def do_analysis(lens, name_prefix): viz.scatter3d(data3, lens, show=False) viz.plt.savefig(name_prefix + "circle.png") viz.plt.close("all") graph = mapper.map(lens, data, clusterer=sklearn.cluster.DBSCAN(eps=0.1, min_samples=5), cover=km.Cover(n_cubes=10, perc_overlap=0.2)) mapper.visualize(graph, color_function=lens, path_html=name_prefix + "_circle_output.html", title=name_prefix + " circle", lens=lens)
clusterer = lk.LinkageGap(verbose=0, metric="precomputed"), precomputed=True, cover=km.Cover(n_cubes=nc, perc_overlap=po)) mapper.visualize(graph, color_function=lens, path_html = name + "_cat.html", title= name + "_cat"); n = 12 p = 0.5 # ********** reference cat ********** cat = trimesh.load_mesh("../0_data/cat/cat-reference-simplified.obj") data = cat.vertices dist_mat = np.load("cat_dists.npy") lens = filt.eccentricity_from_dist(dist_mat,p=1) viz.scatter3d(data, lens, colorsMap='viridis') do_analysis(data, dist_mat, lens, "cat_ecc_ins_", n, p) # ********** seated cat ********** cat = trimesh.load_mesh("../0_data/cat/cat-02-simplified.obj") data = cat.vertices seated_dist_mat = np.load("seated_cat_dists.npy") lens = filt.eccentricity_from_dist(seated_dist_mat,p=1) viz.scatter3d(data, lens, colorsMap='viridis') do_analysis(data, seated_dist_mat, lens, "seated_cat_ecc_ins_", n, p)
import sklearn import kmapper as km import mapperutils.linkage_gap as lk import mapperutils.visualization as viz mapper = km.KeplerMapper(verbose=2) hand = trimesh.load_mesh("../0_data/hand/hand_simplified3k5.stl") data = np.array(hand.vertices) lens = data[:, 1:2] plot = True if plot: viz.scatter3d(data, lens, colorsMap='viridis', show=False) viz.plt.gca().view_init(elev=90, azim=0) viz.axisEqual3D(viz.plt.gca()) viz.plt.show() n = 7 p = 0.2 graph = mapper.map(lens, data, clusterer=lk.LinkageGap(verbose=0), cover=km.Cover(n_cubes=n, perc_overlap=p)) name = "n{}_p{}".format(n, p) mapper.visualize(graph, color_function=lens, path_html="hand_only_" + name + ".html",
name = "{}_n{}_o{}".format(name_prefix, nc, po) graph = mapper.map(lens, data, clusterer = # ****** (1) ******, cover=km.Cover(n_cubes=nc, perc_overlap=po)) mapper.visualize(graph, color_function=lens, path_html = name + "_cat.html", title= name + "_cat"); n = 12 p = 0.5 # ********** reference cat ********** cat = trimesh.load_mesh("../0_data/cat/cat-reference-simplified.obj") data = cat.vertices lens = filt.eccentricity(data,p=1) viz.scatter3d(data, lens,colorsMap='viridis') do_analysis(data, lens, "cat_ecc_", n, p) # ********** seated cat ********** cat = trimesh.load_mesh("../0_data/cat/cat-02-simplified.obj") data = # ****** (2) ****** lens = # ****** (3) ****** viz.scatter3d(data, lens,colorsMap='viridis') do_analysis(data, lens, "seated_cat_ecc_", n, p)