blocks|key|401218|text|如何清除“烦人”的河流：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|401219|如果要对图像进行后处理(而不是直接使用底图)，可以删除未连接到海洋的水体：|401220|import+pylab+as+plt
A+=+plt.imread("world.png")

import+numpy+as+np
import+scipy.ndimage+as+nd
import+collections

#+Get+a+counter+of+the+greyscale+colors
a++++++=+A[:,:,0]
colors+=+collections.Counter(a.ravel())
outside_and_water_color,+land_color+=+colors.most_common(2)

#+Find+the+contigous+landmass
land_idx+=+a+==+land_color[0]

#+Index+these+land+masses
L+=+np.zeros(a.shape,dtype=int)+
L[land_idx]+=+1
L,mass_count+=+nd.measurements.label(L)

#+Loop+over+the+land+masses+and+fill+the+"holes"
#+(rivers+without+outlays)
L2+=+np.zeros(a.shape,dtype=int)+
L2[land_idx]+=+1
L2+=+nd.morphology.binary_fill_holes(L2)

#+Remap+onto+original+image
new_land+=+L2==1
A2+=+A.copy()
c+=+[land_color[0],]*3+%2B+[1,]
A2[new_land]+=+land_color[0]

#+Plot+results
plt.subplot(221)
plt.imshow(A)
plt.axis('off')

plt.subplot(222)
plt.axis('off')
B+=+A.copy()
B[land_idx]+=+[1,0,0,1]
plt.imshow(B)

plt.subplot(223)
L+=+L.astype(float)
L[L==0]+=+None
plt.axis('off')
plt.imshow(L)

plt.subplot(224)
plt.axis('off')
plt.imshow(A2)

plt.tight_layout()++#+Only+with+newer+matplotlib
plt.show()|code-block|syntax|javascript|401221|​|401222|📷|atomic|offset|length|401223|401224|第一幅图像是原始图像，第二幅图像识别陆地块。第三个不是必需的，但很有趣，因为它的ID是每个独立的连续陆地。第四张图片是你想要的，去掉了“河”的图片。|401225|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/41777ba4c2a82bc41da71f90a2f7a060.png|imageAlt^0|0|0|0|0|0|1|0|0|0|0^^$0|@$1|2|3|4|5|6|7|11|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|12|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|13|8|@]|9|@]|A|$G|H]]|$1|I|3|J|5|6|7|14|8|@]|9|@]|A|$]]|$1|K|3|L|5|M|7|15|8|@]|9|@$N|16|O|17|1|18]]|A|$]]|$1|P|3|J|5|6|7|19|8|@]|9|@]|A|$]]|$1|Q|3|R|5|6|7|1A|8|@]|9|@]|A|$]]|$1|S|3|-4|5|6|7|1B|8|@]|9|@]|A|$]]]|T|$U|$5|V|W|X|A|$Y|Z|10|-4]]]]

<h1>How to remove "annoying" rivers:</h1>

If you want to post-process the image (instead of working with Basemap directly) you can remove bodies of water that don't connect to the ocean:

<pre><code>import pylab as plt
A = plt.imread("world.png")

import numpy as np
import scipy.ndimage as nd
import collections

# Get a counter of the greyscale colors
a = A[:,:,0]
colors = collections.Counter(a.ravel())
outside_and_water_color, land_color = colors.most_common(2)

# Find the contigous landmass
land_idx = a == land_color[0]

# Index these land masses
L = np.zeros(a.shape,dtype=int) 
L[land_idx] = 1
L,mass_count = nd.measurements.label(L)

# Loop over the land masses and fill the "holes"
# (rivers without outlays)
L2 = np.zeros(a.shape,dtype=int) 
L2[land_idx] = 1
L2 = nd.morphology.binary_fill_holes(L2)

# Remap onto original image
new_land = L2==1
A2 = A.copy()
c = [land_color[0],]*3 + [1,]
A2[new_land] = land_color[0]

# Plot results
plt.subplot(221)
plt.imshow(A)
plt.axis('off')

plt.subplot(222)
plt.axis('off')
B = A.copy()
B[land_idx] = [1,0,0,1]
plt.imshow(B)

plt.subplot(223)
L = L.astype(float)
L[L==0] = None
plt.axis('off')
plt.imshow(L)

plt.subplot(224)
plt.axis('off')
plt.imshow(A2)

plt.tight_layout() # Only with newer matplotlib
plt.show()
</code></pre>

<img src="https://i.stack.imgur.com/dHyX1.png" alt="enter image description here">

The first image is the original, the second identifies the land mass. The third is not needed but fun as it ID's each separate contiguous landmass. The fourth picture is what you want, the image with the "rivers" removed.

blocks|key|401252|text|由于这样的原因，我经常避免把底图都放在一起，用OGR读取shapefile，然后自己把它们转换成Matplotlib艺术家。这是更多的工作，但也给了更多的灵活性。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|401253|底图有一些非常简洁的功能，比如将输入数据的坐标转换为你的“工作投影”。|401254|如果你想坚持使用底图，那就得到一个不包含河流的shapefile。例如，Natural+Earth在物理部分有一个很好的'Land‘shapefile+(下载'scale+rank’数据并解压缩)。请参阅http://www.naturalearthdata.com/downloads/10m-physical-vectors/|offset|length|401255|您可以使用m.readshapefile()方法从底图中读取shapefile。这允许您获得Matplotlib路径顶点和投影坐标中的代码，然后可以将其转换为新路径。这有点绕道，但它为你提供了Matplotlib的所有样式选项，其中大部分不能通过底图直接使用。这有点老生常谈，但我现在没有另一种方式，同时坚持底图。|401256|所以：|401257|from+mpl_toolkits.basemap+import+Basemap
import+matplotlib.pyplot+as+plt
from+matplotlib.collections+import+PathCollection
from+matplotlib.path+import+Path

fig+=+plt.figure(figsize=(8,+4.5))
plt.subplots_adjust(left=0.02,+right=0.98,+top=0.98,+bottom=0.00)

