{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "efaa81fc-b1ce-4e43-a172-eb29c8ef8f96",
   "metadata": {},
   "source": [
    "# Create the convective ABL case for NREL5MW runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "35607fd1-a434-4bb1-b4c6-8f35ba1c209b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/ascldap/users/lcheung/.local/lib/python3.9/site-packages/pandas/core/computation/expressions.py:21: UserWarning: Pandas requires version '2.8.4' or newer of 'numexpr' (version '2.8.1' currently installed).\n",
      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n",
      "/ascldap/users/lcheung/.local/lib/python3.9/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.4' currently installed).\n",
      "  from pandas.core import (\n"
     ]
    }
   ],
   "source": [
    "# Add any possible locations of amr-wind-frontend here\n",
    "amrwindfedirs = ['/projects/wind_uq/lcheung/amrwind-frontend',\n",
    "                 '/ccs/proj/cfd162/lcheung/amrwind-frontend/']\n",
    "import sys, os, shutil\n",
    "for x in amrwindfedirs: sys.path.insert(1, x)\n",
    "\n",
    "# Load the libraries\n",
    "import amrwind_frontend  as amrwind\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy             as np\n",
    "import math\n",
    "import pandas as pd\n",
    "import postproamrwindsample as ppsample\n",
    "import time\n",
    "import utm\n",
    "import shutil\n",
    "import yaml\n",
    "\n",
    "# Also ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Make all plots inline \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f4890f3e-a3e6-4da1-836a-658739861bc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Start the AMR-Wind case\n",
    "case = amrwind.MyApp.init_nogui()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aca538d8-0df0-4646-b645-6dd065777767",
   "metadata": {},
   "outputs": [],
   "source": [
    "rundir = './'\n",
    "originalinput = 'abl.inp'\n",
    "outputfile    = '../input_files/convective_abl.inp'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "08b8b854-588f-490e-a8be-ff120a869b85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CANNOT update: ABLMeanBoussinesq use forcechange=True in setval()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "OrderedDict([('io.line_plot_int', '1'),\n",
       "             ('CoriolisForcing.turn_off_vertical_force', 'True'),\n",
       "             ('tagging.static_refinement', 'false'),\n",
       "             ('tagging.static_refinement_def', 'static_box.txt')])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the starting point\n",
    "# This assumes that rundir was already set up with setup.sh (see https://github.com/Exawind/exawind-cases/blob/main/16_turb_abl_fsi/setup.sh)\n",
    "os.chdir(rundir)\n",
    "case.loadAMRWindInput(originalinput)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7bd819d6-8ebf-4297-ba07-03b97f5f2a95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'static_box.txt'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove static refinement from the static_box.txt file\n",
    "case.extradictparams.pop('tagging.static_refinement', None)\n",
    "case.extradictparams.pop('tagging.static_refinement_def', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f8185cd3-3a09-4696-8ac7-ac5041241a08",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the tolerances to match other ABL runs\n",
    "case.setAMRWindInput('diffusion.mg_rtol',  1.0e-8)\n",
    "case.setAMRWindInput('diffusion.mg_atol',  1.0e-8)\n",
    "\n",
    "case.setAMRWindInput('mac_proj.mg_rtol',   1.0e-8)\n",
    "case.setAMRWindInput('mac_proj.mg_atol',   1.0e-8)\n",
    "\n",
    "case.setAMRWindInput('nodal_proj.mg_rtol', 1.0e-8)\n",
    "case.setAMRWindInput('nodal_proj.mg_atol', 1.0e-8)\n",
    "\n",
    "case.setAMRWindInput('temperature_diffusion.mg_rtol', 1.0e-8)\n",
