semvec/pypi-packages huggingface dataset:
plutoplots 0.4.8
visualization 

<div align="center">
    <h1>
        <a>Pluto</a>
    </h1>
    Visualization library for <i>easier plotting</i> built on top of Seaborn and Matplotlib.
</div>


![-----------------------------------------------------](https://raw.githubusercontent.com/andreasbm/readme/master/assets/lines/rainbow.png)


<div align="center">
If you like the repo, please feel free to give it a :star:  (top right). Cheers!

For more information about the author, see below links to get in touch:

[![Email Badge](https://img.shields.io/badge/-Gmail-D14836?style=flat&logo=gmail&logoColor=white)](mailto:oluwoleilesanmi@gmail.com)
[![LinkedIn Badge](https://img.shields.io/badge/LinkedIn-0077B5?style=flat&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/oluwoleilesanmi)
[![GitHub Badge](https://img.shields.io/badge/GitHub-100000?style=flat&logo=github&logoColor=white)](https://github.com/oluwoleilesanmi)


</div>

![-----------------------------------------------------](https://raw.githubusercontent.com/andreasbm/readme/master/assets/lines/rainbow.png)

#### How to run
```
$ pip install pluto
```

```
# create custom bar_plot
bar_plot = Barplot()
# configuring custom bar_plot
bar_plot.shape(dim=(10, 5))
    .create('x_col', 'y_col', table, hue='z_col', c=None)
    .grid()
    .spine()
    .tick(left=True, labelleft=True, bottom=True, labelbottom=True)
    .label('x axis', 'y axis', 'title')
    .render()

```
#### Illustrations
- *TBI*
yaqg-ni 2023.12.0
qt-based gui for yaqd-ni daemons
Requires Python: >=3.7

# yaqg-ni

[![PyPI](https://img.shields.io/pypi/v/yaqg-ni)](https://pypi.org/project/yaqg-ni)
[![Conda](https://img.shields.io/conda/vn/conda-forge/yaqg-ni)](https://anaconda.org/conda-forge/yaqg-ni)
[![yaq](https://img.shields.io/badge/framework-yaq-orange)](https://yaq.fyi/)
[![black](https://img.shields.io/badge/code--style-black-black)](https://black.readthedocs.io/)
[![ver](https://img.shields.io/badge/calver-YYYY.0M.MICRO-blue)](https://calver.org/)
[![log](https://img.shields.io/badge/change-log-informational)](https://github.com/yaq-project/yaqg-ni/blob/main/yaqg-ni/CHANGELOG.md)

qtpy guis for yaqd-ni daemons.  These GUIs can be run as standalone programs or integrate with [yaqc-qtpy](https://github.com/yaq-project/yaqc-qtpy) as plugins.

This package contains the following gui(s):

- yaqd-ni-daqmx-tmux

## maintainers
- [Blaise Thompson](https://github.com/untzag)
geopolygonize 0.2.2
Convert geographic rasters into simplified polygons
Requires Python: >=3.10

# Geopolygonize

Convert geographic rasters into simplified polygons. Given an input raster file, this tool produces a gpkg file representation of the raster that simplifies out pixelation. It also maintains shapes' relations to one another such that the output is guaranteed to have no gaps.

## Algorithm

Most existing methods for polygon simplification such as the [Douglas–Peucker simplification algorithm](https://en.wikipedia.org/wiki/Ramer%E2%80%93Douglas%E2%80%93Peucker_algorithm) or [concave hulls](http://lin-ear-th-inking.blogspot.com/2022/04/outer-and-inner-concave-polygon-hulls.html) operate on a single polygon at a time. This presents a problem when run on a collection of polygons that fit together perfectly–simplifying each polygon separately will introduce unpredictable gaps. Our algorithm takes an approach similar to [TopoJSON](https://github.com/topojson/topojson). It identifies the boundaries shared between polygons, simplifies those boundaries, then assigns the simplified boundaries back to the polygons. This results in simplified polygons that fit perfectly together without any gaps.

