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Update computation of the BelAQI index
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5 changed files with 305 additions and 178 deletions
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@ -26,7 +26,7 @@ import aiohttp
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import asyncio
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from datetime import datetime, date
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from open_irceline import IrcelineRioClient, RioFeature, IrcelineForecastClient, ForecastFeature, belaqi_index_actual
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from open_irceline import IrcelineRioClient, RioFeature, IrcelineForecastClient, ForecastFeature, belaqi_index_rio_hourly
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async def get_rio_interpolated_data():
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@ -61,7 +61,7 @@ async def get_current_belaqi():
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"""Get current BelAQI index from RIO interpolated values"""
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async with aiohttp.ClientSession() as session:
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client = IrcelineRioClient(session)
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result = await belaqi_index_actual(
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result = await belaqi_index_rio_hourly(
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rio_client=client,
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timestamp=datetime.utcnow(), # must be timezone aware
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position=(50.85, 4.35) # (lat, lon) for Brussels
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@ -1,5 +1,5 @@
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from .api import IrcelineRioClient, IrcelineForecastClient, IrcelineApiError
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from .belaqi import belaqi_index, belaqi_index_actual, belaqi_index_forecast
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from .belaqi import belaqi_index_rio_hourly, belaqi_index_forecast_daily, belaqi_index_daily, belaqi_index_hourly
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from .data import RioFeature, ForecastFeature, FeatureValue, BelAqiIndex
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__version__ = '0.1.0'
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@ -1,26 +1,31 @@
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"""
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Compute the BelAQI index from concentrations of PM10, PM2.5, O3 and NO2, based on
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https://www.irceline.be/en/air-quality/measurements/belaqi-air-quality-index/information
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> to calculate the actual (hour per hour varying) sub-indexes and the global index, the concentration scales of Table 4
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> are applied to the latest hourly mean O3 and NO2 concentrations and the running 24-hourly mean PM2.5 and PM10
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> concentrations.
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https://www.irceline.be/en/air-quality/measurements/air-quality-index-november-2022/info_nov2022
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"""
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from datetime import datetime, date
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from typing import Tuple, Dict
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from typing import Tuple, Dict, Final
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from .api import IrcelineRioClient, IrcelineForecastClient
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from .data import BelAqiIndex, RioFeature, ForecastFeature
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# Ratio values from Figure 2 at
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# https://www.irceline.be/en/air-quality/measurements/air-quality-index-november-2022/info_nov2022
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NO2_MAX_HMEAN_TO_DMEAN: Final = 1.51
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O3_MAX_HMEAN_TO_MAX8HMEAN: Final = 1.10
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def belaqi_index(pm10: float, pm25: float, o3: float, no2: float) -> BelAqiIndex:
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def belaqi_index_daily(pm10: float, pm25: float, o3: float, no2: float) -> BelAqiIndex:
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"""
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Computes the BelAQI index based on the components
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Computes the daily BelAQI index based on the components
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Raise ValueError if a component is < 0
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:param pm10: PM10 daily mean (or running 24-hourly mean for real-time) (µg/m³)
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:param pm25: PM2.5 daily mean (or running 24-hourly mean for real-time) (µg/m³)
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:param o3: O3 max 1-hourly mean per day (or latest hourly mean for real-time) (µg/m³)
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:param no2: NO2 max 1-hourly mean per day (or latest hourly mean for real-time) (µg/m³)
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Values taken from Table 1 of
