Out of memory error on interpolation

I am new in matlab. Recently i started some work. But i am stack at a point. I have a data of size = 301 241 46 119. Here some data is missing. But i need to fill all of this data. I tried to use interp3 for this. But i am getting out of memory exception. I also tried in https://matlab.mathworks.com/ Still same error, I need help to solve this problem Here is my code

% Check for missing values

missing_indices = find(isnan(temp_data));

% Identify the coordinates (latitude, longitude, depth, and season) of missing values

[lat_missing, lon_missing, depth_missing, season_missing] = ind2sub(size(temp_data), missing_indices);

% Perform data interpolation for missing values using interp3

temperature_interpolated = interp3(lon, lat, depth, season, temp_data, lon_missing, lat_missing, depth_missing, season_missing, 'linear');

% Replace missing values with the interpolated values

temp_data(missing_indices) = temperature_interpolated;

Error:

Out of memory.

Error in internal.matlab.imagesci.nc/read (line 711)

data(data==fillValue) = NaN;

Error in ncread (line 76)

vardata = ncObj.read(varName, varargin{:});

[Torsten]

Where in the examples did you find such a call to "interp3" ?

I only see calls with at most 7 inputs arrays.

Answer 1

[Walter Roberson]

You are not running out of memory in a call to interp3(): you are running out of memory in a call to ncread()

The data file that you are reading is occupies more than 1/2 of your available memory, so when MATLAB tries to compare each data location to the fill value in order to convert fill values to nan, the temporary logical array that is generated fills the rest of remaining memory.

You might possibly be able to do better by using netcdf.open and netcdf.getVar yourself but you might have to disable fill using netcdf.setFill

But you will probably find that in practice what you should be doing is using the ncread start and count parameters so that you are only reading in part of the variable at one time. For example it is often that case that you really only need the data for one timestep at a time -- that even if you are doing an animated plot, the processing on the data often effectively "summarizes" the data down to an array the size of your figure at a time, which can be much more managable.