ohadle

using DataFrames, Distributions, GLM, MixedModels
using BenchmarkTools


n_groups = 1000
samples_per_group = 500

function test_mm(n_groups, samples_per_group)
  group_offset = rand(n_groups)

ohadle

➜  ~ git:(master) ✗ sudo dtruss -p 45999

SYSCALL(args) 		 = return
write(0x9, "x\0", 0x1)		 = 1 0
psynch_cvsignal(0x102700148, 0x2600000026100, 0x26000)		 = 257 0
psynch_cvwait(0x102700148, 0x2600100026100, 0x26000)		 = 0 0
psynch_cvsignal(0x102700148, 0x2610000026200, 0x26100)		 = 257 0
psynch_cvwait(0x102700148, 0x2610100026200, 0x26100)		 = 0 0
select(0x6, 0x7FFF5FBF14D0, 0x7FFF5FBF1450, 0x7FFF5FBF13D0, 0x0)		 = 1 0
psynch_cvsignal(0x102700148, 0x2620000026300, 0x26200)		 = 257 0

ohadle

data = readcsv(joinpath(file_path, in_file); header=true)
map!(replace_nulls, data[1])

out_f = open(joinpath(file_path, out_file), "w")
write(out_f, join(data[2], ",")) #headers
writecsv(out_f, data[1][1:1, :]) #unclear why this is needed
writecsv(out_f, data[1])
close(out_f)

ohadle

using JSON

g_dict = open(JSON.parse, "/home/olevinkr/Documents/julia/nbrs_graph2.json")

using Graphs

#transfer all nodes to ExVertex types
new_vs = Array(ExVertex, length(g_dict["nodes"]))
i = 1
for vertex in g_dict["nodes"]