mirror of
https://github.com/osm-search/Nominatim.git
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3eb4d88057
This adds an installation step for PHP code for the tokenizer. The PHP code is split in two parts. The updateable code is found in lib-php. The tokenizer installs an additional script in the project directory which then includes the code from lib-php and defines all settings that are static to the database. The website code then always includes the PHP from the project directory.
505 lines
18 KiB
Python
505 lines
18 KiB
Python
"""
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Tokenizer implementing normalisation as used before Nominatim 4.
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"""
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from collections import OrderedDict
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import logging
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import re
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import shutil
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from textwrap import dedent
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from icu import Transliterator
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import psycopg2
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import psycopg2.extras
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from nominatim.db.connection import connect
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from nominatim.db import properties
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from nominatim.db import utils as db_utils
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from nominatim.db.sql_preprocessor import SQLPreprocessor
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from nominatim.errors import UsageError
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DBCFG_NORMALIZATION = "tokenizer_normalization"
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DBCFG_MAXWORDFREQ = "tokenizer_maxwordfreq"
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LOG = logging.getLogger()
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def create(dsn, data_dir):
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""" Create a new instance of the tokenizer provided by this module.
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"""
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return LegacyTokenizer(dsn, data_dir)
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def _install_module(config_module_path, src_dir, module_dir):
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""" Copies the PostgreSQL normalisation module into the project
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directory if necessary. For historical reasons the module is
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saved in the '/module' subdirectory and not with the other tokenizer
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data.
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The function detects when the installation is run from the
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build directory. It doesn't touch the module in that case.
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"""
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# Custom module locations are simply used as is.
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if config_module_path:
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LOG.info("Using custom path for database module at '%s'", config_module_path)
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return config_module_path
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# Compatibility mode for builddir installations.
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if module_dir.exists() and src_dir.samefile(module_dir):
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LOG.info('Running from build directory. Leaving database module as is.')
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return module_dir
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# In any other case install the module in the project directory.
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if not module_dir.exists():
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module_dir.mkdir()
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destfile = module_dir / 'nominatim.so'
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shutil.copy(str(src_dir / 'nominatim.so'), str(destfile))
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destfile.chmod(0o755)
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LOG.info('Database module installed at %s', str(destfile))
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return module_dir
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def _check_module(module_dir, conn):
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""" Try to use the PostgreSQL module to confirm that it is correctly
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installed and accessible from PostgreSQL.
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"""
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with conn.cursor() as cur:
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try:
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cur.execute("""CREATE FUNCTION nominatim_test_import_func(text)
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RETURNS text AS '{}/nominatim.so', 'transliteration'
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LANGUAGE c IMMUTABLE STRICT;
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DROP FUNCTION nominatim_test_import_func(text)
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""".format(module_dir))
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except psycopg2.DatabaseError as err:
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LOG.fatal("Error accessing database module: %s", err)
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raise UsageError("Database module cannot be accessed.") from err
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class LegacyTokenizer:
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""" The legacy tokenizer uses a special PostgreSQL module to normalize
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names and queries. The tokenizer thus implements normalization through
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calls to the database.
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"""
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def __init__(self, dsn, data_dir):
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self.dsn = dsn
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self.data_dir = data_dir
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self.normalization = None
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def init_new_db(self, config, init_db=True):
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""" Set up a new tokenizer for the database.
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This copies all necessary data in the project directory to make
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sure the tokenizer remains stable even over updates.
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"""
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module_dir = _install_module(config.DATABASE_MODULE_PATH,
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config.lib_dir.module,
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config.project_dir / 'module')
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self.normalization = config.TERM_NORMALIZATION
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self._install_php(config)
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with connect(self.dsn) as conn:
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_check_module(module_dir, conn)
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self._save_config(conn, config)
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conn.commit()
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if init_db:
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self.update_sql_functions(config)
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self._init_db_tables(config)
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def init_from_project(self):
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""" Initialise the tokenizer from the project directory.
