From ac77bc5c00e4a0e7abbd9ec6d318ed5f38dc28e3 Mon Sep 17 00:00:00 2001
From: Oliver Schmitz <o.schmitz@uu.nl>
Date: Tue, 6 Jun 2023 10:42:22 +0200
Subject: [PATCH] Readme and Jupyter notebook added

---
 notebook/course.ipynb              | 566 +++++++++++++++++++++++++++++
 notebook/utils/__init__.py         |   9 +
 notebook/utils/animate.py          | 141 +++++++
 notebook/utils/generate_inputs.py  |  25 ++
 notebook/utils/plot_luminosity.py  |  20 +
 notebook/utils/plot_population.py  |  44 +++
 notebook/utils/plot_temperature.py |  36 ++
 7 files changed, 841 insertions(+)
 create mode 100644 notebook/course.ipynb
 create mode 100644 notebook/utils/__init__.py
 create mode 100644 notebook/utils/animate.py
 create mode 100644 notebook/utils/generate_inputs.py
 create mode 100644 notebook/utils/plot_luminosity.py
 create mode 100644 notebook/utils/plot_population.py
 create mode 100644 notebook/utils/plot_temperature.py

diff --git a/notebook/course.ipynb b/notebook/course.ipynb
new file mode 100644
index 0000000..a980427
--- /dev/null
+++ b/notebook/course.ipynb
@@ -0,0 +1,566 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Daisyworld model using Campo\n",
+    "\n",
+    "## Overview and introduction\n",
+    "\n",
+    "DaisyWorld was introduced by James Lovelock and Andrew Watson (1983), to\n",
+    "illustrate the Gaia Hypothesis that organisms can create a\n",
+    "self-regulating system by interacting with their surroundings.\n",
+    "\n",
+    "In Daisyworld, only two life forms (agents) are present: black daisies\n",
+    "and white daisies. White daisies have a high albedo, reflecting light,\n",
+    "thereby cooling the surface temperature (a field). In contrast, black\n",
+    "daisies have a low albedo, absorbing light, thereby increasing the\n",
+    "surface temperature.\n",
+    "\n",
+    "Daisies can only reproduce in a certain temperature range; this is where\n",
+    "the interaction between the organisms (agents) and their surroundings\n",
+    "(field) occurs. As you will see when running the model, this leads to an\n",
+    "equilibrium between black and white daisies under a relatively wide\n",
+    "range of luminosity conditions.\n",
+    "\n",
+    "## Representing the Daisyworld model in Campo\n",
+    "\n",
+    "For the Daisyworld model, we represent the daisies and the climate with\n",
+    "two different phenomena. The daisies and the climate will be represented\n",
+    "according to the following scheme: ![Field and agents\n",
+    "scheme](attachment:schema.png)\n",
+    "\n",
+    "Daisies will use point coordinates as their spatial domain, the spatial\n",
+    "domain of the climate will be represented by a bounding box and a number\n",
+    "of rows and columns. The property values of both can change over time.\n",
+    "\n",
+    "### Instantiation of daisies and surface temperature\n",
+    "\n",
+    "To add phenomena to a model it is first required to initialise a model\n",
+    "data object:\n",
+    "\n",
+    "``` python\n",
+    "  daisy_world = campo.Campo()\n",
+    "```\n",
+    "\n",
+    "The `daisy_world` object will provide access to the methods for\n",
+    "constructing agents, and eventually arrange the storage to the LUE\n",
+    "dataset on disk.\n",
+    "\n",
+    "Now the phenomena can be added:\n",
+    "\n",
+    "``` python\n",
+    "  daisies = daisy_world.add_phenomenon('daisies')\n",
+    "  climate = daisy_world.add_phenomenon('climate')\n",
+    "```\n",
+    "\n",
+    "This will add empty phenomena with zero agents to a model. The type of\n",
+    "an agent is defined by its spatial domain obtained from an input file.\n",
+    "The input data will also set the number of agents:\n",
+    "\n",
+    "``` python\n",
+    "  daisies.add_property_set('site', 'location_site.csv')\n",
+    "  climate.add_property_set('surface', 'area_extent.csv')\n",
+    "```\n",
+    "\n",
+    "Properties can then be assigned and initialised using the dot notation:\n",
+    "\n",
+    "``` python\n",
+    "  daisies.site.age = 0\n",
+    "  climate.surface.albedo = 0.4\n",
+    "```\n",
+    "\n",
+    "### Operations on field or agent properties\n",
+    "\n",
