semvec/pypi-packages huggingface dataset:
palaestrai-environments 3.5.5
A set of example environments for palaestrAI.
Requires Python: >=3.10

# The palaestrAI environments package

## Introduction

This package contains a set of environemnts for palaestrAI that are 
ready-to-use.

## Installation

Clone the repository, install the project with pip:

```bash
pip install -e .
```

## Usage

Use one of the environments in your experiment run file. The environments
live in the `palaestrai.environment` namespace. For example, in your 
experiment run file, set 
`palaestrai_environments.tictactoe:TicTacToeEnvironment` as argument to the 
environment `name` key. Also do not forget to define the respective sensors 
and actuators.

Please consult palaestrAI's main documentation at <http://docs.palaestr.
ai> to learn more.

Have a lot of fun!
mm-address-parser 0.1.4
A parser for Myanmar addresses
Requires Python: >=3.9

# Project Title
**Myanmar Address Parsing**

## Project Description

This project parses the given address into different components (state, district, township, ward, village) to search in the Mimu database. The address will be parsed to identify the hierarchical address levels, starting with the highest level (state) and moving down to the lowest level (village or ward). This ensures that the address is accurately parsed even if certain components are ambiguous or missing.

### Tasks

1. **Extracting hierarchical components** from the address input, such as state, district, township, ward, and village.
2. **Handling different types of address formats** including those with missing or incomplete components.
3. **Parsing address in multiple languages** (Myanmar and English), ensuring compatibility with both.
4. **Standardizing parsed data** to be consistent with the Mimu database structure, facilitating easier search and geolocation retrieval.

---

### Installation

Install from [PyPI](https://pypi.org/project/mm-address-parser/):

```
pip install mm_address_parser
```
---

### Usage
```
from mm_address_parser.parser import Parser
parser = Parser()
parsed = parser.parse("ရန်ကုန်တိုင်းဒေသကြီး, လှိုင်မြို့နယ်, သီလဝါလမ်း")
print(parsed)
```
---
## Credits and Licensing

- **Project Lead:** Myo Thida
- **Institution/Affiliation:** MMDT
- **Data Source:** Myanmar Information Management Unit (MIMU), veresion 9.6 resleased in Feb 2025

This project and its outputs are released under the **MIT License**.  
You are free to use, modify, and distribute the code and dataset with proper attribution.

> © 2025 Myo Thida. All rights reserved.  
> If you use this project in your work, please cite or link back to this repository and the parent project:  
> _"From Words to Coordinates: Mapping Myanmar's Unstructured Addresses to Coordinates."_
colab-dc333 0.0.3
colab utils
Requires Python: >=3.11

# Install
pip install colab-dc333

# How to use
>>> from colab_dc333 import nvidia
>>> nvidia.update_12_4()



To update pypi
increment version in pyproject.toml 
>python -m build 
>python3 -m twine upload --repository pypi dist/*

activate a venv with twine installed
NoteClient 0.2.1
Automatically posts articles on note

![](https://github.com/Mr-SuperInsane/NoteClient/blob/main/docs/img/header.png)
<div align="center">Note Client</div>

---

## Usage

Note Client can automate the posting of articles on note. The content of the articles is written using Markdown notation.  

We are currently releasing a beta version. In the beta version, you can use major headings, minor headings, bullet points, paragraph numbers, and horizontal lines. With future updates, customization such as bold text and quotations as well as the insertion of tables of contents will be available.

Note Client's source code is made available under the [MIT license](https://github.com/Mr-SuperInsane/NoteClient/blob/main/LICENSE).  

[PyPI page](https://pypi.org/project/NoteClient) for this project.

## Documentation

[Japanese](https://note.com/naokun_gadget/n/naf129cb5f34b)

## Requirements

- Python >= 3.7

## Installation

```
pip install NoteClient
```

## Quick Example

```
from note_client import Note

EMAIL = 'your email'
PASSWORD = 'your password'
USER_ID = 'your user_id'

TITLE = 'Sample'
CONTENT_PATH = 'content.txt'
TAG_LIST = ['sample_tag']

# > If an image is specified, the index number is entered; if not, no description is given.
# INDEX = 0

# > True if the article is to be published, False if the article is to be saved as a draft; if not specified, the article is saved as a draft.
# POST_SETTING = True

# > True if the execution screen is not displayed, False if it is displayed, or not displayed if not specified.
# HEADLESS = False

# To specify the above three options, add them to the function arguments.

note = Note(email=EMAIL, password=PASSWORD, user_id=USER_ID)
print(note.create_article(title=TITLE, file_name=CONTENT_PATH, input_tag_list=TAG_LIST, image_index=None))

## If successful(Public).
# {'run':'success','title':'Sample','file_path':'content.txt','tag_list':['sample_tag'],'post_setting':'Public','post_url':'https://note.com/USER_ID/n/abc123'}

## If successful(Draft).
# {'run':'success','title':'Sample','file_path':'content.txt','tag_list':['sample_tag'],'post_setting':'Draft'}

## If unsuccessful.
# 'Required data is missing.'
```


