def geocode_batch(self, vals):
"""
@param vals an iterator of "county name, state name" strings
@return iterator of (loctype, latlon or shapeid)
"""
Q = """
select c.shape_id
from __dbtruck_county__ as c, __dbtruck_state__ as s,
__dbtruck_countyname__ as cn, __dbtruck_statename__ as sn
where c.fips = cn.fips and s.fips = sn.fips and c.state_fips = s.fips and
levenshtein(cn.name, %s) < 1 + 0.2*char_length(cn.name) and
levenshtein(sn.name, %s) < 1 + 0.2*char_length(sn.name)
order by levenshtein(cn.name, %s) + levenshtein(sn.name, %s) asc
limit 1
"""
for v in vals:
try:
v = to_utf(v.lower())
idx = v.rindex(',')
c, s = v[:idx], v[idx:] # county, state
args = (c, s, c, s)
shapeids = self.session.bind.execute(Q, args).fetchone()
if not shapeids:
yield None
else:
yield SHAPE, shapeids[0]
except Exception as e:
print e
yield None
def geocode(self, address):
"""
Try to pick the best geocoder for the address. Google has a low
daily limit so try to use other geocoders if possible
If the address looks like a standard address (NUMBER WORDS+),
then use yahoo, bing, geocoder.us
Otherwise if it is a place name, try geonames
If all else fails, use Google
"""
address = address.replace('SOUTH BOSTON', 'BOSTON')
query = to_utf(address).lower()
if not query:
return []
if query in self.cache:
try:
ret = pickle.loads(self.cache[query])
if ret and isinstance(ret[0], basestring):
return [ret]
return ret
except KeyboardInterrupt:
raise
except:
pass
geocoder = self.get_initial_geocoder(address)
print geocoder, address
try:
result = geocoder.geocode(query, exactly_one=False)
self.ncalls += 1
if not result:
raise Exception('no result found for %s' % query)
except Exception as e:
print e
# try:
# geocoder = geocoders.Google()
# result = geocoder.geocode(query, exactly_one=False)
# self.ncalls += 1
# except Exception as e:
# print e
# result = []
self.cache[query] = pickle.dumps(result)
if result and isinstance(result[0], basestring):
result = [result]
return result
def populate_shadow_cols(db, tablemd, schema):
tablename = tablemd.tablename
colnames = schema.columns.keys()
arg = ','.join(colnames)
resproxy = db.execute("""select %s from %s order by id asc""" % (arg, tablename))
annotations = defaultdict(list)
for anno in tablemd.annotations:
annotations[anno.name].append((anno.loctype, anno.extractor()))
def annotate_shadow(shadow_data, loctype, vals):
for sd, v in zip(shadow_data, vals):
sd[loctype] = v
while True:
rows = resproxy.fetchmany(2000)
if not rows:
break
coldatas = zip(*rows)
ids = None
shadow_data = [dict() for row in rows]
for cn, cd in zip(colnames, coldatas):
cd = [ re_badchar.sub(' ', to_utf(v)).lower().strip() for v in cd if v]
annos = annotations[cn]
for loctype, extractor in annos:
extracted = map(extractor, cd)
if loctype == 'latlon':
lats, lons = zip(*extracted)
annotate_shadow(shadow_data, 'latitude', lats)
annotate_shadow(shadow_data, 'longitude', lons)
else:
annotate_shadow(shadow_data, loctype, extracted)
ids = cd if cn == 'id' else ids
print 'saving', len(rows)
save_shadow(db, tablename, ids, shadow_data)
loctypes = set([anno.loctype for anno in tablemd.annotations])
if ('latlon' in loctypes or
('latitude' in loctypes and 'longitude' in loctypes)):
return False
return True
def geocode_batch(self, vals):
"""
@return iterator of (loctype, latlon or shapeid)
"""
Q = """select shape_id
from __dbtruck_state__ s, __dbtruck_statename__ sn
where s.fips = sn.fips and
levenshtein(name, %s) < 1 + 0.2*char_length(name)
order by levenshtein(name, %s) limit 1"""
for v in vals:
try:
v = to_utf(v.lower())
shapeids = self.session.bind.execute(Q, (v,v)).fetchone()
yield SHAPE, shapeids[0]
except Exception as e:
print e
yield None
def _geocode(self, address, restriction=''):
"""
