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        "author": "Robert Sulej, R&D Team",
        "author_email": "dev@rnd.team",
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            "Development Status :: 4 - Beta",
            "Environment :: Win32 (MS Windows)",
            "Environment :: X11 Applications",
            "Intended Audience :: Education",
            "Intended Audience :: End Users/Desktop",
            "Intended Audience :: Science/Research",
            "License :: Free for non-commercial use",
            "Natural Language :: English",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX :: Linux",
            "Programming Language :: Python :: 3 :: Only",
            "Topic :: Artistic Software",
            "Topic :: Multimedia :: Graphics :: 3D Rendering",
            "Topic :: Scientific/Engineering :: Visualization"
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        "description": "PlotOptiX\n=========\n\n.. image:: https://img.shields.io/pypi/v/plotoptix.svg\n   :target: https://pypi.org/project/plotoptix\n   :alt: Latest PlotOptiX version\n.. image:: https://img.shields.io/pypi/dm/plotoptix.svg\n   :target: https://pypi.org/project/plotoptix\n   :alt: PlotOptiX downloads by pip install\n.. image:: https://img.shields.io/badge/support%20project-paypal-brightgreen.svg\n   :target: https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=RG47ZEL5GKLNA&source=url\n   :alt: Support project\n\n**Data visualisation in Python based on NVIDIA OptiX ray tracing framework.**\n\n**Note:** active development is continuing, expect changes.\n\n3D `ray tracing <https://en.wikipedia.org/wiki/Ray_tracing_(graphics)>`__ package for Python, aimed at easy and aesthetic visualization\nof large datasets (and small as well). Data features can be represented on plots as a position, size/thickness and color of markers\nof several basic shapes, or projected onto the surfaces of objects in form of a color textures and displacement maps. All finished with\na photorealistic lighting and depth of field.\n\nNo need to write shaders, intersection algorithms, handle 3D scene technicalities. Basic usage is even more simple than with\n`matplotlib <https://matplotlib.org/gallery/mplot3d/scatter3d.html>`__:\n\n.. code-block:: python\n\n   import numpy as np\n   from plotoptix import TkOptiX\n\n   n = 1000000                                  # 1M points, better not try this with matplotlib\n   xyz = 3 * (np.random.random((n, 3)) - 0.5)   # random 3D positions\n   r = 0.02 * np.random.random(n) + 0.002       # random radii\n\n   plot = TkOptiX()\n   plot.set_data(\"my plot\", xyz, r=r)\n   plot.show()\n\n... but PlotOptiX is much faster on large data and, with all the raytraced shades and DoF, more readable and eye catching.\n\n`Documentation pages <https://plotoptix.rnd.team>`__ are currently generated from the source code docstrings. Please,\nsee `examples on GitHub <https://github.com/rnd-team-dev/plotoptix/tree/master/examples>`__\nfor practical code samples.\n\nPlotOptiX is based on `NVIDIA OptiX <https://developer.nvidia.com/optix>`_ framework wrapped in RnD.SharpOptiX C#/C++ libraries\nand completed with custom CUDA shaders by `R&D Team <https://rnd.team>`_. PlotOptiX makes use of RTX-capable GPU's.\n\n.. image:: https://plotoptix.rnd.team/images/screenshots.jpg\n   :alt: PlotOptiX screenshots, scatter and line plots ray tracing\n\nFeatures\n--------\n\n- progressive path tracing with explicit light sampling\n- pinhole cameras and thin-lens cameras with depth of field simulation\n- geometries: particle (sphere), parallelepiped, parallelogram, tetrahedron, bezier line, surface mesh\n- parameterized materials shading: flat, diffuse, reflective, refractive; including: light dispersion, surface roughness, and nested volumes\n- spherical and parallelogram light sources\n- environmental light and ambient occlusion\n- post-processing: tonal correction curves, levels adjustment, apply mask/overlay, AI