rca_create_baseline_hdf5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pyart\n",
    "#from netCDF4 import Dataset\n",
    "\n",
    "#dataset = Dataset('/home/hunzinger/data/rca_baseline_20181108.nc')\n",
    "#n_baseline = dataset.variables['PDF counts'][:]\n",
    "#bins_baseline = dataset.variables['PDF bins'][:]\n",
    "#p_baseline = dataset.variables['CDF values'][:]\n",
    "#dbz95_baseline = dataset.variables['Baseline 95th reflectivity'][:]\n",
    "#dataset.close()\n",
    "\n",
    "#print(dbz95_baseline)\n",
    "#print(n_baseline)\n",
    "#print(p_baseline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rca_create_baseline(filename, hPCT_on_50, vPCT_on_50, uhPCT_on_50, uvPCT_on_50, hmask, vmask, uhmask, uvmask):\n",
    "    '''Calculates the 95th percentile clutter area reflectivity baseline using clutter map'''\n",
    "    #radar = pyart.io.cfradial.read_cfradial(filename)\n",
    "    radar = pyart.aux_io.read_gamic(filename, file_field_names=True)\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'] > 10000.)[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",
    "    zh = radar.fields['Zh']['data'][sweep_start_idx:sweep_stop_idx,r_start_idx:r_stop_idx]\n",
    "    zv = radar.fields['Zv']['data'][sweep_start_idx:sweep_stop_idx,r_start_idx:r_stop_idx]\n",
    "    uzh = radar.fields['UZh']['data'][sweep_start_idx:sweep_stop_idx,r_start_idx:r_stop_idx]\n",
    "    uzv = radar.fields['UZv']['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",
    "        zv = np.delete(zv,-diff,axis=0)\n",
    "        uzh = np.delete(uzh,-diff,axis=0)\n",
    "        uzv = np.delete(uzv,-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",
    "    zv = zv[sorted_idx]\n",
    "    uzh = uzh[sorted_idx]\n",
    "    uzv = uzv[sorted_idx]\n",
    "    theta = theta[sorted_idx]\n",
    "    \n",
    "    # Create array to store qualifying reflectivities (fall within PCT_on > 0.5)\n",
    "    #zh_car = np.empty((zh.shape))\n",
    "    #zh_car[:,:] = np.nan\n",
    "    #zv_car = np.empty((zv.shape))\n",
    "    #zv_car[:,:] = np.nan\n",
    "    #uzh_car = np.empty((uzh.shape))\n",
    "    #uzh_car[:,:] = np.nan\n",
    "    #uzv_car = np.empty((uzv.shape))\n",
    "    #uzv_car[:,:] = np.nan\n",
    "    \n",
    "    # H POLARIZATION\n",
    "    # Find and store all reflectivity values that fall within the PCT_on > 0.5 grid gate\n",
    "    #zh_car = zh[hmask]\n",
    "    zh_car = zh[np.isfinite(hPCT_on_50)]\n",
    "    #for i in range(0,len(hPCT_on_50[:,0])):\n",
    "    #    for j in range(0,len(hPCT_on_50[0,:])):\n",
    "    #        if np.isfinite(hPCT_on_50[i,j]):\n",
    "    #            zh_car[i,j*10-10:j*10] = zh[i,j*10-10:j*10]\n",
    "                \n",
    "    # Calculate the PDF of the clutter area reflectivity (CAR)\n",
    "    #mask = np.where(np.isfinite(zh_car))  \n",
    "    n,bins=np.histogram(zh_car,bins=525,range=(-40.,65.))\n",
    "    #n,bins=np.histogram(zh_car[mask],bins=525,range=(-40.,65.))\n",
    "    #n,bins,patches=plt.hist(zh_car[mask],bins=525,range=(-40.,65.))\n",
