semvec/pypi-packages huggingface dataset:
coordax 0.2.5
Coordinate axes for scientific computing in JAX
Requires Python: >=3.11

![](https://github.com/neuralgcm/coordax/raw/main/docs/_static/coordax_logo_light.png)

# Coordax: Coordinate Axes for JAX

Authors: Dmitrii Kochkov and Stephan Hoyer

Coordax is a Python library for labeled axes with [JAX](https://github.com/jax-ml/jax/).
Our approach is reminiscent of [Xarray](https://github.com/pydata/xarray),
but tailored to meet the needs of modern physics- and AI-based simulation codes
written in JAX, such as [NeuralGCM](https://github.com/neuralgcm/neuralgcm).

Compared to other libraries for labeled arrays, Coordax provides a handful of key
features:

1. First class integration with JAX, including support for arbitrary JAX transformations
2. Easy wrapping of code not written for labeled arrays with `cmap`,
   inspired by [Penzai](https://penzai.readthedocs.io/en/stable/notebooks/named_axes.html)
3. Optional `Coordinate` objects, for advanced use-cases
4. Lossless conversion to and from [Xarray](https://github.com/pydata/xarray),
   for serialization and data analysis

Coordax was developed to meet the needs of
[NeuralGCM](https://github.com/neuralgcm/neuralgcm), but we hope it will be
useful more broadly!

For more details, **[read the documentation](https://coordax.readthedocs.io/)**.

## Disclaimer

Coordax is an experiment that we are sharing with the outside world in the    hope
that it will be useful. It is not a supported Google product. We welcome feedback,
bug reports and code contributions, but cannot guarantee they will be addressed.
inklog 0.1.2
Analyst-friendly scrapers with TUI and uvx support
Requires Python: >=3.12

# Inklog

Inklog is a collection of uvx-friendly scrapers for analysts. It provides a CLI for automation and a TUI (work in progress) for browsing available scrapers. The current focus is downloading files (PDF/EPUB). Webpage-to-markdown scraping is planned but not implemented yet.

All dates in this repository use the ISO8601 format: `YYYY-MM-DD`.

## Quick start

### Run a scraper (CLI)

```bash
uvx inklog run malaysia_parliament 2025-11-01 2025-11-30
```

### List scrapers

```bash
uvx inklog list
```

### Show example URLs

```bash
uvx inklog info malaysia_parliament
```

### Run the TUI

```bash
uvx inklog
```

Run a single document type:

```bash
uvx inklog run malaysia_parliament:lower_house_hansard 2025-11-01 2025-11-30
```

## TUI

The TUI is a work in progress. It shows the available scrapers, their descriptions, and lets you run a scraper with date ranges and options.
Each Malaysia Parliament document type appears as its own row for quick runs.

Key bindings:
- `Enter` or `Run` button: run the selected scraper
- Example URLs can be opened from the details pane
- `Q`: quit

## Malaysia Parliament scraper

This scraper downloads PDFs from the Malaysia Parliament site for the following document types:

- Dewan Rakyat - Jawapan Lisan (Oral Answers)
- Dewan Rakyat - Jawapan Bukan Lisan (Non-Oral Answers)
- Dewan Rakyat - Penyata Rasmi (Official Report)
- Dewan Negara - Jawapan Bertulis (Written Answers)

### Filename rules

Some sources change their filename patterns over the years. Inklog models this using date-bound filename rules, so each document type can define a list of templates tied to start/end dates. The Malaysia Parliament scraper ships with the current pattern and a rules table that can be extended when historical patterns are known.

## CLI options

`inklog run` supports overriding boolean options with `--set`:

```bash
uvx inklog run malaysia_parliament 2025-11-01 2025-11-30 \
  --set jawapan_lisan_rakyat=true \
  --set jawapan_bukan_lisan_rakyat=true \
  --set penyata_rasmi_rakyat=true \
  --set jawapan_bertulis_negara=false
```

## Adding a new scraper

1) Copy `src/inklog/scrapers/template.py` and rename it.
2) Update `ScraperMeta` and implement `run()`.
3) Ensure the module exports `SCRAPER`.

Scrapers are auto-discovered from the `inklog.scrapers` package.

