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pg_variance.m
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59 lines (52 loc) · 2.09 KB
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function [gc_var, gc_angvar] = pg_variance(grid_cell)
% pg_variance.m
% 17 December 2018
%
% Finds circular variance of grid_cell input azimuths. Applies
% circ_var.m from CircStat2012a library.
%
% INPUTS:
% grid_cell
% 180 x 360 cell array of stroke-station great circle path
% crossings. Each cell is a n x 3 array of format
% stroke index | time | azimuth to stroke
%
% grid_cell variables can be very large and computationally
% expensive; they should not be recalculated with subsequent
% runs of pg_gridcell. pg_gridcell should be run once per input
% file, and the grid_cell variable should be saved to a file with
% format grid_cell_*.mat.
%
% OUTPUTS:
% gc_var, gc_angvar
% 180 x 360 array of skewness of azimuths of stroke-station great
% circle path crossings. Each element refers to all
% paths crossing that lat/lon grid location in the input
% grid_cell; i.e. each element is the size n of the corresponding
% n x 3 cell in grid_cell.
%
% gc_var: variance = 1-R
% gc_angvar: angular variance = 2*(1-R)
%
% gc_var matrices are small in memory relative to
% grid_cell cell arrays -- a whole day grid_cell can be 2-4 GB,
% while the corresponding grid_crossings with 1-10 minute
% resolution (i.e. a 3-D matrix with dimension 180 x 360 x (time
% res)) could be tens of MB. Therefore, whole-day statistics are
% best calculated by running pg_gridcell and pg_gridcross for
% 10-minute input files, then concatenating pg_gridcross into a
% whole-day 3-D matrix.
%
gc_var = zeros(180,360);
gc_angvar = zeros(180,360);
for n = 1:180
for p = 1:360
if size(grid_cell{n,p},1) == 0
gc_var(n,p) = NaN;
gc_angvar(n,p) = NaN;
else
[gc_var(n,p), gc_angvar(n,p)] = circ_var(deg2rad(grid_cell{n,p}(:,3)),[],[],1);
end
end
end
end