Update after MPIEvolBiol workshop (#7)

* update all

* deelte more large files

Author: Datseris

notebooks/Predator_Prey_1.mp4

@@ -9,12 +9,12 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "32c878b0",
    "metadata": {},
    "source": [
-    "This course introduces the [Agents.jl](https://juliadynamics.github.io/Agents.jl/stable/) Julia package for agent based modelling. The notebook is created by [Alejandra Ramirez](https://github.com/MA-Ramirez)\n",
-    "and George Datseris.\n",
+    "This course introduces the [Agents.jl](https://juliadynamics.github.io/Agents.jl/stable/) Julia package for agent based modelling.\n",
     "\n",
     "Agent based modelling are simulations where autonomous agents react to their environment and interact with each other given a predefined set of rules. \n",
     "These rules are formulated based on explicit statements rather than mathematical equations (such as: “If condition X is fulfilled, do action Y, and then perform operation Z on all nearby agents”).\n",
@@ -224,12 +224,20 @@
    ]
   },
   {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "c3126a56",
+   "metadata": {},
+   "source": [
+    "The first version of the model will be agents (rabbit) performing random walks, losing energy, and dying."
+   ]
+  },
+  {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "5c115fca",
    "metadata": {},
    "source": [
-    "The first steps in any simulation with Agents.jl is to decide the space and the agent types. \n",
-    "\n",
     "### 1. Choose the space \n",
     "\n",
     "The space is a grid space so we define"
@@ -622,13 +630,16 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "e5994737",
    "metadata": {},
    "source": [
     "### 5. Visualize the model and animate its time evolution\n",
     "\n",
-    "Alright, things seem to work. All rabbits die, since they eat no food but loose energy. But let's also visualize the model! To do so it is very easy! We choose a color/size/marker for the agents and call `abmplot`!"
+    "Alright, things seem to work. All rabbits die, since they eat no food but loose energy. But let's also visualize the model! To do so it is very easy! We choose a color/size/marker for the agents and call `abmplot`!\n",
+    "\n",
+    "The way this works is that agents are plotted as a scatter plot (each marker is one agent). Then, the user decides how the agents will be plotting by providing a function that given an agent it gives its color or size or marker."
    ]
   },
   {
@@ -642,9 +653,10 @@
     "\n",
     "using GLMakie # this allows videos and interactive apps\n",
     "model = init_rabbits() # refresh model\n",
-    "rabbitcolor(a) = :orange\n",
-    "rabbitmarker(a) = :circle\n",
-    "fig, = abmplot(model; ac = rabbitcolor, am = rabbitmarker)\n",
+    "rabbitcolor(agent) = :orange # all agents have the same color\n",
+    "rabbitmarker(agent) = :circle\n",
+    "rabbitsize(agent) = agent.energy # size depends on agent\n",
+    "fig, = abmplot(model; ac = rabbitcolor, am = rabbitmarker, as = rabbitsize)\n",
     "fig"
    ]
   },
@@ -664,7 +676,7 @@
    "outputs": [],
    "source": [
     "fig, = abmplot(model; \n",
-    "    ac = rabbitcolor, am = rabbitmarker,\n",
+    "    ac = rabbitcolor, am = rabbitmarker, as = rabbitsize,\n",
     "    agent_step! = rabbit_step!,    \n",
     ")\n",
     "\n",
@@ -934,7 +946,7 @@
     "plotkwargs = (;\n",
     "    ac = (\"white\", 0.5),\n",
     "    offset = agent -> (0.25rand(), 0.25rand()),\n",
-    "    as = 20,\n",
+    "    as = agent.energy,\n",
     "    #additional keyword arguments propagated to the scatter! call.\n",
     "    scatterkwargs = (strokewidth = 1.0, strokecolor = :black),\n",
     "    heatarray = grasscolor,\n",
@@ -1537,6 +1549,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "adbefad7",
    "metadata": {},
@@ -1559,21 +1572,10 @@
     "* Reproduction of strategies is only allowed on empty neighbouring sites, to mimic a finite carrying capacity of the system\n",
     "* Mobility is represented via $\\epsilon$, this exchange rate represent the likelihood of agents to swap position with a neighbouring individual or hop onto an empty neighbouring site.\n",
     "\n",
-    "![Spatial RPS instructions](SpatialRPS_instructions.png)\n",
-    "\n",
     "Whether selection, reproduction or mobility occurs is computed according to the rates (probability = rate* number of agents).\n",
     "\n",
-    "Explore the system's evolution for different exchange rate values.\n",
-    "\n"
+    "Explore the system's evolution for different exchange rate values."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "ced17cdc",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {