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[GCC] PAD: Implementação nova versão do GF - Teste Inicial #511

Closed joaomas closed 3 weeks ago

joaomas commented 1 month ago

:people_holding_hands: User Story

COMO (...) QUEREMOS (...) PARA (...)

:clipboard: Critérios de aceite de conclusão da Issue

:pencil: Detalhamento adicional da atividade

:comet: Impactos

:link: Dependências

509

:hammer_and_wrench: Solução

:rotating_light: Problemas encontrados

:white_check_mark: Conclusão

:spiral_calendar: Trabalhos Futuros

N/A

joaomas commented 1 month ago

Dica Paiva.

$ find dir1/ -type f -exec sha256sum {} + > hashes.txt

$ sha256sum -c hashes.txt dir1/file1.txt: OK dir1/dir2/dir3/file3.txt: OK dir1/dir2/file2.txt: OK

$ echo "oi" >> dir1/dir2/file2.txt

$ sha256sum -c hashes.txt dir1/file1.txt: OK dir1/dir2/dir3/file3.txt: OK dir1/dir2/file2.txt: FAILED sha256sum: WARNING: 1 computed checksum did NOT match

cfbastarz commented 1 month ago

Olá @egkhamis, segue um script simples (diff.py) para fazer a diferença entre dois campos de um arquivo NetCDF utilizando o Xarray e o Cartopy:

#! /usr/bin/env python3

import os
import xarray as xr
import cartopy.crs as ccrs
import matplotlib.pyplot as plt 

bpath = '/mnt/beegfs/eduardo.khamis/issues/511/scripts_CD-CT/dataout/zzz/2024020100.teste.binario'
fname = 'MONAN_DIAG_G_POS_GFS_2024020100.00.00.x1024002L55.nc'

ds1 = xr.open_dataset(os.path.join(str(bpath), 'Post', fname), engine='netcdf4')
ds2 = xr.open_dataset(os.path.join(str(bpath), 'z/Post', fname), engine='netcdf4')

# https://docs.xarray.dev/en/latest/generated/xarray.Dataset.equals.html
print('Datasets are equal: ', ds1.equals(ds2))

# https://docs.xarray.dev/en/stable/generated/xarray.Dataset.identical.html
print('Datasets are identical: ', ds1.identical(ds2))

# Dif. entre uma variável de um dataset e outro (para um tempo e nível específicos)
ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_global()

(ds1['zgeo'].isel(time=0, level=0) - ds2['zgeo']).isel(time=0, level=2).plot.contourf(ax=ax, transform=ccrs.PlateCarree())

ax.coastlines()

plt.savefig('zgeo_t0_l0.png')

O resultado é a seguinte figura:

image

egkhamis commented 1 month ago

Oi @cfbastarz , @marcelopaivaramos , muito obrigado!

Fizemos o PR, vou atualizar tudo e testar o python.

cfbastarz commented 1 month ago

@egkhamis , esqueci de comentar sobre o ambiente em que executei esse script:

conda create -n xarray_netcdf
conda activate xarray_netcdf
pip install xarray[complete]
pip install cartopy

Abraço!

joaomas commented 1 month ago

@egkhamis e @marcelopaivaramos

Usando comando "sha256sum" - rodada 11h30 e 20h30 - mesmo GFS e mesma data da analise 05/06


Pre

sha256sum 2024060500/Pre/x1.1024002.init.nc df2695f43a9684738a4984282d1952ca9647290a79573b9ff26152e559706798 2024060500/Pre/x1.1024002.init.nc

sha256sum 2024060500-11h30/Pre/x1.1024002.init.nc df0dded1b509481760d6f0981b8e02e263a0ed536bc5fc3282fdbcfa9e57ea20 2024060500-11h30/Pre/x1.1024002.init.nc


