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Update geolocaliza.py #1

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269 changes: 214 additions & 55 deletions geolocaliza.py
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Original file line number Diff line number Diff line change
@@ -1,56 +1,215 @@
# -*- coding:utf-8 -*-
"""
Script para geolocalizar endereços
requer o modulo geopy para funcionar:
easy_install geopy
"""
from geopy import geocoders

def carrega_csv(filename)
"""
Abre Arquivo csv com endereços para geo-referenciamento
primeira linha deve ser cabeçalho com nomes de variaveis
"""
with open(filename,'r') as f:
ends = f.readlines()
return ends


def georeferencia(enderecos,arqsaida,campo,geoserv='google'):

if geoserv == 'google':
# Usando o Google
g = geocoders.Google('ABQIAAAA_gnHK6RUKt_B9h1XlCw2fRRSj27_XPuJ4H5ID33wYOhGVNZE9hSv8VlUSRnLRlA9aUnXiquLb89oJw')
elif geoserv == 'geonames':
#Usando o geonames que não requer API Key
gn = geocoders.GeoNames()
j=1
varnames = enderecos.pop(0)
try:
for i,e in enumerate(enderecos):
if i == 0:
fs.write('lat,lng,ATIVIDADE,DDD,T1,NOME,CNPJ,LOGR,NR,COMPL ,CEP,BAIRRO ,CIDADE ,UF,RA,GRUPO,QTD_FUNC\n')
continue
# lat lng perto do RJ ---> agua!
lat = -23.40
lng = -43.10
l = e.split(',')
#por endereco
query_string = '%s, %s, %s, rio de janeiro, RJ, Brazil'%(l[5], l[6], l[9])
#por CEP
#query_string = 'rio de janeiro, RJ, %s, Brazil'%(l[8][:5]+'-'+l[8][5:])
#print query_string#place, lat, lng
try:
place, (lat, lng) = g.geocode(query_string)
print i#place, lat, lng
except:
print '==> Não encontrei no Google: %s'%query_string, j
j += 1
l = [str(lat),str(lng)]+l
fs.write(','.join(l))
finally:
fs.close()
if __name__=="__main__":
fs = open('NovaAmostra-loc-endereco.csv','w')
ends = carrega_csv('Dados nova amostra.csv')
import random
import math

# The network, each of which has two available spaces
network=['Zeus','Athena','Hercules','Bacchus','Pluto']

# People, along with their first and second choices
prefs=[('Toby', ('Bacchus', 'Hercules')),
('Steve', ('Zeus', 'Pluto')),
('Karen', ('Athena', 'Zeus')),
('Sarah', ('Zeus', 'Pluto')),
('Dave', ('Athena', 'Bacchus')),
('Jeff', ('Hercules', 'Pluto')),
('Fred', ('Pluto', 'Athena')),
('Suzie', ('Bacchus', 'Hercules')),
('Laura', ('Bacchus', 'Hercules')),
('James', ('Hercules', 'Athena'))]


# Each person is assigned to one open slot and the domain becomes:
# [(0,9),(0,8),(0,7),(0,6),...,(0,0)]
domain=[(0,(len(dorms)*2)-i-1) for i in range(0,len(dorms)*2)]

# Loop through the slots, and every solution.
def printsolution(vec):
slots=[]
# Create two slots for each network
for i in range(len(network)): slots+=[i,i]

# Loop over each person’s space
for i in range(len(vec)):
x=int(vec[i])

# Choose the slot from the remaining ones
network=networks[slots[x]]
# Show the person and assigned space
print prefs[i][0],space
# Remove this slot
del slots[x]

# Slots are removed as they are used up, the cost is calculated by
# comparing person's current assignment to its top choices.
# Increase cost by:
# 0 if the top choice
# 1 if second choice
# 3 if neither of the two top choices
def networkcost(vec):
cost=0
# Create list a of slots
slots=[0,0,1,1,2,2,3,3,4,4]

# Loop over each person
for i in range(len(vec)):
x=int(vec[i])
network=networks[slots[x]]
pref=prefs[i][1]
# First choice costs 0, second choice costs 1
if pref[0]==network: cost+=0
elif pref[1]==network: cost+=1
else: cost+=3
# Not on the list costs 3

# Remove selected slot
del slots[x]

return cost
# A module that implements optimization. From the provided data.
# This is an implementation that visualizes a Network
from PIL import Image,ImageDraw

people=['Charlie','Augustus','Veruca','Violet','Mike','Joe','Willy','Miranda']

links=[('Augustus', 'Willy'),
('Mike', 'Joe'),
('Miranda', 'Mike'),
('Violet', 'Augustus'),
('Miranda', 'Willy'),
('Charlie', 'Mike'),
('Veruca', 'Joe'),
('Miranda', 'Augustus'),
('Willy', 'Augustus'),
('Joe', 'Charlie'),
('Veruca', 'Augustus'),
('Miranda', 'Joe')]

