From 1e63829dfd7d3b7bdc4706de8b6f04aea8a59e61 Mon Sep 17 00:00:00 2001 From: Will Boyd Date: Thu, 8 Oct 2015 16:04:23 -0400 Subject: [PATCH] Revised EnergyGroups class per comments from @paulromano --- .../examples/multi-group-cross-sections.ipynb | 127 ++-- .../examples/pandas-dataframes.ipynb | 672 ++++++++--------- .../pythonapi/examples/tally-arithmetic.ipynb | 676 ++++++++++++++++-- openmc/filter.py | 6 +- openmc/mgxs/groups.py | 63 +- 5 files changed, 1044 insertions(+), 500 deletions(-) diff --git a/docs/source/pythonapi/examples/multi-group-cross-sections.ipynb b/docs/source/pythonapi/examples/multi-group-cross-sections.ipynb index 5e1533781..7c7ad7bbb 100644 --- a/docs/source/pythonapi/examples/multi-group-cross-sections.ipynb +++ b/docs/source/pythonapi/examples/multi-group-cross-sections.ipynb @@ -330,7 +330,7 @@ "cell_type": "code", "execution_count": 12, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ @@ -437,7 +437,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": { "collapsed": false }, @@ -463,7 +463,7 @@ " License: http://mit-crpg.github.io/openmc/license.html\n", " Version: 0.7.0\n", " Git SHA1: 23535afa1c69644bb299bde18a094c3b99d53ae0\n", - " Date/Time: 2015-10-08 14:20:33\n", + " Date/Time: 2015-10-08 16:01:19\n", " MPI Processes: 1\n", "\n", " ===========================================================================\n", @@ -548,20 +548,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.5400E-01 seconds\n", - " Reading cross sections = 1.0100E-01 seconds\n", - " Total time in simulation = 1.4106E+01 seconds\n", - " Time in transport only = 1.4092E+01 seconds\n", - " Time in inactive batches = 2.1000E+00 seconds\n", - " Time in active batches = 1.2006E+01 seconds\n", + " Total time for initialization = 3.9700E-01 seconds\n", + " Reading cross sections = 9.1000E-02 seconds\n", + " Total time in simulation = 1.2622E+01 seconds\n", + " Time in transport only = 1.2608E+01 seconds\n", + " Time in inactive batches = 1.8770E+00 seconds\n", + " Time in active batches = 1.0745E+01 seconds\n", " Time synchronizing fission bank = 4.0000E-03 seconds\n", " Sampling source sites = 3.0000E-03 seconds\n", - " SEND/RECV source sites = 1.0000E-03 seconds\n", - " Time accumulating tallies = 3.0000E-03 seconds\n", - " Total time for finalization = 2.0000E-03 seconds\n", - " Total time elapsed = 1.4572E+01 seconds\n", - " Calculation Rate (inactive) = 11904.8 neutrons/second\n", - " Calculation Rate (active) = 8329.17 neutrons/second\n", + " SEND/RECV source sites = 0.0000E+00 seconds\n", + " Time accumulating tallies = 0.0000E+00 seconds\n", + " Total time for finalization = 3.0000E-03 seconds\n", + " Total time elapsed = 1.3030E+01 seconds\n", + " Calculation Rate (inactive) = 13319.1 neutrons/second\n", + " Calculation Rate (active) = 9306.65 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -579,7 +579,7 @@ "0" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -606,7 +606,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": { "collapsed": false }, @@ -625,7 +625,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": { "collapsed": false }, @@ -645,7 +645,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": { "collapsed": false }, @@ -667,7 +667,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": { "collapsed": false }, @@ -710,7 +710,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": { "collapsed": false }, @@ -751,7 +751,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": { "collapsed": false }, @@ -881,7 +881,7 @@ "54 1 2 2 total 0.266499 0.001265" ] }, - "execution_count": 21, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -900,7 +900,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": { "collapsed": true }, @@ -918,7 +918,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": { "collapsed": true }, @@ -946,7 +946,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": { "collapsed": false }, @@ -985,7 +985,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": { "collapsed": true }, @@ -1019,7 +1019,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "metadata": { "collapsed": false }, @@ -1181,7 +1181,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "metadata": { "collapsed": false }, @@ -1244,7 +1244,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "metadata": { "collapsed": false }, @@ -1278,7 +1278,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "metadata": { "collapsed": true }, @@ -1304,7 +1304,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "metadata": { "collapsed": true }, @@ -1333,7 +1333,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "metadata": { "collapsed": false }, @@ -1382,7 +1382,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "metadata": { "collapsed": false }, @@ -1423,7 +1423,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "metadata": { "collapsed": true }, @@ -1450,7 +1450,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 35, "metadata": { "collapsed": false }, @@ -1480,7 +1480,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 36, "metadata": { "collapsed": false }, @@ -1520,7 +1520,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 37, "metadata": { "collapsed": false }, @@ -1531,7 +1531,7 @@ "0" ] }, - "execution_count": 36, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -1561,7 +1561,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 38, "metadata": { "collapsed": false }, @@ -1582,7 +1582,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 39, "metadata": { "collapsed": false }, @@ -1618,7 +1618,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 40, "metadata": { "collapsed": false }, @@ -1672,7 +1672,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 41, "metadata": { "collapsed": false }, @@ -1714,7 +1714,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 42, "metadata": { "collapsed": false }, @@ -1844,7 +1844,7 @@ "119 10002 1 5 H-1 0.000000 0.000000" ] }, - "execution_count": 41, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -1864,7 +1864,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 43, "metadata": { "collapsed": false }, @@ -1892,7 +1892,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 44, "metadata": { "collapsed": false }, @@ -1901,7 +1901,7 @@ "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWYAAADDCAYAAACxgLv/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ned7lsJ91IAAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1932,7 +1932,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 45, "metadata": { "collapsed": true }, @@ -1954,7 +1954,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 46, "metadata": { "collapsed": false }, @@ -1993,7 +1993,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 47, "metadata": { "collapsed": false }, @@ -2076,7 +2076,7 @@ "2 10000 2 U-235 485.513530 2.418761" ] }, - "execution_count": 46, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -2102,7 +2102,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 48, "metadata": { "collapsed": true }, @@ -2124,7 +2124,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 49, "metadata": { "collapsed": false }, @@ -2172,7 +2172,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 50, "metadata": { "collapsed": false }, @@ -2200,7 +2200,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 51, "metadata": { "collapsed": false }, @@ -2235,7 +2235,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 52, "metadata": { "collapsed": false }, @@ -2292,7 +2292,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 53, "metadata": { "collapsed": false }, @@ -2310,7 +2310,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 54, "metadata": { "collapsed": false }, @@ -2346,6 +2346,15 @@ "* Spatial discretization of OpenMOC's mesh\n", "* Constant-in-angle multi-group cross sections" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/docs/source/pythonapi/examples/pandas-dataframes.ipynb b/docs/source/pythonapi/examples/pandas-dataframes.ipynb index c7da78a6e..cc2bd3b11 100644 --- a/docs/source/pythonapi/examples/pandas-dataframes.ipynb +++ b/docs/source/pythonapi/examples/pandas-dataframes.ipynb @@ -164,7 +164,18 @@ "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/universe.py:223: DeprecationWarning: Cell.add_surface(...) has been deprecated and may be removed in a future version. The region for a Cell should be defined using the region property directly.\n", + "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/universe.py:223: DeprecationWarning: Cell.add_surface(...) has been deprecated and may be removed in a future version. The region for a Cell should be defined using the region property directly.\n", + "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/universe.py:223: DeprecationWarning: Cell.add_surface(...) has been deprecated and may be removed in a future version. The region for a Cell should be defined using the region property directly.\n", + "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/universe.py:223: DeprecationWarning: Cell.add_surface(...) has been deprecated and may be removed in a future version. The region for a Cell should be defined using the region property directly.\n" + ] + } + ], "source": [ "# Create a Universe to encapsulate a fuel pin\n", "pin_cell_universe = openmc.Universe(name='1.6% Fuel Pin')\n", @@ -225,7 +236,20 @@ "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/universe.py:223: DeprecationWarning: Cell.add_surface(...) has been deprecated and may be removed in a future version. The region for a Cell should be defined using the region property directly.\n", + "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/universe.py:223: DeprecationWarning: Cell.add_surface(...) has been deprecated and may be removed in a future version. The region for a Cell should be defined using the region property directly.\n", + "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/universe.py:223: DeprecationWarning: Cell.add_surface(...) has been deprecated and may be removed in a future version. The region for a Cell should be defined using the region property directly.\n", + "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/universe.py:223: DeprecationWarning: Cell.add_surface(...) has been deprecated and may be removed in a future version. The region for a Cell should be defined using the region property directly.\n", + "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/universe.py:223: DeprecationWarning: Cell.add_surface(...) has been deprecated and may be removed in a future version. The region for a Cell should be defined using the region property directly.\n", + "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/universe.py:223: DeprecationWarning: Cell.add_surface(...) has been deprecated and may be removed in a future version. The region for a Cell should be defined using the region property directly.\n" + ] + } + ], "source": [ "# Create root Cell\n", "root_cell = openmc.Cell(name='root cell')\n", @@ -385,7 +409,7 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAAAFzUkdC\nAK7OHOkAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAAAAxQTFRF\n////chIS6YCRTb/E6kGE+wAAAAFiS0dEAIgFHUgAAAAJcEhZcwAAAEgAAABIAEbJaz4AAAPZSURB\nVGje7Zs7buMwEIZ9iey50gyNjQpXKTYudIScgkdQYTfut1idwkdQkQNsYQO2Qj0sPiVK+mlQDmwg\nwIcgg8Cc4fCTSK5W4OeFkM8rHv+2I/rgxPZEPZgR7XtQxKdXYuUXJSUnBQ/9WCgo4vOSJ+WFUvF7\nE08mlia+rn7VcKXP8sRszFX8b2MdX2y6v1Tw6MZUw4H4ojfIjD8mvn/qRL5p4+vvlMqvp2EhR8WB\nzfiz20hXORmP9fi/bM9EeUFvV5H/0yRkeSbiGRfFJErxD9ENdz7Mbhig/h89fvtFdMiI/ePUIXV4\nlXju8K3DKv9NThOZ3q2KmUy6grxFES8rjeyic+FFQav+ncg3fXjH+Ts+/iibztFqOiZuZP/Z3Oaf\nPX40NGgST2r+uvQkXXp6cKvmr+r0e1Eef5um3+JHP3IFF1D/seNZJgaDmvY0Gav1s+2f1fqpIcub\nlfKGt6apotG/NVx3SInWtLX+7Vg/Pv1YqOsnun6JSVdOXT/X7vk75f938QP+8OmSBs0fXtymMhJb\nf8qlPynYmpKCh7OB1fzNalOj1sl0ZAruHLiA+RM73pDe/VjMVP89+aTXwjyc/x5n+u991895/utr\nJTy8/06TXh0r/5JOa2JmYmqi4r/vUm/H4wLmT+z4anhr05X+q6KUXhtzr/9qSff5L5uMT//V/NdU\n4YuBTPa/8P67l/6r44ds+hYuoP5jx9ciy6XTWlibBrmx8V/TdMfjkP+6pOsu/lvM9N90sf7r+f6m\n/65n+S8p/itN15v0UkW3/+48+PRfJX6S9Joo4g+G/1qYG9KroqP/WypcuvyXPf13wH89/hHef7MB\n6R3Cqn55U4rv4kfH3zaSgQuYP7HjVf89tXrbO+hfLdr+Ozv/SP1dgtQ/Ov8C+i/3+q/Zf2D/HWi6\nbjT6rym9I/v/03/b+LHS4cTg/utTsV7/net/Afzz4f0XGX84/2j9xZ4/sePR/of2X7D/o+vPo/sv\n6h9B/Bfxr9j1Hz2eN/hO8/wfff4A848+f/1A/530/I0+/8PvH9D3H9HnT+R49P0b+v4PfP/4E/wX\nfP8Mvf9G37/D/ovuP8SeP7Hj0f0vdP8tqP9O339cyv7p3P1fdP8Z3v9G999j13/seMax8x/o+ZN7\n+O+E8zdP/8XOf8Hnz9Dzb7HnT+x49PxlCp7/BM+fOv13wvnXBfivt2lMvD8TyH/Hnb+Gz3+j589j\nz5/Y8ej9h4D+W7qQmf57efqv239n3T+C7z+h969i13/seMax+3/o/cMcu/8Y2H9n3p+J6r98pv8m\n4fwXuH+M3n+OO3++AX9clR+4PhbRAAAAJXRFWHRkYXRlOmNyZWF0ZQAyMDE1LTEwLTAzVDEzOjAz\nOjU5LTA0OjAwMu4u/QAAACV0RVh0ZGF0ZTptb2RpZnkAMjAxNS0xMC0wM1QxMzowMzo1OS0wNDow\nMEOzlkEAAAAASUVORK5CYII=\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAAAFzUkdC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\nMP8iuCwAAAAASUVORK5CYII=\n", "text/plain": [ "" ] @@ -574,8 +598,8 @@ " Copyright: 2011-2015 Massachusetts Institute of Technology\n", " License: http://mit-crpg.github.io/openmc/license.html\n", " Version: 0.7.0\n", - " Git SHA1: e0c2aace2e73367536fa03e153b67a2d038cd2b3\n", - " Date/Time: 2015-10-03 13:03:59\n", + " Git SHA1: 23535afa1c69644bb299bde18a094c3b99d53ae0\n", + " Date/Time: 2015-10-08 14:21:55\n", " MPI Processes: 1\n", "\n", " ===========================================================================\n", @@ -588,11 +612,11 @@ " Reading materials XML file...\n", " Reading tallies XML file...\n", " Building neighboring cells lists for each surface...\n", + " Loading ACE cross section table: 92235.71c\n", " Loading ACE cross section table: 92238.71c\n", " Loading ACE cross section table: 8016.71c\n", - " Loading ACE cross section table: 92235.71c\n", - " Loading ACE cross section table: 5010.71c\n", " Loading ACE cross section table: 1001.71c\n", + " Loading ACE cross section table: 5010.71c\n", " Loading ACE cross section table: 40090.71c\n", " Initializing source particles...\n", "\n", @@ -602,39 +626,35 @@ "\n", " Bat./Gen. k Average k \n", " ========= ======== ==================== \n", - " 1/1 0.59998 \n", - " 2/1 0.65473 \n", - " 3/1 0.67452 \n", - " 4/1 0.66458 \n", - " 5/1 0.70093 \n", - " 6/1 0.70726 \n", - " 7/1 0.65977 0.68351 +/- 0.02375\n", - " 8/1 0.68457 0.68387 +/- 0.01372\n", - " 9/1 0.70024 0.68796 +/- 0.01053\n", - " 10/1 0.64895 0.68016 +/- 0.01128\n", - " 11/1 0.68744 0.68137 +/- 0.00929\n", - " 12/1 0.68037 0.68123 +/- 0.00786\n", - " 13/1 0.64865 0.67715 +/- 0.00793\n", - " 14/1 0.71415 0.68127 +/- 0.00811\n", - " 15/1 0.65717 0.67886 +/- 0.00764\n", - " 16/1 0.71598 0.68223 +/- 0.00769\n", - " 17/1 0.67285 0.68145 +/- 0.00707\n", - " 18/1 0.69329 0.68236 +/- 0.00656\n", - " 19/1 0.65696 0.68055 +/- 0.00634\n", - " 20/1 0.65500 0.67884 +/- 0.00615\n", - " Triggers unsatisfied, max unc./thresh. is 1.21110 for absorption in tally 10002\n", - " The estimated number of batches is 28\n", + " 1/1 0.54958 \n", + " 2/1 0.67628 \n", + " 3/1 0.70618 \n", + " 4/1 0.66601 \n", + " 5/1 0.70876 \n", + " 6/1 0.69708 \n", + " 7/1 0.68623 0.69166 +/- 0.00543\n", + " 8/1 0.69159 0.69163 +/- 0.00313\n", + " 9/1 0.69908 0.69349 +/- 0.00289\n", + " 10/1 0.63865 0.68253 +/- 0.01120\n", + " 11/1 0.65439 0.67784 +/- 0.01027\n", + " 12/1 0.68518 0.67889 +/- 0.00875\n", + " 13/1 0.69507 0.68091 +/- 0.00784\n", + " 14/1 0.70129 0.68317 +/- 0.00728\n", + " 15/1 0.71336 0.68619 +/- 0.00717\n", + " 16/1 0.68725 0.68629 +/- 0.00649\n", + " 17/1 0.72579 0.68958 +/- 0.00678\n", + " 18/1 0.67149 0.68819 +/- 0.00639\n", + " 19/1 0.67771 0.68744 +/- 0.00596\n", + " 20/1 0.68035 0.68697 +/- 0.00557\n", + " Triggers unsatisfied, max unc./thresh. is 1.09851 for absorption in tally 10002\n", + " The estimated number of batches is 24\n", " Creating state point statepoint.020.h5...\n", - " 21/1 0.67090 0.67835 +/- 0.00577\n", - " 22/1 0.69025 0.67905 +/- 0.00546\n", - " 23/1 0.66113 0.67805 +/- 0.00525\n", - " 24/1 0.67934 0.67812 +/- 0.00496\n", - " 25/1 0.67203 0.67781 +/- 0.00472\n", - " 26/1 0.66928 0.67741 +/- 0.00451\n", - " 27/1 0.70271 0.67856 +/- 0.00445\n", - " 28/1 0.70233 0.67959 +/- 0.00437\n", - " Triggers satisfied for batch 28\n", - " Creating state point statepoint.028.h5...