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

Official implement of Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer

Improving Calibration for Long-Tailed Recognition (CVPR2021)

Python implementation of Lightning-rod Agent, the Stack4Things board-side probe

Deep Learning GPU Training System

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Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks

SANet: A Slice-Aware Network for Pulmonary Nodule Detection

ICSS - Interactive Continual Semantic Segmentation

This is the official implementation for "Do Transformers Really Perform Bad for Graph Representation?".

Code for Robust Contrastive Learning against Noisy Views

Joint learning of images and text via maximization of mutual information

Prompt Tuning with Rules

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A real world application of a Recurrent Neural Network on a binary classification of time series data

Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations.

Code accompanying the paper "Wasserstein GAN"

Reference PyTorch implementation of "End-to-end optimized image compression with competition of prior distributions"

Multivariate Time Series Forecasting with efficient Transformers. Code for the paper "Long-Range Transformers for Dynamic Spatiotemporal Forecasting."

A simple version for graphfpn

Multiple types of NN model optimization environments. It is possible to directly access the host PC GUI and the camera to verify the operation. Intel iHD GPU (iGPU) support. NVIDIA GPU (dGPU) support.