LabNB_12092019.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0,'/cis/home/leebc/Projects/Mouse_Histology/code/pytorch/') # add code directory to path\n",
    "# import lddmm\n",
    "import torch_lddmm\n",
    "# import numpy\n",
    "import numpy as np\n",
    "# import nibabel for i/o\n",
    "import nibabel as nib\n",
    "# import matplotlib for display\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import SimpleITK as sitk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SimpleITK's procedural API provides three methods for performing resampling, with the difference being the way you specify the resampling grid:\n",
    "\n",
    "Resample(const Image &image1, Transform transform, InterpolatorEnum interpolator, double defaultPixelValue, PixelIDValueEnum outputPixelType)\n",
    "\n",
    "Resample(const Image &image1, const Image &referenceImage, Transform transform, InterpolatorEnum interpolator, double defaultPixelValue, PixelIDValueEnum outputPixelType)\n",
    "\n",
    "Resample(const Image &image1, std::vector< uint32_t > size, Transform transform, InterpolatorEnum interpolator, std::vector< double > outputOrigin, std::vector< double > outputSpacing, std::vector< double > outputDirection, double defaultPixelValue, PixelIDValueEnum outputPixelType)\n",
    "\n",
    "Below we use the third resampling method.\n",
    "\n",
    "The goal is to make the target file (the SMI file) the same as the MRI file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.004000000189989805, 0.004000000189989805, 1.0)\n",
      "(220, 116, 14)\n",
      "[98.23226112 59.23475875  6.57543992] target\n",
      "[392.27772958 198.87809045  23.36704865] mri\n"
     ]
    },
    {
     "data": {
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RWSMiN6y43n6M77/eHrUR+OcV296BKZUd45h7QPYCdfFUO5ol8KPAWzGJd+c4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/+02a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UTGpzZP8v1TT1KTXc6l0xzy1zH9eYy1rIovNAeDDT408jx/0bqLSGYYJL908cP88P7HUcLw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\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O872cPWLVMUAWtq0Ku0bRisMiZN2u1shXHsPqra28m6SHwqxnznUKSc9z3WR++XEGlZn8\nxSNnOsTzDbzducQLd+UocRYpnmuBYz3Hx4E7hhTLh+XAX1m+msR/C4up7O9+mHD3k2a2b6gRXpqN\n6rDev921gD48iUg/ave3BrX9MjCeOdZur3QKu2NpioWE0pZaeDksqFWJycoJXg5ziEv5fGjoJsm2\nnAxj4bi7Vlh4fvlxvOY46plPbbacLK+dY22hB9u6a3r1PLblHnE//7nQU15ayDPpygcNotlF0o4S\nZxG5sH7LPWw1d7n7ifDh4hkze2PYAV1h2+nfTkQGZzu1HTut3Yft9e8nIhtQ4ixSPMeBgz3H1wEn\nhhTLh+LuJ8L9aTP7OflwxFPdIWxmdgA4PdQgL81Gddg2/3YiMlDbpu3Yxu0+qO2XQfKMrNXGuvsZ\nt9t4o4mVQvoWx1iSP46iiDhJoLvHcxKvzIPuzomOeo577t1sZdWr8FqPe74L6plTve5K3j2s9+si\n95WVsTOw3lWy13uu1c7fo97A5xc0VFtENuVF4JCZfRR4D/g88K+HG9LFM7NRIHL3+fD4buCbwNPA\n/cC3wv1Tw4vykm1Uh6eBh8L8xDuAWc1xE5FNULu/Najtl8FxB0/xLB/C7O0hx7MJ/YZZbIchGEqc\nRQrG3Ttm9hDwKyAGHnP314cc1qXYD/w8LGaRAH/h7r80sxeBn5jZV4B3gc8NMcYLMrMfkS8Gs8fM\njgOPkH9oWq8OvwA+AxwF6sCXBx6wiGw5aveLR22/iKxlvonNpc3sHWAeSIGOu99mZlPA/wBuAN4B\n/pW7z1jeWv5X8gakDvwbd/9deJ/7gX8f3vY/uvsT/c67y6b8Dvv0JVRL5MN71p98uWcrDREZELX9\nMiy/9eeY8+kLDEYUkStBbb8My2bb/ovZx3mze/Ktu5ddSLQfIR/CcjvwSHfjeBEREREREZGiupjE\nea37yPewI9x/tqf8B577DTARFlD4M+AZd5929xngGc7fWF5ERERERESkUDabOHf35HvZzB4IZav2\nsgMutJfdRuUiIiIiIiIihbXZxPkud7+VfBj2g2b2J31eu9Fedpva487MHjCzl8zspTbNTYYnIiKD\nYGYHzex5MztsZq+b2VdD+ZSZPWNmb4b7yVBuZvYdMztqZr83s1uHWwMREbkYavdFcptKnHv35ANW\n7ckHsMm97Da1x527P+rut7n7bSUqF1cbERG50jrA1939JuBO8i9Tb+Yi170QEZEtQ+2+CJtInM1s\n1MzGu4/J9+R7jZW97OD8vey+FL5tupOVvex+BdxtZpPhG6m7Q5mIiGwR7n6yu1OCu88Dh8mn3Vzs\nuhciIrIFqN0XyW1mH+eL3ZNv3b3s3H3azP4ceDG87pvuPn3ZaiIiIgNlZjcAnwR+y5p1L8zsQute\nnFzzXg+Q90xQZeSKxi0iIpfmcrb74f3U9suWccHE2d3fBj6xTvkHwHmbrXm+MfSDG7zXY8BjFx+m\niIgUiZmNAT8Fvubuc+HL1XVfuk7ZeetbuPujwKOQ7+V5ueIUEZHL43K3+6C2X7aWD7MdlYiI7EBm\nViL/8PRDd/9ZKL7YdS9ERGSLULsvosRZREQuguVdDN8HDrv7t3ueuth1L0REZAtQuy+S28wcZxER\nka67gC8Cr5rZK6HsG8C3uIh1L0REZMtQuy+CEmcREbkI7v7XrD9/DS5y3QsRESk+tfsiOQ3VFhER\nEREREelDibOIiIiIiIhIH0qcRURERERERPpQ4iwiIiIiIiLShxJnERERERERkT6UOIuIiIiIiIj0\nocRZREREREREpA8lziIiIiIiIiJ9KHEWERERERER6UOJs4iIiIiIiEgfSpxFRERERERE+lDiLCIi\nIiIiItKHEmcRERERERGRPpQ4i4iIiIiIiPShxFlERERERESkDyXOIiIiIiIiIn0ocRYRERERERHp\nQ4mziIiIiIiISB9KnEVERERERET6UOIsIiIiIiIi0ocSZxEREREREZE+kmEHICIi0muembPP+pOL\nwNlhxwLsQXGsVZRYrkQc11/m9xORTZpnZuFZf/LIsOMItnM7dymKEgcMse1X4iwiIoXi7nvN7CV3\nv23YsSiO8xUllqLEISKXzZGi/E0XpX1RHOcbZiwaqi0iIiIiIiLShxJnERERERERkT6UOIuISBE9\nOuwAAsVxmXPS9AAABzBJREFUvqLEUpQ4ROTyKNLfdFFiURznG1os5u7DOvcF7bIpv8M+PewwZId6\n1p98uSjzOUREREREZHjU4ywiIiIiIiLShxJnEREpDDO7x8yOmNlRM3t4gOc9aGbPm9lhM3vdzL4a\nyv+Dmb1nZq+E22cGFM87ZvZqOOdLoWzKzJ4xszfD/eQVjuHGnnq/YmZzZva1QV0TM3vMzE6b2Ws9\nZeteA8t9J/ze/N7Mbr0SMYnIlaG2vxjtfjjn0Nr+orf7SpxFRKQQzCwGvgvcC9wMfMHMbh7Q6TvA\n1939JuBO4MGec/8Xd78l3H4xoHgA/jScsztl5GHgOXc/BDwXjq8Ydz/SrTfwj4E68PPw9CCuyePA\nPWvKNroG9wKHwu0B4HtXKCYRuczU9q8y1HYfht72P06B230lziIiUhS3A0fd/W13bwE/Bu4bxInd\n/aS7/y48ngcOA9cO4twX4T7gifD4CeCzAzz3p4G33P3vB3VCd/81ML2meKNrcB/wA8/9BpgwswOD\niVREPiS1/RsbZrsPA277i97uK3EWEZGiuBY41nN8nCF8gDGzG4BPAr8NRQ+FYWCPDWKYXODAX5nZ\ny2b2QCjb7+4nIf+wB+wbUCwAnwd+1HM8jGsCG1+DQvzuiMglKcTfbwHa/qK1+1CMtr8w7b4SZxER\nKQpbp2ygWz+Y2RjwU+Br7j5HPvTr48AtwEngPw8olLvc/VbyoWgPmtmfDOi85zGzMvAvgP8ZioZ1\nTfoZ+u+OiFyyof/9FqTtL0y7D1ui7R/4740SZxERKYrjwMGe4+uAE4M6uZmVyD84/dDdfwbg7qfc\nPXX3DPjv5EMKrzh3PxHuT5PPLbsdONUdhhbuTw8iFvIPcb9z91MhpqFck2CjazDU3x0R+VDU9lO4\ndh+K0/YXpt1X4iwiIkXxInDIzD4avun+PPD0IE5sZgZ8Hzjs7t/uKe+dL/UvgdfW/uwViGXUzMa7\nj4G7w3mfBu4PL7sfeOpKxxJ8gZ6hesO4Jj02ugZPA18Kq6zeCcx2h/aJSOHt+La/gO0+FKftL0y7\nn1zJNxcREdksd++Y2UPAr4AYeMzdXx/Q6e8Cvgi8amavhLJvkK/uegv58K93gH87gFj2Az/PP8+R\nAH/h7r80sxeBn5jZV4B3gc9d6UDMbAT456yu938axDUxsx8BnwL2mNlx4BHgW6x/DX4BfAY4Sr4C\n7JevREwicvmp7QcK1O7D8Nr+orf75l7cKUC7bMrvsE8POwzZoZ71J1/u2Q5ARERERER2KA3VFhER\nEREREelDibOIiIiIiIhIH0qcRURERERERPpQ4iwiIiIiIiLShxJnERERERERkT6UOIuIiIiIiIj0\nocRZREREREREpA8lziIiIiIiIiJ9KHEWERERERER6cPcfdgxbMjM5oEjw45jgPYAZ4cdxIBshbpe\n7+57hx2EiIiIiIgMVzLsAC7giLvfNuwgBsXMXtop9d1JdRURERERka1NQ7VFRERERERE+lDiLCIi\nIiIiItJH0RPnR4cdwIDtpPrupLqKiIiIiMgWVujFwURERERERESGreg9ziIiIiIiIiJDVdjE2czu\nMbMjZnbUzB4edjyXwsweM7PTZvZaT9mUmT1jZm+G+8lQbmb2nVDf35vZrT0/c394/Ztmdv8w6nIh\nZnbQzJ43s8Nm9rqZfTWUb8v6ioiIiIjIzlHIxNnMYuC7wL3AzcAXzOzm4UZ1SR4H7llT9jDwnLsf\nAp4Lx5DX9VC4PQB8D/LEE3gEuAO4HXikm3wWTAf4urvfBNwJPBj+zbZrfUVEREREZIcoZOJMnjAd\ndfe33b0F/Bi4b8gxXTR3/zUwvab4PuCJ8PgJ4LM95T/w3G+ACTM7APwZ8Iy7T7v7DPAM5yfjQ+fu\nJ939d+HxPHAYuJZtWl8REREREdk5ipo4Xwsc6zk+Hsq2g/3ufhLyZBPYF8o3qvOWuxZmdgPwSeC3\n7ID6ioiIiIjI9lbUxNnWKdvuy39vVOctdS3MbAz4KfA1d5/r99J1yrZcfUVEREREZPsrauJ8HDjY\nc3wdcGJIsVxup8KQZML96VC+UZ23zLUwsxJ50vxDd/9ZKN629RURERERkZ2hqInzi8AhM/uomZWB\nzwNPDzmmy+VpoLtS9P3AUz3lXwqrTd8JzIahzb8C7jazybBI1t2hrFDMzIDvA4fd/ds9T23L+oqI\niIiIyM6RDDuA9bh7x8weIk+YYuAxd399yGFdNDP7EfApYI+ZHSdfLfpbwE/M7CvAu8Dnwst/AXwG\nOArUgS8DuPu0mf05+ZcJAN9097ULjhXBXcAXgVfN7JVQ9g22b31FRERERGSHMHdNHxURERERERHZ\nSFGHaouIiIiIiIgUghJnERERERERkT6UOIuIiIiIiIj0ocRZREREREREpA8lziIiIiIiIiJ9KHEW\nERERERER6UOJs4iIiIiIiEgfSpxFRERERERE+vj/ixn2HNsNPn0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x1440 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load mouse brain histology and mri images\n",
    "target_file_name = '/cis/home/jtrieu/Documents/WK0145_SMI_v5_reversed.img'\n",
    "mri_file_name = '/cis/home/jtrieu/Documents//WK0145_20191120.img'\n",
    "\n",
    "# resample target file to make it the same size as the mri file\n",
    "mri_sitk = sitk.ReadImage(mri_file_name)    \n",
    "target_sitk = sitk.ReadImage(target_file_name)\n",
    "\n",
    "# removing the 4th dimension from the target file\n",
    "target_data = sitk.GetArrayFromImage(target_sitk)\n",
    "target_data = np.squeeze(target_data)\n",
    "target_3D = sitk.GetImageFromArray(target_data)\n",
    "target_3D.SetSpacing([target_sitk.GetSpacing()[0],target_sitk.GetSpacing()[1],target_sitk.GetSpacing()[3]])\n",
    "\n",
    "# save the new 3D target image\n",
    "sitk.WriteImage(target_3D,'/cis/home/jtrieu/Documents/WK0145_3D_SMI_v5_reversed.img')\n",
    "\n",
    "# update target file name\n",
    "target_file_name = '/cis/home/jtrieu/Documents/WK0145_3D_SMI_v5_reversed.img'\n",
    "\n",
    "# read in new target that's been reduced from 4->3 dimensions\n",
    "target_sitk = sitk.ReadImage(target_file_name)\n",
    "print(target_sitk.GetSpacing())\n",
    "\n",
    "# resample the target image\n",
    "#output_size = [mri_sitk.GetSize()[0],mri_sitk.GetSize()[1],target_3D.GetSize()[2]]\n",
    "output_spacing = [mri_sitk.GetSpacing()[0]/4,mri_sitk.GetSpacing()[1]/4,target_3D.GetSpacing()[2]]\n",
    "output_size = [int(np.round(target_3D.GetSize()[0]*target_3D.GetSpacing()[0]/output_spacing[0])),\n",
    "               int(np.round(target_3D.GetSize()[1]*target_3D.GetSpacing()[1]/output_spacing[1])),\n",
    "               target_3D.GetSize()[2]]\n",
    "target_resample = sitk.Resample(sitk.SmoothingRecursiveGaussian(target_3D,\n",
    "                                                                target_3D.GetSpacing()[0]*2),\n",
    "                                                                output_size,\n",
    "                                                                sitk.AffineTransform(3),\n",
    "                                                                sitk.sitkLinear,\n",
    "                                                                (0,0,0),\n",
    "                                                                output_spacing,\n",
    "                                                                (1,0,0,0,1,0,0,0,1), 0.0)\n",
    "\n",
    "# pad the resampled target\n",
    "target_resample_np = np.transpose(sitk.GetArrayFromImage(target_resample),(2,1,0))\n",
    "pad_amount = [15,15,4]\n",
    "target_padded_np = np.zeros((target_resample_np.shape[0]+pad_amount[0]*2,target_resample_np.shape[1]+pad_amount[1]*2,target_resample_np.shape[2]+pad_amount[2]*2))\n",
    "target_padded_np[pad_amount[0]:-pad_amount[0],pad_amount[1]:-pad_amount[1],pad_amount[2]:-pad_amount[2]] = target_resample_np\n",
    "\n",
    "target_resample = sitk.GetImageFromArray(np.transpose(target_padded_np,(2,1,0)))\n",
    "target_resample.SetSpacing(output_spacing)\n",
    "\n",
    "print(target_resample.GetSize())\n",
    "\n",
    "# save the new 3D target resampled image\n",
    "sitk.WriteImage(target_resample,'/cis/home/jtrieu/Documents/WK0145_3D_SMI_resampled.img')\n",
    "\n",
    "# update target file name to new 3D target resampled image\n",
    "target_file_name = '/cis/home/jtrieu/Documents/WK0145_3D_SMI_resampled.img'\n",
    "\n",
    "# load mri and target data into numpy arrays\n",
    "target_image_struct = nib.load(target_file_name)\n",
    "mri_image_struct = nib.load(mri_file_name)\n",
    "target_image = np.squeeze(target_image_struct.get_data()).astype(np.float32)\n",
    "mri_image = np.squeeze(mri_image_struct.get_data()).astype(np.float32)\n",
    "\n",
    "mri_image_mirrored = mri_image[:,mri_image.shape[1]:None:-1,:] # mirror mri in y dimension\n",
    "mri_image_mirrored_sitk = sitk.GetImageFromArray(np.transpose(mri_image_mirrored,(2,1,0)))\n",
    "mri_image_mirrored_sitk.SetSpacing(mri_sitk.GetSpacing())\n",
    "mri_output_size = [int(np.round(mri_sitk.GetSize()[0]*mri_sitk.GetSpacing()[0]/output_spacing[0])),int(np.round(mri_sitk.GetSize()[1]*mri_sitk.GetSpacing()[1]/output_spacing[1])),int(np.round(mri_sitk.GetSize()[2]*mri_sitk.GetSpacing()[2]/output_spacing[2]))]\n",
    "mri_resample = sitk.Resample(mri_image_mirrored_sitk, mri_output_size, sitk.AffineTransform(3), sitk.sitkLinear,\n",
    "                             (0,0,0), output_spacing, (1,0,0,0,1,0,0,0,1), 0.0)\n",
    "\n",
    "target_center_of_mass = np.mean(np.where(np.transpose(sitk.GetArrayFromImage(target_resample),(2,1,0)) > np.mean(np.transpose(sitk.GetArrayFromImage(target_resample),(2,1,0)))),axis=1)\n",
    "mri_center_of_mass = np.mean(np.where(np.transpose(sitk.GetArrayFromImage(mri_resample),(2,1,0)) > np.mean(np.transpose(sitk.GetArrayFromImage(mri_resample),(2,1,0)))),axis=1)\n",
    "print(target_center_of_mass, 'target')\n",
    "print(mri_center_of_mass,'mri')\n",
    "\n",
    "# plot translated, resampled target image\n",
    "translation = sitk.TranslationTransform(3)\n",
    "translation.SetOffset((-(target_center_of_mass[0]-mri_center_of_mass[0])*mri_resample.GetSpacing()[0],\n",
    "                       -(target_center_of_mass[1]-mri_center_of_mass[1])*mri_resample.GetSpacing()[1],\n",
    "                       -(target_center_of_mass[2]-mri_center_of_mass[2])*mri_resample.GetSpacing()[2]))\n",
    "mri_transform = sitk.Resample(mri_resample, target_resample.GetSize(), translation, sitk.sitkLinear,\n",
    "                                 (0,0,0), output_spacing, (1,0,0,0,1,0,0,0,1), 0.0)\n",
    "\n",
    "# display slices\n",
    "plt.rcParams[\"figure.figsize\"]=20,20\n",
    "plt.figure()\n",
    "\n",
    "# plot original target image\n",
    "plt.subplot(1,6,1)\n",
    "plt.title('Original Target Image')\n",
    "plt.imshow(np.transpose(sitk.GetArrayFromImage(target_3D),(2,1,0))[:,:,1])\n",
    "\n",
    "# plot original mri image\n",
    "plt.subplot(1,6,2)\n",
    "plt.imshow(mri_image[:,:,36])\n",
    "plt.title('Original MRI Image')\n",
    "\n",
    "# plot resampled target image\n",
    "plt.subplot(1,6,3)\n",
    "plt.title('Resampled Target Image')\n",
    "plt.imshow(np.transpose(sitk.GetArrayFromImage(target_resample),(2,1,0))[:,:,5])\n",
    "\n",
    "# plot mirrored mri image\n",
    "plt.subplot(1,6,4)\n",
    "plt.imshow(np.transpose(sitk.GetArrayFromImage(mri_image_mirrored_sitk),(2,1,0))[:,:,36])\n",
    "plt.title('Mirrored MRI Image')\n",
    "\n",
    "plt.subplot(1,6,5)\n",
    "plt.imshow(np.transpose(sitk.GetArrayFromImage(mri_transform),(2,1,0))[:,:,1])\n",
    "plt.title('Rescaled MRI Image')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "sitk.WriteImage(target_resample,'/cis/home/jtrieu/Documents/target_resample.img')\n",
    "sitk.WriteImage(mri_transform,'/cis/home/jtrieu/Documents/mri_transform.img')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Start with alignment to self"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Current parameters:\n",
      ">    a               = 8 (smoothing kernel, a*(pixel_size))\n",
      ">    p               = 2 (smoothing kernel power, p*2)\n",
      ">    niter           = 150 (number of iterations)\n",
      ">    epsilon         = 0.005 (gradient descent step size)\n",
      ">    epsilonL        = 1e-07 (gradient descent step size, affine)\n",
      ">    epsilonT        = 2e-05 (gradient descent step size, translation)\n",
      ">    minbeta         = 0.0001 (smallest multiple of epsilon)\n",
      ">    sigma           = [20.0] (matching term coefficient (0.5/sigma**2))\n",
      ">    sigmaR          = 10.0 (regularization term coefficient (0.5/sigmaR**2))\n",
      ">    nt              = 5 (number of time steps in velocity field)\n",
      ">    do_lddmm        = 0 (perform LDDMM step, 0 = no, 1 = yes)\n",
      ">    do_affine       = 2 (interleave linear registration: 0 = no, 1 = affine, 2 = rigid)\n",
      ">    checkaffinestep = 1 (evaluate linear matching energy: 0 = no, 1 = yes)\n",
      ">    im_norm_ms      = 0 (normalize image by mean and std: 0 = no, 1 = yes)\n",
      ">    gpu_number      = None (index of CUDA_VISIBLE_DEVICES to use)\n",
      ">    dtype           = float (bit depth, 'float' or 'double')\n",
      ">    energy_fraction = 0.02 (fraction of initial energy at which to stop)\n",
      ">    cc              = 0 (contrast correction: 0 = no, 1 = yes)\n",
      ">    cc_channels     = [] (image channels to run contrast correction (0-indexed))\n",
      ">    we              = 0 (weight estimation: 0 = no, 2+ = yes)\n",
      ">    we_channels     = [] (image channels to run weight estimation (0-indexed))\n",
      ">    sigmaW          = 1.0 (coefficient for each weight estimation class)\n",
      ">    nMstep          = 5 (update weight estimation every nMstep steps)\n",
      ">    v_scale         = 1.0 (parameter scaling factor)\n",
      ">    low_memory      = 0 (low memory mode: 0 = no, 1 = yes)\n",
      ">    update_epsilon  = 0 (update optimization step size between runs: 0 = no, 1 = yes)\n",
      ">    outdir          = ../notebook/ (output directory name)\n",
      ">    optimizer       = gradient descent with reducing epsilon (optimizer type)\n",
      "\n",
      "\n",
      ">    template        = [numpy.ndarray]\n",
      "\n",
      ">    target          = [numpy.ndarray]\n",
      "\n",
      ">    costmask        = None (costmask file name or numpy.ndarray)\n",
      "WARNING: nt set to 1 because settings indicate affine registration only.\n"
     ]
    }
   ],
   "source": [
    "# need to change these to numpy arrays\n",
    "mri_transform_arr = np.transpose(sitk.GetArrayFromImage(mri_transform),[2,1,0])*255.0\n",
    "target_resample_arr = np.transpose(sitk.GetArrayFromImage(target_resample),[2,1,0])\n",
    "\n",
    "lddmm = torch_lddmm.LDDMM(template=[mri_transform_arr],target=[target_resample_arr],costmask=None,outdir='../notebook/',do_affine=2,do_lddmm=0,a=8,niter=150,epsilon=5e-3,sigma=[20.0],sigmaR=10.0,optimizer='gdr',\n",
    "                          dx=output_spacing,cc=0,gpu_number = None,checkaffinestep=1)\n",
    "lddmm._checkParameters()\n",
    "lddmm._load(lddmm.params['template'],lddmm.params['target'],lddmm.params['costmask'])\n",
    "lddmm.initializeVariables()\n",
    "lddmm.initializeKernels()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 10, 11, 12, 13]\n",
      "iter: 1, E= 2119.716, Eim_t= 2119.716, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0000, ep_xy= 0.0000, ep_t= 0.0000.\n",
      "iter: 2, E= 2118.647, Eim_t= 2118.647, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0000, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 3, E= 2115.562, Eim_t= 2115.562, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0000, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 4, E= 2114.664, Eim_t= 2114.664, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0000, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 5, E= 2111.440, Eim_t= 2111.440, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0000, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 6, E= 2110.529, Eim_t= 2110.529, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0000, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 7, E= 2107.362, Eim_t= 2107.362, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0000, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 8, E= 2106.433, Eim_t= 2106.433, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0000, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 9, E= 2103.340, Eim_t= 2103.340, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0000, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 10, E= 2102.391, Eim_t= 2102.391, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0000, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 11, E= 2099.385, Eim_t= 2099.385, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0000, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 12, E= 2098.417, Eim_t= 2098.417, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0000, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 13, E= 2095.515, Eim_t= 2095.515, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0001, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 14, E= 2094.526, Eim_t= 2094.526, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0001, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 15, E= 2091.744, Eim_t= 2091.744, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0001, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 16, E= 2090.735, Eim_t= 2090.735, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0001, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 17, E= 2088.090, Eim_t= 2088.089, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0001, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 18, E= 2087.061, Eim_t= 2087.061, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0001, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 19, E= 2084.573, Eim_t= 2084.573, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0002, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 20, E= 2083.524, Eim_t= 2083.524, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0002, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 21, E= 2081.140, Eim_t= 2081.140, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0002, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 22, E= 2080.055, Eim_t= 2080.054, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0002, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 23, E= 2077.711, Eim_t= 2077.711, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0003, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 24, E= 2076.589, Eim_t= 2076.589, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0003, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 25, E= 2074.313, Eim_t= 2074.313, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0004, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 26, E= 2073.152, Eim_t= 2073.152, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0004, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 27, E= 2070.919, Eim_t= 2070.918, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0004, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 28, E= 2069.713, Eim_t= 2069.713, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0004, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 29, E= 2067.526, Eim_t= 2067.526, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0005, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 30, E= 2066.288, Eim_t= 2066.288, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0005, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 31, E= 2064.100, Eim_t= 2064.099, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0006, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 32, E= 2062.819, Eim_t= 2062.818, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0006, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 33, E= 2060.533, Eim_t= 2060.532, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0007, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 34, E= 2059.195, Eim_t= 2059.194, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0007, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 35, E= 2056.782, Eim_t= 2056.781, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0008, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 36, E= 2055.376, Eim_t= 2055.375, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0008, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 37, E= 2052.888, Eim_t= 2052.887, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0010, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 38, E= 2051.424, Eim_t= 2051.423, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0010, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 39, E= 2048.944, Eim_t= 2048.943, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0011, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 40, E= 2047.423, Eim_t= 2047.422, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0011, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 41, E= 2044.969, Eim_t= 2044.968, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0013, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 42, E= 2043.394, Eim_t= 2043.393, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0013, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 43, E= 2040.982, Eim_t= 2040.981, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0015, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 44, E= 2039.358, Eim_t= 2039.356, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0015, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 45, E= 2036.893, Eim_t= 2036.891, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0017, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 46, E= 2035.208, Eim_t= 2035.207, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0017, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 47, E= 2032.636, Eim_t= 2032.635, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0019, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 48, E= 2030.901, Eim_t= 2030.899, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0019, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 49, E= 2028.270, Eim_t= 2028.268, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0021, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 50, E= 2026.487, Eim_t= 2026.485, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0021, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 51, E= 2023.783, Eim_t= 2023.781, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0024, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 52, E= 2021.947, Eim_t= 2021.945, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0024, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 53, E= 2019.208, Eim_t= 2019.205, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0026, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 54, E= 2017.319, Eim_t= 2017.317, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0026, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 55, E= 2014.514, Eim_t= 2014.511, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0029, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 56, E= 2012.573, Eim_t= 2012.570, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0029, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 57, E= 2009.744, Eim_t= 2009.740, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0033, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 58, E= 2007.750, Eim_t= 2007.746, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0033, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 59, E= 2005.027, Eim_t= 2005.023, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0036, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 60, E= 2002.985, Eim_t= 2002.981, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0036, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 61, E= 2000.368, Eim_t= 2000.364, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0040, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 62, E= 1998.285, Eim_t= 1998.281, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0040, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 63, E= 1995.721, Eim_t= 1995.717, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0044, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 64, E= 1993.589, Eim_t= 1993.585, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0044, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 65, E= 1990.968, Eim_t= 1990.964, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0048, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 66, E= 1988.769, Eim_t= 1988.764, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0048, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 67, E= 1986.132, Eim_t= 1986.127, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0052, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 68, E= 1983.878, Eim_t= 1983.872, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0052, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 69, E= 1981.363, Eim_t= 1981.358, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0057, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 70, E= 1979.053, Eim_t= 1979.048, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0057, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter: 71, E= 1976.705, Eim_t= 1976.699, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0061, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 72, E= 1974.351, Eim_t= 1974.345, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0061, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 73, E= 1972.098, Eim_t= 1972.092, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0066, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 74, E= 1969.687, Eim_t= 1969.680, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0066, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 75, E= 1967.509, Eim_t= 1967.501, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0071, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 76, E= 1965.055, Eim_t= 1965.048, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0071, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 77, E= 1962.984, Eim_t= 1962.976, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0076, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 78, E= 1960.499, Eim_t= 1960.491, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0076, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 79, E= 1958.559, Eim_t= 1958.551, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0082, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 80, E= 1956.038, Eim_t= 1956.030, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0082, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 81, E= 1954.232, Eim_t= 1954.224, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0087, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 82, E= 1951.661, Eim_t= 1951.653, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0087, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 83, E= 1949.892, Eim_t= 1949.883, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0093, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 84, E= 1947.261, Eim_t= 1947.252, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0093, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 85, E= 1945.445, Eim_t= 1945.435, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0098, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 86, E= 1942.746, Eim_t= 1942.736, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0098, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 87, E= 1940.917, Eim_t= 1940.906, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0104, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 88, E= 1938.151, Eim_t= 1938.141, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0104, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 89, E= 1936.385, Eim_t= 1936.374, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0110, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 90, E= 1933.552, Eim_t= 1933.541, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0110, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 91, E= 1931.822, Eim_t= 1931.810, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0116, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 92, E= 1928.922, Eim_t= 1928.910, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0116, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 93, E= 1927.194, Eim_t= 1927.181, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0123, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 94, E= 1924.223, Eim_t= 1924.210, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0123, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 95, E= 1922.485, Eim_t= 1922.472, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0130, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 96, E= 1919.441, Eim_t= 