我想用底图创建一个密度图。我得到的数据是不加筛选和重复的，或者彼此非常接近。我试过将数据网格化，然后使用pcolor绘制存储箱的数量，但即使所有数据集的长度都相等，我仍然会得到缓冲区大小错误。我最初的想法是使用下面的Basemap脚本，但我只能让分散来工作，虽然这并不能给我一个密度图。
m = Basemap(resolution='f',projection='merc',
lon_0=160,
llcrnrlat=-30.0,
urcrnrlat=30.0,
llcrnrlon=100.,
urcrnrlon=270.0,
lat_ts=0.0)
m.drawmapboundary(fill_color='white')
m.fillcontinents(color='#F5DEB3',lake_color='#85A6D9')
m.drawcoastlines(color='black', linewidth=.4)
m.drawcountries(color='#6D5F47', linewidth=.4)
m.drawmeridians(np.arange(0, 360, 20),
labels=[0,0,0,1],
color='black',
dashes=[1,0],
labelstyle='+/-',
linewidth=0.2)
m.drawparallels(np.arange(-90, 90, 10),
labels=[1,0,0,0],
color='black',
dashes=[1,0],
labelstyle='+/-',
linewidth=0.2)
lats =
array([ 14.375, 14.125, 14.125, 9.375, 13.625, 14.375, 5.625,
13.875, 14.625, 5.875, 8.875, 5.625, 8.875, 13.375,
11.125, 8.375, 12.375, 6.125, 5.375, 8.375, 7.375,
7.875, 14.375, 14.875, 9.875, 11.125, 14.875, 7.875,
9.125, 11.625, 5.125, 10.875, 5.125, 12.125, 12.625,
8.625, 5.125, 8.375, 11.625, 11.375, 12.875, 14.375,
8.875, 8.375, 6.375, 8.625, 5.875, 8.125, 9.375,
5.875, 8.125, 8.875, 5.375, 8.875, 5.625, 11.875,
9.875, 9.875, 10.875, 11.375, 9.875, 9.375, 13.125,
14.125, 8.125, 14.875, 9.875, 9.625, 10.625, 12.125,
9.375, 5.625, 13.625, 6.375, 10.125, 14.875, 8.875,
5.125, 6.125, 8.625, 6.875, 9.375, 9.125, 9.375,
13.625, 6.125, 6.125, 6.875, 11.375, 13.375, 10.375,
6.875, 7.625, 7.625, 13.875, 5.125, 6.125, 14.125,
7.375, 5.375])
lons = array([ 122.125, 125.875, 122.375, 132.125, 124.375, 122.625,
206.125, 122.625, 120.375, 187.625, 243.625, 161.625,
220.375, 121.875, 125.625, 130.125, 219.625, 223.875,
223.375, 190.125, 132.875, 185.875, 122.625, 120.875,
259.375, 125.875, 204.875, 131.875, 130.875, 125.375,
234.375, 241.375, 188.125, 124.375, 124.375, 140.375,
205.625, 130.875, 256.875, 239.375, 124.125, 123.125,
131.625, 126.375, 241.125, 130.875, 233.625, 184.375,
205.125, 169.375, 150.375, 183.375, 168.875, 239.625,
180.125, 241.375, 131.375, 244.875, 244.375, 125.625,
131.375, 150.125, 203.625, 122.125, 243.125, 159.125,
125.625, 243.375, 125.875, 127.375, 130.125, 146.625,
203.125, 185.125, 204.625, 120.625, 130.125, 233.875,
131.875, 258.625, 130.125, 173.375, 258.125, 129.125,
137.875, 215.625, 214.125, 234.625, 125.375, 136.625,
231.125, 145.625, 128.625, 232.875, 125.125, 145.125,
239.875, 138.625, 241.375, 169.625])
data =
array([ 84.8125 , 81.6875 , 79.75 , 78.5625 , 70.8125 , 70.75 ,
69.0625 , 69. , 67.3125 , 63.65625, 63.375 , 62.5625 ,
62.46875, 61.09375, 60.4375 , 59.21875, 58.84375, 58.8125 ,
58.75 , 58.4375 , 57.8125 , 57.375 , 57.1875 , 57.15625,
57.15625, 57.09375, 56.96875, 56.875 , 56.3125 , 56.28125,
56.1875 , 55.59375, 55.5 , 55.46875, 55.4375 , 55.375 ,
55.3125 , 55.21875, 55.21875, 55.15625, 54.90625, 54.75 ,
54.6875 , 54.65625, 54.625 , 54.59375, 54.4375 , 54.34375,
54.21875, 54.0625 , 53.9375 , 53.75 , 53.65625, 53.21875,
53.15625, 53.0625 , 52.78125, 52.75 , 52.4375 , 52.25 ,
52.0625 , 51.71875, 51.71875, 51.59375, 51.4375 , 51.3125 ,
51.28125, 51.28125, 51.25 , 51.03125, 51. , 50.96875,
50.9375 , 50.875 , 50.8125 , 50.75 , 50.71875, 50.6875 ,
50.5 , 50.4375 , 50.4375 , 50.28125, 50.21875, 50.125 ,
50. , 49.5 , 49.5 , 49.40625, 49.21875, 49.21875,
49.0625 , 48.96875, 48.53125, 48.46875, 48.375 , 48.28125,
48.21875, 48.0625 , 47.875 , 47.84375])
关于如何得到这些数据的密度图有什么想法吗？感谢你的帮助。
/米
最佳答案
如果您对网格框中每个纬度的频率计数感兴趣，可以使用numpy函数histogram2d。
以下代码可能是您要查找的代码：
nx, ny = 10, 3
# compute appropriate bins to histogram the data into
lon_bins = numpy.linspace(lons.min(), lons.max(), nx+1)
lat_bins = numpy.linspace(lats.min(), lats.max(), ny+1)
# Histogram the lats and lons to produce an array of frequencies in each box.
# Because histogram2d does not follow the cartesian convention
# (as documented in the numpy.histogram2d docs)
# we need to provide lats and lons rather than lons and lats
density, _, _ = numpy.histogram2d(lats, lons, [lat_bins, lon_bins])
# Turn the lon/lat bins into 2 dimensional arrays ready
# for conversion into projected coordinates
lon_bins_2d, lat_bins_2d = numpy.meshgrid(lon_bins, lat_bins)
# convert the xs and ys to map coordinates
xs, ys = m(lon_bins_2d, lat_bins_2d)
plt.pcolormesh(xs, ys, density)
plt.colorbar(orientation='horizontal')
# overlay the scatter points to see that the density
# is working as expected
plt.scatter(*m(lons, lats))
plt.show()
编辑：我添加了更多的内联注释来帮助理解/可读性。