{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run Structure.ipynb\n",
    "%run Aero.ipynb\n",
    "from types import SimpleNamespace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "class Environment():\n",
    "    def __init__(self, aero_model, ref_ground_wind, latitude=32):\n",
    "        self.up = np.array([0,0,1])\n",
    "        self.project_normal = np.identity(3) - np.outer(self.up, self.up)\n",
    "        self.ka        = 1.4                   # Ratio of specific heats, air  \n",
    "        self.Ra        = 287.058                 # Avg. specific gas constant (dry air)\n",
    "        self.latitude  = np.radians(latitude) # degrees north, launch site\n",
    "        self.earth_rad = 6371000 # m\n",
    "        self.mu_earth  = 3.986004418 * 10**14 # m^3/s^2, earth gravitational parameter\n",
    "        \n",
    "        # International Gravity Formula (IGF) 1980, Geodetic Reference System 1980 (GRS80)\n",
    "        self.IGF    = 9.780327 * (1 + 0.0053024 * np.sin(self.latitude)**2 - 0.0000058 * np.sin(2*self.latitude)**2)\n",
    "        # Free Air Correction (FAC)\n",
    "        self.FAC    = -3.086 * 10**(-6)\n",
    "        \n",
    "        # for coriolis accel\n",
    "        self.sinlat = np.sin(self.latitude)\n",
    "        self.coslat = np.cos(self.latitude)\n",
    "        self.erot   = 7.2921150e-5 # Sidearial Earth rotation rate\n",
    "        \n",
    "        #openrocket method for gravitational accel, open rocket uses 6371000 m for earth radius\n",
    "        # comparing is low priority\n",
    "        #sin2lat = self.sinlat**2\n",
    "        #self.g_0 = 9.7803267714 * ((1.0 + 0.00193185138639 * sin2lat) / np.sqrt(1.0 - 0.00669437999013 * sin2lat))\n",
    "        \n",
    "        self.aero_model = aero_model\n",
    "        \n",
    "        # initialize atmospheric model, is it worth calculating this once and for all?\n",
    "        T_0 = 288.15 # K\n",
    "        p_0 = 101325 # Pa\n",
    "        self.layer =  [0, 11000, 20000, 32000, 47000, 51000, 71000, 84852,\n",
    "                       90000, 100000, 110000, 120000, 130000, 140000, 150000, 160000]\n",
    "        self.baseTemp = [288.15, 216.65, 216.65, 228.65, 270.65, 270.65, 214.65, 186.95,\n",
    "                         196.86, 203.06, 224.28, 250.09, 285.13, 364.19, 441.79, 517.94]\n",
    "        self.basePress = [p_0]\n",
    "        for i, alt in enumerate(self.layer):\n",
    "            if i == 0:\n",
    "                pass\n",
    "            else:\n",
    "                self.basePress.append(self.getConditions(self.layer[i] - 1)[1])\n",
    "        \n",
    "        # references for wind model < 150 m\n",
    "        self.u_0   = ref_ground_wind\n",
    "        self.z_0   = 18.3\n",
    "        self.alpha = 1.21 * self.u_0**(-0.75)\n",
    "                \n",
    "    # https://github.com/openrocket/openrocket/blob/b5cde10824440a1e125067b7d149873deec424b4/core/src/net/sf/openrocket/util/GeodeticComputationStrategy.java\n",
    "    # double check, low priority. might not even need\n",
    "    def coriolis(self, v):\n",
    "        v_e = v[0] # note, pretty sure openrocket does this wrong by adding a negative sign here\n",
    "        v_n = v[1]\n",
    "        v_u = v[2]\n",
    "        acc = -2*self.erot*np.cross(np.array([0, self.coslat, self.sinlat]), v)\n",
    "        return acc # acceleration\n",
    "    \n",
    "    # https://www.sensorsone.com/local-gravity-calculator/\n",
    "    # maybe one day i'll use this alternative https://github.com/openrocket/openrocket/blob/unstable/core/src/net/sf/openrocket/models/gravity/WGSGravityModel.java\n",
    "    def g_accel(self, x):\n",
    "        return np.array([0, 0, -self.IGF + self.FAC * x[-1]])\n",
    "        #return self.g_0 * (6356766/(6356766 + x[-1]))**2\n",
    "        \n",
    "    # gravitational torque. inputs in inertial frame, outputs in body frame\n",
    "    def T_gg(self, x, attitude_BI, J):\n",
    "        r = np.linalg.norm(x)\n",
    "        height = r + self.earth_rad\n",
    "        n = frame_rotation(attitude_BI, - x / r)\n",
    "        return (3 * self.mu_earth / height**3) * np.cross(n, J.dot(n)) # kg * m^2 / s^2\n",
    "\n",
    "    # U.S. 1976 Standard Atmosphere, at some point compare more rigorously to openrocket's method, low priority\n",
    "    #def old_std_at(self, x):\n",
    "    #    if x[2] < 11000:\n",
    "    #        T = 15.04 - 0.00649*x[2]\n",
    "    #        p = 101.29*((T + 273.1)/288.08)**5.256\n",
    "    \n",
    "    #    elif 11000 <= x[2] and x[2] <25000:\n",
    "    #        T = -56.46\n",
    "    #        p = 22.65*np.exp(1.73 - 0.000157*x[2])\n",
    "    \n",
    "    #    else:\n",
    "    #        T = -131.21 + 0.00299*x[2]\n",
    "    #        p = 2.488 * ((T + 273.1)/216.6)**(-11.388)\n",