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https://www.irceline.be/en/air-quality/measurements/air-quality-index-november-2022/info_nov2022
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:param pm10: PM10 daily mean (µg/m³)
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:param pm25: PM2.5 daily mean (µg/m³)
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:param o3: O3 maximum running 8-hour mean (µg/m³)
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:param no2: NO2 daily mean (µg/m³)
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:return: BelAQI index from 1 to 10 (Value of BelAqiIndex enum)
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"""
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if pm10 is None or pm25 is None or o3 is None or no2 is None:
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@ -29,39 +34,90 @@ def belaqi_index(pm10: float, pm25: float, o3: float, no2: float) -> BelAqiIndex
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if pm10 < 0 or pm25 < 0 or o3 < 0 or no2 < 0:
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raise ValueError("All the components should have a positive value")
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elif pm10 > 100 or pm25 > 70 or o3 > 320 or no2 > 300:
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elif pm10 > 100 or pm25 > 50 or o3 > 220 or no2 > 50:
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return BelAqiIndex.HORRIBLE
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elif pm10 > 80 or pm25 > 60 or o3 > 280 or no2 > 250:
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elif pm10 > 80 or pm25 > 40 or o3 > 190 or no2 > 40:
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return BelAqiIndex.VERY_BAD
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elif pm10 > 70 or pm25 > 50 or o3 > 240 or no2 > 200:
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elif pm10 > 70 or pm25 > 35 or o3 > 160 or no2 > 35:
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return BelAqiIndex.BAD
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elif pm10 > 60 or pm25 > 40 or o3 > 180 or no2 > 180:
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elif pm10 > 60 or pm25 > 25 or o3 > 130 or no2 > 30:
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return BelAqiIndex.VERY_POOR
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elif pm10 > 50 or pm25 > 35 or o3 > 160 or no2 > 150:
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elif pm10 > 45 or pm25 > 15 or o3 > 100 or no2 > 25:
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return BelAqiIndex.POOR
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elif pm10 > 40 or pm25 > 25 or o3 > 120 or no2 > 120:
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elif pm10 > 35 or pm25 > 10 or o3 > 80 or no2 > 20:
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return BelAqiIndex.MODERATE
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elif pm10 > 30 or pm25 > 15 or o3 > 70 or no2 > 70:
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elif pm10 > 25 or pm25 > 7.5 or o3 > 70 or no2 > 15:
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return BelAqiIndex.FAIRLY_GOOD
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elif pm10 > 20 or pm25 > 10 or o3 > 50 or no2 > 50:
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elif pm10 > 15 or pm25 > 5 or o3 > 60 or no2 > 10:
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return BelAqiIndex.GOOD
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elif pm10 > 10 or pm25 > 5 or o3 > 25 or no2 > 20:
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elif pm10 > 5 or pm25 > 2.5 or o3 > 30 or no2 > 5:
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return BelAqiIndex.VERY_GOOD
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elif pm10 >= 0 or pm25 >= 0 or o3 >= 0 or no2 >= 0:
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return BelAqiIndex.EXCELLENT
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async def belaqi_index_actual(rio_client: IrcelineRioClient, position: Tuple[float, float],
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timestamp: datetime | None = None) -> BelAqiIndex:
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def belaqi_index_hourly(pm10: float, pm25: float, o3: float, no2: float) -> BelAqiIndex:
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"""
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Computes the hourly BelAQI index based on the components
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Raise ValueError if a component is < 0
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Values taken from Table 2 of
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https://www.irceline.be/en/air-quality/measurements/air-quality-index-november-2022/info_nov2022
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:param pm10: PM10 hourly mean (µg/m³)
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:param pm25: PM2.5 hourly mean (µg/m³)
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:param o3: O3 hourly mean (µg/m³)
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:param no2: NO2 hourly mean (µg/m³)
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:return: BelAQI index from 1 to 10 (Value of BelAqiIndex enum)
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"""
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if pm10 is None or pm25 is None or o3 is None or no2 is None:
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raise ValueError("All the components should be valued (at lest one is None here)")
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if pm10 < 0 or pm25 < 0 or o3 < 0 or no2 < 0:
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raise ValueError("All the components should have a positive value")
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elif pm10 > 140 or pm25 > 75 or o3 > 240 or no2 > 75:
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return BelAqiIndex.HORRIBLE
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elif pm10 > 110 or pm25 > 60 or o3 > 210 or no2 > 60:
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return BelAqiIndex.VERY_BAD
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elif pm10 > 95 or pm25 > 50 or o3 > 180 or no2 > 50:
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return BelAqiIndex.BAD
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elif pm10 > 80 or pm25 > 35 or o3 > 150 or no2 > 45:
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return BelAqiIndex.VERY_POOR
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elif pm10 > 60 or pm25 > 20 or o3 > 110 or no2 > 40:
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return BelAqiIndex.POOR
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elif pm10 > 45 or pm25 > 15 or o3 > 90 or no2 > 30:
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return BelAqiIndex.MODERATE
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elif pm10 > 35 or pm25 > 10 or o3 > 75 or no2 > 20:
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return BelAqiIndex.FAIRLY_GOOD
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elif pm10 > 20 or pm25 > 7.5 or o3 > 65 or no2 > 15:
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return BelAqiIndex.GOOD
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elif pm10 > 10 or pm25 > 3.5 or o3 > 30 or no2 > 10:
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return BelAqiIndex.VERY_GOOD
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elif pm10 >= 0 or pm25 >= 0 or o3 >= 0 or no2 >= 0:
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return BelAqiIndex.EXCELLENT
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async def belaqi_index_rio_hourly(rio_client: IrcelineRioClient, position: Tuple[float, float],
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timestamp: datetime | None = None) -> BelAqiIndex:
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"""
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Get current BelAQI index value for the given position using the rio_client
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Raise ValueError if one or more components are not available
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@ -74,23 +130,23 @@ async def belaqi_index_actual(rio_client: IrcelineRioClient, position: Tuple[flo
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timestamp = datetime.utcnow()
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components = await rio_client.get_data(
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timestamp=timestamp,
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features=[RioFeature.PM10_24HMEAN,
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RioFeature.PM25_24HMEAN,
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features=[RioFeature.PM10_HMEAN,
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RioFeature.PM25_HMEAN,
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RioFeature.O3_HMEAN,
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RioFeature.NO2_HMEAN],
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position=position
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)
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return belaqi_index(
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components.get(RioFeature.PM10_24HMEAN, {}).get('value', -1),
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components.get(RioFeature.PM25_24HMEAN, {}).get('value', -1),
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components.get(RioFeature.O3_HMEAN, {}).get('value', -1),
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components.get(RioFeature.NO2_HMEAN, {}).get('value', -1)
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return belaqi_index_hourly(
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pm10=components.get(RioFeature.PM10_HMEAN, {}).get('value', -1),
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pm25=components.get(RioFeature.PM25_HMEAN, {}).get('value', -1),
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o3=components.get(RioFeature.O3_HMEAN, {}).get('value', -1),
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no2=components.get(RioFeature.NO2_HMEAN, {}).get('value', -1)
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)
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async def belaqi_index_forecast(forecast_client: IrcelineForecastClient, position: Tuple[float, float],
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timestamp: date | None = None) -> Dict[date, BelAqiIndex | None]:
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async def belaqi_index_forecast_daily(forecast_client: IrcelineForecastClient, position: Tuple[float, float],
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timestamp: date | None = None) -> Dict[date, BelAqiIndex | None]:
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"""
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Get forecasted BelAQI index value for the given position using the forecast_client.
