SPATZ/tests.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1       21.079909\n",
       "2       21.079909\n",
       "3       21.079909\n",
       "4       21.079909\n",
       "5       21.079909\n",
       "          ...    \n",
       "1362    21.080085\n",
       "1363    21.080085\n",
       "1364    21.080085\n",
       "1365    21.080085\n",
       "1366    21.080085\n",
       "Name: longitude, Length: 1366, dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/simulations/11km.txt', sep='\\t', encoding='latin')\n",
    "df = df.drop([0], axis=0)\n",
    "df = df.astype(float)\n",
    "\n",
    "df['longitude']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1       67.885478\n",
       "2       67.885478\n",
       "3       67.885478\n",
       "4       67.885478\n",
       "5       67.885478\n",
       "          ...    \n",
       "1362    67.959101\n",
       "1363    67.959101\n",
       "1364    67.959101\n",
       "1365    67.959101\n",
       "1366    67.959101\n",
       "Name: latitude, Length: 1366, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['latitude']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1       67.750940\n",
       "2       67.750940\n",
       "3       67.750940\n",
       "4       67.750940\n",
       "5       67.750940\n",
       "          ...    \n",
       "1362    67.824918\n",
       "1363    67.824918\n",
       "1364    67.824918\n",
       "1365    67.824918\n",
       "1366    67.824918\n",
       "Name: declination, Length: 1366, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['declination']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.20214666930979e-13 -1.51393643837011e-12 -2.40063988328838e-12\n"
     ]
    }
   ],
   "source": [
    "print(df.at[2, 'x_FL'], df.at[2, 'y_FL'], df.at[2, 'z_FL'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/simulations/raw/Result_Export.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       X-Acceleration w/o Gravity of STAHR_Rocket in ...\n",
       "1                                         Meter/Second**2\n",
       "2                                    9.67852498145304E+00\n",
       "3                                    9.67852498145304E+00\n",
       "4                                    9.67852498145304E+00\n",
       "                              ...                        \n",
       "1718                                -6.79946483067537E+00\n",
       "1719                                -6.69554885541942E+00\n",
       "1720                                -6.59021972861004E+00\n",
       "1721                                -6.48349967690488E+00\n",
       "1722                                -6.48349967690488E+00\n",
       "Name: acceleration_without_gravity_x~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket, Length: 1723, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['acceleration_without_gravity_x~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Phase', nan)\n",
      "('acceleration_without_gravity_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Meter/Second**2')\n",
      "('acceleration_without_gravity_north~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Meter/Second**2')\n",
      "('acceleration_without_gravity_radial~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Meter/Second**2')\n",
      "('acceleration_without_gravity_x~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket', 'Meter/Second**2')\n",
      "('acceleration_without_gravity_y~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket', 'Meter/Second**2')\n",
      "('acceleration_without_gravity_z~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket', 'Meter/Second**2')\n",
      "('acceleration_without_gravity~STAHR_Rocket', 'Meter/Second**2')\n",
      "('acceleration_x~STAHR_Rocket#J2000@Earth', 'Meter/Second**2')\n",
      "('acceleration_y~STAHR_Rocket#J2000@Earth', 'Meter/Second**2')\n",
      "('acceleration_z~STAHR_Rocket#J2000@Earth', 'Meter/Second**2')\n",
      "('acceleration~STAHR_Rocket#J2000@Earth', 'Meter/Second**2')\n",
      "('acc_aero_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Meter/Second**2')\n",
      "('acc_aero_north~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Meter/Second**2')\n",
      "('acc_aero_r~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Meter/Second**2')\n",
      "('acc_aero~STAHR_Rocket', 'Meter/Second**2')\n",
      "('acc_normal~STAHR_Rocket', 'Meter/Second**2')\n",
