rca_createcluttermap_function.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pyart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rca_cluttermap_function(filename):\n",
    "    '''Creates a clutter map (precip-free day) to be used for the RCA calculation for PPIs'''\n",
    "    radar = pyart.io.cfradial.read_cfradial(filename)\n",
    "    date_time = radar.time['units'].replace('seconds since ', '')\n",
    "    \n",
    "    # Constrain range between 1 - 5 km\n",
    "    r_start_idx = np.where(radar.range['data'] < 1000.)[0][-1]+1\n",
    "    r_start_idx = 0\n",
    "    r_stop_idx = np.where(radar.range['data'] > 5000.)[0][0]\n",
    "    \n",
    "    # Using lowest elevation angle of PPI (0.5 deg)\n",
    "    sweep_start_idx = radar.sweep_start_ray_index['data'][0]\n",
    "    sweep_stop_idx = radar.sweep_end_ray_index['data'][0]+1\n",
    "    \n",
    "    # Get variables (only the rays/gates needed)\n",
    "    zh = radar.fields['reflectivity']['data'][sweep_start_idx:sweep_stop_idx,r_start_idx:r_stop_idx]\n",
    "    zdr = radar.fields['differential_reflectivity']['data'][sweep_start_idx:sweep_stop_idx,r_start_idx:r_stop_idx]\n",
    "    r = radar.range['data'][r_start_idx:r_stop_idx]\n",
    "    theta = radar.azimuth['data'][sweep_start_idx:sweep_stop_idx]\n",
    "    \n",
    "    # Eliminate duplicate azimuths to maintain a total # of azimuths of 360\n",
    "    if len(theta) > 360:\n",
    "        diff = len(theta) - 360\n",
    "        zh = np.delete(zh,-diff,axis=0)\n",
    "        theta = np.delete(theta,-diff)\n",
    "        \n",
    "    # Arrange/sort azimuths to span 0 to 360 deg. from index 0 to 359\n",
    "    sorted_idx = np.argsort(theta)\n",
    "    zh = zh[sorted_idx,:]\n",
    "    theta = theta[sorted_idx]\n",
    "    \n",
    "    # Create a fixed polar grid/array (FPG; 1 deg x 1 km) and create mask to flag clutter\n",
    "    r_grid = np.array((0,1,2,3,4,5))*1000.\n",
    "    theta_grid = theta\n",
    "    r_matrix, theta_matrix = np.meshgrid(r_grid,theta_grid)\n",
    "    clutter_flag = np.zeros((r_matrix.shape))\n",
    "    \n",
    "    # Set reflectivity threshold 55 dBZ\n",
    "    # Flag PPI pixels in FPG box that exceed threshold\n",
    "    z_thresh = 45.\n",
    "    \n",
    "    # Create an empty array to fill with Z from the clutter area reflectivity grid boxes (CAR)\n",
    "    zh_car = np.empty((zh.shape))\n",
    "    zh_car[:,:] = np.nan\n",
    "\n",
    "    for i in range(0,len(theta_matrix[:,0])):\n",
    "        for j in range(0,len(r_matrix[0,:])):\n",
    "            if j == 0:\n",
    "                clutter_flag[:,0] = np.nan\n",
    "            else:\n",
    "                if np.logical_or(zh[i,j*10-10] >= z_thresh, zh[i,j*10-9] >= z_thresh):\n",
    "                    clutter_flag[i,j] = 1\n",
    "                    zh_car[i,j*10-10:j*10-1] = zh[i,j*10-10:j*10-1]\n",
    "                elif np.logical_or(zh[i,j*10-8] >= z_thresh, zh[i,j*10-7] >= z_thresh):\n",
    "                    clutter_flag[i,j] = 1\n",
    "                    zh_car[i,j*10-10:j*10-1] = zh[i,j*10-10:j*10-1]\n",
    "                elif np.logical_or(zh[i,j*10-6] >= z_thresh, zh[i,j*10-5] >= z_thresh):\n",
    "                    clutter_flag[i,j] = 1\n",
    "                    zh_car[i,j*10-10:j*10-1] = zh[i,j*10-10:j*10-1]\n",
    "                elif np.logical_or(zh[i,j*10-4] >= z_thresh, zh[i,j*10-3] >= z_thresh):\n",
    "                    clutter_flag[i,j] = 1\n",
    "                    zh_car[i,j*10-10:j*10-1] = zh[i,j*10-10:j*10-1]\n",
