QuadrotorFTC.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoGenU for Jupyter: Fault-Tolerant Control of a Quadrotor\n",
    "\n",
    "An Automatic C++ Code Generator for Nonlinear Model Predictive Control (NMPC)  \n",
    "\n",
    "---  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Quadrotor Model   \n",
    "#### Variables\n",
    "$\\xi$: position in the inertial frame $\\{ e_1^\\textrm{i}, e_2^\\textrm{i}, e_3^\\textrm{i} \\}$  \n",
    "$v$: velocity in the intertial frame $\\{ e_1^\\textrm{i}, e_2^\\textrm{i}, e_3^\\textrm{i} \\}$  \n",
    "$q$: unit quaternion (in the body frame) representing the rotation of the inertial frame $\\{ e_1^\\textrm{i}, e_2^\\textrm{i}, e_3^\\textrm{i} \\}$ to the body frame $\\{ e_1^\\textrm{b}, e_2^\\textrm{b}, e_3^\\textrm{b} \\}$:   \n",
    "$e_j^\\textrm{b} = q \\otimes e_j^\\textrm{i} \\otimes q^* (j=1,2,3)$ holds, where $\\otimes$ denotes the product of quaternions, and $q^*$ denotes the conjugate quaternion of $q$.   \n",
    "$\\omega$: angular velocity in the body frame $\\{ e_1^\\textrm{b}, e_2^\\textrm{b}, e_3^\\textrm{b} \\}$   \n",
    "$u$: thrust vector   \n",
    "#### Parameters   \n",
    "$m$: mass, $J$: moment of intertia, $d_3$: coefficient of aerodynamic drag in the yaw axis, $\\ell$ distance between the center of mass and each rotor,   \n",
    "$k$: a coefficient representing the relationship between the thrust and reaction torque in each rotor,   \n",
    "$c_1$: thrust reduction factor of rotor No. 1 ($0 \\leq c_1 \\leq 1$) (1: normal, 0: completely failed) \n",
    "#### State Equation\n",
    "$$\\begin{array}{l}\n",
    "    \\dot{\\xi} = v \\\\\n",
    "    \\dot{v} = -g e_3^\\textrm{i} + \\frac{1}{m} e_3^\\textrm{b}(q) s^{\\top} u \\\\   % E_3^\\textrm{b}(q) u \\\\\n",
    "    \\dot{q} = \\frac{1}{2} \\Omega(\\omega) q   \\quad \\textrm{Note that $q$ is in the inertial frame while $\\omega$ is in the body frame. This is correct with $\\Omega(\\omega)$ defined appropriately.} \\\\\n",
    "    \\dot{\\omega} = - J^{-1} \\omega^{\\times} J \\omega - J^{-1} D \\omega + J^{-1} T u \n",
    "\\end{array} $$  \n",
    "where\n",
    "$$\\begin{array}{l}\n",
    "e_3^\\textrm{b}(q) &=& \\left[ 2(q_0 q_2 + q_1 q_3) \\quad 2(q_2 q_3 - q_0 q_1) \\quad q_0^2 - q_1^2 - q_2^2 + q_3^2 \\right]^{\\top} \\in \\mathbb{R}^3, \\\\\n",
    "    s &=& \\left[ c_1 \\; 1 \\; 1 \\; 1 \\right]^{\\top} \\in \\mathbb{R}^4 \\\\\n",
    "    \\omega^{\\times} &=& \\begin{bmatrix}\n",
    "            0    &  -\\omega_3  &   \\omega_2 \\\\\n",
    "        \\omega_3 &       0     &  -\\omega_1 \\\\\n",
    "       -\\omega_2 &   \\omega_1  &      0     \\\\\n",
    "      \\end{bmatrix}  \\in \\mathbb{R}^{3 \\times 3}, \\\\\n",
    "    \\Omega &=& \\begin{bmatrix}\n",
    "           0   &  -\\omega^{\\top} \\\\\n",
    "        \\omega &  -\\omega^{\\times}\n",
    "      \\end{bmatrix}   \\in \\mathbb{R}^{4 \\times 4}, \\\\\n",
    "    D &=& {\\rm diag}[0,0,d_3] \\in \\mathbb{R}^{3 \\times 3} \\\\\n",
    "    T &=& \\begin{bmatrix}\n",
    "           0  &  \\ell &  0   & -\\ell \\\\\n",
    "        - c_1 \\ell &   0   & \\ell &   0  \\\\\n",
    "          c_1 k  &  -k   &  k   &  -k  \n",
    "      \\end{bmatrix} \\in \\mathbb{R}^{3 \\times 4},\n",
    "\\end{array}$$\n",
    "#### Representations by Array x\n",
    "$$\\xi = [x[0],x[1],x[2]], v = [x[3],x[4],x[5]], q = [x[6],x[7],x[8],x[9]], \\omega = [x[10],x[11],x[12]]$$\n",
    "$$e_3^\\textrm{b}(q) = [ 2*(x[6]*x[8] + x[7]*x[9]) \\quad 2*(x[8]*x[9] - x[6]*x[7]) \\quad x[6]**2 - x[7]**2 - x[8]**2 + x[9]**2 ]$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# Generate C++ code for optimal control problem (OCP)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import autogenu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set dimensions and define `AutoGenU`\n",
    "- `nx`: Dimension of the state vector $x$   \n",
    "- `nu`: Dimension of the control input vector $u$  \n",
    "- `ocp_name`: Name of the optimal control problem (OCP). Used as the name of the directory containing the generated C++ source files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "nx = 13\n",
    "nu = 4\n",
    "ocp_name = 'QuadrotorFTC'\n",
    "ag = autogenu.AutoGenU(ocp_name, nx, nu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate t, x, and u, necessary variables to formulate of the optimal control problem\n",
    "- `t`: Time parameter $t$  \n",
    "- `x`: The state vector $x$  \n",
    "- `u`: The control input vector $u$  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "t = ag.define_t()\n",
    "x = ag.define_x()\n",
    "u = ag.define_u()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define variables used in the state equation, constraints, and cost function\n",
    "- Define a scalar variable whose name is \"var\" as  \n",
    "`var = ag.define_scalar_var('var') `\n",
    "\n",
    "- Define scalar variables whose names are \"var\\_1\", \"var\\_2\", ..., \"var\\_n\" as  \n",
    "`var_1, var_2, ..., var_n = ag.define_scalar_vars('var_1', 'var_2', ..., 'var_n')`\n",
    "\n",
    "- Define array variables whose name is \"vec\" and dimension is dim_vec as  \n",
    "`vec = ag.define_array_var('vec', dim_vec)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Variables used in the state function\n",
    "m, g, J1, J2, J3, d3, l, k, c1 = ag.define_scalar_vars('m', 'g', 'J1', 'J2', 'J3', 'd3', 'l', 'k', 'c1')\n",
    "# Variables used in the cost function\n",
    "s = ag.define_array_var('s', nx)\n",
    "s_terminal = ag.define_array_var('s_terminal', nx)\n",
    "x_ref = ag.define_array_var('x_ref', nx)\n",
    "r = ag.define_array_var('r', nu)\n",
    "u_ref = ag.define_array_var('u_ref', nu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the state equation, constraints, the stage cost, and the terminal cost\n",
    "- `f`: The state equation $ f(t, x, u)$  \n",
    "- `C`: The equality constraisnts $C(t, x, u) = 0 $  \n",
    "- `h`: The inequality constraisnts $h(t, x, u) \\leq 0$ considered by semi-smooth Fischer-Burumeister method  \n",
