Add "size" to the title, and use pdf figures

mbjoseph · mbjoseph · commit 811e1ccf2cb0 · 2019-03-25T18:18:09.000Z
diff --git a/.gitignore b/.gitignore
@@ -43,3 +43,4 @@ main.synctex.gz
 slides/very-simple-ecoregions*
 appendix-s1.tex
 appendix-s2.tex
+tex_src.zip
diff --git a/Makefile b/Makefile
@@ -1,14 +1,14 @@
 data-dir = data/processed
 gdb = data/raw/us_pbg00_2007.gdb
-figs = fig/ppc-density-funs.png \
-	fig/burn-area-effs.png fig/ppc-counts.png \
-	fig/all-coefs.png \
-	fig/count-partial-effs.png fig/attribution-plot.png \
-	fig/max-preds-l3.png \
-	fig/maps.png \
-	fig/spline-concept.png \
-	fig/million-acre-probs.png \
-	fig/wallow-local-conditions.png
+figs = fig/figure_7.pdf \
+	fig/figure_8.pdf fig/figure_4.pdf \
+	fig/figure_6.pdf \
+	fig/figure_5.pdf fig/figure_11.pdf \
+	fig/figure_9.pdf \
+	fig/figure_2.pdf \
+	fig/figure_3.pdf \
+	fig/figure_10.pdf \
+	fig/figure_12.pdf
 	
 tables = data/processed/burn-area-loglik.csv data/processed/count-loglik.csv \
 	data/processed/rho_beta.csv data/processed/count_test_intervals.csv \
@@ -17,7 +17,7 @@ tables = data/processed/burn-area-loglik.csv data/processed/count-loglik.csv \
 	data/processed/burn-area-beta.csv \
 	data/processed/million-er-mon.csv
 
-all: main.pdf appendix-s1.pdf appendix-s2.pdf
+all: main.pdf appendix-s1.pdf appendix-s2.pdf tex_src.zip
 
 main.pdf: $(figs) $(tables) main.Rmd library.bib header.sty
 		Rscript -e "rmarkdown::render('main.Rmd')"
@@ -96,40 +96,40 @@ ba_tpareto_fit.rds: R/fit-burn-area-tpareto.R data/processed/stan_d.rds
 ba_weibull_fit.rds: R/fit-burn-area-weibull.R data/processed/stan_d.rds
 		Rscript --vanilla R/fit-burn-area-weibull.R
 
-fig/ppc-density-funs.png data/processed/burn-area-loglik.csv data/processed/area_coverage.csv: R/burn-area-model-comps.R \
+fig/figure_7.pdf data/processed/burn-area-loglik.csv data/processed/area_coverage.csv: R/burn-area-model-comps.R \
 	ba_gamma_fit.rds ba_lognormal_fit.rds ba_pareto_fit.rds \
 	ba_tpareto_fit.rds ba_weibull_fit.rds
 		Rscript --vanilla R/burn-area-model-comps.R
 
-fig/burn-area-effs.png data/processed/burn-area-beta.csv: ba_lognormal_fit.rds R/burn-area-plots.R
+fig/figure_8.pdf data/processed/burn-area-beta.csv: ba_lognormal_fit.rds R/burn-area-plots.R
 		Rscript --vanilla R/burn-area-plots.R
 
-data/processed/count-loglik.csv fig/ppc-counts.png: R/count-model-comps.R \
+data/processed/count-loglik.csv fig/figure_4.pdf: R/count-model-comps.R \
 	pois_fit.rds nb_fit.rds zip_fit.rds zinb_fit.rds
 		Rscript --vanilla R/count-model-comps.R
 
-fig/all-coefs.png fig/count-partial-effs.png data/processed/rho_beta.csv: R/count-effplots.R \
+fig/figure_6.pdf fig/figure_5.pdf data/processed/rho_beta.csv: R/count-effplots.R \
 	zinb_full_fit.rds
 		Rscript --vanilla R/count-effplots.R
 