Try to pick the best geocoder for the address. Google has a low
daily limit so try to use other geocoders if possible
If the address looks like a standard address (NUMBER WORDS+),
then use yahoo, bing, geocoder.us
Otherwise if it is a place name, try geonames
If all else fails, use Google
"""
geocoder = None
format_string = self.get_format_string(restriction)
query = to_utf((format_string % address).lower())
if not query:
return []
if query in self.cache:
try:
ret = pickle.loads(self.cache[query])
if ret and isinstance(ret[0], basestring):
return [ret]
return ret
except KeyboardInterrupt:
raise
except:
pass
if re_addr2.search(address) and restriction:
rand = random.random()
if rand < 0.5:
geocoder = geocoders.Yahoo(settings.YAHOO_APPID)
#elif rand < 0.8:
else:
geocoder = geocoders.Bing(settings.BING_APIKEY)
#else:
# geocoder = geocoders.GeocoderDotUS()
else:
geocoder = geocoders.GeoNames()
try:
result = geocoder.geocode(query, exactly_one=False)
self.ncalls += 1
if not result:
raise Exception('no result found for %s' % query)
except Exception as e:
print geocoder, e
try:
geocoder = geocoders.Google()
result = geocoder.geocode(query, exactly_one=False)
self.ncalls += 1
except Exception as e:
print e
result = []
self.cache[query] = pickle.dumps(result)
if result and isinstance(result[0], basestring):
result = [result]
return result
r = shapefile.Reader("./tl_2008_us_county")
db = create_engine(DBURI)
db_session = scoped_session(sessionmaker(autocommit=False, autoflush=True, bind=db))
seen = set()
for idx in xrange(r.numRecords):
recshape = r.shapeRecord(idx)
rec, shape = recshape.record, recshape.shape
statefp, countyfp, shortname, name = rec[0], rec[1], rec[4].lower(), rec[5].lower()
countyfp = int(statefp + countyfp)
statefp = int(statefp)
shortname = to_utf(shortname)
name = to_utf(name)
print countyfp, "\t", name
shp = get_wkt(shape)
names = [CountyName(fips=statefp, name=name), CountyName(fips=statefp, name=shortname)]
shapeobj = Shape(shp)
county = County(fips=countyfp, state_fips=statefp, names=names, county_shape=shapeobj)
db_session.add_all(names)
db_session.add_all([shapeobj, county])
db_session.commit()
def possible_loc(colname, vals):
def is_ok(new_vals, maxposs=vals, thresh=0.65):
n = 0
for v in new_vals:
if isinstance(v, list) or isinstance(v, tuple):
if filter(lambda s:s, v):
n += 1
else:
if v != None:
n += 1
if float(n) > thresh * len(maxposs):
return n
return False
vals = [ re_badchar.sub(' ', to_utf(v)).lower().strip() for v in vals if v]
nonempty = [v for v in vals if v]
colname = colname.lower().strip()
ret = {}#defaultdict()
if 'lat' in colname:
lats = map(parse_lat, vals)
if is_ok(lats, nonempty, thresh=0.8):
ret['latitude'] = 'parse_lat'
if 'lon' in colname:
lons = map(parse_lon, vals)
if is_ok(lons, nonempty, thresh=0.8):
ret['longitude'] = 'parse_lon'
if 'latitude' in ret and 'longitude' in ret:
return ret
if is_ok(map(parse_coords, vals), nonempty, thresh=0.5):
ret['latlon'] = 'parse_coords'
return ret
if 'zip' in colname:
zips = map(parse_zip, vals)
if is_ok(zips, nonempty):
return {"zipcode" : 'parse_zip'}
if colname.startswith('st'):
states = map(parse_state, vals)
if is_ok(states, nonempty):
return {'state' : 'parse_state'}
zips = map(parse_per_word_zip, vals)
if is_ok(zips, nonempty, thresh=0.8):
ret['zipcode'] = 'parse_per_word_zip'
states = map(parse_per_word_state, vals)
if is_ok(states, nonempty, thresh=0.8):
ret['state'] = 'parse_per_word_state'
# county codes
# countries
# street addresses (number string string suffix)
# column is not a single attribute, lets look for composite data
# ok maybe its embedded in the text??
addrs = map(parse_addrs, vals)
if is_ok(addrs, nonempty, thresh=0.55):
ret['address'] = 'parse_addrs'
return ret
