semvec/pypi-packages huggingface dataset:
xnrl 2022.9.29
xNRL - Read NRL files into xarray Datasets nested within pandas DataFrames
Keywords: flatfile,nrl,xarray,dataset,binary,hdf5,grib

> Distribution Statement A. Approved for public release. Distribution unlimited.
> 
> Author:
> Naval Research Laboratory, Marine Meteorology Division
> 
> This program is free software:
> you can redistribute it and/or modify it under the terms
> of the NRLMMD License included with this program.
> If you did not receive the license, see
> https://github.com/U-S-NRL-Marine-Meteorology-Division/
> for more information.
> 
> This program is distributed WITHOUT ANY WARRANTY;
> without even the implied warranty of MERCHANTABILITY
> or FITNESS FOR A PARTICULAR PURPOSE.
> See the included license for more details.


<div
  align="center"
>

# <p style="text-align:center"><img src="https://github.com/U-S-NRL-Marine-Meteorology-Division/xnrl/blob/main/images/logo.png?raw=true" width=250>

<!-- Badges -->

[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) ![PyPI](https://img.shields.io/pypi/v/xnrl) [![Publish to PyPI](https://github.com/U-S-NRL-Marine-Meteorology-Division/xnrl/actions/workflows/publish-to-pypi.yml/badge.svg)](https://github.com/U-S-NRL-Marine-Meteorology-Division/xnrl/actions/workflows/publish-to-pypi.yml)

<!-- (Badges) -->
</div>

**xNRL** helps you read NRL NWP output into xarray Datasets nested within Pandas DataFrames.

# Install
xNRL requires numpy, pandas, and xarray.

## Install with pip
```bash
pip install xnrl
```

## Clone and install with pip
```bash
git clone https://github.com/U-S-NRL-Marine-Meteorology-Division/xnrl.git
cd xnrl
pip install -e .
```
> The `-e` makes the code editable. 
> 
> You can update the code with 
> ```bash
> cd xnrl
> git pull origin main
> ```

## Install with Conda

Copy the [`environment_xnrl.yml`](./environment_xnrl.yml) file and create the Conda environment. 

```bash
conda env create -f environment_xnrl.yml
```
> Note: This only installs the current main branch and does not let you edit the code. You can update xnrl (and all packages in the environment) with 
> ```
> conda env update -f environment_xnrl.yml
>```

# Examples
```python
import xnrl

# Load COAMPS flatfiles into an xarray Dataset
fp = '<path>/*pre*00120*'
ds = xnrl.open_dataset(fp, model='COAMPS')
```
Pepitoww 0.0.1
A small example package
Requires Python: >=3.7

# M1-2022-git

## Introduction
This repository is a fork from the previous repository.
The bugs in the converter have been fixed and now we want to publish our work as a publicly available python package.

## Getting started
You should be working from **your own repository** that is a fork of M1-2022-git-workflow.
In order to run this project, you will have to follow these steps.
1. Clone this project
2. move into the project folder
``` shell
cd M1-2022-git-workflow
```
3. Set up a virtual environment
 * Create the virtual environment
 ```shell
 python -m venv myvenv
 ```
 * Activate venv
 ```shell
 . venv/bin/activate
 ```
4. Install pytest
```shell
pip install -U pytest
```
5. Execute main script
```shell
python main.py
```

## Instructions
### Merge feature branch into dev
Now that the converter has no more bugs, we want to merge our branch feature into the dev branch.
We will do a pull request (on your own repo). On GitHub, go to the "Pull requests tab" and click "New pull request".
Select *base* and *compare*. Remember : we want to merge *decimal-binary-converter* into *dev*. 
Then, *Create the pull request*. Add some description of the work you have done and publish the Pull Request.

**Note** : We could have just merge *decimal-binary-converter* into *dev* from the command line. However, when working on a real project, you probably won't have the rights to do so. Remember : dev is a public branch. So all the work going down there should be carefully reviewed and tested.


