numpy-stl 1.8.0

Links

• The source: https://github.com/WoLpH/numpy-stl

• Project page: https://pypi-hypernode.com/pypi/numpy-stl

• Reporting bugs: https://github.com/WoLpH/numpy-stl/issues

• Documentation: http://numpy-stl.readthedocs.org/en/latest/

• My blog: http://w.wol.ph/

Requirements for installing:

• numpy any recent version

• python-utils version 1.6 or greater

Installation:

pip install numpy-stl

Initial usage:

• stl2bin your_ascii_stl_file.stl new_binary_stl_file.stl

• stl2ascii your_binary_stl_file.stl new_ascii_stl_file.stl

• stl your_ascii_stl_file.stl new_binary_stl_file.stl

Quickstart

import numpy
from stl import mesh

# Using an existing stl file:
your_mesh = mesh.Mesh.from_file('some_file.stl')

# Or creating a new mesh (make sure not to overwrite the `mesh` import by
# naming it `mesh`):
VERTICE_COUNT = 100
data = numpy.zeros(VERTICE_COUNT, dtype=mesh.Mesh.dtype)
your_mesh = mesh.Mesh(data, remove_empty_areas=False)

# The mesh normals (calculated automatically)
your_mesh.normals
# The mesh vectors
your_mesh.v0, your_mesh.v1, your_mesh.v2
# Accessing individual points (concatenation of v0, v1 and v2 in triplets)
assert (your_mesh.points[0][0:3] == your_mesh.v0[0]).all()
assert (your_mesh.points[0][3:6] == your_mesh.v1[0]).all()
assert (your_mesh.points[0][6:9] == your_mesh.v2[0]).all()
assert (your_mesh.points[1][0:3] == your_mesh.v0[1]).all()

your_mesh.save('new_stl_file.stl')

Plotting using matplotlib is equally easy:

from stl import mesh
from mpl_toolkits import mplot3d
from matplotlib import pyplot

# Create a new plot
figure = pyplot.figure()
axes = mplot3d.Axes3D(figure)

# Load the STL files and add the vectors to the plot
your_mesh = mesh.Mesh.from_file('tests/stl_binary/HalfDonut.stl')
axes.add_collection3d(mplot3d.art3d.Poly3DCollection(your_mesh.vectors))

# Auto scale to the mesh size
scale = your_mesh.points.flatten(-1)
axes.auto_scale_xyz(scale, scale, scale)

# Show the plot to the screen
pyplot.show()

Modifying Mesh objects

from stl import mesh
import math
import numpy

# Create 3 faces of a cube
data = numpy.zeros(6, dtype=mesh.Mesh.dtype)

# Top of the cube
data['vectors'][0] = numpy.array([[0, 1, 1],
                                  [1, 0, 1],
                                  [0, 0, 1]])
data['vectors'][1] = numpy.array([[1, 0, 1],
                                  [0, 1, 1],
                                  [1, 1, 1]])
# Right face
data['vectors'][2] = numpy.array([[1, 0, 0],
                                  [1, 0, 1],
                                  [1, 1, 0]])
data['vectors'][3] = numpy.array([[1, 1, 1],
                                  [1, 0, 1],
                                  [1, 1, 0]])
# Left face
data['vectors'][4] = numpy.array([[0, 0, 0],
                                  [1, 0, 0],
                                  [1, 0, 1]])
data['vectors'][5] = numpy.array([[0, 0, 0],
                                  [0, 0, 1],
                                  [1, 0, 1]])

# Since the cube faces are from 0 to 1 we can move it to the middle by
# substracting .5
data['vectors'] -= .5

# Generate 4 different meshes so we can rotate them later
meshes = []
for _ in range(4):
    meshes.append(mesh.Mesh(data.copy()))

# Rotate 90 degrees over the Y axis
meshes[0].rotate([0.0, 0.5, 0.0], math.radians(90))

# Translate 2 points over the X axis
meshes[1].x += 2

# Rotate 90 degrees over the X axis
meshes[2].rotate([0.5, 0.0, 0.0], math.radians(90))
# Translate 2 points over the X and Y points
meshes[2].x += 2
meshes[2].y += 2

