{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Code provided under BSD 3-Clause license, all other content under a Creative Commons Attribution license, CC-BY 4.0. (c) 2022 Francesco Mario Antonio Mitrotta."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Verification of SOL 106's Nonlinear Buckling Method\n",
    "\n",
    "In this notebook we want to perform a verification study on SOL 106's nonlinear buckling method. This study is inspired by the verification of the nonlinear buckling method presented by [Lee & Herting (1985)](https://onlinelibrary.wiley.com/doi/pdf/10.1002/nme.1620211016). In their paper, the authors mentioned that the nonlinear buckling method should be applicable to linear problems because linear buckling is a special case of nonlinear buckling. For this reason they tested the nonlinear buckling method on Euler's column, analogously to what we did in our [first notebook](01_Buckling_Analysis_of_Euler_Column.ipynb).\n",
    "\n",
    "Since in our [last notebook](05_Sensitivity_Study_of_SOL_106_Nonlinear_Analysis_Parameters.ipynb) we found out that the box beam appears to behave linearly up to the buckling load predicted by SOL 105, here we follow the same reasoning of Lee & Herting and apply the nonlinear buckling method to verify whether we are able to reproduce the results of the linear buckling analysis in the linear region of the structural response. We will also test the nonlinear buckling method beyond the buckling load predicted by SOL 105 to observe how the method behaves.\n",
    "\n",
    "* [Problem definition](#problem)\n",
    "* [Setup of the numerical model](#numerical-model)\n",
    "* [Nonlinear buckling method verification](#verification)\n",
    "* [Tangent stiffness matrix assessment](#tangent-stiffness-matrix)\n",
    "* [Conclusion](#conclusion)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem definition <a name=\"problem\"></a>\n",
    "\n",
    "***\n",
    "\n",
    "We consider the same box beam model that we used in our [last notebook](05_Sensitivity_Study_of_SOL_106_Nonlinear_Analysis_Parameters.ipynb), that is to say with a rigid tip section and a concentrated load applied at the center of such section.\n",
    "\n",
    "![Concentrated tip load.](resources/04_BoxBeamConcentratedLoad.svg \"Concentrated tip load.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Box beam dimensions:\n",
      "- width: 1.0 m\n",
      "- length: 4.5 m\n",
      "- height: 0.2 m\n",
      "- wall thickness: 4.0 mm\n"
     ]
    }
   ],
   "source": [
    "AR = 9.   # aspect ratio - 2*b/w (the length of the box beam corresponds to half the span of the CRM wing)\n",
    "w = 1e3   # width [mm]\n",
    "l = AR*w/2  # length [mm]\n",
    "non_dimensional_height = 0.2  # h/w\n",
    "h = w*non_dimensional_height  # box height [mm]\n",
    "non_dimensional_thickness = 1/50  # t/h\n",
    "t = h*non_dimensional_thickness   # shell thickness [mm]\n",
    "print(f\"\"\"\n",
    "Box beam dimensions:\n",
    "- width: {w/1e3:.1f} m\n",
    "- length: {l/1e3:.1f} m\n",
    "- height: {h/1e3:.1f} m\n",
    "- wall thickness: {t:.1f} mm\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "rho = 2780e-12 # density [ton/mm^3]\n",
    "E = 73.1e3 # Young's modulus [MPa]\n",
    "nu = 0.3 # Poisson's ratio"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup of the numerical model <a name=\"numerical-model\"></a>\n",
    "\n",
    "***\n",
    "\n",
    "We discretize the geometry of our box beam into quadrilateral elements using the function `mesh_box_beam` from the `box_beam_utils` module. For that we use a target shell element edge length of 60 mm, following the results of our previous [mesh convergence study](04_Nonlinear_Buckling_Analysis_of_a_Box_Beam.ipynb#mesh-convergence)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from resources import box_beam_utils\n",
    "\n",
    "shell_element_length = 59.9  # [mm]\n",
    "box_beam_mesh = box_beam_utils.mesh_box_beam(width=w, length=l, height=h, element_length=shell_element_length)\n",
    "nodes_coordinates_array = box_beam_mesh.points\n",
    "nodes_connectivity_matrix = box_beam_mesh.faces.reshape(-1, 5)[:, 1:]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Then we create a base bdf input using the function `create_base_bdf_input` and we print a summary of the bulk data cards defined in the resulting `BDF` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---BDF Statistics---\n",
      "SOL None\n",
      "\n",
      "bdf.spcs[1]: 1\n",
      "  SPC1:    1\n",
      "\n",
      "bdf.params: 0\n",
      "  PARAM    : 1\n",
      "\n",
      "bdf.nodes: 0\n",
      "  GRID     : 3388\n",
      "\n",
      "bdf.elements: 0\n",
      "  CQUAD4   : 3344\n",
      "\n",
      "bdf.properties: 0\n",
      "  PSHELL   : 1\n",
      "\n",
      "bdf.materials: 0\n",
      "  MAT1     : 1\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "subcase=0 already exists...skipping\n"
     ]
    }
   ],
   "source": [
    "box_beam_bdf_input = box_beam_utils.create_base_bdf_input(young_modulus=E, poisson_ratio=nu, density=rho,\n",
    "                                                          shell_thickness=t, nodes_xyz_array=nodes_coordinates_array,\n",
    "                                                          nodes_connectivity_matrix=nodes_connectivity_matrix)\n",
    "print(box_beam_bdf_input.get_bdf_stats())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need to make the tip section of the box beam rigid and apply a concentrated load. For this purpose we add a master node at the center of the tip section, add a `RBE2` element to make the section rigid and add a `FORCE` card to define a force along the $z$-axis applied at the master node."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FORCE         11    3389              1.      0.      0.      1."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Add master node of tip section\n",
    "master_node_id = np.size(nodes_coordinates_array, 0) + 1\n",
    "box_beam_bdf_input.add_grid(master_node_id, [w/2, l, 0.])\n",
    "\n",
    "# Add RBE2 element to make tip section rigid\n",
    "nodes_ids = np.arange(1, np.size(nodes_coordinates_array, 0) + 1)  # find ids of all nodes\n",
    "tip_nodes_ids = nodes_ids[nodes_coordinates_array[:,1] == l]  # find ids of nodes at tip section\n",
    "rbe2_eid = len(box_beam_bdf_input.elements) + 1\n",
    "box_beam_bdf_input.add_rbe2(rbe2_eid, master_node_id, '123456', tip_nodes_ids)\n",
    "\n",
    "# Add concentrated force\n",
    "force_set_id = 11\n",
    "concentrated_force_magnitude = 1.\n",
    "concentrated_force_direction = [0., 0., 1.]\n",
    "box_beam_bdf_input.add_force(sid=force_set_id, node=master_node_id, mag=concentrated_force_magnitude,\n",
    "                             xyz=concentrated_force_direction)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nonlinear buckling method verification <a name=\"verification\"></a>\n",
    "\n",
    "***\n",
    "\n",
    "Given the linear behavior observed for our box beam in our last notebook, with this analysis we want to verify whether we are able to obtain the same buckling load predicted by SOL 105 with SOL 106's nonlinear buckling method. We are going to test this hypothesis for different applied loads ranging from a fraction of the buckling load predicted by SOL 105 to twice its value, as we are also interested in checking the results of the nonlinear buckling method beyond the linear region of the structural response.\n",
    "\n",
    "In order to execute this sweep, we define the function `run_nonlinear_buckling_method_sweep` in an analogous way as we did in our [fourth notebook](04_Nonlinear_Buckling_Analysis_of_a_Box_Beam.ipynb#disrtibuted-load-flexible-tip)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from resources import pynastran_utils\n",
