-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathConsolidate_Apero.m
executable file
·490 lines (393 loc) · 22.3 KB
/
Consolidate_Apero.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
% Consolidate daily processing, merge variables, and export to SeaBASS
% author: Guillaume Bourdin
% created: May 27, 2024
%% Import data
% cd('/Volumes/Data2/TaraEuropa/InLineAnalysis-master')
cd('/Users/gui/Documents/MATLAB/InLineAnalysis/InLineAnalysis-master')
cruise = 'Apero';
% Load InLineAnalysis and the configuration
ila = InLineAnalysis(['cfg' filesep cruise '_cfg.m']);
path_dev = strrep(ila.instrument.FLOW.path.prod, ...
'prod', 'DeviceFiles');
% create Graph folder if it doesn't exist
if ~isfolder([ila.instrument.FLOW.path.prod '/plots'])
mkdir([ila.instrument.FLOW.path.prod '/plots'])
end
% whenever TSG and fdom are processed, load with:
% load([ila.instrument.FLOW.path.prod cruise '_InLine_TSG_prod.mat'])
% load([ila.instrument.SUVF6253.path.prod cruise '_InLine_SUVF6253_prod.mat'])
%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% TSG & GPS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
ila.cfg.days2run = datenum(2023,6,4):datenum(2023,7,11);
ila.cfg.instruments2run = {'SBE459999'};
% populate ila.instrument
ila.Read('prod');
% extract TSG data from obj
tsg_temp = ila.instrument.('SBE459999').prod.a;
tsg_temp.dt = datetime(tsg_temp.dt,'ConvertFrom','datenum');
tsg_temp = round_timestamp(tsg_temp);
% build TSG table: linearly interpolate missing lat, lon when missing data < replace_consecutive_nan
replace_consecutive_nan = 3*60; % 3h
tsg = merge_timeseries(tsg_temp, tsg_temp, {'lat', 'lon'}, '', replace_consecutive_nan);
% Remove NaN
if any(any(isnan([tsg.lat tsg.lon]),2))
warning('%i row with missing lat/lon (longer than %ih consecutive): deleted', ...
sum(any(isnan([tsg.lat tsg.lon]),2)), replace_consecutive_nan/60)
tsg(any(isnan([tsg.lat tsg.lon]),2), :) = [];
end
tsg(:, contains(tsg.Properties.VariableNames, {'_sd', '_n'})) = [];
tsg = movevars(tsg, {'lat', 'lon'}, 'After','dt');
tsg = renamevars(tsg,{'t1','s'},{'sst','sss'});
% add units and precision
tsg.Properties.VariableUnits = {'', 'degrees', 'degrees', 'degreesC', 'PSU'};
tsg.Properties.VariableDescriptions = {'', '%.4f', '%.4f', '%.4f', '%.4f'};
% sort by date
tsg = sortrows(tsg, 'dt');
ila.instrument.('SBE459999').prod.a = renamevars(ila.instrument.('SBE459999').prod.a, {'t1'}, {'sst'});
ila.visProd_timeseries()
saveGraph(fullfile(ila.instrument.FLOW.path.prod, 'plots', [cruise '_TSG_timeseries']), 'jpg')
saveGraph(fullfile(ila.instrument.FLOW.path.prod, 'plots', [cruise '_TSG_timeseries']), 'fig')
close figure 30
SimpleMap(tsg.sst, tsg(:,1:3), 'TSG SST [°C]')
saveGraph(fullfile(ila.instrument.FLOW.path.prod, 'plots', [cruise '_TSG_SST_map']), 'jpg')
saveGraph(fullfile(ila.instrument.FLOW.path.prod, 'plots', [cruise '_TSG_SST_map']), 'fig')
close figure 1
SimpleMap(tsg.sss, tsg(:,1:3), 'TSG SSS [PSU]')
saveGraph(fullfile(ila.instrument.FLOW.path.prod, 'plots', [cruise '_TSG_SSS_map']), 'jpg')
saveGraph(fullfile(ila.instrument.FLOW.path.prod, 'plots', [cruise '_TSG_SSS_map']), 'fig')
close figure 1
filename = sprintf('%s_InLine_%s_%s_%s_Product_v%s.sb', cruise, 'TSG', ...