1919.428, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0130, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 97, E= 1917.729, Eim_t= 1917.715, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0136, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 98, E= 1914.606, Eim_t= 1914.592, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0136, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 99, E= 1913.004, Eim_t= 1912.989, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0143, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 100, E= 1909.803, Eim_t= 1909.788, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0143, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 101, E= 1908.344, Eim_t= 1908.329, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0150, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 102, E= 1905.059, Eim_t= 1905.044, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0150, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 103, E= 1903.763, Eim_t= 1903.748, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0157, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 104, E= 1900.399, Eim_t= 1900.384, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0157, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 105, E= 1899.290, Eim_t= 1899.273, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0163, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 106, E= 1895.846, Eim_t= 1895.829, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0163, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 107, E= 1894.940, Eim_t= 1894.923, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0169, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 108, E= 1891.416, Eim_t= 1891.399, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0169, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 109, E= 1890.697, Eim_t= 1890.680, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0174, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 110, E= 1887.075, Eim_t= 1887.058, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0174, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 111, E= 1886.496, Eim_t= 1886.478, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0179, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 112, E= 1882.774, Eim_t= 1882.756, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0179, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 113, E= 1882.287, Eim_t= 1882.268, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0183, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 114, E= 1878.457, Eim_t= 1878.438, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0183, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 115, E= 1878.057, Eim_t= 1878.039, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0187, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 116, E= 1874.116, Eim_t= 1874.098, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0187, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 117, E= 1873.775, Eim_t= 1873.756, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0190, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 118, E= 1869.718, Eim_t= 1869.699, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0190, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 119, E= 1869.431, Eim_t= 1869.411, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0192, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 120, E= 1865.242, Eim_t= 1865.222, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0192, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 121, E= 1864.985, Eim_t= 1864.965, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0194, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 122, E= 1860.663, Eim_t= 1860.643, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0194, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 123, E= 1860.412, Eim_t= 1860.392, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0196, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 124, E= 1855.942, Eim_t= 1855.922, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0196, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 125, E= 1855.692, Eim_t= 1855.672, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0197, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 126, E= 1851.070, Eim_t= 1851.051, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0197, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 127, E= 1850.823, Eim_t= 1850.803, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0197, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 128, E= 1846.047, Eim_t= 1846.027, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0197, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 129, E= 1845.804, Eim_t= 1845.784, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0198, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 130, E= 1840.872, Eim_t= 1840.852, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0198, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 131, E= 1840.625, Eim_t= 1840.605, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0197, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 132, E= 1835.526, Eim_t= 1835.505, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0197, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 133, E= 1835.263, Eim_t= 1835.243, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0197, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 134, E= 1829.975, Eim_t= 1829.955, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0197, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 135, E= 1829.693, Eim_t= 1829.673, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0196, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 136, E= 1824.205, Eim_t= 1824.185, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0196, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 137, E= 1823.897, Eim_t= 1823.878, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0195, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 138, E= 1818.196, Eim_t= 1818.176, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0195, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 139, E= 1817.861, Eim_t= 1817.841, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0194, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 140, E= 1811.934, Eim_t= 1811.914, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0194, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 141, E= 1811.568, Eim_t= 1811.548, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0192, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 142, E= 1805.397, Eim_t= 1805.377, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0192, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter: 143, E= 1804.988, Eim_t= 1804.968, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0191, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 144, E= 1798.583, Eim_t= 1798.563, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0191, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 145, E= 1798.171, Eim_t= 1798.151, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0189, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 146, E= 1791.548, Eim_t= 1791.529, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0189, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 147, E= 1791.163, Eim_t= 1791.144, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0187, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 148, E= 1784.298, Eim_t= 1784.279, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0187, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 149, E= 1783.928, Eim_t= 1783.909, Eim_a= 0.000, ER_xy= 0.000, ER_t= 0.0185, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 150, E= 1776.773, Eim_t= 1776.755, Eim_a= 0.000, ER_xy= 0.001, ER_t= 0.0185, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 151, E= 1776.364, Eim_t= 1776.346, Eim_a= 0.000, ER_xy= 0.001, ER_t= 0.0182, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 152, E= 1768.867, Eim_t= 1768.848, Eim_a= 0.000, ER_xy= 0.001, ER_t= 0.0182, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 153, E= 1768.420, Eim_t= 1768.402, Eim_a= 0.000, ER_xy= 0.001, ER_t= 0.0180, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 154, E= 1760.624, Eim_t= 1760.605, Eim_a= 0.000, ER_xy= 0.001, ER_t= 0.0180, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 155, E= 1760.231, Eim_t= 1760.213, Eim_a= 0.000, ER_xy= 0.001, ER_t= 0.0178, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 156, E= 1752.153, Eim_t= 1752.134, Eim_a= 0.000, ER_xy= 0.001, ER_t= 0.0178, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 157, E= 1751.764, Eim_t= 1751.746, Eim_a= 0.000, ER_xy= 0.001, ER_t= 0.0175, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 158, E= 1743.356, Eim_t= 1743.338, Eim_a= 0.000, ER_xy= 0.001, ER_t= 0.0175, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 159, E= 1742.901, Eim_t= 1742.883, Eim_a= 0.000, ER_xy= 0.001, ER_t= 0.0172, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 160, E= 1734.051, Eim_t= 1734.033, Eim_a= 0.000, ER_xy= 0.001, ER_t= 0.0172, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 161, E= 1733.398, Eim_t= 1733.380, Eim_a= 0.000, ER_xy= 0.001, ER_t= 0.0170, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 162, E= 1724.030, Eim_t= 1724.012, Eim_a= 0.000, ER_xy= 0.001, ER_t= 0.0170, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 163, E= 1723.417, Eim_t= 1723.400, Eim_a= 0.000, ER_xy= 0.001, ER_t= 0.0167, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 164, E= 1713.701, Eim_t= 1713.684, Eim_a= 0.000, ER_xy= 0.001, ER_t= 0.0167, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 165, E= 1713.154, Eim_t= 1713.137, Eim_a= 0.000, ER_xy= 0.001, ER_t= 0.0164, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 166, E= 1702.912, Eim_t= 1702.895, Eim_a= 0.000, ER_xy= 0.001, ER_t= 0.0164, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 167, E= 1702.314, Eim_t= 1702.297, Eim_a= 0.000, ER_xy= 0.001, ER_t= 0.0161, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 168, E= 1691.674, Eim_t= 1691.657, Eim_a= 0.000, ER_xy= 0.001, ER_t= 0.0161, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 169, E= 1690.876, Eim_t= 1690.859, Eim_a= 0.000, ER_xy= 0.001, ER_t= 0.0158, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 170, E= 1679.711, Eim_t= 1679.695, Eim_a= 0.000, ER_xy= 0.001, ER_t= 0.0158, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 171, E= 1679.005, Eim_t= 1678.988, Eim_a= 0.000, ER_xy= 0.001, ER_t= 0.0155, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 172, E= 1667.621, Eim_t= 1667.605, Eim_a= 0.000, ER_xy= 0.001, ER_t= 0.0155, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 173, E= 1667.141, Eim_t= 1667.124, Eim_a= 0.000, ER_xy= 0.001, ER_t= 0.0153, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 174, E= 1655.194, Eim_t= 1655.177, Eim_a= 0.000, ER_xy= 0.001, ER_t= 0.0153, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 175, E= 1654.575, Eim_t= 1654.558, Eim_a= 0.000, ER_xy= 0.001, ER_t= 0.0151, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 176, E= 1641.856, Eim_t= 1641.839, Eim_a= 0.000, ER_xy= 0.001, ER_t= 0.0151, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 177, E= 1641.247, Eim_t= 1641.230, Eim_a= 0.000, ER_xy= 0.001, ER_t= 0.0149, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 178, E= 1628.631, Eim_t= 1628.615, Eim_a= 0.000, ER_xy= 0.002, ER_t= 0.0149, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 179, E= 1628.588, Eim_t= 1628.572, Eim_a= 0.000, ER_xy= 0.002, ER_t= 0.0147, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 180, E= 1616.404, Eim_t= 1616.388, Eim_a= 0.000, ER_xy= 0.002, ER_t= 0.0147, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 181, E= 1617.031, Eim_t= 1617.015, Eim_a= 0.000, ER_xy= 0.002, ER_t= 0.0145, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 182, E= 1603.553, Eim_t= 1603.536, Eim_a= 0.000, ER_xy= 0.002, ER_t= 0.0145, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 183, E= 1603.577, Eim_t= 1603.560, Eim_a= 0.000, ER_xy= 0.002, ER_t= 0.0144, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 184, E= 1591.637, Eim_t= 1591.621, Eim_a= 0.000, ER_xy= 0.002, ER_t= 0.0144, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 185, E= 1591.531, Eim_t= 1591.515, Eim_a= 0.000, ER_xy= 0.002, ER_t= 0.0143, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 186, E= 1579.914, Eim_t= 1579.898, Eim_a= 0.000, ER_xy= 0.002, ER_t= 0.0143, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 187, E= 1579.662, Eim_t= 1579.646, Eim_a= 0.000, ER_xy= 0.002, ER_t= 0.0141, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 188, E= 1567.463, Eim_t= 1567.447, Eim_a= 0.000, ER_xy= 0.002, ER_t= 0.0141, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 189, E= 1567.044, Eim_t= 1567.028, Eim_a= 0.000, ER_xy= 0.002, ER_t= 0.0140, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 190, E= 1555.411, Eim_t= 1555.394, Eim_a= 0.000, ER_xy= 0.002, ER_t= 0.0140, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 191, E= 1555.152, Eim_t= 1555.136, Eim_a= 0.000, ER_xy= 0.002, ER_t= 0.0138, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 192, E= 1544.376, Eim_t= 1544.360, Eim_a= 0.000, ER_xy= 0.002, ER_t= 0.0138, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 193, E= 1543.894, Eim_t= 1543.878, Eim_a= 0.000, ER_xy= 0.002, ER_t= 0.0136, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 194, E= 1533.773, Eim_t= 1533.757, Eim_a= 0.000, ER_xy= 0.003, ER_t= 0.0136, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 195, E= 1533.309, Eim_t= 1533.293, Eim_a= 0.000, ER_xy= 0.003, ER_t= 0.0134, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 196, E= 1523.851, Eim_t= 1523.835, Eim_a= 0.000, ER_xy= 0.003, ER_t= 0.0134, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 197, E= 1523.333, Eim_t= 1523.317, Eim_a= 0.000, ER_xy= 0.003, ER_t= 0.0132, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 198, E= 1514.253, Eim_t= 1514.237, Eim_a= 0.000, ER_xy= 0.003, ER_t= 0.0132, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 199, E= 1513.895, Eim_t= 1513.879, Eim_a= 0.000, ER_xy= 0.003, ER_t= 0.0130, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 200, E= 1505.162, Eim_t= 1505.146, Eim_a= 0.000, ER_xy= 0.003, ER_t= 0.0130, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 201, E= 1504.830, Eim_t= 1504.814, Eim_a= 0.000, ER_xy= 0.003, ER_t= 0.0128, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 202, E= 1495.760, Eim_t= 1495.744, Eim_a= 0.000, ER_xy= 0.003, ER_t= 0.0128, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 203, E= 1494.853, Eim_t= 1494.837, Eim_a= 0.000, ER_xy= 0.003, ER_t= 0.0126, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 204, E= 1486.056, Eim_t= 1486.040, Eim_a= 0.000, ER_xy= 0.003, ER_t= 0.0126, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 205, E= 1485.433, Eim_t= 1485.417, Eim_a= 0.000, ER_xy= 0.003, ER_t= 0.0124, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 206, E= 1477.412, Eim_t= 1477.396, Eim_a= 0.000, ER_xy= 0.004, ER_t= 0.0124, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 207, E= 1477.048, Eim_t= 1477.032, Eim_a= 0.000, ER_xy= 0.004, ER_t= 0.0121, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 208, E= 1469.754, Eim_t= 1469.739, Eim_a= 0.000, ER_xy= 0.004, ER_t= 0.0121, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 209, E= 1469.596, Eim_t= 1469.580, Eim_a= 0.000, ER_xy= 0.004, ER_t= 0.0119, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 210, E= 1462.686, Eim_t= 1462.670, Eim_a= 0.000, ER_xy= 0.004, ER_t= 0.0119, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 211, E= 1461.542, Eim_t= 1461.526, Eim_a= 0.000, ER_xy= 0.004, ER_t= 0.0117, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 212, E= 1455.179, Eim_t= 1455.163, Eim_a= 0.000, ER_xy= 0.004, ER_t= 0.0117, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 213, E= 1454.223, Eim_t= 1454.207, Eim_a= 0.000, ER_xy= 0.004, ER_t= 0.0116, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 214, E= 1448.282, Eim_t= 1448.266, Eim_a= 0.000, ER_xy= 0.004, ER_t= 0.0116, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter: 215, E= 1447.218, Eim_t= 1447.202, Eim_a= 0.000, ER_xy= 0.004, ER_t= 0.0114, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 216, E= 1441.680, Eim_t= 1441.664, Eim_a= 0.000, ER_xy= 0.005, ER_t= 0.0114, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 217, E= 1440.949, Eim_t= 1440.933, Eim_a= 0.000, ER_xy= 0.005, ER_t= 0.0114, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 218, E= 1435.932, Eim_t= 1435.915, Eim_a= 0.000, ER_xy= 0.005, ER_t= 0.0114, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 219, E= 1435.431, Eim_t= 1435.415, Eim_a= 0.000, ER_xy= 0.005, ER_t= 0.0113, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 220, E= 1430.892, Eim_t= 1430.876, Eim_a= 0.000, ER_xy= 0.005, ER_t= 0.0113, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 221, E= 1430.230, Eim_t= 1430.214, Eim_a= 0.000, ER_xy= 0.005, ER_t= 0.0113, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 222, E= 1426.151, Eim_t= 1426.135, Eim_a= 0.000, ER_xy= 0.005, ER_t= 0.0113, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 223, E= 1425.664, Eim_t= 1425.648, Eim_a= 0.000, ER_xy= 0.005, ER_t= 0.0113, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 224, E= 1421.854, Eim_t= 1421.837, Eim_a= 0.000, ER_xy= 0.005, ER_t= 0.0113, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 225, E= 1421.378, Eim_t= 1421.361, Eim_a= 0.000, ER_xy= 0.005, ER_t= 0.0113, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 226, E= 1417.489, Eim_t= 1417.472, Eim_a= 0.000, ER_xy= 0.005, ER_t= 0.0113, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 227, E= 1416.918, Eim_t= 1416.901, Eim_a= 0.000, ER_xy= 0.005, ER_t= 0.0114, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 228, E= 1413.337, Eim_t= 1413.320, Eim_a= 0.000, ER_xy= 0.006, ER_t= 0.0114, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 229, E= 1412.939, Eim_t= 1412.922, Eim_a= 0.000, ER_xy= 0.006, ER_t= 0.0114, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 230, E= 1409.674, Eim_t= 1409.657, Eim_a= 0.000, ER_xy= 0.006, ER_t= 0.0114, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 231, E= 1409.369, Eim_t= 1409.352, Eim_a= 0.000, ER_xy= 0.006, ER_t= 0.0114, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 232, E= 1406.328, Eim_t= 1406.311, Eim_a= 0.000, ER_xy= 0.006, ER_t= 0.0114, ep_xy= 0.0002, ep_t= 0.0000, time= 0.04.\n",
      "iter: 233, E= 1406.087, Eim_t= 1406.070, Eim_a= 0.000, ER_xy= 0.006, ER_t= 0.0114, ep_xy= 0.0002, ep_t= 0.0000, time= 0.04.\n",
      "iter: 234, E= 1403.285, Eim_t= 1403.268, Eim_a= 0.000, ER_xy= 0.006, ER_t= 0.0114, ep_xy= 0.0002, ep_t= 0.0000, time= 0.04.\n",
      "iter: 235, E= 1403.127, Eim_t= 1403.110, Eim_a= 0.000, ER_xy= 0.006, ER_t= 0.0114, ep_xy= 0.0002, ep_t= 0.0000, time= 0.04.\n",
      "iter: 236, E= 1400.544, Eim_t= 1400.527, Eim_a= 0.000, ER_xy= 0.006, ER_t= 0.0114, ep_xy= 0.0002, ep_t= 0.0000, time= 0.04.\n",
      "iter: 237, E= 1400.361, Eim_t= 1400.343, Eim_a= 0.000, ER_xy= 0.006, ER_t= 0.0113, ep_xy= 0.0002, ep_t= 0.0000, time= 0.04.\n",
      "iter: 238, E= 1397.858, Eim_t= 1397.840, Eim_a= 0.000, ER_xy= 0.006, ER_t= 0.0113, ep_xy= 0.0002, ep_t= 0.0000, time= 0.04.\n",
      "iter: 239, E= 1397.590, Eim_t= 1397.573, Eim_a= 0.000, ER_xy= 0.006, ER_t= 0.0113, ep_xy= 0.0002, ep_t= 0.0000, time= 0.04.\n",
      "iter: 240, E= 1394.934, Eim_t= 1394.916, Eim_a= 0.000, ER_xy= 0.006, ER_t= 0.0113, ep_xy= 0.0002, ep_t= 0.0000, time= 0.04.\n",
      "iter: 241, E= 1394.501, Eim_t= 1394.484, Eim_a= 0.000, ER_xy= 0.006, ER_t= 0.0112, ep_xy= 0.0002, ep_t= 0.0000, time= 0.04.\n",
      "iter: 242, E= 1391.805, Eim_t= 1391.788, Eim_a= 0.000, ER_xy= 0.006, ER_t= 0.0112, ep_xy= 0.0002, ep_t= 0.0000, time= 0.04.\n",
      "iter: 243, E= 1391.429, Eim_t= 1391.412, Eim_a= 0.000, ER_xy= 0.006, ER_t= 0.0110, ep_xy= 0.0002, ep_t= 0.0000, time= 0.04.\n",
      "iter: 244, E= 1388.872, Eim_t= 1388.854, Eim_a= 0.000, ER_xy= 0.007, ER_t= 0.0110, ep_xy= 0.0002, ep_t= 0.0000, time= 0.04.\n",
      "iter: 245, E= 1388.666, Eim_t= 1388.648, Eim_a= 0.000, ER_xy= 0.007, ER_t= 0.0109, ep_xy= 0.0002, ep_t= 0.0000, time= 0.04.\n",
      "iter: 246, E= 1385.951, Eim_t= 1385.933, Eim_a= 0.000, ER_xy= 0.007, ER_t= 0.0109, ep_xy= 0.0002, ep_t= 0.0000, time= 0.04.\n",
      "iter: 247, E= 1385.831, Eim_t= 1385.814, Eim_a= 0.000, ER_xy= 0.007, ER_t= 0.0107, ep_xy= 0.0002, ep_t= 0.0000, time= 0.04.\n",
      "iter: 248, E= 1383.041, Eim_t= 1383.024, Eim_a= 0.000, ER_xy= 0.007, ER_t= 0.0107, ep_xy= 0.0002, ep_t= 0.0000, time= 0.04.\n",
      "iter: 249, E= 1382.966, Eim_t= 1382.949, Eim_a= 0.000, ER_xy= 0.007, ER_t= 0.0106, ep_xy= 0.0002, ep_t= 0.0000, time= 0.04.\n",
      "iter: 250, E= 1380.353, Eim_t= 1380.336, Eim_a= 0.000, ER_xy= 0.007, ER_t= 0.0106, ep_xy= 0.0002, ep_t= 0.0000, time= 0.04.\n",
      "iter: 251, E= 1380.141, Eim_t= 1380.124, Eim_a= 0.000, ER_xy= 0.007, ER_t= 0.0103, ep_xy= 0.0002, ep_t= 0.0000, time= 0.04.\n",
      "iter: 252, E= 1377.416, Eim_t= 1377.399, Eim_a= 0.000, ER_xy= 0.007, ER_t= 0.0103, ep_xy= 0.0002, ep_t= 0.0000, time= 0.04.\n",
      "iter: 253, E= 1377.212, Eim_t= 1377.195, Eim_a= 0.000, ER_xy= 0.007, ER_t= 0.0101, ep_xy= 0.0002, ep_t= 0.0000, time= 0.04.\n",
      "iter: 254, E= 1374.536, Eim_t= 1374.519, Eim_a= 0.000, ER_xy= 0.007, ER_t= 0.0101, ep_xy= 0.0002, ep_t= 0.0000, time= 0.04.\n",
      "iter: 255, E= 1374.375, Eim_t= 1374.358, Eim_a= 0.000, ER_xy= 0.007, ER_t= 0.0099, ep_xy= 0.0002, ep_t= 0.0000, time= 0.04.\n",
      "iter: 256, E= 1371.763, Eim_t= 1371.746, Eim_a= 0.000, ER_xy= 0.007, ER_t= 0.0099, ep_xy= 0.0002, ep_t= 0.0000, time= 0.04.\n",
      "iter: 257, E= 1371.682, Eim_t= 1371.665, Eim_a= 0.000, ER_xy= 0.007, ER_t= 0.0096, ep_xy= 0.0002, ep_t= 0.0000, time= 0.04.\n",
      "iter: 258, E= 1369.145, Eim_t= 1369.128, Eim_a= 0.000, ER_xy= 0.008, ER_t= 0.0096, ep_xy= 0.0003, ep_t= 0.0000, time= 0.04.\n",
      "iter: 259, E= 1369.105, Eim_t= 1369.088, Eim_a= 0.000, ER_xy= 0.008, ER_t= 0.0093, ep_xy= 0.0003, ep_t= 0.0001, time= 0.04.\n",
      "iter: 260, E= 1366.505, Eim_t= 1366.488, Eim_a= 0.000, ER_xy= 0.008, ER_t= 0.0093, ep_xy= 0.0003, ep_t= 0.0001, time= 0.04.\n",
      "iter: 261, E= 1366.487, Eim_t= 1366.470, Eim_a= 0.000, ER_xy= 0.008, ER_t= 0.0091, ep_xy= 0.0003, ep_t= 0.0001, time= 0.04.\n",
      "iter: 262, E= 1363.491, Eim_t= 1363.474, Eim_a= 0.000, ER_xy= 0.008, ER_t= 0.0091, ep_xy= 0.0003, ep_t= 0.0001, time= 0.04.\n",
      "iter: 263, E= 1364.701, Eim_t= 1364.684, Eim_a= 0.000, ER_xy= 0.008, ER_t= 0.0087, ep_xy= 0.0003, ep_t= 0.0000, time= 0.04.\n",
      "iter: 264, E= 1361.493, Eim_t= 1361.476, Eim_a= 0.000, ER_xy= 0.008, ER_t= 0.0087, ep_xy= 0.0003, ep_t= 0.0000, time= 0.04.\n",
      "iter: 265, E= 1362.292, Eim_t= 1362.275, Eim_a= 0.000, ER_xy= 0.008, ER_t= 0.0086, ep_xy= 0.0003, ep_t= 0.0000, time= 0.04.\n",
      "iter: 266, E= 1358.602, Eim_t= 1358.585, Eim_a= 0.000, ER_xy= 0.008, ER_t= 0.0086, ep_xy= 0.0003, ep_t= 0.0000, time= 0.04.\n",
      "iter: 267, E= 1357.327, Eim_t= 1357.311, Eim_a= 0.000, ER_xy= 0.008, ER_t= 0.0082, ep_xy= 0.0003, ep_t= 0.0000, time= 0.04.\n",
      "iter: 268, E= 1354.298, Eim_t= 1354.281, Eim_a= 0.000, ER_xy= 0.008, ER_t= 0.0082, ep_xy= 0.0003, ep_t= 0.0000, time= 0.04.\n",
      "iter: 269, E= 1354.000, Eim_t= 1353.983, Eim_a= 0.000, ER_xy= 0.008, ER_t= 0.0080, ep_xy= 0.0003, ep_t= 0.0000, time= 0.04.\n",
      "iter: 270, E= 1350.764, Eim_t= 1350.747, Eim_a= 0.000, ER_xy= 0.009, ER_t= 0.0080, ep_xy= 0.0003, ep_t= 0.0000, time= 0.04.\n",
      "iter: 271, E= 1350.556, Eim_t= 1350.540, Eim_a= 0.000, ER_xy= 0.009, ER_t= 0.0078, ep_xy= 0.0003, ep_t= 0.0000, time= 0.04.\n",
      "iter: 272, E= 1347.406, Eim_t= 1347.390, Eim_a= 0.000, ER_xy= 0.009, ER_t= 0.0078, ep_xy= 0.0003, ep_t= 0.0000, time= 0.04.\n",
      "iter: 273, E= 1347.240, Eim_t= 1347.223, Eim_a= 0.000, ER_xy= 0.009, ER_t= 0.0076, ep_xy= 0.0003, ep_t= 0.0000, time= 0.04.\n",
      "iter: 274, E= 1344.037, Eim_t= 1344.020, Eim_a= 0.000, ER_xy= 0.009, ER_t= 0.0076, ep_xy= 0.0003, ep_t= 0.0000, time= 0.04.\n",
      "iter: 275, E= 1343.957, Eim_t= 1343.940, Eim_a= 0.000, ER_xy= 0.009, ER_t= 0.0074, ep_xy= 0.0003, ep_t= 0.0000, time= 0.04.\n",
      "iter: 276, E= 1340.881, Eim_t= 1340.864, Eim_a= 0.000, ER_xy= 0.009, ER_t= 0.0074, ep_xy= 0.0004, ep_t= 0.0000, time= 0.04.\n",
      "iter: 277, E= 1340.875, Eim_t= 1340.858, Eim_a= 0.000, ER_xy= 0.009, ER_t= 0.0072, ep_xy= 0.0004, ep_t= 0.0000, time= 0.04.\n",
      "iter: 278, E= 1338.157, Eim_t= 1338.140, Eim_a= 0.000, ER_xy= 0.010, ER_t= 0.0072, ep_xy= 0.0004, ep_t= 0.0000, time= 0.04.\n",
      "iter: 279, E= 1338.122, Eim_t= 1338.105, Eim_a= 0.000, ER_xy= 0.010, ER_t= 0.0070, ep_xy= 0.0004, ep_t= 0.0000, time= 0.04.\n",
      "iter: 280, E= 1335.787, Eim_t= 1335.770, Eim_a= 0.000, ER_xy= 0.010, ER_t= 0.0070, ep_xy= 0.0004, ep_t= 0.0000, time= 0.04.\n",
      "iter: 281, E= 1335.735, Eim_t= 1335.718, Eim_a= 0.000, ER_xy= 0.010, ER_t= 0.0068, ep_xy= 0.0004, ep_t= 0.0000, time= 0.04.\n",
      "iter: 282, E= 1333.610, Eim_t= 1333.594, Eim_a= 0.000, ER_xy= 0.010, ER_t= 0.0068, ep_xy= 0.0004, ep_t= 0.0000, time= 0.04.\n",
      "iter: 283, E= 1333.615, Eim_t= 1333.598, Eim_a= 0.000, ER_xy= 0.010, ER_t= 0.0066, ep_xy= 0.0004, ep_t= 0.0000, time= 0.04.\n",
      "iter: 284, E= 1331.586, Eim_t= 1331.569, Eim_a= 0.000, ER_xy= 0.010, ER_t= 0.0066, ep_xy= 0.0004, ep_t= 0.0000, time= 0.04.\n",
      "iter: 285, E= 1331.582, Eim_t= 1331.565, Eim_a= 0.000, ER_xy= 0.010, ER_t= 0.0065, ep_xy= 0.0004, ep_t= 0.0000, time= 0.04.\n",
      "iter: 286, E= 1329.905, Eim_t= 1329.888, Eim_a= 0.000, ER_xy= 0.010, ER_t= 0.0065, ep_xy= 0.0004, ep_t= 0.0000, time= 0.04.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter: 287, E= 1329.875, Eim_t= 1329.858, Eim_a= 0.000, ER_xy= 0.010, ER_t= 0.0063, ep_xy= 0.0004, ep_t= 0.0000, time= 0.04.\n",
      "iter: 288, E= 1328.512, Eim_t= 1328.495, Eim_a= 0.000, ER_xy= 0.011, ER_t= 0.0063, ep_xy= 0.0005, ep_t= 0.0000, time= 0.04.\n",
      "iter: 289, E= 1328.471, Eim_t= 1328.454, Eim_a= 0.000, ER_xy= 0.011, ER_t= 0.0062, ep_xy= 0.0005, ep_t= 0.0000, time= 0.04.\n",
      "iter: 290, E= 1327.308, Eim_t= 1327.291, Eim_a= 0.000, ER_xy= 0.011, ER_t= 0.0062, ep_xy= 0.0005, ep_t= 0.0000, time= 0.04.\n",
      "iter: 291, E= 1327.268, Eim_t= 1327.251, Eim_a= 0.000, ER_xy= 0.011, ER_t= 0.0061, ep_xy= 0.0005, ep_t= 0.0000, time= 0.04.\n",
      "iter: 292, E= 1326.199, Eim_t= 1326.182, Eim_a= 0.000, ER_xy= 0.011, ER_t= 0.0061, ep_xy= 0.0005, ep_t= 0.0000, time= 0.04.\n",
      "iter: 293, E= 1326.162, Eim_t= 1326.145, Eim_a= 0.000, ER_xy= 0.011, ER_t= 0.0059, ep_xy= 0.0005, ep_t= 0.0000, time= 0.04.\n",
      "iter: 294, E= 1325.194, Eim_t= 1325.177, Eim_a= 0.000, ER_xy= 0.011, ER_t= 0.0059, ep_xy= 0.0005, ep_t= 0.0000, time= 0.04.\n",
      "iter: 295, E= 1325.156, Eim_t= 1325.139, Eim_a= 0.000, ER_xy= 0.011, ER_t= 0.0058, ep_xy= 0.0005, ep_t= 0.0000, time= 0.04.\n",
      "iter: 296, E= 1324.414, Eim_t= 1324.397, Eim_a= 0.000, ER_xy= 0.011, ER_t= 0.0058, ep_xy= 0.0005, ep_t= 0.0000, time= 0.04.\n",
      "iter: 297, E= 1324.362, Eim_t= 1324.345, Eim_a= 0.000, ER_xy= 0.011, ER_t= 0.0056, ep_xy= 0.0005, ep_t= 0.0000, time= 0.04.\n",
      "iter: 298, E= 1323.799, Eim_t= 1323.782, Eim_a= 0.000, ER_xy= 0.011, ER_t= 0.0056, ep_xy= 0.0006, ep_t= 0.0000, time= 0.04.\n",
      "iter: 299, E= 1323.730, Eim_t= 1323.713, Eim_a= 0.000, ER_xy= 0.011, ER_t= 0.0055, ep_xy= 0.0006, ep_t= 0.0000, time= 0.04.\n",
      "iter: 300, E= 1323.291, Eim_t= 1323.274, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0055, ep_xy= 0.0006, ep_t= 0.0000, time= 0.04.\n",
      "iter: 301, E= 1323.218, Eim_t= 1323.201, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0054, ep_xy= 0.0006, ep_t= 0.0000, time= 0.04.\n",
      "iter: 302, E= 1322.845, Eim_t= 1322.828, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0054, ep_xy= 0.0006, ep_t= 0.0000, time= 0.04.\n",
      "iter: 303, E= 1322.773, Eim_t= 1322.757, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0052, ep_xy= 0.0006, ep_t= 0.0000, time= 0.04.\n",
      "iter: 304, E= 1322.397, Eim_t= 1322.380, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0052, ep_xy= 0.0006, ep_t= 0.0000, time= 0.04.\n",
      "iter: 305, E= 1322.335, Eim_t= 1322.318, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0051, ep_xy= 0.0006, ep_t= 0.0000, time= 0.04.\n",
      "iter: 306, E= 1321.956, Eim_t= 1321.939, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0051, ep_xy= 0.0006, ep_t= 0.0000, time= 0.04.\n",
      "iter: 307, E= 1321.922, Eim_t= 1321.905, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0050, ep_xy= 0.0006, ep_t= 0.0000, time= 0.04.\n",
      "iter: 308, E= 1321.605, Eim_t= 1321.588, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0050, ep_xy= 0.0007, ep_t= 0.0000, time= 0.04.\n",
      "iter: 309, E= 1321.595, Eim_t= 1321.578, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0048, ep_xy= 0.0007, ep_t= 0.0000, time= 0.04.\n",
      "iter: 310, E= 1321.356, Eim_t= 1321.339, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0048, ep_xy= 0.0007, ep_t= 0.0000, time= 0.04.\n",
      "iter: 311, E= 1321.363, Eim_t= 1321.346, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0047, ep_xy= 0.0007, ep_t= 0.0000, time= 0.04.\n",
      "iter: 312, E= 1321.177, Eim_t= 1321.160, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0047, ep_xy= 0.0007, ep_t= 0.0000, time= 0.04.\n",
      "iter: 313, E= 1321.191, Eim_t= 1321.175, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0046, ep_xy= 0.0007, ep_t= 0.0000, time= 0.04.\n",
      "iter: 314, E= 1321.040, Eim_t= 1321.024, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0046, ep_xy= 0.0008, ep_t= 0.0000, time= 0.04.\n",
      "iter: 315, E= 1321.054, Eim_t= 1321.037, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0046, ep_xy= 0.0008, ep_t= 0.0000, time= 0.04.\n",
      "iter: 316, E= 1320.918, Eim_t= 1320.902, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0046, ep_xy= 0.0008, ep_t= 0.0000, time= 0.04.\n",
      "iter: 317, E= 1320.927, Eim_t= 1320.911, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0045, ep_xy= 0.0008, ep_t= 0.0000, time= 0.04.\n",
      "iter: 318, E= 1320.794, Eim_t= 1320.777, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0045, ep_xy= 0.0008, ep_t= 0.0000, time= 0.04.\n",
      "iter: 319, E= 1320.800, Eim_t= 1320.783, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0045, ep_xy= 0.0008, ep_t= 0.0000, time= 0.04.\n",
      "iter: 320, E= 1320.654, Eim_t= 1320.637, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0045, ep_xy= 0.0009, ep_t= 0.0000, time= 0.04.\n",
      "iter: 321, E= 1320.657, Eim_t= 1320.641, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0045, ep_xy= 0.0009, ep_t= 0.0000, time= 0.04.\n",
      "iter: 322, E= 1320.492, Eim_t= 1320.476, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0045, ep_xy= 0.0009, ep_t= 0.0000, time= 0.04.\n",
      "iter: 323, E= 1320.494, Eim_t= 1320.477, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0045, ep_xy= 0.0009, ep_t= 0.0000, time= 0.04.\n",
