Class: CurveFit
- Inherits:
-
Object
- Object
- CurveFit
- Defined in:
- lib/curve_fit.rb
Overview
A wrapper around cfityk (fityk.nieto.pl/) to handle fitting a curve to X+Y data, creating confidence intervals, and projecting up to a ceiling.
Also supports basic manipulation of X+Y data files.
Instance Attribute Summary collapse
-
#debug ⇒ Object
Returns the value of attribute debug.
Instance Method Summary collapse
-
#append_xy_file(filename, x, y) ⇒ True
Adds an entry to an x+y style data file.
-
#fit(data, ceiling = nil, guess_list = ["Linear", "Quadratic"], &x_transform) ⇒ Hash
Given an aray of X,Y data points, guesses the most correct curve (as measured by R-Squared) and generates a trend line, top and bottom confidence intervals, and optionally projects the trend to an artifical ceiling.
-
#initialize(debug = false) ⇒ CurveFit
constructor
A new instance of CurveFit.
-
#load_xy_file(filename) ⇒ Array
Loads an x+y style data file as an array of arrays, suitable for passing to the fit method.
-
#string_to_number(string) ⇒ Integer, ...
Takes a string of digits and converts it to an integer or a float, depending on whether it rocks the dot.
-
#write_xy_file(data, filename = nil) ⇒ IO
Writes a data set out to an X+Y file.
Constructor Details
#initialize(debug = false) ⇒ CurveFit
Returns a new instance of CurveFit.
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# File 'lib/curve_fit.rb', line 25 def initialize(debug=false) @debug = debug end |
Instance Attribute Details
#debug ⇒ Object
Returns the value of attribute debug.
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# File 'lib/curve_fit.rb', line 23 def debug @debug end |
Instance Method Details
#append_xy_file(filename, x, y) ⇒ True
Adds an entry to an x+y style data file.
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# File 'lib/curve_fit.rb', line 73 def append_xy_file(filename, x, y) File.open(filename, 'a') do |xy_file| xy_file.puts "#{x} #{y}" end true end |
#fit(data, ceiling = nil, guess_list = ["Linear", "Quadratic"], &x_transform) ⇒ Hash
Given an aray of X,Y data points, guesses the most correct curve (as measured by R-Squared) and generates a trend line, top and bottom confidence intervals, and optionally projects the trend to an artifical ceiling.
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# File 'lib/curve_fit.rb', line 138 def fit(data, ceiling=nil, guess_list=["Linear", "Quadratic"], &x_transform) data_file = Tempfile.new("curvefit") x_pos = 0 data.each do |point| data_file.puts("#{x_pos} #{point[1]}") x_pos += 1 end data_file.close guess_data = Hash.new guess_list.each do |shape| guess_data[shape] = Hash.new puts "Guessing #{shape} fit..." if @debug IO.popen("cfityk -I -q -c '@0 < '#{data_file.path}'; guess #{shape}; fit; info+ formula in @0; info fit in @0; info errors in @0;'") do |fityk_output| fityk_output.each_line do |line| puts "#{shape}: #{line}" if @debug case line when /R-squared = (.+)/ guess_data[shape][:r_squared] = $1.to_f when /(.+) \+ (.+) \* \(x\)/ # 692.1 + 30.633 * (x), linear fit formula first = $1.to_f second = $2.to_f guess_data[shape][:curve_formula] = lambda { |x| first + second * x.to_f } when /(.+) \+ (.+)\*\(x\) \+ (.+)\*\(x\)\^2/ # 1019.43 + 9.543*(x) + 0.202086*(x)^2, quadratic/polynomial fit formula first = $1.to_f second = $2.to_f third = $3.to_f guess_data[shape][:curve_forumla_args] = { 1 => first, 2 => second, 3 => third } guess_data[shape][:curve_formula] = lambda { |x| first + second * x.to_f + third * x.to_f**2 } when /\$_(\d) = (\d+\.\d+) \+\- (\d+\.