CAMP2Ex_APR3_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(456, 1, 9675)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import h5py\n",
    "import math\n",
    "import glob\n",
    "import datetime\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import pandas as pd\n",
    "import matplotlib.colors as cm\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import Normalize\n",
    "from metpy.plots import ctables\n",
    "\n",
    "def roundup(x):\n",
    "    for i in range(len(x)):\n",
    "        x[i] = int(math.ceil(x[i] / 100.0)) * 100 \n",
    "    return x\n",
    "\n",
    "ht_gridded = np.linspace(10000.,-5000.,500)\n",
    "# df = pd.DataFrame(columns=['Lat', 'Lon', 'CTH (km)'])\n",
    "start = 1000\n",
    "end = 10000\n",
    "\n",
    "\n",
    "def grid_altitude(alt3D, W_refl, vel):\n",
    "    z_gridded = np.zeros((len(ht_gridded),np.shape(alt3D)[1]))\n",
    "    z_gridded[:,:] = np.nan\n",
    "    v_gridded = np.zeros((len(ht_gridded),np.shape(alt3D)[1]))\n",
    "    v_gridded[:,:] = np.nan\n",
    "    for i in range(np.shape(alt3D)[1]):\n",
    "        ht = alt3D[:,i]\n",
    "        z = W_refl[:,i]\n",
    "        v = vel[:,i]\n",
    "        for j in range(len(ht)):\n",
    "            if(np.isnan(z[j])==False and ht[j]>-5000. and ht[j]<10000.):\n",
    "                m = int((10000.-ht[j])/30.)\n",
    "                z_gridded[m,i] = z[j]\n",
    "                v_gridded[m,i] = v[j]\n",
    "    return z_gridded, v_gridded\n",
    "\n",
    "# READ AND PARSE HERE\n",
    "cmap = ctables.registry.get_colortable('NWSReflectivity')\n",
    "\n",
    "Wn_files = sorted(glob.glob('F:/CAMP2Ex_analysis/DATA/RF07/APR3/*Wn.h5'))\n",
    "# print(Wn_files[3])\n",
    "hdf = h5py.File(Wn_files[24], 'r')\n",
    "\n",
    "#print out the groups of variables\n",
    "# print(\"Keys: %s\" % hdf.keys())\n",
    "# Keys: <KeysViewHDF5 ['hi2lo', 'hires', 'lores', 'params_W', 'postEng_cal']>\n",
    "\n",
    "params_W = hdf['params_W']\n",
    "#print out the variables within params_W\n",
    "# for key in params_W.keys():\n",
    "#      print(key, params_W[key][:])\n",
    "\n",
    "hires = hdf['hires']\n",
    "#print out the variables within geolocation\n",
    "# for key in hires.keys():\n",
    "#      print(key)\n",
    "\n",
    "lat = hires['lat'][0,:]\n",
    "lon = hires['lon'][0,:]\n",
    "p_vel = hires['gsp_mps'][0,:]\n",
    "\n",
    "W_refl = hires['z95n']\n",
    "# print(W_refl.shape)\n",
    "W_refl_ = W_refl[:,0,start:end]\n",
    "\n",
    "vel = hires['vel95n']\n",
    "vel_ = vel[:,0,start:end]\n",
    "\n",
    "alt3D = hires['alt3D']\n",
    "print(alt3D.shape)\n",
    "alt3D_ = alt3D[:,0,start:end]\n",
    "z_gridded, v_gridded = grid_altitude(alt3D_,W_refl_,vel_)\n",
    "\n",
    "alt_nav = hires['alt_nav'][0,:]\n",
    "\n",
    "time = hires['scantime']\n",
    "time = time[:,start:end]\n",
    "UTC = []\n",
    "for i in range(time.shape[1]):\n",
    "    UTC = np.append(UTC,datetime.datetime.utcfromtimestamp(time[0,i]).strftime('%H:%M:%S'))\n",
    "\n",
    "# cols = ['UTC', 'Lat', 'Lon', 'Plane speed', 'CTH (m)', 'Plane_Alt(m)', 'Max Reflectivity', 'Max Dop Vel']\n",
    "# annot = np.zeros((len(lat),2))\n",
    "# lst = []\n",
    "# z_gridded = z_gridded[np.where(np.logical_and(ht_gridded>-500.,ht_gridded<8000.))[0][:],:]\n",
    "# v_gridded = v_gridded[np.where(np.logical_and(ht_gridded>-500.,ht_gridded<8000.))[0][:],:]\n",
    "# ht_gridded = ht_gridded[np.where(np.logical_and(ht_gridded>-500.,ht_gridded<8000.))[0][:]]\n",
    "# for shot in range(len(lat)-1):\n",
    "#     z2 = z_gridded[np.isnan(z_gridded[:,shot])==False,shot] \n",
    "#     ht2 = ht_gridded[np.isnan(z_gridded[:,shot])==False] \n",
    "#     cth = ht2[np.where(z2>-15.)][0]\n",
    "#     if (alt_nav[shot]-cth>500.):\n",
    "#         if (cth>1000.):\n",
    "# #                 print(shot, cth )\n",
    "#                 annot[shot,0] = shot\n",
    "#                 annot[shot,1] = np.where(abs(ht_gridded-cth)==np.min(abs(ht_gridded-cth)))[0]\n",
    "#                 lst.append([UTC[shot], lat[shot], lon[shot], p_vel[shot], cth, alt_nav[shot],\n",
    "#                            np.nanmax(z_gridded[:,shot]), v_gridded[np.where(abs(v_gridded[:,shot])==\n",
    "#                                                                     np.nanmax(abs(v_gridded[:,shot])))[0],shot][0]])\n",
    "#         else:\n",
    "#                 annot[shot,:] = np.nan\n",
    "#                 lst.append([UTC[shot], lat[shot], lon[shot], p_vel[shot], np.nan, alt_nav[shot], np.nan, np.nan])\n",
    "#     else:\n",
    "#         annot[shot,:] = np.nan\n",
    "#         lst.append([UTC[shot], lat[shot], lon[shot], p_vel[shot], np.nan, alt_nav[shot], np.nan, np.nan])\n",
    "# df = pd.DataFrame(lst, columns=cols)  \n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "ax = plt.gca()\n",
    "plt.imshow(z_gridded, cmap=cmap, norm=Normalize(-25,70), interpolation='nearest', aspect='auto')\n",
    "plt.xlabel('UTC Time [hh:mm]', fontsize=13)\n",
    "plt.ylabel('Altitude [m]', fontsize=13)\n",
    "cb = plt.colorbar(shrink=0.5, orientation='horizontal', pad=0.15, extend='both')\n",
    "cb.ax.set_xlabel('W-band Radar Reflectivity [dBZ]', fontsize=13)\n",
    "\n",
    "# Set the x-ticks to be UTC Time [hh:mm]\n",
    "x_ticks = np.arange(UTC.shape[0])\n",
    "x_labels = [timestamps for timestamps in UTC]\n",
    "ax.set_xticks(x_ticks[::1000])\n",
    "ax.set_xticklabels(x_labels[::1000], fontsize=13, rotation=45)\n",
    "\n",
    "# Set the y-ticks to be height.\n",
    "vert_vals = ht_gridded\n",
    "v_ticks = np.arange(vert_vals.shape[0])\n",
    "ax.set_yticks(v_ticks[::50])\n",
    "ax.set_yticklabels(roundup(vert_vals[::50]).astype(int), fontsize=12)\n",
    "\n",
    "# for i in range(0,np.shape(annot)[0]):\n",
    "#     if (np.isnan(annot[i,0])==False):\n",
    "#         plt.annotate('+', xy=(int(annot[i,0]),int(annot[i,1])), color='k', fontsize=12, weight='bold')\n",
    "# plt.savefig('F:/CAMP2Ex_analysis/DATA/RF07/APR3/APR3_Wn_Refl_RF07_0533_0538.png', \n",
    "#             dpi=400, bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "ax = plt.gca()\n",
    "plt.imshow(v_gridded, cmap='seismic', norm=Normalize(-10,10), interpolation='nearest', aspect='auto')\n",
    "plt.xlabel('UTC Time [hh:mm]', fontsize=13)\n",
    "plt.ylabel('Altitude [m]', fontsize=13)\n",
    "cb = plt.colorbar(shrink=0.5, orientation='horizontal', pad=0.15, extend='both')\n",
    "cb.ax.set_xlabel('W-band Doppler Velocity [m/s]', fontsize=13)\n",
    "\n",
    "# Set the x-ticks to be UTC Time [hh:mm]\n",
    "x_ticks = np.arange(UTC.shape[0])\n",
    "x_labels = [timestamps for timestamps in UTC]\n",
    "ax.set_xticks(x_ticks[::1000])\n",
    "ax.set_xticklabels(x_labels[::1000], fontsize=13, rotation=45)\n",
    "\n",
    "# Set the y-ticks to be height.\n",
    "vert_vals = ht_gridded\n",
    "v_ticks = np.arange(vert_vals.shape[0])\n",
    "ax.set_yticks(v_ticks[::100])\n",
    "ax.set_yticklabels(roundup(vert_vals[::100]).astype(int), fontsize=12)\n",
    "\n",
    "# plt.savefig('F:/CAMP2Ex_analysis/DATA/RF07/APR3/APR3_Wn_Refl_Velocity_RF07_0533_0538.png', \n",
    "#             dpi=400, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(456, 1, 9675)\n",
      "['05:58:32' '05:58:32' '05:58:32' '05:58:32' '05:58:32' '05:58:33'\n",
      " '05:58:33' '05:58:33' '05:58:33' '05:58:33' '05:58:33' '05:58:33'\n",
      " '05:58:33' '05:58:33' '05:58:33' '05:58:33' '05:58:33' '05:58:33'\n",
      " '05:58:33' '05:58:33' '05:58:33' '05:58:33' '05:58:33' '05:58:33'\n",
      " '05:58:33' '05:58:34' '05:58:34' '05:58:34' '05:58:34' '05:58:34']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import h5py\n",
    "import math\n",
    "import glob\n",
    "import datetime\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import pandas as pd\n",
    "import matplotlib.colors as cm\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import Normalize\n",
    "from metpy.plots import ctables\n",
    "\n",
    "def roundup(x):\n",
    "    for i in range(len(x)):\n",
    "        x[i] = int(math.ceil(x[i] / 100.0)) * 100 \n",
    "    return x\n",
    "\n",
    "ht_gridded = np.linspace(10000.,-5000.,500)\n",
    "# df = pd.DataFrame(columns=['Lat', 'Lon', 'CTH (km)'])\n",
    "start = 4500\n",
    "end = 6500\n",
    "\n",
    "\n",
    "def grid_altitude(alt3D, W_refl, vel):\n",
    "    z_gridded = np.zeros((len(ht_gridded),np.shape(alt3D)[1]))\n",
    "    z_gridded[:,:] = np.nan\n",
    "    v_gridded = np.zeros((len(ht_gridded),np.shape(alt3D)[1]))\n",
    "    v_gridded[:,:] = np.nan\n",
    "    for i in range(np.shape(alt3D)[1]):\n",
    "        ht = alt3D[:,i]\n",
    "        z = W_refl[:,i]\n",
    "        v = vel[:,i]\n",
    "        for j in range(len(ht)):\n",
    "            if(np.isnan(z[j])==False and ht[j]>-5000. and ht[j]<10000.):\n",
    "                m = int((10000.-ht[j])/30.)\n",
    "                z_gridded[m,i] = z[j]\n",
    "                v_gridded[m,i] = v[j]\n",
    "    return z_gridded, v_gridded\n",
    "\n",
    "# READ AND PARSE HERE\n",
    "cmap = ctables.registry.get_colortable('NWSReflectivity')\n",
    "\n",
    "Wn_files = sorted(glob.glob('F:/CAMP2Ex_analysis/DATA/RF07/APR3/*Wn.h5'))\n",
    "# print(Wn_files[3])\n",
    "hdf = h5py.File(Wn_files[24],'r')\n",
    "\n",
    "\n",
    "params_W = hdf['params_W']\n",
    "\n",
    "hires = hdf['hires']\n",
    "#print out the variables within geolocation\n",
    "# for key in hires.keys():\n",
    "#      print(key)\n",
    "\n",
    "lat = hires['lat'][0,:]\n",
    "lon = hires['lon'][0,:]\n",
    "p_vel = hires['gsp_mps'][0,:]\n",
    "\n",
    "W_refl = hires['z95n']\n",
    "# print(W_refl.shape)\n",
    "W_refl_ = W_refl[:,0,start:end]\n",
    "\n",
    "vel = hires['vel95n']\n",
    "vel_ = vel[:,0,start:end]\n",
    "\n",
    "alt3D = hires['alt3D']\n",
    "print(alt3D.shape)\n",
    "alt3D_ = alt3D[:,0,start:end]\n",
    "z_gridded, v_gridded = grid_altitude(alt3D_,W_refl_,vel_)\n",
    "\n",
    "alt_nav = hires['alt_nav'][0,:]\n",
    "\n",
    "time = hires['scantime']\n",
    "time = time[:,start:end]\n",
    "UTC = []\n",
    "for i in range(time.shape[1]):\n",
    "    UTC = np.append(UTC,datetime.datetime.utcfromtimestamp(time[0,i]).strftime('%H:%M:%S'))   \n",
    "# print(UTC[15:45])\n",
    "plt.figure(figsize=(12,8))\n",
    "ax = plt.gca()\n",
    "plt.imshow(z_gridded[150:250,:], cmap=cmap, norm=Normalize(-25,70), interpolation='nearest', aspect='auto')\n",
    "plt.xlabel('UTC Time [hh:mm]', fontsize=13)\n",
    "plt.ylabel('Altitude [m]', fontsize=13)\n",
    "cb = plt.colorbar(shrink=0.5, orientation='horizontal', pad=0.15, extend='both')\n",
    "cb.ax.set_xlabel('W-band Radar Reflectivity [dBZ]', fontsize=13)\n",
    "\n",
    "# Set the x-ticks to be UTC Time [hh:mm]\n",
    "x_ticks = np.arange(UTC.shape[0])\n",
    "x_labels = [timestamps for timestamps in UTC]\n",
    "ax.set_xticks(x_ticks[::200])\n",
    "ax.set_xticklabels(x_labels[::200], fontsize=13, rotation=45)\n",
    "\n",
    "# Set the y-ticks to be height.\n",
    "vert_vals = ht_gridded[150:250]\n",
    "v_ticks = np.arange(vert_vals.shape[0])\n",
    "ax.set_yticks(v_ticks[::10])\n",
    "ax.set_yticklabels(roundup(vert_vals[::10]).astype(int), fontsize=12)\n",
    "\n",
    "# plt.savefig('F:/CAMP2Ex_analysis/DATA/RF07/APR3/APR3_Wn_Refl_RF07_0558_0600_zoomed.png', \n",
    "#             dpi=400, bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "ax = plt.gca()\n",
    "plt.imshow(v_gridded[150:250], cmap='seismic', norm=Normalize(-10,10), interpolation='nearest', aspect='auto')\n",
    "plt.xlabel('UTC Time [hh:mm]', fontsize=13)\n",
    "plt.ylabel('Altitude [m]', fontsize=13)\n",
    "cb = plt.colorbar(shrink=0.5, orientation='horizontal', pad=0.15, extend='both')\n",
    "cb.ax.set_xlabel('W-band Doppler Velocity [m/s]', fontsize=13)\n",
    "\n",
    "# Set the x-ticks to be UTC Time [hh:mm]\n",
    "x_ticks = np.arange(UTC.shape[0])\n",
    "x_labels = [timestamps for timestamps in UTC]\n",
    "ax.set_xticks(x_ticks[::200])\n",
    "ax.set_xticklabels(x_labels[::200], fontsize=13, rotation=45)\n",
    "\n",
    "# Set the y-ticks to be height.\n",
    "vert_vals = ht_gridded[150:250]\n",
    "v_ticks = np.arange(vert_vals.shape[0])\n",
    "ax.set_yticks(v_ticks[::10])\n",
    "ax.set_yticklabels(roundup(vert_vals[::10]).astype(int), fontsize=12)\n",
    "\n",
    "# plt.savefig('F:/CAMP2Ex_analysis/DATA/RF07/APR3/APR3_Wn_Refl_Velocity_RF07_0558_0600_zoomed.png', \n",
    "#             dpi=400, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time_Start</th>\n",
       "      <th>Time_Stop</th>\n",