      "iter: 324, E= 1320.303, Eim_t= 1320.287, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0045, ep_xy= 0.0009, ep_t= 0.0000, time= 0.04.\n",
      "iter: 325, E= 1320.304, Eim_t= 1320.287, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0045, ep_xy= 0.0009, ep_t= 0.0000, time= 0.04.\n",
      "iter: 326, E= 1320.090, Eim_t= 1320.073, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0045, ep_xy= 0.0010, ep_t= 0.0000, time= 0.04.\n",
      "iter: 327, E= 1320.090, Eim_t= 1320.073, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0045, ep_xy= 0.0010, ep_t= 0.0000, time= 0.04.\n",
      "iter: 328, E= 1319.866, Eim_t= 1319.850, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0045, ep_xy= 0.0010, ep_t= 0.0000, time= 0.04.\n",
      "iter: 329, E= 1319.866, Eim_t= 1319.849, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0045, ep_xy= 0.0010, ep_t= 0.0000, time= 0.04.\n",
      "iter: 330, E= 1319.653, Eim_t= 1319.636, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0045, ep_xy= 0.0010, ep_t= 0.0000, time= 0.04.\n",
      "iter: 331, E= 1319.653, Eim_t= 1319.637, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0045, ep_xy= 0.0010, ep_t= 0.0000, time= 0.04.\n",
      "iter: 332, E= 1319.468, Eim_t= 1319.451, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0045, ep_xy= 0.0011, ep_t= 0.0000, time= 0.04.\n",
      "iter: 333, E= 1319.469, Eim_t= 1319.452, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0045, ep_xy= 0.0011, ep_t= 0.0000, time= 0.04.\n",
      "iter: 334, E= 1319.320, Eim_t= 1319.303, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0045, ep_xy= 0.0011, ep_t= 0.0000, time= 0.04.\n",
      "iter: 335, E= 1319.320, Eim_t= 1319.303, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0045, ep_xy= 0.0011, ep_t= 0.0000, time= 0.04.\n",
      "iter: 336, E= 1319.213, Eim_t= 1319.196, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0045, ep_xy= 0.0012, ep_t= 0.0000, time= 0.04.\n",
      "iter: 337, E= 1319.213, Eim_t= 1319.196, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0045, ep_xy= 0.0012, ep_t= 0.0000, time= 0.04.\n",
      "iter: 338, E= 1319.144, Eim_t= 1319.127, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0045, ep_xy= 0.0012, ep_t= 0.0000, time= 0.04.\n",
      "iter: 339, E= 1319.144, Eim_t= 1319.127, Eim_a= 0.000, ER_xy= 0.012, ER_t= 0.0045, ep_xy= 0.0012, ep_t= 0.0000, time= 0.04.\n",
      "iter: 340, E= 1319.102, Eim_t= 1319.085, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0013, ep_t= 0.0000, time= 0.04.\n",
      "iter: 341, E= 1319.103, Eim_t= 1319.086, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0013, ep_t= 0.0000, time= 0.04.\n",
      "iter: 342, E= 1319.077, Eim_t= 1319.060, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0013, ep_t= 0.0000, time= 0.04.\n",
      "iter: 343, E= 1319.077, Eim_t= 1319.060, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0013, ep_t= 0.0000, time= 0.04.\n",
      "iter: 344, E= 1319.057, Eim_t= 1319.040, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0014, ep_t= 0.0000, time= 0.04.\n",
      "iter: 345, E= 1319.057, Eim_t= 1319.040, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0014, ep_t= 0.0000, time= 0.04.\n",
      "iter: 346, E= 1319.034, Eim_t= 1319.016, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0014, ep_t= 0.0000, time= 0.04.\n",
      "iter: 347, E= 1319.034, Eim_t= 1319.017, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0014, ep_t= 0.0000, time= 0.04.\n",
      "iter: 348, E= 1318.995, Eim_t= 1318.977, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0015, ep_t= 0.0000, time= 0.04.\n",
      "iter: 349, E= 1318.995, Eim_t= 1318.978, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0015, ep_t= 0.0000, time= 0.04.\n",
      "iter: 350, E= 1318.938, Eim_t= 1318.921, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0015, ep_t= 0.0000, time= 0.04.\n",
      "iter: 351, E= 1318.938, Eim_t= 1318.921, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0015, ep_t= 0.0000, time= 0.04.\n",
      "iter: 352, E= 1318.887, Eim_t= 1318.870, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0016, ep_t= 0.0000, time= 0.04.\n",
      "iter: 353, E= 1318.887, Eim_t= 1318.870, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0016, ep_t= 0.0000, time= 0.04.\n",
      "iter: 354, E= 1318.830, Eim_t= 1318.812, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0017, ep_t= 0.0000, time= 0.04.\n",
      "iter: 355, E= 1318.830, Eim_t= 1318.812, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0017, ep_t= 0.0000, time= 0.04.\n",
      "iter: 356, E= 1319.057, Eim_t= 1319.040, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0011, ep_t= 0.0000, time= 0.04.\n",
      "iter: 357, E= 1319.057, Eim_t= 1319.040, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0011, ep_t= 0.0000, time= 0.04.\n",
      "iter: 358, E= 1319.245, Eim_t= 1319.227, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0007, ep_t= 0.0000, time= 0.04.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter: 359, E= 1319.245, Eim_t= 1319.227, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0007, ep_t= 0.0000, time= 0.04.\n",
      "iter: 360, E= 1318.976, Eim_t= 1318.959, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0008, ep_t= 0.0000, time= 0.04.\n",
      "iter: 361, E= 1318.976, Eim_t= 1318.958, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0008, ep_t= 0.0000, time= 0.04.\n",
      "iter: 362, E= 1318.763, Eim_t= 1318.746, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0008, ep_t= 0.0000, time= 0.04.\n",
      "iter: 363, E= 1318.763, Eim_t= 1318.746, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0008, ep_t= 0.0000, time= 0.04.\n",
      "iter: 364, E= 1318.737, Eim_t= 1318.719, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0008, ep_t= 0.0000, time= 0.04.\n",
      "iter: 365, E= 1318.737, Eim_t= 1318.719, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0008, ep_t= 0.0000, time= 0.04.\n",
      "iter: 366, E= 1318.662, Eim_t= 1318.644, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0009, ep_t= 0.0000, time= 0.04.\n",
      "iter: 367, E= 1318.662, Eim_t= 1318.644, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0009, ep_t= 0.0000, time= 0.04.\n",
      "iter: 368, E= 1318.672, Eim_t= 1318.655, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0006, ep_t= 0.0000, time= 0.04.\n",
      "iter: 369, E= 1318.672, Eim_t= 1318.655, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0006, ep_t= 0.0000, time= 0.04.\n",
      "iter: 370, E= 1318.594, Eim_t= 1318.576, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0006, ep_t= 0.0000, time= 0.04.\n",
      "iter: 371, E= 1318.594, Eim_t= 1318.576, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0006, ep_t= 0.0000, time= 0.04.\n",
      "iter: 372, E= 1318.590, Eim_t= 1318.573, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0006, ep_t= 0.0000, time= 0.04.\n",
      "iter: 373, E= 1318.590, Eim_t= 1318.573, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0006, ep_t= 0.0000, time= 0.04.\n",
      "iter: 374, E= 1318.585, Eim_t= 1318.568, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0006, ep_t= 0.0000, time= 0.04.\n",
      "iter: 375, E= 1318.585, Eim_t= 1318.567, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0006, ep_t= 0.0000, time= 0.04.\n",
      "iter: 376, E= 1318.585, Eim_t= 1318.568, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0004, ep_t= 0.0000, time= 0.04.\n",
      "iter: 377, E= 1318.585, Eim_t= 1318.568, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0004, ep_t= 0.0000, time= 0.04.\n",
      "iter: 378, E= 1318.585, Eim_t= 1318.568, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0004, ep_t= 0.0000, time= 0.04.\n",
      "iter: 379, E= 1318.585, Eim_t= 1318.568, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0004, ep_t= 0.0000, time= 0.04.\n",
      "iter: 380, E= 1318.586, Eim_t= 1318.568, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0003, ep_t= 0.0000, time= 0.04.\n",
      "iter: 381, E= 1318.586, Eim_t= 1318.568, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0003, ep_t= 0.0000, time= 0.04.\n",
      "iter: 382, E= 1318.587, Eim_t= 1318.569, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0002, ep_t= 0.0000, time= 0.04.\n",
      "iter: 383, E= 1318.587, Eim_t= 1318.569, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0002, ep_t= 0.0000, time= 0.04.\n",
      "iter: 384, E= 1318.587, Eim_t= 1318.570, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 385, E= 1318.587, Eim_t= 1318.569, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 386, E= 1318.587, Eim_t= 1318.570, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 387, E= 1318.587, Eim_t= 1318.570, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 388, E= 1318.587, Eim_t= 1318.570, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 389, E= 1318.587, Eim_t= 1318.570, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0001, ep_t= 0.0000, time= 0.04.\n",
      "iter: 390, E= 1318.588, Eim_t= 1318.570, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 391, E= 1318.588, Eim_t= 1318.570, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 392, E= 1318.588, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 393, E= 1318.588, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 394, E= 1318.588, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 395, E= 1318.588, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 396, E= 1318.588, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 397, E= 1318.588, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 398, E= 1318.588, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 399, E= 1318.588, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 400, E= 1318.588, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 401, E= 1318.588, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 402, E= 1318.589, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 403, E= 1318.589, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 404, E= 1318.589, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 405, E= 1318.589, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 406, E= 1318.589, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 407, E= 1318.589, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 408, E= 1318.589, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 409, E= 1318.589, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 410, E= 1318.589, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 411, E= 1318.589, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 412, E= 1318.589, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 413, E= 1318.589, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 414, E= 1318.589, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 415, E= 1318.589, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 416, E= 1318.589, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 417, E= 1318.589, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 418, E= 1318.589, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 419, E= 1318.589, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 420, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 421, E= 1318.589, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 422, E= 1318.589, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 423, E= 1318.589, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 424, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 425, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 426, E= 1318.589, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 427, E= 1318.589, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 428, E= 1318.589, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 429, E= 1318.589, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 430, E= 1318.589, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter: 431, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 432, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 433, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 434, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 435, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 436, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 437, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 438, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 439, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 440, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 441, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 442, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 443, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 444, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 445, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 446, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 447, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 448, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 449, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 450, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 451, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 452, E= 1318.589, Eim_t= 1318.571, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 453, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 454, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 455, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 456, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 457, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 458, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 459, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 460, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 461, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 462, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 463, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 464, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 465, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 466, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 467, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 468, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 469, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 470, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 471, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 472, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 473, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 474, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 475, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 476, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 477, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 478, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 479, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 480, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 481, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 482, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 483, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 484, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 485, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 486, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 487, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 488, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 489, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 490, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 491, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 492, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 493, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 494, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 495, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 496, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 497, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 498, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n",
      "iter: 499, E= 1318.589, Eim_t= 1318.572, Eim_a= 0.000, ER_xy= 0.013, ER_t= 0.0045, ep_xy= 0.0000, ep_t= 0.0000, time= 0.04.\n"
     ]
    }
   ],
   "source": [
    "# Grab the indices of the missing slices\n",
    "missingslices = []\n",
    "for i in range(target_resample_arr.shape[2]):\n",
    "    if np.unique(target_image[:,:,i]).shape[0] < 3:\n",
    "        missingslices.append(i)\n",
    "print(missingslices)\n",
    "\n",
    "\n",
    "#origJ = lddmm.J[0].clone()\n",
    "epsilonxy = 4e-7\n",
    "epsilontheta=2e-7\n",
    "a = None\n",
    "b = None\n",
    "theta = None\n",
    "niter = 500\n",
    "\n",
    "a,b,theta,outtarget,epsilonxy,epsilontheta = lddmm.sa(torch.tensor(target_resample_arr).type(torch.FloatTensor),torch.tensor(target_resample_arr).type(torch.FloatTensor),niter=niter,dim=2,norm=0,missingslices=missingslices,sigma_atlas=99999999999.0,sigma_target=50.0,a=a,b=b,theta=theta,nx=list(target_resample_arr.shape),dx=output_spacing,epsilonxy=epsilonxy*4, epsilontheta=epsilontheta*4, sigma_target_radius=1, sigma_atlas_radius=1)\n",
    "#lddmm.setParams('a',4)\n",
    "outtarget = lddmm.applySA(torch.tensor(target_resample_arr).type(torch.FloatTensor), a, b, theta, dim=2,nx=list(target_resample_arr.shape),dx=output_spacing).numpy().astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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8lNk8xkr5xO+pByPcm2vy8X1qFb1A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      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.subplot(1,2,1)\n",
    "plt.imshow(outtarget[:,:,6])\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(target_resample_arr[:,:,6])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check image\n",
    "outimg = nib.AnalyzeImage(outtarget,None)\n",
    "outimg.header['pixdim'][1:4] = output_spacing\n",
    "nib.save(outimg,'/cis/home/jtrieu/Documents/target_resectioned.img')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Start with rigid alignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "213.75468\n",
      "198.93884\n",
      "0.0\n",
      "-0.1435687\n",
      "\n",
      "Current parameters:\n",
      ">    a               = 8 (smoothing kernel, a*(pixel_size))\n",
      ">    p               = 2 (smoothing kernel power, p*2)\n",
      ">    niter           = 150 (number of iterations)\n",
      ">    epsilon         = 0.005 (gradient descent step size)\n",
      ">    epsilonL        = 1e-07 (gradient descent step size, affine)\n",
      ">    epsilonT        = 2e-05 (gradient descent step size, translation)\n",
      ">    minbeta         = 0.0001 (smallest multiple of epsilon)\n",
      ">    sigma           = [20.0] (matching term coefficient (0.5/sigma**2))\n",
      ">    sigmaR          = 10.0 (regularization term coefficient (0.5/sigmaR**2))\n",
      ">    nt              = 5 (number of time steps in velocity field)\n",
      ">    do_lddmm        = 0 (perform LDDMM step, 0 = no, 1 = yes)\n",
      ">    do_affine       = 2 (interleave linear registration: 0 = no, 1 = affine, 2 = rigid)\n",
      ">    checkaffinestep = 0 (evaluate linear matching energy: 0 = no, 1 = yes)\n",
      ">    im_norm_ms      = 0 (normalize image by mean and std: 0 = no, 1 = yes)\n",
      ">    gpu_number      = 1 (index of CUDA_VISIBLE_DEVICES to use)\n",
      ">    dtype           = float (bit depth, 'float' or 'double')\n",
      ">    energy_fraction = 0.02 (fraction of initial energy at which to stop)\n",
      ">    cc              = 1 (contrast correction: 0 = no, 1 = yes)\n",
      ">    cc_channels     = [] (image channels to run contrast correction (0-indexed))\n",
      ">    we              = 0 (weight estimation: 0 = no, 2+ = yes)\n",
      ">    we_channels     = [] (image channels to run weight estimation (0-indexed))\n",
      ">    sigmaW          = 1.0 (coefficient for each weight estimation class)\n",
      ">    nMstep          = 5 (update weight estimation every nMstep steps)\n",
      ">    v_scale         = 1.0 (parameter scaling factor)\n",
      ">    low_memory      = 0 (low memory mode: 0 = no, 1 = yes)\n",
      ">    update_epsilon  = 0 (update optimization step size between runs: 0 = no, 1 = yes)\n",
      ">    outdir          = ../notebook/ (output directory name)\n",
      ">    optimizer       = gradient descent with reducing epsilon (optimizer type)\n",
      "\n",
      "\n",
      ">    template        = [numpy.ndarray]\n",
      "\n",
      ">    target          = [numpy.ndarray]\n",
      "\n",
      ">    costmask        = numpy.ndarray (costmask file name or numpy.ndarray)\n"
     ]
    }
   ],
   "source": [
    "# need to change these to numpy arrays\n",
    "mri_transform_arr = np.transpose(sitk.GetArrayFromImage(mri_transform),[2,1,0])*255.0\n",
    "target_resample_arr = np.transpose(sitk.GetArrayFromImage(target_resample),[2,1,0])\n",
    "\n",
    "mri_transform_arr[:,:,9:] = 0\n",
    "mri_translated = np.zeros(mri_transform_arr.shape)\n",
    "mri_translated[:,:,3:-3] = mri_transform_arr[:,:,0:8]\n",
    "\n",
    "\n",
    "costmask = np.zeros(target_resample_arr.shape)\n",
    "costmask[np.where(target_resample_arr != 0)] = 1\n",
    "\n",
    "print(np.max(mri_transform_arr))\n",
    "print(np.max(outtarget))\n",
    "print(np.min(mri_transform_arr))\n",
    "print(np.min(outtarget))\n",
    "\n",
    "lddmm = torch_lddmm.LDDMM(template=[mri_translated],target=[outtarget],costmask=costmask,outdir='../notebook/',do_affine=2,do_lddmm=0,a=8,niter=150,epsilon=5e-3,sigma=[20.0],sigmaR=10.0,optimizer='gdr',\n",
    "                          dx=output_spacing,cc=1,gpu_number = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter 'niter' changed to '500'.\n",
      "Parameter 'epsilonL' changed to '1e-99'.\n",
      "Parameter 'epsilonT' changed to '2e-05'.\n",
      "WARNING: nt set to 1 because settings indicate affine registration only.\n",
      "iter: 0, E = 3658.8354, ER = 0.0000, EM = 3658.8354, epd = 0.005000.\n",
      "iter: 1, E= 3649.284, ER= 0.000, EM= 3649.284, epd= 0.005, time= 0.05s.\n",
      "iter: 2, E= 3639.774, ER= 0.000, EM= 3639.774, epd= 0.005, time= 0.04s.\n",
      "iter: 3, E= 3630.312, ER= 0.000, EM= 3630.312, epd= 0.005, time= 0.04s.\n",
      "iter: 4, E= 3620.901, ER= 0.000, EM= 3620.901, epd= 0.005, time= 0.03s.\n",
      "iter: 5, E= 3611.547, ER= 0.000, EM= 3611.547, epd= 0.005, time= 0.03s.\n",
      "iter: 6, E= 3602.253, ER= 0.000, EM= 3602.253, epd= 0.005, time= 0.03s.\n",
      "iter: 7, E= 3593.025, ER= 0.000, EM= 3593.025, epd= 0.005, time= 0.03s.\n",
      "iter: 8, E= 3583.867, ER= 0.000, EM= 3583.867, epd= 0.005, time= 0.03s.\n",
      "iter: 9, E= 3574.783, ER= 0.000, EM= 3574.783, epd= 0.005, time= 0.03s.\n",
      "iter: 10, E= 3565.780, ER= 0.000, EM= 3565.780, epd= 0.005, time= 0.03s.\n",
      "iter: 11, E= 3556.861, ER= 0.000, EM= 3556.861, epd= 0.005, time= 0.03s.\n",
      "iter: 12, E= 3548.031, ER= 0.000, EM= 3548.031, epd= 0.005, time= 0.03s.\n",
      "iter: 13, E= 3539.296, ER= 0.000, EM= 3539.296, epd= 0.005, time= 0.03s.\n",
      "iter: 14, E= 3530.660, ER= 0.000, EM= 3530.660, epd= 0.005, time= 0.03s.\n",
      "iter: 15, E= 3520.669, ER= 0.000, EM= 3520.669, epd= 0.005, time= 0.03s.\n",
      "iter: 16, E= 3510.458, ER= 0.000, EM= 3510.458, epd= 0.005, time= 0.03s.\n",
      "iter: 17, E= 3500.352, ER= 0.000, EM= 3500.352, epd= 0.005, time= 0.03s.\n",
      "iter: 18, E= 3490.355, ER= 0.000, EM= 3490.355, epd= 0.005, time= 0.03s.\n",
      "iter: 19, E= 3480.377, ER= 0.000, EM= 3480.377, epd= 0.005, time= 0.03s.\n",
      "iter: 20, E= 3469.467, ER= 0.000, EM= 3469.467, epd= 0.005, time= 0.03s.\n",
      "iter: 21, E= 3458.662, ER= 0.000, EM= 3458.662, epd= 0.005, time= 0.03s.\n",
      "iter: 22, E= 3447.968, ER= 0.000, EM= 3447.968, epd= 0.005, time= 0.03s.\n",
      "iter: 23, E= 3437.390, ER= 0.000, EM= 3437.390, epd= 0.005, time= 0.03s.\n",
      "iter: 24, E= 3426.934, ER= 0.000, EM= 3426.934, epd= 0.005, time= 0.03s.\n",
      "iter: 25, E= 3416.604, ER= 0.000, EM= 3416.604, epd= 0.005, time= 0.03s.\n",
      "iter: 26, E= 3406.407, ER= 0.000, EM= 3406.407, epd= 0.005, time= 0.03s.\n",
      "iter: 27, E= 3396.347, ER= 0.000, EM= 3396.347, epd= 0.005, time= 0.03s.\n",
      "iter: 28, E= 3386.429, ER= 0.000, EM= 3386.429, epd= 0.005, time= 0.03s.\n",
      "iter: 29, E= 3375.469, ER= 0.000, EM= 3375.469, epd= 0.005, time= 0.03s.\n",
      "iter: 30, E= 3364.133, ER= 0.000, EM= 3364.133, epd= 0.005, time= 0.03s.\n",
      "iter: 31, E= 3352.950, ER= 0.000, EM= 3352.950, epd= 0.005, time= 0.03s.\n",
      "iter: 32, E= 3341.924, ER= 0.000, EM= 3341.924, epd= 0.005, time= 0.03s.\n",
      "iter: 33, E= 3331.062, ER= 0.000, EM= 3331.062, epd= 0.005, time= 0.03s.\n",
      "iter: 34, E= 3320.370, ER= 0.000, EM= 3320.370, epd= 0.005, time= 0.03s.\n",
      "iter: 35, E= 3309.851, ER= 0.000, EM= 3309.851, epd= 0.005, time= 0.03s.\n",
      "iter: 36, E= 3299.454, ER= 0.000, EM= 3299.454, epd= 0.005, time= 0.03s.\n",
      "iter: 37, E= 3288.082, ER= 0.000, EM= 3288.082, epd= 0.005, time= 0.03s.\n",
      "iter: 38, E= 3276.882, ER= 0.000, EM= 3276.882, epd= 0.005, time= 0.03s.\n",
      "iter: 39, E= 3265.859, ER= 0.000, EM= 3265.859, epd= 0.005, time= 0.03s.\n",
      "iter: 40, E= 3255.018, ER= 0.000, EM= 3255.018, epd= 0.005, time= 0.03s.\n",
      "iter: 41, E= 3244.362, ER= 0.000, EM= 3244.362, epd= 0.005, time= 0.03s.\n",
      "iter: 42, E= 3233.896, ER= 0.000, EM= 3233.896, epd= 0.005, time= 0.03s.\n",
      "iter: 43, E= 3223.624, ER= 0.000, EM= 3223.624, epd= 0.005, time= 0.03s.\n",
      "iter: 44, E= 3212.291, ER= 0.000, EM= 3212.291, epd= 0.005, time= 0.03s.\n",
      "iter: 45, E= 3200.778, ER= 0.000, EM= 3200.778, epd= 0.005, time= 0.03s.\n",
      "iter: 46, E= 3189.470, ER= 0.000, EM= 3189.470, epd= 0.005, time= 0.03s.\n",
      "iter: 47, E= 3178.371, ER= 0.000, EM= 3178.371, epd= 0.005, time= 0.03s.\n",
      "iter: 48, E= 3167.486, ER= 0.000, EM= 3167.486, epd= 0.005, time= 0.03s.\n",
      "iter: 49, E= 3156.818, ER= 0.000, EM= 3156.818, epd= 0.005, time= 0.03s.\n",
      "iter: 50, E= 3146.369, ER= 0.000, EM= 3146.369, epd= 0.005, time= 0.03s.\n",
      "iter: 51, E= 3136.143, ER= 0.000, EM= 3136.143, epd= 0.005, time= 0.03s.\n",
      "iter: 52, E= 3126.143, ER= 0.000, EM= 3126.143, epd= 0.005, time= 0.03s.\n",
      "iter: 53, E= 3115.507, ER= 0.000, EM= 3115.507, epd= 0.005, time= 0.03s.\n",
      "iter: 54, E= 3104.700, ER= 0.000, EM= 3104.700, epd= 0.005, time= 0.03s.\n",
      "iter: 55, E= 3094.118, ER= 0.000, EM= 3094.118, epd= 0.005, time= 0.03s.\n",
      "iter: 56, E= 3083.765, ER= 0.000, EM= 3083.765, epd= 0.005, time= 0.03s.\n",
      "iter: 57, E= 3073.642, ER= 0.000, EM= 3073.642, epd= 0.005, time= 0.03s.\n",
      "iter: 58, E= 3063.750, ER= 0.000, EM= 3063.750, epd= 0.005, time= 0.03s.\n",
      "iter: 59, E= 3054.092, ER= 0.000, EM= 3054.092, epd= 0.005, time= 0.03s.\n",
      "iter: 60, E= 3044.668, ER= 0.000, EM= 3044.668, epd= 0.005, time= 0.03s.\n",
      "iter: 61, E= 3034.245, ER= 0.000, EM= 3034.245, epd= 0.005, time= 0.03s.\n",
      "iter: 62, E= 3023.852, ER= 0.000, EM= 3023.852, epd= 0.005, time= 0.03s.\n",
      "iter: 63, E= 3013.696, ER= 0.000, EM= 3013.696, epd= 0.005, time= 0.03s.\n",
      "iter: 64, E= 3003.779, ER= 0.000, EM= 3003.779, epd= 0.005, time= 0.03s.\n",
      "iter: 65, E= 2994.102, ER= 0.000, EM= 2994.102, epd= 0.005, time= 0.03s.\n",
      "iter: 66, E= 2984.667, ER= 0.000, EM= 2984.667, epd= 0.005, time= 0.03s.\n",
      "iter: 67, E= 2975.475, ER= 0.000, EM= 2975.475, epd= 0.005, time= 0.03s.\n",
      "iter: 68, E= 2966.526, ER= 0.000, EM= 2966.526, epd= 0.005, time= 0.03s.\n",
      "iter: 69, E= 2957.820, ER= 0.000, EM= 2957.820, epd= 0.005, time= 0.03s.\n",
      "iter: 70, E= 2948.617, ER= 0.000, EM= 2948.617, epd= 0.005, time= 0.03s.\n",
      "iter: 71, E= 2939.277, ER= 0.000, EM= 2939.277, epd= 0.005, time= 0.03s.\n",
      "iter: 72, E= 2930.179, ER= 0.000, EM= 2930.179, epd= 0.005, time= 0.03s.\n",
      "iter: 73, E= 2921.323, ER= 0.000, EM= 2921.323, epd= 0.005, time= 0.03s.\n",
      "iter: 74, E= 2912.708, ER= 0.000, EM= 2912.708, epd= 0.005, time= 0.03s.\n",
      "iter: 75, E= 2904.333, ER= 0.000, EM= 2904.333, epd= 0.005, time= 0.03s.\n",
      "iter: 76, E= 2896.199, ER= 0.000, EM= 2896.199, epd= 0.005, time= 0.03s.\n",
      "iter: 77, E= 2888.305, ER= 0.000, EM= 2888.305, epd= 0.005, time= 0.03s.\n",
      "iter: 78, E= 2880.649, ER= 0.000, EM= 2880.649, epd= 0.005, time= 0.03s.\n",
      "iter: 79, E= 2873.230, ER= 0.000, EM= 2873.230, epd= 0.005, time= 0.03s.\n",
      "iter: 80, E= 2866.047, ER= 0.000, EM= 2866.047, epd= 0.005, time= 0.03s.\n",
      "iter: 81, E= 2858.886, ER= 0.000, EM= 2858.886, epd= 0.005, time= 0.03s.\n",
      "iter: 82, E= 2850.872, ER= 0.000, EM= 2850.872, epd= 0.005, time= 0.03s.\n",
      "iter: 83, E= 2843.093, ER= 0.000, EM= 2843.093, epd= 0.005, time= 0.03s.\n",
      "iter: 84, E= 2835.549, ER= 0.000, EM= 2835.549, epd= 0.005, time= 0.03s.\n",
      "iter: 85, E= 2828.238, ER= 0.000, EM= 2828.238, epd= 0.005, time= 0.03s.\n",
      "iter: 86, E= 2821.160, ER= 0.000, EM= 2821.160, epd= 0.005, time= 0.03s.\n",
      "iter: 87, E= 2814.312, ER= 0.000, EM= 2814.312, epd= 0.005, time= 0.03s.\n",
      "iter: 88, E= 2807.695, ER= 0.000, EM= 2807.695, epd= 0.005, time= 0.03s.\n",
      "iter: 89, E= 2801.078, ER= 0.000, EM= 2801.078, epd= 0.005, time= 0.03s.\n",
      "iter: 90, E= 2793.913, ER= 0.000, EM= 2793.913, epd= 0.005, time= 0.03s.\n",
      "iter: 91, E= 2786.969, ER= 0.000, EM= 2786.969, epd= 0.005, time= 0.03s.\n",
      "iter: 92, E= 2780.245, ER= 0.000, EM= 2780.245, epd= 0.005, time= 0.03s.\n",
      "iter: 93, E= 2773.740, ER= 0.000, EM= 2773.740, epd= 0.005, time= 0.03s.\n",
      "iter: 94, E= 2767.450, ER= 0.000, EM= 2767.450, epd= 0.005, time= 0.03s.\n",
      "iter: 95, E= 2761.375, ER= 0.000, EM= 2761.375, epd= 0.005, time= 0.03s.\n",
      "iter: 96, E= 2755.512, ER= 0.000, EM= 2755.512, epd= 0.005, time= 0.03s.\n",
      "iter: 97, E= 2749.858, ER= 0.000, EM= 2749.858, epd= 0.005, time= 0.03s.\n",
      "iter: 98, E= 2744.411, ER= 0.000, EM= 2744.411, epd= 0.005, time= 0.03s.\n",
      "iter: 99, E= 2739.168, ER= 0.000, EM= 2739.168, epd= 0.005, time= 0.03s.\n",
      "iter: 100, E= 2734.127, ER= 0.000, EM= 2734.127, epd= 0.005, time= 0.03s.\n",
      "iter: 101, E= 2729.286, ER= 0.000, EM= 2729.286, epd= 0.005, time= 0.03s.\n",
      "iter: 102, E= 2724.639, ER= 0.000, EM= 2724.639, epd= 0.005, time= 0.03s.\n",
      "iter: 103, E= 2720.187, ER= 0.000, EM= 2720.187, epd= 0.005, time= 0.03s.\n",
      "iter: 104, E= 2715.925, ER= 0.000, EM= 2715.925, epd= 0.005, time= 0.03s.\n",
      "iter: 105, E= 2711.849, ER= 0.000, EM= 2711.849, epd= 0.005, time= 0.03s.\n",
      "iter: 106, E= 2707.957, ER= 0.000, EM= 2707.957, epd= 0.005, time= 0.03s.\n",
      "iter: 107, E= 2704.246, ER= 0.000, EM= 2704.246, epd= 0.005, time= 0.03s.\n",
      "iter: 108, E= 2700.712, ER= 0.000, EM= 2700.712, epd= 0.005, time= 0.03s.\n",
      "iter: 109, E= 2697.353, ER= 0.000, EM= 2697.353, epd= 0.005, time= 0.03s.\n",
      "iter: 110, E= 2693.375, ER= 0.000, EM= 2693.375, epd= 0.005, time= 0.03s.\n",
      "iter: 111, E= 2689.430, ER= 0.000, EM= 2689.430, epd= 0.005, time= 0.03s.\n",
      "iter: 112, E= 2685.663, ER= 0.000, EM= 2685.663, epd= 0.005, time= 0.03s.\n",
