[](https://konigcell.readthedocs.io/en/latest/)
[](https://pypi.python.org/pypi/konigcell/)
[](https://konigcell.readthedocs.io/en/latest/?badge=latest)
[](https://lgtm.com/projects/g/anicusan/KonigCell/context:python)
[](https://lgtm.com/projects/g/anicusan/KonigCell/context:cpp)
[](https://github.com/anicusan/konigcell)
[](https://pypi.python.org/pypi/konigcell/)
[](https://pypi.org/project/konigcell/#files)
[](https://pypi.org/project/konigcell/#files)
[](https://pypi.org/project/konigcell/#files)
[](https://pypi.org/project/konigcell/#files)
#### **Quantitative, Fast Grid-Based Fields Calculations in 2D and 3D** - Residence Time Distributions, Velocity Grids, Eulerian Cell Projections etc.
That sounds dry as heck.
#### **Project moving particles' trajectories (experimental or simulated) onto 2D or 3D grids with infinite resolution.**
Better? No? Here are some figures produced by KonigCell:
*Left panel: 2D residence time distribution in a GranuTools GranuDrum imaged using Positron Emission Particle Tracking (PEPT). Two middle panels: 3D velocity distribution in the same system; voxels are rendered as a scatter plot (left) and tomogram-like 3-slice (right). Right panel: velocity vectorfield in a constricted pipe simulating a aneurysm, imaged using PEPT.*
This is, to my knowledge, the only library that accurately projects particle
trajectories onto grids - that is, taking their full projected area / volume into
account (and not approximating them as points / lines). It's also the only one creating
quantitative 3D projections.
And it is *fast* - 1,000,000 particle positions can be rasterized onto a 512x512
grid in 7 seconds on my 16-thread i9 CPU. The code is fully parallelised on
threads, processes or distributed MPI nodes.
## But Why?
Rasterizing moving tracers onto uniform grids is a powerful way of computing statistics about a
system - occupancies, velocity vector fields, modelling particle clump imaging etc. - be it
experimental or simulated. However, the classical approach of approximating particle trajectories
as lines discards a lot of (most) information.
Here is an example of a particle moving randomly inside a box - on a high resolution (512x512)
pixel grid, the classical approach (top row) does not yield much better statistics with increasing
numbers of particle positions imaged. Projecting complete trajectory **areas** onto the grid
(KonigCell, bottom row) preserves more information about the system explored:
A typical strategy for dealing with information loss is to coarsen the pixel grid, resulting in
a trade-off between accuracy and statistical soundness. However, even very low resolutions
still yield less information using line approximations (top row). With area projections,
**you can increase the resolution arbitrarily** and improve precision (KonigCell, bottom row):
## The KonigCell Libraries
This repository effectively hosts three libraries:
- `konigcell2d`: a portable C library for 2D grid projections.
- `konigcell3d`: a portable C library for 3D grid projections.
- `konigcell`: a user-friendly Python interface to the two libraries above.
### Installing the Python Package
This package supports Python 3.6 and above (though it might work with even older
versions).
Install this package from PyPI:
```pip install konigcell```
Or conda-forge:
```conda install konigcell```
If you have a relatively standard system, the above should just download pre-compiled wheels -
so no prior configuration should be needed.
To *build* this package on your specific machine, you will need a C compiler -
the low-level C code does not use any tomfoolery, so any compiler since the
2000s should do.
To build the latest development version from GitHub:
```pip install git+https://github.com/anicusan/KonigCell```
### Integrating the C Libraries with your Code
The C libraries in the `konigcell2d` and `konigcell3d` directories in this repository; they
contain instructions for compiling and using the low-level subroutines. All code is fully
commented and follows a portable subset of the C99 standard - so no VLAs, weird macros or
compiler-specific extensions. Even MSVC compiles it!
You can run `make` in the `konigcell2d` or `konigcell3d` directories to build shared
libraries and the example executables under `-Wall -Werror -Wextra` like a stickler. Running
`make` in the repository root builds both libraries.
Both libraries are effectively single-source - they should be as straightforward as possible
to integrate in other C / C++ codebases, or interface with from higher-level programming
languages.
## Examples and Documentation
The `examples` directory contains some Python scripts using the high-level Python routines
and the low-level Cython interfaces. The `konigcell2d` and `konigcell3d` directories contain
C examples.
Full documentation is available [here](https://konigcell.readthedocs.io/).
