From d199332959e6c415185e33cc8bf34a245b955a23 Mon Sep 17 00:00:00 2001 From: Ilja Kocken Date: Fri, 4 Nov 2022 14:56:52 +0100 Subject: [PATCH] fix axis orientation, make colourlog figure prettier --- 2022_colourlog.Rmd | 63 +- 2022_colourlog.html | 101 +- imgs/colouranalysis.pdf | Bin 24982234 -> 12476390 bytes out/colourdata.csv | 1954 +++++++++++++++++++-------------------- 4 files changed, 1087 insertions(+), 1031 deletions(-) diff --git a/2022_colourlog.Rmd b/2022_colourlog.Rmd index d8d9824..b258450 100644 --- a/2022_colourlog.Rmd +++ b/2022_colourlog.Rmd @@ -184,6 +184,7 @@ imgs |> ggplot(aes(xmin = 0, xmax = 1, ymin = 0, ymax = 1.50)) + # here we turn the filename into the filepath, relative to this project geom_textured_rect(aes(image = paste0("dat/croppedsections/", file))) + + scale_y_reverse() + facet_grid(cols = vars(section), rows = vars(core)) ``` @@ -209,13 +210,14 @@ Now that we have the metadata, we can attach it to the images. ```{r} imgs <- imgs |> - left_join(coresumm |> - mutate(Sect = str_replace(Sect, "CC", "8") |> parse_integer()), - by = c('site' = 'Site', - 'hole' = 'Hole', - 'core' = 'Core', - 'type' = 'Type', - 'section' = 'Sect')) + tidylog::left_join(coresumm |> + mutate(Sect = str_replace(Sect, "CC", "8") |> + parse_integer()), + by = c('site' = 'Site', + 'hole' = 'Hole', + 'core' = 'Core', + 'type' = 'Type', + 'section' = 'Sect')) ``` and make proper plots of our sections @@ -223,10 +225,17 @@ and make proper plots of our sections ```{r fig.width = 1} corepics <- imgs |> ## slice(1) |> # try it out for a single image - ggplot(aes(xmin = 0, xmax = 1, ymin = bot_depth, ymax = top_depth)) + + 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))) + - facet_grid(cols = vars(hole)) + scale_y_reverse() + + facet_grid(cols = vars(hole)) + + 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 ``` @@ -292,11 +301,11 @@ 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 + distance_from_core_top2 * 0.01) + mutate(depth = `Top depth CSF-B (m)` + distance_from_core_top2 * 0.01) # inspect the new depth depthcalc %>% - select(top_depth, distance_from_core_top2, depth) |> + select(`Top depth CSF-B (m)`, top_depth, distance_from_core_top2, depth) |> tail() ``` @@ -334,24 +343,40 @@ colourplot <- colourlog |> ## arrange(depth) |> ggplot(aes(y = depth, x = mean_greyscale_values2, - colour= colour)) + - geom_path(colour = "black") + - geom_point() + + colour= colour, + core = core, section = section)) + + # there are many nice ways to plot this data! + # this will just draw the familiar line + ## geom_line(colour = "black", orientation = "y") + + ## geom_point() + + # this makes it easier to see + ## geom_area(colour = "black", orientation = "y") + + # the rug is like a cleaned-up average colour log geom_rug(aes(y = depth, colour = colour), sides = 'l') + + # make the colours extend to the mean grayscale value + # this is a bit hacky because it's drawing segments for each step + # if you zoom in very far, you should increase the segment thickness + geom_segment(aes(x = 0, xend = mean_greyscale_values2, + y = depth, yend = depth)) + scale_colour_identity() + scale_y_reverse() - colourplot ``` Save plot and data ```{r fig.width = 4.5} +# make sure that corepics and colourplot have the same depth scale (corepics + - coord_cartesian(ylim = c(300, 340)) + - labs(y = "Depth (mbsf)")) + - (Colourplot + + coord_cartesian(ylim = c(312, 274)) + + labs(y = "Depth (mbsf)") + + theme(axis.title.x = element_blank(), + axis.text.x = element_blank(), + axis.ticks.x = element_blank(), + panel.grid = element_blank())) + + (colourplot + labs(x = "Mean grayscale value") + - coord_cartesian(ylim = c(300, 340)) + + coord_cartesian(ylim = c(312, 274)) + + # remove the axis from the right part theme(axis.title.y = element_blank(), axis.text.y = element_blank(), axis.ticks.y = element_blank())) + diff --git a/2022_colourlog.html b/2022_colourlog.html index f9da5e8..d3f85c6 100644 --- a/2022_colourlog.html +++ b/2022_colourlog.html @@ -596,7 +596,7 @@

Read colour data

geom_path() + scale_y_reverse() + facet_grid(cols = vars(section)) -

+

Good! We have the colour information for each section!

