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---
title: 'Paper 3 plots / data exploration'
author: "Anya Leenman"
output:
html_document:
number_sections: no
theme: cerulean
toc: yes
toc_float: yes
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
# Housekeeping
rm(list = ls())
library(dplyr)
library(lubridate)
library(scales)
library(colorspace)
library(RColorBrewer)
library(raster)
library(RStoolbox)
library(tidyr)
write_data_out <- FALSE # should clean datasets be written for Zenodo upload?
drive1 <- "E"
drive2 <- "D"
colz = c("black", brewer.pal(4, "Set1"))
# location to save all files from this document
outfiles <- paste0(drive2, ":/Experiments/writing/3_feed_duration/figures")
```
Nov 2020.
Plots for third PhD paper with constant feed, varied sediment supply. Trying to decide if I should compare runs 1-6-7 or runs 1-7-8-9...
Runs 1, 6 and 7 (varying amplitude of feed variations) compared with runs 1, 7, 8 and 9 (varying duration of on/off phases) - analogous to varying period but holding amplitude constant.
# Feed rates:
```{r, echo = F}
# plot of feed rates.
#-----------
# generate time series of feed rates:
run1 <- data.frame(
minz = 1:40,
feed = 5
)
run6 <- data.frame(
minz = 1:40,
feed = rep(c(rep(6.66, 5), rep(3.33, 5)), 4)
)
run7 <- data.frame(
minz = 1:40,
feed = rep(c(rep(10, 5), rep(0, 5)), 4)
)
run8 <- data.frame(
minz = 1:40,
feed = rep(c(rep(10, 10), rep(0, 10)), 2)
)
run9 <- data.frame(
minz = 1:40,
feed = c(rep(10, 20), rep(0, 20))
)
#-------------
# # plot first set:
# plot(run1,
# type = "l",
# col = colz[1],
# xlab = "Time in cycle (minutes)",
# ylab = "Feed rate (g/s)",
# ylim = c(0, 10),
# lwd = 2)
# lines(run6,
# col = colz[2],
# lwd = 2)
# lines(run7,
# col = colz[3],
# lwd = 2)
# legend("topright",
# legend = c("Run 1", "Run 6", "Run 7"),
# col = colz[1:3],
# lwd = 2)
#--------------------
# plot second set:
pdf(paste0(outfiles, "/feed_rates.pdf"),
width = 6, height = 4)
par(mfrow = c(3, 1),
mai = c(.3,.3,0.1,0.1),
oma = c(2, 2, 3, 0),
xpd = NA)
plot(run1,
type = "l",
col = colz[1],
xlab = "",
ylab = "",
ylim = c(0, 10),
lwd = 2)
lines(run7,
col = colz[3],
lwd = 2)
legend(10, 16,
bty = "n",
ncol = 4,
legend = c("CON", "OSC10", "OSC20", "OSC40"),
col = colz[c(1, 3:5)],
lwd = 2)
plot(run1,
type = "l",
col = colz[1],
xlab = "",
ylab = "",
ylim = c(0, 10),
lwd = 2)
lines(run8,
col = colz[4],
lwd = 2)
plot(run1,
type = "l",
col = colz[1],
xlab = "",
ylab = "",
ylim = c(0, 10),
lwd = 2)
lines(run9,
col = colz[5],
lwd = 2)
mtext(text = "Time in experiment (minutes)", side = 1, outer = T, line = 0)
mtext(text = "Sediment supply rate (g/s)", side = 2, outer = T, line = 0)
dev.off()
```
# Median fan slope:
<!-- Data wrangling: -->
```{r, include=FALSE}
# import slope data for each run
#----------------
run <- 1
run1 <- data.frame()
for(repnum in 1:3){
repe <- repnum
run1_rep_i <- read.csv(
paste0(drive1, "://Experiments/Processing/Run", run, "/Run", run, "_rep", repe,
"/_1min_intervals/t_series/Recalculating_slope/Slope_length_curv.csv")) %>%
dplyr::select(tvec, contains("slope")) %>% # select relevant variables
mutate(t_h = ifelse((tvec %% 100) == 0, tvec / 100, floor (tvec / 100))) %>% # get hour
mutate(t_m = ifelse((tvec %% 100) == 0, 0, tvec - t_h * 100)) %>% # get minute
mutate(time = paste(t_h, t_m, sep = ":")) %>% # concatenate to hh:mm format
mutate(time = hm(time)) %>% # convert to time format using lubridate
mutate(t_sec = period_to_seconds(time)) %>% # Convert to number of seconds since start
mutate(t_min = t_sec / 60) %>% # time in minutes
mutate(t_hr = t_min / 60) %>% # time in hours
dplyr::select(-time, -t_h, -t_m) %>% # remove unnecessary columns
#filter(complete.cases(.)) %>% # get rid of rows with NA.
