def PrepSet(point_set, gdf_sub):
'''
Prepares a small df of a given origin / destination set, expressed as 'item : Nearest Node ID'
'''
Prepared_point_set, gdf_node_pos2, gdf_new = net_p.prepare_newOD(point_set['file'], gdf_sub)
Prepared_point_set = Prepared_point_set['Node']
return Prepared_point_set
# Take only the largest subgraph which all connected links
len_old = 0
for g in nx.connected_component_subgraphs(G_tograph):
if len(list(g.edges())) > len_old:
G1 = g
len_old = len(list(g.edges()))
G_sub = G1.copy()
#print('number of disconnected compoents is', nx.number_connected_components(G_sub))
nx.info(G_sub)
# Save the simplified transport network back into GeoDataFrame
gdf_sub = net_p.graph_to_df(G_sub)
# assign the OD to the closest node of the biggest subgraph:
gdf_points2, gdf_node_pos2, gdf_new = net_p.prepare_newOD(centroid, gdf_sub)
G2_multi = net_p.gdf_to_simplified_multidigraph(gdf_node_pos2,
gdf_new,
simplify=False)
G2 = net_p.multigraph_to_graph(G2_multi)
gdf2 = net_p.graph_to_df(G2)
allNode = G2.nodes()
allEdge = G2.edges()
od = gdf_points2['Node']
################### traffic flow matrix ####################################################
#read OD demand matrix
import scipy.io
mat = scipy.io.loadmat(r'./input/MZ_inputs/traffic_matrix.mat')
def main(adminIsPoint=False):
## Define filepath
path = os.path.realpath(
os.path.abspath(
os.path.split(inspect.getfile(inspect.currentframe()))[0]))
path = os.path.split(path)[0]
## Define dash. This .xlsm includes settings for the criticality script
dash = os.path.join(path, r'dashboard.xlsm')
ctrl = pd.read_excel(dash, sheetname="AGGREGATE", index_col=0)
## Define operative district. Note, this parameter can be anything - it is the sub folder in input, Runtime where files are drawn from
district = ctrl['Weight'].loc['DISTRICT']
## Add logging
logging.basicConfig(filename=os.path.join(path, 'runtime', district,
"PCS_Criticality_log.log"),
level=logging.INFO,
format="%(asctime)s-%(levelname)s: %(message)s")
logging.info("Starting Criticality Process")
print "Running: Criticality Analysis on %s. Do not interrupt" % district
## Path Settings
# outputs
outpath = os.path.join(path, 'Outputs', '%s' % district)
# ensure folders exist
runtime = os.path.join(path, r'PCS\Criticality\runtime\%s\\' % district)
for d in [outpath, runtime]:
if not os.path.isdir(d):
os.mkdir(d)
## Input file setting
# location of road network
NETWORK_IN = os.path.join(path, r'runtime\%s\\' % district)
# location of OD
OD_IN = os.path.join(path, 'PCS\Criticality\input', '%s' % district)
# location of administrative boundaries file
DATA_IN = os.path.join(path, 'PCS\Criticality\Vietnam_Data_Layers')
inAdmin = os.path.join(DATA_IN, 'Poverty_Communes_2009.shp')
# road network import. Must be a .csv including geometry information of roads.
inNetworkFile = os.path.join(NETWORK_IN, 'Network.csv')
# set WGS 84 coordinate reference system
crs_in = {'init': 'epsg:4326'}
# ensure folders exist
for d in [outpath, runtime, OD_IN]:
if not os.path.isdir(d):
os.mkdir(d)
# error checking - Check input data existence
for curFile in [dash, inNetworkFile, inAdmin, DATA_IN, OD_IN, NETWORK_IN]:
if not os.path.exists(curFile):
logging.error("No input found: %s" % curFile)
raise ValueError("No input found: %s" % curFile)
# import input dataframes - road network and control dashboard
inNetwork = pd.read_csv(inNetworkFile)
ctrldf = pd.read_excel(dash, sheetname="CRITICALITY", index_col='COL_ID')
#Inputs
network = os.path.join(runtime, 'Network.shp')
