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import IPython.display as IPd
# Local path on user's machine
path = '/Users/jgaboardi/AAG_16/Data/'
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import pysal as ps
import geopandas as gpd
import numpy as np
import networkx as nx
import shapefile as shp
from shapely.geometry import Point
import shapely
from collections import OrderedDict
import pandas as pd
import qgrid
import gurobipy as gbp
import time
import bokeh
from bokeh.plotting import figure, show, ColumnDataSource
from bokeh.io import output_notebook, output_file, show
from bokeh.models import (HoverTool, BoxAnnotation, GeoJSONDataSource,
GMapPlot, GMapOptions, ColumnDataSource, Circle,
DataRange1d, PanTool, WheelZoomTool, BoxSelectTool,
ResetTool, MultiLine)
import utm
from cylp.cy import CyCbcModel, CyClpSimplex
import matplotlib.pyplot as plt
import matplotlib as mpl
%matplotlib inline
#%matplotlib notebook
mpl.rcParams['figure.figsize']=11,11
output_notebook()
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def c_s_matrix(): # Define Client to Service Matrix Function
global All_Dist_MILES # in meters
All_Neigh_Dist = ntw.allneighbordistances(
sourcepattern=ntw.pointpatterns['Rand_Points_CLIENT'],
destpattern=ntw.pointpatterns['Rand_Points_SERVICE'])
All_Dist_MILES = All_Neigh_Dist * 0.000621371 # to miles
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def Gurobi_PMCP(sites, Ai, AiSum, All_Dist_Miles):
#**************************************************
# Define Global Variables
global pydf_M #--------- median globals
global selected_M
global NEW_Records_PMP
global VAL_PMP
global AVG_PMP
#
global pydf_C #--------- center globals
global selected_C
global NEW_Records_PCP
global VAL_PCP
#
global pydf_CentDian #--------- centdian globals
global selected_CentDian
global NEW_Records_Pcentdian
global VAL_CentDian
#
global pydf_MC #--------- pmcp globals
global VAL_PMCP
global p_dens
#***********************************************
# Initiate Solutions
for p in range(1, sites+1): #--------- for all [p] in p = length(service facilities)
# DATA
# [p] --> sites
# Demand --> Ai
# Demand Sum --> AiSum
# Travel Costs
Cij = All_Dist_MILES
# Weighted Costs
Sij = Ai * Cij
# Total Client and Service nodes
client_nodes = range(len(Sij))
service_nodes = range(len(Sij[0]))
#*************************************************************
# PMP
t1_PMP = time.time()
# Create Model, Add Variables, & Update Model
# Instantiate Model
mPMP = gbp.Model(' -- p-Median -- ')
# Turn off Gurobi's output
mPMP.setParam('OutputFlag',False)
# Add Client Decision Variables (iXj)
client_var = []
for orig in client_nodes:
client_var.append([])
for dest in service_nodes:
client_var[orig].append(mPMP.addVar(vtype=gbp.GRB.BINARY,
lb=0,
ub=1,
obj=Sij[orig][dest],
name='x'+str(orig+1)+'_'+str(dest+1)))
# Add Service Decision Variables (j)
serv_var = []
for dest in service_nodes:
serv_var.append([])
serv_var[dest].append(mPMP.addVar(vtype=gbp.GRB.BINARY,
lb=0,
ub=1,
name='y'+str(dest+1)))
# Update the model
mPMP.update()
# 3. Set Objective Function
mPMP.setObjective(gbp.quicksum(Sij[orig][dest]*client_var[orig][dest]
for orig in client_nodes for dest in service_nodes),
gbp.GRB.MINIMIZE)
# 4. Add Constraints
# Assignment Constraints
for orig in client_nodes:
mPMP.addConstr(gbp.quicksum(client_var[orig][dest]