\d+)/ # $_1 = 692.1 +- 32.0558 guess_data[shape][:curve_error_args] ||= Hash.new guess_data[shape][:curve_error_args][$1.to_i] = [ $2.to_f, $3.to_f ] end end end if $?.exitstatus != 0 raise "cfityk returned status #{$?.exitstatus} when guessing #{shape}, bailing" end if guess_data[shape][:r_squared] == 1 guess_data[shape][:top_confidence_formula] = guess_data[shape][:curve_formula] guess_data[shape][:bottom_confidence_formula] = guess_data[shape][:curve_formula] else case shape when "Quadratic" curve_error_args = guess_data[shape][:curve_error_args] guess_data[shape][:top_confidence_formula] = lambda { |x| (curve_error_args[1][0] + curve_error_args[1][1]) + (curve_error_args[2][0] + curve_error_args[2][1]) * x.to_f + (curve_error_args[3][0] + + curve_error_args[3][1]) * x.to_f**2 } guess_data[shape][:bottom_confidence_formula] = lambda { |x| (curve_error_args[1][0] - curve_error_args[1][1]) + (curve_error_args[2][0] - curve_error_args[2][1]) * x.to_f + (curve_error_args[3][0] + - curve_error_args[3][1]) * x.to_f**2 } when "Linear" curve_error_args = guess_data[shape][:curve_error_args] guess_data[shape][:top_confidence_formula] = lambda { |x| (curve_error_args[1][0] + curve_error_args[1][1]) + (curve_error_args[2][0] + curve_error_args[2][1]) * x.to_f } guess_data[shape][:bottom_confidence_formula] = lambda { |x| (curve_error_args[1][0] - curve_error_args[1][1]) + (curve_error_args[2][0] - curve_error_args[2][1]) * x.to_f } end end end best_fit_name = nil best_fit = nil guess_data.each do |shape, shape_guess| best_fit_name ||= shape best_fit ||= shape_guess if shape_guess[:r_squared] > best_fit[:r_squared] best_fit = shape_guess best_fit_name = shape end end trend_line = [] top_confidence_line = [] bottom_confidence_line = [] ceiling_line = [] x = 0 y = 0 no_ceiling = ceiling.nil? while(no_ceiling ? x < data.length : ceiling >= y) y = best_fit[:curve_formula].call(x) y_top_confidence = best_fit[:top_confidence_formula].call(x) y_bottom_confidence = best_fit[:bottom_confidence_formula].call(x) if x_transform trend_line << [ x_transform.call(x), y ] top_confidence_line << [ x_transform.call(x), y_top_confidence ] bottom_confidence_line << [ x_transform.call(x), y_bottom_confidence ] ceiling_line << [ x_transform.call(x), ceiling ] unless no_ceiling else trend_line << [ x, y ] top_confidence_line << [ x, y_top_confidence ] bottom_confidence_line << [ x, y_bottom_confidence ] ceiling_line << [ x, ceiling ] unless no_ceiling end x += 1 end { :data => data, :trend => trend_line, :top_confidence => top_confidence_line, :bottom_confidence => bottom_confidence_line, :ceiling => ceiling_line, :r_squared => best_fit[:r_squared], :guess => best_fit_name } end |
#load_xy_file(filename) ⇒ Array
Loads an x+y style data file as an array of arrays, suitable for passing to the fit method.
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# File 'lib/curve_fit.rb', line 36 def load_xy_file(filename) xy_data = Array.new File.open(filename, "r") do |xy_file| xy_file.each_line do |line| x, y = line.split(' ') xy_data << [ string_to_number(x), string_to_number(y) ] end end xy_data end |
#string_to_number(string) ⇒ Integer, ...
Takes a string of digits and converts it to an integer or a float, depending on whether it rocks the dot. Returns the raw string if nothing matches.
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# File 'lib/curve_fit.rb', line 53 def string_to_number(string) case string when /^\d+$/ string.to_i when /^\d+.\d$/ string.to_f else string end end |
#write_xy_file(data, filename = nil) ⇒ IO
Writes a data set out to an X+Y file
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# File 'lib/curve_fit.rb', line 88 def write_xy_file(data, filename=nil) data_file = nil if filename data_file = File.open(filename, "w") else data_file = Tempfile.new("curvefit") filename = data_file.path end data.each do |point| data_file.puts("#{point[0]} #{point[1]}") end data_file.close data_file end |