       "      <th>Day_Of_Year</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Pressure_Altitude</th>\n",
       "      <th>True_Air_Speed</th>\n",
       "      <th>Static_Air_Temp</th>\n",
       "      <th>Dew_Point</th>\n",
       "      <th>Static_Pressure</th>\n",
       "      <th>...</th>\n",
       "      <th>nbin13_LAWSON_Hawk</th>\n",
       "      <th>nbin14_LAWSON_Hawk</th>\n",
       "      <th>nbin15_LAWSON_Hawk</th>\n",
       "      <th>nbin16_LAWSON_Hawk</th>\n",
       "      <th>nbin17_LAWSON_Hawk</th>\n",
       "      <th>nbin18_LAWSON_Hawk</th>\n",
       "      <th>nbin19_LAWSON_Hawk</th>\n",
       "      <th>nbin20_LAWSON_Hawk</th>\n",
       "      <th>nbin21_LAWSON_Hawk</th>\n",
       "      <th>AC_Time (UTC)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>85879</td>\n",
       "      <td>85880</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>-9999.00</td>\n",
       "      <td>-9999.00</td>\n",
       "      <td>-9999.00</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>2019-09-08 23:51:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85880</td>\n",
       "      <td>85881</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>-9999.00</td>\n",
       "      <td>-9999.00</td>\n",
       "      <td>-9999.00</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>2019-09-08 23:51:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>85881</td>\n",
       "      <td>85882</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>-9999.00</td>\n",
       "      <td>-9999.00</td>\n",
       "      <td>-9999.00</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>2019-09-08 23:51:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>85882</td>\n",
       "      <td>85883</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>-9999.00</td>\n",
       "      <td>-9999.00</td>\n",
       "      <td>-9999.00</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>2019-09-08 23:51:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>85883</td>\n",
       "      <td>85884</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>-9999.00</td>\n",
       "      <td>-9999.00</td>\n",
       "      <td>-9999.00</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>2019-09-08 23:51:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33003</th>\n",
       "      <td>118882</td>\n",
       "      <td>118883</td>\n",
       "      <td>251</td>\n",
       "      <td>15.182436</td>\n",
       "      <td>120.557288</td>\n",
       "      <td>563.0</td>\n",
       "      <td>-888888.0</td>\n",
       "      <td>23.00</td>\n",
       "      <td>19.25</td>\n",
       "      <td>992.80</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>2019-09-09 09:01:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33004</th>\n",
       "      <td>118883</td>\n",
       "      <td>118884</td>\n",
       "      <td>251</td>\n",
       "      <td>15.182452</td>\n",
       "      <td>120.557246</td>\n",
       "      <td>565.0</td>\n",
       "      <td>-888888.0</td>\n",
       "      <td>22.99</td>\n",
       "      <td>19.06</td>\n",
       "      <td>992.73</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>2019-09-09 09:01:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33005</th>\n",
       "      <td>118884</td>\n",
       "      <td>118885</td>\n",
       "      <td>251</td>\n",
       "      <td>15.182468</td>\n",
       "      <td>120.557205</td>\n",
       "      <td>564.0</td>\n",
       "      <td>-888888.0</td>\n",
       "      <td>22.91</td>\n",
       "      <td>18.91</td>\n",
       "      <td>992.77</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>2019-09-09 09:01:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33006</th>\n",
       "      <td>118885</td>\n",
       "      <td>118886</td>\n",
       "      <td>251</td>\n",
       "      <td>15.182483</td>\n",
       "      <td>120.557165</td>\n",
       "      <td>567.0</td>\n",
       "      <td>-888888.0</td>\n",
       "      <td>22.96</td>\n",
       "      <td>18.82</td>\n",
       "      <td>992.66</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>2019-09-09 09:01:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33007</th>\n",
       "      <td>118886</td>\n",
       "      <td>118887</td>\n",
       "      <td>251</td>\n",
       "      <td>15.182498</td>\n",
       "      <td>120.557123</td>\n",
       "      <td>563.0</td>\n",
       "      <td>-888888.0</td>\n",
       "      <td>22.98</td>\n",
       "      <td>18.69</td>\n",
       "      <td>992.80</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>2019-09-09 09:01:26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33008 rows × 119 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Time_Start  Time_Stop  Day_Of_Year     Latitude    Longitude  \\\n",
       "0           85879      85880        -9999 -9999.000000 -9999.000000   \n",
       "1           85880      85881        -9999 -9999.000000 -9999.000000   \n",
       "2           85881      85882        -9999 -9999.000000 -9999.000000   \n",
       "3           85882      85883        -9999 -9999.000000 -9999.000000   \n",
       "4           85883      85884        -9999 -9999.000000 -9999.000000   \n",
       "...           ...        ...          ...          ...          ...   \n",
       "33003      118882     118883          251    15.182436   120.557288   \n",
       "33004      118883     118884          251    15.182452   120.557246   \n",
       "33005      118884     118885          251    15.182468   120.557205   \n",
       "33006      118885     118886          251    15.182483   120.557165   \n",
       "33007      118886     118887          251    15.182498   120.557123   \n",
       "\n",
       "       Pressure_Altitude  True_Air_Speed  Static_Air_Temp  Dew_Point  \\\n",
       "0                -9999.0         -9999.0         -9999.00   -9999.00   \n",
       "1                -9999.0         -9999.0         -9999.00   -9999.00   \n",
       "2                -9999.0         -9999.0         -9999.00   -9999.00   \n",
       "3                -9999.0         -9999.0         -9999.00   -9999.00   \n",
       "4                -9999.0         -9999.0         -9999.00   -9999.00   \n",
       "...                  ...             ...              ...        ...   \n",
       "33003              563.0       -888888.0            23.00      19.25   \n",
       "33004              565.0       -888888.0            22.99      19.06   \n",
       "33005              564.0       -888888.0            22.91      18.91   \n",
       "33006              567.0       -888888.0            22.96      18.82   \n",
       "33007              563.0       -888888.0            22.98      18.69   \n",
       "\n",
       "       Static_Pressure  ...  nbin13_LAWSON_Hawk  nbin14_LAWSON_Hawk  \\\n",
       "0             -9999.00  ...              -999.0              -999.0   \n",
       "1             -9999.00  ...              -999.0              -999.0   \n",
       "2             -9999.00  ...              -999.0              -999.0   \n",
       "3             -9999.00  ...              -999.0              -999.0   \n",
       "4             -9999.00  ...              -999.0              -999.0   \n",
       "...                ...  ...                 ...                 ...   \n",
       "33003           992.80  ...              -999.0              -999.0   \n",
       "33004           992.73  ...              -999.0              -999.0   \n",
       "33005           992.77  ...              -999.0              -999.0   \n",
       "33006           992.66  ...              -999.0              -999.0   \n",
       "33007           992.80  ...              -999.0              -999.0   \n",
       "\n",
       "       nbin15_LAWSON_Hawk  nbin16_LAWSON_Hawk  nbin17_LAWSON_Hawk  \\\n",
       "0                  -999.0              -999.0              -999.0   \n",
       "1                  -999.0              -999.0              -999.0   \n",
       "2                  -999.0              -999.0              -999.0   \n",
       "3                  -999.0              -999.0              -999.0   \n",
       "4                  -999.0              -999.0              -999.0   \n",
       "...                   ...                 ...                 ...   \n",
       "33003              -999.0              -999.0              -999.0   \n",
       "33004              -999.0              -999.0              -999.0   \n",
       "33005              -999.0              -999.0              -999.0   \n",
       "33006              -999.0              -999.0              -999.0   \n",
       "33007              -999.0              -999.0              -999.0   \n",
       "\n",
       "       nbin18_LAWSON_Hawk  nbin19_LAWSON_Hawk  nbin20_LAWSON_Hawk  \\\n",
       "0                  -999.0              -999.0              -999.0   \n",
       "1                  -999.0              -999.0              -999.0   \n",
       "2                  -999.0              -999.0              -999.0   \n",
       "3                  -999.0              -999.0              -999.0   \n",
       "4                  -999.0              -999.0              -999.0   \n",
       "...                   ...                 ...                 ...   \n",
       "33003              -999.0              -999.0              -999.0   \n",
       "33004              -999.0              -999.0              -999.0   \n",
       "33005              -999.0              -999.0              -999.0   \n",
       "33006              -999.0              -999.0              -999.0   \n",
       "33007              -999.0              -999.0              -999.0   \n",
       "\n",
       "       nbin21_LAWSON_Hawk        AC_Time (UTC)  \n",
       "0                  -999.0  2019-09-08 23:51:19  \n",
       "1                  -999.0  2019-09-08 23:51:20  \n",
       "2                  -999.0  2019-09-08 23:51:21  \n",
       "3                  -999.0  2019-09-08 23:51:22  \n",
       "4                  -999.0  2019-09-08 23:51:23  \n",
       "...                   ...                  ...  \n",
       "33003              -999.0  2019-09-09 09:01:22  \n",
       "33004              -999.0  2019-09-09 09:01:23  \n",
       "33005              -999.0  2019-09-09 09:01:24  \n",
       "33006              -999.0  2019-09-09 09:01:25  \n",
       "33007              -999.0  2019-09-09 09:01:26  \n",
       "\n",
       "[33008 rows x 119 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import necessary modules \n",
    "\n",
    "%matplotlib inline \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import glob\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.io import netcdf\n",
    "import math as math\n",
    "import netCDF4\n",
    "from netCDF4 import Dataset,num2date,date2num\n",
    "\n",
    "directory_P3 = 'C:/Users/arkam/Box/Puja And Arka/Data/'\n",
    "filename_P3 = directory_P3+'camp2ex-mrg1_p3b_20190908_RA_20200322T172757.ict'\n",
    "\n",
    "# Read in P3 file\n",
    "\n",
    "df_P3 = pd.read_csv(filename_P3, delimiter = ',',skiprows=150, names = ['Time_Start','Time_Stop', 'Day_Of_Year', 'Latitude', 'Longitude', \n",
    "            'Pressure_Altitude','True_Air_Speed', 'Static_Air_Temp','Dew_Point','Static_Pressure', 'Wind_Speed', 'Wind_Direction',\n",
    "            'Solar_Zenith_Angle','Mixing_Ratio', 'Part_Press_Water_Vapor', 'Sat_Vapor_Press_H2O', 'Sat_Vapor_Press_Ice', 'Relative_Humidity',\n",
    "            'H2O_DLH_CAMP2EX-DLH-H2O', 'RHi_DLH_CAMP2EX-DLH-H2O', 'RHw_DLH_CAMP2EX-DLH-H2O', 'H2O_DLH_CAMP2EX-DLH-H2O-20Hz', 'RHi_DLH_CAMP2EX-DLH-H2O-20Hz', 'RHw_DLH_CAMP2EX-DLH-H2O-20Hz',\n",
    "            'conc_LAWSON', 'ext_LAWSON', 'lwc_LAWSON', 'sv_LAWSON', 'CNT_LAWSON', \n",
    "            'cbin01_LAWSON','cbin02_LAWSON','cbin03_LAWSON', 'cbin04_LAWSON','cbin05_LAWSON','cbin06_LAWSON','cbin07_LAWSON',\n",
    "            'cbin08_LAWSON','cbin09_LAWSON','cbin10_LAWSON','cbin11_LAWSON','cbin12_LAWSON','cbin13_LAWSON','cbin14_LAWSON',\n",
    "            'cbin15_LAWSON','cbin16_LAWSON','cbin17_LAWSON','cbin18_LAWSON','cbin19_LAWSON','cbin20_LAWSON','cbin21_LAWSON',\n",
    "            'nbin01_LAWSON','nbin02_LAWSON','nbin03_LAWSON','nbin04_LAWSON','nbin05_LAWSON','nbin06_LAWSON','nbin07_LAWSON',\n",
    "            'nbin08_LAWSON','nbin09_LAWSON','nbin10_LAWSON','nbin11_LAWSON','nbin12_LAWSON','nbin13_LAWSON','nbin14_LAWSON',\n",
    "            'nbin15_LAWSON','nbin16_LAWSON','nbin17_LAWSON','nbin18_LAWSON','nbin19_LAWSON','nbin20_LAWSON','nbin21_LAWSON',\n",
    "            'conc_LAWSON_Hawk','ext_LAWSON_Hawk','lwc_LAWSON_Hawk', 'sv_LAWSON_Hawk', 'CNT_LAWSON_Hawk',\n",
