NLS_GNSS_SOFAMESA_conclusions.ipynb

{
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![NLS_logo.jpg](attachment:NLS_logo.jpg)\n",
    "\n",
    "\n",
    "                    Ohjelma Conclusions-tuloksia varten / \n",
    "                    Software for the Conclusions results\n",
    "                    \n",
    "                    Author: Tuukka Mattila\n",
    "                    Do not hesitate to contact via LinkedIn:\n",
    "                    https://www.linkedin.com/in/tuukkamattila/\n",
    "                    \n",
    "                    Thank you for the help: Topi Rikkinen, Marko Ollikainen, Antti Laaksonen,\n",
    "                    Hannu Koivula, Octavian Andrei, Mikael Kauhava, Timo Sallinen and Ari Huvinen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Laitetaan kello päälle ohjelman ajan mittaamiseksi. / Let's start the clock for the program to measure the time spent:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "time_beginning = time.time()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tuodaan tarvittavat kirjastot / Import necessary Python libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'bottleneck' has no attribute '__version__'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-e94799793543>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     40\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig_init\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     41\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 42\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapi\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     43\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msparse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapi\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     44\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapi\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\core\\api.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0malgorithms\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mfactorize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0munique\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue_counts\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmissing\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0misna\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0misnull\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnotna\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnotnull\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marrays\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mCategorical\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mGrouper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformats\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mset_eng_float_format\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\core\\arrays\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mbase\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mExtensionArray\u001b[0m  \u001b[1;31m# noqa\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mcategorical\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mCategorical\u001b[0m  \u001b[1;31m# noqa\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\core\\arrays\\categorical.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     33\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0malgorithms\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mfactorize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtake_1d\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0munique1d\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtake\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     34\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maccessor\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mPandasDelegate\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 35\u001b[1;33m from pandas.core.base import (PandasObject,\n\u001b[0m\u001b[0;32m     36\u001b[0m                               NoNewAttributesMixin, _shared_docs)\n\u001b[0;32m     37\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcommon\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\core\\base.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrors\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mAbstractMethodError\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mcommon\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malgorithms\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnanops\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnanops\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     24\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_libs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlib\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mlib\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumpy\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mfunction\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnv\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\core\\nanops.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     28\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     29\u001b[0m     \u001b[1;32mimport\u001b[0m \u001b[0mbottleneck\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mbn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 30\u001b[1;33m     \u001b[0mver\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__version__\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     31\u001b[0m     _BOTTLENECK_INSTALLED = (LooseVersion(ver) >=\n\u001b[0;32m     32\u001b[0m                              LooseVersion(_MIN_BOTTLENECK_VERSION))\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'bottleneck' has no attribute '__version__'"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import os\n",
    "import sys\n",
    "import math\n",
    "import pathlib\n",
    "import outlier_detection_conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tuodaan data .txt- tai .csv-muodossa, eroteltuna \",\"-merkillä ja muotoiltuna (sarakkeiden järjestyksellä ei ole väliä) [Point Number,Code,Date,Time,Solution,Physical Station ID,Virtual Station ID,Northing,Easting,Ell Ht,Elev,Ant Ht,HRMS,VRMS,HDOP,VDOP,PDOP,GDOP,TDOP,GPS,GLONASS,GALILEO,BEIDOU,SBAS,QZSS]\n",
    "\n",
    "/ \n",
    "\n",
    "Importing the data in .txt or .csv and separated with \",\" and formatted as (the order of the columns doesn't matter) [Point Number,Code,Date,Time,Solution,Physical Station ID,Virtual Station ID,Northing,Easting,Ell Ht,Elev,Ant Ht,HRMS,VRMS,HDOP,VDOP,PDOP,GDOP,TDOP,GPS,GLONASS,GALILEO,BEIDOU,SBAS,QZSS]:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "testmeasurements_df = pd.read_csv(testmeasurements_df_name, delimiter=',')\n",
    "testmeasurements_df.name = testmeasurements_df_name.split('.')[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Luodaan uusi kansio kuvaajille. / Let's create a new folder for the figures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    os.makedirs('{0}{3}{1}{3}{2}'.format(GNSS_device_or_NRTK_method_folder_path, testmeasurements_df.name, '{}_figures'.format(testmeasurements_df.name), os.sep))\n",
    "    session_folder_path = '{0}{3}{1}{3}{2}'.format(GNSS_device_or_NRTK_method_folder_path, testmeasurements_df.name, '{}_figures'.format(testmeasurements_df.name), os.sep)\n",
    "except:\n",
    "    print('Folder already exists. Use the existing folder location of the figures {0}{3}{1}{3}{2}.'.format(GNSS_device_or_NRTK_method_folder_path, testmeasurements_df.name, '{}_figures'.format(testmeasurements_df.name), os.sep))\n",
    "    session_folder_path = '{0}{3}{1}{3}{2}'.format(GNSS_device_or_NRTK_method_folder_path, testmeasurements_df.name, '{}_figures'.format(testmeasurements_df.name), os.sep)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysoidaan satelliittidataa / Let's analyze the satellite data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "x = range(len(testmeasurements_df.loc[:, 'GPS']))\n",
    "y = [testmeasurements_df.loc[:, 'GPS'], testmeasurements_df.loc[:, 'GLONASS'], testmeasurements_df.loc[:, 'GALILEO'], testmeasurements_df.loc[:, 'BEIDOU']]\n",
    "satellites_inc_outl = pd.DataFrame({'GPS': testmeasurements_df.loc[:, 'GPS'], 'GLONASS': testmeasurements_df.loc[:, 'GLONASS'], 'GALILEO': testmeasurements_df.loc[:, 'GALILEO'], 'BEIDOU': testmeasurements_df.loc[:, 'BEIDOU']})\n",
    "satellites_inc_outl['All'] = satellites_inc_outl.sum(axis = 1)\n",
    "mean_all_satellites_inc_outl = round(satellites_inc_outl.loc[:, 'All'].mean(), 1)\n",
    "max_all_satellites_inc_outl = satellites_inc_outl['All'].max()\n",
    "min_all_satellites_inc_outl = satellites_inc_outl['All'].min()\n",
    "date = testmeasurements_df.loc[:, 'Date']\n",
    "\n",
    "mean_GPS_inc_outl = round(testmeasurements_df.loc[:, 'GPS'].mean(), 1)\n",
    "mean_GLONASS_inc_outl = round(testmeasurements_df.loc[:, 'GLONASS'].mean(), 1)\n",
    "mean_GALILEO_inc_outl = round(testmeasurements_df.loc[:, 'GALILEO'].mean(), 1)\n",
    "mean_BEIDOU_inc_outl = round(testmeasurements_df.loc[:, 'BEIDOU'].mean(), 1)\n",
    "\n",
    "sns.set(style=\"darkgrid\")\n",
    "fig = plt.figure(figsize=(15, 12))\n",
    "fig.suptitle('{}'.format(name_of_GNSS_device_or_NRTK_method), fontsize = 20)\n",
    "plt.stackplot(x, y, labels = ['GPS (Mean = {})'.format(mean_GPS_inc_outl),'GLONASS (Mean = {})'.format(mean_GLONASS_inc_outl),'GALILEO (Mean = {})'.format(mean_GALILEO_inc_outl), 'BEIDOU (Mean = {})'.format(mean_BEIDOU_inc_outl)])\n",
    "plt.title('The Number of Satellites (No Outliers Removed) (the Mean of All Satellites = {}). Date: {}'.format(mean_all_satellites_inc_outl, date[1]))\n",
    "plt.xlabel('The Number of the Measurement')\n",
    "plt.ylabel('The Number of the Satellites')\n",
    "plt.legend(loc='upper right')\n",
    "strFileA = '{0}{1}A_The Number of Satellites (No Outliers Removed).png'.format(session_folder_path, os.sep)\n",
    "if os.path.isfile(strFileA):\n",
    "    os.remove(strFileA)\n",
    "fig.savefig('{0}{1}A_The Number of Satellites (No Outliers Removed).png'.format(session_folder_path, os.sep))\n",
    "plt.show()\n",
    "\n",
    "# luodaan DataFrame kuvaajien kuville myöhempää PDF-tiedostoa varten / create a DataFrame for the pictures of the figures for later .pdf file creation:\n",
    "fig1 = {'The Figure File Name': ['A_The Number of Satellites (No Outliers Removed).png']}\n",
    "figures_df = pd.DataFrame(data = fig1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysoidaan DOP-arvoja / Let's analyze the DOP values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = range(len(testmeasurements_df.loc[:, 'HDOP']))\n",
    "y = [testmeasurements_df.loc[:, 'HDOP'], testmeasurements_df.loc[:, 'VDOP'], testmeasurements_df.loc[:, 'PDOP'], testmeasurements_df.loc[:, 'TDOP'], testmeasurements_df.loc[:, 'GDOP']]\n",
    "mean_HDOP_inc_outl = round(testmeasurements_df.loc[:, 'HDOP'].mean(), 1)\n",
    "mean_VDOP_inc_outl = round(testmeasurements_df.loc[:, 'VDOP'].mean(), 1)\n",
    "mean_PDOP_inc_outl = round(testmeasurements_df.loc[:, 'PDOP'].mean(), 1)\n",
    "mean_TDOP_inc_outl = round(testmeasurements_df.loc[:, 'TDOP'].mean(), 1)\n",
    "mean_GDOP_inc_outl = round(testmeasurements_df.loc[:, 'GDOP'].mean(), 1)\n",
    "\n",
    "fig = plt.figure(figsize=(15, 12))\n",
    "fig.suptitle('{}'.format(name_of_GNSS_device_or_NRTK_method), fontsize = 20)\n",
    "plt.stackplot(x, y, labels=['HDOP (Mean = {})'.format(mean_HDOP_inc_outl), 'VDOP (Mean = {})'.format(mean_VDOP_inc_outl), 'PDOP (Mean = {})'.format(mean_PDOP_inc_outl), 'TDOP (Mean = {})'.format(mean_TDOP_inc_outl), 'GDOP (Mean = {})'.format(mean_GDOP_inc_outl)])\n",
    "plt.title('The DOP Values (No Outliers Removed). Date: {}'.format(date[1]))\n",
    "plt.xlabel('The Number of the Measurement')\n",
    "plt.ylabel('DOP Value')\n",
    "plt.legend(loc='upper right')\n",
    "strFileB = '{0}{1}B_The DOP Values (No Outliers Removed).png'.format(session_folder_path, os.sep)\n",