#+MPL+searches+for+ne_10m_land.shp+in+the+directory+'D:\\ne_10m_land'
m+=+Basemap(projection='robin',lon_0=0,resolution='c')
shp_info+=+m.readshapefile('D:\\ne_10m_land',+'scalerank',+drawbounds=True)
ax+=+plt.gca()
ax.cla()

paths+=+[]
for+line+in+shp_info[4]._paths:
++++paths.append(Path(line.vertices,+codes=line.codes))

coll+=+PathCollection(paths,+linewidths=0,+facecolors='grey',+zorder=2)

m+=+Basemap(projection='robin',lon_0=0,resolution='c')
#+drawing+something+seems+necessary+to+'initiate'+the+map+properly
m.drawcoastlines(color='white',+zorder=0)

ax+=+plt.gca()
ax.add_collection(coll)

plt.savefig('world.png',dpi=75)|code-block|syntax|javascript|401258|提供：|401259|​|401260|📷|atomic|401261|401262|entityMap|0|LINK|mutability|MUTABLE|url|http://www.naturalearthdata.com/downloads/10m-physical-vectors/|1|IMAGE|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/dcaff674bb5c98ffd1b8ee08350753b2.png|imageAlt^0|0|0|2U|1R|0|0|0|0|0|0|0|0|1|1|0|0^^$0|@$1|2|3|4|5|6|7|1C|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|1D|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|1E|8|@]|9|@$F|1F|G|1G|1|1H]]|A|$]]|$1|H|3|I|5|6|7|1I|8|@]|9|@]|A|$]]|$1|J|3|K|5|6|7|1J|8|@]|9|@]|A|$]]|$1|L|3|M|5|N|7|1K|8|@]|9|@]|A|$O|P]]|$1|Q|3|R|5|6|7|1L|8|@]|9|@]|A|$]]|$1|S|3|T|5|6|7|1M|8|@]|9|@]|A|$]]|$1|U|3|V|5|W|7|1N|8|@]|9|@$F|1O|G|1P|1|1Q]]|A|$]]|$1|X|3|T|5|6|7|1R|8|@]|9|@]|A|$]]|$1|Y|3|-4|5|6|7|1S|8|@]|9|@]|A|$]]]|Z|$10|$5|11|12|13|A|$14|15]]|16|$5|17|12|18|A|$19|1A|1B|-4]]]]

For reasons like this i often avoid Basemap alltogether and read the shapefile in with OGR and convert them to a Matplotlib artist myself. Which is alot more work but also gives alot more flexibility.

Basemap has some very neat features like converting the coordinates of input data to your 'working projection'. 

If you want to stick with Basemap, get a shapefile which doesnt contain the rivers. Natural Earth for example has a nice 'Land' shapefile in the physical section (download 'scale rank' data and uncompress). See <a href="http://www.naturalearthdata.com/downloads/10m-physical-vectors/" rel="noreferrer">http://www.naturalearthdata.com/downloads/10m-physical-vectors/</a>

You can read the shapefile in with the m.readshapefile() method from Basemap. This allows you to get the Matplotlib Path vertices and codes in the projection coordinates which you can then convert into a new Path. Its a bit of a detour but it gives you all styling options from Matplotlib, most of which are not directly available via Basemap. Its a bit hackish, but i dont now another way while sticking to Basemap.

So:

<pre><code>from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
from matplotlib.collections import PathCollection
from matplotlib.path import Path

fig = plt.figure(figsize=(8, 4.5))
plt.subplots_adjust(left=0.02, right=0.98, top=0.98, bottom=0.00)

# MPL searches for ne_10m_land.shp in the directory 'D:\\ne_10m_land'
m = Basemap(projection='robin',lon_0=0,resolution='c')
shp_info = m.readshapefile('D:\\ne_10m_land', 'scalerank', drawbounds=True)
ax = plt.gca()
ax.cla()

paths = []
for line in shp_info[4]._paths:
 paths.append(Path(line.vertices, codes=line.codes))

coll = PathCollection(paths, linewidths=0, facecolors='grey', zorder=2)

m = Basemap(projection='robin',lon_0=0,resolution='c')
# drawing something seems necessary to 'initiate' the map properly
m.drawcoastlines(color='white', zorder=0)

ax = plt.gca()
ax.add_collection(coll)

plt.savefig('world.png',dpi=75)
</code></pre>

Gives:

<img src="https://i.stack.imgur.com/U5ljJ.png" alt="enter image description here">

blocks|key|401318|text|好的，我想我有一个部分的解决方案。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|401319|其基本思想是，绘制海岸线()使用的路径是按大小/面积排序的。这意味着前N条路径(对于大多数应用程序)是主要的陆地和湖泊，后面的路径是较小的岛屿和河流。|401320|问题是，你想要的前N条路径将取决于投影(例如，全局、极地、区域)，是否已经应用了area_thresh，以及你是想要湖泊还是小岛等。换句话说，你必须对每个应用程序进行调整。|401321|from+mpl_toolkits.basemap+import+Basemap
import+matplotlib.pyplot+as+plt

mp+=+'cyl'
m+=+Basemap(resolution='c',projection=mp,lon_0=0,area_thresh=200000)

fill_color+=+'0.9'

#+If+you+don't+want+lakes+set+lake_color+to+fill_color
m.fillcontinents(color=fill_color,lake_color='white')

#+Draw+the+coastlines,+with+a+thin+line+and+same+color+as+the+continent+fill.
coasts+=+m.drawcoastlines(zorder=100,color=fill_color,linewidth=0.5)

#+Exact+the+paths+from+coasts
coasts_paths+=+coasts.get_paths()

