From e7b7c402fc6d940349efbdb0cc2fbac216cdada1 Mon Sep 17 00:00:00 2001 From: Cyrus Wyett <34195737+cjwyett@users.noreply.github.com> Date: Sat, 15 Aug 2020 06:53:12 +0100 Subject: [PATCH 01/17] Add files via upload --- examples/jupyter/pincell.ipynb | 425 ++++++++++++++++----------------- 1 file changed, 212 insertions(+), 213 deletions(-) diff --git a/examples/jupyter/pincell.ipynb b/examples/jupyter/pincell.ipynb index 7d0f4e65a9..06ac8dc580 100644 --- a/examples/jupyter/pincell.ipynb +++ b/examples/jupyter/pincell.ipynb @@ -173,7 +173,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/sam/openmc/openmc/openmc/mixin.py:71: IDWarning: Another Material instance already exists with id=2.\n", + "/home/master/anaconda3/envs/OMC/lib/python3.8/site-packages/openmc/mixin.py:68: IDWarning: Another Material instance already exists with id=2.\n", " warn(msg, IDWarning)\n" ] } @@ -218,7 +218,7 @@ "metadata": {}, "outputs": [], "source": [ - "mats = openmc.Materials([uo2, zirconium, water])" + "materials = openmc.Materials([uo2, zirconium, water])" ] }, { @@ -245,10 +245,10 @@ } ], "source": [ - "mats = openmc.Materials()\n", - "mats.append(uo2)\n", - "mats += [zirconium, water]\n", - "isinstance(mats, list)" + "materials = openmc.Materials()\n", + "materials.append(uo2)\n", + "materials += [zirconium, water]\n", + "isinstance(materials, list)" ] }, { @@ -294,7 +294,7 @@ } ], "source": [ - "mats.export_to_xml()\n", + "materials.export_to_xml()\n", "!cat materials.xml" ] }, @@ -353,7 +353,7 @@ "water.remove_nuclide('O16')\n", "water.add_element('O', 1.0)\n", "\n", - "mats.export_to_xml()\n", + "materials.export_to_xml()\n", "!cat materials.xml" ] }, @@ -386,14 +386,14 @@ "text": [ "\n", "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " ...\n", " \n", " \n", @@ -493,7 +493,7 @@ "metadata": {}, "outputs": [], "source": [ - "sph = openmc.Sphere(r=1.0)" + "sphere = openmc.Sphere(r=1.0)" ] }, { @@ -511,8 +511,8 @@ "metadata": {}, "outputs": [], "source": [ - "inside_sphere = -sph\n", - "outside_sphere = +sph" + "inside_sphere = -sphere\n", + "outside_sphere = +sphere" ] }, { @@ -555,7 +555,7 @@ "outputs": [], "source": [ "z_plane = openmc.ZPlane(z0=0)\n", - "northern_hemisphere = -sph & +z_plane" + "northern_hemisphere = -sphere & +z_plane" ] }, { @@ -663,7 +663,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 24, @@ -672,7 +672,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -702,7 +702,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 25, @@ -711,7 +711,7 @@ }, { "data": { - "image/png": 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" ] @@ -741,7 +741,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 26, @@ -750,7 +750,7 @@ }, { "data": { - "image/png": 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\n", "text/plain": [ "
" ] @@ -787,9 +787,9 @@ "metadata": {}, "outputs": [], "source": [ - "fuel_or = openmc.ZCylinder(r=0.39)\n", - "clad_ir = openmc.ZCylinder(r=0.40)\n", - "clad_or = openmc.ZCylinder(r=0.46)" + "fuel_outer_radius = openmc.ZCylinder(r=0.39)\n", + "clad_inner_radius = openmc.ZCylinder(r=0.40)\n", + "clad_outer_radius = openmc.ZCylinder(r=0.46)" ] }, { @@ -805,9 +805,9 @@ "metadata": {}, "outputs": [], "source": [ - "fuel_region = -fuel_or\n", - "gap_region = +fuel_or & -clad_ir\n", - "clad_region = +clad_ir & -clad_or" + "fuel_region = -fuel_outer_radius\n", + "gap_region = +fuel_outer_radius & -clad_inner_radius\n", + "clad_region = +clad_inner_radius & -clad_outer_radius" ] }, { @@ -826,9 +826,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/sam/openmc/openmc/openmc/mixin.py:71: IDWarning: Another Cell instance already exists with id=1.\n", + "/home/master/anaconda3/envs/OMC/lib/python3.8/site-packages/openmc/mixin.py:68: IDWarning: Another Cell instance already exists with id=1.\n", " warn(msg, IDWarning)\n", - "/home/sam/openmc/openmc/openmc/mixin.py:71: IDWarning: Another Cell instance already exists with id=2.\n", + "/home/master/anaconda3/envs/OMC/lib/python3.8/site-packages/openmc/mixin.py:68: IDWarning: Another Cell instance already exists with id=2.\n", " warn(msg, IDWarning)\n" ] } @@ -879,7 +879,7 @@ "metadata": {}, "outputs": [], "source": [ - "water_region = +left & -right & +bottom & -top & +clad_or\n", + "water_region = +left & -right & +bottom & -top & +clad_outer_radius\n", "\n", "moderator = openmc.Cell(4, 'moderator')\n", "moderator.fill = water\n", @@ -928,7 +928,7 @@ "metadata": {}, "outputs": [], "source": [ - "water_region = box & +clad_or" + "water_region = box & +clad_outer_radius" ] }, { @@ -965,14 +965,14 @@ } ], "source": [ - "root = openmc.Universe(cells=(fuel, gap, clad, moderator))\n", + "root_universe = openmc.Universe(cells=(fuel, gap, clad, moderator))\n", "\n", - "geom = openmc.Geometry()\n", - "geom.root_universe = root\n", + "geometry = openmc.Geometry()\n", + "geometry.root_universe = root_universe\n", "\n", "# or...\n", - "geom = openmc.Geometry(root)\n", - "geom.export_to_xml()\n", + "geometry = openmc.Geometry(root_universe)\n", + "geometry.export_to_xml()\n", "!cat geometry.xml" ] }, @@ -991,8 +991,9 @@ "metadata": {}, "outputs": [], "source": [ + "# Create a point source\n", "point = openmc.stats.Point((0, 0, 0))\n", - "src = openmc.Source(space=point)" + "source = openmc.Source(space=point)" ] }, { @@ -1008,11 +1009,16 @@ "metadata": {}, "outputs": [], "source": [ + "# OpenMC simulation parameters\n", + "batches = 100\n", + "inactive = 10\n", + "particles = 1000\n", + "\n", + "# Instantiate a Settings object\n", "settings = openmc.Settings()\n", - "settings.source = src\n", - "settings.batches = 100\n", - "settings.inactive = 10\n", - "settings.particles = 1000" + "settings.batches = batches\n", + "settings.inactive = inactive\n", + "settings.particles = particles" ] }, { @@ -1030,11 +1036,6 @@ " 1000\r\n", " 100\r\n", " 10\r\n", - " \r\n", - " \r\n", - " 0 0 0\r\n", - " \r\n", - " \r\n", "\r\n" ] } @@ -1065,8 +1066,8 @@ "source": [ "cell_filter = openmc.CellFilter(fuel)\n", "\n", - "t = openmc.Tally(1)\n", - "t.filters = [cell_filter]" + "tally = openmc.Tally(1)\n", + "tally.filters = [cell_filter]" ] }, { @@ -1082,8 +1083,8 @@ "metadata": {}, "outputs": [], "source": [ - "t.nuclides = ['U235']\n", - "t.scores = ['total', 'fission', 'absorption', '(n,gamma)']" + "tally.nuclides = ['U235']\n", + "tally.scores = ['total', 'fission', 'absorption', '(n,gamma)']" ] }, { @@ -1117,7 +1118,7 @@ } ], "source": [ - "tallies = openmc.Tallies([t])\n", + "tallies = openmc.Tallies([tally])\n", "tallies.export_to_xml()\n", "!cat tallies.xml" ] @@ -1168,165 +1169,164 @@ "\n", " | The OpenMC Monte Carlo Code\n", " Copyright | 2011-2020 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.12.0-dev\n", - " Git SHA1 | dd74b2f43f1d2060486de2823ee30058b8971dbd\n", - " Date/Time | 2020-03-03 13:58:53\n", - " MPI Processes | 1\n", - " OpenMP Threads | 8\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.12.0\n", + " Git SHA1 | 3d90a9f857ec72eae897e054d4225180f1fa4d93\n", + " Date/Time | 2020-08-15 06:49:11\n", + " OpenMP Threads | 4\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Reading U235 from /home/sam/openmc/libs/nndc_hdf5/U235.h5\n", - " Reading U238 from /home/sam/openmc/libs/nndc_hdf5/U238.h5\n", - " Reading O16 from /home/sam/openmc/libs/nndc_hdf5/O16.h5\n", - " Reading Zr90 from /home/sam/openmc/libs/nndc_hdf5/Zr90.h5\n", - " Reading Zr91 from /home/sam/openmc/libs/nndc_hdf5/Zr91.h5\n", - " Reading Zr92 from /home/sam/openmc/libs/nndc_hdf5/Zr92.h5\n", - " Reading Zr94 from /home/sam/openmc/libs/nndc_hdf5/Zr94.h5\n", - " Reading Zr96 from /home/sam/openmc/libs/nndc_hdf5/Zr96.h5\n", - " Reading H1 from /home/sam/openmc/libs/nndc_hdf5/H1.h5\n", - " Reading O17 from /home/sam/openmc/libs/nndc_hdf5/O17.h5\n", - " Reading c_H_in_H2O from /home/sam/openmc/libs/nndc_hdf5/c_H_in_H2O.h5\n", - " Maximum neutron transport energy: 20000000.000000 eV for U235\n", + " Reading U235 from /home/master/data/nuclear/endfb71_hdf5/U235.h5\n", + " Reading U238 from /home/master/data/nuclear/endfb71_hdf5/U238.h5\n", + " Reading O16 from /home/master/data/nuclear/endfb71_hdf5/O16.h5\n", + " Reading Zr90 from /home/master/data/nuclear/endfb71_hdf5/Zr90.h5\n", + " Reading Zr91 from /home/master/data/nuclear/endfb71_hdf5/Zr91.h5\n", + " Reading Zr92 from /home/master/data/nuclear/endfb71_hdf5/Zr92.h5\n", + " Reading Zr94 from /home/master/data/nuclear/endfb71_hdf5/Zr94.h5\n", + " Reading Zr96 from /home/master/data/nuclear/endfb71_hdf5/Zr96.h5\n", + " Reading H1 from /home/master/data/nuclear/endfb71_hdf5/H1.h5\n", + " Reading O17 from /home/master/data/nuclear/endfb71_hdf5/O17.h5\n", + " Reading c_H_in_H2O from /home/master/data/nuclear/endfb71_hdf5/c_H_in_H2O.h5\n", " Minimum neutron data temperature: 294.000000 K\n", " Maximum neutron data temperature: 294.000000 K\n", " Reading tallies XML file...\n", " Preparing distributed cell instances...\n", " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000.000000 eV for U235\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 1.32572\n", - " 2/1 1.46138\n", - " 3/1 1.46068\n", - " 4/1 1.39592\n", - " 5/1 1.37519\n", - " 6/1 1.38777\n", - " 7/1 1.50242\n", - " 8/1 1.42042\n", - " 9/1 1.47458\n", - " 10/1 1.49148\n", - " 11/1 1.39339\n", - " 12/1 1.40637 1.39988 +/- 0.00649\n", - " 13/1 1.42972 1.40983 +/- 0.01063\n", - " 14/1 1.46319 1.42317 +/- 0.01531\n", - " 15/1 1.41538 1.42161 +/- 0.01196\n", - " 16/1 1.38163 1.41494 +/- 0.01182\n", - " 17/1 1.41257 1.41461 +/- 0.01000\n", - " 18/1 1.43455 1.41710 +/- 0.00901\n", - " 19/1 1.33136 1.40757 +/- 0.01241\n", - " 20/1 1.41560 1.40837 +/- 0.01113\n", - " 21/1 1.38911 1.40662 +/- 0.01021\n", - " 22/1 1.28621 1.39659 +/- 0.01370\n", - " 23/1 1.45693 1.40123 +/- 0.01343\n", - " 24/1 1.46839 1.40603 +/- 0.01333\n", - " 25/1 1.46738 1.41012 +/- 0.01306\n", - " 26/1 1.43977 1.41197 +/- 0.01236\n", - " 27/1 1.44066 1.41366 +/- 0.01173\n", - " 28/1 1.39358 1.41254 +/- 0.01112\n", - " 29/1 1.39142 1.41143 +/- 0.01057\n", - " 30/1 1.38525 1.41012 +/- 0.01012\n", - " 31/1 1.38025 1.40870 +/- 0.00973\n", - " 32/1 1.45348 1.41074 +/- 0.00949\n", - " 33/1 1.35893 1.40848 +/- 0.00935\n", - " 34/1 1.32332 1.40493 +/- 0.00963\n", - " 35/1 1.46285 1.40725 +/- 0.00952\n", - " 36/1 1.33760 1.40457 +/- 0.00953\n", - " 37/1 1.41117 1.40482 +/- 0.00917\n", - " 38/1 1.45574 1.40664 +/- 0.00903\n", - " 39/1 1.43472 1.40760 +/- 0.00876\n", - " 40/1 1.30110 1.40405 +/- 0.00918\n", - " 41/1 1.41765 1.40449 +/- 0.00889\n", - " 42/1 1.45300 1.40601 +/- 0.00874\n", - " 43/1 1.40491 1.40597 +/- 0.00847\n", - " 44/1 1.42053 1.40640 +/- 0.00823\n", - " 45/1 1.38805 1.40588 +/- 0.00801\n", - " 46/1 1.34293 1.40413 +/- 0.00798\n", - " 47/1 1.35441 1.40279 +/- 0.00787\n", - " 48/1 1.29370 1.39991 +/- 0.00818\n", - " 49/1 1.48467 1.40209 +/- 0.00826\n", - " 50/1 1.41759 1.40248 +/- 0.00806\n", - " 51/1 1.37151 1.40172 +/- 0.00790\n", - " 52/1 1.42403 1.40225 +/- 0.00773\n", - " 53/1 1.38826 1.40193 +/- 0.00755\n", - " 54/1 1.48944 1.40392 +/- 0.00764\n", - " 55/1 1.41452 1.40415 +/- 0.00747\n", - " 56/1 1.47337 1.40566 +/- 0.00746\n", - " 57/1 1.35700 1.40462 +/- 0.00738\n", - " 58/1 1.40305 1.40459 +/- 0.00722\n", - " 59/1 1.41608 1.40482 +/- 0.00708\n", - " 60/1 1.47254 1.40618 +/- 0.00706\n", - " 61/1 1.36847 1.40544 +/- 0.00696\n", - " 62/1 1.34103 1.40420 +/- 0.00694\n", - " 63/1 1.39510 1.40403 +/- 0.00681\n", - " 64/1 1.40228 1.40399 +/- 0.00668\n", - " 65/1 1.29401 1.40200 +/- 0.00686\n", - " 66/1 1.42693 1.40244 +/- 0.00675\n", - " 67/1 1.36447 1.40177 +/- 0.00666\n", - " 68/1 1.37498 1.40131 +/- 0.00656\n", - " 69/1 1.36958 1.40077 +/- 0.00647\n", - " 70/1 1.38585 1.40053 +/- 0.00637\n", - " 71/1 1.42133 1.40087 +/- 0.00627\n", - " 72/1 1.44900 1.40164 +/- 0.00622\n", - " 73/1 1.37696 1.40125 +/- 0.00613\n", - " 74/1 1.48851 1.40261 +/- 0.00619\n", - " 75/1 1.38933 1.40241 +/- 0.00610\n", - " 76/1 1.41780 1.40264 +/- 0.00601\n", - " 77/1 1.41054 1.40276 +/- 0.00592\n", - " 78/1 1.38194 1.40246 +/- 0.00584\n", - " 79/1 1.38446 1.40219 +/- 0.00576\n", - " 80/1 1.37504 1.40181 +/- 0.00569\n", - " 81/1 1.40550 1.40186 +/- 0.00561\n", - " 82/1 1.49785 1.40319 +/- 0.00569\n", - " 83/1 1.35613 1.40255 +/- 0.00565\n", - " 84/1 1.41786 1.40275 +/- 0.00557\n", - " 85/1 1.38444 1.40251 +/- 0.00550\n", - " 86/1 1.40459 1.40254 +/- 0.00543\n", - " 87/1 1.39923 1.40249 +/- 0.00536\n", - " 88/1 1.44540 1.40304 +/- 0.00532\n", - " 89/1 1.45962 1.40376 +/- 0.00530\n", - " 90/1 1.37057 1.40335 +/- 0.00525\n", - " 91/1 1.38115 1.40307 +/- 0.00519\n", - " 92/1 1.35758 1.40252 +/- 0.00516\n", - " 93/1 1.34508 1.40182 +/- 0.00514\n", - " 94/1 1.31471 1.40079 +/- 0.00519\n", - " 95/1 1.41434 1.40095 +/- 0.00513\n", - " 96/1 1.33895 1.40023 +/- 0.00512\n", - " 97/1 1.44716 1.40077 +/- 0.00509\n", - " 98/1 1.38455 1.40058 +/- 0.00503\n", - " 99/1 1.52127 1.40194 +/- 0.00516\n", - " 100/1 1.35488 1.40141 +/- 0.00513\n", + " 1/1 1.42066\n", + " 2/1 1.39831\n", + " 3/1 1.46207\n", + " 4/1 1.44888\n", + " 5/1 1.42595\n", + " 6/1 1.35549\n", + " 7/1 1.36717\n", + " 8/1 1.45095\n", + " 9/1 1.36061\n", + " 10/1 1.36554\n", + " 11/1 1.36973\n", + " 12/1 1.44276 1.40625 +/- 0.03652\n", + " 13/1 1.35512 1.38920 +/- 0.02711\n", + " 14/1 1.54216 1.42744 +/- 0.04277\n", + " 15/1 1.39353 1.42066 +/- 0.03382\n", + " 16/1 1.38650 1.41497 +/- 0.02820\n", + " 17/1 1.38760 1.41106 +/- 0.02415\n", + " 18/1 1.38413 1.40769 +/- 0.02118\n", + " 19/1 1.39088 1.40582 +/- 0.01877\n", + " 20/1 1.47468 1.41271 +/- 0.01815\n", + " 21/1 1.45695 1.41673 +/- 0.01690\n", + " 22/1 1.40308 1.41559 +/- 0.01547\n", + " 23/1 1.40821 1.41503 +/- 0.01424\n", + " 24/1 1.32301 1.40845 +/- 0.01473\n", + " 25/1 1.36702 1.40569 +/- 0.01399\n", + " 26/1 1.30968 1.39969 +/- 0.01440\n", + " 27/1 1.38099 1.39859 +/- 0.01357\n", + " 28/1 1.42103 1.39984 +/- 0.01285\n", + " 29/1 1.39741 1.39971 +/- 0.01216\n", + " 30/1 1.36548 1.39800 +/- 0.01166\n", + " 31/1 1.41573 1.39884 +/- 0.01112\n", + " 32/1 1.39788 1.39880 +/- 0.01061\n", + " 33/1 1.35942 1.39709 +/- 0.01028\n", + " 34/1 1.40483 1.39741 +/- 0.00985\n", + " 35/1 1.39418 1.39728 +/- 0.00944\n", + " 36/1 1.41492 1.39796 +/- 0.00910\n", + " 37/1 1.49392 1.40151 +/- 0.00945\n", + " 38/1 1.45114 1.40329 +/- 0.00928\n", + " 39/1 1.42619 1.40408 +/- 0.00899\n", + " 40/1 1.35249 1.40236 +/- 0.00885\n", + " 41/1 1.35401 1.40080 +/- 0.00870\n", + " 42/1 1.40220 1.40084 +/- 0.00842\n", + " 43/1 1.36437 1.39974 +/- 0.00824\n", + " 44/1 1.33642 1.39787 +/- 0.00821\n", + " 45/1 1.36953 1.39706 +/- 0.00801\n", + " 46/1 1.30034 1.39438 +/- 0.00824\n", + " 47/1 1.44097 1.39564 +/- 0.00811\n", + " 48/1 1.37981 1.39522 +/- 0.00790\n", + " 49/1 1.34870 1.39403 +/- 0.00779\n", + " 50/1 1.41247 1.39449 +/- 0.00761\n", + " 51/1 1.33382 1.39301 +/- 0.00756\n", + " 52/1 1.37043 1.39247 +/- 0.00740\n", + " 53/1 1.38754 1.39236 +/- 0.00723\n", + " 54/1 1.40160 1.39257 +/- 0.00707\n", + " 55/1 1.37511 1.39218 +/- 0.00692\n", + " 56/1 1.38589 1.39204 +/- 0.00677\n", + " 57/1 1.40630 1.39234 +/- 0.00663\n", + " 58/1 1.29944 1.39041 +/- 0.00677\n", + " 59/1 1.40019 1.39061 +/- 0.00663\n", + " 60/1 1.42384 1.39127 +/- 0.00653\n", + " 61/1 1.36502 1.39076 +/- 0.00643\n", + " 62/1 1.37042 1.39037 +/- 0.00631\n", + " 63/1 1.42295 1.39098 +/- 0.00622\n", + " 64/1 1.40042 1.39116 +/- 0.00611\n", + " 65/1 1.36382 1.39066 +/- 0.00602\n", + " 66/1 1.31659 1.38934 +/- 0.00606\n", + " 67/1 1.36101 1.38884 +/- 0.00597\n", + " 68/1 1.46359 1.39013 +/- 0.00601\n", + " 69/1 1.41012 1.39047 +/- 0.00591\n", + " 70/1 1.27411 1.38853 +/- 0.00613\n", + " 71/1 1.45399 1.38960 +/- 0.00612\n", + " 72/1 1.40455 1.38984 +/- 0.00603\n", + " 73/1 1.33020 1.38890 +/- 0.00601\n", + " 74/1 1.44599 1.38979 +/- 0.00598\n", + " 75/1 1.34985 1.38917 +/- 0.00592\n", + " 76/1 1.36183 1.38876 +/- 0.00584\n", + " 77/1 1.41080 1.38909 +/- 0.00576\n", + " 78/1 1.43991 1.38984 +/- 0.00573\n", + " 79/1 1.35613 1.38935 +/- 0.00566\n", + " 80/1 1.31659 1.38831 +/- 0.00568\n", + " 81/1 1.51344 1.39007 +/- 0.00587\n", + " 82/1 1.38404 1.38999 +/- 0.00579\n", + " 83/1 1.39613 1.39007 +/- 0.00571\n", + " 84/1 1.43037 1.39061 +/- 0.00566\n", + " 85/1 1.47316 1.39172 +/- 0.00569\n", + " 86/1 1.39220 1.39172 +/- 0.00561\n", + " 87/1 1.44400 1.39240 +/- 0.00558\n", + " 88/1 1.42419 1.39281 +/- 0.00552\n", + " 89/1 1.30930 1.39175 +/- 0.00556\n", + " 90/1 1.46976 1.39273 +/- 0.00557\n", + " 91/1 1.38334 1.39261 +/- 0.00550\n", + " 92/1 1.35260 1.39212 +/- 0.00546\n", + " 93/1 1.38505 1.39204 +/- 0.00539\n", + " 94/1 1.38290 1.39193 +/- 0.00533\n", + " 95/1 1.42597 1.39233 +/- 0.00528\n", + " 96/1 1.41624 1.39261 +/- 0.00523\n", + " 97/1 1.42053 1.39293 +/- 0.00518\n", + " 98/1 1.36268 1.39258 +/- 0.00513\n", + " 99/1 1.39175 1.39258 +/- 0.00507\n", + " 100/1 1.38148 1.39245 +/- 0.00502\n", " Creating state point statepoint.100.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 9.7663e-01 seconds\n", - " Reading cross sections = 8.7037e-01 seconds\n", - " Total time in simulation = 1.8046e+00 seconds\n", - " Time in transport only = 1.7523e+00 seconds\n", - " Time in inactive batches = 2.0849e-01 seconds\n", - " Time in active batches = 1.5961e+00 seconds\n", - " Time synchronizing fission bank = 6.7422e-03 seconds\n", - " Sampling source sites = 4.9715e-03 seconds\n", - " SEND/RECV source sites = 1.1323e-03 seconds\n", - " Time accumulating tallies = 5.3810e-04 seconds\n", - " Total time for finalization = 5.1810e-04 seconds\n", - " Total time elapsed = 2.7828e+00 seconds\n", - " Calculation Rate (inactive) = 47962.8 particles/second\n", - " Calculation Rate (active) = 56386.7 particles/second\n", + " Total time for initialization = 6.9980e-01 seconds\n", + " Reading cross sections = 6.8788e-01 seconds\n", + " Total time in simulation = 1.9251e+00 seconds\n", + " Time in transport only = 1.9072e+00 seconds\n", + " Time in inactive batches = 1.5794e-01 seconds\n", + " Time in active batches = 1.7672e+00 seconds\n", + " Time synchronizing fission bank = 4.2579e-03 seconds\n", + " Sampling source sites = 3.4948e-03 seconds\n", + " SEND/RECV source sites = 5.9671e-04 seconds\n", + " Time accumulating tallies = 7.9162e-05 seconds\n", + " Total time for finalization = 5.7305e-05 seconds\n", + " Total time elapsed = 2.6291e+00 seconds\n", + " Calculation Rate (inactive) = 63313.6 particles/second\n", + " Calculation Rate (active) = 50928.0 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.39737 +/- 0.00470\n", - " k-effective (Track-length) = 1.40141 +/- 0.00513\n", - " k-effective (Absorption) = 1.39596 +/- 0.00308\n", - " Combined k-effective = 1.39719 +/- 0.00286\n", + " k-effective (Collision) = 1.39516 +/- 0.00457\n", + " k-effective (Track-length) = 1.39245 +/- 0.00502\n", + " k-effective (Absorption) = 1.40443 +/- 0.00333\n", + " Combined k-effective = 1.40145 +/- 0.00319\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -1356,10 +1356,10 @@ "\r\n", " Cell 1\r\n", " U235\r\n", - " Total Reaction Rate 0.731003 +/- 0.00253759\r\n", - " Fission Rate 0.547587 +/- 0.00210114\r\n", - " Absorption Rate 0.657406 +/- 0.0024539\r\n", - " (n,gamma) 0.109821 +/- 0.000368054\r\n" + " Total Reaction Rate 0.726151 +/- 0.00251702\r\n", + " Fission Rate 0.543836 +/- 0.00205084\r\n", + " Absorption Rate 0.652874 +/- 0.002424\r\n", + " (n,gamma) 0.10904 +/- 0.000385793\r\n" ] } ], @@ -1382,12 +1382,12 @@ "metadata": {}, "outputs": [], "source": [ - "p = openmc.Plot()\n", - "p.filename = 'pinplot'\n", - "p.width = (pitch, pitch)\n", - "p.pixels = (200, 200)\n", - "p.color_by = 'material'\n", - "p.colors = {uo2: 'yellow', water: 'blue'}" + "plot = openmc.Plot()\n", + "plot.filename = 'pinplot'\n", + "plot.width = (pitch, pitch)\n", + "plot.pixels = (200, 200)\n", + "plot.color_by = 'material'\n", + "plot.colors = {uo2: 'yellow', water: 'blue'}" ] }, { @@ -1420,7 +1420,7 @@ } ], "source": [ - "plots = openmc.Plots([p])\n", + "plots = openmc.Plots([plot])\n", "plots.export_to_xml()\n", "!cat plots.xml" ] @@ -1467,12 +1467,11 @@ "\n", " | The OpenMC Monte Carlo Code\n", " Copyright | 2011-2020 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.12.0-dev\n", - " Git SHA1 | dd74b2f43f1d2060486de2823ee30058b8971dbd\n", - " Date/Time | 2020-03-03 13:58:56\n", - " MPI Processes | 1\n", - " OpenMP Threads | 8\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.12.0\n", + " Git SHA1 | 3d90a9f857ec72eae897e054d4225180f1fa4d93\n", + " Date/Time | 2020-08-15 06:49:14\n", + " OpenMP Threads | 4\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", @@ -1534,7 +1533,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "" ] @@ -1563,7 +1562,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMgAAADIAgMAAADQNkYNAAAABGdBTUEAALGPC/xhBQAAACBjSFJNAAB6JgAAgIQAAPoAAACA6AAAdTAAAOpgAAA6mAAAF3CculE8AAAADFBMVEUAAP9yEhL//////wAZPRNOAAAAAWJLR0QCZgt8ZAAAAAd0SU1FB+QIDwcxDg24lJMAAAI7SURBVGje7ZmxkcIwEEUhcAnuhxIIEIGvAwioxiU4QASU4GougA4gsM6SfNxh766sfx7N3Ix+zJv9X7KxtLtaZWVlZWWl1Fr12sQQpXLaRRMRTKFe2kbk+Na8POpNkbbmWlMqtkwxRsJllIotMxQ56F7neWX8LmrdGtNpPWc//Z4cLsbpfp6xN85XNRA904Sd2V/stXlJ16EFcL4O7Q/SnUPOrK/9xfzSvQ44K0dFhjLCmq0nRYYyvDPrq2rfka4RnVlfoyJ9GdGZ9XUdI89aWGYbpTITNUKYgvLlnXFhSsqXd7bjEcKXc8YhvYEjhdzY/Db9lUKebP4+/b6lkK7m8nNRhDAlE8WFoREuig/DpKej+DAbGmGiuDAUUsgItWR8ejZ/yaZ3+SmET+/zL4JIC8Ys2VpI7/JPkSKEbCnkyiNPCilDyI5A+AVzSzZFVAhRBCKssVvlBRB5W8iNwZCThDymiLyT5F5iSCsh3RSRN5/cfgwxoiikkpFmgqgwopZAPmTkcxnkKCO3BZDQs088/RhykpFHRjLyP5AE70uadz/Z/1iK/2TgYwF9klJ8K4GPeJrTBXDsSXMeS3O2RA69wNEaOMAD1wTgMgJceYCLVZobH3IVBS68wLUauLwDLQKgEYG0O4CmCtC6ARpEQBsKaXYBLTWgcQe0B4EmJNLqBBqqQNsWaA4jLWig0Q2005GmPTAaAAYQyJgDGKYgIxtgMISMn4AhFzJKAwZ2yFgQGT4iI86srKysrD/rC4LWcCSWwIp+AAAAJXRFWHRkYXRlOmNyZWF0ZQAyMDIwLTA4LTE1VDA2OjQ5OjE0KzAxOjAwlLKpXwAAACV0RVh0ZGF0ZTptb2RpZnkAMjAyMC0wOC0xNVQwNjo0OToxNCswMTowMOXvEeMAAAAASUVORK5CYII=\n", "text/plain": [ "" ] @@ -1574,7 +1573,7 @@ } ], "source": [ - "p.to_ipython_image()" + "plot.to_ipython_image()" ] } ], @@ -1595,7 +1594,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.8.5" } }, "nbformat": 4, From 8f6caffcf2f2885cde92dad35ac7a6bc309a5c49 Mon Sep 17 00:00:00 2001 From: Cyrus Wyett <34195737+cjwyett@users.noreply.github.com> Date: Sat, 15 Aug 2020 06:55:49 +0100 Subject: [PATCH 02/17] Add files via upload --- examples/jupyter/post-processing.ipynb | 364 ++++++++++++------------- 1 file changed, 182 insertions(+), 182 deletions(-) diff --git a/examples/jupyter/post-processing.ipynb b/examples/jupyter/post-processing.ipynb index 889e16877a..45bba7c61a 100644 --- a/examples/jupyter/post-processing.ipynb +++ b/examples/jupyter/post-processing.ipynb @@ -17,7 +17,6 @@ "from IPython.display import Image\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "\n", "import openmc" ] }, @@ -75,10 +74,10 @@ "outputs": [], "source": [ "# Instantiate a Materials collection\n", - "materials_file = openmc.Materials([fuel, water, zircaloy])\n", + "materials = openmc.Materials([fuel, water, zircaloy])\n", "\n", "# Export to \"materials.xml\"\n", - "materials_file.export_to_xml()" + "materials.export_to_xml()" ] }, { @@ -213,18 +212,18 @@ "particles = 5000\n", "\n", "# Instantiate a Settings object\n", - "settings_file = openmc.Settings()\n", - "settings_file.batches = batches\n", - "settings_file.inactive = inactive\n", - "settings_file.particles = particles\n", + "settings = openmc.Settings()\n", + "settings.batches = batches\n", + "settings.inactive = inactive\n", + "settings.particles = particles\n", "\n", "# Create an initial uniform spatial source distribution over fissionable zones\n", "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n", "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", - "settings_file.source = openmc.Source(space=uniform_dist)\n", + "settings.source = openmc.Source(space=uniform_dist)\n", "\n", "# Export to \"settings.xml\"\n", - "settings_file.export_to_xml()" + "settings.export_to_xml()" ] }, { @@ -241,7 +240,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "" ] @@ -271,7 +270,7 @@ "outputs": [], "source": [ "# Instantiate an empty Tallies object\n", - "tallies_file = openmc.Tallies()" + "tallies = openmc.Tallies()" ] }, { @@ -293,7 +292,7 @@ "tally = openmc.Tally(name='flux')\n", "tally.filters = [mesh_filter]\n", "tally.scores = ['flux', 'fission']\n", - "tallies_file.append(tally)" + "tallies.append(tally)" ] }, { @@ -303,7 +302,7 @@ "outputs": [], "source": [ "# Export to \"tallies.xml\"\n", - "tallies_file.export_to_xml()" + "tallies.export_to_xml()" ] }, { @@ -349,157 +348,160 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2019 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.11.0-dev\n", - " Git SHA1 | 61c911cffdae2406f9f4bc667a9a6954748bb70c\n", - " Date/Time | 2019-07-19 06:22:24\n", + " Copyright | 2011-2020 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.12.0\n", + " Git SHA1 | 3d90a9f857ec72eae897e054d4225180f1fa4d93\n", + " Date/Time | 2020-08-15 06:53:51\n", " OpenMP Threads | 4\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Reading U235 from /opt/data/hdf5/nndc_hdf5_v15/U235.h5\n", - " Reading U238 from /opt/data/hdf5/nndc_hdf5_v15/U238.h5\n", - " Reading O16 from /opt/data/hdf5/nndc_hdf5_v15/O16.h5\n", - " Reading H1 from /opt/data/hdf5/nndc_hdf5_v15/H1.h5\n", - " Reading B10 from /opt/data/hdf5/nndc_hdf5_v15/B10.h5\n", - " Reading Zr90 from /opt/data/hdf5/nndc_hdf5_v15/Zr90.h5\n", - " Maximum neutron transport energy: 20000000.000000 eV for U235\n", + " Reading U235 from /home/master/data/nuclear/endfb71_hdf5/U235.h5\n", + " Reading U238 from /home/master/data/nuclear/endfb71_hdf5/U238.h5\n", + " Reading O16 from /home/master/data/nuclear/endfb71_hdf5/O16.h5\n", + " Reading H1 from /home/master/data/nuclear/endfb71_hdf5/H1.h5\n", + " Reading B10 from /home/master/data/nuclear/endfb71_hdf5/B10.h5\n", + " Reading Zr90 from /home/master/data/nuclear/endfb71_hdf5/Zr90.h5\n", + " Minimum neutron data temperature: 294.000000 K\n", + " Maximum neutron data temperature: 294.000000 K\n", " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000.000000 eV for U235\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 1.04359\n", - " 2/1 1.04323\n", - " 3/1 1.04711\n", - " 4/1 1.03892\n", - " 5/1 1.02459\n", - " 6/1 1.03936\n", - " 7/1 1.03529\n", - " 8/1 1.01590\n", - " 9/1 1.03060\n", - " 10/1 1.02892\n", - " 11/1 1.03987\n", - " 12/1 1.04395 1.04191 +/- 0.00204\n", - " 13/1 1.04971 1.04451 +/- 0.00285\n", - " 14/1 1.03880 1.04308 +/- 0.00247\n", - " 15/1 1.03091 1.04065 +/- 0.00310\n", - " 16/1 1.03618 1.03990 +/- 0.00264\n", - " 17/1 1.04109 1.04007 +/- 0.00223\n", - " 18/1 1.02978 1.03879 +/- 0.00232\n", - " 19/1 1.06363 1.04155 +/- 0.00344\n", - " 20/1 1.06549 1.04394 +/- 0.00390\n", - " 21/1 1.03469 1.04310 +/- 0.00362\n", - " 22/1 1.01925 1.04111 +/- 0.00386\n", - " 23/1 1.03268 1.04046 +/- 0.00361\n", - " 24/1 1.03906 1.04036 +/- 0.00334\n", - " 25/1 1.02632 1.03943 +/- 0.00325\n", - " 26/1 1.03906 1.03940 +/- 0.00304\n", - " 27/1 1.05058 1.04006 +/- 0.00293\n", - " 28/1 1.03248 1.03964 +/- 0.00279\n", - " 29/1 1.04076 1.03970 +/- 0.00264\n", - " 30/1 1.00994 1.03821 +/- 0.00292\n", - " 31/1 1.04785 1.03867 +/- 0.00281\n", - " 32/1 1.03080 1.03831 +/- 0.00270\n", - " 33/1 1.01862 1.03746 +/- 0.00272\n", - " 34/1 1.05370 1.03813 +/- 0.00269\n", - " 35/1 1.02226 1.03750 +/- 0.00266\n", - " 36/1 1.02862 1.03716 +/- 0.00258\n", - " 37/1 1.04790 1.03755 +/- 0.00251\n", - " 38/1 1.03762 1.03756 +/- 0.00242\n", - " 39/1 1.02255 1.03704 +/- 0.00239\n", - " 40/1 1.06094 1.03784 +/- 0.00245\n", - " 41/1 1.03842 1.03786 +/- 0.00237\n", - " 42/1 1.00628 1.03687 +/- 0.00249\n", - " 43/1 1.04916 1.03724 +/- 0.00245\n", - " 44/1 1.06237 1.03798 +/- 0.00248\n", - " 45/1 1.08153 1.03922 +/- 0.00271\n", - " 46/1 1.05649 1.03970 +/- 0.00268\n", - " 47/1 1.06265 1.04032 +/- 0.00268\n", - " 48/1 1.05728 1.04077 +/- 0.00265\n", - " 49/1 1.07343 1.04161 +/- 0.00271\n", - " 50/1 1.04640 1.04173 +/- 0.00265\n", - " 51/1 1.05143 1.04196 +/- 0.00259\n", - " 52/1 1.03639 1.04183 +/- 0.00253\n", - " 53/1 1.04846 1.04199 +/- 0.00248\n", - " 54/1 1.02435 1.04158 +/- 0.00245\n", - " 55/1 1.04806 1.04173 +/- 0.00240\n", - " 56/1 1.04798 1.04186 +/- 0.00235\n", - " 57/1 1.06621 1.04238 +/- 0.00236\n", - " 58/1 1.05734 1.04269 +/- 0.00233\n", - " 59/1 1.04581 1.04276 +/- 0.00228\n", - " 60/1 1.02682 1.04244 +/- 0.00226\n", - " 61/1 1.05971 1.04278 +/- 0.00224\n", - " 62/1 1.02357 1.04241 +/- 0.00223\n", - " 63/1 1.02645 1.04211 +/- 0.00221\n", - " 64/1 1.00711 1.04146 +/- 0.00226\n", - " 65/1 1.06171 1.04183 +/- 0.00225\n", - " 66/1 1.03444 1.04170 +/- 0.00221\n", - " 67/1 1.05875 1.04199 +/- 0.00219\n", - " 68/1 1.04640 1.04207 +/- 0.00216\n", - " 69/1 1.04376 1.04210 +/- 0.00212\n", - " 70/1 1.07078 1.04258 +/- 0.00214\n", - " 71/1 1.03916 1.04252 +/- 0.00210\n", - " 72/1 1.01843 1.04213 +/- 0.00211\n", - " 73/1 1.03666 1.04205 +/- 0.00207\n", - " 74/1 1.04625 1.04211 +/- 0.00204\n", - " 75/1 1.05277 1.04228 +/- 0.00202\n", - " 76/1 1.04944 1.04238 +/- 0.00199\n", - " 77/1 1.01898 1.04203 +/- 0.00199\n", - " 78/1 1.03283 1.04190 +/- 0.00197\n", - " 79/1 1.02304 1.04163 +/- 0.00196\n", - " 80/1 1.01539 1.04125 +/- 0.00196\n", - " 81/1 1.03988 1.04123 +/- 0.00194\n", - " 82/1 1.02138 1.04096 +/- 0.00193\n", - " 83/1 1.02473 1.04073 +/- 0.00192\n", - " 84/1 1.03810 1.04070 +/- 0.00189\n", - " 85/1 1.07438 1.04115 +/- 0.00192\n", - " 86/1 1.03048 1.04101 +/- 0.00190\n", - " 87/1 1.06778 1.04135 +/- 0.00191\n", - " 88/1 1.07341 1.04177 +/- 0.00192\n", - " 89/1 1.06729 1.04209 +/- 0.00193\n", - " 90/1 1.05069 1.04220 +/- 0.00191\n", - " 91/1 1.07675 1.04262 +/- 0.00193\n", - " 92/1 1.06470 1.04289 +/- 0.00193\n", - " 93/1 1.02609 1.04269 +/- 0.00191\n", - " 94/1 1.04761 1.04275 +/- 0.00189\n", - " 95/1 1.08802 1.04328 +/- 0.00194\n", - " 96/1 1.04162 1.04326 +/- 0.00192\n", - " 97/1 1.04573 1.04329 +/- 0.00190\n", - " 98/1 1.03232 1.04317 +/- 0.00188\n", - " 99/1 1.03473 1.04307 +/- 0.00186\n", - " 100/1 1.04505 1.04309 +/- 0.00184\n", + " 1/1 1.06227\n", + " 2/1 1.01195\n", + " 3/1 1.03639\n", + " 4/1 1.04914\n", + " 5/1 1.03064\n", + " 6/1 1.04195\n", + " 7/1 1.00884\n", + " 8/1 1.02835\n", + " 9/1 1.03221\n", + " 10/1 1.03582\n", + " 11/1 1.04925\n", + " 12/1 1.08792 1.06859 +/- 0.01933\n", + " 13/1 1.02809 1.05509 +/- 0.01752\n", + " 14/1 1.06848 1.05843 +/- 0.01283\n", + " 15/1 1.03111 1.05297 +/- 0.01134\n", + " 16/1 1.04506 1.05165 +/- 0.00935\n", + " 17/1 1.07306 1.05471 +/- 0.00848\n", + " 18/1 1.05490 1.05473 +/- 0.00734\n", + " 19/1 1.04172 1.05329 +/- 0.00663\n", + " 20/1 1.01989 1.04995 +/- 0.00681\n", + " 21/1 1.05584 1.05048 +/- 0.00618\n", + " 22/1 1.01345 1.04740 +/- 0.00643\n", + " 23/1 1.05132 1.04770 +/- 0.00592\n", + " 24/1 1.05944 1.04854 +/- 0.00555\n", + " 25/1 1.04176 1.04809 +/- 0.00519\n", + " 26/1 1.05255 1.04836 +/- 0.00486\n", + " 27/1 1.06039 1.04907 +/- 0.00462\n", + " 28/1 1.01259 1.04705 +/- 0.00480\n", + " 29/1 1.07706 1.04863 +/- 0.00481\n", + " 30/1 1.04735 1.04856 +/- 0.00456\n", + " 31/1 1.04396 1.04834 +/- 0.00435\n", + " 32/1 1.08646 1.05007 +/- 0.00449\n", + " 33/1 1.02153 1.04883 +/- 0.00447\n", + " 34/1 1.04064 1.04849 +/- 0.00429\n", + " 35/1 1.04707 1.04844 +/- 0.00412\n", + " 36/1 1.03148 1.04778 +/- 0.00401\n", + " 37/1 1.08468 1.04915 +/- 0.00409\n", + " 38/1 1.05295 1.04929 +/- 0.00395\n", + " 39/1 1.01312 1.04804 +/- 0.00401\n", + " 40/1 1.04195 1.04784 +/- 0.00388\n", + " 41/1 1.05267 1.04799 +/- 0.00375\n", + " 42/1 1.01480 1.04695 +/- 0.00378\n", + " 43/1 1.05585 1.04722 +/- 0.00367\n", + " 44/1 1.06288 1.04768 +/- 0.00359\n", + " 45/1 1.07661 1.04851 +/- 0.00358\n", + " 46/1 1.05277 1.04863 +/- 0.00348\n", + " 47/1 1.04078 1.04842 +/- 0.00340\n", + " 48/1 1.08151 1.04929 +/- 0.00342\n", + " 49/1 1.04320 1.04913 +/- 0.00333\n", + " 50/1 1.04634 1.04906 +/- 0.00325\n", + " 51/1 1.06277 1.04940 +/- 0.00319\n", + " 52/1 1.02976 1.04893 +/- 0.00314\n", + " 53/1 1.03343 1.04857 +/- 0.00309\n", + " 54/1 1.01412 1.04779 +/- 0.00312\n", + " 55/1 1.04377 1.04770 +/- 0.00305\n", + " 56/1 1.04291 1.04759 +/- 0.00299\n", + " 57/1 1.07484 1.04817 +/- 0.00298\n", + " 58/1 1.07670 1.04877 +/- 0.00298\n", + " 59/1 1.05094 1.04881 +/- 0.00292\n", + " 60/1 1.00995 1.04803 +/- 0.00296\n", + " 61/1 1.04516 1.04798 +/- 0.00290\n", + " 62/1 1.03550 1.04774 +/- 0.00286\n", + " 63/1 1.02405 1.04729 +/- 0.00284\n", + " 64/1 1.06253 1.04757 +/- 0.00280\n", + " 65/1 1.06091 1.04781 +/- 0.00276\n", + " 66/1 1.04728 1.04781 +/- 0.00271\n", + " 67/1 1.06461 1.04810 +/- 0.00268\n", + " 68/1 1.05355 1.04819 +/- 0.00263\n", + " 69/1 1.06375 1.04846 +/- 0.00260\n", + " 70/1 1.04041 1.04832 +/- 0.00256\n", + " 71/1 1.04634 1.04829 +/- 0.00252\n", + " 72/1 1.02352 1.04789 +/- 0.00251\n", + " 73/1 1.08586 1.04849 +/- 0.00254\n", + " 74/1 1.04945 1.04851 +/- 0.00250\n", + " 75/1 1.06026 1.04869 +/- 0.00247\n", + " 76/1 1.05078 1.04872 +/- 0.00243\n", + " 77/1 1.02991 1.04844 +/- 0.00241\n", + " 78/1 1.01146 1.04790 +/- 0.00244\n", + " 79/1 1.05221 1.04796 +/- 0.00240\n", + " 80/1 1.01754 1.04752 +/- 0.00241\n", + " 81/1 1.05725 1.04766 +/- 0.00238\n", + " 82/1 1.03596 1.04750 +/- 0.00235\n", + " 83/1 1.04586 1.04748 +/- 0.00232\n", + " 84/1 1.02739 1.04721 +/- 0.00230\n", + " 85/1 1.04171 1.04713 +/- 0.00227\n", + " 86/1 1.05118 1.04719 +/- 0.00224\n", + " 87/1 1.03029 1.04697 +/- 0.00222\n", + " 88/1 1.07150 1.04728 +/- 0.00222\n", + " 89/1 1.02603 1.04701 +/- 0.00221\n", + " 90/1 1.00046 1.04643 +/- 0.00225\n", + " 91/1 1.06313 1.04664 +/- 0.00224\n", + " 92/1 1.09268 1.04720 +/- 0.00228\n", + " 93/1 1.00632 1.04670 +/- 0.00230\n", + " 94/1 1.03899 1.04661 +/- 0.00228\n", + " 95/1 1.05496 1.04671 +/- 0.00225\n", + " 96/1 1.01837 1.04638 +/- 0.00225\n", + " 97/1 1.04465 1.04636 +/- 0.00223\n", + " 98/1 1.04925 1.04639 +/- 0.00220\n", + " 99/1 1.03492 1.04627 +/- 0.00218\n", + " 100/1 1.02914 1.04608 +/- 0.00216\n", " Creating state point statepoint.100.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 6.4445e-01 seconds\n", - " Reading cross sections = 6.1129e-01 seconds\n", - " Total time in simulation = 2.0000e+02 seconds\n", - " Time in transport only = 1.9970e+02 seconds\n", - " Time in inactive batches = 2.9966e+00 seconds\n", - " Time in active batches = 1.9701e+02 seconds\n", - " Time synchronizing fission bank = 4.0040e-02 seconds\n", - " Sampling source sites = 3.1522e-02 seconds\n", - " SEND/RECV source sites = 8.3459e-03 seconds\n", - " Time accumulating tallies = 9.3582e-03 seconds\n", - " Total time for finalization = 4.6582e-02 seconds\n", - " Total time elapsed = 2.0072e+02 seconds\n", - " Calculation Rate (inactive) = 16685.4 particles/second\n", - " Calculation Rate (active) = 2284.19 particles/second\n", + " Total time for initialization = 2.8826e-01 seconds\n", + " Reading cross sections = 2.7725e-01 seconds\n", + " Total time in simulation = 5.6710e+01 seconds\n", + " Time in transport only = 5.6647e+01 seconds\n", + " Time in inactive batches = 8.7405e-01 seconds\n", + " Time in active batches = 5.5836e+01 seconds\n", + " Time synchronizing fission bank = 2.2260e-02 seconds\n", + " Sampling source sites = 1.7941e-02 seconds\n", + " SEND/RECV source sites = 4.1545e-03 seconds\n", + " Time accumulating tallies = 3.7878e-03 seconds\n", + " Total time for finalization = 1.2434e-02 seconds\n", + " Total time elapsed = 5.7021e+01 seconds\n", + " Calculation Rate (inactive) = 57205.0 particles/second\n", + " Calculation Rate (active) = 8059.38 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.04342 +/- 0.00159\n", - " k-effective (Track-length) = 1.04309 +/- 0.00184\n", - " k-effective (Absorption) = 1.04107 +/- 0.00140\n", - " Combined k-effective = 1.04195 +/- 0.00117\n", + " k-effective (Collision) = 1.04543 +/- 0.00195\n", + " k-effective (Track-length) = 1.04608 +/- 0.00216\n", + " k-effective (Absorption) = 1.04242 +/- 0.00147\n", + " Combined k-effective = 1.04347 +/- 0.00134\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -556,10 +558,9 @@ "\tID =\t1\n", "\tName =\tflux\n", "\tFilters =\tMeshFilter\n", - "\tNuclides =\ttotal \n", + "\tNuclides =\ttotal\n", "\tScores =\t['flux', 'fission']\n", - "\tEstimator =\ttracklength\n", - "\n" + "\tEstimator =\ttracklength\n" ] } ], @@ -583,19 +584,19 @@ { "data": { "text/plain": [ - "array([[[0.40767451, 0. ]],\n", + "array([[[0.41112167, 0. ]],\n", "\n", - " [[0.40933814, 0. ]],\n", + " [[0.41090482, 0. ]],\n", "\n", - " [[0.4119165 , 0. ]],\n", + " [[0.410451 , 0. ]],\n", "\n", " ...,\n", "\n", - " [[0.40854327, 0. ]],\n", + " [[0.41289992, 0. ]],\n", "\n", - " [[0.40970805, 0. ]],\n", + " [[0.41195517, 0. ]],\n", "\n", - " [[0.40948065, 0. ]]])" + " [[0.41092952, 0. ]]])" ] }, "execution_count": 17, @@ -629,32 +630,32 @@ { "data": { "text/plain": [ - "(array([[[0.00452972, 0. ]],\n", + "(array([[[0.00456802, 0. ]],\n", " \n", - " [[0.0045482 , 0. ]],\n", + " [[0.00456561, 0. ]],\n", " \n", - " [[0.00457685, 0. ]],\n", + " [[0.00456057, 0. ]],\n", " \n", " ...,\n", " \n", - " [[0.00453937, 0. ]],\n", + " [[0.00458778, 0. ]],\n", " \n", - " [[0.00455231, 0. ]],\n", + " [[0.00457728, 0. ]],\n", " \n", - " [[0.00454978, 0. ]]]),\n", - " array([[[2.03553236e-05, 0.00000000e+00]],\n", + " [[0.00456588, 0. ]]]),\n", + " array([[[1.98396826e-05, 0.00000000e+00]],\n", " \n", - " [[1.83847389e-05, 0.00000000e+00]],\n", + " [[1.81394159e-05, 0.00000000e+00]],\n", " \n", - " [[1.68647098e-05, 0.00000000e+00]],\n", + " [[1.52107867e-05, 0.00000000e+00]],\n", " \n", " ...,\n", " \n", - " [[1.71606078e-05, 0.00000000e+00]],\n", + " [[1.93971958e-05, 0.00000000e+00]],\n", " \n", - " [[1.87645811e-05, 0.00000000e+00]],\n", + " [[1.97108386e-05, 0.00000000e+00]],\n", " \n", - " [[1.94447454e-05, 0.00000000e+00]]]))" + " [[2.17053017e-05, 0.00000000e+00]]]))" ] }, "execution_count": 18, @@ -687,10 +688,9 @@ "\tID =\t2\n", "\tName =\tflux\n", "\tFilters =\tMeshFilter\n", - "\tNuclides =\ttotal \n", + "\tNuclides =\ttotal\n", "\tScores =\t['flux']\n", - "\tEstimator =\ttracklength\n", - "\n" + "\tEstimator =\ttracklength\n" ] } ], @@ -727,7 +727,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 21, @@ -736,7 +736,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -768,7 +768,7 @@ "outputs": [ { "data": { - "image/png": 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gskaHNva3tOgfIm7/R2iSdSj6A2gMIYG/Sks1ICEsPvAg1uMIZL2c6KhDxR84TnXq9MAujh52OapTw0tgNZ1vxUyFso+h/BoYPBuCMuHQK9CyH9z7YMsBKO4CnwdGjQZLBXjqQJ8Lda1Q5ITupaBz4zcEo9WEgaMFpUwHvnZ83u10hrzI/jFHiO+YSqbmSqg+DElZEPcSzMyFAffCtDvwrKmg3+452PwfohTtxtCdjEiZS4PxXmT7AryhH2NrbiB5ezqVU7UkdwfjKe+g4ZbbcB+EiIc0VBVGE7msBI3oglU3ILRd2FK3oi9qQ/T2o7VsQNl+IdohswPTn+6/EDpXg3UU2CZC3MWgWwXbg3Fm7sGYNBIqTRC0C/ZfBoO3gOIAZxkUr0He/A2+929E+etUYnOHIvU7QOPHucONZUAfqNobuGNwTICzfgVz7kdEhhJWuBrHoFS6vngSqy0TGeFDlr+IOBgL+1bD3lWQOwF+9Sr4uqB4DZz1wPH/vB4X5L8Lk+74b72A/jcJ4BT1PAkhZgHnAY1Syrxv9z0G3MTRNQj/KKX8kSGpaoDu+Xxe0J3c9CwSD+K7AXrXcti9GXo/Ds37oPg5iEqB1nhIvRZWPg6Zd4C3CFrDwLIXxJWwfRbEmSE0DM59CMLz8K+ejK5JwJEktPoMoA00epLe+wxaInGb3uFQ8jzS4sIxKAo8+TsY8Rieznc57NyGa3AZmvlmlA9fR0TpkMKH8EsOdXgY+UwjwZc+jPjTSCxrHkfra8ERH0aULpKk/A/xrrqWxrhETGMsKO4MYkuK0HR8A60tUO0CvR5SQtAMHgKHXfDhAzDo3MAgmA4vRI2BpIfB54TsDfBGOZqBIzCUbYQWF7hskGWFZROgfg/y7WUoFjMaWwGdnhZCdroIXrEIecU0uC+OA4MX0OuGDILvPD+QLmh2QMkyCA+DLonc9BrRnxjR9JeUdQaR4klENORAr34w8FxoqwkEaCFg87uw/lWYeC9oj/O2/Oi+QFojBIam710J/aee3OtK9e9ObQv6feAV4IN/2f+ilPKvJ1qJ+pCwp2mpgK7vrCP5+YuBgRHH0tX+Azu9fG/OrYwB0FUMnz0CxRKyXgWjDTQCht+Kj3NxNy+A826D+C1Q0wptmyEnChIU6H82hGYhVv4OGa+DvleD0gJ3PwV/2432pe1orn2KZFcbqd80E7+riZJzmrB/dg7KuCk0Tc2huJck8osvyaqchGLKo7vThe5gPf5WL55rLgGTGf27HyLOSIXg3aCNJ+lgJi5jDf6tK/DP/huaQgPxu1rJqmjG2qylpdWOM3gwDPXAxIHI55ci866GAavh7EWBLoM9q2G/FfKHgCYC1k6GdybDF4egr4+gD9fCVgtEKmD2Q7oNevdGsbkh34/29S7EGzdSO7KF6ndCcT2fg8gIQ1jvISg9Gc32NYiPv0C8PRex5BBCmYIImQgOF/7SvpBxG9q0S0gvr4fQI/iGNAUG78TnQJ+JR/v/w1MhIu2HZ777ZtnRrztboGgt2L7NOc+fBXt/aBi+6qSdwjQ7KWU+8JOnMVQDdE9jjYFXxgVata5O2L4Uth1jhZGmOuRd02DFHLC3gTOwjJNvWxF0+o6W02oh9Wy45WMYMgU23g7ls8FbgW9uCp0hW6kbGAVL74GMZ2B8PrSnQm8/SmYUXm8IfHEFDLkDX7ANvtgIWSPh7Ulw4Nv5rybejxy3CWfVMKwGHb1amqgf0ED+2HdoafyMnL8fINw8CevXLxM1+jxKRR6aynp0W7bge/Fp/MkxdNMAXcvA3wpHFmPu+IoQt4HScVpkphNtn8EItxFRU4mtaimRSheusEp8YeA+fz/e6gvwJw749vcYBbd9AdPnwTmPglsPK9dAWzZkJMGwXEiwQp2AsmboAnLtyO2HUXw7kH0kXJQAs6eg3J9IaIofMFCdk4QyYiR0vkOvJ5uQox0wsR0uCodpWZBRCFMFTJmBiO1P08xZKLX1aKaWIpVx+LsboPNG6LwdHM+Cb3fgenPOBFsCWKOP/t2khNcfhQ3fuQvWGyFlEEy5B7paYcmTSKnBtXAh8sc+yFUnRnuC23/ut0KIvUKIWUKIY8zadZQaoHsagxkm3A/r/wZNJeB1wd5jpMxFxeHfdRD5wPXwxo1Q9Rbu92bhved9hNV2tFxwHETmQspQSB8Dk/6OjJiA/ZCepo+9BFustMTngiUXPloE0QPhho+h0I1Y0oKS/waHz42gw3k/3tBoqCyEuz4HcwTs/QT8gQ8D5wer0d/Vh9LxyexNzyS4oIXhl+xCt2oZhydbca1oxl8fSXj+RzQ9PwNvMQiNHu2vpxF306vI7X9AVhRC6K+gPR3CysBUhtui4NLXQ1gHhB6GBAkhzYisDsKGj4IjFo6YRtHRkYw29rqjP3dHNd5lt+J7qT/+JD/ezlJ8hXNpL99AtdJIce8YvE7wdutwt9uQ6zUoacXgq0UbH4wY5IDoTXh1+9HrfktO9VDc1qEU2Hbg29SESJxP9fzxyL47oM9yCDofpBs6d0D1E4gH78c4UINHCQK9BZ0jCePWLlgRBPq7wfkGtI0E1/tHr9nvhy574OvqMpj3EvT5zpzaO5fAoAtAp0eabfjcQTTf/SYYDIgfG36uOr6Ta0FH/mNit2+3m0/gDK8DGcAAoA54/ngHqH3QPVHfCyBzHHz6O7jg99Bx7DslkazFXxSCtq8FueElHA8a0Nw+ivonZ+JcOYOY++8n9IILwBIFTdugcDF+fzDl74RgdacSmttFx6eQ7MxHNrQj0gywfDJK1iA0BS2IyBgMi9uxXxCLObyGsCX5MOY6MJig9/kQkQLzrqa7d29Ksj9D5PpJbosjI24l1PSGxMNkFphxxhhx3KAnuN82LI4OTNpJMC0Tom5HN+9jlIRqguNWwAbg5TNA1wj+aViVL0hotdDtMhGcchu0WyC5D1QvQNrr4Mt30HiD0BQNRQoHtdpiEiqbketmUDVpFI7kPWj1MSjxmWQu+ARfXCTK5a/Tuu6vJEdNQk8hMssL6R3IdNDsAeHoAnTQ1gZXPUW1tY70nX9BDPiIXHsEbWumURcVQnPqEsL7G+navg3r8BFg7Au2Z+Cxy+CxjxHhYZiuVZChXyM/uBxx+HOQXgg9EzyhEF4GOALdRdIX6Mr6w4Vw7ytgskDxHrjhbhj/nenVd38Ov5kFgO+T1/A1gemmuzCdfz5uauhQ9hElppyOhdF/eU6uD7pZSnlSK0NJKRv+eSoh3gY+P94x6kduT7TmSQhNgmtmw965cHD9MYtqRuUhBpmhz9m4i2IwjA0Dy3pkSTFxDz9M6ORh0FUBDSthzyM45EUUP11CTHQ3MS8vxjgoF19nK7suGoISmgzeTNj0JWLhs8gxEXDFToTHz4CnviBmg4Kh3QUXnQntFVC7GtbNpCazk3LlK1JD4uh/+GzCDlwJmxbBuaOhywylZZhrm4j4dBnsfRPRdZixt15Mc1YlTUFPIJ57haRbr0foPYisGKisgPRe+Ad+g1NjItz2PJETl8Gnr8CWFdDSCFPyITIPWnwoRzoIf/MDIta4iXthDvLjaXgNR2iICSUmLg+rvomIb7YjtNEYxzxLuKOZ8E+OUGf5EuXVifAgkAdilxmRcz/0uwG8BqRHg1z6BhkFVkRNLRgTEAUfELarlM40Dw7velov9tKy9NsZDaSEmffBr+4P3N18fSvSFIR+F/ha14A1BNlvKAy+HD59FF65HFa8Bk1u6HbAxrWAG159CFq3w6evg64Agq2B+u1NYLIi9Sa6n30WuWYWpr8tIeiZv9DIHD5se4ynSoepwfk/9Y8sjhPZ/pPqhfjutIwXAfuPd4zagu5puhphy2sw8g6ISIfp8+H+PlC3H+L+ZXCEoxPRqx+aqnzsc+ejLSqn7akRhNnGE7+kCn3Rq9C9FRLOhfRrsS95Hd3sS8jJyQWfpPHPzxJ9Tijd2xYSsuARSiZdQK+Frwc+tuPNyBwFgm2Iy34NUe3I5i/weUHUXYPG6UEMjodWHQmh4USvWYsuxA02DcivobwczMkwxQ2lBqi101Efgu7gRwQ1b8Iw6SPid4bhNJRRz4OYjP2QpvNRYrvRTr8Eth+kKX0GQT4XdK1H634Vso2gfwD2vAmNhQiHFnwmai5PI1yTjcZ1Ebx0Bzi60Sd30698H/prnqHzo42YMkrw5drorplBa+Qo7A/2I77jXOyxr2DdB+JAP4RPg/JlO5q7X8a/OxPR8QBCaYbGNwJ3IB0NUL8PMf120o2VODqC0SVcTtXmB8E5A249G7ITwOqEtS+gWzMX37QRkHM3mhXX4ByWhLkhDVbPhGQTyG4oPQhFX0BHMxgUMGlg2fzAg9KoNqixw+45gZkCt32M0udcOq+7DsOUszFYMiE2A6p38aHi5t7Db7Nu4L+/pBQkEtAea9CSKuAUZnEIIeYD4wl0hVQDjwLjhRADAAlUALccrx41QPc0bRUQngaOlkCANtsgZTAsnwHnPA7x/f5Z1L9mPu7lazApXnzz1tPxfCLr+yXQe3MBA/x+CGmDfU7a5XAc6xYRYWnEkC0QHje8sI+mmY9S0/Q1f/WVMeaC6Vz8t4fYk5FMVmUFn4y+FEewGa/hHfLCK3HGh6GLHkyf4CJi4s9E2z4ftLeDNxzvmv24VvbGeu1oKHdD+u9g7tXgH4snsQ/iSD66/pJ9h2IZuX4f9IqGM7XQZzrmvR8Q7/89Dt/72NlOa7SW5F+/jQhdRmfNQmJSPwezgOodoH0URpVC/Gj4ZhuU21HOyCAidDDBpdVw4fXQ9hQdYXnoy/IxVjgRt+YRrHVgT7Ri3KxgttUROnI1wftGElKzACkqcHrTMUZEsnfEMDJeaMJ63znIho2I+Ex8tkp0HRqEtxEWnQfJTVA8FdNZCzBteA/2TiWq/yCcc97AXFIEA0LhjbPBEIRMNCHCGhEHXocggW/IJDz2fhjnLoJb8/FrnkPz9TqEcICnEYb0h035YBOwqgj6ARk6EHUAKJsX07HVgPWZZ9FpGqFmHNJlp6TqV6wxfM4LWQpnhB69KW7FzUpqaMbFnT91Xpb/BacwQEspr/qB3Se5ArMaoHue6N6QOhaSvvNgyNoI4++ALx6Fsx/B7Q2l6ZXnCMreQKi7FNmuIyg1k+BmH5Oa7iJkQAqOrCpqo45geud1wtbeijlrOsaBg2B3GWRqQKMl995Habt1EY+nrCWpcCWF0+KIf6aG7lFWrv1wHp29E/Amr0fvTUJTZkFfW4+S3YlO3AicBX+9E/LOwH2oDcv9NwVu/VxV8OVNgAYa6zCEhFJ2zS3EfDCL2KLDFDw4nKH7d8Lv0hEhJlCqECseJSjXhTM7E2OolwbdNPRJ4YRtPIzv7+PQPbkK8eUzcPkcCAqHiH3gfhB8a9E0+5A7twXygt8ch8wQHIoOpm+Vh86QQqzxbjwGA4b9GrSKDcORZvzztdBvDb5yicngpnxyONmb1qCNaGfzVRGc+fZmNG1eNL5qNDovXX20BPlSEUX7wBMBjW3Q8DA07wQZR2TuQRq+WYH5sz1Qsx5qs/HVL8SX7Ueau9FUNKJoR6P/cA+ucw9j6NUfoQ3HubuKoJo0aNgdyLS5bCoc8cLubdBXwEU2cOpAm4DrrRcQxdXY5m1FExIC798GFz5Kbe0DPNQyg8eGBDMk9mhwlkheoJD9tPGuHBVoO6sN6B/XA4d6q33Qp4u/AxqeBE8gFc4tJU7lB9a/ay6ByCxQOo7uSx0OhdPhoptwLnuHxuefJ/qWFMKuewn/+S/j95gxRHWg09qIWF+P2ZJCcNQYgusiqNjehK6/FZm4EXvtCqTiArkGdl2HKLwGc79I4j9fjnZAO22pcYSM64M2vwufw0Lozhoil5QRPDII6765mFqqMdrSEUtuh4WzAB3+SQ9iCjuAdtPdsP9eKP4A9Bmg9UGvvrTaXLgTemE4rMPYoac4LhNXkoI7vRnF3ga1Cvhc0Hso5rSniWcg8dGL6XojFFvmLNyLS2HnAsgcHwjOG74EU1+YuBwufx2PtRNNQxm0FUNLPu4D7cQfLsYTFkzjOYNAK2lpScL0YS1PfPISyqUQtMeO+TUwGryQq8GsddGaHU2i3crADXtp7UpDE2uDsBBddQUzAAAgAElEQVSUEB1mrwdh8EJIBnS0QcJGaHwZmvaCzEQYL0ebWErpqhch7yIIi8dxaTjdgzuwvl+PiB2E9nA1Bs0Wgtw34Rzrx9P8NebN5YiSGmgPg0uGgf1L5J7DkDMAfn8bdE9B1kThWngHmsK/Y/jTE4Hg7PNCVysttgIear2cWyIiGRIVjFScSOnHiY/n2M8oonnek0lE6V9hpQbye0Hrhn9/zakCeuB0o2qAPh3a66HbAyIUitKhfREG4LomWO/8lyDdXAyR2dA54+i+rMuhYwCII5hve5Wkl1/CkP0AkhE4P1iMbsr1CNEFW5tRtm3+52FxUWcw9s6RmBJ8hBkisVYcAud66I6C/M2wXsGcGE1HqR5/+KcY9JMw7y0mNCcEqbjwG8MQ0oz2geVwyA040c7aB+1GaCtCztyH57FpKCY/Mj0XrDYYmwJaBzg6cNYsomHMmfR+80vsZX7cxX5qo0ZiMGTTfGt/qmaG4nzwIXBlQSGgaUd6F+PW7UeZWkZz5V2IJTNw7ppB96gcuotfRnnsV3SylE4+pS1F4FJaMRgEdNjwD7fQaYvAlDYQbaWLjDe/wh0+Ea8pDq37Cy6qWEJ3sx4p3GgGKOiG+/GkW4g94qYrR0to/WasGHDd6UCYOmCyHZHiQ5uggSQrjBkLY0dBaiIM7AcztkKzRByuRQzLozupgZZH++KtnI1ibsL6nhtikmFfMZgr8YdY6F73Kro+d+Lb+BiiwwoRudAVDn0Ww5e98F/rQLlRBwfeB/tysPrRp2ox9DsDUTEH6i6DrbfiyvbwWFkwZx/czVkDQpFlZ0H91dTKLp7wrOX8Q/M4Z8ddZJQ8C8F5kPUEjN4J4WNOwwv+/6n/8kPC/4QaoE8HnwdeOA/uuxPsU6B1FsJ9gKlmGFcHO9zfCdLNJRCRBY63wfPtKtmJA6EjD5z5YD8AX80GwLNkMaYH/4RI64XICUfrtaOxfwWlf4Y9lyOKbkHYDyH8TsSyQhStxJ7mxh6XjezXDyYmQJ9EIt/eRctf5oEpCF9MMBqbDe3EGTR5NWAKgmQfeCVEKJCtC6R/xRvhkyvAakfb24I/rxLZ9z6ojoKRXXgujaOzTwq9/v4p7CzGcPb1mH9zKWFBPlzUk7j/ClLevAjTjTOh1QDuNKiKRaE33oo3McVWoJyVgE+zEV/eCJSv56N/aibYu9FgQkMYRnMaXZZI0KThlm0ciYrHGdGK9M8nKL0L7SQvmqACYgc74aVnGPDKCoTHgP2KeyD3abpfzUHGP4vfayY0vRX3cAVddG+CPnHRZA7HqVPo6mNEWv8IummQeAfsi4bUJyH7CvwlV4PFAH+YR1RbDnkl2zCl6bC3txIyqwFtvUTTdAS69sH0V9C5Qgiy5FMdXY55RytdU/T4fz0kkJ3xwAWwczPaTSPwR5YgG0ZCRxBCHkEb6oSUr6C5F6xIxbdzNc8k5pFmr+JqZQdsHw5169niTOMt+0f8oe4QvZREkHnQ5zWIvQgyZ4DW8jO8+P8f6YEtaLUP+nSITIYZ6+GLF8BjgxUrYMClXJdwDx3hv+FNOwyOlAghAitCh0VBRzb4DoNhNIrRSLf3CPtizyN6451kLGtEnDUd4xWXQsd2cG8GqmFIFJr8SmR9AiJmMix6DKwmIA6QuAePpD23jMSDUQjtisDK3QOWootKwJgXT+rzT+NydtLdJxlp+Qxrthc6EhBKDTIaZHIowu1C9vsVwr0f6S5Ce8ZtiJB8NK7DoDyLpzqL5s5obMFuoorXIorccNV4jDUQ0baZrA/rqDFlkF29CfLGI7Q3Ia0bEdUfQ2cNWkMd5thDGNqD0YdoEIe/ga4o2LEfQrxwycUEzV8Nri58pTuJLG9FmBsRYxXi3q1ERggcvUyIPV5QJmHctAIlTQu/ugxNkw1/6m34f38vssOBSM5DV1iLX++hsymYULoQuj2E1JjomuGgxppCxsclCM87MP5sWPkgXPocMjkXyp7C49+COSQdOt6Fq36DpuFyglaOxZ8ZTmtxOMIrCK9pgyvPAsdf0bTVoaRHEv+XF/BbQnBO7UTTtY3gsG5I2wF7TAhjItqtefiH7EP3UhuEeSFPDwYfRHwOwXfQtN2Msetqfr/sbDi7CUWTzNzcB3H6rTxS8CFaUxTEnw8b7obU8yHqpNJ1/3f1wNnshJQ/0A/aAwwZMkTu2PELXhmrajeU/AbavWwK+i2b+l3BvbFWxEfXwFXzkM75CIxI80VUsQPl9d9QdsvVjH/pEGLLQlpvySa8uw0RNhJEJtTWgqMRpbUMUVSGiNJAixssPuh1OVz3Fsquv4Fej6YtPzBPhTiIfVA41t2RKIWllK/vJni04NCd6aTtCScp9TmELwj5eX844Ec2OBHxIdBXgcSzwLkBoW+H+JtRwv3I0mLWJZvIONRO4vtF6DQJMK0YFA/4BDI0l72Dcihp8XPpoSXgScTfaxLKp1+in3AJZEzB/97vOLLYQcLDUzFULYXMYHBmBiZxsitw1YMQHgcHF9Oy+AGCmz0YC93IPuAeGE3LtHBobSX+yUaER4+TGHxjE9DaCtl6QX9CG5PR7C3Cl5eIkl+GMbybGOHHJ2Lw9jGhlQ78tBOxNYnK8CbCjO0kJD0Br90JY85FXr+A/Z6HyV28B7FjE5q4gZBggCwvOPaBSEbu30W39ddsTG/kzOIv0DWHwrC3oPovyMJ9EOagxZuA6YYXETvvxfJuDWJiGoSXwsRvwJCEb3cfRFUU2jVumHQphIdDw3sgjbC5Aun2gwG67ovnpYgnGFO7lwkln4PGAL1/C1UroKUIRr8CiWeD+GXfLAshCk524Mi/GhIt5I4rTvB8r/CTz3cietjnxf+QpAGQtAMaX2VUzWcELVxLc3YlkXYjQlHo1pcgnZ+xn3WEcjapt31DUrdE2TsJ95A4jGf+HeHuCyVrYO1DsKIJhgZRlyeJadGhc8TDABvE10N8Fmy7G03p1+CrhKhhSF8QXlcLjkIX/romWqaNwxzjIGjucoZWxWB5aB44ffC3m+GSc5ERS5DzAIMduV+LDN2HtqwDZY+CMG4CrxdnSClD/HqCPZ342yMgphnWxEBdAzR7EIMLyXMXUjDoOjCEogw5E7d1HeZYPwzwI9fchtK7hsh+ERjOSQfX4+BaAftdyJZiaDqMKJwCk3Ng3xxKCqIYPsZNV1oH1aMGklxgxPrObrSdndAhkHECbUojushmhM5Mar2RyBX1cOXVLI/sZuSaYDS1K9EOl5jLNDSlpeGv0+HLj8Ab0kDv/BK+vnMcMcsWoDszE9qbqdhxBdH9b8R3qRn9jj2g3wcFrXAwHDweZEglxEGQcTZn7fVjz7fiGBZH+P4NmObugEESkiHUlEfzsheJOJAGka1QrIX7PoVDL4AzGu1+Lb6rg6DKhb8uHt2469AkmSB8KFT/Edw7qLEl8mrIzdz8wUukucOh10XQezTYbNBSEHiYWLUSYkYfXQT4u7rsEGT94QUb/hf1wCwONUCfLl3l4KoPzPtrzQaDDYQWou8AQxz9QudS4I/CWrUb8fnD7JkWRz9fNclsIoaH0BGKe/sb1E6vJ2l9LjpGBh5WZE+EDRZot0NhB+YUG1WDQkmrLobIUeDpgvARdA/sjbawCFdsEs05DlxhTmIKEzEbI7AkpBPWcBPuhD24nSuh2AuPXgHmRPjjm4j5lyHcfmQvwKxFtoN4pxSv3YjrgBdt9iF0uU7QaggJcYLVhPbcYWCKh9pF0CkgKA0ENI0Kp9UUilfXjbZ7LqZtPkRdMkQ8hL9wHc7SOJrtaQRNvhRN2WsQvQOyJ0CQA8ZmIhvWISoG4NWGktBXjzDV4uo3kLTD9Th6m2iPtoDHgPcc0MYHoXE6aR8YjK3OSfTiEoL754FvGxeW7aZ8VDrRr/bBNr4Lv+4AseZrsbz4Mb7wDvSRCUhnOGds2Ir2xsNQO4f2mHhcmr2kvfQK7kF7EV3VMHg0DBkPTQegdB94K6FTA85MZF0NoRdPozy6hSPrN9JvYD/M9l1QZ0aX9RVRDcMpvyqKtEUhaJVUSLgQ4s6BJZMQ8UPg8z14f9dGTftifAlHiIu9DuO2hzGY6hHaLGz9UvnT7jUEx1Ujh78GW99BLL0TfDlgSgFvKESthyHfmXpWSti7BRa8Dhm5cOODP8/7oSfqgV0cPexyfrkUncRf+ii6iq8RiRdD1DhoeR9yXgTHGkTso/TpeI6mM7wUpKYwSVyIha8wyj/TJh5Exwg6RhUQ9FgMWsu3rSEpYfmtMPQWSAhGLv8QY4ugqW87Ke4L0dhKIfJa5MZXqdGHkJEg6IxwkRD/OGbLCIh4GMeg4SiH9sCGIozrl6KLiqW7rAUpyvH9MQvt9gsQSiPYo6G6BhlpRjnHjPamVpwvxqB3VaHRaRFlBoJGhUBYM/T3w85VEBIH416ErCvg0SwUk4sGGc2usMFs6j+GMfvq0TafjYwAx1U3oc3IRDFvwrn5EJT8AdoAczw0uxGRaciWtdDvELJtNrJ4MyEj74MRk4lMGgpSsmnjDQwu2IZZ003begXfKB1hnhhCSjoJ8o7FGJ0LmU3gaQHTMI6YmgmZcQ6GN/MJzovGuDmClggTNlMtmIIhPAVj5wGEvQtv0nmUa5+nX+lA5Bg9hk9WIKKBGgkxWtDGICOrgFaIvxZxOB/ttLcgLo045UnEDjstCd2EDBqPrcYB/gK0QwpJm9WJMOjAbIedK2HwVKTThuINQiMdYIdIZwntRyQuw2E6ktrwTBCQ5IVwBxop0DQpdK+/FKOnk+BwgVDKEdd8Cm/eHcggmjMDrn4MzMHgcsJz94DXA3/+zrgJxQmuIrAMQiJxsRYzE0/7++RndQon7D9V1AB9mmhM6bQPH4s3s44Q242YW4zQHAQ7JoC04Azby76cIaSGpNDurcCvi0CjzcCghKPTXkInr+E3RODEjjCaApV6umD/XEidgGPyVNrffYjuQgcRFjOdQWdiE7WQ9jDNtk/J2vpXRGs0Uf3mQdh4aFwHtn5oiMKX0weZk4YyPA827sWg6USW+NB+uggOW/FbkvC2F6JPtKI1utGtHIxvXDPO5KHouiswHVoHg4fjMYWh7VqLJtYNwZEw+s8QlhXo/4zNBccWYvRt3C+ew2gAX+9+aDa24h0wGfcjL2P92x8wyS9J+40JTdgACMuDA/eA5yw482lE+WvIrz+ExnewG3xE9OmAYG9gNr0X72DvrVGE58aR1V5NcJ4T7TYFXVgvzNUSWnfDhRdA1gyafQ18euRJzmg+iH7JGxjGzUS4voAj22kdlEpJipGhG+1oUzTQ6UI+NoRDf8oja8t+tHIbvrThaF02SGmH9jIYMAIGDgLte/h8n6ENehKR+DW4OpDhkcS8/SXYXXi9gqaJd2Ms3YtoFhg6HGiriiDVAFbg3euRoWtg3Wpc2kz8I8wEN3gwhT1B8oav4cAWZHUTotwPvcbAuTeiDBqBDJ6BJ7wEZ6XEGaQQE30NbPkz/P5dKJ4NxohAcK6vhqfvgMt+CyFhgQUO/qHhcTD1R1oG0sYMNNj+NwN0D4uIv+wnBz1MKPciI/rTqVuOEj0cLGdBqx5MCgZdCEMrLURVdTCqaS8rOu9F0Q+k3fsUbipIZDntpOH9UxieqM5A69nZCv2nUzpgGrONu1k3aCiROi/JW5vxNK/GU9eBKyQF7+aN4DwPKprwbnmK7qLf0K6fSWviKrrk2zj5BL9zJ6aDOzArekzbFJRLXkSTMxQxyYA2vQzTBBPiwusR+zyIRAc6eR6x5i8IqtyB609v4Hx5BbqGCtjiR+YHId19kTs/R3nvSpQPLkJ6TIjMCwl2dJHytYmQlgvwB7XRcWE+9t7Po192Jkp6Mb7RsZjT26FuBXzyCLQfgWFT4PBaWPMFItqCYo7DHGuD8G3QshD5xxScvT4kWluJMPnRd09BWmOoqo6BMx+A5kLoLoGVf4GZk9hUvpOmrkGExd6EbruX6oz3KTtrOjgl8f54qgbGURnfBjmhUCDxtLgIPRhH8NUdcNUuWPUVxI+By5ZCNEijBmfD7bQYXqc1uBS75h7aM5fQHvYs7fpbceVEIYM1aHIVQjrepTttDf6gKqQ1BEZPh3gdpJjB6YVDKyChF+ZHPkO3uwvHTIl/ySNgW48MsaL09uO7NhXx5HxESBjaBe+i+7iG0AYrMWFuYg7mUDnhFogZCF/fA8nnQeWywLqJjwQeSvP0gxD2KhRkQsl0sH8DtS+CrwMve+liNkYG/bxvlp+Dmmb3v02DhRjex8N+mtxXESlz0fZ5HrxL0Ib3hdQnkb5LyOp4H2v9EaqiVxLlTCC08WKkeykmTxlpvutxpq1E/+nTtNbvojzIT/CHd3DFeWfQcRBCLroeTcVSWnP0uP9cj6VzBOH+CxDL18CIaHxJu9HuacZ0yIEm8/d49a/jl4cx6bIQYjR4h4P/c3T6LKiNRdz8Psx9BGLz0VoOIofHQFUBDI0BPxDtRln4ME3Nb5Jy118pCO8ism0RSSFWNGus0LgF35rVKBcreEaGYdptJWhPOWHZk0F3G5b3x6I8/SG+nHq6y67AnevF19AXa9d9aGt+C6HA3PMC6xue/RCEDUJpGYgzfj6WoJV4Dy/Fd7sTnT+S7MYa9BYz+vfW4bkxCn2MHkdtMNabFsHs30LZXhSNZPSsSs7XG/CmXor/8sf4rE8MVc7dPLK2nKAJFia2P0Bx35mkrV2PR6vDF6QlflURGK9BItFU1iC8LVAYC4vbETGzMJVvwBlhxKC7GmvROES/qcg9N+MLM6GrVvBc9ST6pTcQtHsfwSH/x95bB9Z1XHvbz+zDIGa0WDKzZWZKnMRxEscYsEMOMzRpww2nidM4zGjHsR1zbMfMlmQZxLbFjEdw+Jw97x/qvW1v+923/V43ze3t88/R3rPPzGxpZu2l2Wt+y4hwBoFLwpRiODUZ4pzQUIZo2AX9ZyLKvsU0Tof7gBn/ST+N9XbM3nYCdFqk6ofQaDAchyHtEL8EHM9DUSoMa2Rd3Tvc+X09hgQBPUug8QyU7YbxCfB+AdxzFQSnQk0CFBeBd84fRJ5m4GY/YbyJgex/7mT5Z/BvD/rfCLQY5GBC1cdpycrHY26CjGegaQvUfQ4n70V2fkxLagGaSh+OzgY8njPY1AoCzBmI8H60tfRjTYaBc8MSqFgkKVqsw6HxonQeRozPA7oRnS5EcgDWkjb0m95GOkqRBgum/TqMb9nRBIQjmu5HV1uK2xKNOGaAnJ+g6hAY3PDxrdBcAu/eArt2gMuAPOxGaJrAoSLK28DQA9d60WPniRtv53jJbxnaaeVI8jRU/wnk2OsQKUPQZujwTDIh6YHTTZByNex+FvJzYPL9KOX3om+sIGijg8j98wk8NgBl7Wew4EmoAbxamPEC9JyAU0vwNGoIa7gL8dNRtOU2jFtsaBuaCTD34FOsMP1+ZHImQbMTsW3aDCGJqI31VEyYRM5No7CqTUh/Me7338ew9Hqu5zImGMYgTp4AjUpY4VFiS89RNDKD2hkxCKuV2tFOOqbegjrwxt5sLfetga7BUAv4ihE+FxzRE3DLKloufIjXoCD790Ec+wo5chZ601BQBTJXB/luOKODrmOgFIE7ByIbIH04tFyA2n3w2TOIFifG8EFY4pMITQzH4QlERplQHLHQWAXBE6E5Dw68BIoO0tvpTuymPCGUwsUCgrshtxS8Cn5/O+rX+XiXDccXnYBar+3d8NTTAINmQPo9qMYIXOzHzNUoBPwzp8k/j398RpW/i1/Y8+JfGKcTTKY/HEh0ba1EnPPSmv4BgReOYPLZoHAFaELpSQsjoWsWgYnX0exfQX3WairFEtzMZgMVjO7OISo1CQvR9PMOoc+HB3FW7MKVNg519x6Uy+LxbK+j47ogYgpawWYB2QM7zqEGhqKEBMLWEuSYMXiGeBDObrx9Q9Bp+sLc9+HD22DyjXB8EzR2Qt8WSIuBsDPIFis4gJ050M8MGQqaJSrPHfsdlrBIlM6NXHqmG7euE0vZizD2MtRgJ/rg4YjTq5ERErXwG6QlAPeROzDOSUFTfR4qfkLRh8KuRug5BU/nQckWiImGUC107gRdIpy3YulQwdUCAS1wAcgyoiabiKxZhMv/E3J4DmgrMaan0t6YB16FC/d+hXP3b2kfnYWzqABtAWjDu+j+7la0Vz3FtIDRqPMm0TkgG7F/M3FRo9h7WStZx9owWodjdXlp4nuMBdvRjRuN6D8WcWQbBFngUBfS78Nc1o43KwzvkBT2dt1OWFAUA2sEmqn34Wlahj4hGndyONpDxWgCrCiJb0HtN+DMA7sN5oWAVYIvERw10CRg0xHUAbHoXD303HMbUWdOIfwaeP8BiPFB0FAIsULVBkgIh6yP6d/5KcmbNsBxA9ywHA58h9JoRs7xIRoPoDTsAqsWb1QQ4Eac3AtlDbjPv0rwiETkoE8RMdf1Gv3/Tfzbg/5fSl0NrPsSVDd423pfmiXMQZmynYigD/AHNKBq3DD0BwgZQYD3foIdNjR1PxFxNghTVTeyYSc+PmYMOaQZKhnTnEbmOQ/ppy6g3HkbnfvO4j2wBaGz45w0D11pN93TIpBZKt7rLHjvmoFcHIMrOYiu4W5qooIoK6il5b0aTO9HoX3zddQxl4A5EKQKkX3AY4Pc3dA/CNXpQZ5xQ4UfYkdDiBnVFoY/+mHExHwiE5cS7M+D8HQCB7xPecgg7Jc/BimXo9H2Q38kH3+mxD08Fk9/HUpIM+bwRkTpcWT4RNBEgMkMTbmQLGH/UijaD6YbwZ4Ip7Ww+SfU7hDU1IUwdBYk2xEjE5DLtiJ9UQRV/0S0LwA14jWEXaDL3UCfu3bS9fkCPg0ykb70M2btCMRsmUjPkUDcQ6ycz+6h5+3LqPxuBi0D6zkceoaKJUtpXXo7Ed1Z+KUO0SeOkAKF9C98aIoqcZkP4f1+OhQegQkzoM6F8GrQmiQ6WwtxxQZmVoyl38bPOTl6HPvk+7T5G/BrtRjaG/BMTkWNTIG2L6HsJPRbBEmPQb/Pwd0Nrh9A2OC6TagPPQetzSiHunjs6g7wG2FmNviL4Nw+qD8Fk+bDiPnQXUHAjlsJdMYTfDYBFi+BTR9DTxbii9MofUajDTChpA8DW3+0o19Bl/0sysiJ+GJuxne2B5pOI/NeQFa+3jte/zfxC9Ti+IU9L/5F+foDqD4GiVug3w2gD0RapyK73oOypzCGOvFGBaCExaDzXoN01EGEG+IfR+u5C+3huYwsMGO6/Faq+tyAN9iJvjMdZfAC2Psb9EUSfcYoIlPqkGOK0O4OxWNJZoj1bUTQ/egT/NC6H0x+NIP6oPFrYXoKtpgRFFYXEuwzI5VhZMa+iSoHEKPXofvsVhg8B9TvkUonXfuHYYodgu7oIdTG/bS7AzB0dBF41w1giMPii+VE+GWMaD+CMA8m6ms3zvHXYepQkYqbjqgEhFDRGhQMJKO4MpDr16OOCsP/ww9oZ41F03IIhvaB2gpwhELBSYjvD6MGg64LwuIRZz7HGR2K2RIB4fNh7L14W17FuzmZztkriMtfgPrNbDTGMPwJ2aieLJxDi3k0yoypqwfZsh+lLRJN+hSslDK0pYuOFQ+hP/4DId9XMr5a0HRLORWe04z8ogBHvYuKWQdJOdeOZuoE5LtR+KZ4sQVNIe7sWURPN4wZAFEBeAOPoM3Phgl3wZF3MTaHMvrHIlxnWqkYqRBiauJCeDoZVe20jigmanciwjygVwv6eAn4W4Ffw+dPwggtMjwB17xcjKlT2V1tYf2CeNpqthKh3waZ8VDogBmXQ3ch6EZAeiu0+lhw+At44hn46Qx4o2GiDd4MhxFL4Z4WEBrkU/chn/sNmlF1KJOGYJ9YjDp5GTZ2k8hpxP9GbdJ/e9D/onidvWu3fw0poSQfmvZDwRGouR0qbkAUDkep/BhPwoN48iJw1VjRlnRD7HUIRx1sPoF01aLqzfh87ViXvI3mnfuI2lyHqlfwigv4PR46IhfgPbueyBGd6E11uAfehe/MeWK6FMLzqsHZgtvXjKfRD06BfuwDGFp9JHjnk2G6nLD0BgacyGPSuwEEeB5EkSs5MFnHj0v6c7z5O+onBCMMTgoui6GrMJ+TE2fgFwkope00Z0bQ+Zvx1P90B7gbGH7yB1y15ajGJPRXPkGJkog7owq7UAnQmzFbgghqvxmlOxuIRsyYiXaOgn6hgnrkMGrGLAhJgyDgEj3YIpETbkG2VcGWD2DC7fju2Ioj3o3LmQAni/B+upicrka+vDGegJ0HIHAM/jnZaK5+Fs2QlzEcqaXro3iCRAbseQSu2ob7h3wMSxuQogdxViE0Zw2xjSXohocQMOo6MvY1M6axFe01EwhUjbQlG+lOTIWg+WCwYN7oJvL9o9h1xbgfeBMyx4PPi0/xwPgFEJoEHWWI+0/AbQUYLW6yyvLxo6E2IpGjE+7CerIvbcmnofscBHfA5DfhkhdhwGyYkwnRo/CvX4DOMwGl4Uf0ljjmD7TjssyB7DxkYwJSauBIHqTcDglLIPJN1EgbriuXQMFqOPEWvPwNBMfCkt3Q2Q1HXoMjX8Khd1FzOpFTn8CTeAnC60fFTiDL/ncaZ/hFRnH820BfDJrOwvHf//UyISBNA3c8AbWXwMoUZPDdtEXexJF+CymzdKGkjSKooxJxdAaUz4KqPLDXQPO31JCHEj0BXOcgWMVSNZyI7+1Uiq/4OsnKmVdeRXP5BxgO7QNbHbqiM7haarBcdws8fTXUVuOr6kSok8ESh+h3OzxWAK4OAne9yKBiI7Zl97DlhTTM96wmvMbJ0Av1pBUZSVtzgYrkDLo8gQzZ8SPNTy0gZdBy6h0DsN4zn/S3izEkpWLIO8SDSYPZ3X8iPdYOGk7Np73pdjKKq9AXelCMoNeWYTpfClElvSmc1qwEDuJv7KGmqQ/tc4bTFBFCV1gSdcmzqLMtglj29m8AACAASURBVP4eKNlA6yU+mu+KwHFoEsrxe9kwaQF5SRPhsyqUhBqyj+QyuX03vlsl/mH34tdWo1HHoPlyFaK7gaCh7fi33gljn8Dz3G9Q/bFobakQYkcWH0GWHgPZjYhsZ3P/m+Cq7yBhHQRfB5ooBtnqOHOdAf9n80Bfj64rCn2Xiv6B16lVXqYrNgI6zmMPCAD76t6UZQMmg87Um1xX7US2CCyNbmbbBjNp4/cYzjThU2OwP/AexKpw6GHYvAgSYmGgwJ8+AiUnB1vNPuwJYfitd/Oq8g0JF/bDJ3eBquLfr+I/sQ257UUAvF230BF0GK02AxGZBaM0sP9OGP86xI6Cq76F6CFw7GmUCQMQl8wF63m64prQG6ZgYCDB3PczTZpfIP+x1fsivCQUQnwihGgWQvxF3kEhxINCCCmECP+/1fNvA30xaDgJpRuhu+Gvl7uS8A28iQtLxlA0J5FdCQbqotIYpr+bQYGvYMxcDiUDe4WBlAMw5DbEtC2IH/dR6z5ISOIdcOQR5Ixl+A4UozncjXxXw9jtfRn/6pMo79yPDAjEmWXCf/I8ras24D+/Fm79FUptHyybGhB5B5ADM3r7ozHAkPvRTPqYkPxaMjbupsek8MWHgyj/fTXu5ByM+hgCFzzOsD2F6I9oMc39EArKqP70fuIfHoFx+q3QmIspzUJYUSWvnHJjzPotd4//Pa9nv0TJyDewhnTgaDPjturw9/8dvtTrIXYp6mUDcd+SQE+8Fnmyh7i6SoKrqnBfkOzIvJzKejubWy5wPiIO3/xRmBMexdUvHld2PNKoMOFsF8RrcZ7czsZr78V+6Rtk7aglaPcqPN5lqLoT+MR+MAfD9AcJzW6Fc/kQNxrPls29Gs1uFbwJoNVBqBk56mnqy4aR/94n4LYDejjYAP54DN6byHxPpetkC5ptIBqMEFqKvmUJKWWfYOp4EtlVCmYj5LRCwRvImBjkiUmQMwLMDeAR0KFC0DSY/izaKCvvDv6IHsNOVCUUTlZDaH84+xDS0ozXuRbF5cJw9iC1Q80cPbkFk9kGx30w5Aq8zQa0Dy5BxGjwP/E43ueXI3OOYFAfxsBc6K6FCW/AkXPwVBLsexbay6GxGdKX4ViehZpVjGfnSbSiLz2sI4jb/jbv2f8vujZ9cT3oz4DZf9GEEAnATKD6b6nkF7bi8j+UrCuhcj8YQ/54LmcbDJ0BWh2+1iq2hn5IS3glk4utTK8YhxKZDAFWULvBvgKsqWB9EXK/hfg1kBQC05czZt1mRIaCx9bMga+2MkG40bghM7wId9lcWtTLiQofRusD6QTYIlEOFSMUMPWJh4TxEJGObMhDyarCHZiHIosxiD/kpzMEQ9QkeGsLYw+d4OxjC8hY+CTii5vwPFlK51e5GDqDsQ4LxVajwbw7kZCPnqS65VckFZ1DY2uC6c+Dfyaahl1MHLUSb2MdIdEDKdZ08G3oEhZteZei5BiGHnucekLZGptDpG8k4xPthF0woilpR2SEoalvpU/Jt/TZ8i0yVceYB+9HuWYVPZ2X0qx+TELDKjS1r9J2eToRIovjdh8hVcsZIrIIDrkHlt2MZtc8jHtP4prqB//tqAnhKFlmtKVOCKqDHU8jhQbzk88gIgQcWAWBiciOTlTrZl7NvIMXH7kNwnWwaR3umnzqHxlN8s46wsKP4Gk0wnt7IdEJnAdnPiLgJvxrPkM99xYosRDuxzfhJjqjdxGgTEV/6gXQxSFN3eC0I2puhrpxiMiRhBmiULpH4+95DGGKRSRfBhoHos6IoaIYmdyEWdtJBI9xOvccymUKHFVQT23F/uURdMZLUeRUxFV78EWvRdwqMPbfDavvg4kvgCUKgrfCmBSIGwl7nkWe3wep49DZY2l/YAfGjw2otiqCgm9FoP/zcd3eBCGRfy6mZMuBpvWQ+eI/elb9/AjAeHGqklIeEEIk/ZWiN4BHgI1/Sz3/NtB/L456cNRB6DBQ/vC/TkB07yDW/fGvK49tQeZsRETmQksrU04NQqQ/gMX1PsoXC+Hp8t4LPQfA3w4mJ4TfCIcU6J4HsddA4DpU/Qm6v96ILiiCqWmtKJ4IZKENUnQYozYQ0DCMmjFh7Mu8nIFiGamWw0QWrECzez0c2gmJ8Sg3rwXToxjkOBwnl4BxLAbNUEhdBAMmwu+nk3K4Bn2+nbzWV8nWT8B98AjCV4Fl5Ajaq8w4z68j6eNvEJXbsNRnQ8lHOEfdgMmcAjo/7NgClQ1MG3oJRRF6jjtPM0UXTGFqf14Z/AALldVMevEE92VvQ4Q0Q8JZeOIQeGdAYiQEOXEf8uMK0WFwBmD68VnkgJnItU0kbKtGt/x3MOZ2IkrisWc04Q3YT0KuG0PCeDjwKri7YORjoDZj3Hgd/qxgHEkOLI4PIT4SoU7H9dqr+JdHojYcg6G3Q8c5mPkU4uPbUBqXEeNvwJYdTFTLCcT2Y1TYXyPOPRF32ADU/EMYZoQjgleDe0lvgoHIRaDoUPM2ovYEQ1clzv5ZeAL3EXIqCWXLOog0Q6YGoTfhsQRhrPND41rQJnJtWSClYSbGVrhxXD4bS/K43uQOny5GxHbgH2tFWO8gdONpREUo3CToCLBhPWtFSYqBh5bibd2I8qUezVEHTBiGDOuD/O5LWH47QlXB1Q39LwVrGs7tv8cZNgvvF8cwBlaiv8WCOqQd1m/CcOXTuH78DEPfJERLDTTXwLZPoKsN5t4OS5+AngOQdwUM2/Dnc8LjBv0vTMTi/w9/n5pduBDiT/WQP5BSfvDfVi/EXKBOSnla/I0Kgv820H8vphjI+xVUb4RxH0HSNX9xiXrmBLR9jujjAJuC0mgiYOeLyLUfguJFOtzYj92HYg3FHNgO+Rb8GbOR9naU+UYUqYecnVDTiOKyEBzTjYh0Iu31cNALqoQKCSMULGcjEN3nMWRN5WV2c9WgBOZdEQz1ndDjgMzB8N5CEI0oaaOxpF2OWvA6fsNGNOXroN9iUBph6YPEnT7IufhmGgdFYHrqAKbfaLHlunC3jiAu4zyipw02zUOXNh+Z8Sw2ttNRcCkx/gGIRz+Cj5/gxNRr+c6eg9NjInPjj6RXNrPpzZ/wp21BY3EiDCkQXArBvwXDMLjmFdjzKGz3UzpvFuVjEpn3Qznkb0M+uRDThUqUWYMhNAfaAqF+IZYVd5HxxihqWwJI9G9EO2cLiscHOW8gGvZBWAaao2VYzppwLFyC03EEq7aY3J0uhk7qROleDfI26D4NkZmQloVoySc3+AEe+HAQ3PUAPf4SOqzNNFnzUDhO5pABGFufgd1Xgf33MPHF3hjxst+hxAThKawn6LgTfWoMJnM2HMiHU+fg7ikQPQpRXIhrYAnGk4UwOgSG5hDVcZr28icRdQY0k1rg/EE49Xyv9OpZDcXt2VhmDyVGfog+aTDhe1w4Lw1C0+8M+rAo/N5HUUI6UW4fgjh0AULDoOQM1Ong0QUQmwCRybDlXrCdwRE3Fc5sJyQuFG9JBZ4VfjSzncj5rXTMGoErrxJ9ViqB967AMHMuRCZCn76QObx3YNccgoRbIHxW73FDJax9GyJiYdEDP9cM/Mfx90VxtP49etBCCDPwOL3LG38zF8VACyFmAyvpff58JKV86b+UPwDcDPiAFmC5lLLqYrT9syNEr2HWB0HFGnA1Q8ZtoLPQbltDaNVWlNZ9MCAYtXEgXmUWovJV/N9F4k8fidpUjW/ReWoydzDwaEVvppLtEmK34A3qS3dgCz3GVqJiz+FviUNp7o+5uQzZYafzgA+r4keTYITIFIRmNjL6OLK7kWvtYxlq8tLl2sO6zDHMP/UdIjIWJfdLkFqINsLp7YiDm1CkBjXUhT/uAErtKYTHD5YjqD3bGNonkcMDY5iVbaSn3I3m6gJirmxFHOiGtXGQoCDL6yB7KDFJb9N55DLKh4ViPbYb7f59xFwxh1s91Vim3UNkXxOaZ4oQRhOaDd3IlG+g8SQi0g3b8vFFrcBlLsEVFkjbr8ZSMkJPiL6a5hwItY3E13USfeIgZEwuMsqO0E6C8WOQWifJvhMcM04ic9dWmqPGEXTYiCm6EGkBn9Qgh7nR5PswffU+5myQmyE2TINpjwbfjEbUwmtQdDY48TpEpmFT2wnQQqvDgHPSEJpK7ybGN5DojNEoF55DW9eJCN0EY1aBNQJcD0JHDrLVg9KjQ+3wYxy/AZG3HXZvwh8dheax5WA1wKnvkCYzPYl9CK4V4MgE2xOIiFXU1F6BfuRk0trOQMO1qPEBMPQUivtuREoE08zjeFruwDApGVnowWTsxJsn0BV24wq/A4tnAsLxA4x9CvYs7VXWu3wiJCyFtx6EygtwPBfidYTm/YTHLXCMthC8dAh6Yz6OElDe9OJ4VYNpzc2Yr16AbuJE/F1daGYu/eO4b97W+9n/nd5Pnw8endfr8X928mefhv8w/nEuayqQDPyH9xwPnBRCjJJSNv7DuiOE0ACrgBn0bnzNEUJsklIW/cll+cAIKaVDCHE78ArwN+Yu+AWi6GDUSvC74Pwq2D0GtHWYTq3DEzARvTIWOXwBnsUP4K5+G4MqEfVNGIZ2oNwQidrnLGlV1Sgb9XA1iMzrIasV4yVfodl1A75JN1GtbcEQu5aI7u2cEVMwVpWToC2j5VMITnNhbD4H8XuQx9IxlUvE+rvI6G7GPTKUYdt2UjhuIe3aNryRi5jcuBPtxA3IwATo/hKx9U40GW/jyfsSyvYjBgci2n9CZrgwn79AamQsnbHpqHWzCAjowvd+D9rcvRDdjK8xldY1HRibDyLf+gSrr5lYYyv1r64kbKAkwXMenlsNEaNg91lo2wR130LxKQgPQZrbaDTEUDPHhranGGOHimGgAY0mn76njRgMyQSUtiEcHXgfnonx9D5821y4HSkYhzwKX+3AudCKEuCmxxqHyPMSdcaGO1GHejQSKprRCgcoAukW+FXQmMA1XEOEbxiUnMT5ZBuB39Yh00x0Nm5k85R5tJT3J8u6jxaHQtyQ4dhrPiep4DDqia/xaWwQej10GFCDJCJhIH7la8TJMTC4B22rHbV0BiJ6IlSshiFzEaXPoZbPRpn7HXJfHa6Tufh319NzaQbW8z9CxkKk7RUSTu/guaXP83lbJHQdRnIDhYanCZ05lMyjpeQNSeTwNifBuhpeXvY0Y/y1ZBb9QGQPWHuuhfb+EP4pmEeCRwNKGAgjrH8TDqyD6FiYeRnMewShd9Jx6g62TppIpjeZkfkHCYzxo4YFoAu5FOezF3C/cwD7u+/iOX6cwOefx3zDDQhXLdR+BEO+6x3/Tju8sgJufKJXI+VfYXkD/qGC/VLKs0DkfzYlRCW9NrH1v/vexXhejALOSynL/9DwamAu8J8GWkq590+uPwYs5X867nrInQPuRjDGQY+O00P6kFFUSLCMQjnehLGlFeP8qyEiABIvQE8XLqUYR1c0gZsa8PQfjmguQdNdDw0u/Fu2oW63Efz+8wQHhaEb7kCTGMuglJux1/8KY7cPloXjPG/H16MQYInA47Ogv2E5cudR7P2PoDslEAGhDGxuQe2qJqcrhKaoS4gtXAEDE0EzCUxRECbR37oFd8XnKKdXoLG5UAwK7iwDGcKGSGmCvkFwqhrZ3gOXTMXXuQ1nWymBS+wQOglz/2mInE8hOoVk5Qz1jyUgDnQQ9PFyGNAOdXZo+RFiBiBMtcjoyYjqfUSXNxDTaoSfvKhB4ayZfzXjG4JI++xxZH0ZHdeb6U4IIWLvTgjrwXvnlWi+2YNal43MOkvHDD9edybx5eewTZxFcLUfT7sDY0kHjLsMMpORnh+Q7xWhufI3NOiCocmFZriBqsA1RMscpGrDFhiKs8NOY7SHXFsi7517FWPHdA5PzKV/URvSF4InNhBvsBWn7weE04X2gBXlhAGp7UbvG4BmSz5khiMVC7yyFBY9ATobzvowZFMV1renIYQB09tnsHw9FdexJnxuDXpNDubMj8mwZ/DY0fvA0h/8oWgCJ5LEHIqC7iXIFERI4zE8jYH8eqqfgLYX2X96Gr+e+hquFeN5bONnDJgxAdRJYAqFxafhy0T48VnIuA+S0qDThnx8LbS1ghCYJy5nRMEmWiLqaAydQR9NKcpUie7X78Nv9LgfDyaw5mUcb7yJr6wMz4G9GAJ+D/3eBkULLfXw2s2w+A4YNOdfKxvLRdyoIoT4FphM71p1LfCUlPLj//5bf8nF6E4cvZI2/0Et/LdSWDcB2y9Cuz8/Pm9vYGLNZjj1IMhaMA+F+nCoriK79Bi+YCPSV4HqP45YYUDIDdAaA47x+M0plCe6yHrmNCJ6AKq1iMqIULyx5Zhqugk6/zaBi25A4z0DKemIgtth0GoIyiag3Qp+gYUQzJOt2G1WWhf1IyTehTJsH+qEe9EWF0FEFGLue2CXKE+PIzt+GjI8DcQ6cIxDRC6G8WnQuBf7kScwuxS8I6KQg4Jxdy/BXb8V88idiL2z4OxeiO1BpDyBGrgCny4E49gyND0X8EfH4pUgzCEQnI3m65PEVVXjqFVxz1PQn1YQycvgmt9BVyP8NA4l7gIyw4Cs1iL2VYA2gnVThpNZUU/Ctk1gGYG8fSSds7YT5p2MKF4DOUMwzv0I568fQDfvO9jig5FBhGiH400cTVPHRoKbNlI3fxjW3U6UT95Hrs2jYdfnKIoJ0zdvUlHtQtx+Kba4JAIijGQEunDtCMUYaSc4toM7Vtm4PjWCwJoK6q/9AY02DHOHB9s1/UCvYqoKQO8ZhFBNONMr0L72I74XbkVxdkOzE/q+D8WXwzNfw7mdUPg5+jYP9YvHYmnJRLjaEZ9eg3NULCIjmri1tfhqa3CWhiKy2wk1XwPuLKg8BM5HCTyaxDBjOM3JZ1H2LcDdNohAtlC/N4oxmdMY3eHE2z8JT+IkaJsJ216Hm1aCKQJ0aeAphs3PQ3kXUp+J+vS9eL5cA1odymvziLvmVXx7XsDfXEDu6FkMTx+P/PZRDCkrkfYfcTRchyXjarR3PwQlj0LITeDugdNvwd4PYM4oKJgLF2JhxJuQcNW/hqG+iIL9UspF/5fypL+lnp/1JaEQYikwApj0/1F+K3ArQGJi4s/Ys7+RH1bBoCAo/wQcNkheClEzwatFNpbiUVNoHzeawPe+w1jSjDB44ZGVMOQyEIIq9/Mk7IxF+d23yI8vQRfmIG7fdeg6cvD1C6Yzy0OF7nmIdCGxYg2fRPSPu1HkPmhohFYdaKrgnB/zzIX4MvPpvN9G8Gg/VK/A4I+EGU/De4+CQ8LIBGTMlt5NF8Y7EKbf9N5H2CgofJniSbPo69OjdwRDvQd9dS4llywi9ON7wOmA64pA/h404YjWW1FMgagh4/EzHGmJR9m7Cc3RSihaj2tGGt5Yle43O9DMCcRkcxCQdGtv9heNFrKSkeeLkRNVfDYX2rhsjqcqGHwKQ63n4Ln1EJCK31eO1VeB9fxWqB0Fp39CrB+JbnQWXRuikDuchGwYjPnGF0hyFbAnewyZhQcIFSk039aHqC909Dw4haL7h+CMTyXgYBh9n15MVFgAvHIbiBbUCwJdfjvy0lBEuxHv5AKmnN6OjR4cjRayvF6s3V6UOw7BiJGIkHI4dwB0AnNoIvQItLf9DkZ6oUQHO8f1prlaeSl4A0Fnh+ybCf7+EKJ6Nyx/GMybiD6agGg8ghiaha7uNNr0SyjfoOAp24Mn247BEU5of4mYex+iTwMx3y3D5zEwf/BOvBf05E4ZzYy9deiunYqxYz04+8KzE2DJi2A7g/zxOgixIoIH4d/Xhioy0VRXI7QmdG+8i2bKdHpa3iGwpYHBxw9RYx1AypFt7JyewpRfP4vGMh39pxX4Wz/Gcb2bgH2bEN5WaKyBLgllVbDkTQgfAHETIPEa0P0Lqd79Ard6X4zu1AEJf3Ic/4dzf4YQYjrwBDBJSvlXI93/EKbyAfRm9b4Ifbu4HNkMUXfB7IO9x1L2eg41d+Nf9hKquYXAL1/E1N2DZ+h43CnVBKh2lOAoOts2oVw4S8D8bXDyFcSokUhnB55zJZheeQKd6saUejXRBxainjhAxYhUOkcm4xvdj7jOy9BknYNDsTDpeji3D5FzDP09s5G/aaJ9VSOWUXb0xni0BzeCORHMrZA8BQK/B2U86Bf8sc/ucxAUTWZHCi71fozHm1Hy/aiz38R7fBVqQjD1E27Gbd6DEENIcsxGXeNEU/o9ms6vYHAcwhsBwRkw7/cw6DJ0UgVAcYxCOX4rllQbhIzqbbO9Bilr6S7x4K3WYh2nZeu1I2gq6mbZuS9w5I5Hf24rutr9KOe2Ep6qwNDnkTclQ/op/K3dKN/nIJeNxD67hKCmJ+F8AWFlRym7KgbfrKdR+/yKVtEHwzQ7gV9L+u2ppb1fH6jVETVxCrx+HTz1Ejh+S7U/kMSV2/AHB6KraMXxOVw1+DhV0+8i65uVmCKzEN4JEF4Oi96EPsnw5g2QGd4bxXPVJHjhht7wsn7h+Ba/jXvh/XQPvZ8A5yoIjEOb1Af1/E4wWJFbH0MEJ6I7uw//iCkQexgiZiDippIyN5j6NzcgIp30FJ0g+Hs/joCrEEvb0A4IxrhDoigKVZmR9Is2IR2n0Tg+A+2z4IiEsamgWw97v6U1NZTwYbug5CQiZAXaJz9FOAVs+grl2l6HTlm+Bvnyg2jix5A7aChz6s8y/vgejqYmkq6dRuTBvZhv/Q5XfB2q6wSa7tvh2E+9Ylb3fwWaPyzSpi77GSfez8Qv0EBfjJ2EOUC6ECJZCKEHFgKb/vQCIcRQ4H3gCill80Vo8+fH7++NDS048sdzQoC/FkI0+K016BsMWFpqUQL1GCfOxTnt17jyXsLXfJK69pdI7P816PSQ+xr4HPg1SzDH9SCcDWAIhJOLIetGFOtYUhqyyXS8TCJ3o7HEQfK9MKgbDFsQXaWIxZ9h+qoEJvZH/2UqXetMdKzOQyoaUNqhoQgpT0Khiqi/EqHJAHsenImFyuXQ52asNesw5tajOd2COC1RPn0PddRYlFA3YcfK6Cz/lOqW9zjlfIyy2RdoDTyPp0eHKq9CrngOLKch6HNw5/Uq9AkFQ1Iy3oLzMLw37EpKiWPXC/jOtKG7OZGAS/wUx2ey3jiA0bVFMP4Z9AvbcAx8j564gzDIixjxIigmfLuewddgQbQno8m1YX0+j7APaxFrxlKSfwvPjkwiDjPa9CUY2pJJ+aoCTbEB52dvYu12cWGYnpTYPHh2Bgwdhb/tWrw6K7F9P6Do3kVo+pthsp9zo8OJnqxgdR5l6/Ir8SzfAI0nIEKFz38LhQd7NyEt+KY3ge3ZlTAmAjoHgFWiGT4Hf5CJU/Gl+MelQmMZsms3XfMMtN1pxLvkdhh1M6heZH0e0qTiGabiiHoLt341sfO3EZTwDSGNAcjBAu2ZBkxPZ2FpeQfNGAnBV6L2hCFD6tFcWYAI74baHbDzKkhOh6DLIHYOdlMSYkQEPPMAyjPrEVseh8RUqDr/xzHr9iJXPAXTXmLUgLv5MTIMy+S3GcMI9p99l9ynFiCmzMWsvRNN6ofwzUPgbYc7Xvqjcf5X5ReoxfH/3JSU0ieEuAvYQe870E+klIVCiGeBXCnlJuBVwAqs/UOISbWU8or/17Z/VhQFFj4Ccal/ft67C3QzUPkeJXolHRlvEFJXA1ueJTp9E63pU6gvuZaEvm+gBMZCWxEMvQfGPY/9oYcISIyG5hzoXAuDVoEhHrXpDmTxVlztVizt1bD4BQgdCH1uhAH3gP0ROHwbTF+IYddzGC9oME+LoMfmxaEdiWV6FhS8gGjsAHUerH4K7rRA1zoIvhziXobGRnyOL/E3BXNqbBpDNBMRs4cRYd8I+lYMY1+i/7v9kXaJMX83jkGDaHv4DUo0Zfi6m9Aff5eQWiMR7nswaGL/89ehGzcb7+++BkWLt7KS1kceJjjtPMIfgr4nDDWzhrCQal6ufpTAKRPxm/woNckEbC3FMTsSe3cHyrgNyIAQfLISY+dCTAUmyApBW5iPK1oDPRDRUMvKXUs5M3sJ/n0vEXy2FqdFpXZxJKlvfUfuw5eQ/cwutEMl0rOPZncppwYPZ9opgbf9KXTBEShZt0Du02SnlqD4DCR2jCe8qoqilOfoe/1bGL/7FYQJ2PENtJWBqkLYQRh1FFxeaPEhSyGn+Wk0t41heGIsmtA0mNkf//HvMVo7MHuS0F/yAkgVefYItuPlhDo70P62GjFiFB1NafjbuzHFO7Fq8qmJjiTRE4D2mnsg/wuoaEFWriV5kIC0MLC4oDITnC4wdsL6A2D8CV/yMiKKT8A1y+DuJyEwGDn0SijdgvB3QfvLSPdhlEXdsM6J0JTQR4aRG55Mw+7VRFkGMv/EBY7/OoO1bGUuMzGUnQChwohZfzkf3B2gC/zjZq1/FX5ht3NRngVSym3Atv9y7sk/+Xn6xWjnn4oQEBQOnf8lKsazH591CbKrHNn4Ef7AcNRr/Shb7JC3Dp1swTEqE3/5OxA0uddTHvYA0mVHbS5EuWooXHgZUu9B5r+PO28P9qowQvoILLnrITQOrKHQ3gGWvtByHia9Dh9dhbf9A/RJLqgAjdlLyCNLUUUmxBZCwmSwPA7Os1B2AhylkLoepICd7yAvHIdFw9AWp9OV4IYR06HhMiJtCagBd1BreATrkiWE7toHjmCU1jhCGwaR0H8pBIFT3UKrdQ0ltrfx5nYRZB5KWsSdCJcRWVOOc9EcOFdIVFYSosqO58EQDJ2RiLY4Is+0YAhTkNpuZPtB/DoH3tmgHdkfta0G2X4UzTkDhnIvasZx1MHvoQRkUzh0N2VHV1J5bRLX5uQQ1pzMwHfWIMNBXN6ByWggtruI9sskIwpqObmkH1Erd+G8qR+F/YKJtdfRk5aMXQ5Ar7rg9Hdg68aY4Ya0lzAaFmP84W6sJdWUJq4m+pkdhD07DeqaIM6HKDAihB/Q4Q/IpmNxe3YcXwAAIABJREFUKYbPHQw++Cm6Gdkonk1IVyveyD50T00g/Ps6HGOze8eO0CC0WixLl2DPeZeOwgZCjtkI7N+E/+YozC/+QOmkVIKq6/DpO1DffwZfVBDGLIFzrAlTjRlFOxNCdOA+Dw1ngQgYoIU1fqjeiOvBazBn3otath7Vtx510CEUzRyU1GJorkYEORDDbKhbvfhsjyJEIFcY3HjC7NCsoLlpIuMa36BWa2SvpYDZ0ZdASCCUroMwP/gd4HeCzwEdBdB0GOJmwLDnIDjzZ5+SF51f4BLHL6w7v3CCwqHi7B+PpQrSDkUfYCo6BJ5CrMYQZGAPcmAXsuhDlOnx9M3T4XO34Wkcgj56JnjD8f90gsBxeVB1DA5J1DOf4WrQoYuJJay7GGrNYI6FSUth/TVwfh9cuwn2/Q4WfIA3rgGK7aB5BLKqIWMsFNSh7HwIrkiH2auh8TXwVEHy59D0BTSfgW9egmFzUG+7BoUWzJdmMu7MUtSgeJSY9XRVvUpHXC6Orh4628Mw1tSzav6znPVfwyNWGwMAelow7f2AhEXfkqC3IO12PM/eAetSkVqBbPQgRgzA+MYq2L8WebYWNaoNuowowo5h5n5YORZRuh+xaAxKdTV4GmBzKD6LH4kVaj1I/QMYB/0WADt+NgcYuEoTyrDju1AOteH19OCOm4zFvQHZfhuVY+zkhnQR5m9jyj4//tFddI4Zi/d0G+kZ2UQc34smNYXG6DZC/OHQcg4iwsBrhqBrQRsBS75F31ZIZvUOurbPQR1iQ3GBt1JLecwMYg8epHPqfRwOktCUxrjRPxH/ST1KbBzuuBnYItuxGh8h7JvHsS/7Am/Ol7DlcZjTex+GwUbce0KIuKIL05wFyMMn4aEN4AZTs4OWmD7IHbVEvqtgDFThmAlp1KGk9gdjGmimw6kbwBoAjRXgDoXV79AS1k3oE98hN0ahJgxC+eZlhGY0muaTyEltKHXP0ZwxkpozS+jb5xR8OQjNr75GaapEvH4/YnogIuprqC0lvjCP+InXQv4ysAZCn9FwYBdMHQdhl4AuDBzVkHQV9JkHhpC/mCr/I7mIURwXi38b6L+H/+pBu34EXxekzsSbNgL9jvO4IwKxDe9LeM1ziBI32qwH6DG3ErDlVfy+LpojHYQlP0r3p/cSPKgciRM7FryeVIKjqxEr3oPzx2DLq3Dve9ByGNw25MTnkI/NBWMPijEMb3QLphMeSPLBtCegzwBwrYHJLlilQN3v4MbboWk15M8Gpx4K6uD6tyEyCdV+JVp7BCin0JTPxpt+GW1hZRi7zzE9/3s+CpqF/qyKP0rLHXm/oXH5CYxV9dTIkcR89Htk3FAUnUADCJ0O/V3P0N3oRO3qRB97AZE9DdoaoSQXESBRqtrhcxuYVEh/Dk6oMGUY9LsT8p6GShWmVKDVZsDgb5HTkvCsWon3hRkwYAWWK67mseP7YdRd+D7Zhs3hQ+2oxdKtRaYq0LyXc2oWfYqbIdyBu6SEuhuXUzXSyazrzhLRXYNcPBBvw3GkN4LgfZ3QqYfxGdBQCPa1oHahdh7HLw6jcXgJjlBwHFJpvy8ezft+jnkHEjQijPjA0YzNfQeUKrrjwjj623TSVn9LzTOpZPp/jfnsWRiyDFPCVXQc+pzQIzuhuQI83SieAoIeeQ37I5NRdStRzDoYk4rMbaI+PZ4hJhuMc1D2aA+R9xnQ9TMTpLYgbUWI+C3w8XjIrwFpgisykUPmIbdvJmTLejTDBsHdD6KtK0ZsPE7b0v7oOEBeykKkZT8h7dX0r4hF72zHt/sYnAkHcwSa1DTE0G1wYxo4O3rV/L78NehVsDZAezjMvRuOPgkRP0DEwzDqSoiZ/E+aiP8g/u1B/w9D7Y1MwN8NbTtBGwO2ZrA3IW278Lt+hXZtE5plIWg0RvDnYz1ajqF2HpooIz2Xd6ApfpvgKccRV9yIdBRjzF2K89wo7LYuCOpDwPk8fDc/RPCZGkRxBSTGwMFKGHMF7HkYBl6JnP41viumIdp60Ix3oZ56h9bt4STcZoJT38INr4F7M3j3QdNNcG86/PAifN4PRk4H/0hoj4alsyAsGLVmHBpNM4RtAkNf5DUN1LXOQ1PdSKBbkFu5HE61IjMuQRvaDsaT/4e9946Oo8rWvn+nqnOrW2rlnC1HyXLOAXACbMBEk2xMBpPDkPPMMOQhmjiAwTbRBhtwzjkHyZYsy7KsnGPn7qrz/tF35s591/vNnXsvc4f5Fs9aWlpV6/SpUqn206d2PfvZ5P3pI2Szgp7Tg9LeRMBVReDrUUgZhmY3UU6JNdyL0SXw+YMEvngO00AXwuSHlgb0OEG4Qycwvj+ipwObVSJbKqD6QYSSCEGgOQ/6+qHzSUTcp5jv+Q2hl7+CzsvpWncJ1n17MOzejKF/EFecgtulI0tVumQ6Z4YPJb3MTZ+tB1AsGuTA0D0bKe+XS1xUN/JoEOG8BNEcT/+WOmjYD9P/AO+vgue/BYMJlGiUY6CEYyD1bGT+tTi+HkNdxq3EpbzCdc+9SvtzgyjTVrO5IA5ht5PX2Ej/1D9Qs/NZlLVWdk79lCHhXhL7fYaCgdbZw0n/ehEYw8iuo1A+CkK1mEcXU1/WjXOCBWdrNcKuYBxvx1ibh2yegKM4lqpz/4h2YBaje79D03rxrZ9F1PJSZP9UxPl3o294GPn5QpQr3+bMN0n01eZD2yfQsok6rZMy3zZ6YvNQbNFM+KCO+PSDkHYhpF+CcfcN6EmTkSYn6lOXQeN2aPPCjx2Rp8TsQZEnxFeKYfZtkDcd9t8FvlOQ3/+fF5P/SPxK0P9CCIcg7Iddr0DMYWhfDc5xcPokbNoFp4+iZIaRrbGIo9XgaoWcKpTBJsw7tyC+qME+dxTaoMfo4TpsUQ9ijJqGMW8xvgdnEBfUCTbFECzMxdD1Kg0TckieeT3q/uegai1YJXj8yAFXot99OULpQrnuYqR7CT2ne0jroyAKLoHKr8D/I4SXQ9QHEYP2vmVgLoEDPbBsDUw2w7Dz4PTvwPcumrECes0YGiogNxpPTA223QpxZ1rB6kc07IeHH0FJvgeCvbCkEM67F9G0BDVqNJz7JpbYPKg8Ds/fj7ZmDXpKBoazLwa9k0DLfrp2tuFY1wpdm5Ef3IkWrCJ0cYCOhS1kTBgKtz7KibzF9N3QBV2lUAbk74GGYaAug66r4fNFGFo60BxgqlmBqT6Ae4QRU5eZHmFGigDh6Daim1sYuf4LZFYcwckulN4OPJqF2gEJFHadQIRjEOc+GnEirFkHXY1w/j1QVgbeUxDOhai0iCNexbsw9UPImBFxRo4bzoDaIvwBBycG9Sd3qYcJd7zOuL0PEIxL5tSO71l3wyZKbhzDDQu+IH7sZ+wtLCHgfo7xbRmoqVGE3voJ9fXn0PadhKW78UwaQ+kfbmP/BaeoyojhttM/kdJ/CGlH1sFxC3pZC2kfzMUXsx7T+6thdCEGYzNRZ7qRU3T0lb1Q9S1Klgn11QLCzjVgSEA35qEk/wYaK0ht20lU/gs4l7ejHN0LLQ1QNA4CKsQnInOS4MA2+K6McFBgcFwNxUBTA+gmOLIfCoshoRgqPgBrFrj7QZoF3n8E7voUbP8/0kDDL5KghZS/PLkxRHTQ+/fv/88H/qNQuQdaTsGK2+GRssjb3d/PhX27kUuP8JP9Vc55/nNKbrmF4fpxhHEdbDIg7QGwOhDH86ClCkIpyGc24DG8gGnrQcJrWvF9dRxXqor2yJ9QE5YT8sVi8O1ByT2FWKWD6oDo8cgj36Ntj0YpKiakGfB2bcM8ZBy26Cyo2AMX9QX/D9AIjJkDOzdF5IBTP4K4odBRSrizheDXd2HrHgaXaWDtRo8/xglPNtW2Gaim0cSe2oZq205CbQNHhw5mfEoVUfYyFAm0HoLD10PHUDANhtwQ0r6TSouTQNKVOC1pxPfGY/nkddj4GeKSC9AKwzRduo3kQ4doeP4eupcvJev3KuZ8N6aTNyFCMfi89bSctY3MjzyINe0Rjc8coM4Co2ejBzcivvUiBvpZmHcDt5Z/jBYbQ+egHCyxUwllpNMuD7HL28OoY4co2FuGV4tFbQ3iGZTB/gv7MfHjANbMIHy9BZ6YBYM+gsVz4eLXICYPPnwJCvogjy9GSx6IIfgdOCfCjDf+/T5YsRjq1xPq/BLPoEw8o1QSSqdgmvAi/P4q8HvwT+yh3NuDyI4i0Tue5Ikv0dx4lKqvnsZ5qAyjT9AxspiumF4SAxrc/AeS1CRE9eO0fW2m6PBn+N/9gd2WzYx/dh3a0p1YHrZTUphPQlCSbL0cTjyKXgrypAUlTUX4PTArHZJG0ZLQgsFRinP7UAzeDqirg4psmNQDigtG/BFevB5e+BHWLYLud/Aacgg9W4pU3HQPm4p9uAVT3/kYd63C0PQVxndqI02Dt7wFUc1Qtgzm7ISWd0GMgu++hpvfgtjUSCHSPxlCiAP/FXe5/xeGFwu5b+PfN1aJ4398vL8H//wr+0tAZx00lIKnAxLzIXsk2GPh46kRmVX5GtiyBcoOglVBlybGvfQT3cEoBsT3Q/hPQdd8CK2EAYA/FbTxkJIO7mTEW9cSFQdabg+Mria02IWwGDGWfArjozCf8MKMR6B+PqToEFSR21aj7VRQLrqETl3Hs3c76dMLUe5fG9Ec39gPSmphqiOSigkEICYHWkph611wrBn621DPxIJNh8NlaIE7IacFpWcB+eXbyIxehG76mI5AFJWJU9g1Ygxb8kaxWrZyNesZKaYhQh4CpxsJn/4aCo8gtFSo7SAxvBPp3kKPYqHeFeLwZSNJv2UcSscxjNU5qINd1N1RREyGlQHfrUXPqUfrvQkGLARF4QQ7yGsvQ5w3C3a+B/EanJGQJumubqak7wCSXhxBltfI876bOa9HJ7PtM+L27aF+QjtefxbppVmkDx9JeERfQp0bsFmthDce4GhxPAXPHUbr7UXGXItwVkJrLHwwAKa9D6FvgIfQdmyg8asfMdnOkDBrN5xuhPyify9AAhg2HlY8i6FgMrbTLTSdZUSJaiTFoILRDCNnYInvpOj4m3QMHIbzuw2AxBRSSBhzFaZbiqk5vZCkA6WM2XcQrGZ45yO4+mHw+0jJBNHQS0vH9SRYk1CvmI/ctQPZaSI/cALrTi+Yy8EBSjyQ6Ic2wCrA7YKuAK4VVYiuEIbcDeBwwYy7YOI2cB8Gtw5lY+BcFbZOgKPNEC+wpQfQ7jARPJpDulpDoM9U/J1l9AY+Qa+LQc67AJNFYtaOI60+XMMDiJKHofVHiFkNFz2P/tAolEseg/Pv+KeF7s8JqUDwZzLs/7nwK0FD5FGtZSOsfB+yR0F8LqQOihjwD5gNJhv0tYGvB0Y/jPrTN7izfZhSxmMPTYCy1WCKhz63gyUG5CNwzlao2QtbX4YhMZGChoFvIA5MwPWxJHSiDaM9G9GwFS5YAcfmRYLJo6F72tFrTchnxtCy+DRG5yEyri9COMdF9Ni1y+DyMASOQu9MsLcjf3CiRbdj2K/Ak8vgtSFw/gRE6Q70LBU5Iw5Z8hr+SonNfAXGVT9hHDgaEtbhmBFHFqeZpFQw/9RKDM5c3NEe6k3LsGWMxTJ+DOGz8lH6TUR21MG3f4CELESNCWdfBRk4SrLegrtTYA6HMB3fgzbCghKXSJtFUFNQwfiwjmIJAKeBPJq0PRR1dcK2dkgIw9A4OBGAnCDRMZ0MOhymLeprnoh7FJ/ZysEcQdbsY4gfp5KxPkjYuJee/BJyT2ynLD+N/PidBM0Kh286l7w3jpLmsnCm2UT6svcxJbjgVDlYhkJ2EVRehb+sD3rpcZzjJY7CRkRDFFjToGwtrPodnPd45N5IzUSachEBPwY1nxPtUUxbsgu2FUNvKqz7EEYno0x6iLjGEFqwnIYtl6EcGkufe+4HIKv/q8h+OtQMgNt/hKdvg9duRwvtg8YgoWGDOb3cTN9J16EoOzHccQN6w/coNWEoWgDJGbD1t+B2wyEzJDmhoQsa/HDxRYSvOAvzqaWRrjH9h4PuA98IMIfBXgOhNvAnQncDCDMY0qCrCSVlJOYZk5E/vYhZlKDmrMU5xoD6XBvCuQeGgHT70NMHEjQVYM65GgpugPavOZ2Tgjx3PLkrX4eJV4Ej9p8Wvj8XpICw+vfW7un/0HP5M34laABzNPRtgtyzwXkfmAZA1S4YNQs2LYZj34AzAKkTYcy91L0/l6gWDzFjMuH40zD4Vbzu0wS1LmJOdSATe2HpBRCfAAVhyJsHSedFVmWu+Zi3vUZw5IX40rZg26lA23ZQ94HXgd5gRZqCcK8JxbiLxEsdKHYVTJUQKoWDn4HfBE2NUKpD6XLoVZBZFbj9HejNISwrH8Sa1xcRMEFSHKGxhQT3VhIeMhRTbwLKrmqYfi1MngS1P0Jtf2TKYERSP8z1GxFdFbg6DxOT9Dae6Gzac9x4bRsQgVbyH9iMoprhijvgnOsif1OonX6vXU5vbSWbrpjC18p8xnoaWLDqZmLREFdfR1jZQpgQYRrx7PyRdMdixMFW2LIcJg6BaUbYDxw4CPMuITopGtuxF3mm8PecsdtpTDDz5PE13Bw/nvTYg6jkIJNVrIE2hq8/he4zItIhP8aF85gZ2VpJ9uVReCsksqIB0yATYtg0ws2nED062toXsd4yH5G0FeoMkFEE4+6G3MlQfwSCPjBZ0UWI5vGQUlvFiSF5OGQbZF+Ivv4FlBHN0NMFg5ZAQjFi3UBEqQH93X3I1/9joAuhQFpuJLXy4gq8d4+kbngMfaqrwF1AT/8eLI/fCtddhXLRQjheS/uJvUQTj9kYBOsUWP8dYCPkUFj8wlya0+KZt3cJyTWGiGQweSYkLQL30+BQoPYx+PEPYOkDx0rAlQ3xFUhZicxyEO57BO/hKrz+KSR3uiCnAXXhNMTm7yFNBc2DSE9BHfMyaua/t9cLtn7KjtD7jJx4LugKbHwSBsyE3Cm/iHTHfxdSCDTD33v+wX/oufwZ/7pX8+dGwvNwZiSE14NrPBSeD6kFsPdLqOmEC28ETxs9b5xN60ATya0B+KwEZj0EFdUIq87RxOOMX7sccb4LOV5HHNTQJy1FsUf/5TDK6TIQCqaMCzEeWA5uG3L/AugnkMFoNOlHxHah14MhayJKZgX4muB9Fc59FDbUQdkBSM6FufPhmh+gczuKoZmYKIk8loZvnZmOGjCsjsGmnsa0ugHf7KuwDX4cE7lwAeCphrqbIXMw1JqRbRsR/R5BaA5E6hxoqkFYBmCULpS6amxOHUNnHb3jrdSMf4EtBTEY2cJMWURK+3Zip2QRk1PPPC2ReSseYWt7FDed9SX9O5q5p66NuIxBhIxmjCX7KfNvpkgeB/M4OHsDOOIgSoOGozBah29/i7hzJ6aWuWinPuEi5yrmJK3nUF0yv8+bT6/zOualFIE/xDmb34Sjn3FyRAJpA17CkRQguKwW429TUR9ejmnNWJR3TlL3aStpjX+g9dOZJDw4H7v7R2SxiZC3BGOZHzp2QfZXkSeUjCF/+X/1yOM0jm1DX69zeEgrQ04eRUZVwpBh4DdC+kHo/h7sFyNHLqF3xXXYRgzDV1mDTD6IyBwamShwDGZfC2Vb0X96iQNzBuCQ8QhTNWpLCYYbRmM+cD5kXwmtOyEhn/jXD9L8+Dck1eQSCpZQevdQjhfNwlG3lzh/D3OOerHIeKj6GhQJSedE0l/he+CxS+CrZ6A4GRlsRsyejn7eJOSKpej5RtoP9aB90ExCXBfR+W70zBZMLWHERakw9RV4+0Xo9kJmB2x+AWyf/eWaCF8102osOGLTI81pXVkQk/UvTc5/hvYLK2f/17+iPxeMaZBTAl3vQ8uzUKZBTx0UjIN9m+jsriZK5HPw8iiGfXCCQNQkDGYVuheBLwWrJ5bwyBq8R1qQTifhlkrCGysJPzkKddAgnNdEY3GmQvV60DNg8XxED5DpBTfIagH2bsJjNNQoG8ZyP/KzXchtOsKhQD8TrNsJC7+ERXfBvFHgfgMCLoh9AzbeB9sDCBnAFhXAlhNDaN7NeG9ZRtgUSyBpE9FZT6Jp7ahxcaCfgZ5mSBgBIxYg1kyC9csQzXsh4ThyfxsNDz9Bo9iPPSqb6J5YjnYWcfLyQRijYmmnhxuZSOodQ5D5teiTZ2AI5hMuW4baYWGSXsLIKUGO/uDkvl1zSdrnZkrNVRSldNA0xspoLQ6hb430Ni7rgmIHXDoOfCsg2g9vnAOxqTRbdFyOXvBFMyQV3rEqdJ38gpV1a5jb72luShvGW1FHMYzJodb2BXnKB1jjp+K/7xr0IzegXPQS4rtbibf1IkQvCfePxrDqA2TcGYKxSzAdH4wodkHVGeiuBXdjxPdY0ZH2WtTgcqI7u9g1djCDjjWS9nwIcdsxRMcVkCdgazMc/AbsOTSulJjsqQTOqiNpnQURfQD+TNA1i2HZ85BQROXt72Cw+ih84BFIMCBFM1liMLbf3RMZ23AHVGYg1T60y1kE3e+wY+4oCt2NzAlPxnj8Q2SgGREaAYEySBsOeMCjRT5vs8PvvoAr7kJPSkGuXY9oOErQkoJhxSnCy6KITy/GoGZD/XYCc2MwDH4F4Y8BSwYc/xG8nXD+WFi8G26/EM69EexRAOxuf5AxbQqGvr/HP96LRbH9LwfrPwYSgfYLq/X+laD/GgEd2TUEUfUAZA+Avk9A95OQmEONo4GaESbGxT9JwHEf1ieXgu6Cpmeh8RlIfoys1KtpWn4VeZu+hJ7NcJef3nuCmDP3YThcj4x2IuIKofgmqL4DPCHwjkIMrUJ06GjDf6TB8yzZNTvRe8MoBAmkmTBPT0Pc8iUcPAILCmG8AobLIDAYDvdC8DM4+3tonQ0nVQgfB48T43ePEv2becjkyTRUv0rnJeMIngzjfP5VbOeuBL0ADPFI2YvMTEFpaQdXX7T6LXRk65xp2kNF1jjK0gSDOlVGhHSmRF0cuVbhbkTN7wkvsKKejsHQUwW7azAc9+I7ZcIyXEHt6mYs0Yw1/56SkirO5S2MFZ0szLoJ9AbwS+i1Q78WCOfDrJthcQVknoCwBxzHqR/4AGWhJKafUiC3HLZ+R0x3LZfkS6aXrmXvsG8psYxjmD2R01xBB38gUbyINXMdgeVjCQ1diCzwYbn8NuRnHyFKH0DfIRHzrJg25SCGDIWyJTDqIfhiGrhioL4i0tJpwhUY8/3UkcCYd/eTOncY4YubMSxS0OZmI+xB1MKLwLACfd3zaEfyiXrtZRptq0lbtzeimfd7YOUL0FYH127BE+2mxLiX2SeyURpKoLgfosfBwKQIOctQI/V4OWCvo+u2sfQvL6NA83Hp4ZX0DvkdetsxQgY/Qhggug1Dcxey4TBeazpeX4juYDdq5W7iOv9Iz+DBxBg/RrkqgfbHBxJ72zJMcTlY59wBl92IREfbdDsiVUONmhNR0ugacu15MKEfIhAHV94DL9wHDcdh9BBq1UrsvXsxNO5CO2Fm8/nDmcG/lq3O/xckgvCvBP0LxbEvYd19BIaOxzJpL/QshWPnw8UHcIduR5rKae0TjXvRi6QqyaitnkiZcOrTYBsK4VZyTv2O0ugkMBaB2IXYVIXz4bF4AmfQLE7MHcNQowfCqWfBbIakKOgzH5rvg45U6uI34fJZUWUYzn2e7Vl7cKbU039dFWyqwnjZjbDlj/D9MXAuhEmFMHIm7Hoa2mvA7UNPFPh7U7AVpsO+HdC0BWFYi+GtK7FNPhf1t/fhW/Ygin0qlpQwGOKQ4Z2IYDxIkDX7WD9uCuWxBfQNWJi27hOGZ6tkd8cQlbsAPrkDzr8BWf8coZ6tkVVZyiwsNU3I8mbCRi9KtB2pxiGbO6ChBpasZKBXY2XhQT6+YC79E04ggwoov0OMdEHoNxA7El/zW1j6nURIFdL7Q7VOQ7JGasN+WL0cnnwbRrwP353A1p6Dzehj5ulbIeV6uu0x9HKYGKbSwYvEiYcwDXsa/Y8/ERy3mYD2Mia/lbAnCaOvEd0gUac8AdWPQ10jyKdBdoAxEea+Bt58tO0baDm5BznSRWpqK7LsCIbmdoRTwTO2iqiyTPB8TyDnckKNb5A+pIe2ytfIm/w5FDwNNgO8dSXMfBDCGtI1hIPdLzBz0yGU1mgYMRTUHOjTF5Y9CtMfxOevobLdzITY6cQ+ejssuAdqv0X3SOyBZ2iLt+GJjie7uQfd14BbSQUvyDo3jn0P4cleRvqRbWgmE50ON5Wl2aT5AiSNc+F5LAvrri7kVA/0jEHKAcjhWzAeNyATro6oV6QfLgVqO+C1bpgVDcMKkUf20bvjMEe/uZRpx+5Exidxesj5lKpbmM4shLcLbP/aJd8SQfBnqvUWQvwJmAm0SCkH/du+54h0m9KBFuA6KWXD35rnV4IGqNkGBxYiB19Ly5CVpNesRVn7LlzwCHTdy6ExZrqi0xj9+XFaFZ3M6JFQsgXOvjby+UYz9L0OJfYa7G03oBm/RD1YhvTGQu0xTFaJCGkELjuA7tyMxWDE0GKAmD5w8jFIGUU4ro7eni/JrHdBw3gOt+7lh2sGcN8nGzBM7CVUciPSE4eYfCPsfwJq1sL3Cix4CWa8CVungA0U4aXp3SpS5tRhVYBxeVDtwbb+c/zuzzDePAARVYe+aCfB8xPAkocWXI8x6CUclY/xgJfxA35gclMWyvmL0XNuJFR6L1H7l8PRdsiaCAnDEAnfoYcbCK4agnJ0JbLMjXAMJrStBPM0F90XjSDq4A6YMQais1H2fUFmkYncoRWknWpBHLVDdxP0z0a29SAssSimCgIz12L56StIzAB1GfU2O0XWMDgT4HePw4ggNKVEFB+GZkj0QN6VOIWBdHsKjrgy3BcU0hF8llj31+jbazCrGuHYEFqegmHaIsT7F4JfI2wtx+A5AX3HwXmfQ6AbNs1CVnXg/3wpWp9sDt29gGm1X0K9hqhvglTQszRExQaZ4FfhAAAgAElEQVRU8xPojkup/ugo+Q/PpadxP64j21Hlb2HzhzB1Htz4Ony3EJqW4HXEMCLcRugiMJZGozgTwfsDuAogGAtvX4St9jCTgwLurIE5bWB9BfK8KMY4zC4/iUonWm8OxjaJkGEs0WbodwXk1YHHTHZPA3LC2TRWpmKsqGPgFUXEn+NDdncT3p5EU19JytanwG0gPFPD4PgBvngGMeITUNqh4xxweUFbCB1zIXk6fPAe2nWTCMUbGbmwBONdDxAMD6HJ8CLxxOH1NWFf/zJc8Mo/M4r/x/iZUxyfAG8Bi/5q30tSyicAhBB3AU8Ct/6tSX4laIC00TB3M2GqCfI1niOv4jBawTELf4eN7rQvGLOkhviWBLZP0OlWDxB9ZB8MnwTOTOg4Da+OhEvewpbzOGdiviA3fxeeO6xoO1OIuroWpRqsL3kID48nPLANYQijNpZAigad5dQNmEOa43yE537YVI7efwjDNlmIr++EogkQOwv9+BLUAdXw+jQoXwmfbYKcN+C8uyD7dai+G0pbMMW6aN7sJ/MqL6JEhTt/Ipx9iBb5HK6OnaidYZTdfjxvV2J6tgZ1ukRpDWHc5ya48GX0rodRyxJRKaTDeBq/KwX0iVBxBNRmWHsUFA1LoAPTUStSMxO+wYt8oRwlLh2logHzV3sxqLnQUAaWekgoJBjfzJT6OtQeA9J4D8HSjRiObEBEKYRfuQNNi0fIcwkGQih90lDH1lEXdTlTlHLkpycQLy2AmbdA8UTQdWRNFRxbiqg6gzz0Oa6MM8iK17FVm8EcJhgSGPsEkKdB7aegljngu2cQsTbEyHQU74MQmwSrdkPhXlDeI2x+GL55lK5bXqK0SGMk47AEVKRxD3JEDHr+IGT7VhyH20G9j/r1TlJvHIGSXI1BqcBfMAB7w4foUy0I2zr4diOivgHdFc2pMZMoHPIpsiaFUG4VhuR3UD98HrIPgygHYyZ43GCKgoVtELoIfpMRaQabczdCbSVk9OKOe4eEEyXgUyBtMOSbIPV+iC4GQCy7lozx/aC0FAZZIfs2hG0QsWPgxNG7SXm8FH2wCUOTQFGqYfJoOHEVpFvB8QKouZGO9dMj82G20GXLJ2Q7TFLsBfDYfExPvcsA8Tv8vE11561kB/zY/1nx+zPi5yJoKeVWIUT2/7Wv56827cB/WiX4cxj2/+tDNUZ+kYwrNA+zdRLcsBGsNnxfrWK6527iB3SCM8SwAxYOFMQgteOwei7oGoy8Dl0ECJx5jXjMVJmchM1pWB79GMtYSe8AK6Fzx0DRgxgWNWE6oCIPh9HKfMgWM2ExE7cjiGvxq7DahNascHROERdXrUFxxSNayjBFP4fiSI34Z2Q/BMP6wTMgXe8jF81Atn+DHDYG6VRJvCkeNVZBHwqcOgmJKlGHViPCQSydOtb6QuxpxRjzjMgP6zG924Tib0dNMWLd40Nt1jGExiEw4KYJ5+kSOH0MTnRCz344uQz2b4HtdSidsahjnBgW2+ix59H0m2ICNw+k5dULUX6/CvS6iKl81z7ivjzJwGX7YW8YZfcqQn+wIR7qRLnKjun6aVhnx2MYJlCKixBFVmQnNIgEkj8/iXZ9KrLrG+TLV8PJPQDoH70Dgw7A/OHUvXYlXPk9wVETqBg7A6O7L8qJIN03mggbIWw3ocV54EQb/PEjsA8DfzREB8Bhhy13I23PEd59CvWxPZw0/USNLMVZsRb55btoj05BHXEdxLk4EF8MRjt4EklzpuLYFUdLy1ACoSS6B32J6F+OCCSC3gRqLfrQYhqTsul/shux6VnMzRei9pgIe5eBOQnGL4t88WWVQmEKvFAB51wfsRRVZkPa9ZFO8uRhEyNx9o6jc/pZSKMb3DVw6HUItkDt61D1LGxeDgseBG0SpPdAcPNfbvX8Q4L6ayaiD+6D/mAjWsk9yL6vIn39IGYRWGZC/RnY+yxc+ywkpyGlpK2zFnt0FsprT8CM8+H+OWhtzZjkWeTviaJuuIVK3iJE7/96+P5c+HMO+u/5+e9CCPE7IUQtcDWRFfTfxK8r6L+CggWzcQSBYSFMqGB04hl7ElfVQsh/Few/YTXNI3XfbXQMyMLVxwJNz0N8Jtx6LaHOpXTLkeTjwG1SceT+CePoNvQCQX3PKQLTy0kclUr0+kYMOU5CA3QC8T5a3cfJOD0Clm0CQzTu4emMqdiCmhMDvnIwZ4FrPKLje2j+FGmzRyrStkvIPQ2jvNBxBuJjYdx0DCtLiE3NpHNpKgnWA3DgGYxj3ieh63YUSzPGVbsRfdKxX5iP9lEdnh96sacZUerrkeueQUxyQ8o4ADxde0nddhoe2QS7VoHdBGkPQkcsfFUJw4qgvRoOdhFjNxL7SQ3anB6sdQpYzoAtDuxt8MABTCEzbMiB1RKsB4napiMLQ8jkbOg3HfHFc3ieegibx4WpqwcaTxCy27A/dBOK+h6iewQk3wjvPIocNxHZ0YXIXE7I9wIJoSOIgpewpE0g6518xJAOZG40/uThaA/EYf9sHVqKG0PSeCiaA8yB5hx4sRkSdchLQYSCWNJNyONldA7M5OINJ7H99AaBeydgdi0C7w2E23azc9vVjKQErd2AaeQF6HMfpc5zLyk7YjB9cDvoExHRHZHosgzg+Lk3khxcjQhXoPdkI+qHYWhZhrrqHbTkHNSuwxF5nDsbZl0Jp26FwbdC6wl491nIdoBzI6jxkH0r5tF34q5eDEIiffWI9m44dDWcaoPyERDdHx5fAEO+BW8pJLwZucF/eB01IQ8x4yxC3U9gjUlBfmXA1xGNccwiVGMFysDXobcKRj8L2eMg34r/6CG6Cu0U9LsUbdF61B9ugQUr0MsOYpnQF3PIQt/U9+ihlnJ+SxxjSWEmAOIX9tLtbyGS4vi7KTFeCPHXXhTv/1vLvr99DCkfAx4TQjwC3AE89bfG/0rQ/xfMDKSbxTi4AITKjhF3c8W2l+HEV9B7DrLqTxS016IXJyGNLYiEfBAuhKUf9pSLMO54nJYCJ62Ju5G9vURFx2N2V5JlGkn7od1IxUvdJek4K7qwN8XhNpkwxZZjfXcnss9ARMIgoncvJfqMAfrOgqh4SCuHlmaw5EHAB5Y98AUIvwLj0+EQoJmRs+yEhx1EeHOwN2h0vncQbWoG6reHoCSHGNN43BdnoRR6IZyAeH8tBmMY2xQDno81bFMsiGlTEJ51cMOtEBNL6rAmxJV/hNh06C4FtRMS3oX2d6GgHczdyP5PE+Q6TANjEC6JvyOD6B3dMOI3MDQWOh1gyoc9F6J1xhAY4kTvr6ErGroxDWkrIpy7md7XnZj1EpSaY5iGHkNP3I6rrhp10ptwYCdE7YPEa+Gax+Cpc1Hz+hHQ6uk5dApnz9lwehSyLpo9eecxyfsdhvhU4qwvURs7F3F2LjE/HEbGBRG6BluXg9IEQ5Ng/ldw5Hx4ahqs70a7fgEzhj2DeXNf9D4m2tPmkqYkgCsDv6ueybt3INtz8Pe0cGrJB/jOC5GWejWdUzrIKFsGURfDN69Auopu8VAbttDfsgSCLyFjT4KtLGJ8P+My/HHrsajVqAkXQc8o5Dtr0G31yGHXIecaUHdvR7nsOmjtA21HYMs9iM4m4nq70FyJqC1dkYrG91qhzxB4fgOsug3GByFcDPEvRqoHd30LHfXIC6NI7d7JKWs/MsfnYLRP5FRSFTm3BRHLd2F9fi7KyT1wwVowWAHwfbucom/2EmjejDkhGvJHgS1M94DRmPFB0APmKJz0ZxB/oFn7iRL1ERQM9OcJVKz/1Jj+exF5SWj6e4e3/Q+9OBYTaXLyK0H/3fC6MWgOwrYzcGwHlOxgROVPQD5kxkLCIsSlLyJ+DKAs3oLMyUAsmPMfpjCP/RLLuhvYOa0v55+pxpdYSTjFgk1MJr5WRe5fic2sESjOoDm+m84MO6ImhtgZZnQZjSokxBvAYIGh1ohmuvkAGLog/hooW4tI7YXLroWmVrBpMPV25PY5eJR+GKrGYvnuW0gsIW5AJu2t8STaVWiuQig/YnlTRTkUjmS/hsZDo4qaFsCen4dn/TFMZxtQVAGvzIb7lxK1rhPDRQlw4kM4+AbMeBocF0LTYjAnIX09+B97FXXqVETXaUhuRmTEozb0gXV9YFAudK2BkisgvAPNNpJw3ElEzjzUmEEYa95C7F0OOVeiJF5N1P7ddPcvxqGovBnn4JIjX4HnPGT/LxGVH8GhF5DtBqQBehLb6Ci5EntzJ6alIKsbCWd0MiGtln25Cxi7xoRx4hAyWE2XYy50WMHxLfL+fuijzkZNMYEvGq6fDNk+uMoAQxMwnFuE4Yen0V1GZGYaTUoVCluJs07CWPElg1O7MZz1Ps41X2FM2seWpP0Ef7MLZUABSl4DnLwX7KkwfQiKNpvp9asROXPBHIlFGVyDHncQZXc+BvM+es69BlutxO/4BjnLgJI7GWPmvaifPYtyaTmIIATNYHdCsheaXkLE6hh6zkbu3AdFA+CpBXDoT9BZAaZWCNdA7HORSs/ynbD3W7Rb3qDrngsxL7yDWFLpbbqD6I9qyeyppnn+MHIuXQvbH4S8ENTeDll/QtIH5ZslqAVDMfRrQrniQXCshcVXEp81HGNXL7TWwZKbIyZhZhvJzeUYndWcmh5LqeleCk1voISJWLr+giHhHyqzE0L0kVKe/LfNC4Hy/+wzvxI0RMyG1nwO7z2C6FMM1zUhy/shhp3N9isKyDVcFBkX6oUj94KzAen1Iup3w5oPwGyCtlq49HFQFJzpd5Kx8lFQKjH0N+EwPI/P4qRjogUlIxHHjnrMB8txDDTTkZZOcriZ1r52HEfDOBKvR1HC4FMh8QkIn0YPhFAavoOBqVBlAeedMP0hOPIcnPoMuU9DeiS2vdtRbINh6kioUrAOjaOzNIZwazUGnwWyojCcaYF4YNoFYNoApz2wFpS0Q9hzEvE8/RXmGRrMiYPLc6meOoj8T96DunUQMsIN02DVu/DFRkjzIrNGEVq9ArXochjhgB0deIf4ic6ug2+bkLHTkGPuQDn0Goh2TFEHMWU/Ccv/BHIDXDw+svIr68BSuwL6XkObM5W1HCLJEMNZLaeQm59H7vkMYfVClgH3YZWGVweSd08JwtqF4nfCewdpevJJrMX9ibEsYeS7r0JdPwi2YYxyEh8uAM8+qDQTmF2NzPoTiseEmpaGmjASMaoP7HwH3LWwaQnU1iLiehBHL8BpOUnVOe1YDWEc/kxe23A+D5jfhO5OOkcPZOw7XWjPzMez7GXE4VZ8QQ1l6kbMjh4I1yLM58OxW5E1E/Apiwn13YUa1AinHMKQp2Nuc0J6J45ADErRATClABCcZqFr0TxE6SKis/ojO7oRrj7gqQfdC40bEOcIsO2Dro9g4lT46lJQouG0Dmftg7gY+P4lmDcETSlBKzDgefNW7E1pcKYGb2s96u0z6LgoGW/gbQbVfA/DH4U+16IfOYD/3smECwZi/PpblIeuhu6jMOFGyOvE721GrxqMQ3NC0ATtdRD0g7uVuECAuEoIxXUTdt6HqfQAxGfBWTfAwHMiFZu/OPyXUhx/eyYhlgKTiaRC6oislM8TQvQlIrM7w3+i4IBfCTqCxmpAwlW/gX7DUQeuRxt4BQZSUGhBQ0eVAo7vgq+PQvAYMtWHthEM+x8Asw3yRsCZh9B6egmtW8foi2ej2XbSe3Zfeg1rEWIc0fJJAlxB78RO6ICo0yEKXgtiufpLCLxFb8BNo+E3xEyYiX1/Pax7Hgon4te/w5r9CKLqfagNwqwPoLUVDkWjB/PwGmowZg/EHF0MniYoWQwWHRwuEgqHo2+vBmsIMkOIZCfkadCzDaLMkBGEcCqMuQll3ztEPRyMdD05+XtIKyB/7zoY3A7NPnCZoWQz5BTBjXdDvI5WPwLT0ymYrumAwC3I3bcSXWZAxIUhuhVWVKNbkhGV1YhR/eDoSBjuhzH1sK8Z3tsGxeNhxlOwbQNkXcibQCetfK4UgaMH4VmK1P3oAy+jtqCDQM4JEhOiUfrEEUoN4FzeQesTt2GddAkxo9KRNdDWHiCubzeGU4vh8x7wArkKvHsFptY1yG4d4ZyJ6FuAKNsMn22EoeNAK4GqFhjoRozZDLdcT97haIxpjYiCbALZT1Fe3QnVBdC9jdi9VVhKGwn1vkRMZT1ahxk5poCTby0CTcPs2knK7Lex7f4RBndhLn4Ok/s9RPg4ansmpB0FcwzhcAWhpDzMphToPAUnlqDHLYPLjZReFGTcgu6IP7m7FywDofcgmKLBkRPpO3loG+xZjWwGYbHBTfMhykLo85vouPtCtOZvSXzkY2IbCvGdiMfy+pP0WFehbv4W69pVFG9ZS8P0BOr7Wkirvpnw2/cjtbNxJws0XzNCUSBkhqOfR8ryr/yR1orr6Ju5DgqGw8DXQAZB+Y9aDqMWBncH8AZYoiIv5MNBMP3CbOP4eWV2Usor/x+7P/qvzvMrQUPExCYt9y+bZpoIcAwDKdgaW/Gm+HCU7Yc93wJeAsnptCdrpMw8Dc4pcN4r8N6DhMdNpfPxp4lduARF9MD6j3HINIzqx/j1OoIHF2A4XYlFhgls1TEsUBCpt8Hm3yKmxOKsbcRZHUbOvhnG1UNHNTLQivCq6DXrUcKx0LEVXsxA2gQi6EeE/VicEtGdiaQPoiEIRwT4EmHAeIynStEnnA1nbYf4uyD/TiiZHylPrjwLag7AICMc/QQuyCZsaMFwygpWDzJQAlJBP+NCJiTg9mVjqign2Boi2LEZ3XUeIm0R7qp63Bd0Ez36IInT0rH+UIt+vQGZMBAxeSLq50vQpieghjyI86ZBdA5EzYP4b8DUBEd2Q/lVcMFnlIsYPPi4gw0gisAXjyflDN3zomjPPkHS217SrvwcQ8l9EBcgPCwd9YNSEszfw46NSHcAMotJbC3n+FlXMzAcgHkWRNYgOLUdTlQhHPOhZwVhoxHRZzjq2McjpLH4UShZD4O6of/DkDMCvtyMIgTmcCF6y1Gqt1+O6lTxa01YjvdgafBDUMNoqcczJAqLQcdw6wEGBAN4T55g9+b9dFw+m74L38XZ+ixq+SJI1EDEQ/JWsBZB1goMTV8R9j1N8MzNmPZ1wsTHwdSOvXwvRXNO4zt+Bu+4VOI6NTgYhsE6HOmAik4IOAkVGdg/aBIjDm3GMNUCW69B1lfiz9Fpbz1F3lMBlPhulMGNWJJt6Gs/xDV5Gv68zfTkTsB1XCfz5H529uuH4481RA0Iow7agssbwJBhQxycDV11cM8DsPI90F6iLX8OGSvWYMnuhjMXgAxB1kpQ/irnrBogOhEu/+3/elj/d/Brqfe/AMwMxMNq7EwhdcMXuGeOxeFIAosXjFbqi1WaJyYSv3Qspj4COj9BCwZpX3ADcR9+QnBwMa0rrifcrwCH6SC6EiSJATQOH4Y+fBLJK3oJ7fkYcZ4PU7/HEQ7gmAksJuhIRXTsRU/pRSYfR5eVBE0ulJZqDK6xiHXNMOYuxIF9UP4xWoILoh9DXf82+N+GkrqIrrVPC4x7DeIzI1rKzmvA9Qh0noDTnTDmHah9A8wCfHWwWUOvySQ0wYGhZATYtxGOnk54y07Mze14+oxDrT2MtXULvjnvY8zqhOHbUHyDcCZW4h5zNs6JV9Hhug/T1h7UhlhwWCCqA/HMYtQn56Gd04hBeQYqLJBZAh2FMOt6tIpDqKsa4aXZWIefx7vhAM3XHae1chGukJuKEZnoBoXC1ZUopwehxxvwNmVgy9yBDBrRU83IF2NQtnailIShtBqEiagjWyCxGHH52xAQ8NHXsG0v3Ps6JD6FYgjQwwjs2kKMxhmQY4WuFDjRDt1bILwIEgsgcxRS5GLvbGLRlmqW7buEt8athrONEA3s3whxKsINWjge1WAB1YxtxaNYrxxJYVU3Wu4gZPwyRMksZF0jeuqtqDEVkLEEFBMYYzHXTMST9Q3K1E8xdLRh+v5PiLZebG6Vw5vAWBMkkGYgRunCpiXDkS6YIKBPCF+GDaOtB6VVg8JboXILJKVi8m6kT1kxxqefQL93MoHpEzHlj6XjhmeIvciNqY8V0eyH4DG0Y16GffMDe14+i8KWWmLFaERtG6LaBbYKwAwJJijuhv/D3ntH11Fsaby/6pOTdJRzTpZkOeecbRwwtsEYTOYSjMmGCybnfMkZDBgDNmBswAljnHOSJVmWZCvnLB1JJ4fu94e4b5hZ82buzNxhmDf3W6vWOtVVp6qre+3d1bv33t+BYWTG52LQ9kKjAvGXgGMLNN0KsR+C9AdjX/0b8EfMxfFHNAT9j0NLOj6qUfatILvvMEVlX8Gm22HzVyh2N/YBSZiVpRTO/xM79NkcUAdoFkfY9uJi3h8q+Na9h5DGnzAMbEAjtRKEmhaKkDlFYkkHyvRytH+xovVIOPM9YOiBSg20DYJeH7xxKaK+FImbUGu/QdM+Gt25uahsy5FqhiGFXYVIXIjQulD7ulFvfB3SY6DOC4vuRhk3B7oaofZpCHjpW78exR8AvxMOLgZjJfjKwJPUb7rQj4d71qAc2UMgsQ8GukCbiKYqBMPAS5AGpBN07xtYEqyIBbdjnnUM5pdjjL6U4JTnMYw9SsTcDnRRTgxBDyDNsCJ2uxAJOhj6GtiPI6ZGIO0xojhbUdrPw6FclJ5WAkXvoPiP4At041fJRAXtQSQUEVrSCuFeZHWAuB/aGPjpeVTrO5Euc+LvvAb9j+0EZBMqtx91aCxSUReK3Y/ilpGj+qAVPCodJVYb2KsgKAQuewtSBkJlPqgtSISjVoaitK5E6amE0ldgUC4MjIGK03B0F3x8EconM7EG3sGu0TNx5imWjMxH02aC1v2QMBAGzkKZkIqm0t3vK16xG4q+Rk5OojtQzZlr4zB9cgVi5wNwvAr0LmyBL1Da2+GpFbA8Bz68Byr3o39Ph/j0zyg1T+GLCUKcyUK1L4Vkj5aMBpCO9eIJ6cUfGQEOV39aUclLXdgcHGYdDIHW6LO4E6fAZ0dwXLkATfYr8PR9KI9J2A5sgeTFGFc8ivNUHEQuRB3eS6DUBdUO5JUW0hNiODHrMuzZetRjLwbTSWibBe46sGWCci0cqyGhpBuDdxaMeARqjkFlAE7UQtvR/rDx/2VQEHjQ/U3l98I/dtD/CoSiIvK4hDjzIR3heWSXboUxd0GdjRaTjq1Bo8kLxBERGU/G1guElZ3EHBXBDWe6UUZk4Hc/gqajA1PbKnyhH1Nu3klL3QaGWSQ0tQdRtwXhr4qH+h4arwwio7MGaWEtrt2tqINCUeeMRRIR8M3d0NuOseYkctYYRMFihM8K668GjQFMY8F+FIaNRe7djvvPXkTCRrS7UlANCAffMdh6OaraPhx/PoHp6RKEbAZ3O5S2wLDVcPAw3PwoiBak0UaM29wI1zkIiYABDvj5IJgU2PMMUIZ85QYCPUuwyDtRSRP6L5ikh4i1UDiG4JCZCHMS+CugNhVO3A4TN4B9IKLqBViXj/JYFK6cPdSdup300u+gRkGyORATR6G6aDnuI6vQH3EjoiLQtSnogoMRjj48tgw84wZgqdLAtO/wqn1oAyCqahFH41HShkDizwSSklD7K0jPv0Cnu51Phu5iSuG7pHq8ENGMIr2AKNwMxhyMTjuCavzdd6KZ9jg0/ASjsmFOFrQDgQcRpZ+g33I1/ox6pphshA42QkIJ+KbB3nLwFoNTpjs9COMFGe2WJ2HmvUiTbyFCvw3RcYxAsAMp92rE/FcRnU8SZvsSj0aPtn0rIiGArBhRKrvhiBaRKqHUqtA0BUCEoFxzMebi13DUB1N5VmH0QTty+AU8cfHoejpBZyVn70a+mHkpY9NOEPrpEQInvqL+2SRU/mLsa67H9HgOUt8ehNaD58IF9EuW0LVoESI0AXVeEerINFST5uHOTUMV7WciN3Ew5EGGPf8CkcfqIXIr+Czw+rUwfBmEXwKu3TC/BE5vgP17QVML41dB1x6ofbufACFqHoRPBpUOlACIP9YO9bf4I+6g/6Gg3U7Q/yZdouyHE/cgertg+IOk/vIizamTYdRyiAohet2N5B4+ysnRkax2uunccBrTVU1I7pnIR9fSE9iFUh1BUFAkPbkGzA4LSV/eRIYeRKoeR/R0PPYpBJ9cg1+nQ9EFo7KGodS2oc3z4jI6USo6UDd8i+rsCRgXhS8xCH/eOLzVGwjibpi2nF6+xb82H2uohPuSbqRKH6raLDTKCqQjf4bKdFhkgKFfgPttpPLd8MpoSIqDWc9D63tQ2QK1Q6DkeRA7YdD9iH1FcPN7UPwM7NkIQyOhsBLkNTABpDOrMIo0iBpLP8sm9NKFfOEXrOsuIG5Tw7A74L3bIWoGFG6Cs1nQMQMGDiUwU0ZqK8JwLpWssLtRgpKhvQFhCUOcL0IcPIRZDURp0LY1E5g0CdlgxJWhwbusi+COH0DxEWj24w7Wocv3orSpEAdciG0/o+RoUe+oQ/GqCEzUEF7dxfwX38AwzYdn4Yfojh/GF+dB3VeHVONAchzjh7yFTFEVEVSsIEKdIJdD6y7QhIIuFyVHhSgswlwqEZidx5jgjeCcCgueRbHcQ4sUoHZ2GgHFjdERgl6dCN1vIccZMUhZuIItVAdKSVn3IKqXS8DyPPaOUkT7eRquDQEbCLWaUIuGrrkxaC25qNJD0fZEoWuIxObaQHCRA7etj55BI1ANkpE0Rzl/URihnigs3maM024lWnWcJm8SyclFaGZfQ6tuJeFHr+bQvRMZ5/gCZ2sM9uvVBEdawOdDcTpxvFdC8NJM/MdjUKXMwRQ2hy7uwljXRsqag/zw4SQWnT5FeMoe2LAWHlsF1l2wdADURMD+F6BoBwSrICgDRj8GuqB+WXI1Qst2yL+hv+7tgOSbIO7S31PC/2b8ERX0P0wcG9+G7vb+VzJbGRy4CkJGgk8GbRzKku8x+Uf1+5Mmz0PcepyFh6pZumsPcK0AACAASURBVLeXkmX34FrRibq9B/mSWnw3BeHsGY7xdBWekTrMvtdRaRowLenDOXsSsjMBlb0D0dmO6KzCGewhICVCo4Swz0Pkg67Aj/ZwD6p99YAEFT2oAg7U9efoTe6j9KEH8LYWY9y9ge4YFTWRCXxRnUd5RSraB4oQP6ykZ34ULX8eSVtoML27hqNpfBMpIxafJRS6HbD/WSgpgsNXQ2c59PVAhx5Rfgrpp73w7jIY+zws/RLGfAxKGEohsB/4YSecLoO7R6PYu3Hs2EFv2zE6Tj4F+ghIPdofWjw+DhZeA3d+D5e8CDPtCM85VH1dKDYDZDoQu55Fym9CidEi4sPA50JYDCBbIFPA9X5UkzPRpN+IqcJG6K5ONN86kNa7UG/w0TnNimuJESXah+y3wcwM5MRITq4ewl3vvMhPdx7EOSmXCGMv2kPBtPk6aJg4ir5GH+LwBQJKDwfTxvBl8hLMX9ageHcjt3biK4RAoxHv2VDsa3W41zegFEuI+BwCciFVZh37Mzs4adpPfUI4UWdaGX5mNhnfeolviSEraBVJkWVkFFQy0PMkw7TrSQ8eicrXAe+shu5PMVWMxdVpJOY7Dyn2h0h920nwwTaiDnqxNE5ALZqQrbl0i3LaMluoXhnHubtzcUltNO2sxdehkG6rwOisouniYJwN3xBfrdAWbYHUhdCxneFvP0b+8Lux1xSzXqzCUxiCrKho7LsLr66N0E2bEEYjqpt+RKkowXf/nTiaXsNQX0Fg5UzSLTMY2aDjXFwoaBvhtnuhqB5SpsKFE6CrgeadkJwA+nHQbQJ/zT/JliEOUm6CkV/C4Lf637RqPoTqj/6wJpD/7lDv/yj+oaBb6+HdB6HqK9gyFIQVLvwAE1+EMbch5c0luLL2n/oXf4ly8cMklz5JzpImwsLtdN9gwSU6kDJziHKWozWoMdS1omlwI3qmItQjUQUHoRNZ6LMex3T2OEKtwXJVD5GOCzDya7BPhOYJBM4nIzSdYG2BxGSoicI1exFqXThhTQb8QW7OTZ4CZ3ykTf6eeHMMi39eR1lTFqIZbKMfJjh7E9HV1xKuuhVTzDBYdD26pbF07NGjzHsLEq6CK0qgzwB/mgoDl0MgGpQDgAxnWuGFu+H7r+Hr5wAzfZ5Qyk2JuFUSPWdkWkr9tN5yOy1Ll1D74L10mfV03vpB/yvsBwEYbQX71/3XLP5SCJ9DQKvBs7kOueYB/HI67sFjoFZC4IW2bggOg143gtj+VK9VJmjcQSD8bfouUyHaVQQq0/uTBw2GsB02zDsC9J3V4W0JcC5W5vbrV3LaNI4XPn2Q+U4rJm0O7uwAWl81fd8fZUNeDD1uMw6tgWZtC1s1F5FWW4/79DjctXmI4GpUVhsquxNNsB7tdUNQp0ahTH6KTWkDKM7Mo88VzKi3dpH84Es4e+qoHJpNodhHUU48JxNlipS3kTVpqIMehuenorpQCLmfQ2QMyt6PUba9juv50+jlG5FviUUsCIKPnuPC9PEYf6nHeHg7lnoHntJYmgojibm0hYxbyomOSyFihJfAlEZ0AyQ0/gQs0QFSaKCuN4Tw1wvoCEqH3io4lIr0zCMs2HeGGUEnuNERRSB3HupvjdRuc/CeI5p6g5HQtWuRemS0gVa0TzyFdP1bSD+fpO71Ibx/3yrsqpkM9i9Hdhb338voKBhVB+OfhD5nPxVcRxec2ky3eji19z5Aze23E3A6/7mcaUNh7A8w/ud+pf1XUt4/EP4a6v23lN8L/zBxRMTB0Y39nG0jXsJVfwpD62nQWfvbJemfP+09dgIFz2IM7cM/bDAWXxFXGLcgt8OGgg40pz+BGqCgDO43QMCBMiAeo/MUQsoEVxWiaB9KRDiS3odeHQamibBoDKoD36CpbUC50YIw2qCiCnIywWBEjPoM/Z75JN9ZTPszCg59GtaN89GFlxLR7ubSEUV0z43h02ATqoq1LDEPIz79cnCvQeVToPYUlmsfo7fIQXBoF3xzKf5WLSrrMURvDOS8CqX3wA1ZEH4xLFzZv96bE0BrwjhpGuGT3uQu7VmePXU9kfIIiDDjnr0Mz5UrsX58M6HxM/r/09IMD52Alz+C5G1wQkFp+xKRokN4Z6A+9QG2a27AcupMP4N6736QulEaQ0CXiGjpQZhKoMWNe/wo3PIWgs5mIBKbUEWW42vPZcdFMaR1dJK1vxnvVQOw7z9NUWgiz9U+gXW7Fpo9KF9nogRkGpOysAy2oWs9x8qfR+M1jKM2r4hT47LpkqN4vOwT5MeDMDm6Ec4QRNRglO58FJ0adelriLzBNFcepHT8ZAb5zpMuD4O0c6hrmwh6uhoWLEFbexzfkTaap8bCFXrOts9m0HMrMOkleP1yyExBdir4o0GzsQfD5ZH9KUiDu5EPrEd1vgExwYt9jAlj0XHYP5y4q0awKVfhlSvjuWdfGRZ9DQlZMYhSBax1MNwHZ5yo8v1kfV9Aw4J4OoKk/pDwYQFO1jxOjvswit2C+vyrpHp6UKrVREkSebcvI3z7Jk5Mn0/1tbdwacCP6vFbMcyeC2cbkM7VM/EZPzdHRTNfyeURkdh/b6vfgthroWAr6MJBaYJeH325S2led5hAczUDHotAdewzSB0Lyb/Sh53fCaGpEJ7+h1TO8A8Txx8TVz8AYbEw/wiF2dfw+qjlcKyhfxfyV2i04PWAoiB3n0f0luEfL1D3FKPeNJS1f9nK8sPHOaruBHctTF4MH/wAs3dA3kKEdiRq53AY/RYUFCB12vEOtGOrDsFTUQNFf4ZAL6zegLLwajhrQy7WIOtACdaArRiEQDJehdbXQdLdHbSufQdX5uOgGYJi10LsIoI87dyl/5IlTV+xT97C7mMz6RHRKJoKcKow3/gYjm/XIItjKNXV+OZH0jpJjdu9HiVoLYwwwshimGAH2d6/9kAAbngBtbqVEFchT+//gvtGr6M9Ph+58isMs66m2VuNx6DHLnqguwMi42HJHXBcht6vUaI3IlstiIS70N2bjohNR7fuO1RnSmDGrYgCUPKd+NMVxCA93H8XBDtQ2n2IA99j3OFHVHWgxNyAUiFz4RINWd4u0htkOsP8iLpCql7OZOJLezCu9SJCc1CGK4goBSUklwPeuzk7JZvIrl70+R9g/LmR5OMK3SKE23Z8TlDkW1SNeIhtuaP5Ye4rnBh5L61p1+NqKMMx+CZK9ku4zvXx4EfbSCMVTNWI6DTUY5JQjzYhnfiFwLYaVFV2EtaWkHhxHWNvXYOp0o18xo6/qJbOC2W4O+qQZCf+bC2s3w5Jj6B1RmOL7sVjbSasq4vGJfORB2iRQgbhe+ByVjZHs6iokRPjJ1FsDEZFI1q3F9wRkHIjxKhAo0YMGEf8VgXZLuFVGaiLuIgv8+Zg0AejpE+Dga/D/jTE7gpMVVbCZ86DNz5l5Bc/kBcbhxsVe5bdghg+BuNrR9BPmkrqZfN494dPiJYsvOL5Crersd9z5dgXkDkL9OF4Tunoqmmk5/tvyVgSyoDL1Wh6yuGHB+DU+/Dllf1l023w5mjY9TT4XP8jov7vod+LQ/s3ld8L/yd20H6a6OUd9IxGzyQkrP/UKAQEx1PR18xXwX08teckpA+Fiu/A04LSV4pIroONbyGPH4bSthlvgg6tmIKQTiOe34TJmMQSezc8PwQkF8xZ2k+kWXgHNG+FsIuhpxR0MeAOQY4ahpKZgUNzgALrahaYgvtdwWp+Qe3ZhasyFZ1kxr6pnIDnPOqXw1AGPw+/vINal4N/1DlSV1spe/M2Um/tpvsGC+Hlb6Pr9sP6YmI1Bi53H0DKiURVdAZc7RAzF3FiF0ErV9Pb2IjlmSvxfzQDZW8bTXkGQtpqcaZcR1DqNZhFFAIBNUX9yewHzYbSu6DoYyL1Ck8fvoYHp37Ji8lvEHLwRhJ6oqgekIwiV2H5cR1MHglxx6CshsDHETDHixg5FhH/PNS/DSOL0K8vRUy9G16/D3+2HjnMTdvgWOJCkpEqd4DSin+8jkBnJNrjAYTDCZe343AkUWecyZy0ZbT1PIfWUIcrWUVIlyB2von2b7yETboYtXwSn3EUd03+kOu+/46UJ1rQDLKASEA9pBm/7naGVH5NVKAC0/3zyNNrSX/hHQxiLgUFl1BqseO+bDlNrZFcs6mAuptHIW8toHLxcjKDe/qTUw2ajz+lEs2TpRDRhm9kALlUoG73orxyJ46gGnoC1egLztOSPYacwJWoXrkRDtnAJRAvP4x8/W3Yvv4cn28o0v0nsbtrEM2NSHl53BZyJ++8cS9XSh0oEx18OyqULeMuZVr5D0T49UhKB9xSCl9lwzgQrj6ERY3qrIe6VMGNJz5BWhsJESdQnlmMmGGDOUBEDoy/HoRAADnJaTB5FtMeeBryP4CvF6JPmYZ/oIGBb25nUPsoiud+wWrhZ2VpF+kBK95tX+HtK0YfrSVoxBxC50fDyY2QKUFtMwy/Gua/ADpLv4zVnYSYPND88SII/4r/YDa73wV/rLP5b4KaWHSM5wL3ITMCC7NJ5TJUvz4Jm8ZO4z1fGU86hqGrKoS4zP4dtGEpgYanUSkFUH4E2SsovWgQsZ3ZGDraYPROMCb1T2IOgexxwDYoHQLGiyH7NVAHQ/0RsEbBvtuh3odfk4J+UDae42eZI4dC5GDY+wDETUIkz0d7fhecthM0IB5HTDq9nxeC6Uc8uePote3CavJgE4MYMPkEntMK/jGRBBQnzmg9nlFaglRz0DTXw8kuMDtArcDRn0BswVQ7ge5KN2JiHpbZ72H67ArER5UoUxxYOx+lcKqVbo0HBYVhG77CumJLf1azmEugeieMWUJC4rOs1qbwKGm8PtNIzDuzCJWq6Yx+EfbvhPuuQDnagq9+GnJHE7rIn/GbrPTKozF+60LjqyMwwYTUXYfv89dw6R/F9LSRhBtPwqNn8NkU3Eom7moLXYczCS3+gqYOI6nhvZxanER25Y9IypdEDsykucRC6cXjGV0yCzHfTETNnXgPrMYfG8sNuY9zdXAao4IVHD0qnA+dRrVuLZp5UzBogxi75zMOj89FThpJi+s40evupiPkQaKGdtJkHYkraAIzftiDPH4SSZFm1LIg63QMXPk8yoZ4POk7ELpB+J99HE/fNdDnRhqjxm7RUawpQ5GWMkg1HdOmK4hKyIeqg9DmREwLAl887L+Azg2h659DfP457HShz6qk3BlOyMM7SIhuY3XWdTx08BGsbaUIr4kMQzqm5CDqynuJVLdh1Cb0R42OO0zflE1Ua46y/+w4xlzYh+aQCfKSUOta8L0yE61IgZ6hcKIQKi+FWU8gxyQh/L2IhgJ4fApsLgCtEXHvTDRmI0pGgMDh78n54ARPTS3hjaWPMurtraSeOUPyCjUqnwQV28E8FmwB6LTDkCmw7NV/Hs6dOPJ3lvr/HP5oJo7/EwoawMRcsgjnAl9Rxzr6OEcMc1AzjJdHpfDox19hDj0EC+6BE2/DiNVw7hM8EzIQqkb0cj22nIm0pGtJ1A+HbR+C5kVQZNwhYXTHBhPssmC4ugZxYQKo26F4JdR2Q28tNBmhfB0IgW7aIoTnAEGmEWgOPQbdY+Cij+DLa+CMGZXBi6+vF+/lf6I9fwcRvX045E6wdRKf5UDxBiEWtKA5sxxJ/QWaR5rRDA1Do7TiizbRMTSMgCkcE5OxtOaiWvs2bH4fhkQiDHqMwzNxfPIUZr0Z6UwVShIo550I1Axs/YDOvlCkhhICQ6eAJQw8fVBfAjV+nMZaek7/hcxWNw8PnshbAy3cFt2AOiOBpOLdEGxFOdyMuzgS1bBx6O9NgF+CUddOpeMiLZqFp4neMh7PjBrk6gt0bXsce30Xge0N+KyJSB+2kbjAjXZaJ4pmLonDF2JfU83Aa6+moexjgrQ+4uUK5PpoTgg3oTqJ1KZO1J334FdC0agaUTm1XL1oL6uOPMjI8l1QXYhx3iRs87OQNqzFnJ6DekQiOl0aqJ3oG74gNPUqtINacJ/oQmqHnFFGUutCYcdpGBQHTbth4TIYOAEajkPYKETvOVQ9ofi++AYxPYVKFVT6E8mtrGH89CNIDiOK/Rto2N3vylmugWxBYLwTVXEDvPo+tAiCH/2ChpllJPzFhXeXRGPsECLjVjH7p++w7rqF7m312E82k558GcGaw9i26ukor0FZfJLIw/FoQtPx1kdxLvETot3D8U+cg+rsETRZNpT4yXjj+/CKSsJ3j4Sz6+HYNkjMgrOXQnc7cqsPyR+HmLwagt6F7DMweya89RAiNRP14OuQfxD4qs+y9LmfKL98Gmufv51HTlyP3OLFM/8SLIO/ge5G6NsJkdP/kLk2/j38EW3Q/2cUNICBUQxmFC4q6eNNmtnCW3RwlbYFc+0ZFE8UYvEDKCcElNYiTpQgYm2gbUG2SrhiWhh2TsbQ+R4uqZOm6DI8Pg/6PiPWklRUredwXTYb7cNvos7Ngd4noP5gv82t+zAgwKhCCt8BJzOIbDsA7TUweBO8vAJvQwWtyZfSua+cICcoJYWU3n0xc0c/glmlQmnYiOfMdSif9SF+7MNu+AXjFDh73VxGVhUQGmFCY4nE+h0o17yGg4O0RX2Gcg+YgwdiHjoWaU89uiHzCfz5ehzXL8B0xxsoxfdBvJb9UyYz6P1jWNsvoDNFgzwAas/Cmruh4hQcc9BxsZ6miWOJOX6QuJ4CVjnPIQoU7Nv1aIocKCPD8Z6xoll0BeqZc8DnRBkdS8PxOyj3S5AYgmechM7bianRjcaVgTErjohpoB0xE15+DVIllNBOlAtf4Tq5HjLMfCY3MXiSzJDGC6jeslK8dDCD7vyZ0+tGMr69E3+CD19MI76piZw+PJR1dw5D8/ZHcHYtlO1BTF5PWG4Czct/RH3nO2jGj0aTaCDxdA0t6Wayz79N36APyM8sYUjCNCzHd8AXl4LDD3IJxIWCfyNsOgK6aoiKQcR1ozl7Gt8pK66WGMw3D+OSl75BBLeCERTjehiu6eddrIrG/mg72v1edEcDcOdhCBoIfh/Sqa0EH+rFd7EWw8E+5PQmAsfWMFI+RtFiM2F3/wlj7CEwlxKyvZmI69JJLTpNX2wX59JHk3bwGEGHHYz6qZY0/T6ag+NRNQoQPkRvM8akDDwhZ6C4DNp8gAm8VZC2CDFoIt4zq5H9JvSchCA3/kG5SFIb6uRQ6BoAF12JlDMSU9FMMlxOMuyvkHk2hwpVPNrJswjPGd4vYCFxYL2Ov4HJ6Q+JfyjoPwgMpCF4lZc4xC18SybzkbtrKb8sFpXjHeL1XpTI51DNaCZQ0U1AbcRtCsbb66Q3OoKOBDWRBi0JDRLq7nBEaDIYeyHxPJgEnheuwuf1olsyBcmhhmwJZsXATg9c8SrY7oSDZyH+FpT895DvGIY9PBZFp0ajtJL7l1dQRW+h5fkDTJmwCun0Vih/DxE3FG1LCP6xMQTGNyHOuJGrDRhHxPHmqJt48pfNaL/dAb3HEbMbMEdNx8x0ZI0L+6RXcP64AXNBByJ8M21fXYVxw094z+wkeKoPSfIyJv8QruuNaHcMh0YJcibD/pUwcQREAJp8ugZmEGruhEVOVCfqUDaZsPuDcXSpsegUhCEBbbgHsf8VKH4JukoQrjYSkmbSc7AGS4cB5wg7LeHRKLlWrF1OLA4v6gvxcPggTL0IDpyEMVkQouXIsFhiLGXM0/+Iop2HTX8R5sjXyHx3PxVPjiBjdzPS8FsQUZuwl5fzXNZzPDJpDJonZsLeA2A5CYnT4Zlb0YfHEFg3Gx65nb7rb4WmMuKfNbI3bjDiVCi2jGDmOBbgP3kn7SobRMngj4DCNlAkGOOFKiNkjsQ/VIuquh3GvIrq2F9Ql3USMfAynNZeNEkWtPq1IPfBLwIxtAr3UoXONCexRQZE3wAwpkLtcXj1SuitwhIchPey2RiPRWFO+5m2vGbizjaTLYHz6Cakn3vRrGnBMUWg7m0jKFygs04hpspNYPazUPABUthINFu2Q3AAlVvqt/2muzFsPIpYPhgWTIeYeNjyAiQNg6krEUHxBAIX0O79FOmbfLpWGVElXUFQ1Ucw9CH46UC/0CRlgOE12L6MgBGS8KJ29FDoKEfTm02IRYFDL0De1WCN/58U7/80/hrq/UfC38WLQwgxRwhxXghRIYR48F9p1wkhvv61/fi/JFP8vXEGG89xgWsZynBeBtsBVMFe0rsmE9ugJWAvoK9XR4PORMHMdKomxeNMNxAjzSWHD8lqW0GIKwqNrENY3Sj6EgLG88jpKpRlK9FtbkC7agbuT3/EWyijdPlBFQ+TOkH8BKEvQ/bj+F/fgj/jGroWpxAUWo1xdAuuebV0Gn+i59BGjH415g3XwpqV+H+uhUoFKXEc2vtPEhJ4CTnVh8iDsb1t7FMNQPG24XnmInqWLsb9zFwCvzyP/MZ9SOs+I+iOzzHrLoGXZqJM2YcldxbNT07GMtyLoyaaQGMEeo0VfZEVu6kKTlfBhlfh+1PwzVEo7AaNBX/AQ7gmEqXRjq9lCEq3C2N6Ju1XTUBEmeGFbYj5U6HxBOQXQuQsuGYjjhlX4VFZiW4rJ313DeknGxj16mHitlfjsfVSsKCBM8u91F4ejkNWqBUa6mUnczq/ZQinqNeFIhQXxve24Kky0zXBQF+sj+gdHfDjXro2J3G8cRyrt3xOeGI8LBsJvXtgS0e/m+SDL6MkJqK+dRcNHfdifq6T4Fsler4cCM0GVFNzeUvupMYagVp1Kcg2KAUuGQiTNaAHWiIgPh/iIsF+GpUjDKzpaG65Al9YCzbHzyiddfjOV4AhF7ElDvGljL8zi57QbmLOdqKV28FWCr+MhZ8OQvzVkDYY6eIn0CfEolr1ERGMoSncTsm85ZRPX8p5dzaN8SnUNeRiOTQb/QE3vUMj8cfWosSUoeIIqhFx0PA56oU9BM2Zi2+kjBKlhsbTCFMP+m8LUPY9DJpemPEYNJsJBIGDt1Ds+UgBP4H54QSfGkxw6+0IbwhYMv6Zi2lfgQFnqBXF/wzaQhlJ183gtnzCekph51z47lswhv+PyfV/FX/dQf8t5ffCf3kHLYRQAe8AM4EG4KQQ4kdFUUp+0+1GoFtRlHQhxDLgReDy/+rc/xk4CfAAJYwllIEBDeLcWoI3fo1vagaBnx5An+hEyGo06lkYTvxEWFUDkgQGnwK6H/H3bYHwqaiNAeQJr6Fq2IvoPY5Imt+fPN9lAHcNqj43hvsT8Z9Jx/XNYTRBS5DCwlCFXI7y8p3493Yh6w3ofM1EnFcgLhHN+DuJiZxFb+undEVoUBb04D/QSvs1SfSlyMT5u4jsG4Om8gKq9z4nZJofn1XCp/qFJ/at4KdkkAIVWIe2k/5VBNaD+7DddAQhtATZp6Fc9SD1L7+Io/c4vsG3oF9uoD3RjG7MLXhKfDB4OXrbGjy9W3EnXoS+SoIVz4BfD2veBG0Bep+G4Ie+hoAbTGW485IxLr6L5A9WwE3T4MtpYO9Befh7bGlZNIgS7N79JLZ8ipYcHMGZaKxtWCvc2FJy2BeYQ8bm7QydfI4yJY+SkG5S5hhJPXgAb30s7rkxnG+JIrMhFtOTryHSU9AvWsS+RY0kdKpQkksQM+bgkdqYc/BNVNUC5b7FiN4jMPV2OPYa/LIN5CLE/HcJG+ajxPQkrnIrlkse5f3LElix8nFsV7Vyvc9LddNOEh2bMY6Mw3/zENRmG4gYmHwM9JHw0CAU9WEkqwsi56HkP4IUnoMlw435+c1oLssg0NqC6C4CjQf/skw6xvYS/ooaNVrI9aMkOlAOlKMESlGN74a1DRB2BLIzwFmLJjQTW8x+IpvridOuwNZVQmV6OnF77MiTazCmp2B0ReDu8tMV2YVh63YMGj1iznBMzccwV1bgG7ccGj6HBgP0xcCyH+COodBwA9x7Cc7Mg/h7vkXvHYnmUDfKsJH4BpxDG/k+fHgJoIGoYyhyIkpHIa2vrAdJIvq+6xGWufDtTih3ITLduDu/w7JzADx34H+l7fm3+P+jiWMUUKEoShWAEGID/XQuv1XQC4Enfv29EXhbCCEU5feP99xGK5cSyzUkIGQHVJyF8no0KakgciAxHtqK0VQXoyl2w5AwFLUH50XpmHgTyV2OUvsAvug+ZOdE5OgHUbdWIaq+gdZ8lPpQ3FVjMeStRXTUohn7AuphJhz3vYbitWPc9TI+YyfaeXY0LV6oPACKHUaMRJgHoLHk0K1uJSL5XVrUP+DrqyTTfiN1PQdxd9Zja6oi/M3XEQuTEDoJz4QFBJrzGVe7lxOa0WRtb8X+dQNlWyoZvSCKYMshnEcm4sjZjqbkamKGmyl7uANli43ARjWahU+hW5iAp+Mpemu+w2/UgVaNSP8G3ZAFGNTn0YnBiIMHUWYEI87X8MuDK7GraxhfUEzkjAcgeTgts6PprT+OM28STWOm4hVlhNBDPLmk1gxFNr2KJuE87Zla1N4wlJ5kXF1VlDUKXr/wKXFFlcyP34xUDiEJfdgazPi2+Sk8lsOhCaNwXjDz2ogdiAsa7KdOE5KlY9AvOYiM2TB+AYnnHsN/eRZ4BiObSlF9ZIZXX4ABg6EtH6pkmDQPi7uKBO7EnaVnI+OYSRARn7yP9YvxZAw6jttoYl3aTUyKnobP3Eh4RxPYXoDqp6DVC6qB+IftQRzwIucfQErshpOgWAeh7K/lwujpOJyFDIpMousKA57hXfhbTBiERMtl8ZBkRbIMwdgWRlh1M6oth0BWUJbcAfIxxPePYB45kGR3F44oM2r1ZMJ77yNMk4+3Lwit04cw6iDiUgyxo9Ar4DTdSeD7WtT12YhV2+DnFWiyLuCzdiMFdqJqdWKX83HdNJTIjSdhTSXGqbfBORNKTw0CPSImCNHch/LiYIQeCAVkgRJ1Cs+ne7AOuwHDwmdAqobuDyHUQnP2cOzmdhLO98Adc8Foxo8NFUGI/4UhFn9l9f57QAjx8gt6yQAAIABJREFUCTAfaFMUZeCvx14GFgBeoBK4XlEU2781zt9DQccB9b+pNwCj/7/6KIriF0L0AGFAx99h/v8QLiIS81+XrQkChxke3AafrIHaczBsNMy+C4wGKF0EVV2I5Z+j+FehtDyJEK0IbxUaNchiFYpmAkrGCMS5pwiofNiGlxLkvREq7oKecdDqRvzUgXHpJPxRybhXTMQwwo7IBEe2wB8i0Kgz0SaOQ2VO4pjvcTK9x7DoHkJhCRWLV2PecIAU51iOS21oy8/hHe3Gay3GMyIbX8wuOqLHYTL7eV9zP0/39RJ/k43ki/cTqNiG77FFmCOnwuGzeOefxH1ZHBkPWFDV2XDXBtPnMtK85FmMJgORaR1YpHbE9JX4knS4aj7GIR3Be8iH4XQJXfen8cZFd3OFNJ0lt86H0BC8x1ZxLHwcJQsHEeWPYOh5K2O3V6Kb99z/e81dTQc4OHkUE23zCNmzCY+nDUPGk/i3PMED499gWaifLqeXAdUn0HmDUKe3o5SCou8mUl3PzI3bkFPDEKd1MHk2Ii6E8W9tRfX609DTDiEWUBtQYoZARTeU16N0dSOmDwRVB8SNgEAtvDYdLDGERSRxPjuKHs0RxrXJkONBOzUJ2stQ/LFkxj3KN+Ico7RFTLXkwsh82DsLgieBMYSAXkFlG4VS4kM1djHEnkEbuxR7yQt49n+Jf4wJn8OGPKSDyIPJGGwSkjOM4MJuCEyG4PFQfAzyd4B/HM7eEuS/3IBhdCaq0nrEletIIJd2ngFfB2RUIbKCEQdsuJ/SYnj9JnCHgqoFEXBhmrICLC+CuR1Kf4SYJNh8N+qwmfg7jyBs7ZifvRYlLRQcAtJaIToGtt6K0usHRYc40oTGYsU3TUbTqkH0dOA0anAk6QktdCEd24GzcAvC60PtrMXRo6b77jwkjRFv3nX43NvxEEtX36c4LS1YmUkcf0ZDxO8t4v9p/J39oD8D3gY+/82xXcDqX3Xgi8Bq4IF/a5A/1EdCIcTNwM0AiYmJ/y1zmP/lki+6HcITYGo1rPoWws+B8MGVb8LgVDjhgeZvMP3cgkjpAncQTP4er3gGyeVHU7sPmrbg7w3CNsGAtScdddrDIFfAN3uhxglRVoTqGFLVacTU8Uhd5SjdzegsbgI1gpJZ2VhNexHiCOGednp1Ci3iCszMICGwgq7wl6Aln8E9hdTmpCAX2fBLk2i2hmNWoskUr3Ek8xfuON/HOnUZKzLLCGsbgNxVQOMMQXrUjSjy3WjXtCP2WAg8r8UzZzxSRS2hVBFzpgFv9qW09HipOV2Lsa6MiCeuRZ9bjLLzKuTQKiqvy8D9Sw+jOceI9BmwYByBH/fimqki3JjPUG8oZu0UAjntNGT7EKxGwoCEASYKQkQa3vZWDOeLMZvHwCU5iFALis5MiruVuB170I3QQuJToLsZcecVyC+uRRXqRFrgBdsUmL4SWusxHfwBuk/Dw4Pg/q/g/M+QuhjRtwal8iCiLQdlZRji1ElIXAq7z8JNj8CuJ2HZ2+y0yKzVdvDuxy9BRyPUT4OZX0PlSozucEa37Oe7hBzO0cLkgs+R3F2gSoPMm5ErNiJqzbgandSs+5DqsiPMPbeT6qk9dD2aQPz8Qrzo6FvlJ2iLFv2xAmyTBmNsr6JrQC5yy/eYN69Bl5qG+pqt2ObMRJs9gKBhIM4dB10KKDJ6MZ9wfwtK4XWIJhMkj0Rxd+ArOoBh/QHIU0FtIcxeAWMWQ+p18PlyGPEn+PEhqF2HGLgMdVEkcrUd0ejCVOEET3x/1Orxq341txhxWwwYR92EaNqLqrMDv2RHXatg6FFoSoihcVIUA0LmoQsJ55D1M7I3dtOam0BQWyemchea86+h0oYjlh0kMr8VcembBDGxP9jpfxn+jpRXB/7ltzZFUX7+TfUY8O+m9ft7KOhGIOE39fhfj/1rfRqEEGr6eSg6/+VAiqJ8CHwIMGLEiN/H/BH+66mPmgnLloJ0Evblg/R8P/XR8WbInYIoGQ0VlShWP/59LnzvNWC49zOUbhc+bzB9iyIJORuCKu9Z2D8WTp+HeD8EZ6C01BKQvGDyY2w/AgtCEeYY1F9VYRzgo81mR+fxQqgfrz4NoyYJjdDix4IierHWdSF09ejwENEDdX0qGsPqyFZ5iRRrCXgdxNV9RGSPnRdd79Kw4yc6/lREsnsEntnRBDb3oBSmIaYPQlPxI1r1dvTnivC3PI104i3cE2JQZpwgqqmcmNNqXAEVtm8rSVq6AN3i5+Dnq7Cmh3H851ziYnLxtLdjLitDFesluKwTyymJ6sUq0gKD8cXloelahhL5ObI+FxkXfpWD4I59mPI/wbniO8xnm6DkJyRrAK/OhJiUi9i1FTwdEO+A6h448RXSMAOERENbC0rfWUTDjxAdgMV+GBcKUR2g2tCf8EpnQGz+DlKSEA9tQf4oE1RJULYbHGfhk8Mw92r4/n6OXvcQw3WxhNz4LrR/BG0fwPH3IO5ZGHYPUuFqXqz9jDbnGep1ySR5EvDNfARZqofkI/gPh/DeGy9wRrrAQ3lXoGluIXP9TnyL36PVNwXtVUFEHghHPXoUvvgS/Ko+aiZa6W7po2HyQOQpatK/d5Lw/FOEXnEp6oWjofx9yJoCRbvhizkwbhUG9xCUDd+BNh10PrQzpyF3dqJoJETBKRDdsPsTaG4BgwWGXA/5X8DK7VByI0Tfhai5gDR4NN5hn6JkguqMB8YvgFAdlBfgy3PRIvcQcfotNMOXo5/yGn3HF2ORlqOy1RHd62H7UBlX8TeM2X2C3L5IDBo3MQ31hHWYURK7ISYeOboRfcFm1DWJwKTfRXT/3lAQeH+/MO4bgK//vU7iv2oG/lXhXgCm06+ITwJXKopy7jd9VgJ5iqLc+utHwsWKoiz9t8YdMWKEcurUqf/Suf3N8PSBxgQntsDpOyEzBPwtsMcP6mFw7TPQWgY1D+Nv0uF6sRLdrQNQRddgH5dIYOhIrN7bkU7eDOenwqkfILwVpoLXmIxUXIZqB4hR4J+agz/9XjTySFRrJkKWkx5dItWmIAbq76J9xADOa1vwOM6TXV5AZE8Pun3t9EVXUD84Dl+3itiTlQQFDMhLhmDM2gQFd+P3r6W8I4eVqd8T1uXi5bBZqHrNeJssRFWFYcociQjdhvJ9ATSCmGvCm5SLav1ppCgBcx/BJ0cTuGI12nf/jGpMK9QchOZ2sDuRJ77EiQNHCLvifjL69PB4JlyyEPJLwWlEGVYLF5xsHT6NKZojWFQ26MuDoFDQmeDIHsgLgeQVYFoK3z0OU7y4IkBrP4r8RguahGAIagfFDFlTQWsDSxy4OpHPXUCq7oLbfgTDQLA191NNGSuh7hTsqkK5ZBkU70ToUlBq90JiOKKjA0VrAGHAX6BGfaYB98AwdJ8fR3L3gqcUlM1weA+kr4YoN3SfQtGncvSZ99CGB0h5PBg5zI1XrcVc4WT3/8Pee0fXVV1r37+19+lF56gc9WrJkixbcrfcO7hgTLEBgwMYCAQuvYQSOiFgAgSw6aFjMGAwGNuAu3FvsuUi2eq999Pr3t8fyjuS3Pf77pf7hptw39xnjDXOGWuvsfcpez57rbmeOaf5HpJTbsTg2cYo1QDmq+CtaHD6aalWiQrEIM2KcGzqULSKBbt5GjF7y7GH3dTbjGQFvkJyyGg+1iJ64iGpH8xh0GZDcykYUkA1Q1s1NCuQYIQ39sF3z6FOj4KYmxEfPgzCMZgBsKscKuqhrwt1+hjEilXQ+A6Ye+CHIaifvkXPZzHosqqxVL6MpFhh3+MQFihZF9BV3Ia38hRxrX6sY18n8sNWvNlnsGqugM3P4Vf6qbgwk7R6L105PcScDqEr1xHM0qNbYUTmDvQWL9LuHcjFb0Jsxj/Gbv8CQogSVVXH/T3nSByXov7i2C1/09gXxaMN/LWL9u0/TTD/8jNlApv+lw/6L/ofBsYxyIP/IQH/3TPoP/lTbgO2ADLwnqqqZUKIp4Bjqqp+y2A124+FENVAL7Ds773uT4b6PfDtLyFhJJhiIaRCfDGUfQaKA8r3wlOLYLYBeluQrUMwXRGH1NNA/zwNvqJuHOt8SPLrcLIZwjvgiU3QWYraeT1SSxX+9ToCI+yEtvWiaa3DvPwFOFBLcKRER9qldGcnkPjjekIbb8VSuYBxE7MQzpP4c27neIyWiGknhd9Woh3ai73HiuO4B9czBkxiPnxzPgTa6R/5ALGjxvBh92LONOSTvqUfZWQ3LVNS6Vt4HZbWRNj7FmRKkKLA6VzC8RcRtDVgMc1EPfImrOlDf38WYlgfbGmHWR/C/nGQpkXYZ7P7wgqKwmcZOmCDX22GvDkQ9wZseg3iZ8GOCnonGPm+8Ldc3rcHxAI42wE1qyE9HuqiINkCkht6SqE9BrktB2FpQc5VUCUQBVOg+8RgjuopH4EuA7WmCuXpUYhhFoQbsMZBXBycVwDvT4O9vfDAV4hjX0JdM7RWgT4Z4jtRXCECDjvhcYUEJ3YgWQsxxc1A7HsFFv8ebrsVsipQs9IJmr8imDKJUF4iKC3kv5MGzW1IbcXo9H24u1oI9KaxKOd2dMSD6TrwfQ/n9oEtCWwVJEoqNLUjqdHMKD8LTi3h8npC02YQatmKY5SMSH4SzZ5DiMlJkFYAB9eAoxdK2yGkg8WXgeEL6FXwlxWia2pBMmlB8SEO7IZJMwlOXoF260uIUCKcPg3CDTk50OKhb/ft6HNSMNZ8g2ifgli3G92Ll2BKtRMueABd3BOoqRY4cAp6PyJ+s5OBX/+G6mKVjIOvYO88i8ZkJuz5Gk1zLYald1J0bhu1k/UEzjmI/aIO1eyh88WPqdevJFMOow+EBus+/hPI+afCf9IH3f1/8kAQQqxgcPNwzt8ikvhJfNCqqn4HfPfv+h77i/d+4LKf4lp/F3o6oeoMNNXARdfC3jegdhvYR8LEeyBjEqx5Hvx6sITgwnTwj4DvDoLSCgEVYQ4hr3iQcMM+JH0l8cvOoplvh97t4HKDqxaemgUxqahdJsQSA6b7VLyVTly7jBh2+QkcPocqC7SxYJlhJXraZXyf7eaS7p3o96/HKUZSfa0NKzsYzV0E9pfS3qSjLz4JjdOLq9CC5pgTTf2zoHFCtJbYPW+g9l5IoCnIqKt2ESrIQH9Iwt4RpDpuLakxDyIKZ8CRQwRH6dHfewbjjlIi0zRQdJTIC11IGTLS+DuhpAZOnoCzsyAvgCpshLZeRPaoQsafentwtZG5EM5+iNpbClMswBGobmPW5j4+KBpCuG8COFcRbAxA6hBM8wTUWqH/QchtgPQEqOhDc2jz4K6DpIInCSp6waKFM3rI9MAQgXpgByIBKLgAyl4BSxZYM+DgVjjshtUH4YMnYN9aVMkLt76JaP4Oteo7xO0fYrQnQ80XKOEopKRFkH4BSsv1iF1XwRU6nLERIqEGtNHno9elYXEeRAgHnPWBtQh15Hqqnr6D2EX3EDtKBs/DoHpAtxCkWXB6OgSaURULEbsfbUDB15JL6Pwi5JI6JPMeDJpKlJkm2mMXEOuvhhkPwplnYMunkFkA8QoUroTDW6FtGyQ5oLsVFBkR8MJzF4PeB8FOaPsVZVvSKbh1Efqyg2CPhsYeGJmBcCRgSsol8tEjRBJBc20iBM8hTQ6h6ZmC+ocDKC/mo4bawWEigkDbB5LmFLnaSzkz2UfOqSZiujoGK3Kn2uGPq+hblohut5/0Q1H03RKDVtKRuPtN1NlBGqRqzN1NGLLO+2da90+C/0qZnRBiPnA/MENVVe//33j4mW0S/pdCUeDEfvjwJaivgCO7IOwC50FQfeBOgAkBmDAfvnwFZhaBpwJ002F4F9gEtAHm0bBrH5rssdhiHkXJno6aV4oID0BQC+0F8N5JmLMA6ZHbUDY8RMToJaY4kdgr70F8sRXh34SamE9E7cBDNYFPriPObaLDnYtt3HhstXWM7fgjijmJ9gceQGj8OB/MpLChHO1OFX+uFo1fhbQIqicNBQl10WOoPVVoz+zC3Cjwxjag3yew1eWiG6rBfWY5lmAQ4kCODsKjBvh9CGWkHmVzJ4rkQGvpgU23Qqd50C2RGIA4K8EfzfTkLqC4+QtiW3QwYgFYUlBVJ+rAJkQgBcquIBz7McGLEkmr97Bl47dMaqokKt+PPNaGuicO0V0Ks1aBTgfTLoLX30JSo4movQhdFKL9FDQkwdKlMO4KePF+eHQNwiEj7AFITYWJT8P+X8GwmyEtD14/ihKK4GlxYtB2IxJkwmE/evkwysVXIr33Avzil5B/GVLXbmh5G7XiPiAELdGo9gEsbSEkoxhc/XRpYNzbcOptME1FqWkl9NSlZP/iOuSxl/zpZloMXcfh7ENguRWmuwgYlhMs2YupsxHnmDSULjfWhla8lcMRIycTSRjKQM0D5NdVQG46iDVQbxqU7qW0QkIYYrbDZZ2Q9C30N8GbF6LrOg1I8OBO2LMKNv0BZcBLdpqPrpbDpHS3I9QwEAXRWagzLiXy+V2o2ecR6S1FfnUN6kV2OpZdiiS3IJaOQ/foL5DG+iEQQWizUOdej279lxi8YSYY42mRFdRmF1LqGZhtxjMikf40icRVEaTpZkIVAWIaG1HzIc5ai23SZ/QYL8SmLsDyyQeIK69BSP+BzM79J++neRiIn48c76cM9RZCrAVmAnFCiGbgcQZVG3pgmxjMiX1IVdWb/6Pz/OsQtCTB3EsGW0M1ZOQM9ne3QulWaCiHz+8ZdHHY6qAqApYE0GyAWBmcEyDWDhesho8fhYCPoLkJKX8mkr0A0e6Crij49gxMLQTxI8rqzagePxolgtjUCAsrUZUDRIbokE/Wos0eQXj5JCzTO2jSqEwubUYvyiB1At6S72l+fT2Bx4sgJxZXIA231oDnitkkWF6hzxtH7Pu9+AuGEhhoQ/r4LiRvkJDTiH2Ll+ZwLiKlg6jzljL0zFYGEseia69AbxgAHygIQgst0KMl0JqL4aOvEI274Y3rwO4Z3OCM9RFuSMS1qxlDVjcHpmQSe6oMcwjURD00PoTY10akcALeU/W0XBcHAx0sffp9jt07G2uZjGZBPLQn4yleTo++hbR7nkAkvgDzVTDEwbwUIp56tEEZJVtGdCUiGtPB/hr82/Pw/K9g4VLAgug/Cet+DZWH4fvvwJ4FSdn4O/xYw7Uo8SmIqBbkujvxtReith7GNLECtfxOlKOTUfR2wiXNhBt6CHXKGCd7CTUZEHYH1nwP/vkbEBu/QL/lKkhxg20o4fUeQoYGtOPG/NXt5K0+iyFGg1Q6llB3M8K1GefYVDTOGCy985Hqv0dkTiHq7rvB76b+8PX44y0kl7ogHID82XDqV2Awg5gJ+k9AOQe+UbD+Gijzw9B50PUlpEXgD8vA0g4R8PtVzMkh+iQ3iqsOyepAmCWU797n8I1WRieqGE7uhylLCOb/CNvfJ7EmD9NNu1CtnRA3GiUYQuoNo9aW4R9fjTpFj7L9IEqvkaSOdupHpxIVMmItq6dnuoThvi6Mt6h015mx3PQG1K1BfHsEebsZ7WfXodM20jr+ceS4mZirQ2CSINwHYTeE+yE8AEpw8McLtELfbnBcBHkvgSnrH8cD/wF+Sh20qqpX/r90v/ufPc/fvUn4X4V/6CbhX6K7Ed69HWp+gHkJYPND/DToc0PnEVQpDjXnMkR8CpG6nRyPrif/WC1mdwC5IQcGfJCdTDhzCK5JduwpLyD2fALlX4PjHOpAJ8FloFufgvjxNM0vDccgF3IiOJGhUVeQWbuZ6ufvwx3UETcuG7M0B6u+ii65jMSsCkKHdMj9XoRrUEMd6LFRO3068YcrcLRVQb3K3idvZ0jVBronWkjodWEc8RA9hoNkHT6DpqsEMoFOlXCzFbUUVLsf3dzJiJJayLBATw9I3aC3E0l30GRXiWkw0rnwRmJu+h3Rt2eCrwl6DQRrqnGekdHWq7S+NpyY0hC1qRMY3rKPPQtHc2HltsEaeckfUcKdRDzdjLpsA7pL8qFWC4nlRMxOGKtH9OjBMgapdwk0b4YlV8NZK+z9YjBp0a0Pg6sSDq2CE/tQ/REwReHrVlA9AcxX3wmNbxNJuAjn9zKRij3I1CIE6DwSQtIRGVGAdIsdfeI1yM4KGPkMbPkN6kcv4jlPj8l4Ld7mA1hOHked/jDhpgDuRzuxaz74s2ysYyeuJy9HBKKxRLVAWAv9QXxBHf0xZuRZY4g3JUM4Blzvo9Rp2XFlIbPrQ8ijV0Lzt3DuJHRugvgJ0NYKo/pgehv4g7D+PAi3gakVdQBwmRAJuXD+R6jvF3PmoJnCSxLpybdxIHE681IfIfxYMZpTdUi3rEDj90H6FKg6Dv5m+ouz0ex6D/NRD2KYDRzT4cuvwaOitgoCYy1wzyQ03U2E1X7kMx347xzH6R6ZlJY6wr/oQhuJkJoGkZQotMWXgMMPn31O+JobUV/+iuC6frQnZqA9ehDRH4aggGgdOGxgXgx1LrjpCUjPGVyZCg2Ysn8ys/0pNgnjxmWoFx576G8a+4G45e++3t+Cn8/64ucAZw+sfwnMyaBJgtyuwWCWcgvs1kJpPlywj+DOJkIf3obU9w1DA076RyXQP92EL7WNxmdjab4lQPeCPtQYLZ7gF3inDcM11ovqO4eSV4RG9w4sXoM6cyKmM04MziySLXNpFb2QfQO229+k6KVtpARBt3sVvvbNGCL9CF0iXbdM4HThCESVhPY4WDvMjNzeSpKnC422GPnFd5h66ktiTT7GVp6my27jpGY3vfoaWhP7OD28gIhTiy93CMz+ishRGxrZiKenFx4og1AcWGIhfipgJ7RLwqG9jKiJJzFUfYf2UQ2ERyGqehG+atQuK+q8ZHpuGoMcn0eieTK6ng52zZ+GobKb3s6x0NMN/gNkeGajkbR43n2JyJlmVHMHpBcjzn8D4rQISyFqfDNqXjokT4dX7oDe1WA7CKGNsP85OPYteBII64aiVgjojMIwdi6dwSGETn+OEncJwTMuhNmMLt6A9a1fYVm7AtN3RzEuycHcfBxdfy1y83oofBIAdeqvUYMR+pZei1S0FMvJ4zB1BchGtGY3AnWQnEMNcHYZfHAj+h4VWmpQggLy4mHxWE73xeMNhRhIOwfRX0PapzA2QN1t48lMsCFPWwndPjj5MVinQNI0VEMlJEVB2USQzdDTj7onCTV+JYx4HVfbMNSxv4UZLxMq+S0+i42cq4GCecS4J+HLGMFe+SWwxaJEUlE6C+CitwcT5k+/Eczb0e1/D2O5DV92AhGtEarWw4CKOsJM9YEitFeE0Tdtx5+Shn6zQL61Cn1HNxmd1cTtc6F0yCSlpeGckYico0LXF6iN21FjNChHPkaa7UEKxqPVFCHih8HcW+ChAzD3F4O1LndthK3fwTXz4PmHoLwLPtkAfv8/0dj/d/xfmYvj/wrs+xJ6W+HMHrjiEchMQD20HhEeCnXxENcFiybDm6sQ29/HcPfHBN6fieeOu9AvDXBmyRAmlIxBk+vEElWAEnZiPupD6fiOoOFT+mI0GG1u+sYnENtbial+I8I2FC77Cl3jYqTK/SR3ZlA2Ih+/COKInQrH90H1WbQzwxxOKSTHmo2a4EJ/1kHupkqEzgLD0uC8dtS1R8A1FhGrQxx9Ee3oC9F2/QDGKEZ3CdRUJ5VYCdkEsfv8hPJyUIddhHLF4xguAqVbA2lh+uufw96zF85/C0qbUfNH4N7+MrE/HEVNiiU5/Qc8dTbE+jfAIMEi6L0mCY8pjdiGEVSHD7J1gRXbwS4uuGUr9cXD+OjehSzZXkVKyVxiPDpsV+6kwvwCwZlhjA0xRDlnITZ8ipjkJqLv4MEjK8kw7eXmGCvaHBsc3QtjJbB6B8tpLVpFb9mvcU6zkBY1D6mmBHFoN+k3+GnaYCMl241e9z3GYUthbDTqp5tRx86ElMdhzDDwnkGu7YBZxSBpAXCZTyAuX0iK6TnUzb+gYeZMMrVWxNUPo1TtRhy+GXKbwb0Mte0sWItRpqRR+flRiq5diRQog9KVjLU68WgMNGU+A+u+hDu+IFRaTK1eMMfrgv6X4IvPYFYSJG6G+NvoC68h+s1SxOk62FUA1iFEVj5LR+KzJK73MVA1AutjdxMO1OHS7MOidKNJmk5f0fVIa6cz7fBmVNmKydyPMj0Rz/MPIRsDyNMGoO4YaCaiadvDlouv4fxz1bjT3ei7UtEvqIHsTjJP9KGmroAzb6CrOAJDZxJx76fJGiKmIYimM5oh08303zgUbagH0augdjVDfRuq1kTEkoRwNqB7yYtI88IFLw5KJGEwgnPGX9iZ1wNnjsPmdfD+Kvh6DfzuDRj97wOP/3n4ueXi+J8Z9HdvwrOXQUc9PLSOQOZJQjVzONWYT6S/Axa+Bwu/AXkPeHth90vg6UNVVEJtfoxPVGAZdS3hqC4CX4awfLQN20sewj0nEH01RJWEiXtZxvRED+bVfZjWtiM+Wg9rX4F35iJVQsRZw7mWt9lJKa4nVxB6bDqc+Q1M8aDNLcDxroekVivCUoCl8gitt1wGG/pg7sXwbhdEZCL1JSjyMdQbdsGs1whlLUX1uFF0E1BKlyFUGx6HmZS0LoxBH7qH1qMd4kGMaUZK08Jd59DvfJ6ILpmOEaM5c80wvFtfw5Cuhek74CIQ0tWIIWG670mj584raZmSRXTHWDI2lePR72X4Bj8ZewxoeyMcv3Ek/fNSyYtY8IkaOqJsuMem0VV5B8Pe3UTL8EQ0uYtRP3wE8f1e/FaZYGo/zy1xkBTbwSXH72Gv4WlURUY97kUdCWrkKK0xawkVTyPjzFxkZ9ugsmGaD7kD0q/0IIuvUISK2u8GnwFxcxApvRCKv4acSagTLWBLhYEDcPYtIrvuhe/uwDL3ZaSHFxMxJXPu+lvAHAMnNhEeqkNTfCd0rYFNR1BPxqA27sYwPoJkN6Ix9cOk22FWAcQLIjr4FEivAAAgAElEQVRBwvY3YeA41O7h+xFTMEUSEfZ34UwNLC6EJCNU1xK2FOGOPkRkeBEEs8Hqh3G5aFY/gW5fOV52ojj7EGEv6vHFaFUnrjYHakcTZ/Xf0Ln4eVzdSRxzZaM0xSNd8xCmbC3Kyt+gdpQBu6DhIDqLhi8nD+PtRbOxtvegyzmBd46K4pfQdDahKX0bZNAm9eM376F5+12EuiUG3gsR2NKBL7qPcHsFpvxXwHMaJfo+iC5CWjwe4/ynUMctAq8LMg9C82cQ+P+oOWgyw4Rp8MCzUB2E747/rMj5f/mg/5b2j8K/9gy6p3XwdXUpZIxACe/H5XwMw7EO8psdyNOGQ/yfQs7TbofFvdARA4CmYBja9GRUn498w2Tcia/juPEmVO9tcPYk/HE2SuE4vMtTsf74W9q0enzKMKJuG4V210GY8RRE2hAxToK27yl6rRXbuAGitYc5snISo8QdmL6ZiEi/npisjXhtYzF6dqC1N9I0+SpyfE6oPAG3r0J8cRdyHjgNWbhOrSDOk4/B+zlByUpJw48of9hI9IgImqfSUSsiiNN1RFbMRRvjgh4TEeHHOxLMX+noWQbtmrM0sp/uCxNJd5VhDTqIfqMXT8YeNC0C3+wQSYG1WPZqUB1b0Tj78El6TJ+0kpfdj5oynEhWH+4Dp7EtCtAxwUiTdgK+XgPTN5ehxlxK0ZNr6RrzNqHFeZh7a1FEHpIxDlk7nSXFZhblPs9bp6LYqX2Qe9UXsbzYjzqsh8QPnoKwA7WtH8xRiCkeiLGDIw8G6ukpt2H092Bq6UY8+DIYl4PyBrTMR42bAlozIncKlG0F915EWMZw6UuIj56BH3fi/+P9GAnBRY/A6qWE82ai9Rnh8GroUwilCHRtAlJvR2e+lsDxz9A3b4G+vcjjwOebxLmFK5j9xRYidTsZve9Loi1+RPJnYNdA4pt0x+YSabqAmJK3iN7qQTqwA373LXx0CyTbIUpPdI8FX2o+oeB+qN5BhflKPC+uIXNcP23JBsad9qCzhFCULKI3fYfTCSbrKHSzb4QNn+H6KoOo8TdD/DmIOcEju54jyu9E/NAHqWCW/ahBEMl2OAzCbkJtDaDL7ENXbyRwZxpK9wCuSAhfv4vwplQ0uy8jJj+EvGjEYC4QSzKMvhTdyMVgyIQYGfJuhB1XQt9ZSJoKk1aC8d/l4zCa/nE2/p/A/9Qk/LkhNhkWDqpcegMf0+19F4NnFMoV5cT2LIHg5j+PjVkEwz8YLPxaugfNsSOI+ASk9o8wlqykZkQ+OqsBW0cyzJKwrtxKuKwc5d0bIc9M7NLJtDaWUbv3B7JuehVdxVGofAMpKQHV3I3pZB+XH/oRzezRJLXU0a19kHTbUsi5Bd0ljXRc/RAZH8sEEwzUeE4y85NnURaMRg2sQTaqkGTHdqKKqIc6CBaWolhaEFEylmwrWfc5MU6cS6h0I6rJQyBWovdYM3ZXNiHbcEzjTuJ4QkUpcaP/1oP3gp0ssK+m89XlJE91Q34vyGGifmwkYlTQHtXTnJVLTFEL1k/68EcZ0YdA5MXAy/sRh9ehOX4vRp2JnKd6iH4tjoEzYbJ2bMA3YCTsrsJ1vxWSi2gI+hj+eQdyWiEaw2AUlwjZ0TdsYEVQpvWhPp5f/SSpw06wKKaCxMIuqKpFMSj4HRIGp4pUFwDXIaSUHBzjryJw9CnC7gx0ueMgtAkyLkf9ZibYXXi69Vjs6xG5xaDfixRS0Z15BMIT4Pyr8KcpZLm/gcCLsGwh4bZnMXTbIVYLs65GsXWgVkYQ1rfIuD8K1bUbPvCALgvmgtFkpCa2l+GjjWgTd6HNKcByqgQq42Dk1ZBwMU5xkkhsAfFvvIUlzYe4cQwMGwWzfgnn9oAcQOMYinX8B7THjsGVGyL67vew19fzxGOPc3lGGimb74X4HKT2OuJiu2mcXUhL+fVEz83FsaULc81qwt1JqDmgLW9nSKcZtVoPE1RELIQlExqdFzoAJQJRRXD4EJKqkLzYD7PPgHkK4bgQp5dcRaF7L3L9DESTAX67HK6YCmjg7NvQ/DTIceBOhqbtgxuf6fNg2A0g/+MqYP+9+AeHev9N+NcmaCCi9FMeWIkcqSbdvhljtBsn9yMrISD+zwOFgKzfQuP1qE+9jTj/boiKBm8rWBPJGv4OFX1rGVOSD0t8UFuKpmAkRNWBGkFnnk78IoWOV8J07P2IpCteRTP6CiKnLiaSY0K8ci9jbLOAJDK25eOPjkddsB7hacQwYhax014h0HU9ofjtLH3hUSJXXoRi/gapNQLTf4mItIPYiBqXSKS1H21EIFsVsmIbUGJTqHefYojHDVod/ces1Pe5MbQcQ2PxM+LlqYiDbSBXIl0aIfXYGkID60jIk4EcvEnP4h37DVGNmxGOyZj3nyZ+TQuegmGE763Aa5lDzIcHYMRiaN6C6vyBSKpAypWRNp4h+nszlpMRwolWDBNyEBtKOO0eTVtcFnOdYSoW9ZP7yX40U5ZDvgr9JQjnaaLshWjll7m9+EYMZ5N59vSDtLSN4t7zH2WEdx1NmSnsSpzD5PY2ChtbENorYOtr6IbqiRzvhFPvwbt/gGV5EH8SpUUl0BrG4gdVbkXk5UD4LIhmONEL1/8blfIWbEorKhKiZhtKfBhprw9u+BCsKbjEO+jrdDD+BSK6Ktr3fkP2/FdgO9A4F2v5Z8w9UEu/PkymcBIzajHkDAGhhUOPQfJCVAcIb5jACB3KzMWYghmwew7kXQ1Ha2F0Ecx7FW9JCdQa8H32Iol19bQtncW4onY0h87g0ygYd3Yg6tyI6CSCl9xM0oFSurzbcGgCSLoIvW0NhPsgYQKoERO4OlGHaFEdIeT4C6D7KPT0Qlwy6qWvo0ZmITzAUQ8UhiB4HPloPCOlBqT8a1HLr0ZxLkTq64Rt2yFdB2P2Q9YtIL0M5gWQ8b/V6/hvg59SZvdT4V+ToDtL8Fkc7NYfY3L/rcQbLifBvG6QhDEjk0gktA/ZFxisKCH+JK/SJYHuHOolRgL5G1G/9UN5HaopB/OhJaT+2Ip7ZgSLT4VPJ0BAC8OMUCGg7CO0CDJ+s5Gmm57GbynFvP1z/L+OQdbrIeY3/Em8jkg9H5fagunUY2DJQhfUoVw1HH9nFlGNnUR0fkTcbLSbqqFwGdjeRt0RoDN0FXWbvRQ8/AjSqltQTzdjsnQQNiaQUeNBtQginfkkfnuCI+/fTMHmDQTGjqIzw0dCuRnprAb1UIRQswrJXiJXFiAVpuKWH6G72ITtyCg0VQdwxSfhy03FYc5HuA2YO3rQ1xgg6TM4cgghX4vcoSMo21GHrCFUoUMZfTPBY8fRBY+hGx1hwqZuSsYvo4rvSbP20rB0GNmH1sGBFwY16BdmIHJXYlr3IYb9HrrulHjsygTqe8B5oo69vimM9R0hwdHE2cwRWEwasl7djbjxY0T5JdC9HeoPQ6cHNtajDNHg3h1C6PIINDrRN3QQPJiGzpwHjlqw+2D4Vfj63kMJJCMOnEXdfhBuNoLxPPA4IDqXICFEyAaSFcuwQup+/wfIuAWmlsKq95AKTRz67SJG925D8fVB/YfwqRmOAIUaaL8E9fJrkKLj8c+QsBhywZAO2jRoPA1D2yDdAd7TSDoV35HDWLzzaP3VROy6TC5//lu01S5EnJceNFjtiejz+xkaPQHGpmE5tpmwYmDAZ6dPGkWsthnlXCe0t6HeEY1viISpPYzoXg/tGnAGwT4E1i9EHNEgxixHzT4CtUch14JY5EHsb0Ps+DeIGYDqNah5qQhfN+QooL0YjrrBPxZ694JmPsy7D0bN/aeZ99+D/3Fx/AwQsaQQWTuc8dMFurix2HQr/kzCgI5ZBHUbMPZE/1U/Hb1wtBBpoRbtGg8BRy+RjCwUORMlJoLV4uXUxGKKuBSDeB3xaR8U/pZA7Vr6lx8CrZVYkUL6kFjqVlyFbet8WpOHEDeQApUPQ8490LEOETOduiwT8d/+DnGuB2EcjpjThd6yEh/JKOEafNEvY7d0QWMNknqa8GkJm4DiB1ci1r8AtecQRTMQRXp0W8pgyDxU64ccGzWJ4nNHqLthObmXHyMit6NZD66yANjGoMmZj3bmOcIb1hI+qadnWxmhBCO5BdPR3GKD/kqS5HbquiXUg4eI3e8nXDwPraMCoY5DhJzQtBp/zp04d3xPzHngf2AAMW0vUUNqEP1uRFDBYuhhxkMvoRQLIlo//aNGobjqkUoOwNAQnDJAxe+gfAAp7xYchqvxu64hx+fEP2YhRzv6aanpIM9STqvXQ0kwkURHN6YjX6D6dKhODZHmCUhKNYG0SVS/v4/hj3WiflOB+4JZNOtasf9YTzkFjNuo4s3XEvvxPIZe8hj2M+/jO96ENjoGKfd8KHoN3r4Gbv0CSduPkhKFDEhaLWrACWm14DwNMyxIZT60bUHy/LApeRLne7ahv84FS1TYrx9MaNR8CKw9EONADsVA2+9Q9QbUyjYiuZMQohv56Gz07SbyH7Aw0NhCQuQ8+vJLMA3RcO7WO8k59gIxZQNsWXEzCRE9ozqPISnD0VWMgPST+Jo1BE8eQj81hvAPrSjXROOcswoJAxbpA2g/BOYUGDiNerwKpSiCNMmGeuoTsA4MppTv6wJJQpn8JVIggLBeipoeTXj4j2je9cMpB8L3Oty9D9Kege4meHYCvHU9nHc7XHT3YLj4fxP8T9HYnwlaTTLy5LtJPLsWaeS9ED36r47rmILLEsFoWzDY0XYOdr8Oaz+DF7ZC9bfIhgrkznIiaan409/B/HguoV9vpkP3POfUvRTF1hJ6ohDtS3egUR24JQ2NGLA7V1BQUUn071dwwHuSLM9Eko7pwdwB++aCvw3EHEZUtRH2h9H4gyjpJyCQi7HXi7J4O2y4Aav0JZHQ7dSs/ZTEmSHMmYVo5KHg7IauJrDb4cReGK0F1zgo/RwxyUfBptPUFjzJ8AIbDul6DK3NmLPPEThxBv+GgzQMiyd7WDwy2Wi9jZiWb4C2cvjw3yDnR0g/H9f2B+maqyI3mQjftRct7+BOyEDOOY273IwjqQV39ynMxiakAw70y4JQVo6UduFg7pCoDlACqIF4ItoG5P5M4o5/jrquHiXXgOSLoHoDiEjjoO/X3khY+oKOsJuW5FRUfTXp0deQengjA+viSDzvKKGQkY3LFjCvLBlbay/SkDCcO4IaY+LIpk4Cy29g6HvPEFxipDWjDXuGGUOxj2G7WtGFzAy0Kai9btK+/jXhNBNlvy3CZfaS0V2K5dwriPOuJfzldHTLQohZmwdXVvs3kp8xAB4Fyrpg4Q0opvfJ7t1N8IsOcus72PHEdM4vP4acGQ/TWgn7PLhP1GObEMTdAp5nVkGgm7jrA6gaPZH3StHldcBVsQj9PCJxx9EkFyINew5b5cv0J75PdqgZoyeAGhXFgi/X0By/FM/ul1DHLSPqqa/h6z8ysOZpggYP3C1wVYFY48Gm+RxdeTPIXoh2gtcNQ4wwNxNhCMCxTkh1QpUJXEBrFiJcQeX12Qwd04Zm/FOIj+5E06KCD1R7OkI/AI9Og3m3wtUr4fkm+OBKmHnFX09u/hvg50jQ/5IyuzQcJOc/jLTgKNRthDN//KvjElaMmkcgYfmgIW57GTa8D/OuhwPr4fUNMCsRIgG0pa2YXwihxvSjE63kiotxScnIsasxhC5AzHoa4e4g9aiZgnA0DvePHHzNzuEl5yjw92NY+Bm07YSjP4ByFmIsMOUZNEUvoPq9qONnISffiKGqBjAhB/XIahQCI/6aCrIvSCbqylVosofBQ19A7hhIj4a1JTDuPMgaCdnFMH4wD4fWVEt0/UGUH5KJNd+GOWcl2CNoRkQQjZAR34TSsBuGXojaaIL3ZsOPr8Gye8F6DKX2TVyVfWT/sglzRQiuugLNXSr6BA9SZhy6GTchV4ZwzInBYgwiBWKRlkchL85B9SlgjILLPoWYHMIjyxFnGxC9MrS3EbnajCjKRMnMIHhFPs67r6fy+lvZN8lFSeQ4sv5SJrRWUiSGEmt2oq1zE7ezFnNDPPr0+czWRtg1Yj8Bu0AMAfWkj64qlfiRHRQ//hRyWQTLGC351fUkuj/GZD1J7GoXXuc4IslRBHM1hKdYEUPyGfGlmzhnP25dmPL8tXQl34IwtaFrNiMdexM+z4Ha3Rie2QqMgPkvgb6NugWzMCQEqM2MISfYia3WxNmY+1CP9SD2SQhvJmkX+nAk9GC1DJByYT0pQ/XoP9FiaJyB+c5CtDf8HmEeBsndnJlxAdbhf0T1HkZW3iYmoRWN63OkGoXItCLUwm5SYv6AOUdDt6OMdcZKwhnZqH1OcpJ9yMEB7HYrmFSo3oVoKoX+TrDlDubWjugQ/XVIh6MQQxYjFAmhmYTwaxEWC4GsmZyOz6FLsaH+7gLQlEJrJuqVF8FAPeqt38F1f4C2ysFc1jojLHsTPrkOflz9zzDvvws/N5ndvyRBA4NPd60JZqyC4AAc+A14nAAohAjo41DQwduXoZ74AcL2QcnSB8+C3AGRNkiZCpNeQ26bgua2aiTbTPK5gDSKwToPXD8gdSpIC19Cf2IscvlUquIuZkiomDnNE9HN+g0D6+ZQoQ8Q6OyEehU6WqD+FQyf/h5negZS1ExwTEO1J6J2BeHsJ4MBFv0tWNy1aBb9AmEYOvidwkH44k0YmwPvxEP7PnB74dr7YMcUMOgwjXdgP+1n1vpV8PwM2P0q2M5DFDSiHWNBn1aA4ZnNuGfqEGo7qhpEnVmFojQSfnwTfa9V4B+biPzxh8T9Zh8hKR115AXoh+ZgTC3AfvEoQulxULEGOmtAb8BbVAg3GRCdJ6FgLpgy4Lznkc52o1xzHeLAUYQyAdESQa2qRC14Hm3041RKNWgS9jM26yKKo98g7dRH6Hr92Lz3gQwRk4SSp0PrjMfw+Wfo2zazqOwcuqCPiFUicjaMKegk/YcGLK/OQE5REZu8kBOGgRuRNj+JlJ2D6boMYna20NUVhSYxjJx7NdI1b5FocZCsayStox9f/UzOTh6G78dK1Io3Id8DZz+Gyneg8xj0fAjBTjLDNRyJGUtmah9yooVJmzuoSz9NwBWBsIaB4iCtLdF0HY7GUuFCPV+Bqd0oRePg6W+h+AcQGtRGP6GQh7xT69FsngN7HwNlImJgFuHWVITRiq5hAsL/C8T3NqSgiyF1bka2N/FD1CFSLCr6JRKa5pGEfmUjvGkEmmsugzf2wuSLQHVCVAQWrQTTCKgph/4k+FHAzh2gmwADEprvehix/xyGrFsJDMmnN5yGGLsc6cL1iKI7Bol5zo1w/3rInzJ4H+qtEJMBu1+GSPifY9//B1CQCKL/m9o/Cv+SLo6/ghAw5j6o/gr+eBFIY2m7aQaqvgm8jxNY2EnUkRZ0k4ZApQ66tHDBPFAnI4xbUTd9gxg+DXR//tOymA4C0A9HdR2ladx4uudPIXLiW6Y8WYtuUgpMfZ5UKZ8Ux+X0DV+P03I/muoudIYJmD74gEBqKgZ3P76cQoxl9xMpvhth/gqp5LdgmQDvzoO8cUAr9KRA1RYo3wAzpsOx1WCeDrp2iETBt5dCrxOS14C2m4FHT9C7byspai/G7nVwsBQJBf0iP6GuHcjOfgI1NTBxCpF1e+CIHwpO0D1Zj6SRyDo3FunkJiK1L2FsOIcmoECbBrX0KKGODQSXTUDbWgJmEBEdOm8BXkcN+r5jkNg7uCpxbUaZeRnaQzVgV0HyQlw2PevKiB55EjlnKWN7RxAwz8Elv0yv9VXiCtLRilsQWx7AFjWEsNmG0PUQCdRjiMokEmcgEJuAvqQeX246OkMtRjTIz89GRGdBqgQ9O6HKAQ0ybF0HdxUhaz5D/64g1t9Cx047iZYtiIkTMabeREgcwepciu3Ll+iPq6J9cSadGoEjPJp4qQLplWfhxWMQlQh1f0RuuY0898UYL6kB+9NIgVpmrdnE7isnEu0LMfSxarTxQWSdgtoESkBDcJwgeGEzIW4GIRCpBlRjkOjqnWidw6HhMMIoQ78eil5GMapEkrKRsy+GYVYonAfPXQfDu8jd9yq5ERdqfir+X2joM+Vj3eTBcaoLkZwFmkbI7YVJn8CJG1D2fUQotgp9ZwBlzyqkkaOhogR8OugupeuBO4jr/xTrmufQzH+WL6e1kIqBaajIF94Oq+6AUReAGgLn8xD36GAmxOXvwYnPoOEQDJn6TzPv/yx+bi6O/yFoADUMmUWw5Neo11+CufZjoh6bSk94GierOpl48Q78w1X0LdHoascjVjwB9WcQzjbENzsh3A0V2+Df3uHEEB3R9afIaO9GeAJUjCihJLOJMWeM5HXUo8wsJVJVibhnCsKfC9ZUoh3JDCxR8Kdo0W87QMeiaDyjFRJPFSBV3wO9PWhee5FQXgy4goNRcAmTQGeFUCM47gZLEmhi4anrwe+Ge5+Ebc+Dfhi0hyHmG9j+OKTOxJydRXCoF11dI7Rb4JLnwPUioq4X6UQnfLGF+JQIIt4Ly1MJ29voSelHkmzERwrAMQGFYXhfuAnLncugeiUBSzYlzxRTeKqEwIKTyA9ZMRgNCEXCfC4OT3gA3CFovn8wcKF/C9q+K6HlBkgHYtuR858j5u4n6Pr6I7TTughbDqJv68S+1Yhm5KuE8ztxG95EXdaJpXEZ8v53IN2E5O9GHXUpppP1uMM7CSRH4+p1ET/GgiZRhv79kLcCkv0wdSHggXeeQ402433PguF3Npw2CW+HEX9lO2r5N9C5DW2DH11fNOgPwOiZEN1H7NkMos5tpH1ZDmcvMzB8bRKYHaAqwBEYtZsJlmJAgsRL4eULIdtI9qFG9l49A89Tk9G4tMSdqyR83pX0/H45YkkCyZrngEsACQUfTnEXfqrQGmXUPhXh0YBVD0f+gEU2oij9yHv/OPiwUxUYMQzaDkFERjEJQufpGDCZie7NRL8vAqOOgXgRYs+Hy78GSYa2yxE9q9G4IDxjMuEzpzCcqoc6LcweIDx1At60TlJ2e/AkmrBN+yV5/EBj6BgRbxVyMADCB4feBPv7oGigwQBB52DrOQOt+yBjPkx7AexD/1kW/jfh5+iD/pclaDUcpmv1aiIdJ9Cl7SHmcgPCPh3n/UvQbNhJW+18StKOMOfJrUTyirCEhhOs+AjXyiI0jk8wOK5DWr6M+qQAWR1nISYdskeRr3jp3TQfz4AXpXUo4ZEe5r1cTozTj5BsSENjUHRp+ObWoDMcRVKOEUnNw9bdjJQSRdeoIVhb24jtb0WOEYSq+uDaXSh9ZSjVTxASqWgGahAWEwycg54gHLwKQudg63Ww4jYYeTGkDYXvDsD4m+H2JXB+MYw4H5p/QL/7MI6SHqSRKopHRepeDym3IXJHEDn8LZJ+B8G19WiHdlL/cByybiiGkxESbVowd6C6/wBt5ViWm0H9PcFhMXgdZoZvPIu514Qcbyd8eTv++70YzztEuF5hR/UcLvKGwC2g6TmQWsDTD1lXoJa8TyBKwqddjzGvH843IT9XRuwsCeok2LoZWiNo1Wtg9KNEKCWYVvr/sPfeYVaU2f7v563aOfbu3h3oTDdN09A0oQHJSAZRREFBxIA5hxl1HHXMOjqmMYyKYJbBgBIERECiZCQ03UDTOeewc666fzBz5p5z557juZOc+/PzPO+z966qp+qtXXt9d72r1rsWhrQ0RM5x1A4D6gs7EI5GdIWz6M3ZhZxmRjx1O6x5EroLoLMN+mRA5jTUb38B3jB+bQjNNSU092TTmWqnKLiQptRWAuN16He+gTQ9hhp0wryjAPTwEXuqdnN1mQvLjgoCA1QQsXOjsFjXuaomxiFIKtDRDG/Pom26lupwDmOyZ5JyRKU3PZOtyQfw9x1HwuJfYn3+JYz26eDeBi3zwLYUST+HuI/2wPTlqF33wshLoOh6cE6E0CmkA0vxZiRh7//BuR9zwA+LR6DOugNq3ybilAkO0+LRZGA7uwJFJ6NUtaGMG40uuA1CTaDtA4c2IvoIQvOnIH8v0JZZobETcmKEpAr8WgvZq7tovuYBcHdjB4qZTk59I2LXDTD4Qsi3weH1MKMdjJcRSZrIQdsOujU+EkNTKW69GV3GxaD557kF/r+iws9x0D8Jgj7El/eTULOMbpeg9VMLoYoxOH/5G2qmv0dC8d3Y7pzOmMIseh5dgGWjl7ZnvsGZ7sJ262oi1OPtvQt/0UF86QnsOGFmYuHjyIBRMuGZ9SKeNZ/Qr2Qrha0qvLQHxXkUj+9RzCEfAZMXXTQVeZ8O2VWFHKpFGTiEmKMKvaUVU1YApdSMZm0ZwmZGdQyBBCdKbROyzURYC+5RvWiavVjdPUgdeUjjn4QRt4E+7i/n6ekAW1+wDYSFb8OBV+HgUdRTXQitieigAJoJftj5A0xIgcrlaEZOJFA2k7qCDcRdmk6fe0pxXTORlPEvEb7tOnTPLkYpe5xwaA7eISqJTV+gETL6MdlIEROSbMa0oZyII5PQL6vhSJhIYy9fpp7HxaO0EM0DawqYZoM9gj+zD9q9MhGbB+OgBzH0LiR5mouO1x8nOqMYzY1Pw63P/0dEgBbQcj7UboIzz6BWJiFCUZShZ4huGgtk4iiYiCvtFJ7ULhwWLZytg8gLIC6E1kb840vRlqloUwxUB1IJaiLkHvYReL8Mp8tFdFuYiNOBWcpHYzoJe3Jh8Pv0xhWjsx8hkm2jw+Yiscl/Lkd42ZcQtwt6POC5GwJdUPoNyoVxaHQh8iJuiLyDzd+Jpe0CCp1Z1OXso+G+yxjR/2ZEpBM0Y6HPteB+H07mnRsFrf0SDrWjajcjiiKg/xCEINZThaloAfT/03X+6j0oP4376dHIRi3S1BDGjiLSO8dgOLqCoBhErLGR6p35FC19DNY/BTWHILcH1HTkYF/ERytovHwQmUo3whWjxaHF1uhDjLgTsoYS+FNaYgmZhPjpoGyE7R+ijL0O35hUuvBqoEYAACAASURBVM39qbEloxVH0ZFCLoMpMM5C6vvTErz/np+nev800Bth2kXIaSU4PSeJXzAAtfcs/vWFJCfqUVpX4rlyBo7tBzma3s3M8RJmr4bOXWB84xnsN96G4ftReNq+wT43Qs/oDNo835PKuTq4A0yFqL3NhMebaJg8lWwlTG+0lN6kybQpYXyyG6eYR+r8m0GREPXr8bc/iLE5SEn0QvrlDSal+S1Uu4SkjRFbewHy7I/RqSnIPTGEYsfxTSeRhA6kQYLuG5NQ5DoknsfACAQmpN4oxtZqePcZmDQD2tvh3eUQ7YArFkPyR8hHgXZgsgcqP4NIFNkehd2tZM6W8XqTCV40hD6e8YgHbkV7y+3EPl9Ma1Ii0YuipDV3QsZqYspR6F6PIXIBjJ4EVbvQlpWgKYlHbSjDaK9gRvpWGDcJQtshmA+hEtTBn6PrXErPwLF0Obtw+jsx2OYh6q8g/rmn6H3hAZz9S+DUVgj1wMQ7wXGuCnvPF4cwdSvUnIyguS0Bx9dhbAvnoZ07EP7wEglXrsbftgB1hx5R5ID0ItQ9KwhvfxvdUxHE0Fy6esxIjhRsy49gmD0a3ROXwzUPomzfia9iPJp1Hrj+KNQ9gnrgQnKSDQzom4Fk8NOT6aT/qjKYcxfUvAaZhyBzHQScsPJSmL4cJfYSFTYb8cM/xFm/H15djHr2e5yPK+Q8YaRz+UVs4R3GSwsw194PsgXiboDvJZDDkBKG8+JQdBrUhBnIW95GsVqI6qKo9cfR7vgA+vRHNfoJPXUxnXnbsEfsGC3tyGU2dEnDwKVD46tFa9MTrT2B+vxhhLYSin1weggEosi5DrwvJpNyqpSAzoDWDOZ8L7Y4L+LscyQXJBENd0DLLKh1EbYl0DJ7OvWRJNBVM0Q5htW2ikkiG4FA/XN61n8zfnZx/FQQEiTPhuTZiEADsjGDAO0cUW9iYPcCNB9uRflqD9UX2Jn65Vf4x2vQT7KRcnse/sZ11O/dTNKxRPpU+ThwZCr9Hs3mUNIp5qKgqg2IHS8jaEM/+1r8/Yax/dirTIoNI77wBqK52bTzGSox3JH1xLUfg1g5AeGmiunYku+krvljUuLthMfOpHHQQpxf/xrL3iWI0Z+AdR3qnlcQagP69GIkYxpO+S0AYnQR4igu3kbetx3j2V7UtlcRF14BpRvg6gdg2DxEXDI83w0NvYiBs0EjoO9D0CQhSs8ierRoFT1m2wisr65APPUr1HmXozx0OXKzC0O8SvxFryINS0RteBW1YzmGBhs0t4J327lk8UX5iLmLCH9zA9rd67ms6V1UWyFCkwT130HODEToMO4GN6euDpB2zINz/WFYNBt0/dEUmkmYNwg23wutJdATD30LoGM72CbhuHYuXWv3UHf2DJFDqaSEg5jffxl7YzGOw00YIr9BM8FLrK8dzegLUfctw1WVinFJjHBrL3su6c/YZ2rwTBlB/P2taH/7ADxwJxiM+GIvYLS9iMLLRFYsQh7ZRDA+CV3IQ7DDg7bLhJg2GklTAUYV7IfAsABq21F2PUxs6kLkqRcR2vUuvbaLGO01o8ZPBGcGSL1IDzVS/t4yxu/aRVr1anbNPsIAl4ac0DrU4+sgEI+iNRFN2wRD7Wi3+4ge2IhktiCO70ceq0F7+CSq/yPEo1vgufuIPVdOaokLTZsOGsYgVVcC+yHFjCbHQ9QVZlj/Y+dcDeYgpD0Fowyw6S3Emvew22oRNf2I5k/FfeJLesZPIE4/DM4+j/i+H7FwO6Svg1m/peVMOdq08YxRr0ezZRrEz4T0v1RF+XcUZzgn0KG/Uy4OIcR7nCsO2/7nqt5CiMuAx4ECYJSqqv9jRZL/c8Ps/ozx3B1ZgGZkYcSRcAHJv/gQ1/JvaBh6Le3bzfh3a/ENGABF5ZhDI9AUBpFsBxEy9E1so0/tSvI8CgfV7ah1+1Ea16DOfxF6yhj07UmmvPolnbtWEjLq0WAnlZtIi15GXMUT0P46wZ6J1GlTqRwwjsFZ46g1CFRLJ8f6Z1IWfA9zrBdNzu3IlvHQLRNoUxCqgtTjAvNfSprJJGBiOkm8S+LI45DgQHhDUH0Q4lPhyHG4tC8sLkJ8vg1x6d0w/SEouAkargeLEabbkRYaoNOP7exhxIAIvHYNwl1Pz2UynZcnYHIlwQ0XwPbtuLMiwGQkhwSdpefCqp77Bax/D/QmeuclIvrdyKkBswmFs8DVBwIxov1+T0XTL+nWRhi2oZvcYAOkvQjHpkPt13BmCSLXAxktsOAKKI6H9fdA717CDev4rvs5vlwwhaK19zDnjoUMHaMl9ZF+GF0naLMPIqDbS+BRiZbxPjpLDxCcrWB8r57w6G5qB6TSv+0klYqO8tZkjC89h7CNhWAXypjBeEsP46vtA/dsx7fpGF3Tmjn29VAYfgSjK4R7wAjiejvBHIIfnoWgBg42g6cF9YYVRMelEvuoiK5wBflnfk/49EQiRwtRw40EjsRIiesk+vFT7Ni9A9O2amb9egfdbTINNX0JFyuERnUSk91o3wb9Nz2IsBntVVcRa61DXSSj0au4Fy4gnNsHdddKhNSEcfsA1GwVNRyg6ZkSYqZ0qC0BJQVhDiFXSMR0WjBFYZcJvnwC3vkN6KpRBrcRHmdBvXI4mpwzWLp7yf1yDeL4E8T0MdQvKjky+xbIGwE935B1pJXUSD4axQuFqbC/AUp3/YsM+O/Hn7PZ/Zj2I/gAmPVflpUClwK7f2yf/s+8g/4rRPBSzHPoiceFm2O+ncz7rgxlWg7SFDN+UxlK1ZvIY54g6ZNpNOekYvUE0PQcImaOZ6BoZUvrCtqaK0iw9qJZdzdyug3yMhHPridSPIyPpX0sJAMrZghVQ9ZHEHGgfyifvjOtDMzS4jE3Y0h0461TKK7dhFxXC/2LUA/fCgdX0v7Rd8QSCuiTWoGqJiAcM/8f5yJv+RY2foga0oKhB1FxGkpK4TdPQcoDEAXmecHWCatuAFcpDHQDBohfQihpDbodAZhpOjehQe+H6rtJ1JbhHRFHaLSK8Y5TqEvnoFtejK4pAZJUGHUZSvNeiDWg2iaitpRBHy0iZzjmQB1nJrzK0NbP8Nc8TGXLXDK8+bja+xNN+gqhV2CdGRLK4LxcuhLm86ZvBUvKf4n1xGGcQ8fyvWM4gTY3Z3IdzKo/ydR2PYgfIJiBRl+HNnMaJrkGx68vBGkOxuIktB9cQ5taj2GMgchxqBuZSv6RdloS43F4eznirkYMngI7NkN5J7GMo+hab6b1sUnoF08gfEYPWVYyTtUReOTXyLcspkM5SlLMBsUXn4tLL48DSQP1J5BK1qJrO064n0STbSDnVZ3EOy0TtSMNwzu9GEb6kTV+JpU0cyq3gN9dcT2LfM0MkYPISfUoR41ADI3JCZcXo7o2QI2EeG0pcoKDiFGgzhQwegO8bIA9myBvAFJAwvjdbDzKVpIvCCHPeRYOvAO7N4IQSEUgpBB8oqCKRERafxjUAHU2/MSjUyRoE/C9B/m+uwj++l30v/iWzubrCX3cg1q+A5pSzo2OXMvhs9tgohaSfg23DoSXrwRz3LmJUf/G/L1cHKqq7hZCZP+XZaeB/8i582P4WaD/RBJjEUgQjXBsw/VMON6AuuhXdA2YRAZX4+sqRG54FbVtFRqNn4zNblx9jJT97nIKXz9LwvBCpnY8jD9RQTthIOF4M3KjDTybILKXDM8VXKIv4lP5NS6PXITdPAo6TsIXFyLyQ3j1KZxSD2NyBcmKnY8ndoy4g0eJZdzC8f4BcqxRDFI9ep0H66ApxDqNiK0lSNFTCM3A/ziPyNZNRN9+DWNRGnT0og7og5hwMXyyAr75DAYkwHV/hPUPwfbXoGgo2PzgKwBhhLP7scQ1EY2LoR45gPB5ILcItlhguA7TLaeBEF0rb6crvIM+n9dgVLQwKB264oj8cTeSwYLyxR9RazYSJ5nwt35NmrmdrvSrOXtFA4kOHdaGZrpebka6ZSMtK8OIRIE0yIVnrwPlRAmdQSdjAktwde+k9myQ0DAXJQ9NICWWyO1rmpHu24iqMcHJRYiCF6BjGabs38Li1dCyEsavQeTKRFyDSd5TSsvoe+mWNlO08wR+rxXJJpNuczJSncoPCTpGr1wGaRZUhyD+y/twzFLoXN+OY+lIAuFSEkcU0uVsgtur6XwumX7J0849EHQ7oGAykYLxtGq3InWGiPdcRTRUSchrRLqyGY3pOaK/exvfQQnH7+dC90nIrWFAXDtpp97mxQXPcu3618gtCEEVEI2gTuhBZJ1F3ewEdyfhqSqx6V3oSmJEjmsxty1COvwBqt2A0IUhczqx1Cl039lGxsRq1BeWQJIVUeWHtDDkSIg1NqIJCrG5LvRNYViwFmxp4FqNOPQuneN2EhqtoGSsxnQ+SL+fSsKYXPxqH1x9cgmm34MhazAknQd7n4HqEth9ArQ9MOZ8+OIWGHQlzLnjX2TFfxv/Sx+0Uwjxf3dRvKOq6jt/7z79LNB/Qpw6BL3txDa9Te+sJAyPbUAWMWKsPifcCFRpDKKjCcUsI0a4sVUEsQQaOHuBoEiXjWWzwJrVF05MQR68j9iQq5GPrAW1A3SzSAisYrFvGcbm1yGaD9ph53x3mckkffUDaUPuQNt+Bp9rK96oB7SFyG1lFLXcy4nkTcTqjzG0ejLSzDtQEjcROXsUuaEIyTITyf8LxFdfEvhiB+bnXiGCEfWNL5DPn4o6bDTC5YJjJRAug5enwaRh0E+G9hNg7AM5YyBrNZ6yUqLrR2FIHIMoHk901wugdKK59lL49BCS3wdyCHtrPVq/n8ZL40luiBKvVxFn16I6M2lZ9Q7ht5/HXL4FU4OB2MkK3Nc66Ly5m/wuCXNYi2qL4ng2maZeicgjDnweH8mhIdiTNkKZSuaY6RDohN3r6FwyjZ3+ZG7XtGD62glBN0poDWrnq0irq+GxdLj0sXOxvX0GQuJD594D0vxX6U27mKT7PiNxTA9uu43e4TYyUAm+PJhWTTIHyj9mdOU+Qk+loT/bASTi2eAlEgxQf+X12I78EXNGBrHsMhLumw3a07gvfRR5lB/D4FSi1d+giF0kDnwYw3l3Q8VmWk8vJTOwAKzx6Lvm43nnBYxjBDG9guaSbfD5aOSCR4hLCfPUF/fRoYmjJDKc/sntdPVKJDcb0SSeJjZeQ+z8bLS7TUi7vUiJvei2hZHMX6Am6qE+CIZE6N5KW0sDya+sQO78Heq7W4kMbEErRxEaGco0YMpCM/NGeia04FjxIvKyiURjfVCGjqAuU0uvIQN/shPF6qZwXgjlwVa816Vjf3ATw66yI7yXEaqW0MpDkAy7IO4OSEyDU29D3XGwWuHDOyFnOPQbBlrjv9Sm/7eoCGLKjxbozn9G0difBRpg92p45gqYfQNdjy1nktaEAwdRvETxgbcKOZJArLoBTa4FyXMzsfCniFg9Re0H6PFYKct/iQHD+2L3dKKqGk6ZUhnw+q+RGQIzEiDaD8U9C5P2NKJ4JZxdCzWfQXIMwrUYmtxIx/fD7PvRRiZyXPMHMk5NY+DZz+kIrMJjbiTnZDclM1X6fb2MuEId6loTmp1+SKiCvstQ5j2JcqALeeIs1NJSQrMXYcoZgnLod8j+HBhcB+Mi4DRD7xko3gz5QNXVcHQ/HEjAYu8lZFbRDE6FnhrUwnG4i91YK15A7m9G/uI8sMUj2dKpHjSFoTtO0HF+P0TlKqJRB5oJesy9G2i7woi13Yb1zHw6Lj8C/VtYHVjHc/ICEA04TEH8PWMwpdlJr+rAp2+jIfM4RimOZG030s5fERw7AF3eMOT4G0h9aClSdn9EnyiKTaBwHdJmG6JvJmj15xpA3OD/dGltFFGRPRC7sg/zI25qdqdjtwfQbtCh1fcyWvcKqevLIMGD/sBZUOPpLjNjfWM1uoO70Sy9G+ddtxBq+xaNyUtPaD8JmnTUBzNp29eCZVUViXkxNHdUwvYrIX46fHEFvpEO+iZrwPUikfdeQz8yA/PkVmIp+1EDtyFGT4RTqyBtHKJBJindjyZxHOtndpEZKscZHUXkoy6k8V1okloQmUMJ7qjCPMOPPBDUpBzEIYVgvhvD2TJcZRGEGsK45TVYrEGMy0C7poPwvRZ0Rwchug7S01ei+5J0XFKM00uLkbQxHC49yd/X0Xd9GVp3lIg5AemiecjWbsJ9A+i/G4fIbyEj+SXcI78hwil0QSPWfclIAx8BnQEGnSu0QGcFWB6FNdeC3Qbz34OUon+qKf8tqIogFPxpxWv//JCwrf7cUO3R1XDNEyRp03DgAEDGRAwfavs36KUhhEelgeMCUHYgT1+JCGahWZMK+xOwPVrJ6YkGWgZbEAmriPfUcmB0BtHBaahxeURfG0HA9wbC+QrsPR/qP4TZ34AzFda6aUnOgZY/QtXrCFVHFA+f5UWJvbIOt+ssieu7yOoZy7DtXbRePILuMy3okuNgG4ikNsS1r+H99SPokkzw+i/gofmYkjyI+lLUrtMoo6RzjycsCfDHXEh4GF6eBY8PgO5dqAnNkJqCUp1FS2kqmnAYylajLWsjwXsBmm4NSpoPRVOFYrHSlmSg2eZCTPmCpMR3iYUS6cweTIfoR1xoGG7VS6JuCdKZXqyf1bI/63xqnGep7jcb9AJxui/R2F6s1YdAtxOzuZucmjBxJWHa0gvpvcpAODOIpB2FY8ZCBr/+KjpxGnXwXsg/hLRvEmL05zC8/39zcUGsv5eBu1soebYY91oDSd90sbtmPIHEJXC0F0f27eSX1cLEPJSBd9NzYATWVz5GN3Ys0cEyiVm9KOu2oTRrcJTmcHiSjWCwAbnlMLkBPc7738S330jJgS+IjHoSPh+Or38Yv8mIpN0GSiqa+Dbs5yvIATsiZifSmgjNPrCdhdKXwCmDPkpX/jc4ejN4kSfYeCoNcVEXmiwQSjJK8WmMd/tRk4DzQYyoRuS3IQkz/mYvvh/aMJmNUO6GxZtgTxlCTUb3mkJoThXKZS/hz0rDLvWjL1mkpjSQKkLkcRPOlgjafQY4BNrTAunAKjhejc5mJvzm8zB/EVJiHnE8iI07iWqjiOj1cHbff/6ynXlw1SpY8gk4+8O2R6H52N/dZP9RqKogFpV/VPtn8bNAJ2fCtU/CuIvBkfSfVrWzmd7wd8S6tqKLv5OwphS1zo+aeRCa1iF83UiF/XFm3Y36RQ+2689Qk9UHXyRAenkJCa5uTibvR/36dTRdVRhqnLDtIfBpwFJ6bpruzjowzuHbR14GZQhYB6I5+TiFXSe58shvOVuUgudUL4POVEBGIhphpuAPa9AOW0Ll1Dz8WSbC33lQ780l8PmX0HoStWgQQeNgpJc2oNxzF0qxAwJx0HoLtNwG86+DN5bBVZ/BlGLUFD0thyKwJQW5YCBps0dCYDfoYuDogOq3kCyT0SbeR8Q5nu7xVoKJ1aRooigphcT23Yjo9xDJzEEVEfyHnkOkTEMKzAV/DNOD7zC3tYNF2tc56KxlY98ZNJ4/CK02gjBIoDVANBHsV2JU+xHf92lknYTRe5qagWW4OYB83nioGISSJCNaZiFpbYjIcgiWQccGiPkBUFH+cgHdzSimr1HH+hncewjjpzHc6TY6nX15cXp//PMWw21T0F90GZwWuFacwfKrh9CNHg2xKJaSH+iZNpTSsAlt4iQ0h0+gBr1klHdhP6ZBaulAO/Ey7EtmoqT5+EPnCjxCS6fZicUNrB0AR+5H05GAyHVDnB+ts5KQtxxVTISGXDBZQBOGpBh+2cSorm6WnVrC7PgX0PsKkXzjker0yEfykTeCtA6oAV4DLCG0IyRic31YH8nGPK0WtWAfLJ8Ez14Iy19C5EfxHjDyQb8xJO7RY3Y1EGh/m4SKOWTca6W76j3UvS6ER0ts/kjE1x1wcQFqZRSReh4SCtENr6B27MXDh3j5FIf8BsIdhNeX/HWbyhwFC/8IS9ZC6rC/vs1PEZW/m0ALIVYB+4F8IUSjEOJ6IcQlQohGYAywUQjx7f+0n59dHP8NDs7D2+JGbjuFN9BEr9lLtfUDCvdZUNM/R44zILx+5BNb6Pvrq9D730Vs2UU0pkE1CwrNFbQraXizNFgTBaJ5PYopjmigk+5vwxh22ZE2yOiuNjOwqQEiQYifQEwagjDcRDJdVNzuJf1ZK1yyAPasAHsKOOqw9bsa3fYqTj7pIZLuZFSpHX3DCYy/TiK8dg3apA7YdD9y8bXE+o9HbOiGqTdD5gh4+FJ47CrYMwm1/zw8K3ZjMx8lWjwdTU8NulIT3GCFdj20CzBkwdjlcOhG9G0t6LeEcQ9sIynFjdfXD80gP8aEjxD7n8WxtgbjW79DrxEEhw9BX7wCojHMTTcxyhVHp30C2WdLOGyLo9s2lSGHq8g7HUYalAPe9UQNEaQPlmJJuAoRfp2MUCNnC15Djhwhax7ot4UQpjiU8R/h+uw9oqU1WMatQae9jbBdoE++DmEeR0i3j1j3y6j9YhhOX4f80msErkzAoE3HYFK5ctVHdLraSA/oEd4aevZ6MS9/AO3o8879cb57F2LKddgH5WDKvJaefadIiTbSb3cyCYfKwdoHZr4M9y+EjAqGuqLk79/C5wPn0jfWySj8cPFuOOiBMbdB0lHYtQP26tGOTyESeBNdXRcUvAU/PIpq6SH5SCu65A/QBRSMPINo1aD2+xjUVoQMpPZF7a4hpgd1iYQciINaP3qhEJtmRN3QQ8A/FJMzAtFxIHSQJCGVh9A6l+P9dj2xgTvos8yF6JcFXd0kfm2l89lBJH60H7n7MLy1CGboIGcknHcj+nfXENjrIZixBS0DiefFc3HOIy6Gb9/8V5vo3xVVFUQjf7cojiv+X1at+d/s52eB/m/QkUCCmIZI6cJqnUzIMwRnRw3+fndgKnmTQHI2vqZm6u8dSu4zO4mV6QmZQkgJUZpUJ/mjuzD2NnEiaTjjXD8QbATt9DjkXA8Jl7gIXaclNKgQb+tIUp49TGekmuj3M3HVhAi7m2l+z0F69Rj6zFHgm/dhwCi46vew8RKI+DEcKWf4Fd/RvnQ43ntvxjb/FMJwFeFjT2N5ejBE2+Doh4ieEpRuPyQ7kZUuuFRA6e9h/HaUJ+diGqkStRoImPdi7IgQmpuC3KGiRAvQeTuQT9Yg7A+gNtdDQzuipJPucQsp9I0mFrmV6OZisC+B/fvAkQ4Fi3CGttOhr6NPuAxp+zrEd92k0EPSoF60+jgmxHqIdBVQnuRjdXEOWeb+JLT3EC+dxXGgGxHZA3V90My+m371+TQ07qbuC4HhoIzoWY/Y5iHaXku0vR0lVkbTbVayxEyMZzYQjnsa/2gdGv1ILCE9omwHscsdGHK7MNS6GXUsSntlkHGNlbTPy8I0/nIsFw9FO2okVbxO1toOtPn50OcTNHUfkze4iEjpQdQWyAuFIQWor4NiC4SikFoOvnKM9ou4pj2Js4PG8YqjkVm9YYbEH0VkFyHC30PEAG0h5N4KIp+0IM9/H+nwSuioJzJfkKRz0V0Wzxd5D3BL6zugMyPUMagdXjgOypAu1HTwjdVjLIsgPKmIwpvRr7qTXSYbxTcsQ1r2DrEz+xC2ZqTyEDTqKHPkMGn1VmhTsDeCcKswdhLEdSB3HcG5qQbiBGJOCuiaQcqEFAk8H6Im6QmO02H4OgnT3KV/MY7sIXDxA/8q0/wHIVBiPy1J/Gn15ieI3TAbBg9DJUTMqqJracRS+iLhoTPQ7f4eS5cg5e5XCA9djstwCl+kC+Wr1bS+PYC8gwfR5WQwtvQHGkYU4BwxAkn/JfiDyJv0WG77FZbPnoQrroG8B8HTC+ufwmCbQU/Lp+jcJ3EcW4Xfr8V08Suw5veoSf3horWIxlNgNCJb43AMugKe3ISYVkfs2VuQewRCLIHQ7yD9PERvBkgn6HbNwl4bQxe4CEok1LTjiGgDUgtEKnX4bzYTSZcxdzmR2xuJWTrxJ8cRarLTqdGjjBpJsqsOncGONnwGWlZg/DqRcEkFUXM1mlgTdLWiPmogcczF1I0YTnL0OAxuR0px4OuRsfS/Cmo/RGNYij4WT/HalQzv24fK/LOsvWgqVv9AFuZHiBPx8Op6MOWgW/sKWa2F+AaVYF9wPZw8DWu+InjvMMIDB6M9/QPYFuCIewTV0YiIbMJ3aBuJNWUISyfMF0ilQwhoSzFKRfi/8xPUa9jx6o0M1D3OluZlTElMIR4JqfoMh2eUM8S8AjN3g+M3SOFuhHovNeFK+pZVAhFwWmD7Q2AWsBPQA21bYehx+ntv5b6OVaxNW8i24unkGqq4tOcHGDwHdm4g+m0S2iuDxLLOQLsHMUGPLleiUp5I9s6tXJizFREyQXcyVH8LFhn/4iCKrGLcLGNcOQBtvhdaW6BfGDVRx5hNLVROf4uMUf2JrN6Itq8b8pOI3jiLVvQkPddDYl8d2gVucAwCXxN0N0JDB0JqhcQciFsNvqehMUIkqRTOZhNcmIBph43wwgdQW29C2O1/MY7Zd/1rjPIfhQr8E/3LP4afBfp/QCRMANkAxAhjxDM2iYSaEJrWRpru19MlpeCRf48yqwNmyEi9QdS7ZuMNh9h43uVMXrONM0OG0DsyniFVmzGngO0eIDMI4XfAlgRd74CzDRKege4mLGMTqB9QR+Hb1YiYhvZRSXTOrCTjzXoiD+eg87iBApCN8OEN6PsEiMWfQa1xoNKNITMMf3wJUpOgMB7JdRY1KRFd8hLaUz8lZeUhNKETqA9sRUwEUQh6vwdbMBt3wUTchXM4GHwdvSGDSaFn0Z78FHnGQ1jeLyQcV0jwwlHkBN5CX3UB2I4gMq1Q3oDqSEWb7YeYhbi6ACdGVqJp2AzlUXrjZ1GbJjF0317wtSK3vgcdEhx0o3gCJFxiY8lXpbhnP0qz3YG9/SFEwAMbb4UGF5ruMPYDThj9JmQ74frn8Fe+TclYQf5gGxrlU3yl32IKFRHrgdeESwAAIABJREFUfxP+kTLKQ4fh6dtQba0o2YWE/R6Mlh7UbBva7Axyvu7B37GESQtuwraxP0HrHFJ64nFdNoAgrZjpC7o8+PKX6CI2+vo7wacScRShba+FbhlyHGBKhOQeUEzQ1gnqZ2gtehbEQnySpWVtUgYpwWLGln4H4UFoGr5HM8CFemABOA4QS5EQ1Xn4xxbTmmciuUkLx79HjSslOCOTSJofQ0MHuu4YaiZIulngrIeSVWB2IG58Cd1z9zBg9yq8EzKJZA+B6FF6h9hQexoY+VsF79BkNI4KiCgwwA/1iXC8GQzTIScM0SicXAWho6jNdXRfFYft82asw55A3JVJrPZXxM6cQHPexL8Yx79RvcEfhSLOzQr9CfHT6s1PhXDnuckOze+jWgbTExekIbmKiCaCxeKj98YkhF/BsTeINqkJS/dbSKYChOJA8tYRy0ui+KSLzrIyIqFMEgMqhe3lqEYfh9zFTJG6UGY+gcbqgdNLoTwd+rwHjocholJdcwN99cORbrgAPjlI8uzleM48Se9oMDfFYP5jsHo3PPUGxCcghETkhxuRIgrhmmNYn74cqryQbIPcM4h3OxFtIUyLjhE12Ile8VvEqADoFyCNkCCpAExVKOEwif4ZNGlXMnZfAg1ThnHy2AOk/fITbAv7ott6EnmIHdzHkNZIiJJ9qEoHul6IdssoM27D/5un0T/yAPrkH1B1cShFn6DsuhFjbAf9DxehxMlIDkAZBOFygnc6CV0UJK4+hKTYSPn2Q7AIkLsg1AA2CcIRqBJwcQLMvRzaPkX94T0MU1z0+95FW8ZoBodaCCYV0KPsIMgdJBxNRrkuTCxzAqivIIIbsFSrKJqZiDGJDNzZhfN3n6P8Yhz1Z5ZhiJuK0rgXzn+BAnE5FTyBmRwM1XWgRODeD1Hfv5AOpR19vI+4ggvg7MFzOZoTtFBdDseicPVwsJXA637E/KVcNe0BFgVeofpMACXNj2gPolQqiBvsiGlR/CE9miSB2lGJcHxHaoMRoTETGtWfYFYVBt1lWCuHIZrehvBe8JoJvvo+0vR+aL0C9dW7CJ+1ok03oYb9SMub8C12YL5WR6ReId7o4o13LmaK1or6XSGx1o/ROCyQewwkA9SdgqoQ5GbBgK3Q2YNvoB1DyIyxtgXp4pdByJjuD6Jo/P9qy/zH8xMrAPOzQP811CgYMiB+OiJuDPHxU7GHGvFsuQjLiOVoPBuhZAPKgSixK24jbt8raCt8KKE8lIXD0QXuwZu+gJZgKkO3luLIl5CCA5A2NXNe7AiRcfkEbroXy6efEclehLR3PaJRRpszCpr9xA3Jw3p0A3RXgDMdNt+FPGk8Ur6ZYEIWLvspnKoMyan/kZhGe9VSwu+9g2FWJ0r3CqTJi2FfAE6MgRlmlOD7aPfsIT5FpsO8mLjTMbRRGWxGMAURUj512aMZ6DpOYmcvrpQWCpan4f/oAPLSAgxHSogWQKAwiPyNBjlNhYAeJcdJKCeCcVsqvoMnEEOG0jZnBg35i/B0vEzj8YdJs3moScrAV1xIUWMDUkIx2GcRbdqH9zoNpgMhpBCQfz7s2wjFT8LW50HVwKoQ5DiheCi4NLDyAOq4DpQzbRg8kJ4LblsZdfsEOdYPMLTMxZ1ci1l/Gl+BjNd2GyhRLMkKsmcshjYTiuE0CbvK6F6aTcIN/cgMpODfW4LRUEzDqd8TbfmK/iPfpCL6PIPWlyMufxZeGYCk9sXxqwO49lxJKBpGH+iBgl9B29PQboAh3ZB3DHqSUSYnIJUsg9AHaGdayR/8OtTMRFGihPv3QS+aUXw29EVeiCWjRLIQwQDR0cMJpu9Ed9KD7fMgQnwOsWOQ4YC4PMSFf8C4/Ua44i74/Cj4YhiXTka9+CmiKxeiPeQlOMoKZqg3aUiozOGblkUUffUqsvVDYjEtUZGHptsFOQZwHIU6YFYn6GPgmIE54EZUDENiJ7jHQiyIyJuBrGk/9wD136wY7I/mXELonxQ/h9n9NfQpkHwp5L9w7jXoRt5yDXH6xWjMOvBtB0szUr8r0Y5+BnHhTUSKFCRtBbG6nbjPXIJ+dTNFqyrR5gQIlOnhzTJEnIoy1Io+Lxcp1UrgifsxpPrRpQbRBjx4P/HiSbmQRL0GznigyQcTZ6F2HUPe9FuMWfdizE3B8ehXdBfW08DjqIFa6PojctYf0U3bhTxaRTnRgnrgB1j0KPRWIeI2IKdGQPIgmsFcfi0uuxMxeRLk90B9LaI6hhLuBesSRFUd+tdO4X/ld9gX98U0XOD+hRnPkwYM26N4J6TSMWYytOUgAlEM2jCiuBC9s5LA7GmUp8ZwNDcTSkykeeA0Ti58i/gjqQwftgzN+cuhwwmWLkJzNZibHyeyLZGTyWPwpn4LczLh3XmQewnocmHTK/BVG9xxOyy6CrV/X9rjMlAtWoQXVIee/qcq6ZhkoW38RYTyKjD709Ee6MVe6cPZ3YW5NAG/2wbpB4hUb0WtDSAP6CFcrEPpqEYO7cQ6zYo090Kaht5Kb28Hmq+KyX9pNa1FUTjxCeiTYdp96Oo+IzFQji668U+ugjvA0wOXZMJoE2qTnkBrO72TLAQevg7Se1D3HkNNs0JXHoqth8io56BgDqJbT9R0K3J1D9HEEuLszYR5H+vhLAxuLyLdDOOK4cltUHEaFDcEAjDfCt9dAa4gFE2HGz5ASPvRjq0l/EgKu2b0JU4/n6G7jyO9uI19s0YwPsmH1O8XqEoiwUIzLmc54foy1LMFcBz4VAsvS/BRPaLFhFi5Fy69FTylMO0N6DoKm66BrxdD2PuvttB/DH8W6B/T/kn8LND/E7EArLsAzrady6zm2gC5K0Cvws0Pgyyjz3ge72wt7otUZFUgyS7ChaCfG0NtlrCWx0G+BYIaGjOSoPow1kV1mOfsRzUNRKjnwY2lWNaewJS7n/YVMt6mfKJ5w1CHLCSY60YvtSLFaokaO1EyfNjNp7BVvIrv0HDYthaxOkrYO4Jw6SjUXXnE1jXCsyOhqxcqHCgxQaghhXDCWygPb0Nj0xMcdw+0xEFpFE6eIn/lGtj+C4L7I3g7UtE+lgTBjdQP9aHtLxGXXIacZSb8vZ8+j24mlOiD5AEo6dkQOoYmW8Z2dgPTd9Qz8MNljGwtQk0ZSaZzKs64YqgtB2saXLwZYvWY1noxvvocttPtpHjrOOnNJ3CgG8aPhKrHYIiAtg/hD2Phs3th1CJqb3Jg3xxA8sdQRwtiBQmopzUM/7KWco2b2Jl6tN2bUC0qDJYJfJaNocxH/JoO9J2J+K2CbimB8qJC6qUE3Ic7YfMJ6JiPLOUywXqUYeP64M6ejCYUQFSXsN+zH5JSwL4aSu8Feyei0gx1Clh1oFwOGdMgcTCBopOsGHM1jQUzCNi24iu+n+asKfi/mEi424GI92HJNsKp44iaIPqvV0JuId2Dn4WoGUtlFNG+GQJ2aAtA1XF4YDLEvOfcVgfnQm0FFKuwCBjihcrLgBhYrqO8UwJVQd8SRBf1I2kljk0+DzHuKMGmBrTfdWO5fw22t5uRTrQS6awjZrCiduugMx7KWpDqhyEiMrQ/AsOmgs4Mk56DWxtg9IPnpuD//xEViPzI9k/ibxJoIUS8EGKrEKLiT6+Ov7LNUCHEfiFEmRCiRAix8G855j8VNQbHroPeEGTpIf1mSH8erCNgwDsQ6QIgoBzHmywRzI3HN3MM2jIIJxkQ3/lp0WRwJn8w0rhiaLXi0WrB14WI+BCSCq3LICsbkZyGOHE7minLSH7rE4zZaXTs3U/tXdPwuy9FrdZA6VcYfvMDUv8Qmi/92F8pQqkeT/S8++Cml9Hf/DFV93bg3vUbxG9vRB2fg3r5zXD3XtySk4fvvZW1nV8QazyF/ZsA7s67UHYFoN2KWjmSM3Mm4Dp0FL9pLKGH++B32qibN5DMjMcwb8hDlD9O44lM7A1eesc7KLsyjqA3CDIw4mnE+QvQJlTja3oAxbue9HX3kx2Lw6lmIRbdA6tehGgE3J0w+nmEczxkDED0yyPRUMfoLV8Q6ihnz+S5RAbdj9oYhFMVED4I/YwgBKmfd6Cv8yByz0ck90FKGo2U4iQ4TqGw8geOXTOYxnEpBAu1iEYjGslIy5yBSL0qbAhhHduNJimGm3ja4lLYNPlqVs3/DVvkI5RGZI6bb6VO9xj2r38gckUhif52zvPtR7U0E93fg0fpg/dYJn6tllhPlPDBEB3KN7ib3qNFtFIq3YLfosVz/dv411TS/dpLmAYvIlCtRw1VIOxRtGcfhMk6iHmgpxfKG4m3Xkgg+RY4kw06FVQ/BGOwrxu27AJdN5T4YCNgtsERzkWOiCOQcCMkXg0VJwhLIS74xAUf7II0GS5ZQOrtrYi4eRgznMQydKjZmXDhTWj6paENZqIMmUgow07M20MoScChN2FsGRjdUHkImneAxgDWdEgaAvbsf5lJ/kNRgdCPbP8k/lYf9IPAd6qqPieEePBPn3/1X7bxA1erqlohhEgFfhBCfKuqau/feOx/LKoKx6eBphUGDYXzV4LQ/mV9ypUQC6KiEv7wDVKHzEMrPsDX5w5807ZxzF9EepoWpU8PbcUd5O84BgXpaKQoQTWIwZqLYBiR4yfQFClIO+6EhCyQUuCzF5FveoqE9nWUjNmP7YkGQrtkDNe2oE6bSHBpGWZ/F+LYHmwGO2juguR4ZKPAnhOhx/Jb4noChFOrkPbuRDvnJLYGwdXb92CorOL/Yu+846Oq8v7/PvdOr0kmvSckpBAIkBCKNKUIoggoiqJiF117xbK6rIju6to7NiwoKEUQEARE6RBCS0gnvfcyvdzfH3GfR3cfd/f5ucV91vfrNa+5c++ZOfOaOefzOvd8z+d7fJO0SDGTsFqz6JnxHMHrwwgMzsH61ErsZ6VRf1M/7kCA6JMmjHFP0d6zjAjFA2uPU/9aKSy14ZgQjqW9Gk2nBoQObDkEjt9H87V5RKz8lv6HFNRFHUR/uwLOPAKtfmisgUfWQ20HJI+le3YfQY674OBy6BmLiE4gKOcO0r+6l49mTierPYvcpsFg2wv6OtgaiXZDH3jUKGoHSq0dR8jXyCOcyBUKgQgZfb+DQzNyueD0bjiViLBOxLDrawL9AsLacGhMBPm7GFrSxNCjg1G9vwrF201HWCT18V3sP89Ga5ya7AUZ9LZbGK2eSuaaQtBX4R3ah7pFTfeV49BX7kPb2YKqxk9QyFCkChmzw0f3ojuZXFRKcEUo0muFGN4Px1r6GmzrxHldNOqmAKL7DHgGw8K7YMtqON6ITrxBwqyhcM1MCNsD1Y+BehXE9cJwICYKtLGwaz+Bnk6EokJUWuDicGj6FfQexncoH+fssViNTXDHHBTpeVoSdmPochFUNxQGp+FL+IrApG601YfAGoEwlKC2TUB9rJOeoTYMvkqI9cLIoVAcMZCO4D+Fn+Ec9E8V6AuByd8dr2RgRegPBFpRlLLvHTcKIVqBMODnK9C9xVB+O4jjYLsShj39Q3H+I7IOARjCLkFZ+wbMc2D8cjU6UysTA9/QHwjG2K9G6QoZcHWNmYu+cD2eb3RoQxyIJZegdh+HpjVQooYzFhjbB2cOQ7+E5tAXmG0x1DzqJ8t9CPcnN6IacoxAiBvP+RlodV7oBHYdB70VCCKmtRe31IlHK/DtEOhWDcX/3ghEtcQQewFNI++gIKYEtauGySUbcDhicFp6cW85hXWOn7ZFVxJJIv3lW9G3rgZ5Kl86PmWOdifSyUb8qiA6PumGrBAGZV9KoOMDUIfQY2hCjYsQmx33hHRMy0oR+ghEZjj07ANtPyREQXMXzLwM/5DJCPkdWP8k4IM9NTDKTW9HPXuSQ8grd5EmZUNOFqROhePboGQ3TEkC7TX4Mj7CaZNQTnnRlMso6S5Um71kUYbxJgd1WhspFY1g8hBY1U5f9iCsqQ1Ua3KpTTZgTI8nU/0ykk+CmxKI7DxFZFwcQ+OfxedyckdEK/emJGAZ9wHt5iJCTwxCf6qewNKteN58EPOULqQUA6Ldh7QlH16cC/J+SjS7ODf9LvyXP05/UTAajQFv1HE0v4tFHR9HoKYJYQogOuzgOQOTL4Gtz0PrV2h+8w6kzQbP3dDfCTW9A9MYMuCugWwdHAMp3kfvrfOwFJWglJZgr7sUU8UznEmKIjn9BpS8E3i6EpC7ZUxuI6a9YVC7HKW+AilGQTqZAN5K2NEL5yjg+gqsQ9g/9xzO3bkcssJgyCo4swwCAZD+Q2ZCf4YC/VN/+QhFUZq+O24GIv5SYSFEHqBhIOvtz5fyp6GzENJPQfyLIP3lDFeq8RPxN5gIfGVA7FXRNykaZ96V6Cv9tMT4SG32427NBdc+DMNk+vNS8feHwP13I9IexdV7EYE+GZ54C+IToU+Col3gaST6SxlTYRM9uyaicR5G1MtoCk3I4aHQ1grTzHB1EOhDIdOHmJOJbvSVqPrAMFygfqYIURpG24NZVE4I5u0QHdO/XMX4st2cabOif7cfp+LAeIsKLs4mQp5AELkEfMXwRQ/UrWbyN59j72gCUzRp+nastR6MKJifOEDA1Iujrw+HWA5DLWhKk9BH/A7R6kN09wzkykg6H/pCIDwMmj2g7MFrrcPsKweNG9JGoPg8+JUi2s+8zuztDWS+9wryofcgKAR0l4JyEKIyUKjCPvxpeuOrkE7Z0bfaCVwukKtVqKwy6psuINXYTvzpBrznxyE3nSC4tx3P/EwwBogpsxDqsqOWDFTzW5T9nw0YPn57EuYuhe4O7m07wG1CT7raROjGInQOK6K5kUDOBXDdJIxxQzFGzED4r4RED6R44fkuHJrzULk66L5sNNqJd2IZ0Y54uQcX0SivK/i/llDVBQggofT2gesodO0FvQ06WmD2bwfEUK8HdyUkpYH1VvCpIAC0lcIiBf8QK4Hqr/Gfuw6cejSrdkN9JDXzk4irvBXWFNJafD/KQRWmOiPELkCJzMFz1ziUaTMgZBKEToKhiyF7K+xsQQmyEOw7gRQuQ3cYHN8AncXg/vmOo/7u/AyDhH91BC2E2MGAufVPefj7LxRFUYQQyl/4nCjgA2CRoiiBHylzI3AjQHx8/F/7av8YnI2gMsKkItCE/E1vESEhKH6BCB1CwG5C1qRiVD+I9NgiLLsupyE7k0TjepTC8ejTLDgvm4Rz9xeYXl6N+O0V6Hr68WoMuIZMxJR9Af4FkxHrFqKYtSjnZRPTPZfq+FcI2Q2y7jSa1+PwXFaPqicBNhxHuXA6Yn4mvPU89NajJHgJSGk0rR5NhPlhRFEXT9dXcSP3cl/EchQMqMN8RNt7OXnRYBKrOpEtRagqDLQn3IbNuhK/7IT9Cqy7HHWrlQMXXs7ZFTpOVh0nuKkAq8mAp60An0mFFKgh9BUf6vxa8LagpFbgTZ2LOiUB4fwQZm6A2e+A1wGZR2H9m7jn+9Gsz0QJOo4rqgJNYxdKp46k1GxEbBJsfAJyUnG5N2Kvvx9NkEBjrseVLaGt7cayI4AyWIUnJBjDt1EIx2lI1yPpdyK650LjapSQJgLNLpQrwFa9je7cbILe3E62OhLrlPcAG74Rx1FPugxMwfgsIdxHM1cXnSH7tVvgrBvQqOORkq6npPk95L07SB6chS18JJwuhhm3Duy8PVYB61mcKt1GbGgv5rcnoMhfIo8JwVvjIFCfineyieaj+cSMS8DfaUNz8iQidw7YOuHiBsg9CZpQcHfChmup7bWjTo/CGjwIvWYGorQW7EVgk5FSEtB6PZzZvhy9NBTbiGO0m4KxFvtojg/Fm7yfyJJYZHcLHBqE8uh9uI5koB71KnJrFJzeDgcfB5cWiowQOYTOcQkkN+6GlFQoqwLpCYgYCvb8ATPL/9Wldd/nZziC/qsCrSjK1B+7JoRoEUJEKYrS9J0At/5IOQsD4Y2HFUU5+BfqehN4EyA3N/dHxf4fii4Csl/6X71F8fsRNtvAkreECPz+VYhvb0DIkQSN+wP+1rvBHEFguwNtfDMdui1or63Cd+hO1GclQFcA6WAhJ96/kH1XjGFwSSOzayRUtxZgcfVB5z3ExC2j/pkiEnZsQ+oLJqDeBJnxUDgGpbgZX3AdKtsIxJChuD4+iEZyEt2zmELLc6zIuo0rmErsHhPab+rwnSsjqQTab+1k9dbRMdxLa1eA8J4q2vVWGttvZnBJBVwNuEA/cQL25Fz2vLOFzOW/p/Whgax1HW+YMHR5kPoCqOorUdJGQY4ZRvpQe3oQUjYUtkLJXZC9Ct8naxCOFuQda/DfdyHEjMd9qADHOQ1IC2eibS3G17YCVZcKxrnBtRO5rxah9+IYptDvUBF80ocmaDxiXDduQxHtZiMRH5SiTlejZMchlyuIQWOh9Qgi+UXEhGn4g6Lw5+jokf0EySqCSjpwTSvCwAw8nY+gip3ENouWL2llnttDVufr+DVtSN8+idDE4zAF0/ubk8Q+n4vqlv1wcDuU7ARbEqQ8AscfJaBbT7l5MHOXFqA7p47AzEhccRUEBrnxfZSPc2oIqqp+vHkrMHy9C7ehDHnLlyiGPqQ0D3LWmQGB1gTD3Pcxuip5x/ANbSoH13XOJa16FTT6YcJ0RJeHgKmdpOBp9KzcRkenmzN3RpN0spLuYTp0xUFo2gsQTgjkf4FzczTqM72oyl+HoGigA9QuUGVA1sWQEKDfW0CcphDqpsJZjw0MTnrfht4vgLdAsgwItXYayH/bwOXfkn83gf4rbAQWAU999/z5nxYQQmgYyOD0vqIon/3E+v7xiP8PL74kESg+jdJUi3zNYgxF3yBaT0POLPCsxqYdg2IbBpdspiHeSmlwHGn1lYi2IyhjRuP31uOTe8g9dBB9dCiVVlg/YTDj+r4iomAljhcUVGGf4Lz4OK2HYzHu+RJ/chj2TifiyBFEQw6e2AD+0uNox6YjhsQhh8dQ+uwjvPzAAm7svY1RnzggqBVXQgK6nc34b0rDl3UCbWs7UfkmGnsN7E8fhSWim4MhOdhVgqmVBaDuQI4YQ+DULsbkdUGkA/csF/aTTRCRQnjJMbweH957R6M+3AcaM2jfRIQmwcpoiBoEoYuh9hXkhTfhW/YoSuYUsPdSNXoY4ScsVFtext23ky4pgTjzXvpXJhMcamNQRj+ydQyy5wkCXSFYa1rpj0ihO7SPGKcXTaOCqs9F430xxG/qQvY0QnceijQKYR0EW15EzNuP6sQSkFWEijicU+vQV7Si/WYBgaFPE2jZgz98Kr+jgzBUjNbGQeIclMO/RxEKiqEeVc8ZRq4Ip2ViNH6lF5E1FOl4F3wwB0I7UKY56OorIhQrRns1vNyLrL4OVfj9CEM4/vpddElbMcQMw7DiFZTTR5HiFHzqbpquC0MerAbzGmANsjAha2yoNCFcpaRQ7HdSZfFzYFIykzoqifeNQJ62DH/VdOTKNVii/XT3CgJzCvDdaSZjox5FNxyPN4CmuxjHMC0Nqx2IwTF0jxnH8NG3oi1/GSoPQO7l0Pc6NH5AfJoPOjMhJQnqn4C8XWDIAPUoCB0DgS5w7YCeJRDoBPVQ8DeB+Q5QZ/y9e+G/hgDg+ld/iR/yUwX6KWCNEOI6BvxIlwAIIXKBxYqiXP/duYmATQhx9Xfvu1pRlOM/se6fDUIIdPfci3jsKkSQG5V+EWLqDKh6AFKeg6bXEY7pyCf3kFB0iqaZwUjtPgKNSSg+IyqPG0+mFRkX2bu3MsLkx92sYf/tIewfMZqc5D0kjnGQolgou7UPW7wWt96HM8KKv8qCpqsD3aoGPEMUAsM+wVAczqm4UN6dchXP//bX6Ax9KN0dSDE3oCTtgSYfcmEtYvzD+EMep39sBPFrHTiCOuhTjAw3HaU9bgLoC3HWmTn8QQun77yYua2f0rPjWYKdFjo/6CZF9iKPz0bqz8dtakQYclF7rCAYCIq6ZUi5BNgIHTKiJxp1VhiemGSkr74g+dQn7NKNZG/vRYRoLsKm/4zaIC+Wh2eQaj0LzaMXoaz/DdaWfpRXh0FBOKpJ12Ko/gMiopHObj02VzcOW4CeKUZsm2Ig/SzEO5Ogvw/SzXCkCcLt0ODBoOhoHj0NvbERT1Mtmu1foTIMojQxjOWb1jEq53xUR5fDoQ9w3vgUvSHLCVrnxDDsdrpbB2E4dpLA5jxUTXX4jTbkMQ+ixBhxvFuFYZiLCQmN8OxD8NzXUKMgrr8Vdr6EVHQIs1+Da1wptI5FyRoHn25DPT6WqLE70LYeQPRlozR9jOI8jS/5GrxGG779zzI0IRHdlgZ0URspz8vjZE8+TaxnSPJYjPI0pLqZeKYOIji8B/9RO2eKIPHsw+gDbgKdJlTjJXx9c4nJX0OcewdaaTu0f4U9KxmD40VEcxnuwWfjqinDmnIHlN8Do7aBkEBnBXfvQCOXgsEwf+ChKODZCx1Xg3sPWB8D/fx//2mQf8cpjr+EoigdwJT/4Xw+cP13xx8CH/6Uev4dkCPCUVp64MCTyHdVQ+VR2B0BBydCqR7a1yHm3Y0jtJjEjhaE9UbknNnYf/MOugdupDutjuisCwm8dDcYGwicl8Q5b2zGOec69i69jCNHdzPRtICY0Bwq5t2FP6gDSVaR+OoIdNoqeCkMrbMIT2MI7ot8DN7o5enCJuTsaNCU0DgqjZCUxahP7sOXnofatYeGT0s40zKOLHMO8AbpR1rwmRT6wg1sjE6jLdzChkd7iD2vi8zYITjqHqL1mvlE7jqGK9aKurMRVjchMhagGLvwqapQO4fCwb3w2V3Q3wUVBRBQwHsIDPugKxTf2RLawdfh27WNs/UnOMezDpE9D/x5fGs/SVLX44RFH4XL70K8fhHKHAn/pkKkglaU/mNo5k5Fqk5BWV6K+ov1mLqW0yIfQbI1YN37EXK/BVLOpqHBQVjtTjQz1eC6ESlzKFFyOSL3BTg8hpphU2jpqEavO8HwYhWqq9NhXA7KtS+j9dxP+DsuAgefZib5AAAgAElEQVQD9EytQ98VzM5bPmLM9UkomVm0pOjIPPM6FHYgG0ag89igeCdULIXeobB/G8i7YNbDiNg8NA/9AWfzSAJdClJjI8rgYJS6brQ1aoQpHiqWIbr3ICIy0ex4GE1BK0xcDPGPwIFJcImadFsUgdrT2Fqc7NOoUKrfJ88aRtXoOKbr41BFfE2/IYXStY3Ez7ChN9SibpaIT96KaJGhpQcyVuC3n6bMvZsRu96Cr8PojZLQDkqGsH0wsRR00QONWmv5b4H+PkKAdgJE/7xj/f9r/q8J9C98R1sNbH8JMWEYGGvh+QUQkwZTwqE/AGN9cCANtr9PUKQK32VttJoOYD1QhsbowvvcViIyxqFIa3l/yW24qysQGTmMPv0ijpYDWIxuwtwj2NP/DYFuhZTeYAw7HCSWdaA5/hmYvCgfqAh0KmiyLfhUWpw3HKR/Qz62+gBoPEQpw8HbjN87HF95MapDYGnZTWpaFqGenWAJgrldqLb60OxXGDb7BMcTo5h5Ty9Bs2ZR9cVKumJS6Q5qJSnPTkSDh56YmYTE5cNRC/rdRfgTZJRELyIjHa5fAtVz4NxLBj5b8UP9TdC+B4+9A7MxC3FOJ/6NNUhvXo+UMwchxzPU/DAtXXuh/nHIjEK5Rwuf+VEGReGK16ArF0h3lKPUV2GJSEXlj0VlfJG4J0bQPCuAY/wszHo/9B9BGyxT0wvxxlBIm4aj8E6aYzKxW69DSYxA5T2AyqmQ+t429LYJMOtqaNqPYqpCFFbACS1ydAb67fmoGtdjDhWUfNXAkJwAOjkER+Q0DCc2o5s4F4YuApUWWl4c2PHkjA0mPATqSKhdgairwLppMsp9T0N7H5LuXOisJfBsJtKMGYjxd0PNVhAxUKOCCB90Hoa9r0DbSYgdC0o0BE4R7TFx4azX8dJN/t1jCTmuRTVuIcSMwZQ5iyG6Z3FIrVSs9xG5UEE3phZteQb9uw6hXDkVX0gIaeZWaLHgiJ+CO6mQ4OHpkPU2SN+TBJUW2qv/b+ff+D5/R4EWQrwDnA+0KoqS9d25EGA1kAhUA5coitL1Fz9HUf41sbi/Rm5urpKfn//XC/6rOfI5fPwQdLTC5U+CrRlyHxm45u2B9+ZC0Sk40wkI/ElmlMQYhNOL2+ikfUgI0spKoh/9ChEcTVPf1Zh3VKLTz6fPfQJ2Hsar12BXheDuUWiPj6RoQRphyY2IXSrGlx8n3DgV/5adED8aEVZDe0o9/RYJ61QvIVWJiI7bYc9WqDiFIit4g+pQ0YnSqEfW6aCjH4aHQUwLaCz4uiLpvaKOoz25jNLNI6hgB76uk1REDsU0KYKI4qN4LUupf+w2Br/5LJjnwbsLYMNqlKhwRF8yvL8X5D+fzw9ULKQ7dCcha9TgyILx96CsvBhm3YLYmw9RI9hweYDZvVuQtAqURsGmfFC7CET68OapkfoT8H3UAp1O9HmegS3EzDqUmKE0jq8itLwLbauerulv4264mV6/HndMKBmfl+O6Ph9jIBzH6UXoNm9GsQcg6SKcpkmYQ/aCPQSldRecUwVvCMQVl8OnW/DmWvD3lnNyVTDps8+m8JYcxqoeQHy+ALoqYOE+UASsfxxOPg/WySDMA66/gk8gdAbc8wIoJ6F2DXQWQJcPZcwH+G++GGlEN1JmNjS2wfSHIeP8AffeO0vgyWdg5WAImoi/6CgiejbK5p34Ztcj1y5AOv0yikYNPWaUCoGcNQSRN5rAi6vocyi4gu0En38u/nY3HduOYby1j2BnHYSeS2NkKlR8hu/Sk8SLsB/+WesWQf0BuL3sz/7HnxtCiKM/dZdtkZCr8MDfqDm/+sv1CSEmAv0MxN7+KNC/Bzq/Z+wLVhTlT419P+CXEfRPZdSFA48/4v+eUV9lAcMV0LsVXvo9rPg1LF6E2/s0mq8K0bfrCPaH0JfZQU3Ei8glRzAW9tMX68W08jmC58iQ6oWgWJzZ0Sg1JwnvgNSDSZhSFmM4pxmvdRcB5QBKYRvtl5ZS6oxj9NEirB1OZIcNn78TdeGbMDIXsspAY8fr8uDTqDCcckJNMFxyGwR1wpmXYXg28tHDGAvcRDQdpctdTZDuAlTJY2nTVTJo92nUoReg9hXgsieA77s14skZcN3jiJV/QFG68D50L3R1Ik2dgeqSywZSdkpqHEnjcSnb8RntqD7cDm8dRYwKwLHNYJbwHnqBkIU5tOp7idReBBOXg2Eb7J2HUphK0xgTNkuA3r1m1FYLejWQMQ7lSAkUtBF5yEEgwQDWHIw9AnPsLfR/8jlh/V2o1L0YX74GdHrEtBxo3wJamapxv+INTyfP2DshMRZxOh1lswGx+Dkwd8GI9fidvRyas5CkKb1UPNdEmLOGbnMlwdNegVObYNMisI6H+s8g9lJY+DSYQ2DlTSAJ8H0Jh9LAZITISWBOGUhkVTkPeZkO/1IZWk4gDVVg3QJIT4bxd6KMW4Qy8i2UT6oRObWIYi8UtSN+/QkB162oRv2GQEEpqlE+lNOH8UsOXO2NBHY1oovLwDpjIlLyBEqXLiEpxwBN5TQfisM0+lrU6emEf/k49dJEEgn987Y9dAFIP68E9v9Q/o4jaEVRvhVCJP7J6b9q7PtTfhHovzfyd47D2mpYej+cfS68vWbgFtHjR46dhooW3DfuxFFWgCNlOFG+MAL231Gfeg8tgwuwHdThTVCjOuJAXHcDYvsxTmTMxNbUyKDLr8GzbB2qta8jTQxBZfTj7y+jOT2Sz+Ons2jlCnRLarH/ajBy3jLU8ZfDlFIoehk6Dbgbq9E12pHqFAhTweCkge2P9l8KfRqoOIhIsNK4VYtQ9xN9lwXKvsZVE01kQy37r3+eScIPZ/6ALdGDO38x2rhMUAP66aDVIy67GVX6TLz33oZ/w6eIomX4PXnIYy5AmmxERzKqqFp45S14+wXw1cDRk/hDZ9J8nw/5kIqCkfdwnvXOgd9y1FzYHYbsayLhRYHnCjMEdWMZMQ4lbCSBLfsI7C8HqwdZ40PuAD9fI+1SgVVFovsEZeVBGHKiMXZ0IBnKUasrQAOSy0jSlmdRzRrH8ZjbGR45Cy74Gl5aDOd1wsvPE9AH8821KXikPrxn+pH1hZjPFBLIXIX/ZROy7ARvNyhrYfBNcMHTKBoDHf0bCW7ehZyhgbGLoaYORo6DyEXg64SW+eAbj2iwIF/uJnDCj9+Ugrz4Kmhdh9KwjsDeXgL77EgXhiNamvFNABEroap/AGdSOp3FewnZfxDVua8jImNQLboDVf9plF3L4cRRvHIw5pG3kbXhc5TXzqf+tbH0fVZPySg7Wt8BostN9Cy9iTOiBDNBWAhGiw6AruQRBBHgP2ByY4AA4PyH1vC/MvbBLwL99ycQgHdfg0P7YNlzEBXzZ0U0LMTLTlypTeD3gDkMOSqOhMJboPYUvrJPCYTVotREI727G2YOY/iXn9Ms+ZH9wegeuh3nPW/iTr2Q+qgLWGss54bK97l1ZwEEolCalqG7di6e5R+genI8aDsg0k9nVAzH2ocx/sCrBCZq0UcGwNYKW+eAoQ/kUJRwG2e+MCBoYsiQPnztJ1Fsoeh2FxPqsNDfs4rAgR1I1TZizp4C4TtBMqBoshH7y+CsXAjfipR8I5p1m2DvDYgOH9JZT+L/djfKW5vQ14XjjQlDGtKIdNnNiOM3owTF0vzbRMKKzid400u8MMTJeT4XdLSB0w5xI0FREF/sQ/u1FtPyDBqnLyTZfwXSnCak1XdAxT5EVw/csxlqP4LRS8BwBPqvJ+PlpVTU+gi5+21s6fF4tJ/gPfwYxhMeVPovuH/PPm6e+jGr5CLkfRsRKcNh+wug8SMi9QxudBDaXo66qwHGBHPmcRcJd7hwnu/GcCwJUdMNwXrwbcKf30O3bz8eRY8Umgvn/w423gjFTsi4FiwHoOM5kObBV09D2gSEXIN03WMENp7E9+QTCJ0WpdCOtHg8qvXLEHtehDozLJ6MpL8YDr+EjlAKMqIYrgZdaAQi+FyoPgbxeYhiC4GicBzzarF+eRnCX4SoK8NgySbxokHoGIkv+mrs91UQFBROL100U08vXXi+ywbULNdDqkIOFoYxGjWaf3Jn+iejAP6/uXSoEOL78yFvfufj+Nuq+ivGvj/yi0D/vWhugvISeOslmD0f3vjoh4GVvh4wD+znJhBoeYwesRObPRlh+q5VFO2G4VORut9EMqbi7ahDqoB9E35Ndu0yNM1GMJ6D2HUzZ156Hi5agD05knue+QRduhkOfQjNlXgPvok6TIIGH4EvxyBN+A2NGU/wzaFVzHv0QZzaeKweB3QF4ZP7kC19iDoVirGXsg096MzRJNw4F9auQO3X443SoQmoUYZMIvPtT/HkZaF7aC+oNNC9DexH8SlrURXqEffPBH8HFP4GMWQpeNsh7SZERCSq+QuQuRSBQGmox//NepTKh/GVxtFxZQvW0wq6yEr47a3YPQr+56zIg1UwLgciC8HejeO3WYihw7G2nERV9Rre+ntQ68LovuRyDPs1aDwpYAyGnEWw+y5IuAEqP0GYqhmU4KV25RUYFj6EvvAY2sO9+NI1yE0q1Kc7ma5fxRqjmgWlH0GEGfI78MwH1wg1kcEupMZolGA9bq+PiLgcPK99jXmYGffkHpS5Y1FJQxEtlXQ1luAz2oj4phWRLmDP7VCdDzUCjj5FQ3Q6MaZnYe0wduTlkTDuGlKL1yG23YB803ECBwrAbEG+9yHYtQxOrAJfHGRmIJnnIzrXQtjtGHa/iyFFza7bLue8qAhMVdXw6UNgTIfbXkGakIlDF4x58gtIlcOgzERM80nk4Q9A8v2oAKspHOuftuXvKOYYOvSEEY2K/yEXzf9F/vYpjvb/jznvv8nY933+Q7Kg/INxOuHKC+GWK+Gpl+Ciy/486l1dCrEa6FoAPTch+dqx8TRO9bNsM/bwCrt5ZaiKU9W/w2moo0EFG+9fQn+CB/mFxTx3bh5B7cX4NAZqwtJY5d1Nf1IY6d+UojWHQHMxhCfD3CDUYT58LQL1qEF4doaglLxGfdmdXPz5p2iC9Ri8jYjiQXC6E7m1A49FgyfZT9HeOEwmMwldbfDWEehJQlRNQP1EPXRpCDGp8S2ciH/ufSiyGiXQCtYp4CxE+rgc/9VmlNYv8an68EdkomzNw9dlQUm9FcXnw9/WSqCsEN+BnXgLtiD5V+DUT6PhISfaXi/GvflQfwJXdwMNmHg193oIdkLPMQgSKKEWOrNj0XX1Qn0txqPH8DUmUDv4EoQnioPZnfTZCqBpIdQuGViLvXMJJF8OSecgKROJXPwy9S88gHLmfeQ4BW25G1+zQn9XBFePn8W6rPNpuDsB7zAZJcqParefEm8mcgFoKqxoq3swFQShbGuhxDaFluCb0R0/G0lzMR6doChhBs7wSURpPEiqNBrO/g2KSiGQGkYg2kXxED/OYx2w6jrIuwyz10NzmA3GPw6z18C2G5CyAkiuI/B4Nnz9Io7KNoifAJOuQWY8ot0FOg2U72Dst1eRaG/GFBoP9ZXQWQ1LVg/EBCZNRz/2BroCqyA5H6wzkMe8DMOW/U3NOoMRJJGOCct/7dzzf5p/fC6OPxr74EeMfX/KLyPon4qiwIdvwfnzYPr5EPndGtKAHxpOD4ycuupBiYOUGWDQQe+tyP5WZE0eXkc/k0J6OLe6G76twm+rQHLLGJPGE6edR9vNx8h5/GPG/L4BobVC7WkivixgeWMFTJDxhxsI3BOHnJwAmg5INiOibsJRbcNU8xiBEwqcdwM51UeQ5+vwvB+MXN4APQdhkAYRexcqZz4nP95N2Jg6oi66FrY1wGc7IMkFNRUINSgZCuSvRY4ZhrriAdBfD5HbQRUODT2ILgv+IX6U4mLc8mECh4+hP3Qapf0U/pIvcdeEgFUHNgOEaDCYKygKm0Z4dhD+0Ch0qXdB3lxYeyW6o1vIW6TDPigDKq1wuBssl+DKaCHq+FH6w6IxWuYhOd5Gr1UR98Vy2vPmk+f2kD+sh8HNTsITVkKsCo6cDVoNaDQoIyajWvURwU2hHDylJzutHZR+NONCiYjtQdo4nxVyBCsue5Z7QiuRDj+KK0WDqUKF5tsoGJuH36lHOq2gGjkGMTaW0ws3EPHEY2jWvkr/WdXER5VgiZ6D2HqKotShnDqwlrnb8/FHTCNgAeveE4ToPLDwQfj4YcItZgq++h3kH4YwC9S2wcbPQS1gtAlUmZRPu4BhHx9ExE6CjgdBOQ7l62HaVQjNKAIdBbDsWkhKhKm3gcEy0AZ/+yJWaRBV0iZsJMJlr4IhGDpPQcdx0EeAdTCYE/81fefnxt93md3HDAQEQ4UQ9cBj/Iix7y/xi0D/VISAG277ny5AdyMc3wT1p6BNB2mJUKgDVzh4a0B1FKOlD/usFWhLShBDFiDHT4dDj4NxJtS8ii3oVhqWVhCzKRVxaC0cfAbd2nyYfwNc9Qzy1zfDka8hKBKqSqE0HCYsxZICPdvfxRB/Gk9RPtq4YMi4GG/lr/BGJGNMrIRiN66sTkqX7if0zggiEm0Eqlei5E5GHrMKao/B1t9DqAvR10egCzS+4wPBFJYiPIkgK/BRGWLhVQjvIdTmm5Giz0W0v464823qH74fZWEE0ZWnUcUugqgpcPwhGPEW5dajROMgiWVIifqBu4xjGkizcG3m9WzSe6D8SRjejVK8Bu0TEmL6PBRbFptyuhl/eCS2/tMIQxgWdQ2alh7GHjJw9Np7sAsHScbJkHoF9NYTaKvB9+0WVB4FW3sfyQ4FSW3AkACKz47bAJoDYQSNaaGeJMoqdpAenUnRVdEM31gCuYOh/ijS0S4o6SbolVNkTMuiz9JPy1t/oOtyD5lldYjufvqUd1FrVGzKiifU50U78Vw49B522YIpPg/dt/nw6yuh30lsaBx+VT3oE6DhFAweC9VlMKcbLGdBr4/shm8gpH4gDW74Q6C5Ezpeh8a9UNuK2QMca4YHXh9YLfNHUtIQjmZCi8pxOXLQ9cvgD4AuChp3QtbdED76n9BJ/k34O1q9FUW57Ecu/Zmx7y/xi0D/o5AkyDwbVGWwvxpO1cDESEiNANsUCBkBZUeR6k8gN+zGLe1Fs7UcKaQO7Cb4ZCbMjkMaO40I+6U4vcsxBlmhdC9cMwoefHWgnrzHofhCSB0E5Tug2gfvnIfIuQrtr56h+eLp2M58g/qTl5A+fJVA8FDUaachEtraBnN89ltkz1IwTFSjrhmL4i7CKx/A7ytDJUUhpYejJE0CZQsitIPAUejXq9E696Eb1QmFRSAaER1vQlIwlEjI8iCIWwBSCBZ7O8ZtFhrOycXc+CRBp29HSpwAUh1abx+2wDkQKIJ9h2HnN7BsNdCPftMNzHMEQ2cPFFvxLZRQ6XsRh/ZjKVrHmJhgukONhKTpEQEX2u5TUGxBjpnAqJoYCptf5hh/YOiRQ/i9dpRN9YiQMLyaephpJlLtQQQ5wHQuItqFtv4AjvMsyHu6uX/3FTyQt4zb43ToB8Uj158EczMMs0JKAN+4YFSfVmBxF9J8fjzdMV1E39OCY2wi+nE1GINj8bgtnG/ZiqcgFbrdtCUMw1rhxhhzHdAMLafBHIl66s18ntDEPatfg+EXQl8TnN8IwV5wboGkj2HzN1DtAC4AVTQEAROeQfF54Im5FF00iyETMuDV5XDbQ1CzFuo2QfsR8PRh8HYifC5ImAu5z4LGCj47GKL+Zd3jZ8svTsL/EJQAVK6EjmMQkQD1x2DCCzAo87/LfLsCZj+NVleDt3g6ykwNnIga2HFkwvnQUggHP0GTOpH+EblonD2ox0yDgtegswpCkuCrJ2HhaihbBCmZkH4rHNsPSgBd1QsETTLjL9PiuPheTLOGgAigtkYSaFVRvq6SuOEJWG0qxIZ+qNiJCEioBwnsQxpRH65EGQkM2QrF3XDQgL8DtMO9aE7uonu3gjVYQtz1JPS8gbB7UaLyEFuuh4tWwYZ5WMQxxNA7SUi7ElfoPPpP3Yym9Rh2492MF93gugU228AwFR5fOWDOKF4FJ/eg6u8DKRolS8beEYx1cC1k3YT35FtobA5cY20092qI6uuC2lug9Heg3YewxjLI6KNE08KR6+PJLfsAsdyM+4QK7f5QaNGx95JYMhorsPV1IVaXIvqD0N02DSlnPZFr2klRynjwgqvZbEiHxVrY+Cac9RFK52zk/lMwTItAkFpUiSgBJV1CmfsH5IhIKJ2KfugKirr3ck50C8fPfhQPbvKWvw5jF4Bago8WE8i9Bt+JMxBhRKl34NeDlLIXsRU8xaH4DKloo29D1HmRlS4Ul/0HM8FdqjZq7ruKVkM8XvJQb/oYPpwE0RaIOgeyfw3mQch4qem8hIRDDoS9AUxxoLH8s3vEz5+fodX7lyDhPwohweAbYPw7MGsdzF70Q3EOBMDdA4ZgZGk4mswSpNPV0NIJ4YPAY4SMELjyTRi3iOCzPkXVHwTpcyA4GT69Boq/HDASODdDzDVwzlPQ8C50FEKLH3HNRswTRqGdHofS7iPQ1ozKWQinS/F4U8hZ+TbpCXZUpRWI/W10lEoUNQ7Fty0S43uhBDolAnYNbG5HlMuIlHTQCySVGilLwawtoaeihr58PUpVJ/IbjSgd1ZB0Hcrri+DrE3QxDsZfCYAuaCyWEY8hZeXhMXVwwJhJYEUCDL8fFo6Ghmug7CLY/QyEhsOo6TBmJI7RNlQX/g5CddjbtlCbFIs1v4P0D07T0TaEzkA4JGZC5rOQeRUU1KCy3EyMRUXcoTr2jxqF0+lBDuqnc44GDy2Mqj1CTXg0a2+cRtlT76JUdSC/9hmiGeQLBrHk0Cpue+4F1L0OaF4BiX748iqkkHiU+EjINEOaBmlcBopLiy/XjKwcgspXoTYcpf42An1ueqPUNHOAUYz/7/++4EMUovB+tQ3Hsyvwd0j4Qw1Ik/MJWBJRlIm4bh+D9t31qIxpyMFeOCsZEf7DZbMGrNgNPsxo6cMN9y6HQzEwdRsMfRDMgwaaImpiQ94hMO19KFsJp14YiJ38wg/5GW4a+8sI+p+BEHD30z8813gMokf+10tJsaCkZKBc/TWi6k3oOgxdp+CNiXDFZoQ5CKZcAccLBsQ9Yxq8PRumXghnvoZKE2RfBWHJUP0RfPkiTJqLkE1ob3gEufw6lNpa2g75MeWZCLlzBiRMh3Hj8FTloNpix1VvRNcn6Iq0Y4tqwG3Woz9pQbS3gT4Z6rrwXBCN/pgJNA7kjDSsYj+ubY8SMLUgufWI9zaA2Qjn5OK9QofsLsbbdT0qpw+PqhefKQfZmYitYRwT1zyPPzQRyb0d+m+GxDsBHwTvgOJLwFKF3zALzbcVlKl2MuSbZtQJDhJ1iZCsQg4kkZVwK4cT6gknlsTRCwfuXBrfR3N4MaG2UDjVhX7KreyJCyKyoZlhOyuxJ0qoh/oZ7M4m23APdf0vkf/7yVhTF5L68mdw4AS6pZuY/uaVcF0cTNGCQQvjfw9RZyG0dgJlTyIXrIQtFUgX3YqqvhOy7gONCU5eS1fPaWaJTbRrreQ6i/HW7kd2ttJf+hY+bxG+nCQi9p5Ge244MRMm4VXeQu9tQYoahjJsIpr4GMrkl7G5q6iNS2WY6ERb+QKoQmDIEgC0GHDTjxUtvbgJCQmFKRfA2vdg/rU/aG4qggd6+4TXoPQ92H01jHsRtD+2yO4/kP/dOuh/Cr+MoP9ZhPxJnoPSLZB23n+/9tsRo9chtCGQvgQyl4FXhlEzYNl8KM2Hsy+FvRsGtkhyVUNOOrjXQ4cTnKeh7DHwR8BnfohPhsJvB6ZYiq5GNakJOdWHNkjCeNfjUP0UNK6A3kPI9hGIhHOJyfGR3NpJWPIQXHI02no3RSkpcK6P/rNmQtxIVPktiD4PhGXARbchwjLQezoQPVrcnT6cdwiUZ9YgLl6OqrQKjdSNs3Y9baY62s0emrRv0t+4FlGyn977rKiX7IELt4KzBXZeBG0FoMtD2Z1B59GZbB8cRVtQCklvr8HbpUPqUeNPvwNGzwRLMLx1HrmnfNQp+2hqvx2q5oK6jcDIz7G/XYWkSSHIegmD1zXTPCaC7mAt1qo+DHYXZr0dVfFdJB18jpxdZahL36HJdpJDM2Opt0oYBk1GTrXC1cfgunbw2qDzfaTSO5H1I+BkGsx5GMY9gnT2XbBhKfzhcrj4VrqmCfp7LIS1tqJbcxX98cX0B47j2PcIdeMGEx5+KViTYc485jVfQb85AneLRL9fYcOYSTysieSzxiz6VDpyWopRJ9fhLX4Tl6+KwMnnoHglonozSl81E95ZS/T6F+CpmTByOHy9ecDg82OkXQ3D7oVdC6H9OPRV/wMa/L8hP8Mtr35JlvSv4qP5cPmaH88S1l0LTath3wug84IrAkznQeMe6DwGUV66x+ShETEYom6Co5tg0EyUj9cgKvbD8nFQsmVgTz9VMRyWIOcjHM89ht7RjHjuPWhZCubxKIUBGD0eodkONXPg0UshFwh1o1z4IfXNZSzrWIrD3s1LU2IxvmJCXe0HrwkirfDw5+A7DjUf0C+dxhsagkc5Q0CvQ2rqRVenR3+iCSUxjEBiKrqNe+l1TKL61XCGKEtQFZXCsAXQfgIOPURF2kS+jU1g5upnaVhkZigf08uDhPbejvLKDLzW+QQO7UHuKUUd68RRBT5TOvlLMxixroyQJSdpOfErQl5ci9rTB1YbdPehGH30hw/CfLwUFlrAMwV030JhAJq10NGM3xrCJ3+4kEG7TjG4uZeQ8LMh+QRETIRvXoPzV4M6B56eD1lqFP82cFyLSJwMa5bAsn1gi+NYVw7Dvcdp64pCf1xDUXI4Q9aewpkXTOj+DqQeA9zxPNQ/TvuQKJ4xXEpvmx9ruI+phkuY8OylaLqPQoIGOn0wcgKB7F/hlE/gUo4S9OV2hFrD9nnXMON5Lzz0Imx4EI7vArcJStphyXMwKAXCE0qEdQYAACAASURBVP/nNubphc3TBu46LtwH8r+vU/DvkiwpJFdhyt+oOZ/99Pr+Fn6Z4vhX0N8G+pAfFWeltQhlzUQUDShnLUKu3IxQn0LZdwraLQivCvpcaK/9gEL9h+Qqk6DhI8Tmx/F5mpHHpSKFPQLPH4Ge/9feeYZXUW0N+N1zeknvhSSQhBI6ofeuUqWKXanXrijea+/l6rWi3k8REewNu1KkgxSRmhBCEhLSSa8nyWn7+3HiBSRIEMSI8z7PPJmyz561TuasWbNm7bWLwNoBOrSCDgMwj3XApip4YAbcfSW0fxyx+gpQDoBxNPgUwX13wa4lUN8LET6SVvGX89/8Pdiyd1NnVqiZbMfvVTPEt4X8ZFg8GTlsAKLnu+iWd8S4aSeKth3CJx+3sRQqvNAMTkTa6yHtEDJQohm0l/i99dhis/HO7QSVSZD0Ely6ktahvYkTWg5duY1YOQnlqzcxTOyB8O6K8A/C0G4v8ock6KOATUHRj0OTlUrigwfY8c8Yei+IJzClAE3bMGiQkFAP1VaE7wi8igqgswuCu0HWT/BzEYQPhqJseHwlmv17uPKW13Hf/ABVo6KRXr0RX3QFXoMPy6DyCzj6BEQnIXveDXwPizaCwwQzH4EfH6Y20J/aeB11nwZxdLYvQZTRY/cudCVOlDwzFcMD8G31b5SEy6jc+xb3u6ZTZvLiqpQPmJC0HnYuhLIcuP95+PplaBsOCVNQGrKxWPthSSvEHdiHqugS6i0pFF1SSeDy51EihkJET/jsQcgrhBduBdNRmL0QBl1x8oWm84K+//GEPPY8DYkP/nHX/F8BNcShAsD7l3m8lqaeXsoyYdlluKJDqbvGh7r2n1A/IALHmIW4r3+Msjsuo6GHFke0L0ZjFBE5UWR8NRlp3AwjxyC8yymfPQG+fQdMFsAEtToocMBDo6D6MPTUgjTDBitkrAbDAXBkQvVKyFsPg+6Dm/aDdzbcOwr71k+wL52HNao7zjoFr82VyPoiyEqHi8wQdAhH4ZfIVfHoY01oehpQIrIRNidKoUAII+SYEd43QJ5C3ebOlAUNojI0EGly4x52LyS/C61GQ2g/NMLjN4Rr5+AnhiDXLcG81wAHF4LIg107kG49TpOE2FiMQ8rRDShH85ydvnnd2HpFDHazL0y/E+Y8AVO+hoxyyE2FwgMwsCf0ewCMbk9Zz7mfQeJ0qK+D7NXQezzKc//BV+mI0Oiwiasg3ww9+8D3r0CwN7KwAfIfgCUKIlgLlz+I++elyLx96NpOxKwRHJp9C96GEQSne6H31iFCwjBvrsU2QE9xp/8iXXX4ZLVmzhfvEe6sY4L+e1jfALIY2oyCn5dDaSVEhcK+BVCdAbtfgrjLUbrejW9ODD5ZIfjGvY/o0AfKi2HPXtidDW1rPSNcpz0IOQfg8O6TrzUhIGwQDF3sCXm00Kfp80YLDHGoHvSfQc1R6H/LiR50URrsfB9ydiKmLUEX3QuttOOqXo2SmYer8kscvpmYjMU4xtTiOuqDdlkfwkqGse+WAVSt/QmfHx7CLgKoO7oSBj8HfS+DmwdA60C46nLY9jho/TyP9LEJkPQFVC6EVhoojoPaXeD0gzWzwOAHrXygYjMNH8yiPPxOogy+BL7QgOKlw3mtGe3CLIT5EUhZTEMXB2T7oF/sgm5XwYhJsO5+EMkIgiHvZ8jaiqxyYZrcD+/2i0iV++ns+Ii64uGY2vdCKfP1fCfuKqhbiFUWgzMQzZgsNEefB2c8ZDZAbj1EWqmzG7EUl6IcTEOO0iBqdVhWPEdfsx9c4YA+sz2P7eX50HEU6Hxg+GAwfwN5aXCwAHxt4G4AxQZfPgXzX4dDB2DNt/DE1choHWLjz9A9Elr/BJWdoC4JSkLgvTQI6Qo5bri7L5V3GynWDKey+j946Q2097+fQp5B1tdBcSRMKEOs0hD8mh/a3Vtxm8KoHjKU7iGdyTi6AuoUmBoL3d6DN68HSx3M+QyCe8LhDyDrOxjyJphDoDgJcrZiHv0o9QYv9D6DoOMgcDpg4lwIioA1z0NZEkx5AQzW374mdeY/9pr/K9AC0+xUA/1n0HUGdJp0bLu2DP5vLNht0GM2bHwfGt5E1Jaj9Q2F9oNRer6FLn0dfH47rnwrRNQjfQ9ReoeLNo5I3M5qHH0tuIe9StDrr0DEB3BgIfQrg9hKOPwpFJR60tdigmHWF7D3Elh8FFrrIMMMfa+G+FePybViJi7HehpcAr07B964maq2fcitrCI27gCuOd4YSyuQpXasq/JxdqiC0cM8NY9ztyC7jEDsVUBXAG1HI1PWgr4WJXM5vt+2ptuwO9B/vIXqCSUQmYB02HCvuB5R50BUWsH7R/gqGSVOwAB/pDEREZiKLMlHtHKimTCSyu2l+FRswxmjocw7gNor4gg+Eoao2g+bh0LbaSB6wI0fQOkRqJ0PBw/C6tnQxg0dn4TlTwF2aD0S9MUQ9QMs3wPvPElNl8+QByMx3fw9rBwMHYvgiDfkpoPFhOg+BeI7Yd8/jTLfifjm+hP32kKynroeAOPRUtxWPZqUYkiqhkvi0Nv0lI+7GcOuVOxHf0J8uY4prbUg3OBVC1n/hFHtwNUd9n8GOTd6nriiL4K0FZ6bwtq1SC8FHJIcwz46MtzzP9PqIDTKsz56AWTvhKUzYNyTEBgHetUQn5Jf0uxaEKqB/jMY8qsa3Zk/woRnYMc3sPNriOoCVz0H1uOmtz+0DrYvAuFE0+9+iIuEAy/glX8TvHsz7gAXmYkxRAXEU3PvNRie2ADVTmgvIUJCSQqE10DQYE9oYtNo8J8FrW+FCh3kZUHfbri/6YsSOAhXfjo1Pj9jmx6KIbsWTRwQ+hZee24huOowBd/5UO2upfrgcnSXx+NuMxQftw7/bzdgqHZjHPsgWp8ktD06oPywFWfRJziSEjHPc0LAXFh1P4bM70AUosu24PZahtLmWWTqLKTBgvJ+GTIKpF4iNgvcuXtxB+xAiXCgiQB3jBaRm4rh0GHsiQZEbn/sgyMJk3MQyYNAFwEkQ4EZSiNhlBlMyVCxCTK9wWIFU1f47N8w/WnoNgaemgmOaKh8CwLug5kPUWtfhDU2DC4PAbMeJlUhZRHcJBD6m8D+EtK0iPo+Vtr4LkP4mbEHvI+iMeOu/AnTz29S0dafoBIH+Jvgs45wkwHfmp85fE04le6OGAb64mPdAD9aIasA8syeGXiMTs+EDSMeg6hxcPAT0HlDypeQvBgRXk3iK/PJ7TUM+nUFc8DJ11pUT7j6PVh+qye8M+87sAad3E7FQwuLQasG+s9A+6u35Z3Gef52u7Tp9vn7YcNLYKqEix6D3jdAzj5Ii8Lw8Uykvh/u/O2YEmpIcb9OVJIGyreCpgz2BEOqHSIbQIRA/UEIPAqF3vDxzRCkh+heuCqSKbDsxzZQgy7vAzTt3Vj9+xLy3ffICjsExJF34GtyH91HuaEQGWPEb7QXXXq0RfdmEgvuexlH5X6m9g4lLuVz7CVLqfMz4nCvR+keRbWmLQlBSVT7agjNuQ/CqiC1DAq9MWQWI0Ld1NfMw+CooS4Kam8cgF/BHlyrdCiR3mjyapBpVrSV5biDtJApsfXLQzddS4PRiN+mLGK7LaVi/T/wi/aG7Co4MgR8DsHym8B6COzLITwAWVoKXhWIjbugUzfPkPy8DMhMggPDIepS0PjjphJRZEebtwV09eDnggoBY12Q1xcuvRe2Lcdu+BZ9dQ9EXSWIAvIm+VKh30CrbXuojvahLiwBStMhMhgKUqFVEmLnVKILcjjoZ0MfbICcURCYAu20UJoB2mFQuQs0XSD5M0j6FHnoM7CEYAsOQXPtQxh23k/Z0Ntx+hg8cz6eCpMP9J0Nq5+A96+H2V95ShGonMg5rMVxrlANdEtGStj7OWxfAtEhUGyDbo3VCutrIGAtrAERvB9NgYMQHxeF1s3otmdQ2VngwzDoMAVkMCyZD2310L4aajtA/1dgy3CY/AbuNl0o6XAZbp0La3IBoVts5NT5oOkusFc5yN6jIPY+iNM6gYSJ4Xj1zoeMamoDe5PTrxOOhGLua52M/vB3KCt3s7nvzUzc/BysSsBxnRfujSWU1ZYT2GE6uqxXwKxAnRdc+gjs+BDhOIIzVGLMLkeEGLF4e2FpvQ6JD8LoRJvuB899jGbLrbh21VI2yYws8sJvUz61n0v0LiC2BvlRZ2rnG/CKuQdt27Hw8XPgGwlT3sOd/x3K+I9h9S1IryRc1XZ0T6yF4tc81d+CNVB/BJzZIHQgDAhMuFfGIx57CGqiYN+9yNx1IGsQlmGg80P2+ByZPQyDfQrsGoc098Z6JBpb1R5c+8ox9S5FlgRDmg+MckFdPq6kRdSbMnBnHyGkwECFJQhdTi22XuBduJvKviNQCouoG3cx6BrrMDfY0XUajUunpUHZjs/++9DKegJCRqIPCAcR/NvXUuwgiF0BlQVQVw6WJrztvztqiEPljEjfAIunwLh7gDIITfTEEMty4LvB4NvWc8evDIMx92A48Ajdvq/C0aDD4SeQJm9E2ACI7Apt/WDxpfCJDXzc0K4CLr4Fyj5EybiVEHsFHAnAXutLQW0xjtJy7KlFmPxdxF/ih6jQkBmVSVVsCGnVbem4LRvz1cto/8I/cIyaT+5Py9DV/oTWJ4gJ330LVw5EpB9G92w2Dn83oQkLUA59CA43FAA1sbAiBZR6UDrTEF2BqMhG422FMhDVgTi1JkRkD3h+EXULh+LWVlB9pxl9cizWuiO45gyA9kZyDuhJ+HQjoqKK8o86UFj5PQFDFFq3KQWfbsjDgYiw3dQVXY4heT/CX4c9LwCtNQ5huMnzXZssMHE4uLaBric4MrHrjDT4OnBVbkeuvBXsLhjoC6mtIDIfyrcg90/EUF+GiI2F0ipE4v/ha38D35QVlK034qMJxJpxmIajUWh+TEWDHc2cuZjHheII8cfdqYC6dsnUtp6INb0e6fLHkuqFti4Er5W2EzMrnBbkN58gB41AsW0DvQuOHsDXvxvNLtfsoxZIOiVqmp3KGbFtCVx0B5RtgbJC6H2j5we75X7o1RXWGyCnxDN6cNtm8LOjK3ahm7oc+5RhVLY5DEuvgKfj4NA8CAT0kZBdDksXeN705+yCNg9A4qsQFoLDvyOWzj3waafBPGQQmo7tKAgJJ6tzF2xGG8lh9cR/eQBqKnAum4qjtRfadx8l+odk/FMd2Gc8z5Hu+RR/v5u61g3IS6ehLSpE+ekNKCsG5wAICYbZz+CYfDFVrY0UtRmK6bUMnD7hSIMDW5d2VHSKoD6okJqOe8g1XIxtfAWmAknwAg3OLftx9w1Gu8OC0aghJjwX17QbcYwOYe0Do/D3CcDLLxjGLkIOmodb54DtdRifT4J6O+yuRcaWUXzfNWDp8L+v290uDJczhTxrBkfEPRRnz0e7Kwfj9iTshjpKB42CbF9ERg4UfYbb4EXNgOEwKAfqDnoyRhw16Iz5aANiCVz4CfYGDZrguzCEm9C07wyhJtx9onDW9EJTZsPwTjTWTxNRjFOxx4/HEeZL2ciDVI73xT3uFRj/3rHFPAVR3wdlyscw/jboOgsMlr/XxK5/NGqanUqzcLtg8ovwflcwRsGR1WArgYZPoG0efDYYlCVwWQJovMC2C+r1iMDx6OdeTVC8lZQHXLg6zCVg7xaoSodyA0QooLd4alR/ci8MmQmlOwAH0nGQcoMFmyucKtpRdCAfjZ+V2AYzUdt3gXcNOQMiqO4/FEv3NlgW70eaa6HsMIRKtF+48Eu7Hv3UaGr0vji/34ex30u4B/ZCbs7HXV6InTKKnW0Je28MjgYT1k0V6CPTsQ0wsLdHAK2UBrYH+NI9Nw19rB++FTWEL81B0XvjCFewiwr8ezoQ76Vin3kYw/6JSO/RVOp3Udh3EhECWr/2DlWzZ2IvzURHJqLeAYcMiNA6CGrrmbk8Kxf7lB8pdk/FKdrjKsvHSBnW0O4Ei3noDrxN9RvrQBeEsnsl+u6PsSvuR8LDLqXj7mdwVwfgPjAMk34aIt4MzjqoyYF108ErAtHjLjR1G7H2CiL72TVEDg9A+8MqsLgQ1dkoo++AAe+gWTUVY7I/kmEIwyik7gBWe3uk3oqdTRgYd2w2E4MR3ljpecGpnQ577oOMHyFu8p96qV4wqGl2Ks1G0YAtG6JGgLkdJN4BoZ1hz0j4SYJPAtTYIHAg+CmQWgMNHWDSMJj+EHJcJ9yunuTXfUJASiGEzIP4VNyl2bj1VWjD+uMyt6VKDkRsnENlYTXaWQHsbh+AO6KGmCotnTcEEeFdg3KwGHdpBXgrDPk4nUL/VoQrt4O8F659C/wmIGy9qKv9HNPwO/HtMQNvjQ7bf9ogd/ujhGUgQwRKkRZd/0GYumSjODtRvW0GmjvjMO57CJ2lnv4Zu3BaFAYd3YtNF0pVVV+innkdMbYXxJXiKMtHSQRHm+noPv0CcjQoMVegUVy87LaQ2r4b9xS8hghx4120HdfnW2mIB8O1Q6F8BcToEMIHd04aZaNMlHUw4VPlJiA3Gct/v8f1r4Voinch9H1wuV6maq83fu32Q9AoxMN3Mjg1HEe7/Qh7PfaoVjgCU7DuSUdujQZZAzYQQkDHSSA0UL0Ppa4HUd3XY9tTg76VBb1PDaRo4MMHYOfH4NcT2vyMcNpBq0V49cJc0xr8m6jrPnTcsXVTJ+g8Dw59eN4uyQuec/ySUAhxGzAHTwBqkZTyxTPtQw1xtGQs4TD6Leh8BbQaAqmL4RMnbDZ4vOlJd8G0p0FvhBtSIGgYZK6B2HYo726kwzPeaDRByNJqyHoKtu5D2WdkX1lnkl8/RNZ/N2Nf9CzaqC7IG4ZiDEmgQ1UkI9osopNPCK18DSg/HsTR04D7uiBE34cxHbYR8/6PUJINvkGwZi4MfBqmzcc0XAcxi6H0dpSj16O7qwc1JRrqcgw4R7aB275EO+ZDXDl+HHnyZ/R5d6OreRapTUf4VKIMS0c7sIDg6hBCOoYSF9eAu19H3Pk/4V5dhi01AG1CAiatltq3O6DJSARHCP/STeJZ+/V4RWUR/PU+sFgRqw+jfPQCwqWDQysgTHJHxjNU5ySgOL2I+N5E5wWVtPrCC+sGI0T2xhVSgcvxPQ7xLVhc2LbsRw56BiZeDkFWDEVeWPceRdbocPbqgD7meeoHzuDI7FCKrp0KgUYwAGGXgF9fKDsK2WUofh3QOvTU2auRIxSw+MKNX4IN2LEQDjlh/UzPU5P9KKQ3c1Rf9MUQO+WPvgr/PpzDkYRCiE54jHNvoCswTggRd6YiqR50S8bcmK9aVwfPDQenE8a8CIOmwxQf+MeLYPCG0c972nUeAlmPeAY1JPZDs2gF7WwlyP43IyL7wc6FOHzDyZxyOz0ey6aOLGLqH0Sj9cKasQQyPiSgpAgCvoHlNcgDu5G9zLjCCzEEPIQIvhoy30XU1EH6fgitAbuPp65F3ktg0oK1K4Q/DxorhmjQXyxx3hCHXLIX+lyMO3wK9lVlVDn8CO6tw52UhBLlRAz7BrK/QPEZAF5DsJqfxMU2Gm7YjdPfByVPEpDWATHoW6gvwrxtFpr61aQ05HCYSN7p+Bav6QYy6tFFLNnyPr1dOuSG+ei7jaPWfpCtmxrQx4BXkBEKUqAoBFFbjahYBloL4uFkDNpoJLtxyFrcwSbMY8ZgvvYuz+jGdy9GXNkbOUxCFw165XJs7ENz5EsC8jtjjXsJMfVZSL0HfPwh5XbQTIKi9dD3UpR5I3B+8IFnphp/L3j7QYjqDJVuqMmEtZmQ2Q26XgQNZacuonU8QoE2E/6oq+/vybkLcXQAtkspbQBCiA3AZOCZM+lE9aBbOo4GOLgBpjwGN30Ao64DlwP8Q2H4VZ4f6S/ERoHtEKT817MtBBpLEAoa6Ho7+AVSaG5gMAPxoz81JHPUuBK0WjCaoL4WtGWw/xUYsgDni2OgbQWGAm9E5I2em0HvO6BrH0+7rLWwLd3z5ts5BJzXQKs3QHNsWLEQAt3kkeiCR1K/JZHMeeswxtxKh0sn495UifCpQZinICyJULYNdj8P3eZDaQaaH7/H9NFPGHY6UQLjqE+0IakHYzDaIV+RNKoXdxVaeGtPL0aWK9x76HXmJq3g/RALS8pX4jRYEWSyLs2fWZ0+ZHrmYmRttceA6rPBywmYwGKG3Ysb5VXQK9eg8/uA4Nde84Qs8g5Ts+x68m/ojDtiADUJZipYhrE6Eu8NyVi/+QZRWQCGVnDUDknvQMH3gAPuWY5zmAkMefjP3IwSdRuMuAlKUuHbN6D7OHjqKEx6AkoOw9pFsK0MDic17/pojiFXaR7ntmB/EjBICBEghDADY4BWZyqS6kG3dHQGGDb3xH0uJ8xfAlbfE/cHxkOdAcrLTtzf+COuGrUYy9qJ+Dv0oOtFDz7jKF8icSEq94JvDzj4Jjh8cV82GFExDSwKBD0K0gkFi8C4HtgHDbXQ/x548kZ4awH0HAT+UadQQk9t7+swPTuDVtdeQ1VmJiWv/oS5pIG6KzWYIrqgKUmCfT+AWw/FN4B/DBRtRgx/DkPP2ZCyGEL7IaUBJ+sorF3GvzIe5vXsh/AXDlxfPULfMAsjNq0FGcqqh4dxy+gPmbljLZtKFboY0+kwNhtqtJCbDVFhUJwLfkYIbQXeOZD+zAmhBV10NPXJX5JV/QJioB8huzdQ2Nsfc5KboM8Po5QuQAaWIsN08PoTiMM/Q1UuXNMVhqyBgEE4XV/jcm9Br12IEAIR9k/g/6CmHtoGQWku1NVCrzkQ3R+WXux557B+CYQ+AubT1NBQOXecWZpdoBDi+Nqkb0gp3/hfV1KmCCH+DawCaoE9Z9R7I6oH/VfEOwB6jDp5f1E+fFcGBeVNfmyjVzYMfgP23AZSosWLCK5CoIG6XOjYBeICoSEYp1iPohmE4tMF0W4cKDoIuQa8h3oMitEEYfkwNQrYCAdvAOd/4dAcyHoYaho9QClpKMwj480PcL+wHb204XfFVCxahfpul1D0lp7KlY8iv5wB9cWQeCNc9Qlo62HEszBwPtJgQBbvxhlYSZ17BtXOvdx8eBEvbn+HVv4K9L+UtJieWPZXIK010L+Wi/bU8vL+3WwvzWJXr24sinkZc5o37gAbtL0Opr0DsSPgqg9g4tuean/6IKjYDq4G7Pm7OZR+PRk1z2H8yRuD42dsHUFzyIbLreOHeX5kXN4aR+wAxMjncQ8rRE67FR5ZBcM/hoBBuNzrcLk/R699yeOJAyh6GHcDRLSHnleDJR9SvvEcC+4Itx2E2iwo+xqUFpaUe6FzZjHoEillz+OWN07qTsrFUspEKeVgoBw4dKYiqR70X5FTPdZGtoZRN0Po8BP3S8l2dlBPPX5+l4B/GmS+CW3m/O84UkLDDmg7AOLvQkdvhL4GtF8dq4SmsYLSBUZ+CG0mevZtM8HgsfBtLzD3hsT7oD4HUu6E7p/DR3ehGfgAnWd0RNHrke0/oPqyaXh9/hWm0lKq/jmD2q+PILrp8DOYQeOCrY+BJQTCeyHd1djk5Rice3BpuqCVi5m5R3K/70ba9toD/lcCnYnNfI+M+B7EHdwDB0sQ6YUYDUu5bfV6rhn5PmlesXxz3Qt0zXmN3iu+A9sRuGk56E0ePYISwBGJ0xhN+paBOB1V+H9USE27IMqnBVMvIpAmJ7JzfxTv/uTLVTh9e+JKEMQXrkLpNB63aQuKaR5CWHG5d+BwLcKgfRshfpWnrNWBbwyMvQWWz4XVj0O3aZ7h13oLzE+GQ6sgbTV0nnourhiV5uAG6s5dd0KIYCllkRAiCk/8ue+Z9qEa6AuNOfdB1cke9I9spRWtkEhEm3mw8zoI6AveCVBfBKZw0HqD/70g9J7MW3d3cC6FulQwtfN0FDbgxBtEh76wfxOY/GDwDR4jbukArebiOPI6GcP+QftP/wXRPWHCQ9S/+y764SPRREaiCQ4kqHcaXHk79XkfY+vZCvOGh8EUCh3HIbfNoi52KzjrEfVu8pev5/GGTswue4GermI42gasyVD9Mpruk/EefC1u3V0oziDoOw/KiiBjF37mYBLrU9kUUcmWlO60L0nH+2LnsWHUQFH/HlSv+y8VAZVErFQIKi1FDg0iYFAY9YG3Ul+5kRBDAnhNxi3d9LR9gdY4HmHuDj52KH4fJa8Ud+Bg8HoZh3wBg/ZdhGhilpLKMs+glpAYmLcCdr4D2TsgpvH3qzVAwvhzdEGonBHn9qHlMyFEAJ6o9U1Syooz7UANcVxo+AZA1HHZPEd34bYV0KZayyQmoqB4DGzXF2HXjZDxf1C6BXy7gf/9cLxBEXqoWndi/7/23o8cgP+bD2EdToxBB0/EXryWV0pSWBk/CPnNo8jUDTi2bME0tzGmXpEM+mAYPgfDqJdxVppxm6LAPwCi+kD/ZZhDMzA7Xic9pBfPB8ziooBdjIwYDlM+hOvvBUcRDOqAMm0pISHD0fR7HNFOQv1cyHgJus+BK5/BFupDl8p93JKSjNlWCT1vhfS54HaQzfskax/FPWIgCSsS8d5bi3LFPYghdeiVOXg7/CgJbaDeyzMzt6ABrSsZ6hvTWhU9hFyHiFuLW4nAVTMSfUlXROHiptPlFoz3pEZK6fGae197zDir/LnIZi7N6UrKQVLKBCllVynlmt8jzlkZaCGEvxBitRAirfGv32+09RZC5AohXjmbc6qcIbajiPytjNVfjobjHrWFFsxRcGQJ7J0PZT959h2PsR34jj3mPTfF0MvA6gO+ESfuFwJLmxt5RezBoa9m/oJNlGT8iP3VVxG/VFLbuxAGPgwR7RBdx2PN9UfoQsBWD7mpiMYMlbysV9DlHqBz+T6mD7geJv8TPnoCHpsEUQ4wO2HlaNjVBpxPQNx10D4NXswB/zbYa4qo9jOQmLUDbb/BaMddBVv/AwFTqE8dgm7f1/TeOYa4JRswjr0Iuz0eZ8galOD1iIOPrGCxEQAAC8dJREFUIPbMpI17Lod5rVE1E5hf49cFMFxyE3bjdwjz/UjdO8isWyDzNk/a4/EU58Il16gZGCqn5Ww96H8Ba6SU8cCaxu1T8Riw8SzPp3KmRAxExF2K1uB/4n6dF/R+F9r8A3S+EDPz5M8KAdEv/Xb/vkEw+3HQNpFtEDgK7CmM6ziZ+/a/yu2Db2B0XQ0lbjdUZIDOCp06wt5bYcdlKAH+iPXbYexqKD4K2ZvYUfUDpqqjtJm4lbl9Lob198PSsVC0G7oHQNc10Gkd9BsNicnQYRuEXO3xUO0NMHQMaXHRfDJ+JGZjLvS6AaY8DpXlUHUE49Zcwt76EMuRIsTML6Bzd0x3Gah73huhi4f+6yFoFKbc1VhpRzEbPF+NbiAYbzpBXZd7JUbdFnT6+1H8vsHdeTTuQD3Y80/8XmbMhx5DTvOPU1E5+xj0RGBo4/pSYD3wz183EkIkAiHACjzzRaucL/ReMOCJpo8JAa1ng2IAv+5NtzHGnv4cAy+BQ4eb7j/mDij9isD+C5i7/Vne6zGbW6urWbp2Bjp/Pyj7EeLvBHM09JZg6Qr7V8HY16n8fBJKmzj8c5I86WsRfTxL7n4IeQ2KPgTlNtBNAWM8iONuEhoN3PEYRMZQKL+ms0PBcCQQejc+5MVMg0OfQt9/w0Vuz5BrIXDZLkfb1Uz923E409LQxsdDhydBuohEksS9+JGIFitC2+t/p5NSotM88T+vX2jagbYfbvvjCO3ME33tabec/jtVUeHsPegQKWVB43ohHiN8AsJzxT4H3HWW51L5vfiexshGXXV2/TsqQe/b9DH/gVB7EOw7GFK3kjfea8uy1KdwmlvD0FXQdoHHOIPHoE+4D7qOIUtbyfIJc0hM2oWIGgPBx91AIjtDVEfQGqEhDD6+Bd4eAeWZx9oIAdffDoDGncHQoi8R+i7Hjkf0B//xYHsPQiaAuR3S5ZlYVTG+jvXhh6l99NHj+tOgoKU1szjMSRlVnhxnceLPSWN8GI15PdL57YmN1WL5LZRf0jias5wfTnulCCF+EEIkNbFMPL6dlPJU4fMbge+klLnNONdcIcROIcTO4uLiZiuhcpacbSzUUemZkLUpfpm9PONx6H4v9Lof7cbFmDA13RxJsb+Vb9jJFYZxiNEvQmWNp/bI8dTmwbhN0GcBDH8Sus+GAx+Dw3asjUaDxEWgW6I48mDPcX2E9YKjSRD/GBy4FRxVgAmNeTNCE48mPBxNfDz29etPOK0X7VAwUMn+Zn01QjsQoVd9k78G53Yo4bkRScrfvQCpQFjjehiQ2kSb94BsIAsoAaqAp0/Xd2JiolT5i5DztZSH3zv1cadNyh0XSemslbK2SMpnjFK+ECGls+GkpivlbvmS/FpWSduxnaXpUn4wXspdi47ts9c0S7QyeVBmNzws5bOdpPxxzbEDLqeUy6dJWZYuZfKtUm5oL6XLccJn3TU1snzCBOl2uU5UR9rkXnmXdEl7s2RQ+eMBdsqzsGWeLrpKKG3mcvbna85yts9aXwGNczBxLfBlEzeAK6WUUVLKGDxhjmVSyt96majyV6NokyeX+lRoTNBjObjtnkJAV66FthMg6f0TmlVTxxdsJwRfLBiPHfCNAXMg/HA3NFR79ukszRKthD146SZD3N3Q77gBPLVHIXMVpH8DkbM8NU1KVpzwWWGxYJg6lfply5DuY5kYGkxEMJlDPEcdec2SQ+WvQMvzoM/2JeHTwMdCiFnAEWA6gBCiJ/APKeXss+xf5a9AyVbw7vDbbTRmz+LnC35AeL+TwhYHyOFmxpDw65oyigbGL4a4MZD2LXSa0SyxHNRSSQZxYipM7HTiQa9w6HU7mALAuwv03wllG07qw3DppVRcdBGythbzTceyNoyEUcQPWIkjEnW034VBy5uU8KwMtJSyFDipsriUcidwknGWUr4NvH0251RpgQQNhMhxp2/3a8yBJ2z2Jv7Y7CG/RghImNq8OsmN5LGeEnZj4ygWEX5ygz53QVmaZ11jgqCLTz6t0YgmJoaGFStOMNAWYujKS+TzRbPlUWnpSM7nC8DmoL5OVjl7Wl8Jgf3OuptTGucTGjX/haYGPSH0wUITxhlAb4XQU6QX/nI6nQ7vd95Bm5DwyzuV/+FDR2K5odnyqLR0zmHF/nOEWotD5ezx7fhnS9AkRgJJOPlB7owRGg3Wp57yeO+/ukHo8T/Fp1T+elxgIQ4VlZZMIN2a55U3A6HmLv8NaHmzxqoGWuWC5VwZZ5W/C6oHraKiotJCUT1oFRUVlRbKOa7Yfw5QDbSKiooKoIY4VFRUVFo0LSvEob6aVlFRUQHO9VBvIcQdQojkxuJyHwghjKf/1ImoBlpFRUUFOJcGWggRAdwK9JRSdgI0QPNqFByHGuJQUVFRAf6ALA4tYBJCOAAzkH+a9k12oKKioqJyZlkcgUKIncdtvyGl/N9MDlLKPCHEf/CUWq4DVkkpV52pRKqBVlFRUQHOMIujREp5yun7GifQngi0BiqAT4QQV0kp3z0TidQYtIqKigpwjosljQQypZTFUkoHsBzof6YSqR60ioqKCnCO86Czgb5CCDOeEMcIYOdvf+RkVAOtoqKiApzLl4RSyu1CiE+BXY2d7oYmZhs+DaqBVlFRUQHO9VBvKeVDwENn04f4dRHyloIQohjPNFrng0A8E9peaFyIel2IOoGq19kSLaUMOpsOhBAr8MjbHEqklCdPwXOOabEG+nwihNj5W29k/6pciHpdiDqBqpdK06hZHCoqKiotFNVAq6ioqLRQVAPt4Yzfrv5FuBD1uhB1AlUvlSZQY9AqKioqLRTVg1ZRUVFpofwtDbQQwl8IsVoIkdb41+832noLIXKFEK+cTxl/D83RSwjRTQixtbFO7T4hxGV/hqynQwhxsRAiVQiRLoT4VxPHDUKIjxqPbxdCxJx/Kc+cZug1XwhxoPF/s0YIEf1nyHkmnE6n49pNEUJIIYSa1dFM/pYGGvgXsEZKGQ+sadw+FY8BG8+LVGdPc/SyAddIKTsCFwMvCiF8z6OMp0UIoQFeBS4BEoDLhRAJv2o2CyiXUsYBLwD/Pr9SnjnN1Gs3nhrCXYBPgWfOr5RnRjN1QgjhBdwGbD+/Ev61+bsa6InA0sb1pcClTTUSQiQCIcAZlwn8kzitXlLKQ1LKtMb1fKAIOKsE/z+A3kC6lPKwlNIOfIhHt+M5XtdPgRFCCHEeZfw9nFYvKeU6KaWtcXMbEHmeZTxTmvO/Ao+j82+g/nwK91fn72qgQ6SUBY3rhXiM8AkIIRTgOeCu8ynYWXJavY5HCNEb0AMZf7RgZ0gEkHPcdm7jvibbSCmdQCUQcF6k+/00R6/jmQV8/4dKdPacVichRA+glZTy2/Mp2IXABVuLQwjxAxDaxKH7jt+QUkohRFOpLDcC30kpc1uSY3YO9PqlnzDgHeBaKaX73EqpcrYIIa4CegJD/mxZzoZGR+d54Lo/WZS/JBesgZZSjjzVMSHEUSFEmJSyoNFQFTXRrB8wSAhxI2AF9EKIGinlb8Wr/3DOgV4IIbyBb4H7pJTb/iBRz4Y8oNVx25GN+5pqkyuE0AI+QOn5Ee930xy9EEKMxHPDHSKlbDhPsv1eTqeTF9AJWN/o6IQCXwkhJkgpz7j85t+Nv2uI4yvg2sb1a4Evf91ASnmllDJKShmDJ8yx7M82zs3gtHoJIfTA53j0+fQ8ynYm/ATECyFaN8o7A49ux3O8rlOBtbLlJ/WfVi8hRHfgdWCClLLJG2wL4zd1klJWSikDpZQxjb+lbXh0U41zM/i7GuingVFCiDQ8Mx88DSCE6CmEePNPlezsaI5e04HBwHVCiD2NS7c/R9ymaYwp3wysBFKAj6WUyUKIR4UQExqbLQYChBDpwHx+OxOnRdBMvZ7F88T2SeP/5tc3phZFM3VS+Z2oIwlVVFRUWih/Vw9aRUVFpcWjGmgVFRWVFopqoFVUVFRaKKqBVlFRUWmhqAZaRUVFpYWiGmgVFRWVFopqoFVUVFRaKKqBVlFRUWmh/D/zJHMgzJzeJwAAAABJRU5ErkJggg==\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -977,7 +977,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.8.5" } }, "nbformat": 4, From 3cb21a6f03028b455dbb70db21c42bf9e485be91 Mon Sep 17 00:00:00 2001 From: Cyrus Wyett <34195737+cjwyett@users.noreply.github.com> Date: Sat, 15 Aug 2020 07:04:17 +0100 Subject: [PATCH 03/17] Get rid of warnings --- examples/jupyter/pincell.ipynb | 170 +++++++++++++++------------------ 1 file changed, 75 insertions(+), 95 deletions(-) diff --git a/examples/jupyter/pincell.ipynb b/examples/jupyter/pincell.ipynb index 06ac8dc580..9e8775805c 100644 --- a/examples/jupyter/pincell.ipynb +++ b/examples/jupyter/pincell.ipynb @@ -166,18 +166,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/master/anaconda3/envs/OMC/lib/python3.8/site-packages/openmc/mixin.py:68: IDWarning: Another Material instance already exists with id=2.\n", - " warn(msg, IDWarning)\n" - ] - } - ], + "outputs": [], "source": [ "zirconium = openmc.Material(2, \"zirconium\")\n", "zirconium.add_element('Zr', 1.0)\n", @@ -198,7 +189,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -214,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -230,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -239,7 +230,7 @@ "True" ] }, - "execution_count": 10, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -260,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -315,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -377,7 +368,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -425,7 +416,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -446,7 +437,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -489,7 +480,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -507,7 +498,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -524,7 +515,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -550,7 +541,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -567,7 +558,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -576,7 +567,7 @@ "(array([-1., -1., 0.]), array([1., 1., 1.]))" ] }, - "execution_count": 20, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -594,7 +585,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -614,7 +605,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -637,7 +628,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -657,22 +648,22 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 24, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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" ] @@ -696,22 +687,22 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 25, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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"text/plain": [ "
" ] @@ -735,16 +726,16 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 26, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, @@ -783,7 +774,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -801,7 +792,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -819,20 +810,9 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 33, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/master/anaconda3/envs/OMC/lib/python3.8/site-packages/openmc/mixin.py:68: IDWarning: Another Cell instance already exists with id=1.\n", - " warn(msg, IDWarning)\n", - "/home/master/anaconda3/envs/OMC/lib/python3.8/site-packages/openmc/mixin.py:68: IDWarning: Another Cell instance already exists with id=2.\n", - " warn(msg, IDWarning)\n" - ] - } - ], + "outputs": [], "source": [ "fuel = openmc.Cell(1, 'fuel')\n", "fuel.fill = uo2\n", @@ -855,7 +835,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -875,7 +855,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -895,7 +875,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -904,7 +884,7 @@ "openmc.region.Intersection" ] }, - "execution_count": 32, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -924,7 +904,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -940,7 +920,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -987,7 +967,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -1005,7 +985,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -1023,7 +1003,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -1060,7 +1040,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -1079,7 +1059,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -1096,7 +1076,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -1134,7 +1114,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 45, "metadata": { "scrolled": true }, @@ -1172,7 +1152,7 @@ " License | https://docs.openmc.org/en/latest/license.html\n", " Version | 0.12.0\n", " Git SHA1 | 3d90a9f857ec72eae897e054d4225180f1fa4d93\n", - " Date/Time | 2020-08-15 06:49:11\n", + " Date/Time | 2020-08-15 07:03:19\n", " OpenMP Threads | 4\n", "\n", " Reading settings XML file...\n", @@ -1306,20 +1286,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 6.9980e-01 seconds\n", - " Reading cross sections = 6.8788e-01 seconds\n", - " Total time in simulation = 1.9251e+00 seconds\n", - " Time in transport only = 1.9072e+00 seconds\n", - " Time in inactive batches = 1.5794e-01 seconds\n", - " Time in active batches = 1.7672e+00 seconds\n", - " Time synchronizing fission bank = 4.2579e-03 seconds\n", - " Sampling source sites = 3.4948e-03 seconds\n", - " SEND/RECV source sites = 5.9671e-04 seconds\n", - " Time accumulating tallies = 7.9162e-05 seconds\n", - " Total time for finalization = 5.7305e-05 seconds\n", - " Total time elapsed = 2.6291e+00 seconds\n", - " Calculation Rate (inactive) = 63313.6 particles/second\n", - " Calculation Rate (active) = 50928.0 particles/second\n", + " Total time for initialization = 6.9749e-01 seconds\n", + " Reading cross sections = 6.8627e-01 seconds\n", + " Total time in simulation = 1.9684e+00 seconds\n", + " Time in transport only = 1.9468e+00 seconds\n", + " Time in inactive batches = 1.5675e-01 seconds\n", + " Time in active batches = 1.8117e+00 seconds\n", + " Time synchronizing fission bank = 4.5360e-03 seconds\n", + " Sampling source sites = 3.6973e-03 seconds\n", + " SEND/RECV source sites = 6.8224e-04 seconds\n", + " Time accumulating tallies = 1.0140e-04 seconds\n", + " Total time for finalization = 5.5400e-05 seconds\n", + " Total time elapsed = 2.6701e+00 seconds\n", + " Calculation Rate (inactive) = 63796.4 particles/second\n", + " Calculation Rate (active) = 49677.1 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -1345,7 +1325,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -1378,7 +1358,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -1399,7 +1379,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -1434,7 +1414,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -1470,7 +1450,7 @@ " License | https://docs.openmc.org/en/latest/license.html\n", " Version | 0.12.0\n", " Git SHA1 | 3d90a9f857ec72eae897e054d4225180f1fa4d93\n", - " Date/Time | 2020-08-15 06:49:14\n", + " Date/Time | 2020-08-15 07:03:32\n", " OpenMP Threads | 4\n", "\n", " Reading settings XML file...\n", @@ -1510,7 +1490,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -1526,19 +1506,19 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 51, "metadata": { "scrolled": false }, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "" ] }, - "execution_count": 47, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -1557,17 +1537,17 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 52, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "" ] }, - "execution_count": 48, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } From b2412adb740d959a3c1eff421cb6409ed9da418b Mon Sep 17 00:00:00 2001 From: Cyrus Wyett <34195737+cjwyett@users.noreply.github.com> Date: Sat, 15 Aug 2020 07:11:22 +0100 Subject: [PATCH 04/17] Add files via upload --- examples/jupyter/pandas-dataframes.ipynb | 1785 ++++++++++++---------- 1 file changed, 982 insertions(+), 803 deletions(-) diff --git a/examples/jupyter/pandas-dataframes.ipynb b/examples/jupyter/pandas-dataframes.ipynb index 7cc2d92e9b..ddc8ee429b 100644 --- a/examples/jupyter/pandas-dataframes.ipynb +++ b/examples/jupyter/pandas-dataframes.ipynb @@ -14,13 +14,11 @@ "outputs": [], "source": [ "import glob\n", - "\n", "from IPython.display import Image\n", "import matplotlib.pyplot as plt\n", "import scipy.stats\n", "import numpy as np\n", "import pandas as pd\n", - "\n", "import openmc\n", "%matplotlib inline" ] @@ -79,10 +77,10 @@ "outputs": [], "source": [ "# Instantiate a Materials collection\n", - "materials_file = openmc.Materials([fuel, water, zircaloy])\n", + "materials = openmc.Materials([fuel, water, zircaloy])\n", "\n", "# Export to \"materials.xml\"\n", - "materials_file.export_to_xml()" + "materials.export_to_xml()" ] }, { @@ -256,7 +254,7 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJNAAB6JgAAgIQAAPoAAACA6AAAdTAAAOpgAAA6mAAAF3CculE8AAAADFBMVEVyEhLpgJFNv8T///98iBL0AAAAAWJLR0QDEQxM8gAAAAd0SU1FB+MHEhEuBAW6hPoAAAOeSURBVGje7ZtLkqowFIZPrAsDRz247YAluIosgYEO2h20q3AJDFyAA6wSVnkDCAnkBAK/lH0bTpVVX1mCmsfJRx5E7w6hXn/U64PFWKNkkaIriXNMQWajpNDAO9HJQqJzRmGeUpRLHkVeYOJAEnkut+qT5zxpI/EYtZE26qJI3Uy9x2NY4sOBFDafvNWYD2NSo/H9t6FbJRY+qv///E0GbnowNZGqgkxpX1xf4XkYLzWq+rureyRVpfGYUuBEok/VxI6SxJHFQ4MBi2sEsSoIKktqAlJREUVNhZmscTeMzWVN+4l084h082h6atHApG4/DTrar9AtVdiN1my/Rv9huxLXafJ5+u/G/lIOH1b/3fb9aWdRUKv/GiU9BlUi1vVfYlwm7V5s6j8u2p9UnfHZpsbjGnVOO07CcvxsMm2m06uB93rQbGHajJ++Sb+LW91/s6FBp4sh038njX8hcn3WP/7zP7o9/hf/P9ROEfb7R2ZiVI2fRZletfQkXiieSLSrRCq4u7Dwr28XriGkNt02/rWRLKylN8xaeHKjNJH6pJdHT/+Vbun9f/z3zHbamf13PyS9nP/uLOnlsczKGeO/h0m4RimycrT/fsZt//WQXg55/zWa0oWR3tV/u/7L5S/Wf6389Rr/9ZBeHrXIHliMtfSyuMZOldZX5bS7eBQKheSSXt6fLBWmjrR5Se/qvz7+6y4023/9pJfB0nTpW0uvNyar/5YhZJOpOmj7b2AjRanW29TDf3MD1T08pdelwqv/9viDS3pn89+0i6b/Zl2s/Nfwt9H+++6+8xOi678WPkuKR2IqbVB6o37/3Tv89zLWf33aL9p/5vHf/vzxSv8lxH9v1E7abWSk99pFznRH4eKj8Jevp5McWKxMl8HSX4alt0+Fnf7r4W9bdvwc478v8NeX++/I34+WH1p/cPtZeqD5C82fYP5Gxw90/Pot/jvVX1B/gv1t6YE+P4DPL+jz0+/x32nPz+jzOzp/AM9fCInNnyw90Pk7cP4Qnb9E509f56/T5o/R+Wt0/hydv4fXD5Ye6PoVuH6Grt/N47/+65ezzB+NWL9F14/R9Wt0/Rxev196oPtHwP0r8/iv//6d1X+x/WPw/jUhsf1zSw90/+YZ2z+K7l9d/RfbP43u34b3jy890PMLJ+z8xGv8d/r5kdV/sfNL6Pkp+PzW0gM9PwieX0TPT87rv5P2D/4M//U8v4ueH4bPL6Pnp98b/wAXomzv5H3x5AAAACV0RVh0ZGF0ZTpjcmVhdGUAMjAxOS0wNy0xOFQyMjo0NjowNC0wNTowMEOkh1cAAAAldEVYdGRhdGU6bW9kaWZ5ADIwMTktMDctMThUMjI6NDY6MDQtMDU6MDAy+T/rAAAAAElFTkSuQmCC\n", + "image/png": "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\n", "text/plain": [ "" ] @@ -436,645 +434,829 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2019 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.11.0-dev\n", - " Git SHA1 | 61c911cffdae2406f9f4bc667a9a6954748bb70c\n", - " Date/Time | 2019-07-18 22:46:04\n", + " Copyright | 2011-2020 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.12.0\n", + " Git SHA1 | 3d90a9f857ec72eae897e054d4225180f1fa4d93\n", + " Date/Time | 2020-08-15 07:10:20\n", " OpenMP Threads | 4\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Reading U235 from /opt/data/hdf5/nndc_hdf5_v15/U235.h5\n", - " Reading U238 from /opt/data/hdf5/nndc_hdf5_v15/U238.h5\n", - " Reading O16 from /opt/data/hdf5/nndc_hdf5_v15/O16.h5\n", - " Reading H1 from /opt/data/hdf5/nndc_hdf5_v15/H1.h5\n", - " Reading B10 from /opt/data/hdf5/nndc_hdf5_v15/B10.h5\n", - " Reading Zr90 from /opt/data/hdf5/nndc_hdf5_v15/Zr90.h5\n", - " Maximum neutron transport energy: 20000000.000000 eV for U235\n", + " Reading U235 from /home/master/data/nuclear/endfb71_hdf5/U235.h5\n", + " Reading U238 from /home/master/data/nuclear/endfb71_hdf5/U238.h5\n", + " Reading O16 from /home/master/data/nuclear/endfb71_hdf5/O16.h5\n", + " Reading H1 from /home/master/data/nuclear/endfb71_hdf5/H1.h5\n", + " Reading B10 from /home/master/data/nuclear/endfb71_hdf5/B10.h5\n", + " Reading Zr90 from /home/master/data/nuclear/endfb71_hdf5/Zr90.h5\n", + " Minimum neutron data temperature: 294.000000 K\n", + " Maximum neutron data temperature: 294.000000 K\n", " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000.000000 eV for U235\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 0.55921\n", - " 2/1 0.63816\n", - " 3/1 0.68834\n", - " 4/1 0.71192\n", - " 5/1 0.67935\n", - " 6/1 0.68254\n", - " 7/1 0.65804 0.67029 +/- 0.01225\n", - " 8/1 0.66225 0.66761 +/- 0.00756\n", - " 9/1 0.66336 0.66655 +/- 0.00545\n", - " 10/1 0.70686 0.67461 +/- 0.00910\n", - " 11/1 0.71753 0.68176 +/- 0.01031\n", - " 12/1 0.66967 0.68004 +/- 0.00889\n", - " 13/1 0.67800 0.67978 +/- 0.00770\n", - " 14/1 0.65634 0.67718 +/- 0.00727\n", - " 15/1 0.66891 0.67635 +/- 0.00656\n", - " 16/1 0.66281 0.67512 +/- 0.00606\n", - " 17/1 0.68160 0.67566 +/- 0.00556\n", - " 18/1 0.63835 0.67279 +/- 0.00586\n", - " 19/1 0.66200 0.67202 +/- 0.00548\n", - " 20/1 0.67156 0.67199 +/- 0.00510\n", - " Triggers unsatisfied, max unc./thresh. is 68.3537 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 70089 --- greater than max batches\n", + " 1/1 0.53544\n", + " 2/1 0.62631\n", + " 3/1 0.63917\n", + " 4/1 0.67203\n", + " 5/1 0.69300\n", + " 6/1 0.64862\n", + " 7/1 0.63937 0.64399 +/- 0.00463\n", + " 8/1 0.67696 0.65498 +/- 0.01131\n", + " 9/1 0.63216 0.64928 +/- 0.00982\n", + " 10/1 0.70996 0.66141 +/- 0.01433\n", + " 11/1 0.69761 0.66745 +/- 0.01316\n", + " 12/1 0.68662 0.67019 +/- 0.01146\n", + " 13/1 0.64374 0.66688 +/- 0.01046\n", + " 14/1 0.69121 0.66958 +/- 0.00961\n", + " 15/1 0.72125 0.67475 +/- 0.01003\n", + " 16/1 0.72706 0.67950 +/- 0.01024\n", + " 17/1 0.69623 0.68090 +/- 0.00945\n", + " 18/1 0.70953 0.68310 +/- 0.00897\n", + " 19/1 0.69026 0.68361 +/- 0.00832\n", + " 20/1 0.68633 0.68379 +/- 0.00775\n", + " Triggers unsatisfied, max unc./thresh. is 75.24758750489383 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 84938 --- greater than max batches\n", " Creating state point statepoint.020.h5...