      "('acc_thrust_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Meter/Second**2')\n",
      "('acc_thrust_north~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Meter/Second**2')\n",
      "('acc_thrust_r~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Meter/Second**2')\n",
      "('acc_thrust~STAHR_Rocket', 'Meter/Second**2')\n",
      "('aero_area_ref~STAHR_Rocket', 'Meter^2')\n",
      "('aero_bank_angle~STAHR_Rocket', 'Degree')\n",
      "('aero_roll_angle~STAHR_Rocket', 'Degree')\n",
      "('airpath_angle~STAHR_Rocket#LD~STAHR_Rocket:Earth', 'Degree')\n",
      "('airpath_angle~STAHR_Rocket#L~STAHR_Rocket:Earth', 'Degree')\n",
      "('AIRPATH_BANK_ANGLE~Esrange#A~Esrange:Earth', 'Radian')\n",
      "('AIRPATH_BANK_ANGLE~STAHR_Rocket#A~STAHR_Rocket:Earth', 'Radian')\n",
      "('airpath_heading~STAHR_Rocket#LD~STAHR_Rocket:Earth', 'Degree')\n",
      "('airpath_heading~STAHR_Rocket#L~STAHR_Rocket:Earth', 'Degree')\n",
      "('airpath_speed~STAHR_Rocket', 'Kilo-Meter/Second')\n",
      "('altitude_apoapsis~STAHR_Rocket@Earth', 'Kilo-Meter')\n",
      "('altitude_periapsis~STAHR_Rocket@Earth', 'Kilo-Meter')\n",
      "('altitude~STAHR_Rocket@Earth', 'Kilo-Meter')\n",
      "('angle1~IB3:STAHR_Rocket#B~STAHR_Rocket', 'Radian')\n",
      "('angle1~IB3:STAHR_Rocket#J2000', 'Radian')\n",
      "('angle1~STAHR:STAHR_Rocket#B~STAHR_Rocket', 'Radian')\n",
      "('angle1~STAHR:STAHR_Rocket#J2000', 'Radian')\n",
      "('angle2~IB3:STAHR_Rocket#B~STAHR_Rocket', 'Radian')\n",
      "('angle2~IB3:STAHR_Rocket#J2000', 'Radian')\n",
      "('angle2~STAHR:STAHR_Rocket#B~STAHR_Rocket', 'Radian')\n",
      "('angle2~STAHR:STAHR_Rocket#J2000', 'Radian')\n",
      "('angle3~IB3:STAHR_Rocket#B~STAHR_Rocket', 'Radian')\n",
      "('angle3~IB3:STAHR_Rocket#J2000', 'Radian')\n",
      "('angle3~STAHR:STAHR_Rocket#B~STAHR_Rocket', 'Radian')\n",
      "('angle3~STAHR:STAHR_Rocket#J2000', 'Radian')\n",
      "('angle_body_and_local_vertical~STAHR_Rocket#L~STAHR_Rocket:Earth', 'Degree')\n",
      "('angle_of_attack_rate~STAHR_Rocket#A~STAHR_Rocket:Earth', 'Degree/Second')\n",
      "('ANGLE_OF_ATTACK~Esrange#A~Esrange:Earth', 'Radian')\n",
      "('angle_of_attack~STAHR_Rocket#A~STAHR_Rocket:Earth', 'Degree')\n",
      "('angular_velocity~STAHR_Rocket#B~STAHR_Rocket', 'Degree/Second')\n",
      "('arc_length~STAHR_Rocket', 'Kilo-Meter')\n",
      "('ascending_node_rel~STAHR_Rocket#TOD~Earth', 'Degree')\n",
      "('ascending_node~STAHR_Rocket#J2000', 'Degree')\n",
      "('ascending_node~STAHR_Rocket#TOD~Earth', 'Degree')\n",
      "('asymptote_decl~STAHR_Rocket#J2000', 'Degree')\n",
      "('asymptote_decl~STAHR_Rocket#TOD~Earth', 'Degree')\n",
      "('asymptote_right_ascension~STAHR_Rocket#J2000', 'Degree')\n",
      "('asymptote_right_ascension~STAHR_Rocket#TOD~Earth', 'Degree')\n",
      "('atmos_density~STAHR_Rocket', 'Kilogram/Meter**3')\n",
      "('atmos_pressure~STAHR_Rocket', 'Pascal')\n",
      "('atmos_temperature~STAHR_Rocket', 'Kelvin')\n",
      "('bank_angle_rate~STAHR_Rocket#A~STAHR_Rocket:Earth', 'Degree/Second')\n",
      "('bank_angle~STAHR_Rocket#A~STAHR_Rocket:Earth', 'Degree')\n",
      "('bending_moment~STAHR_Rocket', 'Pascal*Radian')\n",
      "('burn_time~IB3:STAHR_Rocket', 'Second')\n",
      "('cross_range~STAHR_Rocket', 'Kilo-Meter')\n",
      "('declination~STAHR_Rocket#J2000@Earth', 'Degree')\n",
      "('declination~STAHR_Rocket#PCPF~Earth@Earth', 'Degree')\n",
      "('declination~STAHR_Rocket#TOD~Earth@Earth', 'Degree')\n",
      "('DECL~STAHR_Rocket', 'Radian')\n",
      "('dimension_x~STAHR_Rocket', 'Meter')\n",
      "('dimension_y~STAHR_Rocket', 'Meter')\n",
      "('dimension_z~STAHR_Rocket', 'Meter')\n",
      "('down_range~STAHR_Rocket', 'Kilo-Meter')\n",
      "('drag_acc~STAHR_Rocket', 'Meter/Second**2')\n",
      "('drag_coeff~STAHR_Rocket', nan)\n",
      "('drag~STAHR_Rocket', 'Kilo-Newton')\n",
      "('dummy_altitude', 'Meter')\n",
      "('dummy_declination', 'Degree')\n",
      "('dummy_latitude', 'Degree')\n",
      "('dummy_longitude', 'Degree')\n",
      "('dummy_radius', 'Meter')\n",
      "('dynamic_pressure~STAHR_Rocket', 'Pascal')\n",
      "('dyn_viscosity~STAHR_Rocket', 'Pascal*Second')\n",
      "('eccentricity~STAHR_Rocket@Earth', nan)\n",
      "('eccentric_anomaly~STAHR_Rocket@Earth', 'Degree')\n",
      "('energy_total~STAHR_Rocket@Earth', 'Kilo-Joule/Kilogram')\n",