Model

sha256sum 2024060500/Model/MONAN_DIAG_G_MOD_GFS_2024060500_2024060501.00.00.x1024002L55.nc b559ccd209ac80f86ff73fb247cbe76b80def0d0615bf77ed2060aca8c686c1a 2024060500/Model/MONAN_DIAG_G_MOD_GFS_2024060500_2024060501.00.00.x1024002L55.nc

sha256sum 2024060500-11h30/Model/MONAN_DIAG_G_MOD_GFS_2024060500_2024060501.00.00.x1024002L55.nc e073222a9bca43b51f65f63f0af936b7e727aae3354cb7847a8952bc874f7f87 2024060500-11h30/Model/MONAN_DIAG_G_MOD_GFS_2024060500_2024060501.00.00.x1024002L55.nc


Post

sha256sum 2024060500/Post/MONAN_DIAG_G_POS_GFS_2024060500.00.00.x1024002L55.nc 53eb75524f80edc0a9f327f5f7ef4ceede1bf850c6b597907803bbdf5dc717d7 2024060500/Post/MONAN_DIAG_G_POS_GFS_2024060500.00.00.x1024002L55.nc

sha256sum 2024060500-11h30/Post/MONAN_DIAG_G_POS_GFS_2024060500.00.00.x1024002L55.nc 0fba34204799f6eb5b749224ace9996d17cdbd6c76b5ed2bfadfb25fb7803767 2024060500-11h30/Post/MONAN_DIAG_G_POS_GFS_2024060500.00.00.x1024002L55.nc

joaomas commented 1 month ago

@joaomas e @egkhamis

Usando comando "cmp" - rodada 11h30 e 20h30 - mesmo GFS e mesma data da analise 05/06


Pre

cmp 2024060500/Pre/x1.1024002.init.nc 2024060500-11h30/Pre/x1.1024002.init.nc 2024060500/Pre/x1.1024002.init.nc 2024060500-11h30/Pre/x1.1024002.init.nc differ: byte 3337, line 7


Model

cmp 2024060500/Model/MONAN_DIAG_G_MOD_GFS_2024060500_2024060501.00.00.x1024002L55.nc 2024060500-11h30/Model/MONAN_DIAG_G_MOD_GFS_2024060500_2024060501.00.00.x1024002L55.nc 2024060500/Model/MONAN_DIAG_G_MOD_GFS_2024060500_2024060501.00.00.x1024002L55.nc 2024060500-11h30/Model/MONAN_DIAG_G_MOD_GFS_2024060500_2024060501.00.00.x1024002L55.nc differ: byte 541, line 4


Post

cmp 2024060500/Post/MONAN_DIAG_G_POS_GFS_2024060500.00.00.x1024002L55.nc 2024060500-11h30/Post/MONAN_DIAG_G_POS_GFS_2024060500.00.00.x1024002L55.nc 2024060500/Post/MONAN_DIAG_G_POS_GFS_2024060500.00.00.x1024002L55.nc 2024060500-11h30/Post/MONAN_DIAG_G_POS_GFS_2024060500.00.00.x1024002L55.nc differ: byte 12428, line 19

egkhamis commented 1 month ago

@cfbastarz , ah, sim, eu consegui rodar o script mas instalei o xarray e cartopy com o conda install -c conda-forge xarray (e cartopy também). Obrigado pelo aviso, vou tentar usar o pip também.

Entretanto, encontrei um script bem completo que faz o diff entre dois arquivos netcdf, e gera estatísticas das variáveis que se repetem nos dois arquivos: média, mínimo, máximo, desvio padrão e correlação. Fiz também a instalação do rioxarray, netCDF4, matplotlib, cftime, cfgrid para usar esse script. Infelizmente está dando um erro no meio da execução e não sei se é bug ou falha na instalação desses pacotes, ou mesmo falta de memória (submeti num nó e o erro foi o mesmo). O erro que talvez seja a falta da palavra chave global no código é o seguinte:

Traceback (most recent call last):
  File "/mnt/beegfs/eduardo.khamis/issues/511/scripts_CD-CT/./compare_netcdf_or_grib.py", line 597, in <module>
    main()
  File "/mnt/beegfs/eduardo.khamis/issues/511/scripts_CD-CT/./compare_netcdf_or_grib.py", line 585, in main
    compareNetcdfsOrGribs(netcdf_or_grib_1,netcdf_or_grib_2, plot , outDir)
  File "/mnt/beegfs/eduardo.khamis/issues/511/scripts_CD-CT/./compare_netcdf_or_grib.py", line 413, in compareNetcdfsOrGribs
    valid_locs = np.where((~np.isnan(arr1_1d))&(~np.isnan(arr2_1d)))
UnboundLocalError: local variable 'arr1_1d' referenced before assignment

Estou investigando e testando nova instalação do ambiente conda.