### Publish your work
We now have an awesome binary/decimal two ways converter ! It's time to publish it so the rest of the world can enjoy it !
We will publish it on PyPI (Python Package Index) which is a public repository of softwares for Python.
1. First, you have to create an account on [Pypi.org](https://pypi.org/)
2. Then, in your repository, you will find a [.github directory](/.github)
3. Inside this directory, you will find .yml files that defines workflows (using GitHub actions)
4. Open the yml files and take time to understand them. Do not hesitate to ask the teacher about them

#### Hands on !
5. We want to create a workflow that will, on publish, test our code and if tests succeed, then publish our package on PyPI
6. In GitHub, click on the *Actions* tab, then click on *new workflow*
7. Choose among the suggestions the template that best suits your use-case and click *Configure*
8. You may find some useful informations in the [Python documentation](https://packaging.python.org/en/latest/tutorials/packaging-projects/)
9. When creating a release on GitHub, do not forget to create a tag corresponding to that release

#### Pull request
10. Once the workflow is setup and tested (you have published your package using it) you can open a pull request to the [original repository](https://github.com/rgt-yncrea/M1-2022-git-workflow)
Aygan3 0.0.1
A small example package
Requires Python: >=3.7

# M1-2022-git

## Introduction
This repository is a fork from the previous repository.
The bugs in the converter have been fixed and now we want to publish our work as a publicly available python package.

## Getting started
You should be working from **your own repository** that is a fork of M1-2022-git-workflow.
In order to run this project, you will have to follow these steps.
1. Clone this project
2. move into the project folder
``` shell
cd M1-2022-git-workflow
```
3. Set up a virtual environment
 * Create the virtual environment
 ```shell
 python -m venv myvenv
 ```
 * Activate venv
 ```shell
 . venv/bin/activate
 ```
4. Install pytest
```shell
pip install -U pytest
```
5. Execute main script
```shell
python main.py
```

## Instructions
### Merge feature branch into dev
Now that the converter has no more bugs, we want to merge our branch feature into the dev branch.
We will do a pull request (on your own repo). On GitHub, go to the "Pull requests tab" and click "New pull request".
Select *base* and *compare*. Remember : we want to merge *decimal-binary-converter* into *dev*. 
Then, *Create the pull request*. Add some description of the work you have done and publish the Pull Request.

**Note** : We could have just merge *decimal-binary-converter* into *dev* from the command line. However, when working on a real project, you probably won't have the rights to do so. Remember : dev is a public branch. So all the work going down there should be carefully reviewed and tested.


### Publish your work
We now have an awesome binary/decimal two ways converter ! It's time to publish it so the rest of the world can enjoy it !
We will publish it on PyPI (Python Package Index) which is a public repository of softwares for Python.
1. First, you have to create an account on [Pypi.org](https://pypi.org/)
2. Then, in your repository, you will find a [.github directory](/.github)
3. Inside this directory, you will find .yml files that defines workflows (using GitHub actions)
4. Open the yml files and take time to understand them. Do not hesitate to ask the teacher about them

#### Hands on !
5. We want to create a workflow that will, on publish, test our code and if tests succeed, then publish our package on PyPI
6. In GitHub, click on the *Actions* tab, then click on *new workflow*
7. Choose among the suggestions the template that best suits your use-case and click *Configure*
8. You may find some useful informations in the [Python documentation](https://packaging.python.org/en/latest/tutorials/packaging-projects/)
9. When creating a release on GitHub, do not forget to create a tag corresponding to that release

#### Pull request
10. Once the workflow is setup and tested (you have published your package using it) you can open a pull request to the [original repository](https://github.com/rgt-yncrea/M1-2022-git-workflow)
gitignore-builder 1.0.1
Build the contents of a single .gitignore from several sources.
Requires Python: >=3.7

# gitignore-builder

[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/gitignore-builder.svg)](https://pypi.org/project/gitignore-builder)
[![PyPI - Version](https://img.shields.io/pypi/v/gitignore-builder.svg)](https://pypi.org/project/gitignore-builder)
[![Hatch project](https://img.shields.io/badge/%F0%9F%A5%9A-Hatch-4051b5.svg)](https://github.com/pypa/hatch)

-----

**Table of Contents**

- [Usage](#usage)
- [Installation](#installation)
- [Changelog](#changelog)
- [License](#license)

-----

## Usage

### CLI command's 'help' output:

```console
Usage: gitignore-builder [OPTIONS] {android|java|python} [OUTPUT]

  Generate .gitignore contents from recipe and write them to the output.

Options:
  -c, --config  Print the location of app config files.
  -h, --help    Show this message and exit.
```

