noise_floor_timeseries.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary things\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pyart\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "def noisefloor_difference(filename):\n",
    "    # Read in file\n",
    "    radar = pyart.io.cfradial.read_cfradial(filename, file_field_names=True) \n",
    "    #radar = pyart.aux_io.read_gamic(filename, file_field_names=True) \n",
    "    # Get string of the date and time\n",
    "    # Format: 'YYYY-MM-DDTHH:MM:SSZ'\n",
    "    date_time = radar.time['units'].replace('seconds since ', '')\n",
    "    # Get variables\n",
    "    #uzh = radar.fields['UZh']['data']\n",
    "    #uzv = radar.fields['UZv']['data']\n",
    "    #snrh = radar.fields['SNRh']['data']\n",
    "    #snrv = radar.fields['SNRv']['data']\n",
    "    #sph = radar.fields['SIGPOWh']['data']\n",
    "    #spv = radar.fields['SIGPOWv']['data']\n",
    "    #sqih = radar.fields['SQIh']['data'] #quality index\n",
    "    #sqiv = radar.fields['SQIv']['data'] #quality index\n",
    "    uzh = radar.fields['reflectivity']['data']\n",
    "    uzv = radar.fields['reflectivity_v']['data']\n",
    "    snrh = radar.fields['signal_to_noise_ratio_copolar_h']['data']\n",
    "    snrv = radar.fields['signal_to_noise_ratio_copolar_v']['data']\n",
    "    #sph = radar.fields['SIGPOWh']['data']\n",
    "    #spv = radar.fields['SIGPOWv']['data']\n",
    "    sqih = radar.fields['normalized_coherent_power']['data'] #quality index\n",
    "    sqiv = radar.fields['normalized_coherent_power_v']['data'] #quality index\n",
    "    r = radar.range['data']\n",
    "    # Find indices of the top 15 deg. in elevation angle        \n",
    "    top15idx = []\n",
    "    for i in range(0,len(radar.elevation['data'])):\n",
    "        if radar.elevation['data'][i] > 82.5 and radar.elevation['data'][i] < 97.5:\n",
    "            top15idx.append(i)\n",
    "    # Find indices at ranges greater than 5 km\n",
    "    r_above5idx = np.where(r > 5000.)[0]\n",
    "    # Pull all noise values from Zh and Zv and bin them in 1 dB increments\n",
    "    # Subtract range term from UZx\n",
    "    # Left with the noise variable\n",
    "    noise_h = []\n",
    "    noise_v = []\n",
    "    noise_hv = []\n",
    "    for i in top15idx:\n",
    "        for j in r_above5idx:\n",
    "            if uzh[i,j] <= -10. and uzv[i,j] <= -10. and sqih[i,j] < 0.3 and sqiv[i,j] < 0.3:\n",
    "                noise_h.append(uzh[i,j] - (20*np.log10(r[j])))\n",
    "                noise_v.append(uzv[i,j] - (20*np.log10(r[j])))\n",
    "                noise_hv.append((uzh[i,j]-(20*np.log10(r[j]))) - (uzv[i,j]-(20*np.log10(r[j]))))\n",
    "    # Histogram the data and find the peak value\n",
    "    b = np.arange(401)*0.125-140\n",
    "    # Horizontal\n",
    "    n_h, bins_h, patches_h = plt.hist(noise_h, bins=b)\n",
    "    pk_h = np.where(n_h == np.max(n_h))\n",
    "    peak_h = bins_h[pk_h]\n",
    "    # Vertical\n",
    "    n_v, bins_v, patches_v = plt.hist(noise_v, bins=b)\n",
    "    pk_v = np.where(n_v == np.max(n_v))\n",
    "    peak_v = bins_v[pk_v]\n",
    "    # H - V\n",
    "    bb = np.arange(401)*0.125-40\n",
