def process_us():
name = 'US'
year = 2014
n_neigh = 6
graph = temperatures.country_network(name, 2014, n_neigh=6)
pos = graph.vertex_properties['pos']
station_values = graph.vertex_properties['station_values']
weight = graph.edge_properties['weights']
x_signal = station_values.a
n_eigs = 500
# n_eigs = graph.num_vertices() - 1
alpha = -1e-3
file_name = '{0}_{1}_k{2}'
file_name = file_name.format(name, year, n_neigh)
if os.path.exists(file_name + '_spec.pickle'):
with open(file_name + '_spec.pickle', 'r') as f:
spec_weighted = pickle.load(f)
factory = pickle.load(f)
else:
factory = spec.PageRankSGFT(graph, n_eigs, alpha, weight=weight)
spec_weighted = factory.compute(x_signal)
with open(file_name + '_spec.pickle', 'w') as f:
pickle.dump(spec_weighted, f)
pickle.dump(factory, f)
palette = sns.cubehelix_palette(256, start=2, rot=0, dark=0.15, light=1)
cmap = colors.ListedColormap(palette)
plt.figure()
show_spectrogram(spec_weighted[0:30, :], cmap=cmap)
plt.savefig(file_name + '_spec.pdf', dpi=300)
temperatures.plot(graph, weight, pos, station_values, file_name)
spec.show_window(factory, .5 * (graph.num_vertices() + 1), weight, pos,
file_name + '_window1.png')
spec.show_window(factory, .25 * (graph.num_vertices() + 1), weight, pos,
file_name + '_window2.png')
spec.show_window(factory, .75 * (graph.num_vertices() + 1), weight, pos,
file_name + '_window3.png')
show_spec_in_graph(graph, 417, spec_weighted, pos, weight,
file_name + '_florida')
def test_circle():
size = 50
periodic = True
# periodic = False
graph = gt_gen.lattice([size, size], periodic=periodic)
if periodic:
figure_title = 'grid_periodic'
else:
figure_title = 'grid_non-periodic'
indices = np.arange(size * size)
rows = np.mod(indices, size)
cols = np.floor(indices / size)
v_pos = graph.new_vertex_property('vector<double>',
vals=np.vstack((rows, cols)).T)
radius = 10
center = (size + 1) * (size / 2)
vertex_w = set([])
for i in range(-radius, radius):
for j in range(-radius, radius):
if i ** 2 + j ** 2 < radius ** 2:
idx = j * size + i + center
vertex_w.add(idx)
jump = 1e-5
weight = graph.new_edge_property('double', vals=1)
for idx in vertex_w:
u = graph.vertex(idx)
for e in u.out_edges():
if e.target() not in vertex_w:
weight[e] = jump
v_color = graph.new_vertex_property('vector<double>')
for u in graph.vertices():
if graph.vertex_index[u] in vertex_w:
v_color[u] = [1, 0, 0, 1]
else:
v_color[u] = [0, 0, 1, 1]
vertex_w = list(vertex_w)
x = np.linspace(-np.pi, np.pi, size)
y = np.linspace(-np.pi, np.pi, size)
xx, yy = np.meshgrid(x, y)
z1 = np.sin(np.pi * xx).flatten()
z2 = np.sin(2 * np.pi * yy).flatten()
x_signal = z1
x_signal[vertex_w] = z2[vertex_w]
palette = sns.color_palette('RdBu', n_colors=256, desat=.7)
cmap = colors.ListedColormap(palette, N=256)
plt.figure()
plt.imshow(np.reshape(x_signal, (size, size)), interpolation='nearest',
cmap=cmap)
plt.savefig(figure_title + '_signal.pdf', dpi=300)
n_eigs = 500 # graph.num_vertices() - 1
alpha = -1e-4
factories = [spec.ConvolutionSGFT(graph, n_eigs, tau=200, weight=weight),
spec.PageRankSGFT(graph, n_eigs, alpha, weight=weight),
spec.ConvolutionSGFT(graph, n_eigs, tau=5, weight=None),
spec.PageRankSGFT(graph, n_eigs, alpha, weight=None)]
sgft.comparison.compare_spectrograms(factories, x_signal,
graph, v_pos,
file_name=figure_title,
show_ncomps=300)
spec.show_window(factories[0], center, weight=weight, pos=v_pos,
vertex_size=20,
file_name=figure_title + '_w_window.png')
spec.show_window(factories[1], center, weight=weight, pos=v_pos,
vertex_size=20,
file_name=figure_title + '_w_window_ppr.png')
spec.show_window(factories[2], center, weight=None, pos=v_pos,
vertex_size=20,
file_name=figure_title + '_u_window.png')
spec.show_window(factories[3], center, weight=None, pos=v_pos,
vertex_size=20,
file_name=figure_title + '_u_window_ppr.png')