05 Simulate Data - Strain Mapping.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial demonstrates different routes to obtain strain maps from scanning electron diffraction data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code functionality is illustrated using synthetic data, which is first generated using pyxem. This synthetic data represents a simple cubic crystal that is distorted to a tetragonal stucture. The intention is for this to provide an easy to understand illustration of the code functionality rather than to model any physical system."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This functionaility has been checked to run in pyxem-0.15.0 (April 2023). Bugs are always possible, do not trust the code blindly, and if you experience any issues please report them here: https://github.com/pyxem/pyxem-demos/issues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Contents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. <a href='#gen'> Setting up & Creating Synthetic Data</a>\n",
    "2. <a href='#aff'> Image Affine Transform Based Mapping</a>\n",
    "3. <a href='#vec'> Vector Based Mapping</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='gen'></a> 1. Setting up & Creating Synthetic Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import pyxem and other required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:silx.opencl.common:Unable to import pyOpenCl. Please install it from: https://pypi.org/project/pyopencl\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pyxem as pxm\n",
    "import numpy as np\n",
    "import hyperspy.api as hs\n",
    "import diffpy.structure\n",
    "from matplotlib import pyplot as plt\n",
    "from pyxem.generators.indexation_generator import IndexationGenerator\n",
    "from diffsims.generators.diffraction_generator import DiffractionGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "hs.set_log_level('ERROR') # removes Warnings from hyperspy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a structure for the creation of synthetic data.  We will start by showing how changing\n",
    "the lattice parameter changes the diffraction spots. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "latt = diffpy.structure.lattice.Lattice(3,3,3,90,90,90)\n",
    "atom = diffpy.structure.atom.Atom(atype='Ni',xyz=[0,0,0],lattice=latt)\n",
    "structure = diffpy.structure.Structure(atoms=[atom],lattice=latt)\n",
    "\n",
    "latt = diffpy.structure.lattice.Lattice(3.2,3.2,3,90,90,90)\n",
    "atom = diffpy.structure.atom.Atom(atype='Ni',xyz=[0,0,0],lattice=latt)\n",
    "structure2 = diffpy.structure.Structure(atoms=[atom],lattice=latt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simulate an electron diffraction pattern.  This just calculates the electron diffraction spots which should be visable based on the paramters given.  We simulate a 300 keV beam here. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ediff = DiffractionGenerator(300.)\n",
    "diffraction = ediff.calculate_ed_data(structure,\n",
    "                                      reciprocal_radius=5.,\n",
    "                                      max_excitation_error=0.025, shape_factor_width=5.0,\n",
    "                                      with_direct_beam=False)\n",
    "strained_diffraction = ediff.calculate_ed_data(structure2,\n",
    "                                               reciprocal_radius=5.,\n",
    "                                               max_excitation_error=0.025, shape_factor_width=5.0,\n",
    "                                               with_direct_beam=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check we have some spots. Notice that the material is strained in both x,y so the diffraction spots are all at smaller reciporical spacing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff7b07541c0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xbNgw921vvvkmXbt2JSQkhC5duvDqq6+etEw++ugjhg4dSkhICO+9995JHxtlZGQwatQoYmNjCQ8PZ8CAASxevNij59nQqLyIiEidhIeHEx4ezvz586v9QO4vrVu3DoBZs2aRnZ3tvgywZ88ePvnkE+bOnesuI8XFxUycOJH169ezZMkS/Pz8uOKKK3A6nTVm+etf/8rzzz/P+vXrCQgI4NZbb3Xf9t133zFmzBjuv/9+tm/fzmuvvUZqaipPPfUUAE6nkyuvvJKgoCDWrFnDzJkzefTRRz1eDrfeeiuzZs1yX3777be55ZZbTpqutue2du1aABYvXkx2dra7EL333ntMmTKFp556ih07dvDPf/6Txx57jNmzZ1e7/0mTJnH//fezY8cOUlJSTnr8oqIiLr30UpYsWcKmTZsYPnw4I0eOrPEXuhsso5EpKCgwAKOgoMDsKCIiHiktLTW2b99ulJaWnpk7LDlmGBlLDaPk+Jm5vxr897//NZo2bWqEhIQYQ4YMMSZPnmxs3ry52jSAMW/evGrXPf7440ZgYKBx+PDhGu//yJEjBmBs2bLFMAzDyMzMNABj06ZNhmEYxtKlSw3AWLx4sXuezz//3ADcy/Oiiy4y/vnPf1a733fffdeIj483DMMwvvrqKyMgIMA4ePCg+/Yvv/zylLl/aezYscaoUaOMw4cPG8HBwUZWVpaRlZVlhISEGEeOHDFGjRpljB07ts7P7YT27dsb77//frXrnnzySWPw4MHV5nvxxRerTTNr1iwjKirqtI9vGIbRrVs34+WXX3ZfTkpKMqZPn17jPL9FTeu6N9tvjbyIeCG/pIL9x0rMjlFnx4sr2HqwAMOiX6xtL6tk477jOJzWzF9W6WDTvuNUOmoeRfhNSo/Dq4PhnVHw6jlQmn/G7towDMoqHTh/sf5cddVVHDp0iE8//ZThw4ezbNky+vbtS2pqaq33l5SURExMTLXrdu/ezQ033EC7du2IjIx0f8xU2+hAz5493edP/Jjv4cOHAdi8eTNPPPGEe6QoPDyc8ePHk52dTUlJCTt27CAxMZGEhAT3fQwePLjW/CfExMRw2WWXkZqayqxZs7jsssto0aLFSdPV5bkVFxeTkZHBbbfdVi3/P/7xj2ofOQH079+/xpxFRUU89NBDdO3alejoaMLDw9mxY4clR14axh5PIhZQWFbJsGeXkV9ayexbBzK0U0ztMzUgZZUOLpn+LUeKKnhiVDfGDG5jdiSvGIbBH19eQVZeCXcObcfkEV3NjuS1W1PXsSojj2v6teLZa3r55kGyN0Nhtut8YTZkp0G7YWfkrg/ll5FXXE5UaCBJzZu4rw8JCeHiiy/m4osv5rHHHuP222/n8ccfr/VImyZNmpx03ciRI0lKSuKNN94gISEBp9NJ9+7da92hNzAw0H3eZrMBuD+OKSoqYurUqVx55ZUnzXemfo7h1ltv5d577wVgxowZp5ymLs+tqKgIgDfeeINBgwZVu83f37/a5VMtz1966KGHWLRoEc899xwdOnQgNDSUq6++2uOdpRsSlRcRD5VWOrCXVQKQay8zOY33Kh1O8ktd+XMKrJffacDRIteLbK4F88PPy92nyz++N0TEu4pLRILr8hlyYsSo0lHzyNfZZ59d7dDowMBAHA5Hrfefl5dHeno6b7zxBueffz4AK1asqHvgn/Tt25f09HQ6dOhwytu7du3K/v37yc7Odo/afP/99149xvDhw6moqMBms51yfxNPnltQUBBAtWUVGxtLQkICP/74IzfddJNXmX5t5cqVjBs3jiuuuAJwFaOsrKzfdJ9mUXkR8VDLiBA+unMwB4+XMrJXQu0zNDARIYF8MP4cth4s4NoBiWbH8Zq/n40Pxp/DqoyjXNPfevkB3ho3gMXbc/ljbx+uP6HR8H/fu0Zc4nu7Lp8hrZqGUlAaQESIa5QjLy+Pa665hltvvZWePXsSERHB+vXreeaZZxg1apR7vjZt2rBkyRLOPfdcgoODadq06Snvv2nTpjRv3pzXX3+d+Ph49u3bx6RJk35z7ilTpnD55ZfTunVrrr76avz8/Ni8eTNbt27lH//4B8nJyXTq1ImxY8fy7LPPYrfb+etf/+rVY/j7+7Njxw73+bo8t5YtWxIaGsrChQtp1aoVISEhREVFMXXqVO677z6ioqIYPnw45eXlrF+/nuPHjzNx4kSPM3bs2JG5c+cycuRIbDYbjz32WK07QjdU2udFxAsD2jRjdJ+z8PezmR2lTvq3aca4c9sSFmTN9y09WkVx59D2NGsSZHaUOmnbognjL2hHbKSPfzk6NNr1UdEZLC4AAf5+NA8PJijAtekIDw9n0KBBTJ8+nQsuuIDu3bvz2GOPMX78eF555RX3fM8//zyLFi0iMTHxpMOHf8nPz48PP/yQDRs20L17dx544AGeffbZ35w7JSWFzz77jK+//poBAwZwzjnnMH36dJKSktyPO2/ePEpLSxk4cCC33367+0gkb0RGRp7215A9eW4BAQG89NJLvPbaayQkJLgL4O23386bb77JrFmz6NGjB0OHDiU1NdV9aLWnXnjhBZo2bcqQIUMYOXIkKSkp9O3b1+vn2RDYDKvuuXca3vyktohIQ1BWVkZmZma17zsRaYxqWte92X5r5EVEREQsReVFRERELEXlRURERCxF5UUansJcOL7X7BTeKTgAhzaZncI7+fshYylY6WiDwhxIXwhVp/4q+gap9DjsXgQVFvpyQ8MJ5YXgrP3w5gbDMKCy1Frrs9SZyos0LIW58FIv+HcvOLjR7DSeKTkGrwyE14fBlv+ancYz5UXw/4bAu6Phu+fNTuMZRxW8dgF8cB18+YjZaTw3+4/w3tUw7w6zk3ju+F7I2wPHM81O4rmiHDiyE45l1D6tWJ41j5eUxquq1PXuCaDcbm4WT1WVQ9VPXzpWetzcLJ5yVEDlTyMBZfmmRvGY4YQK17eNnsmvvPe5E8vXg3WjwRz8eWLExUojL+7MVTVPJ6Y6U+u4yos0LE3bwPhvoMx+xr7S3Oci4+GWLyF/L3S/yuw0nglrBuO+cH2VfJ/f9q2d9SYgCMZ9DlkroNeNZqfx3J/mwe6voNvJX01/womvti8pKSE0NLS+kp1e0yQoK4AQC33dREQCBDVxnaTBOvFTBKf6Ij9v6HteREQagOzsbPLz82nZsiVhYWHu3+cRaSycTieHDh0iMDCQ1q1bn7SOe7P91siLiEgDEBcXB/z8S8gijZGfn98pi4u3VF5ERBoAm81GfHw8LVu2pLKy0uw4Ij4RFBSEn99vP1ZI5UVEpAHx9/f/zfsDiDR2OlRaRERELEXlRURERCxF5UVEREQsReVFRERELEXlRURERCxF5UVEREQsReVFRERELEXlRURERCxF5UVEREQsReVFRERELEXlRURERCxF5UVEREQsReVFRERELEXlRURERCxF5UVEREQsReVFRERELEXlRURERCzFp+Vl+fLljBw5koSEBGw2G/Pnz691nmXLltG3b1+Cg4Pp0KEDqampvowoIiIiFuPT8lJcXEyvXr2YMWOGR9NnZmZy2WWXceGFF5KWlsaECRO4/fbb+eqrr3wZU0RERCwkwJd3PmLECEaMGOHx9DNnzqRt27Y8//zzAHTt2pUVK1Ywffp0UlJSfBVT6tG6rGOUVDgY2inG7Ch18u2uIxw8XsrV/VoRFGC9T10Xbc9l+yE7Nw9OolmTILPjeG1Z+mFW/5jHTQOTaN08zOw4Xtu8P59F23MZ3ecsOrQMNzuO1w7ll/LFlmwu6hpL2xZNzI7