\n", + " 21/1 0.68105 0.68660 +/- 0.00522\n", + " 22/1 0.67168 0.68572 +/- 0.00498\n", + " 23/1 0.67520 0.68514 +/- 0.00473\n", + " 24/1 0.67940 0.68483 +/- 0.00449\n", + " Triggers satisfied for batch 24\n", + " Creating state point statepoint.024.h5...\n", "\n", " ===========================================================================\n", " ======================> SIMULATION FINISHED <======================\n", @@ -643,28 +663,28 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 3.9400E-01 seconds\n", - " Reading cross sections = 8.8000E-02 seconds\n", - " Total time in simulation = 1.0755E+01 seconds\n", - " Time in transport only = 1.0746E+01 seconds\n", - " Time in inactive batches = 1.2680E+00 seconds\n", - " Time in active batches = 9.4870E+00 seconds\n", - " Time synchronizing fission bank = 2.0000E-03 seconds\n", - " Sampling source sites = 2.0000E-03 seconds\n", + " Total time for initialization = 4.3800E-01 seconds\n", + " Reading cross sections = 9.7000E-02 seconds\n", + " Total time in simulation = 9.7830E+00 seconds\n", + " Time in transport only = 9.7730E+00 seconds\n", + " Time in inactive batches = 1.4030E+00 seconds\n", + " Time in active batches = 8.3800E+00 seconds\n", + " Time synchronizing fission bank = 1.0000E-03 seconds\n", + " Sampling source sites = 1.0000E-03 seconds\n", " SEND/RECV source sites = 0.0000E+00 seconds\n", " Time accumulating tallies = 0.0000E+00 seconds\n", " Total time for finalization = 0.0000E+00 seconds\n", - " Total time elapsed = 1.1159E+01 seconds\n", - " Calculation Rate (inactive) = 9858.04 neutrons/second\n", - " Calculation Rate (active) = 3952.78 neutrons/second\n", + " Total time elapsed = 1.0230E+01 seconds\n", + " Calculation Rate (inactive) = 8909.48 neutrons/second\n", + " Calculation Rate (active) = 4474.94 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 0.68196 +/- 0.00427\n", - " k-effective (Track-length) = 0.67959 +/- 0.00437\n", - " k-effective (Absorption) = 0.67957 +/- 0.00402\n", - " Combined k-effective = 0.67943 +/- 0.00295\n", - " Leakage Fraction = 0.34370 +/- 0.00201\n", + " k-effective (Collision) = 0.68264 +/- 0.00405\n", + " k-effective (Track-length) = 0.68483 +/- 0.00449\n", + " k-effective (Absorption) = 0.68225 +/- 0.00336\n", + " Combined k-effective = 0.68275 +/- 0.00346\n", + " Leakage Fraction = 0.34345 +/- 0.00167\n", "\n" ] }, @@ -781,13 +801,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.1127471 ]]\n", + "[[[ 0.18257268]]\n", "\n", - " [[ 0.06599162]]\n", + " [[ 0.07111957]]\n", "\n", - " [[ 0.25310075]]\n", + " [[ 0.40880276]]\n", "\n", - " [[ 0.10150973]]]\n" + " [[ 0.16407535]]]\n" ] } ], @@ -840,8 +860,8 @@ " 1\n", " (0.0e+00 - 6.3e-07)\n", " fission\n", - " 0.000224\n", - " 0.000025\n", + " 0.000202\n", + " 0.000037\n", " \n", " \n", " 1\n", @@ -850,8 +870,8 @@ " 1\n", " (0.0e+00 - 6.3e-07)\n", " nu-fission\n", - " 0.000546\n", - " 0.000062\n", + " 0.000492\n", + " 0.000090\n", " \n", " \n", " 2\n", @@ -860,7 +880,7 @@ " 1\n", " (6.3e-07 - 2.0e+01)\n", " fission\n", - " 0.000071\n", + " 0.000076\n", " 0.000004\n", " \n", " \n", @@ -870,7 +890,7 @@ " 1\n", " (6.3e-07 - 2.0e+01)\n", " nu-fission\n", - " 0.000187\n", + " 0.000204\n", " 0.000010\n", " \n", " \n", @@ -880,8 +900,8 @@ " 1\n", " (0.0e+00 - 6.3e-07)\n", " fission\n", - " 0.000392\n", - " 0.000045\n", + " 0.000375\n", + " 0.000039\n", " \n", " \n", " 5\n", @@ -890,8 +910,8 @@ " 1\n", " (0.0e+00 - 6.3e-07)\n", " nu-fission\n", - " 0.000955\n", - " 0.000110\n", + " 0.000914\n", + " 0.000094\n", " \n", " \n", " 6\n", @@ -900,8 +920,8 @@ " 1\n", " (6.3e-07 - 2.0e+01)\n", " fission\n", - " 0.000096\n", - " 0.000005\n", + " 0.000107\n", + " 0.000013\n", " \n", " \n", " 7\n", @@ -910,8 +930,8 @@ " 1\n", " (6.3e-07 - 2.0e+01)\n", " nu-fission\n", - " 0.000252\n", - " 0.000014\n", + " 0.000278\n", + " 0.000032\n", " \n", " \n", " 8\n", @@ -920,8 +940,8 @@ " 1\n", " (0.0e+00 - 6.3e-07)\n", " fission\n", - " 0.000551\n", - " 0.000053\n", + " 0.000564\n", + " 0.000056\n", " \n", " \n", " 9\n", @@ -930,8 +950,8 @@ " 1\n", " (0.0e+00 - 6.3e-07)\n", " nu-fission\n", - " 0.001343\n", - " 0.000130\n", + " 0.001374\n", + " 0.000137\n", " \n", " \n", " 10\n", @@ -940,8 +960,8 @@ " 1\n", " (6.3e-07 - 2.0e+01)\n", " fission\n", - " 0.000131\n", - " 0.000008\n", + " 0.000149\n", + " 0.000007\n", " \n", " \n", " 11\n", @@ -950,8 +970,8 @@ " 1\n", " (6.3e-07 - 2.0e+01)\n", " nu-fission\n", - " 0.000343\n", - " 0.000019\n", + " 0.000388\n", + " 0.000018\n", " \n", " \n", " 12\n", @@ -960,8 +980,8 @@ " 1\n", " (0.0e+00 - 6.3e-07)\n", " fission\n", - " 0.000688\n", - " 0.000063\n", + " 0.000669\n", + " 0.000044\n", " \n", " \n", " 13\n", @@ -970,8 +990,8 @@ " 1\n", " (0.0e+00 - 6.3e-07)\n", " nu-fission\n", - " 0.001676\n", - " 0.000153\n", + " 0.001631\n", + " 0.000108\n", " \n", " \n", " 14\n", @@ -980,8 +1000,8 @@ " 1\n", " (6.3e-07 - 2.0e+01)\n", " fission\n", - " 0.000151\n", - " 0.000007\n", + " 0.000165\n", + " 0.000011\n", " \n", " \n", " 15\n", @@ -990,8 +1010,8 @@ " 1\n", " (6.3e-07 - 2.0e+01)\n", " nu-fission\n", - " 0.000395\n", - " 0.000019\n", + " 0.000433\n", + " 0.000029\n", " \n", " \n", " 16\n", @@ -1000,8 +1020,8 @@ " 1\n", " (0.0e+00 - 6.3e-07)\n", " fission\n", - " 0.000785\n", - " 0.000065\n", + " 0.000932\n", + " 0.000069\n", " \n", " \n", " 17\n", @@ -1010,8 +1030,8 @@ " 1\n", " (0.0e+00 - 6.3e-07)\n", " nu-fission\n", - " 0.001914\n", - " 0.000158\n", + " 0.002270\n", + " 0.000168\n", " \n", " \n", " 18\n", @@ -1020,8 +1040,8 @@ " 1\n", " (6.3e-07 - 2.0e+01)\n", " fission\n", - " 0.000187\n", - " 0.000008\n", + " 0.000183\n", + " 0.000011\n", " \n", " \n", " 19\n", @@ -1030,8 +1050,8 @@ " 1\n", " (6.3e-07 - 2.0e+01)\n", " nu-fission\n", - " 0.000487\n", - " 0.000019\n", + " 0.000477\n", + " 0.000028\n", " \n", " \n", "\n", @@ -1040,26 +1060,26 @@ "text/plain": [ " mesh 1 energy [MeV] score mean std. dev.\n", " x y z \n", - "0 1 1 1 (0.0e+00 - 6.3e-07) fission 0.000224 0.000025\n", - "1 1 1 1 (0.0e+00 - 6.3e-07) nu-fission 0.000546 0.000062\n", - "2 1 1 1 (6.3e-07 - 2.0e+01) fission 0.000071 0.000004\n", - "3 1 1 1 (6.3e-07 - 2.0e+01) nu-fission 0.000187 0.000010\n", - "4 1 2 1 (0.0e+00 - 6.3e-07) fission 0.000392 0.000045\n", - "5 1 2 1 (0.0e+00 - 6.3e-07) nu-fission 0.000955 0.000110\n", - "6 1 2 1 (6.3e-07 - 2.0e+01) fission 0.000096 0.000005\n", - "7 1 2 1 (6.3e-07 - 2.0e+01) nu-fission 0.000252 0.000014\n", - "8 1 3 1 (0.0e+00 - 6.3e-07) fission 0.000551 0.000053\n", - "9 1 3 1 (0.0e+00 - 6.3e-07) nu-fission 0.001343 0.000130\n", - "10 1 3 1 (6.3e-07 - 2.0e+01) fission 0.000131 0.000008\n", - "11 1 3 1 (6.3e-07 - 2.0e+01) nu-fission 0.000343 0.000019\n", - "12 1 4 1 (0.0e+00 - 6.3e-07) fission 0.000688 0.000063\n", - "13 1 4 1 (0.0e+00 - 6.3e-07) nu-fission 0.001676 0.000153\n", - "14 1 4 1 (6.3e-07 - 2.0e+01) fission 0.000151 0.000007\n", - "15 1 4 1 (6.3e-07 - 2.0e+01) nu-fission 0.000395 0.000019\n", - "16 1 5 1 (0.0e+00 - 6.3e-07) fission 0.000785 0.000065\n", - "17 1 5 1 (0.0e+00 - 6.3e-07) nu-fission 0.001914 0.000158\n", - "18 1 5 1 (6.3e-07 - 2.0e+01) fission 0.000187 0.000008\n", - "19 1 5 1 (6.3e-07 - 2.0e+01) nu-fission 0.000487 0.000019" + "0 1 1 1 (0.0e+00 - 6.3e-07) fission 0.000202 0.000037\n", + "1 1 1 1 (0.0e+00 - 6.3e-07) nu-fission 0.000492 0.000090\n", + "2 1 1 1 (6.3e-07 - 2.0e+01) fission 0.000076 0.000004\n", + "3 1 1 1 (6.3e-07 - 2.0e+01) nu-fission 0.000204 0.000010\n", + "4 1 2 1 (0.0e+00 - 6.3e-07) fission 0.000375 0.000039\n", + "5 1 2 1 (0.0e+00 - 6.3e-07) nu-fission 0.000914 0.000094\n", + "6 1 2 1 (6.3e-07 - 2.0e+01) fission 0.000107 0.000013\n", + "7 1 2 1 (6.3e-07 - 2.0e+01) nu-fission 0.000278 0.000032\n", + "8 1 3 1 (0.0e+00 - 6.3e-07) fission 0.000564 0.000056\n", + "9 1 3 1 (0.0e+00 - 6.3e-07) nu-fission 0.001374 0.000137\n", + "10 1 3 1 (6.3e-07 - 2.0e+01) fission 0.000149 0.000007\n", + "11 1 3 1 (6.3e-07 - 2.0e+01) nu-fission 0.000388 0.000018\n", + "12 1 4 1 (0.0e+00 - 6.3e-07) fission 0.000669 0.000044\n", + "13 1 4 1 (0.0e+00 - 6.3e-07) nu-fission 0.001631 0.000108\n", + "14 1 4 1 (6.3e-07 - 2.0e+01) fission 0.000165 0.000011\n", + "15 1 4 1 (6.3e-07 - 2.0e+01) nu-fission 0.000433 0.000029\n", + "16 1 5 1 (0.0e+00 - 6.3e-07) fission 0.000932 0.000069\n", + "17 1 5 1 (0.0e+00 - 6.3e-07) nu-fission 0.002270 0.000168\n", + "18 1 5 1 (6.3e-07 - 2.0e+01) fission 0.000183 0.000011\n", + "19 1 5 1 (6.3e-07 - 2.0e+01) nu-fission 0.000477 0.000028" ] }, "execution_count": 25, @@ -1084,9 +1104,9 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEaCAYAAADtxAsqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBPCvjMC6bD6xSh4AAAAASUVORK5CYII=\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEaCAYAAADtxAsqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xu4nVV94PHvIYmXEfQYocZc9HDTEi8c7EyMI4XDYDHE\nNrFapbZDOTgzMFrqWHAEpJ2QOlbEavOEDBErJZnpAGIH0RnRcClbmFqTloRAhXCTrbmMQRSmiCgE\nTv/4rb33m529z/vuc9mXs7+f59ns9333WnuvHd7zrr3Wb71rgSRJkiRJkiRJkiRJkiRJkiSpSzwH\nbAPuAu4E3jrF7z8C/O+cNCdOw+e2QxmY2+D4T9tcDvW52Z0ugPrKz4Dj0vYpwKeIC307nQQ8Cfzd\nBPMPpOexqSlOYc0+r93lGM9BwPOdLoSm10GdLoD61suAn6TtAeAzwD3A3cD70vE1wB+n7XcA30pp\nNwCfB/4euB94Z4P3nwvcAGwnKog3AkPA2cAfEi2e4+vyHAbcDPwj8BfUft0Ppc/ZmMq4qEl5R9i/\npbMOOCNtl4FPp/SbgSMzn/nXwJb0+Nfp+CuAmzJlqVRWjXwupbsFODS9952Z14+u26/4MPBd4t/o\nmnTsYOCqVM7twG+m4+9Px+4BLsm8x0+BPyNaj28F/m36ftuI/0deYyRN2D7iYnIf8AS1Vsd7iAvk\nAPBLwPeBVwIvJi6GJwE7gMNT+g3AjWn7KGAn8EL2v2hfRq3COSl9LsAq4Nwm5VsHnJ+230H8aq5U\nGs8BS8Yp7zwOrDQuA34vbT8CXJi2T8+kuxp4W9p+NXBv2l4L/FHaXp4pS73niQs66ftelrb/Bjg2\nbf8p8PsN8u4G5qTtl6bnTxOVUMUgMD99x1cAs4BbgZWZz/+ttH0M8LWUBuDy9F0laUKezGwvJSoE\ngD8HRjOv/XfgN9L2W4nKJnvRu6ou/beIC+QItYvxVuJiX/ED4BCi0jivSfm2Aa/J7P+YWqXxvczx\nzzUp74mMX2lUyjMHeCxtP5o+t/LYCbwkbWfLXylLvX3Ufs0fTq1y/B2ipXYQ8BDw8gZ5vwF8Gfjd\n9JkA/0CtFVSxkqioKz4AfDZtP0utFXQOURFVvssO4L80+Fz1MGMa6pTvEF0phxH98tnulwFqffVv\nAn4ELMh5v0Z96eN16TTTLM9TOenG2P8CDtFSaqby/QaAtwDPtFCWZrL/btcTFeTfEBXB4w3SvxM4\ngajwLiK68Bp97nj/f37O/nGVjcDHWyy3eoj9jeqUXybOv8eAO4DT0v5hwK8S/fuvIbqSjgNOpdY9\nNAC8Nz0fCRxBxByy7iB+QUO0QH5EtHSeJFocjfwttfjEKTT+dV5572x5T0jl/QGwGHgB0a3zb+ry\nnZZ5/nbavomILVRUupRuJ1oLEN+9WVkOIv4tSOnvSNs/BzYB64mWWb0BojusBFxAxJgOJmI62Vbd\nYPpuJ1LrnvptonVX71aiq+qwtD83fYYkTUglplEZdntq5rVLqQWWKxfBm4FfT9tvTq+9kLgIrqcW\nCF+e0pxI9KlDXGS/QgRzvw28IR0/Oh3bRi2WUHEYEUy+B/gCsIfoShpKn53VqLwQMYEHiAv2X7N/\n99Ql6bM3ExUdxIX42nT8u0QcAOKCu4nowvtCyt+oe+pJoqvonlT2V2ReW0p0dzVqscwmKphKcPtj\n6fhLiK6oe4j/R+9Kx387k/ZTmff5p7r3fR/xb7udaOEsQZI67Crg3dPwvi+gFsR9KxEXmSrNLvrT\n6aPA6jZ/pmY4YxpSzauB64gun2eA/zCF793u+ym+QgTG67vIJEmSJHWTMtHdczcRR7iSuJfkG8D/\nJ+IvgyntUiKO8jgRFzgx8z5nEvdi/BPwMHBW5rURYBcR/N9LxFRGp/6rSJKm2yNERXAYcbPbXiLm\ncSwRnL+VuCdhATEibFnK9/a0XwlQL6d2k+IJxFDeyk2OI8R9DxcTsZVT0+svm5ZvJEmaNo9Qu/Ma\nYmTUf8vsn0PEET5G3OyX9U1qo6jqfYXakNsRYn6u7FD4vTgCSV3E+zSk4vZmtp+u2/85cZ/Da4gh\nuI9nHm8jphmBaD18h7jD+3Gi5ZEdJvtj9r9R8WfpfaWu4OgpaeKy9z9URkftBP4H+8cqKl4I/C9i\nUr+vEvNZfYWJ3bkudYQtDWlqVC78f0VMy3EKEZd4EdHttIC4D+QFRIzjeaLVcUq7CypNhpWGNHFj\nddtjxOinlcT8S48SU4ucR1QqTxLxi+uIaeHfT7Q4mr2n1JOWEbNVPkht2uh6a9Pr26mNBCmS9zz2\nn/J5iOgrrkw1cXmDPJKkLjWLmFZ5iJiD5y5izvys5dTWNngLEeQrkncRMaokO73CEDG3jSSpC+V1\nTy0hLvxlYvz4tdQWX6lYQUyHDDER2yAxUiQv7+eoTZImSeoBeZXGAmI0SMUuDlzXoFma+ePkXZn2\n62cOhdpCMiUOXI5TktRBeUNuiwblWhky+GIiSPhrDfLvIbqtHiemwr4BeD37r/gmSeqQvEpjN3ER\nr1hEtBDGS7MwpZnTJO+RROxieyb9nUR31qPUVjDbSszNczR1U1QfeeSRYw8//HBO0SVJk7AdGG41\n02ziwj1EjC/PC4QvpRYIL5IX9g+EH0ptPYMjiEpmsEGeMU29VatWdboIUks8Z6cPTXqa8loa+4g5\ndTali/mVwH3A2en1K1KFsZwIej9FzOI5Xt4DKoDM9gnAnxCB8+fT5zyRU0ZJUpsUmUbkG+mRdUXd\n/jkt5K13RGb7+vRQB5TL5U4XQWqJ52z7eUe4qoaHW+6+lDrKc7b9enWitNTlJkmaDgMDA9CgjrCl\nIUkqzEpDVaVSqdNFkFriOdt+VhqSpMKMaUiSDmBMQ5I0aVYaqrJ/WL3Gc7b9rDQkSYUZ05AkHcCY\nhiRp0qw0VGX/sHrNmjWlTheh71hpSOpZd93V6RL0HysNVY2MjHS6CFJLhoZGOl2EvlNkanRJ6hql\nUjwAVq+uHR8ZiYemV5HRU8uANcRCSl8EPt0gzVrgVOBnwCiwrWDe84DPECv2/SQduxD4APAc8GHg\npgaf5+ipaVAqlWxtqKccdVSJhx4a6XQxZqSJjp6aBawjLv6LgffTeLnXo4i1vM8C1hfMuwj4NeD7\nmWOLgdPS8zLg8gJllNSnfvrTTpeg/+R1Ty0hlnEtp/1rgZXsv2zrCmBj2t5MrOk9Dzg8J+/ngI8B\nX82810rgGmK513LKv4TauuOaRrYy1Auy3VN7945w8cWxbfdUe+T9il8A7Mzs70rHiqSZP07elWn/\n7rr3mp+Oj/d5kqQOyWtpFA0ctHJn+YuBjxNdU0XyG7xoE2Ma6gXZFsXnP1/i4otHOlia/pNXaewm\nYg8Vi9i/JdAozcKUZk6TvEcCQ8D2TPo7gbc0ea/djQo2OjrK0NAQAIODgwwPD1cveJWb1Nxvbb+i\nW8rjvvt5+wcf3F3l6eX9yna5XGY8eS2E2cD9wMnAHmALEdDOxjSWA+ek56XEaKmlBfMCPAL8CjF6\najFwNRHHWADcQgTZ61sbjp6S+lT9kNtVq2LbmMbUajZ6Kq+lsY+oEDYRo6GuJC76Z6fXrwBuJCqM\nh4CngDNz8tbLXv3vBa5Lz/uAD2H3lKSM+sqhEghXezjLrapKxjTUpdKv3gbOoDZ480BeJybOWW4l\n9ayxsbGGDxht+poVxvSwpSGpZw0MgJeC6WFLQ5I0aVYaqsoOvZN6Q6nTBeg7VhqSetYZZ3S6BP3H\nmIYk6QDGNCRJk2aloSpjGuo1nrPtZ6UhSSrMmIYk6QDGNCTNOM471X5WGqqyf1i9ZvXqUqeL0Hes\nNCRJhRnTkNSznHtq+hjTkCRNWpFKYxmwA3gQOL9JmrXp9e3AcQXyfiKlvQu4ldoSr0PA08C29Li8\nQPk0RYxpqPeUOl2AvpNXacwC1hEX/8XEcq3H1KVZTizJejRwFrC+QN5LgWOBYeAGYFXm/R4iKp7j\niJX7JKkh555qv7xKYwlxES8DzwLXAivr0qygtnTWZmAQmJeT98lM/oOBxyZSeE0tV+1Tr9mwYaTT\nReg7eZXGAmBnZn9XOlYkzfycvJ8EfkCs13hJ5vjhRNdUCTg+p3ySpDbKqzSKjkuYyCisi4BXAxuA\nP0/H9hDxjeOAc4GrgUMm8N6aAGMa6jWes+03O+f13dSC1KTtXTlpFqY0cwrkhagYbkzbz6QHwFbg\nYSJWsrU+0+joKENDQwAMDg4yPDxc7V6pnEjut7Zf0S3lcd9999v7918qlSiXy4wnr4UwG7gfOJlo\nBWwhAtr3ZdIsB85Jz0uBNel5vLxHEyOqAP6AiH+cDhwKPA48BxwB3A68AXiirlzepyFJ02ii92ns\nIyqETcC9wJeIi/7Z6QHRSvgeEfS+gtqIp2Z5AT4F3EMMuR0BzkvHTyCG4m4Dvpw+o77CkCTAuac6\nwTvCVVUqlapNVqkXDAyUGBsb6XQxZiTvCJckTZotDUk9y7mnpo8tDUnSpFlpqCo79E7qDaVOF6Dv\nWGlI6lnOPdV+xjQkSQcwpiFJmjQrDVUZ01Cv8ZxtPysNSVJhxjQkSQcwpiFpxnHuqfaz0lCV/cPq\nNatXlzpdhL5jpSFJKsyYhqSe5dxT08eYhiRp0opUGsuAHcRKe+c3SbM2vb6dWN87L+8nUtq7gFvZ\nf1nYC1P6HcApBcqnKWJMQ72n1OkC9J28SmMWsI64+C8mlms9pi7NcuAoYgnXs4D1BfJeChwLDAM3\nAKvS8cXAael5GXB5gTJK6lPOPdV+eRfkJcQyrmXgWeBaYGVdmhXAxrS9GRgE5uXkfTKT/2DgsbS9\nErgmpS+n/EsKfxtNiqv2qdds2DDS6SL0ndk5ry8Admb2dwFvKZBmATA/J+8ngdOBp6lVDPOB7zR4\nL0lSF8hraRQdlzCRUVgXAa8GrgLWTEEZNEnGNNRrPGfbL6+lsZv9g9SLiF//46VZmNLMKZAX4Grg\nxnHea3ejgo2OjjI0NATA4OAgw8PD1e6Vyonkfmv7Fd1SHvfdd7+9f/+lUolyucx48loIs4H7gZOB\nPcAWIqB9XybNcuCc9LyUaDUszcl7NDFCCuAPiO6p04kA+NVpfwFwCxFkr29teJ+GJE2jZvdp5LU0\n9hEVwiZiNNSVxEX/7PT6FUQrYTkRtH4KODMnL8CngNcBzwEPAx9Mx+8FrkvP+4APYfeUpCYuvtj5\np9rNO8JVVSqVqk1WqRcMDJQYGxvpdDFmJO8IlyRNmi0NST3Luaemjy0NSdKkWWmoKjv0TuoNpU4X\noO9YaUjqWc491X7GNCRJBzCmIUmaNCsNVRnTUK/xnG0/Kw1JUmHGNCRJBzCmIWnGcd6p9rPSUJX9\nw+o1q1eXOl2EvmOlIUkqzJiGpJ7l3FPTx5iGJGnSilQay4AdxEp75zdJsza9vh04rkDezxALMm0H\nrgdelo4PAU8D29Lj8gLl0xQxpqHeU+p0AfpOXqUxC1hHXPwXE8u1HlOXZjmxJOvRwFnA+gJ5bwJe\nDxwLPABcmHm/h4iK5zhi5T5Jasi5p9ovr9JYQlzEy8CzwLXAyro0K4CNaXszMAjMy8l7M/B8Js/C\nCZZfU8hV+9RrNmwY6XQR+k5epbEA2JnZ35WOFUkzv0BegA8Q64xXHE50TZWA43PKJ0lqo7xKo+i4\nhImOwroIeAa4Ou3vARYRXVPnpuOHTPC91SJjGuo1nrPtNzvn9d3ERbxiEdFiGC/NwpRmTk7eUSIe\ncnLm2DPpAbAVeJiIlWytL9jo6ChDQ0MADA4OMjw8XO1eqZxI7re2X9Et5XHffffb+/dfKpUol8uM\nJ6+FMBu4n7iw7wG2EAHt+zJplgPnpOelwJr0PF7eZcBngROBxzLvdSjwOPAccARwO/AG4Im6cnmf\nhiRNo4nep7GPqBA2AfcCXyIu+menB0Q84ntE0PsKaiOemuUFuAw4mAiIZ4fWnkgMw90GfDl9Rn2F\nIUmAc091gneEq6pUKlWbrFIvGBgoMTY20ulizEjeES5JmjRbGpJ6lnNPTR9bGpKkSbPSUFV26J3U\nG0qdLkDfsdKQ1LOce6r9jGlIkg5gTEOSNGlWGqoypqFe4znbflYakqTCjGlIkg5gTEPSjOPcU+1n\npaEq+4fVa1avLnW6CH3HSkOSVJgxDUk9y7mnpo8xDUnSpBWpNJYBO4AHgfObpFmbXt9OrO+dl/cz\nxIJM24HrgZdlXrswpd8BnFKgfJoixjTUe0qdLkDfyas0ZgHriIv/YmK51mPq0iwHjiLW8j4LWF8g\n703A64FjgQeIioKU7rT0vIxY0c/WkKSGnHuq/fIuyEuIZVzLwLPAtcDKujQrgI1pezMwCMzLyXsz\n8Hwmz8K0vRK4JqUvp/xLWvlCmjhX7VOv2bBhpNNF6Dt5lcYCYGdmf1c6ViTN/AJ5AT5ArDNOyrOr\nQB5JUgfkVRpFxyVMdBTWRcAzwNVTUAZNkjEN9RrP2fabnfP6bmBRZn8R+7cEGqVZmNLMyck7SsRD\nTs55r92NCjY6OsrQ0BAAg4ODDA8PV7tXKieS+63tV3RLedx33/32/v2XSiXK5TLjyWshzAbuJy7s\ne4AtRED7vkya5cA56XkpsCY9j5d3GfBZ4ETgscx7LSZaHUuIbqlbiCB7fWvD+zQkaRpN9D6NfUSF\nsAm4F/gScdE/Oz0g4hHfI4LWVwAfyskLcBlwMBEQ30aMkiKluy49fyO9l7WDpIace6r9vCNcVaVS\nqdpklXrBwECJsbGRThdjRvKOcEnSpNnSkNSznHtq+tjSkCRNmpWGqrJD76TeUOp0AfqOlYakrjB3\nbnQ3tfKA1vPMndvZ79nrjGlI6grtik8YBynGmIYkadKsNFS1Zk2p00WQWmIcrv2sNFR1112dLoGk\nbmdMQ1XLlsE3v9npUqhfGdPoLs1iGnmz3GqGK5XiAbBpU20un5GReEhSli0NVc2bV+KHPxzpdDHU\npybSApjIfGm2NIqxpaGG1qyBG26I7b17a62Ld70LPvKRjhVLUpeypaGq4WGD4eocYxrdxZaGqgYG\nmv1WuI2BgZOa5rOillRkyO0yYAfwIHB+kzRr0+vbgeMK5H0v8F3gOeDNmeNDwNPEwkzZxZk0hcbG\nxho+xnvNCkPdyPs02i+vpTELWAe8nVir+++Br3Hgcq9HAUcDbwHWE8u9jpf3HuA3iZX+6j3E/hWP\nJKlL5LU0lhAX8TLwLHAtsLIuzQpgY9reDAwC83Ly7gAemFTJNQ1GOl0AqSWuNNl+eZXGAmBnZn9X\nOlYkzfwCeRs5nOiaKgHHF0gvSWqTvEqjaEf2VI3C2gMsIrqnzgWuBg6ZovdWrlKnCyC1xJhG++XF\nNHYTF/GKRUSLYbw0C1OaOQXy1nsmPQC2Ag8TsZKt9QlHR0cZGhoCYHBwkOHh4WpTtXIiud/a/hln\n0FXlcb+/9ivdo9P9eVCiVOr89+22/cp2uVxmPHkthNnA/cDJRCtgC/B+DgyEn5OelwJr0nORvLcB\nHwXuTPuHAo8To6qOAG4H3gA8UVcu79OQZhjv0+guE71PYx9RIWwiRkNdSVz0z06vXwHcSFQYDwFP\nAWfm5IUYObWWqCS+TsQwTgVOBFYTgfPn0+fUVxiSpA7xjnBVlSYwj480VZx7qru4cp8kadJsaUjq\nCsY0uostDeWqrKUhSc1Yaahq9epSp4sgtSQ7XFTtYaUhSSrMmIaq7OtVJxnT6C7GNCRJk2aloYxS\npwsgtcSYRvtZaaiqMveUJDVjTENSVzCm0V2MaUiSJs1KQ1X2D6vXeM62n5WGJKkwYxqSuoIxje5i\nTEO5nHtKUp4ilcYyYAfwIHB+kzRr0+vbifW98/K+F/gusULfm+ve68KUfgdwSoHyaYo495R6jTGN\n9surNGYB64iL/2JiudZj6tIsB44i1vI+C1hfIO89xOp9t9e912LgtPS8DLi8QBklSW2Sd0FeQizj\nWiaWYL0WWFmXZgWwMW1vBgaBeTl5dwAPNPi8lcA1KX055V9S7Kto8kY6XQCpJa402X55lcYCYGdm\nf1c6ViTN/AJ5681P6VrJI0lqk7xKo+gYg+kcheU4h7YpdboAUkuMabTf7JzXdwOLMvuL2L8l0CjN\nwpRmToG8eZ+3MB07wOjoKENDQwAMDg4yPDxcbapWTiT3W9uvzD3VLeVxv7/2K92j0/15UKJU6vz3\n7bb9yna5XGY8eS2E2cD9wMnAHmALEdC+L5NmOXBOel4KrEnPRfLeBnwUuDPtLwauJuIYC4BbiCB7\nfWvD+zSkGcb7NLpLs/s08loa+4gKYRMxGupK4qJ/dnr9CuBGosJ4CHgKODMnL8TIqbXAocDXgW3A\nqcC9wHXpeR/wIeyekqSu4R3hqiqVSpkmvNReE2kBTOSctaVRjHeES5ImzZaGpK5gTKO72NJQLuee\nkpTHSkNVzj2lXpMdLqr2sNKQJBVmTENV9vWqk4xpdBdjGpKkSbPSUEap0wWQWmJMo/2sNGaouXOj\nGd7KA1rPM3duZ7+npPYypjFD2T+snjPQxsuRJ22uic49JUltMcBY+37oTP/HzFh2T6nK/mH1Gs/Z\n9rPSkCQVZkxjhjKmoV7jOdtdvE9DkjRpRSqNZcAO4EHg/CZp1qbXtwPHFcg7F7gZeAC4CRhMx4eA\np4lFmbYBlxcon6aI/cPqNZ6z7ZdXacwC1hEX/8XEcq3H1KVZTizJejRwFrC+QN4LiErjtcCtab/i\nIaLiOY5YuU+S1CXyKo0lxEW8DDwLXAusrEuzAtiYtjcTrYZ5OXmzeTYC75pg+TWFXLVPvcZztv3y\nKo0FwM7M/q50rEia+ePkfSWwN23vTfsVhxNdUyXg+JzySZLaKK/SKDrGoMgorIEm7zeWOb4HWER0\nTZ0LXA0cUrAMmiT7h9VrPGfbL++O8N3ERbxiEdFiGC/NwpRmToPju9P2XqIL64fAq4BH0/Fn0gNg\nK/AwESvZWl+w0dFRhoaGABgcHGR4eLjaVK2cSP2+D62mp6vK735/7bd6vk50H0qUSp3/vt22X9ku\nl8uMJ6+FMBu4HziZaAVsIQLa92XSLAfOSc9LgTXpeby8lwI/Bj5NBMEH0/OhwOPAc8ARwO3AG4An\n6srlfRo5HPOuXuM5210mOvfUPqJC2ESMhrqSuOifnV6/AriRqDAeAp4CzszJC3AJcB3w74hA+fvS\n8ROAPyEC58+nz6mvMCRJHeId4TPURH5NlUqlTBN++j5HasRztrt4R7gkadJsacxQ9g+r17RrOY2X\nvxx+8pP2fFYvcz0NSV1tIj8+/NHSfnZPqSo79E7qDaVOF6DvWGlIkgozpjFDGdNQP/D8mz7GNPrM\nGANt+UkwlvmvpJnP7qkZaoCx+AnWwqN0220t5xmwwlAHnXFGqdNF6DtWGpJ61uhop0vQf4xpzFDG\nNCRNhneES5ImzUpDVd6noV7jOdt+VhqSpMIccjuDtT6Xz0jLn/Hyl7ecRZoypdIILhPeXgbCVWVQ\nW73Gc3b6TCYQvgzYATwInN8kzdr0+nZife+8vHOBm4EHgJuIlfsqLkzpdwCnFCifpkyp0wWQWlTq\ndAH6Tl6lMQtYR1z8FxPLtR5Tl2Y5cBSxlvdZwPoCeS8gKo3XAremfVK609LzMuDyAmXUlLmr0wWQ\nWuQ52255F+QlxDKuZWIJ1muBlXVpVgAb0/ZmotUwLydvNs9G4F1peyVwTUpfTvmXtPKFNBmurKvu\nNDAw0PABf9j0tYF2LdDRZ/IqjQXAzsz+rnSsSJr54+R9JbA3be9N+6Q8u3I+T1KfGRsba/hYtWpV\n09eMe06PvNFTRf/Vi1TpA03ebyznc/w/P8XG+wU2MLC66Wv+EarblMvlTheh7+RVGruBRZn9Rezf\nEmiUZmFKM6fB8d1pey/RhfVD4FXAo+O8124OtH1gYODYnLJritncVzfauHFjfiJNxPaJZJoNPAwM\nAS8gok6NAuE3pu2lwHcK5L2U2miqC4BL0vbilO4FwOEpv1cqSeohpwL3E0HpC9Oxs9OjYl16fTvw\n5py8EENub6HxkNuPp/Q7gHdM1ZeQJEmSJE3Sh4F7gZ8AH5tA/r+d2uJIk/LLRLf1ncARTOz8XA2c\nPJWFkmaS+4jhy9JMcAFwUacLIc1Unwd+AdwNfAS4LB1/L3AP8YvtW+nY64kbMrcR8agj0/GfpucB\n4DMp393A+9LxEWL+hi8TFdRfTccX0YwxRJwnXwD+EdgEvIg4h34lpTkUeKRB3uXA/yNGZN6ajlXO\nz1cBtxPn7z3A24h7zzZQO2f/U0q7AXhP2j4Z2Jpev5IYeANxQ/HFRIvmbuB1rX5RqVc9Qgw2OIOY\nFwzij+BVaful6Xkt8DtpezbxhwzwZHp+DzFQYQD4JeD7xFDpEeL28fnptW8Tf7BSI0PELA9vSvtf\nAn4XuI3awJlmlQbAKuDczH7l/DyPGDgDcR4eTFRCN2XSVs71q4B3E+f4D4ipjyBmpKhULI8Av5+2\nPwj8Rd4X60fO6zRzDWQeEP3AG4F/T+3+nL8j/ug+Rvxh/7zuPY4HriZusHyUaKH8q7S/BdiTtu9K\n+aVmHiF+uED8kh9qMX+jofdbgDOJSuVNRAvkYSLusZYYfflkJv0A0Xp4hBihCfE3cUImzfXpeesE\nytgXrDRmtuwt3B8E/oi4efJOoiVyDfAbwNPEvTYnNchf/8daec9fZI49h2uzaHyNzpd9xMSmUGvl\nQrQKtgH/J+c97wB+lbgBeANwOtECPpbo+vqPwBfr8tRPa1A/U0WlnJ7TTVhpzGzZC/6RxC+zVcCP\niLvtDyf6cS8Dvgq8sS7/HcSswwcBhxG/yLbgDZeaGmVqMY3fyhw/k1hi4ddz8r+aOJe/mB5vBl5B\nVETXA3/M/ks1jBH3jQ1Ri9+dTi3GpwKsSWemsboHxF34RxMX/FuIroLziT+aZ4lg4ycz+QG+AryV\nCJKPAf+Z6KY6hgN/sTkxlcbT6Hz5M+A6YkmFrzdI0yx/Zfsk4KPE+fsk8HvEBKdXUftBfAH7+wVR\nKX2ZuP5tIQaPNPoMz2lJkiRJkiRJkiRJkiRJkiRJkiRJmqG8wVaSZriXEHcw30VMwf0+YiLHb6dj\nm1OaFxFiTxIpAAABF0lEQVR3J99NTIA3kvKPAl8jpvq+DfgXwF+mfFuBFW35FpKktngPsTZExUuJ\n2VUr8ygdTMx/dB61CfNeR0wt/0Ki0tgJDKbX/pSYKpx07H6iIpEkzQBHE9NrX0JMH/9G4P82SHc9\ntdYFxIJBbyTWOfnLzPF/IFos29KjjAsAqUvZnyq17kFi9tR3Av+V6GJqptmMwE/V7b87va/U1Zwa\nXWrdq4gFq/4nMVPrEmJFw3+ZXj+E6J66g1q302uJqbx3cGBFsgn4cGb/OKQuZUtDat0bibXTnwee\nIRa4OohYl+TFwM+AtwOXA+uJQPg+olvqWQ6cdvsTwJqU7iDgexgMlyRJkiRJkiRJkiRJkiRJkiRJ\nkiRJEsA/A3sng0v+IomuAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1109,7 +1129,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 27, @@ -1118,9 +1138,9 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAU0AAAEZCAYAAAAT73clAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nefX/AdmeWI23zkQnAAAAAElFTkSuQmCC\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAU0AAAEZCAYAAAAT73clAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAHqtJREFUeJzt3X+YVNWd5/E3tg3+wEiUCUjTkwbBH61JxMkgG3/QT0xc\n0pnAODNZ4j47KpkZeZLBnd1xEsK6u0IcNaMbs4uuPOwGM0xmhTib6NPZaPBHptXRiPYgREOIoLax\nGwF/MSLyq5veP76n7erqulX3nLpVt+r25/U896Hq1vnWPbdtv33OPfeeAyIiIiIiIiIiIiIiIiIi\nIiIiIjKKnQlsBt4FrgVWAf+5jO9bBvzvBOolIlKT1gDfTrsSFdINfDrtSsjodkzaFZDEfRTYmnYl\nAjTEKDMAjKl0RURk9PgZ0AccwLrnM4G/BW50n08E/h/wDvAW8HhO7FKgx8VtY6hFtxz4fk65+cAv\n3Xf8I3BWzmfdwHXAFmAvsB4YF1HXq4EngduBN4FvAtPdObwJvAH8PXCyK/99oB94H9gH/JXbPwd4\nytVnMzA34ngiIgX9I/DlnPffwxISwC3YNc4Gt13o9p8J/AaY7N7/NpbAAG5gKGmeAbwHXOrivwZs\nB451n78CPO2+58NYi3dxRD2vBo4Af471eI4DTnff3Ygl+MeA7+TEvMLw7nkTlmDnufefce8nRhxT\npGzqnmdTVBf2MHAa0IK12p50+/uxFuE5WML6DfByge9aiLVUH3Ux/w04HvhUTpmVwC6s5fdj4Lwi\n9dwJ/E/gKHAQeMl99xEs+X2H4i3Hfwc8APzUvX8E6ALai8SIlEVJM5sG8t4PJr7bgB3AQ1iCWur2\n7wD+A9YV3w2sw5JrvilYQs09zmtYi2/QrpzXB4DxRer5Wt77SViXvgf4F6yFe2qR+I8CX8QS9OB2\nIUMtZpHEKWmOLu9h1wJPx65N/iVD3d11wMVYIhoA/qZAfK/7fNAYoNntLyQ/eZf6/GasBXsudi3z\njxn+O5pf/jdYYv1wznYScGuJ44oEU9LMpjERr38PmOH2vYslqH7sWuWnsS76Iayr3F/ge/8B+Lwr\n24gN+hzEBmJK1SOO8cB+V7cm7Jpprt1Ywh/098AXgMuwa6zHAW0Mb/mKJEpJM5sG8l4Pvp8BPIyN\nPj+FXU98DEuWt2Aj1q9jAynLCsT/GruOeIcr+3ksafUVqUdUa7PQZyuA87Gu+Y+BH+aVuQW7Uf8d\nrJXcAywA/hOwB2t5Xod+r0VERERERERERERERETEV41OfnDRAPxT2pUQGXXmngCPvV9eXjgOBg7G\nL/4OcEo5x6u2Gk2aDNjteoXcBFw/cvcfneB/lL8qXSTf1Au2+wcBK7jBO2YrrQX3P7X8Z3xqeeEZ\n0v7jsEe142na/rZ3DN/1DwHg7ICYnYV3L38Ull8aERN1E1Qxr/iHPPi3/jHzAlPEmGML71/+HiyP\neu7qI57HeMH+8YsaYeCvYxZ0E73Wah4qKK372eZhM+lsZ+hRPhHJiMaYW4Q4+WGl+3wLMMsj9jps\nroPcP13LXPlt2IMSRUX87aqoBuBObEaaXuBZoAP4VQp1EZEKKCOxxMkP7diDGjOBC7CZu+bEiG0G\nPgu8mvNdrdhENK3Yk2SPYE/IHY2qYBotzdnYBBHd2Gw267GnOmK6uBJ1qivNbdPSrkJN0I/BtI1N\nuwYjHR9zKyBOfpgPrHWvNwITsElaSsXeDnw977sWYPMuHHFxO9z3REojaTYxfHabHryeFb4k4erU\nHyVN0za9dJnRoBaTZhnd8zj5IarMlCKxC9z7X+R91xS3v9jxhkmje15q5hvnppzXF6NkKZK8zveg\nM2rMtQxlJJaY+cFr8Oh4bH6Cz8aML1qHNJJmL3ZtYVAzwzO9U2CEXEQS1TbetkEr3kjme6MGebZS\ncvAiTn7ILzPVlWmMiD0dm3h7S075f8auhxb6rqipDoF0uudd2AXcFmAsdhG2I4V6iEiFHBuxfRz7\nH35wKyBOfugArnSv52DrUe0uEvsCNsH1NLf1YLNp7Xaff8mVn+binyl1btXWBywBNmCjXWvQyLlI\nphS5naiUqPwwuNbUamyJk3Zs0GY/sKhEbL7c7vdW4F73bx/wVWqwew7woNtEJIPKSJpQOD+sznu/\nxCM2X/4Q4s1uiyWtpCkiGRZxO1Em1HDS9PxbdW5lapFvAnuD4l6hxTumoeCKE8X9mjO9Y5pe+7l3\nzIgl0eL6ZEBMyOjuZv+Q3Q/4x3T7hzDmxIAgYHnAz7xpT9ixylXDiaVsWT43EUlJmd3zmqakKSKJ\ny3JiyfK5iUhK1NIUEfGQ5cSS5XMTkZSopSki4kG3HImIeFBLU0TEQ5YTS5bPTURS0hg3s4Ss5ZQy\nJU0RSdyxSpoiIvE1NqRdg8pR0hSRxMVuadahGj41z6p1BRyizT9kHycFHAjeYqJ3zEJ+4B3TErJ4\n9xT/EP4iIAZsrUBf/zogZp1/yKSApZfmBfy4Qy2/0D/mB08mX484Gselc9xqqOGkKSJ1K8OZJcOn\nJiKpyXBmyfCpiUhqMpxZMnxqIpKaDI+ep7EapYhkXdRylPlbYfOAbcB2YGlEmZXu8y3ArBixN7qy\nm4FHGVq2twU4ADzntrvinJqISLLCR88bsHssPoOtP/4stsxu7qqS7cAMbLndC4BV2FK+xWJvBf6L\ni78WuAH4U/d+B8MTb1FqaYpI8sJbmrOxJNYNHAHWAwvyyswH1rrXG4EJwOQSsfty4scDbwadF0qa\nIlIJ4UmzieHL9vW4fXHKTCkRexPwG+Aq4Fs5+6dhXfNO4KLiJ6buuYhUQsRAUOe/2FbEQMwjjPGr\nEADXu+0bwHeARcBO7PrmO8D5wP3AOQxvmQ6jpCkiyYvILG2n2jZoxchliXsZGqTBve4pUWaqK9MY\nIxbgHmBwwebDbgPYBLyEXSvdVPgM1D0XkUoI7553YUmrBRgLLMQGc3J1AFe613OAvcDuErEzc+IX\nYN1xgIkMtYunu3Ivlzo1EZFkhWeWPmAJsAFLZmuw0e/F7vPVWCuxHRv02Y91s4vFAtwCnAn0Y63J\nr7j9lwDfxAaOjrrj7K3MqYmIRClvwo4H3ZZrdd77JR6xAH8UUf5HbouthpPm257lTy1dJN+EkJCi\nf4QiNQTMtvoDFnrH/Fe+6R1TvDMSwfc/z6DfDojx+pV2TvYPeWWzf8y0c/1j3t3uHwPwoeP8Y84O\nO1T5ajizlCvDpyYiqcnwY5RKmiKSvAxnlgyfmoikJsOZJcOnJiKpUfdcRMRDhjNLhk9NRFITMNJf\nL5Q0RSR56p6LiHjIcGbJ8KmJSGoynFkyfGoikhp1z0VEPGQ4s2T41EQkNRnOLDV8ap4TcBwMOMQj\n/iHdZ7UEHAh+ffAM75jbTv66d8whxnrHvNw+2Ttm+p5d3jEAnBUQEzLrRJd/yLT2gOOc6B9y/K0B\nxwG43D/k4/d5BgROJjJCebMc1bQaTpoiUrcynFkyfGoikpoMZ5YMn5qIpEaj5yIiHjKcWbSwmogk\nL3xhNYB5wDZsWGppRJmV7vMtwKwYsTe6spuBRxm+auUyV34bcFmpU1PSFJHkNcTcCkfeiSW/VuAK\nRt4/0Q7MwFaOvAZYFSP2VuATwHnY2uY3uP2t2KqVrS7uLkrkRSVNEUnecTG3kWZjq0x2YytErseW\n3M01H1jrXm/EVvuaXCJ2X078eOBN93oBsM6V73bxs4udWoavPIhIasIzSxPwWs77HuCCGGWagCkl\nYm8C/hg4wFBinAI8XeC7IqmlKSLJC++eD8Q8wpiAWl2PrYf6PeC/FylXtA5qaYpI8iIyS+fzthXR\ny/BBmmas9VeszFRXpjFGLMA9wANFvqu3WAWVNEUkeRGZpW2WbYNWrB9RpAsb4GkBdmKDNFfklekA\nlmDXLOcAe4HdwFtFYmcy9JDoAuC5nO+6B7gd65bPBJ4JODURkTKE39zehyXEDe5b1gC/Aha7z1dj\nrcR2bNBmP7CoRCzALcCZQD/wEvAVt38rcK/7tw/4KiW65yHXBaphgPFxL204SwKOEjLJx3kBMcDc\nq37qHTPxgwG++K7lDu+YVrZ6x/zWnve8Y4ChX2EfG6t0nIBJPphTpeOAtaN8TfcrPuYx+yfgSLkG\nBh6Leby5iRyvqtTSFJHk6THKxHUD72JN5SOUuC9KROpMhptjaZ3aANAGvJ3S8UWkkpQ0K6KurmOI\niIcMJ820bm4fwOZN7wL+LKU6iEilhN/cXvPS+ntwIfA68FvAw9jsIk+kVBcRSVqGW5ppndrr7t83\ngPuwgaDhSfPQ8qHXDW1wbFs16iUyqnTutS1xWiMoUSdgDfN92LJUlwErRpQat7yqlRIZjdom2DZo\nxasJfbFamomahLUuB4//f4CHUqiHiFSKkmaiXiH4uRoRqQtKmiIi8Q3U6ch4HEqaIpK4/gxnlto9\nNd/5IJ4uXSQRLWFhJw2bbT+efZzkHdNAn3dMJ23eMS0f6faOAThv3AveMY2ek04AQ5OA+chfiSaO\nPQExIRPFAMwNiNkceKwyKWmKiHg4NG5szJKHK1qPSlDSFJHE9Tdk96KmkqaIJK6/Xp+RjEFJU0QS\n16ekKSISX3+GU0t2z0xEUpPl7rnWPReRxPXTEGuLMA+b+Ww7sDSizEr3+RYgZ33LyNjbsJWjtgA/\nAk52+1uAA9jqlM8Bd5U6NyVNEUncIcbG2gpoAO7Ekl8rtgRv/h207cAMbLnda4BVMWIfAs4BPgG8\nCCzL+b4dWOKdha1GWZSSpogkrp9jY20FzMaSWDe2fth6bJ3yXPOBte71RmACMLlE7MPA0ZyYqaHn\npqQpIokro3veBLyW877H7YtTZkqMWIAvY2unD5qGdc07gYtKnZsGgkQkcVHXK7s699PV+X6x0IGY\nhwhdY+x67DGke9z7nUAz8A5wPnA/1o2PfO5ZSVNEEhd1n+Z5bR/ivLYPffD+f614M79IL5bEBjVj\nLcZiZaa6Mo0lYq/GrodemrPvMEPPcm4CXsKulW4qeALUdNL8Z7/iL/yO/yG+5B9yzJf2+wcBz/Mx\n75hTGfELVdIeJnnH7GVC6UJ53uJU7xiA7pP9LyVNGr/bO+ZDLx/xjuFC/xA6/EOOvF66TCGNIVN1\nX+FZ3n8+lYLKuE+zC0taLVgrcCEjz6IDWIJds5wD7AV2A28ViZ0HfA2b9iR3ypSJWCuzH5ju4l8u\nVsEaTpoiUq/KuE+zD0uIG7DR8DXYrUKL3eerseuR7digz35gUYlYgDuAsdiAEMDPsZHyudhyO0ew\ngaLFWBKOpKQpIok7XPh2orgedFuu1Xnvl3jEgrUgC/mh22JT0hSRxOnZcxERD3r2XETEQ5afPVfS\nFJHEKWmKiHjQNU0REQ+HGZd2FSpGSVNEEqfuuYiIB3XPRUQ86JYjEREP6p6notGv+MHSRUbwnw+D\no0+fGHAgeHXCWf4xM/xP6p6mf+sd8zGe946ZUPzx3Egzt+dPWBPDyaWL5Ns296PeMWd9+1X/A83x\nD2kMm/PF5ujx5Tuvyv8IOEYBSpoiIh6UNEVEPBzSLUciIvGppSki4iHLSTPOwmr/HvhwpSsiItnR\nR0OsrR7FaWlOAp7F1sy4G5sVOe7iRyIyCmX5Ps04Lc3rgTOwhHk1sB24GTi9ctUSkXpWxhK+NS/u\nuudHgV3Y4kX9WHf9/wK3VaheIlLHykya84BtWANtaUSZle7zLcCsGLG3YesFbQF+xPC7f5e58tuA\ny0qdW5yk+RfY0pC3Ak8C5wJfAX4H+IMY8SIyyhxibKytgAbgTiz5tWKrSZ6dV6YdmIGt+3MNsCpG\n7EPYeuafAF7EEiWu3EL37zzgLkrkxTgXHk7BkmP+4xJHgS/EiBeRUaaMa5qzsVUmu9379cAChlaV\nBJgPrHWvNwITgMnAtCKxD+fEbwT+0L1eAKzDVqPsdvGzgaejKhinpXkDIxPmoK0x4kVklCmje94E\nvJbzvsfti1NmSoxYgC9jywDjYnKf7Y2K+UB2h7hEJDVlDPLEvTNnTOD3Xw8cBu4JrYOSpogkLuoe\nzJ2d29nZuaNYaC/QnPO+meEtwUJlproyjSVir8auh15a4rt6i1WwhpNmS+UPMTUg5qeBx/o9/5Bj\nju33jtlKq3fM+5zgHdOAf90Aemc+5R3T9Njb3jFnve0/Y9HPrvtX3jEn8L53TPPlr5UuVEDIzyGt\n/8OjrmlOajubSW1D4zqbVmzIL9KFDfC0ADuxQZor8sp0AEuwa5ZzgL3YnT1vFYmdB3wNmMvwOdE6\nsFbn7Vi3fCbwTLFzq+GkKSL1qozueR+WEDdgo+FrsIGcxe7z1dj1yHZs0GY/sKhELMAdwFiGBoR+\nDnwVG5e51/3b5/apey4i1XW48O1EcT3otlyr894v8YgFa0FGudltsShpikji6vW58jiUNEUkcVl+\n9jy7ZyYiqanX58rjUNIUkcQpaYqIeNA1TRERD7qmKSLiocxbjmqakqaIJE7dcxERD+qei4h40Oh5\nKrr9ir/3cf9DbPMP4ayAGLDFQTwdnXCid8xpk3Z6x5xO0VlnCuoOnFDlET7jHdMyt9s75gku9o4J\nmXzjNPx/3iexzzsG4P25/hOrNO/PnyCoOpQ0RUQ8ZDlpxl1YLcTd2HRNz+fsOwWbZeRFbM2OCRU8\nvoik5BDjYm31qJJJ83vYHHa5voElzTOAR917EckYLeEb5gngnbx9uQsirQV+v4LHF5GUZDlpVvua\n5iSsy477d1KVjy8iVaD7NCtjgKIzJK/Kef1J4HcrXB2R0efxx+HxJ5L/Xt2nmZzd2PrEu4DTgD3R\nRb9SnRqJjGKXXGLboJtuSeZ767XrHUclr2kW0gFc5V5fBdxf5eOLSBVk+ZpmJZPmOuAp4ExsAfdF\nwLeAz2K3HH3avReRjDl0eGysLcI87NGT7cDSiDIr3edbgFkxYr8I/BLoB87P2d8CHACec9tdpc6t\nkt3z/GU3B/k/EiIidaW/Lzi1NAB3YnmiF3gW66H+KqdMOzADWyztAmwAZE6J2OeByxm5QBvYqpaz\nCuwvKLtXa0UkNf19wV3v2VgS63bv1wMLGJ40c29d3Ig9JDMZmFYkNuSh6YKqfU1TREaB/r6GWFsB\nTdjlvEE9bl+cMlNixBYyDeuadwIXlSpcwy3NX3iWD5iw46B/SPBPLOSB0YD6be6P3cv4wN4G/8od\nwH/yCIDjAybFeJ/jA2L869fiO0kM8DpTvGMOBz4+uI+TvGPaT/yJZ8Qu72MU0nekcEtz4MnHGXiq\n6D1ORW5DHGaMb50i7ASasQdxzscGp8+B6FlVajhpiki9OtofkVrmfNq2Qd8ecY9TL5bEBjVjLcZi\nZaa6Mo0xYvMddhvAJuAl7FrppqgAdc9FJHl9DfG2kbqwpNUCjAUWYoM5uTqAK93rOcBe7B7wOLEw\nvJU6ET6492m6i3+52KmppSkiyTsYnFr6gCXABiyZrcEGcha7z1cDD2Aj6DuA/djtjMViwUbOV2JJ\n8ifYNczPAXOBFcAR4Kg7zt5iFVTSFJHk9ZUV/aDbcuXfKrTEIxbgPrfl+6HbYlPSFJHklZc0a5qS\npogkT0lTRMTDkbQrUDlKmiKSvP60K1A5Spoikjx1z0VEPIQ8bVcnlDRFJHlqaYqIeMhw0kzqofek\nDdhN/z5m+B9l6kz/mHP9Q4Ljij6XECFkfc/x/iHT5/4y4EBwQsCEHSETVUwI+OE1D5sgJ56QyURa\n2eodA/BxnveOWc9Cr/I/G/MFKD8vDPDDmPNu/OGYJI5XVWppikjydMuRiIgH3XIkIuIhw9c0lTRF\nJHm65UhExINamiIiHpQ0RUQ8KGmKiHjQLUciIh4yfMuRFlYTkeQdjLkVNg/YBmwHlkaUWek+3wLk\nrlsdFftF4JdYOj8/77uWufLbgMtKnJmSpohUQF/MbaQG4E4s+bUCVwBn55Vpx56bnglcA6yKEfs8\ntrja43nf1YqtWtnq4u6iRF5U0hSR5B2JuY00G1tlstuVWA8syCszH1jrXm8EJgCTS8RuA14scLwF\nwDpXvtvFzy52ajV8TbPbs/xH/Q/RE3C1+pON/jEQNvlGwBwk9ATEBHh51znVORDAWf4hrx7nH7Nl\n8xzvmOPmve0ds3n8rNKFCvh+4XXCi/rUuKeCjlW28GuaTTBs5pQe4IIYZZqAKTFi800Bni7wXZFq\nOGmKSN0Kv+Uo5vRIFZ0ZqWgdlDRFJHlRSbO3E3Z2FovsBZpz3jczsv+UX2aqK9MYI7bU8aa6fZGU\nNEUkeVFXvj7SZtugrhX5JbqwAZ4WYCc2SHNFXpkOYAl2zXIOdvFrN/BWjFgY3krtAO4Bbse65TOB\nZyJqDyhpikglHAqO7MMS4gZsNHwN8Ctgsft8NTZDeTs2aLMfWFQiFmzkfCUwEfgJ8BzwOWArcK/7\ntw/4Kuqei0jVlfcY5YNuy7U67/0Sj1iA+9xWyM1ui0VJU0SSp8coRUQ8ZPgxSiVNEUmeZjkSEfGg\npCki4kHXNEVEPITfclTzlDRFJHnqnqfBt33/akVqMcI/tVbnOBC2ot+XAmJ2BcRMDoiB6v3P9GZA\nzHv+IQefPsU/JmTyFrC5fDw9uPcPAg9WJnXPRUQ86JYjEREP6p6LiHhQ0hQR8aBrmiIiHnTLkYiI\nB3XPRUQ8qHsuIuJBtxyJiHhQ91xExIOSpoiIhwxf0zwm7QqISAb1xdwKmwdsA7YDSyPKrHSfbwFm\nxYg9BXgYeBF4iKEn+VuAA9hCa88Bd5U6NSVNEaklDcCdWPJrxZbgPTuvTDswA1tu9xpgVYzYb2BJ\n8wzgUfd+0A4s8c7CVqMsqoa75wc8y1fpVN7cHhjY4h9ybKN/zHf9Q4LMqNJxADYHxATMCBT0K/RC\nQEzAbErBx5oXeKz0zMaSWLd7vx5YwNBSvADzgbXu9Ubsv/ZkYFqR2PnAXLd/LdDJ8MQZm1qaIlJL\nmoDXct73uH1xykwpEjsJ2O1e73bvB03DuuadwEWlKljJ5tndwOeBPcDH3L7lwJ8Cb7j3y4CfVrAO\nIpKKqJGgx9wWaSDmAcbELFPo+wZy9u8EmoF3gPOB+4FzgH1RX1rJpPk94A7g73L2DQC3u01EMitq\nlOdCtw366/wCvVgSG9SMtRiLlZnqyjQW2N/rXu/GuvC7gNOwxhzAYbcBbAJewq6Vboo4gYp2z5/A\nsne+OH8hRKSuHYm5jdCFJa0WYCywEOjIK9MBXOlezwH2YkmxWGwHcJV7fRXWogSYiA0gAUx38S8X\nO7M0BoKuxU64C7gOO2ERyRTfgdwP9AFLgA1YMluDDeQsdp+vBh7ARtB3APuBRSViAb4F3Av8CTZQ\n9G/c/kuAb2IZ/Kg7TtGcVOlWXwvwY4auaX6EoeuZN2LN5D8pEDcAl+a8nQ6cXuJQ5wdUL//6chwB\nI9pA1UbPq/UkRjVHz8cHxFRr9DxkraRaGj3v6YTezqH3z66A8vPCwPDxmGKakzheVVW7pbkn5/V3\nsYQa4bOVrouITG2zbZAlzQRk9znKaifN04DX3evLgeerfHwRqYrsPkdZyaS5DruZdCLWVr8BaAPO\nw0bRX2HoOoWIZIpamiGuKLDv7goeT0RqhlqaIiIegkfPa56SpohUgLrnKfD9S/VkwDEuLF0kMd3+\nIX0hf63zJ4SJ4xT/kB0BhwHC/meaVLrICCETq8wMiHk3ICb0f7sT/EPu/EXgscql7rmIiAe1NEVE\nPKilKSLiQS1NEREPammKiHjQLUciIh7U0hQR8aBrmiIiHrLb0qzDhdW6065ADdDkUKYz7QrUiM60\nK1BAeQuf1zIlzboUMhttFnWmXYEa0Zl2BQoIXu6i5ql7LiIVUJ+tyDiUNEWkArJ7y1Gtrs3RiU1g\nLCLV9Rg2WXg54q5dDrZibcCMMSIiIiIiIiIidWEesA2bXXZpynVJUzfwC+A54Jl0q1I1dwO7GX6D\n6inAw8CLwEOErXRebwr9HJYDPdjvw3OUXulcRokGbK7wFqAR2EzYFOVZ8Aqj78L5xcAshieLW4Gv\nu9dLgW9Vu1IpKPRzuAH4y3SqMzrVy83ts7Gk2Y3dEbseWJBmhVJWq3c9VMoT2ChrrvnAWvd6LfD7\nVa1ROgr9HGD0/T6kql6SZhO2dvqgHrdvNBoAHgG6gD9LuS5pmoR1VXH/hiwklBXXAluANYyOyxSp\nqpek6XPfV9ZdiHXRPgf8OdZlG+0GGL2/I6uAacB5wOvAt9OtTvbVS9LsBZpz3jdjrc3R6HX37xvA\nfdili9FoNzDZvT4N2JNiXdK0h6E/Gt9l9P4+VE29JM0ubH3VFmAssBDoSLNCKTkBOMm9PhG4jNE7\n5VEHcJV7fRVwf4p1SdNpOa8vZ/T+PkgBnwN+jQ0ILUu5LmmZht05sBmb6mi0/BzWATuBw9i17UXY\nHQSPMLpuOcr/OXwZ+DvsFrQt2B+O0XxtV0RERERERERERERERERERERERERERERERHz8LvZUyjjs\nEc8XgNZUayRSAZqHT5J0I3AccDz2mN/fpFsdEZHa1oi1Np9Gf5Alo+plliOpDxOxrvl4rLUpkjlq\nDUiSOoB7gOnYlGXXplsdEZHadSXwD+71MVgXvS212oiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiGTV\n/wfC21DgNNvIcQAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1208,144 +1228,144 @@ " 10000\n", " U-235\n", " scatter-Y0,0\n", - " 0.038330\n", - " 0.001119\n", + " 0.037095\n", + " 0.001150\n", " \n", " \n", " 1\n", " 10000\n", " U-235\n", " scatter-Y1,-1\n", - " 0.000008\n", - " 0.000341\n", + " 0.000266\n", + " 0.000323\n", " \n", " \n", " 2\n", " 10000\n", " U-235\n", " scatter-Y1,0\n", - " -0.000342\n", - " 0.000342\n", + " -0.000417\n", + " 0.000274\n", " \n", " \n", " 3\n", " 10000\n", " U-235\n", " scatter-Y1,1\n", - " 0.000201\n", - " 0.000262\n", + " -0.000228\n", + " 0.000237\n", " \n", " \n", " 4\n", " 10000\n", " U-235\n", " scatter-Y2,-2\n", - " 0.000136\n", - " 0.000152\n", + " 0.000026\n", + " 0.000199\n", " \n", " \n", " 5\n", " 10000\n", " U-235\n", " scatter-Y2,-1\n", - " 0.000042\n", - " 0.000131\n", + " -0.000115\n", + " 0.000185\n", " \n", " \n", " 6\n", " 10000\n", " U-235\n", " scatter-Y2,0\n", - " 0.000303\n", - " 0.000185\n", + " 0.000151\n", + " 0.000159\n", " \n", " \n", " 7\n", " 10000\n", " U-235\n", " scatter-Y2,1\n", - " -0.000407\n", - " 0.000184\n", + " -0.000122\n", + " 0.000280\n", " \n", " \n", " 8\n", " 10000\n", " U-235\n", " scatter-Y2,2\n", - " -0.000145\n", - " 0.000120\n", + " 0.000008\n", + " 0.000181\n", " \n", " \n", " 9\n", " 10000\n", " U-238\n", " scatter-Y0,0\n", - " 2.319322\n", - " 0.006166\n", + " 2.328632\n", + " 0.013107\n", " \n", " \n", " 10\n", " 10000\n", " U-238\n", " scatter-Y1,-1\n", - " -0.023638\n", - " 0.001940\n", + " 0.024530\n", + " 0.002272\n", " \n", " \n", " 11\n", " 10000\n", " U-238\n", " scatter-Y1,0\n", - " -0.003463\n", - " 0.001892\n", + " -0.000059\n", + " 0.002804\n", " \n", " \n", " 12\n", " 10000\n", " U-238\n", " scatter-Y1,1\n", - " 0.025099\n", - " 0.002270\n", + " -0.027990\n", + " 0.002536\n", " \n", " \n", " 13\n", " 10000\n", " U-238\n", " scatter-Y2,-2\n", - " -0.000617\n", - " 0.001197\n", + " -0.004861\n", + " 0.001575\n", " \n", " \n", " 14\n", " 10000\n", " U-238\n", " scatter-Y2,-1\n", - " 0.002549\n", - " 0.001187\n", + " 0.000557\n", + " 0.002018\n", " \n", " \n", " 15\n", " 10000\n", " U-238\n", " scatter-Y2,0\n", - " 0.007121\n", - " 0.001646\n", + " 0.006236\n", + " 0.001627\n", " \n", " \n", " 16\n", " 10000\n", " U-238\n", " scatter-Y2,1\n", - " -0.000058\n", - " 0.001323\n", + " -0.000648\n", + " 0.001551\n", " \n", " \n", " 17\n", " 10000\n", " U-238\n", " scatter-Y2,2\n", - " -0.002235\n", - " 0.000867\n", + " -0.001031\n", + " 0.001310\n", " \n", " \n", "\n", @@ -1353,24 +1373,24 @@ ], "text/plain": [ " cell nuclide score mean std. dev.