{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "autoscroll": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "sys.path.append(os.path.join(os.getcwd(), \"language_bindings/python\"))\n",
    "import numpy as np\n",
    "import muSpectre as msp\n",
    "import matplotlib.pyplot as plt\n",
    "font = {'family' : 'DejaVu Sans',\n",
    "        'size'   : 16}\n",
    "plt.rc('font', **font)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "resolution = [5, 5]\n",
    "lengths = [5, 5]\n",
    "formulation = msp.Formulation.small_strain\n",
    "splitness1 = msp.SplittedCell.split\n",
    "cons_strain = np.multiply(np.ones([4, 25]),0.0001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "center = np.array([2.5, 2.5])\n",
    "## define the convergence tolerance for the Newton-Raphson increment\n",
    "tol = 1e-5\n",
    "## tolerance for the solver of the linear cell\n",
    "cg_tol = 1e-8\n",
    "## Macroscopic strain\n",
    "Del0 = np.array([[.00, .00],\n",
    "                 [0.00,  .0005]])\n",
    "Del0 = .5*(Del0 + Del0.T)\n",
    "verbose = 1\n",
    "maxiter = 200 ## for linear cell solver\n",
    "vars = np.linspace(0.0, 1.0, 11, endpoint=True)\n",
    "energy = np.zeros(vars.size)\n",
    "pixel_energy = np.array(np.zeros((resolution[0],resolution[0], vars.size), dtype=float)) \n",
    "pixel_stress = np.array(np.zeros((resolution[0],resolution[0], 2, 2, vars.size), dtype=float)) \n",
    "pixel_strain = np.array(np.zeros((resolution[0],resolution[0], 2, 2, vars.size), dtype=float)) \n",
    "for k,var in enumerate(vars):\n",
    "    halfside_neg = np.array([-1.5 + var, -1.5 ])\n",
    "    halfside_pos = np.array([ 1.5 + var,  1.5 ])\n",
    "    precipitate_vertices = list  ([[center[0] + halfside_neg[0], center[1] + halfside_neg[1]],\n",
    "                                   [center[0] + halfside_neg[0], center[1] + halfside_pos[1]],\n",
    "                                   [center[0] + halfside_pos[0], center[1] + halfside_neg[1]],\n",
    "                                   [center[0] + halfside_pos[0], center[1] + halfside_pos[1]]])\n",
    "    rve = msp.Cell(resolution, lengths, formulation, splitness1)\n",
    "    hard = msp.material.MaterialLinearElastic1_2d.make(rve, \"hard\", 10e9, .33)\n",