jtdIKB19vz6FfUlNaNbXe+iM/82l58dbq1atJTk6udl1KSgoTJkw47Tzl5eWUl5e7L9vtdl/Fk99oR7ada2auBmDWLQO4sHNLkxN5Jz2nkLFvrwXgeEkF91zYweRE3sk4UsT4d9YDsOdwIS/f2NfkRN45bC/j1tR1OA1YnZHHp/eeZ3Ykr1Q6nNz4xvcUVzj4dPMhlj9yodmRvHbnuxvYcrCAt1dmsmrSRWbH8do/Pt/Oe2v2ER8VwurJ1ssvP2tQbx1zcnKIjY2tdl1sbCx2u53S0tJTzjNt2jSioqLcp8TExPqIKnUQ6O+Hn811PtiCoxaB/jZr5/f7efmHBPqbG6YO/P1s+P/0BEIsuPxt4B6tCwm0Xn74OXdIgPXWH/h5vQ+14Pov1TWokZe6mDx5MhMnTnRfttvtKjANVIeW4Xz9wAWUVTrpflaU2XG81i4mnHn/dy7ZBWVccnZs7TM0MK2bhzHnzsHszCnkij5nmR3Ha83Dg5lz52DWZx1ntAXzB/j78cndQ/h21xFGdI83O06dvH5zf5amH+a8Di3MjlInk0Z04cLOLemWEGl2FPmNGlR5iYuLIzc3t9p1ubm5REZGEhoaesp5goODCQ4Oro94cgZ0aBlhdoTfpFdiNL0s3I37t2lG/zbNzI5RZ31aN6VP66Zmx6izdjHhtIux3r4uJzRtEsSVfVuZHaPOAv39OK+jNYuXVNegxi4HDx7MkiVLql23aNEiBg8ebFIiERERaWh8Wl6KiopIS0sjLS0NcB0KnZaWxr59+wDXRz5jxoxxT3/XXXfx448/8sgjj7Bz505effVV5syZwwMPPODLmCIiImIhPi0v69evp0+fPvTp0weAiRMn0qdPH6ZMmQJAdna2u8gAtG3bls8//5xFixbRq1cvnn/+ed58800dJl1XTgd8+yx89wI4nWan8YyjChZNgU/vgzKLHDnmqILPHoB3RkNehtlpPOOognl3w6vnQOZys9N4xul0LecXe8KW/5qdxnPLn4WX+8HGd81O4rlt8+CtSyDtfbOTeO5QGswZA1s/MTuJ5wpz4YtHYOfnZiexHJthGIbZIc4ku91OVFQUBQUFREb+znfKyvgG3r3Cdf6WhZBkgY/fflwG74xynb/seRhwu6lxPPLLzP1vhcunmxrHI1krIfVS1/nW58CtFvgiyJytMPNc1/nIBJi4w9w8nijNh6eTXOeDwuEvB02N47Fn2kPJUQgIgb/l1j59Q/DOaPhxqbUyf/03WPUy+Ae7MttsZicylTfb7wa1z4ucYbE9oFk7aNEJYjqbncYzLc+GiHjXC33iOWan8UzLsyGsOWCDNuebncYzLTpB6E877rYdam4WT0W3hvA413mrLOfgCIjt7jpvlcwAHX76DpR2w0yN4ZVOwwEbdLzE7CSea38RBEfC2aN+98XFWxp5kYbH6XCdAiz0DbAVJVBZAk0sdCRDaT4U5riKrVVeOMsKXB/NxfcCP4t8V0dlGeTthpiu4N+gDvA8PcMA+0GISAA/C73HrSyDgGDrrM9SjTfbb4v8S5LfFT9/62yYTggKc52sJDTadbKSkCg4y1rfDExgCMT1MDuFd2w2iLLgIdGBIWYnkHpioUotIiIiovIiIiIiFqPyIiIiIpai8iIiIiKWovIiIiIilqLyIiIiIpai8iIiIiKWovIiIiIilqLyIiIiIpai8iIiIiKWovIiIiIilqLyIiIiIpai8iIiIiKWovIiIiIilqLyIiIiIpai8iIiIiKWovIiIiIilqLyIiIiIpai8iIiIiKWovIiIiIilqLyIiIiIpai8iIiIiKWovIiIiIilqLyIiIiIpai8mIhTqfBe2v2MnfjAQzDMDuO1xxOg5eX7ObphTspq3SYHcdr5VUO/jZ/C7emriPzaLHZcbxWWuHgjnfWM+zZpXy9LcfsOF4rq3Rw++x19H7ia9787kez43jNMAz+8fl2zv3XN7z2bYbZcerkiy3Z3PD698xZt9/sKHWy53ARk+duYdH2XLOj1ElhWSWvLtvD6ow8s6OYLsDsAOK5ZbsO89d5WwFoFxNO78RocwN5afGOXJ5ftAuA2Ihgxp3b1uRE3lm4NYf/fL8PgLAgf165sa/Jibzzvx8O8fVPL9qPzd/KJd3iTE7knUXbc1m84zAAT32xgxsGtqZJsHVewnZkF/Lmd5kATPtyJ9f0T6RZkyCTU3nOMAwe+CiN8ion32fmMaJHHBEhgWbH8spj87ey+sc85qzfz7apKYQE+psdySszv81gxtIMgvz92Do1haCA3+/4w+/3mVtQ62ZNCA30JzIkgPioELPjeC2peRiB/jZsQIeWEWbH8VrrZmHYbK7z7WLCzQ1TB62bhbnPJzUPq2HKhqlV01AAbEDzJkEEW+yFu0XEz5mjwwIJC7LWhtNms7nXm5YRwZbb8AN0jnO97rRuFkagv7XWH4Cu8ZE/vX6GE+BnMzuOqWyGFT9/qIHdbicqKoqCggIiIyPNjnPGFZVX4W+zEWqxF74TsgtKqahyktS8idlR6mTboQIO28sZ2ikGPwu+eHy3+wi7c4u4su9ZRIdZ513/CcvSD7NpXz5X9DmLNi2stw5tO1TAyj1HueTsOEvmLyipZGXGUQa0aUZMRLDZcbzmdBpsz7bTtkUTS43a/dLx4goiQgIIsGD5qo0322+Vl8auqsL1N8BCG6riPHCUQ2SC2Uk8d2QXFOVC0rngZ5EXlb2r4Wg6dLsCQqLMTuOZzO/g4AbofiVEtzY7jWeOpEPWCuiUAlGtzE7jmTI77PseWvWHsGZmp/GMYUDeHtd6EWChYlVRDAGh1nnd8CFvtt/WrJ7imWM/wmtDwS8A7lwO0YlmJ6rd4Z3w+lBXebnxv9Ax2exEtTu4Ed5MBsMB5z8EFz1mdqLaZa2E1Etd5zd/CLcuNDePJ/avg9kjAQO+fxUmbG34pdx+CF67AKrKYGkLeGArBIaanapmTie8dTEc2QlRiXDfJvC3wL4tXzwM696Alt3grhXWKANb/gtzx0OrAXDrV7g/l5ZaWeD/rtRZzlYot0PpMdcLkRXkbnW90BuG6x22FeRucxUXgEMbzc3iqcPbfz6fu9W8HN44shP4aaC4KBfKC02N45Hje13rM0DJUSg5Zm4eTzjK4ahrx3oK9kNZgbl5PLVvlevv4e1QaZGjAbNWgOGEA+t/Xk/EIxp5acw6j4Chj7pGXtr/wew0nulyOfS7FcoLYMBtZqfxTLfRsPMzyN8Hw/5idhrPdL8KNv3HtZFKnmp2Gs90vRzWzHSVrUF3QZPmZieqXasB0PWPsOsr6DcOos4yO1HtAkPhkqdg7WvQ+0/QpIXZiTwz4llY8YLrNSTYIgcEXPAw+PlDm/Ma/ohcA6N9XkTEOgwDHJUN/+MiEfGaN9tvfWwkItZhs6m4iIjKi4iIiFiLyouIiIhYisqLiIiIWIrKi4iIiFiKyouIiIhYisqLiIiIWEq9lJcZM2bQpk0bQkJCGDRoEGvXrj3ttKmpqdhstmqnkBDr/YKyiIiI+IbPy8tHH33ExIkTefzxx9m4cSO9evUiJSWFw4cPn3aeyMhIsrOz3ae9e/f6OqaIiIhYhM/LywsvvMD48eO55ZZbOPvss5k5cyZhYWG8/fbbp53HZrMRFxfnPsXGxvo6poiIiFiET8tLRUUFGzZsIDn5518G9vPzIzk5mdWrV592vqKiIpKSkkhMTGTUqFFs27bttNOWl5djt9urnURERKTx8ml5OXr0KA6H46SRk9jYWHJyck45T+fOnXn77bdZsGAB//nPf3A6nQwZMoQDBw6ccvpp06YRFRXlPiUmJp7x5yEiIiINR4M72mjw4MGMGTOG3r17M3ToUObOnUtMTAyvvfbaKaefPHkyBQUF7tP+/fvrObGIiIjUpwBf3nmLFi3w9/cnNze32vW5ubnExcV5dB+BgYH06dOHPXv2nPL24OBggoODf3NWERERsQafjrwEBQXRr18/lixZ4r7O6XSyZMkSBg8e7NF9OBwOtmzZQnx8vK9iioiIiIX4dOQFYOLEiYwdO5b+/fszcOBAXnzxRYqLi7nlllsAGDNmDGeddRbTpk0D4IknnuCcc86hQ4cO5Ofn8+yzz7J3715uv/12X0cVERERC/B5ebnuuus4cuQIU6ZMIScnh969e7Nw4UL3Trz79u3Dz+/nAaDjx48zfvx4cnJyaNq0Kf369WPVqlWcffbZvo4qIiIiFmAzDMMwO8SZZLfbiYqKoqCggMjISLPjiIiIiAe82X43uKONRERERGqi8iIiIiKWovIiIiIilqLyIiIiIpai8iIiIiKWovIiIiIilqLyIiIiIpai8iIiIiKWovIi4gXDMKhyOM2OUWf7j5WwNvMYlRZ9DqsyjjJ7VRb78krMjlInx4orWJ2RR2FZpdlR6syq6440Lj7/eQA5c4rLq3jis+2EBvrzl0u7EhRgre55vLiCe97fSEmFg5dv6ENiszCzI3llb14x1732PXnF5Tx3TS9G9T7L7Ehe+W73EcbNWofDaTCobTM+GH8Ofn42s2N57L8bDvDQx5sBeCZoJwsnXGCpdWj/sRIue+k77GVVxEeF8OX95xMdFmR2LI8ZhsHEOZuZt+kgF3aO4c2xA/C30PoDsHBrNs8sTOfis2OZfGlXs+N4LetoMf9auJOBbZpx63ltzY5jKmtt/X7nvtqWw0fr9pO6KotVGUfNjuO1z7Zksyojj7T9+Xy4bp/Zcbw2f9MhcuxlVDoM/t+yDLPjeG3exoOc+DWQNZnHOHCsGCpLTU7luf9tPkRzCuhl20NVRSnf7jpidiTPGAZUFPPVthzsZVUAZBeUsSojz+Rg3sm1lzNv00EAlqYfYWeO3eRE3ntmYTo/Hi3mteU/kmsvMzuO195c8SMLt+bwxGfbKSi17ujdmaDyYiED2jSjZUQwrZuF0eOsqNpncDphQyps+o/rBdRkA9s0IzTQnwA/G+e2b3Hqiaoq4Jt/wJePQml+vearTf82TbH99Ebz3PbNf76hNB8+vAleHwb7vjcjmkd6tIrCaYCfDfqH5ZI4qy88FQdfPNIg1o/apITtYlXwn1kQPIUvgybRranD7Ei1KzkGM8+DfyZwzbZ7CaISPxsE+NnoHBdhdrrTWz0DZpwD3z7jvqp5eBBtmrtGulqEB9G6oY16HdoE8+6GrXNPO8nFZ7t+ELhrfATNmzSAUa/S4/DdC5C1wqPJL+oaS2igP0M7xRAZ8vv+4EQ/zNiYbV8Ac8a4zt84BzqlmJsHKCqvwuEwiAoLPPUEG9+FT+8FbHDu/XDx1HrNV5tduYUcLSznnHbNf/7IZfmzsPSfrgIQ0xnuWWNuyNMwDINPNh5kb14xd+Y9Q/iueWD8VAD+vBGatzc3YC2cs0dhy/wWGwYGYLv0ORg43uxYNVv5EiyaArheZred9zKfVw3koq6x9Etqam6208nfBy/2+Pny3asgthsA9rJK1mcdo1eraJqHB5sU8DRe6gPHfgRs8NAuCG95ysly7WU0bxJEgH8DeO/+v/tdbzD9AuDRLAhuwIW2Hniz/f59V7fGLjwWsIENaBJjdhoAwoNrWeXcLzjGaV98zNQpNoJOsb96gQlrDoYTbH4NZjmfis1m4+p+rVwXvmzBiQ0qNj8IamJaLk/5hUa5shoObAAhHow+mi00GvdyBrq1S6Jbuy6mxfFIYBPwDwJHpWt5B/+8EYkMCeQPXWJNDFeD8FhXeQlqAoGhp50sNjKkHkPVIiLe9Te0mWuZi8c08tLYHUkHmz+06GB2Es9lLIXyQuhyOfg1gHdHtXE6Yd0bYD8I59wDEQ30xf2XSvPhswcgbw+cNwG6X2V2otod3wsf3ghHd7ny/vEV8G/g778cVbDoMchcDt2vhPMm4v7ssSHbu9o1ctspBdpfaHYaz5Qcg/QvIPEc67zeOZ1wYB007wBNmtc+fSPnzfZb5UVERERM58322wJva0VERER+pvIiIiIilqLyIiIiIpai8iIiIiKWovIiIiIilqLyIiIiIpai8iIiIiKWovIiIiIilqLyIiIiIpai8iIiIiKWovIiIiIilqLyIiIiIpai8iIiIiKWovIiIiIilqLyIiIiIpai8iIiIiKWovIiIiIilqLyIiIiIpai8iIiIiKWovIiIiIilqLyIiIiIpai8iIiIiKWovIiIiIilqLyIiIiIpai8iIiIiKWovIiIiIillIv5WXGjBm0adOGkJAQBg0axNq1a2uc/uOPP6ZLly6EhITQo0cPvvjii/qIKSIiIhbg8/Ly0UcfMXHiRB5//HE2btxIr169SElJ4fDhw6ecftWqVdxwww3cdtttbNq0idGjRzN69Gi2bt3q66giIiJiATbDMAxfPsCgQYMYMGAAr7zyCgBOp5PExET+/Oc/M2nSpJOmv+666yguLuazzz5zX3fOOefQu3dvZs6cWevj2e12oqKiKCgoIDIy8sw9ETkjHE4DwzAI8K+hN1dVgOGAwND6C+ah/cdKyC+ppPtZkdhstp9vOLoHig9D4jng1zA/ja2ocpK6KpODx0u5plcM3fe9A/n7oPdN0Pocs+M1ToYBG1Lh4HqM9snMrxzAhr3HGdapJclnx5qd7vTKCuDgRojvBWHN3FeXVjjYeqiALnERRIQEmhjwNMrsEBwBv/y3+SvlVQ6C/P2q//uVBsGb7bdPX2UrKirYsGEDycnJPz+gnx/JycmsXr36lPOsXr262vQAKSkpp52+vLwcu91e7eQTRUdg9h9h3l3gdPjmMWqRay/j8pe+4+r/t4qC0sraZyg4CDMGwf87F4pOPdJVn348UsSApxbTa+rXbNh7/NQTHd4Jz3WEfyVBxjf1G7AWq/YcZdizyxj5ygr++cWOn2/YvRhm9IdZI+CzCablq81Tn29n2hc7+c+afWx7+y6Mb/4Jm96D1MshL8PseI3Tujdd60TaB9j+O47/fTyLD9bs5/Z31vPd7iNmpzu18iLXa8a7o12vHyXHXFdXObj85e+4ZuZqLpm+3LPXoPr09RT4VyK8dQk4Tp1txtI9dPnbQm58Yw0Op0/ft3tm9yJ4rhP893ZX0a3FuqxjnP/MNzz88WZ8PO5wescy4a0U+HKyOY//E5+Wl6NHj+JwOIiNrf4OIzY2lpycnFPOk5OT49X006ZNIyoqyn1KTEw8M+F/bffXkPktbP4A8vb45jFqsXTnYbYesrN+73HWZh6rfYbdX8ORnZC7tUEUgW92HuZYcQXFFQ6+2JJ96ol2LYSyfHCUw9a59ZqvNot3HMaJ6wXjsx9+kX/XQuCnd3E7Pq3/YB5alZGHgWv0axDbsOF0jXA5K+HQJrPjNU57V4HNDwwHTvwY6JeOwzDws8E6T/4Nm+HITijY7zpffNi9buzNKyHjSDEA2QVlbDtUYFbCU9s42/X3wFrXBvYU3vt+Lwaw+sc8Dhwvqb9sp7PxHSjKha0fQ3HtZfbj9fvZf6yUjzcc4FhxRT0EPIXt82H/97DmVSg9zZvQetAwx7e9MHnyZAoKCtyn/fv3++aBOo+ALpfBgPHQvKNvHqMWyWfHMrBNU4Z2imFw++a1z9D5UjirPyQOgg4X+z5gLVK6xREfFUJ0WCCje5916om6joQmLSGoCfS6oX4D1mJEjzgC/Fwl5Zp+rX6+oetI1wYKoOd1JiTzzLDOMQD422ystPXCwAY2fwgIhlb9TU7XSLUbCoYTbP744WSlsxv+