-fig/attribution-plot.png: R/interaction-plots.R zinb_full_fit.rds
+fig/figure_11.pdf: R/interaction-plots.R zinb_full_fit.rds
 		Rscript --vanilla R/interaction-plots.R
 
 count-preds.rds data/processed/area_df.csv data/processed/count_test_intervals.csv: R/plot-predicted-counts.R \
 	zinb_full_fit.rds
 		Rscript --vanilla R/plot-predicted-counts.R
 
-fig/max-preds-l3.png fig/million-acre-probs.png test_preds.rds data/processed/predicted_totals.csv data/processed/million-er-mon.csv data/processed/mev_intervals.csv: count-preds.rds \
+fig/figure_9.pdf fig/figure_10.pdf test_preds.rds data/processed/predicted_totals.csv data/processed/million-er-mon.csv data/processed/mev_intervals.csv: count-preds.rds \
 	ba_lognormal_fit.rds R/mev-plots.R
 		Rscript --vanilla R/mev-plots.R
 		
-fig/maps.png: data/processed/mtbs.rds data/processed/ecoregions.rds R/plot-study-region.R
+fig/figure_2.pdf: data/processed/mtbs.rds data/processed/ecoregions.rds R/plot-study-region.R
 		Rscript --vanilla R/plot-study-region.R
 		
-fig/spline-concept.png: R/spline-concept.R
+fig/figure_3.pdf: R/spline-concept.R
 		Rscript --vanilla R/spline-concept.R
 
-fig/wallow-local-conditions.png: R/wallow-case-study.R data/processed/ecoregions.rds data/raw/climate-data.csv
+fig/figure_12.pdf: R/wallow-case-study.R data/processed/ecoregions.rds data/raw/climate-data.csv
 		Rscript --vanilla R/wallow-case-study.R
 		
 push_fits: 
@@ -138,6 +138,9 @@ push_fits:
 pull_fits:
 	aws s3 cp s3://earthlab-mjoseph/ . --recursive --exclude "*" --include "*_fit.rds"
 
+tex_src.zip: $(figs) main.pdf
+	zip tex_src.zip fig/*.pdf main.tex appendix-s1.tex appendix-s2.tex
+
 
 clean: 
 		find . -type f -name '*.png' -delete
diff --git a/R/burn-area-model-comps.R b/R/burn-area-model-comps.R
@@ -286,7 +286,7 @@ sum_plot <- train_max %>%
 
 cowplot::plot_grid(den_plot, tail_plot, max_plot, sum_plot, 
                    nrow = 4, rel_heights = c(1.3, 1, 1, 1))
-ggsave(filename = 'fig/ppc-density-funs.png', width = 6, height = 4.25)
+ggsave(filename = 'fig/figure_7.pdf', width = 6, height = 4.25)
 
 
 
diff --git a/R/burn-area-plots.R b/R/burn-area-plots.R
@@ -185,4 +185,4 @@ location_ts_plot
 
 q <- (coefplot + p) / location_ts_plot + plot_layout(heights = c(.6, 1), ncol = 1)
 q
-ggsave('fig/burn-area-effs.png', plot = q, width = 8.5, height = 5)
+ggsave('fig/figure_8.pdf', plot = q, width = 8.5, height = 5)
diff --git a/R/count-effplots.R b/R/count-effplots.R
@@ -87,7 +87,7 @@ coefplot <- coef_d %>%
   theme(legend.position = 'none') +
   geom_text_repel(aes(label = ifelse(nonzero, variable, '')),
                   color = 'black', size = 2)
-ggsave('fig/all-coefs.png', plot = coefplot, width = 6, height = 3.5)
+ggsave('fig/figure_6.pdf', plot = coefplot, width = 6, height = 3.5)
 
 
 # Partial effect plots ----------------------------------------------------
@@ -187,7 +187,7 @@ p <- plot_data %>%
   legend.position = 'bottom') + 
   scale_y_continuous(breaks = c(-3, 0, 3))
 p
-ggsave('fig/count-partial-effs.png', plot = p, width = 6, height = 4)
+ggsave('fig/figure_5.pdf', plot = p, width = 6, height = 4)
 
