Last updated: 2022-04-26
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Knit directory: RECCAP2_ROMS_Pacific_ETHZ_submission/
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library(tidyverse)
library(gt)This analysis is based on Table 3 of the RECCAP2-ocean protocol for model output, and statistics of the ETHZ CESM model output.
# read Table 3 from model protocol
table_3 <- read_csv(
here::here(
"data/overview",
"RECCAP2-ocean_data_products_overview - Model_protocol_table3.csv"
)
)
# replace placeholder variable name with actual CESM variable name
table_3_temp <- table_3 %>%
filter(variable_id == "epc100type / epc1000type") %>%
select(-variable_id)
table_3_temp <- expand_grid(
table_3_temp,
variable_id = c("epc100hard","epc1000hard","epc100soft","epc1000soft")
)
table_3 <- table_3 %>%
filter(variable_id != "epc100type / epc1000type")
table_3 <- bind_rows(table_3, table_3_temp)
rm(table_3_temp)The list of files and sizes of the ETHZ CESM model output refers to the content in this folder:
# set path to output
path_ROMS <-
"/net/kryo/work/loher/ROMS/RECCAP2_Jan2022/Pacific_global_proto_runA/submit_20220425"
path_ROMS[1] "/net/kryo/work/loher/ROMS/RECCAP2_Jan2022/Pacific_global_proto_runA/submit_20220425"# create list of CESM output files and sizes
ROMS_files_names <- list.files(path = path_ROMS,
pattern = ".nc")
ROMS_files_sizes <-
file.size(paste(path_ROMS, ROMS_files_names, sep = "/"))
ROMS_files <- bind_cols(file_name = ROMS_files_names,
file_size_MB = round(ROMS_files_sizes * 1e-6, 1))
rm(ROMS_files_names, ROMS_files_sizes)
# extract variable_id and experiment_id from file name
ROMS_files <- ROMS_files %>%
mutate(
variable_id = str_split(file_name,
pattern = "_ROMS",
simplify = TRUE)[, 1],
experiment_id = str_sub(string = file_name, -29, -29)
) %>%
mutate(experiment_id = if_else(
experiment_id %in% c("A", "B", "C", "D"),
experiment_id,
"ancillary"
)) %>%
select(-c(file_name))# join file list and tab 3
overview <- full_join(table_3, ROMS_files) %>%
arrange(variable_id) %>%
filter(!is.na(file_size_MB))
rm(ROMS_files, table_3)
# write overview file
overview %>%
write_csv("data/overview/overview_files.csv")Overview table of output files created. Please note, that for each listed variable, four experiment_id (A-D) versions exist.
overview %>%
group_by(variable_id, dimension, priority) %>%
summarise_at("file_size_MB", sum, na.rm = TRUE) %>%
arrange(dimension, priority) %>%
gt(
rowname_col = "variable_id",
groupname_col = c("dimension", "priority"),
row_group.sep = " | Priority: "
) %>%
summary_rows(groups = TRUE,
fns = list(total = "sum"))| file_size_MB | |
|---|---|
| 2 | Priority: 1 | |
| chlos | 31.1 |
| dissicos | 24.5 |
| fgco2 | 32.4 |
| fgco2_glob | 0.0 |
| fice | 2.6 |
| intphyc | 30.4 |
| intzooc | 30.5 |
| mld | 30.8 |
| sos | 23.9 |
| spco2 | 27.2 |
| talkos | 23.8 |
| tos | 29.4 |
| total | 286.60 |
| 2 | Priority: 2 | |
| no3os | 31.6 |
| o2os | 26.8 |
| pco2atm | 22.1 |
| total | 80.50 |
| 2 | Priority: 3 | |
| alpha | 27.4 |
| total | 27.40 |
| 3 | Priority: 1 | |
| dissic | 159.2 |
| so | 138.5 |
| talk | 154.8 |
| thetao | 191.7 |
| total | 644.20 |
| 3 | Priority: 2 | |
| no3 | 185.2 |
| o2 | 191.9 |
| total | 377.10 |
| NA | Priority: NA | |
| area | 0.3 |
| Area_tot_native | 0.0 |
| Atm_CO2 | 0.0 |
| mask_sfc | 0.3 |
| mask_vol | 35.5 |
| Vol_tot_native | 0.0 |
| volume | 35.5 |
| total | 71.60 |
# create list of CESM output files and sizes
ROMS_files_names_tar <- list.files(path = path_ROMS,
pattern = ".tar")
ROMS_files_sizes_tar <-
file.size(paste(path_ROMS, ROMS_files_names_tar, sep = "/"))
ROMS_files_tar <- bind_cols(
file_name = ROMS_files_names_tar,
file_size_GB = round(ROMS_files_sizes_tar * 1e-9, 1))
rm(path_ROMS, ROMS_files_names_tar, ROMS_files_sizes_tar)
# extract variable_id and experiment_id from file name
ROMS_files_tar# A tibble: 0 × 2
# … with 2 variables: file_name <chr>, file_size_GB <dbl>
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.3
Matrix products: default
BLAS: /usr/local/R-4.1.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.1.2/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] gt_0.3.1 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7
[5] purrr_0.3.4 readr_2.1.1 tidyr_1.1.4 tibble_3.1.6
[9] ggplot2_3.3.5 tidyverse_1.3.1 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.8 here_1.0.1 lubridate_1.8.0 getPass_0.2-2
[5] ps_1.6.0 assertthat_0.2.1 rprojroot_2.0.2 digest_0.6.29
[9] utf8_1.2.2 R6_2.5.1 cellranger_1.1.0 backports_1.4.1
[13] reprex_2.0.1 evaluate_0.14 httr_1.4.2 pillar_1.6.4
[17] rlang_0.4.12 readxl_1.3.1 rstudioapi_0.13 whisker_0.4
[21] callr_3.7.0 jquerylib_0.1.4 checkmate_2.0.0 rmarkdown_2.11
[25] bit_4.0.4 munsell_0.5.0 broom_0.7.11 compiler_4.1.2
[29] httpuv_1.6.5 modelr_0.1.8 xfun_0.29 pkgconfig_2.0.3
[33] htmltools_0.5.2 tidyselect_1.1.1 fansi_1.0.2 crayon_1.4.2
[37] tzdb_0.2.0 dbplyr_2.1.1 withr_2.4.3 later_1.3.0
[41] grid_4.1.2 jsonlite_1.7.3 gtable_0.3.0 lifecycle_1.0.1
[45] DBI_1.1.2 git2r_0.29.0 magrittr_2.0.1 scales_1.1.1
[49] vroom_1.5.7 cli_3.1.1 stringi_1.7.6 fs_1.5.2
[53] promises_1.2.0.1 xml2_1.3.3 bslib_0.3.1 ellipsis_0.3.2
[57] generics_0.1.1 vctrs_0.3.8 tools_4.1.2 bit64_4.0.5
[61] glue_1.6.0 hms_1.1.1 parallel_4.1.2 processx_3.5.2
[65] fastmap_1.1.0 yaml_2.2.1 colorspace_2.0-2 rvest_1.0.2
[69] knitr_1.37 haven_2.4.3 sass_0.4.0