bump version 3.0.1 -> 3.0.2

Implement RIO IFDM client

README.md

@@ -24,9 +24,8 @@ pip install open-irceline
 ```python
 import aiohttp
 import asyncio
-from datetime import datetime, date
-
-from open_irceline import IrcelineRioClient, RioFeature, IrcelineForecastClient, ForecastFeature, belaqi_index_rio_hourly
+from datetime import datetime
+from open_irceline import IrcelineRioClient, IrcelineForecastClient, ForecastFeature, RioFeature
 
 
 async def get_rio_interpolated_data():
@@ -43,12 +42,11 @@ async def get_rio_interpolated_data():
     print(f"PM10   {result[RioFeature.PM10_HMEAN]['value']} µg/m³")
 
 
-async def get_forecast():
+async def get_o3_forecast():
     """Get forecast for O3 concentration for Brussels for the next days"""
     async with aiohttp.ClientSession() as session:
         client = IrcelineForecastClient(session)
         result = await client.get_data(
-            timestamp=date.today(),
             features=[ForecastFeature.O3_MAXHMEAN],
             position=(50.85, 4.35)  # (lat, lon) for Brussels
         )
@@ -57,28 +55,28 @@ async def get_forecast():
         print(f"{feature} {day} {v['value']} µg/m³")
 
 
-async def get_current_belaqi():
+async def get_belaqi_forecast():
     """Get current BelAQI index from RIO interpolated values"""
     async with aiohttp.ClientSession() as session:
-        client = IrcelineRioClient(session)
-        result = await belaqi_index_rio_hourly(
-            rio_client=client,
-            timestamp=datetime.utcnow(),  # must be timezone aware
+        client = IrcelineForecastClient(session)
+        result = await client.get_data(
+            features=[ForecastFeature.BELAQI],
             position=(50.85, 4.35)        # (lat, lon) for Brussels
         )
 
-    print(f"Current BelAQI index for Brussels: {result.get('value')}")
+    for (_, day), value in result.items():
+        print(day, value['value'])
 
 
 if __name__ == '__main__':
-    print("RIO interpolated data")
+    print("\nInterpolated data")
     asyncio.run(get_rio_interpolated_data())
 
     print("\nO3 forecast for Brussels")
-    asyncio.run(get_forecast())
+    asyncio.run(get_o3_forecast())
 
-    print("\nCurrent BelAQI index")
-    asyncio.run(get_current_belaqi())
+    print("\nForecast BelAQI index")
+    asyncio.run(get_belaqi_forecast())
 ```
 
 ## Attribution

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "open-irceline"
-version = "2.0.0"
+version = "3.0.2"
 description = "Get IRCEL - CELINE air quality data 🍃 🇧🇪"
 readme = "README.md"
 authors = [{ name = "Jules Dejaeghere", email = "curable.grass491@mailer.me" }]
@@ -27,7 +27,7 @@ requires-python = ">=3.11"
 Homepage = "https://github.com/jdejaegh/python-irceline"
 
 [tool.bumpver]
-current_version = "2.0.0"
+current_version = "3.0.2"
 version_pattern = "MAJOR.MINOR.PATCH"
 commit_message = "bump version {old_version} -> {new_version}"
 tag_message = "{new_version}"

requirements.txt

@@ -1,4 +1,4 @@
-aiohttp==3.9.5
+aiohttp==3.11.9
 pyproj==3.6.1
 pytest
 async-timeout==4.0.3

src/open_irceline/__init__.py

@@ -1,5 +1,6 @@
-from .api import IrcelineRioClient, IrcelineForecastClient, IrcelineApiError
-from .belaqi import belaqi_index_rio_hourly, belaqi_index_forecast_daily, belaqi_index_daily, belaqi_index_hourly
-from .data import RioFeature, ForecastFeature, FeatureValue, BelAqiIndex
+from .api import IrcelineApiError
+from .data import RioFeature, ForecastFeature, FeatureValue, RioIfdmFeature
+from .forecast import IrcelineForecastClient
+from .rio import IrcelineRioClient, IrcelineRioIfdmClient
 
-__version__ = '2.0.0'
+__version__ = '3.0.2'

src/open_irceline/api.py

@@ -1,42 +1,39 @@
 import asyncio
-import csv
 import socket
 from abc import ABC, abstractmethod
-from datetime import datetime, timedelta, date
-from io import StringIO
-from itertools import product
-from typing import Tuple, List, Dict, Set
+from typing import Tuple, List, Set
 from xml.etree import ElementTree
 
 import aiohttp
 import async_timeout
 from aiohttp import ClientResponse
 
-from .data import RioFeature, FeatureValue, ForecastFeature, IrcelineFeature
-from .utils import SizedDict, epsg_transform, round_coordinates
+from .data import IrcelineFeature
 
 _rio_wfs_base_url = 'https://geo.irceline.be/wfs'
-# noinspection HttpUrlsUsage
-# There is not HTTPS version of this endpoint
-_forecast_base_url = 'http://ftp.irceline.be/forecast'
+_forecast_wms_base_url = 'https://geo.irceline.be/forecast/wms'
+_rio_ifdm_wms_base_url = 'https://geobelair.irceline.be/rioifdm/wms'
 _user_agent = 'github.com/jdejaegh/python-irceline'
 
+
 class IrcelineApiError(Exception):
     """Exception to indicate an API error."""
 
 
 class IrcelineBaseClient(ABC):
-    def __init__(self, session: aiohttp.ClientSession, cache_size: int = 20) -> None:
+    def __init__(self, session: aiohttp.ClientSession) -> None:
         self._session = session
-        self._cache = SizedDict(cache_size)
 
     @abstractmethod
     async def get_data(self,
-                       timestamp: datetime | date,
                        features: List[IrcelineFeature],
                        position: Tuple[float, float]) -> dict:
         pass
 
+    @abstractmethod
+    def get_capabilities(self) -> Set[str]:
+        pass
+
     async def _api_wrapper(self, url: str, querystring: dict = None, headers: dict = None, method: str = 'GET'):
         """
         Call the URL with the specified query string. Raises exception for >= 400 response code
@@ -48,7 +45,6 @@ class IrcelineBaseClient(ABC):
             headers = dict()
         if 'User-Agent' not in headers:
             headers |= {'User-Agent': _user_agent}
-
         try:
             async with async_timeout.timeout(60):
                 response = await self._session.request(
@@ -67,200 +63,39 @@ class IrcelineBaseClient(ABC):
         except Exception as exception:  # pylint: disable=broad-except
             raise IrcelineApiError(f"Something really wrong happened! {exception}") from exception
 
-    async def _api_cached_wrapper(self, url: str, method: str = 'GET'):
-        """
-        Call the API but uses cache based on the ETag value to avoid repeated calls for the same ressource
-        :param url: url to fetch
-        :param method: HTTP method (default to GET)
-        :return: response from the client
-        """
-        if url in self._cache:
-            headers = {"If-None-Match": f'{self._cache.get(url, {}).get("etag")}'}
-        else:
-            headers = None
 
-        r: ClientResponse = await self._api_wrapper(url, headers=headers, method=method)
-        if r.status == 304:
-            return self._cache.get(url, {}).get("response")
-        elif 'ETag' in r.headers:
-            self._cache[url] = {'etag': r.headers['ETag'],
-                                'response': r}
-        return r
-
-
-class IrcelineRioClient(IrcelineBaseClient):
-    """API client for RIO interpolated IRCEL - CELINE open data"""
-
-    async def get_data(self,
-                       timestamp: datetime | date,
-                       features: List[RioFeature],
-                       position: Tuple[float, float]
-                       ) -> Dict[RioFeature, FeatureValue]:
-        """
-        Call the WFS API to get the interpolated level of RioFeature. Raises exception upon API error
-        :param timestamp: datetime for which to get the data for
-        :param features: list of RioFeature to fetch from the API
-        :param position: decimal degrees pair of coordinates
-        :return: dict with the response (key is RioFeature, value is FeatureValue with actual value and timestamp)
-        """
-        # Remove one hour/day from timestamp to handle case where the hour just passed but the data is not yet there
-        # (e.g. 5.01 PM, but the most recent data is for 4.00 PM)
-        if isinstance(timestamp, datetime):
-            timestamp = timestamp.replace(microsecond=0, second=0, minute=0) - timedelta(hours=1)
-            timestamp = timestamp.isoformat()
-            key = 'timestamp'
-        elif isinstance(timestamp, date):
-            timestamp = timestamp - timedelta(days=1)
-            timestamp = timestamp.isoformat()
-            key = 'date'
-        else:
-            raise IrcelineApiError(f"Wrong parameter type for timestamp: {type(timestamp)}")
-
-        lat, lon = epsg_transform(position)
-        querystring = {"service": "WFS",
-                       "version": "1.3.0",
-                       "request": "GetFeature",
-                       "outputFormat": "application/json",
-                       "typeName": ",".join(features),
-                       "cql_filter":
-                           f"{key}>='{timestamp}'"
-                           f" AND "
-                           f"INTERSECTS(the_geom, POINT ({lat} {lon}))"}
-        r: ClientResponse = await self._api_wrapper(_rio_wfs_base_url, querystring)
-        return self._format_result('rio', await r.json(), features)
-
-    async def get_rio_capabilities(self) -> Set[str]:
-        """
-        Fetch the list of possible features from the WFS server
-        :return: set of features available on the WFS server
-        """
-        querystring = {"service": "WFS",
-                       "version": "1.3.0",
-                       "request": "GetCapabilities"}
-        r: ClientResponse = await self._api_wrapper(_rio_wfs_base_url, querystring)
-
-        return self._parse_capabilities(await r.text())
+class IrcelineBaseWmsClient(IrcelineBaseClient, ABC):
+    _default_querystring = {"service": "WMS",
+                            "version": "1.1.1",
+                            "request": "GetFeatureInfo",
+                            "info_format": "application/json",
+                            "width": "1",
+                            "height": "1",
+                            "srs": "EPSG:4326",
+                            "X": "1",
+                            "Y": "1"}
+    _epsilon = 0.00001
+    _base_url = None
 
     @staticmethod
     def _parse_capabilities(xml_string: str) -> Set[str]:
-        """
-        From an XML string obtained with GetCapabilities, generate a set of feature names
-        :param xml_string: XML string to parse
-        :return: set of FeatureType Names found in the XML document
-        """
         try:
             root = ElementTree.fromstring(xml_string)
         except ElementTree.ParseError:
             return set()
-        # noinspection HttpUrlsUsage
-        # We never connect to the URL, it is just the namespace in the XML
-        namespaces = {
-            'wfs': 'http://www.opengis.net/wfs',
-        }
-        path = './/wfs:FeatureTypeList/wfs:FeatureType/wfs:Name'
-        feature_type_names = {t.text for t in root.findall(path, namespaces)}
+
+        path = './/Capability/Layer/Layer/Name'
+        feature_type_names = {t.text for t in root.findall(path)}
         return feature_type_names
 
