PID2_Transfer_Functions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "213bf092-6e7a-496a-8263-87eb3ebe287b",
   "metadata": {},
   "source": [
    "# Stability Analysis of rate controller"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe1daba8-9539-49b8-80ee-652297193e41",
   "metadata": {},
   "source": [
    "## Helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "93504003",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: drake in /home/forge/miniconda3/lib/python3.12/site-packages (1.28.0)\n",
      "Requirement already satisfied: matplotlib in /home/forge/miniconda3/lib/python3.12/site-packages (from drake) (3.8.0)\n",
      "Requirement already satisfied: numpy in /home/forge/miniconda3/lib/python3.12/site-packages (from drake) (1.26.4)\n",
      "Requirement already satisfied: pydot in /home/forge/miniconda3/lib/python3.12/site-packages (from drake) (1.4.2)\n",
      "Requirement already satisfied: PyYAML in /home/forge/miniconda3/lib/python3.12/site-packages (from drake) (6.0.1)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /home/forge/miniconda3/lib/python3.12/site-packages (from matplotlib->drake) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/forge/miniconda3/lib/python3.12/site-packages (from matplotlib->drake) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/forge/miniconda3/lib/python3.12/site-packages (from matplotlib->drake) (4.25.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/forge/miniconda3/lib/python3.12/site-packages (from matplotlib->drake) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/forge/miniconda3/lib/python3.12/site-packages (from matplotlib->drake) (23.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/forge/miniconda3/lib/python3.12/site-packages (from matplotlib->drake) (10.2.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /home/forge/miniconda3/lib/python3.12/site-packages (from matplotlib->drake) (2.4.7)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/forge/miniconda3/lib/python3.12/site-packages (from matplotlib->drake) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in /home/forge/miniconda3/lib/python3.12/site-packages (from python-dateutil>=2.7->matplotlib->drake) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install drake"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8ad63463-db85-440e-9cb7-244cb7ba55e2",
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "/home/forge/miniconda3/lib/python3.12/site-packages/zmq/backend/cython/../../../../.././libstdc++.so.6: version `GLIBCXX_3.4.30' not found (required by /home/forge/miniconda3/lib/python3.12/site-packages/pydrake/common/../lib/libdrake.so)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[3], line 7\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdisplay\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m HTML, display, SVG\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpydrake\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msystems\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mframework\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m      8\u001b[0m     AddMultibodyPlantSceneGraph,\n\u001b[1;32m      9\u001b[0m     DiagramBuilder,\n\u001b[1;32m     10\u001b[0m     LinearQuadraticRegulator,\n\u001b[1;32m     11\u001b[0m     Saturation,\n\u001b[1;32m     12\u001b[0m     StartMeshcat,\n\u001b[1;32m     13\u001b[0m     WrapToSystem\n\u001b[1;32m     14\u001b[0m )\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpydrake\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msystems\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01manalysis\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ControllabilityMatrix, Linearize\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpydrake\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmultibody\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mplant\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m MultibodyPlant, Parser\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.12/site-packages/pydrake/__init__.py:163\u001b[0m\n\u001b[1;32m    157\u001b[0m \u001b[38;5;66;03m# We specifically load `common` prior to loading any other pydrake modules,\u001b[39;00m\n\u001b[1;32m    158\u001b[0m \u001b[38;5;66;03m# in order to a) get assertion configuration done as early as possible, and b)\u001b[39;00m\n\u001b[1;32m    159\u001b[0m \u001b[38;5;66;03m# detect whether we are able to load the shared libraries. But importantly,\u001b[39;00m\n\u001b[1;32m    160\u001b[0m \u001b[38;5;66;03m# we need to do this *after* defining the pure Python helper functions, above,\u001b[39;00m\n\u001b[1;32m    161\u001b[0m \u001b[38;5;66;03m# since they are called while loading `pydrake.common` and things it loads.\u001b[39;00m\n\u001b[1;32m    162\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 163\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m common\n\u001b[1;32m    164\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    165\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcannot open shared object file\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m (e\u001b[38;5;241m.\u001b[39mmsg \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    166\u001b[0m             \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/pydrake/\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m (e\u001b[38;5;241m.\u001b[39mpath \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m)):\n",
      "\u001b[0;31mImportError\u001b[0m: /home/forge/miniconda3/lib/python3.12/site-packages/zmq/backend/cython/../../../../.././libstdc++.so.6: version `GLIBCXX_3.4.30' not found (required by /home/forge/miniconda3/lib/python3.12/site-packages/pydrake/common/../lib/libdrake.so)"
     ]
    }
   ],
   "source": [
    "import math\n",
    "import pydot\n",
    "import matplotlib.pyplot as plt\n",
    "import mpld3\n",
    "import numpy as np\n",
    "from IPython.display import HTML, display, SVG\n",
    "from pydrake.all import (\n",
    "    AddMultibodyPlantSceneGraph,\n",
    "    ControllabilityMatrix,\n",
    "    DiagramBuilder,\n",
    "    Linearize,\n",
    "    LinearQuadraticRegulator,\n",
    "    MeshcatVisualizer,\n",
    "    MultibodyPlant,\n",
    "    Parser,\n",
    "    Propeller,\n",
    "    PropellerInfo,\n",
    "    RigidTransform,\n",
    "    RobotDiagramBuilder,\n",
    "    Saturation,\n",
    "    SceneGraph,\n",
    "    Simulator,\n",
    "    StartMeshcat,\n",
    "    WrapToSystem,\n",
    "    namedview,\n",
    ")\n",
    "from pydrake.examples import (\n",
    "    AcrobotGeometry,\n",
    "    AcrobotInput,\n",
    "    AcrobotPlant,\n",
    "    AcrobotState,\n",
    "    QuadrotorGeometry,\n",
    "    QuadrotorPlant,\n",
    "    StabilizingLQRController,\n",
    ")\n",
    "from pydrake.solvers import MathematicalProgram, Solve\n",
    "import os\n",
    "from underactuated import ConfigureParser, running_as_notebook\n",
    "from underactuated.meshcat_utils import MeshcatSliders\n",
    "from underactuated.quadrotor2d import Quadrotor2D, Quadrotor2DVisualizer\n",
    "from underactuated.scenarios import AddFloatingRpyJoint\n",
    "from pydrake.multibody.tree import QuaternionFloatingJoint\n",
    "if running_as_notebook:\n",
    "    mpld3.enable_notebook()\n",
    "\n",
    "meshcat = StartMeshcat()\n",
    "\n",
    "\n",
    "def MakeMultibodyQuadrotor():\n",
    "    builder = DiagramBuilder()\n",
    "    plant, scene_graph = AddMultibodyPlantSceneGraph(builder, 0.0)\n",
    "    #plant = builder.AddSystem(MultibodyPlant(0.0))\n",
    "    #scene_graph = builder.AddSystem(SceneGraph())\n",
    "    parser = Parser(plant)\n",
    "    \n",
    "    # Your relative path\n",
    "    relative_path = \"quad-v4/quad-v4.urdf\"\n",
    "    # Convert to an absolute path\n",
    "    absolute_path = os.path.abspath(relative_path)\n",
    "    \n",
    "    # Use the absolute path here\n",
    "    (model_instance,) = parser.AddModels(absolute_path)\n",
    "    \n",
    "    # Adding QuaternionFloatingJoint\n",
    "    frame_on_parent = plant.world_frame()\n",
    "    frame_on_child = plant.GetFrameByName(\"quad_v4\", model_instance)\n",
    "    plant.AddJoint(\n",
    "        QuaternionFloatingJoint(\n",
    "            name=\"BaseJoint\",\n",
    "            frame_on_parent=frame_on_parent,  # Corrected parameter name\n",
    "            frame_on_child=frame_on_child,  # Corrected parameter name\n",
    "            angular_damping=0.0,  # Optional, showing how to use it\n",
    "            translational_damping=0.0  # Optional, showing how to use it\n",
    "        )\n",
    "    )\n",
    "    plant.Finalize()\n",
    "\n",
    "    visualizer = MeshcatVisualizer.AddToBuilder(\n",
    "        builder, scene_graph, meshcat\n",
    "    )\n",
    "\n",
    "    body_index = plant.GetBodyByName(\"quad_v4\").index()\n",
    "\n",
    "    L = 0.395 #meters arm length\n",
    "    #prop_offsets = [0.21507, 0.27856, 0.09495]\n",
    "    prop_pos = {'x' : 0.21507, 'y' : 0.27856, 'z' : 0.09495}\n",
    "    kF = 6.5 #0.77 #estimate thrust constant (thrust = kF * angularvelocity^2)\n",
    "    kM = 0.05 #estimate torque constant (torque = kM * angularvelocity^2)\n",
    "\n",
    "    \"\"\"\n",
    "    prop_info = [\n",
    "        PropellerInfo(body_index, RigidTransform([L, 0, 0]), kF, kM),\n",
    "        PropellerInfo(body_index, RigidTransform([0, L, 0]), kF, -kM),\n",
    "        PropellerInfo(body_index, RigidTransform([-L, 0, 0]), kF, kM),\n",
    "        PropellerInfo(body_index, RigidTransform([0, -L, 0]), kF, -kM),\n",
    "    ]\n",
    "    \"\"\"\n",
    "\n",
    "    prop_info = [\n",
    "        PropellerInfo(body_index, RigidTransform([-prop_pos['x'], -prop_pos['y'], prop_pos['z']]), kF, kM), #CW\n",
    "        PropellerInfo(body_index, RigidTransform([prop_pos['x'], -prop_pos['y'], prop_pos['z']]), kF, -kM), #CCW\n",
    "        PropellerInfo(body_index, RigidTransform([prop_pos['x'], prop_pos['y'], prop_pos['z']]), kF, kM), #CW\n",
    "        PropellerInfo(body_index, RigidTransform([-prop_pos['x'], prop_pos['y'], prop_pos['z']]), kF, -kM), #CCW\n",
    "    ]\n",
    "    propellers = builder.AddSystem(Propeller(prop_info))\n",
    "    builder.Connect(\n",
    "        propellers.get_output_port(),\n",
    "        plant.get_applied_spatial_force_input_port(),\n",
    "    )\n",
    "    builder.Connect(\n",
    "        plant.get_body_poses_output_port(),\n",
    "        propellers.get_body_poses_input_port(),\n",
    "    )\n",
    "\n",
    "    #quad_base = plant.GetModelInstanceByName(\"body\")\n",
    "    builder.ExportInput(propellers.get_command_input_port(), \"u\")\n",
    "    builder.ExportOutput(plant.get_state_output_port(), \"state\")\n",
    "\n",
    "\n",
    "    return builder.Build(), plant\n",
    "\n",
    "def matrix_to_latex(matrix, name=\"A\"):\n",
    "    \"\"\"Convert a NumPy matrix to a LaTeX representation.\"\"\"\n",
    "    latex_str = f\"{name} = \\\\begin{{bmatrix}}\"\n",
    "    for i in range(matrix.shape[0]):\n",
    "        row_str = \" & \".join(f\"{matrix[i, j]:.4f}\" for j in range(matrix.shape[1]))\n",
    "        latex_str += row_str\n",
    "        if i < matrix.shape[0] - 1:\n",
    "            latex_str += \"\\\\\\\\\"\n",
    "    latex_str += \"\\\\end{bmatrix}\"\n",
    "    return latex_str"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f97cb7b8-afd0-4466-82d8-f32244908a4f",
   "metadata": {},
   "source": [
    "## Import 2024 UAVF Comp Quad "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a3be14f-1a2e-493b-88a3-4ea6b7fc7f6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "quadrotor_system, quadrotor_plant = MakeMultibodyQuadrotor()\n",
    "\n",
    "quadrotor_plant.set_name(\"quadrotor_base\")\n",
    "\n",
    "quadrotor_system.set_name(\"quadrotor_system\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3fc08b3-6f49-47df-8f34-7f32327b7768",
   "metadata": {},
   "source": [
    "### Print Port information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12f9f113-e22c-4dab-ae57-cff894bd38aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input Ports (Thrust commands)\n",
      "N input ports : 1\n",
      "Actuation port size 4 [u_1, u_2, u_3, u_4] for thrust produced by each prop\n",
      "\n",
      "Output Ports:\n",
      "N output ports : 1\n",
      "State vector port size 13 ['qw', 'qx', 'qy', 'qz', 'x', 'y', 'z', 'wx', 'wy', 'wz', 'vx', 'vy', 'vz'] for state vector of position velocity attitude and angular rates\n"
     ]
    }
   ],
   "source": [
    "# Print information about input ports\n",
    "print(\"Input Ports (Thrust commands)\")\n",
    "print(f\"N input ports : {quadrotor_system.num_input_ports()}\")\n",
    "print(f\"Actuation port size {quadrotor_system.get_input_port(0).size()} [u_1, u_2, u_3, u_4] for thrust produced by each prop\")\n",
    "\n",
    "prefix_to_filter = \"quad-v4_BaseJoint_\"\n",
    "\n",
    "original_state_names = quadrotor_plant.GetStateNames()\n",
    "filtered_state_names = [name.removeprefix(prefix_to_filter) for name in original_state_names]\n",
    "states = filtered_state_names\n",
    "\n",
    "# Print information about output ports\n",
    "print(\"\\nOutput Ports:\")\n",
    "print(f\"N output ports : {quadrotor_system.num_input_ports()}\")\n",
    "print(f\"State vector port size {quadrotor_system.get_output_port(0).size()} {states} for state vector of position velocity attitude and angular rates\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "949b352b-ec74-4961-85bc-e25fe9605369",
   "metadata": {},
   "source": [
    "### Linearize Dynamics about approximate hover"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4d2b0ac-a0e2-4801-accf-2ee0aa936e7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pydrake.all import (\n",
    "    DiagramBuilder,\n",
    "    LinearQuadraticRegulator,\n",
    "    FirstOrderTaylorApproximation,\n",
    "    Simulator,\n",
    ")\n",
    "\n",
    "# Create a context for the entire quadrotor_system (Diagram), not just the plant\n",
    "quadrotor_system_context = quadrotor_system.CreateDefaultContext()\n",
    "\n",
    "# Now, use the diagram context to get the plant context\n",
    "quadrotor_plant_context = quadrotor_system.GetMutableSubsystemContext(quadrotor_plant, quadrotor_system_context)\n",
    "\n",
    "# Set the plant's state to the hover state in the plant's context\n",
    "hover_state = np.array([1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0])  # Hovering at z=1m, level orientation, no velocity\n",
    "quadrotor_plant.SetPositionsAndVelocities(quadrotor_plant_context, hover_state)\n",
    "\n",
    "# Fix the value of the input port on the diagram context, not the plant context\n",
    "input_port = quadrotor_system.get_input_port(0)\n",
    "# Calculate nominal input\n",
    "mass = quadrotor_plant.CalcTotalMass(quadrotor_plant.GetMyContextFromRoot(quadrotor_system_context))\n",
    "gravity = quadrotor_plant.gravity_field().gravity_vector()\n",
    "nominal_input = [(-mass * gravity[2]) / 4] * 4\n",
    "\n",
    "input_port.FixValue(quadrotor_system_context, nominal_input)\n",
    "\n",
    "# Proceed with the first-order Taylor approximation using the diagram context\n",
    "approximation = FirstOrderTaylorApproximation(\n",
    "    system=quadrotor_system,\n",
    "    context=quadrotor_system_context,\n",
    "    input_port_index=input_port.get_index(),\n",
    "    output_port_index=quadrotor_system.get_output_port(0).get_index()\n",
    ")\n",
    "\n",
    "A = approximation.A()\n",
    "B = approximation.B()\n",
    "C = approximation.C()\n",
    "D = approximation.D()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "769134c1-fa90-4920-8238-419e2a100b8f",
   "metadata": {},
   "source": [
    "### State Space Matricies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "674152dd-a957-4a6f-8ec9-6616022bbc0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "A = \\begin{bmatrix}-0.0000 & -0.0000 & -0.0000 & -0.0000 & -0.0000 & -0.0000 & -0.0000 & -0.0000 & -0.0000 & -0.0000 & -0.0000 & -0.0000 & -0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.5000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.5000 & 0.0000 & 0.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.5000 & 0.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 1.0000\\\\-0.0000 & 0.0000 & -2.2450 & 395.2128 & -0.0000 & -0.0000 & -0.0000 & -0.0000 & -0.0000 & -0.0000 & -0.0000 & -0.0000 & -0.0000\\\\-0.0000 & 2.2450 & -0.0000 & 85.2254 & -0.0000 & -0.0000 & -0.0000 & -0.0000 & -0.0000 & -0.0000 & -0.0000 & -0.0000 & -0.0000\\\\0.0000 & -395.2128 & -85.2254 & -0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000\\\\-0.0000 & -0.0000 & 146.8776 & 1.4373 & -0.0000 & -0.0000 & -0.0000 & -0.0000 & -0.0000 & -0.0000 & -0.0000 & -0.0000 & -0.0000\\\\0.0000 & -146.8776 & 0.0000 & -6.1553 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000\\\\-0.0000 & -1.4373 & 6.1553 & 0.0000 & -0.0000 & -0.0000 & -0.0000 & -0.0000 & -0.0000 & -0.0000 & -0.0000 & -0.0000 & -0.0000\\end{bmatrix}"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "B = \\begin{bmatrix}-0.0000 & -0.0000 & -0.0000 & -0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000\\\\-133.8000 & -137.5735 & 148.7605 & 151.7150\\\\123.3404 & -195.1741 & -190.6195 & 127.4999\\\\-1.9261 & -13.2863 & 7.3787 & 7.0671\\\\1.9601 & -2.8587 & -3.0947 & 1.8914\\\\2.0071 & 1.5465 & -1.9919 & -2.0524\\\\3.2543 & 17.7695 & 21.8199 & 7.3106\\end{bmatrix}"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "C = \\begin{bmatrix}1.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000\\\\0.0000 & 1.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 1.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 1.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 0.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 1.0000\\end{bmatrix}"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "D = \\begin{bmatrix}0.0000 & 0.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000\\\\0.0000 & 0.0000 & 0.0000 & 0.0000\\end{bmatrix}"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, Latex\n",
