Metadata-Version: 2.1
Name: tadasets
Version: 0.0.2
Summary: Great data sets for Topological Data Analysis.
Home-page: https://tadasets.scikit-tda.org
Author: Nathaniel Saul
Author-email: nathaniel.saul@wsu.edu
License: MIT
Keywords: topological data analysis,data sets,test data
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Healthcare Industry
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Requires-Python: >=2.7,!=3.1,!=3.2,!=3.3
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: matplotlib

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# TaDAsets
Data sets apt for Topological Data Analysis. This project is a part of scikit-tda.

## Motivation

At SoCG 2018, there was discussion about the need for data sets for two main purposes
- Benchmarking new algorithms
- Demonstrating benefits of TDA

## Data generation

We provide constructors for some synthetic data sets that are popular in development and testing of TDA techniques.

* Torus
* Klein Bottle
* Swiss Roll

## Setup and Usage

You can install from Pypi
```
pip install tadasets
```

or from source

```
git clone https://github.com/scikit-tda/tadasets
cd tadasets
pip install -e .
```

Examples of usage is

```
import tadasets
data = tadasets.sphere(n=1000, r=10)
tadasets.plot3d(data)
```

or 

```
import tadasets
data = tadasets.swiss_roll(n=1000, r=10)
tadasets.plot3d(data)
```

## Contributions

This package is in the very early stages of development. As I think of shapes and data sets, I add them.  If you have ideas, please do suggest them in an issue and submit a pull request! All contributions are welcome.