-    @staticmethod
-    def _format_result(prefix: str, data: dict, features: List[RioFeature]) -> dict:
+    async def get_capabilities(self) -> Set[str]:
         """
-        Format the JSON dict returned by the WFS service into a more practical dict to use with only the latest measure
-        for each feature requested
-        :param prefix: namespace of the feature (e.g. rio), without the colon
-        :param data: JSON dict value as returned by the API
-        :param features: RioFeatures wanted in the final dict
-        :return: reduced dict, key is RioFeature, value is FeatureValue
+        Fetch the list of possible features from the WMS server
+        :return: set of features available on the WMS server
         """
-        if data.get('type', None) != 'FeatureCollection' or not isinstance(data.get('features', None), list):
-            return dict()
-        features_api = data.get('features', [])
-        result = dict()
-        for f in features_api:
-            props = f.get('properties', {})
-            if (f.get('id', None) is None or
-                    props.get('value', None) is None):
-                continue
-            if (props.get('timestamp', None) is None and
-                    props.get('date', None) is None):
-                continue
+        querystring = {"service": "WMS",
+                       "version": "1.1.1",
+                       "request": "GetCapabilities"}
+        r: ClientResponse = await self._api_wrapper(self._base_url, querystring)
 
-            try:
-                if 'timestamp' in props.keys():
-                    timestamp = datetime.fromisoformat(props.get('timestamp'))
-                else:
-                    # Cut last character as the date is written '2024-06-15Z' which is not ISO compliant
-                    timestamp = date.fromisoformat(props.get('date')[:-1])
-                value = float(props.get('value'))
-            except (TypeError, ValueError):
-                continue
-
-            name = f"{prefix}:{f.get('id').split('.')[0]}"
-            if name not in [f'{f}' for f in features]:
-                continue
-            if name not in result or result[name]['timestamp'] < timestamp:
-                result[name] = FeatureValue(timestamp=timestamp, value=value)
-
-        return result
-
-
-class IrcelineForecastClient(IrcelineBaseClient):
-    """API client for forecast IRCEL - CELINE open data"""
-
-    async def get_data(self,
-                       timestamp: date,
-                       features: List[ForecastFeature],
-                       position: Tuple[float, float]
-                       ) -> Dict[Tuple[ForecastFeature, date], FeatureValue]:
-        """
-        Get forecasted concentrations for the given features at the given position. The forecasts are downloaded for
-        the specified day and the 4 next days as well
-        :param timestamp: date at which the forecast are computed (generally today).  If unavailable, the day before will be
-        tried as well
-        :param features: pollutants to get the forecasts for
-        :param position: (lat, long)
-        :return: dict where key is (ForecastFeature, date of the forecast) and value is a FeatureValue
-        """
-        x, y = round_coordinates(position[0], position[1])
-        result = dict()
-
-        for feature, d in product(features, range(5)):
-            url = f"{_forecast_base_url}/BE_{feature}_{timestamp.strftime('%Y%m%d')}_d{d}.csv"
-            try:
-                r: ClientResponse = await self._api_cached_wrapper(url)
-                ts = timestamp
-            except IrcelineApiError:
-                # retry for the day before
-                yesterday = timestamp - timedelta(days=1)
-                url = f"{_forecast_base_url}/BE_{feature}_{yesterday.strftime('%Y%m%d')}_d{d}.csv"
-                try:
-                    r: ClientResponse = await self._api_cached_wrapper(url)
-                    ts = yesterday
-                except IrcelineApiError:
-                    # if it fails twice, just set None and go to the next
-                    result[(feature, timestamp + timedelta(days=d))] = FeatureValue(value=None, timestamp=timestamp)
-                    continue
-
-            result[(feature, ts + timedelta(days=d))] = FeatureValue(
-                value=self.extract_result_from_csv(x, y, await r.text()),
-                timestamp=ts)
-
-        return result
-
-    @staticmethod
-    def extract_result_from_csv(x: float, y: float, csv_text: str) -> float | None:
-        """
-        Find the value of the forecast for the given (x, y) position in the csv text.
-        x, y should already be rounded to match the positions found in the csv
-        :param x: latitude (rounded)
-        :param y: longitude (rounded)
-        :param csv_text: text of the CSV file
-        :return: value matching the position if found, else None
-        """
-        f = StringIO(csv_text)
-        for row in csv.reader(f, delimiter=';'):
-            try:
-                if x == float(row[1]) and y == float(row[2]):
-                    return float(row[3])
-            except (ValueError, IndexError):
-                continue
-        return None
+        return self._parse_capabilities(await r.text())

src/open_irceline/belaqi.py

@@ -1,192 +0,0 @@
-"""
-Compute the BelAQI index from concentrations of PM10, PM2.5, O3 and NO2, based on
-https://www.irceline.be/en/air-quality/measurements/air-quality-index-november-2022/info_nov2022
-"""
-from datetime import datetime, date
-from typing import Tuple, Dict, Final
-
-from .api import IrcelineRioClient, IrcelineForecastClient
-from .data import BelAqiIndex, RioFeature, ForecastFeature, FeatureValue
-
-# Ratio values from Figure 2 at
-# https://www.irceline.be/en/air-quality/measurements/air-quality-index-november-2022/info_nov2022
-NO2_MAX_HMEAN_TO_DMEAN: Final = 1.51
-O3_MAX_HMEAN_TO_MAX8HMEAN: Final = 1.10
-
-
-def belaqi_index_daily(pm10: float, pm25: float, o3: float, no2: float) -> BelAqiIndex:
-    """
-    Computes the daily BelAQI index based on the components
-    Raise ValueError if a component is < 0
-
-    Values taken from Table 1 of
-    https://www.irceline.be/en/air-quality/measurements/air-quality-index-november-2022/info_nov2022
-
-    :param pm10: PM10 daily mean (µg/m³)
-    :param pm25: PM2.5 daily mean (µg/m³)
-    :param o3: O3 maximum running 8-hour mean (µg/m³)
-    :param no2: NO2 daily mean (µg/m³)
-    :return: BelAQI index from 1 to 10 (Value of BelAqiIndex enum)
-    """
-    if pm10 is None or pm25 is None or o3 is None or no2 is None:
-        raise ValueError("All the components should be valued (at lest one is None here)")
-
-    if pm10 < 0 or pm25 < 0 or o3 < 0 or no2 < 0:
-        raise ValueError("All the components should have a positive value")
-
-    elif pm10 > 100 or pm25 > 50 or o3 > 220 or no2 > 50:
-        return BelAqiIndex.HORRIBLE
-
-    elif pm10 > 80 or pm25 > 40 or o3 > 190 or no2 > 40:
-        return BelAqiIndex.VERY_BAD
-
-    elif pm10 > 70 or pm25 > 35 or o3 > 160 or no2 > 35:
-        return BelAqiIndex.BAD
-
-    elif pm10 > 60 or pm25 > 25 or o3 > 130 or no2 > 30:
-        return BelAqiIndex.VERY_POOR
-
-    elif pm10 > 45 or pm25 > 15 or o3 > 100 or no2 > 25:
-        return BelAqiIndex.POOR
-
-    elif pm10 > 35 or pm25 > 10 or o3 > 80 or no2 > 20:
-        return BelAqiIndex.MODERATE
-
-    elif pm10 > 25 or pm25 > 7.5 or o3 > 70 or no2 > 15:
-        return BelAqiIndex.FAIRLY_GOOD
-
-    elif pm10 > 15 or pm25 > 5 or o3 > 60 or no2 > 10:
-        return BelAqiIndex.GOOD
-
-    elif pm10 > 5 or pm25 > 2.5 or o3 > 30 or no2 > 5:
-        return BelAqiIndex.VERY_GOOD
-
-    elif pm10 >= 0 or pm25 >= 0 or o3 >= 0 or no2 >= 0:
-        return BelAqiIndex.EXCELLENT
-
-
-def belaqi_index_hourly(pm10: float, pm25: float, o3: float, no2: float) -> BelAqiIndex:
-    """
-    Computes the hourly BelAQI index based on the components
-    Raise ValueError if a component is < 0
-
-    Values taken from Table 2 of
-    https://www.irceline.be/en/air-quality/measurements/air-quality-index-november-2022/info_nov2022
-
-    :param pm10: PM10 hourly mean (µg/m³)
-    :param pm25: PM2.5 hourly mean (µg/m³)
-    :param o3: O3 hourly mean (µg/m³)
-    :param no2: NO2 hourly mean (µg/m³)
-    :return: BelAQI index from 1 to 10 (Value of BelAqiIndex enum)
-    """
-    if pm10 is None or pm25 is None or o3 is None or no2 is None:
-        raise ValueError("All the components should be valued (at lest one is None here)")
-
-    if pm10 < 0 or pm25 < 0 or o3 < 0 or no2 < 0:
-        raise ValueError("All the components should have a positive value")
-
-    elif pm10 > 140 or pm25 > 75 or o3 > 240 or no2 > 75:
-        return BelAqiIndex.HORRIBLE
-
-    elif pm10 > 110 or pm25 > 60 or o3 > 210 or no2 > 60:
-        return BelAqiIndex.VERY_BAD
-
-    elif pm10 > 95 or pm25 > 50 or o3 > 180 or no2 > 50:
-        return BelAqiIndex.BAD
-
-    elif pm10 > 80 or pm25 > 35 or o3 > 150 or no2 > 45:
-        return BelAqiIndex.VERY_POOR
-
-    elif pm10 > 60 or pm25 > 20 or o3 > 110 or no2 > 40:
-        return BelAqiIndex.POOR
-
-    elif pm10 > 45 or pm25 > 15 or o3 > 90 or no2 > 30:
-        return BelAqiIndex.MODERATE
-
-    elif pm10 > 35 or pm25 > 10 or o3 > 75 or no2 > 20:
-        return BelAqiIndex.FAIRLY_GOOD
-
-    elif pm10 > 20 or pm25 > 7.5 or o3 > 65 or no2 > 15:
-        return BelAqiIndex.GOOD
-
-    elif pm10 > 10 or pm25 > 3.5 or o3 > 30 or no2 > 10:
-        return BelAqiIndex.VERY_GOOD
-
-    elif pm10 >= 0 or pm25 >= 0 or o3 >= 0 or no2 >= 0:
-        return BelAqiIndex.EXCELLENT
-
-
-async def belaqi_index_rio_hourly(rio_client: IrcelineRioClient, position: Tuple[float, float],
-                                  timestamp: datetime | None = None) -> FeatureValue:
-    """
-    Get current BelAQI index value for the given position using the rio_client
-    Raise ValueError if one or more components are not available
-    :param rio_client: client for the RIO WFS service
-    :param position: position for which to get the data
-    :param timestamp: desired time for the data (now if None)
-    :return: BelAQI index value for the position at the time
-    """
-    if timestamp is None:
-        timestamp = datetime.utcnow()
-
-    features = [RioFeature.PM10_HMEAN, RioFeature.PM25_HMEAN, RioFeature.O3_HMEAN, RioFeature.NO2_HMEAN]
-
-    components = await rio_client.get_data(
-        timestamp=timestamp,
-        features=features,
-        position=position
-    )
-
-    ts = min([components.get(f, {}).get('timestamp') for f in features
-              if components.get(f, {}).get('timestamp') is not None])
-
-    belaqi = belaqi_index_hourly(
-        pm10=components.get(RioFeature.PM10_HMEAN, {}).get('value', -1),
-        pm25=components.get(RioFeature.PM25_HMEAN, {}).get('value', -1),
-        o3=components.get(RioFeature.O3_HMEAN, {}).get('value', -1),
-        no2=components.get(RioFeature.NO2_HMEAN, {}).get('value', -1)
-    )
-
-    return FeatureValue(timestamp=ts, value=belaqi)
-
-
-async def belaqi_index_forecast_daily(forecast_client: IrcelineForecastClient, position: Tuple[float, float],
-                                      timestamp: date | None = None) -> Dict[date, FeatureValue]:
-    """
-    Get forecasted BelAQI index value for the given position using the forecast_client.
-    Data is downloaded for the given day and the four next days
-    Value is None for the date if one or more components cannot be downloaded
-    :param forecast_client: client for the forecast data
-    :param position: position for which to get the data
-    :param timestamp: day at which the forecast are issued
-    :return: dict mapping a day to the forecasted BelAQI index
-    """
-    if timestamp is None:
-        timestamp = date.today()
-    components = await forecast_client.get_data(
-        timestamp=timestamp,
-        features=[ForecastFeature.PM10_DMEAN,
-                  ForecastFeature.PM25_DMEAN,
-                  ForecastFeature.O3_MAXHMEAN,
-                  ForecastFeature.NO2_MAXHMEAN],
-        position=position
-    )
-
-    result = dict()
-
-    days = {day for _, day in components.keys()}
-    timestamps = {v.get('timestamp') for v in components.values() if v.get('timestamp') is not None}
-    timestamp = min(timestamps)
-    for day in days:
-        try:
-            belaqi = belaqi_index_daily(
-                pm10=components.get((ForecastFeature.PM10_DMEAN, day), {}).get('value', -1),
-                pm25=components.get((ForecastFeature.PM25_DMEAN, day), {}).get('value', -1),
-                o3=components.get((ForecastFeature.O3_MAXHMEAN, day), {}).get('value', -1) * O3_MAX_HMEAN_TO_MAX8HMEAN,
-                no2=components.get((ForecastFeature.NO2_MAXHMEAN, day), {}).get('value', -1) * NO2_MAX_HMEAN_TO_DMEAN
-            )
-            result[day] = FeatureValue(timestamp=timestamp, value=belaqi)
-        except (ValueError, TypeError):
-            result[day] = FeatureValue(timestamp=timestamp, value=None)
-
-    return result

