## ----setup, include=FALSE-----------------------------------------------------
library(neurobase)
library(oro.nifti)
library(methods)
library(EveTemplate)
library(ggplot2)
knitr::opts_chunk$set(echo = TRUE, cache = TRUE, comment = "",
                      cache.path = "index_cache/html/")


## ---- eval = FALSE------------------------------------------------------------
## packages = installed.packages()
## packages = packages[, "Package"]
## if (!"oro.nifti" %in% packages) {
##   source("https://neuroconductor.org/neurocLite.R")
##   neuroc_install("oro.nifti")
##   ### development version
##   #   neuroc_install("oro.nifti", release = "stable")
## }


## ----nifti_obj----------------------------------------------------------------
library(oro.nifti)
set.seed(20161007)
dims = rep(10, 3)
arr = array(rnorm(10*10*10), dim = dims)
nim = oro.nifti::nifti(arr)
print(nim)
print(class(nim))
oro.nifti::is.nifti(nim)


## ----nifti_slot---------------------------------------------------------------
nim@cal_max
cal_max(nim)
slot(nim, "cal_max")


## ----nifti_data---------------------------------------------------------------
data = slot(nim, ".Data")
class(data)


## ----nifti_data2--------------------------------------------------------------
data = oro.nifti::img_data(nim)
class(data)
dim(data)


## ----nifti_slice--------------------------------------------------------------
slice = data[,,3]
class(slice)


## ----nifti_arr_slice----------------------------------------------------------
slice = data[,,3, drop = FALSE]
class(slice)


## ----slotnames----------------------------------------------------------------
slotNames(nim)


## ----logical------------------------------------------------------------------
above_zero = nim > 0
class(above_zero)
img_data(above_zero)[1]


## ----multi_log----------------------------------------------------------------
class(nim > 0 & nim < 2)


## -----------------------------------------------------------------------------
class(nim * 2)
class(nim + (nim / 4))
class(nim * nim)
class(nim^2)


## ----sum----------------------------------------------------------------------
sum(above_zero)


## ----mean---------------------------------------------------------------------
mean(above_zero)


## ----summs--------------------------------------------------------------------
min(nim)
max(nim)
range(nim)
class(abs(nim))


## ----eve, cache=FALSE---------------------------------------------------------
library(EveTemplate)
eve = readEve(what = "Brain")


## ----ortho--------------------------------------------------------------------
oro.nifti::orthographic(eve)


## ----ortho2-------------------------------------------------------------------
neurobase::ortho2(eve)


## ----ortho2_noorient----------------------------------------------------------
neurobase::ortho2(eve, add.orient = FALSE)


## ----ortho_nona---------------------------------------------------------------
orthographic(eve, y = eve > quantile(eve, 0.9))


## ----ortho2_nona--------------------------------------------------------------
ortho2(eve, y = eve > quantile(eve, 0.9))


## ----eve2, cache=FALSE--------------------------------------------------------
eve2 = eve
eve2[which.max(eve)] = eve2[which.max(eve)] * 5


## ----ortho2_large-------------------------------------------------------------
ortho2(eve2)


## ----ortho2_rob---------------------------------------------------------------
ortho2(robust_window(eve2))


## ----ortho2_zlim--------------------------------------------------------------
ortho2(eve2, zlim = quantile(eve2, probs = c(0, 0.999)))


## ----eve_full, cache = FALSE--------------------------------------------------
eve_full = readEve(what = "T1")


## ----double_ortho-------------------------------------------------------------
double_ortho(eve_full, eve)


## ----all_slices---------------------------------------------------------------
image(eve, z = 90)


## ----one_slice----------------------------------------------------------------
image(eve, z = 90, plot.type = "single")


## ----two_slice----------------------------------------------------------------
image(eve, z = c(90, 100), plot.type = "single")


## ----one_slice_sag------------------------------------------------------------
image(eve, z = 98, plot.type = "single", plane = "sagittal")


## ----one_slice_overlay--------------------------------------------------------
overlay(eve, y = eve > quantile(eve, 0.9), z = 90, plot.type = "single")


## ----one_slice_overlay_right--------------------------------------------------
mask = eve > quantile(eve, 0.9)
mask[ mask == 0] = NA
overlay(eve, y = mask, z = 90, plot.type = "single")
rm(list = "mask")


## ----dd, cache=FALSE----------------------------------------------------------
reduced = dropEmptyImageDimensions(eve)
dim(eve)
dim(reduced)


## ----plot_red-----------------------------------------------------------------
ortho2(reduced)


## ----plot_full_eve------------------------------------------------------------
ortho2(eve)


## ----replace_empty, cache = TRUE----------------------------------------------
dd = dropEmptyImageDimensions(eve, keep_ind = TRUE)
reduced = dd$outimg
reversed = replace_dropped_dimensions(img = reduced, 
                                      inds = dd$inds,
                                      orig.dim = dd$orig.dim)
all(reversed == eve)


## ----nim, cache = FALSE-------------------------------------------------------
dims = dim(eve)
arr = array(rnorm(prod(dims)), dim = dims)
nim = nifti(arr)
nim


## ----copyniftiheader, cache = FALSE-------------------------------------------
nim = copyNIfTIHeader(img = eve, arr = nim)
nim


## ----zeroes, cache=FALSE------------------------------------------------------
zeroes = niftiarr(eve, 0)


## ----masking------------------------------------------------------------------
eve_masked = mask_img(eve, eve > mean(eve))


## ----plot_mask----------------------------------------------------------------
ortho2(eve_masked)


## ----vec, cache=FALSE---------------------------------------------------------
vals = c(eve)
class(vals)


## ----dens---------------------------------------------------------------------
plot(density(vals))


## ----masking_2, cache=FALSE---------------------------------------------------
vals = eve[ eve > 0 ]
mean(vals)
mean(eve)


## ----na_eve-------------------------------------------------------------------
mask = eve > 0
mask[ mask == 0 ] = NA
eve_masked = mask_img(eve, mask)
mean(eve_masked)
mean(eve_masked, na.rm = TRUE)


## ----masking_3----------------------------------------------------------------
vals = eve[ eve > 0 ]


## ----dens_2-------------------------------------------------------------------
plot(density(vals))


## ----eve_df, cache=FALSE------------------------------------------------------
t2 = readEve(what = "T2")
df = data.frame(T1 = c(eve_full),
                T2 = c(t2),
                mask = c(eve > 0))
head(df)


## ----dplyr_df, cache=FALSE----------------------------------------------------
library(dplyr)
df = df %>% 
  filter(mask) %>% 
  select(-mask)


## ----make_hex, cache = TRUE---------------------------------------------------
library(ggplot2)
g = ggplot(df) + stat_binhex()
g + aes(x = T1, y = T2)


## ----make_long, cache = FALSE-------------------------------------------------
long = reshape2::melt(as.matrix(df))
colnames(long) = c("ind", "sequence", "value")
long$ind = NULL


## ----make_dists, cache = TRUE-------------------------------------------------
library(ggplot2)
ggplot(long, aes(x = value)) + 
  geom_line(stat = "density") +
  facet_wrap(~ sequence, 
             scales = "free_x")