Question about two dimensional PCA analysis

1 visualización (últimos 30 días)
Jaime  de la Mota
Jaime de la Mota el 19 de Jun. de 2018
Editada: Jaime de la Mota el 19 de Jun. de 2018
Hello everybody. I have a file containing wind data in directions S-N and W-E for some places and at some times denoted by latitudes and longitudes. I have written the following code:
if true
clear all
close all
clc
ncdisp('solicitud_31082016_00_12-60_ext.nc')
longitud = ncread('solicitud_31082016_00_12-60_ext.nc', 'longitude');
latitud = ncread('solicitud_31082016_00_12-60_ext.nc', 'latitude');
numero = ncread('solicitud_31082016_00_12-60_ext.nc', 'number');
tiempo = ncread('solicitud_31082016_00_12-60_ext.nc', 'time');
mat_u = ncread('solicitud_31082016_00_12-60_ext.nc', 'u');
mat_v = ncread('solicitud_31082016_00_12-60_ext.nc', 'v');
mat_t = ncread('solicitud_31082016_00_12-60_ext.nc', 't');
latitudes = size (mat_u, 2);
longitudes=size (mat_u, 1);
for contador_latitudes = 1:latitudes
for contador_longitudes =1: longitudes
U =(squeeze(mat_u(contador_longitudes,contador_latitudes,:,:)))';
V =(squeeze(mat_v(contador_longitudes,contador_latitudes,:,:)))';
module=sqrt(U.^2+V.^2);
t=size(V,1);
num_realizations=size(V,2);
Autocovariance=cov(module');
[eigenvectors,eigenvalues]=eig(Autocovariance);
[coeff, score, latent, tsquared, explained, mu]=pca(module,'Centered',false);T=score*coeff';TT=T';
useful_pcs=9;
coeff_global(:,:,contador_latitudes, contador_longitudes)=coeff;
score_global(:,:,contador_latitudes, contador_longitudes)=score;
tsquared_global(:,:,contador_latitudes, contador_longitudes)=tsquared;
explained_global(:,contador_latitudes, contador_longitudes)=explained;
mu_global(:, contador_latitudes, contador_longitudes)=mu;
end
end
end
However, I don't want to know just the module of the wind velocity for all these places at all these times. I want to know what is the relationship between both directions and not just the variation of the module of the wind.
Can someone please tell me how to do that?
Edit: I want to see not the unidimensional eigenvectors, instead I want to visualize a surface with the eigenvectors in both directions.
Thanks

Respuestas (0)

Categorías

Más información sobre Dimensionality Reduction and Feature Extraction 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