Veja o script em: https://github.com/gerasimosmichalitsianos/compare_netcdf_or_grib

Um exemplo da saída da variável w:

    MONAN_DIAG_G_POS_GFS_2024020100.00.00.x1024002L55.nc (w) 
      Mean      : -0.0010223837
      Min,      : -1.0604509
      Max.      : 2.2051587
      Std. Dev. : 0.027128834
    MONAN_DIAG_G_POS_GFS_2024020100.00.00.x1024002L55.nc (w) 
      Mean      : -0.0010493156
      Min,      : -1.0604509
      Max.      : 6.2310433
      Std. Dev. : 0.027042756

    MONAN_DIAG_G_POS_GFS_2024020100.00.00.x1024002L55.nc (w) 
    MONAN_DIAG_G_POS_GFS_2024020100.00.00.x1024002L55.nc (w) 
      difference number of elements     : 28551600
      difference Mean,Min.,Max.,St. dev : 0.0023486994, 0.0, 6.1408625, 0.0101217525

A diferença parece grande mas são rodadas com as rotinas de Grell Freitas do MPAS e a nova que o Saulo implementou.

A correlação viria em forma de figura o que deixa o script muito melhor!

Abraço!

egkhamis commented 1 month ago

@joaomas e @egkhamis

Usando comando "cmp" - rodada 11h30 e 20h30 - mesmo GFS e mesma data da analise 05/06

Pre

cmp 2024060500/Pre/x1.1024002.init.nc 2024060500-11h30/Pre/x1.1024002.init.nc 2024060500/Pre/x1.1024002.init.nc 2024060500-11h30/Pre/x1.1024002.init.nc differ: byte 3337, line 7

Model

cmp 2024060500/Model/MONAN_DIAG_G_MOD_GFS_2024060500_2024060501.00.00.x1024002L55.nc 2024060500-11h30/Model/MONAN_DIAG_G_MOD_GFS_2024060500_2024060501.00.00.x1024002L55.nc 2024060500/Model/MONAN_DIAG_G_MOD_GFS_2024060500_2024060501.00.00.x1024002L55.nc 2024060500-11h30/Model/MONAN_DIAG_G_MOD_GFS_2024060500_2024060501.00.00.x1024002L55.nc differ: byte 541, line 4

Post

cmp 2024060500/Post/MONAN_DIAG_G_POS_GFS_2024060500.00.00.x1024002L55.nc 2024060500-11h30/Post/MONAN_DIAG_G_POS_GFS_2024060500.00.00.x1024002L55.nc 2024060500/Post/MONAN_DIAG_G_POS_GFS_2024060500.00.00.x1024002L55.nc 2024060500-11h30/Post/MONAN_DIAG_G_POS_GFS_2024060500.00.00.x1024002L55.nc differ: byte 12428, line 19

@joaomas e @marcelopaivaramos , eu cheguei a testar o comando cmp, mas ele para logo na primeira diferença. Talvez ele tenha uma -flag para mostrar todas as diferenças mas parece que seria preciso fazer um script com uma combinação de flags.

egkhamis commented 1 month ago

@cfbastarz , @joaomas , @marcelopaivaramos , usei uma versão anterior do script que funcionou na configuração do conda na minha conta :)

git clone https://github.com/gerasimosmichalitsianos/compare_netcdf_or_grib
cd compare_netcdf_or_grib
git checkout 240755aff0f58b56f707f734d80415c66295e292
cd bin

Vejam o log da saída entre GF novo ligado e antigo ligado:

comparison_statistics_20240606_121535.txt

Log de saída entre versão 0.6.0 e após o pull request do Saulo, ambos com GF antigo ligado, diferença zero:

comparison_statistics_20240606_132550.txt

Vou tentar incluir a diferença entre figuras que o @cfbastarz enviou.

