debugging_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nThis script takes an array of the modulus of elements at the end of the past iteration and uses the stress \\nperturbation to calculate the new modulus at the end of the next day. \\n'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from define_stress_and_stretch import von_mises, vonH\n",
    "from get_turnover_class import *\n",
    "from read_tav_xplt import StateTimes, nDoms\n",
    "\n",
    "'''\n",
    "This script takes an array of the modulus of elements at the end of the past iteration and uses the stress \n",
    "perturbation to calculate the new modulus at the end of the next day. \n",
    "'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialising parameters: \n",
    "# Original mass values \n",
    "mC0 = 62\n",
    "mE0 = 38\n",
    "m_total = mC0 + mE0\n",
    "frac_C0 = 0.62\n",
    "frac_E0 = 0.38"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "counter = np.loadtxt(\"saved_data/counter.txt\", delimiter = ',')\n",
    "print(counter[1]) \n",
    "\n",
    "if counter[1] == 0.0:\n",
    "    mC0_array = []\n",
    "    for x in range(0,1064):\n",
    "        x = mC0\n",
    "        mC0_array.append(x)\n",
    "    mE0_array = []\n",
    "    for x in range(0,1064):\n",
    "        x = mE0\n",
    "        mE0_array.append(x)\n",
    "    m0_array = []\n",
    "    for x in range(0, 1064): \n",
    "        x = m_total\n",
    "        m0_array.append(x)\n",
    "    frac_C_array = []\n",
    "    for x in range(0, 1064): \n",
    "        x = frac_C0\n",
    "        frac_C_array.append(x)\n",
    "    frac_E_array = []\n",
    "    for x in range(0, 1064): \n",
    "        x = frac_E0\n",
    "        frac_E_array.append(x)\n",
    "    th0 = np.loadtxt(\"saved_data/original_thickness_storage.txt\", delimiter = ',')\n",
    "    thS = th0\n",
    "    np.savetxt(\"saved_data/current_collagen_mass_storage.txt\", mC0_array, delimiter =',')\n",
    "    np.savetxt(\"saved_data/current_elastin_mass_storage.txt\", mE0_array, delimiter =',')\n",
    "    np.savetxt(\"saved_data/current_overall_mass_storage.txt\", m0_array, delimiter = ',')\n",
    "    np.savetxt(\"saved_data/current_collagen_mass_fraction.txt\", frac_C_array, delimiter = ',')\n",
    "    np.savetxt(\"saved_data/current_elastin_mass_fraction.txt\", frac_E_array, delimiter = ',')\n",
    "    np.savetxt(\"saved_data/current_thickness_storage.txt\", thS, delimiter = ',')\n",
    "else: \n",
    "    pass\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Mass Turnover parameters:\n",
    "Amax = 1800\n",
    "half_h = 70 \n",
    "half_u = 1\n",
    "rate = np.log(2)/half_h\n",
    "gain = 0.025\n",
    "\n",
    "#Elastin Damage parameters:\n",
    "Dmax = 1\n",
    "Tdam = 40\n",
    "\n",
    "# initialising turnover class\n",
    "getMass = NewMassTurnover(mC0,gain, rate, half_h, half_u, Amax)\n",
    "# initialising elastin damage class \n",
    "getDamage = ElastinDamage(Dmax, mE0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialing time \n",
    "# This needs to go from 0 to 1, with the time points being equal to the time the stress was extracted \n",
    "# could i use the StateTimes variable ? it only seems to output 0 at the moment. \n",
    "\n",
    "Gtime = np.linspace(0,3650, num = 3651) # 10 years \n",
    "currentT = Gtime[3]\n",
    "\n",
    "tau = np.linspace(0,1, num = 21) + Gtime[0]\n",
    "# need a way to update the above on every interation so that day 1 is Gtime[1] etc\n",
    "\n",
    "mCS = np.loadtxt(\"saved_data/current_collagen_mass_storage.txt\", delimiter = ',')\n",
    "mES = np.loadtxt(\"saved_data/current_elastin_mass_storage.txt\", delimiter = ',')\n",
    "mS = np.loadtxt(\"saved_data/current_overall_mass_storage.txt\", delimiter = ',')\n",
    "th0 = np.loadtxt(\"saved_data/original_thickness_storage.txt\", delimiter = ',')\n",
    "thS = np.loadtxt(\"saved_data/current_thickness_storage.txt\", delimiter = ',')\n",
    "frac_C = np.loadtxt(\"saved_data/current_collagen_mass_fraction.txt\", delimiter = ',')\n",
    "frac_E = np.loadtxt(\"saved_data/current_elastin_mass_fraction.txt\", delimiter = ',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 0\n",
