03_run_cf_simulation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Running a simulation with SHyFT\n",
    "=========\n",
    "\n",
    "### This notebook is guiding through the simulation process of a catchment. The following steps are described in detail:\n",
    "1. **Configuration of a SHyFT simulation run**\n",
    "2. **Running a SHyFT simulation**\n",
    "3. **Post-processing:** Fetching simulation results from the simulator-object."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<i>© Felix Matt, University of Oslo</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "# python modules we use\n",
    "%pylab inline\n",
    "import os\n",
    "import sys\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# setting up the environment\n",
    "shyft_path = os.path.abspath('/geo/nunatak/hycamp/software/shyft_course_2/shyft')\n",
    "sys.path.append(shyft_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from os import path\n",
    "import unittest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from shyft import shyftdata_dir\n",
    "from shyft.repository.default_state_repository import DefaultStateRepository\n",
    "from shyft.orchestration.configuration import yaml_configs\n",
    "from shyft.orchestration.simulators.config_simulator import ConfigSimulator\n",
    "from shyft import api"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Set up configuration\n",
    "config_dir = \"/geo/nunatak/hycamp/software/shyft_course_2/shyft/shyft/tests/netcdf\"\n",
    "cfg = yaml_configs.YAMLConfig(\"neanidelva_simulation.yaml\", \"neanidelva\",config_dir=config_dir, data_dir=shyftdata_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# get a simulator\n",
    "simulator = ConfigSimulator(cfg)\n",
    "n_cells = simulator.region_model.size()\n",
    "state_repos = DefaultStateRepository(cfg.model_t, n_cells)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time\n",
      "series_name\n",
      "x\n",
      "y\n",
      "z\n",
      "crs\n",
      "precipitation\n",
      "time\n",
      "series_name\n",
      "x\n",
      "y\n",
      "z\n",
      "crs\n",
      "temperature\n",
      "time\n",
      "series_name\n",
      "x\n",
      "y\n",
      "z\n",
      "crs\n",
      "wind_speed\n",
      "time\n",
      "series_name\n",
      "x\n",
      "y\n",
      "z\n",
      "crs\n",
      "relative_humidity\n",
      "time\n",
      "series_name\n",
      "x\n",
      "y\n",
      "z\n",
      "crs\n",
      "global_radiation\n"
     ]
    }
   ],
   "source": [
    "# run the simulation\n",
    "simulator.run(cfg.time_axis, state_repos.get_state(0))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Get the discharge for each catchment\n",
    "discharge_ts = []\n",
    "for catch in range(len(simulator.region_model.catchment_id_map)):\n",
    "    dis = simulator.region_model.statistics.discharge(api.IntVector(catch))\n",
    "    ts=[dis.value(t) for t in range(dis.size())]\n",
    "    discharge_ts.append(ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f6c9c1edad0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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FXU7KOP1SkiRJWWfTJujSJTo3Lj8/7mrqV7ZMv1yyZAn77bcfubm5W3Xo7rrrLkaMGBFL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\nLTKfBAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c9c26bfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,10))\n",
    "for i,vals in enumerate(discharge_ts):\n",
    "    ax.plot(vals, label = str(i))\n",
    "ax.legend(title='internal catch. ID')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "##Exercise: Get the snow covered area of each cell for the last time step and use a scatter plot to visualize it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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