    "    \n",
    "    #    p_a = p*1000                 # Ambient air pressure [Pa]\n",
    "    #    rho = p/(0.2869*(T + 273.1)) # Ambient air density [kg/m^3]\n",
    "    #    T_a = T + 273.1              # Ambient air temperature [K]\n",
    "    #    return np.array([p_a, rho, T_a])\n",
    "    \n",
    "    # is there a more intelligent way to do this?\n",
    "    # why are there two formulae?, med priority\n",
    "    def getConditions(self, altitude):\n",
    "        index = 0\n",
    "        g = np.linalg.norm(self.g_accel([altitude]))\n",
    "        altitude = altitude * 6356766 / (altitude + 6356766) # geopotential altitude\n",
    "        for i in range(len(self.layer)-1):\n",
    "            if self.layer[i + 1] > altitude:\n",
    "                break\n",
    "            index += 1\n",
    "        rate = (self.baseTemp[index+1] - self.baseTemp[index])/(self.layer[index+1] - self.layer[index])\n",
    "        t = self.baseTemp[index] + (altitude - self.layer[index])* rate\n",
    "        if altitude > 95000:\n",
    "            Ra = R_univ / 25 # kind of a hack. see miller '57 when you care\n",
    "        else:\n",
    "            Ra = self.Ra\n",
    "        \n",
    "        if abs(rate) > 0.001:\n",
    "            p = self.basePress[index]*(1 + (altitude-self.layer[index])*rate/self.baseTemp[index])**(-g/(rate*Ra))\n",
    "        else:\n",
    "            p = self.basePress[index]*(np.exp(-(altitude-self.layer[index])* g / (Ra * self.baseTemp[index])))\n",
    "        return (t, p)\n",
    "    \n",
    "    def std_at(self, x):\n",
    "        T_a, p_a = self.getConditions(x[-1])\n",
    "        # you know this is a poor method for checking conditions, med priority\n",
    "        if self.Ra == 287.058 and x[-1] > 95000:\n",
    "            self.Ra = R_univ / 25 # rough estimate when you care, see\n",
    "            # Miller, L. E. (1957). “Molecular weight” of air at high altitudes, low priority\n",
    "\n",
    "        rho = p_a/(self.Ra*T_a) # Ambient air density [kg/m^3]\n",
    "        mu = 3.7291*10**-6 + 4.9944*10**-8 * T_a / rho # kinematic viscosity\n",
    "        return np.array([p_a, rho, T_a, mu])\n",
    "    \n",
    "    # calculates drag force, etc\n",
    "    def aero(self, state, rkt, air, wind):\n",
    "        x, q, v, w = state[1:]\n",
    "        roll = w[2]\n",
    "        p_a, rho, T_a, mu = air\n",
    "        \n",
    "        # Check Knudsen number and consider other drag models (e.g. rarified gas dyn vs. quadratic drag), low priority\n",
    "        v0 = np.linalg.norm(v - wind)\n",
    "        v_hat = normalize(v - wind)\n",
    "        v_body = frame_rotation(q, v_hat)\n",
    "        \n",
    "        alpha = np.arccos(np.clip(np.dot(v_body, self.up), -1, 1))\n",
    "        sound_speed = np.sqrt(self.ka * self.Ra * T_a)\n",
    "        fin_flutter = self.aero_model.flutter_velocity(sound_speed, p_a) * 0.85 # 15% factor of safety\n",
    "        Ma = v0 / sound_speed # Mach\n",
    "        #Ma = v0 / (165.77 + 0.606*T_a) # openrocket's approx, < 1% error at low altitude, around 5-7% at high, low priority\n",
    "        dyn_press = 0.5 * rho * v0**2         # Dynamic pressure [Pa]\n",
    "        \n",
    "        # HIGH priority, get parasitic drag from fuel piping :(\n",
    "        # at some point include an option for the old look up table here, med priority\n",
    "        # also med priority, but a simple planform calculation for CoP could be useful for low-fidelity model\n",
    "        # low priority, consider \"Active Control Stabilization of High Power Rocket\" for drag equations\n",
    "        CoP, CN, CDax, Cld_times_d, C_damp_p, C_damp_y = self.aero_model.physics(alpha, Ma, v0, w, mu, rkt.CoM[2])\n",
    "        CoP = np.array([0,0, rkt.length - CoP]) # CoP calculated nose ref, but we work base ref\n",
    "        \n",
    "        direction = -normalize(self.project_normal.dot(v_body)) # normalize might be unnecessary\n",
    "        norm_force= CN*direction\n",
    "        \n",
    "        if not rkt.off_tower:# or v[2] <= 0:\n",
    "            norm_force *= 0\n",
    "            Cld_times_d = 0\n",
    "            \n",
    "        pitch_moment = np.cross(CoP - rkt.CoM, norm_force)\n",
    "        #C_damp_p = min(C_damp_p, pitch_moment[0])\n",
    "        #C_damp_y = min(C_damp_y, pitch_moment[1])\n",
    "        pitch_damp   = np.array([C_damp_p * np.sign(w[0]), C_damp_y * np.sign(w[1]), 0])\n",
    "        #print(pitch_moment, ',  ',pitch_damp)\n",
    "        mult = dyn_press * rkt.frontal_area\n",
    "        force_body = (np.array([0, 0, -CDax]) + norm_force) * mult\n",
    "        torque_body = (np.array([0, 0, -Cld_times_d]) +\n",
    "                       pitch_moment - pitch_damp#+\n",
    "                       #np.array([np.random.uniform(-0.0005,0.0005),\n",