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Data is downloaded for the given day and the four next days
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@ -115,13 +171,13 @@ async def belaqi_index_forecast(forecast_client: IrcelineForecastClient, positio
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for _, day in components.keys():
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try:
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result[day] = belaqi_index(
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components.get((ForecastFeature.PM10_DMEAN, day), {}).get('value', -1),
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components.get((ForecastFeature.PM25_DMEAN, day), {}).get('value', -1),
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components.get((ForecastFeature.O3_MAXHMEAN, day), {}).get('value', -1),
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components.get((ForecastFeature.NO2_MAXHMEAN, day), {}).get('value', -1)
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result[day] = belaqi_index_daily(
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pm10=components.get((ForecastFeature.PM10_DMEAN, day), {}).get('value', -1),
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pm25=components.get((ForecastFeature.PM25_DMEAN, day), {}).get('value', -1),
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o3=components.get((ForecastFeature.O3_MAXHMEAN, day), {}).get('value', -1) * O3_MAX_HMEAN_TO_MAX8HMEAN,
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no2=components.get((ForecastFeature.NO2_MAXHMEAN, day), {}).get('value', -1) * NO2_MAX_HMEAN_TO_DMEAN
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)
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except ValueError:
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except (ValueError, TypeError):
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result[day] = None
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return result
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8
tests/fixtures/rio_wfs_for_belaqi.json
vendored
8
tests/fixtures/rio_wfs_for_belaqi.json
vendored
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@ -3,7 +3,7 @@
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"features": [
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{
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"type": "Feature",
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"id": "pm10_24hmean.fid--d1ce43_19045107e20_1893",
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"id": "pm10_hmean.fid--d1ce43_19045107e20_1893",
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"geometry": {
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"type": "Polygon",
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"coordinates": [
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@ -41,7 +41,7 @@
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},
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{
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"type": "Feature",
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"id": "pm10_24hmean.fid--d1ce43_19045107e20_1894",
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"id": "pm10_hmean.fid--d1ce43_19045107e20_1894",
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"geometry": {
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"type": "Polygon",
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"coordinates": [
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@ -79,7 +79,7 @@
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},
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{
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"type": "Feature",
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"id": "pm25_24hmean.fid--d1ce43_19045107e20_1895",
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"id": "pm25_hmean.fid--d1ce43_19045107e20_1895",
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"geometry": {
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"type": "Polygon",
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"coordinates": [
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@ -117,7 +117,7 @@
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},
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{
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"type": "Feature",
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"id": "pm25_24hmean.fid--d1ce43_19045107e20_1896",
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"id": "pm25_hmean.fid--d1ce43_19045107e20_1896",