joaomas commented 1 month ago

@joaomas e @egkhamis Usando comando "cmp" - rodada 11h30 e 20h30 - mesmo GFS e mesma data da analise 05/06

Pre

cmp 2024060500/Pre/x1.1024002.init.nc 2024060500-11h30/Pre/x1.1024002.init.nc 2024060500/Pre/x1.1024002.init.nc 2024060500-11h30/Pre/x1.1024002.init.nc differ: byte 3337, line 7

Model

cmp 2024060500/Model/MONAN_DIAG_G_MOD_GFS_2024060500_2024060501.00.00.x1024002L55.nc 2024060500-11h30/Model/MONAN_DIAG_G_MOD_GFS_2024060500_2024060501.00.00.x1024002L55.nc 2024060500/Model/MONAN_DIAG_G_MOD_GFS_2024060500_2024060501.00.00.x1024002L55.nc 2024060500-11h30/Model/MONAN_DIAG_G_MOD_GFS_2024060500_2024060501.00.00.x1024002L55.nc differ: byte 541, line 4

Post

cmp 2024060500/Post/MONAN_DIAG_G_POS_GFS_2024060500.00.00.x1024002L55.nc 2024060500-11h30/Post/MONAN_DIAG_G_POS_GFS_2024060500.00.00.x1024002L55.nc 2024060500/Post/MONAN_DIAG_G_POS_GFS_2024060500.00.00.x1024002L55.nc 2024060500-11h30/Post/MONAN_DIAG_G_POS_GFS_2024060500.00.00.x1024002L55.nc differ: byte 12428, line 19

@joaomas e @marcelopaivaramos , eu cheguei a testar o comando cmp, mas ele para logo na primeira diferença. Talvez ele tenha uma -flag para mostrar todas as diferenças mas parece que seria preciso fazer um script com uma combinação de flags.

@egkhamis o curioso é dar diferença, aparentemente no Pré... usei agora o -l - se o pré esta diferente tudo será... convém começar a analise pelo pré então né ?

