interpolation

问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 7 months ago . I have been struggling to inteprolate the data for "empty" pixels in my 2D matrix. Basically, I understand (but not deeply) interpolation techniques such as Inverse Distance Weighting, Kriging, Bicubic etc. I dont know the starting point exactly (either in the statement of the

问题 I have some volumetric imaging data consisting of values sampled on a regular grid in x,y,z, but with a non-cubic voxel shape (the space between adjacent points in z is greater than in x,y). I would eventually like to be able to interpolate the values on some arbitrary 2D plane that passes through the volume, like this: I'm aware of scipy.ndimage.map_coordinates , but in my case using it is less straightforward because it implicitly assumes that the spacing of the elements in the input array

问题 I've been trying to use scipy.interpolate.bisplrep() and scipy.interpolate.interp2d() to find interpolants for data on my (218x135) 2D spherical-polar grid. To these I pass 2D arrays, X and Y, of the Cartesian positions of my grid nodes. I keep getting errors like the following (for linear interp. with interp2d): "Warning: No more knots can be added because the additional knot would coincide with an old one. Probably cause: s too small or too large a weight to an inaccurate data point. (fp>s)

问题 I am trying to implement some function like below For this I am trying to use Cubic interpolation and Catmull interpolation ( check both separately to compare the best result) , what i am not understanding is what impact these interpolation show on image and how we can get these points values where we clicked to set that curve ? and do we need to define the function these black points on the image separately ? I am getting help from these resources Source 1 Source 2 Approx the same focus Edit

问题 i have a matrix consist of 5 columns the first and second columns are for x_start & y_start of the line, the third and fourth are for x_end & y_end the fifth is -concentration of contaminant in this line- for example: % x_start y_start x_end y_end concentration frac= [0 0 1 1 0.3 1 1 3 3 0.6 3 3 10 2 1.2 3 3 10 8 0.5]; if i assume that i have a domain of interest 10mx10m and this domain is divided by finite difference cell size 1mx1m (i.e domain is 10 cells by 10 cells) and i want to

问题 I have this interpolate function taken from this code which I want to optimize: bool interpolate(const Mat &im, float ofsx, float ofsy, float a11, float a12, float a21, float a22, Mat &res) { bool ret = false; // input size (-1 for the safe bilinear interpolation) const int width = im.cols-1; const int height = im.rows-1; // output size const int halfWidth = res.cols >> 1; const int halfHeight = res.rows >> 1; int dim = res.rows * res.cols; float *out = res.ptr<float>(0); for (int j=

问题 this question is related with my previous question How to use numpy interpolation to increase a vector size, but this time I'm looking for a method to do increase the 2D array size and not a vector. The idea is that I have couples of coordinates (x;y) and I want to smooth the line with a desired number of (x;y) pairs for a Vector solution I use the answer of @AGML user with very good results from scipy.interpolate import UnivariateSpline def enlargeVector(vector, size): old_indices = np

问题 I managed connect the end points in the normal polarPlot like data([1:end 1],1) but doing interpolation does not interpolate the extended path data = load('rem_angle_2.dat'); n = 30; phi = interp(data([1:end 1],1)*pi/180, n); H = interp(data([1:end 1], 3), n); mu = 4 * 3.14e-7; ms = 1.2e6; K = 4.5e4; h = mu .* ms .* H / (2 .* K); cosphi = h .* abs(cos( phi )) + (cos( phi ) ) .^2; polar(phi, cosphi, 'r-x'); Example output in the red circle Data 0 0.0314410000000000 0.940571096308908 15 0

问题 I'm writing a filter to format addresses in a single line. The object that will be passed into the filter has the format: { Line1: "123 Main St.", Line2: "Apartment 2", // Optional City: "Chicago", State: "IL", Zip: "60623" } I have the following so far: angular.module('myApp') .filter('address', function ($interpolate) { return function (input, template) { if (input === null || !angular.isDefined(input)) { return input; } // template is optional. If not provided, use the following if(

问题 What are some good libraries for doing (fast) 3D interpolation? Preferably in C++, but I'm intrested in others, too. (Especially Matlab) 回答1: In Matlab, 3D interpolation is performed by INTERP3. For some speed gain, pass your method argument preceded by a star (e.g. '*cubic' instead of 'cubic' ). For some more speed gain, you can just lift the interpolation code out of the function and skip all the error checking. A good C++ implementation is likely to be faster, such as the one presented