{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decay correction\n",
    "\n",
    "This notebook illustrates how to account for decay correction time differences\n",
    "in an interrupted 4-D PET acquisition.\n",
    "\n",
    "Occasionally, 4-D PET acquisitions have to be stopped and resumed after a\n",
    "(short) period of time. This yields two sets of PET acquisitions that have to\n",
    "be combined prior to kinetic modeling. Most reconstruction algorithms are\n",
    "configured to perform radiotracer decay correction to scan start. Because of\n",
    "this, in order to combine the two images, the second image needs to be decay\n",
    "corrected to the scan start of the first image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import requests\n",
    "\n",
    "\n",
    "# download a 4-D PET image from OpenNeuro\n",
    "\n",
    "outdir = Path.cwd() / \"nb_data\"\n",
    "outdir.mkdir(exist_ok=True)\n",
    "\n",
    "petjson_fname = outdir / \"pet.json\"\n",
    "pet_fname = outdir / \"pet.nii\"\n",
    "\n",
    "baseurl = \"https://s3.amazonaws.com/openneuro.org/ds001705/sub-000101/ses-baseline/\"\n",
    "\n",
    "peturl = (\n",
    "    baseurl\n",
    "    + \"pet/sub-000101_ses-baseline_pet.nii\"\n",
    "    + \"?versionId=rMjWUWxAIYI46DmOQjulNQLTDUAThT5o\"\n",
    ")\n",
    "\n",
    "if not petjson_fname.exists():\n",
    "    r = requests.get(\n",
    "        baseurl\n",
    "        + \"pet/sub-000101_ses-baseline_pet.json\"\n",
    "        + \"?versionId=Gfkc8Y71JexOLZq40ZN4BTln_4VObTJR\",\n",
    "        timeout=10,\n",
    "    )\n",
    "    r.raise_for_status()\n",
    "    with open(petjson_fname, \"wb\") as f:\n",
    "        f.write(r.content)\n",
    "\n",
    "if not pet_fname.exists():\n",
    "    with requests.get(peturl, timeout=10, stream=True) as r:\n",
    "        r.raise_for_status()\n",
    "        with open(pet_fname, \"wb\") as f:\n",
    "            for chunk in r.iter_content(chunk_size=8192):\n",
    "                f.write(chunk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dynamicpet.petbids.petbidsimage import load\n",
    "\n",
    "\n",
    "pet = load(pet_fname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to use an uninterrupted acquisition and \"simulate\" an interrupted\n",
    "one by extracting two parts of the scan and changing the decay correction time\n",
    "of the second part to the \"scan start\" of the second part."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Manufacturer': 'Siemens',\n",
       " 'ManufacturersModelName': 'Biograph mMr',\n",
       " 'Units': 'kBq/mL',\n",
       " 'TracerName': 'LondonPride',\n",
       " 'TracerRadionuclide': 'C11',\n",
       " 'BodyPart': 'brain',\n",
       " 'InjectedRadioactivity': 400.0,\n",
       " 'InjectedRadioactivityUnits': 'MBq',\n",
       " 'InjectedMass': 5.0,\n",
       " 'InjectedMassUnits': 'ug',\n",
       " 'SpecificRadioactivity': 35.0,\n",
       " 'SpecificRadioactivityUnits': 'GBq/ug',\n",
       " 'ModeOfAdministration': 'bolus',\n",
       " 'TimeZero': '09:45:00',\n",
       " 'ScanStart': 0,\n",
       " 'InjectionStart': 0,\n",
       " 'FrameTimesStart': [3600.0, 4200.0, 4800.0],\n",
       " 'FrameDuration': [600.0, 600.0, 600.0],\n",
       " 'InjectionEnd': 30,\n",
       " 'AcquisitionMode': '3D',\n",
       " 'ImageDecayCorrected': True,\n",
       " 'ImageDecayCorrectionTime': 0,\n",
       " 'ReconMethodName': 'MLEM',\n",
       " 'ReconMethodParameterLabels': ['iterations'],\n",
       " 'ReconMethodParameterUnits': ['none'],\n",
       " 'ReconMethodParameterValues': [100],\n",
       " 'ReconFilterType': 'PSF',\n",
       " 'ReconFilterSize': 2.5,\n",
       " 'AttenuationCorrection': 'Activity decay corrected'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first part is minutes 0-50\n",
    "pet1 = pet.extract(start_time=pet.start_time, end_time=50)\n",
    "\n",
    "# second part is minutes 60-90\n",
    "pet2 = pet.extract(start_time=60, end_time=pet.end_time)\n",
    "\n",
    "# check out ImageDecayCorrectionTime of second part\n",
    "pet2.json_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that if these two images were acquired separately due to an interruption in\n",
    "the protocol, the timing info for the second image would not look like this, as\n",
    "decay time correction would have been performed to the scan start of the second\n",
    "image. To achieve this, we need to change the decay correction time of the\n",
    "second image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Manufacturer': 'Siemens',\n",
       " 'ManufacturersModelName': 'Biograph mMr',\n",
       " 'Units': 'kBq/mL',\n",
       " 'TracerName': 'LondonPride',\n",
       " 'TracerRadionuclide': 'C11',\n",
       " 'BodyPart': 'brain',\n",
       " 'InjectedRadioactivity': 400.0,\n",
       " 'InjectedRadioactivityUnits': 'MBq',\n",
       " 'InjectedMass': 5.0,\n",
       " 'InjectedMassUnits': 'ug',\n",
       " 'SpecificRadioactivity': 35.0,\n",
       " 'SpecificRadioactivityUnits': 'GBq/ug',\n",
       " 'ModeOfAdministration': 'bolus',\n",
       " 'TimeZero': '09:45:00',\n",
       " 'ScanStart': 0,\n",
       " 'InjectionStart': 0,\n",
       " 'FrameTimesStart': [3600.0, 4200.0, 4800.0],\n",
       " 'FrameDuration': [600.0, 600.0, 600.0],\n",
       " 'InjectionEnd': 30,\n",
       " 'AcquisitionMode': '3D',\n",
       " 'ImageDecayCorrected': True,\n",
       " 'ImageDecayCorrectionTime': 3600.0,\n",
       " 'ReconMethodName': 'MLEM',\n",
       " 'ReconMethodParameterLabels': ['iterations'],\n",
       " 'ReconMethodParameterUnits': ['none'],\n",
       " 'ReconMethodParameterValues': [100],\n",
       " 'ReconFilterType': 'PSF',\n",
       " 'ReconFilterSize': 2.5,\n",
       " 'AttenuationCorrection': 'Activity decay corrected'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# decay_correct input needs to be in unit of seconds, so we convert from min\n",
    "pet2_ = pet2.decay_correct((pet2.start_time - pet1.start_time) * 60)\n",
    "\n",
    "pet2_.json_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, ImageDecayCorrectionTime is 3600 s (= 60 min), correctly reflecting the\n",
    "interrupted acquisition scenario.\n",
    "\n",
    "We can take a look at the impact of ImageDecayCorrectionTime by comparing the\n",
    "mean activity curves in the whole brain. First, we obtain an approximate brain\n",
