Inverse Distance Weighted (IDW) Interpolation with Python
The Question:
What is the best way to calculate inverse distance weighted (IDW) interpolation in Python, for point locations?
The Question:
What is the best way to calculate inverse distance weighted (IDW) interpolation in Python, for point locations?
I’m trying to port a program which uses a hand-rolled interpolator (developed by a mathematician colleage) over to use the interpolators provided by scipy. I’d like to use or wrap the scipy interpolator so that it has as close as possible behavior to the old interpolator.
I am builing my numpy/scipy environment based on blas and lapack more or less based on this walk through.
In numpy/scipy I have an image stored in an array. I can display it, I want to save it using savefig without any borders, axes, labels, titles,… Just pure image, nothing else.
I have a very similar question to this question, but still one step behind. I have only one version of Python 3 installed on my Windows 7 (sorry) 64-bit system.
How do you save/load a scipy sparse csr_matrix in a portable format? The scipy sparse matrix is created on Python 3 (Windows 64-bit) to run on Python 2 (Linux 64-bit). Initially, I used pickle (with protocol=2 and fix_imports=True) but this didn’t work going from Python 3.2.2 (Windows 64-bit) to Python 2.7.2 (Windows 32-bit) and got the error:
I’m trying to use dot products, matrix inversion and other basic linear algebra operations that are available in numpy from Cython. Functions like numpy.linalg.inv (inversion), numpy.dot (dot product), X.t (transpose of matrix/array). There’s a large overhead to calling numpy.* from Cython functions and the rest of the function is written in Cython, so I’d like to avoid this.
I have been trying to install Scipy onto my Python 3.5 (32-bit) install on my Windows 7 machine using the pre-built binaries from:
http://www.lfd.uci.edu/~gohlke/pythonlibs
I’m trying to colorize a Voronoi Diagram created using scipy.spatial.Voronoi. Here’s my code: