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    "<h1> OpenSees Examples Manual Examples for OpenSeesPy</h1>\n",
    "<h2>OpenSees Example 9. Build & Analyze a Section Example</h2>\n",
    "\n",
    "<p>\n",
    "\n",
    "You can find the original Examples:<br>\n",
    "https://opensees.berkeley.edu/wiki/index.php/Examples_Manual<br>\n",
    "Original Examples by By Silvia Mazzoni & Frank McKenna, 2006, in Tcl<br>\n",
    "Converted to OpenSeesPy by SilviaMazzoni, 2020<br>\n",
    "This Example:\n",
    "https://opensees.berkeley.edu/wiki/index.php/OpenSees_Example_9._Build_%26_Analyze_a_Section_Example    \n",
    "<p>\n",
    "\n",
    "    This workbook demonstrates a Moment-Curvature analysis for two types of sections<br>\n",
    "    1. A uniaxial Section (moment-curvature relationship). <br>\n",
    "    For the case of the uniaxial section, moment-curvature and axial force-deformation curves are defined independently, and numerically. The uniaxialMaterial command is used to define the moment-curvature relationship.<br>\n",
    "    2. A fiber section (standard W section).<br>\n",
    "For the case of the fiber sections (steel and RC), uniaxial materials are defined numerically (stress-strain relationship) and are combined into a fiber section where moment-curvature and axial force-deformation characteristics and their interaction are calculated computationally.\n",
    "    <p>\n",
    "        Even though the sections are defined differently, the process of computing the moment-curvature response are the same, as demonstrated in this example.\n",
    "        <p>\n",
    "            For more info on Fiber Recorders, visit the Portwood Digital blog on this topic <a href=\"https://portwooddigital.com/2021/07/25/how-to-record-fiber-response/\"> here </a>\n",
    "\n",
    "    \n",
    "\n",
    "<h3>2D vs. 3D</h3>\n",
    "While this distinction does not affect the section definition itself, it affects the degree-of-freedom associated with moment and curvature in the subsequent analysis. \n",
    "There are two differences between the two models:\n",
    "<ol>\n",
    "    <li>The space defined with the model command (# Define the model builder, ndm=#dimension, ndf=#dofs)</li>\n",
    "    <li>  2D: model BasicBuilder -ndm 2 -ndf 3;</li>\n",
    "    <li>  3D: model BasicBuilder -ndm 3 -ndf 6;</li>\n",
    "    <li>In the 3D model, torsional stiffness needs to be aggregated to the section</li>\n",
    "</ol>\n",
    "This example demonstrates the case of 2D\n",
    "\n",
    "<p>\n",
    "\n",
    "     Objectives of Example\n",
    "    - Build a uniaxialSection: Flexure and axial behavior are uncoupled in this type of section\n",
    "    - Perform a moment-curvature analysis on Section\n",
    "\n",
    "<table>\n",
    "    <tr>\n",
    "        <td><center><h3>uniaxial Section:</h3></center> <br>\n",
    "<img src=\"https://opensees.berkeley.edu/wiki/images/b/b9/ExampleSection_Uniaxial.GIF\">\n",
    "        </td>\n",
    "        <td><center><h3>Fiber Section:</h3></center> <br>\n",
    "<img src=\"https://opensees.berkeley.edu/wiki/images/3/33/ExampleSection_W.GIF\">\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n",
    "    \n",
    "    "
   ]
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    "############################################################\n",
    "#  EXAMPLE: \n",
    "#       pyEx9a.build.UniaxialSection2D.tcl.py\n",
    "#          for OpenSeesPy\n",
    "#  --------------------------------------------------------#\n",
    "#  by: Silvia Mazzoni, 2020\n",
    "#       silviamazzoni@yahoo.com\n",
    "############################################################\n",
    "# This file was obtained from a conversion of the updated Tcl script\n",
    "# The Tcl script was obtained by updating the Examples Manual published in the OpenSees Wiki Page\n",
    "############################################################\n",
    "\n",
    "\n",
    "import openseespy.opensees as ops\n",
    "import os\n",
    "import math\n",
    "import eSEESminiPy\n",
