Skip to main content

SPlisHSPlasH Project Python Bindings

Project description


      Documentation Status

SPlisHSPlasH is an open-source library for the physically-based simulation of fluids. The simulation in this library is based on the Smoothed Particle Hydrodynamics (SPH) method which is a popular meshless Lagrangian approach to simulate complex fluid effects. The SPH formalism allows an efficient computation of a certain quantity of a fluid particle by considering only a finite set of neighboring particles. One of the most important research topics in the field of SPH methods is the simulation of incompressible fluids. SPlisHSPlasH implements current state-of-the-art pressure solvers (WCSPH, PCISPH, PBF, IISPH, DFSPH, PF) to simulate incompressibility. Moreover, the library provides different methods to simulate viscosity, surface tension and vorticity.

The library uses the following external libraries: Eigen, json, partio, zlib, cxxopts, tinyexpr, toojpeg, pybind, glfw, hapPLY, nfd, and imgui. All external dependencies are included.

Furthermore we use our own libraries:

SPlisHSPlasH can export the particle data in the partio and vtk format. If you want to import partio files in Maya or Blender, try out our plugins:

Author: Jan Bender

License

The SPlisHSPlasH library code is licensed under the MIT license. See LICENSE for details.

External dependencies are covered by separate licensing terms. See the extern folder for the code and respective licensing terms of each dependency.

Documentation

Forum

On our GitHub discussions page you can ask questions, discuss about simulation topics, and share ideas.

Build Instructions

This project is based on CMake. Simply generate project, Makefiles, etc. using CMake and compile the project with a compiler of your choice that supports C++11. The code was tested with the following configurations:

  • Windows 10 64-bit, CMake 3.18.3, Visual Studio 2019
  • Debian 11.5 64-bit, CMake 3.18.4, GCC 10.2.1.

Note: Please use a 64-bit target on a 64-bit operating system. 32-bit builds on a 64-bit OS are not supported.

Python Installation Instruction

For Windows and Linux targets there exists prebuilt python wheel files which can be installed using

pip install pysplishsplash

These are available for Python Versions. See also here: pySPlisHSPlasH. If you do not meet these conditions please refer to the build instructions and to the python binding Getting started guide.

The command line simulator is available by running one of the following

splash
splash --help

Features

SPlisHSPlasH implements:

  • an open-source SPH fluid simulation (2D & 3D)
  • neighborhood search on CPU or GPU
  • supports vectorization using AVX
  • Python binding (thanks to Stefan Jeske)
  • supports embedded Python scripts
  • several implicit pressure solvers (WCSPH, PCISPH, PBF, IISPH, DFSPH, PF)
  • explicit and implicit viscosity methods
  • current surface tension approaches
  • different vorticity methods
  • computation of drag forces
  • support for multi-phase simulations
  • simulation of deformable solids
  • rigid-fluid coupling with static and dynamic bodies
  • two-way coupling with deformable solids
  • XSPH velocity filter
  • fluid emitters
  • scripted animation fields
  • a json-based scene file importer
  • automatic surface sampling
  • a tool for volume sampling of closed geometries
  • a tool to generate spray, foam and bubble particles in a postprocessing step
  • a tool to skin a visual mesh to the moving particles of an elastic solid in a postprocessing step
  • partio file export of all particle data
  • VTK file export of all particle data (enables the data import in ParaView)
  • rigid body export
  • a Maya plugin to model and generate scene files
  • a ParaView plugin to import particle data

Pressure Solvers

The SPlisHSPlasH library implements the following pressure solvers:

  • Weakly compressible SPH for free surface flows (WCSPH)
  • Predictive-corrective incompressible SPH (PCISPH)
  • Position based fluids (PBF)
  • Implicit incompressible SPH (IISPH)
  • Divergence-free smoothed particle hydrodynamics (DFSPH)
  • Projective Fluids (PF)
  • Implicit compressible SPH (ICSPH)

Boundary Handling

The SPlisHSPlasH library implements the following boundary handling methods:

  • Nadir Akinci, Markus Ihmsen, Gizem Akinci, Barbara Solenthaler, and Matthias Teschner, "Versatile rigid-fluid coupling for incompressible SPH", ACM Transactions on Graphics 31(4), 2012
  • Dan Koschier and Jan Bender, "Density Maps for Improved SPH Boundary Handling", In Proceedings of ACM SIGGRAPH / EUROGRAPHICS Symposium on Computer Animation (SCA), 2017
  • Jan Bender, Tassilo Kugelstadt, Marcel Weiler, Dan Koschier, "Volume Maps: An Implicit Boundary Representation for SPH", ACM SIGGRAPH Conference on Motion, Interaction and Games, 2019