# Rotate 90 degrees over the X and Y axis
meshes[3].rotate([0.5, 0.0, 0.0], math.radians(90))
meshes[3].rotate([0.0, 0.5, 0.0], math.radians(90))
# Translate 2 points over the Y axis
meshes[3].y += 2


# Optionally render the rotated cube faces
from matplotlib import pyplot
from mpl_toolkits import mplot3d

# Create a new plot
figure = pyplot.figure()
axes = mplot3d.Axes3D(figure)

# Render the cube faces
for m in meshes:
    axes.add_collection3d(mplot3d.art3d.Poly3DCollection(m.vectors))

# Auto scale to the mesh size
scale = numpy.concatenate([m.points for m in meshes]).flatten(-1)
axes.auto_scale_xyz(scale, scale, scale)

# Show the plot to the screen
pyplot.show()

Extending Mesh objects

from stl import mesh
import math
import numpy

# Create 3 faces of a cube
data = numpy.zeros(6, dtype=mesh.Mesh.dtype)

# Top of the cube
data['vectors'][0] = numpy.array([[0, 1, 1],
                                  [1, 0, 1],
                                  [0, 0, 1]])
data['vectors'][1] = numpy.array([[1, 0, 1],
                                  [0, 1, 1],
                                  [1, 1, 1]])
# Right face
data['vectors'][2] = numpy.array([[1, 0, 0],
                                  [1, 0, 1],
                                  [1, 1, 0]])
data['vectors'][3] = numpy.array([[1, 1, 1],
                                  [1, 0, 1],
                                  [1, 1, 0]])
# Left face
data['vectors'][4] = numpy.array([[0, 0, 0],
                                  [1, 0, 0],
                                  [1, 0, 1]])
data['vectors'][5] = numpy.array([[0, 0, 0],
                                  [0, 0, 1],
                                  [1, 0, 1]])

# Since the cube faces are from 0 to 1 we can move it to the middle by
# substracting .5
data['vectors'] -= .5

cube_back = mesh.Mesh(data.copy())
cube_front = mesh.Mesh(data.copy())

# Rotate 90 degrees over the X axis followed by the Y axis followed by the
# X axis
cube_back.rotate([0.5, 0.0, 0.0], math.radians(90))
cube_back.rotate([0.0, 0.5, 0.0], math.radians(90))
cube_back.rotate([0.5, 0.0, 0.0], math.radians(90))

cube = mesh.Mesh(numpy.concatenate([
    cube_back.data.copy(),
    cube_front.data.copy(),
]))

# Optionally render the rotated cube faces
from matplotlib import pyplot
from mpl_toolkits import mplot3d

# Create a new plot
figure = pyplot.figure()
axes = mplot3d.Axes3D(figure)

# Render the cube
axes.add_collection3d(mplot3d.art3d.Poly3DCollection(cube.vectors))

# Auto scale to the mesh size
scale = cube_back.points.flatten(-1)
axes.auto_scale_xyz(scale, scale, scale)

# Show the plot to the screen
pyplot.show()

Creating Mesh objects from a list of vertices and faces

import numpy as np
from stl import mesh

# Define the 8 vertices of the cube
vertices = np.array([\
    [-1, -1, -1],
    [+1, -1, -1],
    [+1, +1, -1],
    [-1, +1, -1],
    [-1, -1, +1],
    [+1, -1, +1],
    [+1, +1, +1],
    [-1, +1, +1]])
# Define the 12 triangles composing the cube
faces = np.array([\
    [0,3,1],
    [1,3,2],
    [0,4,7],
    [0,7,3],
    [4,5,6],
    [4,6,7],
    [5,1,2],
    [5,2,6],
    [2,3,6],
    [3,7,6],
    [0,1,5],
    [0,5,4]])

# Create the mesh
cube = mesh.Mesh(np.zeros(faces.shape[0], dtype=mesh.Mesh.dtype))
for i, f in enumerate(faces):
    for j in range(3):
        cube.vectors[i][j] = vertices[f[j],:]

# Write the mesh to file "cube.stl"
cube.save('cube.stl')