    "while i < nDoms:\n",
    "    c = getMass.newMass(mCS[i], np.mean(von_mises[i]), vonH, Gtime[3], tau)[0]\n",
    "    e = getDamage.damage(Gtime[3],Tdam)\n",
    "    mCS[i] = c\n",
    "    mES[i] = e\n",
    "    mS[i] = c + e\n",
    "    frac_C[i] = c/(c+e)\n",
    "    frac_E[i] = e/(c+e)\n",
    "    thS[i] = ((c + e)/ 100) * th0[i]\n",
    "    i = i + 1 \n",
    "np.savetxt(\"saved_data/current_collagen_mass_storage.txt\", mCS, delimiter = ',')\n",
    "np.savetxt(\"saved_data/current_elastin_mass_storage.txt\", mES, delimiter = ',')\n",
    "np.savetxt(\"saved_data/current_overall_mass_storage.txt\", mS, delimiter = ',')\n",
    "np.savetxt(\"saved_data/current_thickness_storage.txt\", thS, delimiter = ',')\n",
    "np.savetxt(\"saved_data/current_collagen_mass_fraction.txt\", frac_C, delimiter = ',')\n",
    "np.savetxt(\"saved_data/current_elastin_mass_fraction.txt\", frac_E, delimiter = ',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "counter[1] = counter[1] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt(\"saved_data/counter.txt\", counter, delimiter =',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FEBIOtav2.feb\n"
     ]
    }
   ],
   "source": [
    "# practicing file naming\n",
    "\n",
    "counter = 2 \n",
    "\n",
    "name = 'FEBIO' + 'tav' + str(counter) + '.feb'\n",
    "print(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "3.3174643137460342,3.392133842204212,3.397387876028467,3.4147830219013775\n",
      "3.552252600231017,3.6118603318935563,3.4147645230320873,3.397369471393723\n",
      "2.9036765369131747,2.8923094812414027,3.191336004368552,3.2279569486674657\n",
      "2.892342215393989,2.8426091527260477,3.0440559369126516,3.1913721227993075\n",
      "2.8426449166813046,2.9551201205207085,3.0388884108744936,3.04409423534711\n",
      "3.2526300486857656,3.0389058867798937,2.95513711469512,3.1825748552392485\n",
      "3.182584377394151,3.3764655466910454,3.4969187689646546,3.252639780443406\n",
      "2.7479487396635602,3.008267860427342,3.270476160537354,2.7229832151540134\n",
      "3.0081227573770066,2.9246358444029728,3.5007496897787065,3.270318409935027\n",
      "2.924453146163449,3.0827923307117,3.641407828628714,3.5005310024482617\n",
      "3.009017377832124,2.7486333979132196,2.3773330541204554,2.486784699680719\n",
      "2.9254575178790994,3.008967886416712,2.4867437977917772,2.180316177886598\n",
      "3.083413067979642,2.9250420009617724,2.180006497007113,2.4010231590818454\n",
      "3.069250911743264,3.259735932087971,3.3543973801276503,3.2570069488977813\n",
      "3.259686295995262,3.565059566591937,3.696558114634281,3.3543463026223517\n",
      "3.3543236704052295,3.6965331734689078,3.56605691386427,3.2569353792383406\n",
      "2.95658777465085,2.8006339612096443,2.6392379338055503,2.837458773036054\n",
      "2.5651520252107622,2.83600568557776,2.7184570305554545,2.7120434580798904\n",
      "3.5203687991666226,3.7278741493844754,3.399876235163411,3.452181772300302\n",
      "3.727873245084099,3.791747965614174,3.5541326955780237,3.3998754104281232\n",
      "3.399851572815777,3.554107776418585,3.5797138210245243,3.2735036041528534\n",
      "3.452122244603965,3.399817609397603,3.2734709029100433,3.385171416579737\n",
      "3.3851430241776472,3.2734434473721494,3.2908150536465297,3.427370623405274\n",
      "3.2734794129139155,3.5796873668876583,3.6156870244765735,3.290851210051294\n",
      "3.1521956665523776,3.347216016811591,3.1834059416825915,3.130141075670798\n",
      "3.1521578056480744,3.0829201778258932,3.163350384304657,3.3611428455949115\n",
      "3.3543392677142125,3.4103821108450694,3.502021398233362,3.441980672614383\n",
      "3.4103589659450524,3.509348132275365,3.5858766190803544,3.5019976314141363\n",
      "3.5093443120540933,3.611806714951444,3.6494397129294107,3.585872715551373\n",
      "3.61184623494307,3.509382710913382,3.3591388708146344,3.4147511953388823\n",
      "2.382087626912964,2.3231350249595186,2.7003709115058974,2.7614150452037993\n",
      "2.76143536289267,2.700390780049785,3.168129638567069,3.0777613298288022\n",
