I suggest attaching them in a .mat file so we can see directly what they are and demonstrate solutions.
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How to convert char data column to number and split it to cell? and merge two arrays?
조회 수: 2 (최근 30일)
이전 댓글 표시
Hello, I have two questions.
1- I have a 900x2 array with a date column and another char with 720 elements ('00001100').
I want to be able to convert char cells to number and have a 900x 721 array with each char element in a cell like: date/0/0/0/0/1/1/0/0. How to proceed?
2- I have two paintings. A 10x6 (10 date and 5 samples) and a 5x2 (5 locations with latitude and longitude of the positions). I would like to merge them to have a three-variable array with each sample corresponding to its location (lattitude and longitude). So have a table representing the samples according to their dates and location.
THANKS.
댓글 수: 2
Sangare Lassana Meidi
2023년 6월 6일
Thanks Matt,
The first table ''tab_char (900x2)'' is that of the first question. you can see the date in the first column and the char data in the second. this is the one i want to change to a 900x721 table by converting the data in the char to 720 cells for each date.
The second table ''tab_loc (720x2)'' concerns question 2. When I have for each of the dates the 720 data in char in the first table, I want to merge these two tables. each char data for each date corresponds to a location (lat/long). we have 720 positions for 720 samples for the 900 dates. this is what I would like to represent in one table.
THANKS
채택된 답변
Cris LaPierre
2023년 6월 6일
For #1, use cellfun and num2cell.
load tab_char(900x2).mat
% Split each element of data to its own cell
data = cellfun(@num2cell,t.data,'UniformOutput',false);
% merge the timestamps and data into a new table
t = [t(:,1),data];
t = splitvars(t,'Var2')
t = 900×721 table
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Var2_123 Var2_124 Var2_125 Var2_126 Var2_127 Var2_128 Var2_129 Var2_130 Var2_131 Var2_132 Var2_133 Var2_134 Var2_135 Var2_136 Var2_137 Var2_138 Var2_139 Var2_140 Var2_141 Var2_142 Var2_143 Var2_144 Var2_145 Var2_146 Var2_147 Var2_148 Var2_149 Var2_150 Var2_151 Var2_152 Var2_153 Var2_154 Var2_155 Var2_156 Var2_157 Var2_158 Var2_159 Var2_160 Var2_161 Var2_162 Var2_163 Var2_164 Var2_165 Var2_166 Var2_167 Var2_168 Var2_169 Var2_170 Var2_171 Var2_172 Var2_173 Var2_174 Var2_175 Var2_176 Var2_177 Var2_178 Var2_179 Var2_180 Var2_181 Var2_182 Var2_183 Var2_184 Var2_185 Var2_186 Var2_187 Var2_188 Var2_189 Var2_190 Var2_191 Var2_192 Var2_193 Var2_194 Var2_195 Var2_196 Var2_197 Var2_198 Var2_199 Var2_200 Var2_201 Var2_202 Var2_203 Var2_204 Var2_205 Var2_206 Var2_207 Var2_208 Var2_209 Var2_210 Var2_211 Var2_212 Var2_213 Var2_214 Var2_215 Var2_216 Var2_217 Var2_218 Var2_219 Var2_220 Var2_221 Var2_222 Var2_223 Var2_224 Var2_225 Var2_226 Var2_227 Var2_228 Var2_229 Var2_230 Var2_231 Var2_232 Var2_233 Var2_234 Var2_235 Var2_236 Var2_237 Var2_238 Var2_239 Var2_240 