diff --git a/README.md b/README.md index 57197c363e..f0bb67989d 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 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). 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/hexagonal-lattice.ipynb b/examples/jupyter/hexagonal-lattice.ipynb index e0a48e2366..f47b3bc603 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" ] }, { @@ -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(lat.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 through 'list concatenation' as follows: " ] }, { "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", @@ -153,30 +183,34 @@ "\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", - "lat.universes = [outer_ring, middle_ring, inner_ring]\n", - "print(lat)" + "lattice.universes = [outer_ring, \n", + " ring_1, \n", + " ring_2,\n", + " inner_ring]\n", + "print(lattice)" ] }, { @@ -188,14 +222,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "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()" + "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()" ] }, { @@ -207,30 +241,30 @@ }, { "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" } ], "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()" ] }, { @@ -251,28 +285,28 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "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()" ] }, { @@ -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(lat.show_indices(3, orientation='x'))" + "print(lattice.show_indices(4, orientation='x'))" ] }, { @@ -318,32 +356,32 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAGQBAMAAABykSv/AAAABGdBTUEAALGPC/xhBQAAACBjSFJNAAB6JgAAgIQAAPoAAACA6AAAdTAAAOpgAAA6mAAAF3CculE8AAAAElBMVEX///+TUVD//wCAgAA4vPIAAP/pte6jAAAAAWJLR0QAiAUdSAAAAAd0SU1FB+MGCAIxFif9eE4AAAn2SURBVHja7d3tceM4DAbgsANJHVh0BVIFl7E7WPffymV346wlEh8UCVDGAP9u3jkvHhP+JCN/fHh5eXl5eXl5eXl5eXl5eXl5eXl5eXl5eXl5eXl5WatRtPQcQRYyOKS0ZB16syW8IHpLYgYi7VCbLSuQIA8ZHFJS8g6d2VJYEJ0lMQPRcKjMlhVI0IEMDuGWjkN+trILMl/QniYinnssSQ4yz6hkIuOcRBoCdDLjjWISKFaH/GkUlnzHoASMZR0BahSUMONUMuguyI8DaJUdpxJdyIy1solzkgmLJR0BazQj2cQz6kjjQRGybTSRTEXxrAjBHftWp8J4L1GDpJ3MRLyRELEkJFCOV0k2vmCOvWTQWZBsoy+SQ/FWogOZ8VaAeMbvhlkFEhiOpwSMZ9SxlQwKELjRP5LpcDwrQHiO361OFfGrRByCdzIT8YWIpSGBuSDVJT1b3MlqCJFZEisQ5nNWW4jEbJmBwK+GcV2xvr7iBcuTWPbFHX5ZX38X3GpkxNs7QvbFHYSsKyqJrHjVg4wA5NkJNF3MeAEg7WcLgqwruiSRGa9akABA/nWSv8/Z8QJAWs/WQUhc8RXTh4wAZMU7jex4BSCNZyschKwrumIcSNslgSCR6LQgXlQgYz9I29myAgk9IS1nywxk7AlpOFuhL6TdksAQhdeRlpCxL6TdbB2HRHasAQkIJKL3eIM3jS1nC4NQn0dwJ+PzSEvIiEEi2ij1wYr+hNhwtgIKoT6z407yM3vLJSEgEW30KSHizd