      "('equinoctial_f~STAHR_Rocket#TOD~Earth', nan)\n",
      "('equinoctial_g~STAHR_Rocket#TOD~Earth', nan)\n",
      "('equinoctial_h~STAHR_Rocket#TOD~Earth', nan)\n",
      "('equinoctial_k~STAHR_Rocket#TOD~Earth', nan)\n",
      "('equinoctial_l~STAHR_Rocket#TOD~Earth', 'Degree')\n",
      "('equinoctial_p~STAHR_Rocket@Earth', 'Kilo-Meter')\n",
      "('excess_velocity~STAHR_Rocket@Earth', 'Kilo-Meter/Second')\n",
      "('flightpath_angle~STAHR_Rocket#LD~STAHR_Rocket:Earth', 'Degree')\n",
      "('flightpath_angle~STAHR_Rocket#L~STAHR_Rocket:Earth', 'Degree')\n",
      "('flightpath_heading~STAHR_Rocket#LD~STAHR_Rocket:Earth', 'Degree')\n",
      "('flightpath_heading~STAHR_Rocket#L~STAHR_Rocket:Earth', 'Degree')\n",
      "('flightpath_speed~STAHR_Rocket', 'Kilo-Meter/Second')\n",
      "('flightpath_vertical~STAHR_Rocket#L~STAHR_Rocket:Earth', 'Degree')\n",
      "('flight_time', 'Second')\n",
      "('flight_time~STAHR_Rocket', 'Second')\n",
      "('force_aero_axial~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket', 'Kilo-Newton')\n",
      "('force_aero_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Kilo-Newton')\n",
      "('force_aero_lateral~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket', 'Kilo-Newton')\n",
      "('force_aero_normal~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket', 'Kilo-Newton')\n",
      "('force_aero_north~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Kilo-Newton')\n",
      "('force_aero_r~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Kilo-Newton')\n",
      "('gps_time', 'Second')\n",
      "('gravity_force_x~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket', 'Newton')\n",
      "('gravity_force_y~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket', 'Newton')\n",
      "('gravity_force_z~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket', 'Newton')\n",
      "('gravity_potential~Esrange', 'Kilo-Joule/Kilogram')\n",
      "('gravity_potential~STAHR_Rocket@Earth', 'Kilo-Joule/Kilogram')\n",
      "('gravity~STAHR_Rocket', 'Meter/Second**2')\n",
      "('great_circle_distance~STAHR_Rocket', 'Kilo-Meter')\n",
      "('heat_flux_density~STAHR_Rocket', 'Kilo-Watt/Meter**2')\n",
      "('hload_lift~STAHR_Rocket', nan)\n",
      "('inclination~STAHR_Rocket#J2000', 'Degree')\n",
      "('inclination~STAHR_Rocket#TOD~Earth', 'Degree')\n",
      "('inertial_flightpath_vertical~STAHR_Rocket#L~STAHR_Rocket:Earth', 'Degree')\n",
      "('inertial_flightpath~STAHR_Rocket#L~STAHR_Rocket:Earth', 'Degree')\n",
      "('inertial_heading~STAHR_Rocket#L~STAHR_Rocket:Earth', 'Degree')\n",
      "('inertial_speed~STAHR_Rocket', 'Kilo-Meter/Second')\n",
      "('inertia_xx~STAHR_Rocket', 'Kilogram*Meter**2')\n",
      "('inertia_xy~STAHR_Rocket', 'Kilogram*Meter**2')\n",
      "('inertia_xz~STAHR_Rocket', 'Kilogram*Meter**2')\n",
      "('inertia_yy~STAHR_Rocket', 'Kilogram*Meter**2')\n",
      "('inertia_yz~STAHR_Rocket', 'Kilogram*Meter**2')\n",
      "('inertia_zz~STAHR_Rocket', 'Kilogram*Meter**2')\n",
      "('isp_vacuum~IB3:STAHR_Rocket', 'Second')\n",
      "('isp~IB3:STAHR_Rocket', 'Second')\n",
      "('julian_date_tt', 'Day')\n",
      "('julian_date_utc', 'Day')\n",
      "('knudsen_number~STAHR_Rocket', nan)\n",
      "('latitude~STAHR_Rocket#PCPF~Earth@Earth', 'Degree')\n",
      "('lift_acc~STAHR_Rocket', 'Meter/Second**2')\n",
      "('lift_coeff~STAHR_Rocket', nan)\n",
      "('lift_drag_ratio~STAHR_Rocket', nan)\n",
      "('lift~STAHR_Rocket', 'Kilo-Newton')\n",
      "('load_factor_axial~STAHR_Rocket', nan)\n",
      "('load_factor_cross~STAHR_Rocket', nan)\n",
      "('load_factor_normal~STAHR_Rocket', nan)\n",
      "('load_factor~STAHR_Rocket', nan)\n",
      "('local_central_body_radius~STAHR_Rocket', 'Kilo-Meter')\n",
      "('longitude~STAHR_Rocket#PCPF~Earth@Earth', 'Degree')\n",
      "('LONG~STAHR_Rocket', 'Radian')\n",
      "('mach~STAHR_Rocket', nan)\n",
      "('magnetic_flux_density_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Nano-Tesla')\n",
      "('magnetic_flux_density_north~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Nano-Tesla')\n",
      "('magnetic_flux_density_radial~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Nano-Tesla')\n",
      "('magnetic_flux_density_x~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket', 'Nano-Tesla')\n",
      "('magnetic_flux_density_y~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket', 'Nano-Tesla')\n",
