Your problem is that LineCollection does not do what you think it does. What you want is an outer filled shape with 'holes punched in' that have the shapes of the other lines in your LineCollection (i.e. the islands in the north sea). LineCollection, however, fills every line segment in the list, so the filled shapes just overlay each other.
Inspired by this post, I wrote an answer that seems to solve your problem using Patches. However, I'm not entirely sure how robust the solution is: according to the linked (unanswered) post, the vertices of the outer shape should be ordered clockwise while the vertices of the 'punched in' holes should be ordered counter-clock wise (I checked that too and it appears to be correct; this matplotlib example shows the same behaviour). As the shapefiles are binary, it is hard to verify the ordering of the vertices, but the result appears to be correct. In the example below I assume that in each shapefile the longest line segment is the outline of the country (or north sea), while shorter segments are islands or some such. I thus first order the segments of each shapefile by length and create a Path and a PathPatch thereafter. I took the freedom to use a different colour for each shapefile in order to make sure that everything works as it should.
import numpy as np
import matplotlib
matplotlib.use('Agg')
from scipy.interpolate import griddata
from mpl_toolkits.basemap import Basemap, maskoceans
import matplotlib.pyplot as plt
from numpy.random import seed
import shapefile as shp
from matplotlib.collections import LineCollection
from matplotlib.patches import Path, PathPatch
from matplotlib import cm
# Set figure size
fig, ax = plt.subplots(figsize=(15,15), dpi = 80)
# Define map bounds
xMin, xMax = 2.5, 8.0
yMin, yMax = 50.6, 53.8
shapefiles = [
'shapefiles/BEL_adm0',
'shapefiles/FRA_adm0',
'shapefiles/DEU_adm0',
'shapefiles/northsea',
]
colors = ['red', 'green', 'yellow', 'blue']
y = [54.325666666667,52.36,53.269444444444,55.399166666667,54.116666666667,53.614444444444,53.491666666667,53.824130555556,52.918055555556,54.03694,52.139722,52.926865008825,54.853888888889,52.317222,53.240026656696,52.642696895243,53.391265948394,52.505333893732,52.098821802977,52.896643913235,52.457270486008,53.223000488316,52.701902388132,52.0548617826,53.411581103636,52.434561756559,52.749056395511,53.123676213651,52.067534268959,53.194409573306,52.27314817052,51.441334059998,51.224757511326,51.990941918858,51.447744494043,51.960667359998,51.969031121385,51.564889021961,51.857593837453,51.449772459909,51.658528382201,51.196699902606,50.905256257898,51.497306260089,yMin,yMin,yMax,yMax]
x = [2.93575,3.3416666666667,3.6277777777778,3.8102777777778,4.0122222222222,4.9602777777778,5.9416666666667,2.9452777777778,4.1502777777778,6.04167,4.436389,4.7811453228565,4.6961111111111,4.789722,4.9207907082729,4.9787572406902,5.3458010937365,4.6029300588208,5.1797058644882,5.383478899702,5.5196324030324,5.7515738887123,5.8874461671401,5.8723225499118,6.1990994508938,6.2589770334531,6.5729701105864,6.5848470019087,6.6567253619722,7.1493220605216,6.8908745111116,3.5958241584686,3.8609657214986,4.121849767852,4.342014,4.4469005114756,4.9259216999194,4.9352386335384,5.1453989235756,5.3770039280214,5.7065946674719,5.7625447234516,5.7617834850481,6.1961067840608,xMin,xMax,xMin,xMax]
z = [4.8,5.2,5.8,5.4,5,5.3,5.4,4.6,5.8,6.3,4.8,5.4,5.3,4.6,5.4,4.4,4.1,5.5,4.5,4.2,3.9,3.7,4.2,3.2,4,3.8,2.7,2.3,3.4,2.5,3.7,5.2,2.9,5.1,3.8,4.4,4.2,3.9,3.8,3.2,2.6,2.8,2.4,3.1]
avg = np.average(z)
z.extend([avg,avg,avg,avg])
# Create map
m = Basemap(
ax = ax,
projection='merc',
llcrnrlon=xMin,
llcrnrlat=yMin,
urcrnrlon=xMax,
urcrnrlat=yMax,
resolution='h'
)
x,y = m(x,y)
m.drawmapboundary(fill_color='#d4dadc',linewidth=0.25)
# target grid to interpolate to
xis = np.arange(min(x),max(x),2000)
yis = np.arange(min(y),max(y),2000)
xi,yi = np.meshgrid(xis,yis)
# interpolate
zi = griddata((x,y),z,(xi,yi),method='cubic')
# Decide on proper values for colour bar (todo)
vrange = max(z)-min(z)
mult = 2
vmin = min(z)-(mult*vrange)
vmax = max(z)+(mult*vrange)
# Draw contours
cs = m.contour(xi, yi, zi, 5, linewidths=0.25, colors='k')
cs = m.contourf(xi, yi, zi, 5,vmax=vmax,vmin=vmin,cmap=plt.get_cmap('jet'))
for sf_name,color in zip(shapefiles, colors):
print(sf_name)
# Load data
#drawing shapes:
sf = shp.Reader(sf_name)
shapes = sf.shapes()
##print shapes[0].parts
records = sf.records()
##ax = plt.subplot(111)
for record, shape in zip(records,shapes):
lons,lats = zip(*shape.points)
data = np.array(m(lons, lats)).T
if len(shape.parts) == 1:
segs = [data,]
else:
segs = []
for i in range(1,len(shape.parts)):
index = shape.parts[i-1]
index2 = shape.parts[i]
seg = data[index:index2]
segs.append(seg)
segs.append(data[index2:])
##assuming that the longest segment is the enclosing
##line and ordering the segments by length:
lens=np.array([len(s) for s in segs])
order = lens.argsort()[::-1]
segs = [segs[i] for i in order]
##producing the outlines:
lines = LineCollection(segs,antialiaseds=(1,),zorder=4)
##note: leaving the facecolors out:
##lines.set_facecolors('#abc0d3')
lines.set_edgecolors('red')
lines.set_linewidth(0.5)
ax.add_collection(lines)
##producing a path from the line segments:
segs_lin = [v for s in segs for v in s]
codes = [
[Path.MOVETO]+
[Path.LINETO for p in s[1:]]
for s in segs]
codes_lin = [c for s in codes for c in s]
path = Path(segs_lin, codes_lin)
##patch = PathPatch(path, facecolor="#abc0d3", lw=0, zorder = 3)
patch = PathPatch(path, facecolor=color, lw=0, zorder = 3)
ax.add_patch(patch)
plt.axis('off')
fig.savefig("shapefiles.png",bbox_inches='tight',pad_inches=0)
The result looks like this:
Hope this helps.
