Two Story Steel MRF (FGU).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Two Story Steel MRF\n",
    "#### December 2021, By Amir Hossein Namadchi\n",
    "In this notebook, a 2 story steel moment resisting frame is modeled. This is an OpenSeesPy simulation of TCL version of the model, presented by [*Fernando Gutiérrez Urzúa*](https://www.ucl.ac.uk/epicentre/fernando-gutierrez-urzua) in his [YouTube channel](https://www.youtube.com/user/lfgurzua). Some minor modifications are made by me in the python version. According to his, modeling assumptions are:\n",
    "\n",
    "- Columns are modeled as distributed plasticity elements\n",
    "- Beams are modeled as concentrated plasticity elements\n",
    "- Plastic hinges are modeled as two nodes at the same coordinates, connected via a rotational spring.\n",
    "\n",
    "The functions defined in this notebook are as follows:\n",
    "- `W_section`: *Creates W-Section based on nominal dimension and generates fibers over it*\n",
    "- `build_model`: *Builds the 2D Steel Moment resisting Frame Model*\n",
    "- `run_gravity`: *Runs gravity analysis*\n",
    "- `run_modal`: *Runs Modal analysis*\n",
    "- `run_pushover`: *Runs Pushover analysis*\n",
    "- `run_time_history`: *Runs Time history analysis*\n",
    "- `reset_analysis`: *Resets the analysis by setting time to 0,removing the recorders and wiping the analysis.*\n",
    " \n",
    "Please note that some functions use data obtained by running other functions. For example, in order to run Pushover analysis, some components of eigenvectors are needed that is obtained via `run_modal` function.\n",
    "It's highly recommended to watch Dr. Fernando Gutiérrez Urzúa videos for better understanding."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Steel Frame](FGU_2SSMRF_files/FGU_2SSMRF.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import openseespy.opensees as ops\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions\n",
    "Below functions can be encapsulated in a single *.py* file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def W_section(section, sec_tag, mat_tag, nf_dw, nf_tw, nf_bf, nf_tf):\n",
    "    \n",
    "    \"\"\"\n",
    "    Creates W-Section based on nominal dimension and generates\n",
    "    fibers over it\n",
    "    TCL version by: Remo M. de Souza, 08/99\n",
    "    Python version by: Amir Hossein Namadchi, 2022\n",
    "    \n",
    "    Keyword arguments:\n",
    "    section -- a dict type containing section info\n",
    "    sec_tag -- Section tag\n",
    "    mat_tag -- Material Tag\n",
    "    nf_dw -- Number of fibers along web depth \n",
    "    nf_tw -- Number of fibers along web thickness\n",
    "    nf_bf -- Number of fibers along flange width\n",
    "    nf_tf -- Number of fibers along flange thickness    \n",
    "    \"\"\"\n",
    "    # unpack variables for readability\n",
    "    d, bf = section['d'], section['bf']\n",
    "    tf, tw = section['tf'], section['tw']\n",
    "    \n",
    "    dw = d - 2*tf\n",
    "    y1, y2, y3, y4 = -d/2, -dw/2, dw/2, d/2\n",
    "    z1, z2, z3, z4 = -bf/2, -tw/2, tw/2, bf/2\n",
    "    \n",
    "    ops.section('Fiber', sec_tag)\n",
    "    ops.patch('quad', mat_tag, nf_bf, nf_tf,\n",
    "          *[y1,z4], *[y1,z1], *[y2,z1], *[y2,z4])\n",
    "    ops.patch('quad', mat_tag, nf_tw, nf_dw,\n",
    "          *[y2,z3], *[y2,z2], *[y3,z2], *[y3,z3])    \n",
    "    ops.patch('quad', mat_tag, nf_bf, nf_tf,\n",
    "          *[y3,z4], *[y3,z1], *[y4,z1], *[y4,z4])          \n",
    "    \n",
    "    \n",
    "def build_model():\n",
    "    \n",
    "        \n",
    "    \"\"\"\n",
    "    Builds the 2D Steel Moment resisting Frame Model\n",
    "    \n",
    "  \n",
    "    \"\"\"\n",
    "    \n",
    "    ## Units\n",
    "    m = 1.0               # Meters\n",
    "    KN = 1.0              # KiloNewtons\n",
    "    sec = 1.0             # Seconds\n",
    "\n",
    "    mm = 0.001*m          # Milimeters\n",
    "    cm = 0.01*m           # Centimeters\n",
    "    ton = KN*(sec**2)/m    # mass unit (derived)\n",
    "    g = 9.81*(m/sec**2)   # gravitational constant \n",
    "    \n",
    "\n",
    "    ops.wipe()\n",
    "    ops.model('basic','-ndm',2,'-ndf',3)\n",
    "\n",
    "    L_x = 6.0*m    # Span\n",
    "    L_y = 3.5*m    # Story Height\n",
    "\n",
    "    # Node Coordinates Matrix (size : nn x 2)\n",
    "    node_coords = np.array([[0, 0], [L_x, 0],\n",
    "                           [0, L_y], [L_x, L_y],\n",
    "                           [0, 2*L_y], [L_x, 2*L_y],\n",
    "                           [0, L_y], [L_x, L_y],\n",
    "                           [0, 2*L_y], [L_x, 2*L_y]])\n",
    "\n",
    "    # Element Connectivity Matrix (size: nel x 2)\n",
    "    connectivity = [[1,3], [2,4],\n",
    "                    [3,5], [4,6],\n",
    "                    [7,8], [9,10],\n",
    "                    [7,3], [8,4],\n",
    "                    [9,5], [10,6]\n",
    "                   ]\n",
    "\n",
    "    # Get Number of elements\n",
    "    nel = len(connectivity)\n",
    "\n",
    "    # Distinguish beams, columns & hinges by their element tag ID\n",
    "    all_the_beams = [5, 6]\n",
    "    all_the_cols = [1, 2, 3, 4]\n",
    "    all_the_hinges = [7, 8, 9, 10]  \n",
    "\n",
    "    # Columns are modeled using `Steel01` material, beams are \n",
    "    # assumed elastic and plastic hinges are modeled via `Bilin`\n",
    "    # material, proposed by *D. G. Lignos & H. Krawinkler*\n",
    "    # Uniaxial bilinear steel material with kinematic hardening\n",
    "    mat_S355 = {'ID':'Steel01',\n",
    "                'matTag':1,\n",
    "                'Fy':(3.55e5)*(KN/m**2),\n",
    "                'E0':(2.1e8)*(KN/m**2),\n",
    "                'b':0.01}\n",
    "\n",
    "    ## Material used for plastic hinges\n",
    "    # Modified Ibarra-Medina-Krawinkler Deterioration Model with Bilinear Hysteretic Response (Bilin Material)\n",
    "    # https://ascelibrary.org/doi/full/10.1061/%28ASCE%29ST.1943-541X.0000376\n",
    "    mat_lignos = {'ID':'Bilin',\n",
    "                 'matTag':2,\n",
    "                 'K0':(64033.2)*(KN/m),\n",
    "                 'as_Plus':0.00203545752454409,\n",
    "                 'as_Neg':0.00203545752454409,\n",
    "                 'My_Plus':101.175*(KN*m),\n",
    "                 'My_Neg':-101.175*(KN*m),\n",
    "                 'Lamda_S':1.50476106091578,\n",
    "                 'Lamda_C':1.50476106091578,\n",
    "                 'Lamda_A':1.50476106091578,\n",
    "                 'Lamda_K':1.50476106091578,\n",
    "                 'c_S':1, 'c_C':1,\n",
    "                 'c_A':1, 'c_K':1,\n",
    "                 'theta_p_Plus':0.0853883552651735,\n",
    "                 'theta_p_Neg': 0.0853883552651735,\n",
    "                 'theta_pc_Plus':0.234610805942179, \n",
    "                 'theta_pc_Neg':0.234610805942179,\n",
    "                 'Res_Pos': 0.4,\n",
    "                 'Res_Neg': 0.4,\n",
    "                 'theta_u_Plus': 0.4,\n",
    "                 'theta_u_Neg': 0.4,\n",
    "                 'D_Plus':1,\n",
    "                 'D_Neg':1 }\n",
    "\n",
    "\n",
    "    # Main Beams and Columns\n",
    "    sections = {'IPE220':{'d':220.0*mm, 'tw':5.9*mm,\n",
    "                          'bf':110.0*mm, 'tf':9.2*mm,\n",
    "                          'A':33.4*(cm**2),\n",
    "                          'I1':2772.0*(cm**4), 'I2':205.0*(cm**4)},\n",
    "\n",
    "                'HE180B':{'d':180.0*mm, 'tw':8.5*mm,\n",
    "                          'bf':180.0*mm, 'tf':14.0*mm,\n",
    "                          'A':65.3*(cm**2),\n",
    "                          'I1':3830.0*(cm**4), 'I2':1360.0*(cm**4)}\n",
    "               }    \n",
    "    \n",
    "    \n",
    "    # For columns, `nonlinearBeamColumn` and for beams,\n",
    "    # `elasticBeamColumn` is employed. Furthermore, `zeroLength`\n",
    "    # element is used to model plastic hinges.    \n",
    "    \n",
    "    # Main Nodes\n",
    "    [ops.node(n+1,*node_coords[n])\n",
    "     for n in range(len(node_coords))];\n",
    "\n",
    "\n",
    "    # Boundary Conditions\n",
    "    ## Fixing the Base Nodes\n",
    "    [ops.fix(n, 1, 1, 1)\n",
    "     for n in [1, 2]];\n",
    "\n",
    "    ## Tie the displacements (not rotations) in plastic hinges:\n",
    "    ops.equalDOF(3, 7, *[1,2])\n",
    "    ops.equalDOF(4, 8, *[1,2])\n",
    "    ops.equalDOF(5, 9, *[1,2])\n",
    "    ops.equalDOF(6, 10, *[1,2])\n",
    "\n",
    "    # Materials\n",
    "    ## For Columns\n",
    "    ops.uniaxialMaterial(*mat_S355.values())\n",
    "    ## For Plastic Hinges\n",
    "    ops.uniaxialMaterial(*mat_lignos.values())\n",
    "\n",
    "    # Adding Sections\n",
    "    ## For Columns\n",
    "    W_section(sections['HE180B'],\n",
    "              1, mat_S355['matTag'], *[4, 2, 4, 2])\n",
    "\n",
    "    # Transformations\n",
    "    ops.geomTransf('PDelta', 1)\n",
    "\n",
    "\n",
    "    # Adding Elements\n",
    "    ## Beams\n",
    "    [ops.element('elasticBeamColumn', e, *connectivity[e-1],\n",
