figure3.qmd

---
title: "figure_3"
author: "vb"
format: html
editor: source
---

## Density plots - figure 3

### functions and libraries

```{r}

#libraries
library(EBImage)
library(imager)
library(imagerExtra)
library(magick)

library(tidyverse)
library(pipeR)

library(mclust)
library(ggdist)

#percentage_crop

#manual_crop

#get_intermediate_images

```

### set parameters and input

```{r}

#individual_filpath_C08_221207_BF

my_crop_percentage <- 0.05
my_isoblur <- 1
my_k <- 0.1
my_ws <- 39
my_clean <-  0
my_grow <- 0
my_shrink <- 4

```

### plot the densities (figure 3B)

```{r}
#| fig-width: 10.5

tessels_C08 <- 
    individual_filpath_C08_221207_BF %>%  
      get_intermediate_images(.,
        crop_percentage = my_crop_percentage,
        my_isoblur = my_isoblur,
        my_k = my_k, 
        my_ws = my_ws,
        my_clean = my_clean,
        my_grow = my_grow, 
        my_shrink = my_shrink) %>% 
  pluck("shrink") %>% 
  get_features_BF_for_mini() %>% 
  do_tesselation(well_name = "C08", 
                 boundary_box = c(0, pixset_dims %>% nth(1), 
                                0, pixset_dims %>%  nth(2)))

model_C08 <- tessels_C08 %>% 
  pull(dir.area) %>% 
  densityMclust(., G= 3, 
                modelName = "V",
                plot = FALSE, verbose = FALSE) 
  

x <- model_C08 %>% pluck("data") 
x0 <- extendrange(x, f = 0.1)
x0 <- seq(x0[1], x0[2], length = 600)

cdens <- predict(model_C08, newdata = x0, what = "cdens")
cdens_mod3 <- sweep(cdens, 2, model_C08$parameters$pro, "*")

cdens_mod3_w <- cdens_mod3 %>% 
  as_tibble() %>% 
  rename(cluster1 = '1',
         cluster2 = '2',
         cluster3 = '3') %>% 
  mutate(all_clusters = cluster1+cluster2+cluster3) %>% 
  mutate(x0 = x0) %>% 
  pivot_longer(cols = c(-x0), names_to = "class", values_to = "values") 
  
my_class_colors <- c("#E41A1C","#4DAF4A", "#377EB8") #from RColorBrewer::brewer.pal(3, "Set1"))

ggplot() +
  ggdist::stat_slab(
    data = x %>% as_tibble(),
    mapping = aes(x = V1),
    density = "histogram",
    alpha = 0.4,
    side = "top",
    color = "grey40",
    fill = "grey",
    outline_bars = TRUE,
    linewidth = 0.5,
    breaks = 50) +
  geom_line(
    data = cdens_mod3_w,
    mapping = aes(
      x = x0, 
      y = values %>% scales::rescale(., to = c(0, 1)),
      color = class,
      linetype = class),
    linewidth = 1,) +
  scale_color_manual(
    values =
      c("black", my_class_colors)) +
  scale_linetype_manual(
    values =
      c("solid", "5151", "5151", "5151")) +
  #xlim(c(-100, 500)) +
  labs(
    x = "Tile area",
    y = "Density") +
  theme_BF_larger()

ggplot() +
  ggdist::stat_slab(
    data = x %>% as_tibble(),
    mapping = aes(x = V1),
    density = "histogram",
    alpha = 0.4,
    side = "top",
    color = "grey40",
    fill = "grey",
    outline_bars = TRUE,
    linewidth = 0.5,
    breaks = 50) +
  geom_line(
    data = cdens_mod3_w %>% filter(class == "all_clusters"),
    mapping = aes(
      x = x0,
      y = values %>% scales::rescale(., to = c(0, 1)),
      color = class,
      linetype = class),
    linewidth = 1,) +
  scale_color_manual(
    values =
      c("black", RColorBrewer::brewer.pal(9, "Set1"))) +
  scale_linetype_manual(
    values =
      c("solid", "5151", "5151", "5151")) +
  #xlim(c(-100, 500)) +
  labs(
    x = "Tile area",
    y = "Density") +
  theme_BF_larger()


```

### Voronoi colored by classification (figure 3C)

```{r}

tibble(classification =  model_C08 %>% pluck("classification"),
       dir.area = model_C08 %>% pluck("data")) %>% 
  left_join(tessels_C08, by = c("dir.area")) %>% 
  ggplot() +
      ggforce::geom_voronoi_tile(
        aes(x=x,
            y=y,
            fill = as.factor(classification),
            group = -1L),
        color = "black",
        linewidth = 0.5,
        bound = c(0, pixset_dims %>% nth(1), 
                                -pixset_dims %>%  nth(2), 0),
        normalize = FALSE) +
      scale_y_reverse()+
      colorspace::scale_fill_discrete_qualitative(palette = "Dark 3",
                                                 name = "Classification")+
      empty_theme() +
      coord_fixed() 
  