    "\n",
    "# Assuming A_matrix and B_matrix are already defined as the A and B matrices from your linear system\n",
    "\n",
    "# Convert matrices to LaTeX strings\n",
    "A_latex = matrix_to_latex(A, \"A\")\n",
    "B_latex = matrix_to_latex(B, \"B\")\n",
    "C_latex = matrix_to_latex(C, \"C\")\n",
    "D_latex = matrix_to_latex(D, \"D\")\n",
    "\n",
    "# Display matrices in LaTeX format\n",
    "display(Latex(A_latex))\n",
    "display(Latex(B_latex))\n",
    "display(Latex(C_latex))\n",
    "display(Latex(D_latex))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2016d2f0-76b7-4cee-a16a-abc1056b0aa3",
   "metadata": {},
   "source": [
    "### Thrust allocation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8169e317-568c-4c2a-9166-39a9010de644",
   "metadata": {},
   "outputs": [],
   "source": [
    "import control as ctrl\n",
    "\n",
    "\n",
    "degrees = 49.57\n",
    "beta = np.deg2rad(degrees)\n",
    "c_t = 1  # Example value for c\n",
    "c_q = 1\n",
    "l = 0.39 #meters\n",
    "# Define the thrust allocation matrix G1\n",
    "#G1 = np.array([[1, 1, 1, 1],\n",
    "#               [-np.sin(beta), np.cos(beta), np.sin(beta), -np.cos(beta)],\n",
    "#               [np.cos(beta), np.sin(beta), -np.cos(beta), -np.sin(beta)],\n",
    "#               [1/c, -1/c, 1/c, -1/c]])\n",
    "\n",
    "G1 = np.array([[1, 1, 1, 1],\n",
    "               [l*np.sin(beta), -l*np.sin(beta), -l*np.sin(beta), l*np.sin(beta)],\n",
    "               [-l*np.cos(beta), -l*np.cos(beta), l*np.cos(beta), l*np.cos(beta)],\n",
    "               [c_q / c_t, -c_q / c_t, c_q / c_t, -c_q / c_t]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "238abe27-fd6f-401b-b0dd-9bd181493e34",
   "metadata": {},
   "source": [
    "## Dynamics Transfer Function & PID Transfer Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4b30bca-3dc4-4292-8b6d-03ec8b9cbad7",
   "metadata": {},
   "outputs": [
    {
     "ename": "ControlMIMONotImplemented",
     "evalue": "Not implemented for MIMO systems without slycot.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/control/xferfcn.py:1485\u001b[0m, in \u001b[0;36m_convert_to_transfer_function\u001b[0;34m(sys, inputs, outputs, use_prefix_suffix)\u001b[0m\n\u001b[1;32m   1482\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1483\u001b[0m     \u001b[38;5;66;03m# Use Slycot to make the transformation\u001b[39;00m\n\u001b[1;32m   1484\u001b[0m     \u001b[38;5;66;03m# Make sure to convert system matrices to numpy arrays\u001b[39;00m\n\u001b[0;32m-> 1485\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mslycot\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m tb04ad\n\u001b[1;32m   1486\u001b[0m     tfout \u001b[38;5;241m=\u001b[39m tb04ad(\n\u001b[1;32m   1487\u001b[0m         sys\u001b[38;5;241m.\u001b[39mnstates, sys\u001b[38;5;241m.\u001b[39mninputs, sys\u001b[38;5;241m.\u001b[39mnoutputs, array(sys\u001b[38;5;241m.\u001b[39mA),\n\u001b[1;32m   1488\u001b[0m         array(sys\u001b[38;5;241m.\u001b[39mB), array(sys\u001b[38;5;241m.\u001b[39mC), array(sys\u001b[38;5;241m.\u001b[39mD), tol1\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.0\u001b[39m)\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'slycot'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mControlMIMONotImplemented\u001b[0m                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[14], line 8\u001b[0m\n\u001b[1;32m      4\u001b[0m state_space_model \u001b[38;5;241m=\u001b[39m ctrl\u001b[38;5;241m.\u001b[39mStateSpace(A, (B \u001b[38;5;241m@\u001b[39m G1_inv), C, D)\n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m# Convert state space model to transfer function\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m G \u001b[38;5;241m=\u001b[39m \u001b[43mctrl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mss2tf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate_space_model\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mState transfer function has n inputs \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mG\u001b[38;5;241m.\u001b[39mninputs\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mState Transfer function has n outputs \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mG\u001b[38;5;241m.\u001b[39mnoutputs\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/control/xferfcn.py:1818\u001b[0m, in \u001b[0;36mss2tf\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1815\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124moutputs\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m   1816\u001b[0m         kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124moutputs\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m sys\u001b[38;5;241m.\u001b[39moutput_labels\n\u001b[1;32m   1817\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m TransferFunction(\n\u001b[0;32m-> 1818\u001b[0m         \u001b[43m_convert_to_transfer_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1819\u001b[0m \u001b[43m            \u001b[49m\u001b[43msys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_prefix_suffix\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43msys\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_generic_name_check\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m   1820\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1821\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1822\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m   1823\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mss2tf(sys): sys must be a StateSpace object.  It is \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1824\u001b[0m         \u001b[38;5;241m%\u001b[39m \u001b[38;5;28mtype\u001b[39m(sys))\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/control/xferfcn.py:1506\u001b[0m, in \u001b[0;36m_convert_to_transfer_function\u001b[0;34m(sys, inputs, outputs, use_prefix_suffix)\u001b[0m\n\u001b[1;32m   1503\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n\u001b[1;32m   1504\u001b[0m     \u001b[38;5;66;03m# If slycot is not available, use signal.lti (SISO only)\u001b[39;00m\n\u001b[1;32m   1505\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m sys\u001b[38;5;241m.\u001b[39mninputs \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m sys\u001b[38;5;241m.\u001b[39mnoutputs \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m-> 1506\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m ControlMIMONotImplemented(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNot implemented for \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m\n\u001b[1;32m   1507\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMIMO systems without slycot.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;66;03m# Do the conversion using sp.signal.ss2tf\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m     \u001b[38;5;66;03m# Note that this returns a 2D array for the numerator\u001b[39;00m\n\u001b[1;32m   1511\u001b[0m     num, den \u001b[38;5;241m=\u001b[39m sp\u001b[38;5;241m.\u001b[39msignal\u001b[38;5;241m.\u001b[39mss2tf(sys\u001b[38;5;241m.\u001b[39mA, sys\u001b[38;5;241m.\u001b[39mB, sys\u001b[38;5;241m.\u001b[39mC, sys\u001b[38;5;241m.\u001b[39mD)\n",
      "\u001b[0;31mControlMIMONotImplemented\u001b[0m: Not implemented for MIMO systems without slycot."
     ]
    }
   ],
   "source": [
    "# Invert G1\n",
    "G1_inv = np.linalg.inv(G1)\n",
    "# Create the state-space model\n",
    "state_space_model = ctrl.StateSpace(A, (B @ G1_inv), C, D)\n",
    "\n",
    "\n",
    "# Convert state space model to transfer function\n",
    "G = ctrl.ss2tf(state_space_model)\n",
    "\n",
    "print(f\"State transfer function has n inputs {G.ninputs}\")\n",
    "print(f\"State Transfer function has n outputs {G.noutputs}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f78786d3-c3dd-4c7e-86e8-c8cf96b9f27a",
   "metadata": {},
   "source": [
    "### PID Transfer function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "088cf126-b594-488b-a175-0ad9b4199c78",
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe kernel failed to start due to the missing module 'pygments'. Consider installing this module.\n",
      "\u001b[1;31mClick <a href='https://aka.ms/kernelFailuresMissingModule'>here</a> for more info."
     ]
    }
   ],
   "source": [
    "# Define PID parameters for roll, pitch, and yaw\n",
    "Kp_roll, Ki_roll, Kd_roll = 0.5, 0, 0\n",
    "Kp_pitch, Ki_pitch, Kd_pitch = 0.5, 0, 0\n",
    "Kp_yaw, Ki_yaw, Kd_yaw = 0.10000000298023224, 0, 0\n",
    "\n",
    "# Create the PID controllers for each axis\n",
    "s = ctrl.TransferFunction.s\n",
    "pid_roll = Kp_roll + Ki_roll/s + Kd_roll*s\n",
    "pid_pitch = Kp_pitch + Ki_pitch/s + Kd_pitch*s\n",
    "pid_yaw = Kp_yaw + Ki_yaw/s + Kd_yaw*s\n",
    "\n",
    "print(f\"Roll rate transfer\")\n",
    "print(pid_roll)\n",
    "print()\n",
    "print(f\"Pitch rate transfer\")\n",
    "print(pid_pitch)\n",
    "print()\n",
    "print(f\"Yaw rate transfer\")\n",
    "print(pid_yaw)\n",
    "print()\n",
    "\n",
    "print(f\"Roll rate PID transfer function has n inputs {pid_roll.ninputs}\")\n",
    "print(f\"pitch rate PID transfer function has n inputs{pid_pitch.ninputs}\")\n",
    "print(f\"yaw rate PID transfer function has n inputs {pid_yaw.ninputs}\")\n",
    "\n",
    "print(f\"Roll rate PID transfer function has n outputs {pid_roll.noutputs}\")\n",
    "print(f\"pitch rate PID transfer function has n outputs{pid_pitch.noutputs}\")\n",
    "print(f\"yaw rate PID transfer function has n outputs {pid_yaw.noutputs}\")\n",
    "\n",
    "# Since the B_matrix likely has different columns for different inputs (torques for each axis),\n",
    "# You need to define a transfer function from the state space model for each input axis.\n",
    "# For example:\n",
    "G_roll = G[7, 0]  # First output, first input\n",
    "G_pitch = G[8, 1]  # First output, second input\n",
    "G_yaw = G[9, 2]  # First output, third input\n",
    "\n",
    "# Calculate the closed-loop transfer functions for each axis\n",
    "closed_loop_roll = ctrl.feedback(pid_roll, G_roll, 1)\n",
    "closed_loop_pitch = ctrl.feedback(pid_pitch, G_pitch, 1)\n",
    "closed_loop_yaw = ctrl.feedback(pid_yaw, G_yaw, 1)\n",
    "\n",
    "# Display the closed-loop transfer functions\n",
    "print('Closed-loop Transfer Function for Roll:')\n",
    "print(closed_loop_roll)\n",
    "print(closed_loop_roll.poles())\n",
    "print('Closed-loop Transfer Function for Pitch:')\n",
    "print(closed_loop_pitch)\n",
    "print(closed_loop_pitch.poles())\n",
    "print('Closed-loop Transfer Function for Yaw:')\n",
    "print(closed_loop_yaw)\n",
    "print(closed_loop_yaw.poles())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de5904e5-decd-4f3f-8bb4-689560e7a65d",
   "metadata": {},
   "source": [
    "## Plots"
   ]
  },
  {
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      "\u001b[1;31mThe kernel failed to start due to the missing module 'pygments'. Consider installing this module.\n",
      "\u001b[1;31mClick <a href='https://aka.ms/kernelFailuresMissingModule'>here</a> for more info."
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   ],
   "source": [
    "%matplotlib widget\n",
    "import matplotlib.pyplot as plt\n",
    "import control as ctrl\n",
    "import numpy as np\n",
    "\n",
    "# Create a figure and a set of subplots\n",
    "fig, axs = plt.subplots(1, 3, figsize=(12, 6))\n",
    "\n",
    "# Titles for each subplot\n",
    "titles = ['Nyquist Plot for Roll', 'Nyquist Plot for Pitch', 'Nyquist Plot for Yaw']\n",
    "\n",
    "# Transfer functions for each axis\n",
    "transfer_functions = [closed_loop_roll, closed_loop_pitch, closed_loop_yaw]\n",
    "colors = ['red', 'green', 'blue']  # Colors for each plot\n",
    "omega = np.logspace(-5, 5, 1000)  # From 0.001 to 1000 rad/s\n",
    "\n",
    "\n",
    "for ax, tf, title, color in zip(axs, transfer_functions, titles, colors):\n",
    "    # Compute frequency response\n",
    "    mag, phase, omega = ctrl.freqresp(tf, omega)\n",
    "    real = mag * np.cos(phase)\n",
    "    imag = mag * np.sin(phase)\n",
    "    \n",
    "    # Plot Nyquist plot\n",
    "    line, = ax.plot(real.squeeze(), imag.squeeze(), color=color, label=title)\n",
    "    ax.plot(real.squeeze(), -imag.squeeze(), linestyle='--', color=color)  # Mirror image\n",
    "    \n",
    "    # Critical Point (-1, 0)\n",
    "    ax.plot(-1, 0, 'rx', markersize=10, label='Critical Point (-1,0)')\n",
    "    \n",
    "    # Real Axis Intersections (Phase Crossover Points)\n",
    "    # Find points where the imaginary part is close to zero\n",
    "    real_axis_intersections = real.squeeze()[np.isclose(imag.squeeze(), 0, atol=1e-4)]\n",
    "    for x in real_axis_intersections:\n",
    "        ax.plot(x, 0, 'ko', markersize=5, label='Real Axis Intersection' if x == real_axis_intersections[0] else \"\")\n",
    "    \n",
    "    # Add arrows indicating direction of increasing frequency\n",
    "    arrow_length = 0.5  # Length of the arrow\n",
    "    for i in range(0, len(omega) - 1, 20):\n",
    "        ax.annotate('', xy=(real[i + 1], imag[i + 1]), xytext=(real[i], imag[i]),\n",
    "                    arrowprops=dict(arrowstyle='->', color=color),\n",
    "                    size=12)\n",
    "    \n",
    "    ax.set_title(title)\n",
    "    ax.set_xlabel('Real Part')\n",
    "    ax.set_ylabel('Imaginary Part')\n",
    "    ax.legend()\n",
    "    ax.grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
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   "execution_count": null,
   "id": "e903eb92-3cc1-4d22-bbe1-cd1845b95ba6",
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   "outputs": [
    {
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     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe kernel failed to start due to the missing module 'pygments'. Consider installing this module.\n",
      "\u001b[1;31mClick <a href='https://aka.ms/kernelFailuresMissingModule'>here</a> for more info."
     ]
    }
   ],
   "source": [
    "\n",
    "# Bode plot for Roll\n",
    "plt.figure()\n",
    "ctrl.bode_plot(closed_loop_roll, dB=True)\n",
    "plt.suptitle('Bode Plot for Roll')\n",
    "\n",
    "# Bode plot for Pitch\n",
    "plt.figure()\n",
    "ctrl.bode_plot(closed_loop_pitch, dB=True)\n",
    "plt.suptitle('Bode Plot for Pitch')\n",
    "\n",
    "# Bode plot for Yaw\n",
    "plt.figure()\n",
    "ctrl.bode_plot(closed_loop_yaw, dB=True)\n",
    "plt.suptitle('Bode Plot for Yaw')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
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   "execution_count": null,
   "id": "37751985-6b5c-4dd8-ad08-91ff7feabab6",
   "metadata": {},
   "outputs": [
    {
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     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe kernel failed to start due to the missing module 'pygments'. Consider installing this module.\n",
      "\u001b[1;31mClick <a href='https://aka.ms/kernelFailuresMissingModule'>here</a> for more info."
     ]
    }
   ],
   "source": [
    "plt.figure()\n",
    "ctrl.freqplot.nyquist_plot(closed_loop_pitch)\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "ctrl.freqplot.nyquist_plot(closed_loop_roll)\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "ctrl.freqplot.nyquist_plot(closed_loop_yaw)\n",
    "plt.show()"
   ]
  },
  {
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   "execution_count": null,
   "id": "509abd47-0eb1-4c4f-8399-15f093104906",
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe kernel failed to start due to the missing module 'pygments'. Consider installing this module.\n",
      "\u001b[1;31mClick <a href='https://aka.ms/kernelFailuresMissingModule'>here</a> for more info."
     ]
    }
   ],
   "source": [
    "from control.matlab import rss, impulse\n",
    "T = np.linspace(0,0.6)\n",
    "\n",
    "response, T = impulse(closed_loop_pitch, T=T)\n",
    "\n",
    "#help(closed_loop_pitch)\n",
    "print(closed_loop_pitch.ninputs)\n",
    "plt.figure()\n",
    "plt.grid(True)\n",
    "plt.plot(T, response)\n",
    "plt.xlabel(\"time\")\n",
    "plt.ylabel(\"s\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2e722f9-3adf-4f5b-ab7b-f7cf690509f1",
   "metadata": {},
   "source": [
    "# Etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fdcf08c-dc5d-4664-a2ea-1e51ef7138c0",
   "metadata": {},
   "outputs": [
    {
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     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe kernel failed to start due to the missing module 'pygments'. Consider installing this module.\n",
      "\u001b[1;31mClick <a href='https://aka.ms/kernelFailuresMissingModule'>here</a> for more info."
