Import, export, process, analyze and view triangular meshes.
Project Links
Meta
Author: Michael Dawson-Haggerty
Requires Python: >=3.8
Classifiers
Development Status
- 4 - Beta
License
- OSI Approved :: MIT License
Programming Language
- Python
- Python :: 3.8
- Python :: 3.9
- Python :: 3.10
- Python :: 3.11
- Python :: 3.12
Natural Language
- English
Topic
- Scientific/Engineering
- Multimedia :: Graphics
- Multimedia :: Graphics :: 3D Modeling
Trimesh is a pure Python 3.8+ library for loading and using triangular meshes with an emphasis on watertight surfaces. The goal of the library is to provide a full featured and well tested Trimesh object which allows for easy manipulation and analysis, in the style of the Polygon object in the Shapely library.
The API is mostly stable, but this should not be relied on and is not guaranteed: install a specific version if you plan on deploying something using trimesh.
Pull requests are appreciated and responded to promptly! If you'd like to contribute, here a quick development and contributing guide.
Basic Installation
Keeping trimesh easy to install is a core goal, thus the only hard dependency is numpy. Installing other packages adds functionality but is not required. For the easiest install with just numpy:
pip install trimesh
The minimal install can load many supported formats (STL, PLY, OBJ, GLTF/GLB) into numpy.ndarray values. More functionality is available when soft dependencies are installed, including convex hulls (scipy), graph operations (networkx), fast ray queries (embreex), vector path handling (shapely and rtree), XML formats like 3DXML/XAML/3MF (lxml), preview windows (pyglet), faster cache checks (xxhash), etc.
To install trimesh with the soft dependencies that generally install cleanly from binaries on Linux x86_64, MacOS ARM, and Windows x86_64 using pip:
pip install trimesh[easy]
If you are supporting a different platform or are freezing dependencies for an application we recommend you do not use extras, i.e. depend on trimesh scipy versus trimesh[easy]. Further information is available in the advanced installation documentation.
Quick Start
Here is an example of loading a mesh from file and colorizing its faces (nicely formatted notebook version of this example.
import numpy as np
import trimesh
# attach to logger so trimesh messages will be printed to console
trimesh.util.attach_to_log()
# mesh objects can be created from existing faces and vertex data
mesh = trimesh.Trimesh(vertices=[[0, 0, 0], [0, 0, 1], [0, 1, 0]],
faces=[[0, 1, 2]])
# by default, Trimesh will do a light processing, which will
# remove any NaN values and merge vertices that share position
# if you want to not do this on load, you can pass `process=False`
mesh = trimesh.Trimesh(vertices=[[0, 0, 0], [0, 0, 1], [0, 1, 0]],
faces=[[0, 1, 2]],
process=False)
# some formats like `glb` represent multiple meshes with multiple instances
# and `load_mesh` will concatenate irreversibly, load it as a Scene
# if you need instance information:
# `scene = trimesh.load_scene('models/CesiumMilkTruck.glb')`
mesh = trimesh.load_mesh('models/CesiumMilkTruck.glb')
# is the current mesh watertight?
mesh.is_watertight
# what's the euler number for the mesh?
mesh.euler_number
# the convex hull is another Trimesh object that is available as a property
# lets compare the volume of our mesh with the volume of its convex hull
print(mesh.volume / mesh.convex_hull.volume)
# since the mesh is watertight it means there is a volume
# with a center of mass calculated from a surface integral approach
# which we can set as the origin for our mesh. It's perfectly fine to
# alter the vertices directly:
# mesh.vertices -= mesh.center_mass
# although this will completely clear the cache including face normals
# as we don't know that they're still valid. Using the translation
# method will try to save cached values that are still valid:
mesh.apply_translation(-mesh.center_mass)
