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Analysis of Ventricular Volume Curves from CMR42
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{
"metadata": {
"name": ""
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"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Cardiac Volumes Exported From CMR42"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Reading the XML Files"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"CMR42 can export reports either in text or in xml format. Unfortunately, the text form rounds the output and is anyway rather irregular and would require some munging. It seems better to try and parse the xml files.\n",
"\n",
"Python has several modules for this, but the most common interface seems to be the `etree` parser available in the standard library or in `lmxml`.\n",
"\n",
"We have a suitable test file in `volumes.xml`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First a little boiler plate:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from __future__ import division\n",
"from __future__ import print_function\n",
"import json\n",
"import matplotlib as mpl\n",
"mpl.rcParams.update(json.load(open('styles/bmh_matplotlibrc.json')))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is the file to read:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cmr42_volumes_file = 'volumes.xml'"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll try using the `lxml` xml library on the test file."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from lxml import etree\n",
"\n",
"root = etree.parse(cmr42_volumes_file).getroot()\n",
"\n",
"patinfo = root.find('Patient')\n",
"studyinfo = root.find('Study')\n",
"\n",
"# This seems to the analysis\n",
"sax3d = root.find('SAX3DFunction')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use the `find` and `get` methods to drill down to the fields we want. They seem to be `Frame` records of the following form, one for each time point:\n",
"```\n",
"<frame nb=\"3\">\n",
" <Lv>\n",
" <volume val=\"138.089\" unit=\"ml\" id=\"cavum\" basalCorrection=\"0\" apicalCorrection=\"0\"/>\n",
" <volume val=\"281.527\" unit=\"ml\" id=\"myocard\" basalCorrection=\"0\" apicalCorrection=\"0\"/>\n",
" <volume val=\"4.70968\" unit=\"ml\" id=\"papillaries\" basalCorrection=\"0\" apicalCorrection=\"0\"/>\n",
" <volume val=\"165.907\" unit=\"ml\" id=\"cavum\" basalCorrection=\"0\" apicalCorrection=\"0\" excludingPapillaries=\"yes\"/>\n",
" <volume val=\"253.709\" unit=\"ml\" id=\"myocard\" basalCorrection=\"0\" apicalCorrection=\"0\" excludingPapillaries=\"yes\" triggerTime=\"91.8\"/>\n",
" </Lv>\n",
" <Rv triggerTime=\"91.8\"/>\n",
"</frame>\n",
"```\n",
"The attribute `nb` seems to just an index for the record. We have two entries with `id=cavum` and `id=myocard`. The difference\n",
"seems to be that the second excludes the papillaries. Only the last one seems to have the trigger time. Or we could use the `Rv` tag which seems to have a `triggerTime` too.\n",
"\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#\n",
"# The volume curve data\n",
"#\n",
"time,volume = zip(*[(\n",
" float(f.find('Lv').findall('volume')[-1].get('triggerTime')),\n",
" float(f.find('Lv').findall('volume')[0].get('val'))\n",
" )\n",
" for f in sax3d.findall('frame')]\n",
")\n",
"\n",
"units = sax3d.find('frame').find('Lv').find('volume').get('unit')\n",
"region = sax3d.find('frame').find('Lv').find('volume').get('id')\n",
"time_units = 'ms'\n",
"\n",
"#\n",
"# A selection of other stuff (ok, just showing off here)\n",
"#\n",
"\n",
"# The R to R interval in milliseconds (assuming heart rate in beats/min)\n",
"r_to_r = 60000 / float(sax3d.find('LV').find('HR').get('val'))\n",
"assert sax3d.find('LV').find('HR').get('unit') == '1/min'\n",
"patient_name = patinfo.find('DicomPatientName').get('val')\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So here's a plot of the LV volume."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(time, volume, '.')\n",
"plot(time, volume, '-')\n",
"grid(True)\n",
"xlabel('Delay from Trigger (%s)' % time_units)\n",
"ylabel('Volume (%s)' % units)\n",
"text(500, 60, 'RR interval %3.0f ms' % r_to_r)\n",
"title('%s (LV %s Volume)' % (patient_name, region));"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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pl2PJeZ6BZTNXSbdr2dKdLANDFiz+i/8GXiU1XfvnoxJFowzUmVpEG/pi1SFy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\nMdZkXDQYY4w12f8DqTl3ZH6IFS8AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x2b39bd0>"
]
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OK, this seems pretty much as it appeared from within CVI42."
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Fitting the Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's try smoothing it and looking for a consistent baseline and a parabolic minumum so we can define a time for 80% ventricular filling. Note we have a fairly dubious looking pair of points at the end of the series. We'll want to have some degree of rubustness to this sort of stuff. Let's have a look at using *Savitzky-Golay* smoothing and *lowess* regression.\n",
"\n",
"First the *Savitzky-Golay* smoothing:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# We've our own copy of this scipy recipe\n",
"from nummethods import savitzky_golay\n",
"\n",
"sg_volume = savitzky_golay(volume, window_size=5, order=2)\n",
"\n",
"dt_secs = np.mean(diff(time))/1000\n",
"derivative = np.gradient(volume, dt_secs)\n",
"sg_derivative = savitzky_golay(volume, window_size=5, order=2, deriv=1)/dt_secs\n",
"\n",
"min_volume = np.amin(sg_volume)\n",
"max_volume = np.mean([np.amax(sg_volume[:5]), np.amax(sg_volume[-5:])])\n",
"max_gradient = np.amax(sg_derivative)\n",
"\n",
"min_vol_time = time[np.argmin(sg_volume)]\n",
"\n",
"\n",
"\n",
"fig, ax = subplots(1, 2, figsize=(12, 4))\n",
"ax[0].plot(time, volume, '.')\n",
"ax[0].plot(time, sg_volume, '-')\n",
"ax[0].grid(True)\n",
