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

Object tracking and object detection is applied to track golf puts in real time and display stats/games.

Image Data Augmentation in Keras

基于Flask开发后端、VUE开发前端框架，在WEB端部署YOLOv5目标检测模型

A library for Deep Learning Implementations and utils

Referring Video Object Segmentation

Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks

An implementation of the "Attention is all you need" paper without extra bells and whistles, or difficult syntax

sequitur is a library that lets you create and train an autoencoder for sequential data in just two lines of code

Supervised Contrastive Learning for Product Matching

Code for the paper "PortraitNet: Real-time portrait segmentation network for mobile device" @ CAD&Graphics2019

TYolov5: A Temporal Yolov5 Detector Based on Quasi-Recurrent Neural Networks for Real-Time Handgun Detection in Video

Language-Driven Semantic Segmentation

Code for the CIKM 2019 paper "DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting".

AutoDeeplab / auto-deeplab / AutoML for semantic segmentation, implemented in Pytorch

Face2webtoon - Despite its importance, there are few previous works applying I2I translation to webtoon.

Py4fi2nd - Jupyter Notebooks and code for Python for Finance (2nd ed., O'Reilly) by Yves Hilpisch.

An official implementation of the paper Exploring Sequence Feature Alignment for Domain Adaptive Detection Transformers

Code for "Single-view robot pose and joint angle estimation via render & compare", CVPR 2021 (Oral).

Kaggle Ultrasound Nerve Segmentation competition [Keras]

Bridging the Gap between Label- and Reference based Synthesis(ICCV 2021)