V = py.igl.eigen.MatrixXd();
F = py.igl.eigen.MatrixXi();
py.igl.read_triangle_mesh('../tutorial/shared/fertility.off', V, F);

% Alternative discrete mean curvature
HN = py.igl.eigen.MatrixXd();
L = py.igl.eigen.SparseMatrixd();
M = py.igl.eigen.SparseMatrixd();
Minv = py.igl.eigen.SparseMatrixd();


py.igl.cotmatrix(V,F,L)
py.igl.massmatrix(V,F,py.igl.MASSMATRIX_TYPE_VORONOI,M)

py.igl.invert_diag(M,Minv)

% Laplace-Beltrami of position
HN = -Minv*(L*V)

% Extract magnitude as mean curvature
H = HN.rowwiseNorm()

% Compute curvature directions via quadric fitting
PD1 = py.igl.eigen.MatrixXd()
PD2 = py.igl.eigen.MatrixXd()

PV1 = py.igl.eigen.MatrixXd()
PV2 = py.igl.eigen.MatrixXd()

py.igl.principal_curvature(V,F,PD1,PD2,PV1,PV2)

% Mean curvature
H = 0.5*(PV1+PV2)

viewer = py.igl.viewer.Viewer()
viewer.data.set_mesh(V, F)

% Compute pseudocolor
C = py.igl.eigen.MatrixXd()
py.igl.parula(H,true,C)

viewer.data.set_colors(C)

% Average edge length for sizing
avg = py.igl.avg_edge_length(V,F)

% Draw a blue segment parallel to the minimal curvature direction
red = py.iglhelpers.p2e(py.numpy.array([[0.8,0.2,0.2]]))
blue = py.iglhelpers.p2e(py.numpy.array([[0.2,0.2,0.8]]))

viewer.data.add_edges(V + PD1*avg, V - PD1*avg, blue)

% Draw a red segment parallel to the maximal curvature direction
viewer.data.add_edges(V + PD2*avg, V - PD2*avg, red)

% Hide wireframe
viewer.core.show_lines = false

viewer.launch()