    "mask:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 660x350 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from nilearn.image import threshold_img\n",
    "from nilearn.masking import compute_background_mask\n",
    "from nilearn.plotting import plot_anat\n",
    "from scipy.ndimage import binary_fill_holes\n",
    "\n",
    "\n",
    "# get an approximate brain mask\n",
    "mask = compute_background_mask(\n",
    "    threshold_img(pet2.dynamic_mean(), threshold=0.5, two_sided=False),\n",
    "    connected=True,\n",
    "    opening=3,\n",
    ")\n",
    "mask_data = binary_fill_holes(mask.get_fdata())\n",
    "\n",
    "mask = mask.__class__(mask_data.astype(\"float\"), affine=mask.affine)\n",
    "plot_anat(mask);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we calculate the mean TAC in this brain mask:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "pet1_tac = pet1.mean_timeseries_in_mask(mask=mask_data)\n",
    "pet2_tac = pet2.mean_timeseries_in_mask(mask=mask_data)\n",
    "pet2_tac_ = pet2_.mean_timeseries_in_mask(mask=mask_data)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(pet1_tac.frame_mid, pet1_tac.dataobj.flatten(), label=\"1st image TAC\")\n",
    "plt.plot(\n",
    "    pet2_tac.frame_mid,\n",
    "    pet2_tac.dataobj.flatten(),\n",
    "    label=\"2nd image TAC, decay corr. to 1st image start\",\n",
    ")\n",
    "plt.plot(\n",
    "    pet2_tac_.frame_mid,\n",
    "    pet2_tac_.dataobj.flatten(),\n",
    "    label=\"2nd image TAC, decay corr. to 2nd image start\",\n",
    ")\n",
    "plt.legend(loc=\"upper right\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot illustrates that in the real-life scenario, the reconstructed signal\n",
    "in the second image (green curve) is lower than it should be. This is because\n",
    "reconstruction corrects for only a small amount of decay time, meaning that the\n",
    "acquired signal doesn't get at large of a boost at it would if decay correction\n",
    "were done to the scan start of the first image.\n",
    "We can fix this easily using functionality provided in _Dynamic PET_.\n",
    "\n",
    "In practice, you would only have `pet1` and `pet2_`, not `pet2`.\n",
    "\n",
    "Our goal is to concatenate these two scans, taking into account this difference\n",
    "in decay correction.\n",
    "\n",
    "_Dynamic PET_ makes this task easy via the `concatenate` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pet_concat = pet1.concatenate(pet2_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can verify that this works as intended by plotting the mean TAC in the brain\n",
    "mask for the concatenated scan (shown using purple bars below):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0aEKIe5AkLKXo/U1nuJ6lEODmwphmQeic1WTqM0vk3CaTCaPeiK/GF29Pb6oCDXzhgWppTN93gd2XjXhqnHilWQBqlQp9ll66QQu7ZGdn89prr3H27Fk8PT1p27YtK1eulIHZhBAOIQlLKfn79FW+PZgzB8orTQPQlVIvHq1Gi85VZ3n9YLAOtbMLU3bFsulCKlpnZ15tmTNyKbb3WBWCLl26WAaeE0IIR5NGt6UgLcvIpO+PABBZ1YWmlcp2oroOVX2Y3iYEJ+DnuOvMPXhBBtsSQghxR5MSllIwa8MJLiZnUs3Hlb41HPMRdwqqgNGs8Obec6w7exUUE72qS9IihBDiziQlLCXIYDCw/cQlVu7J6Y465aEQ1IrRYfF0Ca7Ia61yqoPWxd5gzZksKWkRQghxR5KEpYQYDAYO7T3Ekt+OAXC/vzNO8Wc4c+oM2dnZDovr4RBfJrYIAuDXC9l8+MdZSVqEEELccSRhKSFGo5GstCxOp+YkA12r+1NRU5HszOwyG269IL1qVuKFRjkDeS3eEc/8LTISqBBCiDuLJCwlKDnLzKUMIyqgZUAFXLXlZ+CzXjUq8GQtLQAfbj7Nx3/GODgiIYQQwnaSsJSgkyk5JSmhPjo8y+FkfV2DNIzvVBOA93+L5rPtZxwckbDF1q1bUalUJCcnF7iPSqVi/fr1ZRbTvSQuLg6VSmWZI0rcfTp27Mi4ceMcHYYogiQsJSg6OSdhaVbZw8GRFGxERDDjO9cB4J0NJ1m2I9bBEd09Zs2aRatWrfD09KRKlSr06tWL6OjoMrl2QkIC3bp1K5NrlaTp06ejUqkKXXKtXr0atVrN6NGj8z1Xamoqr7/+OvXq1cPV1RV/f38iIyNZu3attNsqYyWVQP/zzz/06dOHkJAQVCoVH374oV3HDx06lF69ehW539q1a3nrrbeKF2Q5FxISYvfnVhhHfjmShKUEncxNWCqV34QF4MVOobzwYG0AZvx0nK92n3NwRHeHbdu2MXr0aHbv3s2mTZvIzs7moYceIj09vdSv7e/vj1arLfXrlLQJEyaQkJBgWapVq8abb75ptS7XkiVLmDhxIqtXr0av11udJzk5mbZt27JixQomT57MwYMH2b59O/369WPixImkpKSU9Vu74xkMeUeaVBQFo7Hsej5mZGRQs2ZNZs+ejb+/f6ldp2LFinh6epba+e8G+f08lDVJWErIjQwDFzPMADSt7O7gaIo2vnMdnumQUz00Zf0xvtl33sER3fk2btzI0KFDadiwIU2bNmX58uXEx8dz4MAByz4qlYrPP/+c3r174+bmRmhoKD/++KPVeTZs2ECdOnXQ6XQ88MADxMXFFXntW7/15FZhfPPNN9x///3odDpatWrFqVOn2LdvHy1btsTDw4Nu3bpx5coVyzn27dtH586dqVSpEt7e3nTo0IGDBw9aXefkyZO0a9cOV1dXGjRowObNm/N84zp//jx9+/bFx8eHihUr0rNnzwLfg4eHB/7+/pZFrVbj6elptQ4gNjaWnTt38uqrr1KnTh3Wrl1rdZ7XXnuNuLg49uzZw5AhQ2jQoAF16tRh5MiRREVF4eFh+5eIvXv3EhYWhqurKy1btuTQoUN59jl27BjdunXDw8MDPz8/nnrqKa5evWrZbjabee+996hduzZarZbq1aszc+ZMy/ZJkyZRp04d3NzcqFmzJlOnTrX0JoyLi8PJyYn9+/dbXfPDDz8kODgYs9mcb9xZWVlMmjSJoKAgtFottWvXZsmSJZbt27Zto3Xr1mi1WgICAnj11Vetko+OHTsyZswYxo0bR6VKlejSpYulOvLXX3+lRYsWaLVa/v7770I/v5CQEAB69+6NSqWyvAZYuHAhtWrVQqPRULduXb788stCz9WqVSvef/99+vfvX2BC/t1339G4cWN0Oh2+vr5ERkaSnp7O9OnT+eKLL/jhhx8spXVbt27N9xz/rRIKCQnh7bffZvDgwXh4eBAcHMyPP/7IlStX6NmzJx4eHjRp0sTqHl27do0BAwZQtWpV3NzcaNy4MatXr7a6zs2bNxk4cCDu7u4EBATwwQcf5Ll2VlYWEyZMoGrVqri7u9OmTZsC44acJHL69OlUr14drVZLYGAgL774ouV9nTt3jpdeesmqxNKWWPP7eSjs3pYFSVhKyIH4nG9wwR4aKmjL/1wrKpWKV7vW4+mIGgBMWnuEtQcvODiqAigKGNIds9xGVULut/qKFStarZ8xYwZ9+/blyJEjdO/enYEDB3L9+nUg52H/2GOP0aNHD6KiohgxYgSvvvpqsa7/xhtvMGXKFA4ePIizszNPPvkkEydOZP78+fz111/ExMQwbdo0y/43b95kyJAh/P333+zevZvQ0FC6d+/OzZs5E3iaTCZ69eqFm5sbe/bs4bPPPuP111+3umZ2djZdunTB09OTv/76ix07duDh4UHXrl1v6xvasmXLePjhh/H29mbQoEFWD2Kz2cyaNWsYOHAggYGBeY718PDA2dm2NmVpaWk88sgjNGjQgAMHDjB9+nQmTJhgtU9ycjIPPvggYWFh7N+/n40bN5KUlETfvn0t+0yePJnZs2czdepUjh8/zqpVq/Dz87Ns9/T0ZPny5Rw/fpz58+ezePFiPvjgAyDnYRkZGcmyZcvyfAZDhw7FySn/P9uDBw9m9erVfPTRR5w4cYJFixZZErWLFy/SvXt3WrVqxeHDh1m4cCFLlizh7bfftjrHF198gUajYceOHXz66aeW9a+++iqzZ8/mxIkTNGnSpNDPcN++fZZ4ExISLK/XrVvH2LFjefnllzl27BjPPPMMw4YN488//yz0fIVJSEhgwIABPP3005w4cYKtW7fy2GOPoSgKEyZMoG/fvnTt2tVSWte2bVubz/3BBx8QERHBoUOHePjhh3nqqacYPHgwgwYN4uDBg9SqVYvBgwdbqhv1ej0tWrTgl19+4dixY4waNYqnnnqKvXv3Ws45fvx4duzYwY8//simTZv466+/8nwpGDNmDLt27WLNmjUcOXKEJ554gq5du3L6dP69O7///ns++OADFi1axOnTp1m/fj2NGzcGcqq6/ltqaWuskPfnoaB7W2aUu0BKSooCKCkpKQ6LYeraKCV40s/KC5/8rVzcf1G5uP+icnTzUWXBWwuUY5uP2b2uqMXeY2L+jlG2/bpNSU9Pt4rbbDYrU9YdVYIn/azUePVn5Yeoiw76BHNkZmYqx48fVzIzM/9dmZWmKG94OWbJSivW+zCZTMrDDz+sREREWK0HlClTplhep6WlKYDy66+/KoqiKJMnT1YaNGhgdcykSZMUQLlx40aB1wOUdevWKYqiKLGxsQqgfP7555btq1evVgBly5YtlnWzZs1S6tatW+h78PT0VH766SdFURTl119/VZydnZWEhATLPps2bbK69pdffqnUrVtXMZvNln2ysrIUnU6n/PbbbwVeK1dwcLDywQcf5IkjKChIWb9+vaIoinLlyhVFo9EoZ8+eVRRFUZKSkhRAmTdvXpHnL8qiRYsUX19fq5+/hQsXKoBy6NAhRVEU5a233lIeeughq+POnz+vAEp0dLSSmpqqaLVaZfHixTZf9/3331