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')