src/open_irceline/data.py

@@ -1,5 +1,5 @@
 from datetime import datetime, date
-from enum import StrEnum, Enum
+from enum import StrEnum
 from typing import TypedDict
 
 
@@ -30,27 +30,25 @@ class RioFeature(IrcelineFeature):
     SO2_HMEAN = 'rio:so2_hmean'
 
 
+class RioIfdmFeature(IrcelineFeature):
+    PM25_HMEAN = 'rioifdm:pm25_hmean'
+    NO2_HMEAN = 'rioifdm:no2_hmean'
+    PM10_HMEAN = 'rioifdm:pm10_hmean'
+    O3_HMEAN = 'rioifdm:o3_hmean'
+    BELAQI = 'rioifdm:belaqi'
+
+
 class ForecastFeature(IrcelineFeature):
-    NO2_MAXHMEAN = 'chimere_no2_maxhmean'
-    O3_MAXHMEAN = 'chimere_o3_maxhmean'
-    PM10_DMEAN = 'chimere_pm10_dmean'
-    PM25_DMEAN = 'chimere_pm25_dmean'
-
-
-class BelAqiIndex(Enum):
-    EXCELLENT = 1
-    VERY_GOOD = 2
-    GOOD = 3
-    FAIRLY_GOOD = 4
-    MODERATE = 5
-    POOR = 6
-    VERY_POOR = 7
-    BAD = 8
-    VERY_BAD = 9
-    HORRIBLE = 10
+    NO2_MAXHMEAN = 'forecast:no2_maxhmean'
+    NO2_DMEAN = 'forecast:no2_dmean'
+    O3_MAXHMEAN = 'forecast:o3_maxhmean'
+    O3_MAX8HMEAN = 'forecast:o3_max8hmean'
+    PM10_DMEAN = 'forecast:pm10_dmean'
+    PM25_DMEAN = 'forecast:pm25_dmean'
+    BELAQI = 'forecast:belaqi'
 
 
 class FeatureValue(TypedDict):
     # Timestamp at which the value was computed
-    timestamp: datetime | date
-    value: int | float | BelAqiIndex | None
+    timestamp: datetime | date | None
+    value: int | float | None

src/open_irceline/forecast.py

@@ -0,0 +1,55 @@
+import asyncio
+from datetime import date, timedelta, datetime
+from itertools import product
+from typing import List, Tuple, Dict
+
+from aiohttp import ClientResponse, ClientResponseError
+
+from .api import IrcelineApiError, IrcelineBaseWmsClient, _forecast_wms_base_url
+from .data import ForecastFeature, FeatureValue
+
+
+class IrcelineForecastClient(IrcelineBaseWmsClient):
+    _base_url = _forecast_wms_base_url
+
+    async def get_data(self,
+                       features: List[ForecastFeature],
+                       position: Tuple[float, float]
+                       ) -> Dict[Tuple[ForecastFeature, date], FeatureValue]:
+        """
+        Get forecasted concentrations for the given features at the given position. The forecasts are downloaded for
+        the specified day and the 3 next days as well
+        :param features: pollutants to get the forecasts for
+        :param position: (lat, long)
+        :return: dict where key is (ForecastFeature, date of the forecast) and value is a FeatureValue
+        """
+        timestamp = date.today()
+        result = dict()
+        lat, lon = position
+        base_querystring = (self._default_querystring |
+                            {"bbox": f"{lon},{lat},{lon + self._epsilon},{lat + self._epsilon}"})
+
+        tasks = [asyncio.create_task(self._get_single_feature(base_querystring, d, feature, timestamp))
+                 for feature, d in product(features, range(4))]
+        results = await asyncio.gather(*tasks)
+
+        for r in results:
+            result |= r
+
+        return result
+
+    async def _get_single_feature(self, base_querystring: dict, d: int, feature: ForecastFeature,
+                                  timestamp: date) -> dict:
+        result = dict()
+
+        querystring = base_querystring | {"layers": f"{feature}_d{d}",
+                                          "query_layers": f"{feature}_d{d}"}
+        try:
+            r: ClientResponse = await self._api_wrapper(self._base_url, querystring)
+            r: dict = await r.json()
+            result[(feature, timestamp + timedelta(days=d))] = FeatureValue(
+                value=r.get('features', [{}])[0].get('properties', {}).get('GRAY_INDEX'),
+                timestamp=datetime.fromisoformat(r.get('timeStamp')) if 'timeStamp' in r else None)
+        except (IrcelineApiError, ClientResponseError, IndexError):
+            result[(feature, timestamp + timedelta(days=d))] = FeatureValue(value=None, timestamp=None)
+        return result

src/open_irceline/rio.py

@@ -0,0 +1,176 @@
+import asyncio
+from datetime import datetime, date, UTC, timedelta
+from typing import List, Tuple, Dict, Set
+from xml.etree import ElementTree
+
+from aiohttp import ClientResponse, ClientResponseError
+
+from .api import IrcelineBaseClient, _rio_wfs_base_url, IrcelineApiError, _rio_ifdm_wms_base_url, IrcelineBaseWmsClient
+from .data import RioFeature, FeatureValue, RioIfdmFeature
+from .utils import epsg_transform
+
+
+class IrcelineRioClient(IrcelineBaseClient):
+    """
+    API client for RIO interpolated IRCEL - CELINE open data
+    RIO is more coarse grained for interpolation than RIO IFDM and allows to request multiple features in the same
+        request, which may be faster.
+    """
+
+    async def get_data(self,
+                       features: List[RioFeature],
+                       position: Tuple[float, float],
+                       timestamp: datetime | date | None = None
+                       ) -> Dict[RioFeature, FeatureValue]:
+        """
+        Call the WFS API to get the interpolated level of RioFeature. Raises exception upon API error
+        :param timestamp: datetime for which to get the data for
+        :param features: list of RioFeature to fetch from the API
+        :param position: decimal degrees pair of coordinates
+        :return: dict with the response (key is RioFeature, value is FeatureValue with actual value and timestamp)
+        """
+        if timestamp is None:
+            timestamp = datetime.now(UTC)
+        # Remove one hour/day from timestamp to handle case where the hour just passed but the data is not yet there
+        # (e.g. 5.01 PM, but the most recent data is for 4.00 PM)
+        if isinstance(timestamp, datetime):
+            timestamp = timestamp.replace(microsecond=0, second=0, minute=0) - timedelta(hours=1)
+            timestamp = timestamp.isoformat()
+            key = 'timestamp'
+        elif isinstance(timestamp, date):
+            timestamp = timestamp - timedelta(days=1)
+            timestamp = timestamp.isoformat()
+            key = 'date'
+        else:
+            raise IrcelineApiError(f"Wrong parameter type for timestamp: {type(timestamp)}")
+
+        lat, lon = epsg_transform(position)
+        querystring = {"service": "WFS",
+                       "version": "1.3.0",
+                       "request": "GetFeature",
+                       "outputFormat": "application/json",
+                       "typeName": ",".join(features),
+                       "cql_filter":
+                           f"{key}>='{timestamp}'"
+                           f" AND "
+                           f"INTERSECTS(the_geom, POINT ({lat} {lon}))"}
+        r: ClientResponse = await self._api_wrapper(_rio_wfs_base_url, querystring)
+        return self._format_result('rio', await r.json(), features)
+
+    async def get_capabilities(self) -> Set[str]:
+        """
+        Fetch the list of possible features from the WFS server
+        :return: set of features available on the WFS server
+        """
+        querystring = {"service": "WFS",
+                       "version": "1.3.0",
+                       "request": "GetCapabilities"}
+        r: ClientResponse = await self._api_wrapper(_rio_wfs_base_url, querystring)
+
+        return self._parse_capabilities(await r.text())
+
+    @staticmethod
+    def _parse_capabilities(xml_string: str) -> Set[str]:
+        """
+        From an XML string obtained with GetCapabilities, generate a set of feature names
+        :param xml_string: XML string to parse
+        :return: set of FeatureType Names found in the XML document
+        """
+        try:
+            root = ElementTree.fromstring(xml_string)
+        except ElementTree.ParseError:
+            return set()
+        # noinspection HttpUrlsUsage
+        # We never connect to the URL, it is just the namespace in the XML
+        namespaces = {
+            'wfs': 'http://www.opengis.net/wfs',
+        }
+        path = './/wfs:FeatureTypeList/wfs:FeatureType/wfs:Name'
+        feature_type_names = {t.text for t in root.findall(path, namespaces)}
+        return feature_type_names
+
+    @staticmethod
+    def _format_result(prefix: str, data: dict, features: List[RioFeature]) -> dict:
+        """
+        Format the JSON dict returned by the WFS service into a more practical dict to use with only the latest measure
+        for each feature requested
+        :param prefix: namespace of the feature (e.g. rio), without the colon
+        :param data: JSON dict value as returned by the API
+        :param features: RioFeatures wanted in the final dict
+        :return: reduced dict, key is RioFeature, value is FeatureValue
+        """
+        if data.get('type', None) != 'FeatureCollection' or not isinstance(data.get('features', None), list):
+            return dict()
+        features_api = data.get('features', [])
+        result = dict()
+        for f in features_api:
+            props = f.get('properties', {})
+            if (f.get('id', None) is None or
+                    props.get('value', None) is None):
+                continue
+            if (props.get('timestamp', None) is None and
+                    props.get('date', None) is None):
+                continue
+
+            try:
+                if 'timestamp' in props.keys():
+                    timestamp = datetime.fromisoformat(props.get('timestamp'))
+                else:
+                    # Cut last character as the date is written '2024-06-15Z' which is not ISO compliant
+                    timestamp = date.fromisoformat(props.get('date')[:-1])
+                value = float(props.get('value'))
+            except (TypeError, ValueError):
+                continue
+
+            name = f"{prefix}:{f.get('id').split('.')[0]}"
+            if name not in [f'{f}' for f in features]:
+                continue
+            if name not in result or result[name]['timestamp'] < timestamp:
+                result[name] = FeatureValue(timestamp=timestamp, value=value)
+
+        return result
+
+
+class IrcelineRioIfdmClient(IrcelineBaseWmsClient):
+    """
+    API client for RIO IFDM interpolated IRCEL - CELINE open data
+    RIO IFDM is more fine-grained for interpolation than RIO but only allows one feature to be request at a time, which
+    may be slower
+    """
+    _base_url = _rio_ifdm_wms_base_url
+
+    async def get_data(self,
+                       features: List[RioIfdmFeature],
+                       position: Tuple[float, float]
+                       ) -> Dict[RioIfdmFeature, FeatureValue]:
+        """
+        Get interpolated concentrations for the given features at the given position.
+        :param features: pollutants to get the forecasts for
+        :param position: (lat, long)
+        :return: dict where key is RioIfdmFeature and value is a FeatureValue
+        """
+        result = dict()
+        lat, lon = position
+        base_querystring = (self._default_querystring |
+                            {"bbox": f"{lon},{lat},{lon + self._epsilon},{lat + self._epsilon}"})
+
+        tasks = [asyncio.create_task(self._get_single_feature(base_querystring, feature)) for feature in features]
+        results = await asyncio.gather(*tasks)
+
+        for r in results:
+            result |= r
+
+        return result
+
+    async def _get_single_feature(self, base_querystring: dict, feature: RioIfdmFeature) -> dict:
+        result = dict()
+        querystring = base_querystring | {"layers": f"{feature}", "query_layers": f"{feature}"}
+        try:
+            r: ClientResponse = await self._api_wrapper(self._base_url, querystring)
+            r: dict = await r.json()
+            result[feature] = FeatureValue(
+                value=r.get('features', [{}])[0].get('properties', {}).get('GRAY_INDEX'),
+                timestamp=datetime.fromisoformat(r.get('timeStamp')) if 'timeStamp' in r else None)
+        except (IrcelineApiError, ClientResponseError, IndexError):
+            result[feature] = FeatureValue(value=None, timestamp=None)
+        return result

