def test_overrepresented_clust(self):
""" Test the extraction of units with overrepresentation """
city = clustered_city()
units = mb.overrepresented_units(city)
units_answer = {"A": [0, 1, 3, 6], "B": [2, 4, 5, 7, 8]}
assert set(units["A"]) == set(units_answer["A"])
assert set(units["B"]) == set(units_answer["B"])
def test_overrepresented_check(self):
""" Test the extraction of units with overrepresentation """
city = checkerboard_city()
units = mb.overrepresented_units(city)
units_answer = {"A": [0, 2, 4, 6, 8], "B": [1, 3, 5, 7]}
assert set(units["A"]) == set(units_answer["A"])
assert set(units["B"]) == set(units_answer["B"])
def test_overrepresented_clust(self):
""" Test the extraction of units with overrepresentation """
city = clustered_city()
units = mb.overrepresented_units(city)
units_answer = {"A":[0,1,3,6],
"B":[2,4,5,7,8]}
assert set(units["A"]) == set(units_answer["A"])
assert set(units["B"]) == set(units_answer["B"])
def test_overrepresented_check(self):
""" Test the extraction of units with overrepresentation """
city = checkerboard_city()
units = mb.overrepresented_units(city)
units_answer = {"A":[0,2,4,6,8],
"B":[1,3,5,7]}
assert set(units["A"]) == set(units_answer["A"])
assert set(units["B"]) == set(units_answer["B"])
reader.next()
for rows in reader:
msa[rows[0]] = rows[1]
#
# Extract neighbourhoods and save
#
for i, city in enumerate(msa):
print "Extract neighbourhoods 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:
households[rows[0]] = {c:int(h) for c,h in enumerate(rows[1:])}
## Extract neighbourhoods
neigh = mb.overrepresented_units(households)
## Save the list of areal units per class
with open('extr/neighbourhoods/categories/msa/%s.csv'%city, 'w') as output:
for cat in sorted(neigh.iterkeys()):
for bkgp in neigh[cat]:
output.write("%s\t%s\n"%(cat, bkgp))
classes[rows[0]] =[int(r) for r in rows[1:]]
#
# Extract neighbourhoods and save
#
for i, city in enumerate(msa):
print "Extract neighbourhoods 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:
households[rows[0]] = {c:int(h) for c,h in enumerate(rows[1:])}
## Extract neighbourhoods
neigh = mb.overrepresented_units(households, classes)
## Save the list of areal units per class
with open('extr/neighbourhoods/classes/msa/%s.csv'%city, 'w') as output:
for cat in sorted(neigh.iterkeys()):
for bkgp in neigh[cat]:
output.write("%s\t%s\n"%(cat, bkgp))