NhuGAUM6tDA73am1PBuad3Cdj2oFZ/UDoG2LJpyd4BrKb90sjJ6tok0KeBrn3A3YoN0waN7+lJPcdn47Av1tXNSlJYlNw+o13ikNuB2atoV+t0CTmFonv3FQEp3jIhg3uA3NmgTVQ8BT6HGNa3tywcMQ2tScDPh4n5eKigrCwsL473//y+jRo93Xjx07lvz8fBYsWHDSPK1bt2bixIlMmDDBfd3jjz/O/Pnz2bx5c62PqX1eGgGnEzDAz9/sJCc5UliOvayS9jHh1W8oOAAleRDXs8bP283kcBp8sHYfB/NLuaJnSzrt/dD1DrvndZDQ2+x4jZNhwJaP4cB66JDMwoqebNp3nAs6xXBuQy0vABUlkLsNWnZx7UNy4uoqJ7sPF9I+JpyQwIb37xNHFfgHmJ1C6sib7Xe97LA7cOBAXn75ZcC1w27r1q259957T7vDbklJCf/73//c1w0ZMoSePXtqh10REZFGypvtt88r6sSJExk7diz9+/dn4MCBvPjiixQXF3PLLbcAMGbMGM466yymTZsGwP3338/QoUN5/vnnueyyy/jwww9Zv349r7/+uq+jioiIiAX4vLxcd911HDlyhClTppCTk0Pv3r1ZuHChe6fcffv24feLQ0uHDBnC+++/z9/+9jf+8pe/0LFjR+bPn0/37t19HVVEREQswOcfG9U3fWwkIiJiPQ3me15EREREzjSVFxEREbEUlRcRERGxFJUXERERsRSVFxEREbEUlRcRERGxFJUXERERsRSVFxEREbEUlRcRERGxFJUXERERsRSVFxEREbEUlRcRERGxFJUXERERsRSVFxEREbEUlRcRERGxFJUXERERsRSVFxEREbEUlRcRERGxFJUXERERsRSVFxEREbEUlRcRERGxFJUXERERsRSVFxEREbEUlRcRERGxFJUXEbEOwwBHpdkpRMRkKi+N3aFNkP2D2Sk8Zxiw83NI+wAcVWan8YyjCr57AT5/CPL3m53GM8V58N618FJf2PQfs9N4Ji8DXuoDT7aAj/4EVRVmJ6pdVQV8eh/8uxcsedK1fltBxjew4F5I/9LsJJ4rOgxrXoOcrWYn8ZzT4VrW9kNmJ7GcALMDiA9lfgezL3edv20xJA4wN48ndn4OH93kOl+UC+dNMDWORzbMgiVTweYHOVvgtq/MTlS7Zf+EPYvBcLg2Uu3/AJEJZqeq2aIpkL/PdX7H/2Drf6H3jeZmqk3af2DjbNf5756DpMHQIdncTLUpOgLvXePasKb9B+5Lg6ZtzE5Vuw9ugIPrITAUHkyHkCizE9Vu6VPw3fMQ2gwm7oDAELMTWYZGXizG4TRwOj1891ZW8PP58oLTT1eP8orKybWXnX6CcvupzzcQPxzIZ/H2XKoczp+vLC8EbGAAFYVmRauVw2nwzuosnvjfduz2Xy5bA6pq+H/SQDgqSnAaruVuAFSWmprHI5WlgM19cWPGIf7+6TZW7TlqXqbaOMrBWQUYrpGiyp/Xjbyicj774RA5BQ1wfTnxeldVftqPFg3D4McjRZRVOuoxWA1K811/K4p/Wua1q3Q4MawygudDGnmxkMyjxVwxYyVBAX589ufzaBlZS0vvchlc+Sb4+UP7i+onZA22HizgildX4nAavD1uAMM6tzx5oh7XukZcygvhvAfqP2QNlqUfZtysdQD8aVBr/nFFD9cNA8fD0V1QsB8u+YeJCWv21oof+ecXO/G32VgXfB4LotfiV7Afzr0fmrUzO16t3gm8lmuNlTSxlZPuTMQe/gcGmh2qNn3+BNsXwP615CVezLVLo3DasnhndRYLJ1xAp9gIsxOeLKoVXD4d0t6HbldAyy4AVFQ5ufzlFWQXlBEdFsi3D19IVGigyWF/4dp3YP3brpGtJi1OOcnfP93G7NV7aRfThK8mXECgv8nv3y+eCjGd4az+EBxe6+Rfbcvhnvc20qd1NB/dMRg/P1ut8zRWKi8Wkrb/OPmlrncUO3IKay8vNhv0vKYeknlm84F8Kh2udwzrs46furz4BzS40nLCDwcKsOF6179h3/GfbwiOgCtmmhXLYzuzC/GzgcMw2FLWgoNjVpMYHQJ+1hiAXVbanhfKZxBnO0amEcdUu1/DLy8hUXDb1+B0Mn/VXqp2b/9p2AgyDhc1zPIC0P9W1+kXjpdUkP3TiEt+SSUHj5c2rPISezZc9lyNk6z4acTrxyPFHC+uqP011NeCI2DQnR5PvmL3UaqcBuuyjlNYXtWwln89s8arlgAwons8t5zbhruHtefc9s3NjuO1y3smcMnZsZzXoTk3ndPa7Dheu6pfK5Kah9EkyJ/7L+podhyvXTsgkYCfisrQTjG0ahpqmeICruVfSBi7jVaEhYRw4anKb0Pl58eI7nE0bxIEQJvmYZzb8dSjAw1VbGQIN5+TRICfjct7xtMlroEWrxr87bKz6XFWFBMv7mR+camDO4e248q+Z/GvK3v8rosLgM1oZB+e2e12oqKiKCgoIDIy0uw4Ig3KkULXPkdd4yPxt+CQc9r+fHbnFnJ+xxjioqy38SksqyTzaDGdYiMICfQ3O45Ig+LN9lvlRUREREznzfbbOmPGIiIiIqi8iIiIiMWovIiIiIilqLyIiIiIpai8iIiIiKWovIiIiIilqLyIiIiIpai8iIiIiKWovIiIiIilqLyIiIiIpfi0vBw7doybbrqJyMhIoqOjue222ygqKqpxnmHDhmGz2aqd7rrrLl/GFBEREQsJ8OWd33TTTWRnZ7No0SIqKyu55ZZbuOOOO3j//fdrnG/8+PE88cQT7sthYWG+jCkiIiIW4rPysmPHDhYuXMi6devo378/AC+//DKXXnopzz33HAkJCaedNywsjLi4OF9FExEREQvz2cdGq1evJjo62l1cAJKTk/Hz82PNmjU1zvvee+/RokULunfvzuTJkykpKTnttOXl5djt9monERERabx8NvKSk5NDy5Ytqz9YQADNmjUjJyfntPPdeOONJCUlkZCQwA8//MCjjz5Keno6c+fOPeX006ZNY+rUqWc0u4iIiDRcXpeXSZMm8fTTT9c4zY4dO+oc6I477nCf79GjB/Hx8Vx00UVkZGTQvn37k6afPHkyEydOdF+22+0kJibW+fFFRESkYfO6vDz44IOMGzeuxmnatWtHXFwchw8frnZ9VVUVx44d82p/lkGDBgGwZ8+eU5aX4OBggoODPb4/ERERsTavy0tMTAwxMTG1Tjd48GDy8/PZsGED/fr1A+Cbb77B6XS6C4kn0tLSAIiPj/c2qoiIiDRCPttht2vXrgwfPpzx48ezdu1aVq5cyb333sv111/vPtLo4MGDdOnShbVr1wKQkZHBk08+yYYNG8jKyuLTTz9lzJgxXHDBBfTs2dNXUUVERMRCfPolde+99x5dunThoosu4tJLL+W8887j9ddfd99eWVlJenq6+2iioKAgFi9ezCWXXEKXLl148MEHueqqq/jf//7ny5giIiJiITbDMAyzQ5xJdrudqKgoCgoKiIyMNDuOiIiIeMCb7bd+20hEREQsReVFRERELEXlRURERCxF5UVEREQsReVF5PfKMMDpMDuFd5xOKCswO4X3rLacRRo4lZfGrKocvvorLHocHJVmp/FMRQl8Mh7evw4KT/8bWA1KaT6kXgYv9oSsFWan8UzRYZgxEJ6MgVWvmJ3GM8V58Ep/+FdrWHCv2Wk8U1UB714BTzSD+fe4CqMVLH8Wnu0Ai/5udhLPZXwDb6XA6lfNTuK541kw7y5Ie9/sJJaj8tKYpX8Jq1+BlS+6/mFbwc7PYcsc2LUQ1s8yO41nti9wlZb8va4XfSvYNh+O7gLDAcummZ3GMzs/g2MZrvOb3oXio+bm8cTB9T//20v7D9gPmpvHE5Wl8M1TUHwEVk6HoiNmJ/LMV3+F/d/DV5OhvNDsNJ757gXY/AEsuMe13MVjKi+NWUIfaNICwuMgziLfUJzQB4LCwS8AkoaYncYzCX3AL9B1Puk8c7N4Kr4X2Gyu82f1MzeLp+J6gO2nl6yoVhASZW4eTzRrD0FNXOcj4qFJ7T+tYrqAENeyBmjeAUKbmpvHUx2SXX8T+kJgE3OzeKpDsuu1o90w13IXj+lL6ho7p9P1189CPbW80PUxV1gzs5N4Ln+f651qQt+fS0FDdygN8vZA5xE/b2AbugMb4NBG6DoSIjz/gVdTHcuE/Wug/R8gvKXZaTxTUeJazvG9IDjC7DSeMQywH4LwWPD3+mf7zON0gJ+/2SkaBG+23yovFnOsuAJ/PxtRoYFmR6mTzKPFVFQ56RxnkRfEX9mw9xjZBWUM7xZHgL+FCuFPvtqWw57DRVzbP5GYCOv9GvuXW7LZuO841/RPpFOs9dahjfuOs3zXES7tEW/J/EcKy1mWfpghHVpwVnSo2XG8VuVwsnFfPp1jI4gKs+ZraHZBKU3DgggJbHyFx5vtt4XqqaTnFDLy5e8I8Pdj8cShJFjsxWPrwQJGvbISh2Ewa9wALuxikXehP1mfdYyrZ64G4O5h7Xl0eBeTE3lnxe6j3PnuBmzA4u25zLvnXLMjeWV91jHufm8jfjb4eMMB1v01mUALFcicgjKue201lQ6Dt1Zksu6vyZbaABmGwTUzV5GVV0LzJkGsnnwRQQHWWf4AUxZs4/21+2jVNJRvH74Qfz+LjJL+ZO7GA0ycs5kOLcP5esIF+Fks/5lkrTXvdy67oJQKh0FJhYO8ogqz43gtp6AMx08DfQeOl5icxnvZBWUA2IBD+dbbuS7H7spvANk/nbeSXHs5AE4D7KWVVDqcJifyTn5pBZUO1/pfVFZFWaX1Dp8+8f/geEkFFRZb/gD7jrledw7byyy3/gBkHS0GXK+fVc5G9aGJ1zTyYiFDO8Xw4nW9CQn0o0crC+ys+Ct/6NKSxy4/m5LyKq4dkGh2HK8N7x7HnUPbkZ1fxsMpnc2O47XLe8azcs9R0nMKeXSEtUaNAC4+O5ZRvRNYl3WM/xvWgbAga718dY6N4P6LOvLFlmzGDE4iOizI7EhesdlszLipDx+s3c/IXgmEB1tr+QP884oezF6dxbDOMZYa9Trh7mEdaNYkiF6J0ZYb9TrTtM+LiIiImE6/Ki0iIiKNlsqLiIiIWIrKi4iIiFiKyouIiIhYisqLiIiIWIrKi4iIiFiKyouIiIhYisqLiIiIWIrKi4iIiFiKyouIiIhYisqLiIiIWIrKi4iIiFiKyouIiIhYisqLiIiIWIrKi4iIiFiKyouIiIhYisqLiIiIWIrKi4iIiFiKyouIiIhYisqLiIiIWIrKi4iIiFiKyouIiIhYisqLiIiIWIrKi4iIiFiKyktjZxiuk5VUlkFFsdkpvFOaD/n7zE7hnaIjcHCDtdaP4qOQ+R04Ks1O4rnyIti3BqrKzU7iOacDjqSDo8rsJN4pOWat9RnA6TQ7gSWpvDRmhTnwfBeY3t31om8F9myYfjY80w4ObTI7jWfs2fBiD9dp84dmp/FMYQ683Afe+AMsetzsNJ4pzYdXBsDsy+GT28xO4xmnA14fBm9fAu9fZ3Yaz308DmYMhP9caXYSz614EZ5pC+9dbXYSz+36Gp6KhQ9vNDuJ5ai8NGZHdkJRDtgPQF6G2Wk8c3QXlORBVZl1ykvebii3u84fWGduFk8dy4TyQtf5A2vNzeIp+yEoPeY6f2CDuVk8VVHkWj/ANcplFfu+d/21yvoMkLnc9XfvSnNzeGPvCnBUwJ4l1hsxMlmA2QHEh9pcACn/BL8ASBxodhrPtDkfhv0Fygqgp0XeqSadC0P+7CoE595vdhrPJA6EgXfAoY2QPNXsNJ5p2RUG/xkylsCFfzU7jWdCoiBlGvzwIQy5z+w0nvvjy7D2dejzJ7OTeC7lKVgVB10uNzuJ54bcDzZ/aHMe2Gxmp7EUm2E0rrpnt9uJioqioKCAyMhIs+OIiIiIB7zZfvvsY6OnnnqKIUOGEBYWRnR0tEfzGIbBlClTiI+PJzQ0lOTkZHbv3u2riCIiImJBPisvFRUVXHPNNdx9990ez/PMM8/w0ksvMXPmTNasWUOTJk1ISUmhrKzMVzFFRETEYnxWXqZOncoDDzxAjx49PJreMAxefPFF/va3vzFq1Ch69uzJO++8w6FDh5g/f76vYko923KggLWZx8yOUWdrfsxj3qYDVDmseXjjd7uP8PryDApKLXSo8S98/2MeLy7exaH8UrOj1MmObDszlu5hX16J2VHq5HBhGf/5fi8Hjlszf1mlg6+25XDYrjfEVtdgdtjNzMwkJyeH5ORk93VRUVEMGjSI1atXc/31159yvvLycsrLf/7+BLvd7vOsUje7cgsZ+coKAN69bSDnd4wxOZF3ducWcv3r32MAufZy7hra3uxIXsk8WsyYt9ZiANuzC3nxut5mR/LKkcJybnpzDQ6nwbe7jjDv/841O5JXqhxOrnttNfayKj7ZcIBvHhpmdiSv3fHOBtL259O6WRjLH7nQ7Dhem/bFDmav3kti01C+e/QPZseR36DBHCqdk5MDQGxsbLXrY2Nj3bedyrRp04iKinKfEhMTfZpTfhsr70//y4MBrPg8bO7/NKB/+HVkxeX/SzqwxFw2/Q+wPK9GXiZNmsTTTz9d4zQ7duygS5cuvymUNyZPnszEiRPdl+12uwpMA9UpNoL//fk8SisdDGjTzOw4XuvQMoKP7hzMofxSLu8Zb3Ycr7Vp0YR3bx3Ejmw71w6w3r+RmIhg3rt9EGszj3FN/1Zmx/FagL8fc+4azDc7DzOyZ4LZcerkjTH9+Xp7DsM6tzQ7Sp385bKunNuhBb0To82OIr+RV+XlwQcfZNy4cTVO065duzoFiYuLAyA3N5f4+J83DLm5ufTu3fu08wUHBxMcHFynx5T61/2sKLMj/CYD21qvdP3SeR1bcF7HFmbHqLNz2jXnnHbNzY5RZ13iIukSZ92vcIiJCOamQUlmx6iz4AB/LukWZ3YMOQO8Ki8xMTHExPhmP4W2bdsSFxfHkiVL3GXFbrezZs0ar45YEhERkcbNZx9979u3j7S0NPbt24fD4SAtLY20tDSKiorc03Tp0oV58+YBrs8gJ0yYwD/+8Q8+/fRTtmzZwpgxY0hISGD06NG+iikiIiIW47OjjaZMmcLs2bPdl/v06QPA0qVLGTZsGADp6ekUFBS4p3nkkUcoLi7mjjvuID8/n/POO4+FCxcSEhLiq5giIiJiMfp5ABERETFdg/h5ABERERFfUHkRERERS1F5EREREUtReRERERFLUXkRERERS1F5EREREUtReRERERFLUXkRERERS1F5EREREUtReRERERFLUXkRERERS1F5EREREUtReRERERFLUXkRERERS1F5EREREUtReRERERFLUXkRERERS1F5EREREUtReZGGJ2sF7PwcDMPsJJ77cRmsnwWOSrOTeO7HZbBiOpQVmJ3Ec/u+h2X/gsIcs5N4LncbfPss5O8zO4nnCg7CqlfgeJbZSTxXXggb34Xje81OIvUgwOwAItUc+xFSL3Od/9Mn0CHZ3DyeKDgI74wGDKgqh3PuMjtR7UqOwbtXguFwFYERT5udqHZV5fDOKKgqg5ytcP1/zE7kmfeuBvshyFgCty40O41n5t3hehPxw4dw1wqz03hm0eOw/i1o0QnuXWd2GvExjbxIwxIUAcER4BcATVqancYzQWGuzACR8eZm8VRAMIREuc5HWCSzXwCEtXCdt8pyBohIqP7XCiJbVf9rBVGtqv+VRs1mGFYam6+d3W4nKiqKgoICIiMjzY4jdVF63PXxS7hFygtA8VHXaEZMJ7OTeK7osGuIvVV/sNnMTuOZkmOuj2FaDwZ/iwwclxdB9mZoNQACgsxO4xlHFeRugZbdrJPZMOB4JkQlgn+g2WmkDrzZfqu8iIiIiOm82X7rYyMRERGxFJUXERERsRSVFxEREbEUlRcRERGxFJUXES98t/sI763ZS6XDaXaUOlmx+yivLttDYZmFvkzvF9ZnHWP6ol0cLiwzO0qdpOcU8u/Fu9l/rMTsKHWSU1DG68sz2JtXbHYU+Z2zyLGGIubLtZcx5q21GICfzcYNA1ubHckr9rJKxr69FodhUFBayeQRXc2O5BWH0+Dmt9ZSWukg82gRL93Q1+xIXhv/znr2HSthTWYe748/x+w4Xps09weWpR/hf5uz+d+fzzM7jvyOaeRFxEPhwQG0CA/CZoOkZmFmx/FacIAfLSODAWjbvInJabznZ4PEZqEAtLFgfoD2Ma7cbVtYNX84AO1irJlfGg99z4uIF0oqqigudxATEWx2lDopKq/iSGG5ZTeepRUO9h4rpnNsBDarfLHeL1Q6nGQeLaZDTDh+ftbLbxgGB46XclZ0qCXzS8PmzfZbHxuJeCEsKICwIOv+swkPDiA82Lr5Q4P86RJn3Tclgf5+dIqNMDtGndlsNhItOOoojY8+NhIRERFLUXkRERERS1F5EREREUtReRERERFLUXkRERERS1F5EREREUtReRERERFLUXkRERERS1F5EREREUtReRERERFLUXkRERERS/FZeXnqqacYMmQIYWFhREdHezTPuHHjsNls1U7Dhw/3VUQRERGxIJ/9QltFRQXXXHMNgwcP5q233vJ4vuHDhzNr1iz35eBga/56r4iIiPiGz8rL1KlTAUhNTfVqvuDgYOLi4nyQSERERBqDBrfPy7Jly2jZsiWdO3fm7rvvJi8vr8bpy8vLsdvt1U4iIiLSeDWo8jJ8+HDeeecdlixZwtNPP823337LiBEjcDgcp51n2rRpREVFuU+JiYn1mFhERETqm1flZdKkSSftUPvr086dO+sc5vrrr+ePf/wjPXr0YPTo0Xz22WesW7eOZcuWnXaeyZMnU1BQ4D7t37+/zo8vIiIiDZ9X+7w8+OCDjBs3rsZp2rVr91vynHRfLVq0YM+ePVx00UWnnCY4OFg79YqIiPyOeFVeYmJiiImJ8VWWkxw4cIC8vDzi4+Pr7TFFRESkYfPZPi/79u0jLS2Nffv24XA4SEtLIy0tjaKiIvc0Xbp0Yd68eQAUFRXx8MMP8/3335OVlcWSJUsYNWoUHTp0ICUlxVcxRURExGJ8dqj0lClTmD17tvtynz59AFi6dCnDhg0DID09nYKCAgD8/f354YcfmD17Nvn5+SQkJHDJJZfw5JNP6mMhEXFZ/SpsmQMjnoXEAWanERGT2AzDMMwOcSbZ7XaioqIoKCggMjLS7Dgicib9KwnK8qHPn2DUDLPTiMgZ5M32u0EdKi0iUqPkx6HN+TBgvNlJRMREPvvYSETkjOt/q+skIr9rGnkRERERS1F5EREREUtReRERERFLUXkRERERS1F5EREREUtReRERERFLUXkRERERS1F5EREREUtReRERERFLUXkRERERS1F5EREREUtReRERERFLUXkRERERS2l0vyptGAYAdrvd5CQiIiLiqRPb7RPb8Zo0uvJSWFgIQGJioslJRERExFuFhYVERUXVOI3N8KTiWIjT6eTQoUNERERgs9nq/fHtdjuJiYns37+fyMjIen/8hk7Lp2ZaPjXT8qmZlk/ttIxqZubyMQyDwsJCEhIS8POrea+WRjfy4ufnR6tWrcyOQWRkpP5h1EDLp2ZaPjXT8qmZlk/ttIxqZtbyqW3E5QTtsCsiIiKWovIiIiIilqLycoYFBwfz+OOPExwcbHaUBknLp2ZaPjXT8qmZlk/ttIxqZpXl0+h22BUREZHGTSMvIiIiYikqLyIiImIpKi8iIiJiKSovIiIiYikqL2fAU089xZAhQwgLCyM6OtqjecaNG4fNZqt2Gj58uG+DmqQuy8cwDKZMmUJ8fDyhoaEkJyeze/du3wY1ybFjx7jpppuIjIwkOjqa2267jaKiohrnGTZs2Enrz1133VVPiX1rxowZtGnThpCQEAYNGsTatWtrnP7jjz+mS5cuhISE0KNHD7744ot6SmoOb5ZPamrqSetJSEhIPaatX8uXL2fkyJEkJCRgs9mYP39+rfMsW7aMvn37EhwcTIcOHUhNTfV5TrN4u3yWLVt20vpjs9nIycmpn8A1UHk5AyoqKrjmmmu4++67vZpv+PDhZGdnu08ffPCBjxKaqy7L55lnnuGll15i5syZrFmzhiZNmpCSkkJZWZkPk5rjpptuYtu2bSxatIjPPvuM5cuXc8cdd9Q63/jx46utP88880w9pPWtjz76iIkTJ/L444+zceNGevXqRUpKCocPHz7l9KtWreKGG27gtttuY9OmTYwePZrRo0ezdevWek5eP7xdPuD6ptRfrid79+6tx8T1q7i4mF69ejFjxgyPps/MzOSyyy7jwgsvJC0tjQkTJnD77bfz1Vdf+TipObxdPiekp6dXW4datmzpo4ReMOSMmTVrlhEVFeXRtGPHjjVGjRrl0zwNjafLx+l0GnFxccazzz7rvi4/P98IDg42PvjgAx8mrH/bt283AGPdunXu67788kvDZrMZBw8ePO18Q4cONe6///56SFi/Bg4caNxzzz3uyw6Hw0hISDCmTZt2yumvvfZa47LLLqt23aBBg4w777zTpznN4u3y8eY1qbEBjHnz5tU4zSOPPGJ069at2nXXXXedkZKS4sNkDYMny2fp0qUGYBw/frxeMnlDIy8mWrZsGS1btqRz587cfffd5OXlmR2pQcjMzCQnJ4fk5GT3dVFRUQwaNIjVq1ebmOzMW716NdHR0fTv3999XXJyMn5+fqxZs6bGed977z1atGhB9+7dmTx5MiUlJb6O61MVFRVs2LCh2v93Pz8/kpOTT/v/ffXq1dWmB0hJSWl06wnUbfkAFBUVkZSURGJiIqNGjWLbtm31EdcSfk/rz2/Ru3dv4uPjufjii1m5cqXZcYBG+MOMVjF8+HCuvPJK2rZtS0ZGBn/5y18YMWIEq1evxt/f3+x4pjrxeWpsbGy162NjYxvEZ61nUk5OzklDsAEBATRr1qzG53rjjTeSlJREQkICP/zwA48++ijp6enMnTvX15F95ujRozgcjlP+f9+5c+cp58nJyfldrCdQt+XTuXNn3n77bXr27ElBQQHPPfccQ4YMYdu2bQ3iB2zNdrr1x263U1paSmhoqEnJGob4+HhmzpxJ//79KS8v580332TYsGGsWbOGvn37mppN5eU0Jk2axNNPP13jNDt27KBLly51uv/rr7/efb5Hjx707NmT9u3bs2zZMi666KI63Wd98vXysTpPl09d/XKfmB49ehAfH89FF11ERkYG7du3r/P9SuMyePBgBg8e7L48ZMgQunbtymuvvcaTTz5pYjKxgs6dO9O5c2f35SFDhpCRkcH06dN59913TUym8nJaDz74IOPGjatxmnbt2p2xx2vXrh0tWrRgz549ligvvlw+cXFxAOTm5hIfH+++Pjc3l969e9fpPuubp8snLi7upJ0tq6qqOHbsmHs5eGLQoEEA7Nmzx7LlpUWLFvj7+5Obm1vt+tzc3NMui7i4OK+mt7K6LJ9fCwwMpE+fPuzZs8cXES3ndOtPZGTk737U5XQGDhzIihUrzI6h8nI6MTExxMTE1NvjHThwgLy8vGob64bMl8unbdu2xMXFsWTJEndZsdvtrFmzxusjuszi6fIZPHgw+fn5bNiwgX79+gHwzTff4HQ63YXEE2lpaQCWWX9OJSgoiH79+rFkyRJGjx4NgNPpZMmSJdx7772nnGfw4MEsWbKECRMmuK9btGhRtdGGxqIuy+fXHA4HW7Zs4dJLL/VhUusYPHjwSYfWN9b150xJS0trGK8zZu8x3Bjs3bvX2LRpkzF16lQjPDzc2LRpk7Fp0yajsLDQPU3nzp2NuXPnGoZhGIWFhcZDDz1krF692sjMzDQWL15s9O3b1+jYsaNRVlZm1tPwGW+Xj2EYxr/+9S8jOjraWLBggfHDDz8Yo0aNMtq2bWuUlpaa8RR8avjw4UafPn2MNWvWGCtWrDA6duxo3HDDDe7bDxw4YHTu3NlYs2aNYRiGsWfPHuOJJ54w1q9fb2RmZhoLFiww2rVrZ1xwwQVmPYUz5sMPPzSCg4ON1NRUY/v27cYdd9xhREdHGzk5OYZhGMbNN99sTJo0yT39ypUrjYCAAOO5554zduzYYTz++ONGYGCgsWXLFrOegk95u3ymTp1qfPXVV0ZGRoaxYcMG4/rrrzdCQkKMbdu2mfUUfKqwsND9+gIYL7zwgrFp0yZj7969hmEYxqRJk4ybb77ZPf2PP/5ohIWFGQ8//LCxY8cOY8aMGYa/v7+xcOFCs56CT3m7fKZPn27Mnz/f2L17t7Flyxbj/vvvN/z8/IzFixeb9RTcVF7OgLFjxxrASaelS5e6pwGMWbNmGYZhGCUlJcYll1xixMTEGIGBgUZSUpIxfvx49wtQY+Pt8jEM1+HSjz32mBEbG2sEBwcbF110kZGenl7/4etBXl6eccMNNxjh4eFGZGSkccstt1QrdpmZmdWW1759+4wLLrjAaNasmREcHGx06NDBePjhh42CggKTnsGZ9fLLLxutW7c2goKCjIEDBxrff/+9+7ahQ4caY8eOrTb9nDlzjE6dOhlBQUFGt27djM8//7yeE9cvb5bPhAkT3NPGxsYal156qbFx40YTUtePE4f2/vp0YpmMHTvWGDp06Enz9O7d2wgKCjLatWtX7XWosfF2+Tz99NNG+/btjZCQEKNZs2bGsGHDjG+++cac8L9iMwzDqLdhHhEREZHfSN/zIiIiIpai8iIiIiKWovIiIiIilqLyIiIiIpai8iIiIiKWovIiIiIilqLyIiIiIpai8iIiIiKWovIiIiIilqLyIiIiIpai8iIiIiKWovIiIiIilvL/AUQaVoW6/eg4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "fig, ax = plt.subplots(1,1,)\n",
    "diffraction.plot(ax=ax, label=\"Unstrained Material\")\n",
    "strained_diffraction.plot(ax=ax, label=\"Strained Material\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define a \"detector\" configuration here so that we can simulate the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x581.818 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "diffraction.calibration = 1e-2\n",
    "pattern = diffraction.get_diffraction_pattern(shape = (128,128),\n",
    "                                              sigma = 0)\n",
    "\n",
    "strained_diffraction.calibration = 1e-2\n",
    "pattern2 = strained_diffraction.get_diffraction_pattern(shape = (128,128),\n",
    "                                              sigma = 0)\n",
    "pxm.signals.Diffraction2D([pattern, pattern2]).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a distorted structure and simulate diffraction.  Note that this structure is fairly unphysical but it does give us something to fit to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff7ad7bfe80>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from skimage.draw import disk\n",
    "distortion_x = np.zeros((10, 10))\n",
    "distortion_y = np.zeros((10, 10))\n",
    "\n",
    "xx, yy = disk(center=(5, 5), radius=4, shape=(20, 20))\n",
    "\n",
    "distortion_x[xx,yy]= np.abs(np.abs(xx-10)-5)\n",
    "distortion_y[xx,yy]= np.abs(np.abs(yy-10)-5)\n",
    "\n",
    "fig, axs = plt.subplots(1, 2)\n",
    "axs[0].imshow(distortion_x)\n",
    "axs[1].imshow(distortion_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  0.1s\n"
     ]
    }
   ],
   "source": [
    "data = np.empty((10,10,128,128))\n",
    "\n",
    "for ind in np.ndindex((10,10)):\n",
    "    dx = distortion_x[ind]*.1\n",
    "    dy = distortion_y[ind]*.1\n",
    "    latt = diffpy.structure.lattice.Lattice(3+dx,3+dy,3,90,90,90)\n",
    "    atom = diffpy.structure.atom.Atom(atype='Ni',xyz=[0,0,0],lattice=latt)\n",
    "    structure = diffpy.structure.Structure(atoms=[atom],lattice=latt)\n",
    "    diffraction = ediff.calculate_ed_data(structure, reciprocal_radius=5.,\n",
    "                                          max_excitation_error=0.025,\n",
    "                                          with_direct_beam=True)\n",
    "    diffraction.calibration = 1e-2\n",
    "    data[ind] = diffraction.get_diffraction_pattern((128,128),sigma=4)\n",
    "\n",
    "dp = pxm.signals.ElectronDiffraction2D(data)\n",
    "\n",