datetime(min(tsg.dt), 'Format', 'yyyyMMdd'), datetime(max(tsg.dt), 'Format', 'yyyyMMdd'), ...
datetime('today', 'Format', 'yyyyMMdd'));
% % export product to SeaBASS format
% ila.meta.documents = [cruise '_TSG_ProcessingReport_V2.pdf';
% ila.meta.calibration_files = cell2mat(list_dev(i));
% exportSeaBASS(fullfile(ila.instrument.FLOW.path.prod, filename),...
% ila.meta,...
% tsg,...
% {'', '', ''});
% sprintf('%s_InLine_%s_Product.sb saved', cruise, cell2mat(ila.cfg.instruments2run))
% save TSG prod
fprintf('Export to mat and csv... ');
filename = strrep(filename, '.sb', '');
save(fullfile(ila.instrument.FLOW.path.prod, filename), 'tsg');
writetable(tsg, fullfile(ila.instrument.FLOW.path.prod, [filename '.csv']));
fprintf('Done\n');
%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% SUVF6253 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
ila.cfg.days2run = datenum(2023,6,4):datenum(2023,7,11);
ila.cfg.instruments2run = {'SUVF6253'};
% populate ila.instrument
ila.Read('prod');
% extract SUVF data from obj
suvf_temp = ila.instrument.(ila.cfg.instruments2run{:}).prod.a;
suvf_temp.dt = datetime(suvf_temp.dt,'ConvertFrom','datenum');
suvf_temp = round_timestamp(suvf_temp);
% build suvf table: merge lat, lon, sst, sss
replace_consecutive_nan = 3*60; % 3h
suvf = merge_timeseries(suvf_temp, tsg, {'lat', 'lon', 'sst', 'sss'}, '', replace_consecutive_nan);
% Remove NaN
if any(isnan([suvf.lat suvf.lon]),2)
suvf(any(isnan([suvf.lat suvf.lon]),2), :) = [];
warning('%i row with missing lat/lon (longer than %ih consecutive): deleted', ...
sum(any(isnan([suvf.lat suvf.lon]),2)), replace_consecutive_nan/60)
end
% add units and precision
suvf = renamevars(suvf, {'sst','sss','fdom_avg_n'}, {'t','s','bincount'});
suvf = removevars(suvf, {'swt','swt_avg_sd','swt_avg_n','spd1','spd1_avg_sd','spd1_avg_n'});
suvf.Properties.VariableUnits = {'', 'degrees', 'degrees', 'degreesC', 'PSU', 'v_uncalibrated', 'v_uncalibrated', 'none'};
suvf.Properties.VariableDescriptions = {'', '%.4f', '%.4f', '%.4f', '%.4f', '%.4f', '%.2f', '%.2f'};
% sort by date
suvf = sortrows(suvf, 'dt');
ila.visProd_timeseries()
saveGraph(fullfile(ila.instrument.FLOW.path.prod, 'plots', [cruise '_SUVF_fdom_timeseries']), 'jpg')
saveGraph(fullfile(ila.instrument.FLOW.path.prod, 'plots', [cruise '_SUVF_fdom_timeseries']), 'fig')
close figure 50
SimpleMap(suvf.fdom, suvf(:,1:3), 'SUVF fdom [v uncalibrated]')
saveGraph(fullfile(ila.instrument.FLOW.path.prod, 'plots', [cruise '_SUVF_fdom_map']), 'jpg')
saveGraph(fullfile(ila.instrument.FLOW.path.prod, 'plots', [cruise '_SUVF_fdom_map']), 'fig')
close figure 1
filename = sprintf('%s_InLine_%s_%s_%s_Product_v%s.sb', cruise, ila.cfg.instruments2run{:}, ...
