docs/tutorial1/tutorial1_python.ipynb

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    "# Tutorial 1: Setting up a turbine run\n",
    "## Using python interface of `amrwind_frontend`\n",
    "\n",
    "This tutorial shows how the python interface to `amrwind_frontend` can be used to used to accomplish the same actions as the GUI interface demonstrated in [tutorial1](tutorial1gui.md).\n",
    "\n",
    "For the jupyter notebook version of this tutorial, see [tutorial1_python.ipynb](tutorial1_python.ipynb).  In fact, this markdown was created using the command\n",
    "```bash\n",
    "jupyter nbconvert --to markdown tutorial1_python.ipynb\n",
    "```\n",
    "\n",
    "**Contents**  \n",
    "\n",
    "[[_TOC_]]\n"
   ]
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   "source": [
    "## Initialization\n",
    "First the `amrwind_frontend` needs to be loaded, along with some other libraries.  Note that `amrwindfedir` should be set to wherever `amrwind_frontend` was downloaded and installed."
   ]
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   "source": [
    "%%capture \n",
    "\n",
    "# Important header information\n",
    "amrwindfedir = '/projects/wind_uq/lcheung/amrwind-frontend'\n",
    "import sys, os\n",
    "sys.path.insert(1, amrwindfedir)\n",
    "\n",
    "# Load the libraries\n",
    "import matplotlib.pyplot    as plt\n",
    "import amrwind_frontend as amrwind\n",
    "\n",
    "# Also ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Make all plots inline \n",
    "%matplotlib inline"
   ]
  },
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   "source": [
    "Now the working directory needs to be created where the necessary input files and turbine files will be stored. "
   ]
  },
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   "source": [
    "# Create the working directory and switch to it\n",
    "workingdir='tutorial1run'\n",
    "try:\n",
    "    os.mkdir(workingdir)\n",
    "except:\n",
    "    pass\n",
    "os.chdir(workingdir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "To create an instance of the `amrwind_frontend` app that works without the GUI, instantiate `MyApp` with the `init_nogui()` method:"
   ]
  },
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   "source": [
    "# Start the amrwind_frontend app \n",
    "tutorial1 = amrwind.MyApp.init_nogui()"
   ]
  },
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   "source": [
    "## Adding inputs\n",
    "\n",
    "There are two ways to set AMR-Wind parameters using the python interface.  The first is text based, using the `loadAMRWindInput()` interface.  This can be used by defining a string with the AMR-Wind input, written as it would be in a standard input file, and then loading it in:\n",
    "```python\n",
    "inputstr=\"\"\"\n",
    "time.stop_time                           = 100.0               # Max (simulated) time to evolve\n",
    "time.max_step                            = -1                  \n",
    "time.fixed_dt                            = 0.1                 \n",
    "\"\"\"\n",
    "# Load the input into the app\n",
    "tutorial1.loadAMRWindInput(inputstr, string=True)\n",
    "```\n",
    "\n",
    "The `loadAMRWindInput()` interface can also be used to load an entire input file, simply by providing the filename and making sure `string=False` (or removed from the call):\n",
    "```python\n",
    "tutorial1.loadAMRWindInput(filename)\n",
    "```\n",
    "\n",
    "However, this way does not maximize the full potential of the python interface.  The second method of input uses the `setAMRWindInput(parametername, value)` interface.  The argument `parametername` can be either the AMR-Wind parameter name, or an internal `amrwind_frontend` parameter (more on this later).  We'll demonstrate this method later on."