+    "Campo resembles and extends the map algebra approach to field-agent\n",
+    "modelling, thereby providing operations accepting both fields or agents\n",
+    "as arguments. Arithmetic operations (+, -, /, \\*) and comparisons\n",
+    "(e.g. \\<, \\>=) can be directly used with properties, they are overloaded\n",
+    "operators and are executed for each object in a property. E.g.\n",
+    "increasing the daisie’s age and the surface temperature by one can be\n",
+    "done as follows:\n",
+    "\n",
+    "``` python\n",
+    "  daisies.site.age = daisies.site.age + 1\n",
+    "  climate.surface.temperature = climate.surface.temperature + 1\n",
+    "```\n",
+    "\n",
+    "Other operations on properties are provided by the `campo` module, such\n",
+    "as the `uniform()` operation:\n",
+    "\n",
+    "``` python\n",
+    "  daisies.site.age = campo.uniform(daisies.site.low, daisies.site.up)\n",
+    "  climate.surface.temperature = campo.uniform(climate.surface.low, climate.surface.up)\n",
+    "```\n",
+    "\n",
+    "More information on the operations included in the `campo` module can be\n",
+    "found at\n",
+    "https://campo.computationalgeography.org/documentation/index.html.\n",
+    "\n",
+    "## Running the Daisyworld model\n",
+    "\n",
+    "### Setting up the environment\n",
+    "\n",
+    "First import Campo and additional required Python modules to run this\n",
+    "notebook."
+   ],
+   "attachments": {
+    "schema.png": {
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9WnZ2dianA4Dad+bMGT377LNatWqVJMnR0VFPPfWUZs2aJTc3N5PTAQAAAMDv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zv17rvvGsvHjx9XcnKyEysCYDcwMOAwSrapqUmtra1qbm5Wd3f3E41ft1gs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\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aYwy3bt0SadfV1S3x2I8ePYKxsTFSUlIkylGY0NBQjBo1Cl++fJE5y5s3bzBgwAD4+Phg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\nypRRMakQ6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+    }
+   },
+   "id": "3a2c98c7-2811-4fe2-aa3e-4f2238e48c6c"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datetime\n",
+    "import enum\n",
+    "\n",
+    "from osgeo import gdal, ogr, osr\n",
+    "\n",
+    "import pcraster as pcr\n",
+    "import pcraster.framework as pcrfw\n",
+    "\n",
+    "import campo\n",
+    "import utils"
+   ],
+   "id": "f4f89740-de08-4c4c-a49f-bb07eb8ba5d8"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generating the input files\n",
+    "\n",
+    "For initialisation of the model we will create two input files, one file\n",
+    "holding the locations of the daisies as x,y coordinate pair, and a\n",
+    "second file describing the extent and discretisation of the area. The\n",
+    "extent will be used for the climate’s temperature and albedo fields, and\n",
+    "each raster cell in the area will hold one daisy.\n",
+    "\n",
+    "The input files will be generated when executing the following code\n",
+    "cell. Create e.g. a raster with 10 rows and columns:"
+   ],
+   "id": "9cef6fd4-71ea-4467-aa9c-196edd455d4a"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "utils.generate_input_files(rows=10, cols=10)"
+   ],
+   "id": "a2d8efc6-eec8-455b-a1c2-a2943198f490"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To obtain reproducible results we fix the random seed. You can set the\n",
+    "seed to 0 to get different results each model run."
+   ],
+   "id": "5b6e0a2e-f2c7-4beb-b99f-3cdeecb2777a"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "seed = 1\n",
+    "pcr.setrandomseed(seed)"
+   ],
+   "id": "f4b82b7e-a1cd-43eb-a69e-6a7496388997"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model scenarios\n",
+    "\n",
+    "The model can be run for different scenarios, which reflect different\n",
+    "luminosity conditions. The `our` scenario reflects our current\n",
+    "luminosity conditions. The scenarios `low` and `high` have relatively\n",
+    "low and high luminosity, respectively, compared to current conditions.\n",
+    "In the `ramp` scenario, the luminosity increases and then decreases over\n",
+    "800 time steps. You will later specify the scenario as input to the\n",
+    "model."