for dest in service_nodes) == 1)
# Opening Constraints
for orig in service_nodes:
for dest in client_nodes:
mPMP.addConstr((serv_var[orig][0] - client_var[dest][orig] >= 0))
# Facility Constraint
mPMP.addConstr(gbp.quicksum(serv_var[dest][0] for dest in service_nodes) == p)
# 5. Optimize and Print Results
# Solve
mPMP.optimize()
# Write LP
mPMP.write(path+'LP_Files/PMP'+str(p)+'.lp')
t2_PMP = time.time()-t1_PMP
# Record and Display Results
print '\n*************************************************************************'
selected_M = OrderedDict()
dbf1 = ps.open(path+'Snapped/SERVICE_Snapped.dbf')
NEW_Records_PMP = []
for v in mPMP.getVars():
if 'x' in v.VarName:
pass
elif v.x > 0:
var = '%s' % v.VarName
selected_M[var]=(u"\u2588")
for i in range(dbf1.n_records):
if var in dbf1.read_record(i):
x = dbf1.read_record(i)
NEW_Records_PMP.append(x)
else:
pass
print ' | ', var
pydf_M = pydf_M.append(selected_M, ignore_index=True)
# Instantiate Shapefile
SHP_Median = shp.Writer(shp.POINT)
# Add Points
for idy,idx,x,y in NEW_Records_PMP:
SHP_Median.point(float(x), float(y))
# Add Fields
SHP_Median.field('y_ID')
SHP_Median.field('x_ID')
SHP_Median.field('LAT')
SHP_Median.field('LON')
# Add Records
for idy,idx,x,y in NEW_Records_PMP:
SHP_Median.record(idy,idx,x,y)
# Save Shapefile
SHP_Median.save(path+'Results/Selected_Locations_Pmedian'+str(p)+'.shp')
print ' | Selected Facility Locations -------------- ^^^^ '
print ' | Candidate Facilities [p] ----------------- ', len(selected_M)
val_m = mPMP.objVal
VAL_PMP.append(round(val_m, 3))
print ' | Objective Value (miles) ------------------ ', val_m
avg_m = float(mPMP.objVal)/float(AiSum)
AVG_PMP.append(round(avg_m, 3))
print ' | Avg. Value / Client (miles) -------------- ', avg_m
print ' | Real Time to Optimize (sec.) ------------- ', t2_PMP
print '*************************************************************************'
print ' -- The p-Median Problem -- '
print ' [p] = ', str(p), '\n\n'
#******************************************************************************
# PCP
t1_PCP = time.time()
# Instantiate P-Center Model
mPCP = gbp.Model(' -- p-Center -- ')
# Add Client Decision Variables (iXj)
client_var_PCP = []
for orig in client_nodes:
client_var_PCP.append([])
for dest in service_nodes:
client_var_PCP[orig].append(mPCP.addVar(vtype=gbp.GRB.BINARY,
lb=0,
ub=1,
obj=Cij[orig][dest],
name='x'+str(orig+1)+'_'+str(dest+1)))
# Add Service Decision Variables (j)
serv_var_PCP = []
for dest in service_nodes:
serv_var_PCP.append([])
serv_var_PCP[dest].append(mPCP.addVar(vtype=gbp.GRB.BINARY,
lb=0,
ub=1,
name='y'+str(dest+1)))
# Add the Maximum travel cost variable
W = mPCP.addVar(vtype=gbp.GRB.CONTINUOUS,
lb=0.,
name='W')
# Update the model
mPCP.update()
# 3. Set Objective Function
mPCP.setObjective(W, gbp.GRB.MINIMIZE)
# 4. Add Constraints
# Assignment Constraints
for orig in client_nodes:
mPCP.addConstr(gbp.quicksum(client_var_PCP[orig][dest]
for dest in service_nodes) == 1)
# Opening Constraints
for orig in service_nodes:
for dest in client_nodes:
mPCP.addConstr((serv_var_PCP[orig][0] - client_var_PCP[dest][orig] >= 0))
# Add Maximum travel cost constraints
for orig in client_nodes:
mPCP.addConstr(gbp.quicksum(Cij[orig][dest]*client_var_PCP[orig][dest]
for dest in service_nodes) - W <= 0)
