mdapy-v2 2.1.0

---

✨ Maximum Dissimilarity Algorithm in Python✨

---

mdapy-v2 is a maintained fork of IHCantabria/mdapy, developed by the original author. It keeps the legacy mdapy Python import while evolving the public API, correctness checks, examples, and performance of the algorithm.

Installation

mdapy-v2 requires Python 3.12 or newer.

pip install mdapy-v2

The distribution name changed, but the import remains short and familiar:

from mdapy import DataColumn, max_diss_alg

Quick start

Each DataColumn represents one variable. Mark angular variables as directional so that, for example, the distance between 350° and 10° is 20° rather than 340°.

from mdapy import DataColumn, max_diss_alg

tp = [14.93, 10.42, 7.46, 7.63, 12.20, 12.05, 14.49, 8.62]
hs = [1.82, 1.45, 0.26, 0.90, 1.30, 5.71, 1.40, 2.18]
direction = [268, 272, 289, 293, 276, 273, 276, 292]

points, indices = max_diss_alg(
    DataColumn(tp),
    DataColumn(hs),
    DataColumn(direction, directional=True),
    n_clusters=3,
)

points contains the selected observations in selection order and indices contains their positions in the original input. Use seed_index to choose a different initial observation:

points, indices = max_diss_alg(
    DataColumn(tp),
    DataColumn(hs),
    n_clusters=3,
    seed_index=4,
)

How it works

The implementation follows the formulation described in Appendix 1 of Guanche et al. (2013).

The Maximum Dissimilarity Algorithm builds a diverse subset iteratively:

1. Start with the observation at seed_index (position 0 by default).
2. Measure every unselected observation against its nearest selected one.
3. Select the observation whose nearest distance is greatest.
4. Repeat until n_clusters observations have been selected.

Distances are Euclidean after normalizing each variable by its standard deviation. Directional variables use the shortest angular distance and a circular standard deviation. Exact ties preserve input order.

Examples

Start with the minimal selection, a one-dimensional case small enough to follow in a debugger. The next example animates how the algorithm expands a diverse subset through a two-dimensional sample.

For a focused explanation of circular distances, see the directional variables example. The final example applies the algorithm to select representative sea states from 10,000 directional observations.

Clone the repository and run any example in the project environment:

uv run python examples/1_mda_minimal.py

Legacy tuple API

Projects migrating from the original tuple-based API can use an explicit compatibility import:

from mdapy.legacy import max_diss_alg

points, indices = max_diss_alg(
    (tp, hs, direction),
    3,
    seed_index=0,
    dir_indices=[2],
)

Passing a tuple to mdapy.max_diss_alg raises an error that points to this compatibility module. The published ihc-mdapy==0.1.0 package uses the legacy API; its unpublished v0.1.1 fix remains available in this repository's Git history.

Development

Install the locked development environment and run the project checks:

make install
make format
make lint
make test

Credits and license

Originally developed by Pablo Garcia at IHCantabria and now maintained as the personal mdapy-v2 fork. The historical v0.1.1 fix was contributed by Álvaro Rodríguez, but was never included in the published ihc-mdapy package.

The original IHCantabria copyright notice and MIT terms are preserved in LICENSE.

Reference

Guanche, Y., Guanche, R., Camus, P., Mendez, F. J., & Medina, R. (2013). “A multivariate approach to estimate design loads for offshore wind turbines.” Wind Energy, 16(7), 1091–1106. https://doi.org/10.1002/we.1542