import os import pycountry import random import six import yaml from collections import defaultdict from geodata.address_formatting.formatter import AddressFormatter from geodata.addresses.config import address_config from geodata.addresses.floors import Floor from geodata.addresses.entrances import Entrance from geodata.addresses.house_numbers import HouseNumber from geodata.addresses.po_boxes import POBox from geodata.addresses.postcodes import PostCode from geodata.addresses.staircases import Staircase from geodata.addresses.units import Unit from geodata.configs.utils import nested_get from geodata.coordinates.conversion import latlon_to_decimal from geodata.countries.names import * from geodata.language_id.disambiguation import * from geodata.language_id.sample import sample_random_language from geodata.math.floats import isclose from geodata.math.sampling import cdf, weighted_choice from geodata.names.normalization import name_affixes from geodata.boundaries.names import boundary_names from geodata.osm.components import osm_address_components from geodata.states.state_abbreviations import state_abbreviations this_dir = os.path.realpath(os.path.dirname(__file__)) PARSER_DEFAULT_CONFIG = os.path.join(this_dir, os.pardir, os.pardir, os.pardir, 'resources', 'parser', 'default.yaml') class AddressComponents(object): ''' This class, while it has a few dependencies, exposes a simple method for transforming geocoded input addresses (usually a lat/lon with either a name or house number + street name) into the sorts of examples used by libpostal's address parser. The dictionaries produced here can be fed directly to AddressFormatter.format_address to produce training examples. There are several steps in expanding an address including reverse geocoding to polygons, disambiguating which language the address uses, stripping standard prefixes like "London Borough of", pruning duplicates like "Antwerpen, Antwerpen, Antwerpen". Usage: >>> components = AddressComponents(osm_admin_rtree, language_rtree, neighborhoods_rtree, buildings_rtree, subdivisions_rtree, quattroshapes_rtree, geonames) >>> components.expand({'name': 'Hackney Empire'}, 51.54559, -0.05567) Returns (results vary because of randomness): ({'city': u'London', 'city_district': u'London Borough of Hackney', 'country': 'UK', 'name': 'Hackney Empire', 'state': u'England', 'state_district': u'Greater London'}, u'gb', u'en') ''' iso_alpha2_codes = set([c.alpha2.lower() for c in pycountry.countries]) iso_alpha3_codes = set([c.alpha3.lower() for c in pycountry.countries]) rare_components = { AddressFormatter.SUBURB, AddressFormatter.CITY_DISTRICT, AddressFormatter.ISLAND, AddressFormatter.STATE_DISTRICT, AddressFormatter.STATE, } BOUNDARY_COMPONENTS = ( AddressFormatter.SUBURB, AddressFormatter.CITY_DISTRICT, AddressFormatter.CITY, AddressFormatter.ISLAND, AddressFormatter.STATE_DISTRICT, AddressFormatter.STATE ) ALL_OSM_NAME_KEYS = set(['name', 'name:simple', 'ISO3166-1:alpha2', 'ISO3166-1:alpha3', 'short_name', 'alt_name', 'official_name']) NULL_PHRASE = 'null' ALPHANUMERIC_PHRASE = 'alphanumeric' STANDALONE_PHRASE = 'standalone' sub_building_component_class_map = { AddressFormatter.ENTRANCE: Entrance, AddressFormatter.STAIRCASE: Staircase, AddressFormatter.LEVEL: Floor, AddressFormatter.UNIT: Unit, } def __init__(self, osm_admin_rtree, language_rtree, neighborhoods_rtree, quattroshapes_rtree, geonames): self.config = yaml.load(open(PARSER_DEFAULT_CONFIG)) self.osm_admin_rtree = osm_admin_rtree self.language_rtree = language_rtree self.neighborhoods_rtree = neighborhoods_rtree self.quattroshapes_rtree = quattroshapes_rtree self.geonames = geonames def strip_keys(self, value, ignore_keys): for key in ignore_keys: value.pop(key, None) def osm_reverse_geocoded_components(self, latitude, longitude): return self.osm_admin_rtree.point_in_poly(latitude, longitude, return_all=True) def categorized_osm_components(self, country, osm_components): components = defaultdict(list) for props in osm_components: name = props.get('name') if not name: continue for k, v in props.iteritems(): normalized_key = osm_address_components.get_component(country, k, v) if normalized_key: components[normalized_key].append(props) break return components def address_language(self, components, candidate_languages): ''' Language -------- If there's only one candidate language