From 12d898da3b4b6ed42e2d6032c1ec85fd19b47d1d Mon Sep 17 00:00:00 2001 From: Ilja Kocken Date: Fri, 4 Nov 2022 15:31:34 +0100 Subject: [PATCH] switch to mbsf, fix corepics --- 2022_colourlog.Rmd | 62 +- 2022_colourlog.html | 129 +-- imgs/colouranalysis.pdf | Bin 12476390 -> 12475326 bytes out/colourdata.csv | 1956 +++++++++++++++++++-------------------- 4 files changed, 1078 insertions(+), 1069 deletions(-) diff --git a/2022_colourlog.Rmd b/2022_colourlog.Rmd index b258450..b86d4f7 100644 --- a/2022_colourlog.Rmd +++ b/2022_colourlog.Rmd @@ -200,43 +200,50 @@ coresumm <- read_tsv("dat/coresumm.tsv") coresumm ``` -This still lacks the newest depth scale, however, so it's better to use a custom file. + -```{r} -coresumm <- read_csv("dat/SectionSummary.csv") -``` + + + Now that we have the metadata, we can attach it to the images. ```{r} imgs <- imgs |> tidylog::left_join(coresumm |> - mutate(Sect = str_replace(Sect, "CC", "8") |> + mutate(Sc = ifelse(str_detect(Sc, "CC"), "8", Sc) |> parse_integer()), by = c('site' = 'Site', - 'hole' = 'Hole', - 'core' = 'Core', - 'type' = 'Type', - 'section' = 'Sect')) + 'hole' = 'H', + 'core' = 'Cor', + 'type' = 'T', + 'section' = 'Sc')) ``` and make proper plots of our sections -```{r fig.width = 1} +```{r fig.width = 2} corepics <- imgs |> ## slice(1) |> # try it out for a single image ggplot(aes(xmin = 0, xmax = 1, - ymin = `Bottom depth CSF-B (m)`, - ymax = `Top depth CSF-B (m)`)) + - # here we turn the filename into the filepath, relative to this project - geom_textured_rect(aes(image = paste0("dat/croppedsections/", file))) + + ymin = `Top(mbsf)` + `LL(m)`, + ymax = `Top(mbsf)`)) + scale_y_reverse() + + # here we turn the filename into the filepath, relative to this project + geom_textured_rect(aes(image = paste0("dat/croppedsections/", file)), + # below settings are essential so that section images get + # stretched! + nrow = 1, ncol = 1, + img_width = unit(1, "null"), + img_height = unit(1, "null"), + interpolate = FALSE) + facet_grid(cols = vars(hole)) + + labs(y = "Depth (mbsf)") +corepics + + # show the core and section number geom_text(aes(x = 1.2, label = paste0(core, "-", section), - y = `Top depth CSF-B (m)` + - 0.5 * (`Bottom depth CSF-B (m)` - `Top depth CSF-B (m)`)), + y = `Top(mbsf)` + 0.5 * `LL(m)`), size = 2, hjust = 1) -corepics ``` # agemodel @@ -249,6 +256,7 @@ Unfortunately the agemodel isn't published yet, so here's a very low-res fake ag ```{r} ## agem <- read_excel("dat/Agemodel.xlsx") agem <- tribble( ~ depth_mbsf, ~ Age_GTS12, + 260, 15, 290, 20, 315, 22, 323, 22.7, @@ -260,7 +268,7 @@ agem |> geom_line() + geom_point() + # annotate cores we have pics for - geom_rug(aes(y = top_depth), + geom_rug(aes(y = `Top(mbsf)`), colour = "grey", inherit.aes = FALSE, data = imgs) + @@ -273,9 +281,9 @@ because the rmcd column in the agemodel is empty! ```{r} imgs <- imgs |> mutate(top_age = approx(x = agem$depth_mbsf, y = agem$Age_GTS12, - xout = top_depth)$y, + xout = `Top(mbsf)`)$y, bot_age = approx(x = agem$depth_mbsf, y = agem$Age_GTS12, - xout = bot_depth)$y) + xout = `Top(mbsf)` + `LL(m)`)$y) ``` Add the age information to the colourlog @@ -284,13 +292,13 @@ Add the age information to the colourlog newflat <- flat |> # tidylog is nice because it prints some messages about how the join went. tidylog::left_join(coresumm |> - mutate(Sect = str_replace(Sect, "CC", "8") |> + mutate(Sc = ifelse(str_detect(Sc, "CC"), "8", Sc) |> parse_integer()), by = c('site' = 'Site', - 'hole' = 'Hole', - 'core' = 'Core', - 'type' = 'Type', - 'section' = 'Sect')) + 'hole' = 'H', + 'core' = 'Cor', + 'type' = 'T', + 'section' = 'Sc')) glimpse(newflat) ``` @@ -301,11 +309,11 @@ The above metadata gives us the depth of each top of the section, but our ```{r} depthcalc <- newflat %>% # add the section depth (in mbsf) and the top depth (in cm). - mutate(depth = `Top depth CSF-B (m)` + distance_from_core_top2 * 0.01) + mutate(depth = `Top(mbsf)` + distance_from_core_top2 * 0.01) # inspect the new depth depthcalc %>% - select(`Top depth CSF-B (m)`, top_depth, distance_from_core_top2, depth) |> + select(`Top(mbsf)`, distance_from_core_top2, depth) |> tail() ``` diff --git a/2022_colourlog.html b/2022_colourlog.html index d3f85c6..844b9a3 100644 --- a/2022_colourlog.html +++ b/2022_colourlog.html @@ -666,49 +666,49 @@

