Fill oceans in basemap

Question

I am trying to plot 1x1 degree data on a matplotlib.Basemap, and I want to fill the ocean with white. However, in order for the boundaries of the ocean to follow the coastlines drawn by matplotlib, the resolution of the white ocean mask should be much higher than the resolution of my data.

After searching around for a long time I tried the two possible solutions:

(1) maskoceans() and is_land() functions, but since my data is lower resolution than the map drawn by basemap it does not look good on the edges. I do not want to interpolate my data to higher resolution either.

(2) m.drawlsmask(), but since zorder cannot be assigned the pcolormesh plot always overlays the mask.

This code

import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.basemap as bm

#Make data
lon = np.arange(0,360,1)
lat = np.arange(-90,91,1)
data = np.random.rand(len(lat),len(lon))

#Draw map
plt.figure()
m = bm.Basemap(resolution='i',projection='laea', width=1500000, height=2900000, lat_ts=60, lat_0=72, lon_0=319)
m.drawcoastlines(linewidth=1, color='white')
data, lon = bm.addcyclic(data,lon)
x,y = m(*np.meshgrid(lon,lat))
plt.pcolormesh(x,y,data)
plt.savefig('1.png',dpi=300)

Produces this image:

Adding m.fillcontinents(color='white') produces the following image, which is what I need but to fill the ocean and not the land.

Edit:

m.drawmapboundary(fill_color='lightblue') also fills over land and can therefore not be used.

The desired outcome is that the oceans are white, while what I plotted with plt.pcolormesh(x,y,data) shows up over the lands.

Answer 1

I found a much nicer solution to the problem which uses the polygons defined by the coastlines in the map to produce a matplotlib.PathPatch that overlays the ocean areas. This solution has a much better resolution and is much faster:

from matplotlib import pyplot as plt
from mpl_toolkits import basemap as bm
from matplotlib import colors
import numpy as np
import numpy.ma as ma
from matplotlib.patches import Path, PathPatch

fig, ax = plt.subplots()

lon_0 = 319
lat_0 = 72

##some fake data
lons = np.linspace(lon_0-60,lon_0+60,10)
lats = np.linspace(lat_0-15,lat_0+15,5)
lon, lat = np.meshgrid(lons,lats)
TOPO = np.sin(np.pi*lon/180)*np.exp(lat/90)

m = bm.Basemap(resolution='i',projection='laea', width=1500000, height=2900000, lat_ts=60, lat_0=lat_0, lon_0=lon_0, ax = ax)
m.drawcoastlines(linewidth=0.5)

x,y = m(lon,lat)
pcol = ax.pcolormesh(x,y,TOPO)

##getting the limits of the map:
x0,x1 = ax.get_xlim()
y0,y1 = ax.get_ylim()
map_edges = np.array([[x0,y0],[x1,y0],[x1,y1],[x0,y1]])

##getting all polygons used to draw the coastlines of the map
polys = [p.boundary for p in m.landpolygons]

##combining with map edges
polys = [map_edges]+polys[:]

##creating a PathPatch
codes = [
    [Path.MOVETO] + [Path.LINETO for p in p[1:]]
    for p in polys
]
polys_lin = [v for p in polys for v in p]
codes_lin = [c for cs in codes for c in cs]
path = Path(polys_lin, codes_lin)
patch = PathPatch(path,facecolor='white', lw=0)

##masking the data:
ax.add_patch(patch)

plt.show()

The output looks like this:

Original solution:

You can use an array with greater resolution in basemap.maskoceans, such that the resolution fits the continent outlines. Afterwards, you can just invert the mask and plot the masked array on top of your data.

Somehow I only got basemap.maskoceans to work when I used the full range of the map (e.g. longitudes from -180 to 180 and latitudes from -90 to 90). Given that one needs quite a high resolution to make it look nice, the computation takes a while:

from matplotlib import pyplot as plt
from mpl_toolkits import basemap as bm
from matplotlib import colors
import numpy as np
import numpy.ma as ma

fig, ax = plt.subplots()


lon_0 = 319
lat_0 = 72

##some fake data
lons = np.linspace(lon_0-60,lon_0+60,10)
lats = np.linspace(lat_0-15,lat_0+15,5)
lon, lat = np.meshgrid(lons,lats)
TOPO = np.sin(np.pi*lon/180)*np.exp(lat/90)


m = bm.Basemap(resolution='i',projection='laea', width=1500000, height=2900000, lat_ts=60, lat_0=lat_0, lon_0=lon_0, ax = ax)
m.drawcoastlines(linewidth=0.5)

x,y = m(lon,lat)

pcol = ax.pcolormesh(x,y,TOPO)


##producing a mask -- seems to only work with full coordinate limits
lons2 = np.linspace(-180,180,10000)
lats2 = np.linspace(-90,90,5000)
lon2, lat2 = np.meshgrid(lons2,lats2)
x2,y2 = m(lon2,lat2)
pseudo_data = np.ones_like(lon2)
masked = bm.maskoceans(lon2,lat2,pseudo_data)
masked.mask = ~masked.mask

##plotting the mask
cmap = colors.ListedColormap(['w'])
pcol = ax.pcolormesh(x2,y2,masked, cmap=cmap)

plt.show()

The result looks like this: