figures.Rmd

---
title: "Figure and table creation"
author: "Robert Schlegel"
date: "2019-06-10"
output: workflowr::wflow_html
editor_options:
  chunk_output_type: console
csl: FMars.csl
bibliography: MHWNWA.bib
---

```{r global_options, include = FALSE}
knitr::opts_chunk$set(fig.width = 8, fig.align = 'center',
                      echo = TRUE, warning = FALSE, message = FALSE, 
                      eval = TRUE, tidy = FALSE)
```

## Introduction

In this bonus vignette we will go over the creation of the figures used in the publication for this research. These figures are largely adapted from the techniques seen in @Oliver2018tasmania (https://www.sciencedirect.com/science/article/pii/S0079661117303336) and @Schlegel2017predominant (https://www.frontiersin.org/articles/10.3389/fmars.2017.00323/full).

```{r libraries}
# Install from GitHub
# .libPaths(c("~/R-packages", .libPaths()))
# devtools::install_github("fabrice-rossi/yasomi")

# Load packages and functions from the central functions script
source("code/functions.R")

# Packages used in this vignette
# library(jsonlite, lib.loc = "../R-packages/")
library(tidyverse) # Base suite of functions
library(lubridate) # For convenient date manipulation
library(data.table) # For working with massive dataframes
# library(yasomi, lib.loc = "../R-packages/") # The SOM package of choice due to PCI compliance

# Set number of cores
doMC::registerDoMC(cores = 50)

# Disable scientific notation for numeric values
  # I just find it annoying
options(scipen = 999)

# Individual regions
NWA_coords <- readRDS("data/NWA_coords_cabot.Rda")

# Corners of the study area
NWA_corners <- readRDS("data/NWA_corners.Rda")

# The base map
map_base <- ggplot2::fortify(maps::map(fill = TRUE, col = "grey80", plot = FALSE)) %>%
  dplyr::rename(lon = long) %>%
  mutate(group = ifelse(lon > 180, group+9999, group),
         lon = ifelse(lon > 180, lon-360, lon)) %>% 
  select(-region, -subregion)

# Bathymetry data
  # NB: This was created in a previous version of the polygon-prep vignette
bathy <- readRDS("data/NWA_bathy_lowres.Rda")
```

## Figure 1

The first figure we will want is that of the study area. This figure will have multiple panels so that we can show the overall average synoptic state of the variables used in the SOM. Note that this is still a rough aproximation of what the final figure will look like.

```{r old-code, eval=FALSE}
### TO DO
# Gulf Stream curved vector
# Halifax labelled point
# Text "Labrador Sea"
# Text: "Labrador Current"
# Improve bathymetry contours
  # Look into the new ggfriendly method
# One panel should contain current vectors
# And the other panel should contain bathymetry contours

# Load all clims in one file
  # This file was created in the code/workflow.R 
ALL_clim <- readRDS("data/ALL_clim.Rda")

# Mean variable states
system.time(
var_mean_states <- ALL_clim %>% 
  dplyr::select(-doy) %>% 
  group_by(lon, lat) %>% 
  summarise_all(.funs = "mean") %>% 
  ungroup() %>% 
  arrange(lon, lat)
) # 2 seconds

# The previous wind correction for when that info is brought in
# winds <- mutate(arrow_size = ((abs(u*v)/ max(abs(u*v)))+0.3)/6)

# Reduce wind/ current vectors
lon_sub <- seq(min(var_mean_states$lon), max(var_mean_states$lon), by = 1)
lat_sub <- seq(min(var_mean_states$lat), max(var_mean_states$lat), by = 1)

# currents <- currents[(currents$lon %in% lon_sub & currents$lat %in% lat_sub),]
var_mean_states_sub <- var_mean_states %>% 
  filter(lon %in% lon_sub, lat %in% lat_sub) %>% 
  group_by(lon, lat) %>% 
  mutate(arrow_size = abs(u_clim)+abs(v_clim),
         arrow_size = ifelse(is.na(arrow_size), 0, arrow_size)) %>% 
  ungroup() %>% 
  arrange(lon, lat)
# Creating dynamic arrow sizes does not work as ggplot cannot match up the vectors correctly
var_mean_states_sub$arrow_size <- 0.1

# Establish the vector scalar for the currents
current_uv_scalar <- 2

# Establish the vector scalar for the wind
wind_uv_scalar <- 0.5

# Wind feature vector coordinates
# cyc_atlantic <- data.frame(x = c(14.0, 16.1, 16.0), y = c(-36.0, -34.4, -32.1), 
#                            xend = c(16.0, 16.1, 14.0), yend = c(-34.5, -32.2, -30.6))
# cyc_indian <- data.frame(x = c(36.0, 33.9, 34.0), y = c(-31.5, -33.1, -35.4), 
#                          xend = c(34.0, 33.9, 36.0), yend = c(-33.0, -35.3, -36.9))
# westerlies <- data.frame(x = c(18.0, 21.1, 24.2), y = c(-38.0, -37.8, -37.8), 
#                          xend = c(21.0, 24.1, 27.2), yend = c(-37.8, -37.8, -38.0))

# The top figure (sea)
fig_1_top <- ggplot(data = map_base, aes(x = lon, y = lat)) +
  # The ocean temperature
  geom_raster(data = var_mean_states, aes(fill = sst_clim)) +
  # The bathymetry
  # stat_contour(data = bathy[bathy$depth < -100 & bathy$depth > -300,],
  # aes(x = lon, y = lat, z = depth), alpha = 0.5,
  # colour = "ivory", size = 0.5, binwidth = 200, na.rm = TRUE, show.legend = FALSE) +
  # The current vectors
  geom_segment(data = var_mean_states_sub, aes(xend = lon + u_clim * current_uv_scalar, 
                                               yend = lat + v_clim * current_uv_scalar),
               arrow = arrow(angle = 40, length = unit(var_mean_states_sub$arrow_size, "cm"), type = "open"),
                             linejoin = "mitre", size = 0.4) +
  # The land mass
  geom_polygon(aes(group = group), fill = "grey70", colour = "black", size = 0.5, show.legend = FALSE) +
  # The legend for the vector length
  # geom_label(aes(x = 37.0, y = -38.0, label = "1.0 m/s\n"), size = 3, label.padding = unit(0.5, "lines")) +
  # geom_segment(aes(x = 36.0, y = -38.3, xend = 38.0, yend = -38.3), linejoin = "mitre",
               # arrow = arrow(angle = 40, length = unit(0.2, "cm"), type = "open")) +
  # Halifax point and label
  # geom_point(data = SACTN_site_list, shape = 19,  size = 2.8, colour = "ivory") +
  # geom_text(data = SACTN_site_list[-c(3,4,7:9,18,21,23:24),], aes(label = order), size = 1.9, colour = "red") +
  # Ocean label
  # annotate("text", label = "ATLANTIC\nOCEAN", x = 13.10, y = -34.0, size = 4.0, angle = 0, colour = "ivory") +
  # Gulf stream line and label
  # geom_segment(aes(x = 17.2, y = -32.6, xend = 15.2, yend = -29.5),
               # arrow = arrow(length = unit(0.3, "cm")), size = 0.5, colour = "ivory") +
  # annotate("text", label = "Benguela", x = 16.0, y = -31.8, size = 3.5, angle = 298, colour = "ivory") +
  # Labrador Current line and label
  # geom_segment(aes(x = 33, y = -29.5, xend = 29.8, yend = -33.0),
               # arrow = arrow(length = unit(0.3, "cm")), size = 0.5, colour = "ivory") +
  # annotate("text", label = "Agulhas", x = 31.7, y = -31.7, size = 3.5, angle = 53, colour = "ivory") +
  # Labrador Sea label
  # annotate("text", label = "Agulhas\nBank", x = 22.5, y = -35.5, size = 3.0, angle = 0, colour = "ivory") +
  # Improve on the x and y axis labels
  # scale_x_continuous(breaks = seq(-70, -50, 10),
  #                    labels = scales::unit_format(suffix = "°E", sep = ""),
  #                    position = "top") +
  scale_x_continuous(breaks = seq(-70, -50, 10),
                     labels = c("70°W", "60°W", "50°W"),
                     position = "top") +
  scale_y_continuous(breaks = seq(35, 55, 10),
                     labels = scales::unit_format(suffix = "°N", sep = "")) +
  labs(x = NULL, y = NULL) +
  # Slightly shrink the plotting area
  coord_cartesian(xlim = NWA_corners[1:2], ylim = NWA_corners[3:4], expand = F) +
  # Use viridis colour scheme
  scale_fill_viridis_c(name = "Temp.\n(°C)", option = "D", breaks = seq(0, 25, 5)) +
  # Adjust the theme
  theme_bw() +
  theme(panel.border = element_rect(fill = NA, colour = "black", size = 1),
        axis.text = element_text(size = 12, colour = "black"),
        axis.ticks = element_line(colour = "black"))
fig_1_top

# The bottom figure (air)
fig_1_bottom <- ggplot(data = map_base, aes(x = lon, y = lat)) +
    # The land mass
  geom_polygon(aes(group = group), fill = "grey70", colour = "black", size = 0.5, show.legend = FALSE) +
  # The net downward heatflux
  geom_raster(data = var_mean_states, aes(fill = qnet_clim), alpha = 0.9) +
  # The current vectors
  geom_segment(data = var_mean_states_sub, aes(xend = lon + u10_clim * current_uv_scalar, 
                                               yend = lat + v10_clim * current_uv_scalar),
               arrow = arrow(angle = 40, length = unit(var_mean_states_sub$arrow_size, "cm"), type = "open"),
                             linejoin = "mitre", size = 0.4, alpha = 0.3) +
  # The legend for the vector length
  # geom_label(aes(x = 37.0, y = -38.0, label = "4.0 m/s\n"), size = 3, label.padding = unit(0.5, "lines")) +
  # geom_segment(aes(x = 36.0, y = -38.3, xend = 38.0, yend = -38.3), linejoin = "mitre",
               # arrow = arrow(angle = 40, length = unit(0.2, "cm"), type = "open")) +
  # The sub/regions
  # geom_polygon(data = NWA_coords, aes(group = region, fill = region, colour = region), alpha = 0.2) +
  # South Atlantic Anticyclone
  # annotate("text", label = "SOUTH\nATLANTIC\nANTICYCLONE", x = 13.5, y = -33.5, size = 3.0, angle = 0, colour = "ivory") +
  # geom_curve(data = cyc_atlantic, aes(x = x, y = y, xend = xend, yend = yend), curvature = 0.2, colour = "ivory",
             # arrow = arrow(angle = 40, type = "open", length = unit(0.25,"cm"))) +
  # South Indian Anticyclone
  # annotate("text", label = "SOUTH\nINDIAN\nANTICYCLONE", x = 36.5, y = -34.0, size = 3.0, angle = 0, colour = "ivory") +
  # geom_curve(data = cyc_indian, aes(x = x, y = y, xend = xend, yend = yend), curvature = 0.2, colour = "ivory",
             # arrow = arrow(angle = 40, type = "open", length = unit(0.25,"cm"))) +
  # Westerlies
  # annotate("text", label = "WESTERLIES", x = 22.5, y = -37.0, size = 3.0, angle = 0, colour = "ivory") +
  # geom_curve(data = westerlies, aes(x = x, y = y, xend = xend, yend = yend), colour = "ivory",
             # arrow = arrow(angle = 40, type = "open", length = unit(0.25,"cm")), curvature = -0.01) +
  # Improve on the x and y axis labels
  scale_x_continuous(breaks = seq(-70, -50, 10),
                     labels = c("70°W", "60°W", "50°W")) +
  scale_y_continuous(breaks = seq(35, 55, 10),
                     labels = scales::unit_format(suffix = "°N", sep = "")) +
  labs(x = NULL, y = NULL) +
  # Scale bar
  # scaleBar(lon = 22.0, lat = -29.5, distanceLon = 200, distanceLat = 50, distanceLegend = 90, dist.unit = "km",
           # arrow.length = 100, arrow.distance = 130, arrow.North.size = 3, 
           # legend.colour = "ivory", arrow.colour = "ivory", N.colour = "ivory") +
  # Slightly shrink the plotting area
  coord_cartesian(xlim = NWA_corners[1:2], ylim = NWA_corners[3:4], expand = F) +
  # Use viridis colour scheme
  scale_fill_viridis_c(name = "Net\ndownward\nheat flux", option = "A") +
  # Adjust the theme
  theme_bw() +
  theme(panel.border = element_rect(fill = NA, colour = "black", size = 1),
        axis.text = element_text(size = 12, colour = "black"),
        axis.ticks = element_line(colour = "black"))
fig_1_bottom

# Convert the figures to grobs
fig_1_top_grob <- ggplotGrob(fig_1_top)
# fb_inset_grob <- ggplotGrob(fb_inset)
fig_1_bottom_grob <- ggplotGrob(fig_1_bottom)

# Stick them together
  # NB: This is still a bit of a mess...
fig_1 <- ggplot() +
  # First set the x and y axis values so we know what the ranges are
  # in order to make it easier to place our facets
  coord_equal(xlim = c(1, 10), ylim = c(1, 10), expand = F) +
  # Then we place our facets over one another using the coordinates we created
  annotation_custom(fig_1_top_grob,
                    xmin = 1, xmax = 9.5, ymin = 5.5, ymax = 10) +
  # annotation_custom(fb_inset_grob,
                    # xmin = 3.5, xmax = 5.5, ymin = 7.2, ymax = 8.8) +
  annotation_custom(fig_1_bottom_grob,
                    xmin = 1, xmax = 10, ymin = 1, ymax = 5.5)
fig_1
# save
# ggsave(plot = fig_1, filename = "graph/fig_1.pdf", height = 8, width = 8)
```

## Figure 2

This figure shows all of the nodes arranged in their order displaying the average SST and U+V current vectors for each node.

```{r fig-2, eval=FALSE}
# See fig_2_func() in code/functions.R for the code to make this figure
```

## Figure 3

This figure shows all of the nodes arranged in their order displaying the average surface air temperature and U+V wind vectors for each node.

```{r fig-3, eval=FALSE}
# See fig_3_func() in code/functions.R for the code to make this figure
```

## Figure 4

This figure shows all of the nodes arranged in their order displaying the average mixed layer depth (MLD) and net downward surface heat flux for each node. Note that each pixels MLD has already been scaled to one before running the SOM and so the MLD values shown here are proportional (e.g. on a scale of 0 -- 1).

```{r fig-4, eval=FALSE}
# See fig_4_func() in code/functions.R for the code to make this figure
```

## Figure 5

This figure provides a detailed breakdown of the seasonal and regional meta data behind the synoptic states being clustered into the node panels. This means it shows during which seasons the MHWs in each node were occurring, as well as in which region the MHWs were detected for each node. Something like this is shown effectively in Figure 7 of @Oliver2018tasmania.

```{r fig-5, eval=FALSE}
# See fig_5_func() in code/functions.R for the code to make this figure
```

## Figure 6

The following figure shows when in time (x-axis) each MHW in each node occurred, similar to Figure 5 of @Schlegel2017predominant. It also shows what the cumulative intensity of the event is (y-axis) as well as the season of occurrence.

```{r fig-6, eval=FALSE}
# See fig_6_func() in code/functions.R for the code to make this figure
```

## Figure 7

This figure is the same as Figure 6 above, but instead shows what the max intensity of the event is (y-axis) as well as the region in which it occurred.

```{r fig-7, eval=FALSE}
# See fig_7_func() in code/functions.R for the code to make this figure
```