    "- `L`: The stage cost $L(t, x, u)$  \n",
    "- `phi`: The terminal cost $\\phi (t, x)$  \n",
    "\n",
    "Note: array indices start with 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Import symbolic math from sympy. If you need more functions, import same as below.\n",
    "from sympy import sin, cos, tan, exp, log, sinh, cosh, tanh, diff, sqrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define the state equation\n",
    "f = [𝑥[3],\n",
    "     𝑥[4],\n",
    "     𝑥[5],\n",
    "     2*(x[6]*x[8] + x[7]*x[9]) * (c1*u[0] + u[1] + u[2] + u[3])/m,\n",
    "     2*(x[8]*x[9] - x[6]*x[7]) * (c1*u[0] + u[1] + u[2] + u[3])/m,\n",
    "     -g + (x[6]**2 - x[7]**2 - x[8]**2 + x[9]**2) * (c1*u[0] + u[1] + u[2] + u[3])/m,\n",
    "     (           - x[7]*𝑥[10] - x[8]*𝑥[11] - x[9]*𝑥[12])/2,\n",
    "     (x[6]*𝑥[10]              + x[8]*x[12] - x[9]*x[11])/2,\n",
    "     (x[6]*x[11] - x[7]*x[12]              + x[9]*x[10])/2,\n",
    "     (x[6]*x[12] + x[7]*x[11] - x[8]*x[10]             )/2,\n",
    "     (J2*x[11]*x[12] - J3*x[11]*x[12] + l*u[1] - l*u[3])/J1,\n",
    "     (-J1*x[10]*x[12] + J3*x[10]*x[12] - l*c1*u[0] + l*u[2])/J2,\n",
    "     (J1*x[10]*x[11] - J2*x[10]*x[11] - d3*x[12] + k*c1*u[0] - k*u[1] + k*u[2] - k*u[3])/J3\n",
    "    ]\n",
    "\n",
    "# Define the constraints\n",
    "C = []\n",
    "\n",
    "# Define the inequality constraints considered using the semi-smooth Fischer-Burmeister method\n",
    "h = []\n",
    "\n",
    "# Define the stage cost\n",
    "L = sum(s[i]*(x[i] - x_ref[i])**2 for i in range(nx))/2 + sum(r[i] * (u[i] - u_ref[i])**2 for i in range(nu))/2\n",
    "\n",
    "# Define the terminal cost\n",
    "phi = sum(s_terminal[i]*(x[i] - x_ref[i])**2 for i in range(nx))/2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set functions\n",
    "ag.set_functions(f, C, h, L, phi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the control input bound constraints\n",
    "The box constraints on the control input $u_{\\rm min} \\leq u \\leq u_{\\rm max}$ are treated in a special manner.\n",
    "Specifically, it is transformed into $$u^2 + u_{\\rm dummy}^2 - (\\frac{u_{\\rm max} - u_{\\rm min}}{2})^2 = 0$$.  \n",
    "Further, $u_{\\rm dummy}$ and associated Lagrange multiplier are eliminated in Newton-type iteration efficiently in the multiple-shooting solver.  \n",
    "Set such box constraints by specifying\n",
    "- `uindex`: index of the constrained control input element\n",
    "- `umin`: Minimum value (lower bound) of the control input element\n",
    "- `umax`: Maximum value (upper bound) of the control input element\n",
    "- `dummy_weight`: Weight parameter of the dummy (slack) variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for uindex in range(nu):\n",
    "    ag.add_control_input_bounds(uindex=uindex, umin=0.0065, umax=0.3266, dummy_weight=100.0)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set Parameters\n",
    "Set values for all parameters you defined.\n",
    "- Set `value` for a scalar variable whose name is \"var\" as  \n",
    "`ag.set_scalar_var('var', value) `\n",
    "\n",
    "- Set `value_1`, `value_2`, ..., `value_n`, for scalar variables whose names are \"var\\_1\", \"var\\_2\", ..., \"var\\_n\" as  \n",
    "`ag.set_scalar_vars(['var_1', value_1], ['var_2', value_2], ..., ['var_n', value_n])`\n",
    "\n",
    "- Set array variables `var_1`, `var_2`, ..., `var_n` whose name is \"vec\" and dimension is n as  \n",
    "`ag.define_array_var('vec', [var_1, var_2, ..., var_n])`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ag.set_scalar_vars(['m',0.063], ['g',9.81], ['J1',5.83e-5], ['J2',7.17e-5], ['J3',1.00e-4], ['d3',1.0e-3], ['l',0.0624], ['k',0.0731],['c1',0.0])  \n",
    "ag.set_array_var('s', [5,5,50, 1,1,1, 0,1,1,1, 0.1,0.1,0.1])  # Zero weight for q_0\n",
    "ag.set_array_var('s_terminal', [5,5,50, 1,1,1, 0,1,1,1, 0.1,0.1,0.1])\n",
    "ag.set_array_var('x_ref', [0,0,0, 0,0,0, 1,0,0,0, 0,0,0]) \n",
    "ag.set_array_var('r', [1,1,1,1])\n",
    "u_ref_eq = m*g/(c1+3)\n",
    "ag.set_array_var('u_ref', [u_ref_eq,u_ref_eq,u_ref_eq,u_ref_eq])  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate C++ codes of the definition of the optimal control problem\n",
    "Generate `ocp.hpp` that defines the optimal control problem (OCP).  \n",
    "- `simplification`: The flag for simplification. If `True`, symbolic functions are simplified. However, if functions are too complicated, it takes too much time. Default is `False`.  \n",
    "- `common_subexpression_elimination`: The flag for common subexpression elimination. If `True`, common subexpressions in fxu, phix, hx, and hu are eliminated when `ocp.hpp` is generated. Default is `False`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'ocp.hpp', the definition of the OCP, is generated at C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\n"
     ]
    }
   ],
   "source": [
    "simplification = False\n",
    "common_subexpression_elimination = True\n",
    "\n",
    "ag.generate_ocp_definition(simplification, common_subexpression_elimination)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---  \n",
    "# Generate C++ codes of NMPC solver and numerical simulation  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set NLP type  \n",
    "Set which NLP formulation you use. (hint: `MultipleShooting` works better for typical nonlinear problems.)\n",
    "- `MultipleShooting` (recommended) : The multiple shooting based C/GMRES method with condensing of the state and the Lagragne multipliers with respect to the state equation.\n",
    "- `SingleShooting` : The single shooting method, i.e., the original C/GMRES method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "nlp_type = autogenu.NLPType.MultipleShooting\n",
    "#nlp_type = autogenu.NLPType.SingleShooting\n",
    "ag.set_nlp_type(nlp_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set horizon length \n",
    "\n",