      "3.221770392371652,3.0564995191874718,3.0250353572621482,3.0993156633385643\n",
      "3.2216928098751887,3.7080459475513696,3.74707873850075,3.3784802308758737\n",
      "3.295690041070142,3.250716207060553,3.1409733234603268,3.192405624908031\n",
      "3.250731529270494,3.3628537669096024,3.1632938090936173,3.1409881283985666\n",
      "3.141111451393474,3.1634180078649474,3.0829860820139183,3.2192030664447775\n",
      "3.1924594258644916,3.141026257638772,3.2191157546761175,3.1206812633045424\n",
      "3.2956406470552144,3.3591603464070943,3.5094051470423375,3.4104143724805134\n",
      "3.410399244997848,3.354356120301288,3.2506530681913257,3.295626028671254\n"
     ]
    }
   ],
   "source": [
    "import xml.etree.ElementTree as ET\n",
    "import numpy as np\n",
    "\n",
    "tree = ET.parse('jobs/FEBIO' + 'tav' + '0' + '.feb') \n",
    "root = tree.getroot()\n",
    "\n",
    "modC0 = 999.9929003391316\n",
    "modE0 = 3.2675400356856574\n",
    "collagen = np.loadtxt(\"saved_data/current_collagen_mass_fraction.txt\", delimiter =',')\n",
    "elastin = np.loadtxt(\"saved_data/current_elastin_mass_fraction.txt\", delimiter =',')\n",
    "\n",
    "for material in root.iter('material'):\n",
    "    id = material.get('id')\n",
    "    k1 = material.find('k1')\n",
    "    k1.text =  str((modC0/frac_C0) * collagen[int(id) - 1])\n",
    "    c = material.find('c')\n",
    "    c.text = str((modE0/frac_E0)* elastin[int(id) - 1])\n",
    "    #print(k1.text)\n",
    "    #print(c.text)\n",
    "\n",
    "print(Gtime[1])\n",
    "\n",
    "Th0 = []\n",
    "if Gtime[1] == 0:\n",
    "    for ElementData in root.iter('ElementData'):\n",
    "        part = ElementData.get('elem_set')\n",
    "        e = ElementData.find('e')\n",
    "        num = e.text\n",
    "        numbers = [float(x) for x in num.split(\",\")]\n",
    "        Th0.append(numbers)\n",
    "    np.savetxt(\"saved_data/original_thickness_storage.txt\", Th0, delimiter = ',')\n",
    "else: \n",
    "    thS = np.loadtxt(\"saved_data/current_thickness_storage.txt\", delimiter = ',')\n",
    "    for ElementData in root.iter('ElementData'): \n",
    "        part = ElementData.get('elem_set')\n",
    "        id = int(part[4:])\n",
    "        e = ElementData.find('e') \n",
    "        array = thS[id - 1]\n",
    "        string = \",\".join(map(str, array))\n",
    "        e.text = string\n",
    "        print(e.text)\n",
    "        \n",
    "\n",
    "\n",
    "\n",
    "#for e in root.iter('e'):\n",
    "        #print(e.attrib)\n",
    "        #print(e.text)\n",
    "\n",
    "#for child in root: \n",
    "    #print(child.tag, child.attrib)\n",
    "\n",
    "\n",
    "\n",
    "   \n",
    "#tree.write('FEBIO' + 'tav' + str)\n",
    "   \n",
    "\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         0.23423595 0.2480315  0.38368146 0.4875756  0.7177767\n",
      " 0.53128106 0.46099135 0.47835313 0.52230107 1.12369044 1.0900381\n",
      " 0.78674717 0.80008807 0.86485317 0.95821594 0.84416706 0.2573205 ]\n"
     ]
    }
   ],
   "source": [
    "print(von_mises[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# von mises stress comparison. \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "num_bins = 10 \n",
    "plt.hist(von_mises[3], num_bins)\n",
    "plt.title(\"plotting a histogram\")\n",
    "plt.xlabel(\"von mises stress\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "\n",
    "i = 0\n",
    "while i < 1064:\n",
    "    avg = np.mean(von_mises[i])\n",
    "    stan = np.std(von_mises[i])\n",
    "    count = 0 \n",
    "    for x in von_mises[i]:\n",
    "        if x < avg - (3*stan):\n",
    "            print(x, 'is an outlier at element', i, 'at time step', count)\n",
    "        elif x > avg + (3*stan): \n",
    "            print(x, 'is an outlier at element', i, 'at time step', count)\n",
    "        else: \n",
    "            pass\n",
    "        count = count + 1\n",
    "    i = i + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Just setting some plotting parameters.\n",
    "from matplotlib import rcParams\n",
    "\n",
    "ft = 14\n",
    "plotParams = {\n",
    "    'font.family':'serif',\n",