#+In+order+to+see+which+paths+you+want+to+retain+or+discard+you'll+need+to+plot+them+one
#+at+a+time+noting+those+that+you+want+etc.+
for+ipoly+in+xrange(len(coasts_paths)):
++++print+ipoly
++++r+=+coasts_paths[ipoly]
++++#+Convert+into+lon/lat+vertices
++++polygon_vertices+=+[(vertex[0],vertex[1])+for+(vertex,code)+in
++++++++++++++++++++++++r.iter_segments(simplify=False)]
++++px+=+[polygon_vertices[i][0]+for+i+in+xrange(len(polygon_vertices))]
++++py+=+[polygon_vertices[i][1]+for+i+in+xrange(len(polygon_vertices))]
++++m.plot(px,py,'k-',linewidth=1)
++++plt.show()|code-block|syntax|javascript|401322|一旦你知道了相关的停止绘制的ipoly+(poly_stop)，你就可以这样做了…|401323|from+mpl_toolkits.basemap+import+Basemap
import+matplotlib.pyplot+as+plt

mproj+=+['nplaea','cyl']
mp+=+mproj[0]

if+mp+==+'nplaea':
++++m+=+Basemap(resolution='c',projection=mp,lon_0=0,boundinglat=30,area_thresh=200000,round=1)
++++poly_stop+=+10
else:
++++m+=+Basemap(resolution='c',projection=mp,lon_0=0,area_thresh=200000)
++++poly_stop+=+18
fill_color+=+'0.9'

#+If+you+don't+want+lakes+set+lake_color+to+fill_color
m.fillcontinents(color=fill_color,lake_color='white')

#+Draw+the+coastlines,+with+a+thin+line+and+same+color+as+the+continent+fill.
coasts+=+m.drawcoastlines(zorder=100,color=fill_color,linewidth=0.5)

#+Exact+the+paths+from+coasts
coasts_paths+=+coasts.get_paths()

#+In+order+to+see+which+paths+you+want+to+retain+or+discard+you'll+need+to+plot+them+one
#+at+a+time+noting+those+that+you+want+etc.+
for+ipoly+in+xrange(len(coasts_paths)):
++++if+ipoly+>+poly_stop:+continue
++++r+=+coasts_paths[ipoly]
++++#+Convert+into+lon/lat+vertices
++++polygon_vertices+=+[(vertex[0],vertex[1])+for+(vertex,code)+in
++++++++++++++++++++++++r.iter_segments(simplify=False)]
++++px+=+[polygon_vertices[i][0]+for+i+in+xrange(len(polygon_vertices))]
++++py+=+[polygon_vertices[i][1]+for+i+in+xrange(len(polygon_vertices))]
++++m.plot(px,py,'k-',linewidth=1)
plt.show()|401324|​|401325|📷|atomic|offset|length|401326|401327|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/852435f49e4ecf84988ba5cb4b715c60.png|imageAlt^0|0|0|0|0|0|0|0|0|1|0|0|0^^$0|@$1|2|3|4|5|6|7|15|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|16|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|17|8|@]|9|@]|A|$]]|$1|F|3|G|5|H|7|18|8|@]|9|@]|A|$I|J]]|$1|K|3|L|5|6|7|19|8|@]|9|@]|A|$]]|$1|M|3|N|5|H|7|1A|8|@]|9|@]|A|$I|J]]|$1|O|3|P|5|6|7|1B|8|@]|9|@]|A|$]]|$1|Q|3|R|5|S|7|1C|8|@]|9|@$T|1D|U|1E|1|1F]]|A|$]]|$1|V|3|P|5|6|7|1G|8|@]|9|@]|A|$]]|$1|W|3|-4|5|6|7|1H|8|@]|9|@]|A|$]]]|X|$Y|$5|Z|10|11|A|$12|13|14|-4]]]]

Okay I think I have a partial solution. 

The basic idea is that the paths used by drawcoastlines() are ordered by the size/area. Which means the first N paths are (for most applications) the main land masses and lakes and the later paths the smaller islands and rivers. 

The issue is that the first N paths that you want will depend on the projection (e.g., global, polar, regional), if area_thresh has been applied and whether you want lakes or small islands etc. In other words, you will have to tweak this per application.

<pre><code>from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt

mp = 'cyl'
m = Basemap(resolution='c',projection=mp,lon_0=0,area_thresh=200000)

fill_color = '0.9'

# If you don't want lakes set lake_color to fill_color
m.fillcontinents(color=fill_color,lake_color='white')

# Draw the coastlines, with a thin line and same color as the continent fill.
coasts = m.drawcoastlines(zorder=100,color=fill_color,linewidth=0.5)

# Exact the paths from coasts
coasts_paths = coasts.get_paths()

# In order to see which paths you want to retain or discard you'll need to plot them one
# at a time noting those that you want etc. 
for ipoly in xrange(len(coasts_paths)):
 print ipoly
 r = coasts_paths[ipoly]
 # Convert into lon/lat vertices
 polygon_vertices = [(vertex[0],vertex[1]) for (vertex,code) in
 r.iter_segments(simplify=False)]
 px = [polygon_vertices[i][0] for i in xrange(len(polygon_vertices))]
 py = [polygon_vertices[i][1] for i in xrange(len(polygon_vertices))]
 m.plot(px,py,'k-',linewidth=1)
 plt.show()
</code></pre>

Once you know the relevant ipoly to stop drawing (poly_stop) then you can do something like this...