src/open_irceline/utils.py

@@ -1,4 +1,3 @@
-from collections import OrderedDict
 from typing import Tuple
 
 from pyproj import Transformer
@@ -6,31 +5,6 @@ from pyproj import Transformer
 _project_transform = Transformer.from_crs('EPSG:4326', 'EPSG:31370', always_xy=False)
 
 
-class SizedDict(OrderedDict):
-    """Dictionary with a maximum size.  When more items are added, the least recently accessed element is evicted"""
-
-    def __init__(self, size: int):
-        super().__init__()
-        self._size = size
-
-    def __setitem__(self, key, value):
-        super().__setitem__(key, value)
-        self.move_to_end(key)
-        if len(self) > self._size:
-            self.popitem(False)
-
-    def __getitem__(self, key):
-        self.move_to_end(key)
-        return super().__getitem__(key)
-
-    def get(self, __key, __default=None):
-        self.move_to_end(__key)
-        return super().get(__key, __default)
-
-    def update(self, __m, **kwargs):
-        raise NotImplementedError()
-
-
 def epsg_transform(position: Tuple[float, float]) -> Tuple[int, int]:
     """
     Convert 'EPSG:4326' coordinates to 'EPSG:31370' coordinates
@@ -39,15 +13,3 @@ def epsg_transform(position: Tuple[float, float]) -> Tuple[int, int]:
     """
     result = _project_transform.transform(position[0], position[1])
     return round(result[0]), round(result[1])
-
-
-def round_coordinates(x: float, y: float, step=.05) -> Tuple[float, float]:
-    """
-    Round the coordinate to the precision given by step
-    :param x: latitude
-    :param y: longitude
-    :param step: precision of the rounding
-    :return: x and y round to the closest step increment
-    """
-    n = 1 / step
-    return round(x * n) / n, round(y * n) / n

tests/conftest.py

@@ -27,28 +27,3 @@ def get_mock_session(json_file=None, text_file=None):
     mock_session.request = AsyncMock(return_value=mock_response)
     return mock_session
 
-
-def create_mock_response(*args, **kwargs):
-    etag = 'my-etag-here'
-    mock_response = Mock()
-    if '20240619' not in kwargs.get('url', ''):
-        mock_response.status = 404
-        mock_response.raise_for_status = Mock(side_effect=aiohttp.ClientResponseError(Mock(), Mock()))
-    elif etag in kwargs.get('headers', {}).get('If-None-Match', ''):
-        mock_response.text = AsyncMock(return_value='')
-        mock_response.status = 304
-    else:
-        mock_response.text = AsyncMock(return_value=get_api_data('forecast.csv', plain=True))
-        mock_response.status = 200
-
-    if '20240619' in kwargs.get('url', ''):
-        mock_response.headers = {'ETag': etag}
-    else:
-        mock_response.headers = dict()
-    return mock_response
-
-
-def get_mock_session_many_csv():
-    mock_session = Mock(aiohttp.ClientSession)
-    mock_session.request = AsyncMock(side_effect=create_mock_response)
-    return mock_session

tests/fixtures/forecast_wms_feature_info.json

@@ -0,0 +1,17 @@
+{
+  "type": "FeatureCollection",
+  "features": [
+    {
+      "type": "Feature",
+      "id": "",
+      "geometry": null,
+      "properties": {
+        "GRAY_INDEX": 10.853286743164062
+      }
+    }
+  ],
+  "totalFeatures": "unknown",
+  "numberReturned": 1,
+  "timeStamp": "2024-06-30T13:00:21.520Z",
+  "crs": null
+}

tests/fixtures/forecast_wms_feature_info_invalid.json

@@ -0,0 +1,17 @@
+{
+  "type": "FeatureCollection",
+  "features": [
+    {
+      "type": "Feature",
+      "id": "",
+      "geometry": null,
+      "properties": {
+        "WRONG": 10.853286743164062
+      }
+    }
+  ],
+  "totalFeatures": "unknown",
+  "numberReturned": 1,
+  "timeStamp": "2024-06-30T13:00:21.520Z",
+  "crs": null
+}

tests/fixtures/forecast_wms_feature_info_no_field.json

@@ -0,0 +1,8 @@
+{
+  "type": "FeatureCollection",
+  "features": [],
+  "totalFeatures": "unknown",
+  "numberReturned": 1,
+  "timeStamp": "2024-06-30T13:00:21.520Z",
+  "crs": null
+}

tests/fixtures/ifdm_interpolation_feature_info.json

@@ -0,0 +1,17 @@
+{
+  "type": "FeatureCollection",
+  "features": [
+    {
+      "type": "Feature",
+      "id": "",
+      "geometry": null,
+      "properties": {
+        "GRAY_INDEX": 84.33950805664062
+      }
+    }
+  ],
+  "totalFeatures": "unknown",
+  "numberReturned": 1,
+  "timeStamp": "2024-06-30T15:43:07.222Z",
+  "crs": null
+}

