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Rmd	282c3ac	jens-daniel-mueller	2019-12-19	whole data set RT corrected
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Rmd	c6cfca5	jens-daniel-mueller	2019-12-09	RT correction incl OGB data
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Rmd	f875795	jens-daniel-mueller	2019-11-22	now clean
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Rmd	4131b9c	jens-daniel-mueller	2019-10-21	finisehd read CTD and HydroC, created merging Rmd

library(tidyverse)
library(lubridate)
library(zoo)

1 CTD (ts) + HydroC CO₂ data (th)

1.1 Merging summarized data sets

Before merging the ts and th data set, the time stamp of ts is adjusted to match exactly that of th, based on zeroing pCO₂ values recorded in the analog output (ts) and the internal memory (th).

The th data set used here, is V1 of the post-processed data by KM Contros.

# Load Sensor and HydroC data ---------------------------------------------
ts <- read_csv(here::here("data/intermediate/_summarized_data_files",
                          "ts.csv"),
               col_types = list("pCO2_analog" = col_double()))

th <- read_csv(here::here("data/intermediate/_summarized_data_files",
                          "th.csv"))

# Time offset correction ----------------------------------------------

# Time offset was determined by comparing zeroing reads from Sensor and th
# in the plots produced in the section Time stamp synchronicity below
# before applying this correction

ts <- ts %>% 
  mutate(day = yday(date_time),
         date_time = if_else(day >= 206 & day <= 220,
                             date_time - 80, date_time - 10)) %>% 
  select(-day)

# Merge Sensor and HydroC data --------------------------------------------

ts_th <- full_join(ts, th) %>% 
  arrange(date_time)

rm(th, ts)

A pdf with plots of the zeroing signals to check the time stamp syncronicity, can be found here:

Link to zeroing plots

1.2 Interpolation to common time stamp

CTD (ts) and auxillary recordings (15 sec measurment interval) are interpolated to the HydroC (th) time stamps (first 10 sec, than 1 sec measurement interval). Interpolation of ts data is not done when gaps between observations are larger than 20, indicating that th was running without ts, eg during data download from th. Thereafter, th readings not falling in regular transects/profilings are removed, by removing rows with NA depth values. Furthermore, ts readings without corresponding th readings are removed, except during periods when th was not operating.

# Interpolate Sensor data to HydroC time stamp

ts_th <- ts_th %>%
  mutate(
    dep_maxgap = na.approx(dep, na.rm = FALSE, maxgap = 20),
    dep = approxfun(date_time, dep)(date_time),
    sal = approxfun(date_time, sal)(date_time),
    tem = approxfun(date_time, tem)(date_time),
    pCO2_analog = approxfun(date_time, pCO2_analog)(date_time)
  ) %>%
  #remove HC readings not falling in regular transects/profiling
  filter(!is.na(dep_maxgap)) %>% 
  select(-dep_maxgap) %>%
  fill(ID, type, station) %>%
  # removes CTD readings without corresponding HydroC reading
  filter(!is.na(deployment),!is.na(pCO2_analog))

# Time stamp synchronicity

ts_th_Zero <- ts_th %>%
  filter(Zero == 1 | Flush == 1 & duration < 120)

pdf(
  file = here::here(
    "output/Plots/merging_interpolation",
    "Zero_time_synchronization.pdf"
  ),
  onefile = TRUE,
  width = 5,
  height = 5
)

for (i_deployment in unique(ts_th$deployment)) {
  #i_deployment <- unique(ts_th_Zero$deployment)[1]
  
  ts_th_Zero_deployment <- ts_th_Zero %>%
    filter(deployment == i_deployment)
  
  for (i_Zero_counter in unique(ts_th_Zero_deployment$Zero_counter)) {
    #i_Zero_counter <- unique(ts_th_Zero_deployment$Zero_counter)[1]
    
    print(
      ts_th_Zero_deployment %>%
        filter(Zero_counter == i_Zero_counter) %>%
        ggplot() +
        geom_point(aes(date_time, pCO2_corr, col = "HydroC")) +
        geom_point(aes(date_time, pCO2_analog, col = "analog")) +
        labs(
          title = paste("Depl: ", i_deployment,
                        " | Zero_counter: ", i_Zero_counter)
        )
      
    )
    
  }
}

dev.off()

rm(ts_th_Zero,
   ts_th_Zero_deployment,
   i_deployment,
   i_Zero_counter)

1.3 Read cleaned, processed pCO₂ data

A revised post-processed HydroC pCO₂ data set was provided by KM Contros after applying a drift correction to the cleaned raw data, i.e. those without data recorded during configuration and testing of the sensor. This data set is referred to as V2. The post-processing was still based on pre- and post-deployment calibration results.