    "    soft = msp.material.MaterialLinearElastic1_2d.make(rve, \"soft\", 70e9, .33)\n",
    "    rve.make_precipitate(hard, precipitate_vertices)\n",
    "    rve.complete_material_assignment(soft)\n",
    "    rve.set_uniform_strain(cons_strain)\n",
    "    rve.evaluate_stress_tangent(cons_strain)\n",
    "    #solver = msp.solvers.SolverCGEigen(rve, cg_tol, maxiter, verbose=True)\n",
    "    #result = msp.solvers.newton_cg(rve, Del0, solver, tol, verbose)\n",
    "    stress_res = rve.get_stress()\n",
    "    strain_res = rve.get_strain()\n",
    "    stress_res = stress_res.T.reshape(*resolution, 2, 2)\n",
    "    strain_res = strain_res.T.reshape(*resolution, 2, 2)\n",
    "    pixel_stress[:,:,:,:,k] =stress_res\n",
    "    pixel_strain[:,:,:,:,k] =strain_res\n",
    "    energy[k] = np.einsum('ijkl,ijkl',stress_res,strain_res)\n",
    "    for i in range(resolution[0]):\n",
    "        for j in range(resolution[0]):\n",
    "            pixel_energy[i,j,k] = np.einsum('ij,ij', stress_res[i,j,:,:],strain_res[i,j,:,:])\n",
    "    #plt.pcolormesh(pixel_energy[:, :, k])\n",
    "    #plt.colorbar()\n",
    "    #plt.title('%3.1f'%var)\n",
    "    #plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'ELastic Energy')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.step(vars,energy, where=\"mid\")\n",
    "plt.xlabel(\"shift (length unit)\")\n",
    "plt.ylabel(\"ELastic Energy\")\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'ELastic Energy')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "relative_pixel_energy = (pixel_energy[1,2,:]-pixel_energy[2,2,:])/(pixel_energy[0,0,:]-pixel_energy[2,2,:])\n",
    "plt.step(vars, relative_pixel_energy, where=\"mid\")\n",
    "plt.xlabel(\"shift (length unit)\")\n",
    "plt.ylabel(\"ELastic Energy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'shift (length unit)')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uIjWgdP9e8ifcj3/9BIqtBfkZL5B8+Y1ex5I4onIXibCvZ82g6Ud3kOHWM7vlAM4c/gL9TmrjdSyJMyp3kQjZs2snizPvJG3bu2y0Niy86HVSz7/a61gSp1TuIhEwf+Y7tJ15L363jZyTr6P38Kc5pfmJXseSOKZyFzkOxds2sXzCbfhLZrC2XkeW9X+L9NRLvY4lonIXORYuECB/xhucOuth+ro9ZHf8Jf2GPk7jJid4HU0EULmLHLVtG9ZSOGkkyXv/jxUJp7PzJ1PJ6J3hdSyR71C5i4TJBQLkvvci3Rc8SQ93kJzTb8M3+EHqN2jodTSR71G5i4Rhw5qlbJ98C6kH5lLQoBfNrxtLetc+XscSOSyVu8gRlJeVkTvtKXovfZ5EjFlnPYD/mruol5DgdTSRI1K5ixzGmiVzKX17JOllXzO/iZ+2N4wjLamr17FEwqJyF6nk0MED5E16mJQ1f2OfNSYv+UlSrvxfLfQlUUXlLlLB8nlfkJA1mozAGuY0v5DOw8bga9vR61giR03lLgKU7ttD/oR78W94k52WSP4PxpBy2VCvY4kcM5W7xL3FX02nxb/vIsNtIPekH9Ft+Av0a9na61gix0XlLnFrd8kOCjLvIG37e2ywtiy6OBP/eQO9jiUSESp3iUvzP51Gu8/vw+92kNP2es4e/jTtmyV6HUskYlTuEld2bt3Iygmj8e36hDX1OrFswCuk+y7yOpZIxKncJS64QIC5/3qV03IfpY/bS3bSr0ge8hiNGmuhL4lNKneJeVvWr2b9pJGk7PuK5fW7svMnY8joleZ1LJEapXKXmOUCAXLf/Qs9Fv6JHpQx64zbSRn0gBb6krigcpeYtH5VATunjCD14DwWN+pN4nUvkXZGL69jidQalbvElPKyMnKn/pE+y/5KCxKY3etBfD+9Qwt9SdxRuUvMWF2Qy8F3R5FetpR5J6RzypCxpHY83etYIp5QuUvUO3iglDmTHiRl7avstabk+Z4mZcCvtNCXxDWVu0S1ZXNn0uCD28gIrCUv8RJOG/oCvpM7eB1LxHMqd4lK+/fuZn7mPfg3TWG7tWTeuS/ju2SQ17FE6gyVu0SdRV/+k5af3EW628ys1gPpMew5+p7YyutYInWKyl2ixq7i7XydeTtpO7IosnYsunQSaedc6XUskToprFeczKy/mS01sxVmdt8Rxl1rZs7MfJGLKALzPplM6V98+Lb/k+x2Q2h1dx69VOwih1XtkbuZJQBjgEuBIiDXzLKccwWVxjUHbgNm1URQiU87tqxn1YRb8e3+D6vrdab4ytfJSL7A61gidV44R+6pwArn3Crn3EFgClDVotePAX8CSiOYT+KUCwTI++fL2Ng0zt41k+ykW+hw7yy6qdhFwhLOOfcOQGGF+0XAd1ZdMrN+QCfn3AdmdncE80kc2lS4ks1vjsC3fxZL659Jo2vGktFDZ/pEjkY45W5VPOa+3WhWD3gO+Hm1T2R2M3AzQFJSUngJJW4EysvJffc5ei76M10JkN3tblKvv5+E+nrdX+RohfNbUwR0qnC/I7Chwv3mQC9gppkBtAOyzOwq51xexSdyzo0HxgP4fD6HSEjhioXsmjaCtIMLWdS4Ly2vf4mM03p4HUskaoVT7rlAVzPrAqwHBgE3fLPROVcCfHs1YTObCdxdudhFqlJ26CB5Ux+n7/IxJFoDZvd+BP/Vt2npAJHjVG25O+fKzGw0MANIAF5zzi02s0eBPOdcVk2HlNi0atEsyt8bRXrZcuY1/QEdho4jtX1nr2OJxISwTmY656YD0ys99tBhxl54/LEklh0o3cfcSQ/iW/d3dltT5qQ+S3L/X+hoXSSC9EqV1Kolef+h8fRfkxEoJC/xUs4Y/iIprdt5HUsk5qjcpVbs21PCgszfkLp5KlusFfPP/xu+i67zOpZIzFK5S41b9MX7nPTpPaGFvq6m5/Bn6ZN4ktexRGKayl1qTMnObSzNvI3UnR9SaO0puHwKaRlXeB1LJC6o3KVG5H88kY5f/Y5kV0J2++H0G/YEnU5o5nUskbihcpeI2r65kDUTRpOyZyYrE7pQ/OMJZPQ9z+tYInFH5S4R4QIB5nzwEmfMfZzerpTsLiPw3fAIDRo28jqaSFxSuctx27RueXChr9JcltTvQZNrx5LRPdnrWCJxTeUuxyxQXk7u23+mV8GztMAxq/tv8P3sXi30JVIH6LdQjknh8vnsnjaStEOLWNg4mVaDXyKt85lexxKREJW7HJWyQwfJnfwYySvHkWgNye3zGL6Bo7V0gEgdo3KXsK1cmIN7byQZ5SvJb3YunYaMxd/+VK9jiUgVVO5SrdL9e8mf+AC+okxKrDlz058nuf/PvY4lIkegcpcjWjL73zT56HYyAkXkntifbsNfILlVW69jiUg1VO5Spb27i1mYeTepW95mi7VmwYWv4b/wGq9jiUiYVO7yPQv++w9az/wNqYGt5Lb5KWcNf4Z2LVp6HUtEjoLKXb5VsmMryzJvxV/8L9bV68DSAVNJS7vc61gicgxU7gJA/ow36