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@@ -9,3 +9,5 @@ build/ | |
| .vscode/ | ||
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| *.tsv | ||
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| venv/ | ||
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| ## 애플 클라이언트 JWT 갱신 | ||
| ## 애플 로그인용 Client Secret 갱신 | ||
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| 6 개월에 한번씩 갱신해줘야 합니다 | ||
| `secret.txt` 파일을 열면 team id, service id, key id, 그리고 authKey 가 저장되어 있습니다. | ||
| authKey 에 해당하는 내용을 `authkey.p8` 파일을 만들어 붙여넣고 `generate_client_secret.py` 에 적절한 값을 넣어 실행합니다. | ||
| service id, key id 그리고 authKey 는 테섭용과 실섭용이 구분되어 있으니 각각 스크립트에 입력하고 두번 실행해야 합니다. | ||
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| `secret.txt` 파일에 있는 저장되어 있는 값을 추출해서 `authkey.p8` 파일을 만든 뒤 `generate_client_secret.py` 파일을 실행합니다 | ||
| JWT 의 최대 유효기간이 6개월이라서 6개월마다 갱신해야 합니다. |
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28 changes: 28 additions & 0 deletions
28
...eploying_apps/local_script/src/main/kotlin/club/staircrusher/place/StationLngLatFinder.kt
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| package club.staircrusher.place | ||
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| import club.staircrusher.findLngLat | ||
| import club.staircrusher.place.infra.adapter.out.web.KakaoMapsService | ||
| import club.staircrusher.place.infra.adapter.out.web.KakaoProperties | ||
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| private const val kakaoApiKey = "test key" | ||
| private val kakaoMapsService = KakaoMapsService(KakaoProperties(kakaoApiKey)) | ||
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| val originalStationNames = listOf( | ||
| "압구정로데오","여의도","을지로입구","혜화","숙대입구(갈월)","을지로3가","종각","서초","신논현","충무로","삼성중앙","경복궁(정부서울청사)","상왕십리","종로5가","봉천","영등포시장","동대문역사문화공원","서대문","중랑","광화문(세종문화회관)","회현(남대문시장)","을지로3가","남영","명동","신도림","암사","종로3가","안국","불광","종로3가","시청","용산","신설동","합정","신촌","시청","고속터미널","충무로","이태원","청량리(서울시립대입구)","서울숲","사가정","동대입구","마포","문래","미아사거리","신촌","동대문","수유(강북구청)","영등포","한성대입구(삼선교)","독산","서울역","동대문역사문화공원","신설동","연신내","신설동","구로디지털단지","가산디지털단지","청량리(서울시립대입구)","신당","종로3가","한남","선릉","총신대입구(이수)","서울대입구(관악구청)","국회의사당","동대문","성수","약수","영등포구청","왕십리(성동구청)","왕십리(성동구청)","등촌","금천구청","화곡","논현","오류동","잠실새내","성신여대입구(돈암)","회기","신림","동묘앞","신사","약수","효창공원앞","연신내","삼각지","신당","구의(광진구청)","쌍문","군자(능동)","신림","가산디지털단지","성신여대입구(돈암)","까치산","압구정","디지털미디어시티","한티","언주","역삼","여의도","상봉(시외버스터미널)","공덕","뚝섬","낙성대","녹사평(용산구청)","군자(능동)","삼성(무역센터)","불광","아차산(어린이대공원후문)","신대방삼거리","천호(풍납토성)","건대입구","천호(풍납토성)","사당","안암(고대병원앞)","서울대벤처타운","가양","동묘앞","둔촌동","신논현","구로","홍대입구","선릉","구파발","노량진","석촌","대치","강남구청","신정네거리","공릉(서울과학기술대)","학동","디지털미디어시티","발산","명일","영등포구청","방배","건대입구","강동","신사","길동","공덕","망원","남부터미널(예술의전당)","강남","홍대입구","홍대입구","신도림","선정릉","양재(서초구청)","송정","먹골","송파","이대","상도","상봉(시외버스터미널)","신용산","당산","석계","노원","잠실(송파구청)","송파나루","당산","매봉","창동","선정릉","사당","강남","석촌","교대(법원.검찰청)","상수","강남구청","논현","노원","노량진","수서","잠실(송파구청)","양재(서초구청)","문정","고덕","교대(법원.검찰청)","서울역","가락시장","가락시장","강변(동서울터미널)","오목교(목동운동장앞)","마곡", | ||
| ) | ||
| fun main() { | ||
| val lngLatResults = kakaoMapsService.findLngLat(originalStationNames.map { | ||
| if ("(" in it) { | ||
| it.split("(")[0] | ||
| } else { | ||
| it | ||
| } + "역" | ||
| }) | ||
| println(lngLatResults) | ||
| println(buildString { | ||
| appendLine("지하철역,lng,lat") | ||
| originalStationNames.zip(lngLatResults).forEach { (originalStationName, lngLatResult) -> | ||
| appendLine("$originalStationName,${lngLatResult.lng},${lngLatResult.lat}") | ||
| } | ||
| }) | ||
| } |
1 change: 1 addition & 0 deletions
1
app-server/subprojects/deploying_apps/local_script/src/main/python/.python-version