   ]
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   "source": [
    "### Set the simulation type and properties\n",
    "\n",
    "We'll first set some basic properties of the simulation, like the simulation time and some time marching properties:"
   ]
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   "source": [
    "tutorial1.setAMRWindInput('time_control',['const dt'])\n",
    "tutorial1.setAMRWindInput('time.stop_time',100)\n",
    "tutorial1.setAMRWindInput('time.fixed_dt', 0.1)\n",
    "tutorial1.setAMRWindInput('incflo.physics', ['FreeStream', 'Actuator'])"
   ]
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   "source": [
    "Note that the values of any of these inputs can be queried through the `getAMRWindInput()` command:"
   ]
  },
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   "metadata": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100.0\n"
     ]
    }
   ],
   "source": [
    "# Get the current value of time.stop_time\n",
    "print(tutorial1.getAMRWindInput('time.stop_time'))"
   ]
  },
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   "source": [
    "Now we'll set some other fluid properties and velocities:"
   ]
  },
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   "source": [
    "tutorial1.setAMRWindInput('ConstValue.density.value',  1.0)\n",
    "tutorial1.setAMRWindInput('ConstValue.velocity.value', [10.0,0.0,0.0])\n",
    "tutorial1.setAMRWindInput('turbulence.model',  ['Laminar'])"
   ]
  },
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   "source": [
    "### Set the domain and boundary conditions\n",
    "\n",
    "The domain sizes and mesh counts can be set similarly:"
   ]
  },
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   "source": [
    "tutorial1.setAMRWindInput('geometry.prob_lo', [-1000, -500, -500])\n",
    "tutorial1.setAMRWindInput('geometry.prob_hi', [ 1000,  500,  500])\n",
    "tutorial1.setAMRWindInput('amr.n_cell',       [ 128, 64, 64])"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "To set the Y and Z sides of the domain to be periodic (and X not periodic), use"
   ]
  },
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   "cell_type": "code",
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   "source": [
    "tutorial1.setAMRWindInput('is_periodicx', False)\n",
    "tutorial1.setAMRWindInput('is_periodicy', True)\n",
    "tutorial1.setAMRWindInput('is_periodicz', True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
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   },
   "source": [
    "Note that for the periodicity settings, we're not using the `geometry.is_periodic` parameter.  Instead, we use the `amrwind_frontend` internal parameters called `is_periodicx`, `is_periodicy`, and `is_periodicz`, which are booleans.\n",
    "\n",
    "The x inflow and x outflow boundary conditions can be applied as normal:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
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   "source": [
    "tutorial1.setAMRWindInput('xlo.type', 'mass_inflow')\n",
    "tutorial1.setAMRWindInput('xhi.type', 'pressure_outflow')\n",
    "tutorial1.setAMRWindInput('xlo.density', 1.0)\n",
    "tutorial1.setAMRWindInput('xlo.velocity', [10.0, 0.0, 0.0])"
   ]
  },
  {
   "cell_type": "markdown",
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    "deletable": true,
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   "source": [
    "Finally the wind speed through the domain can be set through"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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   "source": [
    "tutorial1.setAMRWindInput('incflo.velocity', [10.0, 0.0, 0.0])"
   ]
  },
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   "source": [
    "### Plot the domain\n",
    "\n",
    "We can now see what the domain and wind direction look like by creating a plot of it.  To do this, we use the `plotDomain()` command.  Note that the `ax` axes parameter should be set, otherwise it tries to open up the GUI window to make the plot."
   ]
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ba8xmsykmJkZ5eXmumgvPGDqdTgUFBblqLpSenq709HRJ0sGDB70xFQAAgIBR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fffb26f4a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the domain\n",
    "plt.rc('font', size=14)\n",
    "fig, ax = plt.subplots(figsize=(10,10), facecolor='w')\n",