    "import numpy as numpy\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# --------------------------------------------------------------------------------------------------\n",
    "# build a section\n",
    "# Silvia Mazzoni and Frank McKenna, 2006\n",
    "#\n",
    "\n",
    "# SET UP ----------------------------------------------------------------------------\n",
    "dataDir  = 'Data' # set up name of data directory -- simple\n",
    "if not os.path.exists(dataDir):\n",
    "    os.mkdir(dataDir)\n",
    "    \n",
    "# --------------------------------------------------------------------------------------------------\n",
    "# LibUnits.tcl -- define system of units\n",
    "# Silvia Mazzoni and Frank McKenna, 2006\n",
    "#\n",
    "# define UNITS ----------------------------------------------------------------------------\n",
    "inch  = 1.  # define basic units -- output units\n",
    "kip  = 1. # define basic units -- output units\n",
    "sec  = 1. # define basic units -- output units\n",
    "LunitTXT  = 'inch' # define basic-unit text for output\n",
    "FunitTXT  = 'kip' # define basic-unit text for output\n",
    "TunitTXT  = 'sec' # define basic-unit text for output\n",
    "ft  = 12.*inch # define engineering units\n",
    "ksi  = kip/math.pow(inch,2)\n",
    "psi  = ksi/1000.\n",
    "lbf  = psi*inch*inch # pounds force\n",
    "pcf  = lbf/math.pow(ft,3) # pounds per cubic foot\n",
    "psf  = lbf/math.pow(ft,3) # pounds per square foot\n",
    "inch2  = inch*inch # inch^2\n",
    "inch4  = inch*inch*inch*inch # inch^4\n",
    "cm  = inch/2.54 # centimeter, needed for displacement input in MultipleSupport excitation\n",
    "PI  = 2*math.asin(1.0) # define constants\n",
    "g  = 32.2*ft/math.pow(sec,2) # gravitational acceleration\n",
    "Ubig  = 1.e10 # a really large number\n",
    "Usmall  = 1/Ubig # a really small number\n"
   ]
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    "# Define a function to run moment-curvature analysis\n",
    "\n",
    "############################################################\n",
    "#  EXAMPLE: \n",
    "#       pyLibMomentCurvature2D.tcl.py\n",
    "#          for OpenSeesPy\n",
    "#  --------------------------------------------------------#\n",
    "#  by: Silvia Mazzoni, 2020\n",
    "#       silviamazzoni@yahoo.com\n",
    "############################################################\n",
    "# This file was obtained from a conversion of the updated Tcl script\n",
    "# The Tcl script was obtained by updating the Examples Manual published in the OpenSees Wiki Page\n",
    "############################################################\n",
    "\n",
    "def MomentCurvature2D(secTag,axialLoad,maxK,numIncr,fiberRecorderData={}):\n",
    "    ##################################################\n",
    "    # A procedure for performing section analysis (only does\n",
    "    # moment-curvature, but can be easily modified to do any mode\n",
    "    # of section reponse.)\n",
    "    #\n",
    "    # MHS\n",
    "    # October 2000\n",
    "    # modified to 2D and to improve convergence by Silvia Mazzoni, 2006\n",
    "    # converted to OpenSeesPy by Silvia Mazzoni, 2020\n",
    "    #\n",
    "    # Arguments\n",
    "    # secTag -- tag identifying section to be analyzed\n",
    "    # axialLoad -- axial load applied to section (negative is compression)\n",
    "    # maxK -- maximum curvature reached during analysis\n",
    "    # numIncr -- number of increments used to reach maxK (default 100)\n",
    "    #\n",
    "    # Sets up a recorder which writes moment-curvature results to file\n",
    "    # sectionsecTag.out ... the moment is in column 1, and curvature in column 2\n",
    "    \n",
    "    # Define two nodes at (0,0)\n",
    "    ops.node(1001,0.0,0.0)\n",
    "    ops.node(1002,0.0,0.0)\n",
    "    \n",
    "    # Fix all degrees of freedom except axial and bending\n",
    "    ops.fix(1001,1,1,1)\n",
    "    ops.fix(1002,0,1,0)\n",
    "    \n",
    "    # Define element\n",
    "    #    tag ndI ndJ secTag\n",
