Maximum temperature (station)

Key: meteo_tmax | Secondary data | Category: Weather

Derived from local weather stations

Data Layer information

Source file is available (Show file)

from .meteo import Meteo


class MeteoTmax(Meteo):
    def __init__(self) -> None:
        super().__init__()
        self.col_of_interest = "tmax"
        self.format_suffix = "°C"

Data is available locally

Data is loaded into the database

Log | Files

Operation	Maximum
Database unit	Celsius per day
Value type	`value`
Temporal resolution	`date`

Data availability per spatial unit within the integrated time period
Shape type	First	Last	Availability
Country			80.11 %
Region			59.60 %
District			54.68 %
Total	2010-01-01	2025-07-11	55.05 %

Analytical overview

Source information

The Data Layers data can be accessed via an API. Below you see minimal code snippets to query the data. For more information regarding the API please look at the documentation.

import requests
from requests.auth import HTTPBasicAuth
from urllib.parse import urlencode

import pandas as pd
import matplotlib.pyplot as plt

# set host, like http://localhost:8000, to where your Data Hub is running
HOST = ''
# in case of basic auth set to HTTPBasicAuth('user', 'password')
AUTH = None
# API TOKEN
TOKEN = ""

q = {
    'datalayer_key': 'meteo_tmax',

    # Optional filters to narrow down the returned data:
    #'shape_id': '',
    #'shape_type': '',
    #'start_date': '',
    #'end_date': '',
}
response = requests.get(
    f"{HOST}/api/datalayers/data/?{urlencode(q)}",
    headers={"Authorization": f"Bearer {TOKEN}"},
    auth=AUTH,
)
if response.status_code == 200:
    d = response.json()
else:
    raise Exception(f"Request failed ({response.status_code}): {response.text}")

meteo_tmax_df = pd.DataFrame(d['data'])
meteo_tmax_df['date'] = pd.to_datetime(meteo_tmax_df['date'], format="%Y-%m-%d")

There is no ready use snippet for API usage in R yet, though you can adapt the Python example to your R needs.