    "    # Calculate CDF of clutter area reflectivity\n",
    "    cdf = np.cumsum(n)\n",
    "    p = cdf/cdf[-1]*100\n",
    "    # Find coefficients of 13th degree polynomial for CDF\n",
    "    x = np.arange(525)*(1/5)-40\n",
    "    coeff = np.polyfit(p,x,13)\n",
    "    poly_func = np.poly1d(coeff)\n",
    "    #x_poly = np.linspace(p[0],p[-1],105)\n",
    "    #y_poly = poly_func(x_poly)\n",
    "    # Find the value of reflectivity at the 95th percentile of CDF\n",
    "    dbz95 = poly_func(95.)\n",
    "    \n",
    "    # UZH POLARIZATION\n",
    "    # Find and store all reflectivity values that fall within the PCT_on > 0.5 grid gate\n",
    "    #uzh_car = np.empty((uzh.shape))\n",
    "    #uzh_car[:,:] = np.nan\n",
    "    \n",
    "    uzh_car = uzh[np.isfinite(uhPCT_on_50)]\n",
    "    #uzh_car = uzh[uhmask]\n",
    "    #for i in range(0,len(uhPCT_on_50[:,0])):\n",
    "    #    for j in range(0,len(uhPCT_on_50[0,:])):\n",
    "    #        if np.isfinite(uhPCT_on_50[i,j]):\n",
    "    #            uzh_car[i,j*10-10:j*10] = uzh[i,j*10-10:j*10]\n",
    "                \n",
    "    # Calculate the PDF of the clutter area reflectivity (CAR)\n",
    "    #mask = np.where(np.isfinite(uzh_car))  \n",
    "    #uhn,uhbins=np.histogram(uzh_car[mask],bins=525,range=(-40.,65.))\n",
    "    uhn,uhbins=np.histogram(uzh_car,bins=525,range=(-40.,65.))\n",
    "    #uhn,uhbins,uhpatches=plt.hist(uzh_car[mask],bins=525,range=(-40.,65.))\n",
    "    # Calculate CDF of clutter area reflectivity\n",
    "    cdf = np.cumsum(uhn)\n",
    "    uhp = cdf/cdf[-1]*100\n",
    "    # Find coefficients of 13th degree polynomial for CDF\n",
    "    x = np.arange(525)*(1/5)-40\n",
    "    coeff = np.polyfit(uhp,x,13)\n",
    "    poly_func = np.poly1d(coeff)\n",
    "    #x_poly = np.linspace(p[0],p[-1],105)\n",
    "    #y_poly = poly_func(x_poly)\n",
    "    # Find the value of reflectivity at the 95th percentile of CDF\n",
    "    dbz95_uh = poly_func(95.)\n",
    "    \n",
    "    # V POLARIZATION\n",
    "    # Find and store all reflectivity values that fall within the PCT_on > 0.5 grid gate\n",
    "    #zv_car = zv[vmask]\n",
    "    zv_car = zv[np.isfinite(vPCT_on_50)]\n",
    "    #for i in range(0,len(vPCT_on_50[:,0])):\n",
    "    #    for j in range(0,len(vPCT_on_50[0,:])):\n",
    "    #        if np.isfinite(vPCT_on_50[i,j]):\n",
    "    #            zv_car[i,j*10-10:j*10] = zv[i,j*10-10:j*10]\n",
    "                \n",
    "    # Calculate the PDF of the clutter area reflectivity (CAR)\n",
    "    #mask = np.where(np.isfinite(zv_car))  \n",
    "    vn,vbins=np.histogram(zv_car,bins=525,range=(-40.,65.))\n",
    "    #vn,vbins=np.histogram(zv_car[mask],bins=525,range=(-40.,65.))\n",
    "    #vn,vbins,vpatches=plt.hist(zv_car[mask],bins=525,range=(-40.,65.))\n",
    "    # Calculate CDF of clutter area reflectivity\n",
    "    cdf = np.cumsum(vn)\n",
    "    vp = cdf/cdf[-1]*100\n",
    "    # Find coefficients of 13th degree polynomial for CDF\n",
    "    x = np.arange(525)*(1/5)-40\n",