    "- `Tf`, `alpha`: Parameters for the length of the horizon. If $\\alpha > 0$, time-varying horizon length $T(t) = T_f (1 - e^{-\\alpha t})$ is used. Otherwise, $T(t)$ is fixed by $T_f$. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "Tf = 0.4\n",
    "alpha = 1.0\n",
    "\n",
    "ag.set_horizon_params(Tf, alpha)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set parameters for the C/GMRES method\n",
    "\n",
    "- `sampling_time`: Sampling period.\n",
    "- `N`: The number of the discretization grids of the horzion.  \n",
    "- `finite_difference_epsilon`: Step length of a finite difference approximations of hessian-vector products in C/GMRES.   \n",
    "- `zeta`: A stabilization parameter of the C/GMRES method. Basically, must be set by the reciprocal of the sampling time.  \n",
    "- `kmax`: The maximam number of the iteration of the GMRES."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "code_folding": [],
    "tags": []
   },
   "outputs": [],
   "source": [
    "sampling_time = 1.0e-3\n",
    "N = 100\n",
    "finite_difference_epsilon = 1e-8\n",
    "zeta = 1/sampling_time\n",
    "kmax = 10\n",
    "\n",
    "ag.set_solver_params(sampling_time, N, finite_difference_epsilon, zeta, kmax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set parameters for the initialization of the solution  \n",
    "- `solution_initial_guess`: The initial guess of the solution of the optimal control problem (OCP) for initialization of the solution of NMPC.   \n",
    "- `tolerance`: The residual tolerance of the solution of the OCP for the initialization of the solution of NMPC. The Newton iteration terminates when the optimality error is less than this velue.  \n",
    "- `max_iterations`: The maxmum number of Newton iteration for the initialization of the solution of NMPC.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "solution_initial_guess = [0.1,0.11,0.09,0.12]  # Non-uniform values often work well. \n",
    "tolerance = 1e-6\n",
    "max_iterations = 100 \n",
    "\n",
    "ag.set_initialization_params(solution_initial_guess, tolerance, max_iterations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set parameters for numerical simulation\n",
    "- `initial_time`: Initial time of the numerical simulation.  \n",
    "- `initial_state`: Initial state vector of the system.  \n",
    "- `simulation_length`: Simulation length.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "initial_time = 0\n",
    "initial_state = [-1,-1,-1, 0,0,0, 1,0,0,0, 0,0,0]  \n",
    "#import math\n",
    "#initial_state = [-1,-1,-1, 0,0,0, math.cos(math.pi/8),0,math.sin(math.pi/8),0, 0,1,0]  # Initial Pitch Angle theta: Initial Quaternion [cos(theta/2),0,sin(theta/2),0]\n",
    "simulation_length = 10\n",
    "\n",
    "ag.set_simulation_params(initial_time, initial_state, simulation_length) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate main.cpp and CMakeLists.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'main.cpp', the closed-loop simulation code, is generated at C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\n",
      "CMakeLists.txt are generated at C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\\python\n"
     ]
    }
   ],
   "source": [
    "ag.generate_main()\n",
    "ag.generate_cmake()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build and run simulation\n",
    "\n",
    "- `generator`: An optional variable for Windows user to choose the generator of CMake. If 'MSYS', then 'MSYS Makefiles' is used. If 'MinGW', then 'MinGW Makefiles' is used. The default value is 'Auto' and the generator is selected automatically. If sh.exe exists in your PATH, MSYS is choosed, and otherwise MinGW is used. \n",
    "- `vectorize` : If `True`, the vectorization, i.e., `-march=native` compile option is enabled. This can improve speed while can cause numerical issue depending on the compiler.  \n",
    "- `remove_build_dir`: If `True`, the existing build directory is removed. If `False`, the build directory is not removed. Need to be set `True` if you change the generator. Default is `False`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Update git submodules...\n",
      "Successfully updated git submodules\n",
      "\n",
      "CMake options: -DCMAKE_BUILD_TYPE=Release -DVECTORIZE=OFF -DBUILD_MAIN=ON -DBUILD_PYTHON_INTERFACE=OFF\n",
      "-- The CXX compiler identification is GNU 8.1.0\n",
      "-- Detecting CXX compiler ABI info\n",
      "-- Detecting CXX compiler ABI info - done\n",
      "-- Check for working CXX compiler: C:/mingw64/bin/c++.exe - skipped\n",
      "-- Detecting CXX compile features\n",
      "-- Detecting CXX compile features - done\n",
      "-- Configuring done\n",
      "-- Generating done\n",
      "-- Build files have been written to: C:/Users/ohtsu/OneDrive - Kyoto Univ/ToCheck/BenchmarkBook/Test2/QuadrotorFTC_Benchmark/generated/QuadrotorFTC/build\n",
      "\n",
      "\n",
      "[ 50%] \u001b[32mBuilding CXX object CMakeFiles/QuadrotorFTC.dir/main.cpp.obj\u001b[0m\n",
      "In file included from C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\\ocp.hpp:13,\n",
      "                 from C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\\main.cpp:5:\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/types.hpp:6: warning: \"EIGEN_STACK_ALLOCATION_LIMIT\" redefined\n",
      " #define EIGEN_STACK_ALLOCATION_LIMIT 0 // this macro allows unlimited stack memory to avoid OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG static_assertion error.\n",
      "\n",
      "In file included from C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/thirdparty/eigen/Eigen/Core:19,\n",
      "                 from C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/types.hpp:4,\n",
      "                 from C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\\ocp.hpp:13,\n",
      "                 from C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\\main.cpp:5:\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/thirdparty/eigen/Eigen/src/Core/util/Macros.h:55: note: this is the location of the previous definition\n",
