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Results from this study are referred to as JDM. Unique eras information is subtracted.
lm_best_cant <-
read_csv(paste(path_version_data,
"lm_best_cant.csv",
sep = ""))“True” Cant fields directly inferred from the model output are referred to as M.
tref <-
read_csv(paste(path_version_data,
"tref.csv",
sep = ""))
cant_tref_1 <-
read_csv(
paste(
path_preprocessing,
"cant_annual_field_",
params_local$model_runs,
"/cant_",
unique(tref$year[1]),
".csv",
sep = ""
)
)
cant_tref_1 <- cant_tref_1 %>%
rename(cant_tref_1 = cant_total) %>%
select(-year)
cant_tref_2 <-
read_csv(
paste(
path_preprocessing,
"cant_annual_field_",
params_local$model_runs,
"/cant_",
unique(tref$year[2]),
".csv",
sep = ""
)
)
cant_tref_2 <- cant_tref_2 %>%
rename(cant_tref_2 = cant_total) %>%
select(-year)
cant_tref_3 <-
read_csv(
paste(
path_preprocessing,
"cant_annual_field_",
params_local$model_runs,
"/cant_",
unique(tref$year[3]),
".csv",
sep = ""
)
)
cant_tref_3 <- cant_tref_3 %>%
rename(cant_tref_3 = cant_total) %>%
select(-year)cant_M_1 <- left_join(cant_tref_1, cant_tref_2) %>%
mutate(cant = cant_tref_2 - cant_tref_1,
eras = unique(lm_best_cant$eras)[1]) %>%
select(-c(cant_tref_1, cant_tref_2))
cant_M_2 <- left_join(cant_tref_2, cant_tref_3) %>%
mutate(cant = cant_tref_3 - cant_tref_2,
eras = unique(lm_best_cant$eras)[2]) %>%
select(-c(cant_tref_2, cant_tref_3))
cant_M <- full_join(cant_M_1, cant_M_2) %>%
arrange(lon, lat, depth, basin_AIP)
cant_M <- cant_M %>%
mutate(cant_pos = if_else(cant <= 0, 0, cant))
rm(cant_tref_1, cant_tref_2, cant_tref_3, cant_M_1, cant_M_2)cant_inv_M <- m_cant_inv(cant_M)cant_zonal_M <- m_zonal_mean_section(cant_M)cant_M %>%
write_csv(paste(path_version_data,
"cant_M.csv", sep = ""))
cant_inv_M %>%
write_csv(paste(path_version_data,
"cant_inv_M.csv", sep = ""))
cant_zonal_M %>%
write_csv(paste(path_version_data,
"cant_zonal_M", sep = ""))
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] metR_0.9.0 scico_1.2.0 patchwork_1.1.1 collapse_1.5.0
[5] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4
[9] readr_1.4.0 tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.3
[13] tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 here_1.0.1 lubridate_1.7.9
[4] lattice_0.20-41 assertthat_0.2.1 rprojroot_2.0.2
[7] digest_0.6.27 R6_2.5.0 cellranger_1.1.0
[10] backports_1.1.10 reprex_0.3.0 evaluate_0.14
[13] httr_1.4.2 pillar_1.4.7 rlang_0.4.10
[16] readxl_1.3.1 data.table_1.13.6 rstudioapi_0.13
[19] whisker_0.4 blob_1.2.1 Matrix_1.2-18
[22] checkmate_2.0.0 rmarkdown_2.5 RcppEigen_0.3.3.9.1
[25] munsell_0.5.0 broom_0.7.3 compiler_4.0.3
[28] httpuv_1.5.4 modelr_0.1.8 xfun_0.20
[31] pkgconfig_2.0.3 htmltools_0.5.0 tidyselect_1.1.0
[34] fansi_0.4.1 crayon_1.3.4 dbplyr_1.4.4
[37] withr_2.3.0 later_1.1.0.1 grid_4.0.3
[40] jsonlite_1.7.2 gtable_0.3.0 lifecycle_0.2.0
[43] DBI_1.1.0 git2r_0.27.1 magrittr_2.0.1
[46] scales_1.1.1 cli_2.2.0 stringi_1.5.3
[49] fs_1.5.0 promises_1.1.1 RcppArmadillo_0.10.1.2.2
[52] xml2_1.3.2 ellipsis_0.3.1 generics_0.1.0
[55] vctrs_0.3.6 tools_4.0.3 glue_1.4.2
[58] hms_0.5.3 parallel_4.0.3 yaml_2.2.1
[61] colorspace_2.0-0 rvest_0.3.6 knitr_1.30
[64] haven_2.3.1