<pre><code>from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt

mproj = ['nplaea','cyl']
mp = mproj[0]

if mp == 'nplaea':
 m = Basemap(resolution='c',projection=mp,lon_0=0,boundinglat=30,area_thresh=200000,round=1)
 poly_stop = 10
else:
 m = Basemap(resolution='c',projection=mp,lon_0=0,area_thresh=200000)
 poly_stop = 18
fill_color = '0.9'

# If you don't want lakes set lake_color to fill_color
m.fillcontinents(color=fill_color,lake_color='white')

# Draw the coastlines, with a thin line and same color as the continent fill.
coasts = m.drawcoastlines(zorder=100,color=fill_color,linewidth=0.5)

# Exact the paths from coasts
coasts_paths = coasts.get_paths()

# In order to see which paths you want to retain or discard you'll need to plot them one
# at a time noting those that you want etc. 
for ipoly in xrange(len(coasts_paths)):
 if ipoly &gt; poly_stop: continue
 r = coasts_paths[ipoly]
 # Convert into lon/lat vertices
 polygon_vertices = [(vertex[0],vertex[1]) for (vertex,code) in
 r.iter_segments(simplify=False)]
 px = [polygon_vertices[i][0] for i in xrange(len(polygon_vertices))]
 py = [polygon_vertices[i][1] for i in xrange(len(polygon_vertices))]
 m.plot(px,py,'k-',linewidth=1)
plt.show()
</code></pre>

<img src="https://i.stack.imgur.com/xa59B.png" alt="enter image description here">

blocks|key|401411|text|按照user1868739的例子，我只能选择我想要的路径(对于一些湖)：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|401412|📷|atomic|offset|length|401413|​|401414|from+mpl_toolkits.basemap+import+Basemap
import+matplotlib.pyplot+as+plt

fig+=+plt.figure(figsize=(8,+4.5))
plt.subplots_adjust(left=0.02,+right=0.98,+top=0.98,+bottom=0.00)
m+=+Basemap(resolution='c',projection='robin',lon_0=0)
m.fillcontinents(color='white',lake_color='white',zorder=2)
coasts+=+m.drawcoastlines(zorder=1,color='white',linewidth=0)
coasts_paths+=+coasts.get_paths()

ipolygons+=+range(83)+%2B+[84]+#+want+Baikal,+but+not+Tanganyika
#+80+=+Superior%2BMichigan%2BHuron,+81+=+Victoria,+82+=+Aral,+83+=+Tanganyika,
#+84+=+Baikal,+85+=+Great+Bear,+86+=+Great+Slave,+87+=+Nyasa,+88+=+Erie
#+89+=+Winnipeg,+90+=+Ontario
for+ipoly+in+ipolygons:
++++r+=+coasts_paths[ipoly]
++++#+Convert+into+lon/lat+vertices
++++polygon_vertices+=+[(vertex[0],vertex[1])+for+(vertex,code)+in
++++++++++++++++++++++++r.iter_segments(simplify=False)]
++++px+=+[polygon_vertices[i][0]+for+i+in+xrange(len(polygon_vertices))]
++++py+=+[polygon_vertices[i][2]+for+i+in+xrange(len(polygon_vertices))]
++++m.plot(px,py,linewidth=0.5,zorder=3,color='black')

plt.savefig('world2.png',dpi=100)|code-block|syntax|javascript|401415|但这只在大陆使用白色背景时有效。如果我在下面的代码行中将color更改为'gray'，我们会看到其他河流和湖泊的颜色与大陆不同。(此外，玩area_thresh也不会删除那些连接到海洋的河流。)|style|CODE|401416|m.fillcontinents(color='gray',lake_color='white',zorder=2)|401417|401418|401419|401420|白色背景的版本足以进一步彩色绘制各大洲的各种陆地信息，但如果想要保留各大洲的灰色背景，则需要一个更复杂的解决方案。|401421|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/4fe6b70b12036e7c7ee37192f19bcb97.png|imageAlt|1|https://ask.qcloudimg.com/http-save/yehe-900000/4fa0b10a3dd127aea543feb13e8e548e.png^0|0|0|1|0|0|0|0|S|5|10|6|1X|B|0|0|0|0|1|1|0|0|0^^$0|@$1|2|3|4|5|6|7|19|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|1A|8|@]|9|@$E|1B|F|1C|1|1D]]|A|$]]|$1|G|3|H|5|6|7|1E|8|@]|9|@]|A|$]]|$1|I|3|J|5|K|7|1F|8|@]|9|@]|A|$L|M]]|$1|N|3|O|5|6|7|1G|8|@$E|1H|F|1I|P|Q]|$E|1J|F|1K|P|Q]|$E|1L|F|1M|P|Q]]|9|@]|A|$]]|$1|R|3|S|5|K|7|1N|8|@]|9|@]|A|$L|M]]|$1|T|3|H|5|6|7|1O|8|@]|9|@]|A|$]]|$1|U|3|C|5|D|7|1P|8|@]|9|@$E|1Q|F|1R|1|1S]]|A|$]]|$1|V|3|H|5|6|7|1T|8|@]|9|@]|A|$]]|$1|W|3|X|5|6|7|1U|8|@]|9|@]|A|$]]|$1|Y|3|-4|5|6|7|1V|8|@]|9|@]|A|$]]]|Z|$10|$5|11|12|13|A|$14|15|16|-4]]|17|$5|11|12|13|A|$14|18|16|-4]]]]

Following user1868739's example, I am able to select only the paths (for some lakes) that I want:
<img src="https://i.stack.imgur.com/Yo0w3.png" alt="world2">

<pre><code>from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt

fig = plt.figure(figsize=(8, 4.5))
plt.subplots_adjust(left=0.02, right=0.98, top=0.98, bottom=0.00)
m = Basemap(resolution='c',projection='robin',lon_0=0)
m.fillcontinents(color='white',lake_color='white',zorder=2)
coasts = m.drawcoastlines(zorder=1,color='white',linewidth=0)
coasts_paths = coasts.get_paths()

ipolygons = range(83) + [84] # want Baikal, but not Tanganyika
# 80 = Superior+Michigan+Huron, 81 = Victoria, 82 = Aral, 83 = Tanganyika,
# 84 = Baikal, 85 = Great Bear, 86 = Great Slave, 87 = Nyasa, 88 = Erie
# 89 = Winnipeg, 90 = Ontario
for ipoly in ipolygons:
 r = coasts_paths[ipoly]
 # Convert into lon/lat vertices
 polygon_vertices = [(vertex[0],vertex[1]) for (vertex,code) in
 r.iter_segments(simplify=False)]
 px = [polygon_vertices[i][0] for i in xrange(len(polygon_vertices))]
 py = [polygon_vertices[i][2] for i in xrange(len(polygon_vertices))]
 m.plot(px,py,linewidth=0.5,zorder=3,color='black')

plt.savefig('world2.png',dpi=100)
</code></pre>

But this only works when using white background for the continents. If I change <code>color</code> to <code>'gray'</code> in the following line, we see that other rivers and lakes are not filled with the same color as the continents are. (Also playing with <code>area_thresh</code> will not remove those rivers that are connected to ocean.)