@@ -626,8 +626,9 @@

load the core images

ggplot(aes(xmin = 0, xmax = 1, ymin = 0, ymax = 1.50)) + # here we turn the filename into the filepath, relative to this project geom_textured_rect(aes(image = paste0("dat/croppedsections/", file))) + + scale_y_reverse() + facet_grid(cols = vars(section), rows = vars(core)) -

+

Currently we do not have the information for the height/depth of these images, however.

@@ -678,22 +679,36 @@

load section depths

# 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 |>
-  left_join(coresumm |>
-              mutate(Sect = str_replace(Sect, "CC", "8") |> parse_integer()),
-            by = c('site' = 'Site',
-                   'hole' = 'Hole',
-                   'core' = 'Core',
-                   'type' = 'Type',
-                   'section' = 'Sect'))
+ tidylog::left_join(coresumm |> + mutate(Sect = str_replace(Sect, "CC", "8") |> + 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

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 = bot_depth, ymax = top_depth)) +
+  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))) +
-  facet_grid(cols = vars(hole))
+  scale_y_reverse() +
+  facet_grid(cols = vars(hole)) +
+  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
-

+

agemodel

@@ -720,7 +735,7 @@

agemodel

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 |>
@@ -792,21 +807,21 @@ 

calculate depth

section.

depthcalc <- newflat  %>%
   # add the section depth (in mbsf) and the top depth (in cm).
-  mutate(depth = top_depth + distance_from_core_top2 * 0.01)
+  mutate(depth = `Top depth CSF-B (m)` + distance_from_core_top2 * 0.01)
 
 # inspect the new depth
 depthcalc %>%
-  select(top_depth, distance_from_core_top2, depth) |>
+  select(`Top depth CSF-B (m)`, top_depth, distance_from_core_top2, depth) |>
   tail()
-
# # A tibble: 6 × 3
-#   top_depth distance_from_core_top2 depth
-#       <dbl>                   <dbl> <dbl>
-# 1      309.                    24.5  310.
-# 2      309.                    25.5  310.
-# 3      309.                    26.5  310.
-# 4      309.                    27.5  310.
-# 5      309.                    28.5  310.
-# 6      309.                    29.5  310.
+
# # 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.

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

@@ -835,27 +850,43 @@

calculate colour

## arrange(depth) |> ggplot(aes(y = depth, x = mean_greyscale_values2, - colour= colour)) + - geom_path(colour = "black") + - geom_point() + + colour= colour, + core = core, section = section)) + + # there are many nice ways to plot this data! + # this will just draw the familiar line + ## geom_line(colour = "black", orientation = "y") + + ## geom_point() + + # this makes it easier to see + ## geom_area(colour = "black", orientation = "y") + + # the rug is like a cleaned-up average colour log geom_rug(aes(y = depth, colour = colour), sides = 'l') + + # make the colours extend to the mean grayscale value + # this is a bit hacky because it's drawing segments for each step + # if you zoom in very far, you should increase the segment thickness + geom_segment(aes(x = 0, xend = mean_greyscale_values2, + y = depth, yend = depth)) + scale_colour_identity() + scale_y_reverse() - colourplot
-

+

Save plot and data

-
(corepics +
-  coord_cartesian(ylim = c(300, 340)) +
-   labs(y = "Depth (mbsf)")) +
-  (Colourplot +
+
# make sure that corepics and colourplot have the same depth scale
+(corepics +
+  coord_cartesian(ylim = c(312, 274)) +
+   labs(y = "Depth (mbsf)") +
+   theme(axis.title.x = element_blank(),
+         axis.text.x = element_blank(),
+         axis.ticks.x = element_blank(),
+         panel.grid = element_blank())) +
+  (colourplot +
      labs(x = "Mean grayscale value") +
-     coord_cartesian(ylim = c(300, 340)) +
+     coord_cartesian(ylim = c(312, 274)) +
+     # remove the axis from the right part
      theme(axis.title.y = element_blank(),
            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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