# divide into 10 minute periods (coded so that high feed is START):
mutate(cycle = ceiling((t_min - 715) / 10)) %>% # assign cycle
mutate(time_in_cycle = t_min - 705 - 10 * cycle) %>% # assign minute in cycle
mutate(run = run) %>%
mutate(exprep = repe)
run1 <- rbind(run1, run1_rep_i)
}
run <- 6
repe <- 1
run6 <- read.csv(
paste0(drive2, "://Experiments/Processing/Run", run, "/Run", run, "_rep", repe,
"/_1min_intervals/t_series/Recalculating_slope/Slope_length_curv.csv"))%>%
dplyr::select(tvec, contains("slope")) %>%
mutate(t_h = ifelse((tvec %% 100) == 0, tvec / 100, floor (tvec / 100))) %>% # get hour
mutate(t_m = ifelse((tvec %% 100) == 0, 0, tvec - t_h * 100)) %>% # get minute
mutate(time = paste(t_h, t_m, sep = ":")) %>% # concatenate to hh:mm format
mutate(time = hm(time)) %>% # convert to time format using lubridate
mutate(t_sec = period_to_seconds(time)) %>% # Convert to number of seconds since start
mutate(t_min = t_sec / 60) %>% # time in minutes
mutate(t_hr = t_min / 60) %>% # time in hours
dplyr::select(-time, -t_h, -t_m) %>% # remove unnecessary columns
#filter(complete.cases(.)) %>% # get rid of rows with NA.
# divide into 10 minute periods (coded so that high feed is START):
mutate(cycle = ceiling((t_min - 715) / 10)) %>% # assign cycle
mutate(time_in_cycle = t_min - 705 - 10 * cycle) %>% # assign minute in cycle
mutate(run = run) %>%
mutate(exprep = repe)
run <- 7
run7 <- read.csv(
paste0(drive2, "://Experiments/Processing/Run", run, "/Run", run, "_rep", repe,
"/_1min_intervals/t_series/Recalculating_slope/Slope_length_curv.csv"))%>%
dplyr::select(tvec, contains("slope")) %>%
mutate(t_h = ifelse((tvec %% 100) == 0, tvec / 100, floor (tvec / 100))) %>% # get hour
mutate(t_m = ifelse((tvec %% 100) == 0, 0, tvec - t_h * 100)) %>% # get minute
mutate(time = paste(t_h, t_m, sep = ":")) %>% # concatenate to hh:mm format
mutate(time = hm(time)) %>% # convert to time format using lubridate
mutate(t_sec = period_to_seconds(time)) %>% # Convert to number of seconds since start
mutate(t_min = t_sec / 60) %>% # time in minutes
mutate(t_hr = t_min / 60) %>% # time in hours
dplyr::select(-time, -t_h, -t_m) %>% # remove unnecessary columns
#filter(complete.cases(.)) %>% # get rid of rows with NA.
# divide into 10 minute periods (coded so that high feed is START):
mutate(cycle = ceiling((t_min - 715) / 10)) %>% # assign cycle
mutate(time_in_cycle = t_min - 705 - 10 * cycle) %>% # assign minute in cycle
mutate(run = run) %>%
mutate(exprep = repe)
run <- 8
run8 <- read.csv(
paste0(drive2, "://Experiments/Processing/Run", run, "/Run", run, "_rep", repe,
"/_1min_intervals/t_series/Recalculating_slope/Slope_length_curv.csv"))%>%
dplyr::select(tvec, contains("slope")) %>%
mutate(t_h = ifelse((tvec %% 100) == 0, tvec / 100, floor (tvec / 100))) %>% # get hour
mutate(t_m = ifelse((tvec %% 100) == 0, 0, tvec - t_h * 100)) %>% # get minute
mutate(time = paste(t_h, t_m, sep = ":")) %>% # concatenate to hh:mm format
mutate(time = hm(time)) %>% # convert to time format using lubridate
mutate(t_sec = period_to_seconds(time)) %>% # Convert to number of seconds since start
mutate(t_min = t_sec / 60) %>% # time in minutes
mutate(t_hr = t_min / 60) %>% # time in hours
dplyr::select(-time, -t_h, -t_m) %>% # remove unnecessary columns
#filter(complete.cases(.)) %>% # get rid of rows with NA.
# divide into 20 minute periods (coded so that high feed is START):
mutate(cycle = ceiling((t_min - 710) / 20)) %>% # assign cycle
mutate(time_in_cycle = t_min - 690 - 20 * cycle) %>% # assign minute in cycle
mutate(run = run) %>%
mutate(exprep = repe)
run <- 9
run9 <- read.csv(
paste0(drive2, "://Experiments/Processing/Run", run, "/Run", run, "_rep", repe, "/_1min_intervals/t_series/Recalculating_slope/Slope_length_curv.csv"))%>%
dplyr::select(tvec, contains("slope")) %>%
mutate(t_h = ifelse((tvec %% 100) == 0, tvec / 100, floor (tvec / 100))) %>% # get hour
mutate(t_m = ifelse((tvec %% 100) == 0, 0, tvec - t_h * 100)) %>% # get minute
mutate(time = paste(t_h, t_m, sep = ":")) %>% # concatenate to hh:mm format
mutate(time = hm(time)) %>% # convert to time format using lubridate
mutate(t_sec = period_to_seconds(time)) %>% # Convert to number of seconds since start
mutate(t_min = t_sec / 60) %>% # time in minutes
mutate(t_hr = t_min / 60) %>% # time in hours
dplyr::select(-time, -t_h, -t_m) %>% # remove unnecessary columns
#filter(complete.cases(.)) %>% # get rid of rows with NA.