\n", - " 21/1 0.67469 0.67216 +/- 0.00478\n", - " Triggers unsatisfied, max unc./thresh. is 63.9814 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 65503 --- greater than max batches\n", - " 22/1 0.69218 0.67334 +/- 0.00464\n", - " Triggers unsatisfied, max unc./thresh. is 64.4829 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 70692 --- greater than max batches\n", - " 23/1 0.72838 0.67639 +/- 0.00534\n", - " Triggers unsatisfied, max unc./thresh. is 65.1347 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 76371 --- greater than max batches\n", - " 24/1 0.68472 0.67683 +/- 0.00507\n", - " Triggers unsatisfied, max unc./thresh. is 61.6163 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 72140 --- greater than max batches\n", - " 25/1 0.66664 0.67632 +/- 0.00483\n", - " Triggers unsatisfied, max unc./thresh. is 59.0208 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 69675 --- greater than max batches\n", - " 26/1 0.65315 0.67522 +/- 0.00473\n", - " Triggers unsatisfied, max unc./thresh. is 56.5216 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 67094 --- greater than max batches\n", - " 27/1 0.63865 0.67356 +/- 0.00480\n", - " Triggers unsatisfied, max unc./thresh. is 53.8991 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 63918 --- greater than max batches\n", - " 28/1 0.68053 0.67386 +/- 0.00460\n", - " Triggers unsatisfied, max unc./thresh. is 51.504 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 61017 --- greater than max batches\n", - " 29/1 0.71585 0.67561 +/- 0.00474\n", - " Triggers unsatisfied, max unc./thresh. is 49.3115 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 58364 --- greater than max batches\n", - " 30/1 0.67268 0.67549 +/- 0.00455\n", - " Triggers unsatisfied, max unc./thresh. is 47.3457 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 56046 --- greater than max batches\n", - " 31/1 0.67027 0.67529 +/- 0.00437\n", - " Triggers unsatisfied, max unc./thresh. is 48.2456 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 60524 --- greater than max batches\n", - " 32/1 0.67324 0.67522 +/- 0.00421\n", - " Triggers unsatisfied, max unc./thresh. is 47.1077 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 59922 --- greater than max batches\n", - " 33/1 0.66398 0.67481 +/- 0.00408\n", - " Triggers unsatisfied, max unc./thresh. is 45.4352 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 57807 --- greater than max batches\n", - " 34/1 0.66373 0.67443 +/- 0.00395\n", - " Triggers unsatisfied, max unc./thresh. is 44.8243 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 58273 --- greater than max batches\n", - " 35/1 0.68412 0.67476 +/- 0.00383\n", - " Triggers unsatisfied, max unc./thresh. is 43.7412 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 57404 --- greater than max batches\n", - " 36/1 0.66026 0.67429 +/- 0.00374\n", - " Triggers unsatisfied, max unc./thresh. is 43.0549 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 57471 --- greater than max batches\n", - " 37/1 0.67283 0.67424 +/- 0.00362\n", - " Triggers unsatisfied, max unc./thresh. is 42.9634 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 59073 --- greater than max batches\n", - " 38/1 0.69507 0.67487 +/- 0.00356\n", - " Triggers unsatisfied, max unc./thresh. is 41.6527 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 57259 --- greater than max batches\n", - " 39/1 0.68681 0.67522 +/- 0.00347\n", - " Triggers unsatisfied, max unc./thresh. is 40.4174 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 55547 --- greater than max batches\n", - " 40/1 0.65886 0.67476 +/- 0.00340\n", - " Triggers unsatisfied, max unc./thresh. is 39.424 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 54404 --- greater than max batches\n", - " 41/1 0.63736 0.67372 +/- 0.00347\n", - " Triggers unsatisfied, max unc./thresh. is 40.094 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 57877 --- greater than max batches\n", - " 42/1 0.71800 0.67491 +/- 0.00358\n", - " Triggers unsatisfied, max unc./thresh. is 39.0603 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 56457 --- greater than max batches\n", - " 43/1 0.67193 0.67484 +/- 0.00348\n", - " Triggers unsatisfied, max unc./thresh. is 38.8448 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 57344 --- greater than max batches\n", - " 44/1 0.66680 0.67463 +/- 0.00340\n", - " Triggers unsatisfied, max unc./thresh. is 38.227 for absorption in tally 3\n" + " 21/1 0.68310 0.68375 +/- 0.00725\n", + " Triggers unsatisfied, max unc./thresh. is 71.20148627325992 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 81120 --- greater than max batches\n", + " 22/1 0.68679 0.68393 +/- 0.00681\n", + " Triggers unsatisfied, max unc./thresh. is 66.94650483064697 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 76197 --- greater than max batches\n", + " 23/1 0.67440 0.68340 +/- 0.00644\n", + " Triggers unsatisfied, max unc./thresh. is 63.553590826021285 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 72709 --- greater than max batches\n", + " 24/1 0.67483 0.68295 +/- 0.00611\n", + " Triggers unsatisfied, max unc./thresh. is 60.37873858685279 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 69272 --- greater than max batches\n", + " 25/1 0.71558 0.68458 +/- 0.00602\n", + " Triggers unsatisfied, max unc./thresh. is 60.34535216026281 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 72837 --- greater than max batches\n", + " 26/1 0.71853 0.68620 +/- 0.00595\n", + " Triggers unsatisfied, max unc./thresh. is 59.60875760463032 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 74623 --- greater than max batches\n", + " 27/1 0.67455 0.68567 +/- 0.00570\n", + " Triggers unsatisfied, max unc./thresh. is 57.228951643423976 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 72059 --- greater than max batches\n", + " 28/1 0.69435 0.68605 +/- 0.00546\n", + " Triggers unsatisfied, max unc./thresh. is 56.194065573871285 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 72634 --- greater than max batches\n", + " 29/1 0.67706 0.68567 +/- 0.00524\n", + " Triggers unsatisfied, max unc./thresh. is 53.86022066923874 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 69628 --- greater than max batches\n", + " 30/1 0.69294 0.68596 +/- 0.00504\n", + " Triggers unsatisfied, max unc./thresh. is 51.73413206858763 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 66916 --- greater than max batches\n", + " 31/1 0.69108 0.68616 +/- 0.00484\n", + " Triggers unsatisfied, max unc./thresh. is 49.71174462484801 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 64258 --- greater than max batches\n", + " 32/1 0.68089 0.68596 +/- 0.00466\n", + " Triggers unsatisfied, max unc./thresh. is 50.80993117627794 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 69710 --- greater than max batches\n", + " 33/1 0.67698 0.68564 +/- 0.00450\n", + " Triggers unsatisfied, max unc./thresh. is 50.46659333785448 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 71318 --- greater than max batches\n", + " 34/1 0.68167 0.68551 +/- 0.00435\n", + " Triggers unsatisfied, max unc./thresh. is 48.852656603250665 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 69216 --- greater than max batches\n", + " 35/1 0.67760 0.68524 +/- 0.00421\n", + " Triggers unsatisfied, max unc./thresh. is 48.5685583427197 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 70773 --- greater than max batches\n", + " 36/1 0.67628 0.68495 +/- 0.00408\n", + " Triggers unsatisfied, max unc./thresh. is 47.77661998216646 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 70766 --- greater than max batches\n", + " 37/1 0.66736 0.68440 +/- 0.00399\n", + " Triggers unsatisfied, max unc./thresh. is 46.57810773879176 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 69430 --- greater than max batches\n", + " 38/1 0.71026 0.68519 +/- 0.00395\n", + " Triggers unsatisfied, max unc./thresh. is 45.876107560616674 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 69458 --- greater than max batches\n", + " 39/1 0.67674 0.68494 +/- 0.00384\n", + " Triggers unsatisfied, max unc./thresh. is 45.30341918376721 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 69787 --- greater than max batches\n", + " 40/1 0.69360 0.68519 +/- 0.00373\n", + " Triggers unsatisfied, max unc./thresh. is 44.018056562863386 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 67821 --- greater than max batches\n", + " 41/1 0.70987 0.68587 +/- 0.00369\n", + " Triggers unsatisfied, max unc./thresh. is 42.781078099052785 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 65893 --- greater than max batches\n", + " 42/1 0.68780 0.68592 +/- 0.00359\n", + " Triggers unsatisfied, max unc./thresh. is 41.60877069209228 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 64063 --- greater than max batches\n", + " 43/1 0.69223 0.68609 +/- 0.00350\n", + " Triggers unsatisfied, max unc./thresh. is 42.44932759168626 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 68479 --- greater than max batches\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " WARNING: The estimated number of batches is 56996 --- greater than max batches\n", - " 45/1 0.65956 0.67425 +/- 0.00334\n", - " Triggers unsatisfied, max unc./thresh. is 37.2591 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 55535 --- greater than max batches\n", - " 46/1 0.64705 0.67359 +/- 0.00332\n", - " Triggers unsatisfied, max unc./thresh. is 37.802 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 58594 --- greater than max batches\n", - " 47/1 0.67729 0.67368 +/- 0.00324\n", - " Triggers unsatisfied, max unc./thresh. is 36.9727 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 57419 --- greater than max batches\n", - " 48/1 0.68259 0.67389 +/- 0.00317\n", - " Triggers unsatisfied, max unc./thresh. is 36.3752 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 56901 --- greater than max batches\n", - " 49/1 0.64395 0.67320 +/- 0.00317\n", - " Triggers unsatisfied, max unc./thresh. is 35.7676 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 56296 --- greater than max batches\n", - " 50/1 0.68839 0.67354 +/- 0.00312\n", - " Triggers unsatisfied, max unc./thresh. is 34.977 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 55058 --- greater than max batches\n", - " 51/1 0.71108 0.67436 +/- 0.00316\n", - " Triggers unsatisfied, max unc./thresh. is 34.453 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 54608 --- greater than max batches\n", - " 52/1 0.66286 0.67411 +/- 0.00310\n", - " Triggers unsatisfied, max unc./thresh. is 33.9781 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 54268 --- greater than max batches\n", - " 53/1 0.62666 0.67313 +/- 0.00319\n", - " Triggers unsatisfied, max unc./thresh. is 33.4946 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 53856 --- greater than max batches\n", - " 54/1 0.67124 0.67309 +/- 0.00313\n", - " Triggers unsatisfied, max unc./thresh. is 32.8639 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 52927 --- greater than max batches\n", - " 55/1 0.67741 0.67317 +/- 0.00306\n", - " Triggers unsatisfied, max unc./thresh. is 32.2922 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 52145 --- greater than max batches\n", - " 56/1 0.67182 0.67315 +/- 0.00300\n", - " Triggers unsatisfied, max unc./thresh. is 31.9136 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 51948 --- greater than max batches\n", - " 57/1 0.68764 0.67343 +/- 0.00296\n", - " Triggers unsatisfied, max unc./thresh. is 31.3059 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 50969 --- greater than max batches\n", - " 58/1 0.72310 0.67436 +/- 0.00305\n", - " Triggers unsatisfied, max unc./thresh. is 30.8841 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 50558 --- greater than max batches\n", - " 59/1 0.67689 0.67441 +/- 0.00299\n", - " Triggers unsatisfied, max unc./thresh. is 30.5895 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 50534 --- greater than max batches\n", - " 60/1 0.65890 0.67413 +/- 0.00295\n", - " Triggers unsatisfied, max unc./thresh. is 30.0567 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 49693 --- greater than max batches\n", - " 61/1 0.69128 0.67443 +/- 0.00291\n", - " Triggers unsatisfied, max unc./thresh. is 29.8144 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 49784 --- greater than max batches\n", - " 62/1 0.65469 0.67409 +/- 0.00288\n", - " Triggers unsatisfied, max unc./thresh. is 29.3138 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 48986 --- greater than max batches\n", - " 63/1 0.71839 0.67485 +/- 0.00293\n", - " Triggers unsatisfied, max unc./thresh. is 28.9465 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 48604 --- greater than max batches\n", - " 64/1 0.69556 0.67520 +/- 0.00291\n", - " Triggers unsatisfied, max unc./thresh. is 29.1602 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 50174 --- greater than max batches\n", - " 65/1 0.70067 0.67563 +/- 0.00289\n", - " Triggers unsatisfied, max unc./thresh. is 28.9248 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 50204 --- greater than max batches\n", - " 66/1 0.67994 0.67570 +/- 0.00284\n", - " Triggers unsatisfied, max unc./thresh. is 28.7841 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 50545 --- greater than max batches\n", - " 67/1 0.74539 0.67682 +/- 0.00301\n", - " Triggers unsatisfied, max unc./thresh. is 28.4946 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 50346 --- greater than max batches\n", - " 68/1 0.67753 0.67683 +/- 0.00296\n", - " Triggers unsatisfied, max unc./thresh. is 28.1166 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 49810 --- greater than max batches\n", - " 69/1 0.69595 0.67713 +/- 0.00293\n", - " Triggers unsatisfied, max unc./thresh. is 28.0441 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 50340 --- greater than max batches\n", - " 70/1 0.70621 0.67758 +/- 0.00292\n", - " Triggers unsatisfied, max unc./thresh. is 27.708 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 49908 --- greater than max batches\n", - " 71/1 0.71027 0.67807 +/- 0.00292\n", - " Triggers unsatisfied, max unc./thresh. is 27.2979 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 49187 --- greater than max batches\n", - " 72/1 0.63710 0.67746 +/- 0.00294\n", - " Triggers unsatisfied, max unc./thresh. is 27.3359 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 50071 --- greater than max batches\n", - " 73/1 0.70979 0.67794 +/- 0.00294\n", - " Triggers unsatisfied, max unc./thresh. is 29.5308 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 59306 --- greater than max batches\n", - " 74/1 0.65957 0.67767 +/- 0.00291\n", - " Triggers unsatisfied, max unc./thresh. is 29.2344 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 58976 --- greater than max batches\n", - " 75/1 0.66611 0.67751 +/- 0.00287\n", - " Triggers unsatisfied, max unc./thresh. is 28.8289 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 58183 --- greater than max batches\n", - " 76/1 0.66033 0.67726 +/- 0.00284\n", - " Triggers unsatisfied, max unc./thresh. is 28.4986 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 57670 --- greater than max batches\n", - " 77/1 0.68535 0.67738 +/- 0.00280\n", - " Triggers unsatisfied, max unc./thresh. is 28.2548 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 57486 --- greater than max batches\n", - " 78/1 0.71920 0.67795 +/- 0.00282\n", - " Triggers unsatisfied, max unc./thresh. is 28.2853 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 58410 --- greater than max batches\n", - " 79/1 0.67645 0.67793 +/- 0.00278\n", - " Triggers unsatisfied, max unc./thresh. is 27.9534 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 57829 --- greater than max batches\n", - " 80/1 0.68300 0.67800 +/- 0.00275\n", - " Triggers unsatisfied, max unc./thresh. is 27.5813 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 57060 --- greater than max batches\n", - " 81/1 0.69810 0.67826 +/- 0.00272\n", - " Triggers unsatisfied, max unc./thresh. is 27.2164 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 56301 --- greater than max batches\n", - " 82/1 0.68213 0.67831 +/- 0.00269\n", - " Triggers unsatisfied, max unc./thresh. is 26.8628 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 55570 --- greater than max batches\n", - " 83/1 0.68745 0.67843 +/- 0.00265\n", - " Triggers unsatisfied, max unc./thresh. is 26.5172 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 54852 --- greater than max batches\n", - " 84/1 0.65239 0.67810 +/- 0.00264\n", - " Triggers unsatisfied, max unc./thresh. is 26.2016 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 54241 --- greater than max batches\n" + " 44/1 0.69561 0.68633 +/- 0.00342\n", + " Triggers unsatisfied, max unc./thresh. is 41.34743776753899 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 66680 --- greater than max batches\n", + " 45/1 0.67503 0.68605 +/- 0.00334\n", + " Triggers unsatisfied, max unc./thresh. is 40.97332186124358 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 67158 --- greater than max batches\n", + " 46/1 0.67290 0.68573 +/- 0.00328\n", + " Triggers unsatisfied, max unc./thresh. is 40.24678448931756 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 66417 --- greater than max batches\n", + " 47/1 0.67355 0.68544 +/- 0.00321\n", + " Triggers unsatisfied, max unc./thresh. is 40.42620640829592 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 68645 --- greater than max batches\n", + " 48/1 0.71383 0.68610 +/- 0.00320\n", + " Triggers unsatisfied, max unc./thresh. is 39.90662320308606 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 68485 --- greater than max batches\n", + " 49/1 0.68389 0.68605 +/- 0.00313\n", + " Triggers unsatisfied, max unc./thresh. is 39.075369568753906 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 67188 --- greater than max batches\n", + " 50/1 0.73148 0.68706 +/- 0.00322\n", + " Triggers unsatisfied, max unc./thresh. is 38.57054567218653 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 66951 --- greater than max batches\n", + " 51/1 0.69796 0.68730 +/- 0.00316\n", + " Triggers unsatisfied, max unc./thresh. is 38.21572703507316 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 67186 --- greater than max batches\n", + " 52/1 0.70691 0.68771 +/- 0.00312\n", + " Triggers unsatisfied, max unc./thresh. is 37.50971908717773 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 66134 --- greater than max batches\n", + " 53/1 0.69104 0.68778 +/- 0.00306\n", + " Triggers unsatisfied, max unc./thresh. is 36.824732312223716 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 65096 --- greater than max batches\n", + " 54/1 0.74368 0.68892 +/- 0.00320\n", + " Triggers unsatisfied, max unc./thresh. is 36.20814737643575 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 64246 --- greater than max batches\n", + " 55/1 0.67371 0.68862 +/- 0.00315\n", + " Triggers unsatisfied, max unc./thresh. is 35.48607231512293 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 62969 --- greater than max batches\n", + " 56/1 0.67846 0.68842 +/- 0.00310\n", + " Triggers unsatisfied, max unc./thresh. is 35.34421893287461 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 63715 --- greater than max batches\n", + " 57/1 0.66351 0.68794 +/- 0.00307\n", + " Triggers unsatisfied, max unc./thresh. is 34.67062652878957 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 62512 --- greater than max batches\n", + " 58/1 0.67049 0.68761 +/- 0.00303\n", + " Triggers unsatisfied, max unc./thresh. is 34.22135922543247 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 62074 --- greater than max batches\n", + " 59/1 0.66967 0.68728 +/- 0.00299\n", + " Triggers unsatisfied, max unc./thresh. is 33.66881484408945 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 61219 --- greater than max batches\n", + " 60/1 0.70271 0.68756 +/- 0.00295\n", + " Triggers unsatisfied, max unc./thresh. is 33.19914799505717 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 60626 --- greater than max batches\n", + " 61/1 0.70035 0.68779 +/- 0.00291\n", + " Triggers unsatisfied, max unc./thresh. is 32.65594936729897 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 59725 --- greater than max batches\n", + " 62/1 0.66274 0.68735 +/- 0.00289\n", + " Triggers unsatisfied, max unc./thresh. is 32.15622046485561 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 58945 --- greater than max batches\n", + " 63/1 0.68607 0.68733 +/- 0.00284\n", + " Triggers unsatisfied, max unc./thresh. is 31.601225649282494 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 57926 --- greater than max batches\n", + " 64/1 0.66518 0.68695 +/- 0.00282\n", + " Triggers unsatisfied, max unc./thresh. is 31.12129365572805 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 57149 --- greater than max batches\n", + " 65/1 0.65999 0.68650 +/- 0.00281\n", + " Triggers unsatisfied, max unc./thresh. is 30.641988019531464 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 56341 --- greater than max batches\n", + " 66/1 0.67843 0.68637 +/- 0.00276\n", + " Triggers unsatisfied, max unc./thresh. is 30.320463443580458 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 56085 --- greater than max batches\n", + " 67/1 0.69295 0.68648 +/- 0.00272\n", + " Triggers unsatisfied, max unc./thresh. is 30.06051080038397 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 56031 --- greater than max batches\n", + " 68/1 0.69158 0.68656 +/- 0.00268\n", + " Triggers unsatisfied, max unc./thresh. is 29.7400907913873 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 55727 --- greater than max batches\n", + " 69/1 0.69825 0.68674 +/- 0.00264\n", + " Triggers unsatisfied, max unc./thresh. is 29.278619659445805 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 54869 --- greater than max batches\n", + " 70/1 0.73637 0.68750 +/- 0.00271\n", + " Triggers unsatisfied, max unc./thresh. is 28.945044018568716 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 54464 --- greater than max batches\n", + " 71/1 0.64301 0.68683 +/- 0.00275\n", + " Triggers unsatisfied, max unc./thresh. is 28.73677928804667 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 54508 --- greater than max batches\n", + " 72/1 0.71506 0.68725 +/- 0.00274\n", + " Triggers unsatisfied, max unc./thresh. is 29.20796537704291 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 57164 --- greater than max batches\n", + " 73/1 0.69203 0.68732 +/- 0.00270\n", + " Triggers unsatisfied, max unc./thresh. is 29.56297016014581 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 59435 --- greater than max batches\n", + " 74/1 0.69208 0.68739 +/- 0.00267\n", + " Triggers unsatisfied, max unc./thresh. is 29.545241442413783 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 60237 --- greater than max batches\n", + " 75/1 0.65717 0.68696 +/- 0.00266\n", + " Triggers unsatisfied, max unc./thresh. is 29.284013224166248 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 60034 --- greater than max batches\n", + " 76/1 0.70992 0.68728 +/- 0.00265\n", + " Triggers unsatisfied, max unc./thresh. is 29.00034584995327 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 59718 --- greater than max batches\n", + " 77/1 0.65590 0.68685 +/- 0.00264\n", + " Triggers unsatisfied, max unc./thresh. is 28.867967845905174 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 60007 --- greater than max batches\n", + " 78/1 0.64439 0.68626 +/- 0.00267\n", + " Triggers unsatisfied, max unc./thresh. is 29.016012595317935 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 61466 --- greater than max batches\n", + " 79/1 0.66295 0.68595 +/- 0.00265\n", + " Triggers unsatisfied, max unc./thresh. is 28.626245464278142 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 60646 --- greater than max batches\n", + " 80/1 0.66672 0.68569 +/- 0.00263\n", + " Triggers unsatisfied, max unc./thresh. is 28.24218910063624 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 59827 --- greater than max batches\n", + " 81/1 0.69110 0.68576 +/- 0.00260\n", + " Triggers unsatisfied, max unc./thresh. is 27.917349908027763 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 59238 --- greater than max batches\n", + " 82/1 0.67481 0.68562 +/- 0.00257\n", + " Triggers unsatisfied, max unc./thresh. is 28.01946018168837 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 60457 --- greater than max batches\n", + " 83/1 0.72216 0.68609 +/- 0.00258\n", + " Triggers unsatisfied, max unc./thresh. is 27.931394620754766 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 60858 --- greater than max batches\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 85/1 0.64990 0.67775 +/- 0.00263\n", - " Triggers unsatisfied, max unc./thresh. is 25.9705 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 53963 --- greater than max batches\n", - " 86/1 0.68586 0.67785 +/- 0.00260\n", - " Triggers unsatisfied, max unc./thresh. is 25.7908 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 53884 --- greater than max batches\n", - " 87/1 0.63453 0.67732 +/- 0.00262\n", - " Triggers unsatisfied, max unc./thresh. is 25.5271 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 53439 --- greater than max batches\n", - " 88/1 0.65402 0.67704 +/- 0.00261\n", - " Triggers unsatisfied, max unc./thresh. is 25.321 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 53221 --- greater than max batches\n", - " 89/1 0.69063 0.67720 +/- 0.00258\n", - " Triggers unsatisfied, max unc./thresh. is 25.8769 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 56253 --- greater than max batches\n", - " 90/1 0.65729 0.67697 +/- 0.00256\n", - " Triggers unsatisfied, max unc./thresh. is 25.7648 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 56431 --- greater than max batches\n", - " 91/1 0.72355 0.67751 +/- 0.00259\n", - " Triggers unsatisfied, max unc./thresh. is 25.5034 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 55942 --- greater than max batches\n", - " 92/1 0.63010 0.67696 +/- 0.00262\n", - " Triggers unsatisfied, max unc./thresh. is 25.2708 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 55565 --- greater than max batches\n", - " 93/1 0.68610 0.67707 +/- 0.00259\n", - " Triggers unsatisfied, max unc./thresh. is 24.9941 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 54980 --- greater than max batches\n", - " 94/1 0.67618 0.67706 +/- 0.00256\n", - " Triggers unsatisfied, max unc./thresh. is 24.7139 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 54365 --- greater than max batches\n", - " 95/1 0.68946 0.67719 +/- 0.00253\n", - " Triggers unsatisfied, max unc./thresh. is 25.4371 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 58240 --- greater than max batches\n", - " 96/1 0.70557 0.67751 +/- 0.00252\n", - " Triggers unsatisfied, max unc./thresh. is 25.5082 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 59216 --- greater than max batches\n", - " 97/1 0.64689 0.67717 +/- 0.00252\n", - " Triggers unsatisfied, max unc./thresh. is 25.2374 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 58603 --- greater than max batches\n", - " 98/1 0.70194 0.67744 +/- 0.00251\n", - " Triggers unsatisfied, max unc./thresh. is 25.393 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 59972 --- greater than max batches\n", - " 99/1 0.68278 0.67750 +/- 0.00248\n", - " Triggers unsatisfied, max unc./thresh. is 25.5651 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 61441 --- greater than max batches\n", - " 100/1 0.67066 0.67742 +/- 0.00246\n", - " Triggers unsatisfied, max unc./thresh. is 25.3552 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 61079 --- greater than max batches\n", - " 101/1 0.64907 0.67713 +/- 0.00245\n", - " Triggers unsatisfied, max unc./thresh. is 25.3463 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 61679 --- greater than max batches\n", - " 102/1 0.69810 0.67735 +/- 0.00243\n", - " Triggers unsatisfied, max unc./thresh. is 25.1877 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 61544 --- greater than max batches\n", - " 103/1 0.70659 0.67764 +/- 0.00242\n", - " Triggers unsatisfied, max unc./thresh. is 24.9371 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 60948 --- greater than max batches\n", - " 104/1 0.64152 0.67728 +/- 0.00243\n", - " Triggers unsatisfied, max unc./thresh. is 24.6848 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 60330 --- greater than max batches\n", - " 105/1 0.68117 0.67732 +/- 0.00240\n", - " Triggers unsatisfied, max unc./thresh. is 24.4368 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 59721 --- greater than max batches\n", - " 106/1 0.71963 0.67774 +/- 0.00242\n", - " Triggers unsatisfied, max unc./thresh. is 24.2091 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 59200 --- greater than max batches\n", - " 107/1 0.69488 0.67790 +/- 0.00240\n", - " Triggers unsatisfied, max unc./thresh. is 23.9711 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 58616 --- greater than max batches\n", - " 108/1 0.65697 0.67770 +/- 0.00238\n", - " Triggers unsatisfied, max unc./thresh. is 23.8071 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 58384 --- greater than max batches\n", - " 109/1 0.70032 0.67792 +/- 0.00237\n", - " Triggers unsatisfied, max unc./thresh. is 23.5788 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 57825 --- greater than max batches\n", - " 110/1 0.66571 0.67780 +/- 0.00235\n", - " Triggers unsatisfied, max unc./thresh. is 23.5035 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 58009 --- greater than max batches\n", - " 111/1 0.69676 0.67798 +/- 0.00234\n", - " Triggers unsatisfied, max unc./thresh. is 23.3157 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 57629 --- greater than max batches\n", - " 112/1 0.68219 0.67802 +/- 0.00231\n", - " Triggers unsatisfied, max unc./thresh. is 23.1525 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 57361 --- greater than max batches\n", - " 113/1 0.69025 0.67813 +/- 0.00230\n", - " Triggers unsatisfied, max unc./thresh. is 23.0036 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 57156 --- greater than max batches\n", - " 114/1 0.69241 0.67826 +/- 0.00228\n", - " Triggers unsatisfied, max unc./thresh. is 22.792 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 56628 --- greater than max batches\n", - " 115/1 0.68646 0.67834 +/- 0.00226\n", - " Triggers unsatisfied, max unc./thresh. is 22.6864 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 56620 --- greater than max batches\n", - " 116/1 0.69601 0.67850 +/- 0.00224\n", - " Triggers unsatisfied, max unc./thresh. is 22.5007 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 56203 --- greater than max batches\n", - " 117/1 0.68761 0.67858 +/- 0.00222\n", - " Triggers unsatisfied, max unc./thresh. is 22.3093 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 55749 --- greater than max batches\n", - " 118/1 0.71356 0.67889 +/- 0.00223\n", - " Triggers unsatisfied, max unc./thresh. is 22.6651 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 58054 --- greater than max batches\n", - " 119/1 0.69850 0.67906 +/- 0.00221\n", - " Triggers unsatisfied, max unc./thresh. is 22.4712 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 57570 --- greater than max batches\n", - " 120/1 0.70957 0.67933 +/- 0.00221\n", - " Triggers unsatisfied, max unc./thresh. is 22.3266 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 57331 --- greater than max batches\n", - " 121/1 0.69643 0.67947 +/- 0.00220\n", - " Triggers unsatisfied, max unc./thresh. is 22.6029 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 59269 --- greater than max batches\n", - " 122/1 0.67717 0.67945 +/- 0.00218\n", - " Triggers unsatisfied, max unc./thresh. is 22.4667 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 59062 --- greater than max batches\n", - " 123/1 0.68419 0.67949 +/- 0.00216\n", - " Triggers unsatisfied, max unc./thresh. is 22.3764 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 59089 --- greater than max batches\n", - " 124/1 0.69221 0.67960 +/- 0.00214\n", - " Triggers unsatisfied, max unc./thresh. is 22.3341 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 59364 --- greater than max batches\n", - " 125/1 0.73940 0.68010 +/- 0.00218\n", - " Triggers unsatisfied, max unc./thresh. is 22.1478 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 58868 --- greater than max batches\n", - " 126/1 0.66908 0.68001 +/- 0.00217\n", - " Triggers unsatisfied, max unc./thresh. is 22.0085 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 58615 --- greater than max batches\n" + " 84/1 0.68429 0.68607 +/- 0.00254\n", + " Triggers unsatisfied, max unc./thresh. is 27.713137470531738 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 60679 --- greater than max batches\n", + " 85/1 0.65458 0.68567 +/- 0.00254\n", + " Triggers unsatisfied, max unc./thresh. is 27.364539968246927 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 59911 --- greater than max batches\n", + " 86/1 0.69966 0.68585 +/- 0.00252\n", + " Triggers unsatisfied, max unc./thresh. is 27.178113974435043 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 59836 --- greater than max batches\n", + " 87/1 0.64776 0.68538 +/- 0.00253\n", + " Triggers unsatisfied, max unc./thresh. is 26.941566345072534 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 59525 --- greater than max batches\n", + " 88/1 0.62737 0.68468 +/- 0.00259\n", + " Triggers unsatisfied, max unc./thresh. is 26.73959660667411 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 59351 --- greater than max batches\n", + " 89/1 0.69779 0.68484 +/- 0.00257\n", + " Triggers unsatisfied, max unc./thresh. is 26.490234865810894 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 58951 --- greater than max batches\n", + " 90/1 0.67312 0.68470 +/- 0.00254\n", + " Triggers unsatisfied, max unc./thresh. is 26.24036001465229 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 58533 --- greater than max batches\n", + " 91/1 0.69289 0.68480 +/- 0.00251\n", + " Triggers unsatisfied, max unc./thresh. is 25.936795778335345 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 57859 --- greater than max batches\n", + " 92/1 0.69884 0.68496 +/- 0.00249\n", + " Triggers unsatisfied, max unc./thresh. is 25.695465963215582 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 57448 --- greater than max batches\n", + " 93/1 0.71351 0.68528 +/- 0.00248\n", + " Triggers unsatisfied, max unc./thresh. is 25.49001212499821 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 57183 --- greater than max batches\n", + " 94/1 0.65602 0.68495 +/- 0.00248\n", + " Triggers unsatisfied, max unc./thresh. is 25.350859463183905 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 57203 --- greater than max batches\n", + " 95/1 0.72223 0.68537 +/- 0.00248\n", + " Triggers unsatisfied, max unc./thresh. is 25.157803279393637 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 56968 --- greater than max batches\n", + " 96/1 0.67930 0.68530 +/- 0.00246\n", + " Triggers unsatisfied, max unc./thresh. is 24.92205849077747 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 56526 --- greater than max batches\n", + " 97/1 0.66201 0.68505 +/- 0.00244\n", + " Triggers unsatisfied, max unc./thresh. is 24.653967027285237 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 55925 --- greater than max batches\n", + " 98/1 0.71110 0.68533 +/- 0.00243\n", + " Triggers unsatisfied, max unc./thresh. is 24.566957281211884 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 56134 --- greater than max batches\n", + " 99/1 0.69409 0.68542 +/- 0.00241\n", + " Triggers unsatisfied, max unc./thresh. is 24.581943149247135 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 56807 --- greater than max batches\n", + " 100/1 0.71197 0.68570 +/- 0.00240\n", + " Triggers unsatisfied, max unc./thresh. is 24.444112571967217 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 56769 --- greater than max batches\n", + " 101/1 0.71713 0.68603 +/- 0.00240\n", + " Triggers unsatisfied, max unc./thresh. is 24.18957776896016 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 56179 --- greater than max batches\n", + " 102/1 0.68143 0.68598 +/- 0.00237\n", + " Triggers unsatisfied, max unc./thresh. is 23.97797098066553 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 55775 --- greater than max batches\n", + " 103/1 0.69936 0.68612 +/- 0.00235\n", + " Triggers unsatisfied, max unc./thresh. is 24.253000406602812 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 57650 --- greater than max batches\n", + " 104/1 0.65247 0.68578 +/- 0.00235\n", + " Triggers unsatisfied, max unc./thresh. is 24.593482483379837 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 59885 --- greater than max batches\n", + " 105/1 0.66517 0.68557 +/- 0.00234\n", + " Triggers unsatisfied, max unc./thresh. is 24.37904760701804 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 59439 --- greater than max batches\n", + " 106/1 0.67814 0.68550 +/- 0.00232\n", + " Triggers unsatisfied, max unc./thresh. is 24.142311084988883 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 58873 --- greater than max batches\n", + " 107/1 0.67788 0.68542 +/- 0.00229\n", + " Triggers unsatisfied, max unc./thresh. is 23.935477435724106 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 58442 --- greater than max batches\n", + " 108/1 0.68016 0.68537 +/- 0.00227\n", + " Triggers unsatisfied, max unc./thresh. is 24.532504688648594 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 61995 --- greater than max batches\n", + " 109/1 0.66963 0.68522 +/- 0.00226\n", + " Triggers unsatisfied, max unc./thresh. is 24.354532539671386 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 61692 --- greater than max batches\n", + " 110/1 0.67556 0.68513 +/- 0.00224\n", + " Triggers unsatisfied, max unc./thresh. is 24.16165322902175 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 61303 --- greater than max batches\n", + " 111/1 0.68273 0.68511 +/- 0.00222\n", + " Triggers unsatisfied, max unc./thresh. is 24.00069508298176 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 61065 --- greater than max batches\n", + " 112/1 0.69505 0.68520 +/- 0.00220\n", + " Triggers unsatisfied, max unc./thresh. is 23.791656279909404 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 60572 --- greater than max batches\n", + " 113/1 0.69385 0.68528 +/- 0.00218\n", + " Triggers unsatisfied, max unc./thresh. is 23.667941020219764 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 60504 --- greater than max batches\n", + " 114/1 0.65352 0.68499 +/- 0.00218\n", + " Triggers unsatisfied, max unc./thresh. is 23.469658485546123 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 60045 --- greater than max batches\n", + " 115/1 0.68339 0.68497 +/- 0.00216\n", + " Triggers unsatisfied, max unc./thresh. is 23.259123161328624 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 59514 --- greater than max batches\n", + " 116/1 0.65854 0.68474 +/- 0.00215\n", + " Triggers unsatisfied, max unc./thresh. is 23.06250977653337 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 59044 --- greater than max batches\n", + " 117/1 0.66907 0.68460 +/- 0.00214\n", + " Triggers unsatisfied, max unc./thresh. is 22.874382198219536 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 58608 --- greater than max batches\n", + " 118/1 0.68165 0.68457 +/- 0.00212\n", + " Triggers unsatisfied, max unc./thresh. is 22.709602691165983 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 58283 --- greater than max batches\n", + " 119/1 0.70967 0.68479 +/- 0.00211\n", + " Triggers unsatisfied, max unc./thresh. is 22.509869996658225 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 57769 --- greater than max batches\n", + " 120/1 0.65543 0.68453 +/- 0.00211\n", + " Triggers unsatisfied, max unc./thresh. is 22.422104322874098 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 57822 --- greater than max batches\n", + " 121/1 0.67305 0.68444 +/- 0.00209\n", + " Triggers unsatisfied, max unc./thresh. is 22.32834321567902 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 57838 --- greater than max batches\n", + " 122/1 0.68206 0.68441 +/- 0.00207\n", + " Triggers unsatisfied, max unc./thresh. is 22.155196965032374 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 57435 --- greater than max batches\n", + " 123/1 0.71125 0.68464 +/- 0.00207\n", + " Triggers unsatisfied, max unc./thresh. is 21.96683678913398 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 56945 --- greater than max batches\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 127/1 0.66041 0.67985 +/- 0.00216\n", - " Triggers unsatisfied, max unc./thresh. is 21.8274 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 58131 --- greater than max batches\n", - " 128/1 0.69395 0.67996 +/- 0.00214\n", - " Triggers unsatisfied, max unc./thresh. is 21.6537 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 57678 --- greater than max batches\n", - " 129/1 0.68665 0.68002 +/- 0.00212\n", - " Triggers unsatisfied, max unc./thresh. is 21.7739 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 58794 --- greater than max batches\n", - " 130/1 0.64849 0.67976 +/- 0.00212\n", - " Triggers unsatisfied, max unc./thresh. is 21.7492 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 59134 --- greater than max batches\n", - " 131/1 0.69734 0.67990 +/- 0.00211\n", - " Triggers unsatisfied, max unc./thresh. is 21.59 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 58738 --- greater than max batches\n", - " 132/1 0.69482 0.68002 +/- 0.00210\n", - " Triggers unsatisfied, max unc./thresh. is 21.4249 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 58302 --- greater than max batches\n", - " 133/1 0.68884 0.68009 +/- 0.00208\n", - " Triggers unsatisfied, max unc./thresh. is 21.2587 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 57853 --- greater than max batches\n", - " 134/1 0.63042 0.67971 +/- 0.00210\n", - " Triggers unsatisfied, max unc./thresh. is 21.1851 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 57902 --- greater than max batches\n", - " 135/1 0.69209 0.67980 +/- 0.00209\n", - " Triggers unsatisfied, max unc./thresh. is 21.0525 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 57623 --- greater than max batches\n", - " 136/1 0.69873 0.67995 +/- 0.00208\n", - " Triggers unsatisfied, max unc./thresh. is 20.9996 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 57774 --- greater than max batches\n", - " 137/1 0.70270 0.68012 +/- 0.00207\n", - " Triggers unsatisfied, max unc./thresh. is 20.8455 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 57364 --- greater than max batches\n", - " 138/1 0.67295 0.68006 +/- 0.00205\n", - " Triggers unsatisfied, max unc./thresh. is 21.3716 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 60752 --- greater than max batches\n", - " 139/1 0.63853 0.67975 +/- 0.00206\n", - " Triggers unsatisfied, max unc./thresh. is 21.2124 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 60301 --- greater than max batches\n", - " 140/1 0.66645 0.67966 +/- 0.00205\n", - " Triggers unsatisfied, max unc./thresh. is 21.1279 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 60268 --- greater than max batches\n", - " 141/1 0.70730 0.67986 +/- 0.00204\n", - " Triggers unsatisfied, max unc./thresh. is 20.9845 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 59893 --- greater than max batches\n", - " 142/1 0.68838 0.67992 +/- 0.00203\n", - " Triggers unsatisfied, max unc./thresh. is 20.8774 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 59719 --- greater than max batches\n", - " 143/1 0.64900 0.67970 +/- 0.00203\n", - " Triggers unsatisfied, max unc./thresh. is 21.3772 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 63069 --- greater than max batches\n", - " 144/1 0.64490 0.67945 +/- 0.00203\n", - " Triggers unsatisfied, max unc./thresh. is 21.2531 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 62791 --- greater than max batches\n", - " 145/1 0.69221 0.67954 +/- 0.00201\n", - " Triggers unsatisfied, max unc./thresh. is 21.2049 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 62956 --- greater than max batches\n", - " 146/1 0.69481 0.67965 +/- 0.00200\n", - " Triggers unsatisfied, max unc./thresh. is 21.0645 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 62569 --- greater than max batches\n", - " 147/1 0.70394 0.67982 +/- 0.00200\n", - " Triggers unsatisfied, max unc./thresh. is 20.9156 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 62125 --- greater than max batches\n", - " 148/1 0.69482 0.67992 +/- 0.00198\n", - " Triggers unsatisfied, max unc./thresh. is 20.7699 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 61694 --- greater than max batches\n", - " 149/1 0.63886 0.67964 +/- 0.00199\n", - " Triggers unsatisfied, max unc./thresh. is 20.6366 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 61331 --- greater than max batches\n", - " 150/1 0.69377 0.67973 +/- 0.00198\n", - " Triggers unsatisfied, max unc./thresh. is 20.5819 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 61430 --- greater than max batches\n", - " 151/1 0.71045 0.67994 +/- 0.00198\n", - " Triggers unsatisfied, max unc./thresh. is 20.5417 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 61612 --- greater than max batches\n", - " 152/1 0.66093 0.67982 +/- 0.00197\n", - " Triggers unsatisfied, max unc./thresh. is 20.4124 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 61256 --- greater than max batches\n", - " 153/1 0.68564 0.67985 +/- 0.00196\n", - " Triggers unsatisfied, max unc./thresh. is 20.3025 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 61010 --- greater than max batches\n", - " 154/1 0.66961 0.67979 +/- 0.00194\n", - " Triggers unsatisfied, max unc./thresh. is 20.2239 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 60948 --- greater than max batches\n", - " 155/1 0.67099 0.67973 +/- 0.00193\n", - " Triggers unsatisfied, max unc./thresh. is 20.0962 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 60584 --- greater than max batches\n", - " 156/1 0.72742 0.68004 +/- 0.00194\n", - " Triggers unsatisfied, max unc./thresh. is 19.9753 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 60256 --- greater than max batches\n", - " 157/1 0.66458 0.67994 +/- 0.00193\n", - " Triggers unsatisfied, max unc./thresh. is 19.8852 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 60109 --- greater than max batches\n", - " 158/1 0.69052 0.68001 +/- 0.00192\n", - " Triggers unsatisfied, max unc./thresh. is 19.7963 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 59965 --- greater than max batches\n", - " 159/1 0.70643 0.68018 +/- 0.00192\n", - " Triggers unsatisfied, max unc./thresh. is 19.6991 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 59766 --- greater than max batches\n", - " 160/1 0.68576 0.68022 +/- 0.00191\n", - " Triggers unsatisfied, max unc./thresh. is 19.6197 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 59670 --- greater than max batches\n", - " 161/1 0.69854 0.68034 +/- 0.00190\n", - " Triggers unsatisfied, max unc./thresh. is 19.8287 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 61341 --- greater than max batches\n", - " 162/1 0.65983 0.68020 +/- 0.00189\n", - " Triggers unsatisfied, max unc./thresh. is 20.0243 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 62958 --- greater than max batches\n", - " 163/1 0.66316 0.68010 +/- 0.00188\n", - " Triggers unsatisfied, max unc./thresh. is 19.8975 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 62560 --- greater than max batches\n", - " 164/1 0.66179 0.67998 +/- 0.00187\n", - " Triggers unsatisfied, max unc./thresh. is 19.895 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 62940 --- greater than max batches\n", - " 165/1 0.70881 0.68016 +/- 0.00187\n", - " Triggers unsatisfied, max unc./thresh. is 19.8013 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 62740 --- greater than max batches\n", - " 166/1 0.70729 0.68033 +/- 0.00187\n", - " Triggers unsatisfied, max unc./thresh. is 19.6876 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 62410 --- greater than max batches\n", - " 167/1 0.71073 0.68052 +/- 0.00186\n", - " Triggers unsatisfied, max unc./thresh. is 19.5695 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 62046 --- greater than max batches\n" + " 124/1 0.65918 0.68443 +/- 0.00206\n", + " Triggers unsatisfied, max unc./thresh. is 21.78216358933223 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 56467 --- greater than max batches\n", + " 125/1 0.68122 0.68440 +/- 0.00205\n", + " Triggers unsatisfied, max unc./thresh. is 21.681928420505304 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 56418 --- greater than max batches\n", + " 126/1 0.66900 0.68427 +/- 0.00203\n", + " Triggers unsatisfied, max unc./thresh. is 21.60631058168512 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 56492 --- greater than max batches\n", + " 127/1 0.66742 0.68414 +/- 0.00202\n", + " Triggers unsatisfied, max unc./thresh. is 21.468291123480988 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 56234 --- greater than max batches\n", + " 128/1 0.66971 0.68402 +/- 0.00201\n", + " Triggers unsatisfied, max unc./thresh. is 21.313238544974386 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 55879 --- greater than max batches\n", + " 129/1 0.68183 0.68400 +/- 0.00199\n", + " Triggers unsatisfied, max unc./thresh. is 21.314008888585132 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 56337 --- greater than max batches\n", + " 130/1 0.68403 0.68400 +/- 0.00197\n", + " Triggers unsatisfied, max unc./thresh. is 21.159444482258046 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 55971 --- greater than max batches\n", + " 131/1 0.69137 0.68406 +/- 0.00196\n", + " Triggers unsatisfied, max unc./thresh. is 21.24931160989673 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 56899 --- greater