    "case.setAMRWindInput('temperature_diffusion.mg_atol', 1.0e-8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99ef7d4a-763a-478a-8bb6-774dafd2cbfa",
   "metadata": {},
   "source": [
    "## Add a turbine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a7af1af8-a211-4895-9177-44195652037b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is a dummy turbine, just to get the dimensions of the turbine placement correct\n",
    "turbinetype = case.get_default_turbinetypedict()\n",
    "turbinetype['turbinetype_name'] = 'NREL5MW_junk'\n",
    "turbinetype['Actuator_type']    = 'UniformCtDisk'\n",
    "turbinetype['Actuator_rotor_diameter']     = 126.0\n",
    "turbinetype['Actuator_hub_height']         = 90.0\n",
    "turbinetype['Actuator_epsilon']            = [2.5]\n",
    "turbinetype['Actuator_output_frequency']   = 1\n",
    "turbinetype['Actuator_diameters_to_sample']= 5.0 \n",
    "turbinetype['Actuator_num_points_r']       = 20\n",
    "turbinetype['Actuator_num_points_t']       = 3\n",
    "turbinetype['Actuator_thrust_coeff']       = 0.2\n",
    "case.add_populatefromdict('listboxturbinetype', turbinetype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f8dd5493-a3dd-4b9e-8364-f84e70a69ffe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build the CSV input file of turbine layouts for amrwind-frontend\n",
    "options=\"\"\n",
    "\n",
    "turbinescsv=\"\"\"\n",
    "# CSV file should have columns with\n",
    "# name, x, y, type, yaw, hubheight, options\n",
    "T1,  1800, 1800, NREL5MW_junk, 240.0, , \n",
    "\"\"\"\n",
    "case.setAMRWindInput('turbines_csvtextbox',  turbinescsv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d59eae9d-6633-400a-9b70-224173a482b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['T1']\n"
     ]
    }
   ],
   "source": [
    "case.setAMRWindInput('turbines_deleteprev', True)\n",
    "case.setAMRWindInput('turbines_createnewdomain', False)\n",
    "\n",
    "case.turbines_createAllTurbines()\n",
    "\n",
    "# Print out existing list of turbines, just to confirm that the turbines got made\n",
    "print(case.listboxpopupwindict['listboxactuator'].getitemlist())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc45729a-2c9b-4078-9dbd-38151bed1c40",
   "metadata": {},
   "source": [
    "## Add refinement regions (optional)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eb41eeba-059d-419c-b95c-defe8427b5eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Add refinement zones\n",
    "refinementcsv=\"\"\"\n",
    "# CSV file should have columns with\n",
    "# level, upstream, downstream, lateral, below, above, options\n",
    "level, upstream, downstream, lateral, below, above, options\n",
    "#0,     2160,     2160,       2160,    90,   200,   center:specified units:meter orientation:x centerx:2560 centery:2560 centerz:90 name:level1\n",
    "#1,     1600,     1600,       1600,    90,   200,   center:specified units:meter orientation:x centerx:2000 centery:2000 centerz:90 name:level2\n",
    "0,     5,        10,         5,     0.75,   2,\n",
    "1,     2.5,      5,          2,     0.75,   1.5,\n",
    "2,     1.25,     2.0,        1.25,  0.75,   1.0, \n",
    "3,     0.75,     0.75,       1.00,  0.75,   0.75, \n",
    "\"\"\"\n",
    "case.setAMRWindInput('refine_csvtextbox', refinementcsv)\n",
    "case.setAMRWindInput('refine_deleteprev', True)\n",
    "\n",
    "# Uncomment this to create refinement zones\n",
    "#case.refine_createAllZones()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e0a72192-4ce6-48d1-b58c-7aa2ab29b476",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ESTIMATED MESH SIZE\n",
      "   Level       Ncells                      Cell Size\n",
      "       0     50331648             10.0 x 10.0 x 10.0\n",
      "  TOTAL:     50331648\n"
     ]
    }
   ],
   "source": [
    "case.estimateMeshSize()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eceb98fd-d157-4b7f-a3bf-da3335eabfc3",
   "metadata": {},
   "source": [
    "## Add some sampling planes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a4b457bb-42a9-467f-99e2-32efb12b61b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First delete everything that already exists\n",
    "case.listboxpopupwindict['listboxsampling'].deleteall()\n",
    "case.listboxpopupwindict['listboxpostprosetup'].deleteall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "61464cfd-f770-4d14-894e-b0e6474957e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "postprocessing_setup_name: 'metmast_'\n",
      "postprocessing_setup_type: 'Sampling'\n",
      "postprocessing_setup_output_frequency: 1\n",
      "postprocessing_setup_fields: ['velocity', 'temperature', 'tke']\n",
      "postprocessing_setup_derived_fields: None\n",
      "postprocessing_setup_averaging_window: None\n",
      "postprocessing_setup_averaging_start_time: None\n",
      "postprocessing_setup_averaging_stop_time: None\n"
     ]
    }
   ],
   "source": [
    "## virtual metmast measurements\n",
    "metmastpprosetup = case.get_default_postprosetupdict()\n",
    "metmastpprosetup['postprocessing_setup_name'] = 'metmast_'\n",
    "metmastpprosetup['postprocessing_setup_type'] = 'Sampling'\n",
    "metmastpprosetup['postprocessing_setup_output_frequency'] =  1\n",
    "metmastpprosetup['postprocessing_setup_fields']           =  ['velocity', 'temperature', 'tke']\n",
    "case.add_postprosetup(metmastpprosetup, verbose=True)\n",
    "\n",
    "sampledict = case.get_default_samplingdict()\n",
    "sampledict['sampling_name']     = 'virtualmast'\n",
    "sampledict['sampling_outputto'] = 'metmast_'\n",
    "sampledict['sampling_type']     = 'LineSampler'\n",
    "sampledict['sampling_l_num_points'] = 20\n",
    "sampledict['sampling_l_start']      = [1800, 1800, 10.0]\n",
    "sampledict['sampling_l_end']        = [1800, 1800, 200.0]\n",
    "case.add_sampling(sampledict, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "af3a2656-9541-4e8d-abee-063233caac2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "outputoptions=\"outputvars:velocity;tke;temperature outputfreq:100\"\n",
    "samplingcsv=\"\"\"\n",
    "# CSV file should have columns withturbinescsv\n",
    "# name, type, upstream, downstream, lateral, below, above, n1, n2, options\n",
    "name,      type, upstream, downstream, lateral, below, above, n1, n2,  options\n",
    "rotorplaneUP,  rotorplane, 4,     0,       2,    0.7,      1,  11, 11,           usedx:0.05 outputto:rotorplaneUP_  orientation:nacdir {outputoptions} noffsets:4\n",
    "rotorplaneDN,  rotorplane, 0,     10,      2,    0.7,      1,  11, 11,           usedx:0.05 outputto:rotorplaneDN_  orientation:nacdir {outputoptions} noffsets:10\n",
    "turbsw,      streamwise, 4,       10,      0,    0.7,     1.5, 11, 11,           usedx:0.05 outputto:turbsw_        orientation:nacdir {outputoptions} noffsets:0\n",
    "turbhh,      hubheight,  4,       10,      2,       0,    0,   11, 11,           usedx:0.05 outputto:turbhh_        orientation:nacdir {outputoptions} noffsets:0\n",
    "XYdomain027, hubheight,  8,       8,       2,       0,    90,  11, 11, units:meter usedx:10 outputto:XYdomain_027_ orientation:nacdir center:specified centerx:100 centery:100 centerz:27 wholedomain:1  {outputoptions} noffsets:0\n",
    "XYdomain090, hubheight,  8,       8,       2,       0,    90,  11, 11, units:meter usedx:10 outputto:XYdomain_090_ orientation:nacdir center:specified centerx:100 centery:100 centerz:90 wholedomain:1  {outputoptions} noffsets:0\n",
    "XYdomain153, hubheight,  8,       8,       2,       0,    153, 11, 11, units:meter usedx:10 outputto:XYdomain_153_ orientation:nacdir center:specified centerx:100 centery:100 centerz:153 wholedomain:1  {outputoptions} noffsets:0\n",
