def __init__(self,
lat=None,
lon=None,
pop=None,
name=None,
area=None,
forced_id=None):
Node.__init__(self, lat, lon, pop, name, area, forced_id)
self.air_temperature = []
self.land_temperature = []
self.rainfall = []
self.humidity = []
def duplicate_nodeID_check(nodelist):
nodeIDs = pd.Series([n.id for n in nodelist])
dups = nodeIDs.duplicated()
while any(dups):
# In lieu of something more clever, find the first non-unique, find a nearby unused ID,
# and loop until all IDs are unique
ind2fix = dups[dups].index[0]
oldNodeID = nodeIDs[ind2fix]
newNodeID = oldNodeID
shift = 0
while newNodeID == oldNodeID:
shift += 1
for xs in range(-1 * shift, shift):
for ys in range(-1 * shift, shift):
testId = oldNodeID + xs * 2**16 + ys
if not any(nodeIDs.isin([testId])):
newNodeID = testId
nodeIDs[ind2fix] = newNodeID
# nodelist[ind2fix]['NodeID'] = int(newNodeID)
n = deepcopy(nodelist[ind2fix])
nodelist[ind2fix] = Node(lat=n.lat,
lon=n.lon,
pop=n.pop,
forced_id=int(newNodeID),
extra_attributes=n.extra_attributes)
dups = nodeIDs.duplicated()
return nodelist
def fill_nodes(node_info, res=30 / 3600):
out_nodes = []
states2keep = ['katsina', 'kano', 'jigawa', 'kaduna', 'bauchi']
for index, row in node_info.iterrows():
if row['dot_name'].split(':')[1] in states2keep:
pop = int(max(5000, 1000 * row['population']))
lat = row['latitude']
lon = row['longitude']
extra_attributes = {
'Area_deg2': row['area'],
'Area_km2': row['area'] * 111 * 111
}
meta = {'dot_name': row['dot_name']}
if (pop / extra_attributes['Area_deg2']) > 10000000:
extra_attributes['Urban'] = 1
extra_attributes['BirthRate'] = 0.00024
else:
extra_attributes['Urban'] = 0
extra_attributes['BirthRate'] = 0.000288
node = Node(lat=lat,
lon=lon,
pop=pop,
name=int(node_ID_from_lat_long(lat, lon, res)),
forced_id=int(node_ID_from_lat_long(lat, lon, res)),
extra_attributes=extra_attributes,
meta=meta)
out_nodes.append(node)
out_nodes = duplicate_nodeID_check(out_nodes)
return out_nodes
def from_file(cls, base_file):
content = json.load(open(base_file, 'rb'))
nodes = []
# Load the nodes
for node in content["Nodes"]:
nodes.append(Node.from_data(node))
# Load the idref
idref = content['Metadata']['IdReference']
# Create the file
return cls(nodes, idref, base_file)
def from_file(cls,
cb,
population_input_file,
demographics_type='static',
res_in_arcsec=DEFAULT_RESOLUTION,
update_demographics=None,
default_pop=1000):
nodes_list = list()
with open(population_input_file, 'r') as pop_csv:
reader = csv.DictReader(pop_csv)
for row in reader:
# Latitude
if not 'lat' in row:
raise ValueError(
'Column lat is required in input population file.')
lat = float(row['lat'])
# Longitude
if not 'lon' in row:
raise ValueError(
'Column lon is required in input population file.')
lon = float(row['lon'])
# Node label
res_in_deg = cls.arcsec_to_deg(res_in_arcsec)
node_label = row[
'node_label'] if 'node_label' in row else nodeid_from_lat_lon(
lat, lon, res_in_deg)
# Population
pop = int(float(row['pop'])) if 'pop' in row else default_pop
# Append the newly created node to the list
nodes_list.append(Node(lat, lon, pop, node_label))
return cls(cb, nodes_list, demographics_type, res_in_arcsec,
update_demographics, default_pop)