# Facility Constraint
mPCP.addConstr(gbp.quicksum(serv_var_PCP[dest][0] for dest in service_nodes) == p)
# 5. Optimize and Print Results
# Solve
mPCP.optimize()
# Write LP
mPCP.write(path+'LP_Files/PCP'+str(p)+'.lp')
t2_PCP = time.time()-t1_PCP
# Record and Display Results
print '\n*************************************************************************'
selected_C = OrderedDict()
dbf1 = ps.open(path+'Snapped/SERVICE_Snapped.dbf')
NEW_Records_PCP = []
for v in mPCP.getVars():
if 'x' in v.VarName:
pass
elif 'W' in v.VarName:
pass
elif v.x > 0:
var = '%s' % v.VarName
selected_C[var]=(u"\u2588")
for i in range(dbf1.n_records):
if var in dbf1.read_record(i):
x = dbf1.read_record(i)
NEW_Records_PCP.append(x)
else:
pass
print ' | ', var, ' '
pydf_C = pydf_C.append(selected_C, ignore_index=True)
# Instantiate Shapefile
SHP_Center = shp.Writer(shp.POINT)
# Add Points
for idy,idx,x,y in NEW_Records_PCP:
SHP_Center.point(float(x), float(y))
# Add Fields
SHP_Center.field('y_ID')
SHP_Center.field('x_ID')
SHP_Center.field('LAT')
SHP_Center.field('LON')
# Add Records
for idy,idx,x,y in NEW_Records_PCP:
SHP_Center.record(idy,idx,x,y)
# Save Shapefile
SHP_Center.save(path+'Results/Selected_Locations_Pcenter'+str(p)+'.shp')
print ' | Selected Facility Locations -------------- ^^^^ '
print ' | Candidate Facilities [p] ----------------- ', len(selected_C)
val_c = mPCP.objVal
VAL_PCP.append(round(val_c, 3))
print ' | Objective Value (miles) ------------------ ', val_c
print ' | Real Time to Optimize (sec.) ------------- ', t2_PCP
print '*************************************************************************'
print ' -- The p-Center Problem -- '
print ' [p] = ', str(p), '\n\n'
#******************************************************************************
# p-CentDian
t1_centdian = time.time()
# Instantiate P-Center Model
mPcentdian = gbp.Model(' -- p-CentDian -- ')
# Add Client Decision Variables (iXj)
client_var_CentDian = []
for orig in client_nodes:
client_var_CentDian.append([])
for dest in service_nodes:
client_var_CentDian[orig].append(mPcentdian.addVar(vtype=gbp.GRB.BINARY,
lb=0,
ub=1,
obj=Cij[orig][dest],
name='x'+str(orig+1)+'_'+str(dest+1)))
# Add Service Decision Variables (j)
serv_var_CentDian = []
for dest in service_nodes:
serv_var_CentDian.append([])
serv_var_CentDian[dest].append(mPcentdian.addVar(vtype=gbp.GRB.BINARY,
lb=0,
ub=1,
name='y'+str(dest+1)))
# Add the Maximum travel cost variable
W_CD = mPcentdian.addVar(vtype=gbp.GRB.CONTINUOUS,
lb=0.,
name='W')
# Update the model
mPcentdian.update()
# 3. Set Objective Function
M = gbp.quicksum(Sij[orig][dest]*client_var_CentDian[orig][dest]
for orig in client_nodes for dest in service_nodes)
Zt = M/AiSum
mPcentdian.setObjective((W_CD + Zt) / 2, gbp.GRB.MINIMIZE)
# 4. Add Constraints
# Assignment Constraints
for orig in client_nodes:
mPcentdian.addConstr(gbp.quicksum(client_var_CentDian[orig][dest]
for dest in service_nodes) == 1)
# Opening Constraints
for orig in service_nodes:
for dest in client_nodes:
mPcentdian.addConstr((serv_var_CentDian[orig][0] -
client_var_CentDian[dest][orig]
>= 0))
# Add Maximum travel cost constraints
for orig in client_nodes:
mPcentdian.addConstr(gbp.quicksum(Cij[orig][dest]*client_var_CentDian[orig][dest]
for dest in service_nodes) - W_CD <= 0)
# Facility Constraint
mPcentdian.addConstr(gbp.quicksum(serv_var_CentDian[dest][0]