## Table 1

This table will show a synopsis of what each node appears to portray. It will be primarily modelled after Table 4 of @Oliver2018tasmania.

```{r interpretation-table, eval=FALSE}
# Node 1: Warm pulse of GS near NS coast. Shallowing mixed layer, low wind stress, and strong negative heat flux. Mostly gm and ss, almost no nfs. Almost entirely summer and autumn from 2013 - 2016. Mostly smaller evets but a few are massive.

# Node 2: Cold GS with warm LC caused by positive heat flux, low wind stress, and shallow mixed layer. Mostly cbs with some gsl and no mab. Occurred in only 199 in two pulses in spring and summer (June - October). Normal intensity but short duration.

# Node 3: Calm sea state with some positive heatflux into the LC causing events. Shallower mixed layer everywhere. Mostly nfs with progressively fewer events in regions down the coast. Almost none in ls. Smaller events with a couple of large ones. All seasons from 1999 - 2014.

# Node 4: Extremely shallow mixed layer with a strong positive heatflux and low wind stress. Mostly nfs with progressively fewer events further away. Smaller events. Autumn, Winter, and Spring from 1999 - 2014.

# Node 5: Slightly shallow slightly fast push of the GS into the coast becoming slightly deeper near WHOI before coming back away from the coast and chilling out. The core of the pulse has negative heatflux but the surrounding GS has a strong positive heatflux and snall wind stress. Almost exclusively occurs in mab with only a bit everywhere else. Smallish events with a few massive ones. All seasons from 2003 - 2015.

# Node 6: Slightly warmer LS and LC with cooler GS. Minor poitive heat flux into LS and large positive heat flux into GS. Normal mixed layer with low wind stress over the LS and high over the GS. Mostly in the ls with a bit in the mab with almost none elsewhere. Occurred over 1999 - 2010 in spring and summer. Smaller events that have not been increasing over time.

# Node 7: Warm waters from LS to LC to GSL and a cold GS. Strong downward heat flux over northern waters and negative flux over GS. Shallow northern waters with low wind stress while high stress over GS. Equally high in ls and nfs. A bit in cbs but almost none elsewhere. Spring - Autumn from 2000 - 2014. 2006 was a particularly strong year. Events are overall not particularly large.

# Node 8: Warm northern waters with a cold GS. Strong positive flux over LS with weaker positive flux over GSL and negative over GS. Very shallow LS and very deep GS. Affects all northern waters but highest in gsl and ls. No events in mab and almost none in gm. Almost always Autumn and Winter from 2006 - 2013. Some more intense events later on with 2010/11 being a larger year.

# Node 9: Similar to node 5. Strong nearshore GS pulse. Strong negative flux over LS and GS but positive over the rest of the Atlantic. Very strong wind stress over LS and eastern part of Atlantic, weak over the warm heat flux area of the Atlantic. Extremely deep LS and shallow GS. Occurred over 2002 - 2016 for winter and spring, events began occurring in Autumn from 2013. Evens becoming rather intense as time progresses with some massive ones. Increasing in intensity in most regions. 

# Node 10: Very unstable mostly cold GS with warm GM and SS waters. Negative heat flux into shelf waters and positive into GS. High wind stress over LS and low over shelf waters. Deep GS and GM waters but shallow over SS. Spread out over most regions with fewest events in mab and nfs. A few tiny events from 2009 - 2011 but really got going from 2012 - 2013. Spring of 2013 was small while Autumn/WInter of 2012/13 was noteworthy.

# Node 11: Energetic but normal temperature GS with warm inshore waters and slightly warm LS. Positive heat flux into GS and LS but negative into inshore waters. High wind stress above LS and a bit over central AO, but negative everywhere else. Very deep mixed layer next to coast in MAB but relatively normal everywhere else. Relatively equivalent occurrence in all regions. Occurred only from July - October, 2012. A few decent sized events. Mean max intensity is decent.

# Node 12: Warm inshore and LS waters with cold GS and AO. GS is moving fast and consistent. Negative heatflux into GS and inshore waters, slightly positive into LS and AO. High wind stress over GS and AO, negative over inshore waters and LS. Very deep mixed layer along coast in mab and very shallow along coast in ls. Mostly events occurring in gsl, but also in other northern areas. Occurred every even year from 2008 to 2014 from ~June - September. Relatively small (short) events but with decent max intensities.
```

## Appendix

### Figures

It may be good to create a reference multi-panel figure for each event, as seen in @Schlegel2017predominant. But given that there are nearly 700 events being considered, this is likely too much. Perhaps showing the top 100 or some sort of meaningful reduction

```{r appendix-fig, eval=FALSE}
# Create synoptic figure for each event

# Load SACTN data
load("~/data/SACTN/AHW/SACTN_clims.Rdata")
load("data/SACTN/SACTN_events.Rdata")
load("setupParams/SACTN_site_list.Rdata")

# The files for loading
event_idx <- data.frame(event = dir("data/SOM", full.names = TRUE),
                        x = length(dir("data/SOM")))

# Create a synoptic atlas figure for each MHW
system.time(plyr::ddply(event_idx, c("event"), synoptic.fig, .progress = "text")) # 539 seconds
```

## References