    "tutorial1.plotDomain(ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Adding a turbine\n",
    "\n",
    "To add a working turbine to the simulation, we first must include the `AcuatorForcing` term to the `ICNS.source_terms` parameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "tutorial1.setAMRWindInput('ICNS.source_terms', ['ActuatorForcing'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The way the turbines get added is by defining a dictionary which contains all necessary properties for the turbine.  Here we'll get the default dictionary, then start modifying properties about it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actuator_name: 'turbine0'\n",
      "use_turbine_type: ''\n",
      "copy_turb_files: 1\n",
      "edit_fast_files: 1\n",
      "Actuator_type: None\n",
      "Actuator_openfast_input_file: None\n",
      "Actuator_base_position: [0, 0, -90]\n",
      "Actuator_rotor_diameter: None\n",
      "Actuator_hub_height: None\n",
      "Actuator_num_points_blade: None\n",
      "Actuator_num_points_tower: None\n",
      "Actuator_epsilon: None\n",
      "Actuator_epsilon_tower: None\n",
      "Actuator_openfast_start_time: None\n",
      "Actuator_openfast_stop_time: None\n",
      "Actuator_nacelle_drag_coeff: None\n",
      "Actuator_nacelle_area: None\n",
      "Actuator_yaw: 270.0\n",
      "Actuator_output_frequency: None\n",
      "Actuator_density: 1.0\n"
     ]
    }
   ],
   "source": [
    "# Get a copy the default dictionary which defines AMR-Wind turbines\n",
    "turbine = tutorial1.get_default_actuatordict()\n",
    "\n",
    "# Set some basic properties of the turbine, like the name and position\n",
    "turbine['Actuator_name']          = 'turbine0'\n",
    "turbine['Actuator_base_position'] = [0, 0, -90]\n",
    "turbine['Actuator_yaw']           = 270.0\n",
    "turbine['Actuator_density']       = 1.0\n",
    "\n",
    "# Print out the dictionary\n",
    "for k, g in turbine.items(): print('%s: %s'%(k, repr(g)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "However, there are still a lot of properties which are undefined for the turbine.  Rather than specifying each one individually, we can copy over the parameters from one of the previously defined turbines.  In this case, we'll use the `NREL5MW ADM NOSERVO` turbine.  This can be done with the `turbinemodels_applyturbinemodel()` function.\n",
    "\n",
    "Note that both the `docopy` and `updatefast` parameters are set to `True`.  `docopy` creates a copy of the necessary OpenFAST files needed to run the turbine, and puts it into a new directory based on the turbine name.  `updatefast` will modify any `OpenFAST` files so that the settings in `OpenFAST` are consistent with `AMR-Wind`.\n",
    "\n",
    "Finally, the turbine is added to the simulation with `add_turbine()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "docopy = True from /projects/wind_uq/lcheung/amrwind-frontend/turbines/OpenFAST_NREL5MW to turbine0_OpenFAST_NREL5MW\n",
      "turbine0_OpenFAST_NREL5MW/nrel5mw_noservo.fst\n",
      "/gpfs1/lcheung/TCF/TurbineCheckout/test/tutorial1run/turbine0_OpenFAST_NREL5MW/./5MW_Baseline/NRELOffshrBsline5MW_Onshore_AeroDyn15.dat\n",
      "Actuator_name: 'turbine0'\n",
      "use_turbine_type: ''\n",
      "copy_turb_files: 1\n",
      "edit_fast_files: 1\n",
      "Actuator_type: 'TurbineFastDisk'\n",
      "Actuator_openfast_input_file: 'turbine0_OpenFAST_NREL5MW/nrel5mw_noservo.fst'\n",
      "Actuator_base_position: [0, 0, -90]\n",
      "Actuator_rotor_diameter: 126.0\n",
      "Actuator_hub_height: 90.0\n",
      "Actuator_num_points_blade: 64\n",
      "Actuator_num_points_tower: 12\n",
      "Actuator_epsilon: [10.0, 10.0, 10.0]\n",
      "Actuator_epsilon_tower: [5.0, 5.0, 5.0]\n",
      "Actuator_openfast_start_time: 0.0\n",
      "Actuator_openfast_stop_time: 1000.0\n",
      "Actuator_nacelle_drag_coeff: 0.0\n",
      "Actuator_nacelle_area: 0.0\n",
      "Actuator_yaw: 270.0\n",
      "Actuator_output_frequency: 10\n",
      "Actuator_density: 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       1.0 AirDens - Air density (kg/m^3) [EDITED]\n"
     ]
    }
   ],
   "source": [
    "turbine = tutorial1.turbinemodels_applyturbinemodel(turbine, 'NREL5MW ADM NOSERVO', docopy=True, updatefast=True)\n",
    "\n",
    "# Add the turbine to the simulation.\n",
