# = statsample.rb -
# Statsample - Statistic package for Ruby
# Copyright (C) 2008-2014  Claudio Bustos
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
#

require 'matrix'
require 'extendmatrix'
require 'distribution'
require 'dirty-memoize'
require 'reportbuilder'
require 'daru'
require 'statsample/daru'

class Numeric
  def square
    self * self
  end
end

class String
  def is_number?
    if self =~ /^-?\d+[,.]?\d*(e-?\d+)?$/
      true
    else
      false
    end
  end
end

class Module
  def include_aliasing(m, suffix = 'ruby')
    m.instance_methods.each do |f|
      if instance_methods.include? f
        alias_method("#{f}_#{suffix}", f)
        remove_method f
      end
    end
    include m
  end
end

class Array
  def sum
    inject(:+)
  end

  def mean
    sum.fdiv(size)
  end

  # Calcualte sum of squares
  def sum_of_squares(m=nil)
    m ||= mean
    inject(0) {|a,x| a + (x-m).square }
  end

  # Calculate sample variance
  def variance_sample(m=nil)
    m ||= mean
    sum_of_squares(m).quo(size - 1)
  end

  # Calculate sample standard deviation
  def sd
    m ||= mean
    Math::sqrt(variance_sample(m))
  end
end

def create_test(*args, &_proc)
  description = args.shift
  fields = args
  [description, fields, Proc.new]
end

#--
# Test extensions
begin
  require 'gettext'
rescue LoadError
  def bindtextdomain(d) #:nodoc:
    d
  end

  # Bored module
  module GetText  #:nodoc:
    def _(t)
      t
    end
  end
end

# Library for statistical analysis on Ruby
#
# * Classes for manipulation and storage of data:
# * Module Statsample::Bivariate provides covariance and pearson, spearman, point biserial, tau a, tau b, gamma, tetrachoric (see Bivariate::Tetrachoric) and polychoric (see Bivariate::Polychoric) correlations. Include methods to create correlation and covariance matrices
# * Multiple types of regression on Statsample::Regression
# * Factorial Analysis algorithms on Statsample::Factor module.
# * Dominance Analysis. Based on Budescu and Azen papers.link[http://psycnet.apa.org/journals/met/8/2/129/].
# * Module Statsample::Codification, to help to codify open questions
# * Converters to import and export data from databases, csv and excel files.
# * Module Statsample::Crosstab provides function to create crosstab for categorical data
# * Reliability analysis provides functions to analyze scales.
# * Module Statsample::SRS (Simple Random Sampling) provides a lot of functions to estimate standard error for several type of samples
# * Interfaces to gdchart, gnuplot and SVG::Graph
#
module Statsample
  def self.create_has_library(library)
    define_singleton_method("has_#{library}?") do
      cv = "@@#{library}"
      unless class_variable_defined? cv
        begin
          gem library.to_s # activate gem
          require library.to_s
          class_variable_set(cv, true)
        rescue LoadError
          class_variable_set(cv, false)
        end
      end
      class_variable_get(cv)
    end
  end
  
  create_has_library :gsl

  SPLIT_TOKEN = ','
  autoload(:Analysis, 'statsample/analysis')
  autoload(:Database, 'statsample/converters')
  autoload(:Anova, 'statsample/anova')
  autoload(:CSV, 'statsample/converters')
  autoload(:PlainText, 'statsample/converters')
  autoload(:Excel, 'statsample/converters')
  autoload(:GGobi, 'statsample/converters')
  autoload(:SPSS, 'statsample/converter/spss')
  autoload(:Histogram, 'statsample/histogram')
  autoload(:DominanceAnalysis, 'statsample/dominanceanalysis')
  autoload(:HtmlReport, 'statsample/htmlreport')
  autoload(:Mx, 'statsample/converters')
  autoload(:Resample, 'statsample/resample')
  autoload(:SRS, 'statsample/srs')
  autoload(:Codification, 'statsample/codification')
  autoload(:Reliability, 'statsample/reliability')
  autoload(:Bivariate, 'statsample/bivariate')
  autoload(:Multivariate, 'statsample/multivariate')
  autoload(:Multiset, 'statsample/multiset')
  autoload(:StratifiedSample, 'statsample/multiset')
  autoload(:MLE, 'statsample/mle')
  autoload(:Regression, 'statsample/regression')
  autoload(:Test, 'statsample/test')
  autoload(:Factor, 'statsample/factor')
  autoload(:Graph, 'statsample/graph')

