14 Build your own model with PCSE.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img style=\"float: right;\" src=\"data:image/png;base64,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\">\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# Building your own model with PCSE\n",
    "\n",
    "This Jupyter notebook will help you understand the building blocks of PCSE and how to create your own models with it.\n",
    "\n",
    "Allard de Wit, August 2024\n",
    "\n",
    "**Prerequisites for running this notebook**\n",
    "\n",
    "Several packages need to be installed for running PCSE/WOFOST:\n",
    "\n",
    "  1. PCSE and its dependencies. See the [PCSE user guide](http://pcse.readthedocs.io/en/stable/installing.html) for more information;\n",
    "  2. The `pandas` module for processing and storing WOFOST output;\n",
    "  3. The `matplotlib` module for generating charts\n",
    "\n",
    "**Important notice**\n",
    "\n",
    "If you start experimenting with this notebook and change the examples you may need to frequently restart the jupyter kernel. Python loads modules only once and therefore changes that you make to the code may not be reflected in the PCSE model results until you restart the kernel which forces python to reload all modules."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Importing the relevant modules\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This notebook was built with:\n",
      "python version: 3.10.13 | packaged by conda-forge | (main, Oct 26 2023, 18:01:37) [MSC v.1935 64 bit (AMD64)]\n",
      "PCSE version: 6.0.6\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import sys\n",
    "from pathlib import Path\n",
    "import datetime as dt\n",
    "import yaml\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "matplotlib.style.use(\"ggplot\")\n",
    "import matplotlib.pyplot as plt\n",
    "import pcse\n",
    "print(\"This notebook was built with:\")\n",
    "print(f\"python version: {sys.version}\")\n",
    "print(f\"PCSE version: {pcse.__version__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Developing a basic model\n",
    "\n",
    "One of the most basic models in agronomy is the Growing Degree Day model. This model simulates plant phenological development by counting the number of degrees above a base temperature. Usually this calculation is based on the daily average temperature, although some models use hourly temperature. \n",
    "\n",
    "## The model logic\n",
    "\n",
    "In the cell below we implement a GrowingDegreeDay model. This model has one state variable (Growing Degree Days: `GDD`), one rate variable (the daily increase in Growing Degree Days `rGDD`) and one parameter (the `BaseTemperature`). See the comments in the code below for more explanation. \n",
    "\n",
    "We write this model to a file `gddmodel.py` using the `%%writefile` directive at the top of the cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting gddmodel.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile gddmodel.py\n",
    "# Here we import some components from PCSE that are building blocks for\n",
    "# any model in PCSE.\n",
    "from pcse.base import SimulationObject, StatesTemplate, RatesTemplate, ParamTemplate\n",
    "from pcse.traitlets import Float\n",
    "\n",
    "# Models in PCSE always inherit from SimulationObject\n",
    "class GrowingDegreeDayModel(SimulationObject):\n",
    "    \"\"\"A simple model to accumulate growing degree days between start and end of the crop cycle.\n",
    "    \"\"\"\n",
    "\n",
    "    # This defines the model parameters. It inherits from ParamTemplate, this ensures that parameters\n",
    "    # receive their value and it signals when parameters are missing.\n",
    "    class Parameters(ParamTemplate):\n",
    "        BaseTemperature = Float\n",
    "\n",
    "    # State variables always inherit from StatesTemplate. StatesTemplate provides certain behaviour\n",
    "    # such as that state variables must be initialized.\n",
    "    class StateVariables(StatesTemplate):\n",
    "        GDD = Float\n",
    "\n",
    "    # Rate variables are somewhat similar to states but are initialized automatically to zero.\n",
    "    class RateVariables(RatesTemplate):\n",
    "        rGDD = Float\n",
    "\n",
    "    def initialize(self, day, kiosk, parameters):\n",
    "        \"\"\"Initialize runs only once, when the model is started. \n",
