Metadata-Version: 2.1
Name: thunor
Version: 0.1.29
Summary: Dose response curve and drug induced proliferation (DIP) rate fits and visualisation
Home-page: https://www.thunor.net
Author: Alex Lubbock
Author-email: code@alexlubbock.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Description-Content-Type: text/markdown
License-File: LICENSE

# Thunor

**Thunor** (pronounced THOO-nor) is a free software platform for managing,
visualizing, and analyzing high throughput screen (HTS) data, which measure
the dose-dependent response of cells to one or more drug(s).
Thunor has a web interface for drag-and-drop upload of cell count data, 
automatic calculation of dose response curves, and an interactive
multi-panelled plot system.

This repository, Thunor Core, is a Python package which can be used for
standalone analysis or integration into computational pipelines. There
is also a web interface, [Thunor Web](https://github.com/alubbock/thunor-web),
built around this package with added database and multi-user capabilities.

## Implementation

Thunor is written in pure Python and is compatible with Python 3 only.
It makes extensive use of [pandas](http://pandas.pydata.org/) and 
[plotly](http://plot.ly/python/).

## Installation

Install using `pip`:

```
pip install thunor
```

## Examples and documentation

The Thunor Core documentation is available [online](https://core.thunor.net),
or you can build it locally for offline use. To do so, clone this git
repository and change into the `thunor` directory.

To build documentation locally, you'll need a few software dependencies:

    pip install -r doc/requirements.txt

Then, you can build the documentation like so:

    cd doc
    make html

After the build completes, open _build/html/index.html in your web browser.

## Tutorial

To manually work through the tutorial from the documentation above, you can
open the file with Jupyter Notebook:

    jupyter notebook --NotebookApp.iopub_data_rate_limit=1.0e10 doc/tutorial.ipynb

## Citation

Lubbock A.L.R., Harris L.A., Quaranta V., Tyson D.R., Lopez C.F.
[Thunor: visualization and analysis of high-throughput dose–response datasets](https://doi.org/10.1093/nar/gkab424)
Nucleic Acids Research (2021), gkab424.

## Further help and resources

See the [Thunor website](https://www.thunor.net) for further links,
documentation and related projects.