denoiser\n- GPU acceleration using RT Cores, multi-GPU support, and everything else what comes with `OptiX 6.0 <https://developer.nvidia.com/optix>`__\n- callbacks at the scene initialization, start and end of each frame raytracing, end of progressive accumulation\n- image output to `numpy <http://www.numpy.org>`__ array, or save to popular image file formats\n- hardware accelerated video output to MP4 file format using `NVENC 9.0 <https://developer.nvidia.com/nvidia-video-codec-sdk>`__\n- Tkinter based UI or headless raytracer\n\nSystem Requirements\n-------------------\n\n- a `CUDA-enabled GPU <https://developer.nvidia.com/cuda-gpus>`__ with compute capability 5.0 (Maxwell) to latest (Turing)\n- **Python 3 64-bit**\n- Windows:\n   - `.NET Framework <https://dotnet.microsoft.com/download/dotnet-framework>`__ >= 4.6.1 (present in normally updated Windows)\n- Linux:\n   - `Mono <https://www.mono-project.com/download/stable/#download-lin>`__ Common Language Runtime >= 5.2, recommended: 5.20.1\n   - `pythonnet <http://pythonnet.github.io>`__ >= 2.4\n   - `FFmpeg <https://ffmpeg.org/download.html>`__ >= 4.1\n- for video encoding: `CUDA Toolkit v10.x <https://developer.nvidia.com/cuda-downloads>`__ (tested with v10.0 and v10.1)\n\nWhat's Included\n---------------\n\n- OptiX 6.0.0 libraries\n- RnD.SharpOptiX and RnD.SharpEncoder libraries\n- all other supporting 3'rd party libraries: FFmpeg (Windows only), LibTiff, Newtonsoft.Json\n- Python examples\n\nInstallation\n============\n\n**GPU drivers note:** *Get ready for OptiX 7 backend and install the most recent drivers (>= 435)! If you experience problems with older driver releases then drivers 419 (Windows) and 418 (Ubuntu, CentOS) are recommended*.\n\n**Note**, at this point, PlotOptiX binaries are tested in: Windows 10, Ubuntu 18.04, CentOS 7.\n\nPlotOptiX was also successfully tested on the `Google Cloud Platform <https://cloud.google.com/>`__, using Compute Engine instance with 2x V100 GPU's and Ubuntu 18.04 image.\nHere are the `installation steps <https://github.com/rnd-team-dev/plotoptix/blob/master/gcp_install_gpu.txt>`__ so you can save some precious seconds (FFmpeg not included).\n\nWindows should be ready to go in most cases. You need to do some more typing in Linux. For video encoding you need to install CUDA toolkit in both Linux and Windows.\n\nWindows prerequisites\n---------------------\n\n*.NET Framework:*\n\nMost likely you already got the right version with your Windows installation. Just in case, here is the command verifying this::\n\n   C:\\>reg query \"HKEY_LOCAL_MACHINE\\SOFTWARE\\Microsoft\\NET Framework Setup\\NDP\\v4\\full\" /v version\n\n   HKEY_LOCAL_MACHINE\\SOFTWARE\\Microsoft\\NET Framework Setup\\NDP\\v4\\full\n       version    REG_SZ    4.7.03056\n\nIf the number in your output is < 4.6.1, visit `download page <https://dotnet.microsoft.com/download/dotnet-framework>`__ and\ninstall the most recent release.\n\nLinux prerequisites\n-------------------\n\n*Mono runtime:*\n\nCheck if / which Mono release is present in your system::\n\n   mono -V\n\n   Mono JIT compiler version 5.18.1.3 (tarball Tue Apr  9 16:16:30 UTC 2019)\n      Copyright (C) 2002-2014 Novell, Inc, Xamarin Inc and Contributors. www.mono-project.com\n\t   TLS:           __thread\n      ... (output cropped for clarity) ...\n\nIf ``mono`` command is not available, or the reported version is < 5.2, visit `Mono download page <https://www.mono-project.com/download/stable/#download-lin>`__ and follow instructions related to your Linux distribution. You want to install **mono-complete** package.