This package provides access to various data sources published by Environment and Climate Change Canada.
ECWeather provides current conditions and forecasts. It automatically determines which weather station to use based on latitude/longitude provided. It is also possible to specify a specific station code of the form AB/s0000123 based on those listed in this CSV file. For example:
import asyncio
from env_canada import ECWeather
ec_en = ECWeather(coordinates=(50, -100))
ec_fr = ECWeather(station_id='ON/s0000430', language='french')
asyncio.run(ec_en.update())
# current conditions
ec_en.conditions
# daily forecasts
ec_en.daily_forecasts
# hourly forecasts
ec_en.hourly_forecasts
# alerts
ec_en.alertsECRadar provides Environment Canada meteorological radar imagery.
import asyncio
from env_canada import ECRadar
radar_coords = ECRadar(coordinates=(50, -100))
# Conditions Available
animated_gif = asyncio.run(radar_coords.get_loop())
latest_png = asyncio.run(radar_coords.get_latest_frame())ECAirQuality provides Environment Canada air quality data.
import asyncio
from env_canada import ECAirQuality
aqhi_coords = ECAirQuality(coordinates=(50, -100))
asyncio.run(aqhi_coords.update())
# Data available
aqhi_coords.current
aqhi_coords.forecastsECHydro provides Environment Canada hydrometric data.
import asyncio
from env_canada import ECHydro
hydro_coords = ECHydro(coordinates=(50, -100))
asyncio.run(hydro_coords.update())
# Data available
hydro_coords.measurementsECHistorical provides historical daily weather data.
The ECHistorical object is instantiated with a station ID, year, language, format (one of xml or csv) and granularity (hourly, daily data).
Once updated asynchronously, historical weather data is contained with the station_data property. If xml is requested, station_data will appear in a dictionary form. If csv is requested, station_data will contain a CSV-readable buffer. For example:
import asyncio
from env_canada import ECHistorical
from env_canada.ec_historical import get_historical_stations
# search for stations, response contains station_ids
coordinates = [53.916944, -122.749444] # [lat, long]
# coordinates: [lat, long]
# radius: km
# limit: response limit, value one of [10, 25, 50, 100]
# The result contains station names and ID values.
stations = asyncio.run(get_historical_stations(coordinates, radius=200, limit=100))
ec_en_xml = ECHistorical(station_id=31688, year=2020, language="english", format="xml")
ec_fr_xml = ECHistorical(station_id=31688, year=2020, language="french", format="xml")
ec_en_csv = ECHistorical(station_id=31688, year=2020, language="english", format="csv")
ec_fr_csv = ECHistorical(station_id=31688, year=2020, language="french", format="csv")
# timeframe argument can be passed to change the granularity
# timeframe=1 hourly (need to create of for every month in that case, use ECHistoricalRange to handle it automatically)
# timeframe=2 daily (default)
ec_en_xml = ECHistorical(station_id=31688, year=2020, month=1, language="english", format="xml", timeframe=1)
ec_en_csv = ECHistorical(station_id=31688, year=2020, month=1, language="english", format="csv", timeframe=1)
asyncio.run(ec_en_xml.update())
asyncio.run(ec_en_csv.update())
# metadata describing the station
ec_en_xml.metadata
# historical weather data, in dictionary form
ec_en_xml.station_data
# csv-generated responses return csv-like station data
import pandas as pd
df = pd.read_csv(ec_en_csv.station_data)ECHistoricalRange provides historical weather data within a specific range and handles the update by itself.
The ECHistoricalRange object is instantiated with at least a station ID and a daterange. One could add language, and granularity (hourly, daily (default)).
The data can then be used as pandas DataFrame, XML (requires pandas >=1.3.0) and csv
For example :
import pandas as pd
import asyncio
from env_canada import ECHistoricalRange
from env_canada.ec_historical import get_historical_stations
from datetime import datetime
coordinates = ['48.508333', '-68.467667']
stations = pd.DataFrame(asyncio.run(get_historical_stations(coordinates, start_year=2022,
end_year=2022, radius=200, limit=100))).T
ec = ECHistoricalRange(station_id=int(stations.iloc[0,2]), timeframe="daily",
daterange=(datetime(2022, 7, 1, 12, 12), datetime(2022, 8, 1, 12, 12)))
ec.get_data()
#yield an XML formated str.
# For more options, use ec.to_xml(*arg, **kwargs) with pandas options
ec.xml
#yield an CSV formated str.
# For more options, use ec.to_csv(*arg, **kwargs) with pandas options
ec.csvIn this example ec.df will be:
| Date/Time | Longitude (x) | Latitude (y) | Station Name | Climate ID | Year | Month | Day | Data Quality | Max Temp (°C) | Max Temp Flag | Min Temp (°C) | Min Temp Flag | Mean Temp (°C) | Mean Temp Flag | Heat Deg Days (°C) | Heat Deg Days Flag | Cool Deg Days (°C) | Cool Deg Days Flag | Total Rain (mm) | Total Rain Flag | Total Snow (cm) | Total Snow Flag | Total Precip (mm) | Total Precip Flag | Snow on Grnd (cm) | Snow on Grnd Flag | Dir of Max Gust (10s deg) | Dir of Max Gust Flag | Spd of Max Gust (km/h) | Spd of Max Gust Flag | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-07-02 | -68,47 | 48,51 | POINTE-AU-PERE (INRS) | 7056068 | 2022 | 7 | 2 | 22,8 | 12,5 | 17,7 | 0,3 | 0 | 0 | 26 | 37 | ||||||||||||||||
| 2022-07-03 | -68,47 | 48,51 | POINTE-AU-PERE (INRS) | 7056068 | 2022 | 7 | 3 | 21,7 | 10,1 | 15,9 | 2,1 | 0 | 0,4 | 28 | 50 | ||||||||||||||||
| … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … |
| 2022-07-31 | -68,47 | 48,51 | POINTE-AU-PERE (INRS) | 7056068 | 2022 | 7 | 31 | 23,5 | 14,1 | 18,8 | 0 | 0,8 | 0 | 23 | 31 | ||||||||||||||||
| 2022-08-01 | -68,47 | 48,51 | POINTE-AU-PERE (INRS) | 7056068 | 2022 | 8 | 1 | 23 | 15 | 19 | 0 | 1 | 0 | 21 | 35 |
One should note that july 1st is excluded as the time provided contains specific hours, so it yields only data after or at exactly the time provided.
To have all the july 1st data in that case, one can provide a datarange without time: datetime(2022, 7, 7) instead
of datetime(2022, 7, 1, 12, 12)
The code is available under terms of MIT License
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