      "iter: 113, E= 2682.072, ER= 0.000, EM= 2682.072, epd= 0.005, time= 0.03s.\n",
      "iter: 114, E= 2678.150, ER= 0.000, EM= 2678.150, epd= 0.005, time= 0.03s.\n",
      "iter: 115, E= 2674.048, ER= 0.000, EM= 2674.048, epd= 0.005, time= 0.03s.\n",
      "iter: 116, E= 2670.111, ER= 0.000, EM= 2670.111, epd= 0.005, time= 0.03s.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter: 117, E= 2666.337, ER= 0.000, EM= 2666.337, epd= 0.005, time= 0.03s.\n",
      "iter: 118, E= 2662.721, ER= 0.000, EM= 2662.721, epd= 0.005, time= 0.03s.\n",
      "iter: 119, E= 2659.259, ER= 0.000, EM= 2659.259, epd= 0.005, time= 0.03s.\n",
      "iter: 120, E= 2655.948, ER= 0.000, EM= 2655.948, epd= 0.005, time= 0.03s.\n",
      "iter: 121, E= 2652.784, ER= 0.000, EM= 2652.784, epd= 0.005, time= 0.03s.\n",
      "iter: 122, E= 2649.764, ER= 0.000, EM= 2649.764, epd= 0.005, time= 0.03s.\n",
      "iter: 123, E= 2646.884, ER= 0.000, EM= 2646.884, epd= 0.005, time= 0.03s.\n",
      "iter: 124, E= 2644.141, ER= 0.000, EM= 2644.141, epd= 0.005, time= 0.03s.\n",
      "iter: 125, E= 2641.530, ER= 0.000, EM= 2641.530, epd= 0.005, time= 0.03s.\n",
      "iter: 126, E= 2639.049, ER= 0.000, EM= 2639.049, epd= 0.005, time= 0.03s.\n",
      "iter: 127, E= 2636.693, ER= 0.000, EM= 2636.693, epd= 0.005, time= 0.03s.\n",
      "iter: 128, E= 2634.459, ER= 0.000, EM= 2634.459, epd= 0.005, time= 0.03s.\n",
      "iter: 129, E= 2632.345, ER= 0.000, EM= 2632.345, epd= 0.005, time= 0.03s.\n",
      "iter: 130, E= 2630.345, ER= 0.000, EM= 2630.345, epd= 0.005, time= 0.03s.\n",
      "iter: 131, E= 2628.459, ER= 0.000, EM= 2628.459, epd= 0.005, time= 0.03s.\n",
      "iter: 132, E= 2626.680, ER= 0.000, EM= 2626.680, epd= 0.005, time= 0.03s.\n",
      "iter: 133, E= 2625.008, ER= 0.000, EM= 2625.008, epd= 0.005, time= 0.03s.\n",
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      "iter: 135, E= 2621.967, ER= 0.000, EM= 2621.967, epd= 0.005, time= 0.03s.\n",
      "iter: 136, E= 2620.593, ER= 0.000, EM= 2620.593, epd= 0.005, time= 0.03s.\n",
      "iter: 137, E= 2619.312, ER= 0.000, EM= 2619.312, epd= 0.005, time= 0.03s.\n",
      "iter: 138, E= 2618.120, ER= 0.000, EM= 2618.120, epd= 0.005, time= 0.03s.\n",
      "iter: 139, E= 2617.016, ER= 0.000, EM= 2617.016, epd= 0.005, time= 0.03s.\n",
      "iter: 140, E= 2615.998, ER= 0.000, EM= 2615.998, epd= 0.005, time= 0.03s.\n",
      "iter: 141, E= 2615.060, ER= 0.000, EM= 2615.060, epd= 0.005, time= 0.03s.\n",
      "iter: 142, E= 2614.201, ER= 0.000, EM= 2614.201, epd= 0.005, time= 0.03s.\n",
      "iter: 143, E= 2613.418, ER= 0.000, EM= 2613.418, epd= 0.005, time= 0.03s.\n",
      "iter: 144, E= 2612.710, ER= 0.000, EM= 2612.710, epd= 0.005, time= 0.03s.\n",
      "iter: 145, E= 2612.073, ER= 0.000, EM= 2612.073, epd= 0.005, time= 0.03s.\n",
      "iter: 146, E= 2611.504, ER= 0.000, EM= 2611.504, epd= 0.005, time= 0.03s.\n",
      "iter: 147, E= 2611.001, ER= 0.000, EM= 2611.001, epd= 0.005, time= 0.03s.\n",
      "iter: 148, E= 2610.562, ER= 0.000, EM= 2610.562, epd= 0.005, time= 0.03s.\n",
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      "iter: 150, E= 2609.867, ER= 0.000, EM= 2609.867, epd= 0.005, time= 0.03s.\n",
      "iter: 151, E= 2609.466, ER= 0.000, EM= 2609.466, epd= 0.005, time= 0.03s.\n",
      "iter: 152, E= 2608.714, ER= 0.000, EM= 2608.714, epd= 0.005, time= 0.03s.\n",
      "iter: 153, E= 2608.020, ER= 0.000, EM= 2608.020, epd= 0.005, time= 0.03s.\n",
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      "iter: 158, E= 2599.566, ER= 0.000, EM= 2599.566, epd= 0.005, time= 0.03s.\n",
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      "iter: 163, E= 2588.887, ER= 0.000, EM= 2588.887, epd= 0.005, time= 0.03s.\n",
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      "iter: 165, E= 2585.265, ER= 0.000, EM= 2585.265, epd= 0.005, time= 0.03s.\n",
      "iter: 166, E= 2583.577, ER= 0.000, EM= 2583.577, epd= 0.005, time= 0.03s.\n",
      "iter: 167, E= 2581.968, ER= 0.000, EM= 2581.968, epd= 0.005, time= 0.03s.\n",
      "iter: 168, E= 2580.431, ER= 0.000, EM= 2580.431, epd= 0.005, time= 0.03s.\n",
      "iter: 169, E= 2578.967, ER= 0.000, EM= 2578.967, epd= 0.005, time= 0.03s.\n",
      "iter: 170, E= 2577.569, ER= 0.000, EM= 2577.569, epd= 0.005, time= 0.03s.\n",
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      "iter: 172, E= 2574.967, ER= 0.000, EM= 2574.967, epd= 0.005, time= 0.03s.\n",
      "iter: 173, E= 2573.755, ER= 0.000, EM= 2573.755, epd= 0.005, time= 0.03s.\n",
      "iter: 174, E= 2572.601, ER= 0.000, EM= 2572.601, epd= 0.005, time= 0.03s.\n",
      "iter: 175, E= 2571.500, ER= 0.000, EM= 2571.500, epd= 0.005, time= 0.03s.\n",
      "iter: 176, E= 2570.451, ER= 0.000, EM= 2570.451, epd= 0.005, time= 0.03s.\n",
      "iter: 177, E= 2569.451, ER= 0.000, EM= 2569.451, epd= 0.005, time= 0.03s.\n",
      "iter: 178, E= 2568.498, ER= 0.000, EM= 2568.498, epd= 0.005, time= 0.03s.\n",
      "iter: 179, E= 2567.589, ER= 0.000, EM= 2567.589, epd= 0.005, time= 0.03s.\n",
      "iter: 180, E= 2566.725, ER= 0.000, EM= 2566.725, epd= 0.005, time= 0.03s.\n",
      "iter: 181, E= 2565.900, ER= 0.000, EM= 2565.900, epd= 0.005, time= 0.03s.\n",
      "iter: 182, E= 2565.115, ER= 0.000, EM= 2565.115, epd= 0.005, time= 0.03s.\n",
      "iter: 183, E= 2564.367, ER= 0.000, EM= 2564.367, epd= 0.005, time= 0.03s.\n",
      "iter: 184, E= 2563.655, ER= 0.000, EM= 2563.655, epd= 0.005, time= 0.03s.\n",
      "iter: 185, E= 2562.976, ER= 0.000, EM= 2562.976, epd= 0.005, time= 0.03s.\n",
      "iter: 186, E= 2562.331, ER= 0.000, EM= 2562.331, epd= 0.005, time= 0.03s.\n",
      "iter: 187, E= 2561.715, ER= 0.000, EM= 2561.715, epd= 0.005, time= 0.03s.\n",
      "iter: 188, E= 2561.129, ER= 0.000, EM= 2561.129, epd= 0.005, time= 0.03s.\n",
      "iter: 189, E= 2560.572, ER= 0.000, EM= 2560.572, epd= 0.005, time= 0.03s.\n",
      "iter: 190, E= 2560.041, ER= 0.000, EM= 2560.041, epd= 0.005, time= 0.03s.\n",
      "iter: 191, E= 2559.535, ER= 0.000, EM= 2559.535, epd= 0.005, time= 0.03s.\n",
      "iter: 192, E= 2559.054, ER= 0.000, EM= 2559.054, epd= 0.005, time= 0.03s.\n",
      "iter: 193, E= 2558.597, ER= 0.000, EM= 2558.597, epd= 0.005, time= 0.03s.\n",
      "iter: 194, E= 2558.162, ER= 0.000, EM= 2558.162, epd= 0.005, time= 0.03s.\n",
      "iter: 195, E= 2557.747, ER= 0.000, EM= 2557.747, epd= 0.005, time= 0.03s.\n",
      "iter: 196, E= 2557.353, ER= 0.000, EM= 2557.353, epd= 0.005, time= 0.03s.\n",
      "iter: 197, E= 2556.979, ER= 0.000, EM= 2556.979, epd= 0.005, time= 0.03s.\n",
      "iter: 198, E= 2556.622, ER= 0.000, EM= 2556.622, epd= 0.005, time= 0.03s.\n",
      "iter: 199, E= 2556.283, ER= 0.000, EM= 2556.283, epd= 0.005, time= 0.03s.\n",
      "iter: 200, E= 2555.960, ER= 0.000, EM= 2555.960, epd= 0.005, time= 0.03s.\n",
      "iter: 201, E= 2555.654, ER= 0.000, EM= 2555.654, epd= 0.005, time= 0.03s.\n",
      "iter: 202, E= 2555.362, ER= 0.000, EM= 2555.362, epd= 0.005, time= 0.03s.\n",
      "iter: 203, E= 2555.085, ER= 0.000, EM= 2555.085, epd= 0.005, time= 0.03s.\n",
      "iter: 204, E= 2554.822, ER= 0.000, EM= 2554.822, epd= 0.005, time= 0.03s.\n",
      "iter: 205, E= 2554.572, ER= 0.000, EM= 2554.572, epd= 0.005, time= 0.03s.\n",
      "iter: 206, E= 2554.334, ER= 0.000, EM= 2554.334, epd= 0.005, time= 0.03s.\n",
      "iter: 207, E= 2554.108, ER= 0.000, EM= 2554.108, epd= 0.005, time= 0.03s.\n",
      "iter: 208, E= 2553.893, ER= 0.000, EM= 2553.893, epd= 0.005, time= 0.03s.\n",
      "iter: 209, E= 2553.689, ER= 0.000, EM= 2553.689, epd= 0.005, time= 0.03s.\n",
      "iter: 210, E= 2553.496, ER= 0.000, EM= 2553.496, epd= 0.005, time= 0.03s.\n",
      "iter: 211, E= 2553.312, ER= 0.000, EM= 2553.312, epd= 0.005, time= 0.03s.\n",
      "iter: 212, E= 2553.138, ER= 0.000, EM= 2553.138, epd= 0.005, time= 0.03s.\n",
      "iter: 213, E= 2552.972, ER= 0.000, EM= 2552.972, epd= 0.005, time= 0.03s.\n",
      "iter: 214, E= 2552.815, ER= 0.000, EM= 2552.815, epd= 0.005, time= 0.03s.\n",
      "iter: 215, E= 2552.666, ER= 0.000, EM= 2552.666, epd= 0.005, time= 0.03s.\n",
      "iter: 216, E= 2552.524, ER= 0.000, EM= 2552.524, epd= 0.005, time= 0.03s.\n",
      "iter: 217, E= 2552.390, ER= 0.000, EM= 2552.390, epd= 0.005, time= 0.03s.\n",
      "iter: 218, E= 2552.263, ER= 0.000, EM= 2552.263, epd= 0.005, time= 0.03s.\n",
      "iter: 219, E= 2552.142, ER= 0.000, EM= 2552.142, epd= 0.005, time= 0.03s.\n",
      "iter: 220, E= 2552.027, ER= 0.000, EM= 2552.027, epd= 0.005, time= 0.03s.\n",
      "iter: 221, E= 2551.919, ER= 0.000, EM= 2551.919, epd= 0.005, time= 0.03s.\n",
      "iter: 222, E= 2551.817, ER= 0.000, EM= 2551.817, epd= 0.005, time= 0.03s.\n",
      "iter: 223, E= 2551.719, ER= 0.000, EM= 2551.719, epd= 0.005, time= 0.03s.\n",
      "iter: 224, E= 2551.627, ER= 0.000, EM= 2551.627, epd= 0.005, time= 0.03s.\n",
      "iter: 225, E= 2551.540, ER= 0.000, EM= 2551.540, epd= 0.005, time= 0.03s.\n",
      "iter: 226, E= 2551.457, ER= 0.000, EM= 2551.457, epd= 0.005, time= 0.03s.\n",
      "iter: 227, E= 2551.379, ER= 0.000, EM= 2551.379, epd= 0.005, time= 0.03s.\n",
      "iter: 228, E= 2551.305, ER= 0.000, EM= 2551.305, epd= 0.005, time= 0.03s.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter: 229, E= 2551.235, ER= 0.000, EM= 2551.235, epd= 0.005, time= 0.03s.\n",
      "iter: 230, E= 2551.169, ER= 0.000, EM= 2551.169, epd= 0.005, time= 0.03s.\n",
      "iter: 231, E= 2551.107, ER= 0.000, EM= 2551.107, epd= 0.005, time= 0.03s.\n",
      "iter: 232, E= 2551.047, ER= 0.000, EM= 2551.047, epd= 0.005, time= 0.03s.\n",
      "iter: 233, E= 2550.992, ER= 0.000, EM= 2550.992, epd= 0.005, time= 0.03s.\n",
      "iter: 234, E= 2550.939, ER= 0.000, EM= 2550.939, epd= 0.005, time= 0.03s.\n",
      "iter: 235, E= 2550.890, ER= 0.000, EM= 2550.890, epd= 0.005, time= 0.03s.\n",
      "iter: 236, E= 2550.843, ER= 0.000, EM= 2550.843, epd= 0.005, time= 0.03s.\n",
      "iter: 237, E= 2550.799, ER= 0.000, EM= 2550.799, epd= 0.005, time= 0.03s.\n",
      "iter: 238, E= 2550.757, ER= 0.000, EM= 2550.757, epd= 0.005, time= 0.03s.\n",
      "iter: 239, E= 2550.718, ER= 0.000, EM= 2550.718, epd= 0.005, time= 0.03s.\n",
      "iter: 240, E= 2550.680, ER= 0.000, EM= 2550.680, epd= 0.005, time= 0.03s.\n",
      "iter: 241, E= 2550.646, ER= 0.000, EM= 2550.646, epd= 0.005, time= 0.03s.\n",
      "iter: 242, E= 2550.613, ER= 0.000, EM= 2550.613, epd= 0.005, time= 0.03s.\n",
      "iter: 243, E= 2550.582, ER= 0.000, EM= 2550.582, epd= 0.005, time= 0.03s.\n",
      "iter: 244, E= 2550.553, ER= 0.000, EM= 2550.553, epd= 0.005, time= 0.03s.\n",
      "iter: 245, E= 2550.526, ER= 0.000, EM= 2550.526, epd= 0.005, time= 0.03s.\n",
      "iter: 246, E= 2550.500, ER= 0.000, EM= 2550.500, epd= 0.005, time= 0.03s.\n",
      "iter: 247, E= 2550.476, ER= 0.000, EM= 2550.476, epd= 0.005, time= 0.03s.\n",
      "iter: 248, E= 2550.460, ER= 0.000, EM= 2550.460, epd= 0.005, time= 0.03s.\n",
      "iter: 249, E= 2550.450, ER= 0.000, EM= 2550.450, epd= 0.005, time= 0.03s.\n",
      "iter: 250, E= 2550.443, ER= 0.000, EM= 2550.443, epd= 0.005, time= 0.03s.\n",
      "iter: 251, E= 2550.437, ER= 0.000, EM= 2550.437, epd= 0.005, time= 0.03s.\n",
      "iter: 252, E= 2550.434, ER= 0.000, EM= 2550.434, epd= 0.005, time= 0.03s.\n",
      "iter: 253, E= 2550.432, ER= 0.000, EM= 2550.432, epd= 0.005, time= 0.03s.\n",
      "iter: 254, E= 2550.429, ER= 0.000, EM= 2550.429, epd= 0.005, time= 0.03s.\n",
      "iter: 255, E= 2550.429, ER= 0.000, EM= 2550.429, epd= 0.005, time= 0.03s.\n",
      "iter: 256, E= 2550.428, ER= 0.000, EM= 2550.428, epd= 0.005, time= 0.03s.\n",
      "iter: 257, E= 2550.428, ER= 0.000, EM= 2550.428, epd= 0.005, time= 0.03s.\n",
      "iter: 258, E= 2550.427, ER= 0.000, EM= 2550.427, epd= 0.005, time= 0.03s.\n",
      "iter: 259, E= 2550.427, ER= 0.000, EM= 2550.427, epd= 0.005, time= 0.03s.\n",
      "iter: 260, E= 2550.426, ER= 0.000, EM= 2550.426, epd= 0.005, time= 0.03s.\n",
      "iter: 261, E= 2550.427, ER= 0.000, EM= 2550.427, epd= 0.005, time= 0.03s.\n",
      "iter: 262, E= 2550.426, ER= 0.000, EM= 2550.426, epd= 0.005, time= 0.03s.\n",
      "iter: 263, E= 2550.427, ER= 0.000, EM= 2550.427, epd= 0.005, time= 0.03s.\n",
      "iter: 264, E= 2550.426, ER= 0.000, EM= 2550.426, epd= 0.005, time= 0.03s.\n",
      "iter: 265, E= 2550.426, ER= 0.000, EM= 2550.426, epd= 0.005, time= 0.03s.\n",
      "iter: 266, E= 2550.426, ER= 0.000, EM= 2550.426, epd= 0.005, time= 0.03s.\n",
      "iter: 267, E= 2550.426, ER= 0.000, EM= 2550.426, epd= 0.005, time= 0.03s.\n",
      "iter: 268, E= 2550.426, ER= 0.000, EM= 2550.426, epd= 0.005, time= 0.03s.\n",
      "iter: 269, E= 2550.426, ER= 0.000, EM= 2550.426, epd= 0.005, time= 0.03s.\n",
      "iter: 270, E= 2550.426, ER= 0.000, EM= 2550.426, epd= 0.005, time= 0.03s.\n",
      "iter: 271, E= 2550.426, ER= 0.000, EM= 2550.426, epd= 0.005, time= 0.03s.\n",
      "iter: 272, E= 2550.426, ER= 0.000, EM= 2550.426, epd= 0.005, time= 0.03s.\n",
      "iter: 273, E= 2550.426, ER= 0.000, EM= 2550.426, epd= 0.005, time= 0.03s.\n",
      "Early termination: Energy change threshold reached.\n",
      "Total elapsed runtime: 7.51 seconds.\n",
      "Parameter 'epsilonL' changed to '1e-05'.\n",
      "Parameter 'epsilonT' changed to '2e-05'.\n",
      "iter: 0, E = 2550.4263, ER = 0.0000, EM = 2550.4263, epd = 0.005000.\n",
      "iter: 1, E= 2540.039, ER= 0.000, EM= 2540.039, epd= 0.005, time= 0.03s.\n",
      "iter: 2, E= 2535.390, ER= 0.000, EM= 2535.390, epd= 0.005, time= 0.03s.\n",
      "iter: 3, E= 2532.309, ER= 0.000, EM= 2532.309, epd= 0.005, time= 0.03s.\n",
      "iter: 4, E= 2530.752, ER= 0.000, EM= 2530.752, epd= 0.005, time= 0.03s.\n",
      "iter: 5, E= 2529.400, ER= 0.000, EM= 2529.400, epd= 0.005, time= 0.03s.\n",
      "iter: 6, E= 2528.199, ER= 0.000, EM= 2528.199, epd= 0.005, time= 0.03s.\n",
      "iter: 7, E= 2527.105, ER= 0.000, EM= 2527.105, epd= 0.005, time= 0.03s.\n",
      "iter: 8, E= 2526.047, ER= 0.000, EM= 2526.047, epd= 0.005, time= 0.03s.\n",
      "iter: 9, E= 2525.046, ER= 0.000, EM= 2525.046, epd= 0.005, time= 0.03s.\n",
      "iter: 10, E= 2524.101, ER= 0.000, EM= 2524.101, epd= 0.005, time= 0.03s.\n",
      "iter: 11, E= 2523.201, ER= 0.000, EM= 2523.201, epd= 0.005, time= 0.03s.\n",
      "iter: 12, E= 2522.336, ER= 0.000, EM= 2522.336, epd= 0.005, time= 0.03s.\n",
      "iter: 13, E= 2521.492, ER= 0.000, EM= 2521.492, epd= 0.005, time= 0.03s.\n",
      "iter: 14, E= 2520.674, ER= 0.000, EM= 2520.674, epd= 0.005, time= 0.03s.\n",
      "iter: 15, E= 2519.892, ER= 0.000, EM= 2519.892, epd= 0.005, time= 0.03s.\n",
      "iter: 16, E= 2519.150, ER= 0.000, EM= 2519.150, epd= 0.005, time= 0.03s.\n",
      "iter: 17, E= 2518.452, ER= 0.000, EM= 2518.452, epd= 0.005, time= 0.03s.\n",
      "iter: 18, E= 2517.796, ER= 0.000, EM= 2517.796, epd= 0.005, time= 0.03s.\n",
      "iter: 19, E= 2517.175, ER= 0.000, EM= 2517.175, epd= 0.005, time= 0.03s.\n",
      "iter: 20, E= 2516.589, ER= 0.000, EM= 2516.589, epd= 0.005, time= 0.03s.\n",
      "iter: 21, E= 2516.036, ER= 0.000, EM= 2516.036, epd= 0.005, time= 0.03s.\n",
      "iter: 22, E= 2515.511, ER= 0.000, EM= 2515.511, epd= 0.005, time= 0.03s.\n",
      "iter: 23, E= 2515.012, ER= 0.000, EM= 2515.012, epd= 0.005, time= 0.03s.\n",
      "iter: 24, E= 2514.540, ER= 0.000, EM= 2514.540, epd= 0.005, time= 0.03s.\n",
      "iter: 25, E= 2514.094, ER= 0.000, EM= 2514.094, epd= 0.005, time= 0.03s.\n",
      "iter: 26, E= 2513.673, ER= 0.000, EM= 2513.673, epd= 0.005, time= 0.03s.\n",
      "iter: 27, E= 2513.276, ER= 0.000, EM= 2513.276, epd= 0.005, time= 0.03s.\n",
      "iter: 28, E= 2512.898, ER= 0.000, EM= 2512.898, epd= 0.005, time= 0.03s.\n",
      "iter: 29, E= 2512.544, ER= 0.000, EM= 2512.544, epd= 0.005, time= 0.03s.\n",
      "iter: 30, E= 2512.208, ER= 0.000, EM= 2512.208, epd= 0.005, time= 0.03s.\n",
      "iter: 31, E= 2511.890, ER= 0.000, EM= 2511.890, epd= 0.005, time= 0.03s.\n",
      "iter: 32, E= 2511.588, ER= 0.000, EM= 2511.588, epd= 0.005, time= 0.03s.\n",
      "iter: 33, E= 2511.298, ER= 0.000, EM= 2511.298, epd= 0.005, time= 0.03s.\n",
      "iter: 34, E= 2511.023, ER= 0.000, EM= 2511.023, epd= 0.005, time= 0.03s.\n",
      "iter: 35, E= 2510.760, ER= 0.000, EM= 2510.760, epd= 0.005, time= 0.03s.\n",
      "iter: 36, E= 2510.510, ER= 0.000, EM= 2510.510, epd= 0.005, time= 0.03s.\n",
      "iter: 37, E= 2510.271, ER= 0.000, EM= 2510.271, epd= 0.005, time= 0.03s.\n",
      "iter: 38, E= 2510.042, ER= 0.000, EM= 2510.042, epd= 0.005, time= 0.03s.\n",
      "iter: 39, E= 2509.825, ER= 0.000, EM= 2509.825, epd= 0.005, time= 0.03s.\n",
      "iter: 40, E= 2509.616, ER= 0.000, EM= 2509.616, epd= 0.005, time= 0.03s.\n",
      "iter: 41, E= 2509.418, ER= 0.000, EM= 2509.418, epd= 0.005, time= 0.03s.\n",
      "iter: 42, E= 2509.228, ER= 0.000, EM= 2509.228, epd= 0.005, time= 0.03s.\n",
      "iter: 43, E= 2509.046, ER= 0.000, EM= 2509.046, epd= 0.005, time= 0.03s.\n",
      "iter: 44, E= 2508.872, ER= 0.000, EM= 2508.872, epd= 0.005, time= 0.03s.\n",
      "iter: 45, E= 2508.707, ER= 0.000, EM= 2508.707, epd= 0.005, time= 0.03s.\n",
      "iter: 46, E= 2508.546, ER= 0.000, EM= 2508.546, epd= 0.005, time= 0.03s.\n",
      "iter: 47, E= 2508.392, ER= 0.000, EM= 2508.392, epd= 0.005, time= 0.03s.\n",
      "iter: 48, E= 2508.244, ER= 0.000, EM= 2508.244, epd= 0.005, time= 0.03s.\n",
      "iter: 49, E= 2508.102, ER= 0.000, EM= 2508.102, epd= 0.005, time= 0.03s.\n",
      "iter: 50, E= 2507.965, ER= 0.000, EM= 2507.965, epd= 0.005, time= 0.03s.\n",
      "iter: 51, E= 2507.833, ER= 0.000, EM= 2507.833, epd= 0.005, time= 0.03s.\n",
      "iter: 52, E= 2507.706, ER= 0.000, EM= 2507.706, epd= 0.005, time= 0.03s.\n",
      "iter: 53, E= 2507.582, ER= 0.000, EM= 2507.582, epd= 0.005, time= 0.03s.\n",
      "iter: 54, E= 2507.463, ER= 0.000, EM= 2507.463, epd= 0.005, time= 0.03s.\n",
      "iter: 55, E= 2507.348, ER= 0.000, EM= 2507.348, epd= 0.005, time= 0.03s.\n",
      "iter: 56, E= 2507.236, ER= 0.000, EM= 2507.236, epd= 0.005, time= 0.03s.\n",
      "iter: 57, E= 2507.128, ER= 0.000, EM= 2507.128, epd= 0.005, time= 0.03s.\n",
      "iter: 58, E= 2507.024, ER= 0.000, EM= 2507.024, epd= 0.005, time= 0.03s.\n",
      "iter: 59, E= 2506.922, ER= 0.000, EM= 2506.922, epd= 0.005, time= 0.03s.\n",
      "iter: 60, E= 2506.823, ER= 0.000, EM= 2506.823, epd= 0.005, time= 0.03s.\n",
      "iter: 61, E= 2506.728, ER= 0.000, EM= 2506.728, epd= 0.005, time= 0.03s.\n",
      "iter: 62, E= 2506.636, ER= 0.000, EM= 2506.636, epd= 0.005, time= 0.03s.\n",
      "iter: 63, E= 2506.545, ER= 0.000, EM= 2506.545, epd= 0.005, time= 0.03s.\n",
      "iter: 64, E= 2506.458, ER= 0.000, EM= 2506.458, epd= 0.005, time= 0.03s.\n",
      "iter: 65, E= 2506.373, ER= 0.000, EM= 2506.373, epd= 0.005, time= 0.03s.\n",
      "iter: 66, E= 2506.291, ER= 0.000, EM= 2506.291, epd= 0.005, time= 0.03s.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter: 67, E= 2506.211, ER= 0.000, EM= 2506.211, epd= 0.005, time= 0.03s.\n",
      "iter: 68, E= 2506.131, ER= 0.000, EM= 2506.131, epd= 0.005, time= 0.03s.\n",
      "iter: 69, E= 2506.055, ER= 0.000, EM= 2506.055, epd= 0.005, time= 0.03s.\n",
      "iter: 70, E= 2505.981, ER= 0.000, EM= 2505.981, epd= 0.005, time= 0.03s.\n",
      "iter: 71, E= 2505.910, ER= 0.000, EM= 2505.910, epd= 0.005, time= 0.03s.\n",
      "iter: 72, E= 2505.840, ER= 0.000, EM= 2505.840, epd= 0.005, time= 0.03s.\n",
      "iter: 73, E= 2505.773, ER= 0.000, EM= 2505.773, epd= 0.005, time= 0.03s.\n",
      "iter: 74, E= 2505.706, ER= 0.000, EM= 2505.706, epd= 0.005, time= 0.03s.\n",
      "iter: 75, E= 2505.640, ER= 0.000, EM= 2505.640, epd= 0.005, time= 0.03s.\n",
      "iter: 76, E= 2505.577, ER= 0.000, EM= 2505.577, epd= 0.005, time= 0.03s.\n",
      "iter: 77, E= 2505.515, ER= 0.000, EM= 2505.515, epd= 0.005, time= 0.03s.\n",
      "iter: 78, E= 2505.456, ER= 0.000, EM= 2505.456, epd= 0.005, time= 0.03s.\n",
      "iter: 79, E= 2505.398, ER= 0.000, EM= 2505.398, epd= 0.005, time= 0.03s.\n",
      "iter: 80, E= 2505.341, ER= 0.000, EM= 2505.341, epd= 0.005, time= 0.03s.\n",
      "iter: 81, E= 2505.285, ER= 0.000, EM= 2505.285, epd= 0.005, time= 0.03s.\n",
      "iter: 82, E= 2505.231, ER= 0.000, EM= 2505.231, epd= 0.005, time= 0.03s.\n",
      "iter: 83, E= 2505.178, ER= 0.000, EM= 2505.178, epd= 0.005, time= 0.03s.\n",
      "iter: 84, E= 2505.126, ER= 0.000, EM= 2505.126, epd= 0.005, time= 0.03s.\n",
      "iter: 85, E= 2505.076, ER= 0.000, EM= 2505.076, epd= 0.005, time= 0.03s.\n",
      "iter: 86, E= 2505.027, ER= 0.000, EM= 2505.027, epd= 0.005, time= 0.03s.\n",
      "iter: 87, E= 2504.979, ER= 0.000, EM= 2504.979, epd= 0.005, time= 0.03s.\n",
      "iter: 88, E= 2504.932, ER= 0.000, EM= 2504.932, epd= 0.005, time= 0.03s.\n",
      "iter: 89, E= 2504.885, ER= 0.000, EM= 2504.885, epd= 0.005, time= 0.03s.\n",
      "iter: 90, E= 2504.841, ER= 0.000, EM= 2504.841, epd= 0.005, time= 0.03s.\n",
      "iter: 91, E= 2504.796, ER= 0.000, EM= 2504.796, epd= 0.005, time= 0.03s.\n",
      "iter: 92, E= 2504.752, ER= 0.000, EM= 2504.752, epd= 0.005, time= 0.03s.\n",
      "iter: 93, E= 2504.709, ER= 0.000, EM= 2504.709, epd= 0.005, time= 0.03s.\n",
      "iter: 94, E= 2504.667, ER= 0.000, EM= 2504.667, epd= 0.005, time= 0.03s.\n",
      "iter: 95, E= 2504.626, ER= 0.000, EM= 2504.626, epd= 0.005, time= 0.03s.\n",
      "iter: 96, E= 2504.585, ER= 0.000, EM= 2504.585, epd= 0.005, time= 0.03s.\n",
      "iter: 97, E= 2504.546, ER= 0.000, EM= 2504.546, epd= 0.005, time= 0.03s.\n",
      "iter: 98, E= 2504.507, ER= 0.000, EM= 2504.507, epd= 0.005, time= 0.03s.\n",
      "iter: 99, E= 2504.469, ER= 0.000, EM= 2504.469, epd= 0.005, time= 0.03s.\n",
      "iter: 100, E= 2504.432, ER= 0.000, EM= 2504.432, epd= 0.005, time= 0.03s.\n",
      "iter: 101, E= 2504.396, ER= 0.000, EM= 2504.396, epd= 0.005, time= 0.03s.\n",
      "iter: 102, E= 2504.360, ER= 0.000, EM= 2504.360, epd= 0.005, time= 0.03s.\n",
      "iter: 103, E= 2504.324, ER= 0.000, EM= 2504.324, epd= 0.005, time= 0.03s.\n",
      "iter: 104, E= 2504.289, ER= 0.000, EM= 2504.289, epd= 0.005, time= 0.03s.\n",
      "iter: 105, E= 2504.255, ER= 0.000, EM= 2504.255, epd= 0.005, time= 0.03s.\n",
      "iter: 106, E= 2504.222, ER= 0.000, EM= 2504.222, epd= 0.005, time= 0.03s.\n",
      "iter: 107, E= 2504.189, ER= 0.000, EM= 2504.189, epd= 0.005, time= 0.03s.\n",
      "iter: 108, E= 2504.156, ER= 0.000, EM= 2504.156, epd= 0.005, time= 0.03s.\n",
      "iter: 109, E= 2504.125, ER= 0.000, EM= 2504.125, epd= 0.005, time= 0.03s.\n",
      "iter: 110, E= 2504.093, ER= 0.000, EM= 2504.093, epd= 0.005, time= 0.03s.\n",
      "iter: 111, E= 2504.062, ER= 0.000, EM= 2504.062, epd= 0.005, time= 0.03s.\n",
      "iter: 112, E= 2504.032, ER= 0.000, EM= 2504.032, epd= 0.005, time= 0.03s.\n",
      "iter: 113, E= 2504.001, ER= 0.000, EM= 2504.001, epd= 0.005, time= 0.03s.\n",
      "iter: 114, E= 2503.973, ER= 0.000, EM= 2503.973, epd= 0.005, time= 0.03s.\n",
      "iter: 115, E= 2503.944, ER= 0.000, EM= 2503.944, epd= 0.005, time= 0.03s.\n",
      "iter: 116, E= 2503.916, ER= 0.000, EM= 2503.916, epd= 0.005, time= 0.03s.\n",
      "iter: 117, E= 2503.888, ER= 0.000, EM= 2503.888, epd= 0.005, time= 0.03s.\n",
      "iter: 118, E= 2503.860, ER= 0.000, EM= 2503.860, epd= 0.005, time= 0.03s.\n",
      "iter: 119, E= 2503.833, ER= 0.000, EM= 2503.833, epd= 0.005, time= 0.03s.\n",
      "iter: 120, E= 2503.807, ER= 0.000, EM= 2503.807, epd= 0.005, time= 0.03s.\n",
      "iter: 121, E= 2503.781, ER= 0.000, EM= 2503.781, epd= 0.005, time= 0.03s.\n",
      "iter: 122, E= 2503.754, ER= 0.000, EM= 2503.754, epd= 0.005, time= 0.03s.\n",
      "iter: 123, E= 2503.729, ER= 0.000, EM= 2503.729, epd= 0.005, time= 0.03s.\n",
      "iter: 124, E= 2503.712, ER= 0.000, EM= 2503.712, epd= 0.005, time= 0.03s.\n",
      "iter: 125, E= 2503.700, ER= 0.000, EM= 2503.700, epd= 0.005, time= 0.03s.\n",
      "iter: 126, E= 2503.691, ER= 0.000, EM= 2503.691, epd= 0.005, time= 0.03s.\n",
      "iter: 127, E= 2503.685, ER= 0.000, EM= 2503.685, epd= 0.005, time= 0.03s.\n",
      "iter: 128, E= 2503.681, ER= 0.000, EM= 2503.681, epd= 0.005, time= 0.03s.\n",
      "iter: 129, E= 2503.678, ER= 0.000, EM= 2503.678, epd= 0.005, time= 0.03s.\n",
      "iter: 130, E= 2503.677, ER= 0.000, EM= 2503.677, epd= 0.005, time= 0.03s.\n",
      "iter: 131, E= 2503.675, ER= 0.000, EM= 2503.675, epd= 0.005, time= 0.03s.\n",
      "iter: 132, E= 2503.674, ER= 0.000, EM= 2503.674, epd= 0.005, time= 0.03s.\n",
      "iter: 133, E= 2503.674, ER= 0.000, EM= 2503.674, epd= 0.005, time= 0.03s.\n",
      "iter: 134, E= 2503.673, ER= 0.000, EM= 2503.673, epd= 0.005, time= 0.03s.\n",
      "iter: 135, E= 2503.673, ER= 0.000, EM= 2503.673, epd= 0.005, time= 0.03s.\n",
      "iter: 136, E= 2503.673, ER= 0.000, EM= 2503.673, epd= 0.005, time= 0.03s.\n",
      "iter: 137, E= 2503.672, ER= 0.000, EM= 2503.672, epd= 0.005, time= 0.03s.\n",
      "iter: 138, E= 2503.673, ER= 0.000, EM= 2503.673, epd= 0.005, time= 0.03s.\n",
      "iter: 139, E= 2503.672, ER= 0.000, EM= 2503.672, epd= 0.005, time= 0.03s.\n",
      "iter: 140, E= 2503.672, ER= 0.000, EM= 2503.672, epd= 0.005, time= 0.03s.\n",
      "iter: 141, E= 2503.672, ER= 0.000, EM= 2503.672, epd= 0.005, time= 0.03s.\n",
      "iter: 142, E= 2503.672, ER= 0.000, EM= 2503.672, epd= 0.005, time= 0.03s.\n",
      "iter: 143, E= 2503.672, ER= 0.000, EM= 2503.672, epd= 0.005, time= 0.03s.\n",
      "iter: 144, E= 2503.672, ER= 0.000, EM= 2503.672, epd= 0.005, time= 0.03s.\n",
      "iter: 145, E= 2503.672, ER= 0.000, EM= 2503.672, epd= 0.005, time= 0.03s.\n",
      "iter: 146, E= 2503.672, ER= 0.000, EM= 2503.672, epd= 0.005, time= 0.03s.\n",
      "iter: 147, E= 2503.672, ER= 0.000, EM= 2503.672, epd= 0.005, time= 0.03s.\n",
      "iter: 148, E= 2503.672, ER= 0.000, EM= 2503.672, epd= 0.005, time= 0.03s.\n",
      "Early termination: Energy change threshold reached.\n",
      "Total elapsed runtime: 4.06 seconds.\n",
      "Parameter 'do_affine' changed to '1'.\n",
      "WARNING: Affine state has changed. Variables will be initialized.\n",
      "Parameter 'epsilonL' changed to '1e-05'.\n",
      "Parameter 'epsilonT' changed to '2e-05'.\n",
      "iter: 0, E = 2503.6724, ER = 0.0000, EM = 2503.6724, epd = 0.005000.\n",
      "iter: 1, E= 2421.226, ER= 0.000, EM= 2421.226, epd= 0.005, time= 0.03s.\n",
      "iter: 2, E= 2376.315, ER= 0.000, EM= 2376.315, epd= 0.005, time= 0.03s.\n",
      "iter: 3, E= 2344.488, ER= 0.000, EM= 2344.488, epd= 0.005, time= 0.03s.\n",
      "iter: 4, E= 2320.133, ER= 0.000, EM= 2320.133, epd= 0.005, time= 0.03s.\n",
      "iter: 5, E= 2300.859, ER= 0.000, EM= 2300.859, epd= 0.005, time= 0.03s.\n",
      "iter: 6, E= 2284.742, ER= 0.000, EM= 2284.742, epd= 0.005, time= 0.03s.\n",
      "iter: 7, E= 2270.583, ER= 0.000, EM= 2270.583, epd= 0.005, time= 0.03s.\n",
      "iter: 8, E= 2257.706, ER= 0.000, EM= 2257.706, epd= 0.005, time= 0.03s.\n",
      "iter: 9, E= 2245.996, ER= 0.000, EM= 2245.996, epd= 0.005, time= 0.03s.\n",
      "iter: 10, E= 2235.256, ER= 0.000, EM= 2235.256, epd= 0.005, time= 0.03s.\n",
      "iter: 11, E= 2225.283, ER= 0.000, EM= 2225.283, epd= 0.005, time= 0.03s.\n",
      "iter: 12, E= 2216.004, ER= 0.000, EM= 2216.004, epd= 0.005, time= 0.03s.\n",
      "iter: 13, E= 2207.284, ER= 0.000, EM= 2207.284, epd= 0.005, time= 0.03s.\n",
      "iter: 14, E= 2199.026, ER= 0.000, EM= 2199.026, epd= 0.005, time= 0.03s.\n",
      "iter: 15, E= 2191.185, ER= 0.000, EM= 2191.185, epd= 0.005, time= 0.03s.\n",
      "iter: 16, E= 2183.723, ER= 0.000, EM= 2183.723, epd= 0.005, time= 0.03s.\n",
      "iter: 17, E= 2176.602, ER= 0.000, EM= 2176.602, epd= 0.005, time= 0.03s.\n",
      "iter: 18, E= 2169.796, ER= 0.000, EM= 2169.796, epd= 0.005, time= 0.03s.\n",
      "iter: 19, E= 2163.265, ER= 0.000, EM= 2163.265, epd= 0.005, time= 0.03s.\n",
      "iter: 20, E= 2156.998, ER= 0.000, EM= 2156.998, epd= 0.005, time= 0.03s.\n",
      "iter: 21, E= 2151.018, ER= 0.000, EM= 2151.018, epd= 0.005, time= 0.03s.\n",
      "iter: 22, E= 2145.329, ER= 0.000, EM= 2145.329, epd= 0.005, time= 0.03s.\n",
      "iter: 23, E= 2139.916, ER= 0.000, EM= 2139.916, epd= 0.005, time= 0.03s.\n",
      "iter: 24, E= 2134.771, ER= 0.000, EM= 2134.771, epd= 0.005, time= 0.03s.\n",
      "iter: 25, E= 2129.880, ER= 0.000, EM= 2129.880, epd= 0.005, time= 0.03s.\n",
      "iter: 26, E= 2125.195, ER= 0.000, EM= 2125.195, epd= 0.005, time= 0.03s.\n",
      "iter: 27, E= 2120.679, ER= 0.000, EM= 2120.679, epd= 0.005, time= 0.03s.\n",
      "iter: 28, E= 2116.303, ER= 0.000, EM= 2116.303, epd= 0.005, time= 0.03s.\n",
      "iter: 29, E= 2112.031, ER= 0.000, EM= 2112.031, epd= 0.005, time= 0.03s.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter: 30, E= 2107.824, ER= 0.000, EM= 2107.824, epd= 0.005, time= 0.03s.\n",
      "iter: 31, E= 2103.646, ER= 0.000, EM= 2103.646, epd= 0.005, time= 0.03s.\n",
      "iter: 32, E= 2099.487, ER= 0.000, EM= 2099.487, epd= 0.005, time= 0.03s.\n",
      "iter: 33, E= 2095.349, ER= 0.000, EM= 2095.349, epd= 0.005, time= 0.03s.\n",
      "iter: 34, E= 2091.242, ER= 0.000, EM= 2091.242, epd= 0.005, time= 0.03s.\n",
      "iter: 35, E= 2087.178, ER= 0.000, EM= 2087.178, epd= 0.005, time= 0.03s.\n",
      "iter: 36, E= 2083.163, ER= 0.000, EM= 2083.163, epd= 0.005, time= 0.03s.\n",
      "iter: 37, E= 2079.200, ER= 0.000, EM= 2079.200, epd= 0.005, time= 0.03s.\n",
      "iter: 38, E= 2075.303, ER= 0.000, EM= 2075.303, epd= 0.005, time= 0.03s.\n",
      "iter: 39, E= 2071.479, ER= 0.000, EM= 2071.479, epd= 0.005, time= 0.03s.\n",
      "iter: 40, E= 2067.737, ER= 0.000, EM= 2067.737, epd= 0.005, time= 0.03s.\n",
      "iter: 41, E= 2064.073, ER= 0.000, EM= 2064.073, epd= 0.005, time= 0.03s.\n",
      "iter: 42, E= 2060.488, ER= 0.000, EM= 2060.488, epd= 0.005, time= 0.03s.\n",
      "iter: 43, E= 2056.978, ER= 0.000, EM= 2056.978, epd= 0.005, time= 0.03s.\n",
      "iter: 44, E= 2053.540, ER= 0.000, EM= 2053.540, epd= 0.005, time= 0.03s.\n",
      "iter: 45, E= 2050.172, ER= 0.000, EM= 2050.172, epd= 0.005, time= 0.03s.\n",
      "iter: 46, E= 2046.876, ER= 0.000, EM= 2046.876, epd= 0.005, time= 0.03s.\n",
      "iter: 47, E= 2043.661, ER= 0.000, EM= 2043.661, epd= 0.005, time= 0.03s.\n",
      "iter: 48, E= 2040.535, ER= 0.000, EM= 2040.535, epd= 0.005, time= 0.03s.\n",
      "iter: 49, E= 2037.501, ER= 0.000, EM= 2037.501, epd= 0.005, time= 0.03s.\n",
      "iter: 50, E= 2034.559, ER= 0.000, EM= 2034.559, epd= 0.005, time= 0.03s.\n",
      "iter: 51, E= 2031.705, ER= 0.000, EM= 2031.705, epd= 0.005, time= 0.03s.\n",
      "iter: 52, E= 2028.933, ER= 0.000, EM= 2028.933, epd= 0.005, time= 0.03s.\n",
      "iter: 53, E= 2026.237, ER= 0.000, EM= 2026.237, epd= 0.005, time= 0.03s.\n",
      "iter: 54, E= 2023.619, ER= 0.000, EM= 2023.619, epd= 0.005, time= 0.03s.\n",
      "iter: 55, E= 2021.072, ER= 0.000, EM= 2021.072, epd= 0.005, time= 0.03s.\n",
      "iter: 56, E= 2018.593, ER= 0.000, EM= 2018.593, epd= 0.005, time= 0.03s.\n",
      "iter: 57, E= 2016.181, ER= 0.000, EM= 2016.181, epd= 0.005, time= 0.03s.\n",
      "iter: 58, E= 2013.831, ER= 0.000, EM= 2013.831, epd= 0.005, time= 0.03s.\n",
      "iter: 59, E= 2011.545, ER= 0.000, EM= 2011.545, epd= 0.005, time= 0.03s.\n",
      "iter: 60, E= 2009.313, ER= 0.000, EM= 2009.313, epd= 0.005, time= 0.03s.\n",
