Figures_simulations_rician_noise.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dipy Simulations (with Rician noise at different SNRs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First let's load relevant modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import nibabel as nib\n",
    "import matplotlib.pyplot as plt\n",
    "from dipy.core.sphere import disperse_charges, HemiSphere\n",
    "from dipy.sims.voxel import (multi_tensor_dki, single_tensor, multi_tensor, \n",
    "                             _check_directions, all_tensor_evecs)\n",
    "from dipy.data import (get_fnames, get_sphere)\n",
    "from dipy.io.gradients import read_bvals_bvecs\n",
    "from dipy.core.gradients import gradient_table\n",
    "from dipy.reconst.dti import (decompose_tensor, from_lower_triangular)\n",
    "from dipy.sims.voxel import add_noise\n",
    "from dipy.data import get_sphere\n",
    "from dipy.reconst.dti import fractional_anisotropy\n",
    "from dipy.viz import window, actor\n",
    "import dipy.reconst.dki as dki\n",
    "import dipy.reconst.dti as dti\n",
    "import dipy.reconst.msdki as msdki\n",
    "sphere = get_sphere('repulsion724')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The synthetic signals for each set of ground truth parameters are generated according to the gradient directions and b-values of the CFIN dataset. So let's load a object containing the acquisition parameters of the CFIN dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fraw, fbval, fbvec, t1_fname = get_fnames('cfin_multib')\n",
    "bvals, bvecs = read_bvals_bvecs(fbval, fbvec)\n",
    "gtab = gradient_table(bvals, bvecs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "bval1000 = gtab.bvals == 1000\n",
    "bval2000 = gtab.bvals == 2000\n",
    "bval3000 = gtab.bvals == 3000\n",
    "bval_sel = bval1000 | bval2000 | bval3000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "bvals = gtab.bvals[bval_sel]\n",
    "bvecs = gtab.bvecs[bval_sel, :]\n",
    "\n",
    "# add 6 b-values\n",
    "bvals = np.hstack((np.zeros(6), bvals))\n",
    "bvecs = np.vstack((np.zeros((6, 3)), bvecs))\n",
    "gtab = gradient_table(bvals, bvecs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Voxel case 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "eval1 = [1.4e-3, 0.1e-3, 0.1e-3]\n",
    "eval2 = [2.0e-3, 0.5e-3, 0.5e-3]\n",
    "angle1 = (130.0, 0.0)\n",
    "angle2 = (130.0, 0.0)\n",
    "stick1 = _check_directions([angle1])\n",
    "stick2 = _check_directions([angle2])\n",
    "evec1 = all_tensor_evecs(stick1[0])\n",
    "evec2 = all_tensor_evecs(stick2[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ground truth parameters\n",
    "mevals = [eval1, eval2]\n",
    "S0 = 1\n",
    "angles = [angle1, angle2]\n",
    "fractions = [50, 50]\n",
    "\n",
    "signal2_gt, st = multi_tensor(gtab, mevals, S0, angles, fractions, snr=None)\n",
    "\n",
    "dki_model = dki.DiffusionKurtosisModel(gtab, fit_method='NLS')\n",
    "dki_fit = dki_model.fit(signal2_gt)\n",
    "md_gt = dki_fit.md\n",
    "ad_gt = dki_fit.ad\n",
    "rd_gt = dki_fit.rd\n",
    "fa_gt = dki_fit.fa\n",
    "\n",
    "mk_gt = dki_fit.mk()\n",
    "ak_gt = dki_fit.ak()\n",
    "rk_gt = dki_fit.rk()\n",
    "kfa_gt = dki_fit.kfa\n",
    "mkt_gt = dki_fit.mkt()\n",
    "\n",
    "msdki_model = msdki.MeanDiffusionKurtosisModel(gtab)\n",
    "msdki_fit = msdki_model.fit(signal2_gt)\n",
    "msk_gt = msdki_fit.msk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "20\n",
      "30\n",
      "40\n",
      "50\n",
      "60\n",
      "70\n",
      "80\n",
      "90\n",
      "100\n"
     ]
    }
   ],
   "source": [
    "# Produce simulated signals for different SNRs\n",
    "SNRs = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100])\n",
    "nrep = 1000\n",
    "DWI_simulates = np.empty((SNRs.size, nrep, gtab.bvals.size))\n",
    "\n",
    "for si in range(10):\n",
    "    snri =  SNRs[si]\n",
    "    for ri in range(nrep):\n",
    "        signal2, st = multi_tensor(gtab, mevals, 1.0, angles, fractions, snr=snri)\n",
    "        DWI_simulates[si, ri, :] = signal2\n",
    "    print(snri)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's fit DKI using DIPY's implementations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dki_fit = dki_model.fit(DWI_simulates)\n",
    "MD = dki_fit.md\n",
    "AD = dki_fit.ad\n",
    "RD = dki_fit.rd\n",
    "FA = dki_fit.fa\n",
    "\n",
    "MK = dki_fit.mk()\n",
    "AK = dki_fit.ak()\n",
    "RK = dki_fit.rk()\n",
    "KFA = dki_fit.kfa\n",
    "MKT = dki_fit.mkt()\n",
    "\n",
    "msdki_fit = msdki_model.fit(DWI_simulates)\n",
    "MSK = msdki_fit.msk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7, 0.9)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=1, ncols=4, figsize=(12, 3))\n",
    "fig.subplots_adjust(hspace=1, wspace=0.3)\n",
    "\n",
    "# Initializing vectors for statistics\n",
    "median_p = np.empty(SNRs.size)\n",
    "lower_p = np.empty(SNRs.size)\n",
    "upper_p = np.empty(SNRs.size)\n",
    "\n",
    "for snri in range(SNRs.size):\n",
    "    median_p[snri] = np.median(MD[snri, :]*1000)\n",
    "    p25, p75 = np.percentile(MD[snri, :]*1000, [25, 75])\n",
    "    lower_p[snri] = median_p[snri] - p25 \n",
    "    upper_p[snri] = p75 - median_p[snri]\n",
    "\n",
    "axs[0].errorbar(SNRs, median_p, fmt='-', yerr=[lower_p, upper_p],\n",
    "                color=[0.0, 0.0, 0.8], ecolor= [0.0, 0.0, 0.8],\n",
    "                linewidth=1.0)\n",
    "axs[0].hlines(md_gt*1000, xmin=0, xmax=110, colors=[0, 0, 0])\n",
    "\n",
    "# Adjust properties of the first panel of the figure\n",
    "# axs[0].set_ylim([md_gt*1000*0.9, md_gt*1000*1.1])\n",
    "axs[0].set_xlim([0, 110])\n",
    "axs[0].set_xlabel('SNR', fontsize=14)\n",
    "axs[0].set_title('A.1) MD ($x10^{-3}mm^{2}/s$)', fontsize=14, pad=10)\n",
    "\n",
    "\n",
    "for snri in range(SNRs.size):\n",
    "    median_p[snri] = np.median(RD[snri, :]*1000)\n",
    "    p25, p75 = np.percentile(RD[snri, :]*1000, [25, 75])\n",
    "    lower_p[snri] = median_p[snri] - p25 \n",