Var2_241 Var2_242 Var2_243 Var2_244 Var2_245 Var2_246 Var2_247 Var2_248 Var2_249 Var2_250 Var2_251 Var2_252 Var2_253 Var2_254 Var2_255 Var2_256 Var2_257 Var2_258 Var2_259 Var2_260 Var2_261 Var2_262 Var2_263 Var2_264 Var2_265 Var2_266 Var2_267 Var2_268 Var2_269 Var2_270 Var2_271 Var2_272 Var2_273 Var2_274 Var2_275 Var2_276 Var2_277 Var2_278 Var2_279 Var2_280 Var2_281 Var2_282 Var2_283 Var2_284 Var2_285 Var2_286 Var2_287 Var2_288 Var2_289 Var2_290 Var2_291 Var2_292 Var2_293 Var2_294 Var2_295 Var2_296 Var2_297 Var2_298 Var2_299 Var2_300 Var2_301 Var2_302 Var2_303 Var2_304 Var2_305 Var2_306 Var2_307 Var2_308 Var2_309 Var2_310 Var2_311 Var2_312 Var2_313 Var2_314 Var2_315 Var2_316 Var2_317 Var2_318 Var2_319 Var2_320 Var2_321 Var2_322 Var2_323 Var2_324 Var2_325 Var2_326 Var2_327 Var2_328 Var2_329 Var2_330 Var2_331 Var2_332 Var2_333 Var2_334 Var2_335 Var2_336 Var2_337 Var2_338 Var2_339 Var2_340 Var2_341 Var2_342 Var2_343 Var2_344 Var2_345 Var2_346 Var2_347 Var2_348 Var2_349 Var2_350 Var2_351 Var2_352 Var2_353 Var2_354 Var2_355 Var2_356 Var2_357 Var2_358 Var2_359 Var2_360 Var2_361 Var2_362 Var2_363 Var2_364 Var2_365 Var2_366 Var2_367 Var2_368 Var2_369 Var2_370 Var2_371 Var2_372 Var2_373 Var2_374 Var2_375 Var2_376 Var2_377 Var2_378 Var2_379 Var2_380 Var2_381 Var2_382 Var2_383 Var2_384 Var2_385 Var2_386 Var2_387 Var2_388 Var2_389 Var2_390 Var2_391 Var2_392 Var2_393 Var2_394 Var2_395 Var2_396 Var2_397 Var2_398 Var2_399 Var2_400 Var2_401 Var2_402 Var2_403 Var2_404 Var2_405 Var2_406 Var2_407 Var2_408 Var2_409 Var2_410 Var2_411 Var2_412 Var2_413 Var2_414 Var2_415 Var2_416 Var2_417 Var2_418 Var2_419 Var2_420 Var2_421 Var2_422 Var2_423 Var2_424 Var2_425 Var2_426 Var2_427 Var2_428 Var2_429 Var2_430 Var2_431 Var2_432 Var2_433 Var2_434 Var2_435 Var2_436 Var2_437 Var2_438 Var2_439 Var2_440 Var2_441 Var2_442 Var2_443 Var2_444 Var2_445 Var2_446 Var2_447 Var2_448 Var2_449 Var2_450 Var2_451 Var2_452 Var2_453 Var2_454 Var2_455 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Var2_678 Var2_679 Var2_680 Var2_681 Var2_682 Var2_683 Var2_684 Var2_685 Var2_686 Var2_687 Var2_688 Var2_689 Var2_690 Var2_691 Var2_692 Var2_693 Var2_694 Var2_695 Var2_696 Var2_697 Var2_698 Var2_699 Var2_700 Var2_701 Var2_702 Var2_703 Var2_704 Var2_705 Var2_706 Var2_707 Var2_708 Var2_709 Var2_710 Var2_711 Var2_712 Var2_713 Var2_714 Var2_715 Var2_716 Var2_717 Var2_718 Var2_719 Var2_720
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05-May-2023 00:59:00 {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'}
05-May-2023 00:58:00 {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'} {'0'}
댓글 수: 7
Cris LaPierre
2023년 6월 6일
편집: Cris LaPierre
2023년 6월 6일
As an observation looking into Q2, I have not done an exhaustive search, but it would appear your 'data' variable is not as I'd expect. At least not given the request to turn all this data into a nx3 table with datetime, lng and lat.
Some rows of data contain only 0's, while others contain as many as 720 1's.