    "    n_hv, bins_hv, pathces_hv = plt.hist(noise_hv, bins=bb)\n",
    "    pk_hv = np.where(n_hv == np.max(n_hv))\n",
    "    peak_hv = bins_hv[pk_hv]\n",
    "\n",
    "    return date_time, peak_h, peak_v, peak_hv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-10-28 04:10:55 0:00\n",
      "2018-10-28 02:10:55 0:00\n",
      "2018-10-28 02:25:55 0:00\n",
      "2018-10-28 08:07:15 0:00\n",
      "2018-10-28 03:40:55 0:00\n",
      "2018-10-28 07:55:55 0:00\n",
      "2018-10-28 06:25:55 0:00\n",
      "2018-10-28 05:25:55 0:00\n",
      "2018-10-28 00:22:15 0:00\n",
      "2018-10-28 03:55:55 0:00\n",
      "2018-10-28 06:52:15 0:00\n",
      "2018-10-28 07:52:15 0:00\n",
      "2018-10-28 04:40:55 0:00\n",
      "2018-10-28 04:07:15 0:00\n",
      "2018-10-28 00:37:15 0:00\n",
      "2018-10-28 07:37:15 0:00\n",
      "2018-10-28 02:40:55 0:00\n",
      "2018-10-28 08:10:55 0:00\n",
      "2018-10-28 04:52:15 0:00\n",
      "2018-10-28 06:22:15 0:00\n",
      "2018-10-28 00:40:55 0:00\n",
      "2018-10-28 01:55:55 0:00\n",
      "2018-10-28 01:10:55 0:00\n",
      "2018-10-28 00:55:56 0:00\n",
      "2018-10-28 03:25:55 0:00\n",
      "2018-10-28 02:22:15 0:00\n",
      "2018-10-28 06:40:55 0:00\n",
      "2018-10-28 07:10:55 0:00\n",
      "2018-10-28 01:07:15 0:00\n",
      "2018-10-28 01:37:15 0:00\n",
      "2018-10-28 03:37:15 0:00\n",
      "2018-10-28 07:40:55 0:00\n",
      "2018-10-28 03:22:15 0:00\n",
      "2018-10-28 01:25:55 0:00\n",
      "2018-10-28 07:22:15 0:00\n",
      "2018-10-28 05:10:55 0:00\n",
      "2018-10-28 06:07:15 0:00\n",
      "2018-10-28 01:52:15 0:00\n",
      "2018-10-28 05:55:55 0:00\n",
      "2018-10-28 06:37:15 0:00\n",
      "2018-10-28 06:55:55 0:00\n",
      "2018-10-28 00:52:15 0:00\n",
      "2018-10-28 05:37:15 0:00\n",
      "2018-10-28 03:52:15 0:00\n",
      "2018-10-28 04:55:55 0:00\n",
      "2018-10-28 05:07:15 0:00\n",
      "2018-10-28 07:07:15 0:00\n",
      "2018-10-28 02:52:15 0:00\n",
      "2018-10-28 08:22:15 0:00\n",
      "2018-10-28 04:25:55 0:00\n",
      "2018-10-28 00:25:55 0:00\n",
      "2018-10-28 02:55:55 0:00\n",
      "2018-10-28 03:07:15 0:00\n",
      "2018-10-28 05:52:15 0:00\n",
      "2018-10-28 05:40:55 0:00\n",
      "2018-10-28 06:10:55 0:00\n",
      "2018-10-28 03:10:55 0:00\n",
      "2018-10-28 02:07:15 0:00\n",
      "2018-10-28 05:22:15 0:00\n",
      "2018-10-28 07:25:55 0:00\n",
      "2018-10-28 02:37:15 0:00\n",
      "2018-10-28 08:25:55 0:00\n",
      "2018-10-28 04:22:15 0:00\n",
      "2018-10-28 01:40:55 0:00\n",
      "2018-10-28 04:37:15 0:00\n",
      "2018-10-28 01:22:15 0:00\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this function on a bunch of files (try data from one day)\n",
    "import os\n",
    "import glob\n",
    "\n",
    "DATE = []\n",
    "H = []\n",
    "V= []\n",
    "HV = []\n",
    "\n",
    "for filepath in glob.glob(os.path.join('/home/hunzinger/data/hsrhi_1028/', 'corcsapr2cfrhsrhi*.20181028*.nc')):\n",
    "    dt,ph,pv,phv = noisefloor_difference(filepath)\n",
    "    print(dt)\n",
    "    DATE.append(dt)\n",
    "    H.append(ph)\n",
    "    V.append(pv)\n",
    "    HV.append(phv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "66\n",