    "from pyNastran.op2.op2 import read_op2\n",
    "\n",
    "# Function to set up and run the nonlinear analysis with the nonlinear buckling methods for a sequence of applied loads\n",
    "def run_nonlinear_buckling_method_sweep(bdf_input, load_magnitudes, directory_path, input_filename, run_flag=True):\n",
    "    # Set up nonlinear analysis with arc-length method\n",
    "    pynastran_utils.set_up_arc_length_method(bdf=bdf_input, ninc=100, max_iter=25, conv='PUV', eps_p=1e-3,\n",
    "                                             eps_u=1e-3, max_bisect=10, minalr=.01, maxalr=1.0001, desiter=5,\n",
    "                                             maxinc=1000)\n",
    "    # Define parameters for buckling analysis\n",
    "    bdf_input.add_param('BUCKLE', [2])  # PARAM card to request nonlinear buckling analysis\n",
    "    eigrl_sid = force_set_id + 1\n",
    "    bdf_input.add_eigrl(sid=eigrl_sid, v1=0., nd=1)  # calculate only the first positive eigenvalue with the Lanczos method\n",
    "    bdf_input.case_control_deck.subcases[0].add_integer_type('METHOD', eigrl_sid)\n",
    "    # Create the LOAD cards and corresponding subcases for each input load magnitude\n",
    "    for i, scale_factor in enumerate(load_magnitudes):\n",
    "        load_set_id = 21 + i\n",
    "        bdf_input.add_load(sid=load_set_id, scale=1., scale_factors=[scale_factor], load_ids=[force_set_id])  # create LOAD card\n",
    "        pynastran_utils.create_static_load_subcase(bdf=bdf_input, subcase_id=i+1, load_set_id=load_set_id)  # create subcase\n",
    "    # Run analysis\n",
    "    pynastran_utils.run_analysis(directory_path=directory_path, bdf=bdf_input, filename=input_filename,\n",
    "                                 run_flag=run_flag)\n",
    "    # Read op2 file\n",
    "    op2_path = os.path.join(directory_path, input_filename + '.op2')\n",
    "    op2 = read_op2(op2_filename=op2_path, load_geometry=True, debug=None)\n",
    "    # Return op2 object\n",
    "    return op2"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We recall the buckling load predicted by SOL 105 for our model (see [notebook 4](04_Nonlinear_Buckling_Analysis_of_a_Box_Beam.ipynb#mesh-convergence)) and we define 11 load magnitudes equally spaced between 0 and twice the linear buckling load. We discard the load case with null magnitude and we keep the other 10 load cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Applied loads [N]: [ 331.  662.  992. 1323. 1654. 1985. 2316. 2646. 2977. 3308.]\n"
     ]
    }
   ],
   "source": [
    "linear_buckling_load = 1654.  # [N]\n",
    "applied_load_magnitudes = np.linspace(0, 2*linear_buckling_load, 11)[1:]\n",
    "np.set_printoptions(precision=0, suppress=True)\n",
    "print(f\"Applied loads [N]: {applied_load_magnitudes}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's define the analysis folder and run our first nonlinear buckling load sweep."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nastran job nonlinear_buckling_verification.bdf completed\n",
      "Wall time: 914.0 s\n"
     ]
    }
   ],
   "source": [
    "import os  # import operating system module\n",
    "\n",
    "# Define name of analysis directory\n",
    "analysis_directory_name = '06_Verification_of_SOL_106_Nonlinear_Buckling_Method'\n",
    "analysis_directory_path = os.path.join(os.getcwd(), 'analyses', analysis_directory_name)\n",
    "\n",
    "# Run analysis\n",
    "input_name = 'nonlinear_buckling_verification'\n",
    "sol_106_op2 = run_nonlinear_buckling_method_sweep(box_beam_bdf_input.__deepcopy__({}), applied_load_magnitudes,\n",
    "                                                  analysis_directory_path, input_name, run_flag=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analogously to what we did in our fourth notebook, we define the function `plot_buckling_loads` to plot the buckling load predicted by SOL 106, $P_\\text{SOL 106}$, and the critical buckling factor $\\alpha$ against the applied load $P$. Both loads are nondimensionalized with the linear buckling load, $P_\\text{SOL 105}$. Subsequently, we read the nonlinear buckling loads and the critical buckling factors from the f06 file using the function `read_nonlinear_buckling_load_from_f06` and we plot the results of our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Hklp37491xfr163Xu3Dk9++yzpts8+uijmjJlinbs2HHdpDYyMlKRkZFlllmt1jLv2+ttGjRo4BPj9AbEyjXEyzxiZR6xMq+6Y/VfXx7RV8eyJUkll66KX287el7v7cnQxN6tq20c7sD7yjxPxKqsraIVcVtSu3379nLL8vLy3NVNpbzzzjuqV6+ehg8f7nLb6209AACgriqwF2lFSposKp3QXssiaUXKCY1NjPHa1VrUDm5Lah955JHrlv/iF79wV1cuOXfunNavX68hQ4aoSZMmptstX75ckrjNFwAAZdiVlq1MW36F9QxJGbY87UrLVkKM+X+HAVe5Lan9/vvv3XUqJxs2bFBubq4uXbokSfruu++0Zs0aSVf3voaEhGjMmDFavny5jh07pqioKKf2y5cvV2FhYZn3ppWkr776SnPnztXgwYPVsmVLXblyRRs2bNCSJUvUq1cvDRw4sFrmBQCAL7t4ueKEtir1AVd59X1qJWncuHE6ceKE4/Xq1au1evVqSVcT6ejoaNntdtnt9jIvRlu2bJmio6PVp0+fMs9/0003yd/fXy+++KIyMzNlsVjUunVrvfDCC5o8eTLbDwAAKEPD4HrVWh9wlduT2iNHjmjLli3KzMxUeHi4EhMT1bp15TeHp6WlVVgnOTlZycnJZZYdOnToum1btWqlTz/9tBIjAwCg7uoa3Vjh1nrKsuVXuKc23BqkrtGNPTU01FFuW4YsKirS448/rjZt2uill17SP/7xD7300ktq27atfv/736uoqMhdXQEAgBoW6O+nEQnR101opat7akckRHGRGKqd295hL774olJSUrRnzx4dP35cKSkpOn78uHbt2qXt27dr7ty57uoKAAB4gXGJMerV5uptLUveuLP4da82kRqbGOPRcaFucltSu3z5cr333nvq3Lmz0/EuXbpo1apVWrZsmbu6AgAAXiDQ309vD4/T5L6xCrcGOZWFW4M0uW+s3h4exyotPMJte2p//PFHtW/fvsyy9u3b68cff3RXVwAAwEsE+vtpYu/WGpsYo11p2bp4OV8Ng+upa3Rjkll4lNuS2hYtWiglJUV33XVXqbLt27erRYsW7uoKAAB4mUB/P+5Dixrltl+hJk6cqIcffliffvqp49ZahmHok08+0SOPPKKnnnrKXV0BAAAATty2UjthwgRlZ2dr2LBhKiwsVHh4uDIzMxUQEKDnnntO48ePd1dXAAAAgBO33qd2+vTpmjhxolJSUpSVlaUmTZooISFBDRs2dGc3AAAAgBO3bT84ceKETp8+rYYNG6p///4aNGiQtm3bpuHDh2vu3LkqLCx0V1cAAACAE7cltWPGjNHu3bsdr8ePH6+///3vuvXWW/WXv/xFf/zjH93VFQAAAODEbUntv//9b/Xt21eSdOnSJb3//vtas2aNXnnlFf3jH//Q6tWr3dUVAAAA4MRtSW1BQYFCQkIkXb2FV1hYmG677TZJUmxsrLKystzVFQAAAODEbUlt27ZttW7dOknSmjVrHKu2knT27FndcMMN7uoKAAAAcOK2ux/Mnz9fSUlJql+/vvLz87Vt2zZH2d///nfdfffd7uoKAAAAcOK2pPbee+9VWlqaDh8+rNjYWIWGhjrKBgwYoGHDhrmrKwAAAMBJpbYfXLp0SaNHj1ZERIRCQkKUmJiobdu2KTQ0VF27dnVKaCXp1ltvVbNmzdwyYAAAAKCkSiW1zz33nJKTkxUZGanOnTtr9+7d6tu3r7Zu3eru8QEAAAAVqlRS+/HHH2vChAn69ttvlZKSouPHj6tr16569tln3T0+AAAAoEKVSmpPnz6toUOHOl5HRETotdde086dO5Wdne22wQEAAABmVCqpLSoqUv369Z2OdezYUYZh6MyZM24ZGAAAAGBWpe9Te/bsWafXQUFBkqT8/PyqjQgAAABwUaVv6TVkyBCFhISoY8eO6tKli9q3by+LxeLOsQEAAACmVCqp/fTTT7Vnzx7t2bNHu3fv1o4dOxxl99xzjzp16qS4uDjFxcXp9ttvV+fOnd02YAAAAKCkSiW1AwYM0IABAxyvz58/70hwi5PdxYsXyzAMWSwW2e12tw0YAAAAKMktTxQLCwtTnz591KdPH8exnJwc7d69W9988407ugAAwKcU2Iu0Ky1bFy/nq2FwPXWNbqxA/0pfygKgApVKat944w09+OCDuvnmm8utExoaqp49e6pnz56VHhwAAL6mwF6kxVuOaUVKmjJtP188HWEN0vCEKI1LjCG5BapBpX6q5s2bp6ioKN1xxx165ZVXdOzYMXePCwAAn1NgL9LjK3bptS8OK8vmfDegTFueXvvisH7/l90qsBfV0AiB2qtSSe3Zs2e1ceNGdevWTX/+858VGxurLl26aM6cOfruu+/cPUYAAHzC4i3HtPlQhiTJKFFW/HrTwXS9tYXFIMDdKpXU+vn5qWfPnvrv//5vnTlzRtu2bVPPnj21dOlSdezYUW3bttX06dOrvJ/20qVLeuaZZ9SvXz9FRETIYrFo1qxZptomJyfLYrGU+d+5c+dK1d+4caMSEhIUEhKi8PBwjRw5Uunp6VUaPwCg7iiwF2lFSpoqurmlRdKKlBOs1gJu5pZNPXfddZdef/11paWlKSUlRb/61a/097//XV27dlXLli31zDPPVOq8WVlZWrJkifLy8pSUlFSpc7z77rtKSUlx+q9JkyZOdbZu3aoBAwboxhtv1Lp16/TGG29o48aN6t27t/Ly8irVLwCgbtmVlq1MW36pFdqSDEkZtjztSuOx8oA7ueXuB9eKj49XfHy85s+fr7179+qDDz7Qhx9+qAULFrh8rqioKGVnZ8tisSgzM1NLly51+RwdOnRQ165dr1vnD3/4g2JjY7VmzRoFBFwNyS233KK77rpLy5Yt07hx41zuFwBQt1y87NoTNV2tD+D6qvXyyy5duujFF1/Ut99+W6n2xdsFqtOZM2e0c+dODR8+3JHQStKdd96p2NhYrV27tlr7BwDUDg2D61VrfQDX57ak9vvvv9fFixcdr1etWqVHHnlEjzzyiP7617+6qxuXPfDAA/L391dYWJiGDBmi/fv3O5UXv+7UqVOptp06dSpVHwCAsnSNbqxwaz1Te2ojrEHqGt3YE8MC6gy3bT944IEH9PHHH6thw4aaM2eOVq5cqd///vcyDEMvvPCCjh8/rmnTprmruwo1bdpU06ZNU/fu3RUaGqp9+/bp5ZdfVvfu3fX11187Ht2blZUl6eoDJEoKCwtzlJcnPT1dGRkZTseOHj0qSbLZbMrJyXHHdKpFbm6u01eUj1i