def identify_missing_locations(unidentified_grouped_locations_enum,
identified_grouped_locations,
minimum_match_value, t):
#For each group of locations with the same country
for i, item in unidentified_grouped_locations_enum:
country, grouped_locations_list = item
#Get a list of all cities that exist anywhere in that country
all_cities_in_country = geo_data_session.query(
AllCities.city, AllCities.region).filter_by(country=country)
#Construct a name for each location that matches the normal cleaned location format
all_cities_in_country = [
geoalchemy_util.concatenate_location(
x.city, x.region if geoalchemy_util.region_is_a_state(x.region)
else '', country) for x in all_cities_in_country
]
#For each location found in this country, find its closest match
#among the list of all cities from that country
for grouped_location in grouped_locations_list:
cleaned_location = grouped_location["cleaned_location"]
closest_match = geoalchemy_util.get_closest_match_leven(
cleaned_location, all_cities_in_country, minimum_match_value)
#If no match was found or only the trivial match
if closest_match == '' or closest_match == country:
continue
#If we have a good match, add it to the list of matched locations
closest_match_split = re.split(",", closest_match)
city = closest_match_split[0].strip()
if len(closest_match_split) == 3:
region = closest_match_split[1].strip()
country = closest_match_split[2].strip()
matching_location = geo_data_session.query(
AllCities).filter_by(city=city,
region=region,
country=country).first()
else:
country = closest_match_split[1].strip()
matching_location = geo_data_session.query(
AllCities).filter_by(city=city, country=country).first()
if not matching_location:
print 'Warning: all_cities match attempt failed for', cleaned_location.encode(
'utf8'), 'location not found'
grouping_id = u"{0}|{1}".format(matching_location.latitude,
matching_location.longitude)
raw_location = grouped_location["raw_location"]
identified_grouped_locations.append({
"raw_location": raw_location,
"matching_location": matching_location,
"grouping_id": grouping_id
})
print 'all_cities found additional location for', raw_location
def identify_missing_locations(
unidentified_grouped_locations_enum, identified_grouped_locations, minimum_match_value, t
):
# For each group of locations with the same country
for i, item in unidentified_grouped_locations_enum:
country, grouped_locations_list = item
# Get a list of all cities that exist anywhere in that country
all_cities_in_country = geo_data_session.query(AllCities.city, AllCities.region).filter_by(country=country)
# Construct a name for each location that matches the normal cleaned location format
all_cities_in_country = [
geoalchemy_util.concatenate_location(
x.city, x.region if geoalchemy_util.region_is_a_state(x.region) else "", country
)
for x in all_cities_in_country
]
# For each location found in this country, find its closest match
# among the list of all cities from that country
for grouped_location in grouped_locations_list:
cleaned_location = grouped_location["cleaned_location"]
closest_match = geoalchemy_util.get_closest_match_leven(
cleaned_location, all_cities_in_country, minimum_match_value
)
# If no match was found or only the trivial match
if closest_match == "" or closest_match == country:
continue
# If we have a good match, add it to the list of matched locations
closest_match_split = re.split(",", closest_match)
city = closest_match_split[0].strip()
if len(closest_match_split) == 3:
region = closest_match_split[1].strip()
country = closest_match_split[2].strip()
matching_location = (
geo_data_session.query(AllCities).filter_by(city=city, region=region, country=country).first()
)
else:
country = closest_match_split[1].strip()
matching_location = geo_data_session.query(AllCities).filter_by(city=city, country=country).first()
if not matching_location:
print "Warning: all_cities match attempt failed for", cleaned_location.encode(
"utf8"
), "location not found"
grouping_id = u"{0}|{1}".format(matching_location.latitude, matching_location.longitude)
raw_location = grouped_location["raw_location"]
identified_grouped_locations.append(
{"raw_location": raw_location, "matching_location": matching_location, "grouping_id": grouping_id}
)
print "all_cities found additional location for", raw_location
def find_difficult_locations_from_file(inputfilename, outputfilename):
inputfile = open(inputfilename, 'r')
outputfile = open(outputfilename, 'w+')
t = datetime.datetime.now()
all_japan_cities_query = geo_data_session.query(AllCities.city).filter(AllCities.country=='JP').group_by(AllCities.city).all()
all_japan_cities = []
for row in all_japan_cities_query:
all_japan_cities.append(row.city)
print 'list of all_japan_cities created', datetime.datetime.now()-t
for line in inputfile:
line = line.decode('utf8')
line = geoalchemy_util.remove_eol_pattern.sub('', line)
if line.endswith(', JP') or line.endswith(', JA'):
city = line.split(',')[0].strip()
most_similar_city = geoalchemy_util.get_closest_match_leven(city, all_japan_cities, 0.8)
if most_similar_city!='':
outputfile.write('{0}|{1}\n'.format(city.encode('utf8'), most_similar_city.encode('utf8')))
print datetime.datetime.now()-t
def find_difficult_locations_from_file(inputfilename, outputfilename):
inputfile = open(inputfilename, "r")
outputfile = open(outputfilename, "w+")
t = datetime.datetime.now()
all_japan_cities_query = (
geo_data_session.query(AllCities.city).filter(AllCities.country == "JP").group_by(AllCities.city).all()
)
all_japan_cities = []
for row in all_japan_cities_query:
all_japan_cities.append(row.city)
print "list of all_japan_cities created", datetime.datetime.now() - t
for line in inputfile:
line = line.decode("utf8")
line = geoalchemy_util.remove_eol_pattern.sub("", line)
if line.endswith(", JP") or line.endswith(", JA"):
city = line.split(",")[0].strip()
most_similar_city = geoalchemy_util.get_closest_match_leven(city, all_japan_cities, 0.8)
if most_similar_city != "":
outputfile.write("{0}|{1}\n".format(city.encode("utf8"), most_similar_city.encode("utf8")))
print datetime.datetime.now() - t
