semvec/pypi-packages huggingface dataset:
earthml 0.7
A library to Perform different possible operations on Geo-Spatial Dataset

# EarthML: Geospatial Data Analysis Made Easy
<p align="center">
  <img src="https://github.com/akhilchibber/earthml/blob/main/earthml_logo.png?raw=true" width="300" alt="earthml Logo">
</p>

EarthML is an advanced Python library engineered to streamline the analysis of geospatial and remote sensing data. Developed with a vision to encompass a wide array of functionalities, EarthML currently supports geospatial data conversion into geohashes, and pre-processing of Synthetic Aperture Radar (SAR) datasets from Sentinel-1, ALOSPALSAR, and TerraSAR-X. EarthML is committed to expanding its functionalities to include other remote sensing sensors such as LiDAR, Hyperspectral, and Multispectral Optical in the near future.

## Goal

The primary objective of EarthML is to simplify and expedite the geospatial and SAR data analysis process. It is designed to handle diverse file formats, automatically compute geohashes that encapsulate the entire study area, and conduct essential pre-processing steps on SAR data.

## Features

- **Support for Multiple Formats**: Facilitates the conversion of geospatial data in various formats such as Shapefile, GeoJSON, GeoTIFF, LAS, and images.
- **Geohash Calculation**: Automatically computes the geohash that optimally represents the geographical bounds of the dataset.
- **SAR Data Pre-Processing**: Offers functionalities for crucial pre-processing steps on SAR datasets from Sentinel-1, ALOSPALSAR, and TerraSAR-X. This includes radiometric calibration, speckle filtering, and geometric correction.
- **Future-Ready**: EarthML is actively developed with a roadmap that includes the integration of other remote sensing sensors such as LiDAR, Hyperspectral, and Multispectral Optical.

## Installation

To install EarthML, you can use pip:

```
pip install earthml
```





# License
This project is licensed under the MIT License. See the LICENSE file for details.





# Author
Akhil Chhibber






# Note
This README provides a concise overview of the library, elucidates its objectives, highlights key features, and outlines simple installation instructions.

EarthML is under active development. We welcome contributions and suggestions for new features and improvements.
dabpumps 0.1.0
Python library for the DAB Pumps DConnect service
Requires Python: >=3.11

# dabpumps

[![PyPI - Version](https://img.shields.io/pypi/v/dabpumps.svg)](https://pypi.org/project/dabpumps)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/dabpumps.svg)](https://pypi.org/project/dabpumps)

-----

**Table of Contents**

- [Installation](#installation)
- [License](#license)

## Installation

```console
pip install dabpumps
```

## License

`dabpumps` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
rmy 0.1.5
Stateful rpc for Python
Requires Python: >=3.8,<4.0

[![PyPI version](https://img.shields.io/pypi/v/rmy)](https://pypi.org/project/rmy/)
[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![Python Versions](https://img.shields.io/pypi/pyversions/rmy)](https://pypi.org/project/rmy/)
[![test suite](https://github.com/pytek-io/rmy/actions/workflows/main.yml/badge.svg)](https://github.com/pytek-io/rmy/actions/workflows/main.yml)
[![Coverage Status](https://coveralls.io/repos/github/pytek-io/rmy/badge.svg?branch=main)](https://coveralls.io/github/pytek-io/rmy?branch=main)

An asyncio compatible RPC framework for building distributed applications.

RMY provides a high level API allowing to expose objects from one process to the other. It frees programmers from having to handle communication between processes while still providing enough control when needed. The simplest possible use of RMY framework looks as follows.


```python
import rmy

class Demo:
    async def greet(self, name):
        return f"Hello {name}!"


if __name__ == "__main__":
    rmy.run_tcp_server(8080, Demo())
```

This will expose an instance of the `Demo` object which can then be remotely accessed to as follows.

```python
import rmy
from hello_rmy_server import Demo

if __name__ == "__main__":
    with rmy.create_sync_client("localhost", 8080) as client:
        demo_remote = client.fetch_remote_object(Demo)
        while True:
            print('Enter your name:')
            name = input()
            print(demo_remote.greet.eval(name))
```

One can easily also asynchronously iterate through results generated by another object remotely. MY provides robust ways to iterate over (asynchronous) iterators applying back pressure if required. This allows to implement trivially Pub-Sub or any other event notification pattern easily. A trivial example of remote generator iteration might look like this. 

```python
import random

class Demo:
    ...
    async def count(self):
        for i in range(1_000_000):
            await asyncio.sleep(random.uniform())
            yield i
```

One can remotely iterate through the values using a normal loop.

```python
for i in demo_remote.count():
    print(i)
```

For smplicity sake we showcased examples in synchronous code. One can easily make those asynchronous by instanciating an asynchronous client.
pythoncpp 0.2
Python, compiled.
Requires Python: >=3.7,<4.0

# Welcome to PythonC++!

PythonC++ is a full-scale compiler for Python.

The aim of this project is to convert high-level Python code to lower-level C++ code, increasing your program's performance dramatically.

In theory, PythonC++ is faster than other tools (such as PyInstaller), as it leaves no trace of a Python runtime interpreter, or libraries.

The output is just plain C++ code - without any Python - so your program runs at the maximum performance!

Thanks for choosing PythonC++ to simplify your projects!