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        "author": "Sebastian Achim Mueller",
        "author_email": "sebastian-achim.mueller@mpi-hd.mpg.de",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: C",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Astronomy",
            "Topic :: Scientific/Engineering :: Image Recognition",
            "Topic :: Scientific/Engineering :: Medical Science Apps.",
            "Topic :: Scientific/Engineering :: Physics"
        ],
        "description": "# Overlap of a ray and a volume cell (voxel) [![Build Status](https://travis-ci.org/cherenkov-plenoscope/ray_voxel_overlap.svg?branch=master)](https://travis-ci.org/cherenkov-plenoscope/ray_voxel_overlap)\n\nEstimate the euclidean overlap passed by a ray within a rectangular volume cell (voxel).\n\n![img](readme/ray_and_voxel.svg)\n\nFor a given, rectangular space partitioning in 3D, and a given ray the overlap of all voxels with the ray is estimated.\nThe figure shows a ray and its overlap with voxels.\nA brown overlap with voxel ```3```, a red overlap with voxel ```0```, a purple overlap with voxel ```4```, and a green overlap with voxel ```5```. The ray is defined by its support and direction vectors. The space-partitioning is defined by its bin-edges.\n# Interface\nThere is one core function:\n\n```python\nimport ray_voxel_overlap\nray_voxel_overlap.estimate_overlap_of_ray_with_voxels?\n\"\"\"\nReturns the voxel indices and overlap distances for one single ray\n(defined by support and direction) with voxels defined by the bin_edges\nin x,y and z.\n\nsupport         3D support vector of ray.\n\ndirection       3D direction vector of ray.\n\nx_bin_edges     voxel bin edge positions in x.\n\ny_bin_edges     voxel bin edge positions in y.\n\nz_bin_edges     voxel bin edge positions in z.\n\"\"\"\n```\n\nThere are two more functions:\n\n- 2nd ```ray_voxel_overlap.estimate_system_matrix()```\n\nCreate a system-matrix using scipy.sparse matrix which can be used for iterative tomographic reconstructions.\n\n- 3rd ```ray_voxel_overlap.estimate_overlap_of_ray_bundle_with_voxels()```\n\nAverage the overlap of multiple rays representing a single read-out-channel. This is useful when a single ray is not representative enough for the geometry sensed by a read-out-channel in your tomographic setup, e.g. when there is a narrow depth-of-field.\n\n## Tomographic system-matrix\n\n```python\nimport numpy as np\nimport ray_voxel_overlap as rvo\n\nnp.random.seed(0)\n\nN_RAYS = 100\nsupports = np.array([\n    np.random.uniform(-2.5, 2.5, N_RAYS),\n    np.random.uniform(-2.5, 2.5, N_RAYS),\n    np.zeros(N_RAYS)\n]).T\n\ndirections = np.array([\n    np.random.uniform(-0.3, 0.3, N_RAYS),\n    np.random.uniform(-0.3, 0.3, N_RAYS),\n    np.ones(N_RAYS)\n]).T\n\nnorm_directions = np.linalg.norm(directions, axis=1)\ndirections[:, 0] /= norm_directions\ndirections[:, 1] /= norm_directions\ndirections[:, 2] /= norm_directions\n\nN_X_BINS = 8\nN_Y_BINS = 13\nN_Z_BINS = 7\nsystem_matrix = rvo.estimate_system_matrix(\n    supports=supports,\n    directions=directions,\n    x_bin_edges=np.linspace(-100., 100., N_X_BINS+1),\n    y_bin_edges=np.linspace(-100., 100., N_Y_BINS+1),\n    z_bin_edges=np.linspace(0., 200., N_Z_BINS+1),\n)\n```\n## How it is done\nTo be fast, the production-code is written in ```C``` and wrapped in ```cython```. But for development, there is a ```python``` implementation.\n\n## Authors\nSebastian A. Mueller,\n\nETH-Zurich, Switzerland (2014-2019),\n\nMPI-Heidelberg, Germany (2019-)",
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