globals [
  percent-similar  ;; on the average, what percent of a turtle's neighbors
                   ;; are the same color as that turtle?
  percent-unhappy  ;; what percent of the turtles are unhappy?

  current-cost ;; of solar, discounted in cents/kWH

  new-installations ;; the count of new installations occurring during a tick
  neighborhood-size ;; the radius that a residence examines for solar among its neighbors
  new-high-installations new-low-installations ;; See beginning of go for explanation.
  solar-low solar-high  ;; Simply end points for plotting and reporting.
]

turtles-own [
  happy?       ;; for each turtle, indicates whether at least %-similar-wanted percent of
               ;; that turtles' neighbors are the same color as the turtle
  similar-nearby   ;; how many neighboring patches have a turtle with my color?
  other-nearby ;; how many have a turtle of another color?
  total-nearby  ;; sum of previous two variables

  has-solar?
  getting-solar?
  solar-size
]

breed [highs high-guy]
breed [lows low-guy]
breed [nones none-guy]

to setup
  clear-all
  set solar-low -5
  set solar-high 50
  if number-of-residences > count patches
    [ user-message (word "This pond only has room for " count patches " turtles.")
      stop ]
  ;; create turtles on random patches.
  ask n-of number-of-residences patches
    [ let my-type random-float 1
      ;; Here we are assuming three breeds of turtle residences.
      if my-type <= %-highs
      [sprout-highs 1
      [ set color red ] ]
      if my-type > %-highs and my-type <= %-highs + %-lows
      [sprout-lows 1
        [set color blue]]
      if my-type > %-highs + %-lows
      [sprout-nones 1
        [set color yellow]]
    ]

  ask turtles [set shape "dot"
    set has-solar? false
    set getting-solar? false]
  ask patches [set pcolor 7]
  update-variables
  go-setup
  display-logistic
  set current-cost solar-PV-cost
  set neighborhood-size 3 ;; Note this is hard wired. Later,
  ;; consider adding a widget to explore different values.
  reset-ticks
end 

to go-setup
  let mycount 0
  while [not all? turtles [happy?] and mycount < 1000]
  [set mycount (mycount + 1)
    move-unhappy-turtles
  update-variables]
end 

to move-unhappy-turtles
  ask turtles with [ not happy? ]
    [ find-new-spot ]
end 

to find-new-spot
  rt random-float 360
  fd random-float 10
  if any? other turtles-here
    [ find-new-spot ]          ;; keep going until we find an unoccupied patch
  move-to patch-here  ;; move to center of patch
end 

to update-variables
  update-turtles
  update-globals
end 

to update-turtles
  ask turtles [
    ;; in next two lines, we use "neighbors" to test the eight patches
    ;; surrounding the current patch
    set similar-nearby count (turtles-on neighbors)
      with [color = [color] of myself]
    set other-nearby count (turtles-on neighbors)
      with [color != [color] of myself]
    set total-nearby similar-nearby + other-nearby
    set happy? similar-nearby >= ( %-similar-wanted * total-nearby / 100 )
  ]
end 

to update-globals
  let similar-neighbors sum [similar-nearby] of turtles
  let total-neighbors sum [total-nearby] of turtles
  set percent-similar (similar-neighbors / total-neighbors) * 100
  set percent-unhappy (count turtles with [not happy?]) / (count turtles) * 100
end 

to go
  set new-high-installations 0 ;; This will be our counter for the tick, to
  ;; record the number of new installations to the high (large) houses.
  set new-low-installations 0  ;; This will be our counter for the tick, to
  ;; record the number of new installations to the low (small) houses.
  set new-installations 0 ;; This will be our counter for the total number of
  ;; new installations during the tick/period.