5hniZR6zM84VYPXT7TVq47cR16xiSHrq9qS7n2nS5msbhC7HyFsTKPE/GymazudzGbUntyZMn1bJlS0lXL87atGmToqKiJEnDhg3TnXfe6dGktn///urfv7/j9b333qv7779fHTt21IwZM7Ru3Tqn+uVtc6ho+8OiRYs0e/bsMstSU1PLvNOCt0lNTa3pIfgMYuUa4mUesTLPm2MVUyS1a+Sn7y746Wr6eu2/IVdft2tUpJZ5x7R5c/Xf1subY+VtiJV5nojVyZMnXW7jtqS2adOmOnTokG699VYVFBQ4rXw2atTIK1Yso6Ojdffdd2vHjh2OY8V3QihrRfb8+fNlruBea/z48Ro6dKjTsaNHjyopKUnx8fFq27atG0ZePXJzc5Wamqr4+Hg1aNCgpofj1YiVa4iXecTKPF+JVY/EIr2bclp/2/2DMnMLHMfDG9TTb+KaaVTCzdX+RDFfiZU3IFbmeTJWBw4ccLmN25LaP/7xj3rooYf0+uuva8qUKfrtb3+rqVOnyjAMLViwQMOGDXNXV1ViGIb8/H7+MOnQoYMkad++fbrvvvuc6u7bt89RXp7IyEhFRkaWWWa1WhUaGlrFEVe/Bg0a+MQ4vQGxcg3xMo9YmecLsZpyXyM99ct22pWWrYuX89UwuJ66Rjf2+ONxfSFW3oJYmeeJWFmtVpfbuC2pHT16tCIiIjR16lR98803Kiws1KeffqrmzZvrP/7jPzRz5kx3dVVp33//vb7++mv16dPHcax58+aKj4/XypUrNWXKFPn7+0uSduzYoUOHDmnSpEk1NFoAgC8L9PdTQkyTiisCcAu33qd24MCBGjhwoIqKivTjjz8qODhYjRo1qtI5N2zYoNzcXF26dEmS9N1332nNmjWSpPvuu08hISEaM2aMli9frmPHjjn28fbp00f33nuvOnXq5LhQbMGCBbJYLHrxxRed+pg/f7769u2roUOHavz48UpPT9fUqVPVoUMHjRo1qkrjBwAAQPVza1KbnZ2tdevW6dtvv5XNZpPValWHDh00cODACvemlmfcuHE6ceLnK0lXr16t1atXS7q68hodHS273S673S7D+Pk5Lh07dtTf//53vfrqq7p8+bIiIyPVq1cvPf/884qNjXXqIzExUevXr9eMGTM0cOBAhYSE6IEHHtArr7yioKCgSo0bAAAAnuO2pHbjxo0aNmyYOnfurI4dO6pJkybKycnR8uXL9Z//+Z96//331bt3b5fPm5aWVmGd5ORkJScnOx17/fXXXeqnb9++6tu3r0ttAAAA4B3cltQ+8cQTWr58uQYOHFiq7JNPPtH48eN16NAhd3UHAAAAOLjtMszTp0+rX79+ZZb16dNHp0+fdldXAAAAgBO3JbWJiYl64okndPbsWafjP/zwg5588kklJia6qysAAADAiduS2uTkZF28eFHR0dEKDw9Xy5YtHV8vXryo5cuXu6srAAAAwInb9tSGh4fr/fffV25uro4cOeK4+0Hr1q15QgcAAACqlVtv6SVdfcpEly5dnI4VFRVp5cqVGjFihLu7AwAAANy3/eB6CgoKeIgBAAAAqo3bVmpfeOGFcssKCgrc1Q0AAABQituS2jlz5mjw4MEKDQ0tVWa3293VDQAAAFCK25LaDh06aMSIEbr//vtLlV25cqXUE78AAAAAd3HbntoxY8aosLCwzLLAwEDNnDnTXV0BAAAATty2UjthwoRyy/z9/UlqAQAAUG08cvcDAAAAoDqR1AIAAMDneSSpLSoq0ooVKzzRFQAAAOogHr4AAAAAn+ex7QeGYXiqKwAAANQxHktqLRaLp7oCAABAHeO2W3pt37693LK8vDx3dQMA8FEF9iLtSsvWxcv5ahhcT12jGyvQn+uVAbiH25LaRx555Lrlv/jFL9zVFQDAhxTYi7R4yzGtSElTpi3fcTzCGqThCVEalxhDcgugytyW1H7//ffuOhUAoJYosBfp8RW7tPlQhkpuQsu05em1Lw5r76kLent4HIktgCpxW1Jb7MiRI9qyZYsyMzMVERGhxMREtWrVyt3dAAB8wOItx7T5UIYkqeTlwsWvNx1M11tbjmli79YeHRuA2sVtvxYXFRXp8ccfV5s2bfTSSy/pH//4h+bNm6c2bdro97//vYqKitzVFQDABxTYi7QiJa3UCm1JFkkrUk6owM6/EwAqz21J7YsvvqiUlBTt2bNHx48fV0pKio4fP65du3Zp+/btmjt3rru6AgD4gF1p2cq05ZdaoS3JkJRhy9OutGxPDAtALeW2pHb58uV677331LlzZ6fjXbp00apVq7Rs2TJ3dQUA8AEXL+dXXKkK9QHgWm5Lan/88Ue1b9++zLL27dvrxx9/dFdXAAAf0DC4XrXWB4BruS2pbdGihVJSUsos2759u1q0aOGurgAAPqBrdGOFW+uZ2lMbYQ1S1+jGnhgWgFrKbUntxIkT9fDDD+vTTz91PBLXMAx98skneuSRR/TUU0+5qysAgA8I9PfTiIRoU3tqRyREcUsvAFXitlt6TZgwQdnZ2Ro2bJgKCwsVHh6uzMxMBQQE6LnnntP48ePd1RUAwEeMS4zR3lMXtOlguixyvq1X8etebSI1NjGmZgYIoNZw66/F06dP1w8//KB169ZpwYIFWrdunX744QdNmzatUue7dOmSnnnmGfXr108RERGyWCyaNWuWqbYffvihHn74YbVq1UrBwcGKjo7WI488oiNHjpSqm5iYKIvFUuq//v37V2rcAICrAv399PbwOE3uG6twa5BTWbg1SJP7xvLgBQBu4baV2hMnTsjf318333yz+vfvL5vNpnnz5mnRokW644479OyzzyogwLXusrKytGTJEnXu3FlJSUlaunSp6bbz589X06ZNNW3aNLVs2VKnTp3SvHnzdPvtt2vHjh2lLmpr2bKlVq1a5XSsUaNGLo0XAFBaoL+fJvZurbGJMdqVlq2Ll/PVMLieukY3JpkF4DZuS2rHjBmjiRMn6uabb5YkjR8/Xl9//bWGDBmiv/zlL7pw4YJeeeUVl84ZFRWl7OxsWSwWZWZmupTUfvzxx4qMjHQ61qtXL0VHR+v1118vda7g4GB1797dpfEBAMwL9PdTQkyTmh4GgFrKbb8i//vf/1bfvn0lXd028P7772vNmjV65ZVX9I9//EOrV692+ZzF2wAqo2RCK0nNmjXTzTffrFOnTlXqnAAAAPBOblupLSgoUEhIiKSrt/AKCwvTbbfdJkmKjY1VVlaWu7qqtOPHj+vEiRNKSkoqVXbs2DGFhYUpJydHUVFR+s1vfqPp06crODj4uudMT09XRkaG07GjR49Kkmw2m3Jyctw2fnfLzc11+oryESvXEC/ziJV5xMo8YmUesTLPk7Gy2Wwut3FbUtu2bVutW7dOgwYN0po1axyrtpJ09uxZ3XDDDe7qqlIKCws1ZswYWa1WPf30005ld999tx566CG1adNGly9f1oYNG7RgwQL985//1ObNm+XnV/6C9qJFizR79uwyy1JTU3Xu3Dm3zqM6pKam1vQQfAaxcg3xMo9YmUeszCNW5hEr8zwRq5MnT7rcxm1J7fz585WUlKT69esrPz9f27Ztc5T9/e9/19133+2urlxmGIbGjBmjr776Sh988EGpB0HMmTPH6fV9992n6OhoTZkyRevWrdPgwYPLPff48eM1dOhQp2NHjx5VUlKS4uPj1bZtW/dNxM1yc3OVmpqq+Ph4NWjQoKaH49WIlWuIl3nEyjxiZR6xMo9YmefJWB04cMDlNm5Lau+9916lpaXp8OHDio2NVWhoqKNswIABGjZsmLu6colhGHrssce0cuVKLV++XIMGDTLV7tFHH9WUKVO0Y8eO6ya1kZGRZe7flSSr1eoUB2/VoEEDnxinNyBWriFe5hEr84iVecTKPGJlnidiZbVaXW5TqQvFLl26pNGjRysiIkIhISFKTEzUtm3bFBoaqq5du5aa6K233qpmzZpVpqsqKU5o3333XS1dulSPPvqoy+e43tYDAAAAeIdKZWzPPfeckpOTFRkZqc6dO2v37t3q27evtm7d6u7xVZphGPrd736nd999V2+//bZGjRrlUvvly5dLErf5AgAA8AGV2n7w8ccfa8KECXrzzTclSRkZGUpKStKzzz6rHTt2uHWAGzZsUG5uri5duiRJ+u6777RmzRpJV/e+hoSEaMyYMVq+fLmOHTumqKgoSdKTTz6pd955R6NHj1bHjh2dxhUUFOS4M8NXX32luXPnavDgwWrZsqWuXLmiDRs2aMmSJerVq5cGDhzo1vkAAADA/SqV1J4+fdrp4qiIiAi99tpruvPOO5Wdna3GjRu7bYDjxo3TiRMnHK9Xr17tuOft999/r+joaNntdtntdhnGz08V//jjjyVJy5Yt07Jly5zOGRUVpbS0NEnSTTfdJH9/f7344ovKzMyUxWJR69at9cILL2jy5MlsPwAAAPABlUpqi4qKVL9+fadjHTt2lGEYOnPmjFuT2uLk83qSk5OVnJzscjtJatWqlT799FPXBwYAAACvUellyLNnzzq9DgoKkiTl5+dXbUQAAACAiyp9S68hQ4YoJCREHTt2VJcuXdS+fftKP9IWAAAAqIpKJbWffvqp9uzZoz179mj37t1OF2Hdc8896tSpk+Li4hQXF6fbb79dnTt3dtuAAQAAgJIqldQOGDBAAwYMcLw+f/68I8EtTnYXL14swzBksVhkt9vdNmAAAACgJLc8USwsLEx9+vRRnz59HMdycnK0e/duffPNN+7oAgAAACiX2x6TW1JoaKh69uypnj17VlcXAAAAgKQq3P0AAAAA8BYktQAAAPB5JLUAAADweSS1AAAA8HkktQAAAPB5JLUAAADweSS1AAAA8HkktQAAAPB5JLUAAADweSS1AAAA8HkktQAAAPB5JLUAAADweSS1AFBDCuxF2nnigv6dZdHOExdUYC+q6SEBgM8KqOkBAEBdU2Av0uItx7QiJU2ZtnxJ/lp2eJ8irIc1PCFK4xJjFOjPmgMAuIKkFgA8qMBepMdX7NLmQxmylCjLtOXptS8Oa++pC3p7eByJLQC4gE9MAPCgxVuOafOhDEmSUaKs+PWmg+l6a8sxj44LAHwdSS0AeEiBvUgrUtJKrdCWZJG0IuUEe2wBwAUktQDgIbvSspVpyy+1QluSISnDlqddadmeGBYA1AoktQDgIRcv51drfQCoy0hqAcBDGgbXq9b6AFCXkdQCgId0jW6scGs9U3tqI6xB6hrd2BPDAoBagaQWgNcosBcp5ViWPtt/VinHsmrdhVKB/n4akRBtak/tiIQobukFAC7w6k/MS5cu6ZlnnlG/fv0UEREhi8WiWbNmmW6fnp6ukSNHKjw8XCEhIUpISNCXX35ZZt2NGzcqISFBISEhCg8P18iRI5Wenu6mmcAdanvCU1Jdmm+BvUj/9eURJbz0pR7+nx0au3KPHv6fHbrzpU36ry+P1Kq5j0uMUa82kZJUasW2+HWvNpEamxjj0XEBgK/z6ocvZGVlacmSJercubOSkpK0dOlS023z8vLUu3dvXbhwQW+88YYiIyO1cOFC9e/fXxs3blSPHj0cdbdu3aoBAwbo/vvv17p165Senq5nn31WvXv31q5duxQUFFQd06uyAnuRdqVl6+LlfDUMrqeu0Y1r5cpO6acvXRVhDaqVT1+qi/OtSw8jCPT309vD4/TWlmNakXJCGbY8R1m4NUgjEqI0tpZ9jwHAE7w6qY2KilJ2drYsFosyMzNdSmrfeecd7d+/X9u3b1dCQoIkqWfPnurcubOeeeYZ/etf/3LU/cMf/qDY2FitWbNGAQFXQ3LLLbforrvu0rJlyzRu3Dj3TqyK6lLSU9cSnro2X8m1hxFM7N3ao2OrLoH+fprYu7XGJsZo23en9fXOvbqrWxfd2+7mWvN9BQBP8+pPT4vFIouloksqyrZ27VrdeuutjoRWkgICAvToo48qNTVVZ86ckSSdOXNGO3fu1PDhwx0JrSTdeeedio2N1dq1a6s2CTcrTnpe++KwsmzOt/spTnp+/5fdtebPtXXt6Ut1bb51/WEEgf5+6hbVSJ2bGOoW1YiEFgCqoNZ+gu7fv1+dOnUqdbz42Lfffuuod+3xknWLy71FXUp66lrCU9fmK/EwAgCA+3j19oOqyMrKUlhYWKnjxceysrKcvpZXt7i8POnp6crIyHA6dvToUUmSzWZTTk6O64MvR4G9SMu3fy+LSie017JIWr79e/329ojrrvzk5uY6ffU2O09ccNpeUZ7ihGfbd6fVLapRtYzFE7HypvlWldl4nc266NJ5z2ZdVE5EYKXH5Y28/efQmxAr84iVecTKPE/Gymazudym1ia1kq67daFkWXl1K9r+sGjRIs2ePbvMstTUVJ07d66CUZp35KJFWbn+FdYzJGXmFujdj7epdcOK1sCujtMb/TvLIqni+Rb7eude2Y5XPN+qqM5YeeN8q6qieB2/6Nqcjx/cr81nvXvOleWtP4feiFiZR6zMI1bmeSJWJ0+edLlNrU1qmzRpUuYq6/nz5yX9vDLbpEkTSSq3blkruNcaP368hg4d6nTs6NGjSkpKUnx8vNq2bVup8ZfFfjBT+u6A6fot23RQzzbh5Zbn5uYqNTVV8fHxatCggTuG6FbWExe07PA+0/Xv6talWldqqztW3jTfqjIbr7vtRfrriVSdzy2o8K8PTRoEatTAu2rdvlNv/zn0JsTKPGJlHrEyz5OxOnDAfL5TrNYmtR07dtS+faUThOJjHTp0cPq6b98+3XfffaXqFpeXJzIyUpGRkWWWWa1WhYaGujz28tzUpMDF+g1N9d+gQQO3jtNd7m1nVbj1kLIq2HNp0dVbIXniyvHqjJU3zreqzMTrP+68Ra99cfi6dYz/q9ekcSP3Dc7LeOvPoTciVuYRK/OIlXmeiJXVanW5jXf/i1gFgwcP1sGDB51u3VVYWKiVK1fqjjvuULNmzSRJzZs3V3x8vFauXCm73e6ou2PHDh06dEhDhgzx+NjLU9cesVnXnr5U1+ZbjIcRAADcwev/VdywYYPWrFmjjz/+WJL03Xffac2aNVqzZo1++uknSdKYMWMUEBCgEydOONqNHj1a7du319ChQ/Xee+9p48aNGjZsmA4dOqT58+c79TF//nwdPHhQQ4cO1caNG/Xee+9p2LBh6tChg0aNGuW5yVagLiY9dS3hqWvzlX5+GMHkvrEKtzo/6CTcGqTJfWNr1X15AQDVw+u3H4wbN84pWV29erVWr14tSfr+++8VHR0tu90uu90uw/g53QsKCtKXX36pZ555RhMnTtRPP/2kLl26aMOGDU5PE5OkxMRErV+/XjNmzNDAgQMVEhKiBx54QK+88orXPU1sXGKM9p66oE0H00vdBaH4dW1Keura05fq2nyLXfswgrrwlDwAgPt5fVKblpZWYZ3k5GQlJyeXOn7jjTdq+fLlpvrp27ev+vbt6+LoPK8uJj11LeGpa/O9VqC/nxJimtT0MAAAPsjrk1qUVleTnrqW8NS1+QIAUBUktT6MpAcAAOAqktpqkJd3dUtA8ZPFvJXNZtPJkyd14MCBSt06oy4hVq4hXuYRK/OIlXnEyjxiZZ4nY1WcQxXnVGaQ1FaDU6dOSZKSkpJqdiAAAAA+7NSpU7r99ttN1bUY194yAG5x4cIFbd26VS1atPC6uydcq/jJZx999JFatWpV08PxasTKNcTLPGJlHrEyj1iZR6zM82Ss8vLydOrUKfXo0UONGjUy1