datetime(min(suvf.dt), 'Format', 'yyyyMMdd'), datetime(max(suvf.dt), 'Format', 'yyyyMMdd'), datetime('today', 'Format', 'yyyyMMdd'));
% % export product to SeaBASS format
% ila.meta.documents = [cruise '_SUVF_ProcessingReport_V2.pdf'];
% ila.meta.calibration_files = 'SUVF6244_CharSheet.pdf';
% exportSeaBASS(fullfile(ila.instrument.FLOW.path.prod, filename),...
% ila.meta,...
% suvf,...
% {'', '', ''});
% sprintf('%s_InLine_%s_Product.sb saved', cruise, cell2mat(ila.cfg.instruments2run))
suvf = renamevars(suvf, {'t','s','bincount'}, {'sst','sss','fdom_n'});
% save SUVF prod
fprintf('Export to mat and csv... ');
filename = strrep(filename, '.sb', '');
save(fullfile(ila.instrument.FLOW.path.prod, filename), 'suvf');
writetable(suvf, fullfile(ila.instrument.FLOW.path.prod, [filename '.csv']));
fprintf('Done\n');
%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% BB31502 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
ila.cfg.days2run = datenum(2023,6,4):datenum(2023,7,11);
ila.cfg.instruments2run = {'BB31502'};
% populate ila.instrument
ila.Read('prod');
% extract HyperBB data from obj
bb3_lambda = ila.instrument.(ila.cfg.instruments2run{:}).lambda;
bb3_temp = ila.instrument.(ila.cfg.instruments2run{:}).prod.p;
bb3_temp.dt = datetime(bb3_temp.dt,'ConvertFrom','datenum');
bb3_temp = round_timestamp(bb3_temp);
% build hbb table: merge lat, lon, sst, sss
replace_consecutive_nan = 3*60; % 3h
bb3_temp = merge_timeseries(bb3_temp, tsg, {'lat', 'lon', 'sst', 'sss'}, '', replace_consecutive_nan);
% Remove NaN
if any(any(isnan([bb3_temp.lat bb3_temp.lon]),2))
warning('%i row with missing lat/lon (longer than %ih consecutive): deleted', ...
sum(any(isnan([bb3_temp.lat bb3_temp.lon]),2)), replace_consecutive_nan/60)
bb3_temp(any(isnan([bb3_temp.lat bb3_temp.lon]),2), :) = [];
end
% add units and precision
bb3 = bb3_temp(:, ~contains(bb3_temp.Properties.VariableNames, {'gamma_bbp', 'poc', 'cphyto'}));
bb3.Properties.VariableUnits = {'', 'degrees', 'degrees', 'degreesC', 'PSU', '1/m/sr', '1/m', '1/m/sr', 'none'};
bb3.Properties.VariableDescriptions = {'', '%.4f', '%.4f', '%.4f', '%.4f', '%.4f', '%.4f', '%.4f', '%.2f'};
bb3 = renamevars(bb3, {'sst', 'sss', 'betap','betap_sd','betap_n'}, {'t','s','VSF_124ang','VSF_124ang_sd','bincount'});
% sort by date
bb3 = sortrows(bb3, 'dt');
bb3.bbp(bb3.bbp<0)=NaN;
bb3.VSF_124ang_sd(bb3.VSF_124ang<0)=NaN;
bb3.VSF_124ang(bb3.VSF_124ang<0)=NaN;
bb3(all(isnan(bb3.bbp),2),:)=[];
%%% BB3 3D plots %%%
save_figures = true;
ila.SpectralQC('BB', {'prod'}, save_figures); % AC or BB
close all
% plot time series
ila.visProd_timeseries()
saveGraph(fullfile(ila.instrument.FLOW.path.prod, 'plots', [cruise '_BB3_gammabbp_bbp550']), 'jpg')