      "('magnetic_flux_density_z~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket', 'Nano-Tesla')\n",
      "('magnetic_flux_density~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Nano-Tesla')\n",
      "('mass_flow~IB3:STAHR_Rocket', 'Kilogram/Second')\n",
      "('mass_total~STAHR_Rocket', 'Mega-Gram')\n",
      "('mean_anomaly~STAHR_Rocket@Earth', 'Degree')\n",
      "('mean_local_time_of_the_ascending_node~STAHR_Rocket', 'Hour')\n",
      "('mean_motion~STAHR_Rocket@Earth', 'Radian/Second')\n",
      "('mission_time', 'Second')\n",
      "('moment_aero_x~STAHR_Rocket#B~STAHR_Rocket', 'Newton*Meter')\n",
      "('moment_aero_y~STAHR_Rocket#B~STAHR_Rocket', 'Newton*Meter')\n",
      "('moment_aero_z~STAHR_Rocket#B~STAHR_Rocket', 'Newton*Meter')\n",
      "('Norm_Indep_Var', nan)\n",
      "('nozzle_area~IB3:STAHR_Rocket', 'Meter^2')\n",
      "('OMEGA_X~STAHR_Rocket', 'Radian/Second')\n",
      "('omega_x~STAHR_Rocket#B~STAHR_Rocket', 'Degree/Second')\n",
      "('OMEGA_Y~STAHR_Rocket', 'Radian/Second')\n",
      "('omega_y~STAHR_Rocket#B~STAHR_Rocket', 'Degree/Second')\n",
      "('OMEGA_Z~STAHR_Rocket', 'Radian/Second')\n",
      "('omega_z~STAHR_Rocket#B~STAHR_Rocket', 'Degree/Second')\n",
      "('orbital_period~STAHR_Rocket@Earth', 'Second')\n",
      "('periapsis_argument~STAHR_Rocket#J2000', 'Degree')\n",
      "('periapsis_argument~STAHR_Rocket#TOD~Earth', 'Degree')\n",
      "('pitch_rate~STAHR_Rocket#J2000', 'Degree/Second')\n",
      "('pitch_rate~STAHR_Rocket#LO~STAHR_Rocket:Earth', 'Degree/Second')\n",
      "('pitch_rate~STAHR_Rocket#L~STAHR_Rocket:Earth', 'Degree/Second')\n",
      "('PITCH~Esrange#L~Esrange:Earth', 'Radian')\n",
      "('PITCH~STAHR_Rocket', 'Radian')\n",
      "('pitch~STAHR_Rocket#J2000', 'Degree')\n",
      "('pitch~STAHR_Rocket#LD~STAHR_Rocket:Earth', 'Degree')\n",
      "('pitch~STAHR_Rocket#LO~STAHR_Rocket:Earth', 'Degree')\n",
      "('pitch~STAHR_Rocket#L~STAHR_Rocket:Earth', 'Degree')\n",
      "('pitch~STAHR_Rocket#N~STAHR_Rocket', 'Degree')\n",
      "('pitch~STAHR_Rocket#TOD~Earth', 'Degree')\n",
      "('PROP_MASS~STAHR:STAHR_Rocket', 'Kilogram')\n",
      "('quaternion_w~Earth#J2000', nan)\n",
      "('quaternion_w~IB3:STAHR_Rocket#B~STAHR_Rocket', nan)\n",
      "('quaternion_w~STAHR:STAHR_Rocket#B~STAHR_Rocket', nan)\n",
      "('quaternion_w~STAHR_Rocket#J2000', nan)\n",
      "('quaternion_w~STAHR_Rocket#L~STAHR_Rocket:Earth', nan)\n",
      "('quaternion_x~Earth#J2000', nan)\n",
      "('quaternion_x~IB3:STAHR_Rocket#B~STAHR_Rocket', nan)\n",
      "('quaternion_x~STAHR:STAHR_Rocket#B~STAHR_Rocket', nan)\n",
      "('quaternion_x~STAHR_Rocket#J2000', nan)\n",
      "('quaternion_x~STAHR_Rocket#L~STAHR_Rocket:Earth', nan)\n",
      "('quaternion_y~Earth#J2000', nan)\n",
      "('quaternion_y~IB3:STAHR_Rocket#B~STAHR_Rocket', nan)\n",
      "('quaternion_y~STAHR:STAHR_Rocket#B~STAHR_Rocket', nan)\n",
      "('quaternion_y~STAHR_Rocket#J2000', nan)\n",
      "('quaternion_y~STAHR_Rocket#L~STAHR_Rocket:Earth', nan)\n",
      "('quaternion_z~Earth#J2000', nan)\n",
      "('quaternion_z~IB3:STAHR_Rocket#B~STAHR_Rocket', nan)\n",
      "('quaternion_z~STAHR:STAHR_Rocket#B~STAHR_Rocket', nan)\n",
      "('quaternion_z~STAHR_Rocket#J2000', nan)\n",
      "('quaternion_z~STAHR_Rocket#L~STAHR_Rocket:Earth', nan)\n",
      "('radius_apoapsis~STAHR_Rocket@Earth', 'Kilo-Meter')\n",
      "('radius_periapsis~STAHR_Rocket@Earth', 'Kilo-Meter')\n",
      "('radius~STAHR_Rocket@Earth', 'Kilo-Meter')\n",
      "('reynolds_number~STAHR_Rocket', nan)\n",
      "('right_ascension~STAHR_Rocket#J2000@Earth', 'Degree')\n",
      "('right_ascension~STAHR_Rocket#TOD~Earth@Earth', 'Degree')\n",
      "('roll_rate~STAHR_Rocket#J2000', 'Degree/Second')\n",
      "('roll_rate~STAHR_Rocket#LO~STAHR_Rocket:Earth', 'Degree/Second')\n",
      "('roll_rate~STAHR_Rocket#L~STAHR_Rocket:Earth', 'Degree/Second')\n",
      "('ROLL~Esrange#L~Esrange:Earth', 'Radian')\n",
      "('ROLL~STAHR_Rocket', 'Radian')\n",
      "('roll~STAHR_Rocket#J2000', 'Degree')\n",
      "('roll~STAHR_Rocket#LD~STAHR_Rocket:Earth', 'Degree')\n",
      "('roll~STAHR_Rocket#LO~STAHR_Rocket:Earth', 'Degree')\n",
      "('roll~STAHR_Rocket#L~STAHR_Rocket:Earth', 'Degree')\n",
      "('roll~STAHR_Rocket#N~STAHR_Rocket', 'Degree')\n",
      "('roll~STAHR_Rocket#TOD~Earth', 'Degree')\n",
      "('R~STAHR_Rocket', 'Meter')\n",
