Towards more modular system for data files
Also use `lru_cache` to do some memoization on data files loading function, and speed up everything a bit.
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@ -8,6 +8,8 @@ import argparse
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import logging
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import sys
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logging.basicConfig()
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import flatisfy.config
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from flatisfy import cmds
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from flatisfy import data
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@ -118,14 +120,14 @@ def main():
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# Set logger
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if args.vv:
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logging.basicConfig(level=logging.DEBUG)
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logging.getLogger('').setLevel(logging.DEBUG)
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logging.getLogger('sqlalchemy.engine').setLevel(logging.DEBUG)
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elif args.verbose:
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logging.basicConfig(level=logging.INFO)
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logging.getLogger('').setLevel(logging.INFO)
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# sqlalchemy INFO level is way too loud, just stick with WARNING
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logging.getLogger('sqlalchemy.engine').setLevel(logging.WARNING)
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else:
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logging.basicConfig(level=logging.WARNING)
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logging.getLogger('').setLevel(logging.WARNING)
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logging.getLogger('sqlalchemy.engine').setLevel(logging.WARNING)
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# Init-config command
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@ -10,12 +10,27 @@ import json
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import logging
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import os
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import flatisfy.exceptions
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LOGGER = logging.getLogger(__name__)
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MODULE_DIR = os.path.dirname(os.path.realpath(__file__))
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# Try to load lru_cache
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try:
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from functools import lru_cache
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except ImportError:
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try:
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from functools32 import lru_cache
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except ImportError:
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lru_cache = lambda maxsize=None: lambda func: func
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LOGGER.warning(
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"`functools.lru_cache` is not available on your system. Consider "
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"installing `functools32` Python module if using Python2 for "
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"better performances."
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)
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def _preprocess_ratp(output_dir):
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"""
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@ -98,6 +113,18 @@ def _preprocess_laposte(output_dir):
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return True
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DATA_FILES = {
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"ratp.json": {
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"preprocess": _preprocess_ratp,
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"output": ["ratp.json"]
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},
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"laposte.json": {
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"preprocess": _preprocess_laposte,
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"output": ["cities.json", "postal_codes.json"]
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},
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}
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def preprocess_data(config, force=False):
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"""
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Ensures that all the necessary data files have been built from the raw
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@ -115,38 +142,32 @@ def preprocess_data(config, force=False):
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except OSError:
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LOGGER.debug("Opendata directory already existed, doing nothing.")
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is_built_ratp = os.path.isfile(
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os.path.join(opendata_directory, "ratp.json")
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# Build all the necessary data files
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for data_file in DATA_FILES:
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# Check if already built
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is_built = all(
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os.path.isfile(
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os.path.join(opendata_directory, output)
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) for output in DATA_FILES[data_file]["output"]
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)
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if not is_built_ratp or force:
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LOGGER.info("Building from RATP data.")
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if not _preprocess_ratp(opendata_directory):
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raise flatisfy.exceptions.DataBuildError("Error with RATP data.")
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is_built_laposte = (
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os.path.isfile(os.path.join(opendata_directory, "cities.json")) and
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os.path.isfile(os.path.join(opendata_directory, "postal_codes.json"))
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)
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if not is_built_laposte or force:
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LOGGER.info("Building from LaPoste data.")
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if not _preprocess_laposte(opendata_directory):
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if not is_built or force:
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# Build if needed
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LOGGER.info("Building from {} data.".format(data_file))
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if not DATA_FILES[data_file]["preprocess"](opendata_directory):
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raise flatisfy.exceptions.DataBuildError(
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"Error with LaPoste data."
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"Error with {} data.".format(data_file)
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)
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@lru_cache(maxsize=5)
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def load_data(data_type, config):
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"""
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Load a given built data file.
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Load a given built data file. This function is memoized.
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:param data_type: A valid data identifier.
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:param config: A config dictionary.
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:return: The loaded data. ``None`` if the query is incorrect.
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"""
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if data_type not in ["postal_codes", "cities", "ratp"]:
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LOGGER.error("Invalid request. No %s data file.", data_type)
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return None
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opendata_directory = os.path.join(config["data_directory"], "opendata")
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datafile_path = os.path.join(opendata_directory, "%s.json" % data_type)
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data = {}
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