mapping.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\nicho\\\\Downloads'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import smopy\n",
    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
    "\n",
    "# change current working directory to `Downloads` where .SL2 output is located\n",
    "workingDir = r'C:\\Users\\nicho\\Downloads'\n",
    "os.chdir(workingDir)\n",
    "os.getcwd()"
   ]
  },
  {
   "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>Unnamed: 0</th>\n",
       "      <th>channel</th>\n",
       "      <th>upperLimit</th>\n",
       "      <th>lowerLimit</th>\n",
       "      <th>frequency</th>\n",
       "      <th>waterDepth</th>\n",
       "      <th>keelDepth</th>\n",
       "      <th>speedGps</th>\n",
       "      <th>temperature</th>\n",
       "      <th>lng_enc</th>\n",
       "      <th>...</th>\n",
       "      <th>altitude</th>\n",
       "      <th>heading</th>\n",
       "      <th>timeOffset</th>\n",
       "      <th>headingValid</th>\n",
       "      <th>altitudeValid</th>\n",
       "      <th>gpsSpeedValid</th>\n",
       "      <th>waterTempValid</th>\n",
       "      <th>positionValid</th>\n",
       "      <th>waterSpeedValid</th>\n",
       "      <th>trackValid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Primary</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>800 KHz</td>\n",
       "      <td>3.455</td>\n",
       "      <td>0.656168</td>\n",
       "      <td>0.493391</td>\n",
       "      <td>23.160004</td>\n",
       "      <td>-8155749</td>\n",
       "      <td>...</td>\n",
       "      <td>1121.030151</td>\n",
       "      <td>0</td>\n",
       "      <td>81</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Secondary</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>800 KHz</td>\n",
       "      <td>3.455</td>\n",
       "      <td>0.656168</td>\n",
       "      <td>0.493391</td>\n",
       "      <td>23.160004</td>\n",
       "      <td>-8155749</td>\n",
       "      <td>...</td>\n",
       "      <td>1121.030151</td>\n",
       "      <td>0</td>\n",
       "      <td>83</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>DSI (Downscan)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>800 KHz</td>\n",
       "      <td>3.455</td>\n",
       "      <td>0.656168</td>\n",
       "      <td>0.493391</td>\n",
       "      <td>23.160004</td>\n",
       "      <td>-8155748</td>\n",
       "      <td>...</td>\n",
       "      <td>1121.030151</td>\n",
       "      <td>0</td>\n",
       "      <td>84</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Composite</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>800 KHz</td>\n",
       "      <td>3.455</td>\n",
       "      <td>0.656168</td>\n",
       "      <td>0.493391</td>\n",
       "      <td>23.160004</td>\n",
       "      <td>-8155748</td>\n",
       "      <td>...</td>\n",
       "      <td>1121.030151</td>\n",
       "      <td>0</td>\n",
       "      <td>84</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>DSI (Downscan)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>800 KHz</td>\n",
       "      <td>3.437</td>\n",
       "      <td>0.656168</td>\n",
       "      <td>0.502773</td>\n",
       "      <td>23.160004</td>\n",
       "      <td>-8155748</td>\n",
       "      <td>...</td>\n",
       "      <td>1121.030151</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0         channel  upperLimit  lowerLimit frequency  waterDepth  \\\n",
       "0           1         Primary         0.0        10.0   800 KHz       3.455   \n",
       "1           2       Secondary         0.0        10.0   800 KHz       3.455   \n",
       "2           3  DSI (Downscan)         0.0         6.0   800 KHz       3.455   \n",
       "3           4       Composite       -18.0        18.0   800 KHz       3.455   \n",
       "4           5  DSI (Downscan)         0.0         6.0   800 KHz       3.437   \n",
       "\n",
       "   keelDepth  speedGps  temperature  lng_enc  ...     altitude  heading  \\\n",
       "0   0.656168  0.493391    23.160004 -8155749  ...  1121.030151        0   \n",
       "1   0.656168  0.493391    23.160004 -8155749  ...  1121.030151        0   \n",
       "2   0.656168  0.493391    23.160004 -8155748  ...  1121.030151        0   \n",
       "3   0.656168  0.493391    23.160004 -8155748  ...  1121.030151        0   \n",
       "4   0.656168  0.502773    23.160004 -8155748  ...  1121.030151        0   \n",
       "\n",