\n", - "0 10000 U-235 scatter-Y0,0 0.038330 0.001119\n", - "1 10000 U-235 scatter-Y1,-1 0.000008 0.000341\n", - "2 10000 U-235 scatter-Y1,0 -0.000342 0.000342\n", - "3 10000 U-235 scatter-Y1,1 0.000201 0.000262\n", - "4 10000 U-235 scatter-Y2,-2 0.000136 0.000152\n", - "5 10000 U-235 scatter-Y2,-1 0.000042 0.000131\n", - "6 10000 U-235 scatter-Y2,0 0.000303 0.000185\n", - "7 10000 U-235 scatter-Y2,1 -0.000407 0.000184\n", - "8 10000 U-235 scatter-Y2,2 -0.000145 0.000120\n", - "9 10000 U-238 scatter-Y0,0 2.319322 0.006166\n", - "10 10000 U-238 scatter-Y1,-1 -0.023638 0.001940\n", - "11 10000 U-238 scatter-Y1,0 -0.003463 0.001892\n", - "12 10000 U-238 scatter-Y1,1 0.025099 0.002270\n", - "13 10000 U-238 scatter-Y2,-2 -0.000617 0.001197\n", - "14 10000 U-238 scatter-Y2,-1 0.002549 0.001187\n", - "15 10000 U-238 scatter-Y2,0 0.007121 0.001646\n", - "16 10000 U-238 scatter-Y2,1 -0.000058 0.001323\n", - "17 10000 U-238 scatter-Y2,2 -0.002235 0.000867" + "0 10000 U-235 scatter-Y0,0 0.037095 0.001150\n", + "1 10000 U-235 scatter-Y1,-1 0.000266 0.000323\n", + "2 10000 U-235 scatter-Y1,0 -0.000417 0.000274\n", + "3 10000 U-235 scatter-Y1,1 -0.000228 0.000237\n", + "4 10000 U-235 scatter-Y2,-2 0.000026 0.000199\n", + "5 10000 U-235 scatter-Y2,-1 -0.000115 0.000185\n", + "6 10000 U-235 scatter-Y2,0 0.000151 0.000159\n", + "7 10000 U-235 scatter-Y2,1 -0.000122 0.000280\n", + "8 10000 U-235 scatter-Y2,2 0.000008 0.000181\n", + "9 10000 U-238 scatter-Y0,0 2.328632 0.013107\n", + "10 10000 U-238 scatter-Y1,-1 0.024530 0.002272\n", + "11 10000 U-238 scatter-Y1,0 -0.000059 0.002804\n", + "12 10000 U-238 scatter-Y1,1 -0.027990 0.002536\n", + "13 10000 U-238 scatter-Y2,-2 -0.004861 0.001575\n", + "14 10000 U-238 scatter-Y2,-1 0.000557 0.002018\n", + "15 10000 U-238 scatter-Y2,0 0.006236 0.001627\n", + "16 10000 U-238 scatter-Y2,1 -0.000648 0.001551\n", + "17 10000 U-238 scatter-Y2,2 -0.001031 0.001310" ] }, "execution_count": 29, @@ -1404,8 +1424,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.00086668 0.0061658 ]\n", - " [ 0.00011981 0.00111862]]]\n" + "[[[ 0.00131009 0.01310707]\n", + " [ 0.00018089 0.00114976]]]\n" ] } ], @@ -1473,7 +1493,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.03658762]]]\n" + "[[[ 0.04537029]]]\n" ] } ], @@ -1518,141 +1538,141 @@ " 558\n", " 279\n", " absorption\n", - " 0.000081\n", - " 0.000008\n", + " 0.000093\n", + " 0.000013\n", " \n", " \n", " 559\n", " 279\n", " scatter\n", - " 0.013109\n", - " 0.000358\n", + " 0.013504\n", + " 0.000805\n", " \n", " \n", " 560\n", " 280\n", " absorption\n", - " 0.000088\n", + " 0.000084\n", " 0.000010\n", " \n", " \n", " 561\n", " 280\n", " scatter\n", - " 0.014395\n", - " 0.000586\n", + " 0.014215\n", + " 0.000612\n", " \n", " \n", " 562\n", " 281\n", " absorption\n", - " 0.000097\n", - " 0.000010\n", + " 0.000091\n", + " 0.000008\n", " \n", " \n", " 563\n", " 281\n", " scatter\n", - " 0.014637\n", - " 0.000427\n", + " 0.014545\n", + " 0.000590\n", " \n", " \n", " 564\n", " 282\n", " absorption\n", - " 0.000107\n", - " 0.000009\n", + " 0.000112\n", + " 0.000012\n", " \n", " \n", " 565\n", " 282\n", " scatter\n", - " 0.015683\n", - " 0.000552\n", + " 0.016321\n", + " 0.000729\n", " \n", " \n", " 566\n", " 283\n", " absorption\n", - " 0.000110\n", - " 0.000009\n", + " 0.000092\n", + " 0.000007\n", " \n", " \n", " 567\n", " 283\n", " scatter\n", - " 0.016293\n", - " 0.000627\n", + " 0.016163\n", + " 0.000661\n", " \n", " \n", " 568\n", " 284\n", " absorption\n", - " 0.000111\n", - " 0.000007\n", + " 0.000104\n", + " 0.000011\n", " \n", " \n", " 569\n", " 284\n", " scatter\n", - " 0.017032\n", - " 0.000445\n", + " 0.017384\n", + " 0.000599\n", " \n", " \n", " 570\n", " 285\n", " absorption\n", - " 0.000112\n", - " 0.000006\n", + " 0.000111\n", + " 0.000011\n", " \n", " \n", " 571\n", " 285\n", " scatter\n", - " 0.017666\n", - " 0.000425\n", + " 0.018015\n", + " 0.000774\n", " \n", " \n", " 572\n", " 286\n", " absorption\n", - " 0.000123\n", - " 0.000011\n", + " 0.000125\n", + " 0.000012\n", " \n", " \n", " 573\n", " 286\n", " scatter\n", - " 0.017706\n", - " 0.000597\n", + " 0.018294\n", + " 0.000828\n", " \n", " \n", " 574\n", " 287\n", " absorption\n", - " 0.000108\n", - " 0.000011\n", + " 0.000119\n", + " 0.000013\n", " \n", " \n", " 575\n", " 287\n", " scatter\n", - " 0.017339\n", - " 0.000664\n", + " 0.017483\n", + " 0.000757\n", " \n", " \n", " 576\n", " 288\n", " absorption\n", - " 0.000129\n", - " 0.000011\n", + " 0.000113\n", + " 0.000014\n", " \n", " \n", " 577\n", " 288\n", " scatter\n", - " 0.018452\n", - " 0.000523\n", + " 0.018248\n", + " 0.000782\n", " \n", " \n", "\n", @@ -1660,26 +1680,26 @@ ], "text/plain": [ " distribcell score mean std. dev.\n", - "558 279 absorption 0.000081 0.000008\n", - "559 279 scatter 0.013109 0.000358\n", - "560 280 absorption 0.000088 0.000010\n", - "561 280 scatter 0.014395 0.000586\n", - "562 281 absorption 0.000097 0.000010\n", - "563 281 scatter 0.014637 0.000427\n", - "564 282 absorption 0.000107 0.000009\n", - "565 282 scatter 0.015683 0.000552\n", - "566 283 absorption 0.000110 0.000009\n", - "567 283 scatter 0.016293 0.000627\n", - "568 284 absorption 0.000111 0.000007\n", - "569 284 scatter 0.017032 0.000445\n", - "570 285 absorption 0.000112 0.000006\n", - "571 285 scatter 0.017666 0.000425\n", - "572 286 absorption 0.000123 0.000011\n", - "573 286 scatter 0.017706 0.000597\n", - "574 287 absorption 0.000108 0.000011\n", - "575 287 scatter 0.017339 0.000664\n", - "576 288 absorption 0.000129 0.000011\n", - "577 288 scatter 0.018452 0.000523" + "558 279 absorption 0.000093 0.000013\n", + "559 279 scatter 0.013504 0.000805\n", + "560 280 absorption 0.000084 0.000010\n", + "561 280 scatter 0.014215 0.000612\n", + "562 281 absorption 0.000091 0.000008\n", + "563 281 scatter 0.014545 0.000590\n", + "564 282 absorption 0.000112 0.000012\n", + "565 282 scatter 0.016321 0.000729\n", + "566 283 absorption 0.000092 0.000007\n", + "567 283 scatter 0.016163 0.000661\n", + "568 284 absorption 0.000104 0.000011\n", + "569 284 scatter 0.017384 0.000599\n", + "570 285 absorption 0.000111 0.000011\n", + "571 285 scatter 0.018015 0.000774\n", + "572 286 absorption 0.000125 0.000012\n", + "573 286 scatter 0.018294 0.000828\n", + "574 287 absorption 0.000119 0.000013\n", + "575 287 scatter 0.017483 0.000757\n", + "576 288 absorption 0.000113 0.000014\n", + "577 288 scatter 0.018248 0.000782" ] }, "execution_count": 33, @@ -1766,8 +1786,8 @@ " 10000\n", " 0\n", " absorption\n", - " 0.000131\n", - " 0.000014\n", + " 0.000123\n", + " 0.000012\n", " \n", " \n", " 1\n", @@ -1781,8 +1801,8 @@ " 10000\n", " 0\n", " scatter\n", - " 0.018582\n", - " 0.000680\n", + " 0.017805\n", + " 0.000808\n", " \n", " \n", " 2\n", @@ -1796,8 +1816,8 @@ " 10000\n", " 1\n", " absorption\n", - " 0.000220\n", - " 0.000023\n", + " 0.000217\n", + " 0.000020\n", " \n", " \n", " 3\n", @@ -1811,8 +1831,8 @@ " 10000\n", " 1\n", " scatter\n", - " 0.028711\n", - " 0.001186\n", + " 0.028867\n", + " 0.001263\n", " \n", " \n", " 4\n", @@ -1826,8 +1846,8 @@ " 10000\n", " 2\n", " absorption\n", - " 0.000295\n", - " 0.000022\n", + " 0.000318\n", + " 0.000020\n", " \n", " \n", " 5\n", @@ -1841,8 +1861,8 @@ " 10000\n", " 2\n", " scatter\n", - " 0.038782\n", - " 0.001084\n", + " 0.040493\n", + " 0.001269\n", " \n", " \n", " 6\n", @@ -1856,8 +1876,8 @@ " 10000\n", " 3\n", " absorption\n", - " 0.000331\n", - " 0.000022\n", + " 0.000386\n", + " 0.000018\n", " \n", " \n", " 7\n", @@ -1871,8 +1891,8 @@ " 10000\n", " 3\n", " scatter\n", - " 0.045772\n", - " 0.001084\n", + " 0.048576\n", + " 0.001337\n", " \n", " \n", " 8\n", @@ -1886,7 +1906,7 @@ " 10000\n", " 4\n", " absorption\n", - " 0.000419\n", + " 0.000501\n", " 0.000026\n", " \n", " \n", @@ -1901,8 +1921,8 @@ " 10000\n", " 4\n", " scatter\n", - " 0.055975\n", - " 0.001344\n", + " 0.057063\n", + " 0.001715\n", " \n", " \n", " 10\n", @@ -1916,8 +1936,8 @@ " 10000\n", " 5\n", " absorption\n", - " 0.000514\n", - " 0.000024\n", + " 0.000484\n", + " 0.000026\n", " \n", " \n", " 11\n", @@ -1931,8 +1951,8 @@ " 10000\n", " 5\n", " scatter\n", - " 0.063289\n", - " 0.001605\n", + " 0.060822\n", + " 0.001581\n", " \n", " \n", " 12\n", @@ -1946,8 +1966,8 @@ " 10000\n", " 6\n", " absorption\n", - " 0.000591\n", - " 0.000027\n", + " 0.000532\n", + " 0.000039\n", " \n", " \n", " 13\n", @@ -1961,8 +1981,8 @@ " 10000\n", " 6\n", " scatter\n", - " 0.071011\n", - " 0.002058\n", + " 0.069101\n", + " 0.002249\n", " \n", " \n", " 14\n", @@ -1976,8 +1996,8 @@ " 10000\n", " 7\n", " absorption\n", - " 0.000671\n", - " 0.000036\n", + " 0.000577\n", + " 0.000039\n", " \n", " \n", " 15\n", @@ -1991,8 +2011,8 @@ " 10000\n", " 7\n", " scatter\n", - " 0.077891\n", - " 0.001952\n", + " 0.076722\n", + " 0.002335\n", " \n", " \n", " 16\n", @@ -2006,8 +2026,8 @@ " 10000\n", " 8\n", " absorption\n", - " 0.000721\n", - " 0.000031\n", + " 0.000649\n", + " 0.000039\n", " \n", " \n", " 17\n", @@ -2021,8 +2041,8 @@ " 10000\n", " 8\n", " scatter\n", - " 0.086393\n", - " 0.001722\n", + " 0.081564\n", + " 0.001610\n", " \n", " \n", " 18\n", @@ -2036,8 +2056,8 @@ " 10000\n", " 9\n", " absorption\n", - " 0.000748\n", - " 0.000033\n", + " 0.000680\n", + " 0.000032\n", " \n", " \n", " 19\n", @@ -2051,8 +2071,8 @@ " 10000\n", " 9\n", " scatter\n", - " 0.090861\n", - " 0.001669\n", + " 0.087715\n", + " 0.001959\n", " \n", " \n", "\n", @@ -2086,26 +2106,26 @@ " mean std. dev. \n", " \n", " \n", - "0 0.000131 0.000014 \n", - "1 0.018582 0.000680 \n", - "2 0.000220 0.000023 \n", - "3 0.028711 0.001186 \n", - "4 0.000295 0.000022 \n", - "5 0.038782 0.001084 \n", - "6 0.000331 0.000022 \n", - "7 0.045772 0.001084 \n", - "8 0.000419 0.000026 \n", - "9 0.055975 0.001344 \n", - "10 0.000514 0.000024 \n", - "11 0.063289 0.001605 \n", - "12 0.000591 0.000027 \n", - "13 0.071011 0.002058 \n", - "14 0.000671 0.000036 \n", - "15 0.077891 0.001952 \n", - "16 0.000721 0.000031 \n", - "17 0.086393 0.001722 \n", - "18 0.000748 0.000033 \n", - "19 0.090861 0.001669 " + "0 0.000123 0.000012 \n", + "1 0.017805 0.000808 \n", + "2 0.000217 0.000020 \n", + "3 0.028867 0.001263 \n", + "4 0.000318 0.000020 \n", + "5 0.040493 0.001269 \n", + "6 0.000386 0.000018 \n", + "7 0.048576 0.001337 \n", + "8 0.000501 0.000026 \n", + "9 0.057063 0.001715 \n", + "10 0.000484 0.000026 \n", + "11 0.060822 0.001581 \n", + "12 0.000532 0.000039 \n", + "13 0.069101 0.002249 \n", + "14 0.000577 0.000039 \n", + "15 0.076722 0.002335 \n", + "16 0.000649 0.000039 \n", + "17 0.081564 0.001610 \n", + "18 0.000680 0.000032 \n", + "19 0.087715 0.001959 " ] }, "execution_count": 34, @@ -2158,38 +2178,38 @@ " \n", " \n", " mean\n", - " 0.000417\n", - " 0.000020\n", + " 0.000418\n", + " 0.000022\n", " \n", " \n", " std\n", - " 0.000238\n", - " 0.000008\n", + " 0.000239\n", + " 0.000009\n", " \n", " \n", " min\n", - " 0.000020\n", - " 0.000003\n", + " 0.000018\n", + " 0.000004\n", " \n", " \n", " 25%\n", - " 0.000214\n", - " 0.000014\n", + " 0.000202\n", + " 0.000015\n", " \n", " \n", " 50%\n", - " 0.000394\n", - " 0.000019\n", + " 0.000402\n", + " 0.000021\n", " \n", " \n", " 75%\n", - " 0.000627\n", - " 0.000025\n", + " 0.000615\n", + " 0.000027\n", " \n", " \n", " max\n", - " 0.000915\n", - " 0.000049\n", + " 0.000892\n", + " 0.000044\n", " \n", " \n", "\n", @@ -2200,13 +2220,13 @@ " \n", " \n", "count 289.000000 289.000000\n", - "mean 0.000417 0.000020\n", - "std 0.000238 0.000008\n", - "min 0.000020 0.000003\n", - "25% 0.000214 0.000014\n", - "50% 0.000394 0.000019\n", - "75% 0.000627 0.000025\n", - "max 0.000915 0.000049" + "mean 0.000418 0.000022\n", + "std 0.000239 0.000009\n", + "min 0.000018 0.000004\n", + "25% 0.000202 0.000015\n", + "50% 0.000402 0.000021\n", + "75% 0.000615 0.000027\n", + "max 0.000892 0.000044" ] }, "execution_count": 35, @@ -2241,7 +2261,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mann-Whitney Test p-value: 0.498462484897\n" + "Mann-Whitney Test p-value: 0.414863173548\n" ] } ], @@ -2279,7 +2299,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mann-Whitney Test p-value: 1.61253828675e-41\n" + "Mann-Whitney Test p-value: 3.28554363741e-42\n" ] } ], @@ -2325,7 +2345,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 38, @@ -2334,9 +2354,9 