28rxGKxTq+ntrY2FIfD9ckNcuDAAd6IESOsPT09GbpaVycYhsGxY8eU7u7u8qqqKvGCBQv61UYiNTVVMHPmTEdjza/RaF4rq0NBQUFzWFiYTmpmyWQyKCoqeqCLsUyJ10LMdNJpNsvIyCgfPXr09LKysq3Lly9foutgv+6Qy+Um0ZfIwcGhKwAPwzBoaGjoEjYZGRlCBEFUPRV7Dt7eZCGAth1ALQdQDwUw+WwYMgAoAfSSwdIdFTicMmzLFlcbExEyAI8tEvPnz++6IWZnZ/OPHz/egSAILSYmxiStNJmZmcK6ujqZhYWFTl04ubm5srFjx760qptarQYEQaC6ulrl4uJCfDLe7vTp0+3Tpk3j6jpOphMEQeD//u//HM3MzKC0tLT1/v37HYMHDzbfvn17tZ2dnTouLs6kxY2NjQ1RJBIpWCyWwfc/2dnZ4hEjRtC7P7J/0NzcrCgtLZXMnz9fJ5mqNBoNAgICBgJAri7GMxVeKzHzJNnZ2U0Igiyn0WiWsbGxPU7DLigoUPr7+/ephAqdTncQiUQYg8EwmQArBEFAoVDgAQCOHTsmGDp0qMWECRN6fEOsyMxszXdy6ph69iynsqDAjBUfjzr2gwDydgAJAOi1yvPzIAFgpZmZLW6enqajZp5i9OjRHIDHPYv27t1bkZCQYFLxM2KxGBAEwS1cuFCn6/rtt9/4JBIJRVGUCgAvvEZPnjwpFggEajabTdFoNEoul0s9fvw4D0EQbUdHh+X+/fsl3t7eWEREhENfuje/iM5N2MCBA633799ffujQIdr48eNRsVhs8u6Tmpoa+ejRo43y26+srNQyGIx+k3XZHZcvX66eP3++TjNwZTKZUTZ5+uS1FTMAj91PU6ZM+SIsLGyCs7Nzt64GFEXh66+/vnT48OGovrinxowZQ9u3b18Nk8lUqtVqwOPxMHz4cMcng2yNgZOTEy41NbU8PDzc2c7OrleCbWBEhHnt2bOthfPmlVJqaswdTKR+S3e0Gyk9eziK2tT+97/16MqVYIgqqK/Knj17Kq2srLQ0Gq3L/Xjz5k1xbW1tg4WFBXXy5MlGcxXQaDR49OhR+9ixY3VWO+nKlSuioKAgc39/f7vCwsKG8vLyVhsbG4aVlRWZz+e3hIeHc+h0ulllZaVYqVQKp0+f7nb9+vUWDMNo+/fv58XFxbk/KVw0Gg3s2rWrITw8HOfr66u3+v0TJkxwWbBgAdHMzAz27t1b8euvv1atWLHCVV/z9ZajR48KCASCVKlUEj08PBwRBEENVcjzSXg8nnbo0KGEQYMGmWzT1N6gVquhvb1d5yELEydOTBgwYMDphw8f6r1khKH4RzSa/P77779btGjRO90dh6IoREVF5WzatGmQrouKnTp1qgYANFOnTnVDEASUSiWQSKZcQ/f/k3/pkpQ8b54sRK3ud83vMgAaXQAI3n9l9RiS7XPnCpbs3Gk0MdAdra2t2p07d7bOmzeP1tHRob19+3YTm80mu7i44FAUxRUXF0vmzp3raqz1HTx4sHrSpEkONjY2Ott0HThwoNrMzAyPw+E0dnZ2FpGRkTb19fVAIpGAwWBAYmJiFZlM1jAYDMuJEyeyAB4LlsrKSvD0fH5ijlwuh8TExIa1a9caLHNGIpFoU1JSql4U82ZIzp49K/H29sa4XK4VwOOCeWq1GnNycjK4daSlpQX77rvvGry9vTVMJhMmTpzobOg16JLKykqZQCBAR40apVNxhmEYHDly5MS6detiXpesptfaMtNJVlbWvqioqNX29vYvVA9CoVC7a9cu6f79+wOLi4t1HvMybdo0Zx6Ph12/fl0qEonqGxoarEJCQki+vr4m79stSkpqeVutdjX2Ol4FJwAbDKDDGHMjFRVGTdN/GSKRSPv555+LP/vsM1uhUCglk8n4kJAQKxKJhD169EjK4XDs5s6da3AB2ElpaanK0tJSo0shAwAQHx//TJO+J1uILFmyxPXpv+Px+BcKGYDH7Qfs7e1J3XXt1iU0Gs2MxWIZ9f6Noih88MEHwujoaITL5Xa5lOzt7XUa39QbbGxskC+//JINAFBSUqI4d+5cU2hoqA2VSjVd8+gL4PP5kjt37sjnzJmjc5GMIAhMnTr1rRs3bkQCQJauxzcG/4hGk7GxsQ2bNm0KtLa25j7vBnD58mVFenp63Xvvvcei0+lmXC5XLxcjg8FArly50j5nzhynIUOGWObl5TV7enqavDn02vbtknoqtb7AykrUIZEgThjWb5q4lQBonADIli+JjdAVAgB1NYBs96hRTY+GDm2xCQ+neAwfbqnveXsLiqJQXFwsx+FwmuDgYIq9vT2JwWAQbG1tydbW1mQbGxuLiooKMZ/Pl2ZlZTUWFhaKqFQqXZcxYCiKQltbG+Dx+C43XFlZGRw7dqzy7t27YhRF5W+99ZarrubTNx4eHuaZmZnVnp6eBsucw+FwuLy8PJmbm5tRKjkjCAJjx461OH/+fFNgYCDd1GqwMZlMvEqlIp0/f55PIpHMeutaz8nJaSQQCGaWlpYGF2eZmZnVEomEMGXKFL3FHRGJRKSgoOD2hAkT7uhrDkPyj7DMAAB4eno+CggIiH76dQzDwNXVFamvr0eXL19+PzExcfCT2U/Xrl0TNDU15Ts4OIwIDg7ucyBpfHx8125XLpf3i4JYa69e7drN1tfUoLvmzCm3kMmw2QKBp6mnavsCEIsBGlgAenUBYACwa8aM1unvv2+9btAgo3Qz7ykIgkBpaal26dKlz41Fsba2xoeHh9sBAISHh0NBQYGUQqHo7EmVlJTEs7W1tWhra+sgk8koDodTq1Qq3IABA9grV650M7WHYk8gEonQ0tKiqqiokHC5XINsULhcLq2goKAcjBDg3gkej4eFCxe6Hjp0iB8fH29y1cF9fHzwPj4+3EOHDpX1JI6mqqpKcvHixSYKhYL39/dnpqSkNK1evfoZa56+aW5uVs2bN0+v5T3u3btXcf78+QPbtm3T5zQG4x8jZmpra6ue9/rOnTtL1Wr1xQ0bNqytq6vLvXfvnsTS0hL18fGhAwCoVKqqpUuXTv3555+TgoODF+lqPfv27auMiYlx1dV4hoLt7Iybe/OmB4qisH/AgKrlLS2upvzooQNAM4Bc3/MgAODg6Kh1HzTIaCb2noIgCDQ1NSklEgn81Vn6pfj5+VETExNrly5d6vSqc2ZmZrZHRkZaXLlypc3T05MUGhr6WlRmfZL4+HiP3377TWxtbY1ZW1sb5LIgEonkwsJCuZ+fn0GsM/X19WhWVla5UqnEmZubk8PCwth3795VeHt7G80l2RO8vLyc+Xy+lMPhvDAWsrm5WZWbm6taunRpVxySUqlkVFZWyg1p/UJRFDAM0+uGSK1Ww5kzZ74uLCyU6HMeQ9Lv/IivSnNzc9vzXudyuS08Hq/p119//aqgoCDhyy+/nBgfH3+vrKxM2d7eDkKhUAkAcOXKlc/Onz+fpVTqJgyCTCajVlZW/fbzx+FwEHTmjFMeDtdi7LU8zS8AlScAeAcAHqYB8KMADJJy7L1rl1n60aP9Ijtg4cKF9OTk5B71EUMQBMaMGWOVmJhYUV1d3etgwcTERJ5Go5GfPn26ysbGpiM0NPSVRZGps3TpUvo333wjFIvFBilIOGXKFKf29nbR7du3n3t/0yX3799XZWZmVsXFxXklJCR4xMbGOjU3N4tHjBhB8QoIMgAAIABJREFULCoqarxw4QL/3LlzvEePHpncPaG1tVVGIpHwAACd3w2GYZCWllZRVFTUCgBw8+bNxpiYGNun3gcdHR3PveljGAZ37txpunDhQo1YLNZZnKVCoQAajaZX8aRWqyE/P79Yn3MYmn+EZWbjxo3xc+fO/eB5fwsLCxuVnZ1dsXHjxkUbN27sfHlcTExMeFNTk7K4uPj+6tWr4dixY/z4+PiLcrk8ODo6+m9xEN3tcLVa7d+69N6+fVvR2tpq8jv47nD18sKncLmiwPJyo5m5n0QAgF0EqI4H4NCMINTDtVq7XVevVsO8eSZfgp7JZBJpNFqPhZebmxuVTqebp6WlNS1evLjbFORff/21OjIy0t7Dw4Nka2urjYqKYgKAydbc0RU4HA64XG47kUi0N9ScLS0t6MOHDzsQBMF19t7SB7m5ucIZM2a4PvnakCFDbAAAli5d2lWM8YcffuB7eXmZxD2hE4lEAnQ63Tw9Pb3p/v37qg8//NCxqakJzM3NXR4+fNjA4/FEGo3mmXtyWFiY5W+//VZVVFQkmjlzpnt7e7s2NTW1kkAgmGEYBmPHjnUNCgpCfv/99ypXV1eIjIx0ffL9PB6v9caNGy0EAoEwZ84cTk/KNIhEIjmFQtGrGEYQBOzt7Y1epV6X9FvLQG/AMMzezc3NT61Wg0Kh6Hq9uLhYZmFhAYMHD37mPSkpKVeuXr2aIxKJurpHq9XqjKFDhz5TbGj37t31L5t/7969j27evCkBAEhOTha7uLjgQkNDX4sfknzGDBtTCfw5A9AcZyQhA/B4UvyNG2qNxvTrUQmFQo21tXWvLCTW1tb40NBQy5KSkm5r93C5XKyxsbH54sWLIgzDsOLiYnlbW9trkQLaHVOnTuWWlJTIuj9SN8jlcg2KorjW1laxSqW/q3H48OFOaWlplS87BkVR8PT0JJnaNfDWW2/Z7t69uzYkJIQ5dOhQs4cPH7acPn26Ii8vTyiVSjVRUVHus2bNeiY2Bo/Hw6pVq1w9PDxckpKSKk+ePFm3ePFijwULFrjFxcW5sdlsBABg6dKlrgwGg3n79m1h53sxDIOrV6+K4+PjPaZOncpJT09/6WfXiY2NDYXH4+n193P48OE9ycnJZ/U5h6H5R1hmtm3b9kNKSkqqi4uLs5eXl5ulpeWkkJAQ+rVr12pu3bolPnHixOa1a9d2O87Ro0fvfvLJJ5vXr1+/7cmCUAiCdKhUKti5cycPQRDqgAEDLCZOnNi1Ozc3N1cWFhY2t7a2KshksozFYtFZLFa/r1B56rvvGkds325mKidiBtCGGHH3jwGAd22tVf61a4phEREmHRvNZDLx1dXVQgDoVbaVi4sL5V//+pcwLCysVavVEseMGeNmbm4OFAoFNBoNfPfdd9VsNhvn5eWFHz58uGN9fb3y0qVLagRByDwerz0wMNDksrt0DZPJhHv37omhl5/tqzJnzhzuo0ePVGZmZmhFRYXW29v7pRWCCwsLVZWVlXIej4cuXbqUQafTQS6Xw6FDh6opFIo5gUBoJRAIoNVqcR0dHXg6na6xtra2dnR0pE2dOvWlDVNxOBxYWFhQU1JSxA0NDZIVK1a4GKN43tMQiUTYsGGDEwBAZGQk66effqoPCQmxGTZsWI9iU4YOHUoYOnToS889ICDAYt++fQ3Dhw+3EwqF6szMzPpp06a5AQBYWloCgiDEq1evNoSEhLCebI3xNH/++WfVrFmzXHt+dr0Hw7C616W+TCf/CDGDYZgaAHh//XcZABIBAGbOnAkAAO+//36Px9qyZcu3VCo11N/f3yc4ONgLh8MBhmG1mzZtanz//feHMRiMZ0yVSqUSli1bxm1ublbZ29vbAQBUVlZqhEKhJjg42PhX+iuiyM6WBKHoiwtwGBgbAIPrKiGAMtPDowrB47FGV1fy6E2bXHw9PU3e4okgCLi5uZFRFO1VhWISiQTbt2+3AwC7mpoaqUAgkGZlZYlYLJaytraWvH79+r89vNhsNik2Ntb+66+/rtm4cWO/LmDWU/744w/+9OnTDZrZ4+XlRczKyqoeO3Zsl3VBoVAAmUyGe/fuScrLy1Vz5861zcnJaSOTyerCwkKVm5sbubq6up1Op1ukpKTULV261AWHwwGCIH3aEISHh1sAAFy6dEkrFouVLBbLpKqDXrp0SQQAVGdnZ53HpUyePJm7Y8eOKh8fH0ZsbKzLU39zlEgkcPjw4arhw4fTvb29n1tjjMlkonS6fsuPOTg4POuO6Of8I8SMLvlLzUZ7e3sTwsPDY3E4nLawsDBj2LBhw4RC4a/m5uaO7e3taltb2y5RY2ZmRlSr1WBvb9/1sM3MzGwOCwszKb9yb7GcPZstunIF+8XcvOnTjg69lXLvCb8D8KYCGDyw9JchQ4TvZGUNMPS8fSUvL6+utra2Tz4JZ2dnqrOzM/j5+b00TmPfvn0Vy5cv5/THlOvecvbs2XofHx8KhWL40i9lZWVQUVFRZWVlpVQoFDg8Hm/R3t6uolKp2uHDhzsmJiZWubq64keMGOE0aNAgIBAIkJ2dzT9w4ICIz+drMQwDXX1HhYWFHWQyWZ6Tk9NaW1trtnbtWpNIuUdRFFQqVfuSJUsYlpa6N5wxmUxkzZo1ri/6O41Gg4ULF7reuXNHevDgwfIhQ4YwfXx8rJ78bAIDA53+/PPP5lmzZumsjcfTCASCAn2NbSz+Ee0MDMWcOXN85HK54+jRoxf6+fmNcXR0tHdwcMAfOnTobnx8/LAnj8UwDL7//nvhu+++awcAcOzYsebZs2fr7cerDy4dONDSdPKkuJnBoGw8doxtTGV8BODRfACdNmPrCZ+7uNRMP3bMwdXT0+Sb/3UiEAiUt2/froyOjvY2xHwZGRl1Wq1WFRUV5WoKDzR9cfDgwarRo0czXVxcjBIArlargUB4tbwCkUgE2dnZ0hkzZugkgFgsFsPx48d5Hh4ett7e3lbl5eWNoaGhBguKfhHJycm106ZNczI3N426nzU1NVhhYSF/ypQprgAAbW1t6KFDh6oXL17sqi9BLJfLYfXq1REnT568opcJjMQby4wOSU5OLgGAEgC4gCAIwmazWTNnzoyiUCgDURQd9qQ5H0EQkEgkHVqtFrRaLWRnZ7f2NzEzJj7eBuLjbcofPEBvHD/eMhpFjWJp2gFQMRHAKDfKz6qrnX9fsKDM9fZtk3G3vYwLFy5U1tbWWguFQoPFrkyYMMFBLBZDampqRUxMjEl15tYFTU1NcObMmbKQkBC6sYQMALyykLly5UqNUCikTp8+XWe+DTqdDgkJCV31Wi5dutQGRrpG/4oHqrSwsCDW1tYipiJkAACcnZ2R3Nxc3LVr14QPHjyoYLPZnqtWrXLVZ3PavLy8R6dOnbqqtwmMxBsxoyf+ckfVA0BiQEAAPjQ0dFxYWNiQJ49ZsWKFfVJSUsOVK1fObNq0aY5xVto37ly+rDSfNUs6GkV1LsR+trXlU7y8lCSBAOgCgXWURsN8nvnDBkDlDmDwqrsSAOykgwOvdeBAk4oJeBlSqVS5ZMkSek1NjUE/LzqdDjKZwRJ8DEpGRkbz/PnzPftL49gnEYlE2oKCAvzbb7+t1yANf39/px9++IG/du1azsuCX/XB0aNHBQkJCW4tLS1KsydrZJgI06dPdzl48OC5lpaW84sXL/5On0IGAECpVNZhGGaQOkiGxOQDFV8H8vPzNcXFxelPu/SqqqoaMzIyflq5cuVQNzc3vdWH0BcoikLe9u31YXoQMhgAuCgUEH36tNfkvDyvYbW1zBPm5rVPH5cF0OIFYPA0dwGA/PvISEHIlSseCfv2Gbzc+aui0WjMAB7vCA09t0qlIqPoa3cPhcrKSo2hH9C6gsFgmDk5OYkBHl/P+mLgwIGUsWPHcmpqarpN69cVxcXFsiNHjvAbGxsBh8MBk8kkMRgMk/uicnNzH+Tm5v5769atPx05cuTPwsLCFqlUqrf4D09Pz6GTJ082iIvZkPwjGk2aAuvWrbvp6OgY4e7u7gLw2L+9Z8+enfPmzZsTFhbma+z19ZYrBw40502eLEmoqHDSZfU/PoDqGonUmL54cTt12TIrNx8fEgCAmZkZ8KytlQ/u3m2w7OigWP1lVUxms4UKAkE9WKGwNqQyv4PHiyZfv+5gSe1fGhSPx1uKRCK8jY1hPYKXLl1q8fHxwdvZ2RmlKaI+GTRokOW+ffsqgoKC+mXtqObm5o6HDx9Kzp07pxk2bBhFX3FNdnZ2cOzYsXoHBwc8lUrVe9HQK1euVMydO9dj1KhRNFON1ZJKpehPP/20PDEx8fLnn3+OBQcHH3Nzc/tZJBLlNDQ0tDo7O/tTKBSdCjArKytSa2urODQ09JIuxzU2JqdSX1cKCgo64uLiVpSUlEwRiURiHA6HVVZWKj799NORvr6+91avXh08ePDgfhF3AQDgMGSIlXTrVtWeTZuq40UiB0YffksqAMjC4USVy5crfOfOtfflch1Cn1NRedzSpUxYupR5Lztbkb5rVznz5k0zy5AQ3Liff3bdMXp0xVvl5ew8MrmVplbjQ7Xav7XILUYQ0WVPz1Z3Hs8qSqvtsyUpVKOx/zUqiqdlsfBx+/cb3HT+KggEAkVWVlbTqlWrDJoinZmZ2crhcBBPT0+T7t/zqlCpVDBB70WPCQsLcwEAIJFIDfp2cURERDAbGxvlbDZb76LWwsICAQCdZWjpg+zs7PO///572pOvYRgmB4DTAHB6zZo1pwMDA2f6+/tP9fDw0Ek/MxRFQSQSNetiLFPC9O/ArxEHDx7sDBAGAICRI0e63L9//4harZ65d+9e62+//dYTh8PB4cOHb5WUlNwKDQ2dOmnSJJMMmKQxGPiWxMQalr29slkkUjD6UCDsoKMjb9TFi+4jmD0rbzF09Gjy0NGjPVAUhYsXLzaTyWSIu32bey87W8kkk+new4ZRkt57r5pcVtahrq/Hja6qYuExjEyYO9faYsQI8qWZM6vHKBQuMgCQAWhYPbwO8szMhNXm5h1RMhmHDABv5+e71wOguTk56uGjRpl0ewoMwyAzM1OwZs0a9+6P1i0ymUyOomj/CyjpBWw22+RbWHSHu7u7xYkTJwQzZsxw1PXYKIpCampqvZOTExYcHOyg6/GfR1VVFbG4uFg4aNAgo5aNeBlarVb4sr/v2LHjHACce/fdd2evXLlyv729fZ/rkh09evRYamrqji1btvR1KJPijZgxAPHx8b4kEsknIyPjRHV1dVfTshs3blT/9b/JAQEBKb///vvvsbGx8Xl5efD222+vc3R0NNmYpguxsRUbCgvddbHnUU2bZs7soZB5ktLSUrlcLpcBgC0AwNDRo7semHO+/74rjqXo7l1V7f37HTMXLmTgcDi49u23lH2//PLgYXs71XflSqL95s3yoPb2F7bTVQEAAQD4FIo8lMdz/WnatMq5t245HKBQxDQWqx0uXaIOHzXKZPsOSSQS7dGjR2tnzZplcCEDADBjxgzHzMxMobm5uczFxeW1qwBMJBJBpVK1gpGydXSFnZ0dVSKRKLo/svds27ZNsGzZMgcbGxuDmUnWrFnjdvz48QemLGYYDIZvYGAgNTc3V/qy4woKCuopFIquCqwKHzx4YLDYJUPxRswYABsbG5fY2Ngdc+fO/erHH3/MSkpKev/pH29+fr4GQZDFMpms3dfXd4yzs/MLhUxra6saj8ebUalUo4kdgosLghQWdnvcdQKhrolEUnnJZLY+AJYtACgVAFcJ0FZNIsmqGAxl4Lx5r7RTa2hokPUkcNp32DCi77BhXQULR82fbwvz59t2Vr8t8PVV7Dl5Ujjg4EElamaGUuVy6jAUtbYEQC6SSLWFK1eSSVKpguHsTMTj8bDkzBm3yydOtK+OibGv4fOhoqLipTciY9Le3o7l5ua2e3h42DAYxvPyUCgU4pN90V4HFAoF/PHHH3U2NjYyLpdr8Gw6XUMkEkGj0egl8DQkJMTi/PnzooEDByJDhgwxyA/x/v37SicnJ/2W0u0jQUFBgX5+flEAcOxlx4lEopycnJzC8ePH+/V1TgcHB/++jmGKvBEzBuD7778/k5CQMHHEiBHvKpVKOpvNHgAAd58+7q90udUpKSm5LxtPLpdrMjIyeEuWLDFaRDqBwcA1AMhZAF2+77t4fMMjMlk9QKEglHp5tcrd3UmBH37oGD54MIn34IF296+/VtP9/KiioqJ2Rz8/QtiiRY7DXjZJN4wdO9buwYMHqi+//LJ59erV1jY2Nr0KXOiMD/APDSX7h4aSYevWrr8d2rq1GVOrtQNCQ60WjRlDffp9kTExFgAAzhwOXMjKEoY/dYyxwTAMsrKyaurq6tRhYWEce3t7owZ1FBUViRcsWMABAGhtbUWtrQ0ar60TFAoFHDt2rIZIJHZotVpQqVSEuLg4t/4QL9UdGIbB/v37K4ODg/ViXRo9ejQdAGDv3r2CIUOGdHe4TigoKJBMmjTJZC2mAI/bg4waNWosdCNm8vPzNT///HMJAPRZzAQGBo74+OOP/2/r1q2bX6f+TG8qAJsgGzdunB0TE/Ojl5fXCwO+Pvnkk4Lp06c7BwcHP7PL4fP5rRqNRuXu7q43s7eopQVEXl7iIAyjZ1hYVAptbNTMzZvtq9LTxV4zZzICDPxwP3z4cHlsbKyHIefsZPfu3TVMJrODSCQCgiCk8PBwFzKZjPz55588NptNDQ8PN7iZOy8vT8RkMhlOTgbv8PBCEhMTK6dNm+a4d+/eFg6HI4+KinLXdw8aXaJQKKCgoKA1ODi4X2YtPQ8URWH37t1VWq0WYmNjXfVtvUtJSSmPiYnx0Gq1Og2alslksGvXrpp169Y5d/YGKy8vV6pUKoWPj49JW82OHz/+27Jly5a/7JjZs2d7T548OWnGjBnBuphTqVTCgQMH9n744YfLdDGeKdD/txSvIVu3bj0WExPD37Rp0yUHBwfK0xkGGo0GWltby7y8vPwBHu+qPv744yNBQUEdKIqqMjMzVSQSKeinn37Sm5h5lJ/fISASFQ98fB7QV6xgTpk71wYAYPiUKUa5ceDxeI0x5gUAWLlyZVd2EIqicOLEiXqJRKKIiIjg3Llzp97Q68nJyRE0NTWZGWoH3FMIBALzwIEDLRs2bGDj8XjIzs5W1tfX1yxYsMAoIrS35OfnKzgczmsV81NSUiJ2cHCgTJ8+Xe+CWyAQAJvNZtbU1Mh27dolGz9+vEVERMQrb3rkcjkUFxd3BAUFmScnJ/NWrlzpnpmZ2RIREWFz9epVsUQiaRw9erTJNzcdPnz4zPXr1x/96aefsl50TGho6HJdCRmAxxahCRMmzF62bFnSb7/9dk1X4xqTN3VmTJS5c+cKdu3aFZGZmZkVGBg4hEgkIiUlJVK5XK4BAFxFRcWpw4cPCwMCArj/+9//9imVStnp06d3C4VCSmpq6lfTpk2zpFAoziwWSy9mVicul/BHUZFoZWqqu8vgwUavHZKXlydhsVgUCwsLowp0BEFg4MCBVD8/P+sTJ060LFiwgG3oNdTX16tVaswyaf+dRoWizkwkEqucnByIra1iMDc3XpN2Pz8/YkhICBWPxwMOhwM3Nzd8aWmp0svLi9ofUpsbGxvV2dnZtVQqlWxjY2PS2Ws9xc7OjlxYWNjq6elJ0/d3QKPR4Pr169KmpibF6tWr2Tk5ObW+vr5dpqDa2lppTk6O0N3d/aUboj179vDKyspaKisrxZ6enhbXrl2rGTt2rJO1tTVeJBLJm5ubFV5eXhYhISH2hqhn01fa2trac3JyTk6bNq38RcdUVVX9y8PDw1WX81pZWZE0Go2jn5/fQV2OayzeWGZMGDweX3/nzp3U5ORkV61WW3Hu3LlHERERQY2NjRZjxoyJOHfu3Oxffvkl7dq1a+cYDMZWAoFgBgAsDMPUAPDd999/bxsQEOCjr/VFLF5sMr+fuLg47uXLl+vt7OwMLh6eBw6HAxwO1wYAXWJSl12JX4a/v7/NuvVfNEZN/Y/j7Xtnah0c3cinziQK7VhDrNtllU32TKUN05aBl8qUHWqVnLRo0VyjuU08PT2teTyefODAgV2CWCwWa7VaLWpqgmHo0KHmvr6+7gcOHKigUCgsR0dHo4v4vnLt2rU6uVyu/OOPP0Tx8fF6D8ydN29e1/Xg4eHBOHr06CMfHx+Wr68vLTMzs5FOp7/UwiqTyWDw4MG2ISEhXYKHw+F0ZemNGDGiX/W3AwBobGwU3bp169bLjqmurq6+c+dOaWtra9mAAQNGcjicPp9nWVlZLY/He+m8/QmTeRi94VkyMzPF2dnZ5wDgHADARx99BAAA0dHRflKp9P2hQ4eGOzg4DJgwYULbnTt37ty+ffsOANzpfL+FhYXeivBpNBooKSmBMWPG6GuKXvPo0aMOd3d3lE6nI1Qq1eiVsqytrenXr19v5nK5FGtra8qnn37aGBAQIOFyuTb+/v4MPB4PRCKx+4F6gEajAYlEAt9+l1xpZT3QbMqML5zJZAskJHSBMwAAx9X/r4dIqDWKotDeLtZ0qBuJ7q41Ru0vEBgYSE5PT+cNHDjwyaaEFc3NzTg2mw0YhqF+fn4sDodDLSwsrK+trZXh8Xh8XV2dlsVikTo6OrS+vr4OtbW1zTY2NnhfX1+9ukuIRCIkJCRwk5KSyhcvXtwv3GMvo6OjAw0LC3N1d3c3uGksKCjIOigoyLq8vFyRmJhYPX36dPfi4uK2AwcOlE2ZMsWVwWA8I2azsrJqhgwZYsvj8RTu7u7GMzPqkKCgIK+YmJhPAOCDFx1z586d/2ZkZKgyMzNrtm3bti0hIeGDvmyMBAJBW0pKyr++/vrr18IqA/AmANhkWb16dSSZTF7z3XffzXrRMQiCkH/++edr7u7ubocOHfp50KBBg+vr6+vKy8u/LSwsxKelpRW5ubnp5YKXSCTYuXPnJHPmzDGZ4DqFQgHl5eVQVFRU6+bmZj5s2DAbY2eaoCgKaWlp/Lq6OmzRokWulpaWIJfLYffu3Y1MJlMaFxfX6wfirVv5klaxRB01cbTNnTv5kstXHzZ2KPA4pt1gO46rH9XMrGfnnHfvOO/jD6KMUnvmSZKTk6tYLBZlxIgRdmVlZeJbt261LF26tGtde/furSCTyRonJydGeHh41460paUFbGxsgMfjgbOzMxw+fLh29OjRNC6X+2z5aB1z+vRpAYvFwoYNG2Y6EdavwPHjxyujo6Pd9F35t7fs3LmzcsCAAYzIyMi/3V8yMjJqiESiGYlEokul0qYJEyZwjLXGVwHDMLh586YsMDDQsjNQGQCgtra2/YsvvkhITk7+o7sxBg0aRPniiy+ujBkz5pWSQQsKCkpSUlI++fHHH9O6P7r/8EbMmCgffvjhyvHjx7//8ccfD31ZQSUEQZB33nln3Zo1a761tbUlAACUlpY2kUgkKpfL1dvORSgUasrLy9UjR44019ccfUEsFkNGRkYTgUCQREdHG/2B/TwyMzObNRqNdOLEiW5P77Lq6+u1LBbL7Hm7ry+3pPFYDn5WzqxK0s1b/KaQsHWvVCW6ml8kDvRtIw8Z4k/++ZckYeCQgXQvLw+8nR3T4E82jUYDP/74Y31ISAg9JCTklX9Thw8ffhQbG+uly7W9iF9++aVy7dq1boaYSx8IBALVo0eP5GPGjDG5lDKFQgHnzp2rmj59uuuxY8d4fD7ffNy4cZSCggIpgiDqRYsWcSsqKpRNTU2twcHBOinzbwi0Wi188803NWQyGXV2diYOGDAAN2jQIPtTp05VJCcnzyspKRHOmjVrdlNT08PExMRTLxpn48aNCTNnzvzc09Oz12L63Llz9/Lz889s3br1s76djWnxxs1kovB4vOqVK1d6uri4BANA5ouO+6tOwE+pqanzIyIiQgAABg4cyMQwrCtG49y5c9e4XK4LhUKxqq6ubqDRaKzc3Nx97u7uYSNHjhzyKuZKmUyG4fF4k9nOHT58mI/H49Xm5uaMkSNHMqysrMDV1ZXQ1tZmMmt8mnHjxtm2tLTYJCUl8SgUigYAiLNnz3ZDEAQOHjzYCABakcQBsaGjmrfXz3XtdEkRCa1m9vbuti1Sa/XQ4PGv3O6ipZnf4eUVRP/2u/1870HLOIImCZaecaZu9YpRjg4ObDDkbh2Px8N7773Xp3gnjUYDVefPk9KOHHkoaW3FjztyxI39kuKTfUGlUoG/v79J1RbqLXV1dRp/f3+TEzIAAGQyGczMzOD48eP8qVOnuuPxeLhy5Yo0JibGmUJ5HKrE5XJJ9+/fbwMAkxczd+/ebfX19bXG4XDAZrO1CxcudBWLxZCbm9tSWVnJU6lUpM8+++yihYUFxd7eHsfj8VqHDRs29O7du5XPG2/r1q17N23aZOfp6flVb9fC5/Ot+Xz+vr6flWnxRsyYKHfv3r14/vz5k0OHDt02aNCg0OLiYvnLjr93714iDoejAQC+sbEx6+HDh4UymUw5ePDgeSkpKQk+Pj4ezc3N0uTk5DwvLy/mw4cPhX5+frSIiIj50dHRHwYEBDxjvUhPT6+nUqmE0NBQW61WCxqNBurq6qQ3b95strGxYU6YMMFk0lStrKzMpkyZwmlvb4fKykp5eXm5ikqlosOHDzfpnbONjQ2yZMkSdwCAlpYW7KeffuKbm5sjCQkJLo2NQrhbYKOhUm3Mdu0tahU1P2pdtXy06wfvLXT95vvMKv8h01z7Mrd/wCT2dz8dqWSxQy2IJHMgkswRW1tX/JXs3LqShx3Iu+vHsm1tDdtd+1W4lpYmaUxLa7K+eBG3WSJx64xCOh8SUn0rNhZm/O9/LjKpFE79+9/VOKlUMzcxsc/9zohEIjQ1NbXCX600+htqtRqysrLa/f39TTaImcVi2dDpdPNO8TJ+/PhnxKNQKDT5Z5hcLoecnBxlUVERH4/Ho7GxsW4AAHQ6HSIjI20A4JmLzNHRkf6bpP3xAAAgAElEQVTuu+/+smHDhm9++OGHi88bF0GQHrv46+rqOnJzc2+Hh4eH2dvbVx49erTilU/IRHnjZjJx6HQ6zdXVVZ6fn9+jOioIghD+ymbqlg8++GCVg4NDgFgstvHz85s4duzYrpuFXC6H9PT0BjweT8QwTCaTyfASiUTp7e1NioyMNEijuN6wffv2pqVLl9rQ6XSTtcT0lv9+eYDnOyTWjUKhdZ1T3r3USjNoALKFL8HLe5TO4zU6OmRo2cNrQl//iayCe3vKN35oujVgUBSFg8uX82ekpjI4AM+1krQCaPYOGVLjzuMh0yUSVwFA+5VPPpFM+eCDPme9nTp1qiYgIMDa2dm5S9SXlJQ083g88ejRo92srKxMNt+8tLRUQaPRyI6OOu8paVBSU1OlkydPppJIxutjimEYCIVCNCcnp1KhUKA0Go04YcIETltbm5ZGo5nh8XhITk5+OGfOnAE9HVOhUMDZs2evtrS03Pjoo48+ft4xYWFh7KlTp65pamrySEhImMlisV6Y/VdYWNhw+fLlPfX19Xl8Pv9menp6w6ucqylj8qr2n45YLJb05vieChkAgLa2NsW///3vlS0tLapbt241wxMPhIsXL/Kio6Pd/3I1MAAACgsLO5qampQvGM6o2NvbS+l0ukmXLu8N16/fbvcYMNXxSSEDADBkaLReLU3m5pY4v4AoFgCAlfVQ+vc/7ml55+3lJmuecT9/HnmRkAEAsAbAf5CX1/WZOQNYjPzqK+1v+fnVdg0NckJ7Ozh8/bWjf3h4r1xGmZmZ7eHh4c579uypGjFihDQoKIidmZlZTaPRqNOmTfPIyMionzBhQq8FU319vYrNZusmxe0FSCQSbXZ2duPKlSv7VfDs04hEIhAIBC0kEslo7r6ampqOCxcu1HO5XEcOh+MQEBBgLhaLYfPmzY0uLi6oVCrV4HA41NPTs1dB6WZmZnDu3LkdEolE2pnF+jRXr16tB4BPk5KSDr5MyAAAODk52QCActeuXam9WUd/4o2Y+Qfj7OzsxufzFWfOnGkdOHCg2bFjxx62t7eb2dvbY3K5nPB0zISfn585AJhkwG9UVJTboUOHyq2srFAcDof8FS9EiIqKcjW1TI2ecDOnQOLiPsioNXOEwpK2d9fPN8ngaYDHtXwq7O01F2tr6/FaLSkURRk9MYVwAWgfnj3b9XC5NmdO3R9TpzbP3bv3pUJRIBBoT58+3WRraysOCgpyevTokWj+/PlMCoVicezYMeH8+fNdOn9rZmZmr1SR+uTJk7UhISF0Pz8/vdV8SU9P569cubLPrjZjk5aWVrlmzRqjuZHv3bvXVFhYKFu6dOnfPks6nQ4xMTHWgwcPfmVRisPhYNSoUXE7d+6M7u5YS0vLZ+4TSqUSKioqZN7e3pZarRZSUlIOf/bZZ5tfdT39gTdupn8w/v7+5lQq1WXDhg1Hxo0bZ1q173WAQqGAo0eP8iwtLTUDBgyw1ncNEl2iVmvgv1/+Xjkq4l2j3Kw1ahUU5v1U96+Nq0zOpfg8FAoF5Lu710zq6Hil8vX5AO34wkILxyd6Wf2yerVw7c6dXb+Zv7IKOwIDA7v9Hf35559Ce3t7bVhYWK8EaVpaWikAmE+fPt21N+/rKXw+v+Pq1auiuLi4fulfQlEUfvvttwoEQSA4ONjCz89Pby1buuPs2bO1kydP1ltqvlqthg0bNkw7cuTI6Zcdt2HDhjkeHh4h9fX1xFmzZsXn5OQknzp16khhYeG9t9566y0URbW//vrrUQzDtPpaqynQ/7asrynr169fERER0a1Pddq0aRuWLl0aP2LEiD6biKVSqZbFYvnevHmzpa9jmSJkMhkWL17sPmHChAESicRk4xeeB4IAUCycjbbTwBOIQDIPNNng0Kchk8lQ7+v70iD5l8EBIN2Jji67e+FCVxmEwX/+icthseozvvmmFgAAWlvh9jvvyG+eOCHubrxZs2bZkclkelJSUtV3331Xo1Q+3zvb0tKizsnJacvJyREDAKAoiri7u9unpaXpJUAzPT29ub8KGQAAgUCAIQhCW758OdeYQqaxsVFdVlamV3FAIBBgyJAh3daS+eGHH5LXr1//7pYtW9avWLEiYs2aNcvS09OzBAKBeOfOnft379596HUXMgBvxIxJgCAIYdy4ce98/PHHF7ds2bI1NDT0hS0IiouLd7a3t09saWnp08N55cqV47799tucHTt2HHv77bfH9WUsU4dGo4FQKGw19jp6w46dyXz/wBlGdQW4cgOphw+fNHijzFcBRVHQFhdbFgL0KsasE2sA/BIez7P53/8WADy29FAAYJJKxXb65hvk8Ecf1REWLJD86/5917YvvmjsyZhBQUHmixcvdt2wYYPz8ePHK8rKytq3bdtWd/fu3RaAx4Gj586dqxw2bJgVm8223L9/P08ul1MGDx5s7ufn5/ztt99Wv8q5vAwajdah6zENyb59+xpoNJpCKBQaJXavpaVFU1tbqzx37lzVkiVL9BpzVFtb29be3t5jEYJhGHr37t1cfa7JlHkTM2Ma4Gg0mk1AQIBdcHDwR9HR0e/NnTt3xh9//HHm6QMrKipUCILEY6/oH0QQhLRly5Zv161bF+fk5GQFAGBlZTJFfPWGVqs1Wlft3qJSqUChdoCeVvLVFxYWVmYPS0EKACbR7+pliFtbgfDvf1OyioqkvkeO0F610PuQigrbM+HhD6wqK80na7UcAIBQlcox9Lffuo7xqK5m8MvLNRwPjx59QTgcDsLDw93kcrn2ww8/dGhsbES3b99eY2FhgXXWUOFwOPiFCxd2xSe5ubkRHBwcdL7ZpFAoJlNO4VV46623mN7e3vgvvviieePGjSRLS8OcDoZhcPHixYa8vDzcw4cP8Rs3bnSl0fRbaDozM/PQpk2bvtDrJK8RbywzJgCGYcrCwsL0zn/b29vjfX19xyEvqGb3pJCJiYlZNnfu3IBeTKdqamrKU6vVJpmVpC9QFKV2dPSPTWnqibNSn8HjTCLThG7tZfvv/xzit7Z261kxKgwbG5i+apU1WSpV9qUplzeK2sYVFXlPk8k4LzJ9Bmm1zDsJCb1yAzk6OiKenp54AAB7e3vc/PnznRISElwcHR1faGENCgqy//nnn/l79uypfPDgQduLXFU9RaPRgLKvgxgZPz8/PJFIBG9v7w4LCwu9z4dhGCiVSkBRFE6dOoX/4IMP7Hbv3s3w8PDQaxPUzMzMyxkZGVs7/x0REcH98ssvP5s5c2agPuftz7wRMyaCQqGQPfnvJUuWbPjll19SFi1aNHfSpEkv9HG7u7vbKxSKHpm9AR4LoW+//XbvxYsX9/Zlvf2NWbNmOZ4+fbpfFIqyYzJISkW7sZcBAABc96EMv4C37DMuZIuMvZaeoKZQDGLO8isutm1q7PFl9wxsNhvprm+Yh4cHYd26dZzly5e7IQhC+fLLL+u12ud7HS5fvtwik8me+zeAxw/l//znP010Or3fxss8yciRIx1LSkpefMI6Ijc3V7Rjxw5BUlJSxX/+8x9bADBIZeyioqK2yZMnr927d+/vaWlpd3/88cf7a9eu/Xz9+vXpb731Vr/uB6Yv3ogZE6GoqGjHiRMnTpw/fz794sWLBUQiEZk/f/6M77777uiQIUOWvOh927Zt25yWltbruIarV69eUat7XJKmX6HR/N2jJBAItIcOHSoLDQ01+bLnbW0SuHSFX2tJ1Vtmbq+4nn2oWdZ6pm3unLdMY0FP0Pk9Vz58qP525MgaFEWB5OpqkNIBVhgGjdXVCkPMBQAwYMAAwvvvv8/esWMH/2lBg6Io1NfXKxMTE6tf5H2uqqoST5w4kThx4kS91rAxFAqFQlNUVCTct29fxe3bt4X6mOP+/fuis2fPaoYNG0ZPSEjgMhiGuwTeeeed6XFxcR/FxMQsGT169FBnZ2dzDMOARqNRMQx7LQSprnkTM2MiHDx4sAQAogEAEAQhrlmzZp6Hh4c/gUDgHjx4cNtXX/W6BcdLcXd355qZ9asEn2doaWmBjo4OOH/+fI2HhwfBzMwM19HRoaRSqdS2tra2jo6ODhKJRLazs6PGx8d7Gnu9PaGmRqCwtOIYr5zpU4wMi7UtvPcrDwCMljnyIn7/6qs27bVr4qHFxcgnHR0u/12ypMmmokIKAHovnlhsb98cGRRkkIaWndDpdEhISOAcPXq03sHBARMIBOq4uDhOYmJi5fjx413Pnj3b1ccHRdG/WRBqamqk1tbW1oZcrz4ZOHAgceDAgVyNRgM3b95s0vX4OTk5zaWlpW1r1651N6SI6eR5EQYHDhzITEpKWpWXl8cz+IL6AW/EjAmCYZgKAPZ3/nvdunU6HX/JkiUnly1bNqo35lIMwwBFUZBKpWhpaWkFiqJKrVarEQgEQgKBYD1r1qxXakffWy5duiRsbW1tJRAIwGKxGDQajRofH+/c2YTxCUyygV53cDjO5NbmWyZjMkMQBDSYnUnu5u0vXGhYXFzcVc5gy6lTTMQAQgYDAGFEhFEEJ4VCgdmzZ7PxeDwUFRVJDxw4UIaiKOHq1avNy5Yt496+fbuusrJS3dTUhK1bt86186EYEhLi/NVXX7UwmUxzNpvdv3cxT4DD4QBFUfRV36/VaiE3N7f9zp07Qk9PT5xcLsfL5XKNr68va8mSJSbTd6uoqKhBLBafeSNkXswbMfMPJCkpaQaRSJw/fvz4d4KDg4fJ5fL2u3fvXiCTyQ7BwcFBFhYWz2wLqqqqVD///PMPEonk5PHjx69jGIYhCILMmjVrrre3ty8A6F3MHDp0qHLYsGG0MWPG9LjHSX9j0xeHqsdFvetq7HX8DW2tSWaCWVRU/C2VpS+Bv73hmplZY9TmzUYL0O4U7v7+/tTOzt0SiQR27tzJnzRpEjs4OJh44MCBur+qYAPA45ol8fHxVB6P16FSqSw4HI6hPi69wufz1RqN5pWCmltbW2Hfvn0iJpOJmz17tisAQFNTE+rj42MyYk8kEqHvvPPO22Kx+MjVq1f7RdyasXgjZv6BYBiGAsAhBEEOr127dp6VldWYzZs3r0AQBPn000+3vvPOOx8+aea8d+9eSUZGxo9EIvFRaGjoxJiYmA0pKSl2p0+fZgUEBHi1traq9L3m0tJStUaj0ZaUlDRptVrUx8fntenD1MnRoyeFbu4j8S9IYjMaai3NJMuEC957j1a3ebPKAcBgliMtABRMniwfbGIeGxqNBuvXr+8SWFQq1VytVgOJRAKVSgW1tbVqkUikTklJUbDZbMmHH37YLyo7d8eNGzfqmUwmKJVK6E2zyQcPHrTfunVLMXHiRJqnpyehMxibyWSajJABAGAwGDg/Pz/85s2b3wiZbnjTzuANfyMwMJC6cuXKQzNnzpzW1NTUfvLkyZ/y8/P3HjlypPzw4cPJkyZNmm3sNR4+fLgiNja23/eWeZKqKr72XFaH2I071KSaOkokLUqR8JRw5fLZr9QmQJ+oVCoocnISjtNoDNamIhuHa/GsqbEhm5tki7IuZDIZ3Lhxo2HChAmspKSkqtDQUFcGgwEFBQUyLpdr7urqalIP7b7Q2NgIu3btqvvss8+6FWjXr1/n83g87fDhw128vb37xWZ+586dWz755JNPjL0OU6dffJlvMBy5ublSBEGiS0tLl4lEouL9+/dfA3gclHzq1CmDxMV0B4IgJLVaDQSCXks9GAwURWH/oTtVwSMXmVxTx8aG8vb4eRNNTsgAPI6XyPP2bh91/z6QDTSnN4rS0n75pXnWBx+YTDzF87C0tAQ6nW6ZnJz80N/fn+3p+Tj+XavVdhQXF+NfJzGzf//+umXLlrF27NhRuWrVKrfOWEAUReHMmTO1LBbLksPhWKSnp1cHBgbaLVy40GhdtntLWVmZID8//4Kx19EfeGOZeUOPSUxMTJ4xY4bRLTMajQaSkpIqYmJiXE+ePFkTGxvLeU4AcL8hJ+e2srzaHWEyXUzuJDQaNdzM/rZ6+bIZDhyOMx4AoLych3p4uJtEWQcUReGymxt/plRqsBiWJgB1ModTPePaNXeKAQq36ZIzZ85UjRkzxpVC6Tdtt16KXC6H+/fvtw4fPty6vr5ee/jw4TpPT0+Vubk5sbKyUjVv3jz3Y8eOVXp4eNgHBQVR+tN5FxcXV6ekpKzevn37WWOvpT/wxjLzhh4jFAoLAcDoYgaPx8PixYu5GRkZstjYWM7169ebxowZ029jaG7dKWvw8B5lEhV/nwaPJ0BY5McuB478UamQp+Kt6J5yCyrH/mz64ba318f2es1PBqX2hPs5OR13v/mmIWbvXjfaX203+BUV6K29e+vJVlY40YMH6jip1KDtFpgAhNV8vvsPEyZUzMnI4PYnQUMgEKA/PdB7AoZhCAAAm802W716tfPT5zdv3jy3M2fOtPWn866urhbv3r17zoEDB24Zey39hTdi5g09xsHBwdvYa+gEj8fD5MmTLaurqzEAMMlsm57SobBU4PGm7TIbNnyOW3u7GLW0tMYBABQVtLTxq2u0HBfnbt0VGIaBQCDQ8ni8lsrKSlQul6uio6NdGhsbJRkZGeLly5c7WFtbP/deVLJhQ+07ZWWepwYNqq6dPBnVyuXYiPPnLVdotY4SAFQLgDAMl8jUBQ4ANpSWco8MHlw1JDvbhe3sbBKWqu6wt7e3aWpqAoFAIKfRaGZcLtdkahq9CtnZ2fIRI0Z0lWF4nmCxsLAAiUTSDAD9ogldW1ubNjU1ddsbIdM7+sUF+AbjMmzYMI9x48ZNdHR0HGHstTyNi4sLIpVK+0fTpReAx5t2MCnA43oznUIGAMDXP4qz/9D9xpaW7pMsNm/eLBCJRIhMJqMsXryYtWrVKpfi4uIOZ2dn2kcffeRy/Phx/vOqUW8fObJmblmZOwIAb8nlLmv+/NN1/dmzbkFaLRMBACsAnDGETCc4AJjX1uZ6LS1Naqw19BZfX1/qqVOnqiwsLNTXrl2rfbpadn9CJpNBRUVFY0+ymCZOnOjw5Zdf1jY16by+ns65cuVKye+///7t/2PvvOOaurs/fm4WIQNCSMIMe6sMUdQKIirgVly496haW21rbWufaltt+3Q8bX9d1gkoKiriwCpSxQEKqIgICrLDCAESIITs5P7+sPo4UEYSEp7e9+vlH5J77/ckN7n33DM+x9h29DewmhmM1/LOO+9sfPPNN79hs9lEU1UM/u2332rXrVvXL+eVVFfXqJNOVgoCAl89f8tUQVEUivL3Vrz37n87y1QqFSQnJ5ep1WqqWq1WUygUpUwmQ5csWeLxquOo1WqIj48vNzc31wYEBFgNGDCABQBQWV4OyNChHUEAJpvHaQWAy4cOKSImTux3EQ6VSgUJCQnlK1asMLnC8+5w//59OR6Pl/r5+XVLolelUsGpU6eaY2JiWF3NxTIWQqFQsW/fvs1fffXVz8a2pb+B3759u7FtwDBhLl++7E8mk+3VajXN2tq6r5pGuk1RUZGUQqGIXV1dTUv4o5scOXquwcV9kj2RSDItcZlugCAINDaJOgIGWVuQSERoa2vTHj16tGLevHkegYGB9MDAQEtfX19mU1OTmbu7+yuLm3E4HAwePJg5aNAg69raWu2pyZPr6pRKbeH33zdO5/M5phw+NgOAKyoVb1BMTL/7/uHxeHBwcKDfvXtX6ezsbNp5zk6wsbEh3L17t8nd3b1b6SM8Hg9sNpt07NgxkZ+fH8XUHJqioqKGU6dOfbFjx46fjG1LfwSLzGB0i7Fjx9pPnDhxk6urawibzXYeOHCgUQtWpVIpJCcnV3l4eJBGjBjR7wTAqqur1Q8fPmwFIJIlylAqnc7sd84MAIBWo4HszG9qAgO4MhwOZzlt2jSdZzjd5XAE/mo12xoA1x8+lD/J5JphtbXcvpimrG9OnjxZMXHiRDcy2eSeU7rk4sWLjQEBAZY2NjY9iorJ5XKIi4vjv/nmm31aOP4qJBKJ9siRI7tPnDjxr9zc3GZj29NfMS3XFMNkuXTpUj0AbAYAiIqKco2Ojl43evTohW5ubkaZRF1VVaUcOnQo28fHx2RTEK+ioqJCVVZW1uDo6Gh2JClDOGrMVFrXe5kmODweOPZjiO7uiLW//wCd20Xu37wpU+BwGlY/qucbJZdzT8fHt01ctqxfFJg+i1qtVvdHR4bP52sEAkG7jY1NjwUTZTKZxtbW1mQiUTdv3qzbsmXLWyiKarreGuNV9JsLBobpcPHixcr33ntvc2Ji4icdHR1GCe3V1NS0mJub98uwYlZWVlVUVBR34MCBnPnzomzybqVUG9smXfDwHGZ79VoRX9fjKJVKaI2JaZmgVParSBsNAGTHjwuMbUd34PP50tzc3La0tLTq72bNqpJt20Y8tGWLzueur0lPT69etGhRr2p9MjIyaqdPn24SoocZGRlZCQkJkZgjozuYM4PRa3744Yf9P//88yaBQNDRl+uWlpaqhUIhZGZmypKSkspu375d05fr60JdXZ120KBBT1MxA/y8yeK2MvTG9X0Vl9N3m36rxSvokFvpdC3RarVweMyYion9zJF5Ar2uztgmdIm0owNOHT/e2l5crImMjHT2efhQvVEgcB1x4ACuIDu7X3UEWln17vuWlZXFYzKZJhMJLS4uvpuamlpibDv+F8DSTBidgiAIBQDkfw+l7BT0ccHVT3v37g2aOXPmkr6w68SJEzX29vZUCoWC//vpin38+HGek5OTisPh9Gno+Ny5cx0ikag9JCSEc+fOnQYajdYuk8mIOBxOLRaL6WQyWdnc3AwrVqxwbmlpURcUFLR5enrSlUqlDAAsnhxn5xdvuuBwONj1x/GqnJvH1VqNXDkidJFJiui9CiqVw1QqldBbJeZLKSkdfiUl9LsA7UMA6M/WyjQAyB/hcB22Wi3eC4DxyoMYEae6OpZQKARra5MarfWUw1OmlFNKS5Hg5mYrDg6nSN6xozK6udkRAGCIWm2zZ926srxBg4j+a9c6eA0cSKDRTOZ+/xxKpRKuXLnSEhQU1Kt5XFKpFBcREWESJwlFUXBxcQlzcnIy4/F4vZr8jfFfsAJgjJdYsWLFu8uWLdvO4/EeXLly5f3du3dnvm77+fPnr42Jifl03LhxOtfPZGdnKwYPHmz24k2Rx+OhTk5OyKFDh0oXLlzoKRKJ1GfOnKldunSpCwBARkaGBIfDScPDw/U+dPDy5cstQqGw0dzc3OLBgwcQFRXF1Gg0eBwOJw8MDKSVlJTI3N3dzZ+dFaVWq6GxsVF65cqV5vnz5zsdPnz4UWRkpPONGzeaBw8ezORyuZ2Ky3R0SGHvvqQGL7+1tjgTbYV/FrVaBffvpdWrFLXSf21d9Mr26+7Q2toKWrUaLsyaVTb8/n12E4EgkxMIatnWraTRy5dzjkdGlk8rKnIgA+Bs+nBSdncQAED+2bOakJEjTfKkXXZxqZwpFrt2tV01gCqNQmmaXVtrUhEyrVYLcXFx5Vwul+rl5WXh7Ozc4/qsiooKGY1GI+fn58ujoqKMKu4kEokUcXFx36Wlpf2IFf3qByzNhPESHh4ejAEDBtCDg4OHhoWFffaq7UJCQhh79uw5tW3bth8AQKWLAJdAIED37NnD43A4qsTExIpnXztw4EBZY2Nj+6FDh0p9fX0tAQCYTCZh0qRJdvHx8eV37tyR4/F4LZ1O1/uNJCkpqXXQoEHU2bNnexMIBJyfnx9qbW0NBAJBFhQUREMQBHx8fMxfHHpJIBCgsLBQqNFoFPn5+SozMzMim802mzZtmsOrHBkAACKRAB0yezUOj4fK8jtNjYIqkxZk49eXSBfEetrr6sgAADAYDGCyWDD/yhUPfG4uPbiuzjaspsZx/Lp1HDKZDDYzZ5rf+uwz0TUms1YftusTLYASVSpNUoEu+8oVhadY3K1xH84ARFsAzYm1ayvvsNmNcVu3Nhravq6Qy+WwZ8+eyoULF7pHRkba9saRAQDIyclpunjxYlNHR0dLfHx8Rdd7GI7z58/Hf/HFF59gjoz+wJwZjJcQCoV3qqqqhDKZDP7zn//sfdV2kydPXj9z5sxpHA7HLDQ0lLtv376KoqKiXtXPJCcnV61cudLJzc2NZmlpqXz2NRqNph4yZIjFwoULPYODg59GXthsttmSJUvcbWxsoLS0VCQQCAha7X+zYjU1Na9MkXWXgQMHmgkEAjEAwPjx420mT55sz+VyzQICArqcvBsVFcVduHChp0qlkmg0GmVX2wM8doJAW4bPztpT/kaIhirg36rjVd+XaDWmVx+oVimh5EFqE5ut/1pKZ3d3HIFAgGfbnaM2brQfu2GDvdzCwuSchjwarX5YRIRJCucNHz3arPCTTyR8gG6lMqKlUm5YUpJ1gEbDcQsPN3paLzk5uX7VqlWuug6TNTc3RwYPHmzZ2toqQ1FUKxQKn75WUlLSJ9+pBw8eNF24cOFqenr6N32x3j8JTDQP4yWioqJKdu/eTSaTybYIgvhduHBBNX78+PsvbtfU1PSZm5ubK8Djm/CQIUOs0tLSagMCAnosIMbj8Rr9/PxYAABFRUVyPz+/pzUlhYWFLQMHDnxlntvCwoIwePBgBpvNJl29erUmNzdXUFhYKKupqem4ceNGs7u7u1VRUZHM3t6+RzU1dXV16oyMDP7QoUOtKRRKr+rLEAQBBwcH84KCAsWgQYMsurN9WFgIffSoQUwbGw6xubkSFxxoSc3OLRWxOS4mNSnvVnZC2WfbFrv15cTylG++aZyemupANrEHMbpSSTqamcnziolhEogm0/X7lAc5OdLBV69adudMEQCABWBGBYC6s2f5BceP12c/eqTEWVkROQ4OfV5nKZFI1FlZWTVUKpVoZWXVa4fR3d3dUigUSqOiouy8vb2ZycnJDbW1tfy0tDR1RkaGMioqSu+/L61WCwiCQFlZWcuFCxcO//HHH3O++eabX+bNm9ei77X+6WAFwGtt0CMAACAASURBVBidsm3bts8RBNkZERFhtWDBglM//fTTtI0bN85HUVQNALB06dKxmzdvDnlxv+DgYIszZ840T506tUeP62ZmZk+/iwqF4rknyNbW1m6ljxgMBm78+PFOz/5Nq9VCZmamkEqlqtPS0lqio6Pt1Wo1JCcnt4rFYhEOhwMzMzPK8OHDrfh8PnL//n0+i8VicLlccllZWe3KlSv1IvVOoVAs+Hy+ys7Orkd3uqlTophXr91sZ3O8jCIG0tbWpGhuqpa3txU3mJHENJXGDT9g0DhbiaRF4eREMutrFVWCVKq4TybXh8nl3D5duAvsACjrs7Lcfx85smJ+Xp5b13v0LQoeT94bQaaxCoUjlJaCvLQUrhw8yD/xxRf02kuXxG8fPmzfVyKBJBIJUSqVBDs7uy4fBl4HkUiEAQMGWAAAmJubw7Jly2wBwBZFUTh+/HjZr7/+2u7g4ECdPn26Xuru1Go1/Pjjj38AAO/KlStxWVlZ9evXr9fHoTE6AXNmMF7J39oHzbGxsW9/8sknWdOmTYsCgD/Xrl07dvr06Qfs7e1fuj76+fmxbGxsVDdv3mwtKCgQrVmzplsX9paW/z6o0Gi0p1dJtVoNdDq91zkWHA4Ho0aNsgYA2Lt3b1V8fHytUqlEYmNjHchkMoNEIgGKonDr1q1mBoNBXrVqlTOKotDa2qodMWKE3mbWtLe3tzU3N6vEYjHO29u7h6F7lIAnkLq8c6AoCnm3T9eQSESlIzfI1or58vnpCbzqewK2VQVucrQvy8kpxhLg8XC///z4S5W9vTV+5fIZfe5QTNm+nVsYGSk5uGRJxXyRyM2Uqm1xAOBdX29yCnTCpiawPHtWpzQKGQDGK5V2yi1b4AqB0CGRSMDCQiffAgAeP2wAALzoGD26c6fjyo4dzWZcLp4xdqxqyZIlBnMQEQSBOXPmeAAAnDlzRm899mfPnj3/5ZdfrntdRyiG/jCpUC2GaZKUlJR37969nLa2to633347Zu3atSfCw8NfeSOztrYmjhgxguHm5kZKSkp6FBcXV3f8+PHXXiTGjRvnkp6eXg0AoFQqlUVFRbKkpKSSv/76qyo6OtpFH+9j0qRJ9nw+H5YtW+ZgYWHxtI0YQRAICQlhDRo0iEYkEoFEIgGHo9+RQEOGDLGj0+kWV69e7bGeR1radbG5Ob1b4XU6pQl9f+No94L80zoVD7e2CBSoukA6fVoU28mJ+7RTmkajwaefrHIxhiPzhIEjR9KiHjxwO0Onm5zYoIDLNamC7Qt79wpvDh3KmyMU6sUZIAHAELWaeWLGjMqe7isUCkEsFgPA391J8+ZV3bK1bdw7enSlVquF1pYWuHzyZMf+5ct5osmTW7dcveq85tAhR7xQ2K1BkrqCoigUFBQ8tVEXbt++XZySkrIec2T6Diwyg9Etbt68GV9dXV05e/bsz7hcbrciC5GRkU8nWefm5ooEAgHY2HQ+uufo0aON06dPtwAAsLW1NcfhcPLY2FhvvRj/N7a2tiQmk4mkpKQ0zZ49u1vdHfrCx8cHDwBAp9MlPd3X0dEWT8B3nZ1CEAQI5CDSieQL/M+3zXc5f+G64Ob1S43DQ5e7I0jPphzx+SXtyxaO67KV11jUVFeDo0Ri9OLUF1FyTSr7BU3Jya0wc6Z5RmJi3VilUi+T2ZkAxDl5eZyk996rif3++2694Xt5eah83LgONgBaTCDIOiws2sNFIsfBAGajCwshwcmpvh0Av1wqtbF6Zkq6HECr4PNlra2tlgyGYU83giCwdetWhx9++KH63Xff1UnniclksszNzbtsEsDQH5gzg9EteDxe0759++76+Pj06inJzc2NUVJSorC0tDTrbBbMpEmTmHV1dc2urq5WYWFhTp0cQmcQBIHVq1c71NXVyXbt2tXw5ptv9vlcKQqFYt3c3KxgsVjdLmS0oFPxAAAajRp41QViZ5dACxwOByqVAh4WXeZp1PUKMzOyBtWqcSjQtFpL1JJAIMCUyRE2Xp4l7Oy8CoWtrXuPCic9vUawUs8dblswf5ZJzhvK/v33uvUoqpebsz5h5OWR5DIZkM2NKmPylEXnz7sDACTduFE6tkR/QrNcAGpAYmLr9xUV9a5jx5pPX7fO6sVUkVarhZPbttXT//xT7FJVZRsGwMADAKjVdBCJntalkAFgiVRqTwCAF9OGlgC4ygMH0Kr//EcsTkwkjRo/3qBpPARBwNLSUuc13NzcWBMmTNgJAFP0YBZGN8CcGYxuUV1dXcblcpm9HUrHYrFwpaWlrbm5ucTi4mIJhUIhRURE2Dg4OCAAAF5eXmaZmZl0f39/veTiX4eDg4M5nU6vBYA+d2amTZvGPHXqVMn06dO7HXWaM2eq1Rdf7qmg0/Awd85o1xPJeysVaiuNUl5B2frRm06vK8L19vbGnfnzaoOtrXu3nzRRFIXsrL0V77w1yeQKWQEei+vZHT6MN8WJ2uyODprUhJyZJ3ArKvQu6TtCqXQYcfUqyK5ehf/77Tfe6gcPnK6cOdPRVFoqM+dwzFWbN0vmK5V2VIAuBfhe5WnjAGCnSGSHAsCvycnVMH68wZWxR44ciU9NTW2YPHmyTtcHW1tbL33ZhNE1mDOD0S3u378vaGtrU1hbW/e6NXLEiBE2AAAjR45k4nA4KCwsbM3Ozm5WKpXUtrY29bhx4xiGdmQAABobG9Xm5uZGGzQnkUh6VLdKIpHgi+1LnzoW725a0KP0j0Yj71Hxp0IhhWFDOLYslkmovr8EgUAAgokqlz8MC2sdwGT2aQqzK+rq6oCrUhkswmYOADQ8Xp129KiEvWlTa6RC4agCgGfTRbqCAEDQqVPU/TY2QhsXF7zgypW25YcOGcSx8fHxYf31119VOTk5kmHDhvXaCdRoNCp92oXxejBnBqO7KBUKhQpe/RDVbfB/y/QPGjSIMWjQIIZWqwWtVgt91ebLZrMJarVaLJFIrPp6Bo1cLgdHR0fDe2zPoNVoe1TM3N4uVAcNcDIpTZtnodFoYH32rEXcqlWlZiSSZnBFBccbRfukSPR1CAA0nOXLTWoMAACAg4MDJHC5AruaGge2gcZArK2rc2tftw4sHw8RNwhvaDQsv99+02oAoAiPV0va24FGN0xZyuLFi1127drVlp+fXxEUFMQcPHgwo6fXp9ra2tsGMQ6jU7BuJozuIs3MzDz5ggSMXsDhcH3myAA8zovPnj3b+cSJE+V9tujfnD59ulWhUMj7cs0RwywZ/PpHrd3dXsB/1Ojr62NIk3RmQEgIZdq9e57ML790IKOoSbRDswDwdadPm+Tk88X37rkeW7BAIAYwSHcNDgAMXVyFAIAVAI4FgAvTaFiFPj68HUFB9fk3buj992RhYQErV66kr1q1yo1EIqH79u2rqKur6/Znh6IoXLx48ZS+7cJ4NZgzg9EtUBRFt2zZsvT333//QigU9vsJr4+VdsOYx48fr392BIKhiY2NZQQGBjoePny4tK6ursdt2r0hYvQbVsLGS201vAfdWk+jFrX3lSCaLpz+978bNCtXCp0BTCKKhAcARnFxn5zT3jD/55+5qfb2fe7AGwINAIyQyRxXVlezCs6c6baj3hOYTCYOh8NBYGCg1Zo1a9yKioqaCwoKpN3Zt7CwsKWwsPCmIezC6BwszYTRbdDHI9Y/ff/998uXLFnyo6Ojo8m1xvYEd3d3KxaLxTh58mQlHo/Hx8TEGKSL6ln279/P53K52pqaGnMGg9FnEYVN7yx0/un/9jfmN1cIAwdPdnzdtkRCh8l7Mse++6557r//zbE0sQcyskhkSjp+TxE1N8OFVavKxvH5JlXP01MuEwgCHpstUc6ZQyXSaDiFXA7BEyb0SXFXVFQU59KlS1V37961CgoKem0gauDAgVYbNmxIAIDovrANw8QuBBj9g++++y7+4MGDm1taWvp9gZulpSUSExPjyufzDT7JUa1Wg1gsVnd0dCDr1693oFKpfdaQg8PhYNPGlRxUw1NK2lteOfRSLu8ASwuDZwx0orW1Fdr/+qvd1BwZAADewIF9N6iqB5yaPbti3dWrHt4o2q8fQIrnzVNOuX/fffa2bbbT33uPE7t1K2fA4MF9Ngxr7NixLg0NDS0ikei12yEIAgQCob6PzMIAE7wYYPQtM2bMcPj1119///zzzz9FEKTbT5XffPPN3oSEhG+Vym4NgzZp7t+/3zJz5kyDC8QRCAR4++23uQqFQimTyYzywX34wRK3qspbgle9LmiokEVFvqGX2TSGIiEkpP6t3FyTE/RrA9BaBAWZRP3Oi7hv3mwv7Hozk2fQsWPI0UmTyvoyNfwidnZ25EePHr1S6bmxsVGekpJy+ty5c1v70q5/Opgz8w8HRVHbsLCwuWvXrv0sKSkpd/Xq1d0Oi3722WefHDx4MBE10TbZ7tLU1CR+lTKxAdYCNzc3azabrXNXWG/RaiSd1nVoNRooK04WOTq+NgtldEbt3k3PJpFM4qn3HoDwEYBYBgAnPTzKYj78sM+1i7pD8KhR5AoA3XX6jUyYQuG4LCfHfd/YsT0ep6APlEolFBQUKIcPH95pG5VKpYKkpKT/LF++fHpqaqpJfEf/KWDOzD+clJSUOz/99NOKy5cv3/Ty8vJzdHTcERUV1S1BNxRF0T/++OPTxsbGfj1/xN/f32bXrl1VfbEWDocDjUZj1N8diVDLUCpfbgDB4fFgyfTt006r3hA4ejSdkZnJOWVpaZQbGgDADYCaAwBlIgBUBkA8BSBu9PAwqRSTUqmEE9u21SdNnlx6JiSkkqsHWQVT4BEO1xz4wQd93opfWloqOnLkSPXChQs7ra0Ti8Wa//znP59t27btX31tGwbmzGAAwL59+07Gxsa+sWbNmpBt27YNq6urq+nuvmVlZZUnTpz4/t69e8WGtNGQ2NjYkAcOHGjO4/EMHmJis9lQXFysvnTpktGGJG54axnn1s195TKZRKtWKeGJYyNua5JLJDJrgeCVWSiTwdnDg1Dr52cUJ/ovgKoWAOIyAI8IAFYAgPk8AAu7igq9d/kJ6uq097OyutVB8yzSjg44PmAAb+nPP9u/eeOG5/qGBle7/xFnppZMlpXl5vZpR2VhYWFzdXW1esmSJc6ddfrl5eWV/f777+u+/vrr7dhwSeOA9PcUAYZpsHr16ugPPvjgjLW1tUk9nXYXFEUhOTm5ZvLkydzejmzoCQkJCa2BgYGIv7+/UYpttVotfPDhN7zBQZ4MaYcUxZMjcLXVlwUrlkV6cDjsPtX96SnCpia4uHFjRfT589buBpQ3UcDLd/8sgHoEgPpGJ+vWAUgvrlsnnL5jh96mTR6aMqXc9d49fLq3N3HZH3/YPcrJkbQfOyaglZWhotBQs6nffedMoT4vtJudnt7RtmhR+2yl0tYkW6v0QB6CtCHZ2TQXT0+Dv8X09PQqBoNhNXTo0JfOeXt7uyYlJeXA0aNHP7x58+b/QllSvwVzZjD0Rlxc3Ilp06bNNLYdvaWhoUGek5MjnzZtmkE7Ps6ePSsIDAykcblcvcm99waZTAapqak1ISEh9vfvl0jHjh1FNzc3yfrV58hOT+/gxsaqBgIY7DxlAPBuAFBWAzBoAIQrADwtgLkCgDLjNTL9TQCKMwMGVKNTp1pNeecdNomkm29/OTGxZeSGDZSzADUhALYuAFTLx/pxoAWAH0aOrFx+9qxrXWWl+trHH/NI9+5BdEODPffx/Mb/aX4ZPbp8wcmT7oZeJzExsXTBggWeL/4dRVE4fPjw0bfeemueoW3A6BoszYShN65du/Ybn8+XGNuO3pKXl9fk5eVl0MiSRCIBPp8vNbYjAwBgbm4O06ZN4+bk5DRPnhzVLxwZAIDhkZHUi7GxHYY6fgmAqAZAuxWAdRSgZh9ARTSAkw8AI6wLgT42gNmKoiKvxV99xU6aNavixddPvPtu9bGNG3lP/l9ZUqJKWrmyKv7jjxtf3PbbwYNr27ZskVkDmCEAVG8A2hNHBuDxxdsnL49wOytLmbFgQfW6tDS3lQ0Nbv8ER0YFAO1CIV7cahC9vOeYOHGi65kzZ14q5j19+nTa77//vsLgBmB0C8yZwdAb+/fvv7xr165/GbNtUhcmTpzIzc7ObjbkGnl5ecIRI0b06Wym10EkEkEkEhnMMTAUEZs22fzy9ttNR5jMCn3GlusApNcAWhYDuAAAbABwfQvADQcA7gBE9jPOxOsgAUBsZqZD6rff8gEAJO3tcCQkpHxWXJzD2IQExu309HYAgOx166rePHnSJWTvXsi9cOFpt5FSqQRXDke6XCq1JwLAYgC7kwBlL64zWiZzHDhlCn5tcbHBIxSmBBEAVt2/z01LTGwz9FptbW0dKIo+N6z1woUL13///fe5RUVFPa5nwjAMmDODoVcuXrz46/nz59OMbUdv0Gg0oDWwJyYWizsGDRpkMuOos7KypJGRkSan2dIVLl5ehAXbt7M5CQkOuQjSoo9j7geouAXQtgpAL46BDYDZG199RTk5duyjy0OHlq8tK3O3AiB4AFgw581T3HB05M2/e9cNAOANtZoj2rKlAQAg8/hxUSaXK7AvLHwaYSEBgAeA7Q2A527eVADEDgBvuhVOhoMMgJOmpBj04aOurk5ZVFSknjZt2tMOphs3btxPTExckJuba/iwEEa3wZwZDL1SXFys4vF4V4xtR29AEAQ8PT0tExISqgxx/Li4uEpfX1/WpUuX6gxx/N7A5XJxHR0d/Vb5sCghQRCAola6HKMKQLoHgAcAxOkAdvqx7DHuAJYr7t71WtLY6P5sSCdYq2VNkkqdGI9HOgEAQCuViisvLQX6+vXKmSqVzSSp9LkW4BAAmghA9RDg9fKz/xCoAAhBatjAyK1bt0SBgYHWAI+L5o8cOZLy9ddfR6empna74xOjb8CcGQy9c/v27buGmK5taHA4HIwaNcpKo9Gou966Z4hEImhvb8ffvn27saOjw2RC00VFRQ12dnZ9Jgevbxb89pvT7jfeKH8I8EpF1q7IAmj0AaD6AxhV+XhmcTFXM2yYJEytfqXw3mQAVg6AQaMR/YVv2eyaaWlpBk2vTZs2zTYrK6uipqZGfeLEibO7d++ec/36db4h18ToHZgzg6F3UlJSLhcVFfVb9UsSiaT3Fj8mkwkbNmxwio2NdZHJZOQTJ04YTfDtWVpaWlRsNrvfXgdwOBwsSk11z3FxaejtMWYCuFQASIcYWYeFA2A2DIDWVa/xYADnIoB+V+ekb/xkMg2N3qkQr95AEATmzJnj9uDBg7orV6503Lt3z+Az3DB6R7+9iGGYLiiKqpqbm+8Y247eolQqDfq7iI2N5d6+fZtiCsrJXl5eDIFA0O/1GcxwuF5/lmcBmocC2OvTHkPSAYBW/YNTTVIA2Bwe3qD5/vs+G5oZHR3tvHz58tHbt2/f11drYvQMzJnBMAj19fX5xraht1CpVDO1Wu+ZpqccOHCg7KOPPmJzOByj//6GDBnCzsrKaq2srOy3Ds2djAyJZ3V1r29sHgBQCNDryE5fMwKA3PQPvnaXAUhn7tjBHjV7dp9OAA8MDLQ1Nzf/n1BR/l/kH/uDwDAs9fX1tca2obcMGzbMrqLiJYkQvaDVaoHJZFIsLS1N4reHIAjExMRY1dfXNx48eLA0ISGBL5H0L6mg8uxsabBG0+tJoUEALAuAfuXMsQGY/ess6Q8aAAkhEvtc3Pj8+fMXtmzZsriv18XoHiZxQcX43yM/Pz9569ata48dO3bC2Lb0hOTk5Mpr167Vu7i4GOT4OBwO2tvbTareAUEQGDlypM2iRYs8FyxYYHf27Nl+E6UAAJBXVMh1LTe3AqDX9SOHpgFA+L+sjPe6E0EFwKX/9FOjXNbp8HeDoFAo4Pr16/tQFMVqZkwUzJnBMAjp6enC3377bdepU6c2pKamZvSXsRkKhQKNjY3l6ipD/yp++eWX2oiICL22/+oTPB4PSqVSfubMmZLjx4+XpKWlmbxjQ6bTQZfH9EcAonyAFptuCuKZAmEA7EMAj44C1MgBIAtA1F/uskkUSu1FAkFw2Ne3OI9AeG6qKQoA583Na7Z4eNT97ODQae7TBgD38ZEjnItDhpRf3rfvJeVkQ5CRkXFz7969J/tiLYzegc1mwjA4Q4YMIUdHR3+zZs2a9XQ63aQd6KKiIimTyaTY2enf38jMzGx3c3Mj2Nvbm+v94Aairq5OeuPGjabZs2c7G9uWV6FWq+GOs3PNeJms0wGPzQCoAEBeBSBUAmhFAIpWACIeAGUAaN0AmKMAdNKqMRZKAIgD4NsAMNoeKxebdCGzFACObdvWFDhlCsvZzQ3J2LNHoN25UxosFjueHzCgWmNnh5/w66+u1mw2iJqboc7Hp2WUVvvKc7M/IuJRTHKylyFt5vF4sp07dw4/duxYgSHXwdCNf6JwJEYfc/v2bTkAvL1+/fqsyMjILWFhYUE4nGn6NO7u7pSUlJS6efPmOej72Dwer8Pf37/XtR3GwMHBgcJms6nV1dVaZ2dnkzxpBAIBHjo7q8YXF7/02mWAmg4AyyAAiwkAjib5BnSABACr/xb62wcgQAEgE6BOAUB0BTB3BzBs73IPuWZmVjdlwwaHJ7//iFWrbNTLlkFRfr5m3pAhHs9uy2Sx4JS3d8uIhw+tngghNQOoagFUQhxOXjZokNDvvff0/jt9ERaLRXZ0dBwGAJgzY8L8r/22MUyYX3/9NWnDhg3jU1NTbymVpik6SyaToa2tzSCKf66urmZff/11n4TF9QmJRFJRKBSTTsEM+fFHu0cvCOfVAajUAFZTACwc4X//YhcD4LIfoAoPQBsHwDkN0JwFYDJq0wAAEnNzxYsPMgQCAQKGDOk0U7j46lW37+fM4Z+0tKz8i0Sq3xcYWEsrLaU4l5Qw52RkeA584w2DD2xtaWmR1tTU3Db0Ohi6gUVmMPqUmpqaRgRBhsfExEQOHTo00MbGxker1ZKGDRs21dHRkWZs+/7666+K0aNHG6SmZcSIEVaFhYX9rsurtbUVmpqaFGw22yRrTuVyOVz/5Rfh2wCOT/5WByA7CdAUqefxBKYMEwBW/D0gEwDgXQDXPADpPYC2AABL41n2mHoAJX7nzh6l8wgEAqzZtevpOSyMjy+ztu670WYqlQqOHz9eM3bs2BUTJkz46vz58yblHGL8F8yZwehzUBTVAkDa3/8AAODtt99evnXr1n2GKrztLkKhUC2VSqUuLi7mZLL+791UqsEfJPVOaGioTVJSUiWJROJ6eHjo/QTduHFDIBAIWlgsFjUsLKzTupfXcdLTs/6Djg7HZy9maQA1GwAMWkvRH6AA4JoBTKI2uAZAGTJtWq9rk/h8PuDx+D5Nm507d67yrbfe8iEQCD4eHh5R77///mffffddYl/agNE9/tcjrxj9hJ9//jnu+vXrN4xtR2trK5HBYGiSk5OrL126pPcZSng83qTasruDhYUFbtWqVe45OTn8AwcO9GhMhUwmg66aDFpaWhQxMTE+dDrd6vLly61ZWVn8xMTE6vj4+IqjR49WnzlzpmT//v3lFRUVys6GmjsAqHDw33ZeFABwJhCJMAWuA0iCHgdtjI4Uj5dSKJRe729nZwcuLi5mR44cKSktLTX4xOqsrCy+g4MDi0B47CYHBwd7rl27dv/3338fb+i1MXoOFpnBMAlQFNX++OOP1wHgDX0ds6WlRVtWViYNCgqiPbkgdcXy5ctd8Xg84HA4KCoqkmZmZkpDQ0N7fwV+AbFYbJmVlSUYOXJkvyoEBgAIDg6mazSaboer2tvb0f379zd5eXlJIyIiXC5fvlxFIBDwUVFR3MzMzHofHx8Wi8UiKf6eShoYGEjj8/lAIpEsR44c+VKNTmlpKZqYmFhDJpM7PD09WQ0NDe1tbW1K4cKF5vUSCQ9HpeJxWq1WW16usMrIYOnzvfdj2qgAJvFZVLm7twbqOMwzNDSUAQCMH3/8sXbDhg0MPN4w2nnp6emVrq6uDi9GIplMJsnCwgK7b5og2EnBMBmys7N3azQaRUNDQ6Obm5v33bt3rczNza0/+uijCWZmXauICwQCZX5+vlgqlQoJBAJOJBIx2traOoYOHdrtWhwikQgVFRXtSqWSmJOTU798+XKPrvfqPqtWrbItKipS3Lp1SzB06NB+49BUV1ejBQUFreHh4W7P/j05OZmPIIjYzMyMOmnSpKc1KyKRSJGQkNCwceNG5127dlUmJSXVh4WFORYWFtbcvXtXyGAwrE6dOlUTHh7OIRAIT3Nvf7fEd1ps7OnpiXh6enIBAB48eKAKCgqytrGxQSA29rnttFot5NvYNIHGJLIrRoUOYLi5HD3gEUCb87//rbe2cTqdjtdoNGAIZ0Ymk0Frayu5s5RqYWFhdUZGxmerVq3S+7oYuoHpzGCYNAiCIHv27Dnu7+8f6enpafHsayKRSMNkMvEKhQKIRCLExcWVz5kzx51Ge+y7NDU1oUVFRbWjR4/mAgBoNBo4evRolVwuJy1btsy+s/ZwgUCgvHz5cm1YWJibo6PjS6/rg9TUVGFgYCDZ0dGxXxTQlJeXo+Xl5eKSkhIxmUxWDRs2jFNUVMRXqVQsrVbbtnTpUpdHjx6J6uvrkdGjR1uJRCLNyZMnq1auXOn+4rHu3r0rbm1tVUVERFgrlUrIzs6Wjho1Sm+RryccGjOmfHh+PscTgMIAnTT1+jV7ARpWANgauxXtqLNzSfTdu976Ol5TU5P2zz//rMDj8bQpU6bYWFpadvoWa2pqZCkpKS1vv/12tx2pM2fOVEyePNmts+vD7t27v96yZctHOpiOYSAwZwbD5Fm0aNEEIpE4c8mSJRH+/v5uAADbt2/fX15eHj9mzJjlPB4vksvlymbMmOHOYPx39lxaWpoIABRkMpn44MGDdqlUSoyIiGCJRKI2lUqljI6O5iIIArdv326i0WgEV1dXq3379gnWr19v0IjJ41xg6QAAIABJREFU7du3FVQqVeXr60sDAMjOzm4oLy9vHzx4sKWvr69OYXh9UFBQIMnPzxdQKBSVWq22kMlk6qVLlzohCAL19fWyxsbGjsDAQJZarYZn03fZ2dlNRCKRfuvWrcY333zTyYhvAQAAju/cKfD96Sc0TK22NbYtxiIOQLAUwOgRwOPOziXj9OjMADzusvvqq68EAQEBGhqNhkyZMsUOQf7r06hUKjh48GCTj48Pubi4WOTj48MMDAykv65uJz4+vtLBwYEybty4lz4zjUYD8+bNi05PT7+oz/eBoR+wNBOGyXPo0KELYWFh43/++ecVGzZs2Mvlcp2kUunx1NTUawiCZJ4/f75i2LBhL0UBoqOjmVqtFrRaLYSHhz9bN0CWy+Xw66+/VrJYLDMfHx87FEUV586da1m7dq3BL/xCoVBaVlbWZGtr61ZdXd1uYWHBWLBgge2ePXsa3dzcoDspNUNSWVnZsnjxYncAgJaWFg2CIOiTm4S9vb35EwXjF+uQhg8fzv7www/rP/nkE6M7MtKODmD/3//9ox0ZAAA1gFIBAMYe9dxmY6P34BCDwYCAgAByeHi45ZUrV/jPvqbVauHatWuNM2bM4DAYDHjjjTfoIpEILly4UDpjxgzPzo5XWVmpolKp9HHjxnVaY5Senp7Z2Nh4Xd/vA0M/YM4MhtGxt7cn8/l8QFFU3tnr6OPw4TsAAIsXL55paWm5Y8+ePRf+fk174sSJRwDQqdw+DoeDzsLFZDIZrK2tcfPmzXsSfib7+fn1iY5KdHS0FQBYHT58mKdWqzWLFy+2UiqVoFAopMZ2ZCQSCdjb2z+tMbKysupRimbTpk3MvXv31mzcuLHHLdb6hGxuDo1Tp8oyT55sDUBRxgUzs4bZCsU/yrH5A6BSDaC9B9AWokN315PY/eu8kRwEEZEAEAoA6o2iz3VPVQHI3LZuNcj3Yf78+ZYAAHg8XvLE4VapVPDTTz/VTJw40ebZSC2TyQQCgfBSIRWKonDz5s1mmUxGnjFjxiuLpd3d3T0GDRrkBgBFen8jGDqDOTMYRmP06NG24eHhS3///fc5HR0dRDabPaypqem17dAJCQn3AGDKk/+vXLkyPCoqqlezjoYOHerw4MEDiZ+fn1HE+ubPn/80gkEgEMDe3t7oFasnTpwoX7Ro0UtRru7C4XDIlpaWRi86xeFwMGfPHlf5zz/Dl7Gx9XO++87mZGRk1Qyx2MXYthmaZgA4DyAaAkAJ1jHFpAGArx0ceCRnZ41TSQk6VCi0cwMwbwBQnGGx6lc3N7uWA7Txd+3C23h5UbK+/bbG+c8/mc8+FTykUBqHh4UZdLaXSqXCpaWlVchkMlxLS4t648aNHp11MCqVSsrRo0dLNRoNlUAgKBEEUchkMsLAgQNd3njjjU4dd7VaDTwer51KpTJYLFYoYM6MSYI5MxhGo6mpSRYWFrayqamJmZubey0wMDBsy5YtTnw+/0FCQkJWV/tHRUU5fvzxxwcCAgJce7P+9evXaxYvXtyrffWJWq2GK1euyJqbm/u0TlMul8P+/fur29vbcU5OTnKlUkkxNzcn69IhcurUqYpp06a5db1l30Amk+GD06ftAQBu2tlpjiNI5Yi2NjtHAJNUM9YHyQA1ywC4+lA3zEWQ1qXZ2U4UKhUk7e1w9ssv6/1371bcdXERcTdutLiweXPDLRsb5VuzZzuJhEIQiMVmLQBau2c0zHi+vsrherDldXTXAZ81a1a3U6BarRbS09OvlZeXPzpy5Mjn9vb2Nunp6Xk//vhj7w3FMBiYM4NhNIqKito+++yzfXPmzPlXRkZG9qZNm75sb29nZGZmrn7dfgiCIJs2bVr+3nvvbeqtIwMAEBAQwEpJSSmfPn26O5FI7HoHA/F3XY9s2bJlfeYEFBUVSa9duyZYvXq167NPsBod25krKystBg4cqGYymSZ3bfH/5RdH36AgswMffti4ce9esskZqANCALQIhxNlBwWJVQiCJ93WzyihRiq11YdKZQAA0Oh0iN250166dSsMoNEe528WLYKgv+esoSgKzg8eqCQAHfD3gEslABDmzOl36b26urqWs2fPHktKSnorPz9fvXXrVgCAGmPbhfFqMAVgDKNy8uTJH48dO7bTw8ODfOnSpf04HK4lKirqj9TU1Ptnz54t/fTTTz94cZ/58+fP2bx5896QkJABuqwdHBxMj4mJcd+/f3+VTCbT5VA6QSKRQKFQCPvKoZJKpZCXl9ewdu1a1xdD8bpEZUQiEero6Ejw9PQ0ST9hQHCwGQ6Hg9CFCxmPANp6uj8KAFfw+OZcgHYRgNbouTR4PFnzEoEgyDx8WOHb0GC9Kj3dleHvr7dWdBmV+tzbxOFw8ET64AlPRpBYs1gws6zMJe2jj9TFABIAgHw8vmnEhAlGn7nWUy5evJj20UcfvZmfn28KpxmjG2DODIZRuXfvnmzbtm07P/jgg+3ff//9rzt37hyelZX1i4WFBTU0NNRj9erVXycnJ+dv3rx58ZN93NzchulrbhKBQIBhw4axHzx4INbLAXvBsWPH6n19fdl9sdalS5eaDx48WBMcHKz3FvDMzMwKCwsLvY+A0Dfe/v6km++/L8vD4YQ92e/bAQMq7QsLWTZVVfTL+/fLt0ZGCvhGnnu0x8Ojou6bb8xHjR9PfuKYMi5e1NvIDBWC4EpKSkQ92YfFZGopf0f9yzw8hHaOjsaWuekxEydOnP6vf/3rfWPbgdF9MGcGw6TIz89Xf/311z/++9//HnPlypVsKpWKjBkzJmDYsGHrpkyZMmLz5s0r5s6du1afa9bW1ooFAkFLZ3N/+gIGg6Hx8PBgdL2lbojFYtBqteo1a9Zw9V30XFBQ0OLu7m4/fvx4vam8GpIZH39sW+7o2Py6bR7gcMICPL7xyf/N/f0JbBsbsLCwgLHTp1OIcrnazsiCfAvKyhyIUulzXYAClYpcD6BtAtBJRawcoE27aBE5JSVF/ffEiW7RVloqZwOQtQDQ6uZm3MmxvUQul2tycnLSut4Sw1TAnBkMk+TcuXNVu3fvnhUXF/dTZmZmoVqtJjCZTDWDwWCVlpYWnTp1KrGsrKxdH2tNnjzZLjQ01PngwYPld+7caetLIUmtVgsVFRXa+vp6g0c0LCws4NGjRzJDvD9bW1tKZWWlyUdlnkVq+/pSjtvOzsKmxESzXW5u5Q0AKM3X96li88UTJ6Sbrl83uuNmA2DWVl7+nKexpKjIqeD0aU3O8eOqYxYW1U/+3gigOEuh1PwcElJe8ThDBcUIItrN5ZaXAXTcfcZxu44grSkUitx65Egbf39/fE8kA+yDgsgZJFLDORxOxFqwwEEPb7PPKSoqunzx4sX7xrYDo/tgCsAY/ZIBAwawVq1atXvWrFkxVKr+pgJ8++23NbNmzbKVSCQKDw8Pmrl5r7q+e0xCQkLF4sWL3dRqNeDxeHhWyVSfCIVCNCcnp3bixIl61/3IyMioEgqFTBqNhh8zZgz1SS2FqXJk27bGFT//zOnsNi0GgMPOzuWxd++6q9VquHnpknJkZCTpiWZRU2MjCH19W0egqMEjal2xJzi4dFZ6eqdCcInvv18/+MABSguBoGhevVo5+bPPuDgcDs7/8EMDnDzZ2kink5b8+adb6v79bS2HDzfH5OVx/vT2bvD+7jtuwMiRZIDHYnK3b9/mzZ49u9st+1qtFtRqNRw8eLBiyZIlbt0d9GpsCgsLq5uammpu3br1y1dffZVkbHswug/mzGD0WxAEwf3rX//6bdOmTWv0dczz58+3tbW1QVBQkMW9e/eE9vb2+NDQUCt9Hf9V1NTUqNLT02tUKhWJy+VqJ06caDAV3cTExNIFCxZ0evPTlY6ODmhqatKUlpYqIiMj9T5zSZ9otVo4vnlzTcSBA0xvgKcecQaZzKv/4gt6QFSUlQO3c5+v8sEDZf6WLVV2d+5QJsjlhhni1U3iKZT6Uffv2zOsXv6aarVaUCqVQCKROhWPfJaTGzdWaZVKmPXbby4AjyUDjh07Jpw6dar1jRs3aqKionrsAF++fLl+9OjRnc5BMzWuX7+eHR8fvyg5ObnM2LZg9BzT/4ZhYLwanEQiERcXF+utZXLChAmWc+fOtfT29kbmzJnDyszMlDx5TavVQkeH3morn4PL5RKXL1/uxmazccHBwa9UIX0WsVgM+fn5La/bpqysrO3GjRvCu3fvtmo0GmhsbNQoFAqD1XlQqVTo6OiQCoXC19ajmAI4HA5iv/+eyyORWp/9e3lQkHziihWvdGQAAFz9/EgxZ896NYSGGuYL0QM0Pj7tnTkyAI/fI5lM7tKRAQCY8eOPLk8cmUOHDlWeOXOmfMqUKdZnz57lPRnW2hM6OjpQPp+vMmVHpqGhQb1z587fJBKJVqlUajBHpv/SP2J/GBidgKKoGgA++O6776Q+Pj7bDLHGvHnzbBMTExsJBEJrcXExNTQ0lDBw4EBrDodD0EcqSKPRPNcOLRaL5TY2Nl1GNEQikSo+Pl5CpVJb/fz8rDpL6dy+fbuusbERGTdunH17ezskJSUJeTyeOjQ01KAzEwYMGEBnsVj4X3/9tWr9+vUuhlxLVyorK9HsYcO0lOvX24YAWLYAKGxWr+62LoomIMBS+ddfYMyEmlVVlV6d00ePHsmDg4NZvr6+dACAefPm9SpKmJGRUT1v3jwXfdqmb6qrq8uPHTv2bkdHR6ZcLm+ZNWuWsU3C6CWYM4NhciAIgkdRtNstrzwer8JQtjg7OxOdnZ05AMDRarVw5MiRKolEIlYoFBJHR0cqg8EwV6lUHQEBAT1qrUZRFDIyMurOnTtnNnjw4BalUomfP3++m0QiIZaVlYk9PDwsXrf/3r17BR988IGjVqu1OnbsWNncuXM9Xjx+QUGBfPny5e4AANbW1jB//nzrsrIyRXt7+2ujOfqAw+FQtFqtUTt9usONGzfq30xO5raIRJDw88/1aFOTYva0ad0WYpy2ZYvtLhyuhXjihGh1ZaU7DgAKASQ+ALS+kmE0l8nMKh4+VLn5+uplyZycnDpdRloAAAgEAhWJRLIw5agMAEBzc/NDHo+nAIAjxrYFQzfw27dvN7YNGBjPQSQSL+Tk5ISOHDkytTvb//DDD/ympiZqQEDAUF1E37oCQRDw9/dn+Pj4mMvlcnJQUBBdoVAQb968KQoMDOzRIL/U1FSxr68va9asWdSAgADrAQMGWMXFxdWvXr3asaysTJGbm1vv6elp9aqbgUAgEPF4PIWnpyetsbERMjIy6nA4nHlubm7tnTt3xEVFRRo/Pz+Wvb39cw8sZDKZUF5eLnZ1daXr8FF0CYIgcO/ePREejzeztbU1nrxyFxQVFbUEBAQwqFQq+I0ZQx8weXKP6qNwOBwEhIWZW0ZGWpzQaBqy3d1b2L/9ZlVx6FCjq1bbJ2JxHmq15amiospBCxcyu966a3Jzc8XBwcE6FTaTSCR8bm5ug7+/v8HrzXShsrKyZuDAgQeNbQeG7mCRGQyTg8VilUdFRa347LPPGrZt27a9s22WLl06rqqq6s6VK1daLl682Mjlcj8eO3bsEm9vb/21Nr2G4OBgMwAAZ2dnCA0NtTl27FiJra0tg0qlkv39/S1fVPNFURTOnj1bjaKosrm5mTR06FCus7Pz0zwViUSCNWvW2AMADB8+3EKtVlvs37+/gsvl4hQKhfJvJw2xtbU1Hzp0qOPo0aMdExIShNHR0TBmzBgmADALCgpQCoXCmDp16itvIGlpaeLIyMg+aSlevny5a0ZGRnVlZaW9q6urSTo0Wq2WiKKozt1jXDc3wsJvv336uSaEhUm5GRntHgD0hwDtfDxePkqjYb/qglsIIPYBsOjtBZlEp+ulk0OpVIKdnZ3OxxKLxQoKhWLykTkikdgn1wsMw4M5Mxgmx9WrV09NmDDhzRUrVnz6+eefI59++ulL9TAVFRVDcDjcHQRBEBRFUQsLC5VcLm+HZ7pS+goPDw+Sh4eHt1KphJaWFkhISGiZMmUKncPhPP19IQgCQqEQli1b1q0uIgKBAKtXr35pVtOlS5cak5OTixEEsdi4ceNzTom/vz8CAK99Em5tbRWTyeTXprD0BYFAABaLZUmj9VnGpcdMmDDBPjs7u3HEiBF6VURenJzsdisjQ345I6Pe1sfHLGDSJPY369fXclQqmZZAQFSVlcjE4mIOAkA4FRHBD9y0ye6377+v8Ll1Cx8llfZowrQMAHDjxuls/x9//FHr7OysnTBhgs7DVx88eNBqyI48faFSqVTGtgFDP2DODIbJodFo8EQiESGTyTBv3rz34+PjPZVKZSOPx6vdsWPH9yiKoteuXfsaACAkJGTI0qVL31y9enWrjY2NUWfAkEgksLGxgcDAQDqPx+vgcDhPU0+VlZVtRCJRZ+n7sWPHcgCg1zeuqVOnOhw8eLBs+vTpLjgcDm9paWlQqXl7e3vKw4cP29hsdo/ScH0Fk8mEqqoqibu7uyWH06nkTK8ZGhFBHhoR8dThXHvo0HMt3PEffigYEB1NXxIR4QYAEBAa6vbH9OnVUdeu9WgdcwBQVlZ2AIBOn3FERAS7tra2QR9yHVZWVvT29nbQ19gRQ0Gn0+0QBMGhKGoc+W8MvWHa1VkY/3hYLBZl6tSp82bNmvXOlClTvqA9mdb7N7m5ubfNzMxuLliw4D1bW1uTGGgXHBxMuH79evuzwyvz8vKECxcu7LOp2K/C2toaWbZsmceBAwfqExMTG4qKihq73qv3qNVqrVqtNukbxbx589xSUlL4fb3ukq+/thkSEfFc5xqTzdaoAKACQHbBzKzuLwKhcT+Dwetq2iHpzh2d1bC9vLzMOBwOZ8eOHQ26jvbw9vamZGZm6kWh25CEhIR4ffPNN/uNbQeG7mDODIbJcevWrdzi4mLei3+vr6+vkUgkL10gT548eSQ7O/th31jXPTZs2OB45MiRZhRFoaamRt7U1GRSw/beffddp3Xr1tllZWXJDTmTqqysrHXMmDEmXQQKAGBhYWES6QbqkCGW8du3txQcOoR4FhY6DODxOJOKi51OW1pWvm4/57t3rQV83fyxffv2VeXm5tbT6XSlrjVEf0+CN3jXnK4QCATw8/MbYWw7MHQHc2YwTI6KigphSkrK2wKB4LlZPyEhIZ7h4eGhL24fHR39GZ1Ot+k7C7uGQCDApEmTWF9++WV1U1MTunr1ap3rEAzB3LlznTIyMhoMdfygoCD2lStX6o8dO9bnkY+eQCQSTSJ6NHHNGuuZb79tFT5xIplpbQ1kMhlIJBJI3nqLKgZ4Lv+jBYB7AO0lAK0PRoxos7Gz02ltDw8PGoVC0SxdutRGV2fm73EGJvGZdgWfr6MXiGESYM4Mhknyf//3f6f/+OOP1WVlZU9vtEqlUsvlcl/SJ0tMTPxAJpMZXYn1RWxsbGDkyJEWzs7O5qaqt2FhYQF8Pt9g6QALCwt8eHi4vezZnJsJYmFhwWlsbDSJ6ExnTHznHc5uMrmpDEAuB4DtISE1u3x9y0Tnz5N5x47hF545o5MuDABAeHg4a+7cuV4XL16s1fVYKSkpFTNmzHDR9Th9AYFAqDO2DRi6Y5pXWAwMAPjhhx8Sf/nll6mXLl26AQBgaWmJi42N/XLy5MnPSatHRER4Ojg4dGsEQF+Rm5vbePHixZqHDx+2WltbG9ucV3Lu3Dl+aGioQaJaKIpCWlpabVxcXN20adOMXi/0KhITEytxOFyHtbW1yXZdEQgEWFRVxWm8dAl3wMysMeqrrzjzsrI8AocNIw4ZN06vmkEcDsfy9OnTFbqkHwMDA63++uuv8t27d1colUo9Wqd/aDSaSUV1MXoH1s2EYdLEx8ffGjt27AwajXZx2LBh/iNHjgwuKSnZCADvPdlmzpw5/3Z0dOyb8dbdRC6Xq729vR2ioqJM6oFh9+7dlTY2NtaRkZEWSUlJlf7+/jQXFxeDtGo/fPhQ0tbWxly2bBmlurpae/XqVenAgQPN3d3dTUZ/JC0tTTh48GCar69vjxScjQGJRALfoCBSyqRJKseODiUAGGQsxdixY1lSqZR1/PjxxtjY2F51zrm7u1u5u7tbyWQyiI+Pr3J3d6fz+XxNbGwsx9QmaAsEgnvGtgFDd0zqQouB0RmXLl0S3Lx5MwHgsahXSUlJLoIgOFtbW/NVq1ZNGzdu3CRj2/gibW1tJqlhYW1trRozZoxFYWFh6/z5812Dg4MNdhP39fWlBQYGovv27au4du1adVRUFO3MmTMmFdKvr69v9fHxMXlH5lk+3rvXwcnJiZCSklJvqDVSU1NrwsLCdI74mJubg4uLC8XPz48SExPD2blzp17qUy5evFiflJRUo2vxukajgTt37uTqwyYM42JaLjIGxiu4fft2gUqlgjt37lTt2bPnxDvvvPNLTExMrLW1Nd4U0wNTpkyx//3336s9PDx6JIBmaNRqNdDpdAgJCdFJrr47IAgCXl5eVC8vr6cpJicnJ0SpVEJngzH7GrFYDK2trUSVSmUS9vQEJycn8+bmZkldXZ3WwcFB7w+lvr6+zNbWVrm9vb3OEc/IyMin0Z2YmBg4evQob+7cub0W1CsoKOigUCjqESNGcBMSEsrCw8Ode6swXVhY2HjhwoXTvbUFw3TAIjMY/YK8vLyipqYmDYPBYHl5eVkLBIJkLpdLd3BwMEkxNgAADodjcpGZuro6g07MfhUqlQp27NhRV1VV1eVE8L4gLy9Pcu3atYqNGzc69TdHBgBAJpMBn8+XK5VKnYUYO2PQoEHU/Pz8tr/++otXXl6utxZrf39/O4VC0esiGq1WCzweTxgaGupEp9ORpUuXeuTm5tZJpdKud+6ExsbG4vr6enlv7cEwHTBnBqNf0NDQICguLm44d+7c5yUlJY1Hjhy5dP36dZN9orp06RLf3t7e5Cp/6XS6QW5+XUEkEmHAgAHt7733nvWLzsPp06dr8vLy+qQbLT09XRAXF1eWkZEhmjx5spuuLcjG4s6dO0IikWhryJlX8+fPdxkyZIhTZWWlXrvdRo8e7Zibm9srB+L69evSN9544zkl5QkTJjgfOHCgVym3e/funerNfhimB5ZmwugXoCiqmTJlylZXV1f8ypUrFw8fPnyCnZ2dSaVwngVBEHTEiBEmJRaXm5vbHhUVpdcZRD3B3d3d6tChQ2Vubm5sT09Pi9LSUlV1dXXTwIEDiXfv3m0ePHiwwedq3bt3T/H+++97GHodQ0Oj0Yje3t4GT6/W1taq7e3t9TKN+wnOzs7krKwsYUhIyNNZB1qtFoqLi6GhoUHR0dGh8fDwIPr6+r70/srLy5vCw8Of+91LpVLtoEGDemujopf7YZgYmDOD0W9YsWLFB+Hh4X76mHKsL9LS0irFYrGSQCAQY2JintaGNDQ0SIxp14uIxWLg8/nNISEhRhPv8/f3t/H397fJyclprqysbPb29mb7+/s70Gg0yMnJqSguLpY5OTmZUyj6z0SdOHGCh6KobO7cuQ56P7gRcHJyohYUFMhGjx5t0C6+srIyRVhYmN5PiEqlEqenp0uUSqUKh8ORiEQi3tPTkxMYGGj2+eeft/j6+mpSUlJ4tra2zCcPBeXl5eqgoKCXRpbY2tri8/Pz62pqahy4XG6POuVaW1tNTp8Ko3dgzgxGv4FMJtMBwKiOTFxcXKW1tbVGqVSqtVotcLlcu+joaMvMzExJeXl5e3V1tbC0tBQxNzc3idqQuLi4CiKRiLO1tbULCQlx7HoPwzNs2LCXNIFWrlzpdvXq1Zr4+HhSTEwMJSQkRK/aKXK5XDt9+nQvGo1mGl6wjuTk5NRFR0cbfCq1RCJpk0qlRGtra70WFi1ZsuQlp/rcuXMCPp8v//TTT52ZTCZ4eHjQz549yxMKhVb5+fmNRCJROWrUqE6/w+PHj3c6efJkNZ1Od2Yw/p+9845r6t7//+tkQQIkYYSwR8JSEUVEUFQUxU1BrOLE2Wp36+0dvb1dt/Xe3rbf+7PV9tqqtW7FieIW6oaKiBMBGWFvSCCLkPH7g9o6UFZCDvY8Hw8fD0nOeIcczud93uP17nptO4vFIl3zAEXPoJwZin6DwWAwS72HwWBAUlJSsU6n04SGhroNGjToiXTI6NGjrY8ePdoQFBTkOmLECCYZFH+1Wi38/f1tg4ODbVNSUspYLJZL53uZj8jISPfIyEjk5+c3Jycnl8bGxhptsZ4/f77Xli1bCpctWyYmS1SvpxQWFqrUarWtqa+xkpKSNoIgVB2pbpuCyMhIx7179xZXV1dr7ezsGAAwbdo0j5ycHEVAQICNq6vrM6NQM2fO9Ny0aVPR0qVLRV3RspFKpW39ZeQCReeY/45LQdFFDAaDWbqDbty4IddoNAJLS0vnAQMGPLWuIyYmxt7T05NpbW0NU6RKuktOTo5WJpOpLS0t8eKLL7rb29uTRqzuWfj5+XEZDIZRaxloNBpmz54tvn79erMxj2sOxGIx28/PT7d3716JKc9TUFCgiI6Odmtu7ptfmbW1NTFnzhzRzZs3Wx/ox9DpdAwePNiqM0cGaI/YLl26VLR169YuqQ7X1dVpT5w4cbz3llOQAcqZoeg3mMuZGTp0qPXcuXOt3dzc1NnZ2f2ijXP37t1FVlZWmokTJ/Zu+qCZaGpqsklLS3vk+1YoFEhPT6/Pzc2t68kxuVwusrOz64qLi2XGsdJ8DBo0iN/Y2GjoaUtyV5gwYQL/xIkTTd98802fCR3a2NggIiKClZKSUn3jxo1u/60xGAwsWrRI9MMPP0ju3bvX4S/n/Pnz986dO3fj1KlTGx6e/UbRv6GcGYp+w08//ZSzcePG/9bV1fWpQ0EQBJhMJsrKyhpDQkIsO9/DvOzcubMyIiJCIBaLOWSTju8qGo1GERUV9Vs9g0qlwpYtW+pFIpFDVVUVLTMzs0cL7IoVK8SFhYXK69evm0w9t6+Ij48Xbtk+ltieAAAgAElEQVSypbS3KrjPYtGiRU7Dhg3r04cIDw8P5uDBg20vX74s7cn+LBYLr7/+utfmzZsVeXl5TxT4FhYWHp05c2bwBx98sLr31lKQhf55p6P4Q6JQKFLWr1+/p7y8/EJ8fPz/DRkypNeTgrtDW1sb6Z3/EydOKPh8Pt3Dw8OoBbR9jUwmYwLt9UoXLlyoKSsrQ2JiopDL5UIoFNpv2rSp8t69e/e1Wi2ttLSUPWvWLM7gwYO7VPlpY2NDZGRkGDw8PDQODg79TzHvV4RCIWf58uUeGzduLF+5cqVJirv3799fFxMT42WKYz+NkydPVrq5uTlMnDixVzIClpaWOpVKZXj4Nblcrm9ubq7pnYUUZIRyZij6DcePH9/663+TZ86ceXvhwoW7oqKiwvrq/AEBAYLz5883REZGkk4MDwDy8/NbVSpVdW1tLaHT6SCXy3Hz5s268PBwQX9TuWWxWEhLSyu7ceMGli1b5h4aGvpIHdKKFSt+K2bW6/XYsGFD0507d5rnzZv3yNDMvLy8huzs7Hpra2uCzWazuVwuo66ujv7qq6867dy5M3/BggV+ffixjI6lpSWsra1NVhgvlUoVbDa7z2ZXpaenl7e1tTEDAwN7fcG++uqr9idPniwXCARsV1dXOgBkZGRkfvLJJ//tvaUUZIP0T5oUFB1x6NChorVr107btGnTf27duiXpi3Py+Xz65cuXeyzFbmouXrxYEx8fL16yZIlo3759sitXrqjCw8MFmzZtKjYYDJ0fwIxotdpHfl6yZIlHXl4ebfXq1e58Pv+ZBdU0Gg0xMTFcBwcH4qeffipISUn5LYVUVFQknzt3rv+MGTP8Ro4c6c7hcOxmzJjhCABtbW2kmrTeUwwGQ5sJU00GhaJvpFiuX78utbW1tY+JiREa43guLi7MZcuWeZ8/f74QAEpKSuRVVVWUrsxzCkH2mxwFRWfMmjXLJzo6+ltfX1+fYcOGiTrfo/sYDAZs3bq1KDExUUSGtuvHOXv2bB2HwyFGjRr1hIaLXC7H3r17i5cvX242wbzO+OabbyRSqZS1fPlyh5KSEvnVq1dbwsLCuD1RUd69e3dJSEiIzZ07d6Q8Hs96woQJHaYrtmzZUrBkyRKf/t6qrVAocPr06bqZM2caPYKi1+vxzTfflL799tsm17RZv3596euvv27089y4caNWqVSyL168+Prnn3++zdjHpyAHVJqJot9z4MCBAgCTJ02aNOOHH344wuPxjL46lZWVtSqVSvb69etL3nzzTdKMUThy5Eg5j8ezlUgkLStWrOjQkbO2toazs7NZNHq6ws6dO4sjIiLsQ0JCuMeOHVOEh4fb+fv729nb9yybFxYW5tzc3KyPj49/psS9Xq+n9XdHBgCsrKzAZrM1crkc1tZPCOT2ChqNhri4OJczZ87UTJw4UWiK31d2dnadlZUVm8VimUQ6YOjQoY5fffXVIcqReb6hIjMUzw0EQRBffPHFdytWrFhlqnM0NjZq09LSql588UV3U52jq2RlZTWz2WxDU1MTBg8ezOVyuR2uNNevX5fz+XyDSCT6rShYKpXq+Xy+2UJMjY2NmuPHj5cQBEGLiorycnZ27jMNHL1ej40bNxZ5e3vTJk2a5NVX5zUlWq0WW7duLfTw8LCJjo42+vyt8vJyHDlypPjVV1/tdXSvtLRUde/evVpVO/Q7d+5YL1u2TGBtbU0TCoUmuSZv375dsm7duin79u3LNcXxKcwP5cxQ9GsmTpzoHhAQEL9+/fqvASAxMTFi9erVRz09PU025LGyslKVlJRU+/rrr3uas/U5Ozu7hcPhtPn7+z8zApGSklI9bNgwrouLCwcAbt++3XDs2LHWxMREB4lEImtoaJC6u7sLS0tLpSEhIQ6urq4mVfyrr69vPXXqVNWCBQu8THmep1FYWAiCIDQikah/VUV3gbt379aUlZXRp0yZ8kS6sbeUlZXpLly4UDJv3rwup1q1Wi1u3rzZrFKpWocOHeqQnJxcFBAQ4GoOiYNjx46d+v777+dfvHixsa/PTWF66B9//LG5baCg6DEEQewZPXp07OrVq1MSEhIaZ86cWbZ+/frahoYGHY1G49nb2xu9RdnGxoY5fPhw/rZt24oHDRpkS6ebR1hXIBBY3L59u1kkEj0zt+Dn52ednJxcdv369aahQ4fa/vzzz+Uvv/yy19WrV1WhoaG2gYGB9jqdziI0NJR34MCBqqFDh/JMmX45dOhQ8YIFC/q0rf5hkpOTi0ePHu1gru/NlDg6OlpbW1szDh8+XCoQCHg2NjZG+yJ5PB6tpqaGyM7OLqmpqSFKSkqafvnll1qBQMCWSqV6Lpf7hGev0Whw8eLFMmdnZ+7169el7u7u3LCwMOPmwrqIn5+fj0KhsIqIiDhhjvNTmBaqZoai3/L222/PfeONNybW19fL3d3dZwBYCwCfffbZFgBbli1b9vqHH364jsfjGf3cDAYDnp6eLLVaDXO1Pe/atas8ISGhS/oiCxcuFGVmZir27t2bN2zYMA8ajYaJEyf+tqi4urYPk2az2RydTgdjR5w0Gg3S09Orq6qq5KGhoWZN0TGZTG1/a1XvDk5OTqyFCxd679mzR/Liiy96GfO7jI6O5gPgl5SU6AQCAZ3D4SA1NbVZr9fTr169qlAqlU1CodAiOjraDWhvHV+wYIHP8ePHJZMmTfIydk1Pd1Gr1cOCgoK4t27d6vdjLSgehYrMUPRL3njjjQmJiYkbXV1duUeOHNn8ySeffPT4NnFxcZn19fXFPj4+Efb29k+dqdQTFAoFkpKSNMHBwRwrK6Meustcu3atKTQ0tMsjgl1dXVmBgYEO9vb2T50UbG9vb5mamlodEBBg05vozLFjx+psbGxoFRUVcgcHB3Zqamrp+PHjXQcPHmxnb29v1ocooVBoe+XKFYlYLDZZKpIMiMVi/r59+yqCgoK4nW/dPfh8Po3JbL+MRCKRhVgsZp09e7Y2JyfHKiEhwYHD4TwS9vLx8eGfPXu2+ObNmy2DBg0y/tNFFwkNDXUnCEI8cuTI/eaygcI0kK/HlIKiEwiCICIiIj4Qi8VCAJg2bdqy999//z2CIB65ng0Gg2HHjh1bP/jgg9DTp09fNqYNVlZWePPNN+3T0tJqfvzxR4kxj90ZjY2N2LRpU0l0dLTRxfucnZ1pUVFRzsnJySXHjh0rPXz4cJeG9j3M//73v2KNRsM6d+5crU6ns9y2bVspm822I0tLu1AopPF4PF5KSkqxuW0xJWw2GwaDoc/atRYvXuw5Z84cVkfOMkEQmD59urderzfLfLWH7Zg1a9aLK1asmN7Zti+++OLwv/71r38mnoeWtz8AVJqJot8RExMzdvTo0ZEPfhYKhZy33nrrXwaDgQ3gw8e3P336dImfn99cT0/PXH9/f6OFUXg8HpGQkCDcv39/sUQi0Xl5efVJEUZSUlLxypUrvU11j+Xz+RAKhc4hISEsGo2Gzz//vOL999937ex8165dKysvL6d7eXlZTZ06lQeABwCDBg0yuUZJdwkLC7M7ePBgjwZW9icsLS2bAbh0uqER4HK50Ol0T3VW0tLSKgMDA80++NTGxoY2duzYN0aNGnXuypUrHYrovf/++9999tlny2xtbS2cnZ0jCIKYbzAYTDfVk6LXkONRiYKiG9jY2Dg8ntphMplYuXLl3z/44INPOnqSys/PL9+5c+drEonE6LlyjUajNxgMJtdxKSkpMSQnJzfOnz/f09QPiyNHjmSxWCykpqZWz507t9OUU11dnbquro41ZswYl6lTpxq9NdhEPPf3vwEDBtidOXOmti/OpdfrUVRUpAGAEydOSE6dOlUEtAtO7t+/v9Tf399p0KBBJu2U6yrTp0+f7OjoOOFp7/P5/DqhUGjBYrEwc+bM2Pj4+PlEO5YEQVj0pa0UXeO5/2OmeP5obGxUdiQpwOVy6a+88sqHGzduPDRr1qwBj7+/bt26rWfOnNml0xnP7/juu+8kISEhTt7e3iarKJXL5Vi/fr1EqVQqLSwsNFwut0/+bpubm0EQRJuPj88TNRd3795VNDU16QCgvr5ec+jQocqpU6cKeyp0Zw5sbGxIPwG9twQGBjpWVVXJ++JcNBoNISEhttu3b6/x9fV1Ki8vZ2m1Wvz4449FUVFRHq6urqRZbwwGA5ycnByXLl06ddmyZVMefm/o0KEMBweHYQ9+5nK5WLt27Q+7d+++eu7cuap169ad2bx587FJkyaRRjyTgtKZoeiHvPfee6/+5S9/+fZZ21y7dq1wzZo1o8+dO1f98OsEQdAPHjx4bdy4cUONYcvVq1dbKisrVXFxcUaPRqSlpTVoNBqLlpaWmlmzZon7uuZEqVRi7969hTQazTB27FiBt7c3DwCKioparl271mRtbc1ubW1tbmpqwrJly8zWat0T9Ho9hg/fUDphgiO+/PJF0qXBjIVKpUJKSop09uzZXS4UNxYajQbr1q0rGjJkiHVvJ2CbgsrKSrWVlRWjra1Nd+DAgW8yMzMPHjhwIGPEiBHBe/bsuW5r++z68J07d+7ZvXv3wsuXL5NWXfuPBGk8ZQqKruLj4zO4s22GDx8ujo+P/+zx1w0Gg+727dt7jWXLiBEjbAA0Get4D1NTU6OUyWTq2bNn97kjAwAcDgdLly4VL1682Of8+fP1O3bsKDp9+nTRrVu3GubMmeMxbdo0QWxsrHjOnDn9ypEBgH370lokkpVuhw/X0aKjtxZWVdUgJubLCnPbZWxUKpWmubnZLEW3LBYLTk5OWjI6MgDg4uJiyePxGA4ODhYrV67883/+858Lu3btOhkYGOi4cePG5M72nzt37tyEhIR9VIEwOaCcGYp+h4WFRZdaO1944YXFX3755fdDhgx5ZDpyamrq7pKSEqOF3tlstu3BgweN6tBs37690N3dnR0fH290JdeesGTJEvHChQtFKpWKVV9frztw4EAV0J5aMLd2SE84e7amEXCgNTT83S0r6y1xYKBMduVKAjsqan/+Tz8ly2/cyFOb28bekp+f37h///7ypUuXGn0AZVe4f/++cvDgwWYf+9FV7O3tmVOnTp0cFxf3n0WLFo3rbHs6nY7Zs2fPXLly5Zw+MI+iEyhnhqLfwWAwnqqT8jA8Ho+xfPnyl996660jI0aM+G3FPX/+fElJSUlOb+2QSCStO3fuLAJgiI+PN5pmiVQqha+vr83o0aMdHmh5kIXY2Fi3CRMmONTU1PTr0Pr9+1zV7z8RMBjCeAbDELsbN17yW716ktWpU7lSU54/MzOzpqWlRb9169bS5uZm/cPvXbp0qXTt2rUlvTn+sWPHyuRyucXLL79stinvrq6unNzc3HqznLwXjBkzZoizs3OXHpiKi4sVDQ0NQlPbRNE5VGs2RX+kyys8QRCIi4ubqFQqt0ZERMx5kN+uqam5BWBEb4zYvHmz7KOPPhIZWy33xIkTFbNnz3Y16kGNSGZmZkNkZKSTue3oCr/8cqMtNra6MSSkQX7s2O8jFGbMoHPv3r1Qo9ON7WAh4hItLU8G7hoaGmFv/8wxWF2iubnZUFhY2Hro0KGalStXuh04cKDY3t4eWq2WyWQy27hcruPMmTMtd+3add/T05PLYDBYYWFhz3SW1Wo1LC1/r2eWyWTM6dOndypDkJGR0ZKfn980YsQI12vXrjVMmzbN0c6u958RaE9TMhiMDlufnxcGDBhgFRcXNwbAN+a25Y8O5cxQ9DssLS27dbclCAJz586Nr62t/QeATwAgLy/vFwAremPHO++8w//6669Lvby8GLNmzTKaloelpWWLOQdYdsacOXNE27ZtK8vOzq4ZP368J5m6VB4nLGwo09X1YHN6eqLYyelK1Zw5uYZXXx3l9O6701yKi7+u2717DIDHSx6s8b//jbFQqX6szsgQSIODlZyMDH1bebknx9W1VLljR4iXnR2fLhR2L3tz7dq1yqysLI2Tk5PVnDlzPObMmQMajYalS5d2WHPk6enp29zcjCNHjpQGBwfbPhjBcPz48dLw8HBnOzs7JtA+zPHrr7+uBtD2pz/9yZ3BYIBGozG0Wi3S0tKU9fX1qvnz5z/RZtbY2Ijc3Ny6xYsXi+7fv6+aN2+e4759+wpdXFzsGQyGobq62hAXF9djscN3391fmpVVz4yOlsPGpv+lIruCVqtFbm7uPXPbQUF1M1H0Q44fP35v5MiRAd3dLz09vWD16tUDc3Nz2yZMmDDuhRde2DtgwADbkJAQJo1Gw/nz56W3bt2SvvHGG15dPebatWsr7ezslImJiT7dtedp7Nq1K3/+/Pl+xjqeqcjOzpa6uLjwhUJyR9lDQk7nFRcn+Lf/pAWDcb6GxarUKpUj7AH/Z7RnqwCwH3vNADr9TJ2FhVq1enUtZ/XqWZ3WNLW1teHMmTMlDQ0NWLRoUbfbeevq6lBdXa0YPHiwVW5ublNZWRmRlZUlHzVqFCs8PNyxoaEBtbW14PF42pMnT5aOHDlScPHiRRmdTifmzJnj2tbWprl48WJDdHS0c05OjspgMCiysrLkERERDjwez+Dj4/PIMNajR49KvL297Xx9fbnbt28vXLFiRZcLvLVaLd5//2BFWhpdWVgY6w4o2tat+0W3YMGkDrupNBoNXn55f7FcbtDv32++4aM9QS6XGz7//PN/pqamrsnNzTWrsjEFFZmh6GcQBMFITU3tUVFsaGioz/jx4+cB2FZcXHwlMDBQ7+rqSk9LS6tqbW1Vcjgcrlgs7vJNqbKyUj9w4EDbSZMmuVy9erV1xIgRRhHTsra2FqpUKrDZjy+k5CElJaWQzWY7BAcHm9uUZyKTNaO+3vKh74UBrXaCUKvtyt4d/f4J6HSTBEolsGFD8v3Vq9HhtfjOOwfKPDykbR4efG1RUZHlW2+95dnT4ZYCgQBHjx6tZbPZwvv377fGxMQ4RUdH8zUaDQ4cONDU2tqqmjt3roulpSVj1apVosbGRvD5/CoXFxfrX3V/WK6urrSkpKSiYcOGce/du9f85ptvip52vpiYGK8H//f09LQuLCzUi8XiTsMzJSUV+qlTr0qqq5eJHmSCLSyyGubNm/hEylSr1WL58n1FZ896MVWqVd5AifKvf91SO3Kk2DIubqzRZ0mZgpSUlKRvv/32Y3PbQdEOFZmh6Ff4+/t7JycnFwqFwh61Qx49evRQYmJiPAAkJSWdiI6OfkQw69y5cyWtra286Ohovl6vR1NTEwSCjtMJOp0O//3vfyvs7Oy0YWFhtpmZmbWWlpZESEiIu5+fX49F9Jqbm1FYWNgSHBxs0/nWfU9DQ4PmwoULjTNnziR93UxVVQ0CA5uaDIaRRh8qSaNdbbx7l2/3eLpp5sw9BRcuhDgsW/aL8ssvFxot/ZiRkdEaHh7eJYe5pqamTSAQMB+kiHQ6HXJzc1sGDRrU5Wvqzp07zQ0NDURkZGSn+8jlcsTFJRVfv77cC+A/9Lepgb///wqPHJkmTkq61FRQoFC8+eZY59jY9JLy8t+dngcwmanVJSUDnVgsFtatO9j01lsvknYY6J49e7575ZVXXjO3HRTtUM4MRb9iwYIF8evWrTvQU2mH27dvN86aNcu9rq5OuWnTpu9mzZr1yuPbSKVSQ1ZWVjVBEHoej8fJy8urGz58uKefn98TC4lOp4NOp8PDT91bt24tB6COiYnx6W4xpVQqxe3bt+VeXl5Md3d30smm79q1q5TL5VrPmDHDOFWiJiQ+fk/hxYsDbHS6MY6AKcZmGRAe/r+C48fn/ZZibG3VwNs7q4zFajLs2uXiOGrU0H6rMmwwGHDkyJFiJycnYVhY2DPHEOzcear5rbeEOr1+fAfOhwF0+idlOt177kCDkiAq1QbD8KdcPxoMHrymrKHBX63Xa2k5OTGkTT0dPHgwafny5QnmtoOiHSrNRNGv8PPz8+iNRlVAQIDd6NGjxwI4qVQqGzrahs/nExMmTPhtIF5ISIhtUlJSoZ+f3xM3VjqdDjr90YVy8eLFbrdv3+52F8elS5ekNBqNLhQKQUZHBgAsLCz0/cGROXz4svTcuRmugLsJnQkCGRmzPdzdfy799FM9Ryi0ZXz0UXGtWp3ga2Ozo4TJFOlzcopaAYIYONB04y5MBUEQsLCwYAUHB3MA4NatW/qgoKAO003vvsuX6/XjnxKFIqDTedMACwAuHIPB5RmOEQu3b3/iDgAjR27P7eVHMCmlpaXXzG0Dxe+QtguBgqIjhEJhr6TnmUwmhg8fPoggCFpeXp6mq/up1WqGVNp16ZHS0tKW7kRldu7cWVZXV1c/atQoGz8/P1KmlwCgubmZ9PeMyZO3F65aZaswrSPzAAFLoYj3WL16lPWiRba6goIVfoANUVcXLpg3r6oqMlIn++tfT1bFx+8s3LjxbJ8MfDQmIpGI/dNPP9X++OOPRRYWFto9e/Z0+BkGDMjtRGRQ321dorq6Uvbgwd+Xvv9+cml39+0LBg0atJQgCFOE/Ch6AJVmouhX7N69e/+UKVNm9eYY165dK9DpdAw/Pz9PW1vbLoV58vPzWwiCYPv6+nYpmllZWdl6//79Jr1erysqKjLMnj3bjcvtuK5Rq9Xi4sWLDePHjyf9lMbDhw+XyuVyTnR0tJ1QKCSdY/PPf54s/frr4TYGgy+Jai1yVMBANot1pKq6eoxz59uTC61WCxqNBhqNhrKyspbMzEzZjBkz3DZtSpYlJ+tr6+tt9KWlAXY6XcgzetV35QLzu92BCAA0WmbDxYsEb8AAMakyCVqtFjt37tz19ttvLzC3LRRUmomin8HhcHo9FHD48OHdbqPOy8urjomJ8e3q9i4uLhYuLi5OABAZGYmMjIzaiooK1eTJkz23bdtWamNjYxAKhcxJkya5bNy4sTgxMdGruzaZg7i4OA+ZTKY7d+5cc2xsbJ8PL3wWRUVl2p9+YmrJ5cgAwEA2ANBoDG1LS//TXHlY88jd3d3m5s18iMUHKhWK+S4Aj/ekTk9HaHosZS0S/SJNT7enDRggJtX3ymAw4OXlNdDcdlC0Q7onKwqKp0EQBMPKysosE455PB67pqZG3/mWT0Kj0TBy5EjHwsJC2rFjxwpfe+01j8WLF3v6+vqyk5KSChYsWOBpZWXVL4bVXbp0qfTkyZNlZHNkyssrMHVqdplU+uJTW47NjVo9yX3LlpMmHZPQF/z8c71UoVjiAvDRNUcGACwcgJ6NuxoxgslYtmwKqRyZB9y/fz9l9erVCxYvXjzZ3Lb80aEiMxT9hmHDhgWIRCKzKLSNHTvWbdeuXeXz589368n+BoMBYrGYmD179m9FxGKx2FYsJtfTZmfk5ubSw8LCPPR6Pcw186cjEhNPFNfVrfY2tx3PhoFPPw1qu3RpT2FS0lzSdul0hlxu0AHPbG7qAFqPZ3kdP26r02g06KlOjynx9/ePi4mJ8abT6fR///vfP/L5fMEXX3yxvLi4uKWrxyAIgjBQ9R69hjx3IwqKTggODh5sa2vWtV+5ZcuW4n379hUUFBR06zFz+/btRS+88EKPHCEyMX78eHs+n6/ftm1boblteZjgYJZlf7id6XRhgpISqy5J9pERiaTUkJLi3YOi1yG2wK3mnpxTKp0p+vbbw0adSm8sxowZEygUCq0cHBwsV61a9aqXl9f4AQMGBALA3LlzQ2fNmtVhSpsgCBpBEIRAIOBs2LAhIyUl5c6aNWv+07fWP19QkRmKfoNIJHI35/nt7e2dJkyYwGUwGPj444+rfXx85NHR0aLOCmH1ej2qq6ttJBKJ3N/fv38VTDyGWCy2LCoqUltZWZGqi2PVqrGOBw/uLZHJ5niS3akJD1eRV9q5E1auTC9QqVZ2uXbsdwIIYF09MKIH6r4a8Pkccn+pAHJychr1ej39o48+2r99+/aMd999d7RcLpcnJCTE7t27986D7UaMGGG9du3aH+3t7blsNps/bty4ETQaDXw+3youLm7T4cOH75vzc/RXKGeGot8gEAiMpqbaEyZPnvzbjfi9995zsrCwwNatWyWLFy/2etZ+mzZtqlq1apWQx+OR/obcFZKTk2tjYmJI1ZXj6+tFDw8/bzh1qlgJiLubA+lTfv7ZoG1qksLWllRlR0/lq6+OV54/39YSGcmyaWri6bpeJ/Mwx5qAhB5OgmeitbXLKgpmw2Aw0F1dXW08PT1t/fz84n992XH06NEvAXjrwXYtLS3c8ePHv/C4ltSgQYO8Jk6c+NnQoUMX3Lhxo99G78wF5cxQ9BtsbGx6eDM0PhYW7fehkJAQ9vbt26tCQ0P5AQEBvz1x7969u1ij0WDatGleMplMffz48UI6na7XaDSWc+fO9STzVOzOeOeddzzS09PLs7KyVFqtlmhoaOCLRCLDjBkzujdG2khotVqsXbtHdvbsWA7ZHRkAKC8PF7a0KAy2tvx+UfRtackiMjNF1hkZzaygoKO2wEwA3ZXw8bIASqSAY7dr3gSC3cUvvxxH8noowNXV1Wbv3r01K1eufMTRNxgMosTExPkajaZVrVZn5ebmlpaXl1e5u7t7PX6MefPmzUlLS9sN4HBf2f28QOnMUPQbDh8+fCUyMnKkue3oiOvXr1fm5eUp6HQ6VyqVqhISEty5XC59//79RUwmkxkXF+cOtE8JTkpKKlm4cGG3pyeTGZlMpt+7d2/Jyy+/3OeLjl6vh7v7pXKVKraf1CQZYG+/WRIbq2X84x+T3fh8ckdoPvhgT1VyMl1ZXr5YDBTIgEG8nh1pUx2wWPD4PKZnU6n78MODipoaZktQkD1n3rwJzyyaO3nyavPLL9fL3n671XL16pl96lxv3ry5aOnSpaKOCuMfrLO5ubnlMplMM2TIENHTBsl+//33n/ztb3/72JS2Po/038dDij8UBEEQJ0+eJO1iNWzYMJdhw4ahra196DaT2X7Dnj179iOtwiwWC1wut8uTufsDBoMBX375ZcOSJZg7B8gAACAASURBVEvMkgaUy+XQaPjka3V5KgQsLRWG7dtFrD17vm7Yu3cqPyJiBKlqkB6gVquxYYMfQ6eb8Gv3VU8dGQDgs4E2dMeZ4XDWN+7fbyvPyXndG5Aos7L2lr/33kS3o0cvKWbNmmB1/XqeKjIyhA20O7XvvltTa2FhQZszZ0if19dZWlryntbh92AEy4ABAzq9hwUGBk7ZsGGDFZ1Ot8zNzT39f//3f0eNa+nzCeXMUPQLxGKxu5ubm1lrZrrCAyfmWbDZbNKnQroDQRAYOXKkXCQS9Xmaaf36o3UFBcV0ne5V0qsnP0xT00DaP/9ZR1u69D17MrYcP+Ddd/dW6HSvGCm9+4I1sKUciLYFRFZd2UOp/JcgJwe/XldenB9/nGP500/Xm/T6GNu//z21ksGw1ixffpCVmnrOsrJyirSxcY5PUNC2+25ufZuRLi4uVg8ZMsQoxf0RERFhAMIAICUlZTgAypnpApQzQ9EvCA4OHubs7EzKp9fuoNfrUVlZ2TP1MBITHBzsfubMGfXkyZP7ZEq0VqvFjBk7Cq9eTfQGLGlAl9ZG0qBUhnpwuWeayezIAMCf/hTldPfuN8U3b77q3fvfMQsAxwDY96Kby46m10+0BQC1eo4LAHz9tQpAqBQIEgHArVsRLpmZd9ShoYF9NrHc2dnZMjk5+f6NGzdYAJgWFhbNjY2NFitXruxV2pVOp2PEiBH8q1ev9nuxRVNDOTMU/YIBAwb0alo2Wdi1a1dpQkICaVVqe4qTkxPj9OnTEgC/6WrU1dWhvLxcHRwcbPRF5V//2lt39epKcffF28hCXktMTHgvUjam58qVbLVM1kRnsVh6QKEDjNGOT1cAxu7qYwMIeqjwaIDVoUPfVYWGBvZZx52lpSUSEhIebll3OXjwYK9brKdOnRpeUVHxPoA/9/ZYzzuUM0PRL3BycjKrxoyxMBgMbZaWffbA2GfQaDRMnTrV7dixYzVjxowRJiUlFQ4fPtyhtLS0MScnR8fj8Qz5+fksb29vi9jYWKfeqgf//e8Jgk2b0iRy+Qwv43yCvsbCcPFiVqudnb0+LCyQdLozmzen1f75z4MtgSFcwE7cs3bsjlD3QXSVhvR0TgsAs8oHBAYGuufl5en9/f17fLEbDAY0NjaWG9Ou5xXKmaHoF/B4PLPMZDI2BEE8t+2DOTk5WolEomxsbCxMTEwUs1gsDB069JHoQ01Nje7IkSO1cXFxjr05F4PBQGxsNXburG8DHHo8xNB8DOUtWMDUDBz4P9m5cwFssrXqnzkjlQF+vfqOOkbgAkjRPtfJVOgRGanpgTifcXF0dGSmpKQUSKVSQVhYWI+ky9va2lBSUkKlmLrAcyHiRfH8Y2VlRdpOpq6yY8cOSUBAAKnE5oyJTCarfu2117wXLVokflotiFAopA8ePJi5Y8eOkt6eb926WV7OzqckvT2O+RjEysn5yik4+N9V5rbkcVavDnC1tEwrM/6Rx1sB12uMf9xH4fNpZi9G4vP59IULF/oVFhb22BlhMpmoq6t77mrsTAG5HgcoKDqAIAjaiRMn+nVkpq2tDSwWy2b48OH9q1K1GxAE0aVhgmKx2JbNZltdvHhRNWbMmA5TLEqlEgcOHKgYPXq0o7e3NzMnJ0d/8+bNOo1G01JcTNicOcOXWlgA1dWjTBA96Dvo9Ct1M2ZwSZFmyskp1nz0UXqZm5vBIj2dr2htHWGC7jRrAM00oFINuJgo30pDcTHRAsDONMfvHnQ6vcdSDARB4P/+7/+2fvrpp5G3bt36z759+3r9EPC8QkVmKEjPkCFDfMViMWnUf3tCa2srnsdamYdRKBRdvp+4uLiwpFJp7a5duyo6en/Pnj0FCxYscL106ZJk8+bNhWw2W5uQkCBcvHixT34+XXHvXqL/jRuL/Q0G3341dfxxdDop84cfeJrPP9/RmJNT1GpOWw4duipNTX1NvHXra275+Qv9DQY/EzkDcQLgUqVpjt1OTQ3DrL/Lh9FqezeZwN3d3eL1119/ZebMmd/HxsY+Mm2dIAgqIPEr1C+CgvSEhoaGODg49OtWJmtra+h0OplSqbTncPprB86zGThwoHNubq4sICCgS106MTExnnfu3FEWFhYavL29Ca1Wiw0bNtSLRKLmuLg4LxqNhrFjx7q4uLhYPazf89e/DvE6eVLSBnj3Qa1MgwGwN+K1V9AG3GgEgm2BY+XAYi+DgUf74os2ZGRszT98WORnvHN1Hb1ej6NH0QzA0XjFvk/jTAswzqQPJxYWerM9qOt0OmRmZtbxeDxiwIABDgaDwaLzvTpn6tSpk319fa+sX7/+0NWrV1MlEkntjh071v3000/3L1++fPjnn38+VlBQ0GiMc/VHKGeGgvT4+vqSfi5LV3BwcLBQq9V4Xp0Zd3d3659//rm0q84MAPj7+3O2bt1aunHjRksPDw/FrFmzPFxdXR0evO/p6flEWs7CgqO3tr5VKZd7m3gkRGoFkEkDRuuA0Uaq2UqrB4bqgdxK4M2HWvSZaGmhm604fMmSbSX5+at8Ot/SGJTVANEmPJcSI0cy+yTFdOXKlSobGxsLR0dH64KCgubAwECHI0eOFM6cOVN84cKF2ra2NplSqdQb63w+Pj6OPj4+K+fPn79SqVTCysoKAAa/8MIL8SdPnrw6derUGSdOnKgz1vn6E1SaiYL0CASCfu/MKBQKQ0lJCezsSJHGNwm7d++WTJ06tVu1TUwmEytWrPD49NNPHV955RVvV1fXTlt3AwJEjP/+Vy0AJCYapawHcFUBlOuBvzkDd2ntr3UXDYAzit9/bgRA6IFQV2C61+Nbl5VxzPZweeGCr65dr6UvsO5SbVXPoYMgdCZvAU9PT6/m8Xg8a2tr64KCgrbQ0FCHrKys1unTp4utra0xbdo0Rz6fz12xYoXR718EQTxwZH77ecKECSPs7OxIObuuL6CcGQrSw+Vy+70zc/jw4YL58+d3GlrfsWNH3a1bt/rl7KagoCBOXl5ej2ohmEwmuiOK+O67ujrAy0QdK3uKADcrYPGv2ka2bKC7DSV6AN9IAC89cPrX0L8lAA/W09I49fUjHA8cuNLU4dH0elRWVqG0tHcNRs3NzdDr9aipqcOrr+4p+eyzvTVTp24tam4O66NRIc0ArE0cmmzSDxzoYnTPLCcnp/77778vUigUhkOHDkl4PB5v0KBBHG9vb1ZERIQVi8VCVFSUxcMPLB4eHkRvNZW6yvnz5zN37dqV0icnIyGUM0NBagiCIDgcTr92ZjZs2FBbV1fHP3LkSP2ztisuLm5zc3NrtbOz02/cuLGot4WDfYler8fdu3fVarW6TxwxZ2elCaIyrQBS6wCtFfDw2q5QALVy4EQ1cLYSuN2hw/Ekw60BXxtAJQV21wHJBcCEZ3QIedt8801Th23LX3yxpzEwsKVl6FC9MjFxb1HXP9PvaDQaBAT8XOXomNM0cKC0cc+eVZ7//e8q4S+/vC0CBH1Unb67BRhrUgFMGi1X5uXlYjRHt6mpSbtnz558iUSieemll0Q7d+6sjIyM9Bo4cCAputAeEBQUNPjNN9+cbW47zAVVM0NBavz9/T09PT37tfrvqlWrHA0GAxoaGjRHjx5tDgoKsrx48WLDtGnTnFUqFVxdXXH27NlaGxsb/bhx49wAYNiwYTZZWVnKsLAw0hfY6PV6rFmzpvHtt9/2sLGx6ZNzbt48zHPcuKs1Wu0IofGOeqYcmOL25DOeNQFclgELfo2snawEBnfSRZXcAET+qgwXKwIkrYBXp63OhYUOnLq6BggEj87NbGkhWoERdgCB48ebbJVKZbdrr65cud6qVk+wB9zNpMFSDCCE2d6ebTr0+rG2s2evKc3IeN0ocg6nTp2qFIvFzqGhoTYA8PLLL5Oys1IoFFqGhIRMA7DX3LaYAyoyQ0FqRo0aFebk5NTvnW6CIODg4MDy8PDQ3bhxo37ixIn2165dq5LJZMozZ86UW1hYtIaFhTkBwFdffVVaUFDA6A+ODABs2LCh8s9//rNdXzkyADBwoDfrL3+5SwB1RgpfGQAU0duf7x6/Lc52/d2RAYBqBZ5JrgZIA2Dz0HXr1aWOFqVyssdf/pJSCrRHUtLSLmv++c991adOSTUP0lNWVvcUy5enFIwceSTPy+unitzcoqf+DiSSCt1rr20tX778+4o337xfCehNVGfUFY4X9k0H2k3Z/Pmuv3lMH364tb43Uc4XXnjBIysrq18U1VpZWfl2vtXzCWEwPLfq6hTPAV988cXfX3rppTUAsG3btoOBgYGDhg0b5m9uu0zJzp077y9YsKBf3JS0Wi1SU1PLJk+ebJboWWTk9sLbtxd6Ana9dHhLFEChGoiy73zbLbVAoiPwtBrT9SXAYk+gZ84dnX61bs2aO6wPPhCq29rChIAF2v91HFCJiPi28OjR+eKO3nv33b3lP/4425HFymzQaKaYUX26oA2Qa4ChfSQaWSxzcsqqGTKkicjIULJtbBhtt27N7zRdrdPpQKe3f69qtRpVVVWygwcPyt555x2Pvqp96Q0tLS36tWvXfnj8+PEvcnNz+2XtXU/p90+8FM83QqHQB2hfNO/evZt848aNz2/evLlt6dKlAea2zVQwmUwTd3v0nvT09IaqqqoGBwcH3pgxY8yWBjx/fpE4NPTw/cLCpb10/ko0wLguODIAEOMIXGsEwjpoTTsrB2Y69tSRAQCdboTgb3+zVwPiLrW4p6eP5s2du6N416753o8vuGlplirAnmVeR+ayFJDLgMkmbqV/GG9edbU3r7oaAAyQyerVERFH8hYutOK98soEp6fttWPHjhI6nd5mYWGhVygUAm9vb+JPf/pTv1Eft7Gxof3973//zMPDQwRgubnt6UsoZ4aC1HC5XLFer8e2bdt2bNy4cYfBYNCPGTMmis/nb5w5c+Z0c9tnTE6fPt0wfPhw+yFDhgjOnj3bMHHixC4urn1PRkaG6p133jGLwNvjeHhoUVjYmyOUKYCbTcDoLqoJOwA43gRckQETbIAgh9/f0zEBKyOIpIm7XJCr1w9xOH3ax+GTT/7XSKdzNR9+ONsJAC5dutYqkYwmwUJcQwBRZrSDACCwvHdvsf/HH1+uHDUqr3XIEH+LlhY5bGwerd+h0+mahQsX9ouo6MNoNBqcPn36pEKhuM/hcBxVKtUwgiAIwx8o9UKlmShIC0EQ9FOnTpUUFRVlrl27ds7DYdMxY8bYvf7660cmTZoUYU4bjcHPP/9cw2QyrRgMhrqsrExmaWmpt7Cw4AMwTJo0iZSzh3Jzc1XV1dW148aN68On7Sc5fPiy9NVX+S1q9eheRIe25QOJPXDMzjYC6hZgxkO/g+/qgVUO5ilHvCN3ds6qvnhxrM/69UcaNmyw1ajVc52flp7qOzaXA8tJMyjW2vqEhMVS6RQKPXvKlBLdZ59Nd3V1FdLKy8tV9+/f144fP77vir+MxP/7f/9v/aeffvrmA+flj+bIAFRkhoLEhIWFDdZoNPTt27evfDz/e/HixcaoqKg3ZDLZT7Nnzw4yl429ISsrq+3GjRuVw4cPt0xJSZHFx8fbhoeH//aUf+3atbqSkhK9p6cn6ZL1Uqm0raqqymjKpj1FqZQy1eqoXjgycgCsHqZgLAiA95inwJcBNIeOtzc1gdbV1TTb4OCzkpaWcY7AEJJE9qx47QKC5naq2pHLp3o9+H9yMnDp0t4isVjRdv26p62LS4X8xo1e5Aj7EIPBgL1790osLCy0eXl5mx52Xv5ojgxAOTMUJCY0NHRIXV3dmcuXL9d29L6bm5tep9M1KJVKA4fD6Vezm3755ZfGI0eOaD799FNPGo0Gd3d3va2t7SNOS0hIiGDz5s0lK1asMGv043GUSiUkEknVnDlzzF6IXVYmVwHsXhSVynWAYw9XWQ8eUP9YfZN5Z1UYDAPtW1qUesCaHJ4DAGCMNZBVDYx8aq2KOWloSBA1NABAuTw4+L7c3PZ0lTNnzpRGR0d7crlcIiMjYzCAm+a2yZyQ7omPguIBYrFYXFFRcQcA/v3vf/978+bNPyxbtmzIg/e3bdt2c/369TPOnDlz0XxW9oz8/PyGzz77zOlBwaadnR2tIwVctbpz5dmCgoLWjIyM1t27dxcdO3asvKNt0tPTWzWa3nflnj9/vnb//v0VoaGhggddH+bk6FE09W4wYkYF4NjD+6A9DXh47s7ZGsBAgg4SggkwSPSg6koARVJzW/F0atVAZauf3/nyLVsSRJ1vb35++OGHIldXVwd7e3uCwWBAIBCEm9smc0OiC56C4lG4XK6wqKhIsmDBgnFr1qz5i1wu19TW1nq+++67d9Rq9V1PT0+3pUuXDr137x5iY2PNbW63GDJkiPM333xTsmzZMk9ra+sOpfwJgoCLi0trR/s3Nzfjxo0byrFjx3Kys7PLxowZ4yUQCBzz8/PrtVotGAwGtFotkpKSlPX19XUTJ0502rlzZ9HSpUt7fLPOzMxsYjKZ6sTERBIUlT6IECm4BJHfYDD4dTOlYgCwqwSIcgece+jMWAOokQH4ta5JJgdmddgi3bcM4QOflgOfkKZOBdCRZq1xdv6hyNWVramqsqRVVzvYjRp1T/rWWyI3LtfTy9y2PQudTofTp09XyWSy5ujoaG9vb28WAGRmZuacOHHivTVr1pjbRLNCmguMguJxCgsL79TU1Nzw8/MbyePxaDwez3LVqlWTAEwyt229JSgoyJpOp9MuXrwor6ioqH/ppZe8OtqurKzM4u7du8pBgwZxAODq1auy4uLiWkdHR46Hhwdv586d+VFRUa6/CgtaOzo6cjIzMxsNBoNBr9djxowZ9lwu1xMAmpubLbZt21aXmJjYqRLtA06fPl0rl8tVTk5OVgKBwMLX15cUjgwAcDgclJYuES5fvkuSnCyy797t7GxFe6twb8tbaliADkChHhhMEmVYBgA3ks3CoJm9vuoBIhGr7ejRGQFAexcQizXUTDVOXUcmk+mSkpJKEhISRFwu95Ear6qqqozr16+3mMs2skA5MxSkZc2aNesA4LvvvltqbltMgUql0ikUCkZUVNRTnYtly5Z5lpeXyw8ePFhgMBj0gwcPFiQkJPzWOioWix/pwrGxsaGNHDmyw9HcLi4udhqNplsTE1taWhpnzZpFWk0fGo0GpVKH3zPmBgCpFUB1C9BGA4bygeAOOsJqW3rvyADAUk/gWClQpwGW+xjhgEYiyALIbwP8+kBxtzPuawBP0qhZ5+fLfmt7Z7FIVFr0DM6dO1cZFxcn4nK5j7xeWloqzc7OTlqyZIl5DCMRlDNDQWomTpzo/tlnnz2Xw9PUarXBwcGhTSQScZ+2jY2NDQYMGGA9YMCAXi+Uly9fbpw3b95v0YN9+/aV0Ol0lVKpZIjFYruOnKDW1lbS19XRaGxtuzOzrghg2wDxrsCDj/K/QqBEBbS0Amw6YGACFTogwkgichwA1lZAGckiIUOdgSNycjgzl0qBpSRx9Mo0GzYM7HJkkgxkZ2c3iMViB4HgUbNTU1PPp6enr//6669Pmck0UkE5MxSkZtq0ae/7+/sbcZggOdDr9SguLm4ICgpiEwTxVGfGmFRXV2s1Gg3S09MbSktLGydPnuzu6OhoCQBnz54tzc/Pt/bz8/vtUTUlJaU6NDSUlDo3D/P66wNcrl7dVdzUxNQDKx5bqF55rIZFD+P3PUTZt6v1HpMA072MfPAeQgNQroKppzp2CR5p1hmB4EJZZOQUEtQ1dU5mZma1RCJp8fDwEAYHBz8yofvu3bvFBw4ceGX37t33zGUf2SDNRUZB8TgffPDBOy+99NJL5rbD2Jw8ebJKJpPJX3zxRV82m935DkYiLi7O4dChQ/kODg6WixYtekTldOLEiR537txp/PHHH5uioqI879y5UxMUFMT18PAgveZGRMRgTlqarXdwcGYXhgGaKtDkyQByOEC5EnAjQUqFCcCtCYAZoxAVeuB0ITCaJDUpcrzzjsauP8xYAoCysjLp7NmzO0zx1tbWllCOzKP0j2+V4g9JYGDgRBsbmw6v0ebm5r42x2jI5XJZQkJCnzoyAODt7W2VkJDgN2HChA6LeAMDA+2WLl0q/uWXXwqbm5uVZHVkWls1aG39vc3888+PVcyefbAUWGLm9MFUR+ByA5BTb147HhDhBWyVAx3KNPUBe6qApb6AbxfHRJiaQnlAgLuFXt+3tchSqVT3/fffl7S2dtiY2CEGgwFKpfKp+kleXl5Dpk2b1u/GLpgSKjJDQVoUCkVVR69fu3atpby8XNnS0tK6YMEC0k+z1Wg0+Oqrr6ROTk4NABAdHU2ajqDHuXXrVl1NTY3l8OHDSVkZOWvWrsLs7BwujTZS7usr03l5KThJSdPZBsPCDoue+54Ed+B4NUBrAgLMvIi7sIDRAFLuA8vMsPC5K/r+nM9iiHV8/L0Wf//tRefPzxP1VfHvvXv36seMGeO4Y8eOomnTpjk5Ozs/M3Kn1+uxefPmonnz5j1VRsHb29t2wYIFm8aNG5dw7ty5auNb3f+gnBkK0qLX6x95lFEqlTh27Fi9paWlLC4uTqxWq3HixIliBoOhp9PpfLIOZmSxWLCwsGhevHixmAxCc49TVFTUfO/evYbW1lbDsGHDPN58803SFUh+/vnRyjNnlIrs7HkigE8HaIJffgF++cXclnXENCfg4H3zOzMAIGYBKhegFEBf+9D1PKAN7SkvsjDAJi+PYMXGrqs4ceJPJm2lV6lU0Gq1el9fX4e8vLy25cuXi3bs2JE/b948PxqN1qG2FACUl5drIyIivK2tn13uNG3atLFsNvu8j49PSEFBQb9RLjYVlDNDQVqsrKzsb9261Xbr1q0qDoejFAqFdrGxsY4sFssBACwtLTF9+nRvANizZ08BAFI6M0ePHi2fOnWqDRkdGQDgcDjsqqoqblRUFNfLy4t094Rvvz1d88UXk+wBJxdz29J1uCRK0Q2yAn4oBFb2ceErpxlgmqV438npvyVKpdig09EJhSLEGXD+NQxTo/vf/7L0kyYt77Ujc/bs2eKmpiadh4eH7YgRI+wfOCcNDQ1tN2/ebKyoqNASBNHW1NREvPbaa54AMGrUKK+kpKS65ubmltjYWC+hUEhrbW0Fg8EAg8FAW1sbiouLFREREbyu2DBu3Di/9957b09sbOxLycnJHUay/yiQ7sZFQfGA4uJit0GDBukXLlz4zEdKpVIJFovVpT9+c0AQhGrgwIEkUmN9FCcnJ6aTk1OLh4cH6ZzBdeuO1/znP0I14GRhblu6jhYAk0ThCAKA0KLdrr685dubbX1hMh00EslYXwA4efKy4osvKksKChyZBNFAnz07zt0Yqenq6mrtwoULfRsaGrB58+aiFStWiACgrKxM5eHhYRcVFfXENSASiVgikUgAQHD27FlVamqq0sLCgtDpdFq9Xq9lMBhsgUDAZnRxGkVdXZ3KysqKIRAIBgCgnBkKCrJBEARx4sQJN3d3904XsbS0NElcXJxXd45fVVUFW1tbWFpadr5xL6HT6aSfYBscHOx5+/ZtQ3BwMCkGdk6Y8FU5QXB016+vEALCftaan6sHnHox/NIUBAiASvRtqqnGbNc9g8H8rcp3ypQIqylT4KvX6yGTNaO3jkx+fn5jWlpaS2BgoC0A2Nvbw87Ojnn69OmySZMmuQ8dOpS7e/fuPB8fn2cOYp04cSIbQK+6ANLT0w8vWbJk/ty5c3tzmOcCypmhICX+/v6enp6enUYzysrKWnQ6Hevq1auaoqKicktLS71SqbSaM2eO88MFfufPn6+pr69vYrFYNIVCATabzbazs2PW1ta2zpo1y6RTqWtra+mNjY0aOzs7UhbVAkBDQ4Ps124Lvjnt+PDDw+Xp6Sp1dvYiTyazsAEQmt7bNDqBNGBXHaC2AoaQpDDZngXcUAAefehkMc2UVzWguHiU88iRuwvPnIkRP6g9odFosLXt3eXd0NDQdvXqVcWqVaseuWfEx8e7Z2VlNaenp5ePHDnSzcvLy14qlYLPN+2fE4vFIlEE0LxQzgwFKQkPDw8VCoWd3gwlEonB2dmZ4+npyQgPDxcB7d1Dly5dKq+urlbTaDQWjUZTDRgwwDYyMvIJzYYjR47kmsL+h/H19eXV1dUpyezMBAUF8Xfu3FkSHh5uVmfm/Pk21e3br/oCQFtbgJM5bekd892ByyrgjgwIJEEKlAPglxrAygEY1QcijTUGQNMNVeRGtPvRvU3/1OpiYr6pr6gQSdVqNbOr6ZrO0Ov1OHr0aBGTybRZuHChe0fbhISEcC9cuKAvLS1V1NfXN3O5XJPr6xAEQdp7Sl9DOTMUpCQgIMD7adX+DzNmzJgnbswsFgtRUVGdRnUMBgOam5tN/vRYUFAgHTVqFEnk3J+OVqtt0+v1vQ7D9wYvLzpx+7bZTm9kItjADxXkcGaO3Af+7gcUqoAt5cBSE9dwHSsAXupiO/gJKdAsA1p1gIwHvNGL2i1Hek6OV3Nm5sxnpni6S1JSUsELL7zgw+E8Ww9x7Nix/O3bt0tEIhHX1H9HFRUV0tu3b++ZN2+eSc/TXyC3QAfFHxaBQOBt6nMolUrQ6XTW9u3bS6VSqcnOQxAES6sl2eieDoiKinLdvHlzkTltiI524NHp2eZSeTMBKhI4Mq0A5Bbtt3tfNlBmaC8GNiW8LirTbS4EQjhAgieQKAIc5YCyV2cuKgpwUKu7NU/1mRgMBmi1Wk5njswDFi1a5BUREWGyqIxSqcQPP/zww4YNG+b961//2m2q8/Q3KGeGgpTY2NiY3JmxsrLCvHnzPBcsWOBx8+bN6oMHD5pkEU1ISPA4ePCgxBTHNibu7u5sT09Ps0ZrFy4cKwgNvdFkThuMC9OMUtXFUiApF/hWAix/qMbjVXdgk4kdRloXryM7LeD4UKrkBU/gXAXwpQTomXSKhUWlas2aQ0YTksvOdavAhwAAIABJREFUzq6LiIggRRF6a2sr/v3vf3/4t7/9bdX69etPmtseMkE5MxSkhMPhmNyZeQCNRkNkZKTThAkTHH788cdCYx67ubkZu3fvlvRFOqur6PV67NhxUlZVVffE03NVVZX+zp3C1qiow3mxsdvMEqVRqcg/qbtr6AEozOQctgLYoALmBACrvR691W8sAZaZcIDoKQUwpgtF9cfqgMGP2WGJ9lobJ/R0RqZaPcfl22/j7N56a0fFzz9nyX/66XSPneOSkhKZRCJRent7k+LvNzs7u/Lbb7/9zGAwkL5Dsq+hamYoSIdIJHI+dOiQSTuMOoLH49E8PT2ZVVVVemdn514tqIcPHy5jMpkGHo9nkZiY6NWV+p++YuPGkw3vvTfRduDAA0WXLs3zKS2t0h09mtHs4+NglZHRxnznHUYrne5mN2TIXbNEFb79doTnxIlppWp1FGnHPnQNDYARfTIR/UksACyzBNIa2qd6P8xIa9M+x9aoAIdOuqbuaIEGPeDzmEpyuQZwdAbielnY6sLavn2uYOfOyha93sNq375dBSoVT6vV0g08XjHn6NFXunR/yczMbAwODu7ze9HTkMlkeZQj0zGUM0NBOkaPHj26K/oypmDChAkeGRkZVefOnZMDYA0bNkzg7+/f7SnIUqlUs2TJkj5WXO0aKlULDbCl5edzbd3cUss1GieGVjvPCWg2AMMELNbFposXAxx8fRebZazBwIHerH/84wT7H/+o0ACu/bhbgwGgTgHATGMN/G2BrPt4QhnboO36rb9GD5xoAAzKdufjbW9ADWBHEWBlAFxsgAmPRVesLYFmAM/y4wIZwPEOJi+erABeMlJUVsDS6wX2AJCe7vdrAb5E8+abVZ3mr/Lz81U+Pj7s6Ohoj+Tk5BKxWNxnkeKncfr06QsXLlxIpTRlOoZyZihIR2BgoJ85Ixnh4eHO4eHhAIDdu3eX+Pv7d/vJbPr06aLk5OSi2NjYpw6LMxd376rkAM1Wq42312rZaFeIBQA2AYCl0cwVTpmyVVJY6OXVVzaVlpbDxcUJ167d1ty4UaT66CODAXDqx44M0H57VfaumrXXTPEG1hUCbzzkWF9TAU0VQKsckLMAgSUQ59zx/geqgeUu7ZGeX6TtjsxJObBU1P7arQbgn2XAP9x/j/ZMtAbW1wI+jYCeAbjaA/VKQKVvb1l/gH0bUKgG6ATgZQHkNANBju3pueMKYMZD0Z3vCgEnByC+lwXV7qz164cgPj5XGxQU0OH6V1paqigpKaGfPXu2+NVXX/VWq9Vmj4ScOXPm2oULF/7x3XffXTS3LWSF/vHHH5vbBgqKRygsLFzh7+8/xNx2AEBLS4s8NTVV6e7ubsXhcLrsYVlZWREymYypUCg0Dg4OpFmUJZIy/dtvOzIAT1b7AMCOPhIBrVbXWlt7RB4VFWhtqhbTwsIS/eLFKUVcbgN72rSi5h07rtRt2BBulZp6Qm4wvCVsXyz7O3xbIL0O8DWTIrBCC5TKgaEPRYes24CBfCDcCQixBST1wDkpYOAALg8t8GuLgDlCgPfra9fKgSA74LoKCLJsd16EHCCcB3wjAQg64G7R/r3pVMB0DyDQDvBkAwO4gAsHSKoGhv4asqFZAWC0F/qeKAcyWgE7GnCnFvC3btfp8bAAvi8ERDaAq9Wj9vUEGgwGDmFtnSrdteta04QJ/tzHdecuXbpUOmXKFEeNRqNvaWlRVFRUtA4bNsys4offfffdOz/88MMJc9pAdp6TQjuK5wkej/eEuJ25GDVqlNvkyZPtJRKJprv7enh4WGs0Gp0p7OopQqGAFhNzpdHJ6fvi9qfsjtFqQ4SbN7/m5OFxsWLu3P8+MfNFr+9i5y2AQ4d+Vvz8c5b8wT4XLtxUiMXHJKGhhPzy5TfEiYm+Op1uqX1l5V88AR9LYKCiXeTtecD2/7N33uFNle0f/2S06d6L7kULpaVsBGQjQ1CRJQjIRgHFV1R89Sdufd0DFRBEEIpsEGSJbARkFVpGW7r33m3apEnO749Qli0dtE1a+rmuXtDm5DxPmvQ833M/9/29xaCo0N34MXnQ7R7zwWD7u52Vh3vBbC9IT4etKbd/7qrR9nSqxNMM1t+Ai/K7Pzsy4FVPcJbAl/GwJhWqCq3aGqDNTL5JJxkESKGDoVYIdZNAZ3MY4wntTKCTEVzMg/k+EF4OXRvIDdrJ6IcfJtrt2dOZgoLif32QH3nkEddff/012s/Pz1oul8vy8/N1qqrT0tLK5HK5Ti0TmgOi1lyiVvQJOzs70127dqUGBATogT+HliNHjqRUZ8K3Zs2aWAcHB0l5ebl47NixdyWsrlu3LmbEiBEu9vb2D9R/pTHQaDT07r0+9saN+T5w/8CRmdm+hPffLzMdOrS9/bp1ZzJ3764ocnbOMNm+/eUqOw//9dfpssce633rNbdr90dcVlYvh2nTQpT79l0VFRbOUiiVj9/H3XdDEnR2BD+ZdstBbwJb9WBFDDzvW3UErCkQgB/T4YU2tcsqSJVrc2QsjeFRc2hTx4VcDcQWQwHQo4rO4etvwFQ/rZfM+hgQC2AuhnE+95/f0SwY2KAVWBYW2+MjI3t7VdefbfXq1XGzZs3S+Tbx2bNn46ZPnx6YkZFRpuu56DOtkZlW9Ir+/fv39/Pz0xshA1BSUlJa3WNOTk7qkSNHelpbW4tyc3OFhIQEtVKp5Pvvv08cMmSIXgoZ0Jajm5nlyqC4Rve0kpLHPV97LUDasWN6wXff+aiiol5oa2RkUmVoZuXKQ1mTJhmU9+y568apU1fk5eXlKBRiCbQ1O3DAOz8//82y+wsZgMnuoFHAuSL4OwuWJdfvVeoDGnMISdTd+CKgtymsrOWdvYsJTHaDPhZ1FzIAEsDPHC5maYXovdgZalsXrIuDOb5al+CJNQgZgPg7co80wMrsBzXXKypys73XzDI7O1uQ30xz6tKli/nBgwdL6xKFbAyuX7++vVXI1ExrAnArekXnzp0DG6qfSkMhkVRvMaFWqzUAAwcOdPvpp58SXF1dZTdu3FDNmzfPQ99ex51kZGQL16/3BWxrNUlBaG8N7VEqsZLJjqW88sqj/7Kcv3AhQv7RRxZyjWaoZ3Q01hs2fJ/Up0+Q+9NPF0jXroXMzGl1qO4KvJlXUWEByf/a5mo+dLeGS6laYdbVQrvYNzUlxeBaB9MWY8D4AcNh03zgaCYMvsdszt8aTibA8951i1Y97QaboqFcAuXG0N8ajiZDvgz8LCEhHxyMYUAdboScDM6cuV5sb28oDw8PLxKLxYbOzs52+fn5xTKZrMjCwsLC1NRU/O2336YuWrSoyihkY1NUVCRcvnz5vC7Gbm7o79W2lYcSFxcXvcmXqSQwMNBx9+7dkUqlEpVKZeDi4uLYt29fs/Pnz5crlUoZgEgkYvz48Z5JSUkFnTt31mmzxtpQVlauUSjM65mUKhAScjn3n3+i5S+++KRddHRixSuvnEo6fbqPHfT2rDzq2DGrirfe2pi2c6dLFSW4teFYKsQYQi+9/31WT3dDCPLSbrv8EgsdTaBnNZVDjUWpCp5s4sXYBLhUBvlxMO6OrRpvS+1XXbGWwMR7ej21d4NMFSjV4GMLYWXaqE91uboJha6u/2Smp7e3VKsNTfr1O5x1+nSydOrUCW2ee+65O0WXKVrXPjIzM+nevbtOhAyAhYWFqHPnzgOArbqaQ3OhVcy0oldYW1s3aIO4hsDDw8PKw8Pj1oKalZWlPHz4cGr79u3tu3fvfst/wtbWlvDw8CK07X/1mqVLz6QKwqx6mdIpFANd160bCOSqEhN/zg4JCVYoFAv+FXXJyJjqs2KFQP3yRZIqIF6A2TrxumlYjNCujXN8YGd0049fWOfk9YbhNU/4Pg5KBTBtpKQhRym31rE+BrCrCCZUaXDj7X0o68KFsX4//3wk6+OPL5Z26uRu+txzU+28vFyqXQcdHXXfxaBt27aDdD2H5kBrzkwreoOzs7ORtbW13kVm7sXBwcFw8ODBLs7Ozv8KxYt12XK6Fkyf/lv81Km/R2/d6sWD38vYSlevfspUoRh6nw7M9V3DtmfAtPucVwP8IYeVKdUfo2+sKAV3Z9iTBCFR8EMGrImB/yVWnV/SUDjK4JiO+l097Q2J9YzM1RUNEAHsLYXseypbijRJSWqblJR0YfbsQQ5isaV62TIf0ZEjF3TsA1QzXbp08f/www+/1vU89J3WaqZW9IYxY8YMW758+QFDw+ZbvbJs2bKMtm3bys3Nzc3btWtnb2Zmhj7lznh7r04pLu5voFZ31/0t533JVcKPOeCpATTw3M0o0u+FkJ0D18xgsSOUl8MGObxuo42A6DNy4JQC+srunms58FcCPOFZ8zkE4Id4UIqgk/G/c1Lu5WIpXEyD09awttE6OVdPBCBTgXcT/RHIAblGm3T9n5tRUwEbm7UJkZGjPPXpb7EulJaWCitXrlz8wQcffKnruegr9RYzoaGhw6RS6buCIDjRGuF5mFEDf6tUqjldu3Z9oHD2J5988ua8efM+aaB56QSNRoNYLKakpITQ0FBldna2oqioKLd3794W/v7+OjXeqpzfoEH7IsPDp+p5BCxVDilyMDOAuBJwt4EMBRTmwRPe2iTVShTAl0ngWg6PeoGPQXVn1T8yNfBHLFhYQ14RTPECs/uEs24oQGEAQWJtjsjZbHCVwECXu38noF3Yv0qCN9217QV08fHLAZJKoUsTmwZezIGwCpAa9+iRmb927VOeTk72+tMgrR6cPn06+aWXXvKNi4vT0bahflMvmRoaGjpMJpP94OnpqTQ2Ns4Xi8Wt4Z2HFI1GI0pMTHy0oKBgHvDdg5zLzc2tUwNNS2dU7jKZmZnRr18/Q7QmKebnz58v3Lx5c7RUKlUZGBiIJRKJyciRI93ue7IGYty45cmhoYEVI0YkS7/5Zpx7bm6injrSKYCsclCJ4GI+GFhCfjrIJHAwEzobwoQqfD9kaPMlkhQgr4CQQvCXQfcqfE70jW0psOBmYqvGDr5Oglfdq9+eayuDT1IhyAWCjSHYHVTA6lgY6QQVGoguhgAH2JkOL7lrL/O60tEHMmBSDaX4jUFXO+iKRHI068CBJ3TeV6khUCqVKa1CpnrqJWakUum7np6eSlNT09ba94ccsVgsODs7lxQXF0/nAcSMSCQS79u3r3vDzUy/6N69u2X37t1vVXF88803KY8//jiN3YMqNDSiXKlUCwUFT3pv3Khi+/Y/05TK/zSJiKobaUqIKIZUoJ0pZKqhqwqealvjUwFQysBFAvG5MNENNsaCl7m22aE+b1s63JFPIgYmucKvN8BS0OaAyAyggzNcywFvG8hRQr97kqKlwPM+N83uDGGIMxxVwEt68D6Xl+sycC8Ikua5r1QFRkZG5v369bM/ceJEtq7noo/Ua5spLCwsLigoqDUi0woAgiAQHh5uExwcXG+3zFGjRvVbsWLFcVNTHbWwaUI2bdqUMGzYMBdra+tG3Q45fPhCyaRJZqUqlaktdNSTi/rBJMgqA0SQK9VGDJ70gjUpIBaDsyWMq4MnSnXEFEGWEs4Uw8te+le4WQRszwGrYni6hsjB4VIYbAoJAliJmkGx3B3kAUcyoVQJ03QgrhLLJ03anJ+YWKHcs2d+nRvG6htnz569eurUqa8+/PDDtbqei75R379wcauQaaWSm9GFB7r9evTRRx99GISMXC7HyspK1FBCprS0VLhy5UrOI4888q8S5h9/jMlUqebVwaiuKVBroJcX+NwMl2iAVfHQxxHamjRcVY+vBfgC3e1gawwYmsFYHWx3VIUKWJoK/+cColok5Q6++Yfh2QxzPmyALvZgIMDKGHjOt2kTtT2MNm5c3MbQ8EgzNl68Tc+ePQOtra0/mjdvnmLFihWbBUHQrT2xHtGauNuKXuDu7t5V13NoCrZv3544dOjQet0hqlQqYmNjK86ePSs/c+ZMbnp6esVvv/0Wb2dnZ7F169bYyuMEQSArK0sTH5+rh4mwvT0h6g4PeTHwvBd0N9FGHBo6t8MAmOQLwaZwrLCBT15PpMAgM9iUBh8kNW5Ztj7gLQY3CUzwhb9SdTEDQRCaoRCsGj8/P5d33333tyVLlqwXNfY+dTOixYmZRYsWOfv5+QVUft+jRw//pUuX/st6vbEYOnSoz19//fXAIYbqzvPVV1/ZOTo6drS1tQ3+888/b4XjZ8yY4bZq1SrrBx1XF4hEIgN7e/sWmy9zJ6ampuX1taL5/fffY9VqtYFcLjfw9va2TUhIUM+ZM8fb19dXFhAQ4LJv375EtVpNSEhIYnR0tGrDhmHOcL0UEks7dPgx1sRka4q2+EyXmAKqHNif2bTj+prD34VwVdAP8dDbEiY5w2x3+CUF1sdDrJ6IrcbCCnCygRvFTT2yi0tqk4/ZmMhkMmbMmPFsz549g3U9F32hxYkZgOjoaONDhw7VWlBERUUZNpTgOXjwYOxjjz1WbWPCBz3P+++/73rq1KmItLS08O7du98yfFqzZk3ynDlzGsUYKycnR/LBBx80aMfaO3nqqaf6BwYG6kGyYuMSEhKS2q9fv7uiMhUVFZw8eTJt//791Zq/nTt3LmvLli1Rtra25n5+fgwcONDA0dGRXr163YrXd+jQwcjU1NTq119/zXziiSc8+vTpY+jr6y7189uZs27ddfXCha62TzwRKxGJThc05musGSnwpDukFzX92G+6g6kKVsTWfGxT4QzMdoWpXpCggvWxUKHrSTUi3Y3hdDZE5jTdmFkVixebtQAn6buxtLTkjTfeWLt48eIpup6LPtAixYyZmZl6+fLltf7wRkdHy0JCQnRgKFV3iouLJb6+vhUymUywsbFpklvM3NxcyU8//dRoJmu9e/fuZWSk74ZnD45YLBZsbW2NBEHg9OnTxRqNhpCQkPigoCBnExMT2Y4dO6ITExPlcrmcH3/8MT4kJCT5l19+ibGysrKcMGGC/8CBA+8rKPv37285c+ZMRysrbYKooaEh//wz32PUqEcsVq5Mzdm8eVEbQeirB9mjp9LBVwf5KxLAywAGW8J2PezEPdgWJvnAslp2uG6uPOcNYblNNZqBQXjuxIkD9eBz37CIRCIGDBgQ/MQTT7z33//+d1Hv3r3r0fOq5dAgKf75+flNku9gbW19sTbHPf744/kHDhywzsnJuatF7ZIlSxx//vlnB5lMJnz++edJEyZMKOratat/TEyMsVwuF9vZ2QUPGTKkYNOmTYnVnVutVjNlyhSPQ4cOWUqlUuHTTz9NnjZt2q273R49evi/8847aaNGjboV1pw9e7brrl27bDp06CBXKBTi0aNH52dmZhoUFhZKbty4YXTlyhXTp59+OnfNmjXJ1Z1n3rx5Llu3brUDsLOzC7ayslLFxMRcqzx+7Nixnv379y9euHDhrYtE5esVBEH0zjvvpMyfPz8P4JVXXnHesGGDnUQiEd5+++3UBQsW5C1dutT20KFDFgAnTpyw7NmzZ/H+/ftjn3rqKa+///7boqioSGpnZxfcvn17+cmTJxu0v4yrq2uXhjyfvnDq1KnMgIAAW2traynA0KFDXZcvX55kbm6uKCgosJFKpYXjx4/3MjMzo3///vaA/blz5xRHjhxJmjBhgoe9vX2D3Wx4eCgNL17UhxLltUnapNwOOvS68beDa9X+jeuWyHLw1PUkGplvs+DpJmu2aWNTWMzNxpEthatXryZcu3btsLu7e49evXoFBQYGfuXr6zvY3d19TFJSUhO1j9AvWmRkxtbWVjVw4MDCFStW3No6+v333803bdpkFx4efn379u0x8+fP90pOTpZevHgxasOGDbGdO3cuzcnJCbufkAE4c+aMyeHDhy2Tk5Ov7N27N/rgwYNVNjWrJDQ01Gjv3r3WaWlp4ebm5urx48fnvfHGG9kAW7Zssf3oo49SL126dO23336zz8zMlFR3nuXLl6fm5OSEAeTk5ITdKWSqYufOnRabN2+2vXLlyvXjx49Hvv766x6FhYXi6OhowzNnzpjFxMRcPXPmTOS77757a3vnzz//tJ44cWJeYmJi+MWLF83OnDlj/Mcff8T/888/EU5OTsqcnJywhhYyIpFIYmtr2+LETFxcXH56erp89+7dybm5uQqAc+fOpc2dO9d96tSpbUePHm0ZFxdXZmZ2dxVyjx49ZDNmzHBvSCETEnIi+/ffB+mBUd7mODA31K2QqaS9lbY1gj6hBKJz4Kl6WxzoP2eKYIAYvO573WxIsrP9rPr02R01efK2mGPHLjf73Jn8/HzVypUrp7/wwguz33vvvX6rV6/+5dixY+eAtODg4Ba/XV8dDRKZqW3EpCmZP39+1vz58z1tbGxUAHv37rUcP358rr29vdre3l4dHBxccujQIfMZM2bUKc+kffv2CpFIxLx581wHDRpUvHr16vuGq42MjDQajUZUUVEhUiqVYrX6dgLmoEGDCvv37y8HsLW1rSgoKJA4Ojo2SIbmvn37LMaPH59nZ2entrOzU5eWll4CsLS0VH733XfJH3zwgePJkyfNc3Nzb30GAgMDS5999tlCAC8vr/L8/PxGN+cYNWpU38DAwHp1b9YXFAoFpaWlFTExMaWurq6Ghw8fzrSzs5ONGzfOq6SkhL///jt5+PDhbqWlpSWVvWHc3NykEydObPS7xfz8At58U5BrNB115LFxrRCMDcHLGIrNYHaj5V7VjfaWkFGktfzXA23F0RxtAvBzdSilj6mAP1O1rsCCEhI1MFeP3W4jlVBUCMOadMHVaILsIyKC7CMiICFhRfTKlUZmHTq0azZVQKtXrz7v7e2tdHd3D7awsJDu3Llzzfr1648DnDt3rgCYVXns7NmzdTZPXdMiIzMAQ4cOLRWLxUJERMSthiV3VrHVt6LN1tZWHRERcbVfv34lmzZtsunbt69fTcc7ODhUeHl5BVVUVIheeOGFvMrHvL29b4UDG7vCbs2aNdYJCQkGBw4cMBs3bpyPt7e3Yv369Ql3HuPh4dFk86mkT58+vU1M9GExqR8KhYJVq1Yl7tu3L8fDw8Pq1KlT2f7+/nYjRoxwBvjhhx+Shw3TXrwDAgKcU1ObrjK1vLycDh2OpZeWPqUjIXOwAA4ARVL4NRf669GevhK4kK8fQiasDCrkMNunbm7Ff2TAAk8Y2gaGeYC7CFYlahtX6hdi8dupEFEGj+k0chAR8VzbuXPPNqucpHHjxnU7fvz4ulmzZvm+/PLL/d544435up6TPtJixQzArFmzsktKSiQAI0eOLNy6datNTk6O5NKlS0ZhYWGmQ4YMKQZwdHSsyMjIMFCpVGRnZ0tUKlW159y1a5f5s88+6zllypT8L774IuXKlSumGk31ebhbt2616tKlS0lmZmb4kSNHYszNzW8d3JiCYcSIEUXbtm2zyc3NlSQkJBi8/vrr7mKxWDh9+rRpp06dSufOnZu3c+fOuxaX6ubj4OCgKigokBYXF4uLi4vFJSUlDTZxV1fXZltauH///sSNGzcmzJ8/32PKlCltHB0dGT9+vEePHj1u9QR64YUX3H7++edYhUKBvb29LCEhoclWGiMjI1xd88r79v0lytl5VSzElUNsE4bZh1rBXEs4nQvTbbURBH1BBHQ0hM16kAgcmgdD6xidjAaG3RPlGu4Jkz1gj15tpUgk57I3bRpgceyYg/kbb6zKeeaZVfHDh6+94ey8KVrbnqEpUQkzZ9rrvOFrXQgPDz+TkpISFxYWlrl///7zup6PvqJvHt8Nyty5c3M/+OADV4DRo0cXX7x4MTcoKChAJpMJP/74Y4Kbm5sKoHv37uWPPvpokZOTU7BYLBbi4uKumJmZVelwPHLkyOL169fbOjk5BUskEmHJkiUp9/MNGT58eNGbb77ptmfPHhtzc3N1nz59itauXZvUKC/4DsaMGVN08eLFvMDAwACJRMLHH3+c7O7urpo8eXL+pk2bbB0cHIKffPLJPBMTE014ePh9Fxlra2vNggULMry9vQM1Go3o5MmTEQEBAQ98ERKJRNJ9+/Z1ftDz6AqRSKQRiURipVJJddVYVlZWzJo1yyckJCTDwMBAPWnSJJemnOO5czO9QNste9eu46VpaULJe+9llKnVfRpxu6cCrVnd5gTIV0FQk/k81R4DYFgbSJbD7jx4UpcLnJG2gslYAxopzPCs/j7zlzi4agy+ZTC3ityak6kwvEk/Y/dDLN5auGGDkfmQIY8aAXTsGGAH3Koc/fTTPWk//2wmz8vzt4OARq84srU9ED9jxvBmlZN05syZG6WlpRm6noe+U9/eTAnBwcFN6BPQfJk9e7Zrly5d5PPnz88rLi4W9+zZ03/16tUJvXr1alFNOsPCwuyCg4M96/KcuXPnTvzoo482GhjooVFtLUlNTZWvX7++7L///a8eLtj/pqCggJ49dyRlZ7/aQHlKCrRdqyuJL4afisBXBZ1sIa8Ihjo3zFiNgQrYXACTm7h098ckMAYwBosyGHfz/VAAW7JgrAOsTwVrASa4ah87VQ6OFSAyAJ8q1LMKWJ0Bz9+Ri7WvGIabN30QPqvC0XFj2scf+1qOGdO7xt9tWFiU4rXXQpMvXpzkCdaNcpNtZHQi6bff1NYDBnRqBt3U72bXrl2xb731VmBaWpr+7SHqCS06MqMPjBo1qvC1115zf++991ylUqkwePDgwm7durUoIVNfunfv3q85CxmAEydOZCxcuLDZ3OlJpVLy8jo2UKKIBvg8GZwrQCLAeB/Yl6Vt7CgGHMWwSs/vKKWAUT5clEDXJlrkfk6HCc5gX8X1N7sMpAbwcxy86A1/3ozilgMpmdDnPvlPUsBBDt9ngVkhOEqh0Eorcpq2LN/JaWPqH3/0d/Px8ai2QvNOgoP9ZX/95e87evS6uBMnXvC+WyA3DO3bJ5caGDg57N9/XHn6dEp+374elkOHPqozg6vK9ITCwiJWrfrTIyd1AAAgAElEQVQ77ciRvLLt28f5rFhxKOu77+zkwcHRwoIFjsmRkZG7//zzz02tQub+tEZmWmkQ6hOZ+f3330P79+/fbLeZAFJSUpSJiYlCnz599Cgf5P68887PeT/8MMkMXB9ghbuSDy4WsDURnvfWdkc+WgI9zcD1juPeTYX3XLQ5KvrKsSRwsoN2TZANXABEFULPapKh0+RwrQAGOGu3wsKyIUwDJiZQmAMzvWr+XWq4HYlRAiHRIDOCyU2YfHu1cP78s8UffTTGteZjb6PRaHjuuY1x+/Y97QQuDfp+2NisTigo6Gam0bjbaVsrxCkWLTqe9/bbDe958+KLOxL+/NNa5eqaxtGjk33vfTw9PZ1+/U4kFhd7yCoqbGSC4GMGNgZt2szPSk9/XQY+lpAniESvTtFo1vzW0PNribToBOBW9JdevXp5+/r6BtR8pP4il8uJjIwsa05CBiA6WlIkEmXIaz6yOhIUcKwCPi2Ftje3kGyAsfcIGYCXXODHOP226M9WNo2QAbAArmVX/7izCTx2U8gABNvDREfoWAFutYwi3HlZNwRmtoXeNvBVk0XJLCyi8+sqZADEYjEhIZO9R4zYltXQc8rLm+Wp0QTbgTVaQegj+/rrHibh4VENmoS8bdvf+b/9NtQhN3eMb1jYU84LF25MUirvHiIpKVXIzX3aQ6kc4SQIPa3BzgDEpKd/46AVMgA2IkEYMlMkGu0tEk0a0pBzbIm0iplWdEL//v2Hu7i4NCsRcC9HjhxJKy0tld6vmk0f+eWXSZ729hHV2MkL3C7tvZKvTeC9F08ZPGkBSywgtoZaczvAzFK/d7T7u8G2tIY7318FcDATtsbAiXt8rE7lg0Ottl5uYwj42WhLsOsb4SqSw1ALONUk7rBFRT3cT5++UG8F+9ZbPd0gqgm24wMthw0rzXrrrZ0NVtX2ww9pOeB2Uxy7mYSEzHH39T2duHfvuVsmjStWRCVCVWl2xvd8/8RA+OQfWPGnSPT59yLRJ6+IRF2azHCwOdEqZlrRCX5+ft10PYcHxd3d3bRLly6m9e2CrSsWL96VlJX17B3mbHHlcCgVDmXCijhYkQLr0iAXWJ90d6dtBVAMJJbApmjoXou77y7GEBILxXXf024SHGQQZAobGqiTd3Q59HWE8b7gIoE/UmBPHPx+A+wEGKUDYztjMURkaBtbroiFxu7zqRaMjQ3rKNpu06GDv2TJknNF1tab46FxNY1CMdj1wAFNuUqloqysnMWL16fv2XO07E6LjrFjN8T+3/9tqVa4//TTX9lXrtyoePLJX+PCw8ff8/4aUFLytMe0aS4VQ4bsjOradUPs7t2PWdVO4FuIIcAeLMWw+EV482uY8Fp9X2tLpjVnppUGoa45M7t27Qrt169fs86XKSsrY+/evXnjxo1rVr4Vs2Ztjs/KQpWfn2Nw/bqrEvzdoZeRNlEUtBfZLMABKEHbhsBeDE94wifZ4CSDpyzuqLCtBRpgXSn0lEB7Pe0qejgJvNtom1E+CL/EwMx/5UnoDwKwLBYW1MFtuG54ev4Ue+HCOJ8HFfoajYaPP/49ffVqK0VR0UhPqPzoVArseuule6jAxmZTfGBgkuTEiTfdoYi2bdfHnj07xSc5OV3TpUtutqFhkTI1tfe/8o7CwiKVjz0mylOpTC0gwKTh5lQdR8JhcCehPot3C0afY7+ttFDat29vHxIS0k7X83hQCgsLlVlZWcVoE0aaDatXP+MFEBERq+rXj3y1utvNFeLOy0GlDY0ZMMsb3k+G7ESY51G/lysGhpnAtRRor6f9Ywa7w4+xMKeOTrz3EuQI4dnQ0b7BptagiIDH28D6GPBxhN4NXMWVovzuO2/XhohYisViliwZ0+b//k/D55//lvHrryYlRkYq9ejRpVY2NmLDy5fJT0gwVl675mWmVPapc47ObQzIy+tlGx0dUSASxeULgq91dPQYt6CgkPjs7CBDtXqAS1lZkcbN7UjiiBFZ6pUrx3sDqFQqXn31bLJK9Z9GE4b/Rq1Cq5iqd3d9CGle8fFasGjRIuf58+ffMo3q0aOH/7Zt2ywAZsyY4bZq1Srrms6xdOlS27Fjx3o2xJiNOW5zpWfPnoO9vLzu3RxudpiZmRkYGho22wtK+/Y+0gUL/hbBtdKaj37XDWbVU8hU0kYEhXre0fcFH62B3b1va7IKfomGn+NrbhegVkORnr9OLxOY6gsxubCzFBou0G5tfSG5b99uDZoPJxaL+e9/n3aKiBjme+nSSP93353g+NJL46xXrx7nffjwyHb790tsPTzWRENyPfek8tWvvHKy9Nq11929vP65mXzsYpia+pKXUjng5rXdQlxaOtrjjz+8DSMi4ioOHgwtDAg4FBsa+nwTChmAvEju3vtthYcsMrNmzRqdWJfralx9pUOHDn5N1fupsQgNDU2PiYlRz549u4kvZA3LO+9MtTtxYmXC5cvORmDd2PFxQGkAlzKhs2Pjj1UfJMACb9gWC+oK8HKGNmawPRlea6vN3zidD3E5YKiBjm7Q6Z5KqGsFMMtTB5OvB1M9tTlQW6NhVtuGOKOra0mTl6517uxvfOmSf9u9e/8p/uSTI1EREY86g0+NEScjo8PJQUFppba2xYZLlkzyBli7tp3X2LErErKzX/Cs6jkKxRDXkSNDEgYOTJDl5PyfT9PbDgx9Br5MB1pzZ+7goRIzregHbdq08df1HB6UhISE8gkTJuhxh+LaIRaLOXLkBc/Bg39KuHRpnC34NrJx3CQPuFgKB5Pq3o+oqTAAJt4UqTFFIJfDqzffa2NgkLX2C2BNInS6x8hOZaDNS2kOgl2Etly8pzVcKoPOdYqYymTHU+zts0s9PXPFV686SQoKujpFRTlZlJeXV9viozEZOfIR85Ej8T948Fzx++//HXXjhreNWt3b/t95LGrMzLYlHTni7+zr2+XWOqjRaGjTxsqwqMjzvnlTBQWTPXfuTFXq5j22lsCz80Wi13Phn5WCcLKaysSHi4YRMyJR1wY5T00IwsUHefrYsWM9+/fvX7xw4cJbb/57773nuGzZMkcPDw+Fubm5OjAwUO7r66uoqKgQPfnkk14nTpyw7NmzZ/H+/ftj67sHrKtx9RFvb2/DZcuWNftKpqioqKa1VG1kDh9+3vPAgbMlU6Zk5Wk0vRsxB2hDIZRkwYBm0f4BfGsog5UoILwUwlK0u/ZdPaCLM5wugD5N3CLhQQi00zoT10XMZGrWri2yHTZs8K1clY0bT+QePpyuzMx00nh4uOjswjV0aA/zoUPxLy4u4bvvVmeePWtYkJNjKNjaKkVSKdjby2VLlz7taWysFVznz18re//9G8mXLjkYl5W52ULfGlpwiHgw08kHpY0xfPEJHHtOJJrzKYhUYJ0vCJ/t092cdEuLjMysXbvWYcuWLXYAhYWF1YbO8/PzxZ999plzSkpK+DvvvOMkkUiEpUuXpi1dutT2zz//tF6zZk3s+vXrE318fILOnDlj/M477ziHhYWZ3XmOiRMn5hgaGgq1HbM+4/bp06fFtD8YO3bsnO7du/vpeh4PQklJiWBnZ9firMX37LlSrNG80uBuqHfTTQZnDOF4Ppg+oAuxPpBjAOpymHoz2ng+D0QV0K0ZCZlKLMq1uUK1WRYKNT4+u2O7dh1419bUpEn9bCdNqtJARSeYm5vx9tvjHYFqtzUTE1M1jz9eXqpWz2yG16UB7aDfWq242vkP0CpmHogHjJg0NNOnT89atmxZKmiTcas7zsDAQJBKpYJSqRQplUqRoeHt62pgYGDps88+Wwjg5eVVnp+fLz18+HBsVedZtGiRc23HrM+4tX/l+k2nTp2k77zzzozmHGkqLS3lf//7X+aHH37YrHNlquLtt59yunJleWx8vJe0pGTUfXoAPQj+RuDvoV009yWCayON01TYGEPnOxbv7s2qsu1unvaCdYkws8b3xMbmcPz58+MaJMdG17z99skEtXpes+mv9m8qr6eGLeamtz60mIWyPkgkEgIDA+XdunVr7+zsrNyxY8ctseLh4XGrGqGhk1V1Na6uGTp06Px+/fo1zZZkIyESiTAzMzO8ceOGsl27ds08qnA3Tk72ouPHp/i0b78qqaTErQRsxeDaCDb/KuDnWBhQTX8iXXKxAM4Ugr1Ka1YWaHF3XoQAJKi0/ZzUAoxs9nlTt5ECmlp4l2h44onMFvPZLygw0edeG7VEBYSehVG6nojOaL63yA3AsWPHTI2NjTXJyclXzp8/H+Xm5narHrMxhYSuxtUlvXr1sh02bNhLEkkTFMw8IOnp6erQ0NDiqh4zMTFh8eLFNqdOnarWLXb//v0Zv/zyS+yqVauy7nQRbS5cuDDZ/fTpCuN27Q6lNM4I67Nhpg+0q4vrXhORmA0vesAzPmBhBj/FaQWMBvgjHlYnQKkUJnnDbB9o04KuocfTYZxnzccJREXla559dm2iXP4ALb70gNjYRHV2dnYLEGaX02Hdu7qehS55qCMzvXv3ll+9etXE3t4+2MTERB0cHCxft25dQksdV5c888wzH3Xp0qVJXVGVSiU//fRTvLOzs8HYsWNvJSnu2LGj0M/PzywwMPAuZZWXl8euXbti/f39LSMjI4u7dOnyr8qesLCw4qioqLS4uDjTzZs3xxcVFQlKpVLk5ORkI5PJxIWFhUUeHh6qESNG+CiVSnbu3Jk1fvx4h3vPo8+YmprQrp2PpEuXs8YJCdtTy8s7WdSmzLX2PG0PvyRAkDH00bMS7Ttt/j3E8JQX/BQPYmN4zBWeeEB3YH0lqQTyNNpu0jUh4Z9/3vIwM9uR1OjTamQOHLiWl5nZFogrBu9GruRrTLxtYOqLwNe6nomueKjbGXz00UcORUVFks8//zxdoVCIRowY4TNz5sycKVOmFLTEcRuT+7UzGD9+/JOdOnX6wdbWVmlmZmYycuTIGpNMw8PDS27cuJFeWloqnjZtWpX5KSqVCqlUq8ejoqLkoaGh6ZaWlnY2NjaSnJycLLVaLRkyZIhHQUGB/NKlS/mZmZkKS0tLdXp6unmPHj3MevbsaZaZmalJTU0tk8lkosjIyMwxY8Z4/f3332mARURERLa1tbWyqKjIZPLkyW7Z2dmaY8eOpU6dOvUuB1ulUolKpUKtVrNjx464adOm3dp/37JlS8yECRP02Nq+eiobaI4fvzH26NEXGyFH6FoZnEnXdnQO0IOE2aulkCeFfs26AWrtuVYBm/IgIB9yjOAlz9o8y8TkUOKgQfGqadN87QcP7tQimh7u3Xu2aPZsdbFCMdKl5qP1EQGY8xas/vRhbXPwUEdmBg8eXDx79mzPtWvX2ovFYrp37178xBNPNHYHNp2NqwtEIpEoJCTkP48//rgbwK+//pq4ZcuWxAkTJlSZZJibm8uePXviunTpYjZu3Li2+fn56jVr1iTOmDHDo6SkhNLSUsHR0VEE8PHHH2f06tVLWVBQoDAzMzOcNGmST3FxMREREQWjRo26JShMTU1NMjIy8t3d3c07dtRazEdHR+d99dVXqcHBwWq5XK729PQ0HTt2rBeAn5+fdVxcXNGcOXO8RCIRarWavXv3xjs4OJjcK2QADA0NqUzilkqlGkEQbm0XymQyoztFV3NCLBbz5purclSq/EZya+5gDB284fcsuHIDDCTwuM/t/jtNTQdT+DER+jXzpOTa0sEARsjAvR3crxOAAtDqO6n0n/QBA6JV69ZNbNQE+DfffHPvBx98MNLAoGkCYSNH9rSoqAhvfnvCtxABX30Mfm1FogH/FYRjWbqeUVPzUEdmWmk4qorMiEQi0SuvvPJ/ixcv/lAmu32zm5qaWnbgwIFsIyMjY41GU2FsbFxUUVEhVigUsrZt2xr27t27TaUY2LNnT1xqaioGBgYyMzMzRUFBAWZmZhoPDw/3iIiItNmzZ981pq45efJk7qVLl5QTJkywcXJyksnlcrZt2xbft29fVy+vB21g2DRoNBrmzv0xo6jIquzQoWdcwcqgcjFr5JGBXXHwtI4qS66VgiCDwOanPGuFCjgZDilHIDEGDKSQ4wzDB8Lg7tU9y9h4fl55+WxRz56h2Z991tEjKKhto38Yvvzyy/84OTmNc3d3N5FIJCJfX19/R0fHRkhG11JeXo6zc2IxPNKMt5oAziTB1LaCEKPU9Uyamhb6R9uKPvD8889Pee211+4SMgAuLi7Gs2bNcge4GcWo1qBKpVIZaDQa0fjx413MzW9fZxISEio8PT1rMLZqevr27Wvbt29fNmzYcGPy5Ml+JiYmPPfcc16rVq2KA4yeeeYZZyMjI+4sx9cXvv9+Tfnnn1vmBAUlcPbsM8aCEOjUtDMQAwUiOJ8L3XXgVdLBVNtNOsCnZdVGqIFD5+HQcvhynSAId/X1EYl6W4F5GPSo0pHZzCy4cNeuEpdu3cbW2F+uIYiJickODQ3dfeDAgWWCIFQArFixYs0zzzwzvbHG/L//25cI4/S0AWptEYDoaw+jkIFWMdNKI9GjRw+rt99++2Vj4/vvUNRUvTV69OgqLzCenve3G9c1BgYGd4U858yZ411YWKi+fPlyVmJionzq1KmeOppateTlWecplS7Sf/6Z6aQ7K/4ZXvBztG7EDGgrlH6Igxe9W4aguRwHu36A3d8LwkUVfFHFMSPnQod7/s4ERKKLee3aXc796aeuHoGBPk2mvlNSUhL3798ff+fPMjMzb1T+PzExMTc6OjpyyJAhfRpqTGdnmUwsTsjVaIL1tNN5TZxNh+gUWDEBntP1ZHRCS/hrbUUPGTNmzLt9+/Zt1p4yD0JmZqbpvWXZlpaWkn79+jlYWlrqZUdlGxtkFRWmZrrvKdTbFbYnQo7m9s8qgPOFEF7SuGMbAtYy+LOZOzxrgO2H4cMhgvDuN1ohc7+Dje5609u0+TkuLMzE+tSpsW2bUsgAmJiYuNnb25uIRKJb1YZ79+5dfvr06fCrV6/GLFu2bMxHH3009saNG3kNNearr450+vzzSDGkNQPjuchkWL8DcgU4dg0OHIINS2Bqb0E43ch/H/pLq5hppUFp166dwRdffPH9uHHj5ul6Lrri+PHjxaampprdu3dXmYRnYmJiHB8fr6nqMV3xzju/5nz2mXsJ9DGr+ejGJsAYRnjAsQJYFwORedok1FNiCMto/PHFxTBCV1nIDYAG+Gk9LBkhCNvjazycHd/Ct59AaKZ2Swoee0wjdXVtoxNV261bN8cdO3akb968OS4oKMgJ4Ny5cwW7du16aefOnS+vXLnyBIC8gU1uZs4catuhw4407e9PXxGAtZ8JwtSx8GwvGNhREIY/JghLVwuC0IwTmB+c1gTgVhqE0NBQ56+//vrJJ5988pvBgwc/2pxbFjwomzZtujFx4kS/rKysChMTE4mZmdldvwxBENi2bVvy008/7aYvVU49e65KiI5e6Km7SqL7caoQrhaDRAU9TSGoEbcCNMDOBHjKs/nuwm/cDx8+JQjX6+RsKxLZm8Kox6Bnp/Hj44OefrqTIJFIjA0MDIwlEomxRCIxMTU1dfL09HSIi4tLS09PPyMIglIsFhsYGBjY+Pr6dvf09Kx1Am1KSooiNze3KDi46q0djUbDjBkzRu3evXvvnT+fMmVK7+nTp+/r2rVrgztIl5bKmTr199jjx/vaCELHJskRqht5ahj3iCAcuaDrmegbzfWvtVoWLVrkXF5eLqrsk9SQ9OjRw3/x4sXp48aNa5Fl1HVBEASKi4ttAQvAUCKRmE6aNCmkd+/e9+1L9TBQVlYmTkhIKDx69GiJl5eX8YABA+7q15ORkVFWXl5enp+fj729fmzRjxtnbfa//xUqwUj/MpPpY6n9OpxRs5ApEeBCXv27cv+dD0lWzTdofTQUQqbXVcgACEJ2KfD7za8qcXZ2NnJycmp76dKlG4Ig3LVd+sQTT/iOGTNm6ejRo0fUNFZ5eTlmZmaSn376KbOgoKAsOTn5qEQiyXF0dHxk0KBBfUBrDfDII48EA7fEzIQJEwLmzp37c0MKGZVKxbRpW+KuXbOv8PMrEsfGmiMI3nrge1QVp4+3CpmqaXFippWmoby83EQikbjLZDIxgFQqpVXIaMnJybHau3dv3oIFC7w2b94cDdwlZrKzs+W9evXy0BchA/Dzz+JicNRxewENsCYe+luBbxV3xZfLoaMK7Ku5bimAX+Ih0ApWxYFEDV0coNPNha8CSKwAuQIyi2FAG6jMIxeA/amgksIreuZKXBuic+HkBvjjS0HY22geI2lpaeXAlaoe++OPP2KCg4NnBAQERPj5+d03qiESiViyZEmUt7f3hdWrV/+zZ8+elYIgCEOHDu1lbGy8q1evXvYAhoaGAbNmzZro6enpUVRUpHrllVf+065du/uZ4tSLw4f9jZTKId5Jeu1pLABFD/VW0v1oFTOt1AtBECQVFRVymUymBzkW+sXrr79uB9jl5uZibW39L8XSsWNH202bNsVGRkaajBo1qkY35PoSEPB9SqdO1hWBgRayv/7SFFdUIAoOLjMaNszeYdSoQUZisRiNRsPLL29OKivThx5Dx/LAxQFOZIGVKRxNgRygswN0MQMMYU0+zLUFq5vzVaFtQWAD7Em4XYU06KaAjFfDmmhwMoJwAUa7g40BBJjBrwlgWgYKGeRIYKQLtGuG18TfD8IvLwjC7niYqdOZ9OrVK0AQBDlwXzEjk8kwNzeX79+//+Pz58/HVP78r7/+Ouvt7b28V69e7wDMmjVrMjC5MecslUrx8kotjopqzFEelOQC+Oot2LUBntX1ZPSSBrmAiUR0bYqv+s5PrVYzZ84cVwcHh47+/v4Bx48fN4mPjzfw8PAIBLCysuq0Z88e8+eff971f//7X51vl9PT06WDBg3ytbOzCx4wYIBvamqqdPny5TYTJ070OHXqlLFMJuuiUChEgYGB7S9cuKCPSQl1xsTEpNjMzCymoZPwmjNxcXHKI0eO5Fd+X15ejp2dXZWdNSdOnOiTkZEhr2wZEBMTU7Fhw4bYjRs3JjbEXM6evazKyJjqcODAfK8vv+xmGRY2w//69Rl+GzfOc58+vbfB88/fHmfHDk9RScnTOna9zRFAbgTDTWGmF/xUCMHu8Lw3mBnApix42RkW28OBZG0U5mKBtvt2SDqUA+ZVmLl5SWBGWxjhBm+4gz9at1spMMsTJraHfm4QZdK8hEyOEv6OgT8OwsUftEJGt4wfP77/ggULdvr7+9eqJUDXrl2NFApFeZ8+fRw6d+68AEAQBE1kZOS3p06dCqvtuBqNhtDQ0Ey5XF5vG//HHiuw0X6m9BEBOB4hCN8uF4T4hz7FoTr04G6s8fnuu+/srl69apKQkHDliy++SH722Wd9nJycVOXl5eKoqCjDgICA0qtXrxpFR0cbde3aVW5nZxd879fGjRur3aN94YUX3Dp06FCWk5MT1qVLF/mcOXPce/bsKY+NjTUKDw837tq1a/HVq1dlmZmZBp06dWrmJZ+3kUqlaqCsoqJCrVAoFLt27doaFhZW8JC2BiEiIiI/Pz9fcfz48TyANm3acOHChWoT5fv06WOza9euxB07dkTJ5fKiyZMn+zg5OQlff/118oPM45lnfk144YVrSeBuCBK0ZnCViAAXyfbtI23atFmWo1KpMDDI0YPyje3x4HeHmOhrrv1eDATK4DmH24FkXyfYVAR2lvCCD7zYBkISILKsfpe0kwmwsg43MQogprQeAzUQ11Ph42cE4dG2gvDEMEH48A/dzeU2Fy9ePHPkyJE96enptSpvHjt2bND3339/RiqVfmpiYrKi8ufHjh3L37Jly4cKRc3ioqioSL106dKvZsyY4fHZZ599UF5ev8vr3r12+U3jcl1X8tXw4cfw7Shdz0TfaZA7EUHgYkOcp7H4888/LWfMmJFtYmIiPPnkk8Wvv/66+vz588b+/v5l27Ztsxo+fHhhRESEUWJioqxXr15lOTk5Vd4VfPPNN1U6oh4/ftwyPDz8KsCbb76Z6enp2TE4ODguMzPT4Pr168bDhw8vPHDggIWLi4tSX6pXGgpDQ8NMQRCyJBKJyfTp0yd06tSpw7p16y66ubnJAPLz8ykvL8fJyalGg7zmjoWFhWlJSUl6enq6BrARi8UMGDDA9fLly4WdOnX6lxhu3769dfv27e8Kxw8cONCzpKQkduPGjalisbhEKpWa3tnxuyY+//yPjL/+es4VbGr4oHmZKxT/MQsM3BVXVDSySufXpsVSCX53JB/3u08Es5tM+1WJGHiQtha2dUx6vpADp8tgphHYVhl5azyuJcP34wVhxdmmHbdm4uLilMCU4cOHB3bo0KGni4uLi0wmMyssLMzKyMjIkcvlCkNDQ8MOHTp08vHxGdajR4/20dHRFzMzM5+PjIy8y5U4PDz86JEjR0L9/Pz8MjMz88VisQYQpFKpSWZm5umbPc+EM2fOrFu6dOmm999/H5FI9L6Xl5fX9OnT6+Qad/ToCU1hYbS5k9O2GxkZnR3Bp8GrpOrHjXRY944gfPSzSPSuSCTq5ysIJ2Jqft7DSctaWe/DnQtp5f+Dg4Ple/bssfrggw9ST506ZW5gYCCYm5vX6y618pyV/0okEuzt7SuuXLli/Pnnn+e9/PLLbkFBQS1uS8bIyKjyLswEwMPDw7VNmzYygH379p399NNPZ2RkZJQuWbLk+zFjxjxhamraYhVNQUFB5uDBg70PHjyYcubMmdLc3NyM/Px88eTJk73qcp6BAwf6yGQyDAwMWL16ddz+/fvlkZGROdbW1upOnTo5R0ZGFg4ePNjh3gTihIRU9ddfe2hqFjKVmIpycp7VUR8kAW1Cbmg+OJmCqJGaWdbE/nzoVse2DX3s4Ho8RBTDo01Y9RKXC6tn6qOQuZMDBw5cBa7e55Bf/f39TcePHz83JSXlcGRk5L8qry5fvpwnEol6+vj42MXGxuYKglAh0l5cJWv5a2cAACAASURBVHf6qUyaNOnWcwRBEPr27fvFqFGjnrWzs6v12jZwYD9xTEy/NkCbceM2xB45MttSPywKLl2GMqVItPkP2GAE7oEiUbdHBOFCg2xFtzQeCjEzbNiwwrVr19pNmTKl4MiRI6bFxcWS7t27l8XFxRn+8MMPToGBgYqcnBxpu3bt6uX+2L9//8KvvvrK4dtvv0377LPPHPr3718IEBgYKD9z5ox5UFBQ+fnz580nTJjQYI6V+sr58+ePnzt3LqZ3796+ubm5/1y5ciXi5kNPLVy4cHK3bt1GFRYWlg8YMGC0q6urnpY/1p3k5GRVeHi48Y0bN5KHDx/udO7cudQZM2bUq7OwmdntnOpZs2Z5FxcXM2jQIHdDQ0Nu3LhROm7cOIfLly8XHzlyJF0sFpsUFxtY+vq2kc2fn5KsVM5u1G7GDYcAzM2Dl40htgIMdVRJ5WMC+xLBxRCGetbuOVGlUCQFjQjKgKbQYZly+PklQfj6UBMM1uhERUWVAt/c75iboiXjju8FtBnf1dKxY0fbm3l8FvWZ15Ytk3y6dVsfk5Awx7c+z29Ynhmh/QIoUsP6dXBRr+utdEmLFDOrVq1yXLt2rUPl98uXL48PCAgo8/DwCLK2tlb99ttvscbGxsIjjzwit7CwULdp00bl5eWlqE3kZMqUKb7PPffcraSQuLi48BUrViRPnjzZ09bWNjgwMLD0t99+SwDo0qWLPD093dDY2FhwdHRU9ujRQ4f77E1DWlpa+aBBg4Zv3bq1e1RU1F8LFy689djSpUs3ABsAFixYcOnDDz/8rqVsPf31118Jb775pm9ISEh2hw4djFJTU01rflbtuLPBpr+/vylAt27dzLt162ZeXFwszJixOenIkSAHGN6EQuafZIiRQxsrGFyPUua0MuhWAp1taj62MfGTgZ8f/JkHKaXgevN9W5cInU0h6A6RdaEITggw3ARedYMTRfBrFtiVgLoCrpvCe64N3w6iAljzhSB8srGBT9ziEIvFJpXNKeuKSqVi4cLtiRkZHfQweeZqhiAsmAkLdD0RvaXVAbiVBiEsLMwuODjYs7bHe3p6eu3fvz/WycmpRaiZgwcPJg8dOtRt06ZN0RMnTmy7b9++Um9vb3W7du3qdYdYWzQaDb16rYuLjn7Bu2kiBAB/ZYEgwFBHWBELc3y0icaVRCu0vX7c7pOL8m08/KdO22+Nz28J4GMMySpwNIKMXBjvp31MAyyPhWk+UJ0bwW/FMN78tndNXVGjbSkgKoNunrd/vu0AjB8pCIIeJGrrP8uXL182ceLEOrVTUalUBAWtT8rMXOiun/f4yeXwag9B2FKlx08r+vmutfIQMGrUqP4ODg4tQsgAZGZmKuLi4goMDQ0rF3Clo6NjowqZqKj4iscfv5qanz/Lq+mEDEBSIcxqq/3/TB9YFg/TPMDiZinRxSRINIC2htDHBgzEYHPz93I9D8JKoa1JE064lrg7gUEFjLsZCvuuCLIrwN4ALpTA1PsIGYC+JlqzPosKeMQRfGu5jVqkhiMH4cRm2L0RPCxhyGTwbAfG7rD7jVYhUztGjBjh9NJLLw2u6/O6dt0Un5m5wEt/l8SkKHAOoBrDwlb0951rpYXTpk0b25bUv2nQoEFeoaGhBWPGjPEoKipCIpGUWFtbN1pvl9TUTM3w4dfTCguf82ysMaqnvTm8nQIfuWq7TL/kBSujYe5NgZMlgXmeoAQ+TYcuEhCVQHd7OFoGQZbgrYeh/EeNuCvz83kv2Jet9X8LL4EeNRhEuklg/s2E6n0ZcD4aDI3BTAXDPKt+TngCbP5QED7+BUYDXwNkA9/ePmZ0/V7OQ8jjjz/+Zq9evfxKSkpu5Z4plUoMDQ2pqKjg+vXrOR07drS7d3u7sNBL0J/S7ORC+H4JmCgh6CkYPAyWfyMIIZt1PTN9plXMtNLk+Pn5WX/22WcTdT2PhsTFxUXi4uJie/DgwdKEhISsadOmNeoWyqlTl8sEwVxHhj69ncCpDLbEQboYXvYEgztWB3dj7aXFAviy0uHYAf6XBP7G8IgFnMwCV4d/n1ufMALG2EMa0L+OZpqPOwFO2u2pM4WwIQqKrKB9BQxw1eax7tgPW14ShG2xDT71h5TDhw+vjY+Pv3r69OnLXbt2DbSwsLC6cuVKZJ8+fTrl5uZKDh06tHPjxo0XnZycZABFRUUYGhqiUumLZ0aGHLZvFITPvwcQiYbsgmMDIOKkjiem9+jJG9jKw4S/v3//fv36ddP1PBoDtVotmjJlipdM1jh3ecXFJSxatClVoTCsUCo76zC05W0M3t5wKFsbgSkVQTFQIocLAgzgZrX+Hbzqro3kAEQroQ/6UQJbE84P8FwxtxtlAuzNgsgS2LMGXv9P6/ZRw/L7779fAi7d/Pb8HQ/tr/zPe++999mMGTOWREREXPvggw9mtG/f3l2jsRkP/d2bdru2AjhxGUpy4PQR0KRBRaYgfHug8ghBOJQBbGrCSTVbWsVMK03O6NGjn2tJW0wAqamp6szMTMXVq1c1PXr0wMSkcVJCjh69VLJ9+2Nmt5sn6oqwLLhYAnbmWoEy3wd2ZoOyHD5yq/o5d+YDW8q0Iqg5iJmGZJADvLoMlr8sPKxW2Trm/ffffy82NvaMk5OTw+nTpy8AF375ZZ4VSJswWpwshx8XwWc/C4KghqeabugWSquYaaVJmTx5crv58+d30vU8GpqjR4/GeXh4WC9cuNDudg5ww6LRaFi6NDobRuigCuhMCsQqIdUIzJRgLoGZdxjuiYGxddiKyS6GeAkE67g0uympAJZ/3ypkdMvN3/2Bu3+6fwO8awTtR4GrH0iKod8DXqcEIA8QNHDuONg7QPcO2seibwjCpz/Bpw82RCu3aBUzrTQZ06ZN6zFp0qQfAwICbi3GMTEx6VevXv0zJibGfNKkSU+5uLg0u8/k9evXKzp16mQfGBjYaCaAeXkFDBu2LzY29iWf+pf+1hcBuKrQlmA3FAu94WwxHE6BwbVu19B8EYCVv8KrrUJGDxGEBAWwDFgmEokk8P53oLaER720f28aYNUBSIyCxS9rK9CkInCuJsRcASxdAb8sBQsDQTgTLhKN94S2o2Hue2DjJhK1MRaE2vWxaqVmWlasH1i0aJHz/Pnzb3Vt7dGjh/+2bdssAGbMmOG2atWqGitMli5dajt27FjP+o7Zyr+ZMmXKoy+//PK+Pn363MqVUSqVLFu27PUff/xx8YABAwKbo5BJTU1VHz9+PLkxhYxKpWLgwP1xsbHzfe7eqmlokpXai/bBbFgRr12AQZusWgH8mgQnChpuvJ7m4NtG61WzM0HrphuSf3vclsQfJ+CL51uFjP4jCIJaEN55EV4cBBl3GPAZmMPGL2FMX+jsCn194OtftI/JgdMZkKrUfr/nALy2QBCuRQjCmXDtebcmCMIn38LsfvDV85ChbOKX1qJpdovHg7BmzZoH6kbcSv0pKirK0mjuznWUSqVkZWXlP/7443O6devmr6Op1ZsNGzak29nZKebNm9eo/Y0UCiUGBoVi7SLfWNY8ArC0AOwqYI4LdLGHFTFQJoHpbtqcGIDT/9/efcc1de5/AP+cJEzZQ1FkqygC0SJovN46OrC3rqrVOupo3d5rbx1VO7yK1lux1Yp7VGnFWWtVtAo/FetAbylqIA4EkSVDwgyEleT5/RGwDhSBkAD5vl8vXoVznnPON1SS73nO8zzfDOCmAdBDQyscu/DVla8VAA4WAQIVsCFdPYvI0hCwbAU3XJIM4Nfp1Xf/pMXw6aZeY6hGuhmw6jsgvZCx8zMBgOP+tQvY9QiIuQrsCAeG+gCrTgB3I140uJuxs3EA4oC9z+3jOO8OwK0sSnrrrxW8UZCW4MSJE/dOnz6948m/0ezsbGVCQoJEIpH88ssvv5xUKBSoqGg57/fl5eVVgYEvWj9Ec9q0McX06Q7Wc+Z8l2dsfE7DCTkDsPcRkFEGDDcBFjsCNgDsAMzuBMx3Aw6lqNsqAOQbAJKX1sdpGAGAiZbAB7bqD5DoQuBwOiB5pPlradvlQ4ztuafrKEh9dbYDHlTXQjoRCezsA5w/BpzeWtOCsY1XGZu2lLHtJxhjjLETcUBqLJBT7xXyOS7kADBoI+BqXndr8iyN9MwcPFrpp4nz1OWDkYaxjTl+1KhRrv3795fNmzcvr2bb8uXL223ZsqWdi4tLhbm5udLb21veqVOniqqqKm7YsGFuFy9etOzdu7fs9OnT9+s7A0epVGLWrFkdjx8/bmNtba3YsWNHirOzc9WAAQM8U1NTJVZWVj3CwsLuh4eHW7q6ulYsXbo0tzGvr7nbs2fPcrlcnuHj4/Omu7v7gKysrIz79++nJyUlMY7jRhQWFu5JTk6OdnBw8Jg4ceInVlZWBs25dpOpqWm5tq41c+YISwDo3v0yvvnm67T09MUaWnb99xzA0xI4WwBMbV97m052QE4lcKMY6NUOqGeR6Xqb8MQFjt4DvJv5ejQv83/RwKHVwCxdB0LqibGVezlubiLQ+0NAfICxjHIAL6yPxXHe9sDr2wFfV8DmDQBh6u3qN7Haeluq9/GA79YB1p2BjQE0Xb9hWuVjptDQ0LaHDx+2A4CioiL+i9oVFBTw1qxZ0yEjIyNu2bJlDnw+n4WEhGSGhITYRkREWO/Zs+f+3r17Uz08PHyuXr1qsmzZsg5isfipVUA/+OADqaGhYa1dghs2bLCTSCSmKSkp8WfPnjUbP368R1JSkqS8vJyXkJBg6OXlVSqRSIwTExON33///QLN/haan+Tk5EpUD7Lz9fW1dXd396j5A1dPT8SkmraTJk262LVr1zcnTJgwx8LColn+O83MzDSRyWRPFYJsauPG9bMdNSrANjDw80yxeE2Hxj92eqQC2ilfnMgAwFtWQGg20K5N0ycyzypovtlsnYpUQPg3jEXl6zoS0jCMbb4G4Nqrtb4lBXgzgK15QHc7YBk4juMBH34JuMvwTJVwjpv+OhCxFgg7CNwzBrLHUSLTcBr5kGhsj4mmTZky5dGWLVseAuoBwC9qZ2BgwAQCAausrOQqKyu5J6fUent7l44fP74IANzc3MoLCgoE586dq3Wlzvnz59e6qlZERITl1KlTc01NTdmwYcNkixYtUsbExJh4enqWHTlyxGrw4MFFd+7cMU5NTTXq3bu3Xo1qj4uLywOQ96L9P/30UziA8C+//NKxuLj42pAhQyb7+/t7ay/C2v3+++9FUqk0p7i4mB8YGGipzUSmhqGhIfr0cebEYk18zpeUAWNeksjUmKLtLAZAFgMyW/BCNOdPMxYSrusoiHZU35hJAYDj/Aw57rMrwC9ywN4TuPzf548wbwPERgApuxm7WKTlcFudZnnHqy18Ph/e3t7yXr16devQoUPl0aNHHycrLi4ujwdvNOZRx5PH1nwvFArlJ0+etAoKCnp45coVcwMDA2Zubk4ZeS1WrVo1GgBmzZoVnZWVNdfKyqqLs7Ozp6urbp4rp6amVn744YdddPn4a+XK8IdhYXwOKIWBwdmcqqrh7Rp+tmvmwEeaC06jDDigLQOyywGHFpbUVAA4sxt4T9eBEB1gLPYhx3UaCGAlcGY0Y3HPJSuMrTsN4DSwVPsBtkJ6PQD4woULbUxMTFTp6enxMTExCU5OTo8HNmriwyowMLAoNDTUrqysjDt16pSZTCbj+/v7l/n5+ZVeu3bN3Nvbu0IqlQq6du2qV70yDbFt27boyZMnTxg+fLj/5s2bx2RlaX99hr17997v1asXX9fjeG7erJCXl/cV9OgRmvTVV6nmQHYjEuGBMuCWXHPRaZIdgJnOwPFMXUdSf1figB3HdB0F0R3GkioZC15cWyJDNE+vk5m+ffvKJRKJqb29vdDJycl7yJAh7vn5+Q36nezcubOdqalpz5qvvXv3Wn3yySdSLy+vMhcXF5/58+c779+//76JiQnr06eP3MLCQtm+fXuFm5tbRc+ePUs1/dpas127dp1Zu3btqD/++COZMYa7d+8+Ki4uVj7ZJjs7u/zSpUs3srOzizV1XRsbG4WXl5fOV6zt0kVgFBLygH/+/PhOM2aMNTUzu5bW8LONdgc2lmguuqZQ1gLfp3Ju0vgHQrSHa8h0drFYnCIUCus99ay5WbVqVdvi4mJ+cHBwVkVFBffOO+94fPTRR9KJEydqcGEw/SAWi+2EQqGrNq/p6enZRiQSDSksLLxua2vr+9prr/UTCAQcn8+3uHz58q7Q0NDo9evXb5oyZcpcTVzv2LFjCSNGjGhW6+GcPHmuYtIkj3KgZwNrNR1MAUa6Nu1ifI0VnAl81phqj1pWCWDme4ztoZ4ZQrREr8fMvPHGG7Jp06a5hoaG2vN4PPj7+8uGDh2qsTt50rQSEhJKARyq/jERwC81+6ZNmwYA2L59+38HDBgwzdXVtdFlrEtLS5vduI3Bg/sbvf32/qzIyPbGgMMrvsbsMuCODJAWA+7tm3ciAwAOLWyl1JtZQOhvwB5dB0KI3tDrZEYkEpXdunXrjq7jIE3HwcFBXlBQkOfq6tqoO/vo6OgCPp/fBIvFNQ6Px0PbtgY8QPDCJQj+ciEPSJUBDhZAYFsAzXz9lkqoSyikGQKncoF361HIUpfy4xljLSwBI6Rl0+tkhrR+HMd1cnNza9S04qNHj+Z7e3tzffv21WChRc0YMGDffYlkohtgU8e4EgbA0hRwMwNeb3QvlXZseAh87AyYoGUN76to8Y/gCWlpWtI7BCH1FhUVFbN79+6F58+fv5aamtqgDxmRSGRVUlLS7AZzJiamVkkkf7erO5GJzQW25QA8w5aTyADAp47ApSLgcBLQ3MJ+2QQwDxHHffo3rYVCCKFkhrR+K1euXD9q1CjR9u3bP6+qqqr7gGe0b9+el5WV1exWaF627H+przbw164N4G0ECF/hUVRzIgAw3BJQWamrduuaCurHXr+lAT+mAC+aJ+DtBtgKtRgYIXqPkhmiN7Zt27Z7zZo1C7Ozs+u9rorq2ZLfWiaRJFb97W+HE48ciZArFAqoVCpER7c1qPtIAHAwBTKaQzbQQIIi4BVfapM5kQOcSQdGOQH/cAZmugIJ+cDWh+ok50kqABINFwQlhLwMjZkheqO6/tN3X3zxRcmECRO+bd++/eM6WyqVCrUVEs3Kyiq5efNmSnJy8rXbt2+beXl56WSK8LffRuXcubO484wZUsyfH5VmZFSiksnGuT7d6n4pcC8ecHIGvJ+IkwcgXwaghQygfZIKgFTHN123qwBHPuDn9Nc2HoDeNoA3gIg04FE50N0R6NUGSK8CrsToKlpC9FGr65mZP39+hzlz5jg2xbkDAgI8jxw5YlHzM8dxfnK5/PFysN7e3t2SkpLqvIUcNWqUa0hIiG1TxEjq9vXXX29PSUlJq1lj6fLly3+uWbPmu9zc3KeqYJeUlKhCQ0OXjh8/3mf58uXTf/jhh3+cOXPmanZ2ttYXmbt/36Z6xWM7lJS875yXN9UVeHKmeGImsHsS8I++QPAg4OcjQKIcEBeq71lcBM/3IDRXP2YA+xKBg3eBY8nATLfGnU8F4Nlaj3IAN15xxlF0NuBnV/u+NgDecQYmdwHMqoDtD4D7mUBGTsPjJYTUF/XMaJBEIqFp3i3EkSNHvvzzzz9HTZ06dcLly5cPBgcHfzdt2rQzAwcO/EqlUhVWVVWVJCcnxwcHB2+qOWb37t1iAH2//fbbnyZMmPChsbH2lp1JTe1QR5KcWwXsOlNd7C4BwPsc18UBUJYCM+YBvHeB150Bi5efRuf+lAG9zIHuHRt/rltyID4DaGsBmBkDWbmAXAkoOMDJAnAwAyIfAXwDoLAIKOMATqWe+cXZAowPVMqA7q841qirFdDZCgj5gzVkNVJCSINRMkP00u7du38NCgrqFhwcPHfTpk3b1qxZg127dp0FcLauYxctWjT54cOH5/v27TujpKQEI0aMEDV1vAJBWR3dKlwbgPfUByhj97Krv/2a47jVQOkcYNi/gdc6qQshqqCe9tyc/JEL/OMVqni/iqxyoF9HoKNp9Qar59t0rSlY+kyxznKox+nwret3TT6AbrSCOCFappFkpvToIT9NnKcubUaOjW3IcUqlErNmzep4/PhxG2tra8WOHTtSnJ2dqwYMGOCZmpoqsbKy6hEWFnY/PDzc0tXVtWLp0qW5DbmOo6Ojz/nz5xM8PT0rAUChUOC9995zu3btmnmvXr1KsrKyDBcvXpwFABkZGYY9e/bsmpycbPzJJ59kBQUFUbe0li1btmw1AGzcuLFex1XfdYcCCB07duzbr7/++m82NjZNNlNIpVJBoRC85JFwFYDzOxh7cfHN6pg3c9zY08CoTUDcecDMAXhrKmBqBHi0UX8QA3/9VxfmuANXy4DMHKBvI6qBA8CbNkBoKjDFpf7HNqbXTUG11gjRslY3ZqY2GzZssJNIJKYpKSnxa9euTR8/fryHg4ODory8nJeQkGDo5eVVKpFIjBMTE439/PzkdnZ2wme/Dhw4YAkAH3/8sXvNtrque/ToUYuMjAyjnJycuOzsbMPly5c/HDduXBEA/PTTT/ZhYWEPTpw4cW/9+vUauhMl2nbo0KHIsLCwb5vyqUJ5eTnKyqzNXtyCD0D1SgOTGTuUzNiYfzC26ltg6SKgVwdA5Ah8Ng5Y8Qmw8yDwdNFO7ROZACkKIF8DKy7zKxp/jvpSlNfdhhCiSRrpmWloj4m2REREWE6dOjXX1NSUDRs2TLZo0SJlTEyMiaenZ9mRI0esBg8eXHTnzh3j1NRUI5FIVCaVSsW1nWf9+vUOP/zwQ/Lo0aOLAfUA4Jdd18TEhCkUCq6qqoqrqqrilErl48HCY8aMkXbv3r1CpVKhpKSkha3/QZ50+PDhoICAgCF9+vTp3hTnNzU1hZlZcXFREV4wCDWnEsip9d/sy1T31lRUfx2s3hzCcasTgE//o9tHUGUqILcUsGlgAc0aKgt1WQRt1p/icXW3IYRokl70zAAAx3HPfS8UCuUnT560EolEpdnZ2YYGBgbM3NxcY1M+PDw8KuRyOc/JycmnW7duZSNGjCh+ch+AWqcDk5bl1q1b8mvXrm2JioqKlcvlL+2iycnJkZ87d+7msmXLPjl8+PCB2tqrVCoolX91jkgkCQqZzPIlH+rtDYEJ8zjO1/TFberjixXA9TTNnKuhBnUAIvKA4if+HpOKgF2JwNoUdYLyKrxs1MmeNjFKZgjRMr0YABwYGFgUGhpqN3HixMLz58+3kclkfH9//7Lk5GTDTZs2OXh7e1dIpVJB165dXzjmoCF2795tO3HiROmKFSueGw/zZHJFWr4VK1Zs4Thu69GjR/83YMAAfwDIyckpA8CrrKys2rRp03+6d+/ePjU19cq6deuOjR49GhzHbZwyZcpBe3t7/3/9619fmJmZcYWFhYrffvvteF5e3p233nprYteuXV29vT0FY8ceKjlwwMcSsH7B32wfNyBgBID9jX0tjDHGcbuuAiJn9Xic+EdAmgR4d5D6LUMG9SwhYcemmx3lxgfmuQN7MgFBKSAXAN4OwLTO6mnWPyUDk9zr7nFxEADphYCTTRMFWgt+c6u9QEir1yqTmZ07d7YLDQ19XBF469atD7y8vMpcXFx8rK2tFfv3779vYmLC+vTpI7ewsFC2b99e4ebmVuHj41PvlWFfZujQoUWDBw/23LJlSztzc3PlsGHDCr7//vtMTV6DNB+MMfbPf/7zi7y8vDF2dnbCuLi4fTExMb8bGBjg6NGjN2trD+AEx3Gn+vXrN/Xvf/+749mzZ8Pmzp07FQCWL1/+qLCw8N98Pt+0d29l4sGDJ8sZc2DAo/OAvQ+QowIsqoBOfQB3M3XZAk1Z9yFQkgwkxgPHfwUeVgBjhwCGBkByCnBFAuw6CHz8nuauWZuptYwFsgHwkTuwPRl43xGweyZ5eMgARw6IkQN5UmCwc9PG+CyOulsJ0TKuIQMXxWJxilAopMqwdXj77bc9FixYkBMYGFiSk5PD9/Hx6X7nzp1btra2Oh5gqXlisdhOKBS66jqOlmr27Nlv+Pr6To2JiVmze/fu+JrtHMcJACjVvSUcD4ARY+ypHkSOs28D9OwJSGMZu67R3sWX4bj5bwMDFgLD3tLWNZ/340PAmwf4VQ+ij5YBWXLAWA50aQt01mCC96pO/MzYsDHavy4h+qtV9sw0F++9917BjBkzXMvKyniGhoZs4sSJ0taYyJDG27p16zkA52bPnv3UdsaY4onvVQCeS1YYyy0FcLmpY3z+uusiOe5jC+Ddt3QznfsRgMmOwN576mTm/4qAAikwxkMHwTyBZjMRom2UzDSh2bNn58+ePfvZddQJaUVuHgN2HwCmj1P/nFsJcAwwNwL+SAeEjoBFIx+7KAB8nwF0sgSycwAHAByAtvbA2RygzBDYcR8Y1hFw0HEiAwAKHUwHJ0S/UTJDCGkwxmIVHNdvNfDeWMCOB+z7Fki6CjwoAX67BCz+Cgj6T+OmRh/MAuZ0VJcZaGP+9D6RJZAoAywMgXbNZOAtaylFsAhpNSiZIYQ00pVbwJ83gMF+QEkGY5tO1uzhOC4IEGSq14sxsgXc/QGeGRAQABgBqGs2eSIAQfnL23U2f8lOHTBz0HUEhOgbSmYIIY2iHpz8+SqgbDyQG/vMPhWAHU9u4ziOA17zB9bsA97s9PwZLxQDSVJ1D0fvDsCoRlbN1jYTJ11HQIi+oWSGENJojK0+BuAYUPdMbXXy804poMwDUl0BR4G66GWEHOhiCtzNA2a5N3HITcjSleM4Q8aYlhfrI0R/UTJDCNE6xk7f4jiuL9BvKDBpOdDeBhjoDCTLgWktrCfmWe2tgHZtAWToOhJC9EWrW9xp/vz5HebMmeNY8/OQIUPcQ0JCbF/lWEdHR5+EhIQ6Ryru37/f0tHR0cfa2lq4c+dO65rty5Yta7ds2bJGVvr9S0BAWcP8uwAAC7BJREFUgOfJkyeb2XgAQjRlWxhw6hgwvQcwxBkwA+Br2vLvsew5oK+XrqMgRJ+09HcNnVi5cqXjDz/88ODtt98uycvLe7zARlBQ0HNlCwghz1MvBnhJ1HTlEHTJAMDHn3HcEhvGvjlYZ3NCSKO1up4ZbSguLua7ublV8ng82Nvb0yJ4hNTbvz4ARK66jqLpvPsGsDSM4y7c5bhvVus6GkJaO430zAQGBvpp4jx1iYiIiK27Ve2GDRvmBgAXL1607N27t+z06dP3GWOYNGmSc2RkpJVIJJJVVVU9rv64YcMG27Vr13aoqKjglixZkrlgwQLp6tWr7detW9ehoKBA0KdPn258Ph8PHjyIr6m0PX/+/A4AsG7dukwAOHnypHlQUFAHoVBYeuTIEVsPD4/yCxcu3DMzM2OHDh2yXLJkScfi4mLB5MmTc2uOWbhwYfsff/zRvlu3bmUlJSWUbJJWKj8LEMuAnubqBfBepgjAS4qGN1uWfGCAJ3Dfse62hJDG0JsPy4iICOsPPvggPzU1NS42Ntbs6tWrJnv27LGOj483TU1NjR85cmRBbm6uAQDExMQYb968uV1sbOzt69ev316zZk2H9PR0weeff54rlUrFDg4OldeuXbsjlUrFNYnMi9y8ebONm5tbRXZ2dlxJSQn/6NGjlpmZmYKFCxc6RUZGJiYlJcWfOHHC+sqVKyZRUVGmBw4csLt9+/atlStXPrx7925di3AQ0kLtOw+86Qgsnwes+Tew+3vgWvzz7R7KgOlfAxlV2o/xVd18CORVqmdkMagrjT+pc1eOWzxYB4ERojc00jPTmB4TbfH29i4dP358EQC4ubmVFxQUCKKjo82GDx9eYGpqyqZMmVI4b948JQBERkZapKenG3Xv3t0bACoqKjiJRGLs5ORUUt/r2traVi1ZsiSXx+PBy8tLXlhYyL9w4UKbR48eGYpEom4AUFlZyYnFYhOpVCoYNGhQkb29vbJ///7yLl26aK1oICHaVF0xXAZgY802jpvQFUhfDwwcANgZq7f+8RtgdRkwXAD1YJRmoBjAjVsAzwIwNAPWDwG6+QGPFICNDSCpAvZsAKyqbxZfDwCKVnIcF1m97g4hRMNa3QBgY2NjVXFx8ePXVVlZyeXk5Bi4uLg8rpfCcepubcbY4+8BgMfjPd4+cuTIvH379qUBQEFBAc/ExKT+5cUBdOzYsbLmvE9et3fv3rKLFy8mAkBxcTGPx+Ox7777zv6ZeBp0TUJaIsb23QXwDsd9Mw/o8ncguQp4sxfQJw34+XdgbqCuYwQeVQHz5gLys0B4CtDWlrEcKYCbNS3UiwIO9AX+OV3d+a0E0L8XMH0wgN90FDghrVqre8z02muvyS9evGhRUFDAS0tLE9y4caONpaWl8skkoUZAQEBpeHi4dVlZGRcWFmZVVFTEB4DAwMDiCxcuWKalpQkKCgp4Pj4+3a9fv27ckHhqu+6AAQNKb9++bSoWi43Ky8u5/v37dzl27JilSCQqjYqKssjLy+NfvnzZNCEhgR4zEb3D2JIQxka+D0h2AhbWwEeLXpzIVEL9eAcAUkqAH48B2zYDN/KB2xqYXVgEICZPfY2cMuCrcYwd3MnYiQeMMVadyDwTP2PAj3OAnT8AIduAj8YAi8cCZWmNj4cQUptW1zMzcuTI4lOnTsk8PT29OY7DrFmzcoyNjWvt2p0+fXp+VFSUeceOHX179OhRYmdnVwUA/v7+5QsXLswSiUTdlEolN3PmzJy+fftq7JGPo6OjYuPGjSnDhw/vLJfLecOHD8+fOHFiIQCMGDGioEuXLt5ubm7lHh4e9JiJ6C3GQqM47vg5wH1s7S3+kAB7/wuUyIFunYGrJxj7NQEAOM5mKTDWHfjbCmDccIBf+yleSAbgYjSwfTHwv+vAV8FAzlXGtv/yarHHKgBMq+dFCSENxKkfXdePWCxOEQqFz92REP0lFovthEKhq67jIK0Lx21dAcxe9vyecgCfT2Rs3b6XH+8nAPxGAL0CgE5vAG6ugJvNy69aAWDdeuDzxYyxZjzwmBBSo9U9ZiKEtCa/nwIyS5/eVsqAw8eAH4/XdTRjsQrGdhxhbMZnjA3yA347AijqOOrcfSB8NSUyhLQclMwQQpotxg7+AexeACTmA5fuAxFRwPergMkjGcur9+xC4J9zgOANf42zAYBkKXBWou7tAYB+bsDgUZqInxCiHa1uzAwhpHVh7MvtHBcYBUTeZ4wpgUAAXzTwXEzJcZ0+A+w6AC5OgE8v4MAa4Mt1wOwRQEAfwLIdYNwa6ywQ0mpRMkMIafYYi7inuXMlVQIYAwAc5+cJXL9Xve7N0eovQkgLQ8kMIURvMRaboOsYCCGNR2NmCCGEENKiUTJDCCGEkBat1SYzo0aNcg0ODrZv7DlCQkJsNRXTizg6OvokJCQYNvV1CCGEkNao1SYz2iaVSvlBQUFtdR0HIYQQom8omdGQvLw8/vbt29vpOg5CCCFE32hkNtOOHTv8NHGeusyYMSO2ocd++umnHfbt22fHGMOCBQuylixZkgsAX331Vbtdu3a1NTIyYsHBwWljxowpfpXz8Pl89uWXXz6cO3du/tChQ90uX75sUVxcLLCzsxN269ZNfunSpUQA2LBhg+3atWs7VFRUcEuWLMlcsGCBVKlUYtKkSc6RkZFWIpFIVlVV9Xw1SkIIIYS8Er2Ymp2Tk8PftGmTQ2ZmplipVGLy5MmuS5YsyT127Jj5wYMH7eLi4m6npaUZBAYGeopEoltOTk61rneemJhoePXqVbOkpCRJfn4+v1evXl5z587NDw8Pf5CQkGA4aNAgz4cPH8bXtI+JiTHevHlzu9jY2NuVlZWcn5+f15gxYwojIiLM4+PjTVNTU+MPHz5sGR4eXketGEIIIYS8iEaSmcb0mGiDra2t0s3NrXzWrFlOgYGBRYcPH34AAKdOnbJ8//338+zt7ZX29vZKoVBYcvbsWfOpU6cW1Haezp07V27YsCE9KCio3aVLl8zz8vJe+vuLjIy0SE9PN+revbs3AFRUVHASicQ4OjrabPjw4QWmpqZsypQphfPmzVNq/lUTQggh+kEvxswIBALcvHnzzujRowt+//13c19fX6/y8nIOADjuryc8T35fmzNnzpiNHj3aw93dvWLv3r0pdV2XMYaRI0fmSaVSsVQqFaelpcUNHDiwlDH21LV4PL3430AIIYQ0Cb34FI2LizPq16+f59ChQ4s3btyYIZVKDXJycgTvvvtu0c8//2wjlUr5N27cMBaLxW3efPNN2YvOEx0d3aZHjx6lM2bMyP/1118tn9zXtm1bRWFhoUAmk/FkMhmvpKSECwwMLL5w4YJlWlqaoKCggOfj49P9+vXrxgEBAaXh4eHWZWVlXFhYmFVRURG/6X8LhBBCSOukF2NmfH19K/r06SNzdXX1BYApU6Y8cnFxqXJxcamKjY3N8/Hx8TIyMmKbN29OedF4GQCYMGFCwcGDB23btm0rHDZsWL6pqakqLi7OyNfXt8La2lo1d+7cbHd3d2+VSsVdunTpjr+/f/nChQuzRCJRN6VSyc2cOTOnb9++ZQEBAWVRUVHmHTt29O3Ro0eJnZ1dlfZ+G4QQQkjrwqnrq9WPWCxOEQqF0iaIh7RQYrHYTigUuuo6DkIIIfpHLx4zEUIIIaT1omSGEEIIIS1aQ5MZlUqlooXeCAD1rC0AKl3HQQghRD81KJnhOC67rKzMWNPBkJapsrLSgOO4Il3HQQghRD81KJlRKBQrUlJSDEtLS02oh0a/qVQqLjMz00ypVIbqOhZCCCH6qUGzmQDg+vXrgQKB4D+MMQfQ2Bt9pgRwWaFQTPfz86vUdTCEEEL0T4OTGUIIIYSQ5oB6VAghhBDSolEyQwghhJAWjZIZQgghhLRolMwQQgghpEWjZIYQQgghLRolM4QQQghp0SiZIYQQQkiLRskMIYQQQlo0SmYIIYQQ0qJRMkMIIYSQFo2SGUIIIYS0aP8PZok63qrKQNQAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize = (10,10))\n", + "lisa_colors = crm_lisa.GetColors()\n", + "lisa_labels = crm_lisa.GetLabels()\n", + "\n", + "# attach LISA cluster indicators to geodataframe\n", + "gdf['LISA'] = crm_lisa.GetClusterIndicators()\n", + "\n", + "for ctype, data in gdf.groupby('LISA'):\n", + " color = lisa_colors[ctype]\n", + " lbl = lisa_labels[ctype]\n", + " data.plot(color = color,\n", + " ax = ax,\n", + " label = lbl,\n", + " edgecolor = 'black',\n", + " linewidth = 0.2)\n", + "lisa_legend = [matplotlib.lines.Line2D([0], [0], color=color, lw=2) for color in lisa_colors]\n", + "ax.legend(lisa_legend, lisa_labels,loc='lower left', fontsize=12, frameon=True)\n", + "ax.set(title='局部莫兰指数犯罪地图\\n 每百万人犯罪率聚类地图')\n", + "ax.set_axis_off()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git "a/026Boyce-Clark\345\215\212\345\276\204\345\275\242\347\212\266\346\214\207\346\225\260/Boyce-Clark\345\215\212\345\276\204\345\275\242\347\212\266\346\214\207\346\225\260.ipynb" "b/026Boyce-Clark\345\215\212\345\276\204\345\275\242\347\212\266\346\214\207\346\225\260/Boyce-Clark\345\215\212\345\276\204\345\275\242\347\212\266\346\214\207\346\225\260.ipynb" new file mode 100644 index 0000000..360c2aa --- /dev/null +++ "b/026Boyce-Clark\345\215\212\345\276\204\345\275\242\347\212\266\346\214\207\346\225\260/Boyce-Clark\345\215\212\345\276\204\345\275\242\347\212\266\346\214\207\346\225\260.ipynb" @@ -0,0 +1,351 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 利用arcpy包求解BC半径形状指数" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Boyce-Clark半径形状指数：\n", + "\n", + "### 1964年，Boyce和Clark提出的放射状指数。\n", + "### 其基本思想是将研究区形状与标准圆进行比较，得出一个相对指数。\n", + "### 这种方法是一种基于半径测度的，又叫半径形状指数。\n", + "### 此指数用于研究几何图形是否规则，越接近圆形，指数越小（圆的B-C指数为0）\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 代码及说明 " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 导入arcpy包" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import arcpy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 设置数据处理环境，包括工作空间和允许数据覆盖，定义空间参考" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "arcpy.env.workspace = r\"E:\\Other\\1\\data\\PythonDemo\\PythonDemo.gdb\"\n", + "arcpy.env.overwriteOutput = True\n", + "sr = arcpy.SpatialReference(4326)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 创建结果图层，并且添加需要的字段" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

Output

E:\\Other\\1\\data\\PythonDemo\\PythonDemo.gdb\\bci

Messages

Start Time: 2020年12月22日 14:41:47
Adding bci to bci...
