{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Elasticity in 2D for 3 Phases\n",
    "\n",
    "## Introduction\n",
    "\n",
    "This example provides a demonstration of using PyMKS to compute the linear strain field for a three-phase composite material. It demonstrates how to generate data for delta microstructures and then use this data to calibrate the first order MKS influence coefficients. The calibrated influence coefficients are used to predict the strain response for a random microstructure and the results are compared with those from finite element. Finally, the influence coefficients are scaled up and the MKS results are again compared with the finite element data for a large problem.\n",
    "\n",
    "PyMKS uses the finite element tool [SfePy](http://sfepy.org) to generate both the strain fields to fit the MKS model and the verification data to evaluate the MKS model's accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Elastostatics Equations and Boundary Conditions\n",
    "\n",
    "The governing equations for elasticostaics and the boundary conditions used in this example are the same as those provided in the [Linear Elastic in 2D](elasticity_2D.html) example. \n",
    "\n",
    "Note that an inappropriate boundary condition is used in this example because current version of SfePy is unable to implement a periodic plus displacement boundary condition. This leads to some issues near the edges of the domain and introduces errors into the resizing of the coefficients. We are working to fix this issue, but note that the problem is not with the MKS regression itself, but with the calibration data used. The finite element package ABAQUS includes the displaced periodic boundary condition and can be used to calibrate the MKS regression correctly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling with MKS\n",
    "\n",
    "### Calibration Data and Delta Microstructures\n",
    "\n",
    "The first order MKS influence coefficients are all that is needed to compute a strain field of a random microstructure as long as the ratio between the elastic moduli (also known as the contrast) is less than 1.5. If this condition is met we can expect a mean absolute error of 2%  or less when comparing the MKS results with those computed using finite element methods [1]. \n",
    "\n",
    "Because we are using distinct phases and the contrast is low enough to only need the first-order coefficients, delta microstructures and their strain fields are all that we need to calibrate the first-order influence coefficients [2]. \n",
    "\n",
    "Here we use the `make_delta_microstructure` function from `pymks.datasets` to create the delta microstructures needed to calibrate the first-order influence coefficients for a two-phase microstructure. The `make_delta_microstructure` function uses SfePy to generate the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "#PYTEST_VALIDATE_IGNORE_OUTPUT\n",
    "\n",
    "import pymks\n",
    "\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymks.tools import draw_microstructures\n",
    "from pymks.datasets import make_delta_microstructures\n",
    "\n",
    "\n",
    "n = 21\n",
    "n_phases = 3\n",