    "        \n",
    "        It always has three variables:\n",
    "        - day: the day when the model starts\n",
    "        - kiosk: the VariableKiosk which is an object which is shared between model components\n",
    "        - parameters: an object providing the model parameters\n",
    "        \"\"\"\n",
    "        # The code below initializes the parameters, state and rate variables.\n",
    "        # Note that the initial value of state variable (here: GDD) must be provided.\n",
    "        self.params = self.Parameters(parameters)\n",
    "        self.states = self.StateVariables(kiosk, GDD=0.0)\n",
    "        self.rates = self.RateVariables(kiosk)\n",
    "\n",
    "    def calc_rates(self, day, drv):\n",
    "        \"\"\"calc_rates computes the rate of change that occur during the current day.\n",
    "\n",
    "        It always has two variables:\n",
    "        - the current day (a date)\n",
    "        - the driving variables `drv` that contain the meteorological inputs\n",
    "        \"\"\"\n",
    "        # Here we compute the increase in GDD and assign it to rate variable rGDD\n",
    "        self.rates.rGDD = max(0.0, drv.TEMP - self.params.BaseTemperature)\n",
    "\n",
    "    def integrate(self, day, delt):\n",
    "        \"\"\"This performs the integration of rates of change onto the states.\n",
    "\n",
    "        It always has two variables:\n",
    "        - the current day\n",
    "        - the time step, which is fixed at 1.0 day\n",
    "        \"\"\"\n",
    "        # Here we add rGDD multiplied by delt to GDD.\n",
    "        # The multiplication with `delt` is mostly for educational purposes (since delt=1.0) although\n",
    "        # this may change in the future.\n",
    "        self.states.GDD += self.rates.rGDD * delt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the model implementation above does not contain a time loop that steps through the days of the simulation. There is no need to implement this yourself because the PCSE Engine will take care of implementing the time loop and synchronize it with the information on agromanagement.\n",
    "\n",
    "## A model configuration file\n",
    "\n",
    "However, the PCSE Engine needs to know about your model. Moreover, it wants a few details such as the variables it needs to collect during the simulation. For this purpose we make an engine configuration file. This configuration file is written in plain python although it usually has the extenstion \".conf\" to indicate that it is a configuration file rather than a simulation model.\n",
    "\n",
    "See below for the configuration file for the GrowingDegreeDay model implemented above. We write it to `gddmodel.conf`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting gddmodel.conf\n"
     ]
    }
   ],
   "source": [
    "%%writefile gddmodel.conf\n",
    "\n",
    "from gddmodel import GrowingDegreeDayModel\n",
    "from pcse.agromanager import AgroManager\n",
    "\n",
    "# Module to be used for the soil component (not required yet)\n",
    "SOIL = None\n",
    "\n",
    "# Module to be used for the crop simulation itself\n",
    "CROP = GrowingDegreeDayModel\n",
    "\n",
    "# Module to use for AgroManagement actions\n",
    "AGROMANAGEMENT = AgroManager\n",
    "\n",
    "# variables to save at OUTPUT signals\n",
    "# Set to an empty list if you do not want any OUTPUT\n",
    "OUTPUT_VARS = [\"GDD\", \"rGDD\"]\n",
    "# interval for OUTPUT signals, either \"daily\"|\"dekadal\"|\"monthly\"|\"weekly\"\n",
    "# For daily output you change the number of days between successive\n",
    "# outputs using OUTPUT_INTERVAL_DAYS. For dekadal and monthly\n",
    "# output this is ignored.\n",
    "OUTPUT_INTERVAL = \"daily\"\n",
    "OUTPUT_INTERVAL_DAYS = 1\n",
    "# Weekday: Monday is 0 and Sunday is 6\n",
    "OUTPUT_WEEKDAY = 0\n",
    "\n",
    "# Summary variables to save at CROP_FINISH signals\n",
    "# Set to an empty list if you do not want any SUMMARY_OUTPUT\n",
    "SUMMARY_OUTPUT_VARS = [\"GDD\"]\n",
    "\n",
    "# Summary variables to save at TERMINATE signals\n",
    "# Set to an empty list if you do not want any TERMINAL_OUTPUT\n",
    "TERMINAL_OUTPUT_VARS = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining the model inputs\n",
