inverse_distance_weighting | dimensional spaces using inverse distance | Math library

by paulbrodersen Python Version: Current License: GPL-3.0

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kandi X-RAY | inverse_distance_weighting Summary

inverse_distance_weighting is a Python library typically used in Utilities, Math applications. inverse_distance_weighting has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However inverse_distance_weighting build file is not available. You can download it from GitHub.

Interpolation in D-dimensional spaces using inverse distance weighted, k-nearest neighbours

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inverse_distance_weighting has a low active ecosystem.

It has 42 star(s) with 16 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 1 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of inverse_distance_weighting is current.

Quality

inverse_distance_weighting has 0 bugs and 0 code smells.

Security

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

inverse_distance_weighting code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

inverse_distance_weighting is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

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

inverse_distance_weighting has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

inverse_distance_weighting saves you 14 person hours of effort in developing the same functionality from scratch.

It has 39 lines of code, 6 functions and 1 files.

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

Top functions reviewed by kandi - BETA

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

Applies the function to the model
Calculate the weights of the tree

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inverse_distance_weighting Key Features

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Community Discussions

Trending Discussions on inverse_distance_weighting

Create a matrix of neighbours

QUESTION

Create a matrix of neighbours

Asked 2018-Aug-10 at 13:55

I have a sparse matrix and I need to create a new neighbor matrix of each index.

Below I leave a representation of the data in the NxM matrix. For each of the elements of the matrix I need to obtain the neighbors in a section of KxK. With this information, it would generate a NMxKK matrix that contains in each row the indices of the neighboring KKs of the element.

I asked a similar question a while ago but the difference is that now the data is structured, so I can do without KdTree.

This new matrix is used to calculate the distance of non-zero neighbors, and with these distances associate a weight to each neighbor, to finally estimate the desired value as a weighted average of the neighbors.

Thanks in advance!

UPDATE

I have data like the ones in the image (generated with the function generate_data) and I need to perform the following operation.

Given a filter / kernel / NxN matrix, with N being the kernel size defined by me, calculate for nonzero values the distances with respect to the central pixel. Take as an example the value 20 that is in the position (1, 8) of the image. Taking a matrix of 5x5, the nonzero values of interest are 40 (in (0, 6)), 37 (in (1, 6)) and 25 (in (3, 10)), with distances 2.23606798, 2 and 2.82842712 respectively (obtained making the Euclidean norm between the indices).

What I need to get in this step is the matrix res:

...

ANSWER

Answered 2018-Aug-10 at 13:55

This was really simple, although maybe not very efficient. What I had to do was two convolutions:

In the first, it was convolucionar the Gaussian kernel with the matrix

conv_1 = convolve2d(m * mask_clean, k_gauss)
In the second, the Gaussian kernel with the mask

conv_2 = convolve2d(mask_clean, k_gauss)

In each position, conv_1 would have the sum of each value weighed by the corresponding factor of the Gaussian kernel. conv_2 would have in each position the sum of all nonzero values. The only thing left to do was divide them to get the final result

Source https://stackoverflow.com/questions/51086961

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

Vulnerabilities

No vulnerabilities reported

Install inverse_distance_weighting

You can download it from GitHub.
You can use inverse_distance_weighting 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.

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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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