    "            'cbin01_LAWSON_Hawk','cbin02_LAWSON_Hawk','cbin03_LAWSON_Hawk', 'cbin04_LAWSON_Hawk','cbin05_LAWSON_Hawk','cbin06_LAWSON_Hawk','cbin07_LAWSON_Hawk',\n",
    "            'cbin08_LAWSON_Hawk','cbin09_LAWSON_Hawk','cbin10_LAWSON_Hawk','cbin11_LAWSON_Hawk','cbin12_LAWSON_Hawk','cbin13_LAWSON_Hawk','cbin14_LAWSON_Hawk',\n",
    "            'cbin15_LAWSON_Hawk','cbin16_LAWSON_Hawk','cbin17_LAWSON_Hawk','cbin18_LAWSON_Hawk','cbin19_LAWSON_Hawk','cbin20_LAWSON_Hawk','cbin21_LAWSON_Hawk',\n",
    "            'nbin01_LAWSON_Hawk','nbin02_LAWSON_Hawk','nbin03_LAWSON_Hawk','nbin04_LAWSON_Hawk','nbin05_LAWSON_Hawk','nbin06_LAWSON_Hawk','nbin07_LAWSON_Hawk',\n",
    "            'nbin08_LAWSON_Hawk','nbin09_LAWSON_Hawk','nbin10_LAWSON_Hawk','nbin11_LAWSON_Hawk','nbin12_LAWSON_Hawk','nbin13_LAWSON_Hawk','nbin14_LAWSON_Hawk',\n",
    "            'nbin15_LAWSON_Hawk','nbin16_LAWSON_Hawk','nbin17_LAWSON_Hawk','nbin18_LAWSON_Hawk','nbin19_LAWSON_Hawk','nbin20_LAWSON_Hawk','nbin21_LAWSON_Hawk'])\n",
    "# df_P3\n",
    "# Global variables\n",
    "\n",
    "month = 9\n",
    "day = 8\n",
    "\n",
    "# Function for generating UTC time for P3 file\n",
    "\n",
    "import datetime\n",
    "from datetime import timedelta\n",
    "\n",
    "def timeseries(month,day,sec):\n",
    "    timestamp = datetime.datetime(2019, month, day, 0, 0, 0) + timedelta(seconds=sec)\n",
    "    UTC = timestamp.strftime('%Y-%m-%d %H:%M:%S')\n",
    "    return UTC\n",
    "\n",
    "\n",
    "# Time series generated for P3 file\n",
    "###### Time generated already########\n",
    "df_P3['AC_Time (UTC)'] = df_P3['Time_Start'].apply(lambda x: timeseries(month,day,x))\n",
    "df_P3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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inIi4IyKuyG/rIuI9EXE2cHvFeQcC74qIyyPiCuBdwEF52V5kK1O8JyJuiYj3ASJb5sLMzMzMFrdNI+ImgIj4LXB9RNyeP76DbNyXbCoDX0lLgBXAckk/kXS5pPdLumfC6bsB5xYen5sfG5WdFxFRKD+vUF7sw2GSVkpauWbNmn5PxMzMzMwWUrRXqTetK773AzYGng08HtgDeATw2oRzlwLXFR5fByzN83zLZaPyLctBIuL4iFgRESuWL1/e/RmYmZmZ2bQtlbRudAPuXXrcaeviaW1gcVP+97iIuBJA0rvJBr6vaTl3LbCs8HgZsDYiQlK5bFR+/fhdNjMzW/y6TjBLnahVniRWtWFEV6NJZVXntk1wa5pUVzdZrXg8ZdJbVTue2DZxnZcsazKVgW9EXCvpcvpdnl5NNrHt2/nj3fNjo7IjJKmQ7vBw4APj9NfMzMzMFl5EfG3IeNPcsvhjwMsknQXcSrb8xJkAkjblruTkTSRtBtySD2ZPBl4h6fNkA+cjgOPyul8lmxD3ckn/ArwoP/7lyT8dMzMzM5sUSQek1IuIk1NjTnPg+2Zga+BC4GaypShG+yv/mLuWo/iv/O9OwE+BDwE7Az/Ij38kP0ZErJO0b37s7cD5wL4RMSPbS5uZmZlZT68rPd4+/3s1sE1+/zKyi6RJpjbwjYhbgZfkt3LZjg3nBXBkfqsq/x7wyGF6aWZmtjCqNltoqtNUb8g+lfNoJ9Vm+fn3yU1uyw3uGrOYczx63s7pnZ6I2GV0X9KRwI7AKyPiRklbAO8gu0iabJpXfM3MzMzM+jgc2CkibgGIiBskvZJsV7d3pgaZ6pbFZmZmZmY9LAG2Kx3blo4XcT3wNTMzM7OxSFoi6XuSRgsX7CTpW5IukvRpSZuM2cQngS9IOkjS3pIOJlsk4ZNdgnjga2ZmZmbj+juyRQZGjgX+Kc/TvRY4dMz4R5INco8G/jP/+2/UzAGr44GvmZnZBOiaa2onU43KyhO62iZO9ZlYNYpbvHU5t9jnKlXPY5z+dpnA1zZZrerc8rFR/6v67QltaSQ9EHg62Spb5LvrPhE4Ja9yErDvOG1ExG0R8eaIeEhEbB4Ru0TEMfniCck88DUzMzOzOttIWlm4HVZR5z1kV17vyB/fF/hNRNyWP74ceMAU+trKqzqYmZmZWZ2rI+IxdYWS9snrrJK01+hwRdXOu/dK+m1ELEuo9+uIuE9KTA98zczMzKyvxwHPlPQ0YDNgGdkV4HtL2ii/6vtA4Bc9Ym8i6eiEektSA3rga2Zmlqgup7Rq84SqvNBRLml5U4S286ra6pp3WrcJQ/F4lw0eqnJlm8pHx6rygVM2iiieW9f/tvehLmbVc0l5fZ37CxHxauDVAPkV31dGxPMkfRZ4NtkEtAOB03uEPwd4SmK9JB74mpmZmdnQjgL+TdJbgO8BH+0aICL2GrpTHviamZmZ2dgi4qvAV/P7lwB/sJD9qeJVHczMzMxsLviKr5ktOvc46SbuOPCedztWFAc1x9CJ69fRieuXl+PbfKjLIR0ZN6ezLi82JW5T/mtT3a596luvrrwqr7aqTlOfm3KTU+KXVeUU17WzGP0eHWZ7NVg3QIxZ4yu+ZmZmZjYXPPA1MzMzs5km6XmSNh03jge+ZmZmZjbr3gL8QtJ7Je3WN4gHvmZmZmY20yJiJ+CvgPsBKyX9n6RDJG3eJY4iOu8gt+itWLEiVq5cudDdMLNE9zjpJuKgu09AqzOatFaewNakHDv2uRGdufmd9/sYnV+Meef9RT55ZkPStCnFODFn5T0ed7OLherDLLWdGkPSqohYMVZjY5L07dvgUUNNbtsUzmnasnghSLoP8ALgr4HtgE8B742I89vO9RVfMzMzM1tMdgV2J9sK+XvApsC3Jb267UQPfM3MzMxspknaWtIRks4n2/7418CKiNgrIg4GHgm8qi2O1/E1MzMzs1l3BfB/ZJPcTomIW4qFEXGhpP9uC+KBr5lNzCh38s5c2YNq6p1Iaw5van5vH1Wxy/m5xWMpOb/F86vq1+WVNpmVnNFZU34t2zZD6Bq7atOJlM0c6jZJaDvepZ0u/e5i6NcuNV5KvxciX7iuvVnK5Z4Du0fEBU0VIuI5bUGc6mBmZmZms+60qoOSftAliAe+ZmZmZjbrHtjxeCWnOpiZmZnZTJJ0dH53o8L9kd8Bft4lnge+ZovMPU66CbhrndnUNWbLOat3HHjPyrhFfdevrWqzKr+3Lm+3nO/blP9bVTe5fzUx14vf4TUYvR9V+cFD6JMXXGfW8hLHeW7lfOpirNH9IZ/vKFZbfmddWZe+dMkhras7q+/1pF6HsqbzuvbFOb0L4in5340L9wHuAK4CDukSzANfMzMzM5tJEbE3gKTjIuJl48Zzjq+ZmZmZzbQhBr3gK75mZmZmNoMknR4Rz8rv167RGxFPTY3pga+ZmZmZzaJzCve/MURAD3zNFsCdE9QOap5g1TRRqni8XKc8IatqkpkOWn8yW7kfxclzXSbRlTVtWlFVt3IziYpjXRQ3yGh7zdfrT+k5170Gde/FJCe6jWvIiXKd2656TfbpF6fpcznOJLfyxgtN5/ed5FZnFK/rhKtpTRZrilHcnKNJ3XkpEwKrJhf2nQTYdbOMcn1PdJusiHhb4f6bhojpHF8zMzMzm2mSPiLpsePG8cDXzMzMzGbdxsB/SbpA0lGStu0TZGoDX0lflXSzpLX57ceFspdJulTSbyWtlPSHhTJJOlbSNfntHZJUKN9D0ipJN+Z/95jWczIzMzOzyYuIA4H7A+8Ang5cJulMSX/WJc60r/i+NCKW5rffBZC0J/B24NnAvYCPAqdJWpKfcxiwL7A78HCyTLAX5+duApwOfALYCjgJOD0/bjYz7nHSTUh33eKgu+e+Vm7wkOdCNm3KUJUvWZdX2hSn3Kc+uak6c/P1bimbSZTbrTuna47xKI+37u8kxT433tnfcTcBmVVD5zYWPzepdUf32+q2KeaW1uXwjsqKuatdckrr2i3H7xK3az/GPWekKd+5+ByG2MCjSVPubep5Q9a1yYuIGyLihIh4AvB7gIBTusSYhVSHHYHVEbEqIgI4Gdga2CYvPxB4V0RcHhFXAO8CDsrL9iKboPeeiLglIt5H9iI8cXrdNzMzM7NpkLS1pMOBfycbB36qy/nTHvi+TdKvJH1D0l75sS8ASyTtmV/lPQT4Ptk2dAC7AecWYpybHxuVnZcPmEfOK5SbmZmZ2SIn6RmSTgUuB/4S+Gdg24h4fpc401zO7CjgR8A6YH/gjDwf9xKyUfvZZFdrfwP8aWEwuxS4rhDnOmBpnudbLhuVb1luXNJhZGkTbL/99gM9JTMzMzObgg8BHwf2iIgL+gaZ2sA3Ir5VeHiSpL8CngbcTHaVdzfgJ8BTgTMlPSIifgGsBZYVzl0GrI2IkFQuG5VfX9H+8cDxACtWrIhyuVlZn3VOq/IJ2/Jci+vKVq0125YLPKpffDzqSzG/NCXftq+qdYJTpK7xW1wPt+q59mk7RfE1bFozdjHl8aassVql/PwXah3g1Jze9fqauA5109q9dWsCl4835b02tVuVNzvOWrttucp18VPa7NOntjV3h3reVe2W49ui9aCIuH3cIAu5gUWQXeHdHTgjIi7Mj58l6UrgsWQJy6vzOt/Oy3fPj5H/PUKSCleIHw58YAr9NzMzM5s972aYZNbbgFcOEKcnSXsWLpzuWVjUaz0R8c3UmFMZ+Eq6N7An8DWyl/EvgScAh5OlJrxG0nHApcCTgYcAP8xPPxl4haTPkw2WjwCOy8u+CtwOvFzSvwAvyo9/ecJPyczMzMwm64vclb56dk2dAJbUlN1N48BX0tGJcW6LiHc0lG8MvAXYlWygegGwb0T8WNKFwIPJBrFbkSUtv7iQv/EhYGfgB/njj+THiIh1kvbNj70dOD+Puy6x32ZmZmY2gyJiy8L9QRZkaLviewzw9YQ4jyJbULhSRKzJ61SVBfD6/FZXfmR+qyr/HvDIhD6amZmZ2SIk6QUR8fGK48+LiE+mxmkb+N4UEXsndOba1AbNJmUhJtsUJ9L0mdhWZXROygYXbW302YTi7pPnqmOUY3d9rl3q101sK04CrIo/xGYVdRuETGJC26TirtfGGN+TafSvrf0udftMcCuqmnTWNEmqrmw0Wav4t1y/atJh02Swcuy2elVtVE0om9QksL6bTYykTHhLnVxYfj/a4rW9xkNOxrNGHyBb1aHsOCB54Nt22XifxDjPSm3QzMzMzKyju81sk7Qj2dyxZI1XfCPiaylBIuJ/uzRqZmZmZtZG0q3kE9gkledwLSHbyCJZp1UdJD0QeASlDSIi4l+7xDEzMzMzS/Bksqu9nwf+tHD8DuCqiLioS7Dkga+klwDvAa4FiolSAXjga50s1OL3k3K3fNeanN86k9hcomkTjD7GzekdWl0u9MTaK+WHDp3v2jV/tapPk1Du11DPe/QdSfmurLdBStfvVql+Sr5tMWdziHzUcqy6TUTq2honf7SuL0O2MbSUHNqqvNvUzUO6vLd1edd15Ta8UfaBpAdHxJXjxuuyNMRrgWdGxP0iYqfCbedxO2FmZmZm1uAlkh5bPCDpsZLe1CVIl4HvEuC/uwQ3MzMzMxvAocB5pWM/AF7YJUiXge8JwMFdgpuZmZmZDWBz1k+1JX+8tEuQLpPbjgW+JelwYL0ci4h4apdGbfFxHlM3fdbPHUox53WI/NdRjNG6udNQXoO3T7tta/h2Wee3vB5s+diQ2uKvl69ayJGd1vq6Q+X3TuqcunrrvYc1+b19fudS80pTc22L/UhZu7ctZtd1alPrTnPt2qq1dMvlQ+ma312Xr20TcRHwx8AXCseeDFzcJUiXge8nyNZK+zx3H3GbmZmZmU3K24BPS/ogcCGwC/DXdEx16DLw3Qt4UER4lzYzMzMzm5qIOFXSTcBLyTZY+ynw3Ij4fJc4XQa+l5GtmWZmZmZmNlUR8QXWT3XorMvktmOBkyT9vqTtirdxOmBmZmZm1kbSzpKOlvT+/PHvStqtS4wuV3xPzP8+k2zTCsh20giypc5sRjjRfvL6TiaqmkzVd+LWtDeQWEztpUxaS53YNi2jz9No8lpxklvT561Yf1oT3IYy7oS11HpdX5eUjS2GUBevrY3UjRdS46Q8x6pNPyahabOKuo0qyud0fZ+K73Wf93ecyZGWTtJTgFOBr5Cl374U2Jpsn4k/rT9zfV0Gvjt1qGtmZmZmNpS3A8+JiLMkjeabfRf4/S5Bkge+EXFZl8BmZmZmZgN5cEScld8PgIi4SdLGXYI05vhKelVKEElHdmnUzMzMzKyDn0t6WPGApN3JVndI1nbF92iyS8ttXgW8o0vDlnFO0OQMkfNYzrOsKms8/8Tq48XNE1I2WrCFU/UZavtcDbnJxUJuhjLpzTpGOufgVnwf+/axajOG4u9yW75o6sYOfXJ5x9E3dup5Q/U9ZWOPplzk0Xs1bv5133PbNhuxQb0POFXSMcASSfsBb6Tj+LNt4LtU0rqEOLd2adTMzMzMLFVEfFiSgKPIFlV4E/CeiPh4lzhtA9+9E+N4fV8zMzMzm5iIOB44fpwYjQPfiPjaOMHNzMzMzGZFl+XM5ppzcWdbWx7kuPm+Q+dZjruGbDHvd7Smb3Ft35R1fhdiLeAU4/Rr6LV5y2voTivftq29qhzXSfZj1o313W5Ym3Z0vOn3v5zXW5XrmbLObJcc1ZS6qbnJXdobN491Es9xnH4MsS5zymfE+pN0K3ftHVErIjZJjemBr5mZmdkG5KrdYckAW4utW/gZXE8eOqAHvmZmZmY2i14eEfsBSDo4Ij42bsDGdXzNzMzMzBbIkwr33ztEwE5XfCX9IXAAsG1EPEPSI4EtIuJ/h+iMmZmZmVlutaRPAT8ANpF0dFWliPiH1IDJV3wlPRc4HbgZeMKoLeCY1BizpLjodcrNZlfqJJ+uk4GmMnnooH5l48StqjP0pLCudOJdfejyvEfnFc8f0ugzUNzIIXUiVZcJV8XPWvG8pvbKxxfLRLShdXneTZtUVCluTtA2uasqRvH8YnnTJg3lOHWT7rrUSYlRV6+ub33+jayaRDjkBLau/14P1bY3r5io57d38iIAACAASURBVAO/JVtedwnwlIpbpzzgLld8XwP8cUSslPSC/NgPgd26NGhmZmZm1iYiLgVeDCDp+xGRur9ErS45vttFxMpRX/K/t5GNwM3MzMzMJiIi9gBQZtu+cboMfC+W9NjSsccCP+7buJmZmZlZG0lbSPoocBPwk/zYvpLe0CVOl4HvW4DTJb0W2FjSEcCnWKQ5vuBNKRabYr5l8dZ2TlOdYnlqzL765OwWN6RoqzOLm1GkGG1Y0Se3d1pS80j7fnba4pfzfodse56Mk/9Zl3vbtLlDMae1Sx5oW85xW1+byrvGropT116XGE3Kr21dnKaypudZPq9pA5NUzvOdincD9wMeB6zLj30H+MsuQZJzfCPiPyTdALwcuAx4InBIRPxPlwbNzMzMzDraB3hoRFwnKQAi4gpJ23UJ0mk5s3yQ64GumZmZmU2TyNIc7jogLQXWdgnSOPCV9ISm8hGv42tmZmZmE/QN4NXAmwrHXgZ8pUuQthzfL5VuXwG+Wrh9BfhiSkOSvirpZklr89uP8+N7SbqjcHytpAML591H0mmSbpB0Wb6ecDHuc/PjN0j6D0n3aevLqttuS+myLUJd1lkt5kSW8yOnlS9Zlaeaktc7tIXMD05te/R6TON1GX2OunyeRucNIeXzN+85vV3fm67KubvltWxT1/QtnteUA9u2zm/d8aHmqvSJXZUrO6SUeMX3p+r9atMnB7t8rk3NK4DnS7oIWCrpB8CBwKu6BGm84hsRG4/uSzoAeBZwFHApsDPwD8DnOrT30oj4SMXxX0TEA2vO+QBZEvP9gD2A/5R0bkSslrQb8CHg6cB3geOBfwb279AnMzMzM5thEfFzSQ8jy/XdiWy+2ZkRcVPzmevrkuP7JuDhEXF9/vgiSYcC5wIf79JoKklbAPsBD4uItcDZkj4HvIBshP884IxRqoWk1wHnS9qy0E8zMzMzW+Qi4hbg34vHJO0cEZekxuiynNkyYLPSsc2Ae3WI8TZJv5L0DUl7FY5vI+mXki6V9E/5gBfgIcDtEXFhoe653LVb3G75YwAi4mKyq8MPKTcs6TBJKyWtxP97wszMzGzRyFNf71F4fH9JHwTO7xKny8D3TOC0PCd3J0l7A6cAZySefxRZesQDyFISzpD0YOACshSGbcmWSHsk2VptAEuB60pxrgO2TCy/U0QcHxErImIFXm/PzMzMbOZJ2l3SJcAa4HJJe+YZBxcBv0M2dkzWZeD7t2S7tH0BuDj/+xPgpSknR8S3IuL6iLglIk4im533tIi4KiJ+FBF35HsyHwk8Oz9tLdmV5qJlwPWJ5TanypOTxpkIM9iEpRMLMQ9qr7dYN6QYWvF1m/TEtq6fk/IGKMW/VXVTYlXFSZ18Oe8T3urUbS5RV5YSqzwpqm3iWt2GGFWTq1ImutX1ZyjlPjdtnjHaAKKqTkq/ynWKk9RSJgSOM6lunM09vGnF1PwjcA7wDOBs4NPAK4FnRMRTIuIbXYJ12cBiLXCopBcCy4E1ERFdGiuHJFuTren4hcBGknaJiIvyY7sDq/P7q/PHQJbnAWyan2dmZmZmi9sewI4RcYOkbwK/BnbJ01s763LFF4DIXN1l0Cvp3pL+WNJmkjaS9DzgCcB/5akT2yvzIODtwOl5WzcApwLH5Hs0P45sZYnRZLpPAs+Q9Pg8L/gY4FRPbDMzMzPbIGyajweJiN8A1/cd9EKHK76SbiW7Gns3EbFJy+kbA28BdgVuJ8vr3Tcifizp6WQD2K2Aa4D/AI4unPsS4ATg6rz8byJidd7uakl/nZ9/X7I1hQ9OfU5mZmZm1l9+0fJk4P7AHcDxEfHefF+FTwM7Aj8F/iIiru3RxD0kPYZClkD5cUR8M7m/qRduJf1R6dADgL8HPhYR/5za4CzQHnsEX/rSQnfDeirnMY6bg6szNyf2ufHOv1XH+uROxkHr5+vqxLv/bTv/zj6Wzt1QTXXTjsJ7XXxcNom82aq2U9pJqTfJTR1m1SjHtHysTlX+6ihGMbe0Ki93nLzOtvOr+pFS1rW9oeKntJVS3tRPqH8vh3gthj5X0qqIWNGroYFI+vblX+JRS5aMH2vdrbDDUzgnIh7T0N62wLYR8V1JWwKrgH2Bg4BfR8TbJb0K2CoijuraB0l3tFSJiEh+tl1yfL9W0ZlvAv9GtmmEmZmZmc2RiLgSuDK/f72k88kujj4L2CuvdhLZjr+dB74R0Tktt0mXDSyqXAE8dIiOmJmZmdnM2UbSysLj4yPi+KqKknYEHgF8C7hfPigmIq6UtM2kO5qiS47vY0uHtiDbI7nTwsFmZmZmtmhc3ZTqMCJpKdmuaodHxG+lqoW7Fl6XK75nlx7fAKwEDhmuO2ZmZma2mEjamGzQ+8mIODU//EtJ2+ZXe7clW6RgwXXJ8R00x8LWVzWxwupNegLPKH7fiW1QPVFrdCx1EtdoMpsntE2g3Yr3dVobQKRuSDEPyhPTqiaqjY4XlSc+FX9Di5tDpEy4St3MYlIT24r9qJtk1mfiWV3dITb0GHeiX1t/6iYgDtlGW+zy56PNvP67rezS7keB8yPi3YWiz5FlBrw9/3v6AnTvbrqkOrwmIt5acfzVEfG2YbtlZmZmZn3c/49gkFUd1iVVexzwAuAHkr6fHzuabMD7mXx74Z8Bz+nbD0kbAe8FjoiIm/vGgW6pDkcBdxv4Av8P8MDXzMzMbM5ExNlU78QL8KSB2rhN0v7AS8eN1TrwlbRdfvceeY5G8cntAtwybifMzMzMzBqcDuwHnDJOkJS83cuBnwP3LNz/eX7/i2SXnhedttynoXKXuvQlJb+3nG9U9bj4d0M0dH5vMZ93FlTl8y5IHmxMNu9UJy5cfm+VWc+zLX4+Y58bZ+bz2kXb71Xxb91vma65pjL3MjW/slyv6rwh81dHUjZoaGt33BzjtmN1dYbIXW16/lXtles35SVPom/l/lQdt6nbBPiEpC9K+oik40e3LkFSUh12IrvK+31g98LxO4A14+ZamJmZmZm1uBX4VH5/SX7rrHXgGxGX5Xfv3acBMzMzM7NxRMTBQ8RpHPhKek5EfDa//9yGzvzrEJ0xMzMzM6sj6Z7A1hTmnEXEz5LPj4im4D+MiIfl9y+tqRYRsXNqg7NgxYoVsXLlytZ6bWv7Vf1NVaxflafWlO/btuahc4/GozM3Xy9/ctq5n3HQXev33tmHE6tzfyfbkfx5a/xc0lnK5Z1FVetFp+bwFs+b5bzfLvmYdb+pTevctsXr0o/U3/7UtpvW723r07htTSJfeeh+t7VVzoHuE7tqXWCozq+uu58UV1oVESs6dW5gkr592208aqjlzDbdlHNSdm6bNEk7A58A9iyXRUTys2284jsa9Ob3d+rSQTMzMzOzgbyfbHGFw8h2E34ccAxwRpcgybuxSXpNzfFXd2nQzMzMzKyjPYEXRsQPASJiNfBisv0kknXZhviomuOdGjQzMzMz6+gO4Kb8/lpJ9wZ+DWzfJYg3sDAzMzOzWbeaLL3ha8C3gH8CbgDq5qBVSlnH93IgCvdHBNwOvK5Lg4tNVUJ83cLqdQn4xST6qskaVe0Uy9smEtRNbPNkt/5GE4YWYrJQ1USwqU9sg+pJbbF58mS3eZvQVjVBLfW84t++58+SpkloKROU2jZxSPlNK8cfd6JVn0nMo3abft+r2miqk9pWygS0ujZT2hmdW9fGJDZRatrUpO7f6br+lN/Lqs9K1wnuNnEv567x6P8D/gVYRpbukMwbWJiZmZnZTIuI8wr3LwGe2ieON7AwMzMzs5kj6Qkp9SLif1Njtm1gUbtpRalBb2BhZmZmZkP6UunxPVh/rlmQpd1ukhqw7YrvWxNiBLAoB75tC4s35dw25Y6V85Dq6lbdb8oDrutfU35vVZ/L9Zpy1+oW/B43322xmPbmFXdr/8Qp5/e25fAm5Pf2ze3tmyO7kJr6PMq9Xch88RSp3+eq+QptubhV51W1W2XcTQrGye9tyiHtswFGSp5tn342GSdW6ms/zmYZqZttNOXu1r1uKZ/PPnnBdcc9n2ZyImLj0X1JBwDPIltl7FJgZ+AfgM91idm2gYU3rTAzMzOzhfYm4OERcX3++CJJhwLnAh9PDdJlHd+7kbSbpPeNE8PMzMzMrMUyYLPSsc2Ae3UJ0nngK2lTSQdIOhv4AfD7XWOYmZmZmXVwJnCapL0k7SRpb+AUOm5ZrIhorwVIeijZ/sgvADYnGzTvExH/06nbM0B77BHx/e/3Pz8xz6iqblvMpryncp5uW25c17ZTjkFz3m/5fllTvvGsWqi80wVZu7enlLzerjmuba/7KL+2Ls+2eLycb1tXr4vy85nVXN7UPP6637Guvz+TMuk2236DYTJr0xbbTj3eN37KurTQnFc7ZF/KbY0TL/V41zhdz5O0KiJWdA40IEnfvu02HrVkyfix1q2DTTflnIh4zPjRxiNpKfBe4LnApsA6sjlmf1dIf2jVesVX0vMlfR34IfBHwBuBB5BtE3du556bmZmZmXUQEWsj4lCyi6/3B+4ZEYd0GfRC2gYWJwPXAE+PiC+MDkqqP8PMzMzMbGCRpSpc3ff8lBzf1wPXA/8h6TRJz5A01qQ4MzMzM7NUkpZL+qSkqyTdXrx1idM6gI2ItwAPBvbND/07cAXZTm7bde24mZmZmVlH7yNLtT0UuAF4JvBN4PAuQVJSHUaXlb8AfEHStsAL84a/I+m0iPiLLo0utEdu1Py02yYCpC5y3VTWNqEsdTHtqnO6LLRerFe3KUbK5hptm19U1Sm/zott0tvMKm5C0bYhxZjqJrUNPcGrLV6xvDhRrXxe1SS3poltVRPfisdGk+tS+jikpslBdb9f5fop39kux4