# divide into 40 minute periods (coded so that high feed is START):
mutate(cycle = ceiling((t_min - 700) / 40)) %>% # assign cycle
mutate(time_in_cycle = t_min - 660 - 40 * cycle) %>% # assign minute in cycle
mutate(run = run) %>%
mutate(exprep = repe)
slope_dat <- list(run1, run6, run7, run8, run9)
slope_df <- bind_rows(slope_dat) #%>%
#mutate(run.t = paste(t_min, run, sep = "_"))
#export clean versions for FRDR
runs <- c(1,6,7,8,9)
drives <- c(drive1, rep(drive2, 4))
if(write_data_out == T){
for(r_num in 1:length(runs)){
run <- runs[r_num]
drive <- drives[r_num]
cleandata <- slope_dat[[r_num]] %>%
dplyr::select(tvec, slope_50pc, t_sec,t_min,t_hr, run, exprep)%>%
rename(median_slope = slope_50pc, time_step_hhmm_format = tvec, time_in_sec = t_sec,
time_in_min = t_min, time_in_hrs = t_hr) %>%
mutate(median_slope = median_slope * -1)
write.csv(cleandata, file = paste0(drive, "://Experiments/Processing/Run", run, "/Run", run, "_rep", repe, "/_1min_intervals/t_series/Recalculating_slope/Run", run,
"_all_repeats_slope.csv"), row.names = F)
}
}
```
Comparing temporal trends in median slope:
```{r, echo = F, eval = F}
# plots - 1, 6 and 7 (varying amplitude of feed variations)
# compared with 1, 8 and 9 (varying duration of on/off phases)
# median slope temporal trend: amplitude
plot(run1$t_hr, run1$slope_50pc*-1, type = "l",
ylim = c(0.05, 0.08),
main = "Median slope, varying feed amplitude",
xlab = "Time (hrs)",
ylab = "Median slope",
col = colz[1])
lines(run6$t_hr, run6$slope_50pc*-1, col = colz[2])
lines(run7$t_hr, run7$slope_50pc*-1, col = colz[3])
legend("topleft", legend = c("Run 1", "Run 6", "Run 7"),
col = colz[1:3],
lty = 1)
# median slope temporal trend: duration
plot(run1$t_hr, run1$slope_50pc*-1, type = "l",
ylim = c(0.05, 0.08),
main = "Median slope, varying feed duration",
xlab = "Time (hrs)",
ylab = "Median slope",
col = colz[1])
lines(run7$t_hr, run7$slope_50pc*-1, col = colz[3])
lines(run8$t_hr, run8$slope_50pc*-1, col = colz[4])
lines(run9$t_hr, run9$slope_50pc*-1, col = colz[5])
legend("topleft", legend = c("Run 1", "Run 7", "Run 8", "Run 9"),
col = colz[c(1, 3:5)],
lty = 1)
```
And now boxplots:
Statistical tests should not be applied: data are temporally autocorrelated. Have tried to reduce impact by sampling every fifth timestep for box plot. If I increase the sampling interval further, I end up only sampling high/low slope periods, which would be misleading.
```{r, echo = F, eval = F}
# first, test for autocorrelation in run1:
mod1 <- lm(run1$slope_50pc * -1 ~ run1$t_min)
#plot(mod1$residuals, type = "b")
#acf(residuals(mod1))
# # median slope boxplot
# # first for amplitude
# slope50_1_6_7 <- rbind(run1, run6, run7)
# boxplot(slope_50pc*-1 ~ run, data = slope50_1_6_7,
# main = "median slope, varying amplitude",
# col = alpha(colz[1:3], 0.7))
# test <- pairwise.t.test(slope50_1_6_7$slope_50pc,
# slope50_1_6_7$run,
# #p.adjust.method = "bonf",
# pool.sd = FALSE, paired = FALSE)
# knitr::kable(test$p.value, caption = "p values from pair-wise t-tests")
# repeat for duration
# all data
png(paste0(outfiles, "/slope_boxplot.png"),
width = 720, height = 480)
par(mar = c(4, 5, 1, 1))
slope50_1_7_8_9 <- rbind(run1, run7, run8, run9) %>%
filter(t_min <= max(run1$t_min)) # filter so that all runs use same time range
boxplot(slope_50pc*-1 ~ run, data = slope50_1_7_8_9,
#main = "median slope, varying duration, sampling every minute",
col = alpha(colz[c(1, 3:5)], 0.7),
xlab = "Run",
ylab = expression("Median fan slope (m m"^-1*")"),
par(cex.lab=1.5, cex.axis=1.5),
outcex = 1.5
)
dev.off()