    "    coeff = np.polyfit(vp,x,13)\n",
    "    poly_func = np.poly1d(coeff)\n",
    "    #x_poly = np.linspace(p[0],p[-1],105)\n",
    "    #y_poly = poly_func(x_poly)\n",
    "    # Find the value of reflectivity at the 95th percentile of CDF\n",
    "    dbz95_v = poly_func(95.)\n",
    "    \n",
    "    # UV POLARIZATION\n",
    "    # Find and store all reflectivity values that fall within the PCT_on > 0.5 grid gate\n",
    "    #uzv_car = uzv[uvmask]\n",
    "    uzv_car = uzv[np.isfinite(uvPCT_on_50)]\n",
    "    #for i in range(0,len(uvPCT_on_50[:,0])):\n",
    "    #    for j in range(0,len(uvPCT_on_50[0,:])):\n",
    "    #        if np.isfinite(uvPCT_on_50[i,j]):\n",
    "    #            uzv_car[i,j*10-10:j*10] = uzv[i,j*10-10:j*10]\n",
    "                \n",
    "    # Calculate the PDF of the clutter area reflectivity (CAR)\n",
    "    #mask = np.where(np.isfinite(uzv_car))  \n",
    "    uvn,uvbins=np.histogram(uzv_car,bins=525,range=(-40.,65.))\n",
    "    #uvn,uvbins=np.histogram(uzv_car[mask],bins=525,range=(-40.,65.))\n",
    "    #uvn,uvbins,uvpatches=plt.hist(uzv_car[mask],bins=525,range=(-40.,65.))\n",
    "    # Calculate CDF of clutter area reflectivity\n",
    "    cdf = np.cumsum(uvn)\n",
    "    uvp = cdf/cdf[-1]*100\n",
    "    # Find coefficients of 13th degree polynomial for CDF\n",
    "    x = np.arange(525)*(1/5)-40\n",
    "    coeff = np.polyfit(uvp,x,13)\n",
    "    poly_func = np.poly1d(coeff)\n",
    "    #x_poly = np.linspace(p[0],p[-1],105)\n",
    "    #y_poly = poly_func(x_poly)\n",
    "    # Find the value of reflectivity at the 95th percentile of CDF\n",
    "    dbz95_uv = poly_func(95.)\n",
    "    \n",
    "    del radar\n",
    "    return date_time, n, bins, p, dbz95, vn, vbins, vp, dbz95_v, uhn, uhbins, uhp, dbz95_uh, uvn, uvbins, uvp, dbz95_uv\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "/run/media/hunzinger/WININSTALL/csapr2-data/corcsaprM1.00.20181108.141001.raw.cor-ppi-cacti-15tilt_CSAPR2_20181108130003_00.h5\n",
      "/run/media/hunzinger/WININSTALL/csapr2-data/corcsaprM1.00.20181108.151001.raw.cor-ppi-cacti-15tilt_CSAPR2_20181108131503_00.h5\n",
      "/run/media/hunzinger/WININSTALL/csapr2-data/corcsaprM1.00.20181108.151001.raw.cor-ppi-cacti-15tilt_CSAPR2_20181108133003_00.h5\n",
      "/run/media/hunzinger/WININSTALL/csapr2-data/corcsaprM1.00.20181108.151001.raw.cor-ppi-cacti-15tilt_CSAPR2_20181108134504_00.h5\n",
      "/run/media/hunzinger/WININSTALL/csapr2-data/corcsaprM1.00.20181108.151001.raw.cor-ppi-cacti-15tilt_CSAPR2_20181108140003_00.h5\n",
      "/run/media/hunzinger/WININSTALL/csapr2-data/corcsaprM1.00.20181108.161001.raw.cor-ppi-cacti-15tilt_CSAPR2_20181108143008_00.h5\n"
     ]
    }
   ],
   "source": [
    "# Run this function on a bunch of files (try data from one day)\n",
    "import os\n",
    "import glob\n",
    "\n",
    "from netCDF4 import Dataset\n",
    "dataset = Dataset('/home/hunzinger/data/rca_cluttermap_20181108.nc')\n",
    "hPCT_on_50 = dataset.variables['clutter_gate_values_zh'][:,:]\n",
