Data coverage per cell of 3D array

I have a 3D data array of size 150 x 75 x 730 (lat x lon x time (days). Data coverage for each day (3rd dimension) varies with NAN values representing no data.

How do I calculate the data coverage in each cell for the total time duration? I want to use this to select cells with suffiicient data coverage.

[Lisa Hancke]

Thanks. I used

isfinite

and then

sum

along the 3rd dimension to get the same result. Easy :)

Answer 1

[Voss]

% create a 3D matrix data with 15000 elements
data = rand(15,10,100);
% put NaNs at 7500 random indices (which may have repeats)
data(randi(numel(data),7500,1)) = NaN;
% this many elements of data are NaN:
nnz(isnan(data))

ans = 5918

% data coverage is the total number of non-NaN elements
% at each (x,y) point, where x and y correspond to the 
% first 2 dimensions of data.
% sum ~isnan(data) over the third dimension to find
% the data coverage:
data_coverage = sum(~isnan(data),3);
disp(data_coverage);

    61    61    63    61    67    61    56    57    61    69
    67    62    59    67    62    59    60    56    62    56
    57    69    61    60    60    65    61    66    65    58
    60    68    58    62    64    52    57    61    62    55
    65    55    58    60    69    61    65    57    55    60
    59    54    68    52    60    64    60    55    63    58
    64    54    65    63    59    56    62    61    46    58
    68    55    60    64    63    58    63    63    55    58
    54    55    61    59    69    62    60    49    61    60
    59    61    63    64    57    63    63    58    73    55
    64    60    61    62    57    60    65    60    56    67
    52    65    50    53    67    65    62    54    55    64
    61    68    61    58    68    61    69    56    62    62
    52    65    60    63    56    67    57    64    62    67
    62    56    68    58    60    69    57    58    51    58

% say sufficient data coverage is >= 60 non-NaNs
has_sufficient_data = data_coverage >= 60 % using the number of non-NaNs

has_sufficient_data = 15×10 logical array
   1   1   1   1   1   1   0   0   1   1
   1   1   0   1   1   0   1   0   1   0
   0   1   1   1   1   1   1   1   1   0
   1   1   0   1   1   0   0   1   1   0
   1   0   0   1   1   1   1   0   0   1
   0   0   1   0   1   1   1   0   1   0
   1   0   1   1   0   0   1   1   0   0
   1   0   1   1   1   0   1   1   0   0
   0   0   1   0   1   1   1   0   1   1
   0   1   1   1   0   1   1   0   1   0
   1   1   1   1   0   1   1   1   0   1
   0   1   0   0   1   1   1   0   0   1
   1   1   1   0   1   1   1   0   1   1
   0   1   1   1   0   1   0   1   1   1
   1   0   1   0   1   1   0   0   0   0

% which is the same as <= 40 NaNs:
has_sufficient_data = sum(isnan(data),3) <= 40 % using the number of NaNs

has_sufficient_data = 15×10 logical array
   1   1   1   1   1   1   0   0   1   1
   1   1   0   1   1   0   1   0   1   0
   0   1   1   1   1   1   1   1   1   0
   1   1   0   1   1   0   0   1   1   0
   1   0   0   1   1   1   1   0   0   1
   0   0   1   0   1   1   1   0   1   0
   1   0   1   1   0   0   1   1   0   0
   1   0   1   1   1   0   1   1   0   0
   0   0   1   0   1   1   1   0   1   1
   0   1   1   1   0   1   1   0   1   0
   1   1   1   1   0   1   1   1   0   1
   0   1   0   0   1   1   1   0   0   1
   1   1   1   0   1   1   1   0   1   1
   0   1   1   1   0   1   0   1   1   1
   1   0   1   0   1   1   0   0   0   0