    "            sections['IPE220']['A'], mat_S355['E0'],\n",
    "            sections['IPE220']['I1'], 1)\n",
    "     for e in all_the_beams];\n",
    "\n",
    "    ## Columns\n",
    "    [ops.element('nonlinearBeamColumn', e, *connectivity[e-1],\n",
    "                 4, 1, 1)\n",
    "     for e in all_the_cols];\n",
    "\n",
    "    ## Plastic Hinges\n",
    "    [ops.element('zeroLength', e, *connectivity[e-1],\n",
    "                '-mat', mat_lignos['matTag'], '-dir', 6)\n",
    "     for e in all_the_hinges];    \n",
    "    \n",
    "    global m_1\n",
    "    D_L = 20.0*(KN/m)    # Distributed load\n",
    "    C_L = 50.0*(KN)      # Concentrated load\n",
    "    m_1 = 75.0*ton       # lumped mass 1\n",
    "\n",
    "    # Now, loads & lumped masses will be added to the domain.\n",
    "    loaded_nodes = [3,4,5,6]\n",
    "    loaded_elems = [5,6]\n",
    "\n",
    "    ops.timeSeries('Linear',1,'-factor',1.0)\n",
    "    ops.pattern('Plain', 1, 1)\n",
    "\n",
    "    [ops.load(n, *[0,-C_L,0]) for n in loaded_nodes];\n",
    "    ops.eleLoad('-ele', *loaded_elems,'-type', '-beamUniform',-D_L)\n",
    "    [ops.mass(n, *[m_1*0.5,0,0]) for n in loaded_nodes];    \n",
    "    \n",
    "    print('Model built successfully!')\n",
    "    \n",
    "    \n",
    "def run_gravity(steps = 10):\n",
    "        \n",
    "    \"\"\"\n",
    "    Runs gravity analysis.\n",
    "    Note that the model should be built before\n",
    "    calling this function.\n",
    "    \n",
    "    Keyword arguments:\n",
    "    steps -- total number of analysis steps\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    ops.initialize()\n",
    "    # Records the response of a number of nodes at every converged step\n",
    "    ops.recorder('Node', '-file', 'FGU_2SSMRF_files/Gravity_Reactions.out',\n",
    "                 '-time','-node', *[1,2], '-dof', *[1,2,3], 'reaction')\n",
    "\n",
    "    # plain constraint handler enforces homogeneous single point constraints\n",
    "    ops.constraints('Plain')\n",
    "\n",
    "    # RCM numberer uses the reverse Cuthill-McKee scheme to order the matrix equations\n",
    "    ops.numberer('RCM')\n",
    "\n",
    "    # Constructs a profileSPDSOE (Symmetric Positive Definite) system of equation object\n",
    "    ops.system('ProfileSPD')\n",
    "\n",
    "    # Uses the norm of the left hand side solution vector of the matrix equation to\n",
    "    # determine if convergence has been reached\n",
    "    ops.test('NormDispIncr', 1.0e-6, 100, 0, 2)\n",
    "\n",
    "    # Uses the Newton-Raphson algorithm to solve the nonlinear residual equation\n",
    "    ops.algorithm('Newton')\n",
    "\n",
    "    # Uses LoadControl integrator object\n",
    "    ops.integrator('LoadControl', 0.1)\n",
    "\n",
    "    # Constructs the Static Analysis object\n",
    "    ops.analysis('Static')\n",
    "\n",
    "    # Records the current state of the model\n",
    "    ops.record()\n",
    "    # Performs the analysis\n",
    "    ops.analyze(steps)    \n",
    "    \n",
    "    print(\"Gravity analysis Done!\")\n",
    "    \n",
    "    \n",
    "def run_modal(n_evs = 2):\n",
    "    \n",
    "    \"\"\"\n",
    "    Runs Modal analysis.\n",
    "    Note that the model should be built before\n",
    "    calling this function.\n",
    "\n",
    "    Keyword arguments:\n",
    "    n_evs -- number of eigenvalues\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    ops.initialize()\n",
    "\n",
    "    # Records Eigenvector entries for Node 1,3 & 5 @ dof 1 \n",
    "    ops.recorder('Node', '-file',\n",
    "                 'FGU_2SSMRF_files/ModalAnalysis_Node_EigenVectors_EigenVec1.out',\n",
    "                 '-node', *[1,3,5], '-dof', 1, 'eigen 1')\n",
    "    ops.recorder('Node', '-file',\n",
    "                 'FGU_2SSMRF_files/ModalAnalysis_Node_EigenVectors_EigenVec2.out',\n",
    "                 '-node', *[1,3,5], '-dof', 1, 'eigen 2')\n",
    "\n",
    "    # Constructs a transformation constraint handler, \n",
    "    # which enforces the constraints using the transformation method.\n",
    "    ops.constraints('Transformation')\n",
    "\n",
    "    # Constructs a Plain degree-of-freedom numbering object\n",
    "    # to provide the mapping between the degrees-of-freedom at\n",
    "    # the nodes and the equation numbers.\n",
    "    ops.numberer('Plain')\n",
    "\n",
    "    # Construct a BandGeneralSOE linear system of equation object\n",
    "    ops.system('BandGen')\n",
    "\n",
    "    # Uses the norm of the left hand side solution vector of the matrix equation to\n",
    "    # determine if convergence has been reached\n",
    "    ops.test('NormDispIncr', 1.0e-12, 25, 0, 2)\n",
    "\n",
    "    # Uses the Newton-Raphson algorithm to solve the nonlinear residual equation\n",
    "    ops.algorithm('Newton')\n",
    "\n",
    "    # Create a Newmark integrator.\n",
    "    ops.integrator('Newmark', 0.5, 0.25)\n",
    "\n",
    "    # Constructs the Transient Analysis object\n",
    "    ops.analysis('Transient')\n",
    "\n",
    "    # Eigenvalue analysis\n",
    "    lamda = np.array(ops.eigen(n_evs))\n",
    "\n",
    "    # Writing Eigenvalue information to file\n",
    "    with open(\"FGU_2SSMRF_files/ModalAnalysis_Node_EigenVectors_EigenVal.out\", \"w\") as eig_file:\n",
    "        # Writing data to a file\n",
    "        eig_file.write(\"lambda omega period frequency\\n\")\n",
    "        for l in lamda:\n",
    "            line_to_write = [l, l**0.5, 2*np.pi/(l**0.5), (l**0.5)/(2*np.pi)]\n",
    "            eig_file.write('{:2.6e} {:2.6e} {:2.6e} {:2.6e}'.format(*line_to_write))\n",
    "            eig_file.write('\\n')\n",
    "\n",
    "\n",
    "    # Record eigenvectors \n",
    "    ops.record()    \n",
    "    \n",
    "    print(\"Modal analysis Done!\")\n",
    "    \n",
    "    \n",
    "def run_pushover(steps = 5000):\n",
    "    \n",
    "    \"\"\"\n",
    "    Runs Pushover analysis.\n",
    "    Note that the model should be built before\n",
    "    calling this function. Also, Gravity analysis\n",
    "    should be called afterwards. Morover, the function\n",
    "    requires some components of eigenvectors obtained \n",
    "    by calling the `run_modal` function.\n",
    "    \n",
    "    Keyword arguments:\n",
    "    steps -- total number of analysis steps\n",
    "\n",
    "    \"\"\"    \n",
    "    \n",
    "    ## Records the response of a number of nodes at every converged step\n",
    "    # Global behaviour\n",
    "    # records horizontal reactions of node 1 & 2\n",
    "    ops.recorder('Node', '-file',\n",
    "                 'FGU_2SSMRF_files/Pushover_Horizontal_Reactions.out',\n",
    "                 '-time','-node', *[1,2], '-dof', 1, 'reaction')\n",
    "    # records horizontal displacements of node 3 & 5\n",
    "    ops.recorder('Node','-file',\n",
    "                 'FGU_2SSMRF_files/Pushover_Story_Displacement.out',\n",
    "                 '-time','-node', *[3,5], '-dof',1, 'disp')\n",
    "\n",
    "    # Local behaviour\n",
    "    # records Mz_1 & Mz_2 for each hinge. other forces are zero\n",
    "    ops.recorder('Element','-file',\n",
    "                 'FGU_2SSMRF_files/Pushover_BeamHinge_GlbForc.out',\n",
    "                 '-time','-ele', *[7,8,9,10],'force') \n",
    "    # records the rotation of each hinges, ranging from 7 to 10\n",
    "    ops.recorder('Element','-file',\n",
    "                 'FGU_2SSMRF_files/Pushover_BeamHinge_Deformation.out',\n",
    "                 '-time','-eleRange',*[7, 10], 'deformation') \n",
    "\n",
    "    # records Px_1,Py_1,Mz_1,Px_2,Py_2,Mz_2 for elements 1 to 4 \n",
    "    ops.recorder('Element','-file',\n",
    "                 'FGU_2SSMRF_files/Pushover_Column_GlbForc.out',\n",
    "                 '-time','-eleRange',*[1,4], 'globalForce')\n",
    "    # eps, theta_1, theta_2 for elements 1 to 4\n",
    "    ops.recorder('Element','-file', \n",
    "                 'FGU_2SSMRF_files/Pushover_Column_ChordRot.out',\n",
    "                 '-time','-ele', *[1,2,3,4], 'chordRotation')    \n",
    "\n",
    "    # Measure analysis duration\n",
    "    tic = time.time()\n",
    "\n",
    "    # load eigenvectors for mode 1\n",
    "    phi = np.abs(np.loadtxt('FGU_2SSMRF_files/ModalAnalysis_Node_EigenVectors_EigenVec1.out'))\n",
    "\n",
    "    # Apply lateral load based on first mode shape in x direction (EC8-1)\n",
    "    ops.pattern('Plain', 2, 1)\n",
    "    [ops.load(n, *[m_1*phi[1],0,0]) for n in [3,4]];\n",
    "    [ops.load(n, *[m_1*phi[2],0,0]) for n in [5,6]];\n",
    "\n",
    "\n",
    "    # Define step parameters\n",
    "    step = +1.0e-04\n",
    "    number_of_steps = steps\n",
    "\n",
    "    # Constructs a transformation constraint handler, \n",
    "    # which enforces the constraints using the transformation method.\n",
    "    ops.constraints('Transformation')\n",
    "\n",
    "    # RCM numberer uses the reverse Cuthill-McKee scheme to order the matrix equations\n",
    "    ops.numberer('RCM')\n",