    "vPCT_on_50 = dataset.variables['clutter_gate_values_zv'][:,:]\n",
    "uhPCT_on_50 = dataset.variables['clutter_gate_values_uzh'][:,:]\n",
    "uvPCT_on_50 = dataset.variables['clutter_gate_values_uzv'][:,:]\n",
    "h_mask = dataset.variables['clutter_gate_mask_zh'][:,:]\n",
    "v_mask = dataset.variables['clutter_gate_mask_zv'][:,:]\n",
    "uh_mask = dataset.variables['clutter_gate_mask_uzh'][:,:]\n",
    "uv_mask = dataset.variables['clutter_gate_mask_uzv'][:,:]\n",
    "dataset.close()\n",
    "\n",
    "dt = []\n",
    "n = []\n",
    "bins = []\n",
    "p = []\n",
    "dbz95 = []\n",
    "vn = []\n",
    "vbins = []\n",
    "vp = []\n",
    "dbz95_v = []\n",
    "uhn = []\n",
    "uhbins = []\n",
    "uhp = []\n",
    "dbz95_uh = []\n",
    "uvn = []\n",
    "uvbins = []\n",
    "uvp = []\n",
    "dbz95_uv = []\n",
    "\n",
    "for f in glob.glob(os.path.join('/run/media/hunzinger/WININSTALL/csapr2-data/', 'corcsapr*.*cor-ppi*20181108*.h5')):\n",
    "    print(f)\n",
    "    DateTime, N, Bins, P, DBZ95, VN, VBins, VP, DBZ95V, UHN, UHBins, UHP, DBZ95UH, UVN, UVBins, UVP, DBZ95UV = rca_create_baseline(f, PCT_on_50, vPCT_on_50, uhPCT_on_50, uvPCT_on_50, h_mask, v_mask, uh_mask, uv_mask)\n",
    "    \n",
    "    # Put all PPI times into a list\n",
    "    dt.append(DateTime)\n",
    "    n.append(N)\n",
    "    bins.append(Bins)\n",
    "    p.append(P)\n",
    "    dbz95.append(DBZ95)\n",
    "    vn.append(VN)\n",
    "    vbins.append(VBins)\n",
    "    vp.append(VP)\n",
    "    dbz95_v.append(DBZ95V)\n",
    "    uhn.append(UHN)\n",
    "    uhbins.append(UHBins)\n",
    "    uhp.append(UHP)\n",
    "    dbz95_uh.append(DBZ95UH)\n",
    "    uvn.append(UVN)\n",
    "    uvbins.append(UVBins)\n",
    "    uvp.append(UVP)\n",
    "    dbz95_uv.append(DBZ95UV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.404938499119893\n",
      "21.778154961229678\n",
      "57.52864113984983 18.404938499119893\n",
      "58.33197983479645 21.778154961229678\n",
      "(360, 101)\n",
      "(360, 101)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#dataset = Dataset('/home/hunzinger/data/rca_baseline_20181108.nc')\n",
    "#n_baseline = dataset.variables['PDF counts'][:,:]\n",
    "#bins_baseline = dataset.variables['PDF bins'][:,:]\n",
    "#p_baseline = dataset.variables['CDF values'][:,:]\n",
    "#dbz95_baseline = dataset.variables['Baseline 95th refletivity'][:]\n",
    "#vn_baseline = dataset.variables['PDF counts (V)'][:,:]\n",
    "#vbins_baseline = dataset.variables['PDF bins (V)'][:,:]\n",
    "#vp_baseline = dataset.variables['CDF values (V)'][:,:]\n",
    "#vdbz95_baseline = dataset.variables['Baseline 95th refletivity (V)'][:]\n",
    "#dataset.close()\n",
    "\n",
    "#dataset = Dataset('/home/hunzinger/data/rca_baseline_20181109.nc')\n",
    "#n_baseline = dataset.variables['PDF counts'][:]\n",
    "#bins_baseline = dataset.variables['PDF bins'][:]\n",
    "#p_baseline = dataset.variables['CDF values'][:]\n",
    "#dbz95_baseline = dataset.variables['Baseline 95th reflectivity'][:]\n",
