tk3369

for i in 1...100 {
	println([["FizzBuzz","Fizz"],["Buzz","\(i)"]][min(1,i%3)][min(1,i%5)])
}

tk3369

// Estimate PI using monte carlo simulation
// See http://www.davidrobles.net/blog/2014/06/22/estimating-pi-using-monte-carlo-simulations/

import 'dart:math';

Random rnd = new Random();

// Return a random point within [-1, 1] coordinate plane
class UnitPoint {
  double x;

tk3369

"""
Print a subtype tree from the specified `roottype`
"""
function subtypetree(roottype, level=1, indent=4)
    level == 1 && println(roottype)
    for s in subtypes(roottype)
      println(join(fill(" ", level * indent)) * string(s))
      subtypetree(s, level+1, indent)
    end
end

tk3369

# fizz buzz the bit twiddling way
mymod(n,m,f) = mod(n, m) == 0 ? f : 0x00
fb(n) = ["None", "Fizz", "Buzz", "FizzBuzz"][1 + (mymod(n,3,0x01) | mymod(n,5,0x02))]

Results:

julia> fb.(collect(1:15))
15-element Array{String,1}:

tk3369

# see https://discourse.julialang.org/t/allocation-groupby/7505
function groupby(v::AbstractVector{T}) where T
   d = Dict{T,Vector{Int}}()
   i = 1
   for x in v
       if !haskey(d, x)
           d[x] = [i]
       else
           push!(d[x], i)
       end

tk3369

Group By function performance test

Several approaches are compared.

1. Custom function
2. Query package
3. NormalizedQuantiles package

Results:

Method |Function| 8 elements|1,000 elements |10,000 elements|

tk3369

  foo2(filename, parsers; headers=true, delimiter=",", nrows=0) = open(filename) do f
   if headers 
     hdr = split(readline(f), delimiter)
   end
   lines = nrows == 0 ? [] : Vector(undef, nrows)
   r = 0
   while !eof(f)
     line = readline(f)
     row = map(x -> x[1](x[2]), zip(parsers, split(line, delimiter)))
     r += 1

tk3369

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tk3369

uxcplgxnkwbdwhrp
suerykeptdsutidb
dmrtgdkaimrrwmej
ztxhjwllrckhakut
gdnzurjbbwmgayrg
gjdzbtrcxwprtery
fbuqqaatackrvemm
pcjhsshoveaodyko
lrpprussbesniilv
mmsebhtqqjiqrusd

tk3369

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