    "\"\"\".format(outputoptions=outputoptions)\n",
    "\n",
    "case.setAMRWindInput('sampling_csvtextbox', samplingcsv)\n",
    "case.setAMRWindInput('sampling_deleteprev', False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "13224d58-5600-4c35-8f13-cff23d8b05e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['virtualmast', 'T1_rotorplaneUP', 'T1_rotorplaneDN', 'T1_turbsw', 'T1_turbhh', 'Farm_XYdomain027', 'Farm_XYdomain090', 'Farm_XYdomain153']\n"
     ]
    }
   ],
   "source": [
    "case.sampling_createAllProbes(verbose=False)\n",
    "# Print out existing list of turbines\n",
    "print(case.listboxpopupwindict['listboxsampling'].getitemlist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f0369678-410e-43cf-b89b-1110988afdd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 750x750 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,6), facecolor='w', dpi=125)\n",
    "\n",
    "# Set any additional items to plot\n",
    "case.popup_storteddata['plotdomain']['plot_turbines']        = case.listboxpopupwindict['listboxactuator'].getitemlist()\n",
    "case.popup_storteddata['plotdomain']['plot_refineboxes']     = case.listboxpopupwindict['listboxtagging'].getitemlist()\n",
    "case.popup_storteddata['plotdomain']['plot_sampleprobes']    = ['T1_rotorplaneUP', 'T1_rotorplaneDN', 'T1_turbhh', 'T1_turbsw',] #case.listboxpopupwindict['listboxsampling'].getitemlist() #['p_hub']\n",
    "case.popup_storteddata['plotdomain']['plot_sampleprobes_style'] = \"{'markersize':.25, 'marker':'.', 'linestyle':'None', 'alpha':0.1}\"\n",
    "case.popup_storteddata['plotdomain']['plot_chooseview']      = 'XY'\n",
    "case.plotDomain(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "18f30b22-c7e5-485d-b4bc-312624c19177",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x750 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,6), facecolor='w', dpi=125)\n",
    "\n",
    "# Set any additional items to plot\n",
    "case.popup_storteddata['plotdomain']['plot_turbines']        = case.listboxpopupwindict['listboxactuator'].getitemlist()\n",
    "case.popup_storteddata['plotdomain']['plot_refineboxes']     = case.listboxpopupwindict['listboxtagging'].getitemlist()\n",
    "case.popup_storteddata['plotdomain']['plot_sampleprobes']    = case.listboxpopupwindict['listboxsampling'].getitemlist() #['p_hub']\n",
    "case.popup_storteddata['plotdomain']['plot_sampleprobes_style'] = \"{'markersize':.1, 'marker':'.', 'linestyle':'None', 'alpha':0.1}\"\n",
    "case.popup_storteddata['plotdomain']['plot_chooseview'] = 'XZ'\n",
    "case.plotDomain(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c778c8ad-49d1-4b7a-991c-31a5c0e39b1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "case.removeturbines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a4664961-22c3-41cc-a413-0adefe348332",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# --- Simulation time control parameters ---\n",
      "time.stop_time                           = 15000.0             # Max (simulated) time to evolve [s]\n",
      "time.max_step                            = -1                  \n",
      "time.fixed_dt                            = 0.5                 # Fixed timestep size (in seconds). If negative, then time.cfl is used\n",
      "time.checkpoint_interval                 = 10000               \n",
      "incflo.physics                           = ABL                 # 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                      = weno_z              \n",
      "turbulence.model                         = OneEqKsgsM84        \n",
      "TKE.source_terms                         = KsgsM84Src          \n",
      "nodal_proj.mg_rtol                       = 1e-08               \n",
      "nodal_proj.mg_atol                       = 1e-08               \n",