"ax[0].set_xlabel('Delay from Trigger (%s)' % time_units)\n",
"ax[0].set_ylabel('Volume (%s)' % units)\n",
"ax[0].text(500, 60, 'RR interval %3.0f ms' % r_to_r)\n",
"ax[0].axhline(y=min_volume, linestyle='-.')\n",
"ax[0].axvline(x=min_vol_time, linestyle='-.')\n",
"\n",
"ax[1].plot(time, derivative, '.')\n",
"ax[1].plot(time, sg_derivative, '-')\n",
"ax[1].grid(True)\n",
"ax[1].set_xlabel('Delay from Trigger (%s)' % time_units)\n",
"ax[1].set_ylabel('Gradient (%s/sec)' % units)\n",
"ax[1].text(500, -800, 'RR interval %3.0f ms' % r_to_r)\n",
"ax[1].axvline(x=min_vol_time, linestyle='-.')\n",
"\n",
"fig.suptitle('%s (LV %s Volume)' % (patient_name, region), fontsize=14)\n",
"\n",
"show()\n",
"\n",
"print('Minimum Volume = %4.0f ml' % min_volume)\n",
"print('Maximum Volume Estimate = %4.0f ml' % max_volume)\n",
"#print('Time to 80% as proportion of RR = %4.0f ms' % \n",
"print('Maximum Rate of change of Volume = %4.0f ml/sec' % max_gradient)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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fIsRvJm32/Yx5kwZq3beo4Ra0WWXpsxefiiImOQNDXTnTOzthaqCx85SChmlF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I62tjZcuHBBWOfk5CT8PHDgwC7LGo1Gr/FvznCzqqoqne0ymUxnm6ura4/HOnfuHFav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\ncDPGGGOMMWZAPOFmjDHGGGPMgP4PElsgOqZEC/wAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x2f301d0>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Minimum Volume = 48 ml\n",
"Maximum Volume Estimate = 165 ml\n",
"Maximum Rate of change of Volume = 485 ml/sec\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's compare with Lowess smoothing."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from statsmodels.nonparametric.smoothers_lowess import lowess\n",
"\n",
"lowess_volume = lowess(volume, time, frac=0.2).T[1]\n",
"derivative = np.gradient(volume, dt_secs)\n",
"lowess_derivative = np.gradient(lowess_volume, dt_secs)\n",
"\n",
"min_volume = np.amin(lowess_volume)\n",
"max_volume = np.mean([np.amax(lowess_volume[:5]), np.amax(lowess_volume[-5:])])\n",
"max_gradient = np.amax(lowess_derivative)\n",
"\n",
"min_vol_time = time[np.argmin(lowess_volume)]\n",
"\n",
"\n",
"fig, ax = subplots(1, 2, figsize=(12, 4))\n",
"ax[0].plot(time, volume, '.')\n",
"ax[0].plot(time, lowess_volume, '-')\n",
"ax[0].grid(True)\n",
"ax[0].set_xlabel('Delay from Trigger (%s)' % time_units)\n",
"ax[0].set_ylabel('Volume (%s)' % units)\n",
"ax[0].text(500, 60, 'RR interval %3.0f ms' % r_to_r)\n",
"ax[0].axhline(y=min_volume, linestyle='-.')\n",
"ax[0].axvline(x=min_vol_time, linestyle='-.')\n",
"\n",
"ax[1].plot(time, derivative, '.')\n",
"ax[1].plot(time, lowess_derivative, '-')\n",
"ax[1].grid(True)\n",
"ax[1].set_xlabel('Delay from Trigger (%s)' % time_units)\n",
"ax[1].set_ylabel('Gradient (%s/sec)' % units)\n",
"ax[1].text(500, -800, 'RR interval %3.0f ms' % r_to_r)\n",
"ax[1].axvline(x=min_vol_time, linestyle='-.')\n",
"\n",
"fig.suptitle('%s (LV %s Volume)' % (patient_name, region), fontsize=14)\n",
"\n",
"show()\n",
"\n",
"print('Minimum Volume = %4.0f ml' % min_volume)\n",
"print('Maximum Volume Estimate = %4.0f ml' % max_volume)\n",
"print('Maximum Rate of change of Volume = %4.0f ml/sec' % max_gradient)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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TZnHj4L9l9rr3Kbn2SKnZIiMjsbSE4GBljZQo+XgpkeQShkqp54iScz0tbbaZ\n0b1uUmmmFVby71GJlJqrtHTy3ciqVasK3b5s2bJCt4eEhBASElJueZ4Z3JObR09xeFQI7bcswbR2\nzXLblxBCCCEM0+SuTsyOTGCCp4OUk4hSqZBLuxdHXm4u/7z0Npo7ubT6cQ5qE+MyTCeEEI8mNdxC\nKN+lLXtJ2bCdhu8FYO5k//gniArLYGu49U1tZETLb6dzOzGFkx/OyXffrD0XeHdjLFM2x5GRnaun\nhEIIIYTQh7zsHE5MncXhUZPJPJ9EZNeXOTtvOXl3pE8gnsxT2+EGMKlZDfclYSSu+o3E1Zu020tz\nkYSSa4+Umi0yMpL0dAgLMyM9Xd9p/qPk46VEkksYKqWeI0rOVZHb7IzT59jXbzSXtuylzfqFtNnw\nDc3nvM/579awr/co0o6c0Euusia5dOup7nADWDVvjOvnk4iZ9Ln2H1FZXCQhSiYjQ8XMmZXJyCi7\nC2SFEEKUj4rYZms0GhJXbmRfzwAsnJ1ov+0Hqnm4olKpsB3QDc89K7HycOHvfq9y4sOvyb2VWeA1\n5BtyUZSntob7YSemziL1jwjabQ7nTtWqcpGEjiUnq3B1rcbx4zewszPIU1KIEpEabmHIDLnNLmxu\n7Ts3Mzg+8TMub/2LpjPeoc7QvkXOkHbt7yMcn/gZd29n4/rZRKy92mnve3djLNEptwDoVK8aU73q\n6eItCR2QGu4y0vjDcVR2sOPIq+9jroapXvWks61DFhYagoJuY2FhWA23EEI8jQy5zX64bPTGwX/5\ny8ufzLMJtNuyGPuX+j1yOuIabVvSYdtS6gzty6GRkzjy+gdkX74GyDfkomjS4f5/amMjWn7/CZnn\nEjk1ff4Tv46Sa4+Umu1pmtO1LEiuklFqLqEcSj1HlJzLkNtsbae4hhkvHN3N/oFvULtvJ9pu/A6L\nhnWL9RpqUxOcJwbSYdtSsi5eIrKjL4mrNhLcpS6d6lUjrE8D7aCdkn+PSqTUXKUlHe4HmFrXwD08\nlISlv3Jpy159xxFCCCFEGZvc1QmvahqGLZ9PcvhaPH74jKbTxqM2NSnxa1k0rkebdQtwnvw6Jz/4\nmhPD3uadhqbyDbkoQGq4C3HqkwVc2bmf9luXoFLLZxIhRNmTGm4h9OPytr849tYnVHVtiNu8DzCz\nqVUmr5uVfJl/35nB7cRU2v+5mErmZmXyukIZpIa7HNR7w4/Mc0lc2rxH31GEEEJv7t69i7u7OwMG\nDADg2rVQ3cxiAAAgAElEQVRr9OjRg0aNGtGzZ09u3LihfWxoaCjOzs40adKELVu26CuyEI+UduQE\nh14Jxun1l3huzewy62wDmNlZ03LRp2ju3uXUx/PK7HVFxSAd7kKY1KxG3VEvEvdFOJq8vBI9V8m1\nR0rNVtHndC1rkqtklJrLEHz99de4uLhoLyALCwujR48exMbG4uXlRVhYGAAxMTGsWbOGmJgYNm/e\nzJgxY8grYdupT0o9R5ScyxDb7LuZWRx7cxoOw7xp8NaIcvkG26iKOS3mf0jCig3a0lQl/x6VSKm5\nSks63EVwet2XzPMXSf0jQt9RhBBC5xITE/n9998JDAzkfuXhhg0b8Pf3B8Df359169YBsH79enx9\nfTE2NsbJyYmGDRsSFRWlt+xCFOb+qHPjD94s1/1YubvgHBRI9IRPyb50tVz3JQyHdLiLYFLDirqB\nJR/l9vT0LMdUpaPUbJ6enoq84l3Jx0uJJFfF8vbbb/P555+jfmAUMDU1FRsbGwBsbGxITU0F4OLF\ni9jb22sfZ29vT1JSkm4Dl4JSzxEl5zK0Nvvy9n0krNhA83kf6qS2ut6YYVg0rkf0+E/o0L59ue/v\nSSj5/KqI5DLaR3B6zZfz4T+RumkXtgO66TuOEELoxMaNG6lduzbu7u7s2rWr0MeoVKpHzlVc1H1j\nxozB0dERACsrK9zc3LR/YO9/lSy35XZZ3m7dtBn/vj2DtOc7Ep1xlfvdufLcv6pSJTL8e3P8nRnU\nCv8Rp9FDFXM85Hbxbi9cuJDo6Ghte9WzZ09KQ2YpeYzYsG+5tHkPHXb8r1j1XpGRkYr9dKbUbJKr\nZCRXySg1l5JnKQkJCWHZsmUYGRmRlZXFzZs3ef755zlw4AC7du3C1taW5ORkunbtysmTJ7W13MHB\nwQD07t2badOm0aZNm3yvq9RZSpR6jkiukiksl0aj4cioELIvXaX1ugWojXQ7zpiyYQer3ggicOtq\nLF0a6nTfj2NIv0clkFlKypnTa77cTkwhdeMufUcRQgidmDFjBgkJCcTHx7N69Wq6devGsmXL8Pb2\nZunSpQAsXbqUQYMGAeDt7c3q1avJyckhPj6e06dP07p1a32+BSEASFq9iSu7omg+7wOdd7YBbL27\nUbPjcxx9/UPu3s7W+f6FckiH+zFMqlfFabQPcV8Wr5ZbiZ/K7lNqNk9PT0Ve8a7k46VEkqviul8e\nEhwczNatW2nUqBE7duzQjmi7uLjg4+ODi4sLffr0YcGCBY8sN1EapZ4jSs5lCG125vkkTkydTZPp\n4zF3si/iWeXPL/wr8u7cKdUq1uVByedXRSQlJcWQc/0mEa1fwPWLYOwGKvMrYEOXnKzC1bUax4/f\nwM7OIE9JIUpEySUl5UWpJSWi5JTeZmvu3mX/4LGYVK+K+w+f6f0D4I1DMewf+Drui0Op3aODXrOI\nJyMlJTpgUr0qdQN9OPPl4seOcit5/kilZpNcJSO5SkapuYRyKPUckVwl82Cus/OWk3k2AdcvgvXe\n2Y6MjKSahwsN3xvFvxM+JfvyNb3muc8Qfo8ViXS4i8nptaFkJV8iZcMOfUepkCwsNAQF3cbCQnkj\nJUIIIfJTcpuddvQkcV+E02xWCKbWNfQdR6v+m8Op4uxE9PhPMdDiAlEKUlJSAqdnLiJ140467Pwf\nqkqVdLpvIUTFIiUlQpS9u5lZ/NXrFWq0c8d1ZpC+4xRwOzGFvV7+NJw4CqdAH33HESUgJSU65PTa\nULJSLpPym4xyC1ER5WbcYtXLHxA2fRVTNseRkZ2r70hCiBI49ckCNHfzaPzhOH1HKVRle1tcZwYR\nO30B6SfO6DuO0CHpcJeAsZUldUf7EPflEjR37xb6GCXXHik1m+QqGclVMsXNdWXXfiI7D8co5iT/\n5plzIDGd2ZEJ5ZxOKIGhn7u6ptRcG+d+R8L/fqX5vA8xqlJZ33G0Hj5edgO9sBvUnaNv6HeqQKX+\nHpWaq7R00uEOCAjAxsYGNze3Avd9+eWXqNVqrl377yKC0NBQnJ2dadKkCVu2bNFFxGJzenUo2alX\nSN6wXd9RhBBl4E5aOtFvz+Dg8Peo49OXox9N41IdRxrVMmeCp4O+4wkhiiHnWhrx85bTYMJIqnm4\n6DvOYzX99G3ysrKJ/XSBvqMIHdFJDfeePXuwsLBgxIgRREdHa7cnJCQwevRoTp06xcGDB6lRowYx\nMTH4+flx4MABkpKS6N69O7GxsagfWuVRn7WAcV+Ek7x+G567lkstdxlJT4f5880YOzYLS0t9pxFP\ni0tb93I8aCYmNavhNnsKVZs1IiM7l9mRCUzwdMDCtPwWypAabmHIlNRmazQajgROISv5Mm02LNTL\nAjdP4sah4+wb8DqHx03gVssWTO7qVK5tjigdg6jh7tixI9WrVy+w/Z133mHmzJn5tq1fvx5fX1+M\njY1xcnKiYcOGREVF6SJmsdV9dSjZl66RvF5GuYUwRDnXb3Js3HQOB0zG4eVBtPsjnKrNGgFgYWrE\nVK96ev/Dd/nyZb788ku6detGzZo1MTIyombNmnh5efHFF19w+fJlveYTQimSVm/iyo6/9baa5JOq\n5uHK2b79aRS+iGOnU6WErYLTWw33+vXrsbe3p3nz5vm2X7x4EXv7/1aEsre3JykpSdfxHsm4qgVO\nr73Ema8WF6jlVnLtkVKzRUZGYmkJwcH6Hyl5kJKPlxIZSq7UzRHs7TyMjNh42m9ZQsN3XkFtrKw/\n0sHBwXh4eHDq1CkCAwPZunUrJ06cYOvWrQQEBBAbG4uHh4d2pUdROoZy7iqFktrstKMnORb0OUdf\nGsbYdX8p8kLnR/0ekwcOJMPSikFbftZ5CZuSz6+KSC9/ZTIzM5kxYwZbt27VbntUZUtRk9aPGTMG\nR0dHAKysrHBzc9MuCXr/F1ZetxNd7Tk69zwN1m3jmRd6FThBynv/T3I7OjpaUXmUfluOV8U6Xndu\nZlB9w19c+iOCGy90wnagF5ZNG+j0+KSlpQFw4cIFAgMDKYq9vT1xcXGYmpoWuM/Dw4Nhw4aRlZXF\nokWLinwNISq67MvXOBwwmcSOndjSwIObZ44wOzKBqV719B2t2IJ7NOSbq2Nx++B9Mv6MwMK7m74j\niXKis3m4z507x4ABA4iOjiY6Opru3btjbm4OQGJiInXq1GH//v0sWbIEQDty07t3b6ZNm0abNm3y\nvZ4SagHPzFpC0k9/4rl7uUF9jSXE0yZlww5iJn9BZac6uM2agkUjJ31HkhpuIUoh704uB4a8BcBv\ngW8RlXKbRrXMCevTQO/lYE/i3LerOfP1Ujx3Lce0dk19xxGFMIga7oe5ubmRmppKfHw88fHx2Nvb\nc+jQIWxsbPD29mb16tXk5OQQHx/P6dOnad26tT5iPlbdQB/SU67y5YfLZc5eIRQoK+Uyh0eFcOyt\nj6k37mXabvhGEZ3tkggNDS1wHUtUVFSB61+EeJqc/GgOty9cpOX3nxDcoyGd6lUz2M42QN3RPlg0\nrs+/74bJKpQVlE463L6+vrRv357Y2FgcHBy0o9j3PVgy4uLigo+PDy4uLvTp04cFCxYUWVKib0aW\nVYjv0Ys669dx4EIasyMTFF17pNRskZGRpKdDWJgZ6en6TvMfJR8vJVJSLk1eHgnL1hHZaRj/nD1N\nhx3LqPe6r0HOKvT111/j4pJ/mrOmTZsya9YsPSWqeJR07j5Iybn02WYnrtpI4ooNuC8Jw9S6hvZC\n5yMH/tZ9mGIozu9RpVbjNnsK1/46TNKqjTpIpezzqyLSyUfBVatWPfL+s2fP5rsdEhJCSEhIeUYq\nM5d69MBp42+0u3aBCf4tOHJAWRd4GoqMDBUzZ1bG3z8bS0v5dC+eXEbceY6/9xnpJ87Q5KNxmDpU\no0p9w51P+86dO5iYmOTbZmJiQna2/hbMEEJfbfaNQzHEBH+B6xeTsGrRRGf71QXzus/QdPp4Trz/\nNTU8n8Pc0U7fkUQZkpUmSymovytpbVoz4OwhLEyNtBdJKZFSs0mukpFchcu7k8uZr5fyl5c/ptY1\n6LhnJfZ+A+jYsaNec5WWh4cH8+fPz7ftm2++kVroMqTvc7cokiu/7EtXOTxqMg7+g6kzpE+B+yvC\n8arj258a7VoSPf4TNHl55ZiqYhwvQ2KYxU4KYmFqRJ93/Tjw0gRyrqVhUsNK35EMkoWFhqCg21hY\nyOi2KLkbh2L4991Q7lxPo8W3H2PTu5O+I5WZ2bNn0717d5YvX079+vU5e/YsycnJ+WZ5EkLXdN1m\n5+Xc4XDgFKo0cKTxB2N1sk99UKlUNPtqMpFdhnP++7U4vfaSviOJMiIj3GWgeruWVK5jw8Vf/lR0\n7ZFSsylpTtcHKfl4KZE+cuXeus2JD79m/4DXqN6qOZ4RKwt0tpV6vIrL1dWV2NhY3nvvPVq3bs3E\niROJjY3F1dW13PaZkJBA165dcXV1pVmzZsyZMweAa9eu0aNHDxo1akTPnj25ceOG9jmhoaE4OzvT\npEkTtmzZUm7ZyoNSzxEl59J1m31i6myyLl6i5bfTi5wVTMnHqyRMa9fENWwisTO+IeNUfDmlqjjH\ny1BIh7sMqFQq6vj2J3HFb3J1sRA6cnnn3+ztMpwr2/fR6ue5uM6ciHFVC33HKheWlpZ06NABT09P\nXnrpJSwsyvd9GhsbM2vWLI4fP87ff//N/PnzOXHiBGFhYfTo0YPY2Fi8vLwICwsDICYmhjVr1hAT\nE8PmzZsZM2YMeeX8dbh4eiQsX0/Sj7/jsSQUk5rV9B1HJ2y9u2HTtzPHxk0n747MgFYRSIe7jNTx\n6cOt2HM0t1Du/JlKrYuSXCXztOfKvnSVNUOCODB8IqdatqLFpnBqtG2p91zl5cKFC3To0IGmTZvS\nvXt3AH788cdHLpxTWra2trRsee+YWlhY0LRpU5KSktiwYQP+/v4A+Pv7s27dOuDeysG+vr4YGxvj\n5OREw4YNC0xlqGRKPUckF1w/EE1MyFc0+2oyVd0aP/KxFe14NZ3xLtmXrnD266VlnOieina8lE46\n3GXEzNaaWl7tSNTRdD5CPG3ycu4QP38FEe2HormYwvI3JvFTm17M+eeSvqOVq1dffZW+ffuSnp6u\nna2kZ8+eOivbOHfuHIcPH6ZNmzakpqZiY2MDgI2NDampqQBcvHgRe3t77XPs7e1JSpIZm0TpZKVc\n5sioEOqOGsIzg3vqO47OmVSvSrOvQjgz+wfSjpzQdxxRSnLRZBlyGDaAVa9NpPGH4zCqUlnfcQqI\njIxU5CfHyMhIWrTwZP58M8aOVU4dt5KP19OUS6PRcHnrX5z8aA53b2fhOjOI+ZXrcSUpg0a1zJng\n+egp/5R6vIorKiqK33//HbX6v/ERKysr7TLx5SkjI4MXXniBr7/+GsuH/mGqVKpHrpFQ1H1jxozB\n0dERuPc+3NzctL+f+7Wbur59f5u+9l/U7YULFyri+BR2vFq08GTSpCgGDcqhZ8+y319edg5LXxxN\nJRtzOk953eCP1xOfXyZg7+fNsXHT0Xw0GrWp8VNxfinh3+PChQuJjo7Wtlc9e5buQ5/OlnYva0pc\nIjgvN5f5Lt14fnowdYb21XecApTa8ZAOd8k8TbkyTp/j5AdzuPbXIZze8KX+uJcxqmJORnYusyMT\nmODp8NiV5ZR6vIq7TLCLiwu//vorjRs3pnr16ly/fp2YmBheeukljh07Vm757ty5Q//+/enTpw8T\nJkwAoEmTJuzatQtbW1uSk5Pp2rUrJ0+e1NZyBwcHA9C7d2+mTZtGmzZt8r2mEtttUO45ouRc5dlm\nazQajr8bxtW9B2m3eTEm1asWO5dSj1dpcuXeymRvtxHY9O5Ek2lvKSZXeVFqrtIu7S4d7jJ26pMF\n3PgnmjbrFuo7ihAG687NDM58uZjz4T9Su6cnjT98E/O6dfQdq0wVt/FevHgxoaGhTJ48mfHjx/Pd\nd98xY8YMJk2axPDhw8slm0ajwd/fn5o1a+Zb0TIoKIiaNWsyadIkwsLCuHHjBmFhYcTExODn50dU\nVBRJSUl0796duLi4AqPcSm23hbLEf7OKuM++p+2m77B0aajvOIpwPeoYUYPH0urHOdRo767vOE+l\n0na4Hzk0dPnyZf73v/+xadMmjh49SlpaGlZWVrRs2ZI+ffrg7++PtbX1E++8IrL3G0D8vOVkxJ3H\nomFdfccRwqBo8vJIWr2J2BnfYFKzGs+tmkXNjs/pO5ZeBQQEULNmTb755hscHBxYunQp06dPZ9Cg\nQeW2z71797J8+XKaN2+Ou/u9P+6hoaEEBwfj4+NDeHg4Tk5OrF27Frg3Cu/j44OLiwtGRkYsWLDg\nkeUmQhQled02Yj9ZgPuSMOlsP6B66+Y4vf4SR8d8SOuf51GlgaO+I4kSKnKEOzg4mBUrVtCnTx+6\ndOlCkyZNsLS0JD09nRMnTrB7927++OMPhg0bpv06UZeUOlISGRmJ8RcrqfasK43fV9bk/Er9mkZy\nlUxFzXX9QDQnpswi83wSzhMDcRg5uMj5dnWZq7yUdrTEECm53VbiOfK05bq69xD/+L6NS+i7OAzz\nVkyu0iqrXHm5uUS/9QlX9/xDqx/nYNmkviJylbXIyEjaeTzH3ewcNHfukJedQ96dXPJy7pCXc+f/\nt90h74H/mtaqTvU2Lco1V7mNcNvb2xMXF4epqWmB+zw8PBg2bBhZWVksWrToiXdeUdn7DeDU9Pk4\nB7+G2liuSxWiKLm3bnN5614u/rKFK9v3YT/Mm+dWffXUzLVbHCtXrqRly5a4uLhw6tQpRo8eTaVK\nlVi4cCFNmjTRdzwhykT6iTMcfiWY+m++/ESd7aeB2siI5nPf5993w4h6/k1arZ1N1WaN9B2r2DQa\nDXczMslKuUx26lWyU6+QnXqVrNT/v51yhexLV4k6f5aMu8ZFvo6qUiVUJkaoTUxQGxuhNjWhVtc2\n5d7hLi2p4S4HdzOz2NliAG5zpmLTp7O+4xiE9HQUd9GkKB93s7K5svNvktdt4/KWvagrm2E7oCuO\n/oOfqq+QiztaUr9+ffbt24eNjQ39+/enSZMmVKlShT179rBjxw4dJC07Sm63RcmUZZuddfESf/d/\nlZqdW9Psq8nMjkwgMS0LMyM1k7s6PfbC6KfFrD0X7h0XNQzZsY4rv22n1epZWLm76DtakXKu3uDi\nL39y8ac/uRV7jru3swCoVNkMU9