read_spotter_csv.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e4f04199",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "\n",
    "fn = 'D:/crs/src/NOPP_Utilities/data/SPOT-31232C_2023-09-14_2023-09-18_download.csv'\n",
    "\n",
    "%run -i wave_stats.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c075176b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read .csv, convert dashes to NaNs, remove rows with missing values\n",
    "df = pd.read_csv(fn, na_values='-')\n",
    "df = df[~df.iloc[:,5].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "71b65715",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Battery Voltage (V)\n",
      "1 Power (W)\n",
      "2 Humidity (%rel)\n",
      "3 Epoch Time\n",
      "4 Significant Wave Height (m)\n",
      "5 Peak Period (s)\n",
      "6 Mean Period (s)\n",
      "7 Peak Direction (deg)\n",
      "8 Peak Directional Spread (deg)\n",
      "9 Mean Direction (deg)\n",
      "10 Mean Directional Spread (deg)\n",
      "11 Latitude (deg)\n",
      "12 Longitude (deg)\n",
      "13 f_0\n",
      "14 f_1\n",
      "15 f_2\n",
      "16 f_3\n",
      "17 f_4\n",
      "18 f_5\n",
      "19 f_6\n",
      "20 f_7\n",
      "21 f_8\n",
      "22 f_9\n",
      "23 f_10\n",
      "24 f_11\n",
      "25 f_12\n",
      "26 f_13\n",
      "27 f_14\n",
      "28 f_15\n",
      "29 f_16\n",
      "30 f_17\n",
      "31 f_18\n",
      "32 f_19\n",
      "33 f_20\n",
      "34 f_21\n",
      "35 f_22\n",
      "36 f_23\n",
      "37 f_24\n",
      "38 f_25\n",
      "39 f_26\n",
      "40 f_27\n",
      "41 f_28\n",
      "42 f_29\n",
      "43 f_30\n",
      "44 f_31\n",
      "45 f_32\n",
      "46 f_33\n",
      "47 f_34\n",
      "48 f_35\n",
      "49 f_36\n",
      "50 f_37\n",
      "51 f_38\n",
      "52 df_0\n",
      "53 df_1\n",
      "54 df_2\n",
      "55 df_3\n",
      "56 df_4\n",
      "57 df_5\n",
      "58 df_6\n",
      "59 df_7\n",
      "60 df_8\n",
      "61 df_9\n",
      "62 df_10\n",
      "63 df_11\n",
      "64 df_12\n",
      "65 df_13\n",
      "66 df_14\n",
      "67 df_15\n",
      "68 df_16\n",
      "69 df_17\n",
      "70 df_18\n",
      "71 df_19\n",
      "72 df_20\n",
      "73 df_21\n",
      "74 df_22\n",
      "75 df_23\n",
      "76 df_24\n",
      "77 df_25\n",
      "78 df_26\n",
      "79 df_27\n",
      "80 df_28\n",
      "81 df_29\n",
      "82 df_30\n",
      "83 df_31\n",
      "84 df_32\n",
      "85 df_33\n",
      "86 df_34\n",
      "87 df_35\n",
      "88 df_36\n",
      "89 df_37\n",
      "90 df_38\n",
      "91 a1_0\n",
      "92 a1_1\n",
      "93 a1_2\n",
      "94 a1_3\n",
      "95 a1_4\n",
      "96 a1_5\n",
      "97 a1_6\n",
      "98 a1_7\n",
      "99 a1_8\n",
      "100 a1_9\n",
      "101 a1_10\n",
      "102 a1_11\n",
      "103 a1_12\n",
      "104 a1_13\n",
      "105 a1_14\n",
      "106 a1_15\n",
      "107 a1_16\n",
      "108 a1_17\n",
      "109 a1_18\n",
      "110 a1_19\n",
      "111 a1_20\n",
      "112 a1_21\n",
      "113 a1_22\n",
      "114 a1_23\n",
      "115 a1_24\n",
      "116 a1_25\n",
      "117 a1_26\n",
      "118 a1_27\n",
      "119 a1_28\n",
      "120 a1_29\n",
      "121 a1_30\n",
      "122 a1_31\n",
      "123 a1_32\n",
      "124 a1_33\n",
      "125 a1_34\n",
      "126 a1_35\n",
      "127 a1_36\n",
      "128 a1_37\n",
      "129 a1_38\n",
      "130 b1_0\n",
      "131 b1_1\n",
      "132 b1_2\n",
      "133 b1_3\n",
      "134 b1_4\n",
      "135 b1_5\n",
      "136 b1_6\n",
      "137 b1_7\n",
      "138 b1_8\n",
      "139 b1_9\n",
      "140 b1_10\n",
      "141 b1_11\n",
      "142 b1_12\n",
      "143 b1_13\n",
      "144 b1_14\n",
