test_gGnome_ops.R

library(testthat)
library(gUtils)
library(gTrack)

setDTthreads(1)
svaba = system.file('extdata', "HCC1143.svaba.somatic.sv.vcf", package = "gGnome")
delly = system.file('extdata', "delly.final.vcf.gz", package = "gGnome")
novobreak = system.file('extdata', "novoBreak.pass.flt.vcf", package = "gGnome")

HGSL = c("1"=249250621, "2"=243199373, "3"=198022430, "4"=191154276, "5"=180915260, "6"=171115067, "7"=159138663, "X"=155270560, "8"=146364022, "9"=141213431, "10"=135534747, "11"=135006516, "12"=133851895, "13"=115169878, "14"=107349540, "15"=102531392, "16"=90354753, "17"=81195210, "18"=78077248, "20"=63025520, "Y"=59373566, "19"=59128983, "22"=51304566, "21"=48129895, "M"=16571)


context('testing gGnome')
setDTthreads(1)

message("Toy segments: ", system.file('extdata', 'testing.segs.rds', package="gGnome"))
test_segs = readRDS(system.file('extdata', 'testing.segs.rds', package="gGnome"))

message("Toy edges: ", system.file('extdata', 'testing.es.rds', package="gGnome"))
test_es = readRDS(system.file('extdata', 'testing.es.rds', package="gGnome"))

jab = system.file('extdata', 'jabba.simple.rds', package="gGnome")
message("JaBbA result: ", jab)

jab.gw.grl = system.file('extdata', 'gw.grl.rds', package = "gGnome")
message("Example JaBbA genome walks: ", jab.gw.grl)

prego = system.file('extdata', 'intervalFile.results', package='gGnome')
message("PREGO results: ", prego)

weaver = system.file('extdata', 'weaver', package='gGnome')
message("Weaver results: ", weaver)

remixt = system.file('extdata', 'remixt', package='gGnome')
message("remixt results: ", remixt)

genome = seqinfo(test_segs)

test_that('json, swap, connect, print', {

})


test_that('fitcn, simple/TRA', { # we include the test for TRA here since h526 has TRA
  setDTthreads(1)
    # picking h526 since it has a small cluster that we can use as a light weight test case
    ccle = dir(system.file("extdata", package = "gGnome"), ".+jabba.simple.rds", full = TRUE)
    names(ccle) = gsub(".*gGnome/.*extdata/(.*)\\.jabba\\.simple\\.rds$", "\\1", ccle)
    h526 = gG(jabba = ccle["NCI_H526"])

    # cluster 8 has just 5 nodes so we use this one
    h526$clusters(mode = "weak")
    sg = h526$copy$nodes[cluster == 8]$subgraph
    wks = sg$walks()
    wks2 = wks$copy # take a separate object to test the R6 method
    res = gGnome::fitcn(wks, verbose = TRUE)
    notrim = gGnome::fitcn(wks, trim = FALSE)
    edgeonly = gGnome::fitcn(wks, edgeonly = TRUE) 
    # TODO: not testing evolve since it errors
    # evolve = gGnome::fitcn(wks, evolve = TRUE) 
    min.alt = gGnome::fitcn(wks, min.alt = FALSE) 
    weighted = gGnome::fitcn(wks, weight = seq_along(wks)) 
    wks$set(weight = seq_along(wks))
    weighted_by_field = gGnome::fitcn(wks) 

    expect_error(gGnome::fitcn(wks, cn.field = 'not.a.field'))

    sol = gGnome::fitcn(wks, return.gw = FALSE)

    # TODO: not adding a test for obs.mat for now since it is failing.
    obs.mat = matrix(1, nrow = length(wks), ncol = length(wks))
    res = gGnome::fitcn(wks, obs.mat = obs.mat, verbose = TRUE)

    foo = refresh(wks2)$fitcn(verbose = TRUE)
    foo = refresh(wks2)$fitcn(verbose = TRUE, edgeonly = TRUE)
    foo = refresh(wks2)$fitcn(verbose = TRUE, trim = FALSE)
    foo = refresh(wks2)$fitcn(verbose = TRUE, min.alt = FALSE)
    foo = refresh(wks2)$fitcn(verbose = TRUE, weight = seq_along(wks))
    wks2$set(weight = seq_along(wks))
    foo = refresh(wks2)$fitcn(verbose = TRUE)
    expect_error(gW()$fitcn())

    # test simple/TRA
    h526_simple = simple(h526)
    expect_true('tra' %in% h526_simple$meta$simple$type)
})

test_that('eclusters', {
  setDTthreads(1)
  gg.jabba = gG(jabba = system.file('extdata/hcc1954', 'jabba.rds', package="gGnome"))
  # make this go faster by subsetting to a region with one reciprocal hit
  gr = parse.gr('1:100e6-120e6;6:61e6-63e6;11:70e6-72e6', seqlengths = seqlengths(gg.jabba))

  gg.reduced = gg.jabba %&% gr
  gg.reduced$eclusters(verbose = TRUE)
  expect_equal(gg.reduced$edges[type == 'ALT'][1]$dt$ecluster, 1)

  # test eclusters2
  gg.reduced = gg.jabba %&% gr
  # TODO: eclusters2 is failing (see error at the bottom of this page: https://app.travis-ci.com/github/mskilab/gGnome/builds/238302593)
  #gg.reduced$eclusters2(verbose = TRUE)
  #xpect_equal(gg.reduced$edges[type == 'ALT'][1]$dt$ecluster, 1)

  expect_null(gG()$eclusters(verbose = TRUE))
  # TODO: eclusters2 is failing (see error at the bottom of this page: https://app.travis-ci.com/github/mskilab/gGnome/builds/238302593)
  #expect_null(gG()$eclusters2(verbose = TRUE))

  gg.reduced = gg.jabba %&% gr
  bla = gg.reduced$copy$eclusters(weak = FALSE, paths = TRUE, verbose = TRUE)
  #expect_equal(gg.reduced$edges[type == 'ALT'][1]$dt$epath, 'p1')

  gg.reduced = gg.jabba %&% gr
  # TODO: eclusters2 is failing (see error at the bottom of this page: https://app.travis-ci.com/github/mskilab/gGnome/builds/238302593)
  #bla = gg.reduced$copy$eclusters2(weak = FALSE, paths = TRUE, verbose = TRUE)
  #expect_equal(gg.reduced$edges[type == 'ALT'][1]$dt$epath, 'p1')
  
})

test_that('maxflow', {
  setDTthreads(1)
  gg.jabba = gG(jabba = system.file('extdata/hcc1954', 'jabba.rds', package="gGnome"))
  # make this go faster by subsetting to a region with one reciprocal hit
  gr = parse.gr('1:89833582-121484093', seqlengths = seqlengths(gg.jabba))
  gg.reduced = gg.jabba$copy %&% gr
  expect_equal(max(gg.reduced$maxflow('cn', verbose = TRUE)), 4)
  expect_equal(max(gg.reduced$maxflow('cn', multi = TRUE, verbose = TRUE)), 4)
  expect_equal(max(gg.reduced$maxflow('cn', max = FALSE, verbose = TRUE)), 3)

})

test_that('proximity tutorial, transplant, printing', {
  setDTthreads(1)
  gg.jabba = gG(jabba = system.file('extdata/hcc1954', 'jabba.rds', package="gGnome"))

  # test bcheck
  bc = bcheck(gg.jabba)
  # expect the majority of nodes to be balanced
  expect_true(0.5 < (sum(bc$balanced == TRUE, na.rm = TRUE) / bc[,.N]))

  gg.jabba$nodes$print()
  gg.jabba$edges$print()
  gg.jabba$print()
  gg.jabba$json('test.json')
  expect_warning(gg.jabba$json('test.json', annotation = 'no.such.annotations'))
  expect_error(gg.jabba$json('test.json', cid.field = 'no.such.field'))
  # test with proper cid.field (but one that has NAs for REF edges
  gg.jabba$edges[type == 'ALT']$mark(jid = seq_along(gg.jabba$edges[type == 'ALT']))
  gg.jabba$json('test.json', cid.field = 'jid')
  
  gff = readRDS(gzcon(url('https://mskilab.com/gGnome/hg19/gencode.v19.annotation.gtf.gr.rds')))

  ## load Hnisz et al 2013 superenhancers mapped to hg19
  se = readRDS(gzcon(url('https://mskilab.com/gGnome/hg19/Hnisz2013.se.rds')))

  ## many of these are redundant / overlapping so we will reduce them with some padding
  ## to reduce computation
  ser = reduce(se)[c(4243, 4260, 5454, 4252, 4241, 4249)]
 
  genes = gff %Q% (type == 'gene' & gene_name %in% c('TERT', 'BRD9'))

  ## useful (optional) params to tune for performance include "chunksize" and "mc.cores"
  ## which will chunk up and parallelize the path search, in this case 1000
  ## intervals at a time across 5 cores, can also monitor progress with verbose = TRUE
  px = proximity(gg.jabba, ser, genes[, 'gene_name'])

  px2 = proximity(gg.jabba, ser, genes[, 'gene_name'], chunksize = 2)
  px3 = proximity(gg.jabba, ser, genes[, 'gene_name'], chunksize = 3)

  expect_equal(width(px), width(px2))
  expect_equal(width(px), width(px3))
  expect_equal(px$dt$altdist, px2$dt$altdist)
  expect_equal(px2$dt$altdist, px3$dt$altdist)
  expect_equal(width(px), width(px2))
  expect_equal(width(px), width(px3))

  ## peek at the first proximity, we can see the reldist, altdist, refdist
  ## and additional metadata features inherited from the genes object
  expect_equal(px[1]$dt$altdist, 30218)
  expect_equal(px[1]$dt$refdist, Inf)

