Rechercher dans les propriétés de la page

Cette page fournit une interface de navigation pour trouver toutes les valeurs d’une propriété et une page donnée. D’autres interfaces de recherche disponibles incluent la recherche de propriété et le constructeur de requête « ask ».

Liste de résultats

<nowiki>import requests & … <nowiki>import requests from bs4 import BeautifulSoup class AQIRepository: URL = "https://www.atmo-nouvelleaquitaine.org/air-commune/Bordeaux/33063/indice-atmo?date=2025-10-14" def __init__(self): self.cat_to_conc = { 'Bon': {'PM2.5': 5.0, 'PM10': 15.0, 'O3': 0.040, 'NO2': 20.0, 'SO2': 5.0}, 'Moyen': {'PM2.5': 20.0, 'PM10': 40.0, 'O3': 0.060, 'NO2': 60.0, 'SO2': 20.0}, 'Mauvais':{'PM2.5': 40.0, 'PM10': 100.0,'O3': 0.100, 'NO2': 150.0,'SO2': 100.0}, 'Très mauvais':{'PM2.5':120.0,'PM10':300.0,'O3':0.180,'NO2':800.0,'SO2':500.0} } self.breakpoints = { 'PM2.5': [(0.0,12.0,0,50),(12.1,35.4,51,100),(35.5,55.4,101,150),(55.5,150.4,151,200)], 'PM10': [(0,54,0,50),(55,154,51,100),(155,254,101,150),(255,354,151,200)], 'O3': [(0.000,0.054,0,50),(0.055,0.070,51,100),(0.071,0.085,101,150)], 'NO2': [(0,53,0,50),(54,100,51,100),(101,360,101,150),(361,649,151,200)], 'SO2': [(0,35,0,50),(36,75,51,100),(76,185,101,150),(186,304,151,200)], } def linear_scale(self, C, C_low, C_high, I_low, I_high): return round((I_high - I_low) / (C_high - C_low) * (C - C_low) + I_low) def find_aqi_subindex(self, C, pollutant): for (C_low, C_high, I_low, I_high) in self.breakpoints[pollutant]: if C_low <= C <= C_high: return self.linear_scale(C, C_low, C_high, I_low, I_high) return None def scrape_categories(self): response = requests.get(self.URL, timeout=10) soup = BeautifulSoup(response.text, "html.parser") categories = {} blocks = soup.select(".c-indice-polluant") for block in blocks: title_tag = block.select_one(".c-indice-polluant-title span") cat_tag = block.select_one(".home-map-legend-item span") if title_tag and cat_tag: name = title_tag.get_text(strip=True).replace("₂", "2").replace("₃", "3") cat = cat_tag.get_text(strip=True) categories[name] = cat return categories def compute_aqi(self, categories): concentrations = {k: self.cat_to_conc[v][k] for k, v in categories.items() if k in self.cat_to_conc[v]} subindices = {k: self.find_aqi_subindex(concentrations[k], k) for k in concentrations} aqi_global = max(v for v in subindices.values() if v is not None) return aqi_global, subindices import requests class MeteoRepository: def __init__(self, latitude=44.8378, longitude=-0.5792): self.latitude = latitude self.longitude = longitude self.api_url = ( "https://api.open-meteo.com/v1/forecast?" f"latitude={self.latitude}&longitude={self.longitude}" "&hourly=temperature_2m,precipitation,weathercode" "¤t_weather=true&timezone=Europe%2FParis" ) def get_weather(self): response = requests.get(self.api_url, timeout=10) data = response.json() return data.get("current_weather", {}) def compute_weather_score(self, weather): wmo_scores = { 0: 100, 1: 90, 2: 80, 3: 70, 45: 60, 48: 55, 51: 50, 53: 45, 55: 40, 61: 35, 63: 25, 65: 15, 71: 25, 73: 15, 75: 5, 95: 10, 99: 0, } temp = weather.get("temperature", 20) wind = weather.get("windspeed", 10) code = weather.get("weathercode", 0) base_score = wmo_scores.get(code, 60) if temp < 0: temp_penalty = 30 elif temp < 10: temp_penalty = 15 elif temp > 30: temp_penalty = 20 else: temp_penalty = 0 wind_penalty = 0 if wind > 40: wind_penalty = 20 elif wind > 25: wind_penalty = 10 score = base_score - temp_penalty - wind_penalty return round(max(0, min(100, score))) import requests import json class TrafficRepository: BASE_URL = "https://data.bordeaux-metropole.fr/geojson" def __init__(self, api_key="2HH8S7FKPP", typename="ci_trafi_l"): self.api_key = api_key self.typename = typename def get_traffic_by_gid(self, gid=2215): params = { "key": self.api_key, "typename": self.typename, "filter": json.dumps({"gid": gid}), } response = requests.get(self.BASE_URL, params=params, timeout=10) if response.status_code != 200: raise Exception(f"Erreur API {response.status_code} : {response.text}") return response.json() def compute_traffic_score(self, traffic_data): features = traffic_data.get("features", []) if not features: return 50 props = features[0].get("properties", {}) etat = props.get("etat_trafic") or props.get("etat") or "INCONNU" mapping = {"FLUIDE": 100, "RALENTI": 70, "EMBOUTEILLE": 30, "INCONNU": 50} return mapping.get(etat.upper(), 50) import requests import json from datetime import datetime, timezone class TbmRepository: BASE_URL = "https://data.bordeaux-metropole.fr/geojson" def __init__(self, api_key="2HH8S7FKPP", arret_id=4037): self.api_key = api_key self.arret_id = arret_id def get_next_tram(self, max_results=100): params = { "key": self.api_key, "typename": "sv_horai_a", "filter": json.dumps({"rs_sv_arret_p": self.arret_id}), "orderby": json.dumps(["hor_theo"]), "maxfeatures": max_results } response = requests.get(self.BASE_URL, params=params) response.raise_for_status() data = response.json() now = datetime.now(timezone.utc) for feature in data.get("features", []): props = feature.get("properties", {}) hor_theo = props.get("hor_theo") if not hor_theo: continue try: tram_time = datetime.fromisoformat(hor_theo) except ValueError: continue if tram_time > now: diff_sec = int((tram_time - now).total_seconds()) return {"heure": tram_time, "seconds": diff_sec, "etat": props.get("etat"), "type": props.get("type")} return None from AQIRepository import AQIRepository from MeteoRepository import MeteoRepository from TrafficRepository import TrafficRepository from TramRepository import TbmRepository from fastapi import FastAPI app = FastAPI() @app.get("/") def read_root(): longitude = -0.5935101 latitude = 44.8538665 borneTraficGID = 2215 Aqirepo = AQIRepository() categories = Aqirepo.scrape_categories() aqi, details = Aqirepo.compute_aqi(categories) meteo = MeteoRepository(latitude=latitude, longitude=longitude) weather = meteo.get_weather() score_M = meteo.compute_weather_score(weather) traffic = TrafficRepository() data = traffic.get_traffic_by_gid(borneTraficGID) score_T = traffic.compute_traffic_score(data) tbm = TbmRepository(arret_id=4037) prochain = tbm.get_next_tram() if prochain and 'seconds' in prochain: TP_seconds = prochain['seconds'] else: TP_seconds = 3600 max_tp_seconds = 3600 TP_score = max(0, min(100, 100 - (TP_seconds / max_tp_seconds) * 100)) IMR = ( 0.4 * (100 - aqi) + 0.3 * (100 - score_T) + 0.2 * TP_score + 0.1 * score_M ) return {"IMR": round(IMR, 1)} </nowiki>e(data) tbm = TbmRepository(arret_id=4037) prochain = tbm.get_next_tram() if prochain and 'seconds' in prochain: TP_seconds = prochain['seconds'] else: TP_seconds = 3600 max_tp_seconds = 3600 TP_score = max(0, min(100, 100 - (TP_seconds / max_tp_seconds) * 100)) IMR = ( 0.4 * (100 - aqi) + 0.3 * (100 - score_T) + 0.2 * TP_score + 0.1 * score_M ) return {"IMR": round(IMR, 1)} </nowiki>
Analyse des sources disponibles sur les sites de TBM et de Bordeaux Métropole pour identifier les bénéfices environnementaux du tramway et du réseau de bus. +
Bois, LED, papier transparent, éléments imprimés en 3D, ainsi que des cartes électroniques(Arduino ) pour la rotation et l’éclairage des LED. +
Chaque participant partage sa compréhension du thème. Toutes les idées et les mots-clés sont ensuite notés et organisés sous forme de schéma. +
Conception d’une machine capable d’analyser la qualité de l’air et le trafic, afin de proposer le meilleur moyen de transport entre bus, tram, voiture ou taxi. +