YaV2mrQqFEjDRo0qKaHYVqrVq3Uvn37mh6GTyBWriFe5hEr84iVecTKPGJlnqdiZXaFtljtvVQeAAAAdQZJLQAAAHweSS0AAAB8HkltHRYREaGZM2cqIiKipofi9YiVa4iXecTKPGJlHrEyj1iZ5+2x4u4HAAAA8Hms1AIAAMDnkdQCAADA55HUAgAAwOeR1AIAAMDnkdQCAADA55HUAgAAwOeR1AIAAMDnkdQCAADA55HUAgAAwOeR1AIAAMDnkdQCAADA55HUAgAAwOeR1AIAAMDnkdQCAADA55HUAgAAwOeR1AIAAMDnkdQCAADA55HUAgAAwOeR1AIAAMDnkdQCAADA55HUAgAAwOeR1AIAAMDnkdQCAADA55HUAgAAwOeR1AIAAMDnkdQCAADA55HUAgAAwOeR1AIAAMDnkdQCAADA55HUAgAAwOeR1AIAAMDnBdT0AGqjCxcuaOvWrWrRooWCgoJqejgAAAA+JS8vT6dOnVKPHj3UqFEjU21IaqvB1q1blZSUVNPDAAAA8GkfffSRBg0aZKouSW01aNGihaSr34hWrVrV8GjKZ7PZlJqaqvj4eFmt1poejlcjVq4hXuYRK/OIlXnEyjxiZZ4nY3X06FElJSU5ciozSGqrQfGWg1atWql9+/Y1PJry5eTk6Ny5c2rbtq1CQ0NrejhejVi5hniZR6zMI1bmESvziJV5NRErV7ZxcqEYAAAAfB4rtT6swF6kXWnZung5Xw2D66lrdGMF+vN7CgAAqHtIan1Qgb1Ii7cc04qUNGXa8h3HI6xBGp4QpXGJMSS3AACgTiGp9TEF9iI9vmKXNh/KkKVEWaYtT699cVh7T13Q28PjSGwBAECdQdbjYxZvOabNhzIkSUaJsuLXmw6m660txzw6LgAAgJrESq0PKbAXaUVKmiwqndBeyyJpRcoJjWUbAgCgjjMMQ5cuXVJOTo4KCgpkGM7/ghYUFCgsLExnz55VZmZmDY3SN7gjVn5+fgoKCtKNN94oPz/35ihkPD5kV1q2Mm35101opasJb4YtT7vSsj0xLAAAvFJhYaFOnjypM2fO6NKlSyosLCyV1AYEBCgiIkIBAazzVaSqsTIMQ/n5+bpw4YJOnjypoqIi947PrWdDtbp4Ob/iSlWoDwBAbZKdna2ffvpJDRs2VGRkZJnJmN1u16VLl3TDDTfI39+/BkbpO9wRK8MwlJ6ervPnz+vHH3/UTTfd5LbxsVLrQxoG16vW+gAA1CY2m03+/v666aabWIn1EhaLRZGRkfL391deXp5bz01S60O6RjdWuLVeqbselGTR1dt7dY1u7IlhAQDglQzDUEBAgCyWiv7lhCdZLBb5+/u7ffsBSa0PCfT304iEaFN7akckRHGRGAAA8ErV8YsGWY+PGZcYo15tIiWp1Ipt8etebSI1NjHGo+MCAACoSSS1PibQ309vD4/T5L6xCrcGOZWFW4M0uW8sD14AAAB1DrumfVCgv58m9m6tsYkx2pWWrYuX89UwuJ66RjcmmQUAAHUSGZAPC/T3U0JME/XvcJMSYpqQ0AIAAI+5dOmSnnnmGfXr108RERGyWCyaNWtWjY2HLAgAAAAuy8rK0pIlS5SXl6ekpKSaHg7bDwAAAOC6qKgoZWdny2KxKDMzU0uXLq3R8bBSCwAA4MMMw9Bbb72ljh07qn79+mrVqpXWrFmjoqIidejQQfPmzauWfi0Wi1fdA5iVWgAAAB9lGIYeeughffLJJ5o9e7Zuu+02zZ8/XyNHjlROTo6ys7M1adKkMtvZ7XZTffjK09h8Y5QAAABulpycrHfffVd2u13+/v5lrjp26dJFf/7znx2v9+7dW2aSWNKWLVucXicmJpZZb+TIkRo5cqT5QZeQnJys1atXa9WqVfrtb38rSQoKCtK9996rKVOmaMGCBQoJCSnVbuvWrerZs6epPr7//ntFR0dXeoyeQlILAADqpLS0NG3bts2lNhcuXNDWrVtd7qu8NuUlu2b993//tzp06OBIaCWpcePGkqSmTZtq1KhRZbaLi4vTzp07TfXRrFmzKo3RU0hqAQBAnRQdHa177723wpXaazVq1Eg9evRwua/y2lRlBTQ7O1vffPONpk6d6nS8eFvB3Llz5e/vX2Zbq9Vaam7lYfsBAACAFxs5cqSGDx+uS5cu6YYbbig3AbxWly5dSm0tMKMybSpy9OhRGYahW265xen4kiVLJEndunUrty3bDwAAAOAVipPwjIwMx7Hdu3c7klrDMMpty/YDAAAAeIX27dsrIiJCr7/+uqKjo+Xn56f//M//1K9//Wv97W9/05IlS/T444+rRYsWpdrecMMN6tq1a5XHsGHDBuXm5urSpUuSpO+++05r1qyRJN13331lXqRWXUhqAQAAfFBQUJDWrl2rp556SqNGjVKDBg00duxYzZ07V6GhoZo/f76aN2+usWPHVtsYxo0bpxMnTjher169WqtXr5bk+W0LJLUAAAA+6q677tKuXbtKHX/77bf19ttvV3v/aWlp1d6HWTxRDAAAAD6PpBYAAAA+j6QWAAAAPo+kFgAAAD6PpBYAAAAedb176FYWSS0AAKiVLBaLCgsLqyWBQuUZhiG73S4/P/emoSS1AACgVrJarbLb7Tp79qwKCwtrejjQ1YQ2PT1ddrtdQUFBbj0396kFAAC1UuPGjfXTTz/p4sWLunjxogICAuTn5yeLxeKoYxiGCgoKlJWV5XQcpVU1VsUrtHa7XcHBwbrxxhvdOj5WagEAQK0UEBCgX/ziF2revLluuOEGBQQElErGCgsLlZGRwUquCVWNlcViUb169dSoUSP94he/cPv2A1ZqAQBArWWxWBQaGqrQ0NAyy3NycnTw4EF17ty53Dq4yttjxUotAAAAfB5JLQAAAHweSS0AAAB8ns8mtf/85z913333qXHjxgoODlbr1q314osvOtXZs2eP+vTpI6vVqkaNGmnIkCE6fvx4med788031aZNGwUFBemWW27R7NmzVVBQ4ImpAAAAoIp8Mql977331KNHDzVs2FArVqzQ+vXr9eyzzzrdXPngwYNKTExUfn6+3n//fS1btkyHDx/WPffco4yMDKfzzZ07V0899ZSGDBmizz//XOPHj9e8efM0YcIET08NAAAAleBzdz84c+aMHn/8cf3+97/XokWLHMd79uzpVG/GjBkKCgrSJ5984rhCLy4uTq1bt9arr76q+fPnS5KysrI0Z84c/e53v9O8efMkSYmJiSooKND06dM1adIktWvXzkOzAwAAQGX43Ert0qVLlZubq2effbbcOoWFhfrkk0/04IMPOt1yIioqSj179tTatWsdxz777DNduXJFo0aNcjrHqFGjZBiGPvroI7fPAQAAAO7lcyu127ZtU1hYmA4ePKhBgwZp//79CgsL05AhQ7RgwQKFhobq2LFjunz5sjp16lSqfadOnfTFF1/oypUrql+/vvbv3y9J6tixo1O9m266SeHh4Y7y8qSnp5faznD06FFJks1mU05OTlWmW61yc3OdvqJ8xMo1xMs8YmUesTKPWJlHrMzzZKxsNpvLbXwuqT1z5ox++uknDR06VH/84x/15z//WTt37tTMmTO1f/9+ffXVV8rKypIkhYWFlWofFhYmwzCUnZ2tm266SVlZWQoKClKDBg3KrFt8rvIsWrRIs2fPLrMsNTVV586dq8QsPSs1NbWmh+AziJVriJd5xMo8YmUesTKPWJnniVidPHnS5TY+l9QWFRXpypUrmjlzpqZOnSrp6h7YevXqadKkSfryyy8VEhIiSdd9LvG1ZWbrlWX8+PEaOnSo07GjR48qKSlJ8fHxatu2bYVzqim5ublKTU1VfHx8mUk9fkasXEO8zCNW5hEr84iVecTKPE/G6sCBAy638bmktkmTJjpy5Ih++ctfOh0fMGCAJk2apD179mjQoEGSVOYq6/nz52WxWNSoUSPH+a5cuaKffvrJkQxfWzcuLu6644mMjFRkZGSZZVar1SsfI1dSgwYNfGKc3oBYuYZ4mUeszCNW5hEr84iVeZ6IldVqdbmNz10oVtY+WUmO23n5+fkpJiZGwcHB2rdvX6l6+/btU6tWrVS/fn1JP++lLVn33LlzyszMVIcOHdw5fAAAAFQDn0tqH3zwQUnShg0bnI6vX79ektS9e3cFBARo4MCB+vDDD3Xp0iVHnZMnT2rz5s0aMmSI41j//v1Vv359JScnO50vOTlZFotFSUlJ1TMRAAAAuI3PbT/o16+fBg4cqBdeeEFFRUXq3r27du3apdmzZ+uBBx7Q3XffLUmaPXu2unXrpgceeEBTp07VlStXNGPGDIWHh2vy5MmO84WFhWn69Ol6/vnnFRYWpn79+mnnzp2aNWuWHnvsMe5RCwAA4AN8bqVWkv7+979r0qRJWrJkiQYMGKDFixfr6aef1po1axx12rRpoy1btigwMFC//vWvNXLkSLVq1Urbtm1TRESE0/mmTZumP//5z1qzZo369eunN998U1OnTtXChQs9PTUAAABUgs+t1EpScHCwXn75Zb388svXrRcXF6eNGzeaOueTTz6pJ5980h3DQzUpsBdpV1q2Ll7OV8Pgeuoa3ViB/j75exkAAHAzn0xqUbcU2Iu0eMsxrUhJU6Yt33E8whqk4QlRGpcYQ3ILAEAdR1ILr1ZgL9LjK3Zp86EMlbxjcKYtT699cVh7T13Q28PjSGwBAKjDyALg1RZvOabNh64+htgoUVb8etPBdL215ZhHxwUAALwLSS28VoG9SCtS0kqt0JZkkbQi5YQK7EWeGBYAAPBCJLXwWrvSspVpyy+1QluSISnDlqddadmeGBYAAPBCJLXwWhcv51dcqQr1AQBA7UFSC6/VMLhetdYHAAC1B0ktvFbX6MYKt9Yztac2whqkrtGNPTEsAADghUhq4bUC/f00IiHa1J7aEQlR3NILAIA6jCwAXm1cYox6tYmUpFIrtsWve7WJ1NjEGI+OCwAAeBeSWni1QH8/vT08TpP7xircGuRUFm4N0uS+sTx4AQAA8EQxeL9Afz9N7N1aYxNjtCstWxcv56thcD11jW5MMgsAACSR1MKHBPr7KSGmSU0PAwAAeCGWuQAAAODzSGoBAADg80hqAQAA4PNIagEAAODzSGoBAADg80hqAQAA4PNIagEAAODzSGoBAADg80hqAQAA4PNIagEAAODzSGoBAADg80hqAQAA4PNIagEAAODzSGoBAADg80hqAQAA4PNIagEAAODzSGoBAADg80hqAQAA4PNIagEAAODzSGoBAADg80hqAQAA4PNIagEAAODzfD6pXbp0qSwWi6xWa6myPXv2qE+fPrJarWrUqJGGDBmi48ePl3meN998U23atFFQUJBuueUWzZ49WwUFBdU9fAAAALiBTye1Z86c0ZQpU9SsWbNSZQcPHlRiYqLy8/P1/vvva9myZTp8+LDuueceZWRkONWdO3eunnrqKQ0ZMkSff/65xo8fr3nz5mnChAmemgoAAACqIKCmB1AVY8eO1b333quwsDCtWbPGqWzGjBkKCgrSJ598otDQUElSXFycWrdurVdffVXz58+XJGVlZWnOnDn63e9+p3nz5kmSEhMTVVBQoOnTp2vSpElq166dZycGAAAAl/jsSu3KlSu1detWLVq0qFRZYWGhPvnkEz344IOOhFaSoqKi1LNnT61du9Zx7LPPPtOVK1c0atQop3OMGjVKhmHoo48+qrY5AAAAwD18MqlNT0/XpEmT9PLLL+vmm28uVX7s2DFdvnxZnTp1KlXWqVMnHT16VFeuXJEk7d+/X5LUsWNHp3o33XSTwsPDHeUAAADwXj65/WD8+PG69dZbNW7cuDLLs7KyJElhYWGlysLCwmQYhrKzs3XTTTcpKytLQUFBatCgQZl1i89VnvT09FJ7dI8ePSpJstlsysnJMTWnmpCbm+v0FeUjVq4hXuYRK/OIlXnEyjxiZZ4nY2Wz2VxuU61J7csvv6xJkyapfv36bjvnBx98oI8//ljffPONLBbLdeter/zaMrP1yrJo0SLNnj27zLLU1FSdO3fuuu29QWpqak0PwWcQK9cQL/OIlXnEyjxiZR6xMs8TsTp58qTLbao1qV25cqUWLlyoF154QSNHjnRKEE+fPq3169fr8ccfN30+m82mCRMmaOLEiWrWrJkuXLggScrPz5ckXbhwQYGBgWrSpIkklbnKev78eVksFjVq1EiS1KRJE125ckU//fSTQkJCStWNi4u77pjGjx+voUOHOh07evSokpKSFB8fr7Zt25qen6fl5uYqNTVV8fHxZa5U42fEyjXEyzxiZR6xMo9YmUeszPNkrA4cOOBym2pNavft26fk5GT94Q9/0Guvvaa5c+eqa9euMgxDS5Ys0YIFC1xKajMzM/Xjjz/qT3/6k/70pz+VKm/cuLEGDRqkNWvWKDg4WPv27StzTK1atXKsHhfvpd23b5/uuOMOR71z584pMzNTHTp0uO6YIiMjFRkZWWaZ1Wp1ulDNWzVo0MAnxukNiJVriJd5xMo8YmUesTKPWJnniViV9fyBilRrUrt27Vo99dRTstlsOn/+vJKSkpxWa3/3u9+5dL6mTZtq8+bNpY6//PLL2rp1qzZs2KDw8HAFBARo4MCB+vDDD7VgwQLdcMMNkq4uZW/evFlPP/20o23//v1Vv359JScnOyW1ycnJslgsSkpKcnHWAAAA8LRqTWqfffZZde/eXTNmzFDjxo1lt9v19ttv63/+5380dOhQvfXWWy6dr379+kpMTCx1PDk5Wf7+/k5ls2fPVrdu3fTAAw9o6tSpunLlimbMmKHw8HBNnjzZUS8sLEzTp0/X888/r7CwMPXr1087d+7UrFmz9Nhjj3GPWgAAAB9Qrbf0On36tKZOnaq7775b7du3V6dOnbRw4UKlpKRoy5Ytev7556ut7zZt2mjLli0KDAzUr3/9a40cOVKtWrXStm3bFBER4VR32rRp+vOf/6w1a9aoX79+evPNNzV16lQtXLiw2sYHAAAA96nWpLZ169bau3dvqeNxcXH605/+pMWLF7uln+Tk5DJv/RAXF6eNGzcqNzdXFy9e1Nq1axUTE1PmOZ588kkdOnRIeXl5OnHihGbNmqXAwEC3jA8AAADVq1q3H4wfP15TpkxR8+bN9dBDDzmVGYbBPeGA6yiwF2lXWrYuXs5Xw+B66hrdWIH+Pvm8FAAAql21JrVjx47Vpk2b9PDDD2vevHkaOHCgbrnlFmVkZOj1118v9RQvAFeT2cVbjmlFSpoybfmO4xHWIA1PiNK4xBiSWwAASqj2J4q9//77evfdd7Vw4UK99NJLMgxDkhQREaE33nijursHfEqBvUiPr9ilzYcyVPKxH5m2PL32xWHtPXVBbw+PI7EFAOAaHvlXcdSoUdq1a5eysrKUmpqq3bt369SpU0pISPBE94DPWLzlmDYfuvrYZaNEWfHrTQfT9daWYx4dFwAA3s6jSz2NGjVS165dddttt6levXqe7BrwegX2Iq1ISSu1QluSRdKKlBMqsBd5YlgAAPgE/n4JeIldadnKtOWXWqEtyZCUYcvTrrRsTwwLAACfQFILeImLl/MrrlSF+gAA1GYktYCXaBjs2pYcV+sDAFCbkdQCXqJrdGOFW+uZ2lMbYQ1S1+jGnhgWAAA+gaQW8BKB/n4akRBtak/tiIQobukFAMA1+FcR8CLjEmPUq02kJJVasS1+3atNpMYmlv24ZwAA6iqSWsCLBPr76e3hcZrcN1bh