docs/casestudies/SWIFT/Postprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Post processing the SWIFT ABL case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%%capture \n",
    "\n",
    "# Important libraries header information\n",
    "# ----------------------------------\n",
    "\n",
    "# change this directory as necessary\n",
    "amrwindfedir = '/projects/wind_uq/lcheung/amrwind-frontend'         # official version\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",
    "import numpy             as np\n",
    "from matplotlib import cm\n",
    "import re\n",
    "\n",
    "# Also ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Make all plots inline \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Postprocess Met-Mast data\n",
    "\n",
    "Run this section if the met-mast data has not already been post-processed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Helper functions to post-process met-mast data\n",
    "naluhelperdir = '/projects/wind_uq/lcheung/Scitech2021_WakePOD/utilities'\n",
    "sys.path.insert(1, naluhelperdir)\n",
    "import windspectra\n",
    "from scipy.io import loadmat\n",
    "\n",
    "# Some helper functions\n",
    "flatten = lambda x: x[0][0].reshape((len(x[0][0])))\n",
    "\n",
    "def replacenan(x):\n",
    "    xmean=np.mean(x[~np.isnan(x)])\n",
    "    where_are_NaNs = np.isnan(x)\n",
    "    x[where_are_NaNs]=xmean\n",
    "    return x \n",
    "\n",
    "# Average the spectra over multiple locations\n",
    "# given by locations in planefilelist\n",
    "def avgSpectraMetfiles(planefilelist, Nsample=None, dt=0.01, zkey='sonic_32m',avgbins=[], datadir='./', verbose=True):\n",
    "    # set initial values\n",
    "    Suu_avg={}\n",
    "    Svv_avg={}\n",
    "    Sww_avg={}\n",
    "    iplane = 0\n",
    "    all_ulongavgs = []\n",
    "    lent = 0\n",
    "    # Loop through and get spectra\n",
    "    for planefile in planefilelist:\n",
    "        if not os.path.isfile(datadir+'/'+planefile): continue\n",
    "        dat   = loadmat(datadir+'/'+planefile)\n",
    "        torig = flatten(dat[zkey]['Time'])\n",
    "        u     = replacenan(flatten(dat[zkey]['SonicU']))\n",
    "        v     = replacenan(flatten(dat[zkey]['SonicV']))\n",
    "        w     = replacenan(flatten(dat[zkey]['SonicW']))\n",
    "        Nt    = len(torig)\n",
    "        t     = np.arange(Nt)*0.01\n",
    "        if (Nsample is not None) and (Nt<Nsample): continue\n",
    "        #if ((iplane != 0) and (len(torig)<lent)): continue\n",
    "        if verbose: print(\"Loaded plane \"+planefile)\n",
    "        ulong, ulat = windspectra.convertUxytoLongLat(u,v)\n",
    "        all_ulongavgs.append(np.mean(ulong))\n",
    "        if (Nsample is None): Ntake = Nt\n",
    "        else:                 Ntake = Nsample\n",
    "        f, Suu      = windspectra.avgWindSpectra(t[:Ntake], ulong[:Ntake], avgbins)\n",
    "        f, Svv      = windspectra.avgWindSpectra(t[:Ntake], ulat[:Ntake],  avgbins)\n",
    "        f, Sww      = windspectra.avgWindSpectra(t[:Ntake], w[:Ntake],     avgbins)\n",
    "        if iplane == 0:\n",
    "            lent    = Nt\n",
    "            favg    = f\n",
    "            Suu_avg = Suu\n",
    "            Svv_avg = Svv\n",
    "            Sww_avg = Sww\n",
    "        else:\n",
    "            Suu_avg = Suu_avg + Suu\n",
    "            Svv_avg = Svv_avg + Svv\n",
    "            Sww_avg = Sww_avg + Sww\n",
    "        iplane = iplane+1\n",
    "    # Average the spectra\n",
    "    Suu_avg = Suu_avg/iplane\n",
    "    Svv_avg = Svv_avg/iplane\n",
    "    Sww_avg = Sww_avg/iplane\n",
    "    if verbose: print(\"Averaged over %i planes\"%iplane)\n",
    "    return favg, Suu_avg, Svv_avg, Sww_avg, np.mean(all_ulongavgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data from SWIFT_Neutral_metmastprofile.dat\n"
     ]
    }
   ],
   "source": [
    "# Change this to True to generate metMastProfileDataFile\n",