for a given country or region, return that language. In countries that speak multiple languages (Belgium, Hong Kong, Wales, the US in Spanish-speaking regions, etc.), we need at least a road name for disambiguation. If we can't identify a language, the address will be labeled "unk". If the street name itself contains phrases from > 1 language, the address will be labeled ambiguous. ''' language = None if len(candidate_languages) == 1: language = candidate_languages[0]['lang'] else: street = components.get(AddressFormatter.ROAD, None) if street is not None: language = disambiguate_language(street, [(l['lang'], l['default']) for l in candidate_languages]) else: language = UNKNOWN_LANGUAGE return language def pick_random_name_key(self, props, component, suffix=''): ''' Random name ----------- Pick a name key from OSM ''' raw_key = boundary_names.name_key(props, component) key = ''.join((raw_key, suffix)) if ':' not in raw_key else raw_key return key, raw_key def all_names(self, props, languages): names = set() for k, v in six.iteritems(props): if k in self.ALL_OSM_NAME_KEYS: names.add(v) elif ':' in k: k, qual = k.split(':', 1) if k in self.ALL_OSM_NAME_KEYS and qual.split('_', 1)[0] in languages: names.add(v) return names def normalized_place_name(self, name, tag, osm_components, country=None, languages=None): ''' Multiple place names -------------------- This is to help with things like addr:city="New York NY" ''' names = set() components = defaultdict(set) for props in osm_components: component_names = self.all_names(props, languages or set()) names |= component_names is_state = False for k, v in six.iteritems(props): normalized_key = osm_address_components.get_component(country, k, v) if not normalized_key: continue for cn in component_names: components[cn.lower()].add(normalized_key) if normalized_key == AddressFormatter.STATE and not is_state: is_state = True if is_state: for state in component_names: for language in languages: state_code = state_abbreviations.get_abbreviation(country, language, state, default=None) if state_code: names.add(state_code.upper()) phrase_filter = PhraseFilter([(n.lower(), '') for n in names]) tokens = tokenize(name) tokens_lower = [(t.lower(), c) for t, c in tokens] phrases = list(phrase_filter.filter(tokens_lower)) num_phrases = 0 total_tokens = 0 current_phrase_start = 0 current_phrase_len = 0 current_phrase = [] for is_phrase, phrase_tokens, value in phrases: if is_phrase: whitespace = not any((c in (token_types.IDEOGRAPHIC_CHAR, token_types.IDEOGRAPHIC_NUMBER) for t, c in phrase_tokens)) join_phrase = six.u(' ') if whitespace else six.u('') if num_phrases > 0: # Return phrase with original capitalization return join_phrase.join([t for t, c in tokens[:total_tokens]]) elif num_phrases == 0 and total_tokens > 0: phrase = join_phrase.join([t for t, c in phrase_tokens]) if tag not in components.get(phrase, set()): return None elif num_phrases == 0: current_phrase_tokens = tokens_lower[current_phrase_start:current_phrase_start + current_phrase_len] current_phrase = join_phrase.join([t for t, c in current_phrase_tokens]) current_phrase_start = total_tokens current_phrase_len = len(phrase_tokens) current_phrase_tokens = tokens_lower[current_phrase_start:current_phrase_start + current_phrase_len] current_phrase = join_phrase.join([t for t, c in current_phrase_tokens]) # Handles cases like addr:city="Harlem" when Harlem is a neighborhood tags = components.get(current_phrase, set()) if tags and tag not in tags: return None total_tokens += len(phrase_tokens) num_phrases += 1 else: total_tokens += 1 # If the name contains a comma, stop and only use the phrase before the comma if ',' in name: return name.split(',')[0].strip() return name def normalize_place_names(self, address_components, osm_components, country=None, languages=None): for key in list(address_components): name = address_components[key] if key in set(self.BOUNDARY_COMPONENTS): name = self.normalized_place_name(name, key, osm_components, country=country, languages=languages) if name is not None: address_components[key] = name else: address_components.pop(key) def normalize_address_components(self, components): address_components = {k: v for k, v in components.iteritems() if k in self.formatter.aliases} self.formatter.aliases.replace(address_components) return address_components def