    "                       #         np.random.uniform(-0.0005,0.0005), 0]) # openrocket injects noise for realism\n",
    "                      ) * mult\n",
    "        \n",
    "        return np.array([force_body, torque_body, v0, dyn_press, Ma, alpha, CoP[2], fin_flutter])\n",
    "    \n",
    "    # https://en.wikipedia.org/wiki/Wind_profile_power_law\n",
    "    # assuming stable atmosphere over land without too rough\n",
    "    # of terrain and wind measurements at 10 m\n",
    "    def ground_wind_profile(self, z):\n",
    "        if z < 0: z = 0\n",
    "        if z > 150: z = 150\n",
    "        return self.u_0 * (z / self.z_0)**self.alpha\n",
    "    \n",
    "    def wind_vector(self, x):\n",
    "        magnitude = self.ground_wind_profile(x[2] - launch_site_alt)\n",
    "        return np.array([1, 0, 0]) * magnitude\n",
    "\n",
    "# state = [m, x, q, v, w], is it an issue that our CoM changes but we still use F=MA?\n",
    "# parameters = [mdot, thtl, I_body, forces, torques], is it a sin to not update MoI as a state variable?\n",
    "# reference (7.233) on page 435 of Shabana for dynamical equations\n",
    "def derivatives(state, parameters):\n",
    "    def dm_dt(mdot, throttle):\n",
    "        return -mdot*throttle\n",
    "    \n",
    "    # kinematic equations of motion\n",
    "    def dx_dt(v):\n",
    "        return v\n",
    "    \n",
    "    def dq_dt(q, w):\n",
    "        return 1/2 * product(q, np.array([0, w[0], w[1], w[2]]))\n",
    "    \n",
    "    # dynamical equations of motion\n",
    "    def dv_dt(mass, forces, accels):\n",
    "        return accels + forces / mass\n",
    "    \n",
    "    def dw_dt(J_body, w, torque):\n",
    "        return np.linalg.inv(J_body).dot(torque - np.cross(w, J_body.dot(w)))\n",
    "    \n",
    "    return np.array([dm_dt(parameters[0], parameters[1]),\n",
    "                     dx_dt(state[3]),\n",
    "                     dq_dt(state[2], state[4]),\n",
    "                     dv_dt(state[0], parameters[3], parameters[4]),\n",
    "                     dw_dt(parameters[2], state[4], parameters[5])])\n",
    "    \n",
    "def dynamics(env, rkt, state, param):\n",
    "    wind      = np.zeros(3)#env.wind_vector(state[1]) #np.zeros(3) # find a nice deterministic profile, high priority\n",
    "    air       = env.std_at(state[1])\n",
    "    aero      = env.aero(state, rkt, air, wind)\n",
    "    \n",
    "    grav_acc  = env.g_accel(state[1])\n",
    "    grav_torq = env.T_gg(state[1], state[2], rkt.moment)\n",
    "    \n",
    "    if rkt.has_fuel():\n",
    "        throttle = rkt.engine.throttle_engine(np.linalg.norm(aero[0])) # still need a better method, low priority\n",
    "        thrust   = rkt.engine.thrust(air[0], throttle)\n",
    "    else:\n",
    "        throttle = 0.\n",
    "        thrust   = 0.\n",
    "        \n",
    "    forces = sandwich(state[2], sum([aero[0], np.array([0, 0, thrust]), param[0]])) # put forces in inertial frame\n",
    "    accels = sum([grav_acc, env.coriolis(state[3])])\n",
    "    torque = sum([grav_torq, aero[1], param[1]])\n",
    "    return np.array([forces, accels, torque, throttle, air, aero, thrust])\n",
    "\n",
    "# parameters = [C_d, T_param]\n",
    "def runge_kutta(env, rkt, state, parameters, dt):\n",
    "    mdot = rkt.engine.mdot if rkt.has_fuel() else 0\n",
    "    F1 = dynamics(env, rkt, state, parameters)\n",
    "    k1 = derivatives(state, [mdot, F1[3], rkt.moment, F1[0], F1[1], F1[2]])\n",
    "    \n",
    "    state_2 = [sum(pair) for pair in zip(state, dt*k1/2)]\n",
    "    state_2[2] = normalize(state_2[2])\n",
    "    F2 = dynamics(env, rkt, state_2, parameters)\n",
    "    k2 = derivatives(state_2, [mdot, F2[3], rkt.moment, F2[0], F2[1], F2[2]])\n",
    "    \n",
    "    state_3 = [sum(pair) for pair in zip(state, dt*k2/2)]\n",
    "    state_3[2] = normalize(state_3[2])\n",
    "    F3 = dynamics(env, rkt, state_3, parameters)\n",
    "    k3 = derivatives(state_3, [mdot, F3[3], rkt.moment, F3[0], F3[1], F3[2]])\n",
    "    \n",
    "    state_4 = [sum(pair) for pair in zip(state, dt*k3)]\n",
    "    state_4[2] = normalize(state_4[2])\n",
    "    F4 = dynamics(env, rkt, state_4, parameters)\n",
    "    k4 = derivatives(state_4, [mdot, F4[3], rkt.moment, F4[0], F4[1], F4[2]])\n",
    "    \n",
    "    return ((k1 + 2*k2 + 2*k3 + k4) / 6,\n",
    "            (F1 + 2*F2 + 2*F3 + F4) / 6,\n",
    "            None)\n",
    "\n",
    "# parameters = [C_d, T_param]\n",
    "# http://maths.cnam.fr/IMG/pdf/RungeKuttaFehlbergProof.pdf\n",
    "def adaptive_runge_kutta(env, rkt, state, parameters, dt):\n",
    "    mdot = rkt.engine.mdot if rkt.has_fuel() else 0\n",
    "    F1 = dynamics(env, rkt, state, parameters)\n",
    "    k1 = derivatives(state, [mdot, F1[3], rkt.moment, F1[0], F1[1], F1[2]])\n",