+   ],
+   "id": "ce81a756-da46-4794-9a01-d7b0701d2bc8"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Scenarios(enum.Enum):\n",
+    "    low = 1\n",
+    "    high = 2\n",
+    "    our = 3\n",
+    "    ramp = 4"
+   ],
+   "id": "794085a0-cf3f-4e97-88e6-6c381d6b6016"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Implementation of the Daisyworld model"
+   ],
+   "id": "e48b8ce2-5fe2-4f46-893d-8e01835401f7"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class DaisyWorld(pcrfw.DynamicModel):\n",
+    "    def __init__(self, fraction_white=0.2, fraction_black=0.2, albedo_white=0.75, albedo_black=0.25, surface_albedo=0.4, scenario=Scenarios.our):\n",
+    "        pcrfw.DynamicModel.__init__(self)\n",
+    "        # Framework requires a clone\n",
+    "        # set a dummy\n",
+    "        pcr.setclone(5, 4, 10, 0, 0)\n",
+    "        self.fraction_white = fraction_white / 100\n",
+    "        self.fraction_black = fraction_black / 100\n",
+    "        self.albedo_white = albedo_white\n",
+    "        self.albedo_black = albedo_black\n",
+    "        self.surface_albedo = surface_albedo\n",
+    "        self.scenario = scenario\n",
+    "\n",
+    "        # Simple input checking...\n",
+    "        assert self.fraction_white >= 0 and self.fraction_white <= 1, 'fraction_white should be between 0 and 100'\n",
+    "        assert self.fraction_black >= 0 and self.fraction_black <= 1, 'fraction_black should be between 0 and 100'\n",
+    "\n",
+    "        assert self.albedo_white >= 0 and self.albedo_white <= 1, 'albedo_white should be between 0 and 1'\n",
+    "        assert self.albedo_black >= 0 and self.albedo_black <= 1, 'albedo_black should be between 0 and 1'\n",
+    "\n",
+    "        assert self.surface_albedo >= 0, 'surface_albedo should be larger than 0'\n",
+    "\n",
+    "    def initial(self):\n",
+    "        self.daisy_world = campo.Campo()\n",
+    "\n",
+    "        # Daisies\n",
+    "\n",
+    "        self.daisies = self.daisy_world.add_phenomenon('daisies')\n",
+    "\n",
+    "        self.daisies.add_property_set('site', 'location_daisies.csv')\n",
+    "\n",
+    "        # default init all agents\n",
+    "        self.daisies.site.age_min = 0\n",
+    "        self.daisies.site.age_max = 25\n",
+    "        self.daisies.site.age = self.daisies.site.age_min\n",
+    "\n",
+    "        self.daisies.site.albedo_init = 1\n",
+    "        self.daisies.site.breed = -1\n",
+    "        self.daisies.site.albedo = self.daisies.site.albedo_init\n",
+    "\n",
+    "        self.daisies.site.mask_dead = -1\n",
+    "        self.daisies.site.mask_alive = 1\n",
+    "        self.daisies.site.mask = self.daisies.site.mask_dead\n",
+    "\n",
+    "        self.daisies.site.lower = 0\n",
+    "        self.daisies.site.upper = 1\n",
+    "        self.daisies.site.init = campo.uniform(\n",
+    "            self.daisies.site.lower, self.daisies.site.upper, seed)\n",
+    "\n",
+    "        self.daisies.site.w_value = 0\n",
+    "        self.daisies.site.b_value = 1\n",
+    "        self.daisies.site.albedo_white = self.albedo_white\n",
+    "        self.daisies.site.albedo_black = self.albedo_black\n",
+    "\n",
+    "        # cover of the world surface of breeds\n",
+    "        self.daisies.site.whites = self.fraction_white\n",
+    "        self.daisies.site.blacks = 1 - self.fraction_black\n",
+    "        self.daisies.site.breed = campo.where(\n",
+    "            self.daisies.site.init < self.daisies.site.whites, self.daisies.site.w_value, self.daisies.site.breed)\n",
+    "        self.daisies.site.breed = campo.where(\n",
+    "            self.daisies.site.init > self.daisies.site.blacks, self.daisies.site.b_value, self.daisies.site.breed)\n",
+    "\n",
+    "        self.daisies.site.albedo = campo.where(\n",
+    "            self.daisies.site.init < self.daisies.site.whites, self.daisies.site.albedo_white, self.daisies.site.albedo)\n",
+    "        self.daisies.site.albedo = campo.where(\n",
+    "            self.daisies.site.init > self.daisies.site.blacks, self.daisies.site.albedo_black, self.daisies.site.albedo)\n",
+    "\n",
+    "        self.daisies.site.age_init = campo.random_integers(\n",
+    "            self.daisies.site.age_min, self.daisies.site.age_max, seed)\n",