## Development

```bash
uv sync --extra dev
uv run ruff check --fix .
uv run ruff format .
uv run pytest
```

## Roadmap

- Add markdown scraping mode (TBD).
- Expand filename rules for historical Malaysia Parliament patterns.
boomboxlib 0.2.9
Boombox SDK for Python
Requires Python: >=3.11

![boombox_transparent](https://github.com/user-attachments/assets/1c5f25a2-cc27-4349-ae72-91315d43d6a1)

Boombox is a high-level tool for audio & video streaming based on [Membrane Framework](https://membrane.stream).
It's built for easy transformations between any popular media formats -
containers and streaming protocols alike.

You can also use it as a bridge between Python code and external media, allowing
for seamless integration with AI pipelines and other projects that interact with
raw media streams directly.

To see examples and demos see the
[`examples`](https://github.com/membraneframework/boombox/tree/master/python/examples)
directory.

- **Documentation:** <https://boombox.readthedocs.io>
- **Source code:** <https://github.com/membraneframework/boombox/tree/master/python>

## Installation

The package is available on PyPI and can be downloaded with pip:

```bash
pip install boomboxlib
```

Because of some reliance on native libraries for encoding and decoding, the package is available for
the following platforms:

- MacOS on ARM architecture
- Linux on x86 architecture
- Linux on ARM architecture

## Example usage

The following example will read the MP4 file from the provided URL, flip the video and save it to
`output.mp4`

```python
boombox1 = Boombox(
input="https://raw.githubusercontent.com/membraneframework/static/gh-pages/samples/big-buck-bunny/bun33s.mp4",
output=Array(video=True, audio=True),
)
boombox2 = Boombox(input=Array(video=True, audio=True), output="output.mp4")
for b in boombox1.read():
    if isinstance(b, VideoPacket):
        b.payload = np.flip(b.payload, axis=0)
    boombox2.write(b)
boombox2.close(wait=True)
```

## Authors

Boombox is created by Software Mansion.

Since 2012 [Software Mansion](https://swmansion.com/?utm_source=git&utm_medium=readme&utm_campaign=boombox) is a software agency with experience in building web and mobile apps as well as complex multimedia solutions. We are Core React Native Contributors and experts in live streaming and broadcasting technologies. We can help you build your next dream product – [Hire us](https://swmansion.com/contact/projects).

Copyright 2024, [Software Mansion](https://swmansion.com/?utm_source=git&utm_medium=readme&utm_campaign=boombox)

[![Software Mansion](https://logo.swmansion.com/logo?color=white&variant=desktop&width=200&tag=membrane-github)](https://swmansion.com/?utm_source=git&utm_medium=readme&utm_campaign=boombox)

Licensed under the [Apache License, Version 2.0](LICENSE)
eztaox 0.1.1
`EzTaoX` is a flexible framework for multi-wavelength and multi-survey AGN light-curve modeling using Gaussian Processes (GPs).  Built on top of `tinygp`---a scalable GP library in `JAX`---`EzTaoX` is fast, modular, and integrates seamlessly with the `JAX` ecosystem for statistical inference and modern machine learning.
Requires Python: <3.13,>=3.10

[![DOI](https://zenodo.org/badge/928658755.svg)](https://doi.org/10.5281/zenodo.17467662)
## EzTao*X*
`EzTaoX` is a flexible framework for multi-wavelength and multi-survey AGN light-curve modeling using Gaussian Processes (GPs).  Built on top of `tinygp`---a scalable GP library in `JAX`---`EzTaoX` is fast, modular, and integrates seamlessly with the `JAX` ecosystem for statistical inference and modern machine learning.

> #### `EzTaoX` is under active development, breaking API changes are expected.

### Installation
```
pip install git+https://github.com/ywx649999311/EzTaoX.git
```
#### Dependencies
##### Supports Python 3.10, 3.11, 3.12
```
"jax (<=0.4.31)",
"jaxlib (<=0.4.31)",
"tinygp (>=0.3.0,<0.4.0)",
"jaxopt (>=0.8.3,<0.9.0)",
"optax (>=0.2.4,<0.3.0)",
"numpyro (>=0.17.0,<0.20.0)",
```