    "tutorial1.add_turbine(turbine, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Adding a refinement window\n",
    "\n",
    "Refinement windows can be added in a similar fashion as turbines.  First we'll set `AMR-Wind` to use 1 level of refinement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "tutorial1.setAMRWindInput('amr.max_level', 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Then we'll get the default dictionary for refinement windows, and modify it to suit our purposes.  In this example, we're creating a box refinement zone with specified coordinates.  Finally, we'll add it to the simulation with the `add_tagging()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Get the default dictionary for refinement window\n",
    "refinewin = tutorial1.get_default_taggingdict()\n",
    "\n",
    "# Edit the parameters of the refinement window\n",
    "refinewin['tagging_name']         = 'box1'\n",
    "refinewin['tagging_shapes']       = 'box1'\n",
    "refinewin['tagging_type']         = 'GeometryRefinement'\n",
    "refinewin['tagging_level']        = 0\n",
    "refinewin['tagging_geom_type']    = 'box'\n",
    "refinewin['tagging_geom_origin']  = [-200, -200, -200]\n",
    "refinewin['tagging_geom_xaxis']   = [400, 0, 0]\n",
    "refinewin['tagging_geom_yaxis']   = [0, 400, 0]\n",
    "refinewin['tagging_geom_zaxis']   = [0, 0, 400]\n",
    "\n",
    "# Add refinement zone to simulation.\n",
    "tutorial1.add_tagging(refinewin)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Adding a sampling plane\n",
    "\n",
    "We'll follow the same process for adding in a sampling plane.  First, we need to tell AMR-Wind to add sampling functionality to the simulation and tell it to sample velocity fields every 100 iterations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "tutorial1.setAMRWindInput('incflo.post_processing',    ['sampling'])\n",
    "tutorial1.setAMRWindInput('sampling.output_frequency', 100)\n",
    "tutorial1.setAMRWindInput('sampling.fields',           ['velocity'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now we'll create the sampling plane by getting a default sampling probe dictionary and then modifying it.  In this case, we're creating a single sampling plane at hub-height, with specified origin and dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default_alwaysfalse: False\n",
      "sampling_name: 'xyplane'\n",
      "sampling_type: 'PlaneSampler'\n",
      "sampling_pf_probe_location_file: None\n",
      "sampling_l_num_points: 2\n",
      "line_probe_xyzheader: ['X', 'Y', 'Z']\n",
      "sampling_l_start: [0, 0, 0]\n",
      "sampling_l_end: [1, 1, 1]\n",
      "sampling_p_num_points: [101, 51]\n",
      "sample_plane_xyzheader: ['X', 'Y', 'Z']\n",
      "sampling_p_origin: [-1000, -500, 0]\n",
      "sampling_p_axis1: [2000, 0, 0]\n",
      "sampling_p_axis2: [0, 1000, 0]\n",
      "sampling_p_normal: [0, 0, 0]\n",
      "sampling_p_offsets: None\n"
     ]
    }
   ],
   "source": [
    "# Get the default dictionary for sampling probes\n",
    "sampleplane = tutorial1.get_default_samplingdict()\n",
    "\n",
    "# Modify the geometry\n",
    "sampleplane['sampling_name'] = 'xyplane'\n",
    "sampleplane['sampling_type'] = 'PlaneSampler'\n",
    "sampleplane['sampling_p_num_points'] = [101, 51]\n",
    "sampleplane['sampling_p_origin']     = [-1000, -500, 0]\n",
    "sampleplane['sampling_p_axis1']      = [2000, 0, 0]\n",
    "sampleplane['sampling_p_axis2']      = [0, 1000, 0]\n",
    "\n",
    "# Add sampling plane to simuation\n",
    "tutorial1.add_sampling(sampleplane, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Plotting the domain again\n",
    "\n",
    "We can now re-plot the domain, but this time include the turbine, refinement window, and sampling plane.  Again, we'll use the `plotDomain()` function, but this time we'll add the additional features to plot through the `popup_storteddata['plotdomain']` option:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting turbines\n"
     ]
    },
    {
     "data": {
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l4TrIc9LySHLS8pjjCkv10BtuXEnWIvQJLPttLVJ378GuXbuwZMkS1KjpjUtFZZj0ThLS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fffb26f4b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure and axes to plot domain\n",
    "plt.rc('font', size=14)\n",
    "fig, ax = plt.subplots(figsize=(10,10), facecolor='w')\n",
