3_PPK.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "77285988-0d55-4041-9b44-189e79401ab3",
   "metadata": {
    "tags": []
   },
   "source": [
    "# PPK"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecdf1541-fdd1-42e6-abd1-cd6c97efee5a",
   "metadata": {},
   "source": [
    "This python script aims to apply the PPK using the **RTKLIB** package an Open Source Program Package for GNSS Positioning.\n",
    "\n",
    "Package Requirements: \n",
    "\n",
    "- RTKLIB Package.\n",
    "- pandas.\n",
    "- matplotlib."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1112dc92-4501-4c3d-aa14-68e9d838b501",
   "metadata": {},
   "source": [
    "## Importing Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e02a9ff8-1929-403b-9f07-3fe385434308",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys,subprocess,csv\n",
    "import math\n",
    "import datetime, time\n",
    "import pandas as pd\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a471d0b-2233-461f-b9b7-6fec15e8211a",
   "metadata": {},
   "source": [
    "## Specify directories, PPK configuration file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "95ceb106-894e-44b9-9550-3d507fd49025",
   "metadata": {},
   "outputs": [],
   "source": [
    "rover_files='C:/Users/bourriz/GNSS_INS_Processing/UIS_PosPac_HyspexNav_processing/Result/Rover/' #Path of the rover files\n",
    "rnx2rtkp='C:/Users/bourriz/GNSS_INS_Processing/UIS_PosPac_HyspexNav_processing/demo5_b34g/rnx2rtkp' #Path of the rnx2rtkp tool for ppk correction\n",
    "ppkconf='C:/Users/bourriz/GNSS_INS_Processing/UIS_PosPac_HyspexNav_processing/PPK_conf/ppk_2.conf' #Path of the ppk config file\n",
    "corrections='C:/Users/bourriz/GNSS_INS_Processing/UIS_PosPac_HyspexNav_processing/Result/Corrections_files/'#Path of the correction files\n",
    "Result='C:/Users/bourriz/GNSS_INS_Processing/UIS_PosPac_HyspexNav_processing/Result/' #Path of result\n",
    "observation_path='C:/Users/bourriz/GNSS_INS_Processing/UIS_PosPac_HyspexNav_processing/Result/Rover/5905C00224202112030934-5905C00224202112031022.obs'\n",
    "navigation_path='C:/Users/bourriz/GNSS_INS_Processing/UIS_PosPac_HyspexNav_processing/Result/Rover/5905C00224202112030934-5905C00224202112031022.nav'\n",
    "for file in os.listdir(corrections):\n",
    " Permanent_obs_path=os.path.join(corrections,'WIND00NAM_R_20213370000_01D_30S_MO.obs') #Path of the observation file of the permanent station\n",
    " Permanent_nav_path=os.path.join(corrections,'brdc3370.21n') #path of the navigation file of the permanent station\n",
    " Orbital_file_path=os.path.join(corrections,'igs21865.sp3') #path of the orbital file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "286a7191-b290-40c6-87ee-ffa805bbbb92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:/Users/bourriz/GNSS_INS_Processing/UIS_PosPac_HyspexNav_processing/Result/Position/ is already created\n"
     ]
    }
   ],
   "source": [
    "#Create directory of result if does not exist\n",
    "Position=Result+'Position/'\n",
    "if not os.path.exists(Position):\n",
    "   os.mkdir(Position)\n",
    "else:\n",
    "   print(f'{Position} is already created')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bbb6f643-64c3-4377-9f20-3a95c5afdb80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The .pos file already exist\n"
     ]
    }
   ],
   "source": [
    "#Apply the PPK correction using rnx2rtkp tool\n",
    "for name in os.listdir(rover_files):\n",
    "    if name.endswith('.obs'):\n",
    "     pos_out=Position + name.split('.obs')[0]+'.pos'#Path of the position file\n",
    "if not os.path.exists(pos_out):\n",
    "      cmd=rnx2rtkp + ' -k '+ppkconf+' -o '+pos_out+\\\n",