    "X_delta = make_delta_microstructures(n_phases=n_phases, size=(n, n))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at a few of the delta microstructures by importing `draw_microstructures` from `pymks.tools`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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xCc7mQDgIAAAAwDAqB6fFmYMAAAAAMFMqBwEAAAAYRuXgtAgHAQAAABhGODgt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f30299c55c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_microstructures(X_delta[::2])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using delta microstructures for the calibration of the first-order influence coefficients is essentially the same, as using a unit [impulse response](http://en.wikipedia.org/wiki/Impulse_response) to find the kernel of a system in signal processing. Any given delta microstructure is composed of only two phases with the center cell having an alternative phase from the remainder of the domain. The number of delta microstructures that are needed to calibrated the first-order coefficients is $N(N-1)$ where $N$ is the number of phases, therefore in this example we need 6 delta microstructures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating Calibration Data\n",
    "\n",
    "The `make_elasticFEstrain_delta` function from `pymks.datasets` provides an easy interface to generate delta microstructures and their strain fields, which can then be used for calibration of the influence coefficients. The function calls the `ElasticFESimulation` class to compute the strain fields.\n",
    "\n",
    "In this example, lets look at a three phase microstructure with elastic moduli values of 80, 100 and 120 and Poisson's ratio values all equal to 0.3. Let's also set the macroscopic imposed strain equal to 0.02. All of these parameters used in the simulation must be passed into the `make_elasticFEstrain_delta` function. The number of Poisson's ratio values and elastic moduli values indicates the number of phases. Note that `make_elasticFEstrain_delta` does not take a number of samples argument as the number of samples to calibrate the MKS is fixed by the number of phases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymks.datasets import make_elastic_FE_strain_delta\n",
    "from pymks.tools import draw_microstructure_strain\n",
    "\n",
    "\n",
    "elastic_modulus = (80, 100, 120)\n",
    "poissons_ratio = (0.3, 0.3, 0.3)\n",
    "macro_strain = 0.02\n",
    "size = (n, n)\n",
    "\n",
    "X_delta, strains_delta = make_elastic_FE_strain_delta(elastic_modulus=elastic_modulus,\n",
    "                                                      poissons_ratio=poissons_ratio,\n",
    "                                                      size=size, macro_strain=macro_strain)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at one of the delta microstructures and the $\\varepsilon_{xx}$ strain field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DAAAG2aJ9+pIkVfXCHmXnMhXwLVnnUpJLSZ5dUH4x87dmSFVdzcARmoI+AABg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3029bf14a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_microstructure_strain(X_delta[0], strains_delta[0])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because `slice(None)` (the default slice operator in Python, equivalent to array[:]) was passed in to the `make_elasticFEstrain_delta` function as the argument for `strain_index`, the function returns all the strain fields. Let's also take a look at the $\\varepsilon_{yy}$ and $\\varepsilon_{xy}$ strain fields."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calibrating First-Order Influence Coefficients\n",