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"geometry": {
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"type": "Polygon",
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"coordinates": [
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@ -1,150 +1,222 @@
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from datetime import date, timedelta, datetime
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from random import randint, seed
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import pytest
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from freezegun import freeze_time
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from src.open_irceline.api import IrcelineForecastClient, IrcelineRioClient
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from src.open_irceline.belaqi import belaqi_index, belaqi_index_forecast, belaqi_index_actual
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from src.open_irceline.belaqi import belaqi_index_forecast_daily, belaqi_index_rio_hourly, belaqi_index_hourly, \
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belaqi_index_daily
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from src.open_irceline.data import BelAqiIndex
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from tests.conftest import get_mock_session_many_csv, get_mock_session
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def test_belaqi_index():
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# Excellent
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assert belaqi_index(5, 2, 10, 10) == BelAqiIndex.EXCELLENT
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assert belaqi_index(0, 0, 0, 0) == BelAqiIndex.EXCELLENT
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assert belaqi_index(10, 5, 25, 20) == BelAqiIndex.EXCELLENT
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# Very good
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assert belaqi_index(15, 8, 40, 35) == BelAqiIndex.VERY_GOOD
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assert belaqi_index(11, 6, 26, 21) == BelAqiIndex.VERY_GOOD
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assert belaqi_index(20, 10, 50, 50) == BelAqiIndex.VERY_GOOD
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# Good
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assert belaqi_index(25, 12, 60, 60) == BelAqiIndex.GOOD
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assert belaqi_index(21, 11, 51, 51) == BelAqiIndex.GOOD
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assert belaqi_index(30, 15, 70, 70) == BelAqiIndex.GOOD
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# Fairly good
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assert belaqi_index(35, 20, 100, 90) == BelAqiIndex.FAIRLY_GOOD
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assert belaqi_index(31, 16, 71, 71) == BelAqiIndex.FAIRLY_GOOD
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assert belaqi_index(40, 25, 120, 120) == BelAqiIndex.FAIRLY_GOOD
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# Moderate
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assert belaqi_index(45, 30, 140, 130) == BelAqiIndex.MODERATE
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assert belaqi_index(41, 26, 121, 121) == BelAqiIndex.MODERATE
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assert belaqi_index(50, 35, 160, 150) == BelAqiIndex.MODERATE
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# Poor
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assert belaqi_index(55, 38, 170, 160) == BelAqiIndex.POOR
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assert belaqi_index(51, 36, 161, 151) == BelAqiIndex.POOR
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assert belaqi_index(60, 40, 180, 180) == BelAqiIndex.POOR
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# Very poor
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assert belaqi_index(65, 45, 200, 190) == BelAqiIndex.VERY_POOR
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assert belaqi_index(61, 41, 181, 181) == BelAqiIndex.VERY_POOR
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assert belaqi_index(70, 50, 240, 200) == BelAqiIndex.VERY_POOR
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# Bad
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assert belaqi_index(75, 55, 260, 220) == BelAqiIndex.BAD
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assert belaqi_index(71, 51, 241, 201) == BelAqiIndex.BAD
