02 Running with custom input data.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Running PCSE/WOFOST with custom input data\n",
    "\n",
    "This Jupyter notebook will show you how to read inputs from files for running PCSE/WOFOST.\n",
    "\n",
    "Allard de Wit, March 2018\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",
    "\n",
    "## Introduction\n",
    "\n",
    "For running PCSE/WOFOST (and PCSE models in general) with your own data sources you need three different types of inputs:\n",
    "\n",
    " 1. Model parameters that parameterize the different model components. These parameters usually consist of a set of crop parameters (or multiple sets in case of crop rotations), a set of soil parameters and a set of site parameters. The latter provide ancillary parameters that are specific for a location, for example the initial amount of moisture in the soil.\n",
    " 2. Driving variables represented by weather data which can be derived from various sources.\n",
    " 3. Agromanagement actions which specify the farm activities that will take place on the field that is simulated by PCSE.\n",
    "\n",
    "For this example we will run a simulation for sugar beet in Wageningen (Netherlands) and we will read the input data step by step from several different sources instead of using the pre-configured start_wofost() script. For the example we will assume that data files are in the `data` directory within the directory where this notebook is located. This will be the case if you downloaded the notebooks from GitHub.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 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: 5.5.5\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import sys\n",
    "from pathlib import Path\n",
    "import matplotlib\n",
    "matplotlib.style.use(\"ggplot\")\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "data_dir = Path.cwd() / \"data\"\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": [
    "## Reading model parameters\n",
    "### Crop parameters\n",
    "\n",
    "The crop parameters consist of parameter names and the corresponding parameter values that are needed to parameterize the components of the crop simulation model. These are crop-specific values regarding phenology, assimilation, respiration, biomass partitioning, etc. The parameter file for sugar beet is taken from the crop files in the WOFOST Control Centre.\n",
    "\n",
    "As many crop models in Wageningen were written in FORTRAN, the crop parameters for many models in Wageningen are often provided in the CABO format that could be read with the TTUTIL FORTRAN library. This CABO format will be gradually phased out and PCSE will move to a new format based on YAML, see [here for an example](https://github.com/ajwdewit/WOFOST_crop_parameters/blob/master/wheat.yaml). However, PCSE tries to be backward compatible as much as possible and provides the `CABOFileReader` for reading parameter files in CABO format. The `CABOFileReader` returns a dictionary with the parameter name/value pairs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pcse.fileinput import CABOFileReader\n",
    "cropfile = data_dir / \"crop\" / \"SUG0601.crop\"\n",
    "cropd = CABOFileReader(cropfile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Soil parameters\n",
    "The soildata dictionary provides the parameter name/value pairs related to the soil type and soil physical properties. The number of parameters is variable depending on the soil water balance type that is used for the simulation. For this example, we will use the water balance for freely draining soils and use the soil file for medium fine sand: `ec3.soil`. This file is also taken from the soil files in the [WOFOST Control Centre](http://www.wageningenur.nl/wofost)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "soilfile = data_dir / \"soil\" / \"ec3.soil\"\n",
    "soild = CABOFileReader(soilfile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Site parameters\n",
    "\n",
    "The site parameters provide ancillary parameters that are not related to the crop or the soil. Examples are the initial conditions of the water balance such as the initial soil moisture content (WAV) and the initial and maximum surface storage (SSI, SSMAX). Also the atmospheric $CO_{2}$ \n",
    "concentration is a typical site parameter. For the moment, we can define these parameters directly on the Python commandline as a simple python dictionary. However, it is more convenient to use the `WOFOST72SiteDataProvider` that documents the site parameters and provides sensible defaults:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'IFUNRN': 0, 'NOTINF': 0, 'SSI': 0.0, 'SSMAX': 0.0, 'WAV': 10.0, 'SMLIM': 0.4}\n"
     ]
    }
   ],
   "source": [
    "from pcse.util import WOFOST72SiteDataProvider\n",
    "sited = WOFOST72SiteDataProvider(WAV=10)\n",
    "print(sited)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Packaging all parameters\n",
    "Finally, we need to pack the different sets of parameters into one variable using the `ParameterProvider`. This is needed because PCSE expects one variable that contains all parameter values. Using this approach has the additional advantage that parameter value can be easily overridden in case of running multiple simulations with slightly different parameter values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pcse.base import ParameterProvider\n",
    "parameters = ParameterProvider(cropdata=cropd, soildata=soild, sitedata=sited)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agromanagement\n",
    "The agromanagement inputs provide the start date of the agricultural campaign, the start_date/start_type of the crop simulation, the end_date/end_type of the crop simulation and the maximum duration of the crop simulation. The latter is included to avoid unrealistically long simulations for example as a results of a too high temperature sum requirement.\n",
    "\n",
