What's a histogram and why should you care? First read Lies, Damned Lies, and Averages: Perc50, Perc95 explained for Programmers. This library lets you build histograms in pure Ruby.
Add this line to your application's Gemfile:
gem 'mini_histogram'And then execute:
$ bundle install
Or install it yourself as:
$ gem install mini_histogram
Given an array, this class calculates the "edges" of a histogram these edges mark the boundries for "bins"
array = [1,1,1, 5, 5, 5, 5, 10, 10, 10]
histogram = MiniHistogram.new(array)
puts histogram.edges
# => [0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0]It also finds the weights (aka count of values) that would go in each bin:
puts histogram.weights
# => [3, 0, 4, 0, 0, 3]
This means that the array here had three items between 0.0 and 2.0, four items between 4.0 and 6.0 and three items between 10.0 and 12.0
You can plot!
require 'mini_histogram/plot'
array = 50.times.map { rand(11.2..11.6) }
histogram = MiniHistogram.new(array)
puts histogram.plotWill generate:
┌ ┐
[11.2 , 11.25) ┤▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 9
[11.25, 11.3 ) ┤▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 6
[11.3 , 11.35) ┤▇▇▇▇▇▇▇▇▇▇▇▇▇ 4
[11.35, 11.4 ) ┤▇▇▇▇▇▇▇▇▇▇▇▇▇ 4
[11.4 , 11.45) ┤▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 11
[11.45, 11.5 ) ┤▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 5
[11.5 , 11.55) ┤▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 7
[11.55, 11.6 ) ┤▇▇▇▇▇▇▇▇▇▇▇▇▇ 4
└ ┘
Frequency
Integrated plotting is an experimental currently, use with some caution. If you are on Ruby 2.4+ you can pass an instance of MiniHistogram to unicode_plot.rb:
array = 50.times.map { rand(11.2..11.6) }
histogram = MiniHistogram.new(array)
puts UnicodePlot.histogram(histogram)If you're plotting multiple histograms (first, please normalize the bucket sizes), second. It can be hard to compare them vertically. Here's an example:
┌ ┐
[11.2 , 11.28) ┤▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 12
[11.28, 11.36) ┤▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 22
[11.35, 11.43) ┤▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 30
[11.43, 11.51) ┤▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 17
[11.5 , 11.58) ┤▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 13
[11.58, 11.66) ┤▇▇▇▇▇▇▇ 6
[11.65, 11.73) ┤ 0
[11.73, 11.81) ┤ 0
[11.8 , 11.88) ┤ 0
└ ┘
Frequency
┌ ┐
[11.2 , 11.28) ┤▇▇▇▇ 3
[11.28, 11.36) ┤▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 19
[11.35, 11.43) ┤▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 17
[11.43, 11.51) ┤▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 25
[11.5 , 11.58) ┤▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 15
[11.58, 11.66) ┤▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 13
[11.65, 11.73) ┤▇▇▇▇ 3
[11.73, 11.81) ┤▇▇▇▇ 3
[11.8 , 11.88) ┤▇▇▇ 2
└ ┘
Frequency
Here's the same data set plotted side-by-side:
┌ ┐ ┌ ┐
[11.2 , 11.28) ┤▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 12 [11.2 , 11.28) ┤▇▇▇▇ 3
[11.28, 11.36) ┤▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 22 [11.28, 11.36) ┤▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 19
[11.35, 11.43) ┤▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 30 [11.35, 11.43) ┤▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 17
[11.43, 11.51) ┤▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 17 [11.43, 11.51) ┤▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 25
[11.5 , 11.58) ┤▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 13 [11.5 , 11.58) ┤▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 15
[11.58, 11.66) ┤▇▇▇▇▇▇▇ 6 [11.58, 11.66) ┤▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 13
[11.65, 11.73) ┤ 0 [11.65, 11.73) ┤▇▇▇▇ 3
[11.73, 11.81) ┤ 0 [11.73, 11.81) ┤▇▇▇▇ 3
[11.8 , 11.88) ┤ 0 [11.8 , 11.88) ┤▇▇▇ 2
└ ┘ └ ┘
Frequency Frequency