$ cmp -l 2024060500/Pre/x1.1024002.init.nc 2024060500-11h30/Pre/x1.1024002.init.nc 3337 165 156 3338 153 64 3339 165 155 3340 63 142 3341 64 167 3342 162 156 3343 144 60 3344 151 64 3345 143 160 3346 63 147 27201 0 363 27202 0 354 27203 0 101 27204 0 107 27205 0 363 27206 0 354 27207 0 101 27208 0 107 27209 0 363 27210 0 354 27211 0 101 27212 0 107 27213 0 363 27214 0 354 27215 0 101 27216 0 107 27217 0 363 27218 0 354 27219 0 101 27220 0 107 27221 0 363 27222 0 354 27223 0 101 27224 0 107 27225 0 363 27226 0 354 27227 0 101 27228 0 107 27229 0 363 27230 0 354 27231 0 101 27232 0 107 27233 0 363 27234 0 354 27235 0 101 27236 0 107 27237 0 363 27238 0 354 27239 0 101 27240 0 107 27241 0 363 27242 0 354 27243 0 101 27244 0 107 27245 0 363 27246 0 354 27247 0 101 27248 0 107 27249 0 363 27250 0 354 27251 0 101 27252 0 107 27253 0 363 27254 0 354 27255 0 101 27256 0 107 27257 0 363 27258 0 354 27259 0 101 27260 0 107 27261 0 363 27262 0 354 27263 0 101 27264 0 107 27265 0 363 27266 0 354 27267 0 101 27268 0 107 27269 0 363 27270 0 354 27271 0 101 27272 0 107 27273 0 363 27274 0 354 27275 0 101 27276 0 107 27277 0 363 27278 0 354 27279 0 101 27280 0 107 27281 0 363 27282 0 354 27283 0 101 27284 0 107 27285 0 363 27286 0 354 27287 0 101 27288 0 107 27289 0 363 27290 0 354 27291 0 101 27292 0 107 27293 0 363 27294 0 354 27295 0 101 27296 0 107 27297 0 363 27298 0 354 27299 0 101 27300 0 107 27301 0 363 27302 0 354 27303 0 101 27304 0 107 27305 0 363 27306 0 354 27307 0 101 27308 0 107 27309 0 363 27310 0 354 27311 0 101 27312 0 107 27313 0 363 27314 0 354 27315 0 101 27316 0 107 27317 0 363 27318 0 354 27319 0 101 27320 0 107 27321 0 363 27322 0 354 27323 0 101 27324 0 107 27325 0 363 27326 0 354 27327 0 101 27328 0 107 27329 0 363 27330 0 354 27331 0 101 27332 0 107 27333 0 363 27334 0 354 27335 0 101 27336 0 107 27337 0 363 27338 0 354 27339 0 101 27340 0 107 27341 0 363 27342 0 354 27343 0 101 27344 0 107 27345 0 363 27346 0 354 27347 0 101 27348 0 107 27349 0 363 27350 0 354 27351 0 101 27352 0 107 27353 0 363 27354 0 354 27355 0 101 27356 0 107 27357 0 363 27358 0 354 27359 0 101 27360 0 107 27361 0 363 27362 0 354 27363 0 101 27364 0 107 27365 0 363 27366 0 354 27367 0 101 27368 0 107 27369 0 363 27370 0 354 27371 0 101 27372 0 107 27373 0 363 27374 0 354 27375 0 101 27376 0 107 27377 0 363 27378 0 354 27379 0 101 27380 0 107 27381 0 363 27382 0 354 27383 0 101 27384 0 107 27385 0 363 27386 0 354 27387 0 101 27388 0 107 27389 0 363 27390 0 354 27391 0 101 27392 0 107 27393 0 363 27394 0 354 27395 0 101 27396 0 107 27397 0 363 27398 0 354 27399 0 101 27400 0 107 27401 0 363 27402 0 354 27403 0 101 27404 0 107 27405 0 363 27406 0 354 27407 0 101 27408 0 107 27409 0 363 27410 0 354 27411 0 101 27412 0 107 27413 0 363 27414 0 354 27415 0 101 27416 0 107 27417 0 363 27418 0 354 27419 0 101 27420 0 107 27421 0 363 27422 0 354 27423 0 101 27424 0 107 27425 0 363 27426 0 354 27427 0 101 27428 0 107 27429 0 363 27430 0 354 27431 0 101 27432 0 107 27433 0 363 27434 0 354 27435 0 101 27436 0 107 27437 0 363 27438 0 354 27439 0 101 27440 0 107 27441 0 363 27442 0 354 27443 0 101 27444 0 107 27445 0 363 27446 0 354 27447 0 101 27448 0 107 27449 0 363 