semvec/pypi-packages huggingface dataset:
quant-clone 0.1.2
Generate a llama-quantize command to copy the quantization parameters of any GGUF
Requires Python: >=3.8

## quant_clone

This is a simple little script to help you generate a llama-quantize (from llama.cpp) command which will allow you to quantize your own GGUF the same way your target GGUF has been quantized.

## Installation

`pip install quant_clone`

if the published `gguf` package doesn't support your model yet, install the current one with:

`pip install --force-reinstall --upgrade "git+https://github.com/ggml-org/llama.cpp.git#egg=gguf&subdirectory=gguf-py"`

## Usage

`quant_clone input.gguf output.txt`

input.gguf is the GGUF file whose quantization parameters you would like to copy

output.txt parameter is optional, if it's omitted the output will be saved to cmd.txt

## Example

if I take one of unsloth's dynamic 2.0 quants and run:

`quant_clone gemma-3-1b-it-UD-IQ1_S.gguf`

I get this output:

`llama-quantize --imatrix <imatrix_unsloth.dat> --tensor-type token_embd.weight=Q5_1 --tensor-type "blk\.(0|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|16|17|18|19|20|21|22|23|24|25)\.attn_k.weight=IQ4_NL" --tensor-type "blk\.(0|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|16|17|18|19|20|21|22|23|24|25)\.attn_output.weight=IQ2_XXS" --tensor-type "blk\.(0|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|16|17|18|19|20|21|22|23|24|25)\.attn_q.weight=IQ4_NL" --tensor-type "blk\.(0|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|16|17|18|19|20|21|22|23|24|25)\.attn_v.weight=Q5_0" --tensor-type "blk\.(0|2|3|4|25)\.ffn_down.weight=IQ3_S" --tensor-type "blk\.(0|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|16|17|18|19|20|21|22|23|24|25)\.ffn_gate.weight=IQ4_NL" --tensor-type "blk\.(0|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|16|17|18|19|20|21|22|23|24|25)\.ffn_up.weight=IQ4_NL" --tensor-type "blk\.(1)\.ffn_down.weight=Q2_K" --tensor-type "blk\.(5|6|7|8|9|10|16|17|18|19|20|21|22|23|24)\.ffn_down.weight=IQ1_S" --tensor-type "blk\.(11|12|13|14|15)\.ffn_down.weight=IQ2_S" <input.gguf> <output.gguf> Q8_0`

That's the command to run to replicate the quantization. Make sure to edit imatrix path, input gguf path, and output gguf path.
lv-binding-rust-generator 0.2.18
Unofficial, non-intrusive Lvgl binding generator for Rust
Requires Python: >=3.8

# lv_binding_rust

This project aims to provide basic Rust bindings for lvgl.

However, unlike the official Rust bindings project, this project makes minimal assumptions. For example, it makes no assumptions about the lvgl version number, and doesn't even care whether the lvgl codebase has been modified.

Given the complexities of C language conditional compilation, compilers, and compilation environments, this project doesn't attempt to parse C structs in memory, but simply provides wrappers with the same name.

This project also doesn't aim for complete API integrity, and the resulting code may not even be directly compiled by Rust, but it will significantly reduce the workload.

I will strive to maintain consistency in directory structure, file names, function names, parameter names, and type names, and do everything possible to avoid introducing ambiguity or new constructs.