      " #define EIGEN_STACK_ALLOCATION_LIMIT 131072\n",
      "\n",
      "[100%] \u001b[32m\u001b[1mLinking CXX executable QuadrotorFTC.exe\u001b[0m\n",
      "[100%] Built target QuadrotorFTC\n",
      "\n",
      "\n",
      "Start a simulation...\n",
      "End the simulation\n",
      "\n",
      "MPC used in this simulation:\n",
      "Multiple shooting CGMRES solver:\n",
      "  N:    100\n",
      "  kmax: 10\n",
      "\n",
      "OCP_QuadrotorFTC:\n",
      "  nx:  13\n",
      "  nu:  4\n",
      "  nc:  0\n",
      "  nh:  0\n",
      "  nuc: 4\n",
      "  nub: 4\n",
      "\n",
      "  m: 0.063\n",
      "  g: 9.81\n",
      "  J1: 5.83e-005\n",
      "  J2: 7.17e-005\n",
      "  J3: 0.0001\n",
      "  d3: 0.001\n",
      "  l: 0.0624\n",
      "  k: 0.0731\n",
      "  c1: 0\n",
      "\n",
      "  s: [  5,   5,  50,   1,   1,   1,   0,   1,   1,   1, 0.1, 0.1, 0.1]\n",
      "  s_terminal: [  5,   5,  50,   1,   1,   1,   0,   1,   1,   1, 0.1, 0.1, 0.1]\n",
      "  x_ref: [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]\n",
      "  r: [1, 1, 1, 1]\n",
      "  u_ref: [0.206, 0.206, 0.206, 0.206]\n",
      "\n",
      "  ubound_indices: [0, 1, 2, 3]\n",
      "  umin: [0.0065, 0.0065, 0.0065, 0.0065]\n",
      "  umax: [0.3266, 0.3266, 0.3266, 0.3266]\n",
      "  dummy_weight: [100, 100, 100, 100]\n",
      "\n",
      "Horizon: time-varying length\n",
      "  Tf:    0.4\n",
      "  alpha: 1\n",
      "  t0:    0\n",
      "\n",
      "Soler settings:\n",
      "  max iter:                  100\n",
      "  opterr tol:                1e-006\n",
      "  finite difference epsilon: 1e-008\n",
      "  sampling_time:             0.001\n",
      "  zeta:                      1000\n",
      "  min dummy:                 0.001\n",
      "  verbose level:             0\n",
      "  profile solver:            true\n",
      "\n",
      "TimingProfile:\n",
      "  average time: 0.121801 [ms]\n",
      "  max time:     15.7089 [ms]\n",
      "  counts:       10000\n",
      "\n",
      "\n",
      "The log files are generated at  C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\\log\n"
     ]
    }
   ],
   "source": [
    "generator = 'Auto'  \n",
    "vectorize = False\n",
    "remove_build_dir = True # False  # It is safe to remove the build directory if you use different environments.\n",
    "ag.git_submodule_update()\n",
    "ag.build_main(generator=generator, vectorize=vectorize, remove_build_dir=remove_build_dir)\n",
    "ag.run_simulation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the simulation results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'created' timestamp seems very low; regarding as unix timestamp\n",
      "'modified' timestamp seems very low; regarding as unix timestamp\n",
      "'created' timestamp seems very low; regarding as unix timestamp\n",
      "'modified' timestamp seems very low; regarding as unix timestamp\n",
      "'created' timestamp seems very low; regarding as unix timestamp\n",
      "'modified' timestamp seems very low; regarding as unix timestamp\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The graph of the simlation results is generated at C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\\log\\QuadrotorFTC.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 18 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotter = autogenu.Plotter(log_dir=ag.get_ocp_log_dir(), log_name=ag.get_ocp_name())\n",
    "plotter.set_scales(2,5,2)\n",
    "# plotter.show()\n",
    "plotter.save()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run the following 4.1 and 4.2 only when you want to build and install Python interface as it takes long. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Build Python interface\n",
    "\n",
    "- `generator`: An optional variable for Windows user to choose the generator of CMake. If 'MSYS', then 'MSYS Makefiles' is used. If 'MinGW', then 'MinGW Makefiles' is used. The default value is 'Auto' and the generator is selected automatically. If sh.exe exists in your PATH, MSYS is choosed, and otherwise MinGW is used. \n",
    "- `vectorize` : If `True`, the vectorization, i.e., `-march=native` compile option is enabled. This can improve speed while can cause numerical issue depending on the compiler.  \n",
    "- `remove_build_dir`: If `True`, the existing build directory is removed. If `False`, the build directory is not removed. Need to be set `True` if you change the generator. Default is `False`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pybind11 source codes are generated at C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\\python\n",
      "Update git submodules...\n",
      "Successfully updated git submodules\n",
      "\n",
      "CMake options: -DCMAKE_BUILD_TYPE=Release -DVECTORIZE=OFF -DBUILD_MAIN=OFF -DBUILD_PYTHON_INTERFACE=ON\n",
      "-- The CXX compiler identification is GNU 8.1.0\n",
      "-- Detecting CXX compiler ABI info\n",
      "-- Detecting CXX compiler ABI info - done\n",
      "-- Check for working CXX compiler: C:/mingw64/bin/c++.exe - skipped\n",
      "-- Detecting CXX compile features\n",
      "-- Detecting CXX compile features - done\n",
      "-- pybind11 v2.11.0 dev1\n",
      "-- Found PythonInterp: C:/Users/ohtsu/AppData/Roaming/jupyterlab-desktop/jlab_server/python.exe (found suitable version \"3.8.18\", minimum required is \"3.6\")\n",
      "-- Found PythonLibs: C:/Users/ohtsu/AppData/Roaming/jupyterlab-desktop/jlab_server/libs/python38.lib\n",
      "-- pybind11::lto disabled (problems with undefined symbols for MinGW for now)\n",
      "-- pybind11::thin_lto disabled (problems with undefined symbols for MinGW for now)\n",
      "-- pybind11 v2.11.0 dev1\n",
      "-- Configuring done\n",
      "-- Generating done\n",
      "-- Build files have been written to: C:/Users/ohtsu/OneDrive - Kyoto Univ/ToCheck/BenchmarkBook/Test2/QuadrotorFTC_Benchmark/generated/QuadrotorFTC/build\n",
      "\n",
      "\n",