27450 0 354 27451 0 101 27452 0 107 27453 0 363 27454 0 354 27455 0 101 27456 0 107 27457 0 363 27458 0 354 27459 0 101 27460 0 107 27461 0 363 27462 0 354 27463 0 101 27464 0 107 27465 0 363 27466 0 354 27467 0 101 27468 0 107 27469 0 363 27470 0 354 27471 0 101 27472 0 107 27473 0 363 27474 0 354 27475 0 101 27476 0 107 27477 0 363 27478 0 354 27479 0 101 27480 0 107 27481 0 363 27482 0 354 27483 0 101 27484 0 107 27485 0 363 27486 0 354 27487 0 101 27488 0 107 27489 0 363 27490 0 354 27491 0 101 27492 0 107 27493 0 363 27494 0 354 27495 0 101 27496 0 107 27497 0 363 27498 0 354 27499 0 101 27500 0 107 27501 0 363 27502 0 354 27503 0 101 27504 0 107 27505 0 363 27506 0 354 27507 0 101 27508 0 107 27509 0 363 27510 0 354 27511 0 101 27512 0 107 27513 0 363 27514 0 354 27515 0 101 27516 0 107 27517 0 363 27518 0 354 27519 0 101 27520 0 107 27521 0 363 27522 0 354 27523 0 101 27524 0 107 27525 0 363 27526 0 354 27527 0 101 27528 0 107 27529 0 363 27530 0 354 27531 0 101 27532 0 107 27533 0 363 27534 0 354 27535 0 101 27536 0 107 27537 0 363 27538 0 354 27539 0 101 27540 0 107 27541 0 363 27542 0 354 27543 0 101 27544 0 107 27545 0 363 27546 0 354 27547 0 101 27548 0 107 27549 0 363 27550 0 354 27551 0 101 27552 0 107 27553 0 363 27554 0 354 27555 0 101 27556 0 107 27557 0 363 27558 0 354 27559 0 101 27560 0 107 27561 0 363 27562 0 354 27563 0 101 27564 0 107 27565 0 363 27566 0 354 27567 0 101 27568 0 107 27569 0 363 27570 0 354 27571 0 101 27572 0 107 27573 0 363 27574 0 354 27575 0 101 27576 0 107 27577 0 363 27578 0 354 27579 0 101 27580 0 107 27581 0 363 27582 0 354 27583 0 101 27584 0 107 27585 0 363 27586 0 354 27587 0 101 27588 0 107 27589 0 363 27590 0 354 27591 0 101 27592 0 107 27593 0 363 27594 0 354 27595 0 101 27596 0 107 27597 0 363 27598 0 354 27599 0 101 27600 0 107 27601 0 363 27602 0 354 27603 0 101 27604 0 107 27605 0 363 27606 0 354 27607 0 101 27608 0 107 27609 0 363 27610 0 354 27611 0 101 27612 0 107 27613 0 363 27614 0 354 27615 0 101 27616 0 107 27617 0 363 27618 0 354 27619 0 101 27620 0 107 27621 0 363 27622 0 354 27623 0 101 27624 0 107 27625 0 363 27626 0 354 27627 0 101 27628 0 107 27629 0 363 27630 0 354 27631 0 101 27632 0 107 27633 0 363 27634 0 354 27635 0 101 27636 0 107 27637 0 363 27638 0 354 27639 0 101 27640 0 107 27641 0 363 27642 0 354 27643 0 101 27644 0 107 27645 0 363 27646 0 354 27647 0 101 27648 0 107 4123657 0 202 4123658 0 347 4123659 0 45 4123660 0 107 4123661 0 170 4123662 0 350 4123663 0 45 4123664 0 107 4123665 0 156 4123666 0 351 4123667 0 45 4123668 0 107 4123669 0 144 4123670 0 352 4123671 0 45 4123672 0 107 4123673 0 60 4123674 0 353 4123675 0 45 4123676 0 107 4123677 0 46 4123678 0 354 4123679 0 45 4123680 0 107 4123681 0 34 4123682 0 355 4123683 0 45 4123684 0 107 4123685 0 351 4123686 0 355 4123687 0 45 4123688 0 107 4123689 0 336 4123690 0 356 4123691 0 45 4123692 0 107 4123693 0 253 4123694 0 357 4123695 0 45 4123696 0 107 4123697 0 241 4123698 0 360 4123699 0 45 4123700 0 107 4123701 0 227 4123702 0 361 4123703 0 45 4123704 0 107 4123705 0 144 4123706 0 362 4123707 0 45 4123708 0 107 4123709 0 131 4123710 0 363 4123711 0 45 4123712 0 107 4123713 0 117 4123714 0 364 4123715 0 45 