Viscosity

The SPlisHSPlasH library implements explicit viscosity methods:

  • Standard SPH formulation of viscosity

and the implicit methods of the following publications:

  • Jan Bender and Dan Koschier, "Divergence-free SPH for incompressible and viscous fluids", IEEE Transactions on Visualization and Computer Graphics, 2017
  • Andreas Peer, Markus Ihmsen, Jens Cornelis, and Matthias Teschner, "An Implicit Viscosity Formulation for SPH Fluids", ACM Transactions on Graphics, 34(4), 2015
  • Andreas Peer and Matthias Teschner. Prescribed Velocity Gradients for Highly Viscous SPH Fluids with Vorticity Diffusion. IEEE Transactions on Visualization and Computer Graphics, 2016
  • An improved version of: Tetsuya Takahashi, Yoshinori Dobashi, Issei Fujishiro, Tomoyuki Nishita, and Ming C. Lin. Implicit Formulation for SPH-based Viscous Fluids. Computer Graphics Forum, 34, 2015.
  • Marcel Weiler, Dan Koschier, Magnus Brand and Jan Bender. A Physically Consistent Implicit Viscosity Solver for SPH Fluids. Computer Graphics Forum (Eurographics), 37(2), 2018

Surface Tension

The SPlisHSPlasH library implements the surface tension methods of the following publications:

  • Markus Becker and Matthias Teschner. Weakly compressible SPH for free surface flows. In Proceedings of ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 2007. Eurographics Association.
  • Nadir Akinci, Gizem Akinci, and Matthias Teschner. Versatile surface tension and adhesion for SPH fluids. ACM Trans. Graph., 32(6):182:1–182:8, 2013.
  • Xiaowei He, Huamin Wang, Fengjun Zhang, Hongan Wang, Guoping Wang, and Kun Zhou, "Robust simulation of sparsely sampled thin features in SPH-based free surface flows", ACM Transactions on Graphics, 34(1), 2014.
  • F. Zorilla, M. Ritter, J. Sappl, W. Rauch, M. Harders, "Accelerating Surface Tension Calculation in SPH via Particle Classification and Monte Carlo Integration", Computers 9, 23, 2020.

Vorticity

The SPlisHSPlasH library implements the vorticity methods of the following publications:

  • Jan Bender, Dan Koschier, Tassilo Kugelstadt and Marcel Weiler. A Micropolar Material Model for Turbulent SPH Fluids. In Proceedings of ACM SIGGRAPH / EUROGRAPHICS Symposium on Computer Animation, 2017
  • Miles Macklin and Matthias Müller. Position based fluids. ACM Trans. Graph., 32(4):104:1–104:12, July 2013.

Drag Forces

The SPlisHSPlasH library implements the drag force computation of the following publications:

  • Christoph Gissler, Stefan Band, Andreas Peer, Markus Ihmsen and Matthias Teschner. Approximate Air-Fluid Interactions for SPH. In Proceedings of Virtual Reality Interactions and Physical Simulations, 2017
  • Miles Macklin, Matthias Müller, Nuttapong Chentanez and Tae-Yong Kim. Unified Particle Physics for Real-Time Applications. ACM Trans. Graph., 33(4), 2014

Elastic Forces

  • M. Becker, M. Ihmsen, and M. Teschner. Corotated SPH for deformable solids. Proceedings of Eurographics Conference on Natural Phenomena, 2009
  • A. Peer, C. Gissler, S. Band, and M. Teschner. An Implicit SPH Formulation for Incompressible Linearly Elastic Solids. Computer Graphics Forum, 2017
  • Tassilo Kugelstadt, Jan Bender, José Antonio Fernández-Fernández, Stefan Rhys Jeske, Fabian Löschner, and Andreas Longva. Fast Corotated Elastic SPH Solids with Implicit Zero-Energy Mode Control. Proceedings of the ACM on Computer Graphics and Interactive Techniques, 2021

Multi-Phase Fluid Simulation

The SPlisHSPlasH library implements the following publication to realize multi-phase simulations:

  • B. Solenthaler and R. Pajarola. Density Contrast SPH Interfaces. In Proceedings of ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 2008.

