semvec/pypi-packages huggingface dataset:
siri-parser 0.1.8
A Python library for parsing  SIRI (Service Interface for Real-time Information) messages according to the French national profile specification (SIRI Profile France)
Requires Python: >=3.7
Keywords: siri specification, siri specification parser, siri parser python, siri xml parser

# SIRI Profile France Parser 

A Python library for parsing  SIRI (Service Interface for Real-time Information) messages according to the French national profile specification (SIRI Profile France).

## Overview
This library implements the French national adaptation of the SIRI standard, which is used for real-time public transport information exchange in France. It provides tools for parsing and working with SIRI messages that conform to the French profile specifications.


## Key Features

Full support for SIRI Profile France v2.0
XML parsing
Type-safe data structures
Support for all SIRI France service types:

- Stop Monitoring
- Estimated Timetable
- General Message
- Situation Exchange
- Production Timetable
- Facility Monitoring
- Connection Monitoring
- Vehicle Monitoring

## Siri interpretation notice et folders structures.
SIRI (Service Interface for Real-time Information) defines 
- Services (services available)
- Structures (data class/ structures for every object found in the specficication)
- Enums 


Sometimes you will find ``:::`` in the specification, ex: 

| LEADER | :::    | 1:1     | xxx-Delivery     | voir xxxDelivery  |
|--------|--------|---------|------------------|-------------------|
|        |        |         |                  |                   |


For us, it means  single or multiples members of xxx-Delivery/LEADER can be found flattened into the structures where xxx-Delivery/LEADER is implemented.

We have therefore decide to flatten XxxDelivery rust struct each time we found ``:::`` or ``LEADER`` defined into a response data structure and we build a custom deserializer for it.
We have done so everytime we found that a data structure needed to be flattened



## Installation

The parser can be installed using pypi, it requires python to be at version >=3.7

```
pip install siri-parser
```


an then it can be used like this:

```python

import siri_parser as siri

siri_parser = siri.SIRI()


data = """SIRI XML"""

parsed_data = siri_parser.parse(data)
print(str(parsed_data))

if parsed_data.body().notify_production_timetable():
    print("production_timetable")
elif parsed_data.body().notify_estimated_timetable():
    print("estimated_timetable")
else:
    print("other services")
```



## License
MIT license
entity-linking-alpha 0.0.1
A library for entity linking
Requires Python: ==3.11

Lightweight entity linking solution.

**Please consider citing our works if you use code from this repository.**

## Main dependencies
* python==3.11
* numpy==1.26.4
* SPARQLWrapper==2.0.0
* sentence_transformers==3.1.1
* aiohttp==3.9.5
* openai==1.54.2
* beautifulsoup4==4.12.2
* fake-useragent==1.5.1

## Examples & Usage

```
from main import navia

# Your API token which can be found here (https://github.com/marketplace/models/azure-openai/gpt-4o)
api_token = "YOUR_API_TOKEN"

EL(api_token=api_token, sentence="We used PCA and FA for our experiments.", mention="PCA", single="No", combination="No", disambiguation="No", embedding_model="Lajavaness/bilingual-embedding-large")
```

```
Wikipedia: https://en.wikipedia.org/wiki/Principal_component_analysis

Wikidata: https://www.wikidata.org/wiki/Q2873

DBpedia: http://dbpedia.org/resource/Principal_component_analysis

Execution Time: 00:00:18
```

## Licence
This library is licensed under the CC-BY-NC 4.0 license.
mtclabpy 0.6.8
A comprehensive tool for molecular and enzyme calculations
Requires Python: >=3.6

# MTCLabPy

A comprehensive Python package for molecular biology and enzyme calculations.

## Features

- Solubility Analysis
- Molecular Pockets Analysis
- Mutations Analysis
- Affinity Calculations
- Kcat Prediction
- Enzyme Self Calculation
- Molecular Docking
- Developmental Tree Analysis

## Installation

```bash
pip install mtclabpy
```

## Usage

```python
# Example for kcat prediction
from mtclabpy.kcat_prediction import dlkat

# Example for solubility prediction
from mtclabpy.solubility import nessolp

# Predict solubility
result_url = nessolp(
    fasta_file_path="sequence.fasta",
    email="your_email@example.com",
    username="your_username",
    password="your_password"
)

# More examples in documentation
```

## Requirements

- Python >= 3.6
- NumPy
- Pandas
- SciPy
- BioPython
- Requests

## License

MIT License
m_demo 0.1.2
Just a test for using Flit
Requires Python: >=3.6

flit-demo
=========

.. image:: https://img.shields.io/badge/Made%20with-Python3-brightgreen.svg
        :target: https://www.python.org/
        :alt: made-with-python

.. image:: https://img.shields.io/pypi/v/flit_demo.svg
        :target: https://pypi.python.org/pypi/flit_demo

.. image:: https://img.shields.io/badge/License-BSD%202--Clause-brightgreen.svg
        :target: https://github.com/eamontoyaa/flit_demo/blob/master/LICENSE
        :alt: License

.. image:: https://readthedocs.org/projects/flit_demo/badge/?version=latest
        :target: https://flit_demo.readthedocs.io/en/latest/?badge=latest
        :alt: Documentation Status

Just a test for using Flit