# what's the (3, 3) moment of inertia for the mesh?
mesh.moment_inertia
# if there are multiple bodies in the mesh we can split the mesh by
# connected components of face adjacency
# since this example mesh is a single watertight body we get a list of one mesh
mesh.split()
# facets are groups of coplanar adjacent faces
# set each facet to a random color
# colors are 8 bit RGBA by default (n, 4) np.uint8
for facet in mesh.facets:
mesh.visual.face_colors[facet] = trimesh.visual.random_color()
# preview mesh in an opengl window if you installed pyglet and scipy with pip
mesh.show()
# transform method can be passed a (4, 4) matrix and will cleanly apply the transform
mesh.apply_transform(trimesh.transformations.random_rotation_matrix())
# axis aligned bounding box is available
mesh.bounding_box.extents
# a minimum volume oriented bounding box also available
# primitives are subclasses of Trimesh objects which automatically generate
# faces and vertices from data stored in the 'primitive' attribute
mesh.bounding_box_oriented.primitive.extents
mesh.bounding_box_oriented.primitive.transform
# show the mesh appended with its oriented bounding box
# the bounding box is a trimesh.primitives.Box object, which subclasses
# Trimesh and lazily evaluates to fill in vertices and faces when requested
# (press w in viewer to see triangles)
(mesh + mesh.bounding_box_oriented).show()
# bounding spheres and bounding cylinders of meshes are also
# available, and will be the minimum volume version of each
# except in certain degenerate cases, where they will be no worse
# than a least squares fit version of the primitive.
print(mesh.bounding_box_oriented.volume,
mesh.bounding_cylinder.volume,
mesh.bounding_sphere.volume)
Features
- Import meshes from binary/ASCII STL, Wavefront OBJ, ASCII OFF, binary/ASCII PLY, GLTF/GLB 2.0, 3MF, XAML, 3DXML, etc.
- Export meshes as GLB/GLTF, binary STL, binary PLY, ASCII OFF, OBJ, COLLADA, etc.
- Import and export 2D or 3D vector paths with DXF or SVG files
- Preview meshes using an OpenGL
pygletwindow, or in-line in jupyter or marimo notebooks usingthree.js - Automatic hashing from a subclassed numpy array for change tracking using MD5, zlib CRC, or xxhash, and internal caching of expensive values.
- Calculate face adjacencies, face angles, vertex defects, convex hulls, etc.
- Calculate cross sections for a 2D outline, or slice a mesh for a 3D remainder mesh, i.e. slicing for 3D-printing.
- Split mesh based on face connectivity using networkx, or scipy.sparse
- Calculate mass properties, including volume, center of mass, moment of inertia, principal components of inertia, etc.
- Repair simple problems with triangle winding, normals, and quad/triangle holes
- Compute rotation/translation/tessellation invariant identifier and find duplicate meshes
- Check if a mesh is watertight, convex, etc.
- Sample the surface of a mesh
- Ray-mesh queries including location, triangle index, etc.
- Boolean operations on meshes (intersection, union, difference) using Manifold3D or Blender.
- Voxelize watertight meshes
- Smooth watertight meshes using Laplacian smoothing algorithms (Classic, Taubin, Humphrey)
- Subdivide faces of a mesh
- Approximate minimum volume oriented bounding boxes and spheres for meshes.
- Calculate nearest point on mesh surface and signed distance
- Primitive objects (Box, Cylinder, Sphere, Extrusion) which are subclassed Trimesh objects and have all the same features (inertia, viewers, etc)
- Simple scene graph and transform tree which can be rendered (pyglet window, three.js in a jupyter/marimo notebook or exported.
- Many utility functions, like transforming points, unitizing vectors, aligning vectors, tracking numpy arrays for changes, grouping rows, etc.
Additional Notes
- Check out some cool stuff people have done in the GitHub network.
- Generally
trimeshAPI changes should have a one-year period of printing awarnings.DeprecationWarningalthough that's not always possible (i.e. the pyglet2 viewer rewrite that's been back-burnered for several years.) - Docker containers are available on Docker Hub as
trimesh/trimeshand there's a container guide in the docs. - If you're choosing which format to use, you may want to try GLB as a fast modern option.