load section depths

# 9 159 959 A 2 H 2 1.5 1.5 10.6 <NA> # 10 159 959 A 2 H 3 1.5 1.5 12.1 <NA> # # … with 1,063 more rows -

This still lacks the newest depth scale, however, so it’s better to -use a custom file.

-
coresumm <- read_csv("dat/SectionSummary.csv")
-
# Rows: 142 Columns: 10
-# ── Column specification ──────────────────────────────────────────────────────
-# Delimiter: ","
-# chr (3): Hole, Type, Sect
-# dbl (7): Exp, Site, Core, Top depth CSF-B (m), Bottom depth CSF-B (m), top_d...
-# 
-# ℹ Use `spec()` to retrieve the full column specification for this data.
-# ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
+ + + +

Now that we have the metadata, we can attach it to the images.

imgs <- imgs |>
   tidylog::left_join(coresumm |>
-                       mutate(Sect = str_replace(Sect, "CC", "8") |>
+                       mutate(Sc = ifelse(str_detect(Sc, "CC"), "8", Sc) |>
                                 parse_integer()),
                      by = c('site' = 'Site',
-                            'hole' = 'Hole',
-                            'core' = 'Core',
-                            'type' = 'Type',
-                            'section' = 'Sect'))
-
# left_join: added 5 columns (Exp, Top depth CSF-B (m), Bottom depth CSF-B (m), top_depth, bot_depth)
-
#            > rows only in x     0
-
#            > rows only in y  (111)
-
#            > matched rows      31
-
#            >                 =====
-
#            > rows total        31
+ 'hole' = 'H', + 'core' = 'Cor', + 'type' = 'T', + 'section' = 'Sc')) +
# left_join: added 5 columns (Leg, LL(m), CL(m), Top(mbsf), Comment)
+
#            > rows only in x       0
+
#            > rows only in y  (1,042)
+
#            > matched rows        31
+
#            >                 =======
+
#            > rows total          31

and make proper plots of our sections

corepics <- imgs |>
   ## slice(1) |> # try it out for a single image
   ggplot(aes(xmin = 0, xmax = 1,
-             ymin = `Bottom depth CSF-B (m)`,
-             ymax = `Top depth CSF-B (m)`)) +
-  # here we turn the filename into the filepath, relative to this project
-  geom_textured_rect(aes(image = paste0("dat/croppedsections/", file))) +
+             ymin = `Top(mbsf)` + `LL(m)`,
+             ymax = `Top(mbsf)`)) +
   scale_y_reverse() +
+  # here we turn the filename into the filepath, relative to this project
+  geom_textured_rect(aes(image = paste0("dat/croppedsections/", file)),
+                     # below settings are essential so that section images get
+                     # stretched!
+                     nrow = 1, ncol = 1,
+                     img_width = unit(1, "null"),
+                     img_height = unit(1, "null"),
+                     interpolate = FALSE) +
   facet_grid(cols = vars(hole)) +
+  labs(y = "Depth (mbsf)")
+corepics +
+  # show the core and section number
   geom_text(aes(x = 1.2, label = paste0(core, "-", section),
-                y = `Top depth CSF-B (m)` +
-                  0.5 * (`Bottom depth CSF-B (m)` - `Top depth CSF-B (m)`)),
-            size = 2, hjust = 1)
-corepics
-

+ y = `Top(mbsf)` + 0.5 * `LL(m)`), + size = 2, hjust = 1) +

agemodel

@@ -719,6 +719,7 @@

agemodel

low-res fake agemodel.

## agem <- read_excel("dat/Agemodel.xlsx")
 agem <- tribble( ~ depth_mbsf, ~ Age_GTS12,
+                260, 15,
                 290, 20,
                 315, 22,
                 323, 22.7,
@@ -730,36 +731,36 @@ 
agemodel

   geom_line() +
   geom_point() +
   # annotate cores we have pics for
-  geom_rug(aes(y = top_depth),
+  geom_rug(aes(y = `Top(mbsf)`),
            colour = "grey",
            inherit.aes = FALSE,
            data = imgs) +
   scale_y_reverse()
-

+

Apply the agemodel. I’m assuming that the top_age and bot_age are in mbsf, because the rmcd column in the agemodel is empty!

