pairs = []
r = csv.reader(csvfile)
for row in r:
a = names[row[0]]
b = names[row[1]]
t = (a, b, int(row[2]))
pairs.append(t)
return pairs
def get_adjacency(pairs):
maxtuple = max(pairs, key=maxpair)
matsize = max(maxtuple) + 1
mat = [[0 for a in range(matsize)] for a in range(matsize)]
for p in pairs:
mat[p[1]][p[0]] = p[2]
# mat[p[0]][p[1]] = -p[2]
return mat
def maxpair(p):
return max(p[:2])
if __name__ == "__main__":
names = utils.get_name_map("mississippi_county.list")
pairs = get_pairs("mississippi_graph_NS.csv", names)
mat = get_adjacency(pairs)
s = utils.to_matlab(mat, 'adj')
print s
#! /usr/bin/python
import utils
def get_mat(names, population):
mat = []
for t in population:
mat.append(t[1])
return mat
if __name__ == "__main__":
populations = utils.get_population("mississippi_county_pop.csv")
names = utils.get_name_map("mississippi_county.list")
mat = get_mat(names, populations)
s = utils.to_matlab(mat, 'pops')
print s
if dist/rad > 3:
return 1000000
if cat == 1:
return 0.5
th = 0.1 * 2**(dist/rad) / cat
return th
def dist(p1, p2):
return vincenty(p1, p2).kilometers
def compute_thresholds(clat, clong, rad, cat):
positions = pk.load(open("county_locations.dict"))
positions = sorted(positions.items(), key=lambda (k, v) : k)
positions = map(lambda (k, v) : (v[0], v[1]), positions)
distances = map(lambda p: dist(p, (clat, clong)), positions)
thresholds = map(lambda d: threshold(d, rad, cat), distances)
return thresholds
if __name__ == "__main__":
clat = float(sys.argv[1])
clong = float(sys.argv[2])
rad = float(sys.argv[3])
cat = int(sys.argv[4])
thresholds = compute_thresholds(clat, clong, rad, cat)
s = utils.to_matlab(thresholds, '')
s = s.replace('\n', '')
s = s[:-1]
print s