1iCnsnBrkCb3jeXBCwAAlKHanygGwDWB/n6a2Lu1xibGaFdati5ezlfD4HrqGt2YZBYAgHKQ1AJeKtDfTwkxTWp6GAAA+ASWfQAAAODzSGoBAADg80hqAQAA4PNIagEAAHBdBfYi7TxxQf/OsmjniQsqsBfV9JBK4UIxAAAAlKnAXqTFW45pRUqaMm35kvy17PA+RVgPa3hClMYlxnjNnXlIagEAAFBKgb1Ij6/Ypc2HMko9ECjTlqfXvjisvacueM3902t+BAAAAPA6i7cc0+ZDGZJU6hHuxa83HUzXW1uOeXRc5SGpBQAAgJMCe5FWpKSVWqEtySJpRcoJr9hjS1ILAAAAJ7vSspVpyy+1QluSISnDlqddadmeGNZ1kdQCAADAycXL+dVavzqQ1AIAAMBJw+B61Vq/OpDUAgAAwEnX6MYKt9Yztac2whqkrtGNPTGs6yKpBQAAgJNAfz+NSIg2tad2REIUt/QCAACAdxqXGKNebSIlqdSKbfHrXm0iNTYxxqPjKg9JLQAAAEoJ9PfT28PjNLlvrMKtQU5l4dYgTe4b6zUPXpB4ohgAAADKEejvp4m9W2tsYoy2fXdaX+/cq7u6ddG97W72mmS2GEktAAAArivQ30/dohrJdtxQt6hGXpfQSmw/AAAAQC1AUgsAAACfR1ILAAAAn0dSCwAAAJ/nc0ntpk2bNHr0aLVp00YNGjRQ8+bNNWjQIO3evbtU3T179qhPnz6yWq1q1KiRhgwZouPHj5d53jfffFNt2rRRUFCQbrnlFs2ePVsFBQXVPR0AAAC4gc8ltYsXL1ZaWpqeeuoprV+/Xm+88YbS09PVvXt3bdq0yVHv4MGDSkxMVH5+vt5//30tW7ZMhw8f1j333KOMjAync86dO1dPPfWUhgwZos8//1zjx4/XvHnzNGHCBE9PDwAAAJXgc7f0WrhwoSIjI52O9e/fX61atdK8efPUq1cvSdKMGTMUFBSkTz75RKGhoZKkuLg4tW7dWq+++qrmz58vScrKytKcOXP0u9/9TvPmzZMkJSYmqqCgQNOnT9ekSZPUrl07D84QAAAArvK5ldqSCa0kWa1WtWvXTqdOnZIkFRYW6pNPPtGDDz7oSGglKSoqSj179tTatWsdxz777DNduXJFo0aNcjrnqFGjZBiGPvroo+qZCAAAANzG55Lasly8eFF79uxR+/btJUnHjh3T5cuX1alTp1J1O3XqpKNHj+rKlSuSpP3790uSOnbs6FTvpptuUnh4uKMcAAAA3svnth+UZcKECcrNzdW0adMkXd1SIElhYWGl6oaFhckwDGVnZ+umm25SVlaWgoKC1KBBgzLrFp+rPOnp6aX26B49elSSZLPZlJOTU6k5eUJubq7TV5SPWLmGeJlHrMwjVuYRK/OIlXmejJXNZnO5jc8ntc8//7xWrVqlN998U3FxcU5lFoul3HbXlpmtV5ZFixZp9uzZZZalpqbq3Llz123vDVJTU2t6CD6DWLmGeJlHrMwjVuYRK/OIlXmeiNXJkyddbuPTSe3s2bM1Z84czZ07V0888YTjeJMmTSSpzFXW8+fPy2KxqFGjRo66V65c0U8//aSQkJBSdUsmyiWNHz9eQ4cOdTp29OhRJSUlKT4+Xm3btq3M1DwiNzdXqampio+PL3OlGj8jVq4hXuYRK/OIlXnEyjxiZZ4nY3XgwAGX2/hsUjt79mzNmjVLs2bN0nPPPedUFhMTo+DgYO3bt69Uu3379qlVq1aqX7++pJ/30u7bt0933HGHo965c+eUmZmpDh06XHcckZGRZV68Jl29gO3aC9W8VYMGDXxinN6AWLmGeJlHrMwjVuYRK/NcjVWBvUi70rJ18XK+GgbXU9foxgr0rxWXKlXIE+8rq9XqchufTGpffPFFzZo1S9OnT9fMmTNLlQcEBGjgwIH68MMPtWDBAt1www2Sri5lb968WU8//bSjbv/+/VW/fn0lJyc7JbXJycmyWCxKSkqq9vkAAADfUGAv0uItx7QiJU2ZtnzH8QhrkIYnRGlcYkydSW69jc8ltX/60580Y8YM9e/fX/fff7927NjhVN69e3dJV1dyu3XrpgceeEBTp07VlStXNGPGDIWHh2vy5MmO+mFhYZo+fbqef/55hYWFqV+/ftq5c6dmzZqlxx57jHvUAgAASVcT2sdX7NLmQxkqecVNpi1Pr31xWHtPXdDbw+NIbGuAzyW1H3/8saSr95f97LPPSpUbhiFJatOmjbZs2aJnn31Wv/71rxUQEKBevXrp1VdfVUREhFObadOm6YYbbtDChQv16quvqmnTppo6darjbgoAAACLtxzT5kNX73hklCgrfr3pYLre2nJME3u39ujY4INJ7ZYtW0zXjYuL08aNG03VffLJJ/Xkk09WclQA3KEu71GrC/j+wpcV2Iu0IiVNFpVOaK9lkbQi5YTGsg3B43wuqQVQ+7BHrXbj+4vaYFdattP7tzyGpAxbnnalZSshpkn1DwwOJLUAahR71Gq3uvz9ZWW6drl4ueKEtir1UXUktQBqFHvUare6+P1lZbp2ahhcr1rro+r4qQJQY67do3Y9xXvUCuxFnhgW3KQufn+LV6Zf++Kwskr8qbp4Zfr3f9ldK+Za13SNbqxwaz1T7+cIa5C6Rjf2xLBwDZJaADWmeI/a9S66kJz3qMF31MXvrysr0/Atgf5+GpEQber9PCIhitX4GkDEAdQY9qjVbnXt+1sXV6brmnGJMerV5upTREt+n4tf92oTqbGJMR4dF64iqQVQY9ijVrvVte9vXVyZvlaBvUgpx7L02f6zSjmWVSuT9kB/P709PE6T+8Yq3BrkVBZuDdLkvrG18qJHX8GFYgBqTPEetawKEgGLrv6DwR4131LXvr91bWW6WF27MC7Q308Te7fW2MQY7m7hZYg+gBrDHrXara59f+vayrRUty+MC/T3U0JME/XvcJMSYpr4/Pu3NuA7AKBGsUetdqtL39+6eHU8F8bBm5DUAqhR7FGr3erS97eurUxzYRy8DXtqAdQ49qjVbnXp+zsuMUZ7T13QpoPpssh59bL4dW1ZmeaxsfA2JLUAvEbxHjXUTnXh+1u8Mv3WlmNakXJCGbY8R1m4NUgjEqI0tpZcOFVXL4yD9yKpBQDAjerKynRdvDAO3o2kFgCAalDbV6br2i3b4P1qz6+MAADAY+rahXHwfrzDAABApdSlW7bB+5HUAgCASqlLt2yD92NPLQAAqLS6cmEcvB9JLQAAqLLafmEcvB+/QgEAAMDnkdQCAADA55HUAgAAwOeR1AIAAMDnkdQCAADA55HUAkANKbAXaeeJC/p3lkU7T1xQgb2opocEAD6LW3oBgIcV2Iu0eMsxrUhJU6YtX5K/lh3epwjrYQ1PiNK4xBju7wkALiKpBQAPKrAX6fEVu7T5UEapx4pm2vL02heHtffUBZ7CBAAu4hMTADxo8ZZj2nwoQ5JklCgrfr3pYLre2nLMo+MCAF9HUgsAHlJgL9KKlLRSK7QlWSStSDnBHlsAcAFJLQB4yK60bGXa8kut0JZkSMqw5WlXWrYnhgUAtQJJLQB4yMXL+dVaHwDqMpJaAPCQhsH1qrU+ANRlJLUA4CFdoxsr3FrP1J7aCGuQukY39sSwAKBWIKkFAA8J9PfTiIRoU3tqRyREcUsvAHABn5gA4EHjEmPUq02kJJVasS1+3atNpMYmxnh0XADg60hq/4/NZtOkSZPUrFkz1a9fX126dNHf/va3mh4WgFom0N9Pbw+P0+S+sQq3BjmVhVuDNLlvLA9eAIBK4Ili/2fIkCHauXOnXn75ZcXGxuq9997Tww8/rKKiIv32t7+t6eEBqEUC/f00sXdrjU2M0bbvTuvrnXt1V7cuurfdzSSzAFBJJLWS1q9fry+++MKRyEpSz549deLECf3hD3/QQw89JH9//xoeJYDaJtDfT92iGsl23FC3qEYktABQBXyCSlq7dq2sVquGDh3qdHzUqFH64Ycf9K9//auGRgYAAAAzWKmVtH//frVt21YBAc7h6NSpk6P8zjvvLLNtenq6MjIynI4dPXpUkjR8+HA1aNCg3H47duyol19+2fH6//2//6c//vGPFY73008/dXp9//33V9jmpZdecsxHkqZOnaq9e/cqJydHoaGhZa5E//a3v9UjjzzieL1q1Sq999571+2npue0b9++67ap7Jyef/55SVJubm6tmVN1fp/sdrvjvbVgwYJaMadrufP7dG2sSv4c+uqcrqcqc8rNzZV09eewtszpWu6c07Xvq+HDh9eKOV3Lnd+n6/0MSr45p4pUdk4xMVcvYM3Nza32OdlstgrHVBJJraSsrCy1bNmy1PGwsDBHeXkWLVqk2bNnl1n2zTffXLff7Oxsbd682fF63759+uc//1nheK9tI8lUmy1btjjNY9u2bfr222+v26Z58+Zq1qyZ4/XWrVsr7Ku2zik1NVWSlJqaWmvmVBu/T8zpqto+p9TU1Fo3J6n6vk+/+MUvat2cauP3yZfmlJqaWu1zOnnyZIVjKomk9v9YLOXfDv16ZePHjy+1beHo0aNKSkrSbbfdVuFKbc+ePR2vmzRporvvvrvCsV7bRpKpNomJiU6/Nd57770KDQ297m+nPXr0cOrrhx9+0JkzZ67bT03PqXHj69+svrJzio+PV2pqquLj42vNnKrz+3TtykdtmdO13DmnilZqfXFO11OVOeXm5jp+DmvLnK7lzjld+76qLXO6ljvnVNFKrS/OqSKVnVNMTIzjZ7C653TgwIEKx1SSxTCMiu4DXuslJCTIbrc7VuOKffvtt+rQoYPefvttPf7446bPV9xu//79at++vbuH6zY5OTnavHmzevbsqdDQ0JoejlcjVq4hXuYRK/OIlXnEyjxiZZ4nY1WZXIoLxXT1t4IDBw6osLDQ6XjxXpEOHTrUxLAAAABgEkmtpMGDB8tms+mDDz5wOr58+XI1a9ZMd9xxRw2NDAAAAGawp1bSgAED1LdvX40bN045OTlq1aqV/vrXv+qzzz7TypUruUctAACAlyOp/T8ffvihpk2bphkzZuj8+fNq06aN/vrXv+o3v/lNTQ8NAAAAFSCp/T9Wq1VvvPGG3njjjZoeCgAAAFxEUlsN8vLyJP38EAZvZbPZdPLkSR04cEBWq7Wmh+PViJVriJd5xMo8YmUesTKPWJnnyVgV51DFOZUZJLXV4NSpU5KkpKSkmh0IAACADzt16pRuv/12U3W5T201uHDhgrZu3aoWLVooKCiopodTruKHRHz00Udq1apVTQ/HqxEr1xAv84iVecTKPGJlHrEyz5OxysvL06lTp9SjRw81atTIVBtWaqtBo0aNNGjQoJoehmmtWrXy6odEeBNi5RriZR6xMo9YmUeszCNW5nkqVmZXaItxn1oAAAD4PJJaAAAA+DySWgAAAPg8kto6LCIiQjNnzlRERERND8XrESvXEC/ziJV5xMo8YmUesTLP22PF3Q8AAADg81ipBQAAgM8jqQUAAIDPI6kFAACAzyOpBQAAgM8jqfVxNptNkyZNUrNmzVS/fn116dJFf/vb3yps9+GHH+rhhx9Wq1atFBwcrOjoaD3yyCM6cuRIqbqJiYmyWCyl/uvfv391TKnaVDZWycnJZc7fYrHo3Llzpepv3LhRCQkJCgkJUXh4uEaOHKn09PTqmFK1qWysynuvlBWv2vK+unTpkp555hn169dPERERslgsmjVrlun26enpGjlypMLDwxUSEqKEhAR9+eWXZdb19fdWVWJV1z6zqhKruvaZVZVY1bXPrE2bNmn06NFq06aNGjRooObNm2vQoEHavXu3qfbe/nnFY3J93JAhQ7Rz5069/PLLio2N1XvvvaeHH35YRUVF+u1vf1tuu/nz56tp06aaNm2aWrZsqVOnTmnevHm6/fbbtWPHjlKPv2vZsqVWrVrldMzss5i9RWVjVezdd99VmzZtnI41adLE6fXWrVs1YMAA3X///Vq3bp3S09P17LPPqnfv3tq1a5eCgoLcOqfqUtlYLVq0SDk5OU7HfvrpJ/Xv319xcXFq2rSpU1lteF9lZWVpyZIl6ty5s5KSkrR06VLTbfPy8tS7d29duHBBb7zxhiIjI7Vw4UL1799fGzduVI8ePRx1a8N7qyqxqmufWVWJVbG68plVlVjVtc+sxYsXKysrS0899ZTatWunjIwM/elPf1L37t31+eefq1evXuW29YnPKwM+69NPPzUkGe+9957T8b59+xrNmjUzCgsLy237448/ljp25swZIzAw0BgzZozT8R49ehjt27d3z6BrSFVi9e677xqSjJ07d1bYT7du3Yx27doZBQUFjmNff/21IclYtGhR5SfgQVWJVVmSk5MNScbSpUudjteG95VhGEZRUZFRVFRkGIZhZGRkGJKMmTNnmmq7cOFCQ5Kxfft2x7GCggKjXbt2Rnx8vFPd2vDeqkqs6tpnVlViVdc+s6oSq7LU5s+ssn6OLl26ZNx4441G7969r9vWFz6v2H7gw9auXSur1aqhQ4c6HR81apR++OEH/etf/yq3bWRkZKljzZo1080336xTp065faw1rSqxMuvMmTPauXOnhg8froCAn/8Icueddyo2NlZr166tch+e4O5YvfPOO7JarXrooYfcOUyvUfwnyMpYu3atbr31ViUkJDiOBQQE6NFHH1VqaqrOnDkjqfa8t6oSq7r2mVWVWJnF+6pstfkzq6yfI6vVqnbt2lX4c+QLn1cktT5s//79atu2rdObRpI6derkKHfF8ePHdeLEiVJ/xpOkY8eOKSwsTAEBAYqJidG0adN0+fLlyg/ew9wRqwceeED+/v4KCwvTkCFDSrUpfl18zpL9uPr9qCnufF8dOXJEX331lX7zm9/IarWWKvf191VV7d+/v9z3iyR9++23jnrXHi9Z11feW+5Wmz+z3KGufGa5U138zLp48aL27NlT5s/RtXzh84o9tT4sKytLLVu2LHU8LCzMUW5WYWGhxowZI6vVqqefftqp7O6779ZDDz2kNm3a6PLly9qwYYMWLFigf/7zn9q8ebP8/Lz/d6OqxKp4H1/37t0VGhqqffv26eWXX1b37t319ddfq3Pnzk7nKD5nyX5c+X7UJHe+r9555x1J0pgxY0qV1Yb3VVVlZWWV+34pLr/2q6+/t9yptn9mVUVd+8xyp7r4mTVhwgTl5uZq2rRp163nC59XJLU+7np/cjH75xjDMDRmzBh99dVX+uCDD9SiRQun8jlz5ji9vu+++xQdHa0pU6Zo3bp1Gjx4sOsDrwGVjVX//v2drm699957df/996tjx46aMWOG1q1bZ+pc1f2nRHdyx/uqsLBQy5cvV/v27dW9e/dS5bXlfVVVrsS6Nry33KGufGZVVl38zHKHuviZ9fzzz2vVqlV68803FRcXV2F9b/+88s1fKyDp6lWsZf3Gc/78eUll/5ZUkmEYeuyxx7Ry5UolJydr0KBBpvp+9NFHJUk7duxwYcQ1xx2xulZ0dLTuvvtup/kXX1VcXj+u9lFT3BWr9evX69y5c3rsscdM9+1r76uqMhvr2vLecoe68pnlbrX5M8td6tpn1uzZszVnzhzNnTtXTzzxRIX1feHziqTWh3Xs2FEHDhxQYWGh0/F9+/ZJkjp06HDd9sX/OLz77rtaunSp44fTFb7y55aqxqoshmE4zb/4HMXnLNlPZfqoCe6K1TvvvKN69epp+PDhLo/BV95XVdWxY8dy3y/Sz7GuLe+tqqpLn1nVobZ+ZrlLXfrMmj17tmbNmqVZs2bpueeeM9XGJz6vqu2+Cqh269evNyQZf/vb35yO9+/fv8JbLxUVFRljxowxLBaLsWTJEpf7nj9/viHJ+Oijj1xuWxOqEquyHD9+3LBarUZSUpLT8fj4eKNDhw5O50tJSTEkGYsXL678BDzIHbE6e/asERAQYAwbNsylvn3tfVWSq7cTWrRokSHJ2LFjh+NYQUGB0b59e+OOO+5wqlsb3lvXcjVWde0z61ruuE1Vbf7MulZlY1WXPrNeeOEFQ5Ixffp0l9r5wucVSa2P69u3r9G4cWNjyZIlxqZNm4zf/e53hiRj5cqVjjqjR482/P39jbS0NMexJ554wpBkjB492khJSXH6b8+ePY5627ZtM375y18ab731lvG///u/xj/+8Q9j3Lhxhr+/v9GrVy/Dbrd7dL5VUdlY9e7d25g9e7axdu1a48svvzT+/Oc/G82aNTNuuOEGY9++fU59bN682QgICDAGDx5sfPHFF8aqVauMFi1aGB06dDCuXLnisblWVWVjVezll182JBn/+7//W+b5a9P7yjCu/iKwevVqY9myZYYkY+jQocbq1auN1atXG7m5uYZhlB2vK1euGO3btzdatGhhrFq1yvjiiy+MwYMHGwEBAcaWLVuc+qgt763KxqoufmZVNlZ18TOrsrEqVlc+s1599VVDktG/f/9SP0cpKSmOer76eUVS6+MuXbpkPPnkk0bTpk2NevXqGZ06dTL++te/OtX5j//4D0OS8f333zuORUVFGZLK/C8qKspR78iRI8Z9991nNG/e3AgKCjLq169vdOzY0Zg7d65PfeAZRuVjNWnSJKNdu3bGDTfcYAQEBBjNmjUzHn30UePQoUNl9vO///u/Rvfu3Y369esbYWFhxogRI8q84bU3q2ysisXGxhrR0dGOG6KXVJveV4Zx/Z+n4viUF69z584ZI0aMMMLCwoz69esb3bt3N7744osy+6kN763KxqoufmZVNlZ18TOrKj+DhlF3PrN69OhRbpyu/eO9r35eWQzDMFzeswAAAAB4Ed/a2QwAAACUgaQWAAAAPo+kFgAAAD6PpBYAAAA+j6QWAAAAPo+kFgAAAD6PpBYAAAA+j6QWAAAAPo+kFgAAAD6PpBYAAAA+j6QWAAAAPo+kFgA8bP369bJYLI7//P39FRUVpSeeeEI5OTlOde12uyIjI/X666+73BYA6pKAmh4AANQ1e/bskSR98MEHatasma5cuaLVq1dr4cKFstlsSk5OdtTdtm2bMjIyNGTIEJfbAkBdYjEMw6jpQQBAXTJkyBB9/vnnunTpkvz8fv6DWbt27fTjjz8qKyvLcWzChAlKTU3Vzp07XW4LAHUJ2w8AwMN2796tjh07OiWlkhQaGqrc3FzHa8MwtHbtWj344IMutwWAuoakFgA8KCsrSydPnlTnzp2djmdkZGj//v3q1q2b49j27dt19uxZR1LrSlsAqGtIagHAg4r3xHbo0EGFhYXKzc3Vv/71LyUlJSkvL08vvPCCo+6aNWvUsWNHtW7d2qW227ZtU/fu3dWwYUM1adJEvXr10vfff+80joULF+rWW29VcHCwoqOjNWvWLNntdkd5dHS0/vnPf5Y7j//+7//WbbfdpsDAQM2aNatUeUZGhu6//341aNBAsbGx+uKLLxxliYmJql+/vqxWq6xWq3r16uViFAGgNJJaAPCg3bt3S5KefPJJBQYGymq1qnv37srPz9fnn3+unj17Oup++OGHpbYeVNT24sWLGjRokJ555hllZ2frxIkTmjhxovz9/R3nmTNnjl566SX9z//8jy5duqR169ZpzZo1Gjt2rOl5NG/eXC+88IKSkpLKLJ8wYYKaNm2qjIwMvfrqqxo2bJjTft+lS5fKZrPJZrNp06ZNpvsFgPJw9wMA8KA9e/aofv362rZtmywWi+rVq6fmzZurSZMmTvVSU1N18uRJp6TWTNvDhw8rKCjIcbcEq9WqwYMHO8ovXLigefPm6W9/+5vuvfdeSVLnzp21cuVK3X777ZoyZYpuvfXWCudRfM5169aVKrPZbProo490/PhxhYSE6Fe/+pU6d+6sdevWafTo0S5ECwDMY6UWADxoz5496tSpk7p166auXbuqU6dOpRJa6eotu2JjY9WhQweX2sbGxio/P1+PPfaYPvvss1L3rk1JSVFBQYHuv/9+p+NdunRRVFSUtmzZUuU5HjlyRFarVTfffLPjWMeOHfXtt986Xj/99NOKiIhQr169HNsqAKAqSGoBwEMuXryo48ePKy4ursK6H3zwgdMqrdm2DRs21LZt25SXl6eRI0cqIiJCjz76qC5duiTp6sVm4eHhTtsRit14443KzMx0cVal2Ww2hYaGOh0LDQ2VzWaTJC1YsEDff/+9Tp48qQceeEADBgzQhQsXqtwvgLqNpBYAPGTPnj0yDKPCxHTv3r06duxYqa0HZtpKVy8k+8tf/qJz585p+/bt2r59u+bOnStJatKkiTIzM50uCiv2448/Kjw83MVZlWa1WkutEOfk5MhqtUqS4uPjZbVaFRwcrP/8z/9URESEtm/fXuV+AdRtJLUA4CHFf2a//fbbr1vvgw8+UFRUlFMCa7ZtSXFxcRoyZIj2798vSUpISFBgYKA+/fRTp3p79+7ViRMnlJiY6NL5y9K6dWvZbDadPn3acWz//v1q3759mfVL3nMXACqDTxIA8JDJkyfLMAzddttt161XcuuBK20PHjyo119/XT/88IOkqxeOffzxx4qPj5ckNWrUSM8995zGjx+vbdu2qbCwUP/v//0/Pfrooxo9erTTRWL5+fm6cuWK479rV3cLCwsdx679f+nqSu2gQYM0a9YsXb58WZ988on27t2rX/3qV7pw4YK++OIL5eXlKT8/X//1X/+lc+fOKSEhwXwgAaAsBgCg1jh9+rTx61//2mjatKkREhJitGjRwnj66aeNvLw8p3pvvvmm0bp1ayMoKMj4xS9+YcycOdMoKChwlEdFRRmSnP579913HeUzZ868bnl6eroxYMAAIzg42GjVqpXx+eefO47HxcUZDRo0MBo3bmwkJiYaO3furNaYAKgbLIZhGDWXUgMAAABVx/YDAAAA+DySWgAAAPg8kloAAAD4PJJaAAAA+DySWgAAAPg8kloAAAD4PJJaAAAA+DySWgAAAPg8kloAAAD4PJJaAAAA+DySWgAAAPg8kloAAAD4PJJaAAAA+Lz/D4tTPpPyCLnWAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 768x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Function to plot SOL 106 buckling loads and critical buckling factors against applied loads\n",
    "def plot_buckling_loads(applied_loads, sol_105_buckling_load, sol_106_buckling_loads, alphas):\n",
    "    _, axs = plt.subplots(nrows=2, ncols=1, sharex='all')  # figure with 2 subplots\n",
    "    axs[0].plot(applied_loads/sol_105_buckling_load, sol_106_buckling_loads/sol_105_buckling_load, 'o')  # buckling loads vs applied loads\n",
    "    axs[1].plot(applied_loads/sol_105_buckling_load, alphas, 'o')  # critical buckling factors vs applied loads\n",
    "    axs[1].axhline(y=1, color='k', ls='--', label='$\\\\alpha=1$')  # alpha=1 reference line\n",
    "    # Set plot appearance\n",
    "    axs[0].set_ylabel(\"$P_\\mathrm{SOL\\,106}/P_\\mathrm{SOL\\,105}$\")\n",
    "    axs[0].grid(visible=True)\n",
    "    axs[1].set_ylabel(\"$\\\\alpha$\")\n",
    "    axs[1].grid(visible=True)\n",
    "    axs[1].legend()\n",
    "    axs[1].set_xlabel(\"$P/P_\\mathrm{SOL\\,105}$\")\n",
    "    plt.show()\n",
    "\n",
    "# Set default dpi of figures\n",
    "plt.rcParams['figure.dpi'] = 120\n",
    "\n",
    "# Find nonlinear buckling loads and critical buckling factors\n",
    "nonlinear_buckling_load_vectors, critical_buckling_factors = pynastran_utils.read_nonlinear_buckling_load_from_f06(\n",
    "    f06_filepath=os.path.join(analysis_directory_path, input_name + '.f06'), op2=sol_106_op2)  # read buckling loads and critical buckling factors from f06 file\n",
    "nonlinear_buckling_loads = np.linalg.norm(np.sum(nonlinear_buckling_load_vectors[:, :, 0:3], axis=1), axis=1)  # calculate the norm of the nonlinear buckling load vector for each subcase\n",
    "\n",
    "# Plot results\n",
    "plot_buckling_loads(applied_load_magnitudes, linear_buckling_load, nonlinear_buckling_loads, critical_buckling_factors)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results show that the nonlinear buckling method predicts the same buckling load of SOL 105 for $P/P_\\text{SOL 105}<1$, confirming that in the linear regime the nonlinear buckling method is able to predict the same buckling load of SOL 105. For $P/P_\\text{SOL 105}\\geq1$ the nonlinear buckling method predicts a larger buckling load than SOL 105 and we observe a nearly linear relation between the applied load and the nonlinear buckling load.\n",
    "\n",
    "In our [last notebook](05_Sensitivity_Study_of_SOL_106_Nonlinear_Analysis_Parameters.ipynb#sensitivity), we observed that the deformation of the box beam at $P/P_\\text{SOL 105}=1$ featured a buckled-like shape of the top skin, suggesting that some nonlinear effects may come into play and affect the structural response. As a consequence, it is possible that the difference between $P_\\text{SOL 106}$ and $P_\\text{SOL 105}$ for $P/P_\\text{SOL 105}\\geq1$ is caused by this nonlinearity. On the other hand, it is not immediately clear why the nonlinear buckling method predicts the seemingly linear behavior observed between nonlinear buckling load and applied load.\n",
    "\n",
    "As far as the critical buckling factor $\\alpha$ is concerned, we observe initially large values with a linear decreasing trend for $P/P_\\text{SOL 105}<1$, with a minimum at $P/P_\\text{SOL 105}=1$. This behavior makes sense: the applied load is initially very far from the buckling load predicted by SOL 106 and it gradually gets closer. As a consequence $\\alpha$ is initially very large and it gradually decreases. However, all values of $\\alpha$ are one order of magnitude larger than 1 or more.\n",
    "\n",
    "For $P/P_\\text{SOL 105}>1$, $\\alpha$ appears to first decrease slightly and then to increase at a larger rate, as shown in the zoomed version of the plot below. Considering that in this range the nonlinear buckling method predicts a nearly linear relationship between the nonlinear buckling load and the applied load, it is not obvious to explain these results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 768x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot()\n",
    "ax.plot(applied_load_magnitudes/linear_buckling_load, critical_buckling_factors, 'o')  # critical buckling factors vs applied loads\n",
    "ax.axhline(y=1, color='k', ls='--', label='$\\\\alpha=1$')  # alpha=1 reference line\n",
    "ax.set_xlabel(\"$P/P_\\mathrm{SOL\\,105}$\")\n",
    "ax.set_ylabel(\"$\\\\alpha$\")\n",
    "ax.grid(visible=True)\n",