    "\n",
    "    # Construct a BandGeneralSOE linear system of equation object\n",
    "    ops.system('BandGen')\n",
    "\n",
    "    # Uses the norm of the left hand side solution vector of the matrix equation to\n",
    "    # determine if convergence has been reached\n",
    "    ops.test('NormDispIncr', 0.000001, 100)\n",
    "\n",
    "    # Line search increases the effectiveness of the Newton method\n",
    "    # when convergence is slow due to roughness of the residual.\n",
    "    ops.algorithm('NewtonLineSearch',True, 0.8,\n",
    "                  1000, 0.1, 10.0)\n",
    "\n",
    "    # Displacement Control tries to determine the time step that will\n",
    "    # result in a displacement increment for a particular degree-of-freedom\n",
    "    # at a node to be a prescribed value.\n",
    "    # Target node is 5 and dof is 1\n",
    "    ops.integrator('DisplacementControl',5,1, step)\n",
    "\n",
    "    # Constructs the Static Analysis object\n",
    "    ops.analysis('Static')\n",
    "\n",
    "    # Records the current state of the model\n",
    "    ops.record()\n",
    "\n",
    "    # Performs the analysis\n",
    "    ops.analyze(number_of_steps)\n",
    "\n",
    "    toc = time.time()\n",
    "\n",
    "    print('Pushover Analysis Done in {:1.2f} seconds'.format(toc-tic))\n",
    "    \n",
    "\n",
    "\n",
    "def run_time_history(g_motion_id = 1, scaling_id = 1,\n",
    "                     lamda = 1, acc_dir = 'FGU_2SSMRF_files/acc_1.txt'):\n",
    "\n",
    "    \"\"\"\n",
    "    Runs Time history analysis.\n",
    "    Note that the model should be built before\n",
    "    calling this function. Also, Gravity analysis\n",
    "    should be called afterwards.\n",
    "    \n",
    "    Keyword arguments:\n",
    "    g_motion_id -- Groundmotion id (in case you run many GMs, like in an IDA)\n",
    "    scaling_id -- Scaling id (in case you run many GMs, like in an IDA)\n",
    "    lamda -- Scaling of the GM\n",
    "    acc_dir -- file directory of GM to read from\n",
    "    \"\"\"\n",
    "\n",
    "    ## Records the response of a number of nodes at every converged step\n",
    "    # Global behaviour\n",
    "    # records horizontal reactions of node 1 & 2\n",
    "    ops.recorder('Node','-file',\n",
    "                ('FGU_2SSMRF_files/TimeHistory_Horizontal_Reactions.'\n",
    "                 +str(g_motion_id)+'.'+str(scaling_id)+'.out'),\n",
    "                '-time','-node',*[1,2],'-dof',1,'reaction')\n",
    "    # records horizontal displacements of node 3 & 5\n",
    "    ops.recorder('Node','-file',\n",
    "                ('FGU_2SSMRF_files/TimeHistory_Story_Displacement.'\n",
    "                 +str(g_motion_id)+'.'+str(scaling_id)+'.out'),\n",
    "                '-time','-node',*[3,5],'-dof',1,'disp')\n",
    "\n",
    "    # Local behaviour\n",
    "    # records Mz_1 & Mz_2 for each hinge. other forces are zero\n",
    "    ops.recorder('Element','-file',\n",
    "                ('FGU_2SSMRF_files/TimeHistory_BeamHinge_GlbForc.'\n",
    "                 +str(g_motion_id)+'.'+str(scaling_id)+'.out'),\n",
    "                '-time','-ele',*[7,8,9,10],'force')\n",
    "    # records the rotation of each hinges, ranging from 7 to 10\n",
    "    ops.recorder('Element','-file',\n",
    "                ('FGU_2SSMRF_files/TimeHistory_BeamHinge_Deformation.'\n",
    "                 +str(g_motion_id)+'.'+str(scaling_id)+'.out'),\n",
    "                '-time','-eleRange',*[7,10],'deformation')\n",
    "    # records Px_1,Py_1,Mz_1,Px_2,Py_2,Mz_2 for elements 1 to 4 \n",
    "    ops.recorder('Element','-file',\n",
    "                ('FGU_2SSMRF_files/TimeHistory_Column_GlbForc.'\n",
    "                 +str(g_motion_id)+'.'+str(scaling_id)+'.out'),\n",
    "                '-time','-eleRange',*[1,4],'globalForce')\n",
    "    # eps, theta_1, theta_2 for elements 1 to 4\n",
    "    ops.recorder('Element','-file',\n",
    "                ('FGU_2SSMRF_files/TimeHistory_Column_ChordRot.'\n",
    "                 +str(g_motion_id)+'.'+str(scaling_id)+'.out'),\n",
    "                '-time','-ele',*[1,2,3,4],'chordRotation')   \n",
    "\n",
    "\n",
    "    # Reading omega for obraining Rayleigh damping model\n",
    "    omega = np.loadtxt('FGU_2SSMRF_files/ModalAnalysis_Node_EigenVectors_EigenVal.out',skiprows=1)[:,1]\n",
    "    xis = np.array([0.03, 0.03]) \n",
    "    a_R, b_R = 2*((omega[0]*omega[1])/(omega[1]**2-omega[0]**2))*(\n",
    "        np.array([[omega[1],-omega[0]],\n",
    "                  [-1/omega[1],1/omega[0]]])@xis)\n",
    "\n",
    "    ## Analysis Parameters\n",
    "    accelerogram = np.loadtxt(acc_dir)  # Loads accelerogram file\n",
    "    dt = 0.02                               # Time-Step\n",
    "    n_steps = len(accelerogram)             # Number of steps\n",
    "    tol = 1.0e-6                            # prescribed tolerance\n",
    "    max_iter = 5000                         # Maximum number of iterations per step\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    # Uses the norm of the left hand side solution vector of the matrix equation to\n",
    "    # determine if convergence has been reached\n",
    "    ops.test('NormDispIncr', tol, max_iter,0,0)\n",
    "\n",
    "    # RCM numberer uses the reverse Cuthill-McKee scheme to order the matrix equations\n",
    "    ops.numberer('RCM')\n",
    "\n",
    "    # Construct a BandGeneralSOE linear system of equation object\n",
    "    ops.system('BandGen')\n",
    "\n",
    "    # The relationship between load factor and time is input by the user as a \n",
    "    # series of discrete points\n",
    "    ops.timeSeries('Path', 2, '-dt', dt, '-values', *accelerogram, '-factor', lamda)\n",
    "\n",
    "    # allows the user to apply a uniform excitation to a model acting in a certain direction\n",
    "    ops.pattern('UniformExcitation', 3, 1,'-accel', 2)\n",
    "\n",
    "    # Constructs a transformation constraint handler, \n",
    "    # which enforces the constraints using the transformation method.\n",
    "    ops.constraints('Transformation')\n",
    "\n",
    "    # Create a Newmark integrator.\n",
    "    ops.integrator('Newmark', 0.5, 0.25)\n",
    "\n",
    "    # assign damping to all previously-defined elements and nodes\n",
    "    ops.rayleigh(a_R, b_R, 0.0, 0.0)\n",
    "\n",
    "    # Introduces line search to the Newton algorithm to solve the nonlinear residual equation\n",
    "    ops.algorithm('NewtonLineSearch',True,False,False,False,0.8,100,0.1,10.0)\n",
    "\n",
    "    # Constructs the Transient Analysis object\n",
    "    ops.analysis('Transient')\n",
    "\n",
    "    # Measure analysis duration\n",
    "    t = 0\n",
    "    ok = 0\n",
    "    print('Running Time-Histroy analysis with lambda=',lamda)\n",
    "    start_time = time.time()\n",
    "    final_time = ops.getTime() + n_steps*dt\n",
    "    dt_analysis = 0.1*dt\n",
    "\n",
    "    while (ok == 0 and t <= final_time):\n",
    "\n",
    "        ok = ops.analyze(1, dt_analysis)\n",
    "        t = ops.getTime()    \n",
    "        \n",
    "    finish_time = time.time()\n",
    "    \n",
    "    if ok == 0:\n",
    "        print('Time-History Analysis Done in {:1.2f} seconds'.format(finish_time-start_time))\n",
    "    else:\n",
    "        print('Time-History Analysis Failed in {:1.2f} seconds'.format(finish_time-start_time))\n",
    "    \n",
    "    ops.wipe()        \n",
    "    \n",
    "def reset_analysis():\n",
    "    \"\"\"\n",
    "    Resets the analysis by setting time to 0,\n",
    "    removing the recorders and wiping the analysis.\n",
    "    \"\"\"    \n",
    "    \n",
    "    # Reset for next analysis case\n",
    "    ##  Set the time in the Domain to zero\n",
    "    ops.setTime(0.0)\n",
    "    ## Set the loads constant in the domain\n",
    "    ops.loadConst()\n",
    "    ## Remove all recorder objects.\n",
    "    ops.remove('recorders')\n",
    "    ## destroy all components of the Analysis object\n",
    "    ops.wipeAnalysis()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gravity Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Modified Ibarra-Medina-Krawinkler Model with Bilinear Hysteretic Response\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model built successfully!\n",
      "Gravity analysis Done!\n"
     ]
    }
   ],
   "source": [
    "build_model();\n",
    "run_gravity();\n",
    "reset_analysis();\n",
    "ops.wipe();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pseudo-Time</th>\n",
       "      <th>R1_1</th>\n",
       "      <th>R1_2</th>\n",
       "      <th>R1_3</th>\n",
       "      <th>R2_1</th>\n",
       "      <th>R2_2</th>\n",
       "      <th>R2_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.749647</td>\n",
       "      <td>22</td>\n",
       "      <td>-0.884039</td>\n",
       "      <td>-0.749647</td>\n",
       "      <td>22</td>\n",
       "      <td>0.884039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.2</td>\n",
       "      <td>1.499200</td>\n",
       "      <td>44</td>\n",
       "      <td>-1.768080</td>\n",
