Class: MiniHistogram

Inherits:
Object
  • Object
show all
Defined in:
lib/mini_histogram.rb,
lib/mini_histogram/plot.rb,
lib/mini_histogram/version.rb

Overview

Plots the histogram in unicode characters

Thanks to github.com/red-data-tools/unicode_plot.rb it could not be used because the dependency enumerable-statistics has a hard lock on a specific version of Ruby and this library needs to support older Rubies

Example:

require 'mini_histogram/plot'
array = 50.times.map { rand(11.2..11.6) }
histogram = MiniHistogram.new(array)
puts histogram.plot => Generates a plot

Defined Under Namespace

Classes: Error

Constant Summary collapse

INT64_MIN = 
-9223372036854775808
INT64_MAX = 
9223372036854775807
VERSION = 
"0.3.1"

Instance Attribute Summary collapse

Class Method Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(array, left_p: true, edges: nil) ⇒ MiniHistogram

Returns a new instance of MiniHistogram.



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# File 'lib/mini_histogram.rb', line 24

def initialize(array, left_p: true, edges: nil)
  @array = array
  @left_p = left_p
  @edges = edges
  @weights = nil

  @min, @max = array.minmax
end

Instance Attribute Details

#array ⇒ Object (readonly)

Returns the value of attribute array.



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# File 'lib/mini_histogram.rb', line 22

def array
  @array
end

#left_p ⇒ Object (readonly)

Returns the value of attribute left_p.



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# File 'lib/mini_histogram.rb', line 22

def left_p
  @left_p
end

#max ⇒ Object (readonly)

Returns the value of attribute max.



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# File 'lib/mini_histogram.rb', line 22

def max
  @max
end

Class Method Details

.dual_plot {|a, b| ... } ⇒ Object

Yields:

  • (a, b) 


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# File 'lib/mini_histogram/plot.rb', line 66

def self.dual_plot
  a = PlotValue.new
  b = PlotValue.new

  yield a, b

  if b.options[:ylabel] == a.options[:ylabel]
    b.options[:ylabel] = nil
  end

  MiniHistogram.set_average_edges!(a.histogram, b.histogram)
  PlotValue.dual_plot(a.plot, b.plot)
end

.set_average_edges!(*array_of_histograms) ⇒ Object

Given an array of Histograms this function calcualtes an average edge size along with the minimum and maximum edge values. It then updates the edge value on all inputs

The main pourpose of this method is to be able to chart multiple distributions against a similar axis

See for more context: github.com/schneems/derailed_benchmarks/pull/169



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# File 'lib/mini_histogram.rb', line 213

def self.set_average_edges!(*array_of_histograms)
  array_of_histograms.each { |x| raise "Input expected to be a histogram but is #{x.inspect}" unless x.is_a?(MiniHistogram) }
  steps = array_of_histograms.map(&:bin_size)
  avg_step_size = steps.inject(&:+).to_f / steps.length

  max_value = array_of_histograms.map(&:max).max

  max_edge = array_of_histograms.map(&:edges_max).max
  min_edge = array_of_histograms.map(&:edges_min).min

  average_edges = [min_edge]
  while average_edges.last < max_edge
    average_edges << average_edges.last + avg_step_size
  end

  array_of_histograms.each {|h| h.update_values(edges: average_edges, max: max_value) }

  return array_of_histograms
end

Instance Method Details

#bin_size ⇒ Object 



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# File 'lib/mini_histogram.rb', line 57

def bin_size
  return 0 if edges.length <= 1

  edges[1] - edges[0]
end

#closed ⇒ Object 



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# File 'lib/mini_histogram.rb', line 45

def closed
  @left_p ? :left : :right
end

#edges ⇒ Object Also known as: edge

Finds the “edges” of a given histogram that will mark the boundries for the histogram’s “bins”

Example:

a = [1,1,1, 5, 5, 5, 5, 10, 10, 10]
MiniHistogram.new(a).edges
# => [0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0]

There are multiple ways to find edges, this was taken from
https://github.com/mrkn/enumerable-statistics/issues/24

