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What is the preferred and efficient approach for interpolating multidimensional data?

Things I'm worried about:

  1. performance and memory for construction, single/batch evaluation
  2. handling dimensions from 1 to 6
  3. linear or higher-order
  4. ability to obtain gradients (if not linear)
  5. regular vs scattered grid
  6. using as Interpolating Function, e.g. to find roots or to minimize
  7. extrapolation capabilities

Is there efficient open-source implementation of this?

I had partial luck with scipy.interpolate and kriging from scikit-learn.

I did not try splines, Chebyshev polynomials, etc.

Here is what I found so far on this topic:

Python 4D linear interpolation on a rectangular gridPython 4D linear interpolation on a rectangular grid

Fast interpolation of regularly sampled 3D data with different intervals in x,y, and zFast interpolation of regularly sampled 3D data with different intervals in x,y, and z

Fast interpolation of regular grid dataFast interpolation of regular grid data

What method of multivariate scattered interpolation is the best for practical use?What method of multivariate scattered interpolation is the best for practical use?

What is the preferred and efficient approach for interpolating multidimensional data?

Things I'm worried about:

  1. performance and memory for construction, single/batch evaluation
  2. handling dimensions from 1 to 6
  3. linear or higher-order
  4. ability to obtain gradients (if not linear)
  5. regular vs scattered grid
  6. using as Interpolating Function, e.g. to find roots or to minimize
  7. extrapolation capabilities

Is there efficient open-source implementation of this?

I had partial luck with scipy.interpolate and kriging from scikit-learn.

I did not try splines, Chebyshev polynomials, etc.

Here is what I found so far on this topic:

Python 4D linear interpolation on a rectangular grid

Fast interpolation of regularly sampled 3D data with different intervals in x,y, and z

Fast interpolation of regular grid data

What method of multivariate scattered interpolation is the best for practical use?

What is the preferred and efficient approach for interpolating multidimensional data?

Things I'm worried about:

  1. performance and memory for construction, single/batch evaluation
  2. handling dimensions from 1 to 6
  3. linear or higher-order
  4. ability to obtain gradients (if not linear)
  5. regular vs scattered grid
  6. using as Interpolating Function, e.g. to find roots or to minimize
  7. extrapolation capabilities

Is there efficient open-source implementation of this?

I had partial luck with scipy.interpolate and kriging from scikit-learn.

I did not try splines, Chebyshev polynomials, etc.

Here is what I found so far on this topic:

Python 4D linear interpolation on a rectangular grid

Fast interpolation of regularly sampled 3D data with different intervals in x,y, and z

Fast interpolation of regular grid data

What method of multivariate scattered interpolation is the best for practical use?

7 added 273 characters in body
source | link

What is the preferred and efficient approach for interpolating multidimensional data?

Things I'm worried about:

  1. performance and memory for construction, single/batch evaluation
  2. handling dimensions from 1 to 6
  3. linear or higher-order
  4. ability to obtain gradients (if not linear)
  5. regular vs scattered grid
  6. using as Interpolating Function, e.g. to find roots or to minimize
  7. extrapolation capabilities

Is there efficient open-source implementation of this?

I had partial luck with scipy.interpolate and kriging from scikit-learn.

I did not try splines, Chebyshev polynomials, etc.

Here is what I found so far on this topic:

http://stackoverflow.com/questions/14119892Python 4D linear interpolation on a rectangular grid

http://stackoverflow.com/questions/16217995Fast interpolation of regularly sampled 3D data with different intervals in x,y, and z

http://stackoverflow.com/questions/16983843Fast interpolation of regular grid data

http://stackoverflow.com/questions/6238250

http://stackoverflow.com/questions/592026What method of multivariate scattered interpolation is the best for practical use?

What is the preferred and efficient approach for interpolating multidimensional data?

Things I'm worried about:

  1. performance and memory for construction, single/batch evaluation
  2. handling dimensions from 1 to 6
  3. linear or higher-order
  4. ability to obtain gradients (if not linear)
  5. regular vs scattered grid
  6. using as Interpolating Function, e.g. to find roots or to minimize
  7. extrapolation capabilities

Is there efficient open-source implementation of this?