saveGraph(fullfile(ila.instrument.FLOW.path.prod, 'plots', [cruise '_BB3_gammabbp_bbp550']), 'fig')
close figure 11
saveGraph(fullfile(ila.instrument.FLOW.path.prod, 'plots', [cruise '_BB3_BBparticulate_timeseries']), 'jpg')
saveGraph(fullfile(ila.instrument.FLOW.path.prod, 'plots', [cruise '_BB3_BBparticulate_timeseries']), 'fig')
close figure 21
saveGraph(fullfile(ila.instrument.FLOW.path.prod, 'plots', [cruise '_BB3_POCparticulate_timeseries']), 'jpg')
saveGraph(fullfile(ila.instrument.FLOW.path.prod, 'plots', [cruise '_BB3_POCparticulate_timeseries']), 'fig')
close figure 20
% saveGraph(fullfile(ila.instrument.FLOW.path.prod, 'plots', [cruise '_BB3_BBdissolved_timeseries']), 'jpg')
% saveGraph(fullfile(ila.instrument.FLOW.path.prod, 'plots', [cruise '_BB3_BBdissolved_timeseries']), 'fig')
% close figure 25
lambda_id = abs(bb3_lambda - 530) == min(abs(bb3_lambda - 530));
SimpleMap(bb3.bbp(:,lambda_id), bb3(:,1:3), ['bbp (' num2str(bb3_lambda(lambda_id)) 'nm) [m^-^1]'] )
saveGraph(fullfile(ila.instrument.FLOW.path.prod, 'plots', [cruise '_bb3_bbp_map']), 'jpg')
saveGraph(fullfile(ila.instrument.FLOW.path.prod, 'plots', [cruise '_bb3_bbp_map']), 'fig')
close figure 1
filename = sprintf('%s_InLine_%s_%s_%s_Product_v%s.sb', cruise, ila.cfg.instruments2run{:}, ...
datetime(min(bb3.dt), 'Format', 'yyyyMMdd'), datetime(max(bb3.dt), 'Format', 'yyyyMMdd'), ...
datetime('today', 'Format', 'yyyyMMdd'));
% export product to SeaBASS format
ila.meta.documents = sprintf('%s_%s_ProcessingReport_v%s.pdf', cruise, ...
ila.cfg.instruments2run{:}, datetime('today', 'Format', 'yyyyMMdd'));
ila.meta.calibration_files = [ila.cfg.instruments2run{:} '_CharSheet.pdf'];
exportSeaBASS(fullfile(ila.instrument.FLOW.path.prod, filename),...
ila.meta,...
bb3,...
{string(bb3_lambda),string(bb3_lambda),string(bb3_lambda),''});
fprintf('%s_InLine_%s_Particulate_v%s.sb saved\n', cruise, ila.cfg.instruments2run{:}, ...
datetime('today', 'Format', 'yyyyMMdd'))
bb3_temp.Properties.VariableNames = {'dt','lat','lon','sst','sss','VSF124','bbp','VSF124_sd','bincount','gamma_bbp','poc','cphyto_bbp'};
bb3_temp.Properties.VariableUnits = {'','degrees','degrees','degreesC','PSU','m^-1.sr^-1','m^-1','m^-1.sr^-1','none','unitless','ug/L','ug/L'};
bb3_temp.Properties.VariableDescriptions = {'','%.4f','%.4f','%.4f','%.4f','%.4f','%.4f','%.4f','%.2f','%.4f','%.2f','%.2f'};
bb3 = bb3_temp;
% save HBB prod
fprintf('Export to mat and csv... ');
filename = strrep(filename, '.sb', '');
save(fullfile(ila.instrument.FLOW.path.prod, filename), 'bb3', 'bb3_lambda');
writetable(bb3, fullfile(ila.instrument.FLOW.path.prod, [filename '.csv']));
fprintf('Done\n');
%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% AC %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
list_leg = {...
