//! # Unpivoted Grid Query //! Grid queries allow you to request weather and climate data for cells in a rectangular grid, where //! the grid is defined by a bounding box. The function [`query_grid_unpivoted`](`rust_connector_api::APIClient::query_grid_unpivoted`) //! allows to download more than a single parameter (whereas [`query_grid_pivoted`](`rust_connector_api::APIClient::query_grid_pivoted`) //! only allows a single parameter). The bounding box or [`BBox`](`rust_connector_api::BBox`) is defined //! by the upper left (i.e. North Western) corner and lower right(i.e. South Eastern) corner). The cell //! size in turn is defined either in pixels (```res_lat=400``` = 400 pixel heigh cells) or in degrees //! (e.g. ```res_lat=0.1```= 0.1° or about 7 km at the equator). //! //!# The Example //! The example demonstrates how to request current temperature and precipitation data for Switzerland. //! The grid is spaced in 0.1 ° (or about 7 km cell width and cell height). //! There are several optional parameters you can pass to the meteomatics API that will change the //! data you get back. In the example we specify that we would like to receive the parameters based //! on the mix ```model = String::from("model=mix");```. The Meteomatics Mix combines different model //! s and sources into an intelligent blend, such that the best data source is chosen for each time //! and location (). //! //! # The account //! You can use the provided credentials or your own if you already have them. //! Check out to request an //! API package. use chrono::{Utc}; use meteomatics::{APIClient, BBox}; use meteomatics::errors::ConnectorError; use polars::prelude::*; // Demonstrates how to use the rust connector to query the Meteomatics API for gridded data. Also // demonstrates how to work with the resulting ```DataFrame```. #[tokio::main] async fn main(){ // Create Client let api: APIClient = APIClient::new("rust-community", "5GhAwL3HCpFB", 10); let df_unpivoted = example_request(&api).await.unwrap(); // Print the query result println!("{:?}", df_unpivoted); // Do some calculations for col in vec!["t_2m:C", "precip_1h:mm"] { let mean: f64 = df_unpivoted[col].mean().unwrap(); let max: f64 = df_unpivoted[col].max().unwrap(); let min: f64 = df_unpivoted[col].min().unwrap(); println!("{} statistics: mean = {}; max = {}, min = {} in the last 24 h.", col, mean, max, min); } // Do some groupby calculations for col in vec!["t_2m:C", "precip_1h:mm"] { let lat_means = df_unpivoted.groupby(["lat"]).unwrap().select(&[col]).mean().unwrap(); let lon_means = df_unpivoted.groupby(["lon"]).unwrap().select(&[col]).mean().unwrap(); println!("{:?}", lat_means); println!("{:?}", lon_means); } } /// Demonstrates how to query a time series for a single point in time (now), a grid and two parameters. async fn example_request(api: &APIClient) -> std::result::Result{ // Time series definition let datetime = Utc::now(); // Location definition let ch: BBox = BBox { lat_min: 45.8, lat_max: 47.8, lon_min: 6.0, lon_max: 10.5, lat_res: 0.1, lon_res: 0.1, }; // Parameter selection let temp2m = String::from("t_2m:C"); let precip1h = String::from("precip_1h:mm"); let params = vec![temp2m, precip1h]; // Optionals let model_mix = String::from("model=mix"); let optionals = Option::from(vec![model_mix]); let result = api.query_grid_unpivoted(&datetime, ¶ms, &ch, &optionals).await; result }