Succeeded at 2020年12月22日 14:41:47 (Elapsed Time: 0.24 seconds)
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pline = arcpy.CreateFeatureclass_management(arcpy.env.workspace, \"sline\", \"POLYLINE\", spatial_reference = sr)\n", + "arcpy.AddField_management(pline, \"name\", \"TEXT\")\n", + "bcipnt = arcpy.CreateFeatureclass_management(arcpy.env.workspace, \"bci\", \"POINT\", spatial_reference = sr)\n", + "arcpy.AddField_management(bcipnt, \"name\", \"TEXT\")\n", + "arcpy.AddField_management(bcipnt, \"bci\", \"FLOAT\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 定义各种方法" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 子方法1 ： 获取从中心点到extent的最大范围的方法" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def maxdist(pnt, ext):\n", + " point = arcpy.Point(ext.XMin, ext.YMin)\n", + " pg1 = arcpy.PointGeometry(point, sr)\n", + " dist1 = pnt.angleAndDistanceTo(pg1)[1]\n", + " point = arcpy.Point(ext.XMax, ext.YMax)\n", + " pg2 = arcpy.PointGeometry(point, sr)\n", + " dist2 = pnt.angleAndDistanceTo(pg2)[1]\n", + " return max(dist1, dist2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 子方法2：根据角度和距离，创建一条射线" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def createInPnt(cent, dist, angle):\n", + " adpnt = cent.pointFromAngleAndDistance(angle, dist)\n", + " line = arcpy.Polyline(arcpy.Array([cent.centroid, adpnt.centroid]), sr)\n", + " return line" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 子方法3：如果有多个交点，有两种处理方法，一种是取短线，一种是取长线" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def minPoint(cent, pnt):\n", + " p = pnt.centroid\n", + " dmin = cent.angleAndDistanceTo(arcpy.PointGeometry(p, sr))[1]\n", + " for i in range(pnt.pointCount):\n", + " dist1 = cent.angleAndDistanceTo(arcpy.PointGeometry(pnt[i], sr))[1]\n", + " if dmin > dist1:\n", + " dmin = dist1\n", + " p = pnt[i]\n", + " return (dmin, p)\n", + "\n", + "def maxPoint(cent, pnt):\n", + " dmax = 0\n", + " p = pnt.centroid\n", + " for i in range(pnt.pointCount):\n", + " dist1 = cent.angleAndDistanceTo(arcpy.PointGeometry(pnt[i], sr))[1]\n", + " if dmax < dist1:\n", + " dmax = dist1\n", + " p = pnt[i]\n", + " return (dmax, p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 子方法4：将生成的结果的线，写入到数据中" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def createPline(name, line):\n", + " cursor = arcpy.da.InsertCursor(\"sline\", [\"SHAPE@\", \"name\"])\n", + " for l in line:\n", + " row = (l, name)\n", + " cursor.insertRow(row)\n", + " del cursor" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 子方法5：计算BC指数" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def culabcIndex(distlist, n):\n", + " distsum = sum(distlist)\n", + " bci = 0.0\n", + " for d in distlist:\n", + " bci += math.fabs(((d / distsum) * 100) - (100.0 / n))\n", + " return bci" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 子方法6: 主功能" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def bcindex(pline, name, poly, n):\n", + " cent = arcpy.PointGeometry(poly.centroid, sr)\n", + " angle = 360.0 / n\n", + " ext = poly.extent\n", + " dist = maxdist(cent, ext)\n", + " distlist = []\n", + " linelist = []\n", + " for i in range(n):\n", + " a = i * angle\n", + " line = createInPnt(cent, dist, a)\n", + " pnt = line.intersect(poly, 1)\n", + " mp = maxPoint(cent, pnt)\n", + " linelist.append(arcpy.Polyline(arcpy.Array([cent.centroid, mp[1]]), sr))\n", + " distlist.append(mp[0])\n", + " bci = culabcIndex(distlist, n)\n", + " createPline(name, linelist)\n", + " return bci" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### main函数" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(, '黑龙江', 29.884698351976635)\n", + "(, '新疆', 19.13302859943545)\n", + "(, '山西', 28.395275857599124)\n", + "(, '宁夏', 33.60425733789232)\n", + "(, '西藏', 31.864406467251175)\n", + "(, '山东', 20.48993063729425)\n", + "(, '河南', 17.69485721600139)\n", + "(, '江苏', 26.1252011768546)\n", + "(, '安徽', 21.148567965123675)\n", + "(, '湖北', 31.119986172013608)\n", + "(, '浙江', 10.77822823519536)\n", + "(, '江西', 23.675399835449145)\n", + "(, '湖南', 12.251784279362326)\n", + "(, '云南', 18.833784902780536)\n", + "(, '贵州', 15.264387353614495)\n", + "(, '福建', 15.369069543807175)\n", + "(, '广西', 13.844785725800476)\n", + "(, '广东', 30.596713770759365)\n", + "(, '海南', 15.95251890326718)\n", + "(, '吉林', 28.26897058139867)\n", + "(, '辽宁', 21.219037144168265)\n", + "(, '天津', 24.1088541706385)\n", + "(, '青海', 15.679446317587919)\n", + "(, '甘肃', 47.83648772464698)\n", + "(, '陕西', 43.32338889611713)\n", + "(, '内蒙古', 59.584410384713024)\n", + "(, '重庆', 41.88603139581827)\n", + "(, '河北', 25.503963189044164)\n", + "(, '上海', 19.53006223422724)\n", + "(, '北京', 19.62476985447872)\n", + "(, '台湾', 30.918276026141093)\n", + "(, '香港', 28.830644150950974)\n", + "(, '澳门', 74.12285532900415)\n", + "(, '四川', 17.673571971594168)\n" + ] + } + ], + "source": [ + "fields = ['SHAPE@', 'name']\n", + "biclist = []\n", + "geo = [row for row in arcpy.da.SearchCursor(\"cn\", fields)]\n", + "for g in geo:\n", + " bci = bcindex(pline, g[1], g[0], 36)\n", + " biclist.append((g[0].centroid, g[1], bci))\n", + "bcicursor = arcpy.da.InsertCursor(bcipnt, [\"SHAPE@\", \"name\", \"bci\"])\n", + "for b in biclist:\n", + " print(b)\n", + " row = b\n", + " bcicursor.insertRow(row)\n", + "del bcicursor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git "a/026Boyce-Clark\345\215\212\345\276\204\345\275\242\347\212\266\346\214\207\346\225\260/data.zip" "b/026Boyce-Clark\345\215\212\345\276\204\345\275\242\347\212\266\346\214\207\346\225\260/data.zip" new file mode 100644 index 0000000..77c342a Binary files /dev/null and "b/026Boyce-Clark\345\215\212\345\276\204\345\275\242\347\212\266\346\214\207\346\225\260/data.zip" differ diff --git "a/026Boyce-Clark\345\215\212\345\276\204\345\275\242\347\212\266\346\214\207\346\225\260/\345\217\257\350\247\206\345\214\226.ipynb" "b/026Boyce-Clark\345\215\212\345\276\204\345\275\242\347\212\266\346\214\207\346\225\260/\345\217\257\350\247\206\345\214\226.ipynb" new file mode 100644 index 0000000..0f1ab41 --- /dev/null +++ "b/026Boyce-Clark\345\215\212\345\276\204\345\275\242\347\212\266\346\214\207\346\225\260/\345\217\257\350\247\206\345\214\226.ipynb" @@ -0,0 +1,371 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.rcParams['font.family'] = ['sans-serif']\n", + "plt.rcParams['font.sans-serif'] = ['SimHei']" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import arcpy" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "v = arcpy.da.FeatureClassToNumPyArray(\"bci_long\",[\"name\",\"bci\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "p = pandas.DataFrame(v)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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namebci
0黑龙江29.884699
1新疆19.133028
2山西28.395275
3宁夏33.604256
4西藏31.864407
5山东20.489931
6河南17.694857
7江苏26.125200
8安徽21.148567
9湖北31.119986
10浙江10.778228
11江西23.675400
12湖南12.251784
13云南18.833784
14贵州15.264387
15福建15.369069
16广西13.844786
17广东30.596714
18海南15.952518
19吉林28.268970
20辽宁21.219038
21天津24.108854
22青海15.679446
23甘肃47.836487
24陕西43.323387
25内蒙古59.584412
26重庆41.886032
27河北25.503963
28上海19.530062
29北京19.624769
30台湾30.918276
31香港28.830645
32澳门74.122856
33四川17.673573
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" + ], + "text/plain": [ + " name bci\n", + "0 黑龙江 29.884699\n", + "1 新疆 19.133028\n", + "2 山西 28.395275\n", + "3 宁夏 33.604256\n", + "4 西藏 31.864407\n", + "5 山东 20.489931\n", + "6 河南 17.694857\n", + "7 江苏 26.125200\n", + "8 安徽 21.148567\n", + "9 湖北 31.119986\n", + "10 浙江 10.778228\n", + "11 江西 23.675400\n", + "12 湖南 12.251784\n", + "13 云南 18.833784\n", + "14 贵州 15.264387\n", + "15 福建 15.369069\n", + "16 广西 13.844786\n", + "17 广东 30.596714\n", + "18 海南 15.952518\n", + "19 吉林 28.268970\n", + "20 辽宁 21.219038\n", + "21 天津 24.108854\n", + "22 青海 15.679446\n", + "23 甘肃 47.836487\n", + "24 陕西 43.323387\n", + "25 内蒙古 59.584412\n", + "26 重庆 41.886032\n", + "27 河北 25.503963\n", + "28 上海 19.530062\n", + "29 北京 19.624769\n", + "30 台湾 30.918276\n", + "31 香港 28.830645\n", + "32 澳门 74.122856\n", + "33 四川 17.673573" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "D66140B0-0277-45FF-AE18-A787BE3D4AAF:2: UserWarning: FixedFormatter should only be used together with FixedLocator\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f,ax = plt.subplots(figsize = (12,5))\n", + "ax.set_xticklabels(ax.get_xticklabels(), rotation=55)\n", + "seaborn.barplot(x=\"name\",y=\"bci\",data=p)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ArcGISPro", + "language": "Python", + "name": "python3" + }, + "language_info": { + "file_extension": ".py", + "name": "python", + "version": "3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git "a/027hash\347\256\227\346\263\225\346\227\245\345\216\206\346\212\275\345\245\226/.ipynb_checkpoints/\346\227\245\345\216\206\346\212\275\345\245\226_\345\214\272\345\235\227\351\223\276\347\256\227\346\263\225-checkpoint.ipynb" "b/027hash\347\256\227\346\263\225\346\227\245\345\216\206\346\212\275\345\245\226/.ipynb_checkpoints/\346\227\245\345\216\206\346\212\275\345\245\226_\345\214\272\345\235\227\351\223\276\347\256\227\346\263\225-checkpoint.ipynb" new file mode 100644 index 0000000..9520a5c --- /dev/null +++ "b/027hash\347\256\227\346\263\225\346\227\245\345\216\206\346\212\275\345\245\226/.ipynb_checkpoints/\346\227\245\345\216\206\346\212\275\345\245\226_\345\214\272\345\235\227\351\223\276\347\256\227\346\263\225-checkpoint.ipynb" @@ -0,0 +1,1498 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import hashlib,pandas" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.pyplot as plt\n", + "plt.rcParams['font.sans-serif']=['SimHei']\n", + "plt.rcParams['axes.unicode_minus']=False" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def hashKnife(i):\n", + " sha256 = hashlib.sha256()\n", + " sha256.update('{0}'.format(i).encode('utf-8'))\n", + " s1 = sha256.hexdigest()\n", + " sha256 = hashlib.sha256()\n", + " sha256.update('{0}'.format(s1).encode('utf-8'))\n", + " return sha256.hexdigest()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4 \t 033c339a7975542785be7423a5b32fa8047813689726214143cdd7939747709c\n", + "21 \t 053b22ca1fcea7a8de0da76b0f4deaef4aa9fb1100bff13965c3c0da76272862\n", + "31 \t 028f917950de90c724f3dacb96792258929510f54bfd4866dd6dba26e0b4414a\n", + "33 \t 0cca79f951e82323381375324442d5fe77e5bcb5899b87cb2f0bebff1bc0244a\n", + "83 \t 0401167548c0ed9abc4ef94cc0b43b1942030903ca05abf1e938c822d492f8a3\n", + "98 \t 0a23001d74edbe05d7e79524a918f077b3928eb3ee34b3ec13d990f9a4b43e45\n" + ] + } + ], + "source": [ + "for i in range(100):\n", + " h = hashKnife(i)\n", + " if h[0:1] == \"0\":\n", + " print(i,\"\\t\",hashKnife(i))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "pd = pandas.read_csv(\"./加权.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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indexname朋友圈加权留言加权广告加权虾神点赞
01锅醋姜就是我0210
12蔚蓝天空01210
23XYQ011110
34Hi~我是蘇小美0010
45LS0210
56HelloWorld0010
67Yang0210
78壳乐乐0110
89R27210
910浩阳24211
1011Lilly An20210
1112孙宇76210
1213Pz0010
1314默溪9111
1415Pursuit88511
1516A^Hundred^Flowers23110
1617夏天14000
1718蓝袜子-UP6101
1819ChercherᝰACE20110
1920柳好肥36110
2021会跳舞的文艺青年70011
2122HYL-GISer7301
2223其实，不懂你85100
2324白桃大魔王03600
2425CityDast01400
2526筱䓉^_^薇諒01100
2627周浩0600
2728Berton0400
2829阳光的丹尼尔0400
2930城城0200
3031Mr_wu0200
3132汤鹏0200
3233浩阳0200
3334Snow0200
3435含信0200
3536别来无恙0200
3637郭家乐0200
3738M I AO0100
3839期待灵感的hm啊0100
3940🇭 🇪 🇷 🇴 🇮 🇨0100
4041直到世界的尽头0100
4142HelloWorld0100
4243小昭她哥0100
4344炒饭没了？0100
4445七度十二分0100
4546人海0100
4647兔子州0100
4748YYL0100
4849雪落香杉树0200
4950憬0100
5051文献综合征患者0100
5152金喜william0100
5253一一0100
5354虫虫0100
5455Bing0100
5556、Fresh0100
5657轩仔0100
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indexname朋友圈加权留言加权广告加权虾神点赞life
2223其实，不懂你8510018.0
1112孙宇7621017.0
2021会跳舞的文艺青年7001117.0
23XYQ01111013.0
1920柳好肥361109.0
89R272108.0
910浩阳242118.0
1516A^Hundred^Flowers231107.0
1011Lilly An202106.0
1819ChercherᝰACE201106.0
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" + ], + "text/plain": [ + " index name 朋友圈加权 留言加权 广告加权 虾神点赞 life\n", + "22 23 其实，不懂你 85 1 0 0 18.0\n", + "11 12 孙宇 76 2 1 0 17.0\n", + "20 21 会跳舞的文艺青年 70 0 1 1 17.0\n", + "2 3 XYQ 0 111 1 0 13.0\n", + "19 20 柳好肥 36 1 1 0 9.0\n", + "8 9 R 27 2 1 0 8.0\n", + "9 10 浩阳 24 2 1 1 8.0\n", + "15 16 A^Hundred^Flowers 23 1 1 0 7.0\n", + "10 11 Lilly An 20 2 1 0 6.0\n", + "18 19 ChercherᝰACE 20 1 1 0 6.0" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd2.sort_values(\"life\",ascending=False).head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "e = [0 for i in range(26)] + [0.1]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f = plt.figure(figsize=(12,15))\n", + "labels = pd2[pd2[\"life\"] > 1][\"name\"].tolist()+[\"其他(一条命)\"]\n", + "sizes = pd2[pd2[\"life\"] > 1][\"life\"].tolist() + [sum(pd2[pd2[\"life\"] <= 1][\"life\"])]\n", + "explode = (0,0,0,0.1,0,0)\n", + "plt.pie(sizes,labels=labels,explode=tuple(e),shadow=True,autopct='%1.2f%%')\n", + "pass" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "val = {}\n", + "for idx,life in zip(pd2[\"index\"],pd2[\"life\"]):\n", + " if idx <10:\n", + " idx = \"0{}\".format(idx)\n", + " else:\n", + " idx = \"{}\".format(idx)\n", + " val[idx] = life" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "start = 202101081730" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "第 91 轮，攻击被触发，发动攻击的数值是 202101081821 \n", + "被击中战斗的同学是：汤鹏 , 剩余生命值：0.0\n", + "*_* 汤鹏 同学退出战斗……阿门~~~\n", + "还有 55 位同学在继续战斗\n", + "\n", + "第 146 轮，攻击被触发，发动攻击的数值是 202101081876 \n", + "被击中战斗的同学是：小昭她哥 , 剩余生命值：0.0\n", + "*_* 小昭她哥 同学退出战斗……阿门~~~\n", + "还有 54 位同学在继续战斗\n", + "\n", + "第 178 轮，攻击被触发，发动攻击的数值是 202101081908 \n", + "被击中战斗的同学是：ChercherᝰACE , 剩余生命值：5.0\n", + "第 202 轮，攻击被触发，发动攻击的数值是 202101081932 \n", + "被击中战斗的同学是：R , 剩余生命值：7.0\n", + "第 317 轮，攻击被触发，发动攻击的数值是 202101082047 \n", + "被击中战斗的同学是：郭家乐 , 剩余生命值：0.0\n", + "*_* 郭家乐 同学退出战斗……阿门~~~\n", + "还有 53 位同学在继续战斗\n", + "\n", + "第 363 轮，攻击被触发，发动攻击的数值是 202101082093 \n", + "被击中战斗的同学是：金喜william , 剩余生命值：0.0\n", + "*_* 金喜william 同学退出战斗……阿门~~~\n", + "还有 52 位同学在继续战斗\n", + "\n", + "第 380 轮，攻击被触发，发动攻击的数值是 202101082110 \n", + "被击中战斗的同学是：、Fresh , 剩余生命值：0.0\n", + "*_* 、Fresh 同学退出战斗……阿门~~~\n", + "还有 51 位同学在继续战斗\n", + "\n", + "第 393 轮，攻击被触发，发动攻击的数值是 202101082123 \n", + "被击中战斗的同学是：会跳舞的文艺青年 , 剩余生命值：16.0\n", + "第 449 轮，攻击被触发，发动攻击的数值是 202101082179 \n", + "被击中战斗的同学是：HelloWorld , 剩余生命值：0.0\n", + "*_* HelloWorld 同学退出战斗……阿门~~~\n", + "还有 50 位同学在继续战斗\n", + "\n", + "第 493 轮，攻击被触发，发动攻击的数值是 202101082223 \n", + "被击中战斗的同学是：M I AO , 剩余生命值：0.0\n", + "*_* M I AO 同学退出战斗……阿门~~~\n", + "还有 49 位同学在继续战斗\n", + "\n", + "第 649 轮，攻击被触发，发动攻击的数值是 202101082379 \n", + "被击中战斗的同学是：憬 , 剩余生命值：0.0\n", + "*_* 憬 同学退出战斗……阿门~~~\n", + "还有 48 位同学在继续战斗\n", + "\n", + "第 654 轮，攻击被触发，发动攻击的数值是 202101082384 \n", + "被击中战斗的同学是：默溪 , 剩余生命值：4.0\n", + "第 916 轮，攻击被触发，发动攻击的数值是 202101082646 \n", + "被击中战斗的同学是：柳好肥 , 剩余生命值：8.0\n", + "第 926 轮，攻击被触发，发动攻击的数值是 202101082656 \n", + "被击中战斗的同学是：虫虫 , 剩余生命值：0.0\n", + "*_* 虫虫 同学退出战斗……阿门~~~\n", + "还有 47 位同学在继续战斗\n", + "\n", + "第 940 轮，攻击被触发，发动攻击的数值是 202101082670 \n", + "被击中战斗的同学是：城城 , 剩余生命值：0.0\n", + "*_* 城城 同学退出战斗……阿门~~~\n", + "还有 46 位同学在继续战斗\n", + "\n", + "第 1015 轮，攻击被触发，发动攻击的数值是 202101082745 \n", + "被击中战斗的同学是：周浩 , 剩余生命值：1.0\n", + "第 1030 轮，攻击被触发，发动攻击的数值是 202101082760 \n", + "被击中战斗的同学是：HelloWorld , 剩余生命值：1.0\n", + "第 1187 轮，攻击被触发，发动攻击的数值是 202101082917 \n", + "被击中战斗的同学是：筱䓉^_^薇諒 , 剩余生命值：1.0\n", + "第 1279 轮，攻击被触发，发动攻击的数值是 202101083009 \n", + "被击中战斗的同学是：孙宇 , 剩余生命值：16.0\n", + "第 1346 轮，攻击被触发，发动攻击的数值是 202101083076 \n", + "被击中战斗的同学是：锅醋姜就是我 , 剩余生命值：1.0\n", + "第 1457 轮，攻击被触发，发动攻击的数值是 202101083187 \n", + "被击中战斗的同学是：别来无恙 , 剩余生命值：0.0\n", + "*_* 别来无恙 同学退出战斗……阿门~~~\n", + "还有 45 位同学在继续战斗\n", + "\n", + "第 1483 轮，攻击被触发，发动攻击的数值是 202101083213 \n", + "被击中战斗的同学是：会跳舞的文艺青年 , 剩余生命值：15.0\n", + "第 1654 轮，攻击被触发，发动攻击的数值是 202101083384 \n", + "被击中战斗的同学是：Bing , 剩余生命值：0.0\n", + "*_* Bing 同学退出战斗……阿门~~~\n", + "还有 44 位同学在继续战斗\n", + "\n", + "第 1698 轮，攻击被触发，发动攻击的数值是 202101083428 \n", + "被击中战斗的同学是：夏天 , 剩余生命值：3.0\n", + "第 1710 轮，攻击被触发，发动攻击的数值是 202101083440 \n", + "被击中战斗的同学是：直到世界的尽头 , 剩余生命值：0.0\n", + "*_* 直到世界的尽头 同学退出战斗……阿门~~~\n", + "还有 43 位同学在继续战斗\n", + "\n", + "第 1940 轮，攻击被触发，发动攻击的数值是 202101083670 \n", + "被击中战斗的同学是：其实，不懂你 , 剩余生命值：17.0\n", + "第 1951 轮，攻击被触发，发动攻击的数值是 202101083681 \n", + "被击中战斗的同学是：周浩 , 剩余生命值：0.0\n", + "*_* 周浩 同学退出战斗……阿门~~~\n", + "还有 42 位同学在继续战斗\n", + "\n", + "第 2233 轮，攻击被触发，发动攻击的数值是 202101083963 \n", + "被击中战斗的同学是：Mr_wu , 剩余生命值：0.0\n", + "*_* Mr_wu 同学退出战斗……阿门~~~\n", + "还有 41 位同学在继续战斗\n", + "\n", + "第 2302 轮，攻击被触发，发动攻击的数值是 202101084032 \n", + "被击中战斗的同学是：兔子州 , 剩余生命值：0.0\n", + "*_* 兔子州 同学退出战斗……阿门~~~\n", + "还有 40 位同学在继续战斗\n", + "\n", + "第 2305 轮，攻击被触发，发动攻击的数值是 202101084035 \n", + "被击中战斗的同学是：孙宇 , 剩余生命值：15.0\n", + "第 2376 轮，攻击被触发，发动攻击的数值是 202101084106 \n", + "被击中战斗的同学是：XYQ , 剩余生命值：12.0\n", + "第 2430 轮，攻击被触发，发动攻击的数值是 202101084160 \n", + "被击中战斗的同学是：人海 , 剩余生命值：0.0\n", + "*_* 人海 同学退出战斗……阿门~~~\n", + "还有 39 位同学在继续战斗\n", + "\n", + "第 2616 轮，攻击被触发，发动攻击的数值是 202101084346 \n", + "被击中战斗的同学是：YYL , 剩余生命值：0.0\n", + "*_* YYL 同学退出战斗……阿门~~~\n", + "还有 38 位同学在继续战斗\n", + "\n", + "第 2775 轮，攻击被触发，发动攻击的数值是 202101084505 \n", + "被击中战斗的同学是：孙宇 , 剩余生命值：14.0\n", + "第 2902 轮，攻击被触发，发动攻击的数值是 202101084632 \n", + "被击中战斗的同学是：七度十二分 , 剩余生命值：0.0\n", + "*_* 七度十二分 同学退出战斗……阿门~~~\n", + "还有 37 位同学在继续战斗\n", + "\n", + "第 3134 轮，攻击被触发，发动攻击的数值是 202101084864 \n", + "被击中战斗的同学是：蓝袜子-UP , 剩余生命值：2.0\n", + "第 3285 轮，攻击被触发，发动攻击的数值是 202101085015 \n", + "被击中战斗的同学是：孙宇 , 剩余生命值：13.0\n", + "第 3347 轮，攻击被触发，发动攻击的数值是 202101085077 \n", + "被击中战斗的同学是：浩阳 , 剩余生命值：7.0\n", + "第 3463 轮，攻击被触发，发动攻击的数值是 202101085193 \n", + "被击中战斗的同学是：其实，不懂你 , 剩余生命值：16.0\n", + "第 3487 轮，攻击被触发，发动攻击的数值是 202101085217 \n", + "被击中战斗的同学是：Berton , 剩余生命值：0.0\n", + "*_* Berton 同学退出战斗……阿门~~~\n", + "还有 36 位同学在继续战斗\n", + "\n", + "第 3556 轮，攻击被触发，发动攻击的数值是 202101085286 \n", + "被击中战斗的同学是：雪落香杉树 , 剩余生命值：0.0\n", + "*_* 雪落香杉树 同学退出战斗……阿门~~~\n", + "还有 35 位同学在继续战斗\n", + "\n", + "第 3569 轮，攻击被触发，发动攻击的数值是 202101085299 \n", + "被击中战斗的同学是：夏天 , 剩余生命值：2.0\n", + "第 3913 轮，攻击被触发，发动攻击的数值是 202101085643 \n", + "被击中战斗的同学是：会跳舞的文艺青年 , 剩余生命值：14.0\n", + "第 3917 轮，攻击被触发，发动攻击的数值是 202101085647 \n", + "被击中战斗的同学是：ChercherᝰACE , 剩余生命值：4.0\n", + "第 4021 轮，攻击被触发，发动攻击的数值是 202101085751 \n", + "被击中战斗的同学是：XYQ , 剩余生命值：11.0\n", + "第 4080 轮，攻击被触发，发动攻击的数值是 202101085810 \n", + "被击中战斗的同学是：含信 , 剩余生命值：0.0\n", + "*_* 含信 同学退出战斗……阿门~~~\n", + "还有 34 位同学在继续战斗\n", + "\n", + "第 4108 轮，攻击被触发，发动攻击的数值是 202101085838 \n", + "被击中战斗的同学是：默溪 , 剩余生命值：3.0\n", + "第 4293 轮，攻击被触发，发动攻击的数值是 202101086023 \n", + "被击中战斗的同学是：LS , 剩余生命值：1.0\n", + "第 4440 轮，攻击被触发，发动攻击的数值是 202101086170 \n", + "被击中战斗的同学是：ChercherᝰACE , 剩余生命值：3.0\n", + "第 4607 轮，攻击被触发，发动攻击的数值是 202101086337 \n", + "被击中战斗的同学是：Lilly An , 剩余生命值：5.0\n", + "第 4645 轮，攻击被触发，发动攻击的数值是 202101086375 \n", + "被击中战斗的同学是：浩阳 , 剩余生命值：6.0\n", + "第 4672 轮，攻击被触发，发动攻击的数值是 202101086402 \n", + "被击中战斗的同学是：文献综合征患者 , 剩余生命值：0.0\n", + "*_* 文献综合征患者 同学退出战斗……阿门~~~\n", + "还有 33 位同学在继续战斗\n", + "\n", + "第 4917 轮，攻击被触发，发动攻击的数值是 202101086647 \n", + "被击中战斗的同学是：浩阳 , 剩余生命值：0.0\n", + "*_* 浩阳 同学退出战斗……阿门~~~\n", + "还有 32 位同学在继续战斗\n", + "\n", + "第 4975 轮，攻击被触发，发动攻击的数值是 202101086705 \n", + "被击中战斗的同学是：阳光的丹尼尔 , 剩余生命值：0.0\n", + "*_* 阳光的丹尼尔 同学退出战斗……阿门~~~\n", + "还有 31 位同学在继续战斗\n", + "\n", + "第 5015 轮，攻击被触发，发动攻击的数值是 202101086745 \n", + "被击中战斗的同学是：HYL-GISer , 剩余生命值：3.0\n", + "第 5172 轮，攻击被触发，发动攻击的数值是 202101086902 \n", + "被击中战斗的同学是：默溪 , 剩余生命值：2.0\n", + "第 5229 轮，攻击被触发，发动攻击的数值是 202101086959 \n", + "被击中战斗的同学是：CityDast , 剩余生命值：1.0\n", + "第 5429 轮，攻击被触发，发动攻击的数值是 202101087159 \n", + "被击中战斗的同学是：CityDast , 剩余生命值：0.0\n", + "*_* CityDast 同学退出战斗……阿门~~~\n", + "还有 30 位同学在继续战斗\n", + "\n", + "第 5468 轮，攻击被触发，发动攻击的数值是 202101087198 \n", + "被击中战斗的同学是：筱䓉^_^薇諒 , 剩余生命值：0.0\n", + "*_* 筱䓉^_^薇諒 同学退出战斗……阿门~~~\n", + "还有 29 位同学在继续战斗\n", + "\n", + "第 5636 轮，攻击被触发，发动攻击的数值是 202101087366 \n", + "被击中战斗的同学是：Hi~我是蘇小美 , 剩余生命值：1.0\n", + "第 5845 轮，攻击被触发，发动攻击的数值是 202101087575 \n", + "被击中战斗的同学是：柳好肥 , 剩余生命值：7.0\n", + "第 5860 轮，攻击被触发，发动攻击的数值是 202101087590 \n", + "被击中战斗的同学是：浩阳 , 剩余生命值：5.0\n", + "第 5876 轮，攻击被触发，发动攻击的数值是 202101087606 \n", + "被击中战斗的同学是：会跳舞的文艺青年 , 剩余生命值：13.0\n", + "第 5936 轮，攻击被触发，发动攻击的数值是 202101087666 \n", + "被击中战斗的同学是：浩阳 , 剩余生命值：4.0\n", + "第 5996 轮，攻击被触发，发动攻击的数值是 202101087726 \n", + "被击中战斗的同学是：A^Hundred^Flowers , 剩余生命值：6.0\n", + "第 5999 轮，攻击被触发，发动攻击的数值是 202101087729 \n", + "被击中战斗的同学是：孙宇 , 剩余生命值：12.0\n", + "第 6356 轮，攻击被触发，发动攻击的数值是 202101088086 \n", + "被击中战斗的同学是：轩仔 , 剩余生命值：0.0\n", + "*_* 轩仔 同学退出战斗……阿门~~~\n", + "还有 28 位同学在继续战斗\n", + "\n", + "第 6421 轮，攻击被触发，发动攻击的数值是 202101088151 \n", + "被击中战斗的同学是：夏天 , 剩余生命值：1.0\n", + "第 6427 轮，攻击被触发，发动攻击的数值是 202101088157 \n", + "被击中战斗的同学是：ChercherᝰACE , 剩余生命值：2.0\n", + "第 6664 轮，攻击被触发，发动攻击的数值是 202101088394 \n", + "被击中战斗的同学是：Yang , 剩余生命值：1.0\n", + "第 6750 轮，攻击被触发，发动攻击的数值是 202101088480 \n", + "被击中战斗的同学是：柳好肥 , 剩余生命值：6.0\n", + "第 6871 轮，攻击被触发，发动攻击的数值是 202101088601 \n", + "被击中战斗的同学是：XYQ , 剩余生命值：10.0\n", + "第 7210 轮，攻击被触发，发动攻击的数值是 202101088940 \n", + "被击中战斗的同学是：默溪 , 剩余生命值：1.0\n", + "第 7284 轮，攻击被触发，发动攻击的数值是 202101089014 \n", + "被击中战斗的同学是：HelloWorld , 剩余生命值：0.0\n", + "*_* HelloWorld 同学退出战斗……阿门~~~\n", + "还有 27 位同学在继续战斗\n", + "\n", + "第 7400 轮，攻击被触发，发动攻击的数值是 202101089130 \n", + "被击中战斗的同学是：炒饭没了？ , 剩余生命值：0.0\n", + "*_* 炒饭没了？ 同学退出战斗……阿门~~~\n", + "还有 26 位同学在继续战斗\n", + "\n", + "第 7462 轮，攻击被触发，发动攻击的数值是 202101089192 \n", + "被击中战斗的同学是：夏天 , 剩余生命值：0.0\n", + "*_* 夏天 同学退出战斗……阿门~~~\n", + "还有 25 位同学在继续战斗\n", + "\n", + "第 8112 轮，攻击被触发，发动攻击的数值是 202101089842 \n", + "被击中战斗的同学是：Pz , 剩余生命值：1.0\n", + "第 8127 轮，攻击被触发，发动攻击的数值是 202101089857 \n", + "被击中战斗的同学是：会跳舞的文艺青年 , 剩余生命值：12.0\n", + "第 8267 轮，攻击被触发，发动攻击的数值是 202101089997 \n", + "被击中战斗的同学是：会跳舞的文艺青年 , 剩余生命值：11.0\n", + "第 8282 轮，攻击被触发，发动攻击的数值是 202101090012 \n", + "被击中战斗的同学是：孙宇 , 剩余生命值：11.0\n", + "第 8367 轮，攻击被触发，发动攻击的数值是 202101090097 \n", + "被击中战斗的同学是：Snow , 剩余生命值：0.0\n", + "*_* Snow 同学退出战斗……阿门~~~\n", + "还有 24 位同学在继续战斗\n", + "\n", + "第 8396 轮，攻击被触发，发动攻击的数值是 202101090126 \n", + "被击中战斗的同学是：蓝袜子-UP , 剩余生命值：1.0\n", + "第 8576 轮，攻击被触发，发动攻击的数值是 202101090306 \n", + "被击中战斗的同学是：XYQ , 剩余生命值：9.0\n", + "第 9029 轮，攻击被触发，发动攻击的数值是 202101090759 \n", + "被击中战斗的同学是：默溪 , 剩余生命值：0.0\n", + "*_* 默溪 同学退出战斗……阿门~~~\n", + "还有 23 位同学在继续战斗\n", + "\n", + "第 9042 轮，攻击被触发，发动攻击的数值是 202101090772 \n", + "被击中战斗的同学是：Lilly An , 剩余生命值：4.0\n", + "第 9095 轮，攻击被触发，发动攻击的数值是 202101090825 \n", + "被击中战斗的同学是：Hi~我是蘇小美 , 剩余生命值：0.0\n", + "*_* Hi~我是蘇小美 同学退出战斗……阿门~~~\n", + "还有 22 位同学在继续战斗\n", + "\n", + "第 9397 轮，攻击被触发，发动攻击的数值是 202101091127 \n", + "被击中战斗的同学是：Yang , 剩余生命值：0.0\n", + "*_* Yang 同学退出战斗……阿门~~~\n", + "还有 21 位同学在继续战斗\n", + "\n", + "第 9548 轮，攻击被触发，发动攻击的数值是 202101091278 \n", + "被击中战斗的同学是：LS , 剩余生命值：0.0\n", + "*_* LS 同学退出战斗……阿门~~~\n", + "还有 20 位同学在继续战斗\n", + "\n", + "第 9558 轮，攻击被触发，发动攻击的数值是 202101091288 \n", + "被击中战斗的同学是：期待灵感的hm啊 , 剩余生命值：0.0\n", + "*_* 期待灵感的hm啊 同学退出战斗……阿门~~~\n", + "还有 19 位同学在继续战斗\n", + "\n", + "第 9716 轮，攻击被触发，发动攻击的数值是 202101091446 \n", + "被击中战斗的同学是：HYL-GISer , 剩余生命值：2.0\n", + "第 9836 轮，攻击被触发，发动攻击的数值是 202101091566 \n", + "被击中战斗的同学是：一一 , 剩余生命值：0.0\n", + "*_* 一一 同学退出战斗……阿门~~~\n", + "还有 18 位同学在继续战斗\n", + "\n", + "第 10300 轮，攻击被触发，发动攻击的数值是 202101092030 \n", + "被击中战斗的同学是：R , 剩余生命值：6.0\n", + "第 11029 轮，攻击被触发，发动攻击的数值是 202101092759 \n", + "被击中战斗的同学是：白桃大魔王 , 剩余生命值：4.0\n", + "第 11084 轮，攻击被触发，发动攻击的数值是 202101092814 \n", + "被击中战斗的同学是：壳乐乐 , 剩余生命值：1.0\n", + "第 11358 轮，攻击被触发，发动攻击的数值是 202101093088 \n", + "被击中战斗的同学是：其实，不懂你 , 剩余生命值：15.0\n", + "第 11466 轮，攻击被触发，发动攻击的数值是 202101093196 \n", + "被击中战斗的同学是：ChercherᝰACE , 剩余生命值：1.0\n", + "第 11541 轮，攻击被触发，发动攻击的数值是 202101093271 \n", + "被击中战斗的同学是：浩阳 , 剩余生命值：3.0\n", + "第 11655 轮，攻击被触发，发动攻击的数值是 202101093385 \n", + "被击中战斗的同学是：其实，不懂你 , 剩余生命值：14.0\n", + "第 11666 轮，攻击被触发，发动攻击的数值是 202101093396 \n", + "被击中战斗的同学是：锅醋姜就是我 , 剩余生命值：0.0\n", + "*_* 锅醋姜就是我 同学退出战斗……阿门~~~\n", + "还有 17 位同学在继续战斗\n", + "\n", + "第 12224 轮，攻击被触发，发动攻击的数值是 202101093954 \n", + "被击中战斗的同学是：柳好肥 , 剩余生命值：5.0\n", + "第 12308 轮，攻击被触发，发动攻击的数值是 202101094038 \n", + "被击中战斗的同学是：🇭 🇪 🇷 🇴 🇮 🇨 , 剩余生命值：0.0\n", + "*_* 🇭 🇪 🇷 🇴 🇮 🇨 同学退出战斗……阿门~~~\n", + "还有 16 位同学在继续战斗\n", + "\n", + "第 12910 轮，攻击被触发，发动攻击的数值是 202101094640 \n", + "被击中战斗的同学是：浩阳 , 剩余生命值：2.0\n", + "第 13142 轮，攻击被触发，发动攻击的数值是 202101094872 \n", + "被击中战斗的同学是：XYQ , 剩余生命值：8.0\n", + "第 13279 轮，攻击被触发，发动攻击的数值是 202101095009 \n", + "被击中战斗的同学是：浩阳 , 剩余生命值：1.0\n", + "第 13847 轮，攻击被触发，发动攻击的数值是 202101095577 \n", + "被击中战斗的同学是：ChercherᝰACE , 剩余生命值：0.0\n", + "*_* ChercherᝰACE 同学退出战斗……阿门~~~\n", + "还有 15 位同学在继续战斗\n", + "\n", + "第 14068 轮，攻击被触发，发动攻击的数值是 202101095798 \n", + "被击中战斗的同学是：孙宇 , 剩余生命值：10.0\n", + "第 14321 轮，攻击被触发，发动攻击的数值是 202101096051 \n", + "被击中战斗的同学是：R , 剩余生命值：5.0\n", + "第 14636 轮，攻击被触发，发动攻击的数值是 202101096366 \n", + "被击中战斗的同学是：A^Hundred^Flowers , 剩余生命值：5.0\n", + "第 15140 轮，攻击被触发，发动攻击的数值是 202101096870 \n", + "被击中战斗的同学是：柳好肥 , 剩余生命值：4.0\n", + "第 15601 轮，攻击被触发，发动攻击的数值是 202101097331 \n", + "被击中战斗的同学是：白桃大魔王 , 剩余生命值：3.0\n", + "第 15746 轮，攻击被触发，发动攻击的数值是 202101097476 \n", + "被击中战斗的同学是：A^Hundred^Flowers , 剩余生命值：4.0\n", + "第 16350 轮，攻击被触发，发动攻击的数值是 202101098080 \n", + "被击中战斗的同学是：浩阳 , 剩余生命值：0.0\n", + "*_* 浩阳 同学退出战斗……阿门~~~\n", + "还有 14 位同学在继续战斗\n", + "\n", + "第 16363 轮，攻击被触发，发动攻击的数值是 202101098093 \n", + "被击中战斗的同学是：壳乐乐 , 剩余生命值：0.0\n", + "*_* 壳乐乐 同学退出战斗……阿门~~~\n", + "还有 13 位同学在继续战斗\n", + "\n", + "第 16779 轮，攻击被触发，发动攻击的数值是 202101098509 \n", + "被击中战斗的同学是：孙宇 , 剩余生命值：9.0\n", + "第 17301 轮，攻击被触发，发动攻击的数值是 202101099031 \n", + "被击中战斗的同学是：孙宇 , 剩余生命值：8.0\n", + "第 17628 轮，攻击被触发，发动攻击的数值是 202101099358 \n", + "被击中战斗的同学是：R , 剩余生命值：4.0\n", + "第 17748 轮，攻击被触发，发动攻击的数值是 202101099478 \n", + "被击中战斗的同学是：Pz , 剩余生命值：0.0\n", + "*_* Pz 同学退出战斗……阿门~~~\n", + "还有 12 位同学在继续战斗\n", + "\n", + "第 18895 轮，攻击被触发，发动攻击的数值是 202101100625 \n", + "被击中战斗的同学是：白桃大魔王 , 剩余生命值：2.0\n", + "第 18941 轮，攻击被触发，发动攻击的数值是 202101100671 \n", + "被击中战斗的同学是：柳好肥 , 剩余生命值：3.0\n", + "第 19342 轮，攻击被触发，发动攻击的数值是 202101101072 \n", + "被击中战斗的同学是：孙宇 , 剩余生命值：7.0\n", + "第 19704 轮，攻击被触发，发动攻击的数值是 202101101434 \n", + "被击中战斗的同学是：Lilly An , 剩余生命值：3.0\n", + "第 19786 轮，攻击被触发，发动攻击的数值是 202101101516 \n", + "被击中战斗的同学是：蓝袜子-UP , 剩余生命值：0.0\n", + "*_* 蓝袜子-UP 同学退出战斗……阿门~~~\n", + "还有 11 位同学在继续战斗\n", + "\n", + "第 19968 轮，攻击被触发，发动攻击的数值是 202101101698 \n", + "被击中战斗的同学是：孙宇 , 剩余生命值：6.0\n", + "第 20781 轮，攻击被触发，发动攻击的数值是 202101102511 \n", + "被击中战斗的同学是：柳好肥 , 剩余生命值：2.0\n", + "第 22120 轮，攻击被触发，发动攻击的数值是 202101103850 \n", + "被击中战斗的同学是：其实，不懂你 , 剩余生命值：13.0\n", + "第 22202 轮，攻击被触发，发动攻击的数值是 202101103932 \n", + "被击中战斗的同学是：白桃大魔王 , 剩余生命值：1.0\n", + "第 22259 轮，攻击被触发，发动攻击的数值是 202101103989 \n", + "被击中战斗的同学是：R , 剩余生命值：3.0\n", + "第 22264 轮，攻击被触发，发动攻击的数值是 202101103994 \n", + "被击中战斗的同学是：R , 剩余生命值：2.0\n", + "第 22513 轮，攻击被触发，发动攻击的数值是 202101104243 \n", + "被击中战斗的同学是：会跳舞的文艺青年 , 剩余生命值：10.0\n", + "第 22531 轮，攻击被触发，发动攻击的数值是 202101104261 \n", + "被击中战斗的同学是：柳好肥 , 剩余生命值：1.0\n", + "第 22859 轮，攻击被触发，发动攻击的数值是 202101104589 \n", + "被击中战斗的同学是：白桃大魔王 , 剩余生命值：0.0\n", + "*_* 白桃大魔王 同学退出战斗……阿门~~~\n", + "还有 10 位同学在继续战斗\n", + "\n", + "第 23539 轮，攻击被触发，发动攻击的数值是 202101105269 \n", + "被击中战斗的同学是：HYL-GISer , 剩余生命值：1.0\n", + "第 23645 轮，攻击被触发，发动攻击的数值是 202101105375 \n", + "被击中战斗的同学是：会跳舞的文艺青年 , 剩余生命值：9.0\n", + "第 23651 轮，攻击被触发，发动攻击的数值是 202101105381 \n", + "被击中战斗的同学是：XYQ , 剩余生命值：7.0\n", + "第 24135 轮，攻击被触发，发动攻击的数值是 202101105865 \n", + "被击中战斗的同学是：孙宇 , 剩余生命值：5.0\n", + "第 24233 轮，攻击被触发，发动攻击的数值是 202101105963 \n", + "被击中战斗的同学是：A^Hundred^Flowers , 剩余生命值：3.0\n", + "第 24729 轮，攻击被触发，发动攻击的数值是 202101106459 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "被击中战斗的同学是：R , 剩余生命值：1.0\n", + "第 25251 轮，攻击被触发，发动攻击的数值是 202101106981 \n", + "被击中战斗的同学是：HYL-GISer , 剩余生命值：0.0\n", + "*_* HYL-GISer 同学退出战斗……阿门~~~\n", + "还有 9 位同学在继续战斗\n", + "\n", + "第 25735 轮，攻击被触发，发动攻击的数值是 202101107465 \n", + "被击中战斗的同学是：XYQ , 剩余生命值：6.0\n", + "第 26457 轮，攻击被触发，发动攻击的数值是 202101108187 \n", + "被击中战斗的同学是：R , 剩余生命值：0.0\n", + "*_* R 同学退出战斗……阿门~~~\n", + "还有 8 位同学在继续战斗\n", + "\n", + "第 26619 轮，攻击被触发，发动攻击的数值是 202101108349 \n", + "被击中战斗的同学是：XYQ , 剩余生命值：5.0\n", + "第 27033 轮，攻击被触发，发动攻击的数值是 202101108763 \n", + "被击中战斗的同学是：柳好肥 , 剩余生命值：0.0\n", + "*_* 柳好肥 同学退出战斗……阿门~~~\n", + "还有 7 位同学在继续战斗\n", + "\n", + "第 27427 轮，攻击被触发，发动攻击的数值是 202101109157 \n", + "被击中战斗的同学是：XYQ , 剩余生命值：4.0\n", + "第 28500 轮，攻击被触发，发动攻击的数值是 202101110230 \n", + "被击中战斗的同学是：孙宇 , 剩余生命值：4.0\n", + "第 28582 轮，攻击被触发，发动攻击的数值是 202101110312 \n", + "被击中战斗的同学是：其实，不懂你 , 剩余生命值：12.0\n", + "第 28644 轮，攻击被触发，发动攻击的数值是 202101110374 \n", + "被击中战斗的同学是：A^Hundred^Flowers , 剩余生命值：2.0\n", + "第 28749 轮，攻击被触发，发动攻击的数值是 202101110479 \n", + "被击中战斗的同学是：蔚蓝天空 , 剩余生命值：2.0\n", + "第 28820 轮，攻击被触发，发动攻击的数值是 202101110550 \n", + "被击中战斗的同学是：会跳舞的文艺青年 , 剩余生命值：8.0\n", + "第 29021 轮，攻击被触发，发动攻击的数值是 202101110751 \n", + "被击中战斗的同学是：XYQ , 剩余生命值：3.0\n", + "第 29735 轮，攻击被触发，发动攻击的数值是 202101111465 \n", + "被击中战斗的同学是：蔚蓝天空 , 剩余生命值：1.0\n", + "第 29778 轮，攻击被触发，发动攻击的数值是 202101111508 \n", + "被击中战斗的同学是：XYQ , 剩余生命值：2.0\n", + "第 30490 轮，攻击被触发，发动攻击的数值是 202101112220 \n", + "被击中战斗的同学是：XYQ , 剩余生命值：1.0\n", + "第 31624 轮，攻击被触发，发动攻击的数值是 202101113354 \n", + "被击中战斗的同学是：A^Hundred^Flowers , 剩余生命值：1.0\n", + "第 32394 轮，攻击被触发，发动攻击的数值是 202101114124 \n", + "被击中战斗的同学是：A^Hundred^Flowers , 剩余生命值：0.0\n", + "*_* A^Hundred^Flowers 同学退出战斗……阿门~~~\n", + "还有 6 位同学在继续战斗\n", + "\n", + "第 33505 轮，攻击被触发，发动攻击的数值是 202101115235 \n", + "被击中战斗的同学是：孙宇 , 剩余生命值：3.0\n", + "第 33662 轮，攻击被触发，发动攻击的数值是 202101115392 \n", + "被击中战斗的同学是：Lilly An , 剩余生命值：2.0\n", + "第 33871 轮，攻击被触发，发动攻击的数值是 202101115601 \n", + "被击中战斗的同学是：其实，不懂你 , 剩余生命值：11.0\n", + "第 34754 轮，攻击被触发，发动攻击的数值是 202101116484 \n", + "被击中战斗的同学是：孙宇 , 剩余生命值：2.0\n", + "第 37277 轮，攻击被触发，发动攻击的数值是 202101119007 \n", + "被击中战斗的同学是：XYQ , 剩余生命值：0.0\n", + "*_* XYQ 同学退出战斗……阿门~~~\n", + "\n", + "\n", + " 战斗结束……恭喜以下同学获奖：♪(^∇^*)\n", + "蔚蓝天空\t 剩余生命值：1.0\n", + "Lilly An\t 剩余生命值：2.0\n", + "孙宇\t 剩余生命值：2.0\n", + "会跳舞的文艺青年\t 剩余生命值：8.0\n", + "其实，不懂你\t 剩余生命值：11.0\n" + ] + } + ], + "source": [ + "flag = 0\n", + "while True:\n", + " flag +=1\n", + " h = hashKnife(start + flag)\n", + " if h[0:1] == \"0\":\n", + " if h[-2:] in val:\n", + " val[h[-2:]] -=1\n", + " name = pd2[pd2[\"index\"] == int(h[-2:])][\"name\"].tolist()[0]\n", + " print(\"第 {0} 轮，攻击被触发，发动攻击的数值是 {1}\\\n", + " \\n被击中战斗的同学是：{2} , 剩余生命值：{3}\".format(flag,start+flag,\n", + " name,val[h[-2:]]))\n", + " if val[h[-2:]] == 0:\n", + " del val[h[-2:]]\n", + " print(\"*_* {0} 同学退出战斗……阿门~~~\".format(name))\n", + " if len(val) <= 5:\n", + " print(\"\\n\\n 战斗结束……恭喜以下同学获奖：♪(^∇^*)\")\n", + " for v in val:\n", + " name = pd2[pd2[\"index\"] == int(v)][\"name\"].tolist()[0]\n", + " print(\"{0}\\t 剩余生命值：{1}\".format(name,val[v]))\n", + " break\n", + " else:\n", + " print(\"还有 {0} 位同学在继续战斗\\n\".format(len(val)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git "a/027hash\347\256\227\346\263\225\346\227\245\345\216\206\346\212\275\345\245\226/~$\345\212\240\346\235\203.xlsx" "b/027hash\347\256\227\346\263\225\346\227\245\345\216\206\346\212\275\345\245\226/~$\345\212\240\346\235\203.xlsx" new file mode 100644 index 0000000..c5ee44d Binary files /dev/null and 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"b/027hash\347\256\227\346\263\225\346\227\245\345\216\206\346\212\275\345\245\226/\346\227\245\345\216\206\346\212\275\345\245\226_\345\214\272\345\235\227\351\223\276\347\256\227\346\263\225.ipynb" @@ -0,0 +1,1498 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import hashlib,pandas" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.pyplot as plt\n", + "plt.rcParams['font.sans-serif']=['SimHei']\n", + "plt.rcParams['axes.unicode_minus']=False" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def hashKnife(i):\n", + " sha256 = hashlib.sha256()\n", + " sha256.update('{0}'.format(i).encode('utf-8'))\n", + " s1 = sha256.hexdigest()\n", + " sha256 = hashlib.sha256()\n", + " sha256.update('{0}'.format(s1).encode('utf-8'))\n", + " return sha256.hexdigest()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4 \t 033c339a7975542785be7423a5b32fa8047813689726214143cdd7939747709c\n", + "21 \t 053b22ca1fcea7a8de0da76b0f4deaef4aa9fb1100bff13965c3c0da76272862\n", + "31 \t 028f917950de90c724f3dacb96792258929510f54bfd4866dd6dba26e0b4414a\n", + "33 \t 0cca79f951e82323381375324442d5fe77e5bcb5899b87cb2f0bebff1bc0244a\n", + "83 \t 0401167548c0ed9abc4ef94cc0b43b1942030903ca05abf1e938c822d492f8a3\n", + "98 \t 0a23001d74edbe05d7e79524a918f077b3928eb3ee34b3ec13d990f9a4b43e45\n" + ] + } + ], + "source": [ + "for i in range(100):\n", + " h = hashKnife(i)\n", + " if h[0:1] == \"0\":\n", + " print(i,\"\\t\",hashKnife(i))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "pd = pandas.read_csv(\"./加权.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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indexname朋友圈加权留言加权广告加权虾神点赞
01锅醋姜就是我0210
12蔚蓝天空01210
23XYQ011110
34Hi~我是蘇小美0010
45LS0210
56HelloWorld0010
67Yang0210
78壳乐乐0110
89R27210
910浩阳24211
1011Lilly An20210
1112孙宇76210
1213Pz0010
1314默溪9111
1415Pursuit88511
1516A^Hundred^Flowers23110
1617夏天14000
1718蓝袜子-UP6101
1819ChercherᝰACE20110
1920柳好肥36110
2021会跳舞的文艺青年70011
2122HYL-GISer7301
2223其实，不懂你85100
2324白桃大魔王03600
2425CityDast01400
2526筱䓉^_^薇諒01100
2627周浩0600
2728Berton0400
2829阳光的丹尼尔0400
2930城城0200
3031Mr_wu0200
3132汤鹏0200
3233浩阳0200
3334Snow0200
3435含信0200
3536别来无恙0200
3637郭家乐0200
3738M I AO0100
3839期待灵感的hm啊0100
3940🇭 🇪 🇷 🇴 🇮 🇨0100
4041直到世界的尽头0100
4142HelloWorld0100
4243小昭她哥0100
4344炒饭没了？0100
4445七度十二分0100
4546人海0100
4647兔子州0100
4748YYL0100
4849雪落香杉树0200
4950憬0100
5051文献综合征患者0100
5152金喜william0100
5253一一0100
5354虫虫0100
5455Bing0100
5556、Fresh0100
5657轩仔0100
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indexname朋友圈加权留言加权广告加权虾神点赞life
2223其实，不懂你8510018.0
1112孙宇7621017.0
2021会跳舞的文艺青年7001117.0
23XYQ01111013.0
1920柳好肥361109.0
89R272108.0
910浩阳242118.0
1516A^Hundred^Flowers231107.0
1011Lilly An202106.0
1819ChercherᝰACE201106.0
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" + ], + "text/plain": [ + " index name 朋友圈加权 留言加权 广告加权 虾神点赞 life\n", + "22 23 其实，不懂你 85 1 0 0 18.0\n", + "11 12 孙宇 76 2 1 0 17.0\n", + "20 21 会跳舞的文艺青年 70 0 1 1 17.0\n", + "2 3 XYQ 0 111 1 0 13.0\n", + "19 20 柳好肥 36 1 1 0 9.0\n", + "8 9 R 27 2 1 0 8.0\n", + "9 10 浩阳 24 2 1 1 8.0\n", + "15 16 A^Hundred^Flowers 23 1 1 0 7.0\n", + "10 11 Lilly An 20 2 1 0 6.0\n", + "18 19 ChercherᝰACE 20 1 1 0 6.0" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd2.sort_values(\"life\",ascending=False).head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "e = [0 for i in range(26)] + [0.1]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f = plt.figure(figsize=(12,15))\n", + "labels = pd2[pd2[\"life\"] > 1][\"name\"].tolist()+[\"其他(一条命)\"]\n", + "sizes = pd2[pd2[\"life\"] > 1][\"life\"].tolist() + [sum(pd2[pd2[\"life\"] <= 1][\"life\"])]\n", + "explode = (0,0,0,0.1,0,0)\n", + "plt.pie(sizes,labels=labels,explode=tuple(e),shadow=True,autopct='%1.2f%%')\n", + "pass" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "val = {}\n", + "for idx,life in zip(pd2[\"index\"],pd2[\"life\"]):\n", + " if idx <10:\n", + " idx = \"0{}\".format(idx)\n", + " else:\n", + " idx = \"{}\".format(idx)\n", + " val[idx] = life" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "start = 202101081730" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "第 91 轮，攻击被触发，发动攻击的数值是 202101081821 \n", + "被击中战斗的同学是：汤鹏 , 剩余生命值：0.0\n", + "*_* 汤鹏 同学退出战斗……阿门~~~\n", + "还有 55 位同学在继续战斗\n", + "\n", + "第 146 轮，攻击被触发，发动攻击的数值是 202101081876 \n", + "被击中战斗的同学是：小昭她哥 , 剩余生命值：0.0\n", + "*_* 小昭她哥 同学退出战斗……阿门~~~\n", + "还有 54 位同学在继续战斗\n", + "\n", + "第 178 轮，攻击被触发，发动攻击的数值是 202101081908 \n", + "被击中战斗的同学是：ChercherᝰACE , 剩余生命值：5.0\n", + "第 202 轮，攻击被触发，发动攻击的数值是 202101081932 \n", + "被击中战斗的同学是：R , 剩余生命值：7.0\n", + "第 317 轮，攻击被触发，发动攻击的数值是 202101082047 \n", + "被击中战斗的同学是：郭家乐 , 剩余生命值：0.0\n", + "*_* 郭家乐 同学退出战斗……阿门~~~\n", + "还有 53 位同学在继续战斗\n", + "\n", + "第 363 轮，攻击被触发，发动攻击的数值是 202101082093 \n", + "被击中战斗的同学是：金喜william , 剩余生命值：0.0\n", + "*_* 金喜william 同学退出战斗……阿门~~~\n", + "还有 52 位同学在继续战斗\n", + "\n", + "第 380 轮，攻击被触发，发动攻击的数值是 202101082110 \n", + "被击中战斗的同学是：、Fresh , 剩余生命值：0.0\n", + "*_* 、Fresh 同学退出战斗……阿门~~~\n", + "还有 51 位同学在继续战斗\n", + "\n", + "第 393 轮，攻击被触发，发动攻击的数值是 202101082123 \n", + "被击中战斗的同学是：会跳舞的文艺青年 , 剩余生命值：16.0\n", + "第 449 轮，攻击被触发，发动攻击的数值是 202101082179 \n", + "被击中战斗的同学是：HelloWorld , 剩余生命值：0.0\n", + "*_* HelloWorld 同学退出战斗……阿门~~~\n", + "还有 50 位同学在继续战斗\n", + "\n", + "第 493 轮，攻击被触发，发动攻击的数值是 202101082223 \n", + "被击中战斗的同学是：M I AO , 剩余生命值：0.0\n", + "*_* M I AO 同学退出战斗……阿门~~~\n", + "还有 49 位同学在继续战斗\n", + "\n", + "第 649 轮，攻击被触发，发动攻击的数值是 202101082379 \n", + "被击中战斗的同学是：憬 , 剩余生命值：0.0\n", + "*_* 憬 同学退出战斗……阿门~~~\n", + "还有 48 位同学在继续战斗\n", + "\n", + "第 654 轮，攻击被触发，发动攻击的数值是 202101082384 \n", + "被击中战斗的同学是：默溪 , 剩余生命值：4.0\n", + "第 916 轮，攻击被触发，发动攻击的数值是 202101082646 \n", + "被击中战斗的同学是：柳好肥 , 剩余生命值：8.0\n", + "第 926 轮，攻击被触发，发动攻击的数值是 202101082656 \n", + "被击中战斗的同学是：虫虫 , 剩余生命值：0.0\n", + "*_* 虫虫 同学退出战斗……阿门~~~\n", + "还有 47 位同学在继续战斗\n", + "\n", + "第 940 轮，攻击被触发，发动攻击的数值是 202101082670 \n", + "被击中战斗的同学是：城城 , 剩余生命值：0.0\n", + "*_* 城城 同学退出战斗……阿门~~~\n", + "还有 46 位同学在继续战斗\n", + "\n", + "第 1015 轮，攻击被触发，发动攻击的数值是 202101082745 \n", + "被击中战斗的同学是：周浩 , 剩余生命值：1.0\n", + "第 1030 轮，攻击被触发，发动攻击的数值是 202101082760 \n", + "被击中战斗的同学是：HelloWorld , 剩余生命值：1.0\n", + "第 1187 轮，攻击被触发，发动攻击的数值是 202101082917 \n", + "被击中战斗的同学是：筱䓉^_^薇諒 , 剩余生命值：1.0\n", + "第 1279 轮，攻击被触发，发动攻击的数值是 202101083009 \n", + "被击中战斗的同学是：孙宇 , 剩余生命值：16.0\n", + "第 1346 轮，攻击被触发，发动攻击的数值是 202101083076 \n", + "被击中战斗的同学是：锅醋姜就是我 , 剩余生命值：1.0\n", + "第 1457 轮，攻击被触发，发动攻击的数值是 202101083187 \n", + "被击中战斗的同学是：别来无恙 , 剩余生命值：0.0\n", + "*_* 别来无恙 同学退出战斗……阿门~~~\n", + "还有 45 位同学在继续战斗\n", + "\n", + "第 1483 轮，攻击被触发，发动攻击的数值是 202101083213 \n", + "被击中战斗的同学是：会跳舞的文艺青年 , 剩余生命值：15.0\n", + "第 1654 轮，攻击被触发，发动攻击的数值是 202101083384 \n", + "被击中战斗的同学是：Bing , 剩余生命值：0.0\n", + "*_* Bing 同学退出战斗……阿门~~~\n", + "还有 44 位同学在继续战斗\n", + "\n", + "第 1698 轮，攻击被触发，发动攻击的数值是 202101083428 \n", + "被击中战斗的同学是：夏天 , 剩余生命值：3.0\n", + "第 1710 轮，攻击被触发，发动攻击的数值是 202101083440 \n", + "被击中战斗的同学是：直到世界的尽头 , 剩余生命值：0.0\n", + "*_* 直到世界的尽头 同学退出战斗……阿门~~~\n", + "还有 43 位同学在继续战斗\n", + "\n", + "第 1940 轮，攻击被触发，发动攻击的数值是 202101083670 \n", + "被击中战斗的同学是：其实，不懂你 , 剩余生命值：17.0\n", + "第 1951 轮，攻击被触发，发动攻击的数值是 202101083681 \n", + "被击中战斗的同学是：周浩 , 剩余生命值：0.0\n", + "*_* 周浩 同学退出战斗……阿门~~~\n", + "还有 42 位同学在继续战斗\n", + "\n", + "第 2233 轮，攻击被触发，发动攻击的数值是 202101083963 \n", + "被击中战斗的同学是：Mr_wu , 剩余生命值：0.0\n", + "*_* Mr_wu 同学退出战斗……阿门~~~\n", + "还有 41 位同学在继续战斗\n", + "\n", + "第 2302 轮，攻击被触发，发动攻击的数值是 202101084032 \n", + "被击中战斗的同学是：兔子州 , 剩余生命值：0.0\n", + "*_* 兔子州 同学退出战斗……阿门~~~\n", + "还有 40 位同学在继续战斗\n", + "\n", + "第 2305 轮，攻击被触发，发动攻击的数值是 202101084035 \n", + "被击中战斗的同学是：孙宇 , 剩余生命值：15.0\n", + "第 2376 轮，攻击被触发，发动攻击的数值是 202101084106 \n", + "被击中战斗的同学是：XYQ , 剩余生命值：12.0\n", + "第 2430 轮，攻击被触发，发动攻击的数值是 202101084160 \n", + "被击中战斗的同学是：人海 , 剩余生命值：0.0\n", + "*_* 人海 同学退出战斗……阿门~~~\n", + "还有 39 位同学在继续战斗\n", + "\n", + "第 2616 轮，攻击被触发，发动攻击的数值是 202101084346 \n", + "被击中战斗的同学是：YYL , 剩余生命值：0.0\n", + "*_* YYL 同学退出战斗……阿门~~~\n", + "还有 38 位同学在继续战斗\n", + "\n", + "第 2775 轮，攻击被触发，发动攻击的数值是 202101084505 \n", + "被击中战斗的同学是：孙宇 , 剩余生命值：14.0\n", + "第 2902 轮，攻击被触发，发动攻击的数值是 202101084632 \n", + "被击中战斗的同学是：七度十二分 , 剩余生命值：0.0\n", + "*_* 七度十二分 同学退出战斗……阿门~~~\n", + "还有 37 位同学在继续战斗\n", + "\n", + "第 3134 轮，攻击被触发，发动攻击的数值是 202101084864 \n", + "被击中战斗的同学是：蓝袜子-UP , 剩余生命值：2.0\n", + "第 3285 轮，攻击被触发，发动攻击的数值是 202101085015 \n", + "被击中战斗的同学是：孙宇 , 剩余生命值：13.0\n", + "第 3347 轮，攻击被触发，发动攻击的数值是 202101085077 \n", + "被击中战斗的同学是：浩阳 , 剩余生命值：7.0\n", + "第 3463 轮，攻击被触发，发动攻击的数值是 202101085193 \n", + "被击中战斗的同学是：其实，不懂你 , 剩余生命值：16.0\n", + "第 3487 轮，攻击被触发，发动攻击的数值是 202101085217 \n", + "被击中战斗的同学是：Berton , 剩余生命值：0.0\n", + "*_* Berton 同学退出战斗……阿门~~~\n", + "还有 36 位同学在继续战斗\n", + "\n", + "第 3556 轮，攻击被触发，发动攻击的数值是 202101085286 \n", + "被击中战斗的同学是：雪落香杉树 , 剩余生命值：0.0\n", + "*_* 雪落香杉树 同学退出战斗……阿门~~~\n", + "还有 35 位同学在继续战斗\n", + "\n", + "第 3569 轮，攻击被触发，发动攻击的数值是 202101085299 \n", + "被击中战斗的同学是：夏天 , 剩余生命值：2.0\n", + "第 3913 轮，攻击被触发，发动攻击的数值是 202101085643 \n", + "被击中战斗的同学是：会跳舞的文艺青年 , 剩余生命值：14.0\n", + "第 3917 轮，攻击被触发，发动攻击的数值是 202101085647 \n", + "被击中战斗的同学是：ChercherᝰACE , 剩余生命值：4.0\n", + "第 4021 轮，攻击被触发，发动攻击的数值是 202101085751 \n", + "被击中战斗的同学是：XYQ , 剩余生命值：11.0\n", + "第 4080 轮，攻击被触发，发动攻击的数值是 202101085810 \n", + "被击中战斗的同学是：含信 , 剩余生命值：0.0\n", + "*_* 含信 同学退出战斗……阿门~~~\n", + "还有 34 位同学在继续战斗\n", + "\n", + "第 4108 轮，攻击被触发，发动攻击的数值是 202101085838 \n", + "被击中战斗的同学是：默溪 , 剩余生命值：3.0\n", + "第 4293 轮，攻击被触发，发动攻击的数值是 202101086023 \n", + "被击中战斗的同学是：LS , 剩余生命值：1.0\n", + "第 4440 轮，攻击被触发，发动攻击的数值是 202101086170 \n", + "被击中战斗的同学是：ChercherᝰACE , 剩余生命值：3.0\n", + "第 4607 轮，攻击被触发，发动攻击的数值是 202101086337 \n", + "被击中战斗的同学是：Lilly An , 剩余生命值：5.0\n", + "第 4645 轮，攻击被触发，发动攻击的数值是 202101086375 \n", + "被击中战斗的同学是：浩阳 , 剩余生命值：6.0\n", + "第 4672 轮，攻击被触发，发动攻击的数值是 202101086402 \n", + "被击中战斗的同学是：文献综合征患者 , 剩余生命值：0.0\n", + "*_* 文献综合征患者 同学退出战斗……阿门~~~\n", + "还有 33 位同学在继续战斗\n", + "\n", + "第 4917 轮，攻击被触发，发动攻击的数值是 202101086647 \n", + "被击中战斗的同学是：浩阳 , 剩余生命值：0.0\n", + "*_* 浩阳 同学退出战斗……阿门~~~\n", + "还有 32 位同学在继续战斗\n", + "\n", + "第 4975 轮，攻击被触发，发动攻击的数值是 202101086705 \n", + "被击中战斗的同学是：阳光的丹尼尔 , 剩余生命值：0.0\n", + "*_* 阳光的丹尼尔 同学退出战斗……阿门~~~\n", + "还有 31 位同学在继续战斗\n", + "\n", + "第 5015 轮，攻击被触发，发动攻击的数值是 202101086745 \n", + "被击中战斗的同学是：HYL-GISer , 剩余生命值：3.0\n", + "第 5172 轮，攻击被触发，发动攻击的数值是 202101086902 \n", + "被击中战斗的同学是：默溪 , 剩余生命值：2.0\n", + "第 5229 轮，攻击被触发，发动攻击的数值是 202101086959 \n", + "被击中战斗的同学是：CityDast , 剩余生命值：1.0\n", + "第 5429 轮，攻击被触发，发动攻击的数值是 202101087159 \n", + "被击中战斗的同学是：CityDast , 剩余生命值：0.0\n", + "*_* CityDast 同学退出战斗……阿门~~~\n", + "还有 30 位同学在继续战斗\n", + "\n", + "第 5468 轮，攻击被触发，发动攻击的数值是 202101087198 \n", + "被击中战斗的同学是：筱䓉^_^薇諒 , 剩余生命值：0.0\n", + "*_* 筱䓉^_^薇諒 同学退出战斗……阿门~~~\n", + "还有 29 位同学在继续战斗\n", + "\n", + "第 5636 轮，攻击被触发，发动攻击的数值是 202101087366 \n", + "被击中战斗的同学是：Hi~我是蘇小美 , 剩余生命值：1.0\n", + "第 5845 轮，攻击被触发，发动攻击的数值是 202101087575 \n", + "被击中战斗的同学是：柳好肥 , 剩余生命值：7.0\n", + "第 5860 轮，攻击被触发，发动攻击的数值是 202101087590 \n", + "被击中战斗的同学是：浩阳 , 剩余生命值：5.0\n", + "第 5876 轮，攻击被触发，发动攻击的数值是 202101087606 \n", + "被击中战斗的同学是：会跳舞的文艺青年 , 剩余生命值：13.0\n", + "第 5936 轮，攻击被触发，发动攻击的数值是 202101087666 \n", + "被击中战斗的同学是：浩阳 , 剩余生命值：4.0\n", + "第 5996 轮，攻击被触发，发动攻击的数值是 202101087726 \n", + "被击中战斗的同学是：A^Hundred^Flowers , 剩余生命值：6.0\n", + "第 5999 轮，攻击被触发，发动攻击的数值是 202101087729 \n", + "被击中战斗的同学是：孙宇 , 剩余生命值：12.0\n", + "第 6356 轮，攻击被触发，发动攻击的数值是 202101088086 \n", + "被击中战斗的同学是：轩仔 , 剩余生命值：0.0\n", + "*_* 轩仔 同学退出战斗……阿门~~~\n", + "还有 28 位同学在继续战斗\n", + "\n", + "第 6421 轮，攻击被触发，发动攻击的数值是 202101088151 \n", + "被击中战斗的同学是：夏天 , 剩余生命值：1.0\n", + "第 6427 轮，攻击被触发，发动攻击的数值是 202101088157 \n", + "被击中战斗的同学是：ChercherᝰACE , 剩余生命值：2.0\n", + "第 6664 轮，攻击被触发，发动攻击的数值是 202101088394 \n", + "被击中战斗的同学是：Yang , 剩余生命值：1.0\n", + "第 6750 轮，攻击被触发，发动攻击的数值是 202101088480 \n", + "被击中战斗的同学是：柳好肥 , 剩余生命值：6.0\n", + "第 6871 轮，攻击被触发，发动攻击的数值是 202101088601 \n", + "被击中战斗的同学是：XYQ , 剩余生命值：10.0\n", + "第 7210 轮，攻击被触发，发动攻击的数值是 202101088940 \n", + "被击中战斗的同学是：默溪 , 剩余生命值：1.0\n", + "第 7284 轮，攻击被触发，发动攻击的数值是 202101089014 \n", + "被击中战斗的同学是：HelloWorld , 剩余生命值：0.0\n", + "*_* HelloWorld 同学退出战斗……阿门~~~\n", + "还有 27 位同学在继续战斗\n", + "\n", + "第 7400 轮，攻击被触发，发动攻击的数值是 202101089130 \n", + "被击中战斗的同学是：炒饭没了？ , 剩余生命值：0.0\n", + "*_* 炒饭没了？ 同学退出战斗……阿门~~~\n", + "还有 26 位同学在继续战斗\n", + "\n", + "第 7462 轮，攻击被触发，发动攻击的数值是 202101089192 \n", + "被击中战斗的同学是：夏天 , 剩余生命值：0.0\n", + "*_* 夏天 同学退出战斗……阿门~~~\n", + "还有 25 位同学在继续战斗\n", + "\n", + "第 8112 轮，攻击被触发，发动攻击的数值是 202101089842 \n", + "被击中战斗的同学是：Pz , 剩余生命值：1.0\n", + "第 8127 轮，攻击被触发，发动攻击的数值是 202101089857 \n", + "被击中战斗的同学是：会跳舞的文艺青年 , 剩余生命值：12.0\n", + "第 8267 轮，攻击被触发，发动攻击的数值是 202101089997 \n", + "被击中战斗的同学是：会跳舞的文艺青年 , 剩余生命值：11.0\n", + "第 8282 轮，攻击被触发，发动攻击的数值是 202101090012 \n", + "被击中战斗的同学是：孙宇 , 剩余生命值：11.0\n", + "第 8367 轮，攻击被触发，发动攻击的数值是 202101090097 \n", + "被击中战斗的同学是：Snow , 剩余生命值：0.0\n", + "*_* Snow 同学退出战斗……阿门~~~\n", + "还有 24 位同学在继续战斗\n", + "\n", + "第 8396 轮，攻击被触发，发动攻击的数值是 202101090126 \n", + "被击中战斗的同学是：蓝袜子-UP , 剩余生命值：1.0\n", + "第 8576 轮，攻击被触发，发动攻击的数值是 202101090306 \n", + "被击中战斗的同学是：XYQ , 剩余生命值：9.0\n", + "第 9029 轮，攻击被触发，发动攻击的数值是 202101090759 \n", + "被击中战斗的同学是：默溪 , 剩余生命值：0.0\n", + "*_* 默溪 同学退出战斗……阿门~~~\n", + "还有 23 位同学在继续战斗\n", + "\n", + "第 9042 轮，攻击被触发，发动攻击的数值是 202101090772 \n", + "被击中战斗的同学是：Lilly An , 剩余生命值：4.0\n", + "第 9095 轮，攻击被触发，发动攻击的数值是 202101090825 \n", + "被击中战斗的同学是：Hi~我是蘇小美 , 剩余生命值：0.0\n", + "*_* Hi~我是蘇小美 同学退出战斗……阿门~~~\n", + "还有 22 位同学在继续战斗\n", + "\n", + "第 9397 轮，攻击被触发，发动攻击的数值是 202101091127 \n", + "被击中战斗的同学是：Yang , 剩余生命值：0.0\n", + "*_* Yang 同学退出战斗……阿门~~~\n", + "还有 21 位同学在继续战斗\n", + "\n", + "第 9548 轮，攻击被触发，发动攻击的数值是 202101091278 \n", + "被击中战斗的同学是：LS , 剩余生命值：0.0\n", + "*_* LS 同学退出战斗……阿门~~~\n", + "还有 20 位同学在继续战斗\n", + "\n", + "第 9558 轮，攻击被触发，发动攻击的数值是 202101091288 \n", + "被击中战斗的同学是：期待灵感的hm啊 , 剩余生命值：0.0\n", + "*_* 期待灵感的hm啊 同学退出战斗……阿门~~~\n", + "还有 19 位同学在继续战斗\n", + "\n", + "第 9716 轮，攻击被触发，发动攻击的数值是 202101091446 \n", + "被击中战斗的同学是：HYL-GISer , 剩余生命值：2.0\n", + "第 9836 轮，攻击被触发，发动攻击的数值是 202101091566 \n", + "被击中战斗的同学是：一一 , 剩余生命值：0.0\n", + "*_* 一一 同学退出战斗……阿门~~~\n", + "还有 18 位同学在继续战斗\n", + "\n", + "第 10300 轮，攻击被触发，发动攻击的数值是 202101092030 \n", + "被击中战斗的同学是：R , 剩余生命值：6.0\n", + "第 11029 轮，攻击被触发，发动攻击的数值是 202101092759 \n", + "被击中战斗的同学是：白桃大魔王 , 剩余生命值：4.0\n", + "第 11084 轮，攻击被触发，发动攻击的数值是 202101092814 \n", + "被击中战斗的同学是：壳乐乐 , 剩余生命值：1.0\n", + "第 11358 轮，攻击被触发，发动攻击的数值是 202101093088 \n", + "被击中战斗的同学是：其实，不懂你 , 剩余生命值：15.0\n", + "第 11466 轮，攻击被触发，发动攻击的数值是 202101093196 \n", + "被击中战斗的同学是：ChercherᝰACE , 剩余生命值：1.0\n", + "第 11541 轮，攻击被触发，发动攻击的数值是 202101093271 \n", + "被击中战斗的同学是：浩阳 , 剩余生命值：3.0\n", + "第 11655 轮，攻击被触发，发动攻击的数值是 202101093385 \n", + "被击中战斗的同学是：其实，不懂你 , 剩余生命值：14.0\n", + "第 11666 轮，攻击被触发，发动攻击的数值是 202101093396 \n", + "被击中战斗的同学是：锅醋姜就是我 , 剩余生命值：0.0\n", + "*_* 锅醋姜就是我 同学退出战斗……阿门~~~\n", + "还有 17 位同学在继续战斗\n", + "\n", + "第 12224 轮，攻击被触发，发动攻击的数值是 202101093954 \n", + "被击中战斗的同学是：柳好肥 , 剩余生命值：5.0\n", + "第 12308 轮，攻击被触发，发动攻击的数值是 202101094038 \n", + "被击中战斗的同学是：🇭 🇪 🇷 🇴 🇮 🇨 , 剩余生命值：0.0\n", + "*_* 🇭 🇪 🇷 🇴 🇮 🇨 同学退出战斗……阿门~~~\n", + "还有 16 位同学在继续战斗\n", + "\n", + "第 12910 轮，攻击被触发，发动攻击的数值是 202101094640 \n", + "被击中战斗的同学是：浩阳 , 剩余生命值：2.0\n", + "第 13142 轮，攻击被触发，发动攻击的数值是 202101094872 \n", + "被击中战斗的同学是：XYQ , 剩余生命值：8.0\n", + "第 13279 轮，攻击被触发，发动攻击的数值是 202101095009 \n", + "被击中战斗的同学是：浩阳 , 剩余生命值：1.0\n", + "第 13847 轮，攻击被触发，发动攻击的数值是 202101095577 \n", + "被击中战斗的同学是：ChercherᝰACE , 剩余生命值：0.0\n", + "*_* ChercherᝰACE 同学退出战斗……阿门~~~\n", + "还有 15 位同学在继续战斗\n", + "\n", + "第 14068 轮，攻击被触发，发动攻击的数值是 202101095798 \n", + "被击中战斗的同学是：孙宇 , 剩余生命值：10.0\n", + "第 14321 轮，攻击被触发，发动攻击的数值是 202101096051 \n", + "被击中战斗的同学是：R , 剩余生命值：5.0\n", + "第 14636 轮，攻击被触发，发动攻击的数值是 202101096366 \n", + "被击中战斗的同学是：A^Hundred^Flowers , 剩余生命值：5.0\n", + "第 15140 轮，攻击被触发，发动攻击的数值是 202101096870 \n", + "被击中战斗的同学是：柳好肥 , 剩余生命值：4.0\n", + "第 15601 轮，攻击被触发，发动攻击的数值是 202101097331 \n", + "被击中战斗的同学是：白桃大魔王 , 剩余生命值：3.0\n", + "第 15746 轮，攻击被触发，发动攻击的数值是 202101097476 \n", + "被击中战斗的同学是：A^Hundred^Flowers , 剩余生命值：4.0\n", + "第 16350 轮，攻击被触发，发动攻击的数值是 202101098080 \n", + "被击中战斗的同学是：浩阳 , 剩余生命值：0.0\n", + "*_* 浩阳 同学退出战斗……阿门~~~\n", + "还有 14 位同学在继续战斗\n", + "\n", + "第 16363 轮，攻击被触发，发动攻击的数值是 202101098093 \n", + "被击中战斗的同学是：壳乐乐 , 剩余生命值：0.0\n", + "*_* 壳乐乐 同学退出战斗……阿门~~~\n", + "还有 13 位同学在继续战斗\n", + "\n", + "第 16779 轮，攻击被触发，发动攻击的数值是 202101098509 \n", + "被击中战斗的同学是：孙宇 , 剩余生命值：9.0\n", + "第 17301 轮，攻击被触发，发动攻击的数值是 202101099031 \n", + "被击中战斗的同学是：孙宇 , 剩余生命值：8.0\n", + "第 17628 轮，攻击被触发，发动攻击的数值是 202101099358 \n", + "被击中战斗的同学是：R , 剩余生命值：4.0\n", + "第 17748 轮，攻击被触发，发动攻击的数值是 202101099478 \n", + "被击中战斗的同学是：Pz , 剩余生命值：0.0\n", + "*_* Pz 同学退出战斗……阿门~~~\n", + "还有 12 位同学在继续战斗\n", + "\n", + "第 18895 轮，攻击被触发，发动攻击的数值是 202101100625 \n", + "被击中战斗的同学是：白桃大魔王 , 剩余生命值：2.0\n", + "第 18941 轮，攻击被触发，发动攻击的数值是 202101100671 \n", + "被击中战斗的同学是：柳好肥 , 剩余生命值：3.0\n", + "第 19342 轮，攻击被触发，发动攻击的数值是 202101101072 \n", + "被击中战斗的同学是：孙宇 , 剩余生命值：7.0\n", + "第 19704 轮，攻击被触发，发动攻击的数值是 202101101434 \n", + "被击中战斗的同学是：Lilly An , 剩余生命值：3.0\n", + "第 19786 轮，攻击被触发，发动攻击的数值是 202101101516 \n", + "被击中战斗的同学是：蓝袜子-UP , 剩余生命值：0.0\n", + "*_* 蓝袜子-UP 同学退出战斗……阿门~~~\n", + "还有 11 位同学在继续战斗\n", + "\n", + "第 19968 轮，攻击被触发，发动攻击的数值是 202101101698 \n", + "被击中战斗的同学是：孙宇 , 剩余生命值：6.0\n", + "第 20781 轮，攻击被触发，发动攻击的数值是 202101102511 \n", + "被击中战斗的同学是：柳好肥 , 剩余生命值：2.0\n", + "第 22120 轮，攻击被触发，发动攻击的数值是 202101103850 \n", + "被击中战斗的同学是：其实，不懂你 , 剩余生命值：13.0\n", + "第 22202 轮，攻击被触发，发动攻击的数值是 202101103932 \n", + "被击中战斗的同学是：白桃大魔王 , 剩余生命值：1.0\n", + "第 22259 轮，攻击被触发，发动攻击的数值是 202101103989 \n", + "被击中战斗的同学是：R , 剩余生命值：3.0\n", + "第 22264 轮，攻击被触发，发动攻击的数值是 202101103994 \n", + "被击中战斗的同学是：R , 剩余生命值：2.0\n", + "第 22513 轮，攻击被触发，发动攻击的数值是 202101104243 \n", + "被击中战斗的同学是：会跳舞的文艺青年 , 剩余生命值：10.0\n", + "第 22531 轮，攻击被触发，发动攻击的数值是 202101104261 \n", + "被击中战斗的同学是：柳好肥 , 剩余生命值：1.0\n", + "第 22859 轮，攻击被触发，发动攻击的数值是 202101104589 \n", + "被击中战斗的同学是：白桃大魔王 , 剩余生命值：0.0\n", + "*_* 白桃大魔王 同学退出战斗……阿门~~~\n", + "还有 10 位同学在继续战斗\n", + "\n", + "第 23539 轮，攻击被触发，发动攻击的数值是 202101105269 \n", + "被击中战斗的同学是：HYL-GISer , 剩余生命值：1.0\n", + "第 23645 轮，攻击被触发，发动攻击的数值是 202101105375 \n", + "被击中战斗的同学是：会跳舞的文艺青年 , 剩余生命值：9.0\n", + "第 23651 轮，攻击被触发，发动攻击的数值是 202101105381 \n", + "被击中战斗的同学是：XYQ , 剩余生命值：7.0\n", + "第 24135 轮，攻击被触发，发动攻击的数值是 202101105865 \n", + "被击中战斗的同学是：孙宇 , 剩余生命值：5.0\n", + "第 24233 轮，攻击被触发，发动攻击的数值是 202101105963 \n", + "被击中战斗的同学是：A^Hundred^Flowers , 剩余生命值：3.0\n", + "第 24729 轮，攻击被触发，发动攻击的数值是 202101106459 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "被击中战斗的同学是：R , 剩余生命值：1.0\n", + "第 25251 轮，攻击被触发，发动攻击的数值是 202101106981 \n", + "被击中战斗的同学是：HYL-GISer , 剩余生命值：0.0\n", + "*_* HYL-GISer 同学退出战斗……阿门~~~\n", + "还有 9 位同学在继续战斗\n", + "\n", + "第 25735 轮，攻击被触发，发动攻击的数值是 202101107465 \n", + "被击中战斗的同学是：XYQ , 剩余生命值：6.0\n", + "第 26457 轮，攻击被触发，发动攻击的数值是 202101108187 \n", + "被击中战斗的同学是：R , 剩余生命值：0.0\n", + "*_* R 同学退出战斗……阿门~~~\n", + "还有 8 位同学在继续战斗\n", + "\n", + "第 26619 轮，攻击被触发，发动攻击的数值是 202101108349 \n", + "被击中战斗的同学是：XYQ , 剩余生命值：5.0\n", + "第 27033 轮，攻击被触发，发动攻击的数值是 202101108763 \n", + "被击中战斗的同学是：柳好肥 , 剩余生命值：0.0\n", + "*_* 柳好肥 同学退出战斗……阿门~~~\n", + "还有 7 位同学在继续战斗\n", + "\n", + "第 27427 轮，攻击被触发，发动攻击的数值是 202101109157 \n", + "被击中战斗的同学是：XYQ , 剩余生命值：4.0\n", + "第 28500 轮，攻击被触发，发动攻击的数值是 202101110230 \n", + "被击中战斗的同学是：孙宇 , 剩余生命值：4.0\n", + "第 28582 轮，攻击被触发，发动攻击的数值是 202101110312 \n", + "被击中战斗的同学是：其实，不懂你 , 剩余生命值：12.0\n", + "第 28644 轮，攻击被触发，发动攻击的数值是 202101110374 \n", + "被击中战斗的同学是：A^Hundred^Flowers , 剩余生命值：2.0\n", + "第 28749 轮，攻击被触发，发动攻击的数值是 202101110479 \n", + "被击中战斗的同学是：蔚蓝天空 , 剩余生命值：2.0\n", + "第 28820 轮，攻击被触发，发动攻击的数值是 202101110550 \n", + "被击中战斗的同学是：会跳舞的文艺青年 , 剩余生命值：8.0\n", + "第 29021 轮，攻击被触发，发动攻击的数值是 202101110751 \n", + "被击中战斗的同学是：XYQ , 剩余生命值：3.0\n", + "第 29735 轮，攻击被触发，发动攻击的数值是 202101111465 \n", + "被击中战斗的同学是：蔚蓝天空 , 剩余生命值：1.0\n", + "第 29778 轮，攻击被触发，发动攻击的数值是 202101111508 \n", + "被击中战斗的同学是：XYQ , 剩余生命值：2.0\n", + "第 30490 轮，攻击被触发，发动攻击的数值是 202101112220 \n", + "被击中战斗的同学是：XYQ , 剩余生命值：1.0\n", + "第 31624 轮，攻击被触发，发动攻击的数值是 202101113354 \n", + "被击中战斗的同学是：A^Hundred^Flowers , 剩余生命值：1.0\n", + "第 32394 轮，攻击被触发，发动攻击的数值是 202101114124 \n", + "被击中战斗的同学是：A^Hundred^Flowers , 剩余生命值：0.0\n", + "*_* A^Hundred^Flowers 同学退出战斗……阿门~~~\n", + "还有 6 位同学在继续战斗\n", + "\n", + "第 33505 轮，攻击被触发，发动攻击的数值是 202101115235 \n", + "被击中战斗的同学是：孙宇 , 剩余生命值：3.0\n", + "第 33662 轮，攻击被触发，发动攻击的数值是 202101115392 \n", + "被击中战斗的同学是：Lilly An , 剩余生命值：2.0\n", + "第 33871 轮，攻击被触发，发动攻击的数值是 202101115601 \n", + "被击中战斗的同学是：其实，不懂你 , 剩余生命值：11.0\n", + "第 34754 轮，攻击被触发，发动攻击的数值是 202101116484 \n", + "被击中战斗的同学是：孙宇 , 剩余生命值：2.0\n", + "第 37277 轮，攻击被触发，发动攻击的数值是 202101119007 \n", + "被击中战斗的同学是：XYQ , 剩余生命值：0.0\n", + "*_* XYQ 同学退出战斗……阿门~~~\n", + "\n", + "\n", + " 战斗结束……恭喜以下同学获奖：♪(^∇^*)\n", + "蔚蓝天空\t 剩余生命值：1.0\n", + "Lilly An\t 剩余生命值：2.0\n", + "孙宇\t 剩余生命值：2.0\n", + "会跳舞的文艺青年\t 剩余生命值：8.0\n", + "其实，不懂你\t 剩余生命值：11.0\n" + ] + } + ], + "source": [ + "flag = 0\n", + "while True:\n", + " flag +=1\n", + " h = hashKnife(start + flag)\n", + " if h[0:1] == \"0\":\n", + " if h[-2:] in val:\n", + " val[h[-2:]] -=1\n", + " name = pd2[pd2[\"index\"] == int(h[-2:])][\"name\"].tolist()[0]\n", + " print(\"第 {0} 轮，攻击被触发，发动攻击的数值是 {1}\\\n", + " \\n被击中战斗的同学是：{2} , 剩余生命值：{3}\".format(flag,start+flag,\n", + " name,val[h[-2:]]))\n", + " if val[h[-2:]] == 0:\n", + " del val[h[-2:]]\n", + " print(\"*_* {0} 同学退出战斗……阿门~~~\".format(name))\n", + " if len(val) <= 5:\n", + " print(\"\\n\\n 战斗结束……恭喜以下同学获奖：♪(^∇^*)\")\n", + " for v in val:\n", + " name = pd2[pd2[\"index\"] == int(v)][\"name\"].tolist()[0]\n", + " print(\"{0}\\t 剩余生命值：{1}\".format(name,val[v]))\n", + " break\n", + " else:\n", + " print(\"还有 {0} 位同学在继续战斗\\n\".format(len(val)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/image/issues/20230914101754.png b/image/issues/20230914101754.png new file mode 100644 index 0000000..f81bcc5 Binary files /dev/null and b/image/issues/20230914101754.png differ diff --git a/image/issues/20230914102028.png b/image/issues/20230914102028.png new file mode 100644 index 0000000..fcd6bf7 Binary files /dev/null and b/image/issues/20230914102028.png differ