       "      <td>-1.499200</td>\n",
       "      <td>44</td>\n",
       "      <td>1.768080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.3</td>\n",
       "      <td>2.248670</td>\n",
       "      <td>66</td>\n",
       "      <td>-2.652130</td>\n",
       "      <td>-2.248670</td>\n",
       "      <td>66</td>\n",
       "      <td>2.652130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.4</td>\n",
       "      <td>2.998040</td>\n",
       "      <td>88</td>\n",
       "      <td>-3.536180</td>\n",
       "      <td>-2.998040</td>\n",
       "      <td>88</td>\n",
       "      <td>3.536180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.5</td>\n",
       "      <td>3.747320</td>\n",
       "      <td>110</td>\n",
       "      <td>-4.420230</td>\n",
       "      <td>-3.747320</td>\n",
       "      <td>110</td>\n",
       "      <td>4.420230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.6</td>\n",
       "      <td>4.496520</td>\n",
       "      <td>132</td>\n",
       "      <td>-5.304280</td>\n",
       "      <td>-4.496520</td>\n",
       "      <td>132</td>\n",
       "      <td>5.304280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.7</td>\n",
       "      <td>5.245620</td>\n",
       "      <td>154</td>\n",
       "      <td>-6.188340</td>\n",
       "      <td>-5.245620</td>\n",
       "      <td>154</td>\n",
       "      <td>6.188340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.8</td>\n",
       "      <td>5.994630</td>\n",
       "      <td>176</td>\n",
       "      <td>-7.072400</td>\n",
       "      <td>-5.994630</td>\n",
       "      <td>176</td>\n",
       "      <td>7.072400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.9</td>\n",
       "      <td>6.743550</td>\n",
       "      <td>198</td>\n",
       "      <td>-7.956470</td>\n",
       "      <td>-6.743550</td>\n",
       "      <td>198</td>\n",
       "      <td>7.956470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.492370</td>\n",
       "      <td>220</td>\n",
       "      <td>-8.840540</td>\n",
       "      <td>-7.492370</td>\n",
       "      <td>220</td>\n",
       "      <td>8.840540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Pseudo-Time      R1_1  R1_2      R1_3      R2_1  R2_2      R2_3\n",
       "0           0.0  0.000000     0  0.000000  0.000000     0  0.000000\n",
       "1           0.1  0.749647    22 -0.884039 -0.749647    22  0.884039\n",
       "2           0.2  1.499200    44 -1.768080 -1.499200    44  1.768080\n",
       "3           0.3  2.248670    66 -2.652130 -2.248670    66  2.652130\n",
       "4           0.4  2.998040    88 -3.536180 -2.998040    88  3.536180\n",
       "5           0.5  3.747320   110 -4.420230 -3.747320   110  4.420230\n",
       "6           0.6  4.496520   132 -5.304280 -4.496520   132  5.304280\n",
       "7           0.7  5.245620   154 -6.188340 -5.245620   154  6.188340\n",
       "8           0.8  5.994630   176 -7.072400 -5.994630   176  7.072400\n",
       "9           0.9  6.743550   198 -7.956470 -6.743550   198  7.956470\n",
       "10          1.0  7.492370   220 -8.840540 -7.492370   220  8.840540"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reactions = pd.read_table('FGU_2SSMRF_files/Gravity_Reactions.out', header=None,\n",
    "                   sep=\" \", names=[\"Pseudo-Time\",\"R1_1\",\"R1_2\",\"R1_3\",\"R2_1\",\"R2_2\",\"R2_3\"])\n",
    "df_reactions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Modal Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model built successfully!\n",
      "Modal analysis Done!\n"
     ]
    }
   ],
   "source": [
    "build_model();\n",
    "run_modal();\n",
    "reset_analysis();\n",
    "ops.wipe();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lambda</th>\n",
       "      <th>omega</th>\n",
       "      <th>period</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.352407</td>\n",
       "      <td>2.711532</td>\n",
       "      <td>2.317208</td>\n",
       "      <td>0.431554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>102.341000</td>\n",
       "      <td>10.116370</td>\n",
       "      <td>0.621091</td>\n",
       "      <td>1.610071</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       lambda      omega    period  frequency\n",
       "0    7.352407   2.711532  2.317208   0.431554\n",
       "1  102.341000  10.116370  0.621091   1.610071"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_eigs = pd.read_table('FGU_2SSMRF_files/ModalAnalysis_Node_EigenVectors_EigenVal.out', sep=\" \")\n",
    "df_eigs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pushover Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model built successfully!\n",
      "Gravity analysis Done!\n",
      "Pushover Analysis Done in 1.68 seconds\n"
     ]
    }
   ],
   "source": [
    "build_model();\n",
    "run_gravity();\n",
    "reset_analysis();\n",
    "run_pushover();\n",
    "ops.wipe();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Time-History Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model built successfully!\n",
      "Gravity analysis Done!\n",
      "Running Time-Histroy analysis with lambda= 1\n",
      "Time-History Analysis Done in 17.42 seconds\n"
     ]
    }
   ],
   "source": [
    "build_model();\n",
    "run_gravity();\n",
    "reset_analysis();\n",
    "run_time_history();\n",
    "ops.wipe();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pushover Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pover_x_react = np.loadtxt('FGU_2SSMRF_files/Pushover_Horizontal_Reactions.out')\n",
    "pover_story_disp = np.loadtxt('FGU_2SSMRF_files/Pushover_Story_Displacement.out')\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(pover_story_disp[:,2],\n",
    "         -(pover_x_react[:,1]+pover_x_react[:,2]), color = '#2ab7ca', linewidth=1.75)\n",
    "plt.ylabel('Base Shear (KN)', {'fontname':'Cambria', 'fontstyle':'italic','size':14})\n",
    "plt.xlabel('Roof Displacement (m)', {'fontname':'Cambria', 'fontstyle':'italic','size':14})\n",
    "plt.grid(which='both')\n",
    "plt.title('Pushover Curve',{'fontname':'Cambria', 'fontstyle':'normal','size':16})\n",
    "plt.yticks(fontname = 'Cambria', fontsize = 14)\n",
    "plt.xticks(fontname = 'Cambria', fontsize = 14);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Time History Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Ground Motion histroy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEeCAYAAABIapnGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOydd3gc1bn/P+8WadUsS+4VGxewaQYcepGpCZCEhOSSQMKPFEMgNwk3JDfl5pKEkEKSS0gIPYTee+8gm2qwDca4915kq5ft5/fHzOzOVq1kyZat9/M8erQzc2bm3Z0zZ77nnfe8R4wxKIqiKIqiKIrSdTx72gBFURRFURRF2VtRMa0oiqIoiqIo3UTFtKIoiqIoiqJ0ExXTiqIoiqIoitJNVEwriqIoiqIoSjdRMa0oiqIoiqIo3UTFtKIoexwReUpEjIhcvKdt2Z2ISJWI/EtEXheRNhEZs6dt6glE5AL7+9TuaVsUCxEpF5G37fusZk/boyj7EiqmFaWfICK/sR+ky0Wk1v68RkTeEZGgiDS6yhoR+dVuNO/8zgqIyDjbrm/sDoN2E7cD64wxpwLXAh3pBUTkMyLyvIjMFpGPRWStiNyz2y3tAsaYB4Dncm23r+VC+3o+lWW7R0Q22Nv/uav2iMhdIrJyV4+zN2OMaQVm7mk7FGVfRMW0ovQvZgMHGmNq7OU7jDHHA19PK7cM2LG7jDLGhAooFsGyq6mQY4rIcyLym12xqzcRkTLgS8AHAMaYq40xO9LK1AC1wF3GmJOMMdOA64Gzdq+13SKjY+BgjFkL/BPrWn5ORAamFTkDqLI/X9mVk4pIaxbP6xZgVVeOs4+S85ooitJ9VEwrSv9hO/BPY0w8y7angHedBWPMgcaYW3abZQVgjNlk2/VsZ2VFJAActRvM2hUmYbXBWTsSIiLArcAzxphHXZv+AdzQ++b1OiHgJaxO0lfStn0HeBwK7mgBICJHAmXp640xvzDGnNl9UxVFUXKjYlpR+gnGmJvSRJl7mzHGnAVgx/Am4pdF5BgRWS8iS0XkBPuV+TYReVREBovIn0Rkvoh8JCL7u48rIheJyIciMsuO1zysEzMrROSPIjJHRFaIyGfs43hF5KX0eE8R+Ykd/jBbRLaIyO0i4gX+DxgAXGyHtNSKSJG9z8W2vW+JyDwRucB1vJ+JSMw+zvkiskpEQiLyRRGJ2OEwX7XLnmMvbxGRQ9O/iIgU2b/NJ3YozSwROcLZBlxuF73etm9I2iGOAiYDT6Zdq7gx5reu8zxv/y7fFJE/iEidiLxqbxMR+amILLC/7wcicrpr33+kXetDRGSTiKx1lbneLvNtEbna/i4rRSSlsyIiJ4nIe/a1exc4MfdlTtCB1ZFzX4PBwDQsj3xXftOxwFVpv+lNInK2iDRKWvy2vX6OXS/ni8gVdgcGETnd/h1fEpGvishDIrJdRH6d7UuISImIzLV/p1NF5Db7nLfb28vsdR+KyPv25xLX/oNE5D7791slVjjPVHtbhV1+gb3v8yIyocDzFovI30Rkif397y3gmiiK0lWMMfqnf/rXD/8AA/wqy3q/ve1i17prgCDwXXv5dLvM60Clvc8q4GbXPl/Aeo0/1l7+NbAG8OWx50Ogyl5+B3jRtX2iXabGXj7JXt7PXj4beNtVfi3wm7RznAe0AQfYy2fYxzjVVeZdYBFWfOkpwA57/d3A8rTjzQMuyvF9bgAWAOX28h+ARqDaXh7n/j5Z9r/M3n5YJ9exyC43C6gBfgo8Z2+7EtgEDLeXLwGiwOS0fd3X+s/AWtey1y4zBxhkr3sReMFVZn/7d/2WvVwBbARq89h9MXAX8FkgBoy01/8X8Bt7u+mJ3xS4z20LVkclCpxkLx8MhIFvu8rci1V/z7aXzwfizm+Z5ftMts/9DDAduA64yd72BJan3WP/5suA39nbBKvO/dteHgQ0k6znzwIvAz677H1Y95q/gPPeAiwEKuzlH2f7ffRP//Rv1/7UM60oSgrGmEiW1VGgxRjzL3v5ffv/E8aYJnuf+VihCw4/AZ4yxqy3lx/HEjvH5Tn9ncaYBtc53J7uaFrZYvv/RNvu540xJ+Q5NlhC7TljzDJ7n1ewxMb3XGXCwGZjzO3GmDeMMYPt9Q8BE0VkFICIVAPD7fUpiEg58F3gFmMN/IKktzw9Pj0X1fb/1nyFjDFh++MrxphaY8xfjDHn2OuuAO4zxmy1l+8AWrAHorn2ddOedvyY/fEeY8xO+/M8YIKr2HdtO++292khWUc641Ws+HxnEOrFwL/TC+3ib5ped34IzDPGzLbt/RQr5OQyV5kYVufpeXt5HpaYHZ/jHM5v+bAxZq4x5sfGmMvFelvzJewQK/s3fxa40C5/EnAslgjG/o2nAHNEZCJwDnC9MSZqjDF2uf2xOoL5zlsBfAu41b4eAK908jspitINVEwrilIoba7PrVnWtWN5zxwOB062X7fXYnnJ1mF5LXPhFnKtJAVzNt4A7gFeEpG7ReTA/OYDcBiQntVhCanCECxvbjqvYXlBz7WXvwbcn0OQHgAE3OeyRVJdlnPlwokVLnVWiMjhIrLZfq2fHnKQYrOIDAJGp9kQA5Z3wQY37msdJfXaHAKsNqnx+Hk7AWk2PQxcICJHA1tcHTA3PfGbOhRaD9K/M+Svk5BZdw63///RdS+cRvL6Hmn/X+bsYKzxAR22naTZusT+31mdnYzlBV/hWlfQNVEUpWuomFYUpcvYHrKM1VmW7zHG1Nh/Jxhjxrk8fZ2ephMbYsaY/wecgOXFXSgi5xVwTF8nZXKdL4IV3/tle9VFwG15