    "     ' '+ observation_path + ' ' + Permanent_obs_path + ' ' + Permanent_nav_path + ' ' + navigation_path +  ' ' + Orbital_file_path + ' '\n",
    "      cmd=cmd.replace('/','\\\\')\n",
    "      subprocess.call(cmd, shell=False)\n",
    "      print('Process Done')\n",
    "else:\n",
    "    print('The .pos file already exist')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be452767-8e6e-4492-8ab4-7c8520c735d9",
   "metadata": {},
   "source": [
    "The output of *rnx2rtkp* is a list of coordinate with a specific extension **.pos** readable using NotePad++.\n",
    "For manipulation, it's better to convert it into **.csv**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d055c381-d615-48e1-ae23-bdc2e118786a",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GPST</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Height</th>\n",
       "      <th>Q</th>\n",
       "      <th>ns</th>\n",
       "      <th>sdn(m)</th>\n",
       "      <th>sde(m)</th>\n",
       "      <th>sdu(m)</th>\n",
       "      <th>...</th>\n",
       "      <th>ratio</th>\n",
       "      <th>vn(m/s)</th>\n",
       "      <th>ve(m/s)</th>\n",
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       "      <th>sdvn</th>\n",
       "      <th>sdve</th>\n",
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       "      <td>4.0148</td>\n",
       "      <td>7.7656</td>\n",
       "      <td>25.4229</td>\n",
       "      <td>...</td>\n",
       "      <td>1.4</td>\n",
       "      <td>-1.03204</td>\n",
       "      <td>1.91748</td>\n",
       "      <td>0.08522</td>\n",
       "      <td>2.26644</td>\n",
       "      <td>3.64198</td>\n",
       "      <td>10.23567</td>\n",
       "      <td>-2.12832</td>\n",
       "      <td>-5.63846</td>\n",
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       "      <th>1</th>\n",
       "      <td>2186</td>\n",
       "      <td>466543.6</td>\n",
       "      <td>-21.241365</td>\n",
       "      <td>14.876892</td>\n",
       "      <td>901.7332</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3.7037</td>\n",
       "      <td>7.1768</td>\n",
       "      <td>23.5275</td>\n",
       "      <td>...</td>\n",
       "      <td>1.4</td>\n",
       "      <td>-0.33353</td>\n",
       "      <td>0.53270</td>\n",
       "      <td>0.02729</td>\n",
       "      <td>2.10978</td>\n",
       "      <td>3.38555</td>\n",
       "      <td>9.78956</td>\n",
       "      <td>-2.09458</td>\n",
       "      <td>-5.39471</td>\n",
       "      <td>3.87928</td>\n",
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       "      <th>2</th>\n",
       "      <td>2186</td>\n",
       "      <td>466544.2</td>\n",
       "      <td>-21.241366</td>\n",
       "      <td>14.876893</td>\n",
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       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2.9174</td>\n",
       "      <td>5.6605</td>\n",
       "      <td>18.5672</td>\n",
       "      <td>...</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.01021</td>\n",
       "      <td>-0.04879</td>\n",
       "      <td>0.00073</td>\n",
       "      <td>1.38262</td>\n",
       "      <td>2.62462</td>\n",
       "      <td>8.48690</td>\n",
       "      <td>-1.85061</td>\n",
       "      <td>-4.68343</td>\n",
       "      <td>3.36569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>466544.4</td>\n",
       "      <td>-21.241366</td>\n",
       "      <td>14.876893</td>\n",
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       "      <td>4</td>\n",
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       "      <td>2.6713</td>\n",
       "      <td>5.1838</td>\n",
       "      <td>17.0026</td>\n",
       "      <td>...</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.02707</td>\n",
       "      <td>-0.00883</td>\n",