combine_fields(self, address_components, language, country=None, generated_components=None): combo_config = address_config.get_property('components.combinations', language, country=country, default={}) values = [] probs = [] generated_components = generated_components or set() for k, v in six.iteritems(combo_config): values.append(v) probs.append(v['probability']) if not isclose(sum(probs), 1.0): values.append(None) probs.append(1.0 - sum(probs)) probs = cdf(probs) combo = weighted_choice(values, probs) if combo is not None: components = OrderedDict.fromkeys(combo['components']).keys() if not all((c in address_components and (c in generated_components or self.is_numeric(address_components[c])) for c in components)): return None values = [] probs = [] for s in combo['separators']: values.append(s['separator']) probs.append(s['probability']) probs = cdf(probs) separator = weighted_choice(values, probs) new_label = combo['label'] new_value = separator.join([address_components.pop(c) for c in components]) address_components[new_label] = new_value return new_label return None def generated_type(self, component, existing_components, language, country=None): component_config = address_config.get_property('components.{}'.format(component), language, country=country) if not component_config: return None prob_dist = component_config conditionals = component_config.get('conditional', []) if conditionals: for vals in conditionals: c = vals['component'] if c in existing_components: prob_dist = vals['probabilities'] break values = [] probs = [] for num_type in (self.NULL_PHRASE, self.ALPHANUMERIC_PHRASE, self.STANDALONE_PHRASE): key = '{}_probability'.format(num_type) prob = component_config.get(key) if prob is not None: values.append(num_type) probs.append(prob) probs = cdf(probs) num_type = weighted_choice(values, probs) if num_type == self.NULL_PHRASE: return None else: return num_type def is_numeric(self, component, language, country=None): tokens = tokenize(component) return sum((1 for t, c in tokens if c == token_types.NUMERIC or c not in token_types.WORD_TOKEN_TYPES)) == len(tokens) def get_component_phrase(self, cls, component, language, country=None): component = safe_decode(component) if self.is_numeric(component): phrase = cls.phrase(component, language, country=country) if phrase != component: return phrase else: return None else: return component def cldr_country_name(self, country_code, language): ''' Country names ------------- In OSM, addr:country is almost always an ISO-3166 alpha-2 country code. However, we'd like to expand these to include natural language forms of the country names we might be likely to encounter in a geocoder or handwritten address. These splits are somewhat arbitrary but could potentially be fit to data from OpenVenues or other sources on the usage of country name forms. If the address includes a country, the selection procedure proceeds as follows: 1. With probability a, select the country name in the language of the address (determined above), or with the localized country name if the language is undtermined or ambiguous. 2. With probability b(1-a), sample a language from the distribution of languages on the Internet and use the country's name in that language. 3. This is implicit, but with probability (1-b)(1-a), keep the country code ''' cldr_config = nested_get(self.config, ('country', 'cldr')) alpha_2_iso_code_prob = float(cldr_config['iso_alpha_2_code_probability']) localized_name_prob = float(cldr_config['localized_name_probability']) alpha_3_iso_code_prob = float(cldr_config['iso_alpha_3_code_probability']) values = ('localized', 'alpha3', 'alpha2') probs = cdf([localized_name_prob, alpha_3_iso_code_prob, alpha_2_iso_code_prob]) value = weighted_choice(values, probs) country_name = country_code.upper() if language in (AMBIGUOUS_LANGUAGE, UNKNOWN_LANGUAGE): language = None if value == 'localized': country_name = country_names.localized_name(country_code, language) or country_names.localized_name(country_code) or country_name elif value == 'alpha3': country_name = country_names.alpha3_code(country_code) or country_name return country_name def is_country_iso_code(self, country): country = country.lower() return country in self.iso_alpha2_codes or country in self.iso_alpha3_codes def replace_country_name(self, address_components, country, language): address_country = address_components.get(AddressFormatter.COUNTRY) cldr_country_prob = float(nested_get(self.config, ('country', 'cldr_country_probability'))) replace_with_cldr_country_prob = float(nested_get(self.config, ('country', 'replace_with_cldr_country_probability'))) remove_iso_code_prob = float(nested_get(self.config, ('country', 'remove_iso_code_probability'))) is_iso_code = address_country and self.is_country_iso_code(address_country) if (is_iso_code and random.random() < replace_with_cldr_country_prob) or random.random() < cldr_country_prob: address_country = self.cldr_country_name(country, language) if address_country: address_components[AddressFormatter.COUNTRY] = address_country elif is_iso_code and random.random() < remove_iso_code_prob: address_components.pop(AddressFormatter.COUNTRY) def non_local_language(self): non_local_language_prob = float(nested_get(self.config, ('languages', 'non_local_language_probability'))) if random.random() < non_local_language_prob: return sample_random_language() return None def state_name(self, address_components, country, language, non_local_language=None, always_use_full_names=False): ''' States ------ Primarily for the US, Canada and Australia, OSM tends to use the abbreviated state name whereas we'd like to include both forms, so wtih some probability, replace the abbreviated name with the unabbreviated one e.g. CA => California ''' address_state = address_components.get(AddressFormatter.STATE) if address_state and country and not non_local_language: state_full_name = state_abbreviations.get_full_name(country, language, address_state) state_full_name_prob = float(nested_get(self.config, ('state', 'full_name_probability'))) if state_full_name and (always_use_full_names or random.random() < state_full_name_prob): address_state = state_full_name elif address_state and non_local_language: _ = address_components.pop(AddressFormatter.STATE, None) address_state = None return address_state def pick_language_suffix(self, osm_components, language, non_local_language, more_than_one_official_language): ''' Language suffix --------------- This captures some variations in languages written with different scripts e.g. language=ja_rm is for Japanese Romaji. Pick a language suffix with probability proportional to how often the name is used in the reverse geocoded components. So if only 2/5 components have name:ja_rm listed but 5/5 have either name:ja or just plain name, we would pick standard Japanese (Kanji) with probability .7143 (5/7) and Romaji with probability .2857 (2/7). ''' # This captures name variations like "ja_rm" for Japanese Romaji, etc. language_scripts = defaultdict(int) use_language = (non_local_language or language) for c in osm_components: for k, v in six.iteritems(c): if ':' not in k: continue splits = k.split(':') if len(splits) > 0 and splits[0] == 'name' and '_' in splits[-1] and splits[-1].split('_', 1)[0] == use_language: language_scripts[splits[-1]] += 1 elif k == 'name' or (splits[0] == 'name' and splits[-1]) == use_language: language_scripts[None] += 1 language_script = None if len(language_scripts) > 1: cumulative = float(sum(language_scripts.values())) values = list(language_scripts.keys()) probs = cdf([float(c) / cumulative for c in language_scripts.values()]) language_script = weighted_choice(values, probs) if not language_script and not non_local_language and not more_than_one_official_language: return '' else: return ':{}'.format(language_script or non_local_language or language) def add_admin_boundaries(self, address_components, osm_components, country, language, non_local_language=None, language_suffix='', random_key=True, always_use_full_names=False, ): ''' OSM boundaries -------------- For many addresses, the city, district, region, etc. are all implicitly generated by the reverse geocoder e.g. we do not need an addr:city tag to identify that 40.74, -74.00 is in New York City as well as its parent geographies (New York county, New York state, etc.) Where possible we augment the addr:* tags with some of the reverse-geocoded relations from OSM. Since addresses found on the web may have the same properties, we include these qualifiers in the training data. ''' simple_name_key = 'name:simple' international_name_key = 'int_name' if osm_components: name_key = ''.join((boundary_names.DEFAULT_NAME_KEY, language_suffix)) raw_name_key = boundary_names.DEFAULT_NAME_KEY osm_components = self.categorized_osm_components(country, osm_components) poly_components = defaultdict(list) existing_city_name = address_components.get(AddressFormatter.CITY) for component, components_values in osm_components.iteritems(): seen = set() for component_value in components_values: if random_key: key, raw_key = self.pick_random_name_key(component_value, component, suffix=language_suffix) else: key, raw_key = name_key, raw_name_key for k in (key, name_key, raw_key, raw_name_key): name = component_value.get(k) if name and not (name == existing_city_name and component != AddressFormatter.CITY): break # if we've checked all keys without finding a valid name, leave this component out else: continue if (component, name) not in seen: poly_components[component].append(name) seen.add((component, name)) abbreviate_state_prob = float(nested_get(self.config, ('state', 'abbreviated_probability'))) join_state_district_prob = float(nested_get(self.config, ('state_district', 'join_probability'))) replace_with_non_local_prob = float(nested_get(self.config, ('languages', 'replace_non_local_probability'))) for component, vals in poly_components.iteritems(): if component not in address_components or (non_local_language and random.random() < replace_with_non_local_prob): if not always_use_full_names: if component == AddressFormatter.STATE_DISTRICT and random.random() < join_state_district_prob: num = random.randrange(1, len(vals) + 1) val = six.u(', ').join(vals[:num]) elif len(vals) == 1: val = vals[0] else: val = random.choice(vals) if component == AddressFormatter.STATE and random.random() < abbreviate_state_prob: val = state_abbreviations.get_abbreviation(country, language, val, default=val) address_components[component] = val def quattroshapes_city(self, address_components, latitude, longitude, language, non_local_language=None, always_use_full_names=False): ''' Quattroshapes/GeoNames cities ----------------------------- Quattroshapes isn't great for everything, but it has decent city boundaries in places where OSM sometimes does not (or at least in places where we aren't currently able to create valid polygons). While Quattroshapes itself doesn't reliably use local names, which we'll want for consistency, Quattroshapes cities are linked with GeoNames, which has per-language localized names for most places. ''' city = None qs_add_city_prob = float(nested_get(self.config, ('city', 'quattroshapes_geonames_backup_city_probability'))) abbreviated_name_prob = float(nested_get(self.config, ('city', 'quattroshapes_geonames_abbreviated_probability'))) if AddressFormatter.CITY not in address_components and random.random() < qs_add_city_prob: lang = non_local_language or language quattroshapes_cities = self.quattroshapes_rtree.point_in_poly(latitude, longitude, return_all=True) for result in quattroshapes_cities: if result.get(self.quattroshapes_rtree.LEVEL) == self.quattroshapes_rtree.LOCALITY and self.quattroshapes_rtree.GEONAMES_ID in result: geonames_id = int(result[self.quattroshapes_rtree.GEONAMES_ID].split(',')[0]) names = self.geonames.get_alternate_names(geonames_id) if not names or lang not in names: continue city = None if 'abbr' not in names or non_local_language: # Use the common city name in the target language city = names[lang][0][0] elif not always_use_full_names and random.random() < abbreviated_name_prob: # Use an abbreviation: NYC, BK, SF, etc. city = random.choice(names['abbr'])[0] if not city or not city.strip(): continue return city break else: if non_local_language and AddressFormatter.CITY in address_components and ( AddressFormatter.CITY_DISTRICT in address_components or AddressFormatter.SUBURB in address_components): address_components.pop(AddressFormatter.CITY) return city def neighborhood_components(self, latitude, longitude): return self.neighborhoods_rtree.point_in_poly(latitude, longitude, return_all=True) def add_neighborhoods(self, address_components, neighborhoods, language_suffix=''): ''' Neighborhoods ------------- In some cities, neighborhoods may be included in a free-text address. OSM includes many neighborhoods but only as points, rather than the polygons needed to perform reverse-geocoding. We use a hybrid index containing Quattroshapes/Zetashapes polygons matched fuzzily with OSM names (which are on the whole of better