### Sample
![](https://github.com/Mr-SuperInsane/NoteClient/blob/main/docs/img/sample.png)

## Help

If you would like to make an enquiry about NoteClient, click [here](https://gist.github.com/Mr-SuperInsane/e9711c7f8b2722b0a03c9922e3f2ecdc).
EquiForge 0.2.0
A toolkit for equirectangular image processing and conversions.
Requires Python: >=3.8
Keywords: equirectangular, panorama, image processing, 360

<p align="center">
    <img src="src/Logo+Name.svg" alt="EquiForge Logo" width="500"/>
</p>

<h4 align="center">
    A precision toolkit for equirectangular image processing and conversions
</h4>


<!--<img src=".img/equilib.png" alt="equilib" width="720"/>-->

<div align="center">
<a href="https://badge.fury.io/py/equiforge"><img src="https://badge.fury.io/py/equiforge.svg" alt="PyPI version"></a>
<a href="https://pypi.org/project/equiforge"><img src="https://img.shields.io/pypi/pyversions/equiforge"></a>
  <a href="https://github.com/MikkelKappelPersson/EquiForge/actions"><img src="https://github.com/MikkelKappelPersson/EquiForge/actions/workflows/python-package-tests.yml/badge.svg"></a>
  <a href="https://github.com/MikkelKappelPersson/EquiForge/blob/main/LICENSE"><img alt="GitHub license" src="https://img.shields.io/github/license/mikkelkappelpersson/equiforge"></a>
</div>

## Features

- Convert perspective images to equirectangular projection (`pers2equi`)
- Convert equirectangular images to perspective view (`equi2pers`)
- GPU acceleration with CUDA (optional)
- Uses 32-bit floating point precision for all image processing operations

## Installation

### Prerequisites
- Python 3.8 or later
- numpy
- numba
- Pillow

### Using `pip`:

```bash
pip install equiforge
```

### CUDA GPU Support
To enable CUDA GPU support, install the [latest graphics drivers from NVIDIA](https://www.nvidia.com/en-us/drivers/) for your platform. Then install the CUDA Toolkit package.

For CUDA 12, cuda-nvcc and cuda-nvrtc are required:
```bash
$ conda install -c conda-forge cuda-nvcc cuda-nvrtc "cuda-version>=12.0"
```

## Example Usage

```python
from equiforge import pers2equi

# Convert perspective image to equirectangular
equi_image = pers2equi(
    'input.jpg',
    output_height=2048, 
    fov_x=90.0,
    yaw=0.0,
    pitch=0.0,
    roll=0.0
)
```

```python
from equiforge import pers2equi
# Convert equirectangular image to perspective view
pers_image = equi2pers(
    'equirectangular.jpg',
    output_width=1920,
    output_height=1080,
    fov_x=90.0,
    yaw=45.0,
    pitch=15.0,
    roll=0.0
)
```

## Documentation

For more examples and detailed documentation, see the Jupyter notebooks included in the repository.

## Acknowledgements:

- [equilib](https://github.com/haruishi43/equilib)
- [Perspective-and-Equirectangular](https://github.com/timy90022/Perspective-and-Equirectangular)
pyopencap 1.2.7
An open source package for metastable electronic states in molecules.

PyOpenCAP is an open-source package aimed at extending the capabilities of electronic structure packages to 
describe metastable electronic states. 

Currently supported electronic structure packages are:

- Q-Chem
- OpenMolcas
- PySCF
- Psi4
- Columbus

Currently available features:

- Box and Voronoi CAPs
- Trajectory analysis tools

All PyOpenCAP wheels distributed on PyPi are MIT licensed.
spatial-ds-wrapper 0.1.0
A Python package for spatial data science workflows.
Requires Python: >=3.7

# Spatial DS Wrapper

A Python package for spatial data science workflows.

## Features
- Integration with scikit-learn models.
- Support for Kriging and Regression Kriging.
- Easy-to-use wrapper for spatial data workflows.

## Installation
```bash
pip install spatial-ds-wrapper
```

## Usage
```python
from spatial_ds_wrapper import Wrapper
# Example usage here
```

## License
MIT License