semvec/pypi-packages huggingface dataset:
modelmgr 0.1.0
A Model Management package for model archive including upload and download from AWS S3 and Huggingface Hub.
Requires Python: >=3.11

# ModelMgr

ModelMgr is a Model Management package for model archive including upload and download from AWS S3 and Huggingface Hub.

## Installation

You can install ModelMgr using pip:

```bash
pip install modelmgr
umap-jsonl 0.1.0
A command-line tool for generating UMAP plots and KMeans clustering from JSONL data
Requires Python: >=3.8

# UMAP-JSONL

![output](https://github.com/user-attachments/assets/942e4e7b-0382-42bc-8fa3-1fca1b007426)


**UMAP-JSONL** is a command-line tool for generating UMAP plots and KMeans clustering from JSONL data files. This tool leverages UMAP for dimensionality reduction and KMeans for clustering, producing customizable visualizations in PNG format.

## Features

- **UMAP Visualization**: Create 2D UMAP plots from JSONL data.
- **KMeans Clustering**: Apply KMeans clustering with a configurable number of clusters.
- **Customizable Plot Size**: Set the figure size for your output plot.
- **Flexible Command-Line Interface**: Easily configure parameters and specify input/output files.

## Installation

You can install **UMAP-JSONL** using `pip`:

```sh
pip install umap-jsonl
```

Usage

To generate a UMAP plot with KMeans clustering, use the following command:

```sh

umap-jsonl input_file <input_file> --output_file <output_file> --num_results <num_results> --n_neighbors <n_neighbors> --min_dist <min_dist> --n_components <n_components> --n_clusters <n_clusters> --plot_size <width> <height>

Command-Line Arguments

    input_file: Path to the JSONL input file.
    --output_file: Path to the output PNG file (default: output.png).
    --n_neighbors: Number of neighbors for UMAP (default: 15).
    --min_dist: Minimum distance for UMAP (default: 0.1).
    --n_components: Number of components for UMAP (default: 2).
    --n_clusters: Number of clusters for KMeans (default: 3).
    --plot_size: Size of the output plot in inches (default: 8,6). Provide width and height separated by a comma.

```

Basic usage, provide a .jsonl file as a positional argument

```sh

umap-jsonl tests/hf_datasets_100000.jsonl

```

Advanced usage, set details of umap algorithm, change plot size, etc.

```sh

umap-jsonl --input_file data.jsonl --output_file my_plot.png --num_results 10 --n_neighbors 15 --min_dist 0.2 --n_components 2 --n_clusters 4 --plot_size 10 8

```

Development

To contribute to UMAP-JSONL, clone the repository and install the development dependencies:

```sh

git clone https://github.com/david4096/umap-jsonl.git
cd umap-jsonl
pip install -e .

```

Running Tests

To run tests, use:

```sh

python -m unittest discover -s tests

```

License

This project is licensed under the MIT License - see the LICENSE file for details.
Contact

For any questions or issues, please contact David Steinberg at your.email@example.com.
tvrip 3.3
Extract and transcode TV series from DVD or Bluray
Requires Python: >=3.10
Keywords: handbrake dvd bluray tv rip

.. -*- rst -*-

=====
tvrip
=====

tvrip is a small command line script that brings together several other
utilities (HandBrake, Atomic Parsley, mkvtoolnix, VLC, etc.) with the aim of
making it relatively simple to rip whole seasons or series of TV episodes from
DVD to high quality MP4 or Matroska files, along with optional subtitles,
alternate languages and director's commentaries.


Pre-requisites
==============

tvrip depends primarily on the following applications:

* `Handbrake`_

* `Atomic Parsley`_

* `mkvpropedit`_

* `VLC`_

As tvrip is written in the `Python`_ language, you will need a copy of this
installed along with the following Python packages:

* `sqlalchemy`_

* `requests`_

* `rich`_


License
=======

This file is part of tvrip.

tvrip is free software: you can redistribute it and/or modify it under the
terms of the GNU General Public License as published by the Free Software
Foundation, either version 3 of the License, or (at your option) any later
version.

tvrip is distributed in the hope that it will be useful, but WITHOUT ANY
WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
A PARTICULAR PURPOSE.  See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with
tvrip.  If not, see <http://www.gnu.org/licenses/>.


Attributions
============

Episode meta-data in tvrip is (optionally) downloaded from the excellent
`TVDB`_.

.. image:: docs/images/the-tvdb.png
    :align: center
    :width: 200px
    :alt: The TVDB's logo
    :target: `TVDB`_


.. _Python: http://www.python.org/
.. _HandBrake: http://handbrake.fr/
.. _Atomic Parsley: http://atomicparsley.sourceforge.net/
.. _mkvpropedit: https://mkvtoolnix.download/
.. _VLC: https://www.videolan.org/
.. _sqlalchemy: http://www.sqlalchemy.org/
.. _requests: https://requests.readthedocs.io/
.. _rich: https://rich.readthedocs.io/
.. _TVDB: https://thetvdb.com/
anisofilter 1.0.6
Anisotropic filter for 3D point clouds
Requires Python: >=3.6

# Python Wrapper for Anisotropic Denoising of 3D Point Clouds

![Image of Result noisy](https://webpages.tuni.fi/foi/PointCloudFiltering/arma_n.jpg)
![Image of Result denoised](https://webpages.tuni.fi/foi/PointCloudFiltering/arma_d.jpg)

A python implementation for denosing 3D point clouds with Gaussian noise, where the anisotropic neighborhoods were computed to both denoise the smooth regions and to preserve the sharp features, i.e. edges and corners.
 
The implementation is based on *Z. Xu and A. Foi, "Anisotropic Denoising of 3D Point Clouds by Aggregation of Multiple Surface-Adaptive Estimates," in IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 6, pp. 2851-2868, 1 June 2021, doi: 10.1109/TVCG.2019.2959761.*

The package contains the Anisotropic Denoising binaries compiled for:

* Windows (Win10, MinGW-64)
* Linux (Ubuntu 20.04.2 LTS, 64bit)
* Mac OSX (Big Sur, 64-bit)

The binaries are available for non-commercial use only (please see LICENSE for more details).

For the demo, see the demo folder of the full source zip, which also includes the example noisy and noise-free point clouds demonstrated in the paper. You can also download the demo from https://webpages.tuni.fi/foi/PointCloudFiltering/pcd_anisotropic_denoi_py_demo.zip

Authors:<br/>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Zhongwei Xu [xu@noiselessimaging.com](mailto:xu@noiselessimaging.com)<br/>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Alessandro Foi