    "    \n",
    "    state_2 = [sum(pair) for pair in\n",
    "               zip(state, dt[-1]*k1/4)]\n",
    "    state_2[2] = normalize(state_2[2])\n",
    "    F2 = dynamics(env, rkt, state_2, parameters)\n",
    "    k2 = derivatives(state_2, [mdot, F2[3], rkt.moment, F2[0], F2[1], F2[2]])\n",
    "    \n",
    "    state_3 = [sum(pair) for pair in\n",
    "               zip(state, dt[-1] *(3*k1 + 9*k2)/32)]\n",
    "    state_3[2] = normalize(state_3[2])\n",
    "    F3 = dynamics(env, rkt, state_3, parameters)\n",
    "    k3 = derivatives(state_3, [mdot, F3[3], rkt.moment, F3[0], F3[1], F3[2]])\n",
    "    \n",
    "    state_4 = [sum(pair) for pair in\n",
    "               zip(state, dt[-1]*(1932*k1 - 7200*k2 + 7296*k3)/2197)]\n",
    "    state_4[2] = normalize(state_4[2])\n",
    "    F4 = dynamics(env, rkt, state_4, parameters)\n",
    "    k4 = derivatives(state_4, [mdot, F4[3], rkt.moment, F4[0], F4[1], F4[2]])\n",
    "    \n",
    "    state_5 = [sum(pair) for pair in\n",
    "               zip(state, dt[-1]*(439*k1/216 - 8*k2 + 3680*k3/513 - 845*k4/4104))]\n",
    "    state_5[2] = normalize(state_5[2])\n",
    "    F5 = dynamics(env, rkt, state_5, parameters)\n",
    "    k5 = derivatives(state_5, [mdot, F5[3], rkt.moment, F5[0], F5[1], F5[2]])\n",
    "    \n",
    "    state_6 = [sum(pair) for pair in\n",
    "               zip(state, dt[-1]*(-8*k1/27 + 2*k2 - 3544*k3/2565 + 1859*k4/4104 - 11*k5/40))]\n",
    "    state_6[2] = normalize(state_6[2])\n",
    "    F6 = dynamics(env, rkt, state_6, parameters)\n",
    "    k6 = derivatives(state_6, [mdot, F6[3], rkt.moment, F6[0], F6[1], F6[2]])\n",
    "    \n",
    "    order_5 = state + dt[-1] * (16*k1/135 + 6656*k3/12825 + 28561*k4/56430 - 9*k5/50 + 2*k6/55)\n",
    "    order_5[2] = normalize(order_5[2])\n",
    "    return (25*k1/216 + 1408*k3/2565 + 2197*k4/4101 - k5/5,\n",
    "            25*F1/216 + 1408*F3/2565 + 2197*F4/4101 - F5/5,\n",
    "            order_5)\n",
    "\n",
    "def time_step(env, rkt, state, dt, state_list):\n",
    "    if rcs_control and len(state_list) > 1:\n",
    "        num_thrusters, rcs_force, rcs_torque = rkt.rcs.controller(state[2], state_list[-1][1][1][4][0], rkt.CoM)\n",
    "    else:\n",
    "        num_thrusters, rcs_force, rcs_torque = 0, np.zeros(3), np.zeros(3)\n",
    "        \n",
    "    # may even want this up one more step in the hierarchy\n",
    "    parameters = [rcs_force, rcs_torque]\n",
    "    \n",
    "    if rkt.adaptive:\n",
    "        update = adaptive_runge_kutta(env, rkt, state, parameters, dt)\n",
    "    else:\n",
    "        update = runge_kutta(env, rkt, state, parameters, dt)\n",
    "        \n",
    "    new = state + dt[-1] * update[0]\n",
    "    new[2] = normalize(new[2])\n",
    "    \n",
    "    # at some point, adjust this to account for position being at CoM, low priority\n",
    "    if (new[1][2] - state_list[0][0][1][2] >= launch_tower and\n",
    "                    state[1][2] - state_list[0][0][1][2] < launch_tower):\n",
    "        rkt.off_tower = True\n",
    "        rkt.tower_index = len(state_list) + 1\n",
    "        rkt.launch_speed = np.linalg.norm(new[3])\n",
    "        \n",
    "    # at some point organize this logic, med priority\n",
    "    if rkt.has_fuel():\n",
    "        rkt.kludge = True\n",
    "    else:\n",
    "        if rkt.kludge:\n",
    "            rkt.kludge = False\n",
    "            rkt.F_index = len(state_list) - 1\n",
    "    \n",
    "    rkt.rcs.parts[0].drain(num_thrusters*rkt.rcs.mdot * dt[-1])\n",
    "    rkt.rcs.parts[1].exhaust_velocity(rkt.rcs.pressure())\n",
    "    \n",
    "    del_m_o, del_m_f = proportion(dt[-1]*update[0][0], rkt.OF)\n",
    "    rkt.lox_tank.drain(del_m_o)\n",
    "    rkt.ipa_tank.drain(del_m_f)\n",
    "    \n",
    "    rkt.sum_parts()\n",
    "    stability_margin = (rkt.CoM[2] - update[1][5][6]) / rkt.diameter\n",
    "    \n",
    "    if not rkt.adaptive:\n",
    "        m_prop = rkt.lox_tank.parts[-1].mass + rkt.ipa_tank.parts[-1].mass\n",
    "        openrocket_CoM = rkt.eng_sys.CoM[2] # for openrocket engine\n",
    "    else:\n",
    "        m_prop, openrocket_CoM = None, None\n",
    "    \n",
    "    if rkt.adaptive:\n",
    "        diff = 2 * sqrt(sum([np.linalg.norm(i)**2 for i in (update[-1] - new)]))\n",
    "        rkt.error.append(diff/2)\n",
    "        dt.append(dt[-1] * (rkt.tol/diff)**.25)\n",
    "     \n",
    "    state_list.append((new, update[:-1], stability_margin, m_prop, openrocket_CoM))\n",
    "\n",
    "def integration(env, rkt, dt, descend):\n",
    "    stop = False\n",
    "    env.first_time_env = True\n",
    "    rkt.off_tower = False\n",
    "    rkt.error = []\n",
    "    state_list = []\n",
    "    rkt.dt = [dt]\n",
    "    rkt.F_index = 2\n",