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEZCAYAAABvpam5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRCKRSCSB5/8ByMyfADZGvh4AAAAASUVORK5CYII=\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEZCAYAAABvpam5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4FFXbh+/dTdndVGroBAGRIl0ERAwgSBFRxIIgYgV7\nV7Ch2AEriAjyiqgIoqgIvIooEXlFhU+QjigiTXpPT/b3/TGTsAkJBJKwWTj3de2VnZlzzvxmdnKe\nOc9zChgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FQKhkKTAi0CIPBYDBA\nO+AnYD+wB1gItCximQOBH/PsmwQ8W8RySxIfcBg4BGwF3gRCCpn3aeCDkpFlONMp7ENoMJxKooFZ\nwCDgEyAcuBBIC6SoAnABWSV8jsbABqA28AOwFhhbwuc0GAyGoKMlsO84aW4FVgMHgVVAM3v/EOBP\nv/2X2/vrAylAJtZb/D67jHQso3QI+NJOWwX4DNiJVWnf7Xfep4FPsd7oDwA3k/sNPx6rtTAA+AfY\nBTzml98DvA/stfU/Amw+xnX6gLP8tqcBY/y23wA22VqWYLXYALra15VuX9tSe38MMBHYBmzBanU5\n7WN1sIzTflv31GPoMhgMhlJHFLAby6XUFSiT5/hVWBVfC3u7NlDD/t4HqGR/vxrL5RNnb9/A0W6r\n94DhfttO4P+AJ7Ba5rWAv4Au9vGnsSrky+xtNzCMo43HO1gtpsZAKlDPPv4SMB+rEq8KLMeq/AvC\nZ18fwDlYlf4Av+P9sO6PE3gA+BcIs48NAybnKe9z4G0sI1YB+AW4zT72MVb8BruMtsfQZTAYDKWS\nc7Aq9s1ABlaroKJ97BtytwaOxVKOVPQDyd94+Mc8zsdqMfgzFPiP/f1pIDHP8ac52nhU8Tv+C5Yh\nA8sQdfY7djPHb3kcwDKCPqyYx7HYC5ybjy6wjGgqlsHLpi/wvf39fSyjV/U45zAYcpqrBkNpYy1w\nI1AdaIRVGb9uH6uGVQnnxwAsg7HP/jQCyp3AeWva59rn9xnKEcMFVqvneGz3+54MRNrfq5DbWBSm\nrGZ2/muwrq+m37GHsNxf+22tMUD5AsqpCYRitU6yr20cVgsELBeaA/gVWIl1/w2GfDEBc0MwsA7r\nrTjbvbIZyz+fl5rAeKAjsAgQliFx2MeVT568+zYBfwNnF6BF+eTJr9yC+BfLIK61t6ufQN7pQC+s\nFsWNWJ0IHsa63lV2mr0UfL2bseIg5bBaMXnZwZF7fAEwDysGsuEENBrOEEzLw1AaqYflv892n1TH\ncq8ssrffxXrjbo5VUdbBinlEYFWYu7Ge7RuxWh7Z7MBqtYTm2ecfkP4VK8D8CFZcwGWXkd1N2MHR\n5LevID7BasnE2td3FydmfF7CuhfVsGJDmVjXGwY8hdVTLZvtWG60bH3/AnOBV+28Tqx4Snv7+FV2\nuWC1ZET+RsZgMMbDUCo5hBV7+AXL178IK7D8oH38U+B5YApWr6oZWEHj1cArdvrtWJX+Qr9yv8N6\nQ9+O1ZMKrJ5HDbBcODOwKstLgaZYb9y7sFoz2ZVyQS0P5dkuiOFYrqq/sSry6VgB+ILIW9ZKrBjF\nA8DX9ucPYCNWbzL/4Pt0++8erJ5YYLm9wrDu1V47TXYHg5bAzxzpeXaPXa7BcMrpitU8Xw88WkCa\nN+3jv3OkuyXAvcAKrH+We0tQo8EQSG7H6n1lMBhsXFj97eOx3ATLsPra+9MdmGN/Px/rrQesN8YV\nWL1CXMC3HOmuaDAEM5Ww4glOLPfceqw3fIMhqChJt1UrLOOxEaur5VSsYJ8/l2EFQsFyUcRi/XPV\nt7dTsUbv/gD0LkGtBsOpIgyrh9NBLDfaF5jR4oYgpCR7W1Xl6C6J5xciTRWsVsdzQFksA9IDK5Bp\nMAQ7mzgyDsNgCFpK0ngUtgdJfj1V1gIvYwUUk7C6W5peHwaDwVBKKEnjsZXcfdirc/SAqLxpqtn7\nwBrRmz2q9wXymcKhSpUq2rZtW7GINRgMhjOIv8h/rFSpIARLYDyWn/d4AfPWHAmYw5ERvTWANeTu\nv56Ngplhw4YFWkKRMPoDSzDrD2btUvDr58TGFhVYwZcUmVgDoL7B6jE1EcsIDLKPv4NlOLpjBdaT\nyD0dwqdYI2EzgDuwAoynFRs3bgy0hCJh9AeWYNYfzNoh+PUXByU9Pcl/7Y8/7+TZvquAvO0L2G8w\nGAyGAGNGmAeQgQMHBlpCkTD6A0sw6w9m7RD8+ouDE5mTpzRiu+8MBoPBUFgcDgcUsf43LY8AkpiY\nGGgJRcLoDyzBrD+YtUPw6y8OjPEwGAwGwwlj3FYGg8FwhmHcVgaDwWAICMZ4BJBg95sa/YElmPUH\ns3YIfv3FgTEeBoPBYDhhTMzDYDAYzjCKI+ZR0iPMDSeBz+fj999/JzU1laZNm+LxeAItyWAwGHJh\n3FYBJD+/aVpaGgkJPbjwwqvo2vVO6tVrxtatW4/OXAoIdr+v0R84glk7BL/+4sAYj1LGa6+9wZIl\nISQlreXgwd/Ytu1qbrvt/kDLMhgMhlyYmEcpo1+/W5kypQUw2N6zhFq1bmHDhmWBlGUwGE4jzDiP\n05DzzjsXr3c61uq7IjT0Q5o2NauWGgyG0oUxHgEkP7/pXXfdQefOFfF4ahIRUYs6df7H+PGvnXpx\nhSDY/b5Gf+AIZu0Q/PqLA9PbqpQREhLC559PYdOmTaSlpVG7dm1cLlegZRkMBkMuSjrm0RV4HWsl\nwXeBl/NJ8ybQDUgGBgJL7f1Dgf6AD1iBtcpgWp68p13Mw2AwGEqa0h7zcAFjsAxIA6Av+a9hXgeo\nC9wGvG3vjwduBZoD59plXVuCWg0Gg8FwApSk8WiFtTb5Rqx1yKcCvfKkuQx43/7+CxALxGGtV54B\neLFca16gdA52KALB7jc1+gNLMOsPZu0Q/PqLg5I0HlWBzX7bW+x9hUmzF3gF2ARsA/YD80pMqcFg\nMBhOiJIMmBc2GJGf3602cB+W++oAMB3oB3yUN+HAgQOJj48HIDY2lqZNm5KQkAAceTsordvZ+0qL\nHqO/dOk7nfUnJCSUKj2nu/7ExEQmTZoEkFNfFpWSDJi3Bp7GinmAFQD3kTtoPg5IxHJpAawFLgIS\ngM7ALfb+6+3y7sxzDhMwNxgMhhOktAfMl2AFwuOBMOAaYGaeNDOBAfb31ljuqR3AOnvbg3WBFwOr\nS1BrQMh+MwhWjP7AEsz6g1k7BL/+4qAk3VaZwF3AN1i9pSYCa4BB9vF3gDlYPa7+BJKwuuMCLAMm\nYxkgH/AbML4EtRoMBoPhBDBzWxkMBsMZRml3WxlKiF27dnHJJVdSpkxVGjVqw//93/8FWpLBYDjD\nMMYjgJyM31QSXbpcwfz5Ndi//ydWrbqTDh26s3379uIXeByC3e9r9AeOYNYOwa+/ODBzWwUBBw8e\nZMqUKRw8eJA2bdqwevVyMjIWYNn+mjgcn/DTTz/Ru3fvQEs1GAxnCCbmUco5cOAATZu2ZceOc8jI\nqEFo6Eekpe3H59sEVAKyiIxsyYwZI+jcuXOg5RoMhiDArGF+BjBhwgT+/bcJaWlTAMjMvITY2EFk\nZCSQlHQdXu9PnHtueTp06BBgpQaD4UzCxDwCSGH8prt37yM9vZ7fnrNxOsVHH73MkCFpjBrVi8TE\n2YSEnPr3gGD3+xr9gSOYtUPw6y8OTMujlNOtWxdGj+5HcnJ3oAZwN+CkXbt29OqVd55Jg8FgODWY\nmEcQcOeddzF27AdYYy0vIyQknO7dD/Hll1MCLc1gMAQhJuZxhuByhQOPA48AkJn5Bz//3C2gmgwG\nw5mNiXkEkML6Tc86qzpu949YM7WAw7GA6tVrnPD5vv32W5o2bU+dOi0YNuw5srKyTrgMf4Ld72v0\nB45g1g7Br784MC2PIGDw4MFMmfIla9ach9NZCYfjN957b+4JlbFkyRIuv7wfycnjgCqMGnU/GRmZ\nvPDC0yWi2WAwnN6YmEeQkJmZyfz580lKSuKCCy6gQoUKJ5T/4YeHMmpUONYs+QDLqVz5KrZtW1fc\nUg0GQynHxDzOIEJCQoo0CNDrdeNy7eGIp2ov4eHuYtFmMBjOPEzMI4CcSr/pLbfcRFTUp7hcDwOv\n4/X25/nnhxSpzGD3+xr9gSOYtUPw6y8OTMvjDKF69er8/vvPvP76Wxw4sJ5rr33PTGdiMBhOGhPz\nMBgMhjOMYFjPoyvWuuTrgUcLSPOmffx3oJm9rx6w1O9zALinRJUaDAaDodCUpPFwAWOwDEgDoC9Q\nP0+a7kAdrLXObwPetvevwzIkzYAWQDLweQlqDQjB7jc1+gNLMOsPZu0Q/PqLg5I0Hq2w1ibfCGQA\nU4G8kzFdBrxvf/8FiAXi8qS5GPgL2FxSQg0Gg8FwYpRkzKMPcAlwq73dHzgfa2a/bL4CXgR+srfn\nYbm3/NdV/Q+wBBibzzlMzMNgMBhOkNI+zqOwtXreC/DPFwb0pOB4CQMHDiQ+Ph6A2NhYmjZtSkJC\nAnCkaWm2zbbZNttn8nZiYiKTJk0CyKkvSzOtga/9todytBEYB1zrt72W3G6rXnnKyIuCmfnz5wda\nQpEw+gNLMOsPZu1S8Oun8C/3BVKSMY8lWIHweKwWxDXAzDxpZgID7O+tgf3ADr/jfYGPS1CjwWAw\nGE6Ckh7n0Q14Havn1USs+MYg+9g79t/sHllJwI3Ab/b+COAfoBZwqIDybSNqMBgMhsJSHDEPM0jQ\nYDAYzjCCYZCg4RhkB7SCFaM/sASz/mDWDsGvvzgwxsNgMBgMJ4xxWxkMBsMZhnFbGQwGgyEgGOMR\nQILdb2r0B5Zg1h/M2iH49RcHxngYDAaD4YQxMQ+DwWA4wzAxD4PBYDAEBGM8Akiw+02N/sASzPqD\nWTsEv/7iwBiPIGfJkiW0b9+Dhg3b8sQTw8nMzAy0JIPBcAZgYh5BzJ9//knTpm1ISnoJqIvX+yQ3\n3tiSMWNeCbQ0g8FQijExjzOcL774gvT0a4CbgfYkJ3/A++9/EGhZBoPhDMAYjwBSVL9paGgoTudh\nvz2HCA0NK1KZJ0Kw+32N/sARzNoh+PUXB8Z4BDF9+/YlMvI7XK5HgHfxenvzwAN30a/frcTHN+ai\ni3qwbt26QMs0GAynISbmEeRs2bKFF198hZ0793Hlld146633WLy4Mmlp9+BwLKBMmZH88cfvlCtX\nLtBSDQZDKcGs52GMRy727dtHXFwNMjL2kb08fVRUNyZPHsTll18eWHEGg6HUEAwB865Y65Kv5+j1\ny7N50z7+O9DMb38s8CmwBliNtUztaUVx+E03bNjAkCGP88ADj7Bq1SqkLI4svChgL263u8jnyY9g\n9/sa/YEjmLVD8OsvDkJKsGwX1hKzFwNbgcVYa5av8UvTHaiDtdb5+cDbHDESbwBzgD62zogS1BqU\nrF+/nhYt2pGUNACfL4p33ulNjx49+fbbLiQn34DbvZD4eCcdO3YMtFSDwXCaUZJuqzbAMKzWB8AQ\n++9LfmnGAfOBafb2WuAiIBVYCpx1nHOc0W6rW2+9i4kTyyM9be+ZwvnnT+K22/ryww+/4HZDjx7d\nadeuHWXLls3Jd+jQIYYNe56VK/+kdesmPP74I4SHhwfkGgwGw6mntLutqgKb/ba32PuOl6YaUAvY\nBbwH/AZMALwlpjRIOXgwGamS357KJCUlc8MNA9i5cxcfffQ911//GnXqnMvy5csByMjIoF27Sxg7\n9l++/fYqRo1aTM+e1+BvhFetWsWcOXPYtGnTKb4ig8EQLJSk26qwTYK81k9YupoDd2G5u17Hark8\nlTfzwIEDiY+PByA2NpamTZuSkJAAHPFLltbt119//aT01qxZkz59BrJs2c/ADKAeEEV4+G1ccEEX\nJk+ezIIFu0hOfgsIBTbQr99gRo9+gdWrV7Nhw0HS0t4DFpCScjc//tiPzZs3s2HDBiZMmMQXX8wl\nNPRcUlN/ZujQBxk27Kli1V9ato3+wG37xwxKg57TXX9iYiKTJk0CyKkvSzOtga/9todydNB8HHCt\n3/ZaIA6oBPztt78dMCufcyiYmT9//gnnycjIUI0a58jpHCHYK7hDDkcZValyjp599iX5fD499tgT\ngmEC2Z8tio6OkyQtXLhQUVFNBT77WKY8nsrasGGDli1bJq+3imCnfew3eTyxSk1NLTb9pQmjP3AE\ns3Yp+PVT+Jf7gBAC/AXEA2HAMqB+njTdsYLiYBmbn/2OLQDOtr8/DbyczzkC/Ruccv766y9FRFT3\nMwxSTEyC5s6dm5NmxowZiohoKNgt8MnlGqYLL+wmSUpNTVXt2o0VGnqfYK7Cw6/X+ed3lM/n0xdf\nfKHo6B65yvZ44rRly5ZAXa7BYCgBKAbjUZJuq0wst9M3WD2vJmL1tBpkH38Hy3B0B/4EkoAb/fLf\nDXyEZXj+ynPsjCU2NpaMjP3ATqAikEpm5j+5AuIXXHABbvchkpKqAl4iIjxMmfILAOHh4SxaNI/7\n7nuMNWteoFWrJowc+RYOh4NGjRqRmfkrsApoCHyBxxNCXFzcKb9Og8FgKEkCbcCLxMk2fYcOHaaI\niLPlcj2kiIiW6t27v3w+n3bt2qVRo0bp7LMbKSSkn93yWCGPp6UmTZpUqLI/+OAjud3R8nqrqkyZ\nKvr555+LXX9pwegPHMGsXQp+/ZTyloehhHjhhadp3741S5cupXbth+nTpw+7du2icePW7N/fnrS0\nrsAk4F1gHykpcSQmLuKGG244btn9+19H796Xs2vXLqpUqUJoaGgJX43BYAhGzPQkpwnDhj3Diy/+\nS0bGOHvPPcBULO/fCsqWXcTGjauJiooKnEiDwVAqKO3jPAynkH37DpKRUctvzyfAXOBJ4BNSUlrw\n8ccfB0acwWA47TDGI4D49xUvKr16dcfrHQ0swurlfAConnM8K6s6hw8fLiD3yVGc+gOB0R84glk7\nBL/+4sAYj9OETp068fbbL1G58g3ExrajVq26uN2DsOacnElIyFS6du16vGIMBoOhUJiYx2lKcnIy\ngwffz3//O5eyZcsxduzLtGnThk2bNlGlShWio6MDLdFgMAQIs56HMR6FZv78+fTqdQ1SDFlZuxk/\n/i36978u0LIMBkMAMAHzIOdU+U1TUlLo1esaDh36mMOH15OSspBBg+4t8sSHwe73NfoDRzBrh+DX\nXxwY43EGsHXrVny+CKCTvachoaGNWbt2bSBlGQyGIMa4rc4AkpKSqFChGikp32Mt1rgFj6c5y5f/\nRJ06dQItz2AwnGKM28pQKCIiInj//XfxejsTE3MRHk8znnnmMWM4DAbDSWOMRwA5lX7Tq666kvXr\nlzNjxjBWrvyFhx++r8hlBrvf1+gPHMGsHYJff3Fg5rY6g6hSpQpVqlQJtAyDwXAaYGIeBoPBcIZh\nYh6GEmf16tV06dKbc8+9kCFDhpGRkRFoSQaDoRRQ0sajK9bSsus5egnabN60j/+O1RUom43AcmAp\n8GvJSQwcpd1vunXrVtq06ci8eQmsXPkso0cv4tZb7845Xtr1Hw+jP3AEs3YIfv3FwckajwmFSOMC\nxmAZkAZAX/JfhrYOUBe4DXjb75iABCyD0uokdRqKwKxZs8jI6IJ0D5BAcvJUpkyZjHEVGgyG4/m8\nXFgLQ7yWZ39LYMlx8rYBhmEZD4Ah9t+X/NKMA+YD0+zttcBFwA6sqWFbAnuOcQ4T8yhm/vnnH26+\n+V7WrVtP2bIRrF9fnZSUz+yjWwgPb0BKyoFsn6nBYAhCTkXMIwvIbwKk4xkOgKrAZr/tLfa+wqYR\nMM8+162FOJ+hiCQnJ9O27cUkJp7Hli0fs3p1K9LT5xES8gDwPhERPbn//vuN4TAYDIVyWy3Ecj9d\nCDT3+xyPwjYJCqqJ2mG5rLoBd9rnP60oDr9pamoqixcvZuXKlUV2Jy1dupTDh2PIynocaExm5mjC\nwqK56qp99Or1La+/fjcvvPB0Tvpg9/sa/YEjmLVD8OsvDgozzqMZliEYnmd/h+Pk24r/akTW9y3H\nSVPN3gewzf67C/gcK+7xY96TDBw4kPj4eABiY2Np2rQpCQkJwJEfuLRuL1u2rEj5P/nkE+666yFS\nU8uQlbWfhg1r8uKLw+jUqdNJlbd69WrS0/8FMoBQ4BsyMw9y11230qZNG3744Qd++OGHYtMf6G2j\n32yfKduJiYlMmjQJIKe+LGlcwAMnmTcE+AuIB8KAZeQfMJ9jf28N/Gx/9wLZi21HAP8DuuRzDp3J\nXHzx5XK5nhFIkCaP52K9+eboky4vKytLCQk95PF0Fbwhh+M8uVzl5HZX1GWXXauMjIxiVG8wGAIF\nhfcMFUhhYh59T7LsTOAu4BtgNVZQfA0wyP6AZTg2AH8C7wB32PsrYbUylgG/ALOwFuQ2+LFmzTqy\nsq6wt8JISbmU338/uZlyN2/ezPnnd2Lhwu8JC1tKXNwbOBxRZGVtJzX1H+bN28mYMWOLT7zBYAhq\nTibm0YLCxTwA/gvUw+qO+6K97x37k81d9vEmwG/2vg1AU/vTyC/vaUV2s/JkOffchoSETMV6iUjB\n6/2c5s0bnnA5kujYsSdLl3YkM3M7Bw6MZ+fOHfh8D2M1IN0kJ/dh8eIVOXmmTJlKu3aduemmO9iw\nYUORriNQFPX+B5pg1h/M2iH49RcHJRnzMJQwEye+wYUXdmXnzulkZR2iS5eODBp02wmXs2vXLjZv\n3kxW1hNY/Rcuw+k8H+k9fL6uQCYezxwaN24PwBtvjOGxx94kObk3ixaFM2PGBaxcuZhq1aoV6/UZ\nDAZDSRFo12HASU9P14oVK/Tnn3/K5/MddXz69E9VtWo9xcRU1vXX36bk5OSj0iQnJys01CvYnBM/\n8XrPUcWKNRQd3UwREbXVvn03paamSpIqVjxLsNROK4WGDtZLL710QrpXrVqlHj2uUatWnfXii6OU\nlZV1cjfAYDCcMBRDzKMwLY9KwPNY4y+yR4u3ASYW9eSGohMaGkqjRo3yPfbzzz8zYMCdpKR8AsQz\nffq9OBz38/7743Kl83g8PPvsswwffiEZGVcQFvYTF13UiE8+mcSKFSsIDw+nSZMmOJ2WlzMrKxPw\n5OT3+bxkZGQWWvOmTZto3boDhw8PQarHypXPsmvXbl555bT0ThoMZyxfA9dgzTMFVh/OlYGTk4tA\nG/AiMX/+/BIt/6mnhsnheDynhQB/q0yZqgWm//777/Xyyy/r448/VmZmZq5jGzZs0E8//aS9e/dq\nyJCn5PW2EowSjFdERHmtXbu20Lpee+01hYXd6qdroyIjy5/0dZ4sJX3/S5pg1h/M2qXg188panmU\nx+oplT29SAZWTypDKScmJpqwsP8jLS17zwYiI2Nyjk+dOo2ZM7+lUqVyPPLI/XTo0IEOHXKHspYv\nX86DDz5GYuKPuN01ge3MmjWdmJho3n33A2rUqMnLL8+hXr16bNmyhcGDH2Tdur9o0aIxY8eOomzZ\nskfpcjqdOBz+j1AmDoeZ4NlgON1IBMphzW4L1niMHwKmJjeBNuClmn379ql69XoKD79WTudQeTwV\n9fnnn0uSXnpplLzeeoJxCgm5V3Fx8dq9e3eu/J99NkNhYTGCaEFtQRVBe8XGVj4qvpKUlKRq1c6W\ny/Wk4GeFhd2uJk3a5hvL2LZtm2JjK8vpfFrwsSIimujJJ4eX3I0wGAy5oBhaHoWZpKgFMBpoCKwC\nKgB9sKZQDzT2fTAUxIEDB3jvvfc4cOAg3bt347zzzgMgOroihw4tBM4GwOO5lldfTeCCCy7gqade\nZu/egyxZ8j+Sk+OxZop5HUgHLsPhSOTAgd3s37+f/fv3c/bZZ/PLL7/Qs+dDHDyYPXu+D6+3BitW\nLOCss846SteGDRsYNuwlduzYS+/elzBo0C1mziyD4RRRHBMjFpZQrPEW52KNFi8tBNqAF4lA+k3d\n7mjB9py4Q3j4bXriiScUGVlBDsergk8FtQTVBb/6xSfGKSysnAYNukehodGKijpHlSvX1vTp0xUZ\neY4g006XLLe7nLZs2RKwazwewe63Dmb9waxdCn79nIIR5tlkYAXJV2C9fhqCnL59++Px9AcWARMJ\nCZnBoUOHSUnpj3Q/cCXWlGKHgU+xnrUMYBodO57Phx8mkpHxEYcOrWHHjjt54YU3adiwCm731cAE\nvN5L6datK1Wr5p1IOX9SUlL46KOPePvtt1m/fn2JXHNJsm3bNpYsWcKBAwcCLcVgOCUEu5/ANqKG\nEyUjI4PHHnuGmTPnUqFCOd544zlmzZrNs8/uJyvrVTvVzzidF+PzZQFxhIVl0rp1YxISzuPZZzOQ\nXrDT7cTrrc8//6zj+edfYvPmXbRt25x77rmTkJDj98lISkqiZcuL2Ly5LD5fDRyOL5k9e3rOBG+l\nnREjXmPYsGcJC6uB9C+zZk2nffv2gZZlMBSIWcPccNLs3buXzMwkXC7h84n16/+kX7/r8Hg+xOF4\nDZiIw9GV0NCzCQurTXR0KiNGPMz8+bNo0KABXu9cIAkAh+MLypWrRPXqdZgwYTrz5n1Dq1YtCmU4\nAN599102bqxBUtI3pKS8S3LyRG699WTn4zy1LF++nGeeGUlq6nIOHlzGoUOTueyyq/H5fIGWZjAY\njkGgXYdFIlB+023btqlMmcqCKMEIwQcKD6+pcePGa/ny5br88n6qUKGOnM4HBJsEjQXlBeG69NIr\nlZWVpeuuu1lhYeUUHd1SZcpUVXh4jOB3O94xRzExcTkj0gsiMzNTY8a8pUaNzhN0EaT6jUepVuL3\noTju/7Rp0xQV1dsvJiSFh8dq165dRRd4HILZ7x7M2qXg188pGueRH0ux5rwyBCGjR49l376ywADg\nYQDS0uJ56aU7+fvvW/n88w9p0uQidu3qBtyCFf94EtjLrFkt+eqrr/jwwwl06XIhZ599Ntu3b+fG\nG8eSltbYPkM3MjPdbNmyhdq1axeoo2/fm5g9eyPJyTdgxVc6A7MJDx9Gx47BMXVavXr1yMpahLX8\nTBVgHuHhYfmObzEYDKWHQBvwoKRbt8sEkYIn/N6Yf1bFinUkWfNllS8fL7hCUEGwzS/dY3rqqWG5\nylu7dq0rOBSeAAAgAElEQVQ8nji/dMvldsfo8OHDBWrYsmWL3O5ygiQ7T4agqpzOEF1ySW8dOHDg\nmNdw4MAB7du3r8j3ojh44YWRcrvLKjq6uaKiKgb9W6nh9IdT2NvKcJowefKHfPfdYqx4xThgPPAV\n0I+2bZsA8Msvv5CSEgHsBNKwZtYHSMPlmkvt2rnHbdSrV4+hQ+/H42lGTExnvN6O/Oc/7xAREVGg\njpSUFJxOL0fmyAohOroK338/j6+//ozo6Oh882VkZHD11TdQvnxlKlasRvfufUhNTT3Ju1E8DB36\nEOvX/863377Npk3rgibQbzCUFIeBQwV8DgZQlz+BNuBFIhBvqDVrnitIFJwjeM5uXXRUWFhlff31\n19q/f79GjRqliIjGgizB04IIQTO5XNV0ySVX5Mx7lVf/mjVrNGfOHG3cuPG4OjIzM9WgwXkKDb1X\n8JtcrmdVpUqdY7ZWJGn48Bfl9V5st1hS5fFcrvvvH3JS9yLYWwjBrD+YtUvBr58SbnlEYi0Fm98n\n/9fCo+kKrAXWA48WkOZN+/jvHB1HcWHFV74q5PkMxyEl5TCwACveMRpYgcPxM40axePz+TjrrIYM\nH/45yck7sdb8upiwsCuoWzeT//3vU/77389wuVy5ypTEr7/+yoYNG2jevDk1a9Y8rg6Xy0Vi4mx6\n9NhNjRoD6NjxNxYt+u6YrRWAH374leTk27BWKg4nJWUwCxb8esw8BoMhcFwI3Gh/rwDUKkQeF9by\nsvFYI9SPt4b5+RxZwzybB4CPgJkFnCPQBjxoSEtL09NPPy2XK1JwqeAiQWW7VfGknM6H5HTGyuF4\nx45BpMrhaGEfj1KlSnX066+/HlVuVlaWrrzyekVE1FZ0dGdFRlbQ//73vxK7jsGD71Vo6B0Cn0AK\nCRmqq68eWGLnMxhORyiGlkdheBprDfE/7O2qwE+FyNcGazr3bIZwZGbebMZhTfeezVogzv5eDZiH\ntWJhQS2PQP8GQUF6erpateogp7OqINs4+ARnCSb7BcPL+C0IJcFTgvME/wimyO0uo61bt+Yqe8aM\nGYqMbC5IsfN8oerVzylW/ZmZmdq7d698Pp92796tWrUaKiqqnaKiOqhKlTpHacqP3bt366233tKr\nr76qP//8s1j1GQzBBqcoYH4FcBnZI8JgK5ZL63hUBTb7bW+x9xU2zWtY/UhP29FWp2od5FmzZrF6\ndSo+XyxWAw+swaXhQGW/lDVwOCZgPVf7sBp9DiAV6Et6+nn873//y0mdmJjI33//TXp6O8Bt772Y\nf//9u9i0f/rpZ0RHlycurgY1apzDjh07WLnyV6ZOHcqUKQ+wdu1vVKlS5ZhlbN++nQYNWvLQQwsZ\nMuQPmjRpzZIlS4J+Hepg1h/M2iH49RcHhTEeaeSuwI/tlD5CYS1b3iHyDuBSrK4+S/M5bjhB9u3b\nh1Qby/v4EpYx2EZo6EHCw+8H/g9YgNu9m4oVP8TlqozV8DsPawLlDsB2YDNRUVG5ym7evDkhITOx\nxjmA0zmOBg1aFErXnj17uO++R7jiiusZN248yjPVzJ9//smAAYNJTv6OjIxDbNnyMF26XI7H46F7\n9+5ceumlR+nJj5dffpW9e3uRkjKF9PS3SUp6mXvvfaJQGg0GQ/4UZpDgdOAdIBa4DbgJeLcQ+bYC\n1f22q2O1LI6Vppq970qs1k53rFfaaGAyVpQ3FwMHDiQ+Ph6A2NhYmjZtmtNVMvvtoLRuZ+8r6fO1\nb98e6VHgHqzQUxQOh4NrrrmOsLAw5s4dQEhICFdf3Z8OHRLo0eMyrD4M2S2IZkAC1aqJ0NBQ8vL4\n43fw9NNn43SGExMTweefJx5X36FDh2jQoBl79jQjK+sK5s59i7lzv+Oee24nISGBP//8kyFDhuDz\nVeZIP4o67Ny5jT179lC+fPlCX/+OHXvJzGyJ9dhOBFJYs2YfmZmZp+T+B/vzUxLbCQkJpUrP6a4/\nMTGRSZMmAeTUlyWNA6gBdAFG2Z/OhcwbAvyFFTAP4/gB89YcHTAHuAgT8ygy8+bNU/Xq9eX1llGH\nDpdq+/bt+abLyMhQSEi4YHdO7MPhaKcePXooLS2twPIPHjyoTZs2KT09XaNGva6LL+6tm266Q9u2\nbcs3/dSpUxUZeYlffGWbnM5QTZ06Ve++O1EeT3l5vb3sBahutGM0y+V2Ryk9Pb1AHT6fTxMnvqeu\nXa/W9dffpr/++ktTp06T211LUE4wVrBAISEX6uab7zyxm2gwnCZwCgLmDoq2Xnk3YB1Wr6uh9r5B\n9iebMfbx37H6hublIk7T3lalta/43Xc/JK/3PMF7CgkZrNjYOPXpM0DPPPOckpKSctLlp3/QoHvl\n9bYVTFNIyCOKi6uV70jwDz74QJGR2XNCpQguFNRTZOSlgnDBEvvYYUF1eTwXyeOpoA8/nHJM7c8+\n+5K83oaCD+V0DlNsbGVt3bpV3btfKmhon+cmwSqFhnqKfK8CSWl9fgpDMGuXgl8/p6i31ftAq1Nx\nopMg0L9BkSitD6DP59PYseN0+eX9dfbZzeXxtBOMk9vdRy1atFdGRoako/VnZmbarZY9OS2K0NBL\nFBFRVnFxtTVmzNs5aXfs2KEyZarI6RwluFvQ2R6UeFDgzumKC5LH00d33HGH1qxZc1ztsbFVBKtz\n8oaF3ayRI0eqRo36gv6C7wUPCM5ReHhUsd63U01pfX4KQzBrl4JfP6fIeKwDsoANWItBrQCWn4oT\nF4JA/wanNTt37rTXMD9kV8ZZiow8Vz/++GO+6TMyMuRyhQkO+LmjugleFiyR13uWPv30s5z0q1ev\nVvPmF8jrLSfoJGhlp48XjLHzr5DHU1GrVq06ptZDhw5p7NixCg+PFfzlZ7zu0KOPPiqPp4ptnLK7\nKdfRLbcMLtb7ZTAEC5yirrqXALWBjkBP+3NZUU9sKP2kpaXhcoVjjeYGcOJ0xpCamsp3333HXXc9\nwFNPPc3OnTsBCAkJoW/fAXg8V2IN8RmO1WHuJqAFycmP8sknswBrjqrBgx/kjz+SSU31YHXqew24\nHNgFDANigJbcfvv1NGjQIJe2vXv3ctVVA4mPb0xCwqU0atSSBx+cS3p6A7uMb3A4xhAePp0ePXpg\nvf9k2rmFy5XB5ZdfWjI3zmAwlHoCbcCLRGlt+v7xxx+67rqb1bnzlapZs6HCwm4V/CqX6zlVqnSW\nxo0bL6+3quA2hYQMVsWK8dq5c6cka0DiE08MV8uWnVSmTLxgVE4rwOUaojvuuE+S9N577ykiIkHW\nmueRgp1+rZUBghcEe+Tx3Ki33347lz6fz6fmzS9UWNjtgt/kcLxgD3A8YLcu7lBoaJwuueRK/f77\n7/L5fOrcuZecziaC6wQJglqKjY0r0sy8Pp9P69ev14oVK44ZxC8pSuvzUxiCWbsU/Po5RW6r0kyg\nf4MiURofwH/++UfR0XFyOp8TTJHXW09NmrTVWWc1U5cuvbVx40ZVrlxX8JNgvh1XGKhRo0YdVdZP\nP/0kr7e8nM6HFBp6m8qUqaJ//vlHkvTss8/K6RxiG4tygj/9jMfldq+o5fJ44rRs2bJc5W7evFke\nT0U/N5RkjYSfa3/fqqioijnpfT6feva8RlBPcLvdg+sFud1tNXny5JO6TxkZGerR4yp5PJUVGVlH\ndes2LbAHW0lRGp+fwhLM2qXg108AF4MyFAP+/fVLCx9//DEpKVfi8z0OQHJyI7Zu7cmuXRtz0qSm\nJmPNImNNzZ6ZGUdSUvJRZbVp04bFi3/gs89mEB5egf79F+eMBm/Tpg1u9y0kJ9+ONZHAJcAjWJ3u\nvsXhmIXbHcH48WNp0qRJrnLdbjdZWalYkx5EYbmkdmONTWlMePiDXHJJ15z0ixYt4vvvf8MK14UD\njwHn4HR2YcuWLdx338NkZmZx4439aNGicAMcR49+i/nz95GS8jcQxsaNj3Lbbffz5ZdTCpW/OCiN\nz09hCWbtEPz6DUHe8iiNPPfc83K57vF7o1+jsmWr50ozaNC98ng6y1p29gt5POXUps3F8nrLqnr1\n+po7d26hzjVixKsKDfXI5fIKPILLBA8LPlJMTFyBrqDMzEx17txT4eGtBGPkdl+h2rUbq2LFWvJ6\ny6p37/46dOhQTvovv/xS0dHd/K7JJyijyMiydrD+ScFz8nrLa8GCBTnn2LJli1JSUvLV0K/frXbr\nKLvM/1N8fONCXbfBEGgwbqvgNh6lsem7Zs0aOZ2RgjcF4wWV1aJF65w1PCRrht67735Y5ctXV/36\n56tRo9YKDb1dsEPwtbze8lq3bl2hzpeenq4PP/xQ0dE9/Spiye2ukO8Aw4yMDCUk9FBExLkKD2+i\nkJBYDRp0u5KTkws8x7Zt2xQZWUEw0+459rxCQ2NVu/Y5gmf9zvsfJST01LJly1SxYk15PHFyu6P1\n/vsfHFXmyJGvyOPpKkgX+BQS8rh69Li6UNdcXJTG56ewBLN2Kfj1Y4yHMR7FzcyZM+XxNBRcIIgS\n9BHUVadOPXMZEMnSn5mZKaczRJCWUwl7PH3Vt2/fQs9gu3z5cnm9lXVkGduFiowsl2/Lwwq0XyRr\n2VoJPlXt2k2Oe44ff/xRVavWk8MRKoi1A+zVBGUFK+2yZqlly06Ki6sl+NDet1IeTwWtXbs2V3lp\naWnq1KmnvN6aiopqpPj4BoWa3bc4KY3PT2EJZu1S8OvHGI/gNh6lkUmTJikysp+ghqwVB631xSMi\nWuuTTz45Kr3P55PXG6sjA/O2CKIVGnqtwsJuV2RkBT3++BNq1OgCNWnSXp99dmScx9KlS/Xhhx/q\nl19+0fDhL8ntLq+YmLaKiCivOXPm5Ktv+PDhcjqH+rUWtisiolyhrm3mzJn2NCXNZY1cl2CcoJHg\nR3m99TVy5Cv2WJEjraDo6CvyvfasrCytWLFCixcvVmpqqiSrw8GMGTP0008/yefzFUqXwXCqwRgP\nYzyKmz///FMeTzlBqCA5pwIND79Db7zxRr553nnnXXm9VeVyPaqQkDoC/5jJjXI6a9g9oWbK6Syn\nO+64WyNGvCavt7Kioq6R11tdjz/+jP766y/98MMP2rFjR4H6vvnmG3m9Z9lGaoUcjuaKja2lSZMm\nH7eyfueddxQa2lwwxE/fLjkcHtWu3Uyvvz5a6enptjFcbB/fJ6+3pn755Zfj3ruvv7ZcdtHRPRUR\nUUfXXXezMSCGUgnGeAS38SitTd+5c+cqNLScXclmCdbI66181EqC/voXLFig5557Ti1atLdjJdmV\n8/mCWX7b4+V0VrXjKpvsfTvl8VQo9CJNzz8/wp4GxSN4SfCxvN56eu21N4+Zb/ny5faI+YaC/bK6\nGr+qJk3a5Ur3zDPDZa2geIEcjgrq3/+W42ry+XyKja0k+MG+piRFRjbUN998U6hrOhlKy/Nz+PBh\n3XjjHapRo5HOP//io7pW50dp0X6yBLt+jPEwxqOk2Lp1qxo3biuXK0xud5QmTnxPkjUn1QMPPKpr\nrrlJjz/+xFFv1h9/PFVebz3BGrt1UFUwxc94jBRcK2v+qt8EcwTbFBPTJqenU158Pp9mzpypMWPG\n6Oeff5YkPfbY43I67/crd7EqVz77uNf14YdT5HJFCbxyOCqqQoWa+uOPP3KOHwmuTxLMEDyqqlXr\nHhXvyUtaWpocDpf8x554vQM1fvz442o6WYrz+Tl06JCefPIZXX31jXrzzTHHvV5/unfvI7f7GsFS\nwXhFRVXUli1bjpmnND/7hSHY9WOMR3Abj2AgOTlZWVlZkqR9+/apcuXaCg29UzBOXm99DR/+4lF5\nXn75FUVGVrRbBpcKKgpeEzwva0DgNwKvbVg6C8rK7Y7Rrl27jirL5/OpT58BioxsIrd7kLzeqnrj\njbf0xBNPyel82M94/KZKleoW6pr8R4ZnxyqymT17tqKju+SKeXi9RwY3Hos6dZrI4Rgta3LHefJ4\nKun//u//cqXJyMjQ/PnzNXv27CKNbi9O0tPT1bhxG4WHXysYL6+3na6//tZC53U6Q3VkGWIpIuIa\nTZo0qYRVG4oCxngY41EcLFiwQJdeeq26dr2qwEC1JE2YMEFeb2+/ivUveTyx+ab98ccfFRNzvp3u\nR8FttuF4QuHhjeRyxduVrATz5PWW0+zZs7V///5c5SxcuFAREXX9KqcNCguL0LJlyxQRUV4wWvCl\nvN5GevHFkTn5li5dqrvvvls1ajRU1ar1NXDg7Tp8+PBx78XixYsVERHvF1D/R6GhXn399dfas2fP\nMfOuW7dOZcpUljWlfDl5veW1ZMmSnOMpKSlq1aqDIiMbKzq6o8qVq65169Zp3bp1+u677075CPVs\nvvvuO0VGNtORmYwPKjQ0Unv37j1u3szMTIWGenSkp5xPkZGdNG3atFOg3HCyYIxHcBuP0tD0Xbhw\nobzeCoJ3BJPk9VbRl19+mW/aMWPGyO2+yc94fKnQUG++QeEDBw6obNmqgv8IdsvpfFXh4eXUrFmC\nmjdvoZCQ6/zKyRI4FRWVoAoVamr16tUaNOheVapUV1Wr1pfX2yZXSyA8vKy2b9+u3377Td26XaW2\nbbvprbfG5eiYPXu23O6ygmjBRMEyhYT0VvXq9fT6668rNTVVP/zwg9q3v0R9+tyghQsX5uj2+Xzq\n3/9WRUY2ksdzm0JCyik0tKxiYtooKqpigTMKS9KqVavsaVP+sLV+ogoVauToGjlylNzuy2TN5yU5\nHK+rWrUG8ngqKiamvSIiyuu///1voX+74np+5syZo+joBL97nKnw8DKFNmZPPPGMvYbKmwoPH6A6\ndRrnWvclP0rDs18Ugl0/xngY41FUrrxygI5Mf25VeG3bds037caNG+14wHjBIoWFnad+/QoOJi9f\nvlz1658njydGTZu20/r163XLLXfJ7a5vu7I22ud8R1YQW3I6X1RsbHWFhnaVNf7ic1nB6/GCdDmd\nr6hmzQbH7MVUs2Yjwf2Cfn7XlSQIEbRQ5cq15fFUENwrGC2vt0KueIvP59OcOXN03333ye2O15H1\nSWarfPnqBZ536tSpioq6MpehCws74o679da7bPdd9vHlssacZE8KOVNud5QWLlxYqF5axfX8HDhw\nQHFxteRyPS9YpPDwG9W6dadC9xTz+XyaMmWKbrzxdj311DNHtR7zozQ8+0Uh2PUTBMajK7AWa9Kh\nRwtI86Z9/HeOLFbtBn7BWrp2NfBiAXkD/RsEPb17Xy94269C+0ytW19SYPrffvtNF1zQVXXrttR9\n9z16zKVp87Jjxw67t9MB290UKWuQXgXBKvv8v8iKlWzN0eRy3aOIiFg5nS41aNDquL2yypSpKnhD\n0MXPFbPJNkJZslYTHOh3zWPUoUOPo96W3333XUVE3OCXzieHI0R16jRTZGR5tW/fPdco+F9//VVe\nbw0/Y/OTIiLKKjMzU1lZWXriiScUHl5X8LcgSy7XYLlcdey0vwriBK3k9dZWr159c2JNp4K///5b\nl1xyperWbakBAwbpwIEDJ13W0qVLNWHCBH399dcl3lU5LS1NkydP1qhRo47qDWgoGEq58XBhLS8b\nD4Ry/DXMzyf3GubZi0iE2Pvb5XOOQP8GQc/8+fNtV8v7gqnyeqtp+vRPS+RcGzZssKdyP+Jbh3Ps\nt+/askZ9l7eNypKcStvtvkajR48udGV63XW3KDy8l6CB3fp4TVBX1jTvkrWSYB/7+/eCWLlcFeXx\nxOqzz2bklLNo0SJ5vdX9DNnHcjgiBR8L/lVIyFA1aHBergryoYcel8dTWTExneT1ltesWbOUnp6u\nTp16KjKynsLCzhd4FRYWo/r1W8rtriBr8arGgmn2eVLldrdQv379NG3atFNqRIrKxInvyeutJK93\noCIiGurqqweWmAFJT09Xq1YdFBGRoLCwe+T1VtLkyR+WyLlONyjlxqMN1opA2QyxP/6MA67x216L\nNV2rP15gMdCAown0b1AkSkvT99tvv1XHjr3Uvv2lmjFjxvEz2Jyo/szMTNWr11whIY8K1glelTU9\n+pN266Oc4APBc/Zb+EuC/nI6o3XeeR2OmiIkm+TkZG3YsCGn51RSUpKuvnqgPJ5YOZ1eWT27OgpS\nBSvlcJRVWFgZWaPoPYJ5dqW9RF5vuVytieefH6Hw8BhFRZ2jyMiKioi4IFdLJDy8TM5aJtmsXLlS\nX3/9dU531XHjxsnr7SRrHizJ4Rirc89tK5/Pp7feekdhYVGCMMFev7Lvk9PZVhERrdSjx1X5VsCl\n5fnJJj09XeHhkYK19jUkKyLi7Hy7YBeH9mnTpikiop2OdI9eqqioCkUutzCUtnt/olDKjUcfYILf\ndn9gdJ40XwFt/bbnAdlzYruwWiuHgBEFnCPQv0GRCPYHMD/9X375pS6/vL+uv/42rVy58qjj//77\nrzp27Clr3qyLbSMiu5VwjV/lOUvWKPfzBP+Tw/GmypWrdlQPoE8//UweT6wiIqorOjpOP/zwgyTL\nRVa2bFU5ncMFH9nGyCmHw6Onnx6uqKg4wfWCOn7nlGJiLtT333+f6xw7duzQihUr9PXXXysyspGO\nzKv1lxwOr1q27KQBAwbl29VYkh588BFZ3ZSP9FLzn6m4b98b5XBUtI2mT/Cv3RL7RpCmyMj6Odd1\nvPsfSHbv3m27JY8/tUtxaB87dqw8nlv9zpcqpzPklLTUStu9P1EoBuNRkut5FFaco4B8WUBTrLVI\nvwESgMS8mQcOHEh8fDwAsbGxNG3aNGeu/cREK3lp3c7eV1r0FFX/Y489zquvvkta2vM4HLuZPr0t\n77wzmgEDBuTK/+WXUyhTpiKZmfcB24C6QDqwBpgPdADOBXxY4a62SG1JSXmPCRMm8MgjjwAwffp0\nrr/+FtLS5gPNgZF069aL3bu38dVXX5GUVBef70KsR6cLTmcVvv12FpGRkbz66ufA1cAM+7z1gemk\npPxOzZo1j7reihUrsnPnTurUiWT9+s4kJbXF4RiDw9GcJUseZNmy//Ltt62YNGkcbdu2ZeXKlaxd\nu5aaNWvSsmUzIiJeIinpXCCSkJC5NGvWnMTERPbs2cOMGV8g/QhcCrwKHAZuBsKAn3A667Bnz55i\nf36+/fZbfvzxRypVqkRCQkLOcsInW97y5cspUyaWnTvfQLobGEta2ne0bPnKUekTEhKK/Py53W58\nvk+w3kub4nLdSP36zXA6nSdV3olsF4f+U7mdmJjIpEmTAHLqy9JMa3K7rYZydNB8HHCt33Z+biuA\nJ4GH8tkfaANu8KNu3ZZ+LiDJ4Xhc99//cL5pb7rpDnm9bQUT5HBcK6v3VXVZqwiOkMdT13Y57VN2\n99HIyEa53sDnzZunmJiLcr3pRkbW1tq1azVx4kR5vVf5Hduh0FCPfD6f1q1bJ4+nkqzp2SfLirO0\nkttdQSNGvCZJ2r9/v2bMmKEvv/wy19ogGRkZ+s9//qP77rtfISGxOa4oa1r2RnrvvfdUvnwNRUU1\nV0hIRVWpco7eeutt3XHH/QoLi5LHU0nnnNMixzW2fv16RUTUsFscmbIC+/XldN4sKyb0lSIjKxx3\nxPaJkpqaqpYtL1JkZHt5PLfK662gr776qsjlrl+/XnXrNpPTGaLo6IqaNWtWofOuWLFCffoMUOfO\nV+qDDz4qVJ6ZM2eqQoWaCg316KKLuhfY+jPkhlLutgoB/sIKmIdx/IB5a44EzMsDsfZ3D7AA6JTP\nOQL9GxSJYG/65tVfq1ZTwf/8KuzndOed9+ebNysrS2PGjFWfPjfooYeG6JVXXtHQoUN1222DdMcd\n9+nzzz/XoEH3yuttIRglj+dStW7dSRkZGTllrF+/3u5ymx3QXiW3O0YHDhzQrl27VL58dblcTwk+\nk9fbRnfe+UBO3v79b5XbXVcOx1B5PPXVtWvPnDVINm/erLi4WoqK6qKoqA6qUeOcoyqlbdu2yQrs\np+YYD6inSpVqyOHI7vqcJGiusLB4PfTQY9qzZ4/++eefXG6VzMxM1a3bVCEhQwVr5XS+onLlaqhp\n03YKDfWoWrV6WrBggZKSko7q2VaU52fSpEmKiOjkFy+Yr7i4s066vLykpqYeM1CeV/u6desUGVlB\nDscoWcsf19Xo0WOLTU9xE+z/u5Ry4wHQDViH1etqqL1vkP3JZox9/Hcs3wNYPovfsAzOcqx1SvMj\n0L9BkQj2BzCv/ldeecMeLPZfwWR5POW1ePHiky7f5/Pp/fff1+DB9+iVV149aioRSXr++ZHyeOIU\nE9NFHk95vf/+B1q2bJmuueZGdezYSxde2EkXXdRTI0a8mqvS9vl8GjZsmIYPH67PPvssV0V31VU3\nyOV6MscIhoberUGD7s113jVr1sgKxPcUfCprBH1VhYZGyL+bsdUZ4AGFhnoK9MVv27ZNXbr0Vlxc\nbbVte4nWr1+fc+zQoUPq1OkyuVzhCgkJ1/33D8nRWpTnZ8SIEQoN9Z8bbK/Cw6NOurwTJa/2oUOf\nkNP5iJ+eRapevcFxy9mxY4cmTJig8ePHn9IR+sH+v0sQGI+SJtC/gcEPn8+nMWPeVvPmHdSuXXcl\nJiaekvOuWbNGs2fP1oYNG7Rq1Sp72pKRgvfk9dbMdyXAY9GiRUc7WJ1dkU3TxRf3zjmempqqSpXO\nsoPtl8haPvcWQTk5nZFyOEbY+Q4ImgreU0hI+AlNNpjNDTcMVnh4P9s9tksREc00adL7Babfvn27\nRo4cqWeeGa7ly5cXmO7nn3+2XXfLBKkKDb1TnTpddsL6ioshQx6Xw+G/TstiVatW/5h5/v77b5Ut\nW1Ve77XyevuqTJkq+uuvv06R4uAGYzyM8SiNHDx4UL16XSevt4zi4s4q1LiRf/75R++//75mzJiR\nbwvjWEydOk01ajRUhQq11LhxK/ttP7sS+lZnn33eCZX30EOPyePpKWs+rUPyejvkmjdr1apVioys\nK2t8xjmyxqeECe4SvC6nM0rWpI+xgivldnfVtdfeeEIasomPbyKrt9gtghsEd6pr1ys0bdq0XC0U\nyWvgzfoAACAASURBVJoJuXz56goLu0lO58Pyestr/vz52rZtm664or/OOed89e9/W86EjJMnf6io\nqApyOkN04YXdtHv37pPSWBwcMfpjBV/I622oESNePWaea6+9SS7X0zm/tdP5nPr0GXCKFAc3GOMR\n3MYj2Ju+Bem//PLrFB7eX9aa5j/K6407pvtq0aJFiogor8jIaxUZeYEaN26j5ORk+Xw+7dq1K1ec\nIy+JiYn2ErbzBWvtCRf9u8UuUO3azU9If2pqqi677FqFhLjlcoXruutuztVq2LZtm73a4HhZI+Tr\nyBovcq8d+3Dq008/1YUXdlXDhm314IOPndBIfH+aNr3ANkIjZM0EUE4uV5Sio69QWFi0Pv30yMqM\nDz88VC6X/0Jc09S48QWqWbO+QkKGCBYqLOxWNW16gbKyspSRkaGNGzcWajqR4ia/e79kyRJ17dpH\nbdt209tvjz/u4ML27XsKPvO73plq27ZbCSnOzfz583Xw4EHdcMNg1arVVB06HImZnSgpKSn6448/\ndPDgwWJWWTAY42GMRyApSL/XW8Y2HFbQ2Om8WoMHD853TXJJql+/lWBqTuDZ47lMjzzyiOLiaik8\nPFZeb+6R3/7cffcDOjJy3H8U+H8EX8nrPUdvvvnWCenP5vDhwwVO8NeyZXu7xdFR1uDGibIGHY5U\nuXLV9P333+daJySbvBViWlraMY3jFVf0zXN9nwvaKXtOsLCwSH333Xfy+Xy66aY7BK/ncv1Urlxb\nUVHN/fZlyeutqjlz5igurpa83qoKC4vUG2/kf4+Kk6SkpJxrLY5nf+TI1+T1tpY1LmaHvN4L9MIL\nI4+fsRiYP3++Lrqou/2S9KuczldUtmzVQrfefD6fZs+erfvuu89+caoltzum0L3MigrGeAS38Thd\niYs7S9Y07Ntst865Cg+vr8aN2+Tq9ppN2bLVBRv8Krin5fWWlzXaPHvkd3n9/fffR+V98slhCgm5\n3S/vLFWrVk8dOlymVq06a/z4d4tteowff/xRTz01TA8//LA8nqqC3Tn6rNZBH3vxrDKKiWknt7uC\nnnzyWUnSpk2b1LRpOzmdLpUrV90eTPn/7Z13eBRV98e/23dnW8huQhqQ0KQIBEFBUEDpRUQQpRfB\nQm8qKIgUqSIgiqKAIC/4ShWR8tIkIiAQqorgq3T4UQSkJqBkv78/7myymwIbkpCs7/08zz7ZmZ25\n893Jzpy595x7TjvqdEbq9Sb26fMqd+7cycGD3+SoUaN5+vRpkiLVisjT5f1+aylyc3mXLVSUOHbv\n3oerV69Wc2vtIHCYilKH7dt3oc1Wht5MvkAyzWY3o6NLEZilrjtCRYnySx+fm1y4cIHVq9elTmei\nwWDh+PGTcqXdlJQU9u37Go1GK41GK3v2HHBPfqV74fLly9TrFaaFapN2e+OAMjSkpKSwSpWa1GgK\nUwRdrFbb+JkWi5tHjhzJc/2QxkMaj4LI4sVLqCiFqdFUIvBq6hOvydSBr702NMP2zZu3pdH4onoh\nHqfZXIxGo9vnBkk6HE25fPnyDPueOXOGYWFFaTC8SGA4FSV7cwsCZe7ceVSUKGo0Q2k0VqNW+6Sf\nPjFXJJyiQuL39M4tUZRo7tmzh2XKVKFON4oitHcT9XonTabmFHXiL9BkKk2DoRCB4dTrezA0NJon\nT57kmjVraDS61J7ZKgpfyki1/U8JlCZwhVZrLHfu3MmZM2czIqIkCxWKZu/eg5icnMyHH65Ds7k1\ngTm0WBqwceNWatVDT6p+q7UzZ82alevnzePxsFGjVjQYetM7j0VRSmQr9XwgxwjkAcHj8fD69eu5\n8jBx48YN6vVmpqWU8dBqrcZVq1Zluv2JEye4adMmnjx5kt2791QfquZQZD7wzXDQME9+v+mBNB7B\nbTz+qcNWpCiqVLhwaQIJPhfHPDZt2ibDtn/++Sdr1WpMnc5Ig8HCd94ZT5PJTpGSnQQuU1GKZvlk\nfPbsWb7zzhgOHvwmd+zYkSv601OoUBSB3aqeXylqhXgzAS9RnyD7Uwxl+afnmDt3rvqUmnaz1mqL\nENjss64cRdlb8blO15+9evVlZGQcDYZS1GiKUat1sWXL1uq58VZiPJClcf322285ZcoULl68mEOH\nvs0WLTpw7NiJvHXrllq0aqN6vKvU6YoxMrIkn366Hc+cOZPpOfB4PFyzZg2nTZuWaSRdcnIy33tv\nMnv1GsAZM2bw0UfrU6czUqOx+vRySOBttm/f4Y7nO1CDEChbtmyhyxVDrdZIgyGE8fE1/QqfHT9+\nnO3bd2etWk9x/PhJd+3BbNq0iS1btqUoJfAxgeeo1ToyjXCbOfMzWiwuOp2P0WJxqbVmDqgPDiEU\n5ZhJ4AwVJYIHDhzIte+dFZDGQxqP/ORu+l94oRdNpk7qE2cSLZYGHDNmQpbb37x5M3UuxLx582mx\nhNFub0mrNZa9eg26q56zZ89y+fLl3LRpU0D5jbJz/kXCv0sEzhLYRa32Cer1VipKNPV6B4G+FBPu\noggsp6iB/iS12lAuWLCARqOVaUWi/qJWG6puqyPwEIFIirTs3hvsRNpsEQRiCFRSjUVxPvLIk0xO\nTmZ4eDG1V+ch8EPqsN7Jkyd57NgxvvXWaFqtxWk09qailGXhwqVot4fT6SxCk8lKiyWERqOTilKV\nIs9YeQI7qNe/zuLFK2Qa8fbSS31ptZal2dyDihKXOiRHiqSIVarUUotdTaRWW4oaTQ2KiZJbKQIL\nfiKQQoulEQcMyHzyKEmOG/cuLRYn9XoTn322E5OTkwP+P2XGn3/+Sbs9nCJfmofCb+Si2RzOtWvX\n8sKFCwwLK0qdbhiBZVSUmnzllX53bPPbb79l7drNCHSiiIQbQWAYO3Z8KXWbrVu3snLlWtRoFKbl\ncDtIEVzhNdxL1AeRKrRYwjlq1PgcfddAgTQewW08/ulcvXqV1avXpdkcTpOpEJs3b5Ol0zwzDh06\nxIULF/KHH37giRMnOG3aNE6fPp3nzp1LbT8xMZHHjh1jYmIi7fZwOhxNaLOV5xNPNL2jIzo9KSkp\nXLRoESdMmMD169fz1q1bHDJkOKtUeZItWrRn3brNqNNVJ+BUb7QWTps2jUePHuWUKdNUx+0ltadl\noUi1spTAx1QUN9u370CDwU6DoS4tlkrU6WzqTewmRTEuhUAV9Yk0gQZDCLXaGkwbU/+AwAM0GFy8\nfPkyd+3axSJFHqBeb6bN5uJXX33Fhg2fodnsptlcmBqNnSKU2EORXHIQxeTFLyiG1zZTpwunXh9J\nkRImisLZ7qHdXi5D7XVRJTGSYu4KCZylyeRM/V+sXbuWNlsVps1YP08xhJekLrejyfQQbbZqrFq1\ndpbh2EuWLKGilFa1X6HF8nSGCZrZ5YcffqDTWZW+PULgQQLD2aTJ8+pse9/yyheo15uyfAD55JNZ\nVJQQtdfwH5/95rB583ap50v47d5WjX/asU2msjQaixCYT2AkNRoLJ0yYkGXW6LwA0nhI41HQ8Xg8\nPHHihF+a8+xy4MAB2u3hNJtfoNncni5XDFeuXMmQkEg6HPE0m10sVKgoxXwIEvibilKHs2fPDljj\nM8+0p9X6MPX6gVSU4ixf/mFaLA0JrKVON5pOZ2FqtU4Cv9MbAmyzuZicnMyUlBT26NGfOp2ROp1R\nvcl+73PDqE+9PoZa7Ws0Gh9miRLlqNc/nO5mFkaRZbgQ3e44NmrUjP5RVodVg2SnXm+lXm9mv36v\n8+rVq/R4PBw2bKQ6N+UWRdbflhTVFP9Qb3Ien7aaqgbLxTSn/wmKHkgFAkaWKFGJBw8eTD1HCQkJ\ndDpr+Gm220vxl19+IUkuXbqUdntTn89vU6RvuUTgNq3W6uzbty9XrFhxxweIrl170j9AYA+LFatw\nz78dUkwmNJtdTKvY+H/qOZnAevVacM6cObRafStAXiSgo8NRmHPm+E/I3Lx5s1qT5heKkgLlCOyl\n6P0V56JFi0mSo0aNpk43SD3/oUyrTzOJgJ0Gg41udwm2atX+nkN8cwKk8Qhu4/FPH7bKLRo1epYa\nTVr5Vp3uTSpKONPCe/8gEEExBOC9AQzj8OFvB6R/+/bttFpLUkwKFGPPYtJfWpoRs7k6LRbfOt+k\n1VrELzLmr7/+YnJyMkuUqEzhzyCB6+oTuLetW7RYYikc7Nd9jqcQeJl6/UCOHz+eCxYsoNlcicBl\n9cb/JoHiFMWt/iZwgWZzSc6bN48kWa9eS6YVk/JGZT2o3gjN6jGo7luBokCWbwgvKYbORhO4TI1m\nOsPDY1OHjC5evEiHozBFb+oWgVkMCyuW2oM4f/48Q0IiKZz4B6nXv0iNJoQmUy/abI+zRo36fj3B\n114bTJerKK1WFzt2fCn1OMOGvU29vjWBpykKZNVm5cqP5/g3NHToSJpMMQSeUb9nSwIhtNlCuWvX\nLrpcMeqEwxUUPbUnCOymokT51bgfM2YMdbrXKeYVeQgMI+BgTExZfvzxJ6nbTZw4kUZjN/W8fkXR\nYw1XDWoCgT9oNHZjgwbP5Pi73QuQxkMaj/zkful/6KEn6J8u5HMCGqaFn5JabSdqtd6ys2dotZa+\na9SKV//q1avpdNbzad+jXuw/pa5TlNo0GkMpSsiSwFZaraGZjsdPnjyVer2LQH2KioU2+j752+2N\naLdHEniAQE8CsRRDYcOpKHGp8zZefLGP6mx2UwwrOQjs99HZi9269SJJNWS1C9Oy89akRlOIgIMG\ng40GQwSBXgSqUaMpRZPpaXUsfr26z0JVZ7KPzrLcv39/6vfavn07o6NLU6vVsXjxily9erXfvIYf\nf/yRVarUYeHCwvG+ceNGTp06lV988YVfb2PdunU0mcIonsZP02xuxhdf7EtS9BLEDP2xFAEKXVim\nTJV7cp6fOHGCe/bsSZ2rs23bNkZEFKVIGdOOwHfUat9gz579eeTIERYuXJIiCKEpgUcIdKZGM5ij\nRo1KbXPWrFlUlPpM81msZUxMmdTPz58/z+7du7NZs2a02UKp071K4CNaLMVYv34DGo09ff5/V2gw\nWLL9vXIDSOMR3MZDEhgjRoylojxO8fR+hFZrJYaERPs8aV+gxRLHYsUeoMkUSoNB4Vtvjbp7wyrn\nzp1THaqLCUwgEEuNphANhlIEvqReP4iRkSU4YcJ7NJsL0eGoQqvV7Ret4+Xq1assWrQMdboXCHxE\nrbYEHY4o9an2AoHFtNvDmZCQQJvNRZOpNIFoajQO6vWWDJPczpw5w+nTp3Py5MmsUKGGT8ZeD83m\n1hwzZhxJMZfCZHJThO7GUISC7qIos+uiyVSVer2T7dq146RJkzh9+nQuX76cISER1OvNdLuL0mRy\nUaSBJ4FLNJtDefLkyQzfcfHixdRqrRTOfiubN2+drQJMffsOIjDO5yZ6gBERpUgK34nd7juPJYVm\nszt13osv165d4/r16/ndd99lGAobMGAIzeZQOhwP0u0uwp9++okkWaZMNab1CkngI7Zt242bN29W\njbQ3S/IN9bzV5UcfpWX3vXnzJqtUqUWb7XEqSlcqipvr1q0jSZ46dYoGQyiBRwm8SMDOmjVrsW3b\nbvzqq69U34r3AYcEElmoUFSG77Vt2za++eYwTpw4kRcvXgz4vGYHSOMhjcf/Ardv32bPngNosThp\ntYZy2LCRTExMVH0elWg2u9ijR3/Onz+fn3/+eYZqg4Gwfft2hobGqL2ArQS+pcEQzYoVq7N7996p\n4aunT5/m9u3bs7yo//Wvf9Fq9R37P0mDwcJq1erSYnEyNvZBLly4kNevX+fFixe5cuVKbty4kadP\nn2ZSUtIdNR44cICFCkXRbm9Ku70aK1SozuvXr5MUkT02W3kKX0tF+odIT6eICNpNi8XpF4bq8Xh4\n7do1ejwedu3ak1ZrRer1g2i1lmOfPhlrsZw6dYo6nZ1pYcazCTj5wQcZZ6jv3buXo0aN5uTJk/3O\n1+jR79Bo7Oqj7yuWKfMISfK7776jzVaRaY73qzQa7RnO9/HjxxkZWYIOx2O02SoxPr4mr1+/zmXL\nlrFx4+Zqz++42sZsliwZzx07dvCJJ+rRYChG4AcCW6koxbhixQr269dP7Qn69j6jWLx4+QxZBm7d\nusVFixbx008/9fNXNG36FIGaPsZhK/V6Z+rnycnJfPDBalSUxtTrB1FRIrhgwRd+bS9dupSKEkHg\nLRqNnRkVVTJPDAik8Qhu4yGHrXLGtWvXmJiYyJUrV9JuD6fN1oo2Ww1WqFA9y7QivqTX//DD9Qh8\n43MDmcNmzdpmS9PMmTOpKB182riWmlF3xYpvqCihtNmKU1FC+c032Z8Mdv78eS5dupSrV69OfeIl\nhSNXpCHxEKjONH8QKRJFipxXBoMjyxQaP/30E3v16sUuXbrw66+/psfj4YoVKxgeHkeTyca6dZvz\nyy+/VENwfX0l4XzmGf/ztHbtWipKGLXa12gydWBERHGePXuW27Zt47Jly+hyRdFsfo56fX8ajYXo\ncsXQ6Yxku3bdWLnyYzSbWxGYTkWpwU6dXs6gtWnT53ySIqbQbG7DOnUa0GwuQZEHrCWFTyeJIkWO\njhZLOIU/I5yAk253Ec6cKYIq3npruLp+HEVY7XACtkyzGpDit5OUlMR3353El17qw/nz57Ny5WoE\nfLMdXCFg9NsvKSmJn376KceNG8ft27dnaLdo0fJqb9EbmdWJ776b+ylXII2HNB75SUHRX7lyLYrZ\nut7hnJacOPHuF1x6/XXqNKfIiSUuXI1mAtu0eSFbWk6ePKkOgX1CYActlqfZsmUHXrhwgYriIrBd\nbf8HWq2ue+olZaZ/zZo1tNsjKJzkVopoohEUEVdhBH4j8BktFnem/oNFixbTYgmj1dqZFkslVq1a\nS006GUbgOwKXaDD0ZJUqtanVhlMMb+2jNzrLZotks2atOXr0WG7YsIFGYziBYhSZhpNoMHRj0aKl\naLOVocPxGB0ON4cPH85evXrRZAqlcEAfp9ncgm3adOWYMePYseNL/PjjTzIdEitd+mEC23xu1J9S\nozFRRI15ew51KIY251GnK0QRzfY8hS9rHo3GEB47dowkuX//fprNhdTz5yBQkjpdA0ZEFE8NR/Zl\n/fr1fOihx2k2tyAwmYoSzxo1nqDwle2gCIZ4kdHRd04rnx6Rqud3pv0Gh3Ho0Ley1UYgQBqP4DYe\nktxBODoP+txIJmRZwfBOfP/992ps/ihqNENptbr9ZgzPnj2HpUpVYYkSD/GDDz7K0om7b98+1qzZ\niMWLV2aPHgOYnJzMHTt20OHwj25yOOJzVCzLy8KFi6goURQ1TPpQhOCupMj2W101Ji4CUaxSpXaG\n/a9du0adzsq0cNK/CJShwWBW08Z4NScR0LFZs9YUEUtO1dheoghbjaTB8CS1Wpt60/6JYg5JFwJj\nqdOVpIj2IrXa9/jYY405YsRIarW+dTyO0+mMvOt37tjxJRqNL1AEB1ynxfI4NRo903wWpIjYiqDN\nFqamezdS+DLE5wZDG86cOTO1zY0bN6rn8VOfbXpz4MDBqdscPnyY1avXo83mplZblmnDaxeo11vY\nqtXzFJFzOoaHl8jUV3MnunXrTYulqWrsN9BiKZxpDyWnIEiMRyOI2uS/IWMNcy/T1M/3A6isrisC\nYBOAAwB+BtA3k/1y/aRKgo9nn+2k3kj+JvB/VJSyXLLk7jVEMmP37t3s02cgBwx4zW+ew6JFi6ko\nseoT8mYqygOcPXtOwO2eOXNGfbL9Tb3Z/Jdmc6GAqt+dOXOGEydO5KhRo/nzzz+T9Ddk4ml1g89N\ns4fa6xhHkUTxHIFDtFge86tL4kUkX9TRN3pN3PDrUqN5hGlj+HsIOBkdXYqjR4+m0RjvZwyFsRpM\nwNefcZGAlXp9GP2HdH5mZGRpTpkyhWZzG5/1mzK96V64cIENG7akw1GYJUrEc82aNaxW7UmazWE0\nGp18/vkufPLJpyii236hqE0fSqPRyV27drFt2xcImJgWLeehxVKPCxb4Z7EtVaoqhT/Eq+djtmvX\nnaQYcoqKKkmt9l2KnuUTPtvdJmBJzaS8f/9+Tpw4kR999BGvXLkS8O/k5s2b7NatN93uYixW7MFM\n87nlBggC46GDKDEbC8CAu9cxr4a0OuYRAOLV9zaIcrbp982TE3u/KCjDPvdKQdF/+fJlNTeWKNUa\naKRVdvQ3bPgs07L8ksByPvpoo2zpnDFjJi0WN53OOrRY3Pz008wnMX722VyWK/coy5evwcmTpzI0\nNJpGY3fqdIOoKG6OGjVaNWSTKRzXRQmM8tE2gsBzBP5DnS6MWq3I3Nu9e+9MczaJnttD6n4pqpEI\nV9u2EahBkbcrgsACOhxVOG/ePHWynHeuykWKobJ3CTTy0XKAGo3Cbt26U1GqUfRuniDgoMtVnAcP\nHmTRomVoMj1PjWYIAQctlmI0mwv5peqoXr2emlzxFIFltFrDePToUZ4+fZrnz59PnSip14vwZI3G\nRYPBwXffnUpSJDKsVKk6RSTaJOr1bVmyZMXUgAMvAwYMUSeHnifwXyrKA/z3v78kKeqNKErZ1P+/\nCJ/+kCIrwMvUaGI5dOhwrlu3jooSRoOhPy2WVixWrGxqAa6CAoLAeDwK4D8+y0PUly8zADzvs3wI\nQOFM2loOoG66dfn9P8gRBeXme68UNP2+9SICITv6W7XqpN6s08bY69dvefcd03H06FGuX78+S0fs\nggX/pqLEEVhHYC31+lBqtQN9jvsvOp2xqiHblHoj02hiCCQSWE6DIZTh4cUYE1Oa06Z9yNu3b98x\n0V/FijXVm+CDBLQUyR0XUqMZRKczSn1it6jrh1FRSjAxMZEdOrxIq7WyaljiCHSlVjuUWq1DnXPy\nLhUljtOmTefq1atpsbgphnSmElhAnW4oo6JKsWTJyrTb3dTpLBTOblL0IGO4c+dOJicnU6cz0rdn\nZLO1SZ0gSQrDbLVWUG/kB2k2V+Tbb2d8iFi2bBlfeaUvR48ek2mP4NatW+zU6WWaTHZarS6OG/cu\nk5KS+OOPP3LevHmqgbyhnvtE9ZyEEqhK4B2+9FIflixZmWLYUGg1GjtwwoSsc7rlBwgC4/EsgJk+\nyx0AfJBum28A1PBZ3gCgSrptYgEch+iB+JLf/wPJ/wh79+5Vx82HExhNRXFz69atuX6c2rWfIrDI\nx1jUpwi19S5vpc0Wnc6QzWRsbEXGxlZkmTIPMzw8lnb7E7TbGzI8PJbHjx+/4zG3bdtGq9VNRelC\nvf4BAkaazWHqBMWRFMNf3rTw5RkXV54pKSn0eDxcsmQJ33rrLT7+eD3GxVVigwYtuX//fo4cOZo9\ne/bnypUreejQIdWXNIliDoRvOKybwAwKJ7PB5zPSZmvHuXPn8vbt2zQaFQLH6I2ustmq8+uvv079\nDk8+2SLdefs6R1UFd+3axS+++IKLFi2iy1WEdntZNR9ZDIHKFDPxqxF4gWLI8BEqShy/+eYbut2x\nTEuCSQKj+Oqrg+9+0PsIcsF46HPawF0IVKDmDvvZACwB0A/A9fQ7dunSBbGxsQCAkJAQxMfHo06d\nOgCAhIQEAJDLcjnHy/Hx8fjgg4lYufI/iIyMRrdu63DlyhUkJCTk6vGSkq4A+BOCBAAm6HTvICXl\nEQC/wmSagOeffwpffjkWN27sA6CHoqzAggVf46+//sKUKR9izZpq+PvvDwAk4MaNuejffyiWLfvX\nHY//44878OGHH8JiaYWePXuiTZtu2LKlOIBaAOpADAZ8AKANIiO3QqvVIiEhAS6XC6NGjfJrr2LF\niqhYsWLqsvjbHOJSPg7gLwBGAKsgLukWAMIB2AGMhxicOIO//lqHa9cehU6nw5gxYzBsWHXculUP\ninIBZcqYoChK6vl3uZwA1gMIA1AHGs1hkDcz/H9u3LiB8uXLIyYmBlu2bMn0fGzatBWTJs0AWQLJ\nyYkApgPoAuArAN3Uc7EKwJMA6gH4CXr9YXTr1gE2mw1NmzbCwoWDcfNmBwB/QFE+RZMm8/L195uQ\nkIC5c+cCQOr9sqBTHf7DVm8go9N8BoA2Psu+w1YGAGsB9M+i/fw24DmioA37ZBepP/fZtm2b+pQ+\nnsBYms0hbNWqNd3uWBYqFMN+/V7n33//zZ9//pktWz7HXr0GcM+ePan7N2jwLIF/+zz1rmflynWy\npeHMmTPqJLu5Pu0sJ9CQGs0EtmjRPlvtLVq0iDbbYxQBDc8QqE2gG43GCtRqH/Q5xgxqNFY1q66F\nOp2DDke4Wo/kLdat24AtWjzDYcOGccWKFX7JNg8dOkSHI5wGQ0/q9b1ps4WlBhd4ee+992k02qgo\n0YyIKM5ffvmF+/fv58qVK3nixAmS5JEjR2g2uylS76eow3h/p2rUaDpRry9FEc7bnAZDB9rt4X7H\nSkpKYps2L1BRQul2F+Vnn83N1vm6HyAIhq30AA5DDDsZcXeHeXWkOcw1AOYBmHKH9vP7f5AjCuLN\nKztI/XlDYmIiu3fvxUqVHqXJVJhOZ30qiptffeUfeZOZfpFC5XGKeRjJtFiac+DAN7J1fJGUsTqF\nI36tOr5flDpdbTochbOdOvzWrVusUqUWrdZ61OkG0mh0smrVapwxYwYLF46jwdCdwDgqSjQnTZqs\nzodZl2r8hL8llqLqnosajYMORx1arW6uXbs29TjHjh3j+PHjOW7cOB4+fNhPw/bt26koMfTOOtdo\nZtDpjKGiRNPpbEhFcXP58q/5/fff0+mspvo13lSP2YJiwt+ftFpLs3///uzQoQNff/11Tp8+PdXw\nBBMIAuMBAI0hIqV+h+h5AMDL6svLh+rn+wE8pK57DIAHwuDsVV+N0rWd3/8DiSRP2L17t3qz86ZM\n30lFCckynfmWLVv4+ONNGB1dmhqNQ/UfGFi2bNVsF1NatmwZbbZaFPUmqlOkO9FxwoQJPHHiBGfO\nnE2HI5x6vZlNmrTm1atX79rmzZs3OXv2bI4dOzY1S+2xY8f4ySefsEOHjuzdewA3bNjAnTt3AtaC\ncgAAE5lJREFU0uFIHwIcQaANhcP8NsXs8dcJfEeHIyygpIkzZsygonTzaXOb6m/xlpHdQUUpxHPn\nzqm+rWoEWlNkEX6eWq2bZnM4e/d+NVvnsqCCIDEeeUl+/w8kkjxhyZIldDie9ruJms2uTEvE7t+/\nXx3q+oCiJsfvFBP6fqXZ7EqdRR0oSUlJfOCBh2gytSMwjYpSMbX2/KZNm1Sj9iOBqzSZOrBVq47Z\n/n7ffvstrVY37fbWtNkq8cknn+Lt27d56tQpdT7MSfV7n6KIZlrh5wwHmhDwUK+3BGS81q5dS6u1\nLIFrahtvUgQjpJ1fkymEf/zxhxpVFeozXJVCi6UUly5dmu3vWVCBNB7BbTwK6rBJoBQU/adPn+aU\nKVM4adKkLENgM6Og6M+MX3/9lRZLGNNmzi+hyxXDffv2cfHixfzxxx9T9Q8e/CZFXYn9FMWJ0m6I\nTmd1fv/991keJ6uezNWrVzly5Gh27dqD8+fPT326Hzr0LYqIs7QZ4SEhGTPD3g2XK5rAGrWNq9Rq\ni1CrtdFsDmPDhk2oKJG025+hyVRY7Ul1pYjOSiHQkSI8eD4NhhBOnjztrr0Pj8fDLl16UFGK0ums\nR4slRM1CfEjVMJ9ms5t2ezhDQqLV7Lje0GAP7fby3LFjR2p7Bfm3EwiQxkMaj/ykIOg/fPgwQ0Ii\naTJ1o9H4Cu328NT023ejIOi/E3PmfE6z2UFFiWFoaDT79h1IRYmgw9GCihLJF1/sQZIcNmw4tdpB\nqp8jTPUTiKEZq9XN8+fPZ2h7w4YNDA2NpkajZWxseb/Z9Flx9OhRvvLKKzQa6zNt1vkqxsVVvON+\nV65cYaNGrajXm+l0RnDOnM+p15uYVtL2ZbUncYLAFgJOjho1iosWLeL+/fv5xRdf0Gh0UaMpSp2u\nGMXExVCKiYzTqSiV+M47mc+j+O233zhkyFAOGjSYe/fu5e7du7l69WqeOXOGs2fPpclkp6JE0WwW\n6deFhj3UaiOo19chsIpG48ssV+5hP0Nb0H87dwPSeAS38ZDknA4dXqRW682uSmo0U9m4cev8lpVr\nXL9+nUeOHOGxY8doMoUwLc34KZrNoTxx4gQPHz5Muz2cWu1IAq8SUGg0htFqDeWqVasytHn69Gl1\nmGuD+iQ/nRaL+47lYefNm0+LxU2Hoy41GicNhvI0m1+hori5fv36O36H5s3b0mTqrBqLvVSUKJYv\nX4063VB6U5+LMrve3swwxsc/4tfGX3/9xcTERO7atYv9+g2kSHXire++h5GRpblp0yZ+/fXXqcby\nl19+oc0WRq32dYpCW26/qoCkmFh6/PhxFitWgWm5vUhgKsuVq8pq1Rqwe/feOUpgWRBBLhgPbS7c\nwCWSfOP8+T/h8ZROXSZL48KFP++wR3BhtVoRFxeHCxcuwGQqBqCo+kk0jMY4nD59GsWLF8euXd+j\nc+ezeOaZc/jyyzn4/ffduHjx/9CkSZMMbe7duxd//10WImGDFkBPJCd7MH78xEw1XLlyBS+91AvJ\nyQm4enUDyAMAzuC118Kwa9dm1KtX747fYdOmDbh1aywAB4B43LzZGQ0bPoZy5TZCr7cCuAbgiM8e\nv8Fg0Pm1YTAYULVqVVSpUgV2uw1arQIRyQ8A13Hx4iU89VQfdOz4MUqVqoh9+/Zh7NgpuHGjHzye\nCQBGIinpXbz55ji/dhVFQdGiRREaGgqRXk+g1/8XBoMWhw//Fxs3bsbmzZvv+B0lwUd+G/AcEexd\n34Kg/9NPZ1FRKlDUYDhCRanGceMmBbRvQdAfKJcvX6bdHsa0ENaNNJsd9/REnJiYSI2msI/z+CgB\nhc8+m7nj++DBg7TZSqbzpdTmhg0bAjpekSJlfXR7aLE0Z79+/UgK30qPHj0p5k28SuBZajQ2v97M\nd999x9dff4MTJkzgpUuX+Pvvv9NuD6dGM4bALOr1Dup05VTnOgnM5YMPPsqnn25PUazKq/tLut1x\nbNLkeU6e/L5fqvfNmzdTUdzU6/vTbO5As9lFs7mu+rtaT4sl3C+7bTD9djIDcthKGo/8pCDo93g8\nHDlyLJ3OCNrt4Rw06I2AS6IWBP3ZISEhgQ5HOM1mF+32ML733nv31I7H42Fk5AMESlCk14imXl+F\nI0aMznT7GzduqIbL60vZQ0Vx8dSpUwEdb82aNbRY3DSZetBqbcCyZatyzZo1ftu8/fYIxsSUZMmS\nD/rVnl+w4AsqSiSBETQaOzImpjT//PNPHjx4kG3bdqXDEUOd7kGKZIyFCewlcIyFCkVz6dJlagLJ\n7wgkUKNxUqfrSWA+FaVmav33S5cu8e23R7J16/bs3Lkz33//fYaERDF9XY1hw4an6gq23056II1H\ncBsPiSS73L59m2fPnr1josNAOHLkCENCImg2l6LFUoqVKz92x+qLmzZtosMRTqu1KC2WEC5enL2U\n9z///DPff/99zp07967ldn0pXLgERVlgb7hyG06dKjLlinTuzZlWU2M2gVo0GF5l3bpPkyRnzfqM\ncXGV6HIVodFYy6cXcplarYEbN25k0aJl1JT+71FRinPy5GksUqQc0xJPkkZjJ06cODFb37kgA2k8\npPGQSO6Vq1evcu3atUxISLijs9zLzZs3efjw4buW+E1JSeHAgUNos7lpt4dx2LCRAU3kywxRlfFE\n6k1cp3uN77zzDkmyb99BFGlcvAbhVwJ2xsSUzFAnZeHChbTbm/lsm0TAQIMhhHp9E5/1B2mzubl4\n8RK1lvjbNBq7MDKyRJble4MRSOMR3MYj2Lu+Un/+UlD1T5w4Wa3dcYzAYSpKPKdPn+G3TaDaO3R4\nUe1d/EZgDS2WMO7evZukmEhptZajyEN1myLktzEtlsgMGY8vXrxIt7sodbrRapRZUwLtCEwg0MnH\neFyi0WglKWbtDxkylOPGjc9gOArquQ8UyGgriURS0Fi69D9ISnoLQDEAxZGU9AaWLVt7T23NnDkN\nbdvGwO2uh7i4wViyZC4eekhkMGrZsiX69m0NIAYikus3APNx8+YLWLduvV87oaGhSEz8DvXr7wPw\nHICKAD4D0BIiW+6/AfwIs7krWrRoDQCoWbMmxo17B0OGDIbL5bon/f9k0qdCDzZUIyqRSAoKzZu3\nw8qVVUAOAgBotaPQps0JLFgwK0+OV6RIGZw6NQHA0wAAs7k1Jk6shT59+mTYliQiIorj/PkxANoB\nOAiTqSbi4kri+vUbaNy4Ht5/fzwsFkueaC0oaDQaIIf3f2k8JBJJrnLw4EFUr14Ht241B3AbFsta\n7N69BcWLF8+T461atQrPPdcVf//dHgbDUURFHcXevVths6WvHSfYt28fGjRogevXb8HjuYFPPpmO\nzp075om2goo0HkFuPHwL1QQjUn/+UpD1nzx5EsuWLYNGo0Hr1q0RGRnp93lua9+3bx/Wr18Pp9OJ\ndu3aZWk4vNy+fRtnz56Fy+W6p15GQT73gZAbxiOvKwlKJJL/QYoUKYJ+/frdt+PFx8cjPj4+4O31\nej1iYmLyUNE/H9nzkEgk/1gWLVqMsWM/REpKCgYM6I4XXuiS35IKBLLnIZFIJFmwcuVKdO06EElJ\nHwMwok+fntDr9ejUqUN+S/tHcD9CdRtB1CX/DRnrl3uZpn6+H0Bln/WfATgH4Ke8FJhfeAvUBytS\nf/4SzPrvh/YZMxYgKWkUgGYAGiAp6T18/PH8XGk7mM99bpHXxkMHUWK2EYByANoi8xrmJQGUAvAS\ngI99PpuDjKVnJRKJ5K6YzUYAV33WXIHJZMwvOf848trn8SiAt5FmAIaof8f7bDMDwCYAC9XlQwDq\nADirLscC+AZAhUzalz4PiUSSKbt27ULt2o2RlDQIgBGKMh4rVy7EE088kd/S8p1g8HlEAzjps3wK\nQLUAtolGmvGQSCSSbFO1alVs2bIOH3wwE7dvp+CVV5ajRo0a+S3rH0NeG49AuwXpLWDA3YkuXbog\nNjYWABASEoL4+PjU+GvvuGRBXZ46dWpQ6ZX6C9ZyMOv39Rnk9fE+++yj1OUEn/kZwaI/t/TOnTsX\nAFLvlwWd6gD+47P8BjI6zWcAaOOzfAhAYZ/lWGTtMM+ntGK5Q7AnV5P685dg1h/M2sng149cSIyY\n1z4PPYBfIepd/h+AnRBO84M+2zQB0Fv9Wx3AVPWvl1hIn4dEIpHkGrnh88jraKvbEIZhLYBfIJzi\nBwG8rL4AYDVEAePfAXwCoKfP/v8GsA1AaQi/SNc81iuRSCSSALgf8zzWAHgAIhzXW33+E/Xlpbf6\neSUAe3zWtwUQBcAEoAhE6O4/Bt9x02BE6s9fgll/MGsHgl9/biDreUgkEokk28jcVhKJRPI/RjD4\nPCQSiUTyD0Qaj3wk2MdNpf78JZj1B7N2IPj15wbSeEgkEokk20ifh0QikfyPIX0eEolEIskXpPHI\nR4J93FTqz1+CWX8waweCX39uII2HRCKRSLKN9HlIJBLJ/xjS5yGRSCSSfEEaj3wk2MdNpf78JZj1\nB7N2IPj15wbSeEgkEokk20ifh0QikfyPIX0eEolEIskX8tp4NIIoK/sbMpaf9TJN/Xw/gMrZ3Deo\nCfZxU6k/fwlm/cGsHQh+/blBXhoPHYAPIYxAOYjCTmXTbdMEoghUKQAvAfg4G/sGPfv27ctvCTlC\n6s9fgll/MGsHgl9/bpCXxuMRiNKyxwD8DeBLAE+n26Y5gM/V9zsAhACICHDfoOfy5cv5LSFHSP35\nSzDrD2btQPDrzw3y0nhEQ9Qd93JKXRfINlEB7CuRSCSSfCIvjUegYVDBHvF1zxw7diy/JeQIqT9/\nCWb9wawdCH79BZ3qAP7js/wGMjq+ZwBo47N8CEDhAPcFxNAW5Uu+5Eu+5Ctbr99RgNEDOAwgFoAR\nwD5k7jBfrb6vDmB7NvaVSCQSyT+UxgB+hbByb6jrXlZfXj5UP98P4KG77CuRSCQSiUQikUgkeUMo\ngPUA/gtgHUQ4b2ZkNanwXQAHIXo2ywA480xpYHp8KcgTJO9VfxEAmwAcAPAzgL55KzNTcnLuATHP\naC+Ab/JK4F3Iif4QAEsgfvO/QAwH329yov8NiN/OTwC+AGDKO5lZcjf9ZQD8AOAmgEHZ3Pd+cK/6\nC8K1m6tMBPC6+n4wgPGZbKODGN6KBWCAv4+kPtKiysZnsX9ucyc9Xnz9PdWQ5u8JZN+8Jif6IwDE\nq+9tEEOP91N/TrR7GQhgAYAVeaYya3Kq/3MAL6jv9bh/D0tecqI/FsARpBmMhQA6553UTAlEfxiA\nqgDegf/NN1iu3az0Z+vaDYbcVr4TCT8H0CKTbe40qXA9AI/6fgeAmLwSGqAeLwV5guS96i8M4CzE\nDxYArkM8AUflrVw/cqIdEL+PJgBmIX/CyHOi3wngcQCfqZ/dBnAlb+VmICf6r6r7KBCGTwFwOs8V\n+xOI/j8A7FI/z+6+eU1O9Gfr2g0G41EYwDn1/TmkXeS+BDIhERBPZKszWZ/bBPsEyXvVn94wx0IM\nSezIZX13IifnHgCmAHgNaQ8c95ucnPs4iBvDHAB7AMyEuAHfT3Jy/i8BeA/ACQD/B+AygA15pjRz\nAr2X5Pa+uUVuaYjFXa7dgmI81kOMcaZ/NU+3nTdGOT2ZrUvPUAB/QYyj5jWB6AEK7gTJe9Xvu58N\nYuy9H8RTzP3iXrVrADQDcB7C35Ff/5ucnHs9RMTiR+rfGwCG5J60gMjJb78EgP4QN64oiN9Q+9yR\nFTCB6s/tfXOL3NAQ0LWrz4UD5Qb17/DZOYjhnLMAIiEu7vSchnD2eCkCYXG9dIEYiqibI5WBczc9\nmW0To25jCGDfvOZe9XuHGAwAlgKYD2B5HmnMipxobwXxwNIEgBmAA8A8AJ3ySmwm5ES/Rt02UV2/\nBPffeOREfx0A2wBcVNcvA1ADwv90vwhEf17sm1vkVEN+Xru5zkSkRQwMQeYO7ztNKmwEET3gzlOV\ngevxUpAnSOZEvwbihjslz1VmTk60+1Ib+RNtlVP9mwGUVt+PADAhj3RmRU70x0NE+VggfkefA+iV\nt3IzkJ3rbwT8Hc7Bcu16GQF//fl97eY6oRDjnulDdaMArPLZLqtJhb8BOA4xFLEXokt/Pwj2CZL3\nqv8xCH/BPqSd80b3Qa8vOTn3Xmojf6KtgJzprwTR87jfoem+5ET/60gL1f0c4kn4fnM3/REQfoUr\nAP6E8NHY7rDv/eZe9ReEa1cikUgkEolEIpFIJBKJRCKRSCQSiUQikUgkEolEIpFIJBKJRCKRSCQS\niUQikUgkEolEIsl/YiGK68yBmLm7AEADAFshMh88DMAKkQp9B0RG2+Y++24GsFt9PaqurwMgAcBi\niNTX8/P4O0gkEonkPhMLUfugPET+n10AZqufNQfwFYAxSMsAGwJhZBSIPE3e4kalkJa0sA5EyvEo\ntc1tAGrm3VeQSHKXgpJVVyIp6ByFyLkE9a+3zsTPEMYlBsKQvKquN0FkND0LkcepEoAUCAPiZSdE\n3QpA5BOKhejNSCQFHmk8JJLAuOXz3gNRG8b7Xg9Rta8lRCJOX0YAOAOgI0SJ0JtZtJkCeT1KgoiC\nUgxKIgl21gLo67NcWf3rgOh9AKIuiO5+ipJI8gppPCSSwEhfoY3p3o+GSB/+I8RQ1kj1s48AdIYY\nlnoA/pXZ7tSmRCKRSCQSiUQikUgkEolEIpFIJBKJRCKRSCQSiUQikUgkEolEIpFIJBKJRCKRSCQS\niUSS+/w/t3PPI3Lev3cAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2363,7 +2383,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 39, @@ -2372,9 +2392,9 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEZCAYAAAB4hzlwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEZCAYAAAB4hzlwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, diff --git a/docs/source/pythonapi/examples/tally-arithmetic.ipynb b/docs/source/pythonapi/examples/tally-arithmetic.ipynb index bbd900fdd..705946537 100644 --- a/docs/source/pythonapi/examples/tally-arithmetic.ipynb +++ b/docs/source/pythonapi/examples/tally-arithmetic.ipynb @@ -179,10 +179,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/universe.py:199: DeprecationWarning: Cell.add_surface(...) has been deprecated and may be removed in a future version. The region for a Cell should be defined using the region property directly.\n", - "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/universe.py:199: DeprecationWarning: Cell.add_surface(...) has been deprecated and may be removed in a future version. The region for a Cell should be defined using the region property directly.\n", - "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/universe.py:199: DeprecationWarning: Cell.add_surface(...) has been deprecated and may be removed in a future version. The region for a Cell should be defined using the region property directly.\n", - "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/universe.py:199: DeprecationWarning: Cell.add_surface(...) has been deprecated and may be removed in a future version. The region for a Cell should be defined using the region property directly.\n" + "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/universe.py:223: DeprecationWarning: Cell.add_surface(...) has been deprecated and may be removed in a future version. The region for a Cell should be defined using the region property directly.\n", + "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/universe.py:223: DeprecationWarning: Cell.add_surface(...) has been deprecated and may be removed in a future version. The region for a Cell should be defined using the region property directly.\n", + "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/universe.py:223: DeprecationWarning: Cell.add_surface(...) has been deprecated and may be removed in a future version. The region for a Cell should be defined using the region property directly.\n", + "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/universe.py:223: DeprecationWarning: Cell.add_surface(...) has been deprecated and may be removed in a future version. The region for a Cell should be defined using the region property directly.\n" ] } ], @@ -228,12 +228,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/universe.py:199: DeprecationWarning: Cell.add_surface(...) has been deprecated and may be removed in a future version. The region for a Cell should be defined using the region property directly.\n", - "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/universe.py:199: DeprecationWarning: Cell.add_surface(...) has been deprecated and may be removed in a future version. The region for a Cell should be defined using the region property directly.\n", - "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/universe.py:199: DeprecationWarning: Cell.add_surface(...) has been deprecated and may be removed in a future version. The region for a Cell should be defined using the region property directly.\n", - "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/universe.py:199: DeprecationWarning: Cell.add_surface(...) has been deprecated and may be removed in a future version. The region for a Cell should be defined using the region property directly.\n", - "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/universe.py:199: DeprecationWarning: Cell.add_surface(...) has been deprecated and may be removed in a future version. The region for a Cell should be defined using the region property directly.\n", - "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/universe.py:199: DeprecationWarning: Cell.add_surface(...) has been deprecated and may be removed in a future version. The region for a Cell should be defined using the region property directly.\n" + "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/universe.py:223: DeprecationWarning: Cell.add_surface(...) has been deprecated and may be removed in a future version. The region for a Cell should be defined using the region property directly.\n", + "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/universe.py:223: DeprecationWarning: Cell.add_surface(...) has been deprecated and may be removed in a future version. The region for a Cell should be defined using the region property directly.\n", + "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/universe.py:223: DeprecationWarning: Cell.add_surface(...) has been deprecated and may be removed in a future version. The region for a Cell should be defined using the region property directly.\n", + "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/universe.py:223: DeprecationWarning: Cell.add_surface(...) has been deprecated and may be removed in a future version. The region for a Cell should be defined using the region property directly.\n", + "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/universe.py:223: DeprecationWarning: Cell.add_surface(...) has been deprecated and may be removed in a future version. The region for a Cell should be defined using the region property directly.\n", + "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/universe.py:223: DeprecationWarning: Cell.add_surface(...) has been deprecated and may be removed in a future version. The region for a Cell should be defined using the region property directly.\n" ] } ], @@ -393,7 +393,7 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAAAFzUkdC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\nVDEzOjQyOjA2LTA0OjAw+NvbzQAAAABJRU5ErkJggg==\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAAAFzUkdC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\nVDE0OjIyOjIxLTA0OjAw6p8uJQAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] @@ -587,7 +587,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "rm: cannot remove ‘statepoint.*’: No such file or directory\n", "\n", " .d88888b. 888b d888 .d8888b.\n", " d88P\" \"Y88b 8888b d8888 d88P Y88b\n", @@ -605,7 +604,7 @@ " License: http://mit-crpg.github.io/openmc/license.html\n", " Version: 0.7.0\n", " Git SHA1: 23535afa1c69644bb299bde18a094c3b99d53ae0\n", - " Date/Time: 2015-10-08 13:42:07\n", + " Date/Time: 2015-10-08 14:22:21\n", " MPI Processes: 1\n", "\n", " ===========================================================================\n", @@ -661,20 +660,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.6000E-01 seconds\n", - " Reading cross sections = 1.3900E-01 seconds\n", - " Total time in simulation = 1.6630E+01 seconds\n", - " Time in transport only = 1.6613E+01 seconds\n", - " Time in inactive batches = 2.1710E+00 seconds\n", - " Time in active batches = 1.4459E+01 seconds\n", + " Total time for initialization = 4.3800E-01 seconds\n", + " Reading cross sections = 1.0100E-01 seconds\n", + " Total time in simulation = 1.5663E+01 seconds\n", + " Time in transport only = 1.5651E+01 seconds\n", + " Time in inactive batches = 2.2110E+00 seconds\n", + " Time in active batches = 1.3452E+01 seconds\n", " Time synchronizing fission bank = 2.0000E-03 seconds\n", " Sampling source sites = 0.0000E+00 seconds\n", " SEND/RECV source sites = 1.0000E-03 seconds\n", " Time accumulating tallies = 0.0000E+00 seconds\n", - " Total time for finalization = 1.0000E-03 seconds\n", - " Total time elapsed = 1.7100E+01 seconds\n", - " Calculation Rate (inactive) = 5757.72 neutrons/second\n", - " Calculation Rate (active) = 2593.54 neutrons/second\n", + " Total time for finalization = 3.0000E-03 seconds\n", + " Total time elapsed = 1.6114E+01 seconds\n", + " Calculation Rate (inactive) = 5653.55 neutrons/second\n", + " Calculation Rate (active) = 2787.69 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -721,7 +720,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 24, "metadata": { "collapsed": false, "scrolled": true @@ -741,7 +740,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 25, "metadata": { "collapsed": false, "scrolled": true @@ -764,24 +763,45 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { - "ename": "AttributeError", - "evalue": "'CrossScore' object has no attribute 'strip'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mfiss_rate\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_tally\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'fiss. rate'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mabs_rate\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_tally\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'abs. rate'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mkeff\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfiss_rate\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mabs_rate\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[0mkeff\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_pandas_dataframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/tallies.pyc\u001b[0m in \u001b[0;36m__div__\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m 