      "iter: 61, E= 2007.136, ER= 0.000, EM= 2007.136, epd= 0.005, time= 0.03s.\n",
      "iter: 62, E= 2005.012, ER= 0.000, EM= 2005.012, epd= 0.005, time= 0.03s.\n",
      "iter: 63, E= 2002.937, ER= 0.000, EM= 2002.937, epd= 0.005, time= 0.03s.\n",
      "iter: 64, E= 2000.909, ER= 0.000, EM= 2000.909, epd= 0.005, time= 0.03s.\n",
      "iter: 65, E= 1998.928, ER= 0.000, EM= 1998.928, epd= 0.005, time= 0.03s.\n",
      "iter: 66, E= 1996.991, ER= 0.000, EM= 1996.991, epd= 0.005, time= 0.03s.\n",
      "iter: 67, E= 1995.097, ER= 0.000, EM= 1995.097, epd= 0.005, time= 0.03s.\n",
      "iter: 68, E= 1993.245, ER= 0.000, EM= 1993.245, epd= 0.005, time= 0.03s.\n",
      "iter: 69, E= 1991.434, ER= 0.000, EM= 1991.434, epd= 0.005, time= 0.03s.\n",
      "iter: 70, E= 1989.663, ER= 0.000, EM= 1989.663, epd= 0.005, time= 0.03s.\n",
      "iter: 71, E= 1987.932, ER= 0.000, EM= 1987.932, epd= 0.005, time= 0.03s.\n",
      "iter: 72, E= 1986.238, ER= 0.000, EM= 1986.238, epd= 0.005, time= 0.03s.\n",
      "iter: 73, E= 1984.582, ER= 0.000, EM= 1984.582, epd= 0.005, time= 0.03s.\n",
      "iter: 74, E= 1982.964, ER= 0.000, EM= 1982.964, epd= 0.005, time= 0.03s.\n",
      "iter: 75, E= 1981.381, ER= 0.000, EM= 1981.381, epd= 0.005, time= 0.03s.\n",
      "iter: 76, E= 1979.833, ER= 0.000, EM= 1979.833, epd= 0.005, time= 0.03s.\n",
      "iter: 77, E= 1978.321, ER= 0.000, EM= 1978.321, epd= 0.005, time= 0.03s.\n",
      "iter: 78, E= 1976.841, ER= 0.000, EM= 1976.841, epd= 0.005, time= 0.03s.\n",
      "iter: 79, E= 1975.392, ER= 0.000, EM= 1975.392, epd= 0.005, time= 0.03s.\n",
      "iter: 80, E= 1973.977, ER= 0.000, EM= 1973.977, epd= 0.005, time= 0.03s.\n",
      "iter: 81, E= 1972.591, ER= 0.000, EM= 1972.591, epd= 0.005, time= 0.03s.\n",
      "iter: 82, E= 1971.234, ER= 0.000, EM= 1971.234, epd= 0.005, time= 0.03s.\n",
      "iter: 83, E= 1969.907, ER= 0.000, EM= 1969.907, epd= 0.005, time= 0.03s.\n",
      "iter: 84, E= 1968.607, ER= 0.000, EM= 1968.607, epd= 0.005, time= 0.03s.\n",
      "iter: 85, E= 1967.332, ER= 0.000, EM= 1967.332, epd= 0.005, time= 0.03s.\n",
      "iter: 86, E= 1966.084, ER= 0.000, EM= 1966.084, epd= 0.005, time= 0.03s.\n",
      "iter: 87, E= 1964.862, ER= 0.000, EM= 1964.862, epd= 0.005, time= 0.03s.\n",
      "iter: 88, E= 1963.662, ER= 0.000, EM= 1963.662, epd= 0.005, time= 0.03s.\n",
      "iter: 89, E= 1962.487, ER= 0.000, EM= 1962.487, epd= 0.005, time= 0.03s.\n",
      "iter: 90, E= 1961.332, ER= 0.000, EM= 1961.332, epd= 0.005, time= 0.03s.\n",
      "iter: 91, E= 1960.197, ER= 0.000, EM= 1960.197, epd= 0.005, time= 0.03s.\n",
      "iter: 92, E= 1959.079, ER= 0.000, EM= 1959.079, epd= 0.005, time= 0.03s.\n",
      "iter: 93, E= 1957.979, ER= 0.000, EM= 1957.979, epd= 0.005, time= 0.03s.\n",
      "iter: 94, E= 1956.896, ER= 0.000, EM= 1956.896, epd= 0.005, time= 0.03s.\n",
      "iter: 95, E= 1955.825, ER= 0.000, EM= 1955.825, epd= 0.005, time= 0.03s.\n",
      "iter: 96, E= 1954.768, ER= 0.000, EM= 1954.768, epd= 0.005, time= 0.03s.\n",
      "iter: 97, E= 1953.722, ER= 0.000, EM= 1953.722, epd= 0.005, time= 0.03s.\n",
      "iter: 98, E= 1952.687, ER= 0.000, EM= 1952.687, epd= 0.005, time= 0.03s.\n",
      "iter: 99, E= 1951.663, ER= 0.000, EM= 1951.663, epd= 0.005, time= 0.03s.\n",
      "iter: 100, E= 1950.649, ER= 0.000, EM= 1950.649, epd= 0.005, time= 0.03s.\n",
      "iter: 101, E= 1949.644, ER= 0.000, EM= 1949.644, epd= 0.005, time= 0.03s.\n",
      "iter: 102, E= 1948.648, ER= 0.000, EM= 1948.648, epd= 0.005, time= 0.03s.\n",
      "iter: 103, E= 1947.657, ER= 0.000, EM= 1947.657, epd= 0.005, time= 0.03s.\n",
      "iter: 104, E= 1946.674, ER= 0.000, EM= 1946.674, epd= 0.005, time= 0.03s.\n",
      "iter: 105, E= 1945.696, ER= 0.000, EM= 1945.696, epd= 0.005, time= 0.03s.\n",
      "iter: 106, E= 1944.723, ER= 0.000, EM= 1944.723, epd= 0.005, time= 0.03s.\n",
      "iter: 107, E= 1943.755, ER= 0.000, EM= 1943.755, epd= 0.005, time= 0.03s.\n",
      "iter: 108, E= 1942.791, ER= 0.000, EM= 1942.791, epd= 0.005, time= 0.03s.\n",
      "iter: 109, E= 1941.830, ER= 0.000, EM= 1941.830, epd= 0.005, time= 0.03s.\n",
      "iter: 110, E= 1940.872, ER= 0.000, EM= 1940.872, epd= 0.005, time= 0.03s.\n",
      "iter: 111, E= 1939.919, ER= 0.000, EM= 1939.919, epd= 0.005, time= 0.03s.\n",
      "iter: 112, E= 1938.971, ER= 0.000, EM= 1938.971, epd= 0.005, time= 0.03s.\n",
      "iter: 113, E= 1938.025, ER= 0.000, EM= 1938.025, epd= 0.005, time= 0.03s.\n",
      "iter: 114, E= 1937.085, ER= 0.000, EM= 1937.085, epd= 0.005, time= 0.03s.\n",
      "iter: 115, E= 1936.150, ER= 0.000, EM= 1936.150, epd= 0.005, time= 0.03s.\n",
      "iter: 116, E= 1935.220, ER= 0.000, EM= 1935.220, epd= 0.005, time= 0.03s.\n",
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      "iter: 118, E= 1933.375, ER= 0.000, EM= 1933.375, epd= 0.005, time= 0.03s.\n",
      "iter: 119, E= 1932.462, ER= 0.000, EM= 1932.462, epd= 0.005, time= 0.03s.\n",
      "iter: 120, E= 1931.555, ER= 0.000, EM= 1931.555, epd= 0.005, time= 0.03s.\n",
      "iter: 121, E= 1930.653, ER= 0.000, EM= 1930.653, epd= 0.005, time= 0.03s.\n",
      "iter: 122, E= 1929.757, ER= 0.000, EM= 1929.757, epd= 0.005, time= 0.03s.\n",
      "iter: 123, E= 1928.868, ER= 0.000, EM= 1928.868, epd= 0.005, time= 0.03s.\n",
      "iter: 124, E= 1927.983, ER= 0.000, EM= 1927.983, epd= 0.005, time= 0.03s.\n",
      "iter: 125, E= 1927.105, ER= 0.000, EM= 1927.105, epd= 0.005, time= 0.03s.\n",
      "iter: 126, E= 1926.234, ER= 0.000, EM= 1926.234, epd= 0.005, time= 0.03s.\n",
      "iter: 127, E= 1925.368, ER= 0.000, EM= 1925.368, epd= 0.005, time= 0.03s.\n",
      "iter: 128, E= 1924.508, ER= 0.000, EM= 1924.508, epd= 0.005, time= 0.03s.\n",
      "iter: 129, E= 1923.654, ER= 0.000, EM= 1923.654, epd= 0.005, time= 0.03s.\n",
      "iter: 130, E= 1922.806, ER= 0.000, EM= 1922.806, epd= 0.005, time= 0.03s.\n",
      "iter: 131, E= 1921.966, ER= 0.000, EM= 1921.966, epd= 0.005, time= 0.03s.\n",
      "iter: 132, E= 1921.134, ER= 0.000, EM= 1921.134, epd= 0.005, time= 0.03s.\n",
      "iter: 133, E= 1920.310, ER= 0.000, EM= 1920.310, epd= 0.005, time= 0.03s.\n",
      "iter: 134, E= 1919.491, ER= 0.000, EM= 1919.491, epd= 0.005, time= 0.03s.\n",
      "iter: 135, E= 1918.680, ER= 0.000, EM= 1918.680, epd= 0.005, time= 0.03s.\n",
      "iter: 136, E= 1917.877, ER= 0.000, EM= 1917.877, epd= 0.005, time= 0.03s.\n",
      "iter: 137, E= 1917.083, ER= 0.000, EM= 1917.083, epd= 0.005, time= 0.03s.\n",
      "iter: 138, E= 1916.296, ER= 0.000, EM= 1916.296, epd= 0.005, time= 0.03s.\n",
      "iter: 139, E= 1915.517, ER= 0.000, EM= 1915.517, epd= 0.005, time= 0.03s.\n",
      "iter: 140, E= 1914.746, ER= 0.000, EM= 1914.746, epd= 0.005, time= 0.03s.\n",
      "iter: 141, E= 1913.982, ER= 0.000, EM= 1913.982, epd= 0.005, time= 0.03s.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter: 142, E= 1913.226, ER= 0.000, EM= 1913.226, epd= 0.005, time= 0.03s.\n",
      "iter: 143, E= 1912.478, ER= 0.000, EM= 1912.478, epd= 0.005, time= 0.03s.\n",
      "iter: 144, E= 1911.738, ER= 0.000, EM= 1911.738, epd= 0.005, time= 0.03s.\n",
      "iter: 145, E= 1911.005, ER= 0.000, EM= 1911.005, epd= 0.005, time= 0.03s.\n",
      "iter: 146, E= 1910.279, ER= 0.000, EM= 1910.279, epd= 0.005, time= 0.03s.\n",
      "iter: 147, E= 1909.560, ER= 0.000, EM= 1909.560, epd= 0.005, time= 0.03s.\n",
      "iter: 148, E= 1908.848, ER= 0.000, EM= 1908.848, epd= 0.005, time= 0.03s.\n",
      "iter: 149, E= 1908.142, ER= 0.000, EM= 1908.142, epd= 0.005, time= 0.03s.\n",
      "iter: 150, E= 1907.443, ER= 0.000, EM= 1907.443, epd= 0.005, time= 0.03s.\n",
      "iter: 151, E= 1906.750, ER= 0.000, EM= 1906.750, epd= 0.005, time= 0.03s.\n",
      "iter: 152, E= 1906.063, ER= 0.000, EM= 1906.063, epd= 0.005, time= 0.03s.\n",
      "iter: 153, E= 1905.381, ER= 0.000, EM= 1905.381, epd= 0.005, time= 0.03s.\n",
      "iter: 154, E= 1904.704, ER= 0.000, EM= 1904.704, epd= 0.005, time= 0.03s.\n",
      "iter: 155, E= 1904.031, ER= 0.000, EM= 1904.031, epd= 0.005, time= 0.03s.\n",
      "iter: 156, E= 1903.363, ER= 0.000, EM= 1903.363, epd= 0.005, time= 0.03s.\n",
      "iter: 157, E= 1902.701, ER= 0.000, EM= 1902.701, epd= 0.005, time= 0.03s.\n",
      "iter: 158, E= 1902.044, ER= 0.000, EM= 1902.044, epd= 0.005, time= 0.03s.\n",
      "iter: 159, E= 1901.393, ER= 0.000, EM= 1901.393, epd= 0.005, time= 0.03s.\n",
      "iter: 160, E= 1900.745, ER= 0.000, EM= 1900.745, epd= 0.005, time= 0.03s.\n",
      "iter: 161, E= 1900.104, ER= 0.000, EM= 1900.104, epd= 0.005, time= 0.03s.\n",
      "iter: 162, E= 1899.467, ER= 0.000, EM= 1899.467, epd= 0.005, time= 0.03s.\n",
      "iter: 163, E= 1898.836, ER= 0.000, EM= 1898.836, epd= 0.005, time= 0.03s.\n",
      "iter: 164, E= 1898.210, ER= 0.000, EM= 1898.210, epd= 0.005, time= 0.03s.\n",
      "iter: 165, E= 1897.590, ER= 0.000, EM= 1897.590, epd= 0.005, time= 0.03s.\n",
      "iter: 166, E= 1896.975, ER= 0.000, EM= 1896.975, epd= 0.005, time= 0.03s.\n",
      "iter: 167, E= 1896.364, ER= 0.000, EM= 1896.364, epd= 0.005, time= 0.03s.\n",
      "iter: 168, E= 1895.760, ER= 0.000, EM= 1895.760, epd= 0.005, time= 0.03s.\n",
      "iter: 169, E= 1895.161, ER= 0.000, EM= 1895.161, epd= 0.005, time= 0.03s.\n",
      "iter: 170, E= 1894.567, ER= 0.000, EM= 1894.567, epd= 0.005, time= 0.03s.\n",
      "iter: 171, E= 1893.979, ER= 0.000, EM= 1893.979, epd= 0.005, time= 0.03s.\n",
      "iter: 172, E= 1893.395, ER= 0.000, EM= 1893.395, epd= 0.005, time= 0.03s.\n",
      "iter: 173, E= 1892.817, ER= 0.000, EM= 1892.817, epd= 0.005, time= 0.03s.\n",
      "iter: 174, E= 1892.244, ER= 0.000, EM= 1892.244, epd= 0.005, time= 0.03s.\n",
      "iter: 175, E= 1891.677, ER= 0.000, EM= 1891.677, epd= 0.005, time= 0.03s.\n",
      "iter: 176, E= 1891.113, ER= 0.000, EM= 1891.113, epd= 0.005, time= 0.03s.\n",
      "iter: 177, E= 1890.553, ER= 0.000, EM= 1890.553, epd= 0.005, time= 0.03s.\n",
      "iter: 178, E= 1889.999, ER= 0.000, EM= 1889.999, epd= 0.005, time= 0.03s.\n",
      "iter: 179, E= 1889.450, ER= 0.000, EM= 1889.450, epd= 0.005, time= 0.03s.\n",
      "iter: 180, E= 1888.905, ER= 0.000, EM= 1888.905, epd= 0.005, time= 0.03s.\n",
      "iter: 181, E= 1888.364, ER= 0.000, EM= 1888.364, epd= 0.005, time= 0.03s.\n",
      "iter: 182, E= 1887.827, ER= 0.000, EM= 1887.827, epd= 0.005, time= 0.03s.\n",
      "iter: 183, E= 1887.294, ER= 0.000, EM= 1887.294, epd= 0.005, time= 0.03s.\n",
      "iter: 184, E= 1886.765, ER= 0.000, EM= 1886.765, epd= 0.005, time= 0.03s.\n",
      "iter: 185, E= 1886.239, ER= 0.000, EM= 1886.239, epd= 0.005, time= 0.03s.\n",
      "iter: 186, E= 1885.716, ER= 0.000, EM= 1885.716, epd= 0.005, time= 0.03s.\n",
      "iter: 187, E= 1885.196, ER= 0.000, EM= 1885.196, epd= 0.005, time= 0.03s.\n",
      "iter: 188, E= 1884.680, ER= 0.000, EM= 1884.680, epd= 0.005, time= 0.03s.\n",
      "iter: 189, E= 1884.167, ER= 0.000, EM= 1884.167, epd= 0.005, time= 0.03s.\n",
      "iter: 190, E= 1883.657, ER= 0.000, EM= 1883.657, epd= 0.005, time= 0.03s.\n",
      "iter: 191, E= 1883.150, ER= 0.000, EM= 1883.150, epd= 0.005, time= 0.03s.\n",
      "iter: 192, E= 1882.646, ER= 0.000, EM= 1882.646, epd= 0.005, time= 0.03s.\n",
      "iter: 193, E= 1882.145, ER= 0.000, EM= 1882.145, epd= 0.005, time= 0.03s.\n",
      "iter: 194, E= 1881.647, ER= 0.000, EM= 1881.647, epd= 0.005, time= 0.03s.\n",
      "iter: 195, E= 1881.151, ER= 0.000, EM= 1881.151, epd= 0.005, time= 0.03s.\n",
      "iter: 196, E= 1880.658, ER= 0.000, EM= 1880.658, epd= 0.005, time= 0.03s.\n",
      "iter: 197, E= 1880.168, ER= 0.000, EM= 1880.168, epd= 0.005, time= 0.03s.\n",
      "iter: 198, E= 1879.680, ER= 0.000, EM= 1879.680, epd= 0.005, time= 0.03s.\n",
      "iter: 199, E= 1879.194, ER= 0.000, EM= 1879.194, epd= 0.005, time= 0.03s.\n",
      "iter: 200, E= 1878.713, ER= 0.000, EM= 1878.713, epd= 0.005, time= 0.03s.\n",
      "iter: 201, E= 1878.233, ER= 0.000, EM= 1878.233, epd= 0.005, time= 0.03s.\n",
      "iter: 202, E= 1877.756, ER= 0.000, EM= 1877.756, epd= 0.005, time= 0.03s.\n",
      "iter: 203, E= 1877.281, ER= 0.000, EM= 1877.281, epd= 0.005, time= 0.03s.\n",
      "iter: 204, E= 1876.809, ER= 0.000, EM= 1876.809, epd= 0.005, time= 0.03s.\n",
      "iter: 205, E= 1876.340, ER= 0.000, EM= 1876.340, epd= 0.005, time= 0.03s.\n",
      "iter: 206, E= 1875.874, ER= 0.000, EM= 1875.874, epd= 0.005, time= 0.03s.\n",
      "iter: 207, E= 1875.410, ER= 0.000, EM= 1875.410, epd= 0.005, time= 0.03s.\n",
      "iter: 208, E= 1874.948, ER= 0.000, EM= 1874.948, epd= 0.005, time= 0.03s.\n",
      "iter: 209, E= 1874.490, ER= 0.000, EM= 1874.490, epd= 0.005, time= 0.03s.\n",
      "iter: 210, E= 1874.033, ER= 0.000, EM= 1874.033, epd= 0.005, time= 0.03s.\n",
      "iter: 211, E= 1873.580, ER= 0.000, EM= 1873.580, epd= 0.005, time= 0.03s.\n",
      "iter: 212, E= 1873.129, ER= 0.000, EM= 1873.129, epd= 0.005, time= 0.03s.\n",
      "iter: 213, E= 1872.681, ER= 0.000, EM= 1872.681, epd= 0.005, time= 0.03s.\n",
      "iter: 214, E= 1872.235, ER= 0.000, EM= 1872.235, epd= 0.005, time= 0.03s.\n",
      "iter: 215, E= 1871.793, ER= 0.000, EM= 1871.793, epd= 0.005, time= 0.03s.\n",
      "iter: 216, E= 1871.354, ER= 0.000, EM= 1871.354, epd= 0.005, time= 0.03s.\n",
      "iter: 217, E= 1870.917, ER= 0.000, EM= 1870.917, epd= 0.005, time= 0.03s.\n",
      "iter: 218, E= 1870.484, ER= 0.000, EM= 1870.484, epd= 0.005, time= 0.03s.\n",
      "iter: 219, E= 1870.053, ER= 0.000, EM= 1870.053, epd= 0.005, time= 0.03s.\n",
      "iter: 220, E= 1869.626, ER= 0.000, EM= 1869.626, epd= 0.005, time= 0.03s.\n",
      "iter: 221, E= 1869.201, ER= 0.000, EM= 1869.201, epd= 0.005, time= 0.03s.\n",
      "iter: 222, E= 1868.779, ER= 0.000, EM= 1868.779, epd= 0.005, time= 0.03s.\n",
      "iter: 223, E= 1868.359, ER= 0.000, EM= 1868.359, epd= 0.005, time= 0.03s.\n",
      "iter: 224, E= 1867.943, ER= 0.000, EM= 1867.943, epd= 0.005, time= 0.03s.\n",
      "iter: 225, E= 1867.528, ER= 0.000, EM= 1867.528, epd= 0.005, time= 0.03s.\n",
      "iter: 226, E= 1867.117, ER= 0.000, EM= 1867.117, epd= 0.005, time= 0.03s.\n",
      "iter: 227, E= 1866.708, ER= 0.000, EM= 1866.708, epd= 0.005, time= 0.03s.\n",
      "iter: 228, E= 1866.303, ER= 0.000, EM= 1866.303, epd= 0.005, time= 0.03s.\n",
      "iter: 229, E= 1865.900, ER= 0.000, EM= 1865.900, epd= 0.005, time= 0.03s.\n",
      "iter: 230, E= 1865.501, ER= 0.000, EM= 1865.501, epd= 0.005, time= 0.03s.\n",
      "iter: 231, E= 1865.104, ER= 0.000, EM= 1865.104, epd= 0.005, time= 0.03s.\n",
      "iter: 232, E= 1864.710, ER= 0.000, EM= 1864.710, epd= 0.005, time= 0.03s.\n",
      "iter: 233, E= 1864.318, ER= 0.000, EM= 1864.318, epd= 0.005, time= 0.03s.\n",
      "iter: 234, E= 1863.928, ER= 0.000, EM= 1863.928, epd= 0.005, time= 0.03s.\n",
      "iter: 235, E= 1863.541, ER= 0.000, EM= 1863.541, epd= 0.005, time= 0.03s.\n",
      "iter: 236, E= 1863.156, ER= 0.000, EM= 1863.156, epd= 0.005, time= 0.03s.\n",
      "iter: 237, E= 1862.772, ER= 0.000, EM= 1862.772, epd= 0.005, time= 0.03s.\n",
      "iter: 238, E= 1862.391, ER= 0.000, EM= 1862.391, epd= 0.005, time= 0.03s.\n",
      "iter: 239, E= 1862.011, ER= 0.000, EM= 1862.011, epd= 0.005, time= 0.03s.\n",
      "iter: 240, E= 1861.634, ER= 0.000, EM= 1861.634, epd= 0.005, time= 0.03s.\n",
      "iter: 241, E= 1861.258, ER= 0.000, EM= 1861.258, epd= 0.005, time= 0.03s.\n",
      "iter: 242, E= 1860.884, ER= 0.000, EM= 1860.884, epd= 0.005, time= 0.03s.\n",
      "iter: 243, E= 1860.512, ER= 0.000, EM= 1860.512, epd= 0.005, time= 0.03s.\n",
      "iter: 244, E= 1860.141, ER= 0.000, EM= 1860.141, epd= 0.005, time= 0.03s.\n",
      "iter: 245, E= 1859.770, ER= 0.000, EM= 1859.770, epd= 0.005, time= 0.03s.\n",
      "iter: 246, E= 1859.402, ER= 0.000, EM= 1859.402, epd= 0.005, time= 0.03s.\n",
      "iter: 247, E= 1859.036, ER= 0.000, EM= 1859.036, epd= 0.005, time= 0.03s.\n",
      "iter: 248, E= 1858.670, ER= 0.000, EM= 1858.670, epd= 0.005, time= 0.03s.\n",
      "iter: 249, E= 1858.307, ER= 0.000, EM= 1858.307, epd= 0.005, time= 0.03s.\n",
      "iter: 250, E= 1857.944, ER= 0.000, EM= 1857.944, epd= 0.005, time= 0.03s.\n",
      "iter: 251, E= 1857.581, ER= 0.000, EM= 1857.581, epd= 0.005, time= 0.03s.\n",
      "iter: 252, E= 1857.221, ER= 0.000, EM= 1857.221, epd= 0.005, time= 0.03s.\n",
      "iter: 253, E= 1856.862, ER= 0.000, EM= 1856.862, epd= 0.005, time= 0.03s.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter: 254, E= 1856.504, ER= 0.000, EM= 1856.504, epd= 0.005, time= 0.03s.\n",
      "iter: 255, E= 1856.147, ER= 0.000, EM= 1856.147, epd= 0.005, time= 0.03s.\n",
      "iter: 256, E= 1855.792, ER= 0.000, EM= 1855.792, epd= 0.005, time= 0.03s.\n",
      "iter: 257, E= 1855.438, ER= 0.000, EM= 1855.438, epd= 0.005, time= 0.03s.\n",
      "iter: 258, E= 1855.085, ER= 0.000, EM= 1855.085, epd= 0.005, time= 0.03s.\n",
      "iter: 259, E= 1854.733, ER= 0.000, EM= 1854.733, epd= 0.005, time= 0.03s.\n",
      "iter: 260, E= 1854.382, ER= 0.000, EM= 1854.382, epd= 0.005, time= 0.03s.\n",
      "iter: 261, E= 1854.032, ER= 0.000, EM= 1854.032, epd= 0.005, time= 0.03s.\n",
      "iter: 262, E= 1853.683, ER= 0.000, EM= 1853.683, epd= 0.005, time= 0.03s.\n",
      "iter: 263, E= 1853.337, ER= 0.000, EM= 1853.337, epd= 0.005, time= 0.03s.\n",
      "iter: 264, E= 1852.992, ER= 0.000, EM= 1852.992, epd= 0.005, time= 0.03s.\n",
      "iter: 265, E= 1852.647, ER= 0.000, EM= 1852.647, epd= 0.005, time= 0.03s.\n",
      "iter: 266, E= 1852.304, ER= 0.000, EM= 1852.304, epd= 0.005, time= 0.03s.\n",
      "iter: 267, E= 1851.961, ER= 0.000, EM= 1851.961, epd= 0.005, time= 0.03s.\n",
      "iter: 268, E= 1851.621, ER= 0.000, EM= 1851.621, epd= 0.005, time= 0.03s.\n",
      "iter: 269, E= 1851.282, ER= 0.000, EM= 1851.282, epd= 0.005, time= 0.03s.\n",
      "iter: 270, E= 1850.944, ER= 0.000, EM= 1850.944, epd= 0.005, time= 0.03s.\n",
      "iter: 271, E= 1850.608, ER= 0.000, EM= 1850.608, epd= 0.005, time= 0.03s.\n",
      "iter: 272, E= 1850.273, ER= 0.000, EM= 1850.273, epd= 0.005, time= 0.03s.\n",
      "iter: 273, E= 1849.938, ER= 0.000, EM= 1849.938, epd= 0.005, time= 0.03s.\n",
      "iter: 274, E= 1849.604, ER= 0.000, EM= 1849.604, epd= 0.005, time= 0.03s.\n",
      "iter: 275, E= 1849.273, ER= 0.000, EM= 1849.273, epd= 0.005, time= 0.03s.\n",
      "iter: 276, E= 1848.941, ER= 0.000, EM= 1848.941, epd= 0.005, time= 0.03s.\n",
      "iter: 277, E= 1848.610, ER= 0.000, EM= 1848.610, epd= 0.005, time= 0.03s.\n",
      "iter: 278, E= 1848.279, ER= 0.000, EM= 1848.279, epd= 0.005, time= 0.03s.\n",
      "iter: 279, E= 1847.949, ER= 0.000, EM= 1847.949, epd= 0.005, time= 0.03s.\n",
      "iter: 280, E= 1847.620, ER= 0.000, EM= 1847.620, epd= 0.005, time= 0.03s.\n",
      "iter: 281, E= 1847.293, ER= 0.000, EM= 1847.293, epd= 0.005, time= 0.03s.\n",
      "iter: 282, E= 1846.966, ER= 0.000, EM= 1846.966, epd= 0.005, time= 0.03s.\n",
      "iter: 283, E= 1846.641, ER= 0.000, EM= 1846.641, epd= 0.005, time= 0.03s.\n",
      "iter: 284, E= 1846.317, ER= 0.000, EM= 1846.317, epd= 0.005, time= 0.03s.\n",
      "iter: 285, E= 1845.993, ER= 0.000, EM= 1845.993, epd= 0.005, time= 0.03s.\n",
      "iter: 286, E= 1845.672, ER= 0.000, EM= 1845.672, epd= 0.005, time= 0.03s.\n",
      "iter: 287, E= 1845.351, ER= 0.000, EM= 1845.351, epd= 0.005, time= 0.03s.\n",
      "iter: 288, E= 1845.031, ER= 0.000, EM= 1845.031, epd= 0.005, time= 0.03s.\n",
      "iter: 289, E= 1844.712, ER= 0.000, EM= 1844.712, epd= 0.005, time= 0.03s.\n",
      "iter: 290, E= 1844.394, ER= 0.000, EM= 1844.394, epd= 0.005, time= 0.03s.\n",
      "iter: 291, E= 1844.077, ER= 0.000, EM= 1844.077, epd= 0.005, time= 0.03s.\n",
      "iter: 292, E= 1843.761, ER= 0.000, EM= 1843.761, epd= 0.005, time= 0.03s.\n",
      "iter: 293, E= 1843.447, ER= 0.000, EM= 1843.447, epd= 0.005, time= 0.03s.\n",
      "iter: 294, E= 1843.135, ER= 0.000, EM= 1843.135, epd= 0.005, time= 0.03s.\n",
      "iter: 295, E= 1842.823, ER= 0.000, EM= 1842.823, epd= 0.005, time= 0.03s.\n",
      "iter: 296, E= 1842.511, ER= 0.000, EM= 1842.511, epd= 0.005, time= 0.03s.\n",
      "iter: 297, E= 1842.201, ER= 0.000, EM= 1842.201, epd= 0.005, time= 0.03s.\n",
      "iter: 298, E= 1841.893, ER= 0.000, EM= 1841.893, epd= 0.005, time= 0.03s.\n",
      "iter: 299, E= 1841.585, ER= 0.000, EM= 1841.585, epd= 0.005, time= 0.03s.\n",
      "iter: 300, E= 1841.278, ER= 0.000, EM= 1841.278, epd= 0.005, time= 0.03s.\n",
      "iter: 301, E= 1840.974, ER= 0.000, EM= 1840.974, epd= 0.005, time= 0.03s.\n",
      "iter: 302, E= 1840.670, ER= 0.000, EM= 1840.670, epd= 0.005, time= 0.03s.\n",
      "iter: 303, E= 1840.368, ER= 0.000, EM= 1840.368, epd= 0.005, time= 0.03s.\n",
      "iter: 304, E= 1840.069, ER= 0.000, EM= 1840.069, epd= 0.005, time= 0.03s.\n",
      "iter: 305, E= 1839.770, ER= 0.000, EM= 1839.770, epd= 0.005, time= 0.03s.\n",
      "iter: 306, E= 1839.473, ER= 0.000, EM= 1839.473, epd= 0.005, time= 0.03s.\n",
      "iter: 307, E= 1839.177, ER= 0.000, EM= 1839.177, epd= 0.005, time= 0.03s.\n",
      "iter: 308, E= 1838.881, ER= 0.000, EM= 1838.881, epd= 0.005, time= 0.03s.\n",
      "iter: 309, E= 1838.587, ER= 0.000, EM= 1838.587, epd= 0.005, time= 0.03s.\n",
      "iter: 310, E= 1838.294, ER= 0.000, EM= 1838.294, epd= 0.005, time= 0.03s.\n",
      "iter: 311, E= 1838.003, ER= 0.000, EM= 1838.003, epd= 0.005, time= 0.03s.\n",
      "iter: 312, E= 1837.712, ER= 0.000, EM= 1837.712, epd= 0.005, time= 0.03s.\n",
      "iter: 313, E= 1837.423, ER= 0.000, EM= 1837.423, epd= 0.005, time= 0.03s.\n",
      "iter: 314, E= 1837.136, ER= 0.000, EM= 1837.136, epd= 0.005, time= 0.03s.\n",
      "iter: 315, E= 1836.851, ER= 0.000, EM= 1836.851, epd= 0.005, time= 0.03s.\n",
      "iter: 316, E= 1836.566, ER= 0.000, EM= 1836.566, epd= 0.005, time= 0.03s.\n",
      "iter: 317, E= 1836.284, ER= 0.000, EM= 1836.284, epd= 0.005, time= 0.03s.\n",
      "iter: 318, E= 1836.003, ER= 0.000, EM= 1836.003, epd= 0.005, time= 0.03s.\n",
      "iter: 319, E= 1835.723, ER= 0.000, EM= 1835.723, epd= 0.005, time= 0.03s.\n",
      "iter: 320, E= 1835.445, ER= 0.000, EM= 1835.445, epd= 0.005, time= 0.03s.\n",
      "iter: 321, E= 1835.168, ER= 0.000, EM= 1835.168, epd= 0.005, time= 0.03s.\n",
      "iter: 322, E= 1834.892, ER= 0.000, EM= 1834.892, epd= 0.005, time= 0.03s.\n",
      "iter: 323, E= 1834.618, ER= 0.000, EM= 1834.618, epd= 0.005, time= 0.03s.\n",
      "iter: 324, E= 1834.345, ER= 0.000, EM= 1834.345, epd= 0.005, time= 0.03s.\n",
      "iter: 325, E= 1834.073, ER= 0.000, EM= 1834.073, epd= 0.005, time= 0.03s.\n",
      "iter: 326, E= 1833.803, ER= 0.000, EM= 1833.803, epd= 0.005, time= 0.03s.\n",
      "iter: 327, E= 1833.534, ER= 0.000, EM= 1833.534, epd= 0.005, time= 0.03s.\n",
      "iter: 328, E= 1833.266, ER= 0.000, EM= 1833.266, epd= 0.005, time= 0.03s.\n",
      "iter: 329, E= 1833.000, ER= 0.000, EM= 1833.000, epd= 0.005, time= 0.03s.\n",
      "iter: 330, E= 1832.734, ER= 0.000, EM= 1832.734, epd= 0.005, time= 0.03s.\n",
      "iter: 331, E= 1832.470, ER= 0.000, EM= 1832.470, epd= 0.005, time= 0.03s.\n",
      "iter: 332, E= 1832.208, ER= 0.000, EM= 1832.208, epd= 0.005, time= 0.03s.\n",
      "iter: 333, E= 1831.947, ER= 0.000, EM= 1831.947, epd= 0.005, time= 0.03s.\n",
      "iter: 334, E= 1831.687, ER= 0.000, EM= 1831.687, epd= 0.005, time= 0.03s.\n",
      "iter: 335, E= 1831.428, ER= 0.000, EM= 1831.428, epd= 0.005, time= 0.03s.\n",
      "iter: 336, E= 1831.171, ER= 0.000, EM= 1831.171, epd= 0.005, time= 0.03s.\n",
      "iter: 337, E= 1830.915, ER= 0.000, EM= 1830.915, epd= 0.005, time= 0.03s.\n",
      "iter: 338, E= 1830.660, ER= 0.000, EM= 1830.660, epd= 0.005, time= 0.03s.\n",
      "iter: 339, E= 1830.406, ER= 0.000, EM= 1830.406, epd= 0.005, time= 0.03s.\n",
      "iter: 340, E= 1830.153, ER= 0.000, EM= 1830.153, epd= 0.005, time= 0.03s.\n",
      "iter: 341, E= 1829.901, ER= 0.000, EM= 1829.901, epd= 0.005, time= 0.03s.\n",
      "iter: 342, E= 1829.651, ER= 0.000, EM= 1829.651, epd= 0.005, time= 0.03s.\n",
      "iter: 343, E= 1829.401, ER= 0.000, EM= 1829.401, epd= 0.005, time= 0.03s.\n",
      "iter: 344, E= 1829.152, ER= 0.000, EM= 1829.152, epd= 0.005, time= 0.03s.\n",
      "iter: 345, E= 1828.904, ER= 0.000, EM= 1828.904, epd= 0.005, time= 0.03s.\n",
      "iter: 346, E= 1828.657, ER= 0.000, EM= 1828.657, epd= 0.005, time= 0.03s.\n",
      "iter: 347, E= 1828.410, ER= 0.000, EM= 1828.410, epd= 0.005, time= 0.03s.\n",
      "iter: 348, E= 1828.164, ER= 0.000, EM= 1828.164, epd= 0.005, time= 0.03s.\n",
      "iter: 349, E= 1827.919, ER= 0.000, EM= 1827.919, epd= 0.005, time= 0.03s.\n",
      "iter: 350, E= 1827.674, ER= 0.000, EM= 1827.674, epd= 0.005, time= 0.03s.\n",
      "iter: 351, E= 1827.430, ER= 0.000, EM= 1827.430, epd= 0.005, time= 0.03s.\n",
      "iter: 352, E= 1827.188, ER= 0.000, EM= 1827.188, epd= 0.005, time= 0.03s.\n",
      "iter: 353, E= 1826.946, ER= 0.000, EM= 1826.946, epd= 0.005, time= 0.03s.\n",
      "iter: 354, E= 1826.705, ER= 0.000, EM= 1826.705, epd= 0.005, time= 0.03s.\n",
      "iter: 355, E= 1826.465, ER= 0.000, EM= 1826.465, epd= 0.005, time= 0.03s.\n",
      "iter: 356, E= 1826.226, ER= 0.000, EM= 1826.226, epd= 0.005, time= 0.03s.\n",
      "iter: 357, E= 1825.988, ER= 0.000, EM= 1825.988, epd= 0.005, time= 0.03s.\n",
      "iter: 358, E= 1825.750, ER= 0.000, EM= 1825.750, epd= 0.005, time= 0.03s.\n",
      "iter: 359, E= 1825.515, ER= 0.000, EM= 1825.515, epd= 0.005, time= 0.03s.\n",
      "iter: 360, E= 1825.279, ER= 0.000, EM= 1825.279, epd= 0.005, time= 0.03s.\n",
      "iter: 361, E= 1825.045, ER= 0.000, EM= 1825.045, epd= 0.005, time= 0.03s.\n",
      "iter: 362, E= 1824.812, ER= 0.000, EM= 1824.812, epd= 0.005, time= 0.03s.\n",
      "iter: 363, E= 1824.579, ER= 0.000, EM= 1824.579, epd= 0.005, time= 0.03s.\n",
      "iter: 364, E= 1824.348, ER= 0.000, EM= 1824.348, epd= 0.005, time= 0.03s.\n",
      "iter: 365, E= 1824.118, ER= 0.000, EM= 1824.118, epd= 0.005, time= 0.03s.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter: 366, E= 1823.889, ER= 0.000, EM= 1823.889, epd= 0.005, time= 0.03s.\n",
      "iter: 367, E= 1823.660, ER= 0.000, EM= 1823.660, epd= 0.005, time= 0.03s.\n",
      "iter: 368, E= 1823.433, ER= 0.000, EM= 1823.433, epd= 0.005, time= 0.03s.\n",
      "iter: 369, E= 1823.206, ER= 0.000, EM= 1823.206, epd= 0.005, time= 0.03s.\n",
      "iter: 370, E= 1822.981, ER= 0.000, EM= 1822.981, epd= 0.005, time= 0.03s.\n",
      "iter: 371, E= 1822.756, ER= 0.000, EM= 1822.756, epd= 0.005, time= 0.03s.\n",
      "iter: 372, E= 1822.533, ER= 0.000, EM= 1822.533, epd= 0.005, time= 0.03s.\n",
      "iter: 373, E= 1822.311, ER= 0.000, EM= 1822.311, epd= 0.005, time= 0.03s.\n",
      "iter: 374, E= 1822.089, ER= 0.000, EM= 1822.089, epd= 0.005, time= 0.03s.\n",
      "iter: 375, E= 1821.869, ER= 0.000, EM= 1821.869, epd= 0.005, time= 0.03s.\n",
      "iter: 376, E= 1821.649, ER= 0.000, EM= 1821.649, epd= 0.005, time= 0.03s.\n",
      "iter: 377, E= 1821.430, ER= 0.000, EM= 1821.430, epd= 0.005, time= 0.03s.\n",
      "iter: 378, E= 1821.213, ER= 0.000, EM= 1821.213, epd= 0.005, time= 0.03s.\n",
      "iter: 379, E= 1820.995, ER= 0.000, EM= 1820.995, epd= 0.005, time= 0.03s.\n",
      "iter: 380, E= 1820.779, ER= 0.000, EM= 1820.779, epd= 0.005, time= 0.03s.\n",
      "iter: 381, E= 1820.564, ER= 0.000, EM= 1820.564, epd= 0.005, time= 0.03s.\n",
      "iter: 382, E= 1820.350, ER= 0.000, EM= 1820.350, epd= 0.005, time= 0.03s.\n",
      "iter: 383, E= 1820.136, ER= 0.000, EM= 1820.136, epd= 0.005, time= 0.03s.\n",
      "iter: 384, E= 1819.924, ER= 0.000, EM= 1819.924, epd= 0.005, time= 0.03s.\n",
      "iter: 385, E= 1819.712, ER= 0.000, EM= 1819.712, epd= 0.005, time= 0.03s.\n",
      "iter: 386, E= 1819.502, ER= 0.000, EM= 1819.502, epd= 0.005, time= 0.03s.\n",
      "iter: 387, E= 1819.292, ER= 0.000, EM= 1819.292, epd= 0.005, time= 0.03s.\n",
      "iter: 388, E= 1819.083, ER= 0.000, EM= 1819.083, epd= 0.005, time= 0.03s.\n",
      "iter: 389, E= 1818.876, ER= 0.000, EM= 1818.876, epd= 0.005, time= 0.03s.\n",
      "iter: 390, E= 1818.669, ER= 0.000, EM= 1818.669, epd= 0.005, time= 0.03s.\n",
      "iter: 391, E= 1818.463, ER= 0.000, EM= 1818.463, epd= 0.005, time= 0.03s.\n",
      "iter: 392, E= 1818.257, ER= 0.000, EM= 1818.257, epd= 0.005, time= 0.03s.\n",
      "iter: 393, E= 1818.052, ER= 0.000, EM= 1818.052, epd= 0.005, time= 0.03s.\n",
      "iter: 394, E= 1817.848, ER= 0.000, EM= 1817.848, epd= 0.005, time= 0.03s.\n",
      "iter: 395, E= 1817.645, ER= 0.000, EM= 1817.645, epd= 0.005, time= 0.03s.\n",
      "iter: 396, E= 1817.442, ER= 0.000, EM= 1817.442, epd= 0.005, time= 0.03s.\n",
      "iter: 397, E= 1817.240, ER= 0.000, EM= 1817.240, epd= 0.005, time= 0.03s.\n",
      "iter: 398, E= 1817.039, ER= 0.000, EM= 1817.039, epd= 0.005, time= 0.03s.\n",
      "iter: 399, E= 1816.839, ER= 0.000, EM= 1816.839, epd= 0.005, time= 0.03s.\n",
      "iter: 400, E= 1816.639, ER= 0.000, EM= 1816.639, epd= 0.005, time= 0.03s.\n",
      "iter: 401, E= 1816.440, ER= 0.000, EM= 1816.440, epd= 0.005, time= 0.03s.\n",
      "iter: 402, E= 1816.241, ER= 0.000, EM= 1816.241, epd= 0.005, time= 0.03s.\n",
      "iter: 403, E= 1816.043, ER= 0.000, EM= 1816.043, epd= 0.005, time= 0.03s.\n",
      "iter: 404, E= 1815.846, ER= 0.000, EM= 1815.846, epd= 0.005, time= 0.03s.\n",
      "iter: 405, E= 1815.649, ER= 0.000, EM= 1815.649, epd= 0.005, time= 0.03s.\n",
      "iter: 406, E= 1815.453, ER= 0.000, EM= 1815.453, epd= 0.005, time= 0.03s.\n",
      "iter: 407, E= 1815.257, ER= 0.000, EM= 1815.257, epd= 0.005, time= 0.03s.\n",
      "iter: 408, E= 1815.061, ER= 0.000, EM= 1815.061, epd= 0.005, time= 0.03s.\n",
      "iter: 409, E= 1814.866, ER= 0.000, EM= 1814.866, epd= 0.005, time= 0.03s.\n",
      "iter: 410, E= 1814.673, ER= 0.000, EM= 1814.673, epd= 0.005, time= 0.03s.\n",
      "iter: 411, E= 1814.480, ER= 0.000, EM= 1814.480, epd= 0.005, time= 0.03s.\n",
      "iter: 412, E= 1814.288, ER= 0.000, EM= 1814.288, epd= 0.005, time= 0.03s.\n",
      "iter: 413, E= 1814.096, ER= 0.000, EM= 1814.096, epd= 0.005, time= 0.03s.\n",
      "iter: 414, E= 1813.905, ER= 0.000, EM= 1813.905, epd= 0.005, time= 0.03s.\n",
      "iter: 415, E= 1813.715, ER= 0.000, EM= 1813.715, epd= 0.005, time= 0.03s.\n",
      "iter: 416, E= 1813.525, ER= 0.000, EM= 1813.525, epd= 0.005, time= 0.03s.\n",
      "iter: 417, E= 1813.336, ER= 0.000, EM= 1813.336, epd= 0.005, time= 0.03s.\n",
      "iter: 418, E= 1813.147, ER= 0.000, EM= 1813.147, epd= 0.005, time= 0.03s.\n",
      "iter: 419, E= 1812.959, ER= 0.000, EM= 1812.959, epd= 0.005, time= 0.03s.\n",
      "iter: 420, E= 1812.772, ER= 0.000, EM= 1812.772, epd= 0.005, time= 0.03s.\n",
      "iter: 421, E= 1812.585, ER= 0.000, EM= 1812.585, epd= 0.005, time= 0.03s.\n",
      "iter: 422, E= 1812.398, ER= 0.000, EM= 1812.398, epd= 0.005, time= 0.03s.\n",
      "iter: 423, E= 1812.212, ER= 0.000, EM= 1812.212, epd= 0.005, time= 0.03s.\n",
      "iter: 424, E= 1812.027, ER= 0.000, EM= 1812.027, epd= 0.005, time= 0.03s.\n",
      "iter: 425, E= 1811.842, ER= 0.000, EM= 1811.842, epd= 0.005, time= 0.03s.\n",
      "iter: 426, E= 1811.658, ER= 0.000, EM= 1811.658, epd= 0.005, time= 0.03s.\n",
      "iter: 427, E= 1811.474, ER= 0.000, EM= 1811.474, epd= 0.005, time= 0.03s.\n",
      "iter: 428, E= 1811.291, ER= 0.000, EM= 1811.291, epd= 0.005, time= 0.03s.\n",
      "iter: 429, E= 1811.109, ER= 0.000, EM= 1811.109, epd= 0.005, time= 0.03s.\n",