    "For actually running the model we require some more components from PCSE. First of all, the `Engine` is required, next we require the `ParameterProvider` which is the component that encapsulates the model parameters and finally we need a component for reading weather data: the `ExcelWeatherDataProvider`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pcse.engine import Engine\n",
    "from pcse.base import ParameterProvider\n",
    "from pcse.input import ExcelWeatherDataProvider"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we need to define some more inputs for running our simulation. \n",
    "\n",
    "First of all the parameters that the model requires. In this case the only parameter required is the `BaseTemperature` which is specified through a python dictionary. For the current simulation the `BaseTemperature` is set to 3.0 degrees Celsius. Next, the parameter dictionary is packed through the `ParameterProvider`. For the current simulation this has no advantage but when large sets of parameters are necessary this has clear advantages.\n",
    "\n",
    "Next, the agromanagement needs to be defined. The simulation starts at 2006-01-01 while emergence takes place at 2006-03-15. We assume the crop gets harvested at 2006-09-25. In this example the `crop_name` and `variety_name` are just labels which have no impact.\n",
    "\n",
    "Finally, the weather data is imported using the ExcelWeatherDataProvider.\n",
    "\n",
    "We now have everything to run our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cropd = dict(BaseTemperature=3.0)\n",
    "params = ParameterProvider(cropdata=cropd, sitedata={}, soildata={})\n",
    "\n",
    "agro  = yaml.safe_load(\"\"\"\n",
    "- 2006-01-01:\n",
    "    CropCalendar:\n",
    "        crop_name: 'GDDCrop'\n",
    "        variety_name: 'GDDVariety'\n",
    "        crop_start_date: 2006-03-15\n",
    "        crop_start_type: emergence\n",
    "        crop_end_date: 2006-09-25\n",
    "        crop_end_type: harvest\n",
    "        max_duration: 300\n",
    "    TimedEvents: null\n",
    "    StateEvents: null\n",
    "\"\"\")\n",
    "\n",
    "weatherfile = Path.cwd() / \"data\" / \"meteo\" / \"nl1.xlsx\"\n",
    "wdp = ExcelWeatherDataProvider(weatherfile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running the actual model\n",
    "The code below runs the GrowingDegreeDay model by starting the engine and providing it with the parameters, weather data, agromanagement and the path to the configuration file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdd_config = Path.cwd() / \"gddmodel.conf\"\n",
    "gddmodel = Engine(params, wdp, agro, config=gdd_config)\n",
    "gddmodel.run_till_terminate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now retrieve the model output, convert it to a pandas dataframe and show its content. Als you see, the dataframe contains the columns 'GDD' and 'rGDD' which are the variables specified in the OUTPUT in the configuration file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDD</th>\n",
       "      <th>rGDD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-09-21</th>\n",
       "      <td>2253.60</td>\n",
       "      <td>18.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-09-22</th>\n",
       "      <td>2272.25</td>\n",
       "      <td>16.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-09-23</th>\n",
       "      <td>2288.50</td>\n",
       "      <td>17.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-09-24</th>\n",
       "      <td>2305.75</td>\n",
       "      <td>15.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-09-25</th>\n",
       "      <td>2321.35</td>\n",
       "      <td>13.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                GDD   rGDD\n",
       "day                       \n",
       "2006-09-21  2253.60  18.65\n",
       "2006-09-22  2272.25  16.25\n",
       "2006-09-23  2288.50  17.25\n",
       "2006-09-24  2305.75  15.60\n",
       "2006-09-25  2321.35  13.20"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame(gddmodel.get_output()).set_index(\"day\")\n",
    "df1.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summary output is also available:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'GDD': 2321.3500000000004}]\n"
     ]
    }
   ],
   "source": [