# Read Contros corrected data file, based on cleaned recordings and
# without water vapor correction

th_new_withoutAW_all <-
  read_csv2(
    here::here(
      "data/input/TinaV/Sensor/HydroC-pCO2/corrected_Contros",
      "parameter&pCO2s(method 43)_new_withoutAW.txt"
    ),
    col_names = c(
      "date_time",
      "Zero",
      "Flush",
      "p_NDIR",
      "p_in",
      "T_control",
      "T_gas",
      "%rH_gas",
      "Signal_raw",
      "Signal_ref",
      "T_sensor",
      "pCO2_corr",
      "Runtime",
      "nr.ave"
    )
  ) %>%
  mutate(
    date_time = dmy_hms(date_time),
    Flush = as.factor(as.character(Flush)),
    Zero = as.factor(as.character(Zero))
  )

# slive every 10th data point to reduce number for plotting
th_new_withoutAW <- th_new_withoutAW_all %>%
  slice(seq(1, n(), 10))

# load analog pCO2 data (raw)
th_pre_cleaning <-
  read_csv(here::here(
    "data/intermediate/_summarized_data_files",
    "th_pre_cleaning.csv"
  ))

# slive every 10th data point to reduce number for plotting
th_pre_cleaning <- th_pre_cleaning %>%
  slice(seq(1, n(), 10))


# slive every 10th data point to reduce number for plotting
ts_th_sub <- ts_th %>%
  slice(seq(1, n(), 10))

1.3.1 Comparison of pCO₂ time series

1.3.1.1 Analog vs internal

ggplot() +
  geom_path(data = ts_th_sub,
            aes(date_time, pCO2_corr, col = "HydroC V1")) +
  geom_path(data = ts_th_sub,
            aes(date_time, pCO2_analog, col = "analog CTD")) +
  scale_color_brewer(palette = "Set1", name = "pCO2 record") +
  coord_cartesian(ylim = c(0, 600)) +
  labs(y = expression(pCO[2] ~ (µatm)), x = "") +
  facet_wrap(~ deployment, scales = "free_x", ncol = 1)

pCO~2~ record after interpolation to HydroC timestamp (analog output from HydroC and drift corrected data provided by Contos). ID refers to the starting date of each cruise. Please note that pCO2_analog measurement range is technically restricted to 100-500 µatm. Zeroing periods are included.

pCO₂ record after interpolation to HydroC timestamp (analog output from HydroC and drift corrected data provided by Contos). ID refers to the starting date of each cruise. Please note that pCO2_analog measurement range is technically restricted to 100-500 µatm. Zeroing periods are included.

1.3.1.2 Internal V1 vs. V2

th_comparison <- full_join(
  ts_th_sub %>% select(date_time, deployment, pCO2_corr),
  th_new_withAW %>% select(date_time, pCO2_corr) %>% rename(pCO2_withAW = pCO2_corr)
)

th_comparison <- full_join(
  th_comparison,
  th_new_withoutAW %>% select(date_time, pCO2_corr) %>% rename(pCO2_withoutAW = pCO2_corr)
)

th_comparison %>%
  ggplot() +
  #geom_path(data = th_pre_cleaning, aes(date_time, pCO2_corr, col = "pre cleaning")) +
  geom_path(aes(date_time, pCO2_corr, col = "V1")) +
  #geom_path(aes(date_time, pCO2_withAW, col = "withAW")) +
  geom_path(aes(date_time, pCO2_withoutAW, col = "V2")) +
  scale_color_brewer(palette = "Set1", name = "HydroC pCO2 (th)") +
  coord_cartesian(ylim = c(0, 600)) +
  labs(y = expression(pCO[2] ~ (µatm)), x = "") +
  facet_wrap( ~ deployment, scales = "free_x", ncol = 1)

1.3.2 Replace pCO₂ data

th_new_withoutAW_all <- th_new_withoutAW_all %>% 
  select(date_time, pCO2_corr)

ts_th <- ts_th %>% 
  select(-pCO2_corr)

ts_th <- full_join(ts_th, th_new_withoutAW_all) 

rm(th_new_withoutAW_all)

1.3.3 Offset analog vs post-processed pCO₂ (V2)

ts_th %>%
  ggplot() +
  geom_path(aes(date_time, pCO2_corr - pCO2_analog)) +
  ylim(-30, 0) +
  labs(y = expression(pCO[2] ~ (µats_th)), x = "") +
  facet_wrap( ~ ID, scales = "free_x", ncol = 1)

pCO~2~ difference betweeb HydroC and drift corrected data provided by Contos. Please note that pCO2 range is restricted to +/- 50 µatm.

pCO₂ difference betweeb HydroC and drift corrected data provided by Contos. Please note that pCO2 range is restricted to +/- 50 µatm.