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2.35.15
Oct 31, 2018
2.35.14
Oct 31, 2018
2.35.13
Oct 26, 2018
2.35.12
Oct 26, 2018
2.35.11
Oct 26, 2018
2.35.10
Oct 22, 2018
2.35.9
Oct 22, 2018
2.35.7
Oct 19, 2018
2.35.6
Oct 18, 2018
2.35.5
Oct 14, 2018
2.35.4
Oct 14, 2018
2.35.3
Oct 13, 2018
2.35.2
Oct 09, 2018
2.35.1
Oct 07, 2018
2.35.0
Oct 07, 2018
2.34.16
Oct 06, 2018
2.34.15
Oct 06, 2018
2.34.14
Oct 05, 2018
2.34.13
Oct 04, 2018
2.34.12
Oct 04, 2018
2.34.9
Oct 04, 2018
2.34.8
Oct 03, 2018
2.34.7
Oct 02, 2018
2.34.4
Oct 02, 2018
2.34.3
Oct 02, 2018
2.34.2
Oct 01, 2018
2.33.43
Sep 28, 2018
2.33.42
Sep 28, 2018
2.33.41
Sep 26, 2018
2.33.40
Sep 25, 2018
2.33.39
Sep 23, 2018
2.33.38
Sep 23, 2018
2.33.37
Sep 19, 2018
2.33.36
Sep 19, 2018
2.33.34
Sep 15, 2018
2.33.33
Sep 15, 2018
2.33.30
Sep 12, 2018
2.33.29
Sep 11, 2018
2.33.28
Sep 11, 2018
2.33.27
Sep 10, 2018
2.33.26
Sep 08, 2018
2.33.25
Sep 07, 2018
2.33.23
Sep 06, 2018
2.33.22
Sep 06, 2018
2.33.21
Sep 06, 2018
2.33.19
Sep 05, 2018
2.33.18
Sep 05, 2018
2.33.17
Sep 04, 2018
2.33.15
Sep 03, 2018
2.33.14
Sep 03, 2018
2.33.12
Sep 03, 2018
2.33.11
Sep 03, 2018
2.33.7
Sep 02, 2018
2.33.4
Aug 31, 2018
2.33.3
Aug 28, 2018
2.33.2
Aug 28, 2018
2.33.1
Aug 28, 2018
2.33.0
Aug 28, 2018
2.32.13
Aug 27, 2018
2.32.11
Aug 26, 2018
2.32.10
Aug 23, 2018
2.32.8
Aug 20, 2018
2.32.6
Aug 20, 2018
2.32.5
Aug 14, 2018
2.32.4
Aug 14, 2018
2.32.3
Aug 11, 2018
2.32.1
Aug 09, 2018
2.32.0
Aug 07, 2018
2.31.54
Aug 06, 2018
2.31.52
Aug 05, 2018
2.31.51
Aug 05, 2018
2.31.50
Aug 03, 2018
2.31.49
Aug 02, 2018
2.31.48
Aug 01, 2018
2.31.47
Aug 01, 2018
2.31.46
Jul 30, 2018
2.31.45
Jul 30, 2018
2.31.44
Jul 30, 2018
2.31.43
Jul 28, 2018
2.31.42
Jul 26, 2018
2.31.39
Jul 25, 2018
2.31.38
Jul 23, 2018
2.31.37
Jul 23, 2018
2.31.36
Jul 21, 2018
2.31.34
Jul 21, 2018
2.31.33
Jul 20, 2018
2.31.32
Jul 20, 2018
2.31.31
Jul 20, 2018
2.31.29
Jul 20, 2018
2.31.28
Jul 20, 2018
2.31.27
Jul 20, 2018
2.31.26
Jul 18, 2018
2.31.25