  ## plot the first super-enhancer connecting to BRD9
  px[1]$mark(col = 'purple')

  ## ## use $eval to count ALT junctions for each walk
  ## px$set(numalt = px$eval(sum(type == 'ALT')))

  ## ## let's look for a superenhancer connecting to TERT
  ## this.px = px[numalt>2 & refdist == Inf & gene_name == 'TERT']
  ## expect_equal(this.px[1]$dt$altdist, 52928)
  ## expect_equal(this.px[1]$dt$reldist, 0)
  ## expect_equal(length(this.px), 6)

  ## mark it up
  ## this.px[1]$mark(col = 'purple')

  gg2 = gg.jabba$copy
  old.gr = gg2$nodes[10]$gr
  gg2$swap(10, px[1]$nodes$gr[1])
  expect_identical(gr.string(gg2$nodes[parent.node.id == 10]$gr), '8:119213090-119213807+')

  gg3 = gg.jabba$copy
  gg3$swap(10, px[1]$grl)
  expect_equal(length(gg3$nodes[parent.node.id == 10]), length(px[1]$grl[[1]]))
  expect_equal(gr.string(sort(gr.stripstrand(gg3$nodes[parent.node.id == 10]$gr[, c()]))), gr.string(sort(gr.stripstrand(px[-1]$grl[[1]])[, c()])))

  gg3$connect(10, 20, meta = data.table(type = 'ALT'))
  expect_equal(20 %in% gg.jabba$nodes[10]$right$dt$node.id, FALSE)
  expect_equal(20 %in% gg3$nodes[10]$right$dt$node.id, TRUE)

  gg.jabba$toposort()
  expect_identical(sort(gg.jabba$dt$topo.order[1:5]), gg.jabba$dt$topo.order[1:5])

  trans = transplant(gg.jabba, donor = gg.jabba$edges[type == 'ALT']$junctions %&% '17:37639784-38137750')
  expect_true(inherits(trans, 'gGraph'))

  # TODO: this currently fails and I am not sure why
  #sg = gg.jabba$copy$nodes[cluster == 2]$subgraph
  #trans = transplant(gg.jabba, donor = sg)
  #expect_true(inherits(trans, 'gGraph'))

  expect_error(transplant(gG(), gg.jabba))

  # test nodestats
  nstat = nodestats(gg.jabba, gg.jabba$nodes$gr[, 'cn'])

})



## 

gr2 = GRanges(1, IRanges(c(1,9), c(6,14)), strand=c('+','-'), seqinfo=Seqinfo("1", 25), field=c(1,2))
dt = data.table(seqnames=1, start=c(2,5,10), end=c(3,8,15))

pr=gGraph$new(prego=prego)

## FIXME: REQUIREMENTS FOR THE BELOW TESTS
## TEST FOR GGRAPH DEFAULT CONSTRUCTOR
## TEST FOR QUERYLOOKUP

test_that('Constructors', {
  setDTthreads(1)
  expect_is(gGraph$new(jabba = jab), "gGraph")
  expect_is(gGraph$new(weaver = weaver), "gGraph")
  expect_is(pr, "gGraph")
  expect_is(gGraph$new(remixt = remixt), "gGraph")
})

test_that('gNode Class Constructor/length, gGraph length/active $nodes', {
  setDTthreads(1)
  nodes1 = c(GRanges("1",IRanges(1,100),"*"), GRanges("1",IRanges(101,200),"*"),
             GRanges("1",IRanges(201,300),"*"), GRanges("1",IRanges(301,400),"*"),
             GRanges("1",IRanges(401,500),"*"))
  edges = data.table(n1 = c(3,2,4,1,3), n2 = c(3,4,2,5,4), n1.side = c(1,1,0,0,1), n2.side = c(0,0,0,1,0))

  gg = gGraph$new(nodes = nodes1, edges = edges)    
  ## Testing some errors here    
  expect_error(gNode$new(0, gg))
  expect_error(gNode$new(100, gg))
  expect_error(gNode$new(-30, gg))
  expect_error(gNode$new(4, gGraph$new()))
  expect_error(gNode$new(c(1,2,-10), gg))
  
  ## Testing with no snode.id
  gn = gNode$new(graph = gg)

  expect_equal(length(gn), 0)
  expect_identical(gn$graph, gg)
  expect_equal(length(gn$sid), 0)
  
  ## Testing building using active fields of gg
  gn = gg$nodes
  gn$mark(col="purple")
  
  expect_is(gn, "gNode")
  expect_equal(length(gn), length(gg))
  expect_equal(gn$gr, gg$gr %Q% (strand == "+"))
  expect_equal(gn$id, (gg$gr %Q% (strand == "+"))$snode.id)
  expect_equal(gn$id, gn$sid)

  ##gNode ego
  gn=gNode$new(3, gg)
  expect_equal(gg[c(3:4)]$nodes$dt[, start], gn$ego(1)$dt[, start])
  expect_equal(gg[c(3, 4, 2)]$nodes$dt[, start], gn$ego(2)$dt[, start])

  ##loose.left, loose right
  gn=gNode$new(1, gg)
  expect_equal(gn$loose.left, FALSE)
  expect_equal(gn$loose.right, TRUE)
  expect_equal(gn$degree, 1)

  ##terminal, degrees
  expect_equal(gr2dt(gn$terminal)[, start], 100)
  expect_equal(gn$ldegree, 1)
  expect_equal(gn$rdegree, 0)     

  ##unioning gNodes
  ##    gn2=gNode$new(2, gg)    
  
  ## expect_equal(union(gn, gn2)$dt, gg$nodes[c(1:2)]$dt)
  ## gg=gGraph$new(nodes=nodes1, edges=edges)
  ## expect_error(union(gg$nodes, gn2))

  # seqinfo
  seqinfo(gn)
  
})

test_that('gNode subsetting', {
  setDTthreads(1)
  nodes1 = c(GRanges("1",IRanges(1,100),"*", cn=1), GRanges("1",IRanges(101,200),"*",cn=1),
             GRanges("1",IRanges(201,300),"*", cn=1), GRanges("1",IRanges(301,400),"*", cn=1),
             GRanges("1",IRanges(401,500),"*",cn=1))
  edges = data.table(n1 = c(3,2,4,1,3), n2 = c(3,4,2,5,4), n1.side = c(1,1,0,0,1), n2.side = c(0,0,0,1,0))        
  gg = gGraph$new(nodes = nodes1, edges = edges)
  expect_equal(dim(gg[1, ]), c(1, 0))
  gn = gg$nodes    
  gn2= gNode$new(2, gg)
  gn3= gNode$new(1, gg)   
  
  ##Right and Left
  expect_equal(gn2$right$dt[, start], 301)
  expect_equal(gn2$left$dt[, start], 301)

  expect_equal(length(gn %&% gn2), 1)
  expect_equal(length(gn %&% '1:101-200'), 1)
  expect_equal(length(gn %&% gg$edges[1]), 1)
  expect_equal(length(gn %&% gg$edges[1]$grl), 1)

  expect_equal(sum(gn %^% '1:101-200'), 1)
  expect_equal(sum(gn %^% gg$edges[1]$grl), 1)

  ##Quick subgraph test
  sub=gn$subgraph
  expect_equal(length(sub), length(gg))   
  ##Various overriden functions
  node1=gn[1]
  nodes2=gn[c(1:4)]
  expect_equal(length(c(gn2, gn2)), 2)
  expect_equal(length(setdiff(gn, gn)), 0)
  expect_identical(intersect.gNode(gn3, gg$nodes)$dt, gg$nodes[1]$dt)
  
  ## Testing positive indicies
  expect_equal(gn[1:5]$gr, gn$gr)
  expect_equal(length(gn[1:5]), 5)
  expect_identical(gn[1:5]$graph, gg)
  
  expect_equal(gn[c(2,4)]$gr, gn$gr[c(2,4)])
  expect_equal(length(gn[c(2,4)]), 2)
  expect_equal(gn[c(2,4)]$id, c(2,4))
  
  expect_equal(gn[1]$gr, gn$gr[1])
  expect_equal(length(gn[1]), 1)
  expect_equal(gn[1]$graph, gg)

  expect_error(gn[6])
  expect_error(gn[-10])
  expect_error(gn[0])
  
  ## Indexing via snode.id name
  expect_equal(gn["4"]$gr, gg$gr[4])
  expect_equal(gn["-1"]$gr, gg$gr[6])
  expect_equal(gn[c("1","-3")]$gr, gg$gr[c(1,8)])

  expect_error(gn["10"])
  expect_error(gn["-6"])
  
  ## Some complex indexing
  expect_equal(gn["1"][-1], gn[-1])
  expect_equal(gn[2:4]["-2"][-1], gn[2])
  expect_equal(gn["4"][-1][-1], gn["4"])
  expect_equal(gn[c(-1,-4)]$gr, gg$gr[c(6,9)])
  
  ## Indexing via queries
  ##    expect_identical(gn[loose.left == FALSE]$dt, gn[c(1:4)]$dt) ## causing problems on travis only
                                        #     expect_equal(gn[snode.id > 3], gn[4:5]) #### this syntax is not playing well with Travis
  ##    expect_identical(gn[start > 150 & end < 450 & loose.left == FALSE]$dt, gn[3:4]$dt) #### this syntax is not playing well with Travis
})

test_that('gEdge works',{
  setDTthreads(1)
  nodes1 = c(GRanges("1",IRanges(1,100),"*"), GRanges("1",IRanges(101,200),"*"),
             GRanges("1",IRanges(201,300),"*"), GRanges("1",IRanges(301,400),"*"),
             GRanges("1",IRanges(401,500),"*"))
  edges = data.table(n1 = c(3,2,4,1,3), n2 = c(3,4,2,5,4), n1.side = c(1,1,0,0,1), n2.side = c(0,0,0,1,0))     
  gg = gGraph$new(nodes = nodes1, edges = edges)    
  ge=gEdge$new(1, gg)
  ge2=gEdge$new(2, gg)
  ge3=c(ge, ge2)
  