    "                elif np.logical_or(zh[i,j*10-2] >= z_thresh, zh[i,j*10-1] >= z_thresh):\n",
    "                    clutter_flag[i,j] = 1\n",
    "                    zh_car[i,j*10-10:j*10-1] = zh[i,j*10-10:j*10-1]\n",
    "                else:\n",
    "                    clutter_flag[i,j] = 0\n",
    "                    \n",
    "    # Calculate the PDF of the clutter area reflectivity (CAR)\n",
    "    mask = np.where(np.isfinite(zh_car))  \n",
    "    n,bins,patches=plt.hist(zh_car[mask],bins=105,range=(-40.,65.))\n",
    "    \n",
    "    # Calculate CDF of clutter area reflectivity\n",
    "    cdf = np.cumsum(n)\n",
    "    p = cdf/cdf[-1]*100\n",
    "    \n",
    "    x = np.arange(105)-40\n",
    "    coeff = np.polyfit(p,x,10) # Find coefficients of 10th degree polynomial for CDF\n",
    "    poly_func = np.poly1d(coeff)\n",
    "    #x_poly = np.linspace(p[0],p[-1],105)\n",
    "    #y_poly = poly_func(x_poly)\n",
    "    \n",
    "    # Find the value of reflectivity at the 95th percentile of CDF\n",
    "    dbz95 = poly_func(95.)\n",
    "    \n",
    "    del radar\n",
    "    return clutter_flag, r_matrix, theta_matrix, zh, zh_car, date_time, n, bins, p, dbz95"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/hunzinger/data/ppi_cf/corcsapr2cfrppiM1.a1.20181102.231504.nc\n"
     ]
    },
    {
     "data": {
      "image/png": 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7vAm460jXNi5VdX1Vnd79PF4O/GNV/Q5wH/DebthczxGgy5jvJnlDt+tC4JvM+HM5Eyv0Id0LXEzvTcIfAr833XJG9hfAq4Ht3W8jX6+q36+qx5PcTu/FdBC4uqpenmKdI2n8lBEXAFcCjyZ5uNt3A3AjcHuSq+h1ar1vSvVN0oeBbUk+DjxE92binPsQcFu38HiKXsa8ihl+Lv2kqCQ1YqYPuUiSBmegS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUiP8FyFwMMbZINtcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this function on a bunch of files (try data from one day)\n",
    "import os\n",
    "import glob\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import norm \n",
    "\n",
    "clutter_flag = []\n",
    "theta = []\n",
    "r = []\n",
    "zh = []\n",
    "zhCAR = []\n",
    "dt = []\n",
    "n = []\n",
    "bins = []\n",
    "cdf = []\n",
    "p = []\n",
    "dbz95 = []\n",
    "\n",
    "\n",
    "\n",
    "for f in glob.glob(os.path.join('/home/hunzinger/data/ppi_cf/', 'corcsapr2cfrppi*.20181102.2315*.nc')):\n",
    "    print(f)\n",
    "    ClutterFlag, R, Theta, ZH, ZHCAR, DateTime, N, Bins, P, DBZ95 = rca_cluttermap_function(f)\n",
    "    \n",
    "    # Put all PPI times into a list\n",
    "    clutter_flag.append(ClutterFlag)\n",
    "    r.append(R)\n",
    "    theta.append(Theta)\n",
    "    zh.append(ZH)\n",
    "    zhCAR.append(ZHCAR)\n",
    "    dt.append(DateTime)\n",
    "    n.append(N)\n",
    "    bins.append(Bins)\n",
    "    p.append(P)\n",
    "    dbz95.append(DBZ95)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 360, 6)\n",
      "(360, 6)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hunzinger/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:6: RuntimeWarning: invalid value encountered in less_equal\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 489.6x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clutter_flag = np.asarray(clutter_flag)\n",
    "print(clutter_flag.shape)\n",
    "PCT_on = np.sum(clutter_flag,axis=0)/len(clutter_flag[:,0,0])\n",
    "\n",
    "# Mask values less than 0.5\n",
    "mask_u50 = np.where(PCT_on <= 0.5)\n",
    "PCT_on_50 = np.copy(PCT_on)\n",
    "PCT_on_50[mask_u50] = np.nan\n",
    "print(PCT_on_50.shape)\n",
    "\n",