       "   timeOffset  headingValid  altitudeValid  gpsSpeedValid  waterTempValid  \\\n",
       "0          81         False           True           True           False   \n",
       "1          83         False           True           True           False   \n",
       "2          84         False           True           True           False   \n",
       "3          84         False           True           True           False   \n",
       "4         146         False           True           True           False   \n",
       "\n",
       "   positionValid  waterSpeedValid  trackValid  \n",
       "0          False            False       False  \n",
       "1          False            False       False  \n",
       "2          False            False       False  \n",
       "3          False            False       False  \n",
       "4          False            False       False  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load SL2 log data that was exported into CSV\n",
    "def readLogFile(file: str):\n",
    "    df = pd.read_csv(\n",
    "        file,\n",
    "        header=0,\n",
    "        parse_dates=False,\n",
    "    )\n",
    "    return df\n",
    "\n",
    "df = readLogFile('bartlettPondOutput.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "def plot_lat_lon(df):\n",
    "    df.plot(\n",
    "        kind='scatter', \n",
    "        x='lng_enc', \n",
    "        y='lat_enc', \n",
    "        color='black', \n",
    "        figsize=(4,4)\n",
    "    )\n",
    "    plt.show()\n",
    "\n",
    "plot_lat_lon(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SL2 format stores Easting and Northing coordinates in Spherical Mercator Projection,\n",
    "# using WGS84 POLAR Earth radius (and NOT the WGS84 EQUATORIAL Earth Radius, \n",
    "# as used by OpenStreetMap and Google)\n",
    "\n",
    "def convert_easting_to_wsg84_longitude(easting_longitude_value):\n",
    "    POLAR_EARTH_RADIUS = 6356752.3142\n",
    "    longitude = easting_longitude_value / POLAR_EARTH_RADIUS * (180/np.pi)\n",
    "    return longitude\n",
    "\n",
    "def convert_northing_to_wsg84_latitude(northing_latitude_value):\n",
    "    POLAR_EARTH_RADIUS = 6356752.3142;\n",
    "    temp = northing_latitude_value / POLAR_EARTH_RADIUS\n",
    "    temp = np.exp(temp)\n",
    "    temp = (2*np.arctan(temp))-(np.pi/2)\n",
    "    latitude = temp * (180/np.pi)\n",
    "    return latitude"
   ]
  },
  {
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-73.510808</td>\n",
       "      <td>44.101586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-73.510808</td>\n",
       "      <td>44.101586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-73.510799</td>\n",
       "      <td>44.101593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-73.510799</td>\n",
       "      <td>44.101593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-73.510799</td>\n",
       "      <td>44.101593</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude   latitude\n",
       "0 -73.510808  44.101586\n",
       "1 -73.510808  44.101586\n",
       "2 -73.510799  44.101593\n",
       "3 -73.510799  44.101593\n",
       "4 -73.510799  44.101593"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add latitude and longitude to dataframe WGS84\n",
    "df[\"longitude\"] = convert_easting_to_wsg84_longitude(df.lng_enc.values)\n",
    "df[\"latitude\"] = convert_northing_to_wsg84_latitude(df.lat_enc.values)\n",
    "\n",
    "df[[\"longitude\", \"latitude\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44.10130776836456 -73.51162783865469 44.105475973884595 -73.50823881041634\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e945913940>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# (lat_min, lon_min, lat_max, lon_max)\n",
    "latMin = convert_northing_to_wsg84_latitude(df.lat_enc.min())\n",
    "lonMin = convert_easting_to_wsg84_longitude(df.lng_enc.min())\n",
    "latMax = convert_northing_to_wsg84_latitude(df.lat_enc.max())\n",
    "lonMax = convert_easting_to_wsg84_longitude(df.lng_enc.max())\n",
    "print(latMin, lonMin, latMax, lonMax)\n",
    "map = smopy.Map((latMin, lonMin, latMax, lonMax), z=16)\n",
    "# map.show_ipython()\n",
    "x, y = map.to_pixels(df.latitude, df.longitude)\n",
    "ax = map.show_mpl(figsize=(10,10))\n",
    "ax.plot(x, y, 'g', ms=6, mew=2)"
   ]
  }
 ],
 "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": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}