The openspoor package is intended to allow easy transformation between different geographical and topological systems commonly used in Dutch Railway

Overview

Openspoor

alt text

The openspoor package is intended to allow easy transformation between different geographical and topological systems commonly used in Dutch Railway. Its goal is to be publicly available and function as an open source package.

Currently the openspoor package allows the following transformations:

Type of input:

  • Point data

These transformations can be performed between the following systems:

Geographical systems:

  • WGS84 coordinate system (commonly known as GPS coordinates)
  • EPSG:28992 coordinate system (commonly known in the Netherlands as Rijksdriehoek)

Topological systems:

  • Geocode and geocode kilometrering
  • Spoortak and spoortak kilometrering (unavailable on switches)

Getting Started

Installation

Installation using anaconda

  • Clone the "openspoor" repository
    • pip install openspoor
  • create an environment:
    • conda create -n openspoorenv python==3.6.12
  • activate the environment:
    • conda activate openspoorenv
  • If you are installing on Windows OS with Anaconda, first install rtree and geopandas through anaconda with the commands:
    • conda install rtree==0.8.3 -y
    • conda install geopandas==0.6.1 -y
  • In the root directory of the repository, execute the command:
    • pip install -r requirements.txt
  • In the root directory of the repository, execute the command:
    • pip install .
  • In the root directory of the repository, execute the command:
    • python -m pytest
  • If all the test succeed, the openspoor package is ready to use and you are on the right "track"!

Demonstration notebook

In the future a notebook will be added that demonstrates the use of the openspoor package. For now one can take the code in the acceptance tests as example of how to use the package.

Dependencies

The transformations available in the openspoor package rely completely on data and API's made available at https://mapservices.prorail.nl/. Be aware of this dependency and specifically of the following possible issues:

  • The use of API's on mapservices.prorail.nl is changed
  • The output data of the mapservices API's is changed (with added, removed or missing columns for instance)

Furthermore mapservices.prorail.nl only provides current information about the topological systems used in Dutch Railways. As the topological systems tend to change with time, due to changing infrastructure and naming conventions, the current topological system is not necessarily sufficient to provide transformations on historical data. In the future we hope to add historical topological systems as part of the functionality of this package in such a way that it is available publicly.

Structure

The structure of the openspoor package is largely split in two categories.

MapservicesData

The MapservicesData classes use mapservices.prorail.nl API's to retrieve the necessary data to perform transformations. The essentially function as an interface with the topological systems used by ProRail.

  • PUICMapservices provides general data about railway tracks (spoor) and switches (wissel and kruisingbenen). This contains information regarding Geocode, geocodekilometrering, but also Spoortak identificatie.
  • SpoortakMapservices provides information about railway tracks concerning Spoortak identificatie and lokale kilometrering.

Transformers

The various transformers use the geopandas dataframes obtained by MapservicesData objects to add additional geographical or topological systems to a given geopandas input dataframe. The current transformers only function for geopandas dataframes containing Point data. The available transformers are:

  • TransformerCoordinatesToSpoor: transforms WGS84 or EPSG:28992 coordinates to spoortak and lokale kilomtrering as well as geocode and geocode kilometrering.
  • TransformerGeocodeToCoordinates: transforms geocode and geocode kilometrering to WGS84 or EPSG:28992 coordinates.
  • TransformerSpoorToCoordinates: transforms spoortak and lokale kilometrering to WGS84 or EPSG:28992 coordinates.

Release History

  • 0.1.0
    • The first proper release
    • ADD: transform point data between geographical systems.
  • 0.0.1
    • Work in progress

Contributing

The openspoor package stimulates every other person the contribute to the package. To do so:

  • Fork it
  • Create your feature branch (git checkout -b feature/fooBar)
  • Commit your changes (git commit -am 'Add some fooBar')
  • Push to the branch (git push origin feature/fooBar)
  • Create a new Pull Request with 3 obligated reviewers from the developement team.

You could also contribute by thinking of possible new features. The current backlog is:

  • Make the package available for the "spoor" industry.
Depression Asisstant GDSC Challenge Solution

Depression Asisstant can help you give solution. Please using Python version 3.9.5 for contribute.

Ananda Rauf 1 Jan 30, 2022
Contrastive Multi-View Representation Learning on Graphs

Contrastive Multi-View Representation Learning on Graphs This work introduces a self-supervised approach based on contrastive multi-view learning to l

Kaveh 208 Dec 23, 2022
Official implementation of the method ContIG, for self-supervised learning from medical imaging with genomics

ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with Genetics This is the code implementation of the paper "ContIG: Self-s

Digital Health & Machine Learning 22 Dec 13, 2022
Tensorflow Repo for "DeepGCNs: Can GCNs Go as Deep as CNNs?"

