metno-locationforecast 1.2.0

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.

Contents

• Features
• Installation
• Usage
  • Basics
  • Accessing Data
  • Custom URLs
  • Configuration
  • More Examples
• Notes on Licensing
• Dependencies
• Useful Links

Features

• Get weather 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 with pip:

pip install metno-locationforecast

It's recommended to install metno-locationforecast into a virtual environment for your application.

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 where 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

Note: Use an underscore in the name when importing.

Create a Place instance. The first argument is your name for the place, next are the geographic coordinates. Geographic coordinates are given by latitude, longitude (in degrees) and altitude (in metres).

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

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.

Next create a Forecast instance for the place. Here you 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.

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

There are also three optional arguments that you can supply. First is the forecast_type parameter, 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. "compact" is the default.

The second optional parameter is save_location, this is the folder where data will be stored. The default is "./data/". Finally there is the base_url parameter, more on this in the Custom URLs section.

These parameters can be configured for your entire app by using a configuration file, more on this in the configuration section.

Next 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 'metno_locationforecast.data_containers.Data'>

This is a special Data class which stores the weather data information. You can list its attributes like so;

>>> vars(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 an Interval instance. This interval class has a variables attribute which is a dictionary mapping variable names to 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 Data, 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 (metno-locationforecast uses the requests library).
• 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.

The code for the Forecast class can be found here.

Custom URLs

By default the Forecast class will fetch data from 'https://api.met.no/weatherapi/locationforecast/2.0/' if you wish to use a different domain you can pass a base_url parameter to the constructor function. Note that the type for the forecast will be appended to this url when requests are made, if this is not suitable for your application you should pass an empty string for the type.

>>> ny_forecast = Forecast(new_york, "metno-locationforecast/1.0", forecast_type="",  base_url="somewhere.com")
>>> ny_forecast.url
'somewhere.com'

Configuration

If you wish to provide application wide configuration for your module this can be done in either a metno-locationforecast.ini file or in a setup.cfg file in the root directory of your application. Below is an example of the configurations that you can put in there showing their default values.

[metno-locationforecast]
user_agent = None
forecast_type = compact
save_location = ./data
base_url = https://api.met.no/weatherapi/locationforecast/2.0/

Note that regardless of the file, configurations need to be under a [metno-locationforecast] section and settings in a metno-locationforecast.ini file will take precedence.

More Examples

For further usage examples see the examples folder.

To see what can be done with this library you could also checkout Dry Rock. It is another project maintained by myself that uses the metno-locationforecast library. It was in fact the original inspiration for me to create this library.

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.

Dependencies

• Requests

Useful Links

• PyPI page - https://pypi.org/project/metno-locationforecast/
• Github page - https://github.com/Rory-Sullivan/metno-locationforecast
• 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 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/
• Requests library - https://requests.readthedocs.io/en/master/