go.Scatter3d doesn't display a given tensor

Issue

While plotting an np.ndarray of type fp64, it is not displayed.
It is so funny that we could reproduce it only for one specific array.

Thigs that make the script work:

• If we add epsilon (as in the commented line), then the pointcloud is displayed properly.
• If we cast to fp32 it works.
• If we add a small value (0.000000000001 for example), it works.

Things that doesn't work:

• If we add or substract big values, like 0.4, 1 or 2, it doesn't work
• If we substract epsilon, it doesn't work
• if we add numbers larger than 0.000000000001, it doesnt' work even if tehy are small (0.000001 for example)

Environment

We tried this (and reproduced in at least 2 computers) with numpy 1.26.3 and plotly 5.18.0 on ubuntu 22

import numpy as np
import plotly.graph_objects as go

init = np.array(
    [
        [
            -0.063,
            -0.063,
            0.0,
        ],
        [
            -0.063,
            -0.021,
            0.0,
        ],
        [
            -0.063,
            0.021,
            0.0,
        ],
        [
            -0.063,
            0.063,
            0.0,
        ],
        [
            -0.021,
            -0.021,
            0.0,
        ],
        [
            -0.021,
            -0.063,
            0.0,
        ],
        [
            -0.021,
            0.021,
            0.0,
        ],
        [
            -0.021,
            0.063,
            0.0,
        ],
        [
            0.021,
            -0.063,
            0.0,
        ],
        [
            0.021,
            -0.021,
            0.0,
        ],
        [
            0.021,
            0.021,
            0.0,
        ],
        [
            0.021,
            0.063,
            0.0,
        ],
        [
            0.063,
            -0.063,
            0.0,
        ],
        [
            0.063,
            -0.021,
            0.0,
        ],
        [
            0.063,
            0.021,
            0.0,
        ],
        [
            0.063,
            0.063,
            0.0,
        ],
    ]
)

rot_mat = [
    [4.93038066e-32, -1.00000000e00, 2.22044605e-16],
    [2.22044605e-16, 2.22044605e-16, 1.00000000e00],
    [-1.00000000e00, 0.00000000e00, 2.22044605e-16],
]

transformed = (rot_mat @ init.T).T + np.array([0.5, 0.5, 0.5])


x, y, z = 0, 1, 2
idx = list(transformed.shape).index(3)
if idx < 0:
    raise ValueError("Array must be [X,Y,Z] x N")
elif idx == 1:
    array = transformed.transpose()

default_kwargs = dict(
    mode="markers",
    marker=dict(size=3, color="black"),
)

print(array.dtype)

# array[y]+=np.finfo(np.float64).eps
print(array.dtype)
print(array)
plot = go.Scatter3d(x=array[x], y=array[y], z=array[z], **default_kwargs)

default_kwargs = dict(
    scene=dict(
        xaxis=dict(title="X", range=[0, 2]),
        yaxis=dict(title="Y", range=[0, 2]),
        zaxis=dict(title="Z", range=[0, 2]),
    ),
)
layout = go.Layout(**default_kwargs)

fig = go.Figure(data=plot, layout=layout)

fig.show()

Verwende das bereitgestellte Reproduktionsskript als Einstiegspunkt und führe es mit numpy 1.26.3 und plotly 5.18.0 auf Ubuntu 22 aus. Vergleiche das fehlschlagende fp64-Array mit den funktionierenden epsilon- und fp32-Varianten und verfolge anschließend den go.Scatter3d-Renderingpfad. Als erledigt gilt die Aufgabe, wenn die Ursache identifiziert und eine Regression-Prüfung hinzugefügt wurde, die zeigt, dass der angegebene Tensor angezeigt wird.