Sangare Lassana Meidi
2023년 6월 7일
Thank you for your answer, it is really useful. I manage to separate the variables as wanted but I have the impression that it remains in character form and not numeric.
For Q2, it's normal to have 0 and 1. It indicates a true or false depending on the date and the position in the tab_loc array
I want to have by combining the two arrays 4 variables.
- date
- Long
-Lat
- 0 or 1 depending on the date and the long, lat
Cris LaPierre
2023년 6월 7일
In that case, I'd change the code a little bit.
load tab_char(900x2).mat
% Split each element of data to its own cell
data = cellfun(@num2cell,t.data,'UniformOutput',false);
data = vertcat(data{:});
data = str2double(data);
% merge the timestamps and data into a new table
t.data = data;
t = splitvars(t,'data')
t = 900×721 table
timestamp data_1 data_2 data_3 data_4 data_5 data_6 data_7 data_8 data_9 data_10 data_11 data_12 data_13 data_14 data_15 data_16 data_17 data_18 data_19 data_20 data_21 data_22 data_23 data_24 data_25 data_26 data_27 data_28 data_29 data_30 data_31 data_32 data_33 data_34 data_35 data_36 data_37 data_38 data_39 data_40 data_41 data_42 data_43 data_44 data_45 data_46 data_47 data_48 data_49 data_50 data_51 data_52 data_53 data_54 data_55 data_56 data_57 data_58 data_59 data_60 data_61 data_62 data_63 data_64 data_65 data_66 data_67 data_68 data_69 data_70 data_71 data_72 data_73 data_74 data_75 data_76 data_77 data_78 data_79 data_80 data_81 data_82 data_83 data_84 data_85 data_86 data_87 data_88 data_89 data_90 data_91 data_92 data_93 data_94 data_95 data_96 data_97 data_98 data_99 data_100 data_101 data_102 data_103 data_104 data_105 data_106 data_107 data_108 data_109 data_110 data_111 data_112 data_113 data_114 data_115 data_116 data_117 data_118 data_119 data_120 data_121 data_122 data_123 data_124 data_125 data_126 data_127 data_128 data_129 data_130 data_131 data_132 data_133 data_134 data_135 data_136 data_137 data_138 data_139 data_140 data_141 data_142 data_143 data_144 data_145 data_146 data_147 data_148 data_149 data_150 data_151 data_152 data_153 data_154 data_155 data_156 data_157 data_158 data_159 data_160 data_161 data_162 data_163 data_164 data_165 data_166 data_167 data_168 data_169 data_170 data_171 data_172 data_173 data_174 data_175 data_176 data_177 data_178 data_179 data_180 data_181 data_182 data_183 data_184 data_185 data_186 data_187 data_188 data_189 data_190 data_191 data_192 data_193 data_194 data_195 data_196 data_197 data_198 data_199 data_200 data_201 data_202 data_203 data_204 data_205 data_206 data_207 data_208 data_209 data_210 data_211 data_212 data_213 data_214 data_215 data_216 data_217 data_218 data_219 data_220 data_221 data_222 data_223 data_224 data_225 data_226 data_227 data_228 data_229 data_230 data_231 data_232 data_233 data_234 data_235 data_236 data_237 data_238 data_239 data_240 data_241 data_242 data_243 data_244 data_245 data_246 data_247 data_248 data_249 data_250 data_251 data_252 data_253 data_254 data_255 data_256 data_257 data_258 data_259 data_260 data_261 data_262 data_263 data_264 data_265 data_266 data_267 data_268 data_269 data_270 data_271 data_272 data_273 data_274 data_275 data_276 data_277 data_278 data_279 data_280 data_281 data_282 data_283 data_284 data_285 data_286 data_287 data_288 data_289 data_290 data_291 data_292 data_293 data_294 data_295 data_296 data_297 data_298 data_299 data_300 data_301 data_302 data_303 data_304 data_305 data_306 data_307 data_308 data_309 data_310 data_311 data_312 data_313 data_314 data_315 data_316 data_317 data_318 data_319 data_320 data_321 data_322 data_323 data_324 data_325 data_326 data_327 data_328 data_329 data_330 data_331 data_332 data_333 data_334 data_335 data_336 data_337 data_338 data_339 data_340 data_341 data_342 