### Sample CLI command invocations

```shell
# print the command help description
gitignore-builder --help

# print absolute paths to the app config files
gitignore-builder --config

# generate and print .gitignore file contents
gitignore-builder java

# generate and write the contents to '.gitignore' file in current dir
gitignore-builder python .gitignore
```

-----

## Installation

Installing with Pip

```shell
# from PyPI 
pip install gitignore-builder

# from source
git clone git@github.com:Hrissimir/gitignore-builder.git
cd gitignore-builder
pip install .
```

-----

## Changelog

#### Version 1.0.1

- Minor bugfix

#### Version 1.0.0

- Introduced the concepts of 'recipes' and 'templates'
- Implemented usage of recipes.yaml and templates.yaml
    - Created in per-user app-config dir upon first usage
    - Editable by the user to provide extra/custom values
- Implemented support for printing paths to the app data-files
- Improved of the bundled lists of templates and recipes
- Improved CLI command help-description.
- Better unit-tests coverage

#### Version 0.1.0

- Added basic implementation of the CLI command.
- Initial PyPI publication.

#### Version 0.0.1

- Generated project skeleton
- Added README.md
- Added CONTRIBUTING.md
- Configured the GitHub CI/CD pipeline.

-----

## License

`gitignore-builder` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
spartzival 0.0.1
A small example package
Requires Python: >=3.7

# M1-2022-git

## Introduction
This repository is a fork from the previous repository.
The bugs in the converter have been fixed and now we want to publish our work as a publicly available python package.

## Getting started
You should be working from **your own repository** that is a fork of M1-2022-git-workflow.
In order to run this project, you will have to follow these steps.
1. Clone this project
2. move into the project folder
``` shell
cd M1-2022-git-workflow
```
3. Set up a virtual environment
 * Create the virtual environment
 ```shell
 python -m venv myvenv
 ```
 * Activate venv
 ```shell
 . venv/bin/activate
 ```
4. Install pytest
```shell
pip install -U pytest
```
5. Execute main script
```shell
python main.py
```

## Instructions
### Merge feature branch into dev
Now that the converter has no more bugs, we want to merge our branch feature into the dev branch.
We will do a pull request (on your own repo). On GitHub, go to the "Pull requests tab" and click "New pull request".
Select *base* and *compare*. Remember : we want to merge *decimal-binary-converter* into *dev*. 
Then, *Create the pull request*. Add some description of the work you have done and publish the Pull Request.

**Note** : We could have just merge *decimal-binary-converter* into *dev* from the command line. However, when working on a real project, you probably won't have the rights to do so. Remember : dev is a public branch. So all the work going down there should be carefully reviewed and tested.


### Publish your work
We now have an awesome binary/decimal two ways converter ! It's time to publish it so the rest of the world can enjoy it !
We will publish it on PyPI (Python Package Index) which is a public repository of softwares for Python.
1. First, you have to create an account on [Pypi.org](https://pypi.org/)
2. Then, in your repository, you will find a [.github directory](/.github)
3. Inside this directory, you will find .yml files that defines workflows (using GitHub actions)
4. Open the yml files and take time to understand them. Do not hesitate to ask the teacher about them

#### Hands on !
5. We want to create a workflow that will, on publish, test our code and if tests succeed, then publish our package on PyPI
6. In GitHub, click on the *Actions* tab, then click on *new workflow*
7. Choose among the suggestions the template that best suits your use-case and click *Configure*
8. You may find some useful informations in the [Python documentation](https://packaging.python.org/en/latest/tutorials/packaging-projects/)
9. When creating a release on GitHub, do not forget to create a tag corresponding to that release

#### Pull request
10. Once the workflow is setup and tested (you have published your package using it) you can open a pull request to the [original repository](https://github.com/rgt-yncrea/M1-2022-git-workflow)
example-package-Loicpln 0.0.1
A small example package
Requires Python: >=3.7

# M1-2022-git

## Introduction
This repository is a fork from the previous repository.
The bugs in the converter have been fixed and now we want to publish our work as a publicly available python package.