    "    quat = np.array([0.9990482, 0, 0.0436194, 0 ])#np.array([1,0,0,0])# # 10 degree angle normalize(np.array([0.9961947, 0, 0.0871557, 0]))\n",
    "    initial_state = np.array([rkt.mass, np.array([0,0, launch_site_alt]), quat, np.zeros(3), np.zeros(3)])\n",
    "    state_list.append((initial_state, 0)) # kludge, at some point clean initialization up. low priority\n",
    "    \n",
    "    time_step(env, rkt, state_list[-1][0], rkt.dt, state_list)\n",
    "    while (not descend and state_list[-1][0][3][2] > 0) or (descend and state_list[-1][0][1][2] > launch_site_alt):\n",
    "        time_step(env, rkt, state_list[-1][0], rkt.dt, state_list)\n",
    "    \n",
    "    return state_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# array breakdown:\n",
    "# state_list[i]: (state, update, stability_margin, m_prop, openrocket_CoM)\n",
    "#   state: (m, x, q, v, w)\n",
    "#   update: (derivatives, dynamics)\n",
    "#     derivatives: see state\n",
    "#     dynamics: (forces, accels, torque, throttle, air, aero, thrust)\n",
    "#       air: (p_a, rho, T_a, mu)\n",
    "#       aero: (force_body, torque_body, v0, dyn_press, Ma, alpha, CoP[2], flutter)\n",
    "\n",
    "def trajectory(m_prop, mdot, p_e,\n",
    "               throttle_window, min_throttle,\n",
    "               rcs_mdot, rcs_p_e, rcs_p_ch,\n",
    "               ballast, root, tip, sweep, span, thickness,\n",
    "               airfrm_in_rad, OF, p_ch, T_ch, ke, MM,\n",
    "               dt, adaptive=False, tol=0.042, descend=False):\n",
    "    \n",
    "    LV4 = create_rocket(m_prop, mdot, p_e,\n",
    "                    p_ch, T_ch, ke, MM,\n",
    "                    throttle_window, min_throttle,\n",
    "                    airfrm_in_rad, OF,\n",
    "                    rcs_mdot, rcs_p_e, rcs_p_ch,\n",
    "                    ballast, root, tip, sweep, span, thickness)\n",
    "    aero_model = AeroModel(LV4.diameter, LV4.length, nose_l,\n",
    "                           LV4.fin)\n",
    "    env = Environment(aero_model, 17.7)\n",
    "    LV4.adaptive = adaptive\n",
    "    LV4.tol = tol\n",
    "    states = integration(env, LV4, dt, descend)\n",
    "    \n",
    "    sim = SimpleNamespace()\n",
    "    sim.raw_states = states\n",
    "    sim.LV4 = LV4\n",
    "    sim.launch_speed = LV4.launch_speed\n",
    "    sim.F_index = LV4.F_index - 1 # subtract one cus we're throwing away the first entry. pardon the kludge\n",
    "    sim.alt = []\n",
    "    sim.v = []\n",
    "    sim.a = []\n",
    "    sim.thrust = []\n",
    "    sim.drag = []\n",
    "    sim.dyn_press = []\n",
    "    sim.p_a = []\n",
    "    sim.rho = []\n",
    "    sim.t = []\n",
    "    sim.Ma = []\n",
    "    sim.m = []\n",
    "    sim.throttle = []\n",
    "    sim.stability_margin = []\n",
    "    sim.m_prop = []\n",
    "    sim.openrocket_CoM = []\n",
    "    sim.fin_flutter = []\n",
    "    \n",
    "    for i, state in enumerate(states[1:]):\n",
    "        sim.alt.append(state[0][1][2])\n",
    "        sim.v.append(state[0][3][2])\n",
    "        sim.a.append(state[1][0][3][2])\n",
    "        sim.thrust.append(state[1][1][6])\n",
    "        sim.drag.append(abs(state[1][1][5][0][2]))\n",
    "        sim.dyn_press.append(state[1][1][5][3])\n",
    "        sim.fin_flutter.append(state[1][1][5][7] / state[0][3][2])\n",
    "        sim.p_a.append(state[1][1][4][0])\n",
    "        sim.rho.append(state[1][1][4][1])\n",
    "        sim.t.append(i*dt) # kludge? add time as a state variable, low priority\n",
    "        sim.Ma.append(state[1][1][5][4])\n",
    "        sim.m.append(state[0][0])\n",
    "        sim.throttle.append(state[1][1][3])\n",
    "        sim.stability_margin.append(state[2])\n",
    "        sim.m_prop.append(state[3])\n",
    "        sim.openrocket_CoM.append(state[4])\n",
    "    \n",
    "    sim.alt = np.array(sim.alt)\n",
    "    sim.v = np.array(sim.v)\n",
    "    sim.a = np.array(sim.a)\n",
    "    sim.thrust = np.array(sim.thrust)\n",
    "    sim.drag = np.array(sim.drag)\n",
    "    sim.dyn_press = np.array(sim.dyn_press)\n",
    "    sim.p_a = np.array(sim.p_a)\n",
    "    sim.rho = np.array(sim.rho)\n",
    "    sim.t = np.array(sim.t)\n",
    "    sim.fin_flutter = np.array(sim.fin_flutter)\n",
    "    \n",
    "    # note g_n is a global constant in inputs\n",
    "    # this will run after we reach apogee to do last-minute calculations and conversions\n",
    "    sim.TWR = sim.a[0] / g_n                  # Thrust-to-weight ratio constraint\n",
    "    sim.S_crit = LV4.engine.p_e / sim.p_a[0]         # Sommerfield criterion constraint\n",
    "    sim.massratio = LV4.GLOW / sim.m[-1] # Mass ratio\n",
    "    sim.dV1 = LV4.engine.Ve * np.log(sim.massratio) / 1000            # Tsiolkovsky's bane (delta-V)\n",
    "    sim.max_g_force = max(abs(sim.a)) / g_n   # calculates top g-force\n",