data_343 data_344 data_345 data_346 data_347 data_348 data_349 data_350 data_351 data_352 data_353 data_354 data_355 data_356 data_357 data_358 data_359 data_360 data_361 data_362 data_363 data_364 data_365 data_366 data_367 data_368 data_369 data_370 data_371 data_372 data_373 data_374 data_375 data_376 data_377 data_378 data_379 data_380 data_381 data_382 data_383 data_384 data_385 data_386 data_387 data_388 data_389 data_390 data_391 data_392 data_393 data_394 data_395 data_396 data_397 data_398 data_399 data_400 data_401 data_402 data_403 data_404 data_405 data_406 data_407 data_408 data_409 data_410 data_411 data_412 data_413 data_414 data_415 data_416 data_417 data_418 data_419 data_420 data_421 data_422 data_423 data_424 data_425 data_426 data_427 data_428 data_429 data_430 data_431 data_432 data_433 data_434 data_435 data_436 data_437 data_438 data_439 data_440 data_441 data_442 data_443 data_444 data_445 data_446 data_447 data_448 data_449 data_450 data_451 data_452 data_453 data_454 data_455 data_456 data_457 data_458 data_459 data_460 data_461 data_462 data_463 data_464 data_465 data_466 data_467 data_468 data_469 data_470 data_471 data_472 data_473 data_474 data_475 data_476 data_477 data_478 data_479 data_480 data_481 data_482 data_483 data_484 data_485 data_486 data_487 data_488 data_489 data_490 data_491 data_492 data_493 data_494 data_495 data_496 data_497 data_498 data_499 data_500 data_501 data_502 data_503 data_504 data_505 data_506 data_507 data_508 data_509 data_510 data_511 data_512 data_513 data_514 data_515 data_516 data_517 data_518 data_519 data_520 data_521 data_522 data_523 data_524 data_525 data_526 data_527 data_528 data_529 data_530 data_531 data_532 data_533 data_534 data_535 data_536 data_537 data_538 data_539 data_540 data_541 data_542 data_543 data_544 data_545 data_546 data_547 data_548 data_549 data_550 data_551 data_552 data_553 data_554 data_555 data_556 data_557 data_558 data_559 data_560 data_561 data_562 data_563 data_564 data_565 data_566 data_567 data_568 data_569 data_570 data_571 data_572 data_573 data_574 data_575 data_576 data_577 data_578 data_579 data_580 data_581 data_582 data_583 data_584 data_585 data_586 data_587 data_588 data_589 data_590 data_591 data_592 data_593 data_594 data_595 data_596 data_597 data_598 data_599 data_600 data_601 data_602 data_603 data_604 data_605 data_606 data_607 data_608 data_609 data_610 data_611 data_612 data_613 data_614 data_615 data_616 data_617 data_618 data_619 data_620 data_621 data_622 data_623 data_624 data_625 data_626 data_627 data_628 data_629 data_630 data_631 data_632 data_633 data_634 data_635 data_636 data_637 data_638 data_639 data_640 data_641 data_642 data_643 data_644 data_645 data_646 data_647 data_648 data_649 data_650 data_651 data_652 data_653 data_654 data_655 data_656 data_657 data_658 data_659 data_660 data_661 data_662 data_663 data_664 data_665 data_666 data_667 data_668 data_669 data_670 data_671 data_672 data_673 data_674 data_675 data_676 data_677 data_678 data_679 data_680 data_681 data_682 data_683 data_684 data_685 data_686 data_687 data_688 data_689 data_690 data_691 data_692 data_693 data_694 data_695 data_696 data_697 data_698 data_699 data_700 data_701 data_702 data_703 data_704 data_705 data_706 data_707 data_708 data_709 data_710 data_711 data_712 data_713 data_714 data_715 data_716 data_717 data_718 data_719 data_720
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________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________
05-May-2023 00:59:00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
05-May-2023 00:58:00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
You still haven't answered how to take a datetime that has many ones and turn that into a corresponding long/lat data.
