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        "author": "the-lay",
        "author_email": "ilja.gubin@gmail.com",
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        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3 :: Only",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "## voltools\n[![TraviCI build status](https://travis-ci.com/the-lay/voltools.svg?branch=master)](https://travis-ci.com/the-lay/voltools)\n[![PyPI latest version](https://badge.fury.io/py/voltools.svg)](https://pypi.org/project/voltools/)\n\n##### CUDA-accelerated numpy/cupy texture memory 3D affine transformations\n\n#### Features\n1. `transforms` module that offers CUDA-accelerated affine transforms for 3D numpy arrays:\n```python\nimport numpy as np\nfrom voltools import transform\n\nvolume = np.random.random((200, 200, 200)).astype(np.float32)\ntransformed_volume = transform(volume, interpolation='filt_bspline', device='cpu',\n                               translation=(10, 0, -10),\n                               rotation=(0, 45, 0), rotation_units='deg', rotation_order='rzxz')\n```\n\n2. `StaticVolume` class optimized for multiple transformations of the same data.\nThe data transfer is minimized (especially for GPU devices) to just the transformation matrix for each transformation.\n```python\nimport numpy as np\nfrom voltools import StaticVolume\n\nvolume = StaticVolume(np.random.random((200, 200, 200)).astype(np.float32), interpolation='filt_bspline', device='gpu:0')\nfor i in range(0, 180):\n    rotated_vol = volume.rotate(rotation=(0, i, 0), rotation_units='deg', rotation_order='rzxz', profile=True)\n```\n\n3. If you don't need to move data back from GPU to CPU, you can specify `output=some_cupy_array` keyword\nand the result of transformation will be saved there. Works for both `transforms` and `StaticVolume`. If output is not\nspecified, methods will return a numpy array.\n\n4. Support for different devices. Each method accepts `device` keyword with option `cpu` (default).\nHowever, if cupy is present, additional options include `gpu` (cupy auto-selects gpu) and `gpu:X`\nfor each GPU (X is ID of GPU).\n\n5. Various interpolations currently supported:\n- `linear`, tri-linear interpolation\n- `bspline`, cubic b-spline interpolation (optimized, 8 texture lookups)\n- `bspline_simple`, cubic b-spline interpolation (simple implementation, 27 texture lookups)\n- `filt_bspline`, prefiltered cubic b-spline interpolation (8 texture lookups) \n- `filt_bspline_simple`, prefiltered cubic b-spline interpolation (27 texture lookups)\n\n#### Installation\n\nPIP: `pip install voltools`  \nSource: `pip install git+https://github.com/the-lay/voltools`\n\nIf you want to use use GPUs, please install cupy >= 7.0.0b4\n([cupy installation guide](https://docs-cupy.chainer.org/en/stable/install.html#install-cupy)).\n\n\n#### TODO\n- Tests\n- Travis? Other CI?\n- Visualizations? Some kind of easy to launch volume viewer.\n- Return scripts back: projections\n- Develop branch for cleaner separation of code\n\n#### Notes\n- CUDA cubic b-spline interpolation is based on [Danny Ruijters's implementation](https://github.com/DannyRuijters/CubicInterpolationCUDA/)\n- Transformation matrices are based on [Christoph Gohlike's transformations.py](https://www.lfd.uci.edu/~gohlke/code/transformations.py.html)\n\n\n#### Benchmark\nSource: combination of different runs (`device='cpu' and 'gpu'`, `order=1 or 3`, `interpolation='linear' or 'filt_bspline_simple' or 'filt_bspline'`) of `tests/benchmark.py`   \nResults on laptop GTX1050Ti, i7-7700HQ CPU @ 2.80GHz, timings are in ms, first column is the size of volume\n\n##### Linear interpolation (`interpolation='linear'`)\nScipy.affine_transform was run with `order=1`\n```\n                  scipy      np_transform  np_transform_out  cp_transform  cp_transform_out  static_vol  static_vol_out\n5, 5, 5            0.099339      1.707743     0.198648         0.178715       0.149916         0.096302      0.057303\n25, 25, 25         1.317875      0.355751     0.226222         0.193489       0.162452         0.113279      0.057102\n50, 50, 50         9.752507      0.490053     0.290321         0.230922       0.198530         0.151064      0.092354\n100, 100, 100     86.330383      1.569304     1.426492         0.835178       0.773746         0.494033      0.403363\n250, 250, 250   1732.845833     22.273793    21.761067        13.274235      12.677875         9.971454      8.768116\n```\n\n##### Cubic b-spline interpolation optimized lookup (`interpolation='bspline' or interpolation='filt_bspline'`)\nScipy.affine_transform was run with `order=3`\n```\n                   scipy      np_transform   np_transform_out  cp_transform  cp_transform_out  static_vol  static_vol_out\n5, 5, 5           0.185161       1.467492        0.191925      0.176825          0.147542      0.095937        0.055644\n25, 25, 25        5.506060       0.336234        0.208039      0.187378          0.155858      0.112726        0.061163\n50, 50, 50        45.34205       0.571488        0.392732      0.329547          0.290759      0.242434        0.181332\n100, 100, 100   368.032446       2.488342        2.331586      1.689142          1.627921      1.345535        1.250627\n250, 250, 250  6003.420537      48.115719       47.672480     39.183325         38.772662     35.991094       34.685690\n```\n\n##### Cubic b-spline interpolation (`interpolation='bspline_simple' or interpolation='filt_bspline_simple'`)\nScipy.affine_transform was run with `order=3`\n```\n                   scipy      np_transform   np_transform_out  cp_transform  cp_transform_out  static_vol  static_vol_out\n5, 5, 5           0.201232      4.528787          0.207631      0.195885          0.163193    0.109361         0.059611\n25, 25, 25        5.240529      0.332447          0.238356      0.217309          0.194111    0.138756         0.09321\n50, 50, 50       43.515086      0.804508          0.632981      0.560689          0.527334    0.474820         0.416062\n100, 100, 100   375.886700      4.232868          4.114524      3.454444          3.390018    3.091396         2.999451\n250, 250, 250  6189.052927     95.083083         94.479406     85.200392         84.435548    81.808363       80.959284\n```\n\n\n",
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