       "      <td>0.00085</td>\n",
       "      <td>1.31835</td>\n",
       "      <td>2.49828</td>\n",
       "      <td>8.06588</td>\n",
       "      <td>-1.75813</td>\n",
       "      <td>-4.45124</td>\n",
       "      <td>3.19850</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2186</td>\n",
       "      <td>466544.6</td>\n",
       "      <td>-21.241366</td>\n",
       "      <td>14.876893</td>\n",
       "      <td>901.7355</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2.4379</td>\n",
       "      <td>4.7316</td>\n",
       "      <td>15.5182</td>\n",
       "      <td>...</td>\n",
       "      <td>1.4</td>\n",
       "      <td>-0.00599</td>\n",
       "      <td>0.04072</td>\n",
       "      <td>0.00218</td>\n",
       "      <td>1.25539</td>\n",
       "      <td>2.37423</td>\n",
       "      <td>7.65196</td>\n",
       "      <td>-1.66718</td>\n",
       "      <td>-4.22295</td>\n",
       "      <td>3.03414</td>\n",
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       "<p>5 rows × 24 columns</p>\n",
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      "text/plain": [
       "   GPST  Timestamp   Latitude  Longitude    Height  Q  ns  sdn(m)  sde(m)  \\\n",
       "0  2186   466543.4 -21.241363  14.876887  901.7170  4   3  4.0148  7.7656   \n",
       "1  2186   466543.6 -21.241365  14.876892  901.7332  4   3  3.7037  7.1768   \n",
       "2  2186   466544.2 -21.241366  14.876893  901.7352  4   3  2.9174  5.6605   \n",
       "3  2186   466544.4 -21.241366  14.876893  901.7354  4   3  2.6713  5.1838   \n",
       "4  2186   466544.6 -21.241366  14.876893  901.7355  4   3  2.4379  4.7316   \n",
       "\n",
       "    sdu(m)  ...  ratio  vn(m/s)  ve(m/s)  vu(m/s)     sdvn     sdve      sdvu  \\\n",
       "0  25.4229  ...    1.4 -1.03204  1.91748  0.08522  2.26644  3.64198  10.23567   \n",
       "1  23.5275  ...    1.4 -0.33353  0.53270  0.02729  2.10978  3.38555   9.78956   \n",
       "2  18.5672  ...    1.4  0.01021 -0.04879  0.00073  1.38262  2.62462   8.48690   \n",
       "3  17.0026  ...    1.4  0.02707 -0.00883  0.00085  1.31835  2.49828   8.06588   \n",
       "4  15.5182  ...    1.4 -0.00599  0.04072  0.00218  1.25539  2.37423   7.65196   \n",
       "\n",
       "     sdvne    sdveu    sdvun  \n",
       "0 -2.12832 -5.63846  4.05694  \n",
       "1 -2.09458 -5.39471  3.87928  \n",
       "2 -1.85061 -4.68343  3.36569  \n",
       "3 -1.75813 -4.45124  3.19850  \n",
       "4 -1.66718 -4.22295  3.03414  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Processed_position=pos_out #path of the output\n",
    "gnss_data=pd.read_csv(Processed_position,sep='\\s+',header=None,comment='%')\n",
    "gnss_data.columns = [\n",
    "        'GPST','Timestamp', 'Latitude', 'Longitude', 'Height', 'Q', 'ns',\n",
    "        'sdn(m)', 'sde(m)', 'sdu(m)', 'sdne(m)', 'sdeu(m)', 'sdun(m)', 'age(s)', 'ratio',\n",
    "        'vn(m/s)', 've(m/s)', 'vu(m/s)', 'sdvn', 'sdve', 'sdvu', 'sdvne', 'sdveu', 'sdvun'\n",
    "    ]\n",
    "gnss_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "582021fe-23eb-4804-ae88-6b8f1da949c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "gnss_data.to_csv('C:/Users/bourriz/GNSS_INS_Processing/UIS_PosPac_HyspexNav_processing/Result/Position/gnss_data.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31245f20-b141-4af9-b4af-a4b541377026",
   "metadata": {},
   "source": [
    "After the conversion, we can now manipulate the data using Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c3e94c6a-4bea-46cc-b227-67ce6f74b0cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26964"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnssWeek=gnss_data['GPST'].to_list()\n",
    "SOW=gnss_data['Timestamp'].to_list()\n",