tests/fixtures/rio_ifdm_capabilities.xml

@@ -0,0 +1,694 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE WMT_MS_Capabilities SYSTEM "https://geobelair.irceline.be/schemas/wms/1.1.1/WMS_MS_Capabilities.dtd">
+<WMT_MS_Capabilities version="1.1.1" updateSequence="4129">
+    <Service>
+        <Name>OGC:WMS</Name>
+        <Title>IRCEL - CELINE - Web Map Service</Title>
+        <Abstract>A compliant implementation of WMS plus most of the SLD extension (dynamic styling). Can also generate
+            PDF, SVG, KML, GeoRSS
+        </Abstract>
+        <KeywordList>
+            <Keyword>WFS</Keyword>
+            <Keyword>WMS</Keyword>
+            <Keyword>GEOSERVER</Keyword>
+        </KeywordList>
+        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink" xlink:type="simple"
+                        xlink:href="http://geo.irceline.be/rioifdm/wms"/>
+        <ContactInformation>
+            <ContactPersonPrimary>
+                <ContactPerson>IRCEL - CELINE</ContactPerson>
+                <ContactOrganization>Belgian Interregional Environment Agency</ContactOrganization>
+            </ContactPersonPrimary>
+            <ContactPosition/>
+            <ContactAddress>
+                <AddressType/>
+                <Address>Gaucheretstraat 92-94 Rue Gaucheret</Address>
+                <City>Brussels</City>
+                <StateOrProvince/>
+                <PostCode>1030</PostCode>
+                <Country>Belgium</Country>
+            </ContactAddress>
+            <ContactVoiceTelephone>+(32)(0)2 227 57 01</ContactVoiceTelephone>
+            <ContactFacsimileTelephone/>
+            <ContactElectronicMailAddress>info@irceline.be</ContactElectronicMailAddress>
+        </ContactInformation>
+        <Fees>NONE</Fees>
+        <AccessConstraints>NONE</AccessConstraints>
+    </Service>
+    <Capability>
+        <Request>
+            <GetCapabilities>
+                <Format>application/vnd.ogc.wms_xml</Format>
+                <Format>text/xml</Format>
+                <DCPType>
+                    <HTTP>
+                        <Get>
+                            <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink" xlink:type="simple"
+                                            xlink:href="https://geobelair.irceline.be/rioifdm/wms?SERVICE=WMS&amp;"/>
+                        </Get>
+                        <Post>
+                            <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink" xlink:type="simple"
+                                            xlink:href="https://geobelair.irceline.be/rioifdm/wms?SERVICE=WMS&amp;"/>
+                        </Post>
+                    </HTTP>
+                </DCPType>
+            </GetCapabilities>
+            <GetMap>
+                <Format>image/png</Format>
+                <Format>application/atom xml</Format>
+                <Format>application/atom+xml</Format>
+                <Format>application/json;type=utfgrid</Format>
+                <Format>application/openlayers</Format>
+                <Format>application/openlayers2</Format>
+                <Format>application/openlayers3</Format>
+                <Format>application/pdf</Format>
+                <Format>application/rss xml</Format>
+                <Format>application/rss+xml</Format>
+                <Format>application/vnd.google-earth.kml</Format>
+                <Format>application/vnd.google-earth.kml xml</Format>
+                <Format>application/vnd.google-earth.kml+xml</Format>
+                <Format>application/vnd.google-earth.kml+xml;mode=networklink</Format>
+                <Format>application/vnd.google-earth.kmz</Format>
+                <Format>application/vnd.google-earth.kmz xml</Format>
+                <Format>application/vnd.google-earth.kmz+xml</Format>
+                <Format>application/vnd.google-earth.kmz;mode=networklink</Format>
+                <Format>atom</Format>
+                <Format>image/geotiff</Format>
+                <Format>image/geotiff8</Format>
+                <Format>image/gif</Format>
+                <Format>image/gif;subtype=animated</Format>
+                <Format>image/jpeg</Format>
+                <Format>image/png8</Format>
+                <Format>image/png; mode=8bit</Format>
+                <Format>image/svg</Format>
+                <Format>image/svg xml</Format>
+                <Format>image/svg+xml</Format>
+                <Format>image/tiff</Format>
+                <Format>image/tiff8</Format>
+                <Format>image/vnd.jpeg-png</Format>
+                <Format>image/vnd.jpeg-png8</Format>
+                <Format>kml</Format>
+                <Format>kmz</Format>
+                <Format>openlayers</Format>
+                <Format>rss</Format>
+                <Format>text/html; subtype=openlayers</Format>
+                <Format>text/html; subtype=openlayers2</Format>
+                <Format>text/html; subtype=openlayers3</Format>
+                <Format>utfgrid</Format>
+                <DCPType>
+                    <HTTP>
+                        <Get>
+                            <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink" xlink:type="simple"
+                                            xlink:href="https://geobelair.irceline.be/rioifdm/wms?SERVICE=WMS&amp;"/>
+                        </Get>
+                    </HTTP>
+                </DCPType>
+            </GetMap>
+            <GetFeatureInfo>
+                <Format>text/plain</Format>
+                <Format>application/vnd.ogc.gml</Format>
+                <Format>text/xml</Format>
+                <Format>application/vnd.ogc.gml/3.1.1</Format>
+                <Format>text/xml; subtype=gml/3.1.1</Format>
+                <Format>text/html</Format>
+                <Format>application/json</Format>
+                <DCPType>
+                    <HTTP>
+                        <Get>
+                            <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink" xlink:type="simple"
+                                            xlink:href="https://geobelair.irceline.be/rioifdm/wms?SERVICE=WMS&amp;"/>
+                        </Get>
+                        <Post>
+                            <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink" xlink:type="simple"
+                                            xlink:href="https://geobelair.irceline.be/rioifdm/wms?SERVICE=WMS&amp;"/>
+                        </Post>
+                    </HTTP>
+                </DCPType>
+            </GetFeatureInfo>
+            <DescribeLayer>
+                <Format>application/vnd.ogc.wms_xml</Format>
+                <DCPType>
+                    <HTTP>
+                        <Get>
+                            <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink" xlink:type="simple"
+                                            xlink:href="https://geobelair.irceline.be/rioifdm/wms?SERVICE=WMS&amp;"/>
+                        </Get>
+                    </HTTP>
+                </DCPType>
+            </DescribeLayer>
+            <GetLegendGraphic>
+                <Format>image/png</Format>
+                <Format>image/jpeg</Format>
+                <Format>application/json</Format>
+                <Format>image/gif</Format>
+                <DCPType>
+                    <HTTP>
+                        <Get>
+                            <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink" xlink:type="simple"
+                                            xlink:href="https://geobelair.irceline.be/rioifdm/wms?SERVICE=WMS&amp;"/>
+                        </Get>
+                    </HTTP>
+                </DCPType>
+            </GetLegendGraphic>
+            <GetStyles>
+                <Format>application/vnd.ogc.sld+xml</Format>
+                <DCPType>
+                    <HTTP>
+                        <Get>
+                            <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink" xlink:type="simple"
+                                            xlink:href="https://geobelair.irceline.be/rioifdm/wms?SERVICE=WMS&amp;"/>
+                        </Get>
+                    </HTTP>
+                </DCPType>
+            </GetStyles>
+        </Request>
+        <Exception>
+            <Format>application/vnd.ogc.se_xml</Format>
+            <Format>application/vnd.ogc.se_inimage</Format>
+            <Format>application/vnd.ogc.se_blank</Format>
+            <Format>application/json</Format>
+        </Exception>
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+                <Title>belaqi_dm2</Title>
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+                    <Keyword>WCS</Keyword>
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+                <Title>no2_dm1</Title>
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+                    <Abstract>BelAQI index colour scale. Daily mean concentration classes for Nitrogen dioxide (NO2).
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+                <Title>no2_dm2</Title>
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+                    <Title>Nitrogen dioxide (NO2) daily mean concentrations</Title>
+                    <Abstract>BelAQI index colour scale. Daily mean concentration classes for Nitrogen dioxide (NO2).
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+                <Title>no2_hmean</Title>
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+                <Title>o3_dm1</Title>
+                <Abstract/>
+                <KeywordList>
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+                    <Keyword>WCS</Keyword>
+                    <Keyword>GeoTIFF</Keyword>
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+                    <Abstract>BelAQI index colour scale. Daily max 8-hourly mean concentration classes for Ozone (O3).
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+                <Title>o3_dm3</Title>
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+                <BoundingBox SRS="EPSG:31370" minx="18950.0" miny="18650.0" maxx="297550.0" maxy="248050.0"/>
+                <Style>
+                    <Name>pm10_hmean_raster_discrete_belair</Name>
+                    <Title>particulate matter (PM10) hourly mean concentrations</Title>
+                    <Abstract>BelAQI index colour scale. Hourly mean concentration classes for particulate matter
+                        (PM10).
+                    </Abstract>
+                    <LegendURL width="87" height="252">
+                        <Format>image/png</Format>
+                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink" xlink:type="simple"
+                                        xlink:href="https://geobelair.irceline.be/rioifdm/wms?request=GetLegendGraphic&amp;version=1.1.1&amp;format=image%2Fpng&amp;width=20&amp;height=20&amp;layer=pm10_hmean"/>
+                    </LegendURL>
+                </Style>
+            </Layer>
+            <Layer queryable="1" opaque="0">
+                <Name>pm25_dm1</Name>
+                <Title>pm25_dm1</Title>
+                <Abstract/>
+                <KeywordList>
+                    <Keyword>pm25_dmean_dm1</Keyword>
+                    <Keyword>WCS</Keyword>
+                    <Keyword>GeoTIFF</Keyword>
+                </KeywordList>
+                <SRS>EPSG:31370</SRS>
+                <LatLonBoundingBox minx="2.4804079470216474" miny="49.461523892203324" maxx="6.494748595696256"
+                                   maxy="51.54189845090732"/>
+                <BoundingBox SRS="EPSG:31370" minx="18950.0" miny="18650.0" maxx="297550.0" maxy="248050.0"/>
+                <Style>
+                    <Name>pm25_dmean_raster_discrete_belair</Name>
+                    <Title>particulate matter (PM25) daily mean concentrations</Title>
+                    <Abstract>BelAQI index colour scale. Daily mean concentration classes for particulate matter
+                        (PM25).
+                    </Abstract>
+                    <LegendURL width="87" height="252">
+                        <Format>image/png</Format>
+                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink" xlink:type="simple"
+                                        xlink:href="https://geobelair.irceline.be/rioifdm/wms?request=GetLegendGraphic&amp;version=1.1.1&amp;format=image%2Fpng&amp;width=20&amp;height=20&amp;layer=pm25_dm1"/>
+                    </LegendURL>
+                </Style>
+            </Layer>
+            <Layer queryable="1" opaque="0">
+                <Name>pm25_dm2</Name>
+                <Title>pm25_dm2</Title>
+                <Abstract/>
+                <KeywordList>
+                    <Keyword>pm25_dmean_dm2</Keyword>
+                    <Keyword>WCS</Keyword>
+                    <Keyword>GeoTIFF</Keyword>
+                </KeywordList>
+                <SRS>EPSG:31370</SRS>
+                <LatLonBoundingBox minx="2.4804079470216474" miny="49.461523892203324" maxx="6.494748595696256"
+                                   maxy="51.54189845090732"/>
+                <BoundingBox SRS="EPSG:31370" minx="18950.0" miny="18650.0" maxx="297550.0" maxy="248050.0"/>
+                <Style>
+                    <Name>pm25_dmean_raster_discrete_belair</Name>
+                    <Title>particulate matter (PM25) daily mean concentrations</Title>
+                    <Abstract>BelAQI index colour scale. Daily mean concentration classes for particulate matter
+                        (PM25).
+                    </Abstract>
+                    <LegendURL width="87" height="252">
+                        <Format>image/png</Format>
+                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink" xlink:type="simple"
+                                        xlink:href="https://geobelair.irceline.be/rioifdm/wms?request=GetLegendGraphic&amp;version=1.1.1&amp;format=image%2Fpng&amp;width=20&amp;height=20&amp;layer=pm25_dm2"/>
+                    </LegendURL>
+                </Style>
+            </Layer>
+            <Layer queryable="1" opaque="0">
+                <Name>pm25_dm3</Name>
+                <Title>pm25_dm3</Title>
+                <Abstract/>
+                <KeywordList>
+                    <Keyword>pm25_dmean_dm3</Keyword>
+                    <Keyword>WCS</Keyword>
+                    <Keyword>GeoTIFF</Keyword>
+                </KeywordList>
+                <SRS>EPSG:31370</SRS>
+                <LatLonBoundingBox minx="2.4804079470216474" miny="49.461523892203324" maxx="6.494748595696256"
+                                   maxy="51.54189845090732"/>
+                <BoundingBox SRS="EPSG:31370" minx="18950.0" miny="18650.0" maxx="297550.0" maxy="248050.0"/>
+                <Style>
+                    <Name>pm25_dmean_raster_discrete_belair</Name>
+                    <Title>particulate matter (PM25) daily mean concentrations</Title>
+                    <Abstract>BelAQI index colour scale. Daily mean concentration classes for particulate matter
+                        (PM25).
+                    </Abstract>
+                    <LegendURL width="87" height="252">
+                        <Format>image/png</Format>
+                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink" xlink:type="simple"
+                                        xlink:href="https://geobelair.irceline.be/rioifdm/wms?request=GetLegendGraphic&amp;version=1.1.1&amp;format=image%2Fpng&amp;width=20&amp;height=20&amp;layer=pm25_dm3"/>
+                    </LegendURL>
+                </Style>
+            </Layer>
+            <Layer queryable="1" opaque="0">
+                <Name>pm25_hmean</Name>
+                <Title>pm25_hmean</Title>
+                <Abstract/>
+                <KeywordList>
+                    <Keyword>pm25_hmean</Keyword>
+                    <Keyword>WCS</Keyword>
+                    <Keyword>GeoTIFF</Keyword>
+                </KeywordList>
+                <SRS>EPSG:31370</SRS>
+                <LatLonBoundingBox minx="2.4804079470216474" miny="49.461523892203324" maxx="6.494748595696256"
+                                   maxy="51.54189845090732"/>
+                <BoundingBox SRS="EPSG:31370" minx="18950.0" miny="18650.0" maxx="297550.0" maxy="248050.0"/>
+                <Style>
+                    <Name>pm25_hmean_raster_discrete_belair</Name>
+                    <Title>particulate matter (PM25) hourly mean concentrations</Title>
+                    <Abstract>BelAQI index colour scale. Hourly mean concentration classes for particulate matter
+                        (PM25).
+                    </Abstract>
+                    <LegendURL width="87" height="252">
+                        <Format>image/png</Format>
+                        <OnlineResource xmlns:xlink="http://www.w3.org/1999/xlink" xlink:type="simple"
+                                        xlink:href="https://geobelair.irceline.be/rioifdm/wms?request=GetLegendGraphic&amp;version=1.1.1&amp;format=image%2Fpng&amp;width=20&amp;height=20&amp;layer=pm25_hmean"/>
+                    </LegendURL>
+                </Style>
+            </Layer>
+        </Layer>
+    </Capability>
+</WMT_MS_Capabilities>