Jul 11, 2018
2.31.24
Jul 09, 2018
2.31.23
Jul 09, 2018
2.31.22
Jul 08, 2018
2.31.21
Jul 08, 2018
2.31.20
Jul 06, 2018
2.31.19
Jul 06, 2018
2.31.18
Jul 06, 2018
2.31.17
Jul 04, 2018
2.31.16
Jul 04, 2018
2.31.15
Jun 30, 2018
2.31.14
Jun 30, 2018
2.31.13
Jun 30, 2018
2.31.12
Jun 29, 2018
2.31.11
Jun 29, 2018
2.31.9
Jun 27, 2018
2.31.8
Jun 26, 2018
2.31.7
Jun 25, 2018
2.31.6
Jun 24, 2018
2.31.5
Jun 24, 2018
2.31.4
Jun 24, 2018
2.31.2
Jun 23, 2018
2.31.1
Jun 23, 2018
2.30.62
Jun 20, 2018
2.30.61
Jun 20, 2018
2.30.58
Jun 19, 2018
2.30.57
Jun 18, 2018
2.30.55
Jun 17, 2018
2.30.54
Jun 15, 2018
2.30.53
Jun 15, 2018
2.30.52
Jun 14, 2018
2.30.51
Jun 14, 2018
2.30.50
Jun 13, 2018
2.30.49
Jun 13, 2018
2.30.46
Jun 11, 2018
2.30.45
Jun 11, 2018
2.30.44
Jun 11, 2018
2.30.43
Jun 08, 2018
2.30.42
Jun 08, 2018
2.30.41
Jun 08, 2018
2.30.40
Jun 08, 2018
2.30.39
Jun 06, 2018
2.30.38
Jun 06, 2018
2.30.37
Jun 05, 2018
2.30.36
Jun 05, 2018
2.30.35
Jun 04, 2018
2.30.33
Jun 04, 2018
2.30.32
Jun 04, 2018
2.30.31
Jun 04, 2018
2.30.30
May 31, 2018
2.30.28
May 30, 2018
2.30.27
May 30, 2018
2.30.26
May 30, 2018
2.30.25
May 30, 2018
2.30.22
May 30, 2018
2.30.21
May 28, 2018
2.30.20
May 28, 2018
2.30.18
May 28, 2018
2.30.16
May 22, 2018
2.30.14
May 18, 2018
2.30.11
May 15, 2018
2.30.9
May 10, 2018
2.30.8
May 09, 2018
2.30.2
May 08, 2018
2.30.1
May 08, 2018
2.29.32
May 07, 2018
2.29.30
May 05, 2018
2.29.28
May 02, 2018
2.29.27
Apr 30, 2018
2.29.26
Apr 28, 2018
2.29.25
Apr 27, 2018
2.29.24
Apr 27, 2018
2.29.22
Apr 24, 2018
2.29.21
Apr 24, 2018
2.29.20
Apr 23, 2018
2.29.19
Apr 23, 2018
2.29.18
Apr 23, 2018
2.29.17
Apr 21, 2018
2.29.16
Apr 19, 2018
2.29.14
Apr 18, 2018
2.29.12
Apr 17, 2018
2.29.10
Apr 16, 2018
2.29.9
Apr 13, 2018
2.29.8
Apr 12, 2018
2.29.7
Apr 12, 2018
2.29.5
Apr 11, 2018
2.29.4
Apr 11, 2018
2.29.3
Apr 11, 2018
2.29.2
Apr 11, 2018
2.29.1
Apr 10, 2018
2.29.0
Apr 09, 2018
2.28.32
Apr 05, 2018
2.28.31
Apr 04, 2018
2.28.30
Apr 02, 2018
2.28.29
Mar 29, 2018
