semvec/pypi-packages huggingface dataset:
bech32ref 0.1.0
A bech32 implemenation, slightly modified to allow for >90char strings
License: MIT
Requires Python: >=3.10,<4.0

# bech32m for Python

This is a straight port of @sipa's Python reference implementation of bech32 and bech32m.

Original can be found here: [https://github.com/sipa/bech32/tree/master/ref/python](https://github.com/sipa/bech32/tree/master/ref/python).
karaoke-video-generator 0.4.0
Fully automated creation of _acceptable_ karaoke music videos from any music on YouTube, using open source tools and AI (e.g. Whisper and MDX-Net)
License: MIT
Requires Python: >=3.9,<3.11

# KaraokeHunt: Karaoke video generator
Fully automated creation of _acceptable_ karaoke music videos from any music on YouTube, using open source tools and AI (e.g. Whisper and MDX-Net)

## Context
This is one experimental tool as part of the journey towards implementing the [full vision](https://docs.google.com/document/d/19LS1aJI8YwSmkWmDdpCHpmTGiHL9l0VDJ1SxSl4l6Z8/edit#) for KaraokeHunt (https://karaokehunt.com).

Some of the other components include:
- [Lyrics from Genius](https://github.com/karaokenerds/lyrics-from-genius)
- [Karaoke Song Finder (Spreadsheet generator)](https://github.com/karaokenerds/music-data-karaoke-song-sheets)
- [KaraokeHunt Mobile App](https://github.com/karaokenerds/karaokehunt-app)

## Idea steps
- Fetch the requested YouTube video using [yt-dlp](https://github.com/yt-dlp/yt-dlp) and extract the audio to wav using ffmpeg
- Run that audio through an ML-based vocal isolation model tuned for karaoke (e.g. [UVR-MDX-NET Karaoke 2](https://github.com/Anjok07/ultimatevocalremovergui/blob/master/models/MDX_Net_Models/model_data/model_name_mapper.json#L12) to get high quality instrumental audio without lead vocals but retaining backing vocals
- Run the lead vocal track through [whisper-timestamped](https://github.com/linto-ai/whisper-timestamped) to generate a time-synced lyrics file
- Correct the detected lyrics by fetching lyrics from a human-input source (e.g. musicxmatch/spotify using [syrics](https://github.com/akashrchandran/syrics), genius using [lyrics-from-genius](https://github.com/karaokenerds/lyrics-from-genius) and attempting to match up segments with the whisper-heard lyrics whilst maintaining timestamps
  - Potentially also consider splitting words by syllable (e.g. using [python-syllables](https://github.com/prosegrinder/python-syllables) and attempting to guess the sub-word timestamps 
- Generate a new video file using the instrumental audio and a background image, with the synced lyrics “burned” into the video at the correct timestamps
  - Lots of scope to make this really nice, e.g. adjusting kerning dynamically to fit longer lines on one screen, but also lots of gotchas e.g. super long lines needing to be split at a reasonable place
- Publish this video to YouTube
qian 0.1.3
Quantitative Investment Algorithm Nexus
License: MIT
Requires Python: >=3.6
Keywords: quantitative-research time-series value-investing business-analysis valuation cash-flow

# Quantitative Investment Algorithm Nexus (QIAN)

"Nexus" is a term that refers to a connection, link, or central point of intersection. In the context of the acronym, it signifies the integration and interconnectedness of the various quantitative investment algorithms that Qian Capital runs. It represents the idea of algorithms coming together as a cohesive unit, forming a central hub or network, where they collaborate and interact to generate investment insights and drive decision-making. The term "nexus" conveys the notion of a powerful and dynamic network of algorithms working in synergy to analyze data, identify patterns, and optimize investment strategies. For Qian Capital, QIAN represents, and is the name of, our flagship super-intelligent AI trader that accounts for all of Qian Capital's quantitative investing algorithms, including all of the sub-models, inputs, etc. QIAN is the super-intelligent AGI that makes the final alpha generation, portfolio optimization, execution decisions.
translate4colors 0.12
automates text translation using Google Translate
License: MIT
Keywords: google,translate


