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)
