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inTRA_aer_eff_ind_camp.m
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clear
close all
cd '~/Box/grad/research/aerosol_reldisp/datasets/'
load clouds.mat
campaigns={'vocals','mase','post','oracles','gomaccs'};
ctitle={'VOCALS','MASE','POST','ORACLES','GoMACCS'};
drp_instr={'pdi','pdi','pdi','pdi','pdi'};
%%
set(groot, 'DefaultFigurePosition', [1738 403 732 534])
close all
for c = 1
clear b X y y_hat pred
clear slp_raw intcpt_raw rsq_raw
camp = [campaigns{c} drp_instr{c}];
fb = load([camp,'_flight_basics.mat']);
fbvar = [camp,'_flight_basics'];
% maxN = arrayfun(@(x) clouds.(camppdi)(x).maxN, 1:length(clouds.(camppdi)));
min_dtpt = 100;
% exclude the date where not enough data points are present
dtpt_pdi = arrayfun(@(x) length(clouds.(camp)(x).s_t),1:length(clouds.(camp)));
vdate_pdi = find(dtpt_pdi>min_dtpt)';
% close all
epsvar = ['s_disp_' drp_instr{c}];
Nvar = ['s_ntot_' drp_instr{c}];
ndays = length(vdate_pdi);
nbins = 20;
samp_frac = 0.5;
upper_limit = 2e4;
% layers = 3;
layer_seg = [0 0.2 0.8 1.2];
allb = [];
all_pred_dailyreg = [];
allX = [];
ally = [];
all_y_hat_dailyreg = [];
ndays = length(clouds.(camp));
days_analyzed = 1:ndays;
% remove the days that have incomplete flights in vocals
if strcmp(camp,'vocalspdi')
days_analyzed(ismember(days_analyzed,[8,10,11,13]))=[];
elseif strcmp(camp,'oraclespdi')
days_analyzed(ismember(days_analyzed, [2,3,5,14,15,16,17,18]))=[];
end
% close all
for iday = days_analyzed
% get the unfiltered time first for later use
s_t_unfilt = floor(clouds.(camp)(iday).s_t);
a_t_unfilt = clouds.(camp)(iday).a_t;
cloudlegs_i = fb.(fbvar)(iday).ti;
cloudlegs_f = fb.(fbvar)(iday).tf;
% T_BL = fb.(fbvar)(iday).T_BL;
% T_FB = fb.(fbvar)(iday).T_FB;
if ~isempty(cloudlegs_i)
for ileg = 1:length(cloudlegs_i)
% get the initial and final time for each cloud
ti = cloudlegs_i(ileg);
tf = cloudlegs_f(ileg);
% filtering criteria
s_filt_crit = find(s_t_unfilt > ti & s_t_unfilt < tf &...
clouds.(camp)(iday).(Nvar) > 25);
a_filt_crit = find(a_t_unfilt > ti & a_t_unfilt < tf);
s_t_leg = floor(clouds.(camp)(iday).s_t(s_filt_crit));
if c == ~strcmp(camp,'masepdi')
a_t_leg = s_t_leg;
else
a_t_leg = clouds.(camp)(iday).a_t(a_filt_crit);
end
[cmt{c,iday,ileg}, cmt_ipdi{c,iday,ileg}, cmt_ipcasp{c,iday,ileg}] = ...
intersect(s_t_leg,a_t_leg);
s_all_filt_crit = s_filt_crit(cmt_ipdi{c,iday,ileg});
a_all_filt_crit = a_filt_crit(cmt_ipcasp{c,iday,ileg});
reldisp = clouds.(camp)(iday).(epsvar)(s_all_filt_crit);
s_ntot = clouds.(camp)(iday).(Nvar)(s_all_filt_crit);
% s_ap = clouds.(camp)(iday).s_ap(s_all_filt_crit);
if strcmp(camp,'oraclespdi') || strcmp(camp,'gomaccspdi')
normAC = clouds.(camp)(iday).a_normAC(a_all_filt_crit);
try %#ok<TRYNC>
thet = clouds.(camp)(iday).a_thet(a_all_filt_crit);
end
elseif ~strcmp(camp,'gomaccspdi')
normAC = clouds.(camp)(iday).normAC(s_all_filt_crit);
thet = clouds.(camp)(iday).s_thet(s_all_filt_crit);
end
a_ntot = clouds.(camp)(iday).a_ntot(a_all_filt_crit);
a_ntot_ex = clouds.(camp)(iday).a_ntot_ex(a_all_filt_crit);
s_actfrac = clouds.(camp)(iday).s_actfrac(s_all_filt_crit);
s_lwc = clouds.(camp)(iday).s_lwc_pdi(s_all_filt_crit);
% s_qt = clouds.(camp)(iday).s_qt(s_all_filt_crit);
% ent_ratio_T = clouds.(camp)(iday).ent_ratio_T(s_all_filt_crit);
ent_ratio_qt = clouds.(camp)(iday).ent_ratio_qt(s_all_filt_crit);
% if ismember(c,[1 2 5])
% s_rh = clouds.(camp)(iday).s_rh(s_all_filt_crit);
% elseif ismember(c, [3 4])
