require 'pry' # gem install pry

# Pass in an enumeration of data and
# (optionally) a block to extract the grouping aspect of the data.
#
# Optional: sort_by lambda (operates on group key and count)

def puts_hist(data, sort_by:nil, &blk)
  data   = data.map(&blk) if blk
  counts = data.each_with_object(Hash.new(0)) {|k,h| h[k]+=1}
  max    = counts.values.max
  width  = Pry::Terminal.size!.last
  width0 = counts.keys.map{|k|k.to_s.length}.max
  width1 = width - width0 - 30
  div    = [1, max / width1.to_f].max
  puts "max: %d; widths: %d, %d; divisor: %0.3f" % [max, width0, width1, div]
  puts "bucket count    sum  bar"
  counts = counts.sort_by(&sort_by)  if sort_by
  acc = 0
  counts.each do |k,c|
    acc += c
    puts "%#{width0}s (%05s %06s) %-#{width1}s" % [("%.2f" % k), c, acc, "#"*((c/div).round)]
  end
  counts
end

# sample

class RandomGaussian
  def initialize(mean, stddev, rand_helper = lambda { Kernel.rand })
    @rand_helper = rand_helper
    @mean = mean
    @stddev = stddev
    @valid = false
    @next = 0
  end

  def rand
    if @valid then
      @valid = false
      return @next
    else
      @valid = true
      x, y = self.class.gaussian(@mean, @stddev, @rand_helper)
      @next = y
      return x
    end
  end

  private
  def self.gaussian(mean, stddev, rand)
    theta = 2 * Math::PI * rand.call
    rho = Math.sqrt(-2 * Math.log(1 - rand.call))
    scale = stddev * rho
    x = mean + scale * Math.cos(theta)
    y = mean + scale * Math.sin(theta)
    return x, y
  end
end

r=RandomGaussian.new(3, 2)
pp=Array.new(1e4){r.rand}.map{|d|d if d >= 0}.compact
tt = pp.map{|i|
  (i*4).to_i/4.0
}.sort

puts_hist(tt)