Volume Sampling

The SPlisHSPlasH library implements the volume sampling techniques of following publications:

  • M. Jiang, Y. Zhou, R. Wang, R. Southern, J. J. Zhang. Blue noise sampling using an SPH-based method. ACM Transactions on Graphics, 2015
  • Tassilo Kugelstadt, Jan Bender, José Antonio Fernández-Fernández, Stefan Rhys Jeske, Fabian Löschner, and Andreas Longva. Fast Corotated Elastic SPH Solids with Implicit Zero-Energy Mode Control. Proceedings of the ACM on Computer Graphics and Interactive Techniques, 2021

Screenshots

Videos

The following videos were generated using the SPlisHSPlasH library:

A Micropolar Material Model for Turbulent SPH Fluids Density Maps for Improved SPH Boundary Handling
Video Video
Divergence-Free Smoothed Particle Hydrodynamics Divergence-Free SPH for Incompressible and Viscous Fluids
Video Video
A Physically Consistent Implicit Viscosity Solver for SPH Fluids Turbulent Micropolar SPH Fluids with Foam
Video Video
Volume Maps: An Implicit Boundary Representation for SPH Implicit Frictional Boundary Handling for SPH
Video Video
Fast Corotated Elastic SPH Solids with Implicit Zero-Energy Mode Control
Video