 
 
diff --git a/R/count-model-comps.R b/R/count-model-comps.R
@@ -262,4 +262,4 @@ sum_plot <- ppc_counts %>%
 
 cowplot::plot_grid(den_plot, zero_plot, max_plot, sum_plot, 
                    ncol = 1, rel_heights = c(1.3, 1, 1, 1))
-ggsave('fig/ppc-counts.png', width = 6, height = 4.5)
+ggsave('fig/figure_4.pdf', width = 6, height = 4.5)
diff --git a/R/interaction-plots.R b/R/interaction-plots.R
@@ -153,6 +153,6 @@ case_study_plot <- write_attribution_plot(max_d$NA_L3NAME)
 p1 <- case_study_plot$plot +
   geom_vline(aes(xintercept = ym), data = max_d, lty = 'dotted')
 p1
-ggsave(filename = 'fig/attribution-plot.png', plot = p1,
+ggsave(filename = 'fig/figure_11.pdf', plot = p1,
        width = 7, height = 4)
 write_rds(case_study_plot, 'data/processed/case-study-plot.rds')
diff --git a/R/mev-plots.R b/R/mev-plots.R
@@ -219,7 +219,7 @@ ym_million_prob %>%
         legend.position = 'none') + 
   annotate(geom = 'text', x = 2007, y = .15, label = 'Train') + 
   annotate(geom = 'text', x = 2013, y = .15, label = 'Test', color = 'red')
-ggsave('fig/million-acre-probs.png', width = 4, height = 3)
+ggsave('fig/figure_10.pdf', width = 4, height = 3)
 
 
 # Evaluate interval coverage ----------------------------------------------
@@ -297,7 +297,7 @@ interval_df %>%
   ylab('Maximum fire size (hectares)') +
   theme(panel.grid.minor = element_blank(), 
         axis.text.x = element_text(angle = 90))
-ggsave('fig/max-preds-l3.png', width = 7, height = 4)
+ggsave('fig/figure_9.pdf', width = 7, height = 4)
 
 # get overall interval coverage stats for block maxima
 write_csv(interval_df, 'data/processed/mev_intervals.csv')
diff --git a/R/plot-study-region.R b/R/plot-study-region.R
@@ -48,4 +48,4 @@ fire_map <- ecoregions %>%
   ggtitle('A')
 
 p <- (fire_map  + l1_map) / (l2_map + l3_map)
-ggsave(filename = 'fig/maps.png', plot = p, width = 10, height = 7)
+ggsave(filename = 'fig/figure_2.pdf', plot = p, width = 10, height = 7)
diff --git a/R/spline-concept.R b/R/spline-concept.R
@@ -89,4 +89,5 @@ function_plot <- function_val %>%
 
 p <- unweight_plot + weight_plot + function_plot
 p
-ggsave(filename = 'fig/spline-concept.png', plot = p, width = 6, height = 2)
+
+ggsave(filename = 'fig/figure_3.pdf', plot = p, width = 6, height = 2)
diff --git a/R/wallow-case-study.R b/R/wallow-case-study.R
@@ -236,4 +236,4 @@ p2 <- pts_df %>%
 p2
 
 wallow_perim_plot + p2 + plot_layout(widths = c(1, 1))
-ggsave('fig/wallow-local-conditions.png', width = 7, height = 5)
+ggsave('fig/figure_12.pdf', width = 7, height = 5)
diff --git a/appendix-s1.Rmd b/appendix-s1.Rmd
@@ -1,5 +1,5 @@
 ---
-title: 'Spatiotemporal prediction of wildfire extremes with Bayesian finite sample maxima'
+title: 'Spatiotemporal prediction of wildfire size extremes with Bayesian finite sample maxima'
 subtitle: 'Appendix S1: Prior specifications'
 bibliography: library.bib
 output:
diff --git a/appendix-s2.Rmd b/appendix-s2.Rmd
@@ -1,5 +1,5 @@
 ---
-title: 'Spatiotemporal prediction of wildfire extremes with Bayesian finite sample maxima'
+title: 'Spatiotemporal prediction of wildfire size extremes with Bayesian finite sample maxima'
 subtitle: 'Appendix S2: Joint distributions'
 bibliography: library.bib
 output:
diff --git a/header.sty b/header.sty
@@ -71,6 +71,7 @@
 % left justify
 \usepackage[document]{ragged2e}
 
