semvec/pypi-packages huggingface dataset:
paml2html 0.1.1
[P]oker's M[a]de-up [M]arkup [L]anguage to HTML converter
License: MIT
Requires Python: >=3.9,<4.0

paml2html is a proof-of-concept converter making HTML from [P]oker's M[a]de-up [M]arkup [L]anguage. PaML's docs can be found [here](https://paml.pokerfacowaty.com/). You can also track PaML's & paml2html's development on my [Vikunja list!](https://vikunja.pokerfacowaty.com/share/xSbQeLXtVLmTpqdniQhIjmzLUgdxJwtpUStgIbya/auth)

# Prerequisites
- [Python 3](https://www.python.org/downloads/)
- [yattag](https://www.yattag.org/) (`pip install yattag`)

# Installation
Download the files
- Clone the repo:
```
git clone https://github.com/PokerFacowaty/paml2html
```
- ... or simply download `src/paml2html.py`

# Usage
To use paml2html by itself, simply start it when in the same directory:

```
python paml2html.py
```
(or if you have Python 2 installed)
```
python3 paml2html.py
```

...or you can also import it and use in a different file with `import paml2html` by calling the `paml2html.convert_from_file()` or `paml2html.convert_from_text()` function and providing a filepath or text


## Arguments
`paml2html.py [-h] [--indent INDENT] source_file destination_file`

- `-h, --help` - show help
- `--indent <number>` - the optional amount of spaces used to indent the HTML file, indentation is off by default
- `source_file` - source text file containing PaML content
- `destination_file` - file for the resulting HTML content
more-ibm-utils 0.1.0
Python Util package for MORE applications
License: MIT
Requires Python: >=3.7

# MoreUtils

MoreUtils is a Python package to support MORE applications. With moreutils, you can retrieve de-compressed time series / data models from [**ModelarDB**](https://github.com/ModelarData/ModelarDB), save and load models to the cloud object storage, upload timeseries forecast and more.

## Current State:
The release tag v1.0.0 is the first integrated version that works with other MORE applications.

## Installation

It is strongly recommended that a virtual environment (like venv or conda) is created to run MoreUtils to avoid dependency conflicts with already
installed packages.

### Python Version

The package is developed using a python version of atleast 3.6.

### Install PyModelarDB (recommended)

If you want to connect to ModelarDB to retrieve / store time series data, install PyModelarDB from this [**link**](https://github.com/ModelarData/PyModelarDB).

Alternately, You can also install PyModelarDB using the below commands.

```shell
git clone https://github.com/ModelarData/PyModelarDB.git
cd PyModelarDB
pip install .
```

**Note:** Make sure that the above library is installed inside the same python virtual environment.

### Run ModelarDB instance

1. Clone ModelarDB repository by running

```shell
git clone https://github.com/ModelarData/ModelarDB.git
```

2. Follow the installation steps in the Readme (`ModelarDB/README.md`). It is recommended to use `modelardb.interface` as arrow.

3. Start the instance and load your dataset into the ModelarDB.

**Note:** The sample configuration file is present at `ModelarDB/modelardb`.conf

### To use MoreUtils as a python package in your library

MoreUtils can be installed via pip. The github repo can be cloned to the local machine and in the root directory where setup.py is located, the following command needs to be run.

```shell
pip install .
```

This command will install MoreUtils along with its dependencies.

### To install MoreUtils for development purpose, run following

```shell
pip install -e .
```

**Note:** The -e flag specifies the editable mode. Packages install in editable mode do not need to re-install before the changes come into effect. This is useful if you are working on a moreutils package.

## Testing

Unit tests are contained in the [**tests**](https://github.com/IBM/more-utils/tree/main/tests) directory.

## Usage

A series of [**examples**](https://github.ibm.com/Dublin-Research-Lab/more-utils/tree/main/examples) in the repository shows how to use various functions of MoreUtils.

## Acknowledgment
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 957345 for MORE project.