python-为什么Matplotlib底图不显示岛？

概述我一直在使用matplotlib和底图来显示有关纽约市的一些信息.到目前为止,我一直在关注this guide,但遇到了一个问题.我试图在可视化中显示曼哈顿岛,但我不知道为什么底图没有将其显示为岛.这是底图为我提供的可视化效果：这是我正在使用的边界框的屏幕截图：这是生成图像的代码：wl = -74.04006 sl = 40.683092 el = -73.

我一直在使用matplotlib和底图来显示有关纽约市的一些信息.到目前为止,我一直在关注this guide,但遇到了一个问题.我试图在可视化中显示曼哈顿岛,但我不知道为什么底图没有将其显示为岛.

这是底图为我提供的可视化效果：

这是我正在使用的边界框的屏幕截图：

这是生成图像的代码：

wl = -74.04006sl = 40.683092el = -73.834067nl = 40.88378m = Basemap(resolution='f',# c,l,i,h,f or None            projection='merc',area_thresh=50,lat_0=(wl + sl)/2,lon_0=(el + nl)/2,llcrnrlon= wl,llcrnrlat= sl,urcrnrlon= el,urcrnrlat= nl)m.drawmapboundary(fill_color='#46bcec')m.fillcontinents(color='#f2f2f2',lake_color='#46bcec')m.drawcoastlines()m.drawrivers()

我以为它可能会考虑河流之间的水,但是m.drawrivers()似乎没有解决问题.任何帮助显然都非常感激.

提前致谢！

最佳答案一种为您的地块获取质量更好的基础地图的方法是,以适当的缩放级别从Web地图图块构建一个.在这里,我演示了如何从openstreetmap Web地图服务器获取它们.在这种情况下,我将缩放级别设置为10,并获得2个地图图块,以将其合并为单个图像数组.缺点之一是组合图像的范围始终大于我们要求的值.这是工作代码：

from mpl_toolkits.basemap import Basemapimport matplotlib.pyplot as pltimport numpy as npimport mathimport urllib2import StringIOfrom PIL import Image# === Begin block1 ===# Credit: BerndGit,answered Feb 15 '15 at 19:47. And ...# Source: https://wiki.openstreetmap.org/wiki/Slippy_map_tilenamesdef deg2num(lat_deg,lon_deg,zoom):    '''Lon./lat. to tile numbers'''    lat_rad = math.radians(lat_deg)    n = 2.0 ** zoom    xtile = int((lon_deg + 180.0) / 360.0 * n)    ytile = int((1.0 - math.log(math.tan(lat_rad) + (1 / math.cos(lat_rad))) / math.pi) / 2.0 * n)    return (xtile,ytile)def num2deg(xtile,ytile,zoom):    '''Tile numbers to lon./lat.'''    n = 2.0 ** zoom    lon_deg = xtile / n * 360.0 - 180.0    lat_rad = math.atan(math.sinh(math.pi * (1 - 2 * ytile / n)))    lat_deg = math.degrees(lat_rad)    return (lat_deg,lon_deg)   # NW-corner of the tile. def getimageCluster(lat_deg,delta_lat,delta_long,zoom):    # access map tiles from internet    # no access/key or password is needed    smurl = r"http://a.tile.openstreetmap.org/{0}/{1}/{2}.png"    # useful snippet: smurl.format(zoom,xtile,ytile) -> complete URL    # x increases L-R; y top-Bottom    xmin,ymax =deg2num(lat_deg,zoom)  # get tile numbers (x,y)    xmax,ymin =deg2num(lat_deg+delta_lat,lon_deg+delta_long,zoom)    # PIL is used to build new image from tiles    Cluster = Image.new('RGB',((xmax-xmin+1)*256-1,(ymax-ymin+1)*256-1) )     for xtile in range(xmin,xmax+1):        for ytile in range(ymin,ymax+1):            try:                imgurl = smurl.format(zoom,ytile)                print("opening: " + imgurl)                imgstr = urllib2.urlopen(imgurl).read()                # Todo: study,what these do?                tile = Image.open(StringIO.StringIO(imgstr))                Cluster.paste(tile,Box=((xtile-xmin)*256,(ytile-ymin)*255))            except:                 print("Couldn't download image")                tile = None    return Cluster# ===End Block1===# Credit to myselfdef getextents(latmin_deg,lonmin_deg,zoom):    '''Return LL and UR,each with (long,lat) of real extent of combined tiles.       latmin_deg: bottom lat of extent       lonmin_deg: left long of extent       delta_lat: extent of lat       delta_long: extent of long,all in degrees    '''    # Tile numbers(x,y): x increases L-R; y top-Bottom    xtile_LL,ytile_LL = deg2num(latmin_deg,zoom)  #get tile numbers as specifIEd by (x,y)    xtile_UR,ytile_UR = deg2num(latmin_deg + delta_lat,lonmin_deg + delta_long,zoom)    # from tile numbers,we get NW corners    lat_NW_LL,lon_NW_LL = num2deg(xtile_LL,ytile_LL,zoom)    lat_NW_LLL,lon_NW_LLL = num2deg(xtile_LL,ytile_LL+1,zoom) # next down below    lat_NW_UR,lon_NW_UR = num2deg(xtile_UR,ytile_UR,zoom)    lat_NW_URR,lon_NW_URR = num2deg(xtile_UR+1,zoom) # next to the right    # get extents    minLat = lat_NW_LLL    minLon = lon_NW_LL    maxLat = lat_NW_UR    maxLon = lon_NW_URR    return (minLon,maxLon,minLat,maxLat)  # (left,right,bottom,top) in degrees# OP's values of extents for target area to plot# some changes here (with larger zoom level) may lead to better final plotwl = -74.04006sl = 40.683092el = -73.834067nl = 40.88378lat_deg = sllon_deg = wld_lat = nl - sld_long = el - wlzoom = 10   # zoom level# Acquire images. The combined images will be slightly larger that the extentstimg = getimageCluster(lat_deg,d_lat,d_long,zoom)# This computes real extents of the combined tile images,and get (left,top)latmin_deg,delta_long = sl,wl,nl-sl,el-wl(left,top) = getextents(latmin_deg,zoom)   #units: degrees# Set Basemap with proper parametersm = Basemap(resolution='h',# h is nice            projection='merc',lat_0=(bottom + top)/2,lon_0=(left + right)/2,llcrnrlon=left,llcrnrlat=bottom,urcrnrlon=right,urcrnrlat=top)fig = plt.figure()fig.set_size_inches(10,12)m.imshow(np.asarray(timg),extent=[left,top],origin='upper' )m.drawcoastlines(color='gray',linewidth=3.0)  # intentionally thick line#m.fillcontinents(color='#f2f2f2',lake_color='#46bcec',Alpha=0.6)plt.show()

希望能帮助到你.结果图：

编辑

裁剪图像以获得确切的绘图区域并不困难. PIL模块可以处理. Numpy的数组切片也可以.

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