    "if os.path.isfile(strFileB):\n",
    "    os.remove(strFileB)\n",
    "fig.savefig('{0}{1}B_The DOP Values (No Outliers Removed).png'.format(session_folder_path, os.sep))\n",
    "plt.show()\n",
    "\n",
    "figures_df.loc[1, 'The Figure File Name'] = 'B_The DOP Values (No Outliers Removed).png'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysoidaan laitteen ilmoittamia HRMS- ja VRMS-arvoja / Let's analyze the HRMS and VRMS Quality Control (QC) values of the receiver:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = range(len(testmeasurements_df.loc[:, 'HRMS']))\n",
    "y= [testmeasurements_df.loc[:, 'HRMS'], testmeasurements_df.loc[:, 'VRMS']]\n",
    "mean_HRMS = round(testmeasurements_df.loc[:, 'HRMS'].mean(), 4)\n",
    "mean_VRMS = round(testmeasurements_df.loc[:, 'VRMS'].mean(), 4)\n",
    "\n",
    "fig = plt.figure(figsize=(15, 12))\n",
    "fig.suptitle('{}'.format(name_of_GNSS_device_or_NRTK_method), fontsize = 20)\n",
    "plt.stackplot(x, y, labels=['HRMS (Mean = \\xb1 {}m)'.format(mean_HRMS),'VRMS (Mean = \\xb1 {}m)'.format(mean_VRMS)])\n",
    "plt.title('The Reported HRMS and VRMS Values of the Receiver  (No Outliers Removed). Date: {}'.format(date[1]))\n",
    "plt.xlabel('The Number of the Measurement')\n",
    "plt.ylabel('The Reported HRMS and VRMS Values of the Receiver')\n",
    "plt.legend(loc='upper right')\n",
    "strFileC = '{0}{1}C_The Reported HRMS and VRMS Values of the Receiver  (No Outliers Removed).png'.format(session_folder_path, os.sep)\n",
    "if os.path.isfile(strFileC):\n",
    "    os.remove(strFileC)\n",
    "fig.savefig('{0}{1}C_The Reported HRMS and VRMS Values of the Receiver  (No Outliers Removed).png'.format(session_folder_path, os.sep))\n",
    "plt.show()\n",
    "\n",
    "figures_df.loc[2, 'The Figure File Name'] = 'C_The Reported HRMS and VRMS Values of the Receiver  (No Outliers Removed).png'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Käyttäjältä kysyttiin, halutaanko tuottaa analyysiä FIXED-, FLOAT-, CODE_DIFF- ja STANDALONE-parametreistä raportteihin ja taulukoihin. Mittaustiedostoista täytyy löytyä \"Solution\"-sarakkeesta AINOASTAAN FIXED-, FLOAT-, CODE_DIFF- tai STANDALONE-nimisiä soluja, jos näistä halutaan tuottaa analyysiä. / The user was prompted, if the user is willing to receive an analysis of FIXED, FLOAT, CODE_DIFF and STANDALONE parameters in the reports and tables. The test measurement files' column \"Solution\" should ONLY include cells named FIXED, FLOAT, CODE_DIFF or STANDALONE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if solution_analysis == 'yes':\n",
    "    list_solutions_percentages = []\n",
    "    num_all_solutions = len(testmeasurements_df.index)\n",
    "    percentage_fixed_solutions = round((len(testmeasurements_df[testmeasurements_df['Solution'] == 'FIXED'])) / num_all_solutions * 100, 1)\n",
    "    list_solutions_percentages.append(percentage_fixed_solutions)\n",
    "    percentage_float_solutions = round((len(testmeasurements_df[testmeasurements_df['Solution'] == 'FLOAT'])) / num_all_solutions * 100, 1)\n",
    "    list_solutions_percentages.append(percentage_float_solutions)\n",
    "    percentage_code_diff_solutions = round((len(testmeasurements_df[testmeasurements_df['Solution'] == 'CODE_DIFF'])) / num_all_solutions * 100, 1)\n",
    "    list_solutions_percentages.append(percentage_code_diff_solutions)\n",
    "    percentage_standalone_solutions = round((len(testmeasurements_df[testmeasurements_df['Solution'] == 'STANDALONE'])) / num_all_solutions * 100, 1)\n",
    "    list_solutions_percentages.append(percentage_standalone_solutions)\n",
    "    percentage_other_solutions = round((100 - percentage_fixed_solutions - percentage_float_solutions - percentage_code_diff_solutions - percentage_standalone_solutions), 1)\n",
    "    list_solutions_percentages.append(percentage_other_solutions)\n",
    "    \n",
    "    solutions_df_outliers = pd.DataFrame({'Solution': testmeasurements_df.loc[:, 'Solution'], 'The Number of the Measurement': testmeasurements_df.loc[:, 'Point Number']})\n",
    "    solutions_numbers_df_outliers = pd.DataFrame({'Solution': testmeasurements_df.loc[:, 'Solution'], 'The Number of the Measurement': testmeasurements_df.loc[:, 'Point Number']})\n",
    "\n",
    "    i = 0\n",
    "    while i < len(solutions_df_outliers.index):\n",
    "        if solutions_df_outliers.loc[i, 'Solution'] == 'FIXED':\n",
    "            solutions_numbers_df_outliers.loc[i, 'Solution'] = 4\n",
    "        elif solutions_df_outliers.loc[i, 'Solution'] == 'FLOAT':\n",
    "            solutions_numbers_df_outliers.loc[i, 'Solution'] = 3\n",
    "        elif solutions_df_outliers.loc[i, 'Solution'] == 'CODE_DIFF':\n",
    "            solutions_numbers_df_outliers.loc[i, 'Solution'] = 2\n",
    "        elif solutions_df_outliers.loc[i, 'Solution'] == 'STANDALONE':\n",
    "            solutions_numbers_df_outliers.loc[i, 'Solution'] = 1\n",
    "        else:\n",
    "            solutions_numbers_df_outliers.loc[i, 'Solution'] = 0\n",
    "        i += 1\n",
    "    \n",
    "    date = testmeasurements_df.loc[:, 'Date']\n",
    "    sns.set(style=\"darkgrid\")\n",
    "    fig = plt.figure(figsize=(15, 12))\n",
    "    fig.suptitle('{}'.format(name_of_GNSS_device_or_NRTK_method), fontsize = 20)\n",
    "    sns.lineplot(x = 'The Number of the Measurement', y = 'Solution', data = solutions_numbers_df_outliers)\n",
    "    plt.title('The Solutions (No Outliers Removed) (the Total Number of Measurements = {0}). Date: {1}'.format(round(num_all_solutions, 0), date[1]))\n",
    "    plt.xlabel('The Number of the Measurement')\n",
    "    plt.ylabel('The Solution')\n",
    "    plt.yticks(np.arange(6), ('Other', 'Standalone', 'Code Diff', 'Float', 'Fixed', ''))\n",
    "    plt.legend(('Fixed = {0}%\\nFloat = {1}%\\nCode Diff = {2}%\\nStandalone = {3}%\\nOther = {4}%'.format(percentage_fixed_solutions, percentage_float_solutions, percentage_code_diff_solutions, percentage_standalone_solutions, percentage_other_solutions), 'test'), loc = 'upper right', handlelength = 1.5)\n",
    "    strFileA = '{0}{1}D_The Solutions (No Outliers Removed).png'.format(session_folder_path, os.sep)\n",
    "    if os.path.isfile(strFileA):\n",
    "        os.remove(strFileA)\n",
    "    fig.savefig('{0}{1}D_The Solutions (No Outliers Removed).png'.format(session_folder_path, os.sep))\n",
    "    plt.show()\n",
    "\n",
    "figures_df.loc[3, 'The Figure File Name'] = 'D_The Solutions (No Outliers Removed).png'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Luetaan kaikkien pisteiden referenssikoordinaattien .txt- tai .csv-tiedosto muuttujiin. / Let's read the .txt or .csv file of the reference coordinates of the points to the variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "referencepoint_number_df = referencecoordinates_df.loc[referencecoordinates_df['Reference Point Number'] == referencepoint_number].reset_index()\n",
    "referencecoordinate_n = referencepoint_number_df.loc[0,'Northing']\n",
    "referencecoordinate_e = referencepoint_number_df.loc[0,'Easting']\n",
    "referencecoordinate_h = referencepoint_number_df.loc[0,'Elevation']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lisätään testimittausten pohjois-, itä- ja korkeuskoordinaatit omiin muuttujiinsa / Let's add the Norting, Easting and Elevation coordinates of the test measurements to their own variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "northcoordinates = testmeasurements_df.loc[:, 'Northing']\n",
    "eastcoordinates = testmeasurements_df.loc[:, 'Easting']\n",
    "heights = testmeasurements_df.loc[:, 'Elev']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lasketaan koordinaattien erotukset keskiarvoistaan (mahdolliset outlierit mukaan luettuina) / Let's calculate the differences between the coordinates and their means (possible outliers included):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def errors_outlierpossibility(coordinates1, mean1, coordinates2, mean2, coordinates3, mean3):\n",
    "    errors_outlierpossibility = np.zeros(shape=(len(coordinates1), 3))\n",
    "    i = 0     \n",
    "    while i < len(coordinates1):\n",
    "        errors_outlierpossibility[i, 0] = abs(coordinates1.loc[i] - mean1)\n",
    "        errors_outlierpossibility[i, 1] = abs(coordinates2.loc[i] - mean2)\n",
    "        errors_outlierpossibility[i, 2] = abs(coordinates3.loc[i] - mean3)\n",
    "        i += 1\n",
    "    return errors_outlierpossibility\n",
    "\n",
    "mean_n_outlierpossibility = np.mean(northcoordinates, axis = 0)\n",
    "mean_e_outlierpossibility = np.mean(eastcoordinates, axis = 0)\n",
    "mean_h_outlierpossibility = np.mean(heights, axis = 0)\n",
    "\n",
    "errors_outlierpossibility = errors_outlierpossibility(northcoordinates, mean_n_outlierpossibility, eastcoordinates, mean_e_outlierpossibility, heights, mean_h_outlierpossibility)\n",
    "errors_outlierpossibility_df = pd.DataFrame({'North Errors (m)':errors_outlierpossibility[:,0],'East Errors (m)':errors_outlierpossibility[:,1],'Height Errors (m)':errors_outlierpossibility[:,2]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Piirretään KDE-kuvaajat (Kernel Density Estimator) outliereiden visuaalista tunnistamista varten / Let's draw KDE plots (Kernel Density Estimator) for visual outlier detection:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(15, 12))\n",
    "fig.suptitle('{}'.format(name_of_GNSS_device_or_NRTK_method), fontsize = 20)\n",
    "ax1 = sns.kdeplot(errors_outlierpossibility_df['North Errors (m)'], shade=True)\n",
    "ax2 = sns.kdeplot(errors_outlierpossibility_df['East Errors (m)'], shade=True)\n",
    "ax3 = sns.kdeplot(errors_outlierpossibility_df['Height Errors (m)'], shade=True)\n",
    "ax1.set_title('KDE Plot for Outlier Recognition (PRECISION, INNER ACCURACY). Date: {}'.format(date[1]))\n",
    "ax1.set_xlabel('Coordinate Error (m)')\n",
    "ax1.set_ylabel('The Number of Measurements')\n",
    "strFileD = '{0}{1}E_KDE Plot for Outlier Recognition (PRECISION, INNER ACCURACY).png'.format(session_folder_path, os.sep)\n",
    "if os.path.isfile(strFileD):\n",
    "    os.remove(strFileD)\n",
    "fig.savefig('{0}{1}E_KDE Plot for Outlier Recognition (PRECISION, INNER ACCURACY).png'.format(session_folder_path, os.sep))\n",
    "plt.show()\n",
    "\n",
    "figures_df.loc[4, 'The Figure File Name'] = 'E_KDE Plot for Outlier Recognition (PRECISION, INNER ACCURACY).png'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Edelliseen kuvaajaan perustuen tarkistetaan koordinaattien mediaanit, keskiarvot ja keskihajonnat (pohjoinen, itä, korkeus). Lisäksi tunnistetaan outlier-havainnot, jos ne ylittävät käyttäjän määrittämän poikkeaman pohjois- tai itäsuunnassa sekä korkeussuunnassa mediaaneistaan. / Based on the previous figure, let's check the medians, means and standard deviations of the coordinates (Northing, Easting, Elevation). Furthermore, let's recognize the outlier measurements, if they exceed the user-defined tolerances, which are calculated as differences from the medians of Northing or Easting and Elevation coordinates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_tolerance_values_text = 'The user-defined tolerance values for both North and East were {}m and the tolerance value for Height was {}m.'.format(tolerance_north_east, tolerance_height)\n",
    "\n",
    "median_n = np.median(northcoordinates, axis = 0)\n",
    "median_e = np.median(eastcoordinates, axis = 0)\n",
    "median_h = np.median(heights, axis = 0)\n",
    "\n",
    "(northcoordinates, eastcoordinates, heights, abovetolerances, testmeasurements_df_no_outliers, testmeasurements_df_outliers, abovetolerances_text, allcoordinates_amount, abovetolerances_amount, abovetolerances_percentage) = outlier_detection_conclusions.outliers(np, pd, tolerance_north_east, tolerance_height, northcoordinates, median_n, eastcoordinates, median_e, heights, median_h, testmeasurements_df)\n",
    "satellites_no_outl = pd.DataFrame({'GPS': testmeasurements_df_no_outliers.loc[:, 'GPS'],\n",
