Hi Angus,
Your aim is to create a 578x235 grid of ocean temperature data with land pixels represented as NaN values. Additionally, you need to implement a 1-pixel buffer around the coastline, ensuring that all pixels adjacent to land pixels (NaNs) also become NaNs. In order to accomplish this task, I will start by generating a 578x235 grid of random ocean temperature data using the rand function, set the first 100 rows of the grid as land pixels by assigning NaN values to them. Then, create a A logical array nan_mask to identify NaN and non-NaN values in the ocean_data. Now, to implement the 1-pixel buffer around the coastline, I will iterate through each pixel in the grid (excluding borders). For each land pixel, I will check if any of its adjacent pixels are also land pixels using the nan_mask. If any adjacent pixel is land, I will set the current pixel as land as well in the buffered_data. Finally, I will display the ocean temperature data grid with the coastline buffer using imagesc and add a colorbar for reference. Here is the snippet code as an example to illustrate it.
% Create a 578x235 grid of ocean temperature data with land pixels as NaN
ocean_data = rand(578, 235); % Generate random ocean temperature data
ocean_data(1:100, :) = NaN; % Set the first 100 rows as land (NaN)
% Create a logical array to identify NaN and non-NaN values
nan_mask = isnan(ocean_data);
% Implement a 1-pixel buffer around the coastline
buffered_data = ocean_data;
for i = 2:size(ocean_data, 1)-1
for j = 2:size(ocean_data, 2)-1
if ocean_data(i, j) == 0 % Check if the pixel is land
if any(any(nan_mask(i-1:i+1, j-1:j+1))) % Check if any adjacent pixel is land
buffered_data(i, j) = NaN; % Set the pixel as land
end
end
end
end
% Display the ocean temperature data grid with the coastline buffer
imagesc(buffered_data);
colorbar;
title('Ocean Temperature Data with Coastline Buffer');
Please see attached plot.
I hope this answers your question.