    "print(gddmodel.get_summary_output())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally we can plot the results for the GDD variable as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df1.GDD.plot(title=\"Growing degree days\", ylabel=\"Degree days\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see the total number of growing degree days at harvest is 2321. During the first 1.5 months the GDD values accumulate relatively slowly due to low temperatures, but then the accumulation accellerates in late spring and summer until reaching the harvest date at 2006-09-25.\n",
    "\n",
    "# 3. Signaling maturity for a given GDD\n",
    "\n",
    "The model above is useful for computing GDD values between a given emergence and harvest date. However, in many cases we want to apply the model in a prognostic way. E.g. we know the number of degree days a particular crop variety requires to reach maturity and we want to predict the maturity day of that variety in a given environment. To achieve this we need to add a few more details to a our model. \n",
    "\n",
    "First, we need to add a new crop parameter that defines the number of degree days to reach maturity, we call his `GDDatMaturity`. Next, it is convenient to save the day when maturity is reached, so an additional state variable `DayOfMaturity` is introduced. Finally, we can instruct the Simulation Engine that the simulation can be stopped because maturity is reached. This is done by sending a `CROP_FINISH` signal through the `_send_signal()` function. The latter is defined within the SimulationObject definition and because our model inherites from SimulationObject we can use it straight away.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting gddmodel_v2.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile gddmodel_v2.py\n",
    "import datetime as dt\n",
    "from pcse.base import SimulationObject, StatesTemplate, RatesTemplate, ParamTemplate\n",
    "from pcse.traitlets import Float, Instance\n",
    "from pcse import signals\n",
    "\n",
    "class GrowingDegreeDayModelV2(SimulationObject):\n",
    "    \"\"\"A simple model to accumulate growing degree days up till GDDatMaturity. The model\n",
    "    stores the maturity date and triggers a crop_finish signal when maturity is reached.    \n",
    "    \"\"\"\n",
    "\n",
    "    class Parameters(ParamTemplate):\n",
    "        BaseTemperature = Float\n",
    "        GDDatMaturity = Float\n",
    "\n",
    "    class StateVariables(StatesTemplate):\n",
    "        GDD = Float\n",
    "        DayofMaturity = Instance(dt.date)\n",
    "\n",
    "    class RateVariables(RatesTemplate):\n",
    "        rGDD = Float\n",
    "\n",
    "    def initialize(self, day, kiosk, parameters):\n",
    "        self.params = self.Parameters(parameters)\n",
    "        self.states = self.StateVariables(kiosk, GDD=0.0, DayofMaturity=None)\n",
    "        self.rates = self.RateVariables(kiosk)\n",
    "\n",
    "    def calc_rates(self, day, drv):\n",
    "        self.rates.rGDD = max(0, drv.TEMP - self.params.BaseTemperature)\n",
    "\n",
    "    def integrate(self, day, delt):\n",
    "        self.states.GDD += self.rates.rGDD * delt\n",
    "        if self.states.GDD >= self.params.GDDatMaturity:\n",
    "            self.states.DayofMaturity = day\n",
    "            self._send_signal(day=day, signal=signals.crop_finish, crop_delete=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Off course we need to create a configuration file for our new model. The only changes are that we are now importing from the python module that contains the V2 version of our model and we add the DayofMaturity to the SUMMARY_OUTPUT_VARS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting gddmodel_v2.conf\n"
     ]
    }
   ],
   "source": [
    "%%writefile gddmodel_v2.conf\n",
    "\n",
    "from gddmodel_v2 import GrowingDegreeDayModelV2\n",
    "from pcse.agromanager import AgroManager\n",
    "\n",
    "# Module to be used for water balance\n",
    "SOIL = None\n",
    "\n",
    "# Module to be used for the crop simulation itself\n",
    "CROP = GrowingDegreeDayModelV2\n",
    "\n",
    "# Module to use for AgroManagement actions\n",
    "AGROMANAGEMENT = AgroManager\n",
    "\n",
    "# variables to save at OUTPUT signals\n",
    "# Set to an empty list if you do not want any OUTPUT\n",
    "OUTPUT_VARS = [\"GDD\", \"rGDD\"]\n",
    "# interval for OUTPUT signals, either \"daily\"|\"dekadal\"|\"monthly\"|\"weekly\"\n",
    "# For daily output you change the number of days between successive\n",
    "# outputs using OUTPUT_INTERVAL_DAYS. For dekadal and monthly\n",