% s_rh = clouds.(camp)(iday).a_rh(a_all_filt_crit);
% end
% s_ss = (s_rh-100)/100;
% s_ss(s_ss<0) = nan;
indvar_raw = normAC;
depvar_raw = reldisp;
% make sure indvar and depvar are a pair of non-NaNs
indvar = indvar_raw(~isnan(indvar_raw) & ~isnan(depvar_raw));
depvar = depvar_raw(~isnan(indvar_raw) & ~isnan(depvar_raw));
% color = normAC(~isnan(indvar_raw) & ~isnan(depvar_raw));
if sum(~isnan(depvar))>100 && ~strcmp(camp,'gomaccspdi')
% set the maximum subsample size of each bin
max_per_bin = floor(length(indvar)/nbins);
[N_raw,edges,bin] = histcounts(indvar,nbins);
N = ceil(samp_frac*N_raw);
N(N>max_per_bin) = max_per_bin;
N(N<5) = 0;
rand_samp_idx = [];
for ibin = 1:nbins
rand_samp_idx_bin = randsample(find(bin==ibin),N(ibin));
rand_samp_idx = [rand_samp_idx; rand_samp_idx_bin];
end
% SubSampled data
indvar_ss = indvar(rand_samp_idx);
depvar_ss = depvar(rand_samp_idx);
bin_ss = bin(rand_samp_idx);
% to prevent matrix dimension error below
if length(bin_ss) ~= length(N)
ac_arr_raw = accumarray(bin_ss,depvar_ss);
ac_arr_raw(ac_arr_raw==0) = [];
ac_arr(N~=0) = ac_arr_raw;
ac_arr(N==0) = 0;
end
bindep = ac_arr./N;
bincenters=(edges(1:end-1)+edges(2:end))/2;
elseif strcmp(camp,'gomaccspdi')
rand_samp_idx = true(size(s_ntot));
end
% figure
% scatter(indvar(rand_samp_idx), depvar(rand_samp_idx), [], color(rand_samp_idx), '.')
x1 = a_ntot(rand_samp_idx);
x2 = s_ntot(rand_samp_idx);
x3 = s_actfrac(rand_samp_idx);
x4 = normAC(rand_samp_idx);
x5 = ent_ratio_qt(rand_samp_idx);
y = reldisp(rand_samp_idx);
if strcmp(camp,'gomaccspdi')
X = [ones(size(x1)) x3 x5];
else
X = [ones(size(x1)) x3 x4];
end
% X(:,2:end) = X(:,2:end) - mean(X(:,2:end));
% b = regress(y,X);
% y_hat = X*b;
% pred = 1-nansum((y_hat-y).^2)/nansum((y-nanmean(y)).^2);
[b,~,pred] = regress_tt(y, X, 0.7);
y_hat = X*b;
allX = [allX;X];
ally = [ally;y];
all_y_hat_dailyreg = [all_y_hat_dailyreg;y_hat];
allb = [allb;b'];
all_pred_dailyreg = [all_pred_dailyreg;pred];
end
end
end
meanB = regress(ally, allX);
[coeff(c,:), train_rsq(c), test_rsq(c)] = regress_tt(ally, allX, 0.7);
% all_y_hat_campmean = allX*meanB;
notnan_idx_cm = ~isnan(all_y_hat_campmean);
notnan_idx_dr = ~isnan(all_y_hat_dailyreg);
all_y_hat_campmean = allX*coeff(c,:)';
pred_campmean = 1-nansum((all_y_hat_campmean-ally).^2)/nansum((ally(notnan_idx_cm)-nanmean(ally(notnan_idx_cm))).^2);
pred_dailyreg = 1-nansum((all_y_hat_dailyreg-ally).^2)/nansum((ally(notnan_idx_dr)-nanmean(ally(notnan_idx_dr))).^2);
figure('Position',[1004 403 732 534])
dp_color = '#E16B8C';
% close all
sp = scatter(all_y_hat_dailyreg,ally,'filled','MarkerFaceAlpha',0.1,'MarkerFaceColor',dp_color);
ax_lim = [0 1];
xlim(ax_lim)
ylim(ax_lim)
set(gca,'fontsize',16)
refline(1,0)
title(['\Pi_s = ', sprintf('%0.4f \n(predictability of fit)',pred_dailyreg)])
figure
dp_color = '#E16B8C';
% close all
sp = scatter(all_y_hat_campmean,ally,'filled','MarkerFaceAlpha',0.1,'MarkerFaceColor',dp_color);
ax_lim = [0 1];
xlim(ax_lim)
ylim(ax_lim)
set(gca,'fontsize',16)
refline(1,0)
title(['\Pi_c = ', sprintf('%0.4f \n(predictability of fit)',test_rsq(c))])
% nanmean(allpred)
% nanmedian(allpred)
% abs(nanstd(allb)./nanmean(allb))
%
y_hat_combined = [all_y_hat_dailyreg all_y_hat_campmean];
pred_combined = [pred_dailyreg test_rsq(c)];
title_string = {'Self predictability', 'Cross predictability'};
y_hat_string = {'dailyreg', 'campmean'};
end
% saveas(figure(1),'plots/abstract/self_meas_vs_pred_eps.png')
% saveas(figure(2),'plots/abstract/cross_meas_vs_pred_eps.png')