<pre><code>m.fillcontinents(color='gray',lake_color='white',zorder=2)
</code></pre>

<img src="https://i.stack.imgur.com/Fwdlk.png" alt="world3">

The version with white background is adequate for further color-plotting all kind of land information over the continents, but a more elaborate solution would be needed, if one wants to retain the gray background for continents.

blocks|key|183463|text|根据我对@sampo-smolander的评论|type|unstyled|depth|inlineStyleRanges|entityRanges|data|183464|from+mpl_toolkits.basemap+import+Basemap
import+matplotlib.pyplot+as+plt

fig+=+plt.figure(figsize=(8,+4.5))
plt.subplots_adjust(left=0.02,+right=0.98,+top=0.98,+bottom=0.00)
m+=+Basemap(resolution='c',projection='robin',lon_0=0)
m.fillcontinents(color='gray',lake_color='white',zorder=2)
coasts+=+m.drawcoastlines(zorder=1,color='white',linewidth=0)
coasts_paths+=+coasts.get_paths()

ipolygons+=+range(83)+%2B+[84]
for+ipoly+in+xrange(len(coasts_paths)):
++++r+=+coasts_paths[ipoly]
++++#+Convert+into+lon/lat+vertices
++++polygon_vertices+=+[(vertex[0],vertex[1])+for+(vertex,code)+in
++++++++++++++++++++++++r.iter_segments(simplify=False)]
++++px+=+[polygon_vertices[i][0]+for+i+in+xrange(len(polygon_vertices))]
++++py+=+[polygon_vertices[i][1]+for+i+in+xrange(len(polygon_vertices))]
++++if+ipoly+in+ipolygons:
++++++++m.plot(px,py,linewidth=0.5,zorder=3,color='black')
++++else:
++++++++m.plot(px,py,linewidth=0.5,zorder=4,color='grey')
plt.savefig('world2.png',dpi=100)|code-block|syntax|javascript|183465|​|183466|📷|atomic|offset|length|183467|183468|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/fcc3b84a82e0e5716e5f836aea0731b4.png|imageAlt^0|0|0|0|0|1|0|0|0^^$0|@$1|2|3|4|5|6|7|X|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|Y|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Z|8|@]|9|@]|A|$]]|$1|I|3|J|5|K|7|10|8|@]|9|@$L|11|M|12|1|13]]|A|$]]|$1|N|3|H|5|6|7|14|8|@]|9|@]|A|$]]|$1|O|3|-4|5|6|7|15|8|@]|9|@]|A|$]]]|P|$Q|$5|R|S|T|A|$U|V|W|-4]]]]

As per my comment to @sampo-smolander

<pre><code>from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt

fig = plt.figure(figsize=(8, 4.5))
plt.subplots_adjust(left=0.02, right=0.98, top=0.98, bottom=0.00)
m = Basemap(resolution='c',projection='robin',lon_0=0)
m.fillcontinents(color='gray',lake_color='white',zorder=2)
coasts = m.drawcoastlines(zorder=1,color='white',linewidth=0)
coasts_paths = coasts.get_paths()

ipolygons = range(83) + [84]
for ipoly in xrange(len(coasts_paths)):
 r = coasts_paths[ipoly]
 # Convert into lon/lat vertices
 polygon_vertices = [(vertex[0],vertex[1]) for (vertex,code) in
 r.iter_segments(simplify=False)]
 px = [polygon_vertices[i][0] for i in xrange(len(polygon_vertices))]
 py = [polygon_vertices[i][1] for i in xrange(len(polygon_vertices))]
 if ipoly in ipolygons:
 m.plot(px,py,linewidth=0.5,zorder=3,color='black')
 else:
 m.plot(px,py,linewidth=0.5,zorder=4,color='grey')
plt.savefig('world2.png',dpi=100)
</code></pre>

<img src="https://i.stack.imgur.com/l1djt.png" alt="enter image description here">

blocks|key|183522|text|如果你可以绘制轮廓而不是shapefile，那么绘制你可以从任何地方获得的海岸线是相当容易的。我从美国国家海洋与大气局的海岸线提取器上得到了我的海岸线，格式是：http://www.ngdc.noaa.gov/mgg/shorelines/shorelines.html|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|183523|为了编辑海岸线，我将其转换为SVG格式，然后使用Inkscape对其进行编辑，然后将其转换回经度/经度文本文件("MATLAB“格式)。|183524|所有的Python代码都包含在下面。|183525|#+---------------------------------------------------------------
def+plot_lines(mymap,+lons,+lats,+**kwargs)+:
++++"""Plots+a+custom+coastline.++This+plots+simple+lines,+not
++++ArcInfo-style+SHAPE+files.

++++Args:
++++++++lons:+Longitude+coordinates+for+line+segments+(degrees+E)
++++++++lats:+Latitude+coordinates+for+line+segments+(degrees+N)

++++Type+Info:
++++++++len(lons)+==+len(lats)
++++++++A+NaN+in+lons+and+lats+signifies+a+new+line+segment.