datenum(2023,6,4):datenum(2023,7,11);
};
list_dev = repmat({fullfile(path_dev, 'ACS-348_20230504.dev')}, size(list_leg, 1), 1);
list_instru = repmat({'ACS348'}, size(list_leg, 1), 1);
data_AC = struct('particulate', [], 'product', []);
acs = [];
for i=1:size(list_instru,1)
cd('/Users/gui/Documents/MATLAB/InLineAnalysis/InLineAnalysis-master/')
ila = InLineAnalysis(['cfg' filesep cruise '_cfg.m']);
ila.cfg.instruments2run = list_instru(i);
ila.cfg.days2run = list_leg{i};
% populate ila.instrument
ila.Read('prod');
% get wavelength
[ila.instrument.(list_instru{i}).lambda_c, ...
ila.instrument.(list_instru{i}).lambda_a] = importACSDeviceFile(list_dev{i});
% set section name
ref = sprintf('%s_%s_%s', list_instru{i}, datetime(ila.cfg.days2run(1), 'ConvertFrom','datenum', 'Format', 'yyyyMMdd'), ...
datetime(ila.cfg.days2run(end), 'ConvertFrom', 'datenum', 'Format', 'yyyyMMdd'));
% remove flagged data
flag = read_flagbit(ila.instrument.(list_instru{i}).prod.p.flag_bit, 'AC');
% flag_info = InlineFlagInfo('ACS');
% flag.HH_G50_flag(flag.HH_G50_flag & ila.instrument.(list_instru{i}).prod.p.HH_G50 > 0 & ila.instrument.(list_instru{i}).prod.p.HH_G50 <= 500) = false;
% ila.instrument.(list_instru{i}).prod.p.flag_bit = set_flagbit(flag);
% remove flagged products
ila.instrument.(list_instru{i}).prod.p.poc(flag.poc_flag) = NaN;
ila.instrument.(list_instru{i}).prod.p.chl_ap676lh(flag.chl_ap676lh_flag) = NaN;
ila.instrument.(list_instru{i}).prod.p.gamma(flag.cp_bubbles) = NaN;
ila.instrument.(list_instru{i}).prod.p.poc(flag.cp_bubbles) = NaN;
ila.instrument.(list_instru{i}).prod.p.chl_Halh(flag.chl_Halh_flag) = NaN;
ila.instrument.(list_instru{i}).prod.p.chl_Halh(flag.cp_bubbles) = NaN;
ila.instrument.(list_instru{i}).prod.p.HH_mphi(flag.cp_bubbles) = NaN;
ila.instrument.(list_instru{i}).prod.p.HH_G50(flag.HH_G50_flag) = NaN;
ila.instrument.(list_instru{i}).prod.p.HH_G50(flag.cp_bubbles) = NaN;
% ila.instrument.(list_instru{i}).prod.p.chl_Halh(flag.chlratio_flag) = NaN;
% % remove suspicious products
% ila.instrument.(list_instru{i}).prod.p.gamma(flag.gamma_suspicious) = NaN;
% % ila.instrument.(list_instru{i}).prod.p.gamma(acs_prod.gamma > 2) = NaN;
% ila.instrument.(list_instru{i}).prod.p.poc(flag.ap_bubbles) = NaN;
% ila.instrument.(list_instru{i}).prod.p.poc(flag.poc_suspicious) = NaN;
% ila.instrument.(list_instru{i}).prod.p.chl_ap676lh(flag.chl_ap676lh_suspicious) = NaN;
% ila.instrument.(list_instru{i}).prod.p.chl_Halh(flag.chl_Halh_suspicious) = NaN;
% ila.instrument.(list_instru{i}).prod.p.HH_mphi(flag.HH_G50_mphi_suspicious) = NaN;
% ila.instrument.(list_instru{i}).prod.p.HH_G50(flag.HH_G50_mphi_suspicious) = NaN;
ila.visProd_timeseries()
