eurec4a_rvmeteor_instruments_pitch_roll_histogram_daily_resample.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import glob\n",
    "import os\n",
    "import datetime as dt\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker \n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify the date\n",
    "day = 20200216\n",
    "date = dt.datetime(2020, 2, 16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define paths for loading data\n",
    "directory = '/home/jari/Documents/IPSP/Bachelorarbeit/'    \n",
    "seapath = sorted(glob.glob(os.path.join(directory + 'DSHIP/RV*.dat')))\n",
    "arduino_pitch = sorted(glob.glob(os.path.join(directory + 'arduino_clean_data/2*_pitch_cleanest.csv')))\n",
    "arduino_roll = sorted(glob.glob(os.path.join(directory + 'arduino_clean_data/2*_roll_cleanest.csv')))\n",
    "limrad94 = sorted(glob.glob(os.path.join(directory + 'angles/*.csv')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create functions for reading data\n",
    "def read_seapath(day):\n",
    "    df_seapath = pd.read_csv(f'/home/jari/Documents/IPSP/Bachelorarbeit/DSHIP/RV-Meteor_DSHIP_all_1Hz_{day}.dat',\n",
    "        encoding='windows-1252', sep='\\t', na_values='-999.0').loc[:,['date time','FOG.PPPRP.pitch','FOG.PPPRP.roll']]\n",
    "    df_seapath.set_index('date time', inplace=True)\n",
    "    df_seapath.index = pd.to_datetime(df_seapath.index, format=\"%Y-%m-%d %H:%M:%S\")\n",
    "    return df_seapath\n",
    "\n",
    "def read_arduino_pitch(day):\n",
    "    df_arduino_pitch = pd.read_csv(f'/home/jari/Documents/IPSP/Bachelorarbeit/arduino_clean_data/{day}_attitude_arduino_pitch_cleanest.csv',\n",
    "        na_values='-999.0')\n",
    "    df_arduino_pitch.set_index('datetime', inplace=True)\n",
    "    df_arduino_pitch.index = pd.to_datetime(df_arduino_pitch.index, format=\"%Y-%m-%d %H:%M:%S\")\n",
    "    return df_arduino_pitch\n",
    "\n",
    "def read_arduino_roll(day):\n",
    "    df_arduino_roll = pd.read_csv(f'/home/jari/Documents/IPSP/Bachelorarbeit/arduino_clean_data/{day}_attitude_arduino_roll_cleanest.csv',\n",
    "        na_values='-999.0')\n",
    "    df_arduino_roll.set_index('datetime', inplace=True)\n",
    "    df_arduino_roll.index = pd.to_datetime(df_arduino_roll.index, format=\"%Y-%m-%d %H:%M:%S\")\n",
    "    return df_arduino_roll\n",
    "    \n",
    "def read_limrad94(day):\n",
    "    df_limrad94 = (pd.read_csv(f'/home/jari/Documents/IPSP/Bachelorarbeit/angles/RV-Meteor_cloudradar_attitude-angles_{day}.csv',\n",
    "        na_values='-999.0').loc[:,['datetime', 'roll', 'pitch']])\n",
    "    df_limrad94.set_index('datetime', inplace=True)\n",
    "    df_limrad94.index = pd.to_datetime(df_limrad94.index, format=\"%Y-%m-%d\")\n",
    "    return df_limrad94"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list with the days\n",
    "days = []\n",
    "for file in arduino_pitch:\n",
    "    days.append(file[-44:-36])\n",
    "days1 = days[0:9]\n",
    "days2 = days[9:20]\n",
    "days3 = days[20:30]\n",
    "days4 = days[30:40]\n",
    "\n",
    "# Create a list with the days in datetime format\n",
    "dates1 = []\n",
    "dates2 = []\n",
    "dates3 = []\n",
    "dates4 = []\n",
    "for day in days1:\n",
    "    dates1.append(dt.datetime.strptime(day, '%Y%m%d'))\n",
    "for day in days2:\n",
    "    dates2.append(dt.datetime.strptime(day, '%Y%m%d'))\n",
    "for day in days3:\n",
    "    dates3.append(dt.datetime.strptime(day, '%Y%m%d'))\n",
    "for day in days4:\n",
    "    dates4.append(dt.datetime.strptime(day, '%Y%m%d'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jari/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3254: DtypeWarning: Columns (1,2,8) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n",
      "/home/jari/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3254: DtypeWarning: Columns (1,2,8) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n",