      "iter: 430, E= 1810.927, ER= 0.000, EM= 1810.927, epd= 0.005, time= 0.03s.\n",
      "iter: 431, E= 1810.747, ER= 0.000, EM= 1810.747, epd= 0.005, time= 0.03s.\n",
      "iter: 432, E= 1810.566, ER= 0.000, EM= 1810.566, epd= 0.005, time= 0.03s.\n",
      "iter: 433, E= 1810.387, ER= 0.000, EM= 1810.387, epd= 0.005, time= 0.03s.\n",
      "iter: 434, E= 1810.208, ER= 0.000, EM= 1810.208, epd= 0.005, time= 0.03s.\n",
      "iter: 435, E= 1810.030, ER= 0.000, EM= 1810.030, epd= 0.005, time= 0.03s.\n",
      "iter: 436, E= 1809.854, ER= 0.000, EM= 1809.854, epd= 0.005, time= 0.03s.\n",
      "iter: 437, E= 1809.677, ER= 0.000, EM= 1809.677, epd= 0.005, time= 0.03s.\n",
      "iter: 438, E= 1809.501, ER= 0.000, EM= 1809.501, epd= 0.005, time= 0.03s.\n",
      "iter: 439, E= 1809.326, ER= 0.000, EM= 1809.326, epd= 0.005, time= 0.03s.\n",
      "iter: 440, E= 1809.151, ER= 0.000, EM= 1809.151, epd= 0.005, time= 0.03s.\n",
      "iter: 441, E= 1808.978, ER= 0.000, EM= 1808.978, epd= 0.005, time= 0.03s.\n",
      "iter: 442, E= 1808.804, ER= 0.000, EM= 1808.804, epd= 0.005, time= 0.03s.\n",
      "iter: 443, E= 1808.632, ER= 0.000, EM= 1808.632, epd= 0.005, time= 0.03s.\n",
      "iter: 444, E= 1808.460, ER= 0.000, EM= 1808.460, epd= 0.005, time= 0.03s.\n",
      "iter: 445, E= 1808.289, ER= 0.000, EM= 1808.289, epd= 0.005, time= 0.03s.\n",
      "iter: 446, E= 1808.119, ER= 0.000, EM= 1808.119, epd= 0.005, time= 0.03s.\n",
      "iter: 447, E= 1807.950, ER= 0.000, EM= 1807.950, epd= 0.005, time= 0.03s.\n",
      "iter: 448, E= 1807.782, ER= 0.000, EM= 1807.782, epd= 0.005, time= 0.03s.\n",
      "iter: 449, E= 1807.614, ER= 0.000, EM= 1807.614, epd= 0.005, time= 0.03s.\n",
      "iter: 450, E= 1807.447, ER= 0.000, EM= 1807.447, epd= 0.005, time= 0.03s.\n",
      "iter: 451, E= 1807.281, ER= 0.000, EM= 1807.281, epd= 0.005, time= 0.03s.\n",
      "iter: 452, E= 1807.115, ER= 0.000, EM= 1807.115, epd= 0.005, time= 0.03s.\n",
      "iter: 453, E= 1806.950, ER= 0.000, EM= 1806.950, epd= 0.005, time= 0.03s.\n",
      "iter: 454, E= 1806.785, ER= 0.000, EM= 1806.785, epd= 0.005, time= 0.03s.\n",
      "iter: 455, E= 1806.621, ER= 0.000, EM= 1806.621, epd= 0.005, time= 0.03s.\n",
      "iter: 456, E= 1806.458, ER= 0.000, EM= 1806.458, epd= 0.005, time= 0.03s.\n",
      "iter: 457, E= 1806.295, ER= 0.000, EM= 1806.295, epd= 0.005, time= 0.03s.\n",
      "iter: 458, E= 1806.133, ER= 0.000, EM= 1806.133, epd= 0.005, time= 0.03s.\n",
      "iter: 459, E= 1805.972, ER= 0.000, EM= 1805.972, epd= 0.005, time= 0.03s.\n",
      "iter: 460, E= 1805.812, ER= 0.000, EM= 1805.812, epd= 0.005, time= 0.03s.\n",
      "iter: 461, E= 1805.653, ER= 0.000, EM= 1805.653, epd= 0.005, time= 0.03s.\n",
      "iter: 462, E= 1805.493, ER= 0.000, EM= 1805.493, epd= 0.005, time= 0.03s.\n",
      "iter: 463, E= 1805.335, ER= 0.000, EM= 1805.335, epd= 0.005, time= 0.03s.\n",
      "iter: 464, E= 1805.177, ER= 0.000, EM= 1805.177, epd= 0.005, time= 0.03s.\n",
      "iter: 465, E= 1805.020, ER= 0.000, EM= 1805.020, epd= 0.005, time= 0.03s.\n",
      "iter: 466, E= 1804.863, ER= 0.000, EM= 1804.863, epd= 0.005, time= 0.03s.\n",
      "iter: 467, E= 1804.707, ER= 0.000, EM= 1804.707, epd= 0.005, time= 0.03s.\n",
      "iter: 468, E= 1804.551, ER= 0.000, EM= 1804.551, epd= 0.005, time= 0.03s.\n",
      "iter: 469, E= 1804.397, ER= 0.000, EM= 1804.397, epd= 0.005, time= 0.03s.\n",
      "iter: 470, E= 1804.243, ER= 0.000, EM= 1804.243, epd= 0.005, time= 0.03s.\n",
      "iter: 471, E= 1804.089, ER= 0.000, EM= 1804.089, epd= 0.005, time= 0.03s.\n",
      "iter: 472, E= 1803.937, ER= 0.000, EM= 1803.937, epd= 0.005, time= 0.03s.\n",
      "iter: 473, E= 1803.784, ER= 0.000, EM= 1803.784, epd= 0.005, time= 0.03s.\n",
      "iter: 474, E= 1803.633, ER= 0.000, EM= 1803.633, epd= 0.005, time= 0.03s.\n",
      "iter: 475, E= 1803.482, ER= 0.000, EM= 1803.482, epd= 0.005, time= 0.03s.\n",
      "iter: 476, E= 1803.332, ER= 0.000, EM= 1803.332, epd= 0.005, time= 0.03s.\n",
      "iter: 477, E= 1803.183, ER= 0.000, EM= 1803.183, epd= 0.005, time= 0.03s.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter: 478, E= 1803.034, ER= 0.000, EM= 1803.034, epd= 0.005, time= 0.03s.\n",
      "iter: 479, E= 1802.886, ER= 0.000, EM= 1802.886, epd= 0.005, time= 0.03s.\n",
      "iter: 480, E= 1802.740, ER= 0.000, EM= 1802.740, epd= 0.005, time= 0.03s.\n",
      "iter: 481, E= 1802.594, ER= 0.000, EM= 1802.594, epd= 0.005, time= 0.03s.\n",
      "iter: 482, E= 1802.448, ER= 0.000, EM= 1802.448, epd= 0.005, time= 0.03s.\n",
      "iter: 483, E= 1802.303, ER= 0.000, EM= 1802.303, epd= 0.005, time= 0.03s.\n",
      "iter: 484, E= 1802.159, ER= 0.000, EM= 1802.159, epd= 0.005, time= 0.03s.\n",
      "iter: 485, E= 1802.016, ER= 0.000, EM= 1802.016, epd= 0.005, time= 0.03s.\n",
      "iter: 486, E= 1801.873, ER= 0.000, EM= 1801.873, epd= 0.005, time= 0.03s.\n",
      "iter: 487, E= 1801.731, ER= 0.000, EM= 1801.731, epd= 0.005, time= 0.03s.\n",
      "iter: 488, E= 1801.589, ER= 0.000, EM= 1801.589, epd= 0.005, time= 0.03s.\n",
      "iter: 489, E= 1801.448, ER= 0.000, EM= 1801.448, epd= 0.005, time= 0.03s.\n",
      "iter: 490, E= 1801.308, ER= 0.000, EM= 1801.308, epd= 0.005, time= 0.03s.\n",
      "iter: 491, E= 1801.169, ER= 0.000, EM= 1801.169, epd= 0.005, time= 0.03s.\n",
      "iter: 492, E= 1801.030, ER= 0.000, EM= 1801.030, epd= 0.005, time= 0.03s.\n",
      "iter: 493, E= 1800.891, ER= 0.000, EM= 1800.891, epd= 0.005, time= 0.03s.\n",
      "iter: 494, E= 1800.753, ER= 0.000, EM= 1800.753, epd= 0.005, time= 0.03s.\n",
      "iter: 495, E= 1800.616, ER= 0.000, EM= 1800.616, epd= 0.005, time= 0.03s.\n",
      "iter: 496, E= 1800.479, ER= 0.000, EM= 1800.479, epd= 0.005, time= 0.03s.\n",
      "iter: 497, E= 1800.343, ER= 0.000, EM= 1800.343, epd= 0.005, time= 0.03s.\n",
      "iter: 498, E= 1800.208, ER= 0.000, EM= 1800.208, epd= 0.005, time= 0.03s.\n",
      "iter: 499, E= 1800.073, ER= 0.000, EM= 1800.073, epd= 0.005, time= 0.03s.\n",
      "Total elapsed runtime: 14.02 seconds.\n",
      "CPU times: user 31.9 s, sys: 5.55 s, total: 37.5 s\n",
      "Wall time: 25.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "lddmm.setParams('niter',500)\n",
    "# translation\n",
    "lddmm.setParams('epsilonL',1e-99)\n",
    "lddmm.setParams('epsilonT',2e-5)\n",
    "lddmm.run()\n",
    "# translation + rotation\n",
    "lddmm.setParams('epsilonL',1e-5)\n",
    "lddmm.setParams('epsilonT',2e-5)\n",
    "lddmm.run()\n",
    "# affine\n",
    "lddmm.setParams('do_affine',1)\n",
    "lddmm.setParams('epsilonL',1e-5)\n",
    "lddmm.setParams('epsilonT',2e-5)\n",
    "lddmm.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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mjjOGhYU2zWI5Ybolh1OEJZF4tDE2jlts53NCdAkH/YIIUhSYtRE6GqD9Al86QmYJeXyv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\ndiGXm7Mew6yicA0785Ldec4gr7m+12e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JUx+fHpCOeTFHdD0bGQWvg25vlQexBHPvNvgk\n5kgYogFvE2vncLagsiVj126BRGdbtD7P6MTfU/bg0JYxfmm8Mh6FzTa2y3gkkVYr3FIN6JNVGbzp\nFASSQJZ45EUdMeXJPsTMJJKIMoaymbWkzeZEktBNoTiB0Z2I8WwY9lbo+uavVWCp2VfZSDJQnBiW\nxQR7I1K7cb6mJZpkKI0u8nhjdOMSIHqvIybEB6QLmDZgnGCdMgs3cgoZ8k5yDOqHZPohWFZRJH1/\nS6+wyN6KoP2YeqYZZSwC22MHayJONFf0uWMSGzpjacVQDFJWZQ8ZwGY2kyFhSTn3qIzeScSaeMbn\n9VKl8T3EQwTRbn16zPKGgnsSDX4M3SwxCht/K7eG8jgRSqHdYbg2EdWbCPL3MkCUMmPILaE6yWx5\nrwBRqLQGP33OMLqZKOea0w6/HAmVcPq0oX7TcVt2qZ7zdEH9z9beDaVwtOYiLBX/rnEJEH23SFFR\n5xAyRTIiPkAXMJ0lFRHTCdaaIUH1ZtbnQSL6jy120HcDi0hbF8D+94ZkiCLumVmQhgYvN34RBJUB\nRKuJNzoh5vWSl/H+Rrh3D/n1E6599YDq5EXe/nOB2BiYemJjkKArocMom6PdFYoTpxRKtCBP1pIs\nFHN9PUOZSE7RSJfNZI1Had2V3o8jm0POrZrPRsF2WqSLgI+GV5cH3CiPeMIdY4gcxfEZJsllPOTI\ngDRAaltdCWusFhiinkN9TpGo50KiDMaOQ/O2BQIPvmNbeUNDNiDRADxp0lJ5WRoA5u2JWx/bUrMe\nBCpEG7LeOwR0k5nJP+8Lp8tm7dGEFCXysZd440/ukyy0O/qamVboZolX/stvQAiE23cACHfuMvlf\nf5MJYK8ccvvPvkI3cUhMxKIYiqVYQPwTR3Sdpao7YulZndSIjcx2V7py2Fvqv78DEU7+QuCpyRH/\nwt7X+X/vvcKzs3t8ublOWquUo92LpCJhF3o4F6uKG7NTBRWrS4DoPY0UoetyzSJI48BajLW5/tCy\nUJIZpPMhZNP8QnON9M2bARtkI30/N7zq5fI9OUCZiWTmoppSt9axNpGV86xDcZ+JLJw1p7ak3KzF\nASSqTEdpPM70zVo/XbuMRxVptca0kAptoGKhRWyoNqbntknYJhvYL/T8uKVKVQHCZkEZxakMniLF\n6cZeQSf7iX4brx8x+BhJCWqCraylweqhNcxPRno5lMSsWFNXChu00RLd5Vl5TyOmTH3eAES2MKTG\nbLaT9ZL3qOyfGFE3j7jVB9lsoWHSZiBm8ubCYRDGIFvD6s/yDgUAnI2UJlBbz8h2jE3LwlTMUsEk\ntYStWmU7C/Vfm8ye1vpHfRqtiXmT40Vv+X84Y3Q7snxKTe/9SPNOeSwUp9onh6AgUBIFecrTjdrH\ndiopg7xMY52y/FUXcbhGrVraqeYY1yS6kbC+ornNjwS3ShTLyPyGZf4clEdQ34Zu5rh9MiHMhNqp\nIslHgzWRWFz8nHMJED0oUgTsZstQSnrKeljbR6SLGGsGdo9O+zWBSUyD7899QJHebGPg+G6gUa9m\nG0we+99JSue3aauxyzT/qGtpRfJ62pSbPbYYBlYY1k5exvsXMRBu3WL3V1pmX3+a9Q3Hq/+KIN4Q\nxpEwS9hTS5pl47VWCHWW+ex6Rt9StCiJgj7tYcCdqrys201DYZSsmq3ZlRZs0QGFMpaIugEi1Up5\nPFlXvGNnfKc+4MXy5lCUj8pL2ccji21zfG+hC2AMxmYdtYDt378JJMmwgShZUXBoK99s5xzJElPt\nvTIbcdsv5Fz0G8hs9gYZ25ZduzrDGurBoe0wEimMp0awsuVZchnvWbinn+Lr//azTF5XQ2k/SQrq\nFNnsvkyYaccf/vVnqL9V8eLffgO8oj/+9TcgJcLtO+z/rV8/e8e5WHbXr3Hr6EV2lolQaBd37W5A\nksWPdnCriF0Hyj/4OgB31y+x81df5UvzZzASWfqS0gXd1jgO2HtOsdEiYeeGthtzslvzqb3X+e30\n9KN74j6AkfpVzN7rNkTvkbZTCf1QGjjNL1GylB5C1KmpFMomEreVZ3q5xyAj03sRkwdWOQ/g1MMq\ngg68OkMoDF2wtL2ZbLSErQ1Bvc9QSchS1kQQBYe6gUmkkpHSeJzdmuZfxpmQqtI6Fkj+4QKxcbHg\niX+y4juzEe2VkD0zjfoHJWUH9awiExQwKhaJ5TUZ5B7iweVyowePooNQC3aVwEE3EsK+oZv1DMTN\ntsVY6TnrrnjKm043NY4TbinEtmJVeuxOpLRhkAfNu4p0WRK/9xGCXnM6j7QbxmLqGYsAedlG3B5W\n9SyinjmUZWUDa1E2S4Mg56Mow++RjLZvCVpgZRXUqZxn1ClAtOsKuuRosdTJY0TZQ70zlREhpJS9\nieJ9DCJn4/393GU8stj5u79JN/5Jjj8sxCoREthWWF1TJqEaUif8WH3RekNrP1LFT+8dvE3QMDER\nagWAAF0M5BQ4Uv8zrY+bPcF0wt6XT6nfKdn9VkmzZ2knQrEQmmXJvAgUY62Tl23BwXhFLC9+0rkE\niL6XSCmvS03awIWIhIB4g+m2qYWZNm8zzdGCOG3KU5QNSMTmwtYzi3qfoQEY2pKFDBvTTAKbEBcx\nhTZnMRqil7yYEQWJTFIGkVV0KCKDzOyyaHp04Z5+irf/zLOMbyrtEPJEC1heM6wP4Nn/7Nfgd44Z\nfWXMK7df4ubnZhy/EmDqdT1wp6ygMEFlZYA5cayvqalQqmI260v4Pa99/9QT7pUbhlkVSYVRIKjQ\nszMZN5y8Mx0MYf1pQWMjt+KUb5RX+eTkNQAO3ZxZ1TziZ+4DHDnXpNizFT2SKctma7IlUU3yQ2Rg\nEyWbMjgkZ9hEPTijEzjYNvJ7oCyk9xzKNygkUIlnateUoo3F9haPbV3+IDczaj67YU1eJp+HHsZi\nX36BN/6la0SbOH5ZJ/TlkdDuJmwUkhdkDl32E2pfWfHl/+gqxdsFdi08/3/uqJQ6JFJhiV/6ioKU\nQE8D92+/w/7feue+h5eqwn32o9z8sTHF3LH+iZf1cV+ec7dVrUhEuLmccbqqmB0uWH5tj/KeYBth\n/lwk1YGnnrvDtGw48TVPHR4/mufugxwx6cUoJpIPm1xjzJBnJFlC9lgcGIuFEH0GH93ZPLO9WOPM\nNL/3CSGplJbMcBxYA3JGbWAkZo+zNBjIKlOx9yFK2JRUXpYstdEmrzYdU9dSO3+21rrcUDWEffIJ\n0nwJQLh166Hfv/l/fofnmh/l1V+Y0D7fEMQSgsqDYmUQnwEir41aLIVUKAMa1BQ/bXUl0WXgKKps\nLNRgW2VF+7HWxrFKxFGEXO9KZ3B3HKYRQpko7+lB6KaJblmy7Ep2yjWl3QBkj8M0/7GOpLlGGUQe\nnEWagBFltssARktmRAuhSOqdmfNM3AakzwzO5QwgnExSIKlnK4oOxvCGaBPe2wGQ7pKhS1YlrVv1\nzINMFXrmdC0dVZ93XEdpMyDaS/sv49FGSkzf9Jy85EjjRADalGWnjWTmkAy1b3TZN2is+SZkgDlZ\nsI1e6xAFkIaHsAyvcTIQxurBGB1c+x0PInS7JbHQQYoJ+rgpCqOy46BectpV7NS66n78GFyXLgGi\n7zVSVHPHmIZiWkJEQtStLlYLF7JhlUSdqskg2UiDweuZu91iDzEg4myaqnwYh+8bMEWkLD3GJLw3\neLHZRiQbHsX8PkiSQaG0lVwvk9d7HfbKIfd+/mXamXD8J1Ycv1Xr81+qxxA7HkzCuMjb/+5PMXkr\nMvu7vwFf/DI3vrnH7mdf5KX/5Kv841/7EeIsKGssA0UAcccjNpK8QRYqC0h1pJ41rI8rYmcU+LFJ\nN54FwR2usS4yqlrGZcekaDm5N9aD1QnSGIxJrJYlx7Oat7p99t0CS2K/WnLZsr33kWJCom41I5hh\nwp/OgUOQlRypn+wzMIh0qp8UGHKiuaAnQdpc+GQ+dpLe+FHU7wHeFUA2JJyJjG3DjllhJBKTIYgW\nV5vbKRgUMBTopN/2ANFl2nnoYeqa1c99gls/WuDHKXtwCOWRFi2Tj9+j+/WDYRghoSAV4KcWXKK7\n6ukS/NEvTzGNepfFMvHMP/q0nh0gVBu2WrLQjQyhhOZA6dvdjrB8IlI8fcq8dRSlpy49H7v6DnNf\nUduOK9WC19MebVNQj1pimfBTWD0VSWWkmLXcPJqy98SKo27Eqiso37+n9QMTKbOipWcsdhtkx6Tc\nbMeU5WUGUwrBC7EQ9T9zSQnVVhSYNpsGbphX9XJXC7DJOb3RLIkHWlHYbEzdT+vLgbGo4FYgUJuO\nGA21tIxNy8i0VM7r8g4jl83a+RBBrPnjb/eDxG98iefiJ1g8M0YiHL1kmX/IE/bU80o6BQKI2ox1\nVztlwBu0LjrVtkQ9QCA5XUttGmUHmEYBgTALIAmpIuWow5iEMZHVoiL4Un1H5gpINYdJNyx6YdUV\nKvNwidJ4bJV4s748J+91pNAzox3SdOcGXiqhN14IpSG6pF8Xmzwj/ZZoswVI597ozAIOm8Fo8r4e\nkwf2BlL2VwyxX8Tx7kMugJCSMokQYtrcX0mklo4ds2JsW2auYVK0yszPf9dlPPqo/v4/4eDgJ1k8\nZYlWlRTdKOGM9sI+g4imAVMLYZQZ1hl8hAz81AweZ91e1O15mmqIRdIlG3mzWXXXcP2LHeXdNccf\n2aEbC9VxxNd6CMZvJdorFh8Mb5zusjdasVOtOWnqxwKUvgSI+mmZtZC3lWEtUqguH+eQooDCkcqC\nWDlS6YilzR9GE1qpyUyBGBkm99Fpo9YjltvSsQ0zaKuwOkeX1EZPZWUYZQ9ZG3EubDYLRZWf0Dd5\nSQbGkabKDXtJ7/viH8zHNcx4zPwLL/H2z3tYG5gXMAtaAFWRVEBRd4TOEjvD6afXnHzEst7/PLaB\ng//x16n/7zlvf+UGH7qy4O5/3HD05UPkmeXwGL0/TCTCXsQ4vbB1rePg+gnzZU0nCVd5nNOtLuNK\nN0oV2XB63pUUow4RaOclNNAsSlJjGLmO01Bzo7hHQHDvYlJ8GQ8htt+HYjQHZYPqM9Gvo44R42Pe\n2tG/l00GmUUbvmxMr8xBlZuybbrX55xzTdkGmE5qSJxzS+871E/NxqbBSqRLjnUszmwb6qPfZhaJ\nOIkPUrBdxg8QZjLhzX/rkyyejcQiIEFwS8G22ZRRoP2NA0z28FCAR7CnOkhIRaI4NbS7Ok2wrdDt\nBrCJb/8SVLM17bKkHKt7o+8cKYKx+moe7s05mo8QARcF5yLWtuxNVnTB8uZ8l2nZcOxHvDHf5Xg5\nIgZhfncM40BaOtI4gBf8nRp3ZcW8rbgxOrn0PHuvI2UTsmjopfPJB8SoRwIpIalQRpm3SGmJIRGC\nUQl0zOSjANELxm4NoLZqH9DZxra8VEToV11vWETc53fWx9ltiQoOmX6tfZadRemYmJapXbN0JZX1\nOum9PEb3R97U+16E/dgr6oP49deIv/V7TH4LxDlmzz9D99Qeb35hxOqG5qqUGUO+TDr0rHXgKZKI\nI2X2CGwsFEzCN3qQ3I0WY5QdGYLBSOLG/gnWRJZdQekCx0m3oUmExQsBd7AmHVdgE84GDqslI6uG\n6KtQKOBwGe9ZpJiQLGtNXaesaBSIlhhJ3iE+EUuD+ERyChQZr+yO6AQpsj+RlWG71BlG0ZlCYwMS\niWQA2yYIQgyGGA1+S8b6oNDKNw1SMytQSGRsGsamYWrXTFzDpGgQF7NP1mXieb9i9+/8BrtoLrrz\n44ccf0hZh6FOhBK9IE3ULF89iBR8TEU+PAFSIYTE4L+ITZjGYBooOpVhH3w5MvvaKfMXp9z9aEG0\nBbEEt1DQ2zWJbix0O4J0wnJd8sTeqZpVJ6FynvUlQHTBQ2RozMQapCx1o0dRQFGQKgWGYumUer8F\nCkWnk7RY9HRIybTrHhjaol+7rWTWX4h6OmQvHRvokud+7rKHhFWQyDgFhyqXjdHIjxUNoTd7yxuI\nzoBEkg2qLxlE71mY8Zjlz/4Ib33eQoOCer2cB9R3Q6DrjCaqKMjYY8aeo88HXBG499HPU90Tnv4v\nfg2+bbn6l3e5snqd9LEX+cx//yUA/pd/+AXk2RXGwWjcIJKyOWfCB11LX+6oYefuSBu8wgZmxXrQ\n3NcAe3DraDr8jakz2KmaDh91Y+xIC3JnLtfcP7QQuR+MthYKtwGjnSUVTj+cIRVW848zpMIQi5x/\nyk3uOe93poC00mf76ciZPOTS1rQ/bT6s+ptZGynyto/KbrZ9lBIwRAImN26GkISIISRzZvPQtqn1\nZTzckLJk/lxErjeYtypMK0PTXSwS3UQoVnnl8zyxPtSiiAT1XWHxbKR8TRmlScAtBQlqfB/KRCNg\njh2tyUCh1a2YwQupNdxmCknlAMkb0lj3T986muLXBbYMyhbKk9rVca2+ea2huulwS+iuQ3nL0V7z\nFEVgr1rhk+HJ6Qmr9/G5/aGLvmERo55Dfc4R0UGYc4iz6k1ozP0AQr+aOiTEpGH5QRLBoMxoQ8ax\ne1B6W2L2XXzO9O9SSavQb03cMsCn35AYz4BDwOBFVOApxFNLx9SuGbs257OH9gz+8ERKD82D0vzI\nR2ienOLmHaF2vP3JCl/D9PV9NWx90rL/1ZbyH34R83V47s0XiXsTpFUA6Bv/2h68uCAGHZaFUwc7\nHSZL6WeTNV2wrBYlsbHKPvJCGypI4I4dYSdg99d00TAtVQq/bErKUUc7KUhHllQHgjfYhSGMoekc\nPhl8srTRsfTlpUfeI4gUAgSLtF1WOGTQKETER/AO6QzSaZ8lGYCWoL1V6D1Ve5mP3fgMbfscDizp\nlMfjhmHbdBqYQ/kDOQMS9X6KASGQOH8sDAoQ1QQmpmFm1kxtw9jpub208Xj04Z57hjSuSa+/TTw9\nBSB8+avs/ZFl/zMf5eZnZyyeBEwijCKStHbuJalIBg8BLMhahn58+m3L3jc81e0VtglI07F6asbx\nSwXzp3Zpd6C5FpBGcAvBrWQjYbM6nItlxLmIj4anZ0fD2XttfPEvUB9cgOjdwKGqQupKgaGqJNaO\nWDliZbUpK3r647YudsMOikVuyHpDta0P3TzFhkHENsbNGWBoG9ROrmcQxQ17yEZK57GSiEnw0RCs\nblyL2aga2dxpsptm8HKq9t6EqWsWP/8jvP4nhTTpkJXV1y0IZm1IRvX1RDCdJZa9P5SDUaAYdVRV\nR3jRszyp+cbf+Emmrxqu/9e/pg/wT/+Af/oZvVy9mNRI1u7t8uZf+jjHn2kw9wrkesP1w2NiqgCo\nCo/PhZORRGkDE9sSk9BGx+Foyc27O4MPlhSR2XTFO/MZTXD86PQ7XLUnXK9Oud995DK+n+hZQuKc\nNmdFiZQbhqICQxkQKgzJGmUgFkab+SHnKGuwz0XxTK7ZAEN9PoLtfJS2wKI0fA+DNoAm6jYpGyht\nwIlKOWrTUYjPW4QMXXJ0cAYc2vYsshIHJtIljejhhRQlr/6Vj/LEx97mrZt7UOg1orxnKOZ6HVo8\nExm/ZeimiWZffTuKU2h3oZjD9FuWdg8mb8D6QCgWyiIKFdhS6JLDtEK8mcVeSTAZLEoGOHY8+f9F\nvvOnE+VtR7djYbcjzR2mMfiJYX5aYGcdYe2QtYWJ10lcB4sPddDq32cn6kpbu47TrsJcMhYfTvQ1\njhHEufvBaGPAuQGQxlkFpK0FZxSQtqKgtJX8s2yWfx/L8fxjb33uWdKGYfvQMBDLOcdazTmlUbPp\nHozu5WU9ONTL9M8zF8uco8appbZdlpi9B8/p4x4Pg0EkgnvyBq/9wgHzFzzl7fEwdAh1ZPlMZp3u\nLzh5uWb35Z+CpMawh791i/AVNbJ/+fR54rRGVq360qwbUoiQzbOT96R1Q+paMBYzqjE7M2XZhgBl\nwTf/jWe4+sIpi7ZgYUvaPKFv7o4ojwxuDTtfKvFj9SwKI2WrNcHhJBKSMHYt4VJi9nAi5xzItY7J\nK9+LQnNQD0b3eccasGZg3Ujq5WYJ8VE90bzmCpX9qF+ZyuU309e01UxJz0rc/sG71B9GkhpMm5i9\nhbS22Y4wvF/OAtNFlplNTMOuW7JXjCmKoHXY5SD+PQmpKsyo1ve/iHorGOFrf+Vp/JMNT/9v+8y+\n9A7hcIa9dYx/9TukL/4+N+4+z/zj11gdWk6fdzSHOvA2jRAmETs37HxDz2x9L1Es4rD4pX79CL71\nHeJiweIv/ASrK4ZoYf5sIkyiSqY7kxdTZXm27/GAXHtXkb2xjrzmXcXYtaxDcX7h54WMDy5ABFo4\n5Sk+ZaHg0KgijUrCuCTWFj+yhCqDQ0U2aTwD/uR/O4a1eRu69RYwkyf1Q9HS54/tQ7LFNulv03vJ\nyDZ7qPDUzuMkKkBkdV1sDJmZcka6ku/6UpN/JqSqSM3DMV82dc3pn/0kb/ypSDFt8J0FLGZhkU4o\n5rqBzLRCt5uNpttex2oJAt3CkQ4E6f2GnmhYPR342o9/hrR0FPcsL/wfC/3bv/hlpCxJIXL9v/k1\nrqNU7vCTP8Lb/17N6hs7xAJOJVHdtdwslUb52S/8EUYSdTZnPG5HuCLoGtAEeFENf1sQE8Pq8qlt\n4NIV5AePvmFzDilLpCqhrkhVSTovXXV9Q8YABmkekSGf9IyhB+Yjt/k+nAWqk9lmEeVp+8AeSjgb\nKUygNH5o2ArxgMJ2AwAAIABJREFUuVmLtMmqZn9g5m78iPopnMmeIZtc9xhcES96iHDz3/wxli+3\nNHd24bQgVRF7bBUcclAuEnatJtUIiAc/SqSrul4eUTOq6BKhFEY3E34ilEeJUAvdDOzK0ByqDA0U\nVOqmvVGx/tt0AXfkcHMhWkNqSmwjtNe1QaczhHmBVAHpHOnEISFv//CCXRiK5xasTysOD49xEhkV\nHfea8fv8JP8QxDlw6Dw7msxQHEAhYwZQCCMKSluzmdSbnIN6YHlLBn//Y28NovqvhxqILXBIWYtG\ndDtQYXUr0Hau6Zs28wCEuV95X4hnYhpCEqauVd+Sy1Ln/og/GEAkzmGfusGtn36a1bWIXRhioU2R\nXQoSDGGUiGWkOymRHc/x5zqVji0KTl64Rn3nGrZNLJ8Q9r8Smf693wDT0z/OspXt/j7pmRf55i/t\n8/RPvsE3v3kdM7fE/bzqbO15/a0DJrsrbs8dTePoVgVmabCrbIRd5GtfAW6v5WCyxEhi4UvaPD3p\nt6Bdxg8Q59nR7wYKFU5BIaPAEJLzSgalz4PPkv0VTZaUGbMBidXKTM+zRL47IHymr1JvRCda4yhD\nMecZ2fI5yxHOgaoG3exai2dmV+zGFYflnLLwdNli5DK2YjvffL81oLHc/tc/w91PR8xaF1yM3hZd\nWX8kmK7inc/CrU89RfPSmp0vPsOTv1LD3WP8t16l/ua3qUXY/cInOX5pRHUSsU1kdeio7wWmX3wV\n0GUc2xFFMJ/8KKcf3uHeRw3dJGE6vabZuXq9mk6wS6E8heJEpWXNvtbmoQLJ/sQH9ZKxa5n7CiOJ\nUH2/T+ijiw8uQCQZGCoKpCyQuiaNKuKkJo4L/MjiJxZfG0Kl4FBy5wAic+7zNig0AENn5WPJ9DA3\nD6RAJ9m6jUtIGbBFpKw6xlXHpGyZFO0wUfNR18RuWERps766fz/2k7zL3AXw0MAhKUpWP/MJbv7i\nmv3JmqZzIAnvS8x6S+onumoxjCSvrYdY6mTf3LXEAjqp9PVuDcFa7DQw3VkRJobDF5YUn1/nR73K\n7eWEmzd3+eh/8AaUBYhgX73Ns39ViPvgdyvccYN883Xu/PmP42uIXxB8MoxsR0TogsXaSNfZQfK2\nXFc4F7g+ndMly1GYUJuOS4Do4UTPVhzAoXFNHBWEUbGRrxZCKOWMn0cvBerPUt9s9UDRYNh4DjDq\nQSI4B1hvMYc2IPTG36y0gdp6qjzRH5uGWjpq0cLcSsQklX90mYjdGz7avIXIPg4jkscsFr/4Oe59\nrsOUgTDXTT31PQV8QqnT03ZPkOfmdIuSYtKqx1gUQhVIUVhbIEH9jqWb6vrWWOg61/JYV0K7lQ4U\nttWldg0IhEq9ru5+uMA2+hpXR4J4oTlMyCL7CyUo7jlCbRm/qWDD6nok1In6HUe0ifXtES++/DbX\nR6d8YvYGr64PKUcnvPoQAfwPZPSeZkan95SFrjgvC2UqZkBaQWizAYC2GEJDzSL9sosMSm+BQw/0\n29iS0G8YRGlrW2saTInFKCBtRaVk22xFBYk0l4S0YQ/FJAOLqN90VksHFia20SUOl5P8+yMlbcy/\nz7BPP8k7P/cUx68AAnal3meQa5yoRvfiBXtq1fR1GnTbahVYP5VorhqVDdWBds9w9KGfot1RE2mJ\nnGnmk02kSUBcwze/dV2ZYQfZ1NpnKcjaMm8neg1bWW3YGq2zYrkZ3CYLo1HLbrka5EWzoqEJbrPm\n+jK+/5CNZF6cU0VGVWrOcfYsQxE2/c+W/cUZcCgBMWV2Rg8SZVmryMbE/D7/oQf9ffphsseiNRFD\notgCpS3pPhYRZJAoh3pgJ2oJTKRlzy5YupJR2dG4yx7roUYGHcPnP0G7J9TvWMQzeAi1u0Koci3r\nwE8i5p2K1dXEN/7iFXa+eYXD37+OtB5z8x7+V/8Z+7+6ufsqP0bIZzJ9/pMsn6oxPmXbGGH+lKE5\nUIk1JhErzXumE0yrNZJplCHp1glfC7aB9QE0hxFTahE1di3Pj+9w4kd8c3744FV5Fyg+sGlR2UO9\n1EMLqDQqCZMCP3H4saUbC34kWkD3co3+YnMGHEqbhk0236MHiGBz0RtMqvNk3ZxLRkLmWerXpgjY\nIlCWgarYgEM7hYIFEaHxjsZEBYhi9iFJZtgScubxPwhh7H2TqPfqcdKPfYRXfzFRO/UAEoHQWYpT\nLZy6nc3rnxyQ1OPDT9VXyjQGdypQJuzSEEeJ+m1Lc0VoveDHFusidxdjVp2OuSalysReefZtwt+x\nXKlP+cM717j71i5maXniIzf5qWt/yP/+h5/i8B98nP/qP/2b/PbqBX7l9kdx2fTTR5UDeW9IQZAT\nZSK0jaMuO3bKNQHDIpaMTQtM3vvn84c9JE/VqhJGNWlcEyYlYVzgx+eYilseZht5hmxA3u3J/BZg\nFHMjdx4IgrOgdXLnwKFSPbDKyjPKW+6mRcOOW7HrlszsmolpmIgnIHTS0YljvQUUbUuDQjIYlOGo\ntPH34fn+YQtjufnjBjEeEkhrcIsNCL18xuP2WuS1EenbE0yduPbUXd6YH2CnHfLaiNFHjlgc7Woe\nqhKUkArd9pPyQgWAZldBox7jC4UW6KaD8lhvaxotlCRBu5OYvCkUr8HyCUNc6MYhP4lUdyzNZ+bE\nN8bDIoboEu1Vz+z6nJHruFEfc7ubMrENi1BhqopwCRB9fyFyhh29PQBTtqJV6XwGpDesRNmA0QM4\npPd3HpzeZjIOucedzVtwtk7agNgbcNoUkaIIVM4zdi0T1+jq6AxGlwQKIgXKMmqTGYAhS9r6WlkA\nAJXxqnIRlDFzGZv4ASRmdmeHo889yfHLYBswpwbbgl1pnujPSCwE47T+EQ9hZQm1HQaWyWZmSJba\nN4eRWCeY+DMvl9hE6gzm2A1rp/stQibXthIE45WRHUYqC3FzbdAAfA1hlIHJ7KNWW48zgcoEJq6h\ntY5Q/4DP62Wc6akGu466JI1Klc3bDEbbjZzsDKNkyDM9ukz2PdNzJEavQTHfRBlEWZKW6wzO3WXf\nS20YjSkziNS3s2cs2uxxdp6t+G5dhEFx55rAzKwJCOtYKPvVpctN0Q8xln/+c9z8jFH5ligzupcc\n9nWuCYIvk+aGCKnQ7WWpiNy+Khy/MsV0MPvWHvtfua5+V0YII6cKodoQsmH00SuG9dUAVjeTExPS\nQTEXfexOIEF5irKwKx3221avaX4kdBOtnWwLqYxcPzhhWjZEhHmouNuOiUlrqIsej8Gf+B5FzyBy\nTg2pa5WV+Ymjm1q6scGPwE/Ul6GXbTyQIbQ1KeunYwMItM3kGVBzbcwwIHarexLUt0MYtPlV1VEX\nnnHRMS0b9srVABD5ZFi5gpUt6LKJpFjV8A4a3K2L9wci0iPoRkXgxz/GN35xQjlbEKMwX1Wqj75X\nYlrBrXRlYjKJMI6EsSYMO+sY1R3rdUFRBJq3xqRRRFpDcWSIZaI4EXywcOKIwOqJNeuVsngmV1tq\n51l2+u+la3lm55iX9u+w9CWLruT3jp7kpSdu8eS/c8zfeP1PUdqAj4Z5V1Faz531hFXnhq1WEtCp\nXILCBZa+pIkFh3bO+nHIZI9BDF4gVUkaVYRJiZ+WCkZPDKHMRvdbUla2m7Dz4NBwx+du0+eeXgoy\ngNJb+cttgUNFxBaRovQDCD0r1+wUa3bcml27YmZWzMyaWoIWRNms2ubGLRKxKZ4xqe69if5Yk9rL\n+J7i7l/+HN3VDmMjce0w2bwzFYnuik7V01s15bGwfKFD6sDbf3ANJpFwUlJ2sPzGLgLYteCfW8Ot\nCrfSZl8StDM9f36iL1gY6WNHB8VC6CZapPVna1ua4UdkQ9FEfUdod8FHodmPTCrPfBIxK0Ny0F4J\nlHsNz+/fY6dYM3UNy1DSJctz9R2+PXoBTk4e/ZP8wxK911lZqhy5LknjStmKtVMwutScs5GR5RyR\nTaZ7ycZ9crEzrKAtsGibtdj/7nlmtUv5zEYoI64I5wDpNVO7ZmZXTEzD2HTUEimkH7iqrDVKZhAl\nzgFG8b511Y+kHnhcIsbvCyCy169x8i++wJ2PGyQl7GpzH73ETLJ5eXVPiJlwbDo9H7HQ35GggLR0\nudnP7Fc/SsRlSdjvhutV8WpJrFQKazpt0PpaxTZak8dSH0Oiejz6UVLQqoVuynA9DaMEVxqu75xy\npZpTGc+Jr/HREpIM+e4yvs8QGcDoDThUEccVcaRgtHomblhCA8G4R3Rkw0TbKB42votnGNVnmNKb\nLc1wFrTu5bB97pEMSNcZkJ5aBaR7hnTPViyE+wyq4X7AqJDei6ilNOHS9+whRvOnP8utTys4lIxe\nP4zk93u/dj7ngGKuB6bfViYhM9qTbjDzs8SdPTj+8FjzCNBNEnEU9VqUL3LSZp/F3rTa5Hyz1iUf\nptHHN17zjPEpe2MpOORrxQtCDe1uor6y4plsTD2xLU+UJ+y4Nc7s83Z58RvyH7jzE5FvA6foe8en\nlH5cRA6A/xl4Hvg28EsppXs/6GM91Bi0+QoOqcyjZw4Zugn4seBHEKv0rr5CaoS45bEBW0yhtGEH\nRWFw1e+/l/0+xEZ12e9XS4uyUEw2iy1doC47xkU3MIf2ihU7bkXA0ATH3FY4G7DGKgU8ZvRT0Hoq\nwUVzxHpPz82j8DoRw9f/4oT6mROMSXSdJXhDCAa7NEOhHEvw00iyCbvb8nMf+gq/+p0X1QjYBVKC\n8oklMQqhs/iu4uBDd5l/8QrVbcGPE7GEsHRIpYXua1+9Djsde3vqSWRkQkyCkcS0bDhZVxip2KnX\n3FpPWfmC/WqJM5HbqwkHoyWLtuT4ZEIMgkgiFYnRE3OWd8asO8cig08nccTMrIHZe/+cfg/x2Oab\nXEDh1Iw6jnu2oqWdGrqxDFKf/uMM8DP4fjzAW6Nv6M5My/TTULSYLVmZ3YBDptBGrSo8k7JjVjbM\nioY9t2TXLdkxK3bMmol4xgIdSq8uMy27S5YOq35DKQ7by0K/Rjah20re52n+Y3t2gPSFT3Hnx3VR\nQVwoKpNsor5jWDwfIAgyChRvFqyvRXZ/r+DkxyLhoOPqtRNuvblHezWbM047urslqbFc/fAdjv/Z\nFcIoYRshlEmndQllheT1r3GUCN6on9FMhw+m02avf1X9SC8zplP/o+ZKQLxO3RanNZSRlFdYT3fW\nhGDYKdZcr054fbXPxDXcbqa8vtxTIPWCxGN3bvrlG4VT+XFu1sK4JIwdoTZb0vlNc6Urmhkas75J\n244HAdP3rZzeAohiv3UoN2q4pBtZy0BZekZFx6zUumZm1+yYFRNpmUhHLYFaoBChS32tFWlzUovI\n4EMUJFKCMgAuSL1z4c7O98kgmn/+ed78lzvMO2oyHytd6Ww6aPeg3Y1MvqOMouTUe8OutbmqjtUA\nPzoFcqLJHma5244WiEIqIBzAeFcNXeWrJXKqAFPvRdMnG7dKkGB9RZvC6q5o45Yn/O0sHwEDsdQ6\nfXe24kM7t9gvlgCsQkmXDKtQbBrCCxIX7tx8t9jyVqQoFRwa11rjjArNOaXJCzOyhFXehUF0/j5z\npDN5RobaeljYsQUWwXYu6redpZxztNYpXWDkOia2ZeryqnrTUIunFh1xFYAV3c54Bl7Of28gbzQj\nUmf/s8KGd82Zjzoeq7PzXcKtFE2OZcItlDlk1wpGE8F2CZkqu6yYa04iCW7O4JcXbdIhfaEsiSSZ\nOS0Qq6hD0qSb8oDhAmeW2QrEKzBtV+CWYBuVkfWMSUl6Fn29YQ75afZ6vNJwdWdOGyyLrqI0ukhh\nHip2XDMM3y5yPKwq7GdSSre3/v3XgP8rpfTXReSv5X//+w/psb7/MFbXvIrBjGpkPCJNRsRJTZiW\n+Imlmwh+DCEjgWc1zOmc4XR6dx+hLYmYPm5P4dmARgM4lFdKOxcUFMraWGv0c+084zxdm7iWmdO1\nilPb0CVLZT2l8bqS2galrvUfgvoZRTk7/YOLMlV7PM7Nu4T5xCtwpUEE6sKzXFTEkwLqCJWu7F1X\nEKYRs98SVpa67jjuRnhvWc0rdveWLNcbbx8xEMaBo98/pFpBs6/3IxGqd9xmqj8NMHesxwUhmOE+\nRlXH8apmsawQk2i8ZVK1tN6y9g4rCR8NR+sRIQpF6VnfGcHIk4qkW4pcousci7bkNNRUpmNmVg/V\n1PshxONzbraNG0stnmKWsnYTRzdRcGhgKvZSVnc/CyjB2byyzcwZpvrpvgKlb+qSRUHtXDBtwCFP\nXXZMq5adSplDB8WCXbfiwM7Zs0vGpmEsgVoMpDgYOtosWexlZv0GE2DYaibbf+f7H4/P2clhxmNe\n/dkxqewoa093rHLQ8RtOpRQTD2uLKwLtfkQ64fjjHhpDsdtwdDpicrhkvSoJJ4romCsNYe249foe\nMlUDfaVUg1tvhiG2yVPdLI8FZQrESmtluxbanahSNz0aJKt+InalGz+5sSadlFBE6kmL7yzeW16+\ndovKKMB4pZpzu5myV664244V2LhY8dicm36VPVk+n8qCtDUA8yOzmXSW7w4QcbZe3tz3uffy/cxF\nNjmLszVTL2lNLkIGpevCMy0bdge24pKZXTE2DZUEaknUYjCyNe0SCHkzkcneREYiZULX3svWjrOL\nYYx/Yc5OSumf25fJjMcq8blTEsuECYJbwOiWPrfNvpAKHWTFErppZsB7ldhLgPp2fsyeAZA27I7e\nuNzNYfd3S/xI6xnTqa+HbVXi2s1kYOS3O9nn41pg9uwJ/jf3MYVO7v0k6d8bBD+JhEkEmziYLBnZ\njrFpOQ21Xr/ySbmga+4vzLl5YGxvgy4cUpWZObRRZPhxZkcPTKBNfpAzK5u37jad+95WPtowFTfW\nH2cYROeA6v5aJoX2WXXZMSkaZsV6CxzqqCRQSG9CLRRnCq3N/29IiYxpUhKJBMamoTT+IvkPXfyz\n811CfuzjvPX5Gj9OFHM5Aw7bteb+bia4JZhOc8zpDWUiTl8z+FrrFOslD0QF2yoNtSeYmk4Ixdl6\nOVXKUJVGhYam0VzXS1bdOlEdR2wbaWeW9b6hm+o5NFkGG8pEnASuH5zy5PSYke14YnTKQbHgrp/Q\nREf3mFDN3qsx3Z8Dfjp//T8B/5j3+zDmRm1w16/VAySNK8K0pJu6YZLvx6KmvmWePljuA4f6i9q7\nP9a7fC0bYEg/nwWHyrydbFgvbQLWRMauVSqka5lkYGicaZFNLDgxNaUNA6jkTFT0NEEMhtjTNr/b\n33tx4uKdm3cJ+8pLfPUv7VLVc8ZVy2JdYozq5ZPNtMVSkJ2WtHS4wg+v/9fuXtEtZwI+KuMo3q1I\nVUSKSLnb0HWGxW6iuGfxu7qG0a42tO0g4E4t6zSBBO5wTbcq8F1+O0sCb4hFYNmUJKDpCtrGUZSe\nrrOkaNjfXdAsC1JroYjMjzaQdmmD+lvFgpjMRfcEuXjnpi+cCjestJfRiDQdEabVWXBorKD0ZvU8\nAwPwPDjUe7gAQ5W18X9I7/o+H65FveeQyeBQnuBXhWdWN+xUaw6qBQflksNiwb5bsGeXzMyKiXgt\nnkSwqFFkv63MkiglEIc/VKMHqy8QOPRucfHOzrk4/YVPsL4aKKYt7Wmp1nUrS6iUtZPWFnds8c0I\nE8A9Nye1jvGkoWkcZRmY1g3NukBGgdjYzGCNsCqw67xl0QMirJ9SI3IpIuFIgZo4CSSxOolN6AQO\nkCODXev0P4pKQSQ3kMooStR1x7K1mDLQNgWjccPOaM1euRw20txtJ8yKtZrHJiFdPIDofFysc7Pd\nqGXPoX4ra5xW+EkxSFl7cMjXOvHclmLofemnBzU72yDRGUAoS1vvk7VmcKiXQ0oVsWWgrjqmlUrm\nD8oFB0O+UbZiLZGCTc6JCFHUMLZA82P3LrklIhc85byPZycE3SD1PYapa/xnP8zNzzhsk+WlSQ3x\n21n2IvNQ3bS0e+otpsx1QULS7YlVQrzFrdSvo/cOsetsSp0UzGl3dPputsoMibC6KoQMPrmFTvKj\ng9U19XD0v7WvTOsiP7RLmCT4aSRWEVzkyafv8vz0LpZISLpx0yer1zFJF45B9IC4mDmn9ztzbuPl\nOioGcKgbC77KjI5zANFwV1s5Rc2pM+s4bX4mWzlnAIfOLQwCMih0diEHNmFcoix9lpd1TG2jucY0\nTKSlzPmmB4dM7p0MQhyA5jSARAZQ6xo1qy4vCIPoAXGxzs53CXvlkDsf22F9NerCjCW4lbJ32pkw\nf0aobysY3OwlTFAvxO6wQ6pAfLPGrTV39J5Wxm+Y+cVCiP2Wy0TeuJpf60YN9N0KJAqmycxFFIj0\nI6FYCcnqddSPMus/+xN3O4luN1DvrXlyesy16pRfffNF5suK//BT/4CfHX+Tu6Hgb978WUz3fj3D\n33s8DIAoAf9IRBLw36aU/jvgekrpLYCU0lsicu38L4nILwO/DFDzCNba5tWLUpYb2vWoJEwUHOom\ndpjk+5FSZGORpWVuu1lLZ/2FeslYLyGTrSwnW5/J+TT7DYlVCZm1Z2VkI9dR247SBCrrmdiNcWMP\nDKlW1mOJjGxHaTylCbQ26BazJJtkGQzRCPFB0pT3L76vcwPvw9k5F9/+peuk60qDnlUNd4+muMIT\n+40KAdIoUNWe5qSkuVdjxp7VsuTKtQVxZnBWGV/NuiBOvUoM75S0YztcEJMDd2oIdWJ9NQ3bQqRV\nmnfaawlLR0qCOXaEiWgD1xgoI0tf4cpACGpEnRpLnAphqW/7eVnBvBhMilMUaAy+thytagoJG+Ph\nUX1RPEEu/rk5v2K6rgZAOk5r/LS4DxwKtdJht/09hkaN7e+lAfCRXLQMpcvW9L8HjIBNs5Y9hwaq\ndanMoXHZnQGHrhWnXClOOczsoZlpM3uoB4d0zWtv6mglEhCKrNAP+Q8MGHy8UB5EF//snAt75ZBb\nnzKk/D6mNZjGUN0VVjcC46d1W5m5YwkuYa6viFEQk1ic1Hzmxdd4e7HD23d2GY0bFr5mdmXJ6Z0J\nzB2pUNDQNrloyg2VPh+axwCkCnrN86Kj0yKCN3TThFsJfhyxa1376sdRz2ChVf3ytBo25AHsjtbs\n1Sv2itUgR+yBISOJ3WLNaX2hAKKLfW764VdZbgxiJyMFh8blFju6l7LmzXVlBqXPD7TOA0TbPz/P\nIOqBoS324hmJmdGGPblEKhNUQcGhUcusbtivlhyUS64Ucw6c5puJ9PlGmzXIjKF8t/30PqY0yMtg\nIwUJg56NfIjf1+Rzsc7OP6/E7EPP8/rPjPCjRHEqFHMgKYOwOYBuFkkmT+PHuvY+uTwka4xKvlqh\nmyWVVORrU3VPpac9O7/ZVyuHBLiYm7VOQSg/1ua/3Yl00yw1aWRj0N+DB/lSE0v1fCTbQOxcWfDC\nzl1mxRpnIk1yxCTM3JqFrxjZ7v9n701+ZEuy9L6fTXfyIeY35FCVnTV09cBuskV2UwMhCZQa0EqA\nAEFLShAEaKOd/g6tBWmvhbRoSKsGREALAiIJAYIIstlDsbpryMx6+cYYfLj32jUzLY7d6+7xXmbl\nUN3vvaw4QCAmD48Id/Nzzb7zDW/iof7NWjev+j37PaeuULOGuKjFVzHvcYZa4RtFLA79g6b/kDzg\n4uWvja/hKeRiDyBKe/e1nyoNWdI6fs9J31FloCg9s7LnqGgnQPrUrljoLVUegI2yMmAChbQSsCh+\nTh8ZkxX3/4fXWG/82vm8Su9csHpfWEN2I+9VSHQncjbX2V8sWYhVYigjutXoG4N5ZkXamoemKoNA\nZqsmEFj3QFQYCyppSSLr89kqCINa+wxUZzBb+0SxEqbSUCmCE3a06UWSHyoBzUOdMMc9x/MNfY5G\n/Ifv/QXbUHATav5lf86P+wu2wf3KxNz/uymlT/KC+z+VUn/2RX4oL9r/CWCpTv9mXlLGTHGvqSyI\nexTIfeZQqNKECiabdn4+0+FrDxw62FyNBzIBkZQeuxuT8bTOgJKx4QAcmhc9M5uNGl1LrXuJlzb9\nZKLW6C77foTJiLHSnlIHnAm4EIkmENNhj0pJHfz9bwjt+iutG3hNayeX/p0f0J1FjI1UhefR1YKU\nwHcW3SsioGceYyN9a0k6cfHeJattSQiam66gKrwksgTDfNZy2c8oao9XIsNIaFSvGI4HWTtlIHWG\nvpHFZq8kPtb9sCaeBcK2pH6s6U4VsRHGUag0KoFvAgxKfEUaAYHKZUf3vGb7pMGutHhsYXFHHfG5\nIy4UXefYBEkwMyoIsPpm1Ju9bm6DQ0WxSxCaVQwzl8FoLWB0BoeGKrMS9w9gewe0A4bQePiCwxf6\nPjC036/G6egeOGSdMIeawnNcbTkt11wUK87cegKHzsyKY92yUEP2AdFoBAEdJWaamM2qdSZbS68R\n/yGh6appCvjaZa1v9tp5RW1+/0P8Qg7WKQgAHKvI9iGQFOvLGn1t8e91pK2YzvvOcna64jsnT9kM\nBR8snwPw+HLOfLnlDx7+lH/Sf0iXSnQVCDh0BneSTaiVJdUBXYWdVGjU6CuZskVAedm8hVKShVIG\nhVIxsozytU8nqroH4GKx5luL59TGE5PmxK3QJB61S658xZGTzftPyzfHg4i3YN2MfoqUJSonJI4e\nIMJW3E9l3ZPO3zqs7e4wf3nsO+PTn/J+Yrpdesm4et8HbQKHXAIXMWWgrDzzquO42nJWbrgobji3\nN1zYa471hoX2k+8QyEFN74Ea5mCCLz0npBFI0sIgSiMD4bXvdd6stRPTF5aYKVcwnNUMjUi2BAjK\nd1PIc6uieP/sJ2UCqEF+R3El660/jsRG9tKqV3iv8QtyVLSAOkTQMX8MDFoxVMIQUIOiuNZyuAP8\nIgkRKilMK7K22ETpQ1kqX59sOZlvuN/c4HTAENkGx81Q4aOhNj0RRa17WLxx4/w3a928qoxBlaVY\nddQlcS7gkF+4fKbKQT/NDgg8YDinsZ+8+u4nmVncA4lgJzPTL78Bk6xsBIcoIq4cmFU9y0JA6RO3\nEUBaC2P2jkf3AAAgAElEQVSxVIFMpgQEkB7lh2P/0UpNm65XpZvFCZT+Wo/qL6Pe/LXzGWW//T6P\n/v4J7UWU85SVmfdo/QK7AA09sn628qzZrcJsFd1p9irK8fN2K2ujerpbh7YV1mKywlCawEWLmO2n\nnQ+o6UTmarO0rVvqHD4ke10/kz18qBLqouNBTi07LrYMybD1BVpFng5zvl084WpouPYV4Y05Un12\nfe1dWErpk/z+sVLqj4DfBz5VSj3MSOVD4PHX/T1ft5RWIvNwDsqC2JRZk58bWZaUTXrWg6nZHjMI\nDhrcAUsov0mS2J75tI7ZdDpNU9TCDgIO2WFK8FjYjpkVGdnCtDgl2tZK+z1waMDkDtRrQ6k9pR6w\nKorMLGqMTqQUsxdRIpk4gVliPvn6xyVvy7q5XX/+3xxhLzbYPGXXOpEGLfKyhx0EzXzRslpVxK2F\nBJUd8M7we+99xL9+cZ+Hs2s2Q8FlW3PSbPHBkJLCfvuGtnXwvBYq/sYQ60Da5gU5XiBNQg9yOKt/\nboTWuJQ1oTo1rU/dK1SQeNlkIXWaqBLRKlSnKa6kYepWEZuEby0GOQzqMnE5NHyrfMbTYQHuzTiw\nvQ3rZvQ5Q+/RrpucWjYzknbQMDEVQykHqKRkEzSZBMPL4NBtdhC7270EYI+3G2Wt2XPIOQGl92Vl\nIzh0311xljdOx3rLQg3MtFCuDS/3DYmHjRM4ZLIRzX6amezoXv+u6W1YOwelFM9/IPnyY8tWrUy8\nTKfwi4jaGHSniM8K0tFADJpm3hGi4l988i4xKh6eXPP4xYKLkxue38z4Jz/5kBg0ugo0s45Va4h1\n2snHepHAWhcIo2ceMHRy+ksmyaZMiQdRLBNEJaaP2UhfDSK3VVWgrD2zqmdRdny4eMZ6KMB4jt2G\n1VCytC3nxZptcBw7YZOExr0yReZ11Bu/bpTI5qf0oJE5NHP45hVS1tEIv8g9JY19h2mvc8AemnoO\nO+Blrw4AItgxrMl7qQwOjZLWppSDmjAWBZC+sNec6TVHumOhEo2WZ3+c2o/vTTaheRWLaDziv0nJ\niW/c2klfLMVMWQu//T0+/vdqQiXHYdMbMWRtoD9Jky+ICnktFZE47oE7RX8e6COYjRYWQC/XhGQS\nQyNyVN3J4UxFAYHEpyr/ER5sqyguZb12Z4locs9xsvmONtEfp6nvMBuYLVuGQfP9iydcVCtq08tB\nLRb4aBiSRpOISUJeNIlq1v/SH+qvU2/cunlFKWPEc6ipiPN6suoYZnvgUC2sj2QP+8TYc1LcbWU+\nK2l5kiGOr+n9fjRJWG/1HJP3VCMoneWsJ9WGU7fm3K44NaspfGOmI07t9jiBNLEWX7XvuV1xMkD6\n6o/nL6vehrXzWZVmNX6uUFFi68X7MJ9pooAwkNeCkmREfxRkz7xRO2AwA4gj6zEZMbZPWtizIPcX\n85qafImGnJCYr5F2y5RS1p7oCQrQvVwHuxNFf5IY5hF3seX7955idaQyntr4HPSyzX5niTY5vl0+\n5aPmhB++/mP4L6yv5ZSklJoppRbjx8AfAv8K+D+Af5Rv9o+A//3r/J5fSo2x9jliOtaWoTb4WhpZ\nyHr8iao4Hq7GF/zUxD6jA2RvImUkaUabhLFBoqPLgbIYqMueZd3KW9VxUm05q9ZcVCvulTdcFDeT\nvOPc3nDfXXFhb8QoVm841kK/rpSnUn5iE5XaU5gBNzKJTJj8iIzJ1P59hP0111u1bvbKXFxg13ra\nX227gq6z6CKgikA166mXLU3ZEzcWVQUwiY8fH+Ns4Fk3o3GeF13Dk/UMHzQhago7oLV0qLruSQ9b\nYhWJlZgrmpXB3Ji8xiA0kf6hpz+K9EtpksWlmDZqL67+I/06KXDXmuK5pnhm0JeO+HGD3SoxYFtl\nang2ug11JAVNSggNEs0mFKQ3ACB6q9aNFiBWGS0msaVjqMUg1tdyWBtlrDvJGPCqKdQtAHr6fGQT\njRuike1o5UBGGVBVwNTyVtSepumY1x3HzZbzesXD+pqH1TX3ipsDcGihWxotzCFNpll/xuXCkHaR\n90hM7EtR06+53qq1k0v/1q/TXiTSTEzpuXGouEvcSGVEd4pQR+LRQDHvGVbiNaZUwrmBX38g+8A/\n/O6fcVGvUSpRFgOnR2vmc7kW1cct5UlLtewwdYBjTzHvMUbAROdEEiSDj0RqgvSnMgrbtkiEWRAq\nfxWgiDL1rQJV02NtYFb0PGyuuShu+M3Fz3mnvMKpQEDzab/ked9QGxl2tNER7Zuxe3ob1o0aY6Ml\n/pRkDLEwhNIQ6pxWVmYwOjOHot0li91mQU9f25/M7/sMvTJqeowclvUQS3lLZYQyYmoBCud1x2m9\n4X5zzf3yhofFJQ/sFffMDUe6Y6YjldKYDEjrV4AZBjVJQeRNwj30K3qOeo3JiW/k2olJAKJfABKZ\n99/lZ//JEf4ooTIDOVo5eLUXkWEZhOmjYJgnwtGAO+pIRSTZSGzEiNydtoQmSm8YQYEyy+UT+KO4\nM6BVYpYfyiRDEyNrcfMwsb2X8MeBYZZo7wfCMjAso0jZ6iC/66ijWbYcN1t+cP8xF9VqGrhaFeiC\nneKmT4s1temxOnA9lAzDG7I55g1dN68okbMWBz6uIzjkm10KdKiTxI9X8ryGIu3Zd2T/lvHjPfB6\nfNtnPO6UHdzyIErTfkhkrQgbtoi4YpikZefFmjMn0rIzsxJAWot83k1m+FL73eTz+hHsJGbT7O41\n9Z23Ze18Vg0nDZt3JGijuJaLjunSbh3VAsZEB927nmERUUmRXBRfvSaziwYZXIVM/CiuEm6VxGx6\nJNR3uyRG0+W3VtaOpJWBCvnGCtpTxebhTiq5vadoz8V3TR33vHd2yXm14mF9xXm55r3qBfeKax73\nC66GhqtQ8yfb9/h/V9/mo83xW+F79nVPffeBP1LyorHA/5JS+mOl1P8D/K9Kqf8a+Cnwn3/N3/P1\nSyuJmHaWWNk99tAe7XrcON3aNE0msNMXbt13Zg2hE3r0FrJiPm11xFkBbQoj8YoAje1ZuG7yGDoy\nEvHa6E5c9bWfgCCnBpGV5ZUtzUhP37c6Umi5/zHBbJSa6aQgM4im/+v1T/PfnnWzV8//8Dv4ex4X\nFUPSxKiJvcEUAZKS2OZZSz8YiW0dNGbhJ9bYD5+eU7qBIcrPlk7WQkoKoxJkg/LOWlKjSGsZu8RK\neLjTwVAlCIowFzlZtEAlrALT7iYu05TOy0XTdGNcrGKYJYmo1olUR6LPU9fsc7OctcxMzypUHNkt\nqX4j+JBv17rJDKJUWkJlCSMgvSdlTZYdTL83JctDtr3Y6cP3Mj27xRSaQOpdQqLWcsgHJknZzPUc\nlVvOy3WWd6w4MmIQe2w2LFVHoweavQShV7KHsgeRR5hEI4soJC3SszcLJHoz144etRnxpb58+TvH\nDLMovj8JlB99OxLDMqI6Taiz18agCLnv+I3jKmpcMbAdHEZH/s3NOT4avnX6gvNqTRssK18ydx1O\nR7pg0Cpx05ZUbuCoahminjyC1n2BOkrEqOl7Q6yzr9lcvM3GUiZfLI88zbxjUbec1RsuqhWFHgho\nyqxTaXTPkd2wChX3i2t8Mvx2/REP7BX/ePkPeENciN7MdXOr1HjoVwqMIjlNKDRDqbIptXgOpT1w\n6Av5ruwzg/aIjQcStLy3OEhJHFmLRcSWA0U5MK8yOFTf8LC84mFxxX13yYW9zge1OB3UnDKElAiv\nFHNIHSQJJbUHUAt7+g1Iun/j1k76Ih5E2hCPZnvs1kj5xAqQo2T/4C6NxEWTSCc92iTCYFBFJEWF\nrTzDdYG/rOQAp8UXSOUY+lAl2b+sNf5IvIeSk/sb/UD64xxDrUG3GmwkNqB6hWo1LCX8QyVFvWhp\nSk/tPN9avGBmO4ZouPI1vbHUuscnTW08WkUa09NGx9x0vOgbYnxzACLewHXzUo1rSGuS1hKMYxTB\njYbieeA+Bm/cfngnJEXtfT5+LzOBpm+lSc5zm1md9L4ZtXxrZLKqKuCqgXktw/iLasXFOAgzKxa6\nz4D0IUM6voIGNLKJYkoTcBQRk/w+aXyyDMkw+Z69vnrz185nlP6dH/CT/7AhFgE7CLswVLB5iPga\nJoW71rInAsxzt4uqj0pYjNmPCDLxKDCtw0ErbJsk7bKEpJWAiU6M7wGKTcJnKw+zld