saveGraph(fullfile(ila.instrument.(list_instru{i}).path.prod, 'plots', [cruise '_' ref '_ACS_prod_regressions']), 'jpg')
saveGraph(fullfile(ila.instrument.(list_instru{i}).path.prod, 'plots', [cruise '_' ref '_ACS_prod_regressions']), 'fig')
close figure 11
saveGraph(fullfile(ila.instrument.(list_instru{i}).path.prod, 'plots', [cruise '_' ref '_ACS_prod_timeseries']), 'jpg')
saveGraph(fullfile(ila.instrument.(list_instru{i}).path.prod, 'plots', [cruise '_' ref '_ACS_prod_timeseries']), 'fig')
close figure 10
% extract AC data from obj
AC = ila.instrument.(list_instru{i}).prod.p;
AC.dt = datetime(AC.dt,'ConvertFrom','datenum');
% build AC table: merge lat, lon, sst, sss, and fdom
replace_consecutive_nan = 3*60; % 3h
AC = merge_timeseries(AC, suvf, {'fdom'}, '', replace_consecutive_nan);
AC = merge_timeseries(AC, tsg, {'lat', 'lon', 'sst', 'sss'}, '', replace_consecutive_nan);
% rename variables for SeaBASS
AC = renamevars(AC, {'sst','sss'}, {'t','s'});
% Remove NaN
if any(any(isnan([AC.lat AC.lon]),2))
warning('%i row with missing lat/lon (longer than %ih consecutive): deleted', ...
sum(any(isnan([AC.lat AC.lon]),2)), replace_consecutive_nan/60)
AC(any(isnan([AC.lat AC.lon]),2), :) = [];
end
% split into particulate table
id_particulate = logical(sum(categorical(AC.Properties.VariableNames) == {'dt','lat','lon','t','s','fdom','ap','ap_sd','cp','cp_sd','ap_n'}'));
data_AC.particulate.(ref) = AC(:, id_particulate);
% add variable units and description
data_AC.particulate.(ref).Properties.VariableUnits = {'', 'degrees', 'degrees', 'degreesC', 'PSU','ppb', '1/m', '1/m', '1/m', '1/m', 'none'};
data_AC.particulate.(ref).Properties.VariableDescriptions = {'', '%.4f', '%.4f', '%.4f', '%.4f', '%.4f', '%.4f', '%.4f', '%.4f', '%.4f', '%.0f'};
% rename variables for SeaBASS
data_AC.particulate.(ref) = renamevars(data_AC.particulate.(ref), {'fdom','ap_n'}, {'cdomf','bincount'});
% split into product table
id_product = logical(sum(categorical(AC.Properties.VariableNames) == {'ap', 'ap_sd', 'cp', 'cp_sd'}'));
data_AC.product.(ref) = AC(:, ~id_product);
% add variable units and description
data_AC.product.(ref).Properties.VariableUnits = {'', 'degrees', 'degrees', 'degreesC', 'PSU','ppb', ...
'1/m','1/m','1/m','1/m','1/m','1/m','1/m','1/m','1/m','1/m','1/m','1/m','1/m','1/m', ...
'ug/L','unitless','ug/L','unitless','unitless','ug/L','microns','unitless','unitless','unitless','unitless'};
data_AC.product.(ref).Properties.VariableDescriptions = [{''} repmat({'%.4f'}, 1, size(data_AC.product.(ref),2) - 4) ...
repmat({'%i'}, 1, 3)];
% rename variables for SeaBASS
data_AC.product.(ref) = renamevars(data_AC.product.(ref), {'poc','gamma','chl_ap676lh', 'fdom'}, ...
