semvec/pypi-packages huggingface dataset:
zjson 1.3.0
Json Database full async
Requires Python: ~=3.6
Keywords: python,json,database,aio


<p align="center">
    <a href="https://github.com/x72x/zjson/">
        <img src="https://dev-zaiddev.pantheonsite.io/zaid/files/AgACAgIAAxkBAAILyWT_j6PAbt8P3F1JiqznohMs.jpg" alt="zjson" width="256">
    </a>
    <br>
    <b>Json Database full async</b>
    <br>
    <a href="https://github.com/x72x/zjson/tree/main/examples">
        Examples
    </a>
    •
    <a href="https://t.me/Y88F8">
        News
    </a>
</p>

## Aio-Json

> Example

``` python
from zjson import AsyncClient

db = AsyncClient(name="db.json", directory="cache")

async def main():
    await db.set(
        key="key",
        value="value"
    )
    print(await db.get("key")) # value
    await db.set(
        key="key2",
        value=[1, 2, 3],
        expire=10 # expire after 10 seconds
    )
    await db.sleep(10)
    print(await db.get("key2")) # None

db.run(main())

```

### Installing

``` bash
pip3 install -U zjson
```

### Community

- Join the telegram channel: https://t.me/Y88F8
brandpy 0.0.9
This is a simple python wrapper for lookup calls of Brandwatch API.
Requires Python: >=3.7

# brandpy

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

-----

**Table of Contents**

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

## Installation

```console
pip install brandpy
```

## License

`brandpy` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
chrpa 0.0.6
Climate Hazards Return Period Analysis
Requires Python: >=3.10.8

# chrpa
Climate Hazards Return Period Analysis


chrpa contains a series of functions to support climate hazard analyses. The functions facilitate analyses to understand how return periods and exceedance probability for straight-line wind and precipidation at key locations change under future climate conditions predicted by climate models.  The package also contains example datasets and tutorials to help demonstrate how the functions can be used.

Installation
=============
pvops can be installed using `pip`. See more information at [readthedocs](https://chrpa.readthedocs.io/en/latest/).


Package Layout and Documentation
==============

The package is delineated into the following directories. Refer to the `examples` directory for a full run-through of the available functionality.
```

├───chrpa
│   ├───main
│   │ 
│   ├───tests
│      
│   
├───docs
│   
└───Examples
```

More information about these modules is available at [readthedocs](https://chrpa.readthedocs.io/en/latest/).

Citing
======

If using this package, please cite [our paper](https://chrpa.readthedocs.io/en/latest/) using the following:


Contributing
============

The long-term success of chrpa requires community support. Please see the [Contributing page](https://pvops.readthedocs.io/en/latest/) for more on how you can contribute.


Copyright and License
=======

chrpa is copyrighted through Sandia National Laboratories. The software is distributed under the Revised BSD License. See [copyright and license](https://github.com/hmendo/chrpa/blob/main/LICENSE) for more information.
cohere-sagemaker 0.8.1
A Python library for the Cohere endpoints in AWS Sagemaker
Requires Python: >=3.6

# Cohere Python SDK (AWS SageMaker)

This package provides functionality developed to simplify interfacing with the [Cohere models via AWS SageMaker Marketplace](https://aws.amazon.com/marketplace/pp/prodview-6dmzzso5vu5my) in Python >=3.6

## Installation

The package can be installed with pip:
```bash
pip install --upgrade cohere-sagemaker
```

Install from source:
```bash
python setup.py install
```

## Quick Start

To use this library, you need to configure your AWS credentials. You can do this by running `aws configure`. Once that's set up, please refer to one of the Jupyter notebooks to get started. Here is the one for our [medium command model](https://github.com/cohere-ai/cohere-sagemaker/blob/main/notebooks/Deploy%20command%20medium.ipynb)


Note: by default we assume region configured in AWS CLI (`aws configure get region`), to override use `region_name` parameter, e.g.
```python
client = Client(region_name='us-east-1')
```
geometricLib 0.0.3
calculates the area of a circle by radius and a triangle by three sides
Requires Python: >=3.6

# Geometric Library #

## What is this? ##
The module allows you to calculate the area of a circle by radius and a triangle by three sides

### Using ###

Let's import it first:
First, import everything from the library (use the `from `...` import GeometryCalculator` construct).

The library has 2 functions:

calculate the area of a circle by radius:

        circle_area(radius)

calculate the area of a triangle on three sides
        
        triangle_area(side1, side2, side3)

Examples of all operations:

    radius = 5
    circle_area = GeometryCalculator.circle_area(radius)
    print(f"The area of a circle with radius {radius} is equal to {circle_area:.2f}")

    side1 = 3
    side2 = 4
    side3 = 5
    triangle_area = GeometryCalculator.triangle_area(side1, side2, side3)
    print(f"the area of a triangle with sides {side1}, {side2} and {side3} is equal to {triangle_area:.2f}")`



## Developer ##
My github: [link](https://github.com/uhiintezz)
modisconverter 0.2.0
A library for converting MODIS data files
Requires Python: >=3.9, <4
Keywords: MODIS,Converter,HDF4,NetCDF4

# modisconverter

`modisconverter` is a Python package that converts MODIS data from HDF4 to NetCDF4 file format.

## Prequisites

- Python >= 3.9
- GDAL >= 3.1.2, with supported file formats `HDF4` and `netCDF`

## Installation

This package is available on [PyPI](https://pypi.org/project/modisconverter/) and can be installed with [pip](https://pip.pypa.io/en/stable/user_guide/).

Package install depends on having a Python `GDAL` package that matches the version of the underlying GDAL installation. It also requires the `rasterio` package to be built from source.

```bash
$ pip install GDAL==<installed-version-number> --no-binary rasterio modisconverter
```

e.g.

```bash
$ pip install GDAL==3.6.3 --no-binary rasterio modisconverter
```

You can find your installed GDAL version by using the `gdal-config` binary:

```bash
$ gdal-config --version
```

## Environment Considerations

It's recommended to install the package in a [virtual environment](https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/). Otherwise, if your machine can run Docker containers, installing the package in one provides natural isolation.

For potential Docker images, you could consider using one of the [official GDAL images](https://hub.docker.com/r/osgeo/gdal/tags) from DockerHub, which contain a GDAL and Python installation.  For instance, the Ubuntu-based image `ghcr.io/osgeo/gdal:ubuntu-full-3.6.3`.  Note that the `pip` package manager may not be installed in such images.

## Usage

```python
from modisconverter import convert_file

src = 'example-modis.hdf'
dst = 'example-modis.nc'
convert_file(src, dst)
```

## Documentation

Listed below are various documents pertaining to this project.

- [Changelog](CHANGELOG.md) - Information on releases.
- [Usage](docs/USAGE.md) - Detailed usage of the library.