    "dp = dp.shift_diffraction(7,8) #shifting diffraction patterns to simulate real_effects\n",
    "\n",
    "dp = dp+np.random.random((10,10,128,128))*.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x581.818 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x581.818 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "dp.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calibrating the Diffraction Pattern\n",
    "\n",
    "It is important that we keep track of the center of the diffraction pattern as well as the calibration. Lets consider the diffraction pattern above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "dp.set_diffraction_calibration(1e-2) # known from simulation\n",
    "dp.beam_energy = 300 #kEv\n",
    "dp.unit = \"k_A^-1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  0.1s\n",
      "[########################################] | 100% Completed |  0.1s\n"
     ]
    }
   ],
   "source": [
    "shifts, aligned = dp.center_direct_beam(method=\"blur\",half_square_width=20,sigma=5, return_shifts=True, inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "table, th, td {\n",
       "\tborder: 1px solid black;\n",
       "\tborder-collapse: collapse;\n",
       "}\n",
       "th, td {\n",
       "\tpadding: 5px;\n",
       "}\n",
       "</style>\n",
       "<p><b>< Axes manager, axes: (10, 10|128, 128) ></b></p>\n",
       "<table style='width:100%'>\n",
       "\n",
       "<tr> \n",
       "<th>Navigation axis name</th> \n",
       "<th>size</th> \n",
       "<th>index</th> \n",
       "<th>offset</th> \n",
       "<th>scale</th> \n",
       "<th>units</th> </tr>\n",
       "<tr> \n",
       "<td><undefined></td> \n",
       "<td>10</td> \n",
       "<td>0</td> \n",
       "<td>0.0</td> \n",
       "<td>1.0</td> \n",
       "<td><undefined></td> </tr>\n",
       "<tr> \n",
       "<td><undefined></td> \n",
       "<td>10</td> \n",
       "<td>0</td> \n",
       "<td>0.0</td> \n",
       "<td>1.0</td> \n",
       "<td><undefined></td> </tr></table>\n",
       "<table style='width:100%'>\n",
       "\n",
       "<tr> \n",
       "<th>Signal axis name</th> \n",
       "<th>size</th> \n",
       "<th></th> \n",
       "<th>offset</th> \n",
       "<th>scale</th> \n",
       "<th>units</th> </tr>\n",
       "<tr> \n",
       "<td>kx</td> \n",
       "<td>128</td> \n",
       "<td></td> \n",
       "<td>-0.64</td> \n",
       "<td>0.01</td> \n",
       "<td>k_A^-1</td> </tr>\n",
       "<tr> \n",
       "<td>ky</td> \n",
       "<td>128</td> \n",
       "<td></td> \n",
       "<td>-0.64</td> \n",
       "<td>0.01</td> \n",
       "<td>k_A^-1</td> </tr></table>\n"
      ],
      "text/plain": [
       "<Axes manager, axes: (10, 10|128, 128)>\n",
       "            Name |   size |  index |  offset |   scale |  units \n",
       "================ | ====== | ====== | ======= | ======= | ====== \n",
       "     <undefined> |     10 |      0 |       0 |       1 | <undefined> \n",
       "     <undefined> |     10 |      0 |       0 |       1 | <undefined> \n",
       "---------------- | ------ | ------ | ------- | ------- | ------ \n",
       "              kx |    128 |      0 |   -0.64 |    0.01 | k_A^-1 \n",
       "              ky |    128 |      0 |   -0.64 |    0.01 | k_A^-1 "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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usixLjRs31vLly92qleVLAABgzK6bx/bp0+eK19izZ48kaerUqVccX7ly5TU/73A41KxZM+c8TZs21bFjx+RwOJSfn69NmzY5t8woKJoyAAAAG2D5EgAAGLPrPWW+hKQMAADABkjKAACAMZIycyRlAAAANkBSBgAAjJGUmSMpAwAAsAGaMgAAABtg+RIAABhj+dIcSRkAAIANuJ2UHThwQJZlqVq1apKkjRs3aubMmYqNjdU//vEPjxcIAADsj6TMnNtJ2WOPPaYVK1ZI+v2b1++9915t3LhRQ4YM0ciRIz1eIAAAQGngdlP2008/6Y477pAkzZ49W/Xr19e6des0Y8YMTZs2zdP1AQAAH2DXLyT3JW43ZRcuXFDZsmUlSUuXLlWHDh0kSTfffLOOHDni2eoAAABKCbebsltuuUXvvPOO1qxZoyVLlqht27aSpMOHD6tSpUoeLxAAANgfSZk5t5uyV199Ve+++65atGihrl27qmHDhpKkBQsWOJc1AQAA4B63n75s0aKFTpw4oczMTFWoUMF5/B//+IfKlSvn0eIAAIBv4OlLc4XaPNbf39+lIZOkGjVqeKIeAACAUqlATVnjxo21bNkyVahQQX/5y19kWdZVz92yZYvHigMAAL6BpMxcgZqyv/3tb84nLh988MGirAcAAKBUKlBTNmzYsCv+GgAAAJ7h9tOXl3bzv5J3333XqBgAAOCb2BLDnNs3+rdt21a9e/dWUlKSAgICJEknTpzQ3//+d61du1ZPP/10ga6Tm5vr7tS2UaZMoZ6PsIX33nvP2yUYuXjxordLKLTPPvvM2yUU2m+//ebtEoxc6z5Yuzt8+LC3Syi0OnXqeLsEI7789xR8U6GSsk8//VRNmjRRamqqvvjiC9WvX1+ZmZn64YcfiqBEAABgdyRl5txuyu666y798MMPql+/vho3bqyOHTuqb9++WrlypapXr14UNQIAAJR4bjdlkrRr1y5t2rRJ1apVU5kyZbRz506dO3fO07UBAAAfQVJmzu2mbMyYMYqLi9O9996rn376SRs3btT333+vBg0aaP369UVRIwAAQInn9h3rEyZM0Geffab7779fklS/fn1t3LhR//rXv9SiRQudP3/e40UCAAB7Y/NYc243Zdu2bdP111/vciwgIEDjxo1T+/btPVYYAABAaeJ2U/bnhuyP4uPjjYoBAAC+iaTMXKE23Nq0aZNmz56tjIyMy/ZxmTdvnkcKAwAAKE3cvtE/OTlZd911l3bs2KFPP/1UFy5c0Pbt27V8+XKFh4cXRY0AAMAH8OSlGbebsqSkJI0fP14LFy5UYGCgJkyYoLS0NHXu3FnR0dFFUSMAAECJ53ZTlp6eroSEBElSYGCgzp49K8uy1LdvX02ZMsXjBQIAAPtjnzJzbjdlFSpUUFZWliSpatWq+umnnyRJp0+fZgNZAACAQnL7Rv/mzZtryZIluvXWW9WpUye9+OKLWr58uZYsWaJ77rmnKGoEAAAo8dxuyt566y3l5ORIkoYMGaKAgACtW7dODz/8sF5++WWPFwgAAOyPLTHMud2UVaxY0flrPz8/DRo0yKMFAQAAlEaF2qcMAADgj0jKzLl9oz8AAAA8j6QMAAAYIykzR1IGAABgA243ZU899ZRzn7I/Onv2rJ566imPFAUAAHwLm8eac7spmz59un777bfLjv/222/66KOPPFIUAABAaVPge8oyMzOdHWpWVpaCgoKcY3l5efryyy8VERFRJEUCAAB7454ycwVuysqXLy/LsmRZlurUqXPZuGVZGjFihEeLAwAAKC0K3JStWLFCDodDrVq10ty5c102kQ0MDFT16tV1ww03FEmRAADA3kjKzBW4KYuPj5ck7d27V9HR0bIsq8iKAgAAKG3cvtF/x44d+uabb5zv3377bTVq1EiPPfaYTp065dHiAAAASgu3m7J//vOfyszMlCRt27ZN/fr1U7t27bR3717169fP4wUCAAD7Y0sMc27v6L93717FxsZKkubOnasHHnhASUlJ2rJli9q1a+fxAgEAAEoDt5OywMBAnTt3TpK0dOlS3XfffZKkihUrOhM0AABQupCUmXM7KWvWrJn69eunpk2bauPGjUpJSZEk7dq1S9WqVfN4gQAAAKWB20nZW2+9pTJlyuiTTz7Rf/7zH1WtWlWStGjRIrVt29bjBQIAAPsjKTPndlIWHR2tzz///LLj48eP90hBAAAApZHbSdnVXLx4URkZGZ66HAAA8CEkZeY81pRt375dNWvW9NTlAAAAShW3ly8BAAD+jK9ZMlfgpqxx48bXHP/tt9+MiwEAACitCtyUpaam6tFHH73qEuWRI0e0a9cujxUGAAB8B0mZuQI3ZfXr19edd96pXr16XXH8hx9+0HvvveexwgAAAEqTAjdlTZs21c6dO686HhoaqubNm3ukKAAA4FtIyswVuCmbMGHCNcdr166tFStWGBcEAABQGnlsSwwAAAAUHltiAAAAYyxfmiMpAwAAsAGSMgAAYIykzBxJGQAAgA2QlAEAAGMkZeYK1JT95S9/kWVZBbrgli1bCnReuXLlCnSeHR07dszbJRTa6dOnvV2CEV+uPzs729slFNrJkye9XYKR8PBwb5dQaBcvXvR2CYV26tQpb5dgJCwszNsloJQpUFP24IMPOn+dk5OjyZMnKzY2VnFxcZKkDRs2aPv27Xr22WeLpEgAAGB/JTnFKg4FasqGDRvm/HXPnj3Vu3dvjRo16rJzDhw44NnqAAAASgm3b/SfM2eOevTocdnxxx9/XHPnzvVIUQAAwLdcuqesOF4lldtN2XXXXadvvvnmsuPffPONgoKCPFIUAABAaeP205d9+vRRr169tGXLFt1xxx2SpG+//VYffPCBhg4d6vECAQCA/fH0pTm3m7JBgwapVq1amjBhgj7++GNJUr169fThhx+qc+fOHi8QAACgNCjUPmWdO3emAQMAAPCgQu3of/r0aU2dOlX/+te/9Ouvv0r6fX+yQ4cOebQ4AADgG7jR35zbSdnWrVvVunVrhYeHa9++ferZs6cqVqyoefPmKSMjQx999FFR1AkAAFCiuZ2U9evXT08++aR2797t8rRlu3bttHr1ao8WBwAAfANJmTm3m7LvvvtOTz/99GXHq1atqqNHj3qkKAAAgNLG7eXLsmXLKjMz87Lju3btUuXKlT1SFAAA8C1siWHO7aSsQ4cOGjlypC5cuCBJsixLGRkZGjhwoB5++GGPFwgAAFAauN2Uvf7668rOzlZERIR+++03xcfH66abblJoaKheeeWVoqgRAADYHPeUmXN7+TI8PFxLlizR2rVrtXXrVmVnZ6tx48Zq3bp1UdQHAABQKhRq81hJatasmZo1a+bJWgAAgI/injJzhWrKvvvuO61YsULHjh1Tfn6+y9gbb7zhkcIAAABKE7ebsqSkJL388suqW7euIiMjZVmWc+yPvwYAAKUHSZk5t2/0nzBhgj744APt2LFDK1eu1IoVK5yv5cuXF0WNAAAAhTJp0iRFRkbK399flmVp8ODBLuMvvfSSKlWqJD8/P1mWpZSUlMuucfr0ad16663Oc6pWraqffvrJ5Zz169crIiJClmXJz89PTZo0UU5Ojlu1ut2U+fn5qWnTpu5+DAAAlGB2ffry1KlTiomJ0YABA644npmZqYYNG6p79+5XvUbTpk21Y8cOjRs3Tv/973+VmZmpu+66yzmem5urli1bKi8vT8nJyRo+fLg2b96sVq1auVWr28uXffv21dtvv60333zT3Y8CAAAUq8TERCUmJkqSxo4de9n4O++8I0lau3btFb+/OyMjQ6mpqerXr5/69+8vSapYsaISEhI0depU9ezZU2PGjNH58+e1adMm1a9fX5KUlpamWbNmKTs7WyEhIQWq1e2mbMCAAUpISFDt2rUVGxurgIAAl/F58+a5e0kAAAC3HDp0yOUbhsLCwhQWFubxeebMmSNJ6t27t/NYu3bt5O/vr88//1w9e/bU0qVLFRQU5GzIJOmZZ57RrFmztHDhQnXt2rVAc7ndlPXu3VsrVqxQy5YtValSJW7uBwAAxX6jf2xsrMvx+Ph4rVy50uPz/fzzz5Kk6tWruxwPCgrSkSNHJEnHjx9XcHCwy/il+nbv3l3gudxuyqZPn665c+cqISHB3Y8CAAB4RGpqqkJDQ53viyIlK25uN2UVK1ZU7dq1i6IWAADgo4o7KatatWqxNGK1atWSJO3fv98lLcvJyVGVKlUkSZUrV9a+fftcPpeamipJiomJKfBcbj99OXz4cA0bNkznzp1z96MAAAA+pVOnTpKkt956y3ls0aJFysvLU/v27SVJrVu3Vk5OjrZv3+48Z8qUKZLk1sqi20nZxIkTlZ6ersjISNWoUeOyG/23bNni7iUBAICPs+vmsUePHtWqVauc73fs2KGUlBRFR0crLi5O6enp2rRpk9LS0iRJ69atkyTVq1dPDRo0UHR0tGJjYzV+/HhVqVJFERER6tWrl0JCQtSzZ09J0qBBg5SUlKT4+HhNnjxZu3fv1syZMxUXF+dWmud2U/bggw+6+xEAAACvSE5OVt++fZ3v58+fr/nz56t27dras2ePRo8erffff985PnHiRE2cONHlwYFvvvlGd999twYMGCCHw6EqVaroq6++cn4mMDBQy5YtU8eOHdWlSxdZlqXGjRu7vam+203ZsGHD3P0IAAAo4eyalPXp00d9+vS56vjUqVM1derUa16jfPny2rZt2zXPadq0qY4dO+ZWbX/m9j1lAAAA8Dy3k7K8vDyNHz9es2fPVkZGhnJzc13Gf/31V48VBwAAfINdkzJf4nZSNmLECL3xxhvq0qWLzpw5o379+umhhx6Sn5+fhg8fXgQlAgAAlHxuN2UzZszQe++9p/79+6tMmTLq2rWrpk6dqsTERG3YsKEoagQAADZn1y8k9yVuN2VHjx7VrbfeKkkKCQnRmTNnJEnt27fXF1984dnqAAAASgm3m7Jq1ao5v+updu3aWrx4sSTpu+++U9myZT1bHQAAQCnhdlPWsWNHLVu2TJL0wgsvaOjQoYqJiVGPHj301FNPebxAAABgfyxfmnP76csxY8Y4f92lSxdFR0dr/fr1iomJ0QMPPODR4gAAAEoLt5uyP4uLi1NcXJwnagEAAD6KLTHMFaop2717t1asWKFjx44pPz/fZSwxMdEjhQEAAJQmbjdl7733nnr16qXrr79eUVFRsizLOWZZFk0ZAAClVElOsYqD203Zv//9b73yyisaOHBgUdQDAABQKrndlJ06dUqdOnUqiloAAICP4p4yc25vidGpUyfn3mQAAADwDLeTsptuuklDhw7Vhg0bdOuttyogIMBlvHfv3h4rDgAA+AaSMnNuN2VTpkxRSEiIVq1apVWrVrmMWZZFUwYAAFAIbjdle/fuLYo6AACADyMpM+f2PWUAAADwPLeTsry8PE2bNk3Lli274uaxy5cvL9B1AgMD3Z3aNiIiIrxdQqHVqFHD2yUY2bVrl7dLKLSgoCBvl1BoFStW9HYJRq677jpvl1BoISEh3i6h0PLy8rxdgpE///0GFDW3m7IXX3xR06ZNU0JCgurXr++yeSwAACidWL4053ZTlpycrNmzZ6tdu3ZFUQ8AAECp5HZTFhgYqJtuuqkoagEAAD6KpMyc2zf69+/fXxMmTCjRvykAAADFze2kbO3atVqxYoUWLVqkW2655bLNY+fNm+ex4gAAgG8gKTPndlNWvnx5dezYsShqAQAAKLXcbso+/PDDoqgDAAD4MJIyc2weCwAAYAMFTsoqVKhwxT3JwsPDVadOHQ0YMED33nuvR4sDAAC+gaTMXIGbsjfffPOKx0+fPq3Nmzerffv2+uSTT/TAAw94qjYAAIBSo8BN2RNPPHHN8UaNGmn06NE0ZQAAlEIkZeY8dk9Z+/btlZaW5qnLAQAAlCpuP315NefPn/fpLxkHAACFR1JmzmNJ2fvvv69GjRp56nIAAAClSoGTsn79+l3x+JkzZ7Rlyxbt2rVLq1ev9lhhAAAApUmBm7Lvv//+isfDwsJ07733at68eapZs6bHCgMAAL6D5UtzBW7KVqxYUZR1AAAAlGoeu9EfAACUXiRl5viaJQAAABsgKQMAAMZIysyRlAEAANgASRkAADBGUmaOpAwAAMAGSMoAAIAxkjJzJGUAAAA2QFIGAACMkZSZIykDAACwAZoyAAAAG2D5EgAAGGP50hxJGQAAgA2QlAEAAI8oySlWcSApAwAAsAGSMgAAYIx7ysyRlAEAANgASRkAADBGUmaOpAwAAMAGSMoAAIAxkjJzJGUAAAA2QFIGAACMkZSZIykDAACwAZIyAABgjKTMHEkZAACADdCUAQAA2ADLlwAAwBjLl+ZIygAAAGyApAwAABgjKTPntabMz893Q7q8vDxvl1BojzzyiLdLMPLzzz97u4RCCwsL83YJhXbgwAFvl2CkYsWK3i6h0Hr16uXtEgotMDDQ2yUYGT9+vLdLQClDUgYAAIyRlJnz3bgKAACgBCEpAwAAxkjKzJGUAQAA2ABJGQAAMEZSZo6kDAAAwAZIygAAgDGSMnMkZQAAADZAUwYAAGADLF8CAABjLF+aIykDAACwAZIyAABgjKTMHEkZAACADZCUAQAAYyRl5kjKAAAAbICkDAAAGCMpM0dSBgAAYAMkZQAAwBhJmTmSMgAAABsgKQMAAMZIysyRlAEAANgASRkAADBGUmaOpAwAAMAGaMoAAECJNWnSJEVGRsrf31+WZWnw4MEu4/n5+WrevLlzvGLFilqyZInLOV9//bWqVKkiPz8/WZalsLAwvfHGGy7nrF+/XhEREbIsS35+fmrSpIlycnLcqpWmDAAAeMSlJcyifLnr1KlTiomJ0YABA644npCQoDVr1mjgwIGaM2eOypYtq3bt2un06dPOcx544AHl5eVp9uzZWrx4sapXr67+/ftr69atkqTc3Fy1bNlSeXl5Sk5O1vDhw7V582a1atXKrVq5pwwAAJRYiYmJSkxMlCSNHTvWZSw/P1+LFy9WQkKCkpKSJEl33HGHqlevrsTERE2cOFE7d+7UhQsXlJiYqEceeUTS78lZ1apV9dVXX6lBgwYaM2aMzp8/r02bNql+/fqSpLS0NM2aNUvZ2dkKCQkpUK0kZQAAwFhxpGR/TMsOHTqkgwcPOl+ZmZlu17x69Wrl5+ere/fuzmPR0dEKDw/X6tWrJUkxMTEKDAzUW2+9pWPHjiknJ0f/+Mc/ZFmWOnXqJElaunSpgoKCnA2ZJD3zzDOSpIULFxa4HpIyAADgc2JjY13ex8fHa+XKlW5dY+fOnZKkunXruhwPDQ3Vr7/+Kkny8/PT2rVr1aJFC0VGRjqPzZgxQzVr1pQkHT9+XMHBwVesb/fu3QWuh6YMAAAYK+4tMVJTUxUaGuo8HhYWViTz5efnq0OHDgoODtZrr72msLAwjR49Wo8//rjq1aunRo0aeWwumjIAAOBzqlatatyIXUrIdu7c6dJcZWVlqUaNGpKk119/XUePHtWBAwdUrVo1SVK3bt0UGBioQYMG6auvvlLlypW1b98+l2unpqZK+n35s6C4pwwAABgr7nvKPKF58+by8/PTxx9/7Dx28OBBnTlzRs2bN5ck571qZcpcnmPl5+dLklq3bq2cnBxt377dOTZlyhRJvz/dWVA0ZQAAoMQ6evSoUlJSlJKSIknasWOHUlJStH79evn5+em+++7TF198oSFDhmju3Llq0qSJypQpo5EjR0qSHn/8cVmWpdtuu02zZ8/W119/rSZNmujChQvq2bOnJGnQoEEqW7as4uPjNXv2bL3yyiuaOXOm4uLi3ErzWL4EAADG7Po1S8nJyerbt6/z/fz58zV//nzVrl1be/bs0RdffKEWLVpozJgxys/PV4UKFfT555+rfPnykn5f4pw2bZr69eunRx99VA6HQyEhIRoxYoQ6d+4sSQoMDNSyZcvUsWNHdenSRZZlqXHjxlq+fLlbtdKUAQCAEqtPnz7q06fPVcf9/Pyc219cTY8ePdSjR49rntO0aVMdO3asMCU60ZQBAABjdk3KfAn3lAEAANgATRkAAIANsHwJAACMsXxpjqQMAADABkjKAACAMZIycyRlAAAANkBSBgAAjJGUmSMpAwAAsAGSMgAAYIykzBxJGQAAgA2QlAEAAGMkZea81pQdOXLEW1MbCw0N9XYJhRYVFeXtEozs3bvX2yUUWkhIiLdLKLRatWp5uwQjvvzn3pf/3Nxyyy3eLsGIZVneLgGlDEkZAAAwRlJmjnvKAAAAbICmDAAAwAZYvgQAAMZYvjRHUgYAAGADJGUAAMAYSZk5kjIAAAAbICkDAADGSMrMkZQBAADYAEkZAAAwRlJmjqQMAADABkjKAACAMZIycyRlAAAANkBSBgAAjJGUmSMpAwAAsAGSMgAA4BElOcUqDiRlAAAANkBTBgAAYAMsXwIAAGPc6G+OpAwAAMAGSMoAAIAxkjJzJGUAAAA2QFIGAACMkZSZIykDAACwAZIyAABgjKTMHEkZAACADZCUAQAAYyRl5kjKAAAAbICkDAAAGCMpM0dSBgAAYAM0ZQAAADbA8iUAADDG8qU5kjIAAAAbICkDAADGSMrMkZQBAADYAEkZAAAwRlJmjqQMAADABkjKAACAMZIycyRlAAAANkBSBgAAjJGUmSMpAwAAsAGSMgAAYIykzBxJGQAAgA2QlAEAAGMkZeZIygAAAGyApgwAAMAGWL4EAADGWL4057WmrEwZ3+0HL1686O0SCq1cuXLeLsFI3bp1vV1Cofn7+3u7hEILCQnxdglGzp075+0SCm3AgAHeLqHQcnNzvV2CkZycHG+XgFLGdzsjAABgGyRl5rinDAAAwAZIygAAgDGSMnMkZQAAADZAUgYAAIyRlJkjKQMAALABkjIAAOARJTnFKg4kZQAAADZAU+ZF8+fP1yuvvOLWZ9544w3t2bNHJ0+eVP369Qt1jcL45JNPNGLEiCKfBwDgmy7dU1Ycr5KqxCxf9uvXTx06dFBMTIxycnL07bffKjExUXv27PF2aR5zzz33qGvXrurQoYP279+vkydP6oknnvDpbxgAAAC/KzFJWbNmzTRlyhTdc889+tvf/qaAgAB99tlnhfpaofLlyys4OLgIqjRTs2ZN/fLLL/ruu+907Ngx5eXl6fTp08rOzi72WiIjI336a4MAALCbEtOUPfTQQ5o5c6bS0tL0008/6ZlnnlF0dLQaNWpUoM/7+/urTZs2mj59unbt2qWaNWtKkrZt26Znn33W5dyVK1fqpZdecr6fP3++Ro8erWHDhmnPnj1KTU11GZd+/87JyZMna//+/dq+fftl15Qky7LUp08fbdmyRQcPHtSqVav0wAMPSJLeeustvfrqq7rxxht18uRJff/99865/7h8WZBaLMvS888/rw0bNig9PV1LlixRQkKCc/y6667ThAkTtHv3bn3//fd6+umnL6u1W7du2rx5sxITE3XzzTcX6PcYAFBysXxprsQsX/5ZeHi4JOnUqVPXPC82NlbdunVT586dFRAQoHnz5ql9+/b66aef3Jrv0Ucf1eTJk3XfffepSZMmeuutt7Rx40atXLlSkjRixAjddddd6t69u44fP66XX35ZDRs2dJmnb9++6tSpk/r376+ff/5Zd911l9555x2dPHlSgwcP1t69e/XEE0+odevWysvLK3QtL7zwgh5++GENHDhQe/fu1V//+ldNmjRJJ0+e1IYNGzR06FDFxcXp73//u06cOKHBgwfr1ltv1fbt251zvP3229qzZ48eeeQRff3110pLS9Ps2bP16aef6tdff3Xr9w4AAJTQpsyyLI0ZM0br16/Xjh07LhuvWLGiunTpoq5du6pevXpavHix+vXrp6+++koXLlwo1Jzbt2/XuHHjJEk///yzevbsqebNm2vlypUKDg5Wt27d9Mwzz2j16tWSpOeee07btm1zfj4wMFB9+vTRQw89pE2bNkmS9u/frzvvvFNPPPGEnn76aWVnZysvL0/Hjh0rdC2BgYHq3bu3unTpos2bN0uSMjIydMcdd6h79+7aunWrunbtqhdeeEFr166VJL344ovOcy85f/68FixYoAULFqhSpUrq2LGjOnfurKFDh2r58uWaPXu2lixZcs3mEQBQcrB5rLkS2ZS9/vrrqlevntq0aXPF8aefflqDBw/WN998o0aNGunQoUPGc6amprq8/+WXX3T99ddLkmrUqKGyZcu6NDanT592eQihZs2aCg4O1ty5c12uExgY6NK8eaKWcuXKKTk52eWcgIAA/fTTT85aLy2PXqo1PT39qvOdPHlSU6dO1dSpU9WyZUu9+eabatu2re69916XdA0AAFxdiWvKXnvtNbVt21b333+/Dh8+fMVzPvzwQ128eFFdu3bVt99+qwULFig5OVlr1qy5rAPPz8+XZVkuxwICAi675p8TNofDIT+/gt+yFxISIknq2rWrjhw54jJ2/vz5Al/nf9Vy6QGG7t276+jRoy7n5ebmqnz58m7Ndema7du318MPP6y//vWvWr9+vUaNGqVdu3a5fS0AgG8iKTNXopqy1157Te3bt1dCQoL2799/1fOOHj2qcePGady4cbrjjjv02GOP6eOPP1Z2drZmz56t5ORkpaWlSfo9BYqMjHR+NjQ0VNHR0W7VtW/fPuXm5uq2225zpnLh4eGqXbu21q1bJ0nauXOncnJyVK1aNeexorBr1y7l5OSoatWq2rBhw2Xjp0+fVm5urv7yl7+41FqrVi2tX7/eeZ6fn5/i4+P1yCOPqG3btjp8+LDmzJmjvn37eiR5BACgtCkxTdkbb7yhRx55RF27dlVWVpYiIiIkSZmZmcrJybnq5zZu3KiNGzdq4MCBat++vbp166bevXurWbNmSk1N1apVq9StWzctWrRIZ86c0ZAhQ5Sfn+9WbWfPntWMGTM0YsQInTp1ynmj/x+7/ezsbL399tv697//LT8/P23YsEFhYWG68847lZWVddlyY2GdPXtW77zzjkaMGCE/Pz9t3LhRYWFhatKkibKysjRnzhzNmjVLQ4cO1alTp3TixAkNGjTosp+5d+/eevrpp7VgwQJ16dLFeR8cAKB0IikzV2Kasp49e0qSFi1a5HL8mWee0cyZM//n58+fP6+5c+dq7ty5ioqK0tmzZyX93uzVqFFDs2fPVmZmpl555RXVqFHD7fqGDRum4OBgzZgxQ9nZ2Zo8ebJCQ0NdzklKStKJEyfUp08fVa9eXWfOnNHWrVs1fvx4t+e7lrFjx+rkyZN64YUXFB0drczMTG3btk0TJ06UJI0aNUrBwcGaPn26srOz9e67715W6yeffKL//Oc/bi+tAgCAK7McXmg5MzMzVa1ateKe1mOudE+Zryhbtqy3SzDiy99e4Mub7V6659FXnTt3ztslFJo796baTW5urrdLMHLpH+e+6PDhwwoLCyuWuTIzMxUeHq5q1aoVy5/X/Px8HTx4UGfOnCm2n7G4+O5/7QAAACVIiVm+BAAA3sM9ZeZKXFO2cuVKl6cli8Kft8iAq+PHj+v+++/3dhkAAPiUEteURUZGqmrVqt4uAwCAUoWkzFyJa8p++eWXIp+DpOzajh8/7u0SAADwOSWuKWvRokWRz8HTlwAAwNNKXFMGAACKH8uX5tgSAwAAlFiTJk1SZGSk/P39ZVmWBg8e7DKen5+v5s2bO8crVqyoJUuWXHad4cOHKyQkRJZlyc/PT1WqVHEZX79+vSIiIpzjTZo0ueY3Cl0JTRkAADB2KSkrjpc7Tp06pZiYGA0YMOCK4wkJCVqzZo0GDhyoOXPmqGzZsmrXrp1Onz7tPOef//ynRo4cqQ4dOuirr77SZ599pkcffdQ5npubq5YtWyovL0/JyckaPny4Nm/erFatWrlVKzv6FwL3lHkPO/p7Bzv6ew87+nsPO/oXzKUd/aOiooptR/+jR48Wakd/y7I0aNAgjR492nmtgIAA3X///fr8888lSRkZGapevbpeeOEFTZw4UTk5OQoODlb37t01bdq0K1535MiRGjZsmLZt26b69etLkh577DHNmjVLWVlZBf7/UN/9rx0AANhGcSdlhw4d0sGDB52vzMxMt2tevXq18vPz1b17d+ex6OhohYeHa/Xq1ZKkWbNmKT8/X35+fipXrpz8/f1VuXJlffrpp87PLF26VEFBQc6GTPr9u7claeHChQWuh6YMAAD4nNjYWN14443OV4cOHdy+xs6dOyVJdevWdTkeGhqqX3/9VZL0/fffS5I++ugjPffcc5o+fbqCg4P18MMPKz09XdLvW0EFBwdfVp8k7d69u8D18PQlAAAwVtxPX6ampio0NNR5vKiWa/Py8iRJjz76qMaNGydJ6tChg8qXL6/ExETNmDHDY3PRlAEAAJ9TtWpV40bsUkK2c+dONWrUyHk8KytLNWrUkCTVrl1bknTXXXc5x8PCwlSuXDnt27dPklS5cmXnry9JTU2VJMXExBS4HpYvAQCAMbs+fXktzZs3l5+fnz7++GPnsYMHD+rMmTNq3ry5JKlz586SpI0bNzrPOXfunM6dO6eaNWtKklq3bq2cnBxt377dec6UKVMk/f50Z0GRlAEAgBLr6NGjWrVqlfP9jh07lJKSoujoaMXFxem+++7TF198oSFDhqhx48Z6/vnnVaZMGY0cOVKSVK1aNTVq1Ej//e9/VbduXTVq1Eh9+vSRJI0aNUqSNGjQICUlJSk+Pl6TJ0/W7t27NXPmTMXFxbmV5rElRiGwJYb3sCWGd7AlhvewJYb3sCVGwVzaEuP6668vti0xTpw4UeAtMd5880317dv3suO1a9fWnj17lJ+frxYtWuibb75Rfn6+KlSooFmzZqlNmzbOc8+dO6fmzZtry5Ytcjgcqlixoj744AP97W9/c57zzTffqGPHjjp+/Lgsy1Ljxo21du1aBQUFFfhnoykrBJoy76Ep8w6aMu+hKfMemrKCsXtT5kt89792AACAEoR7ygAAgDG+kNwcSRkAAIANkJQBAABjJGXmSMoAAABsgKQMAAAYIykzR1IGAABgA15JykpylwsAgLd56+9Z/n4345WmLCsrS1lZWd6YGgCAEi8rK0vh4eHeLgNu8kpTdsMNN+jAgQMKDQ2VZVneKAEAgBLH4XAoKytLN9xwg1fmLknzeINXmjI/Pz+f/polAADsioTMd/H0JQAAMEZSZo6nLwEAAGyApgwAAMAGWL4EAADGWL40R1IG4H8aPny4GjVqVCTXtixLn332WZFc247zAsDV0JQBBdSiRQv16dPnsuPTpk1T+fLli70eu8nIyFBCQoLKlSuniIgI/fOf/9TFixed40XZ2BXGkSNHdP/993u7DKDEuPQ1S8XxKqlYvgRgLC8vTwkJCYqKitK6det05MgR9ejRQwEBAUpKSvJ2eVcUFRXl7RIAwAVJGeBhTz75pB588EG99tprqlKliipVqqTnnntOFy5ccJ5To0YNJSUl6amnnlJoaKiio6M1ZcoUl+sMHDhQderUUbly5VSrVi0NHTrU5RqXkqcPPvhA0dHRCgkJ0bPPPqu8vDyNHTtWUVFRioiI0CuvvOJy3dOnT6tnz56qXLmywsLC1KpVK/34448u54wZM0aRkZEKDQ3V//3f/yknJ+eaP/PixYuVmpqqjz/+WI0aNdL999+vUaNG6e2331Zubq6mTZumESNG6Mcff5RlWbIsS9OmTXN+/sSJE+rYsaPKlSunmJgYLViw4Jrz/fe//9Xtt9+u0NBQRUVF6bHHHtOxY8ec4yNHjtQNN9ygkydPOo8lJCSoZcuWys/Pl+S6fJmbm6vnn39eVapUUVBQkKpXr67Ro0dfswYArkjKzNGUAUVgxYoVSk9P14oVKzR9+nRNmzbNpQmRpNdff1233367vv/+ez377LPq1auXdu7c6RwPDQ3VtGnTlJqaqgkTJui9997T+PHjXa6Rnp6uRYsW6auvvtKsWbP0/vvvKyEhQQcPHtSqVav06quv6uWXX9a3337r/EynTp107NgxLVq0SJs3b1bjxo11zz336Ndff5UkzZ49W8OHD1dSUpI2bdqkKlWqaPLkydf8edevX69bb71VkZGRzmNt2rRRZmamtm/fri5duqh///665ZZbdOTIER05ckRdunRxnjtixAh17txZW7duVbt27dStWzdnPVdy4cIFjRo1Sj/++KM+++wz7du3T08++aRzfMiQIapRo4Z69uwpSXr77be1bt06TZ8+XX5+l//f3sSJE7VgwQLNnj1bO3fu1IwZM1SjRo1r/swA4HEOAAUSHx/vePHFFy87/uGHHzrCw8Od75944glH9erVHRcvXnQe69Spk6NLly7O99WrV3c8/vjjzvf5+fmOiIgIx3/+85+rzj9u3DjHbbfd5nw/bNgwR7ly5RyZmZnOY23atHHUqFHDkZeX5zxWt25dx+jRox0Oh8OxZs0aR1hYmCMnJ8fl2rVr13a8++67DofD4YiLi3M8++yzLuN33nmno2HDhlet7f/9v//nuO+++1yOnT171iHJ8eWXXzrrvdI1JDlefvll5/vs7GyHJMeiRYuuOt+ffffddw5JjqysLOex9PR0R2hoqGPgwIGO6667zjFjxozL5v30008dDofD8cILLzhatWrlyM/PL/CcAH535swZhyRHcHCwIyQkpMhfwcHBDkmOM2fOePtH9ziSMqAI3HLLLfL393e+r1KlisvymiQ1aNDA+WvLshQVFeVyTkpKipo2baqoqCiFhITo5ZdfVkZGhss1atSoodDQUOf7yMhIxcbGuqRBkZGRzuv++OOPys7OVqVKlRQSEuJ87d27V+np6ZKkHTt26M4773SZJy4urrC/FQXyx9+L4OBghYWFXfb79UebN2/WAw88oOjoaIWGhio+Pl6SXH5/atWqpddee02vvvqqOnTooMcee+yq13vyySf1ww8/qG7duurdu7cWL17sgZ8KANzDjf5AAYWFhenMmTOXHT99+vRl3zUXEBDg8t6yLOe9TAU5Z/369erWrZtGjBihNm3aKDw8XMnJyXr99df/5zWudd3s7GxVqVJFK1euvOznMHmCNCoqShs3bnQ59ssvvzjH/peC/H5dcvbsWbVp00Zt2rTRjBkzVLlyZWVkZKhNmzbKzc11OXf16tXy9/fXvn37dPHiRZUpc+X/y2vcuLH27t2rRYsWaenSpercubNat26tTz755H/WDuB3DvYpM0ZSBhRQ3bp1tWXLlsuOb9myRXXq1PHoXOvWrVP16tU1ZMgQ3X777YqJidH+/fuNr9u4cWMdPXpUZcqU0U033eTyuv766yVJ9erVc7kHTZI2bNhwzevGxcVp27ZtLunWkiVLFBYWptjYWElSYGCg8vLyjH+GtLQ0nTx5UmPGjNHdd9+tm2+++YqpWkpKiubNm6eVK1cqIyNDo0aNuuZ1w8LC1KVLF7333ntKSUnR3Llzr3lfGwB4Gk0ZUEC9evXSrl271Lt3b23dulU7d+7UG2+8oVmzZql///4enSsmJkYZGRlKTk5Wenq6Jk6cqE8//dT4uq1bt1ZcXJwefPBBLV68WPv27dO6des0ZMgQbdq0SZL04osv6oMPPtCHH36oXbt2adiwYdq+ffs1r3vfffcpNjZW3bt3148//qivv/5aL7/8sp577jmVLVtW0u9LrXv37tUPP/ygEydO6Pz584X6GaKjoxUYGKhJkybp559/1oIFCy5ruA4ePKhevXrp1VdfVbNmzfThhx8qKSnpqs3lpf8d09LStGvXLs2ZM0dRUVHsPwe4wcHTl8ZoyoACqlWrllavXq20tDS1bt1ad955p2bPnq05c+aobdu2Hp2rQ4cO6tu3r55//nk1atRI69at09ChQ42va1mWvvzySzVv3lx///vfVadOHT366KPav3+/88nJLl26aOjQoXrppZd02223af/+/erVq9c1r+vv76/PP/9c/v7+iouL0+OPP64ePXpo5MiRznMefvhhtW3bVi1btlTlypU1a9asQv0MlStX1rRp0zRnzhzFxsZqzJgxeu2115zjDodDTz75pO644w49//zzkn5/ErRXr156/PHHlZ2dfdk1Q0NDNXbsWN1+++1q0qSJ9u3bpy+//PKKT2oCQFGxHCW55QQAAEUqMzNT4eHhCgoKkmVZRT6fw+FQTk6Ozpw5o7CwsCKfrzjxz0AAAAAboCkDAACwAbbEAAAAxtgSwxxJGQAAgA2QlAEAAGMkZeZIygAAAGyApAwAABgjKTNHUgYAAGADJGUAAMAYSZk5kjIAAAAbICkDAADGSMrMkZQBAADYAEkZAAAwRlJmjqQMAADABmjKAAAAbIDlSwAAYIzlS3MkZQAAADZAUgYAAIyRlJkjKQMAALABkjIAAOARJTnFKg4kZQAAADZAUwYAAAotMDBQUVFRxTpnVFSUAgMDi3XO4mA5yBoBAICBnJwc5ebmFtt8gYGBCgoKKrb5igtNGQAAgA2wfAkAAGADNGUAAAA2QFMGAABgAzRlAAAANkBTBgAAYAM0ZQAAADZAUwYAAGAD/x8TaL020pa9DQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x581.818 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x581.818 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aligned.plot()\n",
    "aligned.axes_manager"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Template Matching\n",
    "\n",
    "A common way to find spots in a diffraction pattern is through template matching.  Template matching involves creating a template of one of the diffraction spots and convolving it with the entire dataset.  Usually this is done using a flat disk or a summed image of the zero beam.  Often times reducing the noise in the dataset is useful as it \n",
    "\n",
    "There are a couple of ways to acomplish this is pyxem. \n",
    "\n",
    "The first is to use the `template_match_disk` function which uses the normalized_cross_correlation to find shere some diffraction spot is.  We can think of this as the template being slid across the image and multiplied at every point.  The result is that objects similar to the template are very visable in the filtered dataset. \n",
    "\n",
    "There are multiple ways to do this:\n",
    "\n",
    "1. Use the vaccum probe as a template and convolve the vaccum probe with the image\n",
    "2. Use the "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  0.2s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x581.818 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x581.818 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matched = aligned.template_match_disk(disk_r=5) # the disk_r should be equal to the size of the diffraction spots. \n",
    "matched.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Peak Finding\n",
    "We can then find the position of the peaks using the `hyperspy.Signal2D.find_peaks()` method.  There is an interactive form of this method as well which makes defining the proper settings a little bit easier. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b05e296c8b0342cbb429dc1eee5b4263",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Accordion(children=(VBox(children=(HBox(children=(Label(value='Unnamed 0th axis', layout=Layout…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<BaseSignal, title: , dimensions: (10, 10|)>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "matched.find_peaks()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  0.2s\n"
     ]
    }
   ],
   "source": [
    "peaks = matched.find_peaks(threshold=1.0, distance=5, interactive=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting Peaks to Diffraction Vectors\n",
    "\n",
    "We then need to take the peaks identified and transform them into diffraction Vectors. We can construct the signal using the following code.  Note that the center of the diffraction pattern and the calibration need to be passed once again. We can take these from the diffraction signal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  0.1s\n"
     ]
    }
   ],
   "source": [
    "center = [a.offset/a.scale*-1 for a in matched.axes_manager.signal_axes] # should be the center of the pattern now...\n",
    "\n",
    "calibration = matched.axes_manager.signal_axes[0].scale\n",
    "\n",
