numpy-stl

v3.2.0

Library to make reading, writing and modifying both binary and ascii STL files easy. For more information about how to use this package see README

Latest version published 27 days ago

License: BSD-3-Clause

PyPI

GitHub

Package Health Score

85 / 100

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maintenance
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Popular

GitHub Stars: 632
Forks: 107
Contributors: 30

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Based on project statistics from the GitHub repository for the PyPI package numpy-stl, we found that it has been starred 632 times.

Security

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Security and license risk for latest version

Release Date: Nov 25, 2024
Direct Vulnerabilities: 0
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L
Indirect Vulnerabilities: 0
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License Risk: 0
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License

BSD-3-Clause

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Open Issues: 1
Open PR: 0
Last Release: 27 days ago
Last Commit: 27 days ago

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Package

Python Versions Compatibility: >3.9.0

Age: 10 years
Latest Release: 27 days ago
Dependencies: 1 Direct
Versions: 68
Maintainers: 1
Wheels: OS Independent

Readme

numpy-stl

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.

Security contact information

To report a security vulnerability, please use the Tidelift security contact. Tidelift will coordinate the fix and disclosure.

Issues

If you encounter any issues, make sure you report them here. Be sure to search for existing issues however. Many issues have been covered before. While this project uses numpy as it's main dependency, it is not in any way affiliated to the numpy project or the NumFocus organisation.

Links

> - The source: > - Project page: > - Reporting bugs: > - Documentation: > - My blog:

Requirements for installing:

> - numpy any recent version > - python-utils version 1.6 > or greater

Installation:

pip install numpy-stl

Initial usage:

After installing the package, you should be able to run the following commands similar to how you can run pip.

$ 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:

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 = figure.add_subplot(projection='3d')

# 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()

Experimental support for reading 3MF files

import pathlib
import stl

path = pathlib.Path('tests/3mf/Moon.3mf')

# Load the 3MF file
for m in stl.Mesh.from_3mf_file(path):
    # Do something with the mesh
    print('mesh', m)

Note that this is still experimental and may not work for all 3MF files. Additionally it only allows reading 3mf files, not writing them.

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 = figure.add_subplot(projection='3d')

# 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 = figure.add_subplot(projection='3d')

# 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 a single triangle

import numpy
from stl import mesh

# A unit triangle
tri_vectors = [[0,0,0],[0,1,0],[0,0,1]]

# Create the vector data. It’s a numpy structured array with N entries, where N is the number of triangles (here N=1), and each entry is in the format ('normals','vectors','attr')
data = numpy.array([(
    0, # Set 'normals' to zero, and the mesh class will automatically calculate them at initialization
    tri_vectors, # 'vectors'
    0 # 'attr'
)], dtype = mesh.Mesh.dtype) # The structure defined by the mesh class (N x ('normals','vectors','attr'))

# Create the mesh object from the structured array
tri_mesh = mesh.Mesh(data)

# Optionally make a plot for fun
# Load the plot tools
from matplotlib import pyplot
from mpl_toolkits import mplot3d

# Create a new plot
figure = pyplot.figure()
axes = figure.add_subplot(projection='3d')

# Add mesh to plot
axes.add_collection3d(mplot3d.art3d.Poly3DCollection(tri_mesh.vectors)) # Just need the 'vectors' attribute for display

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.

numpy-stl dependencies

numpy

FAQs

What is numpy-stl?

Library to make reading, writing and modifying both binary and ascii STL files easy. Visit Snyk Advisor to see a full health score report for numpy-stl, including popularity, security, maintenance & community analysis.

Is numpy-stl popular?

The python package numpy-stl receives a total of 17,864 weekly downloads. As such, numpy-stl popularity was classified as a popular. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is numpy-stl well maintained?

We found that numpy-stl demonstrated a healthy version release cadence and project activity. It has a community of 30 open source contributors collaborating on the project. See the full package health analysis to learn more about the package maintenance status.

Is numpy-stl safe to use?

The python package numpy-stl was scanned for known vulnerabilities and missing license, and no issues were found. Thus the package was deemed as safe to use. See the full health analysis review.

Last updated on 22 December-2024, at 00:52 (UTC).

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Readme

numpy-stl

Security contact information

Issues

Links

Requirements for installing:

Installation:

Initial usage:

Contributing:

Quickstart

Plotting using matplotlib is equally easy:

Experimental support for reading 3MF files

Modifying Mesh objects

Extending Mesh objects

Creating a single triangle

Creating Mesh objects from a list of vertices and faces

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

Combining multiple STL files

Known limitations

numpy-stl dependencies

FAQs

What is numpy-stl?

Is numpy-stl popular?

Is numpy-stl well maintained?

Is numpy-stl safe to use?

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Scan your app for vulnerabilities

Source Code

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