class CurveFit

# File lib/ms/curvefit.rb, line 9
def self.get_parameters(opts)
  data = Mzml_reader.get_data(opts[:mzml])
  generations = opts[:generations]

  @pts_int_var = []
  @pts_mz_var = []
  @pts_elut = []

  file = File.open(opts[:mzml],"r")

  mzs_in = data[0]
  rts_in = data[1]
  ints_in = data[2]

  @@avg_mz = mzs_in.inject(:+)/mzs_in.size.to_f
  @@avg_rt = rts_in.inject(:+)/rts_in.size.to_f

  ints_in = GenCurvefit.normalize(ints_in)
  #-----------------------overlapRange--------------------------------------------
  mean = mzs_in.inject(:+)/mzs_in.size
  opts[:overlapRange] = (mzs_in.sample_variance(mean)*10**6)/4
  #-------------------------------------------------------------------------------


  #----------------------create points/curve to fit elution-----------------------
  ints_in.each_with_index do |s,i|
    @pts_elut<<[rts_in[i],s]
  end
  opts[:sampling_rate] = rts_in.size/(rts_in.max - rts_in.min)

  a_fit = GenCurvefit.new(@pts_elut)
  a_fit.set_fit_function(lambda{|a,i| 100.0*Math.exp(-(rts_in.index(i)-a[2])**2/((a[1]*rts_in.index(i)+a[0])**2))})
  a_fit.mutation_limits = [[-5,5],[-1,1],[-rts_in.size/2,rts_in.size/2]]
  a_fit.popsize = 10
  a_fit.paramsize = 3
  a_fit.init_population
  a_fit.generations = generations

  best = a_fit.fit
  opts[:front] = best[0]
  opts[:tail] = best[1]
  opts[:mu] = best[2]
  #puts "RMSD = #{best[3]}"
  labels = ["retention time","normalized intensity"]
  a_fit.plot("elution_curvefit.svg",labels)
  #-------------------------------------------------------------------------------


  #-----------------create points/curve to fit m/z variance-----------------------
  wobs = []
  mean = mzs_in.inject(:+)/mzs_in.size
  mzs_in.each do |mz|
    wobs<<(mean-mz).abs
  end

  ints_in.length.times do |d|
    if d >= 3
      sd = wobs[d-3..d].standard_deviation
      @pts_mz_var<<[ints_in[d],sd]
    end
  end

  b_fit = GenCurvefit.new(@pts_mz_var)
  b_fit.set_fit_function(lambda{|a,i| a[0]*i**a[1]})
  b_fit.mutation_limits = [[-1,1],[-1,1]]
  b_fit.popsize = 10
  b_fit.paramsize = 2
  b_fit.init_population
  b_fit.generations = generations

  best = b_fit.fit
  opts[:wobA] = best[0]
  opts[:wobB] = best[1]
  #puts "RMSD = #{best[2]}"
  labels = ["normalized intensity","m/z variance"]
  b_fit.plot("mz_var_curvefit.svg",labels)
  #-------------------------------------------------------------------------------

  #--------------------create points/curve to fit intensity variance--------------
  smooth_ave = GenCurvefit.smoothave(ints_in)

  diff = []
  smooth_ave.each_with_index do |s,i|
    if s == nil
      diff<<0
    else
      diff<<(s-ints_in[i]).abs
    end
  end


  ints_in.each_with_index do |i,d|
    if d >= 3
      sd = diff[d-3..d].standard_deviation
      @pts_int_var<<[i,sd]
    end
  end

  c_fit = GenCurvefit.new(@pts_int_var)
  c_fit.set_fit_function(lambda{|a,i| a[0]*(1-Math.exp(-a[2]*i))+a[1]})
  c_fit.mutation_limits = [[-20,20],[-0.5,0.5],[-0.5,0.5]]
  c_fit.popsize = 10
  c_fit.paramsize = 3
  c_fit.init_population
  c_fit.generations = generations

  best = c_fit.fit
  opts[:jagA] = best[0]
  opts[:jagC] = best[1]
  opts[:jagB] = best[2]
  #puts "RMSD = #{best[3]}"
  labels = ["normalized intensity","intensity variance"]
  c_fit.plot("intensity_var_curvefit.svg",labels)
  #-------------------------------------------------------------------------------

  return opts
end