than max batches\n", + " 132/1 0.67481 0.68399 +/- 0.00195\n", + " Triggers unsatisfied, max unc./thresh. is 21.164512281281944 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 56893 --- greater than max batches\n", + " 133/1 0.70390 0.68414 +/- 0.00194\n", + " Triggers unsatisfied, max unc./thresh. is 21.084176856795946 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 56907 --- greater than max batches\n", + " 134/1 0.67961 0.68411 +/- 0.00192\n", + " Triggers unsatisfied, max unc./thresh. is 21.48684646255342 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 59563 --- greater than max batches\n", + " 135/1 0.65362 0.68387 +/- 0.00192\n", + " Triggers unsatisfied, max unc./thresh. is 21.41235779526348 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 59609 --- greater than max batches\n", + " 136/1 0.63946 0.68353 +/- 0.00194\n", + " Triggers unsatisfied, max unc./thresh. is 21.30299014546295 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 59456 --- greater than max batches\n", + " 137/1 0.64818 0.68327 +/- 0.00194\n", + " Triggers unsatisfied, max unc./thresh. is 21.159761745415484 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 59107 --- greater than max batches\n", + " 138/1 0.68975 0.68331 +/- 0.00193\n", + " Triggers unsatisfied, max unc./thresh. is 21.000094566393475 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 58659 --- greater than max batches\n", + " 139/1 0.67280 0.68324 +/- 0.00191\n", + " Triggers unsatisfied, max unc./thresh. is 20.853656171297644 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 58279 --- greater than max batches\n", + " 140/1 0.66857 0.68313 +/- 0.00190\n", + " Triggers unsatisfied, max unc./thresh. is 20.709591767033707 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 57905 --- greater than max batches\n", + " 141/1 0.68175 0.68312 +/- 0.00189\n", + " Triggers unsatisfied, max unc./thresh. is 20.560813068689416 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 57499 --- greater than max batches\n", + " 142/1 0.72210 0.68340 +/- 0.00190\n", + " Triggers unsatisfied, max unc./thresh. is 20.54814917791921 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 57851 --- greater than max batches\n", + " 143/1 0.67361 0.68333 +/- 0.00188\n", + " Triggers unsatisfied, max unc./thresh. is 20.4177880049802 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 57536 --- greater than max batches\n", + " 144/1 0.65862 0.68315 +/- 0.00188\n", + " Triggers unsatisfied, max unc./thresh. is 21.229890183572195 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 62654 --- greater than max batches\n", + " 145/1 0.69713 0.68325 +/- 0.00187\n", + " Triggers unsatisfied, max unc./thresh. is 21.34513800240435 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 63792 --- greater than max batches\n", + " 146/1 0.72980 0.68358 +/- 0.00188\n", + " Triggers unsatisfied, max unc./thresh. is 21.60165210412777 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 65801 --- greater than max batches\n", + " 147/1 0.70004 0.68370 +/- 0.00187\n", + " Triggers unsatisfied, max unc./thresh. is 21.596734424310384 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 66237 --- greater than max batches\n", + " 148/1 0.68882 0.68374 +/- 0.00186\n", + " Triggers unsatisfied, max unc./thresh. is 21.447240534346236 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 65783 --- greater than max batches\n", + " 149/1 0.70401 0.68388 +/- 0.00185\n", + " Triggers unsatisfied, max unc./thresh. is 21.424993974056104 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 66106 --- greater than max batches\n", + " 150/1 0.72110 0.68413 +/- 0.00186\n", + " Triggers unsatisfied, max unc./thresh. is 21.27792348945665 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 65654 --- greater than max batches\n", + " 151/1 0.65918 0.68396 +/- 0.00185\n", + " Triggers unsatisfied, max unc./thresh. is 21.378637401006184 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 66734 --- greater than max batches\n", + " 152/1 0.67751 0.68392 +/- 0.00184\n", + " Triggers unsatisfied, max unc./thresh. is 21.25974745003047 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 66446 --- greater than max batches\n", + " 153/1 0.69302 0.68398 +/- 0.00183\n", + " Triggers unsatisfied, max unc./thresh. is 21.16055371271148 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 66275 --- greater than max batches\n", + " 154/1 0.67102 0.68389 +/- 0.00182\n", + " Triggers unsatisfied, max unc./thresh. is 21.0227808264386 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 65857 --- greater than max batches\n", + " 155/1 0.64427 0.68363 +/- 0.00183\n", + " Triggers unsatisfied, max unc./thresh. is 20.882547322553506 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 65418 --- greater than max batches\n", + " 156/1 0.68488 0.68364 +/- 0.00181\n", + " Triggers unsatisfied, max unc./thresh. is 20.797424126850476 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 65318 --- greater than max batches\n", + " 157/1 0.67337 0.68357 +/- 0.00180\n", + " Triggers unsatisfied, max unc./thresh. is 20.67015745584828 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 64948 --- greater than max batches\n", + " 158/1 0.66662 0.68346 +/- 0.00180\n", + " Triggers unsatisfied, max unc./thresh. is 20.568519956722266 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 64734 --- greater than max batches\n", + " 159/1 0.62697 0.68309 +/- 0.00182\n", + " Triggers unsatisfied, max unc./thresh. is 20.47085213159483 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 64540 --- greater than max batches\n", + " 160/1 0.68300 0.68309 +/- 0.00181\n", + " Triggers unsatisfied, max unc./thresh. is 20.34586209866351 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 64168 --- greater than max batches\n", + " 161/1 0.68918 0.68313 +/- 0.00180\n", + " Triggers unsatisfied, max unc./thresh. is 20.23505377614212 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 63881 --- greater than max batches\n", + " 162/1 0.70939 0.68330 +/- 0.00179\n", + " Triggers unsatisfied, max unc./thresh. is 20.21114215977674 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 64138 --- greater than max batches\n", + " 163/1 0.69681 0.68338 +/- 0.00179\n", + " Triggers unsatisfied, max unc./thresh. is 20.163170350438893 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 64241 --- greater than max batches\n", + " 164/1 0.66454 0.68326 +/- 0.00178\n", + " Triggers unsatisfied, max unc./thresh. is 20.109882525638955 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 64306 --- greater than max batches\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 168/1 0.69610 0.68061 +/- 0.00185\n", - " Triggers unsatisfied, max unc./thresh. is 19.4797 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 61857 --- greater than max batches\n", - " 169/1 0.67141 0.68056 +/- 0.00184\n", - " Triggers unsatisfied, max unc./thresh. is 19.438 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 61970 --- greater than max batches\n", - " 170/1 0.67727 0.68054 +/- 0.00183\n", - " Triggers unsatisfied, max unc./thresh. is 19.3208 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 61599 --- greater than max batches\n", - " 171/1 0.64150 0.68030 +/- 0.00184\n", - " Triggers unsatisfied, max unc./thresh. is 19.2066 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 61242 --- greater than max batches\n", - " 172/1 0.68758 0.68035 +/- 0.00183\n", - " Triggers unsatisfied, max unc./thresh. is 19.114 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 61018 --- greater than max batches\n", - " 173/1 0.67126 0.68029 +/- 0.00182\n", - " Triggers unsatisfied, max unc./thresh. is 19.1545 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 61644 --- greater than max batches\n", - " 174/1 0.65933 0.68017 +/- 0.00181\n", - " Triggers unsatisfied, max unc./thresh. is 19.0415 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 61281 --- greater than max batches\n", - " 175/1 0.70572 0.68032 +/- 0.00181\n", - " Triggers unsatisfied, max unc./thresh. is 18.9347 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 60954 --- greater than max batches\n", - " 176/1 0.66175 0.68021 +/- 0.00180\n", - " Triggers unsatisfied, max unc./thresh. is 18.8337 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 60660 --- greater than max batches\n", - " 177/1 0.68714 0.68025 +/- 0.00179\n", - " Triggers unsatisfied, max unc./thresh. is 18.7329 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 60364 --- greater than max batches\n", - " 178/1 0.70181 0.68037 +/- 0.00178\n", - " Triggers unsatisfied, max unc./thresh. is 18.6297 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 60048 --- greater than max batches\n", - " 179/1 0.66700 0.68030 +/- 0.00177\n", - " Triggers unsatisfied, max unc./thresh. is 18.5239 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 59711 --- greater than max batches\n", - " 180/1 0.68980 0.68035 +/- 0.00176\n", - " Triggers unsatisfied, max unc./thresh. is 18.4186 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 59374 --- greater than max batches\n", - " 181/1 0.69586 0.68044 +/- 0.00176\n", - " Triggers unsatisfied, max unc./thresh. is 18.3816 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 59473 --- greater than max batches\n", - " 182/1 0.68689 0.68048 +/- 0.00175\n", - " Triggers unsatisfied, max unc./thresh. is 18.2781 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 59139 --- greater than max batches\n", - " 183/1 0.69257 0.68054 +/- 0.00174\n", - " Triggers unsatisfied, max unc./thresh. is 18.1773 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 58819 --- greater than max batches\n", - " 184/1 0.69926 0.68065 +/- 0.00173\n", - " Triggers unsatisfied, max unc./thresh. is 18.2191 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 59422 --- greater than max batches\n", - " 185/1 0.67801 0.68063 +/- 0.00172\n", - " Triggers unsatisfied, max unc./thresh. is 18.1184 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 59096 --- greater than max batches\n", - " 186/1 0.67049 0.68058 +/- 0.00171\n", - " Triggers unsatisfied, max unc./thresh. is 18.0484 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 58965 --- greater than max batches\n", - " 187/1 0.68164 0.68058 +/- 0.00170\n", - " Triggers unsatisfied, max unc./thresh. is 17.9808 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 58848 --- greater than max batches\n", - " 188/1 0.66856 0.68052 +/- 0.00170\n", - " Triggers unsatisfied, max unc./thresh. is 17.9146 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 58736 --- greater than max batches\n", - " 189/1 0.71850 0.68073 +/- 0.00170\n", - " Triggers unsatisfied, max unc./thresh. is 17.8551 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 58665 --- greater than max batches\n", - " 190/1 0.67095 0.68067 +/- 0.00169\n", - " Triggers unsatisfied, max unc./thresh. is 17.8953 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 59250 --- greater than max batches\n", - " 191/1 0.70857 0.68082 +/- 0.00169\n", - " Triggers unsatisfied, max unc./thresh. is 17.8197 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 59068 --- greater than max batches\n", - " 192/1 0.65322 0.68067 +/- 0.00169\n", - " Triggers unsatisfied, max unc./thresh. is 17.8199 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 59387 --- greater than max batches\n", - " 193/1 0.67888 0.68066 +/- 0.00168\n", - " Triggers unsatisfied, max unc./thresh. is 17.8072 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 59620 --- greater than max batches\n", - " 194/1 0.72890 0.68092 +/- 0.00169\n", - " Triggers unsatisfied, max unc./thresh. is 17.7152 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 59319 --- greater than max batches\n", - " 195/1 0.64688 0.68074 +/- 0.00169\n", - " Triggers unsatisfied, max unc./thresh. is 17.6252 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 59029 --- greater than max batches\n", - " 196/1 0.68906 0.68078 +/- 0.00168\n", - " Triggers unsatisfied, max unc./thresh. is 17.5465 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 58810 --- greater than max batches\n", - " 197/1 0.69381 0.68085 +/- 0.00167\n", - " Triggers unsatisfied, max unc./thresh. is 17.4939 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 58764 --- greater than max batches\n", - " 198/1 0.70057 0.68095 +/- 0.00167\n", - " Triggers unsatisfied, max unc./thresh. is 17.4414 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 58717 --- greater than max batches\n", - " 199/1 0.67868 0.68094 +/- 0.00166\n", - " Triggers unsatisfied, max unc./thresh. is 17.4394 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 59008 --- greater than max batches\n", - " 200/1 0.69190 0.68100 +/- 0.00165\n", - " Triggers unsatisfied, max unc./thresh. is 17.3511 for absorption in tally 3\n", - " WARNING: The estimated number of batches is 58712 --- greater than max batches\n", + " 165/1 0.68804 0.68329 +/- 0.00177\n", + " Triggers unsatisfied, max unc./thresh. is 20.033653897136055 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 64221 --- greater than max batches\n", + " 166/1 0.66078 0.68315 +/- 0.00176\n", + " Triggers unsatisfied, max unc./thresh. is 19.916131324861123 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 63867 --- greater than max batches\n", + " 167/1 0.65762 0.68300 +/- 0.00176\n", + " Triggers unsatisfied, max unc./thresh. is 19.85097950868946 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 63843 --- greater than max batches\n", + " 168/1 0.69267 0.68306 +/- 0.00175\n", + " Triggers unsatisfied, max unc./thresh. is 19.729436003984436 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 63453 --- greater than max batches\n", + " 169/1 0.67859 0.68303 +/- 0.00174\n", + " Triggers unsatisfied, max unc./thresh. is 19.61178427698242 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 63084 --- greater than max batches\n", + " 170/1 0.66545 0.68292 +/- 0.00173\n", + " Triggers unsatisfied, max unc./thresh. is 19.495197332905757 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 62716 --- greater than max batches\n", + " 171/1 0.66716 0.68283 +/- 0.00172\n", + " Triggers unsatisfied, max unc./thresh. is 19.47044861415963 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 62936 --- greater than max batches\n", + " 172/1 0.70008 0.68293 +/- 0.00172\n", + " Triggers unsatisfied, max unc./thresh. is 19.382801191970024 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 62746 --- greater than max batches\n", + " 173/1 0.69417 0.68300 +/- 0.00171\n", + " Triggers unsatisfied, max unc./thresh. is 19.270038663528165 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 62390 --- greater than max batches\n", + " 174/1 0.66458 0.68289 +/- 0.00170\n", + " Triggers unsatisfied, max unc./thresh. is 19.281533726364312 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 62836 --- greater than max batches\n", + " 175/1 0.65867 0.68275 +/- 0.00170\n", + " Triggers unsatisfied, max unc./thresh. is 19.234847030404104 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 62902 --- greater than max batches\n", + " 176/1 0.69631 0.68283 +/- 0.00169\n", + " Triggers unsatisfied, max unc./thresh. is 19.13381099709457 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 62609 --- greater than max batches\n", + " 177/1 0.71142 0.68299 +/- 0.00169\n", + " Triggers unsatisfied, max unc./thresh. is 19.022563643493143 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 62245 --- greater than max batches\n", + " 178/1 0.68640 0.68301 +/- 0.00168\n", + " Triggers unsatisfied, max unc./thresh. is 19.03176453708651 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 62667 --- greater than max batches\n", + " 179/1 0.70448 0.68313 +/- 0.00167\n", + " Triggers unsatisfied, max unc./thresh. is 19.088395456136563 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 63405 --- greater than max batches\n", + " 180/1 0.70538 0.68326 +/- 0.00167\n", + " Triggers unsatisfied, max unc./thresh. is 18.98864751831452 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 63105 --- greater than max batches\n", + " 181/1 0.65591 0.68311 +/- 0.00166\n", + " Triggers unsatisfied, max unc./thresh. is 18.911017891051518 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 62948 --- greater than max batches\n", + " 182/1 0.72818 0.68336 +/- 0.00167\n", + " Triggers unsatisfied, max unc./thresh. is 18.808510226366458 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 62621 --- greater than max batches\n", + " 183/1 0.67896 0.68334 +/- 0.00167\n", + " Triggers unsatisfied, max unc./thresh. is 18.825142861337717 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 63086 --- greater than max batches\n", + " 184/1 0.65442 0.68317 +/- 0.00166\n", + " Triggers unsatisfied, max unc./thresh. is 18.79514029258707 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 63239 --- greater than max batches\n", + " 185/1 0.68885 0.68321 +/- 0.00165\n", + " Triggers unsatisfied, max unc./thresh. is 18.76276176864163 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 63373 --- greater than max batches\n", + " 186/1 0.68893 0.68324 +/- 0.00165\n", + " Triggers unsatisfied, max unc./thresh. is 18.690155368597363 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 63233 --- greater than max batches\n", + " 187/1 0.68918 0.68327 +/- 0.00164\n", + " Triggers unsatisfied, max unc./thresh. is 18.590144288270153 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 62904 --- greater than max batches\n", + " 188/1 0.69854 0.68335 +/- 0.00163\n", + " Triggers unsatisfied, max unc./thresh. is 18.61460656150607 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 63416 --- greater than max batches\n", + " 189/1 0.66324 0.68324 +/- 0.00162\n", + " Triggers unsatisfied, max unc./thresh. is 18.518608099237504 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 63106 --- greater than max batches\n", + " 190/1 0.69450 0.68331 +/- 0.00162\n", + " Triggers unsatisfied, max unc./thresh. is 18.425351661292233 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 62812 --- greater than max batches\n", + " 191/1 0.68953 0.68334 +/- 0.00161\n", + " Triggers unsatisfied, max unc./thresh. is 18.328779429843646 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 62491 --- greater than max batches\n", + " 192/1 0.66621 0.68325 +/- 0.00160\n", + " Triggers unsatisfied, max unc./thresh. is 18.28094973389564 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 62500 --- greater than max batches\n", + " 193/1 0.71102 0.68339 +/- 0.00160\n", + " Triggers unsatisfied, max unc./thresh. is 18.19949730061142 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 62275 --- greater than max batches\n", + " 194/1 0.65341 0.68324 +/- 0.00160\n", + " Triggers unsatisfied, max unc./thresh. is 18.159054737369345 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 62328 --- greater than max batches\n", + " 195/1 0.70061 0.68333 +/- 0.00159\n", + " Triggers unsatisfied, max unc./thresh. is 18.082465954324594 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 62131 --- greater than max batches\n", + " 196/1 0.69339 0.68338 +/- 0.00159\n", + " Triggers unsatisfied, max unc./thresh. is 18.043133483791827 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 62186 --- greater than max batches\n", + " 197/1 0.64411 0.68318 +/- 0.00159\n", + " Triggers unsatisfied, max unc./thresh. is 18.019303623546417 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 62347 --- greater than max batches\n", + " 198/1 0.66626 0.68309 +/- 0.00159\n", + " Triggers unsatisfied, max unc./thresh. is 17.968092739058083 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 62316 --- greater than max batches\n", + " 199/1 0.67839 0.68306 +/- 0.00158\n", + " Triggers unsatisfied, max unc./thresh. is 17.91968515142146 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 62302 --- greater than max batches\n", + " 200/1 0.66459 0.68297 +/- 0.00157\n", + " Triggers unsatisfied, max unc./thresh. is 17.82970764669685 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 61996 --- greater than max batches\n", " Creating state point statepoint.200.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 9.3777e-01 seconds\n", - " Reading cross sections = 8.7757e-01 seconds\n", - " Total time in simulation = 4.0652e+01 seconds\n", - " Time in transport only = 3.9022e+01 seconds\n", - " Time in inactive batches = 9.1120e-01 seconds\n", - " Time in active batches = 3.9741e+01 seconds\n", - " Time synchronizing fission bank = 4.0496e-02 seconds\n", - " Sampling source sites = 3.3700e-02 seconds\n", - " SEND/RECV source sites = 6.4404e-03 seconds\n", - " Time accumulating tallies = 2.0272e-03 seconds\n", - " Total time for finalization = 4.0896e-03 seconds\n", - " Total time elapsed = 4.1621e+01 seconds\n", - " Calculation Rate (inactive) = 13718.1 particles/second\n", - " Calculation Rate (active) = 12267.1 particles/second\n", + " Total time for initialization = 2.9309e-01 seconds\n", + " Reading cross sections = 2.8108e-01 seconds\n", + " Total time in simulation = 1.1321e+01 seconds\n", + " Time in transport only = 1.1242e+01 seconds\n", + " Time in inactive batches = 1.6721e-01 seconds\n", + " Time in active batches = 1.1153e+01 seconds\n", + " Time synchronizing fission bank = 2.2958e-02 seconds\n", + " Sampling source sites = 1.8701e-02 seconds\n", + " SEND/RECV source sites = 3.9403e-03 seconds\n", + " Time accumulating tallies = 9.9349e-04 seconds\n", + " Total time for finalization = 5.2200e-07 seconds\n", + " Total time elapsed = 1.1620e+01 seconds\n", + " Calculation Rate (inactive) = 74758.2 particles/second\n", + " Calculation Rate (active) = 43708.5 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 0.68122 +/- 0.00150\n", - " k-effective (Track-length) = 0.68100 +/- 0.00165\n", - " k-effective (Absorption) = 0.68224 +/- 0.00159\n", - " Combined k-effective = 0.68162 +/- 0.00134\n", - " Leakage Fraction = 0.34047 +/- 0.00082\n", + " k-effective (Collision) = 0.68198 +/- 0.00141\n", + " k-effective (Track-length) = 0.68297 +/- 0.00157\n", + " k-effective (Absorption) = 0.68161 +/- 0.00145\n", + " Combined k-effective = 0.68209 +/- 0.00118\n", + " Leakage Fraction = 0.34033 +/- 0.00074\n", "\n" ] } @@ -1128,10 +1310,9 @@ "\tID =\t1\n", "\tName =\tmesh tally\n", "\tFilters =\tMeshFilter, EnergyFilter\n", - "\tNuclides =\ttotal \n", + "\tNuclides =\ttotal\n", "\tScores =\t['fission', 'nu-fission']\n", - "\tEstimator =\ttracklength\n", - "\n" + "\tEstimator =\ttracklength\n" ] } ], @@ -1159,13 +1340,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[0.16617932]]\n", + "[[[0.04508259]]\n", "\n", - " [[0.06455926]]\n", + " [[0.0221707 ]]\n", "\n", - " [[0.32266365]]\n", + " [[0.10763375]]\n", "\n", - " [[0.13355528]]]\n" + " [[0.05107401]]]\n" ] } ], @@ -1233,8 +1414,8 @@ " 0.00e+00\n", " 6.25e-01\n", " fission\n", - " 1.76e-04\n", - " 2.92e-05\n", + " 2.27e-04\n", + " 1.02e-05\n", " \n", " \n", " 1\n", @@ -1244,8 +1425,8 @@ " 0.00e+00\n", " 6.25e-01\n", " nu-fission\n", - " 4.28e-04\n", - " 7.12e-05\n", + " 5.54e-04\n", + " 2.50e-05\n", " \n", " \n", " 2\n", @@ -1255,8 +1436,8 @@ " 6.25e-01\n", " 2.00e+07\n", " fission\n", - " 6.67e-05\n", - " 6.94e-06\n", + " 7.19e-05\n", + " 1.82e-06\n", " \n", " \n", " 3\n", @@ -1266,8 +1447,8 @@ " 6.25e-01\n", " 2.00e+07\n", " nu-fission\n", - " 1.75e-04\n", - " 1.71e-05\n", + " 1.89e-04\n", + " 4.69e-06\n", " \n", " \n", " 4\n", @@ -1277,8 +1458,8 @@ " 0.00e+00\n", " 6.25e-01\n", " fission\n", - " 2.04e-04\n", - " 3.80e-05\n", + " 2.35e-04\n", + " 9.82e-06\n", " \n", " \n", " 5\n", @@ -1288,8 +1469,8 @@ " 0.00e+00\n", " 6.25e-01\n", " nu-fission\n", - " 4.96e-04\n", - " 9.27e-05\n", + " 5.71e-04\n", + " 2.39e-05\n", " \n", " \n", " 6\n", @@ -1299,8 +1480,8 @@ " 6.25e-01\n", " 2.00e+07\n", " fission\n", - " 5.76e-05\n", - " 6.97e-06\n", + " 6.88e-05\n", + " 1.61e-06\n", " \n", " \n", " 7\n", @@ -1310,8 +1491,8 @@ " 6.25e-01\n", " 2.00e+07\n", " nu-fission\n", - " 1.52e-04\n", - " 1.91e-05\n", + " 1.81e-04\n", + " 4.15e-06\n", " \n", " \n", " 8\n", @@ -1321,8 +1502,8 @@ " 0.00e+00\n", " 6.25e-01\n", " fission\n", - " 1.80e-04\n", - " 3.15e-05\n", + " 2.31e-04\n", + " 1.13e-05\n", " \n", " \n", " 9\n", @@ -1332,8 +1513,8 @@ " 0.00e+00\n", " 6.25e-01\n", " nu-fission\n", - " 4.38e-04\n", - " 7.68e-05\n", + " 5.63e-04\n", + " 2.76e-05\n", " \n", " \n", " 10\n", @@ -1343,8 +1524,8 @@ " 6.25e-01\n", " 2.00e+07\n", " fission\n", - " 7.19e-05\n", - " 9.68e-06\n", + " 6.95e-05\n", + " 1.76e-06\n", " \n", " \n", " 11\n", @@ -1354,8 +1535,8 @@ " 6.25e-01\n", " 2.00e+07\n", " nu-fission\n", - " 1.89e-04\n", - " 2.49e-05\n", + " 1.83e-04\n", + " 4.53e-06\n", " \n", " \n", " 12\n", @@ -1365,8 +1546,8 @@ " 0.00e+00\n", " 6.25e-01\n", " fission\n", - " 1.91e-04\n", - " 3.67e-05\n", + " 2.07e-04\n", + " 9.85e-06\n", " \n", " \n", " 13\n", @@ -1376,8 +1557,8 @@ " 0.00e+00\n", " 6.25e-01\n", " nu-fission\n", - " 4.66e-04\n", - " 8.93e-05\n", + " 5.04e-04\n", + " 2.40e-05\n", " \n", " \n", " 14\n", @@ -1387,8 +1568,8 @@ " 6.25e-01\n", " 2.00e+07\n", " fission\n", - " 6.78e-05\n", - " 9.81e-06\n", + " 6.48e-05\n", + " 1.45e-06\n", " \n", " \n", " 15\n", @@ -1398,8 +1579,8 @@ " 6.25e-01\n", " 2.00e+07\n", " nu-fission\n", - " 1.76e-04\n", - " 2.44e-05\n", + " 1.71e-04\n", + " 3.81e-06\n", " \n", " \n", " 16\n", @@ -1409,8 +1590,8 @@ " 0.00e+00\n", " 6.25e-01\n", " fission\n", - " 1.56e-04\n", - " 2.32e-05\n", + " 2.20e-04\n", + " 1.07e-05\n", " \n", " \n", " 17\n", @@ -1420,8 +1601,8 @@ " 0.00e+00\n", " 6.25e-01\n", " nu-fission\n", - " 3.81e-04\n", - " 5.65e-05\n", + " 5.37e-04\n", + " 2.60e-05\n", " \n", " \n", " 18\n", @@ -1431,8 +1612,8 @@ " 6.25e-01\n", " 2.00e+07\n", " fission\n", - " 6.28e-05\n", - " 8.06e-06\n", + " 6.76e-05\n", + " 1.78e-06\n", " \n", " \n", " 19\n", @@ -1442,8 +1623,8 @@ " 6.25e-01\n", " 2.00e+07\n", " nu-fission\n", - " 1.62e-04\n", - " 2.05e-05\n", + " 1.78e-04\n", + " 4.63e-06\n", " \n", " \n", "\n", @@ -1452,49 +1633,49 @@ "text/plain": [ " mesh 1 energy low [eV] energy high [eV] score mean \\\n", " x y z \n", - "0 1 1 1 0.00e+00 6.25e-01 fission 1.76e-04 \n", - "1 1 1 1 0.00e+00 6.25e-01 nu-fission 4.28e-04 \n", - "2 1 1 1 6.25e-01 2.00e+07 fission 6.67e-05 \n", - "3 1 1 1 6.25e-01 2.00e+07 nu-fission 1.75e-04 \n", - "4 2 1 1 0.00e+00 6.25e-01 fission 2.04e-04 \n", - "5 2 1 1 0.00e+00 6.25e-01 nu-fission 4.96e-04 \n", - "6 2 1 1 6.25e-01 2.00e+07 fission 5.76e-05 \n", - "7 2 1 1 6.25e-01 2.00e+07 nu-fission 1.52e-04 \n", - "8 3 1 1 0.00e+00 6.25e-01 fission 1.80e-04 \n", - "9 3 1 1 0.00e+00 6.25e-01 nu-fission 4.38e-04 \n", - "10 3 1 1 6.25e-01 2.00e+07 fission 7.19e-05 \n", - "11 3 1 1 6.25e-01 2.00e+07 nu-fission 1.89e-04 \n", - "12 4 1 1 0.00e+00 6.25e-01 fission 1.91e-04 \n", - "13 4 1 1 0.00e+00 6.25e-01 nu-fission 4.66e-04 \n", - "14 4 1 1 6.25e-01 2.00e+07 fission 6.78e-05 \n", - "15 4 1 1 6.25e-01 2.00e+07 nu-fission 1.76e-04 \n", - "16 5 1 1 0.00e+00 6.25e-01 fission 1.56e-04 \n", - "17 5 1 1 0.00e+00 6.25e-01 nu-fission 3.81e-04 \n", - "18 5 1 1 6.25e-01 2.00e+07 fission 6.28e-05 \n", - "19 5 1 1 6.25e-01 2.00e+07 nu-fission 1.62e-04 \n", + "0 1 1 1 0.00e+00 6.25e-01 fission 2.27e-04 \n", + "1 1 1 1 0.00e+00 6.25e-01 nu-fission 5.54e-04 \n", + "2 1 1 1 6.25e-01 2.00e+07 fission 7.19e-05 \n", + "3 1 1 1 6.25e-01 2.00e+07 nu-fission 1.89e-04 \n", + "4 2 1 1 0.00e+00 6.25e-01 fission 2.35e-04 \n", + "5 2 1 1 0.00e+00 6.25e-01 nu-fission 5.71e-04 \n", + "6 2 1 1 6.25e-01 2.00e+07 fission 6.88e-05 \n", + "7 2 1 1 6.25e-01 2.00e+07 nu-fission 1.81e-04 \n", + "8 3 1 1 0.00e+00 6.25e-01 fission 2.31e-04 \n", + "9 3 1 1 0.00e+00 6.25e-01 nu-fission 5.63e-04 \n", + "10 3 1 1 6.25e-01 2.00e+07 fission 6.95e-05 \n", + "11 3 1 1 6.25e-01 2.00e+07 nu-fission 1.83e-04 \n", + "12 4 1 1 0.00e+00 6.25e-01 fission 2.07e-04 \n", + "13 4 1 1 0.00e+00 6.25e-01 nu-fission 5.04e-04 \n", + "14 4 1 1 6.25e-01 2.00e+07 fission 6.48e-05 \n", + "15 4 1 1 6.25e-01 2.00e+07 nu-fission 1.71e-04 \n", + "16 5 1 1 0.00e+00 6.25e-01 fission 2.20e-04 \n", + "17 5 1 1 0.00e+00 6.25e-01 nu-fission 5.37e-04 \n", + "18 5 1 1 6.25e-01 2.00e+07 fission 6.76e-05 \n", + "19 5 1 1 6.25e-01 2.00e+07 nu-fission 1.78e-04 \n", "\n", " std. dev. \n", " \n", - "0 2.92e-05 \n", - "1 7.12e-05 \n", - "2 6.94e-06 \n", - "3 1.71e-05 \n", - "4 3.80e-05 \n", - "5 9.27e-05 \n", - "6 6.97e-06 \n", - "7 1.91e-05 \n", - "8 3.15e-05 \n", - "9 7.68e-05 \n", - "10 9.68e-06 \n", - "11 2.49e-05 \n", - "12 3.67e-05 \n", - "13 8.93e-05 \n", - "14 9.81e-06 \n", - "15 2.44e-05 \n", - "16 2.32e-05 \n", - "17 5.65e-05 \n", - "18 8.06e-06 \n", - "19 2.05e-05 " + "0 1.02e-05 \n", + "1 2.50e-05 \n", + "2 1.82e-06 \n", + "3 4.69e-06 \n", + "4 9.82e-06 \n", + "5 2.39e-05 \n", + "6 1.61e-06 \n", + "7 4.15e-06 \n", + "8 1.13e-05 \n", + "9 2.76e-05 \n", + "10 1.76e-06 \n", + "11 4.53e-06 \n", + "12 9.85e-06 \n", + "13 2.40e-05 \n", + "14 1.45e-06 \n", + "15 3.81e-06 \n", + "16 1.07e-05 \n", + "17 2.60e-05 \n", + "18 1.78e-06 \n", + "19 4.63e-06 " ] }, "execution_count": 20, @@ -1520,7 +1701,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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v24S9eeVM3JU/YEfHgfzvJTpbE8B8qm5mlmm89KqPj3a1mTVdRBE4G1kaIWmRpPWSeiRdXmP/REm3pP2rJZ1cse+KtH29pPPStpMk3S3pYUnrJL2/Iv1VkjZJWpuW84erW6umB54h6TZJP5L0iKSfb0U9zKxcZU2dkWaivA54GzAfuEjS/KpklwA7I+JU4FrgmpR3PsW0PGdQzJv+uXS8fuCDETEfOBu4tOqY10bEmWl5wSRx1VrV4vwr4J8j4r8BrwIeaVE9zKxEEY0tDVgI9ETEhojYD6wAFlelWQzcmNZvA86VpLR9RUT0RcRjQA+wMCI2R8T9RT1jD0XcmTOS9znqgVPSdOCNpHmLI2J/ROwa7XqYWbkCMTjY0dACzJK0pmJZWnW4OcCTFa83cnCQez5NRPQDu4FjG8mbTuvPAlZXbL5M0oOSbpB0zHDvtRUtzrnA08DfS/pPSV+QdNB8ApKWDn2oA3v2jn4tzSxbNLgA2yJiQcWyfLTqKOko4GvAByLimbT588ApwJnAZuAvhztGKwJnF/Bq4PMRcRawFzjowm9ELB/6UDun5c/TYmajrNzOoU3ASRWvT0zbaqaR1AVMB7YPl1fSBIqgeXNEfP35qkdsjYiBiBgE/o7iUkFdrQicG4GNETHURL6NIpCaWbvLaHIewr3APElzJXVTdPasrEqzErg4rV8A3BURkbYvSb3uc4F5wD3p+uf1wCMR8ZnKA0maXfHyV4GHhqvcqN/HGRFbJD0p6fSIWA+cCzw82vUws/KVdR9nRPRLugy4HegEboiIdZKuBtZExEqKIHiTpB5gB0VwJaW7lSKu9AOXRsSApDcA7wF+KGltKupjqQf905LOpAjrjwO/M1z9WnUD/O8DN6e/JBuA32pRPcysJAEMDpZ3A3wKaKuqtl1Zsd4LXFgn7zJgWdW270Pth+kj4j05dWtJ4IyItcCCVpRtZk0SwDh5csiPXJpZafys+ljSEXROyxsc4eyTH88uZuu+aVnpDwzkDwqxZVdeGQCakp2FY0/ckZ1nT+/E/Dwbj85Kv39G/oAVE3eOYPCNZ/J/gwe681tLg515eSZvzRusBqDj2d7sPNrXl5dhBIOi1OTAaWaWo/Hn0NudA6eZlcctTjOzDAFRYq/6WObAaWYlcuA0M8vjU3Uzs0wOnGZmGXwDvJlZPt8Ab2aWy73qZmZ55BanmVmGxsfabHsOnGZWErlzaCzp6hzkmKOfy8pz/MRnDp2oyq79k7PSj2SQj1986YbsPHv68wffWPPES7LzTJyYN5AKwOTZz2al738kb1AQgL4Z2VnYPy3/F3hS/rgo2XFi26vyR2yZ9UB2Frr6MwcT6Sgp4LnFaWaWKX/wq7bkwGlm5RhH93G2YrI2JP2hpHWSHpL0VUmTWlEPMyuXorGloWNJiyStl9Qj6aCZcNNkbLek/avTXOlD+65I29dLOi9tO0nS3ZIeTvHn/RXpZ0q6Q9Kj6f+xNa+6pDnAHwALIuIVFBMxLRntephZE5Q0y6WkTuA64G3AfOAiSfOrkl0C7IyIU4FrgWtS3vkUMeUMYBHwuXS8fuCDETEfOBu4tOKYlwN3RsQ84E5qTFle6ZCBU9LvHyr6jkAXMDnNhTwF+FnJxzez9rYQ6ImIDRGxH1gBLK5Ksxi4Ma3fBpybpgBeDKyIiL6IeAzoARZGxOaIuB8gIvYAjwBzahzrRuCdw1WukRbn8cC9km5NTefDuogREZuAvwB+CmwGdkfEt6vTSVoqaY2kNf2783rUzaw1SjxVnwM8WfF6I/8V5A5KExH9wG7g2EbyptP6s4DVadPxEbE5rW+hiHt1HTJwRsSfUEzofj3wXuBRSX8m6ZRD5a0ltV4XA3OBE4Cpkt5do9zlEbEgIhZ0TR/BpDtmNrqC4pHLRhaYNdQwSsvS0aqmpKOArwEfiIiD7luMiENeUGioVz0iQtIWikjcDxwD3Cbpjoj4SGa9fwl4LCKeBpD0deD1wJczj2NmY03j93Fui4jhpgjfBJxU8frEtK1Wmo3pst90YPtweSVNoAiaN0fE1yvSbJU0OyI2S5oNPDVc5Ru5xvl+SfcBnwb+DXhlRPwu8Brgfx4qfw0/Bc6WNCWd9p9Lca3BzNpciafq9wLzJM2V1E3R2bOyKs1K4OK0fgFwV2otrgSWpF73uRRnzPekeHM98EhEfGaYY10MfHO4yjXS4pwJ/I+IeKJyY0QMSnp7A/lfICJWS7oNuJ+i9fqfwPLc45jZGFTWLMMR/ZIuA26nuPPmhohYJ+lqYE1ErKQIgjdJ6gF2kO7OSeluBR6miDGXRsSApDcA7wF+KGltKupjEbEK+BRwq6RLgCeAXxuufocMnBHx8WH2jailmI5Z97hm1qZKfOQyBbRVVduurFjvBS6sk3cZsKxq2/epMylSRGynOPttSFs8OTSpq5/TZw57yaEUb3/Rg1npv/LkwuwyJnfuz87T88ys7Dyvn5v/TPz9m086dKIqBw7kPa8/8zX53+PWR/Pff8eB/Js/DkzPzsKUTXm3Qh/9ZOYz5MDA5Pxf0wkH+vMylBDwcm5ub3dtETjNrE14IGMzszxucZqZ5XLgNDPL4GucZmYj4MBpZpZH42Qg45aMx2lm1s7c4jSz8vhU3cwsgzuHzMxGwIHTzCyTA6eZWePE+OlVb4vAORDimQN5E2G+deZD2eW8uGt3VvqXH7Mlu4yjOvuy87x8xtbsPFNHUM6+vd3ZeaYclV9Orhkn78rOs3Nj/ogdk7bm/zooc8yO3hl5g6IAdO7Lj0aD06fmZdhSwg02vsZpZjYCDpxmZpkcOM3M8oyXU/WmPTkk6QZJT0l6qGLbTEl3SHo0/V/2fO1m1krR4NLmmvnI5ReBRVXbLgfujIh5wJ3ptZkdCaLoVW9kaXdNC5wR8V2KCZQqLQZuTOs3Au9sVvlm1gIltjglLZK0XlKPpIMaWWkWy1vS/tWSTq7Yd0Xavl7SeRXbDzoTTtuvkrRJ0tq0nD9c3UZ7kI/jI2JzWt8CHF8voaSlQ5PV79+1b3RqZ2aHpazpgSV1AtcBbwPmAxdJml+V7BJgZ0ScClwLXJPyzqeY8fIMirPez6XjQe0z4SHXRsSZaVlVJw3QwtGR0vzHdT/CiFgeEQsiYkH3jMmjWDMzG7HyWpwLgZ6I2BAR+4EVFGeslSrPYG8Dzk1zpy8GVkREX0Q8BvSk49U7E8422oFzq6TZAOn/5k9daWajo9Gg2VjgnAM8WfF6Y9pWM01E9AO7gWMbzFvLZZIeTKfzw3Zcj3bgXAlcnNYvBr45yuWbWZOIrFP1WUOX4tKytLW15/PAKcCZwGbgL4dL3LT7OCV9FXgTxQe0Efg48CngVkmXAE8Av9as8s1s9GXcx7ktIhYMs38TcFLF6xPTtlppNkrqAqYD2xvM+wIR8fxzzZL+DvjWcOmbFjgj4qI6u85tVplm1mLl3aN5LzBP0lyKoLcE+PWqNENnsP8BXADcFREhaSXwFUmfAU4A5gH3DFeYpNkVHde/Cgw72EVbPDk0vWsf5x/3w6w8L+rak13Ov+09LSv9cd3PZpdxwfQ12Xn+5In8u7ZOn5Y/MMgvnf6j7Dx7MgdfeWJP/jMPu3+Sn0cjuAg1If9HhgnP5UWKwRH8xnX1Zo4kAmh/f16GKCnilXWYiH5JlwG3A53ADRGxTtLVwJqIWAlcD9wkqYeiw2dJyrtO0q3Aw0A/cGlEDEDtM+GIuB74tKQz0zt4HPid4erXFoHTzNpAyaMjpVuCVlVtu7JivRe4sE7eZcCyGttrnglHxHty6ubAaWblOQIep2yEA6eZleZIeJyyEQ6cZlaa8TI6kgOnmZXjCBn5qBEOnGZWHgdOM7PGDT05NB44cJpZaTQ4PiKnA6eZlcPXOM3M8vlU3cwslwOnmVketzjHkGkdfZwz9cdZea7f8QvZ5czu3pWV/kB0HjpRlft6X5qd5/hJ+aNPvHji7uw8m/ryB9M4qrMvK/3TvUdllzFhzt7sPPt7J2TnObAjb8ASgMFuZaWfkD8uDNGRVwZAdGf+aiu/jNoFl3OYsa4tAqeZtYHwI5dmZlnG032cTZs6o9Y0nJL+XNKP0rwe35A0o1nlm1kLRDS2tLlmzjn0RQ6ehvMO4BUR8XPAj4Ermli+mY2ysqYHHuuaFjhrTcMZEd9Os9EB/IBiLhAzOxKUO8vlmNbKa5y/DdxSb2ea9W4pwAlz8nuvzWz0jZfOodGeHhgASX9MMRfIzfXSRMTyiFgQEQuOmdmSappZJg02trS7UY9Ikt4LvB34jYgj4CqxmRWCUjuHJC2StF5Sj6TLa+yfKOmWtH+1pJMr9l2Rtq+XdF7F9oM6rdP2mZLukPRo+n/Ym5pHNXBKWgR8BHhHRDw3mmWbWfOV1TkkqRO4DngbMB+4SNL8qmSXADsj4lTgWuCalHc+xYyXZ1B0UH8uHQ9qd1oDXA7cGRHzgDvT67qaeTvSVynmOz5d0kZJlwCfBaYBd0haK+lvm1W+mbVAeZ1DC4GeiNgQEfuBFcDiqjSLgRvT+m3AuZKUtq+IiL6IeAzoScer2Wld41g3AsPOyd20zqE603Be36zyzKy1Mm+AnyVpTcXr5RGxvOL1HODJitcbgddVHeP5NGke9t3AsWn7D6ryzjlEfY6PiM1pfQtw/HCJ/eSQmZUjImcg420RsaCZ1RmpiAhp+D8BbRE4nx2cyPeeOyUrz2mTtmSX8/rJG7LSP9B3qD9iB5vRmX9pd9qE3uw8q3fOzc7zyqN/lp1nY2/ewCATOgayy+jszO+Gjb78W9j2z8gv5+ievKtdHf35/aF9M/IHLJm44em8DAP530tN5XX3bgJOqnh9YtpWK81GSV3AdGB7g3mrbZU0OyI2S5oNPDVcYt/nY2alKfHJoXuBeZLmSuqm6OxZWZVmJXBxWr8AuCvdqbMSWJJ63ecC84B7DlFe5bEuBr45XGIHTjMrRwCD0dhyqEMVTxheBtwOPALcGhHrJF0t6R0p2fXAsZJ6gD8i9YRHxDrgVuBh4J+BSyNiAOp2WgN8CniLpEeBX0qv62qLU3UzaxMl3pkdEauAVVXbrqxY7wUurJN3GbCsxvZandZExHbg3Ebr5sBpZqU5EgbwaIQDp5mVxtMDm5nlOEJGPmqEA6eZlaK4AX58RE4HTjMrzxEw8lEjHDjNrDRucZqZ5fA1TjOzXFnPqrc1B04zK49P1ceOTg1mD46xfeCo7HJe3j0lK/0g+YNirO3Ln5/u92b9a3aem3ctzM7zs77p2Xn29E/MSr9pd34Zfb3d2XkmH7MvO0/v/qnZeZ59aV6gmP7j7CLo7M3vcdn/kllZ6WNbCaEgjoxpMRrRFoHTzNqEW5xmZpnGR9xs6tQZNSdFSvs+KCkk5Z1PmNmYpsHBhpZ218xh5b5IjUmRJJ0EvBX4aRPLNrPRFhQ3wDeytLmmBc5hJkW6lmKmy3HSqDcbH0SgaGxpd6N6jVPSYmBTRDxQTEY3bNqlwFKAWSfk96qaWQscAUGxEaMWOCVNAT5GcZp+SGnGu+UAp7xy6vj4Nsza3TgJnKM5dcYpwFzgAUmPU0ygdL+kF49iHcysWcbRNc5Ra3FGxA+BFw29TsFzQURsG606mFlzHQk95o1o5u1I9SZFMrMjUhSn6o0sDZC0SNJ6ST2SLq+xf6KkW9L+1ZJOrth3Rdq+XtJ5hzqmpC9KekzS2rScOVzdmtbirDcpUsX+k5tVtpm1QFDaNU5JncB1wFuAjcC9klZGxMMVyS4BdkbEqZKWANcA75I0n2I64TOAE4DvSDot5RnumB+OiNsaqV9bPDk0Vft53aS858K3DUzILue7vXnPXe8YOC67jB39+c/Qf2nn2dl5tvYdnZ1n03P5z5GfdvRTWelfc+oT2WV86dHXZefpGMGsYfs68vN07R3+7pBqozV6kAYyT5nLqlZ5Z+oLgZ6I2AAgaQWwmGLK3yGLgavS+m3AZ1XcrrMYWBERfcBjafrgocEbDnXMhnhedTMrTcZ9nLMkralYllYdag7wZMXrjWlbzTRpHvbdwLHD5D3UMZdJelDStZKGbUW1RYvTzNpE46fq2yJiQTOrkukKYAvQTXEb5EeBq+sldovTzMoRAQODjS2Htgk4qeJWwgPVAAAHwElEQVT1iWlbzTSSuoDpwPZh8tY9ZkRsjkIf8Pf816l9TQ6cZlae8nrV7wXmSZorqZuis2dlVZqVwMVp/QLgroiItH1J6nWfC8wD7hnumJJmp/8FvBM4aHCiSj5VN7PylNSrHhH9ki4Dbgc6gRsiYp2kq4E1EbESuB64KXX+7KAIhKR0t1J0+vQDl0bEAECtY6Yib5Z0HMUsx2uB9w1XPwdOMytHACXeNRARq4BVVduurFjvBS6sk3cZsKyRY6bt5+TUzYHTzEoSEOPjySEHTjMrR9Box0/bc+A0s/KMk9GRHDjNrDwOnGZmORofwKPdOXCaWTkCGCfDyrVF4OyUmNbRmZVnz+BAdjlvzBvjg5V7839Ijp+wKzvP1gP5A3acOS1/LrxNz70yO8/P9uUNDPKdJ07PLmPf3vypU/RU5pcJdA3kDdgBEJmPkPTOzC9j6ubsLHTu3Z+VvrTBR9ziNDPLEe5VNzPLEhDj5D7OZo4Af4OkpyQ9VLX99yX9SNI6SZ9uVvlm1gKD0djS5prZ4vwi8FngS0MbJL2ZYuDQV0VEn6QX1clrZu3I1zgPT0R8t3IOkOR3gU+loZuIiLzhw81s7IoYN73qoz2s3GnAL6aJlf5V0mvrJZS0dGh06G3bx8eXYdb2SpysbSwb7c6hLmAmcDbwWuBWSS9LY+i9QEQspxiJmbNe1d3+n7TZES+IgfzbANvRaAfOjcDXU6C8R9IgMAt4epTrYWZlK3lYubFstE/V/x/wZoA0XWc3sG2U62BmzRKDjS1trmktTklfBd5EMZvdRuDjwA3ADekWpf3AxbVO082s/QQQ46TF2cxe9Yvq7Hp3s8o0sxYKD2RsZpZtvHQOqR3OlCU9DTxRY9csWnuN1OW7/COl/JdGxHGHcwBJ/0xRp0Zsi4hFh1NeK7VF4KxH0ppWTmrv8l3+eC5/PPO86mZmmRw4zcwytXvgXO7yXb7Lt9HW1tc4zcxaod1bnGZmo86B08wsU1sETkmLJK2X1CPp8hr7J0q6Je1fXWMc0MMp+yRJd0t6OI1a//4aad4kabektWm5sqzy0/Efl/TDdOw1NfZL0l+n9/+gpFeXWPbpFe9rraRnJH2gKk2p77/W7AGSZkq6Q9Kj6f9j6uS9OKV5VNLFJZb/52nmggclfUPSjDp5h/2uDqP8qyRtqviMz6+Td9jfFStJRIzpBegEfgK8jGJQkAeA+VVpfg/427S+BLilxPJnA69O69OAH9co/03At5r4GTwOzBpm//nAPwGiGLJvdRO/iy0UN0s37f0DbwReDTxUse3TwOVp/XLgmhr5ZgIb0v/HpPVjSir/rUBXWr+mVvmNfFeHUf5VwIca+H6G/V3xUs7SDi3OhUBPRGyIiP3ACorpNyotBm5M67cB50rKn4e1hojYHBH3p/U9wCPAnDKOXaLFwJei8ANghqTZTSjnXOAnEVHrKa7SRMR3gR1Vmyu/4xuBd9bIeh5wR0TsiIidwB1A9tMptcqPiG9HRH96+QPgxNzjHk75DWrkd8VK0A6Bcw7wZMXrjRwcuJ5Pk364dwPHll2RdAngLGB1jd0/L+kBSf8k6YySiw7g25Luk7S0xv5GPqMyLAG+WmdfM98/wPERMTTD+Bbg+BppRutz+G2KFn4th/quDsdl6VLBDXUuVYzW+x/32iFwjgmSjgK+BnwgIp6p2n0/xenrq4C/oRh3tExviIhXA28DLpX0xpKPf0iSuoF3AP+3xu5mv/8XiOK8tCX30Un6Y6AfuLlOkmZ9V58HTgHOBDYDf1nScW0E2iFwbgJOqnh9YtpWM42kLmA6sL2sCkiaQBE0b46Ir1fvj4hnIuLZtL4KmCCp0cEODikiNqX/nwK+QXFKVqmRz+hwvQ24PyK21qhfU99/snXo8kP6v9ZEf039HCS9F3g78BspeB+kge9qRCJia0QMRDFx+d/VOe5o/BwY7RE47wXmSZqbWj1LgJVVaVYCQz2oFwB31fvBzpWulV4PPBIRn6mT5sVD11QlLaT4XEsJ3JKmSpo2tE7RSfFQVbKVwG+m3vWzgd0Vp7VluYg6p+nNfP8VKr/ji4Fv1khzO/BWScekU9m3pm2HTdIi4CPAOyLiuTppGvmuRlp+5TXrX61z3EZ+V6wMre6damSh6DX+MUWP4R+nbVdT/BADTKI4hewB7gFeVmLZb6A4LXwQWJuW84H3Ae9LaS4D1lH0Yv4AeH2J5b8sHfeBVMbQ+68sX8B16fP5IbCg5M9/KkUgnF6xrWnvnyJAbwYOUFynu4TimvWdwKPAd4CZKe0C4AsVeX87/Rz0AL9VYvk9FNcPh34Ghu7iOAFYNdx3VVL5N6Xv9kGKYDi7uvx6vyteyl/8yKWZWaZ2OFU3MxtTHDjNzDI5cJqZZXLgNDPL5MBpZpbJgdPMLJMDp5lZJgdOK42k16ZBKCalp2jWSXpFq+tlVjbfAG+lkvQJiie5JgMbI+KTLa6SWekcOK1U6Rnpe4FeikcvB1pcJbPS+VTdynYscBTFaPmTWlwXs6Zwi9NKJWklxcjjcykGorisxVUyK11XqytgRw5JvwkciIivSOoE/l3SORFxV6vrZlYmtzjNzDL5GqeZWSYHTjOzTA6cZmaZHDjNzDI5cJqZZXLgNDPL5MBpZpbp/wP7mdM67LtJOQAAAABJRU5ErkJggg==\n", 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\n", "text/plain": [ "
" ] @@ -1600,10 +1781,9 @@ "\tID =\t2\n", "\tName =\tcell tally\n", "\tFilters =\tCellFilter\n", - "\tNuclides =\tU235 U238 \n", + "\tNuclides =\tU235 U238\n", "\tScores =\t['scatter']\n", - "\tEstimator =\ttracklength\n", - "\n" + "\tEstimator =\ttracklength\n" ] } ], @@ -1654,16 +1834,16 @@ " 1\n", " U235\n", " scatter\n", - " 3.80e-02\n", - " 1.33e-04\n", + " 3.81e-02\n", + " 4.13e-05\n", " \n", " \n", " 1\n", " 1\n", " U238\n", " scatter\n", - " 2.33e+00\n", - " 8.12e-03\n", + " 2.34e+00\n", + " 2.41e-03\n", " \n", " \n", "\n", @@ -1671,8 +1851,8 @@ ], "text/plain": [ " cell nuclide score mean std. dev.\n", - "0 1 U235 scatter 3.80e-02 1.33e-04\n", - "1 1 U238 scatter 2.33e+00 8.12e-03" + "0 1 U235 scatter 3.81e-02 4.13e-05\n", + "1 1 U238 scatter 2.34e+00 2.41e-03" ] }, "execution_count": 24, @@ -1704,8 +1884,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[0.00811746]\n", - " [0.00013266]]]\n" + "[[[2.41367509e-03]\n", + " [4.12533801e-05]]]\n" ] } ], @@ -1736,10 +1916,9 @@ "\tID =\t3\n", "\tName =\tdistribcell tally\n", "\tFilters =\tDistribcellFilter\n", - "\tNuclides =\ttotal \n", + "\tNuclides =\ttotal\n", "\tScores =\t['absorption', 'scatter']\n", - "\tEstimator =\ttracklength\n", - "\n" + "\tEstimator =\ttracklength\n" ] } ], @@ -1767,25 +1946,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[0.04347272]]\n", + "[[[0.0131914 ]]\n", "\n", - " [[0.04671736]]\n", + " [[0.01252949]]\n", "\n", - " [[0.04878286]]\n", + " [[0.01241481]]\n", "\n", - " [[0.03059582]]\n", + " [[0.01194961]]\n", "\n", - " [[0.04548096]]\n", + " [[0.01186091]]\n", "\n", - " [[0.04288085]]\n", + " [[0.0127257 ]]\n", "\n", - " [[0.02557663]]\n", + " [[0.01358576]]\n", "\n", - " [[0.0419826 ]]\n", + " [[0.0130368 ]]\n", "\n", - " [[0.05878954]]\n", + " [[0.014031 ]]\n", "\n", - " [[0.04217666]]]\n" + " [[0.0141883 ]]]\n" ] } ], @@ -1877,8 +2056,8 @@ " 3\n", " 279\n", " absorption\n", - " 6.26e-04\n", - " 4.62e-05\n", + " 7.11e-04\n", + " 1.10e-05\n", " \n", " \n", " 559\n", @@ -1891,8 +2070,8 @@ " 3\n", " 279\n", " scatter\n", - " 8.73e-02\n", - " 2.14e-03\n", + " 8.91e-02\n", + " 6.60e-04\n", " \n", " \n", " 560\n", @@ -1905,8 +2084,8 @@ " 3\n", " 280\n", " absorption\n", - " 6.15e-04\n", - " 3.12e-05\n", + " 6.75e-04\n", + " 1.02e-05\n", " \n", " \n", " 561\n", @@ -1919,8 +2098,8 @@ " 3\n", " 280\n", " scatter\n", - " 8.06e-02\n", - " 1.85e-03\n", + " 8.35e-02\n", + " 6.11e-04\n", " \n", " \n", " 562\n", @@ -1933,8 +2112,8 @@ " 3\n", " 281\n", " absorption\n", - " 6.36e-04\n", - " 4.24e-05\n", + " 6.10e-04\n", + " 1.02e-05\n", " \n", " \n", " 563\n", @@ -1947,8 +2126,8 @@ " 3\n", " 281\n", " scatter\n", - " 7.59e-02\n", - " 1.93e-03\n", + " 7.75e-02\n", + " 6.10e-04\n", " \n", " \n", " 564\n", @@ -1961,8 +2140,8 @@ " 3\n", " 282\n", " absorption\n", - " 5.30e-04\n", - " 2.75e-05\n", + " 5.67e-04\n", + " 9.88e-06\n", " \n", " \n", " 565\n", @@ -1975,8 +2154,8 @@ " 3\n", " 282\n", " scatter\n", - " 6.82e-02\n", - " 1.02e-03\n", + " 7.11e-02\n", + " 5.99e-04\n", " \n", " \n", " 566\n", @@ -1989,8 +2168,8 @@ " 3\n", " 283\n", " absorption\n", - " 4.67e-04\n", - " 2.84e-05\n", + " 5.06e-04\n", + " 9.35e-06\n", " \n", " \n", " 567\n", @@ -2003,8 +2182,8 @@ " 3\n", " 283\n", " scatter\n", - " 6.42e-02\n", - " 1.81e-03\n", + " 6.39e-02\n", + " 5.53e-04\n", " \n", " \n", " 568\n", @@ -2017,8 +2196,8 @@ " 3\n", " 284\n", " absorption\n", - " 4.52e-04\n", - " 2.13e-05\n", + " 4.35e-04\n", + " 8.22e-06\n", " \n", " \n", " 569\n", @@ -2031,8 +2210,8 @@ " 3\n", " 284\n", " scatter\n", - " 5.64e-02\n", - " 1.20e-03\n", + " 5.62e-02\n", + " 5.18e-04\n", " \n", " \n", " 570\n", @@ -2045,8 +2224,8 @@ " 3\n", " 285\n", " absorption\n", - " 3.85e-04\n", - " 1.99e-05\n", + " 3.73e-04\n", + " 7.90e-06\n", " \n", " \n", " 571\n", @@ -2059,8 +2238,8 @@ " 3\n", " 285\n", " scatter\n", - " 4.86e-02\n", - " 1.58e-03\n", + " 4.76e-02\n", + " 4.92e-04\n", " \n", " \n", " 572\n", @@ -2073,8 +2252,8 @@ " 3\n", " 286\n", " absorption\n", - " 2.84e-04\n", - " 2.16e-05\n", + " 2.98e-04\n", + " 7.30e-06\n", " \n", " \n", " 573\n", @@ -2087,8 +2266,8 @@ " 3\n", " 286\n", " scatter\n", - " 3.91e-02\n", - " 1.66e-03\n", + " 3.82e-02\n", + " 4.17e-04\n", " \n", " \n", " 574\n", @@ -2101,8 +2280,8 @@ " 3\n", " 287\n", " absorption\n", - " 2.17e-04\n", - " 2.15e-05\n", + " 2.05e-04\n", + " 5.96e-06\n", " \n", " \n", " 575\n", @@ -2115,8 +2294,8 @@ " 3\n", " 287\n", " scatter\n", - " 3.02e-02\n", - " 1.71e-03\n", + " 2.86e-02\n", + " 3.72e-04\n", " \n", " \n", " 576\n", @@ -2129,8 +2308,8 @@ " 3\n", " 288\n", " absorption\n", - " 1.50e-04\n", - " 1.42e-05\n", + " 1.22e-04\n", + " 4.12e-06\n", " \n", " \n", " 577\n", @@ -2143,8 +2322,8 @@ " 3\n", " 288\n", " scatter\n", - " 1.89e-02\n", - " 9.31e-04\n", + " 1.82e-02\n", + " 2.59e-04\n", " \n", " \n", "\n", @@ -2178,26 +2357,26 @@ " mean std. dev. \n", " \n", " \n", - "558 6.26e-04 4.62e-05 \n", - "559 8.73e-02 2.14e-03 \n", - "560 6.15e-04 3.12e-05 \n", - "561 8.06e-02 1.85e-03 \n", - "562 6.36e-04 4.24e-05 \n", - "563 7.59e-02 1.93e-03 \n", - "564 5.30e-04 2.75e-05 \n", - "565 6.82e-02 1.02e-03 \n", - "566 4.67e-04 2.84e-05 \n", - "567 6.42e-02 1.81e-03 \n", - "568 4.52e-04 2.13e-05 \n", - "569 5.64e-02 1.20e-03 \n", - "570 3.85e-04 1.99e-05 \n", - "571 4.86e-02 1.58e-03 \n", - "572 2.84e-04 2.16e-05 \n", - "573 3.91e-02 1.66e-03 \n", - "574 2.17e-04 2.15e-05 \n", - "575 3.02e-02 1.71e-03 \n", - "576 1.50e-04 1.42e-05 \n", - "577 1.89e-02 9.31e-04 " + "558 7.11e-04 1.10e-05 \n", + "559 8.91e-02 6.60e-04 \n", + "560 6.75e-04 1.02e-05 \n", + "561 8.35e-02 6.11e-04 \n", + "562 6.10e-04 1.02e-05 \n", + "563 7.75e-02 6.10e-04 \n", + "564 5.67e-04 9.88e-06 \n", + "565 7.11e-02 5.99e-04 \n", + "566 5.06e-04 9.35e-06 \n", + "567 6.39e-02 5.53e-04 \n", + "568 4.35e-04 8.22e-06 \n", + "569 5.62e-02 5.18e-04 \n", + "570 3.73e-04 7.90e-06 \n", + "571 4.76e-02 4.92e-04 \n", + "572 2.98e-04 7.30e-06 \n", + "573 3.82e-02 4.17e-04 \n", + "574 2.05e-04 5.96e-06 \n", + "575 2.86e-02 3.72e-04 \n", + "576 1.22e-04 4.12e-06 \n", + "577 1.82e-02 2.59e-04 " ] }, "execution_count": 28, @@ -2261,38 +2440,38 @@ " \n", " \n", " mean\n", - " 4.15e-04\n", - " 2.29e-05\n", + " 4.19e-04\n", + " 6.86e-06\n", " \n", " \n", " std\n", - " 2.33e-04\n", - " 9.14e-06\n", + " 2.41e-04\n", + " 2.51e-06\n", " \n", " \n", " min\n", - " 1.84e-05\n", - " 3.31e-06\n", + " 1.68e-05\n", + " 1.07e-06\n", " \n", " \n", " 25%\n", - " 2.08e-04\n", - " 1.58e-05\n", + " 2.06e-04\n", + " 5.09e-06\n", " \n", " \n", " 50%\n", - " 4.10e-04\n", - " 2.24e-05\n", + " 3.98e-04\n", + " 6.90e-06\n", " \n", " \n", " 75%\n", - " 6.25e-04\n", - " 2.93e-05\n", + " 6.17e-04\n", + " 8.44e-06\n", " \n", " \n", " max\n", - " 8.87e-04\n", - " 5.06e-05\n", + " 8.70e-04\n", + " 1.52e-05\n", " \n", " \n", "\n", @@ -2303,13 +2482,13 @@ " \n", " \n", "count 2.89e+02 2.89e+02\n", - "mean 4.15e-04 2.29e-05\n", - "std 2.33e-04 9.14e-06\n", - "min 1.84e-05 3.31e-06\n", - "25% 2.08e-04 1.58e-05\n", - "50% 4.10e-04 2.24e-05\n", - "75% 6.25e-04 2.93e-05\n", - "max 8.87e-04 5.06e-05" + "mean 4.19e-04 6.86e-06\n", + "std 2.41e-04 2.51e-06\n", + "min 1.68e-05 1.07e-06\n", + "25% 2.06e-04 5.09e-06\n", + "50% 3.98e-04 6.90e-06\n", + "75% 6.17e-04 8.44e-06\n", + "max 8.70e-04 1.52e-05" ] }, "execution_count": 29, @@ -2342,7 +2521,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mann-Whitney Test p-value: 0.3531165056829588\n" + "Mann-Whitney Test p-value: 0.47449458604689265\n" ] } ], @@ -2378,7 +2557,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mann-Whitney Test p-value: 2.835784441937541e-42\n" + "Mann-Whitney Test p-value: 2.499381683224802e-42\n" ] } ], @@ -2412,18 +2591,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/.pyenv/versions/3.7.0/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n", + ":4: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", - "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", - " after removing the cwd from sys.path.