    "    upper_p[snri] = p75 - median_p[snri]\n",
    "\n",
    "axs[1].errorbar(SNRs, median_p, fmt='-', yerr=[lower_p, upper_p],\n",
    "                color=[0.0, 0.0, 0.4], ecolor= [0.0, 0.0, 0.4],\n",
    "                linewidth=1.0)\n",
    "axs[1].hlines(rd_gt*1000, xmin=0, xmax=110, colors=[0, 0, 0])\n",
    "\n",
    "# Adjust properties of the first panel of the figure\n",
    "#axs[0].set_ylim([, 1.2])\n",
    "axs[1].set_xlim([0, 110])\n",
    "axs[1].set_xlabel('SNR', fontsize=14)\n",
    "axs[1].set_title('A.2) RD ($x10^{-3}mm^{2}/s$)', fontsize=14, pad=10)\n",
    "\n",
    "\n",
    "\n",
    "for snri in range(SNRs.size):\n",
    "    median_p[snri] = np.median(AD[snri, :]*1000)\n",
    "    p25, p75 = np.percentile(AD[snri, :]*1000, [25, 75])\n",
    "    lower_p[snri] = median_p[snri] - p25 \n",
    "    upper_p[snri] = p75 - median_p[snri]\n",
    "\n",
    "axs[2].errorbar(SNRs, median_p, fmt='-', yerr=[lower_p, upper_p],\n",
    "                color=[0.0, 0.8, 0.8], ecolor= [0.0, 0.8, 0.8],\n",
    "                linewidth=1.0)\n",
    "axs[2].hlines(ad_gt*1000, xmin=0, xmax=110, colors=[0, 0, 0])\n",
    "\n",
    "# Adjust properties of the first panel of the figure\n",
    "#axs[0].set_ylim([, 1.2])\n",
    "axs[2].set_xlim([0, 110])\n",
    "axs[2].set_xlabel('SNR', fontsize=14)\n",
    "axs[2].set_title('A.3) AD ($x10^{-3}mm^{2}/s$)', fontsize=14, pad=10)\n",
    "\n",
    "for snri in range(SNRs.size):\n",
    "    median_p[snri] = np.median(FA[snri, :])\n",
    "    p25, p75 = np.percentile(FA[snri, :], [25, 75])\n",
    "    lower_p[snri] = median_p[snri] - p25 \n",
    "    upper_p[snri] = p75 - median_p[snri]\n",
    "\n",
    "axs[3].errorbar(SNRs, median_p, fmt='-', yerr=[lower_p, upper_p],\n",
    "                color=[1.0, 0.0, 0.0], ecolor= [1.0, 0.0, 0.0],\n",
    "                linewidth=1.0)\n",
    "axs[3].hlines(fa_gt, xmin=0, xmax=110, colors=[0, 0, 0])\n",
    "\n",
    "# Adjust properties of the first panel of the figure\n",
    "axs[3].set_xlim([0, 110])\n",
    "axs[3].set_xlabel('SNR', fontsize=14)\n",
    "axs[3].set_title('A.4) FA', fontsize=14, pad=10)\n",
    "\n",
    "\n",
    "axs[0].set_ylim([0.7, 0.9])\n",
    "axs[1].set_ylim([0.2, 0.4])\n",
    "axs[2].set_ylim([1.6, 2.0])\n",
    "axs[3].set_ylim([0.7, 0.9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.3, 1.0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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clLgjkDSIlAhHWAVmiJk9a2ZvhCvBjE99qCIi+U1tsYhIajWbCEdcBeb7BP18Dibo+P7bVAcqIpLP1BaLiKRelDvCUVaBcaBb+LoEWJW6EEVEBLXFIqnhtcHzR/+G6sp4Y5HYRRks19AqMIfVK1MKPGlmVwCdgRMaOpGZTQYmAwwZMqSlsYqI5DO1xSKpsCOcRv4f34C1ZdDvQBh8BOxxZPDoOiDe+CSjUjVYbhJwl7sPBsYD95rZbudu1eTXIiLSHLXFIs1p3zV4njwPrloDx/8QOvWGt+6C3+0HvxwGD54Hr94Kq1+H2urkrqNFO7JClDvCza4CA1wMnALg7i+ZWQegN7AmFUGKiIjaYpGUa9cZho0LHgDusG4xrHgp6Dox73bY8AEMHBvsr6mCwuJo59aiHVkhyh3hnavAmFk7ggEYs+qV+RA4HsDM9gU6AFr4W0QkddQWi6SbGfQeBQddBBOmwf97F771ERxxVbD/tn3hrXugtibWMCV1mk2EI64C8x3gUjN7C3gAuMjdPV1Bi4jkG7XFIjHp0B1Gnha8nngHvP6HoAvFu3/+bOCdZK1IK8tFWAVmAXBUakPbVemqUkoHlqbzEpIDzGw68Hlgjbvv18D+ccDfgGXhpofcfWq47xTgV0AhcIe7/zgTMYtE1RbaYpG8NmwcXPQ8vP8kPPsDeOFmOG4qjDo9uJssWSdrVpabslorukgkdxH2kWzCC+5+UPioS4KjzNEqIiL5zgz2PhkueQU+d3PQF/gP/wnvzQ76GEtWyZpEWCQKd38eqEji0ChztIqIiATMYNQEmPwaHH0tPPVdmH4kLP1n8gmxZprIOCXCko+OMLO3zOwfZjYm3NbQHK2DGjrYzCab2Twzm1dernFIIiJ5zQpg9Jlw2dtw6BXw2P+Du8cld67n9Ot3pikRlnzzOjDU3Q8EfgM80tITaA5WEZEsd+wNqT9nQSHsfx58fQEc9NVg2yMXwhbNXtiWKRGWvOLuG919c/h6NlBsZr2JNkeriIjkgnTO71tQFEy/BtCpL/x2P5j7O0251kYpEZa8Ymb9zYKhvWZ2KMG/gXVEm6NVRERaycxOMbNFZrbEzK5pYP8QM3vWzN4ws7fNbHwcce4mmbvIJ/0ULnga3r0f7jwCVr2W+rikVSJNnyaSLczsAWAc0NvMVgA3AMUA7n47cBbwP2ZWDWwDzg3nWa02s7o5WguB6e4+P4aPICKSsxJm6DmRYCzGXDObFU79V+f7BPNk/y6cvWc2MCzjwdaX7F3kfvvDRc8FC3HcfxqM/hJ87sZgfmKJnRJhySnuPqmZ/bcCtzayb7c5WkVkd6tWlTJQ87pLcnbO0ANgZnUz9CQmwg50C1+XAKsyGmE6WEHQXWLkBHj6e3DbaDjxp0GfYs0/HCt1jRARkRZZrXndJXlRZugpBc4Pf9WbDVzR0ImycgafTr1gwu/hnIfgpZ/BPcdDeVncUeU1JcIiIiLSlkwC7nL3wcB44F4z2y1fyeoZfAYfDpfOhX2+AH88JrhLXLU17qjykhJhERERyZQoM/RcDPwFwN1fAjoAvTMSXSYVFMFh34D/eRs+XQa/HdP8MfVpAY5WUyIsIiIimRJlhp4PgeMBzGxfgkQ4S/o+JKHrQDjzATjl18H7N++KfqwW4Gg1JcIiIiKSEe5eDdTN0FNGMDvEfDObamYTw2LfAS41s7eAB4CLwtl9ctuoCcHz8zfBk1dp3uEM0awRIiIS2datr8cdgmS5hmbocffrE14vAI7KdFwpl+zqdZe8AjPPhgcmBHeKO5SkNi7Zhe4Ii4hIZJ988ou4QxDJDsnOO9ypF3z5ceixJ9x5OKx7L6Vhya6UCIvkmNIMDJ7IxDWkberZs8mpukUkFQqLYfytcNg34Y9Hw9J/xh1RzsrpRLh0VWncIYhk3JQMDJ5o6TWUOOeObt1OBGD79mUxRyKSB8b+N5z1Z3jofHj1VsiDrtKZltOJ8BRN+i7SrEwkqckk50qe2yazYgAqKmbEHIlInhg2Di7+N8y7Hf5+GdTsiDuinJLTibCINC8Td5CTkYm7zi09Rsn5Zyoq7o87BJH80WPPIBnevBruPRG2ro07opyhRFhyhplNN7M1ZvZuI/u/bGZvm9k7ZvZvMzswYd/ycPubZjYvc1E3T8lXNMkk9C09pq1+aYhDTc0Gtm17J+4wRPJH+25w7iOwx1Hwh0PhkyT//elvyi6UCEsuuQs4pYn9y4Bj3X1/4EZgWr39x7n7Qe4+Nk3xJUXJl7RFPXtOoqLigbjDEMkvVgDH/xCOuxHu+Vxy59DflF0oEZac4e7PAxVN7P+3u68P375MsLSniLTQgAE30LPneVRU3E8+rHMg0uYc8GU477Hg9fM3aRBdKygRlnx1MfCPhPcOPGlmr5nZ5JhiEskKAweW0rHjARQUdGLLlpfiDkckPw06NHhe/HeYeQ7s2BJvPFkq0spyZnYK8CugELjD3X/cQJmzgVKChOItdz8vhXGKpIyZHUeQCB+dsPlod19pZn2Bp8xsYXiHuaHjJwOTAdq3b8+4cePSG/ByGPdsC66R7vK6RsuukUJtqS02s513hbt0OTIdlxDJP8msRnfRnGA2iT8eDec8At2HpjqqnNbsHWEzKwRuA04FRgOTzGx0vTIjgGuBo9x9DPDN1Icq0npmdgBwB3C6u6+r2+7uK8PnNcDDwKGNncPdp7n7WHcfW1xcnO6QJQaOU1VUxebOm6noWcHqAatZPmw59IsvprbYFvfsOYn16/+Ke3U6LyOSP5JZja6oA5z+RzjggmAlug8avIcjjYhyR/hQYIm7LwUwsxnA6cCChDKXArfV9b8MkwmRNsXMhgAPAV9x98UJ2zsDBe6+KXx9EjA1yjlHjRrFnDlz0hHuTjbFmHND9Guku3xbuoa7U1VbxY6aHXT9UVdmfncmNbU1VNdWU+M11NTW7PZct29HzQ6Omn4UZ55yJis2rmDlppWs3LQyeL1xJcWFxQzuNphBXQcxqNsgBncdzE0v3NSyz23Wos/cjDbXFrdvvxft2w9n48anKSk5OZ2XEpGmmMER34K+Y+CvX4JxU2DsZXFHlRWiJMKDgI8S3q8ADqtXZiSAmb1I8JNdqbs/npIIRSIysweAcUBvM1sB3AAUA7j77cD1QC/gt2GCUh3OENEPeDjcVgTcr/qbPrVeS8W2CtZsWdPoo3xrOQAH/O4AdtTs2PmoS3rrHtW11RQXFNOusB0Ao24dRVFBEYVWSGFB4W7PifuKC4K7+QvXLmRwt8GM6TuGQV0HBclvt0F0addlt9hveuGmzP2H2l2bbIt79JhERcX9SoRF2oK9ToKv/gtmnA6fvA2n/CpYrlkaFamPcMTzjCBIQgYDz5vZ/u7+aWKhxL6VQ4YMSdGlU6d0VSmlA0vjDkOS5O6Tmtl/CXBJA9uXAgfufoQkw91Zs2UN88vnM3/NfOaXzweCpHbNljWs27aObu270bdz388enYLnA/sdSN/OfendqTfj7h7HfV+8j3aF7XYmu4mP4sJiiguKd951tSnGuqvXNRHZ7myKcdtpt6X6P0GcMt4W9+x5NqtXl1Jbu42Cgo6tOpeIpECvEXDJy8GyzPeeAF+aCZ37JH++OaXJddnIElES4ZXAHgnvB4fbEq0AXnH3KmCZmS0maIznJhZy92mEc7eOHTu2zc31MWX1FCXCIi2wZsuancnu/DXzWbB2AfPXzMdxxvQZw5g+Y9iv734A3HvGvTuT3OKIdygO6HdAOsPPNm2yLS4uHkCnTmPZsOExevQ4qzWnkjzR3KBPM/sFcFz4thPQ1927ZzTIbFe3+MYzP4A7Dg0G0fVP8n7Pc1PyPhGeC4wws+EEje65QP1RyI8Ak4A/mllvgp/nlqYwThGJWVVNFS9+9CKPLQ7mruzz0z5U11bvTHjH9B3DWaPPYkzfMfTr3G+X/rFX/OMKDky2EZY6bbYtrps9QomwNCdh0OeJBF/c5prZLHff2dfd3b+VUP4K4OCMB5oLrACOvxn6HRDcGT7tdzBa/0brazYRdvdqM7sceILg29t0d59vZlOBee4+K9x3kpktAGqA7yaOyBeR7LR+23oeX/I4jy5+lMeXPM6ePfZkwsgJALx12VsM6DIg1QPCpBFtuS3u3v0MPvrom1RXf0pRUfd0X06yW5RBn4kmEYz3kGTtd07QXeLPZwT9hmUXkfoIu/tsYHa9bdcnvHbg2+FDRLLYe+ve49HFj/Lo4kd5bdVrHDvsWCaMnMDPTvoZA7sOBKD0udKdryVz2mpbXFTUnW7djufTTx+id++vZfLSkn2iDPoEwMyGAsOBZzIQV24bcAhc8irMPDt4v/lj6NI/3pjaiFQNlhORLPb8B8/z6KIg+d24fSOfH/l5vn34tzl+z+PpVNwp7vDajBuSmew+T/TseR7l5b9XIiypdC4w091rGtrZ1gfgtzld+sEFT8ONxfD7Q+ALd8NeJ8YdVey0xHIrla4qjTsEkaR88OkHXP3U1QBc+fiVdCruxH1fvI8V317BtAnTmDBqgpLgekpzeMBIa5WUnMbWrfOoqloddyjStkUZ9FnnXOCBxk6UuLhRnz6tmBUhnxSE9z+/eB/87SJ4+jqoze8FcZQIt9KU1VPiDkGySOmc0liv7+7868N/cdZfzuKQaYdQHTaAb/z3G0w5bgpjB46lwNQsSMsVFHSkpGQiFRV/iTsUadt2Dvo0s3YEye6s+oXMbB+gB/BShuPLD8M/B//9Bqx+De4aBxs+TN25Y/4711L6i5dhuoOc36Y8F88Xp+3V27nnrXsY+4exfPVvX+XYocey/Mrl3HLyLbHEI7mpbvYIkcZ4sB533aDPMuAvdYM+zWxiQtFzgRlhv3dpTGu6a3XuC1+eDaMmwh/+Exbt9n0kOTH9nUuW+ghnmOYqlkz6ZPMn3D7vdm5/7Xb267sfU8dN5dQRp+qub0h9flOrW7fjWb78Aiorl9Chw95xhyNtVHODPsP3pZmMKWu1truWFcBRV8OQY+DBSbDsGTjhJ1DUPiXhZYOc/WuoL5GSz95Y/QYXPXIR+9y2D6s2reKfX/knT33lKU4beVrOJsHJJLXq85taZkX06PEl1q+fEXcoItISexwRdJXY8CFMPxIqlsQdUcbk5F/ET6o+4fCFhwNw/OLj+e6K7/JAxQMsqlxErdfGHF3LtbQ7hbpf5K95q+YBMHHGRPbpvQ9LrljC7yf8njF9x8QcWfopqW0bgu4Rf9LNCJFs07EHnP0gHPRVuPMIeDc/vtDmXCK8qHIRRyw8gvEl4wG4ut/V9CzsyYPrH+TU906l5M0Sjll0DFd+dCV3r7ubd7a9Q7W37RGTLR2QpwF8+Wnj9o2c+ZczAVj6jaVcc/Q19OrUK+aokqduC9mpc+cjqK2tZNu2t+IORURaygwOvRzOfxKevR5mXRp3RGmXU4nwi5tf5NhFx/L9Ad/nhoHBH9GTS07m2gHXMnOvmSzdfykf7v8hUwZMYY/iPXhiwxN86f0vUfJmCYeVBfN5P7nxSXbU7ojzY2RcLt1BNrPpZrbGzN5tZL+Z2a/NbImZvW1mhyTsu9DM3gsfF2Yu6tS4+qmrOXHPYE7I4sLimKNpPd3hzU5mRs+ekzRoTiSbDTgYJr8GNduD93N/C9Xb440pTXImEZ65fiZnvH8Gdw+7m681MaF7j6IefK7b57iq/1Xcv+f9LNxvIR8f8DE/3+PnANyw6gb6v92fLy/7MjPXz2RzzeZMfYTYJHMHuQ1317gLOKWJ/acCI8LHZOB3AGbWk2AZz8MIlgC9wcx6pDXSFHpm2TM89t5j/Pykn8cdSoN0dze/BInwDDwLu6KJSKh9VzjjnuD1e7Ph1pHw2jSoya2bhTmRCP/yk1/yzY++yRMjnuDkkpNbfHzXwq4c3eVoAF7a5yXeHf0ux3Q5hjvW3sHAtwcyYckE7lx7J2uq1qQ69KzVVrtruPvzQEUTRU4H7vHAy0B3MxsAnAw85e4V7r4eeIqmE+o2Y/OOzVwy6xJ+//nfU9KhJO5wGqS7u/mlY8f9KSwsYfPmFwFYlUO/OonkpfP+Dmf9GcoehFtHwRvTc2YhjqxOhGu9lm999C2mrZ3Gi6Ne5OBOB6fkvAPbDeSyPpfx+IjH+XD/Dzmv53k8sfEJRrw7gmMWHcMtn9zC0u1LmzxHtVezoWYDq3asYnHlYt7Y+gYvbHoBgFe2vMK7295l6falfFL1CZtqNlHT8AqSknoNrXM/qIntuzGzyWY2z8zmlZeXpy3QqK7957UcM/QYxo8YH3coIjslzim8WuMWRLLf4MPh/CfgjHvhnT/BrfvAW/dCbQrylxgX4cjaeYQrayv5yrKvsKZ6DS+OepEeRen5Fbt7UXcm9ZzEpJ6TqKyt5JlNz/Dwpw/zk4U/oW9RXwAOLTuULbVb2Fy7mS01wXOVV9G5oDNdCrsEzwXBM8AVH17B1tqtbKndsstzO2tH54LOdCrotPO5U0GwxO0RC4+gxmuopZYar6GGml3fh9vqZsU4YfEJDCoexOB2g3d5HtRuEH2L+mZsCq3ttdvZWLMxI9fKFHefBkwDGDt2bKxD459b/hwPLXyId/+nwS7RaaOuDtKcnj3PZeHCQxky5NdxhyIiqTTkaLjgaVg+JxhQ98LNwcIeY86GgsLkzvnclNbPiZykrEyE11Wv4/QlpzO43WCeHPEk7QsyM/Fzh4IOjC8Zz/iS8dw+5HbmbpnLEYuO4DdDfrMz0a1LfDtYB8xst3PYa8ar+76623Z3p9IrdybGW2qC5Hhr7Vb+a/F/8fPBP6fQCimkkEIrpICCht9bIUPfGcrV/a9m5Y6VrKxaydvb3uYfG//Bih0rWFm1kg01G+hf3J/BxYMZ1C646XnViqt2niPKM8C1K69lY81GNtZsZEPNhp2vN9Z+9r7WaykpbFM/1ze2zv1KYFy97XMyFlUStlZt5eJZF/O7035Hj46Z7c6srg7SnPbth9O+/Qg2bnwq7lBEJFGqbmQMGwcXPQfLng4T4pvg2NLUnDuDYkuEFy1axLhx46IfsAnGdR3Htp7beOfSd+g1vxeFjxVysjfRJzg8pqXXaJFN8L9d/zft1/he1++1qPwPu/5wt80l4f9qC2vZXrKdLSVbeLfkXWgHD3d6GDcHAy/w4HUBuO36um4/W2CGz6CosoiiyiIKKwsprCykaHsRvbb1ou/2vhRVFmHVhmE8t+m5ln/u9JgFXG5mMwgGxm1w99Vm9gTww4QBcicB18YVZBTXPX0dhw0+jImjJjZfWCQGWnJZpA1K5Y0MM9jzBBh+PLz/RJAQA6xfBj2Gp+46aZRVd4Q3Dd7Eu197lyFPD2HQiw1235QICmoK6FjRkY4VHQEo21TGHl33aOaoXa3ctJKhXYemI7xWMbMHCO7s9jazFQQzQRQDuPvtBMt6jgeWAFuBr4b7KszsRmBueKqp7t7UoLtYvfjhi8yYPyPjXSJEWqJHjy+xatX34w5DRNLNDPY+BfY6GaYWBKvTnf0g7HFk3JE1K7ZEeNSoUcyZMydyeXvN+LDoQ2YMmcEZnzsj8jFz/qNl12hJ+Vy5RluMCWiwa0lz3H1SM/sd+Hoj+6YD01t80QzbVrWNr836GreeemtWL5ghua+4uB+dOx/Oxo1PxB2KiGRC3d/tidNhxhfg5F/AAV9O/XXmlKbsznZWzBpRN5fvrL1mcUaPaEmwSK66/tnrOaj/QZw5+sy4QxFpVs+e58Udgohk2ohT4cJn4NnvB90lUj2n+HOpm4kmKxLhLoVdADiiyxExRyLJumGAZhlIhVdWvMK9b9/LrafeGncoIpH06HEOAJs3/yvmSEQko/ruBxe/DEufggcnQdW2uCNqUFYkwtL2tDSxLR1Ymp5A8khldSVf/dtX+dUpv6JP5z5xhyMSSUE4q8+KFdcQ9EySfGdmp5jZonCp+2saKXO2mS0ws/lmphGX2apLP7jwWbBCuPs42Pxx3BHtRolwDmppkprM3Voltpk39bmp7NN7H84ec3bcoYi0WE3NejZunB13GBIzMysEbiNY7n40MMnMRtcrM4Jg1p6j3H0M8M1MxykpVNQBvvgnGDEe7jgcPnk77oh2oUQ4C6T77quS2rbvtVWvcecbd/Lb036b1CBCkbgNGnQzK1d+D091X0HJNocCS9x9qbvvAGYAp9crcylwW7jcPe6+JsMxSqqZwbHXwwk/hntOgMWPxR3RTkqEM0x3XyUZF/3tIm456Rb6d+kfdygiSSkpOZ2Cgo6sXz8j7lAkXlGWsx8JjDSzF83sZTM7JWPRye5SuZLofufCpFnw6KXw8q+gDXSXUiKcYUpqJRnDuw/nvP01+l6yl5kxaNCPWLnyB9TW7og7HGnbioARBHPCTwL+YGbd6xcys8lmNs/M5pWXl2c2wnyS6pVEBx8OF78Eb9wBj/0/qKlK7flbKFIiHKVje1juTDNzMxubuhDbNs2GIOn05sdvAnD7529PW5eIG1L5bV/SKtvb4q5dj6NDh71Zu/aOuEOR+DS2zH2iFcAsd69y92XAYoLEeBfuPs3dx7r72D59NIA4q3QfCl97ETZ8APePjzWUZhPhKB3bw3JdgSuBV1IdZFumO7ySTpMfnQzAwK4D03aN0lR/25e0yJW2eODAH/LxxzdRU7Ml7lAkHnOBEWY23MzaAecCs+qVeYTgbjBm1pugq8TSDMYomdC+W9BNYkD4ff3RybB2YcbDiHJHOErHdoAbgZ8AlSmML6N0d1fakk3bNzG/fH7cYUjbkRNtcefO/0GXLsewZs2v4w5FYuDu1cDlwBNAGfAXd59vZlPNbGJY7AlgnZktAJ4Fvuvu6+KJWNKqoAhO+FHwuusguOtYeGAifPBCxvoPR0mEm+3YbmaHAHu4e9sZBpgE3d2VtmTh2oWM6jUq7jCk7ciZtnjgwBtZs+YWqqsr4g5FYuDus919pLvv5e43h9uud/dZ4Wt392+7+2h339/dNcIyH4y7Aa5cFkyzNutrcOfhMP+vUFuT1su2erCcmRUAtwDfiVBWHdslrZrrQ2lmvzCzN8PHYjP7NGFfTcK++j/VZVzZ2jL26b1P3GFIlsimtrhDh5F0734GH3/8k4xfW0TasOJOMPYy+PpCOOoaeOWX8JsR8OqtsCM93amiJMLNdWzvCuwHzDGz5cDhwKyGBmmoY7ukU5Q+lO7+LXc/yN0PAn4DPJSwe1vdPnefSMzKysvYt/e+cYchbUdOtcUDBlzP2rV/YMeO+uOkRCTvFRTCvmcEA+q+eB8sfxZ+NQye+QFs/iS1l4pQpsmO7e6+wd17u/swdx8GvAxMdPd5KY1UpHlR+1DWmQQ8kJHIklC2tox9+ygRlp2yti0e0MD4i3btBtO79yWsXn1jDBGJSNbY40g4+0H42r9h2zq4LbW/lDabCEfs2C7SFkSZqB0AMxsKDAeeSdjcIfy5+GUz+0LaooyobK3uCMtnsrktHtjI+Iv+/a9h/fqZVFa+t9u+VasaPkZE2rh0TcnZawSc9lu4fHFKT1sUpZC7zwZm19t2fSNlx7U+LJG0OxeY6e6JvfCHuvtKM9sTeMbM3nH39+sfaGaTgckAQ4YMSUtwO2p28MGnHzCi125TZ0oey7W2uKioJ/36fYtVq37AnnvuOh5q9eopjSbQItKGpXtKzs6p7c6lleUkl0SZqL3OudTrFuHuK8PnpcAc4OCGDsxE/8olFUsY2n0o7QrbpeX8Im1F375XsmnTc2zd+nrcoYhIHlIiLLkkykTtmNk+QA/gpYRtPcysffi6N3AUsCAjUTegrFwzRkh+KCzswoAB32flyuviDkVE8pASYckZLehDeS4ww32X2br3BeaZ2VsEE7j/2N3jS4TVP1jySO/el1JZuYhNm+bEHYqI5JlIfYRFskWUPpTuXtrAcf8G9k9rcC1QtraMk/Y8Ke4wRDKioKAdAwdOZeXKaxk16t+YWdwhiUieyOk7wloyWbJVWbmmTpP80rPnJGprt7BhQ+xr2YhIHsnpRFhLJks2qvVaFq1bpD7CklfMChk48IesXHkdu07mIiKSPjmdCItko482fESPDj3o1r5b3KGIZFRJyWkUFnanouJPcYciInlCfYRF2hitKCf5yswYPPjHLFt2ftyhiEgmpWsRjgh0R1ikjSkrL2OfXuoWIfmpS5ej6dLlaADca2OORkQyIt2LcDRBibBIG6M7wpLvhg79AwCrV5fGG4iI5DwlwiJtTGvnEL4hxp+YRFKhoKAjAOvW3ce6dffEHI2kmpmdYmaLzGyJmV3TwP6LzKzczN4MH5fEEafkB/URFmljWjt1WmmMPzGJpNLee/+dxYvH0a7dULp2PTbucCQFzKwQuA04EVgBzDWzWQ0sYPRnd7884wFK3tEdYZE2pHxLOTVeQ7/O/eIORSR2HTuOZvjwP7F06dlUVi6OOxxJjUOBJe6+1N13ADOA02OOSfKYEmGRNmTh2oXs23tfrawlEurW7UQGDryJJUtOo7p6XaPlVq0qzVxQ0hqDgI8S3q8It9V3ppm9bWYzzWyPhk5kZpPNbJ6ZzSsvL09HrNJWpbALoBJhkTakbG2ZFtIQqadPn0vp3v0LvP/+GdTWbm+wzOrVUzIclaTRo8Awdz8AeAq4u6FC7j7N3ce6+9g+ffpkNECJWQq7ACoRFmlDyspbN1BOJFcNGvQTiop688EHl+LucYcjyVsJJN7hHRxu28nd17l73TeeO4D/yFBskoeUCEtOac1oZDO70MzeCx8XZjbygKZOE2mYWQHDh99HZWUZH398U9zhSPLmAiPMbLiZtQPOBWYlFjCzAQlvJwJlGYxP8oxmjZCc0ZrRyGbWE7gBGAs48Fp47PoMhL5Ta6dOE8llBQWd2HvvWSxceDjt2+9Nz56T4g5JWsjdq83scuAJoBCY7u7zzWwqMM/dZwHfMLOJQDVQAVwUW8CS85QISy7ZORoZwMzqRiPXT4QbcjLwlLtXhMc+BZwCPJCmWHezecdmyreUM6z7sExdUiTrFBcPYK+9HuW9946nXbuhdOlyZNwhSQu5+2xgdr1t1ye8vha4NtNxSX5S1wjJJa0ZjRz12LSNVF68bjEjeo2gsKAwZecUyUWdOh3AsGF38/77Z7J9+/txhyMiWUyJsOSbSKORm5KukcoaKCfymQEDmp4eqaRkPAMGfJ8lSz5PdXVGezCJSA5RIiy5pDWjkZs9Nt00dZrIZwYOLG22TN++X6dbt5NZuvSs9AckIjlJibDkktaMRn4COMnMephZD+CkcFvGaKCcSMsNHvxzCgo6AbBjR0a/u4pIDlAiLDnD3auButHIZcBf6kYjhyOQIRiNPN/M3gK+QTgaORwkdyNBMj0XmFo3cC5Tyso1dZpIS5kVsueeMwEoKzuI8vLf417b7HFaiU5EIGIiHGFu1m+b2YJwANLTZjY09aGKNM/dZ7v7SHffy91vDrddH07Jg7tf6+5j3P1Adz/O3RcmHDvd3fcOH3/MZNxVNVUs+3QZI3uNzORlJcuoLW5YQUF7AEaOfJZ16/7I4sXHUVm5qMljtBKdiECERDhhbtZTgdHAJDMbXa/YG8DYcADSTOD/Uh2oSC57f/37DOo6iA5FHeIORdootcXN69hxP0aNepEePc5k4cKjWL36h7hXxR2WiLRhUe4I75yb1d13AHVzs+7k7s+6+9bw7csEA41EJKKFaxeqW4Q0R21xBGaF9O37Dfbd9zU2b36BsrKxbNkyN+6wRKSNipIIR55fNXQx8I+GdqRr/lWRbKep0yQCtcUt0L79UPbeezb9+l3NkiUT+Oij71BTsyXusESkjUnpYDkzO59gidqfNrQ/XfOvimQ7TZ0mqaS2OGBm9Or1ZUaPfofq6k9YsGB/Nm58KunzaYCdSO6JkghHml/VzE4ArgMmJszTKiIRaOo0iUBtcZKKi/swfPh9DBlyGx98cCnLl1+U1Hk0wE4k90RJhKPMzXow8HuChndN6sMUyV3urj7CEoXa4lYqKTmV0aPfpbCwBIAVK66msnJxzFGJSJyaTYQjzs36U6AL8Fcze9PMZjVyOhGpZ8XGFXRp14XuHbrHHYq0YWqLU6OwsAt77PGrne8XLTqGRYuOZd26+6it3ZbSa6krhUjbVxSlkLvPBmbX23Z9wusTUhyXSN5QtwiJSm1xag0e/H8MHHgTGzY8ytq1f+Cjj66kZ89J9O59KZ06Hdjq869ePSXSUtEiEh+tLCcSs4VrFyoRFolJQUE7evQ4kxEjHmfffV+nqKg3S5ZMoKzsPykvn0ZNzcaMxpMPd5GbWxgmodyZZuZmNjaT8Ul+USIsErOycs0YIdIWtG8/lIEDS9l//2UMHDiVjRsf5513hrJ8+cVs3vxSRmLI9QF5EReGwcy6AlcCr2Q2Qsk3SoRFYla2tkwD5URaacCAG1J2LrNCSkpOZa+9HmLMmDI6dBjJ8uUXArB48YmsWPFdKiruZ9u2BbjXpOy6eaLZhWFCNwI/ASozGZzkHyXCklOa+8nNzL5tZgvM7G0ze9rMhibsqwkHGGV0kJH6CIu0Xrr64hYX96d///9lv/2C2SX69fsWhYU9WL/+Yd5/fyJvvtmNsrLD+OCDyygvv50tW16htnZrM2dNnSzsStHswjBmdgiwh7s/lsnAJD9FGiwnkg0SfnI7kaBxnWtms9x9QUKxN4Cx7r7VzP4H+D/gnHDfNnc/KJMxV2yroLK6koFdB2bysiKSpJKS8ZSUjN/5vqZmI1u3vsW2bW+yZcurlJdPo7JyIe3bDwNg1aqpdOy4Lx067Ev79iMoKGif0niSGZC3alVpmx3EZ2YFwC3ARRHKTgYmAwwZMiS9gUnOUiIsuWTnT24AZlb3k9vORNjdn00o/zJwfkYjrKeuf7CZxRmGiCSpsLAbXbseQ9eux+zcVlu7g8rKBZSVHYz7Ntat+xOVlWXs2PEB7doNoUOHIDGuS5A7dNiHwsJuGYs55tksmlsYpiuwHzAnbBf7A7PMbKK7z0s8kbtPA6YBjB071tMZtOQuJcKSSxr6ye2wJspfDPwj4X0HM5sHVAM/dvdHGjoolXchNGOESO4pKGhHp04HATBo0I92bq+t3c727UuorCyjsrKMDRue4JNPfsn27YspLOxBhw5BW7By5Q8oLh5Q79E/5XeTY7JzYRiCBPhc4Ly6ne6+Aehd997M5gBX1U+CRVJFibDkJTM7HxgLHJuweai7rzSzPYFnzOwdd3+//rGpvAuh/sEi+aOgoD0dO46hY8cxu2x3r2XHjg+prCxj06Z/YlbEtm1vs3HjE1RVraaqajXV1Z9QUNBtZ2Lcrt1AiosHAFBR8QBFRX0oKupLcXFfiop6Y9Y2/7y7e7WZ1S0MUwhMr1sYBpjn7loERjKqbf5LEUlOcz+5AWBmJwDXAce6+/a67e6+MnxeGt6FOBjYLRFOpbK1ZRxzyDHNFxSRlErlLBOtZVZA+/bDdvYrHjhw99jca6muXrszMa6qWkVV1WoAPv30b1RXr6Gqqpzq6jVUV6+jsLCEoqI+YWLcN+F1n0x+tAY1tzBMve3jMhGT5C8lwpJLmvzJDcDMDgZ+D5zi7msStvcAtrr7djPrDRxFMJAurcrKNXWaSBza6mCxxpgVUFwc3PGFz1a9W7Xq++y554xdyrrXUFOznqqqNVRXl4dJcvC6snIBIvIZJcKSMyL+5PZToAvw13AgxofuPhHYF/i9mdUSTCv443qzTaTctqptrN68mj177JnOy4hIirSlu8hNMSukqKg3RUW9G9xfXv67DEck0nYpEZac0txPbu5+QiPH/RvYP73R7WrRukXs1WMvigr0z1AkG2TbXWQRaZ7+AovERN0ipC1YtGgR48aNizuMnLRpE3TtOi6tx2TiGiK5TCvLicREU6eJ5Lb27Yc2XygFx4hI8nRHWCQmZWvL+MI+X4g7DMlzo0aNYs6cOXGHIUlKZpW4lh6jBX8kl+mOsEhMNIewiLRWMv2W1ddZ5DNKhEViUF1bzZKKJYzqPSruUERERPKWEmGRGCxbv4z+XfrTqbhT3KGIiIjkLSXCIjFoSbeIG47NjrlLRUREso0SYZEYlJVHT4RLx5WmNxgREZE8pURYJAYL1y3UHMIiIiIxUyIsEoOy8jL26b1P3GGIiIjkNc0jLJJh7q6p00REUkgrJEqyIt0RNrNTzGyRmS0xs2sa2N/ezP4c7n/FzIalPFKRCFpTV83s2nD7IjM7OV0xrt68mvaF7enVqVe6LiE5Sm2x5III9fgyM3vHzN40s3+Z2eg44pT80OwdYTMrBG4DTgRWAHPNbJa7L0godjGw3t33NrNzgZ8A56QjYJHGtKauhg3tucAYYCDwTzMb6e41qY6zrLxM/YOlxdQWSy6IWI/vd/fbw/ITgVuAU5o6r1ZIlOY0tkJilDvChwJL3H2pu+8AZgCn1ytzOnB3+HomcLxpTUbJvNbU1dOBGe6+3d2XAUvC86WcukVIktQWSy5oth67+8aEt50Bz2B8kmeiJMKDgI8S3q8ItzVYxt2rgQ2AfveVTGtNXY1ybEosXLtQibAkQ22x5IJIba2Zfd3M3gf+D/hGhmKTPJTRwXJmNhmYHL7dbmbvtuh4cuLGRm9gbdxBxCCZzz00HYG0Vmvr8W3cBsA3+WZqA8ss1eNocrIO54h8rcOQBfXY3W8DbjOz84DvAxfWL6N6DORvPU5ZThElEV4J7JHwfnC4raEyK8ysCCgB1tU/kbtPA6YBmNk8dx8b4fo5RZ87rVpTV6McC6gegz53TJdXW5xC+fq5IfbPHrmtDc0AftfQDtVjfe5UnCtK14i5wAgzG25m7QgGFM2qV2YWn31bOwt4xt3Vp0cyrTV1dRZwbjjqfjgwAng1Q3GLRKG2WHJBs/XYzEYkvD0NeC+D8UmeafaOsLtXm9nlwBNAITDd3eeb2VRgnrvPAu4E7jWzJUAFQcUWyajW1NWw3F+ABUA18PV0zBghkiy1xZILItbjy83sBKAKWE8D3SJEUsXiullgZpPDnzXyij53bsnVz9Ucfe7ckYufKYp8/dyQm589Fz9TFPrcKTiXfjUTERERkXwUaWU5EREREZFcE0si3NzyirnEzJYnLBU5L9zW08yeMrP3wuceccfZWmY23czWJE5f09jntMCvw///3zazQ+KLPDmqw7lXh0H1OO540ilf6nG+1WFQPVY9bl09zngibJ8tr3gqMBqYZLm/jvhx7n5QwlQf1wBPu/sI4Onwfba7i92XwGzsc55KMCvDCII5IBucGqetUh0GcrMOg+qx6nH2u4s8qcOgehy+Vz1uRT2O445wlGVCc13iMqh3A1+IL5TUcPfnCUapJ2rsc54O3OOBl4HuZjYgI4GmhupwDtZhUD1G9fgL8YWSGnlWh0H1GFSPW1WP40iEM7aUbRvhwJNm9poFq+AA9HP31eHrj4F+8YSWdo19zmyvA9kef0vlcx0G1eNckc/1OFfrMOTGZ2gJ1eNAyupxRpdYzlNHu/tKM+sLPGVmCxN3urubWc5P3ZEvnzNHqQ6H8umz5iDVY/Lnc+Yw1WNS+znjuCPc0uUVs5q7rwyf1wAPE/yM80ndbfvweU18EaZVY58z2+tAtsffInleh0H1OCfkeT3O1ToMufEZIlM9Tn09jiMRjrJMaE4ws85m1rXuNXAS8C67LoN6IfC3eCJMu8Y+5yzggnCk5+HAhoSfO7KB6nD+1GFQPc56qsc5W4dB9Vj1uLX12N0z/gDGA4uB94Hr4oghQ59zT+Ct8DG/7rMCvQhGPL4H/BPoGXesKfisDwCrCZbEXAFc3NjnBIxglO/7wDvA2LjjT+Lzqg7nWB0OP5fqcQ4+8qke51sdDj+H6rHqcdL1WCvLiYiIiEhe0spyIiIiIpKXlAiLiIiISF5SIiwiIiIieUmJsIiIiIjkJSXCIiIiIpKXlAiLiIiISF5SIpwmZtbHzH5rZsvNbLuZfWJmT5vZieH+OWbmZvaVesddZGabE96PC8vVPdaZ2TNmdlSmP5PkF9VhyQWqx5ILVI/TR4lw+jxIsPThxcBI4PPAPwgmhK5TCdxoZu0jnG8MMAAYB5QDj1mw1rhIuqgOSy5QPZZcoHqcJkqE08DMugPHANe4+9Pu/oG7z3X3n7n7jISifwY6Al+PcNo17v6xu78D3ASUAIelOnYRUB2W3KB6LLlA9Ti9lAinx+bwMdHMOjRTbgpwXVjRm2VmnYCLwrdVrYhRpCmqw5ILVI8lF6gep5ES4TRw92qCinU+8KmZvWRmPzOzhr5tTQPWAdc0c9rlYT+fzcC3gXkEa26LpJzqsOQC1WPJBarH6aVEOE3c/UFgIDCBoB/PkcDLZva9euWqgeuAb5jZoCZOeRxwCDAJWAZc6O55+e1NMkN1WHKB6rHkAtXj9FEinEbuXunuT7n7VHc/ErgTKDWzdvXK/RV4B5jaxOmWuftid/8zwU8fD0XsEC+SNNVhyQWqx5ILVI/TQ4lwZi0AioCG+vhcDVxIMJKzOfcCxUTrEC+SSqrDkgtUjyUXqB6ngBLhNDCzXuG8fOeb2QFmNtzMvkRQMZ929431j3H354DHgcubO7+71wK/BK4xs84pDl9EdVhyguqx5ALV4/RSIpwem4GXgSuB54D5wA+B+4FzmjjuGqBdE/sTTSf4Jnhl8mGKNEp1WHKB6rHkAtXjNDJ3jzsGEREREZGM0x1hEREREclLSoRFREREJC8pERYRERGRvKREWERERETykhJhEREREclLSoRFREREJC8pERYRERGRvKREWERERETykhJhEREREclL/x8zF5FYt7tEXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=1, ncols=4, figsize=(12, 3))\n",
    "fig.subplots_adjust(hspace=0.3, wspace=0.3)\n",
    "\n",
    "# Initializing vectors for statistics\n",
    "median_p = np.empty(SNRs.size)\n",
    "lower_p = np.empty(SNRs.size)\n",
    "upper_p = np.empty(SNRs.size)\n",
    "\n",
    "for snri in range(SNRs.size):\n",
    "    median_p[snri] = np.median(MK[snri, :])\n",
    "    p25, p75 = np.percentile(MK[snri, :], [25, 75])\n",
    "    lower_p[snri] = median_p[snri] - p25 \n",
    "    upper_p[snri] = p75 - median_p[snri]\n",
    "\n",
    "axs[0].errorbar(SNRs, median_p, fmt='-', yerr=[lower_p, upper_p],\n",
    "                color=[0.0, 0.8, 0.0], ecolor= [0.0, 0.8, 0.0],\n",
    "                linewidth=1.0)\n",
    "axs[0].hlines(mk_gt, xmin=0, xmax=110, colors=[0, 0, 0])\n",
    "\n",
    "# Adjust properties of the first panel of the figure\n",
    "# axs[0].set_ylim([md_gt*1000*0.9, md_gt*1000*1.1])\n",
    "axs[0].set_xlim([0, 110])\n",
    "axs[0].set_xlabel('SNR', fontsize=14)\n",
    "axs[0].set_title('A.5) MK', fontsize=14, pad=10)\n",
    "\n",
    "\n",
    "for snri in range(SNRs.size):\n",
    "    median_p[snri] = np.median(RK[snri, :])\n",
    "    p25, p75 = np.percentile(RK[snri, :], [25, 75])\n",
    "    lower_p[snri] = median_p[snri] - p25 \n",
    "    upper_p[snri] = p75 - median_p[snri]\n",
    "\n",
    "axs[1].errorbar(SNRs, median_p, fmt='-', yerr=[lower_p, upper_p],\n",
    "                color=[0.0, 0.5, 0.0], ecolor= [0.0, 0.5, 0.0],\n",
    "                linewidth=1.0)\n",
    "axs[1].hlines(rk_gt, xmin=0, xmax=110, colors=[0, 0, 0])\n",
    "\n",
    "# Adjust properties of the first panel of the figure\n",
    "#axs[0].set_ylim([, 1.2])\n",
    "axs[1].set_xlim([0, 110])\n",
    "axs[1].set_xlabel('SNR', fontsize=14)\n",
    "axs[1].set_title('A.6) RK', fontsize=14, pad=10)\n",
    "\n",
    "\n",
    "\n",
    "for snri in range(SNRs.size):\n",
    "    median_p[snri] = np.median(AK[snri, :])\n",
    "    p25, p75 = np.percentile(AK[snri, :], [25, 75])\n",
    "    lower_p[snri] = median_p[snri] - p25 \n",
    "    upper_p[snri] = p75 - median_p[snri]\n",
    "\n",
    "axs[2].errorbar(SNRs, median_p, fmt='-', yerr=[lower_p, upper_p],\n",
    "                color=[0.8, 0.8, 0.0], ecolor= [0.8, 0.8, 0.0],\n",
    "                linewidth=1.0)\n",
    "axs[2].hlines(ak_gt, xmin=0, xmax=110, colors=[0, 0, 0])\n",
    "\n",
    "# Adjust properties of the first panel of the figure\n",
    "#axs[0].set_ylim([, 1.2])\n",
    "axs[2].set_xlim([0, 110])\n",
    "axs[2].set_xlabel('SNR', fontsize=14)\n",
    "axs[2].set_title('A.7) AK', fontsize=14, pad=10)\n",
    "\n",
    "for snri in range(SNRs.size):\n",
    "    median_p[snri] = np.median(KFA[snri, :])\n",
    "    p25, p75 = np.percentile(KFA[snri, :], [25, 75])\n",
    "    lower_p[snri] = median_p[snri] - p25 \n",
    "    upper_p[snri] = p75 - median_p[snri]\n",
    "\n",
    "axs[3].errorbar(SNRs, median_p, fmt='-', yerr=[lower_p, upper_p],\n",
    "                color=[1, 0.5, 0.], ecolor=[1, 0.5, 0.],\n",
    "                linewidth=1.0)\n",
    "axs[3].hlines(kfa_gt, xmin=0, xmax=110, colors=[0, 0, 0])\n",
    "\n",
    "# Adjust properties of the first panel of the figure\n",
    "axs[3].set_xlim([0, 110])\n",
    "axs[3].set_xlabel('SNR', fontsize=14)\n",
    "axs[3].set_title('A.8) KFA', fontsize=14, pad=10)\n",
    "\n",
    "axs[0].set_ylim([0, 1])\n",
    "axs[1].set_ylim([0, 2])\n",
    "axs[2].set_ylim([0, 1])\n",
    "axs[3].set_ylim([0.3, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a reference let's plot here the same plots for MSK and MKT. Note that MSK cannot be computed from pyDesigner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=1, ncols=4, figsize=(12, 3))\n",
    "fig.subplots_adjust(hspace=0.3, wspace=0.3)\n",
    "\n",
    "# Initializing vectors for statistics\n",
    "median_p = np.empty(SNRs.size)\n",
    "lower_p = np.empty(SNRs.size)\n",
    "upper_p = np.empty(SNRs.size)\n",
    "\n",
    "for snri in range(SNRs.size):\n",
    "    median_p[snri] = np.median(MKT[snri, :])\n",
    "    p25, p75 = np.percentile(MKT[snri, :], [25, 75])\n",
    "    lower_p[snri] = median_p[snri] - p25 \n",
    "    upper_p[snri] = p75 - median_p[snri]\n",
    "\n",
    "axs[0].errorbar(SNRs, median_p, fmt='-', yerr=[lower_p, upper_p],\n",
    "                color=[0.0, 0.8, 0.0], ecolor= [0.0, 0.8, 0.0],\n",
    "                linewidth=1.0)\n",
    "axs[0].hlines(mkt_gt, xmin=0, xmax=110, colors=[0, 0, 0])\n",
    "\n",
    "# Adjust properties of the first panel of the figure\n",
    "# axs[0].set_ylim([md_gt*1000*0.9, md_gt*1000*1.1])\n",
    "axs[0].set_xlim([0, 110])\n",
    "axs[0].set_xlabel('SNR', fontsize=14)\n",
    "axs[0].set_title('MKT', fontsize=14)\n",
    "\n",
    "for snri in range(SNRs.size):\n",
    "    median_p[snri] = np.median(MSK[snri, :])\n",
    "    p25, p75 = np.percentile(MSK[snri, :], [25, 75])\n",
    "    lower_p[snri] = median_p[snri] - p25 \n",
    "    upper_p[snri] = p75 - median_p[snri]\n",