+    "        self.daisies.site.age = campo.where(\n",
+    "            self.daisies.site.init < self.daisies.site.whites, self.daisies.site.age_init, self.daisies.site.age)\n",
+    "        self.daisies.site.age = campo.where(\n",
+    "            self.daisies.site.init > self.daisies.site.blacks, self.daisies.site.age_init, self.daisies.site.age)\n",
+    "\n",
+    "        # reuse b_value for mask, -1: dead, 1: alive\n",
+    "        self.daisies.site.mask = campo.where(\n",
+    "            self.daisies.site.init < self.daisies.site.whites, self.daisies.site.b_value, self.daisies.site.mask)\n",
+    "        self.daisies.site.mask = campo.where(\n",
+    "            self.daisies.site.init > self.daisies.site.blacks, self.daisies.site.b_value, self.daisies.site.mask)\n",
+    "\n",
+    "        self.daisies.site.init_breed = self.daisies.site.breed\n",
+    "        self.daisies.site.init_mask = self.daisies.site.mask\n",
+    "\n",
+    "        self.daisies.site.buffersize = 15\n",
+    "        self.daisies.site.neighbours = campo.get_others(\n",
+    "            self.daisies.site, self.daisies.site, self.daisies.site.buffersize)\n",
+    "\n",
+    "        # technical detail\n",
+    "        self.daisies.set_epsg(28992)\n",
+    "\n",
+    "        # Temperature field\n",
+    "        self.climate = self.daisy_world.add_phenomenon('climate')\n",
+    "        self.climate.add_property_set('surface', 'area_extent.csv')\n",
+    "\n",
+    "        self.climate.set_epsg(28992)\n",
+    "        self.climate.surface.temperature = 0\n",
+    "        self.climate.surface.surface_albedo = self.surface_albedo\n",
+    "        self.climate.surface.zeros = 0\n",
+    "        self.climate.surface.ones = 1\n",
+    "        self.climate.surface.window_size = 30\n",
+    "        self.climate.surface.default_local_heating = 80\n",
+    "        self.climate.surface.fraction = 1 / (campo.windowtotal(self.climate.surface.ones,\n",
+    "                                          self.climate.surface.window_size) - 1)\n",
+    "\n",
+    "        if self.scenario == Scenarios.low:\n",
+    "            self.climate.surface.solar_luminosity = 0.6\n",
+    "        elif self.scenario == Scenarios.high:\n",
+    "            self.climate.surface.solar_luminosity = 1.4\n",
+    "        elif self.scenario == Scenarios.our:\n",
+    "            self.climate.surface.solar_luminosity = 1.0\n",
+    "        elif self.scenario == Scenarios.ramp:\n",
+    "            self.climate.surface.solar_luminosity = 0.8\n",
+    "\n",
+    "        self.suface_temperature()\n",
+    "        self.climate.surface.init_temp = self.climate.surface.temperature\n",
+    "\n",
+    "        # create the output lue data set\n",
+    "        self.daisy_world.create_dataset(\"daisy_world.lue\")\n",
+    "\n",
+    "        # create real time settings for lue\n",
+    "        date = datetime.date(2000, 1, 1)\n",
+    "        time = datetime.time(0, 0)\n",
+    "        start = datetime.datetime.combine(date, time)\n",
+    "        unit = campo.TimeUnit.month\n",
+    "        stepsize = 1\n",
+    "        self.daisy_world.set_time(start, unit, stepsize, self.nrTimeSteps())\n",
+    "\n",
+    "        # technical detail to inform lue properties may change over time\n",
+    "        self.daisies.site.mask.is_dynamic = True\n",
+    "        self.daisies.site.age.is_dynamic = True\n",
+    "        self.daisies.site.albedo.is_dynamic = True\n",
+    "        self.daisies.site.breed.is_dynamic = True\n",
+    "        self.climate.surface.temperature.is_dynamic = True\n",
+    "        self.climate.surface.solar_luminosity.is_dynamic = True\n",
+    "\n",
+    "        # write initial data to disk\n",
+    "        self.daisy_world.write()\n",
+    "\n",
+    "    def suface_temperature(self):\n",
+    "        surface_albedo = (1 - self.climate.surface.surface_albedo) * \\\n",
+    "            self.climate.surface.solar_luminosity\n",
+    "\n",
+    "        d_albedo = campo.feature_values_to_raster(\n",
+    "            self.climate.surface, self.daisies.site, self.daisies.site.albedo)\n",
+    "        daisy_albedo = (1 - d_albedo) * self.climate.surface.solar_luminosity\n",
+    "\n",
+    "        condition = campo.feature_values_to_raster(\n",