    "#dataset.close()\n",
    "\n",
    "dbz95_baseline = np.nanmean(dbz95)\n",
    "print(dbz95_baseline)\n",
    "n = np.asarray(n)\n",
    "bins = np.asarray(bins)\n",
    "p = np.asarray(p)\n",
    "n_baseline = np.nanmean(n,axis=0)\n",
    "bins_baseline = bins[0,:]\n",
    "p_baseline = np.nanmean(p,axis=0)\n",
    "\n",
    "#plt.plot(bins_baseline[1:],n_baseline*10)\n",
    "#plt.plot(bins_baseline[1:],p_baseline)\n",
    "#plt.plot(x,p_baseline)\n",
    "#plt.show()\n",
    "\n",
    "vdbz95_baseline = np.nanmean(dbz95_v)\n",
    "print(vdbz95_baseline)\n",
    "vn = np.asarray(vn)\n",
    "vbins = np.asarray(vbins)\n",
    "vp = np.asarray(vp)\n",
    "vn_baseline = np.nanmean(vn,axis=0)\n",
    "vbins_baseline = vbins[0,:]\n",
    "vp_baseline = np.nanmean(vp,axis=0)\n",
    "#plt.plot(vbins_baseline[1:],vn_baseline*10)\n",
    "#plt.plot(vbins_baseline[1:],vp_baseline)\n",
    "#plt.show()\n",
    "#plt.plot(x,p_baseline)\n",
    "\n",
    "uhdbz95_baseline = np.nanmean(dbz95_uh)\n",
    "print(uhdbz95_baseline, dbz95_baseline)\n",
    "uhn = np.asarray(uhn)\n",
    "uhbins = np.asarray(uhbins)\n",
    "uhp = np.asarray(uhp)\n",
    "uhn_baseline = np.nanmean(uhn,axis=0)\n",
    "uhbins_baseline = uhbins[0,:]\n",
    "uhp_baseline = np.nanmean(uhp,axis=0)\n",
    "\n",
    "uvdbz95_baseline = np.nanmean(dbz95_uv)\n",
    "print(uvdbz95_baseline, vdbz95_baseline)\n",
    "uvn = np.asarray(uvn)\n",
    "uvbins = np.asarray(uvbins)\n",
    "uvp = np.asarray(uvp)\n",
    "uvn_baseline = np.nanmean(uvn,axis=0)\n",
    "uvbins_baseline = uvbins[0,:]\n",
    "uvp_baseline = np.nanmean(uvp,axis=0)\n",
    "\n",
    "# H and UZh\n",
    "fig, ax1 = plt.subplots(figsize=[8,6])\n",
    "ax1.plot(bins_baseline[1:],n_baseline,color='darkslategrey')#,linestyle='--')\n",
    "ax1.plot(bins_baseline[1:],p_baseline,color='darkslategrey',label='Corr. H polarization :: dBZ95=18.40')\n",
    "ax1.plot(uhbins_baseline[1:],uhn_baseline,color='k')\n",
    "ax1.plot(uhbins_baseline[1:],uhp_baseline,color='k',label='Uncorr. H polarization :: dBZ95=57.52')\n",
    "ax1.axhline(95.,color='grey',linestyle='--')\n",
    "\n",
    "ax1.set_ylabel('PDF*10 and CDF')\n",
    "ax1.set_xlabel('Reflectivity (dBZ)')\n",
    "ax1.set_title('H baseline case \\n 2018-11-08')\n",
    "ax1.legend(loc='center left')\n",
    "\n",
    "# V and UZv\n",
    "fig, ax1 = plt.subplots(figsize=[8,6])\n",
    "ax1.plot(vbins_baseline[1:],vn_baseline,color='darkslategrey')#,linestyle='--')\n",
    "ax1.plot(vbins_baseline[1:],vp_baseline,color='darkslategrey',label='Corr. V polarization :: dBZ95=21.77')\n",
    "ax1.plot(uvbins_baseline[1:],uvn_baseline,color='k')\n",
    "ax1.plot(uvbins_baseline[1:],uvp_baseline,color='k',label='Uncorr. V polarization :: dBZ95=58.33')\n",
    "ax1.axhline(95.,color='grey',linestyle='--')\n",
    "\n",
    "ax1.set_ylabel('PDF and CDF')\n",
    "ax1.set_xlabel('Reflectivity (dBZ)')\n",
    "ax1.set_title('V baseline case \\n 2018-11-08')\n",
    "ax1.legend(loc='center left')\n",
    "\n",
    "# UZh and UZv\n",
