## Functions | |

def | rand |

Returns a random integer in the range [0, n-1] inclusive. | |

def | sqr_dist |

The square of the distance between the given points. | |

def | sqr_dist_3d |

The square of the distance between the given points. | |

def | sample_poisson_uniform |

Gives a Poisson sample of points of a rectangle. | |

def | sample_poisson |

Gives a Poisson sample of points of a rectangle with an arbitrary distance function between points. | |

def | sample_poisson_3d |

Gives a Poisson sample of points of a box (3D rectangle). |

Tutorial: http://www.luma.co.za/labs/2008/02/27/poisson-disk-sampling/

def poisson_disk.rand | ( | n |
) |

Returns a random integer in the range [0, n-1] inclusive.

Definition at line 27 of file poisson_disk.py.

def poisson_disk.sample_poisson | ( | width, |
||

height, |
||||

r_grid, |
||||

k | ||||

) |

Gives a Poisson sample of points of a rectangle with an arbitrary distance function between points.

**Parameters:**-
*width*The width of the rectangle to sample *height*The height of the rectangle to sample *r_grid*r_grid[x, y] is the mimum distance between points around x, y, in terms of rectangle units. *k*The algorithm generates k points around points already in the sample, and then check if they are not too close to other points. Typically, k = 30 is sufficient. The larger k is, the slower th algorithm, but the more sample points are produced.

**Returns:**- A list of tuples representing x, y coordinates of of the sample points. The coordinates are not necesarily integers, so that the can be more accurately scaled to be used on larger rectangles.

Definition at line 137 of file poisson_disk.py.

def poisson_disk.sample_poisson_3d | ( | width, |
||

height, |
||||

depth, |
||||

r_grid, |
||||

k | ||||

) |

Gives a Poisson sample of points of a box (3D rectangle).

**Parameters:**-
*width*The width of the box to sample *height*The height of the box to sample *depth*The depth of the box to sample. *r_grid*r_grid[x, y, z] is the mimum distance between points around x, y, z, in terms of rectangle units. *k*The algorithm generates k points around points already in the sample, and then check if they are not too close to other points. Typically, k = 30 is sufficient. The larger k is, the slower th algorithm, but the more sample points are produced.

**Returns:**- A list of tuples representing x, y coordinates of of the sample points. The coordinates are not necesarily integers, so that the can be more accurately scaled to be used on larger rectangles.

Definition at line 220 of file poisson_disk.py.

def poisson_disk.sample_poisson_uniform | ( | width, |
||

height, |
||||

r, |
||||

k | ||||

) |

Gives a Poisson sample of points of a rectangle.

**Parameters:**-
*width*The width of the rectangle to sample *height*The height of the rectangle to sample *r*The mimum distance between points, in terms of rectangle units. For example, in a 10 by 10 grid, a mimum distance of 10 will probably only give you one sample point. *k*The algorithm generates k points around points already in the sample, and then check if they are not too close to other points. Typically, k = 30 is sufficient. The larger k is, the slower th algorithm, but the more sample points are produced.

**Returns:**- A list of tuples representing x, y coordinates of of the sample points. The coordinates are not necesarily integers, so that the can be more accurately scaled to be used on larger rectangles.

Definition at line 59 of file poisson_disk.py.

def poisson_disk.sqr_dist | ( | x0, |
||

y0, |
||||

x1, |
||||

y1 | ||||

) |

def poisson_disk.sqr_dist_3d | ( | x0, |
||

y0, |
||||

z0, |
||||

x1, |
||||

y1, |
||||

z1 | ||||

) |

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