    "    'font.serif':'Times New Roman',\n",
    "    'font.size' : ft,\n",
    "    'axes.labelsize' : ft,\n",
    "    'legend.fontsize' : ft,\n",
    "    'xtick.labelsize' : ft, \n",
    "    'ytick.labelsize' : ft, \n",
    "    'lines.markersize' : 3,\n",
    "    'lines.linewidth' : 1,\n",
    "    'axes.linewidth' : 1,\n",
    "    'lines.antialiased': True,\n",
    "    'text.usetex': False,\n",
    "    'figure.figsize': [6,4],\n",
    "    'legend.frameon': True,\n",
    "}\n",
    "\n",
    "rcParams.update(plotParams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([27]),)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## De-bugging the fea by visualising the trends: \n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "time = np.linspace(0,190, num = 191)\n",
    "\n",
    "# rememeber to change the save staments first !!!!\n",
    "\n",
    "von_track1 = np.loadtxt(\"saved_data/trackers/von1.txt\", delimiter = \",\")\n",
    "np.savetxt(\"Saved_Simulation/element8_von_track.txt\", von_track1, delimiter =\",\")\n",
    "c_track1 = np.loadtxt(\"saved_data/trackers/c_density1.txt\", delimiter = \",\")\n",
    "np.savetxt(\"Saved_Simulation/element8_c_track.txt\", c_track1, delimiter =\",\")\n",
    "e_track1 = np.loadtxt(\"saved_data/trackers/e_density1.txt\", delimiter = \",\")\n",
    "np.savetxt(\"Saved_Simulation/element8_e_track.txt\", e_track1, delimiter =\",\")\n",
    "thick_track1 = np.loadtxt(\"saved_data/trackers/thick_av1.txt\", delimiter = \",\")\n",
    "np.savetxt(\"Saved_Simulation/element8_thick_track.txt\", thick_track1, delimiter =\",\")\n",
    "production_track1 = np.loadtxt(\"saved_data/trackers/density_production_track1.txt\", delimiter = \",\")\n",
    "np.savetxt(\"Saved_Simulation/element8_production_track.txt\", production_track1, delimiter =\",\")\n",
    "\n",
    "print(np.where(von_track1[0:191] == min(von_track1[0:151])))\n",
    "\n",
    "plt.plot(time, von_track1[0:191], '-b')\n",
    "plt.ylabel(\"Wall stress (kPa)\")\n",
    "plt.xlabel(\"G&R time (days)\")\n",
    "plt.savefig('Saved_Simulation/wall_stress8.pdf', bbox_inches= \"tight\")\n",
    "plt.show()\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "plt.xlabel(\"G&R time (days)\")\n",
    "ax1.plot(time, c_track1[0:191], '-b')\n",
    "ax1.set_ylabel(\"Relative collagen density\", color = 'blue')\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(time, e_track1[0:191], '-r')\n",
    "ax2.set_ylabel(\"Relative elastin density \", color = 'red')\n",
    "plt.savefig('Saved_Simulation/collagen&Elastin_density8.pdf', bbox_inches= \"tight\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "plt.plot(time, thick_track1[0:191], '-b')\n",
    "plt.ylabel(\"Average wall thickness (mm)\")\n",
    "plt.xlabel(\"G&R time (days)\")\n",
    "plt.savefig('Saved_Simulation/wall_thick8.pdf', bbox_inches= \"tight\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([0, 1]),)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "von_track2 = np.loadtxt(\"saved_data/trackers/von2.txt\", delimiter = \",\")\n",
    "np.savetxt(\"Saved_Simulation/element713_von_track.txt\", von_track2, delimiter =\",\")\n",
    "c_track2 = np.loadtxt(\"saved_data/trackers/c_density2.txt\", delimiter = \",\")\n",
    "np.savetxt(\"Saved_Simulation/element713_c_track.txt\", c_track2, delimiter =\",\")\n",
    "e_track2 = np.loadtxt(\"saved_data/trackers/e_density2.txt\", delimiter = \",\")\n",
    "np.savetxt(\"Saved_Simulation/element713_e_track.txt\", e_track2, delimiter =\",\")\n",
    "thick_track2 = np.loadtxt(\"saved_data/trackers/thick_av2.txt\", delimiter = \",\")\n",
    "np.savetxt(\"Saved_Simulation/element713_thick_track.txt\", thick_track2, delimiter =\",\")\n",
    "production_track2 = np.loadtxt(\"saved_data/trackers/density_production_track2.txt\", delimiter = \",\")\n",
    "np.savetxt(\"Saved_Simulation/element713_production_track.txt\", production_track2, delimiter =\",\")\n",
    "\n",
    "print(np.where(von_track2 == min(von_track2)))\n",
    "\n",
    "plt.plot(time, von_track2[0:181], '-b')\n",
    "plt.ylabel(\"Wall stress \")\n",
    "plt.xlabel(\"G&R time (days)\")\n",
    "plt.savefig('Saved_Simulation/wall_stress713.pdf', bbox_inches= \"tight\")\n",