datGhhef31118rFSpUUPR6vaIoinLgwAFFpVIpsbGx+R4fHR2tAMqmTZvy3f7aa6/luS8ff/yx4uHhoZhMJkVRFKVDhw5KWFiY1XF//vmnAlg+e1vd+jORq23btsrIkSOt1j3xxBNK9+7dbTpnfj8bBw4cUAAlLi4u32OGDBmi9OzZs8hzd+jQQRk7dqzVtQYNGmR5nZCQoADK1KlTLet27dqlAFa/D//18MMPKy+//LKiKIqSmpqquLi4KN9++61le3JysuLm5ma59rlz5xS1Wq1cvGj9d7hTp07K5MmT873G3LlzlTp16igGgyHf7fl9bkXFqij5/zwoSv731hb5/m1X7Ht+SwlLCdkfnwxAY9+ynTfodqlUKmY82pABrYMwK/DS11H8ejSh6ANFoUaPHs2xY8dYs2ZNnm23fkN1d3fHy8uLy5cvA3DixAnatGljtX94eHixYrj1Ornf7nO/eeWuy70uQFJSEiNHjiQ0NBRvb2+8vLxIS0sjPj5n5Obo6GiCgoKs2hK0bt3a6pqHDx8mJiYGT09PPDw88PDwoGLFiuj1es6cKV6vtE2bNpGenk737t0BqFSpEp07d2bp0qUAJdqgNrcEwdX13yEJ/vv5Hz58mD///NPy/jw8PKhXrx4AZ86c4cSJE2RlZdGpU6cCr/P1118TERGBv78/Hh4eTJkyxfI5A/Tq1Qu1Ws26desAWL58OQ888ECBRfBRUVGo1Wo6dOhQ4PsKDw+3asQcERFBWloaFy78W7LaokWLfI9v2bJlge/FVidOnCAiIsJqXUREBCdOnCj2OZs2bUqnTp1o3LgxTzzxBIsXL+bGjRu3Gypg2+8PYPkdMplMvPXWWzRu3JiKFSvi4eHBb7/9ZrmvZ8+eJTs72+p3xtvbm7p161peHz16FJPJRJ06dax+vrZt21bg788TTzxBZmYmNWvWZOTIkaxbt67IdkZFxZqroJ8HRyl/fW/vQKn6bE4mpgHQxFdXxN7lj5OTipm9GmMwKnx/8AIvrD7EQrUTnRv4FX1wWXBxg9cuOe7adhozZgw///wz27dvp1q1anlP6WJdZahSqQpsl3A7br1O7oPqv+tuve6QIUO4du0a8+fPJzg4GK1WS3h4uF1VOWlpabRo0YKVK1fm2Va5cuXivA2WLFnC9evX0en+/d0ym80cOXKEGTNmULlyZXx8fDh58mSxzm+vtLQ0evTowbvvvptnW0BAAGfPni30+F27djFw4EBmzJhBly5d8Pb2Zs2aNcydO9eyj0ajYfDgwSxbtozHHnuMVatWMX/+/ALPeetnczvc3fNvf1fQekdTq9Vs2rSJnTt38vvvv/N///d/vP766+zZs4caNWrc1rlt+f0BLL9D77//PvPnz+fDDz+kcePGuLu7M27cOLt/f9RqNQcOHECtVlttK6gdVlBQENHR0WzevJlNmzbx/PPP8/7777Nt27Y8f2ty2RprebvvUsJSAg7E3cCsgJ9ORSXX8t9+JT9OTiree7wJPZsFYjQrPL/yAH+evFz0gWVBpQKNu2OWW76RFkVRFMaMGcO6dev4448/ivUHs379+nnqkXfv3m33eYpjx44dvPjii3Tv3p2GDRui1WqtGpLWrVuX8+fPk5SUZFn33zrs5s2bc/r0aapUqULt2rWtFm9vb7tjunbtGj/88ANr1qwhKirKshw6dIgbN27w+++/4+TkRP/+/Vm5ciWXLuVNbNPS0mzu2VK/fn2OHDli1Qvpv59/8+bN+eeffwgJCcnzHt3d3QkNDUWn07Fly5Z8r7Fz506Cg4N5/fXXadmyJaGhoZw7l7en3ogRI9i8eTOffPIJRqORxx57rMC4GzdujNlsZtu2bQW+r127dlmVRu3YsQNPT898k+rb5eLikmeE7/r167Njxw6rdTt27KBBgwa3dS2VSkVERAQzZszg0KFDaDQaS8mURqMps5HGd+zYQc+ePRk0aBBNmzalZs2anDp1yrK9Zs2auLi4WP3OpKSkWO0TFhaGyWTi8uXLeX62CuslpdPp6NGjBx999BFbt25l165dHD16FMj/Mygq1sLkd2/LiiQsJWB37DUA6nqX7wKr7OxsMjIyClyy9Jm89UgoD9WvTLZJ4ZkvD7Dl2IVCj/nvUh66vjnK6NGj+eqrr1i1ahWenp4kJiaSmJhIZmamzed49tlnOX36NK+88grR0dGsWrWK5cuXl17QtwgNDeXLL7/kxIkT7Nmzh4EDB1p9c+/cuTO1atViyJAhHDlyhB07djBlyhTg32+bAwcOpFKlSvTs2ZO//vqL2NhYtm7dyosvvmhV9WCrL7/8El9fX/r27UujRo0sS9OmTenevbul8e3MmTMJCgqiTZs2rFixguPHj3P69GmWLl1KWFgYaWlpNl3vySefRKVSMXLkSI4fP86GDRuYM2eO1T6jR4/m+vXrDBgwgH379nHmzBl+++03hg0bhslkwtXVlUmTJjFx4kRWrFjBmTNn2L17tyXW0NBQ4uPjWbNmDWfOnOGjjz6yPGBvVb9+fe677z4mTZrEgAEDCi1FCQkJYciQITz99NOsX7/e8rl/8803ADz//POcP3+eF154gZMnT/LDDz/wxhtvMH78+AIb8RZkwYIFhVZ35cazZcsWEhMTLVU0r7zyCsuXL2fhwoWcPn2aefPmsXbt2jyNmm9lMBgsSarBYODixYtERUURE5MzUveePXt455132L9/P/Hx8axdu5YrV65Qv359SxxHjhwhOjqaq1evluq8bqGhoZbSnhMnTvDMM89YJfeenp4MGTKEV155hT///JN//vmH4cOH4+TkZPn9qVOnDgMHDmTw4MGsXbuW2NhY9u7dy6xZs/jll1/yve7y5ctZsmQJx44d4+zZs3z11VfodDqCg4Mtn8H27du5ePGi5QtIUbEWJr97W1YkYSkBe2NzenjU81EXsafjGLINxETHcGjHIfZv31/gEvX3QfpXzqS5rzMGk5kxqw/zw697Cj3m1uXQ3kP3bNKycOFCUlJS6NixIwEBAZbl66+/tvkc1atX5/vvv2f9+vU0bdqUTz/9lHfeeacUo/7XkiVLuHHjBs2bN+epp57ixRdfpEqVKpbtarWa9evXk5aWRqtWrRgxYoSll1Bumw83Nze2b99O9erVeeyxx6hfvz7Dhw9Hr9fj5eVld0xLly61dKH8rz59+vDjjz9y9epVKlasyO7duxk0aBBvv/02YWFh3H///axevZr333/fUrozffr0Qrtienh48NNPP3H06FHCwsJ4/fXX81T9BAYGsmPHDkwmEw899BCNGzdm3Lhx+Pj4WB7+U6dO5eWXX2batGnUr1+ffv36Wdo6PProo7z00kuMGTOGZs2asXPnTqZOnZpvPMOHD8dgMPD0008X+VktXLiQxx9/nOeff5569eoxcuRIyxhAVatWZcOGDezdu5emTZvy7LPPMnz4cEvCaY+rV68W2R5p7ty5bNq0iaCgIMLCwoCcdjnz589nzpw5NGzYkEWLFrFs2TI6duxY4HkuXbpEWFgYYWFhJCQkMGfOHMLCwhgxYgQAXl5ebN++ne7du1OnTh2mTJnC3LlzLYMojhw5krp169KyZUsqV66cp4SnJE2ZMoXmzZvTpUsXOnbsiL+/f55B6+bNm0d4eDiPPPIIkZGRREREUL9+fas2U8uWLWPw4MG8/PLL1K1bl169erFv3z6qV6+e73V9fHxYvHgxERERNGnShM2bN/PTTz/h6+sLwJtvvklcXBy1atWyVMvaEmtB8ru3ZUWllGSLNQdJTU3F29ublJSUYv1RvB0ZBiNNpv+O0awwt407zaoEoXPN+SZ0Pfk623Zto2N4Ryr4VLBrXVHsPSZ3//AW4Xh7FV00bzCZmbj7PEevZxLg5sKCdsH4aAsvQdJn6blmuEbL9i1xcyte42O9Xk9sbCw1atSw+iUW5dOOHTto164dMTEx1KpVy9HhFGnIkCGoVKoyK7W6XW+99RbffvstR44ccXQoohSkp6dTtWpV5s6dy/Dhwx0dTqkq6G+7Pc/v8l2HcQc4eC4Zo1nB30tLJVfb2zs4ilajtSRUhdEB77arzYgt0VxKN/DmwQTmt6+NRl1Eody9Wbhyz1i3bh0eHh6EhoYSExPD2LFjiYiIuCOSFUVR2Lp1a5EDn5UHaWlpxMXFsWDBgjxjpYg716FDhzh58iStW7cmJSWFN998E4CePXs6OLI7g1QJ3aa9/2u/0irYJ99i6zuZj9aZ99vVxN3ZicNX03nvwHmZk+Ued/PmTUaPHk29evUYOnQorVq14ocffnB0WDZRqVScO3eOoKAgR4dSpDFjxtCiRQs6duxoU3WQuHPMmTOHpk2bWkbk/euvv6hUqZKjw7oj2J2wbN++nR49ehAYGJjvJEgFTWD2/vvvF3jO/CZAyx3XoLzb/b/2Ky2DfRwbSCmp4aXjrfAaOAEbzl3nq+hy0nNIOMTgwYM5deoUer2eCxcusHz5cktduSg5y5cvJysri6+//jpP91Zx5woLC+PAgQOkpaVx/fp1Nm3aZDW2iyic3QlLeno6TZs25eOPP853+62TliUkJLB06VJUKhV9+vQp9LwNGza0Ou5OKLbVZ5uIOp8MQMvqPg6NpTTd5+/FS2E5XR8XHr3EtovJjg1ICCHEPcfuNizdunUrdBr7//YV/+GHH3jggQeoWbNm4YE4O5fqbJyl4ciFFAxGM5U8tIT46rjm6IBKUZ/alYlL1fP9mavM2HOOhQ9oqFvhzhrVVwghxJ2rVNuwJCUl8csvv9jU+vn06dMEBgZSs2ZNBg4cmGeI4FtlZWWRmppqtZQ1g8HA7picfuvNg7zIzMws1T7+5cHYZtVo7eeJ3mRm4o6zXMm8u9+vEEKI8qNUE5YvvvgCT0/PQkdoBGjTpg3Lly9n48aNLFy4kNjYWO6//37LLLH/NWvWLLy9vS1LWTeiMxgMHNp7iH+izwOgSU/h4N8HOXPqzF2dtDg7qXjrvhBCPF25kpnNpB1n0BtLfkh5IYQQ4r9KNWFZunQpAwcOLHI8jW7duvHEE0/QpEkTunTpwoYNG0hOTraM0vhfkydPJiUlxbKcP3++NMIvkNFoJCstC5NZC4C/mzcVNRXJzsx22JDFZcVT48x77WrirVFz8kYmb+07h1l6DgkhhChlpZaw/PXXX0RHR1tGJLSHj48PderUsQy//F9arRYvLy+rxRH0/ytcqODmiqv23hnkrJqHlllta+KsUvHnhWQ+/0dmdxZCCFG6Si1hWbJkCS1atKBp06Z2H5uWlsaZM2cICAgohchKTlp2Tsbi7nLvDWfTrLIHk1rmVMUtP5HExnPXHRzR3Wvr1q2oVCqSk5ML3Ce/IQZEyYiLi0OlUhEVFeXoUEQJseWehoSE8OGHH5ZZTKJodj9p09LSLJNRAcTGxhIVFWXVSDY1NZVvv/22wNKVTp06sWDBAsvrCRMmsG3bNuLi4ti5cye9e/dGrVYzYMAAe8MrU+nGnOofT5d7c5yEh0N8GVQ3Z66ZWfvj+ed6hoMjcqxZs2bRqlUrPD09qVKlCr169SI6OrpMrp2QkFBo773yKr8xmP675Fq9ejVqtZrRo0fne67U1FRef/116tWrh6urK/7+/kRGRrJ27VoZ8LCMlVQCvXjxYu6//34qVKhAhQoViIyMzDObeWnZt28fo0aNKpNrlbWS/IJTlgm93QnL/v37LZNRAYwfP56wsDCmTZtm2WfNmjUoilJgwnHmzBmraesvXLjAgAEDqFu3Ln379sXX15fdu3dbJmoqr3JLWDzu0YQF4NnGgbQP9CbbrDBt30WuZN67jXC3bdvG6NGj2b17N5s2bSI7O5uHHnrIMgFdafL390er1Zb6dUrahAkTrMZfqlatGm+++abVulxLlixh4sSJrF69Gr1eb3We5ORk2rZty4oVK5g8eTIHDx5k+/bt9OvXj4kTJ5KSklLWb+2Ol98kpoqiYDQayyyGrVu3MmDAAP7880927dpFUFAQDz30EBcvXiz1a1euXLnYc6LdK8p6olu7E5aOHTuiKEqe5dbJxEaNGkVGRoZlhtT/iouLY/r06ZbXa9as4dKlS2RlZXHhwgXWrFlzR8xNkp6bsGju3YTFSaXijTbB1PHRkWwwMe9oJmlZZfcHrTzZuHEjQ4cOpWHDhjRt2pTly5cTHx/PgQMHLPuoVCo+//xzevfujZubG6Ghofz4449W59mwYQN16tRBp9PxwAMPEBcXV+S1b/3GlPuN55tvvuH+++9Hp9PRqlUrTp06xb59+2jZsiUeHh5069aNK1euWM6xb98+OnfuTKVKlfD29qZDhw4cPHjQ6jonT56kXbt2uLq60qBBAzZv3pzn29r58+fp27cvPj4+VKxYkZ49exb4Hjw8PPD397csarUaT09Pq3WQU5K7c+dOXn31VerUqcPatWutzvPaa68RFxfHnj17GDJkCA0aNKBOnTqMHDmSqKgoPDw8ivwMc+3du5ewsDBcXV1p2bIlhw4dyrPPsWPH6NatGx4eHvj5+fHUU09ZfQkzm82899571K5dG61WS/Xq1Zk5c6Zl+6RJk6hTpw5ubm7UrFmTqVOnWnoYxsXF4eTkxP79+62u+eGHHxIcHIzZnP+XgqysLCZNmkRQUBBarZbatWuzZMkSy/Zt27bRunVrtFotAQEBvPrqq1bJR8eOHRkzZgzjxo2jUqVKdOnSxVId+euvv9KiRQu0Wm2Rg3rmzoadO8v2rbNjL1y4kFq1aqHRaKhbty5ffvlloedauXIlzz//PM2aNaNevXp8/vnnmM1mtmzZYnW9d955h6effhpPT0+qV6/OZ599ZnUeW+5pfu/j1iohlUrFokWLeOSRR3Bzc6N+/frs2rWLmJgYOnbsiLu7O23btrWayfrMmTP07NkTPz8/PDw8aNWqFZs3b7a6TkJCAg8//DA6nY4aNWqwatWqPNdOTk5mxIgRVK5cGS8vLx588EEOHz5cYOwGg4ExY8YQEBCAq6srwcHBzJo1y/K+IO/9sSXWkJAQ3nrrLQYPHoyXlxejRo2iRo0aQM4oviqVqtDZt2/Xvdf4ooQoikLa/6qE7uUSFgCds5r3Imriq3XmYoaZl7//B6Op5EpaFEUhIzvDIcvtVCXkfquvWLGi1foZM2bQt29fjhw5Qvfu3Rk4cCDXr+e0ATp//jyPPfYYPXr0ICoqihEjRvDqq68W6/pvvPEGU6ZM4eDBgzg7O/Pkk08yceJE5s+fz19//UVMTIxVyejNmzcZMmQIf//9N7t37yY0NJTu3btbhhcwmUz06tULNzc39uzZw2effcbrr79udc3s7Gy6dOmCp6cnf/31Fzt27MDDw4OuXbve1rexZcuW8fDDD+Pt7c2gQYOsHsRms5k1a9YwcOBAAgMD8xzr4eGBs7NtY2SmpaXxyCOP0KBBAw4cOMD06dOZMGGC1T7Jyck8+OCDhIWFsX//fjZu3EhSUhJ9+/a17DN58mRmz57N1KlTOX78OKtWrcLPz8+y3dPTk+XLl3P8+HHmz5/P4sWL+eCDD4Cch0JkZCTLli3L8xkMHToUJ6f8/2wPHjyY1atX89FHH3HixAkWLVpkSdQuXrxI9+7dadWqFYcPH2bhwoUsWbIkz8SKX3zxBRqNhh07dvDpp59a1r/66qvMnj2bEydO0KRJk0I/w3379lniTUhIsLxet24dY8eO5eWXX+bYsWM888wzDBs2jD///LPQ890qIyOD7OzsPL9Tc+fOtSQizz//PM8995ylOtaWe2qr3Id1VFQU9erV48knn+SZZ55h8uTJ7N+/H0VRGDNmjGX/tLQ0unfvzpYtWzh06BBdu3alR48eVk0oBg8ezKVLl9i6dSvff/89n332GZcvW0+B8sQTT3D58mV+/fVXDhw4QPPmzenUqZPl78Z/ffTRR/z444988803REdHs3LlSktiUtD9sSVW+HcupEOHDjF16lRLFd3mzZtJSEjI82WiJMlszcWUZQLz/55lHi5qMrMcG4+jVXHT8GbrqozbcY6/Yq4zc8MJ3ujRsETOnWnMpM2qNiVyLnvteXIPbi72FwubzWbGjRtHREQEjRo1sto2dOhQS3XpO++8w0cffcTevXvp2rWr5Rvo3LlzAahbty5Hjx7l3XfftTuGCRMm0KVLFwDGjh3LgAED2LJlCxEREQAMHz7cqmT0wQcftDr+s88+w8fHh23btvHII4+wadMmzpw5w9atWy0lHzNnzqRz586WY77++mvMZjOff/65pf3JsmXL8PHxYevWrTz00EN2vw+z2czy5cv5v//7PwD69+/Pyy+/bJmq/urVq9y4caNE5h9btWoVZrOZJUuW4OrqSsOGDblw4QLPPfecZZ8FCxYQFhbGO++8Y1m3dOlSgoKCOHXqFAEBAcyfP58FCxYwZMgQAGrVqkW7du0s+0+ZMsXy75CQECZMmMCaNWuYOHEiACNGjODZZ59l3rx5aLVaDh48yNGjRwucaPLUqVN88803bNq0icjISACr0cU/+eQTgoKCWLBggWWutkuXLjFp0iSmTZtmSYJCQ0N57733LMflVsm9+eabVve5MLlV+T4+Plajl8+ZM4ehQ4fy/PPPAznNCXbv3s2cOXN44IEHbDr3pEmTCAwMtLzHXN27d7ecd9KkSXzwwQf8+eef1K1b16Z7aqthw4ZZEtNJkyYRHh7O1KlTrX7Phg0bZtm/adOmVh1P3nrrLdatW8ePP/7ImDFjOHnyJJs3b7aUfAJ8/vnnhIaGWo75+++/2bt3L5cvX7ZU+86ZM4f169fz3Xff5dvOJj4+ntDQUNq1a4dKpSI4ONiyraD7U1SsuR588EFefvlly+vcua58fX1LfbR6KWEppgxTTraiVoGrWj5GgHo+Op6pl9O9e9mOOFbuOefgiBxn9OjRHDt2jDVr1uTZdus3VHd3d7y8vCzfqE6cOEGbNtbJWXh4eLFiuPU6ud/ub51ozc/Pz+qbXFJSEiNHjiQ0NBRvb2+8vLxIS0uzfMOKjo4mKCjI6o9S69atra55+PBhYmJi8PT0xMPDAw8PDypWrIher7cqKrfHpk2bSE9Pp3v37gBUqlSJzp07s3TpUoASbVCbW4Jw69hR//38Dx8+zJ9//ml5fx4eHpZk6cyZM5w4cYKsrCw6depU4HW+/vprIiIi8Pf3x8PDgylTplh9k+3VqxdqtZp169YBOZMhPvDAA1bVK7eKiopCrVbToUOHAt9XeHi4VSPmiIgI0tLSuHDhgmVdixYt8j0+92F6O06cOGFJlm+N4cSJEzYdP3v2bNasWcO6devyjO1168+6SqXC39/f6neqqHtqK1t+p/R6vWX09bS0NCZMmED9+vXx8fHBw8ODEydOWP1OOTs707x5c8s5ateuTYUKFSyvDx8+TFpaGr6+vlY/c7GxsQX+Tg0dOpSoqCjq1q3Liy++yO+//17keysq1lwl8bNQXFLCUkwZxpw/kh4uaqs/Ave61lVceLFKAB9tjWXaD/8QXNGddqG3N3W6zlnHnif3lFCE9l/bXmPGjOHnn39m+/btVKtWLc92FxcXq9cqlarAdgm349br5P6M/nfdrdcdMmQI165dY/78+QQHB6PVagkPD7erKictLY0WLVqwcuXKPNuK24h+yZIlXL9+HZ3u33thNps5cuQIM2bMoHLlyvj4+HDy5Mlind9eaWlp9OjRI99Sr4CAAM6ePVvo8bt27WLgwIHMmDGDLl264O3tzZo1ayylagAajYbBgwezbNkyHnvsMVatWsX8+fMLPOetn83tcHd3t2t9WZkzZw6zZ89m8+bN+VZJlaffKcBy7QkTJrBp0ybmzJlD7dq10el0PP7443b/TgUEBLB169Y823x8fPI9pnnz5sTGxvLrr7+yefNm+vbtS2RkJN99912B17E1Vkf+LEjCUkyZ/2ur5n6Pt1/JzzP3BxOfnMX6qEs8v/IA60ZHUKuy7Y0e/0ulUhWrWqasKYrCCy+8wLp169i6daulMZo96tevn6cR7u7du0sqxELt2LGDTz75xFKScf78eauGpHXr1uX8+fMkJSVZvl3m1n/nat68OV9//TVVqlQpkQEdr127xg8//MCaNWto2PDfKkaTyUS7du34/fff6dq1K/379+fLL7/kjTfeyNOOJS0tDVdXV5vasdSvX58vv/wSvV5v+Ub+38+/efPmfP/994SEhOR7ztDQUHQ6HVu2bMl3aIedO3cSHBxs1f7n3Lm8pZEjRoygUaNGfPLJJxiNxkKnOGncuDFms5lt27blqS7JfV/ff/89iqJYHqo7duzA09Mz36T6drm4uOQZ9bt+/frs2LHDUk2WG0ODBg0KPdd7773HzJkz+e2334r17d6We1paduzYwdChQ+nduzeQ87N4awP0unXrYjQaOXTokKV0KyYmhhs3blj2ad68OYmJiTg7OxdYwpYfLy8v+vXrR79+/Xj88cfp2rUr169fp2LFivnen6JiLYhGowEok1HepS6jmHJLWO7VMVgKo1KpmN2nCS2CK5CqNzJ8+T5upJdt9zdHGD16NF999RWrVq3C09OTxMREEhMTyczMtPkczz77LKdPn+aVV14hOjqaVatWWbUzKU2hoaF8+eWXnDhxgj179jBw4ECrb+6dO3emVq1aDBkyhCNHjrBjxw5LW4zch+DAgQOpVKkSPXv25K+//iI2NpatW7fy4osvWlU92OrLL7/E19eXvn370qhRI8vStGlTunfvbml8O3PmTIKCgmjTpg0rVqzg+PHjnD59mqVLlxIWFkZaWppN13vyySdRqVSMHDmS48ePs2HDBubMmWO1z+jRo7l+/ToDBgxg3759nDlzht9++41hw4ZhMplwdXVl0qRJTJw4kRUrVnDmzBl2795tiTU0NJT4+HjWrFnDmTNn+OijjyxVP7eqX78+9913H5MmTWLAgAGFlqKEhIQwZMgQnn76adavX2/53HOnN3n++ec5f/48L7zwAidPnuSHH37gjTfeYPz48QU24i3IggULCq3uyo1ny5YtJCYmWh6+r7zyCsuXL2fhwoWcPn2aefPmsXbt2kIbwL777rtMnTqVpUuXEhISYvmdsvV+gm33tLSEhoaydu1aoqKiOHz4ME8++aRVyU+9evWIjIxk1KhR7N27l0OHDjFq1Ch0Op3ldyoyMpLw8HB69erF77//bhmv7PXXX8/TkyzXvHnzWL16NSdPnuTUqVN8++23+Pv7W0pk8rs/RcVakCpVqqDT6SyNz0tzCAFJWIopN2GREpb8ubqoWfRUC6r66Ii7lsFzKw9guMsnSly4cCEpKSl07NiRgIAAy/L111/bfI7q1avz/fffs379epo2bcqnn35q1bizNC1ZsoQbN27QvHlznnrqKV588UWqVKli2a5Wq1m/fj1paWm0atWKESNGWEoJcr+5urm5sX37dqpXr85jjz1G/fr1GT58OHq9vlglLkuXLrV0v/yvPn368OOPP3L16lUqVqzI7t27GTRoEG+//TZhYWHcf//9rF69mvfff98yxML06dML/Zbq4eHBTz/9xNGjRwkLC+P111/PU/UTGBjIjh07MJlMPPTQQzRu3Jhx48bh4+NjefhPnTqVl19+mWnTplG/fn369etnaVPx6KOP8tJLLzFmzBiaNWvGzp07mTp1ar7xDB8+HIPBwNNPP13kZ7Vw4UIef/xxnn/+eerVq8fIkSMtYwBVrVqVDRs2sHfvXpo2bcqzzz7L8OHDrRr/2urq1atFtkeaO3cumzZtIigoyDJmV69evZg/fz5z5syhYcOGLFq0iGXLlhXaDXbhwoUYDAYef/xxq98pexIOW+5paZk3bx4VKlSgbdu29OjRgy5duli1VwFYsWIFfn5+tG/fnt69ezNy5Eg8PT0tv1MqlYoNGzbQvn17hg0bRp06dejfvz/nzp2z6nl2K09PT9577z1atmxJq1atiIuLY8OGDZafz/zujy2x5sfZ2ZmPPvqIRYsWERgYSM+ePW/nIyuUSrkLhoBMTU3F29ublJSUMplXKCMjg3dX7+SL01l0qOrNrLY1uZ58nW27ttExvCMVfHIaTN3OuqLYe0xxrmGvTH0mF29epGX7lpYBl6ITb9Jn4U7Ssoz0axnE7D6NC23zo9frLb0/ipo0Uzjejh07aNeuHTExMXfE2ElDhgxBpVKVWanV7Xrrrbf49ttvOXLkiKNDEWXkwoULBAUFsXnz5iJLsu4kBf1tt+f5LW1YiklKWGxT19+T/xsQxvAv9vH1/vPUruLByPY1iz5QlEvr1q3Dw8OD0NBQYmJiGDt2LBEREXdEsqIoClu3bi1y4LPyILf9wIIFC/KMlSLuLn/88QdpaWk0btyYhIQEJk6cSEhICO3bt3d0aOWOVAkVU8b/Gt1KG5aiPVCvCq8/nNOw7p1fT7D5eJKDIxLFdfPmTUaPHk29evUYOnQorVq1KnBskPJGpVJx7tw5goKCHB1KkcaMGUOLFi3o2LGjTdVB4s6VnZ3Na6+9RsOGDenduzeVK1dm69ateXo+CSlhKbZMk5Sw2OPpiBDOXElj1Z54XlxziO+ebUuDwNKvvhMla/DgwQwePNjRYdz1li9ffsdUW4nb06VLF8vAc6JwUsJSTNJLyD4qlYoZjzYkorYvGQYTI77Yx+Wb+qIPFEIIIZCEpdikDYv9XNROfPJkC2pWcudSip5RKw6gzy79vvtCCCHufJKwFJOlhOUenqm5OLzdXFgytBXeOheizifzyndHSnRodSGEEHcnacNSTDLSbf6ys7PJyMgodB8/NxXzn2jIiK8O89PhS1Tz1vBCx5xRYbOysjCbzZhMpgJHTlSpVHYPdiWEEOLOJglLMUkblrwM2QZiomMwm82WWUUL4gQMrq1h6aksFm6PIysxgY6BGlRqFR6+Hugz9JgLGGhO5aTCzd1NkhYhhLiHSMJSTNKGJS+TyYRRb8RX44u3p3eR+w+sC1nKFVaevsby01nU8qlMK38deic9zk7OuKjzdutTzApGs1GqkYQQ4h4jCUsxZJvMZP3vy7+UsOSl1WjRudo2e+zzTYO4YVDYcO46bx24xCcP1qCKu1NOtY/KugTFYDBgyDaQbcoGp5yh4kuTs7OzZWIvUT7FxcVRo0YNDh06RLNmzRwdjhCiFEnCUgzpWf+2rZASltujUql4tWV1rumz2ZN0k/cOXmB2ZN75MQwGA1H7o9Cn6TGZTWhdtTipS7dKSOuhJax1mF1JS2JiIjNnzuSXX37h4sWLVKlShWbNmjFu3LhyNcz29OnTWb9+PVFRUWV+7bJKMnKvU5hly5YxdOhQMjMzqVq1Kk5OTly8eDHfKs3vv/+e//u//+PQoUOYTCZq1qzJ448/zpgxY6hYsWJpvQ0hxP9IwlIMqfqcFreuahXOTgXPiyNs4+yk4u3wGozZepqbBgM39NmY/1PjYzQayUrPopKmEmpnNa4611JNWPRZeq6lXcNoNNqcsMTFxREREYGPjw/vv/8+jRs3Jjs7m99++43Ro0dz8uTJUotX5BUUFERCQoLl9Zw5c9i4cSObN2+2rMudFPH777+nYcOGKIrC+vXr6devn9W5cifMe+mll3jnnXcIDAzk9OnTfPrpp3z55ZeMHTu2bN6UEPcwabVYDGlZOQmLlK6UHHcXNXPur0VlVxeMCiSkG/IkLfBvdVNpL65a+ydefP7551GpVOzdu5c+ffpQp04dGjZsyPjx49m9e7dlv/j4eHr27ImHhwdeXl707duXpKR/pyuYPn06zZo148svvyQkJARvb2/69+/PzZs3LfuYzWbee+89ateujVarpXr16sycOdOyfdKkSdSpUwc3Nzdq1qzJ1KlTyc7OBnJGUZ0xYwaHDx9GpVJZTQaYnJzMiBEjqFy5Ml5eXjz44IMcPnzYrtg2btxIu3bt8PHxwdfXl0ceecRqdt/cUo+wsDBUKpXVbL2ff/459evXx9XVlXr16vHJJ59YfcZ79+4lLCwMV1dXWrZsyaFDhwq8H2q1Gn9/f8vi4eGBs7Oz1TqdLqfqcsmSJQwaNIhBgwaxZMmSPNd85513mDt3Lu+//z5t27YlJCSEzp078/333zNkyJACYxBClBxJWIoht4TFw1k+vpLk6+rCpFbVcVKBwWzmUnoWd0rT2uvXr7Nx40ZGjx6Nu7t7nu0+Pj5ATqLRs2dPrl+/zrZt29i0aRNnz57N843+zJkzrF+/np9//pmff/6Zbdu2MXv2bMv2yZMnM3v2bKZOncrx48dZtWqV1VTznp6eLF++nOPHjzN//nwWL17MBx98AEC/fv14+eWXadiwIQkJCSQkJFiu/8QTT3D58mV+/fVXDhw4QPPmzenUqRPXr1+3Obb09HTGjx/P/v372bJlC05OTvTu3RuzOafh1969ewHYvHkzCQkJrF27FoCVK1cybdo0Zs6cyYkTJ3jnnXeYOnUqX3zxBZAzIeAjjzxCgwYNOHDgANOnT2fChAnFu2H/+ax37dpF37596du3L3/99Rfnzp2zbF+5ciUeHh48//zz+R6fe2+FEKVLqoSKIbeExUNKWEpcVXctV1WZqFCRlm0iKcOAv1v5b/gaExODoijUq1ev0P22bNnC0aNHiY2NtUzCt2LFCho2bMi+ffto1aoVkJPYLF++HE9PTwCeeuoptmzZwsyZM7l58ybz589nwYIFlm/3tWrVol27dpbrTJkyxfLvkJAQJkyYwJo1a5g4cSI6nc6qtCHX33//zd69e7l8+bKlDcecOXNYv3493333HaNGjSoyNoA+ffpYveelS5dSuXJljh8/TqNGjahcuTIAvr6+Vtd/4403mDt3Lo899hiQUxJz/PhxFi1axJAhQ1i1ahVms5klS5bg6upKw4YNuXDhAs8991zRN6gQS5cupVu3blSoUAHImdtl2bJlTJ8+HYDTp09Ts2ZNmYxOCAeTIoJiyC1hcXeRj680aNROVHFzQQUkZxm5mpnt6JCKZGs36xMnThAUFGQ1Y3CDBg3w8fHhxIkTlnUhISGWhAAgICCAy5cvW86RlZVVaCPer7/+moiICEtVyJQpU4iPjy80tsOHD5OWloavry8eHh6WJTY21qpKp7DYIOcBP2DAAGrWrImXlxchISEAhV4/PT2dM2fOMHz4cKtrv/3225ZrnzhxgiZNmuDq+m91XXh4eKHvqSgmk4kvvviCQYMGWdYNGjSI5cuXW0qEpAu9EOWDlLAUQ5peSlhKm5uzE37OziRmGLiqz8Ybo6NDKlRoaCgqlarEGtb+99u8SqWyPEBz210UZNeuXQwcOJAZM2bQpUsXvL29WbNmDXPnzi30uLS0NAICAti6dWuebbdWexQWG0CPHj0IDg5m8eLFBAYGYjabadSoEQaDodBrAyxevJg2bdpYbSvN7uu//fYbFy9ezFMlZzKZ2LJlC507d6ZOnTr8/fffZGdnSymLEA4kRQTFYClhkTYspcpH64yva84DIinTgL6AkW/Lg4oVK9KlSxc+/vhj0tPT82xPTk4GoH79+pw/f57z589bth0/fpzk5GQaNGhg07VCQ0PR6XRs2bIl3+07d+4kODiY119/nZYtWxIaGmrVJgNAo9HkmfqgefPmJCYm4uzsTO3ata2WSpUq2RTbtWvXiI6OZsqUKXTq1In69etz48aNPNcGrK7v5+dHYGAgZ8+ezXPt3Ea69evX58iRI+j1/87yfWtj5uJYsmQJ/fv3Jyoqymrp37+/pfHtk08+SVpaWp4GwLly760QonRJCUsx/NuGRRKW0lZZ54LRrJCZCdf0Rqp6KJTX77gff/wxERERtG7dmjfffJMmTZpgNBrZtGkTCxcu5MSJE0RGRtK4cWMGDhzIhx9+iNFo5Pnnn6dDhw60bNnSpuu4uroyadIkJk6ciEajISIigitXrvDPP/8wfPhwQkNDiY+PZ82aNbRq1YpffvmFdevWWZ0jJCSE2NhYoqKiqFatGp6enkRGRhIeHk6vXr147733qFOnDpcuXeKXX36hd+/eNsVXoUIFfH19+eyzzwgICCA+Pp5XX33Vap8qVaqg0+nYuHEj1apVw9XVFW9vb2bMmMGLL76It7c3Xbt2JSsri/3793Pjxg3Gjx/Pk08+yeuvv87IkSOZPHkycXFxzJkzx/Yb9B9Xrlzhp59+4scff6RRo0ZW2wYPHkzv3r25fv06bdq0YeLEibz88stcvHiR3r17ExgYSExMDJ9++int2rWTbs1ClAF54hbDvyUsUiVUFvzdNbg7q1EUhUupadzMyCRTX7qLPktfdGD/UbNmTQ4ePMgDDzzAyy+/TKNGjejcuTNbtmxh4cKFQE71yQ8//ECFChVo3749kZGR1KxZk6+//tqua02dOpWXX36ZadOmUb9+ffr162dpR/Loo4/y0ksvMWbMGJo1a8bOnTuZOnWq1fF9+vSha9euPPDAA1SuXJnVq1ejUqnYsGED7du3Z9iwYdSpU4f+/ftz7tw5qx5IhXFycmLNmjUcOHCARo0a8dJLL/H+++9b7ePs7MxHH33EokWLCAwMpGfPngCMGDGCzz//nGXLltG4cWM6dOjA8uXLLSUsHh4e/PTTTxw9epSwsDDL2CjFtWLFCtzd3fNtC9SpUyd0Oh1fffUVAO+++y6rVq1iz549dOnSxdJdvUmTJtKtWYgyolLughZlqampeHt7k5KSgpeXV6lfb8TyPWw+eZVxjf3oWy8QgOvJ19m2axsdwztSwafCba8rir3HFOca9iqJaxgxkuqcSvWg6lZjoeizDPz+117SU/U4O6nw93bFpZQTxuKMdCuEECIvvV5PbGwsNWrUsGo4b8/zW6qEiuGmXgaOK2uuWg2R97fi7I00ss0KOmc1NSp7oC7FkYZlLiEhhCg/JGEphptZ0ujWEdy0WkIrOXMuLQuzAtf0EOyrQ6WS6RGEEOJuJ0/cYripl0a3juKiVuGnc8JJpSJVn83F5EwZJ0MIIe4B8sQthpv6nO6YUiXkGK5qFdV8XFEB19MNXL6Z5eiQhBBClDJJWOykKMq/bVikSshhPF2dCfTJGUAtKVXP9XRJWoQQ4m5m9xN3+/bt9OjRg8DAQFQqFevXr7faPnToUMsMsLlL165dizzvxx9/TEhICK6urrRp08YyQVp5k5ltwvS/KggZ6bZ0KIqCLbMe+npoqeKZM+fNxRt6Uu+AIfyFEOJeVBJV93YnLOnp6TRt2pSPP/64wH26du1qmQU2ISGB1atXF3rOr7/+mvHjx/PGG29w8OBBmjZtSpcuXazmJykvUjNzSlecyKmaECXLCSdQIDMr06b9/bxcqeCmQUEh/noGGYbyPYS/EELcizIyMoC8U3vYw+5eQt26daNbt26F7qPVaq1mYS3KvHnzGDlyJMOGDQPg008/5ZdffmHp0qV5Rsl0tJv6nG/xbs4q6Z1SCpxwQmPWcOXqFQB0Wh3c8jErioLRbMRJ72SZY8ZXp0KvN5FhMHE2MZvqFXVoZFA/IYRwOEVRyMjI4PLly/j4+NzW3GCl0q1569atVKlShQoVKvDggw/y9ttv4+vrm+++BoOBAwcOMHnyZMs6JycnIiMj2bVrV2mEd1tSLQmLgwO5i3ngQVp2GpeTLoMKq8TQrJgxmU1otBqcnJxuWa+QfDMLg0nhyiUVlT21pTpGixBCCNv5+PjYVZCRnxJ/7Hbt2pXHHnuMGjVqcObMGV577TW6devGrl278s2srl69islkyjP0t5+fX4Ez32ZlZZGV9W8jy9TU1JJ9E4XIHZbfzVkehqVFhQpPPDGbzZixnvBQn6UnKT2JRi0b5Zm12Cc9ixdWHSIpVU9dfy/mPtEEnUYySyGEcCQXF5cSmXW9xP+a9+/f3/Lvxo0b06RJE2rVqsXWrVvznbOjOGbNmsWMGTNK5Fz2ym3YqZOEpdQ5/e+/W6kVNYpJQavVWg3vDFDV1ZX3+7ekz8Kd/HH6BhPWnmDRUy1wVktvLiGEuNOV+l/ymjVrUqlSJWJiYvLdXqlSJdRqNUlJSVbrk5KSCiw+mjx5MikpKZbl/PnzJR53QaSEpXyrVdmDJUNaonV2YsvJy0z94ZgMLCeEEHeBUk9YLly4wLVr1wgICMh3u0ajoUWLFmzZssWyzmw2s2XLFsLDw/M9RqvV4uXlZbWUlZvShqXcaxFckY8GhOGkgtV7z/N/f+SfLAshhLhz2P3YTUtLsyotiY2NJSoqiooVK1KxYkVmzJhBnz598Pf358yZM0ycOJHatWvTpUsXyzGdOnWid+/ejBkzBoDx48czZMgQWrZsSevWrfnwww9JT0+39BoqT3K7NUsJi+NkZ2dbusgV5P4aXkzpVoc3N5xi3qZTVHBV0Scs0OZryMSHQghRvtidsOzfv58HHnjA8nr8+PEADBkyhIULF3LkyBG++OILkpOTCQwM5KGHHuKtt95Cq9Vajjlz5gxXr161vO7Xrx9Xrlxh2rRpJCYm0qxZMzZu3JinIW55kFvColWZi9hTlAZDtoGY6BjMZrPVz1R+agM9qmv4Kd7AtJ+iuRhzjrZ+to0BoPXQEtY6TJIWIYQoJ+xOWDp27Fhom4DffvutyHPExcXlWTdmzBhLiUt5djPTAED69etkZ2ejc9UVcYQoSSaTCaPeiK/GF29P7yL3H9tEAVUSP51L5rMTeiq7VaRjYOFViPosPdfSrmE0GiVhEUKIckJaYtjJYPxfyUq2EZPJ5Nhg7mFajdbmZHFSqxAUVTw/x13nnYOXcNNq6VDVp/CDDLcfoxBCiJIj/T3tZDLnlC7JmGR3DieVikktq9M1uAImBabuiuPvSymODksIIYQdJGGxkzE3YbFldj5RbqhVKl5vFUznoAoYFYXXd8WyO7HsBhwUQghxeyRhsZPRnFMlJB/cnUetUjG1dTAdq/qQbVZ4dcdZ9iVJ0iKEEHcCee7aKbdKSCUlLHckZycVb94Xwv2B3hjMChN3nOXg5ZuODksIIUQRJGGxk6UNi4PjEMXn7KTirftCaBvgRZZJ4ZW/z3L4apqjwxJCCFEIee7aydKGRSUlLHcyjdqJmeE1aO3nSabJzMt/neHYtXRHhyWEEKIAkrDYySSNbu8aWrUT70bUpEUVDzKMZl7aHsOJ64WPoCuEEMIxJGGxk1QJ3V20aifei6hJs0oepBvNjNsew+kUvaPDEkII8R/y3LWTdGu+++ic1bzfriaNfd25mW1i4q7znE+TQQGFEKI8kYTFTlLCcndyd1Ez9/5aNKjoRmq2idmHM4m5Im1ahBCivJDnrp2k0e3dy8NFzbz7axHqreVmtsKwFVGcuSK9h4QQojyQhMVORss4LOJu5KVx5t37qlPd3Ylr6QaeXLybuKtS0iKEEI4mCYudpJfQ3c9bo2ZiUx21K7uTlJrFk4t3c156DwkhhENJwmInacNyb/DSOLH0qWbUquzOpRQ9Axbv5mJypqPDEkKIe5Y8d+0kvYTuHZU8NKwaeR81Krlz4UYmTy7eTaJ0eRZCCIeQhMVOlhIWacRyT/DzcmXVyDZUr+jGuWsZPLl4N5dvStIihBBlTRIWO0kblntPgLeOVSPbUNVHx9mr6Ty5eA9X07IcHZYQQtxTJGGxk1HasNyTqlVwY/XI+/D3ciXmchqDPt/D9XSDo8MSQoh7hjx37WQ0SQnLvaq6rxurR91HFU8tJxNvMujzPSRnSNIihBBlQRIWO0kvoXtbjUrurBp5H5U8NBxPSGXw0r2k6rMdHZYQQtz15LlrB0VRMCm5A8dJCcu9qnYVD1aOuI+K7hqOXEhhyNK93JSkRQghSpUkLHbILV0B6SV0r6vr78lXw9vgrXPhUHwyTy/fR3qW0dFhCSHEXcvZ0QHcSYy3JixSwnJXy87OJiOj8NFtQ3yc+XxgU57+Mop9cTd46vPdfNy/Md46F5uu4ezsjEajKYlwhRDiricJix2sSlgcGIcoXYZsAzHRMZjNZrRabZH7j2+o4f3DRg6eT+Hxj3fyShMdFV2L/gnRemgJax0mSYsQQthAEhY7SAnLvcFkMmHUG/HV+OLt6V3k/lU9oaqnnsl7LnAxw8jMqCxm3xdEiGfByY4+S8+1tGsYjUZJWIQQwgaSsNjBaDJb/i0lLHc/rUaLzlVn076NXHV89qA74/+K4dzNLMbtiOf9djVpUsmj4IOkR7QQQthMnrt2yK0SUgEqaXQr/iPAXcOnD9Shka87N7NNvLgthm0Xkx0dlhBC3BUkYbFDbpWQWpIVUQBvrTMfta9NuwAvDGaF13fGsv7MVUeHJYQQdzxJWOwgEx8KW7g6O/FO25r0qOGLGXjv4Hk+/ycBRZF2T0IIUVySsNjBKAmLsJGzk4pXWwTxdAN/AJYeT+TdA+etGm4LIYSwnSQsdjCZcxrdSpWQsIVKpWJEwwBeaR6EE/Bj7DVe2xWL3mgu8lghhBDWJGGxg7RhEcXRu1YlZratgcZJxd+XUhi7PYZUg8nRYQkhxB1FEhY7WGZqloRF2KlDVR/md6iNp4uao9fSGbvjHFf1UtIihBC2koTFDpYSFgfHIe5MTSt5sPCBUKroXIhPM/DWwQxOJaU5OiwhhLgj2J2wbN++nR49ehAYGIhKpWL9+vWWbdnZ2UyaNInGjRvj7u5OYGAggwcP5tKlS4Wec/r06ahUKqulXr16dr+Z0pbbhkVKWERx1fTWsejBOgR7aLhhUHhq+SF2n73m6LCEEKLcszthSU9Pp2nTpnz88cd5tmVkZHDw4EGmTp3KwYMHWbt2LdHR0Tz66KNFnrdhw4YkJCRYlr///tve0EqdVAmJkuDnpmF+RDB1vNXczDIyeOlefj2a4OiwhBCiXLN7aP5u3brRrVu3fLd5e3uzadMmq3ULFiygdevWxMfHU7169YIDcXbG39/f3nDKlEka3YoS4qlRM7GJjjVXXNl88irPrzrIjEcbMjg8xNGhCSFEuVTqbVhSUlJQqVT4+PgUut/p06cJDAykZs2aDBw4kPj4+AL3zcrKIjU11WopCzIOiyhJGrWKDx5vxMA21VEUmPbDP7z/20kZYE4IIfJRqgmLXq9n0qRJDBgwAC8vrwL3a9OmDcuXL2fjxo0sXLiQ2NhY7r//fm7evJnv/rNmzcLb29uyBAUFldZbsPJvCYtkLKJkqJ1UvN2rES93rgPAx3+eYeJ3R8g2SQ8iIYS4VaklLNnZ2fTt2xdFUVi4cGGh+3br1o0nnniCJk2a0KVLFzZs2EBycjLffPNNvvtPnjyZlJQUy3L+/PnSeAt5SAmLKA0qlYoXOoUy+7HGOKng2wMXGLViPxkGo6NDE0KIcqNUEpbcZOXcuXNs2rSp0NKV/Pj4+FCnTh1iYmLy3a7VavHy8rJayoKMdCtKU//W1fnsqZa4ujjxZ/QVBizew/V0g6PDEkKIcqHEE5bcZOX06dNs3rwZX19fu8+RlpbGmTNnCAgIKOnwbku29BISpSyygR8rR9yHj5sLh88n8/jCnZy/nuHosIQQwuHsTljS0tKIiooiKioKgNjYWKKiooiPjyc7O5vHH3+c/fv3s3LlSkwmE4mJiSQmJmIw/PtNsVOnTixYsMDyesKECWzbto24uDh27txJ7969UavVDBgw4PbfYQmSXkKiLLQIrsB3z7alqo+Os1fTeWzhTv65lOLosIQQwqHsTlj2799PWFgYYWFhAIwfP56wsDCmTZvGxYsX+fHHH7lw4QLNmjUjICDAsuzcudNyjjNnznD16lXL6wsXLjBgwADq1q1L37598fX1Zffu3VSuXLkE3mLJsbRhcXAc4u5Xu4oHa59vSz1/T67czKLfot3siLla9IFCCHGXsnsclo4dOxba7dKWLplxcXFWr9esWWNvGA4hbVhEScrOziYjo+DqHk9nWD64KS98fYx955IZsnQvs3rV5+FGfjZfw9nZGY1GUxLhCiGEQ9mdsNzLpJeQKCmGbAMx0TGYzWa0Wm2h+z5TXYEMZ/ZdMfLK2uNs3x9DnxoanGzoXq/10BLWOkySFiHEHU8SFjuYJGERJcRkMmHUG/HV+OLt6V3k/jPbKHx+4grfnLnOT/EGrmS5MDksAA+Xgqfi1GfpuZZ2DaPRKAmLEOKOJwmLHXLnEpIqIVFStBotOledTfuOax5MPV9PZu+PZ3dSOi/uiOfdiJpU93Qt+CDpFS2EuEtI+1E7SC8h4WhdgyvyyQOhVNa5cO5mFiO2nGJ3YtlMTSGEEI4kCYsdsv/X6FaqhIQjNajoztLIujTydSct28SEv86wMjpJ5iASQtzVJGGxg0mqhEQ54evqwoIOtelRwxcz8PGRS8zYe44smYNICHGXkoTFDv/2EpKMRTieRu3Eqy2CGB9WDbUKfo+/wXN/nuJyhjRcEULcfSRhsYO0YRHljUql4vHalfmwfW28NWpO3sjk6c3RHLma5ujQhBCiREnCYgcZ6VaUVy2qeLIksi61vV25nmVkzNYYfjmX7OiwhBCixMiz1w4maXQryrFAdy2fPliHjlV9MCoK844ksuKUnmxp1yKEuAtIwmIHo1QJiXLOzVnNzPAQRjbMmel886VsRn51mGtpWQ6OTAghbo8kLHaQkW7FnUClUjGsgT9vtqqKqxr2nkvm0QU7OH5JxmsRQty5JGGxQ7Z0axZ3kAh/T6Y1dyOogo6LyZn0WbiTX44kODosIYQoFklY7CCzNYs7TTV3NV+PaMH9oZXIzDYxetVB5v4ejdksg8wJIe4skrDYQWZrFnciH50Ly4a2YuT9NQD4vz9iGPXlfm7qsx0cmRBC2E4SFjtIGxZxp3JWO/H6ww2Y17cpGmcnNp+4TO9PdhJ7Nd3RoQkhhE0kYbGD9BISd7rHmlfj22fC8fPSEnM5jZ4L/mbbqSuODksIIYokCYsdcucSkg9N3MmaBvnw05h2NK/uQ6reyLBle1m8/axMniiEKNfk2WuHf0tYpIhF3NmqeLmyetR99G1ZDbMCMzecYPw3h9FnmxwdmhBC5EsSFjsYZaRbcRfROqt5t08TZjzaELWTinWHLtJv0S4SU/SODk0IIfJwdnQAdxKZ/FDcabKzs8nIyCh0nyeaVSHI24Xx3/3D4QspdJ+/ndm96tOutq9N13B2dkaj0ZREuEIIUSBJWOxgNEkvIXHnMGQbiImOwWw2o9VqC93XGZjaRMP8Yybi07MZteoIj1TX8FiIBucifuC1HlrCWodJ0iKEKFWSsNhBSljEncRkMmHUG/HV+OLt6V3k/lU9YVEHMwuPX+bHuGR+jjdw9qaaKc0D8XNzyfcYfZaea2nXMBqNkrAIIUqVJCx2kDYs4k6k1WjRueps2lcHvNqqBq0DbjBrXzzHb2TyzPY4Xm9VnfZVffI/yFBioQohRIGk0a0dpIRF3CserFaBLx6qR4OKbtzMNvHqzlg+OHQBg8ns6NCEEPcoSVjsIEPzi3tJoLuWhQ+EMqBOFQC+jbnCM3+c4kJaloMjE0LciyRhsYMMzS/uNS5OTrzQtCrvt6uJt0ZNdHImQzed5Pf4644OTQhxj5GExQ7Z/ysOVzs4DiHKWkSAN190rkezSh5kGM1M33OOWfvj0RulikgIUTYkYbGDlLCIe1kVNw0fdajN0w38UQE/xV5j9N9xXEiX0XGFEKVPEhY7yOSH4l7n7KRiRMMA5neoja+rM3E3DUw/kMH3hy7JXERCiFIlCYsdcktYFLN8oxT3tpZVPPmicz1aVnbHYIapP0Uzdk0UN/XZjg5NCHGXkoTFDqb/jcNy6fxFjEajg6MRwrEqurowq001nqihQa1S8ePhS/T4v785djHF0aEJIe5CkrDYIXcICpM+G6T0WwicVCp6BGtZMTSMqj464q5l8NgnO1m2I1aqiIQQJUoSFjuY//cHWJqwCGEtLMibX15sR+cGfhhMZmb8dJxnvjxAcoYMgyuEKBl2Jyzbt2+nR48eBAYGolKpWL9+vdV2RVGYNm0aAQEB6HQ6IiMjOX36dJHn/fjjjwkJCcHV1ZU2bdqwd+9ee0MrdbkJi/QSEiIvHzcNnz3Vguk9GqBRO/H78SQe/uhvDpy74ejQhBB3AbsTlvT0dJo2bcrHH3+c7/b33nuPjz76iE8//ZQ9e/bg7u5Oly5d0Ov1BZ7z66+/Zvz48bzxxhscPHiQpk2b0qVLFy5fvmxveKUqt4RbJfVBQuRLpVIxNKIGa59vS4ivGxeTM+m7aBcLt57BbJbfGyFE8dmdsHTr1o23336b3r1759mmKAoffvghU6ZMoWfPnjRp0oQVK1Zw6dKlPCUxt5o3bx4jR45k2LBhNGjQgE8//RQ3NzeWLl1qb3ilyix18kLYpFFVb356oR2PNg3EZFZ4d+NJhi7fx1UZ1l8IUUwl2oYlNjaWxMREIiMjLeu8vb1p06YNu3btyvcYg8HAgQMHrI5xcnIiMjKywGOysrJITU21WsqC2VLCIoQoiqerC/P7N+PdPo1xdXFi+6krdJv/Fztjrjo6NCHEHci5JE+WmJgIgJ+fn9V6Pz8/y7b/unr1KiaTKd9jTp48me8xs2bNYsaMGSUQsX0US6NbKWkRIld2djYZGRkFbu/RsBL1Krdg/Pf/cOZKBgM/38OodsE81yEEjdq270zOzs5oNJqSClkIcQcq0YSlrEyePJnx48dbXqemphIUFFTq15USFiGsGbINxETHYDab0Wq1he77aj0nvlK7sC0xm0V/n+OXQ+cZUdeVml5Fz86l9dAS1jpMkhYh7mElmrD4+/sDkJSUREBAgGV9UlISzZo1y/eYSpUqoVarSUpKslqflJRkOd9/abXaIv84lgbp1iyENZPJhFFvxFfji7end5H7T2sF2y6l8tHRJC6km3jzYAaP16rI0LqV0BZQ2qLP0nMt7RpGo1ESFiHuYSXahqVGjRr4+/uzZcsWy7rU1FT27NlDeHh4vsdoNBpatGhhdYzZbGbLli0FHuMoipSwCJEvrUaLzlVn09K1ph+rujbgoeoVMAPfnLnOM9vPcSrNlO/+rlpXR789IUQ5YHfCkpaWRlRUFFFRUUBOQ9uoqCji4+NRqVSMGzeOt99+mx9//JGjR48yePBgAgMD6dWrl+UcnTp1YsGCBZbX48ePZ/HixXzxxRecOHGC5557jvT0dIYNG3bbb7Ak5c4lJG1YhLg9PlpnprcJ4d2ImlRydeZ8WhbP/3maeYfOk2GUubqEEHnZXSW0f/9+HnjgAcvr3LYkQ4YMYfny5UycOJH09HRGjRpFcnIy7dq1Y+PGjbi6/vst6cyZM1y9+m9PgX79+nHlyhWmTZtGYmIizZo1Y+PGjXka4jqSoiiWNEVKWIQoGfcHetOsUn0WHLnET7HX+C7mKjsupfJqyyBa+Xk5OjwhRDlid8LSsWPHQucIUalUvPnmm7z55psF7hMXF5dn3ZgxYxgzZoy94ZSZW9+ylLAIUXI8Nc5MblmdTkE+zN5/noQMA2O3n6FHDV/GNAm8M3sGCCFKnMwlZKNbB42TEhYhSl5rPy++6lKPx2tXAuCn2GsM/O0kOxNvOjgyIUR5IAmLjcxWJSxCiNLg5qxmfFgQn3QMJchDy1V9NlP3XWTh8UxuyESKQtzTJGGxkQzLL0TZaVbZgxUP1WNg3So4AbsuG+nxyV5+OZJQaJW0EOLuJQmLjaQNixBlS6t2YnSTqvxfu2CqujlxPSOb0asO8uxXB7h8s+DJVIUQdydJWGwkbViEcIx6FXS82dKN59qH4Oyk4rd/kug8bzvfH7ggpS1C3EMkYbGRJCxCOI6Lk4oXOtbgxzHtaFTVi5TMbF7+9jBDl+3jYnKmo8MTQpQBSVhsZDb/+2+pEhLCMRoEerH++Qgmdq2LxtmJbaeu0OWD7azccw6zWX4vhbibScJiIylhEaJ8cFY78XzH2mx48X6aV/chLcvI6+uO8eTnuzl3Ld3R4QkhSokkLDaShEWI8qV2FQ++fbYt0x5pgM5Fze6z1+ny4XY+/+usZRoNIcTdQxIWG1mNwyIZixDlgtpJxdPtavDbuPaE1/RFn23m7V9O8PinO4m5LAPOCXE3kVGvbZTbG0FyFSHKXnZ2NhkZGQVur6SDzwc25tuDCby/KYZD8cl0m/8XK4c1p1Gg7XMSOTs7o9FoSiJkIUQJk4TFRrklLFK6IkTZMmQbiImOwWw2o9VqC923JvB2c1eWndKTaYSM09Hsj7H9l1broSWsdZgkLUKUQ5Kw2Ci3DYvUoQlRtkwmE0a9EV+NL96e3kXuX9UT5lVSSDea8XBR23wdfZaea2nXMBqNkrAIUQ5JwmIjs1QJCeFQWo0WnavO5v3dinMRma5IiHJLCgxspEiVkBBCCOEwkrDYKLebpJMkLEIIIUSZk4TFRlIlJIQQQjiOJCw2svQScmwYQgghxD1JEhYbWcZhkYxFCCGEKHOSsNgot4RFPjAhhBCi7Mnz10ZmKWERQgghHEYSFhtJo1shhBDCcSRhsZGMwyKEEEI4jiQsNpKh+YUQQgjHkeevjXIHjpMSFiGEEKLsScJiIxmHRQghhHAcSVhslDsOiwzNL4QQQpQ9SVhsJCUsQgghhONIwmIj6dYshBBCOI4kLDaSgeOEEEIIx5GExUaKVAkJIYQQDiMJi43M0uhWCCGEcBhJWGwkjW6FEEIIx5GExUZmc24Ji6QsQgghRFmThMVG0ktICCGEcJwST1hCQkJQqVR5ltGjR+e7//Lly/Ps6+rqWtJh3TazTH4ohBBCOIxzSZ9w3759mEwmy+tjx47RuXNnnnjiiQKP8fLyIjo62vJaVQ6zAilhEUIIIRynxBOWypUrW72ePXs2tWrVokOHDgUeo1Kp8Pf3L+lQSpQi47AIIYQQDlOqbVgMBgNfffUVTz/9dKGlJmlpaQQHBxMUFETPnj35559/Cj1vVlYWqampVktpy60SkkY/QgghRNkr1efv+vXrSU5OZujQoQXuU7duXZYuXcoPP/zAV199hdlspm3btly4cKHAY2bNmoW3t7dlCQoKKoXorUmVkBBCCOE4pZqwLFmyhG7duhEYGFjgPuHh4QwePJhmzZrRoUMH1q5dS+XKlVm0aFGBx0yePJmUlBTLcv78+dII34qlhEUyFiGEEKLMlXgbllznzp1j8+bNrF271q7jXFxcCAsLIyYmpsB9tFotWq32dkO0i7RhEUIIIRyn1EpYli1bRpUqVXj44YftOs5kMnH06FECAgJKKbLiMZmlSkgIIYRwlFJJWMxmM8uWLWPIkCE4O1sX4gwePJjJkydbXr/55pv8/vvvnD17loMHDzJo0CDOnTvHiBEjSiO0YpOh+YUQQgjHKZUqoc2bNxMfH8/TTz+dZ1t8fDxOTv/mSTdu3GDkyJEkJiZSoUIFWrRowc6dO2nQoEFphFZsZqkSEkIIIRymVBKWhx56yNLm47+2bt1q9fqDDz7ggw8+KI0wSlTu+5FuzUIIIUTZk+evjWRofiGEEMJxJGGxkYzDIoQQQjiOJCw2khIWIYQQwnEkYbGRtGERQgghHEeevzYym6WXkBBCCOEokrDYyCSTHwohhBAOI89fG8nQ/EIIIYTjSMJiI+klJIQQQjiOJCw2kqH5hRBCCMeRhMVGMjS/EEIAJiMc/wGMBkdHIu4xpTI0/91IkUa3Qog7hMFgwGg0lsq51ac3ol03DMW9MsbGAzA2ewrFq1qpXMvZ2RmNRlMq5xZ3HklYbPRvt2YpYhFClF8Gg4FDew+RlZZVKuevfPUYNV0qok2/gsvuj3DevYDrFVpyya87N7zDQFVyX+u0HlrCWodJ0iIASVhsZpYSFiHEHcBoNJKVloWvxhdXrWvJX8BzIBeC++J+ZTteF77F7fpefG/kLNm6aqRWe4zUwJ6YNRVu6zL6LD3X0q5hNBolYRGAJCw2M0kbFiHEHcRV64rOVVdKZ9dhDn6E5OBHuJkWi3vcGtwurMcl8wK+pz+i4plPyQzoQnrIALIrNCv+H05pJiNuIQUGNlKkW7MQQuRh8qhBaqPJJEVu5UbTtzF4N0JlNuB28Scq73iSytt74xb3NSpjuqNDFXc4SVhsJL2EhBCiYIqzjszqfbja/luu3P8t6UGPoThpcUmNxufodPw2dcD76Js4p55ydKjiDiUJi41kHBYhhLBNtk8jUprNJLHzNlIavorRPQQnYzrucaupsq0nvjsG4XrxFzBLnY+wnbRhsVFuCYuTZCxCCGETReNNes0hpNcYjObqbtzPrcE1cQva6wfQXj+ASeNLRvU+ZAT3xeRW1dHhinJOEhYbKVLCIoQQxaNSYagcjqFyOE6ZSbjFf4d7/Deo9ZfxjPkMj5jFZPl1ID24P1lV2oFK7eiIRTkkCYuNcsdhkRIWIYQoPrPOj7S6o0kLfQbXpD9xj1uN9uouXJO24pq0FaOuKhkh/ciq8rCjQxXljCQsNpI2LEIIUYKcnNEHdEYf0Bl1Wizu577B7fw6nDMv4nViHp4n/w/3im1xqjkeQjtIjwchjW5tJb2EhBCidJg8apDacBKJnbdyo9k7GHyaoFKyqXJtG66resLCCNj3OWTddHSowoEkYbGRWcZhEUKI0qV2JTOoN1fv/5rzbb4ioXJnFGdXuPwP/PIyzK0HP4+HpH8cHalwAElYbCQJixBClB2DVwNO13qRzOejoOts8A0FQxrsXwIL28LSrnDkWzCWzpxJovyRNiw2sswlJBmLEEKUHVdvuO85aPMsxP2VUzV08heI35WzbKwEYYOg5TCoEOLoaEUpkoTFRjI0vxBCOJBKBTXa5yw3E+HgCjiwHFIvwo4PYcd8CO0MLYfn/N9JukbfbaRKyEZmc87/pdGtEEI4mKc/dJgIY49Av5VQ60FAgdO/w+p+ML8Z/DUX0q44OlJRgiRhsZG0YRFCiHJG7Qz1H4Gn1sELByF8DOgqQEo8bHkT5tWH74bDuZ3/jv4p7liSsNhI2rAIIUQ55lsLusyE8Seg16dQtSWYs+HYd7CsW05D3b2LQZ/q6EhFMUnCYiNpwyKEEHcAFx00GwAjt8Az26H5EHBxg8vHYcOEnK7RP42DxKOOjlTYSRrd2kgGjhNCiLKVnZ1NRkZG8U/gHQqRs+H+13A+9i3OUV/gdO00HFgGB5ZhqtoKY+MBmOp0A1efEov7v5ydndFoNKV2/nuFJCw2MuVWCTk2DCGEuCcYsg3ERMdgNpvRarUlcMamUHsu3lWOEZC0gUo3dqG+uA/1xX2Yf5vIDe8wrvjez7UKbTA5u5XA9f6l9dAS1jpMkpbbJAmLjaSERQghyo7JZMKoN+Kr8cXb07vkTuxVjZvVupKRdRXPSz/ikfgb2rTT+Cbvwzd5H2YnDRmV2pHm9xAZle9HUetu63L6LD3X0q5hNBolYblNkrDYSNqwCCFE2dNqtOhcby9pyJdrEFneo8mqPxrnm2fQXfoV14sbcEmPxePyH3hc/gOz2g29X0f0Vbujr3w/qIuZcBhKNvR7VYnXcEyfPh2VSmW11KtXr9Bjvv32W+rVq4erqyuNGzdmw4YNJR3WbbOMw+LYMIQQQpQwo2ctbtYdw5UHfuFy+3XcrD0Ko1s1nEwZuF3aQMV9Y/D/PQKfQ6+iTdqe0/tIlLlSKWFp2LAhmzdv/vcizgVfZufOnQwYMIBZs2bxyCOPsGrVKnr16sXBgwdp1KhRaYRXLFIlJIQQdzmVCqN3PW561+NmvXG4pBxDd3EDuksbUesTcbvwA24XfsDs4k1mwENkBnbDUKk1qGRU3bJQKgmLs7Mz/v7+Nu07f/58unbtyiuvvALAW2+9xaZNm1iwYAGffvppaYRXLP+OwyIZixBC3PVUKrJ9GpPt05jUBq+guX4I10u/okv4DXXWVdzjv8U9/ltM2kroc5OXis1BJV0zSkupfLKnT58mMDCQmjVrMnDgQOLj4wvcd9euXURGRlqt69KlC7t27SrwmKysLFJTU62W0iZtWIQQ4h6lcsLg24LUxlNI6ryVq+HLSK/+BGYX75zkJW4VlXY+hd/mB/H6ZzYuN47IyLqloMRLWNq0acPy5cupW7cuCQkJzJgxg/vvv59jx47h6emZZ//ExET8/Pys1vn5+ZGYmFjgNWbNmsWMGTNKOvRC5VYJSe4shBD3MJUaQ6X7MFS6j5TGU9Fe2ZXTYDdxM2p9Eh5nv8Dj7BcY3aqRGdgNU6UHQXF3dNR3hRJPWLp162b5d5MmTWjTpg3BwcF88803DB8+vESuMXnyZMaPH295nZqaSlBQUImcuyC5VUJSIySEEAIAJxey/NqT5dceTNNxvfI3rpd+xTXxT5wzLuAZsxjPmMX4ulbFRd0PmvWDKoV3QhEFK/VuzT4+PtSpU4eYmJh8t/v7+5OUlGS1LikpqdA2MFqttoQGErKdTH4ohBCiQGotev9O6P07oTJmor28Dd3FDWgvb8NNfxF2zstZqjSERr2h4WM58x8Jm5V6DUdaWhpnzpwhICAg3+3h4eFs2bLFat2mTZsIDw8v7dDsIr2EhBBC2EJx1qEP7MqNVh8R12EzJ2uNx1SrMzi5wOV/4I+34f+aw6IOsGM+JBfczlP8q8RLWCZMmECPHj0IDg7m0qVLvPHGG6jVagYMGADA4MGDqVq1KrNmzQJg7NixdOjQgblz5/Lwww+zZs0a9u/fz2effVbSod0WGYdFCCGEvRRnDy5XfoDq7V/BTZUFJ36Gf9bC2W2QEJWzbJoG1VpDoz7QsBd42tbL9l5T4gnLhQsXGDBgANeuXaNy5cq0a9eO3bt3U7lyZQDi4+Nxcvq3YKdt27asWrWKKVOm8NprrxEaGsr69evL1RgscEujW8lYhBBCFIeuAjR/KmdJvwrHf4Bja+HcDriwN2fZ+CqEtIOGvaFBT3Cv5Oioy40ST1jWrFlT6PatW7fmWffEE0/wxBNPlHQoJSq3h5rkK0IIIW6beyVoNTxnSU2A4+tzkpcLeyHur5xlwytQs0NOe5f6j+QkPPcwmUvIRlLCIoQQolR4BcB9z+UsyfHwz7qc5CUhCs78kbP8/BLU7pRTbVS3G2jzDhNyt5OExUbSS0gIIUSp86kOEWNzlmtnctq7HFsLl4/DqY05i7MrhD4EjR6D0C6gcXN01GVCEhYbyTgsQgghypRvLWj/Ss5y+URO4vLPWrgWAyd+zFlc3HNKXBo9lpPEqF0cHXWpkYTFRjI0vxBCiOLIzs4mIyPj9k7iEQz3vQRtxqG6fAznkz+iPrEep9QLcOw7lJM/kznmGGhKb1RdZ2dnNBpNqZ2/yOs77Mp3GJMkLEIIIexkyDYQEx2D2Wwu2QFPVV2g/kN4pp2i8rW/UGHizO4TJXf+fGg9tIS1DnNY0iIJi41yx2GRRrdCCCFsZTKZMOqN+Gp88fb0LvkLeFUjK/BBAKqW/Nkt9Fl6rqVdw2g0SsJS3t3a6Fbm4BRCCGEPrUaLzlXn6DBuj8Gxl5fJh22kSKNbIYQQwmEkYbGRZRwWB8chhBBC3Ivk+WsjmfxQCCGEcBxJWGwkQ/MLIYQQjiMJi41kaH4hhBDCcSRhsZFZSliEEEIIh5GExUYms7RhEUIIIRxFEhYbydD8QgghhONIwmKjfyc/lJRFCCGEKGuSsNhIxmERQgghHEeevzYyy0i3QgghhMNIwmIjRUpYhBBCCIeR56+NZKRbIYQQwnEkYbGRjMMihBBCOI4kLDYyS7dmIYQQwmEkYbGRWQaOE0IIIRxGEhYb5VYJyQcmhBBClD15/tpIGt0KIYQQjiMJi40UKWERQgghHEaevzaSEhYhhBDCcSRhsZH0EhJCCCEcRxIWG8nQ/EIIIYTjSMJig9xh+UFKWIQQQghHkITFBuZ/8xWcJGMRQgghypwkLDYwmaWERQghhHAkSVhsYJYqISGEEMKhJGGxgSJVQkIIIYRDScJiA6sSFklYhBBCiDJX4gnLrFmzaNWqFZ6enlSpUoVevXoRHR1d6DHLly9HpVJZLa6uriUdWrFJlZAQQgjhWCWesGzbto3Ro0eze/duNm3aRHZ2Ng899BDp6emFHufl5UVCQoJlOXfuXEmHVmy39hKShEUIIYQoe84lfcKNGzdavV6+fDlVqlThwIEDtG/fvsDjVCoV/v7+JR1Oibh1HBZpwyKEEEKUvVJvw5KSkgJAxYoVC90vLS2N4OBggoKC6NmzJ//880+B+2ZlZZGammq1lCYpYRFCCCEcq1QTFrPZzLhx44iIiKBRo0YF7le3bl2WLl3KDz/8wFdffYXZbKZt27ZcuHAh3/1nzZqFt7e3ZQkKCiqttwD8t9GtpCxCCCFEWSvVhGX06NEcO3aMNWvWFLpfeHg4gwcPplmzZnTo0IG1a9dSuXJlFi1alO/+kydPJiUlxbKcP3++NMK3MP+viEWqg4QQQgjHKPE2LLnGjBnDzz//zPbt26lWrZpdx7q4uBAWFkZMTEy+27VaLVqttiTCtElulZCTlK4IIYQQDlHiJSyKojBmzBjWrVvHH3/8QY0aNew+h8lk4ujRowQEBJR0eMWSWyUkCYsQQgjhGCVewjJ69GhWrVrFDz/8gKenJ4mJiQB4e3uj0+kAGDx4MFWrVmXWrFkAvPnmm9x3333Url2b5ORk3n//fc6dO8eIESNKOrxi+TdhcXAgQgghxD2qxBOWhQsXAtCxY0er9cuWLWPo0KEAxMfH4+T0b+HOjRs3GDlyJImJiVSoUIEWLVqwc+dOGjRoUNLhFUtum1tpcCuEEEI4RoknLLeOWVKQrVu3Wr3+4IMP+OCDD0o6lBIjJSxCCCGEY8lcQjYw/a/VrUoF2cZsB0cjhBBC3HskYbFBuj4LAJXZROzpWIwmo4MjEkIIIe4tkrDYIF2fU6qiVanIzsyGomu9hBBCCFGCJGGxgcFoBkCjlo9LCCGEcAR5AttAn20CQCOtboUQQgiHkITFBlmmnBIWF/m0hBBCCIeQR7ANsrL/VyUkJSxCCCGEQ0jCYoMsoyQsQgghhCNJwmIDSViEEEIIx5KExQa5CYu0YRFCCCEcQx7BNpBeQkIIIYRjScJiA4OlhEUSFiGEEMIRJGGxgV7asAghhBAOJQmLDf5tdOvgQIQQQoh7lDyCbSC9hIQQQgjHkoTFBvpsacMihBBCOJIkLDYwSJWQEEII4VDyCLaB3vi/bs1qKWERQgghHEESFhtYSlhUkrAIIYQQjiAJiw1y27BICYsQQgjhGJKw2CDLJEPzCyGEEI7k7OgA7gRZ2dKtWQghAFKNqSRlJ+Hm5Ia72h03JzdcVC6opMpclDJJWGyQJUPzCyEEAPvT9vPuhXet1qlR46Z2w83pf4v63//rnHSW5Cb33/ntq3PS4aZ2w1kljyWRP/nJsEGWUSY/FEIIACecqORciQxzBhnmDABMmLhpuslN083bPr9WpUXnpMNV5YrRz8j2K9vxSvHKNwG6NdH5bwLk6uSKk0rq8e8mkrDYQIbmF0KIHB19OtLRpyMAZsWM3qzPSV5MGZYkJvffmeZM0k3pln9b9vnPvpnmTLKULACylCyyTDn/RgNXs65Clv1xqlBZlegUVLqjc9Lh6uRqtVitU/27zsXJpYQ+RVEckrDYQC9tWIQQIg8nlVNOAqB2g9t8lhsVo1Wik5iSyN7jewmtG4rKVZUnAcpNdPJLgEyYUFD+LQUylsz7VaPOm9Dkl+Tcss6kNxHrFot7pju+zr7/7qdyRafOKUnSOmmlNMgGkrAUwWRWMJoVQNqwCCFEaXFWOePl7IUXXgB4671J1CcS7hZOBZ8KNp9HURQMiqHg0p1bE5z/JT16sx69okdvyvm/Zd3/lmwlG8ip+ko3p5NuTrfvzfnCH9f+gGsF76JVaQtMfG5dX1Bi5ObkRiP3RvbFdYeRhKUIue1XQEpYhBCivFOpVGhVWrROWnycfUrknEbFaJXA/DehuXXdf7elZqWSkJyAm5cbRidjnqQoV25VWIoppVgxeqg9+L7+9yXyfssrSViKkFsdBDIOixBC3IucVc54qD3wUHvYfez15OtsO72NjrU75ikpUhSFLCUr30TIlqQoy5xl+bfOSVdSb7fckoSlCLklLGoVqGWcASGEECVEpVJZGvWKokmZQRFyS1ikdEUIIYRwHHkMF0HGYBFCCCEcTxKWIkgJixBCCOF48hguQla2lLAIIYQQjlZqCcvHH39MSEgIrq6utGnThr179xa6/7fffku9evVwdXWlcePGbNiwobRCs4veKCUsQgghhKOVymP466+/Zvz48bzxxhscPHiQpk2b0qVLFy5fvpzv/jt37mTAgAEMHz6cQ4cO0atXL3r16sWxY8dKIzy75JawuKgdHIgQQghxDyuVhGXevHmMHDmSYcOG0aBBAz799FPc3NxYunRpvvvPnz+frl278sorr1C/fn3eeustmjdvzoIFC0ojPLvojTIsvxBCCOFoJT4Oi8Fg4MCBA0yePNmyzsnJicjISHbt2pXvMbt27WL8+PFW67p06cL69evz3T8rK4usrH9nw0pJyRkZMDU19Tajz+vGjWTMWRkoWU7cSHFCn6XHaDRiNpu5lnyNbFPOkM3Jqclk6jNLbF1R7D2mONewl1xDrnGnXgNy/q6kG9JJTU3FaCyhyWccICMjg/T0dK5mX0Wr1To6nGIrq/te2u6W91Favx+5z21FUYrct8QTlqtXr2IymfDz87Na7+fnx8mTJ/M9JjExMd/9ExMT891/1qxZzJgxI8/6oKCgYkZdtPPAb6V2diGEEOLedfPmTby9vQvd544c6Xby5MlWJTJms5nr16/j6+uLqoRHo01NTSUoKIjz58/j5eVVoucW9pP7Ub7I/Sh/5J6UL3I/CqcoCjdv3iQwMLDIfUs8YalUqRJqtZqkpCSr9UlJSfj7++d7jL+/v137a7XaPEWdPj4+xQ/aBl5eXvLDVo7I/Shf5H6UP3JPyhe5HwUrqmQlV4k3utVoNLRo0YItW7ZY1pnNZrZs2UJ4eHi+x4SHh1vtD7Bp06YC9xdCCCHEvaVUqoTGjx/PkCFDaNmyJa1bt+bDDz8kPT2dYcOGATB48GCqVq3KrFmzABg7diwdOnRg7ty5PPzww6xZs4b9+/fz2WeflUZ4QgghhLjDlErC0q9fP65cucK0adNITEykWbNmbNy40dKwNj4+Hienfwt32rZty6pVq5gyZQqvvfYaoaGhrF+/nkaNGpVGeHbRarW88cYbd3Rr+7uJ3I/yRe5H+SP3pHyR+1FyVIotfYmEEEIIIRxIBpwXQgghRLknCYsQQgghyj1JWIQQQghR7knCIoQQQohyTxKWInz88ceEhITg6upKmzZt2Lt3r6NDuuvNmjWLVq1a4enpSZUqVejVqxfR0dFW++j1ekaPHo2vry8eHh706dMnz+CDonTMnj0blUrFuHHjLOvkfpS9ixcvMmjQIHx9fdHpdDRu3Jj9+/dbtiuKwrRp0wgICECn0xEZGcnp06cdGPHdy2QyMXXqVGrUqIFOp6NWrVq89dZbVvPjyP0oAYoo0Jo1axSNRqMsXbpU+eeff5SRI0cqPj4+SlJSkqNDu6t16dJFWbZsmXLs2DElKipK6d69u1K9enUlLS3Nss+zzz6rBAUFKVu2bFH279+v3HfffUrbtm0dGPW9Ye/evUpISIjSpEkTZezYsZb1cj/K1vXr15Xg4GBl6NChyp49e5SzZ88qv/32mxITE2PZZ/bs2Yq3t7eyfv165fDhw8qjjz6q1KhRQ8nMzHRg5HenmTNnKr6+vsrPP/+sxMbGKt9++63i4eGhzJ8/37KP3I/bJwlLIVq3bq2MHj3a8tpkMimBgYHKrFmzHBjVvefy5csKoGzbtk1RFEVJTk5WXFxclG+//dayz4kTJxRA2bVrl6PCvOvdvHlTCQ0NVTZt2qR06NDBkrDI/Sh7kyZNUtq1a1fgdrPZrPj7+yvvv/++ZV1ycrKi1WqV1atXl0WI95SHH35Yefrpp63WPfbYY8rAgQMVRZH7UVKkSqgABoOBAwcOEBkZaVnn5OREZGQku3btcmBk956UlBQAKlasCMCBAwfIzs62ujf16tWjevXqcm9K0ejRo3n44YetPneQ++EIP/74Iy1btuSJJ56gSpUqhIWFsXjxYsv22NhYEhMTre6Jt7c3bdq0kXtSCtq2bcuWLVs4deoUAIcPH+bvv/+mW7dugNyPknJHztZcFq5evYrJZLKMzpvLz8+PkydPOiiqe4/ZbGbcuHFERERYRj5OTExEo9HkmfDSz8+PxMREB0R591uzZg0HDx5k3759ebbJ/Sh7Z8+eZeHChYwfP57XXnuNffv28eKLL6LRaBgyZIjlc8/v75fck5L36quvkpqaSr169VCr1ZhMJmbOnMnAgQMB5H6UEElYRLk2evRojh07xt9//+3oUO5Z58+fZ+zYsWzatAlXV1dHhyPISeRbtmzJO++8A0BYWBjHjh3j008/ZciQIQ6O7t7zzTffsHLlSlatWkXDhg2Jiopi3LhxBAYGyv0oQVIlVIBKlSqhVqvz9HRISkrC39/fQVHdW8aMGcPPP//Mn3/+SbVq1Szr/f39MRgMJCcnW+0v96Z0HDhwgMuXL9O8eXOcnZ1xdnZm27ZtfPTRRzg7O+Pn5yf3o4wFBATQoEEDq3X169cnPj4ewPK5y9+vsvHKK6/w6quv0r9/fxo3bsxTTz3FSy+9ZJngV+5HyZCEpQAajYYWLVqwZcsWyzqz2cyWLVsIDw93YGR3P0VRGDNmDOvWreOPP/6gRo0aVttbtGiBi4uL1b2Jjo4mPj5e7k0p6NSpE0ePHiUqKsqytGzZkoEDB1r+LfejbEVEROTp6n/q1CmCg4MBqFGjBv7+/lb3JDU1lT179sg9KQUZGRlWE/oCqNVqzGYzIPejxDi61W95tmbNGkWr1SrLly9Xjh8/rowaNUrx8fFREhMTHR3aXe25555TvL29la1btyoJCQmWJSMjw7LPs88+q1SvXl35448/lP379yvh4eFKeHi4A6O+t9zaS0hR5H6Utb179yrOzs7KzJkzldOnTysrV65U3NzclK+++sqyz+zZsxUfHx/lhx9+UI4cOaL07NlTutGWkiFDhihVq1a1dGteu3atUqlSJWXixImWfeR+3D5JWIrwf//3f0r16tUVjUajtG7dWtm9e7ejQ7rrAfkuy5Yts+yTmZmpPP/880qFChUUNzc3pXfv3kpCQoLjgr7H/DdhkftR9n766SelUaNGilarVerVq6d89tlnVtvNZrMydepUxc/PT9FqtUqnTp2U6OhoB0V7d0tNTVXGjh2rVK9eXXF1dVVq1qypvP7660pWVpZlH7kft0+lKLcMxSeEEEIIUQ5JGxYhhBBClHuSsAghhBCi3JOERQghhBDlniQsQgghhCj3JGERQgghRLknCYsQQgghyj1JWIQQQghR7knCIoQQQohyTxIWIYQQQpR7krAIIYQQotyThEUIIYQQ5Z4kLEIIIYQo9/4fFdOBayNeHngAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pet_concat_tac = pet_concat.mean_timeseries_in_mask(mask=mask_data)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(pet1_tac.frame_mid, pet1_tac.dataobj.flatten(), label=\"1st image TAC\")\n",
    "plt.plot(\n",
    "    pet2_tac.frame_mid,\n",
    "    pet2_tac.dataobj.flatten(),\n",
    "    label=\"2nd image TAC, decay corr. to 1st image start\",\n",
    ")\n",
    "plt.plot(\n",
    "    pet2_tac_.frame_mid,\n",
    "    pet2_tac_.dataobj.flatten(),\n",
    "    label=\"2nd image TAC, decay corr. to 2nd image start\",\n",
    ")\n",
    "plt.bar(\n",
    "    pet_concat_tac.frame_start,\n",
    "    pet_concat_tac.dataobj.flatten(),\n",
    "    width=pet_concat_tac.frame_duration,\n",
    "    edgecolor=\"black\",\n",
    "    color=\"purple\",\n",
    "    align=\"edge\",\n",
    "    alpha=0.2,\n",
    "    label=\"Concatenated TAC\",\n",
    ")\n",
    "plt.legend(loc=\"upper right\");"
   ]
  }
 ],
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