+    "            self.climate.surface, self.daisies.site, self.daisies.site.mask) == 1\n",
+    "        absorbed_luminosity = campo.where(\n",
+    "            condition, daisy_albedo, surface_albedo)\n",
+    "\n",
+    "        local_heating = campo.where(absorbed_luminosity > 0,\n",
+    "                                    72 * campo.log(absorbed_luminosity) + 80,\n",
+    "                                    self.climate.surface.default_local_heating)\n",
+    "\n",
+    "        self.climate.surface.temperature = (\n",
+    "            self.climate.surface.temperature + local_heating) / 2.0\n",
+    "\n",
+    "    def diffuse_temperature(self, ratio=0.5):\n",
+    "        condition = campo.feature_values_to_raster(\n",
+    "            self.climate.surface, self.daisies.site, self.daisies.site.mask) == 1\n",
+    "        spread = campo.spread(\n",
+    "            condition, self.climate.surface.zeros, self.climate.surface.ones) < 15\n",
+    "\n",
+    "        # Sum of temperatures neighbours, exclude yourself\n",
+    "        temp_neighbours = campo.windowtotal(\n",
+    "            self.climate.surface.temperature, self.climate.surface.window_size) - self.climate.surface.temperature\n",
+    "        temp_neighbours = campo.where(\n",
+    "            spread, temp_neighbours, self.climate.surface.zeros)\n",
+    "\n",
+    "        self.climate.surface.temperature = (\n",
+    "            1 - ratio) * self.climate.surface.temperature + temp_neighbours * self.climate.surface.fraction * ratio\n",
+    "\n",
+    "    def solar_activity(self, timestep):\n",
+    "        if self.scenario == Scenarios.ramp:\n",
+    "            if timestep > 200 and timestep <= 400:\n",
+    "                self.climate.surface.solar_luminosity += 0.005\n",
+    "\n",
+    "            if timestep > 500 and timestep <= 750:\n",
+    "                self.climate.surface.solar_luminosity -= 0.0025\n",
+    "\n",
+    "    def spread_plants(self):\n",
+    "        seed_threshold = 0.1457 * self.climate.surface.temperature - \\\n",
+    "            0.0032 * self.climate.surface.temperature**2 - 0.6443\n",
+    "\n",
+    "        self.daisies.site.init = campo.uniform(\n",
+    "            self.daisies.site.lower, self.daisies.site.upper, seed)\n",
+    "\n",
+    "        campo.spread_neighbours(self.daisies.site.neighbours, seed_threshold, self.daisies.site.init,\n",
+    "                                self.daisies.site.breed, self.daisies.site.mask, self.daisies.site.albedo, self.daisies.site.age, seed)\n",
+    "\n",
+    "    def dynamic(self):\n",
+    "        # Processes executed per time step\n",
+    "        self.suface_temperature()\n",
+    "        self.diffuse_temperature()\n",
+    "        self.spread_plants()\n",
+    "        self.solar_activity(self.currentTimeStep())\n",
+    "\n",
+    "        # Increase age of daisies\n",
+    "        self.daisies.site.age = campo.where(campo.logical_and(self.daisies.site.age <= self.daisies.site.age_max,\n",
+    "                                                                self.daisies.site.mask == 1), self.daisies.site.age + 1, self.daisies.site.age_min)\n",
+    "\n",
+    "        # Reset mask and albedo for dead plants\n",
+    "        self.daisies.site.mask = campo.where(\n",
+    "            (self.daisies.site.age > self.daisies.site.age_max), self.daisies.site.mask_dead, self.daisies.site.mask)\n",
+    "        self.daisies.site.albedo = campo.where(\n",
+    "            self.daisies.site.mask == 1, self.daisies.site.albedo, self.daisies.site.albedo_init)\n",
+    "\n",
+    "        # Write data for current time step to disk\n",
+    "        self.daisy_world.write(self.currentTimeStep())"
+   ],
+   "id": "84accdbc-15dc-48f8-bbc6-50d5a1685222"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Running the model\n",
+    "\n",
+    "You can run the model with default parameter settings, or adapt those to\n",
+    "your own needs. Start e.g. with running the four different scenarios.\n",
+    "800 time steps are recommended for the `ramp` scenario, other scenarios\n",
+    "can use fewer time steps.\n",
+    "\n",
+    "When the model is running you will see some `.` indicating the progress\n",
+    "over time steps."