    "\n",
    "Now that we have the delta microstructures and their strain fields, we will calibrate the influence coefficients by creating an instance of the `MKSLocalizatoinModel` class. Because we are going to calibrate the influence coefficients with delta microstructures, we can create an instance of `PrimitiveBasis` with `n_states` equal to 3, and use it to create an instance of `MKSLocalizationModel`. The delta microstructures and their strain fields will then be passed to the `fit` method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymks import MKSLocalizationModel\n",
    "from pymks import PrimitiveBasis\n",
    "\n",
    "\n",
    "p_basis =PrimitiveBasis(n_states=3, domain=[0, 2])\n",
    "model = MKSLocalizationModel(basis=p_basis)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, pass the delta microstructures and their strain fields into the `fit` method to calibrate the first-order influence coefficients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(X_delta, strains_delta)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it, the influence coefficient have been calibrated. Let's take a look at them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sQUsAAAAA+tPaMIKWOzJI7bgStAQAAACgN22vpW1gRuKuG0Ibt2kAYW0AAAAA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\nAAB2iqAlAAAAALBT/n+tpdPNc1v+hAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f302997f898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pymks.tools import draw_coeff\n",
    "\n",
    "\n",
    "draw_coeff(model.coef_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The influence coefficients for $l=0$ and $l = 1$ have a Gaussian-like shape, while the influence coefficients for $l=2$ are constant-valued. The constant-valued influence coefficients may seem superfluous, but are equally as important. They are equivalent to the constant term in multiple linear regression with [categorical variables](http://en.wikipedia.org/wiki/Dummy_variable_%28statistics%29)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict of the Strain Field for a Random Microstructure\n",
    "\n",
    "Let's now use our instance of the `MKSLocalizationModel` class with calibrated influence coefficients to compute the strain field for a random two-phase microstructure and compare it with the results from a finite element simulation. \n",
    "\n",
    "The `make_elasticFEstrain_random` function from `pymks.datasets` is an easy way to generate a random microstructure and its strain field results from finite element analysis.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ffmxbo23cfZuyuXqV5igbfDro7vtHOl5x3wIz25OfO2ek4abVmNMHAAAAIKSN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\nAW+FrEOHt1jZvMCyuaoY8un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pgYr297n7D/Ovq/f3WynpxjqfD0UfAAAAAJS5+3ozu97M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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3029919b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pymks.datasets import make_elastic_FE_strain_random\n",
    "\n",
    "\n",
    "np.random.seed(101)\n",
    "X, strain = make_elastic_FE_strain_random(n_samples=1, elastic_modulus=elastic_modulus,\n",
    "                                          poissons_ratio=poissons_ratio, size=size, \n",