References

  • Nadir Akinci, Gizem Akinci, and Matthias Teschner. Versatile surface tension and adhesion for SPH fluids. ACM Trans. Graph., 32(6):182:1–182:8, 2013.
  • Nadir Akinci, Markus Ihmsen, Gizem Akinci, Barbara Solenthaler, and Matthias Teschner, "Versatile rigid-fluid coupling for incompressible SPH", ACM Transactions on Graphics 31(4), 2012
  • Markus Becker and Matthias Teschner. Weakly compressible SPH for free surface flows. In Proceedings of ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 2007. Eurographics Association.
  • M. Becker, M. Ihmsen, and M. Teschner. Corotated SPH for deformable solids. Proceedings of Eurographics Conference on Natural Phenomena, 2009
  • Jan Bender and Dan Koschier. Divergence-free smoothed particle hydrodynamics. In Proceedings of ACM SIGGRAPH / Eurographics Symposium on Computer Animation, 2015. ACM.
  • Jan Bender and Dan Koschier. Divergence-free SPH for incompressible and viscous fluids. IEEE Transactions on Visualization and Computer Graphics, 2017.
  • Jan Bender, Dan Koschier, Tassilo Kugelstadt and Marcel Weiler. A Micropolar Material Model for Turbulent SPH Fluids. In Proceedings of ACM SIGGRAPH / EUROGRAPHICS Symposium on Computer Animation, 2017
  • Jan Bender, Dan Koschier, Tassilo Kugelstadt and Marcel Weiler. Turbulent Micropolar SPH Fluids with Foam. IEEE Transactions on Visualization and Computer Graphics 25(6), 2019
  • Jan Bender, Tassilo Kugelstadt, Marcel Weiler, Dan Koschier, "Volume Maps: An Implicit Boundary Representation for SPH", ACM SIGGRAPH Conference on Motion, Interaction and Games, 2019
  • Jan Bender, Tassilo Kugelstadt, Marcel Weiler, Dan Koschier, "Implicit Frictional Boundary Handling for SPH", IEEE Transactions on Visualization and Computer Graphics, 2020
  • Jan Bender, Matthias Müller, Miguel A. Otaduy, Matthias Teschner, and Miles Macklin. A survey on position-based simulation methods in computer graphics. Computer Graphics Forum, 33(6):228–251, 2014.
  • Jan Bender, Matthias Müller, and Miles Macklin. Position-based simulation methods in computer graphics. In EUROGRAPHICS 2015 Tutorials. Eurographics Association, 2015.
  • Christoph Gissler, Stefan Band, Andreas Peer, Markus Ihmsen and Matthias Teschner. Approximate Air-Fluid Interactions for SPH. In Proceedings of Virtual Reality Interactions and Physical Simulations, 2017
  • C. Gissler, A. Henne, S. Band, A. Peer and M. Teschner. An Implicit Compressible SPH Solver for Snow Simulation, ACM Transactions on Graphics 39(4), 2020.
  • Xiaowei He, Huamin Wang, Fengjun Zhang, Hongan Wang, Guoping Wang, and Kun Zhou. Robust simulation of sparsely sampled thin features in SPH-based free surface flows. ACM Trans. Graph., 34(1):7:1–7:9, December 2014.
  • Markus Ihmsen, Nadir Akinci, Gizem Akinci, Matthias Teschner. Unified spray, foam and air bubbles for particle-based fluids. The Visual Computer 28(6), 2012
  • Markus Ihmsen, Jens Cornelis, Barbara Solenthaler, Christopher Horvath, and Matthias Teschner. Implicit incompressible SPH. IEEE Transactions on Visualization and Computer Graphics, 20(3):426–435, March 2014.
  • Markus Ihmsen, Jens Orthmann, Barbara Solenthaler, Andreas Kolb, and Matthias Teschner. SPH Fluids in Computer Graphics. In Eurographics 2014 - State of the Art Reports. The Eurographics Association, 2014.
  • M. Jiang, Y. Zhou, R. Wang, R. Southern, J. J. Zhang. Blue noise sampling using an SPH-based method. ACM Transactions on Graphics, 2015
  • Dan Koschier and Jan Bender, "Density Maps for Improved SPH Boundary Handling", In Proceedings of ACM SIGGRAPH / EUROGRAPHICS Symposium on Computer Animation (SCA), 2017
  • Tassilo Kugelstadt, Jan Bender, José Antonio Fernández-Fernández, Stefan Rhys Jeske, Fabian Löschner, and Andreas Longva. Fast Corotated Elastic SPH Solids with Implicit Zero-Energy Mode Control. Proceedings of the ACM on Computer Graphics and Interactive Techniques, 2021
  • Miles Macklin and Matthias Müller. Position based fluids. ACM Trans. Graph., 32(4):104:1–104:12, July 2013.
  • Miles Macklin, Matthias Müller, Nuttapong Chentanez and Tae-Yong Kim. Unified Particle Physics for Real-Time Applications. ACM Trans. Graph., 33(4), 2014
  • J. J. Monaghan. Smoothed Particle Hydrodynamics. Annual Review of Astronomy and Astrophysics, 1992, 30, 543-574.
  • A. Peer, C. Gissler, S. Band, and M. Teschner. An Implicit SPH Formulation for Incompressible Linearly Elastic Solids. Computer Graphics Forum, 2017
  • Andreas Peer, Markus Ihmsen, Jens Cornelis, and Matthias Teschner. An Implicit Viscosity Formulation for SPH Fluids. ACM Trans. Graph., 34(4), 2015.
  • Andreas Peer and Matthias Teschner. Prescribed Velocity Gradients for Highly Viscous SPH Fluids with Vorticity Diffusion. IEEE Transactions on Visualization and Computer Graphics, 2016.
  • B. Solenthaler and R. Pajarola. Density Contrast SPH Interfaces. In Proceedings of ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 2008.
  • B. Solenthaler and R. Pajarola. Predictive-corrective incompressible SPH. ACM Trans. Graph., 28(3):40:1–40:6, July 2009.
  • Tetsuya Takahashi, Yoshinori Dobashi, Issei Fujishiro, Tomoyuki Nishita, and Ming C. Lin. Implicit Formulation for SPH-based Viscous Fluids. Computer Graphics Forum, 34, 2015.
  • Marcel Weiler, Dan Koschier and Jan Bender. Projective Fluids. Proceedings of the 9th International Conference on Motion in Games, ACM, 2016, 79-84
  • Marcel Weiler, Dan Koschier, Magnus Brand and Jan Bender. A Physically Consistent Implicit Viscosity Solver for SPH Fluids. Computer Graphics Forum (Eurographics), 37(2), 2018
  • F. Zorilla, M. Ritter, J. Sappl, W. Rauch, M. Harders. Accelerating Surface Tension Calculation in SPH via Particle Classification and Monte Carlo Integration. Computers 9, 23, 2020.