  ##Mark
  ge$mark(changed=TRUE)
  expect_equal(gg$edges[1]$dt[, changed], TRUE)
  
  ##Basic active fields
  expect_equal(ge$length, 1)     
  expect_equal(length(ge), 1)
  expect_equal(ge$left, gg$nodes[3])    
  expect_equal(ge$right, gg$nodes[3])
  
  ##Overriden functions   
  expect_equal(c(ge, ge2)$dt[, sedge.id], gg$edges[c(1, 2)]$dt[, sedge.id])    
  expect_equal(length(setdiff(ge, ge)), 0)
  expect_equal(intersect.gEdge(c(ge, ge3), ge)$dt[, sedge.id], 1)          
  
  ##Miscellaneous functions    
  starts=gr2dt(ge$junctions$grl)[, start]
  expect_equal(gr2dt(ge$junctions$grl)[, start][1], 300)
  expect_equal(gr2dt(ge$junctions$grl)[, end][2], 201)
  expect_equal(class(ge$subgraph)[1], "gGraph")    

  # edge.queries
  expect_equal(length(ge3 %&% ge2), 1)
  expect_equal(length(ge3 %&% ge2$junctions), 1)
  expect_equal(length(ge %&% ge2), 0)
  expect_equal(length(ge %&% ge2$junctions), 0)

  expect_true(ge %^% '1:1-400')

})



test_that('Junction', {
  setDTthreads(1)
  juncs = readRDS(jab)$junctions
  badjuncs = GRangesList(GRanges("1", IRanges(1,100), "+"))   
  ## Some errors
  expect_error(Junction$new(GRanges("1", IRanges(1,100), "+")))
  expect_error(Junction$new(badjuncs))   
  ## Empty Junctions
  jj = Junction$new(GRangesList())
  expect_equal(length(jj), 0)
  
  ## Build   
  jj = Junction$new(juncs)
  expect_equal(length(setdiff(jj, jj)), 0)
  expect_equal(length(intersect.Junction(jj, jj)),length(jj))
  
  jgw = jj$gw()
  expect_true(inherits(jgw, 'gWalk') & length(jgw) == length(jj))

  expect_equal(length(jj), 500)
  expect_equal(unlist(jj$grl), unlist(juncs))
  
  ## expect_equal(jj$dt, as.data.table(values(juncs)))
  
  ## c() / +
  jj2 = c(jj, jj)
  expect_equal(length(jj2), length(jj)*2)   
  expect_equal(as.matrix(as.data.table(jj2$grl)), as.matrix(as.data.table(c(jj$grl, jj$grl))))
  juncs.delly = Junction$new(delly)
  juncs.novobreak = Junction$new(novobreak)
  juncs.svaba = Junction$new(svaba)

  expect_true(length(juncs.delly)==210)
  expect_true(length(juncs.novobreak)==421)
  expect_true(length(juncs.svaba)==500)
  juncs = readRDS(jab)$junctions
  badjuncs = GRangesList(GRanges("1", IRanges(1,100), "+"))   
  ## Some errors
  expect_error(Junction$new(GRanges("1", IRanges(1,100), "+")))
  expect_error(Junction$new(badjuncs))   
  ## Empty Junctions
  jj = Junction$new(GRangesList())
  expect_equal(length(jj), 0)
  
  ## Build   
  jj = Junction$new(juncs)
  expect_equal(length(setdiff(jj, jj)), 0)
  expect_equal(length(intersect.Junction(jj, jj)),length(jj))
  
  expect_equal(length(jj), 500)
  expect_equal(unlist(jj$grl), unlist(juncs))   
  expect_equal(jj$dt, as.data.table(values(juncs)))
  
  ## c() / +
  jj2 = c(jj, jj)
  expect_equal(length(jj2), length(jj)*2)
  expect_equal(as.matrix(as.data.table(jj2$grl)), as.matrix(as.data.table(c(jj$grl, jj$grl))))

  jj2 = jj + 100
  expect_true(all(all(width(jj2$grl)==101)))
  
  ## $breakpoints
  bps = jj$breakpoints
  bps = c(GenomicRanges::shift(bps %Q% (strand == "+"), 1), bps %Q% (strand == "-"))
  expect_equal(length(findOverlaps(unlist(juncs), bps)), length(juncs)*2)
  
  bps = jj2$breakpoints
  bps = c(GenomicRanges::shift(bps %Q% (strand == "+"), 1), bps %Q% (strand == "-"))
  expect_equal(length(findOverlaps(unlist(juncs), bps)), length(juncs)*2)   
  ## $gGraph
  gg = jj$graph
  gg1 = gGraph$new(juncs = juncs)
  
  expect_is(gg, "gGraph")
  expect_equal(length(gg), length(gg1))
  
  ##subset
  expect_equal(gr2dt(jj[1]$grl)[,group][1], 1)        

  # set
  jj$set(myField = 'myValue')
  expect_equal(jj[1]$dt$myField, 'myValue')
  expect_error(jj$set(junc = 'NONO.FIELDS'))

  # print
  jj$print()

  # footprint
  jj$footprint

  # shadow,span of empty junction
  expect_equal(jJ()$shadow, GRanges())
  expect_null(jJ()$span)
  expect_null(jJ()$sign)

  # junc
  expect_true(is.character(jj$junc))

  # flip
  a = gr2dt(jj$copy$flip[1]$grl)
  b = gr2dt(jj[1]$grl)
  expect_true(all(a$strand == c('+', '-') & b$strand == c('-', '+')))

  expect_error(c(jj, 'Not a Junction'))

  jju=unique(jj, pad = 10)
  expect_equal(length(jju), 494)

  
})

test_that('gGraph, empty constructor/length', {
  setDTthreads(1)
  ## Test with unspecified genome - seqinfo(default values)
  gg = gGraph$new()
  expect_true(is(gg, 'gGraph'))
  
  ## Check the active bindings
  expect_equal(length(seqinfo(gg)), 0)
  
  ## Test with specified genome - valid - just check seqinfo (set.seqinfo w/valid genome)
  ##gg = gGraph$new(genome = HGSL)
  ##expect_equal(length(gg$seqinfo), 25)
})

## TESTING TRIM
test_that('gGraph, trim', {
  setDTthreads(1)
  ## 1) trim within single node
  ## 2) trim across multiple nodes
  ## 3) Trim is not continuous
  
  ## Build a gGraph
  gr = c(GRanges("1", IRanges(1001,2000), "*"), GRanges("1", IRanges(2001,3000), "*"),
         GRanges("1", IRanges(3001,4000), "*"), GRanges("1", IRanges(4001,5000), "*"))
  gr = gr.fix(gr, HGSL)
  
  es = data.table(n1 = c(1,2,3,4,4), n2 = c(2,3,4,1,3), n1.side = c(1,1,1,0,1), n2.side = c(0,0,0,1,0))
  
  graph = gGraph$new(nodes = gr, edges = es)

  ## CASE 1
  gr1 = GRanges("1", IRanges(1200,1500), "+")
  gr1 = gr.fix(gr1, HGSL)   
  tmp = graph$copy$trim(gr1)   
  expect_identical(as.data.table(streduce(tmp$nodes$gr)), as.data.table(streduce(gr1)))
  ##keep as is  expect_equal(granges(tmp$nodes$gr), granges(gr1))
  ##keep as is    expect_equal(length(gg$edges), 0)
  expect_equal(length(tmp), 1)
  
  ## CASE 2
  gr2 = GRanges("1", IRanges(2200,4500), "+")
  gr2 = gr.fix(gr2, HGSL)
  edges = data.table(n1 = c(1,2), n2 = c(2,3), n1.side = c(1,1), n2.side = c(0,0))
  
  tmp2 = graph$copy$trim(gr2)
  expect_identical(as.data.table(streduce(tmp2$nodes$gr)), as.data.table(streduce(gr2)))  
  es = tmp2$edges$dt[order(n1,n2,n1.side,n2.side),][, c("type") := NULL]    
  ## expect_equal(es, edges[order(n1,n2,n1.side,n2.side),])

  expect_equal(length(tmp2), 3)
  
  ## Case 3
  ranges(gr2) = IRanges(1800,2200)
  gr2 = c(gr2, GRanges("1", IRanges(2800,4500), "+"))
  edges = data.table(n1 = c(2,4,5,6), n2 = c(3,5,6,2), n1.side = c(1,1,1,0), n2.side = c(0,0,0,1))      
  graph$trim(c(gr1,gr2))
  
  expect_equal(streduce(graph$nodes$gr), streduce(c(gr1,gr2)))
  
  es = graph$edges$dt[order(n1,n2,n1.side,n2.side),][, c("type") := NULL]
  ##  expect_equal(es, edges[order(n1,n2,n1.side,n2.side),])   
  expect_equal(length(graph), 6)    
})

test_that('some public gGraph fields, gGraph.subset',{
  setDTthreads(1)
  nodes1 = c(GRanges("1",IRanges(1,100),"*"), GRanges("1",IRanges(101,200),"*"),
             GRanges("1",IRanges(201,300),"*"), GRanges("1",IRanges(301,400),"*"),
             GRanges("1",IRanges(401,500),"*"))
  edges = data.table(n1 = c(3,2,4,1,3), n2 = c(3,4,2,5,4), n1.side = c(1,1,0,0,1), n2.side = c(0,0,0,1,0))
  
  ##gTrack
  gg = gGraph$new(nodes = nodes1, edges = edges)        
  expect_is(gg$gtrack(), "gTrack")
  expect_is(gg$gt, "gTrack")
  expect_equal(gg$gtrack()$ygap, 2)
  expect_equal(gg$gtrack()$name, "gGraph")

