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orbeckst/hole_orderparameter.ipynb

Last active July 28, 2016 21:27
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Example for using HOLE with MDAnalysis to analyze a trajectory of the gramicidin A pore. We study the pore radius as a function of a RMSD order parameter.
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hole_orderparameter.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Using HOLE in MDAnalysis with order parameters\n",
"\n",
"The [MDAnalysis.analysis.hole](http://www.mdanalysis.org/mdanalysis/documentation_pages/analysis/hole.html) module in [MDAnalysis](http://mdanalysis.org) can analyze trajectories of, say ion channels, and associate the per-frame pore radius profiles $R_\\rho(\\zeta)$ with an order parameter $\\rho$ for each frame of the trajectory.\n",
"\n",
"In this example notebook we use a synthetic and short trajectory of the gramicidin A cation pore (gA). The trajectory was generated from the first elastic network mode of gA, generated by the [elNémo](http://www.sciences.univ-nantes.fr/elnemo/) server, using the gA structure [1GRM](http://dx.doi.org/10.2210/pdb1grm/pdb). (The trajectory is included as a test case trajectory `MULTIPDB_HOLE` with the MDAnalysis unit tests and the X-ray structure is included as `PDB_HOLE`.)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load packages "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"MDAnalysis : INFO MDAnalysis 0.15.1-dev0 STARTED logging to 'MDAnalysis.log'\n",
"INFO:MDAnalysis:MDAnalysis 0.15.1-dev0 STARTED logging to 'MDAnalysis.log'\n"
]
}
],
"source": [
"import MDAnalysis as mda\n",
"from MDAnalysis.analysis.hole import HOLEtraj\n",
"from MDAnalysis.analysis.rms import RMSD\n",
"\n",
"from MDAnalysis.tests.datafiles import PDB_HOLE, MULTIPDB_HOLE\n",
" \n",
"mda.start_logging()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"plt.style.use('ggplot')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## System set up\n",
"We take the X-ray structure as the reference and we want to analyze the ENM-mode trajectory so we load them into two separate universes:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"DEBUG:MDAnalysis.core.AtomGroup:Universe.load_new(): loading /Volumes/Data/oliver/Biop/Library/python/mdanalysis/testsuite/MDAnalysisTests/data/1grm_single.pdb...\n",
"DEBUG:MDAnalysis.core.AtomGroup:Universe.load_new(): loading /Volumes/Data/oliver/Biop/Library/python/mdanalysis/testsuite/MDAnalysisTests/data/1grm_elNemo_mode7.pdb.bz2...\n"
]
}
],
"source": [
"ref = mda.Universe(PDB_HOLE) # reference structure\n",
"u = mda.Universe(MULTIPDB_HOLE) # trajectory"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Order parameter calculation\n",
"We calculate the all-atom RMSD from the reference structure as the order parameter $\\rho$:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"MDAnalysis.analysis.rmsd: INFO RMS calculation for 260 atoms.\n",
"INFO:MDAnalysis.analysis.rmsd:RMS calculation for 260 atoms.\n",
"/Volumes/Data/oliver/Biop/Library/python/mdanalysis/package/MDAnalysis/coordinates/base.py:733: UserWarning: Reader has no dt information, set to 1.0 ps\n",
" warnings.warn(\"Reader has no dt information, set to 1.0 ps\")\n",
"RMSD 6.11 A at frame 11/11 [100.0%]\n"
]
}
],
"source": [
"# calculate RMSD\n",
"R = RMSD(u, reference=ref, select=\"protein\", mass_weighted=True)\n",
"R.run()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The timeseries of the orderparameter is `R.rmsd` (an N x 3 array, with each row `(frame, time, RMSD)`):"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"frame, _, rho = R.rmsd.transpose()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For the trajectory from the ENM, time is meaningless so we just plot over frame number."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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Wg49RzVrCenQEdEI8JPPKq/QSyYdrIOlHYA19mmt2eSCHbxO6Z8+eC/7s9495\nnYP3Ux1ugTqZDr1oJqzYWVBB5bsDIPkO/c0WyNZPYU2Z67Gr/fo6h9ZWGjVq1OXfpAqvc+DaSnam\n1o2xX926BHLqBFSvGKhGTYyXBNfQKWV6LCQvF/L1Z5B/rob1lxeM3kjK9Fi4kypZeM80loOdyW98\nnZMNmTwUyM8HGl4NK3a20YLgD4FSJsdC8nKhn38aOH4UqF0P1vQF/L5wE1Wy8B6ROn4UKCwEIEDa\nYeDoYdORyA3Ivh/txQAApzP4feHhWA5Ufg2igMhGgOUHQEFOHTediAyTwgLIh/8AbKGAnz9Qv6H9\n+4Q8lkMnpIl+SwUFw4qdDRw9DCkqhCydA6kXCdW4heloZIBoDVm+AKpWHWDcc1DHUoEGUcbPRVHl\ncM+BKkQFBdunt157PazHR0MvjodknDAdiwyQf66GZJyAGvJnWME1oJq1ZDF4AZYDVZpq3xnqzgeg\nF82E5J4zHYeqkN66CfJ/X8AaFQcVwCmr3oTlQE6h/tgb6ppo6GVzIEVFpuNQFZAfv4e8uwLWmKlQ\nNcNMxyEnYzmQUyiloPo9Cfj5Q1YvhYfNkKZykmOp9uXch02Cqt/IdBxyAZYDOY3y84P11F8gB/ZB\nNr5nOg65iJw9Db1wBtRDg6Cuvd50HHIRlgM5lQoKhjVmKmTzR5Bvt5qOQ04mBfnQCfFQnW+HdXN3\n03HIhVgO5HQqvDas0XHQbyVC/rfXdBxyEtEa+s1XoOrUh+o9wHQccjEj1zlkZGQgISEBWVlZsCwL\n3bt3R8+eXOvdm6ioZrAGjYNOnAUrdg5UnatMR6JKkvf/BpzJgpowA0op03HIxYyUg5+fHwYOHIjG\njRsjLy8PsbGxaNu2LRo0aGAiDrmIatsRKqMv9MIZsCa/CFUjxHQkqiD9xUbId9tgTZ4LVS3AdByq\nAkYOK4WFhaFx48YAgKCgIDRo0ACZmZkmopCLWXf0gmp9A/TS2ZCiQtNxqAIkZSfkg7/DGjsdysa7\nPvoK4+ccTpw4gUOHDqFFCy694K1U38FAYBDkb0s4xdXDyNFD0K/PgzUsFqpe+Vf2JM9ldMnuvLw8\nPPvss3jwwQfRsWPZexMnJycjOTm55HFMTAyX4P1FQEAACgoKTMdwmOSdR85z41DtptsQ9MCjTn1v\nTxsLV3LmWOjTGcieOgrVH34CAbf+0SnvWZX4fVHKZrMhKSmp5HF0dDSio6Mv+xpj5VBcXIzZs2ej\nffv25Tr/WhZAAAARtUlEQVQZzfs52HniWvWSlQE9axJUn8dhderqtPf1xLFwFWeNheTnQc/9K1S7\nm2D1etgJyaoevy9KedT9HBITE9GwYUPOUvIhKiwC1phnIGteh+xLMR2HLkF0MfTrL0NFNoK6t5/p\nOGSIkXLYu3cvvvzyS+zZsweTJk1CbGwsdu3aZSIKVTHVsAmsIePtJ6hPcC/QHck7K4DzuVCPj+aU\nVR/G24R6KE/fZdaffwz55ANYk+dAhVRuBoynj4UzVXYs9JYNkM3rf5l6bHNisqrH74tSHnVYiXyb\n1fVuqHY3QSfOghRyiqs7kN07IB+tgTVmmscXA1Uey4GMUX0GAiGhkFWLOMXVMEk9AP3mfFjDJ0PV\nrW86DrkBlgMZoywL1pDxkONpkPX/MB3HZ8npDOhFM6EGDINqfp3pOOQmWA5klAoMhDU6DrJtM/S2\nzabj+BzJOw+9aAbU7ffA6tjFdBxyIywHMk7VrAVr7DTIO8shP+0xHcdniC6GfnUu1NXNoe55yHQc\ncjMsB3ILKjIK1hNP228zmn7EdByfIGveAIoKoR4ZwSmrVAbLgdyGatUOqs/j0AtnQLLPmI7j1fSm\n9ZAfv4c1PBbK38jizOTmWA7kVqxbe0B17AK9OB5SyHVxXEF2/Qfyr3dhjZ0GFcxl1OniWA7kdtR9\nj0CF14EsXwDR2nQcryKH9kOvXARr1F+hatczHYfcGMuB3I6yLKjB4yCZJyEf/N10HK8hGSehE56H\n9ehIqCbXmI5Dbo7lQG5JVQuANSoOsv1L6K8+MR3H48n5XPuU1T/eB9XhZtNxyAOwHMhtKVuofYrr\ne6sgP35vOo7HkuJi6GVzoJpdB3Xn/abjkIdgOZBbU1c1hDVsEvRrL0HSDpuO43FEBLJ6GaCU/Qpo\nTlklB7EcyO2pa6+HemiwfYrr2dOm43gU+WQd5H8/wnpqEpSfn+k45EFYDuQRrJvvgLr5DuiEeEh+\nvuk4HkG+2wb55J/2KavVg03HIQ/DciCPof7UH6peJPSbr3CK6xXIgf9C/20JrNFxUOF1TMchD8Ry\nII+hlIJ6fAyQnQV5b6XpOG5LTh2HXvwCrIFjoK5ubjoOeSiWA3kUVa0arJF/hez8D/TnH5uO43b0\nuRzohTOg7u4D1a6T6TjkwVgO5HFUSE1Y46ZB/rka+ruvUfTfZEherulYxumcbJyLfxpo3gqq+59M\nxyEPxxW3yCOpupFQg8dDFj2HHACIjIIVOxsqyDdPvOrz5yBxw1CcmwMUFAD55wEfHQtyDu45kMdS\n1avbf6M1kJYKHPXd6yDk3VVAbo79wbEjPj0W5BwsB/JcDaKAyCjAsgA/P4iPLiSnt38F/PAfoH4j\nwM8fqN/QPjZElaDEw+7snpaWZjqCW7DZbMjOzjYdwzjJy0X106dw7qO1QN55WCOnQFm+c7GX7P8R\nenE8rAkzgTr1EJyVgdywCJ89vPZb/DdSKjIystyvMbbnkJiYiCeffBJ/+ctfTEUgL6CCglHtmmhY\ng8YCeecha5ebjlRl5MQx6MRZsIb8GapRE6igYPi3aMViIKcwVg7dunVDXFycqc2Tl1H+1WCNmALZ\n8x305g9Nx3E5OZdtX2W118NQ199oOg55IWPl0LJlS9SoUcPU5skLqRoh9lVcN6yF/LDddByXkaJC\n6CWzoFrfCKtbT9NxyEvxhDR5FVXnKlgjpkCvWAg5/LPpOE4nIpBVCUBwCFTfQabjkBdz6+sckpOT\nkZycXPI4JiYGNpvNYCL3ERAQwLH4RZmxaNcRBUPH4/zieITMXAwrwnvWFsp7dxUKj6chZNorUEHV\ny3yd3xelOBYXSkpKKvl9dHQ0oqOjL/t8ty6Hi/0FOPvAjjMxSl10LKJvALr1xNlZsbBiZ3nFSVr9\nzWeQzR/BmjIXOYVFQGHZ///8vijFsShls9kQExNTrtcYPawkIvCwmbTkQdRdfaCatIB+9SVIcbHp\nOJUi/02GJL0Ba8xUqNBapuOQDzBWDgsWLMDUqVNx7NgxjBgxAlu2bDEVhbyUUgpqwHCguAjy9mse\n+0FE0o9CL50Na+gEqAZXm45DPoIXwXko7jKXutJYSO456DmxUF16wPrjfVWYrPIk+yz07IlQd/WB\nddtdV3w+vy9KcSxKedRFcERVRQXXsE9x3fg+ZNc3puM4TAoLoJfEQ91ws0PFQORMLAfyCSqiLqxR\ncdArEyAH95mOc0UiAlmxEAitBfXAY6bjkA9iOZDPUI1bwHp8NPTieEjGCdNxLkv+uRpyMh3WkPFQ\nFv+ZUtXjdx35FNW+M9SdD0AvmgnJPWc6zkXpbZsg33wGa/QzUAGBpuOQj2I5kM9Rf+wN1SIaetkc\nSFGR6TgXkL0/QN5ZAWvsNKiaYabjkA9jOZDPUUpBPfwkYPlBVi91mymuciwV+tW5sJ6aCFW/kek4\n5ONYDuSTlJ8frGETIQf+C/n3+6bjQM5mQS+cAfXgIKiWbUzHIWI5kO9SQcGwxkyDbPoQ8u1WYzmk\nIB96cTxUp66wbuluLAfRb7EcyKep8NqwRsdBv5UI+d/eKt++aA15cz5U7XpQ9z1S5dsnuhSWA/k8\nFdUM1qBx0ImzICfTq3Tbsu5vkDOZUIPGQilVpdsmuhyWAxEA1bYjVM++0AtnQM7lVMk29Zf/hny7\nDdbIOKhqAVWyTSJHsRyIfmHd0Qsquj300tmQokKXbktSdkLWvQVr7HQoW02XbouoIlgORL+hYoYA\ngUGQt5a4bIqrHD0E/fo8WMNioeqVf0E0oqrAciD6DWX5wXriaUjqQciGtU5/fzlzGnrRTKiYoVDX\nXP5OXEQmsRyIfkcFVYc15hnIFxuh//O5095X8vPtxXDLH2F1vt1p70vkCiwHootQYRH2gljzOmRf\nSqXfT3Qx9BsvQ0U2gurVzwkJiVyL5UB0CaphE1hDxttPUJ+o3E2m5N2VQO45qMdHc8oqeQSWA9Fl\nqNY3QPUeAL1wJiTnbIXeQ3+2AfLDdlgjJkP5V3NyQiLXYDkQXYHV9W6otjfZL5IrLN8UV9n9LeTD\nNbDGTIOqYXNRQiLnYzkQOUA9OBAIqQlZtcjhKa6SegD6zVdgDZ8MVbe+ixMSORfLgcgByrJgDZkA\nOZ4GWf+PKz5fTmdAJ8yEGjAMqvl1VZCQyLlYDkQOUoGBsEbHQbZtht62+ZLPk7zz9mLoeg+sjl2q\nMCGR8/ib2vCuXbuwYsUKiAi6deuG+++/31QUIoepmrVgjZ0G/VIcJKIu1LWtL/i66GLo116CimoG\ndc9DhlISVZ6RPQetNd544w3ExcXh5ZdfxtatW3H06FETUYjKTUVGwXriafttRtOPXPA1SXoTKCyA\nemQEp6ySRzNSDvv370f9+vVRp04d+Pv745ZbbsH27dtNRCGqENWqHdQDj9lXcc0+AwDQm9ZDUnbB\nGh4L5W9sp5zIKYx8B2dmZiIiIqLkcXh4OPbv328iClGFWV3uhD6ZDr1oBlSbmyBbNsCa8iJUcIjp\naESV5jYnpLkLTh7p7geB9DTIB38HgoKAEF7LQN7ByJ5DeHg4Tp06VfI4MzMTtWrVKvO85ORkJCcn\nlzyOiYlBZCSXOP6VzcYfRL8yOhbvOG9xPmfg90UpjkWppKSkkt9HR0cjOvryqwIb2XNo3rw50tPT\ncfLkSRQVFWHr1q248cYbyzwvOjoaMTExJb9++5fzdRyLUhyLUhyLUhyLUklJSRf8LL1SMQCG9hws\ny8LQoUPx/PPPQ0Rwxx13oGHDhiaiEBHRRRibUtGuXTssWLDA1OaJiOgy3OaEtCMc2RXyFRyLUhyL\nUhyLUhyLUhUZCyWuulEuERF5LI/acyAioqrBciAiojI84hp/LtJnl5GRgYSEBGRlZcGyLHTv3h09\ne/Y0HcsorTWmTJmC8PBwxMbGmo5jTG5uLpYuXYrU1FQopTBixAi0aNHCdCwjPvzwQ2zZsgVKKURF\nRWHkyJHw95HlTBITE/Hdd98hNDQUL730EgAgJycH8+fPx8mTJ1G3bl2MHz8ewcHBV3wvt99z4CJ9\npfz8/DBw4EC88soriI+Px8aNG312LH61YcMGNGjQwHQM45YvX4727dvjlVdewdy5c312TDIzM/Hx\nxx9jzpw5eOmll1BcXIytW7eajlVlunXrhri4uAv+bN26dbj++uuxYMECREdH4/3333fovdy+HLhI\nX6mwsDA0btwYABAUFIQGDRogMzPTbCiDMjIysHPnTnTv3t10FKPOnz+PvXv3olu3bgDsHyIc+WTo\nrbTWyMvLQ3FxMfLz8y+6+oK3atmyJWrUqHHBn+3YsQNdu3YFANx+++0O//x0+30tLtJ3cSdOnMCh\nQ4d89tABAKxcuRKPPfYYcnNzTUcx6vjx47DZbFiyZAkOHTqEpk2bYvDgwQgICDAdrcqFh4ejV69e\nGDlyJAIDA9GmTRu0adPGdCyjzpw5g7CwMAD2D5hnz5516HVuv+dwMb6+SF9eXh7mzZuHQYMGISgo\nyHQcI349rtq4cWOIiMP3dfZGWmscOHAAd911F+bMmYPAwECsW7fOdCwjzp07hx07dmDJkiVYtmwZ\n8vLy8NVXX5mO5ZHcvhwcXaTPVxQXF+Pll1/Gbbfdho4dO5qOY8zevXuxY8cOjB49GgsWLEBycjIS\nEhJMxzIiPDwcERERaNasGQCgc+fO+Pnnnw2nMmP37t2oW7cuQkJCYFkWOnXqhJ9++sl0LKPCwsKQ\nlZUFAMjKykJoaKhDr3P7cnB0kT5fkZiYiIYNG/r8LKUBAwYgMTERCQkJ+POf/4zWrVtj9OjRpmMZ\nERYWhoiICKSlpQGw/4D01bXKateujX379qGgoAAigt27d/vcyfnf70l36NABn332GQDgs88+c/jn\np0dcIb1r1y4sX768ZJE+X53KunfvXkyfPh1RUVFQSkEphf79+6Ndu3amoxmVkpKC9evX+/RU1oMH\nD2LZsmUoKipCvXr1MHLkSJ89Kb127Vps27YNfn5+aNy4MYYPH+4zU1kXLFiAlJQUZGdnIzQ0FDEx\nMejYsSNeeeUVnDp1CrVr18aECRPKnLS+GI8oByIiqlpuf1iJiIiqHsuBiIjKYDkQEVEZLAciIiqD\n5UBERGWwHIiIqAyWAxGAtLQ0xMbGYuDAgfj4449NxyEyjtc5EAFYunQpgoOD8fjjj5uOQuQWuOdA\nBODkyZOXXHJCa13FaYjM454D+bwZM2YgJSUFfn5+8Pf3R4cOHRAcHIyTJ0/ixx9/xKRJk1BQUIA1\na9YgPT0dNWrUQLdu3dC3b18A9mIZPXo0RowYgTVr1iA/Px/9+/dH06ZNsXTpUpw6dQpdunTBkCFD\nSra5efNmrF+/HmfOnEHz5s3x1FNPoXbt2qaGgKgsISJ59tlnZfPmzSIisnjxYhk0aJD89NNPIiJS\nWFgoycnJcvjwYREROXTokDz55JOyfft2ERE5ceKExMTEyGuvvSaFhYXy/fffy4ABA2Tu3Lly9uxZ\nycjIkCeeeEJSUlJEROQ///mPjB07Vo4ePSrFxcXy7rvvyjPPPGPgb010aTysRPQL+c1O9I033ohr\nrrkGAODv749WrVqhUaNGAICoqCjcfPPNSElJueD1Dz30EPz9/dGmTRsEBQXhlltugc1mQ3h4OFq2\nbIkDBw4AADZt2oT7778fkZGRsCwL999/Pw4ePHjB0vREpvnGUoVE5fTbuw8C9tvV/v3vf0dqaiqK\niopQVFSEzp07X/CcmjVrlvw+ICDggnXzAwICkJeXB8B+GGrFihVYtWrVBa/PzMzkoSVyGywHoov4\n/d0GFyxYgHvuuQdxcXHw9/fHihUrkJOTU6H3joiIQJ8+fXDrrbc6IyqRS/CwEpED8vLyEBISAn9/\nf+zfvx9bt26t8Hv16NED77//Po4cOQIAyM3NxTfffOOsqEROwT0HIgc88cQTWLVqFd544w20atUK\nf/jDH5Cbm+vw63+7J3LTTTchPz8f8+fPx6lTpxAcHIw2bdqUOUxFZBKnshIRURk8rERERGWwHIiI\nqAyWAxERlcFyICKiMlgORERUBsuBiIjKYDkQEVEZLAciIiqD5UBERGX8P/c/NEHlOtYiAAAAAElF\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x106afb5d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = plt.subplot(111)\n",
"ax.plot(frame, rho, '.-')\n",
"ax.set_xlabel(\"frame\")\n",
"ax.set_ylabel(r\"RMSD $\\rho$ ($\\AA$)\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the ENM trajectory is symmetric to frame 5 (the original X-ray structure) and the mode deforms the structure so that the RMSD increases nearly linearly."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## HOLE\n",
"Run [HOLE](http://www.smartsci.uk/hole/) on all 11 frames of the trajectory in universe `u` and match the frames to the RMSD:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"MDAnalysis.analysis.hole: INFO HOLE analysis frame 0 (orderparameter 6.10501)\n",
"INFO:MDAnalysis.analysis.hole:HOLE analysis frame 0 (orderparameter 6.10501)\n",
"DEBUG:MDAnalysis.analysis.hole:path check: HOLE will not read '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-orderparameters/simple2.rad' because it has more than 70 characters.\n",
"DEBUG:MDAnalysis.analysis.hole:path check: Using relative path: '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-orderparameters/simple2.rad' --> 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpaVfgsb.pdb'\n",
"INFO:MDAnalysis.analysis.hole:Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpaVfgsb.pdb'\n",
"MDAnalysis.analysis.hole: INFO Using radius file 'simple2.rad'\n",
"INFO:MDAnalysis.analysis.hole:Using radius file 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CPOINT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CPOINT\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CVECT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CVECT\n",
