Thesis Plots • picoinjector

library(picoinjector)
library(ggplot2)
library(patchwork)
library(wesanderson)

Global settings:

theme
figure width (= latex textwidth)

textwidth <- 6.14
# wes_palette("Zissou1")

2 Background

Relative sizes of things on a logarithmic scale:

# define locations of vertical dashed lines
vLines <- c("atom" = 0.1e-9, "DNA" = 2e-9, "protein" = 4.2e-9, "phage" = 55e-9, "virus" = 100e-9, "bacteria" = 3e-6, "animal_cell" = 20e-6, "plant_cell" = 80e-6, "drosophilia" = 3e-3, "human" = 1.8)
vLines
#>        atom         DNA     protein       phage       virus    bacteria 
#>     1.0e-10     2.0e-09     4.2e-09     5.5e-08     1.0e-07     3.0e-06 
#> animal_cell  plant_cell drosophilia       human 
#>     2.0e-05     8.0e-05     3.0e-03     1.8e+00

# define locations of range dashed lines
rangesXmin <- c(3e-9, 10e-6, 10e-6, 10e-6)
rangesXmax <- c(6e-9, 30e-6, 100e-6, 1e-3)
rangesY <- c(0.05, 0.1, 0.2, 0.13)

# x coordinates for axis labels
xBreaks <- 10^seq(-10, 1)
names(xBreaks) <- c("0.1 nm", "1 nm", "10 nm", "100 nm", "1 µm", "10 µm", "100 µm", "1 mm", "10 mm", "100 mm", "1 m")


ggplot() + 
  geom_vline(xintercept = vLines, alpha = 0.2, linetype = "dashed") + 
  geom_errorbarh(aes(xmin = rangesXmin, xmax = rangesXmax, y = rangesY), alpha = 0.2, linetype = "dashed", height = 0.05) +
  # geom_rect(aes(xmin = 10e-6, xmax = 30e-6, ymin = 0, ymax = Inf), alpha = 0.2) +
  # geom_tile(aes(x = 20e-6, y = 0, width = 20e-6, height = Inf), color = "red") +
  scale_x_log10(breaks = xBreaks) + 
  scale_y_continuous(breaks = c(), limits = c(0,1), expand = expansion(add = 0)) + 
  theme_pretty_thesis() + 
  theme(axis.title.y = element_blank())

  # annotation_logticks(side = "b", color = "gray")

Save as PDF:

grDevices::pdf(file = "output/plots/02_relative_sizes.pdf", width = textwidth, height = textwidth/3, )
print(last_plot())
grDevices::dev.off()
#> agg_png 
#>       2

Poisson distribution for different \(\lambda\):

p1 <- tidyr::crossing("k" = seq(0,6),
                "lambda" = c(0.1, 0.25, 1, 2, 5)) %>% 
  dplyr::mutate("p" = dpois(k, lambda)) %>% 
  ggplot(aes(x = k, y = p)) + 
  geom_line(aes(color = factor(lambda)), linetype = "dashed") + 
  geom_point(aes(fill = factor(lambda)), shape = 21) + 
  scale_x_continuous(breaks = seq(0,6)) + 
  # scale_y_continuous(breaks = c(0, 0.2, 0.4, 0.6, 0.8, 1)) + 
  theme_pretty_thesis() + 
  scale_fill_manual(values = wes_palette("Darjeeling1")) + 
  scale_color_manual(values = wes_palette("Darjeeling1")) + 
  theme(
    # panel.grid.major = element_line(color = "black", linewidth = 0.1),
        legend.position = c(1,1),
        legend.justification = c(1,1)) + 
  labs(y = expression(p(lambda, k)),
       x = "k",
       color = expression(lambda),
       fill = expression(lambda),
       tag = "A") + 
  ylim(c(0,1)) +
  coord_cartesian(expand =F)
p1