## Network Preparation
# set default iri value as the mean iri of roads for which iri exists.
fillvalue = inNetwork['iri_med'].mean()
# fill iri value where missing
inNetwork['TC_iri_med'] = inNetwork['iri_med'].fillna(fillvalue)
# set cost of traversing segment according to length and IRI, per settings in the excel dashboard
inNetwork['total_cost'] = inNetwork['length'] * (
ctrldf['Base_cost_km'][0] +
(ctrldf['IRI_Coeff'][0] * inNetwork['TC_iri_med']))
# convert the pandas DataFrame to a GeoDataFrame
ginNetwork = gpd.GeoDataFrame(inNetwork,
crs=crs_in,
geometry=inNetwork['Line_Geometry'].map(
shapely.wkt.loads))
# set up Shapefile of road network
ginNetwork.to_file(network, driver='ESRI Shapefile')
logging.info("Successfully loaded data")
# Generate admin boundary centroids
if not adminIsPoint:
prepareAdminCentroids(ginNetwork, inAdmin, crs_in,
os.path.join(OD_IN, 'adm_centroids.shp'))
logging.info("Created admin centroids")
# define function for loading origin files into a dictionary. Paramters controlled from dashboard excel
def makeOrigin(n, ctrldf):
origindict = {
'name':
ctrldf['OName'][n],
'file':
os.path.join(path, 'PCS', 'Criticality', 'input', district,
'%s.shp' % ctrldf['OName'][n]),
'scalar_column':
ctrldf['OScalar'][n]
}
return origindict
# define function for loading destination files into a dictionary. Paramters controlled from dashboard excel
def makeDestination(n, ctrldf):
destdict = {
'name':
ctrldf['DName'][n],
'file':
os.path.join(path, 'PCS', 'Criticality', 'input', district,
'%s.shp' % ctrldf['DName'][n]),
'penalty':
ctrldf['DPenalty'][n],
'importance':
ctrldf['DImportance'][n],
'annual':
ctrldf['DAnnual'][n],
'scalar_column':
ctrldf['DScalar'][n]
}
return destdict
# load origins and destinations into dictionary, create dictionaries of each set
origin_1, origin_2, origin_3, origin_4, origin_5 = makeOrigin(
0, ctrldf), makeOrigin(1, ctrldf), makeOrigin(2, ctrldf), makeOrigin(
3, ctrldf), makeOrigin(4, ctrldf)
originlist = {
'%s' % ctrldf['OName'][0]: origin_1,
'%s' % ctrldf['OName'][1]: origin_2,
'%s' % ctrldf['OName'][2]: origin_3,
'%s' % ctrldf['OName'][3]: origin_4,
'%s' % ctrldf['OName'][4]: origin_5,
}
destination_1, destination_2, destination_3, destination_4, destination_5 = makeDestination(
0, ctrldf), makeDestination(1, ctrldf), makeDestination(
2, ctrldf), makeDestination(3, ctrldf), makeDestination(4, ctrldf)
destinationlist = {
'%s' % ctrldf['DName'][0]: destination_1,
'%s' % ctrldf['DName'][1]: destination_2,
'%s' % ctrldf['DName'][2]: destination_3,
'%s' % ctrldf['DName'][3]: destination_4,
'%s' % ctrldf['DName'][4]: destination_5,
}
logging.debug("Opened origins and destinations")
# Prepation of network via TU Delft code
gdf_points, gdf_node_pos, gdf = net_p.prepare_centroids_network(
origin_1['file'], network)
# Create Networkx MultiGraph object from the GeoDataFrame
G = net_p.gdf_to_simplified_multidigraph(gdf_node_pos, gdf, simplify=False)
# Change the MultiGraph object to Graph object to reduce computation cost
G_tograph = net_p.multigraph_to_graph(G)