for dest in service_nodes)
== p)
# 5. Optimize and Print Results
# Solve
mPcentdian.optimize()
# Write LP
mPcentdian.write(path+'LP_Files/CentDian'+str(p)+'.lp')
t2_centdian = time.time()-t1_centdian
# Record and Display Results
print '\n*************************************************************************'
selected_CentDian = OrderedDict()
dbf1 = ps.open(path+'Snapped/SERVICE_Snapped.dbf')
NEW_Records_Pcentdian = []
for v in mPcentdian.getVars():
if 'x' in v.VarName:
pass
elif 'W' in v.VarName:
pass
elif v.x > 0:
var = '%s' % v.VarName
selected_CentDian[var]=(u"\u2588")
for i in range(dbf1.n_records):
if var in dbf1.read_record(i):
x = dbf1.read_record(i)
NEW_Records_Pcentdian.append(x)
else:
pass
print ' | ', var, ' '
pydf_CentDian = pydf_CentDian.append(selected_CentDian, ignore_index=True)
# Instantiate Shapefile
SHP_CentDian = shp.Writer(shp.POINT)
# Add Points
for idy,idx,x,y in NEW_Records_Pcentdian:
SHP_CentDian.point(float(x), float(y))
# Add Fields
SHP_CentDian.field('y_ID')
SHP_CentDian.field('x_ID')
SHP_CentDian.field('LAT')
SHP_CentDian.field('LON')
# Add Records
for idy,idx,x,y in NEW_Records_Pcentdian:
SHP_CentDian.record(idy,idx,x,y)
# Save Shapefile
SHP_CentDian.save(path+'Results/Selected_Locations_CentDian'+str(p)+'.shp')
print ' | Selected Facility Locations -------------- ^^^^ '
print ' | Candidate Facilities [p] ----------------- ', len(selected_CentDian)
val_cd = mPcentdian.objVal
VAL_CentDian.append(round(val_cd, 3))
print ' | Objective Value (miles) ------------------ ', val_cd
print ' | Real Time to Optimize (sec.) ------------- ', t2_centdian
print '*************************************************************************'
print ' -- The p-CentDian Problem -- '
print ' [p] = ', str(p), '\n\n'
#******************************************************************************
# p-Median + p-Center Method
# Record solutions that record identical facility selection
if selected_M.keys() == selected_C.keys() == selected_CentDian.keys():
pydf_MC = pydf_MC.append(selected_C, ignore_index=True) # append PMCP dataframe
p_dens.append('p='+str(p)) # density of [p]
VAL_PMCP.append([round(val_m,3), round(avg_m,3),
round(val_c,3), round(val_cd,3)]) # append PMCP list
# Instantiate Shapefile
SHP_PMCP = shp.Writer(shp.POINT)
# Add Points
for idy,idx,x,y in NEW_Records_PCP:
SHP_PMCP.point(float(x), float(y))
# Add Fields
SHP_PMCP.field('y_ID')
SHP_PMCP.field('x_ID')
SHP_PMCP.field('LAT')
SHP_PMCP.field('LON')
# Add Records
for idy,idx,x,y in NEW_Records_PCP:
SHP_PMCP.record(idy,idx,x,y)
# Save Shapefile
SHP_PMCP.save(path+'Results/Selected_Locations_PMCP'+str(p)+'.shp')
else:
pass
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# Waverly Hills
STREETS_Orig = gpd.read_file(path+'Waverly_Trim/Waverly.shp')
STREETS = gpd.read_file(path+'Waverly_Trim/Waverly.shp')
STREETS.to_crs(epsg=2779, inplace=True) # NAD83(HARN) / Florida North
STREETS.to_file(path+'WAVERLY/WAVERLY.shp')
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ntw = ps.Network(path+'WAVERLY/WAVERLY.shp')
shp_W = ps.open(path+'WAVERLY/WAVERLY.shp')
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buff = STREETS.buffer(200) #Buffer
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buffU = buff.unary_union #Buffer Union
buff1 = gpd.GeoSeries(buffU)
buff1.crs = STREETS.crs