    "# output this is ignored.\n",
    "OUTPUT_INTERVAL = \"daily\"\n",
    "OUTPUT_INTERVAL_DAYS = 1\n",
    "# Weekday: Monday is 0 and Sunday is 6\n",
    "OUTPUT_WEEKDAY = 0\n",
    "\n",
    "# Summary variables to save at CROP_FINISH signals\n",
    "# Set to an empty list if you do not want any SUMMARY_OUTPUT\n",
    "SUMMARY_OUTPUT_VARS = [\"GDD\", \"DayofMaturity\"]\n",
    "\n",
    "# Summary variables to save at TERMINATE signals\n",
    "# Set to an empty list if you do not want any TERMINAL_OUTPUT\n",
    "TERMINAL_OUTPUT_VARS = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we need to define the parameters and agromanagement for our V2 model. We have one new parameter `GDDatMaturity` which we set at 1500 degree days between emergence and maturity. Moreover, the agromanagement requires an update because we assume to the model to run to maturity instead of harvest. This implies that the crop_end_date is undefined (*null*) and the crop_end_type is *maturity*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "cropd = dict(BaseTemperature=3.0, GDDatMaturity=1500.)\n",
    "params = ParameterProvider(cropdata=cropd, sitedata={}, soildata={})\n",
    "agro  = yaml.safe_load(\"\"\"\n",
    "- 2006-01-01:\n",
    "    CropCalendar:\n",
    "        crop_name: 'GDDCrop'\n",
    "        variety_name: 'GDDVariety'\n",
    "        crop_start_date: 2006-03-15\n",
    "        crop_start_type: emergence\n",
    "        crop_end_date: null\n",
    "        crop_end_type: maturity\n",
    "        max_duration: 300\n",
    "    TimedEvents: null\n",
    "    StateEvents: null\n",
    "\"\"\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can run the V2 model by pointing the engine to the V2 configuration file and running it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdd_config = Path.cwd() / \"gddmodel_v2.conf\"\n",
    "gddmodelv2 = Engine(params, wdp, agro, config=gdd_config)\n",
    "gddmodelv2.run_till_terminate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we can retrieve the output and convert it to a pandas dataframe. When we print the last 5 rows, it is evident that the simulation stopped on 2006-07-30 because the simulation reached 1507 degree days and thus the crop reached maturity which was set at 1500 degrees. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDD</th>\n",
       "      <th>rGDD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-07-26</th>\n",
       "      <td>1428.00</td>\n",
       "      <td>23.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-07-27</th>\n",
       "      <td>1451.10</td>\n",
       "      <td>19.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-07-28</th>\n",
       "      <td>1470.15</td>\n",
       "      <td>19.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-07-29</th>\n",
       "      <td>1489.65</td>\n",
       "      <td>18.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-07-30</th>\n",
       "      <td>1507.70</td>\n",
       "      <td>18.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                GDD   rGDD\n",
       "day                       \n",
       "2006-07-26  1428.00  23.10\n",
       "2006-07-27  1451.10  19.05\n",
       "2006-07-28  1470.15  19.50\n",
       "2006-07-29  1489.65  18.05\n",
       "2006-07-30  1507.70  18.90"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame(gddmodelv2.get_output()).set_index(\"day\")\n",
    "df2.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we plot the GDD we get the same picture as the previous one, except that the simulation is stopped when 1500 degree days were reached."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df2.GDD.plot(ylabel=\"Degree days\", title=\"Growing degree days\")\n",
    "fig = plt.gcf()\n",
    "fig.autofmt_xdate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Interaction between model components\n",
    "\n",
    "A single model on its own can be useful but often we are interested in the interaction between different components of the system. For crops this often involves the interaction between the soil and the crop but it could also be the development of pest & disease which is influenced by crop development stage for example.\n",
    "\n",
    "## Accelerated phenological development under water stress\n",
    "For demonstrating the interaction between a crop and a soil, we will take the case that some crops accelerate their phenological development under water stress. In order to model this we need two new components:\n",
