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‎README.md‎

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[![deploy](https://github.com/lucasimi/tda-mapper-python/actions/workflows/deploy.yml/badge.svg)](https://github.com/lucasimi/tda-mapper-python/actions/workflows/deploy.yml)
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[![docs](https://readthedocs.org/projects/tda-mapper/badge/?version=main)](https://tda-mapper.readthedocs.io/en/main/?badge=main)
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[![codecov](https://codecov.io/github/lucasimi/tda-mapper-python/graph/badge.svg?token=FWSD8JUG6R)](https://codecov.io/github/lucasimi/tda-mapper-python)
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[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.10659652.svg)](https://doi.org/10.5281/zenodo.10659652)
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[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.10642381.svg)](https://doi.org/10.5281/zenodo.10642381)
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[![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://tda-mapper-app.streamlit.app/)
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# tda-mapper
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A simple and efficient Python implementation of Mapper algorithm for Topological Data Analysis
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A simple and efficient Python implementation of Mapper algorithm for
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Topological Data Analysis
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* **Installation**: `pip install tda-mapper`
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* **Documentation**: https://tda-mapper.readthedocs.io/en/main/
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* **Demo App**: https://tda-mapper-app.streamlit.app/
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The Mapper algorithm is a well-known technique in the field of topological data analysis that allows data to be represented as a graph.
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Mapper is used in various fields such as machine learning, data mining, and social sciences, due to its ability to preserve topological features of the underlying space, providing a visual representation that facilitates exploration and interpretation.
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For an in-depth coverage of Mapper you can read [the original paper](https://research.math.osu.edu/tgda/mapperPBG.pdf).
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The Mapper algorithm is a well-known technique in the field of topological
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data analysis that allows data to be represented as a graph.
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Mapper is used in various fields such as machine learning, data mining, and
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social sciences, due to its ability to preserve topological features of the
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underlying space, providing a visual representation that facilitates
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exploration and interpretation. For an in-depth coverage of Mapper you can
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read
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[the original paper](https://research.math.osu.edu/tgda/mapperPBG.pdf).
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This library contains an implementation of Mapper, where the construction
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of open covers is based on *vp-trees* for improved performance and scalability.
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The details about this methodology are contained in
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[our preprint](https://doi.org/10.5281/zenodo.10659652).
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| Step 1 | Step 2 | Step 3 | Step 4 |
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| ------ | ------ | ------ | ------ |
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## Example
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[Here](https://github.com/lucasimi/tda-mapper-python/raw/main/tests/example.py) you can find an example to use to kickstart your analysis.
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In this toy-example we use a two-dimensional dataset of two concentric circles.
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The Mapper graph is a topological summary of the whole point cloud.
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[Here](https://github.com/lucasimi/tda-mapper-python/raw/main/tests/example.py)
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you can find an example to use to kickstart your analysis. In this toy-example
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we use a two-dimensional dataset of two concentric circles. The Mapper graph
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is a topological summary of the whole point cloud.
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```python
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import numpy as np
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| ------- | ---------------------- | ------------------------ |
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| ![Dataset](https://github.com/lucasimi/tda-mapper-python/raw/main/resources/circles_dataset.png) | ![Mapper graph (average)](https://github.com/lucasimi/tda-mapper-python/raw/main/resources/circles_mean.png) | ![Mapper graph (standard deviation)](https://github.com/lucasimi/tda-mapper-python/raw/main/resources/circles_std.png) |
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More examples can be found in the documentation https://tda-mapper.readthedocs.io/en/main/.
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More examples can be found in the
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[documentation](https://tda-mapper.readthedocs.io/en/main/).
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### Demo App
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## Citations
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If you want to use **tda-mapper** in your research please use one of the following citation.
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For the methodology you can use:
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```
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Simi, L. (2024). A Scalable Implementation of Mapper for Topological Data Analysis via Vantage Point Trees. Zenodo. https://doi.org/10.5281/zenodo.10659652
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```
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BibTeX entry:
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```
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@misc{simi_2024_10659652,
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author = {Simi, Luca},
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title = {A Scalable Implementation of Mapper for Topological Data Analysis via Vantage Point Trees},
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month = feb,
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year = 2024,
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publisher = {Zenodo},
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doi = {10.5281/zenodo.10659652},
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url = {https://doi.org/10.5281/zenodo.10659652}
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}
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```
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If you want to use **tda-mapper** in your work or research, you can cite the
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[archive uploaded on Zenodo](https://doi.org/10.5281/zenodo.10642381),
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pointing to the specific version of the software used in your work.
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If you want to refer to the actual library instead, you can reference the Zenodo
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archive [https://doi.org/10.5281/zenodo.10642381](https://doi.org/10.5281/zenodo.10642381>).
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In the archive you can find a permanent reference to the exact version used in your work.
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If you want to cite the methodology on which **tda-mapper** is based, you can
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use the
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[preprint](https://doi.org/10.5281/zenodo.10659651).
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``

‎docs/source/citations.rst‎

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Citations
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=========
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If you want to use **tda-mapper** in your research please use one of the following citation.
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For the methodology you can use:
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If you want to use **tda-mapper** in your work or research, you can cite the
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`archive uploaded on Zenodo <https://doi.org/10.5281/zenodo.10642381>`__,
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pointing to the specific version of the software used in your work, for example:
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Simi, L. (2024). A Scalable Implementation of Mapper for Topological Data Analysis via Vantage Point Trees. Zenodo. https://doi.org/10.5281/zenodo.10659652
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.. code:: RST
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BibTeX entry:
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@software{simi_2024_10883941,
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author = {Simi, Luca},
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title = {tda-mapper},
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month = mar,
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year = 2024,
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publisher = {Zenodo},
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version = {v0.5.2},
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doi = {10.5281/zenodo.10883941},
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url = {https://doi.org/10.5281/zenodo.10883941}
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}
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If you want to cite the methodology on which **tda-mapper** is based,
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you can use the
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`preprint <https://doi.org/10.5281/zenodo.10659651>`__:
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.. code:: RST
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doi = {10.5281/zenodo.10659652},
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url = {https://doi.org/10.5281/zenodo.10659652}
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}
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If you want to refer to the actual library instead, you can reference the Zenodo
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archive `https://doi.org/10.5281/zenodo.10642381 <https://doi.org/10.5281/zenodo.10642381>`__.
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In the archive you can find a permanent reference to the exact version used in your work.

‎docs/source/overview.rst‎

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- **Documentation**: https://tda-mapper.readthedocs.io/en/main/
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- **Demo App**: https://tda-mapper-app.streamlit.app/
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The Mapper algorithm is a well-known technique in the field of
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topological data analysis that allows data to be represented as a graph.
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Mapper is used in various fields such as machine learning, data mining,
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and social sciences, due to its ability to preserve topological features
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of the underlying space, providing a visual representation that
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facilitates exploration and interpretation. For an in-depth coverage of
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Mapper you can read `the original
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paper <https://research.math.osu.edu/tgda/mapperPBG.pdf>`__.
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Mapper you can read
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`the original paper <https://research.math.osu.edu/tgda/mapperPBG.pdf>`__.
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This library contains an implementation of Mapper, where the construction
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of open covers is based on *vp-trees* for improved performance and scalability.
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The details about this methodology are contained in
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`our preprint <https://doi.org/10.5281/zenodo.10659652>`__.
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+-----------------+-----------------+-----------------+-----------------+
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| Step 1 | Step 2 | Step 3 | Step 4 |

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