+   ],
+   "id": "0b1ca2ce-3676-4090-98f2-d1f9f80fc92f"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This might need a moment to complete\n",
+    "if __name__ == '__main__':\n",
+    "    # Time steps  to run\n",
+    "    timesteps = 100\n",
+    "    # Initial fractions of daisy breeds, 0 - 100\n",
+    "    fraction_white = 20\n",
+    "    fraction_black = 20\n",
+    "    # Fraction of daisy albledos, 0 - 1\n",
+    "    albedo_white = 0.75\n",
+    "    albedo_black = 0.25\n",
+    "    # Albedo of the area\n",
+    "    surface_albedo = 0.4\n",
+    "    # Scenatio to run, see above\n",
+    "    scenario = Scenarios.our\n",
+    "\n",
+    "    myModel = DaisyWorld(fraction_white, fraction_black, albedo_white, albedo_black, surface_albedo, scenario)\n",
+    "    dynFrw = pcrfw.DynamicFramework(myModel, timesteps)\n",
+    "    dynFrw.run()"
+   ],
+   "id": "eba3d57b-fc9b-49ce-8d85-db9318ce0909"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model ouput\n",
+    "\n",
+    "During model run data is written for each time step to the\n",
+    "`daisy_world.lue` output file. This dataset can be used to create\n",
+    "animations and time series plots.\n",
+    "\n",
+    "You can visualize your input luminosity over time with:"
+   ],
+   "id": "1bf390ac-7059-40fd-bba5-207015458dee"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "utils.plot_luminosity()"
+   ],
+   "id": "3b5eb05f-39dc-45f2-bb15-e56bf2981dbf"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To evaluate the resulting spatio-temporal pattern of daisies and the\n",
+    "surface temperature you can animate those with:"
+   ],
+   "id": "45b88bd2-91dc-4244-9fd3-3bd1663e5252"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This might need a moment to complete\n",
+    "utils.animate()"
+   ],
+   "id": "d0668920-c3a8-4e70-aa89-a8b2d1af7783"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The resulting average surface temperature over time can be plotted with:"
+   ],
+   "id": "6f3796cd-71d6-436a-a378-7e370cfb19e8"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "utils.plot_temperature()"
+   ],
+   "id": "f0585bdd-270f-4b32-ac4b-c7f44e6a23a8"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The total sizes of the black and white daisy populations over time can\n",
+    "be plotted with:"
+   ],
+   "id": "8b446f52-3498-4ba3-9526-7864dd777395"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "utils.plot_population()"
+   ],
+   "id": "1f18e47b-cb54-4f5f-8ab5-654c48fa8d2a"
+  }
+ ],
+ "nbformat": 4,
+ "nbformat_minor": 5,
+ "metadata": {}
+}
diff --git a/notebook/utils/__init__.py b/notebook/utils/__init__.py
new file mode 100644
index 0000000..a0ea1d2
--- /dev/null
+++ b/notebook/utils/__init__.py
@@ -0,0 +1,9 @@
+import matplotlib as mpl
+
+mpl.rcParams["figure.dpi"] = 150
+
+from .generate_inputs import *
+from .plot_luminosity import *
+from .plot_population import *
+from .plot_temperature import *
+from .animate import *
diff --git a/notebook/utils/animate.py b/notebook/utils/animate.py
new file mode 100644
index 0000000..1ac0b95
--- /dev/null
+++ b/notebook/utils/animate.py
@@ -0,0 +1,141 @@
+import warnings
+
+from matplotlib import animation
+from matplotlib import colors as cls
+from matplotlib import pyplot as plt
+from matplotlib import colorbar as cb
+from matplotlib import cm as cm
+import matplotlib
+import numpy as np
+
+from IPython.display import HTML
+
+import lue.data_model as ldm
+import campo
+
+
+warnings.filterwarnings("ignore", category=matplotlib.MatplotlibDeprecationWarning)
+
+
+def animate(show_notebook=True):
+    # import data
+    dataset = ldm.open_dataset("daisy_world.lue")
+    field_dataframe = campo.dataframe.select(
+        dataset.climate, property_names=["temperature"]
+    )
+    daisy_dataframe = campo.dataframe.select(
+        dataset.daisies, property_names=["age", "mask", "breed"]
+    )
+    x_coords = daisy_dataframe["daisies"]["site"]["breed"]["coordinates"][:, 0].data
+    y_coords = daisy_dataframe["daisies"]["site"]["breed"]["coordinates"][:, 1].data
+
+    nr_timesteps = field_dataframe["climate"]["surface"]["temperature"][0].shape[0]
+
+    field_property = field_dataframe["climate"]["surface"]["temperature"]
+
+    # get metadata
+    cellsize = 10
+    minx = field_property[0].xcoord[0].data
+    miny = field_property[0].ycoord[0].data
+    maxx = field_property[0].xcoord[-1].data + cellsize
+    maxy = field_property[0].ycoord[-1].data + cellsize
+    extent = (minx, maxx, miny, maxy)
+
+    #########
+
+    # create the figure
+    f = plt.figure()
+    # [left, bottom, width, height]
+    ax1 = f.add_axes([0.8, 0.05, 0.1, 0.9])
+    ax2 = f.add_axes([0.05, 0.05, 0.7, 0.9])
+    plt.axis("off")
+
+    # making normalization scheme between -30 and 80 degrees Celcius
+    norm = cls.Normalize(vmin=-30, vmax=80)
+    cmap = cm.coolwarm
+    # and corresponding legend
+    leg = cb.ColorbarBase(ax1, cmap=cmap, norm=norm, orientation="vertical")
+    leg.set_label("Temperature (degrees Celcius)")
+
+    # no agent colour receives full transparency
+    # acmap = cm.get_cmap("binary").copy()  # DID NOT WORK FOR ME (MY MPL VERSION?)