    "# Plot clutter map\n",
    "from matplotlib.colors import BoundaryNorm\n",
    "# Convert to cartesian coords to plot\n",
    "x = (r[0]*np.sin(theta[0]*np.pi/180.))\n",
    "y = (r[0]*np.cos(theta[0]*np.pi/180.))\n",
    "#levels = np.arange(6)/10+0.5\n",
    "levels = np.arange(11)/10\n",
    "# pick the desired colormap, sensible levels, and define a normalization\n",
    "# instance which takes data values and translates those into levels.\n",
    "cmap = plt.get_cmap('nipy_spectral_r')\n",
    "norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=[6.8,6],constrained_layout=True)\n",
    "ax1.plot(x,y,(x**2+y**2)**(0.5),color='darkgrey')\n",
    "ax1.axvline(0,-5000,5000,color='grey')\n",
    "ax1.axhline(0,-5000,5000,color='grey')\n",
    "im = ax1.pcolormesh(x, y, PCT_on_50, cmap=cmap, norm=norm)\n",
    "cbar = fig.colorbar(im, ax=ax1)\n",
    "cbar.ax.set_ylabel('Clutter Probability')\n",
    "ax1.set_title('Clutter Map \\n 2018-11-02')\n",
    "ax1.set_xlabel('W-E dist. from radar (m)')\n",
    "ax1.set_ylabel('N-S dist. from radar (m)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 360, 51) (1, 360, 51) (360, 6)\n",
      "1 1\n",
      "49.541238247840845\n"
     ]
    },
    {
     "data": {
      "image/png": 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7M3DH4a5tXKrqmqo6uft5vBj456r6feBe4P3dsLmeI0CXMd9L8ubu1LnAt5nx13ImVuhDuhs4n94fCX8I/MF0yxnZXwFHA9u730a+WVV/VFWPJbmF3jfTfuDyqnplinWOpPEtI84BLgUeSbKzO3ctcB1wS5LL6HVqfWBK9U3SR4Cbk3wCeIjuj4lz7sPATd3C4yl6GfMaZvi19J2iktSImb7kIkkanIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1Ij/g/ziQyrXaCz+gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dBZ95 baseline = 49.541238247840845\n",
      "49.21285998656316\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dBZ95 baseline CAR = 49.21285998656316\n",
      "(360, 51)\n",
      "(360, 51)\n"
     ]
    }
   ],
   "source": [
    "# Want to calculate the PDF and CDf and get 95th percentile reflectivity\n",
    "# Only use reflecitivity that falls within the PCT_on_50 grid boxes\n",
    "\n",
    "#zh_mean = np.nanmean(zh,axis=0)\n",
    "#Fill a z array for the PCT_on_50 boxes\n",
    "zh = np.asarray(zh)\n",
    "zhCAR = np.asarray(zhCAR)\n",
    "zh_50 = np.empty((zh.shape))\n",
    "zhCAR_50 = np.empty((zhCAR.shape))\n",
    "zh_50[:,:,:] = np.nan\n",
    "zhCAR_50[:,:,:] = np.nan\n",
    "print(zh_50.shape, zhCAR_50.shape, PCT_on_50.shape)\n",
    "print(len(zh[:,0,0]), len(zhCAR[:,0,0]))\n",
    "#print(np.isfinite(PCT_on_50))\n",
    "\n",
    "for k in range(0, len(zh[:,0,0])):\n",
    "    for i in range(0,len(PCT_on_50[:,0])):\n",
    "        for j in range(0,len(PCT_on_50[0,:])):\n",
    "            if np.isfinite(PCT_on_50[i,j]):\n",
    "                zh_50[k,i,j*10-10:j*10] = zh[k,i,j*10-10:j*10]\n",
    "\n",
    "dbz95_baseline = []\n",
    "for k in range(0, len(zh[:,0,0])):            \n",
    "    maskk = np.where(np.isfinite(zh_50[k,:,:])) \n",
    "    n,bins,patches=plt.hist(zh_50[k,:,:][maskk],bins=105,range=(-40.,65.))\n",
    "    cdf = np.cumsum(n)\n",
    "    p_mean = cdf/cdf[-1]*100\n",
    "    x = np.arange(105)-40\n",
    "    coeff = np.polyfit(p_mean,x,13) # Find coefficients of 10th degree polynomial for CDF\n",
    "    poly_func = np.poly1d(coeff)\n",
    "    dbz95 = poly_func(95.)\n",
    "    dbz95_baseline.append(dbz95)\n",
    "    print(dbz95)\n",
    "plt.show()\n",
    "\n",
    "dbz95_baseline_mean = np.nanmean(dbz95_baseline)\n",
    "print('dBZ95 baseline =',dbz95_baseline_mean)\n",
    "\n",
    "\n",
    "for k in range(0, len(zhCAR[:,0,0])):\n",
