#!/usr/bin/env python

""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT.
    See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details.
    Author(s):       Marc Glisse

    Copyright (C) 2019 Inria

    Modification(s):
      - YYYY/MM Author: Description of the modification
"""

__license__ = "MIT"


import numpy as np
import gudhi as gd


points = np.array(gd.read_points_from_off_file("../../data/points/Kl.off"))
rc = gd.RipsComplex(points=points, max_edge_length=0.2)
st = rc.create_simplex_tree(max_dimension=2)
# We are only going to plot the triangles
triangles = np.array([s[0] for s in st.get_skeleton(2) if len(s[0]) == 3])

# First possibility: plotly
import plotly.graph_objects as go

fig = go.Figure(
    data=[
        go.Mesh3d(
            # Use the first 3 coordinates, but we could as easily pick others
            x=points[:, 0],
            y=points[:, 1],
            z=points[:, 2],
            i=triangles[:, 0],
            j=triangles[:, 1],
            k=triangles[:, 2],
        )
    ]
)
fig.show()

# Second possibility: matplotlib
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt

fig = plt.figure()
ax = fig.gca(projection="3d")
ax.plot_trisurf(points[:, 0], points[:, 1], points[:, 2], triangles=triangles)
plt.show()

# Third possibility: mayavi
# (this may take a while)
from mayavi import mlab

mlab.triangular_mesh(points[:, 0], points[:, 1], points[:, 2], triangles)
mlab.show()