Module: ClimbFactor

Defined in:
lib/climb_factor/physiology.rb,
lib/climb_factor.rb,
lib/climb_factor/geometry.rb,
lib/climb_factor/filtering.rb,
lib/climb_factor/low_level_math.rb

Overview

For the cr and cw functions, see Minetti, jap.physiology.org/content/93/3/1039.full

Defined Under Namespace

Modules: CfMath, Filtering, Geom, Phys

Class Method Summary collapse

Class Method Details

.estimate(hv, nominal_distance: nil, filtering: 200.0) ⇒ Object 



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# File 'lib/climb_factor.rb', line 6

def self.estimate(hv,nominal_distance:nil,filtering:200.0)
  # inputs:
  #   All inputs are in units of meters.
  #   hv = list of [horizontal,vertical] coordinate pairs
  #   nominal_distance = if supplied, scale horizontal length of course to equal this value
  #   filtering = get rid of bogus fluctuations in vertical data that occur on horizontal scales less than this
  # output:
  #   cf = a floating-point number representing a climb factor, expressed as a percentage
  hv = hv.map do |a|
    unless a.all? { |v| v.is_a?(Numeric) }
      raise ArgumentError, "error in type of input, got #{a.inspect}, should be pairs of Floats"
    end
    a.map(&:to_f)
  end
  hv = Filtering.resample_and_filter_hv(hv,filtering)
  rescale = 1.0
  if !nominal_distance.nil? then
    rescale = nominal_distance/(hv.last[0]-hv[0][0])
  end
  stats,hv = integrate_gain_and_energy(hv,rescale)
  c,h = stats['c'],stats['h'] # energy cost in joules and (possibly rescaled) horiz distance in meters
  cf = 100.0*(c-h*Phys.minetti(0.0))/c
  return cf
end

.integrate_gain_and_energy(hv, rescale, body_mass: 1.0) ⇒ Object 



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# File 'lib/climb_factor.rb', line 31

def self.integrate_gain_and_energy(hv,rescale,body_mass:1.0)
  # integrate to find total gain, slope distance, and energy burned
  # returns [stats,hv], where:
  #   stats = {'c'=>c,'d'=>d,'gain'=>gain,'i_rms'=>i_rms,...}
  #   hv = modified copy of input hv, with predicted times added, if we have the necessary data
  v = 0 # total vertical distance (=0 at end of a loop)
  d = 0 # total distance along the slope
  gain = 0 # total gain
  c = 0 # cost in joules
  first = true
  old_h = 0
  old_v = 0
  i_sum = 0.0
  i_sum_sq = 0.0
  iota_sum = 0.0
  iota_sum_sq = 0.0
  baumel_si = 0.0 # compute this directly as a check
  t = 0.0 # integrated time, in seconds
  h_reintegrated = 0.0 # if rescale!=1, this should be the same as nominal_h
  k = 0
  hv.each { |a|
    h,v = a
    if !first then
      dh = (h-old_h)*rescale
      dv = v-old_v
      dd = Math::sqrt(dh*dh+dv*dv)
      h_reintegrated = h_reintegrated+dh
      d = d+dd
      if dv>0 then gain=gain+dv end
      i=0
      if dh>0 then i=dv/dh end
      if dh>0 then baumel_si=baumel_si+dv**2/dh end
      # In the following, weight by dh, although normally this doesn't matter because we make the
      # h intervals constant before this point.
      i_sum = i_sum + i*dh
      i_sum_sq = i_sum_sq + i*i*dh
      dc = dd*body_mass*Phys.minetti(i)
          # in theory it matters whether we use dd or dh here; I think from Minetti's math it's dd
      c = c+dc
      #if not ($split_energy_at.nil?) and d-dd<$split_energy_at_m and d>$split_energy_at_m then
      #   $stderr.print "at d=#{$split_energy_at}, energy=#{(c*0.0002388459).round} kcals\n"
      #   # fixme -- implement this in a better way
      #end
      k=k+1
    end
    old_h = h
    old_v = v
    first = false
  }
  n = hv.length-1.0
  h = h_reintegrated # should equal $nominal_h; may differ from hv.last[0]-hv[0][0] if rescale!=1
  i_rms = Math::sqrt(i_sum_sq/h - (i_sum/h)**2)
  i_mean = (hv.last[1]-hv[0][1])/h
  i0,c0,c2,b0,b1,b2 = Phys.minetti_quadratic_coeffs()
  e_q = h*body_mass*(b0+b1*i_mean+b2*i_rms)
  cf = (c-h*body_mass*Phys.minetti(0.0))/c
  stats = {'c'=>c,'h'=>h,'d'=>d,'gain'=>gain,'i_rms'=>i_rms,'i_mean'=>i_mean,'e_q'=>e_q,
          'cf'=>cf,'baumel_si'=>baumel_si,
          't'=>t}
  return [stats,hv]
end