This method might require more scrolling in the github issue, but makes it easier to compare two distributions. Here's how you plot dualing histograms:
require 'mini_histogram/plot'
a = MiniHistogram.new [11.205184, 11.223665, 11.228286, 11.23219, 11.233325, 11.234516, 11.245781, 11.248441, 11.250758, 11.255686, 11.265876, 11.26641, 11.279456, 11.281067, 11.284281, 11.287656, 11.289316, 11.289682, 11.292289, 11.294518, 11.296454, 11.299277, 11.305801, 11.306602, 11.309311, 11.318465, 11.318477, 11.322258, 11.328267, 11.334188, 11.339722, 11.340585, 11.346084, 11.346197, 11.351863, 11.35982, 11.362358, 11.364476, 11.365743, 11.368492, 11.368566, 11.36869, 11.37268, 11.374204, 11.374217, 11.374955, 11.376422, 11.377989, 11.383357, 11.383593, 11.385184, 11.394766, 11.395829, 11.398455, 11.399739, 11.401304, 11.411387, 11.411978, 11.413585, 11.413659, 11.418504, 11.419194, 11.419415, 11.421374, 11.4261, 11.427901, 11.429651, 11.434272, 11.435012, 11.440848, 11.447495, 11.456107, 11.457434, 11.467112, 11.471005, 11.473235, 11.485025, 11.485852, 11.488256, 11.488275, 11.499545, 11.509588, 11.51378, 11.51544, 11.520783, 11.52246, 11.522855, 11.5322, 11.533764, 11.544047, 11.552597, 11.558062, 11.567239, 11.569749, 11.575796, 11.588014, 11.614032, 11.615062, 11.618194, 11.635267]
b = MiniHistogram.new [11.233813, 11.240717, 11.254617, 11.282013, 11.290658, 11.303213, 11.305237, 11.305299, 11.306397, 11.313867, 11.31397, 11.314444, 11.318032, 11.328111, 11.330127, 11.333235, 11.33678, 11.337799, 11.343758, 11.347798, 11.347915, 11.349594, 11.358198, 11.358507, 11.3628, 11.366111, 11.374993, 11.378195, 11.38166, 11.384867, 11.385235, 11.395825, 11.404434, 11.406065, 11.406677, 11.410244, 11.414527, 11.421267, 11.424535, 11.427231, 11.427869, 11.428548, 11.432594, 11.433524, 11.434903, 11.437769, 11.439761, 11.443437, 11.443846, 11.451106, 11.458503, 11.462256, 11.462324, 11.464342, 11.464716, 11.46477, 11.465271, 11.466843, 11.468789, 11.475492, 11.488113, 11.489616, 11.493736, 11.496842, 11.502074, 11.511367, 11.512634, 11.515562, 11.525771, 11.531415, 11.535379, 11.53966, 11.540969, 11.541265, 11.541978, 11.545301, 11.545533, 11.545701, 11.572584, 11.578881, 11.580701, 11.580922, 11.588731, 11.594082, 11.595915, 11.613622, 11.619884, 11.632889, 11.64377, 11.645225, 11.647167, 11.648257, 11.667158, 11.670378, 11.681261, 11.734586, 11.747066, 11.792425, 11.808377, 11.812346]
dual_histogram = MiniHistogram.dual_plot do |x, y|
x.histogram = a
x.options = {}
y.histogram = b
y.options = {}
end
puts dual_histogramAlternatives to this gem include https://github.com/mrkn/enumerable-statistics/. I needed this gem to be able to calculate a "shared" or "average" edge value as seen in this PR mrkn/enumerable-statistics#23. So that I could add histograms to derailed benchmarks: zombocom/derailed_benchmarks#169. This gem provides a MiniHistogram.set_average_edges! method to help there. Also this gem does not require a native extension compilation (faster to install, but performance is slower), and this gem does not extend or monkeypatch an core classes.
After checking out the repo, run bin/setup to install dependencies. Then, run rake test to run the tests. You can also run bin/console for an interactive prompt that will allow you to experiment.
To install this gem onto your local machine, run bundle exec rake install. To release a new version, update the version number in version.rb, and then run bundle exec rake release, which will create a git tag for the version, push git commits and tags, and push the .gem file to rubygems.org.
Bug reports and pull requests are welcome on GitHub at https://github.com/zombocom/mini_histogram. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the code of conduct.
The gem is available as open source under the terms of the MIT License.
Everyone interacting in the MiniHistogram project's codebases, issue trackers, chat rooms and mailing lists is expected to follow the code of conduct.