Contributions are welcome.
sbcli-pre-lvol-del 19.0.6
CLI for managing SimplyBlock cluster


# SBCLI (SimplyBlock CLI)

Simplyblock provides a feature-rich CLI (command line interface) to deploy SimplyBlock Cluster and to manage the SimplyBlock Control plane

## Install

The CLI can be installed using pypi package [sbctl](https://pypi.org/project/sbctl/)

```
pip install --upgrade sbctl
```

# Components

## Simply Block Core
Contains core logic and controllers for the simplyblock cluster

## Simply Block CLI
The list of all the available CLI options can be here [CLI docs](./simplyblock_cli/README.md)

## SimplyBlock Web API

Web app that provides API to manage the cluster. More about this in [README.md](./simplyblock_web/README.md)


### local development

FoundationDB requires a client library (libfdb_c.dylib) for the Python bindings to interact with the database.
Depending on the OS architecture, please install the appropriate version from the official github repo

```
wget https://github.com/apple/foundationdb/releases/download/7.3.3/FoundationDB-7.3.3_arm64.pkg
```
setup the code on a management node and the webApp code can be developed by building the `docker-compose-dev.yml` file.

```
sudo docker compose -f docker-compose-dev.yml up --build -d
```
erics-cameras 1.0.2
Eric's camera library, focused on robotics

# Eric's Camera library
<img width="1736" height="954" alt="image" src="https://github.com/user-attachments/assets/60ced5f0-972f-42f0-823b-60402e7acb74" />

## Work-in-progress! Only works for the exact workflows I use right now.

Provides Python classes to interact with various cameras. So far there is support for
- CSI cameras on Jetson Orin platforms
- USB cameras on Linux platforms
- RTSP cameras on Linux platforms
- Arbitrary gstreamer pipelines

The package also includes a nice calibration utility that can be run with `python3 -m erics_cameras.calibrate`, that will automatically select which images to include in the calibration dataset by criteria like stillness, uniqueness of the camera pose, and reprojection error. It will also show you the reprojections of the calibration target points using the latest calculated intrinsics in real time, and the intrinsics will be printed to the terminal for you to copy into your program with numpy.

This package requires you have a version of OpenCV with GStreamer support. The repo includes a `build_opencv.sh` script that will download the opencv 4.12 source code to `~/Downloads` and install it with gstreamer support. You can test this is successfull with `python3 -c "import cv2; print(cv2.getBuildInformation())" | grep 
GStreamer`. The calibration script will break with opencv versions less than 4.10 and if you don't have the opencv contrib package installed. If another package requires opencv and pip automatically installs it, it will override the version built from source. To make this package work again you will need to `pip uninstall opencv-python`, which only removes the version installed with pip, and leaves the system opencv working.

I've tested this package to work with Ubuntu 22 on an x86 pc and Jetson Orin Nano and NX. If you're doing robotics in 2025 I think this is the happy path in terms of OS and hardware for getting max compatibility with other programs like Isaac Sim, ROS, and Gazebo.
sphinxcontrib-giscus 0.0.4
A sphinx extension that integrate Giscus to your sphinx doc
Requires Python: >=3.8

# sphinxcontrib-giscus
![sphinxcontrib-giscus logo](static/sphinxcontrib-giscus.png)


Description
----------------------

This Sphinx extension allows users to dynamically integrate `Giscus`_ comments into specific pages of a
Sphinx-generated documentation website.

Documentation: [sphinxcontrib-giscus](https://un4gt.github.io/sphinxcontrib-giscus/)

Available on pypi: [sphinxcontrib-giscus · PyPI](https://pypi.org/project/sphinxcontrib-giscus/)
pymfpatch 1.0.0
Fill missing weather measurements using user-provided ERA5 data and a hybrid GRU + XGBoost approach
Requires Python: >=3.7
Keywords: weather, Météo-France, ERA5, encoder-decoder, machine learning

# pymfpatch

A modular, end-to-end pipeline for gap-filling EnergyPlus EPW weather files using high-resolution ERA reanalysis (or other reference series) and a hybrid GRU + XGBoost approach.  
It automatically parses, cleans, feature-engineers, imputes missing meteorological variables, and writes out a fully compliant EPW-ready for building simulations.

