env_canada 0.10.1

A package to access meteorological data from Environment Canada

Environment Canada (env_canada)

This package provides access to various data sources published by Environment and Climate Change Canada.

[!IMPORTANT] If you're using the library in a Jupyter notebook, replace asyncio.run(...) with await ... in the examples below. For example:

asyncio.run(ec_en.update())

becomes

await ec_en.update()

Weather Observations and Forecasts

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.alerts

Weather Radar

ECRadar 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())

Air Quality Health Index (AQHI)

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.forecasts

Water Level and Flow

ECHydro 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.measurements

Historical Weather Data

ECHistorical 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.csv

In 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)

License

The code is available under terms of MIT License