      "66\n",
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\n",
      "text/plain": [
       "<Figure size 1008x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(len(DATE))\n",
    "print(len(HV))\n",
    "HVfix = []\n",
    "for i in range(0,len(HV)):\n",
    "    HVfix.append(HV[i][0])\n",
    "DATE = np.asarray(DATE)\n",
    "TIME = []\n",
    "for i in range(0,len(DATE)):\n",
    "    #TIME.append(DATE[i].replace('2018-10-06T',''))\n",
    "    #TIME.append(DATE[i].replace('2018-10-20',''))\n",
    "    TIME.append(DATE[i][11:19])\n",
    "    \n",
    "# idx works on np.array and not lists.\n",
    "list1 = np.array([3,2,4,1])\n",
    "list2 = np.array([\"three\",\"two\",\"four\",\"one\"])\n",
    "idx   = np.argsort(list1)\n",
    "\n",
    "list1 = np.array(list1)[idx]\n",
    "list2 = np.array(list2)[idx]\n",
    "\n",
    "idxx = np.argsort(TIME)\n",
    "print(len(idxx))\n",
    "TIME_s = np.array(TIME)[idxx]\n",
    "HVfix_s = np.array(HVfix)[idxx]\n",
    "DATE_s = np.array(DATE)[idxx]\n",
    "\n",
    "\n",
    "#TIME_ticks = np.empty(len(TIME_s))\n",
    "TIME_ticks = []\n",
    "for i in range(0,len(TIME_s)):\n",
    "    if i%6 == 0:\n",
    "        TIME_ticks.append(TIME_s[i])\n",
    "        #TIME_ticks[i] = TIME_s[i]\n",
    "    else: \n",
    "        TIME_ticks.append('')\n",
    "        #TIME_ticks[i] = np.nan\n",
    "        \n",
    "print(TIME_ticks)\n",
    "\n",
    "\n",
    "#print(HV[0][0])\n",
    "#print(HVfix)\n",
    "#print(DATE)\n",
    "#plt.plot(HVfix)\n",
    "#plt.show()\n",
    "# Plot time series of the noise floor difference (h-v)\n",
    "plt.figure(figsize=[14,3])\n",
    "plt.scatter(DATE_s,HVfix_s,marker='o')\n",
    "#plt.scatter(DATE,MZDR1,\n",
    "#            marker='*',\n",
    "#            c=NZDR,\n",
    "#            cmap='winter_r',\n",
    "#            label='ZDR1')\n",
    "#plt.legend()\n",
    "plt.ylabel('dB')\n",
    "plt.title('Noise floor difference (H-V) \\n 2018-10-28 \\n 00:22:15 - 08:25:55')\n",
    "\n",
    "# Chnage x-tick labels to make more readable\n",
    "#TIME = DATE.replace('2018-10-05','')\n",
    "\n",
    "#TIME = []\n",
    "#for i in DATE:\n",
    "#    TIME.append(i.replace('2018-10-05T',''))\n",
    "plt.xticks(np.arange(len(TIME)),sorted(TIME),rotation=60)\n",
    "#plt.xticks(np.arange(len(TIME)),TIME,rotation=60)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "from scipy import optimize\n",
    "def test_func(x, a, b):\n",
    "    return a * np.sin(b * x)\n",
    "params, params_covariance = optimize.curve_fit(test_func, np.arange(len(DATE_s)), HVfix_s,\n",
    "                                               p0=[2, 2])\n",
    "\n",
    "#print(params)\n",
    "#plt.figure(figsize=[10,3])\n",
    "#plt.scatter(np.arange(len(DATE)),HVfix_s,marker='o')\n",
    "#plt.ylabel('dB')\n",
    "#plt.title('Noise floor difference (H-V) \\n 2018-10-05 \\n 15:5239 - 22:37:39')\n",
    "#labels = TIME\n",
    "#plt.xticks(np.arange(len(TIME)),sorted(TIME),rotation=60)\n",
    "#plt.xticks(np.arange(len(TIME)),sorted(labels),rotation=60)\n",
    "#plt.plot(np.arange(len(DATE_s)),test_func(HVfix_s, params[0], params[1]),\n",