    "    sim.maxq = max(sim.dyn_press)\n",
    "    # ignoring the end of flight stability cuz it doesn't really matter\n",
    "    sim.min_stability = min(sim.stability_margin[:sim.F_index])\n",
    "    sim.min_fin_flutter = min(sim.fin_flutter[:sim.F_index])\n",
    "    sim.ld_ratio = sim.LV4.length / sim.LV4.diameter\n",
    "    \n",
    "    return sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this creates a list of strings for relevant data of trajectory\n",
    "# arranging in sensible order is low priority. there might be more information to extract also.\n",
    "# maybe get input from on high about most relevant info and how to organize it nicely\n",
    "def print_results(sim, save):\n",
    "    text_base = []\n",
    "    text_base.append('\\nDESIGN VECTOR')\n",
    "    text_base.append('\\n-----------------------------')\n",
    "    text_base.append('\\ndesign total propellant mass               = {:.3f} kg'.format(sim.m_prop[0]))\n",
    "    text_base.append('\\ndesign mass flow rate                      = {:.3f} kg/s'.format(sim.LV4.engine.mdot))\n",
    "    text_base.append('\\ndesign nozzle exit pressure                = {:.3f} Pa'.format(sim.LV4.engine.p_e))\n",
    "    text_base.append('\\ntotal tankage length (after adjustment)    = {:.3f} m'.format(sim.LV4.l_o + sim.LV4.l_f))\n",
    "    text_base.append('\\ndesign airframe diameter                   = {:.3f} m.'.format(sim.LV4.diameter))\n",
    "    text_base.append('\\ndesign airframe total length               = {:.3f} m.'.format(sim.LV4.length))\n",
    "    text_base.append('\\ndesign GLOW                                = {:.3f} kg'.format(sim.LV4.GLOW))\n",
    "    text_base.append('\\ndesign ballast mass                        = {:.3f} kg'.format(sim.LV4.ballast))\n",
    "    text_base.append('\\ndesign fin root chord                      = {:.3f} m'.format(sim.LV4.fin.root))\n",
    "    text_base.append('\\ndesign fin tip chord                       = {:.3f} m'.format(sim.LV4.fin.tip))\n",
    "    text_base.append('\\ndesign fin sweep angle                     = {:.3f} deg'.format(np.degrees(sim.LV4.fin.sweep)))\n",
    "    text_base.append('\\ndesign fin span                            = {:.3f} m'.format(sim.LV4.fin.height))\n",
    "    text_base.append('\\ndesign fin thickness                       = {:.3f} mm'.format(sim.LV4.fin.thickness*1000))\n",
    "    \n",
    "    text_base.append('\\n')\n",
    "    text_base.append('\\nCONSTRAINTS')\n",
    "    text_base.append('\\n-----------------------------')\n",
    "    text_base.append('\\nL/D ratio (c.f. < {})                       = {:.3f}'.format(\n",
    "                                                                            cons_LD, sim.ld_ratio))\n",
    "    text_base.append('\\nfin flutter ratio (c.f. > {})                       = {:.3f}'.format(\n",
    "                                                                            1.0, sim.min_fin_flutter))\n",
    "    text_base.append('\\nSommerfield criterion (c.f. pe/pa >= {})    = {:.3f}'.format(cons_S_crit, sim.S_crit))\n",
    "    text_base.append(\"\\nmax acceleration (c.f. < {})                = {:.3f} gs\".format(\n",
    "                                                                                cons_accel, sim.max_g_force))\n",
    "    text_base.append('\\nTWR at lift off (c.f. > {})                 = {:.3f}'.format(cons_TWR, sim.TWR))\n",
    "    text_base.append('\\nLowest stability margin caliber (c.f. > {}) = {:.3f}'.format(cons_stblty, sim.min_stability))\n",
    "    text_base.append('\\nspeed when leaving launch rail (c.f. > {})  = {:.3f} m/s'.format(cons_ls, sim.launch_speed))\n",
    "    text_base.append('\\naltitude at apogee (c.f. > {})              = {:.3f} km'.format(\n",
    "                                                                                cons_alt/1000, sim.alt[-1]/1000))\n",
    "    text_base.append('\\ndesign thrust (ground level) (c.f. < {})    = {:.3f} kN'.format(\n",
    "                                                                                     cons_thrust, sim.thrust[0]/1000))\n",
    "\n",
    "    text_base.append('\\n')\n",
    "    text_base.append('\\nADDITIONAL INFORMATION')\n",
    "    text_base.append('\\n-----------------------------')\n",
    "    text_base.append('\\ndesign thrust (vacuum)                     = {:.2f} kN'.format(sim.thrust[sim.F_index]/1000))\n",
    "    text_base.append('\\ndesign total dry mass                      = {:.3f} kg'.format(sim.m[-1]))\n",
    "    text_base.append('\\nmission time at apogee                     = {:.3f} s'.format(sim.t[-1]))\n",
    "    text_base.append('\\nmission time at burnout                    = {:.3f} s'.format(sim.t[sim.F_index]))\n",
    "    text_base.append('\\nmax dynamic pressure                       = {:.3f} kPa'.format(sim.maxq/1000))\n",