    "    ops.element('zeroLengthSection',2001,1001,1002,secTag)\n",
    "    \n",
    "    # Create recorder\n",
    "    ops.recorder('Node','-file','Mphi.out','-time','-node',1002,'-dof',3,'disp')     #  output moment (col 1) and curvature (col 2)\n",
    "    for thisLabel,thisData in fiberRecorderData.items():\n",
    "        print(thisData)\n",
    "        ops.recorder('Element','-ele',2001,'-file','FiberResponse_'+thisLabel+'.out','section','fiber',*thisData,'stressStrain')     #  output moment (col 1) and curvature (col 2)\n",
    "    # Define constant axial load\n",
    "    ops.timeSeries('Constant',3001)     # timeSeries Constant 3001;\n",
    "    # define Load Pattern\n",
    "    ops.pattern('Plain',3001,3001) # \n",
    "    ops.load(1002,axialLoad,0.0,0.0)\n",
    "    \n",
    "    # Define analysis parameters\n",
    "    ops.wipeAnalysis()     # adding this to clear Analysis module \n",
    "    ops.constraints('Plain')\n",
    "    ops.integrator('LoadControl',0,1,0,0)\n",
    "    ops.system('SparseGeneral','-piv')     #  Overkill, but may need the pivoting!\n",
    "    ops.test('EnergyIncr',1.0e-9,10)\n",
    "    ops.numberer('Plain')\n",
    "    ops.algorithm('Newton')\n",
    "    ops.analysis('Static')\n",
    "    \n",
    "    # Do one analysis for constant axial load\n",
    "    ops.analyze(1)\n",
    "    \n",
    "    ops.loadConst('-time',0.0)\n",
    "    \n",
    "    # Define reference moment\n",
    "    ops.timeSeries('Linear',3002)     # timeSeries Linear 3002;\n",
    "    # define Load Pattern\n",
    "    ops.pattern('Plain',3002,3002) # \n",
    "    ops.load(1002,0.0,0.0,1.0)\n",
    "    \n",
    "    # Compute curvature increment\n",
    "    dK  = maxK/numIncr\n",
    "    \n",
    "    # Use displacement control at node 1002 for section analysis, dof 3\n",
    "    ops.integrator('DisplacementControl',1002,3,dK,1,dK,dK)\n",
    "    \n",
    "    # Do the section analysis\n",
    "    ok  = ops.analyze(numIncr)\n",
    "    \n",
    "    # ----------------------------------------------if convergence failure-------------------------\n",
    "    IDctrlNode  = 1002\n",
    "    IDctrlDOF  = 3\n",
    "    Dmax  = 'maxK'\n",
    "    Dincr  = dK\n",
    "    TolStatic  = 1.e-9\n",
    "    testTypeStatic  = 'EnergyIncr'\n",
    "    maxNumIterStatic  = 6\n",
    "    algorithmTypeStatic  = 'Newton'\n",
    "    #fmt1 = [%s,Pushover,analysis:,CtrlNode,%.3i,dof,%.1i,Curv=%.4f,/%s] # format for screen/file output of DONE/PROBLEM analysis\n",
    "    global LunitTXT ##xx##   # load time-unit text\n",
    "    if ok != 0 :\n",
    "        # if analysis fails, we try some other stuff, performance is slower inside this loop\n",
    "        Dstep  = 0.0\n",
    "        ok  = 0\n",
    "        while Dstep <= 1.0 and ok == 0 :\n",
    "            controlDisp  = ops.nodeDisp(IDctrlNode,IDctrlDOF) \n",
    "            Dstep  = controlDisp/Dmax\n",
    "            ok  = ops.analyze(1)   # this will return zero if no convergence problems were encountered\n",
    "            if ok != 0 :\n",
    "                Nk  = 4 # reduce step size\n",
    "                DincrReduced  = Dincr/Nk\n",
    "                ops.integrator('DisplacementControl',IDctrlNode,IDctrlDOF,DincrReduced)\n",
    "                for ik in range(1,Nk+1,1):\n",
    "                    ok  = ops.analyze(1)   # this will return zero if no convergence problems were encountered\n",
    "                    if ok != 0 :\n",
    "                        # if analysis fails, we try some other stuff\n",
    "                        # performance is slower inside this loop global maxNumIterStatic ##xx##  # max no. of iterations performed before \"failure to converge\" is ret'd\n",
    "                        print(' \"Trying Newton with Initial Tangent ..')\n",
    "                        ops.test('NormDispIncr',TolStatic,2000,0)\n",
    "                        ops.algorithm('Newton','-initial')\n",
    "                        ok  = ops.analyze(1)\n",
