version bump and clean up readme

LICENSE

@@ -1,11 +1,10 @@
 Copyright (c) 2006, 2007, 2008 The University of Texas at Austin
 Copyright (c) 2009, University of Colorado at Boulder and Howard Hughes
-Medical Institute
+Copyright (c) 2013, Brigham Young University
 
 The above copyright holders are collectively designated "COPYRIGHT HOLDER"
 
-Software by John T. Prince under the direction of Edward M. Marcotte and
-Natalie Ahn.
+Software by John T. Prince
 
 By using this software the USER indicates that he or she has read, understood and will comply with the following:
 

README.rdoc → README.md

@@ -1,14 +1,12 @@
-= histogram
+# histogram
 
-Generates histograms similar to R's hist and numpy's histogram functions.  The
-interface is relatively stable and a decent test/spec suite is in place, but
-please consider this alpha software. Inspired by {Richard Cottons's matlab
-implementation}[http://www.mathworks.com/matlabcentral/fileexchange/21033-calculate-number-of-bins-for-histogram]
-and the wikipedia {histogram article}[http://en.wikipedia.org/wiki/Histogram].
+Generates histograms similar to R's hist and numpy's histogram functions.
+Inspired somewhat by [Richard Cottons's matlab implementation](http://www.mathworks.com/matlabcentral/fileexchange/21033-calculate-number-of-bins-for-histogram)
+and the wikipedia [histogram article](http://en.wikipedia.org/wiki/Histogram).
 
-== Examples
+## Examples
 
-=== Typical usage:
+### Typical usage:
 
     require 'histogram/array'  # enables Array#histogram
 
@@ -18,43 +16,45 @@ and the wikipedia {histogram article}[http://en.wikipedia.org/wiki/Histogram].
     (bins, freqs) = data.histogram 
     # equivalent to:  data.histogram(:fd, :bin_boundary => :avg)  
 
-=== Multiple types of binning behavior:
+### Multiple types of binning behavior:
 
     # :fd, :sturges, :scott, or :middle  (median value between the three methods)
     data.histogram(:middle)
     (bins, freqs) = data.histogram(20)                         # use 20 bins
     (bins, freqs) = data.histogram([-3,-1,4,5,6])              # custom bins
 
+    (bins, freqs) = data.histogram(10, :min => 2, :max => 12)  # 10 bins with set min and max
+
     # bins are midpoints, but can be set as minima
     (bins, freqs) = data.histogram([-3,-1,4,5,6], :bin_boundary => :min) # custom bins with :min
 
     # can also set the bin_width (which interpolates between the min and max of the set)
     (bins, freqs) = data.histogram(:bin_width => 0.5)
 
-=== Multiple Datasets:
+### Multiple Datasets:
       
 Sometimes, we want to create histograms where the bins are calculated based on
 all the data sets.  That way, the resulting frequencies will all line up:
 
     # returns [bins, freq1, freq2 ...]
     (bins, *freqs) = set1.histogram(30, :other_sets => [[3,3,4,4,5], [-1,0,0,3,3,6]])
 
-=== Histograms with weights/fractions:
+### Histograms with weights/fractions:
 
     # histogramming with weights
     data.histogram(20, :weights => [3,3,8,8,9,9,3,3,3,3])
 
-=== Works great with {NArray}[http://narray.rubyforge.org/], too:
+### Works with NArray objects
 
-    require 'histogram/narray'  # enables NArray#histogram
+    require 'histogram/narray'    # enables NArray#histogram
     # if the calling object is an NArray, the output is two NArrays:
-    NArray.float(20).random!(3).histogram(20)
-    # => [bins, freqs]  # are both NArray.float objects
+    (bins, freqs) = NArray.float(20).random!(3).histogram(20)
+    # bins and freqs are both NArray.float objects
 
-== Installation
+## Installation
 
     gem install histogram
 
-== See Also
+## See Also
 
-aggregate[http://github.com/josephruscio/aggregate], rsruby[http://github.com/alexgutteridge/rsruby]
+aggregate(http://github.com/josephruscio/aggregate), rsruby(http://github.com/alexgutteridge/rsruby)

lib/histogram.rb

@@ -4,54 +4,56 @@ class NArray
 
 module Histogram
 
-  # returns (min, max)
-  def self.minmax(obj)
-    if obj.is_a?(Array)
-      obj.minmax
-    else
-      mn = obj[0]
-      mx = obj[0]
-      obj.each do |val|
-        if val < mn then mn = val end
-        if val > mx then mx = val end
+  class << self
+    # returns (min, max)
+    def minmax(obj)
+      if obj.is_a?(Array)
+        obj.minmax
+      else
+        mn = obj[0]
+        mx = obj[0]
+        obj.each do |val|
+          if val < mn then mn = val end
+          if val > mx then mx = val end
+        end
+        [mn, mx]
       end
-      [mn, mx]
     end
-  end
 
