bridson | Poisson Disc Sampling in Python | Data Manipulation library

by emulbreh Python Version: 0.1.0 License: MIT

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | bridson Summary

bridson is a Python library typically used in Utilities, Data Manipulation, Numpy applications. bridson has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install bridson' or download it from GitHub, PyPI.

Poisson Disc Sampling in Python

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bridson has a low active ecosystem.

It has 29 star(s) with 2 fork(s). There are no watchers for this library.

It had no major release in the last 12 months.

bridson has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of bridson is 0.1.0

Quality

bridson has 0 bugs and 0 code smells.

Security

bridson has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

bridson code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

bridson is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

bridson releases are not available. You will need to build from source code and install.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

bridson saves you 38 person hours of effort in developing the same functionality from scratch.

It has 102 lines of code, 8 functions and 3 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed bridson and discovered the below as its top functions. This is intended to give you an instant insight into bridson implemented functionality, and help decide if they suit your requirements.

Generate samples from a poisson distribution .
Compute the euclidean distance between two vectors .

Get all kandi verified functions for this library.

bridson Key Features

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bridson Examples and Code Snippets

No Code Snippets are available at this moment for bridson.

Community Discussions

Trending Discussions on bridson

Fast submatrix lookup with numpy

Poisson Disk Sampling on a real dataset

QUESTION

Fast submatrix lookup with numpy

Asked 2021-Feb-06 at 09:22

I am writing an implementation of Bridson's Poisson disc sampling (https://www.cs.ubc.ca/~rbridson/docs/bridson-siggraph07-poissondisk.pdf) for Python.

A feature of this sampling is that there is a set minimal distance between samples, and it avoids clustering. The neighbour lookup for new candidate points exploits this feature, and uses a background grid to speed up searches.

The background grid consists of a boolean grid M that stores True for non-empty cells and False otherwise, and a grid of points P that stores the exact coordinates. Both of them are implemented as n-dimensional numpy arrays.

The grid size cellsize is selected in such a way that there is at most one sample in each grid cell, and then you only have to check a few of the nearest rows and columns.

For now, I use the following procedure for checking if the point p is close to any of the existing points:

...

ANSWER

Answered 2021-Feb-06 at 09:22

Slicing is the way:

The dimensionality makes it a bit tricky. In the end, the overhead for preparing the sliced into an array was well worth it. Maybe a further optimization would be possible if you knew the dimensions beforehands. It shouldn't be too much of a difference. As an interesting part, this made the M matrix almost redundant - it is only used to check if the point itself is worth the try since the rest of the multiplication is sped up drastically by the slice.

Update:

I re-introduced the M check as per comment and also used the np.square instead of np.power as op mentioned.

Time after the np.power > np.square transition:

Source https://stackoverflow.com/questions/66047540

QUESTION

Poisson Disk Sampling on a real dataset

Asked 2020-Nov-17 at 19:47

I have seen lots of people implementing the 'Fast Poisson Disk Sampling' algorithm a.k.a Bridson (Paper of the algorithm, python Code). However, I haven't found anyone implementing it on a real dataset. Let's say, someone has a predefined dataset.

I would like to give my poisson disk algorithm the coordinates of the points with the amount of points I want to sample with a certain condition on the radius. Has anyone done this before? Is it doable? Any help is appreciated.

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ANSWER

Answered 2020-Nov-17 at 19:47

Below is the code with examples posted on my github.

Poisson Disk Sampling

The code is not finalized yet, however, so far it works.

Random Sampling.

Poisson Disc Sampling

Source https://stackoverflow.com/questions/64837775

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install bridson

You can install using 'pip install bridson' or download it from GitHub, PyPI.
You can use bridson like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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