    "    text_base.append('\\ndesign dV                                  = {:.3f} km/s'.format(sim.dV1))\n",
    "    text_base.append('\\nestimated minimum required dV              = {:.3f} km/s'.format(\n",
    "                                                                                    sqrt(2*g_n*sim.alt[-1])/1000))\n",
    "    \n",
    "    text_base.append(\"\\n\")\n",
    "    text_base.append(\"\\nENGINE SYSTEM DETAILS\")\n",
    "    text_base.append(\"\\n-----------------------------\")\n",
    "    mdot_o, mdot_f = proportion(sim.LV4.engine.mdot, sim.LV4.OF)\n",
    "    text_base.append(\"\\nOx flow:                                   = {:.3f} kg/s\".format(mdot_o))\n",
    "    text_base.append(\"\\nFuel flow:                                 = {:.3f} kg/s\".format(mdot_f))\n",
    "    text_base.append(\"\\nOx mass:                                   = {:.3f} kg\".format(sim.LV4.m_o))\n",
    "    text_base.append(\"\\nFuel mass:                                 = {:.3f} kg\".format(sim.LV4.m_f))\n",
    "    text_base.append(\"\\nOx tank length + ullage:                   = {:.3f} m\".format(sim.LV4.l_o))\n",
    "    text_base.append(\"\\nFuel tank length + ullage:                 = {:.3f} m\".format(sim.LV4.l_f))\n",
    "    text_base.append('\\ndesign chamber pressure                    = {:.3f} kPa'.format(sim.LV4.engine.p_ch/1000))\n",
    "    text_base.append('\\ndesign expansion ratio                     = {:.3f}'.format(sim.LV4.engine.ex))\n",
    "    text_base.append('\\ndesign Exit area                           = {:.3f} in.^2'.format(sim.LV4.engine.A_e/0.0254**2))\n",
    "    text_base.append('\\ndesign throat area                         = {:.3f} in.^2'.format(sim.LV4.engine.A_t/0.0254**2))\n",
    "    text_base.append('\\ndesign Throat pressure                     = {:.3f} kPa'.format(sim.LV4.engine.p_t/1000))\n",
    "    text_base.append('\\ndesign Throat temperature                  = {:.3f} K'.format(sim.LV4.engine.T_t))\n",
    "    text_base.append('\\ndesign Chamber temperature                 = {:.3f} K'.format(sim.LV4.engine.T_ch))\n",
    "    text_base.append('\\ndesign exit velocity                       = {:.3f} m/s'.format(sim.LV4.engine.Ve))\n",
    "    text_base.append('\\ndesign isp                                 = {:.3f} s'.format(sim.LV4.engine.Ve/g_n))\n",
    "    text_base.append('\\ndesign average impulse                     = {:.3f} kN*s'.format(\n",
    "                                                  sim.t[sim.F_index]*(sim.thrust[sim.F_index]/1000 + sim.thrust[0]/1000)/2))\n",
    "    \n",
    "    sim.LV4.read_out()\n",
    "    text_base.append('\\n')\n",
    "    text_base.append('\\nPOST-FLIGHT MASS BUDGET')\n",
    "    text_base.append('\\n-----------------------------\\n')\n",
    "    for line in sim.LV4.description:\n",
    "        text_base.append(line)\n",
    "        \n",
    "    if save:\n",
    "        # create a file with all this info in it\n",
    "        with open(rkt_prefix + 'psas_rocket_' + str(get_index()) + '_traj.txt', 'w') as traj:\n",
    "            for line in text_base:\n",
    "                traj.write(line)\n",
    "        \n",
    "    return text_base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this creates a nice set of plots of our trajectory data and saves it to rocket_farm\n",
    "def rocket_plot(t, alt, v, a, F, q, Ma, m, p_a, D, throttle, fin_v, save):\n",
    "    pylab.rcParams['figure.figsize'] = (10.0, 10.0)\n",
    "    fig, (ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8, ax9, ax10) = plt.subplots(10, sharex=True)\n",
    "    \n",
    "    for n in (ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8, ax9, ax10):\n",
    "        n.spines['top'].set_visible(False)\n",
    "        n.spines['right'].set_visible(False)\n",
    "        n.yaxis.set_ticks_position('left')\n",
    "        n.xaxis.set_ticks_position('bottom')\n",
    "        n.yaxis.labelpad = 20\n",
    "        \n",
    "    ax1.plot(t, alt/1000, 'k')\n",
    "    ax1.set_ylabel(\"Altitude (km)\")\n",
    "    ax1.yaxis.major.locator.set_params(nbins=6)\n",
    "    ax1.set_title('LV4 Trajectory')\n",
    "    \n",
    "    ax2.plot(t, v, 'k')\n",
    "    ax2.yaxis.major.locator.set_params(nbins=6)\n",
    "    ax2.set_ylabel(\"Velocity (m/s)\")\n",
    "    \n",
    "    ax3.plot(t, a/g_n, 'k')\n",
    "    ax3.yaxis.major.locator.set_params(nbins=10)\n",
    "    ax3.set_ylabel(\"Acceleration/g_n\")\n",
    "    \n",
    "    ax4.plot(t, F/1000, 'k')\n",
    "    ax4.yaxis.major.locator.set_params(nbins=6)\n",
    "    ax4.set_ylabel(\"Thrust (kN)\")\n",
    "    \n",
    "    ax5.plot(t, q/1000, 'k')\n",
    "    ax5.yaxis.major.locator.set_params(nbins=6)\n",
    "    ax5.set_ylabel(\"Dynamic Pressure (kPa)\")\n",
    "    \n",