      "[  7%] \u001b[32mBuilding CXX object python/common/CMakeFiles/solver_settings.dir/solver_settings.cpp.obj\u001b[0m\n",
      "In file included from C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/solver_settings.hpp:6,\n",
      "                 from C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\\python\\common\\solver_settings.cpp:10:\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/types.hpp:6: warning: \"EIGEN_STACK_ALLOCATION_LIMIT\" redefined\n",
      " #define EIGEN_STACK_ALLOCATION_LIMIT 0 // this macro allows unlimited stack memory to avoid OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG static_assertion error.\n",
      "\n",
      "In file included from C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/THIRDP~1/eigen/Eigen/Core:19,\n",
      "                 from C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/THIRDP~1/pybind11/include/pybind11/eigen.h:32,\n",
      "                 from C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\\python\\common\\solver_settings.cpp:7:\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/THIRDP~1/eigen/Eigen/src/Core/util/Macros.h:55: note: this is the location of the previous definition\n",
      " #define EIGEN_STACK_ALLOCATION_LIMIT 131072\n",
      "\n",
      "In file included from C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\\python\\common\\solver_settings.cpp:11:\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/solver_settings.hpp:1:50: warning: backslash-newline at end of file\n",
      " #define DEFINE_PYBIND11_MODULE_SOLVER_SETTINGS() \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/solver_settings.hpp:18:21: warning: backslash and newline separated by space\n",
      "         ss << self; \\\n",
      "\n",
      "[ 14%] \u001b[32m\u001b[1mLinking CXX shared library solver_settings.cp38-win_amd64.pyd\u001b[0m\n",
      "[ 14%] Built target solver_settings\n",
      "[ 21%] \u001b[32mBuilding CXX object python/common/CMakeFiles/horizon.dir/horizon.cpp.obj\u001b[0m\n",
      "In file included from C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\\python\\common\\horizon.cpp:10:\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/types.hpp:6: warning: \"EIGEN_STACK_ALLOCATION_LIMIT\" redefined\n",
      " #define EIGEN_STACK_ALLOCATION_LIMIT 0 // this macro allows unlimited stack memory to avoid OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG static_assertion error.\n",
      "\n",
      "In file included from C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/THIRDP~1/eigen/Eigen/Core:19,\n",
      "                 from C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/THIRDP~1/pybind11/include/pybind11/eigen.h:32,\n",
      "                 from C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\\python\\common\\horizon.cpp:7:\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/THIRDP~1/eigen/Eigen/src/Core/util/Macros.h:55: note: this is the location of the previous definition\n",
      " #define EIGEN_STACK_ALLOCATION_LIMIT 131072\n",
      "\n",
      "In file included from C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\\python\\common\\horizon.cpp:12:\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/horizon.hpp:1:42: warning: backslash-newline at end of file\n",
      " #define DEFINE_PYBIND11_MODULE_HORIZON() \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/horizon.hpp:6:24: warning: backslash and newline separated by space\n",
      "     .def(py::init<>()) \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/horizon.hpp:15:21: warning: backslash and newline separated by space\n",
      "         ss << self; \\\n",
      "\n",
      "[ 28%] \u001b[32m\u001b[1mLinking CXX shared library horizon.cp38-win_amd64.pyd\u001b[0m\n",
      "[ 28%] Built target horizon\n",
      "[ 35%] \u001b[32mBuilding CXX object python/common/CMakeFiles/timer.dir/timer.cpp.obj\u001b[0m\n",
      "In file included from C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/timer.hpp:7,\n",
      "                 from C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\\python\\common\\timer.cpp:10:\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/types.hpp:6: warning: \"EIGEN_STACK_ALLOCATION_LIMIT\" redefined\n",
      " #define EIGEN_STACK_ALLOCATION_LIMIT 0 // this macro allows unlimited stack memory to avoid OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG static_assertion error.\n",
      "\n",
      "In file included from C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/THIRDP~1/eigen/Eigen/Core:19,\n",
      "                 from C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/THIRDP~1/pybind11/include/pybind11/eigen.h:32,\n",
      "                 from C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\\python\\common\\timer.cpp:7:\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/THIRDP~1/eigen/Eigen/src/Core/util/Macros.h:55: note: this is the location of the previous definition\n",
      " #define EIGEN_STACK_ALLOCATION_LIMIT 131072\n",
      "\n",
      "In file included from C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\\python\\common\\timer.cpp:11:\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/timer.hpp:1:40: warning: backslash-newline at end of file\n",
      " #define DEFINE_PYBIND11_MODULE_TIMER() \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/timer.hpp:4:24: warning: backslash and newline separated by space\n",
      "     .def(py::init<>()) \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/timer.hpp:14:21: warning: backslash and newline separated by space\n",
      "         ss << self; \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/timer.hpp:18:24: warning: backslash and newline separated by space\n",
      "     .def(py::init<>()) \\\n",
      "\n",
      "[ 42%] \u001b[32m\u001b[1mLinking CXX shared library timer.cp38-win_amd64.pyd\u001b[0m\n",
      "[ 42%] Built target timer\n",
      "[ 50%] \u001b[32mBuilding CXX object python/QuadrotorFTC/CMakeFiles/ocp.dir/ocp.cpp.obj\u001b[0m\n",