## Install

```
pip install geopolygonize
```

## Quickstart

To convert a raster to simplified polygons, run:

```
geopolygonize --input-file="data/input.tif" --output-file="data/output.shp"
```

## Options + API

Refer to the [docs](https://rainflame.github.io/geopolygonize).

## Development

Install the dependencies:

```
pip install -r requirements.txt
```

Then, run the CLI:

```
python -m src.geopolygonize --input-file="data/input.tif" --output-file="data/output.shp"
```

Test against some existing input:

```
./tests/run_e2e_test.sh <input>
```

Find test raster inputs [here](https://drive.google.com/drive/folders/1KiEPwjg9zhowBJEODQTJJ44VPrPkXC3_?usp=drive_link).
geonlplify 0.3.3
GeoNLPlify aims to make variations of an input sentence working on spatial information contained in words

<h1 align="center"> GeoNLPlify
:earth_africa: :book:
</h1>

[![Documentation Status](https://readthedocs.org/projects/geonlplify/badge/?version=latest)](https://geonlplify.readthedocs.io/en/latest/?badge=latest)
![GitHub](https://img.shields.io/github/license/remydecoupes/GeoNLPlify)
![GitHub release (latest by date)](https://img.shields.io/github/v/release/remydecoupes/GeoNLPlify)

A NLP library for data augmentation focusing on spatial information contained in text.

## Usage
```python
import geonlplify

my_text = "My name is Clara and I live in Berkeley."
geonlplify.geonlplify(my_text)
```
```bash
'My name is Clara and I live in Bristol'
```

## Installation
```bash
pip install GeoNLPlify
```
```bash
# Download data. Please visit Simplemaps: https://simplemaps.com/data/world-cities
python3 -c "from geonlplify import download_simplemaps_data; download_simplemaps_data()"
# Download spacy model
python -m spacy download en_core_web_trf  
```

## Contributions
You can install GeoNLPlify in three ways: in a [virtual_env](#virtual-environment), in a [conda environment](#conda-environment)
### Virtual Environment
1. Git clone this repository
  ```bash
  git clone https://github.com/remydecoupes/GeoNLPlify.git
  ```
2. Create a virtual env
  ```bash
  python -m venv geonlplify_venv
  source geonlplify_venv/bin/activate
  pip install --upgrade pip
  ```
3. Install dependencies
  ```bash
  cd GeoNLPlify
  pip install -r virtual_env_requirements.txt
  ```

5. Donwload [world-cities from simple maps](https://simplemaps.com/data/world-cities)
  ```bash
  wget -qO- https://simplemaps.com/static/data/world-cities/basic/simplemaps_worldcities_basicv1.75.zip  | bsdtar -xvf- -C ./geonlplify/simplemaps/
  ```
### Conda environment
1. Git clone this repository
  ```bash
  git clone https://github.com/remydecoupes/GeoNLPlify.git
  ```
2. Create a conda with all the required dependencies
  ```bash
  cd GeoNLPlify
  conda create -n geonlplify_conda --file conda_environment.yml python==3.10.6
  conda activate geonlplify_conda
  ```
3. Install spacy models
  ```bash 
  python -m spacy download en_core_web_trf
  ```
4. Donwload [world-cities from simple maps](https://simplemaps.com/data/world-cities)
  ```bash
  wget -qO- https://simplemaps.com/static/data/world-cities/basic/simplemaps_worldcities_basicv1.75.zip  | bsdtar -xvf- -C ./geonlplify/simplemaps/
  ```

## Acknowledgement
:pray: This library use those terrific tools/libraries/data :muscle::
+ [Spacy](https://spacy.io/)
+ [The Komoot geocoder Photon](https://photon.komoot.io/)
+ [OpenStreeMap](https://www.openstreetmap.org/copyright)
+ [Simplemaps data](https://simplemaps.com/data/world-cities)

## Scientific publication
| Conference   |      paper      |  description |
|----------|:-------------:|------:|
| [EGC'2023](https://egc2023.sciencesconf.org/) |  [short paper (in French)](https://editions-rnti.fr/?inprocid=1002848) | [video](https://youtu.be/-QaTBtjWr9g) |