+\usepackage{setspace}
 
 \usepackage{authblk}
 
diff --git a/main.Rmd b/main.Rmd
@@ -1,5 +1,5 @@
 ---
-title: "Spatiotemporal prediction of wildfire extremes with Bayesian finite sample maxima"
+title: "Spatiotemporal prediction of wildfire size extremes with Bayesian finite sample maxima"
 bibliography: library.bib
 output:
     bookdown::pdf_document2:
@@ -43,6 +43,7 @@ hectares_per_acre <- 0.404686
 \linenumbers
 
 \begin{abstract}
+\onehalfspacing
 Wildfires are becoming more frequent in parts of the globe, but predicting where and when wildfires occur remains difficult. 
 To predict wildfire extremes across the contiguous United States, we integrate a 30 year wildfire record with meteorological and housing data in spatiotemporal Bayesian statistical models with spatially varying nonlinear effects. 
 We compared different distributions for the number and sizes of large fires to generate a posterior predictive distribution based on finite sample maxima for extreme events (the largest fires over bounded spatiotemporal domains). 
@@ -54,8 +55,10 @@ Statistically, these drivers affect the chance of an extreme wildfire in two way
 We conclude that recent extremes should not be surprising, and that the contiguous United States may be on the verge of even larger wildfire extremes.
 
 \bigskip \noindent \textbf{Key words}: fire; wildfire; Bayesian; spatiotemporal; extremes; climate.
-\end{abstract}
 
+\bigskip \noindent \textbf{Running Head:}: Extreme wildfires in the contiguous U.S.
+
+\end{abstract}
 