tamNrUwtSmJqa2tTCrXQtT21osPnub4znG5Bqb8JxTdd7u1gCV\niTFqY2PUJkaoKlXSy3sptxHu4jbk3bp1e+KdV1SVzM2wG9yTxJUbpcNdTBkZKmbOrIy/fzaWlgb5\nGVA8Qt6dXK5GHCB53TYubY4AtRqbvp1xXxJKDc9n+XrfRRLPZmF2IU7+wD7kypUr2NjYcPv2bfbu\n3cvPP/+MsbExNWsqd5EtUfGVVZt952YG/wx7F4vG9XGdGYRKpSIxLYvolFsAzI5MYKpXvbKKbdAe\nPC6Ve7/IC1VMiRryFs+t/IrqrZvrOd1/8nJzubJjP0lrNnFpSySV7W2p81I/qr0/9l7H2qYWlSzM\ni7zO4/bmOK4mptOoljlj+zTApIL8PSjyXQQEBBTropf4+PgyDWTo7tdE2fv1Z1/f0WQlX8bMThkX\nliq5XqtBg476jlGAko+XIeTS3L3LtX2HSV6/ndSNO8nLvkPtPh1pPv9DanVujdrURPvY8vwDq9Tj\nVVzW1tacPn2a6OhoWrVqhampKbdu3cJAv5xUJKWeI0rOVRZtdl52DodfCUZtbEzLRZ9oSzDNjO7N\nOV+cefYfzqXU41UWufIdl46OVPF6i0pmpvzz0tt4/G8mNT2f1Uuu+zJiz5G0ehMXf9pM7q3b2Hp3\no/VPc6nWunmJLqLuYppE5XqOxZr21ZAU+U7OnTunwxgVT9UWTbBs2oCktb/TYLy/vuMonoWFhqCg\n21hYSCfCUGk0GjJOn+P630f4a8NfmB2Lxigzk9rd2+M6Mwhrr/ZUMjcr9LlP+gf2afD+++/z3HPP\noVarWbNmDQDbtm3TLr0uhD6UtM3WlkP8f5lIFWM10RM+5XZCCm03fYdRFXPtYyd3dSr2PPtPk8KO\nS6PJr1PJzJSDw9/FfXEY1t3a6jTTnZsZJK/bRtLqTaQdOk71du40mvIGNv27PvECgJWNKzHVs+J9\nqyE13OXo/KIfOff9GjrtW4vqgVXihDBED//BNK8E6cfjuL7/KNf/PsL1/UfJuXqDKs51OWXnxFGb\n+pxt0ox2TWwfO2JdkoVsKorH1QNmZmZibn6vE3Lr1r3R/ypVqgBw6dIl8vLysLW1Lf+gZcgQ2m1R\nPt7dGKv9FqtTvWoM2fs7ias30va3b2WKuzIQv2AlsWHf0vK76dj07lSu+9JoNHz73RZMf/+T2ocO\nYl67BvZD+1FnaJ8Kt17Cg8p1Hu77jhw5wjvvvMPhw4fJyMjQblepVOTk5Dzxzis6uxd6cWr6fK79\ndbjEX/UIoTQXr9zk2j8x1Dl/hk1fnafGmTju3s6iarNGVG/bAtcXe1O9dXNMalVn8+Y4Tv1/DV5x\nRqwtTI2kTvMhdevWxd3dnb59+9KvXz+cnZ2199WuXVuPyYQouQe/xfI5HcW5xT/S6qe50tkuI/XG\n+KE2NeHI6Km0mP8Rtt5lf31dXnYOyeu2cX7RWhxjzhDr6k7k8Nep1601U3s0KPP9VTTFGnb19fWl\nQ4cOREREcOLECe1PTExMeeczOJGRkdr/N6leFZu+nUlc9ZseE/3nwWxKIrlKRte5bp25QMzkL+ky\nYRwvLJ1H0/OxNOvUAvfwT+ke+yfttyym6cfjOV21Eia1qgP3vvrsVK8aYX0a6H3EWqm/x8dJSkoi\nKCiICxcu4O3tjbOzM+PHj+fPP/+UgY4yptRzpCLlut8mvEci8Z/Mo8U3H1P9OTe959IFXeWqO+pF\nXMLe4+iYD7n40+bHPr64ubIvXeX0zEXsenYwpz6eh3UPT6K+nMXvPq9QuY0HEzqV7SJ/Sv09llax\n/hKmpKTw8ccfy8phT8DebwAHh7/HnU9vYlytqr7jCFEsGo2Gq7ujOP/9Wi7v+Juans/SbMFHLK9k\nx/iu9R/biZYR69IzMTGhe/fudO/ena+++oozZ87w+++/M3v2bIYNG0b79u3p27cvgwcPxsbGRt9x\nhXgkC1Mjxlqmc2DUdJpOn1DuZQ9PK4dh3qhNjIme8Cl3s3NKNad52tGTnP9+Lcnrt2HRuB6Npo7B\nblB3KpmZ8t5TWAZYWsWq4R4/fjytWrVi+PDhushULIZSC6jJyyOirQ9Or/tSN+AFfcdRLJmHWxnu\nZmaR9NNmLiz6kcwLSTzzYm/qjhqCZVP5urCslaYeMDMzkx07dvDHH3/QsmVLRo8eXcbpyoehtNvi\n8UraZmecPsf+Aa9h//IgGk95o/wDPuWS12/n2JvTqNHencqOz1C5jg1mdWyoXMcWM3tbzOysC12Y\nLy83l0t/RHB+0Y9cPxCNTe+O1A30oXq7lk/9oKtOargnT55M27ZtCQ0NzVc7qFKpDG7hBV1TqdXY\n+/YjceUG6XALxbqdlMqFJT+TuHw9ajNTHANexGGYt6z4qCd5eXmPvN/MzIy+ffvSv39/HSUS4snd\nvZ3NoRFBWHdvT6OQ1/Ud56lgN9ALMztrru75h6ykVK79dZjbSalkJaWQl5UDKhWmtrXydcTVpsYk\nrf2D3JsZ2PsNwG3O+5jXfUbfb6XCKFaHe8iQITRo0IDBgwdjZvbftF5P+6edwhQ2r2Wdof04/Xk4\nacdOYdW8sZ6SKX+O0uDgLH1HyUfpx6u0ZkWcJ/3Qv9TbsRXrgwexatkEl7D3sOnXtdCRD13lKmtK\nzfUoRkaPP/4qlYq7d+/qIE3Fp9RzROm5ittmx89fjib3Lq4zJ5Vrv0Hpx0vXfsiuRmJjT8xc/1ux\nU6PRcOfqDW4npRKxdTv1qtbidlIKmeeTuHP9JvXGDKPO0D4YWVTRed77lPp7LK1iz1Jy5coVTE1N\nn2gnAQEBbNq0idq1axMdHQ3AxIkT2bhxIyYmJjRo0IAlS5ZgZWUFQGhoKIsXL6ZSpUrMmTOHnj17\nPtF+lcLsmdpYd21D0srf9NrhFuK+vJw7VJ/5FY0OHeR0Mw+ufPABb7/WQ9+xxP87e/asviMIUSYy\nzydxdu4yWn43vch5+EX5KGxBMZVKhUmt6pjUqk719Cs4VcCOrVIVq4a7b9++fPrpp7i7uz/RTvbs\n2YOFhQUjRozQdri3bt2Kl5cXarWa4OBgAMLCwoiJicHPz48DBw6QlJRE9+7diY2NRf3QPNaGVguY\nsmkX/749g65Hf6NS5Sf74CJEWcjLucPR1z/g/P7jrBwxltqNnRQxm8jTprT1gIbI0NptUXoHX54I\nGg0eyz6Xb8V1bMrmOA78//Ss0saXnk5quJ2cnOjZsyfPP/98gRrujz/++LHP79ixY4GVK3v0+G80\nrU2bNvz8888ArF+/Hl9fX4yNjXFycqJhw4ZERUXRtq1uV08qa7V7dEBtbETqpp0882JvfccRT6n7\nne20Y6fwXD+ff8/nylXmCnfjxg3mzJlT6DoIW7Zs0WMyIR7t0p97uBpxAM+IFdLZ1oOSrtj58OJm\n8nehbBVrHu7MzEz69etHTk4OiYmJJCYmkpCQQEJCQpmEWLx4MX379gXg4sWL2Nvba++zt7cnKSmp\nTPajC0XNH6k2MeYZn74krNDfnNxKndsyMjKS9HQICzMjPV3faf6j5OP1JB7sbLf+ZT61Gjow1ate\nmTWqFe14KcWQIUPYvXs3Xl5eDB06NN+P0mzevJkmTZrg7OzMZ599pu84xabUc0TJuR7XZt/NzCJm\nyizqj3tZZ6sPKvl46cP96VmLauMfznW/BOVAYjqzI8umf/cklPp7LK1i/aX94Ycfyi3Ap59+iomJ\nCX5+fkU+pqhPxmPGjMHR8d4qVVZWVri5uWkL7e//wnR9+77C7r/d2Ja7C1fyfngECWnJDHO3pUfX\nzmB07S0AACAASURBVDrLFx0drffjU9TtHTv2MnOmBf7+LbG01Og9j9KPV0lvR+zcxZmvllAvOZ3W\nv8zn0IUzcOGMHC8dn09paWkAXLhwgcDAQIojKiqKS5cuPfE1NLpy9+5d3nzzTbZt20adOnVo1aoV\n3t7eNG3aVN/RRDnIyFAxc2Zl/P2zsbQsWJl6du4yVJXU1BurnOmExaM9uBpocVYIFiVTZA13ampq\nsRZTKO7jzp07x4ABA7Q13HCvI//999+zfft27ewnYWFhANq67t69ezNt2jTatGmT7/UMtRZwlac/\ncXZO7Ok1iE71qsniIP8vOVmFq2s1jh+/gZ3dYy8rECXw8Mi2uaOdviMJil8P2KdPH8LCwmjRooUO\nUj25ffv2MW3aNDZvvrfC3cNtORhuuy0KelSbfSs+kcjOw3APn0HtHh30lFCUVIYsZvNI5VbD3a1b\nNzp37szLL79MmzZt8l20mJeXx/79+1m2bBm7d+/m+PHjJd7x5s2b+fzzz9m9e3e+qQa9vb3x8/Pj\nnXfeISkpidOnT9O6desSv75SpXbvgdsPS7j6/GD5BPkACwsNQUG3sbCQznZZks624fvhhx/o06cP\n7dq1w8bGhvtjJCqVig8++EDP6f6TlJSEg8N/bZq9vT379+/XYyJRnopqszUaDSemzMK6axvpbBsY\nWSG4fBVZw33o0CGaNm3K6NGjsbCwoFmzZrRr145mzZphaWnJ66+/TrNmzTh8+PBjd+Lr60v79u05\ndeoUDg4OLF68mHHjxpGRkUGPHj1wd3dnzJgxALi4uODj44OLiwt9+vRhwYIFBnWxxeNqj0ZNfBGq\nWvL6pWidf4JUal1UZGQklpYQHKysVSaVfLyKQ9edbUM/XkoVEhJCUlISqampnD59mri4OOLi4jh9\n+rS+o+VjSO30w5R6jig5V1Ft9qXNEVz76yBNPp6gl1xKJLlKRqm5SqvIHp+pqSnjxo1j3LhxXLhw\ngejoaG7cuEH16tVp3rx5vgsbH2fVqlUFtgUEBBT5+JCQEEJCQor9+obEsooZHu+N5MwX4Ti/5kMl\nM2XXZQrD8eAV5pM61OHM+I9lZLsCWLt2LadOneKZZ5S94ludOnXyXUifkJBQ6N8JQ7v2Rp+375dg\nKiXP445XxLYdHHt3On3G+2Ne9xk5XnJ+GfTxWrhwIdHR0dr2qrRrwhRrHm4lMuRawLtZ2US0fpGG\nE0fh8PIgfccRFcS7G2OJTrmFOjcX/43LsEtJlM62ghW3HrB58+Zs374da2trHaR6crm5uTRu3Jjt\n27fzzDPP0Lp1a1atWpXvoklDbrfF48WGfUvK+u102LlMBpNEhaOTebhF2apkZkrdV4cSP38F9n4D\nUFWqpO9IogIwM1Kjzs1l6Lql2F65KJ3tCmLEiBEMHDiQcePGFbhAvVu3bnpKVZCRkRHz5s2jV69e\n3L17l1GjRskMJU+RW2cuEL9gJR4/fCadbSEKUax5uEXxFbf2yNF/MDnX0kj5bWc5J/qPUuuiIiNl\nHu6SKCrXJE97/Dcuw+nKRdroobNtaMfLUMybN4/k5GRCQkIYNWpUvh+l6dOnD6dOnSIuLo7Jkyfr\nO06xKfUcUXKuB9tsjUZDzJSvqN29Pdbd9LdInZKPlxJJLt2SEW49MbKsguMrz3N23jJsB3oZ9AVH\nQhkuf78K24RztPntWxnZrkAeXqVXCCWY/1ciW2Krc3VbKmM5y/X9R+kYsVLfsYRQrBLVcOfl5ZGa\nmoqdnf7/mFeEWsDsy9fY3ep53BeH6XVUQBi+W2cusLfbCFp8+zE2vTvpO44ohtLWAxqiitBui3vu\nXzNilJPN6/M+wfV1Hxq8NULfsYQoN6Vts4tVUnL9+nX8/PwwMzOjQYMGAGzYsIGpU6c+8Y4FmFrX\nwN53AGfnLtN3FGHANBoNx4NmYt29vXS2K4hWrVqxdu1acnJyCr0/JyeHtWvXVqg1CoRhub8qYd/9\n27CqVoV6r72k50RCKFuxOtyvv/46/8fevcflfP9/HH9cHUnkHEpqiErOcmrSyCFz2GyMIYxtMqfZ\nErM5bNSYOc3hu40h5rTNYVjTZCmHnIlEiA4qp6SSUl2/P/p1TQrXpa7r87nqfb/dut1cn+v6fD7P\n63N9vHv3uV6f97tKlSrcvHlTNb1wx44d2bJli1bD6SNNa49sxw3lwfHzpJyMePmLS0iudVEil2ae\nzZWwdR8Pz1/G4ZspEiXKpy/HSx+sX7+erVu3UrduXXr06MGECROYPn06n3zyCT169KBevXr89ttv\nrF+/XuqoZYJczxE555rubksP00c0Dt6P0/xPMTA1kTqWrI+XHIlcuqVWDfeBAwdITEzE2NhYtaxW\nrVrcvn1ba8HKCzObutR9qzsxywOotn6B1HEEPZN15z6X5yzH/otxVKgr72HjBPU5Ojry+++/k5iY\nSFBQEBEREdy7d49q1arh5eVFQEBAkRFLBEGXKpkY4vbnNox6vk7Nru2ljiMIsqdWDXejRo04dOgQ\n9erVo1q1aqSkpBAbG0uPHj2IiorSRc4iylItYNqlaxx+YwSdgzdQ2aGh1HEkkZYGK1ZUYPx4ec02\nKXfnxs8m8+Yt2u9ejcJADDqkT0QNt6DPrm09xLeTE5kX8ga17MUf+0LZp5Ma7jFjxvDOO+8QHBxM\nXl4eR48excvLi48++uiVdyz8p7JDQ2p5dCZmxSapo0gmPV3BggUVSU8Xo7Wo6+6/4STtDsZp4TTR\n2RYEQWfysrI5/+0mfsv6mJzKtaWOIwh6Qa3f0tOmTWPw4MGMHz+eJ0+eMGrUKPr378/kyZO1nU/v\nvGrt0WsThpO4I4hHsYmlnOg/cq2LErk0ExYWRu6jx1z0WYjd+Pdl862InI+XILyIXM8Rueb6Y9a3\n5D3nhl4pyfV4iVyakWuuklKrhluhUDBp0iQmTZqk7TzlVrV2zlRt58yN1ZtxnP+p1HF0ztxciY9P\nJubmao9SWa5dXbQGhaEBDSeNlDqKIAjlyJOH6dzaHkjPL2bic1u02YKgLrXH4b558ybnzp0jPT29\n0PKhQ4dqJdjLlMVawDsHjnLmg+m4nfgD01rVpY4jyNTDi9Ec7TmatlsWU8O1rdRxhFekbj1geHg4\n7dsXvSnt+PHjejcsYFlst8ubK/NXczswlE7B6zEwEnPnCeVHSWu41frf4u/vz9y5c3F0dKRixYqF\nnpOqw10W1XyjA5UaNuDmmu3Y+4r6eKEoZW4uF6f6U29gT9HZLie6d+9OWlpakeU9e/YkJSVFgkRC\nefX41m1u/LiFlj/OE51tQdCQWjXcCxcu5OTJk5w8eZLQ0NBCP0JhJak9UigUvDZhGLFrfycnLaMU\nU+WTa12UyKW+2F/+4ER0FE1mTZA6ShFyPF4g31wvk5eXR25ururfT/9ER0cXGqZVKBm5niNyyxW9\n8GcsWjpyuWKe1FGKJbfjVUDk0oxcc5WUWh3uGjVq0KBBA21nEQDLN90xqW5B3IadUkcRZCYzIZkr\nfv/DZvRATKpbSB1H0DIjIyOMjY3JyMjAyMio0I+DgwPjxo2TOqJQjqRdusatbX/R5KtPUCjEaFKC\noCm1arj37dvHpk2bmDx5cpHJFmxsbLQW7kXKci1g7IadXPtuDV2O/4ZhBVOp4+iEGIf7xZRKJWdG\nTiP3cRZttywRv/DKgJfVA964cQOALl26EBoaSkFTrVAoqFWrFmZmZrqIWarKcrtd1p16fyqGZhVp\n+dM3gGizhfJHJzXc2dnZ/P3332zevLnQcoVCofrKUyg9VoN6c+27Ndza/hf1hw+QOo4gA8n7Qrgb\nchzXfzeKznY5YWtrC0BsbKy0QYRy717Yqfz2J3Tzy18sCEKx1Cop8fb2xt/fn9TUVLKzs1U/WVlZ\n2s6nd0qj9siwgikNPhxMzIpN5OXklEKqfHKtiwoLC6NyZfD1ldeVErkcrycP07k043saTf0AM1tr\n2eR6lsilHffu3WP69On07t2b119/XfXTpUsXqaOVGXI9R+SQS5mXx+WvV1Df6y0q2VkDos3WlMil\nGbnmKim1rnDn5OQwatQoDA0NtZ1H+H82Xm9xfdkGkvf8S90B3aWOI0hkcWgs5it+pJpJRdqMelfq\nOIIEhg4dSnZ2NoMGDSo0SpS2vun4/PPP2bNnDyYmJjRs2JBffvkFC4v8ewb8/PxYu3YthoaGLFu2\njB49egBw6tQpRo4cyePHj/H09GTp0qXP3f4XgVeZ7m6LuakY5UIfJO0+QMa1WNpuWiR1FEHQa2rV\ncC9cuJCsrCy++OIL2XydXR5qAa/4rebOgaN0Clonm+Mu6NbcJXtpu8CPLR9Oxf71lszsZid1JKGU\nqFsPWKVKFW7fvk2FChV0kAqCgoLo1q0bBgYG+Pr6AvlDw0ZGRjJ06FBOnDhBQkIC3bt3Jzo6GoVC\ngYuLCz/88AMuLi54enoyceJEevXqVWTbBw4cwPe0gi52VcW5rAfysrIJfX0o1kPfpOHkkVLHEQRJ\nlbSGW62SkqVLlzJnzhwqVapE/fr1VT9S3TBZXjQYM4iMqze5ezBc6iiCBJR5eThu2sA5l9ep0tKR\nya71pY4kSKB58+bEx8frbH8eHh4YGOT/amjfvr1q37t27WLIkCEYGxtja2tLo0aNCA8PJzExkbS0\nNNUkPCNGjGDnzuePsmRf00ycy3oidsMO8rKyaTB2sNRRBEHvqdXh3rhxI0FBQezbt4+AgADVz4YN\nG7SdT++UZu2Raa3qWA/py/VlpXOc5VoXFRYWRloa+PtXoJj5PSQj9fG6/XcoFvfvwqih+PduqPoK\nXupczyNyaccbb7xB7969mT9/PmvXrmXt2rWsWbOGtWvXan3fa9euxdPTE4Bbt25hbW2tes7a2pqE\nhIQiy62srEhISHjuNp8+l+VCrueIlLmePEzn2uJ1NPIZg1GlwhPeiTZbMyKXZuSaq6TUavW6du1a\nop2MHj2avXv3Urt2bSIiIgC4f/8+gwcP5ubNm9ja2rJt2zaqVq0KPL9OsDyyHTeU0I6DSDkRQbV2\nzlLH0Zr0dAULFlTEyyuLypVfWuVU5imVSq4v3YCN19t49C+7n7vwcocOHcLKyoqgoKAiz40ePfqV\ntunh4UFSUlKR5fPnz6dv374AzJs3DxMTk1KfTVhunW2heDE/bMSkRjWsBnsW+7xoswVBM2q1fF9+\n+SUKhaLQOLAF5s6d+9L1R40axYQJExgxYoRqmb+/Px4eHvj4+PDtt9/i7++vqhPcunUrkZGRqjrB\nK1euqL7ilDtXV9dS3Z6ZTV3qvuXB9eUBtNmwoETbKu1spcXV1ZXERKlTFCXl8boXepK0S9dotf7b\nIs/J+XOUI7nmUte///5b6tssrvP+tHXr1rFv3z4OHDigWmZlZUVcXJzqcXx8PNbW1lhZWRUqeYmP\nj8fKyuq52/b29laVI1pYWODs7Kz6jAqubJ1Q2hCf+ph7V87wfqs6eLi7FXr+2deX1ccFy3S9/7av\n2XPjxy1kTRrMkWPHin19fpv9L8ePp9O/f+dyfbz09bE4Xi9+vGrVKiIiIlTtVUkv/qp10+TIkSML\ndbITExM5dOgQb731Fps2bVJrRzdu3KBv376qK9xNmzYlJCQES0tLkpKS6Nq1K1FRUfj5+WFgYMC0\nadMA6NWrF7Nnz6ZDhw6FtlcebposkBZ1ncPuw+kcvIHKDg2ljqMVYhKFwo6/M4FKr9ngtOBzqaMI\nWqLJDTj37t1j7969JCUl4ePjQ0JCAkqlslApR2kJDAxk6tSphISEULNmTdXygpsmjx8/rroYcvXq\nVRQKBe3bt2fZsmW4uLjQp0+fF940qU67PXXPFSKSMgDEDZYSiJgyn0cx8bjsWPHcG/ZFmy2UNzq5\naXLdunX88ssvqp/AwED++OOPEg0TmJycrJq10tLSkuTkZOD5dYL6Qhu1R5WbvkbtHp2JWbGxRNuR\na12UGNO1sAenLpBy9Cx244v/Kl/On6McyTWXukJCQmjSpAm//vorX3/9NQDR0dFam9p9woQJpKen\n4+HhQatWrfD29gbA0dGRQYMG4ejoSO/evVm5cqWqM7Zy5UrGjBlD48aNadSoUbGdbU1UMMr/1aSr\nGyzleo5IkSvt0jUStu6jyVfjn9vZFm22ZkQuzcg1V0m9cjGdh4cHgwYNKpUQCoXihcPePe85db6a\n1PXjAqW9/eQuzYj6YgmNPh+LWYN6r7S9iIgIyY+PPj2W6nhdX7aBpE5NOR0Xg2sDK9kcD7keL315\nHBERQWpqKpA/e+SYMWNQx6RJk9iyZQvdu3enWrVqAHTo0IHwcO2MXhQdHf3c52bMmMGMGTOKLG/T\npo3q28vSMN3dliVhcUx2rS9qvnXsyjcrqdOnK1VbO0kdRRDKFLVKSq5fv17o8aNHj9i0aRN//vkn\nFy5cUGtHxZWU/Pvvv9SpU4fExETc3d2JiorC398fQDX+a69evZgzZw7t27cvtL3yVFJSIPyt8VRu\n+hqOflOljiJoSdqlaxx+YwSdDwZQuelrUscRtEjdryerVatGSkpKoX/n5uZSu3Zt7t27p+2Ypao8\nttv65F7YKU6+NxnX0M2qWSUFQcink5KSRo0aFfrp0KEDoaGhrF+//pV33K9fP9X669evZ8CAAarl\nW7ZsITs7m5iYGKKjo1Xju5Z3r00cTvzmP8m6c1/qKIKWXP8hgNo9XUVnW1BxcHAgMDCw0LIDBw7g\n7CxGrxFKj2oK9xFvic62IGiBWh3uvLy8Qj/p6emEhYXRpk0btXYyZMgQOnXqxOXLl6lfvz6//PIL\nvr6+BAUFYW9vT3BwsOqK9ovqBPWBNmuPanZtj3ljW27+tO2V1pdrXVRYmBjTFeDRzQSSdh7gtYkj\nXvg6OX+OciTXXOr6/vvvGTZsGCNGjODx48d8+OGHeHl5sWBByUYtEv4j13NEV7mUublc+NSPzLgk\nGk4Z+dLXizZbMyKXZuSaq6R0Uhy3efPmYpf/888/xS5/Xp1gebQ4NJb41MdUMDJgurstdp8M5+Jn\n/th9MgzjKuZSxxNKUcyKTVTv1ErUTgqFdOjQgXPnzrFx40bMzc2xsbHhxIkTWhmhRCh/8nJyiJj4\nDfdCT+Lyxw+Y1qoudSRBKJOeW8Ndv/7L7wxXKBTExsaWeih1lJdawGeHx/qiqw2hrkOwHtqX1yYM\nlzidUFoeJ98lpN1A2v66iBqubaWOI+hASesB9VF5abf1Rd6THM6Nm8WDUxdw+W05lRraSB1JEGSr\npG32c69wBwQEvPJGhdLz7PBYCkND7Ma/T/S3P9FgzCAMK5pKnFAoDTdWb6GKU2Oqd1avTEso28aO\nHctPP/0EwPDhxf9hrVAo2LBhgy5jlYqr363BpGY11Y9preqY1KyGURVzvSof1Hd5Wdmc/XAmDy9E\n037HCsxsxTcmgqBNz+1wl3Q69/Lq6VmbSkNxw2NZvdubqwvXkLBtHzZeb0mWrbSU91zZKQ+JW7+D\n5itnqdXhKO/HS1NyzfUidnb/TfTSsGHDQjP9FtDXzmnqmUiy7qaQ/f8/eVnZACiMjf7rgNeoRmWH\nhjT2/RADE2OtZ5LrOaKtXLmZWZwZPZ2M67G037mSivXryiJXSYlcmhG5dEutGu7s7Gy++eYbAgIC\nuHXrFvXq1WP48OHMnDkTExMTbWcs18xNjYrMsmZgaoLtR+8Rs2IT1u/3xcBIjFOrz2LXbKdi/TrU\n7lH2Ghjh1Tx9D8vs2bOlC6IFbTYtUv1bqVSSm/4ovwN+5z7Zd1NUnfGEzXt4dCOeFv/7GgNj0caV\nlpyMTE57+fA48Q7td66iQt1aUkcShHJBrXG4p0yZwvHjx5k1axY2NjbExsYyd+5c2rZty5IlS3SR\ns4jyXguYk57Bv23extF/KvXe6iF1nBIrr9ME52Q8IqTt2zh8PZl675Rsdj5Bv7yoHjA