Last updated: 2022-12-21
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Knit directory:
RECCAP2_CESM_ETHZ_submission_v2/
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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_CESM <-
"/net/kryo/work/loher/CESM_output/RECCAP2/submit_Nov2022"
path_CESM[1] "/net/kryo/work/loher/CESM_output/RECCAP2/submit_Nov2022"# create list of CESM output files and sizes
CESM_files_names <- list.files(path = path_CESM,
pattern = ".nc")
CESM_files_sizes <-
file.size(paste(path_CESM, CESM_files_names, sep = "/"))
CESM_files <- bind_cols(file_name = CESM_files_names,
file_size_MB = round(CESM_files_sizes * 1e-6, 1))
rm(CESM_files_names, CESM_files_sizes)
# extract variable_id and experiment_id from file name
CESM_files <- CESM_files %>%
mutate(
variable_id = str_split(file_name,
pattern = "_CESM",
simplify = TRUE)[, 1],
experiment_id = str_sub(string = file_name, -19, -19)
) %>%
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, CESM_files) %>%
arrange(variable_id)
rm(CESM_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 | 485.2 |
| dissicos | 485.2 |
| epc100 | 485.2 |
| epcalc100 | 485.2 |
| fgco2 | 485.2 |
| fgco2_glob | 0.0 |
| fgco2_reg | 0.0 |
| fice | 485.2 |
| intphyc | 485.2 |
| intpp | 485.2 |
| intzooc | 485.2 |
| mld | 485.2 |
| sos | 485.2 |
| spco2 | 485.2 |
| talkos | 485.2 |
| tos | 485.2 |
| zeu | 485.2 |
| total | 7,278.00 |
| 2 | Priority: 2 | |
| dfeos | 485.2 |
| epc1000 | 485.2 |
| epc1000hard | 485.2 |
| epc1000soft | 485.2 |
| epc100hard | 485.2 |
| epc100soft | 485.2 |
| intdiac | 485.2 |
| intphynd | 485.2 |
| Kw | 485.2 |
| no3os | 485.2 |
| o2os | 485.2 |
| pco2atm | 485.2 |
| po4os | 485.2 |
| sios | 485.2 |
| total | 6,792.80 |
| 2 | Priority: 3 | |
| alpha | 485.2 |
| total | 485.20 |
| 3 | Priority: 1 | |
| dissic | 2426.0 |
| epc | 2426.0 |
| so | 2426.0 |
| talk | 2426.0 |
| thetao | 2426.0 |
| total | 12,130.00 |
| 3 | Priority: 2 | |
| no3 | 2426.0 |
| o2 | 2426.0 |
| po4 | 2426.0 |
| si | 2426.0 |
| total | 9,704.00 |
| NA | Priority: NA | |
| area | 0.3 |
| Area_tot_native | 0.0 |
| Atm_CO2 | 0.0 |
| mask_sfc | 0.3 |
| mask_vol | 15.6 |
| Vol_tot_native | 0.0 |
| volume | 15.6 |
| total | 31.80 |
# create list of CESM output files and sizes
CESM_files_names_tar <- list.files(path = path_CESM,
pattern = ".tar")
CESM_files_sizes_tar <-
file.size(paste(path_CESM, CESM_files_names_tar, sep = "/"))
CESM_files_tar <- bind_cols(
file_name = CESM_files_names_tar,
file_size_GB = round(CESM_files_sizes_tar * 1e-9, 1))
rm(path_CESM, CESM_files_names_tar, CESM_files_sizes_tar)
# extract variable_id and experiment_id from file name
CESM_files_tar# A tibble: 0 × 2
# … with 2 variables: file_name <chr>, file_size_GB <dbl>
sessionInfo()R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.4
Matrix products: default
BLAS: /usr/local/R-4.2.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.2.2/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_0.8.0 forcats_0.5.2 stringr_1.4.1 dplyr_1.0.10
[5] purrr_0.3.5 readr_2.1.3 tidyr_1.2.1 tibble_3.1.8
[9] ggplot2_3.4.0 tidyverse_1.3.2 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] httr_1.4.4 sass_0.4.4 bit64_4.0.5
[4] vroom_1.6.0 jsonlite_1.8.3 here_1.0.1
[7] modelr_0.1.10 bslib_0.4.1 assertthat_0.2.1
[10] getPass_0.2-2 googlesheets4_1.0.1 cellranger_1.1.0
[13] yaml_2.3.6 pillar_1.8.1 backports_1.4.1
[16] glue_1.6.2 digest_0.6.30 promises_1.2.0.1
[19] rvest_1.0.3 colorspace_2.0-3 htmltools_0.5.3
[22] httpuv_1.6.6 pkgconfig_2.0.3 broom_1.0.1
[25] haven_2.5.1 scales_1.2.1 processx_3.8.0
[28] whisker_0.4 later_1.3.0 tzdb_0.3.0
[31] timechange_0.1.1 git2r_0.30.1 googledrive_2.0.0
[34] generics_0.1.3 ellipsis_0.3.2 cachem_1.0.6
[37] withr_2.5.0 cli_3.4.1 magrittr_2.0.3
[40] crayon_1.5.2 readxl_1.4.1 evaluate_0.18
[43] ps_1.7.2 fs_1.5.2 fansi_1.0.3
[46] xml2_1.3.3 tools_4.2.2 hms_1.1.2
[49] gargle_1.2.1 lifecycle_1.0.3 munsell_0.5.0
[52] reprex_2.0.2 callr_3.7.3 compiler_4.2.2
[55] jquerylib_0.1.4 rlang_1.0.6 grid_4.2.2
[58] rstudioapi_0.14 rmarkdown_2.18 gtable_0.3.1
[61] DBI_1.1.3 R6_2.5.1 lubridate_1.9.0
[64] knitr_1.41 fastmap_1.1.0 bit_4.0.5
[67] utf8_1.2.2 rprojroot_2.0.3 stringi_1.7.8
[70] parallel_4.2.2 Rcpp_1.0.9 vctrs_0.5.1
[73] dbplyr_2.2.1 tidyselect_1.2.0 xfun_0.35