# test <- pairwise.t.test(slope50_1_7_8_9$slope_50pc,
# slope50_1_7_8_9$run,
# p.adjust.method = "bonf",
# pool.sd = FALSE, paired = FALSE)
# knitr::kable(test$p.value, caption = "p values from pair-wise t-tests")
# # sample every 5th minute
# slope50_1_7_8_9 <- rbind(run1, run7, run8, run9) %>%
# filter(time_in_cycle %% 5 == 0)
# boxplot(slope_50pc*-1 ~ run, data = slope50_1_7_8_9,
# main = "median slope, varying duration, sampling every 5 minutes",
# col = alpha(colz[c(1, 3:5)], 0.7))
# test <- pairwise.t.test(slope50_1_7_8_9$slope_50pc,
# slope50_1_7_8_9$run,
# p.adjust.method = "bonf",
# pool.sd = FALSE, paired = FALSE)
# knitr::kable(test$p.value, caption = "p values from pair-wise t-tests")
# # sample randomly
# slope50_1_7_8_9 <- rbind(run1, run7, run8, run9) %>%
# sample_n(., 240)
# boxplot(slope_50pc*-1 ~ run, data = slope50_1_7_8_9,
# main = "median slope, varying duration, sampling 240 obs ",
# col = alpha(colz[c(1, 3:5)], 0.7))
# test <- pairwise.t.test(slope50_1_7_8_9$slope_50pc,
# slope50_1_7_8_9$run,
# p.adjust.method = "bonf",
# pool.sd = FALSE, paired = FALSE)
# knitr::kable(test$p.value, caption = "p values from pair-wise t-tests")
```
Temporal trend through low/high feed periods:
```{r, echo = F}
alphas <- c(0.15, rep(0.4, 4)) # set alpha for run 1 slightly lower
# 1) amplitude case----------------
png(paste0(outfiles, "/Run167_slope_vs_timeincycle.png"),
width = 7, height = 4, units = 'in', res = 300, type = 'cairo')
par(mfrow = c(1, 3),
mai = c(0.4, 0.3, 0.1, 0.1),
oma = c(1, 1.5, 0, 0))
exps <- list(run1, run6, run7)
runnames <- c('CON', 'OSC10LA', 'OSC10')
for(i in 1:length(exps)){
dat <- exps[[i]]
# blank axes
plot(0:10,
abs(dat$slope_50pc)[6:16],
type = "n",
xlab = "",
ylab = "",
ylim = c(0.0555, 0.077))
# line to delineate low/high feed
if(i == 2){
abline(v = 0.5, lty = 2,
col = "gray80")
abline(v = 5.5, lty = 2,
col = "gray50")
# labels
text(3, 0.06, "High supply")
text(8, 0.06, "Low supply")
} else if(i == 3){
abline(v = 0.5, lty = 2,
col = "gray80")
abline(v = 5.5, lty = 2,
col = "gray50")
# labels
text(3, 0.06, "V. High supply")
text(8, 0.06, "Zero supply")
}
nrep <- length(unique(dat$exprep))
for(eachrep in 1:nrep){
dat_i <- dat %>%
dplyr::filter(exprep == eachrep)
# add lines for each cycle
for (j in 2:(max(dat_i$cycle) - 1)){ # prune out incomplete cycles
lines(c(0,
abs(dat_i$time_in_cycle)[dat_i$cycle == j]),
c(abs(dat_i$slope_50pc)[dat_i$cycle == (j-1) &
dat_i$time_in_cycle == 10],
abs(dat_i$slope_50pc)[dat_i$cycle == j]),
#lwd = 0.5,
col = alpha(colz[i], alphas[i]))
}
}
# get mean for group; add to plot
df2 = group_by(dat, time_in_cycle)
df2m = summarize(df2, mean_of_medians = mean(abs(slope_50pc)))
lines(0:10,
c(pull(df2m[10, 2]), pull(df2m[,2])),
col = darken(colz[i], amount = 0.4, space = "combined"),
lwd = 2)
# annotations
Run.no <- runnames[i]
if(i == 1){
legend("topleft",
box.col = 'white',
legend = c("Single cycle", "Ensemble mean"),
lwd = c(1, 2),
col = c(alpha(colz[i], 0.4), darken(colz[i], amount = 0.4, space = "combined")),
text.font = 3,
cex = 1,
bg = 'white',
inset = 0.01
)
}
legend("topright",
box.col = 'white',
legend = c(signif (mean(abs(dat$slope_50pc)), 2),
signif (sd(abs(dat$slope_50pc)),1)),
title.font = 1,
title.col = 'black',
text.font = c(2, 3),
text.col = colz[i],
title = Run.no,
cex = 1,
bg = 'white',
inset = 0.01
)
}
mtext(side = 1, text = "Time in cycle (mins)",
outer = T, line = -0.5)
mtext(text = expression("Median fan slope (m m"^-1*")"),
side = 2, outer = T, line = -0.3)
dev.off()
#-------
# 2) Duration case
exps <- list(run1, run7, run8, run9)# experiments to plot
colz2 <- colz[c(1, 3:5)] # colours to use
runnames <- c('CON', 'OSC10', 'OSC20', 'OSC40')
png(paste0(outfiles, "/slope_vs_timeincycle.png"),
width = 960, height = 480)
par(mai = c(0.5,0.4,0.1,0.1),
oma = c(3, 2.5, 0, 0))
layout(matrix(c(1,2,3,4), 1, 4, byrow = TRUE),
# set unique subplot width for each experiment
widths = c(1, 1, 2, 4))
# for each experiment:
for(i in 1:length(exps)){
dat <- exps[[i]] # get data for exp from main list
rep1sup <- dat %>%
dplyr::filter(exprep == 1)
start.row <- min(which(rep1sup$cycle==2)) # index of first row of first complete cycle (only for rep 1, as only needs calc once)
end.row <- max(which(rep1sup$cycle==2)) # index of last row of first complete cycle
cycle.len <- (end.row + 1) - start.row
# blank axes
plot(0:max(unique(dat$time_in_cycle)),
abs(dat$slope_50pc)[(start.row-1):end.row], # starting at 0, not 1
type = "n",
xlab = "",
ylab = "",
ylim = c(0.0555, 0.077), # tweak
cex.axis = 2,