    "                                          macro_strain=macro_strain)\n",
    "draw_microstructure_strain(X[0] , strain[0])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note that the calibrated influence coefficients can only be used to reproduce the simulation with the same boundary conditions that they were calibrated with.**\n",
    "\n",
    "Now, to get the strain field from the `MKSLocalizationModel`, just pass the same microstructure to the `predict` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "strain_pred = model.predict(X)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally let's compare the results from finite element simulation and the MKS model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tGhmO8Ynr/i7UfvPWLeEcFi7613CMPXn0B8/F+oNDXx/7NytJ2x/ZEI4x/MhD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\nTCgEAADose+iLfLnEFJQbgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3029a580b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pymks.tools import draw_strains_compare\n",
    "\n",
    "\n",
    "draw_strains_compare(strain[0], strain_pred[0])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the difference between the two strain fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vJ448IklmNrtue3+1rM0626LlZtZnZo/VlVfPZULHqRmjBgCkFTxGPTw8fKekZ5vv7vvM\nbESV1uz+U4tOn/HdQp3nJSlSbmaLVZk8Vlven/0/78tDIWhRAwDSqrdnhV8dHX2dpFXVN9kErjU1\n7wezbS3XiZS7+3OSHq27fet2Sc+4+3Cb19YWWtQAgNJx9w1mdpeZrZA0R9KAu99bs8tySaslrW+1\nTrRc0tpspbKTS4yKBU8AAF3T5bW+G63tXZ153U6daHm2pvc9eeUThUQNAMjB7VllwBg1AAAlRosa\nAJDmLi+gRW2FtMrPXyRqAECau+QFrHxIog4hUQMA0ro8mQwVjFEDAFBitKgBAGm0qEuBRA0AyMHt\nWWVA1zcAACVGixoAkEbXdymQqAEAadWHchQRBx1rK1G/v21ER0de6fhg2/6x6dPNmvqbnfub79SC\ne24YDMcYmPJROMbOw8fDMS6bMSUcoyiTZs0Kx/joL/48fiIFfYP/0m//djjGP6xbG45x6MRYOEb/\nlGK+l3/68ovCMba/vzsco4gf8UV+MB5E0tRJFo5x9czYv+Pe6b3hc6jnBS14Qos6hjFqAABKjK5v\nAEAOVzH91rSoI0jUAIA0JpOVAl3fAACUGC1qAEAOFjwpAxI1ACCN27NKgUQNAEhjjLoUGKMGAKDE\naFEDANJoUZcCiRoAkEaiLgW6vgEAKDFa1ACAJNb6LgcSNQAgH0m260jUAIA0xqhLgTFqAABKjBY1\nACCNFnUptJWoPzh8XAcOHe34YAtnTe24btUlUwfCMSRpx+iBcIzByy8Oxxj66FA4xvQvfjkcQ5Im\nzb0wHOOphx8Nx5g7Nf798ZrLLgrHkKTdf/h74RjX/9S14Rj/9PxL4RjvHT4WjiFJR9/cGY7xhZsW\nhWPse/21cIwLZs0Kx5CkV3fuDseYPjnWwTn9+LguCZ9FHRJ1KdD1DQBAidH1DQBIo0VdCiRqAEA+\nkmzXkagBAEkseFIOjFEDAFBitKgBAGldHqM2szskuSST1OfuD0TrFFB+t6S5koYkjbj7PR1dXBto\nUQMA0qqJuohXm7KE2efuG9x9vaTtZrY2UqeA8rXufr+73+Put0kaMrPH2r64NpGoAQBltEbS49U3\n7v6EpJXBOh2Xm1mfpOVmNrtm//sk3WJm81u5oE6RqAEAaV1qUWdJcdDdd9QV9ZtZcrWcZnWi5dmf\nB1Xp8q4ayf4/pAnEGDUAIF93ZmznJb69WdnWDupYpNzdt6oyNl1rgSrj2SOn1SoQiRoAUDZ5a0WP\nNihrVmdfsDxllaTNiVZ4oUjUAIAcnr2KiHNuMbMlkpZKWjLRxyJRAwDSund71mjO9oEGZc3qRMvr\n3SdpibvHn/DUBJPJAABpBU8mW7p06YNm9r26168njjwiSXUzrCWpX/njwY3qbAuWn3JMM3tY0qoz\nkaQlWtQAgDNkeHj4TknPNtvP3feZ2Ygqrdn9pxZ5aiJZszrPS1Kg/OQxs3ut11bHpc1scaPzKgIt\nagBAUnWt7yJeHVinymQtSScT5Jqa94PZtpbrRMvN7BZVWtgLzGxZ9n6VyjTr+8CJMe09NtbxwT49\nJ96Av/RTnwrHkKTpP/uvwjEmX3lVOMbxN14Lx9i58ZvhGJJ06ETnP9uqn/vpxeEYrz//YjjGGzs/\nDMeQpMumxf/Ovv/aG+EYNy68OhyjKD0D9XeotG/y0MJwjAP/8lI4xuZX3wnHkKRf+8Sl4Ri79h0M\n1e/Nu7koootLiLr7BjO7y8xWSJojacDd763ZZbmk1ZLWt1onUp7dZ/2YTp8Z5+7+W21fYBvo+gYA\nlFKjtb2zJT7XJ7Y3XA+803J336cu9UKTqAEA+XhEZdeRqAEAaV1+ehYqSNQAgDQSdSkw6xsAgBKj\nRQ0ASKNFXQokagBAGom6FOj6BgCgxGhRAwBydbiq2CkmYi2W8wmJGgCQRtd3KZCoAQBpJOpSYIwa\nAIASo0UNAEijRV0KJGoAQBqJuhTo+gYAoMRoUQMA8tEa7rq2EvUXfukX5Dd9puODjb2/q+O6J2O8\nV8yD3kc2PBKOcXRsPBxj/jULwjHmfOaz4RiSNP7C1nCMPa++Go6xYMHV4RhbXxkJx5Ckv9h9MBxj\nwaxp4RijI2+HYyz+9LXhGJJkMy4Ix/jgL78fjnH5pReHY3y558NwDEn6yd7435NJZbzZmK7vUqBF\nDQBII1GXAmPUAACUGC1qAECSuxeyhGgRMc5nJGoAQBpd36VA1zcAACVGixoA0ACt4W4jUQMAchTU\n9U2yDyFRAwDSGKMuBcaoAQAoMVrUAIA0WtSlQKIGAKSRqEuBrm8AAEqMFjUAIKnSoC5iZbICTuY8\nRqIGAOTg9qwyIFEDANIYoy4FxqgBACgxWtQAgDRa1KXQVqI+/uPnNPbm6x0f7NjBAx3XrTo6XswP\nfO/R4+EYT48eCsfon/JmOMbx8fFwDEnaffREOMbhsfi5zNn+VjjG1B4Lx5CkOVPi32X3HBsLx+id\nFL+e5//ltXAMSbp0+vZwjLEC/h3vendXOMaB4/GfjSTN6u0Jx/jkkutD9SddNj98DqfpcqI2sztU\nGeA2SX3u/kC0TrQ822eZpFXufltHF9Ymur4BAKWTJcw+d9/g7uslbTeztZE6BZQvy97fKmmwyOtt\nhEQNAEhzfdyqDr06OvoaSY+fPBX3JyStDNYJlbv7U+5+j6TN7VxIFIkaAJCjiCTtajdTm1mfpEF3\n31FX1G9mizqpEy1v6wIKRqIGAJTNUM72vQ3KmtWJlncNs74BAGndm0w2kLN9tEFZszr7guVdQ6IG\nACS5e0FLiHJ7VgSJGgCQ1r0W9WjO9oEGZc3qRMu7hjFqAEDZjEiSmc2u295fLWuzzrZged4xzwha\n1ACAtOrtWUXEkbR06dIHt2zZUj8WvNHdN56yu/s+MxtRpTW7/9Qi35o8ROM6z0tSoDx5zDOFFjUA\nIK2Qe6g/7j4fHh6+092/XvfamHP0dZJWVd9ki5GsqXk/mG1ruU4B5VVzc855QpCoAQCl4+4bJO02\nsxVmdrekobrlPJdLWt1OnWi5mS3OViZbLWmJmT1kZiuKv/pT0fUNAMjR3edRN1rbO1vic307daLl\n7v6cpOck3dMoRtFI1ACANJ6eVQokagBAjvaX/8yPg04xRg0AQInRogYAJFV6votYmayAkzmPtZWo\nR/cd0NHRvR0fbM6U+PeC2VdcHo4hSQPz5odjfHZOfPnX//vNvDsTWneioH8EX7lmXjjG/t3xBXx2\nHxsLx7j28ovDMSTpk5PinU42c2Y4xsxfuTkcY+z9neEYkvRnD20Ixzg6Nh6OMW/m1HCMzy35TDiG\nJB3e9no4xk9+/GKo/tRDJxT/m1aHMepSoOsbAIASo+sbAJBGi7oUSNQAgBzdvY8aFSRqAEAaLepS\nYIwaAIASo0UNAEgr+OlZ6AyJGgCQRtd3KdD1DQBAidGiBgCk0aIuBRI1ACCHF7KEKIPUMSRqAEAa\nLepSYIwaAIASo0UNAEjj9qxSIFEDANLo+i4Fur4BACgxWtQAgDRa1KXQVqIeuPIKjetoxwc7+Nab\nHdetmvmJa8MxJOnIP/xtOMb7hzr/LKq+cPHscIxpV88Px5Ak9fSEQ0zftzccY15v/DxmLP/FcAxJ\nOvx3fx2O8d72t8IxLnr098Mxvr3jw3AMSdpzbCwcY8fB+L+dL11i4RiH/nlrOIYkXd8/IxzjyNh4\nqL6NT0Qy5OlZZUCLGgCQRou6FBijBgCgxGhRAwCS3ItZmayY1c3OXyRqAEAa91GXAl3fAACUGC1q\nAEAak8lKgUQNAMjB7VllQKIGACR59l8RcdA5xqgBACgxWtQAgCSGqMuBRA0ASKLruxzo+gYAoMRo\nUQMActEW7j4SNQAgqdtj1GZ2hyrfFUxSn7s/EK0z0eUTga5vAEDpZAmxz903uPt6SdvNbG2kzkSX\nTxQSNQAgyQt8dWCNpMdPnov7E5JWButMdPmEaKvr+90339Hhbds6PtgV03s7rlv10P/eFI4hSVfO\nmBKOsePg0XCMVTd9MhxjbHR3OIYkPfn6O+EY/VN6wjFm98ZHZA790R+HY0jSZ+fMCMfo741/H/7z\nt0fDMX7mwgvCMSRp+4H43/sixtz+3wcHwjFWLby4gDORxguIcfXll4Tq91w0UMBZnKrS9V3E07Pa\n29/M+iQNuvuOuqJ+M1vk7lvbrSNp+0SWp86pKIxRAwCSAq3h0+K0aShn+96sLJUUm9WxCS4nUQMA\nzht53QOjDcqa1dk3weUThkQNAMjF7VndR6IGACR18fasvEkZAw3KmtWZ6PIJw6xvAMAZsXTp0gfN\n7Ht1r19P7DoiSWY2u257f7WszTrbJrg875wKQYsaAJBU9Frfw8PDd0p6tun+7vvMbESV1ur+U4vS\ns6ub1HlekiawfMImkkm0qAEAOcYljXsBr84Ov07SquqbbLGRNTXvB7NtLdc5A+UTgkQNAMjVpcVO\n5O4bJO02sxVmdrekobrlOpdLWt1OnYkunyh0fQMASqlREsyW8FzfTp0zUT4RSNQAgKQuLniCGiRq\nAECSuxe0hCipOoIxagAASowWNQAgia7vciBRAwCSurgyGWqQqAEAucix3ccYNQAAJUaLGgCQxBh1\nObSVqKNT9V/Zf6TjulVXzZwajiFJyy7vD8eY9sWfD8fY9dTmcIwPjhwPx5Ckz194QTjGK/sPh2N8\ncORYOMYLe+PnIUmTLO9Z8a2bOikeY1oBMV7aF//3J0lfW3BZOMYvXHJpOEbPxfEY//TkU+EYkjRW\nwCDsS2+8Hao/22bpi+GzOBW3Z5UDXd8AAJQYXd8AgCS6vsuBRA0ASOL2rHIgUQMAklwdP6LytDjo\nHGPUAACUGC1qAECSZ/8VEQedI1EDAJJcBY1Rx0Oc1+j6BgCgxGhRAwCSuD2rHEjUAIC0gm7PIlPH\nkKgBAEmVFnURk8kQwRg1AAAlRosaAJDEGHU5kKgBAEksIVoOdH0DAFBitKgBAEl0fZdDW4m6d5Jp\nLPAA+yIerv7Vn74+HEOSeq/9VDjG8ZdfDMeY2RPv1HhrrIhl86U3Dh4Nx7hx/uXhGC+8tTMco7+3\nmM6il/d9FI6xeM7McIy+KfHv1M+OHgrHkKTXPtgTjnHN+Fj8RMbjf++nBX6f1RpXPM5Nc2N/T3r6\npofPoZ67ywv4vV1EjPMZLWoAQC5SbPcxRg0AQInRogYAJI2rmOdRFzM4d/4iUQMAkrg9qxzo+gYA\noMRoUQMAkjz7r4g46ByJGgCQi27r7qPrGwCAEqNFDQBIOttWJjOzO7LDmaQ+d38gWidanu2zTNIq\nd7+tk+uiRQ0ASPICXxMtS5h97r7B3ddL2m5mayN1Cihflr2/VdJgp9dGogYAJFWXEC3idQaskfR4\nzbk/IWllsE6o3N2fcvd7JG1u50LqkagBAGc1M+uTNOjuO+qK+s1sUSd1ouWdXEceEjUAIOks6voe\nytm+t0FZszrR8sIwmQwAkFbQymRnIFMP5GwfbVDWrM6+YHlhaFEDAFBitKgBAEnduj0rm039lQZV\nLStbk40Rj+bsN9CgrFmdaHlh2krUHx49oQNHjnd8sBsXzOu4btWJn7wTjiFJ77z6WjjGJ36z2YTC\n5o7/nz8Kx7hgcjEdI3uPjYVj/M22d8MxvnzT9eEYl736ajiGJP3jBwfDMeZMjX8f/szP3BiO8Ys/\n9/PhGJJ0+K9/GI4x6YLZ8RM5djQcYtGv/HL8PCTt+uGT4RjvHu78d6skTT96QnPCZ3GqopcQXbp0\n6YNbtmyp7zLe6O4bT9m/cqvT+jYOMSJJZjbb3ffXbO+vlrVZZ5ukHYHyvGN2hBY1ACBp3CuvIuJI\n0vDw8J2Sno1HPJW77zOzEVVas/tPLfKtHdR5XpIC5cljdooxagDAuWCdpFXVN1n3+Zqa94PZtpbr\nFFBeNbedC6lHixoAkOtseSaHu28ws7vMbIWkOZIG3P3eml2WS1qtmi71ZnWi5Wa2WNLtkm6RNGhm\nD0l6xt03tHNtJGoAQJIXdHvWmXoCV6O1vfPGvZutBx4pd/fnJD0n6Z5GMZqh6xsAgBKjRQ0ASKrc\nnlXErG9EkKgBAEln22Muz1UkagBAkqugMep4iPMaY9QAAJQYLWoAQC5aw91HogYAJLm7vIC+7yJi\nnM/o+gbW5ZZEAAALKUlEQVQAoMRoUQMAkpj1XQ4kagBAEom6HEjUAICks20J0XMVY9QAAJRYWy3q\no2PjOnxivOOD/f3rb3Vct2rRwMxwDEmFfMX79rrfDcf4pSv6wzGm9xTzfetDPxGOsfRLnw/H2PTk\n34Zj/Oq8OeEYkvSN//FfwzHe/4MHwzH2/viFcIy3n3suHEOS5kyJd8RNnTolHGPXgY/CMQ4Ffp/V\nGivg98mM4L9jC5/B6VxSEZ8QDeoYur4BAEncnlUOdH0DAFBitKgBAEnM+i4HEjUAIIlEXQ4kagBA\nWkG3Z5GpYxijBgCgxGhRAwCSPPuviDjoHIkaAJDkKmhlsniI8xpd3wAAlBgtagBAErO+y4FEDQBI\nIlGXA4kaAJBW0BKiPD4rhjFqAABKjBY1ACCJru9yIFEDAJK8oJXJ6PmOoesbAIASo0UNAEgaz15F\nxEHn2krUn/vc9Rq/or/jgx1//ZWO61a9vfdgOIYk/fPuQ+EYfVN6wjFe3Hs4HOOiab3hGJJ009WX\nhWO88fSz4Ri/tjB+Hnv2F/P35JV1a8MxruufEY6x++jxcIzBr309HEOSdv3V98Mxtr63Jxxjz7ET\n4RgXF/Rv58oZU8IxXt4X+13Qd+ioFobP4nQs/9l9dH0DAFBidH0DAJKYTFYOJGoAQBK3Z5UDiRoA\nkESLuhxI1ACAc4KZ3aFKA94k9bn7A9E6BZTfLWmupCFJI+5+T7vXxWQyAECSF/jfRMsSZp+7b3D3\n9ZK2m1nD2zaa1SmgfK273+/u97j7bZKGzOyxdq+NRA0AyOUFvM6QNZIeP3ne7k9IWhms03G5mfVJ\nWm5ms2v2v0/SLWY2v5ULqiJRAwDOallSHHT3HXVF/Wa2qJM60fLsz4OqdHlXjWT/H1IbGKMGACSd\nRZPJ8hLf3qxsawd1LFLu7ltVGZuutUCVToaR02o1QKIGACSdRbdnDeRsH21Q1qzOvmB5yipJmxOt\n8IZI1ACAJJfLC2gOswypZGZLJC2VtKTduiRqAMAZsXTp0ge3bNlS3xLd6O4bazdks6m/ovzGuGVl\na7LW6WjOfgMNyprViZbXu0/SEnc/kFMvF4kaAJBUdNf38PDwnZKaPrknu9VpfRuHGJEkM5vt7vtr\ntvcrfzy4UZ1tknYEyk85ppk9LGlVJ0laYtY3ACCH6+NHXUZeE93x7e77VEmO9WPDnk3qarfO88Hy\nk8fMegfWVselzWxx3kz0PCRqAMC5YJ0qk7UknUyQa2reD2bbWq4TLTezW1RpYS8ws2XZ+1Vi1jcA\noAhn0e1ZcvcNZnaXma2QNEfSgLvfW7PLckmrVdOl3qxOpDy7z/oxnd6h4O7+W+1cW1uJ+tXnX9TB\nN15up8opbrztlo7rVs35i++FY0jSv543JxzjhT0fhWNcMSP+4PojY8X8K+gdXBCOMbbzw3CM1z7Y\nG44x/4Kp4RiSdN2smeEYM5b9YjhG7xuvhmO885ffD8eQpCt/+evhGHNe+nE4xqG33gzHmDa5JxxD\nkvYcOR6O8Zn+6aH6U2cV83e+VlHLf56pWd+N1vbOG/duth54p+VZ13ghvda0qAEASWdTi/pcxhg1\nAAAlRosaAJB0Fq1Mdk4jUQMAcpFku4+ubwAASowWNQAgyb2gtb6ZTRZCogYAJDFGXQ4kagBAErdn\nlQNj1AAAlBgtagBAEl3f5UCiBgDkOlPLfyIfXd8AAJQYLWoAQNK4pPECGtTj8RDnNRI1ACCJMepy\nIFEDAJK4PascGKMGAKDE2mpRL+ibobG5F3R8sB9+69sd163q6y3mQe87Dh4Nx7h29rRwjO0FnMfn\nb1wUjiFJu555Oh6kgK/O+4+PhWNMX7AwHEOSxt7fFY7x1J98KxzjutnTwzFm9BTzvXznX34/HGNW\nAf+OxwpopU399PXxIJLmL/tqOMbT//2/herPOD6ueeGzOB2N4e6j6xsAkOTZf0XEQefo+gYAoMRo\nUQMAkphMVg4kagBAErdnlQOJGgCQRIu6HBijBgCgxGhRAwByFDPrm87vGBI1ACCJMepyoOsbAIAS\no0UNAEhiMlk5kKgBAEnjKuYRlTzmMoaubwAASowWNQAgh8sL6bem7zuCRA0ASGLWdzmQqAEASSTq\ncmCMGgCAEmurRd1zyaWyE4c7Ptglew91XLfqiplTwzEk6bNXXRaOsfP9D8Mxdh05Ho7xx0/9YziG\nJC2aMzMcY1ZvTzjG566+OBxj45Z/CseQpFuH4udy09z45zo2Hm+THCkghiRZATGmXDkvHGPmwk+G\nY4y9vyscQ5Ke/E//ORxj2X/496H61n9R+BzqcXtWOdD1DQBI8oKWEC1mGdLmzOwOVXraTVKfuz8Q\nrRMpN7M+SbdlbxdI6pe0xt33tXNddH0DAM56WcLsc/cN7r5e0nYzWxupEy2XtE7Sj9x9vbvfk23b\n1O61kagBAGn+cfd35HWGGtRrJD1+8tTdn5C0MlgnWj4oaXnN+22SljU5p9PQ9Q0ASDpbZn1nXcyD\n7r6jrqjfzBa5+9Z260jaHil3963u/tW6sgWSNrd6XVUkagBA0tmSqCUN5Wzfm5WdlqhbqJM3b7LV\n8lOOaWZDqrSml6cqNULXNwDgbDeQs320