    "calcMetProfile = False\n",
    "metMastProfileDataFile = 'SWIFT_Neutral_metmastprofile.dat'\n",
    "\n",
    "if calcMetProfile:\n",
    "    # Calculate the velocity profile straight from the met mast data\n",
    "    metmastdir = '/projects/wind_uq/lcheung/Scitech2021_WakePOD/Neutral_MyraRun/WindShearProfFigData/' \n",
    "    metmastfiles = [\n",
    "        'met.z01.b0.20170123.225000.mat',\n",
    "        'met.z01.b0.20170622.012000.mat',\n",
    "        'met.z01.b0.20170622.013000.mat',\n",
    "        'met.z01.b0.20170622.014000.mat',\n",
    "        'met.z01.b0.20170622.015000.mat',\n",
    "        'met.z01.b0.20170703.140000.mat',\n",
    "           ]\n",
    "    sonicheights = ['sonic_10m', 'sonic_18m', 'sonic_32m', 'sonic_45m', 'sonic_58m']\n",
    "    # Averaging bins\n",
    "    avgbins = [[0.0, 599.0]]\n",
    "    \n",
    "    fmet={}\n",
    "    Suu_met={}\n",
    "    Svv_met={}\n",
    "    Sww_met={}\n",
    "    metUavg={}\n",
    "    zmet={}\n",
    "    # Get the heights\n",
    "    for s in sonicheights:\n",
    "        res = list(filter(None, re.split(',| |_|-|!|m|sonic', s)))\n",
    "        zmet[s]=float(res[0])\n",
    "    \n",
    "    for s in sonicheights:\n",
    "        print(s)\n",
    "        fmet[s], Suu_met[s], Svv_met[s], Sww_met[s], metUavg[s] = avgSpectraMetfiles(metmastfiles, zkey=s,\n",
    "                                                                                     Nsample=59800, verbose=True, \n",
    "                                                                                     datadir=metmastdir)\n",
    "        print(metUavg[s])\n",
    "    \n",
    "    zmetvec = [zmet[s] for s in sonicheights]\n",
    "    Umetvec = [metUavg[s] for s in sonicheights]\n",
    "    \n",
    "    # Save the data into a file\n",
    "    print('Saving data to '+metMastProfileDataFile)\n",
    "    savedat = np.vstack((zmetvec, Umetvec)).transpose()\n",
    "    np.savetxt(metMastProfileDataFile, savedat)\n",
    "else:\n",
    "    # Load it from the previously saved profile\n",
    "    print('Loading data from '+metMastProfileDataFile)\n",
    "    loaddat = np.loadtxt(metMastProfileDataFile)\n",
    "    zmetvec = loaddat[:,0]\n",
    "    Umetvec = loaddat[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Postprocess and compare AMR-Wind velocity profiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    " # Set your run directory here\n",
    "casedir = '/nscratch/lcheung/2021/SWIFT_Neutral_WS8.7_Alpha0.14/AMRWind_Precursor/precursor1'\n",
    "\n",
    "# Average between 15,000 sec to 20,000 sec\n",
    "avgtimes = [15000, 20000]\n",
    "\n",
    "# Hub-height locations\n",
    "plotheights=[32.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading /nscratch/lcheung/2021/SWIFT_Neutral_WS8.7_Alpha0.14/AMRWind_Precursor/precursor1/post_processing/abl_statistics00000.nc\n",
      "Time range: 0.500000 to 20000.000000\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "# Start the amrwind_frontend app \n",
    "tutorial2 = amrwind.MyApp.init_nogui()\n",
    "tutorial2.ABLpostpro_loadnetcdffile(casedir+'/post_processing/abl_statistics00000.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading w'theta'_r\n",
      "Loading theta\n",
      "Loading u\n",
      "Loading v'v'_r\n",
      "Loading v\n",
      "Loading u'u'_r\n",
      "Loading w'w'_r\n",
      "        z       Uhoriz      WindDir       TI_TKE     TI_horiz        Alpha     ObukhovL \n",
      "      ===         ====         ====         ====         ====         ====         ==== \n",
      "    32.10 8.689792e+00 2.700014e+02 7.618349e-02 1.264963e-01 1.115013e-01 1.463800e+05 \n",
      "\n",
      "ustar: 0.395520\n"
     ]
    }
   ],
   "source": [
    "# First, let's look at the hub-height averaged statistics\n",
    "reportstats = tutorial2.ABLpostpro_printreport(avgt=avgtimes, avgz=plotheights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make a plot of this ABL on a shear-TI diagram.  Remember that the SWIFT Neutral case had:\n",