    "                                   'GLONASS': testmeasurements_df_no_outliers.loc[:, 'GLONASS'],\n",
    "                                   'GALILEO': testmeasurements_df_no_outliers.loc[:, 'GALILEO'], \n",
    "                                   'BEIDOU': testmeasurements_df_no_outliers.loc[:, 'BEIDOU']})\n",
    "satellites_no_outl['All'] = satellites_no_outl.sum(axis = 1)\n",
    "mean_all_satellites_no_outl = round(satellites_no_outl.loc[:, 'All'].mean(), 1)\n",
    "max_all_satellites_no_outl = satellites_no_outl['All'].max()\n",
    "min_all_satellites_no_outl = satellites_no_outl['All'].min()\n",
    "\n",
    "mean_GPS_no_outl = round(satellites_no_outl.loc[:, 'GPS'].mean(), 1)\n",
    "mean_GLONASS_no_outl = round(satellites_no_outl.loc[:, 'GLONASS'].mean(), 1)\n",
    "mean_GALILEO_no_outl = round(satellites_no_outl.loc[:, 'GALILEO'].mean(), 1)\n",
    "mean_BEIDOU_no_outl = round(satellites_no_outl.loc[:, 'BEIDOU'].mean(), 1)\n",
    "\n",
    "mean_HDOP_no_outl = round(testmeasurements_df_no_outliers.loc[:, 'HDOP'].mean(), 1)\n",
    "mean_VDOP_no_outl = round(testmeasurements_df_no_outliers.loc[:, 'VDOP'].mean(), 1)\n",
    "mean_PDOP_no_outl = round(testmeasurements_df_no_outliers.loc[:, 'PDOP'].mean(), 1)\n",
    "mean_TDOP_no_outl = round(testmeasurements_df_no_outliers.loc[:, 'TDOP'].mean(), 1)\n",
    "mean_GDOP_no_outl = round(testmeasurements_df_no_outliers.loc[:, 'GDOP'].mean(), 1)\n",
    "\n",
    "median_n = np.median(northcoordinates, axis = 0)\n",
    "median_e = np.median(eastcoordinates, axis = 0)\n",
    "median_h = np.median(heights, axis = 0)\n",
    "\n",
    "mean_n = np.mean(northcoordinates, axis = 0)\n",
    "mean_e = np.mean(eastcoordinates, axis = 0)    \n",
    "mean_h = np.mean(heights, axis = 0)\n",
    "\n",
    "std_n = np.std(northcoordinates, axis = 0)\n",
    "std_e = np.std(eastcoordinates, axis = 0)\n",
    "std_h = np.std(heights, axis = 0)\n",
    "\n",
    "list_solutions_no_outliers_percentages = []\n",
    "num_all_solutions_no_outliers = len(testmeasurements_df_no_outliers.index)\n",
    "percentage_fixed_solutions_no_outliers = round((len(testmeasurements_df_no_outliers[testmeasurements_df_no_outliers['Solution'] == 'FIXED'])) / num_all_solutions_no_outliers * 100, 1)\n",
    "list_solutions_no_outliers_percentages.append(percentage_fixed_solutions_no_outliers)\n",
    "percentage_float_solutions_no_outliers = round((len(testmeasurements_df_no_outliers[testmeasurements_df_no_outliers['Solution'] == 'FLOAT'])) / num_all_solutions_no_outliers * 100, 1)\n",
    "list_solutions_no_outliers_percentages.append(percentage_float_solutions_no_outliers)\n",
    "percentage_code_diff_solutions_no_outliers = round((len(testmeasurements_df_no_outliers[testmeasurements_df_no_outliers['Solution'] == 'CODE_DIFF'])) / num_all_solutions_no_outliers * 100, 1)\n",
    "list_solutions_no_outliers_percentages.append(percentage_code_diff_solutions_no_outliers)\n",
    "percentage_standalone_solutions_no_outliers = round((len(testmeasurements_df_no_outliers[testmeasurements_df_no_outliers['Solution'] == 'STANDALONE'])) / num_all_solutions_no_outliers * 100, 1)\n",
    "list_solutions_no_outliers_percentages.append(percentage_standalone_solutions_no_outliers)\n",
    "percentage_other_solutions_no_outliers = round((100 - percentage_fixed_solutions_no_outliers - percentage_float_solutions_no_outliers - percentage_code_diff_solutions_no_outliers - percentage_standalone_solutions_no_outliers), 1)\n",
    "list_solutions_no_outliers_percentages.append(percentage_other_solutions_no_outliers)\n",
    "\n",
    "# print('North and East coordinates and Heights means are: {}m, {}m and {}m.'.format(round(mean_n, 4), round(mean_e, 4), round(mean_h, 4)))\n",
    "# print('North and East coordinates and Heights medians are: {}m, {}m and {}m.'.format(round(median_n, 4), round(median_e, 4), round(median_h, 4)))\n",
    "# print('North and East coordinates and Heights standard deviations are: {}m, {}m and {}m.'.format(round(std_n, 4), round(std_e, 4), round(std_h, 4)))\n",
    "NEH_means_text = 'North and East coordinates and Heights means are: {}m, {}m and {}m.'.format(round(mean_n, 4), round(mean_e, 4), round(mean_h, 4))\n",
    "NEH_medians_text = 'North and East coordinates and Heights medians are: {}m, {}m and {}m.'.format(round(median_n, 4), round(median_e, 4), round(median_h, 4))\n",
    "NEH_stds_text = 'North and East coordinates and Heights standard deviations are: {}m, {}m and {}m.'.format(round(std_n, 4), round(std_e, 4), round(std_h, 4))\n",
    "\n",
    "# luodaan DataFrame teksteille myöhempää PDF-tiedostoa varten / create a DataFrame for the texts for later .pdf file creation:\n",
    "texts = {'Text': [set_tolerance_values_text]}\n",
    "texts_df = pd.DataFrame(data = texts)\n",
    "texts_df.loc[1, 'Text'] = abovetolerances_text\n",
    "texts_df.loc[2, 'Text'] = NEH_means_text\n",
    "texts_df.loc[3, 'Text'] = NEH_medians_text\n",
    "texts_df.loc[4, 'Text'] = NEH_stds_text\n",
    "\n",
    "# luodaan DataFrame ensimmäiselle taulukolle myöhempää PDF-tiedostoa varten / create a DataFrame for the first table for later .pdf file creation:\n",
    "table1 = {'Parameter': ['Total Number of Measurements (No Outliers Removed)'], 'Value': [allcoordinates_amount]}\n",
    "table1_df = pd.DataFrame(data = table1)\n",
    "table1_df.loc[1] = ['Timespan (No Outliers Removed)', '{0} - {1}'.format(testmeasurements_df['Time'].iloc[0], testmeasurements_df['Time'].iloc[-1])]\n",
    "table1_df.loc[2] = ['Solution Percentages (No Outliers Removed),\\nFixed; Float; Code Diff; Standalone; Other', '{0}%; {1}%; {2}%; {3}%; {4}%'.format(list_solutions_percentages[0], list_solutions_percentages[1], list_solutions_percentages[2], list_solutions_percentages[3], list_solutions_percentages[4])]\n",
    "table1_df.loc[3] = ['Number of All Satellites Mean (No Outliers Removed)', mean_all_satellites_inc_outl]\n",
    "table1_df.loc[4] = ['Number of All Satellites (No Outliers Removed), Min; Max', '{}; {}'.format(min_all_satellites_inc_outl, max_all_satellites_inc_outl)]\n",
    "table1_df.loc[5] = ['Number of GPS Satellites Mean (No Outliers Removed)', mean_GPS_inc_outl]\n",
    "table1_df.loc[6] = ['Number of GLONASS Satellites Mean (No Outliers Removed)', mean_GLONASS_inc_outl]\n",
    "table1_df.loc[7] = ['Number of GALILEO Satellites Mean (No Outliers Removed)', mean_GALILEO_inc_outl]\n",
    "table1_df.loc[8] = ['Number of BEIDOU Satellites Mean (No Outliers Removed)', mean_BEIDOU_inc_outl]\n",
    "table1_df.loc[9] = ['Mean of HDOP Values (No Outliers Removed)', mean_HDOP_inc_outl]\n",
    "table1_df.loc[10] = ['Mean of VDOP Values (No Outliers Removed)', mean_VDOP_inc_outl]\n",
    "table1_df.loc[11] = ['Mean of PDOP Values (No Outliers Removed)', mean_PDOP_inc_outl]\n",
    "table1_df.loc[12] = ['Mean of TDOP Values (No Outliers Removed)', mean_TDOP_inc_outl]\n",
    "table1_df.loc[13] = ['Mean of GDOP Values (No Outliers Removed)', mean_GDOP_inc_outl]\n",
    "table1_df.loc[14] = ['User-Defined Tolerance Values,\\nNorth (m) and East (m); Height (m)', '{}; {}'.format(tolerance_north_east, tolerance_height)]\n",
    "table1_df.loc[15] = ['Number of Measurements Above the Set Tolerance Values', abovetolerances_amount]\n",
    "table1_df.loc[16] = ['Above Tolerance Values Percentage', '{}%'.format(abovetolerances_percentage)]\n",
    "table1_df.loc[17] = ['Timespan (Outliers Removed)', '{0} - {1}'.format(testmeasurements_df_no_outliers['Time'].iloc[0], testmeasurements_df_no_outliers['Time'].iloc[-1])]\n",
    "table1_df.loc[18] = ['Solution Percentages (Outliers Removed),\\nFixed; Float; Code Diff; Standalone; Other', '{0}%; {1}%; {2}%; {3}%; {4}%'.format(list_solutions_no_outliers_percentages[0], list_solutions_no_outliers_percentages[1], list_solutions_no_outliers_percentages[2], list_solutions_no_outliers_percentages[3], list_solutions_no_outliers_percentages[4])]\n",
    "table1_df.loc[19] = ['Number of All Satellites Mean (Outliers Removed)', mean_all_satellites_no_outl]\n",
    "table1_df.loc[20] = ['Number of All Satellites (Outliers Removed), Min; Max', '{}; {}'.format(min_all_satellites_no_outl, max_all_satellites_no_outl)]\n",
    "table1_df.loc[21] = ['Number of GPS Satellites Mean (Outliers Removed)', mean_GPS_no_outl]\n",
    "table1_df.loc[22] = ['Number of GLONASS Satellites Mean (Outliers Removed)', mean_GLONASS_no_outl]\n",
    "table1_df.loc[23] = ['Number of GALILEO Satellites Mean (Outliers Removed)', mean_GALILEO_no_outl]\n",
    "table1_df.loc[24] = ['Number of BEIDOU Satellites Mean (Outliers Removed)', mean_BEIDOU_no_outl]\n",
    "table1_df.loc[25] = ['Mean of HDOP Values (Outliers Removed)', mean_HDOP_no_outl]\n",
    "table1_df.loc[26] = ['Mean of VDOP Values (Outliers Removed)', mean_VDOP_no_outl]\n",
    "table1_df.loc[27] = ['Mean of PDOP Values (Outliers Removed)', mean_PDOP_no_outl]\n",
    "table1_df.loc[28] = ['Mean of TDOP Values (Outliers Removed)', mean_TDOP_no_outl]\n",
    "table1_df.loc[29] = ['Mean of GDOP Values (Outliers Removed)', mean_GDOP_no_outl]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Piirretään jakaumat havaintojen virheistä (ei outliereitä mukana) / Let's draw the distributions of the measurement errors (no outliers):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def errors(coordinates1, mean1, coordinates2, mean2, coordinates3, mean3):\n",
    "    errors = np.zeros(shape=(len(coordinates1), 3))\n",
    "    i = 0     \n",
    "    while i < len(coordinates1):\n",
    "        errors[i, 0] = abs(coordinates1[i] - mean1)\n",
    "        errors[i, 1] = abs(coordinates2[i] - mean2)\n",
    "        errors[i, 2] = abs(coordinates3[i] - mean3)\n",
    "        i += 1\n",
    "    return errors\n",
    "\n",
    "errors = errors(northcoordinates, mean_n, eastcoordinates, mean_e, heights, mean_h)\n",
    "errors_df = pd.DataFrame({'North Errors (m)':errors[:,0],'East Errors (m)':errors[:,1],'Height Errors (m)':errors[:,2]})\n",
    "\n",
    "mean_n_error = round(errors_df.loc[:, 'North Errors (m)'].mean(), 4)\n",
    "mean_e_error = round(errors_df.loc[:, 'East Errors (m)'].mean(), 4)\n",
    "mean_h_error = round(errors_df.loc[:, 'Height Errors (m)'].mean(), 4)\n",
    "\n",
    "std_n_error = round(errors_df.loc[:, 'North Errors (m)'].std(), 4)\n",
    "std_e_error = round(errors_df.loc[:, 'East Errors (m)'].std(), 4)\n",
    "std_h_error = round(errors_df.loc[:, 'Height Errors (m)'].std(), 4)\n",
    "\n",
    "fig = plt.figure(figsize=(15, 12))\n",
    "fig.suptitle('{}'.format(name_of_GNSS_device_or_NRTK_method), fontsize = 20)\n",
    "ax1 = sns.kdeplot(errors_df['North Errors (m)'], shade=True)\n",
    "ax2 = sns.kdeplot(errors_df['East Errors (m)'], shade=True)\n",
    "ax3 = sns.kdeplot(errors_df['Height Errors (m)'], shade=True)\n",
    "ax1.set_title('KDE Plot for the Distribution of Errors (PRECISION, INNER ACCURACY). Date: {}'.format(date[1]))\n",
    "ax1.set_xlabel('Coordinate Error (m)')\n",
    "ax1.set_ylabel('The Number of Measurements')\n",
    "strFileE = '{0}{1}F_KDE Plot for the Distribution of Errors (PRECISION, INNER ACCURACY).png'.format(session_folder_path, os.sep)\n",
    "if os.path.isfile(strFileE):\n",
    "    os.remove(strFileE)\n",
    "fig.savefig('{0}{1}F_KDE Plot for the Distribution of Errors (PRECISION, INNER ACCURACY).png'.format(session_folder_path, os.sep))\n",
    "plt.show()\n",
    "\n",
    "figures_df.loc[5, 'The Figure File Name'] = 'F_KDE Plot for the Distribution of Errors (PRECISION, INNER ACCURACY).png'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testataan, miten hyvin yllä oleva aineisto asettuu normaalijakauman mukaiseksi. Tuotetaan yllä olevan aineiston keskiarvojen ja -hajontojen avulla malli normaalijakaumalle. Hyödynnetään satunnaisen numeron tuottamista mallin luomiseksi ja lasketaan normaalijakaumat. Voit testata NLS GNSS SOFAMESA -pääohjelman avulla eri otoskoolla mallia./ Let's test, how well the data above can be fitted to follow the Gaussian distribution. We can produce a Gaussian distribution model with the help of the means and standard deviations of the test measurements. Let's utilize random number generation to produce the model and calculate distributions. You can test the model with varying sample sizes with NLS GNSS SOFAMESA Main Program:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def maximum_y(plt, normaldistribution_component_error, bins):\n",