{'POC_cp','cp_gamma','Chl_lineheight', 'cdomf'});
%%% AC 3D plots %%%
save_figures = true;
ila.SpectralQC('AC', {'prod'}, save_figures); % AC or BB
close all
filename = sprintf('%s_InLine_%s_Particulate_v%s.sb', cruise, ref, datetime('today', 'Format', 'yyyyMMdd'));
% export particulate to SeaBASS format
ila.meta.documents = sprintf('%s_ACS_ProcessingReport_v%s.pdf', cruise, datetime('today', 'Format', 'yyyyMMdd'));
[~, calfile] = fileparts(list_dev{i});
ila.meta.calibration_files = [calfile '.dev'];
exportSeaBASS(fullfile(ila.instrument.(list_instru{i}).path.prod, filename),...
ila.meta,...
data_AC.particulate.(ref),...
{'', string(ila.instrument.(list_instru{i}).lambda_a),...
string(ila.instrument.(list_instru{i}).lambda_c),...
string(ila.instrument.(list_instru{i}).lambda_a),...
string(ila.instrument.(list_instru{i}).lambda_c),''});
fprintf('%s saved\n', filename)
lambda.a = ila.instrument.(list_instru{i}).lambda_a;
lambda.c = ila.instrument.(list_instru{i}).lambda_c;
acs_part = data_AC.particulate.(ref);
filename = strrep(filename, '.sb', '');
% rename variables for .mat
acs_part = renamevars(acs_part, {'t','s','cdomf'}, {'sst','sss','fdom'});
save(fullfile(ila.instrument.(list_instru{i}).path.prod, filename), 'acs_part', 'lambda');
% ACS merged prod
acs = [acs; data_AC.product.(ref)];
end
% sort by date
acs_prod = sortrows(acs, 'dt');
if any(strcmp(acs_prod.Properties.VariableNames, 'chl_ap676lh'))
acs_prod = renamevars(acs_prod, 'chl_ap676lh', 'Chl_lineheight');
end
if any(strcmp(acs.Properties.VariableNames, 'poc'))
acs_prod = renamevars(acs_prod, 'poc', 'POC_cp');
end
if any(strcmp(acs.Properties.VariableNames, 'gamma'))
acs_prod = renamevars(acs_prod, 'gamma', 'cp_gamma');
end
ila.instrument.(list_instru{1}).prod.p = acs_prod;
% figure()
% subplot(2,3,1); histogram(acs_prod.POC_cp); xlabel('[POC] cp (mg.m^{-3})')
% subplot(2,3,2); histogram(acs_prod.Chl_lineheight); xlabel('a_{p676} line height [chl a] (mg.m^{-3})')
% subplot(2,3,3); histogram(acs_prod.cp_gamma); xlabel('gamma cp (mg.m^{-3})')
% subplot(2,3,4); histogram(acs_prod.chl_Halh); xlabel('Houskeeper [chl] (mg.m^{-3})')
% subplot(2,3,5); histogram(acs_prod.HH_mphi); xlabel('H&H phytoplankton slope size distribution')
% subplot(2,3,6); histogram(acs_prod.HH_G50); xlabel('H&H phytoplankton G50: cross-sectional area (\mum)')
ila.visProd_timeseries()
saveGraph(fullfile(ila.instrument.(list_instru{i}).path.prod, 'plots', [cruise '_ACS_prod_regressions']), 'jpg')
saveGraph(fullfile(ila.instrument.(list_instru{i}).path.prod, 'plots', [cruise '_ACS_prod_regressions']), 'fig')
close figure 11
saveGraph(fullfile(ila.instrument.(list_instru{i}).path.prod, 'plots', [cruise '_ACS_prod_timeseries']), 'jpg')
saveGraph(fullfile(ila.instrument.(list_instru{i}).path.prod, 'plots', [cruise '_ACS_prod_timeseries']), 'fig')
close figure 10
SimpleMap(acs_prod.chl_Halh, acs_prod(:,1:3), 'Houskeeper [chl] (mg.m^{-3})')