 
 # Introduction {-}
@@ -86,7 +89,7 @@ max_plot <- annual_maxima %>%
   xlab('Ignition date') + 
   ylab('Fire size (hectares)') + 
   scale_x_date(date_breaks = '5 years', date_labels = "%Y")
-ggsave('fig/max-plot.png', plot = max_plot, width = 4.5, height = 2)
+ggsave('fig/figure_1.pdf', plot = max_plot, width = 4.5, height = 2)
 ```
 
 Factors driving wildfire extremes vary in space and time [@barbero2014modeling], but it is unclear how best to account for this in a predictive model. 
@@ -582,7 +585,7 @@ A. Progression of the Wallow Fire based on perimeter data collected from the Geo
 ## Figure 1 {-}
 
 \begin{figure}[ht]
-\includegraphics[width=\textwidth]{fig/max-plot.png}
+\includegraphics[width=\textwidth]{fig/figure_1.pdf}
 \label{max-plot}
 \end{figure}
 
@@ -591,7 +594,7 @@ A. Progression of the Wallow Fire based on perimeter data collected from the Geo
 ## Figure 2 {-}
 
 \begin{figure}[ht]
-\includegraphics[width=\textwidth]{fig/maps.png}
+\includegraphics[width=\textwidth]{fig/figure_2.pdf}
 \label{study-region}
 \end{figure}
 
@@ -600,7 +603,7 @@ A. Progression of the Wallow Fire based on perimeter data collected from the Geo
 ## Figure 3 {-}
 
 \begin{figure}[ht] 
-\includegraphics[width=\textwidth]{fig/spline-concept.png}
+\includegraphics[width=\textwidth]{fig/figure_3.pdf}
 \label{spline-concept} 
 \end{figure}
 
@@ -609,7 +612,7 @@ A. Progression of the Wallow Fire based on perimeter data collected from the Geo
 ## Figure 4 {-}
 
 \begin{figure}[htp] 
-\includegraphics[width=\textwidth]{fig/ppc-counts.png}
+\includegraphics[width=\textwidth]{fig/figure_4.pdf}
 \label{ppc-counts} 
 \end{figure}
 
@@ -618,7 +621,7 @@ A. Progression of the Wallow Fire based on perimeter data collected from the Geo
 ## Figure 5 {-}
 
 \begin{figure}[ht]
-\includegraphics[width=\textwidth]{fig/count-partial-effs.png}
+\includegraphics[width=\textwidth]{fig/figure_5.pdf}
 \label{count-partial-effs}
 \end{figure}
 
@@ -627,7 +630,7 @@ A. Progression of the Wallow Fire based on perimeter data collected from the Geo
 ## Figure 6 {-}
 
 \begin{figure}[ht] 
-\includegraphics[width=.9\textwidth]{fig/all-coefs.png}
+\includegraphics[width=.9\textwidth]{fig/figure_6.pdf}
 \label{all-coefs} 
 \end{figure}
 
@@ -636,7 +639,7 @@ A. Progression of the Wallow Fire based on perimeter data collected from the Geo
 ## Figure 7 {-}
 
 \begin{figure}[htp] 
-\includegraphics[width=\textwidth]{fig/ppc-density-funs.png}
+\includegraphics[width=\textwidth]{fig/figure_7.pdf}
 \label{ppc-density-funs}
 \end{figure}
 
@@ -645,7 +648,7 @@ A. Progression of the Wallow Fire based on perimeter data collected from the Geo
 ## Figure 8 {-}
 
 \begin{figure}[ht] 
-\includegraphics[width=\textwidth]{fig/burn-area-effs.png}
+\includegraphics[width=\textwidth]{fig/figure_8.pdf}
 \label{burn-area-effs}
 \end{figure}
 
@@ -654,7 +657,7 @@ A. Progression of the Wallow Fire based on perimeter data collected from the Geo
 ## Figure 9 {-}
 
 \begin{figure}[ht]
-\includegraphics[width=\textwidth]{fig/max-preds-l3.png}
+\includegraphics[width=\textwidth]{fig/figure_9.pdf}
 \label{max-preds-l3}
 \end{figure}
 
@@ -663,7 +666,7 @@ A. Progression of the Wallow Fire based on perimeter data collected from the Geo
 ## Figure 10 {-}
 
 \begin{figure}[ht]
-\includegraphics[width=\textwidth]{fig/million-acre-probs.png}
+\includegraphics[width=\textwidth]{fig/figure_10.pdf}
 \label{million-acre-probs}
 \end{figure}
 
@@ -672,7 +675,7 @@ A. Progression of the Wallow Fire based on perimeter data collected from the Geo
 ## Figure 11 {-}
 
 \begin{figure}[ht] 
-\includegraphics[width=\textwidth]{fig/attribution-plot.png}
+\includegraphics[width=\textwidth]{fig/figure_11.pdf}
 \label{attribution-plot}
 \end{figure}
 
@@ -681,7 +684,7 @@ A. Progression of the Wallow Fire based on perimeter data collected from the Geo
 ## Figure 12 {-}
 
 \begin{figure}[ht] 
-\includegraphics[width=\textwidth]{fig/wallow-local-conditions.png}
+\includegraphics[width=\textwidth]{fig/figure_12.pdf}
 \label{wallow-conditions}
 \end{figure}
 

Original file line number	Diff line number	Diff line change
`@@ -185,4 +185,4 @@ location_ts_plot`
`185`	`185`
`186`	`186`	`q <- (coefplot + p) / location_ts_plot + plot_layout(heights = c(.6, 1), ncol = 1)`
`187`	`187`	`q`
`188`		`-ggsave('fig/burn-area-effs.png', plot = q, width = 8.5, height = 5)`
	`188`	`+ggsave('fig/figure_8.pdf', plot = q, width = 8.5, height = 5)`
Original file line number	Diff line number	Diff line change
`@@ -89,4 +89,5 @@ function_plot <- function_val %>%`
`89`	`89`
`90`	`90`	`p <- unweight_plot + weight_plot + function_plot`
`91`	`91`	`p`
`92`		`-ggsave(filename = 'fig/spline-concept.png', plot = p, width = 6, height = 2)`
	`92`	`+`
	`93`	`+ggsave(filename = 'fig/figure_3.pdf', plot = p, width = 6, height = 2)`