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"""
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with connect(self.dsn) as conn:
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self.normalization = properties.get_property(conn, DBCFG_NORMALIZATION)
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def update_sql_functions(self, config):
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""" Reimport the SQL functions for this tokenizer.
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"""
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with connect(self.dsn) as conn:
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max_word_freq = properties.get_property(conn, DBCFG_MAXWORDFREQ)
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modulepath = config.DATABASE_MODULE_PATH or \
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str((config.project_dir / 'module').resolve())
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sqlp = SQLPreprocessor(conn, config)
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sqlp.run_sql_file(conn, 'tokenizer/legacy_tokenizer.sql',
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max_word_freq=max_word_freq,
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modulepath=modulepath)
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def migrate_database(self, config):
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""" Initialise the project directory of an existing database for
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use with this tokenizer.
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This is a special migration function for updating existing databases
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to new software versions.
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"""
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self.normalization = config.TERM_NORMALIZATION
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module_dir = _install_module(config.DATABASE_MODULE_PATH,
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config.lib_dir.module,
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config.project_dir / 'module')
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with connect(self.dsn) as conn:
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_check_module(module_dir, conn)
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self._save_config(conn, config)
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def name_analyzer(self):
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""" Create a new analyzer for tokenizing names and queries
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using this tokinzer. Analyzers are context managers and should
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be used accordingly:
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```
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with tokenizer.name_analyzer() as analyzer:
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analyser.tokenize()
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```
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When used outside the with construct, the caller must ensure to
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call the close() function before destructing the analyzer.
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Analyzers are not thread-safe. You need to instantiate one per thread.
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"""
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normalizer = Transliterator.createFromRules("phrase normalizer",
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self.normalization)
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return LegacyNameAnalyzer(self.dsn, normalizer)
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def _install_php(self, config):
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""" Install the php script for the tokenizer.
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"""
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php_file = self.data_dir / "tokenizer.php"
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php_file.write_text(dedent("""\
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<?php
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@define('CONST_Max_Word_Frequency', {0.MAX_WORD_FREQUENCY});
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@define('CONST_Term_Normalization_Rules', "{0.TERM_NORMALIZATION}");
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require_once('{0.lib_dir.php}/tokenizer/legacy_tokenizer.php');
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""".format(config)))
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def _init_db_tables(self, config):
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""" Set up the word table and fill it with pre-computed word
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frequencies.
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"""
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with connect(self.dsn) as conn:
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sqlp = SQLPreprocessor(conn, config)
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sqlp.run_sql_file(conn, 'tokenizer/legacy_tokenizer_tables.sql')
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conn.commit()
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LOG.warning("Precomputing word tokens")
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db_utils.execute_file(self.dsn, config.lib_dir.data / 'words.sql')
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def _save_config(self, conn, config):
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""" Save the configuration that needs to remain stable for the given
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database as database properties.
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"""
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properties.set_property(conn, DBCFG_NORMALIZATION, self.normalization)
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properties.set_property(conn, DBCFG_MAXWORDFREQ, config.MAX_WORD_FREQUENCY)
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class LegacyNameAnalyzer:
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""" The legacy analyzer uses the special Postgresql module for
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splitting names.
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Each instance opens a connection to the database to request the
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normalization.
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"""
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def __init__(self, dsn, normalizer):
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self.conn = connect(dsn).connection
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self.conn.autocommit = True
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self.normalizer = normalizer
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psycopg2.extras.register_hstore(self.conn)
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self._cache = _TokenCache(self.conn)
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def __enter__(self):
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return self
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def __exit__(self, exc_type, exc_value, traceback):
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self.close()
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def close(self):
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""" Free all resources used by the analyzer.
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"""
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if self.conn:
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self.conn.close()
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self.conn = None
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def normalize(self, phrase):
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""" Normalize the given phrase, i.e. remove all properties that
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are irrelevant for search.
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"""
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return self.normalizer.transliterate(phrase)
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def add_postcodes_from_db(self):
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""" Add postcodes from the location_postcode table to the word table.
