Choosing the parameter perc

The purpose of this example is to demonstrate the effect of different choices of the perc parameter. Roughly speaking, this parameter tells the algorithm at which geometric scale that data should be looked at when constructing the cohomological coordinates.

import matplotlib.pyplot as plt
from dreimac import CircularCoords, GeometryExamples, PlotUtils, CircleMapUtils
from persim import plot_diagrams

The data consists of a trefoil knot in \(\mathbb{R}^3\). As such, at a small scale, the data is parametrized by a circle wrapping around itself. Keep in mind that, although the \(2\)-dimensional projection we display here seems to have self intersections, these intersections are not present in the point cloud in \(\mathbb{R}^3\), at least when the point cloud is looked at from close enough.

When looked at from further away, though, the data stops looking like a circle wrapped around itself, and starts looking like a single circle without any wrapping.

We will identify this behavior by finding two circle-valued maps using the same cohomology class, but two values of the perc parameter.

X = GeometryExamples.trefoil(n_samples = 2500, horizontal_width=10)
fig = plt.figure(figsize=(10,6))
ax1 = fig.add_subplot(121)
ax1.scatter(X[:,0],X[:,1], s=1)
ax1.set_title("Trefoil 2D projection") ; ax1.set_aspect("equal") ; ax1.axis("off")
ax2 = fig.add_subplot(122,projection='3d')
ax2.scatter(X[:,0],X[:,1],X[:,2], alpha=0.5, s=1)
ax2.set_title("Trefoil in 3D") ;  _ = PlotUtils.set_axes_equal(ax2)

The persistence diagram has a single high-persistence class. It is this class that we use in this example.

cc = CircularCoords(X, 300, prime=3)
plot_diagrams(cc._dgms)

We now choose a small and a large perc parameter and get circle-valued representations that parametrized the small and large scale circularity of the data, respectively.

perc_choices = [0.1, 0.5]

plt.figure(figsize=(15,5))
for i,perc in enumerate(perc_choices):
    circular_coordinate = cc.get_coordinates(perc=perc,cocycle_idx=0)
    plt.subplot(1, len(perc_choices), i+1)
    plt.scatter(X[:,0],X[:,1], s = 10, c = CircleMapUtils.to_sinebow(circular_coordinate))
    plt.title("perc = " + str(perc))
    plt.gca().set_aspect("equal") ; _ = plt.axis("off")