    "plt.show()\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "plt.xlabel(\"G&R time (days)\")\n",
    "ax1.plot(time, c_track2[0:181], '-b')\n",
    "ax1.set_ylabel(\"Relative collagen density\", color = 'blue')\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(time, e_track2[0:181], '-r')\n",
    "ax2.set_ylabel(\"Relative elastin density \", color = 'red')\n",
    "plt.savefig('Saved_Simulation/collagen&Elastin_density713.pdf', bbox_inches= \"tight\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "plt.plot(time, thick_track2[0:181], '-b')\n",
    "plt.ylabel(\"Average wall thickness (mm)\")\n",
    "plt.xlabel(\"G&R time (days)\")\n",
    "plt.savefig('Saved_Simulation/wall_thick713.pdf', bbox_inches= \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([0, 1]),)\n"
     ]
    },
    {
     "data": {
      "image/png": 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ctRh6A88A+wA/iTMoSc6wYTBhAtxxh8p3ixS7TBJE4+i+h97AU+6+Eci8X0ryxn//C1dfDWeeqXEHEcksQfwZeA/YBXjZzPYFcnIMQupu5cowIN2xI9x3nyq0ikgGl6u6+53AnSmb3jez7vGFJPXNHX7xizA4/dpr0KJF0hGJSC7IZJD6q2Y2wsyejZ53Bs6NPTKpNw88EG6Eu/56OPzwpKMRkVyRSRfTg8DzwN7R87cJ90ZIAZg/H/r1gx49YMCApKMRkVySSYJo5e6PAVsA3H0TsDnWqKRebNwI55wDTZrAqFHQQLV9RSRFJiUzPjezPYiuXDKzI4FPYo1K6sW114bpQ8eOhbZtk45GRHJNJgmiPzAe2M/MXgVKgDNijUpi98orcMMNcP75cIZ+myKSRo0JwswaAsdFy0GAAfOieyEkT61ZE7qWOnQIN8SJiKRTY4Jw981m1svdbwdm11NMErP+/WHRojB9qEppiEh1MulietXM7gbGAJ9XbHT36bFFJbF55hkYMQIGD4Yjj0w6GhHJZZkkiG9Gj9embHOgR/bDkTh9/DFceCEccggMHZp0NCKS6zJJEBe4+8LUDdGcEJJn+vULd0s//TQ0bZp0NCKS6zK58v3xNNvGZjsQideTT8LDD8NVV+luaRHJTLUtCDPrBHwN2M3MTk95qQXQLO7AJHtWrAi1lrp2DQlCRCQTNbUgDgJOAVoCp6YshwMXZvLmZtbTzOaZ2QIzG5zm9U5mNsnM1pvZwDSvNzSz183sH5n8PEnvV78K4w+jRkHjxklHIyL5otoWhLs/BTxlZke5+6TavnF0D8U9wAnAYmCqmY139zkpu60C+hHmmkjnMmAuodUidTBuXLhT+oYb4NBDk45GRPJJJmMQfaIpRxub2YtmtsLMzsnguG7AAndf6O4bgNFAr9Qd3H25u08FtrnxzszaAicDf8ngZ0kaH38MF18cxhwGaZJYEamlTBLEd6MpR08htAQOBDL5c9MGWJTyfHG0LVN/BH5NVCSwOmZ2kZmVmVlZeXl5Ld6+8A0YEMYfRoyARplcryYikiKjKUejx+8Bj7r7qgzfO92cZBlNVWpmpwDL3X3a9vZ19/vcvdTdS0tKSjIMrfD9619hnodf/xq6dEk6GhHJR5kkiKfN7C2gFHjRzEqALzI4bjHQLuV5W2BJhnEdDZxmZu8RuqZ6mNnDGR5b9D7/PMwpfeCBMGRI0tGISL7aboJw98HAUUBpVKRvLVXGEqoxFTjAzDqYWRPgLEJV2O1y9yvdva27t4+Oe8ndMxn3EOCaa+Ddd+H++6GZLkgWkTrKqGfa3T9OWf+clJpMNRyzycwuIcxG1xAY6e6zzaxv9PpwM9sLKCNcpbTFzC4HOkdjHlIHU6aECq19+8KxxyYdjYjkM3PPaFggL5SWlnpZWVnSYSRmwwb4v/8LVy/NmQMtdHGwiGyHmU1z99J0r+nalgJy660waxaMH6/kICI7rqZSGzVW7FG579yycGGYQvT00+HUU5OORkQKQU0tiFtreE3lvnOIe7ghrlEjzRAnItlTU6mN7vUZiNTd44/Dc8/B7bdD27ZJRyMihaKmLqbTq3sNwN3/nv1wpLbWrIHLLguVWi+5JOloRKSQ1NTFVFNPtgNKEDng6qth2bIw34PKaYhINtXUxXR+fQYitVdWBvfcA7/8JXTrlnQ0IlJoMvrOaWYnEyYP+vK+XHe/tvojJG6bN4eb4fbcM5TyFhHJtu0mCDMbDuwMdCeU3j4DmBJzXLIdw4fDtGnw6KOw225JRyMihSiTYn3fdPefAh+7++8JdZnabecYiVF5eRh76NEDzjwz6WhEpFBlkiDWRY9rzWxvwuQ+HeILSbbnt7+Fzz6Du+4CS1dUXUQkCzIZg/iHmbUEbgamE65guj/OoKR6U6aECYD694fOnZOORkQKWbXF+qLKqq8Cr7v7pmhbU6CZu39SbxHWQqEX69uyBY44AhYvhnnzVG9JRHZcXYv1tQXuADqZ2ZvA/wgJY1L2Q5RMjBwZLm196CElBxGJ33bLfUeT/ZQC3yQMUB8FrHb3nOvgKOQWxKpVYYa4gw+Gl1/W2IOIZMeOlvveiTChz27RsgSYmb3wJBPXXBPmebj7biUHEakfNdViuo9wc9ynwGRCF9NtqbPLSf2YMSPc9/CrX8FhhyUdjYgUi5ouc90HaAosAz4EFgOr6yEmSeEeivHtvnuY70FEpL7UVIupp5kZoRXxTWAAcIiZrQImufvv6inGovb442HM4d574StfSToaESkmNY5BeBjBnmVmq4FPouUUoBugBBGzdetg0CD4+tfhwguTjkZEik1NYxD9CC2Howl3T1dc4joSDVLXi9tug/ffh5degoYNk45GRIpNTS2I9sDjwBXuvrR+wpEKH34IN94IffpAd83tJyIJqGkMon99BiKVXXklbNwIt9ySdCQiUqwyKdYn9Wzy5HC3dP/+0LFj0tGISLFSgsgx7nD55bDXXqFqq4hIUjSLcY555BF47TV44AFo3jzpaESkmKkFkUO++CK0Grp2hZ/+NOloRKTYqQWRQ+68Ez74ILQeGih1i0jC9GcoR6xYAddfDyefHKYSFRFJmhJEjrjuujCN6E03JR2JiEigBJED5s+HP/0Jfv5zTSMqIrlDCSIHXHklNG0Kv/990pGIiGylBJGw//0Pxo2DX/863PsgIpIrlCAS5A4DB0Lr1jBgQNLRiIhUpstcEzRuHEyaBH/5C+yyS9LRiIhUphZEQjZsgMGD4ZBD4Lzzko5GRGRbsSYIM+tpZvPMbIGZDU7zeiczm2Rm681sYMr2ZmY2xczeMLPZZlZww7f33gvvvBMua9VcDyKSi2LrYjKzhsA9wAmE+aynmtl4d5+TstsqoB/Qu8rh64Ee7v6ZmTUGXjGzZ939tbjirU+rV4f5pb/zHejZM+loRETSi7MF0Q1Y4O4L3X0DMBrolbqDuy9396mEGetSt7u7fxY9bRwtHmOs9eqmm2DVqvBolnQ0IiLpxZkg2gCLUp4vjrZlxMwamtkMYDnwgrtPrma/i8yszMzKysvLdyTeerF0Kfzxj3D22aEon4hIroozQaT7bpxxK8DdN7t7F6At0M3MDqlmv/vcvdTdS0tKSuoWaT269towU9x11yUdiYhIzeJMEIuBdinP2wJLavsm7r4a+DeQ97318+fD/ffDRRfBfvslHY2ISM3iTBBTgQPMrIOZNQHOAsZncqCZlZhZy2h9J+A7wFtxBVpfhgwJJTWuuSbpSEREti+2q5jcfZOZXQI8DzQERrr7bDPrG70+3Mz2AsqAFsAWM7sc6Ay0BkZFV0I1AB5z93/EFWt9mD4dRo+Gq65SSQ0RyQ/mXjAXB1FaWuplZWVJh5FWz54wdSosXAi77ZZ0NCIigZlNc/fSdK+p1EY9mDgRnn8ebrlFyUFE8odKbcTMPZTUaNsWLr446WhERDKnFkTMnnwSpkyBESOgWbOkoxERyZxaEDHatCkMSnfqBD/9adLRiIjUjloQMfrrX2Hu3FDWu5HOtIjkGbUgYvLFFzB0KHTrBn36JB2NiEjt6XttTO67DxYtggceUEE+EclPakHEYO1auOEG6N4dvv3tpKMREakbtSBicM898NFHYexBRCRfqQWRZWvWwLBh4c7po49OOhoRkbpTgsiyO+6AlStVzltE8p8SRBatWhXKafTuDaVpK5uIiOQPJYgsuvVW+PTTMCmQiEi+U4LIkuXLQ/fSmWfCoYcmHY2IyI5TgsiSYcNg3bpwc5yISCFQgsiCJUvgT38K9ZYOOijpaEREskMJIgtuuCEU5hsyJOlIRESyRwliB73/fiirccEF0KFD0tGIiGSPEsQOuu46aNAArr466UhERLJLCWIHzJ8PDz4IffuGGeNERAqJEsQO+P3voWnTMKWoiEihUYKoo9mz4ZFH4NJLYa+9ko5GRCT7lCDq6NprYZddYNCgpCMREYmHEkQdzJoFY8fCZZfBHnskHY2ISDyUIOrguutg113hiiuSjkREJD5KELU0e3ZoPVx6qVoPIlLYlCBq6brrwthD//5JRyIiEi8liFqYPRseewz69VPrQUQKnxJELaj1ICLFRAkiQ2o9iEixUYLIkFoPIlJslCAyoNaDiBQjJYgMqPUgIsVICWI75swJrQfd9yAixUYJYjvUehCRYhVrgjCznmY2z8wWmNk2RbHNrJOZTTKz9WY2MGV7OzObaGZzzWy2mV0WZ5zVmTMHxowJrYdWrZKIQEQkOY3iemMzawjcA5wALAammtl4d5+TstsqoB/Qu8rhm4AB7j7dzJoD08zshSrHxk6tBxEpZnG2ILoBC9x9obtvAEYDvVJ3cPfl7j4V2Fhl+1J3nx6tfwrMBdrEGOs21HoQkWIXZ4JoAyxKeb6YOvyRN7P2QFdgcnbCyoxaDyJS7OJMEJZmm9fqDcx2BcYBl7v7mmr2ucjMysysrLy8vA5hbqui9XDJJWo9iEjxijNBLAbapTxvCyzJ9GAza0xIDn9z979Xt5+73+fupe5eWlJSUudgU113Hey8MwwYkJW3ExHJS3EmiKnAAWbWwcyaAGcB4zM50MwMGAHMdffbYoxxGxp7EBEJYruKyd03mdklwPNAQ2Cku882s77R68PNbC+gDGgBbDGzy4HOwNeBnwAzzWxG9Ja/dfdn4oq3wh/+oNaDiAjEmCAAoj/oz1TZNjxlfRmh66mqV0g/hhGruXNh9Gj4zW/UehAR0Z3UKTT2ICKyVawtiHxRWgrr1oUWhFoPIiKBEgTQqROsXw/f+AYMGpR0NCIiuUEJAnj44aQjEBHJPRqDEBGRtJQgREQkLSUIERFJSwlCRETSUoIQEZG0lCBERCQtJQgREUlLCUJERNIy91rN4ZPTzKwceL+Oh7cCVmQxnLgozuzLl1gVZ3blS5wQb6z7unvayXQKKkHsCDMrc/fSpOPYHsWZffkSq+LMrnyJE5KLVV1MIiKSlhKEiIikpQSx1X1JB5AhxZl9+RKr4syufIkTEopVYxAiIpKWWhAiIpKWEoSIiKRV9AnCzHqa2TwzW2Bmg5OOp4KZtTOziWY218xmm9ll0fahZvahmc2Ilu8lHSuAmb1nZjOjmMqibbub2QtmNj96/ErCMR6Uct5mmNkaM7s8F86pmY00s+VmNitlW7Xnz8yujD6z88zsxByI9WYze8vM3jSzJ8ysZbS9vZmtSzm3wxOOs9rfdVLntJo4x6TE+J6ZzYi21+/5dPeiXYCGwDtAR6AJ8AbQOem4othaA4dH682Bt4HOwFBgYNLxpYn3PaBVlW03AYOj9cHAsKTjrPK7XwbsmwvnFDgWOByYtb3zF30O3gCaAh2iz3DDhGP9LtAoWh+WEmv71P1y4Jym/V0neU7TxVnl9VuBIUmcz2JvQXQDFrj7QnffAIwGeiUcEwDuvtTdp0frnwJzgTbJRlVrvYBR0foooHdyoWzj28A77l7XO++zyt1fBlZV2Vzd+esFjHb39e7+LrCA8FmuF+lidfcJ7r4pevoa0La+4qlONee0Oomd05riNDMDfgg8Wh+xVFXsCaINsCjl+WJy8I+wmbUHugKTo02XRE35kUl326RwYIKZTTOzi6JtX3X3pRASHrBnYtFt6ywq/6fLxXNa3fnL9c/tz4BnU553MLPXzew/ZnZMUkGlSPe7ztVzegzwkbvPT9lWb+ez2BOEpdmWU9f9mtmuwDjgcndfA9wL7Ad0AZYSmp+54Gh3Pxw4CbjYzI5NOqDqmFkT4DRgbLQpV89pdXL2c2tmVwGbgL9Fm5YC+7h7V6A/8IiZtUgqPqr/XefqOT2byl9k6vV8FnuCWAy0S3neFliSUCzbMLPGhOTwN3f/O4C7f+Tum919C3A/9di1UBN3XxI9LgeeIMT1kZm1BogelycXYSUnAdPd/SPI3XNK9ecvJz+3ZnYucArwY486zKMum5XR+jRC3/6BScVYw+86586pmTUCTgfGVGyr7/NZ7AliKnCAmXWIvlWeBYxPOCbgy77HEcBcd78tZXvrlN36ALOqHlvfzGwXM2tesU4YsJxFOJfnRrudCzyVTITbqPStLBfPaaS68zceOMvMmppZB+AAYEoC8X3JzHoCvwFOc/e1KdtLzKxhtN6REOvCZKKs8Xedc+cU+A7wlrsvrthQ7+ezvkbDc3UBvke4Qugd4Kqk40mJ61uEJu6bwIxo+R7wEDAz2j4eaJ0DsXYkXAHyBjC74jwCewAvAvOjx91zINadgZXAbinbEj+nhIS1FNhI+DZ7QU3nD7gq+szOA07KgVgXEPrwKz6rw6N9vx99Jt4ApgOnJhxntb/rpM5pujij7Q8CfavsW6/nU6U2REQkrWLvYhIRkWooQYiISFpKECIikpYShIiIpKUEISIiaSlBSEEws6+a2SNmtjAq9zHJzPqkvH6kmb1hoeLsqCrHnmdm5VF1zLfM7IpqfsbxZvbNlOd9zeynMf17Ho+uc6+6/TwzuzuLP+dQM3swW+8nhaVR0gGI7KjopsIngVHu/qNo276EchoVrieUK5kY3QhV1Rh3v8TM9gDmmdnj7r6oyj7HA58B/wNw91hKLZvZ1wiVRGO/oczdZ5pZWzPbx90/iPvnSX5RC0IKQQ9gQ+ofbHd/393vStlnA1GFUQ/VOtPyUMZgAaHc+peigol9gSuilsYx0dwCA6PX/21mt5vZyxbm8PiGmf3dwlwOf0h5n3PMbEr0Hn+uuCu2ih+Tcte5mZ1vZm+b2X+Ao1O2n2pmk6PCbf+KWlENop9ZEu3TwMIcB63M7AdmNitqSb2c8vOeJlQREKlECUIKwdcId5XW5B3gRjMrrWknM9sHaEa40/ZL7v4eMBy43d27uPt/0xy+wd2PjfZ7CrgYOAQ4z8z2MLODgTMJhQ27AJsJyaCqo4FpUTytgd9H204gzFtQ4RXgSA+F20YDv/ZQY+jhlPf9DvCGu68AhgAnuvthVG5dlRGqhopUogQhBcfM7om+JU+NnvcCdiMU6XvEzA6IatpMTTnsTDObTahrc4e7f1GHH11Rx2smMNvDnB7ro/dsR5iD4v+AqRZmCPs2oUxJVa2B8mj9CODf7l7uYc6SMSn7tQWeN7OZwCBCogQYCVSMjfwMeCBafxV40MwuJEyYVGE5sHft/7lS6JQgpBDMJszIBYC7X0z441sSbToReNHdZxLq8TwFXELlP7Zj3P1rhG/St5rZXnWIY330uCVlveJ5I0JJ6VFRC6SLux/k7kPTvM86Qivmy39SNT/vLuBudz8U+EXFMdHYyUdm1oOQYJ6NtvcFriYkqxnReAvRcetq8w+V4qAEIYXgJaCZmf0yZdvOKeuvE1oIzaKuoScIhdm2maXL3ScRCrpdlubnfEqY/rWuXgTOMLM94cs5p/dNs99cYP9ofTJwfNRF1Rj4Qcp+uwEfRuvnUtlfCF1Nj7n75ujn7efuk919CLCCreWtDyR3KthKDlGCkLznoeJkb+A4M3vXzKYQpuj8TbTLCEK3zwwzKyN04QwEHjezndO85TDg/IoS5imeBvpUDFLXIc45hG/wE8zsTeAFqgyGR/5JuGIKDzPJDQUmAf+i8ljLUGCsmf2X8Ac/1XhgV7Z2LwHcHF3mOwt4mVARFKB79DNFKlE1V5EcY2Y7ARMJg9mb6/gepYQB9RoTmZk1Bf4DfMu3ziktAqgFIZJz3H0d8DvqOCeymQ0mzER4ZQa77wMMVnKQdNSCEBGRtNSCEBGRtJQgREQkLSUIERFJSwlCRETSUoIQEZG0/j9kmUWbAIjJUQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "von_track3 = np.loadtxt(\"saved_data/trackers/von3.txt\", delimiter = \",\")\n",