t(82,:)
ans = 1×721 table
timestamp data_1 data_2 data_3 data_4 data_5 data_6 data_7 data_8 data_9 data_10 data_11 data_12 data_13 data_14 data_15 data_16 data_17 data_18 data_19 data_20 data_21 data_22 data_23 data_24 data_25 data_26 data_27 data_28 data_29 data_30 data_31 data_32 data_33 data_34 data_35 data_36 data_37 data_38 data_39 data_40 data_41 data_42 data_43 data_44 data_45 data_46 data_47 data_48 data_49 data_50 data_51 data_52 data_53 data_54 data_55 data_56 data_57 data_58 data_59 data_60 data_61 data_62 data_63 data_64 data_65 data_66 data_67 data_68 data_69 data_70 data_71 data_72 data_73 data_74 data_75 data_76 data_77 data_78 data_79 data_80 data_81 data_82 data_83 data_84 data_85 data_86 data_87 data_88 data_89 data_90 data_91 data_92 data_93 data_94 data_95 data_96 data_97 data_98 data_99 data_100 data_101 data_102 data_103 data_104 data_105 data_106 data_107 data_108 data_109 data_110 data_111 data_112 data_113 data_114 data_115 data_116 data_117 data_118 data_119 data_120 data_121 data_122 data_123 data_124 data_125 data_126 data_127 data_128 data_129 data_130 data_131 data_132 data_133 data_134 data_135 data_136 data_137 data_138 data_139 data_140 data_141 data_142 data_143 data_144 data_145 data_146 data_147 data_148 data_149 data_150 data_151 data_152 data_153 data_154 data_155 data_156 data_157 data_158 data_159 data_160 data_161 data_162 data_163 data_164 data_165 data_166 data_167 data_168 data_169 data_170 data_171 data_172 data_173 data_174 data_175 data_176 data_177 data_178 data_179 data_180 data_181 data_182 data_183 data_184 data_185 data_186 data_187 data_188 data_189 data_190 data_191 data_192 data_193 data_194 data_195 data_196 data_197 data_198 data_199 data_200 data_201 data_202 data_203 data_204 data_205 data_206 data_207 data_208 data_209 data_210 data_211 data_212 data_213 data_214 data_215 data_216 data_217 data_218 data_219 data_220 data_221 data_222 data_223 data_224 data_225 data_226 data_227 data_228 data_229 data_230 data_231 data_232 data_233 data_234 data_235 data_236 data_237 data_238 data_239 data_240 data_241 data_242 data_243 data_244 data_245 data_246 data_247 data_248 data_249 data_250 data_251 data_252 data_253 data_254 data_255 data_256 data_257 data_258 data_259 data_260 data_261 data_262 data_263 data_264 data_265 data_266 data_267 data_268 data_269 data_270 data_271 data_272 data_273 data_274 data_275 data_276 data_277 data_278 data_279 data_280 data_281 data_282 data_283 data_284 data_285 data_286 data_287 data_288 data_289 data_290 data_291 data_292 data_293 data_294 data_295 data_296 data_297 data_298 data_299 data_300 data_301 data_302 data_303 data_304 data_305 data_306 data_307 data_308 data_309 data_310 data_311 data_312 data_313 data_314 data_315 data_316 data_317 data_318 data_319 data_320 data_321 data_322 data_323 data_324 data_325 data_326 data_327 data_328 data_329 data_330 data_331 data_332 data_333 data_334 data_335 data_336 data_337 data_338 data_339 data_340 data_341 data_342 data_343 data_344 data_345 data_346 data_347 data_348 data_349 data_350 data_351 data_352 data_353 data_354 data_355 data_356 data_357 data_358 data_359 data_360 data_361 data_362 data_363 data_364 data_365 data_366 data_367 data_368 data_369 data_370 data_371 data_372 data_373 data_374 data_375 data_376 data_377 data_378 data_379 data_380 data_381 data_382 data_383 data_384 data_385 data_386 data_387 data_388 data_389 data_390 data_391 data_392 data_393 data_394 data_395 data_396 data_397 data_398 data_399 data_400 data_401 data_402 data_403 data_404 