------------------------------------------------------------------------

## Features

-   **Dual-stage imputation**
    -   **GRU** for sequential, multi-output regression on variables with temporal patterns (temperature, humidity, wind, radiation...)
    -   **XGBoost** for the remaining scalar variables (visibility, ceiling height, albedo, precipitation...)\
-   **Flexible configuration** of
    -   which variables to impute with GRU vs XGB\
    -   model hyperparameters (learning rate, depth, batch size, early stopping, ...)\
-   **EnergyPlus EPW input/output** with native header preservation\
-   **Utility functions** for parsing, cleaning, feature-engineering and final EPW formatting

------------------------------------------------------------------------

## Installation

### 1. Editable install from local source
``` bash
# clone & install in editable mode
git clone https://your-repo.org/your-org/pymfpatch.git
cd pymfpatch
pip install -e .
```

### 2. Install from PyPI
``` bash
pip install pymfpatch
```

### 3. GPU enabling
For fastest training/inference, run on a CUDA-enabled GPU.
By default pip install pymfpatch will pull in the CPU-only torch wheel.
If you have Python 3.12 and a compatible GPU, for example, you can upgrade to the GPU build with:
``` bash
pip install torch==2.7.1+cu128 --index-url https://download.pytorch.org/whl/cu128
```

## Quickstart

``` python
from pymfpatch import WeatherImputer
# 1) Instantiate the imputer with your reference (ERA) and target station EPWs:
imputer = WeatherImputer(
    path_to_ref = 'Data/ERA/marignane-era.epw',
    path_to_stn   = 'Data/MF/marignane-mf.epw',
)

# 2) Run the imputation pipeline:
imputer.process()

# 3) Write out a fully-imputed EPW:
imputer.write("marignane-imputed.epw")
```
pymox-kit 1.0.19
Un module avec plein d'outils utiles pour devs en PyMoX (PY-thon - MO-jo - Flet-X)
Requires Python: >=3.10

# PyMoX - Kit

Trousse à outils utiles pour devs en PyMoX (PY-thon - MO-jo - Flet-X)

---

## Rapide mémo

```bash
py -m venv .venv
.\.venv\Scripts\activate
pip install -r requirements.txt

py -m build
twine check dist/*
twine upload dist/* --verbose

git log --oneline

semantic-release version
semantic-release version --print

semantic-release publish

semantic-release --noop version
Cela te dira si une version serait générée.

semantic-release version --commit --tag --no-push

Cela va :
Lire les commits
Calculer la prochaine version (ex: 0.2.0)
Modifier __version__ dans gc7/__init__.py
Créer un commit et un tag localement

Remove-Item -Recurse -Force dist, src/gc7/gc7.egg-info

git commit --allow-empty -m "feat: add dummy feature for version bump"
git commit --allow-empty -m "fix: déclenchement de la release 1.0.1" 
git push origin main

voir conventional commits: https://www.conventionalcommits.org/en/v1.0.0/

En cas de volonté de revenir sur une version précédente (Problème avec PyPI: Interdit de reculer):
git revert <hash du commit> (pour ramener la main du dépôt GH)
dans le __ini__.py, mettre : __version__='x.y.z' (> au dernier de PyPI)
refaire le build + renvoi:
py -m build    
twine upload dist/*

Désinstaller tous les packages de l'env global :
pip freeze | ForEach-Object { pip uninstall -y $_ }

# 1. Crée une branche temporaire pour garder les fichiers
git checkout --orphan temp_branch

# 2. Ajoute tous les fichiers à cette nouvelle branche
git add .

# 3. Fais un commit initial
git commit -m "Initial commit"

# 4. Supprime l'ancienne branche main
git branch -D main

# 5. Renomme la branche temporaire en main
git branch -m main

# 6. Force le push vers GitHub (⚠️ cela écrase l’historique distant)
git push -f origin main

git log main --decorate --oneline

---

from pymox_tools1 import greetings as gt
from pymox_tools1 import tokens as tk

if __name__ == "__main__":
    tk.tokens()
    print(gt.hello(), "\n" + gt.bye())

```