xaxt = "n")
# x-axis intervals
axis(1, at = seq(0, max(dat$time_in_cycle), by = 5), cex.axis = 2, padj = 0.2)
# line to delineate low/high feed
if(unique(dat$run)>1){
abline(v = 0.5, lty = 2,
col = "gray80")
abline(v = cycle.len/2 + 0.5, lty = 2,
col = "gray50")
# labels
text(cycle.len/4, 0.0555, "H-S", cex = 2)
text(cycle.len/2 + cycle.len/4, 0.0555, "0-S", cex = 2)
}
nrep <- length(unique(dat$exprep))
for(eachrep in 1:nrep){
dat_i <- dat %>%
dplyr::filter(exprep == eachrep)
# plot a line for each cycle
for (j in 2:(max(dat_i$cycle) - 1)){ # prune out incomplete cycles
lines(c(0, # paste a 0 on the front, so t10 (or t_final) value in prev cycle
# = t0 value in this cycle
abs(dat_i$time_in_cycle)[dat_i$cycle == j]),
c(abs(dat_i$slope_50pc)[dat_i$cycle == (j-1) & dat_i$time_in_cycle == cycle.len],
abs(dat_i$slope_50pc)[dat_i$cycle == j]),
#lwd = 0.5,
col = alpha(colz2[i], alphas[i]))
}
}
# get mean for group; add to plot
df2 = group_by(dat, time_in_cycle) # groups = min 1, min 2... finds mean at each minute
df2m = summarize(df2, mean_of_medians = mean(abs(slope_50pc)))
lines(0:cycle.len, # paste 0 on front again
c(pull(df2m[cycle.len, 2]), pull(df2m[,2])),
col = darken(colz2[i], amount = 0.4, space = "combined"),
lwd = 2)
# annotations
Run.no <- runnames[i]
if(i == 4){
legend("topleft",
box.col = 'white',
legend = c("Single cycle", "Ensemble mean"),
lwd = c(1, 2),
col = c(alpha(colz2[i], 0.6), darken(colz2[i], amount = 0.4, space = "combined")),
text.font = 3,
cex = 2,
bg = 'white',
inset = 0.01
)
}
legend("topright",
box.col = 'white',
legend = c(signif (mean(abs(dat$slope_50pc)), 2),
signif (sd(abs(dat$slope_50pc)),1)),
title.font = 1,
title.col = 'black',
text.font = c(2, 3),
text.col = colz2[i],
title = Run.no,
cex = 2,
bg = 'white',
inset = 0.01
)
}
# axis labels for whole plot, not subplots
mtext(side = 1, text = "Time in cycle (mins)",
outer = T, line = 0, cex = 1.5)
mtext(text = expression("Median fan slope (m m"^-1*")"),
side = 2, outer = T, line = 0, cex = 1.5)
dev.off()
```
Comparing axial (45 degree) slope to mid-flank (e.g. 22.5 degree) slope:
```{r axial_Vs_flank_wrangling, echo = F}
# import axial, 23 and 67 degrees slope for all fans, all timesteps
# empty list
mylist <- list()
poi <- c('p23', 'p45', 'p67') # profiles of interest
# vector of runs/reps/drives
repz <- c(3, 1, 1, 1, 1)
runnames <- c("CON", "OSC10LA", "OSC10", "OSC20", "OSC40")
for(i in 1:length(runs)){
run <- runs[i]
nreps <- repz[i]
drive <- drives[i]
if(i == 2){
next
}
df_all <- data.frame()
for(repnum in 1:nreps){
repe <- repnum
t_list <- list.files(
paste0(drive, "://Experiments/Processing/Run", run, "/Run", run, "_rep", repe,
"/_1min_intervals/t_series/Recalculating_slope/profile_summaries"))
repdat <- data.frame()
# loop through each timestep to grab relevant slopes
for(tstep in 1:length(t_list)){
# time wrangling
tvec <- substr(t_list[tstep], 10, 13)
hr <- substr(tvec, 1, 2)
mn <- substr(tvec, 3, 4)
t_sec <- period_to_seconds(hm(paste0(hr, ':', mn))) # number of seconds since 0
# import relevant slope data, extracted from DEMs prior
stepdat <- read.csv(
paste0(drive, "://Experiments/Processing/Run", run, "/Run", run, "_rep", repe,
"/_1min_intervals/t_series/Recalculating_slope/profile_summaries/",
t_list[tstep])) %>%
dplyr::filter(prof_names %in% poi) %>%
dplyr::select(prof_names, slope) %>%
mutate(slope = abs(slope), t_sec = t_sec) %>%
pivot_wider(names_from = prof_names, values_from = slope) %>%
cbind(data.frame(tvec = tvec), .) # export to df row
repdat <- rbind(repdat, stepdat) # bind the rows
}
# assign timestamps / data identifiers
repdat <- repdat %>%
mutate(t_min = t_sec/60, t_hr = t_min / 60) %>%
mutate(exprep = repe, run = run) %>%
mutate(flank_av_slope = (p23 + p67)/2) %>% # average flank slope
mutate(ax_minus_flank = p45 - flank_av_slope)
# divide into high/low feed minute periods (coded so that high feed is START):
if(run < 8){
repdat <- repdat %>%
mutate(cycle = ceiling((t_min - 715) / 10)) %>% # assign cycle
mutate(time_in_cycle = t_min - 705 - 10 * cycle) %>% # assign minute in cycle
