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

CrossNorm and SelfNorm for Generalization under Distribution Shifts (ICCV 2021)

New AidForBlind - Various Libraries used like OpenCV and other mentioned in Requirements.txt

Development of IP code based on VIPs and AADM

Turi Create simplifies the development of custom machine learning models.

GLANet - The code for Global and Local Alignment Networks for Unpaired Image-to-Image Translation arxiv

This is the code for HOI Transformer

In this tutorial, you will perform inference across 10 well-known pre-trained object detectors and fine-tune on a custom dataset. Design and train your own object detector.

Code to reproduce the results in "Visually Grounded Reasoning across Languages and Cultures", EMNLP 2021.

Equivariant CNNs for the sphere and SO(3) implemented in PyTorch

LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT

Adaptive Denoising Training (ADT) for Recommendation.

A python interface for training Reinforcement Learning bots to battle on pokemon showdown

Implementation of various Vision Transformers I found interesting

Twin-deep neural network for semi-supervised learning of materials properties

A collection of random and hastily hacked together scripts for investigating EU-DCC

Official Implementation of Swapping Autoencoder for Deep Image Manipulation (NeurIPS 2020)

Labels4Free: Unsupervised Segmentation using StyleGAN

CCCL: Contrastive Cascade Graph Learning.

PyTorch Code for the paper "VSE++: Improving Visual-Semantic Embeddings with Hard Negatives"

FAVD: Featherweight Assisted Vulnerability Discovery