DeepGCNs: Can GCNs Go as Deep as CNNs? In this work, we present new ways to successfully train very deep GCNs. We borrow concepts from CNNs, mainly re

Guohao Li 612 Nov 15, 2022
Code for ACM MM 2020 paper "NOH-NMS: Improving Pedestrian Detection by Nearby Objects Hallucination"

NOH-NMS: Improving Pedestrian Detection by Nearby Objects Hallucination The offical implementation for the "NOH-NMS: Improving Pedestrian Detection by

Tencent YouTu Research 64 Nov 11, 2022
Implementation of Learning Gradient Fields for Molecular Conformation Generation (ICML 2021).

[PDF] | [Slides] The official implementation of Learning Gradient Fields for Molecular Conformation Generation (ICML 2021 Long talk) Installation Inst

MilaGraph 117 Dec 09, 2022
Here I will explain the flow to deploy your custom deep learning models on Ultra96V2.

Xilinx_Vitis_AI This repo will help you to Deploy your Deep Learning Model on Ultra96v2 Board. Prerequisites Vitis Core Development Kit 2019.2 This co

Amin Mamandipoor 1 Feb 08, 2022
Interactive Image Generation via Generative Adversarial Networks

iGAN: Interactive Image Generation via Generative Adversarial Networks Project | Youtube | Paper Recent projects: [pix2pix]: Torch implementation for

Jun-Yan Zhu 3.9k Dec 23, 2022
Code for "Optimizing risk-based breast cancer screening policies with reinforcement learning"

Tempo: Optimizing risk-based breast cancer screening policies with reinforcement learning Introduction This repository was used to develop Tempo, as d

Adam Yala 12 Oct 11, 2022
Inhomogeneous Social Recommendation with Hypergraph Convolutional Networks

Inhomogeneous Social Recommendation with Hypergraph Convolutional Networks This is our Pytorch implementation for the paper: Zirui Zhu, Chen Gao, Xu C

Zirui Zhu 3 Dec 30, 2022
This is project is the implementation of the DeepShift: Towards Multiplication-Less Neural Networks paper

DeepShift This is project is the implementation of the DeepShift: Towards Multiplication-Less Neural Networks paper, that aims to replace multiplicati

Mostafa Elhoushi 88 Dec 23, 2022
Dynamica causal Bayesian optimisation

Dynamic Causal Bayesian Optimization This is a Python implementation of Dynamic Causal Bayesian Optimization as presented at NeurIPS 2021. Abstract Th

nd308 18 Nov 22, 2022
OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers (NeurIPS 2021)

OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers (NeurIPS 2021) This is an PyTorch implementation of OpenMatc

Vision and Learning Group 38 Dec 26, 2022
Codebase for Attentive Neural Hawkes Process (A-NHP) and Attentive Neural Datalog Through Time (A-NDTT)

Introduction Codebase for the paper Transformer Embeddings of Irregularly Spaced Events and Their Participants. This codebase contains two packages: a

Alan Yang 28 Dec 12, 2022
g2o: A General Framework for Graph Optimization

g2o - General Graph Optimization Linux: Windows: g2o is an open-source C++ framework for optimizing graph-based nonlinear error functions. g2o has bee

Rainer Kümmerle 2.5k Dec 30, 2022
Add-on for importing and auto setup of character creator 3 character exports.

CC3 Blender Tools An add-on for importing and automatically setting up materials for Character Creator 3 character exports. Using Blender in the Chara

260 Jan 05, 2023
rliable is an open-source Python library for reliable evaluation, even with a handful of runs, on reinforcement learning and machine learnings benchmarks.

Open-source library for reliable evaluation on reinforcement learning and machine learning benchmarks. See NeurIPS 2021 oral for details.

Google Research 529 Jan 01, 2023
RL and distillation in CARLA using a factorized world model

World on Rails Learning to drive from a world on rails Dian Chen, Vladlen Koltun, Philipp Krähenbühl, arXiv techical report (arXiv 2105.00636) This re

Dian Chen 131 Dec 16, 2022
An educational resource to help anyone learn deep reinforcement learning.

Status: Maintenance (expect bug fixes and minor updates) Welcome to Spinning Up in Deep RL! This is an educational resource produced by OpenAI that ma

OpenAI 7.6k Jan 09, 2023
FlowTorch is a PyTorch library for learning and sampling from complex probability distributions using a class of methods called Normalizing Flows

FlowTorch is a PyTorch library for learning and sampling from complex probability distributions using a class of methods called Normalizing Flows.

Meta Incubator 272 Jan 02, 2023