    "vectors = pxm.signals.DiffractionVectors.from_peaks(peaks,center = center, calibration=calibration)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filter the Diffraction Vectors\n",
    "\n",
    "We want to only return the diffraction vectors which are within some distance of a basis set of diffraction vectors.  This basis set can be just be a single set of diffraction spots but ideally more than 2 spots will be used.  This allows for the least squares fitting to also return a residual.  In many cases this is prefered as it allows outliers to be identified as points with high residuals.  Note that in some cases, using too many spots can also be probalematic as low intensity spots are harder to fit.  In those cases a weight parameter is also allowed to help refine the fitting. \n",
    "\n",
    "Defining the basis set of vectors can be done in multiple different ways.  \n",
    "\n",
    "1. Define a basis from a known basis set of vectors.  Using the known lattice parameter for the material it is possible to define the proper basis set for the material.\n",
    "\n",
    "2. Define the basis from an unstrained part of the material. \n",
    "\n",
    "\n",
    "In most cases the secound way is easier and prefered over the first.  It is less dependent on the calibration and things like elliptical distortion in the diffraction pattern will show up as strains in the first case and a very high quality of experiment is necessary.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  0.1s\n"
     ]
    }
   ],
   "source": [
    "# Get the basis vectors from an unstrained part of the material. \n",
    "basis = vectors.inav[1:2,1:2]\n",
    "basis.filter_magnitude(min_magnitude =0.1, max_magnitude=1)# remove the zero_beam\n",
    "basis_array = basis.data[0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  0.1s\n"
     ]
    }
   ],
   "source": [
    "filtered_vectors = vectors.filter_basis(basis_array, distance =.2, inplace= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyxem.generators.displacement_gradient_tensor_generator import get_DisplacementGradientMap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  0.1s\n"
     ]
    }
   ],
   "source": [
    "strain_map = get_DisplacementGradientMap(filtered_vectors, basis_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  0.1s\n",
      "[########################################] | 100% Completed |  0.1s\n",
      "[########################################] | 100% Completed |  0.1s\n"
     ]
    }
   ],
   "source": [
    "maps = strain_map.get_strain_maps()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_device_pixel_ratio', {\n",
       "                device_pixel_ratio: fig.ratio,\n",
       "            });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute('tabindex', '0');\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;' +\n",
       "            'z-index: 2;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box;' +\n",
       "            'pointer-events: none;' +\n",
       "            'position: relative;' +\n",
       "            'z-index: 0;'\n",
       "    );\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box;' +\n",
       "            'left: 0;' +\n",
       "            'pointer-events: none;' +\n",
       "            'position: absolute;' +\n",
       "            'top: 0;' +\n",
       "            'z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            /* This rescales the canvas back to display pixels, so that it\n",
       "             * appears correct on HiDPI screens. */\n",
       "            canvas.style.width = width + 'px';\n",
       "            canvas.style.height = height + 'px';\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        /* User Agent sniffing is bad, but WebKit is busted:\n",
       "         * https://bugs.webkit.org/show_bug.cgi?id=144526\n",
       "         * https://bugs.webkit.org/show_bug.cgi?id=181818\n",
       "         * The worst that happens here is that they get an extra browser\n",
       "         * selection when dragging, if this check fails to catch them.\n",
       "         */\n",
       "        var UA = navigator.userAgent;\n",
       "        var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n",
       "        if(isWebKit) {\n",
       "            return function (event) {\n",
       "                /* This prevents the web browser from automatically changing to\n",
       "                 * the text insertion cursor when the button is pressed. We\n",
       "                 * want to control all of the cursor setting manually through\n",
       "                 * the 'cursor' event from matplotlib */\n",
       "                event.preventDefault()\n",
       "                return fig.mouse_event(event, name);\n",
       "            };\n",
       "        } else {\n",
       "            return function (event) {\n",
       "                return fig.mouse_event(event, name);\n",
       "            };\n",
       "        }\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    canvas_div.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    canvas_div.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.canvas_div.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "function getModifiers(event) {\n",
       "    var mods = [];\n",
       "    if (event.ctrlKey) {\n",
       "        mods.push('ctrl');\n",
       "    }\n",
       "    if (event.altKey) {\n",
       "        mods.push('alt');\n",
       "    }\n",
       "    if (event.shiftKey) {\n",
       "        mods.push('shift');\n",
       "    }\n",
       "    if (event.metaKey) {\n",
       "        mods.push('meta');\n",
       "    }\n",
       "    return mods;\n",
       "}\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    // from https://stackoverflow.com/q/1114465\n",
       "    var boundingRect = this.canvas.getBoundingClientRect();\n",
       "    var x = (event.clientX - boundingRect.left) * this.ratio;\n",
       "    var y = (event.clientY - boundingRect.top) * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        modifiers: getModifiers(event),\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"699.9999895691873\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "f= plt.figure(figsize=(7,7))\n",
    "hs.plot.plot_images(maps,per_row=2,fig=f,\n",
    "                    label=[\"e11\",\"e22\", \"e12\", \"theta\"],tight_layout=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "hyperspy-dev",
   "language": "python",
   "name": "hyperspy-dev"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  },
  "widgets": {
   "state": {
    "280e931f7b274209a009d92f04098e5c": {
     "views": [
      {
       "cell_index": 54
      }
     ]
    },
    "3d704cd8716e4cb1bf00a7c4e9fe1330": {
     "views": [
      {
       "cell_index": 34
      }
     ]
    },
    "477fdf6035284f3ca253bc694d701370": {
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      {
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      }
     ]
    },
    "48d9b9d421a14ddc9e73f084d5fc5e37": {
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    },
    "635d08b3b596415ab27d7bed3b684f91": {
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      }
     ]
    },
    "64f45a95642f400ea7d3bed5fecff420": {
     "views": [
      {
       "cell_index": 52
      }
     ]
    },
    "7618040a46fc4203b76648a4d1383a11": {
     "views": [
      {
       "cell_index": 28
      }
     ]
    },
    "bb0e63a793d34b2bb255c5dca59a5aa3": {
     "views": [
      {
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      }
     ]
    }
   },
   "version": "1.2.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}