    "    # y, x, _ = plt.hist(normaldistribution_component_error, bins)\n",
    "    # return y.max()\n",
    "\n",
    "fig = plt.figure(figsize=(15, 12))\n",
    "fig.suptitle('{}\\nGaussian Distribution Models for North and East Coordinate Errors, and Height Errors. Precision (Inner Accuracy):\\nUser-Defined Sample Size (n): {}'.format(name_of_GNSS_device_or_NRTK_method, size), fontsize = 20)\n",
    "ax1 = fig.add_subplot(221)\n",
    "normaldistribution_n_error = np.random.normal(mean_n_error, std_n_error, size=size)\n",
    "# max_y1 = maximum_y(plt, normaldistribution_n_error, 100)\n",
    "ax1.hist(normaldistribution_n_error, bins=100)\n",
    "ax1.set_title('North Coordinate Errors (PRECISION, INNER ACCURACY). Date: {}'.format(date[1]))\n",
    "ax1.set_xlabel('North Coordinate Error (m)')\n",
    "ax1.set_ylabel('Samples (n)')\n",
    "ax1.legend(('Std \\xb1 {}m'.format(str(std_n_error)), 'test'), loc = 1, shadow=True, handlelength=1.5, fontsize=15)\n",
    "# ax1.plot([0, 0], [0, max_y1], color = 'red', linestyle = '--', linewidth = 3)\n",
    "\n",
    "ax2 = fig.add_subplot(222)\n",
    "normaldistribution_e_error = np.random.normal(mean_e_error, std_e_error, size=size)\n",
    "ax2.hist(normaldistribution_e_error, bins=100)\n",
    "ax2.set_title('East Coordinate Errors (PRECISION, INNER ACCURACY). Date: {}'.format(date[1]))\n",
    "ax2.set_xlabel('East Coordinate Error (m)')\n",
    "ax2.set_ylabel('Samples (n)')\n",
    "ax2.legend(('Std \\xb1 {}m'.format(str(std_e_error)), 'test'), loc = 1, shadow=True, handlelength=1.5, fontsize=15)\n",
    "\n",
    "ax3 = fig.add_subplot(223)\n",
    "normaldistribution_h_error = np.random.normal(mean_h_error, std_h_error, size=size)\n",
    "ax3.hist(normaldistribution_h_error, bins=100)\n",
    "ax3.set_title('Height Errors (PRECISION, INNER ACCURACY). Date: {}'.format(date[1]))\n",
    "ax3.set_xlabel('Height Error (m)')\n",
    "ax3.set_ylabel('Samples (n)')\n",
    "ax3.legend(('Std \\xb1 {}m'.format(str(std_h_error)), 'test'), loc = 1, shadow=True, handlelength=1.5, fontsize=15)\n",
    "strFileF = '{0}{1}G_Gaussian Distribution Models for the Distributions of Errors (PRECISION, INNER ACCURACY).png'.format(session_folder_path, os.sep)\n",
    "if os.path.isfile(strFileF):\n",
    "    os.remove(strFileF)\n",
    "fig.savefig('{0}{1}G_Gaussian Distribution Models for the Distributions of Errors (PRECISION, INNER ACCURACY).png'.format(session_folder_path, os.sep))\n",
    "plt.show()\n",
    "\n",
    "figures_df.loc[6, 'The Figure File Name'] = 'G_Gaussian Distribution Models for the Distributions of Errors (PRECISION, INNER ACCURACY).png'"
   ]
  },
  {
   "attachments": {
    "Sieppaa.PNG": {
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"
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lasketaan RMSE:t sisäisille ja ulkoisille tarkkuuksille: horisontaalivirheet ja korkeusvirheet.\n",
    "RMSE:n laskenta. / Let's calculate the RMSE values for inner (precisions) and outer (accuracy) accuracies: horizontal and vertical errors. The calculation of RMSEs follows these guidances: ![Sieppaa1.PNG](attachment:Sieppaa1.PNG) ![Sieppaa.PNG](attachment:Sieppaa.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lasketaan sisäisiä tarkkuuksia: vaaka-RMSE ja -2dRMSE. / Let's calculate inner accuracies (precision): Horizontal RMSE and 2dRMSE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def RMSE_horizontal_inner(coordinates1, mean1, coordinates2, mean2):\n",
    "    horizontalerror_square = 0\n",
    "    i = 0     \n",
    "    while i < len(coordinates1):\n",
    "        horizontalerror_square += (np.sqrt((coordinates1[i] - mean1)**2 + (coordinates2[i] - mean2)**2))**2\n",
    "        i += 1\n",
    "    RMSE = np.sqrt(horizontalerror_square/len(coordinates1))\n",
    "    return RMSE\n",
    "\n",
    "RMSE_horizontal_inner = RMSE_horizontal_inner(northcoordinates, mean_n, eastcoordinates, mean_e)\n",
    "TWOdRMSE_horizontal_inner = RMSE_horizontal_inner * 2\n",
    "\n",
    "# print('The precision of the point (inner accuracy) Horizontal RMSE is {}m (63-68% of the measurements, depends on the shape of the distribution) and Horizontal 2dRMSE {}m (95-98% of the measurements, depends on the shape of the distribution).'.format(round(RMSE_horizontal_inner, 3), round(TWOdRMSE_horizontal_inner, 3)))\n",
    "precision_horizontal_text = 'The precision of the point (inner accuracy) Horizontal RMSE is {}m (63-68% of the measurements, depends on the shape of the distribution) and Horizontal 2dRMSE {}m (95-98% of the measurements, depends on the shape of the distribution).'.format(round(RMSE_horizontal_inner, 3), round(TWOdRMSE_horizontal_inner, 3))\n",
    "texts_df.loc[5, 'Text'] = precision_horizontal_text\n",
    "\n",
    "# luodaan DataFrame toiselle taulukolle myöhempää PDF-tiedostoa varten / create a DataFrame for the second table for later .pdf file creation:\n",
    "table2 = {'Parameter': ['Horizontal RMSE*** \\xb1 (m)'], 'Precision*': [round(RMSE_horizontal_inner, 3)], 'Accuracy**': [None]}\n",
    "table2_df = pd.DataFrame(data = table2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lasketaan sisäisiä tarkkuuksia: korkeus-RMSE ja -2dRMSE. / Let's calculate inner accuracies (precision): Vertical RMSE and 2dRMSE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def RMSE_vertical_inner(coordinates, mean):\n",
    "    verticalerror_square = 0\n",
    "    i = 0     \n",
    "    while i < len(coordinates):\n",
    "        verticalerror_square += (np.sqrt((coordinates[i] - mean)**2))**2\n",
    "        i += 1\n",
    "    RMSE = np.sqrt(verticalerror_square/len(coordinates))\n",
    "    return RMSE\n",
    "\n",
    "RMSE_vertical_inner = RMSE_vertical_inner(heights, mean_h)\n",
    "TWOdRMSE_vertical_inner = RMSE_vertical_inner * 2\n",
    "\n",
    "# print('The precision of the point (inner accuracy) Vertical RMSE is {}m (63-68% of the measurements, depends on the shape of the distribution) and Vertical 2dRMSE {}m (95-98% of the measurements, depends on the shape of the distribution).'.format(round(RMSE_vertical_inner, 3), round(TWOdRMSE_vertical_inner, 3)))\n",
    "precision_vertical_text = 'The precision of the point (inner accuracy) Vertical RMSE is {}m (63-68% of the measurements, depends on the shape of the distribution) and Vertical 2dRMSE {}m (95-98% of the measurements, depends on the shape of the distribution).'.format(round(RMSE_vertical_inner, 3), round(TWOdRMSE_vertical_inner, 3))\n",
    "texts_df.loc[6, 'Text'] = precision_vertical_text\n",
    "\n",
    "table2_df.loc[1] = ['Vertical RMSE**** \\xb1 (m)', round(RMSE_vertical_inner, 3), None]\n",
    "table2_df.loc[2] = ['Horizontal 2dRMSE*** \\xb1 (m)', round(TWOdRMSE_horizontal_inner, 3), None]\n",
    "table2_df.loc[3] = ['Vertical 2dRMSE**** \\xb1 (m)', round(TWOdRMSE_vertical_inner, 3), None]\n",
    "table2_df.loc[4] = ['North Coordinate Std \\xb1 (m)', round(std_n, 4), None]\n",
    "table2_df.loc[5] = ['East Coordinate Std \\xb1 (m)', round(std_e, 4), None]\n",
    "table2_df.loc[6] = ['Height Std \\xb1 (m)', round(std_h, 4), None]\n",
    "table2_df.loc[7] = ['North Coordinate Mean (m)', round(mean_n, 4), None]\n",
    "table2_df.loc[8] = ['East Coordinate Mean (m)', round(mean_e, 4), None]\n",
    "table2_df.loc[9] = ['Height Mean (m)', round(mean_h, 4), None]\n",
    "table2_df.loc[10] = ['North Coordinate Median (m)', round(median_n, 4), None]\n",
    "table2_df.loc[11] = ['East Coordinate Median (m)', round(median_e, 4), None]\n",
    "table2_df.loc[12] = ['Height Median (m)', round(median_h, 4), None]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Piirretään kuvaajat sisäisille tarkkuuksille: vaakatarkkuudet. / Let's draw the figures for inner accuracies (precisions): horizontal precisions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def horizontal_errors_inner(coordinates1, mean1, coordinates2, mean2):\n",
    "    horizontal_errors = np.zeros(shape=(len(coordinates1), 2))\n",
    "    i = 0     \n",
    "    while i < len(coordinates1):\n",
    "        horizontal_errors[i, 0] = coordinates1[i] - mean1\n",
    "        horizontal_errors[i, 1] = coordinates2[i] - mean2\n",
    "        i += 1\n",
    "    return horizontal_errors\n",
    "\n",
    "horizontal_errors_inner = horizontal_errors_inner(northcoordinates, mean_n, eastcoordinates, mean_e)\n",
    "horizontal_errors_inner_df = pd.DataFrame({'North Errors (m)':horizontal_errors_inner[:,0],'East Errors (m)':horizontal_errors_inner[:,1]})\n",
    "\n",
    "fig = plt.figure(figsize=(15, 12))\n",
    "fig.suptitle('{}'.format(name_of_GNSS_device_or_NRTK_method), fontsize = 20)\n",
    "sns.scatterplot(x = 'East Errors (m)', y = 'North Errors (m)', data = horizontal_errors_inner_df)\n",
    "plt.title('The PRECISION of the point (INNER accuracy) (m), number of measurements {}/{}: HORIZONTAL. Date: {}'.format(len(northcoordinates), len(northcoordinates) + len(abovetolerances), date[1]))\n",
    "\n",
    "an = np.linspace(0, 2 * np.pi, 100)\n",
    "plt.plot(RMSE_horizontal_inner * np.cos(an), RMSE_horizontal_inner * np.sin(an), color = 'lime', marker = '+', linestyle = '--', linewidth = 3)\n",
    "plt.plot(TWOdRMSE_horizontal_inner * np.cos(an), TWOdRMSE_horizontal_inner * np.sin(an), color = 'orange', marker = '+', linestyle = '--', linewidth = 3)\n",
    "# plt.axis('equal')\n",
    "plt.axis([-(tolerance_north_east), tolerance_north_east, -(tolerance_north_east), tolerance_north_east])\n",
    "plt.legend(('Horizontal RMSE: \\xb1 {}m'.format(round(RMSE_horizontal_inner, 3)), 'Horizontal 2dRMSE: \\xb1 {}m'.format(round(TWOdRMSE_horizontal_inner, 3)), 'Measurement'), loc = 1, shadow=True, handlelength=1.5, fontsize=10)\n",
    "strFileG = '{0}{1}H_The PRECISION of the point (INNER accuracy) (m) HORIZONTAL.png'.format(session_folder_path, os.sep)\n",
    "if os.path.isfile(strFileG):\n",
    "    os.remove(strFileG)\n",
    "fig.savefig('{0}{1}H_The PRECISION of the point (INNER accuracy) (m) HORIZONTAL.png'.format(session_folder_path, os.sep))\n",
    "plt.show()\n",
    "\n",
    "figures_df.loc[7, 'The Figure File Name'] = 'H_The PRECISION of the point (INNER accuracy) (m) HORIZONTAL.png'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Piirretään kuvaajat sisäisille tarkkuuksille: pohjoistarkkuudet. / Let's draw the figures for inner accuracies (precisions): North precisions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def RMSE_north_inner(coordinates, mean):\n",
    "    northerror_square = 0\n",
    "    i = 0     \n",
    "    while i < len(coordinates):\n",
    "        northerror_square += (np.sqrt((coordinates[i] - mean)**2))**2\n",
    "        i += 1\n",
    "    RMSE = np.sqrt(northerror_square/len(coordinates))\n",
    "    return RMSE\n",
    "\n",
    "RMSE_north_inner = RMSE_north_inner(northcoordinates, mean_n)\n",
    "TWOdRMSE_north_inner = RMSE_north_inner * 2\n",
    "\n",
    "def north_errors_inner(coordinates, mean):\n",
    "    north_errors = np.zeros(shape=(len(coordinates), 2))\n",
    "    i = 0     \n",
    "    while i < len(coordinates):\n",
    "        north_errors[i, 0] = coordinates[i] - mean\n",
    "        north_errors[i, 1] = i + 1\n",
    "        i += 1\n",
    "    return north_errors\n",
    "\n",
    "north_errors_inner = north_errors_inner(northcoordinates, mean_n)\n",
    "north_errors_inner_df = pd.DataFrame({'North Errors (m)':north_errors_inner[:,0], 'The Number of the Measurement':north_errors_inner[:,1]})\n",
    "\n",
    "fig = plt.figure(figsize=(15, 12))\n",