4123716 0 107 4123717 0 105 4123718 0 365 4123719 0 45 4123720 0 107 4123721 0 22 4123722 0 366 4123723 0 45 4123724 0 107 4123725 0 336 4123726 0 366 4123727 0 45 4123728 0 107 4123729 0 324 4123730 0 367 4123731 0 45 4123732 0 107 4123733 0 241 4123734 0 370 4123735 0 45 4123736 0 107 4123737 0 227 4123738 0 371 4123739 0 45 4123740 0 107 4123741 0 144 4123742 0 372 4123743 0 45 4123744 0 107 4123745 0 60 4123746 0 373 4123747 0 45 4123748 0 107 4123749 0 46 4123750 0 374 4123751 0 45 4123752 0 107 4123753 0 363 4123754 0 374 4123755 0 45 4123756 0 107 4123757 0 300 4123758 0 375 4123759 0 45 4123760 0 107 4123761 0 266 4123762 0 376 4123763 0 45 4123764 0 107 4123765 0 202 4123766 0 377 4123767 0 45 4123768 0 107 4123769 0 170 4123771 0 46 4123772 0 107 4123773 0 105 4123774 0 1 4123775 0 46 4123776 0 107 4123777 0 22 4123778 0 2 4123779 0 46 4123780 0 107 4123781 0 7 4123782 0 3 4123783 0 46 4123784 0 107 4123785 0 324 4123786 0 3 4123787 0 46 4123788 0 107 4123789 0 241 4123790 0 4 4123791 0 46 4123792 0 107 4123793 0 227 4123794 0 5 4123795 0 46 4123796 0 107 4123797 0 144 4123798 0 6 4123799 0 46 4123800 0 107 4123801 0 131 4123802 0 7 4123803 0 46 4123804 0 107 4123805 0 46 4123806 0 10 4123807 0 46 4123808 0 107 4123809 0 363 4123810 0 10 4123811 0 46 4123812 0 107 4123813 0 22 4123814 0 12 4123815 0 46 4123816 0 107 4123817 0 336 4123818 0 12 4123819 0 46 4123820 0 107 4123821 0 253 4123822 0 13 4123823 0 46 4123824 0 107 4123825 0 241 4123826 0 14 4123827 0 46 4123828 0 107 4123829 0 156 4123830 0 15 4123831 0 46 4123832 0 107 4123833 0 144 4123834 0 16 4123835 0 46 4123836 0 107 4123837 0 60 4123838 0 17 4123839 0 46 4123840 0 107 4123841 0 375 4123842 0 17 4123843 0 46 4123844 0 107 4123845 0 363 4123846 0 20 4123847 0 46 4123848 0 107 4123849 0 351 4123850 0 21 4123851 0 46 4123852 0 107 4123853 0 266 4123854 0 22 4123855 0 46 4123856 0 107 4123857 0 253 4123858 0 23 4123859 0 46 4123860 0 107 4123861 0 170 4123862 0 24 4123863 0 46 4123864 0 107 4123865 0 156 4123866 0 25 4123867 0 46 4123868 0 107 4123869 0 144 4123870 0 26 4123871 0 46 4123872 0 107 4123873 0 60 4123874 0 27 4123875 0 46 4123876 0 107 4123877 0 46 4123878 0 30 4123879 0 46 4123880 0 107 4123881 0 34 4123882 0 31 4123883 0 46 4123884 0 107 4123885 0 351 4123886 0 31 4123887 0 46 4123888 0 107 4123889 0 336 4123890 0 32 4123891 0 46 4123892 0 107 4123893 0 324 4123894 0 33 4123895 0 46 4123896 0 107 4123897 0 241 4123898 0 34 4123899 0 46 4123900 0 107 4123901 0 227 4123902 0 35 4123903 0 46 4123904 0 107 4123905 0 215 4123906 0 36 4123907 0 46 4123908 0 107 4123909 0 202 4123910 0 37 4123911 0 46 4123912 0 107 4123913 0 117 4123914 0 40 4123915 0 46 4123916 0 107 4123917 0 105 4123918 0 41 4123919 0 46 4123920 0 107 4123921 0 73 4123922 0 42 4123923 0 46 4123924 0 107 4123925 0 60 4123926 0 43 4123927 0 46 4123928 0 107 4123929 0 375 4123930 0 43 4123931 0 46 4123932 0 107 4123933 0 363 4123934 0 44 4123935 0 46 4123936 0 107 4123937 0 351 4123938 0 45 4123939 0 46 4123940 0 107 4123941 0 266 4123942 0 46 4123943 0 46 4123944 0 107 4123945 0 253 4123946 0 47 4123947 0 46 4123948 0 107 4123949 0 241 4123950 0 50 4123951 0 46 4123952 0 107 4123953 0 227 4123954 