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"""
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with self.conn.cursor() as cur:
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cur.execute("""SELECT count(create_postcode_id(pc))
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FROM (SELECT distinct(postcode) as pc
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FROM location_postcode) x""")
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def update_special_phrases(self, phrases):
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""" Replace the search index for special phrases with the new phrases.
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"""
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norm_phrases = set(((self.normalize(p[0]), p[1], p[2], p[3])
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for p in phrases))
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with self.conn.cursor() as cur:
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# Get the old phrases.
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existing_phrases = set()
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cur.execute("""SELECT word, class, type, operator FROM word
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WHERE class != 'place'
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OR (type != 'house' AND type != 'postcode')""")
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for label, cls, typ, oper in cur:
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existing_phrases.add((label, cls, typ, oper or '-'))
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to_add = norm_phrases - existing_phrases
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to_delete = existing_phrases - norm_phrases
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if to_add:
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psycopg2.extras.execute_values(
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cur,
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""" INSERT INTO word (word_id, word_token, word, class, type,
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search_name_count, operator)
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(SELECT nextval('seq_word'), make_standard_name(name), name,
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class, type, 0,
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CASE WHEN op in ('in', 'near') THEN op ELSE null END
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FROM (VALUES %s) as v(name, class, type, op))""",
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to_add)
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if to_delete:
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psycopg2.extras.execute_values(
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cur,
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""" DELETE FROM word USING (VALUES %s) as v(name, in_class, in_type, op)
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WHERE word = name and class = in_class and type = in_type
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and ((op = '-' and operator is null) or op = operator)""",
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to_delete)
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LOG.info("Total phrases: %s. Added: %s. Deleted: %s",
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len(norm_phrases), len(to_add), len(to_delete))
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def add_country_names(self, country_code, names):
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""" Add names for the given country to the search index.
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"""
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with self.conn.cursor() as cur:
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cur.execute(
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"""INSERT INTO word (word_id, word_token, country_code)
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(SELECT nextval('seq_word'), lookup_token, %s
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FROM (SELECT ' ' || make_standard_name(n) as lookup_token
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FROM unnest(%s)n) y
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WHERE NOT EXISTS(SELECT * FROM word
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WHERE word_token = lookup_token and country_code = %s))
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""", (country_code, names, country_code))
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def process_place(self, place):
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""" Determine tokenizer information about the given place.
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Returns a JSON-serialisable structure that will be handed into
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the database via the token_info field.
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"""
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token_info = _TokenInfo(self._cache)
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names = place.get('name')
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if names:
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token_info.add_names(self.conn, names)
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country_feature = place.get('country_feature')
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if country_feature and re.fullmatch(r'[A-Za-z][A-Za-z]', country_feature):
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self.add_country_names(country_feature.lower(), list(names.values()))
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address = place.get('address')
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if address:
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hnrs = []
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addr_terms = []
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for key, value in address.items():
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if key == 'postcode':
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self._add_postcode(value)
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elif key in ('housenumber', 'streetnumber', 'conscriptionnumber'):
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hnrs.append(value)
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elif key == 'street':
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token_info.add_street(self.conn, value)
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elif key == 'place':
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token_info.add_place(self.conn, value)
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elif not key.startswith('_') and \
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key not in ('country', 'full'):
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addr_terms.append((key, value))
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if hnrs:
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token_info.add_housenumbers(self.conn, hnrs)
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if addr_terms:
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token_info.add_address_terms(self.conn, addr_terms)
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return token_info.data
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def _add_postcode(self, postcode):
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""" Make sure the normalized postcode is present in the word table.
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"""
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def _create_postcode_from_db(pcode):
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with self.conn.cursor() as cur:
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cur.execute('SELECT create_postcode_id(%s)', (pcode, ))
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if re.search(r'[:,;]', postcode) is None:
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self._cache.postcodes.get(postcode.strip().upper(), _create_postcode_from_db)
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class _TokenInfo:
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""" Collect token information to be sent back to the database.
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"""
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def __init__(self, cache):
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self.cache = cache
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self.data = {}
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def add_names(self, conn, names):
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""" Add token information for the names of the place.
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"""
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with conn.cursor() as cur:
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# Create the token IDs for all names.