+    acmap = cm.binary
+    acmap.set_under("green", alpha=0.0)
+
+    # use imshow to plot the field over time
+    # and scatter to plot the agents
+    def init():
+        # temperature field
+        field_values = field_dataframe["climate"]["surface"]["temperature"][0][0].data
+        im = ax2.imshow(
+            field_values, norm=norm, extent=extent, cmap=cmap, animated=True
+        )
+
+        # daisies, mask the alives
+        agent_values = daisy_dataframe["daisies"]["site"]["breed"]["values"][:, 0]
+        agent_active = daisy_dataframe["daisies"]["site"]["mask"]["values"][:, 0]
+        agent_values = np.where(agent_active == 1, agent_values, -99)
+        agents = ax2.scatter(
+            x_coords,
+            y_coords,
+            marker="o",
+            c=agent_values,
+            s=50,
+            cmap=acmap,
+            vmin=0,
+            vmax=1,
+            animated=True,
+        )
+
+        # time label
+        month = ax2.text(0.02, 0.975, "step = 0", transform=ax2.transAxes, fontsize=10)
+        return im, agents, month
+
+    def animate_ts(i):
+        ax2.clear()
+        ax2.axis("off")
+
+        # temperature field
+        field_values = field_dataframe["climate"]["surface"]["temperature"][0][i].data
+        im = ax2.imshow(
+            field_values, norm=norm, extent=extent, cmap=cmap, animated=True
+        )
+
+        # daisies, mask the alives
+        agent_values = daisy_dataframe["daisies"]["site"]["breed"]["values"][:, i]
+        agent_active = daisy_dataframe["daisies"]["site"]["mask"]["values"][:, i]
+        agent_values = np.where(agent_active == 1, agent_values, -99)
+
+        agents = ax2.scatter(
+            x_coords,
+            y_coords,
+            marker="o",
+            c=agent_values,
+            s=50,
+            cmap=acmap,
+            vmin=0,
+            vmax=1,
+            animated=True,
+        )
+
+        # time label
+        month = ax2.text(
+            0.02, 0.975, f"step = {i+1}", transform=ax2.transAxes, fontsize=10
+        )
+
+        return im, agents, month
+
+    im_ani = animation.FuncAnimation(
+        f, animate_ts, interval=75, blit=True, frames=nr_timesteps, init_func=init
+    )
+    # im_ani.save("im.mp4", dpi=100,  metadata={"artist":"Judith Verstegen"})
+
+    if show_notebook:
+        plt.close(im_ani._fig)
+        return HTML(im_ani.to_html5_video())
+    else:
+        plt.show()
+
+
+if __name__ == "__main__":
+    animate(False)
diff --git a/notebook/utils/generate_inputs.py b/notebook/utils/generate_inputs.py
new file mode 100644
index 0000000..b4adf43
--- /dev/null
+++ b/notebook/utils/generate_inputs.py
@@ -0,0 +1,25 @@
+def generate_input_files(rows=30, cols=30):
+    cellsize = 10
+    x0 = 0
+    y0 = 0
+
+    x1 = x0 + cols * cellsize
+    y1 = y0 + rows * cellsize
+
+    with open("area_extent.csv", "w") as content:
+        content.write(f"{x0},{y0},{x1},{y1},{cols},{rows}")
+
+    agent_id = 0
+
+    with open("location_daisies.csv", "w") as content:
+        for r in range(rows, 0, -1):
+            r -= 1
+            for c in range(cols):
+                x = x0 + c * cellsize + cellsize / 2
+                y = y0 + r * cellsize + cellsize / 2
+                content.write(f"{x},{y}\n")
+                agent_id += 1
+
+
+if __name__ == "__main__":
+    generate_input_files(19, 19)
