examples/05_init_single_node_model.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Copyright 2020 Marco Dal Molin et al.\n",
    "\n",
    "This file is part of SuperflexPy.\n",
    "\n",
    "SuperflexPy is free software: you can redistribute it and/or modify\n",
    "it under the terms of the GNU Lesser General Public License as published by\n",
    "the Free Software Foundation, either version 3 of the License, or\n",
    "(at your option) any later version.\n",
    "\n",
    "SuperflexPy is distributed in the hope that it will be useful,\n",
    "but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
    "MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
    "GNU Lesser General Public License for more details.\n",
    "\n",
    "You should have received a copy of the GNU Lesser General Public License\n",
    "along with SuperflexPy. If not, see <https://www.gnu.org/licenses/>.\n",
    "\n",
    "This file is part of the SuperflexPy modelling framework. For details about it,\n",
    "visit the page https://superflexpy.readthedocs.io\n",
    "\n",
    "CODED BY: Marco Dal Molin\n",
    "DESIGNED BY: Marco Dal Molin, Fabrizio Fenicia, Dmitri Kavetski"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SuperflexPy example 05: Initialize a single node model\n",
    "\n",
    "Author: Marco Dal Molin\n",
    "\n",
    "Collaborators: Fabrizio Fenicia, Dmitri Kavetski"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What's in this example\n",
    "\n",
    "This example will cover the following aspects:\n",
    "- [Initialize a single node model](https://superflexpy.readthedocs.io/en/latest/demo.html#multiple-units-configuration)\n",
    "\n",
    "By clicking on the items you will be redirected to the documentation page (when available) that explains the arguments in a more detailed way. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What's not in this example\n",
    "\n",
    "The following aspects are already covered in other examples:\n",
    "\n",
    "- [Initialize the units and the single elements contained](./04_init_single_unit_model.ipynb)\n",
    "- [Run the model](./01_run_simple_model.ipynb)\n",
    "- [Change states and parameters](./01_run_simple_model.ipynb)\n",
    "\n",
    "For this reason we will put only the code need, without further explanations. You can check the other examples by clicking on the items above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Do you want to use this example as a script?\n",
    "\n",
    "Examples of SuperflexPy are created and distributed using Jupyter notebooks because they enable to conjugate runnable code with text explanations. We have decided to not provide the content of the notebooks as script because this would mean duplicating the same content in multiple places, bringing to maintainability problems.\n",
    "\n",
    "If the user wants to download the content of this notebook as a python script, it is possible following the steps:\n",
    "1. If not already done, open this notebook using [Binder](https://mybinder.org/v2/gh/dalmo1991/superflexPy/master?filepath=examples%2F05_init_single_node_model.ipynb)\n",
    "2. Go on File -> Download as -> Python (.py)\n",
    "3. Select the saving path\n",
    "\n",
    "The result is a Python script that contains all the markdown text as comment and the code contained inside the cells."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STEP 1: Initialize the single node model\n",
    "\n",
    "A single node model is composed by several units that operate in parallel within the node. In this example, we will consider a case where there are 2 units operating in parallel."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 01. Import an initialize the numerical routines, the elements, and the units\n",
    "\n",
    "All the units contained in the node must be, first, initialized. This can be done with the following code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from superflexpy.implementation.root_finders.pegasus import PegasusPython\n",
    "from superflexpy.implementation.numerical_approximators.implicit_euler import ImplicitEulerPython\n",
    "from superflexpy.implementation.elements.hbv import PowerReservoir\n",
    "from superflexpy.framework.unit import Unit\n",
    "\n",
    "# Initialize numercal routines\n",
    "root_finder = PegasusPython()\n",
    "numeric_approximator = ImplicitEulerPython(root_finder=root_finder)\n",
    "\n",
    "# Initialize the elements\n",
    "e1 = PowerReservoir(\n",
    "    parameters={'k': 0.1, 'alpha': 1.0},\n",
    "    states={'S0': 10.0},\n",
    "    approximation=numeric_approximator,\n",
    "    id='E1'\n",
    ")\n",
    "\n",
    "e2 = PowerReservoir(\n",
    "    parameters={'k': 0.1, 'alpha': 1.0},\n",
    "    states={'S0': 10.0},\n",
    "    approximation=numeric_approximator,\n",
    "    id='E2'\n",
    ")\n",
    "\n",
    "e3 = PowerReservoir(\n",
    "    parameters={'k': 0.1, 'alpha': 1.0},\n",
    "    states={'S0': 10.0},\n",
    "    approximation=numeric_approximator,\n",
    "    id='E3'\n",
    ")\n",
    "\n",
    "u1 = Unit(\n",
    "    layers=[\n",
    "        [e1],\n",
    "        [e2]\n",
    "    ],\n",
    "    id='U1'\n",
    ")\n",
    "\n",
    "u2 = Unit(\n",
    "    layers=[\n",
    "        [e3],\n",
    "    ],\n",
    "    id='U2'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 02. Initialize the node\n",
    "\n",
    "Now that the units are all initialized, they can be added to a node to contribute together to its outflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from superflexpy.framework.node import Node\n",
    "\n",
    "model = Node(\n",
    "    units=[u1, u2],\n",
    "    weights=[0.7, 0.3],\n",
    "    area=1.0,\n",
    "    id='node'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STEP 2: Run the model\n",
    "\n",
    "Now that the model has been initialized, it can be run. This can be done running the code in the following cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams.update({'font.size': 20})\n",
    "\n",
    "# Fix the seed\n",
    "SEED = 2\n",
    "rng = np.random.RandomState(seed=SEED)\n",
    "\n",
    "# Generate the input\n",
    "P = np.zeros(100)\n",
    "P[:10] = rng.randint(10, size=10)\n",
    "P[25:30] = rng.randint(20, size=5)\n",
    "P[40:60] = rng.randint(5, size=20)\n",
    "P[80:83] = rng.randint(30, 50, size=3)\n",
    "\n",
    "# Assign the input\n",
    "model.set_input([P])\n",
    "\n",
    "# Set the timestep\n",
    "model.set_timestep(1.0)\n",
    "\n",
    "# Run the model\n",
    "output = model.get_output()\n",
    "\n",
    "# Plot\n",
    "fig, ax = plt.subplots(2, 1, figsize=(20, 12), sharex=True)\n",
    "ax[0].bar(x=np.arange(len(P)), height=P, color='royalblue')\n",
    "ax[0].set_ylabel('Precipitation [mm/day]')\n",
    "ax[0].grid(True)\n",
    "ax[1].plot(np.arange(len(P)), output[0], lw=2, label='Total outflow')\n",
    "ax[1].set_xlabel('Time [days]')\n",
    "ax[1].set_ylabel('Streamflow [mm/day]')\n",
    "ax[1].legend()\n",
    "ax[1].grid(True)\n",
    "pass"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "just_sfpy_RC",
   "language": "python",
   "name": "just_sfpy_rc"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}