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self.data['names'] = cur.scalar("SELECT make_keywords(%s)::text",
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(names, ))
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def add_housenumbers(self, conn, hnrs):
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""" Extract housenumber information from the address.
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"""
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if len(hnrs) == 1:
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token = self.cache.get_housenumber(hnrs[0])
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if token is not None:
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self.data['hnr_tokens'] = token
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self.data['hnr'] = hnrs[0]
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return
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# split numbers if necessary
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simple_list = []
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for hnr in hnrs:
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simple_list.extend((x.strip() for x in re.split(r'[;,]', hnr)))
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if len(simple_list) > 1:
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simple_list = list(set(simple_list))
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with conn.cursor() as cur:
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cur.execute("SELECT (create_housenumbers(%s)).* ", (simple_list, ))
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self.data['hnr_tokens'], self.data['hnr'] = cur.fetchone()
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def add_street(self, conn, street):
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""" Add addr:street match terms.
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"""
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def _get_street(name):
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with conn.cursor() as cur:
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return cur.scalar("SELECT word_ids_from_name(%s)::text", (name, ))
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self.data['street'] = self.cache.streets.get(street, _get_street)
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def add_place(self, conn, place):
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""" Add addr:place search and match terms.
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"""
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def _get_place(name):
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with conn.cursor() as cur:
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cur.execute("""SELECT (addr_ids_from_name(%s)
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|| getorcreate_name_id(make_standard_name(%s), ''))::text,
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word_ids_from_name(%s)::text""",
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(name, name, name))
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return cur.fetchone()
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self.data['place_search'], self.data['place_match'] = \
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self.cache.places.get(place, _get_place)
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def add_address_terms(self, conn, terms):
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""" Add additional address terms.
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"""
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def _get_address_term(name):
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with conn.cursor() as cur:
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cur.execute("""SELECT addr_ids_from_name(%s)::text,
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word_ids_from_name(%s)::text""",
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(name, name))
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return cur.fetchone()
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tokens = {}
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for key, value in terms:
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tokens[key] = self.cache.address_terms.get(value, _get_address_term)
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self.data['addr'] = tokens
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class _LRU:
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""" Least recently used cache that accepts a generator function to
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produce the item when there is a cache miss.
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"""
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def __init__(self, maxsize=128, init_data=None):
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self.data = init_data or OrderedDict()
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self.maxsize = maxsize
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if init_data is not None and len(init_data) > maxsize:
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self.maxsize = len(init_data)
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def get(self, key, generator):
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""" Get the item with the given key from the cache. If nothing
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is found in the cache, generate the value through the
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generator function and store it in the cache.
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"""
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value = self.data.get(key)
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if value is not None:
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self.data.move_to_end(key)
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else:
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value = generator(key)
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if len(self.data) >= self.maxsize:
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self.data.popitem(last=False)
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self.data[key] = value
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return value
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class _TokenCache:
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""" Cache for token information to avoid repeated database queries.
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This cache is not thread-safe and needs to be instantiated per
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analyzer.
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"""
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def __init__(self, conn):
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# various LRU caches
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self.streets = _LRU(maxsize=256)
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self.places = _LRU(maxsize=128)
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self.address_terms = _LRU(maxsize=1024)
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# Lookup houseunumbers up to 100 and cache them
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with conn.cursor() as cur:
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cur.execute("""SELECT i, ARRAY[getorcreate_housenumber_id(i::text)]::text
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FROM generate_series(1, 100) as i""")
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self._cached_housenumbers = {str(r[0]) : r[1] for r in cur}
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# Get postcodes that are already saved
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postcodes = OrderedDict()
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with conn.cursor() as cur:
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cur.execute("""SELECT word FROM word
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WHERE class ='place' and type = 'postcode'""")
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for row in cur:
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postcodes[row[0]] = None
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self.postcodes = _LRU(maxsize=32, init_data=postcodes)
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def get_housenumber(self, number):
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""" Get a housenumber token from the cache.
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"""
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return self._cached_housenumbers.get(number)
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