    "fig.suptitle('{}'.format(name_of_GNSS_device_or_NRTK_method), fontsize = 20)\n",
    "sns.lineplot(x = 'The Number of the Measurement', y = 'North Errors (m)', data = north_errors_inner_df)\n",
    "plt.title('The PRECISION of the point (INNER accuracy) (m), number of measurements {}/{}: NORTH. Date: {}'.format(len(northcoordinates), len(northcoordinates) + len(abovetolerances), date[1]))\n",
    "plt.plot([north_errors_inner_df.loc[0, 'The Number of the Measurement'], len(north_errors_inner_df)], [RMSE_north_inner, RMSE_north_inner], color = 'lime', linestyle = '--', linewidth = 3)\n",
    "plt.plot([north_errors_inner_df.loc[0, 'The Number of the Measurement'], len(north_errors_inner_df)], [TWOdRMSE_north_inner, TWOdRMSE_north_inner], color = 'orange', linestyle = '--', linewidth = 3)\n",
    "plt.plot([north_errors_inner_df.loc[0, 'The Number of the Measurement'], len(north_errors_inner_df)], [-RMSE_north_inner, -RMSE_north_inner], color = 'lime', linestyle = '--', linewidth = 3)\n",
    "plt.plot([north_errors_inner_df.loc[0, 'The Number of the Measurement'], len(north_errors_inner_df)], [-TWOdRMSE_north_inner, -TWOdRMSE_north_inner], color = 'orange', linestyle = '--', linewidth = 3)\n",
    "plt.axis([0, len(northcoordinates), -(tolerance_north_east), tolerance_north_east])\n",
    "plt.legend(('North Errors (m)', 'North RMSE: \\xb1 {}m'.format(round(RMSE_north_inner, 3)), 'North 2dRMSE: \\xb1 {}m'.format(round(TWOdRMSE_north_inner, 3))), loc = 1, shadow=True, handlelength=1.5, fontsize=10)\n",
    "strFileH = '{0}{1}I_The PRECISION of the point (INNER accuracy) (m) NORTH.png'.format(session_folder_path, os.sep)\n",
    "if os.path.isfile(strFileH):\n",
    "    os.remove(strFileH)\n",
    "fig.savefig('{0}{1}I_The PRECISION of the point (INNER accuracy) (m) NORTH.png'.format(session_folder_path, os.sep))\n",
    "plt.show()\n",
    "\n",
    "figures_df.loc[8, 'The Figure File Name'] = 'I_The PRECISION of the point (INNER accuracy) (m) NORTH.png'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Piirretään kuvaajat sisäisille tarkkuuksille: itätarkkuudet. / Let's draw the figures for inner accuracies (precisions): East precisions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def RMSE_east_inner(coordinates, mean):\n",
    "    easterror_square = 0\n",
    "    i = 0     \n",
    "    while i < len(coordinates):\n",
    "        easterror_square += (np.sqrt((coordinates[i] - mean)**2))**2\n",
    "        i += 1\n",
    "    RMSE = np.sqrt(easterror_square/len(coordinates))\n",
    "    return RMSE\n",
    "\n",
    "RMSE_east_inner = RMSE_east_inner(eastcoordinates, mean_e)\n",
    "TWOdRMSE_east_inner = RMSE_east_inner * 2\n",
    "\n",
    "def east_errors_inner(coordinates, mean):\n",
    "    east_errors = np.zeros(shape=(len(coordinates), 2))\n",
    "    i = 0     \n",
    "    while i < len(coordinates):\n",
    "        east_errors[i, 0] = coordinates[i] - mean\n",
    "        east_errors[i, 1] = i + 1\n",
    "        i += 1\n",
    "    return east_errors\n",
    "\n",
    "east_errors_inner = east_errors_inner(eastcoordinates, mean_e)\n",
    "east_errors_inner_df = pd.DataFrame({'East Errors (m)':east_errors_inner[:,0], 'The Number of the Measurement':east_errors_inner[:,1]})\n",
    "\n",
    "fig = plt.figure(figsize=(15, 12))\n",
    "fig.suptitle('{}'.format(name_of_GNSS_device_or_NRTK_method), fontsize = 20)\n",
    "sns.lineplot(x = 'The Number of the Measurement', y = 'East Errors (m)', data = east_errors_inner_df)\n",
    "plt.title('The PRECISION of the point (INNER accuracy) (m), number of measurements {}/{}: EAST. Date: {}'.format(len(eastcoordinates), len(eastcoordinates) + len(abovetolerances), date[1]))\n",
    "plt.plot([east_errors_inner_df.loc[0, 'The Number of the Measurement'], len(east_errors_inner_df)], [RMSE_east_inner, RMSE_east_inner], color = 'lime', linestyle = '--', linewidth = 3)\n",
    "plt.plot([east_errors_inner_df.loc[0, 'The Number of the Measurement'], len(east_errors_inner_df)], [TWOdRMSE_east_inner, TWOdRMSE_east_inner], color = 'orange', linestyle = '--', linewidth = 3)\n",
    "plt.plot([east_errors_inner_df.loc[0, 'The Number of the Measurement'], len(east_errors_inner_df)], [-RMSE_east_inner, -RMSE_east_inner], color = 'lime', linestyle = '--', linewidth = 3)\n",
    "plt.plot([east_errors_inner_df.loc[0, 'The Number of the Measurement'], len(east_errors_inner_df)], [-TWOdRMSE_east_inner, -TWOdRMSE_east_inner], color = 'orange', linestyle = '--', linewidth = 3)\n",
    "plt.axis([0, len(eastcoordinates), -(tolerance_north_east), tolerance_north_east])\n",
    "plt.legend(('East Errors (m)', 'East RMSE: \\xb1 {}m'.format(round(RMSE_east_inner, 3)), 'East 2dRMSE: \\xb1 {}m'.format(round(TWOdRMSE_east_inner, 3))), loc = 1, shadow=True, handlelength=1.5, fontsize=10)\n",
    "strFileI = '{0}{1}J_The PRECISION of the point (INNER accuracy) (m) EAST.png'.format(session_folder_path, os.sep)\n",
    "if os.path.isfile(strFileI):\n",
    "    os.remove(strFileI)\n",
    "fig.savefig('{0}{1}J_The PRECISION of the point (INNER accuracy) (m) EAST.png'.format(session_folder_path, os.sep))\n",
    "plt.show()\n",
    "\n",
    "figures_df.loc[9, 'The Figure File Name'] = 'J_The PRECISION of the point (INNER accuracy) (m) EAST.png'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Piirretään kuvaajat sisäisille tarkkuuksille: vertikaalitarkkuudet. / Let's draw the figures for inner accuracies (precisions): Vertical precisions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def vertical_errors_inner(coordinates, mean):\n",
    "    vertical_errors = np.zeros(shape=(len(coordinates), 2))\n",
    "    i = 0     \n",
    "    while i < len(coordinates):\n",
    "        vertical_errors[i, 0] = coordinates[i] - mean\n",
    "        vertical_errors[i, 1] = i + 1\n",
    "        i += 1\n",
    "    return vertical_errors\n",
    "\n",
    "vertical_errors_inner = vertical_errors_inner(heights, mean_h)\n",
    "vertical_errors_inner_df = pd.DataFrame({'Vertical Errors (m)':vertical_errors_inner[:,0], 'The Number of the Measurement':vertical_errors_inner[:,1]})\n",
    "\n",
    "fig = plt.figure(figsize=(15, 12))\n",
    "fig.suptitle('{}'.format(name_of_GNSS_device_or_NRTK_method), fontsize = 20)\n",
    "sns.lineplot(x = 'The Number of the Measurement', y = 'Vertical Errors (m)', data = vertical_errors_inner_df)\n",
    "plt.title('The PRECISION of the point (INNER accuracy) (m), number of measurements {}/{}: VERTICAL. Date: {}'.format(len(northcoordinates), len(northcoordinates) + len(abovetolerances), date[1]))\n",
    "plt.plot([vertical_errors_inner_df.loc[0, 'The Number of the Measurement'], len(vertical_errors_inner_df)], [RMSE_vertical_inner, RMSE_vertical_inner], color = 'lime', linestyle = '--', linewidth = 3)\n",
    "plt.plot([vertical_errors_inner_df.loc[0, 'The Number of the Measurement'], len(vertical_errors_inner_df)], [TWOdRMSE_vertical_inner, TWOdRMSE_vertical_inner], color = 'orange', linestyle = '--', linewidth = 3)\n",
    "plt.plot([vertical_errors_inner_df.loc[0, 'The Number of the Measurement'], len(vertical_errors_inner_df)], [-RMSE_vertical_inner, -RMSE_vertical_inner], color = 'lime', linestyle = '--', linewidth = 3)\n",
    "plt.plot([vertical_errors_inner_df.loc[0, 'The Number of the Measurement'], len(vertical_errors_inner_df)], [-TWOdRMSE_vertical_inner, -TWOdRMSE_vertical_inner], color = 'orange', linestyle = '--', linewidth = 3)\n",
    "plt.axis([0, len(northcoordinates), -(tolerance_height), tolerance_height])\n",
    "plt.legend(('Vertical Errors (m)', 'Vertical RMSE: \\xb1 {}m'.format(round(RMSE_vertical_inner, 3)), 'Vertical 2dRMSE: \\xb1 {}m'.format(round(TWOdRMSE_vertical_inner, 3))), loc = 1, shadow=True, handlelength=1.5, fontsize=10)\n",
    "strFileJ = '{0}{1}K_The PRECISION of the point (INNER accuracy) (m) VERTICAL.png'.format(session_folder_path, os.sep)\n",
    "if os.path.isfile(strFileJ):\n",
    "    os.remove(strFileJ)\n",
    "fig.savefig('{0}{1}K_The PRECISION of the point (INNER accuracy) (m) VERTICAL.png'.format(session_folder_path, os.sep))\n",
    "plt.show()\n",
    "\n",
    "figures_df.loc[10, 'The Figure File Name'] = 'K_The PRECISION of the point (INNER accuracy) (m) VERTICAL.png'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lasketaan ulkoisia tarkkuuksia: vaaka-RMSE ja -2dRMSE. / Let's calculate outer accuracies (accuracies): Horizontal RMSE and 2dRMSE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def RMSE_horizontal_outer(coordinates1, referencecoordinate1, coordinates2, referencecoordinate2):\n",
    "    horizontalerror_square = 0\n",
    "    i = 0     \n",
    "    while i < len(coordinates1):\n",
    "        horizontalerror_square += (np.sqrt((coordinates1[i] - referencecoordinate1)**2 + (coordinates2[i] - referencecoordinate2)**2))**2\n",
    "        i += 1\n",
    "    RMSE = np.sqrt(horizontalerror_square/len(coordinates1))\n",
    "    return RMSE\n",
    "\n",
    "RMSE_horizontal_outer = RMSE_horizontal_outer(northcoordinates, referencecoordinate_n, eastcoordinates, referencecoordinate_e)\n",
    "TWOdRMSE_horizontal_outer = RMSE_horizontal_outer * 2\n",
    "\n",
    "# print('The accuracy of the point (outer accuracy) Horizontal RMSE is {}m (63-68% of the measurements, depends on the shape of the distribution) and Horizontal 2dRMSE {}m (95-98% of the measurements, depends on the shape of the distribution).'.format(round(RMSE_horizontal_outer, 3), round(TWOdRMSE_horizontal_outer, 3)))\n",
    "accuracy_horizontal_text = 'The accuracy of the point (outer accuracy) Horizontal RMSE is {}m (63-68% of the measurements, depends on the shape of the distribution) and Horizontal 2dRMSE {}m (95-98% of the measurements, depends on the shape of the distribution).'.format(round(RMSE_horizontal_outer, 3), round(TWOdRMSE_horizontal_outer, 3))\n",
    "texts_df.loc[7, 'Text'] = accuracy_horizontal_text\n",
    "\n",
    "table2_df.loc[0, 'Accuracy**'] = [round(RMSE_horizontal_outer, 3)]\n",
    "table2_df.loc[2, 'Accuracy**'] = [round(TWOdRMSE_horizontal_outer, 3)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lasketaan ulkoisia tarkkuuksia: korkeus-RMSE ja -2dRMSE. / Let's calculate outer accuracies (accuracies): Vertical RMSE and 2dRMSE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def RMSE_vertical_outer(coordinates, referencecoordinate):\n",
    "    verticalerror_square = 0\n",
    "    i = 0     \n",
    "    while i < len(coordinates):\n",
    "        verticalerror_square += (np.sqrt((coordinates[i] - referencecoordinate)**2))**2\n",
    "        i += 1\n",
    "    RMSE = np.sqrt(verticalerror_square/len(coordinates))\n",
    "    return RMSE\n",
    "\n",
    "RMSE_vertical_outer = RMSE_vertical_outer(heights, referencecoordinate_h)\n",
    "TWOdRMSE_vertical_outer = RMSE_vertical_outer * 2\n",
    "\n",
    "# print('The accuracy of the point (outer accuracy) Vertical RMSE is {}m (63-68% of the measurements, depends on the shape of the distribution) and Vertical 2dRMSE {}m (95-98% of the measurements, depends on the shape of the distribution).'.format(round(RMSE_vertical_outer, 3), round(TWOdRMSE_vertical_outer, 3)))\n",
    "accuracy_vertical_text = 'The accuracy of the point (outer accuracy) Vertical RMSE is {}m (63-68% of the measurements, depends on the shape of the distribution) and Vertical 2dRMSE {}m (95-98% of the measurements, depends on the shape of the distribution).'.format(round(RMSE_vertical_outer, 3), round(TWOdRMSE_vertical_outer, 3))\n",
    "texts_df.loc[8, 'Text'] = accuracy_vertical_text\n",
    "\n",
    "table2_df.loc[1, 'Accuracy**'] = [round(RMSE_vertical_outer, 3)]\n",
    "table2_df.loc[3, 'Accuracy**'] = [round(TWOdRMSE_vertical_outer, 3)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Piirretään kuvaajat ulkoisille tarkkuuksille: vaakatarkkuudet. / Let's draw the figures for outer accuracies (accuracies): Horizontal accuracies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def horizontal_errors_outer(coordinates1, referencecoordinate1, coordinates2, referencecoordinate2):\n",