    "len(SOW)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d40b7d3-d388-4bc4-b1dd-ca1a42f32ee3",
   "metadata": {},
   "source": [
    "We need to convert the GPS time to UTC time (ISO Format) for a later use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5585e216-3284-40f9-9e23-122b3b77773c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting GPST to UTC\n",
    "secsInWeek = 604800\n",
    "secsInDay = 86400\n",
    "gnssEpoch = (1980, 1, 6, 0, 0, 0)  # (year, month, day, hh, mm, ss)\n",
    "leapSecs=18\n",
    "UTC_time=[]\n",
    "for i in range(len(gnssWeek)):\n",
    " secFract = SOW[i] % 1\n",
    " epochTuple = gnssEpoch + (-1, -1, 0)\n",
    " # mktime is localtime, correct for UTC 145\n",
    " t0 = time.mktime(epochTuple) - time.timezone\n",
    " tdiff = (gnssWeek[i] * secsInWeek) + SOW[i] - leapSecs\n",
    " t = t0 + tdiff\n",
    " (year, month, day, hh, mm, ss, dayOfWeek,\n",
    " julianDay, daylightsaving) = time.gmtime(t)\n",
    " UTC=(year,month,day,hh,mm,round(ss))\n",
    " time_utc=datetime(*UTC)\n",
    " time_str = time_utc.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    " UTC_time.append(time_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3f893bb2-a849-4396-8f82-5c5f559e8161",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding UTC to the dataframe\n",
    "gnss_data.insert(2, 'UTC_Time', UTC_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ba80af67-f9eb-45db-9cc7-a7692a8b6884",
   "metadata": {},
   "outputs": [
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       "      <td>9.78956</td>\n",
       "      <td>-2.09458</td>\n",
       "      <td>-5.39471</td>\n",
       "      <td>3.87928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2186</td>\n",
       "      <td>466544.2</td>\n",
       "      <td>2021-12-03 09:35:26</td>\n",
       "      <td>-21.241366</td>\n",
       "      <td>14.876893</td>\n",
       "      <td>901.7352</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2.9174</td>\n",
       "      <td>5.6605</td>\n",
       "      <td>...</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.01021</td>\n",
       "      <td>-0.04879</td>\n",
       "      <td>0.00073</td>\n",
       "      <td>1.38262</td>\n",
       "      <td>2.62462</td>\n",
       "      <td>8.48690</td>\n",
       "      <td>-1.85061</td>\n",
       "      <td>-4.68343</td>\n",
       "      <td>3.36569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2186</td>\n",
       "      <td>466544.4</td>\n",
       "      <td>2021-12-03 09:35:26</td>\n",
       "      <td>-21.241366</td>\n",
       "      <td>14.876893</td>\n",
       "      <td>901.7354</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2.6713</td>\n",
       "      <td>5.1838</td>\n",
       "      <td>...</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.02707</td>\n",
       "      <td>-0.00883</td>\n",
       "      <td>0.00085</td>\n",
       "      <td>1.31835</td>\n",
       "      <td>2.49828</td>\n",
       "      <td>8.06588</td>\n",
       "      <td>-1.75813</td>\n",
       "      <td>-4.45124</td>\n",
       "      <td>3.19850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2186</td>\n",
       "      <td>466544.6</td>\n",
       "      <td>2021-12-03 09:35:26</td>\n",
       "      <td>-21.241366</td>\n",
       "      <td>14.876893</td>\n",
       "      <td>901.7355</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2.4379</td>\n",
       "      <td>4.7316</td>\n",
       "      <td>...</td>\n",
       "      <td>1.4</td>\n",
       "      <td>-0.00599</td>\n",
       "      <td>0.04072</td>\n",
       "      <td>0.00218</td>\n",
       "      <td>1.25539</td>\n",
       "      <td>2.37423</td>\n",
       "      <td>7.65196</td>\n",
       "      <td>-1.66718</td>\n",