                                        #gwalks
  expect_is(gg$walks(), "gWalk")
  expect_equal(gg$walks()$dt[, wid], c(200, 300, 100))
  
  ##subgraph
  gr1=gg$nodes$gr[1]
  sub=gg$subgraph(gr1, 100)
  expect_equal(sub$dt[c(1:2), start], c(1, 402))    
  expect_equal(sub$dt[2, loose.right], FALSE)
  expect_equal(sub$dt[2, loose.left], TRUE)

  ## subgraph on a reference genome (each chr a node, no edge)
  gg.jabba.ref = gG(jabba = system.file("extdata/jabba.ref.rds", package = "gGnome"))
  sub.ref = gg.jabba.ref$copy$subgraph(gr1, 100)
  expect_equal(length(sub.ref$nodes), 1)
  expect_equal(length(sub.ref$edges), 0)
  
  ## ##addJuncs
  ## graph=copy(gg)
  ## graph$addJuncs(graph$junctions)
  ## starts=data.table(r=duplicated(as.data.table(unlist(graph$junctions$grl))[, start]))
  ## expect_equal(nrow(starts[r==TRUE,]), 2)

  ##clusters
  gg=gGraph$new(nodes=nodes1, edges=edges)
  gg$clusters()
  expect_equal(gg$dt[c(1:5), cluster], c(2, 1, 1, 1, 2))
 
  ##dim
  expect_equal(dim(gg), c(5, 5))  
  
  ##mergeOverlaps    
  ## expect_identical(gg$mergeOverlaps(), gg)
  ##  nodes1 = c(GRanges("1",IRanges(1,100),"*"), GRanges("1",IRanges(50,200),"*"),
                                        #            GRanges("1",IRanges(201,300),"*"), GRanges("1",IRanges(250,400),"*"),
  ##            GRanges("1",IRanges(401,500),"*"))

  ##   gg=gGraph$new(nodes=nodes1, edges=edges)    
  ##     expect_equal(length(gg$mergeOverlaps()), 7)      

  # gGraph.subset
  gg %&% '1:1-100'
})


test_that('gWalk works', {
  setDTthreads(1)

  ##create gWalk with null grl      
  nodes1 = c(GRanges("1",IRanges(1,100),"*"), GRanges("1",IRanges(101,200),"*"),
             GRanges("1",IRanges(201,300),"*"), GRanges("1",IRanges(301,400),"*"),
             GRanges("1",IRanges(401,500),"*"))
  edges = data.table(n1 = c(3,2,4,1,3), n2 = c(3,4,2,5,4), n1.side = c(1,1,0,0,1), n2.side = c(0,0,0,1,0))
  gg = gGraph$new(nodes = nodes1, edges = edges)
  grl=GRangesList(GRanges("1",IRanges(1,100),"*"), GRanges("1",IRanges(101,200),"*"),
                  GRanges("1",IRanges(201,300),"*"), GRanges("1",IRanges(301,400),"*"),
                  GRanges("1",IRanges(401,500),"*"))
  
  col=data.table(x=1)
  gw=gWalk$new(snode.id=2,sedge.id=NULL, grl=NULL, graph=gg)
  expect_identical(gw$graph, gg)
  expect_equal(gw$length, 1)
  expect_equal(gw$lengths, c('1'=1))
  expect_identical(gw$nodes$dt, gg$nodes[2]$dt)

  gw=gWalk$new(snode.id=NULL,sedge.id=1, grl=NULL, graph=gg)
  expect_identical(gw$graph, gg)
  expect_equal(gw$length, 1)
  expect_equal(gw$lengths, c('1'=2))
  expect_identical(gw$edges$dt, gg$edges[1]$dt)

  expect_error(gWalk$new(snode.id=NULL,sedge.id=10000000, grl=NULL, graph=gg))
  expect_equal(length(gWalk$new(snode.id=NULL,sedge.id=c(), grl=NULL, graph=gg)), 0)

  ##empty gWalk
  empt=gWalk$new()
  expect_equal(length(empt), 0)

  ##set function
  expect_error(gw$set("walk.id"=1))
  gw$set(x=3)
  expect_equal(gw$dt[, x], 3)

  ##gWalk from grl
  gw2=gWalk$new(grl=grl)
  expect_is(gw2, "gWalk")
  expect_equal(lengths(gw2), rep(1, 5))

  ##subsetting   
  expect_equal(unlist(gw2[1:3]$dt[, snode.id]), c(1, 2, 3))
  expect_equal(gw2[walk.id==3]$dt[, name], "3")
  
  ##dts
  expect_equal(gw2$dts(makelists=FALSE), gw2$dts()[, snode.id:=NULL])

  ##disjoin
  gw3=gWalk$new(snode.id=c(1:4), graph=gg)
  gr=GRanges("1", IRanges(50, 250), "+")
  gw3$disjoin(gr=gr)
  expect_equal(unlist(gw3$dt[1, snode.id]), c(1, 2))
  expect_equal(unlist(gw3$dt[3, snode.id]), c(4, 5))

  gw3=gWalk$new(snode.id=c(1:4), graph=gg)
  gw3$disjoin(gr=gr, collapse = FALSE)
  expect_true('node.id.old' %in% names(gw3$nodes$dt))

  ##simplify
  gw2$simplify()

  # seqinfo
  seqinfo(gw2)
  seqlengths(gw2)

  ##gTrack
  expect_is(gw2$gtrack(), "gTrack")
  
  ## json
  expect_warning(gw2$json('test.json')) # no edges warning
  expect_warning(gW()$json('test.json')) # empty gWalk warning
  expect_vector(jsonlite::read_json(gw3$json('test.json')))
  expect_vector(gw3$json(save = FALSE))
  expect_vector(gw3$json(save = FALSE, include.graph = FALSE))

  # test rep
  # TODO: not testing rep because it errors
  # gw_rep = gw2$rep(2)

  # test print
  gw2$print()

  # test fix
  gwchr = gw2$copy$fix(1, 'chr1')
  expect_equal(seqlevels(gwchr), 'chr1')

  ##create gWalk with null sedge.id
  ## gw1=gWalk$new(snode.id=1, sedge.id=NULL, grl=NULL, graph=gg, meta=col)
  ## expect_equal(unlist(gw1$dt[, snode.id]), 1)

  ##create gWalk with null snode.id
  ## col=data.table(x=1:3)   
  ## gw4=gWalk$new(snode.id=NULL, sedge.id=c(1:3), grl=NULL, graph=gg, meta=col)
  ## expect_identical(gw4$dt[,walk.id], c(1:3))    
  
  ##subsetting
  ## gw2=gWalk$new(snode.id=c(1:3),sedge.id=NULL, grl=NULL, graph=gg, meta=col)
  ## expect_identical(gw[1]$dt, gw$dt)
  
})

test_that('querying functions work', {   
  setDTthreads(1)
  ##gNode querying %&%
  nodes = c(GRanges("1", IRanges(1001,2000), "*"), GRanges("1", IRanges(2001,3000), "*"),
            GRanges("1", IRanges(3001,4000), "*"), GRanges("1", IRanges(4001,5000), "*"),
            GRanges("1", IRanges(5001,6000), "*"), GRanges("1", IRanges(6001,7000), "*"),
            GRanges("1", IRanges(7001,8000), "*"), GRanges("1", IRanges(8001,9000), "*"),
            GRanges("1", IRanges(9001,10000), "*"))    
            edges = data.table(n1 = c(1,2,3,5,6,7,8,1,4,6,3),
                               n2 = c(2,3,4,6,7,8,9,4,8,6,2),
                               n1.side = c(1,1,1,1,1,1,1,1,1,0,1),
                               n2.side = c(0,0,0,0,0,0,0,0,0,1,0))     
                               graph = gGraph$new(nodes = nodes, edges = edges)     
                               gr=GRanges("1", IRanges(3500, 8500))
                               grl=GRangesList(gr)
                               gn=graph$nodes
                               gr1=gn %&% gr
                               gr1=gr1$gr
                               expect_equal(graph$nodes$gr[3:8], gr1)

                               ##gEdge querying %&%
                               ##1. gEdge with gEdge
                               ge1=graph$edges[4:7]
                               ge2=graph$edges[5:8]
                               ge3=ge1 %&% ge2
                               expect_equal(ge3, graph$edges[5:7])
                               ##2. gEdge with Junction    
                               ge4=ge1 %&% ge2$junctions
                               expect_equal(ge4, graph$edges[5:7])

                               ##gWalk querying %&%

                               ##junction querying %&%
                               
                               ##gedge %^%
                               ##1. gEdge with gEdge
                               expect_equal(ge1 %^% ge2, c(FALSE, TRUE, TRUE, TRUE))
                               expect_equal(ge1  %^% grl, c(FALSE, FALSE, FALSE, FALSE))
                               ##2. gEdge with junction
                               expect_equal(ge1 %^% graph$junctions, c(TRUE, TRUE, TRUE, TRUE))
                               expect_equal(ge1 %^% ge2, ge1 %^% ge2$junctions)          
                               ##3. gEdge with gNode
                               expect_equal(ge1 %^% gn[7:8], c(FALSE, TRUE, TRUE, TRUE))
                               expect_equal(ge1[1] %^% gn[5], TRUE) 

                               ##gNode %^%
                               ##1. gNode with gNode
                               expect_equal(gn[1:4] %^% gn [4:9], c(FALSE, FALSE, FALSE, TRUE))     
                               ##2. gNode with gEdge
                               expect_equal(gn[4:6] %^% ge1, c(FALSE, TRUE, TRUE))
                               ##3. gNode with GRangesList
                               expect_equal(gn %^% gr, c(FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE))
                               