    "\n",
    "axs[1].errorbar(SNRs, median_p, fmt='-', yerr=[lower_p, upper_p],\n",
    "                color=[0.2, 0.0, 0.4], ecolor= [0.2, 0.0, 0.4],\n",
    "                linewidth=1.0)\n",
    "axs[1].hlines(msk_gt, xmin=0, xmax=110, colors=[0, 0, 0])\n",
    "\n",
    "# Adjust properties of the first panel of the figure\n",
    "#axs[0].set_ylim([, 1.2])\n",
    "axs[1].set_xlim([0, 110])\n",
    "axs[1].set_xlabel('SNR', fontsize=14)\n",
    "axs[1].set_title('MSK', fontsize=14)\n",
    "\n",
    "axs[2].axis('off')\n",
    "axs[3].axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now run pydesigner using its default fitting routine with no parameter constraints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from designer.fitting.dwipy import DWI\n",
    "\n",
    "# We have to save the data in nii, bvec and bvals files according to pydesigner requirements\n",
    "np.savetxt('dwi.bvals', bvals)\n",
    "np.savetxt('dwi.bvecs', bvecs.T)\n",
    "nib.save(nib.Nifti1Image(DWI_simulates.reshape((10, 100, 10, gtab.bvals.shape[0])), np.eye(4)), \"sim.nii.gz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No brain mask supplied\n",
      "Image sim.nii.nii loaded successfully\n",
      "Processing with 8 workers...\n"
     ]
    }
   ],
   "source": [
    "my_dwi = DWI(\"sim.nii.gz\", bvecPath='dwi.bvecs', bvalPath='dwi.bvals')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Unconstrained Tensor Fit: [100%]\n"
     ]
    }
   ],
   "source": [
    "my_dwi.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DTI Parameters: [100%]\n"
     ]
    }
   ],
   "source": [
    "md, rd, ad, fa, fe, trace  = my_dwi.extractDTI()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DKI Parameters: [100%]\n"
     ]
    }
   ],
   "source": [
    "mk, rk, ak, kfa, mkt, trace = my_dwi.extractDKI()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reshape to previous data format (matrix with dimensions corresponding to SNRs testes and simulation repetitions)\n",
    "MDpy = md.reshape((10, 1000))\n",
    "ADpy = ad.reshape((10, 1000))\n",
    "RDpy = rd.reshape((10, 1000))\n",
    "FApy = fa.reshape((10, 1000))\n",
    "\n",
    "MKpy = mk.reshape((10, 1000))\n",
    "AKpy = ak.reshape((10, 1000))\n",
    "RKpy = rk.reshape((10, 1000))\n",
    "KFApy = kfa.reshape((10, 1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7, 0.9)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=1, ncols=4, figsize=(12, 3))\n",
    "fig.subplots_adjust(hspace=1, wspace=0.3)\n",
    "\n",
    "# Initializing vectors for statistics\n",
    "median_p = np.empty(SNRs.size)\n",
    "lower_p = np.empty(SNRs.size)\n",
    "upper_p = np.empty(SNRs.size)\n",
    "\n",
    "for snri in range(SNRs.size):\n",
    "    median_p[snri] = np.median(MDpy[snri, :])\n",
    "    p25, p75 = np.percentile(MDpy[snri, :], [25, 75])\n",
    "    lower_p[snri] = median_p[snri] - p25 \n",
    "    upper_p[snri] = p75 - median_p[snri]\n",
    "\n",
    "axs[0].errorbar(SNRs, median_p, fmt='-', yerr=[lower_p, upper_p],\n",
    "                color=[0.0, 0.0, 0.8], ecolor= [0.0, 0.0, 0.8],\n",
    "                linewidth=1.0)\n",
    "axs[0].hlines(md_gt*1000, xmin=0, xmax=110, colors=[0, 0, 0])\n",
    "\n",
    "# Adjust properties of the first panel of the figure\n",
    "# axs[0].set_ylim([md_gt*1000*0.9, md_gt*1000*1.1])\n",
    "axs[0].set_xlim([0, 110])\n",
    "axs[0].set_xlabel('SNR', fontsize=14)\n",
    "axs[0].set_title('B.1) MD ($x10^{-3}mm^{2}/s$)', fontsize=14, pad=10)\n",
    "\n",
    "\n",
    "for snri in range(SNRs.size):\n",
    "    median_p[snri] = np.median(RDpy[snri, :])\n",
    "    p25, p75 = np.percentile(RDpy[snri, :], [25, 75])\n",
    "    lower_p[snri] = median_p[snri] - p25 \n",
    "    upper_p[snri] = p75 - median_p[snri]\n",
    "\n",
    "axs[1].errorbar(SNRs, median_p, fmt='-', yerr=[lower_p, upper_p],\n",
    "                color=[0.0, 0.0, 0.4], ecolor= [0.0, 0.0, 0.4],\n",
    "                linewidth=1.0)\n",
    "axs[1].hlines(rd_gt*1000, xmin=0, xmax=110, colors=[0, 0, 0])\n",
    "\n",
    "# Adjust properties of the first panel of the figure\n",
    "#axs[0].set_ylim([, 1.2])\n",
    "axs[1].set_xlim([0, 110])\n",
    "axs[1].set_xlabel('SNR', fontsize=14)\n",
    "axs[1].set_title('B.2) RD ($x10^{-3}mm^{2}/s$)', fontsize=14, pad=10)\n",
    "\n",
    "\n",
    "\n",
    "for snri in range(SNRs.size):\n",
    "    median_p[snri] = np.median(ADpy[snri, :])\n",
    "    p25, p75 = np.percentile(ADpy[snri, :], [25, 75])\n",
    "    lower_p[snri] = median_p[snri] - p25 \n",
    "    upper_p[snri] = p75 - median_p[snri]\n",
    "\n",
    "axs[2].errorbar(SNRs, median_p, fmt='-', yerr=[lower_p, upper_p],\n",
    "                color=[0.0, 0.8, 0.8], ecolor= [0.0, 0.8, 0.8],\n",
    "                linewidth=1.0)\n",
    "axs[2].hlines(ad_gt*1000, xmin=0, xmax=110, colors=[0, 0, 0])\n",
    "\n",
    "# Adjust properties of the first panel of the figure\n",
    "#axs[0].set_ylim([, 1.2])\n",
    "axs[2].set_xlim([0, 110])\n",
    "axs[2].set_xlabel('SNR', fontsize=14)\n",
    "axs[2].set_title('B.3) AD ($x10^{-3}mm^{2}/s$)', fontsize=14, pad=10)\n",
    "\n",
    "for snri in range(SNRs.size):\n",
    "    median_p[snri] = np.median(FApy[snri, :])\n",
    "    p25, p75 = np.percentile(FApy[snri, :], [25, 75])\n",
    "    lower_p[snri] = median_p[snri] - p25 \n",
    "    upper_p[snri] = p75 - median_p[snri]\n",
    "\n",
    "axs[3].errorbar(SNRs, median_p, fmt='-', yerr=[lower_p, upper_p],\n",
    "                color=[1.0, 0.0, 0.0], ecolor= [1.0, 0.0, 0.0],\n",
    "                linewidth=1.0)\n",
    "axs[3].hlines(fa_gt, xmin=0, xmax=110, colors=[0, 0, 0])\n",
    "\n",
    "# Adjust properties of the first panel of the figure\n",
    "axs[3].set_xlim([0, 110])\n",
    "axs[3].set_xlabel('SNR', fontsize=14)\n",
    "axs[3].set_title('B.4) FA', fontsize=14, pad=10)\n",
    "\n",
    "\n",
    "axs[0].set_ylim([0.7, 0.9])\n",
    "axs[1].set_ylim([0.2, 0.4])\n",
    "axs[2].set_ylim([1.6, 2.0])\n",
