semvec/pypi-packages huggingface dataset:
dynamo-json 1.2.0
Swap between DynamoDB JSON and normal JSON
Keywords: dynamo json dynamodb

[![Build](https://travis-ci.org/adilosa/dynamo-json.svg?branch=master)]()

# dynamo-json

Sick of DynamoDB using its own data type descriptors? Swap between DynamoDB and normal JSON!

## Installation

Install from PyPI via `pip`:

    pip install dynamo_json

## Usage

Use as a library

```python
import dynamo_json

dynamo_json.marshall({"some": ["json", "document"]})

# {"some": {"L": [{"S": "json"}, {"S": "document"}]}}

dynamo_json.unmarshall({"some": {"L": [{"S": "json"}, {"S": "document"}]}})

# {"some": ["json", "document"]}
```

or as a CLI tool

```
$ dynamo-json '{"my": "json"}'
{"my": {"S": "json"}}

$ dynamo-json '{"my": {"S": "dynamo-json"}}'
{"my": "dynamo-json"}
```
greetuvm 0.0.1
Say hello to someone (or thing)

# greetuvm
> Greet You Very Much

An example project to help say hello to people or things.

This is actually a code-along for the talk [Publishing (Perfect) Python Packages on PyPi](https://youtu.be/GIF3LaRqgXo).

## Installation
Run the following to install:

```bash
$ pip install greetuvm
```

## Usage
```python
from hello import say_hello

# Say the generic "Hello"
say_hello()

# Say "Hello, Abel"
say_hello('Abel')
```

# Developing Greeter
To install greetuvm, along with the tools you need to develop and run tests,
run the following in your virtualenv:
```bash
$ pip install -e .[dev]
```
ldis 0.0.1
Local dictionary server.
Requires Python: >=3.7

# ldis
> ldis.

One to two paragraph statement about your product and what it does.

## Installation

OS X & Linux:

Clone the repository:

```sh
git clone https://github.com/polyrand/setDNS.git
```

Run:

```sh
chmod +x setdns.py
./setdns.py
```

## Development setup

Dependency management is done with pip-tools + venv (stdlib).

Framework requirements are in requirements/main.in (main.txt are the pinned ones). Development and documentation requirements are in requirements/dev.in (main.txt are the pinned ones)

## Documentation

Documentation is written in markdown using mkdocs. In order to work on it, run:

```sh
mkdocs serve
```

Now it will be available at [localhost:8000](localhost:8000).

## Usage example

A few motivating and useful examples of how your product can be used. Spice this up with code blocks and potentially more screenshots.

_For more examples and usage, please refer to the [Wiki][wiki]._

## Release History

* 0.2.1
    * CHANGE: Update docs (module code remains unchanged)
* 0.2.0
    * CHANGE: Remove `setDefaultXYZ()`
    * ADD: Add `init()`
* 0.1.1
    * FIX: Crash when calling `baz()` (Thanks @GenerousContributorName!)
* 0.1.0
    * The first proper release
    * CHANGE: Rename `foo()` to `bar()`
* 0.0.1
    * Work in progress

## Meta


Ricardo Ander-Egg Aguilar – [@ricardoanderegg](https://twitter.com/ricardoanderegg) –

- [ricardoanderegg.com](http://ricardoanderegg.com/)
- [github.com/polyrand](https://github.com/polyrand/)
- [linkedin.com/in/ricardoanderegg](http://linkedin.com/in/ricardoanderegg)

Distributed under the XYZ license. See ``LICENSE`` for more information.

## Contributing

1. Fork it (<https://github.com/polyrand/yourproject/fork>)
2. Create your feature branch (`git checkout -b feature/fooBar`)
3. Commit your changes (`git commit -am 'Add some fooBar'`)
4. Push to the branch (`git push origin feature/fooBar`)
5. Create a new Pull Request


MIT License

Copyright (c) [year] [fullname]

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
midea-ac-py-quastmaster 0.1.1
A library to control Midea appliances via the cloud API
Requires Python: >=3.6

# midea-ac-py 

This is a library to allow communicating to a Midea AC via the Midea Cloud.
This is a fork from https://github.com/NeoAcheron/midea-ac-py

This library would not have been possible without the amazing work of @NeoAcheron (https://github.com/NeoAcheron/midea-ac-py) and its Python library.  
Many thanks also to @mac-zhou (https://github.com/mac-zhou/midea-msmart) who found out more about the command-bytes. So it is possible for me to avoid that the device is switched into fan mode at 17Â°C when the data is initially retrieved.

Translated with www.DeepL.com/Translator (free version)

# ! Attention !
This is still an early work in progress and simply serves as a proof of concept.

The code for homeassistant has been removed because I don't need it for my application.  
**The classes have been renamed, making them incompatible with the library from NeoAcheron.**
mmcv-nightly 1.0rc0.dev20200630
OpenMMLab Computer Vision Foundation
Keywords: computer vision

MMCV
====

.. image:: https://img.shields.io/pypi/v/mmcv
  :target: https://pypi.org/project/mmcv

.. image:: https://github.com/open-mmlab/mmcv/workflows/build/badge.svg
  :target: https://github.com/open-mmlab/mmcv/actions

.. image:: https://codecov.io/gh/open-mmlab/mmcv/branch/master/graph/badge.svg
  :target: https://codecov.io/gh/open-mmlab/mmcv

.. image:: 	https://img.shields.io/github/license/open-mmlab/mmcv.svg
  :target: https://github.com/open-mmlab/mmcv/blob/master/LICENSE


Introduction
------------

MMCV is a foundational python library for computer vision research and supports many
research projects in MMLAB, such as `MMDetection <https://github.com/open-mmlab/mmdetection>`_
and `MMAction <https://github.com/open-mmlab/mmaction>`_.

It provides the following functionalities.

- Universal IO APIs
- Image/Video processing
- Image and annotation visualization
- Useful utilities (progress bar, timer, ...)
- PyTorch runner with hooking mechanism
- Various CNN architectures
- High-quality implementation of common CUDA ops

See the `documentation <http://mmcv.readthedocs.io/en/latest>`_ for more features and usage.

Note: MMCV requires Python 3.6+.


Installation
------------

Try and start with

.. code::

    pip install mmcv


or install from source

.. code::

    git clone https://github.com/open-mmlab/mmcv.git
    cd mmcv
    pip install -e .

Note: If you would like to use :code:`opencv-python-headless` instead of :code:`opencv-python`,
e.g., in a minimum container environment or servers without GUI,
you can first install it before installing MMCV to skip the installation of :code:`opencv-python`.