      "In file included from C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\\python\\QuadrotorFTC\\ocp.cpp:10:\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/types.hpp:6: warning: \"EIGEN_STACK_ALLOCATION_LIMIT\" redefined\n",
      " #define EIGEN_STACK_ALLOCATION_LIMIT 0 // this macro allows unlimited stack memory to avoid OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG static_assertion error.\n",
      "\n",
      "In file included from C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/THIRDP~1/eigen/Eigen/Core:19,\n",
      "                 from C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/THIRDP~1/pybind11/include/pybind11/eigen.h:32,\n",
      "                 from C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\\python\\QuadrotorFTC\\ocp.cpp:7:\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/THIRDP~1/eigen/Eigen/src/Core/util/Macros.h:55: note: this is the location of the previous definition\n",
      " #define EIGEN_STACK_ALLOCATION_LIMIT 131072\n",
      "\n",
      "[ 57%] \u001b[32m\u001b[1mLinking CXX shared library ocp.cp38-win_amd64.pyd\u001b[0m\n",
      "[ 57%] Built target ocp\n",
      "[ 64%] \u001b[32mBuilding CXX object python/QuadrotorFTC/CMakeFiles/zero_horizon_ocp_solver.dir/zero_horizon_ocp_solver.cpp.obj\u001b[0m\n",
      "In file included from C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/zero_horizon_ocp_solver.hpp:8,\n",
      "                 from C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\\python\\QuadrotorFTC\\zero_horizon_ocp_solver.cpp:10:\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/types.hpp:6: warning: \"EIGEN_STACK_ALLOCATION_LIMIT\" redefined\n",
      " #define EIGEN_STACK_ALLOCATION_LIMIT 0 // this macro allows unlimited stack memory to avoid OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG static_assertion error.\n",
      "\n",
      "In file included from C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/THIRDP~1/eigen/Eigen/Core:19,\n",
      "                 from C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/THIRDP~1/pybind11/include/pybind11/eigen.h:32,\n",
      "                 from C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\\python\\QuadrotorFTC\\zero_horizon_ocp_solver.cpp:7:\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/THIRDP~1/eigen/Eigen/src/Core/util/Macros.h:55: note: this is the location of the previous definition\n",
      " #define EIGEN_STACK_ALLOCATION_LIMIT 131072\n",
      "\n",
      "In file included from C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\\python\\QuadrotorFTC\\zero_horizon_ocp_solver.cpp:11:\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/zero_horizon_ocp_solver.hpp:1:67: warning: backslash-newline at end of file\n",
      " #define DEFINE_PYBIND11_MODULE_ZERO_HORIZON_OCP_SOLVER(OCP, KMAX) \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/zero_horizon_ocp_solver.hpp:5:43: warning: backslash and newline separated by space\n",
      "     .def(py::init<OCP, SolverSettings>(), \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/zero_horizon_ocp_solver.hpp:7:24: warning: backslash and newline separated by space\n",
      "     .def(py::init<>()) \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/zero_horizon_ocp_solver.hpp:13:24: warning: backslash and newline separated by space\n",
      "         self.set_u(u); \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/zero_horizon_ocp_solver.hpp:16:26: warning: backslash and newline separated by space\n",
      "         self.set_uc(uc); \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/zero_horizon_ocp_solver.hpp:19:32: warning: backslash and newline separated by space\n",
      "         self.set_dummy(dummy); \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/zero_horizon_ocp_solver.hpp:22:26: warning: backslash and newline separated by space\n",
      "         self.set_mu(mu); \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/zero_horizon_ocp_solver.hpp:39:21: warning: backslash and newline separated by space\n",
      "         ss << self; \\\n",
      "\n",
      "[ 71%] \u001b[32m\u001b[1mLinking CXX shared library zero_horizon_ocp_solver.cp38-win_amd64.pyd\u001b[0m\n",
      "[ 71%] Built target zero_horizon_ocp_solver\n",
      "[ 78%] \u001b[32mBuilding CXX object python/QuadrotorFTC/CMakeFiles/single_shooting_cgmres_solver.dir/single_shooting_cgmres_solver.cpp.obj\u001b[0m\n",
      "In file included from C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/single_shooting_cgmres_solver.hpp:8,\n",
      "                 from C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\\python\\QuadrotorFTC\\single_shooting_cgmres_solver.cpp:10:\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/types.hpp:6: warning: \"EIGEN_STACK_ALLOCATION_LIMIT\" redefined\n",
      " #define EIGEN_STACK_ALLOCATION_LIMIT 0 // this macro allows unlimited stack memory to avoid OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG static_assertion error.\n",
      "\n",
      "In file included from C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/THIRDP~1/eigen/Eigen/Core:19,\n",
      "                 from C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/THIRDP~1/pybind11/include/pybind11/eigen.h:32,\n",
      "                 from C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\\python\\QuadrotorFTC\\single_shooting_cgmres_solver.cpp:7:\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/THIRDP~1/eigen/Eigen/src/Core/util/Macros.h:55: note: this is the location of the previous definition\n",
      " #define EIGEN_STACK_ALLOCATION_LIMIT 131072\n",
      "\n",
      "In file included from C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\\python\\QuadrotorFTC\\single_shooting_cgmres_solver.cpp:11:\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/single_shooting_cgmres_solver.hpp:1:76: warning: backslash-newline at end of file\n",