4T3C7pU/fI\nOi+hXyb8fY+tBi5Objir1hy7LSeZGd2Yjvfdcxa65UftPWIyHNktWJjbbpLKvcn1tQCilNJfAr/7\niq8/A/7h17nvu/rm1t26uauvUnfr5q6+at2tnbv6KnW3bu7qq9bd2rmrr1J36+auvmrdrZ27+mXW\n69eN/HXXaBpbFKiiIJYFscpyj0oRKkWoR/qioNEHKWUH98Uhyp0n9pO0bC+23mYD6sIOFEYSySSy\nXqanS9dOsfUL0zI3ErdYKU+lexyBSntmqsdl/aImEVEEFJ6EUwOV9ixMy9K1DBmmjyiGqHGZuRKj\nlsmuenNkZm9b2W+/z/WHGmUjSsu0TOuIbXoGb3M4SGLbO7ZrMZtWJlLXPYUdhNUVNYUN9K0lRkXl\nBmJS3JuvuGxrNr2jHywxalKvxbehCsRoISrclYxI/DJI1r2SKZwajHgWDeT4WEHGTZtTRxDX/1CI\nqSTIWleDIjSAVzKhGddzr2mcJ6K4GmoCmmTvFs4XLqVRaox+NSRnCFleJnJWCE0Sk7r9MfeY2gEH\nErP9pKDJS2ykVN9iDk0sRhOwmcVYuQGAWdGzKMQE/7xcce5W3HPXOa1szUK3LJSnUpFKQaX0njH1\nq0uriE6RkG2qR7Nq1OHPvAHT/DeyzNESgLjZkLpdvrP5ze9z874mFQOqDFAF0pUT1o5N6FYT6+wt\n5yKu9hiTYAExKPG+U4mrtuLebEVjex5U16yHkmO34dI3nBRbnA4UOrAZCtpgZX0ULVZJQqbPF4yh\nNqyGkm6QLcNVV7HtHSFqfGmENeCCJKiVMK87LmYrzitpQO9WlxgVcSpwatYAPA8zfLR8q3jKJpbc\nxIpTs+JxWLxpiZtvTclUVBPNKC1Tk8Rs2uOMXmbTD43vx5F9OmQtwo5JNE3+OfAAQaVdSqJJKCd7\nD1sOVJWkB51U24k99K3yGQ/cFadmxYXecGYSpTI4DE6NTsfCDIrZD2TfH0Ss8jmQmY2MxsMH5K7x\nHFSMv9BYwn7wPn/6Xy1RMWJahb6RiHp3De2FmL/aGyXyUgtpa0kqYReeISiUi4TeyFqIO2aZvtKS\nLqYgLCKqDnBTEq2EwuhePIhik1MUTcJstDADTnvso5Iwyz5nLuEyM03rSOUGGue519xQ6kH2zCZh\nVUCrxCqUlDoQk5Z01rxOztwan8zEsr2rL1FZRi+yU0V0imgVocgson0J2B5RCHa9ZUyZul37Ko40\nqh5etScaDamzGT4gcsY6UFSeWb1jLd4vr3lYCGPx1KxYqIEmMxYlnVX2Ljqb4MMoM9v97kDKzEYI\niXwW07TJ0QcjDP27lvOVKhkjyoYqwkZjW5GV+WXCbBTlc4VfyG1VkD10cvmaoyBVHnpNrCJq0NiN\nJB4OTaJF7VhCK2GuJZ2IxXge2v0d1WWkPdH4pbCIQi1rTEXxI/ILkVOHJlEvW07nG95bXHLktsSk\n+fPVfd6tLrlvrwgofu6PaUxPSJqnfk4XrYR0vAXoy1vwJ371UtaKTtYYcdpvKmLjGGaWUCmGvajX\nSRM7+nncblD7oFEGhdAcAEOjrMyZQOkGShOorKfK4FBtPDMrh4B5Bocak0Eh1YsBdZZlRDQ+GVos\nMEBO6wCmOEWnAgvdcm5vACj0wPO+QVNLZHAwKJUYAocSs7v60vWX/+X7dL/WSRpdrqE3GCeJKfPj\nLd4bPjh9zl+lU9QMYlRT4ks3yHPReYvRkcLGSc4xLzpq5wlRc3nd7K6mJhHXFtUaUh3wp3LQV15j\nrjWmzY7+WkCj5qeWoZYDgArSzGIBJDC9RNOqLD1Tg5rSQnSfU4mCQgVNnAXawbIaCha25dLXdwDR\nFyxljJjhFw5VS88JTTF5D4Vyb/Pk9jY9GWBEqV0S8QRAMyUpHgBD5rAHaS2G+NaKrLWwgTKb4AMs\nipYj13LsNi+llTW6Y6YGKhWnuFcYqdUvS8WcgkoFoMfoSJscLU7Aofz/GOJdu/lFZeR1pZQ62Cev\nv3NMfyLPcYoKBo0OivKFor2nBBwyEt+rVCIGQ1l2dBtHiorzixt+bfmce5VcG/poWdqW3539jDY5\nfqzOaUyPj4aZ6Xjaz+mCFZDIdhy7DVolfObtH9ktV0PNeijRKvKsmLEZCjZepKdjTD3Asmx5WF9j\ndeCd8oqjvDNrdE+hpIdVynNmV6xjySf+mJg0N6Hix/5C7s/drZwvVVqJpFUpsFoOa27nPRSdDAii\nZe+UxisPZ1PdBoemoIvxc3b9Sed+lI3wjZUDd12JGf5xlniM4ND7xTMuzA1nuuNIKxa6wGIwe8Cy\nT8DnScyUkoOjItvkK3ridPi/q1fUL5KYKUWyBrsVAEh78TVMGlbfSoRZxGxFFjb6n4HsSWIGh4yN\nBK9ZXqxYrytCLwPK6EY5LZCU+HR2TOlEYRZ2SZ75drFOpDKgdWI49dhmoGk6QtRcLFakpCjNwFm1\npjQD58WKxvRoEptYcGS2vBgaMHBiN/hkiEmx2suYPnEb6vLNMql+K0ppAaSNSMzi6CF04BeUpvPT\nhLPcnr1/XqtXu/cT1jv2nvw+umxInQEiVQVJLas7juuWs2rNvcnv7JJ75oZj3bPQikYJMjDud0j6\nlbLWUWLmMzgUGSVmGp80fTJijh+BmETKeVe7Gvt6enU/17/9A378nx4xzAN6ZVCDgDBmqyTFlcNg\nhVCJp1kqogwjoiK1RiSsWm5n15qhTsRSvIb8jMnbUEVJJUsWQrFbgKEEFRWmTwyNIuUEPu1Fni3B\nMolhlojLYfLUq40kU89sx3ebT4lJ8n0/6s84sWteDDN+tDln5UsKE5iZt6PffHMBIpVjpqtS0j1m\nDaEpCTMnk/xKwKGw18xGJHqa1o+l0yE4ZA6n9cYGbD6QuZxMVtqB2noq45nZfvIaanJOZ2Pk45nu\ncDmdrFAS0hrGWIe0//QMBx5EIAewhRZNY6U9TsmLLyZNHw8zYHQ+TE5118C+cClXYFpInUYv5DEO\nnYFeM7QJVUaZnCvQKrGoOzpvCVri4hd1Kxp3K4c5kqKwgSPXsu4LrruK67ZkyCASgGkGwtrJWjvt\nSPljELAg2kSaMW2m3KWhPxLzR90L8h6TRE+7VQaLrPgyRAM6iJl1KFNG3NXUXLGRzlv6aLn0NQDR\nma/naP+rUsag6krAoVlNWFb4uZ1SPcKUlph7Sm40Oz/8hELAgklfP5pPK3YTen3oM2QySO3MrgfV\n1lNbz9wJKH3stiztliO75chsMmtoK4b3+WA1bnz6tJvaiw/24S6uUgqtA1UKrFPEHJgySt8a+9FU\nr9Ew9k2teHkFQLr12LTHhqGJzM42DIPG95YYFP2xIjYBVQa0TVycXnO5ahi8oe8trhpICW62Fc+K\nGQvXUhvPO+Ul99w177oXPBqO+K3mYy5Dg08GQ8KpgE9GpukqcK+4plIDbb4GnZo13hmeDgs2oWBp\nWwB8MmyDY4iGF31NTJrG9pmhdjPdN8BSbwF4YK9wGShq4xkX9oY+WR73C37U3uM71WOG6g4g+sKl\nstfZGDU9cwyNYShlmj8Zu04BHHmtZZ+e3crbsYz3h2L7h7GRsfgSUG3SBAwVRaAuhCk9LzuOipaz\ncs15ueJecb2Ll1aeUnHAUgxpHJAlfArTwWwsMcsfB2VJEttz5L0mUWQPommQc7fPOaz4+Slm5vyc\nH/8X94kmUVzKEGr9rQF7bSavj6TFgBwFyeUAFGC+aAlBo3ViedpyVLZcVx1X2wqAdQLroqQhBkX6\ntJRUIoV4xtQDKSrmx9InZhm02faOh8trumDpg5mSf7eDY+FaKjMQUXyrfk6pBhrT4ZPhyG7YhBKn\nAu+WLzg1K9pU8NQveOZnBDSP+iVnbs2y6j7rIbmrzyqtwBiS1UQr7KEJGBrBocwgGo1cp/nqCBil\nHat9SmvdB6BfoeIYQeuk897ISYKeKjJrMTOHjuuWi3rFvUrAoXfdC+6ZG05Ny7GGRjlKJde3SNoN\nt5D9zuhFtD8eG8GhnfdQHuQnhw9GQiTeKOvFt6NUCOQtAaZVNI8UupeUMLMVw+ntUUT14sNIRHpQ\nQozoAD33xNZMXlVxLaljMv3OS1DDMFMUVwkVxZhaDengOYvZN2v0bh0j70e2bGgS6n7HyWLDab2h\nMp5CD8zymf575SN+1p/hVODIbHBq4GlacFGsqI3n0+2SlS/finXyDQaI9C76ta6II3uoNgyV3hk3\n7knLxoaG3ucyspOW7YFD+8wh50RKVrphOphVxtNYYQwtbTuxhUaAaCcn8+jcgozK4BAS1erRExgE\nHBhVj5+PhtbHaS2pMEnTR8tmcNNEF4SdIBu/u433ly31Gx/SnidwidgbQpT4esoAXmPLga4t0Cbw\nV89PqQuPNZEwCHXZqETpRGrYuJ6rrsLl1LLfPf+En65O6INBF57OW7SOrDYVNANhmxteITHBAHFj\nhUHgIsWnbmLAyTTPYIyg4EkJc0iNDS6T0fxRxG60JJ0l0BtNnAdSI5RwZRI+yKTNKKFph/LzhEZ3\nNZaA0hVpVhOXNX7u8HMxww9VZg/tSVnHsZoMwtUEEsEem9FmxqJNB2whY8OUUuhMBqh1PACGlq5l\nkQ/zC9NmM/ytyMn0NvchoeSPJdRpue76fPgypCk5SBI9oNSaKkV0HEBDH/MUTYFGY1QUk+M7zvWX\nKnN2ip9BagIpkeWsiWgjwxJwkbS2BOC5naF1Yj5r8cHQtg4FHDcr/vbpR5y7G2LSHBlh8VyGhgdW\nQKmQ9MTiAWgzz3o0V3RqoE3yNZ8sLg08dIHgNF10tMnSRUewmhdDQ0RRG89qKDBE3nGXEwOpUp5T\ns5pk03909W+xDY4PqmeUWhi094obroaan3Znn89suauDUkajypJYlYRlgV/Yg+CN6ZCWGT87JVkS\n/EQpptQymBiLuwSzPVBon0Gd90KjpNW5QFV46syKBTgut5wWOVra3XBhbyaD2EpFirwfkcOZng5p\nIcmBLe4D1dksf7wSxSknWyb84886Fe6Y0p9RseswP/74M7+vCodfiMyrXUgwxn/7D/4v/sd/+h9g\nrg3YRNSZ/Qo5RTNRHrU8XNwQUSxcSx8tVkVmricB/WBh2WJ1ZK0LnAtshhl65ol5+FXUnkWzY2nP\nip53Z5cM0WB1YIiGhWvZBkepA100tMExJM1JseGhuyTs7VJOzYouOo7sVvbOKlHRTyx9Q8TpRBct\ny7LlDiL6kqWNBHDUFj/LQ/ds6PvK1FV2vYcoex2lch/au/3U+vdlrezuZ+pL496okCh7VwjCMK87\njmthLAo4JFLWhd7SaI8j7UDmvb4x9pzbNcrNAgIMyQBNZ2mZpcfQRscQtfyJd6D0YaXblLHDiv/+\n3+Fn/52n/TSgOhlSdydyVvVLSSyza4VdaTGuNzlgxyR0GZgtcl/ZlvhBC+M6qGmfrX0elK+FdxFK\n8AtF6OVzNcpgGdPvFEMjKoziSuSv3bHcR6gT4VjO+uOZTqtEHy1dtLxbvuCH3QMWuqWNjqvQ0EaH\nUZHroebfXJ9jdeQPzn7MnzTv/c08/l+jvrEAkdJKfEByclkqc+xrmbX5+y77++CQ2VvMe5TGfZDo\ns8ChxsnBrLH9AWtI0sl65qalUgIQFSocgEMgm3CQjfuYVNYrQ1Aar+yUaDaCRFpFqvFzBd5Ynps5\nte5lqrJnOKRN3P0Pv8JllkvitiX5L0bxsx9+wM/+41PCYkAXgRSzv4cRsEhVgaIcaLcFSkFdeI6q\nllVf0Gefoq231G7guNpSGc+yaOmDzeuk46Je4XKjue4qNt5Nmng9D/jLShI8ruWwpmYDaWtBi8Ss\neCFU3xHMVIMSSVl+dUeXNbNFwgYl6R/rQthDOXK2r5TEo4Mg5lEkIwsrG7Lo7uChL1LKGCgccV7i\nF3JY840kl+3YQ+MBLFOis/dQ2mMPoW6BQy6ibdxjLMYsVTz0ORvlrAvXsrQtJ3bDwowA0Q4Y2qUk\nBlyOpgdhJ468H5+Xg7BAZKNUqAwOKYtTRg5p9PgYKAh4ZaZ1KElmd5ulL10PLhhmssnZrktSUNBn\nWofN14tKPM5i0KSYWA8lRTlgbWAYDL9x8ikAj/slP6h/zkx3LMyWB+aagOJ5mPPAXWaPO9lY34SK\nTSyplCegmKnAmREPoXUs2aSSPpnpOlXiibrl6bCYesXcdBzbDfeKa2G4mi3Pw3xiERUELmPN3539\nFU4N/Kw/w+RroE+GJlOv9z0B7uoXlNbgLLFx+Lmln+/krDv2UE4a29sDTAevvIFPScasE9No7FH7\nXme32Iuj52JhBRyaFz1HxZYjJz3ntFhzYjecuxuOzVr8zvQ2y1m5lR4Up0NaIBFvHbRGcGj0KPJJ\npK6kSKHG9CHxuVLj/3p3WHupUnj16FrPZnz8n33AcOwhKMzCE7aG//mP/yP+7X/nz/hn/9/30duc\nnrrwhI0MypSJKAWlHTgr19TGsw2Od6tL/mJ1j0UhB7hH66VYHkTNvOrwx4a68ujllpgU7x5dEaLm\nvdklIPKd31v+lC46Su35WXvKmVvTJcu5XfFRf8LH22Pul9c86edsYsk77gULs2UdSwyJ++4KTWSm\ne2F5JCseRCrxbvmCp37BKpRUxt8BRF+mlEZZQyysDN1rLd6KxR6Asy9HvX32GPtJ3EmrJ3+z2/1p\n/z50ZlNnprty8cDnDOCk2nJerbhf3nCvuOa+veKevTnoO7d7zvjxfml27KF9WVmbjMjK0BM45JMl\nJGG23A03vlwVP32O/afvYu6LpNW0ooJY/3qPfepwK6ieyBBjmCOD8DJOe6LSBmZFT9s79EzWQLd1\nxJDw3m4AACAASURBVFZjWi1MoCx/1L0AP0MFaSb3KSwi+VtGppDu5GduPhCASQ3ZD+kkMDvZUhce\npyN9MEBBTMJgvBoaKu15YK+4DA2bWNBGx8ftMR+tjyl04H5zzZ/d3Kf6+Zu/yfnGAkTAzkQtv42N\nK2Vnw2Qgmr3I18mcUaIzx0rjFFyPm6Pdxmj0G2qcxEc3GRga/YYa3WcJWWZ/5AlHL3uZqQKakBQR\nnQEiuZ0h0io3AUpjtL1WEZNz/AwJrRKaiFMDVkdKM0gUe26EOsvkftXNP8PNzZfaNP7sf6ip3BNW\nTxeMPjEJhdJ5lBAVfW8oq57ChulAbFSiyD4M1ogXTB8MD+srSi3daEiGe+4GQ+S0WLMeSta+wA8G\nZwLDoAnBQJGb4Ti1U5lOuXLZByARipQNqmGYJ1hLvP32XqR8rrFbiKU8+fZRMV10i0tNqDNQcWMP\nJszAFHU9/uxd/YKaNPnZB2TfqHGUbOwDzrdAaJnS58d6BKxHXw8TsS5M/kK3DfArM0x9Z2nbzBja\nAUQz3dGobuojI9AcUQdsQ+CAuWhIBCIum5ZoJZspjUVjMChcPpi5EV5KTKCT/JK7XdMXrWFZ4edM\nG2XtIilfd1wxoBR4b4iDJvQGXQh7IwTNMBiqyvOsa/it+cd8WDyeQB4B+iLrWE3DhhHsGQMPxkPU\nUrcc6w1V3jnNjOdZjNzEmkp5ZlqOU8+GOSEbv86zv55m513kk+XYbFjoLYbEOoln0YW5nr73ZFji\nkxiRXA3in/c2GDi+MaUUqXCExtHPDX6W2YolL/WfSboBB7IPodKnHTA9HsJe4beodULvAUOjpHVZ\nthwXW06LNWdOjMhP7ZpTu8qsoa1ES6uBhZZoaZGMjazphCfg02dz782exMwpg08i69Ypiex1lDTe\nAdOvrs/b+4QgfScocJE4KBg0qMQ//+e/DoVE0Acgei1gdVKk3qDnHcfFhpnp6aLl9xY/5f3iGe8V\nz/moP6XRPe9Wl/zp9QN+7fQ5msRZvWHtC6wWWeD3l48nhj3AiVtP7EefLH+w+BGXYcZVqDkyaza2\n4OdqyTYW/Nb858JEzNe2mel5NBzxm+XHbGLJsd7SJss/236H75aPuO+uMEReDDMAFrbj6q/3kf/m\nlTHEwjDUKnuzqImxiEovgUT7bKBEbj9pt6c9MMDfr5HdODIYbURZkbRaN9BUPcuq47gUaeIYZ/+w\nuJoMqeUa5GkUuH3WYtpjLe5F2e/Xq2RlfTambqOT98kxBLNjEMW3QD/0JpQ2hOM5m3ciYRYpnhqS\nkutW9eNCvH4ctGdJAMhZRDUDaWPBJsraY03gui2py54hGIyOhKAYjiD5ArySgJ6gprWoe1C9+BKx\nR3JX+VyuIsKUNOCrzFKdB9yyZ1G3VHYQj8ZoeLe54tht+LXyCX2yxKT4v2++y9K2k9/Z6DmkVGLl\nSwkFeTC8rkf9C9evxjZsBIm0gETRyNtknravsx9BojzN390HU1KQ1rvN0Sgrm7meue2EOZSBocb0\nlNkbSJg/OwkZCtq0A4NiUvn9jj0UUJOHg1MDVZKLX5PlaUUGh4KShLMxJabSoon02kwHP60jw90Z\n/0tPFM0/PuHJ3/LgEsrIZDK2mtDL86hdFPAtKXSWjTkdGLL5rFYC3o3MMqOE0nzm1hxZuaAFNF20\nlGbgqNiy8Y62FxaR1onkxLvI9/JyHbaWNMi0N5YJ5RV2I6aSoRE/oVACShGbSNgqTKcwW4RhlBF1\nyMh5Ik9kkqx/nRgGzU1f4WtDqQfCnWnsL66cmDj2mgmYvj0RA/YNYidAegKNdgc1TEJb8fYQtuKu\n51R5TVVmYGZ76gxMN7o/SEYcD/O7nrHbvEhKkPxh4db7sQQgUoQ8Q5OelBgILyWcmQwdoeLB77mr\nL15+6RiaNHmOqcw0i61hUFBWPVrLMGGUQxsTOWq23GQvsxtfEZNmqVvWscRjeGCuqZQk+WgVOdUt\nT2JDGwS0WceSF8OMMoNC+x5SR9oDIlOTgURkE0uMihzZDV20XA3iV3bl6906zGy1m1hP7FdN5Fmc\ncxNrDBGtIg6msIVPuuNfeabrlyq921N8Jkt477KXcaHpk/1k07HnYBIqsxd13vMonbBZ1mp1PAji\naGzPUdFy5Lac2A2nVgCio+xzJib4HjeNyPKBLIFXQaQbWU4WEbBoNI79PO6qURncVhqfIub2De6A\n6ZdKfY7NQH+SZDqfwFUD/dqKMWyRMCvNsBSvoHEB6SKgtAxM2+CY257vzx7xfvEMTeTC3nATqyxt\nFibREDU/257wnfoFq1Dy8eaYi2rF9+pPWegtRe47I8N+qVv6ZLiMDYZIo3s+6s9YmJaH1TXb4AhJ\nc2GvWceSRnUszIYH9opKBbwaeBYbDIn33XOMinzqjyZLhvVQUhv/N/LYf1NKaQVGk5zOZvji6Tom\nlh3seUZQemQH7YFEcmf53HXLH233y3ZgtdrzOnNOvM6WlRhRn5fScy6KG+65ax5YAYiWqqPRA41K\nuFGmqtQUwPFZwBCMsrPDxLJXgUObWBCi2tGM7uoLlb13zqd/d4nZgvKGUItxtF0rGWo78Mskfgcq\noTqNags49igrQJBRibPZho134AZ8EGuPGAxhGYhOi+WGzZKyAHYj5yElGOFObWGRa1JOS9Me/ElE\nzz1N3XNvuZp62Mz1LF3Le/UL5qZloVuehTkf9afMTcf1UOVQj8SVr1i4ljY43qmvKbXn3yzuvbbH\n/YvWrwZARG5aej/Om0NG0a3m9dJ0f2QP6STxmSZOk7OROTTKOko9ZICoy75BL0stRqQx5LQySVfQ\n0+Q1ZPbPOBEzKtJmgCigiVoDXd5chwMgyalAqQcGvdsuGRMZXmX69k0sbcSb4JdAL7dr2U3byhOD\nkvjWhEzZbKIoPUrJekhJETLjptDCJopJYXWkMp4j12JV4KLYMDctp2bN02HBuVuh3e5vXfuSEDVD\nrxkGjbWRrnUytYO8NpXIknISVoyK9KAjbS3qxqCCsInmP7Kg9hpgljhpL/cR6pRBI1kYSitSUMSo\nWfuCm6GiLFYHTv939RXq1n7ndiW1B0iPTCKTUFZSYZwTYKgu/EHPGWWs4nHWS9/RnYDIe4xDEFnr\nvkRVQJ88TUt6DyB6GfSRJEUyMyjiU2YUqd0Ga4yZDuNVFw5S/+7q5bptTg2InHOcdNkomvqETE11\npN0URG8w1YBzEpDQdZazsw1n9YYfPT3D6cBNqPh4OOF994x7aoVTkb/0pzS644FZ0yYz6eOP9Raf\nPYVA1oZTcYq5B2j0wDoJ2EMSQCkkzbHZ8FQtuPI1R27LzPREFE/9gs64PK1PPPJnVFoGHAAP7KWA\nREEYBM8zG2lmurt+82XrC3gLysSewz3AxCrK+x+TwO4Yizp7nI2gUGGDgEN2mJiLje2Z256FbTm1\n62yAL2DiwmyZqZ6Z6ilVoFARo7KvhxIjWH0QKc0UI80eSPSq0plaoPNBz5MZRtlj5q71fLka/uA3\nCPd6iJJG5rdOBhcG6vMNWxooIvWsp90Uwi4CnBNZa6EDhR74sHjCdagoVODCXvP36hUfDyc5kCWP\nFVTKA9SBG1cxMwImL03Lx/4EgAfqChScmg03saDInps/689oTMf3ykcEFI/7JVeh5p65mfa/C+V5\n4Dp8gidphlOBf9m+z9+r/5JPhhPxW9NwPVQs7ZZtLOQ1dCdJ/HI1vmjT3tteTT3n4It7/WbfzywD\nQC+95POQdQQirQ1UeUg2LzqOiy3n5WpiLYrX2TXHZs1SdZQq4EgTeDwC0/Ln32YLvQokOkwsuw0O\ntdHRjR5Ekbs19CUrOMUwj6gB1KBoPhEfq1CAiiNQlIfffTaevnLE2UBz1lHaAU2icR6nAyFqGue5\nKTwrW5HmCn9VknKCWfnUMDSJpGWoTlI7u4eopqTnMIswGzg5XdEUHq0SR0VLGyxRKfpg+N7xYyrt\nuZ99HR/7JQsjyeJPhwVXpiagqU3Po3bJT5+e0Niem74Sf9k3vL7ZAJHSEiOcJR8jGBRHuvU+YDK+\nHzdQ+6/xvFlXekwMilgT9nw/hNI6HtLKTNe/DQ6NYBAwgUEHb9FMbKKJ+ZOboxjqFTS6392PAZMi\nYa+jGpV2AJExk3zEqHSA7H+jK352NO6XKeUK/ELo1kNrd7IyBaoKGBeZVT2tz95RUVMXHT4Yhnyw\nKnTA6MgQhc0lzvZb3iueYUg8HRY0uufD8jHPhjkvhgZnshwxKspyYF519GUvxtVADBrVROZNx4tH\nS1RvJPbxSYnKyQ4jcyUWYlStPRL/iKz9UIDphTIeU8JdS+ONZSJYRfSaIe5YUHex01+ilCLlx/8l\n7f1YCVRSMjkbv7SnvxdT6oi2CWPjxBxqnKQi7htQH5ntQSriuBEXGdGOMTQyP0ZGEOyAoZG92KeX\nL1rjRDcg5q8+RTwpm8Tu+ZxlmavJbKjxoAZ3h7UvXEpRf7zm5E+WXH3X0b+XJpAoDYoBiy4Cxojf\n0AgOAfzkxYlsjkrPcbHlvrvigb2kUp6F9vRJ8237gk2yPAoirai0p8LzPMwJSVFqTxcd61iKr5AW\ncLFNerq2jKbWASXT01Dgk8GqyPO+YYiGh9UVlfY8H2a00XFqVwT0JDdxKvBRf8aR2WQGpaPSnitf\nsw0FoXo9D/83pW5f58eD2ksg0chU3AOktRNA2rnhABRyez5nRWYtjgD1yBDZ9zkDkbXOlKdUgSqD\nQxODaM/8df+KfTCAz0DSZ22lNSqziA6Th+7qy9eT361hFaAOaJMInZn8NwZvRFLmFc4GtkEWUBo0\nwSpmtec7syfMTcuZWfGBa/lhf59Z9tz8wD1hHUuOzZonw5ITu6HSnmAUP1WnfFg/4YPi6SR9BXhg\nrjnVPZ8EkZ8udMvH/QkBzTvuBc/CHJ8MJ27NCz+jTY77ZkWlIh7FQmnWRD60V3gUf9yd8LcrQ6M6\nfrf5CX/eviMG1YUMdXXZENv29Tz4b3OlL3F9n9hCO5aishmEtgL+3B6mayXnLq3ErmF/QHbkWo7d\nhnO3mhioxzmh9VhvafRApXLQxt45ye/1ic9iKEZ28rL9xDKfDB6zBxQVbEJBCKNJNXcg0X4phS5k\n8BTbXadXZYn/8AHbBykn9YJulQR05CF2MkmO5EFhVjL4jlUeSDbSJ2or+x2AUg+86GuMjjSu5/58\nxZP1nBsT6TcFyWv6IyU+RLPst+ciqZK/S7WGVAfmJxtmZU9hAveam2nov/IllRmYu4571Yr3iud8\n6o9YmpbHw5K5afmweIxPlqvQ0JieTSi49DV9sHxw+py/eHbBcd2iy1/OOfWvs765AJHSIvPQmmQ1\nyaj8NjKJXiH5QBDEA7Do4D6zekRHjE6yYbIDhQ5YLZuoUeo1Ho7EW2h3EJu8GaI9AIe6aAVEQInZ\n2V6NXh5WBVrj5GeMQROplM8Nay+ZIXseWRUotNyf1vFXgz30S6z0d36d9jxh6wFXDMSo6NaFAIU2\noXQkJhgGeU6PZlsU0AVLF+Rry5zm0kdDbTxnbk2pPTFpCu35dvEUrSKaSEBz6RsBkrKcrWudGGDr\nSMqATUqK0Fle3JTolUEFRTzx0BlUr1BBoiBNP+rBmcxKo02ogIBFDkyUyPuhTEIld0mkZlGx7gp8\nNLKRK1/DE/A21igty0zFQ5bi3u2STCvVAZVapK0jcD31GhOxJlLu+Q3NbMfMZAP8W+BQpYSab15x\nWIpJ543PISgUc2JiSC9vl8bvgYBMLRKF3qaAU+klVrUAUPnf3EOl092m6ReWvX+P1bsz+iMlr8Wg\niNHAoPKkKzOKgH5TQNNzutzQect6W2BMmlQ1Ac2zbBB9EyOPwhJN5MysOdNbfNKsk+NZmE9GrjdR\nZGKf+mPOzIpzI5OxLq+lgjjF+oak2cSCq6ERI/sk0ugn7ZzVUHJZNqxDwWmx4ZmfsQrllG50Udxw\n4tYENDPdTdetUnue6gWx+Jt/7N/2Snty1v06jLK//TP5RDOyFV3EFQNV4ScJWWkGnAlUxk8+Z6Oc\n9TYwNMvsxREYqFTAqTgd0sYa+8OONfTy36Y/Z79ilCZkryKdWUSjF9Fdffkyv/E9Vt+KpNmAtpHQ\nyv4lKUSmvnEiOXQyHFVKSNq6CFSVpyl7QtJUauAde8OFVlTqEwKKH/b3ODbC7ghJ0yfLwrQ4NfB8\nmPNufUmpPe+aFadaUykxqf6u1TyN8I7Z8K/9OU+GJW10xKT5k+17fKt4RqUGbkLFuVvR6I5CRW6S\n5R0TcErzdDAstOd5qPj9+V9yE2s0kZsgfe5Bec0mFMJQqUq4A4i+ev2il944LBvN7p30G5vB6NLK\neWoMbIFx6JRQeVBe6DCpNeam48RtODLbyecMOPA6a9QhMLTPUhwrwGcyFce9Usxs69Gjr83mw20q\n2MRCzm+DoQjcgUO3S2kwL0P8+jvf5qP/fmD4kQy11aAwHYRKpF/9UWb1IAqz5GS/rHvFMA/ErWV5\nei3DsPKaVSgZouGiWnHlxctQ0sR7fsIpQ+lpe0dfWUJvcI3HZvuOk5kATN1geX/5grnrcoJzpDZ9\n3jfLGpllr8XTzFj7zfpjNJF33XMe+WMuQyPJ1dpz313xfJgD8K/6hiFqLuZr/uqTc7i6M6l+vaVG\nw1iTQSKIZndwkwNZrrRHG3rV63s6sCWZpJowUWpLM1DqYQJm9ifn+8DQCAoBEyjkk6GPFh8NXTQH\nyWO3S6vIkISJEpLGkCYZSVC71DKtIk4HXAxEpYk6YEZKJ9wZqH3B6k9KQZmjwvcWVwwUjZfkoKTQ\nOtEPdooZFwNr8FHTD4YhmCl2vLQDtRFvmNGTwSiRnmkinwwnPPZLLoobXvQ1L9oaZwLBjqAQDHlq\nF70mdVqAnCKhNwq1MaQikeqEvTaMXo8q7JZ2LJJIypIg8gISJbTPEpYBOYQajZ55UlL8fLPktFgT\n7R26+IVKZW2+VkSrdubU+0yilLXP0+f5A51E4jcZN8rhTWvZlI9sxTq/7SRlPVVOo3JqEHAo96CY\nY+dh71A29SQ7gUI7c/yXn+d9gAgySJQEDI9E+r0NkckeItPvHutuz/S5pbQiRYj3T3n6tyybbw8y\nZc3JKGrQwvQA4saCi7gqp49txXdo6C0UAzFqnncNP9zex9Qxy8gSf7/6CQHFJ8OCZ0mA3/E5WuqW\ny9hMAKNPhpA0z7M2NSaFVok2Wa5jzSpU022cCmxDwcebI55tZmx7hzOBx27Oouzog+F5O+NqWxGT\n4nS24UkxZ1lsOXZb3i3lgHgVGrSKdNHyCiLbXX1Gqcnz7BYYvb+VGCfbYW+fo2RtyRLIUmOVDgDp\nygooVNld36n1odfZTtLaT16Lt0s8PHa1f4vbvQUOE4QMIv3QJEjCYoxpF1V9u+LYcO8m+a+sVwH1\n4U9/SDJn0BpYRGw9UDcdN48WqEGhioD6tCTMJU7aFIEYFc4FvLeYuuV6qHhYXOJIPAoKnzSlCvyg\n+JQf+TNmusNHsVZYmK2EquS01pg062R54t1kjP809jwKJWe640yvOS5Etvi/vfh9PqiesoklN7kP\nmSSed+tked9EPgmaCxMolWKhFc9i5MJc8yzMeR6O0ERO7YpSe14oYTmqqoI7q+pfeqksPUuwY0xr\n0DZhXaDI0vnaeRZFR6HFANiqmBUUkrKqSVgdmBsJ4TiyGxa6PWAMATRqoFKRSkGlsgH+nreZfL7r\nL9L+MitlDygKKUnfSmpiD/V5OOJzmmeX5WXbWEjCcQR153t2WJ8ROqBeXBP+xYek45ivQ4lQQv1E\nrklDI2snuiSR9WUkaY1dydnH1OI3VBuxXZmbjq0qeKe8ZG46fDJsQ8GgA/fnwi67bGuubIVWiXnV\n8f5CwGib98taRd6rLtnEgpPsreh0YDWUWB2ZZduYZ37G9VDxqT7iwl5TKQn4eODk/hyBkBQf9adC\nAMjr9wdHn3LjKxbf7vjzx3ceRK+vtBLUUmvIDKJo1OEGav/AJt3rwLzxYP+RD2sqH9hc1uFXZhCQ\nSGdZWQaK9n0+BAySptJmj4eRMdRFSxcFJOqDOQB5YHfIiiisCgzREJya5EqNrmVTn3Yme6Mf0ZiW\nFVEYncnXX8Cr4G0vZa14e3xNqVl3YgmziM2pLR+eP6MLlh9/eoY2gabqSUnRecugBDQaPVdSlgn6\nqKmBs3ItZs+oybQ1JMW77gU+WfpkaXTPTahogyQihKTwXk5K1gZMTkVTKjF4Db1GDYpkEmariTER\ni4j2AkAMVW6wheyX9aDEdM9J0lCyMnVWIeFWwjzy80QqFCkq+s6y8Y7n/ezugP9lSimSVXs9Rx32\nkrxhUlEdPK4JdcjVzoC0zf1GpvjDznNIiwl+pfqDZDKjDi/Ifu+0PTKEPCavO5MBozFJ6BUAkdIE\npacDp8m+IY6BSJoMHF9VMack3dUXqzAr8ItxEy3MDnqdx5ygXSBiICiGzu688RRUTc/pXA5ROsvB\nGt3xneIxlRr48XAkU37tOdZdZoNZKuV5HBbchBr//7P3bqGWZWm+12/c5pzrtm8ROyIyqzKzuqrr\nVNOl3eXRY3OOgogcpF8aBEF90IOC+uKD4JugD+KTKIIgB46IjyIiB0QEX5QjzaGhb/bldFVlVlbl\nLTIiY8e+rsu8jJsPY8y55tqxMyszKytzZXV8sGLHvq299lpjf3OM//e/ZODoNXPOA7Vik69XV2FK\nFxWbUNIGk5lDE9qg+WBzxIerQ1ZNifcSaxXeSIz2PFvOebc+AUDpgLOKujGsFwUfcsA3F1cc6Ibn\nds4rxfUQQe2mLxfNZyqRGdJyu8e5DUr3JyJBYi/GMQpDuq4ImSb1YzlZYiwm1uLYX7FnLfahGb2k\ndbz/gdxTRu2h7zHhVs+QWfqqRidKKbZ+RTaG4XuS4lkiETtAUT/x7+V0L+sT6pbnjgiAikQvcEvD\n8qZALRX+niVahYoCddAhZQTt0QKqYrvvPDYb2mC4DoarMKGJhlf1NY+Up4lXrGPBPbViGapsEC1Q\nIg0zSmlpomIdC7osKDy3U76lr1kGw0NV85Y9pomGv3v4l1z5WWLGizDskb+tN5wFTRMDJxLmomAt\nOuai4Eh2XPiKmWz5SfuIV80lV77k2k3Z+IK5aqF6SZP+TCVGLOm7pPR9zxF5XyNEWnJyOwATIqJk\nGJKXK5Uk9DoPufUwdE99KXnYddkUOHlWJcZQQ5Wl8L3PGWw9hWyMO6zFHcbizt7sRaAoIIZIe4vK\nLLit+sNHic0WEul3ji+H8J+iYl0zexxpXgnIWqJaQTCRLhtEJ+/U7AUUgCjQpw3BThCVT8FLXjLT\n7bB3eNIdsvQJAJpIyzfKK360fsSpWrF2BbU2HE9rJtoyMy0nxYYTs94J5Kik5b5YIUUYvIHPwoIj\nuSGQzuxHpmbtSt4on3Mgk0H1Q9XxR+2j7KOY2GzHej34En1zepXO5KbhretTQtj/s/ivLkDUV+8F\nosQACsXRpkn00/z+y/t/hNgaqdGrRlLEq5K5qQmPkX5gD8ls0tpXiEnC0TOHNj5RESFFnLde0wZN\nFxSdT29vx01DBhsQKKFyAk1iMa28Y6oqFqFkJrrBPyRJlmIy7EJS4NKma//X4xdTQmZJ1ue/Czmd\n4nK0u+8Uooz8+MOHuDrHQk9tvrhFrE8Xuko7hIjU1mC9SsbRUWQZYgLrFrIhRMnSVyxUg4160Oav\nfEUbdNqQFx3OV0MyWoyCbpMOa2KpoYhQBIKKCCvBgWoFxaUeJskiJC2vyzH20kKPQfhZcuYPNwVO\nJE2utOCrnGTWKqyXbCZFWpf7z4b86ktIEHI4qI29zsaSVsG25/Rnob5S3DTDNL/vN4mx6Lbsocwc\nmo3MqO8yoYYtKDSWkdmoh4lYH3WevucuiVlauypqFJFOeEz0NEgKQr7PjweIUp99eVr7NBUKRSjy\nuDUbd+IkspXJ80mQJUFkA3NPDFtwuvNqkAMZ4fNrLjHZiPoqJBDoVG4oROA8iCENM8Xeqyw3a5Ei\nDteULiqu/JRrP8Mj2PiC592cJ5sD3j0/wbucxNkq6CReGrpWIwT4ViFkxLtk5B+d5jJITOFY50OZ\nEZ5rPxnkHsMh4mV9uhIZHMoS+jsHYNk/L4YMEhGzBHTkx5H3Nv0AbCznmOstKN2zFmeyo5LdDkBU\n3ArlCJmqPU5LvGufk7wW49D/ZJ76K3ZNrfsLuxEg73AnSgwiXg41PmNFAaIIRCsRM0dsFP7AJYAa\nCJOUQOe9TKmJsxofJIXyGJUO8jYqnvl5Nr7fsBAOH9OQYhNKmmg4Uhu6qPAxbSqaYHhmD/h9/z1+\nZ/qTgUHUoHnbHnNPrXnLHqc+JQLv23tA6hlHasM6lCgCSghOpWMZBadSYETisj3xHWd+ylN3RCU7\nTvSKD+0xc9VkcMqxCQXR/Oofh76wysycKLbMxaHniNG+ZtR3hq8blZQp4KUftve+ZqVM+5zey7X3\nTiyl3cpYs5Q1SVod03zIN4KhK/SsIc8WGBpkZohb/WaYwwy1NadOw32f/99fV3sCgIsyMYheAtOf\nvoSkOxAIKwhHltAqZC0JhpRmZgLFpUJYRagiYe6wywKOXZJNB4kPkmub5KI/mL3HtZ8wlV02DtdI\nEXiyOeC3jx9zJSesbMlEW0IUnFYrFrrhvllykgGdQvhBlp98ERue2CNOzBobFVPVcdYtUsBQN2Mm\nW96zJzzQS5pYMxUtB6rhz9vXmMmWazfBCM+zboEUgftmyZ/dvMaD6ZL3f3b6VT77n6p+pTuiEGJn\nj3BXzHSKvdud5COSeVWiXeS/+D7BTIYMDiXUe0uDjLdkHWEwom6jHhZsDxB1QdNmUKhnD9k7AKLY\nT9tiYgFJEWm8xkhDIQs2vmSZWUTjUiKkKb/wBCHypps7PQpe1ot1/Xu/xdnf9ojKU0yTXksIcI0G\nn4Cf6+WUsrRIGTOmmNMWRKTQjs7pwXC8y9Tq/lBeScupugHg3M9Z+YpNKFCkNWWU57v3znBBi0Q/\nOgAAIABJREFU8myz4GI1ReoMFh1a6BPNVCSKAEogaoWuQXaZopn6JuYmpZVFtQWGhJMoHbAqkjxn\nI2QUXzqFLyNx5ojAxhUvJR+ftrJxRswgc3pnICCmuM0QET7RkccVZbzVh9hO2HK/MTJtxBNTMQHT\n/cZZjugAO9KxEVAUkAMolNai3mEY3SVxDXlSSEieSDKY1AeJn3jo2wGbIi+nap+ihAuEKiImfjCm\nFoFBXhadhEYhgqB4sCFGiCLgvSQEifOSw6phbto0fMiv75mfoYisQ8mBbNhEzftuRkBihONIrTNA\npAfQuveoAmjiNqD8WXfAO5t7nDVzztdTvJOJzSRiSkCyMhk/Okkx6xAiEnxiJcbct0IQOCd55/kJ\nH94c8J2Tc06rNOX77vzZS0D6s5TcNcW/8zofxVb6oLYgkQhx23KGfvMiIH2gGxaqGQ5olbDZ88UP\nxsI9c2gMDvlb/aFPTrzdZ6QIqBgJRExGz5XYsogUDFLWnhUgkYyVzyHGfBgUGfj6gp7fvwYlqwqz\nErho8IeO2EnkWhGqkHyHzg3uIEWLN8sSVTluRIVWaT88Mx0ftkf81ux9PnTHnKgVRnmWUbOMqX8A\nnKgVZ+4ggUXBsMmG96W0HKoNisiZTxSCdSj5jjljnXtPJZIM1gjPIqcuAnghWIYJZz4B4VehoBIt\na19zHkoKAutQcuYWTGVLEwyltCgCc9VwqDY8sUdQvGw6n6kGcEjsDt77yuer/muH/U1PIGK7vzEy\nyVlnquNA1zuJrH06c5HZROP+Uwmfb5H+1evZP37oBz3QMx6abfuSGhz8dw30PdukVxv1wLr2WY7f\nEwACAhtU8gZ8ucX59BVDTlWO2WQIzDIpGaKC8qmmftUnAKkIyUC68oiVgoOk6jieptTU18sLrvyU\n14tzntgjAoK5aln5ivvVGkUCgl+Z3BAQ1N7QhaTaeKSvB8XOgWx4354QouRJd8jr5QUbX/CguOHS\nzjjWa2xIzLFCOs7dnO+WTwdlUEByHmbcUysq2fGhPeLCzZAiUrsincej4NA0PHrj/Kt89j9V/UoD\nREA2i901b9we1kj+DoFdI0eZIPBhD7MzzU/Ttf7QZqTP04utMTXc9h5SAzjUZYCo9ZrGp/etV3RB\n4UOawo73Nf2hK0aByj4QUiTZSSEdG1VkLfYEKQJdVDk5LQy0uSASuDRA27/q0/wYhgnH563yyiM6\nCdP0ugsR6VqDmXYYk0waQxAU2meqcxwO8KVytF6zbEsq7bLRWYpgBAa/l6swHSLIr/2ElSu5sDOu\nugkuSD5cHeKjYNMWdK0h+hwdrgJ6ZrGNhoy667VArwWqzpIyldZ1MOlCHBX4MlLdq7m3WPP8ZoaU\nEasiok7xnKoBaQXdUY4JbhRNXdAuXnqCfK4axwXlSVoCpNmd8OdPi9ADwr2cNe/BxiBR3iRtb+4F\nQ+oxONQbUAPD9GsMEPU+RP2B7bZUbPAEiSmhTEaTY+41nfA7h7B+M/WyPn+JvGSiE4hGDdPNWETU\nRuKUSul3laddF5hJYjIGK+m8wBjHxhpWtuS0WLEJBcsw4UhukrxVWG5ChRQhb7hbbNTJE43IGenw\ntoklRfSsY3KLtnnI8U5zjx/ePOKmrbjaJATae0nsZAKHvEhG+VYRJDTXZQInGomwgjgNiMoTloYY\nClhYusuKv9yUvHr/im8dnLNyJdG83Gl/3tom6Wxvopd69H+wGSTqpa4xb9DjaKM0TlDt9zeJKZ3e\n3galw0BbffHa2/eYsTl++hn5+/O+ZZAeZfDZiB6I3i1LROIxH5dv1velX/W9zuepO4D66BxmJWhP\nPXqamNBuo5CthFYmqf1SEe+BKrO8w6nBk3PZlbRl2tteuDSBr4TlaUjXi6fuKLESQ1pLPat1EwoC\ngjYY7pWrvMYyY1UYHvtD/mj9bYzwPDTXLFTNkVonD868372nV8xCl697ilPZcuaL4dplcq9bqIZK\nWJ5k9tAH3ckwbLFBEYpf/ePQF12xH4K9MHzP4HOvLhX9eSt7LI7+LIczjQjorMhILMV2eM22AJHL\nbEW3Y4BvACN66erdrKGe6TweavUMoh6M7n+Pvqv4EajdezSmj23Z2Aks6pnSvOw5d5W84zwWIkEB\nOj33wgrcNCJcGoi19yPVU0XzyIOJ0Alk5YnZ3NyYxFzU0rMJxcBgLKVlaSssihs3YaLsIBXT0qNE\nxAbFP/rD36T4nT/ne9WTIam330tXssMFxeP2iDokIsaNm/CN8pJ5Doe5b5YsVE2IkiO14cqn85zM\n/W0hk+H96+U5z+wBbdD8yc3ryS5GeprOcPAlvgSfp/76dsS8aZJeELL8SrBFwaPMoJEgs4e20zUl\nw5BclozTkqQLGDTRHrGTVOZCurU+PeWN1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EpYRDi0FB8kmLZ+xRNrlTyKnEjBHRuNOugQ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R6zdnyXgqU65vEt2BMNTUh34B9mRHvMwi6BZvujOqDgkkiggAkCKoBRjagkMBReMN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Tu39UHXD2QQich/B/wh4K6IvAX8h6g7eg38sujU6R9lZ/Q/CPxHIuJRoupfSCmd/Ziu/ftX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lVeyRON3OqvGcpUlj6iFWsLU9nW5HK3bjDS9pZhO9mgE06TWuWg9oofQeGjE5KChfKXnm\ni6/SvPEmqCKf/Bjf/qUBbuTQbcXvO7K3V+ilLfa9LaXYW97rKUsQHZFIWhC8R1OCqO3nNCsZ9aqj\nWhXq1dgouO3FMZyh1JiN7qqFYHYxJiAurmgum1UNdY2o+2lLWUwOjVjPxlzKhmxkQ9bdiJ7UeAIt\nwlg9tSpb7SoHVREbuS1xdtIcn1YV+ZZHHfhRPCnPh0q1CeVNz/jGZbRU6o0WadL4zlwRHxt8rpYV\nQYVJm1GrZ6fpk7vZGVrparbSrTwnSikN3x4+Ev/uYC+ID1w37r4bQe090rbxzhugzseeZEHRPDbE\n19wTyoym52kGjnqQSqZ7aUtrt611bovZtGJornIx9j1jdkGWz/qedVtbu+rFS8WIq+U+j+S7bPoh\nAzchT2f2tXpE5a69Scz54J/9AL6OlSN+AvlBjMnJpgcH+W4s7w9jnW4H0HTXtllRmrWWlUcO2K9K\n1vMxpWvYqftUIWPU5IzqnN1xyaCoqVvPwU6PR5Zw/Ou50F241fVs+rxq7BFSZ2gWk9OauZh8SVtb\ncULIfbx482lLfarImO9/BswqjO7WGD+7yxj7ru9ZPmuI3yWHNsoxV3sHPFTE9eeK32fHD/BZa9XS\nZ0C7vcPgH77EU8PnuPbTJaMn4+859hWKzTxcNUvOhEGAPJD1G/Kioak9qoJM3L2TQ70e1ac+zLWf\nFmQcMwzZfuw3lB/A6JHYQ8RXSrUqVBvCwRMtq6/6aa8j12JbWh80DahKGsYRkLqBicc5h4qQ6azJ\nb0wOxQrFu5ofvHHo8cMPzieaumrqtpRpQ/xD1UJ39l/MFClasqKlV9Zs9Mdc6R3wWLnD4+Vt1soJ\n28Xs3MosFxmOKXaFg42AH8UEcbkFrg24YVwDmlKmr1Ounr3Xcaws85PYK7FeZboFss3TTY8sVrNp\npmgRCEEoXiv4wBdfp3n9DSBWWr/x7zRwHfJ9QRpH/0asZmoG8cZa00vJ0SVdbH5kgkhEfgX454Dr\nqvqx9Nj/AHwofckmsK2qPyEizwAvAd9Of/ZVVf3LD/qgF8o58C6+zzNC4Wl7PlUOuVg5NIgnwzEx\nlDrgZ4cTRCLpY4ll9Ag4Fw5tKRvkNWv5hPVixHo2YT0bcSk/YMOPWHcj1nxMEHUXaDFB5LnVrjKu\ns9iAKzWHW4QX9XmAHxeR37fYORm9G0LbB9cq28+lPh+khS4DWiFPZZVkcVqZ93Ex6vmaKnjeGq8T\nVHiyf5vv60MAXM33uFGtkktgt8nYcz1uVwMK19LWJ3s2/qI+z02uAXy0e+zCxI3OJYlgendKvIcs\nNeyA2Gy88LR9T9t3NL2YHGoGsWooJoj0cP+Q+WTQtFmsTquG4pSymBgqioZe3rBSVKwWE9bzMZvF\niMv5AZezA65mMUG07sb0JL66BnWp1HtxGSJbc07W9iceol4R8r1UPVQrkzVHyOMJlXqghXwYQ7Xt\nQbUGoyfaODGxbHEuIKLcGK8CMKxzelnDsM7ZG/UYHRTsax92cvJ9h6vGJ/68XtTn2WebixY30y2t\npCRR1wutrpEsi2tOnsXeaJmPyaLUE0o0Xtx1DfJDJrM79F2CaDolca6CaG5iovpYyk+XHMriVE2f\nxxsZZd6wUlZs9kZcLmNy6OFij8vZfjz/cTWuq8SGhTT7tDXn/oW9PYr/70We2v0x3vj5dQ6eadHL\nFTrKIGtjL72kuDTmkc04dGVU59y8vslzf+V37vmzJS8IP/Ecb/yxguxACI3GLRx1vNDr3Q4UOzFQ\n6hWhTf1nyluefF/xk3hX/7SSjRd1zYm9z0K8NmkCrmqm/3+dl9QvSN75e+iqEbO5Lavde5n93ua3\nsx5el9KW+2yuGf60Cf5c5eJcM/x+WbHem8RJrb09rub7bPrhtMfroqqlbc15d7o/pH9dOXiSaUVZ\n11oh5IKrA+oEXyvZ6I6tjFVMJgPT85ppZX5f0/l1fJ1xtZDfzHnqvxnjt96i+cFr8fGPfZg/+EsF\nsu+nN+j7t2KfMzlQih1AIBspvVsVyzqE434qiL4A/FfAF7sHVPXPdx+LyH8B7Mx9/fdU9Sce1AEu\nHZG47SNt8QhlRtv304uz+BYnNWgZG5yRppFNk0HTnxV/nIgiEpvA3i05tJkqhzayIWtuzIqb0HM1\nPt33q9UzfZ1/RwAAIABJREFU1IxKPWPNuV6vM5lk9OtYQbSoy7X38TTX+MHL849d6Nh5gNzGOtlQ\nGH3qgPX/a0DwMUsdMqg2A4NrjtAIfjTXhyYIBGE0LHFzcZj7lkvlkLcnseNaUOENv8mtyQoOJSBM\nmozct+yroEGQvEDr6kSe2/t4mif5IL/Dl6ePXYi4mU4IimOoYxY5XcBlGdq0cR2BOK0sd7SFo0ll\nsm3a0hoKTfvq0x0u3+3Fn21xPWRuYmKWx0kdg6JmpajYKEZsFGMu5wdcyodcyg644ve57PfjOiQN\nHsWnOLnXdKPTYmvOyaoHQrUOva24fXmy5qg24l3fbBjvjtUDptNm1Mt0KlG5MaZfxmRilrap7ox7\nNK1jZ9gnBGG0V5K9XVBuC/lemkIzrGhP+ML/fTzNDrcYsjd97NzHTVe12BIv2jRWE+E96lxKEGVI\n5iHLII8JI8mz2P+s7NaVbstZSg4Vs5L9rgH1oe0b8+Pr/eGqoW4NKsqGflqDNstR3AqdKhcvZ3H9\n2XRDypQgumtFwSmxNedowniM/O53eGTtY2z97AHbr23G/h2tIIOGrIgVg4Nexc29FR7Z2OOhlX0u\n/5Xv3PNnSlkiH/4Ab/70amw+XYHbiltK1MWKgeBT/7MVUgWkUuxKSg7Fnmlxq+LpvIBd2DUnvUnT\nphn06bREASfT9eTOATsq6fejqSLezVcuyqxSupvWOp8Y6ra2ZofXoW5iGV7RPEAR8EWgKGtWehXr\nvVg59HCaFn01i5WLg6yaTspaBFtz3p0eHLD6Zs3NP+Jp+4HswMcdjkU3WEHIJkpVSDxvSb2kqs3Y\nJN/VOr2mz/egXYF2oDQDjQntg4zeWxlP/297uJ0h7Xe+R5fe8c9+gO/+q5dAG+RWQbHtKLbjJFeI\nTdRDJkgTk5SjqwUri/ln+pF+ZIJIVb+SMpDvICIC/EvAH3+wh7WkJFYOSZahRZ62l/l4976U+AKT\n9rTGhtRdE+r5n5HedYkhF/BecS4cSg6t5BWDrKLva0rXzG3jyDgIJbV6HAGfrsbGIWesOcNQ8P3h\nFephwepEkbpFF1S+dkkeAqXhLpelFy52HrD21hYhB/neAFcBBay8qWx/CPJdh5tAWFOycWzKOLms\n5FsZzSDQ9gP7ocfBQS9uN+tPADioCjIXaFWY1HFp2Nvvs7oyZj99LUD2doF4h9Yn89wuyUOM9OCu\nf3ah4maaMApxq5kT8A71PlYPlY62F9ed+BbvbHTbCWMfj7kKoq4Z/nwGx5GmJcaJiWXRsNqbsJpX\nbJSjQz3PNvyINRfv2nuJa8pYM1pi5dB21Ufq1Kzf1pxzqdwNjEc+TvYYxxNxP45bziTEqUAhh3p1\n9qLXlrMy7CY41vtj9iYlB5OC0ahAgxD2c/Jtz/rbgrSQ7ynZRGnK0zkDvyQPIffIMpzruFGdJaSb\nZnZHXBzifVzn8xwpciTPkbKIPQ2LjK43TLe17FD/s66CqBtdP1cxNOuBNtf3LE9bW1PPs0FZsdEb\ns1HE5NBDxT5X8z0ezXbY9EPW3Ig1V7HiJodvui2ArTlHp3VF/vzLXP3PP8j6euAn/+Nv8LUbz3Dt\nxgahjVf6T25u0/wLLTocEqq7n2xIXuDe/yTNlVWu/dGVuO11P1ZQ97YCromJn2pVmFwSir1ZQ9pi\nD1RicrEthGpdqDYhZI6NU+grc+HWnBDPR2KVYptKTFukFgjgW50O31DhHdPB1AkSPC2xgpHUByYm\nephOmGrzwxVDhyYkdg2ru6Ec8+9zxZexenqlV7HZH6Xk0B6PFru8r4jDONbdmJ6vp9v1F8HWnHcX\nhkP6L98g23kf9WZLKJU6jdfM99NksrHixxJfrwbx+6QFVynBxx7C6uMWs1B0u4ICIlBsed7/pZu0\nL71Mq4qUJZOf/8O88dmK/IUVVJTetRypYeVafH3Kh4q0yvAhT/9WOj8W6L89Wcw/0n14rz2IfhZ4\nW1XnM5nvF5F/AuwC/76q/r93+0YR+RzwOYAeg/d4GKdHfNxapmVGW3rawqVpZWlcYtHta01bN9zs\nlnqsFGL6sfOp10fWkrnYc2iQ16xksefQSjZh1U8Y+Ipe2iRZq6cNwpCCoI4WoQ4Zw1AwanP225I3\nDjaRg7inmrCcexu5gLHzoLkKJMSmai5NMCu3hGYlVpJkw9ShvySWXBcxLmXiCE0eY3NNubm/xi1Z\nA1F8HgitEPbyaTJze5Tht3JGGw1StqxsxRfxBbl4cSMO8Skxnadx9+X81jJmfYdScihkzJpP39n3\nzGnqe0YaX9+Q5+10S9l6OY5byvIR69korUETBm6CF6VSz0EoOaCk1tjDaqw5b4/W4ujiKix0m9m7\nuHix84CF1HvI1d1deQj9eILfXXhlY2BfaVNs5gdCsx4Xk/GooGk8g148KWr3csq3M9wkTgoZvB3w\nk9i/qCmF/laL7I8WvUf/4sTNXBVjvEBL67wITG+MFYR+RtuLN8fasut7dnhkNMxdmN1ZMTQ3KVF8\nTA7leUuRNwzKitVUvXipiNXTa37MwFU46fot5hBgq1mlrj1lC7Kc5zoXJ3aOKOztIb/1TfqDAS/8\nm/8U67tjNvZvQkoGtcQJaPckgnzkg7z+H0Lz9RXyg7iFZHA9TvB1bbwInGw4mpXYGy9OU4yvlU2P\naTN9aWH8kFJvBKpNWYbR5ec3bjSgaQq0SNxKHydCO8TFCa2x1+vsd6AiSNp6JnOTw6bVQ1lM8gHT\nHkPT5FCh016M096LaWmLW+5jz6H55NCscuhwcuiKP2AgDX1fzyqIFh8rdzq/sXMEenubKy88xlt/\nDJp+TAq5StA1oc2FfNhS7AcmG462jN9T7Cq7P6asfy8lJFuoNpS2n67lMyUrGqrHHO23ZhWN7see\nYfev7sBBj3pdyYbC4C3Fj2cTXX2luEopdwIhA18DCpMrBf3F/BP9SO81QfSLwK/NfX4NeEpVb4nI\nTwL/s4h8VFV37/xGVf088HmAdbm8lFcTd4p38FNz6iKLF2npBCmU84sR05Ofd5QgziWLnIsTy3If\nL856WUM/qw8lh/q+ppRYQdQiKUGUU6tnEjImIWMUCvabgmFTsF+XXN9bjZMbJgpNu5B9+ffhQsXO\nSTg04hOmkzmy/bjXtVoHaQAfT4BCAdKmRpDBEXqB9iAn28poe3Hxa5tYCZCP4glVvDvj497ZVKPr\nR8Rtlotx8eLGzba1UuSzxtRlTBDFNYhZz7PsjubTXS7Px8RQNy3RuUCWBcruouyOqqFL2ZCBn9CT\nmp6Lvc48gaCOPe1Tp0TRMBTstz1uD/v4seAaXVjfsx/h4sXOA7byxpj+DU9xaxirUzNH6OVIG3Dj\nJm4dGE3ijYnuJF813hV2gkzqWKnStDCZECYTtKreNaG4BC2qL27cOBcv1nxce7QsCKsFzSCjGTia\nvksJ6llyqC10ejc/bvW4o5rxLpMS8zxWT68Udex5VozZyOP2+g0/YuAqComRUGtGq44DSm7Wq9RV\nRr8h9jVZvmafSx87/rkP8vjffovXfuruVbsnLQyHyG//LkfqwOE82ZPv49VPX6J6Senvw9obLSET\nsnFARahWHSFzSGC6jSTkTF8Tm0GM2WaQpitercn7NfV4KWb3LH3cHIWGuFtC2xYJ2WxLq6bBG+/o\nN9RtM+u2nMXeZ20/Vk43fUdbxKRQTAbFra0wq2ScVg7NVS92OzqmvRfT1nqXB8q0rWyjN+ZSOeRq\nccCV/CD2O3Nj8m79wVGFDNI26iU81zlXsXNc7fYOl796jZsffx8EKLfTMJ8D4v//Mp6rStrpCKny\nWeIU1lAAorSrAS1Si5iR4/L/M6B/c3ZW4p/7IG/8J47dmwNWvl1SbSpNP/Y0m2wK/VtKKrhnfMnH\nm7gC+08KK2/G5vzL6tgroYhkwD8P/GT3mKpOgEn6+Bsi8j3gOeD593icSyVuoYhdzLvgchX4nOne\n2Tb42dYOmCvFjo+1Tqm9InlgnAWGeUueN+zlJdtZn35WU2YNhWvo+SaNsnc06miCY9zmcQJV6xk3\nGZM6o2o8dZVR75SsbAvFfovcozR3kS5y7BzHtN+P6zZZx1MpV8Po0UC+71CnZONYXt320p2yKt4l\nQ1OztZHgx56QK24ihNLRlo58L95F82M33SKSjeLPcE18cS22lVDEO8W924s5Cb/QceN8TE7ncXtZ\nKOVwcqi7S9Ztbe0aUc9XEElXPcQ7kkNrxYTNcsTlYjhtRL3hh6nXWcClV7hpxVDIGYaSrWaF3abH\ndt1n/6BHMSZVEC3XhdqFjp0HyP2jb+KIU2PnKRztAu+MuLBx001rTckhyTK0zNF+TjPIaVY99UCo\nB8TJiV31dKHTRtTAXPXQXNVQSgy5LFZP53k77Te01jXET9WLG9mIVT8+NIyjUk+tcZv9rXqFUHlc\nrUtXLX1WYqd9+RV++DNnZxyTv3KZ8MxjvPJnN+L2xULpbcVkQXm7pVr3+FpxjVKtS0oQxSoCzVIb\nCGE6abjeDLBes7YxYq03YWtvBRFZWO/OsxI3921+QqtoTBJplgbouHiOMn+DQCRudZU4rTVkDk39\nFttePGdtejJNEMV1Zzq/Y9praJYc6vqcdRXVTKuGXNHi87itdbU3Sc3wh1wt97mSH3A1Tw3xpxOj\nlVoddYg9bRYWJPdw7mLnPdLhiHJLGD7eMs6EYidWC7kaqjUhGwmuVaRN1Wc5rLzumGzGhtRtqWi/\nRdLwlqf/W0f2j79L2It9w7Knn+Rbv3wJt9fSf6Vk8JYyeCtWsPW2W/KDNPlTlPGmo7cdGG+4eAMl\n3YCfrDtWF/hv9G7eS6r8TwJ/oKqvdw+IyEPAlqq2IvIB4Fnglfd4jEtDg6JVjYzGOO8oWsVVBdkw\no9j30z5EbQlt4Q5VD2lX0jjttN/thZ29TTJl3I1czPTw/thEgqSzcZk2ApVWpokq3wjlEFbfDPRu\nTNDheOku1riAsfNeTJtBh9kl2Hc+/0fAVWS3ctp+PAEKWbxTpl4YX413Zdq+0n/bQUr0qMREUe+W\n0gziftxqI25Nw81eZOOdNaXcFupVIDUgLnaUYi8s6mT8YsZN0Pi7rxukavDjhmzoKHxcVOK+acGP\nu+aNQOqtcGjNSZ+HTGk8NJkyygLbRZiWV/eKerrVtZ/VZNIeamhehYyq9bTqGNU5ozqjajImk4zw\n+oDeLSXbr9B6Ceo+DruYsWPeK4sbJ9OhHO10a6ubbS8rIZSzyqFp41eIzV/dXHIoVTE6r2l7faDI\nYvX0Sl5Nk0Ob+ZA1P54mqV1KSQZ1jDWfVi7emqzAxOGatE1luZyN2FFFm6Vbr+/KX7nMR3/zNr/+\nv3+IfA/6b0F/C4rdBmkCxfYE1/ZoS0c5DuQjodiOU7JC7nBVSL1sNE5MkxjDEjJCvo4E5dFRuGfP\no1NyNuLmKOYa4sfkkKIhINMtranquKsk6iaU+ZQcKn1aa1y6zkoj6/PZdrKuajH4uX5D2Z3b7Zlt\ns88DPm8pyzgpcaMcc7kc8nBvj4dTYuhyFpvhdwM5nCjjkFEFjwSJx79c11fnL3beg7C1zWO/NeK7\n/3JOO2hhu5ieD7elIK3SMqs+c1XsOYQo7SDElhx5QIcZl36rxH3lq4QUs/7KZV79xSdxO4HBtYJ8\nL1YKlbttnJA21rjW1IGmdORDpe7HmO3dDrjGUa0Jro79RZcrjKL7GXP/a8DPAVdF5HXgP1DVvwX8\nBQ6XsQH8s8B/JCIN8YbiX1bVrQd7yAukAa0qdOQgBFxVkx/kZGWB9jJCHsfAhtwR8sN7aCFdoHUP\nSbcAcmhkY+imfLi5UY8Sv1cUplOCNG4pklSiGZNF8X02DhS3K7Lru+h4vLB+IC/o1wA+TOybdrFj\n5wF7+pkbvPr61bitKBOaQdzjKk1sGpsNY0Jn/ftQ7AYkKK6N3fmlUfJhw94TJUgsg+w66xfDGCsh\nh3xPKPaVbBgXUz+JP6MtJE6yOSEv6Ne4zQ2A0uKGtO7UICME8E2LTFqyg5yi72l73cmTxP343UlW\nWjcOfT6/tnhiw+u5KR/DXNnP5u7831H6LQEIcfthTE7HUZ9ZA/2byuobDX57OB2ffdpszTHH8YJ+\nrZsm9CGLG1KFdIjNZOsGHU+QPCOT+H/f1Uo2dtQHMVE0P95e59eg7kZYt/Z40o2IeBE3cTDKldvF\nbIpQ16w6ywJ51uJdiFvy07lTm5qdt61juDVg5dWMwY0GJu++VfGk2JpzetpbW/zeJ+D9/PZd/1yB\nPL3d6c4aqUXXTF24NWeaJGqRto0nHW0LPm5jVQlpkdBYouq7daMbW99dJ8125RzanTNttSDQpk8a\nwamiQaCVlByKk+oUaJ2n8UrlPVXuGbdZ7OXqSrwEWhy1z9gOzXRq9F7o8dbBOn5Mqly0NWdZaV2R\nf+tVBj/8MOOHA/Wqxr6tZTx3jcni2Y1xkVhpSEouUgRQeP+XWsrffnGWHLp0idf+jQ9TbSjr33X0\nbwWaXkz2uDo2oi52GlwtjDd93J7WKk1PyA+UybrD13HM/bJVoc0TXYJmoutyWX9K/sSiD+P+OD/t\nRSTep/cu7tF3HpzM9uzfzXzSqPvYzSWTutJKd/jzqfmMO2m7W/d599YGdFKh4zE6Gi307tA/0C99\nQ1U/eVI//0zFzv3q+na8i+/9nY/Te6HP+vdjwzMkNuvr32rx45bJ5ZzB2xPy3/sBsr5K2NqGEGJi\np4uZJx5l/MQ66oRs1OCHDW53NNsb3oZpLxHNPNJVhYwntDdvnXhcnWTsnKm4EUGybpJQBnmBlEVs\nll/k07v7mvu4xRXmphEd/jkxST0bIzsdUe1TBVkmhxPXd2yPjsnp2ERYQlx/YqJIyfca8lsHcOM2\n4fbtha07tuaY4/iafpld3TqxhgBnLm6cn04yk6JABn3olWhZoL3YCy2UGaGIN8W60dKxpD7+iGmC\nWuRwosjNXez5LkEdE0zdmOppQkkO31yLSer4Pj+AlWstaz8Y4r77Ou3t2wv5p7I1xxzHhVtzunOZ\nPEOKHLIsXkO5dA2Vd43ws7lzmnTTPZO4zmQSt5ZN1wuZ7sKAw2vMtCreza9FpPNlUnNrRbveaWni\nphRtTFSnIULOzc7Hm8ZTv7bC5reEq7+7h3zrFcJweOr/lLbm3B/JC9qf/ig/+HQP9bF5ffCQ70tK\nEs1iJ5SxVUOcRh4gV574+47B3/va9Of5S5d45a/9Ier1QP8tR/+G4hrIDwLZONAWDtE4Ca2LTZRZ\nvOZxW3b/ViB4IR8pq1/6+qEdIifpKGvOUnRjO1PC3EjYRR+LOZ/uI2n7wX/ln7zrn3d30FqAe500\nf2uX/Ftzf2339Wa5qKJ1hTY1SBrxKm46PUNEps1k77rq32PinMwnqCH+zEOf3+M1ZD4+pwnqOJlE\n6xptmjOzZcEYcw8at4SgAVVF2hYZjyHLcOmNdHNMfdevyN31JpiKpAaxs7Vl+liaXNRVNt6ZwI7f\nf/jQuipqP2nJtkfI9h7tAi7SjDFHoLEHEU6gAtoQq4e6m+5tkW6wp3UkgLgWV7mY4PEuVQDJbM3o\nbmrN7c6ISWWZSzAfTlp3XzfdteG7C3hJyaj8UAuQ+ROrLMDaTWXttQp/a492+bbTmzlaV2Rf/zYb\nz/044yvx9+2r2GcVJU59ThdM1dWAGwt4xR942sv14eTQ5gYv/afPInVL/01PNoTediBkQn4QG6/7\nEJhsevJhQEJMBmUTJbRxjoJr4m6MalXID2B41bF2VreYGWOMWQKqoO07XkgsUW2MeeDm15umQSeT\nE/3r5nfgH4Xd1DDmDNEQexSmbeg6d2PK9dtUTZSmJ4oiIcxuSt25netuN7Pmk9Iw62vUSedP058b\n4rYznIvf4wV1bpbQlsNJalFwBxNk94Cwsxtv3JmlFkYjHvlfXmH8kSfw4wY3aQi9HH9Q0Q5ymtWY\nIarWPK6JE8uy/Ypqcy5F4jz6zOO41Zrei33K27GnlQSl2GnRLA776apd/USpV2J1frUq5MNAbzuw\n/2g2HQTUlql3ltz9Ju6iWYLIGGOMMcYYY8zJuceNLkhJnbxA8gyV2ARa2tQLrU0JnbZFu4b03Q+5\nS9X9O5LNc9XOhNiYXesmJniO0GrFKu3PIFWat94me+vt+CkxPkJ63+24uLN32fznkme8+mc3GXwT\nyh2l2FX2n3AU2w357RHqXOyt5Rz9N+Pf2RMhFLHC1tUttIof99j+sYLB9YbBa7u0L357aW/yWoLI\nGGOMMcYYY8xCqCpSVzB2SJPSMG2LhpjUiY3zwywxdMQG0ao6ncCrbctS7usxS0nrhkd+p6L/+h5M\nKmRSs7k2gGs30MkkJiS72JqreJM0NbGLtRx46CsObVvaU+o7dFyWIDLmorpXj5m7fu1cCaSGI91x\nMcYYY4wx5p7aNk5sDQrdQJWu4mcuuTPbcnb0BM/8xbsGtXNZc39CS/F/PH+hqscsQWTMIh0lSXOn\n9/LCdtS/15JCxhhjjDHmBGjbouMJ4hsY3fmHYe7DB3Auaue0xrwrSxAZs0iLeoGyF0ZjjDHGGLMM\npv2JLlKdhjHLaTlbZxtjjDHGGGOMMcaYU2MJImOMMcYYY4wxxpgLzhJExhhjjDHGGGOMMRecJYiM\nMcYYY4wxxhhjLjhLEBljjDHGGGOMMcZccJYgMsYYY4wxxhhjjLngLEFkjDHGGGOMMcYYc8FZgsgY\nY4wxxhhjjDHmgrMEkTHGGGOMMcYYY8wF9yMTRCLypIj83yLykoi8KCL/Vnr8soj8poi8nN5fSo+L\niPx1EfmuiPyeiHzipJ+EWU5jHQI8Z7FjjmKsQ76h/xDgoxY35ihszTHHMdYhQ/awuDFHZWuOOQ5b\nc8xx2PmxOS33U0HUAP+2qv4h4FPAXxWRjwC/DHxZVZ8Fvpw+B/g08Gx6+xzwNx/4UZszQRCA1y12\nzFEIwrP8YYAXsbgxR2BrjjkOQSjpY3FjjsrWHHMctuaY47DzY3NafmSCSFWvqeo/Th/vAS8BjwOf\nAX41fdmvAn8uffwZ4IsafRXYFJHHHviRm6VXSh9gCBY75v6V0mc93vywuDFHYmuOOY5S+ngywOLG\nHI2tOeY4bM0xx2Hnx+a0HKkHkYg8A3wc+BrwiKpeg5hEAh5OX/Y48MO5b3s9PWYuMIsdcxwWN+a4\nLHbMcVjcmOOy2DHHYXFjjsPixpyk+04Qicgq8D8Cf01Vd9/tS+/ymN7l531ORJ4XkedrJvd7GOYM\nstgxx+SwuDHHYGuOOQ6LG3NcFjvmOCxuzDE90PNjsNgxh91XgkhEcmIg/h1V/Z/Sw293ZWrp/fX0\n+OvAk3Pf/gTw5p0/U1U/r6qfVNVP5pTHPX6z/ASLHXNEQQPAB7G4MUdna445Mo3nzBY35jhszTFH\nZmuOOY6TOD8Gix1z2P1MMRPgbwEvqep/OfdHvwF8Nn38WeDX5x7/i6lz+qeAna7szVwsqgrwNBY7\n5ghUlW/xPMDY4sYcha055jhUlXFsI2NxY47E1hxzHLbmmOOw82NzWrL7+JqfAf414AUR+WZ67N8F\n/jPg74rILwGvAf9i+rO/D/xp4LvExn1/6YEesTkzdrgFcAX44xY75n7tcIu3eA1gzeLGHIWtOeY4\ndrhFQwUWN+aIbM0xx2FrjjkOOz82p0XS3Y+FWpfL+lPyJxZ9GOYE/AP90jdU9ZMn9fMtds6vk4wd\ni5vzy9Yccxxf0y+zq1t369fwQFjcnF+25pjjsDXHHJetOeY4jrLmHGmKmTHGGGOMMcYYY4w5fyxB\nZIwxxhhjjDHGGHPBWYLIGGOMMcYYY4wx5oKzBJExxhhjjDHGGGPMBWcJImOMMcYYY4wxxpgLzhJE\nxhhjjDHGGGOMMRecJYiMMcYYY4wxxhhjLjhLEBljjDHGGGOMMcZccJYgMsYYY4wxxhhjjLngLEFk\njDHGGGOMMcYYc8FZgsgYY4wxxhhjjDHmgrMEkTHGGGOMMcYYY8wFZwkiY4wxxhhjjDHGmAvOEkTG\nGGOMMcYYY4wxF5wliIwxxhhjjDHGGGMuOFHVRR8DInIDOABuLvpYHpCr2HPpPK2qDz2og7mTiOwB\n3z6pn3/KzlPcwBLHjq05S21p4wZszVly7+X5nHTc2JqzvGzNOT3nKW7A1pzTdJ5ix9ac02NxM3Pf\ncbMUCSIAEXleVT+56ON4EOy5nJ5lP76jOE/PBZb/+Sz78R2FPZfTs+zHdxTn6bnA8j+fZT++o7Dn\ncnqW/fiO4jw9F1j+57Psx3cU9lxOz7If31HYczke22JmjDHGGGOMMcYYc8FZgsgYY4wxxhhjjDHm\nglumBNHnF30AD5A9l9Oz7Md3FOfpucDyP59lP76jsOdyepb9+I7iPD0XWP7ns+zHdxT2XE7Psh/f\nUZyn5wLL/3yW/fiOwp7L6Vn24zsKey7HsDQ9iIwxxhhjjDHGGGPMYixTBZExxhhjjDHGGGOMWYCF\nJ4hE5BdE5Nsi8l0R+eVFH89xiMgPROQFEfmmiDyfHrssIr8pIi+n95cWfZx3IyK/IiLXReT35x67\n67FL9NfT7+r3ROQTizvysx87FjeLYXGzWBY7i3OWY8fiZnHOctyAxc4ineXYsbhZnLMcN2Cxs0hn\nOXaWKW4WmiASEQ/8DeDTwEeAXxSRjyzymN6Dn1fVn5gbP/fLwJdV9Vngy+nzZfQF4BfueOxex/5p\n4Nn09jngb57SMb7DOYodi5tTZHGzFL6Axc4indXY+QIWN4t0VuMGLHYW7azGzhewuFmksxo3YLGz\naGc1dr7AksTNoiuI/mngu6r6iqpWwH8PfGbBx/SgfAb41fTxrwJ/boHHck+q+hVg646H73XsnwG+\nqNFXgU0Reex0jvQdzmvsWNycLIubBbPYWTpnInYsbpbOmYgbsNhZQmcidixuls6ZiBuw2FlCZyJ2\nliluFp0gehz44dznr6fHzhoF/k8R+YaIfC499oiqXgNI7x9e2NEd3b2OfZl+X8t0LMdlcXP6lulY\njutJuRpTAAACV0lEQVS8xQ1Y7JyW8xY7Fjen47zFDVjsnJbzFjsWN6fjvMUNWOyclvMWOwuJm+xB\n/aBjkrs8dhbHqv2Mqr4pIg8Dvykif7DoAzohy/T7WqZjOS6Lm9O3TMdyXBclbmC5fl/LdCzHdVFi\nZ5l+V8t0LMd1UeIGluv3tUzHclwXJXaW6Xe1TMdyXBclbmC5fl/LdCzHdVFi50R/V4uuIHodeHLu\n8yeANxd0LMemqm+m99eBv0cs0Xu7K/VK768v7giP7F7Hvky/r2U6lmOxuFmIZTqWYzmHcQMWO6fi\nHMaOxc0pOIdxAxY7p+Icxo7FzSk4h3EDFjun4hzGzkLiZtEJoq8Dz4rI+0WkAP4C8BsLPqYjEZEV\nEVnrPgb+FPD7xOfx2fRlnwV+fTFHeCz3OvbfAP5i6pz+KWCnK3tbgDMdOxY3FjfHcU7jBix2Ttw5\njR2LmxN2TuMGLHZO3DmNHYubE3ZO4wYsdk7cOY2dxcSNqi70DfjTwHeA7wH/3qKP5xjH/wHgd9Pb\ni91zAK4Qu42/nN5fXvSx3uP4fw24BtTEbOQv3evYieVsfyP9rl4APmmxY3FjcWNxY7Gz/G9nPXYs\nbixuLHYsdixuLG6WPW4sdix2zkPcSPpLjDHGGGOMMcYYY8wFtegtZsYYY4wxxhhjjDFmwSxBZIwx\nxhhjjDHGGHPBWYLIGGOMMcYYY4wx5oKzBJExxhhjjDHGGGPMBWcJImOMMcYYY4wxxpgLzhJExhhj\njDHGGGOMMRecJYiMMcYYY4wxxhhjLjhLEBljjDHGGGOMMcZccP8/tiSM4/NXjZYAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<Figure size 1440x1440 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# apply the contrast adjustment to the MRI\n",
