semvec/pypi-packages huggingface dataset:
OBP-security-pillar-2 0.0.4
Provides AWS compliance details
Requires Python: >=3.7

# OBP_security_pillar

OBP_security_Pillar package can be used to get the compliance details of Operational Best Practices for AWS Well Architected Security Pillar.

Release Notes:
# v0.0.1
1. Added following compliance checks:
   - RdsAutomaticMinorVersionUpgradeEnabled
   - RdsInstancePublicAccessCheck
   - RdsSnapshotEncrypted
   - RdsSnapshotsPublicProhibited
   - RdsStorageEncrypted
   - S3BucketLoggingEnabled
   - S3BucketVersioningEnabled
DeepPhysX.Sofa 22.12.1
A SOFA compatibility layer for DeepPhysX framework.

# DeepPhysX.Sofa

![logo](docs/source/_static/image/logo.png)

This python package is part of the [DeepPhysX](https://github.com/mimesis-inria/DeepPhysX) project.
It contains adaptations of some Core components that are compatible with the [SOFA](https://www.sofa-framework.org/) 
framework.

### Quick install

The package requires [DeepPhysX](https://github.com/mimesis-inria/DeepPhysX) to be installed.
Furthermore, it will require [SOFA bindings](https://sofapython3.readthedocs.io/en/latest/) to be used.

The easiest way to install is using `pip`, but there are a several way to install and configure a **DeepPhysX**
environment (refer to the [**documentation**](https://deepphysx.readthedocs.io) for further instructions).

```bash
$ pip install DeepPhysX.Sofa
```

If cloning sources, clone it in the same repository as other `DeepPhysX` packages.
It must be cloned in a directory with the corresponding name as shown below:

``` bash
$ mkdir DeepPhysX
$ cd DeepPhysX
$ git clone https://github.com/mimesis-inria/DeepPhysX.git Core             # Clone default package
$ git clone https://github.com/mimesis-inria/DeepPhysX.Sofa.git Sofa        # Clone simulation package
$ ls
Core Sofa
```
DeepPhysX 22.12.1
A Python framework interfacing AI with numerical simulation.

## DeepPhysX

![logo](docs/source/_static/image/logo.png)

### Interfacing AI with simulation

The **DeepPhysX** project provides Python packages allowing users to easily interface their **numerical simulations**
with **learning algorithms**.

**DeepPhysX** provides a **Core** package with no dependency on any **simulation** or **AI** framework.
Then other packages are compatible with this **Core** and a specific **simulation** or **AI** framework:
* [DeepPhysX.Sofa](https://github.com/mimesis-inria/DeepPhysX_Sofa) as simulation packages;
* [DeepPhysX.Torch](https://github.com/mimesis-inria/DeepPhysX_Torch) as AI packages.


### Features

**DeepPhysX** is a full Python3 projects with 3 main features:
* Generate a dataset with synthetic data from numerical simulations;
* Train an artificial neural network with a synthetic dataset;
* Use the predictions of a trained network in a numerical simulation.

The full list of features is detailed in the [**documentation**](https://deepphysx.readthedocs.io).


### Quick install

The project was initially developed using [SOFA](https://www.sofa-framework.org/) as the **simulation package** and
[PyTorch](https://pytorch.org/) as the **AI framework**. 
Thus, **DeepPhysX** is mainly designed for these frameworks, but obviously **other frameworks** can also be used.
The packages corresponding to these frameworks will therefore be used for the default installation.

The easiest way to install is using `pip`, but there are a several way to install and configure a **DeepPhysX**
environment (refer to the [**documentation**](https://deepphysx.readthedocs.io/en/latest/presentation/install.html) 
for further instructions).

``` bash
$ pip install DeepPhysX             # Install default package
$ pip install DeepPhysX.Sofa        # Install simulation package
$ pip install DeepPhysX.Torch       # Install AI package
```


### Demos

**DeepPhysX** includes a set of detailed tutorials, examples and demos.
As these scripts are producing data, they cannot be run in the python site-packages, thus they should be run locally.
Use the *command line interface* to get the examples or to run **interactive demos**:

``` bash
$ DPX --get             # Get the full example repository locally
$ DPX --run <demo>      # Run one of the demo scripts
```

|          **Armadillo**<br>`DPX -r armadillo`          |          **Beam**<br>`DPX -r beam`          |          **Liver**<br>`DPX -r liver`          |
|:-----------------------------------------------------:|:-------------------------------------------:|:---------------------------------------------:|
| ![armadillo](docs/source/_static/image/armadillo.png) | ![beam](docs/source/_static/image/beam.png) | ![liver](docs/source/_static/image/liver.png) |


### References

Did this project help you for your research ? Please cite us as:

R. Enjalbert, A. Odot and S. Cotin, *DeepPhysX, a python framework to interface AI with numerical simulation*, 
Zenodo, 2022, [**DOI**](https://doi.org/10.5281/zenodo.7389505)