    "np.savetxt(\"Saved_Simulation/element1_von_track858.txt\", von_track3, delimiter =\",\")\n",
    "c_track3 = np.loadtxt(\"saved_data/trackers/c_density3.txt\", delimiter = \",\")\n",
    "np.savetxt(\"Saved_Simulation/element1_c_track858.txt\", c_track3, delimiter =\",\")\n",
    "e_track3 = np.loadtxt(\"saved_data/trackers/e_density3.txt\", delimiter = \",\")\n",
    "np.savetxt(\"Saved_Simulation/element1_e_track858.txt\", e_track3, delimiter =\",\")\n",
    "thick_track3 = np.loadtxt(\"saved_data/trackers/thick_av3.txt\", delimiter = \",\")\n",
    "np.savetxt(\"Saved_Simulation/element1_thick_track858.txt\", thick_track3, delimiter =\",\")\n",
    "production_track3 = np.loadtxt(\"saved_data/trackers/density_production_track3.txt\", delimiter = \",\")\n",
    "np.savetxt(\"Saved_Simulation/element1_production_track858.txt\", production_track3, delimiter =\",\")\n",
    "\n",
    "print(np.where(von_track3 == min(von_track3)))\n",
    "\n",
    "plt.plot(time, von_track3[0:181], '-b')\n",
    "plt.ylabel(\"Wall stress \")\n",
    "plt.xlabel(\"G&R time (days)\")\n",
    "plt.savefig('Saved_Simulation/wall_stress858.pdf', bbox_inches= \"tight\")\n",
    "plt.show()\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "plt.xlabel(\"G&R time (days)\")\n",
    "ax1.plot(time, c_track3[0:181], '-b')\n",
    "ax1.set_ylabel(\"Relative collagen density\", color = 'blue')\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(time, e_track3[0:181], '-r')\n",
    "ax2.set_ylabel(\"Relative elastin density \", color = 'red')\n",
    "plt.savefig('Saved_Simulation/collagen&Elastin_density858.pdf', bbox_inches= \"tight\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "plt.plot(time, thick_track3[0:181], '-b')\n",
    "plt.ylabel(\"Average wall thickness (mm)\")\n",
    "plt.xlabel(\"G&R time (days)\")\n",
    "plt.savefig('Saved_Simulation/wall_thick858.pdf', bbox_inches= \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "999.9929007193864\n",
      "3.2555437074029885\n"
     ]
    }
   ],
   "source": [
    "c = 0.6200000002357596 * (999.9929003391316/0.62)\n",
    "e = 0.37999999976424037 * (3.255543709422793/0.38)\n",
    "print(c)\n",
    "print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1064,)\n",
      "error\n",
      "no error\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "\n",
    "a = np.zeros(1064)\n",
    "print(a.shape)\n",
    "\n",
    "b = np.loadtxt(\"saved_data/homeostatic_stress.txt\", delimiter =',')\n",
    "\n",
    "if all (x == 0 for x in a): \n",
    "    print(\"error\")\n",
    "else: \n",
    "    pass\n",
    "\n",
    "# check there wasn't an error termination on previous step\n",
    "a = np.loadtxt(\"saved_data/stress_data/current_von_mises.txt\", delimiter = ',')\n",
    "\n",
    "if all (x == 0 for x in a): \n",
    "    print(\"error\")\n",
    "    print(\"simulation stopped\")\n",
    "else: \n",
    "    print(\"no error\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "on day 25: [0.         0.47267564 0.46977105 0.48883229 0.80382547 0.87627206\n",
      " 1.34061753 1.47564977 2.02142366 2.17420567 1.44929665 1.18482898\n",
      " 0.91981288 0.86897437 1.26102132 1.21902924 1.14753315 0.38503949]\n",
      "1.0310449572925549\n",
      "on day 0: [0.         0.43225728 0.56829173 0.63654502 1.12975432 1.23570345\n",
      " 1.84663004 2.05033129 2.61210781 2.65692271 1.4745691  1.14785542\n",
      " 0.89652842 0.79595015 1.19353292 1.15269287 1.04106062 0.27454548]\n",
      "1.174737702611553\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "from Read_XPLTfuncs import * \n",
    "\n",
    "homeostatic = np.loadtxt(\"saved_data/homeostatic_stress.txt\")\n",
    "\n",
    "xpltname = './jobs/FEBIO'+ 'tav' + '25' + '.xplt'\n",