mutate(run = run) %>%
mutate(feed = ifelse(time_in_cycle > 5, "low", "high"))
} else if(run == 8){
repdat <- repdat %>%
mutate(cycle = ceiling((t_min - 710) / 20)) %>% # assign cycle
mutate(time_in_cycle = t_min - 690 - 20 * cycle) %>% # assign minute in cycle
mutate(run = run) %>%
mutate(feed = ifelse(time_in_cycle > 10, "low", "high"))
} else if(run == 9){
repdat <- repdat %>%
mutate(cycle = ceiling((t_min - 700) / 40)) %>% # assign cycle
mutate(time_in_cycle = t_min - 660 - 40 * cycle) %>% # assign minute in cycle
mutate(run = run) %>%
mutate(feed = ifelse(time_in_cycle > 20, "low", "high"))
}
df_all <- rbind(df_all, repdat) # bind all repeats into one df
}
df <- df_all
# save df to list
mylist[[i]] <- df
#export clean versions for FRDR
if(write_data_out == T){
cleandata <- df %>%
dplyr::select(tvec, t_sec,t_min,t_hr,
cycle, time_in_cycle, run, exprep,
p45, flank_av_slope, ax_minus_flank) %>%
rename(time_step_hhmm_format = tvec, time_in_sec = t_sec,
time_in_min = t_min, time_in_hrs = t_hr, min_in_cycle = time_in_cycle,
flank_gradient = flank_av_slope, axial_gradient = p45, gradient_difference = ax_minus_flank)
write.csv(cleandata,
file = paste0(drive, "://Experiments/Processing/Run", run, "/Run", run,
"_rep", repe,
"/_1min_intervals/t_series/Recalculating_slope/Run",
run, "_all_repeats_flank_vs_axial_slope.csv"), row.names = F)
}
}
```
```{r axial_vs_flank_plot, echo = F}
# plot cycles through time, as for other variables
png(paste0(outfiles, "/flank_minus_axial_slope_vs_timeincycle.png"),
width = 960, height = 480)
par(mai = c(0.5,0.4,0.1,0.1),
oma = c(3, 2.5, 0, 0))
layout(matrix(c(1,2,3,4), 1, 4, byrow = TRUE),
widths = c(1, 1, 2, 4))
for(i in c(1, 3:5)){
dat <- mylist[[i]]
rep1sup <- dat %>%
dplyr::filter(exprep == 1)
start.row <- min(which(rep1sup$cycle==2)) # index of first row of first complete cycle (only for rep 1, as only needs calc once)
end.row <- max(which(rep1sup$cycle==2)) # index of last row of first complete cycle
cycle.len <- (end.row + 1) - start.row
# blank axes
plot(0:max(unique(dat$time_in_cycle)),
abs(dat$ax_minus_flank)[(start.row-1):end.row],
type = "n",
xlab = "",
ylab = "",
ylim = c(-0.01, 0.01),
cex.axis = 2,
xaxt = "n")
axis(1, at = seq(0, max(dat$time_in_cycle), by = 5), cex.axis = 2, padj = 0.2)
# line to delineate low/high feed
if(unique(dat$run)>1){
abline(v = 0.5, lty = 2,
col = "gray80")
abline(v = cycle.len/2 + 0.5, lty = 2,
col = "gray50")
# labels
text(cycle.len/4, -0.01, "H-S", cex = 2)
text(cycle.len/2 + cycle.len/4, -0.01, "0-S", cex = 2)
}
nrep <- length(unique(dat$exprep))
for(eachrep in 1:nrep){
dat_i <- dat %>%
dplyr::filter(exprep == eachrep)
# add lines for each cycle
for (j in 2:(max(dat_i$cycle) - 1)){ # prune out incomplete cycles
lines(c(0,
dat_i$time_in_cycle[dat_i$cycle == j]),
c(dat_i$ax_minus_flank[dat_i$cycle == (j-1) &
dat_i$time_in_cycle == cycle.len],
dat_i$ax_minus_flank[dat_i$cycle == j]),
#lwd = 0.5,
col = alpha(colz[i], alphas[i]))
}
}
# get mean for group; add to plot
df2 = group_by(dat, time_in_cycle)
df2m = summarize(df2, mean_of_means = mean(ax_minus_flank))
lines(0:cycle.len,
c(pull(df2m[cycle.len, 2]), pull(df2m[,2])),
col = darken(colz[i], amount = 0.4, space = "combined"),
lwd = 2)
# annotations
Run.no <- runnames[i]
if(i == 5){
legend("topleft",
box.col = 'white',
legend = c("Single cycle", "Ensemble mean"),
lwd = c(1, 2),
col = c(alpha(colz[i], 0.6), darken(colz[i], amount = 0.4, space = "combined")),
text.font = 3,
cex = 2,
bg = 'white',
inset = 0.01
)
}
legend("topright",
box.col = 'white',
legend = c(format(round(mean(dat$ax_minus_flank), 3), nsmall = 1),
signif (sd(dat$ax_minus_flank),1)),
title.font = 1,
title.col = 'black',
text.font = c(2, 3),
text.col = colz[i],
title = Run.no,
cex = 2,
bg = 'white',
inset = 0.01
)
}
mtext(side = 1, text = "Time in cycle (mins)",
outer = T, line = 0, cex = 1.5)
mtext(text = expression("Slope differential (m m"^-1*")"),
side = 2, outer = T, line = 0, cex = 1.5)
dev.off()
```
# Lateral instability:
Did lateral instability rate (Fn) vary between exps?
