Extracting Data from Tall Arrays using Logical Indexing
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I have a dataset made up of tall arrays [X Y Z]. Ultimately, I would like to create some kind of mask or sorting function to extract specific regions of interest. The issue I am running into is how to create a filter using tall arrays and more spcifically, how to do so without using for loops.
For example, I would like to know all of the [X, Y, Z] data points that reside within a cylinder of radius (R) angled 30 degrees from the center (0,0,0). All other values can be removed.
The dataset currently resides in a datastore, to assist with this analysis, I imported a small amount of data for processing and converted [X Y Z] to spherical coordinates [azimuth, elevation, r].
I believe the correct approach is to use Logical Indexing for the three conditions [azimuth, elevation, r]. If someone could help me with the initial set-up of these conditions, i would greatly appreciate it.
Benjamin Großmann on 26 Feb 2020
Edited: Benjamin Großmann on 26 Feb 2020
I think you mentioned everything that you need for this task. Try this as a starting point:
clearvars, close all
% some random data, shall be [x,y,z]
data = rand(100,3);
% transform to cylindrical coordinates
[theta,rho,z] = cart2pol(data(:,1), data(:,2), data(:,3));
% define the mask(s)
rho_min = 0;
rho_max = 0.5;
rho_mask = (rho >= rho_min) & (rho <= rho_max);
theta_min = 0;
theta_max = pi/6;
theta_mask = (theta >= theta_min) & (theta <= theta_max);
z_min = -inf;
z_max = inf;
z_mask = (z >= z_min) & (z <= z_max);
% combine the masks
mask = rho_mask & theta_mask & z_mask;
% Apply the mask
data_filtered = data(mask,:);
Edit: Corrected the theta_mask and added a z_mask, which is inactive via the infinite borders. Code is optimised for clearness, i.e. the z mask is superfluous. Let me know if you have performance issues or need further help.