logging.debug(
'Loaded road network: number of disconnected components is: %d' %
nx.number_connected_components(G_tograph))
# Observe the properties of the Graph object
nx.info(G_tograph)
# Take only the largest subgraph with all connected links
len_old = 0
for g in nx.connected_component_subgraphs(G_tograph):
if len(list(g.edges())) > len_old:
G1 = g
len_old = len(list(g.edges()))
G_sub = G1.copy()
nx.info(G_sub)
# Save the simplified transport network into a GeoDataFrame
gdf_sub = net_p.graph_to_df(G_sub)
blank, gdf_node_pos2, gdf_new = net_p.prepare_newOD(
origin_1['file'], gdf_sub)
#Road Network Graph prep
G2_multi = net_p.gdf_to_simplified_multidigraph(gdf_node_pos2,
gdf_new,
simplify=False)
# Dump files to runtime if dump = 1
Filedump(gdf_new, 'Road_Lines', runtime)
Filedump(gdf_node_pos2, 'Road_Nodes', runtime)
G2 = net_p.multigraph_to_graph(G2_multi)
gdf2 = net_p.graph_to_df(G2)
nLink = len(G2.edges())
# open empty lists
Outputs, cost_list, iso_list = [], [], []
## Run the calculateOD function for each combination of origins and destinations specified in the control excel
# append all outputs to the Outputs, cost_list and iso_list objects just created
for z in ctrldf.index:
if (((ctrldf['ComboO'][z]) != 0) & ((ctrldf['ComboD'][z]) != 0) &
(pd.notnull(ctrldf['ComboO'][z])) &
(pd.notnull(ctrldf['ComboO'][z]))):
Q = int(ctrldf['ComboNumber'][z])
logging.info(
'Computing | combination %s as origin and %s as destination ' %
(ctrldf['ComboO'][z], ctrldf['ComboD'][z]))
xx = calculateOD(originlist['%s' % ctrldf['ComboO'][z]],
destinationlist['%s' % ctrldf['ComboD'][z]], Q,
gdf_sub, G2, nLink, gdf2, runtime, ctrldf)
Outputs.append(xx)
cost_list.append("Social_Cost_%s" % Q)
iso_list.append("Isolated_Trips_%s" % Q)
# drop unneccessary columns
Output = inNetwork.drop(["geometry", 'TC_iri_med', 'total_cost'], axis=1)
# for each object in the Outputs list:
for o_d_calc in range(0, len(Outputs)):
# Merge the objects together. This creates multiple columns showing each scenario
Output = Output.merge(Outputs[o_d_calc]['summary'],
how='left',
on='ID')
# sum across the relevant columns - the 'Social_Cost' columns generated above in calculateOD for each O-D file combo
Output['Cost_total'] = Output[cost_list].sum(axis=1)
# sum across the relevant columns - the 'Isolated_Trips' columns generated above in calculateOD for each O-D file combo
Output['Iso_total'] = Output[iso_list].sum(axis=1)
# Generate an overall criticality score for each road based on user input weights between isolated trips and disrupted trips
Output['CRIT_SCORE'] = (
ctrldf['Disrupt_Weight'][0] * Output['Cost_total'] +
ctrldf['Isolate_Weight'][0] * Output['Iso_total'])
# normalize for each road
Output['CRIT_SCORE'] = (
(Output['CRIT_SCORE'] - Output['CRIT_SCORE'].min()) /
(Output['CRIT_SCORE'].max() - Output['CRIT_SCORE'].min()))
logging.info("Calculated PCS Criticality")
FileOut(Output, 'criticality_output', outpath)
def main(adminIsPoint=False):
path = os.path.realpath(
os.path.abspath(
os.path.split(inspect.getfile(inspect.currentframe()))[0]))