Buff = gpd.GeoDataFrame(buff1, crs=STREETS.crs)
Buff.columns = ['geometry']
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np.random.seed(352)
x = np.random.uniform(shp_W.bbox[0], shp_W.bbox[2], 1000)
np.random.seed(850)
y = np.random.uniform(shp_W.bbox[1], shp_W.bbox[3], 1000)
coords0= zip(x,y)
coords = [shapely.geometry.Point(i) for i in coords0]
Rand = gpd.GeoDataFrame(coords)
Rand.crs = STREETS.crs
Rand.columns = ['geometry']
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Inter = [Buff['geometry'].intersection(p) for p in Rand['geometry']]
INTER = gpd.GeoDataFrame(Inter, crs=STREETS.crs)
INTER.columns = ['geometry']
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# Add records that are points within the buffer
point_in = []
for p in INTER['geometry']:
if type(p) == shapely.geometry.point.Point:
point_in.append(p)
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CLIENT = gpd.GeoDataFrame(point_in[:100], crs=STREETS.crs)
CLIENT.columns = ['geometry']
SERVICE = gpd.GeoDataFrame(point_in[-15:], crs=STREETS.crs)
SERVICE.columns = ['geometry']
CLIENT.to_file(path+'CLIENT')
SERVICE.to_file(path+'SERVICE')
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g = nx.Graph() # Roads & Nodes
g1 = nx.MultiGraph() # Edges and Vertices
GRAPH_client = nx.Graph() # Clients
g_client = nx.Graph() # Snapped Clients
GRAPH_service = nx.Graph() # Service
g_service = nx.Graph() # Snapped Service
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points_client = {}
points_service = {}
CLI = ps.open(path+'CLIENT/CLIENT.shp')
for idx, coords in enumerate(CLI):
GRAPH_client.add_node(idx)
points_client[idx] = coords
GRAPH_client.node[idx] = coords
SER = ps.open(path+'SERVICE/SERVICE.shp')
for idx, coords in enumerate(SER):
GRAPH_service.add_node(idx)
points_service[idx] = coords
GRAPH_service.node[idx] = coords
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# Client Weights for demand
np.random.seed(850)
Ai = np.random.randint(1, 5, len(CLI))
Ai = Ai.reshape(len(Ai),1)
AiSum = np.sum(Ai) # Sum of Weights (Total Demand)
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client = shp.Writer(shp.POINT) # Client Shapefile
# Add Random Points
for i,j in CLI:
client.point(i,j)
# Add Fields
client.field('client_ID')
client.field('Weight')
counter = 0
for i in range(len(CLI)):
counter = counter + 1
client.record('client_' + str(counter), Ai[i])
client.save(path+'Simulated/RandomPoints_CLIENT') # Save Shapefile
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service = shp.Writer(shp.POINT) #Service Shapefile
# Add Random Points
for i,j in SER:
service.point(i,j)
# Add Fields
service.field('y_ID')
service.field('x_ID')
counter = 0
for i in range(len(SER)):
counter = counter + 1
service.record('y' + str(counter), 'x' + str(counter))
service.save(path+'Simulated/RandomPoints_SERVICE') # Save Shapefile
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# Snap
Snap_C = ntw.snapobservations(path+'Simulated/RandomPoints_CLIENT.shp',
'Rand_Points_CLIENT', attribute=True)
Snap_S = ntw.snapobservations(path+'Simulated/RandomPoints_SERVICE.shp',
'Rand_Points_SERVICE', attribute=True)
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# Create Lat & Lon lists of the snapped service locations
y_snapped = []
x_snapped = []
for i,j in ntw.pointpatterns['Rand_Points_SERVICE'].snapped_coordinates.iteritems():
