r"""
Concentration of the eigenvalues.
================================

The eigenvalues of the graph Laplacian concentrates to the same value as the
graph becomes full.
"""

import numpy as np
from matplotlib import pyplot as plt

import pygsp2 as pg

n_neighbors = [1, 2, 5, 8]
fig, axes = plt.subplots(3, len(n_neighbors), figsize=(15, 8))

for k, ax in zip(n_neighbors, axes.T):

    graph = pg.graphs.Ring(17, k=k)
    graph.compute_fourier_basis()
    graph.plot(graph.U[:, 1], ax=ax[0])
    ax[0].axis('equal')
    ax[1].spy(graph.W)
    ax[2].plot(graph.e, '.')
    ax[2].set_title('k={}'.format(k))
    #graph.set_coordinates('line1D')
    #graph.plot(graph.U[:, :4], ax=ax[3], title='')

    # Check that the DFT matrix is an eigenbasis of the Laplacian.
    U = np.fft.fft(np.identity(graph.n_vertices))
    LambdaM = (graph.L.todense().dot(U)) / (U + 1e-15)
    # Eigenvalues should be real.
    assert np.all(np.abs(np.imag(LambdaM)) < 1e-10)
    LambdaM = np.real(LambdaM)
    # Check that the eigenvectors are really eigenvectors of the laplacian.
    Lambda = np.mean(LambdaM, axis=0)
    assert np.all(np.abs(LambdaM - Lambda) < 1e-10)

fig.tight_layout()
plt.show()