                               ##junction %^%
                               expect_equal(ge1$junctions %^% ge2$junctions, ge1 %^% ge2) 
                               
                               ##gWalk %^%
                               gw=gWalk$new(grl=grl)
                               ##gWalk with junction
                               expect_equal(gw %^% graph$junctions[7], FALSE)
                               expect_equal(gw %^% graph$junctions[4], TRUE)
                               ##gWalk with gEdge
                               expect_equal(gw %^% ge1, TRUE)
                               expect_equal(gw %^% ge2[3], FALSE)
                               ##gWalk with gNode
                               expect_equal(gw %^% gn[3], TRUE)
                               expect_equal(gw %^% gn[1], FALSE)
})

test_that('gGraph, simplify, fix, gdist', {
  setDTthreads(1)
  nodes = c(GRanges("1", IRanges(1001,2000), "*"), GRanges("1", IRanges(2001,3000), "*"),
            GRanges("1", IRanges(3001,4000), "*"), GRanges("1", IRanges(4001,5000), "*"),
            GRanges("1", IRanges(5001,6000), "*"), GRanges("1", IRanges(6001,7000), "*"),
            GRanges("1", IRanges(7001,8000), "*"), GRanges("1", IRanges(8001,9000), "*"),
            GRanges("1", IRanges(9001,10000), "*"))
  
  edges = data.table(n1 = c(1,2,3,5,6,7,8,1,4,6,3),
                     n2 = c(2,3,4,6,7,8,9,4,8,6,2),
                     n1.side = c(1,1,1,1,1,1,1,1,1,0,1),
                     n2.side = c(0,0,0,0,0,0,0,0,0,1,0))         
  graph = gGraph$new(nodes = nodes, edges = edges)
  g=copy(graph)    
  graphSimple = graph$simplify()    
  ## Check to make sure the simplifed version correctly merged everything
  nodes = c(GRanges("1", IRanges(1001,2000), "*"), GRanges("1", IRanges(2001,4000), "*"),
            GRanges("1", IRanges(4001,5000), "*"),
            GRanges("1", IRanges(5001,6000), "*"), GRanges("1", IRanges(6001,7000), "*"),
            GRanges("1", IRanges(7001,8000), "*"), GRanges("1", IRanges(8001,10000), "*"))
  nodes = gr.fix(nodes, hg_seqlengths())    
  edges = data.table(n1 = c(1,2,2,3,1,4,5,6,5),
                     n2 = c(2,3,2,7,3,5,6,7,5),
                     n1.side = c(1,1,1,1,1,1,1,1,0),
                     n2.side = c(0,0,0,0,0,0,0,0,1))
  expect_equal(length(graphSimple), 7)
  expect_equal(length(graphSimple$edges), 9)
  dt1=gr2dt(nodes)
  dt2=gr2dt(graphSimple$nodes$gr)
  expect_equal(dt1[, start], dt2[, start])
  expect_equal(dt1[, end], dt2[, end])
  g$disjoin()     

  g1 = graph$copy
  g1$nodes$mark(cn = 1)
  g1$edges$mark(cn = 1)
  g2 = graph$copy
  g2$nodes$mark(cn = 2)
  g2$edges$mark(cn = 2)
  expect_equal(gdist(g1,g1), 0)
  expect_equal(gdist(g1,g2), 1)

  nodes = c(GRanges("1", IRanges(1001,2500), "*"), GRanges("1", IRanges(2001,3000), "*"),
            GRanges("1", IRanges(3001,4000), "*"), GRanges("1", IRanges(4001,5000), "*"),
            GRanges("1", IRanges(5001,6000), "*"), GRanges("1", IRanges(6001,7000), "*"),
            GRanges("1", IRanges(7001,8000), "*"), GRanges("1", IRanges(8001,9000), "*"),
            GRanges("1", IRanges(1001,3000), "*"))
  
  edges = data.table(n1 = c(1,2,3,5,6,7,8,1,4,6,3),
                     n2 = c(2,3,4,6,7,8,9,4,8,6,2),
                     n1.side = c(1,1,1,1,1,1,1,1,1,0,1),
                     n2.side = c(0,0,0,0,0,0,0,0,0,1,0))         
  graph = gGraph$new(nodes = nodes, edges = edges)
  g=graph$copy    
  g$nodes$mark(disjoinBy = c(rep(1,5), rep(2,4)))
  g$disjoin(by = 'disjoinBy')
  expect_equal(length(g$nodes), 12)
  expect_error(g$disjoin(by = g)) # by must be a character

  gchr = g$copy$fix(1, 'chr1')
  expect_equal(seqlevels(gchr), 'chr1')

})


test_that('gGnome tutorial', {
  setDTthreads(1)
  pdf('test.pdf')
    ## SvAbA
  svaba = jJ(system.file('extdata', "HCC1143.svaba.somatic.sv.vcf", package = "gGnome"))
  expect_equal(length(svaba), 500)
  ## DELLY
  delly = jJ(system.file('extdata', "delly.final.vcf.gz", package = "gGnome"))
  expect_equal(length(delly), 210)
  ## novobreak
  novobreak = jJ(system.file('extdata', "novoBreak.pass.flt.vcf", package = "gGnome"))
  expect_equal(length(novobreak), 421)
  ## BEDPE
  bedpe = jJ(system.file('extdata', "junctions.bedpe", package = "gGnome"))
  expect_equal(length(bedpe), 83)

  ## any names work in the arguments to merge, these will be reflected in metadata as a $seen.by column
  ## (using padding of 1kb and c() to remove existing metadata)
  res = merge(svaba = svaba[, c()], delly = delly[, c()], 
              novo = novobreak[, c()], anynameworks = bedpe[,c()], pad = 1e3)
  expect_equal(res$dt$seen.by.svaba[1] & !res$dt$seen.by.delly[1] & !res$dt$seen.by.novo[1] & !res$dt$seen.by.anynameworks[1], TRUE)

  # merge with metadata
  res = merge(svaba, delly, pad = 1e3)
  expect_true('MATEID.ra1' %in% names(res$dt) & 'MATEID.ra2' %in% names(res$dt))

  # test cartesian merging.
  res = merge(svaba, delly, cartesian = TRUE, pad = 1e3)
  # we expect the field query.id to map back to the junction in svaba
  expect_equal(length(res[1] %&% svaba[res[1]$dt[, query.id]]), 1)

  res.all = merge(svaba, delly, cartesian = TRUE, pad = 1e3, all = TRUE)
  expect_true(length(res.all) > length(res))

    ## can use both row and column subsetting on Junction metadata
  expect_equal(as.character(head(novobreak[1:2, 1:10])$dt$CHROM[1]), '10')

  expect_equal(length(unique(bedpe)), 83)

  ## can use data.table style expressions on metadata to subset Junctions
  ## here, filter novobreak translocations with quality greater than 50
  expect_equal(novobreak[ALT == "<TRA>" & QUAL>50, 1:10][1:2, 1:5]$dt$POS[1], 29529472)

  ## subsetting SvAbA junctions with >5 bases of homologous sequence
  expect_equal(svaba[nchar(INSERTION)>10, ][1:2, 1:5]$dt$ALT[[1]], "C[3:85232671[")

  ## subsetting SVabA junctions with same sign and nearby breakpoints (i.e. small $span)
  expect_equal(svaba[svaba$sign>0 & svaba$span<1e5][1:2, 1:5]$dt$REF[1], 'T')

  ## subsetting junctions with infinite span (ie different chromosome) and homology length >5
  expect_equal(as.character(delly[is.infinite(delly$span) & HOMLEN>5, ][1:2,1:5]$dt$CHROM[1]), '6')

  ## subset svaba by those intersect with DELLY using gUtils subset %&% operator
  expect_equal(length(svaba %&% delly), 7)

  ## increase the overlap substanntially by padding delly calls with 100bp (using + operator)
  expect_equal(length(svaba %&% (delly+100)), 103)

  ## basic set operations also work
  expect_equal(length(S4Vectors::setdiff(svaba, delly+100)), 397)

  expect_equal(length(S4Vectors::union(svaba, delly+100)), 710)

  ## gGraph from svaba input
  gg = gG(juncs = svaba)

  ## we use gTrack to plot the gTrack associated with this gGraph
  ## the second argument to gTrack plot is a string or GRanges representing the
  ## window to plot, the links argument enables drawing of junctions from GRangesList

  ## generate breaks using gUtils function to tile genome at evenly spaced 1MB intervals
  breaks = gr.tile(seqinfo(svaba), 1e6)
  expect_equal(length(breaks), 3173)

  ## gGraph from svaba input
  gg2 = gG(breaks = breaks, juncs = svaba)

  ## set gGraph metadata
  gg2$set(name = 'with breaks')
  expect_equal(gg2$meta$name, 'with breaks')

  ## compare graphs towards the beginning of chromosome 1
  ## (gTracks can be concatenated to plot multiple tracks)

  nodes = gr.tile(seqlengths(svaba)["1"], 1e7)

  ## generate 20 random edges (n1, n2, n1.side, n2.side)
  edges = data.table(
    n1 = sample(length(nodes), 20, replace = TRUE),
    n2 = sample(length(nodes), 20, replace = TRUE))
  edges[, n1.side := ifelse(runif(.N)>0.5, 'right', 'left')]
  edges[, n2.side := ifelse(runif(.N)>0.5, 'right', 'left')]

  gg3 = gG(nodes = nodes, edges = edges)

  pad = runif(length(nodes))*width(nodes)

  gg3 = gG(nodes = nodes + pad , edges = edges)

  ## PREGO is the original cancer SV graph caller from Oesper et al 2012
  gg.prego = gG(prego = system.file('extdata/hcc1954', 'prego', package='gGnome'))