       "      <td>-4.22295</td>\n",
       "      <td>3.03414</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   GPST  Timestamp             UTC_Time   Latitude  Longitude    Height  Q  \\\n",
       "0  2186   466543.4  2021-12-03 09:35:25 -21.241363  14.876887  901.7170  4   \n",
       "1  2186   466543.6  2021-12-03 09:35:25 -21.241365  14.876892  901.7332  4   \n",
       "2  2186   466544.2  2021-12-03 09:35:26 -21.241366  14.876893  901.7352  4   \n",
       "3  2186   466544.4  2021-12-03 09:35:26 -21.241366  14.876893  901.7354  4   \n",
       "4  2186   466544.6  2021-12-03 09:35:26 -21.241366  14.876893  901.7355  4   \n",
       "\n",
       "   ns  sdn(m)  sde(m)  ...  ratio  vn(m/s)  ve(m/s)  vu(m/s)     sdvn  \\\n",
       "0   3  4.0148  7.7656  ...    1.4 -1.03204  1.91748  0.08522  2.26644   \n",
       "1   3  3.7037  7.1768  ...    1.4 -0.33353  0.53270  0.02729  2.10978   \n",
       "2   3  2.9174  5.6605  ...    1.4  0.01021 -0.04879  0.00073  1.38262   \n",
       "3   3  2.6713  5.1838  ...    1.4  0.02707 -0.00883  0.00085  1.31835   \n",
       "4   3  2.4379  4.7316  ...    1.4 -0.00599  0.04072  0.00218  1.25539   \n",
       "\n",
       "      sdve      sdvu    sdvne    sdveu    sdvun  \n",
       "0  3.64198  10.23567 -2.12832 -5.63846  4.05694  \n",
       "1  3.38555   9.78956 -2.09458 -5.39471  3.87928  \n",
       "2  2.62462   8.48690 -1.85061 -4.68343  3.36569  \n",
       "3  2.49828   8.06588 -1.75813 -4.45124  3.19850  \n",
       "4  2.37423   7.65196 -1.66718 -4.22295  3.03414  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnss_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9e736e0a-38fd-474e-8e3a-5f7d9745ffc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save the dataframe to a Csv file\n",
    "gnss_data.to_csv('C:/Users/bourriz/GNSS_INS_Processing/UIS_PosPac_HyspexNav_processing/Result/Position/gnss_data.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c441d08-e440-4d4c-aba6-f616eae50b64",
   "metadata": {},
   "source": [
    "# Calculation of GDOP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a90f3180-be38-4ee0-83b8-ae4e22264d43",
   "metadata": {},
   "source": [
    "The main idea is to analyze drone positioning data by estimating its accuracy,the **GDOP** value (*Indicator of the accuracy*) of the drone can be calculated."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3676610e-d2cc-4245-be26-0a86caa2bcbc",
   "metadata": {},
   "source": [
    "## Importing the necessary modules and packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "79e3455a-79c1-401b-bf33-cdcde31b66ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from Modules.calc_manager import Calc_manager\n",
    "from Modules.fov_models import FOV_view_match\n",
    "from Modules.calcs import Calc_gdop\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "%matplotlib widget"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dd17f45-9c4e-48d4-a956-380764c4854e",
   "metadata": {},
   "source": [
    "## Calculating the GDOP value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "74100a4f-ba14-4989-ab2b-4fc76006204f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[DEBUG] process_data() Setting up...\n",
      "[DEBUG] process_data() Done. 0.780s\n",
      "\n",
      "[DEBUG] process_data() Sampling positions...\n",
      "[DEBUG] process_data() Done. 0.021s\n",
      "\n",
      "[DEBUG] process_data() Aquiring satellite info...\n",
      "[DEBUG] get_sats_pos() Done. (1.421s for 3072 positions)\n",
      "\n",
      "[DEBUG] process_data() Calculating visible satellites...\n",
      "[DEBUG] process_data() Done. 3.334s\n",
      "\n",
      "[DEBUG] process_data() Performing calculations...\n",
      "[DEBUG] process_data() Done. 3.417s\n",