    "\n",
    "# Set additional items to plot\n",
    "tutorial1.popup_storteddata['plotdomain']['plot_sampleprobes']    = ['xyplane']\n",
    "tutorial1.popup_storteddata['plotdomain']['plot_turbines']        = ['turbine0']\n",
    "tutorial1.popup_storteddata['plotdomain']['plot_refineboxes']     = ['box1']\n",
    "\n",
    "# Plot the figure \n",
    "tutorial1.plotDomain(ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Validating inputs\n",
    "\n",
    "Before running this case, we can check that the inputs have been set up correctly.  Use the `validate()` function, and hopefully the output shows that all things passed, with no failures or warnings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- Checking inputs --\n",
      "[ PASS] max_level:           max_level = 1 >= 0\n",
      "[ PASS] Actuator physics:    incflo.physics and ICNS.source_terms OK for Actuators\n",
      "[ PASS] Actuator FST:turbine0 [turbine0_OpenFAST_NREL5MW/nrel5mw_noservo.fst] exists\n",
      "[ PASS] Actuator FST:turbine0 CompInflow OK\n",
      "[ PASS] Actuator FST:turbine0 [turbine0_OpenFAST_NREL5MW/./5MW_Baseline/NRELOffshrBsline5MW_Onshore_AeroDyn15.dat] exists\n",
      "[ PASS] Actuator FST:turbine0 WakeMod=0 OK\n",
      "[ PASS] Actuator FST:turbine0 AirDens=1.000000, matches incflo.density=1.000000\n",
      "[ PASS] Sampling probes:xyplane \n",
      "\n",
      "Results: \n",
      " 8 PASS\n",
      " 0 SKIP\n",
      " 0 FAIL\n",
      " 0 WARN\n"
     ]
    }
   ],
   "source": [
    "# Validate the inputs\n",
    "checkoutput=tutorial1.validate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Saving the input file\n",
    "\n",
    "Finally, all of these parameters can be saved into an input file through the `writeAMRWindInput()` function.  However, first we'll take a look at what the input file will look like by passing in the null string `''` and printing it to the screen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# --- Simulation time control parameters ---\n",
      "time.stop_time                           = 100.0               # Max (simulated) time to evolve [s]\n",
      "time.max_step                            = -1                  \n",
      "time.fixed_dt                            = 0.1                 # Fixed timestep size (in seconds). If negative, then time.cfl is used\n",
      "incflo.physics                           = FreeStream Actuator # List of physics models to include in simulation.\n",
      "incflo.verbose                           = 0                   \n",
      "io.check_file                            = chk                 \n",
      "incflo.use_godunov                       = true                \n",
      "incflo.godunov_type                      = ppm                 \n",
      "turbulence.model                         = Laminar             \n",
      "incflo.gravity                           = 0.0 0.0 -9.81       # Gravitational acceleration vector (x,y,z) [m/s^2]\n",
      "incflo.density                           = 1.0                 # Fluid density [kg/m^3]\n",
      "transport.viscosity                      = 1.872e-05           # Fluid dynamic viscosity [kg/m-s]\n",
      "transport.laminar_prandtl                = 0.7                 # Laminar prandtl number\n",
      "transport.turbulent_prandtl              = 0.3333              # Turbulent prandtl number\n",
      "ConstValue.density.value                 = 1.0                 \n",
      "ConstValue.velocity.value                = 10.0 0.0 0.0        \n",
      "\n",
      "# --- Geometry and Mesh ---\n",
      "geometry.prob_lo                         = -1000.0 -500.0 -500.0\n",
      "geometry.prob_hi                         = 1000.0 500.0 500.0  \n",
      "amr.n_cell                               = 128 64 64           # Number of cells in x, y, and z directions\n",
      "amr.max_level                            = 1                   \n",
      "geometry.is_periodic                     = 0 1 1               \n",
      "xlo.type                                 = mass_inflow         \n",
      "xlo.density                              = 1.0                 \n",
      "xlo.velocity                             = 10.0 0.0 0.0        \n",
      "xhi.type                                 = pressure_outflow    \n",
      "\n",
      "# --- ABL parameters ---\n",
      "ICNS.source_terms                        =    ActuatorForcing  \n",
