{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9bd6dd9a-a0f6-4248-89d4-657c318e0d79",
   "metadata": {},
   "source": [
    "# Tutorial 04: Links and Data Collection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c18bd9e-7470-4b09-b7a7-fac8bb2dec17",
   "metadata": {},
   "source": [
    "## Tutorial Description"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7e48aef-41be-4597-be37-1bd196bc97a5",
   "metadata": {},
   "source": [
    "This tutorial covers how to:\n",
    "1. Extracting state data of a particular link of a URDF object.\n",
    "2. Extracting the mass of a particular link of a URDF object.\n",
    "3. Storing data collected during simulation.\n",
    "4. Creating a plot of the simulation data after the simulation has terminated."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be64a904-72de-4411-8a77-37a630f0c3a4",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ca5c838-ffae-45eb-9e60-0ad1e0ac8698",
   "metadata": {},
   "source": [
    "To begin, we import the same modules for the same reasons as tutorial 00. We also include `numpy` and `matplotlib.pyplot` for data collection and visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b2b6dc1e-8420-4242-91a3-e73422ce713e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from condynsate import Simulator as con_sim\n",
    "from condynsate import __assets__ as assets\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dea83d70-5877-4057-b588-983d28579958",
   "metadata": {},
   "source": [
    "## Building the Project Class"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "507fb740-3a04-4ff7-af5e-fbbd3c73f962",
   "metadata": {},
   "source": [
    "We now create a `Project` class with `__init__` and `run` functions. In `__init__` a pendulum is loaded using the same technique as tutorial 02. In `run`, we cover one way to collect data during the simulation that is available to users after the simulation completes. Futhermore, `Project` includes two additional functions, `_add_data_point` and `plot_data`. `_add_data_point` is what we use to collect and store simulation data during the simulation and `plot_data` is what we use to generate a plot of those data after the simulation is complete."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "098a10b0-6cec-4676-8cd2-1ac6b47ad94b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Project():\n",
    "    def __init__(self):\n",
    "        '''\n",
    "        ##################################################################\n",
    "        Differently that the previous tutorials, we have asked the \n",
    "        simulator to use a smaller time step than the default value of \n",
    "        0.01 seconds. Doing this will increase the simulation accuracy at\n",
    "        the expense of real-time execution time.\n",
    "        ##################################################################\n",
    "        '''\n",
    "        # Create a instance of the simulator\n",
    "        self.s = con_sim(animation = False,\n",
    "                         keyboard = False,\n",
    "                         dt = 0.0075)\n",
    "        \n",
    "        # Load the pendulum in the orientation we want\n",
    "        self.pendulum = self.s.load_urdf(urdf_path = assets['pendulum'],\n",
    "                                         position = [0., 0., 0.05],\n",
    "                                         yaw = 1.571,\n",
    "                                         wxyz_quaternion = [1., 0., 0., 0],\n",
    "                                         fixed = True,\n",
    "                                         update_vis = True)\n",
    "\n",
    "        '''\n",
    "        ##################################################################\n",
    "        Differently that the previous tutorials, we set the initial \n",
    "        velocity instead of initial angle.\n",
    "        ##################################################################\n",
    "        '''\n",
    "        # Set the initial angular velocity of the pendulum arm\n",
    "        self.s.set_joint_velocity(urdf_obj = self.pendulum,\n",
    "                                  joint_name = 'chassis_to_arm',\n",
    "                                  velocity = 1.571,\n",
    "                                  initial_cond = True,\n",
    "                                  physics = False)\n",
    "        \n",
    "    def run(self, max_time=None):\n",
    "        '''\n",
    "        ##################################################################\n",
    "        This run function does all the same basic functions as in \n",
    "        tutorial 02 but with the added functionality of data collection.\n",
    "        ##################################################################\n",
    "        '''\n",
    "        # Create an empty dictionary of data to store simulation data\n",
    "        data = {'angle' : [],\n",
    "                'angle_vel' : [],\n",
    "                'KE' : [],\n",
    "                'PE' : [],\n",
    "                'time' : [],}\n",
    "        \n",
    "        # Reset the simulator.\n",
    "        self.s.reset()\n",
    "\n",
    "        '''\n",
    "        ##############################################################\n",
    "        To add a data point at the initial time step to the data\n",
    "        structure, we call the _add_data_point function.\n",
    "        ##############################################################\n",
    "        '''\n",
    "        # Add the simulation data at the current time\n",
    "        data = self._add_data_point(data)\n",
    "        \n",
    "        # Run the simulation loop until done\n",
    "        while(not self.s.is_done):            \n",
    "            '''\n",
    "            ##############################################################\n",
    "            Note that, compared to the previous tutorials, this time when\n",
    "            we call condynsate.simulator.step we store its return value. \n",
    "            condynsate.simulator.step gives a different return value \n",
    "            based on different situations. These return values are as \n",
    "            follows:\n",