    "    horizontal_errors = np.zeros(shape=(len(coordinates1), 2))\n",
    "    i = 0     \n",
    "    while i < len(coordinates1):\n",
    "        horizontal_errors[i, 0] = coordinates1[i] - referencecoordinate1\n",
    "        horizontal_errors[i, 1] = coordinates2[i] - referencecoordinate2\n",
    "        i += 1\n",
    "    return horizontal_errors\n",
    "\n",
    "horizontal_errors_outer = horizontal_errors_outer(northcoordinates, referencecoordinate_n, eastcoordinates, referencecoordinate_e)\n",
    "horizontal_errors_outer_df = pd.DataFrame({'North Errors (m)':horizontal_errors_outer[:,0],'East Errors (m)':horizontal_errors_outer[:,1]})\n",
    "\n",
    "fig = plt.figure(figsize=(15, 12))\n",
    "fig.suptitle('{}'.format(name_of_GNSS_device_or_NRTK_method), fontsize = 20)\n",
    "sns.scatterplot(x = 'East Errors (m)', y = 'North Errors (m)', data = horizontal_errors_outer_df)\n",
    "plt.title('The ACCURACY of the point (OUTER accuracy) (m), number of measurements {}/{}: HORIZONTAL. Date: {}'.format(len(northcoordinates), len(northcoordinates) + len(abovetolerances), date[1]))\n",
    "\n",
    "an = np.linspace(0, 2 * np.pi, 100)\n",
    "plt.plot(RMSE_horizontal_outer * np.cos(an), RMSE_horizontal_outer * np.sin(an), color = 'lime', marker = '+', linestyle = '--', linewidth = 3)\n",
    "plt.plot(TWOdRMSE_horizontal_outer * np.cos(an), TWOdRMSE_horizontal_outer * np.sin(an), color = 'orange', marker = '+', linestyle = '--', linewidth = 3)\n",
    "# plt.axis('equal')\n",
    "plt.axis([-(tolerance_north_east), tolerance_north_east, -(tolerance_north_east), tolerance_north_east])\n",
    "plt.legend(('Horizontal RMSE: \\xb1 {}m'.format(round(RMSE_horizontal_outer, 3)), 'Horizontal 2dRMSE: \\xb1 {}m'.format(round(TWOdRMSE_horizontal_outer, 3)), 'Measurement'), loc = 1, shadow=True, handlelength=1.5, fontsize=10)\n",
    "strFileK = '{0}{1}L_The ACCURACY of the point (OUTER accuracy) (m) HORIZONTAL.png'.format(session_folder_path, os.sep)\n",
    "if os.path.isfile(strFileK):\n",
    "    os.remove(strFileK)\n",
    "fig.savefig('{0}{1}L_The ACCURACY of the point (OUTER accuracy) (m) HORIZONTAL.png'.format(session_folder_path, os.sep))\n",
    "plt.show()\n",
    "\n",
    "figures_df.loc[11, 'The Figure File Name'] = 'L_The ACCURACY of the point (OUTER accuracy) (m) HORIZONTAL.png'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Piirretään kuvaajat ulkoisille tarkkuuksille. pohjoistarkkuudet. / Let's draw the figures for outer accuracies (accuracies): North accuracies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def RMSE_north_outer(coordinates, referencecoordinate):\n",
    "    northerror_square = 0\n",
    "    i = 0     \n",
    "    while i < len(coordinates):\n",
    "        northerror_square += (np.sqrt((coordinates[i] - referencecoordinate)**2))**2\n",
    "        i += 1\n",
    "    RMSE = np.sqrt(northerror_square/len(coordinates))\n",
    "    return RMSE\n",
    "\n",
    "RMSE_north_outer = RMSE_north_outer(northcoordinates, referencecoordinate_n)\n",
    "TWOdRMSE_north_outer = RMSE_north_outer * 2\n",
    "\n",
    "def north_errors_outer(coordinates, referencecoordinate):\n",
    "    north_errors = np.zeros(shape=(len(coordinates), 2))\n",
    "    i = 0     \n",
    "    while i < len(coordinates):\n",
    "        north_errors[i, 0] = coordinates[i] - referencecoordinate\n",
    "        north_errors[i, 1] = i + 1\n",
    "        i += 1\n",
    "    return north_errors\n",
    "\n",
    "north_errors_outer = north_errors_outer(northcoordinates, referencecoordinate_n)\n",
    "north_errors_outer_df = pd.DataFrame({'North Errors (m)':north_errors_outer[:,0], 'The Number of the Measurement':north_errors_outer[:,1]})\n",
    "\n",
    "fig = plt.figure(figsize=(15, 12))\n",
    "fig.suptitle('{}'.format(name_of_GNSS_device_or_NRTK_method), fontsize = 20)\n",
    "sns.lineplot(x = 'The Number of the Measurement', y = 'North Errors (m)', data = north_errors_outer_df)\n",
    "plt.title('The ACCURACY of the point (OUTER accuracy) (m), number of measurements {}/{}: NORTH. Date: {}'.format(len(northcoordinates), len(northcoordinates) + len(abovetolerances), date[1]))\n",
    "plt.plot([north_errors_outer_df.loc[0, 'The Number of the Measurement'], len(north_errors_outer_df)], [RMSE_north_outer, RMSE_north_outer], color = 'lime', linestyle = '--', linewidth = 3)\n",
    "plt.plot([north_errors_outer_df.loc[0, 'The Number of the Measurement'], len(north_errors_outer_df)], [TWOdRMSE_north_outer, TWOdRMSE_north_outer], color = 'orange', linestyle = '--', linewidth = 3)\n",
    "plt.plot([north_errors_outer_df.loc[0, 'The Number of the Measurement'], len(north_errors_outer_df)], [-RMSE_north_outer, -RMSE_north_outer], color = 'lime', linestyle = '--', linewidth = 3)\n",
    "plt.plot([north_errors_outer_df.loc[0, 'The Number of the Measurement'], len(north_errors_outer_df)], [-TWOdRMSE_north_outer, -TWOdRMSE_north_outer], color = 'orange', linestyle = '--', linewidth = 3)\n",
    "plt.axis([0, len(northcoordinates), -(tolerance_north_east), tolerance_north_east])\n",
    "plt.legend(('North Errors (m)', 'North RMSE: \\xb1 {}m'.format(round(RMSE_north_outer, 3)), 'North 2dRMSE: \\xb1 {}m'.format(round(TWOdRMSE_north_outer, 3))), loc = 1, shadow=True, handlelength=1.5, fontsize=10)\n",
    "strFileL = '{0}{1}M_The ACCURACY of the point (OUTER accuracy) (m) NORTH.png'.format(session_folder_path, os.sep)\n",
    "if os.path.isfile(strFileL):\n",
    "    os.remove(strFileL)\n",
    "fig.savefig('{0}{1}M_The ACCURACY of the point (OUTER accuracy) (m) NORTH.png'.format(session_folder_path, os.sep))\n",
    "plt.show()\n",
    "\n",
    "figures_df.loc[12, 'The Figure File Name'] = 'M_The ACCURACY of the point (OUTER accuracy) (m) NORTH.png'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Piirretään kuvaajat ulkoisille tarkkuuksille: itätarkkuudet. / Let's draw the figures for outer accuracies (accuracies): East accuracies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def RMSE_east_outer(coordinates, referencecoordinate):\n",
    "    easterror_square = 0\n",
    "    i = 0     \n",
    "    while i < len(coordinates):\n",
    "        easterror_square += (np.sqrt((coordinates[i] - referencecoordinate)**2))**2\n",
    "        i += 1\n",
    "    RMSE = np.sqrt(easterror_square/len(coordinates))\n",
    "    return RMSE\n",
    "\n",
    "RMSE_east_outer = RMSE_east_outer(eastcoordinates, referencecoordinate_e)\n",
    "TWOdRMSE_east_outer = RMSE_east_outer * 2\n",
    "\n",
    "def east_errors_outer(coordinates, referencecoordinate):\n",
    "    east_errors = np.zeros(shape=(len(coordinates), 2))\n",
    "    i = 0     \n",
    "    while i < len(coordinates):\n",
    "        east_errors[i, 0] = coordinates[i] - referencecoordinate\n",
    "        east_errors[i, 1] = i + 1\n",
    "        i += 1\n",
    "    return east_errors\n",
    "\n",
    "east_errors_outer = east_errors_outer(eastcoordinates, referencecoordinate_e)\n",
    "east_errors_outer_df = pd.DataFrame({'East Errors (m)':east_errors_outer[:,0], 'The Number of the Measurement':east_errors_outer[:,1]})\n",
    "\n",
    "fig = plt.figure(figsize=(15, 12))\n",
    "fig.suptitle('{}'.format(name_of_GNSS_device_or_NRTK_method), fontsize = 20)\n",
    "sns.lineplot(x = 'The Number of the Measurement', y = 'East Errors (m)', data = east_errors_outer_df)\n",
    "plt.title('The ACCURACY of the point (OUTER accuracy) (m), number of measurements {}/{}: EAST. Date: {}'.format(len(eastcoordinates), len(eastcoordinates) + len(abovetolerances), date[1]))\n",
    "plt.plot([east_errors_outer_df.loc[0, 'The Number of the Measurement'], len(east_errors_outer_df)], [RMSE_east_outer, RMSE_east_outer], color = 'lime', linestyle = '--', linewidth = 3)\n",
    "plt.plot([east_errors_outer_df.loc[0, 'The Number of the Measurement'], len(east_errors_outer_df)], [TWOdRMSE_east_outer, TWOdRMSE_east_outer], color = 'orange', linestyle = '--', linewidth = 3)\n",
    "plt.plot([east_errors_outer_df.loc[0, 'The Number of the Measurement'], len(east_errors_outer_df)], [-RMSE_east_outer, -RMSE_east_outer], color = 'lime', linestyle = '--', linewidth = 3)\n",
    "plt.plot([east_errors_outer_df.loc[0, 'The Number of the Measurement'], len(east_errors_outer_df)], [-TWOdRMSE_east_outer, -TWOdRMSE_east_outer], color = 'orange', linestyle = '--', linewidth = 3)\n",
    "plt.axis([0, len(eastcoordinates), -(tolerance_north_east), tolerance_north_east])\n",
    "plt.legend(('East Errors (m)', 'East RMSE: \\xb1 {}m'.format(round(RMSE_east_outer, 3)), 'East 2dRMSE: \\xb1 {}m'.format(round(TWOdRMSE_east_outer, 3))), loc = 1, shadow=True, handlelength=1.5, fontsize=10)\n",
    "strFileM = '{0}{1}N_The ACCURACY of the point (OUTER accuracy) (m) EAST.png'.format(session_folder_path, os.sep)\n",
    "if os.path.isfile(strFileM):\n",
    "    os.remove(strFileM)\n",
    "fig.savefig('{0}{1}N_The ACCURACY of the point (OUTER accuracy) (m) EAST.png'.format(session_folder_path, os.sep))\n",
    "plt.show()\n",
    "\n",
    "figures_df.loc[13, 'The Figure File Name'] = 'N_The ACCURACY of the point (OUTER accuracy) (m) EAST.png'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Piirretään kuvaajat ulkoisille tarkkuuksille: vertikaalitarkkuudet. / Let's draw the figures for outer accuracies (accuracies): Vertical accuracies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def RMSE_vertical_outer(coordinates, referencecoordinate):\n",
    "    verticalerror_square = 0\n",
    "    i = 0     \n",
    "    while i < len(coordinates):\n",
    "        verticalerror_square += (np.sqrt((coordinates[i] - referencecoordinate)**2))**2\n",
    "        i += 1\n",
    "    RMSE = np.sqrt(verticalerror_square/len(coordinates))\n",
    "    return RMSE\n",
    "\n",
    "RMSE_vertical_outer = RMSE_vertical_outer(heights, referencecoordinate_h)\n",
    "TWOdRMSE_vertical_outer = RMSE_vertical_outer * 2\n",
    "\n",
    "def vertical_errors_outer(coordinates, referencecoordinate):\n",
    "    vertical_errors = np.zeros(shape=(len(coordinates), 2))\n",
    "    i = 0     \n",
    "    while i < len(coordinates):\n",
    "        vertical_errors[i, 0] = coordinates[i] - referencecoordinate\n",
    "        vertical_errors[i, 1] = i + 1\n",
    "        i += 1\n",
    "    return vertical_errors\n",
    "\n",
    "vertical_errors_outer = vertical_errors_outer(heights, referencecoordinate_h)\n",
    "vertical_errors_outer_df = pd.DataFrame({'Vertical Errors (m)':vertical_errors_outer[:,0], 'The Number of the Measurement':vertical_errors_outer[:,1]})\n",
    "\n",
    "fig = plt.figure(figsize=(15, 12))\n",
    "fig.suptitle('{}'.format(name_of_GNSS_device_or_NRTK_method), fontsize = 20)\n",
    "sns.lineplot(x = 'The Number of the Measurement', y = 'Vertical Errors (m)', data = vertical_errors_outer_df)\n",
    "plt.title('The ACCURACY of the point (OUTER accuracy) (m), number of measurements {}/{}: VERTICAL. Date: {}'.format(len(heights), len(heights) + len(abovetolerances), date[1]))\n",
    "plt.plot([vertical_errors_outer_df.loc[0, 'The Number of the Measurement'], len(vertical_errors_outer_df)], [RMSE_vertical_outer, RMSE_vertical_outer], color = 'lime', linestyle = '--', linewidth = 3)\n",
    "plt.plot([vertical_errors_outer_df.loc[0, 'The Number of the Measurement'], len(vertical_errors_outer_df)], [TWOdRMSE_vertical_outer, TWOdRMSE_vertical_outer], color = 'orange', linestyle = '--', linewidth = 3)\n",
    "plt.plot([vertical_errors_outer_df.loc[0, 'The Number of the Measurement'], len(vertical_errors_outer_df)], [-RMSE_vertical_outer, -RMSE_vertical_outer], color = 'lime', linestyle = '--', linewidth = 3)\n",
    "plt.plot([vertical_errors_outer_df.loc[0, 'The Number of the Measurement'], len(vertical_errors_outer_df)], [-TWOdRMSE_vertical_outer, -TWOdRMSE_vertical_outer], color = 'orange', linestyle = '--', linewidth = 3)\n",
    "plt.axis([0, len(northcoordinates), -(tolerance_height), tolerance_height])\n",