y_snapped.append(j[0])
x_snapped.append(j[1])
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service_SNAP = shp.Writer(shp.POINT) # Snapped Service Shapefile
# Add Points
for i,j in ntw.pointpatterns['Rand_Points_SERVICE'].snapped_coordinates.iteritems():
service_SNAP.point(j[0],j[1])
# Add Fields
service_SNAP.field('y_ID')
service_SNAP.field('x_ID')
service_SNAP.field('LAT')
service_SNAP.field('LON')
counter = 0
for i in range(len(ntw.pointpatterns['Rand_Points_SERVICE'].snapped_coordinates)):
counter = counter + 1
service_SNAP.record('y' + str(counter), 'x' + str(counter), y_snapped[i], x_snapped[i])
service_SNAP.save(path+'Snapped/SERVICE_Snapped') # Save Shapefile
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# Call Client to Service Matrix Function
c_s_matrix()
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# PANDAS DATAFRAME OF p/y results
p_list = []
for i in range(1, len(SER)+1):
p = 'p='+str(i)
p_list.append(p)
y_list = []
for i in range(1, len(SER)+1):
y = 'y'+str(i)
y_list.append(y)
In [ ]:
pydf_M = pd.DataFrame(index=p_list,columns=y_list)
pydf_C = pd.DataFrame(index=p_list,columns=y_list)
pydf_CentDian = pd.DataFrame(index=p_list,columns=y_list)
pydf_MC = pd.DataFrame(index=p_list,columns=y_list)
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# p-Median
P_Med_Graphs = OrderedDict()
for x in range(1, len(SER)+1):
P_Med_Graphs["{0}".format(x)] = nx.Graph()
# p-Center
P_Cent_Graphs = OrderedDict()
for x in range(1, len(SER)+1):
P_Cent_Graphs["{0}".format(x)] = nx.Graph()
# p-CentDian
P_CentDian_Graphs = OrderedDict()
for x in range(1, len(SER)+1):
P_CentDian_Graphs["{0}".format(x)] = nx.Graph()
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# PMP
VAL_PMP = []
AVG_PMP = []
# PCP
VAL_PCP = []
# CentDian
VAL_CentDian = []
# PMCP
VAL_PMCP = []
p_dens = [] # when the facilities for the p-median & p-center are the same
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Gurobi_PMCP(len(SER), Ai, AiSum, All_Dist_MILES)
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# PMP Total
PMP_Tot_Diff = []
for i in range(len(VAL_PMP)):
if i == 0:
PMP_Tot_Diff.append('0%')
elif i <= len(VAL_PMP):
n1 = VAL_PMP[i-1]
n2 = VAL_PMP[i]
diff = n2 - n1
perc_change = (diff/n1)*100.
PMP_Tot_Diff.append(str(round(perc_change, 2))+'%')
# PMP Average
PMP_Avg_Diff = []
for i in range(len(AVG_PMP)):
if i == 0:
PMP_Avg_Diff.append('0%')
elif i <= len(AVG_PMP):
n1 = AVG_PMP[i-1]
n2 = AVG_PMP[i]
diff = n2 - n1
perc_change = (diff/n1)*100.
PMP_Avg_Diff.append(str(round(perc_change, 2))+'%')
# PCP
PCP_Diff = []
for i in range(len(VAL_PCP)):
if i == 0:
PCP_Diff.append('0%')
elif i <= len(VAL_PCP):
n1 = VAL_PCP[i-1]
n2 = VAL_PCP[i]
diff = n2 - n1
perc_change = (diff/n1)*100.
PCP_Diff.append(str(round(perc_change, 2))+'%')
# p-CentDian
CentDian_Diff = []
for i in range(len(VAL_CentDian)):
if i == 0:
CentDian_Diff.append('0%')
elif i <= len(VAL_CentDian):
n1 = VAL_CentDian[i-1]
n2 = VAL_CentDian[i]
diff = n2 - n1
perc_change = (diff/n1)*100.
CentDian_Diff.append(str(round(perc_change, 2))+'%')
# PMCP
PMCP_Diff = []
counter = 0
for i in range(len(VAL_PMCP)):
PMCP_Diff.append([])
for j in range(len(VAL_PMCP[0])):
if i == 0:
PMCP_Diff[i].append('0%')
elif i <= len(VAL_PMCP):
n1 = VAL_PMCP[i-1][j]
n2 = VAL_PMCP[i][j]
diff = n2 - n1
perc_change = (diff/n1*100.)