zkN3z2Wzxv6fEE3GmI8A5+3AtZ3s3xM2FIoHy5PbXe7HClH4A5b3PBs9+X26XQ+6eS6AC1z3wjRjzJS0ctl+v135zs7zfVeui6IoBaBiWlGU3mIt3fOAFoSIVItIwBgzxxjzeeBp4OeuImEsT6ab1aSGomAvLyzwtI8AJ4nI8UCzMSZX7uI1WEIocS4RqQIGd+Fcs7C8oaenrQ8BGGOC+XY2xtRjedLdNniwQgTcNkSBEtdyRjaMAtgIpE84U17ozsaYOVje10OxrmM2CvlNnbcE6dc9nV2tB11hrf0/173gbB+VZdtq+7/bVudzZ7ZutP+7r0vB10RRlMJRMa0o/RCxchyDK4TAtc15jV3kWl3sXhYr9RxYAw8dAmnLdwJfEJFJ9j5FIvIX17nd58x1vHQb3HZdhTVYziEMbHUtrwaOsY8/1M7UcBtWXuOD7PU1WALuplzfNY3XsQaH3YWVti4rdtz3Y8AlduwqwI+Abdgp30gK2BKyYKyc0zcA37bDHxwxnBIXnuN6OdyOFT4x0l6+COuau2OSVwPH28caBXwe13VwXRt3eEP6tXkcGCsiM+x9DrSP6b6e6RSlbf8zcI0rbMbJvuKHgn/T7VihGcfa+w532e629zbgcBE51S43BWsgpDvlYCDLd07YlYVc1+Ej4GPgR2JlmkFEJovIz+ztL9p2/6ezg4jMFJERxpiPsfKQ/8j5HbDi/j/Byhmf87zGmC3Ae8A37XoDVspByH9dFEXpKnt6BKT+6Z/+7d4/rIfuPVhevo3YGQ1c26+xty3G8oJNBtbb635il/m7vbwAGIElXhqxvKbn22V8WFkZFgFvYs2Id2IOm663j7cQGIk1AGuDve5Ku8x99vK7wBDgm1jxo/OwBru9CkxwHfNYLKH4FvBXLI+rB/gZlhh5Hyt7yOdd+3ze/g6NwOdy2HoH1iQg/k5+50qskIxPgLlYGRkOdG1/lmSWjBE5juG1f8NV9vd+HfgtsMpV5o+u325y2v7FWCJ1oX2et4Dj0sqciyVIZ2F59n+Sdq3/6qoPo+36sMFdxi73v1gx8U9jidwbsDLAZGQ7Afaz60498Nks2yfaNhvgH4X+pnaZy+zv8wzwA+Az9nkipGYt+RaWyH3f/u/O5HG8XQciJOvzw7Y9bwEDs9j8oL19NjA0y/d91rb7deBm7Kw19vYT7N/jU3v7f7q2jcTqRCzAEuaPYWc+KeC8E+16sxCr83e8XfZNYNiebov0T//2lT8xJm9YoqIoiuJCRC4Fxhhjdud064qiKEofRcW0oihKgdihIu8DXzHGbNjT9iiKoih7nt01mllRFGWvRUQuwhqodyDwkQppRVEUxUEHICqKonTOMKxBe9OwYooVRVEUBdAwD0VRFEVRFEXpNnt1mMfgwYPNuHHjdvt529raKCvrTipWpbfQa9L30GvSt9Dr0ffQa9L30GvSt+hL12PevHk7jDFDsm3bq8X0uHHjmDt37m4/b21tLTU1Nbv9vEpu9Jr0PfSa9C30evQ99Jr0PfSa9C360vUQkXW5tmnMtKIoiqIoiqJ0ExXTiqIoiqIoitJNVEwriqIoiqIoSjdRMa0oiqIoiqIo3UTFtKIoiqIoiqJ0ExXTiqIoiqIoitJNVEwriqIoiqIoSjfZq/NMK4rS9zDGsHZ7bE+boSiKoii7BfVMK4rSo8xfHeEPjzcTN3vaEkVRFEXpfVRMK4rSo0SiloqOxLx72BJFURRF6X1UTCuK0qOI3arEjOxZQxRFURRlN6BiWlGUHiWm4dKKoihKP0LFtKIoPUoyVlo904qiKMq+j4ppRVF6FGMsNa3jDxVFUZT+gIppRVEURVEURekmKqYVRekVjLqmFUVRlH6AimlFURRFURRF6SYqphVF6VHUIa0oiqL0J1RMK4qiKIqiKEo3UTGtKEqvYDQ1nqIoitIPUDGtKEqPohJaURRF6U/0OTEtFu+KyG/2tC2KoiiKoiiKko8+J6aB7wEH7mkjFEXZNTQ1nqIoitIf6FNiWkRGAF8Gnt3TtiiKoiiKoihKZ/QpMQ38HbgKza6lKPsAGj2tKIqi7PuI6SPvYkXkHOB8Y8w3ReQuYK0x5jdZyl0CXAIwbNiwIx966KHdaidAa2sr5eXlu/28Sm70mvQdVjZUM2fLaE4Z8REjqrx72hzFRu+Rvodek76HXpO+RV+6HjNmzJhnjJmebZtvdxuTDREpA/4MnJ62/kxgmTFmrbPOGHMbcBvA9OnTTU1Nze4z1Ka2tpY9cV4lN3pN+g6exUHmbGmntLSUmppj97Q5io3eI30PvSZ9D70mfYu95Xr0CTENnA10ADeKCMAR9vKXsGKoFUVRFEVRFKXP0SfEtDHmEeARZzlfmIeiKH0bjZRWFEVR+hN9bQCigw/QYEtF2YvpI8MxFEVRFKVX6XNiWkSmAWcAXxWRcXvWGkVRFEVRFEXJTZ8I83BjjPkYGLqn7VAUZdcwGvChKIqi9AP6nGdaURRFURRFUfYWVEwritKjaKi0oiiK0p9QMa0oiqIoiqIo3UTFtKIoPYoTKa0eakVRFKU/oGJaUZSCaQ/Fmb04WFhhVdOKoihKP0DFdB/hkXfauebRpj1thqLk5cX5Qe6tbU9Z1x6Ks2h9JEtpzeahKIqi7PuomO4jzF4UZF1dbE+boSh5icYy3c0PvtXO9c+1ZKxXx7SiKIrSH1Ax3UfweNSLp/R9YvHMdQ2tqStVRCuKoij9CRXTiqIUTDYx3R5Ok8+qphVFUZR+hIppRVEKJhbPVMqRaHb1rJpaURRF6Q+omO4jiEZ5KHsB2TzT6aiIVhRFUfoTKqYVRSmYaAFjZI2jplVVK4qiKP0AFdOKohRMtjCP3OjrFkVRFGXfR8W0oigF05UwD3VMK4qiKP0BFdOKohRMIWJaVbSiKIrSn1AxrShKwcRN50pZPdOKoihKf0LFtKIoBVNQmIeqaEXpda59opkbns+ceVRRlN2Pb08boFjoUC1lbyDbdOI5UVGtKL3Gyq3RPW2Coig26plWFKVgsiXzUM2sKIqi9GfyeqZF5GDgWLvcdmAlsNgYE9kNtimK0scoJITDKWP0fYuiKIrSD8gppkXkCuAHwEIgAOwHTASiIvIR8AbwjDHmg91hqKIoey9bGmJEY4YxgzWyTFEURdm3yPdkOx2YZIxJDDkSkQBwCHAccArwIxFZA1xujHm7Vy1VFGWPk80xne6tzlbmqgebALj98uoet0lRFEVR9iT5YqZ/7RbSAMaYoDHmQ2PM340xXwSGAn8H7hWRml60c59mfV2UtpBGnip7AXmq6dyVYauIra61RiuKoij9gZxi2hgzt7OdjTEdxpg7gBOA/+pJw/oTv3u0eU+boCgFkU8g3/pK626zoytsro/taRMURVGUfZicYlpEBojID0XkchHJ+27WGLMJ2NHj1imK0qdIDC4saCTirg1AXL45Qiiya/7tpZsi/PqhJjrC6idXFEVReod8YR73Aj8BfgksE5GTnQ0iUioip4pIiav8pb1ko6IofYx80jSZzWPX+MtTLTz+XvsuHaOlw4pUi2fL6acoiqIoPUA+MV0FjDPGjAa+A9wtIqfb2yLAV4EGp7AxRjPIK8o+jtnNc4W3BnftRI69qqUVRVGU3iKfmN7mDEA0xjwDzACuE5ET7DzT1wNFu8FGRVH6MIVk8+gusV1Uwc7uhUyDriiKoijdIZ+YDovIX50FY8wa4LPA9SJyAtYzU/09itKPKMQxXUg4daH0lAjeVVGuKIqiKLnIJ6avAeIi4nVW2AMNzwauBb7Sy7b1W+asCLFyi04yqfQ9kgMQCyibY31LR5z5q8MFnS++i2Ja7DGQ6plWFEVReot8qfGWGGP+2xgTS1u/DWtCl4m9bVx/5V+vtnHtky172gxFyULnKrozof3Q2+3c/NLuSaPn5BNRx7SiKIrSW+TzTCcQke+LyDnOsjGm3RjzLeC0XrNMUZQ+R1cydZhdTI1X6Hny4VjQk6EniqIoiuKmIDENfBdoAxCRCSJyn4jcBSzvLcMURdn91H4a5OG32zovmEecdqZbfd5OCvQCcVXTiqIoSi9RqJiuNca8KSKDgdexHD5zgOt6yhAR+baIPC8iD4jIIhH5c08dW1GUwrh/djuvfRLq0j6S7oB26dZw1GRM8LLr/uquo1paURRF6S18BZYbLiL/jZVber4x5kIAEZncg7b8H/BZY8wcEZkELBeRV4wxr/XgOfYqZi8OctLUwJ42Q1ESxAsI83Bv++V9jRwwyt/t8/WUCNaYaUVRFKW3yDedeIVr8b+AkcDTwAX29inAET1oy1HGmDkAxpgVwHZgvx48/l7HvbW7NvubovQWhWbzaGo3LFzX/cw0Zlejpm03uHqmFUVRlN4iX5jHEhG5VEQ8xpitxpgrjDHXGGOC9vbPAu/2lCG2gAZARIZizcD4Xk8dX1GU3iE9zGN3CtcX53ewcF2eNHvODIiaGk9RFEXpJfKFeZyMlU/6P0XkSmPMK+6Nxpi/9aJdvwNuNcYsTt8gIpcAlwAMGzaM2traXjQjO62trT183kMTn6zjpi8rndHz16S/YtW9XL9le/sBQDGz33oLvyeess7Zb03dUGA4HR1WvzsajQLexPatW0cD1QVcr0Opr2+gtnZ+1q072kt4ee0kvBLna1M+zVpmbdNAYCzz5s1jXWlHJ+fbt9F7pO+xa9ck/72qdA+9T/oWe8v1yCmmjTGrgK+IyIlY04hfAfzYGLO0Nw0SkfOBA4Azcth1G3AbwPTp001NTU1vmpOV2tpaevK89y+uT3yuqanJWFY6p6evSX/FqXu5fstXNjbSGolz4gknEiiyXNKvb26kJRxP7Nf8QTsL64IESkoA8Hq9RGzPcE1NDevebGNVY6jT63X/4nqqq6qoqcke7TXzJstWEU/OY72/PMQ7m9o4/IgjmDC8+7Hb+wJ6j/Q9duWadHavKt1D75O+xd5yPTrN5mGMecsY8xngYeAlEblRRAb1hjEiciTwS+DLxpjCpkjby4nGNJhT2XtwauuWhljecu7CuWp4epaPfOfrbhnnFDoAUVEURektCk2NhzHmbmAKUAcsEJErRaTHXD0iMgC4G/iaMabeXleRf6+9n1/c17inTVCUgnHE6R8eb8455X2Gbs0hZPNp6UKEtkO+eOhE9hEV04qiKEovUbCYBjDGdBhjfgMcDRwCLBKRL/eQLb8BXgGMiBwoIt/Fip3ep2lsy/+U74qoUJTdSWuwk7rbyf75vMVdqfXqmVYURVH2JF0S0w7GmE3GmIuBrwM/EpHZItLtNHkiUgV8DysF3xL77/buHm9fQkWA0pdw9+0cj3Bn2TxyhnkUeJ5dwemMap9UURRF6S26JaYdjDHzjDEnA38HHtyF4zQYY0qNMZL2d8Wu2LcvoCJA2RO8u7TzWRBjOSpnoVXWGPhodfahEaaHwjOczqimxlMURVF6i4LEtIhcKyKrRORtEbleRP5DRCaIyOUAxpjHgYN71VJFUXYbd77RlnV9imc68TndNZ26PpcgXrE5yk0vtVLXnDmYMSGmC7I2N8njaK9UURRF6R0K9Ux/G/gFcAsQA67ECsW4wSlgjOn+NGdKTlQCKH2JFFGaa2Bh/s0J6R211fiT73cQT4tn6qnwJqOeaWUfp7FNK7ei7GkKFdOPAW8bY+4zxlxpjDkaGAic1num7dvEjckQENnQMA+lr5MeM50gh3fZWXZ2+3BlmLrmeNYyu9qbjPeQh1tR+io/vVszQinKnibfDIhu/hu4TUT+ZYx5HcAY0w682WuW7eNc93QLm+ozX2+nx6qqmFb6Ein1sYsiOnkMk3d75xsLRz3TiqIoSm9TqJh+BSsd3vkishaYDcwD5htj3ukl2/Zplm2OZl2fK1ZVUfoC2Tp3nWbvyCGMY3nmfYkXIrgLIK7ZPBRFUZReptAwjyHAIGAk8ANgPXAO8Ewv2aXYqAZQ+gpxY2juyKyR7lzoxrijqu0BiDmOF3OFOeVKr1fXFOfljzq6Z7DrOJpiUlEURektChXT1wLTjTFbjTHPG2OuMsacaYzplWnFFRcqApQ+wluLO0+XZwyd1llncyxP6IVTpqEtzmPv7YKYTtilN5KiKIrSOxQqpg8FvisifxKR/XvTICWVuIoApY/Q0Jpd/bq9vnGTmc0jIwzEXs4rpnOl09sSYcHa7Lmps9oWz7RRURRFUXqSQsX0GUAUOAtYLiKbROQZEfl175mmKEpfwuvJNeIwSSGeaYdYHoWbS0z/+ckW/vlCa879rnm0ibveSG7vqclfFEVRFCUXOcW0iFziWjzHGHOhMeZQoBK4EHgbOKSX7ev3qAhQdid+b+5tubR0+kQu6WntMlLjFeCZ7q4neV1djHeWJj3XzmHUM60oiqL0FvmyeZwN3AZgjFnhrDTGtAG19p+iKPsQFSUe6nOEc+RMddfJZ7fYdscudyXMIx43eArwjKfj5HLXTqmiKIrSW+QL83hHRB4QkYG7zRolA/WoKbsTb6GBX5D0PKeI5fxTdxvjzrCRLJdez9MHDOYT3gWYqGMPFEVRlF4j36Pz/4A2YL2I3CUiXxKR6lyFReSunjZOUZTdi8+b9P6+/JE1zXd9a5xw1GQI3nhWMe2O88j0JM9aFGLOinDK/unHgEwveHfFdDYbFaU/srk+pp1KReklcoppY0zMGDMTOAloAa4D6kRkqYjcJyI/E5Gvi8iXReRPwIDdZHO/Qts+ZXfi8yb/P/ZeB2vrYvzsnkbufL0tw1vsHtx3xrQAkD2bh5sn5yTT3LkFckZYR9pyvsGK+dA804pi8euHmnhzYefpLRVF6TqdvtQ1xnxsjPmBMWY8sD/wv8AG4Djg+8APsVxQ3+5NQ/szz8/tYPnmyJ42Q+kHGAOVpUJVmdU0OCJ2S0MsZ4o7g6G63JNcV6BwjaeI6exC3UE904rSOW3BOLe83JLTA72tKc+0o4qidJtCpxMHwBizDlgHPNo75ijpGOCpDzoYMsDDD86u4KoHmwC4+muVjKjOk3pBUbqJSDJ2OsV7nFbOPVW3MzYwJcqjE9wP/M7EbrdjptUzrfQjlmyMMm9VhLagoaIkGWbl3GtdGRKhKErh6L3Vx3GLjC0NSa/CpxsK91Rv3BGlNdhNNaL0KyxhLIg9v3fMrnIiuSdfAfDYLUm21Hhu3ALaLZBzxWMny6Z7rtMnLc+O5plW+gNrtkX5y1PNFNnusXA0+wDe7mTEURSlc1RM93FSRIDrczBcuDr47SPN3PB8S88ZpezTeDw5pt/OIXjjtgCHtMGIWY4dzyGg8wn19LJQmKf6njfbeHVB0N7fOkAkamhsi7OtUV93K/sOsxYFWb45mjOvetSu7qqlFaV36FKYh7L7cQ/mcreP0VjXXG07W9QzrXRO3A7ZcB7G4nr45pp8BZJhIfFOsnmkTPCSxzOdETOdpn1TyucQCG8tSQ62co53+W0NiXW3X54zOZGi7FV0dv9EbE+1TyMDFaVXKMgzLSKDReQsETlSRHzu9b1nmgLws3saAatxdGuGaIGOtWDEakS7G3Oq9DcMHsleX3J5i7sSM+3eFncpgD883sw7KeI3/TV16nK8i/W5kJjpaMzw1uJQdq+8ovRh0sOZ4gbeWhzitldagWTYhzv1paIoPUehYR7vAzcDrwBNIjJbRP4GPN5rlimZuNrBcIGe6TtftxpT1QdKIRiTPT46X8y0McmYaXc2j86qXHoVvuvNtsTn9Dcp6WWzpcrbnidTQSFi+tP1Ee6pbaM1qDeLsneRXr+b2+PcU9vGhyutnO6RqLVePdOK0jsUKqY3GGP2M8YMAg4D7gDKAPVM7yE6QoU98DfXWwJD0hwSl9xcT+2nwZ42S9kH8IhkpNYSIJ4mjxPZPHDFTJNfRKeEduTwLn+6PswNL7SmrIvGDKFI8sgNWaY8/5/7m3Ket5DOpONdby/w3lKUvkJ6/b7+2dQxMo7zxadB04rSKxQaM/03EfkPY8wjxpiVwErg7l60S+mEiMtVF4ka2kKGgWWZfSNxvX53Ywy8vzxMzcGB3jRT2cuIG/B7kmEeiU6YJL1bDtk803GTX7imhHnkKNfQmrnhxflBFqxNZrD57SPNic+F6IO46XxKcee7pmdCUJS+TnrdjqS9pIlonVaUXqVQz/R3gW/ZMx+eLCLFvWmUkkm+SSzuerONn97dmHU/SXdJu9CpZZVseFwhHffUWqEXApQVp9alZMy0SQxANHlmNUwn96yGmes37Ni17BvGmE7jrB1z3B5wRdkbcOp2rnsubHeEtc1XlN6hUDE9AFgAVOYexYgAACAASURBVAOPAS0iskBE7uw1y5QM3O2gewDixp2pQmPjjmgiJ3U+p92abZoeTEnFiZl2Oms7mpMKVAQmj0y+zEqIaZLe4fRQkHx0ZRBhnj5h3jqeOJfpfBCu43kPR/OXU5S+hvtezIbzJtN5hmxrjKmwVpQepNAwj3ONMQnXp4jsDxwFTO8Vq5SsuBu/qN16vrEwmIiLdvjjE834vcL136nKK0IA6lvjiamgFQWc1HiZD9q4SabAg7QwD1ee6eSe+StfLsd0tkw1eetxAWramHyecAtHcLjDPNpDcVqDhqGVOnJL6bu4M+tkw6nTTrlfPdDEV48r4YxpJbvBOkXZ9ylIRRljGsViuIh4jTGrjTEPGWN+0tsGKkncWsDxsr25MHMQYTgKbfYgqoTHMGXf5EKsi/mqlX2buLFmScv2UI7HwesKUH76g47EZydm+v5Z7by12Epx12k2jxzi9oG32jPWdaaX/9HJpERWzHT+YzhiOuQS0/fWtucd2BiLm05FuqL0Ns79Gs1RF523Lu77emuj9RD5+b2NzFqkg9EVZVcoNM/0d4BWYBPQLiIvi8ixvWrZPkZjW3yX8teKpL4WL1gE2yrEPcmL2/MXyjIwpSNsWLdd33X3V7LlmRZJTuiSjnv9yq2F15uuaNB8IRoCLFwXyV0AS0R0FlaSCPNwHWprJzMlXv1IM1e7BkM6NLXHmb86nP+EitJDOB3BXPMPOJ7phrY4t7xsZcpxOoE7W+K8sTCUfUdFUQqi0Pf7FwDTgGHADGAO8IiInNVbhu1r/PTuRuavzv/A7wy3+IhmEQbxbOrEXuUe3e3OBBLKYtK9tW1c81imQFD2XYIRQzRmEjHT6VVJsOqXN1uLYToJw8hBV2Km83l/Czl33JjOY6azhHl4OmkhN9fHMsKsAB6Y3cbNL7Wyckv2ez5uTPb7VVG6gZOtI9fMuE77X/tpiHmrrE6e+/7Llu2jpSNOR1jrqKIUQqFierUxZoUxZocx5l1jzFVY4npmL9q2z3HLy620Bbs/FaG78cvmgUgfONXcHs8qINwpzrKlAWvu0OkS+wt1TTFm3lTPD25v4FZ7tjR3No8EkgwBScc9ADFlfSfP4Z7yTBeCKSA1nnNfPPR2MszE+V4rtkTszkZhRjsTv1z7ZGb4yZptUS69uYEr78qegUdRuorT9md7Lsy8qT5rh88Yqy5C9pCsH9/ZyDWPpoY4aQdQUbJTqJguFZGvuFcYY3YCW3vepH0P90P8pY+C3RbU7uPE4oYn3mtPxL0B7GxNbTBvfrk1q2fN8cD5vUkxvWprhJk31bNwXbigvL3K3k8sbmhqT9afJRsiKYMJM8uT4ZmOxx1vduY+i3cO7fT8hduae1t3s3k491PcGFZtjaS8sXFw7oU/P9nCZbc28OqCZGzp/bPaMso7BPN49B5/3xLrOtOiUgjrd0RpcTk4/vB4EwvXpYYQOe14rntq8YbMNyRxA3943HoDmest0fam1A2X3tLAnBUaEqIo6RQqpn8C/EJEFovI30TkchH5H2ByL9q2z+BuqF76KMij72YOsOqMnS3xlDRlsTi8+FHqoJFVW6M880Hy2B0hQ0XAUgNjBiezETiioSwgiZy6zoQYb34aSrw2j8QMi9bnD01pD8WZeVM9M2+qT3nF2BaM79aBWa1hv76S7CLfu6UhxXMaM2AwWTtTgpO1I3V9JJZ9PUBH1J/3/D0V5lF4No/0Y1r/F62P8KcnWmi2OxYVJckDpncsVtuevFjcULsoKSqe+bAjpVyuuhiMGLzaWVUKJG4Mv3ukmRtfTM4IumZbjDfTYpwdMf3ou6n10CFb7nR3e13fGs/bAXSzJIswB6tuPzmnXb3XSr+k0GweW4DPANcCw4FvAgcBl/WeafsO6W1LsIDQ6XFDM1NxveQSzztbMpXIvbXtPDs3WUbEiq0eVe2loTWeaHCj9ivBsmJPImZ6ZLV1vqGVnoSAuPzWBq5/Ln+WhOb25JdraEvadMW/G/neLQ159+1Jnlt1ADe9mN9WxSIcNbwwL/OhG4+nzmaYsd1kxkxHYwZDN2Ome3AAYuaxUw8ej2e+oo7ZL3KceNJ3loYpD0jKd0z/LZx7I32GuWfTxLQ7VMotWm54voXFG6NUlAgDSjIt3xUhEoubnKEs25tiLKsfRGObhnDtTfzpCctzvGprNKUepdfLznKjZxtonl6Hf/CvBuqaMsNBookc1fnr5uqtUV6YF2RjWkjJNY825fVmG2NYqwPelb2cnGJaRG4UkYPsz2KMiRtj7jbGfN0Yc6wx5gJjzPLdZ+rex4YdUdpD8QwPXElR58rjC5/pXv7P0mK3V80SDAG/0Bo0/Pt1y7sRjhl8Hij2u18PWvtEopnCqD0UZ9aiYNbGtN3lzXC8H24v4rtLQwTTvCLGGD5ZG+6ycFi/I8o/X8gumGPGw9JN2iB3xqfrw3z/tgaenJNFTNuXI5swNjhhHqkbnYe4UFi4Rcr5sui6h9/OHjqRV0xnMTi9vMHyvLtxvMfOfQHW/eN8p5aOOMs3p9Yp5zfKNdALrPvAPbDX7aV2jnfwWD/NHSYxGAysV/GX3tLAyx918P6yEJ+uD/PzextTXvHn43u3NHD/rOxvvX79UBNzt47i5pdaeH9ZiG2uLCWvfNyR0RlI5+M1YW5+STuru4vm9jhb6mMpE2s9NacjUZfSO7XZxr64yRZLXd+aWa+yhR45s4+m31N1TbEU8e3YtjFtttJ1dTFq82QLeWF+kN8/1pzxnFCUvYl8nuliYH/7c4uIvCUi14nIBSIyEUBETuspQ+w81n8WkYdE5E0RubKnjr272LgjyqxFQV5bEKSxLc7VjzRzzaPNrEpLF/b2ks5jzrrj6QMYVpm8pJZn2lBsv23f2mC1huEIFPmFYr8kGuF629MdipgMUfTesjD3zWpn/upIiuerLRinPZRsYR0x3R5KlrnzjTbeSfu+G3fGuOGFVlZujXLD8y28vzz/77FkoxXP+sK8IAvWRlLOCfmFzcYdUe6tzR3b2l2CYatDkI31O6Lc/WYbO1v63gyTtZ/m/61zhWyAMwAxdZ3zAJRuqOlYls7Za5/0TDxmulCPxzOnE39lQQdxkyp8y4qF9pChuT3O3W9m1hunQ+kWJ8ceUGSvs7ZtaYil3L/u+8Fnv3AaNtD64K77jnfusfc6uOP1Nt5aHGJnS5wf39lIc3ucS2+uTwjr7U0xVtiZQj5eE07cY3Ny3EuOvau3xbjj9baUsIFH3+3gmQ878r7mf/qDjpRsRAvXhZl5Uz3LNmV/zfbOkhDr67Rz213+8HgzVz2UOvjv5Y+D3GDnU08PP4p1o6nJNnbHufedGXQhmSr12bnJDldrMM4v72/il64c7E6Y1IadyevutA9F9vRwP7i9PmNuhKfsjn2T/dbkd482cdkt9Yn2sy0YZ8OO5DE/XhNmycbCM2NFYyYjxlxRepqcYtoY811jzLP24qvAPVgC+8fAIhFpBZ7rQVsuBcYbY74GfBH4HxE5tQeP36vMXhzkt480c9+sdh5+p52f3m2N1K9rjncaKpGNXIPAAM4+MpBzm88VkCliPUSddY7XOhw1FPmgyGfFTEdjJhHzGYqaDCHviORbXm5l3spko3TFvxv5+3OtDBngobRYEqKktSP1oTxneSjFW+2MLP/LUy18si6SMvkHQF1zLPGQXrs9ynXPtHD5rQ2Jh/0HKywbPlwRIhJLHUTn8MBbbTzzQTsvzA8ye3F2gfHxmjDPzc3ukVu4LszD77SxtSGW1YP+4vwObnihNTFBiYOxYxzfXhLK6ESksyuZXbpKNGZYXxdNxMbnwsrMkb3uxeNkxPsmOgzSM57p7pDN3PQYa8eznk5bmidu7Xbr+zz2XnuKCHZwPIVuoXjOdOstUihieH95iN8/1owx8OvzBwCpnulyewzD/sMsdVHsS71fc3HlXY3EDYmO4Z+eaObPT7YQjRlufLGVu2zhH4paEzlFooZN9ZaN2UI/ssWg/+BfDbR0xLn+2Ra2p73ud75D1B5H8Y/nLTH+iZ3fu6E1nhKXe9ebbVkn0mkPxXn9k2BCeKWzeEOk0851Z0Rjlsd/V/L6g/VmYleP0VW21FvZdbKF8QGs2GJd07mrUsVhd6zMFkft3MT3uBwQG3dGueaxZl6YlxTB7s9LNkYwxvDYe9ZbkbUub/qv7reeg4s3RglGDMGINSmTI7KjsaTzprHNcs6sr4sRjSdzxz87t4OrH2lOdFZvfLGVO15LdgY747cPN/GP51sz0v/Vfhpk4w7t8Ck9Q6EDEK82xtxujPm+MWY6UAGcijUwsae4FFucG2OasQT8N3rw+L1CNGZYVxfl3tquDyrMR64H69QxPg4dV5R1W3W5B39aqHVbKJ5ouFZsidIajHPTS1ZDVOQTQlGTmC0RbM902rkbXQ++95dbItP9wPP7hIBfEmK3NRTH54U/fKOSiSN8rNkeS/HGp6dp2tGcHMRY3xrntlda+evTLRhj+L0r37XzgL9/ttUY3/ZqG89+0JESB/roO9Z1eHNhiGfnBhPf5Z2lIW5+qYWZN9Vzm50C7pkPO3j6gw4WrA2zqT7K/NXhRCN9w/OtvLYgxP8+2MQT71uC2xjDgrVh4sawrs6y5bm5HSkP3DrXINFn5wYzvOaxuOGqBxuZeVM9V/zb+u9gjOGZD9r5/WNWjOHiDRFWbol0mtKtMyIxw2W3NvC7RzvPHd5ZzHR6ajynA+Wh629TdjXdXVeOvXFnLOV3nDraRzSW+ao7UOR0OJNeZDcNbZZovOGF5MM84Lf2CUUMd7xmiZBTDilm9CAfAX+qmPaIcNmZ5UwZ7efgsX7KAskfLX3QYra89B+tifD6J0FaOlIHDrt54K12Lr+tgd88ZF3v9MFqlh3Jz+6wswdmt7NoQ4TbX21N1Gu3uPvp3Y0pzoFXPg7yztIQ/31PYyIEyxHqTfZ4im2NMW59uZVtjTFufLGVh95u58q7GtlcH6O+JZYQdS0dcf72bAt3vNbG/z7QyNMftPPEe+2sr7Pe+K3fEWXmTfVc90wzkZjhjtda6QgbdrbEuPTmem59uZUlGyNcdmsDt7zcyootloCb7xKe8bjh0XfbuX9WG+2hOM3tcVZtjfJQWnjR+h1RfnxnY0H3TE+ybPOuzUXQFbLNVfDOkhDPftiReMNZWSo0ZAkHqSxN1pk7XmtlR0s8ER61Zns04YBoco2peeXjpAB33sI89UEHxj5eU3s8ca5R1V5Wbonyydowr9tvq7Y2xPh4TTjjuM982ME/nmvh+mdbCEUMD7/dlnhWNLbFExmvdtrHrm+Jcdcbrdw/u537ZlvPi7qmWEpbvWRjhBfmddAWjDN/VTgxwD4YNvzyvkZeXWCFPT7wluVwmbUomPVNZGNbnA9WhFKOva4uyluLQ4QimWMc4nHL6ZErBDISMzlDeprb411+VrSH4lnj5JWu4yuw3LXAGc6CMSYsIqOBW3vCCBEpAg4BVrpWLwM+2xPH70lWbY0yf9twVr7aypwVvffqKNerdo8IvixiZ78hXtbVxRgyILnR8bIdO9mXiNXctNNpZIwV5hEhJSYzHIFAWhKGRldj+sm6CKwDSD6gf/yFCv7v6ZaEaG9pN5QHhCEDvFxUU8ZVDzalCIXNDTEmDPMRjSdFqcOHK0IJuy+5OXUAo1uoOo3uix8FWbcjSrE3SijmY8XW1IeR48W+643kw/LDlWFmnm4Snsd/vpDq5TjlkGiKp+flj4MsXB9JdAJ+8sUKVmyJcMzkIt5fHubtJSGiMTh6chG3vNzKwDLh0jPKufbJFtZuj7KzNU5ZsXDw2CJufLGVLQ2pD6elmyIcOMrP6m3RxADSf72a+nA/68gAYwb52FQfxecV5iwP09IR53NHBOgIW6EKlaWCIZnfeVtjjDnLw1kfmvnIWveMFebhxGqOrPZaDxfHu9Udz3QPOf2yD0BMXV5fF0vxhAeKhEjUsLM59cepOaiYFz8K2hPUZP9G7s7nuKFe/LZ3OeyqyidNLQaswcY3vtjCP2dWA9br8XJ74OHAMg9vLAzxuSNKGFjmSQjkznDnwXZms8uFMYaXP8qcKto515aGWMq96Xg8126P8Yv7mjjvmNSxG9liap17a+mmKP94riVlFsxrHm1iU32MaMwalLl8c5TDx/v5aE2EJRsjPPR2OweO8jF5pD8lI8rWxjjP2ffCKwuCKZ2jJRujXH6r1TYcOaEoEbIyd1U4xWP7zxdaCUetyXpqDirmtMMC/OqBZFiCOxsLWHVmzCAvHWHDnOXWcTbsiPH+shAvfRSkutzDgFLhwNF+/F5hZ0ssby719M6lU9Z9j67aMYS2eR1sa4zx3rLdG4qQLTTkTTsMrKRIOHFKMSu2RFKEK1gDdU+cWmwLYENTu2GD3Y4PLBMa2wzLNkeZPDJVXrjj8ne2xBN1c+wQL+UBYeOOWCLT1QlTivlgZShhD8CNL7Uy1pWV6s1PgzwwO9WJ9Y/nW1i+Ocprn4T4+ZcHJAZwFvngvaUhTphSzL2z2liy0aqjPi8sWBtOPAN+em4Fn9YN4f7FVsdwzfZo4i3KA7PbOWisn7rmOI+80877y0Osr0tmVhk9yMuphwbweeGht9pT2olTD4ky45AAA0o9XGN30J6b20F9a5zRg7z8z1cGcPebbby/PFkHvndmOXOWh2hqtybPKS32sHpblGIfjB7k42snljK00ssDs9tYuz2a6DSMqvby4y9U0Bo0LNkY4bVPghT5hPKAUOSzrms0bohE4d5ZbcTicMzkIs46ooSXPu6gqc3QHorz5WNL2bQzxqL1EUqLBRGYNr6Ij9eECRRZYaJDB3jY3hS3Bz/D1DF+Kks9rNgS5bUFQQYP8HD6YQGrYxs2TB3jp741zqqtESYO99PUHufjNWEOGutn+oQi2kKGhesiiTSs25tilEQGU2Hf163BOPUtcSaO8HHIftmdinsKyfcaS0R+CnwM/NQYc0batmnApcaYXc7oISIjgM3ANGPMAnvdlcD3jDGT0speAlwCMGzYsCMfeuihXT19l9jQPIBP66ppi5YSihXaF+k6p+23itfWTchYP6q8mWlDt/D86gNS1g8pbaWuvTzrsU4as5bZG8YBUDNmDbUbxgNwQPUOQjEvEwbW87p9roHFHcSN0BxOhpIMCrSzM1ia9dgzxq5mZHkrL62ZQFVxkBJflIU7hgFw4dRPAHh302j8njifGbEZgGdXTmbq4DrGVzbw7qaxrGseWOjPwmFDtrKsYRBHj9jELPs7ORw1YiPL6wfxuf1X8OCSQ/MeZ/rwTczdOorhZS1sbavI2F7kjRK2r69H4sRNspMysryZbW3lnDtpCY8vPyiLjVs4eEgdr67dn1DMR1PI+i2nDNrOkp1DKfFFOH3cKsr9YR6w7Tywuo6l9UMYVd7MZ0Zs4rlVk4nGs7hGbUZXNOHBsL5lIOX+ENUlHQSjPmsgoCTv6YHFQeqDJWzPUTfS8XtijKloYnVTdcr6QYF2Ar4oAwMdLNoxjMriIMbAlEE7mLNlNGfvv4wX10xK+Z3cTBy4k5WNg1LWVRSFaAkXF2RXPtzXyuHcSUt4asUUAAK+CMGon1PGruaN9dYwkP0r61ndVM2YiiY2tFQCIBi+NmUhDy45lEOGbGVnRymbWwdknO+cCct4bpV1/10w5RMM8OCSQ/nc/st5cbWVLfS8yYsI+GI8veIAonEP5x2whGhceHjpIZwzYRmVxSFmb9iPDS2VnDxmDQZh9oZxjK9sYG3TQDJHLqRSFeigxBdJsW+/AY0Z99Kw0ha2tVv1e0LFZuojVTQELYF88pi1LN45mLr2cr4wcSnPrDww5/nc7YbDmeNW8vLaiXntzMZ5kxexZOeQrHnIqwPtnDJ2DY9lua8AvBInlqWOuds4IOd9DVZdDPiifGq3UwA+iRE1qffbOROW8f7m0ezoKANgQFGQyuIQ9cES4kYo94cRyX6lcj1VxVVCBGKxGF6vl+1tZcQLflFcGOX+EK2R7t9f1YF2jBH83hjN4WKCaaku3b9xVaCDhmBJ4r4bXtbCxIH1vL1pv4yyw8taGFzSzoSB9Ty9cgrnTlrCsp2DaYv6WW/X35PHrGXO5lGU+SM0hgKMrmhiXXMVAIcO2condcO79F1KfJGUVJ0Bb4RDh27jgy2jc+5z/Kj1vLNpLACDS9oS9cD9PEg/bjbS7S32Rjl0yFY+3Jp5bq/EOWLYFj7cOipjm88T4+DB24nEPaxprKI9miomDxuylWjcw6K0+6qiKMSYCqsT2RgKpLQZ4ysbCMW8iXVDS1sZUBxiZUOyrZ5ctYOo8RCOednYUkmJL8LAYquj2x71U+YPE4sLXo9ha2s5cTyU+sJMqt7JppYB7OgoY2hpK5G4l/aInxJfhMriEI2hYkAYWtrGzo4SGoIleMQwpLSN8iLrmK3hYjoiQihejEcMAV8EEI4ctpkR5YWH+vQUM2bMmGdHZ2TQmRocBNwBjBSRhcDbwDvAR0AY+AI9kx4vm2qIABl9Z2PMbcBtANOnTzc1NTU9cPquUVtbS01NDVsbY2yoi/LGwlCKN6YnOPzwaby2LjPmcMiQQRxzzBieX506OKWsvJK6duvnOnCULyWzxbRDD2b0+DgPzG5nwgEHU7uhjV+eN4CPVgfYVB/j9XVVibKNoaQn6ktHl/DxmjD1rWWMGexJjOp2qCrzcME5Vr36qLmZxRvLEttOnFKMc20a57Tz/Lwg0w8Zy4yDAzyxsoFjj5zK1DF+jmiLU/tpkFmLQjknsbjtsqqEl/qUYybxyXMtVI2YwgGxCGOGeHltQYjDhmzlpKOm8MHTLQkhfcU5FVz/XAuDKjxcVFNGkQ8GlHr41f1WZoNzpgf44lHVKaEWDtUVRfz265WEIvDJ2jD/eq2Nc6YHeG5ukM2tAxhV7eWzp53IqkgLH69JesMPGOnjP8+1hMDQDRH+9mzyGi6xG7nrvzsUj8d6kK8zrbyzNMzS+iGUFQszzx7NqEHjOPt0a4pfv08wxvDusjB3vdHGkRP8fPmYUoZWWmK3pSNOwC8J72gu4sbw2LsdKZOOZCMS9zJixHBWN6V6ycorKqgo8TB+qJdFO4KUlZVhjGHS5MnM2dLOUUcdxSvrmojneGM4cuRIVjamegIDgRJawrse61Hk9xNOC6c56qhjeGqFdY+MqA5Q1xzn4EMO5Y31VgM8buwIVi8MJYQ0gEE4ZUYNS4ItjKwaR1FTjM2tEcqKJcXL5KmeBqssL9uMGTUAPLy0nikHH8mLq63rfcapJ1hvkYaFeG1BkJqaGna2xHh4aRMzTjyaAaUePnm+hQ0tEaZOPYSbbQ/zyUeM5rwBHv76dP5xFsMHV/C5I0q47plkuSOnjmDd+6ljABwhPX1CEWM9jXzu9IN5d2mIO99oY9aGcRx3YBF1S8Occ/qxvLS2gXAUxg/1Yki+2QI446Rp1LoGm/3t2wNpDx3Jy2tT2yGwws2+c1oZwbAVDvO5IwL4vMKzH3Zw+mEBPnv8icyIGP7n/sYMr+cV545gRPVozjzNekX+zIcdKeMSzjyilNMPC7BsUzTFK/+1sw7nkPURnprTwYUnlzJ6UBU//JfVZlz1HwO47pkWWoOGC08qpeZg696ZtShIU1ucGYcEqCjx8On6MH9/zjrmd04t45gDjmVGe5wPV4Y5alIRFSU9K3Yh+SwBWL45wl+e6rmMKRNHl6e0TV5P10KrDthvIFsbkxlFrv1mJU/O6Uh4TyeOHUzDihChKDQESzhxSjFnzzie0uFBZi3y8vYmq+599bgSNuwoYuvyML8+fwAvzi/igxUVic7MWacdh39BkLkrw9BsnWvGcYcy6+FmgjE/V36xgrLigVz9iOXRveCzB7DhiRYa2uKcMS1AzUHFlBYLd7zexsYdMX5/YSVrtkdTfss//b8h3PlGW+L3uPbiIZQUDWXHE80E/MKPzqkgEoP/+ncDEwfWcfFZE6kqP4yydzvweeHco5PPoKMnB/jmyWUEI4aKEg+NbfFESJjXI3yyNsy08UWJQZci1byxMEhlqYf61jiTR/oYO3gIX20zVJQISzdFeG1BiCMm+DlpagAYzCVY+e8DRUJFieUFHlAiiAwBrPDSd5eGmLsqzMzTy+26adXrWNwwd1WYicN9BPxCWcADjEj8FsGwFSpSWiz4vNY+2xpjfLo+wolTqyjyCfG4YUdLnJIioaIk6VjZ2RKjutyTNYMSkBiDVVIseGQ48bihNWgYUFqdtbwbY4x93MEp62trazn55JPt39I578hOj7e7ySumjTE/F5GrgZuBR4HjgO8A04FS4MUessN5n+92A5UD23vo+L3C8IFehg/08plJxSxcF2ZnS5x1dbGCsnV0Rq74U4+QNczj4LH+xMPPl2VWiMPHF/HA7HbagoaqMg/jh/lYtCGSGECUjVMPDbB8c5Q122NMGO7hghNLueml1qyvowNp6f4OG5/srVeWWgY/MLs98VrOibkbWObh3KNLmTrGz2sLgny0JsKU0T6KfJKIBxURrv56JT6PFc9rjBVr95mJRZx/fBnnH19Gbe0njBmc6lkbWe3lK8eWcPB+fkZVJ6v6+SeUsq4uyuft9IN/+mYl5QEPxX7r1e3P721i0kg/HhFKiqxXyaXFwiH7FTF1jJ9/vdrGGdMsb/NFNWWs2dbEKYcEmLc6zMwzkh7gqWP8/HNmFQvWhjl0XBG/vK+RH55dkRJ3fNGMMgJFwub6GP95VgVFLlHsCGQR4fgDizn+wEwvU6EPeI8I/3F8KScfVMyvHmhi2nh/yoM2pWyOKcPj8dSYaRFJSae3p2Kms53XnUPX77NCOtyhH+n11U2xPZYgHIVzpgdYsjGakpEnfYIW6xzJbAZ/vmhgYhBnRYnQ0mG9pnXuGydOeupoPwvXRVKEelW5h2EDvRT5UnMHez1w/IHFbNwZZfW2GKXFHspc+dNq6QAAIABJREFUaTBPPbSYM6YFGFrpzQj9mHFwMRecVEZtrXXAYw4oYntTjOfnBekIGT57eAAR4c8XDSQWtzqca7dHU8YrVJYl69lRk4ooD3jwew0lRZIIE/nOqVZdnjY+6TH758wqiv1CfWucZz/s4IBR9sBLv/DHbwxkZ0uc95eHmL04xM++NCCR5QSstuGimjI+WRsmFoe/fTvZ6T9yQhH/9fkKnvqgnUnDrbCLaeOLUs79w7PLmTjCT0mR8H8XD2RrYzyRTx/g5INSB3IfPLaI2y+vZn1dlLFDLDsHlHo49dDcA757kskj/dx+efbOfXdIv7+K/ZJ1UO1nJhbx4crMEJMLTypLDCKtKBGqK7wp8bolRcI1Fw5MDLYfNtCqI9UVHja5xsWcMa2Eu96w6uSAEg/HTC5KhN+dMc2qe5VlHtbaoSK/v7CSAaXJ+jaq2ktJkVBSJFzx+QoGVXgpLxEa2uDLx5QkwrF+eHbyTcT+w3ycOLWYoZUepo0rorTYw/c/V8GHK6xUraXF1vF/cV6yM13sgZsuraa29hOqK6w3T189PvlG9k/frKSxLc64oT68nqQDY2BZaht89OTMdvqUQzLrUFW5tf/BY4s4eGxmyMJBY3N7vH1e4aSDApx0UOZxvR7h6Em530gEiiSj/Rs20Jty73k8wtDKTB/noIrcb0vBqmPFfld6Xo8woLSwB0Mugd7Ztr5Cp3EKxph2EfmeMaYDV/YOERlgDxTcZYwxbSKyDpgEvGWvHg/M74nj7w6c+J0tDTE8Am8tCSXi4q771kB+fGdjl46Xq+p4RLLGcg4Z4GX0IC8bd8YyBk5FYiQqeEfYJLa7K306R+zvp9gvCaGyeEOEyz5bwXXfqko09iOqko1I+s3pznftbhgd0hugySP9TB6ZbDyiMUNzh0kMghpRZRntHpF96qGpDUZpsYfrvz2QK/7dyPnHl1JV7uHMwzPzdac/HN0NxKAKLzfMrEoZyOnzSuL6Thrh59qLkq/SK0o8/PVi6yF/1pGZ5yr2C0fZDdt136rK2O4R4WsnlGWs7y2cBjPgF6rLLU9JwJ86kVC2UGGTFjPtFHHikDtr6rK1hemDZU6aWsyHK8MFzWQ5qtrLpBG+RNxrdbmH6nJP4g1Rh8vj7fcK4VhqjGjAVff93lTx7YwlCEescQWXnFHOz+7Jf/8W+YRmWyy7B/SVBzy0Bk3CS1rkS+bqPu2wAPNXh1MyJ4ys9jKwzMONl6SKqj9fNJABpR7WbIvyh8ebKStOfWgdOaEIr0eYMtpq0scO9rLefpP0tRNSQ7Q8Ipx8UIDn51md168ca+1jebAsxg31cfXXK7nqwSYuOb0MvxfOPDzA/sN8HGw/5Iv9wj++W8X2phjPftjB0ZOLMh56jo3V5R5umFlFseuJ4/cJw6u8nHt0KecenT2MDOC3X6vMun7qGD9Tx2TfBqTEVHo8kiKk8+EI6T3FUZOKiMUNQwd4efGjIMcdWMS7Sy3xedOlVYl48a5SUpRdTM88vSyrmC72JzvLP/2i5edyC/SyYmFgmYfvf66cG19s5Ti7s+/u5H3vTMu5UF1u1a3ygNWW/vEblfzivqbE5GQjq7yJ5+WgCk/KM85xGPzju1Upx23piOcc1+DzChfVZLarn8kjMjtjUIW3UzGp9F8KbTVOEJGPjTF1zoqeEtIuHgS+AvxbRIqB07FS5O1VjKjy8s2aMqZPLGL11ihlAUnxHg4b6GFbY+cuuZyeaU/2LANeDwkB6EtrYCpLJbHP0x90JIRP+uQxziBGcE/iYrVw6bM2Th7p4xsnJxurQJowr3A9mKeN9/PzLw9g/FAvD7zVzqxFoRSxnQ2fV6guzyzjDmeYMDyz514W8HDTpVX4d2HO5vTvsi+y/zAfI6u9PPF+B8V+SZkwoRAxDVYdTejhTgYgZh0kmHYbeL2ZAvuX5w1g9uJQ1rc9R0woonZRCI9Y+/ldrZl7Uhq/z7Iz4goFcXf+Jo7wJQYkART5oanNEIoain1Wp+Oq/xhAR9jkfA3v9wottme6yFUtywKS4hFPn6nOfR9c962BKW3FT75YwV+fbuGUQ4oTHVLHq13sF4ZUejjryAAvzAsmxIpzTx93YDFfHeSlLZiZgQWsgWIO2baD1Zb9/sJKhgywXut+5djsgndopZfvnNZ5TH537yu3yO8PzDzd+i1DEcO4YT6mjvEnxHRX2rX0kqXFws4s1Tef18/JQlEacN6SJbftZwvhaeOLuPnSqsQb0bLi5PU6ZD/rZjjtsACjBvkSdW3wAC/XfWtgIlXkmME+hlZag9kcgezzZM84Alady+Y5VZQ9RaFi+r+AM0VkLTDH/psPnGeMuaKHbLka+JeIPG0vX+UMRtwbmTLaz5TRqWJPsEbhbmssZNR29gbOI5mzXwEpr53cYntElZcJw/0pIsX55LyWA7h4Rhnjhnr5zcNWH8kR0840tE7OXOfzyCpvykPYESdOA+hO+eX1CBOGW1XtfDvUYFde23zrlDLWbMsdo74rQro/cMv3qvBIMlesFVqSX0zHjSV+3TmoRZJp0DxCl9V0+oPS6yEjO8L4YT7eXZYppDfVx5gy2s8ZhwVYuD5Meyg1+0ZTW6pnGkh4jiE1v3N6WFTAL9RF44TsyY3Aetinc+KUpJeryGdlqyj2p/5G5YH8dTFbDmqHA0ZZ7Yf7ejji2++1zvOlo0s5Z3pJ4juKCD/5YgUThvuyhns5iAg3X1rFZbc2UJFlWnMHFSx7jmK/cMT+lnf9gJE+ltkZmX5wVnlKasZcpDexpQXMvAswaYSP8+yOU8RuZp16566L+7k8+O665rT936wpTYStlRZ7OHJCaihDeojaz740ICUN6+8uqOx0mnRF6SsUKqabgKOAgcCJWPmlt2GFZfSImLbDSC7siWP1Vbye3Cnv0smZGs+TPSbaI7gGQSTXjx7ktbdn7jNphJ/xQ72s2R4jUPT/27vzODnqct/jn6d71sxk34AAYQtZCFsIibI5HBdW2VxQELkiRyDiri+4XvXiUblc9LgcPC7A1asHDqi4IIIez0GCHkUUvIgSWQXkICKQxJCQhCTz3D+qqrumu7qnpqd7qmb6+369YKaru6t/mV9V99O/en7Pz4bk60Z1Rlcu6ObF7VvZdWb5UIn/HpkVXv46dEEXW7f5kGA6rrPDEgOTkThsUXfpkqKMXBR0RiOmlZMXLeHLmruzo6IG9dAQfJhYOuHOynqpxYIlLuSS9OXxoDAnf7dZRe59vHrUfFPCqoPf/Hm5jFZ8FLuzGFy9iYLXaDGjF8OR6VrefHT5ykxnmOZRebVnuABmt1nFUtnKWl8wZ00pB7TR/uIpUZVfHqN/x3A6isFchJ2mtdfI73gUP+/mzWzsC05vnauB7z95Mrf9fgsLd+nkiCXdpWMqupoT/7IGQepQrfkak3uNM4+axKH7jOw9esqkwpCUwPhxL5J3aaOaF9z97vD3W83scuBDTPDgt9lGEkzXEuRMV283K7/hxT9ckx4bt3R+F4/+dTO9XVYafbjg2H4OiF2ee8WBw0/AmRMG3zMnFzh5Re38R8mPncKc98pjMumLl5dGpsvbzGI50xZ90CbnOycd9tsrKn8UC5C0MnxSXuTcaMTUosmRQ9sWnySbdKViyETPopXy3iEYEdy6zdm6zemuEZdW/om6ilaqrBJXKATn1fYdzsfPmFa60hN5wxF9nLJyEmtrrHh3yelTSvMFov19+YLpdVdIHYn4viW/4t2dtu+rRqbrBNML53UmfgmrPEcX79rB3Y+8WHdSppkxsHRsJm2K5EXaIYkuM3tDdMPdXwA+AnyuJa2aoIpFq7m6XKV61TySAvKOYjlw7qiYPFdPNFmkOyyh86Xzp7Nsr65hn1dpVrhYjC7LjR/L9+7ic2+dVnVMJuZMk7w6YqmaxzCvleZoKhYscRGMpEMxOj+ivO1Br33OTCod4+Vt8X9j5RyE7k5j6/Zg5Lyrxsh05d+osyMoJZdUorCv25g6qcD0/gI7TasOXns6a0+Oi+eZll9baUzt5syj+njjkcEgRaPd39898isQxy3rYf7s8rF51JJurlo1fJkzkXaT9ux6F/BOM/udmV1uZhcS5Dgf1LqmTTzxkelle9W/FFvr/bJQKF9qi3/GdnWUK2/ES+okTVaMi1ZEnB5OYKo1O3o4UXWOpNnikk9mxqTuQlWwWmsC4o5YzrSFX+pSl8ZLcVhFXwYrc4eLCcdwKZgmCqZrr1hYKASlI+P3xtv6ZMXy9t0dFtZira54E1WyqHypaH+VtdghyCGd2qdUCmncnKnFUnm1RlMFh/ssSDKwtIcPva5cMWU8lCgTyUKqd3h3XwscAXyMoFr26wmW/z6jdU2beIJgOngziibk1VJvZDpSawR67tQiJ4U1lJNqUse9+tBejl7aXaoG0KhiIViu9JC90+VrSn5UjXwm5kwHgfOQhxql0oUNzD+sEh2rlcd+UpAcH5mO2lfvc747LP+3YOcOLjl9ypDR3S0Vpfi6O2FtuFx9Zc70u06czMoFXVXl5qIvkUkTDvu6C0xLKA8p0oi0Vzcrr2CkfZ6IjFyqnGkz+yPwGXe/Avhma5s0cRULxt1/DCp51Lp8XJIimO4sWmkSV2cHWPikzo5yOklxmHSN6f0FzjiqOXWO44sqyPhRmVefNPrkBKO/8Q/kAhbLmbYmjExXX3FJuh3fZhjPbBis+bhIVC6yt8uYN7OD52O1HivbHZ9EOGNydQRy7iury8BFOdqfPHta1X0zJxfqVswQGYm0R9JrD+vlrkfKlaPi53Xlqp4iMjppv6s+TVAHWho0c3KBgaXdpRGs4YLp2mkesYlTsa9Cs6cUS4FOZzx/OqGHLz5tSvVGaVtpgtdoAmL8u5lZecKgGTWXg4fyF716onSO6pHphP3F0k3i7Unk5X1HV3Pmxkq+VaZyRKujLd29s+7CRnGzwwm4SXMNzjhqUmm1TZHRin8G/MMbay9aU7nASPS0tx/Xz6fPqf7SJyKNSxtMXwyc1cqGTHSXnTWNY2Or8XUOc00gXZpHcOOEQ3ro7SqHKx1FKz0u6cN9uBQTaS+VQXC9RVuGpIQYeKyaRz1pUi2jUe7Ky9NJaR7lkenqbUlKC0GE58P0/kIp/3nVsZOHPDZeyzmtswf6+Mjrk7+kFgvJq5aKNCLVF8jQ7Cnlj/josfF0QxFpjrTB9NXAmWb2MzN7s5nt3cpGtYOGR6aHpHkEP0uL0Fl5e6mW8KAu5Ul9UX5wJCm3ctCdwcHKEl3lFQubUc0jWtK4chXExAmI0c94e+oErNHodnyUO3qd6RXzBaIKNyOJN2ZPLY66frpIGhXfZ+u69E3lEehCKZhWIC3SbGmD6d8BV4Y/3wmsMbN1ZnZby1o2Qb1sv6CQfaM50/G7X7JvWBQ/jD2iIKhQKOev/vLBNKstSjurrMBSe2TaS8FodHzFq3nUZXDyivqpDl1hLLq1Yun64Urj1Ws3BOdFsZQCVX7Qcct6WbxrdQAclbdT5QLJo5GMTMeVr1Y2tz0ikn7Rlte5e6nmk5l1AcuA5S1p1QQWrfA0XDBdGCaavnLVDDZtGeS7d24uj0zH7o8mJqbN+RSJJE5A9OA7W9WiLSkvfBiwbK8ubvzV5pqPiVIwdlTsNDHNI2EYoFa1gvhCR/FR7kXzOllUZ7XAHhWmkRwaych09Ph4itZwC3mJyMilPa2OMbMvmdkxAO7+orv/0t0/38K2TUhRKbGu4b7G1HiXjIcZpUod0ch0LAjaui3YWG+lKpEkSSO8GzY7z2/2cp3p8L9ynnP9fRrJo2j/+6zyBKpa+0icgBj9jI9Mhz8/+JqhucsFi41Mp7zEfeHx/bzuMK3kKfkz0pHpS980lY+dMbV0zijNQ6T50gbTnwL+BnzazA4GMLOvmNmuLWvZBDdczc+ab3exaDoKEKJNJ63o5bUvDS6lR8vAvnRhV6NNlDZV79iM3xetPpiKJR/Tk7oLpS+W9dI0qnaXMAEx2laZA71o147Ekel6Dtyji74eDeFJ/sQHTdIE0zMnF4esvKmRaZHmS3ta3e7uFwEnAW8Ot30C+HRLWjWBLQwvK0e1N2pV1kjzJlkZTE/rK3BMWDEkCnI0CiHDiddVhvqjzNVpHl76vR4r/a9ie8IoW2WAnlzNI6k0nlW18apVM9h7p86qah4iE0F5tHno9t1mVX9rjM7VqOb66YdP4sgl3a1snkjbSBtMm5m93N0fAfoAwt/3bVnLJqhF8zq5atWMUhCw8/TkobI0wXRphCJhdPC4ZT28eUCXqWV4lZPw6gbT8ZFpLDYBcZjqNFZj8ZXY/o5aEqQkVR7O8UChcgJk0gREM1h1bD87Ty9UPW8k5e5Eci88AQ7co5zg/5lzpnHRqdVlGksDLOE58IoDe3jzQHMW7BJpd2knIH4IuNXMngR6zGwA2AdQtNagpJzPNJKuqidt6+spcOSSofnSe8/tSFzRTdrb8ct6eeLZHaWVBOuN3pYCYgtHpkdZZzr+Ugt2Cd+OqkamY/upaEd8MZjoNQoGB+/VxcF7lVOcon30aEKuTCAFgy+eN52CwXlfWgdAf430pMHS1cqxap1I+0h1Wrn7s8BK4CdAL3A9cG74nzQiIedzyN0j+cxPmbd68Wum8LZXVS+FLO1t/pwOLn3TNN55QnBsRKWzjlxcfQl4SL4msdJ4KV4n6TFJI8vVI9PlB5VGsocZma4UfUHo17LeMsF0FK1ujfXIoFL/RFqm5si0mX0YuBP4lbuvd/ctBBMRPzVWjZvIijUCh0itt7ukx1dOuBJpRGmFtITZfcUC7BgsB7NRdY60daZr50zHAuVRVvMop35U7yjaR/dw9d1FxpGRxMWukWmRlqmX5vFeYB2wu5k9TBBY3wncAdzr7jvMbLG7/2EM2jnhREFJrRUK045Mf+LMqcxS6oY0QTTqXDoiHRbO6+CBJ7eXHlOsCF495QqImA1fPi9FMF3vvIineVTvI9jYpTQPaVNR/fZOTcIVabp6Udip7r4XMIcgsH4cOBW4DXjezO4Abml9Eyem0nLfNYam05TGA5gztZjqEp/IcHrCqh7x4HXejCDnIxrVsorAdkQj08OIRpQrq3nEj+9CxQvFD/2kCh+R6N807MqjIuPAPjsH42AjSQecG5bH69ZiRCJNV3Nk2t1Xhz/XEgTNt0BQ1gNYAryUII9aGtDXE6zKtsecDu54IGHJby1lLGNszzlF3nfyZPp7yseeV/wsVuRMbx9Mt29LqDNd63JzvWoeSVU8IvVypqMAfdjFkkTGgb7u2l8ca1m5oIuD9uwatvKOiIxcqvwAM1tmZheb2f4euM/dr3b3v291AyeqzqLxpfNnML0vuQtGkjMt0gxmxqJ5nckf0NEqmzUWbUm3AqJVbYv/rHytSDyYrnydxJzphNePLnFrZFomgmiS8EiOZjNTNRuRFkmbbHstcDDwAzNbAGBmnzWzqfWfJsOpNUgw0u0izVKgInc69ntl8DqYtpyHVR+7tcqkO3D8IT0cc1BQ2jGpVF+9CYhJgf2OcAS9W8GETAD1UppEZOylDaZ/6u6nE+RMnxNu+xe0AuKopQ2ao4U1OjTXUFpsyLFXOUo85Li01KXxhiuLV+nUlZN47WFBGfuu2EIrpeckjELvNbd2HmkUTGvRFpkIyilNiqZF8iBtaNZtZvu5+2+A2QDufjfBaLW0QOVb5HtPCla0qlz6WaTp6hxi0WTAaEXD0ayAWC8tI64zlppRL81j/uyOcHXR6j1u2RY0tEfnj0wA8RKVIpK9tNNxPgr82Mz+HZhtZvOBBcD0lrWsTdR6M0yKTd5xfD9LdtNUbGmtusuJV9w36I1fak77tHiec3WqSO374o5c3M3GLYNV1UBExqN6k21FZOylXQHxUeBwYAewAngUuAb4QOua1h5G8mZ4wB5ddZd6FmmG6Ahz96p85spFW3wwbdm7+oFwPZ2xr/zR8uFJkxfrfQmYMqnA6Yf3pXo9kbxTOVSRfEldzQPY6u7vcPedgUnuvpO739Da5rUvvVdKVuIx7qSKtIihdZ1h0D3VF8Io+K71OvXER5PL1USsqj0apZN2ER33+pwQyYe0OdO3EFtG3N23mNmxrWlSe1EAIHkTX5b7xOW9fOCUyaX74iNim190Hv3rjvQj0xXbGgkE6gXkOpekXRSSLs2gOuoiWUl76n3X3S+o2DbJzD7o7pc2u1HtxGqEIgoMJCvxNI/ODmPfXcp5+vEA+LePbQseP4bHalVJvIqRcpF2UMqZjm27+LTJTO5VuSeRLKQ982aY2az4Bnf/DnBm85vUXuKByFFLumPbFRlINuode0kjYqnSPMzo7jSm9SVMGEzx/Hed2J/4WvE3