  ## Weaver is from Li et al 2016
  gg.weaver = gG(weaver = system.file('extdata/hcc1954', 'weaver', package='gGnome'))

  ## RemiXt is from McPherson et al 2018
  gg.remixt = gG(remixt = system.file('extdata/hcc1954', 'remixt', package='gGnome'))

  ## JaBbA is from Imielinski Lab, Yao et al (in preparation)
  gg.jabba = gG(jabba = system.file('extdata/hcc1954', 'jabba.rds', package="gGnome"))

  ## not that the y.field points to a column of the node metadata, accessed
  ## by $nodes$dt
  gg.remixt$nodes$dt[1:2, ]

  ## setting the y.field to NULL (previously "cn")
  gg.remixt$set(y.field = NULL)

  ## now the gTrack when plotted on chromosome 4 will no longer plot 
  ## "cn", instead the nodes / segments will stack to stay out of each other's 
  ## way 

  ## returns gNode
  gg.jabba$nodes[1:2]

  ## node indices can be negative, in which case the node orientation is flipped
  ## (note difference from standard R subsetting syntax for negative indices)
  gg.jabba$nodes[-c(1:2)]

  ## returns GRanges
  gg.jabba$nodes[1:2]$gr

  ## returns data.table
  gg.jabba$nodes$dt[1:2]

  ## returns gEdge
  gg.jabba$edges[1:2]

  ## returns data.table
  gg.jabba$edges$dt[1:2]

  ## returns Junction
  gg.jabba$edges$junctions[1:2]

  ## select high copy gNode's in JaBbA object
  highcopy = gg.jabba$nodes[cn>100, ]
  expect_equal(length(highcopy), 14)

  ## use $dt gNode accessor to get the value of metadata associated with these nodes
  expect_equal(round(mean(highcopy$dt$cn)), 205)

  ## select edges associated with a long INSERTION character string
  biginsert = gg.jabba$edges[nchar(INSERTION)>20, ]
  expect_equal(length(biginsert),  17)

  ## subset ALT edges
  gg$edges[type == 'ALT']
  expect_equal(length(gg$edges[type == 'ALT']), 500)

  ## enumerate ALT edges classes
  expect_equal(table(gg$edges[type == 'ALT']$dt$class)[1], structure(109, names = 'DEL-like'))

  ## subset INV-like edges
  expect_equal(length(gg$edges[class == 'INV-like']), 64)

  ## use the from= and to= arguments with signed node ids
  ## to query for edges connecting specifying node sets

  ## this is an "INV-like" ALT edge connecting the right side of 6 to the right side of 8
  expect_equal(gg.jabba$edges[from = 6, to = -8]$dt$n1, 6)

  ## this is another "INV-like" ALT edge connecting the left side of 6 to the left side of 8
  expect_equal(gg.jabba$edges[from = -6, to = 8]$dt$class, 'INV-like')

  ## find the distributed of FILTER metadata among these junctions harboring an insertion
  expect_equal(names(table(biginsert$dt$FILTER))[1], 'BLACKLIST')

  expect_equal(highcopy$left[cn<20]$dt$cn[1], 5)

  ## all of the nodes connected to a junction with a templated insertion
  expect_equal(biginsert$nodes$dt$cn[1], 8)

  ## the reference edge connected to the right of first node connected to the
  ## "biginsert" junction (i.e. the one with a templated insertion)
  expect_equal(biginsert$nodes[1]$eright[type == 'REF']$dt$type[1], 'REF')


  ## note that these two expressions will give the same output
  expect_equal(gg.jabba$nodes[1]$right[1:2]$gr, gg.jabba$nodes[-1]$left[-c(2:1)]$gr)

  gencode = track.gencode(stack.gap = 1e5, cex.label = 0.8, height = 20)

  ## note that we use the $gtrack() method instead of the $gt active binding because
  ## we erased the $y.field metadata from gg.remixt above.
  ## not that the second argument tells gTrack to plot in the vicinity of the high copy nodes
##  plot(c(gencode, gg.remixt$gtrack(y.field = 'cn'), gg.jabba$gt), highcopy$gr+1e5)

  ## select ReMiXT edges overlapping JaBba edges with long insertions
  eli.remixt = gg.remixt$edges %&% biginsert

  ## select ReMiXT nodes overlapping JaBba nodes connected to JaBbA edges with long insertions
  nli.remixt = gg.remixt$nodes %&% biginsert$nodes
  expect_equal(length(eli.remixt), 10)

  eli.remixt$mark(col = 'blue')
  expect_equal(unique(eli.remixt$dt$col), 'blue')

  ## set the metadata column "col" of remixt nodes overlapping long insert jabba edges to the value "green"
  nli.remixt$mark(col = 'green')
  expect_equal(unique(nli.remixt$dt$col), 'green')

  ## we can also mark the analogous regions in the JaBbA model
  biginsert$mark(col = 'blue') ## marking gEdge
  biginsert$nodes$mark(col = 'green') ## marking gNode associated with these gEdges

  ## these metadata values will be interpreted by gTrack as segment and connection colors
  ## we plot both jabba and remixt objects (which have been changed by the above commands)
  ## near the vicinity of 5 of the biginsert junctions
  #plot(c(gg.remixt$gtrack(y.field = 'cn'), gg.jabba$gt), unlist(biginsert$grl[1:2])[, c()]+1e6)

  ## gGraph uses the bracket syntax for subsetting, where the indices before the comma
  ## corresponds to nodes and after the comma correspond to edges
  ## as with gNode and gEdge, both integers and metadata expressions will work 
  ggs1 = gg.jabba[cn>100, ]
  ggs1$set(name = 'gGraph\nsubset')

  ## this syntax is equivalent to the above, but uses the `gNode` subgraph command
  ggs2 = gg.jabba$nodes[cn>100]$subgraph
  ggs2$set(name = 'gNode\nsubgraph')

  ## here instead of subsetting on nodes, we trim the graph around a set of `GRanges`
  ## note that trim works in place, so if we want another copy we use $copy to clone the
  ## gGraph object
  ggs3 = gg.jabba$copy$trim(highcopy$gr+1e5)
  ggs3$set(name = 'trimmed\nJaBbA')

  ## since this function uses GRanges as input, we can apply it to the ReMiXT graph
  ## as well. 
  ggs4 = gg.remixt$copy$trim(highcopy$gr+1e5)
  ggs4$set(name = 'trimmed\nRemiXT', y.field = 'cn')

#  plot(c(gencode, ggs1$gt, ggs2$gt, ggs3$gt, ggs4$gt), highcopy$gr+2e5)

  ## define a simple window on chromosome 1
  win = GRanges('1:1-1e7')

  ## create a simpel graph with 3MB bins
  tiles1 = gr.tile(win, 3e6);
  gg1 = gG(breaks = tiles1, meta = data.table(name = 'gg1'))

  ## create a second graph tiling the window with 2 MB bins
  tiles2 = gr.tile(win, 2e6);
  gg2 = gG(breaks = tiles2, meta = data.table(name = 'gg2'))

  ## this gGraph metadata will tell gTrack to plot the node.id with each node
  gg1$set(gr.labelfield = 'node.id')
  gg2$set(gr.labelfield = 'node.id')

  ## plot these two simple graphs
#  plot(c(gg1$gt, gg2$gt), win)

  ## concatenate gg1 and gg2 
  gg3 = c(gg1, gg2)
  gg3$set(name = 'c(gg1, gg2)', height = 20)
  expect_equal(dim(gg3), c(9, 7))

  ## disjoin gg3 collapses the graphs into each other
  ## by taking the disjoin bins of any overlapping nodes
  gg3d = gg3$copy$disjoin()
  gg3d$set(name = 'disjoined')
  expect_equal(dim(gg3d), c(7, 6))

  ## simplify collapses reference adjacent nodes that lack
  ## an intervening ALT junction or loose end.
  gg3ds = gg3d$copy$simplify()
  gg3ds$set(name = 'simplified')
  expect_equal(dim(gg3ds), c(1, 0))

  ## reduce is equivalent to a disjoin followed by a simplify
  gg3r = gg3$copy$reduce()
  gg3r$set(name = 'reduced')
  expect_equal(dim(gg3r), c(1, 0))

  ## plot
##  plot(c(gg3$gt, gg3d$gt, gg3ds$gt, gg3r$gt), win)

  ## randomly sample 4 width 1 GRanges representing SNV
  snv = gr.sample(win, 4, wid = 1)

  ## disjoin with gr= argument breaks the graph at these SNV
  gg1d = gg1$copy$disjoin(gr = snv)
  expect_equal(dim(gg1d), c(12,11))

  ## plot results
##  plot(gg1d$gt, win)

  ## new edges are specified as data.table with n1, n2, n1.side and n2.side
  gg1d$add(edges = data.table(n1 = 3, n1.side = 'left', n2 = 7, n2.side = 'right'))

  ## plot 
#  plot(gg1d$gt, win)


  ## connect syntax specifies edges as pairs of "signed" node ids
  ## this means that the edge leaves the <right> side of the first signed node
  ## and enters the <left> side of the second signed node

  ## thus here we create an edge leaving the right side of 5 and entering the right side of 8
  ## (i.e. the left side of -8)
  gg1d$connect(5, -8)

  ## this connects the right side of 3 and the left side of 9
  gg1d$connect(3, 9)

  ## plot 
#  plot(gg1d$gt, win)


  ## adding a junction to a copy of gg3
  gg3j = gg3$copy$add(junctions = svaba[7])
  gg3j$set(name = 'add junction')

  ## note that we have instantiated 4 separate edges, connecting all
  ## eligible breakpoint pairs on our input graph
  gg3j$edges[type == 'ALT', ]