2.28.28
Mar 29, 2018
2.28.27
Mar 29, 2018
2.28.26
Mar 28, 2018
2.28.25
Mar 28, 2018
2.28.24
Mar 28, 2018
2.28.23
Mar 27, 2018
2.28.22
Mar 26, 2018
2.28.20
Mar 25, 2018
2.28.19
Mar 25, 2018
2.28.18
Mar 25, 2018
2.28.16
Mar 23, 2018
2.28.14
Mar 23, 2018
2.28.13
Mar 23, 2018
2.28.12
Mar 22, 2018
2.28.11
Mar 22, 2018
2.28.9
Mar 22, 2018
2.28.8
Mar 21, 2018
2.28.7
Mar 21, 2018
2.28.3
Mar 19, 2018
2.27.0
Mar 15, 2018
2.26.24
Mar 12, 2018
2.26.23
Mar 12, 2018
2.26.22
Mar 11, 2018
2.26.18
Mar 06, 2018
2.26.17
Mar 06, 2018
2.26.16
Mar 01, 2018
2.26.15
Feb 28, 2018
2.26.13
Feb 28, 2018
2.26.12
Feb 23, 2018
2.26.11
Feb 22, 2018
2.26.10
Feb 21, 2018
2.26.9
Feb 18, 2018
2.26.8
Feb 16, 2018
2.26.7
Feb 16, 2018
2.26.6
Feb 16, 2018
2.26.5
Feb 14, 2018
2.26.4
Feb 14, 2018
2.26.3
Feb 14, 2018
2.26.2
Feb 13, 2018
2.26.1
Feb 10, 2018
2.26.0
Feb 09, 2018
2.25.5
Feb 06, 2018
2.25.4
Feb 06, 2018
2.25.3
Feb 05, 2018
2.25.2
Feb 02, 2018
2.25.1
Feb 02, 2018
2.24.18
Feb 01, 2018
2.24.17
Jan 31, 2018
2.24.16
Jan 30, 2018
2.24.15
Jan 30, 2018
2.24.14
Jan 30, 2018
2.24.12
Jan 30, 2018
2.24.10
Jan 30, 2018
2.24.9
Jan 25, 2018
2.24.8
Jan 24, 2018
2.24.4
Jan 24, 2018
2.24.3
Jan 23, 2018
2.24.2
Jan 22, 2018
2.22.27
Jan 16, 2018
2.22.26
Jan 12, 2018
2.22.25
Jan 10, 2018
2.22.24
Jan 10, 2018
2.22.23
Jan 10, 2018
2.22.22
Jan 10, 2018
2.22.14
Jan 07, 2018
2.22.13
Jan 07, 2018
2.22.11
Jan 05, 2018
2.22.10
Jan 04, 2018
2.22.9
Jan 04, 2018
2.22.8
Jan 04, 2018
2.22.6
Jan 03, 2018
2.22.5
Dec 31, 2017
2.22.4
Dec 30, 2017
2.22.3
Dec 27, 2017
2.22.0
Dec 22, 2017
2.21.36
Dec 22, 2017
2.21.34
Dec 19, 2017
2.21.33
Dec 18, 2017
2.21.31
Dec 17, 2017
2.21.30
Dec 15, 2017
2.21.29
Dec 15, 2017
2.21.28
Dec 15, 2017
2.21.27
Dec 15, 2017
2.21.26
Dec 14, 2017
2.21.25
Dec 14, 2017
2.21.24
Dec 14, 2017
2.21.23
Dec 14, 2017
2.21.22
Dec 14, 2017
2.21.21
Dec 14, 2017
2.21.20
Dec 13, 2017
2.21.18
Dec 11, 2017
2.21.17
Dec 10, 2017
2.21.16
Dec 09, 2017
2.21.15
Dec 06, 2017
2.21.14
Dec 06, 2017
2.21.13