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from pydrake.all import LeafSystem, BasicVector, PortDataType, AbstractValue, Quaternion, RollPitchYaw\n",
    "from pydrake.math import RotationMatrix\n",
    "from pydrake.systems.framework import LeafSystem_, BasicVector_, PublishEvent, TriggerType\n",
    "from pydrake.systems.scalar_conversion import TemplateSystem\n",
    "from pydrake.symbolic import if_then_else, Expression\n",
    "from pydrake.all import DiagramBuilder, Simulator, ConstantVectorSource\n",
    "from pydrake.systems.primitives import LogVectorOutput\n",
    "\n",
    "\n",
    "@TemplateSystem.define(\"QuaternionDynamicsSystem_\")\n",
    "def QuaternionDynamicsSystem_(T):\n",
    "\n",
    "    class Impl(LeafSystem_[T]):\n",
    "\n",
    "        def _construct(self, converter=None):\n",
    "            LeafSystem_[T].__init__(self, converter)\n",
    "            # Declare system state: assuming 10 states as per your dynamics\n",
    "            self.DeclareContinuousState(10)\n",
    "            # Input port for control inputs: assuming 4 inputs\n",
    "            self.DeclareVectorInputPort(\"u\", BasicVector_[T](4))\n",
    "            # Output port for system state\n",
    "            self.DeclareVectorOutputPort(\"x\", BasicVector_[T](10), self.CopyStateOut)\n",
    "\n",
    "        def _construct_copy(self, other, converter=None):\n",
    "            # Constructor used for scalar conversion\n",
    "            Impl._construct(self, converter=converter)\n",
    "\n",
    "        def CopyStateOut(self, context, output):\n",
    "            # Copy the state to the output port\n",
    "            x = context.get_continuous_state_vector().CopyToVector()\n",
    "            output.SetFromVector(x)\n",
    "\n",
    "        def DoCalcTimeDerivatives(self, context, derivatives):\n",
    "            x = context.get_continuous_state_vector().CopyToVector()\n",
    "            u = self.EvalVectorInput(context, 0).CopyToVector()\n",
    "            # Calculate dynamics here. Placeholder for actual dynamics calculation\n",
    "            dx_dt = self.calculate_dynamics(x, u)\n",
    "            derivatives.get_mutable_vector().SetFromVector(dx_dt)\n",
    "    \n",
    "        def calculate_dynamics(self, x, u):\n",
    "            # Extract the state components\n",
    "            p = x[:3]  # Position (px, py, pz)\n",
    "            q = x[3:7]  # Orientation quaternion (qw, qx, qy, qz)\n",
    "            v = x[7:]  # Velocity (vx, vy, vz)\n",
    "        \n",
    "            # Extract control inputs\n",
    "            omega_des = u[:3]  # Desired body rates (wx_des, wy_des, wz_des)\n",
    "            c_des = u[3]  # Desired thrust\n",
    "        \n",
    "            # Construct the quaternion from state\n",
    "            q_w, q_x, q_y, q_z = q\n",
    "        \n",
    "            # Quaternion kinematics\n",
    "            Qx = np.array([\n",
    "                [ q_w, -q_x, -q_y, -q_z],\n",
    "                [ q_x,  q_w, -q_z,  q_y],\n",
    "                [ q_y,  q_z,  q_w, -q_x],\n",
    "                [ q_z, -q_y,  q_x,  q_w]\n",
    "            ])\n",
    "\n",
    "            augmented_omega = np.concatenate(([0], omega_des)).reshape(4,)\n",
    "            # Quaternion derivative\n",
    "            q_dot = 0.5 * Qx @ augmented_omega\n",
    "        \n",
    "            # Compute the gravity vector\n",
    "            g = 9.81  # Acceleration due to gravity\n",
    "            g_vec = np.array([0, 0, -g])\n",
    "        \n",
    "            # Rotation matrix from quaternion\n",
    "            R = np.array([\n",
    "                [1 - 2*q_y**2 - 2*q_z**2, 2*q_x*q_y - 2*q_z*q_w,     2*q_x*q_z + 2*q_y*q_w],\n",
    "                [2*q_x*q_y + 2*q_z*q_w,     1 - 2*q_x**2 - 2*q_z**2, 2*q_y*q_z - 2*q_x*q_w],\n",
    "                [2*q_x*q_z - 2*q_y*q_w,     2*q_y*q_z + 2*q_x*q_w,     1 - 2*q_x**2 - 2*q_y**2]\n",
    "            ])\n",
    "        \n",
    "            # Velocity dynamics\n",
    "            v_dot = g_vec + R @ np.array([0, 0, c_des])\n",
    "            dx_dt = np.concatenate((v, q_dot, v_dot))\n",
    "        \n",
    "            return dx_dt\n",
    "\n",
    "    return Impl"
   ]
  },
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",
       "_figure_label": "Figure 7",
       "_model_module_version": "^0.11",
       "_size": [
        640,
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       ],
       "_view_module_version": "^0.11",
       "layout": "IPY_MODEL_14d6c3efeb0241f5b67f4f7a70d11859",
       "toolbar": "IPY_MODEL_b7de2d4aa8a54cdfba0c3f1a8d8ef7b9",
       "toolbar_position": "left"
      }
     },
     "3eb3344af14f4fe2ac6db91bd1b2a42f": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "3eb47fee6ca2478286cbe3225ea2f45f": {
      "model_module": "jupyter-matplotlib",
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      "model_name": "MPLCanvasModel",
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D/vOICCwuOW6vr4qQ2q4pR6VdC8bmU0r3Psv01qVdLXz70WeFw+qT4GUV8pAKjM8+H3zz//HA899BAWL16MW2+9Fc8991xRKwUFBbjjjjswceJEXZX+6U9/wu23386KQgoAFk0aRA1Wl2eW3LYonKsJhGqHz6IbpF/x2Pg1seViKZwrfW3z242qJb1QfS+OPppfgBXbsrFg4z4s2LhXL/ort2WXWOzVol4r4TB6tW+B0xtUxekNq+mFPkGd7jMOk3i4fFI+g6ivFACMZHENWblyJQ4fPoxx48bhwIEDJQqAMWPGYPTo0Zg8eTL27duHjh076mKgdevWZBhSAJAUFQGowSoFAJ9LW6SNFi518nqGyI2fwgVxgbDVNmQfq2/qkbp5G/Zg5trdmL1uN+Zu2KOfdQ8djaqnon1GjWN/GtdAm/RqqJqaHLgivizqKwWAqwz34OeRRx7B3r17SxQA/fv3x5AhQzBo0CDt8Z577kFKSgoeffRRsgUpAEiKpACw3P3gM8xHUhMn35P3s87wNkxi4mIpXHkpAPbn5OGndccW/Flrd2HRpn3Iyz+2l6+28Ts2qYFOTWr+suBX1zfjRTooPl0Wh5y2KEwQ9ZUCwOvs4sAuUgHQtm1bvPLKK+jRo4du4aWXXsKMGTOgdgaoQwoAiqHjv1ODNd6Th2qPisnVBEK1w2fRDdKveGz8mthysRTOlb6cXHKjHM+L2tJ/Zfxk5NZsjmmrd2L+xr1Qj+Spo2pqEro2q4WuJx370ya9OpITE1iOKT7jPYapeIKorxQArFQzA6nFe9WqVRGN5s2bh4yMDP2bbQHwzDPPQP0JHWo3Yfz48aXazcnJQWpq5Co60m+x8GZMuEH7EY+NT1NbLp7Cmeqo2C+v+kbrOzcjKS2K++FiKZwrfW37zuUoGq6wsBA7DgNL9xRi2e5CLN9TiJxfXn6Xmgi0rFkBp9WsgFNqVEB6ZSCh2LPyJm1TfMbyZWrLwVOYIOo7fPjwiJ+QN9EhKNgy9zlguQTASx2qsuZ5KYmy8Wlqy8VTOFdnEFQ7Xvi0sfErHhu/JrZcLIVzpa/SgmrLRq9Itnn5BZi1dje+XLYNXy/fjvW7DmmYuidPXbtvlLAPN/Trjg4ZNdhn+FSMNn00teXgKUwQ9ZUdACrLfPw9UgEwatQovPnmmyVuApwwYQLUpQHqkEsAFEPHf6cGayxPprZcPIVzNYFQ7fBZdIP0Kx4bvya2XCyFc6VvvAqAPQeP4NuV2/Hlsu2YumKHft+9OtRNe+eeVg+9TqmDHifX0c/ZU333kkk2Pk1tOXgKE0R9pQDwknmWNl999RWGDRuG/fv3Q22XVa9eXV/rv+yyy5Cfn48RI0Zg0qRJ+jFA9Xf1WCDnkAKAw9IxDDVYpQDgc2mLtNHCpU7FfZnExMVSuCAuEOH8bth1CJlLtuDLpdsxZ/3uokfz1Fn+hafVwwWn18fpDauWerEO1XcvOWTj09SWg6cwQdRXCgAvmRdQGykA+MJQg9XlwsJti8K5mkCodvgsukH6FY+NXxNbLpbCudLXtsANV1W9Snfioi2YtHiLfgmPOtTd+medUgcXnl4P551WT78u1+WY4WQWxafLeDhtUZgg6isFACfTyghGCgC+UNRgjffkwZm0XU0gNn3nM8xH+hWPjV8TWy6WwrnSl5NLlDqrt2dj4qKteuFXr9pVR7XUJPRu1QAXtWmgF//wt+u5HDNUfLZ9pLQIb5+DpzBB0jfUPykAOJlWRjBSAPCFogary8mM2xaFczWBUO3wWXSD9CseG78mtlwshXOlr9fFUS36ny7YgkmLtmDV9gNaXPVlu75q0W/bAGeeXAcpSbxH9LwsoKbZRPEZ7zFMxXOi9Y3EhxQAplkXYLwUAHxxqMEa78mDM2m7mkBs+s5nmI/0Kx4bvya2XCyFc6UvJ5dC6mzZdxifLdiMj+dtxtItx7b3a1dOQd82DXBxm4bo1ryWk7v2qb7zs+U40sanqS0HT2FOhL4Ur1IAUAyVod+lAOCLRQ1WKQD4XNoibbRwqVNxXyYxcbEULl4LhPpc7qTFW/HJ/E36bXyFhcdeyKMW/N90aIRuzWsjkflOfa72VN+5frxqZLsjwYmfwsRLXxMupQCIwlZ2dja+//57/ZKEtLQ0tG/fHu3atTPhNu5YKQD4lFOD1eXCwm2LwrmaQKh2+Cy6QfoVj41fE1sulsK50jfSDkBOXr5+Rv+T+Zvx7Yrt+tW7ajtf3cR3Wft0nHdaXVRMKvnFPDfqHvNC9d1LWzY+TW05eArjp75e+FM2UgCEMbd+/Xr9pT71GJ569r5Bgwb6bWrLli2D+lKfejf/TTfd5JVvX+2kAODTSw1WKQD4XNoibbRwqZPXs0tu/BTO9QLRu3cffYY/fm4WMhdvxYHco/rFPD1b1MFl7Rvpbf5qqcm28rHsqb6znISBbHya2nLwFMa1vn379vVCWwkbKQDCKOzVqxfuvvtuXHzxxUhKSirx67p16zBy5Eikp6ezP9FrrZCBAykA+GRRg9XlwsJti8K5mkCodvgsukH6FY+NXxNbLpbCudJ34+5DeOr9bzF/Xyqy9hzWIrVrXB2/6ZCOS9s1jPpxHTdqRvZC9d1L2zY+TW05eArjSl+XOypSAHjJvIDaSAHAF4YarFIA8Lm0Rdpo4VKnsrwDcDD3qH5k74OfsvRZvzrqVa2IKzs1xoDO6WhRr6qtTFb2fmhs49PUloOnMFIAWKUQaezLtwByc3OxePFiNGvWDLVr1yaDOJEAKQD47FOD1eXCwm2LwrmaQKh2+Cy6QfoVj41fE1sulsKZ6ltQUIgX3/8C6xMb6sX/0JF8fV2/T6v6OAnbcMc1fZHE/LKeGyWje6H67qV9G5+mthw8hTHVN9aZPtUWl0/ZAQhj6scff9Sv3q1VqxYef/xxXHHFFfoegIMHD2Ls2LG4/PLLudzGHScFAJ9ymwFkasvFUzhXEwjVDp9FN0i/4rHxa2LLxVI4rr5qi19d11d/Nu4+tsWvXsU7oHNjXNauEapX8ufd+zZqU3334tvGp6ktB09huPpydqKotrh8SgEQxlS3bt1w5513Qn1a98EHH8R7772H888/HwsWLMCNN96IuXPncrmNO04KAD7lNgPI1JaLp3CuJhCqHT6LbpB+xWPj18SWi6VwsX6fMCkThY3a4d3ZG/DD6l2aeLXF36HmEfzlqrNxSv2SW/xUW26U43vxIx4bn6a2HDyFcTV+Y+0M8BU5hpQCIIyxDh06YP78+fpfmzdvjjVr1hQhiv9mSnQ88FIA8FmmBmssT6a2XDyFczWBUO3wWXSD9CseG78mtlwshYv0++rtB/DurA14d+ZaHMgDkhIqoHer+ri6SwbOblEHX305BZHuBqfacqMc34sf8dj4NLXl4CmMq/ErBUDkvHNyD0CnTp2KzvKL/101Gf7//PSPD1IKAD7P1GCVAoDPpS3SRguXOhX3ZRITF0vhQr8fPpKvr+mrs/3Z6/YcO9tPA2469zRc1akx6latWBRqNJ9UW7aamdr7EY+NT1NbDp7CSAFgmjVmeCcFQL169XD99dfrltU1/9Df1Wd73377bWzbts0sqjiipQDgk00NVpcLC7ctCudqAqHa4bPoBulXPDZ+TWy5WAr33/GZ2JDUGB/N24TsnKP6hr6L2zTAoK5NsHflbPTr168U4VIAeHsWntIinGgOnsK4Gr+yA+DjDsDf//73mLPaww8/7GbW88GLFAB8UqnBKgUAn0tbpI0WLnU6ETsA6uU86l38apt/QdY+HULL+lUxqGsGruiYjhqVUvS/mS70fnHqVWs/4rHxaWrLwVMYKQC8Zg/PzskOAK+pYKKkAODrQg1WlwsLty0K52oCodrhs+gG6Vc8Nn5NbLnYEE7tJqrFXi36ny7YrB/fS0tORKc6Bbjrih7omFEDFSpUKEGuFAClc43Le6QsNbXl4CmMq/ErOwA+7gCMGTMm5qw2dOhQ61nvP//5D1555RU9yNUf9Xrh0KUG5fzRRx/FG2+8odsZNGgQHnvsMVabUgCwaIp5RsXxQA30cB9cPIVzNYFQ7XA4cInxKx4bvya2XOwnn2ciu/bpGDtjPZZvzdYUtk2vrs/21at5p3/3dcQb+mQHIHK2cXmXAiD6aJWnAMK4GThwoP6X/fv347vvvsNZZ52lF+lp06bhnHPOQWZmpvXc99VXX+GMM85A9erVsXHjRnTs2BEzZ87EySefjKlTp+LWW2/FrFmz9KuIe/bsCXVZon///mS7UgCQFBUBgjZ5cKp6KQD4+nL4jOXNJD8o7PKt+/WiP272BuTmA1UqJuHyjo0wqEsTtEmvzspJ2QEorRbFuyt9ublExeNq/HLj4YwWKQCisKReAPTPf/4Tbdq00YglS5bojwSNHz+ew6sRRrXxwgsv4Nxzz8Vtt92GjIwM3HvvvdrHSy+9hOnTp+sbEqlDCgCKoeO/U4M13pMHZ1C7mkBs+s5nmI/0Kx4bvya2kbC5R/P1B3jUwh+6kz+jCnBr77b6k7uVK5b8zgilvxQAUgAUZ8AkP2ONRCkAorCjvgS4aNGiEr+qzwEvXLiQP7MxkF9++SWGDRuGlStXonLlyrj00ksxePBgXHvttdp64sSJePLJJ/XOAHVIAUAxJAUAtdDwGXSHdDWZhUdk49fEtjhWvaXv7Vkb8P7sjdh18Ii+k/+Stg1xXfem2LFsZsQ7+UNxuyrwyovGJhrZ5ganLQoTRH2lAIgyj6mt99/97nd6cVbH6NGj9XV7dTZOHT169MCqVasiwubNm6fP8NWhCgz11cF33nlHX2pQh0kB8Mwzz0D9CR3q7YWRdijUq4xTU1MjxhPpt1h4qu9+/O5HPDY+TW25eApnqqPSorzqG63v3PyktCju59DhHPx8KAXfbSrE4l2FKARQJxXolZ6Ang0qoErKsRv6KJ+u9OW0xeXBFY7qu5d2bHya2nLwFCaI+g4fPhxZWVle6A+cjdOnAFasWIEhQ4boVwCrQ12nV0VAy5YtnXR86dKluOiii/Df//4XvXv3LvIplwBK00tV1l4EsfFpasvFUzhXZxBUO174tLHxKx4bvxzbXQdy8f6cLPz32+XYlQMkVADOP60+hvRoqt/Sl6D+odhB+XSlr2qSastGLy+2fsRj49PUloOnMEHUV3YAiGzOzj52t27Vqu4+p7ls2TK9+I8cObLUXb/ffvutvg+g+E2AjzzyCC655BJy3MklAJKiIgA1WGN5MrXl4imcqwmEaofPohukX/HY+I1mqx7h+2n9Hrw5Yz0mLdqKI/kFqJYCDO3ZAtd2a4L0GmlRSaHicaWvFAB0XlJahHvg4ClMEPWVAiBMaXVXfmiLPloabd68GY0aNaKzLApCnfHPmTMHTZs2LUI89dRTRcXAP/7xD4waNUr/ds011+CJJ55gtSUFAIsmDaIGqxQAfC5tkTZauNSpuK/wmNQLe9Qb+t4q9ghft5Nq4fruTZGweSH6X1T6LX2mi0gQFwhbbUP2fmhs49PUloOnMEHUVwqAsAzv1auX3uZXN+KpLwNWqlRJI9RHgdQjgOo9AX/961/1Z4KDdkgBwFeEGqwuFxZuWxTO1QRCtcNn0Q3Sr3hs/IZsQ4/wfTR3Ew4eyUfVikm4slO6vqnv1F++wMdth8K50te2wHWjakkvVN+9tGnj09SWg6cwQdRXCoAImTdu3Di9Pf/DDz/oZ/Fzc3ORnp6OAQMGYMSIEeQOgZdkdmEjBQCfRWqwSgHA59IWaaOFS51CvtQjfP/z9mQsPFS96BG+Vg2r6bP9SI/wceOncEFcIGy1lR2A4wwGUV8pAGJk+NGjR7Fz5069C1CtWjVXY8E3P1IA8KmlJmOXCwu3LQrnagKh2uGz6AbpVzymfmM9wtepSenX85oublQ8rvSVHQA6Lyktwj1w8BQmiPpKAUDnSplBSAHAl4oarFIA8Lm0RdpoYatTfkEhpq7coW/q+2bFdhQWAk1qVcIZNXPw4OALUKvysY/x2LbDWZSDuEBQfef+7of8yEFVAAAgAElEQVTGNj5NbTl4ChNEfaUA4GZwGcBJAcAXiRqsLib8oJ4h2vSdzzAf6Vc8sfzu1I/wbcTbMzcga8/hUo/wTZkyOep7+b2cHUoB8AWbT27m2OSNqS0HT2GkAOAq6w3n9D0A3kI4sVZSAPD5pwarFAB8Lm2RNlqY6BTpEb46VSpiUJeMUo/wmcTExVK4IC4QttqaFsEm7VF8muQG1S6nLQoTRH1lB4BSvgz9LgUAXyxqsMZ78ojnGaJN3/kM85F+xRPyG+sRvr6tG+jX9Xo9q+foxl0Eg7hA8FWMjfRDYxufprYcPIUJor5SAETJ23379uGBBx7AunXrMGHCBKg396m3Aobe0e9qYLj0IwUAn01qsEoBwOfSFmmjRay2Xx+fiTWJ6Yj1CF80e5OYuFgKF8QFwlZbbvHjpR2Kz3iPYSqeIOorBUCULBk0aJD+EuC7776LxYsX4/Dhw1Dv+J8/f76XXI2LjRQAfJqpwRrvyYNzJulqArHpO59hPtJlPJG+whfrET4pAPg62SBdauyiqDCNh4OnMK7GL2eu4GolBUAUpjp16oS5c+fqbwCoD/ioo3379kXfBuASHE+cFAB8tqnBKgUAn0tbpI0WobYjPcLXqXYh/nJlD8R6hE8KAFv1ePYuNA5vycanqS0HT2GkAODlileU05sAu3fvjhkzZhQVAGoHQL0Z0PXngL12NpKdFAB8NqnBKgUAn0tbpFctoj3Cd123Jhh4RgZmT/vG853nJjFxsRQuiAuErbYuztZdFGm2xQOlHeesPIj6yg5AlOy67777UKVKFYwdOxbPP/88nn32WXTp0gXqwzxBPaQA4CvDGdCuJh5uWxTO1QRCtcNn0Q3SNJ4d2cce4XtnVuRH+EJf4TP1W7w3JrZcLIVzpS9nMXKjHN8L1Xe+p+NIG5+mthw8hQmivlIARMk89RbAp59+Gh9//DHU40OXX365/gZAYmKil1yNi40UAHyaqcEqOwB8Lm2RHC3UGJyxZjfGzlyPyUu2Ii+/EHWrVsQ1Z5R+hM/FWScnJtN2KJ9BXCBstTXlyKQ9is94j2EqniDqKwWAScYFHCsFAF8garDGe/LgnLW5mkBs+s5nmI+MFc++Q3n4YG4W3pq5Hmt2HNROe7aojeu6NUXvVvWRnFj6ET4Xi44JR1wshXOlLyeX+Oq4QVJ999KKjU9TWw6ewgRRXykAwjJPfYo31vHQQw95ydW42EgBwKeZGqxSAPC5tEWGa6HO9udv3Iu3Zm7AZws2I/doAaqnJWNg58b6hT0n163CajJeGnPboXBBXCBYRDNAVN8ZLkpBbHya2nLwFCaI+koBEJZWf/nLX/S/ZGVl4auvvsJll12GChUq4NNPP8UFF1yAt99+20uuxsVGCgA+zdRglQKAz6UtMqTFwdyj+GT+Zn22v2Tzfu1W3cGvvsJ3cduGSE02u/wWL4257VC4IC4Qttq62I2JFgPFZ7zHMBVPEPWVAiBKlvTp0wejRo1Co0aNNGLLli244YYboEQM6iEFAF8ZarDGe/JQ7VExuZpAqHb4LLpBqhf2rE1sjI/mbYJ6a1/llERc3jFdb/O3auT9K5w2/TSx5WIpnCt9ObnkRjm+F6rvfE/HkTY+TW05eAoTRH2lAIiSeeolQOoFQMWPSP/mJXH9spECgM8sNVilAOBz6QWZk5ePSYu3YOyMDfhp/R7t4vSG1XB99yb4TYd0VKmY5MVtCZt4acxth8IFcYGwFuEXB1TfvbRj49PUloOnMEHUVwqAKJnXr18//dz/b3/7W4147bXX9HsBMjMzveRqCZsXX3wRL7/8sn6iQD1tcPPNN2PEiBEaU1BQgDvuuAMTJ07Ulx7+9Kc/4fbbb2e1KQUAiyYNogarFAB8Lk2Qa3cexNsz12PcT1nYeygPFZMS0KnOsRf2dMyooXPe1REvjbntULggLhBB0CJaDBSf8R7DVDxB1FcKgChZsnXrVr0oq/sA1NG7d28899xzaNCggfWYUN8ZqF69uvazf/9+/crhTz75RL90aMyYMRg9ejQmT54MhVP/poqB1q1bk+1KAUBSVASgBmu8Jw9OUeJqArHpO5/h48i8/AJ8uXSbvqlv2uqd+ofmdSpjcLcmGNC5MWZ+7/2FPS51Ku7LhCMulsK50peTS150tLGh+u7Ft41PU1sOnsIEUV8pALxknkMbdW+Beu2wWuTVYt+/f38MGTIE6lsE6rjnnnuQkpKCRx99lGxVCgCSIikALHc/+AwDm/cexruzNuDd2RuxPTsXSQkVoL6+p97U1+Pk2kVn+9TEadKm10U8vA2TmLhYChfEBcIr9zZ8ctuk+HRZHHLaojBB1FcKgChZos7EIx1Dhw7l5mdM3AcffICHH34Yq1evxuOPP4677rpL49u2bYtXXnlFf3hIHS+99JK+9BAtnuKNSAHAl4YarPGePDhnba4mEJu+UwwXFBTiu1U78NaMDfh6+TYUFALpNdJwbdcMXN0lA/WqppZy4Vc8Nn5NbLlYCudKX04uUTq6/p3qu5f2bHya2nLwFCaI+koBECXzBg4cWPRLTk4Opk2bBvV9gEmTJpG5qhbvVatWRcSpDwtlZGQU/aY+N3zFFVforw62bNnSqAB45plnoP6Ejr1792L8+PGl2lXxp6aWnngVMNJvsfBk530A+BGPjU9TWy6ewpnqGE999x8pxPQthZi6uQC7cgB1Jb9N7Qro1aiC/m9CjGv7VL+9ppSNXxNbLpbCudI3mu5eeXRhR/XdSxs2Pk1tOXgKE0R9hw8frh95/zUcTj8GFE7I2rVr8cADD/jyHoBbbrkFp5xyit4FkEsApVORqqy9JK+NT1NbLp7CuTqDoNrh8qle2DNz7W59bT9z8Rb9et46VSrimi6NcW3XJmhcsxLLlat4whuz8Wtiy8VSOFf6yg4AnXaUFl5yifIZRH1lB4DOlSKEq88BL126FK1atdJ+d+zYgZ49e0I9GaBuNFTvHnjzzTdL3AQ4YcIEvTNAHXIJgGLo+O/UYI3lydSWi6dwriYQqh2KxX2H8/Chfj3vBqzefkDDezSvrV/Yo17Pm5IU/fW8kXzbxhMtXhu/JrZcLIVzpa8UAFQGmz8FRGnH4TyI+koBECVX1Jv/Qkd+fj5mzpyJr7/+GrNmzaKzi0D8/ve/x/fff69v7lNnUer///CHP2gr1ZZ6+kBdalCPRKm/q8cCOYcUAByWjmE4A9rVwsJti8K5mkCodiL1W+Xpgqx9eGvGeny2cDNy8o69nlfdxa/O9lvU472eVwqAvlGT1JW+tvnNH0V8pJeco7zb+DS15eApTBD1lQIgSpadd955Rb8kJSWhRYsW+o78k046icrLE/a7FAB86qnBGsuTqS0XT+FcTSBUO8X7fujI8dfzLt507PW8HZvU0G/pu6Sd+et5pQCQAoA/SmMjTfI43JOpLQdPYVyNX5cFnhQArrIxAH6kAOCLQA3W8l4ArNqWjbEz1uPDuZuQnXsUlYpez9sErRsde4eFq8NGC5c6FfdlEhMXS+GCuEAEWWOKT5e5wWmLwgRRXykAomRJ165dS233R/o3VwPEhR8pAPgsUoM13pMHp6p3NYFE83PkaAEyl2zVC/+stbs1BS3rV9Wv51Xv5q+amswn2ABpo4VLnaQAMBDNEOqHxjY+TW05eArjavxy5gquPFIARGFKvZxn7ty5Rb+qV/aqG/GWLVvG5TbuOCkA+JRTg9XlwsJti8K5mkDC/WTtOYR3Zm3Ae7M3YueBI0hJTMBFbRtgSPem6Ny0ptPX80bileo3X9WSSBu/JrZcLIVzpa/LBcIr9+F2VN+9tGPj09SWg6cwQdRXCoCwzHvqqafw5JNP4sCBA6hatWrRr4cPH4Z6CdDIkSO95GpcbKQA4NNMDdZfewFwYe8+mLpyhz7b/3rFdhQWAhm10jC4a1NcfUZj1K5SkU+mJdJGC5c6yQ6ApZAxzP3Q2ManqS0HT2GkAPAvv5RnJ+8BUO/f37NnD2699Vb9wZ7QUa1aNdSsWdPfHlh6lwKATyA1WF0uLNy2KJyLCWTngVw89vbXmL2nIrL2HEZCBeD80+rrbf5ep9RFgvqHOB9Uv72GY+PXxJaLpXAu9A1xRbXllVOvdn7EY+PT1JaDpzBB1Fd2ALxmdADtpADgi0IN1l9TAdCnTx/MXrdHn+2rT/CqF/bUrVoRg7pkYFDXJvpVvSfysNHCpU6yA+BfFvihsY1PU1sOnsJIAeBffjnbAbj22mvxzjvv6A/zRPo0afH7Avztjrl3KQD4nFGD1eXCwm2LwplOINk5eXjsrS8xd38lrNx2/IU9bdP24C+D+yI50eyFPXx2zZBUv828HUfb+DWx5WIpnKm+qqfRbKi2vHLq1c6PeGx8mtpy8BQmiPrKDkBYRv/000/o3Lkzvvvuu4i5fs4553gdA77bSQHAp5garGW5AFi6eT/enLEen8zfhENH8lE1NUm/sEc9u69e2GPTdz7DfKRf8dj4NbHlYilcEBcIvoqxkVTfvbRj49PUloOnMEHUVwoAL5kXUBspAPjCUIO1rBUAEydloiC9HcZMX49Z6449wteucXV0qJKN+wb3QVpKYlGXbPrOZ5iP9CseG78mtlwshQviAsFXUQqAsqivFABR8lZ9pU89EfDzzz9DPQIYOtTrgIN6SAHAV4YarGWlANienYN3Z23E69+txN4j0O/hv7RdIwzt0RTtM2pEPNu36TufYT7Sr3hs/JrYcrEUTgoAfs4oJMVnvMcwFU8Q9ZUCIEqWqJf+XHDBBVCf9k1MPH72pL7WF9RDCgC+MtRgjffkwZnQQjGr9/LP27gXY6avw+eLjt3UV7Mi8NtzW+ob+4o/whepnzZ95zPMR/oVj41fE1sulsIFcYHgqyg7AGVRXykAouRtu3btsHDhQlf5Hxc/UgDwaaYGaxALgAkTM5FTvw3G/LgOC7P26RDVV/iGndkUBRsX4OKL+pUKWwqA6O/ed6UxN5conBQA/PHLKZhd6cttqyzqKwVAlCwZPHiwfiFQkyZNzLLyBKKlAOCTTw3WeE8esSaZTXsP66/wjfnhZxzIA9KSE3Flp3QM7dEMLRsce1lVtP5IASAFAH9U+Ie0GW/RorLxaWrLwVOYIBZ4UgBEya7evXtjzpw5+hJAampqEerDDz/0b5RYepYCgE8gNVhPdAGgtvl/XLNL39Q3eelWFBQC9dKA31/QSt/Rrz7FW/yQAqC0YvHSmNsOhQviAsEfUbGRVN+9tGPj09SWg6cwQdRXCoAomTd69OiIvwwbNsxLrsbFRgoAPs3UYD1RBcBZ516Aj+Zt0tv8oWf3z21ZF8PObIactXNxUb/S2/yyAxBZrXhpzG2HwgVxgeCPKCkAyqK+UgC4yvAA+JECgC8CNVjjXQCs23kQ/3z3O8zamYjsnKP62f2BnTP03fzN6lSOuc0vBYAUAOEM2OQ3fxTxkX7EY+PT1JaDpzBBLPCkAIiSwzfddFOpX2rUqKEvCQwcOJCf+TGQ27dv118YVD4//vjjIuSjjz6KN954Q///oEGD8Nhjj7HakwKARRO5mFJeqIHOnYwLCgrx3aodGD19Hb5dsUObnVq/ij7bv7xDOipXTCrhytUEYho/xYft737FY+PXxJaLpXCu9I1VENpq5dWe6rsXvzY+TW05eAoTRH2lAIiSebfccgumT5+Oa665Rr8S+P3330eHDh30ewHOPvtsPP74415ytoTNFVdcgVq1amHXrl1FBcDUqVP1h4hmzZqFpKQk9OzZE3//+9/BefxQCgC+JNRg9XMHYN/hPIybs1G/rW/9rkP6gzx9WjVAq+Tt+OM1/aJ+ftfVBGLTdz7DfKRf8dj4NbHlYimcK32lAKBzj9KCW8QXx1E+g6ivFABRckW98vfzzz9HlSpVNEJ9Hvjiiy9GZmYmzjjjDCxdupTOshiI1157DUuWLIF63FCd/Yd2AG677TZkZGTg3nvv1dYvvfSSLkTGjh1LticFAElREYAarH4UACu2ZmP0j+vw0dxNOJyXj1qVU/Rz+9d1b6o/yEPF5GoCodrhs+gG6Vc8Nn5NbLlYCudKXykA6LyktJACgOYwaAgnnwMOdapVq1alFvnQv6kPBc2bN89z/9euXYurr74a6mz/vffeK1EAXHrppVCPIKqPEqlj4sSJ+nFEhaUOKQAoho7/bjoBmFT6xbFH8wvw9DtfYMGhGpix5tgretumV9fb/Je0a4jUZP4rel0tEDZ95zPMR/oVj41fE1sulsK50lcKADr3KC2kAKA5DBrCaQGgrr1XqlQJN954o+6neiogOzsbY8aM0dvy6hHBaIe6pr9q1aqIP6vCYejQoXpR79atG0aNGuW5AHjmmWeg/oSOvXv3Yvz48aXazcnJKfEoY3FApN9i4U+E6H7EY+OTY5t9pBDTthTiu00F2JMLJFYAOtergPPSE3BSNUTc5qf8muqotCqv+kbrOzd/KS2oMRSpHcqnK31t+87lyARH9d3EVwhr49PUloOnMEHUd/jw4cjKyvJCf+BsnBYAasv/H//4B7766ivd0fPPPx8PPfQQ0tLSsGfPHtStW9cTAfv27UPz5s1RteqxF7iodg4dOqRvBFRtySWA0rSaVuscYWx8xrJdmLUXo6avw4QFW3AkvwD1q1VEt9p5eHDweahX9fj7JCLFSMXk6gyRaofDn0uMX/HY+DWx5WIpnCt9lTZUWy714/jyIx4bn6a2HDyFCaK+cg8AJ3t9xITvAHz77be6CCh+E+AjjzyCSy65hIxCLgGQFBUBqMEay1O4be7RfExatFUv/PM37tWmXZvVwtAzm6Jv6wb4+ssp6NuXfiMdFZOrCYRqh8+iG6Rf8dj4NbHlYimcK32lAKDzktIi3AMHT2GCqK8UADFyRS3C8+fP19uooWPEiBF0dhkgwgsAZap2HtS/q0M9hfDEE0+wPEoBwKJJg6jByikAtu7Lwdsz1+PtWRux80AuUpMT9ON7Q3o0RetG1Y2LDSomVxMI1Q6fRTdIv+Kx8Wtiy8VSOFf62ua3G1VLeqH67qVNG5+mthw8hQmivlIARMk89ZjfBx98gA0bNkA9ETBlyhT9dcCPPvrIS67