Evaluate Poisson PDF as function of lambda:

p2 <- tibble::tibble("lambda" = seq(0,2,0.01),
               "single" = stats::dpois(1, lambda),
               "empty" = stats::dpois(0, lambda),
               "multiple" = 1 - stats::ppois(1, lambda)) %>% 
  tidyr::pivot_longer(!lambda) %>% 
  ggplot(aes(x = lambda, y = value, linetype = name)) + 
  geom_line() +
  scale_linetype_discrete(labels = c(expression(p(k == 0)), expression(p(k==1)), expression(p(k >= 1))),
                          guide = guide_legend(title = NULL, title.position = "left")) + 
  theme_pretty_thesis() + 
  theme(legend.position = c(1,1),
        legend.justification = c(1,1),
        legend.key.width = unit(0.5, "cm")) +
  coord_cartesian(expand = F) + 
  labs(x = expression(lambda),
       y = expression(p(lambda, k)),
       tag = "B")
p2

Patchwork:

p1 + p2 &
  theme(plot.margin = margin(r = 4, t = 3))

grDevices::pdf(file = "output/plots/02_poisson_distribution.pdf", width = textwidth, height = textwidth/2, )
print(last_plot())
grDevices::dev.off()
#> agg_png 
#>       2

4 Results

fads <- fads_read_tsv("data/fads/220916/pi30v4_6.txt")
fads
#> # A tibble: 413,790 × 6
#>           time  blue  green   red width spacing
#>          <dbl> <dbl>  <dbl> <dbl> <dbl>   <dbl>
#>  1 3647025511.     0 0.0961 0.723 0.32    0.915
#>  2 3647025516.     0 0.0958 0.763 0.37    0.665
#>  3 3647025520.     0 0.125  0.772 0.37    0.495
#>  4 3647025522.     0 0.357  0.759 0.355   0.025
#>  5 3647025526.     0 0.101  0.748 0.345   0.255
#>  6 3647025530.     0 0.0995 0.714 0.285   0.585
#>  7 3647025534.     0 0.0967 0.730 0.33    0.41 
#>  8 3647025538.     0 0.0946 0.763 0.4     0.435
#>  9 3647025543.     0 0.0958 0.752 0.355   0.695
#> 10 3647025547.     0 0.0977 0.769 0.405   0.415
#> # … with 413,780 more rows

fads %>% 
  fads_remove_outliers() %>% 
  ggplot(aes(x = green, y = red)) +
  geom_bin2d(bins = 100, drop = T) + 
  scale_fill_gradientn(colours = wes_palette("Zissou1", 100, type = "continuous")) + 
  theme_pretty_thesis() + 
  theme(legend.position = c(1, 0),
        legend.justification = c(1,0),
        legend.box.background = element_rect(fill = "white")) + 
  labs(x = "Green PMT signal [V]",
       y = "Red PMT signal [V]",
       fill = "Droplets")

grDevices::pdf(file = "output/plots/04_fads_green_red.pdf", width = textwidth, height = textwidth/3, )
print(last_plot())
grDevices::dev.off()
#> agg_png 
#>       2

Supplementary Information

Voltage amplifier:

d <- readr::read_csv("data/boexle_sorter_output.csv", show_col_types = FALSE)
p <- ggplot(d, aes(x = input_vpp, y = output_vpp)) + 
  geom_point(aes(color = factor(frequency)), shape = 17) + 
  geom_smooth(method = "lm", formula = "y ~ x", fullrange = TRUE, color = "black", linewidth = 0.5, alpha = 0.3) + 
  xlim(c(0, 12.5)) + 
  labs(x = "Input signal peak-to-peak voltage [V]",
       y = "Output signal peak-to-peak voltage [V]") + 
  theme_pretty_thesis() + 
  theme(panel.grid.major = element_line(),
        panel.grid.minor = element_line(),
        legend.box.background = element_rect(fill = "white")) + 
  labs(color = "Frequency [Hz]")
p