    "\n",
    "|Case    | WS   | Shear | TI   |\n",
    "|---     | ---  | ---   | ---  |\n",
    "|Neutral | 8.69 | 0.14  | 0.108 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fffa77ed150>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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ImAYAgELCNAAAFBKmAQCgkDANAACFhGkAACgkTAMAQCFhGgAACgnTAABQSJgGAIBCwjQA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fffa778e450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5,5), facecolor='w', dpi=150)\n",
    "\n",
    "# Plot the SWIFT data\n",
    "SWIFT_alpha = 0.14\n",
    "SWIFT_TI    = 0.108\n",
    "plt.plot(SWIFT_alpha, SWIFT_TI, '*', label='SWIFT')\n",
    "\n",
    "# Plot the AMR-Wind results\n",
    "plt.plot(reportstats['Alpha'][0], reportstats['TI_horiz'][0], 's', label='AMR-Wind')\n",
    "\n",
    "plt.xlim([0, 0.4])\n",
    "plt.ylim([0, 0.25])\n",
    "plt.xlabel(r'Shear $\\alpha$ [-]')\n",
    "plt.ylabel('TI [-]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Plot velocity profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading u\n",
      "Loading v\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fffa77d4510>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BAA49NkeOHAEA9OnTx+E8g8GAG264ARaLBSdOnGhwPRp6oyeEQEFpRbP7UesG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fffa764e710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot velocity\n",
    "fig, ax = plt.subplots(figsize=(6,6), facecolor='w', dpi=150)\n",
    "\n",
    "# This command will make the plot directly onto ax\n",
    "#tutorial2.ABLpostpro_plotprofiles(ax=ax, plotvars=['Uhoriz'], avgt=avgtimes)\n",
    "\n",
    "# This command returns the data, and then you can plot it individually\n",
    "amrdat = tutorial2.ABLpostpro_plotprofiles(plotvars=['Uhoriz'], avgt=avgtimes)\n",
    "plt.plot(amrdat['Uhoriz']['data'], amrdat['Uhoriz']['z'], label='AMR-Wind')\n",
    "\n",
    "# Plot the met mast\n",
    "plt.plot(Umetvec, zmetvec, 's', color='k', label='Met mast')\n",
    "\n",
    "# Plot the turbine dimensions\n",
    "plt.hlines(plotheights[0], 0,              14, linestyles='dashed', color='gray', linewidth=1.0) # Plot the HH line\n",
    "\n",
    "#ax.plot(DanAeroWS, DanAeroZ, 's', color='k', label='DanAero')\n",
    "ax.legend()\n",
    "ax.set_xlim([0, 11])\n",
    "ax.set_xlabel('U [m/s]')\n",
    "ax.grid(linestyle=':', linewidth=0.5)\n",
    "ax.set_ylim([0,125])\n",
    "ax.set_ylabel('z [m]')\n",
    "ax.set_title('Horizontal wind speed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Compare AMR-Wind temperature and veer profiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading theta\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fffa7721450>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dygKecYigAwAAgCFnGIZONbh1qMqlA9UuHaxq0sEqlw5WN+l4bct5VWokKT0l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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fffa7779d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,6), facecolor='w', dpi=150)\n",
    "tutorial2.ABLpostpro_plotprofiles(ax=ax, plotvars=['Temperature'], avgt=avgtimes);\n",
    "ax.set_xlabel('Temperature [K]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading u\n",
      "Loading v\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fffa76c65d0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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l4+lux6raHkEHAAAAcFIZOZf10N/2qLLGLKl2Y9C/zk9QmL+XnStrewQdAAAA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\nREREREQGHAUdEREREREZcP4HOIWkuMONgwkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fffa778ec50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,6), facecolor='w', dpi=150)\n",
    "tutorial2.ABLpostpro_plotprofiles(ax=ax, plotvars=['WindDir'], avgt=avgtimes);\n",
    "ax.set_xlabel('Wind Dir [deg]')"
   ]
  },
  {
   "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
}