return [tot,posi,prob] #Normal approx to binomial #Get prob of higher #Return prob ######Prep for maps####### year = '0910' width = 4700000 height = 3100000 #Import 0910 data metros, mg, nodedata = importnetwork(year) #String FIPS code metros['id'] = metros['MSACode'].apply(str) #Draw setup #Take out AK/HI for now for drawing akhi = metros[(metros['lon'] > -60) | (metros['lon'] <-125)].index mgdraw = mg.copy() mgdraw.remove_nodes_from(akhi) #Projection project = pyproj.Proj('+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=38.5 +lon_0=-97 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs') t = project(metros['lon'],metros['lat']) pos = dict(zip(metros.index,zip(t[0]+width / 2,t[1] + height / 2)))
import matplotlib.pyplot as plt import pyproj import mpl_toolkits.basemap as bm #New Modules sys.path.append('/Users/Eyota/projects/thesis/code/python/modules') from rowtodict import * from drawnetworks import netplot import weighted_eigenvector as we from importnetworks import importnetwork os.chdir("/Users/Eyota/projects/thesis") year = '0910' metros, mg = importnetwork(year) #Compute statistics stats = metros #Degree degree = mg.degree() stats = dict2column(stats, degree, 'degree') #Weighted Degree wdegree = mg.degree(weight = 'exmptgross') stats = dict2column(stats, wdegree, 'wdegree') #Current Closeness Centrality flowcloseness = nx.current_flow_closeness_centrality(mg, weight = 'exmptgross')