<!-- data Wrangling -->
```{r, echo = F}
# Load Fn data
run <- 1
run1 <- data.frame()
for(i in 1:3){
repe <- i
run1_rep_i <- read.csv(
file = paste0(drive1, ":/Experiments/Processing/Run", run, "/Run", run, "_rep",
repe, "/_1min_intervals/t_series/change_detection/Run", run,
"_rep", repe, "_lag1_overlap_and_antioverlap.csv")) %>%
# divide into 10 minute periods (coded so that high feed is START):
mutate(cycle = ceiling((t_min - 715) / 10)) %>% # assign cycle
mutate(time_in_cycle = t_min - 705 - 10 * cycle) %>% # Assign min in cycle
mutate(run = run) %>%
mutate(exprep = repe)
run1 <- rbind(run1, run1_rep_i)
}
run <- 6
repe <- 1
run6 <- read.csv(
file = paste0(drive2, ":/Experiments/Processing/Run", run, "/Run", run, "_rep",
repe, "/_1min_intervals/t_series/change_detection/Run", run,
"_rep", repe, "_lag1_overlap_and_antioverlap.csv")) %>%
# divide into 10 minute periods (coded so that high feed is START):
mutate(cycle = ceiling((t_min - 715) / 10)) %>% # assign cycle
mutate(time_in_cycle = t_min - 705 - 10 * cycle) %>% # assign minute in cycle
mutate(run = run) %>%
mutate(exprep = repe)
run <- 7
run7 <- read.csv(
file = paste0(drive2, ":/Experiments/Processing/Run", run, "/Run", run, "_rep",
repe, "/_1min_intervals/t_series/change_detection/Run", run,
"_rep", repe, "_lag1_overlap_and_antioverlap.csv")) %>%
# divide into 10 minute periods (coded so that high feed is START):
mutate(cycle = ceiling((t_min - 715) / 10)) %>% # assign cycle
mutate(time_in_cycle = t_min - 705 - 10 * cycle) %>% # assign minute in cycle
mutate(run = run) %>%
mutate(exprep = repe)
run <- 8
run8 <- read.csv(
file = paste0(drive2, ":/Experiments/Processing/Run", run, "/Run", run, "_rep",
repe, "/_1min_intervals/t_series/change_detection/Run", run,
"_rep", repe, "_lag1_overlap_and_antioverlap.csv")) %>%
# divide into 10 minute periods (coded so that high feed is START):
mutate(cycle = ceiling((t_min - 710) / 20)) %>% # assign cycle
mutate(time_in_cycle = t_min - 690 - 20 * cycle) %>% # assign minute in cycle
mutate(run = run) %>%
mutate(exprep = repe)
run <- 9
run9 <- read.csv(
file = paste0(drive2, ":/Experiments/Processing/Run", run, "/Run", run, "_rep",
repe, "/_1min_intervals/t_series/change_detection/Run", run,
"_rep", repe, "_lag1_overlap_and_antioverlap.csv")) %>%
# divide into 10 minute periods (coded so that high feed is START):
mutate(cycle = ceiling((t_min - 700) / 40)) %>% # assign cycle
mutate(time_in_cycle = t_min - 660 - 40 * cycle) %>% # assign minute in cycle
mutate(run = run) %>%
mutate(exprep = repe)
fn_list <- list(run1, run6, run7, run8, run9)
fn_df <- bind_rows(fn_list) #%>%
#mutate(run.t = paste(t_min, run, sep = "_"))
#export clean versions for FRDR
runs <- c(1,6,7,8,9)
repes <- rep(1, 5)
drives <- c(drive1, rep(drive2, 4))
if(write_data_out == T){
for(r_num in 1:length(runs)){
run <- runs[r_num]
repe <- repes[r_num]
drive <- drives[r_num]
cleandata <- fn_list[[r_num]] %>%
dplyr::select(new_area_raw, new_area_normby_t2_fan, t_sec,t_min,t_hr,
cycle, time_in_cycle, time, run, exprep) %>%
rename(time_at_t2 = time, time_in_sec = t_sec,
time_in_min = t_min, time_in_hrs = t_hr,
new_area_m2 = new_area_raw,
new_area_normalized_by_fan_area = new_area_normby_t2_fan,
cycle_number = cycle, min_in_cycle = time_in_cycle)