quality). ''' neighborhood_levels = defaultdict(list) add_prefix_prob = float(nested_get(self.config, ('neighborhood', 'add_prefix_probability'))) add_neighborhood_prob = float(nested_get(self.config, ('neighborhood', 'add_neighborhood_probability'))) name_key = ''.join((boundary_names.DEFAULT_NAME_KEY, language_suffix)) raw_name_key = boundary_names.DEFAULT_NAME_KEY for neighborhood in neighborhoods: place_type = neighborhood.get('place') polygon_type = neighborhood.get('polygon_type') neighborhood_level = AddressFormatter.SUBURB if place_type == 'borough' or polygon_type == 'local_admin': neighborhood_level = AddressFormatter.CITY_DISTRICT # Optimization so we don't use e.g. Brooklyn multiple times city_name = address_components.get(AddressFormatter.CITY) if name == city_name: name = neighborhood.get(name_key, neighborhood.get(raw_name_key)) if not name or name == city_name: continue key, raw_key = self.pick_random_name_key(neighborhood, neighborhood_level, suffix=language_suffix) name = neighborhood.get(key, neighborhood.get(raw_key)) if not name: name = neighborhood.get(name_key, neighborhood.get(raw_name_key)) name_prefix = neighborhood.get('name:prefix') if name and name_prefix and random.random() < add_prefix_prob: name = six.u(' ').join([name_prefix, name]) if not name: continue neighborhood_levels[neighborhood_level].append(name) for component, neighborhoods in neighborhood_levels.iteritems(): if component not in address_components and random.random() < add_neighborhood_prob: address_components[component] = neighborhoods[0] def generate_sub_building_component(self, component, address_components, language, country=None, **kw): existing = address_components.get(component, None) component_class = self.sub_building_component_class_map[component] if existing is None: generated_type = self.generated_type(component, address_components, language, country=country) if generated_type == self.ALPHANUMERIC_PHRASE: num = component_class.random(language, country=country, **kw) address_components[component] = num return num elif generated_type == self.STANDALONE_PHRASE: return None return None def add_sub_building_phrase(self, component, address_components, language, country, generated_components=None, **kw): num = address_components.get(component) if not num: return generated_components = generated_components or set() component_class = self.sub_building_component_class_map[component] if component in generated_components: phrase = component_class.phrase(num, language, country=country, **kw) if phrase: address_components[component] = phrase else: phrase = self.get_component_phrase(existing, language, country=country) if phrase and phrase != existing: address_components[component] = phrase def add_sub_building_components(self, address_components, language, country=None, num_floors=None, num_basements=None, zone=None): generated_components = set() if self.generate_sub_building_component(AddressFormatter.ENTRANCE, address_components, language, country=country): generated_components.add(AddressFormatter.ENTRANCE) if self.generate_sub_building_component(AddressFormatter.STAIRCASE, address_components, language, country=country): generated_components.add(AddressFormatter.STAIRCASE) if self.generate_sub_building_component(AddressFormatter.LEVEL, address_components, language, country=country, num_floors=num_floors, num_basements=num_basements): generated_components.add(AddressFormatter.LEVEL) if self.generate_sub_building_component(AddressFormatter.UNIT, address_components, language, country=country, num_floors=num_floors, num_basements=num_basements): generated_components.add(AddressFormatter.UNIT) # Combine fields like unit/house_number here combined = self.combine_fields(address_components, language, country=country, generated_components=generated_components) if combined: generated_components -= set([combined]) self.add_sub_building_phrase(AddressFormatter.ENTRANCE, address_components, language, country=country, generated_components=generated_components) self.add_sub_building_phrase(AddressFormatter.STAIRCASE, address_components, language, country=country, generated_components=generated_components) self.add_sub_building_phrase(AddressFormatter.LEVEL, address_components, language, country=country, generated_components=generated_components, num_floors=num_floors) self.add_sub_building_phrase(AddressFormatter.UNIT, address_components, language, country=country, generated_components=generated_components, zone=zone) def