2051\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2052\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mother\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTally\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2053\u001b[1;33m \u001b[0mnew_tally\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_outer_product\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mother\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbinary_op\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'/'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2054\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2055\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mother\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mReal\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/tallies.pyc\u001b[0m in \u001b[0;36m_outer_product\u001b[1;34m(self, other, binary_op)\u001b[0m\n\u001b[0;32m 1548\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mself_score\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother_score\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mitertools\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproduct\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mall_scores\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1549\u001b[0m \u001b[0mnew_score\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCrossScore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself_score\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother_score\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbinary_op\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1550\u001b[1;33m \u001b[0mnew_tally\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_score\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1551\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1552\u001b[0m \u001b[1;31m# Generate nuclide \"outer products\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/tallies.pyc\u001b[0m in \u001b[0;36madd_score\u001b[1;34m(self, score)\u001b[0m\n\u001b[0;32m 434\u001b[0m \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 435\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 436\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_scores\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 437\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 438\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mnum_score_bins\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetter\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mAttributeError\u001b[0m: 'CrossScore' object has no attribute 'strip'" - ] + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
nuclidescoremeanstd. dev.
0total(nu-fission / absorption)1.0401660.009069
\n", + "
" + ], + "text/plain": [ + " nuclide score mean std. dev.\n", + "0 total (nu-fission / absorption) 1.040166 0.009069" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -803,11 +823,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
energy [MeV]nuclidescoremeanstd. dev.
0(0.0e+00 - 6.2e-01)totalabsorption0.959380.008187
\n", + "
" + ], + "text/plain": [ + " energy [MeV] nuclide score mean std. dev.\n", + "0 (0.0e+00 - 6.2e-01) total absorption 0.95938 0.008187" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Compute resonance escape probability using tally arithmetic\n", "therm_abs_rate = sp.get_tally(name='therm. abs. rate')\n", @@ -825,11 +883,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
nuclidescoremeanstd. dev.
0totalnu-fission1.0908990.010602
\n", + "
" + ], + "text/plain": [ + " nuclide score mean std. dev.\n", + "0 total nu-fission 1.090899 0.010602" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Compute fast fission factor factor using tally arithmetic\n", "therm_fiss_rate = sp.get_tally(name='therm. fiss. rate')\n", @@ -848,11 +942,51 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
energy [MeV]cellnuclidescoremeanstd. dev.
0(0.0e+00 - 6.2e-01)10000totalabsorption0.8034130.007031
\n", + "
" + ], + "text/plain": [ + " energy [MeV] cell nuclide score mean std. dev.\n", + "0 (0.0e+00 - 6.2e-01) 10000 total absorption 0.803413 0.007031" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Compute thermal flux utilization factor using tally arithmetic\n", "fuel_therm_abs_rate = sp.get_tally(name='fuel therm. abs. rate')\n", @@ -869,11 +1003,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
energy [MeV]nuclidescoremeanstd. dev.
0(0.0e+00 - 6.2e-01)total(nu-fission / absorption)1.2370530.011765
\n", + "
" + ], + "text/plain": [ + " energy [MeV] nuclide score mean std. dev.\n", + "0 (0.0e+00 - 6.2e-01) total (nu-fission / absorption) 1.237053 0.011765" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Compute neutrons produced per absorption (eta) using tally arithmetic\n", "eta = therm_fiss_rate / fuel_therm_abs_rate\n", @@ -889,11 +1061,52 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
energy [MeV]nuclidescoremeanstd. dev.
0(0.0e+00 - 6.2e-01)total(((absorption * nu-fission) * absorption) * (n...1.0401660.019018
\n", + "
" + ], + "text/plain": [ + " energy [MeV] nuclide \\\n", + "0 (0.0e+00 - 6.2e-01) total \n", + "\n", + " score mean std. dev. \n", + "0 (((absorption * nu-fission) * absorption) * (n... 1.040166 0.019018 " + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "keff = res_esc * fast_fiss * therm_util * eta\n", "keff.get_pandas_dataframe()" @@ -910,7 +1123,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": { "collapsed": false, "scrolled": true @@ -926,11 +1139,131 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
cellenergy [MeV]nuclidescoremeanstd. dev.
010000(0.0e+00 - 6.3e-07)(U-238 / total)(nu-fission / flux)6.657029e-077.377419e-09
110000(0.0e+00 - 6.3e-07)(U-238 / total)(scatter / flux)2.099891e-012.303838e-03
210000(0.0e+00 - 6.3e-07)(U-235 / total)(nu-fission / flux)3.564204e-013.951669e-03
310000(0.0e+00 - 6.3e-07)(U-235 / total)(scatter / flux)5.555330e-036.101004e-05
410000(6.3e-07 - 2.0e+01)(U-238 / total)(nu-fission / flux)7.154887e-038.053460e-05
510000(6.3e-07 - 2.0e+01)(U-238 / total)(scatter / flux)2.277701e-011.079289e-03
610000(6.3e-07 - 2.0e+01)(U-235 / total)(nu-fission / flux)8.066738e-035.254797e-05
710000(6.3e-07 - 2.0e+01)(U-235 / total)(scatter / flux)3.366802e-031.647058e-05
\n", + "
" + ], + "text/plain": [ + " cell energy [MeV] nuclide score \\\n", + "0 10000 (0.0e+00 - 6.3e-07) (U-238 / total) (nu-fission / flux) \n", + "1 10000 (0.0e+00 - 6.3e-07) (U-238 / total) (scatter / flux) \n", + "2 10000 (0.0e+00 - 6.3e-07) (U-235 / total) (nu-fission / flux) \n", + "3 10000 (0.0e+00 - 6.3e-07) (U-235 / total) (scatter / flux) \n", + "4 10000 (6.3e-07 - 2.0e+01) (U-238 / total) (nu-fission / flux) \n", + "5 10000 (6.3e-07 - 2.0e+01) (U-238 / total) (scatter / flux) \n", + "6 10000 (6.3e-07 - 2.0e+01) (U-235 / total) (nu-fission / flux) \n", + "7 10000 (6.3e-07 - 2.0e+01) (U-235 / total) (scatter / flux) \n", + "\n", + " mean std. dev. \n", + "0 6.657029e-07 7.377419e-09 \n", + "1 2.099891e-01 2.303838e-03 \n", + "2 3.564204e-01 3.951669e-03 \n", + "3 5.555330e-03 6.101004e-05 \n", + "4 7.154887e-03 8.053460e-05 \n", + "5 2.277701e-01 1.079289e-03 \n", + "6 8.066738e-03 5.254797e-05 \n", + "7 3.366802e-03 1.647058e-05 " + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "fuel_xs = fuel_rxn_rates / flux\n", "fuel_xs.get_pandas_dataframe()" @@ -945,11 +1278,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[ 6.65702880e-07]\n", + " [ 3.56420449e-01]]\n", + "\n", + " [[ 7.15488656e-03]\n", + " [ 8.06673774e-03]]]\n" + ] + } + ], "source": [ "# Show how to use Tally.get_values(...) with a CrossScore\n", "nu_fiss_xs = fuel_xs.get_values(scores=['(nu-fission / flux)'])\n", @@ -965,11 +1310,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[ 0.00555533]]\n", + "\n", + " [[ 0.0033668 ]]]\n" + ] + } + ], "source": [ "# Show how to use Tally.get_values(...) with a CrossScore and CrossNuclide\n", "u235_scatter_xs = fuel_xs.get_values(nuclides=['(U-235 / total)'], \n", @@ -979,11 +1334,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[ 0.22777006]\n", + " [ 0.0033668 ]]]\n" + ] + } + ], "source": [ "# Show how to use Tally.get_values(...) with a CrossFilter and CrossScore\n", "fast_scatter_xs = fuel_xs.get_values(filters=['energy'], \n", @@ -1001,11 +1365,81 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
cellenergy [MeV]nuclidescoremeanstd. dev.
010000(0.0e+00 - 6.3e-07)U-238nu-fission0.0000021.283958e-08
110000(0.0e+00 - 6.3e-07)U-235nu-fission0.8685536.880390e-03
210000(6.3e-07 - 2.0e+01)U-238nu-fission0.0821498.837250e-04
310000(6.3e-07 - 2.0e+01)U-235nu-fission0.0926185.195308e-04
\n", + "
" + ], + "text/plain": [ + " cell energy [MeV] nuclide score mean std. dev.\n", + "0 10000 (0.0e+00 - 6.3e-07) U-238 nu-fission 0.000002 1.283958e-08\n", + "1 10000 (0.0e+00 - 6.3e-07) U-235 nu-fission 0.868553 6.880390e-03\n", + "2 10000 (6.3e-07 - 2.0e+01) U-238 nu-fission 0.082149 8.837250e-04\n", + "3 10000 (6.3e-07 - 2.0e+01) U-235 nu-fission 0.092618 5.195308e-04" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# \"Slice\" the nu-fission data into a new derived Tally\n", "nu_fission_rates = fuel_rxn_rates.get_slice(scores=['nu-fission'])\n", @@ -1014,11 +1448,131 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
cellenergy [MeV]nuclidescoremeanstd. dev.
010002(1.0e-08 - 1.1e-07)H-1scatter4.6193980.040124
110002(1.1e-07 - 1.2e-06)H-1scatter2.0307570.011239
210002(1.2e-06 - 1.3e-05)H-1scatter1.6584880.009777
310002(1.3e-05 - 1.4e-04)H-1scatter1.8530020.007378
410002(1.4e-04 - 1.5e-03)H-1scatter2.0507730.012484
510002(1.5e-03 - 1.6e-02)H-1scatter2.1317590.007821
610002(1.6e-02 - 1.7e-01)H-1scatter2.2137100.015159
710002(1.7e-01 - 1.9e+00)H-1scatter2.0119250.009406
810002(1.9e+00 - 2.0e+01)H-1scatter0.3712800.003949
\n", + "
" + ], + "text/plain": [ + " cell energy [MeV] nuclide score mean std. dev.\n", + "0 10002 (1.0e-08 - 1.1e-07) H-1 scatter 4.619398 0.040124\n", + "1 10002 (1.1e-07 - 1.2e-06) H-1 scatter 2.030757 0.011239\n", + "2 10002 (1.2e-06 - 1.3e-05) H-1 scatter 1.658488 0.009777\n", + "3 10002 (1.3e-05 - 1.4e-04) H-1 scatter 1.853002 0.007378\n", + "4 10002 (1.4e-04 - 1.5e-03) H-1 scatter 2.050773 0.012484\n", + "5 10002 (1.5e-03 - 1.6e-02) H-1 scatter 2.131759 0.007821\n", + "6 10002 (1.6e-02 - 1.7e-01) H-1 scatter 2.213710 0.015159\n", + "7 10002 (1.7e-01 - 1.9e+00) H-1 scatter 2.011925 0.009406\n", + "8 10002 (1.9e+00 - 2.0e+01) H-1 scatter 0.371280 0.003949" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# \"Slice\" the H-1 scatter data in the moderator Cell into a new derived Tally\n", "need_to_slice = sp.get_tally(name='need-to-slice')\n", diff --git a/openmc/filter.py b/openmc/filter.py index 63034fe57..bff1c62f0 100644 --- a/openmc/filter.py +++ b/openmc/filter.py @@ -161,7 +161,7 @@ class Filter(object): raise ValueError(msg) # If the bin edge is a single value, it is a Cell, Material, etc. ID - if not cv._isinstance(bins, Iterable): + if not isinstance(bins, Iterable): bins = [bins] # If the bins are in a collection, convert it to a list @@ -200,7 +200,7 @@ class Filter(object): msg = 'Unable to add bins "{0}" to a mesh Filter since ' \ 'only a single mesh can be used per tally'.format(bins) raise ValueError(msg) - elif not cv._isinstance(bins[0], Integral): + elif not isinstance(bins[0], Integral): msg = 'Unable to add bin "{0}" to mesh Filter since it ' \ 'is a non-integer'.format(bins[0]) raise ValueError(msg) @@ -443,7 +443,7 @@ class Filter(object): if self.type == 'mesh': # Construct 3-tuple of x,y,z cell indices for a 3D mesh - if (len(self.mesh.dimension) == 3): + if len(self.mesh.dimension) == 3: nx, ny, nz = self.mesh.dimension x = bin_index / (ny * nz) y = (bin_index - (x * ny * nz)) / nz diff --git a/openmc/mgxs/groups.py b/openmc/mgxs/groups.py index f0adc5101..dfaca9be9 100644 --- a/openmc/mgxs/groups.py +++ b/openmc/mgxs/groups.py @@ -17,28 +17,21 @@ class EnergyGroups(object): Parameters ---------- - group_edges : ndarray + group_edges : Iterable of Real The energy group boundaries [MeV] - num_groups : Integral - The number of energy groups Attributes ---------- - group_edges : ndarray + group_edges : Iterable of Real The energy group boundaries [MeV] - num_groups : Integral - The number of energy groups """ - def __init__(self, group_edges=None, num_groups=None): + def __init__(self, group_edges=None): self._group_edges = None - self._num_groups = None if group_edges is not None: self.group_edges = group_edges - if num_groups is not None: - self.num_groups = num_groups def __deepcopy__(self, memo): existing = memo.get(id(self)) @@ -47,7 +40,6 @@ class EnergyGroups(object): if existing is None: clone = type(self).__new__(type(self)) clone._group_edges = copy.deepcopy(self.group_edges, memo) - clone._num_groups = self.num_groups memo[id(self)] = clone @@ -77,47 +69,13 @@ class EnergyGroups(object): @property def num_groups(self): - return self._num_groups + return len(self.group_edges) - 1 @group_edges.setter def group_edges(self, edges): cv.check_type('group edges', edges, Iterable, Real) cv.check_greater_than('number of group edges', len(edges), 1) self._group_edges = np.array(edges) - self._num_groups = len(edges)-1 - - def generate_bin_edges(self, start, stop, num_groups, spacing='linear'): - """Generate equally or logarithmically-spaced energy group boundaries. - - Parameters - ---------- - start : Real - The lowest energy in MeV - stop : Real - The highest energy in MeV - num_groups : Integral - The number of energy groups - spacing : {'linear', 'logarithmic'} - The spacing between groups - - """ - - cv.check_type('first edge', start, Real) - cv.check_type('last edge', stop, Real) - cv.check_type('number of groups', num_groups, Integral) - cv.check_type('spacing', spacing, basestring) - cv.check_greater_than('first edge', start, 0, True) - cv.check_greater_than('last edge', stop, start, False) - cv.check_greater_than('number of groups', num_groups, 0) - cv.check_value('spacing', spacing, ('linear', 'logarithmic')) - - if spacing == 'linear': - self.group_edges = np.linspace(start, stop, num_groups + 1) - elif spacing == 'logarithmic': - self.group_edges = \ - np.logspace(np.log10(start), np.log10(stop), num_groups + 1) - - self._num_groups = num_groups def get_group(self, energy): """Returns the energy group in which the given energy resides. @@ -144,7 +102,7 @@ class EnergyGroups(object): 'the group edges have not yet been set'.format(energy) raise ValueError(msg) - index = np.where(self.group_edges > energy)[0] + index = np.where(self.group_edges > energy)[0][0] group = self.num_groups - index return group @@ -173,6 +131,9 @@ class EnergyGroups(object): 'the group edges have not yet been set'.format(group) raise ValueError(msg) + cv.check_greater_than('group', group, 0) + cv.check_less_than('group', group, self.num_groups, equality=True) + lower = self.group_edges[self.num_groups-group] upper = self.group_edges[self.num_groups-group+1] return lower, upper @@ -205,7 +166,7 @@ class EnergyGroups(object): raise ValueError(msg) if groups == 'all': - indices = np.arange(self.num_groups) + return np.arange(self.num_groups) else: indices = np.zeros(len(groups), dtype=np.int) @@ -229,7 +190,7 @@ class EnergyGroups(object): The energy groups of interest - a list of 2-tuples, each directly corresponding to one of the new coarse groups. The values in the 2-tuples are upper/lower energy groups used to construct a new - coarse group. For example, if [(1,2), (2,4)] was used as the coarse + coarse group. For example, if [(1,2), (3,4)] was used as the coarse groups, fine groups 1 and 2 would be merged into coarse group 1 while fine groups 3 and 4 would be merged into coarse group 2. @@ -255,8 +216,8 @@ class EnergyGroups(object): cv.check_less_than('lower group', group[0], group[1], False) # Compute the group indices into the coarse group - group_bounds = [group[0] for group in coarse_groups] - group_bounds.append(coarse_groups[-1][1]) + group_bounds = [group[1] for group in coarse_groups] + group_bounds.insert(0, coarse_groups[0][0]) # Determine the indices mapping the fine-to-coarse energy groups group_bounds = np.asarray(group_bounds)