      "('semimajor~STAHR_Rocket@Earth', 'Kilo-Meter')\n",
      "('sideslip_rate~STAHR_Rocket#A~STAHR_Rocket:Earth', 'Degree/Second')\n",
      "('sideslip~STAHR_Rocket#A~STAHR_Rocket:Earth', 'Degree')\n",
      "('side_force~STAHR_Rocket', 'Kilo-Newton')\n",
      "('SIDE_SLIP_ANGLE~Esrange#A~Esrange:Earth', 'Radian')\n",
      "('SIDE_SLIP_ANGLE~STAHR_Rocket#A~STAHR_Rocket:Earth', 'Radian')\n",
      "('solar_beta_angle~STAHR_Rocket#PF~STAHR_Rocket:Earth', 'Degree')\n",
      "('sonic_velocity~STAHR_Rocket', 'Meter/Second')\n",
      "('static_stability_x~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket', 'Meter')\n",
      "('thrust_to_ew~STAHR_Rocket', nan)\n",
      "('thrust_vacuum~IB3:STAHR_Rocket', 'Kilo-Newton')\n",
      "('thrust_x~STAHR_Rocket#J2000', 'Kilo-Newton')\n",
      "('thrust_y~STAHR_Rocket#J2000', 'Kilo-Newton')\n",
      "('thrust_z~STAHR_Rocket#J2000', 'Kilo-Newton')\n",
      "('thrust~IB3:STAHR_Rocket', 'Kilo-Newton')\n",
      "('thrust~STAHR_Rocket', 'Kilo-Newton')\n",
      "('Time', 'Second')\n",
      "('torque_x~IB3:STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket', 'Newton*Meter')\n",
      "('torque_x~STAHR_Rocket#B~STAHR_Rocket', 'Newton*Meter')\n",
      "('torque_y~IB3:STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket', 'Newton*Meter')\n",
      "('torque_y~STAHR_Rocket#B~STAHR_Rocket', 'Newton*Meter')\n",
      "('torque_z~IB3:STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket', 'Newton*Meter')\n",
      "('torque_z~STAHR_Rocket#B~STAHR_Rocket', 'Newton*Meter')\n",
      "('total_angle_of_attack_slope_normal_coefficient~STAHR_Rocket#TA~STAHR_Rocket@STAHR_Rocket', 'None/Degree')\n",
      "('total_angle_of_attack~STAHR_Rocket#A~STAHR_Rocket:Earth', 'Degree')\n",
      "('total_lift~STAHR_Rocket', 'Kilo-Newton')\n",
      "('total_solar_irradiance_at_earth~STAHR_Rocket', 'Watt/Meter**2')\n",
      "('TRAJ_SMOOTHNESS~STAHR_Rocket', 'Radian^2/Second')\n",
      "('true_anomaly~STAHR_Rocket@Earth', 'Degree')\n",
      "('u_length~STAHR_Rocket#LO~STAHR_Rocket:Earth', nan)\n",
      "('u_length~STAHR_Rocket#L~STAHR_Rocket:Earth', nan)\n",
      "('u_n~STAHR_Rocket#LO~STAHR_Rocket:Earth', nan)\n",
      "('u_r~STAHR_Rocket#LO~STAHR_Rocket:Earth', nan)\n",
      "('u_t~STAHR_Rocket#LO~STAHR_Rocket:Earth', nan)\n",
      "('u_x~STAHR_Rocket#L~STAHR_Rocket:Earth', nan)\n",
      "('u_y~STAHR_Rocket#L~STAHR_Rocket:Earth', nan)\n",
      "('u_z~STAHR_Rocket#L~STAHR_Rocket:Earth', nan)\n",
      "('velocity_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Kilo-Meter/Second')\n",
      "('velocity_north~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Kilo-Meter/Second')\n",
      "('velocity_rel_apoapsis~STAHR_Rocket@Earth', 'Kilo-Meter/Second')\n",
      "('velocity_rel_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Kilo-Meter/Second')\n",
      "('velocity_rel_periapsis~STAHR_Rocket@Earth', 'Kilo-Meter/Second')\n",
      "('velocity_r~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Kilo-Meter/Second')\n",
      "('VI_EAST~STAHR_Rocket', 'Meter/Second')\n",
      "('VI_NORTH~STAHR_Rocket', 'Meter/Second')\n",
      "('VI_RADIAL~STAHR_Rocket', 'Meter/Second')\n",
      "('vload_lift~STAHR_Rocket', nan)\n",
      "('vx~STAHR_Rocket#J2000@Earth', 'Kilo-Meter/Second')\n",
      "('vx~STAHR_Rocket#TOD~Earth@Earth', 'Kilo-Meter/Second')\n",
      "('vy~STAHR_Rocket#J2000@Earth', 'Kilo-Meter/Second')\n",
      "('vy~STAHR_Rocket#TOD~Earth@Earth', 'Kilo-Meter/Second')\n",
      "('vz~STAHR_Rocket#J2000@Earth', 'Kilo-Meter/Second')\n",
      "('vz~STAHR_Rocket#TOD~Earth@Earth', 'Kilo-Meter/Second')\n",
      "('wind_gust_p~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Degree/Second')\n",
      "('wind_gust_q~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Degree/Second')\n",
      "('wind_gust_r~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Degree/Second')\n",
      "('wind_velocity_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Meter/Second')\n",
      "('wind_velocity_north~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Meter/Second')\n",
      "('wind_velocity_r~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Meter/Second')\n",
      "('x_offset_CoM~Esrange', 'Meter')\n",
      "('x_offset_CoM~Main_Stage:STAHR_Rocket', 'Meter')\n",
      "('x_offset_CoM~STAHR_Rocket', 'Meter')\n",
      "('x_offset_CoP~STAHR_Rocket', 'Meter')\n",
      "('x_offset~IB3:STAHR_Rocket', 'Meter')\n",