    "    for i in range(0,len(PCT_on_50[:,0])):\n",
    "        for j in range(0,len(PCT_on_50[0,:])):\n",
    "            if np.isfinite(PCT_on_50[i,j]):\n",
    "                zhCAR_50[k,i,j*10-10:j*10] = zhCAR[k,i,j*10-10:j*10]\n",
    "\n",
    "dbz95_baselineCAR = []\n",
    "for k in range(0, len(zhCAR[:,0,0])):            \n",
    "    maskk = np.where(np.isfinite(zhCAR_50[k,:,:])) \n",
    "    n,bins,patches=plt.hist(zhCAR_50[k,:,:][maskk],bins=105,range=(-40.,65.))\n",
    "    cdf = np.cumsum(n)\n",
    "    p_mean = cdf/cdf[-1]*100\n",
    "    x = np.arange(105)-40\n",
    "    coeff = np.polyfit(p_mean,x,13) # Find coefficients of 10th degree polynomial for CDF\n",
    "    poly_func = np.poly1d(coeff)\n",
    "    dbz95CAR = poly_func(95.)\n",
    "    dbz95_baselineCAR.append(dbz95CAR)\n",
    "    print(dbz95CAR)\n",
    "plt.show()\n",
    "\n",
    "dbz95_baseline_meanCAR = np.nanmean(dbz95_baselineCAR)\n",
    "print('dBZ95 baseline CAR =',dbz95_baseline_meanCAR)\n",
    "\n",
    "\n",
    "print(zh[0,:,:].shape)\n",
    "print(zh[0,:,0:].shape)\n",
    "#maskk = np.where(np.isfinite(zh_50[0,:,:])) \n",
    "#n,bins,patches=plt.hist(zh_50[0,:,:][maskk],bins=105,range=(-40.,65.))\n",
    "#plt.show()\n",
    "#cdf = np.cumsum(n)\n",
    "#p_mean = cdf/cdf[-1]*100\n",
    "#x = np.arange(105)-40\n",
    "#coeff = np.polyfit(p_mean,x,13) # Find coefficients of 10th degree polynomial for CDF\n",
    "#poly_func = np.poly1d(coeff)\n",
    "#dbz95_mean = poly_func(95.)\n",
    "#print(dbz95_mean)\n",
    "#plt.plot(bins[1:],n)\n",
    "#plt.show()\n",
    "\n",
    "#p_mean = cdf/cdf[-1]*100\n",
    "#x = np.arange(105)-40\n",
    "#coeff = np.polyfit(p_mean,x,13) # Find coefficients of 10th degree polynomial for CDF\n",
    "#poly_func = np.poly1d(coeff)\n",
    "#x_poly = np.linspace(p[0],p[-1],105)\n",
    "#y_poly = poly_func(x_poly)\n",
    "#plt.plot(p_mean,x)\n",
    "#plt.show()\n",
    "    \n",
    "# Find the value of reflectivity at the 95th percentile of CDF\n",
    "#dbz95_mean = poly_func(95.)\n",
    "#print(dbz95_mean)\n",
    "#print(zh_50)            \n",
    "#print(zh_mean[np.isfinite(zh_mean)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_scaled = n\n",
    "plt.plot(bins[1:],n_scaled,color='b')\n",
    "plt.plot(bins[1:],p_mean,color='k')\n",
    "plt.axhline(95,linestyle='--',color='grey')\n",
    "plt.axvline(dbz95_mean,linestyle='--',color='grey')\n",
    "plt.ylim(0,101)\n",
    "plt.ylabel('PDF*100 and CDF (%)')\n",
    "plt.xlabel('Reflectivity (dBZ)')\n",
    "plt.title('PDF and CDF of Clutter Area Reflectivity \\n (baseline: 2018-11-02)')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "from netCDF4 import Dataset\n",
    "dataset = Dataset('/home/hunzinger/data/rca_cluttermap_baseline_20181102.nc',\n",
    "                  'w',format='NETCDF4_CLASSIC')\n",
    "azi = dataset.createDimension('azi', 360)\n",
    "rang = dataset.createDimension('rang', 6)\n",
    "value = dataset.createDimension('value',1)\n",
    "\n",
    "PCT_ON_50 = dataset.createVariable('Flagged clutter grid gates', np.float64, ('azi','rang'))\n",
    "DBZ95_BASE = dataset.createVariable('Baseline 95th reflectivity', np.float64, ('value',))\n",
    "DBZ95_BASECAR = dataset.createVariable('Baseline 95th reflectivity CAR', np.float64, ('value',))\n",
    "\n",
    "PCT_ON_50[:,:] = PCT_on_50\n",
    "DBZ95_BASE[:] = dbz95_baseline_mean\n",
    "DBZ95_BASECAR[:] = dbz95_baseline_meanCAR\n",
    "\n",
    "dataset.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}