saveGraph(fullfile(ila.instrument.FLOW.path.prod, 'plots', [cruise '_ACS_chl_Houskeeper_map']), 'jpg')
saveGraph(fullfile(ila.instrument.FLOW.path.prod, 'plots', [cruise '_ACS_chl_Houskeeper_map']), 'fig')
close figure 1
SimpleMap(acs_prod.HH_G50, acs_prod(:,1:3), 'H&H phytoplankton G50: cross-sectional area (\mum)')
saveGraph(fullfile(ila.instrument.FLOW.path.prod, 'plots', [cruise '_ACS_H&H_map']), 'jpg')
saveGraph(fullfile(ila.instrument.FLOW.path.prod, 'plots', [cruise '_ACS_H&H_map']), 'fig')
close figure 1
SimpleMap(acs_prod.POC_cp, acs_prod(:,1:3), '[POC] cp (mg.m^{-3})')
saveGraph(fullfile(ila.instrument.FLOW.path.prod, 'plots', [cruise '_ACS_POC_map']), 'jpg')
saveGraph(fullfile(ila.instrument.FLOW.path.prod, 'plots', [cruise '_ACS_POC_map']), 'fig')
close figure 1
SimpleMap(acs_prod.Chl_lineheight, acs_prod(:,1:3), 'a_{p676} line height [chl a] (mg.m^{-3})')
saveGraph(fullfile(ila.instrument.FLOW.path.prod, 'plots', [cruise '_ACS_chl_map']), 'jpg')
saveGraph(fullfile(ila.instrument.FLOW.path.prod, 'plots', [cruise '_ACS_chl_map']), 'fig')
close figure 1
SimpleMap(acs_prod.cp_gamma, acs_prod(:,1:3), 'gamma cp (unitless)')
saveGraph(fullfile(ila.instrument.FLOW.path.prod, 'plots', [cruise '_ACS_gamma_map']), 'jpg')
saveGraph(fullfile(ila.instrument.FLOW.path.prod, 'plots', [cruise '_ACS_gamma_map']), 'fig')
close figure 1
% acs_prod.chl_Halh = [];
% acs_prod.HH_G50 = [];
filename = sprintf('%s_InLine_ACS_%s_%s_Products_full_v%s', cruise, ...
datetime(min(acs_prod.dt), 'Format', 'yyyyMMdd'),...
datetime(max(acs_prod.dt), 'Format', 'yyyyMMdd'),...
datetime('today', 'Format', 'yyyyMMdd'));
% rename variables for .mat
acs_prod = renamevars(acs_prod, {'t','s','cdomf'}, {'sst','sss','fdom'});
% save AC prod
fprintf('Export to mat and csv... ');
save(fullfile(ila.instrument.(list_instru{i}).path.prod, filename), 'acs_prod');
writetable(acs_prod, fullfile(ila.instrument.(list_instru{i}).path.prod, [filename '.csv']));
fprintf('Done\n');
% rename variables for SeaBASS
acs_prod = renamevars(acs_prod, {'sst','sss','fdom'}, {'t','s','cdomf'});
% keep only old variables
acs_prod = acs_prod(:, contains(acs_prod.Properties.VariableNames, {'dt', 'lat', 'lon', ...
't','s','cdomf','Chl_lineheight','POC_cp','cp_gamma','ap_n','cp_n','flag_bit'}) & ...
~contains(acs_prod.Properties.VariableNames, {'agaus'}));
% export product to SeaBASS format
filename = [filename '.sb'];
filename = strrep(filename, 'Products_full', 'Products');
exportSeaBASS(fullfile(ila.instrument.(list_instru{i}).path.prod, filename), ila.meta, acs_prod);
fprintf('%s saved\n', filename)
% rename variables for .mat
acs_prod = renamevars(acs_prod, {'t','s','cdomf'}, {'sst','sss','fdom'});
% save AC prod
fprintf('Export to mat and csv... ');
filename = strrep(filename, '.sb', '');
save(fullfile(ila.instrument.(list_instru{i}).path.prod, filename), 'acs_prod');
writetable(acs_prod, fullfile(ila.instrument.(list_instru{i}).path.prod, [filename '.csv']));
fprintf('Done\n');