## Getting started
You should be working from **your own repository** that is a fork of M1-2022-git-workflow.
In order to run this project, you will have to follow these steps.
1. Clone this project
2. move into the project folder
``` shell
cd M1-2022-git-workflow
```
3. Set up a virtual environment
 * Create the virtual environment
 ```shell
 python -m venv myvenv
 ```
 * Activate venv
 ```shell
 . venv/bin/activate
 ```
4. Install pytest
```shell
pip install -U pytest
```
5. Execute main script
```shell
python main.py
```

## Instructions
### Merge feature branch into dev
Now that the converter has no more bugs, we want to merge our branch feature into the dev branch.
We will do a pull request (on your own repo). On GitHub, go to the "Pull requests tab" and click "New pull request".
Select *base* and *compare*. Remember : we want to merge *decimal-binary-converter* into *dev*. 
Then, *Create the pull request*. Add some description of the work you have done and publish the Pull Request.

**Note** : We could have just merge *decimal-binary-converter* into *dev* from the command line. However, when working on a real project, you probably won't have the rights to do so. Remember : dev is a public branch. So all the work going down there should be carefully reviewed and tested.


### Publish your work
We now have an awesome binary/decimal two ways converter ! It's time to publish it so the rest of the world can enjoy it !
We will publish it on PyPI (Python Package Index) which is a public repository of softwares for Python.
1. First, you have to create an account on [Pypi.org](https://pypi.org/)
2. Then, in your repository, you will find a [.github directory](/.github)
3. Inside this directory, you will find .yml files that defines workflows (using GitHub actions)
4. Open the yml files and take time to understand them. Do not hesitate to ask the teacher about them

#### Hands on !
5. We want to create a workflow that will, on publish, test our code and if tests succeed, then publish our package on PyPI
6. In GitHub, click on the *Actions* tab, then click on *new workflow*
7. Choose among the suggestions the template that best suits your use-case and click *Configure*
8. You may find some useful informations in the [Python documentation](https://packaging.python.org/en/latest/tutorials/packaging-projects/)
9. When creating a release on GitHub, do not forget to create a tag corresponding to that release

#### Pull request
10. Once the workflow is setup and tested (you have published your package using it) you can open a pull request to the [original repository](https://github.com/rgt-yncrea/M1-2022-git-workflow)
example-package-solenedfn 0.0.1
A small example package
Requires Python: >=3.7

# M1-2022-git

## Introduction
This repository is a fork from the previous repository.
The bugs in the converter have been fixed and now we want to publish our work as a publicly available python package.

## Getting started
You should be working from **your own repository** that is a fork of M1-2022-git-workflow.
In order to run this project, you will have to follow these steps.
1. Clone this project
2. move into the project folder
``` shell
cd M1-2022-git-workflow
```
3. Set up a virtual environment
 * Create the virtual environment
 ```shell
 python -m venv myvenv
 ```
 * Activate venv
 ```shell
 . venv/bin/activate
 ```
4. Install pytest
```shell
pip install -U pytest
```
5. Execute main script
```shell
python main.py
```

## Instructions
### Merge feature branch into dev
Now that the converter has no more bugs, we want to merge our branch feature into the dev branch.
We will do a pull request (on your own repo). On GitHub, go to the "Pull requests tab" and click "New pull request".
Select *base* and *compare*. Remember : we want to merge *decimal-binary-converter* into *dev*. 
Then, *Create the pull request*. Add some description of the work you have done and publish the Pull Request.

**Note** : We could have just merge *decimal-binary-converter* into *dev* from the command line. However, when working on a real project, you probably won't have the rights to do so. Remember : dev is a public branch. So all the work going down there should be carefully reviewed and tested.


### Publish your work
We now have an awesome binary/decimal two ways converter ! It's time to publish it so the rest of the world can enjoy it !
We will publish it on PyPI (Python Package Index) which is a public repository of softwares for Python.
1. First, you have to create an account on [Pypi.org](https://pypi.org/)
2. Then, in your repository, you will find a [.github directory](/.github)
3. Inside this directory, you will find .yml files that defines workflows (using GitHub actions)
4. Open the yml files and take time to understand them. Do not hesitate to ask the teacher about them

#### Hands on !
5. We want to create a workflow that will, on publish, test our code and if tests succeed, then publish our package on PyPI
6. In GitHub, click on the *Actions* tab, then click on *new workflow*
7. Choose among the suggestions the template that best suits your use-case and click *Configure*
8. You may find some useful informations in the [Python documentation](https://packaging.python.org/en/latest/tutorials/packaging-projects/)
9. When creating a release on GitHub, do not forget to create a tag corresponding to that release

#### Pull request
10. Once the workflow is setup and tested (you have published your package using it) you can open a pull request to the [original repository](https://github.com/rgt-yncrea/M1-2022-git-workflow)
drb-impl-netcdf 1.0.5
DRB Netcdf implementation
Requires Python: >=3.8

# Netcdf Implementation
This module implements netcdf format access with DRB data model. It is able to navigates among the netcdf contents.