      "mac_proj.mg_rtol                         = 1e-08               \n",
      "mac_proj.mg_atol                         = 1e-08               \n",
      "diffusion.mg_rtol                        = 1e-08               \n",
      "diffusion.mg_atol                        = 1e-08               \n",
      "temperature_diffusion.mg_rtol            = 1e-08               \n",
      "temperature_diffusion.mg_atol            = 1e-08               \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                      = 0.0                 # 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",
      "\n",
      "# --- Geometry and Mesh ---\n",
      "geometry.prob_lo                         = 0.0 0.0 0.0         \n",
      "geometry.prob_hi                         = 5120.0 5120.0 1920.0\n",
      "amr.n_cell                               = 512 512 192         # Number of cells in x, y, and z directions\n",
      "amr.max_level                            = 0                   \n",
      "geometry.is_periodic                     = 1 1 0               \n",
      "zlo.type                                 = wall_model          \n",
      "zlo.temperature_type                     = wall_model          \n",
      "zlo.tke_type                             = zero_gradient       \n",
      "zhi.type                                 = slip_wall           \n",
      "zhi.temperature_type                     = fixed_gradient      \n",
      "zhi.temperature                          = 0.003               \n",
      "\n",
      "# --- ABL parameters ---\n",
      "ICNS.source_terms                        = ABLForcing BoussinesqBuoyancy CoriolisForcing ABLMeanBoussinesq\n",
      "ABL.stats_output_frequency               = 1                   \n",
      "ABL.stats_output_format                  = netcdf              \n",
      "ABL.tendency_forcing                     = false               \n",
      "ABL.bndry_io_mode                        = 0                   \n",
      "ABL.bndry_file                           = bndry_file          \n",
      "ABL.bndry_planes                         = xlo ylo             \n",
      "ABL.bndry_output_start_time              = 15000.0             \n",
      "ABL.bndry_var_names                      = velocity temperature tke\n",
      "ABL.bndry_output_format                  = native              \n",
      "incflo.velocity                          = 9.8726896031426 5.7 0.0\n",
      "ABLForcing.abl_forcing_height            = 90.0                \n",
      "ABL.kappa                                = 0.41                \n",
      "ABL.normal_direction                     = 2                   \n",
      "ABL.surface_roughness_z0                 = 0.01                \n",
      "ABL.reference_temperature                = 300.0               \n",
      "ABL.surface_temp_rate                    = 0.0                 \n",
      "ABL.surface_temp_flux                    = 0.005               # Surface temperature flux [K-m/s]\n",
      "ABL.log_law_height                       = 5.0                 \n",
      "CoriolisForcing.latitude                 = 40.0                \n",
      "CoriolisForcing.rotational_time_period   = 86400.0             \n",
      "CoriolisForcing.north_vector             = 0.0 1.0 0.0         \n",
      "CoriolisForcing.east_vector              = 1.0 0.0 0.0         \n",
      "BoussinesqBuoyancy.reference_temperature = 300.0               \n",
      "ABL.temperature_heights                  = 0.0 750.0 850.0 2000.0\n",
      "ABL.temperature_values                   = 300.0 300.0 308.0 311.45\n",
      "ABLMeanBoussinesq.read_temperature_profile = false               \n",
      "ABL.perturb_velocity                     = true                \n",
      "ABL.perturb_ref_height                   = 50.0                \n",
      "ABL.Uperiods                             = 4.0                 \n",