\n\n**Note:** pythonnet 2.4.0 is not compatible with Mono 6.0; install Mono 5.20.1 instead::\n\n   apt -y install gnupg ca-certificates\n   apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-keys 3FA7E0328081BFF6A14DA29AA6A19B38D3D831EF\n\n   echo \"deb https://download.mono-project.com/repo/ubuntu stable-bionic/snapshots/5.20.1 main\" | sudo tee /etc/apt/sources.list.d/mono-official-stable.list\n\n   apt update\n   apt -y install mono-complete\n\n*pythonnet:*\n\nThe `pythonnet <http://pythonnet.github.io>`__ package is available from `PyPI <https://pypi.org/project/pythonnet>`__, however, some prerequisities are needed. Instuctions below are based on APT, replace ``apt`` with ``yum`` depending on your OS::\n\n   apt update\n   apt install clang libglib2.0-dev python-dev\n\nYou may also need to install development tools, if not already present in your system, e.g. in Ubuntu::\n\n   apt install build-essential\n\nor in CentOS::\n\n   yum group install \"Development Tools\" \n\nThen, update required packages and install ``pythonnet``::\n\n   pip install -U setuptools wheel pycparser\n   pip install -U pythonnet\n\nAfter successful installation you should be able to do python's import:\n\n.. code-block:: python\n\n   import clr\n   print(clr.__version__)\n\n*FFmpeg:*\n\nFFmpeg shared libraries >= 4.1 are required to enable video encoding features in PlotOptiX. Uninstall older version first. Visit `FFmpeg site <https://ffmpeg.org/download.html>`__ and download the most recent release sources. Unpack it to a new folder, cd to it. Configure, compile and install as below::\n\n   ./configure --enable-shared\n   make\n   sudo make install\n\nAdd FFmpeg's shared library path to your config::\n\n   export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib\n   sudo ldconfig\n\nCUDA Toolkit\n------------\n\nCUDA libraries are not included in the package and required only for the video encoding features of PlotOptiX. Visit\n`CUDA download page <https://developer.nvidia.com/cuda-downloads>`__, select your operating system and CUDA version **10.x**.\nDownload and run the installer.\n\n*Linux note:* Install the GPU driver before installing CUDA toolkit, it makes things easier.\n\nPlotOptiX\n---------\n\nUsing pip::\n\n   pip install -U plotoptix\n\nFrom GitHub sources::\n\n   git clone https://github.com/rnd-team-dev/plotoptix.git\n   cd plotoptix\n   python setup.py install\n\nThen, try running code from the top of this readme, or one of the examples. You may also need to install ``tkinter`` and/or ``PyQt`` packages, if not shipped with your Python environment.\n\nDenoiser binaries are optional and can be downloaded after PlotOptiX installation (the package size is ~370 MB, administrator rights are required for the installation)::\n\n   python -m plotoptix.install denoiser\n\nDevelopment path\n================\n\nThis is an early version. There are some important features not available yet, eg. ticks and labels on plot axes.\n\nPlotOptiX is basically an interface to RnD.SharpOptiX library which we are developing and using in our Studio. RnD.SharpOptiX offers\nmuch more functionality than it is now available through PlotOptiX. We'll progressively add more to PlotOptiX if there is interest in\nthis project (download, star, and `support <https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=RG47ZEL5GKLNA&source=url>`__\nif you like it!).\n\nThe idea for development is:\n\n1. Binaries for Linux (done in v0.3.0).\n2. Migrate to OptiX 7.0.\n3. Complete the plot layout / cover more raytracing features.\n4. Convenience functions for various plot styles. Other GUI's.\n\n   *Here, the community input is possible and warmly welcome!*\n\nExamples\n========\n\nLooking at examples is the best way to get started and explore PlotOptiX features. Have a look at the\n`readme and sample codes here <https://github.com/rnd-team-dev/plotoptix/tree/master/examples>`__.\n\nExamples in the repository head may use features not yet available in the PyPI release. In order to download examples\ncompatible with PyPI release install the package::\n\n\tpython -m plotoptix.install examples\n\nThis will create a folder with examples in the current directory.\n\n.. image:: https://plotoptix.rnd.team/images/surface_plot.jpg\n   :alt: Surface plot ray tracing with PlotOptiX\n\n\n",
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