PMCP_Diff[i].append(str(round(perc_change, 2))+'%')
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# PMP
pydf_M = pydf_M[len(SER):]
pydf_M.reset_index()
pydf_M.index = p_list
pydf_M.columns.name = 'Decision\nVariables'
pydf_M.index.name = 'Facility\nDensity'
pydf_M['Tot. Obj. Value'] = VAL_PMP
pydf_M['Tot. % Change'] = PMP_Tot_Diff
pydf_M['Avg. Obj. Value'] = AVG_PMP
pydf_M['Avg. % Change'] = PMP_Avg_Diff
pydf_M = pydf_M.fillna('')
#pydf_M.to_csv(path+'CSV') <-- need to change squares to alphanumeric to use
# PCP
pydf_C = pydf_C[len(SER):]
pydf_C.reset_index()
pydf_C.index = p_list
pydf_C.columns.name = 'Decision\nVariables'
pydf_C.index.name = 'Facility\nDensity'
pydf_C['Worst Case Obj. Value'] = VAL_PCP
pydf_C['Worst Case % Change'] = PCP_Diff
pydf_C = pydf_C.fillna('')
#pydf_C.to_csv(path+'CSV') <-- need to change squares to alphanumeric to use
pydf_CentDian = pydf_CentDian[len(SER):]
pydf_CentDian.reset_index()
pydf_CentDian.index = p_list
pydf_CentDian.columns.name = 'Decision\nVariables'
pydf_CentDian.index.name = 'Facility\nDensity'
pydf_CentDian['CentDian Obj. Value'] = VAL_CentDian
pydf_CentDian['CentDian % Change'] = CentDian_Diff
pydf_CentDian = pydf_CentDian.fillna('')
#pydf_CentDian.to_csv(path+'CSV') <-- need to change squares to alphanumeric to use
# PMCP
pydf_MC = pydf_MC[len(SER):]
pydf_MC.reset_index()
pydf_MC.index = p_dens
pydf_MC.columns.name = 'D.V.'
pydf_MC.index.name = 'F.D.'
pydf_MC['Min.\nTotal'] = [VAL_PMCP[x][0] for x in range(len(VAL_PMCP))]
pydf_MC['Min.\nTotal\n%\nChange'] = [PMCP_Diff[x][0] for x in range(len(PMCP_Diff))]
pydf_MC['Avg.\nTotal'] = [VAL_PMCP[x][1] for x in range(len(VAL_PMCP))]
pydf_MC['Avg.\nTotal\n%\nChange'] = [PMCP_Diff[x][1] for x in range(len(PMCP_Diff))]
pydf_MC['Worst\nCase'] = [VAL_PMCP[x][2] for x in range(len(VAL_PMCP))]
pydf_MC['Worst\nCase\n%\nChange'] = [PMCP_Diff[x][2] for x in range(len(PMCP_Diff))]
pydf_MC['Center\nMedian'] = [VAL_PMCP[x][3] for x in range(len(VAL_PMCP))]
pydf_MC['Center\nMedian\n%\nChange'] = [PMCP_Diff[x][3] for x in range(len(PMCP_Diff))]
pydf_MC = pydf_MC.fillna('')
#pydf_MC.to_csv(path+'CSV') <-- need to change squares to alphanumeric to use
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# Create Graphs of the PMCP results
PMCP_Graphs = OrderedDict()
for x in pydf_MC.index:
PMCP_Graphs[x[2:]] = nx.Graph()
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plt.figure(1)
plt.subplot(221)
# Draw Network Actual Roads and Nodes
for e in ntw.edges:
g.add_edge(*e)
nx.draw(g, ntw.node_coords, node_size=2, alpha=0.1, edge_color='r', width=2)
# PMP
size = 700
for i,j in P_Med_Graphs.iteritems():
size=size-50
# p-Median
P_Med = ps.open(path+'Results/Selected_Locations_Pmedian'+str(i)+'.shp')
points_median = {}
for idx, coords in enumerate(P_Med):
P_Med_Graphs[i].add_node(idx)
points_median[idx] = coords
P_Med_Graphs[i].node[idx] = coords
nx.draw(P_Med_Graphs[i],
points_median,
node_size=size,
alpha=.1,
node_color='k')
# Title
plt.title('PMP', family='Times New Roman',
size=30, color='k', backgroundcolor='w', weight='bold')