replace_name_affixes(self, address_components, language): ''' Name normalization ------------------ Probabilistically strip standard prefixes/suffixes e.g. "London Borough of" ''' replacement_prob = float(nested_get(self.config, ('names', 'replace_affix_probability'))) for component in list(address_components): if component not in self.BOUNDARY_COMPONENTS: continue name = address_components[component] if not name: continue replacement = name_affixes.replace_suffixes(name, language) replacement = name_affixes.replace_prefixes(replacement, language) if replacement != name and random.random() < replacement_prob: address_components[component] = replacement def replace_names(self, address_components): ''' Name replacements ----------------- Make a few special replacements (like UK instead of GB) ''' for component, value in address_components.iteritems(): replacement = nested_get(self.config, ('value_replacements', component, value), default=None) if replacement is not None: new_value = repl['replacement'] prob = repl['probability'] if random.random() < prob: address_components[component] = new_value def prune_duplicate_names(self, address_components): ''' Name deduping ------------- For some cases like "Antwerpen, Antwerpen, Antwerpen" that are very unlikely to occur in real life. Note: prefer the city name in these cases ''' name_components = defaultdict(list) for component in (AddressFormatter.CITY, AddressFormatter.STATE_DISTRICT, AddressFormatter.CITY_DISTRICT, AddressFormatter.SUBURB): name = address_components.get(component) if name: name_components[name].append(component) for name, components in name_components.iteritems(): if len(components) > 1: for component in components[1:]: address_components.pop(component, None) def cleanup_house_number(self, address_components): ''' House number cleanup -------------------- This method was originally used for OSM nodes because in some places, particularly Uruguay, we see house numbers that are actually a comma-separated list. It seemed prudent to retain this cleanup in the generalized version in case we see similar issues with other data sets. If there's one comma in the house number, allow it as it might be legitimate, but if there are 2 or more, just take the first one. ''' house_number = address_components.get(AddressFormatter.HOUSE_NUMBER) if not house_number: return if ';' in house_number: house_number = house_number.replace(';', ',') address_components[AddressFormatter.HOUSE_NUMBER] = house_number if house_number and house_number.count(',') >= 2: house_numbers = house_number.split(',') random.shuffle(house_numbers) for num in house_numbers: num = num.strip() if num: address_components[AddressFormatter.HOUSE_NUMBER] = num break else: address_components.pop(AddressFormatter.HOUSE_NUMBER, None) def add_house_number_phrase(self, address_components, language, country=None): house_number = address_components.get(AddressFormatter.HOUSE_NUMBER, None) phrase = HouseNumber.phrase(house_number, language, country=country) if phrase and phrase != house_number: address_components[AddressFormatter.HOUSE_NUMBER] = phrase def add_postcode_phrase(self, address_components, language, country=None): postcode = address_components.get(AddressFormatter.POSTCODE, None) if postcode: phrase = PostCode.phrase(postcode, language, country=country) if phrase and phrase != postcode: address_components[AddressFormatter.POSTCODE] = phrase def expanded(self, address_components, latitude, longitude, num_floors=None, num_basements=None, zone=None): ''' Expanded components ------------------- Many times in geocoded address data sets, we get only a few components (say street name and house number) plus a lat/lon. There's a lot of information in a lat/lon though, so this method "fills in the blanks" as it were. Namely, it calls all the methods above to reverse geocode to a few of the R-tree + point-in-polygon indices passed in at initialization and adds things like admin boundaries, neighborhoods, ''' try: latitude, longitude = latlon_to_decimal(latitude, longitude) except Exception: return None, None, None country, candidate_languages, language_props = self.language_rtree.country_and_languages(latitude, longitude) if not (country and candidate_languages): return None, None, None