tests/test_api_forecast.py

@@ -0,0 +1,128 @@
+from datetime import datetime
+from itertools import product
+from unittest.mock import call
+
+from freezegun import freeze_time
+
+from src.open_irceline import IrcelineForecastClient, ForecastFeature, FeatureValue
+from src.open_irceline.api import _user_agent, _forecast_wms_base_url
+from tests.conftest import get_api_data, get_mock_session
+
+
+def test_parse_capabilities():
+    data = get_api_data('forecast_wms_capabilities.xml', plain=True)
+    result = IrcelineForecastClient._parse_capabilities(data)
+
+    expected = {'o3_maxhmean_wl_d3', 'pm25_dmean_wl_d0', 'o3_max8hmean_chimv2022_d2', 'no2_maxhmean_tf_d2',
+                'belaqi_forecast_chimv2022_d2', 'pm25_dmean_chimv2022_d3', 'pm10_dmean_chimv2022_d0',
+                'no2_maxhmean_wl_d0', 'no2_maxhmean_d2', 'no2_dmean_chimv2022_d2', 'o3_maxhmean_chimv2022_d3',
+                'pm25_dmean_wl_d3', 'o3_maxhmean_chimv2022_d0', 'pm25_dmean', 'pm25_dmean_tf_d0', 'no2_dmean_wl_d2',
+                'o3_max8hmean_chimv2022_d3', 'pm25_dmean_d2', 'o3_max8hmean_chimv2022_d0', 'o3_maxhmean_wl_d2',
+                'no2_maxhmean_wl_d1', 'pm10_dmean_tf_d2', 'pm25_dmean_d1', 'o3_maxhmean_chimv2022_d2',
+                'pm10_dmean_chimv2022_d2', 'o3_maxhmean_vl', 'belaqi_wl_d2', 'pm10_dmean_wl', 'pm10_dmean_d2',
+                'no2_dmean_wl_d0', 'no2_dmean_d1', 'o3_maxhmean_d2', 'o3_maxhmean_wl', 'pm25_dmean_wl_d2',
+                'o3_maxhmean_d3', 'o3_max8hmean_wl_d3', 'belaqi_d0', 'no2_maxhmean_wl_d2', 'no2_maxhmean_wl',
+                'pm10_dmean_wl_d1', 'no2_dmean_chimv2022_d3', 'o3_maxhmean_tf_d1', 'pm25_dmean_vl', 'pm10_dmean_d0',
+                'o3_max8hmean_d0', 'o3_max8hmean_d2', 'no2_maxhmean_vl', 'o3_max8hmean_chimv2022_d1', 'pm10_dmean',
+                'pm10_dmean_wl_d2', 'euaqi_d3', 'belaqi_d1', 'o3_max8hmean_d1', 'o3_maxhmean_chimv2022_d1', 'belaqi_vl',
+                'belaqi_wl_d0', 'no2_dmean_chimv2022_d0', 'pm25_dmean_wl_d1', 'pm25_dmean_tf_d2', 'no2_dmean_d2',
+                'o3_maxhmean', 'belaqi_wl', 'no2_maxhmean_d0', 'no2_maxhmean_d3', 'o3_max8hmean_d3', 'euaqi_forecast',
+                'o3_max8hmean_wl_d1', 'pm10_dmean_chimv2022_d3', 'no2_maxhmean_wl_d3', 'o3_maxhmean_d1',
+                'no2_dmean_wl_d1', 'o3_maxhmean_wl_d1', 'no2_dmean_d3', 'belaqi_d3', 'belaqi', 'pm25_dmean_d3',
+                'belaqi_forecast', 'no2_dmean_d0', 'pm25_dmean_chimv2022_d1', 'belaqi_wl_d1', 'pm10_dmean_d3',
+                'no2_dmean_wl_d3', 'pm25_dmean_tf_d1', 'euaqi_d0', 'o3_maxhmean_wl_d0', 'belaqi_forecast_chimv2022_d3',
+                'no2_dmean_chimv2022_d1', 'o3_max8hmean_wl_d0', 'o3_max8hmean_wl_d2', 'pm10_dmean_chimv2022_d1',
+                'pm10_dmean_wl_d3', 'pm25_dmean_wl', 'belaqi_forecast_chimv2022_d1', 'euaqi_d2', 'pm10_dmean_d1',
+                'belaqi_wl_d3', 'belaqi_forecast_chimv2022_d0', 'o3_maxhmean_tf_d0', 'euaqi_d1', 'no2_maxhmean',
+                'pm25_dmean_chimv2022_d2', 'belaqi_d2', 'pm25_dmean_d0', 'no2_maxhmean_tf_d0', 'pm10_dmean_tf_d0',
+                'pm25_dmean_chimv2022_d0', 'o3_maxhmean_d0', 'pm10_dmean_tf_d1', 'pm10_dmean_vl', 'no2_maxhmean_tf_d1',
+                'o3_maxhmean_tf_d2', 'pm10_dmean_wl_d0', 'no2_maxhmean_d1'}
+
+    assert result == expected
+
+    for f, d in product(ForecastFeature, range(4)):
+        assert f"{f.split(':')[1]}_d{d}" in result
+
+
+async def test_aget_capabilities():
+    session = get_mock_session(text_file='forecast_wms_capabilities.xml')
+
+    client = IrcelineForecastClient(session)
+    _ = await client.get_capabilities()
+
+    session.request.assert_called_once_with(
+        method='GET',
+        url=_forecast_wms_base_url,
+        params={"service": "WMS",
+                "version": "1.1.1",
+                "request": "GetCapabilities"},
+        headers={'User-Agent': _user_agent}
+    )
+
+
+@freeze_time(datetime.fromisoformat("2024-06-30T13:00:21.520Z"))
+async def test_api_forecast_error():
+    pos = (50.4657, 4.8647)
+    session = get_mock_session('forecast_wms_feature_info_invalid.json')
+
+    client = IrcelineForecastClient(session)
+
+    features = [ForecastFeature.NO2_DMEAN, ForecastFeature.O3_MAXHMEAN]
+    result = await client.get_data(features, pos)
+
+    for k, v in result.items():
+        assert v == FeatureValue(timestamp=datetime.fromisoformat("2024-06-30T13:00:21.520Z"), value=None)
+
+
+async def test_api_forecast_no_field():
+    pos = (50.4657, 4.8647)
+    session = get_mock_session('forecast_wms_feature_info_no_field.json')
+
+    client = IrcelineForecastClient(session)
+
+    features = [ForecastFeature.NO2_DMEAN, ForecastFeature.O3_MAXHMEAN]
+    result = await client.get_data(features, pos)
+
+    for k, v in result.items():
+        assert v == FeatureValue(timestamp=None, value=None)
+
+
+async def test_api_forecast():
+    pos = (50.4657, 4.8647)
+    lat, lon = pos
+    session = get_mock_session('forecast_wms_feature_info.json')
+
+    client = IrcelineForecastClient(session)
+
+    features = [ForecastFeature.NO2_DMEAN, ForecastFeature.O3_MAXHMEAN]
+    result = await client.get_data(features, pos)
+
+    base = {"service": "WMS",
+            "version": "1.1.1",
+            "request": "GetFeatureInfo",
+            "info_format": "application/json",
+            "width": "1",
+            "height": "1",
+            "srs": "EPSG:4326",
+            "bbox": f"{lon},{lat},{lon + 0.00001},{lat + 0.00001}",
+            "X": "1",
+            "Y": "1"}
+
+    calls = [call(
+        method='GET',
+        url=_forecast_wms_base_url,
+        params=base | {"layers": f"{feature}_d{d}",
+                       "query_layers": f"{feature}_d{d}"},
+        headers={'User-Agent': _user_agent},
+    )
+        for feature, d in product(features, range(4))]
+
+    session.request.assert_has_calls(calls, any_order=True)
+
+    for k, v in result.items():
+        assert v['value'] == 10.853286743164062
+
+
+def test_parse_capabilities_with_error():
+    result = IrcelineForecastClient._parse_capabilities("wow there no valid XML")
+    assert result == set()

tests/test_api_forecasts.py

@@ -1,90 +0,0 @@
-from datetime import date
-from unittest.mock import call
-
-from src.open_irceline.api import _forecast_base_url, _user_agent
-from src.open_irceline.api import IrcelineForecastClient
-from src.open_irceline.data import ForecastFeature
-from tests.conftest import get_api_data, get_mock_session_many_csv
-
-
-def test_extract_from_csv():
-    data = get_api_data('forecast.csv', plain=True)
-    x, y = 50.45, 4.85
-
-    result = IrcelineForecastClient.extract_result_from_csv(x, y, data)
-    assert result == 13.0844
-
-    result = IrcelineForecastClient.extract_result_from_csv(23, 4, data)
-    assert result is None
-
-
-async def test_cached_calls():
-    session = get_mock_session_many_csv()
-    client = IrcelineForecastClient(session)
-
-    _ = await client.get_data(
-        timestamp=date(2024, 6, 19),
-        features=[ForecastFeature.NO2_MAXHMEAN],
-        position=(50.45, 4.85)
-    )
-
-    calls = [
-        call(method='GET',
-             url=f"{_forecast_base_url}/BE_{ForecastFeature.NO2_MAXHMEAN}_20240619_d{i}.csv",
-             params=None,
-             headers={'User-Agent': _user_agent}
-             ) for i in range(5)
-    ]
-
-    assert session.request.call_count == 5
-    session.request.assert_has_calls(calls)
-
-    _ = await client.get_data(
-        timestamp=date(2024, 6, 19),
-        features=[ForecastFeature.NO2_MAXHMEAN],
-        position=(50.45, 4.85)
-    )
-
-    calls += [
-        call(method='GET',
-             url=f"{_forecast_base_url}/BE_{ForecastFeature.NO2_MAXHMEAN}_20240619_d{i}.csv",
-             params=None,
-             headers={'User-Agent': _user_agent, 'If-None-Match': 'my-etag-here'}
-             ) for i in range(5)
-    ]
-
-    assert session.request.call_count == 10
-    session.request.assert_has_calls(calls)
-
-
-async def test_missed_cached_calls():
-    session = get_mock_session_many_csv()
-    client = IrcelineForecastClient(session)
-
-    r = await client.get_data(
-        timestamp=date(2024, 6, 21),
-        features=[ForecastFeature.NO2_MAXHMEAN],
-        position=(50.45, 4.85)
-    )
-
-    calls = list()
-
-    for i in range(5):
-        calls += [
-            call(method='GET',
-                 url=f"{_forecast_base_url}/BE_{ForecastFeature.NO2_MAXHMEAN}_20240621_d{i}.csv",
-                 params=None,
-                 headers={'User-Agent': _user_agent}
-                 ),
-            call(method='GET',
-                 url=f"{_forecast_base_url}/BE_{ForecastFeature.NO2_MAXHMEAN}_20240620_d{i}.csv",
-                 params=None,
-                 headers={'User-Agent': _user_agent}
-                 )
-        ]
-
-    assert session.request.call_count == 10
-    session.request.assert_has_calls(calls)
-
-    for value in r.values():
-        assert value['value'] is None