      "145 b1_15\n",
      "146 b1_16\n",
      "147 b1_17\n",
      "148 b1_18\n",
      "149 b1_19\n",
      "150 b1_20\n",
      "151 b1_21\n",
      "152 b1_22\n",
      "153 b1_23\n",
      "154 b1_24\n",
      "155 b1_25\n",
      "156 b1_26\n",
      "157 b1_27\n",
      "158 b1_28\n",
      "159 b1_29\n",
      "160 b1_30\n",
      "161 b1_31\n",
      "162 b1_32\n",
      "163 b1_33\n",
      "164 b1_34\n",
      "165 b1_35\n",
      "166 b1_36\n",
      "167 b1_37\n",
      "168 b1_38\n",
      "169 a2_0\n",
      "170 a2_1\n",
      "171 a2_2\n",
      "172 a2_3\n",
      "173 a2_4\n",
      "174 a2_5\n",
      "175 a2_6\n",
      "176 a2_7\n",
      "177 a2_8\n",
      "178 a2_9\n",
      "179 a2_10\n",
      "180 a2_11\n",
      "181 a2_12\n",
      "182 a2_13\n",
      "183 a2_14\n",
      "184 a2_15\n",
      "185 a2_16\n",
      "186 a2_17\n",
      "187 a2_18\n",
      "188 a2_19\n",
      "189 a2_20\n",
      "190 a2_21\n",
      "191 a2_22\n",
      "192 a2_23\n",
      "193 a2_24\n",
      "194 a2_25\n",
      "195 a2_26\n",
      "196 a2_27\n",
      "197 a2_28\n",
      "198 a2_29\n",
      "199 a2_30\n",
      "200 a2_31\n",
      "201 a2_32\n",
      "202 a2_33\n",
      "203 a2_34\n",
      "204 a2_35\n",
      "205 a2_36\n",
      "206 a2_37\n",
      "207 a2_38\n",
      "208 b2_0\n",
      "209 b2_1\n",
      "210 b2_2\n",
      "211 b2_3\n",
      "212 b2_4\n",
      "213 b2_5\n",
      "214 b2_6\n",
      "215 b2_7\n",
      "216 b2_8\n",
      "217 b2_9\n",
      "218 b2_10\n",
      "219 b2_11\n",
      "220 b2_12\n",
      "221 b2_13\n",
      "222 b2_14\n",
      "223 b2_15\n",
      "224 b2_16\n",
      "225 b2_17\n",
      "226 b2_18\n",
      "227 b2_19\n",
      "228 b2_20\n",
      "229 b2_21\n",
      "230 b2_22\n",
      "231 b2_23\n",
      "232 b2_24\n",
      "233 b2_25\n",
      "234 b2_26\n",
      "235 b2_27\n",
      "236 b2_28\n",
      "237 b2_29\n",
      "238 b2_30\n",
      "239 b2_31\n",
      "240 b2_32\n",
      "241 b2_33\n",
      "242 b2_34\n",
      "243 b2_35\n",
      "244 b2_36\n",
      "245 b2_37\n",
      "246 b2_38\n",
      "247 varianceDensity_0\n",
      "248 varianceDensity_1\n",
      "249 varianceDensity_2\n",
      "250 varianceDensity_3\n",
      "251 varianceDensity_4\n",
      "252 varianceDensity_5\n",
      "253 varianceDensity_6\n",
      "254 varianceDensity_7\n",
      "255 varianceDensity_8\n",
      "256 varianceDensity_9\n",
      "257 varianceDensity_10\n",
      "258 varianceDensity_11\n",
      "259 varianceDensity_12\n",
      "260 varianceDensity_13\n",
      "261 varianceDensity_14\n",
      "262 varianceDensity_15\n",
      "263 varianceDensity_16\n",
      "264 varianceDensity_17\n",
      "265 varianceDensity_18\n",
      "266 varianceDensity_19\n",
      "267 varianceDensity_20\n",
      "268 varianceDensity_21\n",
      "269 varianceDensity_22\n",
      "270 varianceDensity_23\n",
      "271 varianceDensity_24\n",
      "272 varianceDensity_25\n",
      "273 varianceDensity_26\n",
      "274 varianceDensity_27\n",
      "275 varianceDensity_28\n",
      "276 varianceDensity_29\n",
      "277 varianceDensity_30\n",
      "278 varianceDensity_31\n",
      "279 varianceDensity_32\n",
      "280 varianceDensity_33\n",
      "281 varianceDensity_34\n",
      "282 varianceDensity_35\n",
      "283 varianceDensity_36\n",
      "284 varianceDensity_37\n",
      "285 varianceDensity_38\n",
      "286 direction_0\n",
      "287 direction_1\n",
      "288 direction_2\n",
      "289 direction_3\n",
      "290 direction_4\n",
      "291 direction_5\n",
      "292 direction_6\n",
      "293 direction_7\n",
      "294 direction_8\n",