imgs <- imgs |>
   mutate(top_age = approx(x = agem$depth_mbsf, y = agem$Age_GTS12,
-                          xout = top_depth)$y,
+                          xout = `Top(mbsf)`)$y,
          bot_age = approx(x = agem$depth_mbsf, y = agem$Age_GTS12,
-                      xout = bot_depth)$y)
+ xout = `Top(mbsf)` + `LL(m)`)$y)

Add the age information to the colourlog

newflat <- flat |>
   # tidylog is nice because it prints some messages about how the join went.
   tidylog::left_join(coresumm |>
-                       mutate(Sect = str_replace(Sect, "CC", "8") |>
+                       mutate(Sc = ifelse(str_detect(Sc, "CC"), "8", Sc) |>
                                 parse_integer()),
                      by = c('site' = 'Site',
-                            'hole' = 'Hole',
-                            'core' = 'Core',
-                            'type' = 'Type',
-                            'section' = 'Sect'))
-
# left_join: added 5 columns (Exp, Top depth CSF-B (m), Bottom depth CSF-B (m), top_depth, bot_depth)
-
#            > rows only in x     0
-
#            > rows only in y  (134)
-
#            > matched rows     977
-
#            >                 =====
-
#            > rows total       977
+ 'hole' = 'H', + 'core' = 'Cor', + 'type' = 'T', + 'section' = 'Sc')) +
# left_join: added 5 columns (Leg, LL(m), CL(m), Top(mbsf), Comment)
+
#            > rows only in x       0
+
#            > rows only in y  (1,065)
+
#            > matched rows       977
+
#            >                 =======
+
#            > rows total         977
glimpse(newflat)
# Rows: 977
 # Columns: 37
@@ -795,11 +796,11 @@ 
agemodel

 # $ mean_blue_values2                       <dbl> 42.14176, 44.79482, 41.49701, …
 # $ standard_deviation_of_blue_values2      <dbl> 13.73924, 16.90331, 14.81062, …
 # $ median_of_blue_values2                  <dbl> 41, 41, 38, 38, 34, 35, 35, 35…
-# $ Exp                                     <dbl> 159, 159, 159, 159, 159, 159, …
-# $ `Top depth CSF-B (m)`                   <dbl> 274, 274, 274, 274, 274, 274, …
-# $ `Bottom depth CSF-B (m)`                <dbl> 275.489, 275.489, 275.489, 275…
-# $ top_depth                               <dbl> 300.02, 300.02, 300.02, 300.02…
-# $ bot_depth                               <dbl> 301.509, 301.509, 301.509, 301…
+# $ Leg <dbl> 159, 159, 159, 159, 159, 159, … +# $ `LL(m)` <dbl> 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, … +# $ `CL(m)` <dbl> 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, … +# $ `Top(mbsf)` <dbl> 274, 274, 274, 274, 274, 274, … +# $ Comment <chr> NA, NA, NA, NA, NA, NA, NA, NA…

calculate depth

The above metadata gives us the depth of each top of the section, but @@ -807,21 +808,21 @@

calculate depth

section.

depthcalc <- newflat  %>%
   # add the section depth (in mbsf) and the top depth (in cm).
-  mutate(depth = `Top depth CSF-B (m)` + distance_from_core_top2 * 0.01)
+  mutate(depth = `Top(mbsf)` + distance_from_core_top2 * 0.01)
 
 # inspect the new depth
 depthcalc %>%
-  select(`Top depth CSF-B (m)`, top_depth, distance_from_core_top2, depth) |>
+  select(`Top(mbsf)`, distance_from_core_top2, depth) |>
   tail()
-
# # A tibble: 6 × 4
-#   `Top depth CSF-B (m)` top_depth distance_from_core_top2 depth
-#                   <dbl>     <dbl>                   <dbl> <dbl>
-# 1                  283.      309.                    24.5  284.
-# 2                  283.      309.                    25.5  284.
-# 3                  283.      309.                    26.5  284.
-# 4                  283.      309.                    27.5  284.
-# 5                  283.      309.                    28.5  284.
-# 6                  283.      309.                    29.5  284.
+
# # A tibble: 6 × 3
+#   `Top(mbsf)` distance_from_core_top2 depth
+#         <dbl>                   <dbl> <dbl>
+# 1        283.                    24.5  284.
+# 2        283.                    25.5  284.
+# 3        283.                    26.5  284.
+# 4        283.                    27.5  284.
+# 5        283.                    28.5  284.
+# 6        283.                    29.5  284.

We now have our entire colourlog and the associated core depth!

@@ -868,7 +869,7 @@

calculate colour

scale_colour_identity() + scale_y_reverse() colourplot -

+

Save plot and data

# make sure that corepics and colourplot have the same depth scale
 (corepics +
@@ -886,7 +887,7 @@ 
calculate colour

            axis.text.y = element_blank(),
            axis.ticks.y = element_blank())) +
   plot_layout(ncol = 2, widths = c(.1, .9))
-

+

ggsave('imgs/colouranalysis.pdf', width = 10, height = 25, units = c('cm'))
 
 write_csv(colourlog, file = "out/colourdata.csv")
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