Here's my approach to the problem, although my need for owslib is very specific, so it may not be applicable to your situation. My objective was to extract weather forecast data at a specific point using the WebMapService of owslib. As far as I know, this is the only way to achieve this with Environment Canada forecasts. To get the data, I made a request through wms.getfeatureinfo(). This returns a wrapper function that can be read and decoded without asyncio. However, to make it work with asyncio, I needed to use the method .geturl() from the wrapper, which gets the query URL. Then, I used the function async with session.get(url) as resp to obtain the necessary data.
Here is basically the necessary function. A lot of it has blabla stuff to handle whatif cases.
import re import numpy as np import pandas as pd from datetime import datetime import warnings from owslib.util import ServiceException from owslib.wms import WebMapService warnings.filterwarnings("ignore") import aiohttp import asyncio async def request(layer: str, time: datetime.date, coor: list) -> list: info = [] pixel_value = [] # Create an aiohttp client session for making HTTP requests async with aiohttp.ClientSession() as session: # Iterate through each timestep for timestep in time: # Get the request URL using the OWSLib library url = wms.getfeatureinfo(layers=[layer], srs='EPSG:4326', bbox=tuple(coor), size=(100, 100), format='image/jpeg', query_layers=[layer], info_format='text/plain', xy=(1, 1), feature_count=1, time=str(timestep.isoformat()) + 'Z' ).geturl() try: # Make an asynchronous GET request to the URL async with session.get(url) as resp: # Check if the response status is OK (200) if resp.status == 200: # Read the text content of the response text = await resp.text() # Extract the value using a regex pattern pixel_value.append(str(re.findall(r'value_0\s+\d*.*\d+', text))) try: # Convert the extracted value to a float pixel_value[-1] = float( re.sub('value_0 = \'', '', pixel_value[-1]) .strip('[""]') ) except ValueError: # Handle any issues with data extraction and print a message print( f'Problem with the extract data (most likely empty output) at time = {timestep} and layer = {layer}') print('Returning empty float instead') pixel_value[-1] = [np.nan] else: # If the response status is not OK, print an error message print(f'Request could not be made for some reason at time = {timestep} and layer = {layer}') pixel_value.append(np.nan) except ServiceException: # Handle any ServiceException errors print(f'Request could not be made for some reason at time = {timestep} and layer = {layer}') pixel_value.append(np.nan) return pixel_value
The following is a reproducible code, just in case.
Stations_info = pd.DataFrame({'ID': {0: 'COMPTN', 1: 'DUNHM'}, 'Lat': {0: 45.2608, 1: 45.0959}, 'Lon': {0: -71.8337, 1: -72.8126}, 'Alt': {0: 245, 1: 225}, 'Name': {0: 'Compton', 1: 'Dunham'}, 'Lon2': {0: -71.7337, 1: -72.71260000000001}, 'Lat2': {0: 45.1608, 1: 44.9959}}) address = "geo.weather.gc.ca/geomet?service=WMS" # for operational use # "http://collaboration.cmc.ec.gc.ca/rpn-wms" # for experimental use # Version = "1.3.0" HRDPS_varlist = ['HRDPS.CONTINENTAL_TT', 'HRDPS.CONTINENTAL_HR', "HRDPS.CONTINENTAL_PR"] wms = WebMapService('https://geo.weather.gc.ca/geomet?SERVICE=WMS' + '&REQUEST=GetCapabilities', version=Version, timeout=300) async def process_request(arg): info = pd.DataFrame(arg).T print(f'Acquiring weather forecast for {arg.iloc[0]}') coor = (info[['Lon', 'Lat2', 'Lon2', 'Lat']].iloc[0].tolist()) # Can add nb_timesteps to define how far we want to go. Must add as function argument nb_timestep = 2 # HRDPS_df = await run_HRDPS(coor,nb_timestep) datetime_str = '23-03-31 13:00:00' # Hardcoded for the sake of the example This should change to always be tomorrow. time_utc = [datetime.strptime(datetime_str, '%y-%m-%d %H:%M:%S')] time_local = time_utc.copy() # Make async requests for each layer in GDPS_varlist and create a dictionary pixel_value_dict_HRDPS = { layer: await request(layer, time_utc[:nb_timestep], coor) for layer in HRDPS_varlist} # Convert the dictionary to a pandas DataFrame HRDPS_df = pd.DataFrame.from_dict(pixel_value_dict_HRDPS, orient='index').transpose() HRDPS_df['Date'] = time_local[:nb_timestep] HRDPS_df['HRDPS.ECONTINENTAL_PR'] = HRDPS_df['HRDPS.CONTINENTAL_PR'].diff() print(HRDPS_df) async def main(): tasks = [process_request(row) for _, row in Stations_info.iterrows()] await asyncio.gather(*tasks) if __name__ == "__main__": asyncio.run(main())
