Prometheus Exporter for data scraped from datenplattform.darmstadt.de

Related tags

Deep Learningdarmstadt-opendata-exporter
Overview

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
Owner
Martin Weinelt
I'm hexa- on IRC and Matrix!
Martin Weinelt
GitHub Repository
Code for "CloudAAE: Learning 6D Object Pose Regression with On-line Data Synthesis on Point Clouds" @ICRA2021

CloudAAE This is an tensorflow implementation of "CloudAAE: Learning 6D Object Pose Regression with On-line Data Synthesis on Point Clouds" Files log:

Gee 35 Nov 14, 2022
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting

Autoformer (NeurIPS 2021) Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting Time series forecasting is a c

THUML @ Tsinghua University 847 Jan 08, 2023
Official implementation of the ICML2021 paper "Elastic Graph Neural Networks"

ElasticGNN This repository includes the official implementation of ElasticGNN in the paper "Elastic Graph Neural Networks" [ICML 2021]. Xiaorui Liu, W

liuxiaorui 34 Dec 04, 2022
Hashformers is a framework for hashtag segmentation with transformers.

Hashtag segmentation is the task of automatically inserting the missing spaces between the words in a hashtag. Hashformers applies Transformer models

Ruan Chaves 41 Nov 09, 2022
ICLR 2021: Pre-Training for Context Representation in Conversational Semantic Parsing

SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing This repository contains code for the ICLR 2021 paper "SCoRE: Pre-Tr

Microsoft 28 Oct 02, 2022
[NeurIPS 2020] Official Implementation: "SMYRF: Efficient Attention using Asymmetric Clustering".

SMYRF: Efficient attention using asymmetric clustering Get started: Abstract We propose a novel type of balanced clustering algorithm to approximate a

Giannis Daras 46 Dec 22, 2022
Optimising chemical reactions using machine learning

Summit Summit is a set of tools for optimising chemical processes. We’ve started by targeting reactions. What is Summit? Currently, reaction optimisat

Sustainable Reaction Engineering Group 75 Dec 14, 2022
[NeurIPS'21] "AugMax: Adversarial Composition of Random Augmentations for Robust Training" by Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Animashree Anandkumar, and Zhangyang Wang.

[NeurIPS'21] "AugMax: Adversarial Composition of Random Augmentations for Robust Training" by Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Animashree Anandkumar, and Zhangyang Wang.

VITA 112 Nov 07, 2022
A python-image-classification web application project, written in Python and served through the Flask Microframework

A python-image-classification web application project, written in Python and served through the Flask Microframework. This Project implements the VGG16 covolutional neural network, through Keras and

Gerald Maduabuchi 19 Dec 12, 2022
Contains code for Deep Kernelized Dense Geometric Matching

DKM - Deep Kernelized Dense Geometric Matching Contains code for Deep Kernelized Dense Geometric Matching We provide pretrained models and code for ev

Johan Edstedt 83 Dec 23, 2022
Deep learning operations reinvented (for pytorch, tensorflow, jax and others)

This video in better quality. einops Flexible and powerful tensor operations for readable and reliable code. Supports numpy, pytorch, tensorflow, and

Alex Rogozhnikov 6.2k Jan 01, 2023
The codes and related files to reproduce the results for Image Similarity Challenge Track 2.

ISC-Track2-Submission The codes and related files to reproduce the results for Image Similarity Challenge Track 2. Required dependencies To begin with

Wenhao Wang 89 Jan 02, 2023
Experimental code for paper: Generative Adversarial Networks as Variational Training of Energy Based Models

Experimental code for paper: Generative Adversarial Networks as Variational Training of Energy Based Models, under review at ICLR 2017 requirements: T

Shuangfei Zhai 18 Mar 05, 2022
Physics-Aware Training (PAT) is a method to train real physical systems with backpropagation.

Physics-Aware Training (PAT) is a method to train real physical systems with backpropagation. It was introduced in Wright, Logan G. & Onodera, Tatsuhiro et al. (2021)1 to train Physical Neural Networ

McMahon Lab 230 Jan 05, 2023
Multistream CNN for Robust Acoustic Modeling

Multistream Convolutional Neural Network (CNN) A multistream CNN is a novel neural network architecture for robust acoustic modeling in speech recogni

ASAPP Research 37 Sep 21, 2022
I will implement Fastai in each projects present in this repository.

DEEP LEARNING FOR CODERS WITH FASTAI AND PYTORCH The repository contains a list of the projects which I have worked on while reading the book Deep Lea

Thinam Tamang 43 Dec 20, 2022
QA-GNN: Question Answering using Language Models and Knowledge Graphs

QA-GNN: Question Answering using Language Models and Knowledge Graphs This repo provides the source code & data of our paper: QA-GNN: Reasoning with L

Michihiro Yasunaga 434 Jan 04, 2023
Ladder Variational Autoencoders (LVAE) in PyTorch

Ladder Variational Autoencoders (LVAE) PyTorch implementation of Ladder Variational Autoencoders (LVAE) [1]: where the variational distributions q at

Andrea Dittadi 63 Dec 22, 2022
This repository contains a re-implementation of the code for the CVPR 2021 paper "Omnimatte: Associating Objects and Their Effects in Video."

Omnimatte in PyTorch This repository contains a re-implementation of the code for the CVPR 2021 paper "Omnimatte: Associating Objects and Their Effect

Erika Lu 728 Dec 28, 2022
Robust Consistent Video Depth Estimation

[CVPR 2021] Robust Consistent Video Depth Estimation This repository contains Python and C++ implementation of Robust Consistent Video Depth, as descr

Facebook Research 213 Dec 17, 2022