      " #define DEFINE_PYBIND11_MODULE_SINGLE_SHOOTING_CGMRES_SOLVER(OCP, N, KMAX) \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/single_shooting_cgmres_solver.hpp:5:52: warning: backslash and newline separated by space\n",
      "     .def(py::init<OCP, Horizon, SolverSettings>(), \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/single_shooting_cgmres_solver.hpp:7:24: warning: backslash and newline separated by space\n",
      "     .def(py::init<>()) \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/single_shooting_cgmres_solver.hpp:13:24: warning: backslash and newline separated by space\n",
      "         self.set_u(u); \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/single_shooting_cgmres_solver.hpp:16:26: warning: backslash and newline separated by space\n",
      "         self.set_uc(uc); \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/single_shooting_cgmres_solver.hpp:19:36: warning: backslash and newline separated by space\n",
      "         self.set_u_array(u_array); \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/single_shooting_cgmres_solver.hpp:22:38: warning: backslash and newline separated by space\n",
      "         self.set_uc_array(uc_array); \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/single_shooting_cgmres_solver.hpp:25:44: warning: backslash and newline separated by space\n",
      "         self.set_dummy_array(dummy_array); \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/single_shooting_cgmres_solver.hpp:28:38: warning: backslash and newline separated by space\n",
      "         self.set_mu_array(mu_array); \\\n",
      "\n",
      "In file included from C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\\python\\QuadrotorFTC\\single_shooting_cgmres_solver.cpp:11:\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/single_shooting_cgmres_solver.hpp:44:21: warning: backslash and newline separated by space\n",
      "         ss << self; \\\n",
      "\n",
      "[ 85%] \u001b[32m\u001b[1mLinking CXX shared library single_shooting_cgmres_solver.cp38-win_amd64.pyd\u001b[0m\n",
      "[ 85%] Built target single_shooting_cgmres_solver\n",
      "[ 92%] \u001b[32mBuilding CXX object python/QuadrotorFTC/CMakeFiles/multiple_shooting_cgmres_solver.dir/multiple_shooting_cgmres_solver.cpp.obj\u001b[0m\n",
      "In file included from C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/multiple_shooting_cgmres_solver.hpp:8,\n",
      "                 from C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\\python\\QuadrotorFTC\\multiple_shooting_cgmres_solver.cpp:10:\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/types.hpp:6: warning: \"EIGEN_STACK_ALLOCATION_LIMIT\" redefined\n",
      " #define EIGEN_STACK_ALLOCATION_LIMIT 0 // this macro allows unlimited stack memory to avoid OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG static_assertion error.\n",
      "\n",
      "In file included from C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/THIRDP~1/eigen/Eigen/Core:19,\n",
      "                 from C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/THIRDP~1/pybind11/include/pybind11/eigen.h:32,\n",
      "                 from C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\\python\\QuadrotorFTC\\multiple_shooting_cgmres_solver.cpp:7:\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/THIRDP~1/eigen/Eigen/src/Core/util/Macros.h:55: note: this is the location of the previous definition\n",
      " #define EIGEN_STACK_ALLOCATION_LIMIT 131072\n",
      "\n",
      "In file included from C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\\python\\QuadrotorFTC\\multiple_shooting_cgmres_solver.cpp:11:\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/multiple_shooting_cgmres_solver.hpp:1:78: warning: backslash-newline at end of file\n",
      " #define DEFINE_PYBIND11_MODULE_MULTIPLE_SHOOTING_CGMRES_SOLVER(OCP, N, KMAX) \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/multiple_shooting_cgmres_solver.hpp:5:52: warning: backslash and newline separated by space\n",
      "     .def(py::init<OCP, Horizon, SolverSettings>(), \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/multiple_shooting_cgmres_solver.hpp:7:24: warning: backslash and newline separated by space\n",
      "     .def(py::init<>()) \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/multiple_shooting_cgmres_solver.hpp:13:24: warning: backslash and newline separated by space\n",
      "         self.set_u(u); \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/multiple_shooting_cgmres_solver.hpp:16:26: warning: backslash and newline separated by space\n",
      "         self.set_uc(uc); \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/multiple_shooting_cgmres_solver.hpp:19:24: warning: backslash and newline separated by space\n",
      "         self.set_x(x); \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/multiple_shooting_cgmres_solver.hpp:22:28: warning: backslash and newline separated by space\n",
      "         self.set_lmd(lmd); \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/multiple_shooting_cgmres_solver.hpp:25:36: warning: backslash and newline separated by space\n",
      "         self.set_u_array(u_array); \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/multiple_shooting_cgmres_solver.hpp:28:38: warning: backslash and newline separated by space\n",
      "         self.set_uc_array(uc_array); \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/multiple_shooting_cgmres_solver.hpp:31:36: warning: backslash and newline separated by space\n",
      "         self.set_x_array(x_array); \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/multiple_shooting_cgmres_solver.hpp:34:40: warning: backslash and newline separated by space\n",
      "         self.set_lmd_array(lmd_array); \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/multiple_shooting_cgmres_solver.hpp:37:44: warning: backslash and newline separated by space\n",
      "         self.set_dummy_array(dummy_array); \\\n",
      "\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/multiple_shooting_cgmres_solver.hpp:40:38: warning: backslash and newline separated by space\n",
