Skip to main content

Library to make reading, writing and modifying both binary and ascii STL files easy.

Project description

numpy-stl test status numpy-stl test status numpy-stl Pypi version numpy-stl code coverage https://img.shields.io/pypi/pyversions/numpy-stl.svg

Simple library to make working with STL files (and 3D objects in general) fast and easy.

Due to all operations heavily relying on numpy this is one of the fastest STL editing libraries for Python available.

Requirements for installing:

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

Contributing:

Contributions are always welcome. Please view the guidelines to get started: https://github.com/WoLpH/numpy-stl/blob/develop/CONTRIBUTING.rst

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()
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]])
# Front 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 = [mesh.Mesh(data.copy()) for _ in range(4)]

# 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()
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]])
# Front 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()
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')

Evaluating Mesh properties (Volume, Center of gravity, Inertia)

import numpy as np
from stl import mesh

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

volume, cog, inertia = your_mesh.get_mass_properties()
print("Volume                                  = {0}".format(volume))
print("Position of the center of gravity (COG) = {0}".format(cog))
print("Inertia matrix at expressed at the COG  = {0}".format(inertia[0,:]))
print("                                          {0}".format(inertia[1,:]))
print("                                          {0}".format(inertia[2,:]))

Combining multiple STL files

import math
import stl
from stl import mesh
import numpy


# find the max dimensions, so we can know the bounding box, getting the height,
# width, length (because these are the step size)...
def find_mins_maxs(obj):
    minx = obj.x.min()
    maxx = obj.x.max()
    miny = obj.y.min()
    maxy = obj.y.max()
    minz = obj.z.min()
    maxz = obj.z.max()
    return minx, maxx, miny, maxy, minz, maxz


def translate(_solid, step, padding, multiplier, axis):
    if 'x' == axis:
        items = 0, 3, 6
    elif 'y' == axis:
        items = 1, 4, 7
    elif 'z' == axis:
        items = 2, 5, 8
    else:
        raise RuntimeError('Unknown axis %r, expected x, y or z' % axis)

    # _solid.points.shape == [:, ((x, y, z), (x, y, z), (x, y, z))]
    _solid.points[:, items] += (step * multiplier) + (padding * multiplier)


def copy_obj(obj, dims, num_rows, num_cols, num_layers):
    w, l, h = dims
    copies = []
    for layer in range(num_layers):
        for row in range(num_rows):
            for col in range(num_cols):
                # skip the position where original being copied is
                if row == 0 and col == 0 and layer == 0:
                    continue
                _copy = mesh.Mesh(obj.data.copy())
                # pad the space between objects by 10% of the dimension being
                # translated
                if col != 0:
                    translate(_copy, w, w / 10., col, 'x')
                if row != 0:
                    translate(_copy, l, l / 10., row, 'y')
                if layer != 0:
                    translate(_copy, h, h / 10., layer, 'z')
                copies.append(_copy)
    return copies

# Using an existing stl file:
main_body = mesh.Mesh.from_file('ball_and_socket_simplified_-_main_body.stl')

# rotate along Y
main_body.rotate([0.0, 0.5, 0.0], math.radians(90))

minx, maxx, miny, maxy, minz, maxz = find_mins_maxs(main_body)
w1 = maxx - minx
l1 = maxy - miny
h1 = maxz - minz
copies = copy_obj(main_body, (w1, l1, h1), 2, 2, 1)

# I wanted to add another related STL to the final STL
twist_lock = mesh.Mesh.from_file('ball_and_socket_simplified_-_twist_lock.stl')
minx, maxx, miny, maxy, minz, maxz = find_mins_maxs(twist_lock)
w2 = maxx - minx
l2 = maxy - miny
h2 = maxz - minz
translate(twist_lock, w1, w1 / 10., 3, 'x')
copies2 = copy_obj(twist_lock, (w2, l2, h2), 2, 2, 1)
combined = mesh.Mesh(numpy.concatenate([main_body.data, twist_lock.data] +
                                    [copy.data for copy in copies] +
                                    [copy.data for copy in copies2]))

combined.save('combined.stl', mode=stl.Mode.ASCII)  # save as ASCII

Known limitations

  • When speedups are enabled the STL name is automatically converted to lowercase.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

numpy-stl-2.15.1.tar.gz (771.7 kB view details)

Uploaded Source

Built Distributions

numpy_stl-2.15.1-py3-none-any.whl (18.6 kB view details)

Uploaded Python 3

numpy_stl-2.15.1-cp38-cp38-macosx_10_15_x86_64.whl (42.7 kB view details)

Uploaded CPython 3.8 macOS 10.15+ x86-64

File details

Details for the file numpy-stl-2.15.1.tar.gz.

File metadata

  • Download URL: numpy-stl-2.15.1.tar.gz
  • Upload date:
  • Size: 771.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: Python-urllib/3.8

File hashes

Hashes for numpy-stl-2.15.1.tar.gz
Algorithm Hash digest
SHA256 f57fdb3c0e420f729dbe54ec3add9bdbbd19b62183aa8f4576e00e5834b2ef52
MD5 69ef82110dd3492d1334785d62b3e3db
BLAKE2b-256 8be6bafc169da6a5fb0c38e499d085809287b2d2b5f9ff30ed2abde11ee6acc5

See more details on using hashes here.

File details

Details for the file numpy_stl-2.15.1-py3-none-any.whl.

File metadata

  • Download URL: numpy_stl-2.15.1-py3-none-any.whl
  • Upload date:
  • Size: 18.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.8.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.59.0 CPython/3.6.7

File hashes

Hashes for numpy_stl-2.15.1-py3-none-any.whl
Algorithm Hash digest
SHA256 cd0e7f9e9abf848cdd75e43cb1ba5c0bd979770450f91eedbf6683e1dd35b3c1
MD5 54c0c2cb96ceb3daefad65a7c0350681
BLAKE2b-256 eb4d16f81f6dca8a6945441836ef7c6f35264af8ac4bff7dc51cb4fb86c26db6

See more details on using hashes here.

File details

Details for the file numpy_stl-2.15.1-cp38-cp38-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for numpy_stl-2.15.1-cp38-cp38-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 d72d7fb71def2cc5ed3cb1f2865f096666c07719551a869c1acc04b01dfa3e78
MD5 f5a6957126a47edca568dc9ed6785507
BLAKE2b-256 fb35a17f73914141193ef02878b4433708cd7c5151cac9932a995fd305610e5a

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page