4OFitbbzx\nxhulGUnrNGm3H8Umcvwtb6q2dqL5qtniwkIpyEnP4NSwz3iS8pB225dhWrvGK2+rvLbZQvmltRru\np23bto1z585Rs2ZNIH/SmtatW9O8eXPJOtzlnZF5JRqMHkjM8o3UHeCht18vF0hPV7BgQUW8vLKo\nXPmlfwOWGXEBuzAyr0SdAd2ljiLIyOjRowv9n46Pj8fAwIAaNWpw79498vLyqF+/fpFJycoSM5u6\nuPy+nPAB3kRM/Ibmy79EYWgodSy99SQ1jZNDPyUvKxuXP1ZgUqNqibZXXttsQXhVao3DLahPl+NH\nNvjgXTJi4rjzzxG1Xi/XsS3La668rGxurNqM3SfDNLp6V16P16uSa64XuXHjBjExMcTExDB27Fgm\nTpxISkoKt27dIiUlhUmTJqk9Nbw+M7O1xuX3H7gfdoqIyfNR5uVpZT9yPUeezbU4NJape67wReBV\n0rNy1N5O9v1UTrw7EXLzaLd9eYk72/pyvORC5NKMXHOVlFod7nfffZd+/foRGBjIpUuX+Ouvv+jf\nvz/vvvuutvMJL2BSsxrWQ/sS88NGqaOUmLm5Eh+fTMzNy8+VkoRt+0CpxGqwp9RRBBn7/vvv8fPz\nw8zMDAAzMzPmz5/P999/L3Ey3ajU0IZ2vy3n7sFjXPzsW611uvVBfOpjIpIyOBGfxpKwOLXWybpz\nnxPvTMDA1IS225ZiUq1KqWQpj222IJTEC2u48/LyMDAwICsri3nz5vHrr7+qbpocMmQIM2fOxNRU\nmmHpynsNd4HM+CQOdXgXl99/oFr7FlLHEdSUl5NDmOsQ6g8fgN3496WOI0hA3XrABg0asGnTpkJ3\n7R8+fJihQ4dy8+ZNbUYsdSVpt9MuXeP4wAnUedMdx28/0/syulfxReBVTsSnYV/TDP/eDVUjVz3P\n4+S7nHhnIqa1qtM6YAFGlcx0lFQQyh6t1nBbWVmpphSeO3cuc+fOfeUdCdpR0boOdd/uyfVlGwrd\n/S/IW9KfwWSnPKS+1wCpowgy980339C7d2/69u2LtbU1cXFx7NmzhxUrVkgdTacqOzSk3falnBj4\nCQpjQxy+mVLuOt3FDRNbQKlU8jghmdQzkTw4HUnqmUjunblESsNGRHmNw9HIBDE3sSBI54UlJatX\nryYmJgYXFxdat27N0qVLuXPnjq6y6SUpao9eG/8+d4KPkRZ59YWvk2tdVHnLpVQqub4sgAYfvIuR\neSWN1y9vx6uk5JpLXcOHDyc8PJymTZvy8OFDHBwcOHbsGCNGjJA6ms5VcWpM261LubU9kMuzlxcZ\nm/xVyfUceTZXwTCx5qZGPElN427Ica4tWcdpLx8ONu9LSNu3uTRzCY9uxFPzjQ6c+WQSAYM/IvxO\n9ktLUDSpD9eX4yUXIpdm5JqrpF54hbt///7079+flJQUtm3bxoYNG/j888/p1asXXl5e9OvXD2Nj\n7U9KILyYeRM7avd6nevLA2ixao7UcYSXuBN0hMwbCTQYI+6BENTj6OjIV199JXUMWbBo0ZS2W5Zw\ncvAkFMZG2H8xTq+vdC8OjSU+9TEVjAyY7m77wjKR1DOR3FzzG6lnI8m4GothJTMsWjTFopUD9QZ5\nUrWVI6Z1a6mOx6PAq+T9fwnKZNf6L8xRUB8OsCQsrsj8D4IglIxa43A/7dq1a2zcuJGff/6ZR48e\nce/ePW1leyFRw13Yg9MXOfbmR3Q5uhWzBlZSx9FYeRnTValUcuzND6nathkOcyZJHUeQkCb1gLt2\n7SIkJIR79+4VuqqrzandFy1axOeff87du3epXr06AH5+fqxduxZDQ0OWLVtGjx75cwCcOnWKkSNH\n8vjxYzw9PVm6dGmx2yzNdjvlRAQn35uC7YeDaTxtbKlsUwpT91xRdXS72FUttqOrVCqJXfs7UXOW\nU6dPV2q83g6LVg6Y29u+cKjE9Kyc55agPEvT+vDy0mYLQoGS1nBrNCxgdnY2J0+e5Pjx4yQnJ9O8\nefNX3rFQuqq2diLDwYFNPis1HjJK0J0f/xdIytkotjXtKD4jQS1z5szho48+Ii8vj23btlGjRg3+\n/vtvqlYt2dBuLxIXF0dQUBANGjRQLYuMjGTr1q1ERkYSGBiIt7e3qvM/btw41qxZQ3R0NNHR0QQG\nBmotW4Fq7Zxps+k7bqzezNXvf9H6/rSlglH+r+HnXYXOyXjEuXGzuOK3mhYrZtFi1Rysh75JZYeG\nLx2X/OkSlJeZ7m5LF7uqanW2BUHQnFod7tDQUMaOHYulpSUzZ86kQ4cOREdHc/DgQW3n0ztS1h5d\n6tWHemGHiDkZVWy9nlzrosLCwqhcGXx95XWlRBvHy3zrH1xs2Z7DGcZqD+v1LDl/jnIk11zqWrNm\nDUFBQSxZsgRTU1MWL17Mn3/+SUxMjNb2+emnn7JgwYJCy3bt2sWQIUMwNjbG1taWRo0aER4eTmJi\nImlpabi4uAAwYsQIdu7cqbVsT6veoSWtAxZyffkGYn/5/ZW3I+U58qKObtDm7Rzt9QHpl2Po9Pda\n6vTV3syimnTOy1ObXRpELs3INVdJvbDDPWvWLBo2bEjfvn1RKBTs2bOH6Ohovvzyy0JXPgR5yHRu\nxuVmren352YmdawndRzhGQ9OR1I9KpITXTzUqqkUBIDU1FScnZ0BMDExITs7GxcXF0JCQrSyv127\ndmFtbV3kG8xbt25hbW2temxtbU1CQkKR5VZWViQkJGglW3FquLah2aLpXP56JY8T9e+m/ud1dG/9\nsZ+LPguwaOlIx70/UamhjUQJBUEoDS/8UzY8PJx58+bRv39/KlasqKtMeu3psXJ1bbq7LT88/oBa\nX/hyd/3vVB43tNDzUmZ7kfKS6/qy9dTu143mbe3VqqnUVa7SInJpx2uvvcbFixdxcnLCycmJVatW\nUa1aNVVd9avw8PAgKSmpyPJ58+bh5+fH/v37VctKaySQAt7e3tjY5HceLSwscHZ2Vn1GBVe2NH3c\n+S0P4jfuZsv4aTT6dHSJtyfl47wnT6gReJKELXuxGf0Oqd07YWhWQTb55Py4YJlc8sj9sTheMOL+\nqQAAIABJREFUL368atUqIiIiVO1VwT0rr0rjmyblQtw0+XyJuw4QMfkbOgcHUMnO+uUrCFqXduka\nh7t50fnAeio7NJQ6jiAD6t6As3fvXszNzXFzcyM8PJyhQ4eSnp7OypUrGThwYKlmunDhAt26dVPN\nahkfH4+VlRXh4eH88kt+nbSvry8AvXr1Ys6cOTRo0AB3d3cuXboEwObNmwkJCWH16tVFtq+tdntx\naCwpkVfpMHcWzTd9j3XXdqW+D114FJvI2bFf8CTlIS1/nodF8yZSRxIE4f/p9KZJ4eXkUHtUp98b\n1HRz4eJU/0JXp+SQrThhYWGkpYG/fwXS0qRO85/SPF7Xl22gdk/XUulsy/lzlCO55lJHXl4eFStW\npEOHDgC0b9+ea9eukZycXOqdbYBmzZqRnJxMTEwMMTExWFtbc/r0aSwtLenXrx9btmwhOzubmJgY\noqOjcXFxoU6dOlSpUoXw8HCUSiUBAQEMGKDbCZ3iUx9zzLAaZ9q7ceKzheQ90eyGZDmcI7eDDnO0\nx0gq1KlJp/1rsWjeRBa5ilMe2uzSJHJpRq65Skp0uMsghUKBo/9nPLxwhfhNu6WOo5b0dAULFlQk\nPV1/x9N9noyYeBJ3HaDhJC+powh6xsDAgH79+mFqairJ/p8e39rR0ZFBgwbh6OhI7969Wblyper5\nlStXMmbMGBo3bkyjRo3o1auXTnMWjPRx5913qJL1iNi1v+l0/yWRl5PDFb/VnBnli90nw2m17luM\nq1aROtZLleU2WxC0QZSUlGHxv/5J1KxluIZsokK92lLHeaHERAVOTlW5ePEBdevq5Sn5XBc+9SPz\nVjLttiyROoogI+p+Penp6cmXX35Jx44ddZBKu7TVbj893nTq7iAipy+iy5GtmNauUer7Kk1Zt+9x\nbtwsMqJv0mL1XKp3aiV1JLWV5TZbEIpT0pISMdhmGWY15M38O92nLaT1hgWyno3N3FyJj08m5uZl\nq+HOTEgmYftftNtW/EQggvAyDRo0oHfv3gwYMID69f8b2UahUDB37lwJk8lHwUgfAJXe6UX8xt1c\nnruC5j/Id3bOu4dOcN57Nub2dnT6Z53s/zh4VlltswVBW0RJSSmTU+2RQqGg2SJf7oedImnXP7LK\n9rSyPKZrzMpNWLRypFqHlqWQKJ+cP0c5kmsudWVmZqpqouPj44mPjycuLo64uFcbx72sUygUOMz/\nlMQdQaSEn1NrHV2eI3k5OVzx/x+nhn6Kzci3abd96XM723I9d8tym60NIpdm5JqrpCS/wu3n58fG\njRsxMDDA2dmZX375hYyMDAYPHszNmzextbVl27ZtWp1VrSwza2BF4+kfETljMQYLJ0gdp1zJunOf\n+E27abXGT9bfLgjytm7dOqkj6J0qTo2p7/UWkdMX0XH/WgyMJP9VB8DjW7c55z2LR9fjabtlCTVc\n20gdSRAEHZG0hvvGjRu88cYbXLp0CVNTUwYPHoynpycXL16kZs2a+Pj48O2335KSkoK/v3+hdUUN\nt/qUubkc6/sxZrZWtFg5W+o45cbleau4F3Kcjn+vFR1uoQh16gGzs7MxMTEB/n+M5rw81XMdO3bE\n2NhYqxlLmy7b7SepaYR2GkzDT0fT4IN3dLLPF7nzzxHOT/yaKs2b0Hz5V5jWevVx1AVB0D29Hhaw\nSpUqGBsb8+jRI3Jycnj06BH16tVj9+7deHnlj+jg5eWls2mCyyqFoSHNvp9O0p6D3N5/WOo45cKT\nBw+J/eV3Xps4QnS2hVeyatUqRo8erXrco0cP3n//fd5//30GDBjA+vXrJUwnf8YWlbGf6U30gp/I\nunNfshx5T3K4PHcFp0dOw27cENr++r3obAtCOSRph7t69epMnToVGxsb6tWrR9WqVfHw8CA5ORlL\nS0sALC0tSU5OljKmRuRae1S56Ws8GODKxWkLyEnLkDpOIWVxTNeba3+nQt3aWHq6lWKifHI9x0Su\n0rV+/Xo+++wz1WNTU1NV7XZwcDA///yzhOn0g9VgTyo1tOHK/KKT8DxNW+dIZlwi4QPGkbgzCJff\nf+C1CSNQGKj/a1eu525ZbLO1SeTSjFxzlZSkhW3Xrl1jyZIl3LhxAwsLC9599102btxY6DUKheK5\nVwi1MUVwSR8XkHpK0uIe321cF6uIWC5/vYKUfp0kz/P046NHw4iNNQFcZJEnLCyMiIiIV1o/J+MR\n+1b8hM2ogapfrnJ4P3I9XuXlcUREBKmpqQDExsYyZswYXiQmJoaWLf+72dbBwUH17+bNm3P9+vUX\nri+AwsAAR7+pHPMcS/1h/ajappnO9p0ceIgLk+dRtV1z2gR8h0l1C53tWxAE+ZG0hnvr1q0EBQWp\nrtQEBARw7NgxgoODOXjwIHXq1CExMRF3d3eioqIKrStquF9N6plIjvX9iLZbl1Kjszh+2hCz6ldi\nf/mD149skc3NWoL8vKwe0NzcnOTkZCpVqlTkubS0NOrUqUNGhry+rXoZqdrtiz4LST17iY5//YTC\n0FCr+1ocfB2zteupfzCYhtM/xn78UFFWJghlgF7XcDdt2pRjx46RmZmJUqnkn3/+wdHRkb59+6rq\nE9evX6/zaYLLMotWjth++B4XP/Mn99FjqeOUObmPs7ixajN2nwwTnW2hRJycnPj777+LfW7//v00\na6a7q7X6rrHvh2TG3SJu059a3c+9sFNYf+5L1ZOn2DJmCtsdOonOtiAIgMQd7hYtWjBixAjatm1L\n8+bNAfjwww/x9fUlKCgIe3t7goOD8fX1lTKmRuRce1SQrdFnHwBw9bs1UsZRkesxe5VcCVv2gkKB\n9WBPLSTKV5aOly7INdfLTJkyBW9vb3bs2KEanSQvL48//viD8ePHM2nSJIkT6g+T6hbYz/iYaL/V\nZN9PLfJ8Sc+RRzcTOPPBDE6+N5mHTk5sHD+dKq2cmOxa/+Urv4Bcz12RSzMil2bkmqukJL8E5+Pj\ng4+PT6Fl1atX559//pEoUdlnaFaBZoumc2LQROr0dceilaPUkcqEvCc5XP9hI7bjhmBgaiJ1HEHP\nvffeeyQkJDB8+HCysrKoWbMmd+/exdTUlFmzZjF06FCpI+oV66F9idu4m2j//+G0wOflK6ghJz2D\n68sCiFm9mRqubel8MABsrEn8/2nmzU0l/xUrCIJMSFrDXRKihrvkLvosJOX4OTrt/wUDE2nH801L\ngxUrKjB+vLxmLtNEwra/iJq9DLcTf2BUqaLUcQSZU7ceMDU1laNHj3L37l1q1KhBx44d9XYiMKnb\n7QenLxLe92M67P0Ri5YOL1/hOZR5edzaHsiVeaswqlKJprMnUqt7p1JMKn9loc0WBE3odQ23IK0m\nX3qTk5bBtSXSj+ebnq5gwYKKpKfrZ72jMi+P68s3YDt2kOhsC6XKwsKCXr16MWzYMHr37q23nW05\nqNraCavBnkROX/TKY3OnnIjgmOdYLn25BLvx79M5OKDcdbZB/9tsQdA10eEuZXKuPXo2m1HlSjh9\nN43ryzfw8GK0RKnke8w0yZW891+yku5iM1r7M9qVheOlS3LNJUjDfsbHZCXf5aDzmxxw9CT8rfFs\n9JpI7Lo/uH/sLNkpD4tdL39a9tkcH+BNleZN6XJkK7YfvafVbwfleu6KXJoRuTQj11wlJQrMyrHF\nobHEZ1THuUNHzk38hs6BazAwluaUMDdX4uOTibm5/lU4KZVKri/bgM2ogRhbiO9WBUHOTGpWw+3k\nH2TGJZJ+OYa0qOtEHTpE3MZdZETfJC8rG9PaNTBv+hrmTewwb2LH48Q73Fj5KxatHOm4fy1VnBpL\n/TYkp89ttiBIQdRwl2NT91whIikD08xHjF0xD6ePB9Fw8kipY+mdO/8c4czYL+h64g9MalaTOo6g\nJ0paD6iP5N5u5+XkkHnzlqojnn75OulR11HmKWns+yGWnm5imD9BKKdEDbfwyioY5X/8DerXpMWi\naVz9/hfSosTsdZpQKpVcW7qe+sP6i862UGYsX74cBwcHmjVrxrRp01TL/fz8aNy4MU2bNmX//v2q\n5adOncLZ2ZnGjRvr9XCFBkZGVGpog6WnG40+HUXL/32Na8gmXg/9lTp9uorOtiAIr0x0uEuZnGuP\nns023d2WLnZV8e/dkAZ93KjT150LU+aTl5MjaS65UCfX7b9DST0Xhd043Q3Rps/HSwpyzSVXBw8e\nZPfu3Zw/f54LFy7w2WefARAZGcnWrVuJjIwkMDAQb29vCr4gHTduHGvWrCE6Opro6GgCAwOlfAsa\nk+s5InJpRuTSjMilW6LDXY6Zmxoxs5udaqxYh6+nkBmXyM0ft0mcTD88SU3j+JRvudKnH1+ff0h6\nlm7/UBEEbVi1ahXTp0/H2Dj/ZsBatWoBsGvXLoYMGYKxsTG2trY0atSI8PBwEhMTSUtLw8XFBYAR\nI0awc+dOyfILgiDIkehwlzJXV1epIzzXy7KZVLfA0f8zohf8SMa1WB2lys+Vlgb+/hVIS9PZbl/q\nZccravZyMitXYW9LN07Ep7EkLE4WuaQicpUN0dHRHDp0iA4dOtC1a1dOnjwJwK1bt7C2tla9ztra\nmoSEhCLLraysSEhI0HluTSwOjWXqnit8EXiV9Kwc2Z4jcs6lj222VEQuzcg1V0mJUUqEQuq86U7i\nzn+ImDKf9jtXojAQf5MV527IcW5t/4urX84mz9AQ+5pmJZ7GWRB0xcPDg6SkpCLL582bR05ODikp\nKRw7dowTJ04waNAgrl8vvXs7vL29sbGxAfLHGHd2dlb9gi34Klnbj+NTaxORlMHDa2e5e+UM/5v4\nrk73X1Yex8Ye4ujRbHr0kEce8Vg8Ls3Hq1atIiIiQtVe9ejRg5IQo5SUsrCwMNn+daZutqw79wlz\ne5+Gn47Cdswg2eTSteflysl4RJjbMOq905N6Uz5giY6ncda34yU1ueaS6yglvXv3xtfXFzc3NwAa\nNWrEsWPH+PnnnwHw9fUFoFevXsyZM4cGDRrg7u7OpUuXANi8eTMhISGsXr26yLbl0m5/EXiVE/Fp\n2Nc0w793Q86eOCbLc0Su567IpRmRSzNyzSVGKRFKnWmt6jh8M4Xoeat5dFPeXw1L4cq81RhWrECj\nKaOK1MELgr4bMGAAwcHBAFy5coXs7Gxq1qxJv3792LJlC9nZ2cTExBAdHY2Liwt16tShSpUqhIeH\no1QqCQgIYMCAARK/ixd7+oZx8X9XEARdEFe4hWIplUrOjJxGTvoj2m1fhsLAIH+inNTHVDAyYLq7\nbbn8RZUSfo7jb39C+92rqNqmmdRxBD0m1yvcT548YfTo0Zw9exYTExMWLVpE165dAZg/fz5r167F\nyMiIpUuX0rNnTyB/WMCRI0eSmZmJp6cny5YtK3bbot0WBEFflbTNLn89JkEtCoUCx28/J8xtGHEb\nd2MzYgDxqY+JSMoAYElYHDO72UmcUrdyM7OI+NQPmzHviM62UGYZGxsTEBBQ7HMzZsxgxowZRZa3\nadOGiIgIbUcTBEHQW6KkpJTJefxITbNVqFOLpnMmcnnuD2TGJ6kmyintGwTDwsJkecf7s8fr6qI1\nKHNysJ/2kUSJ8sn1HBO5BH0l13NEzrn0oc2WC5FLM3LNVVKiwy28kNVgT6q1c+bi59/i27WB1uoe\n09MVLFhQkfR0ec7klnr2EjdWb6bZoukYmlWQOo4gCIKk5N5mC4LciBpu4aUy45MI6zoMh68nYz3k\nTa3sIzFRgZNTVS5efEDduvI6JfOyn3Ck52iqtXPGaYGP1HGEMkKuNdzaJNrtskPObbYgaIOo4Ra0\nrqJ1HZp89QlRs5ZR082FCvVql/o+zM2V+PhkYm4uv4b7+vIAclLTaPLleKmjCIKgY+Jm8eLJuc0W\nBDkSJSWlTM61RyXJVn94f6q2ceLc+Dkoc3NLMVV+rsqVwdf3MZUrl+qmSyQsLIy0S9e4tmQdTgt8\nMKpcSepIgHzPMZFL0FcvOkcKbhbX5WyyBeR67sq5zZYjkUszcs1VUqLDLahFoVDgvOxLMq7e5NrS\nDVLH0Ym83FwuTJlP3f7dqdW9k9RxBEGQgLZuFhcEoXwRHe5SJsfZkQqUNJtpreo0X/4l175fS0r4\nuVJKJd9jVv9iApnxSTSdO0nqKIW4urpSs2ZN3NzccHV1ZcSIEaSnpwMQGxtLvXr1cHNzo1OnTkyc\nOJG8vLwi20hMTGTkyJEv3df333+vUa5X1bdvX86ePVtkeZ8+fXBzc8PNzQ0nJyeGDx8O5F8BadCg\ngeq57777TrXO6tWr6dy5M506dWL16tWyPb8E+XjROSLlJDlyPXdFLs2IXJqRa66SEh1uQSM1u7bH\n9sP3OOc9mycPHkodR2syrscRveBHHP2mYlLdQuo4RZiZmRESEvL/X+1WZt26darn7OzsVM/Fxsay\nZ8+eIuvXrVu30DrPs2TJEo2zFdfBfxmFQoFCUXS0g7179xISEkJISAht27alb9++quc6d+6seu6z\nzz4DIDIykoCAAA4cOEBoaCh///03MTExGucRhAJiNllBEEqD6HCXMjnXHpVWtsa+H2JaqzoXpvpT\nGoPcyG1MV2VeHhc+9SOheQPq9H1D6jhFPPs5tmvXjhs3bhR5nYGBAa1bty72udjYWDp37gzAr7/+\nyogRI3j33Xdp164ds2fPBmDOnDlkZmbi5ubGxx9/DMC2bdvo3r07bm5ufPrpp6rOdf369Rk9ejRd\nunRh8eLFjBo1qlDeIUOGADB16lS6detGp06d8Pf3V/s9P3z4kNDQUDw9PVXLijv3oqOjadOmDRUq\nVMDQ0JDOnTuzfPnyIq8bP348n332GT169KB169aEhYXh7e1Nhw4dGD8+/+bY3Nxcxo8fT+fOnXF1\ndWXVqlVq5xX0i1zbbTnnklObXUDOx0uORC7dkrzD/eDBA9555x0cHBxwdHQkPDyc+/fv4+Hhgb29\nPT169ODBgwdSxxSeYmBiTIvVc7gbcpy4gF1Sxyl1cet3kB51Dduxg6WO8lK5ubkEBwfj4OBQ5LnH\njx9z+PDhYp971oULF1i7di1hYWHs2LGDW7duMWvWLCpWrEhISAirV6/m8uXL7Ny5k7///puQkBAM\nDAzYvn07AI8ePaJJkyYcOnSIyZMnc+rUKTIzMwHYsWMHAwcOBODLL79UXX0+cuQIkZGRar3Pffv2\n4ebmhrm5OZB/Rfz48eO8/vrrDBo0iKioKAAcHBw4duwYKSkpPHr0iP3793Pv3r1it5mamsr+/fuZ\nN28eQ4cOZcKECRw9epRLly5x4cIFIiIiSExM5PDhw4SFhfH++++rlVUQBEEQniV5h3vSpEl4enpy\n6dIlzp8/T9OmTfH398fDw4MrV67QrVs3ja6ESU3OtUelmc3M1hqnBT5EfbWEtEvXSrQtV1dX2dzx\nnrTnIJdmLcNh/lTc+3q+fAUJuLq6qq48Ozg4cOvWrUJXlG/cuIGbmxtNmzbF0tISDw+Pl26zS5cu\nVK5cGVNTU5o0aUJcXNHRGA4dOsS5c+d44403cHNzIzQ0lJs3bwJgaGiIj4+P6t/dunXjr7/+Iicn\nh6CgIHr37g3kd77d3d3p2rUrUVFRXL58Wa33/Pvvv6s67QDNmzcnIiKC0NBQxo4dq6rttre3Z+LE\niQwcOJBBgwbRvHlzrKysit1mr169gPxOuqWlJQ4ODigUCpo2bUpcXBx2dnbcvHkTX19fDhw4QGWp\nT05Ba+Tabss5l1za7KfJ+XjJkcilW5J2uFNTUwkNDWX06NEAGBkZYWFhwe7du/Hy8gLAy8uLnTt3\nShlTeI56b/egTr/unPv4K3Izs6SOU2K3fgvk3LhZOM6bQr23e0gd54UKrjyfO3cOU1NT9u3bp3rO\n1taWkJAQTp8+TXR0NGfOnHnp9kxNTVX/NjQ0JPc5Qz++9957qrrp8PBwVSe7QoUKhWqw3377bXbu\n3EloaCgtW7akUqVK3Lx5kxUrVrBr1y5CQ0Px8PAgK+vl5829e/c4c+YMPXr895lUrlwZMzMzADw8\nPHjy5AkpKSkADBs2jODgYPbs2YOFhQWNGzcudrvGxsZAfumNiYmJarlCoeDJkydYWFhw6NAhOnfu\nzLp165g4ceJLswqCIAhCcSTtcMfExFCrVi1GjRpF69atGTt2LBkZGSQnJ2NpaQmApaUlycnJUsbU\niJxrj7SRzdHvU/Ke5BA1a9krb0MOxywuYCcRU+bjvHgG9YcPAOSRqzhP56pYsSL+/v588803RWqa\nq1evzsyZM/n6669feV9GRkbk5OQA+VfBd+/ezd27dwFISUkhPj6+2FydO3fm/PnzbNiwQXVlOi0t\nDTMzMypXrszt27c5cOCAWhl2795Nz549C3WKb9++rXq/p06dQqlUUq1aNQDu3LkDQHx8PHv27Hnu\nFe6XuX//Prm5ufTt25fp06dz/vz5V9qOIH/68H9dTkQuzYhcmpFrrpKStMOdk5PD6dOn8fb25vTp\n01SqVKlI+cjzRi8A8Pb2xt/fH39/f1atWlXoQwoLCxOPn3kcERFR6ts3qmRGi9Vz2b9xC7sXLi/x\n9qR4fOPHrfw2bS5ZE96h3ju9WBway9CFm/HfdpD0rBzJ8xX3ODc3V/XY2dmZqlWr4u/vr/r/UvD6\nPn36cPfuXVV9doETJ07w6NEjIP//WGJiYqHnz58/T1hYGF5eXri6uvL2229z584dZsyYwcCBA2nd\nujU9e/ZU/TGcm5tb6Pw6cuQIzs7OHDhwgJ49exIWFsaDBw9wdnamffv2DB48mEaNGqlen5qaWmhY\nwKff744dO2jatGmhfIsXL6ZVq1Z06dKFGTNmMGHCBNXzI0eOpEWLFvTv35/vvvsOMzOzIsfv9u3b\nqrpvyK9Bf/r5qKgo9u7dS//+/XFzc2P48OGFSlpe5fNbtWqVqr3y9vZGEITyQ45DuZaENodyLasU\nytIYZuIVJSUl0bFjR9WwXWFhYfj5+XH9+nUOHjxInTp1SExMxN3dvdAvR4ADBw7QunVrKWILxbjx\n41auLlpL5wPrqWhdR+P109JgxYoKjB+v25rAa0vWcW3xOlr+PI/aHvmjdkzdc4WIpAwAuthVZWY3\nO90FEsqN06dP061bN6lj6JRot8sOqdpsfWVjY0NsbCyQP0qSg4MDn3zyCbGxsQwZMoTDhw+Tl5fH\n22+/zejRo+nXr1+J96OuvLw8DAw0u/7ar18/vv76a1q0aPHc13h5edGnTx8GDRpEWFgYK1eu5Ndf\nfy30msjISMaOHcuBAwcwNjbmnXfe4fvvv8fOTn6/d0vaZkt6hbtOnTrUr1+fK1euAPDPP//g5ORE\n3759Wb9+PQDr169nwIABUsYU1NBg7CCqtW3GOe/Z5P1/CYIm0tMVLFhQkfT04r/NKG1KpZIr81dz\nfekG2mz8TtXZBjGznCAIwsvous0uS+QylOuXX34py6Fc//zzzyKvKwtDuUo+Ssny5ct5//33adGi\nBefPn+eLL77A19eXoKAg7O3tCQ4OxtfXV+qYapNz7ZE2sykUCpyXziTzRgLXvl+n0bq6PmZKpZKo\nL5cQ+8vvtN26hBqvty30fMHMcv0tkmQ52YVczzGRq2w4fvw4Li4utGrVinbt2nHixAnVc35+fjRu\n3JimTZuyf/9+1fJTp07h7OxM48aNmTRJXjOzqkOu54jIpRl9yCWnoVzNzc1lOZTrqVOnit2mvg/l\nKnlvokWLFoUa9AL//POPBGmEkjCpWY3mK2ZxcsgUari2oXqnVmqva26uxMcnE3Nz7VY4KXNzuTht\nIcl7/6Xd9mVYtCzasBXMLBcWlqDVLIIgRz4+Pnz99df07NmTv/76Cx8fHw4ePEhkZCRbt24lMjKS\nhIQEunfvTnR0NAqFgnHjxrFmzRpcXFzw9PQkMDBQNeyiUDbpqs0uKwquPCcmJmJjY1PsUK43b96k\nS5cuGg3lCqiGcq1Xr16h1zw9lCvkd+hr164N5I9G1alTJ9W/C4Zy7devH0FBQcydOxfI73xv2LCB\nnJwckpOTuXz5Mo6Oji/N9/vvv6tGm4P/hnI1MzMjKCiI4cOHc+LEiUJDuZqZmdG8eXPVjfnPKm4o\nV0A1lGunTp1UQ7l6eHio3rdcSH6Fu6yR8/iRushW4/W22Hm/z7nxs8m+n6rWOroa0zUvJ4fzE7/m\ndmAoLn+sKLaz/WwuORK5NCPXXHJVt25dUlPz/+8+ePBANcrLrl27GDJkCMbGxtja2tKoUSPCw8NJ\nTEwkLS0NFxcXAEaMGKF3Q7nK9RyRcy4xDrf6XF1dZTmU6+uvv656nZyGcnVzcyt2u/o+lKvocAul\nrtHnY6hQrzbnxn1FTlqG1HEAyMt+wrmPvuL+kTO47FxJZYeGUkcSBFny9/dn6tSp2NjY8Pnnn+Pn\n5wfArVu3sLa2Vr3O2tqahISEIsutrKxISBDfDglCceQ0lOvT5DSU69MjQmlC7kO5Sl5SUtaEhYXJ\n9q9sXWUzMDai5Y/fcNrLhyM9RtHyp2+o0sxesly5mVmcHTOD9OibtN+5CrMG9V6+kg5yvSqRSzNy\nzSUlDw8PkpKSiiyfN28ey5YtY9myZbz11lts376d0aNHExQUVGr79vb2xsbGBgALCwucnZ1Vn09B\nrauuHxcsk2r/z3u8atUqWRwfcbxKfryeff61117D39+fJk2aqIY+DgsLw8LCgrt37xaqqS7YXsHw\npU8P//rs9rt164arqystW7Zk2LBhDBw4kIEDB5KXl0dWVhYff/yxarLBp4+XgYEBzs7O7N+/X3Wz\n4YMHD7C0tKR9+/ZYWVnRqFEj1SAXAGfPniUtLa3I+92xYweTJ08u9H53797NDz/8gKGhITVr1uTn\nn39WPe/n58f9+/fJzs7mgw8+4Pz588WeX1FRUVSvXh0bG5si71+hULB3716WLVumupI+cODAQu3/\nq5xPERERqvbq6Sv2r0LSYQFLQq7DS8n5l7uus+U+ziLqq6UkbN2HwzeTsR7Wv9gx1bX9qf6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v311zkzX9bEmT01zuyZ48w2D2e2eayR2c/cgjsqKgrXr19HW1sbjEYjSkpKkJCQYNOaxh76AMDk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\nAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x2bec750>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Minimum Volume = 50 ml\n",
"Maximum Volume Estimate = 157 ml\n",
"Maximum Rate of change of Volume = 500 ml/sec\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Not a huge difference. The most noticeable change is that that the high penultimate point has been rejected completely as an outlier. Other than that the results are similar. We'll try using lowess. We should probably interpolate up the smoothed curves so that we can use `argmax` without large errors. "
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Determining the Filling Time"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We interpolate up by a factor three in the smooth lowess curve so we can get reasonably accurate time positions for the minumum and 80% volume points."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from scipy.interpolate import InterpolatedUnivariateSpline\n",
"\n",
"spline = InterpolatedUnivariateSpline(time, lowess_volume)\n",
"\n",
"interpolated_time = np.linspace(0, time[-1], len(time)*3)\n",
"interpolated_lowess = spline(interpolated_time)\n",
"\n",
"min_volume = np.amin(interpolated_lowess)\n",
"max_volume = np.mean([np.amax(interpolated_lowess[:15]), np.amax(interpolated_lowess[-15:])])\n",
"min_vol_time = interpolated_time[np.argmin(interpolated_lowess)]\n",
"\n",
"def find_idx_nearest(array, value):\n",
" return np.abs(array-value).argmin()\n",
"\n",
"eighty_per_cent_time = interpolated_time[\n",
" find_idx_nearest(interpolated_lowess, 0.2*min_volume + 0.8*max_volume)\n",
"]\n",
"\n",
"fig, ax = subplots(1, 1, figsize=(9, 6))\n",
"ax.plot(time, volume, '.')\n",
"ax.plot(interpolated_time, interpolated_lowess, '-')\n",
"ax.grid(True)\n",
"ax.set_xlabel('Delay from Trigger (%s)' % time_units)\n",
"ax.set_ylabel('Volume (%s)' % units)\n",
"ax.text(500, 60, 'RR interval %3.0f ms' % r_to_r)\n",
"ax.axhline(y=min_volume, linestyle='-.')\n",
"ax.axhline(y=max_volume, linestyle='-.')\n",
"ax.axhline(y=0.2*min_volume + 0.8*max_volume, linestyle='-.')\n",
"ax.axvline(x=min_vol_time, linestyle='-.')\n",
"ax.axvline(x=eighty_per_cent_time, linestyle='-.')\n",
"\n",
"fig.suptitle('%s (LV %s Volume)' % (patient_name, region), fontsize=14)\n",
"\n",
"show()\n",
"\n",
"print('Time of min. volume = %3.0f ms' % min_vol_time)\n",
"print('Time of 80%% filling = %3.0f ms' % eighty_per_cent_time)\n",
"print('Time difference = %3.0f ms' % (eighty_per_cent_time - min_vol_time))\n",
"print('R-R interval = %3.0f ms' % r_to_r)\n",
"print('Filling time ratio = %0.3f' % ((eighty_per_cent_time - min_vol_time)/r_to_r))\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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hRU4OjXdBiLKh61I21OTEkoYuHWEfNA33v1yCvPgPzUsiTTUs7GVFxQwhhBCi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\nP6Aw4S4e7T6KpK9CIFBVhemIgTAf7Q8d+86vtd+KrMfIi76CvHOX8TT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LzMxkTk5OzMLC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\n",
"text": [
"<matplotlib.figure.Figure at 0x36bb5d0>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Time of min. volume = 318 ms\n",
"Time of 80% filling = 685 ms\n",
"Time difference = 367 ms\n",
"R-R interval = 759 ms\n",
"Filling time ratio = 0.484\n"
]
}
],
"prompt_number": 8
}
],
"metadata": {}
}
]
}
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