QVmzOtHyk7Kx7O9IWuXub+bUy0WLGgCQVBljLmDWdxZi\n6dKlD27ZsqV+xvNGd98YPkiJZRPN1pvZk2Z2g7vf3059EjUAIKnoru/h4eE7JT3bbP+sBfqVBoe3\nrGxNNkY8mrPfQIOyZnWi5SnrJG02s02Jse1cJGoAQKlUW6BtVBmRJDOb7e77a7b3V8varLNN0o5I\neTZZbb2kFTXl1XNZLmlDqxfHGDUAIMldGi/gNdErk2ULiIzo9HFjT834bqHO89FyVSaULasr78/+\nn/flIYlEDQBI8gJfZ8A6Sauqb7Lu8zU17wezbS3XiZS7+3OSHq3r4r5d0jPuPtzOhdH1DQBIOpuW\nEHX3DWZ2l5mtkDRH0oC731uzy3JJq1XTpd6sTrRc0tpspbKTS4yKBU8AAOerRmt75417N1sPPFKe\ndY/fk1feKhI1ACDJVdDTs+IhzmskagBALpJs9zGZDACAEqNFDQBI8oJurZro27POdW0l6n1vv61j\nI290fLCj4/Gf1odHjodjSNKMo0fCMaZPjndIfOHi2eEYX77mk+EYknT41VfCMaYNLQjHsKnTwjH+\n7bR4DEn69tMvh2MMTO0Nx/hXF88Kx5jek/cMgfZMu/zyeJDJ8c9kx1/9VTjGa/sPh2NI0pcWzgvH\nODz8g1D9SfMWqHfZN8LnUetsmvV9LqNFDQBIOouennVOY4waAIASo0UNAEhijLocSNQAgCS6vsuB\nrm8AAEqMFjUAIM1dTt9315GoAQBJ49mriDjoHIkaAJDEGHU5MEYNAECJ0aIGACRxe1Y5kKgBADmK\nWUKUzu8Yur4BACgxWtQAgCRXQV3f8RDnNRI1ACDp0gXXFJJkL11wTQFRzl8kagBAvQ8lfXTH7z40\no8CYH2Vx0aZWE/U0Seq9Yn7oYDMLeJb09Mk94RiS1DMr/vdvypH4M617euLTBCZdNj8cQ5ImH4t/\nd550efy5vDZlSjiGZsSf8y1JFx6I/3xmT4l/H548Z2Y4RlEmXXRROIZNnRqOMe1Y/GfTd+hoOIYk\n9Vx1STxIb+wZ3ZMuubL6xyIexv6WpOskXVhArKoPs7hok7W4PNw3JH1rgs8FABD3G5L+tNsngeK0\nmqjnSvqqpB2S4s1IAEDRpkmaL+kHknZ391RQpFYTNQAA6ALuowYAoMRI1AAAlBiJGgCAEiNRAwBQ\nYiRqAABKjEQNAECJkagBACix/w+3aW6u3xnEQQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3028bac828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pymks.tools import draw_differences\n",
    "\n",
    "\n",
    "draw_differences([strain[0] - strain_pred[0]], ['Finite Element - MKS'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The MKS model is able to capture the strain field for the random microstructure after being calibrated with delta microstructures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resizing the Coefficeints to use on Larger Microstructures \n",
    "\n",
    "The influence coefficients that were calibrated on a smaller microstructure can be used to predict the strain field on a larger microstructure though spectral interpolation [3], but accuracy of the MKS model drops slightly. To demonstrate how this is done, let's generate a new larger random microstructure and its strain field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(63, 63)\n"
     ]
    },
    {
     "data": {
      "image/png": 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r9hyn9Tnv8u08cOA5Tu2mb3+Xtn161jO0frKUb8/WRd2d2ozfPEXbZnVo4dSG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\nyHQRyTLGv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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3029b5b390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = 3 * n \n",
    "size = (m, m)\n",
    "print(size)\n",
    "X, strain = make_elastic_FE_strain_random(n_samples=1, elastic_modulus=elastic_modulus,\n",
    "                                          poissons_ratio=poissons_ratio, size=size, \n",
    "                                          macro_strain=macro_strain)\n",