      "ABL.Vperiods                             = 4.0                 \n",
      "ABL.deltaU                               = 1.0                 \n",
      "ABL.deltaV                               = 1.0                 \n",
      "ABL.perturb_temperature                  = true                \n",
      "ABL.theta_amplitude                      = 0.8                 \n",
      "ABL.cutoff_height                        = 50.0                \n",
      "time.plot_interval                       = 10000               \n",
      "io.plot_file                             = plt                 \n",
      "io.KE_int                                = -1                  \n",
      "\n",
      "#---- postprocessing defs ----\n",
      "incflo.post_processing                   = metmast_ rotorplaneUP_ rotorplaneDN_ turbsw_ turbhh_ XYdomain_027_ XYdomain_090_ XYdomain_153_\n",
      "metmast_.type                            = Sampling            \n",
      "metmast_.output_frequency                = 1                   \n",
      "metmast_.fields                          = velocity temperature tke\n",
      "rotorplaneUP_.type                       = Sampling            \n",
      "rotorplaneUP_.output_frequency           = 100                 \n",
      "rotorplaneUP_.fields                     = velocity temperature tke\n",
      "rotorplaneDN_.type                       = Sampling            \n",
      "rotorplaneDN_.output_frequency           = 100                 \n",
      "rotorplaneDN_.fields                     = velocity temperature tke\n",
      "turbsw_.type                             = Sampling            \n",
      "turbsw_.output_frequency                 = 100                 \n",
      "turbsw_.fields                           = velocity temperature tke\n",
      "turbhh_.type                             = Sampling            \n",
      "turbhh_.output_frequency                 = 100                 \n",
      "turbhh_.fields                           = velocity temperature tke\n",
      "XYdomain_027_.type                       = Sampling            \n",
      "XYdomain_027_.output_frequency           = 100                 \n",
      "XYdomain_027_.fields                     = velocity temperature tke\n",
      "XYdomain_090_.type                       = Sampling            \n",
      "XYdomain_090_.output_frequency           = 100                 \n",
      "XYdomain_090_.fields                     = velocity temperature tke\n",
      "XYdomain_153_.type                       = Sampling            \n",
      "XYdomain_153_.output_frequency           = 100                 \n",
      "XYdomain_153_.fields                     = velocity temperature tke\n",
      "\n",
      "#---- sample defs ----\n",
      "metmast_.labels                          = virtualmast         \n",
      "rotorplaneUP_.labels                     = T1_rotorplaneUP     \n",
      "rotorplaneDN_.labels                     = T1_rotorplaneDN     \n",
      "turbsw_.labels                           = T1_turbsw           \n",
      "turbhh_.labels                           = T1_turbhh           \n",
      "XYdomain_027_.labels                     = Farm_XYdomain027    \n",
      "XYdomain_090_.labels                     = Farm_XYdomain090    \n",
      "XYdomain_153_.labels                     = Farm_XYdomain153    \n",
      "metmast_.virtualmast.type                = LineSampler         \n",
      "metmast_.virtualmast.num_points          = 20                  \n",
      "metmast_.virtualmast.start               = 1800.0 1800.0 10.0  \n",
      "metmast_.virtualmast.end                 = 1800.0 1800.0 200.0 \n",
      "rotorplaneUP_.T1_rotorplaneUP.type       = PlaneSampler        \n",
      "rotorplaneUP_.T1_rotorplaneUP.num_points = 81 35               \n",
      "rotorplaneUP_.T1_rotorplaneUP.origin     = 1489.523196492643 1329.7615982463215 1.8000000000000114\n",