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assert belaqi_index(80, 60, 280, 250) == BelAqiIndex.BAD
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# Very bad
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assert belaqi_index(85, 65, 300, 270) == BelAqiIndex.VERY_BAD
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assert belaqi_index(81, 61, 281, 251) == BelAqiIndex.VERY_BAD
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assert belaqi_index(100, 70, 320, 300) == BelAqiIndex.VERY_BAD
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# Horrible
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assert belaqi_index(110, 75, 330, 310) == BelAqiIndex.HORRIBLE
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assert belaqi_index(101, 71, 321, 301) == BelAqiIndex.HORRIBLE
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assert belaqi_index(150, 100, 400, 400) == BelAqiIndex.HORRIBLE
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@pytest.mark.parametrize("pm10, pm25, o3, no2, expected", [
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(5, 2, 25, 5, BelAqiIndex.EXCELLENT),
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(15, 5, 50, 12, BelAqiIndex.VERY_GOOD),
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(30, 9, 70, 18, BelAqiIndex.GOOD),
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(40, 13, 80, 25, BelAqiIndex.FAIRLY_GOOD),
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(55, 18, 100, 35, BelAqiIndex.MODERATE),
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(70, 25, 130, 43, BelAqiIndex.POOR),
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(90, 45, 160, 48, BelAqiIndex.VERY_POOR),
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(100, 55, 200, 55, BelAqiIndex.BAD),
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(130, 70, 230, 70, BelAqiIndex.VERY_BAD),
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(150, 80, 250, 80, BelAqiIndex.HORRIBLE),
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(150, 80, 300, 80, BelAqiIndex.HORRIBLE),
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(95, 5, 25, 5, BelAqiIndex.VERY_POOR),
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(145, 5, 25, 5, BelAqiIndex.HORRIBLE),
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(5, 55, 25, 5, BelAqiIndex.BAD),
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(5, 85, 25, 5, BelAqiIndex.HORRIBLE),
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(5, 5, 190, 5, BelAqiIndex.BAD),
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(5, 5, 260, 5, BelAqiIndex.HORRIBLE),
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(5, 5, 25, 65, BelAqiIndex.VERY_BAD),
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(5, 5, 25, 85, BelAqiIndex.HORRIBLE),
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(45, 15, 150, 10, BelAqiIndex.POOR),
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(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
|
||||
|
||||
|
||||
def test_belaqi_single_component():
|
||||
# Tests with only PM10 varying
|
||||
assert belaqi_index(5, 0, 0, 0) == BelAqiIndex.EXCELLENT
|
||||
assert belaqi_index(15, 0, 0, 0) == BelAqiIndex.VERY_GOOD
|
||||
assert belaqi_index(25, 0, 0, 0) == BelAqiIndex.GOOD
|
||||
assert belaqi_index(35, 0, 0, 0) == BelAqiIndex.FAIRLY_GOOD
|
||||
assert belaqi_index(45, 0, 0, 0) == BelAqiIndex.MODERATE
|
||||
assert belaqi_index(55, 0, 0, 0) == BelAqiIndex.POOR
|
||||
assert belaqi_index(65, 0, 0, 0) == BelAqiIndex.VERY_POOR
|
||||
assert belaqi_index(75, 0, 0, 0) == BelAqiIndex.BAD
|
||||
assert belaqi_index(85, 0, 0, 0) == BelAqiIndex.VERY_BAD
|
||||
assert belaqi_index(110, 0, 0, 0) == BelAqiIndex.HORRIBLE
|
||||
|
||||
# Tests with only PM2.5 varying
|
||||
assert belaqi_index(0, 2, 0, 0) == BelAqiIndex.EXCELLENT
|
||||
assert belaqi_index(0, 8, 0, 0) == BelAqiIndex.VERY_GOOD
|
||||
assert belaqi_index(0, 12, 0, 0) == BelAqiIndex.GOOD
|
||||
assert belaqi_index(0, 20, 0, 0) == BelAqiIndex.FAIRLY_GOOD
|
||||
assert belaqi_index(0, 30, 0, 0) == BelAqiIndex.MODERATE
|
||||
assert belaqi_index(0, 38, 0, 0) == BelAqiIndex.POOR
|
||||
assert belaqi_index(0, 45, 0, 0) == BelAqiIndex.VERY_POOR
|
||||
assert belaqi_index(0, 55, 0, 0) == BelAqiIndex.BAD
|
||||
assert belaqi_index(0, 65, 0, 0) == BelAqiIndex.VERY_BAD
|
||||
assert belaqi_index(0, 75, 0, 0) == BelAqiIndex.HORRIBLE
|
||||
|
||||
# Tests with only O3 varying
|
||||
assert belaqi_index(0, 0, 10, 0) == BelAqiIndex.EXCELLENT
|
||||
assert belaqi_index(0, 0, 40, 0) == BelAqiIndex.VERY_GOOD
|
||||