    "#mri_adjusted = (mri_transform_arr - lddmm.ccIbar[0].cpu().numpy()) * lddmm.ccCovIJ[0].cpu().numpy() / lddmm.ccVarI[0].cpu().numpy() + lddmm.ccJbar[0].cpu().numpy()\n",
    "\n",
    "deformed_mri = (lddmm.applyThisTransform(mri_translated))[0][-1].cpu().numpy()\n",
    "\n",
    "plt.figure()\n",
    "plt.subplot(1,8,1)\n",
    "plt.imshow(deformed_mri[:,:,4])\n",
    "plt.title('MRI Image')\n",
    "plt.subplot(1,8,2)\n",
    "plt.imshow(outtarget[:,:,4])\n",
    "plt.title('Target Image')\n",
    "plt.subplot(1,8,3)\n",
    "plt.imshow(deformed_mri[:,:,5])\n",
    "plt.title('MRI Image')\n",
    "plt.subplot(1,8,4)\n",
    "plt.imshow(outtarget[:,:,5])\n",
    "plt.title('Target Image')\n",
    "plt.subplot(1,8,5)\n",
    "plt.imshow(deformed_mri[:,:,6])\n",
    "plt.title('MRI Image')\n",
    "plt.subplot(1,8,6)\n",
    "plt.imshow(outtarget[:,:,6])\n",
    "plt.title('Target Image')\n",
    "plt.subplot(1,8,7)\n",
    "plt.imshow(deformed_mri[:,:,7])\n",
    "plt.title('MRI Image')\n",
    "plt.subplot(1,8,8)\n",
    "plt.imshow(outtarget[:,:,7])\n",
    "plt.title('Target Image')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iKr1MPXMX9br6Lb2O/eh4f2w5ljq+mskqu1o2rIwyaWPtcbrvvLFtw3yS5LJN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\nO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      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# resample mri back to original target size\n",
    "# apply the contrast adjustment to the MRI\n",
    "\n",
    "## previously had an error saying the orignisslsize not defined\n",
    "orignisslsize = target_resample_arr.shape # not sure about this\n",
    "\n",
    "mri_adjusted = (mri_resample - lddmm.ccIbar[0].cpu().numpy()) * lddmm.ccCovIJ[0].cpu().numpy() / lddmm.ccVarI[0].cpu().numpy() + lddmm.ccJbar[0].cpu().numpy()\n",
    "mri_adjusted_arr = sitk.GetArrayFromImage(mri_adjusted)\n",
    "deformed_mri = (lddmm.applyThisTransform(mri_adjusted_arr))[0][-1].cpu().numpy()\n",
    "\n",
    "mri_sitk = sitk.GetImageFromArray(np.transpose(deformed_mri,(2,1,0)))\n",
    "mri_sitk.SetSpacing((0.08,0.08,0.08))\n",
    "mri_sitk_upsampled = sitk.Resample(mri_sitk,\n",
    "                                   orignisslsize,\n",
    "                                   sitk.AffineTransform(3),sitk.sitkLinear,(0,0,0),[0.08,0.04,0.08],(1,0,0,0,1,0,0,0,1),0.0)\n",
    "mri_upsampled = np.transpose(sitk.GetArrayFromImage(mri_sitk_upsampled),(2,1,0))\n",
    "plt.figure()\n",
    "plt.subplot(1,4,1)\n",
    "\n",
    "plt.imshow(mri_upsampled[:,:,4])\n",
    "plt.title('MRI Image')\n",
    "plt.subplot(1,4,2)\n",
    "plt.imshow(target_image[:,:,4])\n",
    "\n",
    "\n",
    "plt.title('Target Image')\n",
    "plt.subplot(1,4,3)\n",
    "plt.imshow(mri_upsampled[96,:,:])\n",
    "plt.title('MRI Image')\n",
    "plt.subplot(1,4,4)\n",
    "plt.imshow(target_image[:,:,4])\n",
    "plt.title('Target Image')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(16490361.)\n"
     ]
    }
   ],
   "source": [
    "print(torch.sum(lddmm.It[0][0] - lddmm.It[0][-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'mri_upsampled' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-23-d07879e2447b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0moutmri\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAnalyzeImage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmri_upsampled\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0moutmri\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheader\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'pixdim'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.08\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.04\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.08\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mnib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutmri\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'/cis/home/jtrieu/Documents/mri_rigid_upsampled.img'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'mri_upsampled' is not defined"
     ]
    }
   ],
   "source": [
    "outmri = nib.AnalyzeImage(mri_upsampled,None)\n",
    "outmri.header['pixdim'][1:4] = [0.08,0.04,0.08]\n",
    "nib.save(outmri, '/cis/home/jtrieu/Documents/mri_rigid_upsampled.img')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Do slice alignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115]\n",
      "torch.Size([220, 116, 14])\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'mri_upsampled' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-24-e148630549de>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget_image\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFloatTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmri_upsampled\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFloatTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0;31m#origJ = lddmm.J[0].clone()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0mepsilonxy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m4e-8\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'mri_upsampled' is not defined"
     ]
    }
   ],
   "source": [
    "directoryname = '/cis/home/leebc/Projects/Mouse_Histology/data/registration/BNBoutput/M519/'\n",
    "\n",
    "# Grab the indices of the missing slices\n",
    "missingslices = []\n",
    "for i in range(target_image.shape[1]):\n",
    "    if np.unique(target_image[:,i,:]).shape[0] < 3:\n",
    "        missingslices.append(i)\n",
    "print(missingslices)\n",
    "\n",
    "\n",
    "print(torch.tensor(target_image).type(torch.FloatTensor).cuda().shape)\n",
    "print(torch.tensor(mri_upsampled).type(torch.FloatTensor).cuda().shape)\n",
    "#origJ = lddmm.J[0].clone()\n",
    "epsilonxy = 4e-8\n",
    "epsilontheta=2e-8\n",
    "a = None\n",
    "b = None\n",
    "theta = None\n",
    "\n",
    "for i in range(1,8):\n",
    "    if i == 1:\n",
    "        niter = 600\n",
    "    elif i < 3:\n",
    "        niter = 100\n",
    "    elif i == 7:\n",
    "        niter = 800\n",
    "    else:\n",
    "        #epsilonxy = 2e-10\n",
    "        #epsilontheta=1e-10\n",
    "        niter = 200\n",
    "    \n",
    "    #a,b,theta,outtarget,epsilonxy,epsilontheta = lddmm.sa(origJ,lddmm.It[0][-1],niter=niter,dim=1,epsilonxy=epsilonxy, epsilontheta=epsilontheta,norm=0,missingslices=missingslices,sigma_atlas=0.75,sigma_target=10.0,a=a,b=b,theta=theta,min_sigma_atlas=1.0)\n",
    "    a,b,theta,outtarget,epsilonxy,epsilontheta = lddmm.sa(torch.tensor(target_image).type(torch.FloatTensor).cuda(),torch.tensor(mri_upsampled).type(torch.FloatTensor).cuda(),niter=niter,dim=1,norm=0,missingslices=missingslices,sigma_atlas=4.0,sigma_target=50.0,a=a,b=b,theta=theta,nx=list(target_image.shape),dx=[0.08,0.04,0.08],epsilonxy=epsilonxy*4, epsilontheta=epsilontheta*4)\n",
    "    #lddmm.setParams('a',4)\n",
    "    outtarget = torch.transpose(lddmm.applySA(torch.tensor(target_image).type(torch.FloatTensor).cuda(), a, b, theta, dim=1,nx=list(target_image.shape),dx=[0.08,0.04,0.08]),2,1).cpu().numpy().astype(np.float32)\n",
    "    lddmm.setParams('energy_fraction_from',len(lddmm.EAll)-1)\n",
    "    \n",
    "    # resample the target image\n",
    "    #outtarget_ds = sitk.GetArrayFromImage(sitk.Resample(sitk.GetImageFromArray(np.transpose(outtarget,(2,1,0))), [outtarget.shape[0],int(np.round(outtarget.shape[1]/2.0)), outtarget.shape[2]], sitk.AffineTransform(3), sitk.sitkLinear, (0,0,0),[0.08,0.08,0.08],(1,0,0,0,1,0,0,0,1),0.0)\n",
    "    outtarget_np = sitk.GetImageFromArray(np.transpose(outtarget,(2,1,0)))\n",
    "    outtarget_np.SetSpacing((0.08,0.04,0.08))\n",
    "    outtarget_ds = np.transpose(sitk.GetArrayFromImage(sitk.Resample(outtarget_np, deformed_mri.shape, sitk.AffineTransform(3), sitk.sitkLinear, (0,0,0),[0.08,0.08,0.08],(1,0,0,0,1,0,0,0,1),0.0)),(2,1,0))\n",
    "    \n",
    "    #lddmm.J[0] = torch.transpose(outtarget,2,1)\n",
    "    lddmm.J[0] = torch.tensor(outtarget_ds).type(torch.FloatTensor).cuda()\n",
    "    # reset beta\n",
    "    if i > 1:\n",
    "        lddmm.setParams('epsilonL',lddmm.GDBetaAffineR*lddmm.params['epsilonL']*4) # reduce step size, here we set it to the current size\n",
    "    \n",
    "    lddmm.setParams('epsilonT',lddmm.GDBetaAffineT*lddmm.params['epsilonT']*4) # reduce step size, here we set it to the current size\n",
    "    #lddmm.GDBeta = torch.tensor(1.0).type(lddmm.params['dtype']).to(device=lddmm.params['cuda'])\n",
    "    #lddmm.GDBetaAffineR = float(1.0)\n",
    "    #lddmm.GDBetaAffineT = float(1.0)\n",
    "    lddmm.setParams('niter',40)\n",
    "    lddmm.run()\n",
    "    \n",
    "    # resample the mri image\n",
    "    deformed_mri = (lddmm.applyThisTransform(mri_adjusted))[0][-1].cpu().numpy()\n",
    "    mri_sitk = sitk.GetImageFromArray(np.transpose(deformed_mri,(2,1,0)))\n",
    "    mri_sitk.SetSpacing((0.08,0.08,0.08))\n",
    "    mri_sitk_upsampled = sitk.Resample(mri_sitk,orignisslsize,sitk.AffineTransform(3),sitk.sitkLinear,(0,0,0),[0.08,0.04,0.08],(1,0,0,0,1,0,0,0,1),0.0)\n",
    "    mri_upsampled = np.transpose(sitk.GetArrayFromImage(mri_sitk_upsampled),(2,1,0))\n",
    "    \n",
    "    ##save\n",
    "    #outimg = nib.AnalyzeImage(lddmm.It[0][-1].to('cpu').numpy(),None)\n",
    "    #outimg.header['pixdim'][1:4] = [0.04,0.04,0.04]\n",
    "    #nib.save(outimg,directoryname + 'atlas_lddmm_rnaiter' + str(i) + '.img')\n",
    "    #outimg = nib.AnalyzeImage(np.transpose(outtarget.to('cpu').numpy(),(0,2,1)),None)\n",
    "    #outimg.header['pixdim'][1:4] = [0.04,0.04,0.04]\n",
    "    #nib.save(outimg,directoryname + 'target_lddmm_rnaiter' + str(i) + '.img')\n",
    "    ## transform annotation\n",
    "    #outanno = lddmm.applyThisTransform(anno,interpmode='nearest')\n",
    "    #outimg = nib.AnalyzeImage(outanno[0][-1].to('cpu').numpy(),None)\n",
    "    #outimg.header['pixdim'][1:4] = [0.04,0.04,0.04]\n",
    "    #nib.save(outimg,directoryname + 'anno_lddmm_rnaiter' + str(i) + '.img')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.subplot(1,4,1)\n",
    "plt.imshow(mri_upsampled[:,:,100])\n",
    "plt.title('MRI Image')\n",
    "plt.subplot(1,4,2)\n",
    "plt.imshow(outtarget[:,:,100])\n",
    "plt.title('Target Image')\n",
    "plt.subplot(1,4,3)\n",
    "plt.imshow(mri_upsampled[96,:,:])\n",
    "plt.title('MRI Image')\n",
    "plt.subplot(1,4,4)\n",
    "plt.imshow(outtarget[96,:,:])\n",
    "plt.title('Target Image')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write out transforms\n",
    "aout = a.cpu().numpy()\n",
    "bout = b.cpu().numpy()\n",
    "tout = theta.cpu().numpy()\n",
    "aout = np.delete(aout,missingslices)\n",
    "bout = np.delete(bout,missingslices)\n",
    "tout = np.delete(tout,missingslices)\n",
    "np.savetxt('/cis/home/leebc/Projects/Mouse_Histology/data/registration/BNBoutput/M519/M519_final_a.txt',aout,delimiter='\\n')\n",
    "np.savetxt('/cis/home/leebc/Projects/Mouse_Histology/data/registration/BNBoutput/M519/M519_final_b.txt',bout,delimiter='\\n')\n",
    "np.savetxt('/cis/home/leebc/Projects/Mouse_Histology/data/registration/BNBoutput/M519/M519_final_theta.txt',tout,delimiter='\\n')\n",
    "SAR\n",
    "# write out rotation center\n",
    "rotcenter = [outtarget.shape[0]/2.0*0.08, outtarget.shape[2]/2.0*0.08]\n",
    "np.savetxt('/cis/home/leebc/Projects/Mouse_Histology/data/registration/BNBoutput/M519/M519_rotcenter.txt',rotcenter,delimiter='\\n')\n",
    "\n",
    "# write out the reconstructed target image\n",
    "outimg = nib.AnalyzeImage(outtarget,None)\n",
    "outimg.header['pixdim'][1:4] = [0.08,0.04,0.08]marmoset\n",
    "nib.save(outimg, '/cis/home/jtrieu/Documents/M519_orig_target_STS.img')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aout = np.loadtxt('/cis/home/leebc/Projects/Mouse_Histology/data/registration/BNBoutput/M519/M519_final_a.txt',delimiter='\\n')\n",
    "bout = np.loadtxt('/cis/home/leebc/Projects/Mouse_Histology/data/registration/BNBoutput/M519/M519_final_b.txt',delimiter='\\n')\n",
    "tout = np.loadtxt('/cis/home/leebc/Projects/Mouse_Histology/data/registration/BNBoutput/M519/M519_final_theta.txt',delimiter='\\n')\n",
    "rotcenter = np.loadtxt('/cis/home/leebc/Projects/Mouse_Histology/data/registration/BNBoutput/M519/M519_rotcenter.txt',delimiter='\\n')\n",
    "file = open('/cis/home/leebc/Projects/Mouse_Histology/data/registration/BNBoutput/M519/M519_XForm_matrix.txt','w')\n",
    "for i in range(len(aout)):\n",
    "    file.write(str(np.cos(tout[i])) + ',' + str(-1*np.sin(tout[i])) + ',' + str(np.sin(tout[i])) + ',' + str(np.cos(tout[i])) + ',' + str(aout[i]) + ',' + str(bout[i]) + ',' + str(rotcenter[0]) + ',' + str(rotcenter[1]) + '\\n')\n",
    "\n",
    "file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Map atlas to reconstructed target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# i/o\n",
    "target_image = nib.load('/cis/home/leebc/Projects/Mouse_Histology/data/registration/BNBoutput/M519/M519_orig_target_STS.img').get_data().astype(np.float32)\n",
    "template_image = np.squeeze(nib.load('/cis/home/leebc/Projects/Mouse_Histology/data/marmoset/hashikawa/atlas_80_flip_masked_eroded_refined.img').get_data().astype(np.float32))\n",
    "dx = [0.08,0.08,0.08]\n",
    "\n",
    "# downsample the target\n",
    "target_sitk = sitk.GetImageFromArray(np.transpose(target_image,(2,1,0)))\n",
    "target_sitk.SetSpacing((0.08,0.04,0.08))\n",
    "target_ds = np.transpose(sitk.GetArrayFromImage(sitk.Resample(target_sitk, [target_image.shape[0],int(np.round(target_image.shape[1]/2.0)),target_image.shape[2]], sitk.AffineTransform(3), sitk.sitkLinear, (0,0,0),[0.08,0.08,0.08],(1,0,0,0,1,0,0,0,1),0.0)),(2,1,0))\n",
    "\n",
    "# pad the atlas and the target\n",
    "templatepadsize = [max(target_ds.shape[0],template_image.shape[0]), max(int(np.round(target_image.shape[1]/2.0)),template_image.shape[1]), max(target_ds.shape[2],template_image.shape[2])]\n",
    "target_padded = np.zeros(templatepadsize)\n",
    "target_padded[0:target_ds.shape[0],0:target_ds.shape[1],0:target_ds.shape[2]] = np.copy(target_ds)\n",
    "template_padded = np.zeros(templatepadsize)\n",
    "template_padded[0:template_image.shape[0],0:template_image.shape[1],0:template_image.shape[2]] = np.copy(template_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Do affine alignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "lddmm = torch_lddmm.LDDMM(template=[template_padded],target=[target_padded],costmask=None,outdir='../notebook/',do_affine=2,do_lddmm=0,a=8,niter=150,epsilon=5e-3,sigma=[20.0],sigmaR=10.0,optimizer='gdr',dx=dx,cc=1,epsilonL=4e-8,epsilonT=8e-6)\n",
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lddmm.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lddmm.setParams('do_affine',1)\n",
    "#lddmm.setParams('epsilonL',lddmm.GDBetaAffineR*lddmm.params['epsilonL']*4) # reduce step size, here we set it to the current size\n",
    "#lddmm.setParams('epsilonT',lddmm.GDBetaAffineT*lddmm.params['epsilonT']*4) # reduce step size, here we set it to the current size\n",
    "lddmm.setParams('epsilonL',4e-8)\n",
    "lddmm.setParams('epsilonT',8e-6)\n",
    "lddmm.setParams('niter',500)\n",
    "lddmm.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "plt.rcParams[\"figure.figsize\"]=20,20\n",
    "plt.figure()\n",
    "plt.subplot(1,4,1)\n",
    "plt.imshow(target_padded[:,:,100])\n",
    "plt.title('Target Image')\n",
    "plt.subplot(1,4,2)\n",
    "plt.imshow(lddmm.It[0][-1].cpu().numpy()[:,:,100])\n",
    "plt.title('Deformed Template')\n",
    "plt.subplot(1,4,3)\n",
    "plt.imshow(target_padded[96,:,:])\n",
    "plt.title('Target Image')\n",
    "plt.subplot(1,4,4)\n",
    "plt.imshow(lddmm.It[0][-1].cpu().numpy()[96,:,:])\n",
    "plt.title('Deformed Template')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# save the affine transform\n",
    "A = lddmm.affineA.cpu().numpy()\n",
    "file = open('/cis/home/leebc/Projects/Mouse_Histology/data/registration/BNBoutput/M519/M519_affine_transform.txt','w')\n",
    "file.write(str(A[0,0]) + '\\n')\n",
    "file.write(str(A[0,1]) + '\\n')\n",
    "file.write(str(A[0,2]) + '\\n')\n",
    "file.write(str(A[1,0]) + '\\n')\n",
    "file.write(str(A[1,1]) + '\\n')\n",
    "file.write(str(A[1,2]) + '\\n')\n",
    "file.write(str(A[2,0]) + '\\n')\n",
    "file.write(str(A[2,1]) + '\\n')\n",
    "file.write(str(A[2,2]) + '\\n')\n",
    "file.write(str(A[3,0]) + '\\n')\n",
    "file.write(str(A[3,1]) + '\\n')\n",
    "file.write(str(A[3,2]) + '\\n')\n",
    "file.write(str(target_padded.shape[0]/2.0) + '\\n')\n",
    "file.write(str(target_padded.shape[1]/2.0) + '\\n')\n",
    "file.write(str(target_padded.shape[2]/2.0) + '\\n')\n",
    "file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## crop images back to original size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "template_affine = lddmm.It[0][-1].cpu().numpy()[0:target_ds.shape[0],0:target_ds.shape[1],0:target_ds.shape[2]]\n",
    "outimg = nib.AnalyzeImage(template_affine,None)\n",
    "outimg.header['pixdim'][1:4] = [0.08,0.08,0.08]\n",
    "nib.save(outimg, '/cis/home/leebc/Projects/Mouse_Histology/data/registration/BNBoutput/M519/M519_affine.img')\n",
    "\n",
    "# transform the annotation\n",
    "anno = np.squeeze(nib.load('/cis/home/leebc/Projects/Mouse_Histology/data/marmoset/hashikawa/annotation_80_flip.img').get_data().astype(np.float32))\n",
    "anno_padded = np.zeros(templatepadsize)\n",
    "anno_padded[0:anno.shape[0],0:anno.shape[1],0:anno.shape[2]] = np.copy(anno)\n",
    "\n",
    "deformed_anno,_,_,_ = lddmm.applyThisTransform(anno_padded,interpmode='nearest')\n",
    "deformed_anno = (deformed_anno[-1].cpu().numpy())[0:target_ds.shape[0],0:target_ds.shape[1],0:target_ds.shape[2]]\n",
    "outimg = nib.AnalyzeImage(deformed_anno,None)\n",
    "outimg.header['pixdim'][1:4] = [0.08,0.08,0.08]\n",
    "nib.save(outimg, '/cis/home/leebc/Projects/Mouse_Histology/data/registration/BNBoutput/M519/M519_annotation_affine.img')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Do LDDMM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "template_affine = nib.load('/cis/home/leebc/Projects/Mouse_Histology/data/registration/BNBoutput/M519/M519_affine.img').get_data().astype(np.float32)\n",
    "# i/o\n",
    "target_image = nib.load('/cis/home/leebc/Projects/Mouse_Histology/data/registration/BNBoutput/M519/M519_orig_target_STS.img').get_data().astype(np.float32)\n",
    "#template_image = np.squeeze(nib.load('/cis/home/leebc/Projects/Mouse_Histology/data/marmoset/hashikawa/atlas_80_flip_masked_eroded_refined.img').get_data().astype(np.float32))\n",
    "dx = [0.08,0.08,0.08]\n",
    "\n",
    "# downsample the target\n",
    "target_sitk = sitk.GetImageFromArray(np.transpose(target_image,(2,1,0)))\n",
    "target_sitk.SetSpacing((0.08,0.04,0.08))\n",
    "target_ds = np.transpose(sitk.GetArrayFromImage(sitk.Resample(target_sitk, [target_image.shape[0],int(np.round(target_image.shape[1]/2.0)),target_image.shape[2]], sitk.AffineTransform(3), sitk.sitkLinear, (0,0,0),[0.08,0.08,0.08],(1,0,0,0,1,0,0,0,1),0.0)),(2,1,0))\n",
    "\n",
    "lddmm = torch_lddmm.LDDMM(template=[template_affine],target=[target_ds],costmask=None,outdir='../notebook/',do_affine=0,do_lddmm=1,a=8,niter=250,epsilon=2e-2,sigma=[20.0],sigmaR=10.0,optimizer='gdr',dx=dx,cc=1,nt=4,low_memory=1)\n",
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lddmm.run()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "anno_sitk = sitk.GetImageFromArray(np.transpose(deformed_anno,(2,1,0)))\n",
    "anno_sitk.SetSpacing((0.08,0.08,0.08))\n",
    "anno_us = sitk.Resample(anno_sitk,orignisslsize,sitk.AffineTransform(3),sitk.sitkNearestNeighbor,(0,0,0),[0.08,0.04,0.08],(1,0,0,0,1,0,0,0,1),0.0)\n",
    "anno_us = np.transpose(sitk.GetArrayFromImage(anno_us),(2,1,0))\n",
    "anno_out = nib.AnalyzeImage(anno_us,None)\n",
    "anno_out.header['pixdim'][1:4] = [0.08,0.04,0.08]\n",
    "nib.save(anno_out,'/cis/home/leebc/Projects/Mouse_Histology/data/registration/BNBoutput/M519/M519_annotation.img')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "evels = np.unique(anno_us[:,:,100])\n",
    "\n",
    "plt.figure()\n",
    "plt.subplot(1,2,1)\n",
    "plt.imshow(target_image[:,:,100],cmap='gray')\n",
    "plt.title('Target')\n",
    "for i in range(levels.shape[0]):\n",
    "    plt.contour((anno_us[:,:,100]==levels[i])*1.0,0.999,colors='r')\n",
    "\n",
    "levels = np.unique(anno_us[96,:,:])\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(target_image[96,:,:],cmap='gray')\n",
    "plt.title('Target')\n",
    "for i in range(levels.shape[0]):\n",
    "    plt.contour((anno_us[96,:,:]==levels[i])*1.0,0.999,colors='r')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# save transforms\n",
    "vt0,vt1,vt2 = lddmm.outputTransforms()\n",
    "np.save('/cis/home/leebc/Projects/Mouse_Histology/data/registration/BNBoutput/M519/M519_vt0.npy',vt0)\n",
    "np.save('/cis/home/leebc/Projects/Mouse_Histology/data/registration/BNBoutput/M519/M519_vt1.npy',vt1)\n",
    "np.save('/cis/home/leebc/Projects/Mouse_Histology/data/registration/BNBoutput/M519/M519_vt2.npy',vt2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Warp MRI to reconstructed target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_file_name = '/cis/home/leebc/Projects/Mouse_Histology/data/registration/BNBoutput/M519/M519_orig_target_STS.img'\n",
    "mri_file_name = '/cis/home/leebc/Projects/Mouse_Histology/data/registration/BNBoutput/M519/M519_mri_full.img'\n",
    "\n",
    "target_image = nib.load(target_file_name).get_data().astype(np.float32)\n",
    "mri_image = nib.load(mri_file_name).get_data().astype(np.float32)\n",
    "\n",
    "mripadsize = [max(target_image.shape[0],int(np.round(mri_image.shape[0]*0.06/0.08))), max(int(np.round(target_image.shape[1]/2.0)),int(np.round(mri_image.shape[1]*0.06/0.08))), max(target_image.shape[2],int(np.round(mri_image.shape[2]*0.06/0.08)))]\n",
    "\n",
    "target_sitk = sitk.ReadImage(target_file_name)\n",
    "target_sitk_resample = sitk.Resample(target_sitk,mripadsize,sitk.AffineTransform(3),sitk.sitkLinear,(0,0,0),[0.08,0.08,0.08],(1,0,0,0,1,0,0,0,1),0.0)\n",
    "print(target_sitk_resample.GetSize())\n",
    "target_resample = np.transpose(sitk.GetArrayFromImage(target_sitk_resample),(2,1,0))\n",
    "print(target_resample.shape)\n",
    "\n",
    "mri_sitk = sitk.ReadImage(mri_file_name)\n",
    "print(mri_sitk.GetSize())\n",
    "mri_sitk_resample = sitk.Resample(mri_sitk,target_resample.shape,sitk.AffineTransform(3),sitk.sitkLinear,(0,0,0),[0.08,0.08,0.08],(1,0,0,0,1,0,0,0,1),0.0)\n",
    "print(mri_sitk_resample.GetSize())\n",
    "mri_resample = np.transpose(sitk.GetArrayFromImage(mri_sitk_resample),(2,1,0))\n",
    "print(mri_resample.shape)\n",
    "\n",
    "#target_padded = np.zeros(mripadsize)\n",
    "#target_padded[0:target_image.shape[0],0:target_image.shape[1],0:target_image.shape[2]] = np.copy(target_image)\n",
    "#mri_padded = np.zeros(mripadsize)\n",
    "#mri_padded[0:mri_image.shape[0],0:mri_image.shape[1],0:mri_image.shape[2]] = np.copy(mri_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dx=[0.08,0.08,0.08]\n",
    "lddmm = torch_lddmm.LDDMM(template=[mri_resample],target=[target_resample],costmask=None,outdir='../notebook/',do_affine=2,do_lddmm=0,a=8,niter=150,epsilon=5e-3,sigma=[20.0],sigmaR=10.0,optimizer='gdr',dx=dx,cc=1)\n",
    "lddmm.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lddmm.setParams('do_affine',1)\n",
    "lddmm.run()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save transforms\n",
    "# save the affine transform\n",
    "A = lddmm.affineA.cpu().numpy()\n",
    "file = open('/cis/home/leebc/Projects/Mouse_Histology/data/registration/BNBoutput/M519/M519_MRI_affine_transform.txt','w')\n",
    "file.write(str(A[0,0]) + '\\n')\n",
    "file.write(str(A[0,1]) + '\\n')\n",
    "file.write(str(A[0,2]) + '\\n')\n",
    "file.write(str(A[1,0]) + '\\n')\n",
    "file.write(str(A[1,1]) + '\\n')\n",
    "file.write(str(A[1,2]) + '\\n')\n",
    "file.write(str(A[2,0]) + '\\n')\n",
    "file.write(str(A[2,1]) + '\\n')\n",
    "file.write(str(A[2,2]) + '\\n')\n",
    "file.write(str(A[3,0]) + '\\n')\n",
    "file.write(str(A[3,1]) + '\\n')\n",
    "file.write(str(A[3,2]) + '\\n')\n",
    "file.write(str(target_resample.shape[0]/2.0) + '\\n')\n",
    "file.write(str(target_resample.shape[1]/2.0) + '\\n')\n",
    "file.write(str(target_resample.shape[2]/2.0) + '\\n')\n",
    "file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# crop the MRI back to original target size\n",
    "\n",
    "target_sitk = sitk.ReadImage(target_file_name)\n",
    "target_sitk_resample = sitk.Resample(target_sitk,[target_sitk.GetSize()[0],int(np.round(target_sitk.GetSize()[1]/2.0)),target_sitk.GetSize()[2]],sitk.AffineTransform(3),sitk.sitkLinear,(0,0,0),[0.08,0.08,0.08],(1,0,0,0,1,0,0,0,1),0.0)\n",
    "print(target_sitk_resample.GetSize())\n",
    "target_resample = np.transpose(sitk.GetArrayFromImage(target_sitk_resample),(2,1,0))\n",
    "print(target_resample.shape)\n",
    "\n",
    "mri_affine = lddmm.It[0][-1].cpu().numpy()\n",
    "mri_affine = mri_affine[0:target_resample.shape[0], 0:target_resample.shape[1],0:target_resample.shape[2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "lddmm = torch_lddmm.LDDMM(template=[mri_affine],target=[target_resample],costmask=None,outdir='../notebook/',do_affine=0,do_lddmm=1,a=8,niter=250,epsilon=2e-2,sigma=[20.0],sigmaR=10.0,optimizer='gdr',dx=dx,cc=1,nt=4,low_memory=1)\n",
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lddmm.run()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "deformed_mri,_,_,_ = lddmm.applyThisTransform(mri_affine,interpmode='bilinear')\n",
    "deformed_mri = deformed_mri[-1].cpu().numpy()\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"]=20,20\n",
    "plt.figure()\n",
    "plt.subplot(1,4,1)\n",
    "plt.imshow(target_resample[:,:,100])\n",
    "plt.title('Target Image')\n",
    "plt.subplot(1,4,2)\n",
    "plt.imshow(deformed_mri[:,:,100])\n",
    "plt.title('Deformed Template')\n",
    "plt.subplot(1,4,3)\n",
    "plt.imshow(target_resample[96,:,:])\n",
    "plt.title('Target Image')\n",
    "plt.subplot(1,4,4)\n",
    "plt.imshow(deformed_mri[96,:,:])\n",
    "plt.title('Deformed Template')\n",
    "plt.show()\n",
    "\n",
    "out_mri = nib.AnalyzeImage(deformed_mri, None)\n",
    "out_mri.header['pixdim'][1:4] = [0.08,0.08,0.08]\n",
    "nib.save(out_mri, '/cis/home/leebc/Projects/Mouse_Histology/data/registration/BNBoutput/M519/M519_MRI_lddmm.img')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save transforms\n",
    "vt0,vt1,vt2 = lddmm.outputTransforms()\n",
    "np.save('/cis/home/leebc/Projects/Mouse_Histology/data/registration/BNBoutput/M519/M519_MRI_vt0.npy',vt0)\n",
    "np.save('/cis/home/leebc/Projects/Mouse_Histology/data/registration/BNBoutput/M519/M519_MRI_vt1.npy',vt1)\n",
    "np.save('/cis/home/leebc/Projects/Mouse_Histology/data/registration/BNBoutput/M519/M519_MRI_vt2.npy',vt2)"
   ]
  }
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