JT9EP3cLrI73Ei/YU/QuElTr2OJyDFSuce5bZvWsW7CKJL3fs7KhNMovmoSGX3O9TqWiBwnlXuccoEAeVlj6Tbvj5zlDpJ92mh8gx/SQl8iMULlHoc2rl3K1skj8ZfmsaRBT0742TgyuvX1OpaIRJDKPY4EysvJfetP9P76ORKBWT3vx3/tPdRLSPA6mohEmMo9TqxdOo99b48g7VABC5uk0HrwONJO1UJfIrFK5R7jDh08QN7kR0heNZ5Sa0hu3z/iu2qEFvoSiXEq9xi2Yv6XWNZoMspXMbf5+SQNHYu/XSevY4lILVC5x6DSfXvIn3g//vUTKbYWzM14keTLh3kdS0Rqkco9xnw9awbNPrqdDLeB3JYDggt9ndTG61giUstU7jFiz66dLM68k7Rt77LBTmbRRW/gP/8nXscSEY+o3GPAgs/e4uT/3offbSen7XX0HvY07Zuf6HUsEfGQyj2KFW/bxPIJt+Iv+Zi19Tqx7Iq3Sfdf4nUsEakDVO5RyAUC5M94g1NnPUxft4ecTr+g39A/0qjxCV5HE5E6IqxyN7P+wPNAAvCKc+7JStvvBH4FlAFbgV8659ZGOKsA2zaspXDSCJL3fsnyhDMo/sk00nunex1LROqYaj/JYmYJwBjgCqAnMNjMelYalg/4nHNnA28Df4p00HjnAgFy332ehuPT6bFnNjmn/5ou92VzuopdRKoQzpF7KrDCObcKwMymAAOBgm8GOOc+qzA+BxgayZDxbsPqJWyfcgv+A/kUNOxN85+NIb1rH69jiUgdFk65dwAKK9wvAtKOMP4m4F9VbTCzm4GbAZKSksKMGL/Ky8rInfYkZy99gUSMWWc9gP+au7TQl4hUK5xytyoec1UONBsK+IALqtrunBsPjAfw+XxVPocErf16DvvfGUV62dfMb+Kn7ZCXSOt0htexRCRKhFPuRUDFBUk6AhsqDzKzS4AHgAuccwciEy/+HDp4gLxJD5Gy5hX2WhPykp8k5cr/1UJfInJUwin3XKCrmXUB1gODgBsqDjCzfsDLQH/n3JaIp4wTy+d9QULWaDICa8hrcRFdhv4VX9uOXscSkShUbbk758rMbDQwg+BbIV9zzi02s0eBPOdcFvA00Ax4y8wA1jnnrqrB3DGldN8e8ifcS+qGSWy3luSfMxbfpUO8jiUiUSys97k756YD0ys99lCF2/pY5DEq+Go6Lf59JxluI7Nb/Zgzh/2Ffi1bex1LRKKcPqHqkd0lOyjIvIO07e+x3tqy6JIJpJ6r/9kRkchQuXtgwadTafv5/fjcDnLaDebsYU/RoVmi17FEJIao3GvRzq0bWTlhNL5dn7CmXidWDHiFdN9FXscSkRikcq8FLhBg7r9e5bTcR+nj9pKd9D8kD3lUC32JSI1RudewretXUzRpJCn7vmJZ/W4UX/0iGWcd6QO+IiLHT+VeQ4ILff2FHov+RHdXTk7XO/Fd/1vqN2jgdTQRiQMq9xqwftViiqeMIPXgfBY3PJvE68aRfkYvr2OJSBxRuUdQeVkZuVP/SJ9lf6UFCczq/TD+q2+nXoKWDhCR2qVyj5DVBbkcenck6WXLmHdCBqcMHUtah9O8jiUicUrlfpwOHihlzqQHSVn7KnutKXm+P5My4CYt9CUinlK5H4dlc/9Lgw9uJSOwljmJFwcX+jq5g9exRERU7sdi/97dzM+8B/+mKWy3lsw772VSLh7kdSwRkW+p3I/Soi//SctP7iLdbWZW64H0GPYcfU9s5XUsEZHvULmHaVfxdr7OvJ20HVkU2SksvvRN0s75kdexRESqpHIPw7xPJtP+/36Lz+0k55Qh9Bn2FB2bNvc6lojIYancj2DHlvWsnjCalN2fsrpeZ4qvfIP05PO9jiUiUi2VexVcIMCcD//G6XMeo7fbR/apt5Ay5BEaNmrsdTQRkbCo3CvZVLiCzW+OxLd/Fkvrn0mja8aS0cPndSwRkaOicg8JlJeT9+6z9Fz0DC0IkNPtLvzX/5aE+vorEpHoo+YCClcsZNe0EaQeXMiixn1pef1LpJ/Ww+tYIiLHLK7LvezQQfKm/IG+K8aSaA3I7f17fFf/WksHiEjUi9tyX7VoFuXvjSK9bDn5TX9Ax6Hj8Lfv7HUsEZGIiLtyP1C6j/yJvyOl8HV2WTPmpD5Hcv+f62hdRGJKXJX7krz/0Hj6r0kPFJJ74mV0HfZXUlq38zqWiEjExUW579tTwoLMe0jdPI0t1or5F7yC/4c/8zqWiEiNiflyX/jF+7T69J7gQl9tfkrPYc/QJ/Ekr2OJiNSomC33kp3bWPrGraQWT6fQ2lNw+RTSMq7wOpaISK2IyXLP/3giHb/6HcmuhOz2w+k37Ak6ndDM61giIrUmpsp926ZC1k0cRfKe/7IyoQvFP55ARt/zvI4lIlLrYqLcXSBA3j9fomv+4/RypeR0GUXKDQ/ToGEjr6OJiHgirHI3s/7A80AC8Ipz7slK2xsBmUAKsB243jm3JrJRq7Zp3XK2vDkCf2kuSxr0oMk1Y0nvnlwbuxYRqbOqLXczSwDGAJcCRUCumWU55woqDLsJ2OmcO8PMBgFPAdfXROBvBMrLyX37aXoVPEcLHDnd78V/3b0kJCTU5G5FRKJCOEfuqcAK59wqADObAgwEKpb7QOD3odtvAy+amTnnXASzfmvdsnnsfWskaYcWs7BxMq0Gv0R65zNrYlciIlEpnHLvABRWuF8EpB1ujHOuzMxKgFbAtkiErCj33ec5e/5jlFpDZvd5HP/AkVo6QESkknDK3ap4rPIReThjMLObgZsBkpKSwtj19zXr0J3FKzNIGjaG1HbH9hwiIrEunHIvAjpVuN8R2HCYMUVmVh9IBHZUfiLn3HhgPIDP5zumUzY90i6HtMuP5UtFROJGOOczcoGuZtbFzBoCg4CsSmOygBtDt68FPq2p8+0iIlK9ao/cQ+fQRwMzCL4V8jXn3GIzexTIc85lAa8CE8xsBcEj9kE1GVpERI4srPe5O+emA9MrPfZQhdulgJZZFBGpI/Q2ExGRGKRyFxGJQSp3EZEYpHIXEYlBKncRkRhkXr0d3cy2AmuP8ctbUwNLG9RxmnN80Jzjw/HM+VTnXJvqBnlW7sfDzPKccz6vc9QmzTk+aM7xoTbmrNMyIiIxSOUuIhKDorXcx3sdwAOac3zQnONDjc85Ks+5i4jIkUXrkbuIiBxBnS53M+tvZkvNbIWZ3VfF9kZmNjW0fZaZda79lJEVxpzvNLMCM1tgZv8xs1O9yBlJ1c25wrhrzcyZWdS/syKcOZvZdaHv9WIze7O2M0ZaGD/bSWb2mZnlh36+B3iRM1LM7DUz22Jmiw6z3czshdDfxwIzS45oAOdcnfxDcHnhlcBpQENgPtCz0piRwEuh24OAqV7nroU5/xA4IXR7RDzMOTSuOfA5kAP4vM5dC9/nrkA+0DJ0/2Svc9fCnMcDI0K3ewJrvM59nHM+H0gGFh1m+wDgXwSvZJcOzIrk/uvykfu3F+Z2zh0Evrkwd0UDgTdCt98GLjazqi75Fy2qnbNz7jPn3L7Q3RyCV8aKZuF8nwEeA/4ElNZmuBoSzpz/BxgEqYKZAAAFnklEQVTjnNsJ4JzbUssZIy2cOTugReh2It+/4ltUcc59ThVXpKtgIJDpgnKAE83slEjtvy6Xe1UX5u5wuDHOuTLgmwtzR6tw5lzRTQT/5Y9m1c7ZzPoBnZxzH9RmsBoUzve5G9DNzL40sxwz619r6WpGOHP+PTDUzIoIXj/i1tqJ5pmj/X0/KmFdrMMjEbswdxQJez5mNhTwARfUaKKad8Q5m1k94Dng57UVqBaE832uT/DUzIUE/+/sCzPr5ZwrruFsNSWcOQ8GXnfOPWNmGQSv7tbLOReo+XieqNH+qstH7kdzYW6OdGHuKBLOnDGzS4AHgKuccwdqKVtNqW7OzYFewEwzW0Pw3GRWlL+oGu7P9vvOuUPOudXAUoJlH63CmfNNwDQA51w20JjgGiyxKqzf92NVl8s9Hi/MXe2cQ6coXiZY7NF+HhaqmbNzrsQ519o519k515ng6wxXOefyvIkbEeH8bL9H8MVzzKw1wdM0q2o1ZWSFM+d1wMUAZtaDYLlvrdWUtSsLGB5610w6UOKc2xixZ/f6FeVqXm0eACwj+Cr7A6HHHiX4yw3Bb/5bwApgNnCa15lrYc6fAJuBeaE/WV5nruk5Vxo7kyh/t0yY32cDngUKgIXAIK8z18KcewJfEnwnzTzgMq8zH+d8JwMbgUMEj9JvAm4BbqnwPR4T+vtYGOmfa31CVUQkBtXl0zIiInKMVO4iIjFI5S4iEoNU7iIiMUjlLiISg1Tu4ikzWxN6H3flx6/6ZuVAM2sTWvUz38zOM7PfHuH5zMw+NbMWoft7aiBz34orFprZ783s7hrYT3sze/sw+7zSzB6J9D4ldqjcpU5yzmU5554M3b0YWOKc6+ec+wI4bLkTfC/1fOfcrhqM1ze0nxrlnNvgnLv2MPv