      "/home/jari/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3254: DtypeWarning: Columns (1,2,8) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n",
      "/home/jari/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3254: DtypeWarning: Columns (1,2,8) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n",
      "/home/jari/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3254: DtypeWarning: Columns (1,2,8) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n",
      "/home/jari/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3254: DtypeWarning: Columns (1,2,8) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n",
      "/home/jari/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3254: DtypeWarning: Columns (1,2,8) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n",
      "/home/jari/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3254: DtypeWarning: Columns (1,2,8) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n",
      "/home/jari/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3254: DtypeWarning: Columns (1,2,8) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n",
      "/home/jari/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3254: DtypeWarning: Columns (1,2,8) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n",
      "/home/jari/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3254: DtypeWarning: Columns (1,2,8) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n",
      "/home/jari/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3254: DtypeWarning: Columns (1,2,8) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n",
      "/home/jari/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3254: DtypeWarning: Columns (1,2,8) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n",
      "/home/jari/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3254: DtypeWarning: Columns (1,2,8) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n",
      "/home/jari/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3254: DtypeWarning: Columns (1,2,8) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n",
      "/home/jari/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3254: DtypeWarning: Columns (1,2,8) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n",
      "/home/jari/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3254: DtypeWarning: Columns (1,2,8) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n",
      "/home/jari/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3254: DtypeWarning: Columns (1,2,8) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n",
      "/home/jari/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3254: DtypeWarning: Columns (1,2,8) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n",
      "/home/jari/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3254: DtypeWarning: Columns (1,2,8) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n"
     ]
    }
   ],
   "source": [
    "for day in days4:\n",
    "    for date in dates4:\n",
    "        # Create DataFrame\n",
    "        seapath = read_seapath(day)\n",
    "        seapath = seapath.set_index(seapath.index.round('S')).resample('2S').mean()\n",
    "        arduino_pitch = read_arduino_pitch(day)\n",
    "        arduino_pitch = arduino_pitch.set_index(arduino_pitch.index.round('S')).resample('2S').mean()\n",
    "        arduino_roll = read_arduino_roll(day) \n",
    "        arduino_roll = arduino_roll.set_index(arduino_roll.index.round('S')).resample('2S').mean()\n",
    "        limrad94 = read_limrad94(day)\n",
    "        limrad94 = limrad94.set_index(limrad94.index.round('S')).resample('2S').mean()\n",
    "\n",
    "        instruments = pd.concat([seapath['FOG.PPPRP.pitch'], seapath['FOG.PPPRP.roll'], arduino_pitch['pitch_deg'],\n",