  ;; Process first the highs, that is, the larger residences.
  ask highs [
    ;; Does the residence already have solar? If so, then just skip
    ;; what follows and go on to the next one.
    if not has-solar?
    [ ;; OK, this residence does not have solar installed at this time.
      ;; Now, we will determine whether it will adopt solar during this period.
      ;; First, we need to determine if a neighborhood effect will happen.
      ;; From the Interface tab, neighborhood-effect is a positive floating
      ;; number, representing a cents per kWH effective cost reduction for solar
      ;; to the potential adopter. That is, we do this to model a positive effect
      ;; from having neighbors with solar already.
      ;; So, the value we want to use is either 0 if no one has solar or
      ;; neighborhood-effect if at least one residence nearby has solar.
      ;; Just to be a little clearer, the agent's probability of adopting solar
      ;; is a function of the solar cost - the utility price - the neighborhood effect.
      ;; Here, we either have 0 neighborhood effect, or the value neighborhood-effect (from the
      ;; Interface slider) depending upon whether there is at least one neighbor within 3 patches who
      ;; already has solar.
      let ne 0
      if count turtles in-radius neighborhood-size with [has-solar? = true] > 0
      [set ne neighborhood-effect]
      ;; Now determine whether this guy will in fact adopt solar this period.
      ;; Use the logistic (simple-mirrored-logistic) function/reporter to
      ;; determine its probability, draw the random number and if (and only if)
      ;; it is < the probabilty, install solar.
      ;; See below for the the-score reporter, but basically it calcuates
      ;; x = solar PV cost - current price from the utility - the neighborhood effect.
      ;; Then simple-mirrored-logistic, calculates the probability of adoption, given x and
      ;; k, the scaling parameter, and then L-scale compresses it overall.
      ;; We promise: more information in the Info tab.
      if random-float 1 <
        (simple-mirrored-logistic(the-score(solar-PV-cost)(current-price)(ne))(k) * L-scale)
      ;; So if indeed the guy installs PV this period, set its flag to say "I'm going to get solar"
      ;; (that is, getting-solar?) to true. Increment the number new high installations by 1.
      ;; And finally, call the get-solar-size reporter to determine what size solar the residence
      ;; is getting, and set its solar-size attribute to that value.
      [set getting-solar? true
       set new-high-installations (new-high-installations + 1)
       set solar-size get-solar-size(Prob_big_solar_given_high)
       ]
    ]
  ] ;; end of ask highs [
  ;; OK, that completes the initial processing of the larger residences.

  ;; Now we repeat the same process for the smaller residences, the lows.
  ask lows [
    if not has-solar?
    [ let ne 0
      if count turtles in-radius neighborhood-size with [has-solar? = true] > 0
      [set ne neighborhood-effect
        ]
      if random-float 1 <
      (simple-mirrored-logistic(the-score(solar-PV-cost)(current-price)(ne))(k) * L-scale)
      [set getting-solar? true
       set new-low-installations (new-low-installations + 1)
       set solar-size get-solar-size(Prob_big_solar_given_low)
       ]
    ]
  ]

  ;; Finally, we process all of the turtles, highs or lows, who
  ;; have adopted solar PV this period (if any).
  ask turtles with [getting-solar? = true] [
    set new-installations (new-installations + 1)
    set has-solar? true
    set shape "sun"
    set size 1.1
    set getting-solar? false]
  ;; Last of all, advance the tick counter, so we'll start a new period.
  tick
end 

to-report get-solar-size [the-prob]
  let sol-sz 0
  ifelse random-float 1 <= the-prob
    [set sol-sz 4]
    [set sol-sz 2]
  report sol-sz
end 


;;;;;;;; Below, from sok's Reporter Library NetLogo model.  ;;;;;;;;;;;;;;

to plotvectors [x y]
;  Assumes a plot has been selected and
;  made the current plot and been cleared.
  let dalength length x
  let dacount 1
  while [dacount < dalength]
  [plotxy item dacount x item dacount y
    set dacount (dacount + 1)]
end 

to-report linspace [dastart dastop  numpoints]
  ;; Modeled after MATLAB's linspace.
  ;; Returns a list numpoints long consisting
  ;; of evenly spaced floating point numbers,
  ;; the first of which is start and the last
  ;; of which is stop.
  ;; Note that this reporter does no error checking.
  ;; However, it works properly with negative numbers
  ;; and going from both high to low and low to high.
  let toreport (list dastart)
  let increment ((dastop - dastart)) / (numpoints - 1)
  repeat numpoints - 1
  [set toreport lput ((last toreport) + increment) toreport]
  report toreport
end 

to-report simple-mirrored-logistic [x the-k]
  ;; Given the inputs, returns a probability value.
  ;; Fixing base and temperature, varying score yields
  ;; an "s"-shaped (sigmoid) curve, increasing in score.
  ;; See logistic curve.
  ;; See SOK's book Agents, Games, and Evolution, appendix B.4
  ;; for explanation. See also:
  ;; http://mathworld.wolfram.com/LogisticEquation.html
  ;; Most directly, the logistic function:
  ;; http://en.wikipedia.org/wiki/Logistic_function
  ;; f(x) = 1 / (1 + e^(-k*x))
  ;; where L is a proportionality constant. k is a constant
  ;; and x0 is a constant at which f(x) = 0.5 * L.
  report 1 - (1 / (1 + e ^ ( -1 * the-k * x)))
  ;; Notice we have 1 minus the normal function's value, because
  ;; as the cost of solar decreases, the probability of installation
  ;; increases.
end 

