semvec/pypi-packages huggingface dataset:
fetch-meditation 0.4.0
Fetch Daily meditations
License: MIT
Requires Python: <4.0,>=3.11

# Fetch Meditation Py

* Python library for reading and parsing daily meditations

### Basic Usage
```py
from fetch_meditation.jft_language import JftLanguage
from fetch_meditation.jft_settings import JftSettings
from fetch_meditation.jft import Jft

settings = JftSettings(JftLanguage.Russian)
jft_instance = Jft.get_instance(settings)
jft_entry = jft_instance.fetch()

print(jft_entry.quote)
```
neutralocean 2.4.1
Neutral Surfaces in the Ocean
License: MIT
Requires Python: >=3.8
Keywords: oceanography

# neutralocean

<a href="https://neutralocean.readthedocs.org">
    <img alt="latest docs" src="https://img.shields.io/badge/documentation-latest-blue">
</a>

<a href="https://badge.fury.io/py/neutralocean">
    <img alt="pypi package" src="https://badge.fury.io/py/neutralocean.svg">
</a>

<a href="https://anaconda.org/conda-forge/neutralocean">
    <img alt="conda-forge package" src="https://img.shields.io/conda/vn/conda-forge/neutralocean.svg">
</a>

<a href="https://github.com/geoffstanley/neutralocean/actions/workflows/python-package.yml">
    <img alt="tests" src="https://github.com/geoffstanley/neutralocean/actions/workflows/python-package.yml/badge.svg">
</a>

Calculate neutral surfaces in the ocean, using Python.

The major task of this software is to calculate **omega-surfaces**, following the algorithm of [Stanley et al. (2021)](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020MS002436).  Omega surfaces are highly accurate approximately neutral surfaces that work by iteratively reducing the neutrality error. 

There are also routines to calculate potential density surfaces, specific volume anomaly surfaces, neutral trajectories, the Veronis density label, and measures of neutrality error.

**Topobaric surfaces** [(Stanley, 2019a)](https://www.sciencedirect.com/science/article/pii/S1463500318302221) in their modified form [(Stanley 2019b)](https://www.sciencedirect.com/science/article/pii/S1463500318302233) are the most accurate approximately neutral surfaces that posses an exact geostrophic streamfunction (furnishing an Ertel potential vorticity with no baroclinic production term).  Software to compute topobaric surfaces is significantly more complicated and is currently only available in the original MATLAB [neutral-surfaces](https://github.com/geoffstanley/neutral-surfaces) toolbox.

**How to cite?** If you use this software, the most appropriate paper to cite is [Stanley et al. (2021)](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020MS002436).

# Installation
Simply execute either
```
$ pip install neutralocean
```
if you use pip, or
```
$ conda install -c conda-forge neutralocean
```
if you use conda.

# Documentation
See <https://neutralocean.readthedocs.org>
FastArray 0.1.1
A lightweight Python package for preprocessing geospatial optical imagery for machine learning
License: MIT
Requires Python: >=3.8

# fastarray

**fastarray** is a fast, lightweight Python package for preprocessing geospatial optical imagery (e.g., Sentinel-2) to prepare it for machine learning workflows. It simplifies operations like index computation (NDVI, NDWI, etc.), reprojection, patching, and transformation into PyTorch-ready tensors—while preserving geospatial coordinates.

---

## 🚀 Features

- ✅ Compute vegetation and water indices (NDVI, NDWI, etc.)
- ✅ Read raster data into coordinate-aware arrays
- ✅ Clip and reproject rasters using geometries (e.g., GeoJSON or Shapely)
- ✅ Split imagery into fixed-size patches
- ✅ Convert to PyTorch tensors for ML pipelines
- ✅ Fully Pythonic (no heavy GDAL/C++ dependencies)
- ✅ Fast and async-friendly where possible

---

## 📦 Installation

```bash
pip install fastarray