Other research projects using SPlisHSPlasH

  • Diogo Schaffer, Andre Antonitsch, Amyr Neto, Soraia Musse. Towards Animating Virtual Humans in Flooded Environments. Motion, Interaction and Games, 2020 https://dl.acm.org/doi/10.1145/3424636.3426900
  • Byungsoo Kim, Vinicius C. Azevedo, Markus Gross, Barbara Solenthaler. Lagrangian neural style transfer for fluids. ACM Transactions on Graphics 39, 4, 2020 https://dl.acm.org/doi/abs/10.1145/3386569.3392473
  • Fernando Zorilla, Marcel Ritter, Johannes Sappl, Wolfgang Rauch, Matthias Harders. Accelerating Surface Tension Calculation in SPH via Particle Classification and Monte Carlo Integration. Computer Graphics and Visual Computing (CGVC), 2019 https://diglib.eg.org/handle/10.2312/cgvc20191260
  • H. R. Abbasia and R. Lubbad. A numerical model for the simulation of oil–ice interaction. Physics of Fluids 33, 2021 https://aip.scitation.org/doi/10.1063/5.0065587
  • Uljad Berdica, Yuewei Fu, Yuchen Liu, Emmanouil Angelidis, Chen Feng. Mobile 3D Printing Robot Simulation with Viscoelastic Fluids. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021 https://ieeexplore.ieee.org/document/9636114
  • Arnaud Schoentgen, Pierre Poulin, Emmanuelle Darles, Philippe Meseure. Particle-based liquid control using animation templates. ACM SIGGRAPH/Eurographics Symposium on Computer Animation 2020 https://dl.acm.org/doi/10.1111/cgf.14103
  • B. Ummenhofer, L. Prantl, N. Thuerey, V. Koltun. Lagrangian Fluid Simulation with Continuous Convolutions. ICLR 2020 https://ge.in.tum.de/publications/2020-ummenhofer-iclr/
  • Stefan Reinhardt, Tim Krake, Bernhard Eberhardt, Daniel Weiskopf. Consistent Shepard Interpolation for SPH-Based Fluid Animation. ACM Transactions on Graphics 38, 6, 2019 https://dl.acm.org/doi/10.1145/3355089.3356503
  • Zhongyao Yang, Maolin Wu, Shiguang Liu. Helmholtz decomposition-based SPH. Virtual Reality & Intelligent Hardware 3, 2, 2021 https://www.sciencedirect.com/science/article/pii/S2096579621000176
  • Min Li, Hongshu Li, Weiliang Meng, Jian Zhu, Gary Zhang. An efficient non-iterative smoothed particle hydrodynamics fluid simulation method with variable smoothing length. Visual Computing for Industry, Biomedicine, and Art 6, 1, 2023
  • Yalmar Ponce Atencio, Manuel J. Ibarra, Juan José Oré Cerrón, Roberto Quispe Quispe, Richard Flores Condori, Julio Huanca Marín, Mary Huaman Carrión. Particle-Based Physics for Interactive Applications. Lecture Notes in Networks and Systems book series (LNNS,volume 216), 2021

Project details


Download files

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

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

pySPlisHSPlasH-2.12.2-cp310-cp310-win_amd64.whl (3.8 MB view details)

Uploaded CPython 3.10Windows x86-64

pySPlisHSPlasH-2.12.2-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (4.5 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.12+ x86-64

pySPlisHSPlasH-2.12.2-cp39-cp39-win_amd64.whl (3.8 MB view details)

Uploaded CPython 3.9Windows x86-64

pySPlisHSPlasH-2.12.2-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (4.5 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.12+ x86-64

pySPlisHSPlasH-2.12.2-cp38-cp38-win_amd64.whl (3.8 MB view details)

Uploaded CPython 3.8Windows x86-64

pySPlisHSPlasH-2.12.2-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (4.5 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.12+ x86-64

pySPlisHSPlasH-2.12.2-cp37-cp37m-win_amd64.whl (3.8 MB view details)

Uploaded CPython 3.7mWindows x86-64

pySPlisHSPlasH-2.12.2-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (4.5 MB view details)

Uploaded CPython 3.7mmanylinux: glibc 2.12+ x86-64

pySPlisHSPlasH-2.12.2-cp36-cp36m-win_amd64.whl (3.8 MB view details)

Uploaded CPython 3.6mWindows x86-64

pySPlisHSPlasH-2.12.2-cp36-cp36m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (4.5 MB view details)

Uploaded CPython 3.6mmanylinux: glibc 2.12+ x86-64

File details

Details for the file pySPlisHSPlasH-2.12.2-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for pySPlisHSPlasH-2.12.2-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 9dd00a15c55dd79b9958a3fbaef8017c2a772acf6f721d3ad0ad2650c87a34b8
MD5 ea4aa5ea55cc08db173bf41d1927dff7
BLAKE2b-256 092185859d0700ae8d4d44bd933b8b9b32b8d2e87b4f4f8dfcd82783a12de1ce

See more details on using hashes here.

File details

Details for the file pySPlisHSPlasH-2.12.2-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for pySPlisHSPlasH-2.12.2-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 7489555e3c8991e4e507e0bf8db890329544f8d15c06f499e6047771cb32a9bc
MD5 6889a06edccca81398bd33d1abf9f5b5
BLAKE2b-256 0e39203f16df6b773ef2d93cce741f416a40d4b961a632a7b38f66e233db774e

See more details on using hashes here.