```

### voronoi with image background (figure 5E)

```{r}

#pixset_dims

```

```{r}
#| fig-width: 8.136
#| fig-height: 5.992

raster <- individual_filpath_C08_221207_BF %>% 
                 EBImage::readImage() %>%
                 percentage_crop() %>%   as.raster()

 tessels_C08 %>%
  ggplot() +
      annotation_raster(raster,0, 1017, -749, 0 )+
      ggforce::geom_voronoi_segment(
        aes(x=x,
            y=y,
            group = -1L),
        color = "black",
        #fill = "white",
        linewidth = 0.7,
        bound = c(0, pixset_dims %>% nth(1), 
                                -pixset_dims %>%  nth(2), 0),
        normalize = FALSE) +
      scale_y_reverse() +
  theme_void()
      #scale_fill_gradientn(colours = rev(colorspace::heat_hcl(7)))

```

### for illustrator (figure 3A)

```{r}

#screenshot is loesoe....
dims_ill <- "/Users/vincentdeboer/Desktop/Screenshot 2024-03-22 at 14.08.06.png" %>% 
  EBImage::readImage() %>%  dim()

illustrator_xy <- 
  "/Users/vincentdeboer/Desktop/Screenshot 2024-03-22 at 14.08.06.png" %>% 
  EBImage::readImage() %>%
  magick::image_read() %>%
  magick::image_negate() %>% 
    imager::magick2cimg() %>%
    imager::grayscale() %>% 
    imager::threshold() %>% 
    EBImage::Image() %>% 
    EBImage::getFrame(1) %>% 
    EBImage::bwlabel() %>% 
    EBImage::computeFeatures.moment() %>% 
    tidyr::as_tibble() %>% 
    select(x = m.cx, y = m.cy)

tessels_ill <- illustrator_xy %>% 
  do_tesselation(well_name = "well",
                 boundary_box = c(0, dims_ill %>%  nth(1),
                                  0, dims_ill %>%  nth(2)))

tessels_ill %>%
  ggplot() +
      ggforce::geom_voronoi_segment(
        aes(x=x,
            y=y,
            group = -1L),
        color = "black",
        #fill = "white",
        linewidth = 0.7,
        bound = c(0, dims_ill %>% nth(1), 
                                -dims_ill %>%  nth(2), 0),
        normalize = FALSE) +
      scale_y_reverse() +
  theme_void()


```

### crop for cell size count (figure 3E)

#### functions and libraries

```{r}

#libraries
library(EBImage)
library(imager)
library(imagerExtra)
library(magick)

library(tidyverse)
library(pipeR)

#percentage_crop

#manual_crop

#get_intermediate_images

```

#### set parameters and input

```{r}

#individual_filpath_C08_221207_BF

my_crop_percentage <- 0.05
my_isoblur <- 1
my_k <- 0.1
my_ws <- 39
my_clean <-  0
my_grow <- 0
my_shrink <- 4

```

#### run

```{r}
#| fig-width: 8.285
#| fig-height: 7.1
#| 
individual_filpath_C08_221207_BF %>%  
      get_intermediate_images(.,
        crop_percentage = my_crop_percentage,
        my_isoblur = my_isoblur,
        my_k = my_k, 
        my_ws = my_ws,
        my_clean = my_clean,
        my_grow = my_grow, 
        my_shrink = my_shrink) %>% 
      keep(names(.) %in% c("shrink")) %>% 
    purrr::map(.x = .,
        .f = ~.x %>% 
          EBImage::Image() %>%  
          manual_crop(crop_y_left = 0.1,
              crop_y_right = 0.6,
              crop_x_low = 0.25,
              crop_x_up = 0.4) %>%  plot)


```

#### my used function in this file

```{r}

#qmd code copied to R file
#NCmisc::list.functions.in.file(here::here("prepration.R"))

```

all functions and packages in this files:

Show in New Window \$.GlobalEnv \[1\] "do_tesselation" "get_features_BF_for_mini" \[3\] "manual_crop" "percentage_crop"\
\[5\] "theme_BF_larger"

\$`c("package:dplyr", "package:stats")` \[1\] "filter"

\$`c("package:dplyr", "package:tidyr", "package:tibble")` \[1\] "as_tibble" "tibble"

\$`character(0)` \[1\] "brewer.pal" "densityMclust"\
\[3\] "empty_theme" "geom_voronoi_segment"\
\[5\] "geom_voronoi_tile" "get_intermediate_images"\
\[7\] "list.functions.in.file" "scale_fill_discrete_qualitative" \[9\] "stat_slab"

\$`package:base` \[1\] "as.factor" "c" "dim" "library" "names" "seq"\
\[7\] "sweep"

\$`package:dplyr` \[1\] "left_join" "mutate" "nth" "pull" "rename" "select"

\$`package:EBImage` \[1\] "bwlabel" "computeFeatures.moment" "getFrame"\
\[4\] "Image" "readImage"

\$`package:ggplot2` \[1\] "aes" "annotation_raster" "coord_fixed"\
\[4\] "geom_line" "ggplot" "labs"\
\[7\] "scale_color_manual" "scale_linetype_manual" "scale_y_reverse"\
\[10\] "theme_void"

\$`package:grDevices` \[1\] "as.raster" "extendrange"

\$`package:here` \[1\] "here"

\$`package:imager` \[1\] "grayscale" "magick2cimg" "threshold"

\$`package:magick` \[1\] "image_negate" "image_read"

\$`package:purrr` \[1\] "keep" "map" "pluck"

\$`package:spatstat.geom` \[1\] "rescale"

\$`package:stats` \[1\] "predict"

\$`package:tidyr` \[1\] "pivot_longer"