thediagonal.nlogo

globals [
  initial-trees   ;; how many trees (green patches) we started with
  burned-trees    ;; how many have burned so far
  chopped
  avg-percent-lost
  curr-run
  the-list
]

breed [fires fire]    ;; bright red turtles -- the leading edge of the fire
breed [embers ember]  ;; turtles gradually fading from red to near black

to setup
  clear-all
  init
  set the-list []
  reset-ticks
end


; reset the experiment and relevant global variables
to reset
  ; These other clears don't
  ; seem relevant to our experiment
  ; but it doesn't hurt to call them regardless
  clear-turtles
  ;clear-drawing
  clear-output

  ; I pretty much only need this clear
  clear-patches

  set initial-trees 0
  set burned-trees 0
  set chopped 0

  init
end

to run-once
  if not any? turtles [
    stop
  ]
  ask fires [
    ask neighbors4 with [pcolor = green] [ignite]
    set breed embers
  ]
  fade-embers
  tick
end

to run-exp
  show (word "current run #: " curr-run)
  ; reset when not setup
  ifelse curr-run < runs [
    if curr-run != 0 [reset]
    while [any? turtles] [
      ask fires [
        ask neighbors4 with [pcolor = green]
        [ignite]
        set breed embers
      ]
      fade-embers
      tick
    ]

  ; report stats
  let perc-lost ((burned-trees) / initial-trees) * 100
  show (word "% burned: " ((burned-trees / initial-trees) * 100))
  set avg-percent-lost avg-percent-lost + perc-lost
  set the-list lput perc-lost the-list
  set curr-run curr-run + 1
  ] [
    set avg-percent-lost avg-percent-lost / runs

    show (word "average percent lost: " (precision avg-percent-lost 3) "%")

    let stdev 0
    ifelse runs = 1 [
      set stdev 0
    ] [
      set stdev (standard-deviation the-list)
    ]

    ;;show (word "average percent lost: " (precision avg-percent-lost 3) "%")
    show (word "standard deviation: " (precision stdev 3))
    report-results stdev
    stop
  ]
end

to report-results [stdev]
  file-open "results.csv"
  if not file-exists? "results.csv" [
    file-print (word "group,density(%),# runs,grid size,mean % loss,standard deviation")
  ]

  ifelse group = "Experimental" [
    file-print (word group "," density "," runs "," grid-size "," (precision avg-percent-lost 3) "," (precision stdev 3))
  ] [
    file-print (word group "," density "," runs "," "N/A" "," (precision avg-percent-lost 3) "," (precision stdev 3))
  ]
  file-close
end

; contains common logic between setup and reset
; plant forest -> create grid(if needed) -> choose a burn site
to init
  set-default-shape turtles "square"

  ;; make some green trees
  ask patches with [(random-float 100) < density] [
    set pcolor green
  ]

  set initial-trees count patches with [pcolor = green]
  set chopped 0

  if group = "Experimental" [create-grid]

  ;; Choose a point to ignite
  ask one-of patches [ ignite ]
  ;; set tree counts

  set burned-trees 0
end

to create-grid
  let cell-size floor((max-pxcor - min-pxcor) / grid-size)
  let x min-pxcor + cell-size
  let y min-pycor + cell-size

  repeat grid-size - 1 [
    ask patches with [pxcor = x] [
      ; increment when a tree is chopped
      if pcolor = green [set chopped chopped + 1]
      set pcolor orange
    ]
    set x x + cell-size
  ]

  repeat grid-size - 1 [
    ask patches with [pycor = y] [
      ; increment when a tree is chopped
      if pcolor = green [set chopped chopped + 1]
      set pcolor orange
    ]
    set y y + cell-size

    ;;let percentage-turned-yellow (green-turned-yellow-count / total-green-cells) * 100
    ;;print (word "Percentage of green cells turned yellow: " percentage-turned-yellow "%")
  ]
end

;; creates the fire turtles
to ignite  ;; patch procedure
  sprout-fires 1
    [ set color red ]
  set pcolor black
  set burned-trees burned-trees + 1
end

;; achieve fading color effect for the fire as it burns
to fade-embers
  ask embers
    [ set color color - 0.3  ;; make red darker
      if color < red - 3.5     ;; are we almost at black?
        [ set pcolor color
          die ] ]
end
; Copyright 1997 Uri Wilensky.
; See Info tab for full copyright and license.
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@#$#@#$#@
## WHAT IS IT?

This project simulates the spread of a fire through a forest.  It shows that the fire's chance of reaching the right edge of the forest depends critically on the density of trees. This is an example of a common feature of complex systems, the presence of a non-linear threshold or critical parameter.

## HOW IT WORKS

The fire starts on the left edge of the forest, and spreads to neighboring trees. The fire spreads in four directions: north, east, south, and west.

The model assumes there is no wind.  So, the fire must have trees along its path in order to advance.  That is, the fire cannot skip over an unwooded area (patch), so such a patch blocks the fire's motion in that direction.