    "                             arduino_roll['roll_deg'], limrad94['pitch'], limrad94['roll']], axis=1).dropna()\n",
    "        instruments.rename(columns = {'FOG.PPPRP.pitch':'seapath_pitch', 'FOG.PPPRP.roll':'seapath_roll',\n",
    "                                      'pitch_deg':'arduino_pitch', 'roll_deg':'arduino_roll',\n",
    "                                      'pitch':'limrad94_pitch', 'roll':'limrad94_roll'}, inplace=True)\n",
    "        \n",
    "        # Plot histogram\n",
    "        fig, axs = plt.subplots(2, 1, sharex=False, figsize=(15,7))\n",
    "\n",
    "        axs[0].set(xlim=(-5, 5))\n",
    "        axs[0].set(ylim=(0, 5))\n",
    "        axs[1].set(xlim=(-5, 5))\n",
    "        axs[1].set(ylim=(0, 5))\n",
    "                   \n",
    "        sns.set_theme(style='darkgrid', palette='bright')\n",
    "\n",
    "        sns.kdeplot(instruments['seapath_pitch'], ax=axs[0], shade=True, label='Seapath (10Hz)')\n",
    "        sns.kdeplot(instruments['arduino_pitch'], ax=axs[0], shade=True, label='Arduino (4Hz)')\n",
    "        sns.kdeplot(instruments['limrad94_pitch'], ax=axs[0], shade=True, label='LIMRAD94 (0.5Hz)')\n",
    "        \n",
    "        # Upper plot\n",
    "        axs[0].axvline(x=0, lw=0.5, color='black')\n",
    "\n",
    "        axs[0].tick_params(which=\"both\", bottom=True)\n",
    "        x_lims = axs[0].get_xlim()\n",
    "        axs[0].xaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "        axs[0].set_xticks(np.arange(x_lims[0],x_lims[1], 0.5), minor=True)\n",
    "        axs[0].tick_params(axis='x', which='minor', length=3)\n",
    "        axs[0].tick_params(axis='x', which='major', length=7, width=1)\n",
    "\n",
    "        axs[0].set_ylabel('Prob. density')\n",
    "        axs[0].set_xlabel('Pitch motion [°]')\n",
    "        axs[0].set_title('Pitch (top) vs. roll (bottom) motion for Seapath & Arduino & LIMRAD94 sensors \\n '\n",
    "                     f'EUREC4A - RV-Meteor - {date:%Y-%m-%d}')\n",
    "        axs[0].legend()\n",
    "\n",
    "        sns.kdeplot(instruments['seapath_roll'], ax=axs[1], shade=True, label='Seapath (10Hz)')\n",
    "        sns.kdeplot(instruments['arduino_roll'], ax=axs[1], shade=True, label='Arduino (4Hz)')\n",
    "        sns.kdeplot(instruments['limrad94_roll'], ax=axs[1], shade=True, label='LIMRAD94 (0.5Hz)')\n",
    "        \n",
    "        # Bottom plot\n",
    "        axs[1].axvline(x=0, lw=0.5, color='black')\n",
    "\n",
    "        axs[1].tick_params(which=\"both\", bottom=True)\n",
    "        x_lims = axs[1].get_xlim()\n",
    "        axs[1].xaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "        axs[1].set_xticks(np.arange(x_lims[0],x_lims[1], 0.5), minor=True)\n",
    "        axs[1].tick_params(axis='x', which='minor', length=3)\n",
    "        axs[1].tick_params(axis='x', which='major', length=7, width=1)\n",
    "\n",
    "        axs[1].set_xlabel('Roll motion [°]')\n",
    "        axs[1].set_ylabel('Prob. density')\n",
    "        axs[1].legend()\n",
    "\n",
    "        #plt.suptitle('Pitch (top) vs. roll (bottom) motion for Seapath & Arduino & LIMRAD94 sensors \\n '\n",
    "        #             f'EUREC4A - RV-Meteor - {date:%Y-%m-%d}')\n",
    "\n",
    "        fig.tight_layout()\n",
    "        plt.subplots_adjust(top=0.91)\n",
    "        \n",
    "        # Save plot\n",
    "#        plt.savefig(f'/home/jari/Desktop/test/eurec4a_rvmeteor_{day}_instruments_pitch_roll_histogram_resample.pdf')\n",
    "        plt.savefig(f'/home/jari/Desktop/test/eurec4a_rvmeteor_{day}_instruments_pitch_roll_histogram_resample.png')\n",
    "        plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Create dataframes\n",
    "seapath = read_seapath(day)\n",
    "arduino_pitch = read_arduino_pitch(day)\n",
    "arduino_roll = read_arduino_roll(day)\n",