    "#         label='Fitted function')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = TIME\n",
    "#labels[2::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 10\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "scatter() missing 2 required positional arguments: 'x' and 'y'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-188-51cc14fbe1c2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0mfill\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: scatter() missing 2 required positional arguments: 'x' and 'y'"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "TIME2 = ['01:45:53Z','02:15:53Z','03:30:53Z','04:15:53Z','05:45:53Z','06:30:53Z','07:00:53Z','08:15:53Z','09:15:53Z']\n",
    "zdr = [1.596704,0.43311208,0.44385442,0.5068437,0.56104374,0.96925306,0.95256126,0.962417,0.77882046]\n",
    "TIME2 = ['00:15:03','02:00:04','02:45:03','03:45:03','04:30:04','05:30:04','06:45:04Z','07:00:04Z','07:15:04Z','07:30:04Z','07:45:04Z','08:45:04Z','09:45:04Z']\n",
    "zdr = [5.31109,2.9233,3.2109,3.02735,2.777413,2.9158909,0.7316049,0.5489517,0.58511617,0.5869472,0.6027405,0.66133606,0.8819849]\n",
    "TIME2 = ['00:22:15','02:22:15','03:22:15','03:55:55','04:10:55','04:52:15','05:37:15','06:22:15','07:07:15','07:52:15']\n",
    "zdr = [3.1439,3.779485,3.7617485,4.3780,2.3487,0.602416,0.62928,0.611893,0.58557,0.76525]\n",
    "print(len(TIME2), len(zdr))\n",
    "plt.scatter(TIME2,zdr)\n",
    "#sidx = [8,10,15,18,24,28,30,35,39]\n",
    "#sidx = [1,15,21,29,35,43,53,55,57,59,69,75]\n",
    "sidx = [0,16,24,29,31,36,42,48,54,60]\n",
    "fill = np.empty((len(DATE)))\n",
    "fill[:] = np.nan\n",
    "for i in range(0,len(TIME2)):\n",
    "    fill[sidx[i]] = zdr[i]\n",
    "    \n",
    "fill\n",
    "plt.scatter()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot time series of the noise floor difference (h-v)\n",
    "#plt.figure(figsize=[10,3])\n",
    "#plt.scatter(DATE_s,HVfix_s,marker='o')\n",
    "#plt.scatter(DATE_s,fill,marker='*')\n",
    "#plt.ylabel('dB')\n",
    "#plt.title('Noise floor difference (H-V) \\n 2018-10-20 \\n 00:07:39 - 10:37:39')\n",
    "#plt.xticks(np.arange(len(TIME)),sorted(TIME),rotation=60)\n",
    "#plt.show()\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=[10,3])\n",
    "hv = ax1.scatter(DATE_s,HVfix_s,marker='o',label='H-V')\n",
    "ax1.set_ylabel('Noise floor difference H-V (dB)')\n",
    "ax1.set_title('2018-10-28 \\n 00:22:15 - 08:25:55 Z')\n",
    "ax1.set_xticklabels(sorted(TIME),rotation=60)\n",
    "#ax1.flat.set_major_locator(plt.MaxNLocator())\n",
    "ax1.locator_params(nbins=12)\n",
    "ax2 = ax1.twinx()\n",
    "zd = ax2.scatter(DATE_s,fill,marker='*',color='r',label='ZDR')\n",
    "ax2.set_ylabel('Mean ZDR in stratiform (dB)')\n",
    "#ax2.set_xticklabels(sorted(TIME),rotation=60)\n",
    "ax2.set_xticklabels(TIME_ticks,rotation=60)\n",
    "ax2.locator_params(nbins=12)\n",
    "ax2.legend()\n",
    "#ax1.legend()\n",
    "#var = hv+zd\n",
    "#labs = [l.get_label() for l in var]\n",
    "#ax1.legend(var,labs,prop={'size':16})\n",
    "\n",
    "plt.show()\n",
    "#print(sorted(TIME))\n",
    "\n",
    "#plt.xticks(np.arange(len(TIME)),sorted(labels),rotation=60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}