166 lines
5.4 KiB
Python
166 lines
5.4 KiB
Python
#!/usr/bin/env python3
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# inspiration :
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# https://towardsdatascience.com/loading-data-from-openstreetmap-with-python-and-the-overpass-api-513882a27fd0
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# https://geo.api.gouv.fr/adresse
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# https://wiki.cartocite.fr/doku.php?id=umap:10_-_je_valorise_les_donnees_openstreetmap_avec_umap
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# https://sites-formations.univ-rennes2.fr/mastersigat/Cours/Intro_Overpass.pdf
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# usage des tags : https://taginfo.openstreetmap.org/tags/?key=amenity&value=bicycle_parking#combinations
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# exemple URL données pour umap : https://www.velocite63.fr/velocite63/OSM/stationnements_velos_publics.json
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# penser à cocher "proxy" dans la rubrique "données distantes" du calque
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# export ODS :
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# https://pythonhosted.org/pyexcel-ods/
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# pip3 install pyexcel-ods3
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import requests
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import json
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import time
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from pyexcel_ods3 import save_data
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from collections import OrderedDict
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import os
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from osm_vc63 import errors
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from osm_vc63 import requetes
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from osm_vc63.utils import Utils
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overpass_url = "http://overpass-api.de/api/interpreter"
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geo_api_url = "https://api-adresse.data.gouv.fr"
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dossier_sauvegarde = "resultats/"
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# nombre maxi de retries quand echec API
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max_retry = 4
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# delai en secondes entre les tentatives
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retry_delay = 120
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# id du département "Puy de Dôme" : 7406
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# id Riom : 1693144
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# id Clermont : 110866
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# id Romagnat : 138269
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# l'id de l'area se calcule en ajoutant 3600000000 au numéro de l'objet OSM
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aire_de_recherche = str(3600000000 + 110866)
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# ----------------------------------------------
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trad_bicycle_parking = {
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"stands": "Arceaux",
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"wall_loops": "Pince roues",
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"rack": "Râteliers",
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"anchors": "Ancrage",
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"shed": "Abri collectif",
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"bollard": "Potelet",
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"lockers": "Abris individuels",
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"wide_stands": "Arceaux espacés",
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"ground_slots": "Fente dans le sol",
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"building": "Bâtiment",
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"informal": "Informel",
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"wave": "Râteliers",
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"streetpod": "Arceaux",
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"tree": "Arbre à bicyclettes",
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"crossbar": "Barre",
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"rope": "Câble",
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"two-tier": "Deux étages",
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"floor": "Sol",
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"handlebar_holder": "Accroche-guidons",
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}
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def executer_requete_et_exporter_resultats(
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nom_req, critere, aire_de_recherche, overpass_query_fields
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):
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utils = Utils(overpass_url, geo_api_url, dossier_sauvegarde)
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data = utils.run_overpass_query(critere, aire_de_recherche)
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nb_elements = len(data["elements"])
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print("Nombre d'elements : " + str(nb_elements))
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"""
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print("Géocodage inversé : ", end="", flush=True)
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# @TODO : optimiser en faisant un appel au service /reverse/csv/ plutot que le service unitaire /reverse/
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for element in data["elements"]:
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if (element["type"] == "node") :
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rev_geocode = run_reverse_geocoding(element["lat"], element["lon"])
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else :
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rev_geocode = run_reverse_geocoding(element["center"]["lat"], element["center"]["lon"])
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api_adresse = rev_geocode["features"][0]
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element["tags"]["api_adresse:geometry:coordinates:lon"] = api_adresse["geometry"]["coordinates"][0]
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element["tags"]["api_adresse:geometry:coordinates:lat"] = api_adresse["geometry"]["coordinates"][1]
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element["tags"]["api_adresse:properties:label"] = api_adresse["properties"]["label"]
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element["tags"]["api_adresse:properties:score"] = api_adresse["properties"]["score"]
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if ("housenumber" in api_adresse["properties"]) :
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element["tags"]["api_adresse:properties:housenumber"] = api_adresse["properties"]["housenumber"]
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element["tags"]["api_adresse:properties:type"] = api_adresse["properties"]["type"]
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element["tags"]["api_adresse:properties:name"] = api_adresse["properties"]["name"]
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element["tags"]["api_adresse:properties:postcode"] = api_adresse["properties"]["postcode"]
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element["tags"]["api_adresse:properties:citycode"] = api_adresse["properties"]["citycode"]
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element["tags"]["api_adresse:properties:city"] = api_adresse["properties"]["city"]
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if ("street" in api_adresse["properties"]) :
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element["tags"]["api_adresse:properties:street"] = api_adresse["properties"]["street"]
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element["tags"]["api_adresse:properties:attribution"] = rev_geocode["attribution"]
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element["tags"]["api_adresse:properties:licence"] = rev_geocode["licence"]
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# traduction
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if "bicycle_parking" in element["tags"]:
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element["tags"]["bicycle_parking"] = trad_bicycle_parking[element["tags"]["bicycle_parking"]]
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print("X", end="", flush=True)
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#else :
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# print("-", end="", flush=True)
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print()
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"""
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export_json = utils.nettoyage_json_pour_umap(data, overpass_query_fields)
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# Sauvegarde
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os.makedirs(dossier_sauvegarde, exist_ok=True)
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utils.save_as_json(export_json, nom_req)
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utils.save_as_ods(overpass_query_fields, data, nom_req)
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def main():
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for req in requetes.reqs:
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for nb_essai in range(max_retry): # on tente max_retry fois
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try:
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executer_requete_et_exporter_resultats(
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req.nom, req.critere, aire_de_recherche, req.champs
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)
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break
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except errors.ApiError:
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if nb_essai == max_retry:
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print("trop d'erreurs d'API - abandon")
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exit()
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print("erreur API - on retente dans " + str(retry_delay) + "s")
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time.sleep(retry_delay)
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print("Fini")
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if __name__ == "__main__":
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main()
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