    "plt.legend(('Vertical Errors (m)', 'Vertical RMSE: \\xb1 {}m'.format(round(RMSE_vertical_outer, 3)), 'Vertical 2dRMSE: \\xb1 {}m'.format(round(TWOdRMSE_vertical_outer, 3))), loc = 1, shadow=True, handlelength=1.5, fontsize=10)\n",
    "strFileN = '{0}{1}O_The ACCURACY of the point (OUTER accuracy) (m) VERTICAL.png'.format(session_folder_path, os.sep)\n",
    "if os.path.isfile(strFileN):\n",
    "    os.remove(strFileN)\n",
    "fig.savefig('{0}{1}O_The ACCURACY of the point (OUTER accuracy) (m) VERTICAL.png'.format(session_folder_path, os.sep))\n",
    "plt.show()\n",
    "\n",
    "figures_df.loc[14, 'The Figure File Name'] = 'O_The ACCURACY of the point (OUTER accuracy) (m) VERTICAL.png'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Piirretään kuvaajat sisäisille tarkkuuksille ja DOP-arvoille havaintojen funktiona: vaakatarkkuudet. / Let's draw the figures for inner accuracies (precisions) and DOP values as a function of the measurements: horizontal precisions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "horizontal_errors_inner_df['Horizontal Errors (m)'] = ((horizontal_errors_inner_df.loc[:, 'East Errors (m)'])**2 + (horizontal_errors_inner_df.loc[:, 'North Errors (m)'])**2).pow(1./2)\n",
    "horizontal_errors_inner_df['The Number of the Measurement'] = range(0, len(horizontal_errors_inner_df))\n",
    "x = range(len(testmeasurements_df_no_outliers.loc[:, 'HDOP']))\n",
    "y = [testmeasurements_df_no_outliers.loc[:, 'HDOP']]\n",
    "mean_HDOP = round(testmeasurements_df_no_outliers.loc[:, 'HDOP'].mean(), 1)\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(15, 12))\n",
    "fig.suptitle('{}'.format(name_of_GNSS_device_or_NRTK_method), fontsize = 20)\n",
    "ax1.stackplot(x, y, labels=['HDOP (Mean = {})'.format(mean_HDOP)], alpha=0.5)\n",
    "ax1.set_xlabel('The Number of the Measurement', fontsize=14)\n",
    "ax1.set_ylabel('DOP Value', fontsize=14)\n",
    "ax1.legend(bbox_to_anchor=(-0.005, -0.1), loc='lower left', fontsize=12)\n",
    "ax1.tick_params(axis='y', labelsize = 12)\n",
    "\n",
    "color = (0.8941176470588236, 0.10196078431372549, 0.10980392156862745)\n",
    "ax2 = ax1.twinx()\n",
    "ax2 = sns.lineplot(x = 'The Number of the Measurement', y = 'Horizontal Errors (m)', data = horizontal_errors_inner_df, color = color, linewidth = 1)\n",
    "ax2.set_title('The Horizontal PRECISION of the point (INNER accuracy) (m) with DOP values, number of measurements {}/{}: HORIZONTAL. Date: {}'.format(len(horizontal_errors_inner_df), len(horizontal_errors_inner_df) + len(abovetolerances), date[1]), fontsize=15)\n",
    "ax2.set_ylabel('Horizontal Errors (m)', fontsize = 14)\n",
    "ax3 = plt.plot([len(horizontal_errors_inner_df) - (len(horizontal_errors_inner_df)) / 10, len(horizontal_errors_inner_df)], [RMSE_horizontal_inner, RMSE_horizontal_inner], color = 'lime', linestyle = '--', linewidth = 5)\n",
    "ax4 = plt.plot([len(horizontal_errors_inner_df) - (len(horizontal_errors_inner_df)) / 10, len(horizontal_errors_inner_df)], [TWOdRMSE_horizontal_inner, TWOdRMSE_horizontal_inner], color = 'gold', linestyle = '--', linewidth = 5)\n",
    "ax2.legend(('Horizontal Errors \\xb1 (m)', 'Horizontal RMSE \\xb1 {}m'.format(round(RMSE_horizontal_inner, 3)), 'Horizontal 2dRMSE \\xb1 {}m'.format(round(TWOdRMSE_horizontal_inner, 3))), shadow=True, handlelength=1.5, fontsize=12, bbox_to_anchor=(0.775, -0.15), loc = 'lower left')\n",
    "ax2.tick_params(axis='y', labelcolor=color, labelsize = 12)\n",
    "strFileO = '{0}{1}P_The Horizontal PRECISION of the point (INNER accuracy) (m) with DOP values.png'.format(session_folder_path, os.sep)\n",
    "if os.path.isfile(strFileO):\n",
    "    os.remove(strFileO)\n",
    "fig.savefig('{0}{1}P_The Horizontal PRECISION of the point (INNER accuracy) (m) with DOP values.png'.format(session_folder_path, os.sep))\n",
    "plt.show()\n",
    "\n",
    "figures_df.loc[15, 'The Figure File Name'] = 'P_The Horizontal PRECISION of the point (INNER accuracy) (m) with DOP values.png'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Piirretään kuvaajat sisäisille tarkkuuksille ja DOP-arvoille havaintojen funktiona: 3D-tarkkuudet. / Let's draw the figures for inner accuracies (precisions) and DOP values as a function of the measurements: 3D precisions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def THREED_errors_inner(coordinates1, mean1, coordinates2, mean2, coordinates3, mean3):\n",
    "    THREED_errors = np.zeros(shape=(len(coordinates1), 3))\n",
    "    i = 0     \n",
    "    while i < len(coordinates1):\n",
    "        THREED_errors[i, 0] = coordinates1[i] - mean1\n",
    "        THREED_errors[i, 1] = coordinates2[i] - mean2\n",
    "        THREED_errors[i, 2] = coordinates3[i] - mean3\n",
    "        i += 1\n",
    "    return THREED_errors\n",
    "\n",
    "THREED_errors_inner = THREED_errors_inner(northcoordinates, mean_n, eastcoordinates, mean_e, heights, mean_h)\n",
    "THREED_errors_inner_df = pd.DataFrame({'North Errors (m)':THREED_errors_inner[:,0], 'East Errors (m)':THREED_errors_inner[:,1], 'Height Errors (m)':THREED_errors_inner[:,2]})\n",
    "\n",
    "# ----------------------------------------------------------------------------------\n",
    "def RMSE_THREED_inner(coordinates1, mean1, coordinates2, mean2, coordinates3, mean3):\n",
    "    THREEDerror_square = 0\n",
    "    i = 0     \n",
    "    while i < len(coordinates1):\n",
    "        THREEDerror_square += (np.sqrt((coordinates1[i] - mean1)**2 + (coordinates2[i] - mean2)**2) + (coordinates3[i] - mean3)**2)**2\n",
    "        i += 1\n",
    "    RMSE = np.sqrt(THREEDerror_square/len(coordinates1))\n",
    "    return RMSE\n",
    "\n",
    "RMSE_THREED_inner = RMSE_THREED_inner(northcoordinates, mean_n, eastcoordinates, mean_e, heights, mean_h)\n",
    "TWOdRMSE_THREED_inner = RMSE_THREED_inner * 2\n",
    "\n",
    "# ----------------------------------------------------------------------------------\n",
    "\n",
    "THREED_errors_inner_df['3D Errors (m)'] = ((THREED_errors_inner_df.loc[:, 'East Errors (m)'])**2 + (THREED_errors_inner_df.loc[:, 'North Errors (m)'])**2 + (THREED_errors_inner_df.loc[:, 'Height Errors (m)'])**2).pow(1./2)\n",
    "THREED_errors_inner_df['The Number of the Measurement'] = range(0, len(THREED_errors_inner_df))\n",
    "x = range(len(testmeasurements_df_no_outliers.loc[:, 'HDOP']))\n",
    "y = [testmeasurements_df_no_outliers.loc[:, 'PDOP']]\n",
    "mean_PDOP = round(testmeasurements_df_no_outliers.loc[:, 'PDOP'].mean(), 1)\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(15, 12))\n",
    "fig.suptitle('{}'.format(name_of_GNSS_device_or_NRTK_method), fontsize = 20)\n",
    "ax1.stackplot(x, y, labels=['PDOP (Mean = {})'.format(mean_PDOP)], color = (0.30196078431372547, 0.6862745098039216, 0.2901960784313726), alpha=0.7)\n",
    "ax1.set_xlabel('The Number of the Measurement', fontsize=14)\n",
    "ax1.set_ylabel('DOP Value', fontsize=14)\n",
    "ax1.legend(bbox_to_anchor=(-0.005, -0.1), loc='lower left', fontsize=12)\n",
    "ax1.tick_params(axis='y', labelsize = 12)\n",
    "\n",
    "color = (0.596078431372549, 0.3058823529411765, 0.6392156862745098)\n",
    "ax2 = ax1.twinx()\n",
    "ax2 = sns.lineplot(x = 'The Number of the Measurement', y = '3D Errors (m)', data = THREED_errors_inner_df, color = color, linewidth = 1)\n",
    "ax2.set_title('The 3D PRECISION of the point (INNER accuracy) (m) with DOP values, number of measurements {}/{}: 3D. Date: {}'.format(len(THREED_errors_inner_df), len(THREED_errors_inner_df) + len(abovetolerances), date[1]), fontsize=15)\n",
    "ax2.set_ylabel('3D Errors (m)', fontsize = 14)\n",
    "ax3 = plt.plot([len(THREED_errors_inner_df) - (len(THREED_errors_inner_df)) / 10, len(THREED_errors_inner_df)], [RMSE_THREED_inner, RMSE_THREED_inner], color = 'lime', linestyle = '--', linewidth = 5)\n",
    "ax4 = plt.plot([len(THREED_errors_inner_df) - (len(THREED_errors_inner_df)) / 10, len(THREED_errors_inner_df)], [TWOdRMSE_THREED_inner, TWOdRMSE_THREED_inner], color = 'gold', linestyle = '--', linewidth = 5)\n",
    "ax2.legend(('3D Errors \\xb1 (m)', '3D RMSE \\xb1 {}m'.format(round(RMSE_THREED_inner, 3)), '3D 2dRMSE \\xb1 {}m'.format(round(TWOdRMSE_THREED_inner, 3))), shadow=True, handlelength=1.5, fontsize=12, bbox_to_anchor=(0.775, -0.15), loc = 'lower left')\n",
    "ax2.tick_params(axis='y', labelcolor=color, labelsize = 12)\n",
    "strFileP = '{0}{1}Q_The 3D PRECISION of the point (INNER accuracy) (m) with DOP values.png'.format(session_folder_path, os.sep)\n",
    "if os.path.isfile(strFileP):\n",
    "    os.remove(strFileP)\n",
    "fig.savefig('{0}{1}Q_The 3D PRECISION of the point (INNER accuracy) (m) with DOP values.png'.format(session_folder_path, os.sep))\n",
    "plt.show()\n",
    "\n",
    "figures_df.loc[16, 'The Figure File Name'] = 'Q_The 3D PRECISION of the point (INNER accuracy) (m) with DOP values.png'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Piirretään kuvaajat sisäisille tarkkuuksille ja DOP-arvoille havaintojen funktiona: vertikaalitarkkuudet. / Let's draw the figures for inner accuracies (precisions) and DOP values as a function of the measurements: vertical precisions::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vertical_errors_inner_df['The Number of the Measurement'] = range(0, len(vertical_errors_inner_df))\n",
    "x = range(len(testmeasurements_df_no_outliers.loc[:, 'HDOP']))\n",
    "y = [testmeasurements_df_no_outliers.loc[:, 'VDOP']]\n",
    "mean_VDOP = round(testmeasurements_df_no_outliers.loc[:, 'VDOP'].mean(), 1)\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(15, 12))\n",
    "fig.suptitle('{}'.format(name_of_GNSS_device_or_NRTK_method), fontsize = 20)\n",
    "ax1.stackplot(x, y, labels=['VDOP (Mean = {})'.format(mean_VDOP)], alpha=0.7, color = (1.0, 0.4980392156862745, 0.0))\n",
    "ax1.set_xlabel('The Number of the Measurement', fontsize=14)\n",
    "ax1.set_ylabel('DOP Value', fontsize=14)\n",
    "ax1.legend(bbox_to_anchor=(-0.005, -0.1), loc='lower left', fontsize=12)\n",
    "ax1.tick_params(axis='y', labelsize = 12)\n",
    "\n",
    "color = (0.21568627450980393, 0.49411764705882355, 0.7215686274509804)\n",
    "ax2 = ax1.twinx()\n",
    "ax2 = sns.lineplot(x = 'The Number of the Measurement', y = 'Vertical Errors (m)', data = vertical_errors_inner_df, color = color, linewidth = 1)\n",
    "ax2.set_title('The Vertical PRECISION of the point (INNER accuracy) (m) with DOP values, number of measurements {}/{}: VERTICAL. Date: {}'.format(len(vertical_errors_inner_df), len(vertical_errors_inner_df) + len(abovetolerances), date[1]), fontsize=15)\n",
    "ax2.set_ylabel('Vertical Errors (m)', fontsize = 14)\n",
    "ax3 = plt.plot([vertical_errors_inner_df.loc[0, 'The Number of the Measurement'], len(vertical_errors_inner_df)], [RMSE_vertical_inner, RMSE_vertical_inner], color = 'lime', linestyle = '--', linewidth = 5)\n",
    "ax4 = plt.plot([vertical_errors_inner_df.loc[0, 'The Number of the Measurement'], len(vertical_errors_inner_df)], [TWOdRMSE_vertical_inner, TWOdRMSE_vertical_inner], color = 'gold', linestyle = '--', linewidth = 5)\n",
    "ax5 = plt.plot([vertical_errors_inner_df.loc[0, 'The Number of the Measurement'], len(vertical_errors_inner_df)], [-RMSE_vertical_inner, -RMSE_vertical_inner], color = 'lime', linestyle = '--', linewidth = 5)\n",
    "ax6 = plt.plot([vertical_errors_inner_df.loc[0, 'The Number of the Measurement'], len(vertical_errors_inner_df)], [-TWOdRMSE_vertical_inner, -TWOdRMSE_vertical_inner], color = 'gold', linestyle = '--', linewidth = 5)\n",
    "ax2.legend(('Vertical Errors (m)', 'Vertical RMSE \\xb1 {}m'.format(round(RMSE_vertical_inner, 3)), 'Vertical 2dRMSE \\xb1 {}m'.format(round(TWOdRMSE_vertical_inner, 3))), shadow=True, handlelength=1.5, fontsize=12, bbox_to_anchor=(0.775, -0.15), loc = 'lower left')\n",
    "ax2.tick_params(axis='y', labelcolor=color, labelsize = 12)\n",
    "strFileQ = '{0}{1}R_The Vertical PRECISION of the point (INNER accuracy) (m) with DOP values.png'.format(session_folder_path, os.sep)\n",
