SPATZ/conversion.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/simulations/raw/bending_Sim.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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",
       "1723                                -9.80224056912612E+00\n",
       "1724                                -9.78800336589294E+00\n",
       "1725                                -9.77015060305640E+00\n",
       "1726                                -9.74868887106344E+00\n",
       "1727                                -9.74868887106344E+00\n",
       "Name: acceleration_without_gravity_x~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket, Length: 1728, dtype: object"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['acceleration_without_gravity_x~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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', 'Radian')\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",
      "('V~STAHR_Rocket', '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": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['Time'], df['u_x~STAHR_Rocket#L~STAHR_Rocket:Earth'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5452011601288"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(df['mach~STAHR_Rocket'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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",
       "1723    68.132323\n",
       "1724    68.132323\n",
       "1725    68.132323\n",
       "1726    68.132323\n",
       "1727    68.132323\n",
       "Name: latitude~STAHR_Rocket#PCPF~Earth@Earth, Length: 1726, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['latitude~STAHR_Rocket#PCPF~Earth@Earth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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",
       "1723    67.998970\n",
       "1724    67.998970\n",
       "1725    67.998970\n",
       "1726    67.998970\n",
       "1727    67.998970\n",
       "Name: declination~STAHR_Rocket#PCPF~Earth@Earth, Length: 1726, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['declination~STAHR_Rocket#PCPF~Earth@Earth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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",
       "1723    21.039219\n",
       "1724    21.039219\n",
       "1725    21.039219\n",
       "1726    21.039219\n",
       "1727    21.039219\n",
       "Name: longitude~STAHR_Rocket#PCPF~Earth@Earth, Length: 1726, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['longitude~STAHR_Rocket#PCPF~Earth@Earth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2308a356780>]"
      ]
     },
     "execution_count": 61,
     "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": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "318.99999999906896"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.at[2, 'altitude~STAHR_Rocket@Earth'] * 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "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'] * pi / 180\n",
    "init_longitude = df.at[2, 'longitude'] * pi / 180\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'] * pi / 180\n",
    "\n",
    "B_to_L = T1(yaw) @ T2(pi / 2 - pitch) @ T1(-roll)\n",
    "G_to_LF = T2(-pi/2 - init_latitude) @ T3(init_longitude)\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",
    "ax_FL, ay_FL, az_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'] * pi / 180\n",
    "    long = df.at[i, 'longitude'] * pi / 180\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 + 1/2*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",
    "    acc_LF = -(L_to_LF @ acc_L)\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",
    "    ax_FL.append(acc_LF[0])\n",
    "    ay_FL.append(acc_LF[1])\n",
    "    az_FL.append(acc_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()\n",
    "\n",
    "print('x_FL')\n",
    "plt.plot(df['Time'], x_FL)\n",
    "plt.show()\n",
    "\n",
    "print('y_FL')\n",
    "plt.plot(df['Time'], y_FL)\n",
    "plt.show()\n",
    "\n",
    "print('z_FL')\n",
    "plt.plot(df['Time'], z_FL)\n",
    "plt.show()\n",
    "\n",
    "print('vx_FL')\n",
    "plt.plot(df['Time'], vx_FL)\n",
    "plt.show()\n",
    "\n",
    "print('vy_FL')\n",
    "plt.plot(df['Time'], vy_FL)\n",
    "plt.show()\n",
    "\n",
    "print('vz_FL')\n",
    "plt.plot(df['Time'], vz_FL)\n",
    "plt.show()\n",
    "\n",
    "print('acc')\n",
    "plt.plot(df['Time'], ax_FL, label='ax')\n",
    "plt.plot(df['Time'], ay_FL, label='ay')\n",
    "plt.plot(df['Time'], az_FL, label='az')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.7964371935639445"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(np.array(acc_total) / g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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",
       "1723      9.596509\n",
       "1724      6.411547\n",
       "1725      3.227070\n",
       "1726      0.043079\n",
       "1727      0.043079\n",
       "Name: altitude, Length: 1726, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['altitude'] * 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35196.311584186704"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(df['altitude'] * 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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",
       "1723    577.348034\n",
       "1724    577.800240\n",
       "1725    578.252446\n",
       "1726    578.704652\n",
       "1727    578.704652\n",
       "Name: Time, Length: 1726, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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/predicted_flight.csv', 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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