## HOW TO USE IT

Click the SETUP button to set up the trees (green) and fire (red on the left-hand side).

Click the GO button to start the simulation.

The DENSITY slider controls the density of trees in the forest. (Note: Changes in the DENSITY slider do not take effect until the next SETUP.)

## THINGS TO NOTICE

When you run the model, how much of the forest burns. If you run it again with the same settings, do the same trees burn? How similar is the burn from run to run?

Each turtle that represents a piece of the fire is born and then dies without ever moving. If the fire is made of turtles but no turtles are moving, what does it mean to say that the fire moves? This is an example of different levels in a system: at the level of the individual turtles, there is no motion, but at the level of the turtles collectively over time, the fire moves.

## THINGS TO TRY

Set the density of trees to 55%. At this setting, there is virtually no chance that the fire will reach the right edge of the forest. Set the density of trees to 70%. At this setting, it is almost certain that the fire will reach the right edge. There is a sharp transition around 59% density. At 59% density, the fire has a 50/50 chance of reaching the right edge.

Try setting up and running a BehaviorSpace experiment (see Tools menu) to analyze the percent burned at different tree density levels. Plot the burn-percentage against the density. What kind of curve do you get?

Try changing the size of the lattice (`max-pxcor` and `max-pycor` in the Model Settings). Does it change the burn behavior of the fire?

## EXTENDING THE MODEL

What if the fire could spread in eight directions (including diagonals)? To do that, use `neighbors` instead of `neighbors4`. How would that change the fire's chances of reaching the right edge? In this model, what "critical density" of trees is needed for the fire to propagate?

Add wind to the model so that the fire can "jump" greater distances in certain directions.

Add the ability to plant trees where you want them. What configurations of trees allow the fire to cross the forest? Which don't? Why is over 59% density likely to result in a tree configuration that works? Why does the likelihood of such a configuration increase so rapidly at the 59% density?

The physicist Per Bak asked why we frequently see systems undergoing critical changes. He answers this by proposing the concept of [self-organzing criticality] (https://en.wikipedia.org/wiki/Self-organized_criticality) (SOC). Can you create a version of the fire model that exhibits SOC?

## NETLOGO FEATURES

Unburned trees are represented by green patches; burning trees are represented by turtles.  Two breeds of turtles are used, "fires" and "embers".  When a tree catches fire, a new fire turtle is created; a fire turns into an ember on the next turn.  Notice how the program gradually darkens the color of embers to achieve the visual effect of burning out.

The `neighbors4` primitive is used to spread the fire.

You could also write the model without turtles by just having the patches spread the fire, and doing it that way makes the code a little simpler.   Written that way, the model would run much slower, since all of the patches would always be active.  By using turtles, it's much easier to restrict the model's activity to just the area around the leading edge of the fire.

See the "CA 1D Rule 30" and "CA 1D Rule 30 Turtle" for an example of a model written both with and without turtles.

## RELATED MODELS

* Percolation
* Rumor Mill

## CREDITS AND REFERENCES

https://en.wikipedia.org/wiki/Forest-fire_model

## HOW TO CITE

If you mention this model or the NetLogo software in a publication, we ask that you include the citations below.

For the model itself:

* Wilensky, U. (1997).  NetLogo Fire model.  http://ccl.northwestern.edu/netlogo/models/Fire.  Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

Please cite the NetLogo software as:

* Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

## COPYRIGHT AND LICENSE

Copyright 1997 Uri Wilensky.

![CC BY-NC-SA 3.0](http://ccl.northwestern.edu/images/creativecommons/byncsa.png)

This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License.  To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-sa/3.0/ or send a letter to Creative Commons, 559 Nathan Abbott Way, Stanford, California 94305, USA.

Commercial licenses are also available. To inquire about commercial licenses, please contact Uri Wilensky at uri@northwestern.edu.

This model was created as part of the project: CONNECTED MATHEMATICS: MAKING SENSE OF COMPLEX PHENOMENA THROUGH BUILDING OBJECT-BASED PARALLEL MODELS (OBPML).  The project gratefully acknowledges the support of the National Science Foundation (Applications of Advanced Technologies Program) -- grant numbers RED #9552950 and REC #9632612.

This model was developed at the MIT Media Lab using CM StarLogo.  See Resnick, M. (1994) "Turtles, Termites and Traffic Jams: Explorations in Massively Parallel Microworlds."  Cambridge, MA: MIT Press.  Adapted to StarLogoT, 1997, as part of the Connected Mathematics Project.

This model was converted to NetLogo as part of the projects: PARTICIPATORY SIMULATIONS: NETWORK-BASED DESIGN FOR SYSTEMS LEARNING IN CLASSROOMS and/or INTEGRATED SIMULATION AND MODELING ENVIRONMENT. The project gratefully acknowledges the support of the National Science Foundation (REPP & ROLE programs) -- grant numbers REC #9814682 and REC-0126227. Converted from StarLogoT to NetLogo, 2001.

<!-- 1997 2001 MIT -->
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