semvec/pypi-packages huggingface dataset:
gptools-stan 1.0.0
Gaussian processes on graphs and lattices in Stan.
Requires Python: >=3.8
Keywords: Gaussian process, Stan, Bayesian inference, graph, Fourier

# gptoolsStan [![gptools: Python](https://github.com/onnela-lab/gptools/actions/workflows/python.yml/badge.svg)](https://github.com/onnela-lab/gptools/actions/workflows/python.yml) [![](https://img.shields.io/pypi/v/gptools-stan)](https://pypi.org/project/gptools-stan)

*gptools* is a minimal package to publish Stan code for efficient Gaussian process inference. The package can be used with the [*cmdstanpy*](https://cmdstanpy.readthedocs.io/) interface for Stan in Python.

## Getting Started

1. Install *cmdstanpy* and *cmdstan* if you haven't already (see [here](https://cmdstanpy.readthedocs.io/en/v1.2.5/installation.html) for details).
2. Install *gptools* from [PyPI](https://pypi.org/project/gptools-stan/) by running `pip install gptools-stan` from the command line.
3. Compile your first model.

```python
from cmdstanpy import CmdStanModel
from gptools.stan import get_include

model = CmdStanModel(
  stan_file="path/to/your/model.stan",
  stanc_options={"include-paths": get_include()},
)
```

For an end-to-end example, see [this notebook](https://gptools-stan.readthedocs.io/docs/getting_started/getting_started.html).
geoclass 0.1.0
A Python module for satellite image classification and land valuation
Requires Python: >=3.8

# GeoClass - Land Classification and Valuation System

A Python module for satellite image classification and land valuation using machine learning.

## Features

- Satellite image classification into different land categories
- Automated land scoring based on proximity to key features
- Real estate data scraping from major property websites
- Machine learning-based land price prediction

## Installation

```bash
pip install -r requirements.txt
```

## Project Structure

```
geoclass/
├── geoclass/
│   ├── satellite/          # Satellite image processing
│   ├── scoring/            # Land scoring system
│   ├── scraper/           # Web scraping modules
│   ├── ml/                # Machine learning models
│   └── utils/             # Helper functions
├── tests/                 # Unit tests
└── examples/              # Usage examples
```

## Usage

```python
from geoclass.satellite import ImageClassifier
from geoclass.scoring import LandScorer
from geoclass.scraper import PropertyScraper
from geoclass.ml import PricePredictor

# Initialize components
classifier = ImageClassifier()
scorer = LandScorer()
scraper = PropertyScraper()
predictor = PricePredictor()

# Process satellite image
classified_image = classifier.classify("path/to/satellite_image.tif")

# Score land parcels
land_scores = scorer.score_land(classified_image)

# Scrape property data
property_data = scraper.scrape_properties(latitude, longitude)

# Predict land prices
predictions = predictor.predict(property_data, land_scores)
```

## Development

To run tests:
```bash
pytest tests/
```

## License

MIT License
LxmlSoup 1.6
f is a set of tools for fast and easy parsing
Requires Python: >=3.6
Keywords: BeautifulSoup, bs4, lxml, Soup, LxmlSoup

# LxmlSoup

LxmlSoup is an analogue of BeautifulSoup, containing the most basic and necessary methods. Its speed exceeds bs4 by 5 times. The syntax is the same.

```
0.020386695861816406 - LxmlSoup
0.10153651237487793 - BeautifulSoup
```

## Installation

LxmlSoup requires Python >= 3.7

Install with `pip` from PyPI:

```
pip install LxmlSoup
```

Or cloning the repository:

```
git clone git@github.com:gladkihaa-28/LxmlSoup.git
```

### Examples

```python
from LxmlSoup import LxmlSoup
import requests

html = requests.get('https://sunlight.net/catalog').text
soup = LxmlSoup(html)

links = soup.find_all('a', class_='cl-item-img cl-item-img_loaded')
for link in links:
    print(link.text(), link.get('href'))
```

```python
from LxmlSoup import LxmlSoup
import requests

html = requests.get('https://sunlight.net/catalog').text
soup = LxmlSoup(html)

links = soup.find_all('a', attrs={'itemprop': 'url'})
for link in links:
    print(link.text(), link.get('href'))
```


You can support the author so that updates come out more often.
Sberbank - 2202 2062 9710 1995
csv2sepa 1.2.4
CSV to SEPA Convertor
Keywords: banking, batch, credit, csv, debit, excel, iso20022, sepa, xml, xsd

CSV to SEPA Convertor
=====================

This is a python implementation to generate SEPA XML files from a CSV file.

Limitations
-----------

Supported standards:

* SEPA PAIN.001.001.03 (v2009, not authorized after november 2025)
* SEPA PAIN.001.001.09 (v2019, last version)
* SEPA PAIN.008.001.02 (v2009, not authorized after november 2025)
* SEPA PAIN.008.001.08 (v2019, last version)

Usage
-----
1. Configure the `csv2sepa.ini` file with your informations.
2. Create a CSV file with data based on the example file corresponding to the desired SEPA format (provided in the ./csv directory).
3. Run `py csv2json.py` and answer to the questions (which SEPA format? Which CSV file?).
4. The generated XML file will be saved in the ./xml directory.

Credits and License
-------------------

Maintainer: Philippe-Alexandre PIERRE <pap@httpap.dev>

The source code is released under MIT license.