    "draw_microstructure_strain(X[0] , strain[0])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The influence coefficients that have already been calibrated on a $n$ by $n$ delta microstructures, need to be resized to match the shape of the new larger $m$ by $m$ microstructure that we want to compute the strain field for. This can be done by passing the shape of the new larger microstructure into the `resize_coeff` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.resize_coeff(X[0].shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now take a look that ther resized influence coefficients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+5EDmtDRX3FYELQEAAADoz2g0kJ6WA6hjh1w9AAAAAGCn6GnJkVULliBftDL5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\nAAAA7BRBSwAAAABgpwhaAgAAAAA75f8HGGbXoHdA6jcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3029a83470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_coeff(model.coef_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the coefficients have been resized, they will no longer work for our original $n$ by $n$ sized microstructures they were calibrated on, but they can now be used on the $m$ by $m$ microstructures. Just like before, just pass the microstructure as the argument of the `predict` method to get the strain field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WnOT7vniXLbveYJuTAGBwf5sPcjZxU5pWLWwZhw5aYxwASAw4fiwfdOrE71fb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\nDJvgSYkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3029b64f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "strain_pred = model.predict(X)\n",
    "\n",
    "draw_strains_compare(strain[0], strain_pred[0])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, let's plot the difference between the two strain fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rG7hC/urQtSDWYKjYl2d5GzYYat5Gcu79Kld9r148R+P//ONXaPy+ttAGssZQ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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3029e37f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_differences([strain[0] - strain_pred[0]], ['Finite Element - MKS'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the results from the strain field computed with the resized influence coefficients is not as accurate as they were before they were resized. This decrease in accuracy is expected when using spectral interpolation [4]."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## References\n",
    "\n",
    "[1] Binci M., Fullwood D., Kalidindi S.R., A new spectral framework for establishing localization relationships for elastic behavior of composites and their calibration to finite-element models. Acta Materialia, 2008. 56 (10) p. 2272-2282 [doi:10.1016/j.actamat.2008.01.017](http://dx.doi.org/10.1016/j.actamat.2008.01.017).\n",
    "\n",
    "\n",
    "[2] Landi, G., S.R. Niezgoda, S.R. Kalidindi, Multi-scale modeling of elastic response of three-dimensional voxel-based microstructure datasets using novel DFT-based knowledge systems. Acta Materialia, 2009. 58 (7): p. 2716-2725 [doi:10.1016/j.actamat.2010.01.007](http://dx.doi.org/10.1016/j.actamat.2010.01.007).\n",
    "\n",
    "\n",
    "[3] Marko, K., Kalidindi S.R., Fullwood D., Computationally efficient database and spectral interpolation for fully plastic Taylor-type crystal plasticity calculations of face-centered cubic polycrystals. International Journal of Plasticity 24 (2008) 1264–1276 [doi:10.1016/j.ijplas.2007.12.002](http://dx.doi.org/10.1016/j.ijplas.2007.12.002).\n",
    "\n",
    "\n",
    "[4] Marko, K. Al-Harbi H. F. , Kalidindi S.R., Crystal plasticity simulations using discrete Fourier transforms. Acta Materialia 57 (2009) 1777–1784 [doi:10.1016/j.actamat.2008.12.017](http://dx.doi.org/10.1016/j.actamat.2008.12.017)."
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