    "axs[3].set_ylim([0.7, 0.9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.3, 1.0)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=1, ncols=4, figsize=(12, 3))\n",
    "fig.subplots_adjust(hspace=0.3, wspace=0.3)\n",
    "\n",
    "# Initializing vectors for statistics\n",
    "median_p = np.empty(SNRs.size)\n",
    "lower_p = np.empty(SNRs.size)\n",
    "upper_p = np.empty(SNRs.size)\n",
    "\n",
    "for snri in range(SNRs.size):\n",
    "    median_p[snri] = np.median(MKpy[snri, :])\n",
    "    p25, p75 = np.percentile(MKpy[snri, :], [25, 75])\n",
    "    lower_p[snri] = median_p[snri] - p25 \n",
    "    upper_p[snri] = p75 - median_p[snri]\n",
    "\n",
    "axs[0].errorbar(SNRs, median_p, fmt='-', yerr=[lower_p, upper_p],\n",
    "                color=[0.0, 0.8, 0.0], ecolor= [0.0, 0.8, 0.0],\n",
    "                linewidth=1.0)\n",
    "axs[0].hlines(mk_gt, xmin=0, xmax=110, colors=[0, 0, 0])\n",
    "\n",
    "# Adjust properties of the first panel of the figure\n",
    "# axs[0].set_ylim([md_gt*1000*0.9, md_gt*1000*1.1])\n",
    "axs[0].set_xlim([0, 110])\n",
    "axs[0].set_xlabel('SNR', fontsize=14)\n",
    "axs[0].set_title('B.5) MK', fontsize=14, pad=10)\n",
    "\n",
    "\n",
    "for snri in range(SNRs.size):\n",
    "    median_p[snri] = np.median(RKpy[snri, :])\n",
    "    p25, p75 = np.percentile(RKpy[snri, :], [25, 75])\n",
    "    lower_p[snri] = median_p[snri] - p25 \n",
    "    upper_p[snri] = p75 - median_p[snri]\n",
    "\n",
    "axs[1].errorbar(SNRs, median_p, fmt='-', yerr=[lower_p, upper_p],\n",
    "                color=[0.0, 0.5, 0.0], ecolor= [0.0, 0.5, 0.0],\n",
    "                linewidth=1.0)\n",
    "axs[1].hlines(rk_gt, xmin=0, xmax=110, colors=[0, 0, 0])\n",
    "\n",
    "# Adjust properties of the first panel of the figure\n",
    "#axs[0].set_ylim([, 1.2])\n",
    "axs[1].set_xlim([0, 110])\n",
    "axs[1].set_xlabel('SNR', fontsize=14)\n",
    "axs[1].set_title('B.6) RK', fontsize=14, pad=10)\n",
    "\n",
    "\n",
    "\n",
    "for snri in range(SNRs.size):\n",
    "    median_p[snri] = np.median(AKpy[snri, :])\n",
    "    p25, p75 = np.percentile(AKpy[snri, :], [25, 75])\n",
    "    lower_p[snri] = median_p[snri] - p25 \n",
    "    upper_p[snri] = p75 - median_p[snri]\n",
    "\n",
    "axs[2].errorbar(SNRs, median_p, fmt='-', yerr=[lower_p, upper_p],\n",
    "                color=[0.8, 0.8, 0.0], ecolor= [0.8, 0.8, 0.0],\n",
    "                linewidth=1.0)\n",
    "axs[2].hlines(ak_gt, xmin=0, xmax=110, colors=[0, 0, 0])\n",
    "\n",
    "# Adjust properties of the first panel of the figure\n",
    "#axs[0].set_ylim([, 1.2])\n",
    "axs[2].set_xlim([0, 110])\n",
    "axs[2].set_xlabel('SNR', fontsize=14)\n",
    "axs[2].set_title('B.7) AK', fontsize=14, pad=10)\n",
    "\n",
    "for snri in range(SNRs.size):\n",
    "    median_p[snri] = np.median(KFApy[snri, :])\n",
    "    p25, p75 = np.percentile(KFApy[snri, :], [25, 75])\n",
    "    lower_p[snri] = median_p[snri] - p25 \n",
    "    upper_p[snri] = p75 - median_p[snri]\n",
    "\n",
    "axs[3].errorbar(SNRs, median_p, fmt='-', yerr=[lower_p, upper_p],\n",
    "                color=[1, 0.5, 0.], ecolor=[1, 0.5, 0.],\n",
    "                linewidth=1.0)\n",
    "axs[3].hlines(kfa_gt, xmin=0, xmax=110, colors=[0, 0, 0])\n",
    "\n",
    "# Adjust properties of the first panel of the figure\n",
    "axs[3].set_xlim([0, 110])\n",
    "axs[3].set_xlabel('SNR', fontsize=14)\n",
    "axs[3].set_title('B.8) KFA', fontsize=14, pad=10)\n",
    "\n",
    "axs[0].set_ylim([0, 1])\n",
    "axs[1].set_ylim([0, 2])\n",
    "axs[2].set_ylim([0, 1])\n",
    "axs[3].set_ylim([0.3, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now run pydesigner using its default fitting routine with positive kurtosis constraints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Constrained Tensor Fit: [100%]\n"
     ]
    }
   ],
   "source": [
    "my_dwi.fit(constraints=[0, 1, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DTI Parameters: [100%]\n"
     ]
    }
   ],
   "source": [
    "md, rd, ad, fa, fe, trace  = my_dwi.extractDTI()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DKI Parameters: [100%]\n"
     ]
    }
   ],
   "source": [
    "mk, rk, ak, kfa, mkt, trace = my_dwi.extractDKI()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reshape to previous data format (matrix with dimensions corresponding to SNRs testes and simulation repetitions)\n",
    "MDpy = md.reshape((10, 1000))\n",
    "ADpy = ad.reshape((10, 1000))\n",
    "RDpy = rd.reshape((10, 1000))\n",
    "FApy = fa.reshape((10, 1000))\n",
    "\n",
    "MKpy = mk.reshape((10, 1000))\n",
    "AKpy = ak.reshape((10, 1000))\n",
    "RKpy = rk.reshape((10, 1000))\n",
    "KFApy = kfa.reshape((10, 1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7, 0.9)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=1, ncols=4, figsize=(12, 3))\n",
    "fig.subplots_adjust(hspace=1, wspace=0.3)\n",
    "\n",
    "# Initializing vectors for statistics\n",
    "median_p = np.empty(SNRs.size)\n",
    "lower_p = np.empty(SNRs.size)\n",
    "upper_p = np.empty(SNRs.size)\n",
    "\n",
    "for snri in range(SNRs.size):\n",
    "    median_p[snri] = np.median(MDpy[snri, :])\n",
    "    p25, p75 = np.percentile(MDpy[snri, :], [25, 75])\n",
    "    lower_p[snri] = median_p[snri] - p25 \n",
    "    upper_p[snri] = p75 - median_p[snri]\n",
    "\n",
    "axs[0].errorbar(SNRs, median_p, fmt='-', yerr=[lower_p, upper_p],\n",
    "                color=[0.0, 0.0, 0.8], ecolor= [0.0, 0.0, 0.8],\n",
    "                linewidth=1.0)\n",
    "axs[0].hlines(md_gt*1000, xmin=0, xmax=110, colors=[0, 0, 0])\n",
    "\n",
    "# Adjust properties of the first panel of the figure\n",
    "# axs[0].set_ylim([md_gt*1000*0.9, md_gt*1000*1.1])\n",
    "axs[0].set_xlim([0, 110])\n",
    "axs[0].set_xlabel('SNR', fontsize=14)\n",
    "axs[0].set_title('C.1) MD ($x10^{-3}mm^{2}/s$)', fontsize=14, pad=10)\n",
    "\n",
    "\n",
    "for snri in range(SNRs.size):\n",
    "    median_p[snri] = np.median(RDpy[snri, :])\n",
    "    p25, p75 = np.percentile(RDpy[snri, :], [25, 75])\n",