      "('x_offset~STAHR:STAHR_Rocket', 'Meter')\n",
      "('x~Earth#J2000@SSB', 'Kilo-Meter')\n",
      "('x~STAHR_Rocket#J2000@Earth', 'Kilo-Meter')\n",
      "('x~STAHR_Rocket#TOD~Earth@Earth', 'Kilo-Meter')\n",
      "('x~Sun#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Kilo-Meter')\n",
      "('yaw_rate~STAHR_Rocket#J2000', 'Degree/Second')\n",
      "('yaw_rate~STAHR_Rocket#LO~STAHR_Rocket:Earth', 'Degree/Second')\n",
      "('yaw_rate~STAHR_Rocket#L~STAHR_Rocket:Earth', 'Degree/Second')\n",
      "('YAW~Esrange#L~Esrange:Earth', 'Radian')\n",
      "('YAW~STAHR_Rocket', 'Radian')\n",
      "('yaw~STAHR_Rocket#J2000', 'Degree')\n",
      "('yaw~STAHR_Rocket#LD~STAHR_Rocket:Earth', 'Degree')\n",
      "('yaw~STAHR_Rocket#LO~STAHR_Rocket:Earth', 'Degree')\n",
      "('yaw~STAHR_Rocket#L~STAHR_Rocket:Earth', 'Degree')\n",
      "('yaw~STAHR_Rocket#N~STAHR_Rocket', 'Degree')\n",
      "('yaw~STAHR_Rocket#TOD~Earth', 'Degree')\n",
      "('y_offset_CoM~Esrange', 'Meter')\n",
      "('y_offset_CoM~Main_Stage:STAHR_Rocket', 'Meter')\n",
      "('y_offset_CoM~STAHR_Rocket', 'Meter')\n",
      "('y_offset_CoP~STAHR_Rocket', 'Meter')\n",
      "('y_offset~IB3:STAHR_Rocket', 'Meter')\n",
      "('y_offset~STAHR:STAHR_Rocket', 'Meter')\n",
      "('y~Earth#J2000@SSB', 'Kilo-Meter')\n",
      "('y~STAHR_Rocket#J2000@Earth', 'Kilo-Meter')\n",
      "('y~STAHR_Rocket#TOD~Earth@Earth', 'Kilo-Meter')\n",
      "('y~Sun#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Kilo-Meter')\n",
      "('z_offset_CoM~Esrange', 'Meter')\n",
      "('z_offset_CoM~Main_Stage:STAHR_Rocket', 'Meter')\n",
      "('z_offset_CoM~STAHR_Rocket', 'Meter')\n",
      "('z_offset_CoP~STAHR_Rocket', 'Meter')\n",
      "('z_offset~IB3:STAHR_Rocket', 'Meter')\n",
      "('z_offset~STAHR:STAHR_Rocket', 'Meter')\n",
      "('z~Earth#J2000@SSB', 'Kilo-Meter')\n",
      "('z~STAHR_Rocket#J2000@Earth', 'Kilo-Meter')\n",
      "('z~STAHR_Rocket#TOD~Earth@Earth', 'Kilo-Meter')\n",
      "('z~Sun#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Kilo-Meter')\n",
      "('z~Sun#L~STAHR_Rocket:Earth@STAHR_Rocket.1', 'Kilo-Meter')\n"
     ]
    }
   ],
   "source": [
    "print('\\n'.join(map(str, zip(df.columns.values, df.loc[1]))))\n",
    "\n",
    "df = df.drop([0, 1], axis=0)\n",
    "df = df.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.67663011276857"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(df['mach~STAHR_Rocket'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2       67.885478\n",
       "3       67.885478\n",
       "4       67.885478\n",
       "5       67.885478\n",
       "6       67.885478\n",
       "          ...    \n",
       "1718    68.026390\n",
       "1719    68.026390\n",
       "1720    68.026390\n",
       "1721    68.026390\n",
       "1722    68.026390\n",
       "Name: latitude~STAHR_Rocket#PCPF~Earth@Earth, Length: 1721, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['latitude~STAHR_Rocket#PCPF~Earth@Earth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2       67.750938\n",
       "3       67.750938\n",
       "4       67.750938\n",
       "5       67.750938\n",
       "6       67.750938\n",
       "          ...    \n",
       "1718    67.892466\n",
       "1719    67.892466\n",
       "1720    67.892466\n",
       "1721    67.892466\n",
       "1722    67.892466\n",
       "Name: declination~STAHR_Rocket#PCPF~Earth@Earth, Length: 1721, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['declination~STAHR_Rocket#PCPF~Earth@Earth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2       21.079909\n",
       "3       21.079909\n",
       "4       21.079909\n",
       "5       21.079909\n",
       "6       21.079909\n",
       "          ...    \n",
       "1718    21.080597\n",
       "1719    21.080597\n",
       "1720    21.080597\n",
       "1721    21.080597\n",
       "1722    21.080597\n",
       "Name: longitude~STAHR_Rocket#PCPF~Earth@Earth, Length: 1721, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['longitude~STAHR_Rocket#PCPF~Earth@Earth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x24cf15adfa0>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['altitude~STAHR_Rocket@Earth'] * 1000\n",
    "\n",
    "plt.plot(df['Time'], df['altitude~STAHR_Rocket@Earth'] * 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "318.99999999906896"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.at[2, 'altitude~STAHR_Rocket@Earth'] * 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "from spatz.dataset import T1, T2, T3\n",