"MDAnalysis.analysis.hole: INFO Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpaVfgsb.pdb' (trajectory: None)\n",
"INFO:MDAnalysis.analysis.hole:Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpaVfgsb.pdb' (trajectory: None)\n",
"DEBUG:MDAnalysis.analysis.hole:/Volumes/Data/oliver/Biop/Library/hole2/exe/hole <(input) >hole.out\n",
"MDAnalysis.analysis.hole: INFO HOLE finished: output file 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:HOLE finished: output file 'hole.out'\n",
"MDAnalysis.analysis.hole: INFO Collecting HOLE profiles for run with id 1\n",
"INFO:MDAnalysis.analysis.hole:Collecting HOLE profiles for run with id 1\n",
"MDAnalysis.analysis.hole: INFO Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"DEBUG:MDAnalysis.analysis.hole:Started reading data\n",
"DEBUG:MDAnalysis.analysis.hole:Collected HOLE profile for frame 0 (459 datapoints)\n",
"DEBUG:MDAnalysis.analysis.hole:Finished with frame 0, saved as './run_1/radii_1_0000.dat.gz'\n",
"MDAnalysis.analysis.hole: INFO Collected HOLE radius profiles for 1 frames\n",
"INFO:MDAnalysis.analysis.hole:Collected HOLE radius profiles for 1 frames\n",
"MDAnalysis.analysis.hole: INFO HOLE analysis frame 1 (orderparameter 4.88398)\n",
"INFO:MDAnalysis.analysis.hole:HOLE analysis frame 1 (orderparameter 4.88398)\n",
"DEBUG:MDAnalysis.analysis.hole:path check: HOLE will not read '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-orderparameters/simple2.rad' because it has more than 70 characters.\n",
"DEBUG:MDAnalysis.analysis.hole:path check: Using relative path: '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-orderparameters/simple2.rad' --> 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmptVN7Uk.pdb'\n",
"INFO:MDAnalysis.analysis.hole:Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmptVN7Uk.pdb'\n",
"MDAnalysis.analysis.hole: INFO Using radius file 'simple2.rad'\n",
"INFO:MDAnalysis.analysis.hole:Using radius file 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CPOINT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CPOINT\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CVECT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CVECT\n",
"MDAnalysis.analysis.hole: INFO Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmptVN7Uk.pdb' (trajectory: None)\n",
"INFO:MDAnalysis.analysis.hole:Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmptVN7Uk.pdb' (trajectory: None)\n",
"DEBUG:MDAnalysis.analysis.hole:/Volumes/Data/oliver/Biop/Library/hole2/exe/hole <(input) >hole.out\n",
"MDAnalysis.analysis.hole: INFO HOLE finished: output file 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:HOLE finished: output file 'hole.out'\n",
"MDAnalysis.analysis.hole: INFO Collecting HOLE profiles for run with id 1\n",
"INFO:MDAnalysis.analysis.hole:Collecting HOLE profiles for run with id 1\n",
"MDAnalysis.analysis.hole: INFO Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"DEBUG:MDAnalysis.analysis.hole:Started reading data\n",
"DEBUG:MDAnalysis.analysis.hole:Collected HOLE profile for frame 0 (417 datapoints)\n",
"DEBUG:MDAnalysis.analysis.hole:Finished with frame 0, saved as './run_1/radii_1_0000.dat.gz'\n",
"MDAnalysis.analysis.hole: INFO Collected HOLE radius profiles for 1 frames\n",
"INFO:MDAnalysis.analysis.hole:Collected HOLE radius profiles for 1 frames\n",
"MDAnalysis.analysis.hole: INFO HOLE analysis frame 2 (orderparameter 3.66304)\n",
"INFO:MDAnalysis.analysis.hole:HOLE analysis frame 2 (orderparameter 3.66304)\n",
"DEBUG:MDAnalysis.analysis.hole:path check: HOLE will not read '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-orderparameters/simple2.rad' because it has more than 70 characters.\n",
"DEBUG:MDAnalysis.analysis.hole:path check: Using relative path: '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-orderparameters/simple2.rad' --> 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmp_jNh5g.pdb'\n",
"INFO:MDAnalysis.analysis.hole:Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmp_jNh5g.pdb'\n",
"MDAnalysis.analysis.hole: INFO Using radius file 'simple2.rad'\n",
"INFO:MDAnalysis.analysis.hole:Using radius file 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CPOINT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CPOINT\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CVECT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CVECT\n",
"MDAnalysis.analysis.hole: INFO Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmp_jNh5g.pdb' (trajectory: None)\n",
"INFO:MDAnalysis.analysis.hole:Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmp_jNh5g.pdb' (trajectory: None)\n",
"DEBUG:MDAnalysis.analysis.hole:/Volumes/Data/oliver/Biop/Library/hole2/exe/hole <(input) >hole.out\n",
"MDAnalysis.analysis.hole: INFO HOLE finished: output file 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:HOLE finished: output file 'hole.out'\n",
"MDAnalysis.analysis.hole: INFO Collecting HOLE profiles for run with id 1\n",
"INFO:MDAnalysis.analysis.hole:Collecting HOLE profiles for run with id 1\n",
"MDAnalysis.analysis.hole: INFO Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"DEBUG:MDAnalysis.analysis.hole:Started reading data\n",
"DEBUG:MDAnalysis.analysis.hole:Collected HOLE profile for frame 0 (433 datapoints)\n",
"DEBUG:MDAnalysis.analysis.hole:Finished with frame 0, saved as './run_1/radii_1_0000.dat.gz'\n",
"MDAnalysis.analysis.hole: INFO Collected HOLE radius profiles for 1 frames\n",
"INFO:MDAnalysis.analysis.hole:Collected HOLE radius profiles for 1 frames\n",
"MDAnalysis.analysis.hole: INFO HOLE analysis frame 3 (orderparameter 2.44202)\n",
"INFO:MDAnalysis.analysis.hole:HOLE analysis frame 3 (orderparameter 2.44202)\n",
"DEBUG:MDAnalysis.analysis.hole:path check: HOLE will not read '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-orderparameters/simple2.rad' because it has more than 70 characters.\n",
"DEBUG:MDAnalysis.analysis.hole:path check: Using relative path: '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-orderparameters/simple2.rad' --> 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpCiwXmX.pdb'\n",
"INFO:MDAnalysis.analysis.hole:Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpCiwXmX.pdb'\n",
"MDAnalysis.analysis.hole: INFO Using radius file 'simple2.rad'\n",
"INFO:MDAnalysis.analysis.hole:Using radius file 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CPOINT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CPOINT\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CVECT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CVECT\n",
"MDAnalysis.analysis.hole: INFO Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpCiwXmX.pdb' (trajectory: None)\n",
"INFO:MDAnalysis.analysis.hole:Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpCiwXmX.pdb' (trajectory: None)\n",
"DEBUG:MDAnalysis.analysis.hole:/Volumes/Data/oliver/Biop/Library/hole2/exe/hole <(input) >hole.out\n",
"MDAnalysis.analysis.hole: INFO HOLE finished: output file 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:HOLE finished: output file 'hole.out'\n",
"MDAnalysis.analysis.hole: INFO Collecting HOLE profiles for run with id 1\n",
"INFO:MDAnalysis.analysis.hole:Collecting HOLE profiles for run with id 1\n",
"MDAnalysis.analysis.hole: INFO Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"DEBUG:MDAnalysis.analysis.hole:Started reading data\n",
"DEBUG:MDAnalysis.analysis.hole:Collected HOLE profile for frame 0 (423 datapoints)\n",
"DEBUG:MDAnalysis.analysis.hole:Finished with frame 0, saved as './run_1/radii_1_0000.dat.gz'\n",
"MDAnalysis.analysis.hole: INFO Collected HOLE radius profiles for 1 frames\n",
"INFO:MDAnalysis.analysis.hole:Collected HOLE radius profiles for 1 frames\n",
"MDAnalysis.analysis.hole: INFO HOLE analysis frame 4 (orderparameter 1.22101)\n",
"INFO:MDAnalysis.analysis.hole:HOLE analysis frame 4 (orderparameter 1.22101)\n",
"DEBUG:MDAnalysis.analysis.hole:path check: HOLE will not read '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-orderparameters/simple2.rad' because it has more than 70 characters.\n",
"DEBUG:MDAnalysis.analysis.hole:path check: Using relative path: '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-orderparameters/simple2.rad' --> 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpE1fLE2.pdb'\n",
"INFO:MDAnalysis.analysis.hole:Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpE1fLE2.pdb'\n",
"MDAnalysis.analysis.hole: INFO Using radius file 'simple2.rad'\n",
"INFO:MDAnalysis.analysis.hole:Using radius file 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CPOINT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CPOINT\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CVECT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CVECT\n",
"MDAnalysis.analysis.hole: INFO Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpE1fLE2.pdb' (trajectory: None)\n",
"INFO:MDAnalysis.analysis.hole:Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpE1fLE2.pdb' (trajectory: None)\n",
"DEBUG:MDAnalysis.analysis.hole:/Volumes/Data/oliver/Biop/Library/hole2/exe/hole <(input) >hole.out\n",
"MDAnalysis.analysis.hole: INFO HOLE finished: output file 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:HOLE finished: output file 'hole.out'\n",
"MDAnalysis.analysis.hole: INFO Collecting HOLE profiles for run with id 1\n",
"INFO:MDAnalysis.analysis.hole:Collecting HOLE profiles for run with id 1\n",
"MDAnalysis.analysis.hole: INFO Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"DEBUG:MDAnalysis.analysis.hole:Started reading data\n",
"DEBUG:MDAnalysis.analysis.hole:Collected HOLE profile for frame 0 (465 datapoints)\n",
"DEBUG:MDAnalysis.analysis.hole:Finished with frame 0, saved as './run_1/radii_1_0000.dat.gz'\n",
"MDAnalysis.analysis.hole: INFO Collected HOLE radius profiles for 1 frames\n",
"INFO:MDAnalysis.analysis.hole:Collected HOLE radius profiles for 1 frames\n",
"MDAnalysis.analysis.hole: INFO HOLE analysis frame 5 (orderparameter 3.74062e-07)\n",
"INFO:MDAnalysis.analysis.hole:HOLE analysis frame 5 (orderparameter 3.74062e-07)\n",
"DEBUG:MDAnalysis.analysis.hole:path check: HOLE will not read '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-orderparameters/simple2.rad' because it has more than 70 characters.\n",
"DEBUG:MDAnalysis.analysis.hole:path check: Using relative path: '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-orderparameters/simple2.rad' --> 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpCGetxf.pdb'\n",
"INFO:MDAnalysis.analysis.hole:Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpCGetxf.pdb'\n",
"MDAnalysis.analysis.hole: INFO Using radius file 'simple2.rad'\n",
"INFO:MDAnalysis.analysis.hole:Using radius file 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CPOINT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CPOINT\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CVECT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CVECT\n",
"MDAnalysis.analysis.hole: INFO Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpCGetxf.pdb' (trajectory: None)\n",
"INFO:MDAnalysis.analysis.hole:Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpCGetxf.pdb' (trajectory: None)\n",
"DEBUG:MDAnalysis.analysis.hole:/Volumes/Data/oliver/Biop/Library/hole2/exe/hole <(input) >hole.out\n",
"MDAnalysis.analysis.hole: INFO HOLE finished: output file 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:HOLE finished: output file 'hole.out'\n",
"MDAnalysis.analysis.hole: INFO Collecting HOLE profiles for run with id 1\n",
"INFO:MDAnalysis.analysis.hole:Collecting HOLE profiles for run with id 1\n",
"MDAnalysis.analysis.hole: INFO Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"DEBUG:MDAnalysis.analysis.hole:Started reading data\n",
"DEBUG:MDAnalysis.analysis.hole:Collected HOLE profile for frame 0 (403 datapoints)\n",
"DEBUG:MDAnalysis.analysis.hole:Finished with frame 0, saved as './run_1/radii_1_0000.dat.gz'\n",
"MDAnalysis.analysis.hole: INFO Collected HOLE radius profiles for 1 frames\n",
"INFO:MDAnalysis.analysis.hole:Collected HOLE radius profiles for 1 frames\n",
"MDAnalysis.analysis.hole: INFO HOLE analysis frame 6 (orderparameter 1.221)\n",
"INFO:MDAnalysis.analysis.hole:HOLE analysis frame 6 (orderparameter 1.221)\n",
"DEBUG:MDAnalysis.analysis.hole:path check: HOLE will not read '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-orderparameters/simple2.rad' because it has more than 70 characters.\n",
"DEBUG:MDAnalysis.analysis.hole:path check: Using relative path: '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-orderparameters/simple2.rad' --> 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpecnD_g.pdb'\n",
"INFO:MDAnalysis.analysis.hole:Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpecnD_g.pdb'\n",
"MDAnalysis.analysis.hole: INFO Using radius file 'simple2.rad'\n",
"INFO:MDAnalysis.analysis.hole:Using radius file 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CPOINT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CPOINT\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CVECT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CVECT\n",
"MDAnalysis.analysis.hole: INFO Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpecnD_g.pdb' (trajectory: None)\n",
"INFO:MDAnalysis.analysis.hole:Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpecnD_g.pdb' (trajectory: None)\n",
"DEBUG:MDAnalysis.analysis.hole:/Volumes/Data/oliver/Biop/Library/hole2/exe/hole <(input) >hole.out\n",
"MDAnalysis.analysis.hole: INFO HOLE finished: output file 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:HOLE finished: output file 'hole.out'\n",
"MDAnalysis.analysis.hole: INFO Collecting HOLE profiles for run with id 1\n",
"INFO:MDAnalysis.analysis.hole:Collecting HOLE profiles for run with id 1\n",
"MDAnalysis.analysis.hole: INFO Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"DEBUG:MDAnalysis.analysis.hole:Started reading data\n",
"DEBUG:MDAnalysis.analysis.hole:Collected HOLE profile for frame 0 (381 datapoints)\n",
"DEBUG:MDAnalysis.analysis.hole:Finished with frame 0, saved as './run_1/radii_1_0000.dat.gz'\n",
"MDAnalysis.analysis.hole: INFO Collected HOLE radius profiles for 1 frames\n",
"INFO:MDAnalysis.analysis.hole:Collected HOLE radius profiles for 1 frames\n",
"MDAnalysis.analysis.hole: INFO HOLE analysis frame 7 (orderparameter 2.44202)\n",
"INFO:MDAnalysis.analysis.hole:HOLE analysis frame 7 (orderparameter 2.44202)\n",
"DEBUG:MDAnalysis.analysis.hole:path check: HOLE will not read '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-orderparameters/simple2.rad' because it has more than 70 characters.\n",
"DEBUG:MDAnalysis.analysis.hole:path check: Using relative path: '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-orderparameters/simple2.rad' --> 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpTnio9_.pdb'\n",
"INFO:MDAnalysis.analysis.hole:Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpTnio9_.pdb'\n",
"MDAnalysis.analysis.hole: INFO Using radius file 'simple2.rad'\n",
"INFO:MDAnalysis.analysis.hole:Using radius file 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CPOINT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CPOINT\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CVECT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CVECT\n",
"MDAnalysis.analysis.hole: INFO Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpTnio9_.pdb' (trajectory: None)\n",
"INFO:MDAnalysis.analysis.hole:Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpTnio9_.pdb' (trajectory: None)\n",
"DEBUG:MDAnalysis.analysis.hole:/Volumes/Data/oliver/Biop/Library/hole2/exe/hole <(input) >hole.out\n",
"MDAnalysis.analysis.hole: INFO HOLE finished: output file 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:HOLE finished: output file 'hole.out'\n",
"MDAnalysis.analysis.hole: INFO Collecting HOLE profiles for run with id 1\n",
"INFO:MDAnalysis.analysis.hole:Collecting HOLE profiles for run with id 1\n",
"MDAnalysis.analysis.hole: INFO Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"DEBUG:MDAnalysis.analysis.hole:Started reading data\n",
"DEBUG:MDAnalysis.analysis.hole:Collected HOLE profile for frame 0 (321 datapoints)\n",
"DEBUG:MDAnalysis.analysis.hole:Finished with frame 0, saved as './run_1/radii_1_0000.dat.gz'\n",
"MDAnalysis.analysis.hole: INFO Collected HOLE radius profiles for 1 frames\n",
"INFO:MDAnalysis.analysis.hole:Collected HOLE radius profiles for 1 frames\n",
"MDAnalysis.analysis.hole: INFO HOLE analysis frame 8 (orderparameter 3.66303)\n",
"INFO:MDAnalysis.analysis.hole:HOLE analysis frame 8 (orderparameter 3.66303)\n",
"DEBUG:MDAnalysis.analysis.hole:path check: HOLE will not read '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-orderparameters/simple2.rad' because it has more than 70 characters.\n",
"DEBUG:MDAnalysis.analysis.hole:path check: Using relative path: '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-orderparameters/simple2.rad' --> 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmphkambF.pdb'\n",
"INFO:MDAnalysis.analysis.hole:Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmphkambF.pdb'\n",
"MDAnalysis.analysis.hole: INFO Using radius file 'simple2.rad'\n",
"INFO:MDAnalysis.analysis.hole:Using radius file 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CPOINT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CPOINT\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CVECT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CVECT\n",
"MDAnalysis.analysis.hole: INFO Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmphkambF.pdb' (trajectory: None)\n",
"INFO:MDAnalysis.analysis.hole:Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmphkambF.pdb' (trajectory: None)\n",
"DEBUG:MDAnalysis.analysis.hole:/Volumes/Data/oliver/Biop/Library/hole2/exe/hole <(input) >hole.out\n",
"MDAnalysis.analysis.hole: INFO HOLE finished: output file 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:HOLE finished: output file 'hole.out'\n",
"MDAnalysis.analysis.hole: INFO Collecting HOLE profiles for run with id 1\n",
"INFO:MDAnalysis.analysis.hole:Collecting HOLE profiles for run with id 1\n",
"MDAnalysis.analysis.hole: INFO Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"DEBUG:MDAnalysis.analysis.hole:Started reading data\n",
"DEBUG:MDAnalysis.analysis.hole:Collected HOLE profile for frame 0 (355 datapoints)\n",
"DEBUG:MDAnalysis.analysis.hole:Finished with frame 0, saved as './run_1/radii_1_0000.dat.gz'\n",
"MDAnalysis.analysis.hole: INFO Collected HOLE radius profiles for 1 frames\n",
"INFO:MDAnalysis.analysis.hole:Collected HOLE radius profiles for 1 frames\n",
"MDAnalysis.analysis.hole: INFO HOLE analysis frame 9 (orderparameter 4.88398)\n",
"INFO:MDAnalysis.analysis.hole:HOLE analysis frame 9 (orderparameter 4.88398)\n",
"DEBUG:MDAnalysis.analysis.hole:path check: HOLE will not read '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-orderparameters/simple2.rad' because it has more than 70 characters.\n",
"DEBUG:MDAnalysis.analysis.hole:path check: Using relative path: '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-orderparameters/simple2.rad' --> 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpEcOhD9.pdb'\n",
"INFO:MDAnalysis.analysis.hole:Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpEcOhD9.pdb'\n",
"MDAnalysis.analysis.hole: INFO Using radius file 'simple2.rad'\n",
"INFO:MDAnalysis.analysis.hole:Using radius file 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CPOINT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CPOINT\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CVECT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CVECT\n",
"MDAnalysis.analysis.hole: INFO Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpEcOhD9.pdb' (trajectory: None)\n",
"INFO:MDAnalysis.analysis.hole:Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpEcOhD9.pdb' (trajectory: None)\n",
"DEBUG:MDAnalysis.analysis.hole:/Volumes/Data/oliver/Biop/Library/hole2/exe/hole <(input) >hole.out\n",
"MDAnalysis.analysis.hole: INFO HOLE finished: output file 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:HOLE finished: output file 'hole.out'\n",
"MDAnalysis.analysis.hole: INFO Collecting HOLE profiles for run with id 1\n",
"INFO:MDAnalysis.analysis.hole:Collecting HOLE profiles for run with id 1\n",
"MDAnalysis.analysis.hole: INFO Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"DEBUG:MDAnalysis.analysis.hole:Started reading data\n",
"DEBUG:MDAnalysis.analysis.hole:Collected HOLE profile for frame 0 (417 datapoints)\n",
"DEBUG:MDAnalysis.analysis.hole:Finished with frame 0, saved as './run_1/radii_1_0000.dat.gz'\n",
"MDAnalysis.analysis.hole: INFO Collected HOLE radius profiles for 1 frames\n",
"INFO:MDAnalysis.analysis.hole:Collected HOLE radius profiles for 1 frames\n",
"MDAnalysis.analysis.hole: INFO HOLE analysis frame 10 (orderparameter 6.10502)\n",
"INFO:MDAnalysis.analysis.hole:HOLE analysis frame 10 (orderparameter 6.10502)\n",
"DEBUG:MDAnalysis.analysis.hole:path check: HOLE will not read '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-orderparameters/simple2.rad' because it has more than 70 characters.\n",
"DEBUG:MDAnalysis.analysis.hole:path check: Using relative path: '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-orderparameters/simple2.rad' --> 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmphXute1.pdb'\n",
"INFO:MDAnalysis.analysis.hole:Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmphXute1.pdb'\n",
"MDAnalysis.analysis.hole: INFO Using radius file 'simple2.rad'\n",
"INFO:MDAnalysis.analysis.hole:Using radius file 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CPOINT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CPOINT\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CVECT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CVECT\n",
"MDAnalysis.analysis.hole: INFO Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmphXute1.pdb' (trajectory: None)\n",
"INFO:MDAnalysis.analysis.hole:Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmphXute1.pdb' (trajectory: None)\n",
"DEBUG:MDAnalysis.analysis.hole:/Volumes/Data/oliver/Biop/Library/hole2/exe/hole <(input) >hole.out\n",
"MDAnalysis.analysis.hole: INFO HOLE finished: output file 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:HOLE finished: output file 'hole.out'\n",
"MDAnalysis.analysis.hole: INFO Collecting HOLE profiles for run with id 1\n",
"INFO:MDAnalysis.analysis.hole:Collecting HOLE profiles for run with id 1\n",
"MDAnalysis.analysis.hole: INFO Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"DEBUG:MDAnalysis.analysis.hole:Started reading data\n",
"DEBUG:MDAnalysis.analysis.hole:Collected HOLE profile for frame 0 (387 datapoints)\n",
"DEBUG:MDAnalysis.analysis.hole:Finished with frame 0, saved as './run_1/radii_1_0000.dat.gz'\n",
"MDAnalysis.analysis.hole: INFO Collected HOLE radius profiles for 1 frames\n",
"INFO:MDAnalysis.analysis.hole:Collected HOLE radius profiles for 1 frames\n"
]
}
],
"source": [
"# HOLE analysis with order parameters\n",
"H = HOLEtraj(u, orderparameters=R.rmsd[:,2], \n",
" executable=\"~/hole2/exe/hole\")\n",
"H.run()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualization of pore profiles "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As a quick check, plot the pore profiles $R_\\rho(\\zeta)$:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.Axes3DSubplot at 0x10759b1d0>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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b+rW0tTrRNFAUDZNJzK+hFlZ73cRS6aLxS417qqHZbGcgvry5ujc43o2qqQSz\no6w2bWY0c5Yt1jfkHw8pOSOc4cxJNljm+h5omkZWUelyz96uDqVPENPCXG29AjOHybIJFT8iAYLK\nGMFUBwCrrbOiKjGKQgu6r5TMObKsXopLvmyRZZmuri4GBwd55zurv4OoxOzm0KFDXH311UDO+CaR\nSNSVLbFid7rVeOrOd4JfK5Wa3hSW05ZrMeTr9BAYqWynW5r+VVgR53TaEAQYHZ0Vt1rjzr0tuaq0\nWiruyj3eYXEt+0EagFdq48XnhkkNeRnPDhQ9FlTGkZDz4ltKPJ1BAzqaZne6/xH5F34V+TZp7Uos\nwrOANpPBMMVkdpChuIpJ1BAL3oPCeK7IFAIKKi0Nv1aDxjCf2U0oFMLr9eb/7vF4mJ6ufLOkb7YG\nBwd54IEHVq7oVkKjUrIqoRbTG2+Hh6nhxf/x9DHKHfjpc8iyxOBQfT4Hmqax1udGA2LpTEXXoIv7\nfM9Za3cTzCRQl9nZ3yu1cfJAEjXkIKxMkVZnszsCygirzZuLeq4VMjoT03ZaZ1PG0loSCZkkNyMx\nisxxFHxk1VFiaojBeBKnXPweyEWHaKfIsB5Ympzyy5l0Oo2ppHqwWhYyuym3ealGQ/QcXZPJdOmm\njFWbklXvXLX6MPg7vAQrCC8UxlXny66wWmRGRmu7jdffL0VRMMsyoiBwdirckPer2+5GEkSm0uXz\niEtRNY2/eO0XHAyPLf7kBfDJnYTGNWwWM365kwlltjX6WKafXvOVRJRg2Q/U+WAEWRTzu9ZcV2Eh\n5+mgmciwCZlBVPycSZ/KtRJKJfCXFFNIwjDZmXQxs/AqGYxiiKWg3sKIxcxuPB4PweDsHen09HTF\nNpLJZJJoNHen19bWxkc/+tGVK7rlPHVrMb6pNfa50K6zUnydHqYWEN3SXNiFvjzsDjMT49W5apXe\nCeghF6tJ4vRkYw6/2i0OTILIYGLu2hIFhROKpvLZI//JtwcO8HJohG8PvFrXvDbRSWLcgmaL0yqv\nYTyT696raRpj2X66zRsxCeZ89+BChkIRrAXeE9PKOK3yamTBTFyLoNCei9dqrexLnmO77Ram01m6\nbcXZDnp4QSCEjZ+T1HbXdU0G5anXYWwxs5utW7eyb1+ug3N/fz82m21Rkdc/s8888ww/+clPgJwP\n7/3337+yD9IKb7ur7XBQ6y6uMP2r3jQzX6e3bPZC6fUU/jcfTU02pgKVGYaXHvTp75f+i+K0mBmo\noEFl6ZhPg6N0AAAgAElEQVTlyOXqagwlw1xJe9Hz//Dlx/Gb7Xxtx9v47vmDvBoeY29wiM3OZg6F\nx4lkU7jk8r6mi5HJ5MQ86wzQJq/lVPplruRNnEsfwiRYcIhuXKKPiBKY480wGophL9i1hpVJ3FIL\nAgJhZYJmsR2TcIppZQfTSooOeTNp9RXW2YstGmUGUejCKXyXNK8nS23eAAYLU4/DWCVmN5s3b+bI\nkSP8/d//PWazmfe///2Ljqt/Vnfs2EEymQtteb1eNmzYsHJFtzDvtVQ8lmq+ejMf5hyktXsIjk4X\nHQqWE8OFcmF1vB47o6MLl++Wy6oo55nrtVsZmW7M4VerxUFKUea4jb0YHEIURAaTYY5FJnli9ARf\n2Xo7Hz/4c7ptTfgtdp6ePMcd7RtrmjfnLyyQsIyzxXwrz8R+wLnUYX4W+SYbLTlzcZfkI6IGaGNt\n0WvHo3E8tlmxDykTuMVmJCQCyghrTG1YeZZzmc2sNmUZS8aQBI1OW+HuJ4XEKFlWYeIgEe3ParoO\ng8UJhUI1hxcqMbsBeO9731v12JqmFWVBbNq0iU2bNq1s0S28LV7KBpOlTRlL83xrxWI3Y7aZCU9F\ncfkc84phJUUgzc0OTp4aL7t+/fWVpn+1Ou0MhSoX3ULLy1KskoxFkos6A2uaxr+ff5UPr34dWU3l\nTw48AeS8GqyijF02s8vVxd7gUM2iGwzGQYCgOIRZtHGT4338IvL/AdAprwfI73RLCcSSdBTk6IbV\nKbpMG7GJLsaz51G4DolRzmUm6TUFORudQNU0Oi2zH3yZgRnPBRmZfrKsq+k6DBYnEok0pFVPI9E/\nd0ePHmViYgKXy4UgCNjt9pUrupCLcVayC5yPhU7ey4ltYTijEWiahq/Dw+TgFHa3ta727O1tTSQS\ns+0NC9da6pu7UHkzQLe3iUMjkxWtX/8yEEVx3n+LDquTE9FZk/XDkQmC6QRvbFmLJIhsaWohkk0j\nCAJpNbfWDquzrlSzyakYkiQwmc2V7W6yvp5DyWcZy/bnuzq4JF/ZtLFwMsWV3a35v4eUSTZbr0dE\n4kjyeRTtnQiMcS59gpvdCj8Mv4KKQJt19oMvc4Ys6xAZR8OBxsVforpSuZgdxo4cOcKRI0ewWCw4\nHA6GhoZW7kEa1B6XXYhKMh9qzYEtRE8x87a7CY6G6m7109HhJpOZbbteLvRS6fi9rd58gUQ5Sg/g\ngAXvNNbZPKSULJMzGQy/GDvF29o3Igkiw4kId+/7EQ7JRCiTJKFkCWaStFucjNXh2RAIxjHJEikt\nQUKNomgZAsoImyy7mJ4RWt1vt/C6vnLqBSKpNJ2e2fhsWJmkSWymVV5NSJngqegPOZ72YhHM+OV2\nzsZDWKQsFnHWIMcknCGr9czsctfWfB0Gi9OoppSNRP+c3XLLLdx9993ceeedeDweTCbTyhbdRlJL\n5kOtFKaY+Tt9TI/Vn561epUXRZktzigMvVR72LelvYW0os75YimX91yJkHfb3XjNVo5FJsmoCk9P\n9nNbS+5QaW8w59r1g6HDvBQcZktTCyeiU/gtdqYzyZptIaeDcSxmmdWmPs6mX+NU6gCt8mraTT2E\nlFwYxim6iamzcfADoTF+NHyUjKKSNGVIqwoZLU1KS+AQmxAFCZfk41TqZR6PNPNG57Vk2MJYQsVm\nShf5O8icIMMGQ3SXgYt5p+v3++nt7WXt2rWcOXOGu+66a+WGFwpNb+rZdRZWkS3lYVxh3LOwNLia\nqrSFcLtzt8zj42Gam53IspxPZ6sW3XNgcDrMKq973gM+/boWo8vqwiLKvBoaRUNjrd1DuzW3k3w2\nMMCfrbua/3P+NV4JjfKB7u18/exLvLz3GB3/q5/xq2J02ao/mQ6Fk1isMhssV/Fy4lcoWpZr7L+H\nWbRxPLkXoMj6MaupfP3si7y9ZSO/ZpCvj7zEsDnMh9auoUny5R3BrIKDIKP8nsvFKpNEUt1MKHOC\nTmccp9CPRh8gYOIoGbZiFZ4mo22db5kGdZBOp/nCF76A1Wrl+PHj2Gw2urq6aG5urvj3/qGHHuLw\n4cO4XC4+/elPz3k8mUzyne98h2Awl9N9yy23cM011fW106tTBwcHV2434Eagi4Uet62n3fhi8xR2\nbIDibhf+Ti+BCqrSFhpf/9KQJJHhkXDd1yEIAhZZ4tDwZJFhT63v0SpbE2jwm8l+Hhs5zptbewHo\nj09zIjrF73f08fGea7i1pYfb29az3d3G4aFhrJpUc1w3Eklit5lZa96KT+qg07SeteateMSWfHjB\nLrpJqBFUTWE0GWU6k+Rd/j40NN7a1cuvJs4wmByhSZzteRZRA7TKa4hqTUiMcS7dTFYVud17A23S\nX2Dnccy8hsIaNJzGTncJkWWZe++9l3Q6jdVqZd++fXzrW9+qaoxrrrmGe++9d97H9+zZQ3t7O5/6\n1Ke47777ePzxx6s+R9KznN71rnfxyCOPrPydbi2vK911ynJtb8NC5a/zHcSVZiF4OzyLmt6U282X\nS/+yWGQGB4PsvLJ+UxWX1czx0UnetHF1XQd8AF22JsbTMXZ5OhlLx/Ki+53zr/FfurZgl0y8pW19\n/vnbm9o4OPYatiYro8nKco9LicZS2O1mJMHEra4P5H9uF91ktBQpNYFFtGERHMTVCCPJGF1WF6Oh\nGAjQ6/QiiAJPTZynw+Hlxj3f4jMbryMhRbnK9mb6079lh3WUl6NezKJGry2XPG8RXsLMYRLa7YA6\nY3KzZp5VGtSDKIq0tLRw+vRp7rvvvortHAvp6ekhEJi/EEgQBFKpnOteKpXC4XDU7Nty66235iwk\na3r1RUQ1pjelZjS1CsliFW7VlCD7O6oPL8xnquOwV96gciEURcFntzEYjNR9wAfgli3Igshf9F7L\nv7zuDqxS7kvueGSSa71zfUl3uNs4PxnA2WSveaebiGdwueYWVgiCgFtqmY3rSrkQw0gySofVlSsK\nEcAkStzSvIbHBoP8ZCDLjqY2Hh0+QpPkZ7V5M2fTQVRthMPRM0iCSI/1d0yr92PiIGZeJsFbkBgz\nMheWgWg0umQx3RtvvJGxsTH++3//73zxi1/kXe96V81jmUwm7rzzzpUvupUwnxlNI3uslfrOVnoQ\n56sivLCYz4OryVrUoLKaeHdprm2X18VYJFZRZd9icwiCwHqnj5Ox2bSxlJJlJBVljX3uqfMGp59o\nOI7DbWO0xgyGRDJDk8ta9jGP1Mq0OpvBEFNDjKSidFidnAlOI8gwmAiz09PBNm+Wt7R38ZVttzOe\nipHNtNEk+WmXu3gqGmQoESWrCqyyTWITfg7IpNmGhh2Z4zMmNwZLiR76WgqOHTtGV1cXn/vc5/ir\nv/orHn300fzOt1oGBwf55je/eWmLri6CtZjRVEqpUflicc9SkbI32VBVlXhk/q65+vMX83nwee0E\npyszlikcu3BnDrnbtnV+D8F4cpFXV84Gh49Tsdkd/avhMTY6fJjEuR8WWRBpyZjRHCZGkrXt3FOp\nbP5wsRSf1J7P33WKHqJqkJFkJLfTDYWxmiUOhsexiDJbmsfotrRgEiV2+kycmM4d6u1yvJv9SQta\nZhWgYZOuQcNOhk1IDAMaZuEgGW17Tes3uDjYu3cv27fn/g2bm5vx+XxzTM4rIRKJ8NBDDzExMbGy\nRXc+T93CHWGtZjSVMN9tfjUIgoCvw1PWbazQ/QsWt6ZsbXXNaVA5H4vtzPva/cTTmUVGmXst89Hr\n8HEqOhs7e25qgBv888ee/RkzEYvC+URtzmmZtILPV74VT69lJ0eTLxDMjuKYSRsbSUZptziZiiVo\ncdk5l5hmIhXh+ye7+Ntjuc4T6z0hXplKk1EVWuS1/HWzmclklnZLlKhwH9Pa5whqXwLAzEtYeIk0\nr6tp/QbLx0J3aj6fjxMnTgA54ZyYmMDv91c99tjYGGfPnuWzn/3syhbdUso5fy2FpaN+GFZ4SFaP\nqPs6vUW+uqW7z0pvnTra3SQS8xc1FK6/9MuidO197X4UVSOeXny8Stjk9HMsmqty0zSNZwPnucE3\nv+haExC1qMSUNHFlrvh/69wr/M3R38z7+qyi0tLiLPuYX+5gnXkbe+M/wy01E1ImGUlG6LS6SKSy\nbPL7eXNLL3958Bc0W1O4ZQtjyQgZqZ/VdjfPB3I2kXH1jYylUqyzS6joFWwiMe39uIX/AWTJcEVl\nb5BBTdQbHnzwwQf5h3/4B8bHx3nggQfYu3cvzz33HM8//zwAt912G2fPnuULX/gC//RP/8Qdd9yB\nw1HaxmlxCuPOKzZ7AYqzB3SBqsb5q9oc31KrRT0uXC++jlnjm/nyYStJU1nV7SGdnv951ZQEN1kt\nCAKcGg+yvbut7HMKKXxfyrHG7iGWTTOWjBLJpjEJImvLxHPzxDKEzCa6rB7OJ0JscjYXPfxicIhD\nkQk+EA2w3ukreiyXNaLROo/oAlzneAf/J/i3XGl7E+OZMWJKNwKgKho7O9q4Zm0nXzw9xWq3xmio\ni59PvIrVaecd7Vfws7GT3Ny8llejV+E2/Youe7FPbpJbMfMqSe0mDNPypSUej2O3l7+jqYQPfvCD\nCz7udrv5sz+r36zIarXS1tZGLBZb2Tvd0sOfpWwwWbqDrieFqhRfh4fJoUDd+bCrV/tRVY10Olv0\n89Jc4Uo7QthMMkdH5/dgKPReWOzLSxQErvZ28nzgPHumBrjet2rB+bPRDAFzlk3OZo6E565hOpPk\ntpYevnjyuTldKXIOYxTFdPvj0/xk5Hj+7zbRhVmwIQsmRpJh2i0OXp4eAU1gQ4uPJpOF96y2c4Wr\nhWua7fxo5BC9piu5pXktB0KjRLIpXp6ewCk30W0r/kIAmbD2KdJcu+B7YlA/9XrpLjX67/imTZt4\n//vfTyKRWPmiW2izuNSFDY0Q9dLdtaZpeNqamBoK1hwX1nE6c7vT4eFQfmw9bgvVfym5bVbOTJbP\nrNBDFLroVvIl8XttG/ne4CEeHT7Cm1oX9pZNhhOYXRbWO7xzukhkVIXxVJxPb7iB/sQ0E+niXN6J\nyRiCAHKBEfmXTz7Pl049TzQ7Gy5xSy3E1RDJjJs2q42nR3NG56u8udvAKWUIv9xJTHoOk2BDylyJ\nTTKxvamNfcFhngucR9M0VtdQMWfQGOrx0l0O9M/Hvn37GBoaorm5eWWLLjSmm28lHgONNr0p3D37\nOr1Mj4YacthnMkmc6Z8s2oHW+mXR6rIzGCzOHihNW6vm/b/K08FtrT28ubWXza6FGzTGwwn8Xhet\nFievlYjuSDJKi8WORZLZ6PAzVNKVYnwigiTN/mp/4+w+Xg2PscbmLvL1dYu5eG4648Nn1tg/PIIg\naEyrIwBMZM/TIq9iWh3jBu86js84pb25tZd/OLOX8VSMQDrBKtvFZbZyOREOhy9a34VC9u7dy/79\n+4EVHtOtV6BKX19LB4pq0eO2+iGcJEk0d/kIjNbXVFLHbjNz/nxgTteJWljlbeLF/uH8ugsr7HQR\nr+aLRxAE/nTtVYs+T9M04uEEbb5chVFKVRhLRmmb8Ws4l5jOC12XrSmfU6szNRnDbJr9MjgUHufz\nV+zOlfUmwvS5cuGAJqmZkDpJPO3CbI5gSclk5AyDmZM0Sc2ElUkcgpssaTa7OvldIJdmdmvLOk5E\np+hzNvP/nPodXlP5fGCDpSWVShEOh2t2GFvMdwHg5MmTPPbYYyiKgtPp5GMf+1hVc+ifveuvv54D\nBw5w8uTJlS26paY3tYrLQgdYjUIXLX3NhXP4OryLlgLrYyxUdqwoCi6XhfGJKJIk1W203tvs5amj\n/fkwQqmv8FKRTqSRTDLtThdj6Rjbm1o5GB7Pi+7R8CQbZw7PuqwuhpLFaWWBYByLdfZXeywVo9fh\n5Wi0qSgFzS21MJ46x0SyCatpEiHtw2JRGc6cxCO10CqvIaxO4pFa2Wj18+D5XN82SRD5WM/rORAa\nZY3dveTvh0F5vvGNbzA5OYkoijzxxBN0dXWxefNmrNbKvgSvueYabrzxRr773e+WfTyRSPDoo49y\n77334vF48g0mq0H/zPb39/Pkk09y6NChlR9eqIdCz9mlMr0pTf8qt/tsanaSiCRJJ+fPi11IbAsP\n+fx+J4GpxSvJFppH/xLa1OYjmc2SVZQl7ahcSiyUwN5kY5WtifPxENua2vIhhvOJEN8ZfA2/KXdI\n1m1rmhNeCIUS2Gbap2dUhal0nDaLk25rE4MFAu2V2hhOjTGeSpMijJLK0uZoYjhzijOp11hl7suF\nGKRVrLa7mUzHCWdmq5GOR6fY5Kw8Z9OgsXz84x9n48aNrFq1CpPJxIEDB/L9yCqhp6dnwcyH/fv3\ns3379ryng9M5fzbMfOjnM2vXruWee+7h+uuvX9k73XrQb5VhNvWrGkEpZ15TyHy758J5dURRnDEz\nn6Zt7cKxzsLxy93ut7a6GByszypSX3en24EATMWTdHrMFb/2S0+9yCd370KWavtOj4cT2N02Vtnc\nPDM5wDs7NvHQoUOstrlJzOTsjqZyh2ddNleRkAKEw0kcjtx6c/FfB7KYS1F7dPhI/nleqY3+aIYr\nnH6sGihp8DXLOEQ3J1P7+APv/bwQ/ylt8lpkQWSnu4MXg0P5Q8BjkUl2eTtrukaD+hEEgVAoxJYt\nW3jjG9/Y8PEnJiZQFIWvfe1rpFIpbrrpJnbt2lXTWDt37mTnzp0AK3unW4unbmlpsP76Ru7gSosP\nKvJgaF/cbQwWN9Tp7HQTi9dW0FAYAhFFEY/DjiAInJqobF2hRIo//d4v+NWxfg4Mze3XVinxUBx7\nkw23yUIkm2KD08+H17yOfz67j4eHDuORrXkjnG5rbqdb+O8fjaZwzfguDCcjdFlzBy3rHT4GEiFS\nSu6AVBQk0pkWOuwysYwJNSMSsR0mqIzillpwSB5Gs2dpM+Vcwq71dfNCcDA/z7HoJH3O0nQxg+Vk\nKQ/SVFVlcHCQj3zkI9x77708+eSTTEzMbe9U6Vj5TVKD13nRshylwfMZ0lSCr9NTth174dj6+AsZ\n6qxd7SeVys4zyvxjF2ZrCIKQf2+cFhNHF+iXVjj/jw+coH+mdfueU+erWkMh8XACR5MNl2whMpPi\n9fsdffzrle8gqWa5q3sL5+K5LwKHbMYmyUylZ70r4okMHk8u/DCUDNM5I7oWScYtW/j+0KH8c8Mp\nLy5LiMlMBDUr846u3+Ma+zsIK1NMKxMk1RjNUhcA13q7eSEwiKKpTGeSBNIJVi9U4GGw5NRzkLYY\nbrebvr4+TCYTDoeD3t5ehoeHqxqjtJBKFMWVLbqVWjoudWlwuTmqFfSFDtMKy44Xa7/T09OcL5BY\n7A6gnP9C6br9Tjsnx+f3Gy3k8MgEPoeVZoeNZ08PEl2gz9pC6DFdl2RmNBXlhZmsAY/JgkmQeHfn\nFQwno/kda5etOFabTGbw6qKbiNA10xo9kkkxno7z6PDR/PUPRDWS8otE01k0Dba3r2Wn/U00y138\nIvyvrDNvQ+8a0W514jPbOBaZ4neB8+zydCILK/ojtOKpt1XPQp+Pbdu2cebMGVRVJZ1Oc+7cuaKW\n6pVQ7nN6SfzGzGd6U0lhQ635tvrrqi2emG8+Xxkz89LdeeEOdD6sVtPMaenUvM+B2TDLYgeIq7yu\nObm6ZcfTNI6PBehr9SGJAtf3dPHvLxxa9HXl0GO6DjkXlz0cmcj//wpXMzbJxDqHhyOR3A682+pi\nqCArIZ3O5n0XhpIRuqy55Pn+xDQdFifTmST/PnCA+4/+Bo/Jxls970aN5XZLzY6cWF/juIMmyc81\njjuK1nadr5unJ8/y87FT3NK8tqbrM2gc9ex0F/NdaGtro6+vjy9+8Yt85Stf4brrrqO9vb2qOZ5+\n+umiv09OTl56B2nzdWxYijl0GjGHr9PD8b2nisYvPCSrJv3LYpE4fXaC9evnHspV478AsL7Fx+/O\nDC2akjcwNY3LakaWRALxJO/Ytp7P/fx57rt5Z8Xr1omHcztdURC4b90u+mdCCS8EBrnKkzu4utLd\nzo9GjtJmdczsdGe/GLJZlbbWGaGNT7PWnjt9Ph6Z4mpPJ09NnOGHQ0fY3bKOD67ewbHpScLhkzgk\nE9JMOKjT1EunqXfO2t7TuZkP7P8xfrPNEN2LgEQiUXGKWCmL+S4A7N69m927d9c0fiKR4JFHHuGW\nW24Bcj3dHnzwwUtjpwvVd2yodY7C23FoXPmxryPnNLZYJVwluFxWBgaK48O1+C8A9LZ40TQIxMr7\n/cZSaTKKwo9eOc4NPd2MhGJ0uZ1EkmmSmSzjkerb7eiiC7CtqY1Xw2NMZ5L8duocb5wRut/v6ONA\naJT/uv8xOizO/E43EEmgadDW5sy3fe+cCS8ciUywuamF3S3r+PCaK/nL9dex0ennYHgcMSniMJsW\nXVurxcH3r34P/+/r7kBugNmRQf1crHnSsViMeHzW3zocDnPgwIGVL7qFBQfVdGyohnJG5Y10qtf9\nFwIjwYZ8Yfh9jiL/hXqEvNvrQhKFOR4Meujj0z/6T9769UfYc3qQO6/qY3A6wtWrOzgwNM6GVl9F\nmQ+lxEOzorvZ1Uyr2cEdLzzE7uZ1eQHtsLp44tr302pxIAlivrz3v/3oaTRgLJFgIBGi2+ripeAQ\nJ6NTvBIaZXtTGxucPk7HZuPUk+k4QlLAa69sx+Q1z4Y+DAzmo9QBLZVK4fF4Vr7oVmu6UspiMd3F\njMrr9fPUxbypxUV4KopA/elrHR1upqaiNfkvlL4fnW4XGUXl1EQwP1bhjjmSSnPT+lV89q3XE0qk\naHbauHZdJ6+cH6en2cPZqVDV16PHdPX1/HnP6/GZbNy7bm4J8Uann3AmxWAyTDqbZeB8bp1feGpv\nPrTwqcNP8Yev/ASvycpqu5sNDj9HI7PevieiU3SLbjrc1Se/GxiUon9+otFoXisymQwTExO5TeGF\nXFwj0G+Rq22LvBiLxT7rFUY9ZquPL8syLp+T6fEw/k5vXWOvWeXjdy+cLSo5rhWzLOGymjkyPJH/\nAtLDE8mswmgoyj/fdTsmSeTx107S1+Znc0czk9E4Flmif0Z09f/01LeDQ+M8e3qQP75+x5wiisLw\nAsB6p4/Hr72r7Po2Ov0MJENYRJkfHjyOCdBEOBcI88r4GGtdHuySifvW7eKKGc+Fza4WRlNRJlNx\nUmoWqygRiqdYs+HidasymEs2m22In3Wj0T936XSa8fFxPv/5zyOKIpFIhGAwuPJFt1AM6/Ff0CkU\n22oM0aul0KVLH9/fmWvbU050F6uAK1x7b6+fVCqDJEkN+TLqdDs5Mzmd3+3rDT2fPXmO7V2tTMYS\nhOJJnj4xwHt39mExyXzw2q08dfQc04lkUfhHv5anjp/jZ4dOk1ZUruvp4vVrOvLPiYXiODyVufNv\ndPp5Zuocm20tPPjsYVxWFU2E1e0uXjk3xgev2opTNvOOjk3518iiyE3+Nfxk9DirbW422v3sz0zR\n21x9C2+DC8PAwABTU1O0tFRWwVlKJWY3+jxf/epX+dCHPsSOHTsqGlv/Iti4cSN//dd/TTQaJRqN\nkkqlSKfTK190ob5dZ+Huq1xZbaMoHB8o65vrbfcwNRxk/VXrah5bFEV6e1vQNAiHE/ly2FrXrCgK\nvc0efnHkLOLMmlVVJZFO8+ALh/jwddv4x9/s48VzI1hliWvXdSIIAm/oXcU3nnmFjKqSVTVMUnFI\nZiqa4AOv38J3XjzMr46e5Wf33Qnk/j1iwTgun6Poy3RwOsJ4OMbO1cUpOxucPk7FArzdsomDrim8\nYSthKcG0FCc8mSGtKmwtYyP5wVU7+JMDT7DG7qZPasEkBWlzVd+GxeDCMDY2xvPPP4/ZbOaBBx6g\nq6uLW2+9lZ6ehX2adRYzu4Hc3egTTzxBX19fTWu02+1ceeWVc35+8e3Nq6TemKr++loOmirJ8S2X\nVaG/thRfZ2VuY+XGLly7LMvIksjxE7WV4pbGbde3+pBFkbFwLB/f/vpvX6an2cP1Pd0cHJ7gf995\nG/9w522YZg4YPXYrfqedZoeNgUC4KMQgiiKDoQi3bFzD9//wnVhNcj7Uomka0ekYDneuBFnVND7x\nw1/z6cee5tOP/5aJaKLovXPJFjqtLpIxhZQlQ2AqjmrSCGkpxJjI4fAE291zE9o7bS4+uu5qDobH\ncaTNKJpGp8eI6a4Udu3axa233orL5eK+++5j165dVRnSLGZ2A7Bnzx527NhRk9GNTmH572VXBlyK\nvjustNNureMvVrZbiL9MgUSlY5eu3e4wc/rM/OW7C42r/1lfc7e3CVkSOD2e8+kdnI7w7OlBPrF7\nF6cnp2lvcrC1s4UNrcW9ynqbPXjsVs5MFV9TRlEYC8fo8rhocdlRNY1QMo0oiihZlUwyg73JhqZp\nDE9HODwyyVWr27lqVRtnZ8YSBCF/t/Dm1l6eOX8ed5MFa0rC6jBhzZpwOEzs6Z+/Aebb2jZw37pd\npGMqkiDSZLVU9X4ZXFhCoRAej4eWlhZe97rX0drauviLqhj74MGD3HDDDXWNU1T+eymUAUP1pjfl\n0r8azWKGN/Ot1dvhITCysENYpWY6Xo+d81W4jenjlh6+ZbNZut1OsorK8bFcldtzpwd5Q283TTYL\nBwbH2N5d/pe9w+3EapLpL0k308eB3HvR0+zl9Ex2RDycwOG2539B+wNhrlvXxf916+tZ7XNzPjBb\neabvjt/btQVLSua/rN6KmlLZsqqNbtwE3TGcARvtVmfZ91sQBO7q3srZyRB+h2FEvtKotwR4IX78\n4x9zxx13LMl5zooX3WpYLP2rXuoxvAHwd3oJDJff6RZaRVYydlubi7HR8LyPz7dmfUz9Z6qq4ndY\n0YBXBscQRZHfnBhg96a1APzu7BA7V5UvjdRv10t3uj/Yf4yMouaFtqfZw+kZYY4GYkWHaGcmp1nn\nz5V5rvY1cX46ks9YyWdEKBqppMqhs5NYEFm/ys9QIMJDb3435rTMbf/4ML882j9vMcjgdJRu78Xf\n8tFYiIkAACAASURBVMWgmKVsSnn+/HkefPBBPve5z/Hqq6/ywx/+kEOHqitrD4fDBAIBwuEwsViM\nZDJ56RykLUa1pa+1jN+IQzhfx1ynsdIDuEpzkdeu8XP4yEjVa9ZTwgp3hoIgsMbXxKnxIEdHJgnG\nk2ztbOad//xDrCaZ69Z1lZ2j0+0ins4QiBUbS58cD+Iwmzg+HuCKjmZ6Wzy8cj5nUh6bjuH02vPz\nn5mc5uYNuc7Bq31u/vPEufyadM5PR+j0OHltaBxPVmPdWj89mQnGAjG+8p5beWj/UV4dGudtW3vn\npK+pmkYgluDWTeVDEEtJrb4fBjkikQhdXeV/9yphoff+/vvvz//5e9/7Hlu2bGHr1q0Vjat/Vh98\n8EFOnz5NU1MTJpMJi8WCzWZb+aK7UHhhOdK/Cv1n662E83V4CI6F8v3NCvu16elflY7ft6mdRx59\nJb9DLswEKDVX1zMS9Peq9D3VQwAacP8Tz/DGjWs4NxUikkrzzbvfgiSWb5fU6XYyEYmTyGSJJNO4\nrGYUVWUiGuf2zevYPzCKRZZocdrzu97odC5dTF/D2akQH75+e86wOp7k6MjUnLnOTE7js1tpspgY\nU2L09rTwumSQfefH+JPV7bxz+wYe+Omeot8P/c9j4RiyJLKu2ZsPNVXaVr5eDMGtj3A4XHNmwYMP\nPsipU6eIxWI88MADvPWtb80fHF9//fV1rUu/W3zb297G2NgYiqKQyWRIJBJEo9GVL7rlWI70r0Jx\nalTZsdlmxuG2MzUcxNPWlBfFWnZEPT1+NE0jFE7Q7M95CpRLWSv06NWvDSjaEQKsa3ajahpbOlp4\n/67N7Dl1ntuvWEery172tQB+h5VIKs2GFi+nJoJcuaqNE2MBNE3jg9ds455v/4TnTg+yc1UbQ9NR\nUpkskUAUh8eGIAiksgoTkTirPLlbyEMjE2RVlX0DI+xaM9ux4fREEKtJxiGYGAN8Xjtv6lvHR7//\nS46NTvKH1+9gOpEinEzjsVuL3suBQAhJEOhyO+fkQeuZFvq1Ff6bG1x46nEYq8TsRufuu++uaY6e\nnh56enpIp9OcPXuWTCaD2+1e+aJbunvR45CFgrUYhTu6xSisyioVpkqZT0Q1TaN1TTOjZ8bwd3rr\nCoPY7RYkSeDkiQn817rKhlcK07T09fzBB/+d9b3N/N3fFlsarvW5+d2ZIf7Xe98EwKHhCa5c1V4k\nSoXXASAKAh1NTtqbnBwfm2JrRzNf++1+Wlx22poc/N0dNxFOpvnH3+yj3e3gXCDEucFJftk/yL3A\nuUCIbq+LP/nuf/BXt13L0dEpzJLI86eHikT35EQQl8WMJSlgMs30pPK7+aPrt/PTg6f57ckBtnW1\n8OK5Ed58xbqi93QgGCGtqPOmixUKceG/R2F+tyHEF4alPEhrFA8//DB79+7FarXm3QJXvOjC0u08\nS+coFa56u+2WG7t1bTOT54MNOeBz2C2cPDXBrl1rinb8hSXIhbvTcDgXez11ehJFUZEKCho2tvk5\nMR5AUVUkUeTQ8CQfuGZb/vFyJdKaprHG14TLambv2WEmo3HGI3HeuqUHVVXzVWjfffEQbU47Zyan\nOXFuHNUq0z8V4thogA63k+dOD/J3//Ec0VSabV2tvFbQCkjVNM5MTLOhxYs3KmOzzRaDvOfKPrZ0\ntPCFJ3/Hn920k//9m33csmE1Znk2Y+XURABZFGiyWooEVV+/Tqm4lt4R6H82hHj5CIfDS3aQ1gie\nfPJJnn/+eW6//Xa6u7sxmUwEAoFLQ3T14gCoz2egHAvFhesV3nJhkPZ1rYyfmz+/tpIduf6h9/rs\nDJwPFoUSysVt9f+fLTA+7++ford3tpLLbbPQ7LBzdjKE22Yhmc2yapETf0EQaHc7cVnNjEfjvDY0\nQavTzo6u4hSzLR3NRJJpDgyOcbJ/jOZVXn59vJ/xSByn2cTNG1bz2tA4775yE16blW/seSX/2pFQ\nFIfl/2/vvOOjqtL//56SSe+995BAgJCEHqQJiigoggprwXV1+emqX8uqX1fXVcFddd3V1a+rIjZE\ncFFRWREVparUEJJASCGNJKT3ZFImM78/4r3MTO5MJsmkEPN5vXgxmbn3nufce8/nPOdpx4bypha8\nG+1xcTYM/Yrx9aCutZ1wTzfCPF35T2oWN0+Lv+ioq2rAx9lRclVhaiIxvs/6fwvHShGx8fljGBgG\nc3+0gUKj0fDVV1+xdu3aHllpoyJkTCCV/mq35pxwg7HNjzAIpZIbfMO8qSjsufmdpe0K5g+tVkuA\nvyuVlU1iW0J7puy2+QXVqFQK7O1tyDjdM/IhIdiXY0UXSC+tZIK/l0UyuTvY0dTWwYZlc/n36iso\nqmsg0tvNoP3ZkUGUN7awP/c8jl1QrulkZ3oex4suUNuiJinEl+2/u5a1MyZy5YQIOrq6yPxF282r\nqiPCy42aFjXNDW24exhmGcllMiYH+XCqtIJ75ibxnxNZNPxSD0Kn03GhsZlQT1eL769xZp3+TtL6\nURHCP2FFYXxOf01TY7iIrq4ubGx6r4E8HGhtbaW+vp4pU6YYjLtRk5EmvMzW0CL0ydZUttdAoL+s\nl0pu8A3zoqKwb5lkgtz68bYKhYKICC/qG9Q9ag6DNImf+8Ws0NWl5cCBXLRaw/s5JyqYvTlF7Msp\nJtHPxyItXyGXsT31LJ2aLrIravB1dhSX8sK/GeGBaLRaNF1ammpbsHW2xUYhp7VTw5nyGmb9EpKm\n0+lQKeQEuTnz5/8epL1TQ3Z59zUDXJ1oaGwTd4zQR0KQD8eLyvFzcWRyoA8/55cik8moa22jS6sl\nwgqFbqTI2BQRG4fjCc9L/5wxmEZ2djb//Oc/iY2N5eeff6a4uJjOzk6Lz9+6dStPPPEEzz//vOTv\nJ06c4IUXXuCFF17glVde6fNmlNBdO9fZ2bk7LlepNBiDo4J0rQFhFupL2m5fr6+/3xkgabc1pema\nu67UjhA6nY7oSG/a2zV0dPSsNKY/8wra8dmz5Tg72+Hqao9SqWD9c7sNzkkI8sFWqeBE8QW2/+sw\n/37zEE1NbWRkmn4phRje1PPlHCkoI/EX55v+fVXI5by++ko+WHs1IfYOPLVqPpMCfYj19eA30ybg\n4eRgQEiXRQfjaKtie2oWWeU12NsoCXZ3oaWlnYAAlx6T75yoYH7KL6GprZ2UyCAO5pUgk8nI+SVe\nOMR9cOyCxs5WqXfJnEY8RsTSCAsLY9myZTQ3N5Ofn8+2bdv44osvLD5/+vTprFu3zuTvnp6e3Hvv\nvTzyyCMsXryYjz/+uM8yOjg4MG7cOF599VU0Go34LOVy+eiw6VoaeWDufIG8+pKlZkkol5TdVvhO\nCs6eTmg1WprrW3AyU95QKt5WkEew2/r7uyCTQXlFI6EhHj3ukb7seeeqcHBU4efngo+3M56ejmz/\n5CT19a24unaHcCnkcl5edTkarZa1hzdz6MdzpJ0qobm5nc3v3YZSKe9x/QBXJx5aOJVNP6VT26Lm\nhRXS+03Z2yixd3Wmo1FNfWMHf16a0uMYQf44Py8ySqvYcuwMTrYqxvt7EezuQl57F6GhHj20SQ8H\nOxbEhHLtm5/x+e+v57X9qTS3dXC2ogYtEDxIpCtAyqRj/Lu5z/r2Yf137tfqsLO1tSUiIoKSkhJe\nfvlloG+28oiICGprTe9wHRYWJn4ODQ2loaGhzzI6OjqydOlSNm7cyBNPPEFsbCxubm50dHSMPk23\nLzffOEa1r2m7vV27P9vkyGQyfELNmxikdvIFxBRnYTA6Odkil8s5d65asl19DSzz9AW8vZwI9Hdj\nSkIwR48WEhbqTn5BteHEIZOh0+hQKmU8+D8LuP22GQQHu1NUdNEJp7+M3rL1GNriNuJd3ZnY7tTr\nUr6hupk33zlMZaXpHYjj/DwpqKnn/duu5oPbrqahrR1PO1t0Oh1hIZ6Sfb1vfjJhnq6U1DUy3t+T\n1PPl/JxfQkt7J4GDlAIspcH29hwstREL5+lrxAPxa1xqaGtrM9iQcrD6ffjwYeLi4vp1blhYGHfe\neSfjxo2joKCAI0eOcPTo0dGh6ULfbrqx9tmXOF1Lri2lgfYFvmFeVBZVE5kQ2uM3gcR7i7cVfnNx\nsSXvXBULF4zrcS19ZGSWYatSEhDgSsLkQI6fKObgoTzyzlUzJSHYoH9Hjxei0ehQqRQEBrgSHubJ\n2ewKIiK8xGME7Pr6DAD2djao2zrpXNeFjY10kaHO9k60nV2gkHP0WCFXL50oeZyHoz2OKhVtnV34\nONvQ0NpOp6wTmQzs7Q0dK/rENSXIl1OllUwN9eejY6epaGzFzcEWW4WhP2Cg74GU/byv17QkcsL4\nszGxmzp+NGAowsVyc3M5evQo9913X7/Ol8vlYoIEdO8koVKpRgfpWvpCGZOtQIj62sNAYLwFT3+1\nZr8IHy6cq+ghN1wsaSjILRVvq/+/t5czJb1UGzv04znOF9cRGOhGQIArSqWCdXelkHm6jOzsih4E\nkJXVLdvhI4Xs259LUKAb6rZOllw53uC46upm3FzteeLxKykorOHTHWlUVjYRGCit7WamFYNKCTIZ\n2TmVXL3UtMxxfp6cLa9GpVTwY34JIf62qFRKk0Qnk8mYHOzLrsw8nroqhbyqeqaHBXL6QlUPM5GU\ns9FS0uzNlDAQ9IWIjc8bbaaJwU6MKCsr4+OPP2bdunW91t3Vh8AtaWlp7Nu3Dy8vL2xtbfH09MTR\nsTu9fdSZF6Qg5SQzjrcdyAsoXNvSCmC9tRcY409ZTnmPSAq46HwTwsKklq/6gzM4xJ2qqmaTbV24\n0MA77/3M7+6YRWlpPSHBF2vihgS7U3y+p+2rO4bXi337c7GzU1Jb10p2drkB2eh0OvLzq/EPcGXv\n/hxmzgjDyVFFQWG1ZN/rG9T8sPs0No52ODqqyO+lFnCcvxdfZuTx8fFuTVrT1IGjg8rsMn5yoDen\ny7qJ+rErZqJSKoj0drd4WS+1ohCg//1QhoP1x1knnHcpmyYaGhr6nQIM5hW1uro63nnnHW6++Wa8\nvLz6dF19J7mtrS1qtZq6ujp27tzJtm3b2Ldv3+jSdKXMBPppu9Yu5ShAar+zgSAwxo9db35vEJEg\nk8lEYpeCqXbjxvmxd2+OSfNJfkE18RP82bsvl+hoH1xcLtrJIiO9ycgso7GpzSDpoLKqmcWXx3L7\nbTNwcbbj9TcPcv58HdXVLeQXVJOReYG7fjebwqJaOto1fLXrNDHRPuSdq0a1P5dZMyN6aJMbnttN\nyaki7GxtCAv1IDe3iuaWdpwcpQuLTw8L4N8HUmlUt/PBrUvZvPEwrr/UbDAFV3s7fF0cya2sI9bP\nk7MVNSyIMTTh9GdZb3z+cJCXNZx1l5JGPBBNt7diN9988w2tra188skn4qr1wQcftOjawn0fP348\nERERaDQa3NzcePHFF/Hx8eG6664bHaQrBX0vvrXLOeprPiC939lAru0T7sWFc5U9TAnmNGR984MA\nmUxGTHT3fmlVVc34+PR8Sc+fr0eng8qqJp780xKD34KD3LCzs6GgoIbJk7pDvzo6NKjVHcTH+xMe\n1u20mjUzgs8rTnE2u4LPv0invKKRO26fSW5eJeUVjUyMD+Cfr+zFxcWO8vJGAxm7n5OWmtoWrl86\nni83/0xkpDflFU0UFtQQHx+AFILdndlz301iP2vrWvH3613zmRzkQ1pJBeN8Pci6UM3dlyX2eo4p\nIpZ6FvrartT51oY505I+BmojNu73cBNxQ0NDv0m3t2I3N910EzfdJL37tKVQKpUG2/xotVqCg4Nx\ncXEZfeYFISLBOG7VWk4yYzMFWGdQ6ZsSHJztcXJ3oLa0Xvxen+RNebp3/ONrvn//oMFy0sPDAbm8\n21EmNVAKCmuormlm5YopeLgb2q6CAt3Rdmkp/CU9WKfTkZPbbc+NjvIR2509M4K29k4++PAoFZVN\nKJVyDhzMIze3Cm8vJ/5w91xmzYxgxfLJNDa2GQxkuVxO2YVGZDLY+dlJAsO8CAhwRWWjoKCopsey\nXv+z/nUaG9vw8+vdsTI5yJdTJZUU1zVio1Dg59L3zSiN76PQj/6aJvoLKe22L++i8XOwpA/6/R3O\nOOKmpqYBmReGCsLzaW1tFUl4VJCu8cshkG1fl/vmBoNAfvq7Thgvxfort1RoWWCMP+ezyyyy2wp/\nf/mvb3nvf/9jMBDkcjnu7g6kZ5T2IACtVktBYTXl5Y0kJQb3kM/X15n2ji4xbEyn0/HzzwW4utij\nUl2MynBwUPHk40t+CWGT4eCgYuOmH+nUdHHbLdNxcbHj3nvmMnt2BBqNlvr6VoN2ysrq6erS4WZv\nQ/KsSIIC3Ojo7CInp7IHAUg9J51Oh1rdQVCgBZpuoDeZZVXsTM8lOdSvz2RhbLvVn9D7Q2L9IWL9\nycdYhoGir33Ql1sg4oGm5VuCkV5hrLa2lurqaurqup3YLS0tYsryqDEvCKQlLMn7AnMvh76Zwlp2\nW+F8/SLlxskNAVG+lGRfYNL8OPGc3toNjPGj+HQp1edr8dJziIWFepBfcDFWVxg0VVXNaLt0BAW5\nYWtr6PkHUCjkeHs5kl9wsXD4ybQSJk7sueQPC/PkuWeXoW7rpKOji80fHuHO36UQFnpRDicnO2xs\nFJw4eZ6F8y+GsJWWNqDT6Qj1c8Y/xJPAQDcaG9WcPnMBtbpTMgxM6IdwHzs7tYT9khhhfJw+XO3t\nWD45mj1nC3np+oVm76c+LF3GG6O/y3qp840nnaHSMI3l6c20oi/bYJkmBupIGywI0QtvvvkmlZWV\nODk54eDgQFlZGbt37+bkyZOjg3R1Oh02NjZWK7UoXHOwCqHrk4WpeFv/KF/y04r6NLBqy+oZnxLD\n6UPZzF09U/x+woQAUk+W0NWlRalUUFFQhVar4/jZSgICXQkKdDdJBKGhHhw/UUxnZxdqdScNjW3M\nmR0p2b6//8VBsOHZZZLH+Pg4kZpqSLpFxbVotTrUTa24eLtgZ2eDj7czrm72HDlayNzLogDpMDCA\n6poWAHx9XSwisd/NTuB3sxNM3cYe6M1J1Vf0h4iNzx/q5bwgi7mJx5I+6Mve2wpGClqtlv3796NS\nqVCpVL0eb4ytW7dy+vRpnJ2defTRRyWP+fTTT8nKykKlUrFmzRqCgoIsvr7g21m9ejX19fW0trbS\n3NxMYmIiNTU1NDU1jQ7SBQyIayDQX/Lra6DWgCkHnFS8bWCMH4e2H7W47bbWdjrUHcy8NpnTBw1J\nNyrSG4VCTlFRLZGR3rzwm9epLKzG7poZhLqoCA4KF18W4/sXHOxO1tlyCgprOH++e6kUFu7Zw1lk\nKRImB3HgYJ7BdyWldXh5OtGQfx5X7+4lY0SEFwqFnB2fnyIuzg8/X9P22sKiWuRyGba2FzXi/mqT\n+rA22ZqDKSIeCc46Y1lM3QtLJhNzz8ISU1JnZyd1dXU0Nzfz4Ycf8uWXXxITE8ONN95oUT+mT5/O\nnDlz2LJli+TvZ86coaamhieeeILCwkK2b9/OAw88YNG19RGml0psjFFh07UGBMI2Tq/t7WW2xKYr\nZbcFw/he4xjKoBh/ynLLLZr9AWpL6/AIdGfSvDjS92XR1tIu/hYU6EZXl5ac3O5yiApVNznNCXAg\ne8sBbFtaRTn1+yWXywkOcsfRwZY932fz7Z6zODnZ4uig6rddctHlcTQ1tVNd0yzeg6qqZoKC3Gio\nasTFq3vbnOnTwjhfUse0qaF8tiPN7DXzz1Xh4GCo9ZizTQp9NWdfHUrCtQT6tltr24jNYaD2495s\nxEIbxnZi/XP0V4N2dnasWLGCnJwcnnzySe66664e9WrNISIiwmyyQ2ZmJsnJyUA3carVapqaTKek\n9wapZzRGuhgGj1t7a3appAwwbRsTXkQnD0cUSgX1lb1vow5QnFVGQJQvnoHujE+JYcP1r1BZ3J1g\nYGdng4uzHekZpQA0N6pROtrx3Zs/YBvoSfnpEsmBdfZwHj9u/AFdl5bUf39DR1ktkyYGmh38wiRi\navD7+jhjb2fDnu+zge5QNpVKia+3E20t7Ti6dQ+I8XF+tKk7ae/QmK1iBt2arqdn71EIfRn8Uhrx\nUEGKfIy1QUsdXQMh4sGaeKT60Fs/AM6dO0dlZSVffvklZ8+eRaVS4evrS0xMjFXkgm5bsbu7u/i3\nm5sb9fX1/b6eZF+tIehIQH9mdmMN1JqZOfoEpB/toG9e0IfUyxYQ7Utp9gWLBk3eiQKiksIB+O3z\nN9HZ3sm3b+8Xf4+I8CS/oDsEq7mqEc8lScSsnsMtTy7nxO50AxkEHNp+lBM7U6k4VUhHdSPK5lYm\n/hI3a2rw/2nh3/jo6R1mB79gJwYoLq7Dzs4GJwW4+7mKE569vYo/P3EVP/6Uj0ajpba2xWTfKyqa\nCAjou1NFytljjMHWJE21ZyyfOVibiAeq3fYXxv0wbvPEiRO89tpr/PDDD9x88818/fXXtLa2mrha\n/2DKhm5NjBrSBcvTeaWW+4NRXUywCetvlSMVAmYc4iX8HRjjR1lehUWDJv9kEZFTurOrnNwduef1\ntaT9cFqUK35CAC0tHWSlnUenVFCr1nD/U9cw5+rJKBRy8o4X9HCKnPkph6vvuRx5djdBVpd2L/dN\noaGqidKccioLq80O/oSEQMrLG2lqUlP4S3UylVaLV5Bh+UkXFzsmjPfHw92B/ALTacH1DWoiwvqW\nrqkPY6LT176GaklvbaLrLxEbm7lGgqYvl8vZs2cPH3zwAYsWLWLDhg0sWbIEZ2fnfjnTzMHNzU0M\n8wKor6+3epTEqHGkCQ+ot2ME8hMI0VovlYHNRq+YjtCe8aDs7YWWyWQERPtRlltudsddgC5NF0Wn\nSwibGCzeB/9IX2pKatF0aFCqlERH+WBnp+Tl53bh6O2CrZMdLi7dabNz18zkh80/EjjOn9/HPsLb\neS9RmlsOwKrHrsHBxZ6C0gZKjuRiZ2d6e5RzJwuxd7YzMIno91H4HBPti62tgtS0EoqKa2lr60TX\n2oZnYM+Qrwnj/amqbuZcfjXJST0JX6PR0t7WSVycr0m5TMHS5bNUH0w9CymzhLnnLBw/FERnrh9S\nk4Yp09dgwviZNDc38+STTwKwZcsWsbLYuHHjGDfOfOU8c22YQnx8PIcOHSIxMZHCwkLs7e2tHg88\nakhXH1IEbK0KYFIwF287kEEVGO1H6jcZ4t+mBs2pH87gF+GDvbOd+NLKlXI8Az24kF9J0Dh/goPd\n6OjQ4K7V4B7jh0vYxbqz81bP4sGZf8Hdv7v6V+q3GaT/cIa5N81EoVSw7L4raGlo5f6kJ8VJRQr5\nJ4uYc8N09m/92eRxOp2OsFAPOju1fLjlKM3NHSiVclprW/AO8uhBYLGxvnz+ZTrn8qsln2tRcS06\nMCjU0xusQXTWIOLBspn2BcarQ2MZBjKh9AVS9+LQoUM899xz3HfffSxatMgq7fRWd2H8+PGcOXOG\n9evXo1KpWL16tVXa1ceoIl2pF0Cf/AarBgMY1l8wV9+2LwiI9hM1TlNoV3fwziPb+P3LN/fYJy4g\n2pfSnAsExvhhY6MgKNAdZ40arasTUZEXl+OObg4suv0yPv/nbpbffwVbn/2czrYOblm/8uIxrg44\nujpQfb4Wn9CeS3mdTsfJ7zK5df0qju06RXVJLT4hXga/C7LZ2dkQG+tL/Hh/HBxUfPX1aWrL6hg3\nPbKHVh8c5I5G00VebhUdHRqxFq9wL9PTS7CzU5qs0Ssl52ARXX+IeDhhyb2wtmZviRxqtZoNGzZQ\nXV3N+++/j4eH5RNqb+it7gLAypUrez1mIBg1Nl2ppZGUbdXcEtLSgWB8bUCMtzVXcrGv8Ahwo721\ng5Z6086CY/9NIyIhhEnzx4v9EP51mycu1uVNSgqhMP08F1o6iY8PMJDz+j8uZdO5l1j56NUs/X8L\nufetO3B0NarFEOvP+bM9IwkK0s9zS8C9tNS3EjMtgqAYP0pzysV7JTW4L0uJ4vDRQoqKa3F3c6C6\npBavoIuDSzhOoZATF+uHu7sDaadKe9gkc3Ir8fBw7LNzaKg0y96cQ8byDYWzTmivL5NPf23E5voh\n9UxOnDjBqlWrSEpK4vXXX7cq4Y4UjBrS1Ye52rkDgT7ZAgYhYJ2dnZLxtgMZ3DKZjIAoX7Pa7rmT\nhYyfZRgyI7zMAVG+XMirEAfKxEhPmutacA3yFPdM0x8wNnY26HQ6Ft8xl/GzY3oMmOC4AIpPl/aQ\nYd+WH4lICOXBD36PXCEnMMa/R9SFMenMmhlB7Dhf8vKqmDkznJrSOgPS1UdcrB8+vs588OER6upa\nDQZ+aWkDwUFuJge+PgEI93Q4lvLG98LYYTcUzjpBDms57AZCxMbPpKOjg+eee45XX32VjRs3snz5\n8gH1cyRj1JCufiiWVqu16m6+wksiFW9rLgNO/6Xr72DpNhGYJt3CjPOETwrp0R4gRj8I96C+uJqJ\nKdE88fgSFArpiAlzAyZmWiRZP+f2kKH4TClrnrqO0And6ZJBsf4UZpw3q1XK5TJuvXk6f92wnIXz\nY6i9UI+Hv/SOErGxvlSUN7J4URzPbvgajaZL7G9tXSvjYnwl+yEFazyTvkKKcI3lGwxN0pQM+u1Z\nG5b2A7q3r/noo4/48ssv+c1vfkNISAibNm3C17fvTtFLCaOGdAGxwLc14woF7VaIt9Xf4se4ypK+\n5mCtwRIyPpDiMyXS/dV0cf5MGSHxgZJaZUC0H+XnKsWJoeBUMTFJ4T0KyOjLb2rAAIybHkH+ySLa\nWtsN+lFZXIN3iKf4d9ysaM78mGtAMubQWN2Mg4s9Knvp8J+wUE/kCjlt6k6USgU5Od2ZdcKOGMLe\nbL31yZJnYk0M1KRhLSI2t+IYCpjqd1dXF2q1mhMnTjB58mROnjzJxo0bh0yu4cKoIl2BEPvzQhnb\ndHU66bq8lthtram1REwOpeBUsaTMF85V4u7vip2jreSAsneyw9G9214KUJBeTPikniUce7svOD9U\nAAAAIABJREFUwnUdXR0JiPHjXGqh2I/21nZaG1px9XEW5fcO8cTBxY7zZ8xnkgmoKa3FM9Dd5O9y\nuYz/9/s5fLEzA2Rw8lT3JCRs/ROgV2jHlFbZ24Ri7eW8lFZpDaLr77ulf/5QQ+qZnDt3jltuuQWA\nJ598kscff5znnnuO6667bsjlG2qMKtLVf/H6C327rUC2gilB32bb1wHV38ESEh/I+TNlaDoNt+nR\n6XQUnCoibGKwWRkCovwoTD9Pl6aLvNQiMWutv0i+chJH/5sm9qO6tA6PAPce4WHxl8WSvu+MRQRW\nU1qHZ4Bp0gWIjPBiwzNX096uIe0X0s3IvICNjQLnX0Ll+qpVDsZy3po2U0sh9W6ZwlA666An4ep0\nOt566y0ee+wx/va3v/Hb3/5WlNfGxgZvb+9BlWckYFSR7kAgvHxSdlvBvGDtZZol2pedoy0ege6U\nnC3rQQCFGSUGpCuFGcsT+fJf35KfVoRXoDvOHk6Sx1mKCZfFknP0nHgfqoqq8Q7xNOiDXC5n0vzx\nHP86vYcNVWrQG0cumEJIiAdNjW00NKipqm4mK6ucAH9Xq2mVA1mhmJqMhxpSMgy1s05fDv1JsLi4\nmNWrV9PW1sZHH31EZKR0idDRjlETp2sN7RYuVr8H68Xb9gX6JgpBtojJIeSfKiJkQqB4nLpJTdqe\n06x77VYD2Yxx2U0z+HbTfl6/+33m/WbWgOULHR9IRWE1rU1q7J3sqC6pxTvYs0fbkxeM59MXv+Lg\nx0eYt2aW2BcB+p8F0jXXD+guqh4V5Y1Wp2Pf/lyqq5tZMD/GYttxf6B/Tf1nItUPY/kFuYYKvb2n\nA+lLX/phLAfAhx9+yPbt29mwYQPjx4+3+FoDRWVlJe+//7440dTU1LBkyRLmzp07ZDIY41et6erb\nbQWiHYx424FAJpMxbkYkZ3/OE/9O/TaD38c+imeQOxEJIWY1FrlczgPv3cVVdy/kqnULBiSLTted\n6RY6IZDC9PPIZDKToV4KpYIb/ncZez/80aAvUtpXbVkdnr8UUu9N84qJ8cXF2Y7PdqTh5m5PeLjX\nkD8XY/u98W+DrUUaw5Qduy990V+59dfWLSVHeXk5t956K6WlpXz88cdDSrgAPj4+/PGPf+Thhx/m\noYceQqVSMWnSpCGVwRijjnQtselK2W2FF06qvq1w3aEmXOEFjpsZTdZPuaIc3769n3X/upXHPv5D\nj80BpQa9V5AHl982B6Wqfwsb44EWlRROztF8ZDKZWdPAhJQYSrIvoG5uM3ltkbiDPS1aAkdHelFf\nr+ZvG5ah7dIRHt5Tyx4KSJmaBPmHulBOfwlXCv01sej3R2j/s88+48477+T+++/nj3/8o9WL0/QV\nOTk5eHl5GZRuHA6MOtI1B+HlMGW3NYehdD4Isgr/fMO9kcllVBRUoensIj+tiCmL4w0cJuYGy0AG\nvBS5JF0xiWNfpaHT6SjKLDVJukqVktD4YM6lFppto6a0Dq9fohdM9UNAVJQ3BYU1aHU6tDodAf4u\nQ/5cLF39DMRG3NfnMphKgSX9ELB79242btzIww8/zPHjx3n33Xf7VGR8MHHy5EkSExOHW4zRRbrm\nXlTBZGAu3lbfDiWVLTQUy0apQS2Xyxk/K4YzP+Zy/kw3yRmn6EphIAPeHLnETI9A3dTGv//wAZoO\nDWETTYehxUwNJ+dYvsnf21raaWtpx9lT2sFnfF8dHW0ZF+PL089+zawZEWK9iaFYzhu/I/0hOnME\npt+Gsfao34+hJFxz/RBk0e8XQGlpKREREfj4+PD888+Tnp4+pLJJoauri8zMTBISLN8bb7Awahxp\nAoxnXsGUoNPpTG4Cqf/i6P+vf039/42vb/xZymHRG4RzpV5igLjZ0WTsy6K9tZ2YaREWXdMYUnKZ\n6ou5c+VyOXe/fhu7N+7jjpfWYOtgetkYPTWCPe8eMPl7aW45/lG+JiuSSd2P22+bwd79OVxz9cQe\nhCXVl96eb28wJYe10Nf3S+rcoYbxPWlsbOSJJ55ApVLx5z//GSen7klUP1N0OJGVlUVwcLAo13Bi\nVJGuvj1JX+sRIhIEQh5oycW+kJe+TMbn6h/TmxwTUsax9ZnPKcutYMXDV1kkpyUwbktfdmONWP/4\nqKRw7k3unfyjk8J5494P0HZpkSt6EmvJ2TKCxvn3kMEcUfr5ubD6xmSzfZGafKWeudS5+ucYHz8U\nJCcllyniEr7vz0TfH0i9q/v37+f555/ngQceYMECQ2dtb3HDQ4UTJ06MCNMCjDLSBcN4W5nMuvVt\nzcEcEZvSuowJzpwcXkEezP/NLNK+P82keXEDllcf+nIak5wUeRl/NjfgXbyccfd1peh0qWQ2XGn2\nBQL1SNeaWmVfJ0dTxw3H8t1YLkEO/f8HY8XVmyz6125tbeWZZ56hubmZDz74YNgdVKbQ0dFBTk6O\nxTsGDzZkOjNryrIyy9I4Rwr068kORn3bgUKKhPUxVNqKsUz9uSd9Wfa+++g2/CJ8uGrdwh7HPb/6\n/1j828tIuDx+2J5NX/oy1HL19Z4MRl+k5Dhy5AjPPPMMd911F1dffbVF17EW1Go127Zt48KFC8jl\ncm666SbCwsKGVIbeEBAQYPK3UafpKhQKcTuevththwPmlvaWmCUGgoFOQH3RIhMuj+eLV75hye8X\n9Di3NPsCATF+wzoZSslv6vvB0iL1MZD3dSDavVQ7xrK0t7fz/PPPU1RUxKZNm4Ylbfezzz5j/Pjx\n3H777XR1ddHZ2TnkMgwEw29ssSJksosB3iMp3hZMV3rS/zcU0RLGcljzfpjqy6T5cdRXNLD5yU9Q\nN7eJfWmqa6a1UY1noLvBPRlqCPdX/57oPwdLoj8G+lz0ZTGegAZ6T8y9Y6beM+PEILlcTnp6OqtW\nrWLcuHG89dZbw0K4bW1t5OfnM336dKBbybKzsxtyOQaCUaXpPvTQQ1RWVpKQkEB0dDRNTU2sXLkS\nG5uLpQyNHQ+DPcj7o7UMlu1uOMwrMpkMpY2SR7f9gVfv2sS3m/az7L7FABRllhA8PtAg7Guol/J9\nuSfGslnTpjrUqzFLfRAZGRn89NNPNDc3k5mZyQsvvEBMjGHR/KFETU0Njo6OfPTRR5SVlREcHMx1\n11037IkXfcGo0nRfeOEFHn/8cVpaWjhw4AB5eXmsXLmShx56iI8++oi8vDyDF8uaGqQxTNmQ+zOQ\n+qqpGLdtrMkNh0YZEOXL715aw887jovfFWWWEDYxqFety9rPBvqW5GAK/dEgpfoyGNptf2FM/HK5\nnAsXLiCTyUhOTua9995jx44dwyIbdCtNJSUlpKSk8PDDD2NjY8P3338/bPL0B6NK04XuIOiUlBRS\nUlJQKBRotVry8/NJTU3l7bffJjs7Gzs7OyZPnkxSUhJTp07F3d3drKbS1wEwFBplX7Wu4YZOpyM0\nPoj6ykbqKxrx8HejKLOUCXNi+myHHKg2PJjPpz82Vf3jh5Ns9e+JVqvlzTffZO/evfz1r38lPDxc\nPK6jo2NYZARwc3PD3d2dkJDu3VISEhLGSHe4ERcXR1zcxZAquVxOVFQUUVFR3HDDDQC0traSnp5O\namoq27Zto7q6mqCgIBITE5k6dSrjx4/Hxsamx3Krt8E+nA47KRKWIlwpGY0/WxP691ChVPyyq0QO\nKSunUZhxnqV394xoMCWb1PK3L30ZDvOKKdmk4m6H+tkIbQr/C+0UFhbyyCOPMG/ePLZs2SKmyguy\n2NraDpo8vcHZ2Rk3NzcqKyvx8fEhJyfnktveZ1SFjPUXOp2OsrIyTpw4wcmTJzl9+jQ6nY74+HgS\nExOZNm0afn5+kucaa5nCdyNFY9H/X/hd6rPxcdaQX4rkvnvvAPkni7h1wyrumfi/vJXzd5QWbp8u\ndX2pzwL0yVr/OY2k5zNcz0ZKFoD333+fzz//nOeee45x48ZZpR1ro7S0lG3bttHV1YWXlxerV6/G\n3t5+uMUygLmQsTHSNYGOjg7OnDlDamoqqamplJSU4OnpSVJSEsnJySQkJGBnZ0dDQwPOzs6SBDeU\nA1tKK7ekfUuJy/hzb9c0RfwXzlXw1xte5bbnbuCbt/fx+Pb7LLqmpbC0P8NBun3VtAfj2ehfS1+7\nLSsr449//CMJCQncd999Bs7nocDTTz+Nvb09MpkMhULBgw8+OKTtWxtjpGsl1NTUkJqaysmTJ8nI\nyMDDwwMPDw/RJCHYvYwx2ERs7WVzfwe7JcSv0+l4ePYzKBRy5t88W4zdtTakTBGmViVSn60ti7We\nz0CJWGpC3L59O5s3b+aZZ54Ztlqzzz77LA899BAODr0XcroU8KtKjhhMeHp6smjRImJjY2loaGDe\nvHkEBARw6tQpXnvtNc6dO4ejoyNTpkwhOTmZpKQkXFxcrOqk08dg2ZD749gyJjdTcshkMn739zUc\n2ZnK/FtmD1hWKZgjOUucjtYkYmtPiP1x1Ek9H4Dq6moee+wxgoOD2bZt27DGu1o7OmUkY0zT7Qe0\nWi1NTU24urr2+K2pqYm0tDROnjxJamoq9fX1hIaGipES48aNQ6lUDkjj6q8pwZqQ0iT1MRxmloFM\nQtZeyg+X006/fanPeXl5ZGVl0dLSwrZt23jsscfERIPhxLPPPitqubNmzWLmzJnDLNHAMGZeGEbo\ndDqKi4tF23BWVhYKhYKJEyeSlJTEtGnT8Pb2tnigD/dg1ocpb7vwmzEGk4gH47701z48Up6Rse1W\np9NRUFDAzp07qaioQKvVolQqmTdvXo/qYEONxsZGXFxcaG5u5vXXX2flypVERPSvhOlIwBjpjjCo\n1WoyMzNFbbi8vBxfX19RG46Pj8fW1tbscmu4B7OlGuVge+SHmuB60/AFjLQJUSaT8f333/PSSy/x\n8MMPM3fuXHQ6HbW1tWg0mhEVdrV7925sbW2ZP3/+cIvSb4yR7iWA8vJykYQzMjLo7OwkLi6OxMRE\nvLy8UCgUpKSk9DhvKJfx1jBrWGsZPxJMLEL7vU2OUp8HE8aE29LSwl/+8hfa29t5+umnJc1iw4mO\njg50Oh22tra0t7fzxhtvcMUVVxAbGzvcovUbY6R7CUKj0XDo0CG+++47tFotbW1t1NXViU66xMRE\nHB0dhywsajA1yr4S8UhZvoP0fRmsUK/+yPLTTz+xfv167rnnHq688kqrtWVN1NTUsGnTJmSy7ky4\npKQkLr/88uEWa0AYI91LFLt27cLd3Z3p06cjl8upq6sjLS2N1NRU0tLSaGpqIioqSowdjo6OlhzE\nAxnkgxUhYUm7Up+NMVy7EvRV0x5qM0tbWxt/+9vfKC0t5bnnnsPT07Nf1x0ItFot//jHP3B1deXO\nO+8c8vaHE2OkO0qhX1fi5MmTZGdnY2trS0JCgkjEHh4e/da2RpJG2ds+W0NtZrHGfTFnH+6LmcVY\nltTUVP785z+zdu1arr322mF7bvv27eP8+fO0tbWNka4eLrk43V27dpGZmYlMJsPZ2Zk1a9bg4uIC\nwKeffkpWVhYqlYo1a9YQFBQ0zNIOLiytKxEYGCg66YzrSkDPIjLCdzD8ZGtO07Y03tZa8htHAwz0\n2v3tj/DZ+N50dnbyz3/+k4yMDN544w38/Q33nhtK1NfXc+bMGRYtWsS+ffusdl1T0TKXEi45Tbe9\nvV0suHHgwAHKy8u54YYbOHPmDIcOHeKuu+6isLCQHTt28MADDwyztMMPnc58XYmpU6eKg9NccsBw\nO6ksJf/BWsYPl9bfm5mlrKwMR0dHLly4wP/+7/+yYsUKfvOb3ww7Kb377rssWrSItrY29u7daxVN\nV9hkFrqLmatUqhGx6aUURpWmq1/hqKOjQ7zpmZmZJCd37xAbFhaGWq2mqakJZ2fnYZFzpEAmkxEY\nGEhgYCDLli0DDOtKrF+/npKSEry9vYmIiCAkJIRVq1ZhZ2dnQDRDadcdCMFZO/vM2tptX2FOuwU4\nfvw4hw8fpqWlhfnz5+Pr60tNTQ1eXl5DJqMxTp8+jbOzM0FBQeTm5lrlmjqdThzre/bs4cyZM6xc\nuRI/P78RS7ymcMmRLsBXX33FsWPHsLe35w9/+AMADQ0NBruRurm5UV9f/6snXSmoVCoSEhJISEjg\nt7/9Lbt37+bAgQMEBARQUlLC2rVraWtrIyYmRtSGhUB1UyRs/Lk/GIwwMFMJJsZtGrcr9d1IMrPI\nZDLOnTvH5s2bufzyy1mxYgXFxcUUFxfT3Nw8rKRbUFBAZmYmWVlZdHZ20tbWxocffsjNN9/c72vK\nZN37s73zzjs0NTWxbNkyfH19LznChRFKuq+//jpNTU09vl+6dCnx8fEsXbqUpUuXsmfPHg4cOMCS\nJUt6dUaMwTTCw8OZPXu2wQTV1dVFbm4uqamp/N///Z9BXYnExESSk5NxdXXtl/YohaFcvveFiI1l\nHAlmFp1Ox6ZNm/jqq6/461//SnR0NABeXl4kJiYOuXzGuPrqq8UdgvPy8ti7d2+/CFffnAAXzZ2P\nPPIIAHV1dWi1WhwdHS+pfdJGJOnefffdFh2XlJTEW2+9xZIlS3Bzc6Ourk6s9FVfXz/igsBHKqTq\npioUCmJjY4mNjWXNmjWAYV2J999/36K6EsZEZcphN9wapRT5Ct+bM7MYf7YmpJyIJSUlPPLII0yd\nOpVt27ahVI7IITwgCGQrl8vp6uoSi6jb2tpSV1fH119/jVqtJj09HR8fH9zd3bnyyisNVrojGZfc\nE6uqqhJ3Ic3IyBDTF+Pj4zl06BCJiYkUFhZib28/INPCl19+SWZmJkqlEi8vL9asWSPOpt999x1H\njhxBLpezYsWKSzpzpi9wdnZmzpw5zJkzBzCsK/HRRx/1qCsxdepUfHx8TGrD+t+PhOW78L+x7XY4\noiWkCHfr1q1s3bqV9evXEx8fP+A2LIVGo+Ff//qXuLv25MmTLU60EKJr+gJBuz169CjHjx/H39+f\n4OBgkpOTueKKKygoKKCrq4v77ruPkpIS9u/fT1tbW5/7NVy45KIX3n33XSorK5HJZHh4eLBq1SpR\no/3kk084e/YsKpWK1atXExwc3O92srOziY6ORi6Xs3PnTgCuueYaysvL2bx5Mw8++CD19fX8+9//\n5k9/+tOYKeMXmKorIZgkJk2ahFKppL6+Hg8PD4NzhytSYiCmDWvE2pq6nnBOZWUljz76KFFRUTz4\n4IPDsl1OR0cHKpUKrVbLK6+8wooVKwgNDR209r799luOHDnC4sWLkclkfPbZZ6xYsYJp06YZHJed\nnc2uXbtYu3btiNJ0R1X0wu23327yt5UrV1qtHf0ld2hoKOnp6UB3lMSUKVNQKBR4enri5eVFUVER\nYWFhVmv7Uoa9vT1Tp05l6tSp4ndCXYndu3fz8ssv4+fnR0BAAOPGjWPixIniJoNDvYS3RmTCQGNt\njeUxJv8vvviCjRs38uSTT4rROcMBYYtzjUaDVqu16vMwtt12dHRQUFDA7bffTlBQEDqdjk8//ZT8\n/HwSExPp6OigsrKS/fv3k5WVxapVq0YU4faGS450hwNHjhwRHRQNDQ0GBOvm5kZDQ8MwSXZpwM/P\njyVLlhAQEEB7ezvLli3D1taWkydP8o9//IOCggJcXV1JTEwkKSmJpKSkHnUlrE3Eg+W460+0hNT5\ndXV1PP7443h4eLB169Zh31FBq9Xy0ksvUV1dTUpKijhRDgRC/40jEDo7O8UMxP379/PNN98wa9Ys\nli9fTnt7O3Z2dlRUVKBSqXjyySdxdHQcsCxDiUvOvGBN9BYlAd3LnJKSEn77298C3SaM8PBwkpKS\nANi2bRvjx48ftm1OLiW0t7ej0WgkB4mpuhJCyJpg6hlowoO5DLehhJRZoqysjG+++QaVSsWOHTu4\n5557mDdv3pDLZg5tbW1s2rSJ66+/3uRmrX3F6dOnSU1NJTg4mOnTp6NQKHj77bepq6tDLpdzyy23\nEBQUhEaj4bPPPmPJkiU4OjqO6HCxUWVesCZ6i5I4evQoWVlZ3HPPPeJ3QpSEgPr6ejENeQzmYWtr\na9Ie6e7uzvz588Uaqvp1Jd555x2L6kr0pg2PpFoS+hCciiqViqamJurr65k2bRpfffUVubm5I6pu\ngZ2dHZGRkWRlZfWLdAUNViDM3NxcPv74Y1JSUjh48CD19fUsXryY+fPns337dpYtW0ZQUBBlZWV8\n+OGH+Pj4jOhMNEvwqyZdc8jKyuKHH37g3nvvNQjLiY+PZ/PmzcybN4+Ghgaqq6v77VBIS0tj9+7d\nVFRU8OCDDxo4/n6tERICpOpKtLS0kJ6ezsmTJ9m2bRtVVVUEBQWJJDxhwgTJuhL6GE7tVoAU+f/4\n449s2LCB++67j8WLFwPdy+z6+vphk1NAc3MzCoUCe3t7Ojo6yMnJ6VfpRf2sspKSEtRqNeXl5dx6\n661ERUURHh7OwYMHOXz4MAsWLGDBggUcPnyYQ4cOUV1dzYwZM1i0aJG1uzfk+FWbF8xh/fr1dHV1\niUvh0NBQVq1aBViPECsqKpDL5fznP/9h2bJlIumORUhYBlN1JSZMmCASsUzWXaM1MDDQ4NyhiLOV\nklf4X2hTrVazYcMGqqurWb9+fY+IjpGAsrIyPvroI7RaLTqdjilTpogTg6XQ7/N3333Hrl27iIyM\n5Ny5c6xdu5bJkycD3Tbc3NxcZs+eTVxcHB0dHTQ2NmJra3tJZZeOlXYc4XjttddYvny5SLp79uwB\nELWJN954gyuvvHIsQsICCHUljh8/Tnp6Ora2tnh5eeHs7Cxm09nb25t1Yg1WpISxdnv8+HGeeuop\n7rjjDpYvX271Nkcaqqqq2L9/P66ursyYMQMHBwdeeeUVgoKCuPrqq3FwcKCjo4PPP/8ctVrNwoUL\nL9lKgWM23UsMYxES/YdQV+L06dNMmzaN6667DrVaTWpqKgcPHuTVV1+VrCuhn6wx2JESHR0d/P3v\nfycnJ4eNGzcO+f5k9fX1bNmyhcbGRuRyOTNmzGDu3LlWbUPqHra3t/Pjjz8SEREhmglWr17Nm2++\nSWRkJJMnT0alUpGcnExGRgZubm5WlWmk4FdDuoIBf6gdKJZESBhjrI7EwHHDDTdgY2MDgKOjI4sX\nLxaXxKbqSug76axRV0JKu83MzOTxxx/nxhtv5NFHHx2W5yqXy1m+fDlBQUG0t7fz97//ndjYWKuR\nv37crX4p1qCgIJYtW8Y333wjmhv8/f1JSUlh7969+Pn5ERgYSERExCW9E3BvGNWkq1arxYIYpryd\nQqD3YL38ltaR0MdgREhkZWWxY8cOdDod06dPv+T3oOoNAuFKoa91JZKSkoiLi5OsKwHSZgljwu3q\n6uLVV1/lyJEj/Otf/xpQtuRA4eLiIr5Ptra2+Pr60tDQYDXSFcbat99+S25uLg4ODsyaNYvo6Gjm\nz5/PyZMnee+998REp8svv1wMFfT397+kIxMswagkXWEWPXz4MEePHmXKlCnk5OQwceJEpk+fblCR\naCQ+YGtGSED3xPLpp59y99134+rqyksvvcTEiRNH1Lbbww2puhJFRUXiDhxnzpxBoVAwadKkHnUl\nTKUCd3V1YWNjQ25uLo888ghXXXUVmzdvHlHvXE1NDaWlpQNO6TUuMP7uu+/S2NjIsmXLOHXqFD//\n/DONjY1MnTqVNWvW8NJLL3Hq1CnRgXbPPfdcckkO/cWoJt2amhoqKyuxtbVl1qxZ7N27F1tbW2bM\nmAFAcXExVVVVhISEiEV0jKtiGccVWhPp6el89tlntLS0sHHjRgICAli3bh1+fn4kJCTwt7/9Dblc\nzsqVKwekiRcXF+Pl5SV6xhMTEw2KBY2hJ2QyGWFhYYSFhbFixQrAsK7EF198QXl5OT4+PqI27OHh\nQWtrKxMnTqSlpYWnn34aT09PcnJyuPPOO5kxY8aIItz29nbee+89VqxY0ed6Dl1dXZw6dYrY2Fgc\nHBzEftXV1VFXV0dwcLBY3lEul7N582bUajV+fn4EBwezZMkS3nvvPZ5//nlUKtWwZ9wNJUYl6Qov\nQH5+PosWLWLmzJmoVCrq6+s5efIkU6dOZffu3RQVFdHZ2UltbS2XXXYZ8+bNQ6FQUF1djZubG0ql\nUryWQMZC2IxcLh+wSWLSpEkmM9kWLVpktZhEqQLvRUVFVrn2rwmm6kocO3aMTz75BI1Gg4ODA9u2\nbSMkJISKigrCw8NZuXIlRUVF/PjjjzzxxBNiHYPhRFdXF++++y7JyclMnDixT+dWVlbyyiuvkJyc\nTEJCAtCt3b711lsEBASwYMECpk+fDsDOnTs5fPgwM2bMoKKigmPHjuHn58eCBQuQy+Xivfg1+SxG\nJelCd0C3Wq0mKioKlUqFTqcjNjaW/fv3U1xczE8//cStt97KuHHjaGpq4oUXXiAyMpKwsDDee+89\nMbe8tLSUNWvWiFrhSNJULMWYY27w4Ofnh1KpJD4+nhUrVqBSqTh79izbt2/nlltuYeHCheKxw1UE\nXQpbt27F19e3X1ELgiZ73XXXAd2hpUePHjX4Drr9COfOneOBBx7Ay8uLV199VcxkmzVr1ohLcR4q\njDrSFV7ssrKyHtWQysvL0el01NTU4O/vz7hx49BqtTg4OBAYGEhVVRVhYWF0dHSQn5/P8uXLcXZ2\nFh1be/fu5fz58wQFBTFv3jw8PT2HsaeWYyx1eXBxxRVXGEzG8fHxkpEpI4Vw8/PzOXHiBP7+/rz4\n4ovIZDKWLl1KXFycReer1WqUSiUZGRkcOnQIhUJBYWEhl112GdBdiUypVFJQUICbmxteXl7k5ubi\n6OjI3LlzmTBhwmB2b8Rj1JJuYWEhzc3NFBUVERkZSWlpKSdOnGDKlCnU19eLMYByuZy6ujqcnJxo\na2ujpqaGzs5ObrjhBuLi4oiLi6Oqqoo33niD5ORkFixYQGZmJt988w033nijWNXelBwCjMvXDSVC\nQkKorq6mtrYWFxcXUlNTufXWW61y7a1bt4obET766KNA9zbw77//PrW1tXh4eLB27VrSRKzSAAAM\n5klEQVTs7e2t0t5IxHCvfqSegTlERETwz3/+s9/tJSQksHfvXj744AMmTJjAihUr+Pjjj6mvr6e1\ntVW0z0ZFRXHgwAFef/11CgsLufnmm8cKQzEKSVdAXl4e8+bNIz8/n2PHjqFQKAgJCeGqq65i165d\nqNVq8diioiLUajXBwcEUFRXh7u4uZsK0tbVx9OhRPD09WbJkCQBxcXG8+uqrlJWVmQz9kclkFBUV\n4eLigru7e4+BOZgOOmPI5XKuv/56/v3vf6PT6ZgxY4bVKkRNnz6dOXPmsGXLFvG7PXv2EBMTw8KF\nC9mzZw979uzhmmuusUp7Y+gJqWcwmGhubqa9vR1PT0/i4uJwcXEhKSmJEydOkJ6eLjqqY2JiuOee\ne6itreX2228f1RNvXzDqSFffizp37lwWL15Mfn4+7e3txMfHo1QqmTlzJjt27ODLL78kIiKC//73\nv0yZMoWwsDCOHDlCQECA6M1tb2+ntLSU3Nxc/vKXv+Dp6Ym/vz/29vacP3+e4OBgUavVj5r4/vvv\nKS0tpba2FpVKxeLFi5k+fTo6nQ6tVmtSQ4bBsf3FxcXxpz/9yarXhG6tqba21uC7zMxM7r33XgCm\nTZvGa6+9Nka6gwipZzCYcHJy4rHHHuPo0aMcO3YMNzc3EhMTKS0tJS8vDx8fHzG5ITg4eFhjkkci\nRh3pQrdhv6WlBXd3d2xtbXvYqgTv6ZEjR9i7dy9LliwRbXDZ2dksXLhQDK53dXWlubmZe++9F39/\nf1JTUyksLEQmk4nONYEkheiG7777jsrKSm677TY8PDzIz89HJpOhVqs5ePAgx44dQ6PRMHv2bNGL\nqw/94tcDTT8dDjtic3OzWJzExcWF5ubmIZdhDIOPadOmkZ2dzfHjx/Hz8yMlJYXPPvtMjFD4NYWB\n9QWjknQBZsyYITqLpLLOxo0b12MXXI1GQ0xMDEFBQQbH+vv7c/r0aSIiIpg9ezazZ882OE+fNMvL\nyykvL2fJkiV4eHig0WiIiIhAq9WydetWampquP322+no6GDXrl14eXmJYTfQraFXV1cTEBBgMljc\nnGlC2MsKRo7jZgyjF4sXL+Y///kPJ06cEEPFFArFGOGawagk3YCAAJYtWyb+LUVOUvG2SqWSm266\nqcexy5Yt46OPPmL9+vX4+voSEhJCdHS0ZH64TqejsbFRDDkT2i4tLaWkpIQbb7xRrEDk5OREXl4e\nkydPRiaTceTIEdLT06mtraWhoYGUlBQWL16MUqk0cFAY90fYprquro7t27eTmJhIfHw8R44cYcaM\nGUO+kaGTkxNNTU04OzvT2NiIk5PTkLY/hqGDr68vU6ZM4dChQ0RGRg7pLsWXKkYl6VpST0GKiIWU\nTuPfHB0dufPOOzl//jx5eXlotVpxB2JjODg4oFaraWhowN7eXrxWY2MjMpnMoFSdv78/VVVV4jY2\nX3/9NfPmzWPevHm0tbXxj3/8g+DgYCZOnMj27dtRKBT4+vpSVFTEkiVLxBqxgn24oaEBhUKBnZ0d\nXV1d1NTUGBQcGSwYxwELhH/55Zdz9OjRPgffm4Kp6li/tmgJKZip0DroSElJwdvbe1B3Bx5NGJWk\n29+IgN6I2hKngIuLC3PmzGH37t0sWrQIZ2dndDodSqWS5uZmcRcKjUZDY2MjdnZ22NnZkZqaipOT\nE/PmzUOn02FnZ4ePjw/V1dVA964JZWVlREZG4uDggFKppKWlhd27d1NSUkJKSop4fTc3NxwdHcX0\nVTA9oQwUH3zwAXl5ebS0tPCXv/yFJUuWcPnll/Pee+9x5MgR3N3dWbt2rVXaMlUd68iRI7/qaAmp\nZyBkhA0VjE11YzCNUUm6gwWBuMyRs0KhYNasWXz//fe89dZbODk5sWzZMnx9ffH29iY1NZXExEQO\nHz5MXl6emMFTV1cnmg9kMhnt7e24ubnR3NyMRqOhsrKSpUuXMnPmTGbOnElHRwd//etfxV0Szp07\nJ2p6gYGB7N+/n5iYGPz9/cVrGlfB6ovN19TxpuJ9+1NdrTdIVceqr6//1UdLWCvmegxDgzHS7QMs\nLQHp5ubG9ddfz/XXX49Go0Gr1aJSqZg1axY7d+5kx44dBAcHM3fuXGJiYoBuk4h+7HBFRQWNjY0k\nJCSI201HRUUB3c6yo0ePYmdnx8qVK4HuojYvv/wyV155JTKZjF27duHm5oafnx8ymYxt27Yxe/Zs\ndDodPj4+BpXWBEjVHO5tkhkuCNWxwsLCRPsxjI5oiV9bGc5fG8ZIdxAgkBdgsKmlUI1K2GxQvzJ+\nQkIChYWF7N69m0mTJrFz504cHBxITExk3759eHl5idEMnZ2dlJaWEh0dLZ5va2tLUFAQfn5+VFdX\n4+Ligq+vLzKZjObmZo4dO0ZbWxtNTU2UlZUxc+ZM0dkoOOmkEjjkcjnHjx8nMjLSoGjOcMK4OtZI\nmxAGgrEynKMfY6Q7CDBXMF0ul0tuQ+Lt7U1KSgr79+8nNTWV5ORkkpKSADh9+jQhISFiKJijoyOV\nlZViLVLoDlVzcHDAx8eHgoICXFxcRHNFYWEhzs7OREREcNlll3Hu3Dm2b9/OnDlzsLOz4+uvvyYz\nMxN7e3uSk5OZM2cONjY2Yj8++eQTbr31VgPSHa4YYKnqWKMpWmKsDOfoh1nSNbe52hisj4CAALEq\nlT6peXt7M3nyZDEMTfiuublZfEYffvgh9vb2TJo0iWPHjokV0+zs7NizZw8TJkzgmmuuwdHRkcbG\nRgICAujs7CQ6OporrriC2267jbS0NPbs2SMW9KmtrWXbtm3Y2tpiZ2dn8n0YSgJ+7bXXiIyMZPXq\n1eJ3M2bMICsri2uvvZajR48yc+bMAb+7nZ2dPPXUU2g0Grq6upgxYwarVq0Syxo2NzcTHh7Ovffe\naza7sK8oLi4mMDBQlD8sLIy8vLyxsTiKcOnVKRzF0Gq1BnZVAf/zP/8j5rMLuPnmm8nOzuaBBx7g\n7bff5syZM3h6eqJUKikuLiYyMlI0beTn5xMaGiqGjjU0NCCXy3F2dkalUhEUFISLiwuXXXYZ4eHh\npKWlAeDh4YFSqUSr1fL+++/z8MMPk5eXR2trK2lpaVRVVfWQdaAwF/p09uxZDh48SGZmJo888giP\nPvooaWlpXHvttWRkZHD//feTkZHBtddeO2A5bGxseOqpp3jhhRd48cUXSUtLIzc3ly1btnD11Vfz\nyiuv4OjoyA8//DDgtnrDaDKfjGHMvDCi0JtZQh8+Pj48++yzZGdn09bWhq+vL7a2trS1tVFQUICP\nj49IunV1dYSGhop/V1VVIZfL8fHx4csvv+T48eOUl5cTHh5OdXU1U6ZMEe28Wq2W+fPnc/PNNwPw\n/fffs2vXLsrLy6mqqmLChAncfvvtJuOW+wpzBBMbG8vHH38s+duTTz5plfb1IUxSnZ2ddHV1IZPJ\nOH36NPfffz8Ac+fOZfv27VYrNg/dE50QJghQW1s7YmzpY7AOxkj3EoApMrazszOw60J3/O8jjzwi\nmiIKCwtpbGwUB257e7tY2rKqqootW7bw4osvolKpKCwsZNOmTbi7u4v24/z8fBYuXIhWq6W1tZXd\nu3ezfPlyUlJSAHj66ac5cuSIuNOuOdTV1SGXyw0IWjBNVFdXc/DgQaKjow2ymjQaDVlZWYSEhEie\nN5jQarU89thjVFRUcMUVV+Dr62uwyamnp6dBnWJrICoqSpzQ3N3d+fHHH0WSH8PowBjpXuLQ3xhR\nLpejVCrFWg46nY6wsDA2bNiAl5cX0G1aqKysFEnZx8cHDw8PnJycKC8vR6VS4evrKyZftLS0EBoa\nilwuJzMzk/LycoqKinBwcGDChAnMnDmTsrIys5lv1dXVNDU1oVQqkclkODg4YGNjY0CcX3/9NXV1\ndVxxxRXiebW1tZSXl1NfX097ezvJyclDGsIml8t54YUXaG1t5e9//zulpaU9jrG2HHK5nDvuuIP1\n69ej0+lYsGCBQRbjGC59jJHuJQ4pAjImJn0njI+PD+vWrUOj0WBvb8+ECRO44447mDx5Mp2dnQbR\nFQUFBWJmHHSnMnt5eSGTydiyZQu1tbVotVqioqJMEm5bWxtOTk7Y29v3KOAjyLlv3z5qamq46qqr\nRJOGTCYTl9oajUaMK5bJZJw6dQqdTmdQKGgw4eDgwPjx48nJyaGlpUU099TU1AzK0j8hIYFXXnnF\n6tcdw8jAGOmOQvSmfelXgFq3bh3r1q2jsrKSqqoqqqqqRNJ1dHTEy8uLAwcOcO2116JUKnF3d2fN\nmjWsWbMGgAsXLtDZ2Qn0XPI3NjZSWFhocrcAYZm+f/9+pk2bRnh4uMH30L1jclhYmEFMck1NDfn5\n+YwfP37QNnlsbGxEqVTi4OBAR0cHGRkZLF++nAkTJnD48GFmzZrF/v37SU5OHpT2xzB68f8B0HfC\nPsXvvicAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x107e66150>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"H.plot3D(rmax=2.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The 2D plot clearly shows how the pore profile opens up with increasing order parameter (i.e., increasing strength of the deformation along the mode):"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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aGzZs4O2336Z///4sWrSIKVOmsG7dOrTaxvc8aHwzriMSUuNwlFWh18mUlTow\n6bTklp/KHpGgN3HQHhoLBRAjJ1fryisw46YrBn7AyeBzmp//7ElwMiZKEI2dEVikt6kQkSWpDXxD\nnGYe5b45OLj+nK6tUh1ekqXRlIvZuOhPpJWMgW/RUIqD4YEyCSUks8jZ4UPPz5TzfxFbFHtzUfCi\nlfR4a8iofzGNy/ny+eefM3/+fD744IOArtJJhBBMnz4dvV7PE088Uesxz5Q0t9lsAfXaqKioEGmI\nysrKEHXbcJyUk//jH/+IxWLBYrEwceJE1qxZUysD1bnzqQDswYMHM2rUKFauXEnv3r1Zt24dTzzx\nBJ988gmdO3dm27ZtTJ48mffee4/MzEyMRiNZWVmBLcDf//73LFiwgOzs7JAVYWOgyW7xxTaLwV5W\nhRBQWGQLkn4HSDVFc9wZmu4IIEFOwS2cOH2Rf025RH8MUmguvtpilNbhJRVdIKYKqsStGPjhhJdg\nKAbWEy29hkv0QpaOn/O1VSIjU4hGqsAq/ROrtCBsGwk70dKrVAZpO3nRcggJN1oix9FVh4YCFFIj\n5t8DKPbk4BGuBu8kcTZ8/fXXzJkzh3feeSfs9t2DDz5ISUkJixYtQpbDu96Ho3379uzefer7tWvX\nrsD4GRkZ7NmzJ6j9nj17yMjIqHHcSy+9FL2+7s4AJUkKnFXt3r2bvn37BoxYt27d6NGjB+vX+xNN\nd+zYMaLjRWOkyRoorU7GmmBBsTspKrKRGGUKnEEBXGKOpThCLFSs3AyjFFXtKspFvxPKup6znxsH\n0VDuH0M6ZYwEVsrF/xEjzQ/RDNJQQLT0MmXiIZxiCDKqgboQyOTiEt0pFv/AwBZMfBbSxiItwkWv\noEwPOn5BoQUOrsEofRHSpzZoOYRTRJZFByhUjhGliWnwDylFUXA6nSiKgtfrxeVyoSihoRnffvst\n06dP54033qBr11BBzzlz5rB//37efvvtsEahOtn2MWPGsHDhQo4fP87x48dZuHAht9xyCwB9+/ZF\nlmXefPNN3G43b731FpIk0b9/f8C/anO5XHg8Hnw+X+D/4HfeGDFiBK+99hpVVVXk5uby/vvvc/XV\nVweu7XK5AnNxOp1B8/rss8+w2+0IIfjmm29YtmwZ1157LeB3zti0aVPA5X3nzp1s2rQpsDoaPXo0\nW7du5dtvv8Xn8wUUcNu1i5y3sSHTZA0UQGJaPD67k/yCSppFRwWk3wHamuPwIajwhG6TBDz5vJE9\n+Xwk4KWVgO7kAAAgAElEQVQ1en6O2CYSRmk1Dq72O1uwNajOTR9cZGGV/s6pAF4XcdLD2MUYPHRF\noQUykYOJVc4dmTwUUhBEUSqewCK9EbTKNbAWI+upFPcG9dOxCzddcIjrMLEazjpXnn8F5mRIxBZu\nnxOnsBEvn/sZ18ViwYIFpKen89prr7Fs2TLS09N55ZVXOHbsGO3btw+oYi9YsACbzcakSZMCMUGT\nJvlzWR47dox//etf7Nq1i27dugXq//Of/wSuU51s+6RJkxg6dChDhgzh6quvZujQoUyYMAHwewAu\nXryYjz76iMzMTD788EPefPPNwDnOxo0bufTSS7n99tvJzc0lPT2d8ePHB677xBNPYDab6dmzJzfe\neCOjR48OGD/wr7IyMjKQJImBAwcGvP0AFi9eTO/evcnMzOTJJ5/kueeeo0+fPgBcfvnlzJw5k7vv\nvpsOHTpwzz33cP/99zNgwIDAuK+++ipz5syhU6dOrF69mrfffrtRnj9BE5V8P8lrf3ibn3JttLui\nAy0GNOe9H3az8o9j0UgSuyoKuX/HSv7WdRgdrMGZx0u9x1lW/jJt9F0YbJ0QcXwzH6OT9uCzPFNr\n+W2JSpKkiRSLV/GRRJI0ikKxLHDQ7m9jJ16ajsCCmy7o2YJCK8rFXEBCJo946X4KxYe1umZDoLFI\nlFukhQhhogr/Q9LA90RLz2IXo9FIpRj5hlIxH+8ZOk2x0jyc4gqcDCVOmoFTDMHBDbW+rp5NxFpd\nFFQOiNjmuOcQn1e8QbqhJ9e3uKNR3E+VpoMq+X6WxLeIwygERUVVNI+2IGukwCqqlTkar88X1tU8\nWk7EJRwUeSO7mgM4uAYDGzmbLBBR0gc46YdCKwQmvLRDx86gNgIzxeJ1qsRNgIxdjKJczOHkgb1C\nEhpKOZftRZXqObmCOomLfpSKZ9FIlfhELMViYYhxAoGOXXjwnxvYxF0nVHNrL4lhkr7ES9tq25Qo\nuWglPbFyaFCqikpjpEkbqITUOHSKl7JyB4lRJmRJE8hqbtUa0Go0ZFeVhPSTJS0WTSwlynF8YdIh\nnURgxUV/dOyv1Xw0FGJmOTZxR6DMTU/00tYwrfW4GIBNTMbJNQS/lVoUEpCJpP6rcq5oyQ1xE/fS\nnkrxR6q4PUTnC0BDPuBDoTng1w3z0IEoPqrVNTUUYGAjnhqyT5R48/DhI06uPgxBRaWx0OQNlMbt\nxeHwkGA2IRAU2U45SiTpzRyoCo2FAr+jhF4yViMB78cufoeOfdScm89LjPQ0VWIMPk79AnaJHhG9\n9qpDobl6DnUBkMnDS+QtiXDo2Xli9XTKcaFS/IEo6SO0ZNfYP0r6N3aGA8Zq2xUrubiEnVjVQKn8\nRmjyBspT6UCn06D3gUdRKKwMdjUPt8UHfkcJk8ZCUQ3SBh46ITBhZE3ENloOEif9GYGeKoLPtDxk\nouUwEuFd3iPhd5RQPfnqEn9Gck+QRHtt0Ek7cYtOQWUKLSgXM4iT/oSRVUTaBtZQiIkvsYuba7xO\nkecYPqFg1pzd/FRUGipN2kDFp8TiKK1CljVUVbrRyTJHy04d4rWLSqDYEz6wMk5uhgaZ0gjquqeQ\ncNETq7QwrJHRs4k4aSZu0ZUy8RihsdP6ExpT28/qtSmihRoLVcfIHD9x/nR2LtwGtoRN7upiEOVi\nLmbpv8RKT4fta5Vex84IfMRXew2HrxIvHuLkZg3exVxFpbY0aQNliYtC8XgRXoXSUjsxJgO5ZaeM\nSKY1EYfiweML3Z6LkZNR8FKq5Nd4HR/NcdKfWOlxTncv1lBCjPQ85eIvVDERCB/c5xaRzqEio27x\n1T1ymPOnmtBQgIZyvKSHrXfTmxLxPFoOnlhJnULPZnTsxiYm1XidEm8eUZpoYrXq9p7Kb4cmbaAk\nSSI+JQ6vzUlpqZ2kKDMFtlPBuW2iYtFIEnlhMkrEysk4fTZKldqtUirFNIATQbbl6PiZeGkGdjEc\nN72q7evm7M+h/AZKXUHVJWd68NUGA1tw0ZPqv2oGysSjWKXXiOLfaNlHFP8mRnqSCjGTms6eAIqV\nPHSSgViNaqBUfjs0aQMFkJQWj6/KSWGRjRYxUUHS7y2MVnxCcDCMJ59FE4NHuClVCqr15DuFljIx\nz39N6VaipZexiQlUcUeNPT1kIHMcifC5AcOhrqDqHq2UiyLObgWll7biFtX/AAHw0pYS8SpaKZsY\naT6ylEupeOaEmm7NlJw4C43Vqi7mKr8dGmd4cR2SkBKLfm8RBYU2Wl6axJpfjiCEQJIktJIGi1bP\nzsoCBiUFu/hKkoYYOQmHr5IKXzGxclKN1xKYKRcPn8MstbjpgoHNOLm65uaAj0Q0VOKPtfltSi9c\nbGTycNL/LHoI9GzBVosfIQAKLSkXfz2nuRV783ALJ3FqDJTKb4gmv4KKT43D4FMoLrKRGmtFI0G5\n81QAZbIhiv0RXc2TMWmslHov/FaaQ4zAIr1L7VPkaFBopm7z1SFnewZl4Bt8JJ71tuDZIoSg2HsM\nu6+cGE3NP5QaAo1d8v3FF1+kTZs2QbLsR48eBeDgwYNMmTKFrl270rlzZyZOnMiBAweCxl64cCE9\nevQgMzOTWbNmBfL4Afz444/ccMMNZGRkMHTo0IBAI8CGDRto2bJl0HWXLl0aNPby5csZNGgQ7dq1\no3///kH9Gxv1YqCKi4t59NFHmTFjBg8++GCQINdJdu/ezR133MGcOXOYM2cOH3/88QWZS0JqHFrF\nS2mZgySrGVmjCYqFamOO5VgYZV3wGyitpKv1OdT54KIvPuIw8lWt+6jnUHWJgkwBCrVdoXixSoup\nFHdxtl5/Z4vNV4aMDoPGjF5T83lVQ6CxS74DjBgxIkiW/aS6bkVFBddeey3r169n27ZtdOvWjSlT\npgT6rV27ltdff52PPvqIjRs3cujQoYDGVVlZGVOmTOEPf/gDe/fu5d577+WOO+6gouJUuEvz5s2D\nrjtmzJhA3bp163j66ad5+eWXyc7O5pNPPqFVq1a1fl8aGvVioGRZ5vbbb+ell17iySef5Isvvgj6\nUJ2kY8eOPPPMMzzzzDPcdNNNF2QuCSlx4HRjs7lItkSh+EQgmwRAB0siJe7wruaxcjICcVEMFEjY\nxCQs0hLAXaseaixU3aGhEB+x1Ha71MRKFBJx0/vCTgz/+ZNFjm1UAbqNXfK9Orp3784tt9xCTEwM\nsixz1113ceDAAcrKygLXvfXWW0lPTyc6OpoHHniAjz7yZxXZvHkzycnJDBs2DEmSGD16NPHx8axc\nubJW9/WFF15gxowZdO/eHSDw2hor9WKgYmNjadOmDeAX90pNTaWkJNQR4WLksY1PicVrc+LzCcyy\njM/n43j5qWDdTGsSHqFQ5Q01CrFyMh7hpOSiGChw0wsvbYgOymQeGUU0R5ZUR4m6QHsWHnwyeVik\nxVSKP3ChV0/gFyk0SKZGZaDOlYYk+f7ll1/SuXNnhgwZwrvvRlYn3rhxI82aNQsY43379oVct7Cw\nkLKyMoQQIc89IUSQbHtxcTE9evSgX79+PPLIIzgc/h/QPp+P7du3U1RURP/+/cnKyuLhhx8OkvJo\nbNS7k0RBQQGHDx8Oq1eSnZ3N7NmziYuLY9KkSaSlpdX59eNT4k4E60qUlTkwG3QcKT21nG4b5Vfv\nPGIvp2N08P5+rJyMzVeOT5TgEz400oW29xLlYg5x0p9JkO6kVDyLj4SIrRWaY6hFKh2VmpHJxVuL\n8ycjXwek3r1cHA2eIuUYkqQ56ySxquT7uUu+jxgxgokTJ5KUlMSWLVu4++67iYmJYeTIkUF9cnNz\neeihh5g3b16gzG63B40dHR2NEIKqqip69+5Nfn4+y5cvZ/jw4XzyySccPnw4YITS09NZtWoV6enp\n5OTkcP/99/Poo48yf/58CgsL8Xg8rFixguXLlyPLMpMnT2bBggXMnh1ZhbshU68Gyul08uKLL3LH\nHXcEZJJP0rZtW1577TUMBgM//fQTzz33HAsWhCqY7tq1K+jw8uabbw760NWE1Qq3P3oLSkoicXEx\nTLu6H1EGXWAMKzCj80DMVkvIuFasXKUbiwYNeqOMSWMJew29Xn9Wc6phxnh4Cz0/k8hOHAwl0raT\nhv4YkLFSV9e+cNTtPap79LRH0LHaeymThwE7TpagJbnO73qke9Re2w1Z0pKiS8cq++troyyrSr6f\nu+T76fpNvXv3ZurUqXz22WdBBqq4uJgJEyYwefJkRowYESg3m81BY1dWViJJElFRUcTGxvLmm2/y\n2GOP8dBDDzFo0CAGDBhAixb+H0dJSUkkJfl/KKelpfHQQw9x++23M3/+/MAzdMqUKSQm+pMW3333\n3bzyyisNwkDJshzxO35yaxWgU6dOdOrkTw1WbwZKURReeOEFBgwYQFZWVkj96QarR48eLFq0CJvN\nhsUSbAROfzEnOVsdnC/e/ZpjsXHoTfBFwRGKqhxcnnpqZfLh/q38bEng0Y5XhfTdUfY9AD6TRDtD\n+POGC6N11JZo6T9IbKBCzA3bQoMdg/QSlWJQHV+77mnoelAx0r9xib44I0heyBwjQbqXUvEIbkxA\n3b+WcPfIK9x8XvwuJk0UN8Y8gCxXBtr+Vjgp+b5kyZJqJd+XLFlyTpLv3bp1A0Il3xcuXBjUfs+e\nPUyePDnsWKfLsgOUl5czfvx4rr32WqZNmxbUNiMjg927d3PDDTcErpuUlBTYAuzTpw+ffeZXalYU\nhX79+nHPPfdEfB0nrxsTExMwZA0RRVHCfsetVis33xw+12S9uZm//vrrpKWlMWzYsLD1Jw8UAfbv\n98tVnGmc6orEtHiEw0V+YSUpMVbKHMF7tq3MsRyyl4ftG69tjl4yVquue2HQUCnuxMi3EQN4fcQh\n4UQivHS9Su2pKYuEgU04uSJI5v1iUOLNI1qTiNNnJ1pTfb6+hkRjl3xftWoV5eX+Z8JPP/3E4sWL\nue666wD/Smz8+PFcdtllzJ0b+uNxzJgx/L//9//Izs6mrKyMV155JegBvXPnTrxeL5WVlTz66KOk\npKQEFHM3bNgQcCg7duwYTz/9dOC6ALfccgtvvfUWxcXFlJWVsWjRIoYOHVrdW9GgqZcV1N69e1m/\nfj2tWrVi9uzZSJLEuHHjKCwsRJIkrr76ajZu3Mjq1auRZRm9Xs8DDzxwweaTkBKLoSCX48craN2m\nOVW7PYFgXYAOlgS2V4TPuRcvt6BSKaXQe/SCzS8SglicDMYivX/iQP5MpICreU1idyrVo+VYQM8p\nHHppM04RWY79QlGkHMMqx6GVtEgX/Ay07liwYAEvvvhi4Du2bNkyZs6cyc0338xVV13F2rVrSUlJ\nCZJ8P/mdvOyyy1iyZElA8t1gMARWQZIk8cwzz3DjjTcCfsn3kw/0k3LuGzduJDU1lUmTJnH06FGG\nDBmCJElMmDAhRPJ91qxZPPXUU7Rr147FixcHpNOXL1/OzJkz8Xg8tGjRgunTpwc8jVeuXMn27dvJ\nzs7mgw8+CMzr66+/JiUlhUGDBnHvvfcyduxYXC4Xw4cP58EHHwzcm9dff501a9YgSRKDBg1i8eLF\ngbodO3Ywbdo0KioqiIuL47rrrgsygg888AAlJSVceeWVGI1Gfve73zF9+vS6fwMvEmcl+V5WVsb2\n7ds5dOgQdrsds9lMmzZt6Nq1a43uoheT2kq+n+S/f1vNyv9tp/kVmVw/qTt/+e96Ppw6kmiT/2xn\ne/lxpm1fyTdX3BHirnrUvY9N9v9SoRQxOf7psO6sF3L7SkMxidJkisRCfGEeoLHSXBxiBC76XZDr\n1xUNeYtPwkaSNIYCsZJwXnkmVmCR3qRIvIkgOnSAOiLcPVpn+xCHz4Ysabnaelu1bVVU6pNzkXyv\n1QoqJyeHDz74gF27dtG2bVtSU1OJjY3F4XCwbt063n77bTp16sQtt9xyQTztLjTxLWLRehRKyxwk\nW81oJIkCmz1goDpakxBAnstGijF4bz9e25wKpQiBwO4rJ0q+uIbaRwJ2RmKV3qJc/CmkXs3Jd/74\nM0hEltkwSZ9RLuZeUOMUiSLvMcyaaOLkyKs7FZXGSq0M1GuvvcaIESO477770Ol0IfVer5cff/yR\n119/PSRdSGMgISUOyeWmstJJksWMInwcr6giPcnvLaTTyBg0MltKc0lpkRHU1yxF40MhXk6hUDl6\n0Q0UQJW4lURpIlr2h8g6+GOhjtcmbEolAqcM1Jl4MfMpWg7gITNM/YVFCB/FyjFkSUu83HAPx1VU\nzpVaGainnnqq+kG0Wvr27Uvfvn3rZFIXm/iUWDyVDlwuL0atjCxpOFJSAaeFUyToTOysKOB3Zxgo\nSZKIk1tg1cST7zlMG32Xizx7EETh4HcYpa+wiTMMFC3Qs/Oiz+m3hP/8KdRA6dhHtOYVPKI9AvNF\nn1epko9RslCmFBCvVVdQKr89anWqejI5Y3VkZzfegNC45rE4yu1oJInyCidWo57DJcFee2mmaA7a\nwyeNjZebo5MM5HsPXYTZhsctuqJnd0i5mo/v/JGlXLwiNaRczzacoh+VYmo9zAqOew+RpG2J02fD\nqokcsK2i0lipcQX1/fffU1ZWRkxMDP3790cIQX5+Pjk5OYF/R48e5ejRo7z//vsXY851js6gxRxj\nxub2UFpqJyHKRN5p6Y4AMiyJ/Pf4L2H7x2tbUOQ9Rr73MEL46sWbykNHtPyCP9v5qbfVn49PPYM6\nH2RycTIopFwnbcchrsNNn4s/KSDfewiLJpZYudlFyGKionLxqdFA2e12unXrxo4dO3j55ZfZsmUL\nXq+X5ORkWrRogaIoDB8+nKKioosx3wtGQkocFXYnJSVVNI+OYm9+cVB9z9gW/CtnR9i+iXIq+10/\nYZSiKK2n7RZBFAop6Pgl6DxEYAUkJMoQNBxPy8aEllwUzlxB+dCzkwrqL0I/33OI1rpMErWNzzFJ\nRaU21GigBg4cyI4dOxg8eDBDhw7liy++oKKiguHDh2O1Wlm9ejUDBgwI0jNpjCSmxnHkuJ3cvApa\nJljZdDh41dElOhkfgkJnFUnGqKC6BG0axcoxWus6k+/9td7OA/zS8FvPOLCX8NISLTl4VAN1DrjR\nUIJCcCJWLb/iIxYf9RMc6xYuypVCnNoq1UCp/GapcV9Ap9PRs2dP9Ho9siwzbNgwhg8fzvLly/n4\n448DkdnhvPsaE/EpsegVxR+sGx+DV/Hh8JzSfjHIWvSSzI9loTFWRo0Zo2QhRk6s53OonuilzSHl\nCmloufiBxL8FZI6jkMSZv+X0bMNNaGaDi0Wh9wgJ2hSKlVySwhioi6EEoKJyoTmnjWur1crEiRPJ\nysoiOzub7777juPHG/dBfHxKHDrFS2GhjWbRUcgaiYLK4BRBcXojOyoKwvZP1Kaikwzkevaf0/Xd\nwoUiQlO9nNUYdEdHNhIVQeVe0RJZyjmvsZsqctjtPTBI3+EW56f1JISPAu+Rc+qb69lPc+0lFHtz\nSZRD5+f2nd9nSUWlIXBeJ6utWrVixowZyLJcoyt6QychJRbZ41fWbWY1IwQUVAY7SqQareyvCtWt\nAkjUpuEWDpw+OxVKcdg2kVCEwidlL/J+6eMcdG0/59cgMOOmF0bWBY+vrqDOGS1H8Z5hoDQUo2Mf\nzvPIzpHr2c8HZc/wUdlz5HsOn3X/w+5dxMstMGui0WtMIfU2pXailvVFTXLtkRg7dixpaWlhPYs3\nbNhAWloazz33XKCsOsl48GfHmTp1Ku3atePyyy/nP//5T6Duq6++YtSoUWRmZtKzZ09mz54dkNs4\nnbKyMrp06cLo0aMDZTk5OaSlpQVJs4dTYwjXtyap+u+//56xY8fSsWPHsKE9u3btYvTo0XTs2JGs\nrCxeeumlSLezwVMnrj+XX345999/f10MVW/Ep8QhnG5slU7io0z4hCCnLDgtR3tLAnnO8OljEuU0\nirzHaKXvyBF3qLt3dWx3fI1ZY+UqyzjW2Jbg9IV+CWqLQ1yDSQpW3/TSGi2HznnMpoxW+hWvuCSo\nzCx9ipOrgHOXV99QtZxOxv5cETWaH+0rzqqv02ejVDmORtKQpG0Zto0tjMBmQ6I2cu1nsmzZMnw+\nX9h0Yl6vl3nz5tGzZ3Cy3uok4wH+/Oc/YzAY2L59O6+++ip/+tOfAiEzlZWVPPDAA2zdupW1a9eS\nl5cXVs7jqaeeIiMjI6RckiT27t0bkGYP94yM1Lc6qXqz2cy4ceP4y1/+EuYuwbRp0+jbty979uxh\n6dKlLFmyhNWrV4dt29CplYFasWJFjU4QrVq1YsWKs/uiNSROBus6nB40koTVoOfXouAs4d1jmlPh\ndeEVob/ekrRpFCk5tNZncti9K6Q+ElW+crY6VjPAcgtp+gxa6zuzx7nhnF+Hi75oKEbLKQVOL61O\nxEI1XmXN+kLLr2ck2lUws4wqEV4eoDYUeI9g85XRydifTsYrKFMKOOSufTD1Ec9eUnTplCjHSdSG\nbu8BOBRv2PKGQk1y7WdSWVnJSy+9xMMPPxy2/p///CeDBg0K0mmC6iXjHQ4HK1euZPbs2ZhMJrKy\nshg6dChLly4F4MYbb2TgwIEYjUaio6MZP358oO9JNm/ezL59+8LKRQghqo0hjdS3Jqn67t27M3r0\naFq2DP/jJCcnh1GjRgHQunVrsrKyAkrBjY1aGaiysjLuu+8+Fi5cyLfffsvBgwfJzc3l4MGDfPvt\ntyxcuJD77ruPioqKmgdroMQmR+OyOfF5FZwuDwkWIzlltqA26VHxSEgcDSO9YdHEoQgviXIax7z7\n8YraeTX+ZP+K9oYsYmW/CFkX40B2OtfjC2MEa4d84izq4GllOrykouXst5KaNr4TBqpNoEQmDx9m\nFMI/HGrDDsc3dDEOQCPJaCUdV1rGst62tNafmSPu3bTWdaLQe5TECCuoxnYGVZNc+/z587n99tsD\nYn2nczJX6IwZM2p0DjldMv7AgQPIskybNm0C9afLvp/Jhg0bglY7Pp+Phx9+OGJ6N0mS6NOnD1lZ\nWcycOZOSkpJa9z0f7rzzTj766CO8Xi/79+9n69atAbmOxkatUh2NHz+eG264gbVr17JmzRqOHDlC\nVVUVFouFVq1a0aNHD8aNG9eoRdJkrYw1wUqZ20tpqYOUGCvZBcGZI5IMfvfyXRWFXBIVrOopSRKJ\n2jQqfSUkyqkcde/lEkP1aY+KvcfY5/qBW2JPpctvrmuDUWMh27WFDGOokGNtEJhDNKC8tEXLQbyE\nfwCohCKTiyD6RCyZH7/BuqSaXtVTrhTyq3sH/eJGBcpa6zNJ1Kbyk+NLsszXV9tfEQqH3bvJMg1j\nk/2zsB58Lp8XpRY/cIa++sHZv4AzWD39lvMeoya59m3btrF582aeeOKJsFuAf/3rXwOroOo4UzL+\nTOl18Muvn66ye5J169bx8ccfB4QEARYvXkyvXr3o3Lkzu3cHb+vHx8ezYsUKOnXqRGlpKX/605+Y\nPn06//rXv2rse74MGTKE+++/n3/84x/4fD5mzJhBly4XPwVbXVBrPajo6GhGjBgRJF38WyMxNY4S\nu4viYhtt4mPYfEYslEaSiNEZ2FZxnBtahD7oT55DtTf0Zp/rh2oNlCK8rK58l35RI7GckWB2QNRY\nPqv4Jy31HTBrzt7oV/l0bLLv46jnRUbHzgTAI9qhk/bhFNfV0FvlJDp246Fj4O99zh/I83xJP3MK\n+toLtwYQwsc3tg/oYRqCSRMsvtk/6iY+KnuWNF0GLXSRtbuOevYSKychSzISEmYpNIP6IXsZWnP1\nD2uoG+NyvtQk1y6E4KGHHuKxxx4LUa0Fv3CgzWYLqNNGIpxk/JnS6+DfSjxTGHXLli1MmzaNN954\nI7Days/P58033+Tzzz8PzPN0zGZzwCgkJCTw5JNP0qNHD6qqqqisrKy27/lQVlbGxIkTeeqpp7jx\nxhspKCjgrrvuIjExkdtuu63mARoY9Sb53hBJSI1De6ScozllpKfH4fIqeBQF3Wky0qnGaLJt4T35\nkrRpHHLvZKDlVjbYP8Xps2PUhCYR9QkfKyoWEisn08FweUh9M10bOhj78H3VsiCNn9qyrPwIVllD\nhc9BsTePBG0LPGRiomYvKZVT6KTduMWpoOefHWtI1tp4t6yCW2NLschx1fQOZYtjNV7hprvp6pC6\naDmeq62TWFGxkGutk0nThx6cA+xzbqKdoTeF3hyStGlhzyj2VRaTaWocwbs1ybVXVlayfft27r33\nXoQQKIqCEILevXvzz3/+k++++44dO3bQo0cPACoqKtBqtezduzcg9BdJMv7SSy9FURQOHToUMDy7\nd+8OarNz506mTp3KSy+9RL9+p7w2f/75ZwoKCrjqqqsQQuB0OnE6nfTs2ZMtW7aEfV9OGtht27ad\ndd/acuTIEWRZDngFNm/enJEjR7JmzZpGaaDUBF6nEZ8Si07xkXe8grQ4CxpJosjmCGrT3hJPriu8\nJ1+StiUF3iMYNWZa6jqw37U1bLsD7p9wCTvXWEMFEE/Sy3Qtv7p3cNS9l3zPYVw+R9h2Z1LoPUq5\nz83vrImk63uy37UFAA8ZyBxGdZSoPXp2B7JyFHuP4RRV3GQtJsPQkd3O72s1hhCCYm8e31R+yHb7\nWq6xTkaWwi+/Wus7cUXUTWx1hPe48ggXRz17yDBkUeA9HNGDb3tFPtrzeMhdLGqSawf/zs3WrVtZ\ntWoVq1evZsmSJYB/RdSjRw/mzJnD+vXrWb16NatXr+aaa65h/PjxvPjii0D1kvEmk4nrr7+e559/\nHofDwY8//sjq1asZM2YM4Ff+njhxIo8//jhDhgSrJQ8ePJhNmzYF5jVr1iw6d+7M6tWrkSSJn376\niQMHDiCEoKSkhL/+9a/069cPi8VSY1+oXqpeCIHL5cLj8eDz+QL/B2jb1r/6Xr58OUIICgoK+PTT\nT+nUqdN5vVf1hWqgTiOhRSw6rz9Yt3l0FD4hyCsP3o/OtCbh8fkodTtD+sfJzXCKKhw+G5nGvux0\nrlsiJ/YAACAASURBVEOccRYghGCrfTW9TNeiifCgAjBoTKT9f/bOPDyq8uzD9zlzZs9kmewLSYBs\nZIGwWQUEBBWrdRdbF9x3q9XPrfardlWrX6XVam1Ba9W6FOuC+4KogKIIhCWBhCyE7Ps2+8yZOd8f\nQyYZZhISZG/u68p1wdnmncnkPOd9n+f5/aRcPuhbxirri7zb9zQ93nYs3m58wzT0bnesYYouE0lw\nkq2dRpW7ZO8SghaZ8WEVz8cIRcCGijo8e3N2Fa7vyNFOQy00ka9bRKlzHR5l6FJuWXHT5+2k3PkN\nK3ufpNS1hpnGH+531pWlLaZDbqRLDhX4bfHUkqWdhlY00CLvJkkdmq8BqLR2oRUPYA3yMNJv115W\nVsaUKVMCvUJvv/02jY2N5ObmBpyx4+LiAj+xsbH+fG9cHJIkYTAYgvbrdDoMBgNRUVEAQZbx/a+x\nZMmSwDgeeughHA4HkydP5qc//SmPPPII2dnZACxbtoyuri7uvvtucnJyyMnJCQQqtVod9Lomkwm1\nWk1srF9Vvq6ujssvv5zc3FxOO+00tFotTz/99IjOBb9VfXZ2Nq2trVx22WVkZWUF8m/ffPMNEydO\n5Morr6SpqYmsrCwuvfRSACIiIli+fDnLli2joKCAM844g0mTJh2ztu9jS3yDMKfEIHpkurvtaCUJ\njaSiqqObaekD2nqZhmgkQaTa1sUMTbBHkCCIJEjptMp7yFDnoxLep9K9memcEjimzrMDH14yNft/\nosnQFrDbs42Fxst5z/JXVnT/AUlQEy+l86PIm0NmX06fjRr3FubFLETgaxKkDLyKTJe3iVgpFTfT\n0QgbcStTv+cndfyjphSZXMDvqlzp2sR5kWfjJYlYKYNk9QR2OL9iiv6UkHMb3bt437IMlaLCi0yq\nOptebwed3qF7fPpRCWqm6E/hO/uHLIq8JrDd5u2hXa5jmn4RPsVHm7yHJCkz5HxFUWhyWtCr1HAU\nF/KlpqbS0DC0uklFRUXY7WlpadTXD910vm9T6uuvvz7sOKKjowNLgfuydOnSwExsf1x88cVB5eLn\nnnsu55577gGdC4Q05w7mpJNOGvazmzVrVlAxx7HMqGZQO3bs4J577uHLL78M2t7d3R2y7VjEnBIN\nDheWPv/sKEqvpWafXqhxhig8Pi+7rOHVIhKlDFo9tQiCwMnGC/nK+iYun7+izuLt5gvra5xkPHdE\nlhw93lbS1Xl8an0BgxjFOM0krjQ/hMXXRbnL/wXu83byqeUFfIqPnc5vyNAUohfNCNgRBIEk9Xja\nZf+X2a1MR0uoVt8YoWiErbgpBsDtc+D02UhQ9QUq+Ir1C9ju8M+Qq10lNHr8zZ0exc0a2+ssjLiM\nUyOvRBI0aAQ9PzCeRa27bEQJ8SL9PNrkPVS5SgB/5d5q6yskSBlEqsx0eZswilHoRGPIuV0eB17F\n5w9QY4xxjDOqGdS6deu4+OKLWbNmDQUFBfT09NDa2orJZKK9vf1QjfGwYU6JwW1x4HH413MTTQYa\nuoOX+AwqNQZJTaklvCZfopRJqXMtAEnqCcwwLGKH82sarbXUuLYyzXAamZrCEY2n1l3KgojLSVJn\n4vBZebn7N7gUO2eYruXt3iepc++kSa7Co7hp9lSz3bmG001XoWANlJlHi/H0ev2/GzeFqGhGRTNe\nxizCh0PDVqyKfwbT6+sgShWHJLQi73XWTZImoBa01HvK2WD/AJuvhxmGH1LrLiVWSmW8egpv9f2Z\n2cbzydWdAMB62zt0eBuGzB0FXlvQssh0De/1/Y169046vU3oRRMp6ixsLhstnt0kSeEr/apt3WhE\nFTqVhHI0T6HGGGMEjCpA9XclT506leXLl9PT04PVaqWuro5TTgld6jjWiIyLQHZ68Dg9+HwK46Ij\n+aY2VL18nC6SqiEq+RLVmXxm/VfAuLBIPw9Rp2C1Wjkr6iYSpPQRjaXX247TZyNx7/F6MYIc7Qls\ntH/E3IjFXBLzC3a7S5mqX0iNeyubHZ8SqYolST0ehWoE/EUVUaoEat39PlZqnJyCjk+xcexV9Bwu\nBBxIVOPGvwzb620nShWPINhQFH8JsiAIFOnnUuJYhd3Xx48ib6bM+RWZmkKKdHPZ4y7FrTjI1g4I\nymZqitjt3r7fAAWQoM5gcfQ91Li3kabJIUszLWBK2CzXkKrODnteta0Lj8+HVlQRmiUdY4xji1EF\nqH5LDUmSmDhxIqeffvohGdSRQhRFohOjaHe76etzkp0QwyfltSHH5ZpieadlFx6fF/U+yWiDGIlG\n0NHjbSNmry+UUYykWL8w5DrDUesuJVNTGLQUOMNwBq91P0yOdjpJ6gnk6/xCkU7FxmbHKi6M8vc8\nKegR+2dQqnh6fQOzW4dyOlHCH7ApS4Cjv9LrSODPP2XRr7UXCFDU4mNAySBbO4OvbG+RIKWTpJ5A\n0t7+JZ/iZb39HWYZzw1yuh2vLeIr65ucYDhzROMwqcxM0c8P2d7sqWG6flHYc8otfuNQacxhd4zj\ngFF9i9esWcPGjRtxuVxhRQyPB+JSY1AcbtraLIyPi8Kr+LC6gqu1xhvM6EU1e8JIHgGkqXOp85SH\n3TdS+gPUYAyiiXkRP+ZTy4u4fQPPx9WurYiI6EV/06YPAwJ9aCghSuVf4uvPffjLpn2o2fm9xnc8\noxdW4VQGpGF6vR1EqeIRseFjIO+jFjRECNF0yI1YvAOqIxvtH2EQI8lQB//+kqUJ9Pk6sXqDFUpG\nQ6+3A6/iIUYV/u9vl7WLZF3E9+qlGWOMo4VRBSiDwcDXX3/NXXfdxSuvvMKKFSvYsmULdrud1atX\nH6oxHlbMKdFIskxdfTep0SYEBJr2KTXPNEShEoQhrTcyNYXsGYX45744fTZa5T2kafJC9k3UFjNO\nk8d7fc+wy7WRzy2v0OApZ7xmMnV7RWoV9IiCgxjhXvSiBwERh9L/HgTsytkYhBUHPL7jGYEetHyF\nk9MC23q97USJ8QjYUBhovHb6rFiVHqbpT+U/vf/HRvtHrLO+SYVrQ9geN1FQkaEpGJUw7L7Uu8tJ\n0+SGV/T2+WhxWZloHF0D8RhjHK2MaonvoosuCjSCNTY2UlpayurVq3n66adxu90sWLDgkAzycGJO\niUFd0U5zcy9ROi0CUNPeQ07CgLV3uiEKp08eMkClqXNZZX0Jt8+JRhy9JUO1ewvp6kloBG3Y/XON\nF7PN+QU1ri3EqJJZHH0Pla5NtMv95bcDTY8G3idalUCvty0gm+TgXCJ4FZFWfByfM+EDJUJ4AQeL\n8DFwk+/xtQVmUMqgGVSH3ESsKoWphlNJUWdT6fJXSJ4XdQcGMVSCCGC8poidzm8o1J98QONr8FQM\nWWRT5+jFoFKTaRgLUGMcH4wqQPUHJ/D3MaSmprJo0SIUReHVV1896IM7EsSmRCPJXtrarQiCQKRO\nQ0VbF2cUDLx3s1qPgECFtSPsNTSijiQpkwZPBRO0U0Y9hkrXJop0Q6sPi4JIsX4B6AceCOKkNMpd\nG/b+z/903af8lAjheaJVP6LH207y3sZOBT1uCtFQhnMsQA3Ch57P6FCWB7Z4FBcun4NosRMBG75B\nM6hObxOxkr+qL1GdQaI6Y7+vkK7OZ7X1FVw+O9owMljDoSgKDZ4K5hgvDLu/0taFRhQZb4wOu3+M\nMY41DkomVRAEZs+efTAudcQxp8Qguj10dfmLDBJMBvZ0BduICIJAuj6SKmv3kH0tGZrCA1rKsXp7\n6JDryRhBI+9gYlWpdMnNAZWJDt9yHJyDm5nEqroCpeb9eJR81ML3y5Mdb0jU4CMyaFbpzz9FEyfe\ngMSeoBlUp9xIrCol3KWGRCPqyFQXsNM1dCPmUNh9vRjEyBBx4X4qLB24fF4mjM2gxjhOOGilPhkZ\n+396PBYwp0SjOFz0WfxFCOPMUbRaQh1uJxrNeBUfnZ7wGnmZe3MN+0od7Y9K1ybGayYjCaNrtNSI\nOgxiJD17A5FMNv5807kkqcrp9Qb3bXnIQz0mexSEhhLcBKts9HrbMaucCIIPQfAEB6hBM6jRUKSf\nF2jyHQ3d3jYyNPlD7t9hacfplUnVH/22N//tlu933nkn48ePD9o/+GH3lVdeYfbs2QFpptbW1sC+\npUuXkpmZGXTuYHWNe++9l7lz5zJu3Lj9Kmkc7YzVou5DbEoM7j4Hdru/ci83PoZeR6jAaoYhGpNa\nS/UQ/VBRqnh0opE2eWhZln1RFB9lznXk6w5sNhonpdLpDZZA8VCAWQV93rp9tuchUQUc3c6rhxON\nsBm3EmwZ3uttJl7VhFvx2270L/Epio8uuRnzKGdQAEnSeDSCjj2ekVdSKopCl7eZLM20sPu9io8q\nWxcZhihUx0CJ+ZjlO9xyyy1UVFQE9ve/r/Xr1/Poo4/ywgsvUFZWRlpaGrfeemvQueecc07QuYPd\ndQsKCnjkkUdCxHGPRY7+b/JhxhhtQPH5cNtdKIrCpORYXLIXtze4K3+8MRoRKB8iDwX+hPhu99YR\nv3a1ewtqQUuSdGCGeHGS348qGAGDeAbd3q6gJzQFI16SkNh9QK91/CGjYVtA3qgfm3cbUao43ExF\nUST6C1B6fZ3oxQi04v59l/ZFEASK9QvZaP9wxF5And5GFHwkSOFXKursvehVarIjYsPuP9oYs3wf\nmlWrVnH22WeTlZWFJEnccccdfPPNN9TV1e3/ZODKK69k9uzZQyrEH0uMBah9EAQBc3I0isON3e4m\n3exXRW4KY/9ukd2UWYaWeJqoKabKVTKim5DDZ+Ur25vMibjwgHtY4lSpYQIU+MQfohFk7L7g2ZyH\nSWPLfHtRswsviUHVewC9vkaM4gy8SuLeEnP/76ZrbwXfgZKjnY4PLztd60d0fJVrM2ZV8pDfjXJr\nBxGS5pjNP/23Wb4DvPDCCxQWFnLmmWfywQcfBLYrihL0PvqDXHn5QM541apVFBYWsnDhQl588cVh\n3/OxzKjVzEtLS0lISCAhIYHu7m5efvllRFHk0ksvJTr6+Kgeiksz02Jz09DQQ25uImqVSHlLJ5mx\nUYFj4jUGBATK+vxNsOFuHPF7ZYqsvm5g6JyST/HxieV5srUzhpSwGdG4pTTa5fqQ8SiYiFHpcXg/\nwKi6KbDdo+SjEbbjUEamunwsINCHQvgS7+HQsBk3wctDAj30eN0YVSfjoz6oSbfD24j5APJPgWsL\nIgsjLuft3r+Qqs4mShV64+3Hp/jY5drILOlH+Iaw86qwduJTlFH1QI1Zvh85y/drr72WX/3qV0RG\nRvLFF19w8803k5CQwIwZM1iwYAG33HILS5YsISMjgz/96U+IoojD4c93n3POOVx++eXEx8ezadMm\nbrjhBqKiokasnn4sMeoA9dxzz/G///u/AIHIrVKp+Pvf/8599913cEd3hDCnRCPtbKe2rovc3ESi\ndNqQUnNBEMiOMFNj66bJaSFVH3pT9C/lLKDZU0My4R1SAbY4PkNB4UTD2d9r3BFiDKKgotfXQfQ+\nN7wo1QRsvg3EcSP9swA3xUTwIqAwWtmjSGEpNmUxXvavK3e4EHAQL1xGj/KbkGCzPzTCZuzKRUHb\nJOUjrD4Jo5iMBwkPA5WVrZ7dFOgOrJepn1gplRmGRXzc9w/Oi75jyL63Wvd2DGIkRjEKC+HNMnda\n2ul1O5kwigA1Zvl+ZCzfjUYjhYUDvWwLFizg/PPP58MPP2TGjBnMmTOHu+66i+uuuw6r1cr1119P\nREQEKSn+B6LBy5gzZszg2muv5f333x8LUABdXV3ExcXh9XrZunUrf/3rX5EkiRtvvPFQjO+IEJsc\ng7q0hcYGv9VGosnAnq5QWaMso5lej4syS3vYAAWQpzuRWqWEDrmBOCnUhtvq7abEsYqLou8e1sBw\nJAiCQLI0gRZPdUiAilTl0OHdRhbb8eBPnnpJAxRUNO7998gQacUgvAN46VPu+V5jPpjo+BQBNzrh\ns5Bih+FxoWYnboKTyk7fKiJViYiCCh9J9Cr+/IdP8dEi7+ZU9ZXfe8yTdfPpkBv4yvYmp0RcErLf\np/jYYP9gWP0+l0+m2taNVlBhVo8+J3Yk+W+0fA/HvvuuvPJKrrzS//2qqanhiSeeCFuIsb/rHuuM\nOgel1+vp6elhx44dpKWlodP5lRJk+fipBuuXO2pt90/1M+OiaOkLX2quEgR2DJOHkgQ1SdJ4vrW/\nF3b/OtubFOpOHnaJZzQkqyfS7KkJ2R6tSqTDG49OWDdoq4CbYjRsGdVr6FiLU5mFji8RCe+LdSTQ\nC5/Sp9yCjrWAZ8TnadiBTCYKA0/OIi10ylZiVKFFCZ3eRoxiNHoxImTfaBEEgdnGC9nt2kpnmPzh\nLtd3qAUt4zVDV2RVWDqJ1xjJiog9pjT4/lst3wHef/997HY7iqLw5Zdf8tZbb7FokV8A2OVyBQwb\nGxsbuffee7nuuusCy5GffPIJvb3+B+aSkhKee+45zjjjjMDYPB4PTqcTRVHweDy4XK5jNoCNOkCd\nccYZ3H///Tz55JOBD7S8vDxQjXM84G/Wlenq8gel/KQ4esKUmmdFxGCT3ezoG94LK1HKoMfbHpDC\n6afCuYEubxPTDQdPFT5ZPYFmuTpke5Qqnk6viJZ1+Jf0/LiVqWiEzaN6DZ2wFofyI5ws3DuTOvKI\ndCBRi4MzkRmHhk0jPlcjhOaftHxDi3c8MaqkkOObPNUBVY6DgU40MMNwBmttb+Ab1Bvl8tn51v4e\ns4znDRt4Si1txGh0x5QG33+z5Tv4UyUzZswgPz+fhx56iP/7v//jBz/4AeAPULfeeis5OTmcffbZ\nzJw5k3vuGVipWLlyZaBH6s477+S2227jwgsH1EUuueQSsrKy2LRpE/fddx9ZWVl8++23h+LXeMgR\nlAMIrU1NTYiiSFJSUuD/siyTnj4yr6NDTf8X+0BpqGjm4Uv+iu/EQv721x/T3GPlipfe552bLkSv\nHlgVdflkfvj1y4iCwPsnXopWFX7F1GQyUdO9g3d6n+aUiEv3lp9v4wvrq5wTdRtx0sEL7j7Fy3Nd\n93F5zK+DnvBlxc2znffyi7hm+ngEGX8+TUUTZuFW2pU3GUkeSqSbOOFy2pQ30bAdo/AvupU/f+9x\nm0ymkHzAaNCxCp2whh7ltxh4A7WwM7Aktz9ihRuwKDcHNenGCP/D672JjNOcEjAc7OejvucYrykK\n2f598Cpe3u17imhVAvOMP8GhWPnU8k9iVInMjfCXMA/1Gf1ix2f0elycmZjFWUk5wx47xhhHiqG+\nk/25tXAcUJl5SkpKIDj1//9oCU4Hg9iUGFy9dhx7Z02JUUYEoLo92CZBK0qk6SNJ0kawyzb8Ule8\nNI6zIm/ga9tbLO+6m69tb3NW5E0HNTiBXzE7Ucqk2RM8i5IEDQYxkjbfCXtnUX68JKNgQk3ZiK6v\n42NczAK0yGQisedgDv+AkYQ9ATt2BwvRsh6B8HYog1HRjEgbbooC20S6UFNBh1fGLAU7DyuKQrOn\nmmR1eEfbA0UlqDjTdCPtcgMvdj/Ay92/IU5KZfYQunuDx1Pa10avx8lEo3nYY8cY41hj1EUS//73\n0KWpP/7xka8KOhjoTToktYTLJeN0edBp1Rg0arY2tlGYEpwryjKa6XQ7KOtrpyhyeOHVJPUELot5\nAJfiQCsYDlm+IENTSI17a4hQbYwqkRY5jUTt+9iUfkddAYeyCL3wMR5lf1b0bozCf+hWHgHARywC\nbgR6UDiyLQYSe3Aq8wBQiMbFHAy8g40lw56nYzUu5jD4T0HHlziUE+nxdhCtSgg6vlXejUbUYxIP\nfkOsRtRxYdRd9Pk6MIpRqIeo6htMk9OCJIg0u6xkGo6PNo8xxuhn1DOozs7OoJ/q6mrefffdIK2o\n44HY1Bhwumlt9QvFxkXoqWgNlTWaaDQjCcKwDbuDEQQRnWg8pMnsLE0xte7tyEqw0WKMKpk2WYuK\nFkQGtPkcnIaOL4Ahmmz2omcVMuP36vwBCHtnUSPrcD+UqKhDZqCgwapcilH4D8IQZdl+FPTChziU\nHwZt1Qmf0+qdiUE0hQSJCtd35GhnHLLfnyiIRKsSRhScALb3tTHeEEOi1ohuiCXmMcY4Vhn1N/qW\nW24J2bZlyxbWrVsX5uhjl9iUaJp6ZWpqushIj2W8OYqqjlAn1OwIM2s6a6m2dw/ZsHu4MaqiSZYm\nUuH6joJBun5mKYkmTxUuTkLHOuz4xS19JOAhGx1f4SS8p5eAhQjhBXqVnwdtl8lAojZQun5kkJFo\nQh7Uk+UlAwcLiRSW0qs8SLj8mpb1KGj2ugz7EWlHYjetckJIgYTd10elaxM/jr7/kL2T0VLa5y+Q\nMEijExceY4xjgYMidTR58uQQjapjHXNKDFqfj/p6/6wpPzmOTpsz5Lgso5k6ey+KAs2u0A70I0WR\nfi47ncEyOmZVEt3eFpzKHLTCV0H77Mq5e112w9XMuIgWfo+TWSFq37KSiSQc2TyUv48rDgiedViU\nG1DRSrTwIHo+BAbrKToxCc9gVa5jcPDS8QVOZtPt7QwJUNsda8jWTsekOnqq5bb3tSHAMStxNMYY\nwzHqANXa2hr0U1dXx2uvvUZcXNyhGN8Rw5wSjUbx0dTsX+KbkZGEwyPj3Uf80azRoxElsiLM+y03\nP5xEqRKw+4KXt8yqZLrkFpzKVNTsRKAnsM/FyQi40PIl4L/pxwh3YRKWkiBcjA8TFiVYURkGZlBH\nEonaQIFEMDq6lT/iViajF97FLPwMDWsRsBEt/A4Pebg4adDxCnrhY5zKAjq9TUEFEoqiUOH6jnzd\nrNCXOUK4vDINzj663M5jqsR8jDFGyqiX+G6//fag/2s0GjIzM0Pk4I91YlNiUHu9dHT4Z0Vp0SYE\noKK1i/zk4GA80RhDlKRll62TUzm41V0HilbQ41bsQds0op4IVTSd3l6ixIUYhX9jVfoVQET6lLuJ\nFn5Jn6JBJ3yEhk2IdNKhLBvSGv5oqOTzB6jMsPsUDNhZjF1ZRIJwCTHCr1DQ4OQ0+pTbGDx70vIN\n4MPNDNrlTyjWDzRnNnoqkQQ1caqRK24camrs3aTro6h19JA1VsE3xnHIQa3iO54wp0QjuNwB40JB\nEIjQathU1xISoLIjzHS47FQO4Q11JNAIOtyKE0XxIQzyB0qQMmiT95AgLSFOuAYblwUUFDwU0Kv8\nHJPwLBJVuJiDxB58DH3z85GAgA0BCwpHxihPEmpxKcPPbHR8i4d8RHqwKj/BxcJ9jlAwCi9iVa7A\no3iweLswqwZmUFscn1GsX3BU5Bj7qbJ1k6mP4qvuepJ0waoWx6pywBhjDGZEAWrHjh3k5/sTyaWl\nQ9uYDxZAHI7Ozk6eeuopenp6EEWRhQsXcuaZoVpj//jHP9iyZQtarZZbb701SBb/UGNOjsZjceJ0\nDkg4JUUa2dka2u+UZTRTa+uh0tp5VBRK1Nn9/T8iGjyKC40woM+WIKXTJu/BxyxczMTAu9gY0IBz\n8wM8fIWbyViUnxIj/I9f2miI4gl/JV8GEnVBYqqHE4nd2Lh02GN0wlqcygJ8RBEhLMOlzGFwzkrH\nZwg4cDGXDnk3ZikZ1V5tRK/iocGzi0WR1wL+Bm2teOQr5qqsnZjUWiYYYhD3+c45PMeP9NgY/72M\nKAfVL7oI8Mwzz4T9GexSuT9UKhVXXnklf/rTn3jooYf4+OOPQ2T0S0pKaG1t5cknn+SGG25g+fLl\nI77+wcCcHI2ty4rP68Nm85dfT4iLoq4rtGx5gjGGekcfKkGkzR2q2Xe4eazyK27c+h4f1GRj9wUv\n8yVIGbR5/GXhVuUqDMIK9LxHf3GEjk/Q8i1W5SpAwK1MQRJCtf0G4z2ieaj+Cr7hGsVlNHyHk1m4\nOAmZ8UQJjwD+fKLEbiKFv+xVnhBp9uwmcZAxYJe3hShVHFt7Orhq89ucuf4VLPLwJfmHg13WTlSC\nEDb/1Od0hznj6OG2225j2rRp5OXlMXfuXF599dUhj62rq+PKK68kNzeXyZMn8/DDDwftX7lyJfPn\nzyc7O5vZs2cHCrYqKys588wzKSgooKCggEsuuSTgltvPQw89RGFhIUVFRUG+Tl1dXZx33nkUFhZS\nUFDAueeeO2QhWDgb+oaGBhYvXkxWVhbz589n7dq1Qec8+uijTJ8+nfz8fBYvXhzkQfW73/2OOXPm\nkJeXx/z58wMGiv2sW7eOM844g7y8PGbPnh2w8DgeGdFj4OOPPx7492A9qQMlOjo64B2l0+lITU2l\nq6srSM/vu+++Y948f+NldnY2drudnp6ew+Y5pdFr0EdocXtk6uq6mTQpieK0RD7fFdrzk66PotVt\nY0pkIrusnSRqv7+I6EjxKQqV1k5yTf5lR6dXZpe1k5Un/oQrtvyDzzt2c37SQFNpnJRGl7cFWfGA\nkEG3spQo4SH0fIyCGokGupTHAst1XjLQsXrYMQQq+Y7AqpKKBrwksG8FX/AxTfiIQcGvz9ar/Byz\ncA9m4Q5k0tDyNX3Krcj4bQyaPVXk6n4QOL9DbiRalcIfK77muoxpvNe6i5KeFubG+YPYLmsnJklD\nsu7wLXHKPh9VNn8OKscU2jRsc3kw6r6fOv6h5Pbbb2fp0qWo1Wqqq6u56KKLKCoqClmF8Xg8XHLJ\nJVx99dX8/e9/RxRFamoGHpjWrFnDI488wt/+9jeKi4uD+jGTkpJYvnw5qampKIrC888/z80338yq\nVasAeOmll/jkk0/47LPPAPjJT35CRkYGl19+OUajkaVLlzJhgj+n/PHHH3PVVVexfft2RHHguX4o\nG/pbbrmFmTNn8q9//YvPPvuMG2+8kXXr1mE2m3nnnXdYsWIFK1euJDU1lT/84Q/cfvvtAesOo9HI\niy++yIQJEygpKeHyyy9n/PjxTJ8+HVmWuf7663nggQe49NJL2bp1K4sXL2batGlMmjTpIP6Gjg6O\nuKNuW1sbe/bsCQg09tPV1RUQXgS/Adi+jpSHGnNKDBrFR3WN39b9hIxk3F4fdlewUrZaVDFOF1I1\n1AAAIABJREFUH0mcxnBY81D1jl7uKfuUG7a8x+p2v3X79r5WsiLM6FVqZie6WF67hQZX7cBYBQ3R\nqgQ65AYAZMbTqTyDTbkYu3IB7cpLeAcVHPiX74YvgjiSlXz+AokMFEXBrYSf1YQWUWjpUpZiUy7E\no+TRpTyJE79gr0/x0SxXkzJIDLZTbqS0I4o0fSSnJkzghJhUNvb49R673A7uKf2Um7a8z4t1Ww9b\n7qfa3kWKLoI9jt6wEkeuo9xdIDs7G7Xa37vVvyxeW1sbctyKFStISkriuuuuQ6fTodFoyMvLC+x/\n/PHHufPOOykuLgYgMTGRxER/QY/JZAo89Hq9XkRRZM+ege/yf/7zH2688cbAOTfeeCMrVqwAQKvV\nBoJT//j6+vro6RmofB3Khr6mpoaysjLuuusutFotZ555Jnl5eQHX3IaGBk444QTS0tIQBIELL7ww\naGb3P//zP4HXnjp1KieccAKbNvnFj3t6erBarVxwgb+HccqUKWRnZw/pAnysM6IZ1EgLI0YrdeR0\nOlm6dClXXXVVwLZjOA53bsecEk2n3Uf9Xl+oSL0WSRT5rq6ZednBS0oTjWa0osQu6+Gxn9jW28qv\nyr/gopRJXJNezP07PiNKreXb7kZmRu81NjOp2aDr5dm6b/h1dmbg3BT1ROo95SSp+0uz1bgIb74n\nk4aKZvz2FcHNoO1yPc2eGqbqc5AYfhnwUKEWdiMznp2u9exybeS8qNtDjglf5SfhYl7IsS1yDRGi\nGYM44O+1tqOVda0mlhWfCsDM6BQeLP8Ct8/Lnds/4vyUPE6OTefRXV/R5rJx28QTDnmOaqelg9yI\nOL7orGVimB4oeZ92iKORX/ziF6xYsQKn00lRUVGIpQXA5s2bSU1NZcmSJWzZsoW8vDx+97vfkZeX\nh8/nY9u2bZx++unMnj0bt9vNokWLeOCBB9BqB2bU+fn52O12fD5fkCr4rl27Arn1/uP2vdGfeuqp\nVFdXI8syl156KWbzwMPAUDb0u3btIj09HYPBEPba5557Lu+++y41NTWMGzeOFStWsGBB+Byvw+Fg\n69atXHXVVYBf2f28887jtdde44orrmDz5s00NjZywgkHT7j4aGJEf0WdnQM3XbfbzbfffktWVhZx\ncXF0dHRQVVUVkIofKV6vl8cff5y5c+cyc+bMkP1msznodTs7OwNOmIM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DHmP3eviwtYo/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\nu/ePEKugVAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x107d25fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = H.plot()\n",
"ax.set_ylim(1, 3)\n",
"ax.set_xlim(-15, 35)\n",
"plt.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Analysis of the minimum pore radius\n",
"Is the motion one that could possibly gate the pore, i.e., change the pore radius to a radius that makes more ions pass through? (Note: gA is already open so scientifically this is not a great question...)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We collect the minimum pore radius from each profile \n",
"\n",
"$$\n",
"r(\\rho) = \\min_\\zeta R_\\rho(\\zeta)\n",
"$$\n",
"\n",
"and store it together with the corresponding order parameter:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"r_rho = np.array([[rho, profile.radius.min()] for rho, profile in H])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The plot shows quantitatively that the pore opens up. "
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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UFxfj3LlzOHfuHACgd+/eqs6TkJCA/fv3w9vbG7NmzapwPCUlBTNnzkSzZs0AAOHh4Xjo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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1080b7c90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = plt.subplot(111)\n",
"ax.plot(r_rho[:, 0], r_rho[:, 1], lw=2)\n",
"ax.set_xlabel(r\"order parameter RMSD $\\rho$ ($\\AA$)\")\n",
"ax.set_ylabel(r\"minimum HOLE pore radius $r$ ($\\AA$)\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Comparison to the stacked pore profile plots it is clear that the constriction itself changes position. We can calculate the constriction point along the pore axis\n",
"$$\n",
"\\zeta_0(\\rho) = \\text{arg} \\min_\\zeta R_\\rho(\\zeta)\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"zeta0_rho = np.array([[rho, profile.rxncoord[np.argmin(profile.radius)]] \n",
" for rho, profile in H])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The plot clearly shows that for $\\rho < 2.5$ A, the constriction is near the end but for larger distortions it is close to the center of the pore."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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WzeuCcWyYcxERaV28vtzpjTfeaHT5smXLTjuMiIj4L5/N6X2i5SIi0jJ4Pae3aZpnZE5v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\nISIiHlHBEBERj6hgiIiIR/4/KD3NNfKIj5gAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x107a49550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = plt.subplot(111)\n",
"ax.plot(zeta0_rho[:, 0], np.abs(zeta0_rho[:, 1]), lw=2)\n",
"ax.set_xlabel(r\"order parameter RMSD $\\rho$ ($\\AA$)\")\n",
"ax.set_ylabel(r\"position of constriction $|\\zeta_0|$ ($\\AA$)\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(For the plot I symmetrized $\\zeta_0$ using `np.abs(zeta0)` because for small $\\rho$, either of the two constriction sites near the ends is closed. However, the HOLE search procedure will typically not exactly reproduce the symmetry. For real work one might want to average over the two lowest values near the inside and the outside.)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## See Also\n",
"* [MDAnalysis.analysis.hole](http://pythonhosted.org/MDAnalysis/documentation_pages/analysis/hole.html) documentation\n",
"* notebook [MDAnalysis HOLE Basics](http://nbviewer.jupyter.org/gist/orbeckst/64c0bd5a037b3e434cc8ee6732030252)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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