####################
# Testing
if __name__ == "__main__":
Npnts = 50
pnts = np.random.random((Npnts, 2))
vals = np.random.random(Npnts)
mt = tri.Triangulation(pnts[:, 0], pnts[:, 1])
import pylab
pylab.figure()
# tricontour:
ax1 = pylab.subplot(2, 2, 1)
tri.tricontourf(pylab.gca(), mt, vals)
pylab.axis("equal")
pylab.title("tri.Triangulation: tricontour")
# delaunay - linear interpolation
ax2 = pylab.subplot(2, 2, 2, sharex=ax1, sharey=ax1)
d = tri_to_delaunay(mt)
lin_interp = d.linear_interpolator(vals)
lin_field = lin_interp[0.0:1.0:100j, 0.0:1.0:100j]
pylab.imshow(lin_field, origin="lower", extent=[0, 1, 0, 1], interpolation="nearest")
pylab.title("delaunay.Triangulation: linear interp")
# delaunay - nearest neighbors interpolation
pylab.subplot(2, 2, 3, sharex=ax1, sharey=ax1)
d = tri_to_delaunay(mt)
nn_interp = d.nn_interpolator(vals)
Npnts = 5000
pnts = np.random.random((Npnts, 2))
mt = tri.Triangulation(pnts[:, 0], pnts[:, 1])
# fabricate some data:
point_vals = (pnts[:, 0] - 0.5)**2 + (pnts[:, 1] - 0.5)**2
# make an island by masking a few triangles
island = ((mt.x[mt.triangles].mean(axis=1) - 0.75)**2 +
(mt.y[mt.triangles].mean(axis=1) - 0.75)**2) < 0.03
mt.set_mask(island)
import pylab
pylab.figure()
contour = tri.tricontourf(pylab.gca(), mt, point_vals)
pylab.axis('equal')
from shapely import geometry
import wkb2shp
geoms = []
vals = [] # tuples of vmin,vmax
for colli, coll in enumerate(contour.collections):
vmin, vmax = contour.levels[colli:colli + 2]
for p in coll.get_paths():
p.simplify_threshold = 0.0
polys = p.to_polygons()
Npnts = 5000
pnts = np.random.random((Npnts,2))
mt = tri.Triangulation( pnts[:,0],pnts[:,1])
# fabricate some data:
point_vals = (pnts[:,0]-0.5)**2 + (pnts[:,1]-0.5)**2
# make an island by masking a few triangles
island = ((mt.x[mt.triangles].mean(axis=1) - 0.75)**2 + (mt.y[mt.triangles].mean(axis=1) - 0.75)**2) < 0.03
mt.set_mask( island )
import pylab
pylab.figure()
contour=tri.tricontourf(pylab.gca(),mt , point_vals)
pylab.axis('equal')
from shapely import geometry
import wkb2shp
geoms = []
vals = [] # tuples of vmin,vmax
for colli,coll in enumerate(contour.collections):
vmin,vmax = contour.levels[colli:colli+2]
for p in coll.get_paths():
p.simplify_threshold = 0.0
polys = p.to_polygons()