      "295 direction_9\n",
      "296 direction_10\n",
      "297 direction_11\n",
      "298 direction_12\n",
      "299 direction_13\n",
      "300 direction_14\n",
      "301 direction_15\n",
      "302 direction_16\n",
      "303 direction_17\n",
      "304 direction_18\n",
      "305 direction_19\n",
      "306 direction_20\n",
      "307 direction_21\n",
      "308 direction_22\n",
      "309 direction_23\n",
      "310 direction_24\n",
      "311 direction_25\n",
      "312 direction_26\n",
      "313 direction_27\n",
      "314 direction_28\n",
      "315 direction_29\n",
      "316 direction_30\n",
      "317 direction_31\n",
      "318 direction_32\n",
      "319 direction_33\n",
      "320 direction_34\n",
      "321 direction_35\n",
      "322 direction_36\n",
      "323 direction_37\n",
      "324 direction_38\n",
      "325 directionalSpread_0\n",
      "326 directionalSpread_1\n",
      "327 directionalSpread_2\n",
      "328 directionalSpread_3\n",
      "329 directionalSpread_4\n",
      "330 directionalSpread_5\n",
      "331 directionalSpread_6\n",
      "332 directionalSpread_7\n",
      "333 directionalSpread_8\n",
      "334 directionalSpread_9\n",
      "335 directionalSpread_10\n",
      "336 directionalSpread_11\n",
      "337 directionalSpread_12\n",
      "338 directionalSpread_13\n",
      "339 directionalSpread_14\n",
      "340 directionalSpread_15\n",
      "341 directionalSpread_16\n",
      "342 directionalSpread_17\n",
      "343 directionalSpread_18\n",
      "344 directionalSpread_19\n",
      "345 directionalSpread_20\n",
      "346 directionalSpread_21\n",
      "347 directionalSpread_22\n",
      "348 directionalSpread_23\n",
      "349 directionalSpread_24\n",
      "350 directionalSpread_25\n",
      "351 directionalSpread_26\n",
      "352 directionalSpread_27\n",
      "353 directionalSpread_28\n",
      "354 directionalSpread_29\n",
      "355 directionalSpread_30\n",
      "356 directionalSpread_31\n",
      "357 directionalSpread_32\n",
      "358 directionalSpread_33\n",
      "359 directionalSpread_34\n",
      "360 directionalSpread_35\n",
      "361 directionalSpread_36\n",
      "362 directionalSpread_37\n",
      "363 directionalSpread_38\n",
      "364 Wind Speed (m/s)\n",
      "365 Wind Direction (deg)\n",
      "366 Surface Temperature (°C)\n",
      "367 Partition0 Start Frequency (hz)\n",
      "368 Partition0 End Frequency (hz)\n",
      "369 Partition0 Significant Wave Height (m)\n",
      "370 Partition0 Mean Period (s)\n",
      "371 Partition0 Mean Direction (deg)\n",
      "372 Partition0 Mean Directional Spread (deg)\n",
      "373 Partition1 Start Frequency (hz)\n",
      "374 Partition1 End Frequency (hz)\n",
      "375 Partition1 Significant Wave Height (m)\n",
      "376 Partition1 Mean Period (s)\n",
      "377 Partition1 Mean Direction (deg)\n",
      "378 Partition1 Mean Directional Spread (deg)\n",
      "379 Mean Barometric Pressure (hPa)\n",
      "380 Processing Source\n"
     ]
    }
   ],
   "source": [
    "# Get a list of column numbers and names\n",
    "for i, cname in enumerate( df.columns.tolist()):\n",
    "    print(i,cname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cc565f56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-09-17T22:45:00\n",
      "Checking values: Theirs, ours\n",
      "Hs (m): 0.949 0.9481778828958203\n",
      "Peak period (s) 10.24 10.239606799098915\n",
      "Mean period, TM01, TM02 (s) 4.896 4.979336474253237 4.304775702370635\n",
      "Peak direction (deg): 130.303 130.3026601\n",
      "Peak Directional Spread 36.829 36.82941604\n",
      "Mean Direction (deg) 166.268 166.80670144146495\n",