++++See:
++++++++giss.noaa.drawcoastline_file()
++++"""

++++#+Project+onto+the+map
++++x,+y+=+mymap(lons,+lats)

++++#+BUG+workaround:+Basemap+projects+our+NaN's+to+1e30.
++++x[x==1e30]+=+np.nan
++++y[y==1e30]+=+np.nan

++++#+Plot+projected+line+segments.
++++mymap.plot(x,+y,+**kwargs)


#+Read+"Matlab"+format+files+from+NOAA+Coastline+Extractor.
#+See:+http://www.ngdc.noaa.gov/mgg/coast/

lineRE=re.compile('(.*?)\s%2B(.*)')
def+read_coastline(fname,+take_every=1)+:
++++nlines+=+0
++++xdata+=+array.array('d')
++++ydata+=+array.array('d')
++++for+line+in+file(fname)+:
#++++++++if+(nlines+%25+10000+==+0)+:
#++++++++++++print+'nlines+=+%25d'+%25+(nlines,)
++++++++if+(nlines+%25+take_every+==+0+or+line[0:3]+==+'nan')+:
++++++++++++match+=+lineRE.match(line)
++++++++++++lon+=+float(match.group(1))
++++++++++++lat+=+float(match.group(2))

++++++++++++xdata.append(lon)
++++++++++++ydata.append(lat)
++++++++nlines+=+nlines+%2B+1


++++return+(np.array(xdata),np.array(ydata))

def+drawcoastline_file(mymap,+fname,+**kwargs)+:
++++"""Reads+and+plots+a+coastline+file.
++++See:
++++++++giss.basemap.drawcoastline()
++++++++giss.basemap.read_coastline()
++++"""

++++lons,+lats+=+read_coastline(fname,+take_every=1)
++++return+drawcoastline(mymap,+lons,+lats,+**kwargs)
#+=========================================================
#+coastline2svg.py
#
import+giss.io.noaa
import+os
import+numpy+as+np
import+sys

svg_header+=+"""<?xml+version="1.0"+encoding="UTF-8"+standalone="no"?>


<svg
+++xmlns:dc="http://purl.org/dc/elements/1.1/"
+++xmlns:cc="http://creativecommons.org/ns#"
+++xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
+++xmlns:svg="http://www.w3.org/2000/svg"
+++xmlns="http://www.w3.org/2000/svg"
+++version="1.1"
+++width="360"
+++height="180"
+++id="svg2">
++<defs
+++++id="defs4"+/>
++<metadata
+++++id="metadata7">
++++<rdf:RDF>
++++++<cc:Work
+++++++++rdf:about="">
++++++++<dc:format>image/svg%2Bxml</dc:format>
++++++++<dc:type
+++++++++++rdf:resource="http://purl.org/dc/dcmitype/StillImage"+/>
++++++++<dc:title></dc:title>
++++++</cc:Work>
++++</rdf:RDF>
++</metadata>
++<g
+++++id="layer1">
"""

path_tpl+=+"""
++++<path
+++++++d="%25PATH%25"
+++++++id="%25PATH_ID%25"
+++++++style="fill:none;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
"""

svg_footer+=+"</g></svg>"




#+Set+up+paths
data_root+=+os.path.join(os.environ['HOME'],+'data')
#modelerc+=+giss.modele.read_modelerc()
#cmrun+=+modelerc['CMRUNDIR']
#savedisk+=+modelerc['SAVEDISK']

ifname+=+sys.argv[1]
ofname+=+ifname.replace('.dat',+'.svg')

lons,+lats+=+giss.io.noaa.read_coastline(ifname,+1)

out+=+open(ofname,+'w')
out.write(svg_header)

path_id+=+1
points+=+[]
for+lon,+lat+in+zip(lons,+lats)+:
++++if+np.isnan(lon)+or+np.isnan(lat)+:
++++++++#+Process+what+we+have
++++++++if+len(points)+>+2+:
++++++++++++out.write('\n<path+d="')
++++++++++++out.write('m+%25f,%25f+L'+%25+(points[0][0],+points[0][1]))
++++++++++++for+pt+in+points[1:]+:
++++++++++++++++out.write('+%25f,%25f'+%25+pt)
++++++++++++out.write('"\n+++id="path%25d"\n'+%25+(path_id))
#++++++++++++out.write('style="fill:none;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"')
++++++++++++out.write('+/>\n')
++++++++++++path_id+%2B=+1
++++++++points+=+[]
++++else+:
++++++++lon+%2B=+180
++++++++lat+=+180+-+(lat+%2B+90)
++++++++points.append((lon,+lat))


out.write(svg_footer)
out.close()

#+=============================================================
#+svg2coastline.py

import+os
import+sys
import+re

#+Reads+the+output+of+Inkscape's+"Plain+SVG"+format,+outputs+in+NOAA+MATLAB+coastline+format

mainRE+=+re.compile(r'\s*d=".*"')
lineRE+=+re.compile(r'\s*d="(m%7CM)\s*(.*?)"')

fname+=+sys.argv[1]


lons+=+[]
lats+=+[]
for+line+in+open(fname,+'r')+:
++++#+Weed+out+extraneous+lines+in+the+SVG+file
++++match+=+mainRE.match(line)
++++if+match+is+None+:
++++++++continue

++++match+=+lineRE.match(line)

++++#+Stop+if+something+is+wrong
++++if+match+is+None+:
++++++++sys.stderr.write(line)
++++++++sys.exit(-1)

++++type+=+match.group(1)[0]
++++spairs+=+match.group(2).split('+')
++++x+=+0
++++y+=+0
++++for+spair+in+spairs+:
++++++++if+spair+==+'L'+:
++++++++++++type+=+'M'
++++++++++++continue

++++++++(sdelx,+sdely)+=+spair.split(',')
++++++++delx+=+float(sdelx)
++++++++dely+=+float(sdely)
++++++++if+type+==+'m'+:
++++++++++++x+%2B=+delx
++++++++++++y+%2B=+dely
++++++++else+:
++++++++++++x+=+delx
++++++++++++y+=+dely
++++++++lon+=+x+-+180
++++++++lat+=+90+-+y
++++++++print+'%25f\t%25f'+%25+(lon,+lat)
++++print+'nan\tnan'|code-block|syntax|javascript|183526|entityMap|0|LINK|mutability|MUTABLE|url|http://www.ngdc.noaa.gov/mgg/shorelines/shorelines.html^0|28|1J|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@$A|V|B|W|1|X]]|C|$]]|$1|D|3|E|5|6|7|Y|8|@]|9|@]|C|$]]|$1|F|3|G|5|6|7|Z|8|@]|9|@]|C|$]]|$1|H|3|I|5|J|7|10|8|@]|9|@]|C|$K|L]]|$1|M|3|-4|5|6|7|11|8|@]|9|@]|C|$]]]|N|$O|$5|P|Q|R|C|$S|T]]]]

If you're OK with plotting outlines rather than shapefiles, it's pretty easy to plot coastlines that you can get from wherever. I got my coastlines from the NOAA Coastline Extractor in MATLAB format:
 <a href="http://www.ngdc.noaa.gov/mgg/shorelines/shorelines.html" rel="nofollow">http://www.ngdc.noaa.gov/mgg/shorelines/shorelines.html</a>

To edit the coastlines, I converted to SVG, then edited them with Inkscape, then converted back to the lat/lon text file ("MATLAB" format).