aS+rs7tPLvXJ+JfH0nSI2zUccQ8Zv+zZjExLamuENNMqt6/5pi932e057kOeeeCPx/EXG1pDsi4j8l/YBsZYfjUoMkDXyLIuJK4M2S3gL8KdlKD2ZmZmY2AzZ6H2Pu1JC7bYAYw9kYWJPfv0nSFhHxM0m7dgnSeeA7kl8F/nx+MzMzMzOblAvJ9o5YRbaq2NGSrgN+2SXI1CapSfqqpJslrc1vP86PP13S2ZJ+kycsf1jSloXzNpV0gqTf5uWvKMV9kqQLJN0o6SuSdpjWczIzMzOzqTiabP3e0f3nAH8PHNElyLRXZ3hpRCzNb7+bH7sX8BayiXK/BzwQeGfhnDcCuwA7AHsDR0r6EwBJWwOnAq8D7gOsBD49biebco+GkppzVZf3Ws5Ha1qgviq3tutzaVtsvJhbXO5L+dymHK9i31Jyp4rt9ilPEfvcuCCbEqRsCpEkJrMBx6Tze0eve1O8urIuOcHlx03tlvN5+25+0VfbZ7nqN6Kc+18Xa1ZyFWctz79Lfm+X42312vJR+7w+Q7/HQ71HVZ/bru0Onbs7K58/u0tEfDkivpnf/25EPCQito2Iz3WJs+DLkkXEv0bEWRFxY0RcC3wYeFyhygHAmyPi2og4Py8/KC/7c2B1RHw2Im4mGyTv3jXfw8zMzMwWF0kPknRCl3OmPfB9m6RfSfqGpL1q6jwBWA0gaSuyK8HFHeLOBXbL7+9WLIuIG4CLC+VmZmZmtohJOkDS+yS9RNI9JC2V9G7gIrJMgWS9J7f1cBTwI7K9lfcHzpC0R0RcPKog6SnAgcCe+aGl+d/rCnGuA7YslK9hfcXyO0k6jHwFiu23336sJ2JmZmZmkyfpDcCRZBdFDwUeCuwF/Ap4ckSc3SXe1K74RsS3IuL6iLglIk4CvgE8bVQu6dHAvwLPjogL88Nr87/LCqGWke0kNyovlpXLi+0fHxErImLF8uXLK/tYt0Zt8XHX/NiU+nU5eOU2U/Jrm9poqz/OmrxVdetyeNtiV5W3vTapsbq42/tfyv0sPq7K/Syf16ntg7LbWAbO7R3l9OrEu+f3puTjTkuXPsxKn0fK65AWv0t1a15XnV/3nWjL6035Xpfr9pkz0NZmnzW9y3MKxpHadtu/GXXH6tqse9+afjcnkYvaFHOc39u++btNZV1iDr0msU3NC4A/jog/APYBXgJ8PCL26jrohYXN8Q1AAJIeAXwOOCQivnRnhSzn90pg98J5u5OnQuR/7yyTtAXZLnOrMTMzM7PFbnlhgPtVssyBd/UNNpWBr6R7S/pjSZtJ2kjS88hyef9L0sOAs4CXRcQZFaefDLxW0lb5pLUXASfmZacBD5O0n6TNgNcD50XEBRN/UmZmZmY2aRrdyfeQuDEiem+tMa0c343JlizbFbgduADYNyJ+LOljwHLgo5I+mte/LCJGE9TeAHwQuAy4CTg2Is4CiIg1kvYD3g98AvgWWf6wmZmZmS1+95T034XHS0uPiYinpgabysA3ItYAj6opOxg4uOHcW4BD8ltV+RfJBtRmZmZmtmF5S+nxN8YJpuyq8XxZsWJFrFy5cr1jTZPHuipPQGmK3Tfpvjz5ozwRom5i2dDJ/SmT6vrWqZrg0rZwe9XrXZ6MkTopoe8i8SlSNj0YbHKbhpm81bShxixNEGujMzfv3N/i+1U+t0+8qolM5Y1fyp/lob+/i920fuf69KHvuW3xhprIO+5rNNTz7vMajNt+qq5t3Pn9lVZFxIoJdq29L9K3eTePGiSZ9TbglZwTEY8ZINpMWPANLMzMzMzMpsEDXzMzMzObCx74mpmZmdlc8MA31zefc7R4e1XuVdNGDsW6qXlE4y5Kn5o7VvW4SjmPtvg6VOXkFmM2HUuV8vpWPfemjUDK504qj6xq84vB6cap5Pd2VfdeTCsvc9bzkVM3mOm7ccS0F95P/Y43PZ8uGxeMk3vaVZff1JRzmzYUKf4ejdPuENo210jtR9uGKynt92m3WLfuc+d8+g2XB75mZmZmNhemtY6vmZmZmVkySXeQ7fTbKCKWpMb0wNfMzMzMZtHjC/dXAH9Ntl3xpcDOwOHAh7oEdKpDSVWuT1X+azF3tEuebl0bdeUpuU99csna2h7l5qbkS1XlntXlblblrJXPGbVblf9VjlnXXp/1kcvvaVWb08j5Xa9PJ951Kxsda6rTVzFeStzROrcp6xO3rcM8i8bJC67KwS9/PrusZ9onL7qYFzzt17juu1ns16hsGn1ryzPue27Z0LnGKfm9qVJydLvEavqNLpdN+j1OiT+Jf79n9bdrsYuIb4xuwEHAPhHxkYj4UkR8GHgmDZugVfHA18zMzMxm3YOBn5eOXUF25TeZB75mZmZmNutWAf8oaTOA/O/bge91CeIcXzMzMzObdS8CzgCulXQ1sA1wGVm6QzIPfM3MzMxspkXETyQ9DHg08ACyNIdzIuL2LnHmOtWharOFlEXF646lTgZrS4Ifd1H/oRYwb2u7PBGubVHx8kS2pgXomyb9jI5XTZooToyrm0TT9H6XY9T1r/x3yIkNo0lu5UllxUlsdRPPhpzg1jVeysS2xvOnvLFCV3WbjXSZ+Fb3mamabJTyfUrdPKBpYmzX1z1lAnBdWfl7lbpx0JCTh1N+d6ZpkpM9U97fpknExTgpMVLKht4sosvnseq3ve/GSbYwIuL2fLLbZ/K/nQa94Cu+ZmZmZhuWPYHklW0b3DpAjIFI2gJ4OdmyZlsWyyLiqalxPPA1MzMzs1l3AvAI4D+AG/oG8cDXzMzMzGbdU4GHRMSacYLMdY7vSGoua0qcLotnV90v52R1yT+qy00dJ3eyS+5WU7/GzYtqy/mt6lNVLm45L3l0v3xe0yYY5dzwpgXNh8gb7Lp5QtNmFsXjQ296Ae19rdqspOr92VCl5OSm5Fy2qctxT52v0NR+18/zuBtupNZtKh83Tp/c0K6b/9SdP04O6hA5wkPGGSJWWdtneiE2SbGJuQZYO24QD3zNzMzMbNYdDbxP0n3GCeJUBzMzMzObdZ8km7J3iKT1VnOIiE1Sg3jga2ZmZmaz7slDBJnLVIdVt9125/26PKqmnKRxc4Lr1rGsyglNyUeqWxu0LT+r6jn2zQ9Ozbst5tulxq56Hl3yIMt5pMXXvG0NyKb1f7u8V33y7orrxtatIdukvO5vce3fcq7vuFL71vR6p6zvOav5eXV5hk255Cnnj6TkqXb9PhXbGHdt1XHmFTQ99z7r9zZ9ZlLWzK1bI7zLZzF1XkPV6z/Oc29qo+38aeXY953D0lfbv399Prtd17K2YUTE1+puXeL4iq+ZmZmZzTxJuwJ7AcsBjY5HxDGpMTzwNTMzM7OZJumvgBOB84CH5393B/63S5y5THUwMzMzs0XlNcALIuJRwI35378GvtsliAe+ZmZmZjbrtgc+Wzp2MvCCLkHmcuD7yI3unuEx1ESCrrH61C/2o8+khtRFyVMmaEx6cfWmCWR1k4XKE0bqJm00xa567imTjaompiyGSQ9dNrMYTbQrT2hL3byi6nHT+1A8NtSmCkOpm5w0zsSwujpNm7NUxanbhKXLJhYp+kyqSzm36ySotsml05zANeS/AV0mJ6dskNLUxqQmk44z+bFsqPevy0TTujqT3JjDKv0GuFd+/5eSfg+4D7BFlyBzOfA1MzMzs0Xli8Cf5fc/kz/+NvCFLkE8uc3MzMzMZlpEHFJ4+Abgx8CWwEld4njga2ZmZmaLRkQE2U5unc11qkPbgvJ1OWazlL/Ztoh63TlNuWCjW5dcp67tV7XZJUZXqXm/5fpVz6/qc1H+fBSfUzG/sCp+St7z3XLvGja06LrRRVGXXN/Utqq+Z0PkXJfrTHKzi7rPQzmHtvjep+Qn1x3r+rnvmvvc9v2vqlt+XlVtjLuRQuq55d+plNesSz/bcmZTDfnvQ9Pr3nSs6XjbZj1d+pT6+U79PaiLlfo5Guo71MR5vdMlaYmkoyRdIGlt/vdVkpZ0ieMrvmZmZmY2644FngG8A/gpsBNwBNlmFkekBvHA18zMzMxm3fOBx0TEpaMDkr4M/B8dBr5znepgZmZmZovCHcAVpWO/yI8nm/rAV9Iukm6W9In8sSS9RtLPJP1W0r9JWlaov6mkE/KyqyS9ohTvSXmex42SviJph+S+tKwh2Sf3clrGzR1rej6TWruxqd4kXt/UnNBynm5V/lmx7qi/5bzPqnzf1OfR9rrf2bdSbm/TWrpV+bfFNXib8oSb6MzNa9usPaeUN96Us1lebzXlfeyypum46vKzq+ql5us25QTXfa7qnnM5J7epflWf28pS1pxNyeFsm0fQ9D5WfQe7/N40qcrX76vt/C6xU57fuH3t0laxXlsO/hDrb4+7NnKXHPCyrmtQ28S8B3iXpE0BJG0GvB14d5cgC3HF9wPAdwqPDyDbdeNxwHbAPYHjCuVvBHYBdgD2Bo6U9CcAkrYGTgVeR7aI8Urg05PtvpmZmZlNmqSLJF0o6ULghcCLgd9Iugy4FngJ8KIuMaea4ytpf7KdN74J/E5++BnARyPi53mdY4EvS/qbiLiRbGB8cERcC1wr6cPAQcBZwJ8DqyPis/m5bwR+JWnXiLhges/MzMzMzAb2lqEDTm3gm6cvHAM8CTi0WJTfio83BXaR9DOyq8DnFsrPBfbN7+9WLIuIGyRdnB9fb+Ar6TDgMIDtt99+gGdkZmZmZpMSEZ02p0gxzVSHN1O4slvwBeCFknaUdC/gqPz45sDS/P51hfrXke3UQV5eLCuX3ykijo+IFRGxYvny5WM8DTMzMzMbkfQnkn4s6SeSXjWF9n7Q99ypDHwl7QE8GfiniuITgE8BXwVWA1/Jj18OrM3vLyvUXwZcn99fWyorlzfqMrFi0gvNd5UymaPPJJ+mCUVNr0VVva6mfd5I3SS1urptk9eKE2/Kk4uqJoP0+Ryt13bFJLXR42JZ04YXXSe9lSe4NdZNXHi+aQOKpg1F2mJ2mcjVpZ26jUyqJj5WnTMq77NxQF3MtglhTX1pi1kuG3diVpdJZkNL+e1PqVuly/e57vekbRORunpd1f3O17VT99wmNcm7zwYZKWVtbaa8xt684i75BhIfAP4UeCjwV5IeOuFmkxcyKJvWFd+9gB2Bn0m6CnglsJ+k70bEHRHxhojYMSIeSDb4vQK4Is/rvRLYvRBr97wO+d87yyRtATy4UG5mZmZmk/MHwE8i4pKIWAf8G/CsCbep9irVppXjezzZCzHySrKB8N9Iug+wFXAJ8Htky1IcExGjddlOBl4raSVwP7LZewfnZacB75S0H/CfwOuB8zyxzczMzObWnkCnjXxrrEuq9QCgmMZ6ed6DSfqHvidO5YpvRNwYEVeNbmQpCjdHxBpga+DzwA1k+b4nRMTxhdPfAFwMXAZ8DXhnRJyVx10D7Ae8lWxZiz2B/afxnMzMzMzmwDaSVhZuh5XKq66+xlCNS3p6/n/07woe8ba+8RZk57aIeGNEPD+/f2FE/G5EbB4RO0TEu0t1b4mIQyJiWUTcr6L8ixGxa0TcMyL2ioiftrW/6rbb1nucsjB8nxy1Kk25s03Hx839GyIXuWueZV2OWFv8rvqcl/Kep8Sv20ihKX7VeeWyXp+tHptR9I03TlspGxN0Uc6vTYmb+h5X9bUtj7fcp2KdYt3UBfWbcnPb8g/7bHrQt19DG72Xde1MIr9zKF03T2g7v+qzW35t2jYE6dp+3b95fT5TbZ/7tu9f8X6X/Olim6mbifTJ8V7oz9sUXD1aHCC/HV8qvxx4UOHxA8l2VBvKKcCvJX1d0hskPS7PK+7FWxabmZmZWV/fIVuCdidJm5D9n/fPDRh/K+DpwNeBp5EthvBrSWdI+jtJu3UJNtUNLMzMzMxswxERt0l6KfBfZJnFJ0TEYIsMRMTNwBfz22hfiL3I9oU4CngXHcazHviamZmZWW8R8Xmy+VoTJWkrYG/giWTL5G4GnNklhge+ZmZmZjaTJD2V7Oruk8mWrP0m2Z4PzwO+GxGdJtI5x5fmZPa2iWd9F4Vvar/q+Cgxv++i8XUbctRNQmibCFB1bkr7xcdtC5J3eS3HnTwylKrXnwwjkgAAIABJREFUr2oSStMi8RPpV8/JaOUNLVI2t0hR9f1pWjS/fKx8btNmF1WbS6R8tqvq1b2XVX2omhBXrt/2GW/6vqZ8V5tew7ZJm0NMiE0pq6pX7uO4G3L00bXPdWV1E8K6bsgA3TbeSInXpY0u/R36s1UXu+rf6r4Ts8fp87jPyVqdRbY5xluBrSPiaRHxzohY1XXQCx74mpmZmdnsOpxsWduPAOdKOk7Sn+VpD5154GtmZmZmMyki3hcRf0a278PBZLv7/g1wmaRVkt7RJZ4HvmZmZmY20yLijoj4dkS8Hfg74BhgW+CILnE88C1pWni76ng5L7FvDlGKvjltXRf+rmsvtTzlNahbbL0pL3h0XlVZ6nPsk5PWtW6xj22vRWq7k94sIKkPY+b2tuUI1m0y0faZqYrdd5H9qs0p6jarKPc9JQ81Nce4qp/l16f4ehTzh6t+J5pew7rHXXNGh8jrrGs75f0c5zsyTg5xymYTdeeV348u+bV9X5O+8wrafq/rvt9D5Io3xWnaJKaPtn+bhvg3xLqT9CBJB0n6uKQrgB8ChwKnAc/uEsurOpiZmZnZTJJ0EbAzcBXwZeBo4EsRcXmfeB74mpmZmdmsOg74YkT8aIhgHviamZmZ2UyKiPcNGW8uc3wfuVH9eL9LjtWo/hC5PV3zdcv3U2KWcxXrcpmGXL+zrl7d+osp61hWra2asu5nud1Jacp9bsofKz+3qtdoqM/bQuvzHNpy+Ore+6bXu3xe11zvqu9jVa5t3Xubmm9bPpayLm/bZ6VP/uWkpPRj3HzUceunxuzyeziJHP8uawG3fT76xG86J3UN61Hd4nepLZ+4b9/KmvpWzpefle+PdTOXA18zMzMzmz8e+JqZmZnZXPDA18zMzMzmgge+ZmZmZjYXPPBNlJpY3zfxv+8mE03aJur12YCiTztt9cZdeLxtYfWqvjTpuwlGl/O6brBQnPy2GCZU1E2g6bPRRJe2mtpra2uoiZFNG0IUJ+ykbN5R1beqyT6p/emzMURd/apJpm3Kk5z69qMpdt9zhpowlTphbMjNJVLb7xunbULc6DNZt6FT1W983W9022e03FaXCd9DKn4HF8Nvst3FA18zMzMzmwse+JqZmZnZXPDA18zMzMzmgge+DbpuzJC6yHaTSeQoVfWjaROIrrGGrJ+yKUdbTm9Vm0Ns1JGq7vVO2VCg6n5KDvM4uubI9cmpHj334uLwKYvYl+M1vbaj+22bgDTFT+1HUTnPL2WR++JrkZIn2LSBSd8NCcb9TpT73eWzM9Tnt0vObFuMpjkIfX8rmj6PbW2Ns2HGtHNOm/o6Tm58029N+fekqz7/3tV9V6eZW2zj88DXzMzMzOZC/d69ZmZmZrb4bHQ9sGSAQOuAew8QZ3b4iq+ZmZmZzQUPfBsMtc5tajuTysuqip+aT1UXK7XdPjmW4+YRVz3f1LzfFG25gOW8s5TXtiqvb6j1lNukvvbF17VLP8v5ueU4dethll/DYpxyPm1KHuUk8hDb6tTlKNa9FnXxUtpt+96k5MD2+QylvP9Nz6/pvC5lfedUDP173vTdT+lj6vcptX5f03w9p52T3Oe70qXcZpsHvmZmZmY2FzzwNTMzM7O54IGvmZmZmc0FD3zNzMzMbC544Ntg3IT7rguQ19UfYuH9LoZqL3Xx7/Ii/nWbIXSdIDPu5LWmTRPazm3b0KHp3PL9qolbKYvuD61uE46mDRqK9UeTusoT2Jo2U+m7ucY434txXruq16bqOdRtQtFls4Omtvt8dtv0jVU34altE526z3rxNR3ic17Vj7qyFE2T2bpsMJISv2udPr9l0zDkd67OuO/l0LFt4Xjga2ZmZmZzwQNfMzMzM5sLUx/4StpF0s2SPlE4tlzSv0r6jaRrJX2yULappBMk/VbSVZJeUYr3JEkXSLpR0lck7TDN52NmZmZmi8NCXPH9APCd0rFTgauAHYBtgH8slL0R2CUv2xs4UtKfAEjaOj/3dcB9gJXAp4fqaJdNB8bJqWvLxxpi4f1i3XEW5x4i76suv7Euflu/UxaIHzfntk5bjmrXWFV/m16vqvNTVb2uKW3VbUhQzumt61/q+1n3WUjJ+x0i17er8kYaxX6M+jJ0f6ad4z9ku3Wfn7Z6xfabcqJTf5PL71FKTvZCa8pLTjm+WFTlW4/7nPpsptJm1j4f1myjaTYmaX/gN8A3gd/Jjz0VeBCwV0Tcnlf9XuG0A4CDI+Ja4FpJHwYOAs4C/hxYHRGfzWO9EfiVpF0j4oLJPyMzMzMzWyymdsVX0jLgGOCIUtGjgR8DJ0m6RtJ3JP1Rfs5WwHbAuYX65wK75fd3K5ZFxP/f3pmHTVJVefo9VcVaBRabrAooIu3C1qhtqy00Iu3StggzYiPiMIIgrTMKAooKgmLTLjiDLIILItgqCqgPS7uhoCKLICiIguwKWMpaQAEFp/+4N6kgzIyMyIwlM+/vfZ54vozt/u6JOJnfychz7n0A+H1mvxBCCCGEEEC7qQ5HAJ9391tz2zcAXgmcD6wDfBL4VkxjWBCPuTdz/L3AKvH1gty+/P4nMLO9zewyM7ts0aJFYxkihBBCCCGmj1YCXzPbEngFcHSf3Q8BN7n75939UXf/KnAr8BJgcTxm1czxqwL3x9eLc/vy+5/A3U90923cfZu11lor9GtIrlCT4/613VaWuvK+huWZNTWWbBGjaBaN29lPL59XOCwvtkwOcPa8fI5h2XzkMrnOVY8tQ76Pg16XyZnvtdUvR3eUvMZxfLDK50OZfpa1vwrD7uMo+fxl8pKH5XE3nVuaf68UjSU9jDr6WlfOc1kfL+snddcHjGPnuP9PR8m37vdZ0K+dYZ/z2fdBWz4umqOtJ77bAhsBt5jZHcABwM5mdjlwFeD9Top5vbcDW2Q2bwFcHV9fnd1nZvOBZ2b2CyGEEEIIAbQX+J5ICEi3jMsJwNnAjsCZwGpmtoeZzTWzXYD1gZ/Gc08BPmBmq5nZZsBewMlx35nA88xsZzNbEfgQcJUK24QQQgghRJ5WRnVw9weBB3vrZrYYWOLui+L664DjCEOdXQv8i7v/OR5+KHA8cDMhLeIodz8vtrvIzHYGPgOcClwM7NqGTUIIIYQQYrpodTizHu5+WG79QuD5A459GNgzLv32fx/YrOYuCiGEEEKIGSPpKYuHFQ4NGwS/zED6k0adifllCydGoWqBVNlCpH6TL2Tvc5FN2XOHFVYNan+QDcMmDclfj1EK4Aa1XaUvg47tV9BXpiiq7OQlvddl2y0zWUTVQsmiNst8FgwrXhv3/VOXT+TbLOrXoILWJj8P+92Xqu+fQe0VTZZRpU/Dtvdre9jnT9G5dfpO0WfbOBPDNFloWkazzP/6MvsndVITUZ6kA18hhBBCCJEOCnyFEEIIIUQSKPAVQgghhBBJoMC3BONOilC0rSvqsKmMPVXPKcqRLaJsjlZRzmKdOnnyucHDJrkYtD2bZ5fPTS470UDZyRby16yo//00y+YDDsuB7peTWUW/Xxtl8xXL5lHm+140cP6g9kdhnLzFqv0Ylmtetb1Bx1a9JlXzmatel6L7No6d+b7U2a9B24re+0XnDKqLKKNfpv1hx1TJM+93b+rISS6yeZL+t4vhKPAVQgghhBBJ0MlwZkIIIYQQoiG+AFbDo01fCrxt/HYmCT3xFUIIIYQQSZBs4NtkTk6//KthY00WtdXv7ziMOv7gKOP2lh1HMXt83XmP47TXL6e293qUvMIq+cX5PNt8vwb1JXter/9FY9KW1Snq/yD/LJvLnD+2TN7cOGPiDspXHNb/QecNy1Me1o+q+fJ1f36V9ecqOamj5k4P60vPN0Z5/4163eoYr3jcPvRrK8s4Y0xn28h/XhTl1OfvQ1W/Lzqn6hjG+fdeWRuqonze6SfZwFcIIYQQQqSFAl8hhBBCCJEECnyFEEIIIUQSKPAVQgghhBBJkHTgmx/4v9+g3YPOK2JQoVKZtoYVUdSVoD9Is2wRXZkCsrKFUflzhhXEjTLA/ajnFmlXKTApuq6jDq6ebTM7yUS/NvNFK8OKVbKFImUKWIr8dJSB7wcVjo1a/Nhv8oVBx41D3YVZw/pZ1rerFq6VbaNsUWNVrX5+nC/GzF7rqoVjZT/bexp1fu6WLRSrcr2L2h3GqP/neppN/E8qo1s3oxZjNtUf0RxJB75CCCGEECIdFPgKIYQQQogkUOArhBBCCCGSINnAt1++46ABr8vkr406wcQ4k1qMcs6w/KRBuYOj5OuWHYC8ShtF+XZVJw8YRb/KhAz9zhvFnkH6Ve5B0T0edG/L5qKOmldZNW9u3Dz3UfODR9Ep2tdVjmDVSR+y50H/61d0T6pOSNKPYf5edfKLcSaeqMK4tubXiyZnyB5TxceLrmfdec09ytSOjDpRS5G/jZvjXtS28nunj2QDXyGEEEIIkRYKfIUQQgghRBIo8BVCCCGEEEmgwHcAo+YCVskBLcrhLJOnViVvrsoYncNyS8cZQ7Jsfumw84ftG2RP0/lYVdsfZs+wcX+LxlEt60vZe9Iv7z0/Zmq+L1Xu/bhjzY7LsBzJtiibizksx7XK2KNVfLOu8aazjJof348yn4+jUEddQrad/Ht1WBuD9MvWAVTNzR10fB2fk6P8n8j7adXxmcvqjINyeqcfBb5CCCGEECIJFPgKIYQQQogkUOArhBBCCCGSQIGvEEIIIYRIAgW+fcgn/JcpeKhalJDfly+CyCf1D0ruHyX5v0yBRVFB2LDrMeqg42UpU3iTtWFYYV5+36BjsvubmCwjr9Nrv6c3THPQBBHjTrox7jFlzy0q+Gqz8KxOvbonAckfO+qEJqPoVi2QG9Z+U0Voo1A0uco4mvn7M2qBYZn39rDixzLF1GU0y/S313Zd76NR/HbQtSnzOV+lCLHtzyYxPgp8hRBCCCFEEijwFUIIIYQQSaDAVwghhBBCJEHyge+gvKc68yCLtHrbB+Wkls3nLNPnohzRKnm8w+hqkohxc7PKTp4xSq5jPwb1t1+OdZHmoH1l8+KG5QEWbS97TJmc6KLc7SZ9apQJW8pSNse/7Pt8WJtljsvmOI46kUzVz5p+vlg1r7mOXOlhtRL91puYzKOIMjaUvT+jTiyUfV32vVtUb5K9f2WvXdE9H+RPVXN/R9mf19SEFtNH8oGvEEIIIYRIAwW+QgghhBAiCeZ13QEhhBBCCFEjr3kQnzt3/HYeeWT8NiaM1p/4mtmzzGyJmZ0a17czs1+Z2T1m9hczO9PM1s8cv4KZfcHM7jOzO8zsPbn2tjeza83sQTM738w2LN2XMfNzyuQ0Zl8PGmdxUB5cln45aFXHSayaX1m0vYpG1Xy+cahyP+vK062bKn5ZZRznQX5WlNeX3V533vegnL9+OahNMiyfs+w1LpuPOGrfqmhWbbtKXmOZ45u4b1XyoAcdN+5YvOPoDztnUJ51vt9l6hSq5PxXqYsoOqaoRiW/rWq+bxn9YTnG/c4paq/fef1qL0QxZvbxGKNdFeO7hZl97zOz683st2a2Y1t96iLV4Vjg0sz6NcCO7r4QWA+4Djg+s/8w4FnAhsB2wIFm9k8AZrYmcAbwQWB14DLgaw33XwghhBBCDOd7wPPcfXPgd8D7AMzsOcCuwHOBfwKOM7MaHlEPp9XA18x2Be4BftDb5u53uvsfM4c9BmySWX8LcIS73+3uvwFOAt4a970BuNrdT3f3JYQgeQsz26w5K4QQQgghxDDc/bvuvjSu/hzYIL7+F+Cr7v6wu98IXA+8sI0+tRb4mtmqwOHA/n32Pd3M7gEeAg4A/iNuX43wFPjKzOFXEr4hEP8+sc/dHwB+n9mf1djbzC4zs8sWLVpUi01CCCGEEKIUewLnxtfrA7dm9t0WtzVOm8VtRwCfd/dbzexJO9z9FmChma0O7AVcG3ctiH/vzRx+L7BKZn8+is3uz2qcCJwIsM022/joZgghhBBCJMNTzeyyzPqJMaYCwMy+D6zT57xD3P1b8ZhDgKXAab3T+hzfSmzWyhNfM9sSeAVwdNFx7n4X8CXgW2Y2D1gcd62aOWxV4P74enFuX37/4D6NUOhQVHyQp2zhSJk+jDuZRpXCp3xRXlEBXdMFa2WLB6F/oUHZ4r+qg/eXObbsNR+1OGKciQ3KUKVwcpRJJor6X6bYs6idcRj2Xi0qyqlqf1HhTJntVTXLnNOvT/nPhqJ+lrGp12Zdk3YUfdbmi5HK6g1j3Pdav35W8ftBVPm8K5pcJN/WuBNDVCnQyx5TtSC7aJKfUSgqBE6MP7n7NpnlxOxOd3+Fuz+vz9ILevcAXgvs5u694PY24GmZZjYAsmmvjdFWqsO2wEbALWZ2ByGdYWczu7zPsfOApwKruvvdwO3AFpn9WwBXx9dXZ/eZ2XzgmZn9QgghhBCiA+JgBAcBr3P3BzO7vg3sGkfu2pgwiMElbfSprcD3REJAumVcTgDOBnY0szeY2bPNbI6ZrQV8CrgiPv0FOAX4gJmtFovW9gJOjvvOBJ5nZjub2YrAh4Cr3P1ahBBCCCFEl3yGkH76PTP7pZmdAODuVwNfJ4zsdR6wn7s/1kaHWsnxjVH+E5G+mS0Glrj7ojhm7ycJT3nvB34E7JQ5/VDC8GY3E4rfjnL382K7i8xsZ8KFPRW4mDA8hhBCCCGE6BB336Rg30eBj7bYHaCjKYvd/TB3f3N8fYy7b+zu8919HXff1d1vzhz7sLvv6e6ruvva7v6pXFvfd/fN3H0ld9/W3W8q1Ychg9KXGQC8KlVzpspQZnD9uvKbhjFooopRc1nHzY0ue1xRXmC/9XF1h/nBsFzEfrnX4+aXDupfmbaGXb9+ZH2izAQabTDsno16vXvnD9IqO8HNsDZH6UeeYRPQ9Js4p6j/2ftZ5+QXZfOMq9QoNDFBybD3+ij58Xntsn7ZuzZlr3+/SScG2TPqJBiD/jeMk1OccB6uKEknga8QQgghhBBto8BXCCGEEEIkgQJfIYQQQgiRBAp8hRBCCCFEEiQf+I6aWD9KoUuZSS1GLeqpK6G/yoDhwwoAq9pStuBqWHHWqBo9qhRYtVmENcoED4PaKVM4OEph4Dj96tfGsHtdZaKNcfszzoQjoxZq1lHgOc45WV8o8/k1rIipqMBrlH71zs8X35XtU7+/g/o17vUe9b3Sxmd7EUXXNru/zvehCtREkyQf+AohhBBCiDRQ4CuEEEIIIZJAga8QQgghhEgCBb59GDfvqszA6GUGQx93cPO6qTrwfJ39HrXtuvP0evmEZQe7HzZBRdf3touJIoaRz5+vY8KDOqjyPi6zfVi/6743TdUPjPpeHPe4rP4ok6iUabcK2c+GSXxfwfA85jLn5LePm/8uRNso8BVCCCGEEEmgwFcIIYQQQiSBAl8hhBBCCJEECnxHoGpeX9H4jcPGKJ0E2hwrNUvR9Sw75u8413DQfSuTUzgp964qTY6PXJZJvXb97vko49mOqpfXrkq/+oFh+ahl6hWq6Jd5j1cZR3tQW0340KTm8Nb9GSfErKPAVwghhBBCJIECXyGEEEKI2WEJd99dT0t33RXamyEU+AohhBBCzA6f44QT6mkptPO5ehqbDBT4CiGEEELMDl/hBz+AcXPRFy2CCy4A+GodnZoUFPiOwLDii2zx1bDikFGLC6oUpIzDKJNoDLK5aFKHfhRpjlpwV3biibJkB3Efdv4kFcRA/+KsUQbtH+W8WaCMzVUmvajSzqgTNvRrp8r7rO77PE0TH2QLW4cVvk6yTZNapDetTOL1dPel7LMPHH/8eA0deyzsuy/u/lg9PZsMFPgKIYQQQswS++233FhPfXtPe/fdd169HeseBb5CCCGEEDPE2E99Z/RpLyjwFUIIIYSYPUZ96jvDT3sBzN277kPr2JZbuv/yl3+9vU/OY9Eg9YUaA84btb26aFo/3/64elXO7x3b9TXuxyT2qYgm+jtt12BSGHbdivZn99V9/Qfld4/yni27vUnq+KzK0pWvT0o/UsXMfuHu23Tdjx523HHOddfBBz5Q/qTDDoPnPhffZx9rrGMdoie+QgghhBCzSNWnvjP+tBcU+AohhBBCzCSVc31nOLe3hwJfIYQQQohZpexT3wSe9kKige/fzut/T4flQlUZo29QW03nW1UdK3dcrWH5ZEW5h2VoIs+0zfN65POex2HYuJFVxpUc5C9t5gXWNdbtrDLsXgwbhzdPXde2jvFqu/qc7JF9n9RhyySM4zsp/RCTQ+mnvgk87YVEA18hhBBCiGQY9tQ3kae9oMBXCCGEEGKmGfrUN5GnvaDAVwghhBBi9hn01Dehp72gwFcIIYQQYuYZ+NQ3oae9oAkslm0rUdxQ1wQXbUzq0OYA8HUMWJ/f19TA9k1fu2mcpGHQRARFx0+bjUII0QaTNoFFHjObx/Of/yjf+AassUZ42vvGN8JVV81LJfDVE18hhBBCiAT4q6e+iT3tBQW+QgghhBDp0Mv1/d3vksrt7aHAVwghhBAiEZ546vv61yf3tBdSzfE1WwQ8APy5A/k1O9LtUjtFm7vUls3SnlXdLrVTtLlL7Wm1eUN3X6vOzjSBmc0DDgA+rsA3Eczssi4S0LvS7VI7RZu71JbN0p5V3S61U7S5S+0UbRbtoFQHIYQQQgiRBAp8hRBCCCFEEqQc+J6YmG6X2ina3KW2bJb2rOp2qZ2izV1qp2izaIFkc3yFEEIIIURapPzEVwghhBBCJIQCXyGEEEIIkQQKfIUQQgghRBIo8BVCCCGEEEmgwLciZrZSatop2twlKdos0kH+LWYZ+ffko8C3AmZ2MnCkmS1MRTtFm6P2l8zsPR3onkxiNnepLZtb1z6ZxPxbPpaU9sl05N+iAu6upcQC/A2wGHgc+ASwILPPZlE7RZtj+y+Iuo8D+yfiY53Y3PH1ls3t2pycf8vH0rA5anfm31oq3quuOzBNC/Ax4O3AEuAY4Clx+7xZ1U7U5uWBzwLvBB4BDsrsmyubZ0NbNrdrc9RIyr/lY2nYnNHo7P+llvLLPMRQzMyAucDWwI+BlwMXAvea2fnA35nZJ919yaxop2hzhjnA84EzgLcCXzazu4CLgS3N7D/d/dE6BVO0eQK0ZXNLugn7t3wsAZsnwL9FBZTjW0B0Zgg/UywFvgZs7e4XA1sBBwH/Bfy5ocCzdW0zmwPggVZt7tGFdu96m9mc2Pa5wDru/hXgdcDxwC+BB5v44EzN5oz23Da1M7rWxX3uQrtr34a0/Lsr385pd+LfXeim6t9idBT4FjMXwN0fj+v3ADvH1wbcR/g5ZWszW1CXaAw+V25b28xWcffHzSz7S0BbNu9gZvNzm1vRhvDBFf/2rvedwP+Ir+8F7gceBp5Vp27vi0aGmbc50vuC9Vhb2ma2srt7DEh6c7W3dZ+36L1uW7vL+5yof7fu29Cdf3fp21nN1PxbjEE+90GLAxwKfBE4Fdgls32duH0H4A5gN+AlhDfa2jVpfwr4EnA28Ma2tIGzgLt6bQHLxb9rt2Dz94BzM+tz4t91W9D+MPAF4JPATpntLwA+D7woau8F7A78GVijBt2/y9vb4vXen5B/9jFgu8z2bYDPNWVz1PgIcEJctm9LG/gmcFmvrYx/t2HzD4Bv9/HvFzRscye+nap/d+XbXfp3V76dsn9rqeH+dd2BSVsIAeCFwLsJSfJXAVvFfXOBawlVm/tlzlmpJu0zgZ8Aryf8XHNmr+2ofV2D2hcAdwO3AuvHbXOi7m8a1D0L+OmAfU3bfBYh9+tQwpeN+4HD4775wM1R+98y56xag+5p8UP47ZltvX8YTftYz7//Pb4+F9goY/ONTdic8e8LCcUflwOfyeybD9zS0PXu+fBv4/vrqTndJm0u8u8mbe7Et1P17658u0v/7sq3U/ZvLfUsnXdgkhbgKODHmfWNgUuBHTLbXg68Lnfe2EOVEPKQLsysnx4/yLYAXp7R3qlObWKlKyHI34/wDfqPwNPi9uWj7j83YPMXgVsy628ADgaO6NkZtV/fgPZLgauJ1bbACoSfxx4Cjorb9gB2rtnHDgGuAY4j/KPcJ7Ov95SmKR/7dM7HXghcT/xil7F5p3G1+mifAlyQWT+H8IRqQ5Z9sdyDzC8sNenOjcu58dqfBvyUkAPYu+9N2fw14JrM+quAfeLysgZ9rBPfju0m599d+XZstxP/7sq3Y7tJ+reW+haN6hAxs9UIP61/Oq7Pc/cbzewvwKaEn+MhfMNdGhPpH4cn5TWNo303MTfIzD4MvJoQAG4NnGBmh7v7R/Pnjqvty/LQfkEYh/AQwnX4qZm9l/Bz0VHufmeduma2BvBs4BIz25TwwbUH8DNgDWBfM1vN3b/Qp89jaUeWAo8CC83sbuARdz/dzB4GTjezq9z9SzXoPEHM8VoJeD/wK+B/A28xM9z9BF9WeNGEj60HPJXw82DPvy+J/r0JcEU89GvuviQWqdRxnTGztQjXO+vf2xK+aO4EHGBmH+p3r8el599m9v3Yh6OBI4HTzOx4wpfbE9393jp1zWxtYDvgHDNbDjgQeAvhydzawHwze1/dPhZp3bchTf/u0rehG//u2LchQf8WNdN15D1JCyH5ff3ctnOAAzPrz2xIe0VCMvwzCE8QNsnsewshMN6sQdt3Ac7LrP+U8HPNoXG99jEQ4/X+DuHJ9g3As+L25QnVsBcAa9LAt+V4ne+n/89VhxJ+SltQtzawOsvy8DYm/JP6GbBv5pi/aegev4hcnlm8z2/NrD+tZk2Lf1eK/r05ocq5d69XJfwjuQbYoIl7HXX+L3B6xu8uiv79wbitNv/O2Lw54R/k7+KyWdy+PuEL9pd716VmWzvx7dh+Mv49Kb4dtVrx7659O2ok6d9a6ls0qkMGd7/O3f8AYSiauPl+oLftncD3zWxhZrixurSXeOAGwhv6ejNbPu6+lRAY3l2nZo7zCN+iMbN/JDyN/SVwsJlv3usMAAAP1klEQVSt68ueDNdC/DZ8HfAeQk7cnu5+nZkt5+6PENIt5gH3e/w0qVn7BuB9hOkld8kd8jvC06OH69Z297vc/S/xidONwImEcR93N7M3mtm7gB834WPAJR6f3GdG7lgC3B63vQP4ipmtUpd27/q5+0PRv68i/OTbu9f3EXIQHwTuqft6Zzgj8/rpLPPvXcxszTr92909+thVhOKWPxHe09fGJ5F/IFzzDXvXpS7tLn0b0vLvCfJtaMm/u/RtSN6/RU0kn+qQ/ckr+9ME0HvTzAduNbM9gcMI+b73NKy9BCAGgBDGA7yXMCRLU7rzgJXM7CPA/yJ8c/468P8J355roaftYdi05eI/iX0ITycg/IwFYRSL24lDytWtHTd9lfCE4hgzm+/Lfh5bi5AvtgLxy0AdutltmX+aN5nZiYRhcE4g2Lt9Ez6W64PHD+aVWebfRxKq4O+vWzvH4vi3d6+fRgxOGtR9GFjDzA4kPB37ECEP8z0E//5zndrRv+e6+1Vm9s8ss7UXgCwHXG9my2fe57Xoxk2t+HZWO7tt1v27K98u0G7cv7vy7bx23JSEf4uG8Al47DwJC2GKw1f02f5N4NeEob7+tmXtlYF3EJ70btGULst+JjoCeIAnV6M2MtVi1N6B3M9RhJEk3gH8Bdi8Ie29gGfH1+sT0iqWEr65fyfe660b0H0bA34GIwzJcx/w3IZsfht9UmUIRTE/ij5Wu81FdhN+Hm7sXvd0o08ZoXBzcc+/47ZGqq2H3Ot9gUXA8xrQ7cS3S9g8k/7dlW9ntdv27658O7afpH9rqfledt2BSVkIeUEbZtZ7UxBeQhiA+jltacdt6wKfIQzLslUbuoTxD7ft4nrHbasBHyeMf1jbhxfLRq6YS3j68UPg6bljtgbeBexNzNFrSHeDPse9gpBOs03DNm+Q2T+HkFd+E+Gn2Nr+UZWxm5AfdzRwW133uoTNrwJeE1/XnVdbxuZ1CGPLLqrR5uX76Dbu2wXabfl3oXZT/l3G5iZ8u6TNjfh3SZub8O3ssGzWpn8XaLfi31qaXXqJ6slgZlu5+xWZ9WyKAfHn90cz668Gbnb3qzvQ3gq4291valh3BXd/OLNeZ0V/VZu3Idj8+xq0DyVUGW8KvNvdfxW3z3X3x3o/xdVpb0ndvM3rEp6s39qWds9mM9uLUIV8TVvacdsCQjHSjR5y9prUfZJ/10lFm1cC/gG4wUN++zi6RxP+Gb8Q2M3df53TbcS3S2o36d+ltOv27yo21+nbJbUb8e+KNtfm27G9LxFG49jD3a/PbG/Dv4dpN+bfoh2SKm4zs28CvzCz3XrbskFYXO+9kTeKb65zagp6q2hvGLWvqCHoLaP7cEZ3uRqD3lFsvqymoPcsYHvCBBj3Az8xs6dGzcfi317+2erj6lXU7dm8RvwicHtNQUFpbcLTdYDP1RT0VrF7dXdf7O4/qCHoLaPb8+/a7nMF7azND7n7f9UQ9J5JCKy+Qfg16tMWC2Gb9O0K2k35d2ltavTvijbX5tsVtGv37xFsrsW3e+0BzwdeDHzezJ6R2d3739S7z3X7dxntpfHYWv1btIhPwGPnNhbCWIOXEsZXfATYveDYnQljEtY100sn2iPoXjPtNsf2/p3MjEKEnzwvAnbsc+ybCEMerTituiNq/2zatVO83oSB8y/KrJ9KKGL6e2DThm2eJu2urned76tOrneXNmfa3Z8wlv0lhP8jvSHi5meO2bVOH5sEbS3tLJ13oBUjw881ewJvjuv7EypQd4/rc/qcs+k0a6doc2xnPeAkls12N5eQo3U5mVl2MsevSw3jLnalm6p2ojZvQJhgZvW4/jFCMdMHgP9DeBK1b926qWrL5nZtzvXj08DhhBEifkMoXtuVMHzYmrOqraWdpfMOtGZocNRVMuu9YOwtmW2bZY+Zdu0UbY7t/iOwXnzdy2M/B9g7c8zas6KbqnaiNvcGz1+HMG3sMzP7DiYMq9SUzclpy+bWbe4Vib4cOCmz/UbCkJeHNKHbtbaWdpfOO9CocWE4sGzwlR866wBCMPYa4L2EKtzVplk7RZsz2gNTJQi5am+Irw8kpHWsPK26qWrL5ie2zWFZtf3K8e+LCT9Jr1OHvalqy+ZObF6Q2/as+P5ZDtiGMFHGzYQnsBvNgraW7paZncDCzD5NcOCVzezn7v4+j14d95u7f8LCXN/fIRSn7OjuY8+O1pV2ijb30b7Y3Q/uc9jawF9ilfcBwGvd/cFp1E1VWzaH9xU8UST6SHzd03gZYaKbse1NVVs2T4bNhOHBrgR2J4wvf6C7n2xmFxHSiqZaW3RM15F3Ewth+safAK8k/DRzOWEqyX7HHkQY3Lyu3KxOtFO0uYo24WfonxEGlB97nMmudFPVls1D31fvpMbJElLUls2TZTNwCiHFYL869CZFW0v3S+cdqN0g+CDwo8z6ysB3gYP6HLt5fCO/aJq1U7S5ijbhW/pF1DSjUFe6qWrL5kLdTQhFN9dR00Q3KWrL5smzGXgG8A+588aemKNLbS2TsczUOL5mNheYB5we11f08BPNlYQ5vPPcTJj+8OJp1U7R5qraHj61TgW28zgI+7Tppqotm4fqXh+P28EzE8VIe/J1u9SeBpvd/QZ3vyAe39vm+famRVtMDjOV4+thVpX/AFaJ60virhWAhb3jzOzv3f1n7n7vtGunaHNF7Ze6+0/c/dhp1k1VWzaX0v1eHbqpasvmibW593/jsVnQFpPDTD3xBXD3B9z9Dgv07HsYuBPAzP4NONvM1p8V7RRtrqD9HTPbYBZ0U9WWzYW6Xb6vZkZbNk+kzTP3P0tMBjMX+JrZivDEzxK9Csy5wG8sTJ17OLC9u/9hVrRTtLmi9m2zoJuqtmye2PfVzGjL5jRs7lpbTAYzlepgZu8H7jWz4zzQ+5liDnAM8BAhT+nyWdFO0eYutVO0uUtt2ZyGzV1qy+Y0bO5aW0wOU/vE18wONbNn5zYvBc6L3+SyrAKsBLzM3X8xrdop2tyldoo2d6ktm9vTTVVbNrenm7K2mGzsr+//5GNm3wEWuvvL4vocD4Nt9/Yv7+6PZNafTpiB5vpp1U7R5i61U7S5S23ZnIbNXWrL5jRs7lpbTD5TF/ia2VnAWu7+krg+B1jO3R/OHGPu7ma2FXCzu981zdop2tyldoo2d6ktm9OwuUtt2ZyGzV1riynBJ2Aw4bIL8DHg8cz624GTgEuAQ4H14vY5wIsIM4StPc3aKdqs652OtmxOw2Zdb9k869papmeZmuI2M5sP3Aj81swOJgw/8g7gs8BNwLuBdYF9PPykcbGZPcfd75xW7RRt7lI7RZu71JbNadjcpbZsTsPmrrXFlNF15F1mAZYD5sXX/wrcSvimtlHmmBcBjwBbAXPitjqmN+xEO0Wbdb3T0ZbNadis6y2bZ11by/QtE//E18xOADYEVjOzbwOnAAcAf3T3m8xsBUKl5h2EnzPu8pjE7h48e9q0U7S5S+0Ube5SWzanYXOX2rI5DZu71hbTyUQHvmb2VWAj4EhgfeAoYHPCeHuXA3hMWDez1xC+9S2eZu0Ube5SO0Wbu9SWzWnY3KW2bE7D5q61xRTT9SPnQQvwDOBSMonnwMbAlcAZwLpx26rA/oSfNbacZu0Ubdb1TkdbNqdhs663bJ51bS3TvXTegYEdg7WAq4Gd4vrc+Hcjwk8Wx8T1/YHLgK2mXTtFm3W909GWzWnYrOstm2ddW8t0L513YGDH4CnAjwkVmb1E9F7y+vbAHwkVmhsA68yCdoo263qnoy2b07BZ11s2z7q2luleOu9AYefghYQhSY7IbX8mcBFxTL5Z0k7RZl3vdLRlcxo263rL5lnX1jK9y0QXt7n7JWb2P4HTzWwhcJK7XwXsACwEHp017RRt7lI7RZu71JbNadjcpbZsTsPmrrXF9DIVUxab2cuAzxGc+AHg6cBr3f0Xs6qdos1daqdoc5fasjkNm7vUls1p2Ny1tpg+piLwBTCzdQgVm/OB37r7rbOunaLNXWqnaHOX2rI5DZu71JbNadjctbaYLqYm8BVCCCGEEGIc5nTdASGEEEIIIdpAga8QQgghhEgCBb5CCCGEECIJFPgKIYQQQogkUOArhBBCCCGSQIGvEEIIIYRIAgW+QgghhBAiCRT4CiFmAjNbbGYvblHvrWb2eNTdLW7b1syWDjnPzeyl7fSyPGb2XTN7aFj/hRBimlHgK4QojZndZGZvHrTdzN4fA8HFZvZADPIeyGx7fzx++Xjs1XH/HWZ2vpnt0qftl2XOXxyDzSWZ9XMB3H2Bu1/U/FV4EjdE3dNa1q0dd38l8Kqu+yGEEE0yr+sOCCFmB3c/EjgSwMw2AG4FnuvuN/WOMbO5wNnABsB+wEXAo8A/AHsB38i1eSGwIHP+9cBH3P3kBk0RQggxg+iJrxCibd5ECHJf5+4/dPeH3H1pfP2mURvNphDENITrzezdZnabmd1vZp8wszXM7Jtmdp+ZXZtPOTCzvczs12Z2r5ldYWavHLEvbzSz38d2vm5mq+QO2dzMLo39+rmZbVbQVmVbzOxkM/uymX3BzO4xsz+Y2ZvMbMuM7vlmtt4o9gkhxLSiwFcI0TavBi519+sa1tkQWAg8A3gp8E7gXODjwGrAGcAXeweb2d7AQcBucf8hwBlmtklF3bnAK4EtgE2BrYB35Y55K7AzsCbhqfgxmX78q5ndM44tkV2AbwKrA0cAJwGHAzsBawMOHFbRNiGEmGoU+Aoh2mYt4A8t6DwEfNjdH3H3K4ErCQH3z939MeBUYBMze0o8/l3A4e5+pbs/7u7nAOcDu46gfbC7L3b3O4GzgG1y+z/u7re4+8PAydn97v4Vd184pi0AP3T3s939ceAUYD7wZXe/zd0fJKSUvGAE24QQYmpR4CuEqMKjwHJ9ti8X95VhEbB+bT0azJ9i0NfjQeD23DpALw1hY+DYmBpwT3zquh3V+/qYuy/KrD+Q0ehx+5D9eara8iSNGOjmdR8soSuEEDOFAl8hRBVuAp7007+ZLSD8dH5DyTbOAV4wQgpB09wM7OnuCzPLAnfft+uOCSGEqAcFvkKIKpwM7B2HGJtrZqsB/w+4GriiZBv/CVwIfCuOe7tibOvlZvaVZrpdiqOBw2IBmJnZSmb20qLCMyGEENOFhjMTQpTG3U8zs5WBYwkFV4uBHwOvdfdSEx+4+2Nm9mrgvcBxwEbAfcA1ZIq82sbdTzKzRwhFYhsTUjcuBw5osx9xMozPuvuCoQcLIYSohLl7130QQoipw8x2Bz4LPAK8w927fFo9NnEikJcAcxR0CyFmFQW+QgghhBAiCZTjK4QQQgghkkCBrxBCCCGESAIFvkIIIYQQIgkU+AohhBBCiCRQ4CuEEEIIIZJAga8QQgghhEgCBb5CCCGEECIJ/hs06UTYG/Km0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grab a particular research flight data and plot T vs Concn data along with Relative Humidty\n",
    "%matplotlib inline \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "from scipy.io import netcdf\n",
    "import math as math\n",
    "import datetime\n",
    "\n",
    "def discrete_cmap(N, base_cmap=None):\n",
    "    \"\"\"Create an N-bin discrete colormap from the specified input map\"\"\"\n",
    "\n",
    "    # Note that if base_cmap is a string or None, you can simply do\n",
    "    #    return plt.cm.get_cmap(base_cmap, N)\n",
    "    # The following works for string, None, or a colormap instance:\n",
    "\n",
    "    base = plt.cm.get_cmap(base_cmap)\n",
    "    color_list = base(np.linspace(0, 1, N))\n",
    "    cmap_name = base.name + str(N)\n",
    "    return base.from_list(cmap_name, color_list, N)\n",
    "\n",
    "\n",
    "# Plot CT Air Temperature,Static Air Temp and CDNC vs time for a research flight.\n",
    "\n",
    "RFn = 7\n",
    "\n",
    "\n",
    "#fig, ax1 = plt.subplots(figsize=(10,8))\n",
    "\n",
    "############### Input the timings here ####################\n",
    "# Required variables : \n",
    "month = 9\n",
    "day = 9\n",
    "sh = 5\n",
    "sm = 21\n",
    "ss = 24\n",
    "eh = 5\n",
    "em = 24\n",
    "es = 20\n",
    "\n",
    "\n",
    "def time_subset_resample(month,day,sh,sm,ss,eh,em,es): # n = no of sec for resampling\n",
    "    \n",
    "    t_start = datetime.datetime(2019,month,day,sh,sm,ss)\n",
    "    t_end = datetime.datetime(2019,month,day,eh,em,es)\n",
    "    \n",
    "    df_P3_sub = df_P3.loc[(df_P3['AC_Time (UTC)'] >= str(t_start)) & (df_P3['AC_Time (UTC)'] <= str(t_end))]\n",
    "    return df_P3_sub\n",
    "############################################################################################################\n",
    "\n",
    "df_P3_sub = time_subset_resample(month,day,sh,sm,ss,eh,em,es)\n",
    "SAT = df_P3_sub['Static_Air_Temp']\n",
    "RH = df_P3_sub['Relative_Humidity'] # percent\n",
    "CDNC = df_P3_sub['conc_LAWSON']/1000 # #/cc\n",
    "LWC = df_P3_sub['lwc_LAWSON'] # g/m3\n",
    "Time = df_P3_sub['AC_Time (UTC)']\n",
    "\n",
    "x_ticks = np.arange(len(Time))\n",
    "x_labels = [datetime.datetime.strptime(ts, '%Y-%m-%d %H:%M:%S').strftime('%H:%M:%S') for ts in Time]\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "cmap=discrete_cmap(20,base_cmap=plt.cm.jet)\n",
    "plt.scatter(np.arange(df_P3_sub.shape[0]),SAT,s = 8, marker = 'o', \n",
    "                     c=CDNC, cmap=cmap, clim=(0.0, 100.0), vmin=0, vmax=40)\n",
    "cb=plt.colorbar()\n",
    "cb.set_label('FCDP Cloud Droplet No. Concn (#/cc)', fontsize = 14)\n",
    "plt.xlabel('Time (UTC)', fontsize=13)\n",
    "plt.ylabel('Static Air Temperature (C)', fontsize=13)\n",
    "plt.gca().invert_yaxis()\n",
    "plt.ylim(5,-15)\n",
    "plt.gca().set_xticks(x_ticks[::50])\n",
    "plt.gca().set_xticklabels(x_labels[::50], fontsize=13, rotation=45)\n",
    "plt.show()\n",
    "\n",
    "import h5py\n",
    "import math\n",