    "                -4: Keyboard LOS indicating simulation step failure. No \n",
    "                    simulation step taken. No further simulation steps are to\n",
    "                    be taken. is_done flag is now true.\n",
    "                -3: User initiated simulation termination. No simulation step \n",
    "                    taken. No further simulation steps are to be taken. \n",
    "                    is_done flag is now true.\n",
    "                -2: User initiated pause is occuring. No simulation step taken.\n",
    "                    paused flag remains true.\n",
    "                -1: User initiated pause has ended. No simulation step taken. \n",
    "                    paused flag is now false.\n",
    "                0:  User initiated simulation reset. No simulation step taken.\n",
    "                1:  Normal and successful simulation step.\n",
    "                2:  User initiated pause has started. Normal simulation step \n",
    "                    taken. paused flag is now true.\n",
    "                3:  max_time is now reached. Normal simulation step taken. No \n",
    "                    further simulation steps are to be taken. is_done flag is\n",
    "                    now true.\n",
    "            We want to keep track of these because if the simulation is \n",
    "            reset we will need to empty out all of the simulation data \n",
    "            that we have already collected, and if no simulation step was\n",
    "            taken, we don't want to collect any new data.\n",
    "            ##############################################################\n",
    "            '''\n",
    "            # Take a single physics step.\n",
    "            ret_code = self.s.step(max_time = max_time)\n",
    "\n",
    "            '''\n",
    "            ##############################################################\n",
    "            A ret_code greater than 0 indicates that a simulation step was\n",
    "            taken and the new data was created and can be added.\n",
    "            ##############################################################\n",
    "            '''\n",
    "            # Add the simulation data at the current time if step was taken\n",
    "            if ret_code > 0:\n",
    "                data = self._add_data_point(data)\n",
    "            \n",
    "            '''\n",
    "            ##############################################################\n",
    "            A ret_code of 0 indicates the simulation was reset and we need\n",
    "            to reset the data also.\n",
    "            ##############################################################\n",
    "            '''\n",
    "            # Reset data collection if sim is reset.\n",
    "            if ret_code == 0:\n",
    "                data = {'angle' : [],\n",
    "                        'angle_vel' : [],\n",
    "                        'KE' : [],\n",
    "                        'PE' : [],\n",
    "                        'time' : [],}\n",
    "        \n",
    "        # Return the data\n",
    "        return data\n",
    "\n",
    "    '''\n",
    "    ######################################################################\n",
    "    This function is what we use to collect data points during the \n",
    "    simulation. It collects joint and link state data, calculates \n",
    "    energies, and then appends these data to our data structure. \n",
    "    ######################################################################\n",
    "    '''\n",
    "    def _add_data_point(self, data):\n",
    "        # Collect the state of the joint\n",
    "        state = self.s.get_joint_state(urdf_obj = self.pendulum,\n",
    "                                       joint_name = 'chassis_to_arm')\n",
    "\n",
    "        # Extract angle and angular velocity of the joints\n",
    "        angle = state['position'] * 57.296\n",
    "        angle_vel = state['velocity'] * 57.296\n",
    "        \n",
    "        '''\n",
    "        ##################################################################\n",
    "        To collect the state of a specific link in a URDF object we call\n",
    "        condynsate.simulator.get_link_state. This works for either the \n",
    "        base link or any children links and takes the following \n",
    "        arguments:\n",
    "            urdf_obj : URDF_Obj\n",
    "                A URDF_Obj whose state is being measured.\n",
    "            link_name : string\n",
    "                The name of the link whose state is measured. The link\n",
    "                name is specified in the .urdf file.\n",
    "            body_coords : bool\n",
    "                A boolean flag that indicates whether the passed \n",
    "                velocities are in world coords or body coords.\n",
    "        \n",
    "        It then returns:\n",
    "            state : a dictionary with the following keys:\n",
    "                'position' : array-like, shape (3,)\n",
    "                    The (x,y,z) world coordinates of the link.\n",
    "                'roll' : float\n",
    "                    The Euler angle roll of the link\n",
    "                    that defines the link's orientation in the world. \n",
    "                    Rotation of the link about the world's x-axis.\n",
    "                'pitch' : float\n",
    "                    The Euler angle pitch of the link\n",
    "                    that defines the link's orientation in the world.\n",
    "                    Rotation of the link about the world's y-axis.\n",
    "                'yaw' : float\n",
    "                    The Euler angle yaw of the link\n",
    "                    that defines the link's orientation in the world. \n",
    "                    Rotation of the link about the world's z-axis.\n",
    "                'R of world in link' : array-like, shape(3,3):\n",
    "                    The rotation matrix that takes vectors in world \n",
    "                    coordinates to link coordinates. For example, \n",
    "                    let V_inL be a 3vector written in link coordinates. \n",
    "                    Let V_inW be a 3vector written in world coordinates.\n",