I had partial luck with scipy.interpolate and kriging from scikit-learn.

I did not try splines, Chebyshev polynomials, etc.

Here is what I found so far on this topic:

http://stackoverflow.com/questions/14119892

http://stackoverflow.com/questions/16217995

http://stackoverflow.com/questions/16983843

http://stackoverflow.com/questions/6238250

http://stackoverflow.com/questions/592026

What is the preferred and efficient approach for interpolating multidimensional data?

Things I'm worried about:

  1. performance and memory for construction, single/batch evaluation
  2. handling dimensions from 1 to 6
  3. linear or higher-order
  4. ability to obtain gradients (if not linear)
  5. regular vs scattered grid
  6. using as Interpolating Function, e.g. to find roots or to minimize
  7. extrapolation capabilities

Is there efficient open-source implementation of this?

I had partial luck with scipy.interpolate and kriging from scikit-learn.

I did not try splines, Chebyshev polynomials, etc.

Here is what I found so far on this topic:

Python 4D linear interpolation on a rectangular grid

Fast interpolation of regularly sampled 3D data with different intervals in x,y, and z

Fast interpolation of regular grid data

What method of multivariate scattered interpolation is the best for practical use?

    Tweeted twitter.com/#!/StackSciComp/status/635903671730106368
6 added 11 characters in body
source | link

What is the preferred and efficient approach for interpolating multidimensional data?

Things I'm worried about:

  1. performance and memory for construction, single/batch evaluation
  2. handling dimensions from 1 to 6
  3. linear or higher-order
  4. ability to obtain gradients (if not linear)
  5. regular vs scattered grid
  6. using as Interpolating Function, e.g. to find roots or to minimize
  7. extrapolation capabilities

Is there efficient open-source implementation of this?

I had partial luck with scipy.interpolate and kriging from scikit-learn.

I did not try splines, Chebyshev polynomials, RBF, IDW, etc.

Here is what I found so far on this topic:

http://stackoverflow.com/questions/14119892

http://stackoverflow.com/questions/16217995

http://stackoverflow.com/questions/16983843

http://stackoverflow.com/questions/6238250

http://stackoverflow.com/questions/592026

What is the preferred and efficient approach for interpolating multidimensional data?

Things I'm worried about:

  1. performance for construction, single/batch evaluation
  2. handling dimensions from 1 to 6
  3. linear or higher-order
  4. ability to obtain gradients (if not linear)
  5. regular vs scattered grid
  6. using as Interpolating Function, e.g. to find roots or to minimize
  7. extrapolation capabilities

Is there efficient open-source implementation of this?

I had partial luck with scipy.interpolate and kriging from scikit-learn.

I did not try splines, Chebyshev polynomials, RBF, IDW, etc.

Here is what I found so far on this topic:

http://stackoverflow.com/questions/14119892

http://stackoverflow.com/questions/16217995

http://stackoverflow.com/questions/16983843

http://stackoverflow.com/questions/6238250

http://stackoverflow.com/questions/592026

What is the preferred and efficient approach for interpolating multidimensional data?

Things I'm worried about:

  1. performance and memory for construction, single/batch evaluation
  2. handling dimensions from 1 to 6
  3. linear or higher-order
  4. ability to obtain gradients (if not linear)
  5. regular vs scattered grid
  6. using as Interpolating Function, e.g. to find roots or to minimize
  7. extrapolation capabilities

Is there efficient open-source implementation of this?

I had partial luck with scipy.interpolate and kriging from scikit-learn.

I did not try splines, Chebyshev polynomials, etc.

Here is what I found so far on this topic:

http://stackoverflow.com/questions/14119892

http://stackoverflow.com/questions/16217995

http://stackoverflow.com/questions/16983843

http://stackoverflow.com/questions/6238250

http://stackoverflow.com/questions/592026

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