\n" + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " scatter['rel. err.'] = scatter['std. dev.'] / scatter['mean']\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 32, @@ -2432,7 +2611,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", 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" ] @@ -2508,7 +2687,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.8.5" } }, "nbformat": 4, From 407093c0e70a9e9154a0c229a6f4497234a1324c Mon Sep 17 00:00:00 2001 From: Cyrus Wyett <34195737+cjwyett@users.noreply.github.com> Date: Sat, 15 Aug 2020 07:20:11 +0100 Subject: [PATCH 05/17] Add files via upload --- tally-arithmetic.ipynb | 1746 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1746 insertions(+) create mode 100644 tally-arithmetic.ipynb diff --git a/tally-arithmetic.ipynb b/tally-arithmetic.ipynb new file mode 100644 index 0000000000..6aed6e757b --- /dev/null +++ b/tally-arithmetic.ipynb @@ -0,0 +1,1746 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook shows the how tallies can be combined (added, subtracted, multiplied, etc.) using the Python API in order to create derived tallies. Since no covariance information is obtained, it is assumed that tallies are completely independent of one another when propagating uncertainties. The target problem is a simple pin cell." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import glob\n", + "\n", + "from IPython.display import Image\n", + "import numpy as np\n", + "import openmc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Input Files" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we need to define materials that will be used in the problem. We'll create three materials for the fuel, water, and cladding of the fuel pin." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# 1.6 enriched fuel\n", + "fuel = openmc.Material(name='1.6% Fuel')\n", + "fuel.set_density('g/cm3', 10.31341)\n", + "fuel.add_nuclide('U235', 3.7503e-4)\n", + "fuel.add_nuclide('U238', 2.2625e-2)\n", + "fuel.add_nuclide('O16', 4.6007e-2)\n", + "\n", + "# borated water\n", + "water = openmc.Material(name='Borated Water')\n", + "water.set_density('g/cm3', 0.740582)\n", + "water.add_nuclide('H1', 4.9457e-2)\n", + "water.add_nuclide('O16', 2.4732e-2)\n", + "water.add_nuclide('B10', 8.0042e-6)\n", + "\n", + "# zircaloy\n", + "zircaloy = openmc.Material(name='Zircaloy')\n", + "zircaloy.set_density('g/cm3', 6.55)\n", + "zircaloy.add_nuclide('Zr90', 7.2758e-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With our three materials, we can now create a materials file object that can be exported to an actual XML file." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate a Materials collection\n", + "materials = openmc.Materials([fuel, water, zircaloy])\n", + "\n", + "# Export to \"materials.xml\"\n", + "materials.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's move on to the geometry. Our problem will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces -- in this case two cylinders and six planes." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Create cylinders for the fuel and clad\n", + "fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.39218)\n", + "clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.45720)\n", + "\n", + "# Create boundary planes to surround the geometry\n", + "# Use both reflective and vacuum boundaries to make life interesting\n", + "min_x = openmc.XPlane(x0=-0.63, boundary_type='reflective')\n", + "max_x = openmc.XPlane(x0=+0.63, boundary_type='reflective')\n", + "min_y = openmc.YPlane(y0=-0.63, boundary_type='reflective')\n", + "max_y = openmc.YPlane(y0=+0.63, boundary_type='reflective')\n", + "min_z = openmc.ZPlane(z0=-100., boundary_type='vacuum')\n", + "max_z = openmc.ZPlane(z0=+100., boundary_type='vacuum')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the surfaces defined, we can now create cells that are defined by intersections of half-spaces created by the surfaces." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a Universe to encapsulate a fuel pin\n", + "pin_cell_universe = openmc.Universe(name='1.6% Fuel Pin')\n", + "\n", + "# Create fuel Cell\n", + "fuel_cell = openmc.Cell(name='1.6% Fuel')\n", + "fuel_cell.fill = fuel\n", + "fuel_cell.region = -fuel_outer_radius\n", + "pin_cell_universe.add_cell(fuel_cell)\n", + "\n", + "# Create a clad Cell\n", + "clad_cell = openmc.Cell(name='1.6% Clad')\n", + "clad_cell.fill = zircaloy\n", + "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n", + "pin_cell_universe.add_cell(clad_cell)\n", + "\n", + "# Create a moderator Cell\n", + "moderator_cell = openmc.Cell(name='1.6% Moderator')\n", + "moderator_cell.fill = water\n", + "moderator_cell.region = +clad_outer_radius\n", + "pin_cell_universe.add_cell(moderator_cell)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "OpenMC requires that there is a \"root\" universe. Let us create a root cell that is filled by the pin cell universe and then assign it to the root universe." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Create root Cell\n", + "root_cell = openmc.Cell(name='root cell')\n", + "root_cell.fill = pin_cell_universe\n", + "\n", + "# Add boundary planes\n", + "root_cell.region = +min_x & -max_x & +min_y & -max_y & +min_z & -max_z\n", + "\n", + "# Create root Universe\n", + "root_universe = openmc.Universe(universe_id=0, name='root universe')\n", + "root_universe.add_cell(root_cell)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now must create a geometry that is assigned a root universe, put the geometry into a geometry file, and export it to XML." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Create Geometry and set root Universe\n", + "geometry = openmc.Geometry(root_universe)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Export to \"geometry.xml\"\n", + "geometry.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the geometry and materials finished, we now just need to define simulation parameters. In this case, we will use 5 inactive batches and 15 active batches each with 2500 particles." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# OpenMC simulation parameters\n", + "batches = 20\n", + "inactive = 5\n", + "particles = 2500\n", + "\n", + "# Instantiate a Settings object\n", + "settings = openmc.Settings()\n", + "settings.batches = batches\n", + "settings.inactive = inactive\n", + "settings.particles = particles\n", + "settings.output = {'tallies': True}\n", + "\n", + "# Create an initial uniform spatial source distribution over fissionable zones\n", + "bounds = [-0.63, -0.63, -100., 0.63, 0.63, 100.]\n", + "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", + "settings.source = openmc.Source(space=uniform_dist)\n", + "\n", + "# Export to \"settings.xml\"\n", + "settings.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us also create a plot file that we can use to verify that our pin cell geometry was created successfully." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate a Plot\n", + "plot = openmc.Plot(plot_id=1)\n", + "plot.filename = 'materials-xy'\n", + "plot.origin = [0, 0, 0]\n", + "plot.width = [1.26, 1.26]\n", + "plot.pixels = [250, 250]\n", + "plot.color_by = 'material'\n", + "\n", + "# Show plot\n", + "openmc.plot_inline(plot)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see from the plot, we have a nice pin cell with fuel, cladding, and water! Before we run our simulation, we need to tell the code what we want to tally. The following code shows how to create a variety of tallies." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate an empty Tallies object\n", + "tallies = openmc.Tallies()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Create Tallies to compute microscopic multi-group cross-sections\n", + "\n", + "# Instantiate energy filter for multi-group cross-section Tallies\n", + "energy_filter = openmc.EnergyFilter([0., 0.625, 20.0e6])\n", + "\n", + "# Instantiate flux Tally in moderator and fuel\n", + "tally = openmc.Tally(name='flux')\n", + "tally.filters = [openmc.CellFilter([fuel_cell, moderator_cell])]\n", + "tally.filters.append(energy_filter)\n", + "tally.scores = ['flux']\n", + "tallies.append(tally)\n", + "\n", + "# Instantiate reaction rate Tally in fuel\n", + "tally = openmc.Tally(name='fuel rxn rates')\n", + "tally.filters = [openmc.CellFilter(fuel_cell)]\n", + "tally.filters.append(energy_filter)\n", + "tally.scores = ['nu-fission', 'scatter']\n", + "tally.nuclides = ['U238', 'U235']\n", + "tallies.append(tally)\n", + "\n", + "# Instantiate reaction rate Tally in moderator\n", + "tally = openmc.Tally(name='moderator rxn rates')\n", + "tally.filters = [openmc.CellFilter(moderator_cell)]\n", + "tally.filters.append(energy_filter)\n", + "tally.scores = ['absorption', 'total']\n", + "tally.nuclides = ['O16', 'H1']\n", + "tallies.append(tally)\n", + "\n", + "# Instantiate a tally mesh\n", + "mesh = openmc.RegularMesh(mesh_id=1)\n", + "mesh.dimension = [1, 1, 1]\n", + "mesh.lower_left = [-0.63, -0.63, -100.]\n", + "mesh.width = [1.26, 1.26, 200.]\n", + "meshsurface_filter = openmc.MeshSurfaceFilter(mesh)\n", + "\n", + "# Instantiate thermal, fast, and total leakage tallies\n", + "leak = openmc.Tally(name='leakage')\n", + "leak.filters = [meshsurface_filter]\n", + "leak.scores = ['current']\n", + "tallies.append(leak)\n", + "\n", + "thermal_leak = openmc.Tally(name='thermal leakage')\n", + "thermal_leak.filters = [meshsurface_filter, openmc.EnergyFilter([0., 0.625])]\n", + "thermal_leak.scores = ['current']\n", + "tallies.append(thermal_leak)\n", + "\n", + "fast_leak = openmc.Tally(name='fast leakage')\n", + "fast_leak.filters = [meshsurface_filter, openmc.EnergyFilter([0.625, 20.0e6])]\n", + "fast_leak.scores = ['current']\n", + "tallies.append(fast_leak)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# K-Eigenvalue (infinity) tallies\n", + "fiss_rate = openmc.Tally(name='fiss. rate')\n", + "abs_rate = openmc.Tally(name='abs. rate')\n", + "fiss_rate.scores = ['nu-fission']\n", + "abs_rate.scores = ['absorption']\n", + "tallies += (fiss_rate, abs_rate)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Resonance Escape Probability tallies\n", + "therm_abs_rate = openmc.Tally(name='therm. abs. rate')\n", + "therm_abs_rate.scores = ['absorption']\n", + "therm_abs_rate.filters = [openmc.EnergyFilter([0., 0.625])]\n", + "tallies.append(therm_abs_rate)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Thermal Flux Utilization tallies\n", + "fuel_therm_abs_rate = openmc.Tally(name='fuel therm. abs. rate')\n", + "fuel_therm_abs_rate.scores = ['absorption']\n", + "fuel_therm_abs_rate.filters = [openmc.EnergyFilter([0., 0.625]),\n", + " openmc.CellFilter([fuel_cell])]\n", + "tallies.append(fuel_therm_abs_rate)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# Fast Fission Factor tallies\n", + "therm_fiss_rate = openmc.Tally(name='therm. fiss. rate')\n", + "therm_fiss_rate.scores = ['nu-fission']\n", + "therm_fiss_rate.filters = [openmc.EnergyFilter([0., 0.625])]\n", + "tallies.append(therm_fiss_rate)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate energy filter to illustrate Tally slicing\n", + "fine_energy_filter = openmc.EnergyFilter(np.logspace(np.log10(1e-2), np.log10(20.0e6), 10))\n", + "\n", + "# Instantiate flux Tally in moderator and fuel\n", + "tally = openmc.Tally(name='need-to-slice')\n", + "tally.filters = [openmc.CellFilter([fuel_cell, moderator_cell])]\n", + "tally.filters.append(fine_energy_filter)\n", + "tally.scores = ['nu-fission', 'scatter']\n", + "tally.nuclides = ['H1', 'U238']\n", + "tallies.append(tally)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# Export to \"tallies.xml\"\n", + "tallies.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we a have a complete set of inputs, so we can go ahead and run our simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", + "\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2020 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.12.0\n", + " Git SHA1 | 3d90a9f857ec72eae897e054d4225180f1fa4d93\n", + " Date/Time | 2020-08-15 07:12:56\n", + " OpenMP Threads | 4\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading materials XML file...\n", + " Reading geometry XML file...\n", + " Reading U235 from /home/master/data/nuclear/endfb71_hdf5/U235.h5\n", + " Reading U238 from /home/master/data/nuclear/endfb71_hdf5/U238.h5\n", + " Reading O16 from /home/master/data/nuclear/endfb71_hdf5/O16.h5\n", + " Reading H1 from /home/master/data/nuclear/endfb71_hdf5/H1.h5\n", + " Reading B10 from /home/master/data/nuclear/endfb71_hdf5/B10.h5\n", + " Reading Zr90 from /home/master/data/nuclear/endfb71_hdf5/Zr90.h5\n", + " Minimum neutron data temperature: 294.000000 K\n", + " Maximum neutron data temperature: 294.000000 K\n", + " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", + " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000.000000 eV for U235\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 0.99100\n", + " 2/1 1.00834\n", + " 3/1 1.06764\n", + " 4/1 1.02113\n", + " 5/1 0.99556\n", + " 6/1 1.02501\n", + " 7/1 1.03920 1.03210 +/- 0.00709\n", + " 8/1 1.00744 1.02388 +/- 0.00918\n", + " 9/1 1.04889 1.03014 +/- 0.00902\n", + " 10/1 1.07235 1.03858 +/- 0.01096\n", + " 11/1 1.04400 1.03948 +/- 0.00899\n", + " 12/1 1.02556 1.03749 +/- 0.00786\n", + " 13/1 1.00755 1.03375 +/- 0.00776\n", + " 14/1 1.02346 1.03261 +/- 0.00694\n", + " 15/1 1.03215 1.03256 +/- 0.00621\n", + " 16/1 1.01060 1.03057 +/- 0.00596\n", + " 17/1 1.01665 1.02941 +/- 0.00556\n", + " 18/1 1.04273 1.03043 +/- 0.00522\n", + " 19/1 0.98780 1.02739 +/- 0.00571\n", + " 20/1 1.04402 1.02849 +/- 0.00543\n", + " Creating state point statepoint.20.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 2.8516e-01 seconds\n", + " Reading cross sections = 2.7562e-01 seconds\n", + " Total time in simulation = 1.6686e+00 seconds\n", + " Time in transport only = 1.6528e+00 seconds\n", + " Time in inactive batches = 2.2163e-01 seconds\n", + " Time in active batches = 1.4469e+00 seconds\n", + " Time synchronizing fission bank = 2.4642e-03 seconds\n", + " Sampling source sites = 1.9299e-03 seconds\n", + " SEND/RECV source sites = 5.0275e-04 seconds\n", + " Time accumulating tallies = 3.8964e-05 seconds\n", + " Total time for finalization = 2.0000e-04 seconds\n", + " Total time elapsed = 1.9609e+00 seconds\n", + " Calculation Rate (inactive) = 56401.3 particles/second\n", + " Calculation Rate (active) = 25917.0 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.02606 +/- 0.00624\n", + " k-effective (Track-length) = 1.02849 +/- 0.00543\n", + " k-effective (Absorption) = 1.02154 +/- 0.00530\n", + " Combined k-effective = 1.02503 +/- 0.00501\n", + " Leakage Fraction = 0.01557 +/- 0.00106\n", + "\n" + ] + } + ], + "source": [ + "# Remove old HDF5 (summary, statepoint) files\n", + "!rm statepoint.*\n", + "\n", + "# Run OpenMC!\n", + "openmc.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tally Data Processing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our simulation ran successfully and created a statepoint file with all the tally data in it. We begin our analysis here loading the statepoint file and 'reading' the results. By default, the tally results are not read into memory because they might be large, even large enough to exceed the available memory on a computer." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Load the statepoint file\n", + "sp = openmc.StatePoint('statepoint.20.h5')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have a tally of the total fission rate and the total absorption rate, so we can calculate k-eff as:\n", + "$$k_{eff} = \\frac{\\langle \\nu \\Sigma_f \\phi \\rangle}{\\langle \\Sigma_a \\phi \\rangle + \\langle L \\rangle}$$\n", + "In this notation, $\\langle \\cdot \\rangle^a_b$ represents an OpenMC that is integrated over region $a$ and energy range $b$. If $a$ or $b$ is not reported, it means the value represents an integral over all space or all energy, respectively." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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nuclidescoremeanstd. dev.
0total(nu-fission / (absorption + current))1.0226610.006992
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" + ], + "text/plain": [ + " nuclide score mean std. dev.\n", + "0 total (nu-fission / (absorption + current)) 1.02e+00 6.99e-03" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get the fission and absorption rate tallies\n", + "fiss_rate = sp.get_tally(name='fiss. rate')\n", + "abs_rate = sp.get_tally(name='abs. rate')\n", + "\n", + "# Get the leakage tally\n", + "leak = sp.get_tally(name='leakage')\n", + "leak = leak.summation(filter_type=openmc.MeshSurfaceFilter, remove_filter=True)\n", + "\n", + "# Compute k-infinity using tally arithmetic\n", + "keff = fiss_rate / (abs_rate + leak)\n", + "keff.get_pandas_dataframe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that even though the neutron production rate, absorption rate, and current are separate tallies, we still get a first-order estimate of the uncertainty on the quotient of them automatically!\n", + "\n", + "Often in textbooks you'll see k-eff represented using the six-factor formula $$k_{eff} = p \\epsilon f \\eta P_{FNL} P_{TNL}.$$ Let's analyze each of these factors, starting with the resonance escape probability which is defined as $$p=\\frac{\\langle\\Sigma_a\\phi\\rangle_T + \\langle L \\rangle_T}{\\langle\\Sigma_a\\phi\\rangle + \\langle L \\rangle_T}$$ where the subscript $T$ means thermal energies." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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energy low [eV]energy high [eV]nuclidescoremeanstd. dev.
00.00.625total((absorption + current) / (absorption + current))0.6933880.005475
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" + ], + "text/plain": [ + " energy low [eV] energy high [eV] nuclide \\\n", + "0 0.00e+00 6.25e-01 total \n", + "\n", + " score mean std. dev. \n", + "0 ((absorption + current) / (absorption + current)) 6.93e-01 5.47e-03 " + ] + }, + "execution_count": 22, + "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", + "thermal_leak = sp.get_tally(name='thermal leakage')\n", + "thermal_leak = thermal_leak.summation(filter_type=openmc.MeshSurfaceFilter, remove_filter=True)\n", + "res_esc = (therm_abs_rate + thermal_leak) / (abs_rate + thermal_leak)\n", + "res_esc.get_pandas_dataframe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The fast fission factor can be calculated as\n", + "$$\\epsilon=\\frac{\\langle\\nu\\Sigma_f\\phi\\rangle}{\\langle\\nu\\Sigma_f\\phi\\rangle_T}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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energy low [eV]energy high [eV]nuclidescoremeanstd. dev.
00.00.625total(nu-fission / nu-fission)1.2037180.010102
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" + ], + "text/plain": [ + " energy low [eV] energy high [eV] nuclide score \\\n", + "0 0.00e+00 6.25e-01 total (nu-fission / nu-fission) \n", + "\n", + " mean std. dev. \n", + "0 1.20e+00 1.01e-02 " + ] + }, + "execution_count": 23, + "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", + "fast_fiss = fiss_rate / therm_fiss_rate\n", + "fast_fiss.get_pandas_dataframe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The thermal flux utilization is calculated as\n", + "$$f=\\frac{\\langle\\Sigma_a\\phi\\rangle^F_T}{\\langle\\Sigma_a\\phi\\rangle_T}$$\n", + "where the superscript $F$ denotes fuel." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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energy low [eV]energy high [eV]cellnuclidescoremeanstd. dev.
00.00.6251total(absorption / absorption)0.7486130.006949
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" + ], + "text/plain": [ + " energy low [eV] energy high [eV] cell nuclide score \\\n", + "0 0.00e+00 6.25e-01 1 total (absorption / absorption) \n", + "\n", + " mean std. dev. \n", + "0 7.49e-01 6.95e-03 " + ] + }, + "execution_count": 24, + "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", + "therm_util = fuel_therm_abs_rate / therm_abs_rate\n", + "therm_util.get_pandas_dataframe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next factor is the number of fission neutrons produced per absorption in fuel, calculated as $$\\eta = \\frac{\\langle \\nu\\Sigma_f\\phi \\rangle_T}{\\langle \\Sigma_a \\phi \\rangle^F_T}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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energy low [eV]energy high [eV]cellnuclidescoremeanstd. dev.
00.00.6251total(nu-fission / absorption)1.6635490.015328
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" + ], + "text/plain": [ + " energy low [eV] energy high [eV] cell nuclide score \\\n", + "0 0.00e+00 6.25e-01 1 total (nu-fission / absorption) \n", + "\n", + " mean std. dev. \n", + "0 1.66e+00 1.53e-02 " + ] + }, + "execution_count": 25, + "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", + "eta.get_pandas_dataframe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are two leakage factors to account for fast and thermal leakage. The fast non-leakage probability is computed as $$P_{FNL} = \\frac{\\langle \\Sigma_a\\phi \\rangle + \\langle L \\rangle_T}{\\langle \\Sigma_a \\phi \\rangle + \\langle L \\rangle}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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energy low [eV]energy high [eV]nuclidescoremeanstd. dev.
00.00.625total((absorption + current) / (absorption + current))0.9859730.006036
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" + ], + "text/plain": [ + " energy low [eV] energy high [eV] nuclide \\\n", + "0 0.00e+00 6.25e-01 total \n", + "\n", + " score mean std. dev. \n", + "0 ((absorption + current) / (absorption + current)) 9.86e-01 6.04e-03 " + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p_fnl = (abs_rate + thermal_leak) / (abs_rate + leak)\n", + "p_fnl.get_pandas_dataframe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The final factor is the thermal non-leakage probability and is computed as $$P_{TNL} = \\frac{\\langle \\Sigma_a\\phi \\rangle_T}{\\langle \\Sigma_a \\phi \\rangle_T + \\langle L \\rangle_T}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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energy low [eV]energy high [eV]nuclidescoremeanstd. dev.
00.00.625total(absorption / (absorption + current))0.9978670.009335
\n", + "
" + ], + "text/plain": [ + " energy low [eV] energy high [eV] nuclide \\\n", + "0 0.00e+00 6.25e-01 total \n", + "\n", + " score mean std. dev. \n", + "0 (absorption / (absorption + current)) 9.98e-01 9.33e-03 " + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p_tnl = therm_abs_rate / (therm_abs_rate + thermal_leak)\n", + "p_tnl.get_pandas_dataframe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can calculate $k_{eff}$ using the product of the factors form the four-factor formula." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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energy low [eV]energy high [eV]cellnuclidescoremeanstd. dev.
00.00.6251total(((((((absorption + current) / (absorption + c...1.0226610.021177
\n", + "
" + ], + "text/plain": [ + " energy low [eV] energy high [eV] cell nuclide \\\n", + "0 0.00e+00 6.25e-01 1 total \n", + "\n", + " score mean std. dev. \n", + "0 (((((((absorption + current) / (absorption + c... 1.02e+00 2.12e-02 " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "keff = res_esc * fast_fiss * therm_util * eta * p_fnl * p_tnl\n", + "keff.get_pandas_dataframe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the value we've obtained here has exactly the same mean as before. However, because of the way it was calculated, the standard deviation appears to be larger.\n", + "\n", + "Let's move on to a more complicated example now. Before we set up tallies to get reaction rates in the fuel and moderator in two energy groups for two different nuclides. We can use tally arithmetic to divide each of these reaction rates by the flux to get microscopic multi-group cross sections." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Compute microscopic multi-group cross-sections\n", + "flux = sp.get_tally(name='flux')\n", + "flux = flux.get_slice(filters=[openmc.CellFilter], filter_bins=[(fuel_cell.id,)])\n", + "fuel_rxn_rates = sp.get_tally(name='fuel rxn rates')\n", + "mod_rxn_rates = sp.get_tally(name='moderator rxn rates')" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cellenergy low [eV]energy high [eV]nuclidescoremeanstd. dev.
010.0006.250000e-01(U238 / total)(nu-fission / flux)6.656489e-076.107780e-09
110.0006.250000e-01(U238 / total)(scatter / flux)2.099890e-011.936009e-03
210.0006.250000e-01(U235 / total)(nu-fission / flux)3.563899e-013.286347e-03
310.0006.250000e-01(U235 / total)(scatter / flux)5.554974e-035.124024e-05
410.6252.000000e+07(U238 / total)(nu-fission / flux)7.166537e-037.141716e-05
510.6252.000000e+07(U238 / total)(scatter / flux)2.275268e-019.822400e-04
610.6252.000000e+07(U235 / total)(nu-fission / flux)8.007796e-035.184629e-05
710.6252.000000e+07(U235 / total)(scatter / flux)3.366335e-031.396517e-05
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" + ], + "text/plain": [ + " cell energy low [eV] energy high [eV] nuclide \\\n", + "0 1 0.00e+00 6.25e-01 (U238 / total) \n", + "1 1 0.00e+00 6.25e-01 (U238 / total) \n", + "2 1 0.00e+00 6.25e-01 (U235 / total) \n", + "3 1 0.00e+00 6.25e-01 (U235 / total) \n", + "4 1 6.25e-01 2.00e+07 (U238 / total) \n", + "5 1 6.25e-01 2.00e+07 (U238 / total) \n", + "6 1 6.25e-01 2.00e+07 (U235 / total) \n", + "7 1 6.25e-01 2.00e+07 (U235 / total) \n", + "\n", + " score mean std. dev. \n", + "0 (nu-fission / flux) 6.66e-07 6.11e-09 \n", + "1 (scatter / flux) 2.10e-01 1.94e-03 \n", + "2 (nu-fission / flux) 3.56e-01 3.29e-03 \n", + "3 (scatter / flux) 5.55e-03 5.12e-05 \n", + "4 (nu-fission / flux) 7.17e-03 7.14e-05 \n", + "5 (scatter / flux) 2.28e-01 9.82e-04 \n", + "6 (nu-fission / flux) 8.01e-03 5.18e-05 \n", + "7 (scatter / flux) 3.37e-03 1.40e-05 " + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fuel_xs = fuel_rxn_rates / flux\n", + "fuel_xs.get_pandas_dataframe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that when the two tallies with multiple bins were divided, the derived tally contains the outer product of the combinations. If the filters/scores are the same, no outer product is needed. The `get_values(...)` method allows us to obtain a subset of tally scores. In the following example, we obtain just the neutron production microscopic cross sections." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[6.65648937e-07]\n", + " [3.56389890e-01]]\n", + "\n", + " [[7.16653669e-03]\n", + " [8.00779649e-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", + "print(nu_fiss_xs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The same idea can be used not only for scores but also for filters and nuclides." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[0.00555497]]\n", + "\n", + " [[0.00336633]]]\n" + ] + } + ], + "source": [ + "# Show how to use Tally.get_values(...) with a CrossScore and CrossNuclide\n", + "u235_scatter_xs = fuel_xs.get_values(nuclides=['(U235 / total)'], \n", + " scores=['(scatter / flux)'])\n", + "print(u235_scatter_xs)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[0.22752681]\n", + " [0.00336633]]]\n" + ] + } + ], + "source": [ + "# Show how to use Tally.get_values(...) with a CrossFilter and CrossScore\n", + "fast_scatter_xs = fuel_xs.get_values(filters=[openmc.EnergyFilter], \n", + " filter_bins=[((0.625, 20.0e6),)], \n", + " scores=['(scatter / flux)'])\n", + "print(fast_scatter_xs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A more advanced method is to use `get_slice(...)` to create a new derived tally that is a subset of an existing tally. This has the benefit that we can use `get_pandas_dataframe()` to see the tallies in a more human-readable format." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cellenergy low [eV]energy high [eV]nuclidescoremeanstd. dev.
010.0006.250000e-01U238nu-fission0.0000021.030465e-08
110.0006.250000e-01U235nu-fission0.8544305.572280e-03
210.6252.000000e+07U238nu-fission0.0822067.786594e-04
310.6252.000000e+07U235nu-fission0.0918565.222837e-04
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" + ], + "text/plain": [ + " cell energy low [eV] energy high [eV] nuclide score mean \\\n", + "0 1 0.00e+00 6.25e-01 U238 nu-fission 1.60e-06 \n", + "1 1 0.00e+00 6.25e-01 U235 nu-fission 8.54e-01 \n", + "2 1 6.25e-01 2.00e+07 U238 nu-fission 8.22e-02 \n", + "3 1 6.25e-01 2.00e+07 U235 nu-fission 9.19e-02 \n", + "\n", + " std. dev. \n", + "0 1.03e-08 \n", + "1 5.57e-03 \n", + "2 7.79e-04 \n", + "3 5.22e-04 " + ] + }, + "execution_count": 34, + "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", + "nu_fission_rates.get_pandas_dataframe()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cellenergy low [eV]energy high [eV]nuclidescoremeanstd. dev.
031.000000e-021.080060e-01H1scatter4.5542610.038132
131.080060e-011.166529e+00H1scatter2.0090360.012481
231.166529e+001.259921e+01H1scatter1.6325360.009018
331.259921e+011.360790e+02H1scatter1.8456690.011216
431.360790e+021.469734e+03H1scatter2.0474760.011371
531.469734e+031.587401e+04H1scatter2.1266120.012823
631.587401e+041.714488e+05H1scatter2.2117440.012935
731.714488e+051.851749e+06H1scatter2.0149320.007407
831.851749e+062.000000e+07H1scatter0.3723360.003747
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" + ], + "text/plain": [ + " cell energy low [eV] energy high [eV] nuclide score mean \\\n", + "0 3 1.00e-02 1.08e-01 H1 scatter 4.55e+00 \n", + "1 3 1.08e-01 1.17e+00 H1 scatter 2.01e+00 \n", + "2 3 1.17e+00 1.26e+01 H1 scatter 1.63e+00 \n", + "3 3 1.26e+01 1.36e+02 H1 scatter 1.85e+00 \n", + "4 3 1.36e+02 1.47e+03 H1 scatter 2.05e+00 \n", + "5 3 1.47e+03 1.59e+04 H1 scatter 2.13e+00 \n", + "6 3 1.59e+04 1.71e+05 H1 scatter 2.21e+00 \n", + "7 3 1.71e+05 1.85e+06 H1 scatter 2.01e+00 \n", + "8 3 1.85e+06 2.00e+07 H1 scatter 3.72e-01 \n", + "\n", + " std. dev. \n", + "0 3.81e-02 \n", + "1 1.25e-02 \n", + "2 9.02e-03 \n", + "3 1.12e-02 \n", + "4 1.14e-02 \n", + "5 1.28e-02 \n", + "6 1.29e-02 \n", + "7 7.41e-03 \n", + "8 3.75e-03 " + ] + }, + "execution_count": 35, + "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", + "slice_test = need_to_slice.get_slice(scores=['scatter'], nuclides=['H1'],\n", + " filters=[openmc.CellFilter], filter_bins=[(moderator_cell.id,)])\n", + "slice_test.get_pandas_dataframe()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From 1ebcbd5d2d6738f08c02a95e38032b0a3c51d1c4 Mon Sep 17 00:00:00 2001 From: Cyrus Wyett <34195737+cjwyett@users.noreply.github.com> Date: Tue, 18 Aug 2020 02:44:58 +0100 Subject: [PATCH 06/17] Delete tally-arithmetic.ipynb Wrong directory --- tally-arithmetic.ipynb | 1746 ---------------------------------------- 1 file changed, 1746 deletions(-) delete mode 100644 tally-arithmetic.ipynb diff --git a/tally-arithmetic.ipynb b/tally-arithmetic.ipynb deleted file mode 100644 index 6aed6e757b..0000000000 --- a/tally-arithmetic.ipynb +++ /dev/null @@ -1,1746 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook shows the how tallies can be combined (added, subtracted, multiplied, etc.) using the Python API in order to create derived tallies. Since no covariance information is obtained, it is assumed that tallies are completely independent of one another when propagating uncertainties. The target problem is a simple pin cell." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import glob\n", - "\n", - "from IPython.display import Image\n", - "import numpy as np\n", - "import openmc" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generate Input Files" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First we need to define materials that will be used in the problem. We'll create three materials for the fuel, water, and cladding of the fuel pin." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# 1.6 enriched fuel\n", - "fuel = openmc.Material(name='1.6% Fuel')\n", - "fuel.set_density('g/cm3', 10.31341)\n", - "fuel.add_nuclide('U235', 3.7503e-4)\n", - "fuel.add_nuclide('U238', 2.2625e-2)\n", - "fuel.add_nuclide('O16', 4.6007e-2)\n", - "\n", - "# borated water\n", - "water = openmc.Material(name='Borated Water')\n", - "water.set_density('g/cm3', 0.740582)\n", - "water.add_nuclide('H1', 4.9457e-2)\n", - "water.add_nuclide('O16', 2.4732e-2)\n", - "water.add_nuclide('B10', 8.0042e-6)\n", - "\n", - "# zircaloy\n", - "zircaloy = openmc.Material(name='Zircaloy')\n", - "zircaloy.set_density('g/cm3', 6.55)\n", - "zircaloy.add_nuclide('Zr90', 7.2758e-3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With our three materials, we can now create a materials file object that can be exported to an actual XML file." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Instantiate a Materials collection\n", - "materials = openmc.Materials([fuel, water, zircaloy])\n", - "\n", - "# Export to \"materials.xml\"\n", - "materials.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's move on to the geometry. Our problem will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces -- in this case two cylinders and six planes." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Create cylinders for the fuel and clad\n", - "fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.39218)\n", - "clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.45720)\n", - "\n", - "# Create boundary planes to surround the geometry\n", - "# Use both reflective and vacuum boundaries to make life interesting\n", - "min_x = openmc.XPlane(x0=-0.63, boundary_type='reflective')\n", - "max_x = openmc.XPlane(x0=+0.63, boundary_type='reflective')\n", - "min_y = openmc.YPlane(y0=-0.63, boundary_type='reflective')\n", - "max_y = openmc.YPlane(y0=+0.63, boundary_type='reflective')\n", - "min_z = openmc.ZPlane(z0=-100., boundary_type='vacuum')\n", - "max_z = openmc.ZPlane(z0=+100., boundary_type='vacuum')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With the surfaces defined, we can now create cells that are defined by intersections of half-spaces created by the surfaces." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a Universe to encapsulate a fuel pin\n", - "pin_cell_universe = openmc.Universe(name='1.6% Fuel Pin')\n", - "\n", - "# Create fuel Cell\n", - "fuel_cell = openmc.Cell(name='1.6% Fuel')\n", - "fuel_cell.fill = fuel\n", - "fuel_cell.region = -fuel_outer_radius\n", - "pin_cell_universe.add_cell(fuel_cell)\n", - "\n", - "# Create a clad Cell\n", - "clad_cell = openmc.Cell(name='1.6% Clad')\n", - "clad_cell.fill = zircaloy\n", - "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n", - "pin_cell_universe.add_cell(clad_cell)\n", - "\n", - "# Create a moderator Cell\n", - "moderator_cell = openmc.Cell(name='1.6% Moderator')\n", - "moderator_cell.fill = water\n", - "moderator_cell.region = +clad_outer_radius\n", - "pin_cell_universe.add_cell(moderator_cell)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "OpenMC requires that there is a \"root\" universe. Let us create a root cell that is filled by the pin cell universe and then assign it to the root universe." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Create root Cell\n", - "root_cell = openmc.Cell(name='root cell')\n", - "root_cell.fill = pin_cell_universe\n", - "\n", - "# Add boundary planes\n", - "root_cell.region = +min_x & -max_x & +min_y & -max_y & +min_z & -max_z\n", - "\n", - "# Create root Universe\n", - "root_universe = openmc.Universe(universe_id=0, name='root universe')\n", - "root_universe.add_cell(root_cell)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now must create a geometry that is assigned a root universe, put the geometry into a geometry file, and export it to XML." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Create Geometry and set root Universe\n", - "geometry = openmc.Geometry(root_universe)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Export to \"geometry.xml\"\n", - "geometry.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With the geometry and materials finished, we now just need to define simulation parameters. In this case, we will use 5 inactive batches and 15 active batches each with 2500 particles." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# OpenMC simulation parameters\n", - "batches = 20\n", - "inactive = 5\n", - "particles = 2500\n", - "\n", - "# Instantiate a Settings object\n", - "settings = openmc.Settings()\n", - "settings.batches = batches\n", - "settings.inactive = inactive\n", - "settings.particles = particles\n", - "settings.output = {'tallies': True}\n", - "\n", - "# Create an initial uniform spatial source distribution over fissionable zones\n", - "bounds = [-0.63, -0.63, -100., 0.63, 0.63, 100.]\n", - "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", - "settings.source = openmc.Source(space=uniform_dist)\n", - "\n", - "# Export to \"settings.xml\"\n", - "settings.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us also create a plot file that we can use to verify that our pin cell geometry was created successfully." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Instantiate a Plot\n", - "plot = openmc.Plot(plot_id=1)\n", - "plot.filename = 'materials-xy'\n", - "plot.origin = [0, 0, 0]\n", - "plot.width = [1.26, 1.26]\n", - "plot.pixels = [250, 250]\n", - "plot.color_by = 'material'\n", - "\n", - "# Show plot\n", - "openmc.plot_inline(plot)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we can see from the plot, we have a nice pin cell with fuel, cladding, and water! Before we run our simulation, we need to tell the code what we want to tally. The following code shows how to create a variety of tallies." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Instantiate an empty Tallies object\n", - "tallies = openmc.Tallies()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Create Tallies to compute microscopic multi-group cross-sections\n", - "\n", - "# Instantiate energy filter for multi-group cross-section Tallies\n", - "energy_filter = openmc.EnergyFilter([0., 0.625, 20.0e6])\n", - "\n", - "# Instantiate flux Tally in moderator and fuel\n", - "tally = openmc.Tally(name='flux')\n", - "tally.filters = [openmc.CellFilter([fuel_cell, moderator_cell])]\n", - "tally.filters.append(energy_filter)\n", - "tally.scores = ['flux']\n", - "tallies.append(tally)\n", - "\n", - "# Instantiate reaction rate Tally in fuel\n", - "tally = openmc.Tally(name='fuel rxn rates')\n", - "tally.filters = [openmc.CellFilter(fuel_cell)]\n", - "tally.filters.append(energy_filter)\n", - "tally.scores = ['nu-fission', 'scatter']\n", - "tally.nuclides = ['U238', 'U235']\n", - "tallies.append(tally)\n", - "\n", - "# Instantiate reaction rate Tally in moderator\n", - "tally = openmc.Tally(name='moderator rxn rates')\n", - "tally.filters = [openmc.CellFilter(moderator_cell)]\n", - "tally.filters.append(energy_filter)\n", - "tally.scores = ['absorption', 'total']\n", - "tally.nuclides = ['O16', 'H1']\n", - "tallies.append(tally)\n", - "\n", - "# Instantiate a tally mesh\n", - "mesh = openmc.RegularMesh(mesh_id=1)\n", - "mesh.dimension = [1, 1, 1]\n", - "mesh.lower_left = [-0.63, -0.63, -100.]\n", - "mesh.width = [1.26, 1.26, 200.]\n", - "meshsurface_filter = openmc.MeshSurfaceFilter(mesh)\n", - "\n", - "# Instantiate thermal, fast, and total leakage tallies\n", - "leak = openmc.Tally(name='leakage')\n", - "leak.filters = [meshsurface_filter]\n", - "leak.scores = ['current']\n", - "tallies.append(leak)\n", - "\n", - "thermal_leak = openmc.Tally(name='thermal leakage')\n", - "thermal_leak.filters = [meshsurface_filter, openmc.EnergyFilter([0., 0.625])]\n", - "thermal_leak.scores = ['current']\n", - "tallies.append(thermal_leak)\n", - "\n", - "fast_leak = openmc.Tally(name='fast leakage')\n", - "fast_leak.filters = [meshsurface_filter, openmc.EnergyFilter([0.625, 20.0e6])]\n", - "fast_leak.scores = ['current']\n", - "tallies.append(fast_leak)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# K-Eigenvalue (infinity) tallies\n", - "fiss_rate = openmc.Tally(name='fiss. rate')\n", - "abs_rate = openmc.Tally(name='abs. rate')\n", - "fiss_rate.scores = ['nu-fission']\n", - "abs_rate.scores = ['absorption']\n", - "tallies += (fiss_rate, abs_rate)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Resonance Escape Probability tallies\n", - "therm_abs_rate = openmc.Tally(name='therm. abs. rate')\n", - "therm_abs_rate.scores = ['absorption']\n", - "therm_abs_rate.filters = [openmc.EnergyFilter([0., 0.625])]\n", - "tallies.append(therm_abs_rate)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Thermal Flux Utilization tallies\n", - "fuel_therm_abs_rate = openmc.Tally(name='fuel therm. abs. rate')\n", - "fuel_therm_abs_rate.scores = ['absorption']\n", - "fuel_therm_abs_rate.filters = [openmc.EnergyFilter([0., 0.625]),\n", - " openmc.CellFilter([fuel_cell])]\n", - "tallies.append(fuel_therm_abs_rate)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Fast Fission Factor tallies\n", - "therm_fiss_rate = openmc.Tally(name='therm. fiss. rate')\n", - "therm_fiss_rate.scores = ['nu-fission']\n", - "therm_fiss_rate.filters = [openmc.EnergyFilter([0., 0.625])]\n", - "tallies.append(therm_fiss_rate)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Instantiate energy filter to illustrate Tally slicing\n", - "fine_energy_filter = openmc.EnergyFilter(np.logspace(np.log10(1e-2), np.log10(20.0e6), 10))\n", - "\n", - "# Instantiate flux Tally in moderator and fuel\n", - "tally = openmc.Tally(name='need-to-slice')\n", - "tally.filters = [openmc.CellFilter([fuel_cell, moderator_cell])]\n", - "tally.filters.append(fine_energy_filter)\n", - "tally.scores = ['nu-fission', 'scatter']\n", - "tally.nuclides = ['H1', 'U238']\n", - "tallies.append(tally)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "# Export to \"tallies.xml\"\n", - "tallies.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we a have a complete set of inputs, so we can go ahead and run our simulation." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", - "\n", - " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2020 MIT and OpenMC contributors\n", - " License | https://docs.openmc.org/en/latest/license.html\n", - " Version | 0.12.0\n", - " Git SHA1 | 3d90a9f857ec72eae897e054d4225180f1fa4d93\n", - " Date/Time | 2020-08-15 07:12:56\n", - " OpenMP Threads | 4\n", - "\n", - " Reading settings XML file...\n", - " Reading cross sections XML file...\n", - " Reading materials XML file...\n", - " Reading geometry XML file...\n", - " Reading U235 from /home/master/data/nuclear/endfb71_hdf5/U235.h5\n", - " Reading U238 from /home/master/data/nuclear/endfb71_hdf5/U238.h5\n", - " Reading O16 from /home/master/data/nuclear/endfb71_hdf5/O16.h5\n", - " Reading H1 from /home/master/data/nuclear/endfb71_hdf5/H1.h5\n", - " Reading B10 from /home/master/data/nuclear/endfb71_hdf5/B10.h5\n", - " Reading Zr90 from /home/master/data/nuclear/endfb71_hdf5/Zr90.h5\n", - " Minimum neutron data temperature: 294.000000 K\n", - " Maximum neutron data temperature: 294.000000 K\n", - " Reading tallies XML file...\n", - " Preparing distributed cell instances...\n", - " Writing summary.h5 file...\n", - " Maximum neutron transport energy: 20000000.000000 eV for U235\n", - " Initializing source particles...\n", - "\n", - " ====================> K EIGENVALUE SIMULATION <====================\n", - "\n", - " Bat./Gen. k Average k\n", - " ========= ======== ====================\n", - " 1/1 0.99100\n", - " 2/1 1.00834\n", - " 3/1 1.06764\n", - " 4/1 1.02113\n", - " 5/1 0.99556\n", - " 6/1 1.02501\n", - " 7/1 1.03920 1.03210 +/- 0.00709\n", - " 8/1 1.00744 1.02388 +/- 0.00918\n", - " 9/1 1.04889 1.03014 +/- 0.00902\n", - " 10/1 1.07235 1.03858 +/- 0.01096\n", - " 11/1 1.04400 1.03948 +/- 0.00899\n", - " 12/1 1.02556 1.03749 +/- 0.00786\n", - " 13/1 1.00755 1.03375 +/- 0.00776\n", - " 14/1 1.02346 1.03261 +/- 0.00694\n", - " 15/1 1.03215 1.03256 +/- 0.00621\n", - " 16/1 1.01060 1.03057 +/- 0.00596\n", - " 17/1 1.01665 1.02941 +/- 0.00556\n", - " 18/1 1.04273 1.03043 +/- 0.00522\n", - " 19/1 0.98780 1.02739 +/- 0.00571\n", - " 20/1 1.04402 1.02849 +/- 0.00543\n", - " Creating state point statepoint.20.h5...\n", - "\n", - " =======================> TIMING STATISTICS <=======================\n", - "\n", - " Total time for initialization = 2.8516e-01 seconds\n", - " Reading cross sections = 2.7562e-01 seconds\n", - " Total time in simulation = 1.6686e+00 seconds\n", - " Time in transport only = 1.6528e+00 seconds\n", - " Time in inactive batches = 2.2163e-01 seconds\n", - " Time in active batches = 1.4469e+00 seconds\n", - " Time synchronizing fission bank = 2.4642e-03 seconds\n", - " Sampling source sites = 1.9299e-03 seconds\n", - " SEND/RECV source sites = 5.0275e-04 seconds\n", - " Time accumulating tallies = 3.8964e-05 seconds\n", - " Total time for finalization = 2.0000e-04 seconds\n", - " Total time elapsed = 1.9609e+00 seconds\n", - " Calculation Rate (inactive) = 56401.3 particles/second\n", - " Calculation Rate (active) = 25917.0 particles/second\n", - "\n", - " ============================> RESULTS <============================\n", - "\n", - " k-effective (Collision) = 1.02606 +/- 0.00624\n", - " k-effective (Track-length) = 1.02849 +/- 0.00543\n", - " k-effective (Absorption) = 1.02154 +/- 0.00530\n", - " Combined k-effective = 1.02503 +/- 0.00501\n", - " Leakage Fraction = 0.01557 +/- 0.00106\n", - "\n" - ] - } - ], - "source": [ - "# Remove old HDF5 (summary, statepoint) files\n", - "!rm statepoint.*\n", - "\n", - "# Run OpenMC!\n", - "openmc.run()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Tally Data Processing" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our simulation ran successfully and created a statepoint file with all the tally data in it. We begin our analysis here loading the statepoint file and 'reading' the results. By default, the tally results are not read into memory because they might be large, even large enough to exceed the available memory on a computer." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Load the statepoint file\n", - "sp = openmc.StatePoint('statepoint.20.h5')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have a tally of the total fission rate and the total absorption rate, so we can calculate k-eff as:\n", - "$$k_{eff} = \\frac{\\langle \\nu \\Sigma_f \\phi \\rangle}{\\langle \\Sigma_a \\phi \\rangle + \\langle L \\rangle}$$\n", - "In this notation, $\\langle \\cdot \\rangle^a_b$ represents an OpenMC that is integrated over region $a$ and energy range $b$. If $a$ or $b$ is not reported, it means the value represents an integral over all space or all energy, respectively." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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nuclidescoremeanstd. dev.