    "import glob\n",
    "import datetime\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import pandas as pd\n",
    "import matplotlib.colors as cm\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import Normalize\n",
    "from metpy.plots import ctables\n",
    "\n",
    "def roundup(x):\n",
    "    for i in range(len(x)):\n",
    "        x[i] = int(math.ceil(x[i] / 100.0)) * 100 \n",
    "    return x\n",
    "\n",
    "ht_gridded = np.linspace(10000.,-5000.,500)\n",
    "# df = pd.DataFrame(columns=['Lat', 'Lon', 'CTH (km)'])\n",
    "start = 6500\n",
    "end = 10000\n",
    "\n",
    "\n",
    "def grid_altitude(alt3D, W_refl, vel):\n",
    "    z_gridded = np.zeros((len(ht_gridded),np.shape(alt3D)[1]))\n",
    "    z_gridded[:,:] = np.nan\n",
    "    v_gridded = np.zeros((len(ht_gridded),np.shape(alt3D)[1]))\n",
    "    v_gridded[:,:] = np.nan\n",
    "    for i in range(np.shape(alt3D)[1]):\n",
    "        ht = alt3D[:,i]\n",
    "        z = W_refl[:,i]\n",
    "        v = vel[:,i]\n",
    "        for j in range(len(ht)):\n",
    "            if(np.isnan(z[j])==False and ht[j]>-5000. and ht[j]<10000.):\n",
    "                m = int((10000.-ht[j])/30.)\n",
    "                z_gridded[m,i] = z[j]\n",
    "                v_gridded[m,i] = v[j]\n",
    "    return z_gridded, v_gridded\n",
    "\n",
    "# READ AND PARSE HERE\n",
    "cmap = ctables.registry.get_colortable('NWSReflectivity')\n",
    "\n",
    "Wn_files = sorted(glob.glob('F:/CAMP2Ex_analysis/DATA/RF07/APR3/*Wn.h5'))\n",
    "# print(Wn_files[3])\n",
    "hdf = h5py.File(Wn_files[20],'r')\n",
    "\n",
    "\n",
    "params_W = hdf['params_W']\n",
    "\n",
    "hires = hdf['hires']\n",
    "#print out the variables within geolocation\n",
    "# for key in hires.keys():\n",
    "#      print(key)\n",
    "\n",
    "lat = hires['lat'][0,:]\n",
    "lon = hires['lon'][0,:]\n",
    "p_vel = hires['gsp_mps'][0,:]\n",
    "\n",
    "W_refl = hires['z95n']\n",
    "# print(W_refl.shape)\n",
    "W_refl_ = W_refl[:,0,start:end]\n",
    "\n",
    "vel = hires['vel95n']\n",
    "vel_ = vel[:,0,start:end]\n",
    "\n",
    "alt3D = hires['alt3D']\n",
    "# print(alt3D.shape)\n",
    "alt3D_ = alt3D[:,0,start:end]\n",
    "z_gridded, v_gridded = grid_altitude(alt3D_,W_refl_,vel_)\n",
    "\n",
    "alt_nav = hires['alt_nav'][0,:]\n",
    "\n",
    "time = hires['scantime']\n",
    "time = time[:,start:end]\n",
    "UTC = []\n",
    "for i in range(time.shape[1]):\n",
    "    UTC = np.append(UTC,datetime.datetime.utcfromtimestamp(time[0,i]).strftime('%H:%M:%S'))   \n",
    "# print(UTC[15:45])\n",
    "plt.figure(figsize=(12,8))\n",
    "ax = plt.gca()\n",
    "plt.imshow(z_gridded[100:200,:], cmap=cmap, norm=Normalize(-25,70), interpolation='nearest', aspect='auto')\n",
    "plt.xlabel('UTC Time [hh:mm]', fontsize=13)\n",
    "plt.ylabel('Altitude [m]', fontsize=13)\n",
    "cb = plt.colorbar(orientation='vertical', extend='both')\n",
    "cb.ax.set_ylabel('W-band Radar Reflectivity [dBZ]', fontsize=13)\n",
    "\n",
    "# Set the x-ticks to be UTC Time [hh:mm]\n",
    "x_ticks = np.arange(UTC.shape[0])\n",
    "x_labels = [timestamps for timestamps in UTC]\n",
    "ax.set_xticks(x_ticks[::200])\n",
    "ax.set_xticklabels(x_labels[::200], fontsize=13, rotation=45)\n",
    "\n",
    "# Set the y-ticks to be height.\n",
    "vert_vals = ht_gridded[100:200]\n",
    "v_ticks = np.arange(vert_vals.shape[0])\n",
    "ax.set_yticks(v_ticks[::10])\n",
    "ax.set_yticklabels(roundup(vert_vals[::10]).astype(int), fontsize=12)\n",
    "\n",
    "# plt.savefig('F:/CAMP2Ex_analysis/DATA/RF07/APR3/APR3_Wn_Refl_RF07_0558_0600_zoomed.png', \n",
    "#             dpi=400, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time (1 Hz intervals)</th>\n",
       "      <th>Mean valid Z from top 2 gates (dBZ)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00:18:51</td>\n",
       "      <td>-6.632868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00:18:52</td>\n",
       "      <td>-5.895613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00:18:53</td>\n",
       "      <td>-6.309994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00:18:54</td>\n",
       "      <td>-5.494394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00:18:55</td>\n",
       "      <td>-7.066450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15498</th>\n",
       "      <td>07:52:08</td>\n",
       "      <td>-29.049698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15499</th>\n",
       "      <td>07:52:09</td>\n",
       "      <td>-29.138392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15500</th>\n",
       "      <td>07:52:10</td>\n",
       "      <td>-29.868713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15501</th>\n",
       "      <td>07:52:11</td>\n",
       "      <td>-29.730220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15502</th>\n",
       "      <td>07:52:12</td>\n",
       "      <td>-28.859263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15503 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Time (1 Hz intervals)  Mean valid Z from top 2 gates (dBZ)\n",
       "0                  00:18:51                            -6.632868\n",
       "1                  00:18:52                            -5.895613\n",
       "2                  00:18:53                            -6.309994\n",
       "3                  00:18:54                            -5.494394\n",
       "4                  00:18:55                            -7.066450\n",
       "...                     ...                                  ...\n",
       "15498              07:52:08                           -29.049698\n",
       "15499              07:52:09                           -29.138392\n",
       "15500              07:52:10                           -29.868713\n",
       "15501              07:52:11                           -29.730220\n",
       "15502              07:52:12                           -28.859263\n",
       "\n",
       "[15503 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime\n",
    "import glob\n",
    "import h5py\n",
    "lst = []\n",
    "Wn_files = sorted(glob.glob('F:/CAMP2Ex_analysis/DATA/RF07/APR3/*Wn.h5'))\n",
    "cols = ['Time (1 Hz intervals)', 'Mean valid Z from top 2 gates (dBZ)']\n",
    "for n_files in range(len(Wn_files)):\n",
    "    hdf = h5py.File(Wn_files[n_files],'r')\n",
    "    params_W = hdf['params_W']\n",
    "\n",
    "    hires = hdf['hires']\n",
    "    W_refl = hires['z95n']\n",
    "    W_refl_ = W_refl[:,0,:]\n",
    "    alt3D = hires['alt3D']\n",
    "    alt3D_ = alt3D[:,0,:]\n",
    "    alt_nav = hires['alt_nav'][0,:]\n",
    "\n",
    "    time = hires['scantime']\n",
    "    time = time[:,:]\n",
    "    UTC = []\n",
    "    for i in range(time.shape[1]):\n",
    "        UTC = np.append(UTC,datetime.datetime.utcfromtimestamp(time[0,i]).strftime('%H:%M:%S'))\n",
    "    UTC1 = np.unique(UTC)\n",
    "    for i in range(len(UTC1)):\n",
    "        Ze = W_refl_[:,np.where(UTC==UTC1[i])]\n",
    "        Ze = Ze[np.where(Ze>-30.)]\n",
    "        if (len(Ze)>0):\n",
    "            lst.append([UTC1[i], (Ze[0]+Ze[1])/2.])\n",
    "df = pd.DataFrame(lst, columns=cols)\n",
    "df.to_csv('F:/CAMP2Ex_analysis/DATA/RF07/APR3/two_gates_Ze.csv', index=False)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(456, 25, 352)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import h5py\n",
    "import math\n",
    "import glob\n",
    "import datetime\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import pandas as pd\n",
    "import matplotlib.colors as cm\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import Normalize\n",
    "from metpy.plots import ctables\n",
    "\n",
    "def roundup(x):\n",
    "    for i in range(len(x)):\n",
    "        x[i] = int(math.ceil(x[i] / 100.0)) * 100 \n",
    "    return x\n",
    "\n",
    "ht_gridded = np.linspace(10000.,-5000.,500)\n",
    "# df = pd.DataFrame(columns=['Lat', 'Lon', 'CTH (km)'])\n",
    "start = 0\n",
    "end = 20000\n",
    "\n",
    "\n",
    "def grid_altitude(alt3D, W_refl, vel):\n",
    "    z_gridded = np.zeros((len(ht_gridded),np.shape(alt3D)[1]))\n",
    "    z_gridded[:,:] = np.nan\n",
    "    v_gridded = np.zeros((len(ht_gridded),np.shape(alt3D)[1]))\n",
    "    v_gridded[:,:] = np.nan\n",
    "    for i in range(np.shape(alt3D)[1]):\n",
    "        ht = alt3D[:,i]\n",
    "        z = W_refl[:,i]\n",
    "        v = vel[:,i]\n",
    "        for j in range(len(ht)):\n",
    "            if(np.isnan(z[j])==False and ht[j]>-5000. and ht[j]<10000.):\n",
    "                m = int((10000.-ht[j])/30.)\n",
    "                z_gridded[m,i] = z[j]\n",
    "                v_gridded[m,i] = v[j]\n",
    "    return z_gridded, v_gridded\n",
    "\n",
    "# READ AND PARSE HERE\n",
    "cmap = ctables.registry.get_colortable('NWSReflectivity')\n",
    "\n",
    "Wn_files = sorted(glob.glob('F:/CAMP2Ex_analysis/DATA/RF07/APR3/*KAsWn.h5'))\n",
    "# print(Wn_files[3])\n",
    "hdf = h5py.File(Wn_files[12], 'r')\n",
    "\n",
    "#print out the groups of variables\n",
    "# print(\"Keys: %s\" % hdf.keys())\n",
    "# Keys: <KeysViewHDF5 ['hi2lo', 'hires', 'lores', 'params_W', 'postEng_cal']>\n",
    "\n",
    "params_W = hdf['params_KUKA']\n",
    "#print out the variables within params_W\n",
    "# for key in params_W.keys():\n",
    "#      print(key, params_W[key][:])\n",
    "\n",
    "hires = hdf['lores']\n",
    "#print out the variables within geolocation\n",
    "# for key in hires.keys():\n",
    "#      print(key)\n",
    "\n",
    "lat = hires['lat'][0,:]\n",
    "lon = hires['lon'][0,:]\n",
    "p_vel = hires['gsp_mps'][0,:]\n",
    "\n",
    "W_refl = hires['zhh35']\n",
    "# print(W_refl.shape)\n",
    "W_refl_ = W_refl[:,12,start:end]\n",
    "\n",
    "vel = hires['vel35']\n",
    "vel_ = vel[:,12,start:end]\n",
    "\n",
    "alt3D = hires['alt3D']\n",
    "print(alt3D.shape)\n",
    "alt3D_ = alt3D[:,12,start:end]\n",
    "z_gridded, v_gridded = grid_altitude(alt3D_,W_refl_,vel_)\n",
    "\n",
    "alt_nav = hires['alt_nav'][0,:]\n",
    "\n",
    "time = hires['scantime']\n",
    "time = time[:,start:end]\n",
    "UTC = []\n",
    "for i in range(time.shape[1]):\n",
    "    UTC = np.append(UTC,datetime.datetime.utcfromtimestamp(time[0,i]).strftime('%H:%M:%S'))\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "ax = plt.gca()\n",
    "plt.imshow(z_gridded, cmap=cmap, norm=Normalize(-25,70), interpolation='nearest', aspect='auto')\n",
    "plt.xlabel('UTC Time [hh:mm]', fontsize=13)\n",
    "plt.ylabel('Altitude [m]', fontsize=13)\n",
    "cb = plt.colorbar(shrink=0.5, orientation='horizontal', pad=0.15, extend='both')\n",
    "cb.ax.set_xlabel('KA-Band Nadir Radar Reflectivity [dBZ]', fontsize=13)\n",
    "\n",
    "# Set the x-ticks to be UTC Time [hh:mm]\n",
    "x_ticks = np.arange(UTC.shape[0])\n",
    "x_labels = [timestamps for timestamps in UTC]\n",
    "ax.set_xticks(x_ticks[::50])\n",
    "ax.set_xticklabels(x_labels[::50], fontsize=13, rotation=45)\n",
    "\n",
    "# Set the y-ticks to be height.\n",
    "vert_vals = ht_gridded\n",
    "v_ticks = np.arange(vert_vals.shape[0])\n",
    "ax.set_yticks(v_ticks[::50])\n",
    "ax.set_yticklabels(roundup(vert_vals[::50]).astype(int), fontsize=12)\n",
    "\n",
    "# for i in range(0,np.shape(annot)[0]):\n",
    "#     if (np.isnan(annot[i,0])==False):\n",
    "#         plt.annotate('+', xy=(int(annot[i,0]),int(annot[i,1])), color='k', fontsize=12, weight='bold')\n",
    "plt.savefig('F:/CAMP2Ex_analysis/DATA/RF07/APR3/APR3_Ka_Refl_RF07_0515_0526.png', \n",
    "            dpi=400, bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "ax = plt.gca()\n",
    "plt.imshow(v_gridded, cmap='seismic', norm=Normalize(-10,10), interpolation='nearest', aspect='auto')\n",
    "plt.xlabel('UTC Time [hh:mm]', fontsize=13)\n",
    "plt.ylabel('Altitude [m]', fontsize=13)\n",
    "cb = plt.colorbar(shrink=0.5, orientation='horizontal', pad=0.15, extend='both')\n",
    "cb.ax.set_xlabel('KA-band Doppler Velocity [m/s]', fontsize=13)\n",
    "\n",
    "# Set the x-ticks to be UTC Time [hh:mm]\n",
    "x_ticks = np.arange(UTC.shape[0])\n",
    "x_labels = [timestamps for timestamps in UTC]\n",
    "ax.set_xticks(x_ticks[::50])\n",
    "ax.set_xticklabels(x_labels[::50], fontsize=13, rotation=45)\n",
    "\n",
    "# Set the y-ticks to be height.\n",
    "vert_vals = ht_gridded\n",
    "v_ticks = np.arange(vert_vals.shape[0])\n",
    "ax.set_yticks(v_ticks[::100])\n",
    "ax.set_yticklabels(roundup(vert_vals[::100]).astype(int), fontsize=12)\n",
    "\n",
    "plt.savefig('F:/CAMP2Ex_analysis/DATA/RF07/APR3/APR3_KaRefl_Velocity_RF07_0515_0526.png', \n",