language = None more_than_one_official_language = len(candidate_languages) > 1 language = self.address_language(address_components, candidate_languages) non_local_language = self.non_local_language() # If a country was already specified self.replace_country_name(address_components, country, non_local_language or language) address_state = self.state_name(address_components, country, language, non_local_language=non_local_language) if address_state: address_components[AddressFormatter.STATE] = address_state osm_components = self.osm_reverse_geocoded_components(latitude, longitude) neighborhoods = self.neighborhood_components(latitude, longitude) all_languages = set([l['lang'] for l in candidate_languages]) all_osm_components = osm_components + neighborhoods language_suffix = self.pick_language_suffix(all_osm_components, language, non_local_language, more_than_one_official_language) self.normalize_place_names(address_components, all_osm_components, country=country, languages=all_languages) self.add_admin_boundaries(address_components, osm_components, country, language, non_local_language=non_local_language, language_suffix=language_suffix) city = self.quattroshapes_city(address_components, latitude, longitude, language, non_local_language=non_local_language) if city: address_components[AddressFormatter.CITY] = city self.add_neighborhoods(address_components, neighborhoods, language_suffix=language_suffix) street = address_components.get(AddressFormatter.ROAD) self.replace_name_affixes(address_components, non_local_language or language) self.replace_names(address_components) self.prune_duplicate_names(address_components) self.cleanup_house_number(address_components) self.add_house_number_phrase(address_components, language, country=country) self.add_postcode_phrase(address_components, language, country=country) self.add_sub_building_components(address_components, language, country=country, num_floors=num_floors, num_basements=num_basements, zone=zone) return address_components, country, language def limited(self, address_components, latitude, longitude): try: latitude, longitude = latlon_to_decimal(latitude, longitude) except Exception: return None, None, None country, candidate_languages, language_props = self.language_rtree.country_and_languages(latitude, longitude) if not (country and candidate_languages): return None, None, None remove_keys = NAME_KEYS + HOUSE_NUMBER_KEYS + POSTAL_KEYS + OSM_IGNORE_KEYS for key in remove_keys: _ = value.pop(key, None) language = None more_than_one_official_language = len(candidate_languages) > 1 language = self.address_language(value, candidate_languages) address_components = self.normalize_address_components(value) non_local_language = self.non_local_language() self.replace_country_name(address_components, country, non_local_language or language) address_state = self.state_name(address_components, country, language, non_local_language=non_local_language, always_use_full_names=True) if address_state: address_components[AddressFormatter.STATE] = address_state street = address_components.get(AddressFormatter.ROAD) osm_components = self.osm_reverse_geocoded_components(latitude, longitude) neighborhoods = self.neighborhood_components(latitude, longitude) all_languages = set([l['lang'] for l in candidate_languages]) all_osm_components = osm_components + neighborhoods language_suffix = self.pick_language_suffix(all_osm_components, language, non_local_language, more_than_one_official_language) self.normalize_place_names(address_components, all_osm_components, country=country, languages=all_languages) self.add_admin_boundaries(address_components, osm_components, country, language, language_suffix=language_suffix, non_local_language=non_local_language, random_key=False, always_use_full_names=True) city = self.quattroshapes_city(address_components, latitude, longitude, language, non_local_language=non_local_language, always_use_full_names=True) if city: address_components[AddressFormatter.CITY] = city neighborhoods = self.neighborhood_components(latitude, longitude) self.add_neighborhoods(address_components, neighborhoods, language_suffix=language_suffix) self.replace_name_affixes(address_components, non_local_language or language) self.replace_names(address_components) self.prune_duplicate_names(address_components) return address_components, country, language