    "1. a model that prescribes the water stress that the crop is experiencing. In fact a model for simulating the water availability in the soil.\n",
    "2. a description of the impact of water stress on the rate of phenological development.\n",
    "\n",
    "For the former, we will use an extremely simple soil water model which models the soil moisture as piecewise linear function which keeps the soil wet until the first of April, then gradually dries out the soil up till a given depletion value is reached at 15 august. From that point we assume that soil moisture linearly increases again until it is fully wet again on 1 November.\n",
    "\n",
    "For the latter, we will use a linear function that describes the relative increase in development rate as a function of the water stress factor.\n",
    "\n",
    "### The soil water model\n",
    "\n",
    "The soil water model is decribed by a few parameters:\n",
    "- the field capacity  which defines the relative soil moisture content when the soil is wet.\n",
    "- the wilting point which defines the relative soil moisture content at which plants cannot draw water from the soil anymore.\n",
    "- a depletion factor which defines how much we let the soil dry out, defined as a fraction (0-1) between field capacity and wilting point.\n",
    "\n",
    "Moreover, we will assume that plants start to experience water stress when the soil water content is half way between field capacity and wilting point (the critical level) and this water stress is increasing linearly between this critical level and the wilting point. The water stress is expressed in a `WaterStressFactor` that has value 0 when no stress occurs and linearly increases to 1 when the wilting point is reached.\n",
    "\n",
    "Note that this soil water model does not define any dynamics that are happening in practice (soil water flow, water extraction by roots, infiltration, capillary rise, etc.) but it does serve the purpose of demonstrating interaction between model components in a PCSE model.\n",
    "\n",
    "The figure below shows the prescribed soil water levels. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pcse.util import doy\n",
    "FieldCapacity = 0.35\n",
    "WiltingPoint = 0.1\n",
    "DepletionFactor = 0.9\n",
    "SM_depleted = FieldCapacity - (FieldCapacity-WiltingPoint) * DepletionFactor\n",
    "# The seasonal soil moisture is provided in a tabular format\n",
    "seasonal_soil_moisture = \\\n",
    "    [doy(dt.date(2006,1,1)), FieldCapacity,\n",
    "     doy(dt.date(2006,4,1)), FieldCapacity,\n",
    "     doy(dt.date(2006,8,15)), SM_depleted,\n",
    "     doy(dt.date(2006,11,1)), FieldCapacity,\n",
    "     doy(dt.date(2006,12,31)), FieldCapacity,\n",
    "    ]\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(seasonal_soil_moisture[::2], seasonal_soil_moisture[1::2], label=\"Prescribed soil moisture\")\n",
    "ax.set_ylim(0, 0.5)\n",
    "ax.set_xlabel(\"Day of year\")\n",
    "ax.set_ylabel(\"Relative soil moisture\")\n",
    "ax.plot([0,365], [FieldCapacity, FieldCapacity], linestyle=\"--\", color=\"black\", linewidth=0.5)\n",
    "ax.text(0, 0.36, \"Field capacity\", fontsize=\"x-small\")\n",
    "ax.plot([0,365], [WiltingPoint, WiltingPoint], linestyle=\"--\", color=\"black\", linewidth=0.5)\n",
    "ax.text(0, 0.11, \"Wilting point\", fontsize=\"x-small\")\n",
    "plt.arrow(doy(dt.date(2006,8,15)), FieldCapacity, 0, -1 * (FieldCapacity - SM_depleted), width=1, head_length=0.01, color=\"black\", length_includes_head=True)\n",
    "ax.text(160, 0.30, \"Depletion factor\", fontsize=\"x-small\")\n",
    "r = ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The GDD model with water stress impact\n",
    "\n",
    "The modified GDD model now includes one additional parameter `MaxRelativeGDDincrease` which specifies the relative increase in the rate of GDD under full water stress (with soil moisture at wilting point). This parameter is multiplied with the WaterStressFactor supplied by the soil water model to simulate the increase in GDD rates.\n",
    "\n",