  class << self
    # Load a object saved on a file.
    def load(filename)
      if File.exist? filename
        o = false
        File.open(filename, 'r') { |fp| o = Marshal.load(fp) }
        o
      else
        false
      end
    end

    # Create a matrix using vectors as columns.
    # Use:
    #
    #   matrix=Statsample.vector_cols_matrix(v1,v2)
    def vector_cols_matrix(*vs)
      # test
      size = vs[0].size

      vs.each do |v|
        fail ArgumentError, 'Arguments should be Vector' unless v.instance_of? Daru::Vector
        fail ArgumentError, 'Vectors size should be the same' if v.size != size
      end

      Matrix.rows((0...size).to_a.collect { |i| vs.collect { |v| v[i] } })
    end

    # Returns a duplicate of the input vectors, without missing data
    # for any of the vectors.
    #
    #  a = Daru::Vector.new([1,2,3,6,7,nil,3,5])
    #  b = Daru::Vector.new([nil,nil,5,6,4,5,10,2])
    #  c = Daru::Vector.new([2,4,6,7,4,5,6,7])
    #  a2,b2,c2=Statsample.only_valid(a,b,c)
    #  => [#<Daru::Vector:0xb748c8c8 @data=[3, 6, 7, 3, 5]>,
    #        #<Daru::Vector:0xb748c814 @data=[5, 6, 4, 10, 2]>,
    #        #<Daru::Vector:0xb748c760 @data=[6, 7, 4, 6, 7]>]
    #
    def only_valid(*vs)
      i = 1
      h = vs.inject({}) { |acc, v| acc["v#{i}".to_sym] = v; i += 1; acc }
      df = Daru::DataFrame.new(h).dup_only_valid
      df.map { |v| v }
    end

    # Cheap version of #only_valid.
    # If any vectors have missing_values, return only valid.
    # If not, return the vectors itself
    def only_valid_clone(*vs)
      if vs.any?(&:has_missing_data?)
        only_valid(*vs)
      else
        vs
      end
    end
  end

  module Util
    # Reference: http://www.itl.nist.gov/div898/handbook/eda/section3/normprpl.htm
    def normal_order_statistic_medians(i, n)
      if i == 1
        u = 1.0 - normal_order_statistic_medians(n, n)
      elsif i == n
        u = 0.5**(1 / n.to_f)
      else
        u = (i - 0.3175) / (n + 0.365)
      end
      u
    end

    def self.nice(s, e) # :nodoc:
      reverse = e < s
      min = reverse ? e : s
      max = reverse ? s : e
      span = max - min
      return [s, e] if span == 0 || (span.respond_to?(:infinite?) && span.infinite?)

      step = 10**((Math.log(span).quo(Math.log(10))).round - 1).to_f
      out = [(min.quo(step)).floor * step, (max.quo(step)).ceil * step]
      out.reverse! if reverse
      out
    end
  end

  module Writable
    def save(filename)
      fp = File.open(filename, 'w')
      Marshal.dump(self, fp)
      fp.close
    end
  end
  # Provides method summary to generate summaries and include GetText
  module Summarizable
    include GetText
    bindtextdomain('statsample')
    def summary(method = :to_text)
      ReportBuilder.new(no_title: true).add(self).send(method)
    end
  end
  module STATSAMPLE__ #:nodoc:
  end
end

#--
begin
  require 'statsamplert'
rescue LoadError
  module Statsample
    OPTIMIZED = false
  end
end

require 'statsample/vector'
require 'statsample/dataset'
require 'statsample/crosstab'
require 'statsample/matrix'
require 'statsample/shorthand'
require 'statsample/version'