data_405 data_406 data_407 data_408 data_409 data_410 data_411 data_412 data_413 data_414 data_415 data_416 data_417 data_418 data_419 data_420 data_421 data_422 data_423 data_424 data_425 data_426 data_427 data_428 data_429 data_430 data_431 data_432 data_433 data_434 data_435 data_436 data_437 data_438 data_439 data_440 data_441 data_442 data_443 data_444 data_445 data_446 data_447 data_448 data_449 data_450 data_451 data_452 data_453 data_454 data_455 data_456 data_457 data_458 data_459 data_460 data_461 data_462 data_463 data_464 data_465 data_466 data_467 data_468 data_469 data_470 data_471 data_472 data_473 data_474 data_475 data_476 data_477 data_478 data_479 data_480 data_481 data_482 data_483 data_484 data_485 data_486 data_487 data_488 data_489 data_490 data_491 data_492 data_493 data_494 data_495 data_496 data_497 data_498 data_499 data_500 data_501 data_502 data_503 data_504 data_505 data_506 data_507 data_508 data_509 data_510 data_511 data_512 data_513 data_514 data_515 data_516 data_517 data_518 data_519 data_520 data_521 data_522 data_523 data_524 data_525 data_526 data_527 data_528 data_529 data_530 data_531 data_532 data_533 data_534 data_535 data_536 data_537 data_538 data_539 data_540 data_541 data_542 data_543 data_544 data_545 data_546 data_547 data_548 data_549 data_550 data_551 data_552 data_553 data_554 data_555 data_556 data_557 data_558 data_559 data_560 data_561 data_562 data_563 data_564 data_565 data_566 data_567 data_568 data_569 data_570 data_571 data_572 data_573 data_574 data_575 data_576 data_577 data_578 data_579 data_580 data_581 data_582 data_583 data_584 data_585 data_586 data_587 data_588 data_589 data_590 data_591 data_592 data_593 data_594 data_595 data_596 data_597 data_598 data_599 data_600 data_601 data_602 data_603 data_604 data_605 data_606 data_607 data_608 data_609 data_610 data_611 data_612 data_613 data_614 data_615 data_616 data_617 data_618 data_619 data_620 data_621 data_622 data_623 data_624 data_625 data_626 data_627 data_628 data_629 data_630 data_631 data_632 data_633 data_634 data_635 data_636 data_637 data_638 data_639 data_640 data_641 data_642 data_643 data_644 data_645 data_646 data_647 data_648 data_649 data_650 data_651 data_652 data_653 data_654 data_655 data_656 data_657 data_658 data_659 data_660 data_661 data_662 data_663 data_664 data_665 data_666 data_667 data_668 data_669 data_670 data_671 data_672 data_673 data_674 data_675 data_676 data_677 data_678 data_679 data_680 data_681 data_682 data_683 data_684 data_685 data_686 data_687 data_688 data_689 data_690 data_691 data_692 data_693 data_694 data_695 data_696 data_697 data_698 data_699 data_700 data_701 data_702 data_703 data_704 data_705 data_706 data_707 data_708 data_709 data_710 data_711 data_712 data_713 data_714 data_715 data_716 data_717 data_718 data_719 data_720
____________________ ______ ______ ______ ______ ______ ______ ______ ______ ______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________
04-May-2023 23:38:00 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 1 1 0 0 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 1 0 0 1 1 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 1 0 1 1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 1 1 1 0 0 0 0 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 0 0 0 0 0 1 1 1 0 0 1 1 0 1 1 1 1 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1
Sangare Lassana Meidi
2023년 6월 8일
Thanks,
Datetimes are not related to long/lat data. it is the data_1, data_2, etc... which are linked to the long/lat.