### Documentation
Please see tutorials in the `tutorials` folder.

### Citation
If you find `EzTaoX` useful for your research, please consider citing the following paper [arXiv:2511.21479](https://arxiv.org/abs/2511.21479):
```
@ARTICLE{Yu2025,
       author = {{Yu}, Weixiang and {Ruan}, John J. and {Burke}, Colin J. and {Assef}, Roberto J. and {Ananna}, Tonima T. and {Bauer}, Franz E. and {De Cicco}, Demetra and {Horne}, Keith and {Hern{\'a}ndez-Garc{\'\i}a}, Lorena and {Ili{\'c}}, Dragana and {Kova{\v{c}}evi{\'c}}, Andjelka B. and {Marculewicz}, Marcin and {Panda}, Swayamtrupta and {Ricci}, Claudio and {Richards}, Gordon T. and {Riffel}, Rogemar A. and {Schneider}, Donald P. and {S{\'a}nchez-S{\'a}ez}, Paula and {Satheesh Sheeba}, Sarath and {Tombesi}, Francesco and {Temple}, Matthew J. and {Vogeley}, Michael S. and {Yoon}, Ilsang and {Zou}, Fan},
        title = "{Scalable and Robust Multiband Modeling of AGN Light Curves in Rubin-LSST}",
      journal = {arXiv e-prints},
     keywords = {Astrophysics of Galaxies, Instrumentation and Methods for Astrophysics},
         year = 2025,
        month = nov,
          eid = {arXiv:2511.21479},
        pages = {arXiv:2511.21479},
          doi = {10.48550/arXiv.2511.21479},
archivePrefix = {arXiv},
       eprint = {2511.21479},
 primaryClass = {astro-ph.GA},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2025arXiv251121479Y},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
```



### Acknowledgment
`EzTaoX` is built on top of (and inspired by) [`tinygp`](https://github.com/dfm/tinygp)---a general purpose GP modeling framework written in `JAX`. For more general GP modeling tasks, experienced users can directly explore `tinygp`.
Itomori 1.1.2
Blazingly fast, minimalistic quick Note taking TUI app. Easy to store, run, share.
Requires Python: <3.15,>=3.10
Keywords: TUI app, Note taker, Textual app, Quick note taking app

<!--
 SPDX-FileCopyrightText: 2025-present Ahum Maitra theahummaitra@gmail.com

 SPDX-License-Identifier: 	GPL-3.0-or-later
-->

# Itomori


<div align="center">

![Itomori](https://img.shields.io/badge/Itomori-cba6f7?style=for-the-badge&labelColor=cba6f7&color=cba6f7)
![Ahum's Project](https://img.shields.io/badge/An_Ahum's_Project-cba6f7?style=for-the-badge&labelColor=cba6f7&color=50fa7b) ![GitHub last commit](https://img.shields.io/github/last-commit/TheAhumMaitra/Itomori?style=for-the-badge&color=b4befe) ![GitHub repo size](https://img.shields.io/github/repo-size/TheAhumMaitra/Itomori?style=for-the-badge&color=cba6f7)

<a href="https://pypi.org/project/Itomori/">
  <img src="https://img.shields.io/pypi/pyversions/Itomori?style=for-the-badge&color=b4befe" />
</a>
<br />
<br />
    
<br/>
</div>

Itomori is a clean, minimal, and fully keyboard-powered notes app built with Python. It focuses on simplicity, speed, and staying out of your way—so you can just write. It's designed for taking quick notes , that's why it is great for quick notes.

# Preview of Itomori

https://github.com/user-attachments/assets/f90ba1e9-8df4-4adf-9014-ef3128682a2c

# ✨ Features

1. Minimal and distraction-free

2. Fully keyboard-driven UI

3. Lightweight and fast

4. Stores note simply in json format

5. Easy for quick notes

# 🛠️ Installation

## Windows, Linux, Mac OS

**Installation or update with `uv`:**

```
uv tool install -U Itomori
```

_or_

```
uv tool install -U git+https://github.com/TheAhumMaitra/Itomori.git
```

**_Done , now launch with just ``Itomori` command_**

**Installation using PIP:**

```
pip install itomori
```

**_Done , now launch with just ``Itomori` command_**

**Manual installation:**

**Pre-requirements : `python` installed**
**\*Note : It can work on older Python version but it is not recommended.**

**_Steps :_**

1. Clone this repo

```
git clone https://github.com/TheAhumMaitra/Itomori.git
```

2. Go to the Itomori folder

It's saved where you cloned this repo

```
cd Itomori
```

3. Install it

```
pip install .
```

**_Done , now launch with just ``Itomori` command_**

**Installation with `curl`: (Only for Linux)**

**_Note : This script does not support Fish shell_**

```
curl -sSL "https://raw.githubusercontent.com/TheAhumMaitra/Itomori/refs/heads/main/Linux%20Install%20Scripts/install.sh" | bash
```

# 💬 Contribute

Feel free to contribute ideas, improvements, or bug fixes. PRs are always welcome!