    "fig, ax1 = plt.subplots(figsize=[8,6])\n",
    "ax1.plot(uhbins_baseline[1:],uhn_baseline,color='k')\n",
    "ax1.plot(uhbins_baseline[1:],uhp_baseline,color='k',label='H pol. :: dBZ95=57.52')\n",
    "ax1.plot(uvbins_baseline[1:],uvn_baseline,color='darkslategrey')#,linestyle='--')\n",
    "ax1.plot(uvbins_baseline[1:],uvp_baseline,color='darkslategrey',label='V pol. :: dBZ95=58.33')\n",
    "ax1.axhline(95.,color='grey',linestyle='--')\n",
    "\n",
    "ax1.set_ylabel('PDF and CDF')\n",
    "ax1.set_xlabel('Reflectivity (dBZ)')\n",
    "ax1.set_title('Baseline case \\n 2018-11-08')\n",
    "ax1.legend(loc='center left')\n",
    "\n",
    "# UZh only\n",
    "fig, ax1 = plt.subplots(figsize=[8,6])\n",
    "ax1.plot(uhbins_baseline[1:],uhn_baseline,color='k')\n",
    "ax1.plot(uhbins_baseline[1:],uhp_baseline,color='k',label='H pol. :: dBZ95=57.52')\n",
    "ax1.axhline(95.,color='grey',linestyle='--')\n",
    "\n",
    "ax1.set_ylabel('PDF and CDF')\n",
    "ax1.set_xlabel('Reflectivity (dBZ)')\n",
    "ax1.set_title('Baseline case \\n 2018-11-08')\n",
    "ax1.legend(loc='center left')\n",
    "\n",
    "print(PCT_on_50.shape)\n",
    "print(uhPCT_on_50.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "#d1 = Dataset('/home/hunzinger/data/rca_cluttermap_20181108.nc')\n",
    "#PCT_on_50_1 = d1.variables['Flagged clutter grid gates'][:,:]\n",
    "#d1.close()\n",
    "\n",
    "d = Dataset('/home/hunzinger/data/rca_baseline_20181108.nc',\n",
    "                  'w',format='NETCDF4_CLASSIC')\n",
    "\n",
    "azi = d.createDimension('azi', 360)\n",
    "rang = d.createDimension('rang', 11)\n",
    "value = d.createDimension('value',1)\n",
    "nbins = d.createDimension('nbins',525)\n",
    "binsdim = d.createDimension('binsdim',526)\n",
    "zrang = d.createDimension('zrang',101)\n",
    "\n",
    "HPCT_ON_50 = d.createVariable('clutter_gate_values_zh', np.float64, ('azi','zrang'))\n",
    "HDBZ95_BASE = d.createVariable('baseline_dbz95_zh', np.float64, ('value',))\n",
    "HN_BASE = d.createVariable('pdf_counts_zh', np.float64, ('nbins',))\n",
    "HBINS = d.createVariable('pdf_bins_zh', np.float64, ('binsdim',))\n",
    "HP_BASE = d.createVariable('cdf_values_zh', np.float64, ('nbins',))\n",
    "VPCT_ON_50 = d.createVariable('clutter_gate_values_zv', np.float64, ('azi','zrang'))\n",
    "VDBZ95_BASE = d.createVariable('baseline_dbz95_zv', np.float64, ('value',))\n",
    "VN_BASE = d.createVariable('pdf_counts_zv', np.float64, ('nbins',))\n",
    "VBINS = d.createVariable('pdf_bins_zv', np.float64, ('binsdim',))\n",
    "VP_BASE = d.createVariable('cdf_values_zv', np.float64, ('nbins',))\n",
    "UHPCT_ON_50 = d.createVariable('clutter_gate_values_uzh', np.float64, ('azi','zrang'))\n",
    "UHDBZ95_BASE = d.createVariable('baseline_dbz95_uzh', np.float64, ('value',))\n",
    "UHN_BASE = d.createVariable('pdf_counts_uzh', np.float64, ('nbins',))\n",
    "UHBINS = d.createVariable('pdf_bins_uzh', np.float64, ('binsdim',))\n",
    "UHP_BASE = d.createVariable('cdf_values_uzh', np.float64, ('nbins',))\n",