    "limrad94 = read_limrad94(day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Unify the index\n",
    "seapath = seapath.set_index(seapath.index.round('S')).resample('2S').mean()\n",
    "arduino_pitch = arduino_pitch.set_index(arduino_pitch.index.round('S')).resample('2S').mean()\n",
    "arduino_roll = arduino_roll.set_index(arduino_roll.index.round('S')).resample('2S').mean()\n",
    "#arduino_pitch = arduino_pitch.groupby('datetime').first()\n",
    "#arduino_roll = arduino_roll.groupby('datetime').first()\n",
    "limrad94 = limrad94.set_index(limrad94.index.round('S')).resample('2S').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Merge dataframes\n",
    "instruments = pd.concat([seapath['FOG.PPPRP.pitch'], seapath['FOG.PPPRP.roll'], arduino_pitch['pitch_deg'],\n",
    "                         arduino_roll['roll_deg'], limrad94['pitch'], limrad94['roll']], axis=1).dropna()\n",
    "\n",
    "instruments.rename(columns = {'FOG.PPPRP.pitch':'seapath_pitch', 'FOG.PPPRP.roll':'seapath_roll',\n",
    "                              'pitch_deg':'arduino_pitch', 'roll_deg':'arduino_roll',\n",
    "                              'pitch':'limrad94_pitch', 'roll':'limrad94_roll'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 1, sharex=False, figsize=(15,10))\n",
    "\n",
    "axs[0].set(xlim=(-4, 4))\n",
    "axs[0].set(ylim=(0, 5))\n",
    "axs[1].set(xlim=(-4, 4))\n",
    "axs[1].set(ylim=(0, 5))\n",
    "\n",
    "#fig = plt.gcf()\n",
    "#fig.set_size_inches(15, 7)\n",
    "sns.set_theme(style='darkgrid', palette='bright')\n",
    "\n",
    "sns.kdeplot(instruments['seapath_pitch'], ax=axs[0], shade=True, label='Seapath (10Hz)')\n",
    "sns.kdeplot(instruments['arduino_pitch'], ax=axs[0], shade=True, label='Arduino (4Hz)')\n",
    "sns.kdeplot(instruments['limrad94_pitch'], ax=axs[0], shade=True, label='LIMRAD94 (0.5Hz)')\n",
    "\n",
    "axs[0].axvline(x=0, lw=0.5, color='black')\n",
    "\n",
    "axs[0].tick_params(which=\"both\", bottom=True)\n",
    "x_lims = axs[0].get_xlim()\n",
    "axs[0].xaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "axs[0].set_xticks(np.arange(x_lims[0],x_lims[1], 0.5), minor=True)\n",
    "axs[0].tick_params(axis='x', which='minor', length=3)\n",
    "axs[0].tick_params(axis='x', which='major', length=7, width=1)\n",
    "\n",
    "axs[0].set_ylabel('Prob. density')\n",
    "axs[0].set_xlabel('Pitch motion [°]')\n",
    "axs[0].legend()\n",
    "\n",
    "sns.kdeplot(instruments['seapath_roll'], ax=axs[1], shade=True, label='Seapath (10Hz)')\n",
    "sns.kdeplot(instruments['arduino_roll'], ax=axs[1], shade=True, label='Arduino (4Hz)')\n",
    "sns.kdeplot(instruments['limrad94_roll'], ax=axs[1], shade=True, label='LIMRAD94 (0.5Hz)')\n",
    "\n",
    "#plt.title(f\"Seapath vs. Arduino vs. LIMRAD94 probability density for roll motion \\n EUREC4A - RV-METEOR - {date:%Y-%m-%d}\")\n",
    "#plt.xlabel(\"Roll motion [°]\")\n",
    "#plt.ylabel(\"Prob. density\")\n",
    "#plt.legend()\n",
    "\n",
    "axs[1].axvline(x=0, lw=0.5, color='black')\n",
    "\n",
    "axs[1].tick_params(which=\"both\", bottom=True)\n",
    "x_lims = axs[1].get_xlim()\n",
    "axs[1].xaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "axs[1].set_xticks(np.arange(x_lims[0],x_lims[1], 0.5), minor=True)\n",
    "axs[1].tick_params(axis='x', which='minor', length=3)\n",
    "axs[1].tick_params(axis='x', which='major', length=7, width=1)\n",
    "\n",
    "axs[1].set_xlabel('Roll motion [°]')\n",
    "axs[1].set_ylabel('Prob. density')\n",
    "axs[1].legend()\n",
    "\n",
    "plt.suptitle(f'Pitch (top) vs. roll (bottom) motion for Seapath & Arduino & LIMRAD94 sensors \\n EUREC4A - RV-Meteor - {date:%Y-%m-%d}')\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.subplots_adjust(top=0.93)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}