All Python code is included below.

<pre><code># ---------------------------------------------------------------
def plot_lines(mymap, lons, lats, **kwargs) :
 """Plots a custom coastline. This plots simple lines, not
 ArcInfo-style SHAPE files.

 Args:
 lons: Longitude coordinates for line segments (degrees E)
 lats: Latitude coordinates for line segments (degrees N)

 Type Info:
 len(lons) == len(lats)
 A NaN in lons and lats signifies a new line segment.

 See:
 giss.noaa.drawcoastline_file()
 """

 # Project onto the map
 x, y = mymap(lons, lats)

 # BUG workaround: Basemap projects our NaN's to 1e30.
 x[x==1e30] = np.nan
 y[y==1e30] = np.nan

 # Plot projected line segments.
 mymap.plot(x, y, **kwargs)


# Read "Matlab" format files from NOAA Coastline Extractor.
# See: http://www.ngdc.noaa.gov/mgg/coast/

lineRE=re.compile('(.*?)\s+(.*)')
def read_coastline(fname, take_every=1) :
 nlines = 0
 xdata = array.array('d')
 ydata = array.array('d')
 for line in file(fname) :
# if (nlines % 10000 == 0) :
# print 'nlines = %d' % (nlines,)
 if (nlines % take_every == 0 or line[0:3] == 'nan') :
 match = lineRE.match(line)
 lon = float(match.group(1))
 lat = float(match.group(2))

 xdata.append(lon)
 ydata.append(lat)
 nlines = nlines + 1


 return (np.array(xdata),np.array(ydata))

def drawcoastline_file(mymap, fname, **kwargs) :
 """Reads and plots a coastline file.
 See:
 giss.basemap.drawcoastline()
 giss.basemap.read_coastline()
 """

 lons, lats = read_coastline(fname, take_every=1)
 return drawcoastline(mymap, lons, lats, **kwargs)
# =========================================================
# coastline2svg.py
#
import giss.io.noaa
import os
import numpy as np
import sys

svg_header = """&lt;?xml version="1.0" encoding="UTF-8" standalone="no"?&gt;
&lt;!-- Created with Inkscape (http://www.inkscape.org/) --&gt;

&lt;svg
 xmlns:dc="http://purl.org/dc/elements/1.1/"
 xmlns:cc="http://creativecommons.org/ns#"
 xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
 xmlns:svg="http://www.w3.org/2000/svg"
 xmlns="http://www.w3.org/2000/svg"
 version="1.1"
 width="360"
 height="180"
 id="svg2"&gt;
 &lt;defs
 id="defs4" /&gt;
 &lt;metadata
 id="metadata7"&gt;
 &lt;rdf:RDF&gt;
 &lt;cc:Work
 rdf:about=""&gt;
 &lt;dc:format&gt;image/svg+xml&lt;/dc:format&gt;
 &lt;dc:type
 rdf:resource="http://purl.org/dc/dcmitype/StillImage" /&gt;
 &lt;dc:title&gt;&lt;/dc:title&gt;
 &lt;/cc:Work&gt;
 &lt;/rdf:RDF&gt;
 &lt;/metadata&gt;
 &lt;g
 id="layer1"&gt;
"""

path_tpl = """
 &lt;path
 d="%PATH%"
 id="%PATH_ID%"
 style="fill:none;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
"""

svg_footer = "&lt;/g&gt;&lt;/svg&gt;"




# Set up paths
data_root = os.path.join(os.environ['HOME'], 'data')
#modelerc = giss.modele.read_modelerc()
#cmrun = modelerc['CMRUNDIR']
#savedisk = modelerc['SAVEDISK']

ifname = sys.argv[1]
ofname = ifname.replace('.dat', '.svg')

lons, lats = giss.io.noaa.read_coastline(ifname, 1)

out = open(ofname, 'w')
out.write(svg_header)

path_id = 1
points = []
for lon, lat in zip(lons, lats) :
 if np.isnan(lon) or np.isnan(lat) :
 # Process what we have
 if len(points) &gt; 2 :
 out.write('\n&lt;path d="')
 out.write('m %f,%f L' % (points[0][0], points[0][1]))
 for pt in points[1:] :
 out.write(' %f,%f' % pt)
 out.write('"\n id="path%d"\n' % (path_id))
# out.write('style="fill:none;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"')
 out.write(' /&gt;\n')
 path_id += 1
 points = []
 else :
 lon += 180
 lat = 180 - (lat + 90)
 points.append((lon, lat))


out.write(svg_footer)
out.close()

# =============================================================
# svg2coastline.py

import os
import sys
import re

# Reads the output of Inkscape's "Plain SVG" format, outputs in NOAA MATLAB coastline format

mainRE = re.compile(r'\s*d=".*"')
lineRE = re.compile(r'\s*d="(m|M)\s*(.*?)"')

fname = sys.argv[1]


lons = []
lats = []
for line in open(fname, 'r') :
 # Weed out extraneous lines in the SVG file
 match = mainRE.match(line)
 if match is None :
 continue

 match = lineRE.match(line)