0total(nu-fission / (absorption + current))1.0226610.006992
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" - ], - "text/plain": [ - " nuclide score mean std. dev.\n", - "0 total (nu-fission / (absorption + current)) 1.02e+00 6.99e-03" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Get the fission and absorption rate tallies\n", - "fiss_rate = sp.get_tally(name='fiss. rate')\n", - "abs_rate = sp.get_tally(name='abs. rate')\n", - "\n", - "# Get the leakage tally\n", - "leak = sp.get_tally(name='leakage')\n", - "leak = leak.summation(filter_type=openmc.MeshSurfaceFilter, remove_filter=True)\n", - "\n", - "# Compute k-infinity using tally arithmetic\n", - "keff = fiss_rate / (abs_rate + leak)\n", - "keff.get_pandas_dataframe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice that even though the neutron production rate, absorption rate, and current are separate tallies, we still get a first-order estimate of the uncertainty on the quotient of them automatically!\n", - "\n", - "Often in textbooks you'll see k-eff represented using the six-factor formula $$k_{eff} = p \\epsilon f \\eta P_{FNL} P_{TNL}.$$ Let's analyze each of these factors, starting with the resonance escape probability which is defined as $$p=\\frac{\\langle\\Sigma_a\\phi\\rangle_T + \\langle L \\rangle_T}{\\langle\\Sigma_a\\phi\\rangle + \\langle L \\rangle_T}$$ where the subscript $T$ means thermal energies." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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energy low [eV]energy high [eV]nuclidescoremeanstd. dev.
00.00.625total((absorption + current) / (absorption + current))0.6933880.005475
\n", - "
" - ], - "text/plain": [ - " energy low [eV] energy high [eV] nuclide \\\n", - "0 0.00e+00 6.25e-01 total \n", - "\n", - " score mean std. dev. \n", - "0 ((absorption + current) / (absorption + current)) 6.93e-01 5.47e-03 " - ] - }, - "execution_count": 22, - "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", - "thermal_leak = sp.get_tally(name='thermal leakage')\n", - "thermal_leak = thermal_leak.summation(filter_type=openmc.MeshSurfaceFilter, remove_filter=True)\n", - "res_esc = (therm_abs_rate + thermal_leak) / (abs_rate + thermal_leak)\n", - "res_esc.get_pandas_dataframe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The fast fission factor can be calculated as\n", - "$$\\epsilon=\\frac{\\langle\\nu\\Sigma_f\\phi\\rangle}{\\langle\\nu\\Sigma_f\\phi\\rangle_T}$$" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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energy low [eV]energy high [eV]nuclidescoremeanstd. dev.
00.00.625total(nu-fission / nu-fission)1.2037180.010102
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" - ], - "text/plain": [ - " energy low [eV] energy high [eV] nuclide score \\\n", - "0 0.00e+00 6.25e-01 total (nu-fission / nu-fission) \n", - "\n", - " mean std. dev. \n", - "0 1.20e+00 1.01e-02 " - ] - }, - "execution_count": 23, - "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", - "fast_fiss = fiss_rate / therm_fiss_rate\n", - "fast_fiss.get_pandas_dataframe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The thermal flux utilization is calculated as\n", - "$$f=\\frac{\\langle\\Sigma_a\\phi\\rangle^F_T}{\\langle\\Sigma_a\\phi\\rangle_T}$$\n", - "where the superscript $F$ denotes fuel." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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energy low [eV]energy high [eV]cellnuclidescoremeanstd. dev.
00.00.6251total(absorption / absorption)0.7486130.006949
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" - ], - "text/plain": [ - " energy low [eV] energy high [eV] cell nuclide score \\\n", - "0 0.00e+00 6.25e-01 1 total (absorption / absorption) \n", - "\n", - " mean std. dev. \n", - "0 7.49e-01 6.95e-03 " - ] - }, - "execution_count": 24, - "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", - "therm_util = fuel_therm_abs_rate / therm_abs_rate\n", - "therm_util.get_pandas_dataframe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The next factor is the number of fission neutrons produced per absorption in fuel, calculated as $$\\eta = \\frac{\\langle \\nu\\Sigma_f\\phi \\rangle_T}{\\langle \\Sigma_a \\phi \\rangle^F_T}$$" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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energy low [eV]energy high [eV]cellnuclidescoremeanstd. dev.
00.00.6251total(nu-fission / absorption)1.6635490.015328
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" - ], - "text/plain": [ - " energy low [eV] energy high [eV] cell nuclide score \\\n", - "0 0.00e+00 6.25e-01 1 total (nu-fission / absorption) \n", - "\n", - " mean std. dev. \n", - "0 1.66e+00 1.53e-02 " - ] - }, - "execution_count": 25, - "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", - "eta.get_pandas_dataframe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are two leakage factors to account for fast and thermal leakage. The fast non-leakage probability is computed as $$P_{FNL} = \\frac{\\langle \\Sigma_a\\phi \\rangle + \\langle L \\rangle_T}{\\langle \\Sigma_a \\phi \\rangle + \\langle L \\rangle}$$" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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energy low [eV]energy high [eV]nuclidescoremeanstd. dev.
00.00.625total((absorption + current) / (absorption + current))0.9859730.006036
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" - ], - "text/plain": [ - " energy low [eV] energy high [eV] nuclide \\\n", - "0 0.00e+00 6.25e-01 total \n", - "\n", - " score mean std. dev. \n", - "0 ((absorption + current) / (absorption + current)) 9.86e-01 6.04e-03 " - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "p_fnl = (abs_rate + thermal_leak) / (abs_rate + leak)\n", - "p_fnl.get_pandas_dataframe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The final factor is the thermal non-leakage probability and is computed as $$P_{TNL} = \\frac{\\langle \\Sigma_a\\phi \\rangle_T}{\\langle \\Sigma_a \\phi \\rangle_T + \\langle L \\rangle_T}$$" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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energy low [eV]energy high [eV]nuclidescoremeanstd. dev.
00.00.625total(absorption / (absorption + current))0.9978670.009335
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" - ], - "text/plain": [ - " energy low [eV] energy high [eV] nuclide \\\n", - "0 0.00e+00 6.25e-01 total \n", - "\n", - " score mean std. dev. \n", - "0 (absorption / (absorption + current)) 9.98e-01 9.33e-03 " - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "p_tnl = therm_abs_rate / (therm_abs_rate + thermal_leak)\n", - "p_tnl.get_pandas_dataframe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can calculate $k_{eff}$ using the product of the factors form the four-factor formula." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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energy low [eV]energy high [eV]cellnuclidescoremeanstd. dev.
00.00.6251total(((((((absorption + current) / (absorption + c...1.0226610.021177
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" - ], - "text/plain": [ - " energy low [eV] energy high [eV] cell nuclide \\\n", - "0 0.00e+00 6.25e-01 1 total \n", - "\n", - " score mean std. dev. \n", - "0 (((((((absorption + current) / (absorption + c... 1.02e+00 2.12e-02 " - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "keff = res_esc * fast_fiss * therm_util * eta * p_fnl * p_tnl\n", - "keff.get_pandas_dataframe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We see that the value we've obtained here has exactly the same mean as before. However, because of the way it was calculated, the standard deviation appears to be larger.\n", - "\n", - "Let's move on to a more complicated example now. Before we set up tallies to get reaction rates in the fuel and moderator in two energy groups for two different nuclides. We can use tally arithmetic to divide each of these reaction rates by the flux to get microscopic multi-group cross sections." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Compute microscopic multi-group cross-sections\n", - "flux = sp.get_tally(name='flux')\n", - "flux = flux.get_slice(filters=[openmc.CellFilter], filter_bins=[(fuel_cell.id,)])\n", - "fuel_rxn_rates = sp.get_tally(name='fuel rxn rates')\n", - "mod_rxn_rates = sp.get_tally(name='moderator rxn rates')" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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cellenergy low [eV]energy high [eV]nuclidescoremeanstd. dev.
010.0006.250000e-01(U238 / total)(nu-fission / flux)6.656489e-076.107780e-09
110.0006.250000e-01(U238 / total)(scatter / flux)2.099890e-011.936009e-03
210.0006.250000e-01(U235 / total)(nu-fission / flux)3.563899e-013.286347e-03
310.0006.250000e-01(U235 / total)(scatter / flux)5.554974e-035.124024e-05
410.6252.000000e+07(U238 / total)(nu-fission / flux)7.166537e-037.141716e-05
510.6252.000000e+07(U238 / total)(scatter / flux)2.275268e-019.822400e-04
610.6252.000000e+07(U235 / total)(nu-fission / flux)8.007796e-035.184629e-05
710.6252.000000e+07(U235 / total)(scatter / flux)3.366335e-031.396517e-05
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" - ], - "text/plain": [ - " cell energy low [eV] energy high [eV] nuclide \\\n", - "0 1 0.00e+00 6.25e-01 (U238 / total) \n", - "1 1 0.00e+00 6.25e-01 (U238 / total) \n", - "2 1 0.00e+00 6.25e-01 (U235 / total) \n", - "3 1 0.00e+00 6.25e-01 (U235 / total) \n", - "4 1 6.25e-01 2.00e+07 (U238 / total) \n", - "5 1 6.25e-01 2.00e+07 (U238 / total) \n", - "6 1 6.25e-01 2.00e+07 (U235 / total) \n", - "7 1 6.25e-01 2.00e+07 (U235 / total) \n", - "\n", - " score mean std. dev. \n", - "0 (nu-fission / flux) 6.66e-07 6.11e-09 \n", - "1 (scatter / flux) 2.10e-01 1.94e-03 \n", - "2 (nu-fission / flux) 3.56e-01 3.29e-03 \n", - "3 (scatter / flux) 5.55e-03 5.12e-05 \n", - "4 (nu-fission / flux) 7.17e-03 7.14e-05 \n", - "5 (scatter / flux) 2.28e-01 9.82e-04 \n", - "6 (nu-fission / flux) 8.01e-03 5.18e-05 \n", - "7 (scatter / flux) 3.37e-03 1.40e-05 " - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fuel_xs = fuel_rxn_rates / flux\n", - "fuel_xs.get_pandas_dataframe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We see that when the two tallies with multiple bins were divided, the derived tally contains the outer product of the combinations. If the filters/scores are the same, no outer product is needed. The `get_values(...)` method allows us to obtain a subset of tally scores. In the following example, we obtain just the neutron production microscopic cross sections." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[[6.65648937e-07]\n", - " [3.56389890e-01]]\n", - "\n", - " [[7.16653669e-03]\n", - " [8.00779649e-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", - "print(nu_fiss_xs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The same idea can be used not only for scores but also for filters and nuclides." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[[0.00555497]]\n", - "\n", - " [[0.00336633]]]\n" - ] - } - ], - "source": [ - "# Show how to use Tally.get_values(...) with a CrossScore and CrossNuclide\n", - "u235_scatter_xs = fuel_xs.get_values(nuclides=['(U235 / total)'], \n", - " scores=['(scatter / flux)'])\n", - "print(u235_scatter_xs)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[[0.22752681]\n", - " [0.00336633]]]\n" - ] - } - ], - "source": [ - "# Show how to use Tally.get_values(...) with a CrossFilter and CrossScore\n", - "fast_scatter_xs = fuel_xs.get_values(filters=[openmc.EnergyFilter], \n", - " filter_bins=[((0.625, 20.0e6),)], \n", - " scores=['(scatter / flux)'])\n", - "print(fast_scatter_xs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A more advanced method is to use `get_slice(...)` to create a new derived tally that is a subset of an existing tally. This has the benefit that we can use `get_pandas_dataframe()` to see the tallies in a more human-readable format." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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cellenergy low [eV]energy high [eV]nuclidescoremeanstd. dev.
010.0006.250000e-01U238nu-fission0.0000021.030465e-08
110.0006.250000e-01U235nu-fission0.8544305.572280e-03
210.6252.000000e+07U238nu-fission0.0822067.786594e-04
310.6252.000000e+07U235nu-fission0.0918565.222837e-04
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" - ], - "text/plain": [ - " cell energy low [eV] energy high [eV] nuclide score mean \\\n", - "0 1 0.00e+00 6.25e-01 U238 nu-fission 1.60e-06 \n", - "1 1 0.00e+00 6.25e-01 U235 nu-fission 8.54e-01 \n", - "2 1 6.25e-01 2.00e+07 U238 nu-fission 8.22e-02 \n", - "3 1 6.25e-01 2.00e+07 U235 nu-fission 9.19e-02 \n", - "\n", - " std. dev. \n", - "0 1.03e-08 \n", - "1 5.57e-03 \n", - "2 7.79e-04 \n", - "3 5.22e-04 " - ] - }, - "execution_count": 34, - "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", - "nu_fission_rates.get_pandas_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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cellenergy low [eV]energy high [eV]nuclidescoremeanstd. dev.
031.000000e-021.080060e-01H1scatter4.5542610.038132
131.080060e-011.166529e+00H1scatter2.0090360.012481
231.166529e+001.259921e+01H1scatter1.6325360.009018
331.259921e+011.360790e+02H1scatter1.8456690.011216
431.360790e+021.469734e+03H1scatter2.0474760.011371
531.469734e+031.587401e+04H1scatter2.1266120.012823
631.587401e+041.714488e+05H1scatter2.2117440.012935
731.714488e+051.851749e+06H1scatter2.0149320.007407
831.851749e+062.000000e+07H1scatter0.3723360.003747
\n", - "
" - ], - "text/plain": [ - " cell energy low [eV] energy high [eV] nuclide score mean \\\n", - "0 3 1.00e-02 1.08e-01 H1 scatter 4.55e+00 \n", - "1 3 1.08e-01 1.17e+00 H1 scatter 2.01e+00 \n", - "2 3 1.17e+00 1.26e+01 H1 scatter 1.63e+00 \n", - "3 3 1.26e+01 1.36e+02 H1 scatter 1.85e+00 \n", - "4 3 1.36e+02 1.47e+03 H1 scatter 2.05e+00 \n", - "5 3 1.47e+03 1.59e+04 H1 scatter 2.13e+00 \n", - "6 3 1.59e+04 1.71e+05 H1 scatter 2.21e+00 \n", - "7 3 1.71e+05 1.85e+06 H1 scatter 2.01e+00 \n", - "8 3 1.85e+06 2.00e+07 H1 scatter 3.72e-01 \n", - "\n", - " std. dev. \n", - "0 3.81e-02 \n", - "1 1.25e-02 \n", - "2 9.02e-03 \n", - "3 1.12e-02 \n", - "4 1.14e-02 \n", - "5 1.28e-02 \n", - "6 1.29e-02 \n", - "7 7.41e-03 \n", - "8 3.75e-03 " - ] - }, - "execution_count": 35, - "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", - "slice_test = need_to_slice.get_slice(scores=['scatter'], nuclides=['H1'],\n", - " filters=[openmc.CellFilter], filter_bins=[(moderator_cell.id,)])\n", - "slice_test.get_pandas_dataframe()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} From afbf0e6216fa72b6e398f9af41de6b94bb70f4a7 Mon Sep 17 00:00:00 2001 From: Cyrus Wyett <34195737+cjwyett@users.noreply.github.com> Date: Fri, 21 Aug 2020 07:25:34 +0100 Subject: [PATCH 07/17] Add files via upload --- examples/jupyter/post-processing.ipynb | 352 ++++++++++++------------- 1 file changed, 173 insertions(+), 179 deletions(-) diff --git a/examples/jupyter/post-processing.ipynb b/examples/jupyter/post-processing.ipynb index 45bba7c61a..44a2c2d4e8 100644 --- a/examples/jupyter/post-processing.ipynb +++ b/examples/jupyter/post-processing.ipynb @@ -207,15 +207,10 @@ "outputs": [], "source": [ "# OpenMC simulation parameters\n", - "batches = 100\n", - "inactive = 10\n", - "particles = 5000\n", - "\n", - "# Instantiate a Settings object\n", "settings = openmc.Settings()\n", - "settings.batches = batches\n", - "settings.inactive = inactive\n", - "settings.particles = particles\n", + "settings.batches = 100\n", + "settings.inactive = 10\n", + "settings.particles = 5000\n", "\n", "# Create an initial uniform spatial source distribution over fissionable zones\n", "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n", @@ -240,7 +235,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": "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\n", "text/plain": [ "" ] @@ -348,160 +343,157 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2020 MIT and OpenMC contributors\n", - " License | https://docs.openmc.org/en/latest/license.html\n", - " Version | 0.12.0\n", - " Git SHA1 | 3d90a9f857ec72eae897e054d4225180f1fa4d93\n", - " Date/Time | 2020-08-15 06:53:51\n", + " Copyright | 2011-2019 MIT and OpenMC contributors\n", + " License | http://openmc.readthedocs.io/en/latest/license.html\n", + " Version | 0.11.0-dev\n", + " Git SHA1 | 61c911cffdae2406f9f4bc667a9a6954748bb70c\n", + " Date/Time | 2019-07-19 06:22:24\n", " OpenMP Threads | 4\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Reading U235 from /home/master/data/nuclear/endfb71_hdf5/U235.h5\n", - " Reading U238 from /home/master/data/nuclear/endfb71_hdf5/U238.h5\n", - " Reading O16 from /home/master/data/nuclear/endfb71_hdf5/O16.h5\n", - " Reading H1 from /home/master/data/nuclear/endfb71_hdf5/H1.h5\n", - " Reading B10 from /home/master/data/nuclear/endfb71_hdf5/B10.h5\n", - " Reading Zr90 from /home/master/data/nuclear/endfb71_hdf5/Zr90.h5\n", - " Minimum neutron data temperature: 294.000000 K\n", - " Maximum neutron data temperature: 294.000000 K\n", - " Reading tallies XML file...\n", - " Preparing distributed cell instances...\n", - " Writing summary.h5 file...\n", + " Reading U235 from /opt/data/hdf5/nndc_hdf5_v15/U235.h5\n", + " Reading U238 from /opt/data/hdf5/nndc_hdf5_v15/U238.h5\n", + " Reading O16 from /opt/data/hdf5/nndc_hdf5_v15/O16.h5\n", + " Reading H1 from /opt/data/hdf5/nndc_hdf5_v15/H1.h5\n", + " Reading B10 from /opt/data/hdf5/nndc_hdf5_v15/B10.h5\n", + " Reading Zr90 from /opt/data/hdf5/nndc_hdf5_v15/Zr90.h5\n", " Maximum neutron transport energy: 20000000.000000 eV for U235\n", + " Reading tallies XML file...\n", + " Writing summary.h5 file...\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 1.06227\n", - " 2/1 1.01195\n", - " 3/1 1.03639\n", - " 4/1 1.04914\n", - " 5/1 1.03064\n", - " 6/1 1.04195\n", - " 7/1 1.00884\n", - " 8/1 1.02835\n", - " 9/1 1.03221\n", - " 10/1 1.03582\n", - " 11/1 1.04925\n", - " 12/1 1.08792 1.06859 +/- 0.01933\n", - " 13/1 1.02809 1.05509 +/- 0.01752\n", - " 14/1 1.06848 1.05843 +/- 0.01283\n", - " 15/1 1.03111 1.05297 +/- 0.01134\n", - " 16/1 1.04506 1.05165 +/- 0.00935\n", - " 17/1 1.07306 1.05471 +/- 0.00848\n", - " 18/1 1.05490 1.05473 +/- 0.00734\n", - " 19/1 1.04172 1.05329 +/- 0.00663\n", - " 20/1 1.01989 1.04995 +/- 0.00681\n", - " 21/1 1.05584 1.05048 +/- 0.00618\n", - " 22/1 1.01345 1.04740 +/- 0.00643\n", - " 23/1 1.05132 1.04770 +/- 0.00592\n", - " 24/1 1.05944 1.04854 +/- 0.00555\n", - " 25/1 1.04176 1.04809 +/- 0.00519\n", - " 26/1 1.05255 1.04836 +/- 0.00486\n", - " 27/1 1.06039 1.04907 +/- 0.00462\n", - " 28/1 1.01259 1.04705 +/- 0.00480\n", - " 29/1 1.07706 1.04863 +/- 0.00481\n", - " 30/1 1.04735 1.04856 +/- 0.00456\n", - " 31/1 1.04396 1.04834 +/- 0.00435\n", - " 32/1 1.08646 1.05007 +/- 0.00449\n", - " 33/1 1.02153 1.04883 +/- 0.00447\n", - " 34/1 1.04064 1.04849 +/- 0.00429\n", - " 35/1 1.04707 1.04844 +/- 0.00412\n", - " 36/1 1.03148 1.04778 +/- 0.00401\n", - " 37/1 1.08468 1.04915 +/- 0.00409\n", - " 38/1 1.05295 1.04929 +/- 0.00395\n", - " 39/1 1.01312 1.04804 +/- 0.00401\n", - " 40/1 1.04195 1.04784 +/- 0.00388\n", - " 41/1 1.05267 1.04799 +/- 0.00375\n", - " 42/1 1.01480 1.04695 +/- 0.00378\n", - " 43/1 1.05585 1.04722 +/- 0.00367\n", - " 44/1 1.06288 1.04768 +/- 0.00359\n", - " 45/1 1.07661 1.04851 +/- 0.00358\n", - " 46/1 1.05277 1.04863 +/- 0.00348\n", - " 47/1 1.04078 1.04842 +/- 0.00340\n", - " 48/1 1.08151 1.04929 +/- 0.00342\n", - " 49/1 1.04320 1.04913 +/- 0.00333\n", - " 50/1 1.04634 1.04906 +/- 0.00325\n", - " 51/1 1.06277 1.04940 +/- 0.00319\n", - " 52/1 1.02976 1.04893 +/- 0.00314\n", - " 53/1 1.03343 1.04857 +/- 0.00309\n", - " 54/1 1.01412 1.04779 +/- 0.00312\n", - " 55/1 1.04377 1.04770 +/- 0.00305\n", - " 56/1 1.04291 1.04759 +/- 0.00299\n", - " 57/1 1.07484 1.04817 +/- 0.00298\n", - " 58/1 1.07670 1.04877 +/- 0.00298\n", - " 59/1 1.05094 1.04881 +/- 0.00292\n", - " 60/1 1.00995 1.04803 +/- 0.00296\n", - " 61/1 1.04516 1.04798 +/- 0.00290\n", - " 62/1 1.03550 1.04774 +/- 0.00286\n", - " 63/1 1.02405 1.04729 +/- 0.00284\n", - " 64/1 1.06253 1.04757 +/- 0.00280\n", - " 65/1 1.06091 1.04781 +/- 0.00276\n", - " 66/1 1.04728 1.04781 +/- 0.00271\n", - " 67/1 1.06461 1.04810 +/- 0.00268\n", - " 68/1 1.05355 1.04819 +/- 0.00263\n", - " 69/1 1.06375 1.04846 +/- 0.00260\n", - " 70/1 1.04041 1.04832 +/- 0.00256\n", - " 71/1 1.04634 1.04829 +/- 0.00252\n", - " 72/1 1.02352 1.04789 +/- 0.00251\n", - " 73/1 1.08586 1.04849 +/- 0.00254\n", - " 74/1 1.04945 1.04851 +/- 0.00250\n", - " 75/1 1.06026 1.04869 +/- 0.00247\n", - " 76/1 1.05078 1.04872 +/- 0.00243\n", - " 77/1 1.02991 1.04844 +/- 0.00241\n", - " 78/1 1.01146 1.04790 +/- 0.00244\n", - " 79/1 1.05221 1.04796 +/- 0.00240\n", - " 80/1 1.01754 1.04752 +/- 0.00241\n", - " 81/1 1.05725 1.04766 +/- 0.00238\n", - " 82/1 1.03596 1.04750 +/- 0.00235\n", - " 83/1 1.04586 1.04748 +/- 0.00232\n", - " 84/1 1.02739 1.04721 +/- 0.00230\n", - " 85/1 1.04171 1.04713 +/- 0.00227\n", - " 86/1 1.05118 1.04719 +/- 0.00224\n", - " 87/1 1.03029 1.04697 +/- 0.00222\n", - " 88/1 1.07150 1.04728 +/- 0.00222\n", - " 89/1 1.02603 1.04701 +/- 0.00221\n", - " 90/1 1.00046 1.04643 +/- 0.00225\n", - " 91/1 1.06313 1.04664 +/- 0.00224\n", - " 92/1 1.09268 1.04720 +/- 0.00228\n", - " 93/1 1.00632 1.04670 +/- 0.00230\n", - " 94/1 1.03899 1.04661 +/- 0.00228\n", - " 95/1 1.05496 1.04671 +/- 0.00225\n", - " 96/1 1.01837 1.04638 +/- 0.00225\n", - " 97/1 1.04465 1.04636 +/- 0.00223\n", - " 98/1 1.04925 1.04639 +/- 0.00220\n", - " 99/1 1.03492 1.04627 +/- 0.00218\n", - " 100/1 1.02914 1.04608 +/- 0.00216\n", + " 1/1 1.04359\n", + " 2/1 1.04323\n", + " 3/1 1.04711\n", + " 4/1 1.03892\n", + " 5/1 1.02459\n", + " 6/1 1.03936\n", + " 7/1 1.03529\n", + " 8/1 1.01590\n", + " 9/1 1.03060\n", + " 10/1 1.02892\n", + " 11/1 1.03987\n", + " 12/1 1.04395 1.04191 +/- 0.00204\n", + " 13/1 1.04971 1.04451 +/- 0.00285\n", + " 14/1 1.03880 1.04308 +/- 0.00247\n", + " 15/1 1.03091 1.04065 +/- 0.00310\n", + " 16/1 1.03618 1.03990 +/- 0.00264\n", + " 17/1 1.04109 1.04007 +/- 0.00223\n", + " 18/1 1.02978 1.03879 +/- 0.00232\n", + " 19/1 1.06363 1.04155 +/- 0.00344\n", + " 20/1 1.06549 1.04394 +/- 0.00390\n", + " 21/1 1.03469 1.04310 +/- 0.00362\n", + " 22/1 1.01925 1.04111 +/- 0.00386\n", + " 23/1 1.03268 1.04046 +/- 0.00361\n", + " 24/1 1.03906 1.04036 +/- 0.00334\n", + " 25/1 1.02632 1.03943 +/- 0.00325\n", + " 26/1 1.03906 1.03940 +/- 0.00304\n", + " 27/1 1.05058 1.04006 +/- 0.00293\n", + " 28/1 1.03248 1.03964 +/- 0.00279\n", + " 29/1 1.04076 1.03970 +/- 0.00264\n", + " 30/1 1.00994 1.03821 +/- 0.00292\n", + " 31/1 1.04785 1.03867 +/- 0.00281\n", + " 32/1 1.03080 1.03831 +/- 0.00270\n", + " 33/1 1.01862 1.03746 +/- 0.00272\n", + " 34/1 1.05370 1.03813 +/- 0.00269\n", + " 35/1 1.02226 1.03750 +/- 0.00266\n", + " 36/1 1.02862 1.03716 +/- 0.00258\n", + " 37/1 1.04790 1.03755 +/- 0.00251\n", + " 38/1 1.03762 1.03756 +/- 0.00242\n", + " 39/1 1.02255 1.03704 +/- 0.00239\n", + " 40/1 1.06094 1.03784 +/- 0.00245\n", + " 41/1 1.03842 1.03786 +/- 0.00237\n", + " 42/1 1.00628 1.03687 +/- 0.00249\n", + " 43/1 1.04916 1.03724 +/- 0.00245\n", + " 44/1 1.06237 1.03798 +/- 0.00248\n", + " 45/1 1.08153 1.03922 +/- 0.00271\n", + " 46/1 1.05649 1.03970 +/- 0.00268\n", + " 47/1 1.06265 1.04032 +/- 0.00268\n", + " 48/1 1.05728 1.04077 +/- 0.00265\n", + " 49/1 1.07343 1.04161 +/- 0.00271\n", + " 50/1 1.04640 1.04173 +/- 0.00265\n", + " 51/1 1.05143 1.04196 +/- 0.00259\n", + " 52/1 1.03639 1.04183 +/- 0.00253\n", + " 53/1 1.04846 1.04199 +/- 0.00248\n", + " 54/1 1.02435 1.04158 +/- 0.00245\n", + " 55/1 1.04806 1.04173 +/- 0.00240\n", + " 56/1 1.04798 1.04186 +/- 0.00235\n", + " 57/1 1.06621 1.04238 +/- 0.00236\n", + " 58/1 1.05734 1.04269 +/- 0.00233\n", + " 59/1 1.04581 1.04276 +/- 0.00228\n", + " 60/1 1.02682 1.04244 +/- 0.00226\n", + " 61/1 1.05971 1.04278 +/- 0.00224\n", + " 62/1 1.02357 1.04241 +/- 0.00223\n", + " 63/1 1.02645 1.04211 +/- 0.00221\n", + " 64/1 1.00711 1.04146 +/- 0.00226\n", + " 65/1 1.06171 1.04183 +/- 0.00225\n", + " 66/1 1.03444 1.04170 +/- 0.00221\n", + " 67/1 1.05875 1.04199 +/- 0.00219\n", + " 68/1 1.04640 1.04207 +/- 0.00216\n", + " 69/1 1.04376 1.04210 +/- 0.00212\n", + " 70/1 1.07078 1.04258 +/- 0.00214\n", + " 71/1 1.03916 1.04252 +/- 0.00210\n", + " 72/1 1.01843 1.04213 +/- 0.00211\n", + " 73/1 1.03666 1.04205 +/- 0.00207\n", + " 74/1 1.04625 1.04211 +/- 0.00204\n", + " 75/1 1.05277 1.04228 +/- 0.00202\n", + " 76/1 1.04944 1.04238 +/- 0.00199\n", + " 77/1 1.01898 1.04203 +/- 0.00199\n", + " 78/1 1.03283 1.04190 +/- 0.00197\n", + " 79/1 1.02304 1.04163 +/- 0.00196\n", + " 80/1 1.01539 1.04125 +/- 0.00196\n", + " 81/1 1.03988 1.04123 +/- 0.00194\n", + " 82/1 1.02138 1.04096 +/- 0.00193\n", + " 83/1 1.02473 1.04073 +/- 0.00192\n", + " 84/1 1.03810 1.04070 +/- 0.00189\n", + " 85/1 1.07438 1.04115 +/- 0.00192\n", + " 86/1 1.03048 1.04101 +/- 0.00190\n", + " 87/1 1.06778 1.04135 +/- 0.00191\n", + " 88/1 1.07341 1.04177 +/- 0.00192\n", + " 89/1 1.06729 1.04209 +/- 0.00193\n", + " 90/1 1.05069 1.04220 +/- 0.00191\n", + " 91/1 1.07675 1.04262 +/- 0.00193\n", + " 92/1 1.06470 1.04289 +/- 0.00193\n", + " 93/1 1.02609 1.04269 +/- 0.00191\n", + " 94/1 1.04761 1.04275 +/- 0.00189\n", + " 95/1 1.08802 1.04328 +/- 0.00194\n", + " 96/1 1.04162 1.04326 +/- 0.00192\n", + " 97/1 1.04573 1.04329 +/- 0.00190\n", + " 98/1 1.03232 1.04317 +/- 0.00188\n", + " 99/1 1.03473 1.04307 +/- 0.00186\n", + " 100/1 1.04505 1.04309 +/- 0.00184\n", " Creating state point statepoint.100.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 2.8826e-01 seconds\n", - " Reading cross sections = 2.7725e-01 seconds\n", - " Total time in simulation = 5.6710e+01 seconds\n", - " Time in transport only = 5.6647e+01 seconds\n", - " Time in inactive batches = 8.7405e-01 seconds\n", - " Time in active batches = 5.5836e+01 seconds\n", - " Time synchronizing fission bank = 2.2260e-02 seconds\n", - " Sampling source sites = 1.7941e-02 seconds\n", - " SEND/RECV source sites = 4.1545e-03 seconds\n", - " Time accumulating tallies = 3.7878e-03 seconds\n", - " Total time for finalization = 1.2434e-02 seconds\n", - " Total time elapsed = 5.7021e+01 seconds\n", - " Calculation Rate (inactive) = 57205.0 particles/second\n", - " Calculation Rate (active) = 8059.38 particles/second\n", + " Total time for initialization = 6.4445e-01 seconds\n", + " Reading cross sections = 6.1129e-01 seconds\n", + " Total time in simulation = 2.0000e+02 seconds\n", + " Time in transport only = 1.9970e+02 seconds\n", + " Time in inactive batches = 2.9966e+00 seconds\n", + " Time in active batches = 1.9701e+02 seconds\n", + " Time synchronizing fission bank = 4.0040e-02 seconds\n", + " Sampling source sites = 3.1522e-02 seconds\n", + " SEND/RECV source sites = 8.3459e-03 seconds\n", + " Time accumulating tallies = 9.3582e-03 seconds\n", + " Total time for finalization = 4.6582e-02 seconds\n", + " Total time elapsed = 2.0072e+02 seconds\n", + " Calculation Rate (inactive) = 16685.4 particles/second\n", + " Calculation Rate (active) = 2284.19 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.04543 +/- 0.00195\n", - " k-effective (Track-length) = 1.04608 +/- 0.00216\n", - " k-effective (Absorption) = 1.04242 +/- 0.00147\n", - " Combined k-effective = 1.04347 +/- 0.00134\n", + " k-effective (Collision) = 1.04342 +/- 0.00159\n", + " k-effective (Track-length) = 1.04309 +/- 0.00184\n", + " k-effective (Absorption) = 1.04107 +/- 0.00140\n", + " Combined k-effective = 1.04195 +/- 0.00117\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -558,9 +550,10 @@ "\tID =\t1\n", "\tName =\tflux\n", "\tFilters =\tMeshFilter\n", - "\tNuclides =\ttotal\n", + "\tNuclides =\ttotal \n", "\tScores =\t['flux', 'fission']\n", - "\tEstimator =\ttracklength\n" + "\tEstimator =\ttracklength\n", + "\n" ] } ], @@ -584,19 +577,19 @@ { "data": { "text/plain": [ - "array([[[0.41112167, 0. ]],\n", + "array([[[0.40767451, 0. ]],\n", "\n", - " [[0.41090482, 0. ]],\n", + " [[0.40933814, 0. ]],\n", "\n", - " [[0.410451 , 0. ]],\n", + " [[0.4119165 , 0. ]],\n", "\n", " ...,\n", "\n", - " [[0.41289992, 0. ]],\n", + " [[0.40854327, 0. ]],\n", "\n", - " [[0.41195517, 0. ]],\n", + " [[0.40970805, 0. ]],\n", "\n", - " [[0.41092952, 0. ]]])" + " [[0.40948065, 0. ]]])" ] }, "execution_count": 17, @@ -630,32 +623,32 @@ { "data": { "text/plain": [ - "(array([[[0.00456802, 0. ]],\n", + "(array([[[0.00452972, 0. ]],\n", " \n", - " [[0.00456561, 0. ]],\n", + " [[0.0045482 , 0. ]],\n", " \n", - " [[0.00456057, 0. ]],\n", + " [[0.00457685, 0. ]],\n", " \n", " ...,\n", " \n", - " [[0.00458778, 0. ]],\n", + " [[0.00453937, 0. ]],\n", " \n", - " [[0.00457728, 0. ]],\n", + " [[0.00455231, 0. ]],\n", " \n", - " [[0.00456588, 0. ]]]),\n", - " array([[[1.98396826e-05, 0.00000000e+00]],\n", + " [[0.00454978, 0. ]]]),\n", + " array([[[2.03553236e-05, 0.00000000e+00]],\n", " \n", - " [[1.81394159e-05, 0.00000000e+00]],\n", + " [[1.83847389e-05, 0.00000000e+00]],\n", " \n", - " [[1.52107867e-05, 0.00000000e+00]],\n", + " [[1.68647098e-05, 0.00000000e+00]],\n", " \n", " ...,\n", " \n", - " [[1.93971958e-05, 0.00000000e+00]],\n", + " [[1.71606078e-05, 0.00000000e+00]],\n", " \n", - " [[1.97108386e-05, 0.00000000e+00]],\n", + " [[1.87645811e-05, 0.00000000e+00]],\n", " \n", - " [[2.17053017e-05, 0.00000000e+00]]]))" + " [[1.94447454e-05, 0.00000000e+00]]]))" ] }, "execution_count": 18, @@ -688,9 +681,10 @@ "\tID =\t2\n", "\tName =\tflux\n", "\tFilters =\tMeshFilter\n", - "\tNuclides =\ttotal\n", + "\tNuclides =\ttotal \n", "\tScores =\t['flux']\n", - "\tEstimator =\ttracklength\n" + "\tEstimator =\ttracklength\n", + "\n" ] } ], @@ -727,7 +721,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 21, @@ -736,7 +730,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -768,7 +762,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] From 284871e0ba1aa25103e203a3aaf089ec8f85dede Mon Sep 17 00:00:00 2001 From: Cyrus Wyett <34195737+cjwyett@users.noreply.github.com> Date: Tue, 25 Aug 2020 12:19:44 +0100 Subject: [PATCH 08/17] cleanup hexagonal lattice --- examples/jupyter/hexagonal-lattice.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/jupyter/hexagonal-lattice.ipynb b/examples/jupyter/hexagonal-lattice.ipynb index e0a48e2366..2a4f549ac0 100644 --- a/examples/jupyter/hexagonal-lattice.ipynb +++ b/examples/jupyter/hexagonal-lattice.ipynb @@ -364,7 +364,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.8.5" } }, "nbformat": 4, From befc9e6fe90041d0cd7502854f81313dc907461e Mon Sep 17 00:00:00 2001 From: Cyrus Wyett <34195737+cjwyett@users.noreply.github.com> Date: Tue, 25 Aug 2020 12:40:41 +0100 Subject: [PATCH 09/17] Clean up criticality search (pls read comment) Did a bit more for this one: a) Got rid of warning messages that came from setting the root universe's ID to 0. b) Moved source building to above the settings definition (rather than in the middle of it.) c) Removed `bracketed_method='bisect'` as this is the default argument. Perhaps there should be a default tolerance as well? And is there a good reason to default `print_iterations` to false? --- examples/jupyter/search.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/jupyter/search.ipynb b/examples/jupyter/search.ipynb index bfdc206955..efcfac3ccb 100644 --- a/examples/jupyter/search.ipynb +++ b/examples/jupyter/search.ipynb @@ -344,7 +344,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.8.5" } }, "nbformat": 4, From 59ce6d1a7b7535d0f354d50eff349e6be36c8932 Mon Sep 17 00:00:00 2001 From: Cyrus Wyett <34195737+cjwyett@users.noreply.github.com> Date: Tue, 25 Aug 2020 13:35:54 +0100 Subject: [PATCH 10/17] Mistake: Uploaded the correct files for hexagonal and search --- examples/jupyter/hexagonal-lattice.ipynb | 48 +++---- examples/jupyter/search.ipynb | 157 +++-------------------- 2 files changed, 42 insertions(+), 163 deletions(-) diff --git a/examples/jupyter/hexagonal-lattice.ipynb b/examples/jupyter/hexagonal-lattice.ipynb index 2a4f549ac0..a390dd5fa0 100644 --- a/examples/jupyter/hexagonal-lattice.ipynb +++ b/examples/jupyter/hexagonal-lattice.ipynb @@ -36,8 +36,8 @@ "water.add_nuclide('O16', 1.0)\n", "water.set_density('g/cm3', 1.0)\n", "\n", - "mats = openmc.Materials((fuel, fuel2, water))\n", - "mats.export_to_xml()" + "materials = openmc.Materials((fuel, fuel2, water))\n", + "materials.export_to_xml()" ] }, { @@ -80,7 +80,7 @@ "metadata": {}, "outputs": [], "source": [ - "lat = openmc.HexLattice()" + "lattice = openmc.HexLattice()" ] }, { @@ -96,9 +96,9 @@ "metadata": {}, "outputs": [], "source": [ - "lat.center = (0., 0.)\n", - "lat.pitch = (1.25,)\n", - "lat.outer = outer_universe" + "lattice.center = (0., 0.)\n", + "lattice.pitch = (1.25,)\n", + "lattice.outer = outer_universe" ] }, { @@ -130,7 +130,7 @@ } ], "source": [ - "print(lat.show_indices(num_rings=3))" + "print(lattice.show_indices(num_rings=3))" ] }, { @@ -175,8 +175,8 @@ "outer_ring = [big_pin_universe] + [pin_universe]*11\n", "middle_ring = [big_pin_universe] + [pin_universe]*5\n", "inner_ring = [big_pin_universe]\n", - "lat.universes = [outer_ring, middle_ring, inner_ring]\n", - "print(lat)" + "lattice.universes = [outer_ring, middle_ring, inner_ring]\n", + "print(lattice)" ] }, { @@ -193,9 +193,9 @@ "outputs": [], "source": [ "outer_surface = openmc.ZCylinder(r=4.0, boundary_type='vacuum')\n", - "main_cell = openmc.Cell(fill=lat, region=-outer_surface)\n", - "geom = openmc.Geometry([main_cell])\n", - "geom.export_to_xml()" + "main_cell = openmc.Cell(fill=lattice, region=-outer_surface)\n", + "geometry = openmc.Geometry([main_cell])\n", + "geometry.export_to_xml()" ] }, { @@ -223,14 +223,14 @@ } ], "source": [ - "p = openmc.Plot.from_geometry(geom)\n", - "p.color_by = 'material'\n", - "p.colors = colors = {\n", + "plot = openmc.Plot.from_geometry(geometry)\n", + "plot.color_by = 'material'\n", + "plot.colors = colors = {\n", " water: 'blue',\n", " fuel: 'olive',\n", " fuel2: 'yellow'\n", "}\n", - "p.to_ipython_image()" + "plot.to_ipython_image()" ] }, { @@ -268,11 +268,11 @@ ], "source": [ "# Change the orientation of the lattice and re-export the geometry\n", - "lat.orientation = 'x'\n", - "geom.export_to_xml()\n", + "lattice.orientation = 'x'\n", + "geometry.export_to_xml()\n", "\n", "# Run OpenMC in plotting mode\n", - "p.to_ipython_image()" + "plot.to_ipython_image()" ] }, { @@ -304,7 +304,7 @@ } ], "source": [ - "print(lat.show_indices(3, orientation='x'))" + "print(lattice.show_indices(3, orientation='x'))" ] }, { @@ -335,15 +335,15 @@ ], "source": [ "main_cell.region = openmc.model.hexagonal_prism(\n", - " edge_length=3*lat.pitch[0],\n", + " edge_length=3*lattice.pitch[0],\n", " orientation='x',\n", " boundary_type='vacuum'\n", ")\n", - "geom.export_to_xml()\n", + "geometry.export_to_xml()\n", "\n", "# Run OpenMC in plotting mode\n", - "p.color_by = 'cell'\n", - "p.to_ipython_image()" + "plot.color_by = 'cell'\n", + "plot.to_ipython_image()" ] } ], diff --git a/examples/jupyter/search.ipynb b/examples/jupyter/search.ipynb index efcfac3ccb..74de8f82b7 100644 --- a/examples/jupyter/search.ipynb +++ b/examples/jupyter/search.ipynb @@ -95,22 +95,22 @@ " moderator_cell.region = +clad_outer_radius & (+min_x & -max_x & +min_y & -max_y)\n", "\n", " # Create root Universe\n", - " root_universe = openmc.Universe(name='root universe', universe_id=0)\n", + " root_universe = openmc.Universe(name='root universe')\n", " root_universe.add_cells([fuel_cell, clad_cell, moderator_cell])\n", "\n", " # Create Geometry and set root universe\n", " geometry = openmc.Geometry(root_universe)\n", " \n", + " # Create an initial uniform spatial source distribution over fissionable zones\n", + " bounds = [-0.63, -0.63, -10, 0.63, 0.63, 10.]\n", + " uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", + " \n", " # Finish with the settings file\n", " settings = openmc.Settings()\n", " settings.batches = 300\n", " settings.inactive = 20\n", " settings.particles = 1000\n", " settings.run_mode = 'eigenvalue'\n", - "\n", - " # Create an initial uniform spatial source distribution over fissionable zones\n", - " bounds = [-0.63, -0.63, -10, 0.63, 0.63, 10.]\n", - " uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", " settings.source = openmc.source.Source(space=uniform_dist)\n", "\n", " # We dont need a tallies file so dont waste the disk input/output time\n", @@ -129,7 +129,7 @@ "\n", "To perform the search we imply call the `openmc.search_for_keff` function and pass in the relvant arguments. For our purposes we will be passing in the model building function (`build_model` defined above), a bracketed range for the expected critical Boron concentration (1,000 to 2,500 ppm), the tolerance, and the method we wish to use. \n", "\n", - "Instead of the bracketed range we could have used a single initial guess, but have elected not to in this example. Finally, due to the high noise inherent in using as few histories as are used in this example, our tolerance on the final keff value will be rather large (1.e-2) and a bisection method will be used for the search." + "Instead of the bracketed range we could have used a single initial guess, but have elected not to in this example. Finally, due to the high noise inherent in using as few histories as are used in this example, our tolerance on the final keff value will be rather large (1.e-2) and the default 'bisection' method will be used for the search." ] }, { @@ -137,148 +137,27 @@ "execution_count": 3, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", - " warn(msg, IDWarning)\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "Iteration: 1; Guess of 1.00e+03 produced a keff of 1.08853 +/- 0.00158\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", - " warn(msg, IDWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration: 2; Guess of 2.50e+03 produced a keff of 0.95372 +/- 0.00148\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", - " warn(msg, IDWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration: 3; Guess of 1.75e+03 produced a keff of 1.01328 +/- 0.00169\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", - " warn(msg, IDWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration: 4; Guess of 2.12e+03 produced a keff of 0.98150 +/- 0.00158\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", - " warn(msg, IDWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration: 5; Guess of 1.94e+03 produced a keff of 0.99886 +/- 0.00146\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", - " warn(msg, IDWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration: 6; Guess of 1.84e+03 produced a keff of 1.00759 +/- 0.00162\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", - " warn(msg, IDWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration: 7; Guess of 1.89e+03 produced a keff of 1.00063 +/- 0.00166\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", - " warn(msg, IDWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration: 8; Guess of 1.91e+03 produced a keff of 0.99970 +/- 0.00150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", - " warn(msg, IDWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration: 9; Guess of 1.90e+03 produced a keff of 0.99935 +/- 0.00164\n", - "Critical Boron Concentration: 1902 ppm\n" + "Iteration: 1; Guess of 1.00e+03 produced a keff of 1.08504 +/- 0.00169\n", + "Iteration: 2; Guess of 2.50e+03 produced a keff of 0.95243 +/- 0.00158\n", + "Iteration: 3; Guess of 1.75e+03 produced a keff of 1.01269 +/- 0.00163\n", + "Iteration: 4; Guess of 2.12e+03 produced a keff of 0.98165 +/- 0.00155\n", + "Iteration: 5; Guess of 1.94e+03 produced a keff of 0.99773 +/- 0.00158\n", + "Iteration: 6; Guess of 1.84e+03 produced a keff of 1.00872 +/- 0.00170\n", + "Iteration: 7; Guess of 1.89e+03 produced a keff of 1.00462 +/- 0.00154\n", + "Iteration: 8; Guess of 1.91e+03 produced a keff of 1.00202 +/- 0.00154\n", + "Iteration: 9; Guess of 1.93e+03 produced a keff of 0.99816 +/- 0.00155\n", + "Critical Boron Concentration: 1926 ppm\n" ] } ], "source": [ "# Perform the search\n", "crit_ppm, guesses, keffs = openmc.search_for_keff(build_model, bracket=[1000., 2500.],\n", - " tol=1e-2, bracketed_method='bisect',\n", - " print_iterations=True)\n", + " tol=1e-2, print_iterations=True)\n", "\n", "print('Critical Boron Concentration: {:4.0f} ppm'.format(crit_ppm))" ] @@ -297,7 +176,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] From 148ca44c4505bdd087b0e42cbf148eac0131cc43 Mon Sep 17 00:00:00 2001 From: Cyrus Wyett <34195737+cjwyett@users.noreply.github.com> Date: Tue, 25 Aug 2020 15:11:46 +0100 Subject: [PATCH 11/17] Incorporate feedback on settings definition Also got rid of warnings by removing manually set ID's for cells. The ID for zirconium, for example was set to 2 which conflicted with an already defined material with an automatically generated ID of 2. Same for some others. There is a part of the notebook that talks about ID's so I left that one alone. After the central point about ID's is made there's little point in keeping them in for the cells (unless you like looking at pink warning messages). --- examples/jupyter/pincell.ipynb | 198 ++++++++++++++++----------------- 1 file changed, 99 insertions(+), 99 deletions(-) diff --git a/examples/jupyter/pincell.ipynb b/examples/jupyter/pincell.ipynb index 9e8775805c..0f11ca6559 100644 --- a/examples/jupyter/pincell.ipynb +++ b/examples/jupyter/pincell.ipynb @@ -166,15 +166,15 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "zirconium = openmc.Material(2, \"zirconium\")\n", + "zirconium = openmc.Material(name=\"zirconium\")\n", "zirconium.add_element('Zr', 1.0)\n", "zirconium.set_density('g/cm3', 6.6)\n", "\n", - "water = openmc.Material(3, \"h2o\")\n", + "water = openmc.Material(name=\"h2o\")\n", "water.add_nuclide('H1', 2.0)\n", "water.add_nuclide('O16', 1.0)\n", "water.set_density('g/cm3', 1.0)" @@ -189,7 +189,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -205,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -221,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -230,7 +230,7 @@ "True" ] }, - "execution_count": 12, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -251,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -266,7 +266,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -274,7 +274,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -306,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -321,7 +321,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -329,7 +329,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -368,7 +368,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -416,7 +416,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -437,7 +437,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -480,7 +480,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -498,7 +498,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -515,7 +515,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -541,7 +541,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -558,7 +558,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -567,7 +567,7 @@ "(array([-1., -1., 0.]), array([1., 1., 1.]))" ] }, - "execution_count": 22, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -585,7 +585,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -605,7 +605,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -628,7 +628,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -648,22 +648,22 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 26, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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" ] @@ -687,22 +687,22 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 27, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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FP8dle6ft08PO+1rQOouI2t8kfVjdk3H+RVJnyDznSXpO0i9LWiTpoKRVVfd9nuW6XdL2bHi7pL8aMt9xSUuq7u88yzLv6y9po6QH1T136RpJ36+63wmX7TpJ91fd1wUs28ckrZF0eMj0sddZI/ZUovjlAHXVpssU8rz+myTdHV37JF2YnZ9Ud03ctnKJiL2SfjRilrHXWSNCJadBlwNcVlFf8nrHZQqShl2mEJIesb0/u1yhjvK8/k1cR1L+fl9r+6DtB21/pJyuTdzY66yMa39yqcvlAKmNWq4xHmZdRJzMro161PbT2X+YOsnz+tdyHeWQp98HJL0/In5ie6Okb0laOemOlWDsdVabUInJXg5QmVHLZTvXZQoRcTL7e9r2LnV3x+sWKnle/1quoxzm7XdEvN4zvMf2V20viYimX2w49jpr0+HPqMsB6mreyxRsL7Z9wdywpBvU/R2busnz+u+W9MnsG4VrJL02d/hXc/Mum+1LbTsbXqvue+vV0nua3vjrrOpPn3N+Qr1Z3cT8qaSXJT2cjX+vpD19n1T/UN1P6m+tut85lutidX+c6tns70X9y6XuNw4Hs9uROi/XoNdf0i2SbsmGre4Pdj0n6ZCGfJNXx1uOZduWrZ+DkvZJ+rWq+5xzue6RdErS/2bvsc8UXWecpg8gqTYd/gCoAUIFQFKECoCkCBUASREqAJIiVAAkRagASOr/AWK2ZMJSs3TpAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] @@ -726,16 +726,16 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 28, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, @@ -774,7 +774,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -792,7 +792,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -810,18 +810,18 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ - "fuel = openmc.Cell(1, 'fuel')\n", + "fuel = openmc.Cell(name='fuel')\n", "fuel.fill = uo2\n", "fuel.region = fuel_region\n", "\n", - "gap = openmc.Cell(2, 'air gap')\n", + "gap = openmc.Cell(name='air gap')\n", "gap.region = gap_region\n", "\n", - "clad = openmc.Cell(3, 'clad')\n", + "clad = openmc.Cell(name='clad')\n", "clad.fill = zirconium\n", "clad.region = clad_region" ] @@ -835,7 +835,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -855,13 +855,13 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "water_region = +left & -right & +bottom & -top & +clad_outer_radius\n", "\n", - "moderator = openmc.Cell(4, 'moderator')\n", + "moderator = openmc.Cell(name='moderator')\n", "moderator.fill = water\n", "moderator.region = water_region" ] @@ -875,7 +875,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -884,7 +884,7 @@ "openmc.region.Intersection" ] }, - "execution_count": 36, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -904,7 +904,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -920,7 +920,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -929,10 +929,10 @@ "text": [ "\r\n", "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -967,7 +967,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -985,25 +985,20 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ - "# OpenMC simulation parameters\n", - "batches = 100\n", - "inactive = 10\n", - "particles = 1000\n", - "\n", - "# Instantiate a Settings object\n", "settings = openmc.Settings()\n", - "settings.batches = batches\n", - "settings.inactive = inactive\n", - "settings.particles = particles" + "settings.source = source\n", + "settings.batches = 100\n", + "settings.inactive = 10\n", + "settings.particles = 1000" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -1016,6 +1011,11 @@ " 1000\r\n", " 100\r\n", " 10\r\n", + " \r\n", + " \r\n", + " 0 0 0\r\n", + " \r\n", + " \r\n", "\r\n" ] } @@ -1040,7 +1040,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -1059,7 +1059,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -1076,7 +1076,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -1086,7 +1086,7 @@ "\r\n", "\r\n", " \r\n", - " 1\r\n", + " 3\r\n", " \r\n", " \r\n", " 1\r\n", @@ -1114,7 +1114,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 41, "metadata": { "scrolled": true }, @@ -1152,7 +1152,7 @@ " License | https://docs.openmc.org/en/latest/license.html\n", " Version | 0.12.0\n", " Git SHA1 | 3d90a9f857ec72eae897e054d4225180f1fa4d93\n", - " Date/Time | 2020-08-15 07:03:19\n", + " Date/Time | 2020-08-25 14:58:51\n", " OpenMP Threads | 4\n", "\n", " Reading settings XML file...\n", @@ -1286,20 +1286,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 6.9749e-01 seconds\n", - " Reading cross sections = 6.8627e-01 seconds\n", - " Total time in simulation = 1.9684e+00 seconds\n", - " Time in transport only = 1.9468e+00 seconds\n", - " Time in inactive batches = 1.5675e-01 seconds\n", - " Time in active batches = 1.8117e+00 seconds\n", - " Time synchronizing fission bank = 4.5360e-03 seconds\n", - " Sampling source sites = 3.6973e-03 seconds\n", - " SEND/RECV source sites = 6.8224e-04 seconds\n", - " Time accumulating tallies = 1.0140e-04 seconds\n", - " Total time for finalization = 5.5400e-05 seconds\n", - " Total time elapsed = 2.6701e+00 seconds\n", - " Calculation Rate (inactive) = 63796.4 particles/second\n", - " Calculation Rate (active) = 49677.1 particles/second\n", + " Total time for initialization = 6.9022e-01 seconds\n", + " Reading cross sections = 6.7913e-01 seconds\n", + " Total time in simulation = 1.7892e+00 seconds\n", + " Time in transport only = 1.7650e+00 seconds\n", + " Time in inactive batches = 1.5005e-01 seconds\n", + " Time in active batches = 1.6391e+00 seconds\n", + " Time synchronizing fission bank = 4.2308e-03 seconds\n", + " Sampling source sites = 3.4593e-03 seconds\n", + " SEND/RECV source sites = 6.2601e-04 seconds\n", + " Time accumulating tallies = 9.5555e-05 seconds\n", + " Total time for finalization = 7.4948e-05 seconds\n", + " Total time elapsed = 2.4836e+00 seconds\n", + " Calculation Rate (inactive) = 66645.8 particles/second\n", + " Calculation Rate (active) = 54907.5 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -1325,7 +1325,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -1334,7 +1334,7 @@ "text": [ " ============================> TALLY 1 <============================\r\n", "\r\n", - " Cell 1\r\n", + " Cell 3\r\n", " U235\r\n", " Total Reaction Rate 0.726151 +/- 0.00251702\r\n", " Fission Rate 0.543836 +/- 0.00205084\r\n", @@ -1358,7 +1358,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -1379,7 +1379,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -1393,7 +1393,7 @@ " 1.26 1.26\r\n", " 200 200\r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", "\r\n" ] @@ -1414,7 +1414,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -1450,7 +1450,7 @@ " License | https://docs.openmc.org/en/latest/license.html\n", " Version | 0.12.0\n", " Git SHA1 | 3d90a9f857ec72eae897e054d4225180f1fa4d93\n", - " Date/Time | 2020-08-15 07:03:32\n", + " Date/Time | 2020-08-25 14:58:54\n", " OpenMP Threads | 4\n", "\n", " Reading settings XML file...\n", @@ -1490,7 +1490,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -1506,19 +1506,19 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 47, "metadata": { "scrolled": false }, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "" ] }, - "execution_count": 51, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -1537,17 +1537,17 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 48, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "" ] }, - "execution_count": 52, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } From b1d309faaa92e416c3df41cab5fed0dbfc7600de Mon Sep 17 00:00:00 2001 From: Cyrus Wyett <34195737+cjwyett@users.noreply.github.com> Date: Wed, 26 Aug 2020 05:16:47 +0100 Subject: [PATCH 12/17] Clearer naming in candu and triso There is a loop in candu that isn't incredibly readable, although I'm not quite sure how/if it can be improved: `for i, (r, n, a) in enumerate(zip(ring_radii, num_pins, angles)): for j in range(n):` Also de-abbreviated some of triso, e.g: `trisos = [openmc.model.TRISO(outer_radius, triso_univ, c) for c in centers]` Became: `trisos = [openmc.model.TRISO(outer_radius, triso_univ, center) for center in centers]` Let me know if you don't like it and it'll be changed. --- examples/jupyter/candu.ipynb | 22 +++++++++++----------- examples/jupyter/triso.ipynb | 26 +++++++++++++------------- 2 files changed, 24 insertions(+), 24 deletions(-) diff --git a/examples/jupyter/candu.ipynb b/examples/jupyter/candu.ipynb index 672d56f899..b078d8ac62 100644 --- a/examples/jupyter/candu.ipynb +++ b/examples/jupyter/candu.ipynb @@ -304,11 +304,11 @@ "metadata": {}, "outputs": [], "source": [ - "geom = openmc.Geometry(root_universe)\n", - "geom.export_to_xml()\n", + "geometry = openmc.Geometry(root_universe)\n", + "geometry.export_to_xml()\n", "\n", - "mats = openmc.Materials(geom.get_all_materials().values())\n", - "mats.export_to_xml()" + "materials = openmc.Materials(geometry.get_all_materials().values())\n", + "materials.export_to_xml()" ] }, { @@ -329,14 +329,14 @@ } ], "source": [ - "p = openmc.Plot.from_geometry(geom)\n", - "p.color_by = 'material'\n", - "p.colors = {\n", + "plot = openmc.Plot.from_geometry(geometry)\n", + "plot.color_by = 'material'\n", + "plot.colors = {\n", " fuel: 'black',\n", " clad: 'silver',\n", " heavy_water: 'blue'\n", "}\n", - "p.to_ipython_image()" + "plot.to_ipython_image()" ] }, { @@ -1078,8 +1078,8 @@ } ], "source": [ - "t = sp.get_tally()\n", - "t.get_pandas_dataframe()" + "output_tally = sp.get_tally()\n", + "output_tally.get_pandas_dataframe()" ] }, { @@ -1107,7 +1107,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/examples/jupyter/triso.ipynb b/examples/jupyter/triso.ipynb index 1934433e98..882463efc9 100644 --- a/examples/jupyter/triso.ipynb +++ b/examples/jupyter/triso.ipynb @@ -143,7 +143,7 @@ "metadata": {}, "outputs": [], "source": [ - "trisos = [openmc.model.TRISO(outer_radius, triso_univ, c) for c in centers]" + "trisos = [openmc.model.TRISO(outer_radius, triso_univ, center) for center in centers]" ] }, { @@ -199,7 +199,7 @@ } ], "source": [ - "centers = np.vstack([t.center for t in trisos])\n", + "centers = np.vstack([triso.center for triso in trisos])\n", "print(centers.min(axis=0))\n", "print(centers.max(axis=0))" ] @@ -293,20 +293,20 @@ } ], "source": [ - "univ = openmc.Universe(cells=[box])\n", + "universe = openmc.Universe(cells=[box])\n", "\n", - "geom = openmc.Geometry(univ)\n", - "geom.export_to_xml()\n", + "geometry = openmc.Geometry(universe)\n", + "geometry.export_to_xml()\n", "\n", - "mats = list(geom.get_all_materials().values())\n", - "openmc.Materials(mats).export_to_xml()\n", + "materials = list(geometry.get_all_materials().values())\n", + "openmc.Materials(materials).export_to_xml()\n", "\n", "settings = openmc.Settings()\n", "settings.run_mode = 'plot'\n", "settings.export_to_xml()\n", "\n", - "p = openmc.Plot.from_geometry(geom)\n", - "p.to_ipython_image()" + "plot = openmc.Plot.from_geometry(geometry)\n", + "plot.to_ipython_image()" ] }, { @@ -334,9 +334,9 @@ } ], "source": [ - "p.color_by = 'material'\n", - "p.colors = {graphite: 'gray'}\n", - "p.to_ipython_image()" + "plot.color_by = 'material'\n", + "plot.colors = {graphite: 'gray'}\n", + "plot.to_ipython_image()" ] } ], @@ -357,7 +357,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.8.5" } }, "nbformat": 4, From 7eab3055f52b329303964ab3be5567814b540e5c Mon Sep 17 00:00:00 2001 From: Cyrus Wyett <34195737+cjwyett@users.noreply.github.com> Date: Fri, 28 Aug 2020 08:55:12 +0100 Subject: [PATCH 13/17] Another iteration on hexagonal lattice. In the original example the rings were constructed as 'inner_ring', 'outer_ring' and 'middle_ring'. This naming convention only really works for 3-ringed lattices. The example has been slightly modified to contain a fourth ring. The lattice is constructed as: `lattice.universes = [outer_ring, ring_1, ring_2, inner_ring]` Which is much more translatable to lattices of any size. This commit also incorporates feedback on the criticality search tutorial changes. --- examples/jupyter/hexagonal-lattice.ipynb | 126 +++++++++++++++-------- examples/jupyter/search.ipynb | 16 +-- 2 files changed, 91 insertions(+), 51 deletions(-) diff --git a/examples/jupyter/hexagonal-lattice.ipynb b/examples/jupyter/hexagonal-lattice.ipynb index a390dd5fa0..ff1e5d692c 100644 --- a/examples/jupyter/hexagonal-lattice.ipynb +++ b/examples/jupyter/hexagonal-lattice.ipynb @@ -117,33 +117,63 @@ "name": "stdout", "output_type": "stream", "text": [ - " (0, 0)\n", - " (0,11) (0, 1)\n", - "(0,10) (1, 0) (0, 2)\n", - " (1, 5) (1, 1)\n", - "(0, 9) (2, 0) (0, 3)\n", - " (1, 4) (1, 2)\n", - "(0, 8) (1, 3) (0, 4)\n", - " (0, 7) (0, 5)\n", - " (0, 6)\n" + " (0, 0)\n", + " (0,17) (0, 1)\n", + " (0,16) (1, 0) (0, 2)\n", + "(0,15) (1,11) (1, 1) (0, 3)\n", + " (1,10) (2, 0) (1, 2)\n", + "(0,14) (2, 5) (2, 1) (0, 4)\n", + " (1, 9) (3, 0) (1, 3)\n", + "(0,13) (2, 4) (2, 2) (0, 5)\n", + " (1, 8) (2, 3) (1, 4)\n", + "(0,12) (1, 7) (1, 5) (0, 6)\n", + " (0,11) (1, 6) (0, 7)\n", + " (0,10) (0, 8)\n", + " (0, 9)\n" ] } ], "source": [ - "print(lattice.show_indices(num_rings=3))" + "print(lattice.show_indices(num_rings=4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let's set up a lattice where the first element in each ring is the big pin universe and all other elements are regular pin universes. From the diagram above, we see that the outer ring has 12 elements, the middle ring has 6, and the innermost degenerate ring has a single element." + "Let's set up a lattice where the first element in each ring is the big pin universe and all other elements are regular pin universes. \n", + "\n", + "From the diagram above, we see that the outer ring has 18 elements, the first ring has 12, and the second ring has 6 elements. The innermost ring of any hexagonal lattice will have only a single element. \n", + "\n", + "We build these rings though 'list concatenation' as folows: " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, + "outputs": [], + "source": [ + "outer_ring = [big_pin_universe] + [pin_universe]*17 # Adds up to 18\n", + "\n", + "ring_1 = [big_pin_universe] + [pin_universe]*11 # Adds up to 12\n", + "\n", + "ring_2 = [big_pin_universe] + [pin_universe]*5 # Adds up to 6\n", + "\n", + "inner_ring = [big_pin_universe]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now assign the rings (and the universes they contain) to our lattice. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -153,29 +183,33 @@ "\tID =\t4\n", "\tName =\t\n", "\tOrientation =\ty\n", - "\t# Rings =\t3\n", + "\t# Rings =\t4\n", "\t# Axial =\tNone\n", "\tCenter =\t(0.0, 0.0)\n", "\tPitch =\t(1.25,)\n", "\tOuter =\t3\n", "\tUniverses \n", - " 2\n", - " 1 1\n", - "1 2 1\n", - " 1 1\n", - "1 2 1\n", - " 1 1\n", - "1 1 1\n", - " 1 1\n", - " 1\n" + " 2\n", + " 1 1\n", + " 1 2 1\n", + "1 1 1 1\n", + " 1 2 1\n", + "1 1 1 1\n", + " 1 2 1\n", + "1 1 1 1\n", + " 1 1 1\n", + "1 1 1 1\n", + " 1 1 1\n", + " 1 1\n", + " 1\n" ] } ], "source": [ - "outer_ring = [big_pin_universe] + [pin_universe]*11\n", - "middle_ring = [big_pin_universe] + [pin_universe]*5\n", - "inner_ring = [big_pin_universe]\n", - "lattice.universes = [outer_ring, middle_ring, inner_ring]\n", + "lattice.universes = [outer_ring, \n", + " ring_1, \n", + " ring_2,\n", + " inner_ring]\n", "print(lattice)" ] }, @@ -188,11 +222,11 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "outer_surface = openmc.ZCylinder(r=4.0, boundary_type='vacuum')\n", + "outer_surface = openmc.ZCylinder(r=5.0, boundary_type='vacuum')\n", "main_cell = openmc.Cell(fill=lattice, region=-outer_surface)\n", "geometry = openmc.Geometry([main_cell])\n", "geometry.export_to_xml()" @@ -207,17 +241,17 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -251,17 +285,17 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -284,27 +318,31 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " (0, 8) (0, 9) (0,10)\n", + " (0,12) (0,13) (0,14) (0,15)\n", "\n", - " (0, 7) (1, 4) (1, 5) (0,11)\n", + " (0,11) (1, 8) (1, 9) (1,10) (0,16)\n", "\n", - "(0, 6) (1, 3) (2, 0) (1, 0) (0, 0)\n", + " (0,10) (1, 7) (2, 4) (2, 5) (1,11) (0,17)\n", "\n", - " (0, 5) (1, 2) (1, 1) (0, 1)\n", + "(0, 9) (1, 6) (2, 3) (3, 0) (2, 0) (1, 0) (0, 0)\n", "\n", - " (0, 4) (0, 3) (0, 2)\n" + " (0, 8) (1, 5) (2, 2) (2, 1) (1, 1) (0, 1)\n", + "\n", + " (0, 7) (1, 4) (1, 3) (1, 2) (0, 2)\n", + "\n", + " (0, 6) (0, 5) (0, 4) (0, 3)\n" ] } ], "source": [ - "print(lattice.show_indices(3, orientation='x'))" + "print(lattice.show_indices(4, orientation='x'))" ] }, { @@ -318,24 +356,24 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "main_cell.region = openmc.model.hexagonal_prism(\n", - " edge_length=3*lattice.pitch[0],\n", + " edge_length=4*lattice.pitch[0],\n", " orientation='x',\n", " boundary_type='vacuum'\n", ")\n", diff --git a/examples/jupyter/search.ipynb b/examples/jupyter/search.ipynb index 74de8f82b7..6c3e2169e3 100644 --- a/examples/jupyter/search.ipynb +++ b/examples/jupyter/search.ipynb @@ -47,6 +47,7 @@ "# Create the model. `ppm_Boron` will be the parametric variable.\n", "\n", "def build_model(ppm_Boron):\n", + " \n", " # Create the pin materials\n", " fuel = openmc.Material(name='1.6% Fuel')\n", " fuel.set_density('g/cm3', 10.31341)\n", @@ -101,18 +102,19 @@ " # Create Geometry and set root universe\n", " geometry = openmc.Geometry(root_universe)\n", " \n", - " # Create an initial uniform spatial source distribution over fissionable zones\n", - " bounds = [-0.63, -0.63, -10, 0.63, 0.63, 10.]\n", - " uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", - " \n", - " # Finish with the settings file\n", + " # Instantiate a Settings object\n", " settings = openmc.Settings()\n", + " \n", + " # Set simulation parameters\n", " settings.batches = 300\n", " settings.inactive = 20\n", " settings.particles = 1000\n", - " settings.run_mode = 'eigenvalue'\n", + " \n", + " # Create an initial uniform spatial source distribution over fissionable zones\n", + " bounds = [-0.63, -0.63, -10, 0.63, 0.63, 10.]\n", + " uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", " settings.source = openmc.source.Source(space=uniform_dist)\n", - "\n", + " \n", " # We dont need a tallies file so dont waste the disk input/output time\n", " settings.output = {'tallies': False}\n", " \n", From 111ab1e72c777ba651f1458c69ecc00928b02f59 Mon Sep 17 00:00:00 2001 From: Cyrus Wyett <34195737+cjwyett@users.noreply.github.com> Date: Sun, 30 Aug 2020 17:09:31 +0100 Subject: [PATCH 14/17] Update README.md Changed links to mailing list to links to discourse. --- README.md | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 57197c363e..a47fe227c0 100644 --- a/README.md +++ b/README.md @@ -12,9 +12,7 @@ project started under the Computational Reactor Physics Group at MIT. Complete documentation on the usage of OpenMC is hosted on Read the Docs (both for the [latest release](http://openmc.readthedocs.io/en/stable/) and [developmental](http://openmc.readthedocs.io/en/latest/) version). If you are -interested in the project or would like to help and contribute, please send a -message to the OpenMC User's Group [mailing -list](https://groups.google.com/forum/?fromgroups=#!forum/openmc-users). +interested in the project or would like to help and contribute, please get in touch on the OpenMC [discussion forum](https://openmc.discourse.group/). ## Installation @@ -35,11 +33,9 @@ citing the following publication: ## Troubleshooting If you run into problems compiling, installing, or running OpenMC, first check -the [Troubleshooting -section](http://openmc.readthedocs.io/en/stable/usersguide/troubleshoot.html) in +the [Troubleshooting section](http://openmc.readthedocs.io/en/stable/usersguide/troubleshoot.html) in the User's Guide. If you are not able to find a solution to your problem there, -please send a message to the User's Group [mailing -list](https://groups.google.com/forum/?fromgroups=#!forum/openmc-users). +please post to the [discussion forum](https://openmc.discourse.group/). ## Reporting Bugs From 6fa8086d0e3b1218b2c8b458540b487f3bb6e666 Mon Sep 17 00:00:00 2001 From: Cyrus Wyett <34195737+cjwyett@users.noreply.github.com> Date: Sun, 30 Aug 2020 17:11:17 +0100 Subject: [PATCH 15/17] Update README.md comma --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index a47fe227c0..f0bb67989d 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,7 @@ project started under the Computational Reactor Physics Group at MIT. Complete documentation on the usage of OpenMC is hosted on Read the Docs (both for the [latest release](http://openmc.readthedocs.io/en/stable/) and [developmental](http://openmc.readthedocs.io/en/latest/) version). If you are -interested in the project or would like to help and contribute, please get in touch on the OpenMC [discussion forum](https://openmc.discourse.group/). +interested in the project, or would like to help and contribute, please get in touch on the OpenMC [discussion forum](https://openmc.discourse.group/). ## Installation From e6aada59753afbff03f7d3c3aea96fa044244500 Mon Sep 17 00:00:00 2001 From: Cyrus Wyett <34195737+cjwyett@users.noreply.github.com> Date: Mon, 31 Aug 2020 14:07:03 +0100 Subject: [PATCH 16/17] Update examples/jupyter/hexagonal-lattice.ipynb Co-authored-by: Paul Romano --- examples/jupyter/hexagonal-lattice.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/jupyter/hexagonal-lattice.ipynb b/examples/jupyter/hexagonal-lattice.ipynb index ff1e5d692c..f47b3bc603 100644 --- a/examples/jupyter/hexagonal-lattice.ipynb +++ b/examples/jupyter/hexagonal-lattice.ipynb @@ -145,7 +145,7 @@ "\n", "From the diagram above, we see that the outer ring has 18 elements, the first ring has 12, and the second ring has 6 elements. The innermost ring of any hexagonal lattice will have only a single element. \n", "\n", - "We build these rings though 'list concatenation' as folows: " + "We build these rings through 'list concatenation' as follows: " ] }, { From 5dd2165df8632bb493e71529d372283055187c3c Mon Sep 17 00:00:00 2001 From: Cyrus Wyett <34195737+cjwyett@users.noreply.github.com> Date: Mon, 31 Aug 2020 14:13:41 +0100 Subject: [PATCH 17/17] quick typo fix in publications.rst --- docs/source/publications.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/publications.rst b/docs/source/publications.rst index 5691fd2183..b4f4e6aeb0 100644 --- a/docs/source/publications.rst +++ b/docs/source/publications.rst @@ -81,7 +81,7 @@ Coupling and Multi-physics 264-274 (2017). - Tianliang Hu, Liangzhu Cao, Hongchun Wu, Xianan Du, and Mingtao He, "`Coupled - neutrons and thermal-hydraulics simulation of molten salt reactors based on + neutronics and thermal-hydraulics simulation of molten salt reactors based on OpenMC/TANSY `_," *Ann. Nucl. Energy*, **109**, 260-276 (2017).