Error in interp2 (interpolation) command.

9 visualizaciones (últimos 30 días)
Ivan Mich
Ivan Mich el 22 de Oct. de 2022
Comentada: Torsten el 24 de Oct. de 2022
I woyld like to intepolate my data. I have an ascii file with 3 columns (x,y,z). Each column has 700 lines (number). I am using the following commands, in order to interpolate (smooth) my data:
clc
clear
filename1= 'mydata.csv';
[d1,tex]= xlsread(filename1);
y=d1(:,3);
x=d1(:,4);
z=d1(:,5);
[xq, yq] = meshgrid(...
linspace(min(x),max(x)),...
linspace(min(y),max(y)));
zq = interp2(x,y,z,xq,yq,'cubic');
[c,h]= contourf(xq,yq,zq);
but command window shows me:
Error using griddedInterpolant
The grid vectors must be strictly monotonically increasing.
Error in interp2>makegriddedinterp (line 228)
F = griddedInterpolant(varargin{:});
Error in interp2 (line 128)
F = makegriddedinterp({X, Y}, V, method,extrap);
could you please help me?
  3 comentarios
Ivan Mich
Ivan Mich el 22 de Oct. de 2022
I am uploading a sample of data file
Torsten
Torsten el 22 de Oct. de 2022
No, the two inputs being provided to the INTERP2 are not vectors, but are in fact the (matrix) outputs from MESHGRID.
The inputs x,y and z to interp2 the OP provides are vectors (which is wrong). The query values xq and yq are matrices obtained from "meshgrid".

Iniciar sesión para comentar.

Respuestas (2)

Torsten
Torsten el 22 de Oct. de 2022
Editada: Torsten el 22 de Oct. de 2022
Read about the requirements for input arguments x, y and z for interp2:
Paragraph "Input Arguments".
  4 comentarios
Torsten
Torsten el 22 de Oct. de 2022
Your data are not gridded. Thus you will have to use "scatteredInterpolant":
F = scatteredInterpolant(x,y,z);
[xq,yq] = meshgrid(linspace(min(x),max(x)),linspace(min(y),max(y)));
zq = F(xq,yq)
Steven Lord
Steven Lord el 22 de Oct. de 2022
Confirm that your x and y vectors are sorted.
issorted(x, 'strictascend')
issorted(y, 'strictascend')
One or both of those will return false. Once you know which one(s) are not strictly ascending, you need to determine why. Plotting that vector may help you identify where the flat, decreasing, or missing segments are.

Iniciar sesión para comentar.


Torsten
Torsten el 22 de Oct. de 2022
d1 = readmatrix("https://de.mathworks.com/matlabcentral/answers/uploaded_files/1165673/mydata.csv");
y=d1(:,3);
x=d1(:,4);
z=d1(:,5);
F = scatteredInterpolant(x,y,z,'nearest');
Warning: Duplicate data points have been detected and removed - corresponding values have been averaged.
[xq,yq] = meshgrid(linspace(min(x),max(x)),linspace(min(y),max(y)));
zq = F(xq,yq);
contourf(xq,yq,zq)
colorbar
  3 comentarios
Stephen23
Stephen23 el 24 de Oct. de 2022
@Ivan Mich: you could try a thin-plate spline: https://www.mathworks.com/help/curvefit/tpaps.html
Torsten
Torsten el 24 de Oct. de 2022
Your z-data are not continuous - they only take values 1 1.5 2 2.5 3 ... 9. So would continuous values as 3.1865 even make sense for your application ?
And if you look at the contour plot from your raw data above: the only thing you can learn is that the values for the south-east part are lower than those for the north-west part.
Everything else like further smoothing would not be appropriate in my opinion.

Iniciar sesión para comentar.

Categorías

Más información sobre Contour Plots en Help Center y File Exchange.

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by