0 51 4123955 0 46 4123956 0 107 4123957 0 215 4123958 0 52 4123959 0 46 4123960 0 107 4123961 0 131 4123962 0 53 4123963 0 46 4123964 0 107 4123965 0 46 4123966 0 54 4123967 0 46 4123968 0 107 4123969 0 105 4123970 0 55 4123971 0 46 4123972 0 107 4123973 0 22 4123974 0 56 4123975 0 46 4123976 0 107 4123977 0 7 4123978 0 57 4123979 0 46 4123980 0 107 4123981 0 324 4123982 0 57 4123983 0 46 4123984 0 107 4123985 0 241 4123986 0 60 4123987 0 46 4123988 0 107 4123989 0 300 4123990 0 61 4123991 0 46 4123992 0 107 4123993 0 215 4123994 0 62 4123995 0 46 4123996 0 107 4123997 0 131 4123998 0 63 4123999 0 46 4124000 0 107 4124001 0 117 4124002 0 64 4124003 0 46 4124004 0 107 4124005 0 105 4124006 0 65 4124007 0 46 4124008 0 107 4124009 0 22 4124010 0 66 4124011 0 46 4124012 0 107 4124013 0 7 4124014 0 67 4124015 0 46 4124016 0 107 4124017 0 324 4124018 0 67 4124019 0 46 4124020 0 107 4124021 0 312 4124022 0 70 4124023 0 46 4124024 0 107 4124025 0 227 4124026 0 71 4124027 0 46 4124028 0 107 4124029 0 144 4124030 0 72 4124031 0 46 4124032 0 107 4124033 0 131 4124034 0 73 4124035 0 46 4124036 0 107 4124037 0 46 4124038 0 74 4124039 0 46 4124040 0 107 4124041 0 34 4124042 0 75 4124043 0 46 4124044 0 107 4124045 0 351 4124046 0 75 4124047 0 46 4124048 0 107 4124049 0 266 4124050 0 76 4124051 0 46 4124052 0 107 4124053 0 253 4124054 0 77 4124055 0 46 4124056 0 107 4124057 0 170 4124058 0 100 4124059 0 46 4124060 0 107 4124061 0 156 4124062 0 101 4124063 0 46 4124064 0 107 4124065 0 73 4124066 0 102 4124067 0 46 4124068 0 107 4124069 0 60 4124070 0 103 4124071 0 46 4124072 0 107 4124073 0 375 4124074 0 103 4124075 0 46 4124076 0 107 4124077 0 312 4124078 0 104 4124079 0 46 4124080 0 107 4124081 0 300 4124082 0 105 4124083 0 46 4124084 0 107 4124085 0 215 4124086 0 106 4124087 0 46 4124088 0 107 4124089 0 131 4124090 0 107 4124091 0 46 4124092 0 107 4124093 0 117 4124094 0 110 4124095 0 46 4124096 0 107 4124097 0 34 4124098 0 111 4124099 0 46 4124100 0 107 4124101 0 22 4124102 0 112 4124103 0 46 4124104 0 107 4124105 0 336 4124106 0 112 4124107 0 46 4124108 0 107 4124109 0 324 4124110 0 113 4124111 0 46 4124112 0 107 4124113 0 312 4124114 0 114 4124115 0 46 4124116 0 107 4124117 0 227 4124118 0 115 4124119 0 46 4124120 0 107 4124121 0 144 4124122 0 116 4124123 0 46 4124124 0 107 4124125 0 131 4124126 0 117 4124127 0 46 4124128 0 107 4124129 0 117 4124130 0 120 4124131 0 46 4124132 0 107 4124133 0 34 4124134 0 121 4124135 0 46 4124136 0 107 4124137 0 22 4124138 0 122 4124139 0 46 4124140 0 107 4124141 0 336 4124142 0 122 4124143 0 46 4124144 0 107 4124145 0 324 4124146 0 123 4124147 0 46 4124148 0 107 4124149 0 312 4124150 0 124 4124151 0 46 4124152 0 107 4124153 0 300 4124154 0 125 4124155 0 46 4124156 0 107 4124157 0 215 4124158 0 126 4124159 0 46 4124160 0 107

//

$ echo "teste 1 - arquivo a sera igual ao b" > a.txt $ echo "teste 1 - arquivo a sera igual ao b" > b.txt

$ cmp -l a.txt b.txt $ (blank)

egkhamis commented 1 month ago

@joaomas , para analisarmos o pré teremos que pós processá-lo também. Não sei se convém fazermos isso agora pois temos que fechar as tarefas atuais. Melhor deixarmos para a próxima sprint e levantarmos essas observações.