    "from math import pi\n",
    "\n",
    "# Rename the columns\n",
    "df = df.rename({\n",
    "    'acceleration_without_gravity_x~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket': 'ax_B',\n",
    "    'acceleration_without_gravity_y~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket': 'ay_B',\n",
    "    'acceleration_without_gravity_z~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket': 'az_B',\n",
    "    'latitude~STAHR_Rocket#PCPF~Earth@Earth': 'latitude',\n",
    "    'longitude~STAHR_Rocket#PCPF~Earth@Earth': 'longitude',\n",
    "    'declination~STAHR_Rocket#PCPF~Earth@Earth': 'declination',\n",
    "    'altitude~STAHR_Rocket@Earth': 'altitude',\n",
    "    'pitch~STAHR_Rocket#L~STAHR_Rocket:Earth': 'pitch_l',\n",
    "    'yaw~STAHR_Rocket#L~STAHR_Rocket:Earth': 'yaw_l',\n",
    "    'roll~STAHR_Rocket#L~STAHR_Rocket:Earth': 'roll_l',\n",
    "    'atmos_pressure~STAHR_Rocket': 'atmos_pressure',\n",
    "    'atmos_temperature~STAHR_Rocket': 'atmos_temperature',\n",
    "    'sonic_velocity~STAHR_Rocket': 'sonic_velocity',\n",
    "    'OMEGA_X~STAHR_Rocket': 'OMEGA_X',\n",
    "    'OMEGA_Y~STAHR_Rocket': 'OMEGA_Y',\n",
    "    'OMEGA_Z~STAHR_Rocket': 'OMEGA_Z',\n",
    "    'drag~STAHR_Rocket': 'drag',\n",
    "    'flightpath_speed~STAHR_Rocket': 'flightpath_speed',\n",
    "    'mass_total~STAHR_Rocket': 'mass_total',\n",
    "    'mach~STAHR_Rocket': 'mach'\n",
    "}, axis=1)\n",
    "\n",
    "g = 9.81\n",
    "t0 = df.at[2, 'Time']\n",
    "\n",
    "vel = np.array([0, 0, 0], dtype='float64')\n",
    "pos = np.array([318.99999999906896, 0, 0], dtype='float64')\n",
    "time = t0\n",
    "\n",
    "init_latitude = df.at[2, 'latitude']\n",
    "init_longitude = df.at[2, 'longitude']\n",
    "\n",
    "t0 = df.at[2, 'Time']\n",
    "omega_E = (2*pi) / (24*60*60)\n",
    "\n",
    "altitudes = [318.99999999906896]\n",
    "velocities = [0]\n",
    "acc_total = [0]\n",
    "\n",
    "pitch, yaw, roll = df.at[2, 'pitch_l'] * pi / 180, df.at[2, 'yaw_l'] * pi / 180, df.at[2, 'roll_l'] * pi / 180\n",
    "decl = df.at[2, 'declination']\n",
    "\n",
    "B_to_L = T1(yaw) @ T2(pi / 2 - pitch) @ T1(-roll)\n",
    "L_to_G = np.linalg.inv(T2(-decl) @ T3(init_longitude + omega_E * t0))\n",
    "G_to_LF = T2(-pi/2 - init_latitude) @ T3(init_longitude)\n",
    "L_to_LF = G_to_LF @ L_to_G\n",
    "\n",
    "x_FL, y_FL, z_FL = [8.20214666930979e-13], [-1.51393643837011e-12], [-2.40063988328838e-12]\n",
    "vx_FL, vy_FL, vz_FL = [0], [0], [0]\n",
    "\n",
    "for i in range(3, len(df)+2):\n",
    "    dt = df.at[i, 'Time'] - df.at[i-1, 'Time']\n",
    "\n",
    "    # Fetch values for the current time step.\n",
    "    acc = np.array([df.at[i, 'ax_B'], df.at[i, 'ay_B'], df.at[i, 'az_B']])\n",
    "    pitch, yaw, roll = df.at[i, 'pitch_l'] * pi / 180, df.at[i, 'yaw_l'] * pi / 180, df.at[i, 'roll_l'] * pi / 180\n",
    "    decl = df.at[i, 'declination']\n",
    "    long = df.at[i, 'longitude']\n",
    "\n",
    "    B_to_L = T1(yaw) @ T2(pi / 2 - pitch) @ T1(-roll)\n",
    "    L_to_G = np.linalg.inv(T2(-decl) @ T3(long + omega_E * t0))\n",
    "    G_to_LF = T2(-pi/2 - init_latitude) @ T3(init_longitude)\n",
    "    L_to_LF = G_to_LF @ L_to_G\n",
    "\n",
    "    acc_L = B_to_L @ acc + np.array([-g, 0, 0])\n",
    "    pos += dt * vel + dt**2 * acc_L\n",
    "    vel += dt * acc_L\n",
    "\n",
    "    pos_LF = L_to_LF @ pos\n",
    "    vel_LF = L_to_LF @ vel\n",
    "\n",
    "    x_FL.append(pos_LF[0])\n",
    "    y_FL.append(pos_LF[1])\n",
    "    z_FL.append(pos_LF[2])\n",
    "\n",
    "    vx_FL.append(vel_LF[0])\n",
    "    vy_FL.append(vel_LF[1])\n",
    "    vz_FL.append(vel_LF[2])\n",
    "\n",
    "    altitudes.append(pos[0])\n",
    "    velocities.append(np.sqrt(vel[0]**2 + vel[1]**2 + vel[2]**2))\n",
    "    acc_total.append(np.sqrt(acc[0]**2 + acc[1]**2 + acc[2]**2))\n",
    "\n",
    "plt.plot(df['Time'], df['altitude'] * 1000, label='true')\n",
    "\n",
    "plt.plot(df['Time'], altitudes, label='integrated')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.plot(df['Time'], velocities, label='velocity')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.plot(df['Time'], np.array(velocities) / 340, label='mach')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2        319.000000\n",