    "                    Then: V_inL = R_ofWorld_inLink @ V_inW\n",
    "                'velocity' : array-like, shape (3,)\n",
    "                    The (x,y,z) linear velocity in world coordinates of \n",
    "                    the link.\n",
    "                'angular velocity' : array-like, shape (3,)\n",
    "                    The (x,y,z) angular velocity in world coordinates of \n",
    "                    the link.\n",
    "        ##################################################################\n",
    "        '''\n",
    "        # Retrieve the state of the mass at the end of the rod\n",
    "        state = self.s.get_link_state(urdf_obj = self.pendulum,\n",
    "                                      link_name = 'mass',\n",
    "                                      body_coords = True)\n",
    "        \n",
    "        '''\n",
    "        ##################################################################\n",
    "        To collect the mass of a specific link in a URDF object we call\n",
    "        condynsate.simulator.get_link_mass. This works for either the \n",
    "        base link or any children links and takes the following \n",
    "        arguments:\n",
    "            urdf_obj : URDF_Obj\n",
    "                A URDF_Obj that contains that link whose mass is \n",
    "                measured.\n",
    "            link_name : string\n",
    "                The name of the link whose mass is measured. The link \n",
    "                name is specified in the .urdf file.\n",
    "                \n",
    "        It then returns:\n",
    "            mass : float\n",
    "                The mass of the link in Kg. If link is not found, \n",
    "                returns none.\n",
    "        ##################################################################\n",
    "        '''\n",
    "        # Get the mass of the mass\n",
    "        mass = self.s.get_link_mass(urdf_obj = self.pendulum,\n",
    "                                    link_name = 'mass')\n",
    "            \n",
    "        '''\n",
    "        ##################################################################\n",
    "        Finally, we use the collected link and joint data to add a single \n",
    "        data point to our data structure. This is done by appending the \n",
    "        calculated data to the end of its respective list.\n",
    "        ##################################################################\n",
    "        '''\n",
    "        # Calculate the energy of the mass\n",
    "        height = state['position'][2]\n",
    "        vel = state['velocity']\n",
    "        KE = 0.5*mass*vel.T@vel\n",
    "        PE = mass*9.81*height\n",
    "        \n",
    "        # Append the data to the list\n",
    "        data['angle'].append(angle)\n",
    "        data['angle_vel'].append(angle_vel)\n",
    "        data['KE'].append(KE)\n",
    "        data['PE'].append(PE)\n",
    "        data['time'].append(self.s.time) # This is how we get the current \n",
    "                                         # simulation time\n",
    "        \n",
    "        # Return the new data list\n",
    "        return data\n",
    "    \n",
    "    '''\n",
    "    ######################################################################\n",
    "    The specifics of _plot_data are outside the scope of a condynsate\n",
    "    tutorial. See https://matplotlib.org/ for more information.\n",
    "    ######################################################################\n",
    "    '''\n",
    "    def plot_data(self, data):\n",
    "        # Make the plot and subplots\n",
    "        fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1)\n",
    "        \n",
    "        # Plot the phase space\n",
    "        ax1.plot(data['angle'], data['angle_vel'], c='k', lw=2.5)\n",
    "        ax1.set_xlabel('Angle [Deg]')\n",
    "        ax1.set_ylabel('Angle Rate [Deg / Sec]')\n",
    "        \n",
    "        # Plot the energy\n",
    "        ax2.plot(data['time'], data['KE'], label='KE', c='m', lw=2.5)\n",
    "        ax2.plot(data['time'], data['PE'], label='PE', c='c', lw=2.5)\n",
    "        total_E = np.array(data['KE']) + np.array(data['PE'])\n",
    "        ax2.plot(data['time'], total_E, label='Total', c='k', lw=2.5, ls=':')\n",
    "        ax2.legend(fancybox=True, shadow=True)\n",
    "        ax2.set_xlabel('Time [Sec]')\n",
    "        ax2.set_ylabel('Energy [J]')\n",
    "        \n",
    "        # Figure settings\n",
    "        fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "420433c6-c1d2-4702-aba4-52295bf753e2",
   "metadata": {},
   "source": [
    "## Running the Project Class"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da509ec5-56c1-4bc4-a9d0-0db238bd069a",
   "metadata": {},
   "source": [
    "Now that we have made the `Project` class, we can test it by initializing it and then calling the `run` function. Remember to press the enter key to start the simulation and the esc key to end the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a9461fb2-213e-4b6e-92c0-d0fb1ad00176",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You can open the visualizer by visiting the following URL:\n",
      "http://127.0.0.1:7005/static/\n"
     ]
    }
   ],
   "source": [
    "# Create an instance of the Project class. \n",
    "proj = Project()\n",
    "\n",
    "# Run the simulation.\n",
    "data = proj.run(max_time = 5.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aa752c78-5346-4107-93c0-ffce15ec37f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the data collected\n",
    "%matplotlib inline\n",
    "proj.plot_data(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36d1e2de-e3f4-4dfb-a530-766a4420f876",
   "metadata": {},
   "source": [
    "## Challenge"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ff6e017-b96d-4668-879d-5ebaf083ae88",
   "metadata": {},
   "source": [
    "This tutorial is now complete. For an additional challenge, try creating a similar project but using the double pendulum provided in the condynsate default assests."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.13.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}