MJ0z0i4smrsQO+T33qmTOVNV+1EkC2mD6c8BvzKzD5jZYjPrMbMDUDWPpjpk767S7woLJCvRB/SmrdXrbRYS3jFSpXkQLL7yybOn8+4TJw99XoplPZfuHpwblSNyUVt1eVvaSTFatCXjdohIIG01j18AxxPUlb4H2ATcCVzWuqa1h5qDaXqXlIxEx+Q9j26ruq+Q9CE+wmN1v907h7zOSBQqouloNPoNR0wa+c5EximVxhPJl7TVPOa6+/3ufgYwBdgbmOnu/9TS1rUZMzh+WbCEst4jJSsjPfbi1TZGUjd9JGke5dcafr8iE105zUMngEgepE3zuNfMvgXg7lvd/TFgn5a1qo1UBgf94QQSvUdKVqJjb/aUkU1m+uw503jHCf2pH19rAZWpk+rkbFes/FbehU4YaR+abCuSL2kzDW9x97dUbDvczFa4+9WjaYCZ9QPvBVYCLwL7Ah939+tGs9/xqoHBOpGmigLUIxZ3139gaDBcU7yvpzBk6e+4tB/+F506mSmTagfx0ZpFqrMr7ayoA18kV9IG071m1u3uW2PbrgR+A4wqmCYY4T4fWOLu683sXOCrZvZ9d980yn3nXq26uRqZlqxEh17SZMPyY8oH6I7B6ufW3Gl8U8K2fXburN4YE+VMW8UItUg70eeDSL6kvY57LUE1j9eaWbQm7wxgVp3npHU/sNLd14e3fw50Azs1Yd/jit4gJQ9KX+hSPn4wRTWOJKNZtEUTsKSdaWBaJF9SjUy7+01mthH4BHCdmT1HMBHxitE2wN23AE/ENi0BngQeG+2+x4OqurkKEiRjpYod9Y7B2H3xYHokx20jx3hUAq+yjTpfpJ0csbibdZsGh3+giIyJ1NVZ3f024DAz2xOYD/wXsFszG2NmXcAlwLvdfUeNx7wNeBvA3LlzWb16dTObkMrGjRub9rrPvDCJaC7nPffcw4atPcA8fnr77Rj742HUksW/czxpZp+0u22DBWApf3zkEVavfxaA/s6FbNzWHf6ND2DjhvVAebJh9Ld/elMfQbGfoR568CHs2ediWw5g8+bNrF59Dzt27AcUh+2/o3fvp7/zRdawiDX3/Z7nn9jApm2dwGIeeOB+tj+9ruF/czvQOZI/o+mTXQB1Z/PpPMmX8dIfNYNpM3slMBWYFv6M/z4NmENQd7pnuBcxs92BNQl3PenuC2O3Pwvc7O431NqXu19JkK/N8uXLfWBgYLiXb7rVq1fTrNd95C/b+PFjzwNw8EEH8dS6Hfz6Ly8wMPAyjj7a+PsvrAVo2utNVM3sk3a3dZvzzfvXsWDBPgwcsBSAW/+8no3rBxkYGODaNWuZOXMaT7+wvfSc6G//4J+38R+PP1+1zwX7LmBg6f6l29euWcukSb0MDAxww4Nr2T6Y7hh3d3rv3crRSw+mo2is2zjI9x5az6KFizg85YTJdqVzJH/UJ/mjPsmX8dIf9Uamv0OwOMu9wLbw9+eB5cCN4fYpaV7E3f9EfBgrgZmtAmYDb0+zz4lj6PXp+bODLlH9UMlKqVJGbEZFZfWAKb3J0y1qHrYJedXRQ6f2Ffjr39JdsjYzXnlgT+x2qqeJiIi0TL1gel+ClIspwCXu/gCAmX3V3T8Y/n5VMxphZocC5wBHubuH6R5WUT1kQqoMBvac28FVq2Zk0xgRkicgFmOx8/tPnszuszt45C/beWbD0CB4JLFt9IXxolOnsGVbY7MYSzG+gmoREclIzWoe7v6Uu58H/ANwmZl9xczmExtjCkecm+GLwD8Cu5vZYoKJjcc1ad+5phhA8io+GB0PphfO66S3y3j/KakuTAUSDvRo/1MmFZgztdhQGzUyLSIiWRt2AqK7/wE41cyOAq4BZobLiz9tZjPcfe1oGmBmxwKHAP9acdcPR7Pf8ULBgORNFORO7y9H0EmLRBRHtkBiS+j8ERGRrI2kmsdPgSPN7A3ArWb2W2ApcOBoGuDuP6KNB2jb9h8uuVUoGJedNZWZk8ujxa8+tJefrtky5HFJx+6IcqabcPDr/BERkayNeGzJ3a8nqOJxN0k1sGRkYtGARtkkL+KBNMCS3To5/5jJQ7YlH6/pD+KmBNMjXGBGRESk2VKPTMe5+zbg02aWVO5ORkBBgIxXUebH0t3LS4C3etGW6n3oDBIRkWyNKusxTNGQ0VAsIONUFMf2dg1/ECfV6mjGoa9YWkREspaDKUTtTR0gE8lIYttCEw5+xdIiIpI1xXJZUzQg41QhHBZOMzqc9JBCEw7+Us60ziMREcmIgumMKQaQ8SoKYAsNTqI1vfuIiMgEoI+zrCmalnFqJKPCSTnTCaWrR6yjCDMnF1g4r3P4B4uIiLRAQ9U8RERKK3lb9bY0mhFMF8y47Kxpo9+RiIhIgzQyLSINSazxPIIAuRnBtIiISNYUTGfMlOch41Q5Z7p8DI/kaF6wi1IzRERk/FOaR45opE7Gk8Sc6RrHsFckTX/hvOl06Ku8iIhMAAqmc0TlvWQ8ScqZrqXyi2JnUQe7iIhMDBobytjgYHnITksjy3hiCXWmax3BOrRFRGSiUjCdsW07yr8r3pDxSMG0iIi0MwXTOaKcaRmPhrzYK5Z1AAANVElEQVSJ1DiGFUyLiMhEpWA6Y3vMKXLSob1AcsDR26UoRPIt1aItSau2iIiITACagJgxM2PJbp18/9ebE4OST5w5VYGI5JqlKI03qGNYREQmKAXTOeBhtJwUTE/u1cUDybc0pfEGB8ekKSIiImNOkVoORIN2yiuV8WjoBMTkg3jWFL3ViIjIxKSR6Rzo7w4Cjd4uBRwy/sSP2qRQ+rPnTKOvR8e2iIhMTAqmc2DnGUU+efY0pvUp4JDxZ6fpxbr3K5AWEZGJTJ9yOaFAWsajT79lGi9d2FXeEA5Nn7yil2ndm7NplIiIyBjSyLSINKxygmyU5jF7SoGjdnucDd0Hjn2jRERExpCCaRFpnljS9OSuF3n1y/qya4uIiMgYUG6BiIiIiEiDFEyLSNOouqOIiLQbBdMi0jSlmtOKqkVEpE0omBaRplMsLSIi7ULBtIg0jQamRUSk3SiYFpHmUzQtIiJtQsG0iDSPRT8UTYuISHtQMC0iTVNK81AsLSIibULBtIg0jYJoERFpN7kKps1sJzNbb2YDWbdFRBqnmFpERNpFroJp4JNoiXOR8U/RtIiItIncBNNmdjzwF+DZrNsiIo1RaTwREWk3uQimzawP+J/Ax7Jui4iMgpKmRUSkzeQimAY+Clzl7huyboiINK4jfEcZ9GzbISIiMlbMvfWfema2O7Am4a4ngdOBK4GXuPugmT0G/Ddgobt/OWFfbwPeBjB37txDrr/++lY1u6aNGzfS398/5q8rtalP8mHbjgLffGAph837E7OL/6U+yRGdI/mjPskf9Um+5Kk/jj766LvdfXnSfWMSTNdjZp8CVgLPhZteBfwO2ObuR9R77vLly/2uu+5qcQurrV69moGBgTF/XalNfZIP23c4F3x5HW99eR9bnrpDfZIjOkfyR32SP+qTfMlTf5hZzWA688oZ7v7++O1wZPoid1+dSYNEpGEdReOtr+hj+d5d/OdTWbdGRESk9TIPphN0AMWsGyEijXnJvt1ZN0FERGTM5GUCIgBmdiYwD3iPmeUx0BcRERERKclVMO3u17q7ufuJ7r496/aIiIiIiNSTq2BaRERERGQ8UTAtIiIiItIgBdMiIiIiIg1SMC0iIiIi0iAF0yIiIiIiDVIwLSIiIiLSoMyXEx8NM3sGeDyDl54FPJvB60pt6pP8UZ/ki/ojf9Qn+aM+yZc89cd8d5+ddMe4DqazYmZ31VqfXbKhPskf9Um+qD/yR32SP+qTfBkv/aE0DxERERGRBimYFhERERFpkILpxlyZdQOkivokf9Qn+aL+yB/1Sf6oT/JlXPSHcqZFRERERBqkkWkRERERkQYpmBYRERERaZCC6RGwwOVmdr2Z3WZm78u6Te3GzM4xs5vN7F/N7D4zuzx23wwz+4aZfd3Mfm5mr8yyre0kPDd+YWaXxLapPzJkZi8xs1vM7HYz2yXcpj7JgJkdYGY3hn/3m8PfdwvvU5+MATMrmNmFZnZrxfa6f38zO8jMfmBm15jZrWa2x1i2eyJL6hMz6zezj4TnyXfDz/k3Vjwvf33i7vov5X/A+cC3wt+nAGuBl2fdrnb6D1gHrAx/XwA48Irw9vXA28PfjwT+BkzPus3t8B9wQXg+XBLbpv7Irj/eBNwH7FexXX0y9n3RDfwFOD227Vrg39QnY9YHBwI3An8C7qq4r+bfP+y7x6LzCPgw8LOs/z0T4b9afQIcBPwZmBbePhfYAvTluU80Mj0y5wE/AHD3DcC/E3xoydhZ4e53Arj7Q8BfgflmNgN4LeX++RmwGTgpq4a2CzPbGTgNuCm2Tf2RETNbDHweONnd74ttV59kY1dgLnBvbNsvgSXqkzGzK3Am8NX4xhR//2ODzaXz6AbgCDObPxaNnuAS+wS4n2DAbH14++cEAfRO4e1c9omC6ZTMrAvYH3g4tvkBYL9sWtSewgAaADObA0wH7gAODu72+PLyD6L+GQufAz5CcJUgov7IzgeB77n7wxXb1SfZeIwgQDgNgkvbwPHofWvMuPvN7r6Roe9RMPzffzlDP/MfBgZR/4xarT5x9y3u/kRs0xLgSYLzCHLaJwqm05sJFIGNsW1/IwjmJBsfA77s7muAOQztG1D/tJyZnQhsdfc7Ku5Sf2TAzAw4AfiTmV0V5rF/P8wpVJ9kwN13AK8CLjSz1QTpN30EVzrVJ9ka7u8/5H533wa8gPpnTISDmJcA7w7PI8hpnyiYTq+YsG0bsCNhu7SYmZ0OLASiSaDqnzFmZn3A5cDFFduPAfZOeIr6o/VmEnyo9AAXAkcRvM9/FZ0jWboSuMrdBwhGQwcJvvSoT7I13N9f/ZOtzwI3u/sNsW257BMF0+mtC39OiW3rJ8jZlTFkZocQXMo+zd1fDDevA/rDS6gR9U9rnUCQX/jPZvY94O+ANxIE2OqPbFj48xp33+ru24H/QxBUq08yYGb7EOR5fhmCy9jAt4BPoD7J2nB//3UM/cyH4KqC+qfFzGwVMBv47xV35bJPFEyn5O6bgMcJKkhE9gR+k02L2pOZTQG+BrzB3deG2yYTXDrtAPaIPVz900Lu/k13P8TdT3H3U4CfANe5+4HAzag/xpy7P0NwyXNGbPOLwHZgDeqTLEwLf8ZHzjYAs9D7VtaG+/vfR+wz38zmhY//7Ri1ry2Z2aHAOcDZ7u5m1mVm3eHduewTBdMjcx3BzF/Cjn0l1TNRpbUuAX4MuJktMrNzgY+5+2MEM+Sj/lkK9AI/yqid7aiD8BKc+iNT1wEnx26/GrjR3R9FfZKFewlKfZ0a23YC8AOdJ2Ouh6AyBJDqfeomYLqZrQhvnwp8192fG6sGt4EhfRL6IvCPwO5hdaIrgOPC+3LZJxbW6ZMUzKwXuJrgMhDAt9396xk2qa2Y2XSCWb29FXd9zt3fHV5OvRp4gmBk7iPufvcYN7MtmdlBBF9y1gLHuvtj6o9smNlUgtJ4RnCubALe5e7r1CfZMLP9gc8QXKLuBp4B3ufu69UnrWdmK4GzCdLQpgLfAH7k7l8b7u9vZscDHyKo8jEJWOXuz47xP2HCqdUnwNPADxOecqq7fy98bu76RMG0iIiIiEiDlOYhIiIiItIgBdMiIiIiIg1SMC0iIiIi0iAF0yIiIiIiDVIwLSIiIiLSIAXTIiIiIiINUjAtIpIRM3uNmW03s++b2V1m9qyZXWNmD5vZb8PHfNjMkuquNrMdHWZ2kZndZGbnN3G/l4f7nN+sfYqI5I3qTIuIZMTMLgOedPcrzOyfgenufoaZvQY4zt3PNbO/A7a4+y9a2I6rgMnAGe4+2OR9vxU43t1f08z9iojkRUfWDRARaWO/BG4Ofz8UuD78/fcEq+Xh7j9pZQPC1SvfCMxvdiAd+g3w8RbsV0QkF5TmISKSEXf/nrtvM7NO4ADg1+H2B9z9J2Z2hZm5me1sZrPCVJCbzewUM/udmT1oZnPM7FIz+7OZfSO+fzO70Mz+r5ndZmZfqdGMc4HvuPtzsed91My+GKaf/DS2/fQwDeUmM/s3MyuG2xeb2XVm9gUze8LMTo7tvwcN3IjIBKY3OBGR7O0PdBKM4sbdBJzm7k8BmNmPCYLuycBBwLPA14H3AzcCvzAzc3c3sw8Bi4CzgMXAPcA5Ca99IvC+6IaZHQyc7u6LwmD5U+H2s4C3AMeEbd0EzDWzrcCPCNJS1pjZc8DDsf3PB/7a0F9FRGQcUDAtIpK9Q4E/uPumiu0HEY5Wh/YHXnD3fzGzAtBNMKr8ezM7Eng6DKR3Bj4I7EswMrwK+E7li5rZHgTB7u2xzeuA3c3sOHf/IfAeM+sBPkMQeDvwLoIUlaeAS4Fb3H0NgLt/uOJl9mdocC0iMqEozUNEJHvLGRo0R1YCd8VurwCuCn9fEv68Jvx5cOyxJwPrgQ8Dnw/3/aaE/a8AHnL3Z6MN7v4YcAHwbTNbFW4eIAjc3wJcCWwAXu7BDPYzgR/U+bcdTDAqLiIyIWlkWkQke8uBqxO2H0oQvGJmewIzgZ+H960E7nL3F8LbRwBRxY89gf9w9/OGed1lDA3WAXD3r5nZ08CNZvb1cH//r3J/4ej4bsCjSTs3MwNeAvzTMO0QERm3NDItIpIhM+sHlgK/q9g+lyBQjUZ1VwD3x4LnlcDdsaccHrv9OLCXBbrN7JNmtizh5ZfEX9fM9jSz/xHe/BkwGP73OEHqR5eZFc3svWZ2Ulj9488E6SSY2cvM7OzY/g8G+oD/TPv3EBEZbxRMi4hk6zMEVwlPqNi+Ivx5XOx2PHheSThh0cz2AnYhmBwIwaTEDQS5yj8BfuHulZMbIRjp/lPs9i7AGWb2BYKR8rPD4P1HBAHxg8AdwEZ3/374nPcAnzezh4BTgHhFkVcDt7n7xnp/ABGR8UyLtoiItCkzux24wt1vaMG+O4CHgA+0Yv8iInmhkWkRkfZ1J+UR8Gb7MHA/8O0W7V9EJBcUTIuItK//BSwIJxk2hZmdZmbfIciVfr3r8qeITHBK8xARERERaZBGpkVEREREGqRgWkRERESkQQqmRUREREQapGBaRERERKRBCqZFRERERBr0/wH0Q2zaHxKvEAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "G_M =np.loadtxt('FGU_2SSMRF_files/acc_1.txt')\n",
    "times = np.arange(0,0.02*len(G_M),0.02)\n",
    "plt.figure(figsize=(12,4))\n",
    "plt.plot(times,G_M, color = '#6495ED', linewidth=1.2)\n",
    "plt.ylabel('Acceleration (m/s2)', {'fontname':'Cambria', 'fontstyle':'italic','size':14})\n",
    "plt.xlabel('Time (sec)', {'fontname':'Cambria', 'fontstyle':'italic','size':14})\n",
    "plt.grid(which='both')\n",
    "plt.title('Time history of Ground Motion record',{'fontname':'Cambria', 'fontstyle':'normal','size':16})\n",
    "plt.yticks(fontname = 'Cambria', fontsize = 14);\n",
    "plt.xticks(fontname = 'Cambria', fontsize = 14);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Time history of story displacement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "story_disp_th = np.loadtxt('FGU_2SSMRF_files/TimeHistory_Story_Displacement.1.1.out')\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.plot(story_disp_th[:,0], story_disp_th[:,1], color = '#DE3163', linewidth=1.2)\n",
    "plt.plot(story_disp_th[:,0], story_disp_th[:,2], color = '#FFBF00', linewidth=1.2)\n",
    "plt.ylabel('Horizontal Displacement (m)', {'fontname':'Cambria', 'fontstyle':'italic','size':14})\n",
    "plt.xlabel('Time (sec)', {'fontname':'Cambria', 'fontstyle':'italic','size':14})\n",
    "plt.grid(which='both')\n",
    "plt.title('Time history of horizontal dispacement',{'fontname':'Cambria', 'fontstyle':'normal','size':16})\n",
    "plt.yticks(fontname = 'Cambria', fontsize = 14);\n",
    "plt.xticks(fontname = 'Cambria', fontsize = 14);\n",
    "plt.legend(['Story 1', 'Story 2'],prop={'family':'Cambria','size':14});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n",
    "\n",
    "- <blockquote>Fernando Gutiérrez Urzúa's YouTube channel (https://www.youtube.com/user/lfgurzua) </blockquote>\n",
    "- <blockquote>OpenseesPy Documentation (https://openseespydoc.readthedocs.io/en/latest/) </blockquote>\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}