semvec/pypi-packages huggingface dataset:
environ_secretmanager 0.2.0
Get secrets from Google Cloud Secret Manager or Environment.
Requires Python: <4.0,>=3.10

# environ_secretmanager

The `environ_secretmanager` module is designed to be used to download Google Cloud Secrets Manager secrets and set them 
as environment variables. This is useful for using secrets in various environments, and works especially well with App 
Engine and Cloud Functions where the correct environment variables can be created in an env file during CI/CD and used 
in the final build files for production.


## Usage

```python
from environ_secretmanager.env_secrets import EnvironSecretManager

secrets = EnvironSecretManager(GOOGLE_CLOUD_PROJECT_ID="my-project-id")

# To create a .env file with the secrets
secrets.create_dot_env_file()

# To use the secrets in the current environment
MY_SECRET = secrets.get_env_secret("MY_SECRET", 1)
```

## Build For Local Distribution
Build the files then extract the setup.py file from the tarball and run it to install the package locally.
```shell
poetry build --format sdist
tar -xvf dist/*-`poetry version -s`.tar.gz -O '*/setup.py' > setup.py
```
Then on the machine you want to install the package on, run
```shell
pip install -e .
```

## Development

This project uses [Poetry](https://python-poetry.org/) in development to create a virtual environment and manage
dependencies.
To install poetry, run

```shell
curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python -
```

Then, from the same directory as `pyproject.toml`, run

```shell
poetry install
```

Run `poetry` commands from this same directory to manage your development environment and/or setup the virtual environment created in the last step in your IDE.


See [Poetry Docs](https://python-poetry.org/docs/cli/) for more info.
votekit 3.3.1
A Swiss army knife for computational social choice research.
Requires Python: <3.14,>=3.11

## VoteKit

`VoteKit` is a Swiss army knife for computational social choice research.

**Helpful links:** [Source Repository](https://github.com/mggg/VoteKit) | [Documentation](https://votekit.readthedocs.io/en/latest/) | [Issues & Feature Requests](https://votekit.readthedocs.io/en/latest/package_info/issues/) | [Contributing](https://votekit.readthedocs.io/en/latest/package_info/contributing/) | [MGGG.org](https://mggg.org/)


[![PyPI badge](https://badge.fury.io/py/votekit.svg)](https://badge.fury.io/py/votekit)
![Test badge](https://github.com/mggg/VoteKit/workflows/Test%20&%20Lint/badge.svg)

## Installation
Votekit can be installed through any standard package management tool:

    pip install votekit

For more detailed instructions, please see the [installation](https://votekit.readthedocs.io/en/latest/#installation) section of the VoteKit documentation.


## Issues and Contributing
This project is in active development in the [mggg/VoteKit](https://github.com/mggg/VoteKit) GitHub repository, where [bug reports and feature requests](https://votekit.readthedocs.io/en/latest/package_info/issues/), as well as [contributions](https://votekit.readthedocs.io/en/latest/package_info/contributing/), are welcome.

Currently VoteKit uses `poetry` to manage the development environment. If you want to make a pull request, first `pip install poetry` to your computer. Then, within the Votekit directory and with a virtual environment activated, run `poetry install` This will install all of the development packages you might need. Before making a pull request, run the following:
- `poetry run pytest tests --runslow` to check the test suite,
- `poetry run black .` to format your code,
- `poetry run ruff check src tests` to check the formatting, and then
- `poetry run mypy src` to ensure that your typesetting is correct.

Then you can create your PR! Please do not make your PR against `main`.
fast-rs 1.0.0
Fast sphericity and roundness in 2D and 3D using local thickness
Requires Python: >=3.8

# Fast sphericity and roundness approximation in 2D and 3D 

Fast approximation of Wadell sphericity and roundness in 2D and 3D, described in our paper [(link)](https://doi.org/10.1109/CVPRW67362.2025.00453), [(arXiv)](https://arxiv.org/abs/2504.05808).

The method uses the output from local thickness algorithm to approximate the two measures. Resulting sphericity values closely match those from exact approaches. Roundness values no longer retain the original 0-1 range, but correlate closely to it.

The execution speed is dependent primarily on image volume, and not on number of objects, enabling fast evaluation of thousands of objects at once.

**For in-depth introduction to the library, visit the [CodeOcean capsule](https://codeocean.com/capsule/9246661/).**

## Installation

```
pip install fast_rs
```

## How to use

>For in-depth introduction, visit the [CodeOcean capsule](https://codeocean.com/capsule/9246661/).

Both sphericity and roundness can be calculated together, from a binary mask. The process is exactly the same in 2D and 3D.

``` python
from fast_rs import rs

mask = ... # Load/provide binary mask (can be both a 2D and 3D np.array)
roundness_vec, sphericity_vec, label_img = rs.rs_calculate(mask)
```

Returned label image indices can be used to connect measure values to specific objects in the mask.

**Example result:**

<img src="doc_img\demo_img.png" alt="Demo result of roundness and sphericity" width="500"/>

## Paper

The fast sphericity and roundness approximation method is described and evaluated in our contribution to CVMI (CVPR 2025 Workshop). Please cite our paper if you use the method in your work.

```
@article{pieta2025a,
  author = {Pieta, Pawel Tomasz and Rasmussen, Peter Winkel and Dahl, Anders Bjorholm and Christensen, Anders Nymark},
  title = {Fast Sphericity and Roundness Approximation in 2D and 3D Using Local Thickness},
  language = {eng},
  format = {article},
  journal = {Proceedings of 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
  pages = {4667-4677},
  year = {2025},
  publisher = {IEEE},
  doi = {10.1109/CVPRW67362.2025.00453}
}
```

## License

MIT License (see LICENSE file).
CAPEsolo 0.5.15
Standalone CAPEMON
Requires Python: <3.13,>=3.10

Python GUI to run capemon in standalone VM. Provides a subset of CAPE (Configuration And Payload Extraction) processing and results.

* Create a Windows 10 VM that's suitable for running malware.
  * Use the CAPEv2 guest guide for configuration details.
  * https://capev2.readthedocs.io/en/latest/installation/guest/index.html
* Install Python in VM, tested on 64-bit Python versions 3.11 and 3.12, and add Python to path.
* Download and install both Microsoft Visual C++ Redistributables:
  * https://aka.ms/vs/17/release/vc_redist.x86.exe
  * https://aka.ms/vs/17/release/vc_redist.x64.exe
* Install CAPEsolo.
  * pip install CAPEsolo
* Snapshot your VM.

Quick Start 
* Open an administrator command window.
* Type capesolo <return> to run.

Alternatively, create a shortcut to CAPEsolo.exe, 
which will be in the Scripts subdirectory of same location as your python.exe file. 
* Under Advanced, check 'Run as administrator'
* An icon file is available in the CAPEsolo install folder under site-packages.

Analysis results are found in C:\Users\Public\CAPEsolo\analysis.
* Can be configured in python-path\site-packages\CAPEsolo\cfg.ini

Revert the VM after each analysis.