      "         self.set_mu_array(mu_array); \\\n",
      "\n",
      "In file included from C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\\generated\\QuadrotorFTC\\python\\QuadrotorFTC\\multiple_shooting_cgmres_solver.cpp:11:\n",
      "C:/Users/ohtsu/ONEDRI~1/ToCheck/BENCHM~1/Test2/QUADRO~1/include/cgmres/python/multiple_shooting_cgmres_solver.hpp:68:21: warning: backslash and newline separated by space\n",
      "         ss << self; \\\n",
      "\n",
      "[100%] \u001b[32m\u001b[1mLinking CXX shared library multiple_shooting_cgmres_solver.cp38-win_amd64.pyd\u001b[0m\n",
      "[100%] Built target multiple_shooting_cgmres_solver\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# If you don't want to run this cell, change the cell type from \"Code\" to \"Raw\".\n",
    "# If you want to run this cell, change the cell type from \"Raw\" to \"Code\". \n",
    "generator = 'Auto'  \n",
    "vectorize = False\n",
    "remove_build_dir = True  #  It may be safe to remove the build directory if you run this notebook on different environments\n",
    "ag.generate_python_bindings()\n",
    "ag.git_submodule_update()\n",
    "ag.build_python_interface(generator=generator, vectorize=vectorize, remove_build_dir=remove_build_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install Python interface\n",
    "\n",
    "- `install_prefix` : Prefix of the install destination of the python interfaces. Default is None.\n",
    "    -  If this is None (default), then it is `~/.local/lib/python3.x` (`python3.x` is your Python version). \n",
    "        - If you use Ubuntu, `~/.local/lib/python3.x` is the default Python path, and you do not need to set `PYTHONPATH` additonally.  \n",
    "        - If you use MacOSX or Windows, add it to the Python PATH, as `export PYTHONPATH=$PYTHONPATH:$HOME/.local/lib/python3.x/site-packages`\n",
    "    -  If `install_prefix=os.getcwd()`, the python interfaces will be installed to the `cgmres` directory under the current working directory\n",
    "\n",
    "If you encounter \"Permission denied\" error, delete existing interfaces manually. \n",
    "\n",
    "The usage of Python interfaces is introduced in `examples/python` directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collected Python shared libs:  ['C:\\\\Users\\\\ohtsu\\\\OneDrive - Kyoto Univ\\\\ToCheck\\\\BenchmarkBook\\\\Test2\\\\QuadrotorFTC_Benchmark\\\\generated\\\\QuadrotorFTC\\\\build\\\\python\\\\QuadrotorFTC\\\\multiple_shooting_cgmres_solver.cp38-win_amd64.pyd', 'C:\\\\Users\\\\ohtsu\\\\OneDrive - Kyoto Univ\\\\ToCheck\\\\BenchmarkBook\\\\Test2\\\\QuadrotorFTC_Benchmark\\\\generated\\\\QuadrotorFTC\\\\build\\\\python\\\\QuadrotorFTC\\\\ocp.cp38-win_amd64.pyd', 'C:\\\\Users\\\\ohtsu\\\\OneDrive - Kyoto Univ\\\\ToCheck\\\\BenchmarkBook\\\\Test2\\\\QuadrotorFTC_Benchmark\\\\generated\\\\QuadrotorFTC\\\\build\\\\python\\\\QuadrotorFTC\\\\single_shooting_cgmres_solver.cp38-win_amd64.pyd', 'C:\\\\Users\\\\ohtsu\\\\OneDrive - Kyoto Univ\\\\ToCheck\\\\BenchmarkBook\\\\Test2\\\\QuadrotorFTC_Benchmark\\\\generated\\\\QuadrotorFTC\\\\build\\\\python\\\\QuadrotorFTC\\\\zero_horizon_ocp_solver.cp38-win_amd64.pyd']\n",
      "Collected Python common shared libs:  ['C:\\\\Users\\\\ohtsu\\\\OneDrive - Kyoto Univ\\\\ToCheck\\\\BenchmarkBook\\\\Test2\\\\QuadrotorFTC_Benchmark\\\\generated\\\\QuadrotorFTC\\\\build\\\\python\\\\common\\\\horizon.cp38-win_amd64.pyd', 'C:\\\\Users\\\\ohtsu\\\\OneDrive - Kyoto Univ\\\\ToCheck\\\\BenchmarkBook\\\\Test2\\\\QuadrotorFTC_Benchmark\\\\generated\\\\QuadrotorFTC\\\\build\\\\python\\\\common\\\\solver_settings.cp38-win_amd64.pyd', 'C:\\\\Users\\\\ohtsu\\\\OneDrive - Kyoto Univ\\\\ToCheck\\\\BenchmarkBook\\\\Test2\\\\QuadrotorFTC_Benchmark\\\\generated\\\\QuadrotorFTC\\\\build\\\\python\\\\common\\\\timer.cp38-win_amd64.pyd']\n",
      "Collected Python files:  ['C:\\\\Users\\\\ohtsu\\\\OneDrive - Kyoto Univ\\\\ToCheck\\\\BenchmarkBook\\\\Test2\\\\QuadrotorFTC_Benchmark\\\\generated\\\\QuadrotorFTC\\\\python\\\\QuadrotorFTC\\\\__init__.py']\n",
      "Collected Python common files:  ['C:\\\\Users\\\\ohtsu\\\\OneDrive - Kyoto Univ\\\\ToCheck\\\\BenchmarkBook\\\\Test2\\\\QuadrotorFTC_Benchmark\\\\generated\\\\QuadrotorFTC\\\\python\\\\common\\\\__init__.py']\n",
      "\n",
      "Python interfaces have been installed at C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\n",
      "To use Python interfaces, run \n",
      "\n",
      "    export PYTHONPATH=$PYTHONPATH:C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark\n",
      "\n",
      "in the terminal to recognize the PYTHONPATH temporary.\n",
      "Or set the PATH in Ubuntu as\n",
      "\n",
      "    echo export PYTHONPATH=$PYTHONPATH:C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark >> ~/.bashrc\n",
      "\n",
      "or in Mac OSX as\n",
      "\n",
      "    echo export PYTHONPATH=$PYTHONPATH:C:\\Users\\ohtsu\\OneDrive - Kyoto Univ\\ToCheck\\BenchmarkBook\\Test2\\QuadrotorFTC_Benchmark >> ~/.zshrc\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# If you don't want to run this cell, change the cell type from \"Code\" to \"Raw\".\n",
    "# If you want to run this cell, change the cell type from \"Raw\" to \"Code\" \n",
    "import os\n",
    "ag.install_python_interface(install_prefix=os.getcwd())  # Store generated files to the cgmres directory under the current working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Documentation\n",
    "The documentations for the generated code are available.  \n",
    "`Doxygen` is required for the docs generation."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "tags": []
   },
   "source": [
    "# autogenu.generate_docs()\n",
    "# autogenu.open_docs()"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.18"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "vscode": {
   "interpreter": {
    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
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 },
 "nbformat": 4,
 "nbformat_minor": 4
}