    "    ax6.plot(t, Ma, 'k')\n",
    "    ax6.yaxis.major.locator.set_params(nbins=6) \n",
    "    ax6.set_ylabel(\"Mach number\")\n",
    "    ax6.set_xlabel(\"t (s)\")\n",
    "    \n",
    "    #ax7.plot(t, np.array(m)*0.666*np.array(a), 'k')\n",
    "    ax7.plot(t[:-500], fin_v[:-500], 'k')\n",
    "    ax7.yaxis.major.locator.set_params(nbins=6) \n",
    "    ax7.set_ylabel(\"Fin Flutter Ratio\")\n",
    "    #ax7.set_ylabel(\"LOX Tank Axial Load\")\n",
    "    ax7.set_xlabel(\"t (s)\")\n",
    "    \n",
    "    ax8.plot(t, D, 'k')\n",
    "    ax8.yaxis.major.locator.set_params(nbins=6)\n",
    "    ax8.set_ylabel(\"Drag (N)\")\n",
    "    \n",
    "    ax9.plot(t, p_a/1000, 'k')\n",
    "    ax9.yaxis.major.locator.set_params(nbins=6)\n",
    "    ax9.set_ylabel(\"Air Pressure (kPa)\")\n",
    "    \n",
    "    ax10.plot(t, throttle, 'k')\n",
    "    ax10.yaxis.major.locator.set_params(nbins=6)\n",
    "    ax10.set_ylabel(\"Mass Flow Rate Throttle (%)\")\n",
    "    \n",
    "    # we save the nice figures we make and then display them\n",
    "    if save:\n",
    "        plt.savefig(rkt_prefix + 'psas_rocket_' + str(get_index()) + '_traj.svg')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cory/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:360: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "/home/cory/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:167: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "/home/cory/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:226: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "/home/cory/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:206: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n"
     ]
    }
   ],
   "source": [
    "# this block is so that our notebook won't automatically run a sim when it is imported ;)\n",
    "if __name__ == '__main__' and not '__file__' in globals():\n",
    "    test_run = trajectory(141.88397561701234, 2.8728568968933215, 71035.22181877575, throttle_window, min_throttle, rcs_mdot, rcs_p_e, rcs_p_ch, \n",
    "                          ballast, root, tip, sweep, span, thickness, airfrm_in_rad, OF, p_ch, T_ch, ke, MM, 0.05, True, 0.02, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "ename": "NameError",
     "evalue": "name 'textlist' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-a26ac948c2f9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Trajectory'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtextlist\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'textlist' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# array breakdown:\n",
    "# state_list[i]: (state, update, stability_margin, m_prop, openrocket_CoM)\n",
    "#   state: (m, x, q, v, w)\n",
    "#   update: (derivatives, dynamics)\n",
    "#     derivatives: see state\n",
    "#     dynamics: (forces, accels, torque, throttle, air, aero, thrust)\n",
    "#       air: (p_a, rho, T_a, mu)\n",
    "#       aero: (force_body, torque_body, v0, dyn_press, Ma, alpha, CoP[2], flutter)\n",
    "\n",
    "# this block is so that our notebook won't automatically run a sim when it is imported ;)\n",
    "if __name__ == '__main__' and not '__file__' in globals():\n",
    "    #from matplotlib import pyplot as plt\n",
    "\n",
    "    fig, ax1 = plt.subplots()\n",
    "    #ax1.plot(test.LV4.dt[:])\n",
    "    #ax1.plot(test.LV4.error)\n",
    "    #axs.plot(test.w) # test.LV4.length-\n",
    "    #ax1.plot([test.LV4.length- state[2][3][6] for state in test.states[1:]])\n",
    "    #ax1.plot([2*np.arccos(state[0][2][0])*180/np.pi for state in test_run.raw_states[1:]])\n",
    "    ax1.plot(#[state[0][1][0] for state in test_run.raw_states[1:]],\n",
    "             [state[0][1][2] for state in test_run.raw_states[1:]])\n",
    "    #plt.show()\n",
    "    #print(max([test.LV4.length- state[2][3][6] for state in test.states[1:]]))\n",
    "    #print(min([test.LV4.length- state[2][3][6] for state in test.states[1:2000]]))\n",
    "    #print(test.LV4.length)\n",
    "    #textlist = print_results(test_run, False)\n",
    "    rocket_plot(test_run.t, test_run.alt, test_run.v, test_run.a, test_run.thrust, test_run.dyn_press,\n",
    "                test_run.Ma, test_run.m, test_run.p_a, test_run.drag, test_run.throttle, test_run.fin_flutter, False)\n",
    "    \n",
    "    from mpl_toolkits.mplot3d import Axes3D\n",
    "    fig = plt.figure(1)\n",
    "    ax = fig.add_subplot(111, projection='3d')\n",
    "    xyz = [state[0][1] for state in test_run.raw_states]\n",
    "    x, y, z = [state[0] for state in xyz], [state[1] for state in xyz], [state[2] for state in xyz]\n",
    "    ax.plot(x, y, z)\n",
    "    ax.grid()\n",
    "    plt.title('Trajectory')\n",
    "    for line in textlist:\n",
    "        print(line)"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}