semvec/pypi-packages huggingface dataset:
TESTE-topologic 0.0.1
...
Requires Python: >=3.7
Keywords: VASP,topology,topological,materials,science,DFT

![pytopomat](https://github.com/ncfrey/pytopomat/workflows/pytopomat/badge.svg)
[![Python 3.7](https://img.shields.io/badge/python-3.7-blue.svg)](https://www.python.org/downloads/release/python-370/)
[![Python 3.8](https://img.shields.io/badge/python-3.8-blue.svg)](https://www.python.org/downloads/release/python-380/)
[![Python 3.6](https://img.shields.io/badge/python-3.9-blue.svg)](https://www.python.org/downloads/release/python-390/)

# <img alt="pytopomat" src="pytopomat_logo.png" width="250">

Python Topological Materials (pytopomat) is a code for easy, high-throughput analysis of topological materials.

### Using pytopomat

See the example jupyter notebook provided for how to use pytopomat.
pyquotegen 0.0.4
A Random Quote Generator Python Package
Requires Python: >=3.0

# pyquotegen

[![PyPI version](https://badge.fury.io/py/pyquotegen.svg)](https://badge.fury.io/py/pyquotegen)
[![Downloads](https://pepy.tech/badge/pyquotegen)](https://pepy.tech/project/pyquotegen)
[![GitHub license](https://img.shields.io/github/license/Armanidrisi/pyquotegen)](https://github.com/Armanidrisi/pyquotegen/blob/main/LICENSE)
[![GitHub issues](https://img.shields.io/github/issues/Armanidrisi/pyquotegen)](https://github.com/Armanidrisi/pyquotegen/issues)


`pyquotegen` is a Python package that provides a simple way to generate random quotes. 

## Installation

To install `pyquotegen`, simply run: 

```bash
pip install pyquotegen
```

## Usage
To use `pyquotegen`, import the package and call the `get_quote` function: 

```python
import pyquotegen

# get a random quote
quote = pyquotegen.get_quote()
print(quote)

# get a quote by specific category
quote_by_category = pyquotegen.get_quote("inspirational")
print(quote_by_category)
```

Note: The default category is "motivation" if you don't provide any category. 

## List Of Some Basic Categories
- motivational
- friendship
- technology
- inspirational
- funny
- nature
- duccess
- attitude
- coding

## Feature
- Generates random quotes
- Provides quotes by specific category
- Supports multiple categories
- Lightweight and easy to use

## Contributing
Contributions are welcome! To contribute, please fork the repository and submit a pull request.

## Credits
This package was created by Arman Idrisi.

## License
This project is licensed under the MIT License. See the LICENSE file for details.
eotransform 1.8.0
Protocol definition for streamed source/transform/sink process

![Coverage badge](https://raw.githubusercontent.com/TUW-GEO/eotransform/python-coverage-comment-action-data/badge.svg)
# eotransform

Defines the basic transform protocol to be used in the streamed source to sink concept. Also provides some generic 
transformer implementations such as `Compose` or `Map`.

WIP: More Doc
scrapse 0.3.9
Package to download and manage judgments
Requires Python: >=3.8,<4.0
Keywords: scraping,judgments

# ScrapSE

## Package description

ScrapSE downloads and manage the judgments.

Currently supported platforms: LEGGI D'ITALIA PA.

### Install scrapse
```
pip install scrapse
```
The package creates the `scrapse` folder in `/Users/your_username`, where it will save all judgments in the  
appropriate subfolders.

### How to use

#### Saving cookies - important!
```
scrapse leggitalia save-cookie 'your_cokies'
```
This command saves session cookies in a special file, containing  `your_cookie`.

#### Show filter values
```
scrapse leggitalia show-filters
```
This command shows the possible values to be assigned to sentence search filters.

#### Download the judgments
Make sure you have **saved** platform-related cookies before downloading the judgments!.
```
scrapse leggitalia scrap-judgments -l torino -s 'Sez. lavoro, Sez. V'
```
This command creates a folder in `/Users/your_username/scrapse/leggitalia` named `sez.lavoro&sez.v_torino` containing the judgments.

#### Dump judgments to json format
```
scrapse leggitalia dump-judgments -d 'folder_path'
```
This command creates the json files by saving them in the `/Users/your_username/scrapse/leggitalia/judgments_dump` folder.