    "                        ops.test(testTypeStatic,TolStatic,maxNumIterStatic,0)\n",
    "                        ops.algorithm(algorithmTypeStatic)\n",
    "                    if ok != 0 :\n",
    "                        print(' \"Trying Broyden ..')\n",
    "                        ops.algorithm('Broyden',8)\n",
    "                        ok  = ops.analyze(1)\n",
    "                        ops.algorithm(algorithmTypeStatic)\n",
    "                    if ok != 0 :\n",
    "                        print(' \"Trying NewtonWithLineSearch ..')\n",
    "                        ops.algorithm('NewtonLineSearch',0.8)\n",
    "                        ok  = ops.analyze(1)\n",
    "                        ops.algorithm(algorithmTypeStatic)\n",
    "                    if ok != 0 :\n",
    "                        print('PROBLEM')\n",
    "                        return \n",
    "                ops.integrator('DisplacementControl',IDctrlNode,IDctrlDOF,Dincr)     #  bring back to original increment\n",
    "        # -----------------------------------------------------------------------------------------------------\n",
    "        \n",
    "        if ok != 0  :\n",
    "            print('PROBLEM at NodeDisp=' + str(ops.nodeDisp(IDctrlNode,IDctrlDOF)) +  LunitTXT)\n",
    "        else :\n",
    "            print('DONE! at NodeDisp=' + str(ops.nodeDisp(IDctrlNode,IDctrlDOF)) +  LunitTXT)\n",
    "        \n",
    "        \n",
    "    \n"
   ]
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   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# UNIAXIAL SECTION IN 2d\n",
    "ops.wipe()     #  clear memory of all past model definitions\n",
    "ops.model('BasicBuilder','-ndm',2,'-ndf',3)     #  Define the model builder, ndm= dimension, ndf= dofs\n",
    "# Define your SECTION\n",
    "# MATERIAL parameters -------------------------------------------------------------------\n",
    "SecTagFlex  = 2 # assign a tag number to the column flexural behavior\n",
    "SecTagAxial  = 3 # assign a tag number to the column axial behavior\n",
    "SecTag  = 1 # assign a tag number to the column section tag\n",
    "\n",
    "# COLUMN section\n",
    "# calculated stiffness parameters\n",
    "EASec  = Ubig # assign large value to axial stiffness\n",
    "MySec  = 130000*kip*inch # yield moment\n",
    "PhiYSec  = 0.65e-4/inch # yield curvature\n",
    "EICrack  = MySec/PhiYSec # cracked section inertia\n",
    "b  = 0.01  # strain-hardening ratio (ratio between post-yield tangent and initial elastic tangent)\n",
    "ops.uniaxialMaterial('Steel01',SecTagFlex,MySec,EICrack,b)     #  bilinear behavior for flexural moment-curvature\n",
    "ops.uniaxialMaterial('Elastic',SecTagAxial,EASec)     #  this is not used as a material, this is an axial-force-strain response\n",
    "ops.section('Aggregator',SecTag,SecTagAxial,'P',SecTagFlex,'Mz')     #  combine axial and flexural behavior into one section (no P-M interaction here)\n"
   ]
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     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Perform Moment-Curvature Analysis \n",
    "# set AXIAL LOAD --------------------------------------------------------\n",
    "P  = -1800*kip #+Tension,-Compression\n",
    "\n",
    "# set maximum Curvature:\n",
    "Ku  = 0.01/inch\n",
    "numIncr  = 1000 # Number of analysis increments to maximum curvature (default=100)\n",
    "# Call the section analysis procedure\n",
    "MomentCurvature2D(SecTag,P,Ku,numIncr)\n",
    "ops.wipe()\n",
    "fname3 = 'Mphi.out'\n",
    "dataDFree = numpy.loadtxt(fname3)\n",
    "plt.plot(dataDFree[:,1],dataDFree[:,0])\n",
    "plt.ylabel('Pseudo-Time (~Moment)')\n",
    "plt.xlabel('Curvature')\n",
    "plt.title('Ex9.analyze.MomentCurvature2D.tcl')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# FIBER SECTION IN 2D\n",
    "# Steel W Section\n",
    "\n",
    "# SET UP ----------------------------------------------------------------------------\n",
    "ops.wipe()     #  clear memory of all past model definitions\n",