write.csv(cleandata, file = paste0(drive, "://Experiments/Processing/Run", run, "/Run", run, "_rep", repe, "/_1min_intervals/t_series/change_detection/Run", run,
"_all_repeats_Fn.csv"), row.names = F)
}
}
```
Comparing temporal trends in newly inundated area (Fn), expressed as a percentage of fan area:
```{r, echo = F, eval = F}
# plots - 1, 6 and 7 (varying amplitude of feed variations)
# compared with 1, 8 and 9 (varying duration of on/off phases)
# Fn temporal trend: amplitude
plot(run1$t_hr, run1$new_area_normby_t2_fan * 100,
type = "l",
ylim = c(0, 15),
main = "Fn, varying feed amplitude",
xlab = "Time (hrs)",
ylab = "Fn (%)",
col = colz[1])
lines(run6$t_hr, run6$new_area_normby_t2_fan * 100, col = colz[2])
lines(run7$t_hr, run7$new_area_normby_t2_fan * 100, col = colz[3])
legend("topleft", legend = c("Run 1", "Run 6", "Run 7"),
col = colz[1:3],
lty = 1)
# Fn temporal trend: duration
plot(run1$t_hr, run1$new_area_normby_t2_fan * 100,
type = "l",
ylim = c(0, 15),
main = "Fn, varying feed duration",
xlab = "Time (hrs)",
ylab = "Median slope",
col = colz[1])
lines(run7$t_hr, run7$new_area_normby_t2_fan * 100, col = colz[3])
lines(run8$t_hr, run8$new_area_normby_t2_fan * 100, col = colz[4])
lines(run9$t_hr, run9$new_area_normby_t2_fan * 100, col = colz[5])
legend("topleft", legend = c("Run 1", "Run 7", "Run 8", "Run 9"),
col = colz[c(1, 3:5)],
lty = 1)
```
<!-- And now boxplots: -->
<!-- Question reliability of t-tests... -->
```{r, echo = F, eval = F}
# Fn boxplot
# first for amplitude
F_1_6_7 <- rbind(run1, run6, run7)
boxplot(new_area_normby_t2_fan * 100 ~ run, data = F_1_6_7,
main = "Fn, varying amplitude",
col = alpha(colz[1:3], 0.7))
test <- pairwise.t.test(F_1_6_7$new_area_normby_t2_fan,
F_1_6_7$run,
#p.adjust.method = "bonf",
pool.sd = FALSE, paired = FALSE)
knitr::kable(test$p.value, caption = "p values from pair-wise t-tests")
# repeat for duration
F_1_7_8_9 <- rbind(run1, run7, run8, run9)
boxplot(new_area_normby_t2_fan * 100 ~ run, data = F_1_7_8_9,
main = "Fn, varying duration",
col = alpha(colz[c(1, 3:5)], 0.7))
test <- pairwise.t.test(F_1_7_8_9$new_area_normby_t2_fan,
F_1_7_8_9$run,
#p.adjust.method = "bonf",
pool.sd = FALSE, paired = FALSE)
knitr::kable(test$p.value, caption = "p values from pair-wise t-tests")
```
Temporal trend through low/high feed periods:
```{r, echo = F}
# 1) amplitude case-----
# par(mfrow = c(1, 3))
# exps <- list(run1, run6, run7)
#
# for(i in 1:length(exps)){
# dat <- exps[[i]]
# # blank axes
# plot(0:10,
# abs(dat$new_area_normby_t2_fan)[6:16],
# type = "n",
# xlab = "",
# ylab = "",
# ylim = c(0, 15))
#
# # line to delineate low/high feed
# if (i == 2){
# abline(v = 0.5, lty = 2,
# col = "gray80")
# abline(v = 5.5, lty = 2,
# col = "gray50")
# # labels
# text(3, 0.06, "High feed")
# text(8, 0.06, "Low feed")
# } else if (i == 3){
# abline(v = 0.5, lty = 2,
# col = "gray80")
# abline(v = 5.5, lty = 2,
# col = "gray50")
# # labels
# text(3, 0.06, "V. High feed")
# text(8, 0.06, "No feed")
# }
#
#
# # add lines for each cycle
# for (j in 2:(max(dat$cycle) - 1)){ # prune out incomplete cycles
# lines(c(0, (dat$time_in_cycle)[dat$cycle == j]),
# c((dat$new_area_normby_t2_fan * 100)[dat$cycle == (j-1) & dat$time_in_cycle == 10],
# (dat$new_area_normby_t2_fan * 100)[dat$cycle == j]),
# #lwd = 0.5,
# col = alpha(colz[i], 0.4))
# }
#
# # get mean for group; add to plot
# df2 = group_by(dat, time_in_cycle)