Skip to content

fingerprint

filtered_histogram(mesh, internal, external, bins=200, xrange=None, yrange=None, samples_per_edge=4)

Create histogram with multiple samples per face.

Source code in chmpy/crystal/fingerprint.py 
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
def filtered_histogram(
    mesh, internal, external, bins=200, xrange=None, yrange=None, samples_per_edge=4
):
    """Create histogram with multiple samples per face."""
    di = mesh.vertex_attributes["d_i"]
    de = mesh.vertex_attributes["d_e"]
    if xrange is None:
        xrange = np.min(di), np.max(di)
    if yrange is None:
        yrange = np.min(de), np.max(de)

    di_atom = mesh.vertex_attributes["nearest_atom_internal"]
    de_atom = mesh.vertex_attributes["nearest_atom_external"]
    vertex_mask = (de_atom == external) & (di_atom == internal)

    face_mask = np.any(vertex_mask[mesh.faces], axis=1)
    filtered_faces = mesh.faces[face_mask]

    if len(filtered_faces) == 0:
        return np.histogram2d([], [], bins=bins, range=(xrange, yrange))

    vertices = np.stack([di, de], axis=1)

    interpolated, weights = sample_face_points(
        vertices, filtered_faces, samples_per_edge
    )

    di_samples = interpolated[..., 0].flatten()
    de_samples = interpolated[..., 1].flatten()

    weights_tiled = np.tile(weights, len(filtered_faces))

    return np.histogram2d(
        di_samples, de_samples, bins=bins, range=(xrange, yrange), weights=weights_tiled
    )

fingerprint_histogram(mesh, bins=200, xrange=None, yrange=None, samples_per_edge=4)

Create histogram for all faces with multiple samples per face.

Source code in chmpy/crystal/fingerprint.py 
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
def fingerprint_histogram(mesh, bins=200, xrange=None, yrange=None, samples_per_edge=4):
    """Create histogram for all faces with multiple samples per face."""
    di = mesh.vertex_attributes["d_i"]
    de = mesh.vertex_attributes["d_e"]
    if xrange is None:
        xrange = np.min(di), np.max(di)
    if yrange is None:
        yrange = np.min(de), np.max(de)

    vertices = np.stack([di, de], axis=1)

    interpolated, weights = sample_face_points(vertices, mesh.faces, samples_per_edge)

    di_samples = interpolated[..., 0].flatten()
    de_samples = interpolated[..., 1].flatten()

    weights_tiled = np.tile(weights, len(mesh.faces))

    return np.histogram2d(
        di_samples, de_samples, bins=bins, range=(xrange, yrange), weights=weights_tiled
    )

sample_face_points(vertices, faces, samples_per_edge=4)

Generate sample points within triangle faces using barycentric coordinates.

Source code in chmpy/crystal/fingerprint.py 
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
def sample_face_points(vertices, faces, samples_per_edge=4):
    """Generate sample points within triangle faces using barycentric coordinates."""
    points = []
    weights = []

    for i in range(samples_per_edge + 1):
        for j in range(samples_per_edge + 1 - i):
            a = i / samples_per_edge
            b = j / samples_per_edge
            c = 1.0 - a - b
            points.append([a, b, c])
            weights.append(1.0 / ((samples_per_edge + 1) * (samples_per_edge + 2) / 2))

    points = np.array(points)
    weights = np.array(weights)

    face_vertices = vertices[faces]

    points = points[:, None, :]

    interpolated = np.sum(points[..., None] * face_vertices[None, ...], axis=2)

    return interpolated, weights