metno-locationforecast 0.0.1b1

A Python interface for MET Norway's Locationforecast 2.0 service.

MET Norway Location Forecast

A Python interface for the MET Norway Locationforecast/2.0 service. This is a free weather data service provided by the Norwegian Meteorological Institute and Yr.

Contents

1. Features
2. Installation
3. Usage
   1. Basics
   2. Accessing Data
   3. More Examples
4. Notes on Licensing
5. Useful Links

Features

• Get weather forecast data for anywhere in the world.
• Automatically take care of caching data.
• Helpful classes for managing forecast data.
• Convert between units of measurement.

Installation

Installing from PyPI:

pip install metno-locationforecast

Usage

Basics

Before using this package you should be aware of the terms of service for using the MET Weather API. The metno-locationforecast package will not make requests unless current data has expired and will send requests with the appropriate If-Modified-Since header if possible. Identification can be provided by passing a User-Agent string to the Forecast class, see more on this below.

After installing metno-locationforecast the following commands can be run in a python console. Start by importing the Place and Forecast classes, these are the main classes you will need to interact with.

>>> from metno-locationforecast import Place, Forecast

Create a Place instance. Geographic coordinates are given by latitude, longitude (in degrees) and altitude (in metres). The altitude parameter is optional but recommended. Note that latitude and longitude are rounded to four decimal places and altitude is rounded to the nearest integer, this is required by the MET API. GeoNames is a helpful website for finding the geographic coordinates of a place.

>>> new_york = Place("New York", 40.7, -74.0, 10)

Next create a Forecast instance for the place. Here you need to supply the type of forecast, options are "compact" (a limited set of variables suitable for most purposes) or "complete" (an extensive set of weather data). For more details on the differences check out the this page. We also need to supply a User-Agent string, typically this will include the name and version of your application as well as contact information (email address or website) more details on what is expected here. Do NOT use the string supplied here as this does not apply to your site. Optionally, you can provide a save_location parameter, this is the folder where data will be stored. The default save_location is "./data/".

>>> ny_forecast = Forecast(new_york, "compact", "metno-locationforecast/1.0 https://github.com/Rory-Sullivan/metno-locationforecast")

Then run the update method. This will make a request to the MET API for data and will save the data to the save location. If data already exists for the forecast, this will only request new data if the data has expired and will make the request using the appropriate If-Modified-Since header. It returns a string describing which process occurred, this will be one of "Data-Not-Expired", "Data-Not-Modified" or "Data-Modified". Only in the case of "Data-Modified" has any change to the data occurred.

>>> ny_forecast.update()
'Data-Modified'
>>> ny_forecast.update()
'Data-Not-Expired'

Finally we can print the forecast.

>>> print(ny_forecast)
Forecast for New York:
        Forecast between 2020-07-21 14:00:00 and 2020-07-21 15:00:00:
                air_pressure_at_sea_level: 1016.7hPa
                air_temperature: 28.7celsius
                cloud_area_fraction: 1.6%
                ...

Accessing Data

Printing forecasts to the terminal is great but most likely you want to use the forecast data in your own application. When the update method is run it parses the returned data which can then be accessed through attributes of the forecast instance.

The most notable of these is the data attribute.

>>> type(ny_forecast.data)
<class 'dict'>
>>> ny_forecast.data.keys()
dict_keys(['last_modified', 'expires', 'updated_at', 'units', 'intervals'])

'last_modified', 'expires' and 'updated_at' are datetime.datetime objects for when the data was last modified, when it is expected to expire and when the forecast was updated, respectively.

'units' contains a dictionary mapping variable names to the units in which they are provided by the API.

'intervals' is where we find the actual weather data. It is a list of intervals. Note that the MET API usually supplies multiple intervals for each time point in the data set, the forecast parser takes the shortest supplied interval for each time point.

>>> type(ny_forecast.data["intervals"])
<class 'list'>
>>> type(ny_forecast.data["intervals"][0])
<class 'metno-locationforecast.data_containers.Interval'>
>>> print(ny_forecast.data["intervals"][0])
Forecast between 2020-07-21 14:00:00 and 2020-07-21 15:00:00:
        air_pressure_at_sea_level: 1016.7hPa
        air_temperature: 28.7celsius
        cloud_area_fraction: 1.6%
        relative_humidity: 56.0%
        wind_from_direction: 349.7degrees
        wind_speed: 1.4m/s
        precipitation_amount: 0.0mm

Each interval is a metno-locationforecast.data_containers.Interval instance. This interval class has a 'variables' attribute which is a dictionary mapping variable names to metno-locationforecast.data_containers.Variable instances.

>>> first_interval = ny_forecast.data["intervals"][0]
>>> first_interval.start_time
datetime.datetime(2020, 7, 21, 14, 0)
>>> first_interval.end_time
datetime.datetime(2020, 7, 21, 15, 0)
>>> first_interval.duration
datetime.timedelta(0, 3600)
>>> first_interval.variables.keys()
dict_keys(['air_pressure_at_sea_level', 'air_temperature', 'cloud_area_fraction', 'relative_humidity', 'wind_from_direction', 'wind_speed', 'precipitation_amount'])
>>>
>>> rain = first_interval.variables["precipitation_amount"]
>>> print(rain)
precipitation_amount: 0.0mm
>>> rain.value
0.0
>>> rain.units
'mm'

For a full overview of the Interval and Variable classes see the code.

Other attributes of the Forecast class that could be useful are;

• response: This is the full requests.Response object received from the MET API.
• json_string: A string containing all data in json format. This is what is saved.
• json: An object representation of the json_string.

The Forecast class also has additional methods that may be of use.

• save: Save data to save location.
• load: Load data from saved file.

More Examples

For further usage examples see the examples folder.

Notes on Licensing

While the code in this package is covered by an MIT license and is free to use the weather data collected from the MET Weather API is covered by a separate license and has it's own terms of use.

Useful Links

• The Norwegian Meteorological Institute - https://www.met.no/en
• MET Weather API - https://api.met.no/
• MET Weather API Terms of Service - https://api.met.no/doc/TermsOfService
• Locationforecast/2.0 documentation - https://api.met.no/weatherapi/locationforecast/2.0
• Full list of variables and their names - https://api.met.no/doc/locationforecast/datamodel
• Yr - https://www.yr.no/en
• Yr Developer Portal - https://developer.yr.no/
• Yr Terms of Service (same as the MET API terms of service but perhaps more readable) - https://developer.yr.no/doc/TermsOfService/
• GeoNames - http://www.geonames.org/