    "ops.model('BasicBuilder','-ndm',2,'-ndf',3)     #  Define the model builder, ndm= dimension, ndf= dofs\n",
    "\n",
    "# MATERIAL parameters -------------------------------------------------------------------\n",
    "# define MATERIAL properties ----------------------------------------\n",
    "Fy  = 60.0*ksi\n",
    "Es  = 29000*ksi # Steel Young's Modulus\n",
    "nu  = 0.3\n",
    "Gs  = Es/2./(1+nu) # Torsional stiffness Modulus\n",
    "Hiso  = 0\n",
    "Hkin  = 1000\n",
    "matIDhard  = 1\n",
    "ops.uniaxialMaterial('Hardening',matIDhard,Es,Fy,Hiso,Hkin)\n",
    "\n",
    "# Structural-Steel W-section properties -------------------------------------------------------------------\n",
    "SecTag  = 1\n",
    "WSec  = 'W27x114'\n",
    "\n",
    "# from Steel Manuals:\n",
    "# in × lb/ft Area (in2) d (in) bf (in) tf (in) tw (in) Ixx (in4) Iyy (in4)\n",
    "# W27x114 33.5 27.29 10.07 0.93 0.57 4090 159\n",
    "d  = 27.29*inch # nominal depth\n",
    "tw  = 0.57*inch # web thickness\n",
    "bf  = 10.07*inch # flange width\n",
    "tf  = 0.93*inch # flange thickness\n",
    "nfdw  = 16 # number of fibers along web depth\n",
    "nftw  = 4 # number of fibers along web thickness\n",
    "nfbf  = 16 # number of fibers along flange width (you want this many in a bi-directional loading)\n",
    "nftf  = 4 # number of fibers along flange thickness\n",
    "\n",
    "dw  = d-2 * tf\n",
    "y1  = -d/2\n",
    "y2  = -dw/2\n",
    "y3  = dw/2\n",
    "y4  = d/2\n",
    "\n",
    "z1  = -bf/2\n",
    "z2  = -tw/2\n",
    "z3  = tw/2\n",
    "z4  = bf/2\n",
    "\n",
    "#\n",
    "# define Section\n",
    "ops.section('Fiber',SecTag,'-GJ',1e9) # \n",
    "#   nfIJ nfJK yI zI yJ zJ yK zK yL zL\n",
    "ops.patch('quadr',matIDhard,nfbf,nftf,y1,z4,y1,z1,y2,z1,y2,z4)\n",
    "ops.patch('quadr',matIDhard,nftw,nfdw,y2,z3,y2,z2,y3,z2,y3,z3)\n",
    "ops.patch('quadr',matIDhard,nfbf,nftf,y3,z4,y3,z1,y4,z1,y4,z4)\n",
    "\n",
    "fiberRecorderData = {}\n",
    "fiberRecorderData['topCtr'] = [y4,0.] # no need for a material tag since there is only one material\n",
    "fiberRecorderData['origin'] = [0.,0.]\n",
    "fiberRecorderData['botCtr'] = [y1,0.]\n",
    "fiberRecorderData['topLeft'] = [y4,z4] # no need for a material tag since there is only one material\n",
    "fiberRecorderData['botLeft'] = [y1,z4]\n",
    "fiberRecorderData['topRight'] = [y4,z1] # no need for a material tag since there is only one material\n",
    "fiberRecorderData['botRight'] = [y1,z1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13.645, 0.0]\n",
      "[0.0, 0.0]\n",
      "[-13.645, 0.0]\n",
      "[13.645, 5.035]\n",
      "[-13.645, 5.035]\n",
      "[13.645, -5.035]\n",
      "[-13.645, -5.035]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PERFORM ANALYSIS.\n",
    "# This block of commands is the same as above\n",
    "# Perform Moment-Curvature Analysis \n",
    "# set AXIAL LOAD --------------------------------------------------------\n",
    "P  = -1800*kip #+Tension,-Compression\n",
    "P  = 0*kip #+Tension,-Compression\n",
    "\n",
    "# set maximum Curvature:\n",
    "Ku  = 0.01/inch\n",
    "numIncr  = 1000 # Number of analysis increments to maximum curvature (default=100)\n",
    "# Call the section analysis procedure\n",
    "MomentCurvature2D(SecTag,P,Ku,numIncr,fiberRecorderData)\n",
    "ops.wipe()\n",
    "fname3 = 'Mphi.out'\n",
    "dataDFree = numpy.loadtxt(fname3)\n",
    "plt.plot(dataDFree[:,1],dataDFree[:,0])\n",
    "plt.xlabel('Curvature')\n",
    "plt.ylabel('Pseudo-Time (~Moment)')\n",
    "plt.title('Ex9.analyze.MomentCurvature2D.tcl')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# look at fiber response:\n",
    "for thisLabel,thisData in fiberRecorderData.items():\n",
    "    fname3 = 'FiberResponse_'+thisLabel+'.out'\n",
    "    dataDFree = numpy.loadtxt(fname3)\n",
    "    plt.plot(dataDFree[:,1],dataDFree[:,0])\n",
    "    plt.xlabel('Strain')\n",
    "    plt.ylabel('Stress')\n",
    "    plt.title('Fiber Response:' + thisLabel)\n",
    "    plt.grid(True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}