Another good set of implementations is in numpy
https://github.com/numpy/numpy/blob/d9b1e32cb8ef90d6b4a47853241db2a28146a57d/numpy/lib/histograms.py#L222



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# File 'lib/mini_histogram.rb', line 122

def edges
  return @edges if @edges

  return @edges = [0.0] if array.empty?

  lo = @min
  hi = @max

  nbins = sturges.to_f

  if hi == lo
    start = lo
    step = 1.0
    divisor = 1.0
    len = 1
  else
    bw = (hi - lo) / nbins
    lbw = Math.log10(bw)
    if lbw >= 0
      step = 10 ** lbw.floor * 1.0
      r = bw/step

      if r <= 1.1
        # do nothing
      elsif r <= 2.2
        step *= 2.0
      elsif r <= 5.5
        step *= 5.0
      else
        step *= 10
      end
      divisor = 1.0
      start = step * (lo/step).floor
      len = ((hi - start)/step).ceil
    else
      divisor = 10 ** - lbw.floor
      r = bw * divisor
      if r <= 1.1
        # do nothing
      elsif r <= 2.2
        divisor /= 2.0
      elsif r <= 5.5
        divisor /= 5.0
      else
        divisor /= 10.0
      end
      step = 1.0
      start = (lo * divisor).floor
      len = (hi * divisor - start).ceil
    end
  end

  if left_p
    while (lo < start/divisor)
      start -= step
    end

    while (start + (len - 1)*step)/divisor <= hi
      len += 1
    end
  else
    while lo <= start/divisor
      start -= step
    end
    while (start + (len - 1)*step)/divisor < hi
      len += 1
    end
  end

  @edges = []
  len.times.each do
    @edges << start/divisor
    start += step
  end

  return @edges
end

#edges_max ⇒ Object 



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# File 'lib/mini_histogram.rb', line 37

def edges_max
  edges.max
end

#edges_min ⇒ Object 



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# File 'lib/mini_histogram.rb', line 33

def edges_min
  edges.min
end

#histogram(*_) ⇒ Object 



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# File 'lib/mini_histogram.rb', line 41

def histogram(*_)
  self
end

#plot(nbins: nil, closed: :left, symbol: "▇", **kw) ⇒ Object 



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# File 'lib/mini_histogram.rb', line 201

def plot
  raise "You must `require 'mini_histogram/plot'` to get this feature"
end

#sturges ⇒ Object

Weird name, right? There are multiple ways to calculate the number of “bins” a histogram should have, one of the most common is the “sturges” method

Here are some alternatives from numpy: github.com/numpy/numpy/blob/d9b1e32cb8ef90d6b4a47853241db2a28146a57d/numpy/lib/histograms.py#L489-L521



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# File 'lib/mini_histogram.rb', line 69

def sturges
  len = array.length
  return 1.0 if len == 0

  # return (long)(ceil(Math.log2(n)) + 1);
  return Math.log2(len).ceil + 1
end

#update_values(edges:, max:) ⇒ Object

Sets the edge value to something new, also clears any previously calculated values



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# File 'lib/mini_histogram.rb', line 51

def update_values(edges:, max: )
  @edges = edges
  @max = max
  @weights = nil # clear memoized value
end

#weights ⇒ Object

Given an array of edges and an array we want to generate a histogram from return the counts for each “bin”

Example:

a = [1,1,1, 5, 5, 5, 5, 10, 10, 10]
edges = [0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0]

MiniHistogram.new(a).weights
# => [3, 0, 4, 0, 0, 3]

This means that the `a` array has 3 values between 0.0 and 2.0
4 values between 4.0 and 6.0 and three values between 10.0 and 12.0



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# File 'lib/mini_histogram.rb', line 90

def weights
  return @weights if @weights
  return @weights = [] if array.empty?

  lo = edges.first
  step = edges[1] - edges[0]

  max_index = ((@max  - lo) / step).floor
  @weights = Array.new(max_index + 1, 0)

  array.each do |x|
    index = ((x - lo) / step).floor
    @weights[index] += 1
  end

  return @weights
end