      "Mean Directional Spread (deg) 57.203 57.05425306487272\n",
      "Spectral bandwidth () 0.5813399953134963\n",
      "m0, m1, m2: 0.05619008110079999 0.011284652361081293 0.0030322053065265555\n",
      "TM01, TM02 4.979336474253237 4.304775702370635\n",
      "sigf 0.20544335767574115\n",
      "SpecBw, Qp 0.5813399953134963 1.3240313379613362\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check calcs for one set of data\n",
    "i = 1\n",
    "# calculate statistics\n",
    "epoch = df.iloc[i,3]\n",
    "dt = np.datetime64(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(epoch)))\n",
    "lat = df.iloc[i,11]\n",
    "lon = df.iloc[i,12]\n",
    "f = df.iloc[i,13:52].values.astype(np.float64)\n",
    "delf = df.iloc[i,52:91].values.astype(np.float64)\n",
    "a1 = df.iloc[i,91:130].values.astype(np.float64)\n",
    "b1 = df.iloc[i,130:169].values.astype(np.float64)\n",
    "a2 = df.iloc[i,169:208].values.astype(np.float64)\n",
    "b2 = df.iloc[i,208:247].values.astype(np.float64)\n",
    "S = df.iloc[i,247:286].values.astype(np.float64)\n",
    "bdir = df.iloc[i,286:325].values.astype(np.float64)\n",
    "dspd = df.iloc[i,325:364].values.astype(np.float64)\n",
    "m0 = calc_m0_1d( S, f)\n",
    "m1 = calc_m1_1d( S, f)\n",
    "m2 = calc_m2_1d( S, f)\n",
    "SpecBw = calc_SpecBw_1d( m0, m1, m2 )\n",
    "Qp = calc_Qp_1d( S, f, m0 )\n",
    "\n",
    "sigf = calc_sigmaf_1d( S, f )\n",
    "\n",
    "plt.plot(f,S)\n",
    "print(dt)\n",
    "print('Checking values: Theirs, ours')\n",
    "print('Hs (m):', df.iloc[i,4], calc_Hs_1d(S, f))\n",
    "print('Peak period (s)', df.iloc[i,5], 1./f[np.argmax(S)] )\n",
    "print('Mean period, TM01, TM02 (s)', df.iloc[i,6], m0/m1, np.sqrt(m0/m2))\n",
    "print('Peak direction (deg):', df.iloc[i,7], bdir[np.argmax(S)])\n",
    "print('Peak Directional Spread', df.iloc[i,8], dspd[np.argmax(S)])\n",
    "print('Mean Direction (deg)', df.iloc[i,9], calc_weighted_theta_mean_a1b1( a1, b1, S, f))\n",
    "print('Mean Directional Spread (deg)', df.iloc[i,10], calc_sigma_theta_a1b1( a1, b1, S, f))\n",
    "print('Spectral bandwidth ()', np.sqrt(m0*m2/(m1*m1)-1.) )\n",
    "print('m0, m1, m2:', m0, m1, m2)\n",
    "print('TM01, TM02', m0/m1, np.sqrt(m0/m2))\n",
    "print('sigf', sigf)\n",
    "print('SpecBw, Qp',SpecBw, Qp )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6bd028c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make arrays of bulk statistics\n",
    "# (caution...re-using variable names)\n",
    "# stuff provided by Spotter\n",
    "\n",
    "# convert epoch time to datetime64[us]\n",
    "nrows = df.shape[0]\n",
    "epoch = df.iloc[:,3]\n",
    "dta = np.empty((nrows,), dtype='datetime64[us]') \n",
    "for i, ep in enumerate( epoch ):\n",
    "    dta[i] = np.datetime64( time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(ep) ) )\n",
    "\n",
    "lon = df.iloc[:,12].values\n",
    "lat = df.iloc[:,11].values\n",
    "Hs = df.iloc[:,4].values\n",
    "Tp = df.iloc[:,5].values\n",
    "Dirp = df.iloc[:,7].values\n",
    "Dsprd = df.iloc[:,8].values\n",
    "\n",
    "# stuff we have to calculate\n",
    "sigf = np.ones(nrows)*np.NaN\n",
    "\n",
    "for i in range(nrows):\n",
    "    f = df.iloc[i,13:52].values.astype(np.float64)\n",
    "    S = df.iloc[i,247:286].values.astype(np.float64)\n",
    "    sigf[i] = calc_sigmaf_1d( S, f ) \n",
    "    \n",
    "#export to .csv    \n",
    "pd.DataFrame((np.vstack((epoch, lon, lat, Hs, sigf, Tp, Dirp, Dsprd )).T).round(decimals=6)).to_csv('./wave_stats.csv',header=None, index=None)\n"
   ]
  }
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