Basic Use

Generating data

import matplotlib.pyplot as plt
import numpy as np

theta = np.linspace(0, 2 * np.pi, 50)
data = np.c_[np.cos(theta), np.sin(theta)]
plt.scatter(data[:, 0], data[:, 1])
plt.show()

Projecting our data

projection = data[:, 0]
plt.scatter(data[:, 0], data[:, 1], label="data")
plt.scatter(projection, np.zeros_like(projection), label="projection")
plt.legend()
plt.show()

Covering our data

import zen_mapper as zm

cover_scheme = zm.Width_Balanced_Cover(n_elements=3, percent_overlap=0.4)
cover = cover_scheme(projection)


for i, c in enumerate(cover):
    plt.scatter(
        projection[c],
        np.full_like(c, i),
        label=f"Cover element: {i + 1}",
    )

plt.legend()
plt.show()

Pulling back

for i, c in enumerate(cover):
    plt.scatter(
        data[c, 0],
        data[c, 1] + 0.5 * i,
        label=f"Cover element: {i + 1}",
    )

plt.legend()
plt.show()

Defining a clusterer

from sklearn.cluster import AgglomerativeClustering

sk = AgglomerativeClustering(
    linkage="single",
    n_clusters=None,
    distance_threshold=0.2,
)
clusterer = zm.sk_learn(sk)

Computing the mapper graph

result = zm.mapper(
    data=data,
    projection=projection,
    cover_scheme=cover_scheme,
    clusterer=clusterer,
    dim=1,
)

Visualizing the mapper graph

import networkx as nx

graph = zm.to_networkx(result.nerve)
nx.draw(graph)