pyaemet 1.1.1

Python module to interact with the AEMET API to download meteorological data

pyAEMET

A python library developed to download daily climatological values from the Spanish National Meteorological Agency (AEMET) through its OpenData API. The library contains several methods to facilitate downloading and filtering the climatological data.

The information that this library collects and uses is property of the Spanish State Meteorological Agency, available through its AEMET OpenData REST API.

Installation

$ pip install pyaemet

To use the pyAEMET module, you need to get an API key from the AEMET (Spanish State Meteorological Agency) OpenData platform. You can apply for a key here.

Usage

Once the module is installed and you have your API key, you can start using the module by importing it in your Python script. To use the module's functions, you need to initialize the client with your API key:

import pyaemet

aemet = pyaemet.AemetClima(api_key)

The AemetClima class takes an API key as a parameter in its constructor and allows you to get information about the available monitoring sites, filter sites based on different parameters (e.g., city, province, autonomous community), and get nearby sites to a specific location.

Here is a summary of some of the methods provided by the AemetClima class:

• sites_info: Retrieves information about all the available monitoring sites. The method returns an instance of the SitesDataFrame class, which is a subclass of the pandas DataFrame.

aemet.sites_info(update=True)

• sites_in: Filters the available monitoring sites based on specified parameters (e.g., city, province, autonomous community). The method returns an instance of the SitesDataFrame class.

aemet.sites_in(subregion="Cantabria")

• near_sites: Retrieves the n_near monitoring sites closest to a specified latitude and longitude, within a maximum distance of max_distance kilometers. The method returns an instance of the NearSitesDataFrame class.

aemet.near_sites(latitude=43.47,
                 longitude=-3.798,
                 n_near=5, max_distance=50)

• sites_curation: Retrieves the amount of available data of certain variables in the monitoring sites in a period of time defined by start_dt and end_dt. The function returns a SitesDataFrame or NearSitesDataFrame (depends of the type of the sites parameter given) with a column with the average amount between all variables and has_enough boolean if the amount is greater or equal to a threshold.

• daily_clima: Retrieves daily climate data for a given site or a list of sites over a specified date range defined by start_dt and end_dt. The function returns a ObservationsDataFrame object, which is a data structure that holds the retrieved climate data along with any associated metadata.

import datetime
aemet.daily_clima(site=aemet.sites_in(city="Santander"),
                  start_dt=datetime.date(2022, 6, 3),
                  end_dt=datetime.date.today())

The module also provides three deprecated methods estaciones_info, estaciones_loc and clima_diaria that perform similar functionality as the sites_info, sites_in and daily_clima methods, respectively.

You can find the complete documentation of the module's functions in the GitHub repository, under the docs directory.

FAQ

Contributing

References

• "Estimating changes in air pollutant levels due to COVID-19 lockdown measures based on a business-as-usual prediction scenario using data mining models: A case-study for urban traffic sites in Spain", published in Science in Total Environment by J. González-Pardo et al. (2021)