# Loops through all pairs of links and uses the current
# coordinates of their endpoints to determine whether they cross.
def crosscount(v):
# Convert the number list into a dictionary of person:(x,y)
loc=dict([(people[i],(v[i*2],v[i*2+1])) for i in range(0,len(people))])
total=0

# Loop through every pair of links
for i in range(len(links)):
for j in range(i+1,len(links)):

# Get the locations
(x1,y1),(x2,y2)=loc[links[i][0]],loc[links[i][1]]
(x3,y3),(x4,y4)=loc[links[j][0]],loc[links[j][1]]

den=(y4-y3)*(x2-x1)-(x4-x3)*(y2-y1)

# den==0 if the lines are parallel
if den==0: continue

# Otherwise ua and ub are the fraction of the
# line where they cross
ua=((x4-x3)*(y1-y3)-(y4-y3)*(x1-x3))/den
ub=((x2-x1)*(y1-y3)-(y2-y1)*(x1-x3))/den

# If the fraction is between 0 and 1 for both lines
# then they cross each other
if ua>0 and ua<1 and ub>0 and ub<1:
total+=1

# A way to penalize a solution that has two nodes too close together
# Calculate the distance and divide with desired distance.
for i in range(len(people)):
for j in range(i+1,len(people)):
# Get the locations of the two nodes
(x1,y1),(x2,y2)=loc[people[i]],loc[people[j]]

# Find the distance between them
dist=math.sqrt(math.pow(x1-x2,2)+math.pow(y1-y2,2))
# Penalize any nodes closer than 50 pixels
if dist<50:
total+=(1.0-(dist/50.0))
return total

# Draws the Network
def drawnetwork(sol):
# Create the image
img=Image.new('RGB',(400,400),(255,255,255))
draw=ImageDraw.Draw(img)

# Create the position dict
pos=dict([(people[i],(sol[i*2],sol[i*2+1])) for i in range(0,len(people))])

for (a,b) in links:
draw.line((pos[a],pos[b]),fill=(255,0,0))

for n,p in pos.items():
draw.text(p,n,(0,0,0))

img.show()

domain=[(10,370)]*(len(people)*2)
import dorm
dorm.printsolution([0,0,0,0,0,0,0,0,0,0])
Toby Zeus
Steve Zeus
Karen Athena
Sarah Athena
Dave Hercules
Jeff Hercules
Fred Bacchus
Suzie Bacchus
Laura Pluto
James Pluto

reload(dorm)
<module 'dorm' from 'dorm.py'>
import optimization
s=optimization.randomoptimize(dorm.domain,dorm.dormcost)
dorm.dormcost(s)
18
dorm.printsolution(s)
Toby Bacchus
Steve Zeus
Karen Zeus
Sarah Athena
Dave Pluto
Jeff Athena
Fred Bacchus
Suzie Hercules
Laura Hercules
James Pluto

s=optimization.annealingoptimize(dorm.domain,dorm.dormcost)
>>> dorm.printsolution(s)
Toby Bacchus
Steve Zeus
Karen Athena
Sarah Pluto
Dave Bacchus
Jeff Hercules
Fred Pluto
Suzie Zeus
Laura Hercules
James Athena

>>> import socialnetwork
>>> import optimization
>>> s=optimization.randomoptimize(socialnetwork.domain,socialnetwork.crosscount)
>>> socialnetwork.crosscount(s)
3
>>> s=optimization.annealingoptimize(socialnetwork.domain,
... socialnetwork.crosscount,step=50,cool=0.99)
>>> socialnetwork.crosscount(s)
2
>>> s[196.0, 214, 12.0, 361, 247, 67, 163, 10, 72, 330.0, 298, 44, 220.0, 131, 238, 274.0]

>>> reload(socialnetwork)
<module 'socialnetwork' from 'socialnetwork.py'>
>>> s=optimization.annealingoptimize(socialnetwork.domain,
... socialnetwork.crosscount,step=50,cool=0.99)
>>> socialnetwork.drawnetwork(s) // The image is saved as network1.jpg

>>> import socialnetwork
>>> import optimization
>>> s=optimization.annealingoptimize(socialnetwork.domain,
... socialnetwork.crosscount,step=50,cool=0.99)
>>> socialnetwork.crosscount(s)
5
>>> socialnetwork.drawnetwork(