semvec/pypi-packages huggingface dataset:
particula-ngam 0.0.11
a simple, fast, and powerful particle simulator
Requires Python: >=3.6

# *PARTICULA*

Particula is a simple, fast, and powerful particle simulator, or at least two of those three, we hope. It is a simple particle system that is designed to be easy to use and easy to extend. The goal is to have a robust aerosol (gas phase + particle phase) simulation system that can be used to answer simple questions that arise for experiments and research discussions.

## Goals & conduct

The main goal is to develop an aerosol particle model that is usable, efficient, and productive. In this process, we all will learn developing models in Python and associated packages. Let us all be friendly, respectful, and nice to each other. Any code added to this repository is automatically owned by all. Please speak up if something (even if trivial) bothers you. Talking through things always helps. This is an open source project, so feel free to contribute, however small or big your contribution may be.

We follow the Google Python style guide [here](https://google.github.io/styleguide/pyguide.html).

## Usage & development

The development of this model will be illustrated through Jupyter notebooks that will be put together in the form of a Jupyter book on our website. The model can be run in a container or a virtual environment. With Docker, simply create the container defined in `.devcontainer/`. If Conda, [miniforge](https://github.com/conda-forge/miniforge) is recommended; once installed you will need to create the environment like in the command below.

```bash
conda create -n particula --file requirements.txt
```
dogsled 0.0.5
Macenko medical slide normalisation
Requires Python: >=3.8

<img src="https://raw.githubusercontent.com/RhDm/dogsled/main/docs/source/_static/dogsled_logo.svg" width="300">

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[![tests workflow](https://github.com/RhDm/dogsled/actions/workflows/main.yml/badge.svg)](https://github.com/RhDm/dogsled/actions/workflows/main.yml)
[![codecov](https://codecov.io/gh/RhDm/dogsled/branch/main/graph/badge.svg?token=WMIQ6MD1UK)](https://codecov.io/gh/RhDm/dogsled)
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[![Codacy Badge](https://app.codacy.com/project/badge/Grade/2434dda69e6d44079069e23520cf04cb)](https://www.codacy.com/gh/RhDm/dogsled/dashboard?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=RhDm/dogsled&amp;utm_campaign=Badge_Grade)

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`dogsled` is an open-source Python package that does only one thing: Macenko [1] stain normalisation of large medical slides (OpenSlide formats). It generates either JPEG or TIFF normalised image.

_It is crucial to have a dataset of high quality when, for example, training deep learning models. When working with medical slides, the colour might differ from slide to slide, making it harder to work with them as it causes heterogenicity in the data set. Stain normalisation is one of the techniques used to mitigate this hurdle and homogenise the slide colour. However, the size of the medical slides, which can go beyond gigapixel, makes it challenging to perform such a task. No worries, `dogsled` is there to help you._

Why *dogsled*? Well, first of all, because of the dogs. Second, because together many dogs can push a cargo too heavy for one dog to handle. Similarily, dogsled divides heavy computations into smaller ones. As with many algorithms and life situations, divide and conquer, right?

# `dogsled` is in late alpha phase
if you spot a bug or have a suggestion feel free to open an issue

if wish to test `dogsled` and need the data, feel free to drop me an email at: dmitri.stepanov1@gmail.com

## Quirks and features

Currently, `dogsled` can:
-   normalise all slides located in a specified folder
-   normalise slides specified by name
-   normalise slides defined in a QuPath library (either all or the ones specified by name and/or index)
-   generate JPEG equivalents of the normalised slides
-   generate TIFF equivalents of the normalised slides (also for large slides not fitting in RAM)
-   create hematoxylin/eosin decoupled normalised images
-   create thumbnails of all slides (pre-normalised and normalised)

## Documentation
`dogsled` about page, quickstart, installation and API can be found at [dogsled.readthedocs.io](https://dogsled.readthedocs.io)

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<img src="https://raw.githubusercontent.com/RhDm/dogsled/main/docs/source/_static/graph.jpeg" width="800">

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[1] M. Macenko, M. Niethammer, J. S. Marron, D. Borland, J. T. Woosley, Guan Xiaojun, C. Schmitt, and N. E. Thomas. A method for normalizing histology slides for quantitative analysis. In 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 1107–1110. 2009. doi:10.1109/ISBI.2009.5193250.