path = os.path.split(path)[0]
dash = os.path.join(path, r'dashboard.xlsm')
ctrl = pd.read_excel(dash, sheetname="AGGREGATE", index_col=0)
district = ctrl['Weight'].loc['DISTRICT']
logging.basicConfig(filename=os.path.join(path, 'runtime', district,
"PCS_Criticality_log.log"),
level=logging.INFO,
format="%(asctime)s-%(levelname)s: %(message)s")
logging.info("Starting Criticality Process")
print "Running: Criticality Analysis on %s. Do not interrupt" % district
# Path Settings
outpath = os.path.join(path, 'Outputs', '%s' % district)
runtime = os.path.join(path, r'PCS\Criticality\runtime\%s\\' % district)
for d in [outpath, runtime]:
if not os.path.isdir(d):
os.mkdir(d)
NETWORK_IN = os.path.join(path, r'runtime\%s\\' % district)
OD_IN = os.path.join(path, 'PCS\Criticality\input', '%s' % district)
DATA_IN = os.path.join(path, 'PCS\Criticality\Vietnam_Data_Layers')
inAdmin = os.path.join(DATA_IN, 'Poverty_Communes_2009.shp')
inNetworkFile = os.path.join(NETWORK_IN, 'Network.csv')
crs_in = {'init': 'epsg:4326'} #WGS 84
#Create folders for analysis
for d in [outpath, runtime, OD_IN]:
if not os.path.isdir(d):
os.mkdir(d)
#Error checking - Check input data
for curFile in [dash, inNetworkFile, inAdmin, DATA_IN, OD_IN, NETWORK_IN]:
if not os.path.exists(curFile):
logging.error("No input found: %s" % curFile)
raise ValueError("No input found: %s" % curFile)
inNetwork = pd.read_csv(inNetworkFile)
ctrldf = pd.read_excel(dash, sheetname="CRITICALITY", index_col='COL_ID')
#Inputs
network = os.path.join(runtime, 'Network.shp')
#Network Prep
fillvalue = inNetwork['iri_med'].mean()
inNetwork['TC_iri_med'] = inNetwork['iri_med'].fillna(fillvalue)
inNetwork['total_cost'] = inNetwork['length'] * (
ctrldf['Base_cost_km'][0] +
(ctrldf['IRI_Coeff'][0] * inNetwork['TC_iri_med']))
ginNetwork = gpd.GeoDataFrame(inNetwork,
crs=crs_in,
geometry=inNetwork['Line_Geometry'].map(
shapely.wkt.loads))
ginNetwork.to_file(network, driver='ESRI Shapefile')
logging.info("Successfully loaded data")
if not adminIsPoint:
prepareAdminCentroids(ginNetwork, inAdmin, crs_in,
os.path.join(OD_IN, 'adm_centroids.shp'))
logging.info("Created admin centroids")
def makeOrigin(n, ctrldf):
origindict = {
'name':
ctrldf['OName'][n],
'file':
os.path.join(path, 'PCS', 'Criticality', 'input', district,
'%s.shp' % ctrldf['OName'][n]),
'scalar_column':
ctrldf['OScalar'][n]
}
return origindict
def makeDestination(n, ctrldf):
destdict = {
'name':
ctrldf['DName'][n],
'file':
os.path.join(path, 'PCS', 'Criticality', 'input', district,
'%s.shp' % ctrldf['DName'][n]),
'penalty':
ctrldf['DPenalty'][n],
'importance':
ctrldf['DImportance'][n],
'annual':
ctrldf['DAnnual'][n],
'scalar_column':
ctrldf['DScalar'][n]
}
return destdict
origin_1, origin_2, origin_3, origin_4, origin_5 = makeOrigin(
0, ctrldf), makeOrigin(1, ctrldf), makeOrigin(2, ctrldf), makeOrigin(
3, ctrldf), makeOrigin(4, ctrldf)
originlist = {
'%s' % ctrldf['OName'][0]: origin_1,
'%s' % ctrldf['OName'][1]: origin_2,
'%s' % ctrldf['OName'][2]: origin_3,
'%s' % ctrldf['OName'][3]: origin_4,