# North Arrow and 'N' --> Must be changed for different spatial resolutions, etc.
plt.arrow(624000, 164050, 0.0, 500, width=50, head_width=125,
head_length=75, fc='k', ec='k',alpha=0.75,)
plt.annotate('N', xy=(623900, 164700), fontstyle='italic', fontsize='xx-large',
fontweight='heavy', alpha=0.75)
################################################################################
plt.subplot(222)
# Draw Network Actual Roads and Nodes
for e in ntw.edges:
g.add_edge(*e)
nx.draw(g, ntw.node_coords, node_size=2, alpha=0.1, edge_color='r', width=2)
# PCP
size = 700
for i,j in P_Cent_Graphs.iteritems():
size=size-50
# p-Center
P_Cent = ps.open(path+'Results/Selected_Locations_Pcenter'+str(i)+'.shp')
points_center = {}
for idx, coords in enumerate(P_Cent):
P_Cent_Graphs[i].add_node(idx)
points_center[idx] = coords
P_Cent_Graphs[i].node[idx] = coords
nx.draw(P_Cent_Graphs[i],
points_center,
node_size=size,
alpha=.1,
node_color='k')
# Title
plt.title('PCP', family='Times New Roman',
size=30, color='k', backgroundcolor='w', weight='bold')
# North Arrow and 'N' --> Must be changed for different spatial resolutions, etc.
plt.arrow(624000, 164050, 0.0, 500, width=50, head_width=125,
head_length=75, fc='k', ec='k',alpha=0.75,)
plt.annotate('N', xy=(623900, 164700), fontstyle='italic', fontsize='xx-large',
fontweight='heavy', alpha=0.75)
###############################################################################
plt.subplot(223)
# Draw Network Actual Roads and Nodes
for e in ntw.edges:
g.add_edge(*e)
nx.draw(g, ntw.node_coords, node_size=2, alpha=0.1, edge_color='r', width=2)
# CentDian
size = 700
for i,j in P_CentDian_Graphs.iteritems():
size=size-50
P_CentDian = ps.open(path+'Results/Selected_Locations_CentDian'+str(i)+'.shp')
points_centdian = {}
for idx, coords in enumerate(P_CentDian):
P_CentDian_Graphs[i].add_node(idx)
points_centdian[idx] = coords
P_CentDian_Graphs[i].node[idx] = coords
nx.draw(P_CentDian_Graphs[i],
points_centdian,
node_size=size,
alpha=.1,
node_color='k')
# Title
plt.title('CentDian', family='Times New Roman',
size=30, color='k', backgroundcolor='w', weight='bold')
# North Arrow and 'N' --> Must be changed for different spatial resolutions, etc.
plt.arrow(624000, 164050, 0.0, 500, width=50, head_width=125,
head_length=75, fc='k', ec='k',alpha=0.75,)
plt.annotate('N', xy=(623900, 164700), fontstyle='italic', fontsize='xx-large',
fontweight='heavy', alpha=0.75)
###################################################################################
plt.subplot(224)
# Draw Network Actual Roads and Nodes
# PM+CP
size = 700
#shape = 'sdh^Vp<8>'
#counter = -1
for i,j in PMCP_Graphs.iteritems():
size = size - 300
if int(i) <= len(SER)-1:
counter = counter+1
pmcp = ps.open(path+'Results/Selected_Locations_PMCP'+str(i)+'.shp')
points_pmcp = {}
for idx, coords in enumerate(pmcp):
PMCP_Graphs[i].add_node(idx)
points_pmcp[idx] = coords
PMCP_Graphs[i].node[idx] = coords
nx.draw(PMCP_Graphs[i],
points_pmcp,
node_size=size,
alpha=.75,
node_color='k')
else:
pass
for e in ntw.edges:
g.add_edge(*e)
nx.draw(g, ntw.node_coords, node_size=2, alpha=0.1, edge_color='r', width=2)
# Legend (Ordered Dictionary)
LEGEND = OrderedDict()
for i in PMCP_Graphs:
if int(i) <= len(SER)-1:
LEGEND['PMP/PCP == '+str(i)]=PMCP_Graphs[i]
plt.legend(LEGEND,
loc='lower right',
frameon=False,
scatterpoints=1)
# Title
plt.title('PM+CP', family='Times New Roman',
size=30, color='k', backgroundcolor='w', weight='bold')
# North Arrow and 'N' --> Must be changed for different spatial resolutions, etc.
plt.arrow(624000, 164050, 0.0, 500, width=50, head_width=125,
head_length=75, fc='k', ec='k',alpha=0.75,)
plt.annotate('N', xy=(623900, 164700), fontstyle='italic', fontsize='xx-large',
fontweight='heavy', alpha=1)
plt.show()
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PMCP_Graphs
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for i,j in PMCP_Graphs.iteritems():
print i, j
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