    "UVPCT_ON_50 = d.createVariable('clutter_gate_values_uzv', np.float64, ('azi','zrang'))\n",
    "UVDBZ95_BASE = d.createVariable('baseline_dbz95_uzv', np.float64, ('value',))\n",
    "UVN_BASE = d.createVariable('pdf_counts_uzv', np.float64, ('nbins',))\n",
    "UVBINS = d.createVariable('pdf_bins_uzv', np.float64, ('binsdim',))\n",
    "UVP_BASE = d.createVariable('cdf_values_uzv', np.float64, ('nbins',))\n",
    "HMASK = d.createVariable('clutter_gate_mask_zh','i1',('azi','zrang'))\n",
    "VMASK = d.createVariable('clutter_gate_mask_zv','i1',('azi','zrang'))\n",
    "UHMASK = d.createVariable('clutter_gate_mask_uzh','i1',('azi','zrang'))\n",
    "UVMASK = d.createVariable('clutter_gate_mask_uzv','i1',('azi','zrang'))\n",
    "\n",
    "HPCT_ON_50.long_name = 'Flagged clutter grid gates (Zh)'\n",
    "VPCT_ON_50.long_name = 'Flagged clutter grid gates (Zv)'\n",
    "UHPCT_ON_50.long_name = 'Flagged clutter grid gates (UZh)'\n",
    "UVPCT_ON_50.long_name = 'Flagged clutter grid gates (UZv)'\n",
    "HDBZ95_BASE.long_name = 'Baseline 95th reflectivity (H)'\n",
    "VDBZ95_BASE.long_name = 'Baseline 95th reflectivity (V)'\n",
    "UHDBZ95_BASE.long_name = 'Baseline 95th reflectivity (UZh)'\n",
    "UVDBZ95_BASE.long_name = 'Baseline 95th reflectivity (UZv)'\n",
    "HN_BASE.long_name = 'PDF counts (H)'\n",
    "VN_BASE.long_name = 'PDF counts (V)'\n",
    "UHN_BASE.long_name = 'PDF counts (UZh)'\n",
    "UVN_BASE.long_name = 'PDF counts (UZv)'\n",
    "HBINS.long_name = 'PDF bins (H)'\n",
    "VBINS.long_name = 'PDF bins (V)'\n",
    "UHBINS.long_name = 'PDF bins (UZh)'\n",
    "UVBINS.long_name = 'PDF bins (UZv)'\n",
    "HP_BASE.long_name = 'CDF values (H)'\n",
    "VP_BASE.long_name = 'CDF values (V)'\n",
    "UHP_BASE.long_name = 'CDF values (UZh)'\n",
    "UVP_BASE.long_name = 'CDF values (UZv)'\n",
    "HMASK.long_name = 'Flagged clutter grid mask (H)'\n",
    "VMASK.long_name = 'Flagged clutter grid mask (V)'\n",
    "UHMASK.long_name = 'Flagged clutter grid mask (UZh)'\n",
    "UVMASK.long_name = 'Flagged clutter grid mask (UZv)'\n",
    "\n",
    "HPCT_ON_50[:,:] = hPCT_on_50\n",
    "DBZ95_BASE[:] = dbz95_baseline\n",
    "N_BASE[:] = n_baseline\n",
    "BINS[:] = bins_baseline\n",
    "P_BASE[:] = p_baseline\n",
    "VPCT_ON_50[:,:] = vPCT_on_50\n",
    "VDBZ95_BASE[:] = vdbz95_baseline\n",
    "VN_BASE[:] = vn_baseline\n",
    "VBINS[:] = vbins_baseline\n",
    "VP_BASE[:] = vp_baseline\n",
    "UHPCT_ON_50[:,:] = uhPCT_on_50\n",
    "UHDBZ95_BASE[:] = uhdbz95_baseline\n",
    "UHN_BASE[:] = uhn_baseline\n",
    "UHBINS[:] = uhbins_baseline\n",
    "UHP_BASE[:] = uhp_baseline\n",
    "UVPCT_ON_50[:,:] = uvPCT_on_50\n",
    "UVDBZ95_BASE[:] = uvdbz95_baseline\n",
    "UVN_BASE[:] = uvn_baseline\n",
    "UVBINS[:] = uvbins_baseline\n",
    "UVP_BASE[:] = uvp_baseline\n",
    "HMASK[:,:] = PCT_on_50\n",
    "VMASK[:,:] = vPCT_on_50\n",
    "UHMASK[:,:] = uhPCT_on_50\n",
    "UVMASK[:,:] = uvPCT_on_50\n",
    "\n",
    "d.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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