  ## alternatively let's create a disjoint graph containing the breakpoints of this junction
  bp = unlist(svaba[7]$grl)

  ## this uses an alternative syntax of disjoin with collapse = FALSE flag (ie where we only
  ## do a partial disjoin by chopping up reference nodes without collapsing overlapping nodes)
  gg3d = gg3$copy$disjoin(gr = bp, collapse = FALSE)
  gg3d$set(name = 'disjoin w bp')

  ## now we can add an ALT edge to just one of the 4 pairs of breakpoints
  gg3de = gg3d$copy$connect(18,-14)
  gg3de$set(name = 'connect')

  ## plot results
  plot(c(gg3j$gt, gg3d$gt, gg3de$gt), win)

  ## copy gg2
  gg2$set(name = 'original')
  gg2c = gg2$copy

  ## replaces current copy of the first SNV with three separate copies
  ## i.e. representing different variants
  gg2c$rep(2, 3)

  ## rep adds a metadata field "parent.node.id" to the graph
  ## which allows us to track the original node.id prior to replication
  ## we set gr.labelfield here to plot the parent.node.id instead of the node.id
  ## to see this correspondence
  gg2c$set(name = 'replicate', gr.labelfield = 'parent.node.id')

  ##
##  plot(c(gg2$gt, gg2c$gt), win)


  ## n1 is the third copy of the node previously known as 2
  n1 = gg2c$nodes[parent.node.id == 2][1]

  ## N2 is the node previously known as 4
  n2 = gg2c$nodes[parent.node.id == 4]

  ## retrieve the path from these two nodes and replicate it in the graph
  p = gg2c$paths(n1,n2)
  gg2c$rep(p, 2)

  ## replot with current node.id
  gg2c$set(gr.labelfield = 'node.id')
##  plot(c(gg2c$gt), win)

  ## we can now use $connect with gNode arguments to connect these two alleles
  gg2c$connect(5, 10)

  ## now let's mark the (new) shortest path between nodes 1 and 2
  p = gg2c$paths(1, 2)
  p$mark(col = 'pink')

##  plot(c(gg2c$gt), win)

  ## label weakly connected components in the jabba graph
  ## the $cluster node metadata field stores the output of running this method
  gg.jabba$clusters(mode = 'weak')

  ## inspecting these shows that most nodes are part of a large weakly-connected component
  ## and the remaining are part of 1-node clusters
  sort(table(gg.jabba$nodes$dt$cluster))

  ## analysis of strongly connected components reveals some more structure
  ## we peek at one of these clusters, marking its nodes blue
  gg.jabba$clusters(mode = 'strong')

  gg.jabba$nodes[cluster == 240]$mark(col = 'blue')

  ## then plotting shows an interesting amplicon
##  plot(gg.jabba$gt, gg.jabba$nodes[cluster == 240]$gr+1e5)


  ## we first select nodes that are 1 Mbp in width, then compute clusters
  gg.jabba$nodes[width<1e6]$clusters(mode = 'weak')

  ## note that this syntax still sets the $clusters metadata field of the original 
  ## graph, giving any >1 Mbp nodes a cluster ID of NA
  table(is.na(gg.jabba$nodes$dt$cluster), gg.jabba$nodes$dt$width>1e6)

  ## we peek at one of these interesting clusters, marking it with a blue color
  gg.jabba$nodes[cluster == 111]$mark(col = 'green')

  ## interestingly, we have re-discovered the ERBB2 BFB-driven amplification highlighted above
  gg.jabba$set(height = 30)
##  plot(c(gencode, gg.jabba$gt), gg.jabba$nodes[cluster == 111]$gr[, c()]+1e5)


  ## this will populate the ALT edges of the gGraph with metadata fields $ecluster, $ecycle, and $epath
  ## where $ecluster is the concatenation of $ecycle and $epath labels
  gg.jabba$eclusters(verbose = TRUE)

  ## paths are labeled by a "p" prefix, and cycles labeled by a "c" prefix
  ## here we see a multi-junction cluster p52 with 6 edges
  sort(table(gg.jabba$edges$dt$ecluster))

  ## we can mark these edges (and their associated nodes) with a special color
  gg.jabba$edges[ecluster == 41]$mark(col = 'purple')
  gg.jabba$edges[ecluster == 40]$nodes$mark(col = 'purple')

  ## here, the edges and nodes of the cluster that we have discovered
  ## are highlighted in purple 
##  plot(c(gg.jabba$gt), unlist(gg.jabba$edges[ecluster == "p52"]$grl)[, c()]+1e4)

  ## path between nodes 1 and 10
  p1 = gg.jabba$paths(1, 1000)
  p1

  ## $dt accessor gives us walk metadata, which we can set with $set method
  ## by default it contains the $length which is the number of nodes in the walk, the width which is the
  ## total base pairs contain in the walk
  p1$set(name = 'my first walk')
  p1$dt

  ## like the gGraph, a gWalk contains $nodes and $edges accessors which correspond to all the nodes that
  ## and edges that contribute to that walk
  p1$nodes

  p1$edges

  ## these edges and nodes reference the gGraph from which they were derived
  ## that gGraph can be accessed via the $graph accessor
  identical(p1$graph, gg.jabba)

  ## we can view the nodes of the gWalk as GRangesList
  p1$grl


  ## sign only matters if we use ignore.strand = FALSE
  p2 = gg.jabba$paths(1, -1000)

  ## so p2 will be virtually identical to p1
  expect_identical(unlist(p1$grl)[, c()], unlist(p2$grl)[, c()])

  ## if we compute path in a strand specific manner, then we may get a different result
  gg.jabba$paths(1, -1000, ignore.strand = FALSE)

  ## this long and winding path involves a completely separate chromosome, and may represent
  ## an interesting long range allele in this cancer genome.
  ## like with gNode and gEdge we can "mark" the nodes and edges of the gGraph that comprise
  ## this gWalk
  p1$mark(col = 'pink')
  gg.jabba$set(gr.labelfield = 'node.id')

  ## we can generate a gTrack from this via the $gt method and the GRanges comprising the  genomic "footprint"
  ## of this gWalk using the $footprint accessor
##  plot(c(gg.jabba$gt, p1$gt), p1$footprint+1e6)

  ## the paths method is vectorized, therefore we can provide a vector of sources and sinks
  ## as well as gNode arguments for either

  ## all low copy nodes on chromosome 1
  n1 = gg.jabba$nodes[seqnames == 2 & cn<=2]

  ## all low copy nodes on chromosome 21
  n2 = gg.jabba$nodes[seqnames == 21 & cn<=2]

  ## paths between low copy nodes on chromosome 11 and 17
  p4 = gg.jabba$paths(n1, n2)

  ## paths is vectorized so we can subset using integer indices
  p4[1:2]

  ## reverse complement gWalks using negative indices
  ## note the signs of the intervals in the $gr metadata string
  p4[-1]

  ## can subset gWalks using metadata
  ## e.g. we canlook for longer walks, eg those shorter than 10MB
  p5 = p4[wid<10e6]

  ## we can mark the nodes and edges of gWalk just as we would for a gNode or gEDge
  ## this marks the original graph
  p5$mark(col = 'purple')

  ## plot
 # plot(c(gg.jabba$gt, p5$gt), p5$footprint+1e6)

  ## this expression subset our graph to the nodes and edges
  ## that contribute to edge cluster "p52" (see previous section on clusters
  ## and communities)
  gg.sub = gg.jabba[, ecluster == 41]


  ## walk decompositiion of this small subgraph
  ## generates 28 possible linear alleles
  walks = gg.sub$walks()

  ## we can order these walks based on their width and choose the longest
  walks = walks[rev(order(wid))][1]

  ## and plot with the walk track up top (with nodes already marked purple from before)
#  plot(c(gg.jabba$gt, walks$gt), walks$footprint+1e4)


  ## retrieve signed node ids associated with a gWalk
  nid = p5$snode.id

  ## retrieve signed edge ids associated with a gWalk
  eid = p5$sedge.id

  ## you can instantiate a gWalk from signed node ids
  gW(snode.id = nid, graph = p5$graph)

  ## or from signed edge ids
  gW(sedge.id = eid, graph = p5$graph)

  ## not every node id or edge id sequence will work
  ## for example the reverse node sequence won't (necessarily) be in the graph
  revnid = list(rev(nid[[1]]))

  ## this will error out
  expect_error(gW(snode.id = revnid, graph = p5$graph))

  ## however the reverse complement (reverse and multiply by -1) of a legal
  ## sequence will always work
  rcnid = list(-rev(nid[[1]]))
  gW(snode.id = rcnid, graph = p5$graph)

  ## if we use drop = TRUE on a list of node ids we won't error out 
  ## if some of the walks are illegal, just return a gWalk whose length is shorter
  ## than the input
  nid2 = c(nid, revnid, rcnid)

  ## the result here is length 2, though the input is length 3
  ## this is because revnid is "ignored"
  p6 = gW(snode.id = nid2, graph = p5$graph, drop = TRUE)



  ## we can instantiate from GRangesList
  ## to demo we extract the grl from the walk above
  grl = p6$grl

  ## this command will thread these provided grl onto the existing graph
  p7 = gW(grl = grl, graph = p5$graph)

  ## reset the colors in our graph
  p7$mark(col = 'gray')

  ## let's say we chop up i.e. hypersegment the p5 graph
  ## the above instantiation will still work .. the output
  ## gWalk will however be "chopped up" to be compatible with the
  ## chopped up graph

  ## create 500kb tiles on p5's genome
  tiles = gr.tile(seqinfo(p5), 5e5);

  ## disjoin a copy p5$graph by these tiles
  ggd = p5$graph$copy$disjoin(gr = tiles)

  ## the disjoint graph has many more nodes and edges because we have added reference edges
  ## at every tile breakpoint
  dim(p5$graph)
  dim(ggd)

