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Code for "A density-driven method for the placement of biological cells over two-dimensional manifolds"

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rougier/density-driven

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A density-driven method for the placement of biological cells over two-dimensional manifolds
Copyright 2017 Nicolas P. Rougier, BSD License.

We introduce a graphical method originating from the computer graphics domainthat is used for the arbitrary placement of cells over a two-dimensionalmanifold. Using a bitmap image whose luminance provides cell density, thismethod guarantees a discrete distribution of cell position re- specting thelocal density. is method scales to any number of cells, allows to specifyarbitrary shapes and provides a scalable and versatile alternative to the moreclassical assumption of a non- uniform spatial distribution. e method isillustrated on a discrete homogeneous neural eld, on the distribution of conesand rods in the retina and on the neural density on a a ened piece of cortex.

Figure 1

Please go tohttps://github.com/ReScience-Archives/Rougier-2017

Figure 2

Before runningfigure-2.py, you'll need to run thestippler.py script on thegradient-1024x256.png image as follows:

$ ./stippler.py --n_iter 25 --n_point 1000 --channel red data/gradient-1024x256.png$ mv data/gradient-1024x256-stipple-1000.npy output$$ ./stippler.py --n_iter 25 --n_point 2500 --channel red data/gradient-1024x256.png$ mv data/gradient-1024x256-stipple-2500.npy output$$ ./stippler.py --n_iter 25 --n_point 5000 --channel red data/gradient-1024x256.png$ mv data/gradient-1024x256-stipple-5000.npy output$$ ./stippler.py --n_iter 25 --n_point 10000 --channel red data/gradient-1024x256.png$ mv data/gradient-1024x256-stipple-10000.npy output

Figure 3

Run the scriptfigure-3.py.

Figure 5

Run the scriptfigure-5.py.

Figure 6

Run the scriptfigure-6.py.

Figure 7

Run the scriptfigure-7.py.

Figure 8

Run scriptsfigure-8A.py,figure-8B.py andfigure-8C.py.

Figure 9

Run the scriptfigure-9AC.py, then run:

$ ./stippler.py --n_iter 25 --n_point 25000 --channel red output/galago-patch.png$ ./stippler.py --n_iter 25 --n_point 25000 --channel red output/galago-inter.png

Then run the scriptfigure-9BD.py.

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