library(gapctd)
vessel <- 94
cruise <- c(202201, 202202)
region <- "BS"
processing_method <- "gapctd"
ctd_dir <- "G:/RACE_CTD/data/2022/ebs/v94_8091"
channel <- gapctd::get_connected(schema = "AFSC")
# Setup directory and retrieve haul data. Running this more than once will remove all files from the working directory and require reprocessing from scratch.
gapctd:::setup_gapctd_directory(processing_method = processing_method,
ctd_dir = ctd_dir)
# Get haul data from RACEBASE and write to an .rds file in /output/
haul_df <- gapctd:::get_haul_data(channel = channel,
vessel = vessel,
cruise = cruise,
tzone = "America/Anchorage")
# Load haul data
haul_df <- readRDS(file = here::here("output",
paste0("HAUL_DATA_", vessel, "_", paste(cruise, collapse = "_"), ".rds")))
# Round 1: Estimate alignment parameters, use typical CTM parameters -------------------------------
gapctd:::wrapper_run_gapctd(cnv_dir_path = here::here("cnv"),
processing_method = processing_method,
haul_df = haul_df,
ctm_pars = list(alpha_C = 0.04, beta_C = 0.125),
gapctd_round = 1)
# Make metadata and bottom averages file
gapctd:::make_metadata_file(rds_dir_path = here::here("output", "gapctd"),
in_pattern = "_full.rds",
output_path = here::here("metadata",
paste0("CTD_HAUL_DATA_", vessel, "_", paste(cruise, collapse = "_"), ".rds")))
# Move 'bad' files to bad_cnv
gapctd:::move_bad_rds(rds_dir_path = here::here("output", processing_method),
in_pattern = "_full.rds")
# Move 'bad' files to bad_cnv
gapctd:::move_bad_rds(rds_dir_path = here::here("output", processing_method),
in_pattern = "_split.rds")
# Visually inspect, flag, and interpolate
gapctd:::wrapper_flag_interpolate(rds_dir_path = here::here("output", processing_method),
review = c("density", "salinity"))
# Review profiles
gapctd:::review_profiles(rds_dir_path = here::here("output", processing_method),
threshold = -1e-5,
in_pattern = "_qc.rds")
# Round 2: Optimize alignment parameters, optimize CTM parameters using T-S area ------------------
gapctd:::wrapper_run_gapctd(cnv_dir_path = here::here("cnv"),
processing_method = processing_method,
haul_df = haul_df,
ctm_pars = list(),
gapctd_round = 2)
# Move 'bad' files to bad_cnv
gapctd:::move_bad_rds(rds_dir_path = here::here("output", processing_method),
in_pattern = "_full.rds")
# Visually inspect, flag, and interpolate
gapctd:::wrapper_flag_interpolate(rds_dir_path = here::here("output", processing_method),
review = c("density", "salinity"))
# Review profiles
gapctd:::review_profiles(rds_dir_path = here::here("output", processing_method),
threshold = -1e-5,
in_pattern = "_qc.rds")
# Round 3: Optimize alignment parameters, optimize CTM parameters using S path distance ------------
gapctd:::remedial_ctm(rds_path = here::here("output", processing_method),
haul_df = haul_df)
gapctd:::wrapper_flag_interpolate(rds_dir_path = here::here("output", processing_method),
review = c("density", "salinity"))
gapctd:::review_profiles(rds_dir_path = here::here("output", processing_method),
threshold = -1e-5,
in_pattern = "_qc.rds")
# Finalize data product ----------------------------------------------------------------------------
gapctd::finalize_data(rds_dir_path = here::here("output", processing_method))
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