2 Merges sensor (ts_th) + track (tt) data

tt <- read_csv(here::here("data/intermediate/_summarized_data_files",
                          "tt.csv"))


tm <- full_join(ts_th, tt) %>% 
  arrange(date_time)

# interpolate tt data and than remove columns that originate from tt time stamp
tm <- tm %>%
  mutate(lat = approxfun(date_time, lat)(date_time),
         lon = approxfun(date_time, lon)(date_time)) %>% 
  filter(!is.na(dep))

3 Write merged file

tm %>% write_csv(here::here("data/intermediate/_merged_data_files/merging_interpolation",
                            "tm.csv"))

rm(tm, ts_th, tt)

sessionInfo()

R version 4.0.3 (2020-10-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)

Matrix products: default

locale:
[1] LC_COLLATE=English_Germany.1252  LC_CTYPE=English_Germany.1252   
[3] LC_MONETARY=English_Germany.1252 LC_NUMERIC=C                    
[5] LC_TIME=English_Germany.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] zoo_1.8-8         lubridate_1.7.9.2 forcats_0.5.0     stringr_1.4.0    
 [5] dplyr_1.0.2       purrr_0.3.4       readr_1.4.0       tidyr_1.1.2      
 [9] tibble_3.0.4      ggplot2_3.3.3     tidyverse_1.3.0   workflowr_1.6.2  

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5         here_1.0.1         lattice_0.20-41    ps_1.5.0          
 [5] assertthat_0.2.1   rprojroot_2.0.2    digest_0.6.27      R6_2.5.0          
 [9] cellranger_1.1.0   backports_1.2.1    reprex_0.3.0       evaluate_0.14     
[13] highr_0.8          httr_1.4.2         pillar_1.4.7       rlang_0.4.10      
[17] readxl_1.3.1       rstudioapi_0.13    whisker_0.4        rmarkdown_2.6     
[21] labeling_0.4.2     munsell_0.5.0      broom_0.7.3        compiler_4.0.3    
[25] httpuv_1.5.4       modelr_0.1.8       xfun_0.19          pkgconfig_2.0.3   
[29] htmltools_0.5.0    tidyselect_1.1.0   fansi_0.4.1        crayon_1.3.4      
[33] dbplyr_2.0.0       withr_2.3.0        later_1.1.0.1      grid_4.0.3        
[37] jsonlite_1.7.2     gtable_0.3.0       lifecycle_0.2.0    DBI_1.1.0         
[41] git2r_0.27.1       magrittr_2.0.1     scales_1.1.1       cli_2.2.0         
[45] stringi_1.5.3      farver_2.0.3       fs_1.5.0           promises_1.1.1    
[49] xml2_1.3.2         ellipsis_0.3.1     generics_0.1.0     vctrs_0.3.6       
[53] RColorBrewer_1.1-2 tools_4.0.3        glue_1.4.2         hms_0.5.3         
[57] yaml_2.2.1         colorspace_2.0-0   rvest_0.3.6        knitr_1.30        
[61] haven_2.3.1

Merging and interpolation of observations

Jens Daniel Müller

11 February, 2021

1 CTD (ts) + HydroC CO₂ data (th)

1.1 Merging summarized data sets

1.2 Interpolation to common time stamp

1.3 Read cleaned, processed pCO₂ data

1.3.1 Comparison of pCO₂ time series

1.3.1.1 Analog vs internal

1.3.1.2 Internal V1 vs. V2

1.3.2 Replace pCO₂ data

1.3.3 Offset analog vs post-processed pCO₂ (V2)

2 Merges sensor (ts_th) + track (tt) data

3 Write merged file

Merging and interpolation of observations

Jens Daniel Müller

11 February, 2021

1 CTD (ts) + HydroC CO2 data (th)

1.1 Merging summarized data sets

1.2 Interpolation to common time stamp

1.3 Read cleaned, processed pCO2 data

1.3.1 Comparison of pCO2 time series

1.3.1.1 Analog vs internal

1.3.1.2 Internal V1 vs. V2

1.3.2 Replace pCO2 data

1.3.3 Offset analog vs post-processed pCO2 (V2)

2 Merges sensor (ts_th) + track (tt) data

3 Write merged file

1 CTD (ts) + HydroC CO₂ data (th)

1.3 Read cleaned, processed pCO₂ data

1.3.1 Comparison of pCO₂ time series

1.3.2 Replace pCO₂ data

1.3.3 Offset analog vs post-processed pCO₂ (V2)