      "\n",
      "[DEBUG] process_data() Writing to file...\n",
      "[DEBUG] process_data() Done. 0.011s\n",
      "\n",
      "[DEBUG] process_data() Total runtime: 9.070 for 96 output rows\n"
     ]
    }
   ],
   "source": [
    "#Credits to Gdoper \n",
    "def test():\n",
    "\n",
    "  drone_data = '/gnss_data.csv' #Path of the drone data (Position, number of satellite)\n",
    "\n",
    "  gdoper = Calc_manager(drone_data, ts=30)  # ts is the sampling time from position data\n",
    "  gdoper.set_FOV(FOV_view_match()) # filtering by Field of view\n",
    "  gdoper.add_calc(Calc_gdop()) # Calculating the GDOP\n",
    "  gdoper.process_data()\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "  test()\n",
    "  pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c7712dd-6028-4480-856c-a8b7538dfe94",
   "metadata": {},
   "source": [
    "## GDOP visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a38752b4-dab6-44eb-81e3-c28b5568bde3",
   "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",
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       "        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>Height</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>UTC_Time</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>ns</th>\n",
       "      <th>HDOP</th>\n",
       "      <th>VDOP</th>\n",
       "      <th>GDOP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>901.6736</td>\n",
       "      <td>14.876892</td>\n",
       "      <td>2021-12-03 09:35:55</td>\n",
       "      <td>-21.241366</td>\n",
       "      <td>9</td>\n",
       "      <td>1.561903</td>\n",
       "      <td>0.452160</td>\n",
       "      <td>1.545527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>901.6869</td>\n",
       "      <td>14.876891</td>\n",
       "      <td>2021-12-03 09:36:25</td>\n",
       "      <td>-21.241366</td>\n",
       "      <td>9</td>\n",
       "      <td>1.561903</td>\n",
       "      <td>0.452160</td>\n",
       "      <td>1.545527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>901.6254</td>\n",
       "      <td>14.876891</td>\n",
       "      <td>2021-12-03 09:36:55</td>\n",
       "      <td>-21.241366</td>\n",
       "      <td>9</td>\n",
       "      <td>1.561903</td>\n",
       "      <td>0.452160</td>\n",
       "      <td>1.545527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>901.5948</td>\n",
       "      <td>14.876891</td>\n",
       "      <td>2021-12-03 09:37:25</td>\n",
       "      <td>-21.241366</td>\n",
       "      <td>9</td>\n",
       "      <td>1.561903</td>\n",
       "      <td>0.452160</td>\n",
       "      <td>1.545527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>901.5544</td>\n",
       "      <td>14.876891</td>\n",
       "      <td>2021-12-03 09:37:55</td>\n",
       "      <td>-21.241366</td>\n",
       "      <td>9</td>\n",
       "      <td>1.561903</td>\n",
       "      <td>0.452160</td>\n",
       "      <td>1.545527</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>900.3524</td>\n",
       "      <td>14.876903</td>\n",
       "      <td>2021-12-03 10:20:55</td>\n",
       "      <td>-21.241332</td>\n",
       "      <td>8</td>\n",
       "      <td>1.923937</td>\n",
       "      <td>0.551139</td>\n",
       "      <td>1.707392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>900.8684</td>\n",
       "      <td>14.876901</td>\n",
       "      <td>2021-12-03 10:21:25</td>\n",
       "      <td>-21.241332</td>\n",
       "      <td>8</td>\n",
       "      <td>1.923937</td>\n",
       "      <td>0.551139</td>\n",
       "      <td>1.707392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>900.8676</td>\n",
       "      <td>14.876901</td>\n",
       "      <td>2021-12-03 10:21:55</td>\n",