    "ax.set_ylim([0, 300])  # set y-axis limits to zoom in on the plot\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Let's plot the equilibrium diagram of the structure in terms of applied load vs tip displacement $u_{z, tip}$. We read the load and displacement history from the `OP2` object using the function `read_load_displacement_history_from_op2`, collect together the applied loads and the tip displacements from all subcases and make the plot. The tip displacement is expressed as a percentage of the length $l$ of the box beam."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 768x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Find load and displacement history\n",
    "_, loads, displacements = pynastran_utils.read_load_displacement_history_from_op2(\n",
    "    op2=sol_106_op2, node_ids=[master_node_id])\n",
    "\n",
    "# Collect load and displacement history\n",
    "load_component_index = 2  # select z-axis component of the load\n",
    "displacement_component_index = 2  # select z-axis component of the displacement\n",
    "load_history = np.concatenate([loads[subcase_id][:, load_component_index] for subcase_id in loads])  # concatenate applied load arrays of all subcases\n",
    "displacement_history = np.concatenate(\n",
    "    [displacements[master_node_id][subcase_id][:,displacement_component_index] for subcase_id in loads])  # concatenate displacement arrays of all subcases\n",
    "\n",
    "# Plot load-displacement diagram\n",
    "_, ax = plt.subplots()\n",
    "ax.plot(displacement_history/l*100, load_history/linear_buckling_load, 'o')\n",
    "plt.xlabel(\"$u_{z, tip}/l$, %\")\n",
    "plt.ylabel(\"$P/P_\\mathrm{SOL\\,105}$\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "We observe a change in the slope of the load-displacement diagram approximately for $P/P_\\text{SOL 105}=1$. This can be most probably ascribed to the buckled-like deformation of the top skin that we observed in our [last notebook](05_Sensitivity_Study_of_SOL_106_Nonlinear_Analysis_Parameters.ipynb#sensitivity).\n",
    "\n",
    "Let's plot the deformation of the box beam at the end of the analysis ($P/P_\\text{SOL 105}=2$) to verify the state of the top skin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 768x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from resources import plot_utils  # module with functions to plot deformed shapes\n",
    "\n",
    "valid_subcase_ids = [subcase_id for subcase_id in sol_106_op2.load_vectors if hasattr(\n",
    "    sol_106_op2.load_vectors[subcase_id], 'lftsfqs')]  # find ids of the subcases associated with static nonlinear analysis\n",
    "_, ax, cbar = plot_utils.plot_deformation(\n",
    "    op2=sol_106_op2, subcase_id=valid_subcase_ids[-1], displacement_component='tz', length_unit=\"mm\")  # plot static deformation at the end of the last subcase\n",
    "ax.locator_params(axis='x', nbins=3)  # set number of x-axis ticks\n",
    "ax.locator_params(axis='z', nbins=2)  # set number of z-axis ticks\n",
    "ax.tick_params(axis='y', which='major', pad=20)  # set distance of y-axis tick labels\n",
    "ax.tick_params(axis='z', which='major', pad=6)  # set distance of z-axis tick labels\n",
    "ax.yaxis.labelpad = 60  # set distance of y-axis label\n",
    "ax.zaxis.labelpad = 10  # set distance of z-axis label\n",
    "cbar.ax.set_position(cbar.ax.get_position().shrunk(1.0, .66))  # decrease colorbar size\n",
    "cbar.ax.set_position(cbar.ax.get_position().translated(0, .14))  # move colorbar upwards\n",
    "plt.show()  # show plot"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the top skin develops a clear buckled-like shape for $P/P_\\text{SOL 105}=2$. As a consequence we can conclude that the nonlinearity observed in the load-displacement diagram must be caused by the deformation state of the top skin for $P/P_\\text{SOL 105}\\geq1$, which affects the global stiffness of the box beam.\n",
    "\n",
    "Given the difficult interpretation of the nonlinear buckling method results for $P/P_\\text{SOL 105}\\geq1$ let's repeat our analysis for a set of applied loads more zoomed around $P/P_\\text{SOL 105}=1$. We define 10 applied loads ranging from $P/P_\\text{SOL 105}=0.75$ to $P/P_\\text{SOL 105}=1.25$, run the analysis and plot the nonlinear buckling loads and the critical bukling factors against the applied loads."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Applied loads [N]: [1240. 1332. 1424. 1516. 1608. 1700. 1792. 1884. 1976. 2068.]\n",
      "Nastran job nonlinear_buckling_verification_zoomed.bdf completed\n",
      "Wall time: 971.0 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 768x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define applied loads\n",
    "applied_load_magnitudes = np.linspace(.75*linear_buckling_load, 1.25*linear_buckling_load, 10)\n",
    "print(f\"Applied loads [N]: {applied_load_magnitudes}\")\n",
    "\n",
    "# Run analysis\n",
    "input_name = \"nonlinear_buckling_verification_zoomed\"\n",
    "sol_106_op2 = run_nonlinear_buckling_method_sweep(box_beam_bdf_input.__deepcopy__({}), applied_load_magnitudes,\n",
    "                                                  analysis_directory_path, input_name, run_flag=False)\n",
    "\n",
    "# Find nonlinear buckling loads and critical buckling factors\n",
    "nonlinear_buckling_load_vectors, critical_buckling_factors = pynastran_utils.read_nonlinear_buckling_load_from_f06(\n",
    "    f06_filepath=os.path.join(analysis_directory_path, input_name + '.f06'), op2=sol_106_op2)  # read buckling loads and critical buckling factors from f06 file\n",
    "nonlinear_buckling_loads = np.linalg.norm(np.sum(nonlinear_buckling_load_vectors[:, :, 0:3], axis=1), axis=1)  # calculate the norm of the nonlinear buckling load vector for each subcase\n",
    "\n",
    "# Plot SOL 106 buckling loads and critical buckling factors\n",
    "plot_buckling_loads(applied_load_magnitudes, linear_buckling_load, nonlinear_buckling_loads, critical_buckling_factors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's zoom in the critical buckling factor plot to better observe how close is $\\alpha$ to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 768x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot()\n",
    "ax.plot(applied_load_magnitudes/linear_buckling_load, critical_buckling_factors, 'o')  # critical buckling factors vs applied loads\n",
    "ax.axhline(y=1, color='k', ls='--', label='$\\\\alpha=1$')  # alpha=1 reference line\n",
    "ax.set_xlabel(\"$P/P_\\mathrm{SOL\\,105}$\")\n",
    "ax.set_ylabel(\"$\\\\alpha$\")\n",
    "ax.grid(visible=True)\n",
    "ax.set_ylim([0, 800])  # set y-axis limits to zoom in on the plot\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These results confirm the ability of the nonlinear buckling method to predict the same buckling load of the linear analysis for $P/P_\\text{SOL 105}<1$ and suggests a slightly more gradual change transition to the seemingly linear relation for $P/P_\\text{SOL 105}\\geq1$. However, the most interesting resutls are obtained for the critical buckling factor $\\alpha$. We can see that the smallest value is obtained for the first applied load, and for all other applied loads $\\alpha$ is larger. Why does this happen? This can be ascribed to the fact that the load applied in the first subcase is much larger than the total incremental load applied in the second subcase. As a consequence, the last load increment vector $\\boldsymbol{\\Delta P}$ in the first subcase is much larger than the one in the second subcase and it requires a smaller $\\alpha$ to achieve the same critical buckling load.\n",
    "\n",