```python
import numpy as np
import konigcell as kc
# Generate a short trajectory of XY positions to pixellise
positions = np.array([
[0.3, 0.2],
[0.2, 0.8],
[0.3, 0.55],
[0.6, 0.8],
[0.3, 0.45],
[0.6, 0.2],
])
# The particle radius may change
radii = np.array([0.05, 0.03, 0.01, 0.02, 0.02, 0.03])
# Values to rasterize - velocity, duration, etc.
values = np.array([1, 2, 1, 1, 2, 1])
# Pixellise the particle trajectories
pixels1 = kc.dynamic2d(
positions,
mode = kc.ONE,
radii = radii,
values = values[:-1],
resolution = (512, 512),
)
pixels2 = kc.static2d(
positions,
mode = kc.ONE,
radii = radii,
values = values,
resolution = (512, 512),
)
# Create Plotly 1x2 subplot grid and add Plotly heatmaps of pixels
fig = kc.create_fig(
nrows = 1, ncols = 2,
subplot_titles = ["Dynamic 2D", "Static 2D"],
)
fig.add_trace(pixels1.heatmap_trace(), row = 1, col = 1)
fig.add_trace(pixels2.heatmap_trace(), row = 1, col = 2)
fig.show()
```
## Contributing
You are more than welcome to contribute to this library in the form of library
improvements, documentation or helpful examples; please submit them either as:
- GitHub issues.
- Pull requests (superheroes only).
- Email me at <a.l.nicusan@bham.ac.uk>.
## Acknowledgements
I would like to thank the Formulation Engineering CDT @School of Chemical
Engineering and the Positron Imaging Centre @School of Physics and
Astronomy, University of Birmingham for supporting my work.
And thanks to Dr. Kit Windows-Yule for putting up with my bonkers ideas.
## Citing
If you use this library in your research, you are kindly asked to cite:
> [Paper after publication]
This library would not have been possible without the excellent `r3d` library
(https://github.com/devonmpowell/r3d) which forms the very core of the C
subroutines; if you use KonigCell in your work, please also cite:
> Powell D, Abel T. An exact general remeshing scheme applied to physically conservative voxelization. Journal of Computational Physics. 2015 Sep 15;297:340-56.
## Licensing
KonigCell is MIT licensed. Enjoy.
Raw data
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"description": "\n\n[](https://konigcell.readthedocs.io/en/latest/)\n\n[](https://pypi.python.org/pypi/konigcell/)\n[](https://konigcell.readthedocs.io/en/latest/?badge=latest)\n[](https://lgtm.com/projects/g/anicusan/KonigCell/context:python)\n[](https://lgtm.com/projects/g/anicusan/KonigCell/context:cpp)\n[](https://github.com/anicusan/konigcell)\n[](https://pypi.python.org/pypi/konigcell/)\n\n[](https://pypi.org/project/konigcell/#files)\n[](https://pypi.org/project/konigcell/#files)\n[](https://pypi.org/project/konigcell/#files)\n[](https://pypi.org/project/konigcell/#files)\n\n#### **Quantitative, Fast Grid-Based Fields Calculations in 2D and 3D** - Residence Time Distributions, Velocity Grids, Eulerian Cell Projections etc.\n\nThat sounds dry as heck.\n\n#### **Project moving particles' trajectories (experimental or simulated) onto 2D or 3D grids with infinite resolution.**\n\nBetter? No? Here are some figures produced by KonigCell:\n\n\n\n\n*Left panel: 2D residence time distribution in a GranuTools GranuDrum imaged using Positron Emission Particle Tracking (PEPT). Two middle panels: 3D velocity distribution in the same system; voxels are rendered as a scatter plot (left) and tomogram-like 3-slice (right). Right panel: velocity vectorfield in a constricted pipe simulating a aneurysm, imaged using PEPT.*\n\n\nThis is, to my knowledge, the only library that accurately projects particle\ntrajectories onto grids - that is, taking their full projected area / volume into\naccount (and not approximating them as points / lines). It's also the only one creating\nquantitative 3D projections.\n\nAnd it is *fast* - 1,000,000 particle positions can be rasterized onto a 512x512\ngrid in 7 seconds on my 16-thread i9 CPU. The code is fully parallelised on\nthreads, processes or distributed MPI nodes.\n\n\n\n## But Why?\n\nRasterizing moving tracers onto uniform grids is a powerful way of computing statistics about a\nsystem - occupancies, velocity vector fields, modelling particle clump imaging etc. - be it \nexperimental or simulated. However, the classical approach of approximating particle trajectories\nas lines discards a lot of (most) information.