      "rotorplaneUP_.T1_rotorplaneUP.axis1      = -251.99999999999994 436.47680350735703 -0.0\n",
      "rotorplaneUP_.T1_rotorplaneUP.axis2      = 0.0 0.0 214.2       \n",
      "rotorplaneUP_.T1_rotorplaneUP.offset_vector = 0.8660254037844386 0.4999999999999999 0.0\n",
      "rotorplaneUP_.T1_rotorplaneUP.offsets    = 0.0 126.0 252.0 378.0 504.0\n",
      "rotorplaneDN_.T1_rotorplaneDN.type       = PlaneSampler        \n",
      "rotorplaneDN_.T1_rotorplaneDN.num_points = 81 35               \n",
      "rotorplaneDN_.T1_rotorplaneDN.origin     = 1926.0 1581.7615982463215 1.8000000000000114\n",
      "rotorplaneDN_.T1_rotorplaneDN.axis1      = -251.99999999999994 436.47680350735703 -0.0\n",
      "rotorplaneDN_.T1_rotorplaneDN.axis2      = 0.0 0.0 214.2       \n",
      "rotorplaneDN_.T1_rotorplaneDN.offset_vector = 0.8660254037844386 0.4999999999999999 0.0\n",
      "rotorplaneDN_.T1_rotorplaneDN.offsets    = 0.0 126.0 252.0 378.0 504.0 630.0 756.0 882.0 1008.0 1134.0 1260.0\n",
      "turbsw_.T1_turbsw.type                   = PlaneSampler        \n",
      "turbsw_.T1_turbsw.num_points             = 281 45              \n",
      "turbsw_.T1_turbsw.origin                 = 1363.523196492643 1548.0 1.8000000000000114\n",
      "turbsw_.T1_turbsw.axis1                  = 1527.6688122757496 881.9999999999998 0.0\n",
      "turbsw_.T1_turbsw.axis2                  = 0.0 0.0 277.2       \n",
      "turbsw_.T1_turbsw.offset_vector          = 0.0 0.0 0.0         \n",
      "turbhh_.T1_turbhh.type                   = PlaneSampler        \n",
      "turbhh_.T1_turbhh.num_points             = 281 81              \n",
      "turbhh_.T1_turbhh.origin                 = 1489.523196492643 1329.7615982463215 90.0\n",
      "turbhh_.T1_turbhh.axis1                  = 1527.6688122757496 881.9999999999998 0.0\n",
      "turbhh_.T1_turbhh.axis2                  = -251.99999999999994 436.47680350735703 -0.0\n",
      "turbhh_.T1_turbhh.offset_vector          = 0.0 0.0 0.0         \n",
      "XYdomain_027_.Farm_XYdomain027.type      = PlaneSampler        \n",
      "XYdomain_027_.Farm_XYdomain027.num_points = 513 513             \n",
      "XYdomain_027_.Farm_XYdomain027.origin    = 0.0001 0.0001 27.0  \n",
      "XYdomain_027_.Farm_XYdomain027.axis1     = 5119.9998 0.0 0.0   \n",
      "XYdomain_027_.Farm_XYdomain027.axis2     = 0.0 5119.9998 0.0   \n",
      "XYdomain_027_.Farm_XYdomain027.offset_vector = 0.0 0.0 0.0         \n",
      "XYdomain_090_.Farm_XYdomain090.type      = PlaneSampler        \n",
      "XYdomain_090_.Farm_XYdomain090.num_points = 513 513             \n",
      "XYdomain_090_.Farm_XYdomain090.origin    = 0.0001 0.0001 90.0  \n",
      "XYdomain_090_.Farm_XYdomain090.axis1     = 5119.9998 0.0 0.0   \n",
      "XYdomain_090_.Farm_XYdomain090.axis2     = 0.0 5119.9998 0.0   \n",
      "XYdomain_090_.Farm_XYdomain090.offset_vector = 0.0 0.0 0.0         \n",
      "XYdomain_153_.Farm_XYdomain153.type      = PlaneSampler        \n",
      "XYdomain_153_.Farm_XYdomain153.num_points = 513 513             \n",
      "XYdomain_153_.Farm_XYdomain153.origin    = 0.0001 0.0001 153.0 \n",
      "XYdomain_153_.Farm_XYdomain153.axis1     = 5119.9998 0.0 0.0   \n",
      "XYdomain_153_.Farm_XYdomain153.axis2     = 0.0 5119.9998 0.0   \n",
      "XYdomain_153_.Farm_XYdomain153.offset_vector = 0.0 0.0 0.0         \n",
      "\n",
      "#---- extra params ----\n",
      "io.line_plot_int                         = 1                   \n",
      "CoriolisForcing.turn_off_vertical_force  = True                \n",
      "#== END AMR-WIND INPUT ==\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(case.writeAMRWindInput(outputfile))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c209b7a-e2ad-4731-8bac-2e6c7cfd4bf4",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}