 # Stop if something is wrong
 if match is None :
 sys.stderr.write(line)
 sys.exit(-1)

 type = match.group(1)[0]
 spairs = match.group(2).split(' ')
 x = 0
 y = 0
 for spair in spairs :
 if spair == 'L' :
 type = 'M'
 continue

 (sdelx, sdely) = spair.split(',')
 delx = float(sdelx)
 dely = float(sdely)
 if type == 'm' :
 x += delx
 y += dely
 else :
 x = delx
 y = dely
 lon = x - 180
 lat = 90 - y
 print '%f\t%f' % (lon, lat)
 print 'nan\tnan'
</code></pre>

blocks|key|183615|text|我经常修改Basemap的绘图海岸线()，以避免那些“破碎”的河流。为了保持数据源的一致性，我还修改了drawcountries()。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|183616|以下是我为了支持Natural+Earth数据中的不同分辨率而使用的：|183617|from+mpl_toolkits.basemap+import+Basemap


class+Basemap(Basemap):
++++"""+Modify+Basemap+to+use+Natural+Earth+data+instead+of+GSHHG+data+"""
++++def+drawcoastlines(self):
++++++++shapefile+=+'data/naturalearth/coastline/ne_%25sm_coastline'+%25+\
++++++++++++++++++++{'l':110,+'m':50,+'h':10}[self.resolution]
++++++++self.readshapefile(shapefile,+'coastline',+linewidth=1.)
++++def+drawcountries(self):
++++++++shapefile+=+'data/naturalearth/countries/ne_%25sm_admin_0_countries'+%25+\
++++++++++++++++++++{'l':110,+'m':50,+'h':10}[self.resolution]
++++++++self.readshapefile(shapefile,+'countries',+linewidth=0.5)


m+=+Basemap(llcrnrlon=-90,+llcrnrlat=-40,+urcrnrlon=-30,+urcrnrlat=%2B20,
++++++++++++resolution='l')++#+resolution+=+(l)ow+%7C+(m)edium+%7C+(h)igh
m.drawcoastlines()
m.drawcountries()|code-block|syntax|javascript|183618|下面是输出：|183619|📷|atomic|offset|length|183620|​|183621|请注意，默认情况下，底图使用分辨率=‘c’(粗略)，这在所示代码中不受支持。|183622|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/a4f677b254dad3b2c62dd01ab58774a1.png|imageAlt^0|0|0|0|0|0|1|0|0|0|0^^$0|@$1|2|3|4|5|6|7|12|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|13|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|14|8|@]|9|@]|A|$G|H]]|$1|I|3|J|5|6|7|15|8|@]|9|@]|A|$]]|$1|K|3|L|5|M|7|16|8|@]|9|@$N|17|O|18|1|19]]|A|$]]|$1|P|3|Q|5|6|7|1A|8|@]|9|@]|A|$]]|$1|R|3|S|5|6|7|1B|8|@]|9|@]|A|$]]|$1|T|3|-4|5|6|7|1C|8|@]|9|@]|A|$]]]|U|$V|$5|W|X|Y|A|$Z|10|11|-4]]]]

I frequently modify Basemap's drawcoastlines() to avoid those 'broken' rivers. I also modify drawcountries() for the sake of data source consistency.

Here is what I use in order to support the different resolutions available in Natural Earth data:

<pre><code>from mpl_toolkits.basemap import Basemap


class Basemap(Basemap):
 """ Modify Basemap to use Natural Earth data instead of GSHHG data """
 def drawcoastlines(self):
 shapefile = 'data/naturalearth/coastline/ne_%sm_coastline' % \
 {'l':110, 'm':50, 'h':10}[self.resolution]
 self.readshapefile(shapefile, 'coastline', linewidth=1.)
 def drawcountries(self):
 shapefile = 'data/naturalearth/countries/ne_%sm_admin_0_countries' % \
 {'l':110, 'm':50, 'h':10}[self.resolution]
 self.readshapefile(shapefile, 'countries', linewidth=0.5)


m = Basemap(llcrnrlon=-90, llcrnrlat=-40, urcrnrlon=-30, urcrnrlat=+20,
 resolution='l') # resolution = (l)ow | (m)edium | (h)igh
m.drawcoastlines()
m.drawcountries()
</code></pre>

Here is the output:
<a href="https://i.stack.imgur.com/d92g6.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/d92g6.png" alt="enter image description here"></a>

Please note that by default Basemap uses resolution='c' (crude), which is not supported in the code shown.

Would there be a way to plot the borders of the continents with Basemap (or without Basemap, if there is some other way), without those annoying rivers coming along? Especially that piece of Kongo River, not even reaching the ocean, is disturbing.

EDIT: I intend to further plot data over the map, like in the <a href="http://matplotlib.org/basemap/users/examples.html">Basemap gallery</a> (and still have the borderlines of the continents drawn as black lines over the data, to give structure for the worldmap) so while the solution by Hooked below is nice, masterful even, it's not applicable for this purpose.

<img src="https://i.stack.imgur.com/QFIK3.png" alt="world map">

Image produced by:

<pre><code>from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt

fig = plt.figure(figsize=(8, 4.5))
plt.subplots_adjust(left=0.02, right=0.98, top=0.98, bottom=0.00)
m = Basemap(projection='robin',lon_0=0,resolution='c')
m.fillcontinents(color='gray',lake_color='white')
m.drawcoastlines()
plt.savefig('world.png',dpi=75)
</code></pre>

world map without rivers with matplotlib / Basemap?

图像识别

Python

有没有一种方法可以用底图绘制大陆的边界(如果有其他方法，也可以不用底图)，而不需要那些恼人的河流？特别是那段金戈河，甚至没有到达海洋，是令人不安的。编辑:我打算在地图上进一步绘制数据，就像在中一样(仍然将各大洲的边界线绘制为数据上的黑线，以给出世界地图的结构)，所以虽然下面挂钩的解决方案很好，甚至很精通，但它不适用于此...

问有matplotlib / Basemap的没有河流的世界地图？
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