semvec/pypi-packages huggingface dataset:
vision-oslo-extension 0.5.3
This is the package to support additional features on VISION-OSLO, software owned by Network Rail Infrastructure Limited.
Requires Python: >=3.8

# Project Name

This is VISION-OSLO extension package in order to support the simulation data post processing.

Please refer to the document manual for instructions.

## Copyright

Copyright @ Engineering Services, Network Rail

This package is developed by Jieming Ye. Senior Design Engineering, Engineering Services, Network Rail.
The source code is open to public.

Network Rail Infrastructure Limited are registered in England and Wales under company number 02904587.

## Installation
The repository of this package is saved on GitHub:
https://github.com/NR-JYe/Vision-Oslo-Extension

## Usage
Pre-installtion of Python and VISION-OSLO required.

## Feature
Version 0.5.3 (Beta version): Bug Fix and Improve Packaging

Version 0.5.0 (Beta version): This version introduced model checking.

Version 0.4.1 (Beta version): This version introduced full post-processing.

Version 0.1.1 (Beta version): First version published for user testing.

## Contributing
Engineering Services, part of Route Services, Network Rail

## License
InsuficientGibbs 1.0.0
Insufficient Gibbs Sampling
Requires Python: >=3.8

# Insufficient-Gibbs-Sampling


`InsufficientGibbs` is a package that enables users to sample from the posterior parameters when only certain robust statistics of the data are accessible. The paper that comprehensively describes the theory behind these methods can be found on arXiv (https://arxiv.org/abs/2307.14973). Additionally, the code responsible for generating all the paper figures can be located in the `figures` folder.

## Robust Gibbs package

We propose here three main functions named `Gibbs_med_MAD`, `Gibbs_med_IQR` and `Gibbs_Quantile` to cover the case when we observe the pairs (median, MAD) or (median, IQR) or a sequence of quantiles. 

## Install

Install latest release via `pip`

```shell
pip install InsufficientGibbs
```

For latest development version clone the repository and install via pip

```shell
git clone https://github.com/antoineluciano/Insufficient-Gibbs-Sampling
cd InsufficientGibbs
pip install .
```

## Available models/likelihood
* Normal distribution (`distribution="normal"`)
* Cauchy distribution (`distribution="cauchy"`)
* Weibull distribution (`distribution="weibull"`)
* Translated distribution (`distribution="translated_weibull"`)

## Available location priors
* Normal (`par_loc="normal"`)
* Cauchy (`par_loc="cauchy"`)
* Gamma (`par_loc="gamma"`)
## Available scale priors
* Gamma (`par_loc="gamma"`)
* Jeffreys (`par_loc="jeffreys"`)
## Available shape priors
* Gamma (`par_loc="gamma"`)
noload 1.2.8
solving constrained optimization problem for the design of engineering systems

<!--
SPDX-FileCopyrightText: 2020 G2Elab / MAGE

SPDX-License-Identifier: Apache-2.0
-->

NoLOAD: Non Linear Optimization by Automatic Differentiation 
============================================================

We are happy that you will use or develop the NoLOAD.
It is an **Open Source** project located on GitLab at https://gricad-gitlab.univ-grenoble-alpes.fr/design_optimization/noload
It aims at **solving constrained optimization** problem for the design of engineering systems

Project Presentation
====================

**NoLOAD:** Please have a look to NoLOAD presentation : https://noload.readthedocs.io/en/latest/

A scientific article presenting NoLOAD is available here:

Agobert Lucas, Hodencq Sacha, Delinchant Benoit, Gerbaud Laurent, Frederic Wurtz, “NoLOAD, Open Software for Optimal Design and Operation using Automatic Differentiation”, OIPE 2020, Poland, 09-2021. https://hal.archives-ouvertes.fr/hal-03352443

Please cite us when you use NoLOAD.

NoLoad' Community
====================

Please use the git issues system to report an error: https://gricad-gitlab.univ-grenoble-alpes.fr/design_optimization/noload
Otherwise you can also contact de developer team using the following email adress: benoit.delinchant@G2ELab.grenoble-inp.fr

Installation Help
=================
You can install the library as a user or as a developer. Please follow the corresponding installation steps below.

Prerequisite
------------

Please install Python 3.6 or later
https://www.python.org/downloads/

Installation as a user
----------------------
Please install NoLOAD with pip using the command prompt.   

If you are admin on Windows or working on a virtual environment
    
    pip install noload

If you want a local installation or you are not admin
    
    pip install --user noload

If you are admin on Linux:
    
    sudo pip install noload

Launch the examples to understand how the NoLOAD works:
	
	python noload/01-UnconstrainedMonoObjective.py
	python noload/02-ConstrainedMonoObjective.py
	python noload/03-ConstrainedMultiObjective.py
	
Enjoy your time using NoLOAD !



Library Installation Requirements
---------------------------------
Autograd >= 1.3
Matplotlib >= 3.0
Scipy >= 1.2
Pandas >= 1.3.5
tk >= 0.1.0
openpyxl >= 3.1.2

Main Authors: 
=============
B. DELINCHANT, L. GERBAUD, F. WURTZ


Partners:
=========
Vesta-System: http://vesta-system.fr/

Acknowledgments:
================


Licence
=======
This code is under the Apache License, Version 2.0