'%s' % ctrldf['OName'][4]: origin_5,
}
destination_1, destination_2, destination_3, destination_4, destination_5 = makeDestination(
0, ctrldf), makeDestination(1, ctrldf), makeDestination(
2, ctrldf), makeDestination(3, ctrldf), makeDestination(4, ctrldf)
destinationlist = {
'%s' % ctrldf['DName'][0]: destination_1,
'%s' % ctrldf['DName'][1]: destination_2,
'%s' % ctrldf['DName'][2]: destination_3,
'%s' % ctrldf['DName'][3]: destination_4,
'%s' % ctrldf['DName'][4]: destination_5,
}
logging.debug("Opened origins and destinations")
# Prepation of network
gdf_points, gdf_node_pos, gdf = net_p.prepare_centroids_network(
origin_1['file'], network)
# Create Networkx MultiGraph object from the GeoDataFrame
G = net_p.gdf_to_simplified_multidigraph(gdf_node_pos, gdf, simplify=False)
# Change the MultiGraph object to Graph object to reduce computation cost
G_tograph = net_p.multigraph_to_graph(G)
logging.debug(
'Loaded road network: number of disconnected components is: %d' %
nx.number_connected_components(G_tograph))
# Observe the properties of the Graph object
nx.info(G_tograph)
# Take only the largest subgraph with all connected links
len_old = 0
for g in nx.connected_component_subgraphs(G_tograph):
if len(list(g.edges())) > len_old:
G1 = g
len_old = len(list(g.edges()))
G_sub = G1.copy()
nx.info(G_sub)
# Save the simplified transport network into a GeoDataFrame
gdf_sub = net_p.graph_to_df(G_sub)
blank, gdf_node_pos2, gdf_new = net_p.prepare_newOD(
origin_1['file'], gdf_sub)
#Road Network Graph prep
G2_multi = net_p.gdf_to_simplified_multidigraph(gdf_node_pos2,
gdf_new,
simplify=False)
Filedump(gdf_new, 'Road_Lines', runtime)
Filedump(gdf_node_pos2, 'Road_Nodes', runtime)
G2 = net_p.multigraph_to_graph(G2_multi)
gdf2 = net_p.graph_to_df(G2)
nLink = len(G2.edges())
Outputs, cost_list, iso_list = [], [], []
for z in ctrldf.index:
if (((ctrldf['ComboO'][z]) != 0) & ((ctrldf['ComboD'][z]) != 0) &
(pd.notnull(ctrldf['ComboO'][z])) &
(pd.notnull(ctrldf['ComboO'][z]))):
Q = int(ctrldf['ComboNumber'][z])
logging.info(
'Computing | combination %s as origin and %s as destination ' %
(ctrldf['ComboO'][z], ctrldf['ComboD'][z]))
xx = calculateOD(originlist['%s' % ctrldf['ComboO'][z]],
destinationlist['%s' % ctrldf['ComboD'][z]], Q,
gdf_sub, G2, nLink, gdf2, runtime, ctrldf)
Outputs.append(xx)
cost_list.append("Social_Cost_%s" % Q)
iso_list.append("Isolated_Trips_%s" % Q)
Output = inNetwork.drop(["geometry", 'TC_iri_med', 'total_cost'], axis=1)
for o_d_calc in range(0, len(Outputs)):
Output = Output.merge(Outputs[o_d_calc]['summary'],
how='left',
on='ID')
Output['Cost_total'] = Output[cost_list].sum(axis=1)
Output['Iso_total'] = Output[iso_list].sum(axis=1)
Output['CRIT_SCORE'] = (
ctrldf['Disrupt_Weight'][0] * Output['Cost_total'] +
ctrldf['Isolate_Weight'][0] * Output['Iso_total'])
Output['CRIT_SCORE'] = (
(Output['CRIT_SCORE'] - Output['CRIT_SCORE'].min()) /
(Output['CRIT_SCORE'].max() - Output['CRIT_SCORE'].min()))
logging.info("Calculated PCS Criticality")
FileOut(Output, 'criticality_output', outpath)