Last updated: 2022-06-07
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Knit directory: emlr_obs_preprocessing/
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Rmd | 651fc9a | jens-daniel-mueller | 2021-11-15 | rerun with Key 2004 |
path_key_2004 <- "/nfs/kryo/work/updata/glodapv1_1/GLODAP_gridded.data/"
path_preprocessing <- paste(path_root, "/observations/preprocessing/", sep = "")
library(marelac)
# read text files
pCFC_12_data <-
read_csv(
paste(path_key_2004,
"CFC.data/pCFC-12.data.txt",
sep = ""),
col_names = FALSE,
na = "-999",
col_types = list(.default = "d")
)
# read respective depth layers and convert to vector
Depth_centers <-
read_file(paste(path_key_2004,
"Depth.centers.txt",
sep = ""))
Depth_centers <- Depth_centers %>%
str_split(",") %>%
as_vector()
# read respective latitudes and convert to vector
Lat_centers <-
read_file(paste(path_key_2004, "Lat.centers.txt",
sep = ""))
Lat_centers <- Lat_centers %>%
str_split(",") %>%
as_vector()
# read respective longitudes and convert to vector
Long_centers <-
read_file(paste(path_key_2004, "Long.centers.txt",
sep = ""))
Long_centers <- Long_centers %>%
str_split(",") %>%
as_vector()
# match lon, lat and depth vectors with Cant value file
names(pCFC_12_data) <- Lat_centers
Long_Depth <-
expand_grid(depth = Depth_centers, lon = Long_centers) %>%
mutate(lon = as.numeric(lon),
depth = as.numeric(depth))
pCFC_12_3d <- bind_cols(pCFC_12_data, Long_Depth)
# adjust file dimensions
pCFC_12_3d <- pCFC_12_3d %>%
pivot_longer(1:180, names_to = "lat", values_to = "pCFC_12") %>%
mutate(lat = as.numeric(lat))
pCFC_12_3d <- pCFC_12_3d %>%
drop_na()
# harmonize coordinates
pCFC_12_3d <- pCFC_12_3d %>%
mutate(lon = if_else(lon < 20, lon + 360, lon))
rm(pCFC_12_data,
Long_Depth,
Depth_centers,
Lat_centers,
Long_centers)
# use only three basin to assign general basin mask
# ie this is not specific to the MLR fitting
basinmask <- basinmask %>%
filter(MLR_basins == "2") %>%
select(lat, lon, basin_AIP)
pCFC_12_3d <- inner_join(pCFC_12_3d, basinmask)
pCFC_12_inv_layers <- m_pCFC_12_inv(pCFC_12_3d)
pCFC_12_inv <- pCFC_12_inv_layers %>%
filter(inv_depth == params_global$inventory_depth_standard)
pCFC_12_zonal <- m_zonal_mean_sd(pCFC_12_3d)
p_map_cant_inv(
df = pCFC_12_inv,
var = "pCFC_12_pos",
breaks = seq(0,max(pCFC_12_inv$pCFC_12_pos),5))
p_map_climatology(
df = pCFC_12_3d,
var = "pCFC_12")
p_section_global(
df = pCFC_12_3d,
var = "pCFC_12")
p_section_climatology_regular(
df = pCFC_12_3d,
var = "pCFC_12")
pCFC_12_3d %>%
write_csv(paste(path_preprocessing,
"K04_pCFC_12_3d.csv", sep = ""))
# pCFC_12_inv %>%
# write_csv(paste(path_preprocessing,
# "K04_pCFC_12_inv.csv", sep = ""))
pCFC_12_zonal %>%
write_csv(paste(path_preprocessing,
"K04_pCFC_12_zonal.csv", sep = ""))
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] colorspace_2.0-2 marelac_2.1.10 shape_1.4.6 ggforce_0.3.3
[5] metR_0.11.0 scico_1.3.0 patchwork_1.1.1 collapse_1.7.0
[9] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
[13] readr_2.1.1 tidyr_1.1.4 tibble_3.1.6 ggplot2_3.3.5
[17] tidyverse_1.3.1 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] fs_1.5.2 bit64_4.0.5 lubridate_1.8.0 gsw_1.0-6
[5] httr_1.4.2 rprojroot_2.0.2 tools_4.1.2 backports_1.4.1
[9] bslib_0.3.1 utf8_1.2.2 R6_2.5.1 DBI_1.1.2
[13] withr_2.4.3 tidyselect_1.1.1 processx_3.5.2 bit_4.0.4
[17] compiler_4.1.2 git2r_0.29.0 cli_3.1.1 rvest_1.0.2
[21] xml2_1.3.3 isoband_0.2.5 labeling_0.4.2 sass_0.4.0
[25] scales_1.1.1 checkmate_2.0.0 SolveSAPHE_2.1.0 callr_3.7.0
[29] digest_0.6.29 rmarkdown_2.11 oce_1.5-0 pkgconfig_2.0.3
[33] htmltools_0.5.2 highr_0.9 dbplyr_2.1.1 fastmap_1.1.0
[37] rlang_1.0.2 readxl_1.3.1 rstudioapi_0.13 jquerylib_0.1.4
[41] generics_0.1.1 farver_2.1.0 jsonlite_1.7.3 vroom_1.5.7
[45] magrittr_2.0.1 Rcpp_1.0.8 munsell_0.5.0 fansi_1.0.2
[49] lifecycle_1.0.1 stringi_1.7.6 whisker_0.4 yaml_2.2.1
[53] MASS_7.3-55 grid_4.1.2 parallel_4.1.2 promises_1.2.0.1
[57] crayon_1.4.2 haven_2.4.3 hms_1.1.1 seacarb_3.3.0
[61] knitr_1.37 ps_1.6.0 pillar_1.6.4 reprex_2.0.1
[65] glue_1.6.0 evaluate_0.14 getPass_0.2-2 data.table_1.14.2
[69] modelr_0.1.8 vctrs_0.3.8 tzdb_0.2.0 tweenr_1.0.2
[73] httpuv_1.6.5 cellranger_1.1.0 gtable_0.3.0 polyclip_1.10-0
[77] assertthat_0.2.1 xfun_0.29 broom_0.7.11 later_1.3.0
[81] viridisLite_0.4.0 ellipsis_0.3.2