semvec/pypi-packages huggingface dataset:
SolidsOpt 0.1
This package is made to perfome topology optimization of 2D solids
License: MIT
Keywords: finite-elements,scientific-computing,deep learning,topology,optimization


The code is written in Python and it depends on [Numpy](https://numpy.org), [scipy](https://scipy.org), [TensorFlow](https://www.tensorflow.org) and [SolidsPy](https://solidspy.readthedocs.io/en/latest/).


### Installation

```sh
pip install solidsopt
```

### Load weights for neural networks

```sh
load model
```

## Repositories 

Two repositories were created from this project, one contains the structural optimization algorithms and the other has everything related to the development of deep learning methods.


- {{< icon "github" >}}&nbsp;[kssgarcia/DeepLearningOpt](https://github.com/kssgarcia/DeepLearningOpt)
- {{< icon "github" >}}&nbsp;[kssgarcia/OptTopolgy](https://github.com/kssgarcia/OptTopolgy)


## Topology optimization repo

- BESO method [BESO.py](https://github.com/kssgarcia/OptTopolgy/blob/main/BESO.py)
- ESO stress based method  [ESO_stress_based.py](https://github.com/kssgarcia/OptTopolgy/blob/main/ESO_stress_based.py)
- ESO stiff based method [ESO_stiff_based.py](https://github.com/kssgarcia/OptTopolgy/blob/main/ESO_stiff_based.py)
- SIMP method [SIMP.py](https://github.com/kssgarcia/OptTopolgy/blob/main/SIMP.py)``

### Instructions

### 1. Clone repository

```sh
git clone https://github.com/kssgarcia/OptTopolgy.git
```

### 2. Download the required packages running the following command

```sh
conda env create -f environment.yml
```

### 3. Install solidspy

```sh
pip install solidspy
```

## Optimization with deep learning repo

- [SIMP_multi.py](https://github.com/kssgarcia/DeepLearningOpt/blob/main/simp/SIMP_multi.py) code used for generate training dataset.
- [CNN.py](https://github.com/kssgarcia/DeepLearningOpt/blob/main/neural_network/CNN.py) code used for training neural network.
- [load_model.py](https://github.com/kssgarcia/DeepLearningOpt/blob/main/neural_network/CNN2.py) code used for load neural network.
- [SIMP_multi_dist.py](https://github.com/kssgarcia/DeepLearningOpt/blob/main/neural_network/SIMP_multi_dist.py) code used for generate dataset with a distributed load.


### Instructions


### 1. Clone repository

```sh
git clone https://github.com/kssgarcia/DeepLearningOpt.git
```

### 2. Download the required packages running the following command

```sh
conda env create -f environment.yml
```

### 3. Install solidspy

```sh
pip install solidspy
```
fedora-sig-onboard 0.1.1
Onboard a package onto the appropriate Fedora SIG
License: MIT
Requires Python: >=3.9

# fedora-sig-onboard

`fedora-sig-onboard` is a simple tool to onboard a Fedora package onto the relevant SIG. It will attempt to add the SIG to the package ACL, update the Bugzilla assignee and add the package to [Anitya](https://release-monitoring.org). Rust and Golang packages are currently supported, and will be respectively onboarded onto the [Rust SIG](https://fedoraproject.org/wiki/SIGs/Rust) and the [Go SIG](https://fedoraproject.org/wiki/SIGs/Go).

# Installation

On Fedora Linux 38 or later:

``` console
dnf install fedora-sig-onboard
```

otherwise

``` console
pip install 'git+https://pagure.io/fedora-sig-onboard.git'
```

## Usage

Add your dist-git and Anitya to the configuration file in `~/.config/fedora-sig-onboard/fedora-sig-onboard.conf`:

```
[api]
dist_git_token = YOUR_DIST_GIT_TOKEN_HERE
anitya_token = YOUR_ANITYA_TOKEN_HERE
```

To onboard a package:

```
$ fedora-sig-onboard onboard rust-escape_string
[rust-escape_string] updating ACL
[rust-escape_string] updating Bugzilla assignees
[rust-escape_string] adding to release monitoring
$
```

Multiple packages can also be specified, and will be processed in sequence. If no package is specified, `fedora-sig-onboard` will attempt to infer the package name from the current directory.

## License
`fedora-sig-onboard` is MIT licensed.