       "      <td>-21.241332</td>\n",
       "      <td>8</td>\n",
       "      <td>1.923937</td>\n",
       "      <td>0.551139</td>\n",
       "      <td>1.707392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>900.8847</td>\n",
       "      <td>14.876901</td>\n",
       "      <td>2021-12-03 10:22:25</td>\n",
       "      <td>-21.241333</td>\n",
       "      <td>8</td>\n",
       "      <td>1.928642</td>\n",
       "      <td>0.509510</td>\n",
       "      <td>1.702455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>900.8753</td>\n",
       "      <td>14.876901</td>\n",
       "      <td>2021-12-03 10:22:55</td>\n",
       "      <td>-21.241333</td>\n",
       "      <td>8</td>\n",
       "      <td>1.928642</td>\n",
       "      <td>0.509510</td>\n",
       "      <td>1.702455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Height  Longitude             UTC_Time   Latitude  ns      HDOP  \\\n",
       "1   901.6736  14.876892  2021-12-03 09:35:55 -21.241366   9  1.561903   \n",
       "2   901.6869  14.876891  2021-12-03 09:36:25 -21.241366   9  1.561903   \n",
       "3   901.6254  14.876891  2021-12-03 09:36:55 -21.241366   9  1.561903   \n",
       "4   901.5948  14.876891  2021-12-03 09:37:25 -21.241366   9  1.561903   \n",
       "5   901.5544  14.876891  2021-12-03 09:37:55 -21.241366   9  1.561903   \n",
       "..       ...        ...                  ...        ...  ..       ...   \n",
       "91  900.3524  14.876903  2021-12-03 10:20:55 -21.241332   8  1.923937   \n",
       "92  900.8684  14.876901  2021-12-03 10:21:25 -21.241332   8  1.923937   \n",
       "93  900.8676  14.876901  2021-12-03 10:21:55 -21.241332   8  1.923937   \n",
       "94  900.8847  14.876901  2021-12-03 10:22:25 -21.241333   8  1.928642   \n",
       "95  900.8753  14.876901  2021-12-03 10:22:55 -21.241333   8  1.928642   \n",
       "\n",
       "        VDOP      GDOP  \n",
       "1   0.452160  1.545527  \n",
       "2   0.452160  1.545527  \n",
       "3   0.452160  1.545527  \n",
       "4   0.452160  1.545527  \n",
       "5   0.452160  1.545527  \n",
       "..       ...       ...  \n",
       "91  0.551139  1.707392  \n",
       "92  0.551139  1.707392  \n",
       "93  0.551139  1.707392  \n",
       "94  0.509510  1.702455  \n",
       "95  0.509510  1.702455  \n",
       "\n",
       "[95 rows x 8 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Gdop_path='C:/Users/bourriz/GNSS_INS_Processing/UIS_PosPac_HyspexNav_processing/Result/Position/gnss_data_gdop.csv' #The output of GDOP calculation\n",
    "Gdop_df=pd.read_csv(Gdop_path,sep=',')\n",
    "Gdop_to_plot=Gdop_df.iloc[1:]\n",
    "Gdop_to_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "924131dd-ff4a-4388-804a-facf6213709d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Time UTC')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "28846b096d504eeabfc115703d6b6fe7",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Ploting GDPOP value and nb of satellite in fonction of time\n",
    "\n",
    "fig,(ax1,ax2)=plt.subplots(2,sharex=True)\n",
    "fig.suptitle('Variation of DOPs and Number of satellite by time')\n",
    "ax1.plot(Gdop_to_plot['UTC_Time'],Gdop_to_plot['ns'])\n",
    "locator=mdates.HourLocator(interval=60*10)\n",
    "ax1.xaxis.set_major_locator(locator)\n",
    "ax1.set_ylabel('Number of Satellite')\n",
    "ax1.set_title('Variation of Number of satellite by time')\n",
    "ax2.plot(Gdop_to_plot['UTC_Time'],Gdop_to_plot['GDOP'])\n",
    "ax2.set_ylabel('GDOP values')\n",
    "ax2.set_title('Variation of GDOP by Time')\n",
    "ax2.set_xlabel('Time UTC')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.4"
  },
  "toc-autonumbering": true
 },
 "nbformat": 4,
 "nbformat_minor": 5
}