File details

Details for the file pySPlisHSPlasH-2.12.2-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for pySPlisHSPlasH-2.12.2-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 677da552bd5b2c5b3545bbebc96d366865fec51f67a1f628b05a4bf2e9a29983
MD5 7612149eabe1d3fe110cd7cc5b3a1b56
BLAKE2b-256 1007e7db77cfaff6fc0f379a4d5c34f76876de022448ad68e7c46eb2c4744f9c

See more details on using hashes here.

File details

Details for the file pySPlisHSPlasH-2.12.2-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for pySPlisHSPlasH-2.12.2-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 e6d6237a3bbc07b670fa27a513ad214fcbbdc937f549df69f50cfae0232b4805
MD5 a53867455dcc0bf022fa2c61fa28d56b
BLAKE2b-256 01452e24f8a59ba92a066d449736c5d13bcf56fabec48ec60acb93c5074d3748

See more details on using hashes here.

File details

Details for the file pySPlisHSPlasH-2.12.2-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for pySPlisHSPlasH-2.12.2-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 505248c5519f1c7d945b676cc14cdf071bb29dd200839cbae541c2bd49dfd9af
MD5 9798ac63ec8d72f11503997fd9acf7a0
BLAKE2b-256 2fdbc28e045a6f6978a81b1690390787aba9432ffc30a21ed890b535671da67c

See more details on using hashes here.

File details

Details for the file pySPlisHSPlasH-2.12.2-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for pySPlisHSPlasH-2.12.2-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 d8c87212f894b2c2a1de83f288d985b1a73c79c83cd1773ff219efd505adf848
MD5 7362816ce3ce6023ceb74eda5720ca90
BLAKE2b-256 01f7a6c8856d000c8cd57ed0c31e89d7d60ac9bf8240dd3643cd44eb2a7c5e40

See more details on using hashes here.

File details

Details for the file pySPlisHSPlasH-2.12.2-cp37-cp37m-win_amd64.whl.

File metadata

File hashes

Hashes for pySPlisHSPlasH-2.12.2-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 0c7ba7933ce192486a49c3afa2bc6edec93f1953b201c7c8958a1bd3efd5ebf4
MD5 9ec6ccd91d7423f298bee8045c188139
BLAKE2b-256 8e7c7d41535840bf7c9aa87d83615e26321d659fc67a54579b138583f39fa344

See more details on using hashes here.

File details

Details for the file pySPlisHSPlasH-2.12.2-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for pySPlisHSPlasH-2.12.2-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 105d1d7fe079bc682efe70d274b1d653f29cfbebd45c3804bef2edaef1c7c413
MD5 220bf7ed07a2d81f4ed17552fa175c97
BLAKE2b-256 73b69c1de5b34577d615e992ca56cd81d5f069c378f281e4640575712a94f9ea

See more details on using hashes here.

File details

Details for the file pySPlisHSPlasH-2.12.2-cp36-cp36m-win_amd64.whl.

File metadata

  • Download URL: pySPlisHSPlasH-2.12.2-cp36-cp36m-win_amd64.whl
  • Upload date:
  • Size: 3.8 MB
  • Tags: CPython 3.6m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.9.6 readme-renderer/34.0 requests/2.27.1 requests-toolbelt/0.10.1 urllib3/1.26.14 tqdm/4.64.1 importlib-metadata/4.8.3 keyring/23.4.1 rfc3986/1.5.0 colorama/0.4.5 CPython/3.6.8

File hashes

Hashes for pySPlisHSPlasH-2.12.2-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 c42a8b9bd1630dbd773bef17937e03f729dcd04705a701e08a089d9a7539ba4f
MD5 3fb3e02c198e1143c413cfe047d27e5a
BLAKE2b-256 41c01c0b6f6e5e4183ed73648d22fa215b8f1379f5190c2f05236907d227088e

See more details on using hashes here.

File details

Details for the file pySPlisHSPlasH-2.12.2-cp36-cp36m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for pySPlisHSPlasH-2.12.2-cp36-cp36m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 9979bc0b177b45ed7312eb264d082468efbc1ed5506e03c7733b209526c4a220
MD5 f586ed9343c29b80c6b85db0df617aef
BLAKE2b-256 05015afac7afb60eafaceb543df33e3361ffc364078e0a81df03ca5803b5f755

See more details on using hashes here.

Supported by

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