    "if os.path.isfile(strFileQ):\n",
    "    os.remove(strFileQ)\n",
    "fig.savefig('{0}{1}R_The Vertical PRECISION of the point (INNER accuracy) (m) with DOP values.png'.format(session_folder_path, os.sep))\n",
    "plt.show()\n",
    "\n",
    "figures_df.loc[17, 'The Figure File Name'] = 'R_The Vertical PRECISION of the point (INNER accuracy) (m) with DOP values.png'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tehdään .pdf-tiedosto./ Let's produce a .pdf-file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://www.reportlab.com/docs/reportlab-userguide.pdf\n",
    "# https://leanpub.com/reportlab\n",
    "# https://www.blog.pythonlibrary.org/2010/03/08/a-simple-step-by-step-reportlab-tutorial/\n",
    "\n",
    "import glob\n",
    "import re\n",
    "\n",
    "from reportlab.lib import colors\n",
    "from reportlab.lib.styles import ParagraphStyle as PS\n",
    "from reportlab.platypus import SimpleDocTemplate, Paragraph, Image, PageBreak, Table, TableStyle\n",
    "from reportlab.platypus.doctemplate import PageTemplate, BaseDocTemplate\n",
    "from reportlab.platypus.doctemplate import Frame\n",
    "from reportlab.platypus.tableofcontents import TableOfContents\n",
    "from reportlab.lib.pagesizes import A1, A2, A3, A4\n",
    "from reportlab.lib.units import cm\n",
    "import string\n",
    "import datetime\n",
    "from time import strftime\n",
    "\n",
    "# ------------------------------------------------------------------------------------------\n",
    "class MyDocTemplate(BaseDocTemplate):\n",
    "    \n",
    "    def __init__(self, filename, **kw):\n",
    "        self.allowSplitting = 0\n",
    "        BaseDocTemplate.__init__(self, filename, **kw)\n",
    "        template = PageTemplate('normal', [Frame(2.5*cm, 2.5*cm, 15*cm, 25*cm, id='F1')])\n",
    "        self.addPageTemplates(template)\n",
    "\n",
    "    def afterFlowable(self, flowable):\n",
    "        \"Registers TOC entries.\"\n",
    "        if flowable.__class__.__name__ == 'Paragraph':\n",
    "            text = flowable.getPlainText()\n",
    "            style = flowable.style.name\n",
    "            if style == 'Heading1':\n",
    "                self.notify('TOCEntry', (0, text, self.page))\n",
    "            if style == 'Heading2':\n",
    "                self.notify('TOCEntry', (1, text, self.page))\n",
    "\n",
    "h_title = PS(name = 'h_title', fontSize = 36, leading = 18)             \n",
    "h1 = PS(name = 'Heading1', fontSize = 14, leading = 16)\n",
    "h2 = PS(name = 'Heading2', fontSize = 12, leading = 14, leftIndent = 1*cm)\n",
    "# ------------------------------------------------------------------------------------------\n",
    "def create_pdf(fname, front_cover, back_cover, path_figures, path_session, testmeasurements_name, encrypt_pswd):\n",
    "    filename = '{0}{3}{1}{2}'.format(path_session, fname, '.pdf', os.sep)\n",
    "    doc = SimpleDocTemplate(filename, pagesize = A4, rightMargin = 0, leftMargin= 0, topMargin = 0, bottomMargin= 0)\n",
    "    Story = []\n",
    "    toc = TableOfContents()\n",
    "    width = 12.5*cm\n",
    "    height = 6*cm    \n",
    "    figs = []\n",
    "    toc.levelStyles = [h1, h2]\n",
    "    Story.append(Paragraph('This PDF document was produced:', PS('body')))\n",
    "    now1 = datetime.datetime.now()\n",
    "    now1 = now1.strftime('%Y-%m-%d %H:%M:%S')\n",
    "    now2 = str(datetime.date.today())\n",
    "    Story.append(Paragraph(now1, PS('body')))\n",
    "    Story.append(Paragraph('<br/><br/><br/><br/><br/><br/><br/>NLS GNSS SOFAMESA<br/><br/>', h_title))\n",
    "    Story.append(Paragraph('GNSS Position Solution Analysis Software of the National Land Survey of Finland<br/>Beta Version 0.2<br/>{}<br/><br/><br/><br/><br/><br/><br/>'.format(now2), PS('body')))\n",
    "    Story.append(Paragraph('Measurement Report of <br/><br/>the File: <br/>', h_title))\n",
    "    Story.append(Paragraph('<br/><br/>{0}<br/><br/>Timespan: {1} - {2}<br/><br/><br/><br/>'.format(testmeasurements_name, testmeasurements_df['Time'].iloc[0], testmeasurements_df['Time'].iloc[-1]), PS('body')))\n",
    "    Story.append(Image(front_cover, width, height))\n",
    "    credits = ('\\nAuthor: Tuukka Mattila.\\nDo not hesitate to contact via LinkedIn: https://www.linkedin.com/in/tuukkamattila/.\\n\\nThank you for the help: Topi Rikkinen, Marko Ollikainen, Antti Laaksonen, Hannu Koivula, Octavian Andrei, Mikael Kauhava, Timo Sallinen and Ari Huvinen.').replace('\\n','<br />\\n')\n",
    "    Story.append(Paragraph(credits, PS('body')))\n",
    "    Story.append(PageBreak())\n",
    "    Story.append(toc)\n",
    "    Story.append(PageBreak())\n",
    "    Story.append(Paragraph('Statistics Behind the Figures <br/><br/>', h1))\n",
    "    tb1 = Table(np.vstack((list(table1_df), np.array(table1_df))).tolist())\n",
    "    tb1style = TableStyle([('FONT', (0, 0), (-1, 0), 'Helvetica-Bold', 12),\n",
    "                           ('FONT', (0, 1), (0, -1), 'Helvetica-Bold'),\n",
    "                           ('GRID', (0, 0), (-1,-1), 0.5, colors.blue),\n",
    "                           ('LINEABOVE', (0, 0), (-1, 0), 2.5, colors.blue),\n",
    "                           ('ALIGN', (-1, 0), (-1, -1), 'CENTER'),\n",
    "                          ('LINEBELOW', (0, 0), (-1, 0), 2.5, colors.blue),\n",
    "                          ('LINEBEFORE', (0, 0), (0, -1), 2.5, colors.blue),\n",
    "                           ('LINEAFTER', (-1, 0), (-1, -1), 2.5, colors.blue)])\n",
    "    tb1.setStyle(tb1style)\n",
    "    tb2 = Table(np.vstack((list(table2_df), np.array(table2_df))).tolist())\n",
    "    tb2style = TableStyle([('FONT', (0, 0), (-1, 0), 'Helvetica-Bold', 12),\n",
    "                           ('FONT', (0, 1), (0, -1), 'Helvetica-Bold'),\n",
    "                           ('GRID', (0, 0), (-1,-1), 0.5, colors.green),\n",
    "                           ('LINEABOVE', (0, 0), (-1, 0), 2.5, colors.green),\n",
    "                           ('ALIGN', (-2, 0), (-1, -1), 'CENTER'),\n",
    "                          ('LINEBELOW', (0, 0), (-1, 0), 2.5, colors.green),\n",
    "                          ('LINEBEFORE', (0, 0), (0, -1), 2.5, colors.green),\n",
    "                           ('LINEAFTER', (-1, 0), (-1, -1), 2.5, colors.green)])\n",
    "    tb2.setStyle(tb2style)\n",
    "    Story.append(tb1)\n",
    "    Story.append(Paragraph('<br/><br/>', PS('body')))\n",
    "    Story.append(PageBreak())\n",
    "    Story.append(tb2)\n",
    "    Story.append(Paragraph('<br/><br/>', PS('body')))\n",
    "    Story.append(Paragraph('* The precision of the measurements = inner accuracy.<br/><br/>', PS('body')))\n",
    "    Story.append(Paragraph('** The accuracy of the measurements = outer accuracy.<br/><br/>', PS('body')))\n",
    "    Story.append(Paragraph('*** Horizontal RMSE is 63-68% of the measurements and it depends on the shape of the distribution. Horizontal 2dRMSE is 95-98% of the measurements and it also depends on the shape of the distribution.<br/><br/>', PS('body')))\n",
    "    Story.append(Paragraph('**** Vertical RMSE is 63-68% of the measurements and it depends on the shape of the distribution. Vertical 2dRMSE is 95-98% of the measurements and it also depends on the shape of the distribution.<br/><br/>', PS('body')))\n",
    "    Story.append(PageBreak())\n",
    "    Story.append(Paragraph('Figures <br/><br/>', h1))\n",
    "    Story.append(Paragraph('Please find the figures, which NLS GNSS SOFAMESA produces, on the upcoming pages.', PS('body')))\n",
    "    Story.append(PageBreak())\n",
    "    for index, row in figures_df.itertuples():\n",
    "        fig_title = figures_df.loc[index, 'The Figure File Name'].split('.')[0]\n",
    "        fig_title = fig_title.split('_')[1]\n",
    "        Story.append(Paragraph('{}'.format(fig_title), h2))\n",
    "        Story.append(Paragraph('<br/><br/>', PS('body')))\n",
    "        width1 = 20*cm\n",
    "        height1 = 15*cm\n",
    "        im = Image(pathlib.Path(path_figures).joinpath(row), width1, height1)\n",
    "        im.hAlign = 'CENTER'\n",
    "        Story.append(im)\n",
    "        Story.append(PageBreak())\n",
    "    Story.append(Paragraph('Conclusions', h1))\n",
    "    Story.append(PageBreak())\n",
    "    Story.append(Paragraph('This PDF document was produced:', PS('body')))\n",
    "    now1 = datetime.datetime.now()\n",
    "    now1 = now1.strftime('%Y-%m-%d %H:%M:%S')\n",
    "    Story.append(Paragraph(now1, PS('body')))\n",
    "    Story.append(Paragraph('<br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/>NLS GNSS SOFAMESA<br/><br/>', h_title))\n",
    "    Story.append(Paragraph('GNSS Position Solution Analysis Software of the National Land Survey of Finland<br/>Beta Version 0.2<br/>{}<br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/>'.format(now2), PS('body')))\n",
    "    Story.append(Image(back_cover, width, height))\n",
    "    Story.append(Paragraph(credits, PS('body')))\n",
    "    \n",
    "    doc = MyDocTemplate(filename,\n",
    "                        title = '{} analyzed by NLS GNSS SOFAMESA: GNSS Position Solution Analysis Software of the National Land Survey of Finland'.format(testmeasurements_name),\n",
    "                        author = 'Tuukka Mattila. Do not hesitate to contact via LinkedIn: https://www.linkedin.com/in/tuukkamattila/',\n",
    "                        encrypt = encrypt_pswd)\n",
    "    doc.multiBuild(Story)\n",
    "        \n",
    "    print(\"{} created\".format(filename))\n",
    "    return filename\n",
    "\n",
    "#----------------------------------------------------------------------\n",
    "if __name__ == \"__main__\":\n",
    "    path_figures = '{0}{3}{1}{3}{2}'.format(GNSS_device_or_NRTK_method_folder_path, testmeasurements_df.name, '{}_figures'.format(testmeasurements_df.name), os.sep)\n",
    "    path_pdf_accessories = pathlib.Path(pathlib.Path(pathlib.Path.cwd()).joinpath(\"for_the_pdf_report\"))\n",
    "    path_session = '{0}{2}{1}'.format(GNSS_device_or_NRTK_method_folder_path, testmeasurements_df.name, os.sep)\n",
    "    front_cover = os.path.join(path_pdf_accessories, \"NLS_logo1.jpg\")\n",
    "    back_cover = os.path.join(path_pdf_accessories, \"NLS_logo2.jpg\")\n",
    "    filename = \"{}_Measurement_Report\".format(testmeasurements_df.name)\n",
    "    encrypt_pswd = None\n",
    "# encrypt_doc = input(\"Do you want to set up a password for the measurement meport .pdf file? Enter 'yes' or 'no': \")\n",
    "# if encrypt_doc == 'yes':\n",
    "#    encrypt_pswd = input(\"Enter a password for the measurement report .pdf file: \")\n",
    "#    filename_path = create_pdf(filename, front_cover, back_cover, path_figures, path_session, testmeasurements_df.name, encrypt_pswd)\n",
    "# elif encrypt_doc == 'no':\n",
    "#    encrypt_pswd = None\n",
    "#    filename_path = create_pdf(filename, front_cover, back_cover, path_figures, path_session, testmeasurements_df.name, encrypt_pswd)\n",
    "    filename_path = create_pdf(filename, front_cover, back_cover, path_figures, path_session, testmeasurements_df.name, encrypt_pswd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_ending = time.time()\n",
    "display(Markdown('**This program took {}s to finish!**'.format(round(time_ending-time_beginning, 2))))"
   ]
  },
  {
   "attachments": {
    "NLS_logo.jpg": {
     "image/jpeg": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![NLS_logo.jpg](attachment:NLS_logo.jpg)\n",
    "\n",
    "                    Author: Tuukka Mattila\n",
    "                    Do not hesitate to contact via LinkedIn:\n",
    "                    https://www.linkedin.com/in/tuukkamattila/\n",
    "                    \n",
    "                    Thank you for the help: Topi Rikkinen, Marko Ollikainen, Antti Laaksonen,\n",
    "                    Hannu Koivula, Octavian Andrei, Mikael Kauhava, Timo Sallinen and Ari Huvinen"
   ]
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