# automates text translation using Google Translate

## pip install translate4colors 

## Example Usage

```python
textx = r"""Python. was created in the early 1990s by Guido van Rossum at Stichting Mathematisch 
Centrum (CWI, see https://www.cwi.nl/) in the Netherlands as a successor of a 
language called ABC. Guido remains Python’s principal author, although it includes
many contributions from others.

In 1995, Guido. continued his work on Python at the Corporation for 
National Research Initiatives 
(CNRI, see https://www.cnri.reston.va.us/) in Reston, 
Virginia where he released several versions of the software."""

from translate4colors import Gtranslate
with Gtranslate("en", "de") as go:
    data = go.translate(textx)
print(data)

google = Gtranslate("en", "de")
translated = google.translate(textx)
print(translated)
google.quit_translator(soft_kill_first=True)

# [[('Python. was created in the early 1990s by Guido van Rossum at Stichting Mathematisch Centrum (CWI, see https://www.cwi.nl/) in the Netherlands as a successor of a language called ABC. Guido remains Python’s principal author, although it includes many contributions from others. In 1995, Guido. continued his work on Python at the Corporation for National Research Initiatives (CNRI, see https://www.cnri.reston.va.us/) in Reston, Virginia where he released several versions of the software.', 'Python. wurde Anfang der 1990er Jahre von Guido van Rossum am Stichting Mathematisch Centrum (CWI, siehe https://www.cwi.nl/) in den Niederlanden als Nachfolger einer Sprache namens ABC entwickelt. Guido bleibt der Hauptautor von Python, obwohl es viele Beiträge anderer enthält. Im Jahr 1995, Guido. setzte seine Arbeit an Python bei der Corporation for National Research Initiatives (CNRI, siehe https://www.cnri.reston.va.us/) in Reston, Virginia fort, wo er mehrere Versionen der Software veröffentlichte.')]]
# [[('Python. was created in the early 1990s by Guido van Rossum at Stichting Mathematisch Centrum (CWI, see https://www.cwi.nl/) in the Netherlands as a successor of a language called ABC. Guido remains Python’s principal author, although it includes many contributions from others. In 1995, Guido. continued his work on Python at the Corporation for National Research Initiatives (CNRI, see https://www.cnri.reston.va.us/) in Reston, Virginia where he released several versions of the software.', 'Python. wurde Anfang der 1990er Jahre von Guido van Rossum am Stichting Mathematisch Centrum (CWI, siehe https://www.cwi.nl/) in den Niederlanden als Nachfolger einer Sprache namens ABC entwickelt. Guido bleibt der Hauptautor von Python, obwohl es viele Beiträge anderer enthält. Im Jahr 1995, Guido. setzte seine Arbeit an Python bei der Corporation for National Research Initiatives (CNRI, siehe https://www.cnri.reston.va.us/) in Reston, Virginia fort, wo er mehrere Versionen der Software veröffentlichte.')]]
```
stitch3d 1.0.3
A computational method for tissue 3D reconstruction by joint modeling of multiple spatial transcriptomics slices.
License: MIT
Requires Python: >=3.7

# STitch3D

### Construction of a 3D whole organism spatial atlas by joint modeling of multiple slices

We developed STitch3D, a deep learning-based method for 3D reconstruction of tissues or whole organisms. Briefly, STitch3D characterizes complex tissue architectures by borrowing information across multiple 2D tissue slices and integrates them with a paired single-cell RNA-sequencing atlas.

With innovations in model designs, STitch3D enables two critical 3D analyses: First, STitch3D detects 3D spatial tissue regions which are related to biological functions, for example cortex layer structures in brain; Second, STitch3D infers 3D spatial distributions of fine-grained cell types in tissues, substantially improving the spatial resolution of seq-based ST approaches. The output of STitch3D can be further used for various downstream tasks like inference of spatial trajectories, denoising of spatial gene expression patterns, identification of genes enriched in specific biologically meaningful regions and detection of cell type gradients in newly generated virtual slices.

Installation
------------
* STitch3D can be downloaded from GitHub:
```bash
git clone https://github.com/YangLabHKUST/STitch3D.git
cd STitch3D
conda config --set channel_priority strict
conda env update --f environment.yml
conda activate stitch3d
```
Normally the installation time will be less than ten minutes. We have tested our package on Linux (Ubuntu 18.04.5 LTS). Software dependencies are listed in [this website](https://stitch3d-tutorial.readthedocs.io/en/latest/installation.html#software-dependencies).

Tutorials and reproducibility
-----------------------------
We provided codes for reproducing the experiments of the paper "Construction of a 3D whole organism spatial atlas by joint modeling of multiple slices", and comprehensive tutorials for using STitch3D. Please check the [tutorial website](https://stitch3d-tutorial.readthedocs.io/en/latest/index.html) for more details. 

Interactive 3D results
----------------------
Interactive 3D analysis results from STitch3D are available at the [website](https://stitch3d-tutorial.readthedocs.io/en/latest/index.html).

Reference
----------------------
Gefei Wang, Jia Zhao, Yan Yan, Yang Wang, Angela Ruohao Wu, Can Yang. Construction of a 3D whole organism spatial atlas by joint modeling of multiple slices. bioRxiv 2023.02.02.526814; doi: https://doi.org/10.1101/2023.02.02.526814