    "Below is the implementation of the soil water model and the modified GDD model with accelerated phenology under water stress. An important detail here is the that the WaterStressFactor in the soil water model is a published variable which allows the GrowingDegreeDay model to pick it up from the [VariableKiosk](https://pcse.readthedocs.io/en/stable/reference_guide.html#the-variablekiosk)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting gddmodel_v3.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile gddmodel_v3.py\n",
    "import datetime as dt\n",
    "from pcse.base import SimulationObject, StatesTemplate, RatesTemplate, ParamTemplate\n",
    "from pcse.util import doy, limit, AfgenTrait\n",
    "from pcse.traitlets import Float, Instance\n",
    "from pcse import signals\n",
    "\n",
    "class GrowingDegreeDayModelV3(SimulationObject):\n",
    "    \"\"\"A simple model to accumulate growing degree days up till GDDatMaturity. The model\n",
    "    stores the maturity date and triggers a crop_finish signal when maturity is reached.\n",
    "\n",
    "    The rate of change (rGDD) is modified by a factor that increases rGDD under water stress.\n",
    "    \"\"\"\n",
    "\n",
    "    class Parameters(ParamTemplate):\n",
    "        BaseTemperature = Float\n",
    "        GDDatMaturity = Float\n",
    "        MaxRelativeGDDincrease = Float        \n",
    "\n",
    "    class StateVariables(StatesTemplate):\n",
    "        GDD = Float\n",
    "        DayofMaturity = Instance(dt.date)\n",
    "\n",
    "    class RateVariables(RatesTemplate):\n",
    "        rGDD = Float\n",
    "\n",
    "    def initialize(self, day, kiosk, parameters):\n",
    "        self.params = self.Parameters(parameters)\n",
    "        self.states = self.StateVariables(kiosk, GDD=0.0, DayofMaturity=None)\n",
    "        self.rates = self.RateVariables(kiosk)\n",
    "\n",
    "    def calc_rates(self, day, drv):\n",
    "        # compute the increased rate of GDD. Note that the WaterStressFactor is provided\n",
    "        # by the soil module and can be accessed through self.kiosk\n",
    "        relGDDinc = 1.0 + self.params.MaxRelativeGDDincrease * self.kiosk.WaterStressFactor\n",
    "        self.rates.rGDD = max(0, drv.TEMP - self.params.BaseTemperature) * relGDDinc\n",
    "\n",
    "    def integrate(self, day, delt):\n",
    "        self.states.GDD += self.rates.rGDD * delt\n",
    "        if self.states.GDD >= self.params.GDDatMaturity:\n",
    "            self.states.DayofMaturity = day\n",
    "            self._send_signal(day=day, signal=signals.crop_finish, crop_delete=True)\n",
    "\n",
    "\n",
    "class BasicSoilWaterModel(SimulationObject):\n",
    "\n",
    "    class Parameters(ParamTemplate):\n",
    "        FieldCapacity = Float\n",
    "        WiltingPoint = Float\n",
    "        SeasonalSoilMoisture = AfgenTrait  # Afgen is a linear interpolator object\n",
    "\n",
    "    class StateVariables(StatesTemplate):\n",
    "        SoilMoisture = Float\n",
    "        WaterStressFactor = Float\n",
    "\n",
    "    def initialize(self, day, kiosk, parameters):\n",
    "        self.params = self.Parameters(parameters)\n",
    "        # compute the critical level which is half-way between Field Capacity and Wilting Point\n",
    "        self._critical_level = (self.params.FieldCapacity + self.params.WiltingPoint)/2.0\n",
    "    \n",
    "        SM = self.params.SeasonalSoilMoisture(doy(day))\n",
    "        self.states = self.StateVariables(kiosk, SoilMoisture=SM, WaterStressFactor=0.0, publish=[\"WaterStressFactor\"])\n",
    "\n",
    "    def calc_rates(self, day, drv):\n",
    "        # This model has no dynamics so no rates of change need to be computed\n",
    "        pass\n",
    "\n",
    "    def integrate(self, day, delt):\n",
    "        # Retrieve the soil moisture by linear interpolation based on the day-of-year, Afgen takes care of that.\n",
    "        self.states.SoilMoisture = self.params.SeasonalSoilMoisture(doy(day))\n",
    "        # Compute water stress factor and limit it between 0 and 1.\n",
    "        WSF = (self._critical_level - self.states.SoilMoisture)/(self._critical_level - self.params.WiltingPoint)\n",
    "        self.states.WaterStressFactor = limit(0.0, 1.0, WSF)\n",
    "        \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A configuration for crop and soil components\n",
    "The new configuration file imports both the GDD model and the soil water model and assigns them to the CROP and SOIL variables. Moreover, two additional outputs are included in the list of OUTPUT_VARS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting gddmodel_v3.conf\n"