    "These results suggest that the hard threshold of $\\alpha=1$ suggested by [Lee & Herting (1985)](https://onlinelibrary.wiley.com/doi/pdf/10.1002/nme.1620211016) to assess whether the predicted buckling point is close to the last converged solution (and consequently whether the prediction can be deemed reliable) is actually arbitrary. In fact, we can see that the calculated value of $\\alpha$ is strongly dependent on both the total incremental load applied between successive subcases and, consequently, the latter should be taken into consideration when interpreting the value of $\\alpha$.\n",
    "\n",
    "Going back to the buckling load results, how can we interpret those? We can try to give an explanation by taking inspiration from Euler's column. In our [second notebook](02_Supercritical_Pitchfork_Bifurcation_of_Euler_Column.ipynb) we observed the \"break\" of the supercritical pitchfork bifurcation when we applied a compression load with some eccentricity. In an analogous way, it is possible that under a bending load the bifurcation potentially corresponding to the critical buckling load of the box beam breaks down and we are left with an equilibrium path that is analogous to a broken supercritical pitchfork. In order to verify this, we need to repeat our nonlinear analysis monitoring the lowest eigenvalue $\\lambda$ of the tangent stiffness matrix for each converged iteration."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Tangent stiffness matrix assessment <a name=\"tangent-stiffness-matrix\"></a>\n",
    "\n",
    "***\n",
    "\n",
    "Let's create a copy of the original base bdf input, set up the nonlinear analysis with the arc-length method, define the parameters for the calculation of the lowest eigenvalue $\\lambda$ of the tangent stiffness matrix, set the applied load magnitude to twice the buckling load predicted by SOL 105 and run the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nastran job tangent_stiffness_matrix.bdf completed\n",
      "Wall time: 206.0 s\n"
     ]
    }
   ],
   "source": [
    "# Create deep copy of bdf object and set up nonlinear analysis with the arc-length method\n",
    "box_beam_tangent_stiffness_matrix_input = box_beam_bdf_input.__deepcopy__({})\n",
    "pynastran_utils.set_up_arc_length_method(bdf=box_beam_tangent_stiffness_matrix_input, ninc=100, max_iter=25,\n",
    "                                         conv='PUV', eps_p=1e-3, eps_u=1e-3, max_bisect=10, minalr=.01, maxalr=1.0001,\n",
    "                                         desiter=5, maxinc=1000)\n",
    "\n",
    "# Define parameters for the calculation of the lowest eigenvalue of the tangent stiffness matrix\n",
    "box_beam_tangent_stiffness_matrix_input.add_param('BUCKLE', [2])  # request nonlinear buckling method\n",
    "eigrl_set_id = force_set_id + 1  # set identification number of EIGRL card\n",
    "box_beam_tangent_stiffness_matrix_input.add_eigrl(sid=eigrl_set_id, v1=0., nd=1)  # calculate only the first positive eigenvalue\n",
    "box_beam_tangent_stiffness_matrix_input.case_control_deck.subcases[0].add_integer_type('METHOD', eigrl_set_id)  # add EIGRL card id to case control deck\n",
    "box_beam_tangent_stiffness_matrix_input.executive_control_lines[1:1] = [\n",
    "    'include \\'' + os.path.join(os.pardir, os.pardir, 'resources', 'kllrh_eigenvalues_nobuckle.dmap') + '\\'']  # include DMAP sequence\n",
    "\n",
    "# Define force magnitude and create subcase\n",
    "box_beam_tangent_stiffness_matrix_input.loads[force_set_id][0].mag = linear_buckling_load*2\n",
    "first_subcase_id = 1\n",
    "pynastran_utils.create_static_load_subcase(bdf=box_beam_tangent_stiffness_matrix_input,\n",
    "                                           subcase_id=first_subcase_id, load_set_id=force_set_id)\n",
    "\n",
    "# Run analysis\n",
    "input_name = 'tangent_stiffness_matrix'\n",
    "pynastran_utils.run_analysis(directory_path=analysis_directory_path, bdf=box_beam_tangent_stiffness_matrix_input,\n",
    "                             filename=input_name, run_flag=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can read the load history from the op2 file, the lowest eigenvalues from the f06 file and plot the latter against the former."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 768x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read load history from op2 file\n",
    "op2_filepath = os.path.join(analysis_directory_path, input_name + '.op2')\n",
    "sol_106_op2 = read_op2(op2_filename=op2_filepath, debug=None, load_geometry=True)\n",
    "_, loads, _ = pynastran_utils.read_load_displacement_history_from_op2(op2=sol_106_op2)\n",
    "load_history = np.concatenate([loads[subcase_id][:, load_component_index] for subcase_id in loads])  # concatenate applied load arrays of all subcases\n",
    "\n",
    "# Read the lowest eigenvalue of KLLRH matrices from f06 file\n",
    "f06_filepath = os.path.join(analysis_directory_path, input_name + '.f06')  # path to .f06 file\n",
    "lowest_eigenvalues = pynastran_utils.read_kllrh_lowest_eigenvalues_from_f06(f06_filepath)\n",
    "\n",
    "# Create new figure and plot load history vs lowest eigenvalues\n",
    "_, ax = plt.subplots()\n",
    "ax.plot(load_history/linear_buckling_load, lowest_eigenvalues[0, :], 'o')\n",
    "plt.xlabel(\"$P/P_\\mathrm{SOL\\,105}$\")\n",
    "plt.ylabel(\"$\\lambda$, N/mm\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
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    "We observe that the lowest eigenvalue of the tangent stiffness matrix is initially nearly constant, hence the linear regime, it drops after $P/P_\\text{SOL 105}=0.75$, reaches a minimum just before $P/P_\\text{SOL 105}=1$, increases just after that, and then it decreases again. We note that the lowest eigenvalue is always positive for the investigated load range and, consequently, the tangent stiffness matrix is always positive definite. This means that our box beam does not encounter any critical point, where the lowest eigenvalue should become null, and it is always in a stable equilibrium. This last result seems to confirm the idea that equilibrium diagram of the box beam under a bending load is characterized by something analogous to a broken supercritical pitchfork rather than a supercritical pitchfork bifurcation."
   ]
  },
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   "source": [
    "## Conclusion <a name=\"conclusion\"></a>\n",
    "\n",
    "***\n",
    "\n",
    "In this notebook we have verified that SOL 106's nonlinear buckling method predicts a similar buckling load to SOL 105 for an applied load falling in the linear region of the structural response of the box beam. We also noted that the nonlinear buckling load trend predicted by SOL 106 transitions to a linear relation with the applied load as the latter increases above the linear buckling load predicted. In analogy with the behavior of the Euler's column, we have supposed that this may be caused by the break of the supercritical pitchfork bifurcation that characterizes the response of a non-perfectly symmetric problem. In fact, by monitoring the lowest eigenvalue of the tangent stiffness matrix duuring the nonlinear analysis, we have verified that the structure is always stable and does not encounter any critical point, which seems to confirm our hypothesis.\n",
    "\n",
    "In our [next notebook](07_Verification_of_SOL_106_Nonlinear_Buckling_Method_for_the_Imperfect_Euler_Column.ipynb) we are going to repeat the verification of SOL 106's nonlinear buckling method and of the positive definiteness of the tangent stiffness matrix for the imperfect Euler's column, that is to say an Euler's column with a slight eccentricity in the applied compression load."
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