    "            dpi=400, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('F:/CAMP2Ex_analysis/DATA/RF09/APR3/APR3_CTH_1.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def cc_flag(x):\n",
    "#     if np.isnan(x):\n",
    "#         cc_flag = 0\n",
    "#     else:\n",
    "#         cc_flag = 1\n",
    "        \n",
    "#     return cc_flag\n",
    "\n",
    "# #Calculate physical distance between two geographical coordinates\n",
    "# def haversine(coord1, coord2):\n",
    "#     R = 6372800  # Earth radius in meters\n",
    "#     lat1, lon1 = coord1\n",
    "#     lat2, lon2 = coord2\n",
    "#     phi1, phi2 = math.radians(lat1), math.radians(lat2)\n",
    "#     dphi       = math.radians(lat2 - lat1)\n",
    "#     dlambda    = math.radians(lon2 - lon1)\n",
    "#     a = math.sin(dphi/2)**2 + \\\n",
    "#         math.cos(phi1)*math.cos(phi2)*math.sin(dlambda/2)**2\n",
    "#     return 2*R*math.atan2(math.sqrt(a), math.sqrt(1 - a))\n",
    "\n",
    "# cols = ['Start_UTC', 'Stop_UTC', 'Start_Lat', 'Stop_Lat','Start_Lon', 'Stop_Lon', 'Along Track Extent (m)',\n",
    "#         'Clear/Cloud [0/1]', 'Mean CTH (m)', 'SD CTH (m)', 'Mean Plane Altitude (m)', 'Mean Plane Speed (m/s)',\n",
    "#         'Max Reflectivity (dBZ)', 'Max Doppler Velocity (m/s)']\n",
    "# lst = []\n",
    "# i = 0\n",
    "# j = df.shape[0]\n",
    "# while i < 10000:\n",
    "#         cc = cc_flag(df['CTH (m)'][i])\n",
    "#         start_utc = df['UTC'][i]\n",
    "#         start_lat = df['Lat'][i]\n",
    "#         start_lon = df['Lon'][i]\n",
    "#         start_coord = [start_lat, start_lon]\n",
    "# #         print(cc,start_utc,start_lat,start_lon)\n",
    "#         for j in range(i+1, df.shape[0]):\n",
    "#             if cc_flag(df['CTH (m)'][j]) != cc and (j-i)>=40:\n",
    "#                 stop_utc = df['UTC'][j]\n",
    "#                 stop_lat = df['Lat'][j]\n",
    "#                 stop_lon = df['Lon'][j]\n",
    "#                 if cc == 1:\n",
    "#                     mean_cth = np.nanmean(df['CTH (m)'][i:j])\n",
    "#                     sd_cth = np.nanstd(df['CTH (m)'][i:j])\n",
    "#                 else:\n",
    "#                     mean_cth = np.nan\n",
    "#                     sd_cth = np.nan\n",
    "#                 mean_nav = np.nanmean(df['Plane_Alt(m)'][i:j])\n",
    "#                 mean_spd = np.nanmean(df['Plane speed'][i:j])\n",
    "#                 max_refl = np.nanmax(df['Max Reflectivity'][i:j])\n",
    "#                 max_dopp = np.nanmax(df['Max Dop Vel'][i:j])\n",
    "#                 stop_coord = [stop_lat, stop_lon]\n",
    "#                 extent = haversine(start_coord,stop_coord)\n",
    "# #                 print(j,stop_lat,stop_lon,mean_cth)\n",
    "#                 lst.append([start_utc,stop_utc,start_lat,stop_lat,start_lon,stop_lon,extent,cc,mean_cth,\n",
    "#                                 sd_cth,mean_nav,mean_spd,max_refl,max_dopp])\n",
    "#                 i = j\n",
    "#                 break\n",
    "#             else:\n",
    "#                 j = j+1\n",
    "# df1 = pd.DataFrame(lst, columns=cols) \n",
    "# df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find clear and cloudy remote-sensing patches from P3 Navigation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a001851_E20190909a002355_KUsKAsWn.h5\n",
      "Number of scans : 6076\n",
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a002608_E20190909a003754_KUsKAsWn.h5\n",
      "Number of scans : 14101\n",
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a003810_E20190909a004557_KUsKAsWn.h5\n",
      "Number of scans : 9352\n",
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a010225_E20190909a010243_Wn.h5\n",
      "Number of scans : 576\n",
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a012429_E20190909a013621_KUsKAsWn.h5\n",
      "Number of scans : 14201\n",
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a013631_E20190909a014820_KUsKAsWn.h5\n",
      "Number of scans : 14176\n",
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a014832_E20190909a020020_KUsKAsWn.h5\n",
      "Number of scans : 14151\n",
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a020020_E20190909a020033_Wn.h5\n",
      "Number of scans : 11\n",
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a020034_E20190909a021422_KUsKAsWn.h5\n",
      "Number of scans : 16577\n",
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a021457_E20190909a022649_KUsKAsWn.h5\n",
      "Number of scans : 14226\n",
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a022657_E20190909a023851_KUsKAsWn.h5\n",
      "Number of scans : 14251\n",
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a023857_E20190909a025052_KUsKAsWn.h5\n",
      "Number of scans : 14301\n",
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a025056_E20190909a030239_KUsKAsWn.h5\n",
      "Number of scans : 14026\n",
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a030239_E20190909a030453_Wn.h5\n",
      "Number of scans : 108\n",
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a035213_E20190909a040413_Wn.h5\n",
      "Number of scans : 13824\n",
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a040413_E20190909a041023_Wn.h5\n",
      "Number of scans : 7104\n",
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a041041_E20190909a041734_Wn.h5\n",
      "Number of scans : 7944\n",
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a041819_E20190909a042820_KUsKAsWn.h5\n",
      "Number of scans : 12027\n",
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a042952_E20190909a044419_Wn.h5\n",
      "Number of scans : 16656\n",
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a051151_E20190909a051559_Wn.h5\n",
      "Number of scans : 199\n",
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a051559_E20190909a052650_KUsKAsWn.h5\n",
      "Number of scans : 13001\n",
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a053324_E20190909a053852_KUsKAsWn.h5\n",
      "Number of scans : 6550\n",
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a054246_E20190909a055133_KUsKAsWn.h5\n",
      "Number of scans : 10526\n",
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a055133_E20190909a055447_KUsKAsWn.h5\n",
      "Number of scans : 3876\n",
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a055446_E20190909a060251_KUsKAsWn.h5\n",
      "Number of scans : 9675\n",
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a060642_E20190909a061615_KUsKAsWn.h5\n",
      "Number of scans : 11425\n",
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a062508_E20190909a062633_KUsKAsWn.h5\n",
      "Number of scans : 1675\n",
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a063403_E20190909a063704_Wn.h5\n",
      "Number of scans : 3480\n",
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a070733_E20190909a071146_Wn.h5\n",
      "Number of scans : 4872\n",
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a071202_E20190909a072651_KUsKAsWn.h5\n",
      "Number of scans : 17750\n",
      "F:/CAMP2Ex_analysis/DATA/RF07/APR3\\CAMP2Ex-APR3-L2ZV_P3B_20190908_R0_S20190909a073807_E20190909a075212_KUsKAsWn.h5\n",
      "Number of scans : 16901\n"
     ]
    }
   ],
   "source": [
    "import h5py\n",
    "import math\n",
    "import glob\n",
    "import datetime\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import pandas as pd\n",
    "import matplotlib.colors as cm\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import Normalize\n",
    "from metpy.plots import ctables\n",
    "\n",
    "def roundup(x):\n",
    "    for i in range(len(x)):\n",
    "        x[i] = int(math.ceil(x[i] / 100.0)) * 100 \n",
    "    return x\n",
    "\n",
    "def grid_altitude(alt3D, W_refl, vel):\n",
    "    z_gridded = np.zeros((len(ht_gridded),np.shape(alt3D)[1]))\n",
    "    z_gridded[:,:] = np.nan\n",
    "    v_gridded = np.zeros((len(ht_gridded),np.shape(alt3D)[1]))\n",
    "    v_gridded[:,:] = np.nan\n",
    "    for i in range(np.shape(alt3D)[1]):\n",
    "        ht = alt3D[:,i]\n",
    "        z = W_refl[:,i]\n",
    "        v = vel[:,i]\n",
    "        for j in range(len(ht)):\n",
    "            if(np.isnan(z[j])==False and ht[j]>-5000. and ht[j]<10000.):\n",
    "                m = int((10000.-ht[j])/30.)\n",
    "                z_gridded[m,i] = z[j]\n",
    "                v_gridded[m,i] = v[j]\n",
    "    return z_gridded, v_gridded\n",
    "\n",
    "def cc_flag(x):\n",
    "    if np.isnan(x):\n",
    "        cc_flag = 0\n",
    "    else:\n",
    "        cc_flag = 1\n",
    "        \n",
    "    return cc_flag\n",
    "\n",
    "#Calculate physical distance between two geographical coordinates\n",
    "def haversine(coord1, coord2):\n",
    "    R = 6372800  # Earth radius in meters\n",
    "    lat1, lon1 = coord1\n",
    "    lat2, lon2 = coord2\n",
    "    phi1, phi2 = math.radians(lat1), math.radians(lat2)\n",
    "    dphi       = math.radians(lat2 - lat1)\n",
    "    dlambda    = math.radians(lon2 - lon1)\n",
    "    a = math.sin(dphi/2)**2 + \\\n",
    "        math.cos(phi1)*math.cos(phi2)*math.sin(dlambda/2)**2\n",
    "    return 2*R*math.atan2(math.sqrt(a), math.sqrt(1 - a))\n",
    "\n",
    "Wn_files = sorted(glob.glob('F:/CAMP2Ex_analysis/DATA/RF07/APR3/*Wn.h5'))\n",
    "# print(Wn_files[3])\n",
    "# hdf = h5py.File(Wn_files[1], 'r')\n",
    "\n",
    "#print out the groups of variables\n",
    "# print(\"Keys: %s\" % hdf.keys())\n",
    "# Keys: <KeysViewHDF5 ['hi2lo', 'hires', 'lores', 'params_W', 'postEng_cal']>\n",
    "cols = ['UTC', 'Lat', 'Lon', 'Plane speed', 'CTH (m)', 'Plane_Alt(m)', 'Mean Reflectivity', 'Mean Dop Vel']\n",
    "ht_gridded = np.linspace(10000.,-5000.,500)\n",
    "# df = pd.DataFrame(columns=['Lat', 'Lon', 'CTH (km)'])\n",
    "start = 0\n",
    "end = 20000\n",
    "\n",
    "def file_data(hdf):\n",
    "    ht_gridded = np.linspace(10000.,-5000.,500)\n",
    "#     start = 0\n",
    "#     end = 16576\n",
    "    params_W = hdf['params_W']\n",
    "    #print out the variables within params_W\n",
    "    # for key in params_W.keys():\n",
    "    #      print(key, params_W[key][:])\n",
    "\n",
    "    hires = hdf['hires']\n",
    "    #print out the variables within geolocation\n",
    "    # for key in hires.keys():\n",
    "    #      print(key)\n",
    "\n",
    "    lat = hires['lat'][0,:]\n",
    "    lon = hires['lon'][0,:]\n",
    "    p_vel = hires['gsp_mps'][0,:]\n",
    "\n",
    "    W_refl = hires['z95n']\n",
    "    # print(W_refl.shape)\n",
    "    W_refl_ = W_refl[:,0,start:end]\n",
    "\n",
    "    vel = hires['vel95n']\n",
    "    vel_ = vel[:,0,start:end]\n",
    "\n",
    "    alt3D = hires['alt3D']\n",
    "    print('Number of scans : '+str(alt3D.shape[2]))\n",
    "    alt3D_ = alt3D[:,0,start:end]\n",
    "    z_gridded, v_gridded = grid_altitude(alt3D_,W_refl_,vel_)\n",
    "\n",
    "    alt_nav = hires['alt_nav'][0,:]\n",
    "\n",
    "    time = hires['scantime']\n",
    "    time = time[:,start:end]\n",
    "    UTC = []\n",
    "    for i in range(time.shape[1]):\n",
    "        UTC = np.append(UTC,datetime.datetime.utcfromtimestamp(time[0,i]).strftime('%H:%M:%S'))\n",
    "\n",
    "    annot = np.zeros((len(lat),2))\n",
    "    lst = []\n",
    "    z_gridded = z_gridded[np.where(np.logical_and(ht_gridded>-500.,ht_gridded<8000.))[0][:],:]\n",
    "    v_gridded = v_gridded[np.where(np.logical_and(ht_gridded>-500.,ht_gridded<8000.))[0][:],:]\n",
    "    ht_gridded = ht_gridded[np.where(np.logical_and(ht_gridded>-500.,ht_gridded<8000.))[0][:]]\n",
    "    for shot in range(len(lat)-1):\n",
    "        try:\n",
    "            z2 = z_gridded[np.isnan(z_gridded[:,shot])==False,shot] \n",
    "            v2 = v_gridded[np.isnan(z_gridded[:,shot])==False,shot]\n",
    "            ht2 = ht_gridded[np.isnan(z_gridded[:,shot])==False] \n",
    "            cth = ht2[np.where(z2>-10.)][0]\n",
    "            if (alt_nav[shot]-cth>500.):\n",
    "                if (cth>1000.):\n",
    "        #                 print(shot, cth )\n",
    "                        annot[shot,0] = shot\n",
    "                        annot[shot,1] = np.where(abs(ht_gridded-cth)==np.min(abs(ht_gridded-cth)))[0]\n",
    "                        lst.append([UTC[shot], lat[shot], lon[shot], p_vel[shot], cth, alt_nav[shot],\n",
    "                                   np.nanmean(z2[np.where(ht2>1000.)]), np.nanmean(v2[np.where(ht2>1000.)])])\n",
    "                else:\n",
    "                        annot[shot,:] = np.nan\n",
    "                        lst.append([UTC[shot], lat[shot], lon[shot], p_vel[shot], np.nan, alt_nav[shot], np.nan, np.nan])\n",