     ]
    }
   ],
   "source": [
    "%%writefile gddmodel_v3.conf\n",
    "\n",
    "from gddmodel_v3 import GrowingDegreeDayModelV3, BasicSoilWaterModel\n",
    "from pcse.agromanager import AgroManager\n",
    "\n",
    "# Module to be used for water balance\n",
    "SOIL = BasicSoilWaterModel\n",
    "\n",
    "# Module to be used for the crop simulation itself\n",
    "CROP = GrowingDegreeDayModelV3\n",
    "\n",
    "# Module to use for AgroManagement actions\n",
    "AGROMANAGEMENT = AgroManager\n",
    "\n",
    "# variables to save at OUTPUT signals\n",
    "# Set to an empty list if you do not want any OUTPUT\n",
    "OUTPUT_VARS = [\"GDD\", \"rGDD\", \"SoilMoisture\", \"WaterStressFactor\"]\n",
    "# interval for OUTPUT signals, either \"daily\"|\"dekadal\"|\"monthly\"|\"weekly\"\n",
    "# For daily output you change the number of days between successive\n",
    "# outputs using OUTPUT_INTERVAL_DAYS. For dekadal and monthly\n",
    "# output this is ignored.\n",
    "OUTPUT_INTERVAL = \"daily\"\n",
    "OUTPUT_INTERVAL_DAYS = 1\n",
    "# Weekday: Monday is 0 and Sunday is 6\n",
    "OUTPUT_WEEKDAY = 0\n",
    "\n",
    "# Summary variables to save at CROP_FINISH signals\n",
    "# Set to an empty list if you do not want any SUMMARY_OUTPUT\n",
    "SUMMARY_OUTPUT_VARS = [\"GDD\", \"DayofMaturity\"]\n",
    "\n",
    "# Summary variables to save at TERMINATE signals\n",
    "# Set to an empty list if you do not want any TERMINAL_OUTPUT\n",
    "TERMINAL_OUTPUT_VARS = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model inputs\n",
    "The new model requires additional input parameters. So instead of passing only crop parameters, we also need to pass the parameters for the soil model as well.\n",
    "\n",
    "Note that I also updated the agromanagement definition: I added a [\"trailing empty campaign\"](https://pcse.readthedocs.io/en/stable/reference_guide.html#finding-the-start-and-end-date-of-a-simulation) to force the system to run to 31 December 2006. This ensure that we can plot the full year and see the soil water availability and water stress factor for the entire year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "cropd = dict(BaseTemperature=3.0, GDDatMaturity=1500., MaxRelativeGDDincrease=0.5)\n",
    "soild = dict(FieldCapacity=FieldCapacity, WiltingPoint=WiltingPoint, SeasonalSoilMoisture=seasonal_soil_moisture)\n",
    "params = ParameterProvider(cropdata=cropd, sitedata={}, soildata=soild)\n",
    "agro  = yaml.safe_load(\"\"\"\n",
    "- 2006-01-01:\n",
    "    CropCalendar:\n",
    "        crop_name: 'GDDCrop'\n",
    "        variety_name: 'GDDVariety'\n",
    "        crop_start_date: 2006-03-15\n",
    "        crop_start_type: emergence\n",
    "        crop_end_date: null\n",
    "        crop_end_type: maturity\n",
    "        max_duration: 300\n",
    "    TimedEvents: null\n",
    "    StateEvents: null\n",
    "- 2006-12-31:\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's run the the model with the interaction between soil and GDD model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdd_config = Path.cwd() / \"gddmodel_v3.conf\"\n",
    "gddmodelv3 = Engine(params, wdp, agro, config=gdd_config)\n",
    "gddmodelv3.run_till_terminate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'GDD': 1523.618529411765, 'DayofMaturity': datetime.date(2006, 7, 26)}]\n"
     ]
    }
   ],
   "source": [
    "df3 = pd.DataFrame(gddmodelv3.get_output()).set_index(\"day\")\n",
    "print(gddmodelv3.get_summary_output())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see from the summary output in the results above, the model now reaches maturity at 2006-07-26, 4 days earlier than the model without the impact of water stress. This is also evident from the figure below showing the difference in GDD accumulation, the soil moisture levels and the water stress factor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=3, figsize=(8,8), sharex=True)\n",
    "df2.GDD.plot(ax=axes[0], label=\"No water stress\")\n",
    "df3.GDD.plot(ax=axes[0], label=\"With water stress\")\n",
    "df3.SoilMoisture.plot(ax=axes[1])\n",
    "df3.WaterStressFactor.plot(ax=axes[2])\n",
    "axes[0].set_ylabel(\"GDD\")\n",
    "axes[1].set_ylabel(\"Soil Moisture\")\n",
    "axes[2].set_ylabel(\"Water stress\")\n",
    "fig.autofmt_xdate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}