## Netcdf Factory and Netcdf Node
The module implements the basic factory model defined in DRB in its node resolver. Based on the python entry point mechanism, this module can be dynamically imported into applications.

The entry point group reference is `drb.impl`.<br/>
The implementation name is `netcdf`.<br/>
The factory class is decribed with implementation `drb_impl_netcdf.drb_impl_signature`.<br/>

The netCDF 4.0 data model is based on recusive structure of Groups each containing a set of Attributes, Dimensions and Variables. It also includes User-defined set of types.
The Drb structure implemented here, reproduces this representation.
Attributes are reported in node as node attributes whereas Dimensions and Values are reported as node children. The raw content is available in Variable node requesting array (xarray, or numpy ndarray types)

## limitations
The current version does not manage child modification and insertion. `DrbNetcdfNode` is currently read only.
The factory to build DrbNetcdfNode supports file directly opening it with path, for other implementation ByteIO or BufferedIOBase, they are manged with a local temporary file, removed when the node is closed..

## limitations HDF-EOS file
When open a HDF-EOS file in HDF4 format
If fails with message:`[Errno -128] NetCDF: Attempt to use feature that was not turned on when netCDF was built`
This means that NetCDF system library linked with netCDF4 python wasn't compiled with HDF4 support.
You can try to install netCDF4 python by conda
```commandline
conda install -c conda-forge netcdf4
```
Or rebuild netCDF4 locally with netCDF library with support hdf4 turned ON

## Using this module
To include this module into your project, the `drb-impl-netcdf` module shall be referenced into `requirements.txt` file, or the following pip line can be run:
```commandline
pip install drb-impl-netcdf
```
AudioAI 1.2.2
Create audio files from text, audio to text and voice typing(only in english at the moment)
Requires Python: >=3.7

# AudioAI
[![PyPI version shields.io](https://img.shields.io/pypi/v/anonfile)](https://pypi.python.org/pypi/anonfile/)
[![CI](https://github.com/nstrydom2/anonfile-api/actions/workflows/python-package.yml/badge.svg?branch=master)](https://github.com/nstrydom2/anonfile-api/actions/workflows/python-package.yml)
[![PyPI download total](https://img.shields.io/pypi/dm/anonfile)](https://pypi.python.org/pypi/anonfile/)
[![PyPI pyversions](https://img.shields.io/pypi/pyversions/anonfile)](https://pypi.python.org/pypi/anonfile/)
[![PyPI license](https://img.shields.io/pypi/l/anonfile)](https://pypi.python.org/pypi/anonfile/)

A library to create audio files from text, audio to text and voice typing(only in english at the moment)

This Python package was created to make more easy to create audio files from text, audio to text and voice typing. The goal of
the project is to create an intuitive library for everyone can do this without paying.

## Getting Started

These instructions will get you a copy of the project up and running on your local
machine for development and testing purposes. See deployment for notes on how to
deploy the project on a live system.

## Prerequisites to run this application

 Python 3.7+ [Click here to download](https://www.python.org/ftp/python/3.10.7/python-3.10.7-amd64.exe)
 #### Speech Recognition 3.8.1+
 ```bash
pip install SpeechRecognition
```
 #### gTTS 3.8.1+
 ```bash
pip install gTTS
```

### Installing

To install the library is as simple as running

```bash
pip install AudioAI
```
in the command line

Navigate to [tags on this repository](https://github.com/mariodaniely2003/AudioAI/tags)
to see all available versions.

## Authors

| Name             | Mail Address                | GitHub Profile                                |
|------------------|-----------------------------|-----------------------------------------------|
| Mario Salamanca  | python@mariosalamanca.pt    | [mariodaniely2003](https://github.com/mariodaniely2003)|

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for more details.