    "            else:\n",
    "                annot[shot,:] = np.nan\n",
    "                lst.append([UTC[shot], lat[shot], lon[shot], p_vel[shot], np.nan, alt_nav[shot], np.nan, np.nan])\n",
    "        except:\n",
    "            continue\n",
    "    return lst\n",
    "\n",
    "df = pd.DataFrame(columns=cols)\n",
    "for i in range(0,len(Wn_files)):\n",
    "    print(Wn_files[i])\n",
    "    hdf = h5py.File(Wn_files[i], 'r')\n",
    "    lst = file_data(hdf)\n",
    "    df_ = pd.DataFrame(lst, columns=cols)\n",
    "    df = pd.concat([df,df_])\n",
    "\n",
    "\n",
    "cols = ['Start_UTC', 'Stop_UTC', 'Start_Lat', 'Stop_Lat','Start_Lon', 'Stop_Lon', 'Along Track Extent (m)',\n",
    "        'Clear/Cloud [0/1]', 'Mean CTH (m)', 'SD CTH (m)', 'Mean Plane Altitude (m)', 'Mean Plane Speed (m/s)',\n",
    "        'Max Reflectivity (dBZ)', 'Max Doppler Velocity (m/s)']\n",
    "lst = []\n",
    "i = 0\n",
    "j = df.shape[0]\n",
    "while i < df.shape[0]-100:\n",
    "        cc = cc_flag(df['CTH (m)'].values[i])\n",
    "        start_utc = df['UTC'].values[i]\n",
    "        start_lat = df['Lat'].values[i]\n",
    "        start_lon = df['Lon'].values[i]\n",
    "        start_coord = [start_lat, start_lon]\n",
    "#         print(cc,start_utc,start_lat,start_lon)\n",
    "        for j in range(i+1, df.shape[0]):\n",
    "            if cc_flag(df['CTH (m)'].values[j]) != cc and (j-i)>=40:\n",
    "                stop_utc = df['UTC'].values[j]\n",
    "                stop_lat = df['Lat'].values[j]\n",
    "                stop_lon = df['Lon'].values[j]\n",
    "                if cc == 1:\n",
    "                    mean_cth = np.nanmean(df['CTH (m)'].values[i:j])\n",
    "                    sd_cth = np.nanstd(df['CTH (m)'].values[i:j])\n",
    "                    max_refl = np.nanmax(df['Mean Reflectivity'].values[i:j])\n",
    "                    max_dopp = np.nanmax(df['Mean Dop Vel'].values[i:j])\n",
    "                else:\n",
    "                    mean_cth = np.nan\n",
    "                    sd_cth = np.nan\n",
    "                    max_refl = np.nan\n",
    "                    max_dopp = np.nan\n",
    "                mean_nav = np.nanmean(df['Plane_Alt(m)'].values[i:j])\n",
    "                mean_spd = np.nanmean(df['Plane speed'].values[i:j])\n",
    "                stop_coord = [stop_lat, stop_lon]\n",
    "                extent = haversine(start_coord,stop_coord)\n",
    "                lst.append([start_utc,stop_utc,start_lat,stop_lat,start_lon,stop_lon,extent,cc,mean_cth,\n",
    "                                sd_cth,mean_nav,mean_spd,max_refl,max_dopp])\n",
    "                i = j\n",
    "                break\n",
    "            else:\n",
    "                j = j+1\n",
    "                \n",
    "        if (df.shape[0]-100-j<100):\n",
    "            break\n",
    "                \n",
    "df1 = pd.DataFrame(lst, columns=cols) \n",
    "df1 = df1.drop(np.where(df1['Along Track Extent (m)'].values>80000.)[0][:])\n",
    "df1.to_csv('F:/CAMP2Ex_analysis/DATA/RF07/APR3/APR3_Cloud_Clear_Patches_RF07.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df1 = df1.drop(np.where(df1['Along Track Extent (m)'].values>80000.)[0][:])\n",
    "plt.hist(df1['Along Track Extent (m)'],edgecolor='k',linewidth=1.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start_UTC</th>\n",
       "      <th>Stop_UTC</th>\n",
       "      <th>Start_Lat</th>\n",
       "      <th>Stop_Lat</th>\n",
       "      <th>Start_Lon</th>\n",
       "      <th>Stop_Lon</th>\n",
       "      <th>Along Track Extent (m)</th>\n",
       "      <th>Clear/Cloud [0/1]</th>\n",
       "      <th>Mean CTH (m)</th>\n",
       "      <th>SD CTH (m)</th>\n",
       "      <th>Mean Plane Altitude (m)</th>\n",
       "      <th>Mean Plane Speed (m/s)</th>\n",
       "      <th>Max Reflectivity (dBZ)</th>\n",
       "      <th>Max Doppler Velocity (m/s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>00:18:51</td>\n",
       "      <td>00:19:56</td>\n",
       "      <td>15.823375</td>\n",
       "      <td>15.863778</td>\n",
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       "      <th>1</th>\n",
       "      <td>00:19:56</td>\n",
       "      <td>00:19:58</td>\n",
       "      <td>15.863778</td>\n",
       "      <td>15.865074</td>\n",
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       "      <td>121.701125</td>\n",
       "      <td>281.104676</td>\n",
       "      <td>1</td>\n",
       "      <td>4347.115283</td>\n",
       "      <td>237.590813</td>\n",
       "      <td>5080.650913</td>\n",
       "      <td>137.040737</td>\n",
       "      <td>4.482952</td>\n",
       "      <td>0.545308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00:19:58</td>\n",
       "      <td>00:20:00</td>\n",
       "      <td>15.865074</td>\n",
       "      <td>15.866320</td>\n",
       "      <td>121.701125</td>\n",
       "      <td>121.703299</td>\n",
       "      <td>270.761325</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5084.299597</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00:20:00</td>\n",
       "      <td>00:20:02</td>\n",
       "      <td>15.866320</td>\n",
       "      <td>15.867630</td>\n",
       "      <td>121.703299</td>\n",
       "      <td>121.705584</td>\n",
       "      <td>284.636737</td>\n",
       "      <td>1</td>\n",
       "      <td>4336.673347</td>\n",
       "      <td>225.430861</td>\n",
       "      <td>5088.936998</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>4.120612</td>\n",
       "      <td>-0.061759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>00:43:02</td>\n",
       "      <td>00:43:04</td>\n",
       "      <td>17.289216</td>\n",
       "      <td>17.292473</td>\n",
       "      <td>122.779595</td>\n",
       "      <td>122.779651</td>\n",
       "      <td>362.304963</td>\n",
       "      <td>1</td>\n",
       "      <td>4889.779559</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5809.000000</td>\n",
       "      <td>180.400000</td>\n",
       "      <td>-20.619116</td>\n",
       "      <td>0.625350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1642</th>\n",
       "      <td>07:39:19</td>\n",
       "      <td>07:39:22</td>\n",
       "      <td>17.033476</td>\n",
       "      <td>17.032715</td>\n",
       "      <td>125.198826</td>\n",
       "      <td>125.193971</td>\n",
       "      <td>523.254081</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6167.604756</td>\n",
       "      <td>171.613570</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1643</th>\n",
       "      <td>07:39:22</td>\n",
       "      <td>07:39:27</td>\n",
       "      <td>17.032715</td>\n",
       "      <td>17.031492</td>\n",
       "      <td>125.193971</td>\n",
       "      <td>125.186231</td>\n",
       "      <td>834.215930</td>\n",
       "      <td>1</td>\n",
       "      <td>4691.964574</td>\n",
       "      <td>14.833272</td>\n",
       "      <td>6139.628230</td>\n",
       "      <td>172.125598</td>\n",
       "      <td>-6.551546</td>\n",
       "      <td>1.507586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1644</th>\n",
       "      <td>07:39:27</td>\n",
       "      <td>07:39:29</td>\n",
       "      <td>17.031492</td>\n",
       "      <td>17.030987</td>\n",
       "      <td>125.186231</td>\n",
       "      <td>125.183037</td>\n",
       "      <td>344.387273</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6115.232600</td>\n",
       "      <td>172.441860</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1645</th>\n",
       "      <td>07:39:29</td>\n",
       "      <td>07:39:34</td>\n",
       "      <td>17.030987</td>\n",
       "      <td>17.029729</td>\n",
       "      <td>125.183037</td>\n",
       "      <td>125.175030</td>\n",
       "      <td>862.911652</td>\n",
       "      <td>1</td>\n",
       "      <td>4941.182365</td>\n",
       "      <td>52.634404</td>\n",
       "      <td>6090.776396</td>\n",
       "      <td>172.717984</td>\n",
       "      <td>-13.875753</td>\n",
       "      <td>0.181894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1647</th>\n",
       "      <td>07:50:33</td>\n",
       "      <td>07:50:35</td>\n",
       "      <td>16.867655</td>\n",
       "      <td>16.867034</td>\n",
       "      <td>124.129568</td>\n",
       "      <td>124.125620</td>\n",
       "      <td>425.778662</td>\n",
       "      <td>1</td>\n",
       "      <td>3666.666667</td>\n",
       "      <td>788.566898</td>\n",
       "      <td>4657.921949</td>\n",
       "      <td>170.507805</td>\n",
       "      <td>-13.840153</td>\n",
       "      <td>2.790931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1642 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Start_UTC  Stop_UTC  Start_Lat   Stop_Lat   Start_Lon    Stop_Lon  \\\n",
       "0     00:18:51  00:19:56  15.823375  15.863778  121.627374  121.698869   \n",
       "1     00:19:56  00:19:58  15.863778  15.865074  121.698869  121.701125   \n",
       "2     00:19:58  00:20:00  15.865074  15.866320  121.701125  121.703299   \n",
       "3     00:20:00  00:20:02  15.866320  15.867630  121.703299  121.705584   \n",
       "5     00:43:02  00:43:04  17.289216  17.292473  122.779595  122.779651   \n",
       "...        ...       ...        ...        ...         ...         ...   \n",
       "1642  07:39:19  07:39:22  17.033476  17.032715  125.198826  125.193971   \n",
       "1643  07:39:22  07:39:27  17.032715  17.031492  125.193971  125.186231   \n",
       "1644  07:39:27  07:39:29  17.031492  17.030987  125.186231  125.183037   \n",
       "1645  07:39:29  07:39:34  17.030987  17.029729  125.183037  125.175030   \n",
       "1647  07:50:33  07:50:35  16.867655  16.867034  124.129568  124.125620   \n",
       "\n",
       "      Along Track Extent (m)  Clear/Cloud [0/1]  Mean CTH (m)  SD CTH (m)  \\\n",
       "0                8872.285303                  0           NaN         NaN   \n",
       "1                 281.104676                  1   4347.115283  237.590813   \n",
       "2                 270.761325                  0           NaN         NaN   \n",
       "3                 284.636737                  1   4336.673347  225.430861   \n",
       "5                 362.304963                  1   4889.779559    0.000000   \n",
       "...                      ...                ...           ...         ...   \n",
       "1642              523.254081                  0           NaN         NaN   \n",
       "1643              834.215930                  1   4691.964574   14.833272   \n",
       "1644              344.387273                  0           NaN         NaN   \n",
       "1645              862.911652                  1   4941.182365   52.634404   \n",
       "1647              425.778662                  1   3666.666667  788.566898   \n",
       "\n",
       "      Mean Plane Altitude (m)  Mean Plane Speed (m/s)  Max Reflectivity (dBZ)  \\\n",
       "0                 5022.973784              137.392552                     NaN   \n",
       "1                 5080.650913              137.040737                4.482952   \n",
       "2                 5084.299597              137.000000                     NaN   \n",
       "3                 5088.936998              137.000000                4.120612   \n",
       "5                 5809.000000              180.400000              -20.619116   \n",
       "...                       ...                     ...                     ...   \n",
       "1642              6167.604756              171.613570                     NaN   \n",
       "1643              6139.628230              172.125598               -6.551546   \n",
       "1644              6115.232600              172.441860                     NaN   \n",
       "1645              6090.776396              172.717984              -13.875753   \n",
       "1647              4657.921949              170.507805              -13.840153   \n",
       "\n",
       "      Max Doppler Velocity (m/s)  \n",
       "0                            NaN  \n",
       "1                       0.545308  \n",
       "2                            NaN  \n",
       "3                      -0.061759  \n",
       "5                       0.625350  \n",
       "...                          ...  \n",
       "1642                         NaN  \n",
       "1643                    1.507586  \n",
       "1644                         NaN  \n",
       "1645                    0.181894  \n",
       "1647                    2.790931  \n",
       "\n",
       "[1642 rows x 14 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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