    "    lower_p[snri] = median_p[snri] - p25 \n",
    "    upper_p[snri] = p75 - median_p[snri]\n",
    "\n",
    "axs[1].errorbar(SNRs, median_p, fmt='-', yerr=[lower_p, upper_p],\n",
    "                color=[0.0, 0.0, 0.4], ecolor= [0.0, 0.0, 0.4],\n",
    "                linewidth=1.0)\n",
    "axs[1].hlines(rd_gt*1000, xmin=0, xmax=110, colors=[0, 0, 0])\n",
    "\n",
    "# Adjust properties of the first panel of the figure\n",
    "#axs[0].set_ylim([, 1.2])\n",
    "axs[1].set_xlim([0, 110])\n",
    "axs[1].set_xlabel('SNR', fontsize=14)\n",
    "axs[1].set_title('C.2) RD ($x10^{-3}mm^{2}/s$)', fontsize=14, pad=10)\n",
    "\n",
    "\n",
    "\n",
    "for snri in range(SNRs.size):\n",
    "    median_p[snri] = np.median(ADpy[snri, :])\n",
    "    p25, p75 = np.percentile(ADpy[snri, :], [25, 75])\n",
    "    lower_p[snri] = median_p[snri] - p25 \n",
    "    upper_p[snri] = p75 - median_p[snri]\n",
    "\n",
    "axs[2].errorbar(SNRs, median_p, fmt='-', yerr=[lower_p, upper_p],\n",
    "                color=[0.0, 0.8, 0.8], ecolor= [0.0, 0.8, 0.8],\n",
    "                linewidth=1.0)\n",
    "axs[2].hlines(ad_gt*1000, xmin=0, xmax=110, colors=[0, 0, 0])\n",
    "\n",
    "# Adjust properties of the first panel of the figure\n",
    "#axs[0].set_ylim([, 1.2])\n",
    "axs[2].set_xlim([0, 110])\n",
    "axs[2].set_xlabel('SNR', fontsize=14)\n",
    "axs[2].set_title('C.3) AD ($x10^{-3}mm^{2}/s$)', fontsize=14, pad=10)\n",
    "\n",
    "for snri in range(SNRs.size):\n",
    "    median_p[snri] = np.median(FApy[snri, :])\n",
    "    p25, p75 = np.percentile(FApy[snri, :], [25, 75])\n",
    "    lower_p[snri] = median_p[snri] - p25 \n",
    "    upper_p[snri] = p75 - median_p[snri]\n",
    "\n",
    "axs[3].errorbar(SNRs, median_p, fmt='-', yerr=[lower_p, upper_p],\n",
    "                color=[1.0, 0.0, 0.0], ecolor= [1.0, 0.0, 0.0],\n",
    "                linewidth=1.0)\n",
    "axs[3].hlines(fa_gt, xmin=0, xmax=110, colors=[0, 0, 0])\n",
    "\n",
    "# Adjust properties of the first panel of the figure\n",
    "axs[3].set_xlim([0, 110])\n",
    "axs[3].set_xlabel('SNR', fontsize=14)\n",
    "axs[3].set_title('C.4) FA', fontsize=14, pad=10)\n",
    "\n",
    "\n",
    "axs[0].set_ylim([0.7, 0.9])\n",
    "axs[1].set_ylim([0.2, 0.4])\n",
    "axs[2].set_ylim([1.6, 2.0])\n",
    "axs[3].set_ylim([0.7, 0.9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.3, 1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=1, ncols=4, figsize=(12, 3))\n",
    "fig.subplots_adjust(hspace=0.3, wspace=0.3)\n",
    "\n",
    "# Initializing vectors for statistics\n",
    "median_p = np.empty(SNRs.size)\n",
    "lower_p = np.empty(SNRs.size)\n",
    "upper_p = np.empty(SNRs.size)\n",
    "\n",
    "for snri in range(SNRs.size):\n",
    "    median_p[snri] = np.median(MKpy[snri, :])\n",
    "    p25, p75 = np.percentile(MKpy[snri, :], [25, 75])\n",
    "    lower_p[snri] = median_p[snri] - p25 \n",
    "    upper_p[snri] = p75 - median_p[snri]\n",
    "\n",
    "axs[0].errorbar(SNRs, median_p, fmt='-', yerr=[lower_p, upper_p],\n",
    "                color=[0.0, 0.8, 0.0], ecolor= [0.0, 0.8, 0.0],\n",
    "                linewidth=1.0)\n",
    "axs[0].hlines(mk_gt, xmin=0, xmax=110, colors=[0, 0, 0])\n",
    "\n",
    "# Adjust properties of the first panel of the figure\n",
    "# axs[0].set_ylim([md_gt*1000*0.9, md_gt*1000*1.1])\n",
    "axs[0].set_xlim([0, 110])\n",
    "axs[0].set_xlabel('SNR', fontsize=14)\n",
    "axs[0].set_title('C.5) MK', fontsize=14, pad=10)\n",
    "\n",
    "\n",
    "for snri in range(SNRs.size):\n",
    "    median_p[snri] = np.median(RKpy[snri, :])\n",
    "    p25, p75 = np.percentile(RKpy[snri, :], [25, 75])\n",
    "    lower_p[snri] = median_p[snri] - p25 \n",
    "    upper_p[snri] = p75 - median_p[snri]\n",
    "\n",
    "axs[1].errorbar(SNRs, median_p, fmt='-', yerr=[lower_p, upper_p],\n",
    "                color=[0.0, 0.5, 0.0], ecolor= [0.0, 0.5, 0.0],\n",
    "                linewidth=1.0)\n",
    "axs[1].hlines(rk_gt, xmin=0, xmax=110, colors=[0, 0, 0])\n",
    "\n",
    "# Adjust properties of the first panel of the figure\n",
    "#axs[0].set_ylim([, 1.2])\n",
    "axs[1].set_xlim([0, 110])\n",
    "axs[1].set_xlabel('SNR', fontsize=14)\n",
    "axs[1].set_title('C.6) RK', fontsize=14, pad=10)\n",
    "\n",
    "\n",
    "\n",
    "for snri in range(SNRs.size):\n",
    "    median_p[snri] = np.median(AKpy[snri, :])\n",
    "    p25, p75 = np.percentile(AKpy[snri, :], [25, 75])\n",
    "    lower_p[snri] = median_p[snri] - p25 \n",
    "    upper_p[snri] = p75 - median_p[snri]\n",
    "\n",
    "axs[2].errorbar(SNRs, median_p, fmt='-', yerr=[lower_p, upper_p],\n",
    "                color=[0.8, 0.8, 0.0], ecolor= [0.8, 0.8, 0.0],\n",
    "                linewidth=1.0)\n",
    "axs[2].hlines(ak_gt, xmin=0, xmax=110, colors=[0, 0, 0])\n",
    "\n",
    "# Adjust properties of the first panel of the figure\n",
    "#axs[0].set_ylim([, 1.2])\n",
    "axs[2].set_xlim([0, 110])\n",
    "axs[2].set_xlabel('SNR', fontsize=14)\n",
    "axs[2].set_title('C.7) AK', fontsize=14, pad=10)\n",
    "\n",
    "for snri in range(SNRs.size):\n",
    "    median_p[snri] = np.median(KFApy[snri, :])\n",
    "    p25, p75 = np.percentile(KFApy[snri, :], [25, 75])\n",
    "    lower_p[snri] = median_p[snri] - p25 \n",
    "    upper_p[snri] = p75 - median_p[snri]\n",
    "\n",
    "axs[3].errorbar(SNRs, median_p, fmt='-', yerr=[lower_p, upper_p],\n",
    "                color=[1, 0.5, 0.], ecolor=[1, 0.5, 0.],\n",
    "                linewidth=1.0)\n",
    "axs[3].hlines(kfa_gt, xmin=0, xmax=110, colors=[0, 0, 0])\n",
    "\n",
    "# Adjust properties of the first panel of the figure\n",
    "axs[3].set_xlim([0, 110])\n",
    "axs[3].set_xlabel('SNR', fontsize=14)\n",
    "axs[3].set_title('C.8) KFA', fontsize=14, pad=10)\n",
    "\n",
    "axs[0].set_ylim([0, 1])\n",
    "axs[1].set_ylim([0, 2])\n",
    "axs[2].set_ylim([0, 1])\n",
    "axs[3].set_ylim([0.3, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}