    "nDoms = 1064\n",
    "\n",
    "#Run GetFeb, using .xplt filename, the number of domains and your to output the data tress using True or False \n",
    "feb, _,nStates, _= GetFEB(xpltname,nDoms,False)\n",
    "Nodes, nElems, nVar, StateTimes, VarNames, VarType = GetMeshInfo(feb)\n",
    "\n",
    "stress = GetData(feb,'stress',nStates,nVar)\n",
    "stress = np.array(stress)\n",
    "\n",
    "all_von_data = []\n",
    "\n",
    "n = 0\n",
    "while n < len(stress.T): \n",
    "    von_data = []\n",
    "    i = 0 \n",
    "    while i < nStates : \n",
    "        a = float((stress[i,n]-stress[i,n+1])**2 + (stress[i,n+1]-stress[i,n+2])**2 + (stress[i,n+2]-stress[i,n])**2)\n",
    "        b = (stress[i,n+3])**2 + (stress[i,n+4])**2 + (stress[i,n+5])**2\n",
    "        v = (0.5*a) + (float(3)*b)\n",
    "        von = np.sqrt(v)\n",
    "        von_data.append(von)\n",
    "        i = i + 1\n",
    "    all_von_data.append(von_data) \n",
    "    n = n + 6  \n",
    "von_mises = np.array(all_von_data) \n",
    "\n",
    "print(\"on day 25:\", von_mises[7])\n",
    "print(np.mean(von_mises[7]))\n",
    "\n",
    "\n",
    "xpltname = './jobs/FEBIO'+ 'tav' + '0' + '.xplt'\n",
    "nDoms = 1064\n",
    "\n",
    "#Run GetFeb, using .xplt filename, the number of domains and your to output the data tress using True or False \n",
    "feb, _,nStates, _= GetFEB(xpltname,nDoms,False)\n",
    "Nodes, nElems, nVar, StateTimes, VarNames, VarType = GetMeshInfo(feb)\n",
    "\n",
    "stress = GetData(feb,'stress',nStates,nVar)\n",
    "stress = np.array(stress)\n",
    "\n",
    "all_von_data = []\n",
    "\n",
    "n = 0\n",
    "while n < len(stress.T): \n",
    "    von_data = []\n",
    "    i = 0 \n",
    "    while i < nStates : \n",
    "        a = float((stress[i,n]-stress[i,n+1])**2 + (stress[i,n+1]-stress[i,n+2])**2 + (stress[i,n+2]-stress[i,n])**2)\n",
    "        b = (stress[i,n+3])**2 + (stress[i,n+4])**2 + (stress[i,n+5])**2\n",
    "        v = (0.5*a) + (float(3)*b)\n",
    "        von = np.sqrt(v)\n",
    "        von_data.append(von)\n",
    "        i = i + 1\n",
    "    all_von_data.append(von_data) \n",
    "    n = n + 6  \n",
    "von_mises = np.array(all_von_data) \n",
    "\n",
    "print(\"on day 0:\", von_mises[7])\n",
    "print(np.mean(von_mises[7]))\n",
    "\n",
    "von = []\n",
    "i = 0 \n",
    "while i < nDoms: \n",
    "    x = np.mean(von_mises[i])\n",
    "    von.append(x) \n",
    "    i = i + 1\n",
    "von = np.asarray(von)\n",
    "\n",
    "difference = np.subtract(von, homeostatic)\n",
    "#for x in difference: \n",
    "    #print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    }
   ],
   "source": [
    "xpltname = './jobs/FEBIO'+ 'tav' + '400' + '.xplt'\n",
    "nDoms = 1064\n",
    "\n",
    "#Run GetFeb, using .xplt filename, the number of domains and your to output the data tress using True or False \n",
    "feb, _,nStates, _= GetFEB(xpltname,nDoms,False)\n",
    "Nodes, nElems, nVar, StateTimes, VarNames, VarType = GetMeshInfo(feb)\n",
    "\n",
    "stress = GetData(feb,'stress',nStates,nVar)\n",
    "stress = np.array(stress)\n",
    "\n",
    "all_von_data = []\n",
    "\n",
    "n = 0\n",
    "while n < len(stress.T): \n",
    "    von_data = []\n",
    "    i = 0 \n",
    "    while i < nStates : \n",
    "        a = float((stress[i,n]-stress[i,n+1])**2 + (stress[i,n+1]-stress[i,n+2])**2 + (stress[i,n+2]-stress[i,n])**2)\n",
    "        b = (stress[i,n+3])**2 + (stress[i,n+4])**2 + (stress[i,n+5])**2\n",
    "        v = (0.5*a) + (float(3)*b)\n",
    "        von = np.sqrt(v)\n",
    "        von_data.append(von)\n",
    "        i = i + 1\n",
    "    all_von_data.append(von_data) \n",
    "    n = n + 6  \n",
    "von_mises = np.array(all_von_data) \n",
    "\n",
    "von = []\n",
    "i = 0 \n",
    "while i < nDoms: \n",
    "    x = np.mean(von_mises[i])\n",
    "    von.append(x) \n",
    "    i = i + 1\n",
    "von = np.asarray(von)\n",
    "\n",
    "difference = np.subtract(von, homeostatic)\n",
    "\n",
    "for x in difference: \n",
    "    print(x)"
   ]
  }
 ],
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