assert belaqi_index(0, 0, 60, 0) == BelAqiIndex.GOOD
|
||||
assert belaqi_index(0, 0, 100, 0) == BelAqiIndex.FAIRLY_GOOD
|
||||
assert belaqi_index(0, 0, 140, 0) == BelAqiIndex.MODERATE
|
||||
assert belaqi_index(0, 0, 170, 0) == BelAqiIndex.POOR
|
||||
assert belaqi_index(0, 0, 200, 0) == BelAqiIndex.VERY_POOR
|
||||
assert belaqi_index(0, 0, 260, 0) == BelAqiIndex.BAD
|
||||
assert belaqi_index(0, 0, 300, 0) == BelAqiIndex.VERY_BAD
|
||||
assert belaqi_index(0, 0, 330, 0) == BelAqiIndex.HORRIBLE
|
||||
|
||||
# Tests with only NO2 varying
|
||||
assert belaqi_index(0, 0, 0, 10) == BelAqiIndex.EXCELLENT
|
||||
assert belaqi_index(0, 0, 0, 35) == BelAqiIndex.VERY_GOOD
|
||||
assert belaqi_index(0, 0, 0, 60) == BelAqiIndex.GOOD
|
||||
assert belaqi_index(0, 0, 0, 90) == BelAqiIndex.FAIRLY_GOOD
|
||||
assert belaqi_index(0, 0, 0, 130) == BelAqiIndex.MODERATE
|
||||
assert belaqi_index(0, 0, 0, 160) == BelAqiIndex.POOR
|
||||
assert belaqi_index(0, 0, 0, 190) == BelAqiIndex.VERY_POOR
|
||||
assert belaqi_index(0, 0, 0, 220) == BelAqiIndex.BAD
|
||||
assert belaqi_index(0, 0, 0, 270) == BelAqiIndex.VERY_BAD
|
||||
assert belaqi_index(0, 0, 0, 310) == BelAqiIndex.HORRIBLE
|
||||
@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_random():
|
||||
seed(42)
|
||||
# Generate random test values and their expected indices
|
||||
test_cases = [
|
||||
(randint(0, 10), randint(0, 5), randint(0, 25), randint(0, 20), BelAqiIndex.EXCELLENT),
|
||||
(randint(11, 20), randint(6, 10), randint(26, 50), randint(21, 50), BelAqiIndex.VERY_GOOD),
|
||||
(randint(21, 30), randint(11, 15), randint(51, 70), randint(51, 70), BelAqiIndex.GOOD),
|
||||
(randint(31, 40), randint(16, 25), randint(71, 120), randint(71, 120), BelAqiIndex.FAIRLY_GOOD),
|
||||
(randint(41, 50), randint(26, 35), randint(121, 160), randint(121, 150), BelAqiIndex.MODERATE),
|
||||
(randint(51, 60), randint(36, 40), randint(161, 180), randint(151, 180), BelAqiIndex.POOR),
|
||||
(randint(61, 70), randint(41, 50), randint(181, 240), randint(181, 200), BelAqiIndex.VERY_POOR),
|
||||
(randint(71, 80), randint(51, 60), randint(241, 280), randint(201, 250), BelAqiIndex.BAD),
|
||||
(randint(81, 100), randint(61, 70), randint(281, 320), randint(251, 300), BelAqiIndex.VERY_BAD),
|
||||
(randint(101, 150), randint(71, 100), randint(321, 400), randint(301, 400), BelAqiIndex.HORRIBLE)
|
||||
]
|
||||
|
||||
# Test each case
|
||||
for pm10, pm25, o3, no2, expected in test_cases:
|
||||
assert belaqi_index(pm10, pm25, o3, no2) == expected
|
||||
|
||||
|
||||
def test_belaqi_value_error():
|
||||
def test_belaqi_hourly_value_error():
|
||||
with pytest.raises(ValueError):
|
||||
belaqi_index(-1, 0, 12, 8)
|
||||
belaqi_index_hourly(-1, 0, 12, 8)
|
||||
|
||||
with pytest.raises(ValueError):
|
||||
belaqi_index(1, -20, 12, 8)
|
||||
belaqi_index_hourly(1, -20, 12, 8)
|
||||
|
||||
with pytest.raises(ValueError):
|
||||
belaqi_index(1, 0, -12, 8)
|
||||
belaqi_index_hourly(1, 0, -12, 8)
|
||||
|
||||
with pytest.raises(ValueError):
|
||||
belaqi_index(1, 0, 12, -8888)
|
||||
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"))
|
||||
|
|
@ -153,13 +225,13 @@ async def test_belaqi_index_forecast():
|
|||
client = IrcelineForecastClient(session)
|
||||
pos = (50.55, 4.85)
|
||||
|
||||
result = await belaqi_index_forecast(client, pos)
|
||||
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 == BelAqiIndex.GOOD
|
||||
assert v == BelAqiIndex.MODERATE
|
||||
|
||||
|
||||
async def test_belaqi_index_forecast_missing_day():
|
||||
|
|
@ -167,7 +239,7 @@ async def test_belaqi_index_forecast_missing_day():
|
|||
client = IrcelineForecastClient(session)
|
||||
pos = (50.55, 4.85)
|
||||
|
||||
result = await belaqi_index_forecast(client, pos, date(2024, 6, 21))
|
||||
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
|
||||
|
|
@ -181,9 +253,8 @@ async def test_belaqi_index_actual():
|
|||
client = IrcelineRioClient(session)
|
||||
pos = (50.55, 4.85)
|
||||
|
||||
result = await belaqi_index_actual(client, pos)
|
||||
print(result)
|
||||
assert result == BelAqiIndex.FAIRLY_GOOD
|
||||
result = await belaqi_index_rio_hourly(client, pos)
|
||||
assert result == BelAqiIndex.GOOD
|
||||
|
||||
|
||||
@freeze_time(datetime.fromisoformat("2024-06-23T12:30:09.581Z"))
|
||||
|
|
@ -193,4 +264,4 @@ async def test_belaqi_index_actual_missing_value():
|
|||
pos = (50.55, 4.85)
|
||||
|
||||
with pytest.raises(ValueError):
|
||||
_ = await belaqi_index_actual(client, pos)
|
||||
_ = await belaqi_index_rio_hourly(client, pos)
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue