darmstadt-opendata-exporter

Scrapes data from https://datenplattform.darmstadt.de and presents it in the Prometheus Exposition format.

Pull requests welcome.

Example

# HELP darmstadt_environment_no2_micrograms_per_cubic_meter Environment measurement for no2
# TYPE darmstadt_environment_no2_micrograms_per_cubic_meter gauge
darmstadt_environment_no2_micrograms_per_cubic_meter{sensor="3ac073c4-b5b8-421d-b91a-5aa001234f7e"} 9.5615
# HELP darmstadt_environment_o3_micrograms_per_cubic_meter Environment measurement for o3
# TYPE darmstadt_environment_o3_micrograms_per_cubic_meter gauge
darmstadt_environment_o3_micrograms_per_cubic_meter{sensor="3ac073c4-b5b8-421d-b91a-5aa001234f7e"} 70.83670000000001
# HELP darmstadt_environment_pm2_5_micrograms_per_cubic_meter Environment measurement for dust_pm2.5
# TYPE darmstadt_environment_pm2_5_micrograms_per_cubic_meter gauge
darmstadt_environment_pm2_5_micrograms_per_cubic_meter{sensor="3ac073c4-b5b8-421d-b91a-5aa001234f7e"} 0.1
# HELP darmstadt_environment_so2_micrograms_per_cubic_meter Environment measurement for so2
# TYPE darmstadt_environment_so2_micrograms_per_cubic_meter gauge
darmstadt_environment_so2_micrograms_per_cubic_meter{sensor="3ac073c4-b5b8-421d-b91a-5aa001234f7e"} 0.8
# HELP darmstadt_environment_pm10_micrograms_per_cubic_meter Environment measurement for dust_pm10
# TYPE darmstadt_environment_pm10_micrograms_per_cubic_meter gauge
darmstadt_environment_pm10_micrograms_per_cubic_meter{sensor="3ac073c4-b5b8-421d-b91a-5aa001234f7e"} 0.1
# HELP darmstadt_environment_co_milligrams_per_cubic_meter Environment measurement for co
# TYPE darmstadt_environment_co_milligrams_per_cubic_meter gauge
darmstadt_environment_co_milligrams_per_cubic_meter{sensor="3ac073c4-b5b8-421d-b91a-5aa001234f7e"} 0.10592399999999999

Prometheus Exporter for data scraped from datenplattform.darmstadt.de

Related tags

Overview

darmstadt-opendata-exporter

Example

Owner

Martin Weinelt

Artificial Intelligence search algorithm base on Pacman

Official pytorch implementation of paper Dual-Level Collaborative Transformer for Image Captioning (AAAI 2021).

MPI Interest Group on Algorithms on 1st semester 2021

Deep learned, hardware-accelerated 3D object pose estimation

Code release for the paper “Worldsheet Wrapping the World in a 3D Sheet for View Synthesis from a Single Image”, ICCV 2021.

Deep learning for spiking neural networks

a reimplementation of LiteFlowNet in PyTorch that matches the official Caffe version

Add gui for YoloV5 using PyQt5

Application of the L2HMC algorithm to simulations in lattice QCD.

Semi-supevised Semantic Segmentation with High- and Low-level Consistency

Torchyolo - Yolov3 ve Yolov4 modellerin Pytorch uygulamasıdır

State-of-the-art language models can match human performance on many tasks

Official implementation of deep-multi-trajectory-based single object tracking (IEEE T-CSVT 2021).

Spam your friends and famly and when you do your famly will disown you and you will have no friends.

An OpenAI-Gym Package for Training and Testing Reinforcement Learning algorithms with OpenSim Models

smc.covid is an R package related to the paper A sequential Monte Carlo approach to estimate a time varying reproduction number in infectious disease models: the COVID-19 case by Storvik et al

ACAV100M: Automatic Curation of Large-Scale Datasets for Audio-Visual Video Representation Learning. In ICCV, 2021.

Semantic Scholar's Author Disambiguation Algorithm & Evaluation Suite

Graph parsing approach to structured sentiment analysis.

Jremesh-tools - Blender addon for quad remeshing