tests/test_api_rio.py

@@ -2,7 +2,7 @@ from datetime import datetime, date
 
 from freezegun import freeze_time
 
-from src.open_irceline.api import IrcelineRioClient
+from src.open_irceline import IrcelineRioClient
 from src.open_irceline.api import _rio_wfs_base_url, _user_agent
 from src.open_irceline.data import RioFeature, FeatureValue
 from src.open_irceline.utils import epsg_transform
@@ -64,7 +64,7 @@ async def test_format_result_dmean():
 
 
 def test_parse_capabilities():
-    data = get_api_data('capabilities.xml', plain=True)
+    data = get_api_data('rio_capabilities.xml', plain=True)
     result = IrcelineRioClient._parse_capabilities(data)
 
     expected = {'rio:so2_anmean_be', 'rio:o3_hmean', 'rio:bc_anmean_vl', 'rio:o3_anmean_be', 'rio:pm10_hmean_vl',
@@ -99,7 +99,7 @@ async def test_api_rio():
 
     d = date(2024, 6, 18)
     features = [RioFeature.NO2_HMEAN, RioFeature.O3_HMEAN]
-    _ = await client.get_data(d, features, pos)
+    _ = await client.get_data(features, pos, d)
     session.request.assert_called_once_with(
         method='GET',
         url=_rio_wfs_base_url,
@@ -117,10 +117,10 @@ async def test_api_rio():
 
 
 async def test_api_rio_get_capabilities():
-    session = get_mock_session(text_file='capabilities.xml')
+    session = get_mock_session(text_file='rio_capabilities.xml')
 
     client = IrcelineRioClient(session)
-    _ = await client.get_rio_capabilities()
+    _ = await client.get_capabilities()
 
     session.request.assert_called_once_with(
         method='GET',

tests/test_api_rio_ifdm.py

@@ -0,0 +1,102 @@
+from datetime import datetime
+from unittest.mock import call
+
+from freezegun import freeze_time
+
+from src.open_irceline.api import _rio_ifdm_wms_base_url, _user_agent
+from src.open_irceline.data import RioIfdmFeature, FeatureValue
+from src.open_irceline.rio import IrcelineRioIfdmClient
+from tests.conftest import get_api_data, get_mock_session
+
+
+def test_parse_capabilities():
+    data = get_api_data('rio_ifdm_capabilities.xml', plain=True)
+    result = IrcelineRioIfdmClient._parse_capabilities(data)
+
+    expected = {'no2_dm3', 'belaqi_dm2', 'pm10_hmean', 'belaqi_dm1', 'pm25_dm3', 'pm25_dm2', 'pm10_dm1', 'o3_dm3',
+                'no2_dm1', 'pm10_dm3', 'pm25_dm1', 'belaqi', 'belaqi_dm3', 'pm10_dm2', 'o3_dm2', 'pm25_hmean', 'o3_dm1',
+                'o3_hmean', 'no2_dm2', 'no2_hmean'}
+
+    assert result == expected
+
+    for f in RioIfdmFeature:
+        assert f"{f.split(':')[1]}" in result
+
+
+async def test_aget_capabilities():
+    session = get_mock_session(text_file='rio_ifdm_capabilities.xml')
+
+    client = IrcelineRioIfdmClient(session)
+    _ = await client.get_capabilities()
+
+    session.request.assert_called_once_with(
+        method='GET',
+        url=_rio_ifdm_wms_base_url,
+        params={"service": "WMS",
+                "version": "1.1.1",
+                "request": "GetCapabilities"},
+        headers={'User-Agent': _user_agent}
+    )
+
+
+@freeze_time(datetime.fromisoformat("2024-06-30T13:00:21.520Z"))
+async def test_api_forecast_error():
+    pos = (50.4657, 4.8647)
+    session = get_mock_session('forecast_wms_feature_info_invalid.json')
+
+    client = IrcelineRioIfdmClient(session)
+
+    features = [RioIfdmFeature.NO2_HMEAN, RioIfdmFeature.O3_HMEAN]
+    result = await client.get_data(features, pos)
+
+    for k, v in result.items():
+        assert v == FeatureValue(timestamp=datetime.fromisoformat("2024-06-30T13:00:21.520Z"), value=None)
+
+
+async def test_api_forecast():
+    pos = (50.4657, 4.8647)
+    lat, lon = pos
+    session = get_mock_session('forecast_wms_feature_info.json')
+
+    client = IrcelineRioIfdmClient(session)
+
+    features = [RioIfdmFeature.NO2_HMEAN, RioIfdmFeature.O3_HMEAN]
+    result = await client.get_data(features, pos)
+
+    base = {"service": "WMS",
+            "version": "1.1.1",
+            "request": "GetFeatureInfo",
+            "info_format": "application/json",
+            "width": "1",
+            "height": "1",
+            "srs": "EPSG:4326",
+            "bbox": f"{lon},{lat},{lon + 0.00001},{lat + 0.00001}",
+            "X": "1",
+            "Y": "1"}
+
+    calls = [call(
+        method='GET',
+        url=_rio_ifdm_wms_base_url,
+        params=base | {"layers": f"{feature}",
+                       "query_layers": f"{feature}"},
+        headers={'User-Agent': _user_agent},
+    )
+        for feature in features]
+
+    session.request.assert_has_calls(calls, any_order=True)
+
+    for k, v in result.items():
+        assert v['value'] == 10.853286743164062
+
+
+async def test_api_forecast_no_field():
+    pos = (50.4657, 4.8647)
+    session = get_mock_session('forecast_wms_feature_info_no_field.json')
+
+    client = IrcelineRioIfdmClient(session)
+
+    features = [RioIfdmFeature.NO2_HMEAN, RioIfdmFeature.O3_HMEAN]
+    result = await client.get_data(features, pos)
+
+    for k, v in result.items():
+        assert v == FeatureValue(timestamp=None, value=None)