-  # returns (mean, standard_dev)
-  # if size == 0 returns [nil, nil]
-  def self.sample_stats(obj)
-    _len = obj.size
-    return [nil, nil] if _len == 0
-    _sum = 0.0 ; _sum_sq = 0.0
-    obj.each do |val|
-      _sum += val
-      _sum_sq += val * val
+    # returns (mean, standard_dev)
+    # if size == 0 returns [nil, nil]
+    def sample_stats(obj)
+      _len = obj.size
+      return [nil, nil] if _len == 0
+      _sum = 0.0 ; _sum_sq = 0.0
+      obj.each do |val|
+        _sum += val
+        _sum_sq += val * val
+      end
+      std_dev = _sum_sq - ((_sum * _sum)/_len)
+      std_dev /= ( _len > 1 ? _len-1 : 1 )
+      [_sum.to_f/_len, Math.sqrt(std_dev)]
     end
-    std_dev = _sum_sq - ((_sum * _sum)/_len)
-    std_dev /= ( _len > 1 ? _len-1 : 1 )
-    [_sum.to_f/_len, Math.sqrt(std_dev)]
-  end
 
-  # still need to spec this method!
-  def self.iqrange(obj)
-    srted = obj.sort
-    sz = srted.size
-    if sz % 2 == 0
-      median_index_hi = sz / 2
-      median_index_lo = (sz / 2) - 1
-      # need to check this line for accuracy:
-      dist = median_index_hi / 2 
-      fq = srted[median_index_hi + dist]
-      tq = srted[median_index_lo - dist]
-    else
-      median_index = sz / 2
-      dist = (median_index + 1) / 2
-      fq = srted[median_index - dist]
-      tq = srted[median_index + dist]
+    # still need to spec this method!
+    def iqrange(obj)
+      srted = obj.sort
+      sz = srted.size
+      if sz % 2 == 0
+        median_index_hi = sz / 2
+        median_index_lo = (sz / 2) - 1
+        # need to check this line for accuracy:
+        dist = median_index_hi / 2 
+        fq = srted[median_index_hi + dist]
+        tq = srted[median_index_lo - dist]
+      else
+        median_index = sz / 2
+        dist = (median_index + 1) / 2
+        fq = srted[median_index - dist]
+        tq = srted[median_index + dist]
+      end
+      (tq - fq).to_f
     end
-    (tq - fq).to_f
   end
 
   # returns(integer) takes :scott|:sturges|:fd|:middle
@@ -62,9 +64,9 @@ def self.iqrange(obj)
   # implementation}[http://www.mathworks.com/matlabcentral/fileexchange/21033-calculate-number-of-bins-for-histogram]
   # and the {histogram page on
   # wikipedia}[http://en.wikipedia.org/wiki/Histogram]
-  def number_bins(methd=:fd)
+  def number_of_bins(methd=:fd)
     if methd == :middle
-      [:scott, :sturges, :fd].map {|v| number_bins(v) }.sort[1]
+      [:scott, :sturges, :fd].map {|v| number_of_bins(v) }.sort[1]
     else
       range = (self.max - self.min).to_f
       nbins = 
@@ -180,7 +182,7 @@ def histogram(*args)
     all = [self] + other_sets 
 
     if bins.is_a?(Symbol)
-      bins = number_bins(bins)
+      bins = number_of_bins(bins)
     end
 
     weights = 
@@ -196,16 +198,15 @@ def histogram(*args)
       calc_min, calc_max = 
         unless opts[:min] && opts[:max]
           (mins, maxs) = all.map {|ar| Histogram.minmax(ar) }.transpose
-        end
           [mins.min, maxs.max]
         end
-      _min = opts[:min] || calc_min
-      _max = opts[:max] || calc_max
+    end
+    _min = opts[:min] || calc_min
+    _max = opts[:max] || calc_max
 
-      if opts[:bin_width]
-        bins = []
-        _min.step(_max, opts[:bin_width]) {|v| bins << v }
-      end
+    if opts[:bin_width]
+      bins = []
+      _min.step(_max, opts[:bin_width]) {|v| bins << v }
     end
 
     _bins = nil

lib/histogram/array.rb

@@ -1,4 +1,3 @@
-
 require 'histogram'
 
 class Array

lib/histogram/narray.rb

@@ -1,4 +1,3 @@
-
 require 'histogram'
 
 class NArray

lib/histogram/version.rb

@@ -1,3 +1,3 @@
 module Histogram
-  VERSION = "0.0.10"
+  VERSION = "0.1.0"
 end

spec/histogram_spec.rb

@@ -129,7 +129,7 @@
       [:sturges, :scott, :fd, :middle].zip(answers) do |mth, answ|
         ar = [0,1,2,2,2,2,2,3,3,3,3,3,3,3,3,3,5,5,9,9,10,20,15,15,15,16,17].extend(Histogram)
         # these are merely frozen, not checked to see if correct
-        ar.number_bins(mth).should == answ
+        ar.number_of_bins(mth).should == answ
       end
     end
   end