diff --git a/notebook/utils/plot_luminosity.py b/notebook/utils/plot_luminosity.py
new file mode 100644
index 0000000..ab9452a
--- /dev/null
+++ b/notebook/utils/plot_luminosity.py
@@ -0,0 +1,20 @@
+import matplotlib.pyplot as plt
+
+import lue.data_model as ldm
+import campo
+
+
+def plot_luminosity():
+    dataset = ldm.open_dataset("daisy_world.lue")
+    prop = campo.dataframe.select(dataset.climate, property_names=["solar_luminosity"])
+
+    values = prop["climate"]["surface"]["solar_luminosity"][0].data[:, 0, 0]
+
+    plt.plot(values)
+    plt.ylabel("Solar luminosity (-)")
+    plt.xlabel("Time step")
+    plt.show()
+
+
+if __name__ == "__main__":
+    plot_luminosity()
diff --git a/notebook/utils/plot_population.py b/notebook/utils/plot_population.py
new file mode 100644
index 0000000..ac187c9
--- /dev/null
+++ b/notebook/utils/plot_population.py
@@ -0,0 +1,44 @@
+import numpy
+
+import matplotlib.pyplot as plt
+
+import lue.data_model as ldm
+import campo
+
+
+def plot_population():
+    dataset = ldm.open_dataset("daisy_world.lue")
+
+    daisy_dataframe = campo.dataframe.select(
+        dataset.daisies, property_names=["mask", "breed"]
+    )
+
+    breed = daisy_dataframe["daisies"]["site"]["breed"]["values"]
+    mask = daisy_dataframe["daisies"]["site"]["mask"]["values"]
+
+    nr_timesteps = breed.shape[1]
+    nr_agents = breed.shape[0]
+
+    daisy_b = numpy.zeros(nr_timesteps)
+    daisy_w = numpy.zeros(nr_timesteps)
+
+    for ts in range(0, nr_timesteps):
+        breed_ts = breed[:, ts]
+        mask_ts = mask[:, ts]
+        breeds_alive = numpy.ma.masked_where(mask_ts != 1, breed_ts)
+        w = numpy.ma.masked_where(breeds_alive == 1, numpy.ones(breed.shape[0]))
+        b = numpy.ma.masked_where(breeds_alive == 0, numpy.ones(breed.shape[0]))
+        daisy_b[ts] = len(numpy.ma.compressed(b))
+        daisy_w[ts] = len(numpy.ma.compressed(w))
+
+    plt.plot(daisy_b, label="black daisies", color="k")
+    plt.plot(daisy_w, label="white daisies", color="lightgrey")
+    plt.ylim([0, nr_agents])
+    plt.ylabel("Population size (#)")
+    plt.xlabel("Time step")
+    plt.legend()
+    plt.show()
+
+
+if __name__ == "__main__":
+    plot_population()
diff --git a/notebook/utils/plot_temperature.py b/notebook/utils/plot_temperature.py
new file mode 100644
index 0000000..dc5b551
--- /dev/null
+++ b/notebook/utils/plot_temperature.py
@@ -0,0 +1,36 @@
+import numpy
+import matplotlib.pyplot as plt
+
+import lue.data_model as ldm
+import campo
+
+
+def plot_temperature():
+    dataset = ldm.open_dataset("daisy_world.lue")
+    init_properties = campo.dataframe.select(
+        dataset.climate, property_names=["init_temp"]
+    )
+    properties = campo.dataframe.select(dataset.climate, property_names=["temperature"])
+
+    t_init = init_properties["climate"]["surface"]["init_temp"][0].data
+    t_init_mean = numpy.mean(t_init)
+
+    values = properties["climate"]["surface"]["temperature"][0].data
+
+    nr_timesteps = values.shape[0]
+
+    avg_temp = numpy.zeros(nr_timesteps + 1)
+
+    avg_temp[0] = t_init_mean
+
+    for ts in range(0, nr_timesteps):
+        avg_temp[ts + 1] = numpy.mean(values[ts])
+
+    plt.plot(avg_temp)
+    plt.ylabel("Temperature (degrees Celcius)")
+    plt.xlabel("Time step")
+    plt.show()
+
+
+if __name__ == "__main__":
+    plot_temperature()