tests/test_belaqi.py

@@ -1,267 +0,0 @@
-from datetime import date, timedelta, datetime
-
-import pytest
-from freezegun import freeze_time
-
-from src.open_irceline.api import IrcelineForecastClient, IrcelineRioClient
-from src.open_irceline.belaqi import belaqi_index_forecast_daily, belaqi_index_rio_hourly, belaqi_index_hourly, \
-    belaqi_index_daily
-from src.open_irceline.data import BelAqiIndex
-from tests.conftest import get_mock_session_many_csv, get_mock_session
-
-
-@pytest.mark.parametrize("pm10, pm25, o3, no2, expected", [
-    (5, 2, 25, 5, BelAqiIndex.EXCELLENT),
-    (15, 5, 50, 12, BelAqiIndex.VERY_GOOD),
-    (30, 9, 70, 18, BelAqiIndex.GOOD),
-    (40, 13, 80, 25, BelAqiIndex.FAIRLY_GOOD),
-    (55, 18, 100, 35, BelAqiIndex.MODERATE),
-    (70, 25, 130, 43, BelAqiIndex.POOR),
-    (90, 45, 160, 48, BelAqiIndex.VERY_POOR),
-    (100, 55, 200, 55, BelAqiIndex.BAD),
-    (130, 70, 230, 70, BelAqiIndex.VERY_BAD),
-    (150, 80, 250, 80, BelAqiIndex.HORRIBLE),
-    (150, 80, 300, 80, BelAqiIndex.HORRIBLE),
-    (95, 5, 25, 5, BelAqiIndex.VERY_POOR),
-    (145, 5, 25, 5, BelAqiIndex.HORRIBLE),
-    (5, 55, 25, 5, BelAqiIndex.BAD),
-    (5, 85, 25, 5, BelAqiIndex.HORRIBLE),
-    (5, 5, 190, 5, BelAqiIndex.BAD),
-    (5, 5, 260, 5, BelAqiIndex.HORRIBLE),
-    (5, 5, 25, 65, BelAqiIndex.VERY_BAD),
-    (5, 5, 25, 85, BelAqiIndex.HORRIBLE),
-    (45, 15, 150, 10, BelAqiIndex.POOR),
-    (20, 25, 180, 15, BelAqiIndex.VERY_POOR),
-    (10, 7, 250, 70, BelAqiIndex.HORRIBLE),
-    (110, 3, 30, 25, BelAqiIndex.BAD),
-    (5, 0, 0, 0, BelAqiIndex.EXCELLENT),
-    (15, 0, 0, 0, BelAqiIndex.VERY_GOOD),
-    (30, 0, 0, 0, BelAqiIndex.GOOD),
-    (40, 0, 0, 0, BelAqiIndex.FAIRLY_GOOD),
-    (55, 0, 0, 0, BelAqiIndex.MODERATE),
-    (70, 0, 0, 0, BelAqiIndex.POOR),
-    (90, 0, 0, 0, BelAqiIndex.VERY_POOR),
-    (100, 0, 0, 0, BelAqiIndex.BAD),
-    (130, 0, 0, 0, BelAqiIndex.VERY_BAD),
-    (150, 0, 0, 0, BelAqiIndex.HORRIBLE),
-    (0, 2, 0, 0, BelAqiIndex.EXCELLENT),
-    (0, 5, 0, 0, BelAqiIndex.VERY_GOOD),
-    (0, 9, 0, 0, BelAqiIndex.GOOD),
-    (0, 13, 0, 0, BelAqiIndex.FAIRLY_GOOD),
-    (0, 18, 0, 0, BelAqiIndex.MODERATE),
-    (0, 25, 0, 0, BelAqiIndex.POOR),
-    (0, 45, 0, 0, BelAqiIndex.VERY_POOR),
-    (0, 55, 0, 0, BelAqiIndex.BAD),
-    (0, 70, 0, 0, BelAqiIndex.VERY_BAD),
-    (0, 80, 0, 0, BelAqiIndex.HORRIBLE),
-    (0, 0, 25, 0, BelAqiIndex.EXCELLENT),
-    (0, 0, 50, 0, BelAqiIndex.VERY_GOOD),
-    (0, 0, 70, 0, BelAqiIndex.GOOD),
-    (0, 0, 80, 0, BelAqiIndex.FAIRLY_GOOD),
-    (0, 0, 100, 0, BelAqiIndex.MODERATE),
-    (0, 0, 130, 0, BelAqiIndex.POOR),
-    (0, 0, 160, 0, BelAqiIndex.VERY_POOR),
-    (0, 0, 200, 0, BelAqiIndex.BAD),
-    (0, 0, 230, 0, BelAqiIndex.VERY_BAD),
-    (0, 0, 250, 0, BelAqiIndex.HORRIBLE),
-    (0, 0, 0, 5, BelAqiIndex.EXCELLENT),
-    (0, 0, 0, 12, BelAqiIndex.VERY_GOOD),
-    (0, 0, 0, 18, BelAqiIndex.GOOD),
-    (0, 0, 0, 25, BelAqiIndex.FAIRLY_GOOD),
-    (0, 0, 0, 35, BelAqiIndex.MODERATE),
-    (0, 0, 0, 43, BelAqiIndex.POOR),
-    (0, 0, 0, 48, BelAqiIndex.VERY_POOR),
-    (0, 0, 0, 55, BelAqiIndex.BAD),
-    (0, 0, 0, 70, BelAqiIndex.VERY_BAD),
-    (0, 0, 0, 80, BelAqiIndex.HORRIBLE)
-])
-def test_belaqi_index_hourly(pm10, pm25, o3, no2, expected):
-    assert belaqi_index_hourly(pm10, pm25, o3, no2) == expected
-
-
-@pytest.mark.parametrize("pm10, pm25, o3, no2, expected_index", [
-    (5, 0, 0, 0, BelAqiIndex.EXCELLENT),
-    (15, 0, 0, 0, BelAqiIndex.VERY_GOOD),
-    (25, 0, 0, 0, BelAqiIndex.GOOD),
-    (35, 0, 0, 0, BelAqiIndex.FAIRLY_GOOD),
-    (45, 0, 0, 0, BelAqiIndex.MODERATE),
-    (60, 0, 0, 0, BelAqiIndex.POOR),
-    (70, 0, 0, 0, BelAqiIndex.VERY_POOR),
-    (80, 0, 0, 0, BelAqiIndex.BAD),
-    (100, 0, 0, 0, BelAqiIndex.VERY_BAD),
-    (101, 0, 0, 0, BelAqiIndex.HORRIBLE),
-    (0, 2.5, 0, 0, BelAqiIndex.EXCELLENT),
-    (0, 5, 0, 0, BelAqiIndex.VERY_GOOD),
-    (0, 7.5, 0, 0, BelAqiIndex.GOOD),
-    (0, 10, 0, 0, BelAqiIndex.FAIRLY_GOOD),
-    (0, 15, 0, 0, BelAqiIndex.MODERATE),
-    (0, 25, 0, 0, BelAqiIndex.POOR),
-    (0, 35, 0, 0, BelAqiIndex.VERY_POOR),
-    (0, 40, 0, 0, BelAqiIndex.BAD),
-    (0, 50, 0, 0, BelAqiIndex.VERY_BAD),
-    (0, 51, 0, 0, BelAqiIndex.HORRIBLE),
-    (0, 0, 30, 0, BelAqiIndex.EXCELLENT),
-    (0, 0, 60, 0, BelAqiIndex.VERY_GOOD),
-    (0, 0, 70, 0, BelAqiIndex.GOOD),
-    (0, 0, 80, 0, BelAqiIndex.FAIRLY_GOOD),
-    (0, 0, 100, 0, BelAqiIndex.MODERATE),
-    (0, 0, 130, 0, BelAqiIndex.POOR),
-    (0, 0, 160, 0, BelAqiIndex.VERY_POOR),
-    (0, 0, 190, 0, BelAqiIndex.BAD),
-    (0, 0, 220, 0, BelAqiIndex.VERY_BAD),
-    (0, 0, 221, 0, BelAqiIndex.HORRIBLE),
-    (0, 0, 0, 5, BelAqiIndex.EXCELLENT),
-    (0, 0, 0, 10, BelAqiIndex.VERY_GOOD),
-    (0, 0, 0, 15, BelAqiIndex.GOOD),
-    (0, 0, 0, 20, BelAqiIndex.FAIRLY_GOOD),
-    (0, 0, 0, 25, BelAqiIndex.MODERATE),
-    (0, 0, 0, 30, BelAqiIndex.POOR),
-    (0, 0, 0, 35, BelAqiIndex.VERY_POOR),
-    (0, 0, 0, 40, BelAqiIndex.BAD),
-    (0, 0, 0, 50, BelAqiIndex.VERY_BAD),
-    (0, 0, 0, 51, BelAqiIndex.HORRIBLE),
-    (3, 1, 20, 4, BelAqiIndex.EXCELLENT),
-    (10, 3, 50, 8, BelAqiIndex.VERY_GOOD),
-    (20, 6, 65, 12, BelAqiIndex.GOOD),
-    (30, 8, 75, 18, BelAqiIndex.FAIRLY_GOOD),
-    (40, 12, 90, 22, BelAqiIndex.MODERATE),
-    (50, 20, 110, 28, BelAqiIndex.POOR),
-    (65, 30, 140, 33, BelAqiIndex.VERY_POOR),
-    (75, 38, 180, 38, BelAqiIndex.BAD),
-    (90, 45, 200, 45, BelAqiIndex.VERY_BAD),
-    (110, 55, 230, 55, BelAqiIndex.HORRIBLE),
-    (3, 30, 20, 8, BelAqiIndex.VERY_POOR),
-    (110, 6, 65, 12, BelAqiIndex.HORRIBLE),
-    (3, 6, 230, 12, BelAqiIndex.HORRIBLE),
-    (3, 6, 65, 55, BelAqiIndex.HORRIBLE),
-    (50, 5, 65, 12, BelAqiIndex.POOR),
-    (10, 20, 65, 12, BelAqiIndex.POOR),
-    (10, 5, 110, 12, BelAqiIndex.POOR),
-    (10, 5, 65, 28, BelAqiIndex.POOR),
-    (75, 5, 30, 8, BelAqiIndex.BAD),
-    (10, 38, 30, 8, BelAqiIndex.BAD),
-    (10, 5, 180, 8, BelAqiIndex.BAD),
-    (10, 5, 30, 38, BelAqiIndex.BAD),
-    (65, 3, 20, 22, BelAqiIndex.VERY_POOR),
-    (3, 30, 20, 22, BelAqiIndex.VERY_POOR),
-    (3, 3, 140, 22, BelAqiIndex.VERY_POOR),
-    (3, 3, 20, 33, BelAqiIndex.VERY_POOR),
-    (90, 6, 20, 22, BelAqiIndex.VERY_BAD),
-    (10, 45, 20, 22, BelAqiIndex.VERY_BAD),
-    (10, 6, 200, 22, BelAqiIndex.VERY_BAD),
-    (10, 6, 20, 45, BelAqiIndex.VERY_BAD),
-    (3, 30, 20, 4, BelAqiIndex.VERY_POOR),
-    (110, 1, 20, 4, BelAqiIndex.HORRIBLE),
-    (3, 1, 230, 4, BelAqiIndex.HORRIBLE),
-    (3, 1, 20, 55, BelAqiIndex.HORRIBLE),
-    (50, 3, 20, 4, BelAqiIndex.POOR),
-    (3, 20, 20, 4, BelAqiIndex.POOR),
-    (3, 1, 110, 4, BelAqiIndex.POOR),
-    (3, 1, 20, 28, BelAqiIndex.POOR),
-])
-def test_belaqi_index_daily(pm10, pm25, o3, no2, expected_index):
-    assert belaqi_index_daily(pm10, pm25, o3, no2) == expected_index
-
-
-def test_belaqi_hourly_value_error():
-    with pytest.raises(ValueError):
-        belaqi_index_hourly(-1, 0, 12, 8)
-
-    with pytest.raises(ValueError):
-        belaqi_index_hourly(1, -20, 12, 8)
-
-    with pytest.raises(ValueError):
-        belaqi_index_hourly(1, 0, -12, 8)
-
-    with pytest.raises(ValueError):
-        belaqi_index_hourly(1, 0, 12, -8888)
-
-
-def test_belaqi_daily_value_error():
-    with pytest.raises(ValueError):
-        belaqi_index_daily(-1, 0, 12, 8)
-
-    with pytest.raises(ValueError):
-        belaqi_index_daily(1, -20, 12, 8)
-
-    with pytest.raises(ValueError):
-        belaqi_index_daily(1, 0, -12, 8)
-
-    with pytest.raises(ValueError):
-        belaqi_index_daily(1, 0, 12, -8888)
-
-
-def test_belaqi_hourly_value_error_none():
-    with pytest.raises(ValueError):
-        belaqi_index_hourly(None, 0, 12, 8)
-
-    with pytest.raises(ValueError):
-        belaqi_index_hourly(1, None, 12, 8)
-
-    with pytest.raises(ValueError):
-        belaqi_index_hourly(1, 0, None, 8)
-
-    with pytest.raises(ValueError):
-        belaqi_index_hourly(1, 0, 12, None)
-
-
-def test_belaqi_daily_value_error_none():
-    with pytest.raises(ValueError):
-        belaqi_index_daily(None, 0, 12, 8)
-
-    with pytest.raises(ValueError):
-        belaqi_index_daily(1, None, 12, 8)
-
-    with pytest.raises(ValueError):
-        belaqi_index_daily(1, 0, None, 8)
-
-    with pytest.raises(ValueError):
-        belaqi_index_daily(1, 0, 12, None)
-
-
-@freeze_time(datetime.fromisoformat("2024-06-19T19:30:09.581Z"))
-async def test_belaqi_index_forecast():
-    session = get_mock_session_many_csv()
-    client = IrcelineForecastClient(session)
-    pos = (50.55, 4.85)
-
-    result = await belaqi_index_forecast_daily(client, pos)
-
-    expected_days = {date(2024, 6, 19) + timedelta(days=i) for i in range(5)}
-
-    assert set(result.keys()) == expected_days
-    for v in result.values():
-        assert v.get('value') == BelAqiIndex.MODERATE
-
-
-async def test_belaqi_index_forecast_missing_day():
-    session = get_mock_session_many_csv()
-    client = IrcelineForecastClient(session)
-    pos = (50.55, 4.85)
-
-    result = await belaqi_index_forecast_daily(client, pos, date(2024, 6, 21))
-
-    expected_days = {date(2024, 6, 21) + timedelta(days=i) for i in range(5)}
-    assert set(result.keys()) == expected_days
-    for v in result.values():
-        assert v.get('value') is None
-
-
-@freeze_time(datetime.fromisoformat("2024-06-23T12:30:09.581Z"))
-async def test_belaqi_index_actual():
-    session = get_mock_session(json_file='rio_wfs_for_belaqi.json')
-    client = IrcelineRioClient(session)
-    pos = (50.55, 4.85)
-
-    result = await belaqi_index_rio_hourly(client, pos)
-    assert result.get('value') == BelAqiIndex.GOOD
-
-
-@freeze_time(datetime.fromisoformat("2024-06-23T12:30:09.581Z"))
-async def test_belaqi_index_actual_missing_value():
-    session = get_mock_session(json_file='rio_wfs.json')
-    client = IrcelineRioClient(session)
-    pos = (50.55, 4.85)
-
-    with pytest.raises(ValueError):
-        _ = await belaqi_index_rio_hourly(client, pos)

tests/test_utils.py

@@ -1,44 +1,4 @@
-import pytest
-
-from src.open_irceline.utils import SizedDict, round_coordinates, epsg_transform
-
-
-def test_sized_dict():
-    s_dict = SizedDict(5)
-    assert len(s_dict) == 0
-
-    s_dict['a'] = 1
-    s_dict['b'] = 2
-    s_dict['c'] = 3
-    s_dict['d'] = 4
-    s_dict['e'] = 5
-    assert len(s_dict) == 5
-
-    s_dict['f'] = 6
-    assert 'a' not in s_dict
-    assert s_dict['f'] == 6
-    assert len(s_dict) == 5
-
-    s_dict['b'] = 42
-    s_dict['g'] = 7
-    assert s_dict.get('f') == 6
-    assert s_dict['g'] == 7
-    assert s_dict['b'] == 42
-    assert 'c' not in s_dict
-    assert len(s_dict) == 5
-
-    del s_dict['b']
-    assert len(s_dict) == 4
-    assert 'b' not in s_dict
-
-    with pytest.raises(NotImplementedError):
-        s_dict.update({'a': 1})
-
-
-def test_round_coord():
-    x, y = round_coordinates(50.4657, 4.8647)
-    assert x == 50.45
-    assert y == 4.85
+from src.open_irceline.utils import epsg_transform
 
 
 def test_epsg_transform():