    "The agromanagement inputs are defined with a special syntax called [YAML](http://yaml.org/) which allows to easily create more complex structures which is needed for defining the agromanagement. The agromanagement file for sugar beet in Wageningen `sugarbeet_calendar.agro` can be read with the `YAMLAgroManagementReader`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "!!python/object/new:pcse.fileinput.yaml_agro_loader.YAMLAgroManagementReader\n",
      "listitems:\n",
      "- 2006-01-01:\n",
      "    CropCalendar:\n",
      "      crop_end_date: 2006-10-20\n",
      "      crop_end_type: harvest\n",
      "      crop_name: sugar-beet\n",
      "      crop_start_date: 2006-04-05\n",
      "      crop_start_type: emergence\n",
      "      max_duration: 300\n",
      "      variety_name: sugar-beet-601\n",
      "    StateEvents: null\n",
      "    TimedEvents: null\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from pcse.fileinput import YAMLAgroManagementReader\n",
    "agromanagement_file = data_dir / \"agro\" / \"sugarbeet_calendar.agro\"\n",
    "agromanagement = YAMLAgroManagementReader(agromanagement_file)\n",
    "print(agromanagement)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Daily weather observations\n",
    "Daily weather variables are needed for running the simulation. There are several data providers in PCSE for reading weather data, see the section on [weather data providers](http://pcse.readthedocs.io/en/stable/reference_guide.html#weather-data-providers) to get an overview.\n",
    "\n",
    "For this example we will use weather data from an Excel file which provides daily weather data for Wageningen for the period 2004 to 2008. We will read the data from the file using the `ExcelWeatherDataProvider`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weather data provided by: ExcelWeatherDataProvider\n",
      "--------Description---------\n",
      "Weather data for:\n",
      "Country: Netherlands\n",
      "Station: Wageningen, Location Haarweg\n",
      "Description: Observed data from Station Haarweg in Wageningen\n",
      "Source: Meteorology and Air Quality Group, Wageningen University\n",
      "Contact: Peter Uithol\n",
      "----Site characteristics----\n",
      "Elevation:    7.0\n",
      "Latitude:  51.970\n",
      "Longitude:  5.670\n",
      "Data available for 2004-01-02 - 2008-12-31\n",
      "Number of missing days: 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from pcse.fileinput import ExcelWeatherDataProvider\n",
    "weatherfile = data_dir / \"meteo\" / \"nl1.xlsx\"\n",
    "weatherdataprovider = ExcelWeatherDataProvider(weatherfile)\n",
    "print(weatherdataprovider)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing, initializing and running a PCSE model\n",
    "\n",
    "Internally, PCSE uses a simulation engine to run a crop simulation. This engine takes a configuration file that specifies the components for the crop, the soil and the agromanagement that need to be used for the simulation. So any PCSE model can be started by importing the engine and initializing it with a given configuration file and the corresponding parameters, weather data and agromanagement.\n",
    "\n",
    "However, as many users of PCSE only need a particular configuration (for example the WOFOST model for potential production), preconfigured Engines are provided in `pcse.models`. For the sugarbeet example we will import the WOFOST model for water-limited simulation under freely draining soil conditions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pcse.models import Wofost72_WLP_FD, Wofost72_PP\n",
    "wofsim = Wofost72_WLP_FD(parameters, weatherdataprovider, agromanagement)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then run the simulation and retrieve the time series of daily simulation output using the get_output() method on the WOFOST object. Finally, we convert the simulation reults to a pandas dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "            DVS       LAI          TAGP          TWSO         TWLV  \\\n",
       "day                                                                  \n",
       "2006-10-16  2.0  1.048787  21557.705173  14410.185356  3408.824045   \n",
       "2006-10-17  2.0  1.055901  21628.846417  14474.212476  3412.381108   \n",
       "2006-10-18  2.0  1.049414  21658.771700  14501.145230  3413.877372   \n",
       "2006-10-19  2.0  1.052133  21685.964928  14525.619135  3415.237033   \n",
       "2006-10-20  2.0  1.043584  21717.141971  14553.678474  3416.795885   \n",
       "\n",
       "                   TWST         TWRT       TRA     RD        SM      WWLOW  \n",
       "day                                                                         \n",
       "2006-10-16  3738.695772  2805.686837  0.052878  120.0  0.283340  34.000845  \n",
       "2006-10-17  3742.252834  2807.138699  0.030516  120.0  0.282876  33.945161  \n",
       "2006-10-18  3743.749098  2807.749419  0.030184  120.0  0.284605  34.152545  \n",
       "2006-10-19  3745.108759  2808.304383  0.043053  120.0  0.284840  34.180768  \n",
       "2006-10-20  3746.667611  2808.940650  0.039061  120.0  0.284271  34.112520  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wofsim.run_till_terminate()\n",
    "df_results = pd.DataFrame(wofsim.get_output())\n",
    "df_results = df_results.set_index(\"day\")\n",
    "df_results.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing simulation results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can generate some figures of WOFOST variables such as the development (DVS), total biomass (TAGP), leaf area index (LAI) and root-zone soil moisture (SM) using the MatPlotLib plotting package:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=4, ncols=1, figsize=(12,12), sharex=True)\n",
    "for var, ax in zip([\"DVS\", \"TAGP\", \"LAI\", \"SM\"], axes.flatten()):\n",
    "    ax.plot_date(df_results.index, df_results[var], 'b-')\n",
    "    ax.set_title(var)\n",
    "fig.autofmt_xdate()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
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