to-report the-score [the-solarPVcost the-utilityPrice the-neighborhood-effect]
  report the-solarPVcost - the-utilityPrice - the-neighborhood-effect
end 

to display-logistic
;;  The general form of the logistic is
;;  y = f(x) = L / (1 + e^(-k*x))
;;  where L is a scaling constant, which we set to 1 so we get a probability,
;;  and k is a free parameter that affects the slope of the curve.
;;  Notice that at x = 0, f(x) = 0.5 (assuming as we shall that L = 1).
;;  And we want g(x),
;;  g(x) = 1 - f(x).
;;  We need to introduce more parameters here, as follows:
;;  x = cs - pu - ne
;;  With the following interpretations:
;;  cs = cost of solar PV power in cents per kWH
;;  pu = price of utility power in cents per kWH
;;  Finally, ne is the neighborhood effect (a positive number),
;;  which serves to reduce the value of x, and hence increase the probability
;;  of adoption (as it increases).

  ;; So now we initialize the variables.
  ;; current-price  taken from the Interface slider corresponds to pu, price from the utility
  ;; neighborhood-effect corresponds to  ne and is taken from the Interface slider.

  set-current-plot "Probability of Adoption Function"
  clear-plot
  plot-simple-mirrored-logistic(L-scale)
  clear-output
  let solar-costs linspace(solar-low)(solar-high)(51)
  output-print (word "solar cost" "   " "net score (x)" "      " "p(x)")
  output-print (word "--------------------------------------")
  foreach solar-costs [ [?1] ->
    let x the-score(?1)(current-price)(0) ;; neighborhood-effect
  output-print (word precision ?1 2 "\t\t" precision x 2 "\t\t" precision (simple-mirrored-logistic(x)(k) * L-scale) 3) ]
end 

to plot-simple-mirrored-logistic [da-scale]
  let the-solar-costs linspace(solar-low)(solar-high)(51)
  let davalues []
  let a-score 0
  foreach the-solar-costs [ [?1] ->
    set a-score the-score(?1)(current-price)(0) ;; neighborhood-effect
    set davalues lput (simple-mirrored-logistic(a-score)(k) * 10 * da-scale) davalues ]
  plotvectors(the-solar-costs)(davalues)
end 

to scaled-display-logistic
;;  The general form of the logistic is
;;  y = f(x) = L / (1 + e^(-k*x))
;;  where L is a scaling constant, which we set to 1 so we get a probability,
;;  and k is a free parameter that affects the slope of the curve.
;;  Notice that at x = 0, f(x) = 0.5 (assuming as we shall that L = 1).
;;  And we want g(x),
;;  g(x) = 1 - f(x).
;;  We need to introduce more parameters here, as follows:
;;  x = cs - pu  - ne
;;  With the following interpretations:
;;  cs = cost of solar PV power in cents per kWH
;;  pu = price of utility power in cents per kWH
;;  Finally, ne is the neighborhood effect (a positive number),
;;  which serves to reduce the value of x, and hence increase the probability
;;  of adoption (as it increases).

  ;; So now we initialize the variables.
  ;; current-price  taken from the Interface slider corresponds to pu, price from the utility
  ;; neighborhood-effect corresponds to  ne and is taken from the Interface slider.

  ;;plot-mirrored-logistic(-50)(50)(0)(temperature)(k-scale)
  set-current-plot "L-Scaled Probability of Adoption Function"
  clear-plot
  plot-simple-mirrored-logistic(L-scale)
  clear-output
  let solar-costs linspace(solar-low)(solar-high)(51)
  ;print percentages
  output-print (word "solar cost" "   " "net score (x)" "      " "p(x)")
  output-print (word "--------------------------------------")
  foreach solar-costs [ [?1] ->
    let x the-score(?1)(current-price)(0)  ;; neighborhood-effect
  output-print (word precision ?1 2 "\t\t" precision x 2 "\t\t" precision (simple-mirrored-logistic(x)(k) * L-scale) 3) ]
end