Dec 06, 2017
2.21.12
Dec 06, 2017
2.21.11
Dec 05, 2017
2.21.10
Dec 04, 2017
2.21.9
Dec 02, 2017
2.21.7
Dec 02, 2017
2.21.6
Dec 01, 2017
2.21.5
Dec 01, 2017
2.21.4
Nov 30, 2017
2.21.3
Nov 29, 2017
2.21.1
Nov 29, 2017
2.21.0
Nov 26, 2017
2.20.29
Nov 21, 2017
2.20.28
Nov 21, 2017
2.20.26
Nov 21, 2017
2.20.25
Nov 20, 2017
2.20.24
Nov 20, 2017
2.20.23
Nov 20, 2017
2.20.21
Nov 15, 2017
2.20.20
Nov 13, 2017
2.20.19
Nov 13, 2017
2.20.18
Nov 13, 2017
2.20.17
Nov 12, 2017
2.20.16
Nov 11, 2017
2.20.15
Nov 11, 2017
2.20.14
Nov 10, 2017
2.20.12
Nov 09, 2017
2.20.11
Nov 07, 2017
2.20.10
Nov 03, 2017
2.20.8
Oct 27, 2017
2.20.7
Oct 27, 2017
2.20.6
Oct 23, 2017
2.20.5
Oct 21, 2017
2.20.2
Oct 14, 2017
2.20.1
Oct 14, 2017
2.19.6
Oct 11, 2017
2.19.4
Oct 10, 2017
2.19.3
Oct 09, 2017
2.19.2
Oct 09, 2017
2.19.1
Oct 09, 2017
2.18.6
Oct 03, 2017
2.18.5
Oct 03, 2017
2.18.4
Oct 03, 2017
2.18.3
Oct 03, 2017
2.18.1
Oct 03, 2017
2.18.0
Oct 03, 2017
2.17.0
Sep 30, 2017
2.16.3
Sep 29, 2017
2.16.1
Sep 29, 2017
2.16.0
Sep 28, 2017
2.15.1
Sep 20, 2017
2.14.20
Sep 05, 2017
2.14.17
Aug 27, 2017
2.14.16
Aug 27, 2017
2.14.15
Aug 27, 2017
2.14.14
Aug 27, 2017
2.14.13
Aug 24, 2017
2.14.12
Aug 22, 2017
2.14.10
Aug 12, 2017
2.14.9
Aug 07, 2017
2.14.8
Aug 05, 2017
2.14.7
Aug 04, 2017
2.14.5
Aug 03, 2017
2.14.4
Jul 24, 2017
2.14.2
Jul 18, 2017
2.13.15
Jul 06, 2017
2.13.14
Jul 04, 2017
2.13.13
Jul 04, 2017
2.13.12
Jun 27, 2017
2.13.11
Jun 19, 2017
2.13.10
Jun 19, 2017
2.13.9
Jun 16, 2017
2.13.8
Jun 10, 2017
2.13.7
Jun 10, 2017
2.13.5
Jun 10, 2017
2.13.4
Jun 06, 2017
2.13.3
Jun 02, 2017
2.13.2
Jun 02, 2017
2.13.1
May 29, 2017
2.12.2
May 29, 2017
2.12.0
May 28, 2017
2.11.1
May 27, 2017
2.11.0
May 22, 2017
2.10.18
May 17, 2017
2.10.17
May 16, 2017
2.10.16
May 15, 2017
2.10.14
May 14, 2017
2.10.13
May 09, 2017
2.10.12
Apr 27, 2017
2.10.11
Apr 27, 2017
2.10.10
Apr 24, 2017
2.10.9
Apr 24, 2017
2.10.8
Apr 12, 2017
2.10.6
Apr 11, 2017
2.10.5
Apr 09, 2017
2.10.4
Apr 02, 2017
2.10.3
Apr 01, 2017
2.10.1
Mar 28, 2017
2.10.0
Mar 25, 2017
2.9.4
Mar 20, 2017
2.9.3
Mar 18, 2017
2.9.2
Mar 18, 2017
2.8.5
Mar 12, 2017
2.8.4
Mar 12, 2017
2.8.3
Mar 11, 2017
2.8.2
Mar 11, 2017
2.8.1
Mar 11, 2017
2.8.0
Mar 11, 2017
2.7.24
Mar 06, 2017
2.7.22
Mar 05, 2017
2.7.21
Mar 05, 2017
2.7.20
Mar 05, 2017
2.7.18
Mar 02, 2017
2.7.16
Mar 01, 2017
2.7.15
Feb 27, 2017
2.7.14
Feb 25, 2017
2.7.12
Feb 25, 2017
2.7.11
Feb 24, 2017
2.7.10
Feb 23, 2017
2.7.8
Feb 23, 2017
2.7.7
Feb 22, 2017
2.7.6
Feb 20, 2017
2.7.5
Feb 20, 2017
2.7.4
Feb 18, 2017
2.7.3
Feb 18, 2017
2.7.2
Feb 17, 2017
2.7.0
Feb 16, 2017
2.6.2
Feb 15, 2017
2.5.36
Feb 13, 2017
2.5.35
Feb 12, 2017
2.5.34
Jan 29, 2017
2.5.33
Jan 26, 2017
2.5.32
Jan 21, 2017
2.5.31
Jan 19, 2017
2.5.30
Jan 18, 2017
2.5.28
Jan 14, 2017
2.5.27
Jan 12, 2017
2.5.26
Jan 12, 2017
2.5.25
Jan 08, 2017
2.5.24
Jan 08, 2017
2.5.23
Jan 08, 2017
2.5.22
Jan 07, 2017
2.5.21
Jan 06, 2017
2.5.20
Jan 06, 2017
2.5.19
Jan 06, 2017
2.5.18
Jan 04, 2017
2.5.17
Jan 03, 2017
2.5.15
Jan 03, 2017
2.5.14
Jan 02, 2017
2.5.12
Dec 31, 2016
2.5.11
Dec 31, 2016
2.5.10
Dec 31, 2016
2.5.9
Dec 30, 2016
2.5.8
Dec 30, 2016
2.5.7
Dec 26, 2016
2.5.6
Dec 24, 2016
2.5.5
Dec 24, 2016
2.4.6
Dec 21, 2016
2.4.4
Dec 19, 2016
2.4.1
Dec 19, 2016
2.3.13
Dec 11, 2016
2.3.12
Dec 09, 2016
2.3.11
Dec 09, 2016
2.3.10
Dec 07, 2016
2.3.9
Nov 29, 2016
2.3.8
Nov 27, 2016
2.3.7
Nov 23, 2016
2.3.6
Nov 20, 2016
2.3.5
Nov 18, 2016
2.3.3
Nov 16, 2016
2.3.2
Nov 16, 2016
2.3.0
Nov 13, 2016
2.2.17
Nov 12, 2016
2.2.16
Nov 11, 2016
2.2.15
Nov 03, 2016
2.2.14
Oct 31, 2016
2.2.13
Oct 31, 2016
2.2.12
Oct 29, 2016
2.2.11
Oct 14, 2016
2.2.10
Oct 12, 2016
2.2.9
Oct 12, 2016
2.2.8
Oct 10, 2016
2.2.7
Oct 04, 2016
2.2.6
Oct 02, 2016
2.2.5
Oct 01, 2016
2.2.4
Oct 01, 2016
2.2.2
Sep 30, 2016
2.2.1
Sep 22, 2016
2.2.0
Sep 22, 2016
2.1.15
Sep 21, 2016
2.1.14
Sep 20, 2016
2.1.13
Sep 19, 2016
2.1.12
Sep 16, 2016
2.1.11
Sep 15, 2016
2.1.10
Sep 15, 2016
2.1.9
Sep 15, 2016
2.1.8
Sep 15, 2016