      "incflo.velocity                          = 10.0 0.0 0.0        \n",
      "ABLForcing.abl_forcing_height            = 0.0                 \n",
      "time.plot_interval                       = 1000                \n",
      "io.plot_file                             = plt                 \n",
      "io.KE_int                                = -1                  \n",
      "incflo.post_processing                   = sampling            \n",
      "\n",
      "# --- Sampling parameters ---\n",
      "sampling.output_frequency                = 100                 \n",
      "sampling.fields                          = velocity            \n",
      "\n",
      "#---- sample defs ----\n",
      "sampling.labels                          = xyplane             \n",
      "sampling.xyplane.type                    = PlaneSampler        \n",
      "sampling.xyplane.num_points              = 101 51              \n",
      "sampling.xyplane.origin                  = -1000.0 -500.0 0.0  \n",
      "sampling.xyplane.axis1                   = 2000.0 0.0 0.0      \n",
      "sampling.xyplane.axis2                   = 0.0 1000.0 0.0      \n",
      "sampling.xyplane.normal                  = 0.0 0.0 0.0         \n",
      "\n",
      "#---- tagging defs ----\n",
      "tagging.labels                           = box1                \n",
      "tagging.box1.type                        = GeometryRefinement  \n",
      "tagging.box1.shapes                      = box1                \n",
      "tagging.box1.level                       = 0                   \n",
      "tagging.box1.box1.type                   = box                 \n",
      "tagging.box1.box1.origin                 = -200.0 -200.0 -200.0\n",
      "tagging.box1.box1.xaxis                  = 400.0 0.0 0.0       \n",
      "tagging.box1.box1.yaxis                  = 0.0 400.0 0.0       \n",
      "tagging.box1.box1.zaxis                  = 0.0 0.0 400.0       \n",
      "\n",
      "#---- actuator defs ----\n",
      "Actuator.labels                          = turbine0            \n",
      "Actuator.turbine0.type                   = TurbineFastDisk     \n",
      "Actuator.turbine0.openfast_input_file    = turbine0_OpenFAST_NREL5MW/nrel5mw_noservo.fst\n",
      "Actuator.turbine0.base_position          = 0.0 0.0 -90.0       \n",
      "Actuator.turbine0.rotor_diameter         = 126.0               \n",
      "Actuator.turbine0.hub_height             = 90.0                \n",
      "Actuator.turbine0.num_points_blade       = 64                  \n",
      "Actuator.turbine0.num_points_tower       = 12                  \n",
      "Actuator.turbine0.epsilon                = 10.0 10.0 10.0      \n",
      "Actuator.turbine0.epsilon_tower          = 5.0 5.0 5.0         \n",
      "Actuator.turbine0.openfast_start_time    = 0.0                 \n",
      "Actuator.turbine0.openfast_stop_time     = 1000.0              \n",
      "Actuator.turbine0.nacelle_drag_coeff     = 0.0                 \n",
      "Actuator.turbine0.nacelle_area           = 0.0                 \n",
      "Actuator.turbine0.yaw                    = 270.0               \n",
      "Actuator.turbine0.output_frequency       = 10                  \n",
      "Actuator.turbine0.density                = 1.0                 \n",
      "\n",
      "#---- extra params ----\n",
      "#== END AMR-WIND INPUT ==\n",
      "\n"
     ]
    }
   ],
   "source": [
    "outstr=tutorial1.writeAMRWindInput('')\n",
    "print(outstr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Then we'll actually write it to a file by specifying the filename `tutorial1.inp` (this can be anything):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "outstr=tutorial1.writeAMRWindInput('tutorial1.inp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "That's it!  Now we are ready to run the case."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Submitting the job\n",
    "We can also have `amrwind_frontend` create a submission script and submit it to the queue.  To do this, edit the `submitscript` dictionary like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Set some of the submission script parameters\n",
    "tutorial1.popup_storteddata['submitscript']['submitscript_filename'] = 'submit.sh'\n",
    "tutorial1.popup_storteddata['submitscript']['submitscript_jobname']  = 'amrwind_test1'\n",
