The library is designed to primarily focus on physically based simulations, geometric shape modeling, and optimization. The source code extends VegaFEM and is designed for academic research purposes.

The wheel package of following platform have been provided for ease of use. They are in ./dist folder:

• Ubuntu 24.04: ubuntu24.04/pypgo-0.0.3-cp312-cp312-linux_x86_64.whl. Note that you still need to install gmp and mpfr as suggested in the prerequisites. You may apt install them if needed.
• Ubuntu 22.04: ubuntu22.04/pypgo-0.0.2-cp311-cp311-linux_x86_64.whl. Note that you still need to install gmp and mpfr as suggested in the prerequisites. You may apt install them if needed. This version depends on a lower version of the libc, so it should be more compatible.
• Windows: win11/pypgo-0.0.3-cp312-cp312-win_amd64.whl. The package is built under Windows 11, Visual Studio 2022. In theory, it supports other windows platforms.
• MacOS Arm: pypgo-0.0.3-cp312-cp312-macosx_26_0_arm64.whl. The package is built under Tahoe 26.0.1 on Apple M3.

Do pip install ./dist/your-chosen.whl to install the package. Note that the packages are experimental.

1. CMake >= 3.28
   We use several functionalities that are only supported by 3.28+.

   In most cases, both system's CMake and Conda Environment's CMake have a lower version of CMake unfortunately. In this sitation, please install a new CMake into your system. The latest CMake, either pre-built binaries or source files, can be obtained directly from the official website. Once installed, hook cmake to the newly installed one, either by adding the your-new-cmake/bin to the front of the PATH or by replacing the existing cmake executable with the new one.

2. Compilers

   1. GCC 11, 12, 13 for Ubuntu
      We use C++20, so only GCC 11, 12, and 13 are supported. You can get new gcc using apt or compile a new one from its source code.

   2. Apple Clang (We tested on 15.0.0, Mac OS 14.5)
      Earlier versions might work if it supports C++20.

   3. Visual Studio 2022 (We tested on 17.9.5, Windows)
      Earlier Visual Studio 2022 versions might work.

3. GMP and MPFR for Ubuntu and Mac OS
   This can be installed on Ubuntu by

       sudo apt install libgmp-dev libmpfr-dev

   Or it can be installed on Mac OS by

       brew install gmp mpfr

4. (Optional) Ninja
   It can be installed by

       pip install ninja

   for better compilation performance

5. (Optional) numpy
   This is used for running tests.

Going forward, it is assumed that all specified prerequisites are installed and that a Conda environment is used for python.

Install prerequisites:

    conda install tbb tbb-devel mkl mkl-devel
    conda install conda-forge::imath

Install libpgo:

    cd libpgo
    pip install .

If ninja has been installed, it will compile source files in parallel. If it is not installed, set CMAKE_BUILD_PARALLEL_LEVEL to n, where n is the number of threads for compilation, to control the parallel compilation.

Install libpgo

    cd libpgo
    pip install .

We provide three python scripts to test the installation.

1. pgo_test_01.py. It runs a few basic pgo APIs.

       cd examples
       python ../src/python/pypgo/pgo_test_01.py

   The expected result will look like

   Opening file torus.veg.
   #vtx:564
   #tets:1950
   164,134,506,563
   L Info:
   10067040
   (10067040,)
   (10067040,)
   125.0
   GTLTLG Info:
   503400
   (503400,)
   (503400,)
   9695578.0
   [[  6.958279    0.          0.        -17.495821    0.          0.
      13.10052     0.          0.         -2.5629783   0.          0.       ]
    [  0.          6.958279    0.          0.        -17.495821    0.
       0.         13.10052     0.          0.         -2.5629783   0.       ]
    [  0.          0.          6.958279    0.          0.        -17.495821
       0.          0.         13.10052     0.          0.         -2.5629783]
    [ -5.1109824   0.          0.         10.111505    0.          0.
       8.160282    0.          0.        -13.160804    0.          0.       ]
    [  0.         -5.1109824   0.          0.         10.111505    0.
       0.          8.160282    0.          0.        -13.160804    0.       ]
    [  0.          0.         -5.1109824   0.          0.         10.111505
       0.          0.          8.160282    0.          0.        -13.160804 ]
    [ 23.97409     0.          0.         -6.634346    0.          0.
      -1.4866991   0.          0.        -15.853046    0.          0.       ]
    [  0.         23.97409     0.          0.         -6.634346    0.
       0.         -1.4866991   0.          0.        -15.853046    0.       ]
    [  0.          0.         23.97409     0.          0.         -6.634346
       0.          0.         -1.4866991   0.          0.        -15.853046 ]]
   

2. pgo_run_sim.py. It reads input config file and run simulation. You can try box, box-with-sphere, dragon, and dragon-dyn to test different simulation results. Take the box example for illustration. You can run the box example using the following commands.

       cd examples/box
       python ../../src/python/pypgo/pgo_run_sim.py box.json

   The expected result will look like the first image. The time integrator is hard-coded as implicit backward Euler (BE). You are free to change it to implicit Newmark (NW) or TR-BDF2 integrator (not support friction).

   Box (NM)	Box with Sphere (NM)
   Dragon (BE)	Bunny (BE)
   Rest Dragon	Deformed Dragon

3. pgo_dump_abc.py. It creates the abc file that can be used for blender/maya from config file anim.json. Essentially, it takes the simulation output .obj sequences and output a .abc file.

       cd examples/box
       python ../../src/python/pypgo/pgo_dump_abc.py anim.json ./

If you want to use the library with your C++ code or modify the source code, you may build it without python.

To compile the lib with a basic functionality,

    cd libpgo
    mkdir build
    cd build
    cmake ..

To enable all functionalities, Install MKL. Then,

    cd libpgo
    mkdir build
    cd build
    cmake .. -DPGO_USE_MKL=1 -DPGO_ENABLE_FULL=1

On Windows, a few extra steps are need before running the above commands. First, the library should be configured in "x64 Native Tools Command Prompt for VS 2022". In addition, before running the commands above, run c:\Program Files (x86)\Intel\oneAPI\setvars.bat to setup the environments for MKL, where c:\Program Files (x86)\Intel\oneAPI is the path to the oneAPI installation. Once setup, run above commands.

On Ubuntu, a similar procedure is needed. Before configuring the library, run bash /opt/intel/oneapi/setvars.sh to setup the MKL environments for the subsequent cmake configuration.

To have a basic functionality, use CMake to compile it like on Windows & Ubuntu.

To enable all functionalities,

    cd libpgo
    mkdir build
    cd build
    cmake .. -DPGO_ENABLE_FULL=1 -DDPGO_ENABLE_ALEMBIC=1 -DPGO_ENABLE_GMSH=1

The last two flags work only if you have imath and gmesh libs.

This library use the following third-party libraries:
alembic, argparse, autodiff, boost, ceres, cgal, fmt, geogram, gmesh, json, knitro, libigl, mkl, pybind11, spdlog, suitesparse, tbb, tinyobj-loader

This library is developed using VegaFEM along with various third-party libraries, each governed by their respective licenses. Detailed copyright and license information is included within the majority of the source files.

In instances where specific licensing details are not provided within a source file, the copyright remains with the author. The licensing for those source files adhere to the principles of the pre-existing license framework. For instance, if a source file without licensing details incorporates components that fall under the GPL parts of CGAL, then that file will adhere to the GPL. All other source files default to the MIT License unless stated otherwise.

• Functional and compilable on three major platforms.
• Documentation
• More python interface
• Cleanup source code with non-MIT/non-FreeBSD licence.
• GUI