8ELjKzE6o6RwSnVTuUivMrKmZfWhm881skZldX2HzrWY218wWmln30Pifm9mLZtaX4GqQA8xsnpk9BTQJ3Z5Uxa6GAO8fJsM9ZpYbWjv7kdBjnc3sazP7W2jd9I/NrElomz80NtvMng7lbkjwgzfXhzJ8M4+eZjbTzFaZ2W2H2f+eCrevNbPXQ7dfD63r/VXo66+tkK3KfbrgB1RmAleG8dcvcUjlLrWlP7DBOdfHOdcL+KjCtm3OuWRgHPCd0xvOuXnAQwTXre/rnLsX2B+6PaSK/ZwDzKn8oJldRnBtllSCR8EpZnZ+aHNXgsvrngUUA9eEHv87wU8TZgDloTwHK+WZGhrbHbg89PwPm1mDsP9mgk4BziVY1k9W3HCEfeYB5x3lfiROqNyltiwELjGzp8zsPOdcSYVt74b+OwfofJz7Ock5t7uKxy8L/ckH5hIs428W4lod+kfk2wxmdiLQ3Dn3Vejx6q6E9KFz7oBzbhuwBWh7lLnfc84FnHMFR/G1W4D2R7kfiRMqd6kVzrllQArBkn/CzB6qsPmblS3LOf5lqMtCywRXZsAToSPfvs65M5xzr1baf8UMR3vRl6qeo7KKa300PsLXh7vvxsD+MMdKnFG5S60ws/bAPufcRODPBC8/dqwOHeG0x1KCl3KrbAbwSzNrFsrTwcxOPtwOXPAKSLtDq/VBcBXDb+wmuBTx0dpsZj1C//hcfZRfW9U+uwFVXp9TROUutaU3MNvM5hFci/4Px/Fc44EFh3lB9UOCF7j4DufcxwRPrWSb2UKCl2WsrqBvAsabWTbBo+lvTiV9RvAF1IovqIbjPuAD4FOCqwUejar2+UOC8xX5Hq0KKTHFgtegzHTOXRqB52rmnNsTun0fcIpz7tfH+7yRYGZtgTedcxd7nUXqprp8mT2Ro+ac2xh6W2OLCLzX/Udmdj/B35O11K1L/SUBd3kdQuouHbmLiMQgnXMXEYlBKncRkRikchcRiUEqdxGRGKRyFxGJQSp3EZEY9P8U/HHYaGTZUQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit_pixel_energy = np.poly1d(np.polyfit(vars, relative_pixel_energy,2))\n",
    "xp =np.linspace(0,1,100)\n",
    "plt.plot(vars, relative_pixel_energy,xp, fit_pixel_energy(xp))\n",
    "plt.xlabel(\"shift (length unit)\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'shift (length unit)')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "relative_pixel_stress = (pixel_stress[1,2,1,1,:]-pixel_stress[2,2,1,1,:])/(pixel_stress[0,0,1,1,:]-pixel_stress[2,2,1,1,:])\n",
    "plt.step(vars, relative_pixel_stress, where=\"mid\")\n",
    "plt.xlabel(\"shift (length unit)\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'shift (length unit)')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.step(vars, pixel_strain[1,2,0,0,:], where=\"mid\")\n",
    "plt.xlabel(\"shift (length unit)\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'shift (length unit)')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.step(vars, pixel_strain[1,2,0,0,:], where=\"mid\")\n",
    "plt.xlabel(\"shift (length unit)\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.7"
  },
  "name": "Untitled.ipynb"
 },
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}