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| 3.9.9 |
66 changes: 66 additions & 0 deletions
66
...ps/local_script/src/main/python/club_quests_target_places/crawl_places_nearby_stations.py
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| import csv | ||
| import requests | ||
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| # API endpoint and authorization token | ||
| API_URL = "https://api.staircrusher.club/admin/places/startCrawling" | ||
| AUTH_TOKEN = "eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzUxMiJ9.eyJfYiI6IlwiYWRtaW5cIiIsImlzcyI6Im91ci1tYXAtc2VydmVyIiwiZXhwIjoxNzYwNTQzODIwfQ.IkHTLjaKS8iuhTCuLgCBQ-Y2ClxthrjV4opanjrIoJK5yFFCFPFL_bWl7N--MoEIIzeWq0w5pHdWA91QM-kGfQ" | ||
| HEADERS = { | ||
| "authorization": AUTH_TOKEN, | ||
| "content-type": "application/json" | ||
| } | ||
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| # Function to calculate boundary vertices for 250m squares within the given area | ||
| def calculate_boundary_vertices(lng, lat, distance_km=1): | ||
| delta = distance_km / 111.32 # Approx. km per degree latitude | ||
| eighth_delta = delta / 8 | ||
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| # Define offsets for the 16 smaller 250m squares | ||
| offsets = [ | ||
| (-3 * eighth_delta, -3 * eighth_delta), (-1 * eighth_delta, -3 * eighth_delta), (1 * eighth_delta, -3 * eighth_delta), (3 * eighth_delta, -3 * eighth_delta), | ||
| (-3 * eighth_delta, -1 * eighth_delta), (-1 * eighth_delta, -1 * eighth_delta), (1 * eighth_delta, -1 * eighth_delta), (3 * eighth_delta, -1 * eighth_delta), | ||
| (-3 * eighth_delta, 1 * eighth_delta), (-1 * eighth_delta, 1 * eighth_delta), (1 * eighth_delta, 1 * eighth_delta), (3 * eighth_delta, 1 * eighth_delta), | ||
| (-3 * eighth_delta, 3 * eighth_delta), (-1 * eighth_delta, 3 * eighth_delta), (1 * eighth_delta, 3 * eighth_delta), (3 * eighth_delta, 3 * eighth_delta), | ||
| ] | ||
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| # Generate boundary vertices for each square | ||
| squares = [] | ||
| for offset_lng, offset_lat in offsets: | ||
| square = [ | ||
| {"lng": lng + offset_lng - eighth_delta, "lat": lat + offset_lat - eighth_delta}, | ||
| {"lng": lng + offset_lng + eighth_delta, "lat": lat + offset_lat - eighth_delta}, | ||
| {"lng": lng + offset_lng + eighth_delta, "lat": lat + offset_lat + eighth_delta}, | ||
| {"lng": lng + offset_lng - eighth_delta, "lat": lat + offset_lat + eighth_delta}, | ||
| {"lng": lng + offset_lng - eighth_delta, "lat": lat + offset_lat - eighth_delta}, | ||
| ] | ||
| squares.append(square) | ||
| return squares | ||
|
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| # Function to send a crawling request for a given set of boundary vertices | ||
| def send_crawling_request(boundary_vertices): | ||
| payload = {"boundaryVertices": boundary_vertices} | ||
| response = requests.post(API_URL, headers=HEADERS, json=payload) | ||
| if response.status_code == 200: | ||
| print("Crawling started successfully.") | ||
| else: | ||
| print(f"Failed to start crawling: {response.status_code}, {response.text}") | ||
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| # Main function to process the CSV and initiate crawling | ||
| def main(): | ||
| input_csv = "subway_stations.csv" # Replace with your CSV file name | ||
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| with open(input_csv, newline='', encoding='utf-8') as csvfile: | ||
| reader = csv.DictReader(csvfile) | ||
| for row in reader: | ||
| station_name = row["지하철역"] | ||
| longitude = float(row["lng"]) | ||
| latitude = float(row["lat"]) | ||
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| print(f"Processing station: {station_name} ({longitude}, {latitude})") | ||
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| # Calculate boundary vertices for smaller squares | ||
| squares = calculate_boundary_vertices(longitude, latitude, 1) # 1 km as total area | ||
| for square in squares: | ||
| send_crawling_request(square) | ||
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| if __name__ == "__main__": | ||
| main() |
39 changes: 39 additions & 0 deletions
39
...ript/src/main/python/club_quests_target_places/generate_subway_stations_insert_queries.py
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| import csv | ||
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| def generate_chunked_insert_queries(csv_file, table_name, chunk_size=1000): | ||
| insert_queries = [] | ||
| values = [] | ||
| with open(csv_file, newline='', encoding='utf-8') as csvfile: | ||
| separator = ',\n' | ||
| reader = csv.DictReader(csvfile) | ||
| for i, row in enumerate(reader, start=1): | ||
| station_name = row['지하철역'] | ||
| lng = float(row['lng']) | ||
| lat = float(row['lat']) | ||
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| # Prepare value for the query | ||
| value = f"('{station_name}', {lng}, {lat}, ST_SetSRID(ST_MakePoint({lng}, {lat}), 4326))" | ||
| values.append(value) | ||
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| # When chunk_size is reached, generate an INSERT query | ||
| if i % chunk_size == 0: | ||
| query = f"INSERT INTO {table_name} (name, lng, lat, geom) VALUES\n{separator.join(values)};" | ||
| insert_queries.append(query) | ||
| values = [] # Reset values for the next chunk | ||
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| # Add remaining rows as the final query | ||
| if values: | ||
| query = f"INSERT INTO {table_name} (name, lng, lat, geom) VALUES\n{separator.join(values)};" | ||
| insert_queries.append(query) | ||
|
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| return insert_queries | ||
|
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| if __name__ == "__main__": | ||
| # Usage | ||
| csv_file = "subway_stations.csv" # Replace with your CSV file name | ||
| table_name = "data_subway_station" # Replace with your table name | ||
| queries = generate_chunked_insert_queries(csv_file, table_name, chunk_size=1000) | ||
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| # Print queries | ||
| for query in queries: | ||
| print(query) |
21 changes: 21 additions & 0 deletions
21
...deploying_apps/local_script/src/main/python/club_quests_target_places/subway_stations.csv
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| 지하철역,lng,lat | ||
| 강남,127.02800140627488,37.49808633653005 | ||
| 석촌,127.107004062699,37.5054141216925 | ||
| 교대(법원.검찰청),127.013867969161,37.4927431676548 | ||
| 상수,126.92242989321456,37.54776618366393 | ||
| 강남구청,127.0413109462156,37.51721617197854 | ||
| 논현,127.02165424259898,37.51120000205266 | ||
| 노원,127.063449137455,37.6563403513278 | ||
| 노량진,126.940893179777,37.5135856714992 | ||
| 수서,127.104469788175,37.4852951142482 | ||
| 잠실(송파구청),127.10023101886318,37.51331105877401 | ||
| 양재(서초구청),127.03416413380752,37.48457681195669 | ||
| 문정,127.122484886901,37.4860310539381 | ||
| 고덕,127.15416500963447,37.55504766830918 | ||
| 교대(법원.검찰청),127.013867969161,37.4927431676548 | ||
| 서울역,126.96974961781686,37.55332892758497 | ||
| 가락시장,127.118262745146,37.4930992522183 | ||
| 가락시장,127.118262745146,37.4930992522183 | ||
| 강변(동서울터미널),127.094741101863,37.5351180385975 | ||
| 오목교(목동운동장앞),126.875307312696,37.5245340839144 | ||
| 마곡,126.82469691791624,37.56022863776301 |
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