2.1.7
Sep 15, 2016
2.1.6
Sep 12, 2016
2.1.5
Sep 10, 2016
2.1.4
Sep 09, 2016
2.1.3
Sep 09, 2016
2.1.2
Sep 07, 2016
2.1.1
Sep 07, 2016
2.0.8
Sep 03, 2016
2.0.7
Sep 02, 2016
2.0.6
Sep 01, 2016
2.0.5
Sep 01, 2016
2.0.4
Aug 31, 2016
2.0.3
Aug 30, 2016
2.0.2
Aug 29, 2016
2.0.1
Aug 28, 2016
1.16.1
Aug 27, 2016
1.16.0
Aug 25, 2016
1.15.16
Aug 20, 2016
1.15.15
Aug 18, 2016
1.15.14
Aug 14, 2016
1.15.13
Aug 09, 2016
1.15.12
Aug 07, 2016
1.15.11
Aug 06, 2016
1.15.10
Aug 06, 2016
1.15.9
Jul 30, 2016
1.15.8
Jul 16, 2016
1.15.7
Jul 16, 2016
1.15.6
Jul 14, 2016
1.15.5
Jul 11, 2016
1.15.4
Jul 04, 2016
1.15.3
Jul 04, 2016
1.15.1
Jul 04, 2016
1.15.0
Jul 02, 2016
1.14.20
Jun 28, 2016
1.14.19
Jun 27, 2016
1.14.18
Jun 26, 2016
1.14.14
Jun 25, 2016
1.14.13
Jun 25, 2016
1.14.12
Jun 24, 2016
1.14.11
Jun 13, 2016
1.14.10
Jun 11, 2016
1.14.9
Jun 06, 2016
1.14.8
Jun 05, 2016
1.14.7
Jun 04, 2016
1.14.6
Jun 04, 2016
1.14.5
May 29, 2016
1.14.4
May 23, 2016
1.14.3
May 21, 2016
1.14.2
May 19, 2016
1.14.1
May 15, 2016
1.14.0
May 14, 2016
1.13.1
May 11, 2016
1.13.0
May 09, 2016
1.12.13
Apr 25, 2016
1.12.12
Apr 24, 2016
1.12.11
Apr 23, 2016
1.12.10
Apr 22, 2016
1.12.9
Apr 20, 2016
1.12.8
Apr 13, 2016
1.12.7
Apr 03, 2016
1.12.6
Apr 02, 2016
1.12.5
Mar 31, 2016
1.12.4
Mar 31, 2016
1.12.3
Mar 26, 2016
1.12.2
Mar 24, 2016
1.12.1
Mar 18, 2016
1.12.0
Mar 17, 2016
1.11.16
Mar 17, 2016
1.11.15
Mar 15, 2016
1.11.12
Mar 07, 2016
1.11.11
Mar 04, 2016
1.11.10
Mar 03, 2016
1.11.9
Mar 01, 2016
1.11.8
Feb 23, 2016
1.11.7
Feb 21, 2016
1.11.6
Feb 21, 2016
1.11.5
Feb 17, 2016
1.11.4
Feb 16, 2016
1.11.3
Feb 15, 2016
1.11.2
Feb 13, 2016
1.11.1
Feb 13, 2016
1.11.0
Feb 13, 2016
1.10.1
Feb 11, 2016
1.10.0
Feb 10, 2016
1.9.22
Feb 07, 2016
1.9.21
Feb 04, 2016
1.9.20
Jan 31, 2016
1.9.19
Jan 24, 2016
1.9.18
Jan 23, 2016
1.9.17
Jan 23, 2016
1.9.16
Jan 17, 2016
1.9.15
Jan 13, 2016
1.9.14
Jan 11, 2016
1.9.13
Jan 10, 2016
1.9.12
Jan 10, 2016
1.9.11
Jan 10, 2016
1.9.10
Jan 10, 2016
1.9.7
Dec 02, 2015