    "tutorial1.popup_storteddata['submitscript']['submitscript_numnodes'] = 2\n",
    "tutorial1.popup_storteddata['submitscript']['submitscript_runtime']  = '1:00:00'\n",
    "tutorial1.popup_storteddata['submitscript']['submitscript_wcid']     = 'FY190020'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "To print out what the submission script would look like use `submitscript_makescript()`.  Note that the argument is the AMR-Wind input filename (`savefile` is the filename that was set in `writeAMRWindInput()` above)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Preview what the submission script looks like\n",
    "print(tutorial1.submitscript_makescript(tutorial1.savefile))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "To save the job, use `submitscript_savescript()`.  Add the keyword `submit=True` to actually submit the job to the queue:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved submit.sh\n",
      "Executing: sbatch submit.sh\n",
      "sbatch: INFO: Adding filesystem licenses to job: gpfs1:1,nscratch:1,pscratch:1\n",
      "Submitted batch job 24558098\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tutorial1.submitscript_savescript(submit=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Postprocessing\n",
    "### Plotting the FAST outputs\n",
    "\n",
    "First, let's load the FAST output file and see what data is in there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading turbine0_OpenFAST_NREL5MW/nrel5mw_noservo.out\n"
     ]
    }
   ],
   "source": [
    "# Load the FAST output file\n",
    "headers=tutorial1.FAST_loadallfiles(None, outfile='turbine0_OpenFAST_NREL5MW/nrel5mw_noservo.out')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "There's a lot of variables we could plot in that file, let's take a look at the first 10:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Time', 'Wind1VelX', 'Wind1VelY', 'Wind1VelZ', 'OoPDefl1', 'IPDefl1', 'TwstDefl1', 'BldPitch1', 'Azimuth', 'RotSpeed']\n"
     ]
    }
   ],
   "source": [
    "print(headers[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's choose to plot the `Wind1VelX` variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "tutorial1.popup_storteddata['plotfastout']['plotfastout_vars']     = ['Wind1VelX']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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m8/ZUAQDwOQRWLWez2TSmazP3fbR2HVPyZz9q2fcZ+mx7piKC/PTrXzXX+O6X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\ng0VERERkY2ywiIiIiGzsLyW58fojEZaEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fffb251ded0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot and plot it.\n",
    "plt.rc('font', size=14)\n",
    "fig, ax = plt.subplots(figsize=(10,10), facecolor='w')\n",
    "tutorial1.FAST_plotoutputs(ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Plotting the sample plane\n",
    "After the job is completed, we can also examine the flow fields through the python interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading post_processing/sampling00000.nc\n"
     ]
    }
   ],
   "source": [
    "tutorial1.Samplepostpro_loadnetcdffile('post_processing/sampling00000.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2wJdY/e+hiylC5bYiYnHLNf5Mp7aZeTPwvsqm50bEa9rUDODDrPkMxI7BW2X/3AjAewOf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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fffb24cfd50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the libraries\n",
    "import numpy as np\n",
    "from matplotlib import cm\n",
    "\n",
    "# Create the figure with 2 subplots (one for image, one for colorbar)\n",
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(10,5), gridspec_kw={'width_ratios': [1,  0.05]}, dpi=150)\n",
    "\n",
    "# Set the plot parameters\n",
    "levels     = np.linspace(0,12,41)\n",
    "plotvar    = 'velocityx'  # Plot the velocityx variable\n",
    "plotindex  = 10           # Plot the 10th step in netcdf file (corresponding to time 100.0)\n",
    "planei     = 0            # Plot the 0th plane (the only one in this instance)\n",
    "# Create the image\n",
    "im1 = tutorial1.plotSamplePlane('xyplane', plotvar, plotindex, planei, 'X','Y',ax=ax1, colorbar=False, \n",
    "                                levels=levels, cmap=cm.jet)\n",
    "fig.colorbar(im1[0], cax=ax2)\n",
    "ax1.set_xlim([-750, 750]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}