GxUYKAD7N1GCN5km9ovfF97/Asvx6+GLxVhwtKETjmmn6hT1Xn5GBGpVSSply26JwriYQqh0+i26QfsVj49fEloulcK70lQKAzktKC9kBoDkMGsLpPQBt2rTRN/p1795d7wKsWLEC999/f8Sb7IJChBQAfCVMJ4DDR/LxyfxNGP3jeizbsl83dPYpdTCsRzOcd1o9JKqH+aMc3LYonKsFgmqHz6IbpF/x2Pg1seViKZwrfaUAoPOS0kIKAJrDoCGcFgDqWX9VAKjn9BcsWKDv2lYvAlLFQFAPKQD4ynAngI27D+kX9rw3eyPUC3yqVExClzr5eOCaXmhR79g7IqiD2xaFc7VAUO1Q/XH9u1/x2Pg1seViKZwrfaUAoDOU0kIKAJrDoCGcFgDqbX9ff/011GMS6o5/tbi+/vrrWLRoUdD6XRSPFAB8aWJNAGqbf9rqnfoVvV8t347CQuDkupX1s/tXdEzH9O++Zt3UF4qGO9lQOFcLBNUOn0U3SL/isfFrYsvFUjhX+koBQOclpYUUADSHQUM4LQAWL16Mk046ST+ip7b+1aN/Dz74oN4FCOohBQBfmUgTQHZOHj6cq7b512HNjoOoUAG44LT6uOHMZujZonbRs/t+TB6cSdvVAmEaP59Vb0i/4rHxa2LLxVI4V/pycsmbUt6tqL578Wzj09SWg6cwQdRXbgL0knkBtZECgC9M8cG4evsBvPnjOnzwUxYOHslH9bRk/Yre67s3RUatSqWcUgPdy9kDZ9J2NYGYxs9n1RvSr3hs/JrYcrEUzpW+nFzyppR3K6rvXjzb+DS15eApTBD1lQIgLPPuvPPOmLlY/M17XpLWTxspAPjsTsrMRFKTjnqbX233q+P0htVww5lNcVn7dKSlHH9Fr9cFPWRHTQxcnKsJhBsPn007pF/x2Pg1seViKZwrfaUAoPOR0sLLmKd8BlFfKQDClE5ISECXLl3Qr18/qL+HHw8//DCdXScIIQUATfyeg0fw/pyNeOWb5diVAyQlVEC/Ng309f0zmtaM+iW+4p6pge5l8uBM2q4mENP4aVbtEH7FY+PXxJaLpXCu9OXkkp1i5tZU3809unmXB7ddTvwUJoj6SgEQlgHffPONvtlvxowZ+oM9N910E04++WRunpxQnBQA0elfsnkfxkxfj4/nb0Lu0QJUSwFuOPsUXNetCepXi/2KXq8LOvfMnotzNYFQE1W8k9iveGz8mthysRTOlb5SANAZTGnhZcxTPoOorxQAUXJl//79eOedd/DGG2/o9/+HPt5Dp9aJQ0gBUJL7vPwCZC7eijE/rsPsdXv0jx2b1NA39SVtWYT+F/XzJBY10L1MHpxJ29UEYhq/J5IMjPyKx8aviS0XS+Fc6cvJJQN5nECpvntpxManqS0HT2GCqK8UADEyb+vWrboA+M9//qMLgGHDhnnJ07jZSAFwjOrt2Tl4Z+ZGvDVzPbZn5yIlKQGXtmuEYWc2RbvGNTSGGqyxRDO15eIpnKsJhGonbgn7S0N+xWPj18SWi6VwrvS1zW8/9Kf67qVNG5+mthw8hQmivlIAhGVefn4+Pv30U7z22mtYv349hgwZov80bNjQS47G1aY8FwDq2f25G/bqs/2Ji7YgL78Qjaqn4rruTfUd/bWrVCyhBTVYpQCIX+raaOFSp+K+TGLiYilcEBcIV1lA9d1LOzY+TW05eAoTRH2lAAjLvAYNGqBJkya48cYb0bNnz1J5qd4MGNSjPBYAOXn5+GzBZoz5cT0WbdqnpenRvLa+qe/C0+shKbH0jZy2Z0jUQA/PDy6ewrmaQKh24p3ffsVj49fEloulcK70tc1vP/Sn+u6lTRufprYcPIUJor5SAIRlXrNmzYruBFev/1VnlqFD/f+aNWu85GpcbMpTAbBp72GMnbEe787agD2H8pCWnIgrO6Xrhf/U+lVJvqnB6vLMktsWhXM1gVDtkOQ5BvgVj41fE1sulsK50lcKADpBKS28FPGUzyDqKwUAnStlBvFrLwBUMfbjz7v0m/qmLN2GgkKgWe1KGNKjGQZ0bqxf4MM9qMEqBQCXSXucjRYudSruyyQmLpbCBXGBsFf3mAeq717asfFpasvBU5gg6isFgJfMC6jNr7UA+OTzTOyvfTrGTF+HVdsPaPbPa1kXQ89shnNOqYuEGF/iiyYVNVhdLizctiicqwmEaife6e1XPDZ+TWy5WArnSl+/FlybvKD67sW3jU9TWw6ewgRRXykAvGReQG1+bQXA2p0H9U1978xYh5x8oGpqEq4+IwNDujdFszqVrVSgBqsUAFb0GhnbaOFSJ9kBMJLNCOyHxjY+TW05eAojBYBRyhiDnX4MyLj1ABj8GgqAgoJCfLdyB0ZNX6f/q45GlYHb+rTB5R3SUblikhOmqcHqcmHhtkXhXE0gVDtOCDZw4lc8Nn5NbLlYCudKX9kBoJOP0iLcAwdPYYKor+wA0LniHPHII4/gxRdfRHp6uvbdunVrvPXWW/rvBQUFuOOOOzBx4kR9M+Kf/vQn3H777awYynIBsO9wHsbN2Yg3Z6zH+l2HkJhQAX1a1cfQHs2wb9Vs/Wpmlwc1WKUAcMl2bF82WrjUSXYA/NPcD41tfJracvAURgoA//JLeS4zOwCqANi7dy+ee+65UoyMGTMGo0ePxuTJk7Fv3z507NhRFwOqSKCOslgALN+6Xz/C99HcTTicl49alVNwbdcMXNetKRrVSNNdpgYWxUuk3218mtpy8RTO1QRCteOFTxsbv+Kx8Wtiy8VSOFf6+jVmgqYxxafL4pDTFoUJor6yA2CT1R5tYxUA/fv31y8eGjRokPZ+zz33ICUlBY8++ijZWlkpAI7mF+i7+NXd/DPW7Nb9ate4Oob1aIb+7RoiNbnkl/iogUUSEwFg49PUlouncK4mEKodL3za2PgVj41fE1sulsK50lcKADobKS3CPXDwFCaI+koBQOeKc4QqAF599VXUqVMHtWvXxt/+9jecd955up22bdvilVdeQY8ePfT/v/TSS/rDRGpngDqCXgDsPJCL92Zv1M/vb9mXg+TECujftqF+dr9DRo2oX+KjBhbFi+wA9C1BgR98etEgZONXPDZ+TWy5WAoXxAXCRtfitlTfvbRj49PUloOnMEHUVwoAL5lH2KjFe9WqVRFR8+bNQ3Jysl741X9/+OEHXHHFFZg9ezaaNm1qVAA888wzUH9Ch7qsMH78+FLt5uTkIDU18hfvIv0WC++FrnX7C/HNpgLM2VaIo4VAjRSgV3oCzm5UAdVSKpAuXcejGrTxaWrLxVM4Ux2j9ZNqhxTEMcCveGz8mthysRTOlb62+e1YXu2O6ruXNm18mtpy8BQmiPoOHz4cWVlZXugPnE2ZuQcgnLm+ffvi5ptvxlVXXYVfyyWA3KP5+p38o6evx/yNe3WXuzarpc/2+7Suj+Qor+h1fbYeLUupaj1WdpvacvEUztUZBNVOvEe2X/HY+DWx5WIpnCt9lX5UW78GjW36aGrLwVOYIOorOwDxHgmArrgU8epQOwVnnXUWvv/+e5x66qkYNWoU3nzzzRI3AU6YMEHvDFBHEC4BbN2Xo7/C986sDdh54AhSkxP043vqbv5WjapRXYj4OzWwvDi18Wlqy8VTOFcTCNWOFz5tbPyKx8aviS0XS+Fc6SsFAJ2NlBbhHjh4ChNEfaUAoHPFOUJ9Vvinn35CUlISEhMTcd9992HAgAG6HfU1whEjRmDSpEn6mrj6u3oskHOcqAJAvaJ31trd+m7+zCVbkV9QiMY10zC0R1P94p4alVI44UfFUAPLi3Mbn6a2XDyFczWBUO144dPGxq94bPya2HKxFM6VvlIA0M/UTGEAACAASURBVNlIaSEFAM1h0BBl9hKAKyLjXQAcPpKPj+dvwujp67B8a7buxtmn1NF38593Wj39LL+Lw3Swctq08Wlqy8VTOFcLBNUOhz+XGL/isfFrYsvFUjhX+koBQGcnpYUUADSHQUNIAdC4ccQbOkwnFmpwbNh1CG/OWKfv6N+fcxRVKibpj/Fc370pWtSr4jwvqHi8NGjj09SWi6dwpjpGWwiodrzwaWPjVzw2fk1suVgK50pfKQDobKS0kAKA5jBoCCkAfCwA1Ct6p63eqc/2v16xHeorySfXraxv6ruyU2NdBPh1mA5WThw2Pk1tuXgK52qBoNrh8OcS41c8Nn5NbLlYCudKXykA6OyktJACgOYwaAgpAHwoALJz8jD+pyx9fX/NzoOoUAG48PT6epu/Z4vaUZ/dd5kcpoOV07aNT1NbLp7CuVogqHY4/LnE+BWPjV8TWy6WwrnSVwoAOjspLaQAoDkMGkIKAIcFwMkdz9SP8H04NwsHj+SjeloyBnXJ0Nv8GbUqxVV708HKCc7Gp6ktF0/hXC0QVDsc/lxi/IrHxq+JLRdL4VzpKwUAnZ2UFlIA0BwGDSEFgGUBoO7mV6/ofWbCXCzfU6j1Pb1hNdxwZlNc1j4daSklX9EbrwQwHaycuGx8mtpy8RTO1QJBtcPhzyXGr3hs/JrYcrEUzpW+UgDQ2UlpIQUAzWHQEFIAOCgALvr391i5LRsXt22IG85shs5Na8Zlmz9WMpkOVk5i2vg0teXiKZyrBYJqh8OfS4xf8dj4NbHlYimcK32lAKCzk9JCCgCaw6AhpACwLACUoAuz9mLZ3Bm45jK3n9+1SRbTwcppy8anqS0XT+FcLRBUOxz+XGL8isfGr4ktF0vhXOkrBQCdnZQWUgDQHAYNIQWAgwJAJg86rf2YPDi8u1ogTOOnGbFD+BWPjV8TWy6WwrnSl5NLdoqZW1N9N/do97pj03g4eAoTRH3lTYBeMi+gNvF+EVC8aKAGlpc4bHya2nLxFM7VBEK144VPGxu/4rHxa2LLxVI4V/pKAUBnI6WF7ADQHAYNITsAsgPAzknTCaC4Y1NbLp7CuVogqHbYJDoC+hWPjV8TWy6WwrnSVwoAOjEpLaQAoDkMGkIKACkA2DlpOgFIAcCm1hhoo0Wsxmz8mthysRROCgCz1KH4dJkbnLYoTBD1lUsAZjkXaHTFihVRt27dUjEePnwYaWlpEWOP9NuBAwdQpYr7V/p6JS9W/CfCp2k8XDyFM9VRcVNe9Y3Wd26+UFoU98PFUjhX+qrYZAzHVprSItyag6cwQdR3x44dyM3N5Q6LQOPK/Q5ANHWuvvpqvP/++xF/jvRb0KrCWPF7zUgbn6a2XDyFM9VRcVNe9Y3Wd26+UFoU98PFUjhX+qrYZAzHVprSItyag6cwv2Z9uePKT5wUAFHYHTduHAYOHBjx10i/BW3yiBW/14Sy8Wlqy8VTOFMdFTflVd9ofefmC6VFcT9cLIVzpW8QCwCq71xdvPAeybdpPBw8hfk16+tFP9c2UgA4YjRoBYCjbombXxgQfX/9qSAa/7o1Fn1L6ysFgKOcf+aZZ3DnnXc68iZugsaA6Bs0RdzHIxq75zRIHkVfKQCClI8SizAgDAgDwoAwcMIYkB2AE0a9NCwMCAPCgDAgDJw4BqQAOHHcS8vCgDAgDAgDwsAJY0AKgBNGvTQsDAgDwoAwIAycOAakADhx3EvLwoAwIAwIA8LACWNACgCfqf/jH/+I+fPn46KLLsL999/vc2viPp4M7N69G71798aKFSv0W+Tk+HUxMG3aNNx9991ISEjAVVddhbvuuuvX1UHpDWbNmoU///nPKCwsxPnnn49HH320XLEiBYCPcs+ZMwdvvfUWnn32WQwYMAAvvvgi6tev72OL4jqeDOTl5SE7O1u/OfDLL7+MZ9PSVhwY2Lx5M+rUqYOUlBScd955+Pzzz1GpUqU4tCxNxIsBNYaTk5N1cxdccAE++ugjVKtWLV7Nn/B2pADwUQK14Ddp0gSXXnopnn/+eZx88sm4+OKLfWxRXJ8IBi688EIpAE4E8XFsU2k8YcIEpKamxrFVaSpeDOTn5+OGG27AG2+8gaSkpHg1e8LbkQKAKcGIESPw6aefYv369Zg3bx46dOhQZLlq1SoMGzYMO3fuRPXq1TFq1Ci0bt0ajz32GM466yycc845+t/UmcTgwYOZLQosngx40TcUnxQA8VTKW1s2+k6ZMgUffPABRo4c6a1xsYoLA141fvvtt/HII4+gb9+++kStPB1SADDVnjp1Kpo3b64X9I8//rhEAaCuHQ0dOlRXkGqieOqppzB79my95S87AEyCTzDMi75SAJxg0Qya96pvVlaWHtuq+A/S1z4Nul5uoF41VgQVFBTob7+oQqBt27blhjMpAAylbtasWYkCYPv27WjRogXUDWFq60jdTNKwYUOoG4j27NmDd955B+oVlCq5XnjhBbkHwJDveMNN9FW6q0N2AOKtkvf2TPTNyMhA//79dSHfsmVL742KZVwZMNVYfRJeHeoE7oEHHsApp5wS13hPZGNSABiyH55cP/30k97WV3eCh46uXbviySef1HeV3nbbbVi4cKHeXnrwwQcNWxN4vBkw1Vct/uqSUMeOHfHcc8+hTZs28Q5Z2jNgwERfdblPLQinnnqqbkHd0Juenm7QmkBPBAMmGu/atUsXeGoHoFevXvIUwIkQrCy1aZJcqgCQo2wxIPqWLb1MoxV9TRkre3jRmK+Z7ADwudJIk+2l0BaxYRMCP4EMiL4nkPw4NC36xoHkE9yEaMwXQAoAPlcRCwD1j+eee66+fhS6CVBt/6t3AMhR9hgInzxE37KnYayIRd9fl56ReiMa8zWWAoDJ1e9//3v9IpCtW7eidu3aqFq1KlavXq2t1fV/tfir60nqJRLqWdLydCcpk8JAw0TfQMtjHZzoa01h4B2IxuYSSQFgzplYCAPCgDAgDAgDZZ4BKQDKvITSAWFAGBAGhAFhwJwBKQDMORMLYUAYEAaEAWGgzDMgBUCZl1A6IAwIA8KAMCAMmDMgBYA5Z2IhDAgDwoAwIAyUeQakACjzEkoHhAFhQBgQBoQBcwbKfQGg3gNdt27dUszl5uYi9I7o8B8j/RYLby6LvYUf8dj4NLXl4imcqY6K+fKqb7S+c7OR0qK4Hy6WwrnS17bvXI5McFTfTXyFsDY+TW05eAoTRH137Nih54hfw1HuC4DGjRtDffEr/Pjiiy/0+/sjHZF+i4U/EYniRzw2Pk1tuXgKZ6qj0qq86hut79z8pbQo7oeLpXCu9LXtO5cjExzVdxNfIayNT1NbDp7CBFHfaGuGFz1OtI0UAFIAsHOQGqyxHJnacvEUztUEQrXDJtER0K94bPya2HKxFM6VvlIA0IlJaWFyEsUtSIKorxQAdK6UGYTsAPClMp0AvJzxcScGLs7VBGLTdz7DfKRf8dj4NbHlYimcK32lAKBzj9JCCgCaw6AhZAdAdgDYOWk6AUgBwKbWGGijhcudGq8ac+OncFIAmKUOxafL3OC0RWGCqK/sAJjlXKDRsgPAl4carPGePDhnba4mEJu+8xnmI/2Kx8aviS0XS+Fc6cvJJb46bpBU3720YuPT1JaDpzAu9M0vKETm4q3YvHIBfjegnxfaSthIAWBN4Yl3MG7cOKg/mZmZ+r/hR05ODlJTUyMGGum3WPgT0Vs/4rHxaWrLxVM4Ux2VVuVV32h95+YvpUVxP1wshXOlr23fuRyZ4Ki+m/gKYW18mtpy8BTGRt8j+YX4cWshpmwswI7DQLtaBbitfYoX2krYDB8+POKN49aOT4ADuQQglwDYaUdV67IDwKbSGmijhUudivsyiYmLpXAuzhBDfaDashbN0IEf8dj4NLXl4CmMF30//jwTmyq1wBs/rMXOA0dQPS0Zw3o0RZPctRhwqewAFE9DKQCkAGBPS9RgdbmwcNuicF4mkEg2VDtsEh0B/YrHxq+JLRdL4Vzpq2Sh2nIkHduNH/HY+DS15eApjIm+B3KPYvT0dXjpqxU4eBRIr5GG4WedhGu6ZKByxSRn+solAHYKBx8o9wDwNaIGqxQAfC5tkTZauNRJdgBslYxu74fGNj5NbTl4CsMpAEIL/6vfr8HeQ3mokwrc078druiUjuTEhCKCqba4SkoBwGWqDOCkAOCLZDOATG25eArHmUDCGZAdgMgvwKIyhdLCS7FA+XSlr+wAUOqa75BQ2nE4j+Xjs4mZ2FT5FIz87mfsOZSHxjXT8MfzW6DqzqW4+KLSW/2ceGgWACkAOCyVEYwUAHyhbAaQqS0XT+FcLRBUO3wW3SD9isfGr4ktF0vhXOnLWYzcKMf3QvWd7+k40sanqS0HT2Ei/a7u6v9o3iY89ukC7Mk9ttV/+/ktcFWnxkhJSoi61U+1xeVTCgAuU2UAJwUAXySbAWRqy8VTOFcLBNUOn0U3SL/isfFrYsvFUjhX+koBQOclpQVnJ80UU7zNwsJCTF21E09MXIblW7NROQm4s18rXN+9CSomJZJb/abxR2NECgA6V8oMQgoAvlQ2A8jUlouncK4WCKodPotukH7FY+PXxJaLpXCu9JUCgM5LSgvTxZ3DeajNFVuz8c8JSzFt9U5UTErATWedhFOPrsMVl/C3+k3jlwKAzokyj5ACgC+hzQAyteXiKZyrBYJqh8+iG6Rf8dj4NbHlYimcK305i5Eb5fheqL7zPZXdSwDqkb6FBU0w+sd1KCgsxJUdG+OuPqeiUY00461+V3zKDoCXzAuojRQAfGFsBpCpLRdP4VwtEFQ7fBbdIP2Kx8aviS0XS+Fc6SsFAJ2XlBYudwAKCgoxfm4W/vHJQmTnAR0yauAfv2mNdo1reN7qN41fdgDonCjzCCkA+BLaDCBTWy6ewrlaIKh2+Cy6QfoVj41fE1sulsK50lcKADovKS1cFQCLN+3D3z5ZjHkb9qJqMvDQb9rpG/wSEiqUaCJaPKb/Tve8JEJ2AEwZCzBeCgC+OKYTQHHPprZcPIVztUBQ7fBZdIP0Kx4bvya2XCyFc6WvFAB0XlJa2BYAh4/k49kvV+K1aWu1q6E9mqJdhY0Rr/PH0ksKAFrLEELeBChvAmRni+kEIAUAm1pjoI0WsRqz8Wtiy8VSOCkAzFKH4tNlbnDaCmGmrdqJ+z9ahA27D6F94+p44sp2aNWoWsy395ku9Jx4OGzKDgCHpTKCkR0AvlA2A8jUlouncK4WCKodPotukH7FY+PXxJaLpXCu9JUdADovKS287ACMn5CJ6Yca6uv9acmJuLtvS9xwZjMk/rLdH0R9pQCgc6XMIKQA4EtlOgHIDgCfW1OkjRYuz/K8asyNn8IFcYEw1TIanuq7l3ZsfJraUvgvlmzF3e/+pG/yO+fUunj08jbIqFWpRLeCqK8UAF4yL6A2UgDwhaEGtMuFhdsWhXM1gVDt8Fl0g/QrHhu/JrZcLIVzpa/sANB5SWnB3QHYn5OHv3+6VJ/1V0oCHr+qA37ToREqVCh5kx+lSbR4TP+d7nlJhBQApowFGC8FAF8c0wnA69khNfBN/LpaIGz6zmeYj/QrHhu/JrZcLIVzpa9JzvFVskNSfffi3canqW0k/PTVO3H3uAXYvC9Hn/VfXGc3rrks+id6g6ivFABeMi+gNlIA8IUxnQBMFmru2YMpztUEYtN3PsN8pF/x2Pg1seViKZwrfaUAoHOP0iLW2FR3+D+VuRyjpq9DpZREPNi/Fa7tmoHJkyejb9/oH58Kor5SANC5UmYQUgDwpTKdAKQA4HNrirTRIlZbNn5NbLlYChfEBcJUy2h4qu9e2rHxaWobwi/bsh9/fGceVm8/gC7NauJfA9ujae3KOnzKZxD1lQLAS+YF1EYKAL4w1GB1ubBw26JwriYQqh0+i26QfsVj49fEloulcK705SxGbpTje6H6zvd0HGnj09Q2MzMTO2qcrt/hr97sd1eflri5V/OiO/w5nAdRXykAvGReQG2kAOALYzoByA4An1tTpI0WLgs1rxpz46dwQVwgTLX8Ne4A7DuUhxv+bwrm7ShERq00PH9tJ/063/CjLOorBYCrDA+AHykA+CJQg9XlwsJti8K5WiCodvgsukH6FY+NXxNbLpbCudKXczbqRjm+F6rvfE/x3QH4af1ujHhnPjbtPYz+7RriiSvbolpqcsRwqT4GUV8pALxkXkBtpADgC0MNVikA+FzaIm20cKmT7ADYKhnd3g+NbXxStoWFhRg5dQ2e/mIFkhMrYGBz4B839Iv4eF+o15RPKQD8yy/lWV4FLK8CZmcYNVhdLizctiicqwmEaodNoiOgX/HY+DWx5WIpnCt9ZQeATsxYXGfn5OnH+75Ysg2n1KuCF6/rhLXzp8e8w5/DeRD1lR0AOlfKDEJ2APhSUZOxFAB8Lm2RNlq41El2AGyVLPs7ACu3ZeOWN3/Cmp0HcWn7RnjyyraoXDGJvMNfCgD/cofrudzuAIwbNw7qj7pTVf03/MjJyUFqampEHiP9FgvPFcMlzo94bHya2nLxFM5UR6VBedU3Wt+5eUlpUdwPF0vhXOlr23cuRyY4qu8mvkJYG5+RbGdvK8CY5QU4WggMbJGA89IrFG35c9qiMEHUd/jw4cjKyvJCf+Bsym0BEFJCdgD4OWlz1mlqy8VTOFdbiFQ7fBbdIP2Kx8aviS0XS+Fc6cs5G3WjHN8L1Xe+p+NIG5/FbfPyC/DExOV4/Ye1qFu1Il66rhO6NKtVIiROWxQmiPrKJQAvmRdQGykA+MJQgzWWJ1NbLp7CuZpAqHb4LLpB+hWPjV8TWy6WwrnSVwoAOi9DXO86kItb35qLWWt3o2uzWnhhcEfUq1Z6t5TSjsN5EPWVAoDOlTKDkAKALxVnQEfzZmrLxVM4VxMI1Q6fRTdIv+Kx8Wtiy8VSOFf6chYjN8rxvVB953tytwPQpF0P/Hb0HP2In/ps7wP9T0dyYkLEUDjxU5gg6isFgJfMC6iNFAB8YajBKjsAfC5tkTZauNSpuC+TmLhYChfEBcJW25A91Xcv7dj4fGrsJIxeWQFq+/+xy9vi6i4ZMUPgtEVhgqivFAARZM/Ozsbbb7+Nr776St8gkZaWhvbt22PAgAE488wzveRqXGykAODTTA1WlwsLty0K52oCodrhs+gG6Vc8Nn5NbLlYCudKX9kBiJ6X6vn+F79ZjX9NXonalVPw8pDOpa73R7KmtONwHkR9pQAIU/uJJ57Aa6+9pp/57N69Oxo0aKDvpF62bJm+yz4/Px8jR47Eaaed5mb2c+hFCgA+mZwBHc2bqS0XT+FcTSBUO3wW3SD9isfGr4ktF0vhXOnLWYzcKMf3QvWd7+k40tSn+orfPeMX4rMFm9G4MvDu7eehcc1KrKY5bVGYIOorBUCY/C+++CJuvvlmJCdHft2jKgTUrkDv3r1ZiRNPkBQAfLapwSo7AHwubZE2WrjUqbgvk5i4WAoXxAXCVtuQPdV3L+2Y+Ny6Lwe/GzMHizbtQ9/W9XFxrZ34Tf9+7GY5bVGYIOorBQA7BYIPlAKArxE1WF0uLNy2KJyrCYRqh8+iG6Rf8dj4NbHlYimcK31lB6BkXi7fuh83vD4bW/fnYMT5LfCnC0/FlCmTyTf7mRaEZVFfKQCizGFjxowp9UuNGjXQuXNnpKenu5n5HHuRAoBPKDVYpQDgc2mLtNHCpU6mE77p2S3VTykAzDKJ4lN5m756J37/5k/IOZqPpwe0x+Udj83dHFvTfKB8BlFfKQCi5Jy6B2Dq1Kk466yz9Nugpk2bhq5du2LlypV47rnncPXVV5tlaxzQUgDwSaYGq8uFhdsWhXM1gVDt8Fl0g/QrHhu/JrZcLIVzpa+XBc6NktG9UH330j7l85P5m/Q7/VOTEjFySGec2aJOUTOUbXg8HDyFCaK+UgBEybyrrroKjz32WNHNfitWrMB9992HZ555BpdddhkWLlzoJWd9tZECgE8vNVilAOBzaYu00cKlTqZnfLIDwFfeD42j+VR3+r/83Ro8lbkcDaqlYtRNXXBag2olgjWNh4OnMFIA8PPFC9Lpq4Dbtm2LRYsWlYijXbt2euHv0KED5s+f7yVGX22kAODTSw1WlwsLty0K52oCodrhs+gG6Vc8Nn5NbLlYCudK3/K8A5BfUIhHPl2CN2esR8v6VfXi37B6WqlEpbSQHQA3YzueXpwWAOp5/1tuuQVDhgzRfXjzzTfx8ssvY/r06VIAxFNVD9frOOGZTgBezw5NJmMqJlcLBNUOhz+XGL/isfFrYsvFUjhX+prknEsdXRbNnLjC+VKP+Y14dx6mLN2GHs1rY+TQzqiWGvlpLkoLKQA4CgQL47QAUFv+avFXZ/rqHgD1IqDRo0ejSZMmugiQxwDjJ77pYOVEZuPT1JaLp3CuFgiqHQ5/LjF+xWPj18SWi6VwrvQtjwWAeqf/8NFzMH/jXlzeoRH+Z0B7pCRFfq2vF34o7Tg+g6iv3ANAzGTqrYDqqFq1qss5zxdfcgmATytnQEfzZmrLxVM4VxMI1Q6fRTdIv+Kx8Wtiy8VSOFf6chYjN8rxvVB953s6jgz5XL/rIIa9Pgvrdh3CreeejL/0aYmEhAoxXZrGw8FTmCDqKwVAlDQ5evQo/v3vf+Pnn3/GSy+9pP+7fv16nH/++V5yNS42UgDwaaYGq8vtTG5bFM7VBEK1w2fRDdKveGz8mthysRTOlb7lqQCo36obho+ajT2HjuDvl7XGkB7NWElJaRHuhIOnMEHUVwqAKOmirv+r1/6qx//U2//27t2LCy+8EHPmzGEl2IkASQHAZ50arFIA8Lm0Rdpo4VKn4r5MYuJiKVwQFwhbbUP2VN+9tPP0W5Pw2vJjlv8Z1BF9WjdguzGNh4OnMEHUVwqAKCkTutO/Y8eOmDdvnkap+wAWLFjATrJ4A6UA4DNODVaXCwu3LQrnagKh2uGz6AbpVzw2fk1suVgK50rf8rAD8NbM9Xjwo8WoWTkF/x12Bjo1qWmUjJQWsgNgRGcgwE5vAlQfApoxYwZCBYDaDVBFQfijgYHo+S9BSAHAV8N0AvB6dmgyGVMxuVogqHb4LLpB+hWPjV8TWy6WwrnS1yTn3ChIe6H6Tns4hlDP+P/v5JV44ZvVqJsGvH/buTipTmWueRHONB4OnsIEUV/ZAYiSOuqDQL169cLTTz+N8ePH46mnnkJqaiqef/5542SLZrB9+3ao9w306NEDH3/8sYaNGjUKd9xxB0466ST9/zVr1sQ333zDalMKABZNGkQN1lieTG25eArnagKh2uGz6AbpVzw2fk1suVgK50pf2/x2o2pJL1TfOW0eOVqAez9ciA/nbkL7jBq4vkk2Bl7K/6CP30U81ccg6isFQJTMO3DgAO66666ihfnyyy/XbwGsXNm82oyW3FdccQVq1aqFXbt2lSgAVDEQKgg4AyOEkQKAzxY1WKUA4HNpi7TRwqVOXhcIbvwULogLhK22IXuq71Q72Tl5uHXsXExbvRMXnl4P/7m2I77/5iujD/p41ZdbUFF9DKK+UgBQmefT76+99hqWLFkC9XbB4gu+2gGQAsD92UO4jNRgdbmwcNuicK4mEKodn1I+qlu/4rHxa2LLxVI4V/pyF6x46kz1PVYs6lO+N7wxC8u3ZuO6bk303f5JiQmB28Wj+hhEfaUACMu8SF8BLA4ZOnSo9bhZu3at/piQ+tjQe++9V6oAuPvuu6GEqVSpEv785z9j4MCBrDZlB4BFkwZRg1UKAD6XtkgbLVzq5PUMkRs/hQviAmGrre0OwMpt2bjh9VnYvC8H9/RriVvPOVm/mC2IY7gs6isFQFiGhxbb/fv347vvvivxNcBzzjkHmZmZ5JhQ1/RXrVoVEaeeKFBFxJNPPolu3brpa/7Fz/h37typF371Rz1+2KdPH4wbNw7qpsTwQ12SUH9Ch3pUUd2vEH7k5OTo+xciHZF+i4UnO+8DwI94bHya2nLxFM5URyVFedU3Wt+56UlpUdwPF0vhXOlr23cuRyY4qu+RfK3YU4D/W1yAI/nA0NMS0L1ByTf7efEZasfUloOnMEHUd/jw4cjKyjKRMrBYp08BqOvz//znP9GmTRvdYbVd/9BDD0VcYE0Y2bdvH5o3b170ZkF1r8GhQ4f0jYBfffVVKVe///3vceqpp+r7EahDdgAoho7/TlXrsTyZ2nLxFM7VGSLVDp9FN0i/4rHxa2LLxVI4V/oqVai23CjH92Iaz6cLNuPu9xegYlICXh7SGT2Lfco31Kqpz+LRmtpy8BQmiPrKDkCUHI71NUB+2tPI8B2ATZs2IT09XRtu27ZN70CMHDmS9QZCKQBovoM6eXAmbVcTCDVR8Vl0g/QrHhu/JrZcLIVzpS8nl9wox/dC9T3kST3m9+r3a/D4xGOf8n3jxi44vWHJT/kGdQxTfQyivlIARMnhnj174ne/+x2GDRumEepDQK+88or+EJDLI7wAuP/++/HJJ58gOTkZBQUF+ouEf/jDH1hNSgHAokmDqMEqOwB8Lm2RNlq41MnrGSI3fgoXxAXCVluTxVp9yvefE5Zi1PR1OLV+FYy6sSsa1Sj9KV8Tn9Hip7QIt+PgKUwQ9ZUCIEqGhL4GGHrzn3ohkCoCWrZs6WpMOPcjBQCfUmqwulxYuG1ROFcTCNUOn0U3SL/isfFrYsvFUjhX+toWuG5ULemF6ntOXj7+9O58ZC7Ziu7Na2HkkDNQPS3yp3ylALA7gSmujBQARLbL1wD9mA7MfFKTh5m3Y2gbn6a2XDyFc7VAUO144dPGxq94bPya2HKxFM6Vvrb5baOllzPuPQeP4Ldj5uCn9XtwWftGeHpgO1RMSiTDoPiMdxFPxRNEfaUACMuSjRs3IiMjI2bybd68GY0aNSITNN4A2QHgM04N1nhPHpxJ29UEYtN3PsN8pF/x2Pg1seViKZwrfTm5xFfH+6Z/GAAAIABJREFUDTJa3zbsOqSf8V+z8yBuOedk3NOX/pSv7ADYncDIDkCMnFav/1Xb/IMHD9aP6anH8dSxZs0a/Qigek/AX//6V6inBIJ2SAHAV4SajKUA4HNpi7TRwqVOxX2ZxMTFUrjyVgDM37i36FO+D1/aGsPO5H3KVwoAKQAijXtnjwGq5+7Vnfc//PADkpKSkJubq+/MHzBgAEaMGEHuENhOiF7tpQDgM0dNxi4XFm5bFM7VAkG1w2fRDdKveGz8mthysRTOlb5lYQdg8pKtGPHusa+sPn9tJ/RuVd84mSg+4z2GqXiCqK9cAoiRJUePHkXoxTzVqkV+FMU4a300kAKATy41WOM9eXAmbVcTiE3f+QzzkX7FY+PXxJaLpXCu9OXkEl8dN8jifRs9fR0e+WwJautP+XZBh4wanhqh+Iz3GKbiCaK+UgB4Sr1gGkkBwNeFGqzxnjw4k7arCcSm73yG+Ui/4rHxa2LLxVI4V/pycomvjhuk6lvv3n3wxKRlePX7tWhep7J+zK9J7WOXWL0cFJ/xHsNUPEHUVwoAL5kXUBspAPjCUIM13pMHZ9J2NYHY9J3PMB/pVzw2fk1suVgK50pfTi7x1XGD/GxiJibtqYuJi7aiS7OaeGXIGahZOcXKOcVnvMcwFU8Q9ZUCwCoFg2UsBQBfD2qwxnvy4EzariYQm77zGeYj/YrHxq+JLRdL4Vzpy8klvjr2yN0Hj2Dgv6fg5/1A/3YN8b8D2yM1mX7Mj2qZ4jPeY5iKJ4j6SgFAZVkZ+l0KAL5Y1GCN9+TBmbRdTSA2feczzEf6FY+NXxNbLpbCudKXk0t8deyQ63YexI2jZmPtzoO4uVdz3NvvNCQkHPuan+1B8RnvMUzFE0R9pQCIkiXqoz0PPPAA1q1bhwkTJmDp0qVQbwW89tprbfPWN3spAPjUUoM13pMHZ9J2NYHY9J3PMB/pVzw2fk1suVgK50pfTi7x1fGOnLFmF24Z+xP2H87D1S0S8OTwi7w7i2BJ8RnvMUzFE0R9pQCIkiWDBg3SXwJ89913sXjxYhw+fFh/sW/+/PlOk9ilMykA+GxSgzXekwdn0nY1gdj0nc8wH+lXPDZ+TWy5WArnSl9OLvHV8YZ8f/ZGPPDxIqQkJuD5wR2Rt34++vbt681ZFCuKz3iPYSqeIOorBUCULOnUqRPmzp0L9Q2AefOOPa/avn17vQsQ1EMKAL4y1GCN9+TBmbRdTSA2feczzEf6FY+NXxNbLpbCudKXk0t8dcyQ6oM+T2UuxytT1yC9Rhpev6ELWjaoavXq7WgRUHzGewxT8QRRXykAomRJ9+7dMWPGjKICQO0AqDcDLly40GxExBEtBQCfbGqwxnvy4EzariYQm77zGeYj/YrHxq+JLRdL4Vzpy8klvjp85IHco/jTu/Pw5bLt6Ny0JkYO6Yw6VSpqB1Tf+a0cR9r4NLXl4ClMEPWVAiBK5t13332oUqUKxo4di+effx7PPvssunTpgkceecRLrsbFRgoAPs3UYJUCgM+lLdJGC5c6FfdlEhMXS+GCuEBwtc3acwi/HT0Hy7dm48qO6Xj8yrYl7vSn+s5tx6tG4f5N4+HgKUwQ9ZUCIErmqbcAPv300/j4449RWFiIyy+/XH8DIDHR/vEVL8nOsZECgMPSMQw1WF0uLNy2KJyrCYRqh8+iG6Rf8dj4NbHlYimcK31t89tU1Tnrduub/XYeOIK/9G2JP5x7MipUKHmnP9V30zZt+2gaDwdPYYKorxQAXjIvoDZSAPCFoQarFAB8Lm2RNlq41Mnr2SU3fgoXxAUiFr/qxOitmRvw98+WICkhAc9e0x792jSMaEL13UsO2fg0teXgKUwQ9ZUCIErmPfTQQ7jzzjtRvXp1XHLJJZg5c6b+QNBVV13lJVfjYiMFAJ9marC6XFi4bVE4VxMI1Q6fRTdIv+Kx8Wtiy8VSOFf62p4dc1TNycvHw58swXtzNqJJrUr6ev/pDaN/L4XqO6fNcIyNT1NbDp7CBFFfKQCiZF7ojv8pU6bghRdewP/8z/9APRoYeiLAS8L6bSMFAJ9harBKAcDn0hZpo4VLnWQHgKfkln2HccvYuViwcS96nVoX/xnUATUqxX6trx8a2/g0teXgKYwUALz88opy9jlgFUDo8b8HH3wQp512Gq6//nqEHg30GqDfdlIA8BmmBqvLhYXbFoVzNYFQ7fBZdIP0Kx4bvya2XCyFc6WvnzsAs9buxh/eOna9X13rv6tPSyQy3uxH9d1LJtn4NLXl4ClMEPWVHYAomXfWWWfh0ksvxauvvooffvgB9erVQ9u2bfVLgYJ6SAHAV4YarFIA8Lm0Rdpo4VIn2QGIzqa63j9q+jo89vkypCQl4F8D2+PitpGv90fy4ofGNj5NbTl4CiMFgO1MEdve6Q7A6tWr9dZ/r169cOWVV0L9/wcffIB7773X31548D5u3DioP5mZmfq/4UdOTg5SU1Mjeo70Wyy8h/CsTfyIx8anqS0XT+FMdVTEl1d9o/Wdm4yUFsX9cLEUzpW+tn0P5+hQXiHGrCjAvB2FqJcG3NImEelVzN7nT/Wdq4sX3iP5No2Hg6cwQdR3+PDhyMrK8kJ/4GycFgCB6x0jINkBYJD0C4Sq1l2eWXLbonCuziCodvgsukH6FY+NXxNbLpbCudJXqUK1xVVuUdY+3Pb2XGzYfUh/ye/JK9uiamoy17wI5yqe4g3b+DS15eApTBD1lUsAUVJZvflPvQBIvftfVW6h48MPPzRO/ngZSAHAZ5oarFIA8Lm0Rdpo4VInr4sLN34KF6QFQm35vzljPR6dsEzT8rdLTsf13ZuWer6fqz3Vd64frxqF+zeNh4OnMEHSN8SHFABRMk/d9FetWjVMnDgRd911F0aNGqUvB6g3Agb1kAKArww1WF0uLNy2KJyrCYRqh8+iG6Rf8dj4NbHlYimcK31tdwD25+Thvg8X4fOFW/Qjfi8O7oS2jatbiU313YtzG5+mthw8hQmKvsW5lgIgSuapG/4WLVqEdu3a6ff/Z2dno3///pg6daqXXI2LjRQAfJqpwSoFAJ9LW6SNFi518np2yY2fwgVhgVB3+f/5vfnYtPcw+v1/e1cCHFWVtU/2BELYwk4iBAQEhKDIJrIIDCBQjoqguKAyM4KopaOOis4MshRY5TY6ojgz4pQKfyFu/AgIKKv6CyKICLLKEtnCEiBk63Ty17lJx6Tpzjn33dudF3JeVao73d85597vu++d8+67/V6npvD86C5QN0F/yt/0jJszpig+bY4NTiwK4wZ9/TmRAiDIKOnRowds3LhR/fRv/fr1ULt2bbjiiitg586SKTE3blIA8FWhdtZwHzw4Z222DiAmfeczzEeGqj0mfnVsuVgKZ0tfzljyV8fjLYJXVu2GN9bsU6v8nxnREe7smep4yl8KgIvHf1XqG2xvlAIgCDPDhg2DBQsWwIsvvghr1qyB5ORkuHDhAuCNgdy6SQHAV4Y6GEsBwOfSFGmihU2dauoMwP7MbHXW/0PGWejcIgleGdsN2jZONJW1gn0oNDbxqWvLwVMYKQCsDqmLnFn9FYDX61UP/lH3u37/fcjKyoK7775brQtw6yYFAF8Zame1mVi4sSicrQMIFYfPoh1kqNpj4lfHloulcLb05c4A4LHtfzYdhmn/uwPyCr0wqX8beGRwOzUDYHuj+u4knolPXVsOnsKEW18OpzIDwGGpmmCkAOALRe2sUgDwuTRFmmhhU6eaNAOA1/hxod+63ZnQol4CvDSmK/RMa2gqZVD7UGhs4lPXloOnMFIAhGx4KcdWZwC+//57mDJlCuzfvx/w0cC+Df936yYFAF8Zame1mVi4sSicrQMIFYfPoh1kqNpj4lfHloulcLb0rWwGoKioGBZsOgSzlv4M2fmFMKZ7S3W938ZCP5v7DGdkUXzabA8nFoUJh74c3spjZAYgCGP4K4AHH3wQevfurS4F+LZOnTrpchw2vBQAfKqpnTXcB4/KDtq+ttg6gJj0nc8wHxmq9pj41bHlYimcLX2DjaVDp3LgyQ+3wTf7T0HzuvEw65Yu0L9dI75QBkiq705cm/jUteXgKUyo9XXCoRQAQVhLT09XNwGqTpsUAHy1qJ1VCgA+l6ZIEy1s6lTel06buFgKF6oEUegtUvfxf3HFbsj1eGFcz1R4engHR3f0c6o11Xcnfk186tpy8BQmVPo64c5nIwVAEPYmT54M9957L3Tv3t2E37DaSgHAp5vaWW0mFm4sCmfrAELF4bNoBxmq9pj41bHlYimcLX3LzwBsPngGnv1kO+w8eg5SGiTA7Ju7wLVtk+0Ip+GF6ruGqzKoiU9dWw6ewoRCXye8lbeRAsCPQXwMcEREBHg8Hti1axe0bdu2woN0cG2AWzcpAPjKUDurFAB8Lk2RJlrY1OlSmgH4cMly2JTfXK3yj4mKgPv7tYHJA9tCQuxvlzNNddOxD4XGJj51bTl4CiMFgM6I0cdaWQS4du3aSiP3799fv2VhspACgE80tbPaTCzcWBTO1gGEisNn0Q4yVO0x8atjy8VSOBv64iK/RZszYNribZDtAeid1hCm/76z9d/16ypP9V3XX/lZjnDYctpPYWzo6+srFYvLicwABGBq+/btsHv3bujatSu0adOGy2WV46QA4EtgsgPp2nLxFM7WAYSKw2fRDjJU7THxq2PLxVI4U32/3X8Kpn+2A7b/eg7qxABMvzkdbkxvbu1ufiZqU3134tvEp64tB09hTPV1OkNVGbdSAPixM2fOHPXzv/bt26tLAPPmzYObbrrJyfgMu40UAHzKqZ1VZgD4XJoiTbSwqZPTAyy3/RTOaYLo0L2v+lnf8p+Oqen+e/q0go7Fh+CmkcNMpbFmT/XdSSATn7q2HDyFcarv0KFDL6KHisXlUwoAP6Y6d+6sngCYmpqqHgY0adIk2LBhA5fPKsVJAcCn32QH0rXl4imcrQMIFYfPoh1kqNpj4lfHloulcLr6ns3xwBPzVsKaIxFQ4C2CoZ2awNPDr4BWybWBimVHOb6XULTHxKeuLQdPYXT1RXaD2VCxuMpIAeDHFC4C3LJlS9mn+DAgNy/8K998KQC4wz74jsXxoLvzcfEUztYBhIrD4cAmJlTtMfGrY8vFUjiuvjkFhTDvqwMwd+0+OJdXCJ2aJ8GzIzpC7za/3cmPimVTP46vULTHxKeuLQdPYbj6cmaiqFgcTRAjBYAfU/jEv4ULF6pnAOA2duzYCv/j44HdukkBwFfGZAfSteXiKZytAwgVh8+iHWSo2mPiV8eWi6VwlL4DBg2G+d8egtdX74WT2QXqZ32DmuTDX+8aBlGRERXEoGLZUY7vJRTtMfGpa8vBUxhKX52pfioWVxkpAPyYatWqVdBFM/jzQLkVMHdo2cPZGuycyprTat32cPEUztYBhIrD4cAmJlTtMfGrY8vFUrhg3+cXemH6uytg9fE4wHv4N64TBw8NuhzGdk+B1V+sBJ3EYVM3HV9U33V8+bAmPnVtOXgKY2v/xf5Tsbh8SgHAZaoa4GQGgC+SyQ6ka8vFUzhbBxAqDp9FO8hQtcfEr44tF0vh/L/Hqf7/2XgY3lq3H46dy4N6tWLggQFt4K5ercp+zx/MJxXLjnJ8L6Foj4lPXVsOnsLY2n+lAAg87qzcB4A/pN2HlAKArwm1s1bmSdeWi6dwtg4gVBw+i3aQoWqPiV8dWy6Wwvm+P5vrgXe/OQBvf3UATl8ogOTEOOjXxAPT7h4CiXHRrKl+KpYd5fheQtEeE5+6thw8hbG1/0oBIAVAQAakAAjPAYna0f1bwcVTOFsHECoOn0U7yFC1x8Svji0XS+Hmf7ocDsRcBgu+PQTn8wvVY3on9k+DW7unwNovV2lN9VOx7CjH9xKK9pj41LXl4CmMrf1XCgApAKQA4B97AiKpnVVmAAwJ1jA30cKmTuV96bSJiw2Ew8XGmw6cgbc3/AKf/3QMcOlxm0a1YdKAtuomPjFRkapZwWLofq4hi1UolyOdoCY+dW05eAojBYCOuvrYGnsJ4IMPPgD8W758uXr13/Ly8io8z6D894G+qwyvL4u5RSjaY+JT15aLp3C6OiLzNVXfYH3njkZKC2ofChSnvM8CbzFsPlEMX2QUweHsEvQV9YpgSGo0XNEgAiIjKq7qD9Ye3c+5/beN0+GTG9vEp64tB09hbO2/pmO7PL8TJkyAjIwMLuWuxtXYAsCnilwC4I9Pqlq3eWbJjUXhbJ1BUHH4LNpBhqo9Jn51bLlYxF3Wtbda2PfR9xnqN/wJMVEw+uqWML5PK9i35auA0/wyAxB4nHF5D2Sta8vBUxhb+29l40F3j5RfAegy5mK8FAB8caidVQoAPpemSBMtbOpU3pdOmyjshfxCWLLtCLy58kf45VxJlMsbJ8JtPVJh9FUtoW6tGPWZGxOEqbY+e4ojJ3FMfOracvAUxo36SgHgZOS51EYKAL4w1M5qM7FwY1E4WwcQKg6fRTvIULXHxK+ObSBsobcINuw9CZ9uPaKu7ecUeCEmEuDGbi3h9h4pcFVq/YvuN2JLX6qYsKOanhcdPrmeTXzq2nLwFMaN+koBwB1t1QAnBQBfJGpnlQKAz6Up0kQLmzqZzgDggr6th7NU0sczfrxbH27pKfXUNH/iqZ3w+xHBH9DjxgRhqq3MAPzGoBv1lQLA1gh3gR8pAPgimCQdXVsunsLZOoBQcfgs2kGGqj0mfrm2RUXFMHfR55BV5zJYvv0YHDyVo0hJS64NN6a3UCv58eE8nLNyW/pyYtlRju+Fyyffo9nd8HTbw8FTGDfqKwWAzohzOVYKAL5A1M5q88ySG4vC2TqAUHH4LNpBhqo9Jn4rs/V4i+D/9p9SU/srdxyH4+fyFRFNkuJgZJfm8Pv0FtC5RZLWFD+VtIO1R/dzO4rpezHRIlg0E5+6thw8hbG1/1JjRUcdKQB02HI5VgoAvkDUzioFAJ9LU6SJFjZ1Ku/Lv02Z5/Nh/Z5MWLs7E1b/fEKt4Ped6V9eKwcmjuwNXVvWg0i/h/JU5tO/7W5MEKba+uxDobGJT11bDp7CuFFfKQBsjXAX+JECgC8CtbPaTCzcWBTO1gGEisNn0Q4yVO0x8bt02XJI7tAD1u4+oZL+9l9Ll+8DqLP7oR2bwrDOTaFt40RYsWJF0J/vSQFQwoCJFjIDMPQiCmzxKQWAnWOYK7xIAcCXwWQH0rXl4imcFAB8fXWTDq7a33H0HHy7/zR8+8sp2LD7BOR5S+LVrxUD113eCPq3awTXtUuGxnXiKzSE0o17FmxLX92+67HqDM3lSMe7iU9dWw6ewrhRXykAdEacy7FSAPAFonZWmQHgc2mKNNHCqU54Hf/HX8+WJfzvDpyB7PySaf3oyAhITSyGG3u0g/7tG8GVLepClMHUvhQAMgNAFWXB9gHdz3X3RSkAdBlzMV4KAL44JklH15aLp3C2ziCoOHwW7SBD1R6fX/x53q9ZueonelsPZalXTP75hUWqAzFREeqnej1bN4SeaQ3g6svqw/rVX7Cm9akDe3mGqH7a0lenTXYUpL1Qfac9XIww8alry8FTGDfqKwWAk5HnUhspAPjCUDur0zPLQHbcWBTO1gGEisNn0Q7SZnsw2eOCPZzO/3jNZrgQ30gl/JPZJSv1casTH60SfrfU+tCrdQP1mhAbVaEzOm3iYimcLX2lAKDHJaWFvwcOnsK4UV8pAOixUm0QUgDwpaJ2VikA+FyaIp1qgdft95+8ADuOnIOdR8+ppI+vvhvwYLtwOr9Dszoq4aen1Fev+Bv9ylbr6yZQbvspnBsThKm2Pnuq707imPjUteXgKYwb9ZUCwMnIc6mNFAB8YaidVQoAPpemSEqLnIJC2J95AfZlZsO+E9mwr/Q9Jv+C0ml8bEN8TCS0b5oEHZvhXx04f/hnuO+mIRAfU/HsntNeqk3lfXCxFM6NCYLDFQdD9Z3jw8lZeTC/uu3h4CmMG/WVAsDJyHOpjRQAfGGonVUKAD6XpkjU4rqBg+Dw6Vw4dDpH/R0+naMSPiZ+vH5ffsMn5abUrwVtGtWGDirZJ8EVzZKgdXLtCov1wqUxNw6Fc2OCMNVWZgB+Y9CN+koBYGuEu8CPFAB8EaiDsRQAfC45SHwi3tGzeXDsbB4cOZurErwv0e89lgXnSm6bX2GLi46EtEaJ6rf2mOzblL7HRM85qw+Xxtw4FM6NCYKjLQdD9Z3jQ2YAeMWEDpdSAOiw5XKsFAB8gUwOSLq2XDyFs5UgqDh8FgHwOvzpCwWQmZ0PJ87nqwRfkuhzyxL+sXN5cL70znn+vpMTY6FOZAF0SWsOqQ1qQQr+1cfXBGheN4G8Vm+zUCvvS4cjLpbC2dIX+0HF0tHYBjYU7THxqWvLwVMYN+orBYCN0e0SH1IA8IWgdlabiYUbi8LZOoBU5gdX0Z/PL4SzOR61ch4X1J1SryXvS17z4VTp+zM5nqBU4TV5TOJN68ZD06R49doM39dNUMm+Zf0EqB0XHbJkRfFpS2NuHApnS18pAOjjAKWFk9kGyqcb9ZUCgB4r1QYhBQBfKmpntZUcdA7GVJu4BxBM4vgbdzzrXrZqNXTp3guycgrgbK4HsnI88N22nZDcIlUl+Sz1WYF69f3vLSqulMi6CTHQMDEWkhPjAM/g8bVh7ThonBRXluSbJSVAUkL0RQ/ECeSY6jdf1YpIE786tlwshePqy5mpoGI55dSpXSjaY+JT15aDpzBu1FcKAKcj2sDu+PHjMGnSJNi7dy94PB64//774ZFHHinzOGPGDJg3b576/7bbboOZM2eyokkBwKJJgaidNZwFACbsPE8RfPb5SujVtx/kebyQU+CF3AIv5Hi8kFfgVXep++6H7dCyVVv1Hs/SMcFn53nU/0czzwDEJqj32XmFUEgkcV//YqMj1a1u6yXEQl31GgP18LVWLDSoXTHJY6LHz9DG5maihU2dOInVpICh+unGBGFLZ6rvTuKY+NS15eApjBv1lQLAycgztLnjjjugdevWgIn+woULcO2118K//vUvuOaaa2DdunWqONi4cSNER0er75577jkYMWIEGVUKAJKiMkD5nRETMOZLvD1sgbcI8j0lr/gTs/xCr3r1/eGZ9bffbYaOV3aFfI+3HK48ptSm1NeBjCOQ1CBZJfXyyT3XU5Lk8dXphgvl8MY2kd4CaNwgCRLjoiExLkZ9hu8zjxyC9E7tyxJ73YRY2LF1E9wwuL9K+v43wHHaDhM76sDp1LeJXx1bLpbCuTFBOOXe347qu5M4Jj51bTl4CuNGfaUAcDLyDG06d+4Mb731FvTp00d5evjhh9Xrq6++CpMnT4aUlBR46qmn1Gdz5syBr7/+Gt577z0yqo0CYH9mNnyxdgP07NVLJUWcDsYEia/eYnxf8lmRSprFUFQE6vMi9VnJex8e/8fP1fflMeXsvUVQ8bty9r54+/b/Ai1TUsFTVKwWneHZbaG3GAqLikpfi1Xyxnbh556ikvcexJR+jp+V2JR8lpvvgYjIKOUDcaHeEmKiVLIN9Fqr9POTx49Au7RWgP/Hx0ZBrVIbXPGOCX3HD1vg+uv6lCV3vH7uOxsPdnAJ9Dl1oAo1F+FIDhjDpJ86tlwshXNjgrA1Fqi+O4lj4lPXloOnMG7UVwoAJyPP0Gb8+PFQp04dlfBPnToFffv2hfbt28PixYth1KhRMG7cOLj99ttVlKVLl8Ls2bPVzAC12SgARr62vsKjT6mYVf09PqQF7/YWExWpfgOO93WPjiz3PipSfR9d+rnv/dkzZ6BJ42SFRZsS20iIxb/oSMAza3z1/cVFR5V8HhUJP+/YDtdclX7Rd+Vty9uv/XIVDB82jKQqXAcQKg7ZUMuAULXHxK+OLRdL4dyYIGxJTfXdSRwTn7q2HDyFcaO+UgA4GXmETe/evWHPnj0BUVu2bIGEhAR4/PHHAd83btwY0tLSIDMzEz766COtAuCll14C/PNtWVlZ8OGHH14UNy8vD+LjKz7C1Afy/27DkSI4neOB2JgYwBuu4NVe9RoBEAGlr6Wfq8/8MIHwFWwhosSXv+9gsQDAU5APteLjICoSICrit78SP9gq/a0yTihvurZcPIXT0TGYvvg5FYfqv+3vQ9UeE786tlwshbOlb03RmOKzsnGqa8vBUxg36jthwgTIyMiwvUtXib+IYpx7robbxIkToVGjRjB9+vQqvwSA9FGVbLgpDkV7THzq2nLxFM7WGQQV51LQ13Qc63DExVI4W/qa9j0U+lN9dxLTxKeuLQdPYdyor8wAOBl5hjY47Z+UlAQxMTFqFmD48OHqtVmzZrBmzRpVBJRfBDh16lQYOXIkGdXGJQA5eJA0axdI1IHBF5HC2TqAUHFoBuwiQtUeE786tlwshbOlr+zD9PiktPD3wMFTGDfqKwUAPVasI5YtW6YW/uEqf1wL8MILL0C/fv3K4kybNg3eeecd9f/YsWNh1qxZrDZIAcCiSYGonbUyT7q2XDyFs3UAoeLwWbSDDFV7TPzq2HKxFM6Wvqbj246qFb1QfXcS08Snri0HT2HcqK8UAE5GnkttpADgC0PtrFIA8Lk0RZpoYVOn8r502sTFUjg3JghTbbmzW07iUHzaHBucWBTGjfpKAeBk5LnURgoAvjDUzhrugwfnrM3WAcSk73yG+chQtcfEr44tF0vhbOnLGUt8dewgqb47iWLiU9eWg6cwbtRXCgAnI8+lNnFxcWoxof+Wm5urfnkQaAv0XXZ2NiQmJrqml5W132kjTXzq2nLxFE5XR+SmpuobrO/c8UJpUd4PF0vhbOmLbZN9uHKlKS10jqE+LOXTjfrir8/y8/O5u4WrcdX2VwChZnXMmDGwcOHCgGECfee2qrCy9jvlzsSnri0XT+F0dURuaqq+wfrOHS+UFuX9cLEUzpa+2DbZhytXmtLC35qDpzCXsr7c/SqUOCkAgrBu5+d3AAAMSklEQVT7wQcfwK233hrw20Dfue3gUVn7nQ4oE5+6tlw8hdPVEbmpqfoG6zt3vFBalPfDxVI4W/q6sQCg+s7VxQnvgXzrtoeDpzCXsr5O9LNtIwWAJUbdVgBY6pa4KWVA9L30h4JofGlrLPperK8UAJbGPN5d8M9//rMlb+LGbQyIvm5TxH57RGP7nLrJo+grBYCbxqO0RRgQBoQBYUAYqDIGZAagyqiXwMKAMCAMCAPCQNUxIAVA1XEvkYUBYUAYEAaEgSpjQAqAKqNeAgsDwoAwIAwIA1XHgBQAIeb+oYcegq1bt6qHF02ZMiXE0cR9OBk4ffo0DBkyBHbt2qVuIiPbpcXAhg0b1CPIIyMj4ZZbboHHHnvs0uqg9EY9QO7RRx8FfCju9ddfDzNmzKhRrEgBEEK5v/vuO3j//ffh5ZdfhtGjR8Prr78OTZo0CWFEcR1OBjweD5w/f17dOGjVqlXhDC2xwsDAkSNHIDk5GWJjY2HgwIHw2WefQa1atcIQWUKEiwHch/EJs7gNGjQIPv74Y/XU2ZqySQEQQqUx4aempsKoUaPgtddegzZt2sANN9wQwojiuioYGDx4sBQAVUF8GGOixkuWLIH4+PgwRpVQ4WLA6/XCPffcA/PmzVNPnK0pmxQATKXxUcSLFy+GgwcPwpYtWyA9Pb3Mcs+ePTB+/Hg4efIk1K1bVz2WuFOnTjBz5kzo27cv9O/fX32GZxLjxo1jRhRYOBlwoq+vfVIAhFMpZ7FM9F25ciUsWrQI5s6d6yy4WIWFAacaz58/H6ZOnQpDhw5VJ2o1aZMCgKn2unXrIC0tTSX0Tz75pEIBgNeO7r77blVB4oHi+eefh02bNqkpf5kBYBJcxTAn+koBUMWiaYR3qm9GRobat7H4d9PDvjS6XmOgTjVGgoqKitSt37EQuPLKK2sMZ1IAaErdqlWrCgXAiRMnoG3btoALwnDqCBeTNGvWDHAB0ZkzZ2DBggWAd6DCwfXPf/5T1gBo8h1uuI6+qDtuMgMQbpWcx9PRNyUlBUaMGKEK+fbt2zsPKpZhZUBXY3wiLG54AvfMM8/A5ZdfHtb2VmUwKQA02fcfXJs3b1bT+rgS3Lf16NEDZs+erVaVTp48GbZt26aml5599lnNaAIPNwO6+mLyx0tC3bp1g1deeQU6d+4c7iZLPA0GdPTFy32YENq1a6ci4ILeFi1aaEQTaFUwoKPxqVOnVIGHMwD9+vWTXwFUhWDVKabO4MICQLbqxYDoW7300m2t6KvLWPXDi8Z8zWQGgM+VQupML/mmiDVDCLwKGRB9q5D8MIQWfcNAchWHEI35AkgBwOcqYAGAHw4YMEBdP/ItAsTpf7wHgGzVjwH/g4foW/00rKzFou+lpWeg3ojGfI2lAGBydf/996sbgRw7dgwaNmwIderUgb179yprvP6PyR+vJ+FNJPC3pDVpJSmTQlfDRF9Xy2PcONHXmELXOxCN9SWSAkCfM7EQBoQBYUAYEAaqPQNSAFR7CaUDwoAwIAwIA8KAPgNSAOhzJhbCgDAgDAgDwkC1Z0AKgGovoXRAGBAGhAFhQBjQZ0AKAH3OxEIYEAaEAWFAGKj2DEgBUO0llA4IA8KAMCAMCAP6DEgBoM+ZWAgDwoAwIAwIA9WeASkAqr2E0gFhQBgQBoQBYUCfASkA9DkTC2FAGBAGhAFhoNozIAVAtZdQOlCVDOBtR/FxogkJCaoZ3bt3h3//+99V2aSwxF6yZAng35tvvmkcb/To0TBy5Eh1N03cPB6PeiTr7t27ITY21tg/OsjLy4Nrr70WvvzyS6hbt64Vn+JEGKjuDEgBUN0VlPZXKQOB7jvu3yCv1wtRUVFV2k7bwfFW11gAXHbZZRe5LiwshOjoaHZI/wJg5cqV8Pbbb8OCBQvYPjhAfFzz6dOnYdq0aRy4YISBS54BKQAueYmlg6FkIFAB8M4778B///tfaNCggTqLfeutt1RCfPLJJ+HcuXOABcGUKVPg1ltvVU2bO3cuvPDCC5CYmAg333wz/O1vf4Pi4mL1XUREBJw5cwbq1aun/k9OTlYPmsK4e/bsgUceeQROnDgB+fn58Kc//QkefPDBMruZM2fCJ598ApmZmcrnvffeq77buXOnsjt69Kj6/4EHHlAzF3feeaf6DmPi1qdPH/jrX/8Kw4cPr0Dh+vXrVfvxFbc1a9bA5MmToVevXrB582Z45pln1Fn8P/7xDygoKFDPWp8xYwaMGjVK4X/++We477774OzZs+pMPycnB8aNG1c2A4C++vfvD1gYPPzww/DFF1+omQDk8KuvvoL4+Hj4/PPPYfr06ZCbm6uKq+effx4GDhyo/OOzODA2chgTEwOLFi1SfOFzPLp16wZHjhwp62Mox4b4FgbczoAUAG5XSNrnagb8LwH8/e9/V4kNk+qWLVugffv2kJWVpZLT0qVLoVmzZnDy5Em46qqr4JtvvlHJffDgwQqL32FinTVrFlkApKSkQM+ePeG9996DDh06qCSKCfg///kPXHPNNSrBYVHx2GOPqYSLn2Es3Dp27AjPPfcc3H777ep/bA8WFjhFju3/3e9+p9ozZswYVcD4CgKfEJh4sY/o31cAXH/99bB69WqVuHHDB2NhAYS2Bw4cUG07ePCgulyCbZk4cSJMmDABfvzxR1V8YBGElwAwaeNjtLdu3aoetoWFwU8//QSRkZEqJj6EC/3dcccdqgjAh28h7rrrrlOfI6dY6Hz99deKT+QFt1q1aqnXtLQ0WLx4MXTu3NnV40oaJwyEgwEpAMLBssS4ZBkINgOAiXnVqlWq35j4Mdm2bt26jAecisaZgu3bt6tki2etuB0+fBhSU1PJAgAT29VXX60KDN+GCXLq1Kkwfvx4lXjxDL9p06bq6/r166tkixg8oz906NBFmixcuFAVFJgg//CHP0CnTp3g0UcfvQiHT13DJP3EE0+UFQCI9z0dEz/EWYpnn30WMjIy1Jn7jh07YNu2bdC8eXP1NE28Ju+7LDJo0CC46667VAGwceNGVZzgkzexrVgcYGGCBdSIESNUoTJnzhxVqLRo0aKsbTjLgTMRONuC6zGCTfPjrAbOhgwbNuySHZPSMWGAy4AUAFymBCcMBGAgWAGAU+/4hxsmM5yOx7NS/+3VV1+tUABgwsSze98lAEyex48fV0kTN7xMgEXDhQsX1Jn6r7/+GlCXYJcO0C5YAYDX7jGxf/zxxzB06FB19u+79FA+CBYFeHaPlwdww8SLlxTwrN23oZ/Zs2eraXzcEI8L8PAM3L8AwBkQvPyABcDTTz+tMH/84x+VHV7aWLt2rZpdwMsq69atU2f+eClg/vz5F/Udi5LKCgCceXn55ZfLZipkUAsDNZkBKQBqsvrSd2MGOAUATr3jtPu7776rpvtxw2SJn2GSHTJkiCoC8Gwdz5qxWPAVADi9/9JLL8ENN9wAH330Edxyyy3wyy+/QMuWLQEX4v3lL38pu7aPZ+CYaH1T74HWDqAdTn/jGbT/JQBsFybt119/XcXDaflAG85W4HV5nC0IVgBgkl+xYoWapUAcnuFjH9PT06FHjx4wadIk1W6c3kcM/poACwCcdcBk37hxY7V2AWcJsD/IB84EPPXUU4o3PJPHGZYuXbqoNuDMAfrFAgH9YIHgfwkA116gL+QPX2UTBmo6A1IA1PQRIP03YoBTAGCA77//Hh5//HF1bRwXyOE0P84Q4IK2yhYBLlu2TC2Ew2vfOAX+xhtvlC0C3LdvnzrzxmvrmNxwehzPinFqvLLFg7t27YKHHnpIXSLAa+u4XgGn9XHDpNukSRP44YcfVIERaEMMJlssODBBB5oBwKSPMwQ4g4DrA3Ah3qeffqoKAFyTgMn//PnzahEgzkrgtX5cJ4CXEjZs2KDCImc4E4B8Yf+wAMDiBBf2YfJH/3gpBBca4uI+34wAzhS8+OKLigNcPIix8dcKOJOA6xd8l2aMhBdjYeASYEAKgEtAROnCpcNAdna2Sva+GYBw9wyTJRYZeIZf2YYr9QcMGFD2SwYb7cTZB7zkgYVSKLbbbrtNLTzEGRfZhAFhAEAKABkFwoCLGKjKAgAXxuElCVwD0LVr10pZwZkMnJ3Aa/fVYcNFh3jpAi89yCYMCAMlDEgBICNBGBAGhAFhQBiogQxIAVADRZcuCwPCgDAgDAgDUgDIGBAGhAFhQBgQBmogA/8P9phExEWeeAYAAAAASUVORK5CYII=",
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",
       "_figure_label": "Figure 2",
       "_model_module_version": "^0.11",
       "_size": [
        640,
        480
       ],
       "_view_module_version": "^0.11",
       "layout": "IPY_MODEL_c51097a1a8434a85866f9ed639399ac2",
       "toolbar": "IPY_MODEL_99c9308ef5ff40c6bff45f3aaa4cb0ad",
       "toolbar_position": "left"
      }
     },
     "6466b05b971942e6829cd065eb831174": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "653cc9e0830b48ef969696c72f26f562": {
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      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
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     },
     "762a67a96b5b450b9d17b85e19ec65bb": {
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      "model_name": "ToolbarModel",
      "state": {
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       "_view_module_version": "^0.11",
       "collapsed": true,
       "layout": "IPY_MODEL_4cb2394a1a2241d5aec0f27159619614",
       "orientation": "vertical",
       "toolitems": [
        [
         "Home",
         "Reset original view",
         "home",
         "home"
        ],
        [
         "Back",
         "Back to previous view",
         "arrow-left",
         "back"
        ],
        [
         "Forward",
         "Forward to next view",
         "arrow-right",
         "forward"
        ],
        [
         "Pan",
         "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect",
         "arrows",
         "pan"
        ],
        [
         "Zoom",
         "Zoom to rectangle\nx/y fixes axis",
         "square-o",
         "zoom"
        ],
        [
         "Download",
         "Download plot",
         "floppy-o",
         "save_figure"
        ]
       ]
      }
     },
     "79911859bacc48c8bb00c28440e42000": {
      "model_module": "jupyter-matplotlib",
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      "model_name": "ToolbarModel",
      "state": {
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       "layout": "IPY_MODEL_1449a8baa0bc45089b6df570903c39a0",
       "orientation": "vertical",
       "toolitems": [
        [
         "Home",
         "Reset original view",
         "home",
         "home"
        ],
        [
         "Back",
         "Back to previous view",
         "arrow-left",
         "back"
        ],
        [
         "Forward",
         "Forward to next view",
         "arrow-right",
         "forward"
        ],
        [
         "Pan",
         "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect",
         "arrows",
         "pan"
        ],
        [
         "Zoom",
         "Zoom to rectangle\nx/y fixes axis",
         "square-o",
         "zoom"
        ],
        [
         "Download",
         "Download plot",
         "floppy-o",
         "save_figure"
        ]
       ]
      }
     },
     "83869d25e6a04c698eadd3e885c3ec32": {
      "model_module": "jupyter-matplotlib",
      "model_module_version": "^0.11",
      "model_name": "MPLCanvasModel",
      "state": {
       "_data_url": 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",
       "_figure_label": "Figure 6",
       "_model_module_version": "^0.11",
       "_size": [
        640,
        480
       ],
       "_view_module_version": "^0.11",
       "layout": "IPY_MODEL_11c437e14a2f4999a4bd5e1c44e3290e",
       "toolbar": "IPY_MODEL_f30d5e3c4755433292a9e321688aff62",
       "toolbar_position": "left"
      }
     },
     "9569762de26541bdba1a30d9fa043663": {
      "model_module": "jupyter-matplotlib",
      "model_module_version": "^0.11",
      "model_name": "MPLCanvasModel",
      "state": {
       "_data_url": 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",
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        ],
        [
         "Download",
         "Download plot",
         "floppy-o",
         "save_figure"
        ]
       ]
      }
     },
     "be87df53dc6d4b9c84a870b0c9e9fb32": {
      "model_module": "jupyter-matplotlib",
      "model_module_version": "^0.11",
      "model_name": "MPLCanvasModel",
      "state": {
       "_cursor": "default",
       "_data_url": 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",
       "_figure_label": "Figure 1",
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       "toolbar_position": "left"
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",
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",
       "_figure_label": "Figure 4",
       "_model_module_version": "^0.11",
       "_size": [
        640,
        480
       ],
       "_view_module_version": "^0.11",
       "layout": "IPY_MODEL_6466b05b971942e6829cd065eb831174",
       "toolbar": "IPY_MODEL_432484db1bb9496eb8a4785281fb269c",
       "toolbar_position": "left"
      }
     },
     "f30d5e3c4755433292a9e321688aff62": {
      "model_module": "jupyter-matplotlib",
      "model_module_version": "^0.11",
      "model_name": "ToolbarModel",
      "state": {
       "_model_module_version": "^0.11",
       "_view_module_version": "^0.11",
       "collapsed": true,
       "layout": "IPY_MODEL_f9129d46a15346ce9b76097e84f8ec66",
       "orientation": "vertical",
       "toolitems": [
        [
         "Home",
         "Reset original view",
         "home",
         "home"
        ],
        [
         "Back",
         "Back to previous view",
         "arrow-left",
         "back"
        ],
        [
         "Forward",
         "Forward to next view",
         "arrow-right",
         "forward"
        ],
        [
         "Pan",
         "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect",
         "arrows",
         "pan"
        ],
        [
         "Zoom",
         "Zoom to rectangle\nx/y fixes axis",
         "square-o",
         "zoom"
        ],
        [
         "Download",
         "Download plot",
         "floppy-o",
         "save_figure"
        ]
       ]
      }
     },
     "f9129d46a15346ce9b76097e84f8ec66": {
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      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "fecc37c944e54c2e8843a39b09336c2f": {
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      "model_module_version": "2.0.0",
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    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}