\n\nHere is an example of a particle moving randomly inside a box - on a high resolution (512x512)\npixel grid, the classical approach (top row) does not yield much better statistics with increasing\nnumbers of particle positions imaged. Projecting complete trajectory **areas** onto the grid\n(KonigCell, bottom row) preserves more information about the system explored:\n\n\n\n\nA typical strategy for dealing with information loss is to coarsen the pixel grid, resulting in\na trade-off between accuracy and statistical soundness. However, even very low resolutions\nstill yield less information using line approximations (top row). With area projections,\n**you can increase the resolution arbitrarily** and improve precision (KonigCell, bottom row):\n\n\n\n\n\n\n## The KonigCell Libraries\n\nThis repository effectively hosts three libraries:\n\n- `konigcell2d`: a portable C library for 2D grid projections.\n- `konigcell3d`: a portable C library for 3D grid projections.\n- `konigcell`: a user-friendly Python interface to the two libraries above.\n\n\n\n### Installing the Python Package\n\nThis package supports Python 3.6 and above (though it might work with even older\nversions).\n\nInstall this package from PyPI:\n\n```pip install konigcell``` \n\n\nOr conda-forge:\n\n```conda install konigcell```\n\n\nIf you have a relatively standard system, the above should just download pre-compiled wheels -\nso no prior configuration should be needed.\n\n\nTo *build* this package on your specific machine, you will need a C compiler -\nthe low-level C code does not use any tomfoolery, so any compiler since the\n2000s should do.\n\n\nTo build the latest development version from GitHub:\n\n```pip install git+https://github.com/anicusan/KonigCell```\n\n\n\n### Integrating the C Libraries with your Code\n\nThe C libraries in the `konigcell2d` and `konigcell3d` directories in this repository; they\ncontain instructions for compiling and using the low-level subroutines. All code is fully\ncommented and follows a portable subset of the C99 standard - so no VLAs, weird macros or\ncompiler-specific extensions. Even MSVC compiles it!\n\nYou can run `make` in the `konigcell2d` or `konigcell3d` directories to build shared\nlibraries and the example executables under `-Wall -Werror -Wextra` like a stickler. Running\n`make` in the repository root builds both libraries.\n\nBoth libraries are effectively single-source - they should be as straightforward as possible\nto integrate in other C / C++ codebases, or interface with from higher-level programming\nlanguages.\n\n\n\n## Examples and Documentation\n\nThe `examples` directory contains some Python scripts using the high-level Python routines\nand the low-level Cython interfaces. The `konigcell2d` and `konigcell3d` directories contain\nC examples.\n\nFull documentation is available [here](https://konigcell.readthedocs.io/).\n\n```python\nimport numpy as np\nimport konigcell as kc\n\n# Generate a short trajectory of XY positions to pixellise\npositions = np.array([\n [0.3, 0.2],\n [0.2, 0.8],\n [0.3, 0.55],\n [0.6, 0.8],\n [0.3, 0.45],\n [0.6, 0.2],\n])\n\n# The particle radius may change\nradii = np.array([0.05, 0.03, 0.01, 0.02, 0.02, 0.03])\n\n# Values to rasterize - velocity, duration, etc.\nvalues = np.array([1, 2, 1, 1, 2, 1])\n\n# Pixellise the particle trajectories\npixels1 = kc.dynamic2d(\n positions,\n mode = kc.ONE,\n radii = radii,\n values = values[:-1],\n resolution = (512, 512),\n)\n\npixels2 = kc.static2d(\n positions,\n mode = kc.ONE,\n radii = radii,\n values = values,\n resolution = (512, 512),\n)\n\n# Create Plotly 1x2 subplot grid and add Plotly heatmaps of pixels\nfig = kc.create_fig(\n nrows = 1, ncols = 2,\n subplot_titles = [\"Dynamic 2D\", \"Static 2D\"],\n)\n\nfig.add_trace(pixels1.heatmap_trace(), row = 1, col = 1)\nfig.add_trace(pixels2.heatmap_trace(), row = 1, col = 2)\n\nfig.show()\n```\n\n\n\n\n\n## Contributing\nYou are more than welcome to contribute to this library in the form of library\nimprovements, documentation or helpful examples; please submit them either as:\n\n- GitHub issues.\n- Pull requests (superheroes only).\n- Email me at <a.l.nicusan@bham.ac.uk>.\n\n\n\n## Acknowledgements\nI would like to thank the Formulation Engineering CDT @School of Chemical\nEngineering and the Positron Imaging Centre @School of Physics and\nAstronomy, University of Birmingham for supporting my work.\n\nAnd thanks to Dr. Kit Windows-Yule for putting up with my bonkers ideas.\n\n\n\n## Citing\nIf you use this library in your research, you are kindly asked to cite:\n\n> [Paper after publication]\n\n\nThis library would not have been possible without the excellent `r3d` library\n(https://github.com/devonmpowell/r3d) which forms the very core of the C\nsubroutines; if you use KonigCell in your work, please also cite:\n\n> Powell D, Abel T. An exact general remeshing scheme applied to physically conservative voxelization. Journal of Computational Physics. 2015 Sep 15;297:340-56.\n\n\n\n## Licensing\nKonigCell is MIT licensed. Enjoy.\n",
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