X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/predictops.git/blobdiff_plain/1aa2671e4f047322d6957a58a7b44a568b25d67d..66b4627c14e9f89a2e5ab73bbf48819f8f3a1455:/predictops/source/meteofrance.py diff --git a/predictops/source/meteofrance.py b/predictops/source/meteofrance.py index 2326e16..afe18ad 100644 --- a/predictops/source/meteofrance.py +++ b/predictops/source/meteofrance.py @@ -16,12 +16,19 @@ import gzip fileConfig((Path.cwd() / 'config') / 'logging.cfg') logger = getLogger() +CSV_FILE = Path.cwd() / 'config' / 'features' / 'meteofrance_features.csv' + + class MeteoFrance: - def __init__(self, latitude = 47.25, longitude = 6.0333, nb_stations = 3, - start = datetime.strptime('19960101000000', '%Y%m%d%H%M%S'), - end = datetime.now(), - features = []): + _latitude = None + _longitude = None + _nb_stations = None + _start = None + _end = None + _features = None + + def __init__(self, config_file): ''' Constructor of the MeteoFrance source of feature. @@ -34,33 +41,80 @@ class MeteoFrance: https://donneespubliques.meteofrance.fr/?fond=produit&id_produit=90&id_rubrique=32 Parameters: + - in config file: latitude (float): The latitude from which we want the meteo features. longitude (float): The longitude from which we want the meteo features. nb_stations (int): Number of closest stations to consider. + - provided to the constructor features (list): Weather features that have to be integrated, according to their names in meteofrance_features.csv (cf. config directory) ''' - self._latitude = latitude - self._longitude = longitude - self._nb_stations = nb_stations - self._start = start - self._end = end - self._features = features + self._config = ConfigParser() + self._config.read(config_file) self._data_directory = (Path.cwd() / 'data') / 'features' / 'meteo_france' self._dated_features = None # Re-creating data directory architecture for MeteoFrance, if asked - config = ConfigParser() - config.read((Path.cwd() / 'config') / 'features.cfg') - if eval(config['meteofrance']['regenerate']): + if self._config['GENERAL'].getboolean('regenerate'): self._regenerate_directory() # Collecting the closest meteo station + self._nb_stations = self._config['STATIONS'].getint('nb_stations') self._stations = self._get_stations() + # Collecting meteofrance features + with open(CSV_FILE, "r") as f: + reader = DictReader(f, delimiter=',') + self._features = [row['name'] for row in reader + if self._config['FEATURES'].getboolean(row['name'])] + + + @property + def start(self): + return self._start + + @start.setter + def start(self, x): + self._start = x + + + @property + def end(self): + return self._end + + @end.setter + def end(self, x): + self._end = x + + + @property + def latitude(self): + return self._latitude + + @latitude.setter + def latitude(self, x): + self._latitude = x + + + @property + def longitude(self): + return self._longitude + + @longitude.setter + def longitude(self, x): + self._longitude = x + + + @property + def nb_stations(self): + return self._nb_stations + + @nb_stations.setter + def nb_stations(self, x): + self._nb_stations = x def _regenerate_directory(self): @@ -193,10 +247,9 @@ class MeteoFrance: dict: the dictionary of features per datestamp ''' if self._dated_features == None: - csv_file = Path.cwd() / 'config' / 'features' / 'meteofrance_features.csv' - logger.info(f'Collecting meteo feature information from {csv_file}') + logger.info(f'Collecting meteo feature information from {CSV_FILE}') # A dictionary for the features - with open(csv_file, "r") as f: + with open(CSV_FILE, "r") as f: reader = DictReader(f, delimiter=',') dico_features = {row["abbreviation"]: { @@ -204,17 +257,22 @@ class MeteoFrance: 'type': row['type'] # qualitative (2) or quantitative (1) } for row in reader if row['name'] in self._features} + #print([row for row in reader]) + #print([row for row in reader if row['name'] in self._features]) dir_data = Path.cwd() / 'data' / 'features' / 'meteo_france' / 'historical' self._dated_features = {} for csv_meteo in listdir(dir_data): date = datetime.strptime(csv_meteo.split('.')[1], '%Y%m') - if date >= self._start and date <= self._end: + if (date >= self._start and date <= self._end)\ + or (date.year == self._start.year and date.month == self._start.month)\ + or (date.year == self._end.year and date.month == self._end.month): logger.info(f'Inserting {csv_meteo} in intervention dictionary') with open(dir_data / csv_meteo, "r") as f: reader = DictReader(f, delimiter=';') for row in reader: if row['numer_sta'] in self._stations: date = datetime.strptime(row['date'], '%Y%m%d%H%M%S') - self._dated_features.setdefault(date,{}).update({dico_features[feat]['name']+'_'+str(self._stations.index(row['numer_sta'])): eval(row[feat].replace('mq','None')) for feat in dico_features}) + if date >= self._start and date <= self._end: + self._dated_features.setdefault(date,{}).update({dico_features[feat]['name']+'_'+str(self._stations.index(row['numer_sta'])): eval(row[feat].replace('mq','None')) for feat in dico_features}) return self._dated_features