def test_maximum_isolation(city):
city = segregated_city()
exp = mb.exposure(city)
N_cl = {i: sum([city[au][i] for au in city]) for i in [1, 2, 3]}
N_tot = sum(N_cl.values())
for c in exp:
assert_almost_equal(exp[c][c][0], N_tot / N_cl[c], places=3)
def test_minimum_isolation(city):
city = uniform_city()
exp = mb.exposure(city)
for c in [1,2,3]:
assert_almost_equal(exp[c][c][0],
1.0,
places=3)
def test_maximum_exposure(city):
city = two_way_city()
exp = mb.exposure(city)
N_cl = {i: sum([city[au][i] for au in city]) for i in [1,2,3]}
N_tot = sum(N_cl.values())
assert_almost_equal(exp[2][1][0],
N_tot/(N_cl[1]+N_cl[2]),
places=3)
def test_maximum_exposure(city):
city = two_way_city()
exp = mb.exposure(city)
N_cl = {i: sum([city[au][i] for au in city]) for i in [1, 2, 3]}
N_tot = sum(N_cl.values())
assert_almost_equal(exp[2][1][0],
N_tot / (N_cl[1] + N_cl[2]),
places=3)
def test_maximum_isolation(city):
city = segregated_city()
exp = mb.exposure(city)
N_cl = {i: sum([city[au][i] for au in city]) for i in [1,2,3]}
N_tot = sum(N_cl.values())
for c in exp:
assert_almost_equal(exp[c][c][0],
N_tot/N_cl[c],
places=3)
def test_class_segregated(self):
city = segregated_city()
exp = mb.exposure(city)
classes = mb.uncover_classes(city, exp)
assert_equal(len(classes), 3)
def test_class_uniform(self):
city = uniform_city()
exp = mb.exposure(city)
classes = mb.uncover_classes(city, exp)
assert_equal(len(classes), 2)
def test_minimum_exposure(city):
city = segregated_city()
exp = mb.exposure(city)
for c0,c1 in itertools.permutations([1,2,3], 2):
assert_almost_equal(exp[c0][c1][0],
0.0)
def test_minimum_exposure(city):
city = segregated_city()
exp = mb.exposure(city)
for c0, c1 in itertools.permutations([1, 2, 3], 2):
assert_almost_equal(exp[c0][c1][0], 0.0)
def test_minimum_isolation(city):
city = uniform_city()
exp = mb.exposure(city)
for c in [1, 2, 3]:
assert_almost_equal(exp[c][c][0], 1.0, places=3)
for i, city in enumerate(msa):
print "Compute exposure for %s (%s/%s)"%(msa[city],
i+1,
len(msa))
## Import households data
households = {}
with open('data/income/msa/%s/income.csv'%city, 'r') as source:
reader = csv.reader(source, delimiter='\t')
reader.next()
for rows in reader:
num_cat = len(rows[1:])
households[rows[0]] = {c:int(h) for c,h in enumerate(rows[1:])}
## Compute the exposure (and isolation)
exp = mb.exposure(households)
## Save the exposure values
with open('extr/exposure/categories/msa/%s_values.csv'%city, 'w') as output:
output.write("CATEGORIES\t" + "\t".join(map(str,range(num_cat))) + "\n")
for c0 in sorted(exp.iterkeys()):
output.write("%s\t"%c0)
output.write("\t".join(map(str,[exp[c0][c1][0] for c1 in
sorted(exp[c0].iterkeys())])))
output.write("\n")
## Save the exposure variance
with open('extr/exposure/categories/msa/%s_variance.csv'%city, 'w') as output:
output.write("CATEGORIES\t" + "\t".join(map(str,range(num_cat))) + "\n")
for c0 in sorted(exp.iterkeys()):
output.write("%s\t"%c0)
for i, city in enumerate(msa):
print "Compute exposure for %s (%s/%s)"%(msa[city],
i+1,
len(msa))
## Import households data
households = {}
with open('data/income/msa/%s/income.csv'%city, 'r') as source:
reader = csv.reader(source, delimiter='\t')
reader.next()
for rows in reader:
num_cat = len(rows[1:])
households[rows[0]] = {c:int(h) for c,h in enumerate(rows[1:])}
## Compute the exposure (and isolation)
exp = mb.exposure(households, classes)
## Save the exposure values
with open('extr/exposure/classes/msa/%s_values.csv'%city, 'w') as output:
output.write("CLASSES\t" + "\t".join(map(str,sorted(exp.iterkeys()))) + "\n")
for c0 in sorted(exp.iterkeys()):
output.write("%s\t"%c0)
output.write("\t".join(map(str,[exp[c0][c1][0] for c1 in
sorted(exp[c0].iterkeys())])))
output.write("\n")
## Save the exposure variance
with open('extr/exposure/classes/msa/%s_variance.csv'%city, 'w') as output:
output.write("CLASSES\t" + "\t".join(map(str,sorted(exp.iterkeys()))) + "\n")
for c0 in sorted(exp.iterkeys()):
output.write("%s\t"%c0)
def test_class_segregated(self):
city = segregated_city()
exp = mb.exposure(city)
classes = mb.uncover_classes(city, exp)
assert_equal(len(classes),3)
def test_class_uniform(self):
city = uniform_city()
exp = mb.exposure(city)
classes = mb.uncover_classes(city, exp)
assert_equal(len(classes),2)
# Extract neighbourhoods and save
#
for i, city in enumerate(msa):
print "Compute exposure for %s (%s/%s)" % (msa[city], i + 1, len(msa))
## Import households data
households = {}
with open('data/income/msa/%s/income.csv' % city, 'r') as source:
reader = csv.reader(source, delimiter='\t')
reader.next()
for rows in reader:
num_cat = len(rows[1:])
households[rows[0]] = {c: int(h) for c, h in enumerate(rows[1:])}
## Compute the exposure (and isolation)
exp = mb.exposure(households)
## Save the exposure values
with open('extr/exposure/categories/msa/%s_values.csv' % city,
'w') as output:
output.write("CATEGORIES\t" + "\t".join(map(str, range(num_cat))) +
"\n")
for c0 in sorted(exp.iterkeys()):
output.write("%s\t" % c0)
output.write("\t".join(
map(str,
[exp[c0][c1][0] for c1 in sorted(exp[c0].iterkeys())])))
output.write("\n")
## Save the exposure variance
with open('extr/exposure/categories/msa/%s_variance.csv' % city,