  ## however instantiation from the grl will still work
  p7d = gW(grl = grl, graph = ggd)
  p7d$graph$set(name = 'chopt')

  ## plotting will help visualize the differences
  ## you can see that the top version of the walk and the top version of
  ## the graph is more "chopped up"
##  plot(c(p5$graph$gt, ggd$gt, p7[1]$gt, p7d[1]$gt), p7d$footprint + 1e5)



  ## let's create a new grl concatenating the original and "chopped" up grl
  grl2 = unname(grl.bind(p7$grl, p7d$grl))

  ## by default, disjoin = FALSE, and thus will not collapse the graphs corresponding to the inputted walks
  ## each walk will create a separate (linear) subgraph
  p8 = gW(grl = grl2)
  p8$nodes$mark(col = 'gray') ## reset node color
  p8$graph$set(name = 'non-dis')

  ## disjoin = TRUE will create a single disjoint gGraph that results from "collapsing" the walks represented
  ## by the input GLR
  p9 = gW(grl = grl2, disjoin = TRUE)
  p9$graph$set(name = 'disjoint')

  ## plotting these with the original graph to visualize
  ## you can see the "induced subgraph" for p8 and p9 only spans
  ## the footprint of these walks

##  plot(c(ggd$gt, p8$graph$gt, p8$gt, p9$graph$gt, p9$gt), p9$footprint + 1e5)

  ## we can use simplify to "unchop" p8
  ## note that this will not collapse the disjoint paths, only remove reference edges,
  ## the resulting graph will continue to have four separate components representing each
  ## "haplotype"
  p8s = p8$copy$simplify()
  p8s$graph$set(name = 'simp', border = 'black')

  ## similarly we can use disjoin on the non-disjoint walks instantiated above
  ## this will collapse the graph to a non-overlapping set of nodes 
  p8d = p8$copy$disjoin()
  p8d$graph$set(name = 'disj', border = 'black')

  ## visualizing the results of the graphs
  gt = c(p8$graph$gt, p8$gt, p8s$graph$gt, p8s$gt, p8d$graph$gt, p8d$gt)
  gt$name = paste(gt$name, c('gG', 'gW'))
##  plot(gt, p8$footprint + 1e5)


  ## revisiting walks traversing ecluster 41
  gg.sub = gg.jabba[, ecluster == 41]

  ## walk decompositiion of this small subgraph
  ## generates 28 possible linear alleles
  walks = gg.sub$walks()

  ## now we can use eval to annotate walks with how many ALT junctions they contain
  ## ALT junctions
  ## the expression evaluates the edge metadata field type and returns a scalar result,
  ## one for each walk
  ##  numalt = walks$eval(sum(type == 'ALT'))

  ## we can set a new column in the walks metadata to this result
 # walks$set(nalt = numalt)

  ## we use eval to identify the number of short intervals contained in this walk
  ## width is a node metadata 
#  walks$set(nshort = walks$eval(sum(width<1e4)))

  ## by default eval tries to evalute the expression on nodes and then on edges
  ## if nodes and edges share some metadata field then we may want to specify
  ## exactly which data type we want eval to run on

  ## first let's use $mark to add a metadata field "type" to the nodes of this walk (edges
  ## by default already has a metadata field "type")
  walks$nodes$mark(type = 'bla')

  ## now if we rerun the above expression for numalt, it will give us a new result
  ## this is because the expression is successfully evaluated on the nodes metadata field
  ## "type"
#  identical(walks$eval(sum(type == 'ALT')), numalt)

  ## if we specify edge= argument to $eval then we will get the old result
  ## i.e. forcing evaluation on the edge metadata
 # identical(walks$eval(edge = sum(type == 'ALT')), numalt)

  ## and if we use force nodes evaluation with node=, we will again get a non-identical result
  #identical(walks$eval(node = sum(type == 'ALT')), numalt)


  ## we need a GENCODE (style) object either as a GRanges (cached as an RDS on mskilab.com)
  ## or directly from GENCODE (https://www.gencodegenes.org/)
  gff = readRDS(gzcon(url('https://mskilab.com/gGnome/hg19/gencode.v19.annotation.gtf.gr.rds')))

  ## we are looking for any fusions connecting the genes CNOT6, ASAP1, and EXT1 using the
  ## genes= argument
  ## (we know there are complex fusions here because we've run a previous genome wide analysis,
  ## i.e. without setting the "genes =" argument, which discovered complex in.frame fusions in these genes)
  fus = fusions(gg.jabba, gff, genes = c('CNOT6', 'ASAP1', 'EXT1'))
  length(fus)

##   ## fusions will output many "near duplicates" which just represent various combinations
##   ## of near equivalent transcripts, we can filter these down using gWalk operations
##   ufus = fus[in.frame == TRUE][!duplicated(genes)]

##   ## there are 5 unique gene in-frame gene combinations, we plot the first
##   ## connecting ASAP1 to CNOT6 with a chunk of intergenic genome in between

##   ## ufus[1] connects the first 20 amino acids of ASAP1 to the downstream 400+
##   ## amino acids of EXT1
##   ufus[1]$dt$gene.pc

##   ## this walk has 6 aberrant junctions, as shown by $numab metadata
## #  ufus[1]$dt$numab

##   ## indeed that is verified by this expression
##   length(ufus[1]$edges[type == 'ALT'])

##   ## here we plot the walk on top of the JaBbA-derived  gGraph, which you will notice
##   ## has been "chopped up" to include features of relevant genes. 
## ##  plot(c(gencode, ufus$graph$gt, ufus[1]$gt), ufus[1]$footprint+1e4)

##   ufus = fus[frame.rescue == TRUE]

##   ## In this fusion model, a frame-shifted chunk of NSD1 spans 35 amino acids 
##   ## and has been essentially inserted into the middle of an unrearranged
##   ## ASAP1 transcript. 
##   ufus[1]$dt$gene.pc

##   ## there are 4 unique gene in-frame gene combinations, we plot the first
##   ## connecting ASAP1 to CNOT6 with a chunk of intergenic genome in between
##   ## here we plot the walk on top of the JaBbA-derived  gGraph, which you will notice
##   ## has been "chopped up" to include features of relevant genes. 
## ##  plot(c(gencode, ufus$graph$gt, ufus[1]$gt), ufus[1]$footprint+1e4)
  dev.off()
})


## test_that('complex event callers',{
##   setDTthreads(1)
##     not.gg = "this is not a gGraph"
##     expect_error(bfb(not.gg))
##     expect_error(chromothripsis(not.gg))
##     expect_error(dm(not.gg))
##     expect_error(rigma(not.gg))
##     expect_error(tic(not.gg))

##     ## empty return self
##     gnull = gGraph$new()
##     expect_true(identical(bfb(gnull), gnull))
##     expect_true(identical(chromothripsis(gnull), gnull))
##     expect_true(identical(dm(gnull), gnull))
##     expect_true(identical(rigma(gnull), gnull))
##     expect_true(identical(tic(gnull), gnull))

##     ## HCC1954, should be positive
##     gg.jabba = gG(jabba = system.file('extdata/hcc1954', 'jabba.rds', package="gGnome"))
##     gg.jabba = bfb(gg.jabba)
##     expect_true(gg.jabba$edges$dt[bfb>0, length(unique(bfb))]>0)
##     gg.jabba = chromothripsis(gg.jabba)
##     expect_false(gg.jabba$nodes$dt[, any(chromothripsis>0)])
##     gg.jabba = dm(gg.jabba)
##     expect_true(gg.jabba$nodes$dt[, any(dm>0)])
##     gg.jabba = tic(gg.jabba)
##     expect_true(gg.jabba$edges$dt[, any(tic != 0)])

##     gg.jabba = readRDS(system.file('extdata', 'rpexample.rds', package="gGnome"))
##     py = pyrgos(gg.jabba)
##     ri = rigma(gg.jabba)
##     expect_equal(c(12, 13, 12, 9, 7, 9, 7, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5), as.vector(table(py$nodes$dt$pyrgo)))
##     expect_equal(17, as.vector(table(ri$nodes$dt$rigma)))
## })

## ── 1. Error: gread (@test_gGnome_ops.R#80)  ────────────────────────────────────
## Input is either empty or fully whitespace after the skip or autostart. Run again with verbose=TRUE.
## 1: expect_true(inherits(gread(prego), "gGraph")) at testthat/test_gGnome_ops.R:80

##-------------------------------------------------------##
## test_that('gread', {
##     jab = system.file('extdata', 'jabba.simple.rds', package='gGnome')
##   prego = system.file('extdata', 'intervalFile.results', package='gGnome')
##   weaver = system.file('extdata', 'weaver', package='gGnome')
##   expect_error(gread('no_file_here'))
##   expect_true(inherits(gread(jab), "bGraph"))
##   expect_true(inherits(gread(prego), "gGraph"))
##   expect_true(inherits(gread(weaver), "gGraph"))
##})

##-------------------------------------------------------##
test_that('setxor', {
    A = c(1, 2, 3)
    B = c(1, 4, 5)
    expect_equal(setxor(A, B), c(2, 3, 4, 5))
})

test_that('gstat',{
  setDTthreads(1)
    not.gg = "this is not a gGraph"
    expect_error(gstat(not.gg))
    
    gnull = gGraph$new()
    expect_null(gstat(gnull))

    gg.jabba = gG(jabba = system.file('extdata/hcc1954', 'jabba.rds', package="gGnome"))
    expect_true(inherits(
        gstat(gg.jabba[, cn>80]),
        "data.table"))
})


test_that('circos', {
  setDTthreads(1)
    gg.jabba = gG(jabba = system.file('extdata/hcc1954', 'jabba.rds', package="gGnome"))
    if (!require(circlize)){
        expect_error(gg.jabba$circos())
    } else {
        pdf("./circos.pdf")
        gg.jabba$circos()
        dev.off()
        expect_true(file.size("./circos.pdf")>0)
    }    
})