We have for each long/lat a data (0 or 1) for a given datetime.
in the end it's like combinations: For this date and this position (long/lat), we have this data_x.
This is a table that represents what I want to have by combining the two tables. (the one generated from the Q1 code and the one with the long/lat). For each 900 datetime, we have data_x which represent (1 or 0) according to 720 positions (lat/long)
Example:
04- may-2023 23:38:00 at position 1 (lat1/long1) we have data_1
......
04- may-2023 23:38:00 at position 720 (lat2/long2) we have data_720
04- may-2023 23:39:00 at position 1 (lat1/long1) we have data_1
.....
04- may-2023 23:39:00 at position 720 (lat2/long2) we have data_720
...
THANKS
Cris LaPierre
2023년 6월 8일
편집: Cris LaPierre
2023년 6월 8일
Maybe something like this? If I'm understanding correctly, the final table should have 900*720 rows, or 648000.
load tab_char(900x2).mat
load tab_loc(720x2).mat
% Same as before
data = cellfun(@num2cell,t.data,'UniformOutput',false);
data = vertcat(data{:});
data = str2double(data);
t.data = data;
t = splitvars(t,'data');
% restructure the table to a 648000x2 table
T2 = stack(t,2:width(t),"NewDataVariableName","data","IndexVariableName","Position")
T2 = 648000×3 table
timestamp Position data
____________________ ________ ____
05-May-2023 00:59:00 data_1 0
05-May-2023 00:59:00 data_2 0
05-May-2023 00:59:00 data_3 0
05-May-2023 00:59:00 data_4 0
05-May-2023 00:59:00 data_5 0
05-May-2023 00:59:00 data_6 0
05-May-2023 00:59:00 data_7 0
05-May-2023 00:59:00 data_8 0
05-May-2023 00:59:00 data_9 0
05-May-2023 00:59:00 data_10 0
05-May-2023 00:59:00 data_11 0
05-May-2023 00:59:00 data_12 0
05-May-2023 00:59:00 data_13 0
05-May-2023 00:59:00 data_14 0
05-May-2023 00:59:00 data_15 0
05-May-2023 00:59:00 data_16 0
% Add Long/Lat data
T2.Long = repmat(L.lng,height(t),1);
T2.Lat = repmat(L.lat,height(t),1);
% Reorganize variables
T2 = movevars(T2,["data","Position"])
T2 = 648000×5 table
timestamp Long Lat data Position
____________________ _______ ______ ____ ________
05-May-2023 00:59:00 -80.126 26.012 0 data_1
05-May-2023 00:59:00 -80.127 26.012 0 data_2
05-May-2023 00:59:00 -80.128 26.013 0 data_3
05-May-2023 00:59:00 -80.13 26.013 0 data_4
05-May-2023 00:59:00 -80.131 26.013 0 data_5
05-May-2023 00:59:00 -80.133 26.013 0 data_6
05-May-2023 00:59:00 -80.134 26.014 0 data_7
05-May-2023 00:59:00 -80.135 26.014 0 data_8
05-May-2023 00:59:00 -80.137 26.014 0 data_9
05-May-2023 00:59:00 -80.138 26.014 0 data_10
05-May-2023 00:59:00 -80.14 26.014 0 data_11
05-May-2023 00:59:00 -80.141 26.014 0 data_12
05-May-2023 00:59:00 -80.141 26.015 0 data_13
05-May-2023 00:59:00 -80.141 26.017 0 data_14
05-May-2023 00:59:00 -80.142 26.018 0 data_15
05-May-2023 00:59:00 -80.143 26.019 0 data_16
Sangare Lassana Meidi
2023년 6월 8일
Thanks your Cris, it's working!!
I just need some arguments for the function movevars like this :
T2 = movevars(T2,["data","Position"],'after','Lat');
Cris LaPierre
2023년 6월 8일
I didn't, as you can see above, but it may depend on what version of MATLAB you are using.
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