#### For more help
For more information for each command.
```
command-name --help
```
duonijo-commons 0.6.6b1
Python Library implementing common tools for our projects

# Commons Library Documentation

## How to work with the library Locally
From root directory run
``` shell
pip install -e ./
```

## Unit tests
Before building your new features be sure that they would not break any parts of the entire code.
To do so, you will need to deploy containers in ````docker```` folder.
Run the following command to deploy the stack to test :
```shell
cd docker/
docker compose up -d 
```
### Initialize Topics
1. Attach shell to `commons-kafka-1` container.
2. Move to kafka directory
3. Run script 
````bash
./kafka1_init.sh
```` 

### Run pytest
Finally, to execute unit tests you have to move to root directory and then run :
```shell
pytest commons
```

## How to build and push on Repsy.io

If you don't have already, create .pypirc configuration file.
```shell
touch $HOME/.pypirc
```

Add repository configuration
```shell
[distutils]
index-servers =
    repsy-optalp

[repsy-commons]
repository=https://repsy.io/pypi/optalp/optalp
username=optalp
```

Add repository password

```shell
python -m keyring set https://repsy.io/pypi/optalp/optalp optalp
```

From root directory build code with

```shell
python setup.py bdist_wheel
```

Then push the code with following command
``` shell
python -m twine upload --repository repsy-optalp dist/<new_version_file.whl>
```

## How to install library

### Latest Version
To get the latest stable version run following command 
```shell
pip install commons --index-url https://<repsy_username>:<repsy_password>@repo.repsy.io/pypi/optalp/optalp/simple --extra-index-url https://pypi.org/simple
```
### Specific version
To get specific version run following command 
```shell
pip install commons==<version> --index-url https://<repsy_username>:<repsy_password>@repo.repsy.io/pypi/optalp/optalp/simple --extra-index-url https://pypi.org/simple
```

## How to update library if already installed
To update to the last version run following command
```shell
pip install commons --index-url https://<repsy_username>:<repsy_password>@repo.repsy.io/pypi/optalp/optalp/simple --extra-index-url https://pypi.org/simple --upgrade
```

To update to specific version run following command
```shell
pip install commons==<version> --index-url https://<repsy_username>:<repsy_password>@repo.repsy.io/pypi/optalp/optalp/simple --extra-index-url https://pypi.org/simple --upgrade
```
text2mapviewer 0.2.2
A python package to map your own csv files data using Atlas from NOMIC
Requires Python: >=3.7
Keywords: Embedding, Visualization, Map, Text, CSV,  Search keywords, Dynamic

This is a vesy simple way to map your text data using [Altas from NOMIC](https://docs.nomic.ai/index.html) using the lib `click`.

You have to create an account to get API_KEY NOMIC. 

<< Atlas enables you to:
* Store, update and organize multi-million point datasets of unstructured text, images and embeddings.
* Visually interact with your datasets from a web browser.
* Run semantic search and vector operations over your datasets.
Use Atlas to:

    - Visualize, interact, collaborate and share large datasets of text and embeddings.
    - Collaboratively clean, tag and label your datasets
    - Build high-availability apps powered by semantic search
    - Understand and debug the latent space of your AI model trains  >>


# How to use
### Installation

To install the necessary dependencies, run the following command:

```bash
python -m venv mymapenv 
source mymapenv/bin/activate
pip install --upgrade pip 
pip install text2mapviewer
```

### Login NOMIC server

Login/create your Nomic account:

```bash
nomic login
```
If you have already your account : 

```bash
nomic login [YOUR_API_TOKEN_NOMIC_HERE] 
```
## Examples : 
#### from NOMIC and with lib text2mapviewer 
```python
from text2mapviewer.examples.map_embedding import project

# Use the projet from the lib text2mapviewer 
print(project) 
```

#### With the lib `click` after clone this ripo

```python
python scr/text2mapviewer/examples/map_embedding_click.py --num_embeddings 10000 --embedding_dim 256
```

#### [ The Animation Ouput](https://atlas.nomic.ai/map/0b4c0459-98f2-4aab-8d47-875765832049/54017477-907d-46e8-8d56-dddf7ab7fcfc)




## Supported Transformer Models from Hugging Face 

This project supports a variety of transformer models, including models from the Hugging Face Model Hub and sentence-transformers. Below are some examples:
    - Hugging Face Model: 'prajjwal1/bert-mini'
    - Hugging Face Model: 'Sahajtomar/french_semantic'  (french version for semantic search embedding) 
    - Sentence-Transformers Model: 'sentence-transformers/all-MiniLM-L6-v2' etc...

Please ensure that the model you choose is compatible with the project requirements and adjust the `--transformer_model_name` option accordingly.

## To map your text/csv  files

```bash
pip install -r requirements.txt
python main.py --transformer-model-name MODEL_NAME --cache_dir CACHE_DIR --batch-size BATCH_SIZE --file-path FILE_PATH
```
NOTE: for the CACHE_DIR : you can setup it like ==> 

```bash
export TRANSFORMERS_CACHE=/path_to_your/transformers_cache
```

Give a fidback.