# License

[`License`](License.md)

**⚠ Logs warning :** _This app is deigned to take logs, please clear the `.logs/app.log` , well it might me depends on your OS. We are developing a command to clear logs, but it is not ready yet!_

# Thanks

❤️ Thanks for checking out Itomori!

Made with love and minimalism and coffee and Textual.
topsis-vansh-102303922 0.1.1
Command line implementation of TOPSIS

# TOPSIS

A Python implementation of the **TOPSIS** multi-criteria decision-making (MCDM) method.

---

## Features
- Implements the standard TOPSIS algorithm
- Accepts CSV input files
- Supports custom weights and impacts
- Outputs TOPSIS score and rank
- Command-line based and easy to integrate

---

## What is TOPSIS?
TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is a multi-criteria decision analysis method.  
It identifies solutions from a finite set of alternatives based on their distance from an **ideal best** and an **ideal worst** solution.

---

## Input File Format
The input file must be a **CSV file** with:
- **First column**: Names/IDs of alternatives (non-numeric)
- **Remaining columns**: Criteria values (numeric)

### Sample Input (Table)

| Name | P1 | P2 | P3 | P4 | P5 |
|-----:|---:|---:|---:|---:|---:|
| M1 | 0.71 | 0.50 | 3.0 | 40.4 | 11.15 |
| M2 | 0.60 | 0.36 | 4.7 | 41.2 | 11.72 |
| M3 | 0.65 | 0.42 | 3.2 | 53.5 | 14.44 |
| M4 | 0.84 | 0.71 | 3.9 | 56.8 | 15.56 |
| M5 | 0.60 | 0.36 | 6.3 | 50.4 | 14.42 |
| M6 | 0.65 | 0.42 | 5.0 | 64.3 | 17.59 |
| M7 | 0.94 | 0.88 | 4.1 | 57.4 | 15.83 |
| M8 | 0.71 | 0.50 | 4.1 | 37.3 | 10.65 |

---

## Installation

Make sure you have Python 3 installed. Then install the required libraries:

```bash
pip install pandas numpy
```

---

## Usage

Run the script from the command line:

```bash
python topsis.py <InputDataFile> <Weights> <Impacts> <OutputResultFileName>
```

### Arguments
- **InputDataFile**: CSV file containing the dataset
- **Weights**: Comma-separated numeric weights (e.g., `1,1,1,1,1`)
- **Impacts**: Comma-separated impacts (`+` for benefit, `-` for cost)
- **OutputResultFileName**: Name of the output CSV file

### Example
```bash
python topsis.py data.csv 1,1,1,1,1 +,+,+,+,+ result.csv
```

---

## Output
The output CSV file will contain:
- **Topsis Score**: Performance score of each alternative
- **Rank**: Ranking based on TOPSIS score (higher is better)

### Sample Output (Table)

| Name | P1 | P2 | P3 | P4 | P5 | Topsis Score | Rank |
|-----:|---:|---:|---:|---:|---:|-------------:|-----:|
| M1 | 0.71 | 0.50 | 3.0 | 40.4 | 11.15 | 0.1961 | 8 |
| M2 | 0.60 | 0.36 | 4.7 | 41.2 | 11.72 | 0.2386 | 7 |
| M3 | 0.65 | 0.42 | 3.2 | 53.5 | 14.44 | 0.2661 | 5 |
| M4 | 0.84 | 0.71 | 3.9 | 56.8 | 15.56 | 0.5743 | 2 |
| M5 | 0.60 | 0.36 | 6.3 | 50.4 | 14.42 | 0.4278 | 4 |
| M6 | 0.65 | 0.42 | 5.0 | 64.3 | 17.59 | 0.4689 | 3 |
| M7 | 0.94 | 0.88 | 4.1 | 57.4 | 15.83 | 0.6954 | 1 |
| M8 | 0.71 | 0.50 | 4.1 | 37.3 | 10.65 | 0.2503 | 6 |

---

## Error Handling
The program validates:
- File existence
- Numeric criteria columns
- Correct number of weights and impacts
- Valid impact symbols (`+` or `-`)

Meaningful error messages are shown if validation fails.

---

## Dependencies
- pandas
- numpy
- sys (standard library)

---