def dists_to_pt(pts, pt, use_great_circle=True):
""" Returns a list of distances (in km) from each of the points to
to the pt (uses great_circle_distance)
pts: """
rpy2.robjects.numpy2ri.activate()
np_pts = numpy.array(pts)
np_pt = numpy.array(pt)
dists_to_pt = r.spDistsN1(np_pts, np_pt, longlat=use_great_circle)
return dists_to_pt
#activate r to numpy array auto-conversion
rpy2.robjects.numpy2ri.activate()
#load sp library
r.library('sp')
to_x = numpy.random.rand(100) * 100
to_y = numpy.random.rand(100) * 100
xy = zip(to_x, to_y)
xy_array = numpy.array(xy)
dists = r.spDists(xy_array)
tree = r.hclust(r('as.dist')(dists), "average")
clusts = r.cutree(tree, h=100.0)
np_clusts = numpy.array(clusts)
clusters = [[] for i in range(max(np_clusts))]
for i in range(0, len(np_clusts)):
clusters[np_clusts[i] - 1].append(xy_array[i])
cluster1 = clusters[len(clusters) - 1]
cluster1_cols = numpy.array(cluster1)
print cluster1_cols
print cluster1[0]
dists = r.spDistsN1(cluster1_cols, cluster1[0])
print dists