       "3        319.000000\n",
       "4        319.000000\n",
       "5        319.000000\n",
       "6        319.000000\n",
       "           ...     \n",
       "1718   -2741.434597\n",
       "1719   -2744.446628\n",
       "1720   -2747.458250\n",
       "1721   -2750.469463\n",
       "1722   -2750.469463\n",
       "Name: altitude, Length: 1721, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['altitude'] * 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19335.3982925117"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(df['altitude'] * 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2         0.000000\n",
       "3         0.025000\n",
       "4         0.050000\n",
       "5         0.050000\n",
       "6         0.050000\n",
       "           ...    \n",
       "1718    578.496241\n",
       "1719    578.997494\n",
       "1720    579.498747\n",
       "1721    580.000000\n",
       "1722    580.000000\n",
       "Name: Time, Length: 1721, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.insert(0, 'x_FL', np.array(x_FL) / 1000)\n",
    "df.insert(0, 'y_FL', np.array(y_FL) / 1000)\n",
    "df.insert(0, 'z_FL', np.array(z_FL) / 1000)\n",
    "\n",
    "df.insert(0, 'vx_FL', np.array(vx_FL) / 1000)\n",
    "df.insert(0, 'vy_FL', np.array(vy_FL) / 1000)\n",
    "df.insert(0, 'vz_FL', np.array(vz_FL) / 1000)\n",
    "\n",
    "df.insert(0, 'acc_total', np.array(acc_total))\n",
    "\n",
    "df_new = df[[\n",
    "    'Time',\n",
    "    'Phase',\n",
    "    'declination',\n",
    "    'longitude',\n",
    "    'latitude',\n",
    "    'altitude',\n",
    "    'x_FL',\n",
    "    'y_FL',\n",
    "    'z_FL',\n",
    "    'vx_FL',\n",
    "    'vy_FL',\n",
    "    'vz_FL',\n",
    "    'pitch_l',\n",
    "    'yaw_l',\n",
    "    'roll_l',\n",
    "    'atmos_pressure',\n",
    "    'atmos_temperature',\n",
    "    'sonic_velocity',\n",
    "    'mach',\n",
    "    'OMEGA_X',\n",
    "    'OMEGA_Y',\n",
    "    'OMEGA_Z',\n",
    "    'mass_total',\n",
    "    'flightpath_speed', \n",
    "    'acc_total', \n",
    "    'drag'\n",
    "]]\n",
    "\n",
    "descriptions = pd.DataFrame.from_dict({\n",
    "    'Time': ['Second'],\n",
    "    'Phase': [None],\n",
    "    'declination': ['Degree'],\n",
    "    'longitude': ['Degree'],\n",
    "    'latitude': ['Degree'],\n",
    "    'altitude': ['Kilo-Meter'],\n",
    "    'x_FL': ['Kilo-Meter'],\n",
    "    'y_FL': ['Kilo-Meter'],\n",
    "    'z_FL': ['Kilo-Meter'],\n",
    "    'vx_FL': ['Kilo-Meter / Second'],\n",
    "    'vy_FL': ['Kilo-Meter / Second'],\n",
    "    'vz_FL': ['Kilo-Meter / Second'],\n",
    "    'pitch_l': ['Degree'],\n",
    "    'yaw_l': ['Degree'],\n",
    "    'roll_l': ['Degree'],\n",
    "    'atmos_pressure': ['Pascal'],\n",
    "    'atmos_temperature': ['Kelvin'],\n",
    "    'sonic_velocity': ['Meter / Second'],\n",
    "    'mach': [None],\n",
    "    'OMEGA_X': ['Radian / Second'],\n",
    "    'OMEGA_Y': ['Radian / Second'],\n",
    "    'OMEGA_Z': ['Radian / Second'],\n",
    "    'mass_total': ['Mega-Gram'],\n",
    "    'flightpath_speed': ['Kilo-Meter / Second'], \n",
    "    'acc_total': ['Meter/Second**2'], \n",
    "    'drag': ['Kilo-Newton']\n",
    "}, dtype=str)\n",
    "\n",
    "df_new = pd.concat([descriptions, df_new], axis=0)\n",
    "df_new.to_csv('data/simulations/19km.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'Time', yes\n",
    "\n",
    "'Phase', yes\n",
    "\n",
    "'declination', yes\n",
    "\n",
    "'longitude', yes\n",
    "\n",
    "'latitude', yes\n",
    "\n",
    "'altitude', yes\n",
    "\n",
    "'mach', yes\n",
    "\n",
    "'sonic_velocity', yes\n",
    "\n",
    "'x_FL', yes\n",
    "\n",
    "'y_FL', yes\n",
    "\n",
    "'z_FL', yes\n",
    "\n",
    "'vx_FL', yes\n",
    "\n",
    "'vy_FL', yes\n",
    "\n",
    "'vz_FL', yes\n",
    "\n",
    "'OMEGA_X', yes\n",
    "\n",
    "'OMEGA_Y', yes\n",
    "\n",
    "'OMEGA_Z', yes\n",
    "\n",
    "'pitch_l', yes\n",
    "\n",
    "'yaw_l', yes\n",
    "\n",
    "'roll_l', yes\n",
    "\n",
    "'flightpath_speed', yes\n",
    "\n",
    "'acc_total',\n",
    "\n",
    "'atmos_pressure', yes\n",
    "\n",
    "'atmos_temperature', yes\n",
    "\n",
    "'drag', yes\n",
    "\n",
    "'mass_total', yes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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