Last updated: 2021-06-17
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Knit directory: RECCAP2_CESM_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_CESM <-
"/net/kryo/work/loher/CESM_output/RECCAP2/submit_June2021"
path_CESM[1] "/net/kryo/work/loher/CESM_output/RECCAP2/submit_June2021"# 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 | |
|---|---|
| 2D | Priority: 1 | |
| chlos | 245.6 |
| dissicos | 187.7 |
| epc100 | 234.4 |
| epcalc100 | 237.8 |
| fgco2 | 258.8 |
| fgco2_glob | 0.0 |
| fgco2_reg | 0.0 |
| fice | 68.8 |
| intphyc | 236.4 |
| intpp | 239.6 |
| intzooc | 235.2 |
| mld | 237.8 |
| sos | 187.4 |
| spco2 | 204.3 |
| talkos | 184.2 |
| tos | 218.4 |
| zeu | 26.4 |
| total | 3,002.80 |
| 2D | Priority: 2 | |
| dfeos | 243.4 |
| epc1000 | 211.6 |
| epc1000hard | 213.8 |
| epc1000soft | 211.4 |
| epc100hard | 237.2 |
| epc100soft | 234.0 |
| intdiac | 242.0 |
| intphynd | 240.6 |
| Kw | 244.0 |
| no3os | 238.1 |
| o2os | 201.0 |
| pco2atm | 168.0 |
| po4os | 235.1 |
| sios | 232.4 |
| total | 3,152.60 |
| 2D | Priority: 3 | |
| alpha | 201.4 |
| total | 201.40 |
| 3D | Priority: 1 | |
| dissic | 733.1 |
| epc | 985.2 |
| so | 679.4 |
| talk | 700.2 |
| thetao | 923.0 |
| total | 4,020.90 |
| 3D | Priority: 2 | |
| no3 | 895.4 |
| o2 | 876.8 |
| po4 | 895.6 |
| si | 915.2 |
| total | 3,583.00 |
| NA | Priority: NA | |
| area | 0.0 |
| Area_tot_native | 0.0 |
| Atm_CO2 | 0.0 |
| mask_sfc | 0.0 |
| mask_vol | 0.4 |
| Vol_tot_native | 0.0 |
| volume | 0.2 |
| total | 0.60 |
# 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 x 2
# … with 2 variables: file_name <chr>, file_size_GB <dbl>
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.2
Matrix products: default
BLAS: /usr/local/R-4.0.3/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.0.3/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_0.2.2 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.5
[5] purrr_0.3.4 readr_1.4.0 tidyr_1.1.2 tibble_3.0.4
[9] ggplot2_3.3.3 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 lubridate_1.7.9 here_0.1 utf8_1.1.4
[5] assertthat_0.2.1 rprojroot_2.0.2 digest_0.6.27 R6_2.5.0
[9] cellranger_1.1.0 backports_1.1.10 reprex_0.3.0 evaluate_0.14
[13] httr_1.4.2 pillar_1.4.7 rlang_0.4.10 readxl_1.3.1
[17] rstudioapi_0.11 whisker_0.4 blob_1.2.1 checkmate_2.0.0
[21] rmarkdown_2.5 munsell_0.5.0 broom_0.7.5 compiler_4.0.3
[25] httpuv_1.5.4 modelr_0.1.8 xfun_0.18 pkgconfig_2.0.3
[29] htmltools_0.5.0 tidyselect_1.1.0 fansi_0.4.1 crayon_1.3.4
[33] dbplyr_1.4.4 withr_2.3.0 later_1.1.0.1 grid_4.0.3
[37] jsonlite_1.7.1 gtable_0.3.0 lifecycle_1.0.0 DBI_1.1.0
[41] git2r_0.27.1 magrittr_1.5 scales_1.1.1 cli_2.1.0
[45] stringi_1.5.3 fs_1.5.0 promises_1.1.1 xml2_1.3.2
[49] ellipsis_0.3.1 generics_0.0.2 vctrs_0.3.5 tools_4.0.3
[53] glue_1.4.2 hms_0.5.3 yaml_2.2.1 colorspace_1.4-1
[57] rvest_0.3.6 knitr_1.30 haven_2.3.1 sass_0.2.0