0gBRlxyF/JPKOtEvF2OTP/oAqkeQlBwhkgLWZrPAOkxZJYgYRzQOpnywwkd6uTLOQiMlv6DpmzmvpPvr9LYLa6OCReE/s42r8DDp0czT+UJJBJC3JpC+nmyEhaQhQdqaThZKk6EknNbHV6oD+r3ZL0dbTbhAudHc32RTeQqQfk0gbS3bGTtIB0cmwlDSarm2MjOTpbnR/oz6pfklMsSIPjs+eYrAazZQZyhuesHeTQbIUzQo4sCQLBT5XOET9VSsTJrddCwJf1iO3rzN87Hofj9NZrX9whSMR3qGZ8h4qKM7deCQHfMB7YFEwbJbccIUj5bEGQ7D4sO2ZvAr/ElZARgLD3m5cDcfbWN5Di2QoAhH8CYD0QZ299CyldEgaEf7gkC1m4cR1kPCukdLYgyMqHLMVx/tarIIEDWfBOCMjKjXeQstkyAxnPCymarXBmSMmSmIGMLMiMt7IUx6xnraLZsgIJMCTyITPe6cKNEwh/tniQhej0QBwbQ0YYMrMhC9EpO04g7NkKPMiMt7IciBlv40uWBIVEvJMZd1Jx7taPQ0YMwj02uhyKc7eeQrizhUNmvBMqjujdkIsPQwIBmdFGyThijlycgfBma6QgypWB8JbECiS8A4QzW2Yg4ztAOLNlBZKbrMq90OsnHt/w//2W64ieLQHH7YZKyDgr6fDk+9XJjYgRydfdcPvvACSIOBDJ3xiUXP/EGQk1W0HGAUquvPi/YoiUA2j1yo1TSSmkzvGvk+z0vMRZyUtcCAlijpxkE2ckm7hstgQdGUlRfCtaEklH0mphfCuA7CZrqoMkndyIeCNJ7ob9A35gQ9o47o+v+pVKnu29xi+S60v8yEowSHvHH8ZLr/tGmfEjJ2EuSFvHvtV9o1Sckww8SJ3junc8W/0EGt1K0viRPuBhSPMnrEemlU+g0VfoLb0bnlDWbMk6XiS5Rsk4lTAma6qD5Dv5mZ5b3vkdfwJ3Q/owGWiIyIL8PI0CTipOlgSCSD7SN61ATiJOH+/0c1YLyANsBXJS8a89ZDgD5AF2Wgtp/OR7xzvNxg/c+eA9ATeGPPBO70fiXxwI/LK+lm4EcCAPuNMCyFACWYu3Zq5gJ/86PRQ/OJARgDD30tYyyB3ptAAyEguSvThQHpLbpr3CnTx4ECj+tYcMbMiR/eaukBGA8M8qFEEeSKclkLEV5CVeTgEJACRmO6Uhd6LTg/EjgQxtIPF0kLEesnaBjOiCvBNkcIhDNCBmHux2nn7NvCCaeYti501jACARnawTvo2HIG/3wcrMR107Xz6AkHf7OqjiC7q4+28O5H4kTiAfbSFzDnIHO+FAar4yDW0h/b7EVobcy2MmxMxGj52ttxfIVCcBWvnuRHwz1Mz2tJ0DA6G15A40Kn2EYwOZ6iTJnbppVPhQjZljTnYOnm0hb3wU0MzhTDvHZYOg5BOP2x5gNnOk3M4h/yAlIZaMiFPHUAx50z+EMfOnSXb+WCwISD7xWObP98z8QaUdSHgHCGOy7EDM/GG+HUigIMi2DyOOpZsUGQhrsuxcTsTOBV4CBvnZM1zwTs5wyR0Ugm+7n+siSGYuS2XnQmE8yBtcus3MxfTsQAILsuCdEBC/cmYJxMxFWe1cJtcMxMylpO1c3DucFVI4WXYugG/mJwns/EgEBHm7n+0w80Mqdn7aJkCQd/uxIQSiW7UQMz/IZecn0sxAzPyMoJ0fdjzHbNVP1jmet6qfs3aQ9/452tBdUvuGMQd5559sNvMj2nZ+1nw7W2/8Q/PwV6f6jpoFwfYX1B11kNBNkjiqJiuzwasFGdtCMocHOjnqJit7wqaPoxYSukgy/2jlZPU59HTJ/KO1jh5HHHOOekhutoSHK/cvVk+WHYjA0ewjkHqHHYj+E7DMZHV4TZRZEBoS8X2CmREvKpCAQyK+czOz4o1EaLIoCL7Vxj2UqgHB3wFzNwUBaC4WmiziQ8mB/eacc8UgjRYEhUS8U/75gkUBgv2V5Yp3GtnxikBaOSog64qumDYk9IU0mywEEolOC+JFAfLRF9LOwbuwphSk4YLYgbCuECoFaemwAwn9IE0nC4QovI60hXCuELrgnS4HIW0djCuECr1pbLwgRyHU23h9CHiFUPwepz5YxZxTcrLoS51Cn2WZ8aIGCQCE+sweWTH4mb35ZFGXOoUaJQ+lxnS9ZCHIBeoifuo0rvix0iQWnayWF9akSnZB7ED0TgbKTpYhSNCHiExWwfFZIr4QsfCC8GfrgkOJWHyy2M9bF3zJiFj8OeuDe6nTCwElZlP4Zf1vjQzJhYASj7LNPyHlYF3qlIAS8UUHEmgJEb80SjsGMQj4h6/ZyZiIRimH3ILQx2cvOJSIRb/Q2lbAJbt7dN8qEYt+VUpARrTRPTSJJzSWhKCXOk0dW0kmnrBY0oFejGfMFTtO7wZRSDJb/yTjiLZKxKljEIXA194CGn3GFygHY1kHeFZzBGtCHX8luVgYEvKtII3+lhCx2AmtQsiINvrVKhFLHWLEa1QqaUd+SdqX+ILYgSjNlrzDDiRoOAaHFJQGRMOhsSQqC2IHojBbOg47kCDtGBxSWNIQLYf0kqgtiB2I8GzpOby8vLy8vLy8vLy8vLy8vLy8vLy8vLy8vLy8vLy8lOp/nb6TXPV6EWgAAAAldEVYdGRhdGU6Y3JlYXRlADIwMTktMDYtMDhUMDc6NDk6MjItMDU6MDA/X9I4AAAAJXRFWHRkYXRlOm1vZGlmeQAyMDE5LTA2LTA4VDA3OjQ5OjIyLTA1OjAwTgJqhAAAAABJRU5ErkJggg==\n", + "image/png": "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\n", "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*lat.pitch[0],\n", + " edge_length=4*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()" ] } ], @@ -364,7 +402,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.8.5" } }, "nbformat": 4, 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": 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+ "image/png": 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"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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\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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/DfC/3e2TgG8DJ4lIrfs1xd33c+AUEXkHOMV9jarWAQuBtcD/AJeoaqJU5csnszmsR0WAW886zGopxpguT1SL1gLU6VRXV2tNTU3Jrr9lR5SGrbup6teT/pWRrNfGGNMZichrqlrtt6+UfSrdXv/KSFPwWFy7gasWraYiELDRYMaYLsvStJSBTY40xnQXFlTKINfkyIatu9vpjowxpjQsqJSB3+RIGw1mjOmKLKiUgY0GM8Z0F9ZRXybeyZE2+ssY01VZTcUYY0zRWE2lTPINKbb5K8aYrsKCShl4hxTvwemwn71oNZNGDmB5/Sc2f8UY02VY81cZ5F5vZbvNXzHGdCkWVMog15BiUJu/YozpUiyolEGuIcVjB/ex+SvGmC7F+lTKJNeQ4lvPOozZGX0q1llvjOmsLKiUkTfBZIrNXzHGdCUWVEqo0KHCfsHGGGM6IwsqJWKp7o0x3ZEFlRLINS9lzKDe7IwlrJnLGNNllXT0l4hMFpG3RKReRK722S8icpe7f7WIHOHZ94CIfCwiazLOecSzxPD7IlLrbh8uIrs9++4tZdny8ZuXokllyq+Wc8F9rzDplmdZUruhne7OGGNKp2Q1FREJAnNx1pFvAFaKyBJVXes57FRglPs1EbjH/Rfgt8DdwHzvdVX1W573uAPY5tn9rqpOKGpBWsFvXko0oYASi6fPqLcaizGmKyllTeUooF5V16lqDHgYmJZxzDRgvjpWAH1FZBCAqr4AfJrr4iIiwHTgoZLcfRtkzksJB4UeFTbJ0RjT9ZWyT2UIsN7zuoG9tZB8xwwBNhVw/eOAj1T1Hc+2ESLyd2A78H9U9cXMk0RkBjADYNiwYQW8Tet4hwr3Cgc5/e7lafttkqMxpisqZU1FfLZpK47J5VzSaymbgGGqejhwGfB7EemddXHVeapararVAwcOLPCtWqd/ZYTxQ/sy8oB9bZEuY0y3UMqaSgMw1PO6CtjYimOyiEgI+F/AkaltqhoFou73r4nIu8BooKY1N19sNsnRGNMdlLKmshIYJSIjRCQMnAMsyThmCXChOwrsaGCbqhbS9PVV4E1VbUhtEJGB7uAARORgnM7/dcUoSLGkai4WUIwxXVXJgoqqxoFZwNPAG8BCVa0TkZkiMtM9bBnOg78e+A3wv1Pni8hDwMvAF0SkQUQu8lz+HLI76I8HVovIKuAxYKaq5uzo72i27Iiyav1nlvbeGNOpiWqhXRhdT3V1tdbUtH/rmM2+N8Z0JiLymqpW++2z1PftzDv73hbqMsZ0dhZU2lmuVSG9c1isacwY01lY7q92lmtVyNQcFmsaM8Z0JlZT6QAuOWEkkVD2HBZrGjPGdDZWU2lH3loIKDOOP5jzJg5rGnKcahpLZTqGvU1jNizZGNMRWU2lnWTWQqJxZe7z9WnH+DWNxRJJtu1utNqKMaZDalVQEZF5xb6R7qaQDvrMxJQVQSGRTHLJgtctfb4xpkPKG1REJCgi/+6z6z9LdD/dRnMd9ClTJwzhpatOYu75hxMQiCex/hVjTIeVN6ioaoLsdPWo6mslu6Nuon9lhGtPG0M4KPQKB/MmmexfGaFPzzDhYDBtu6XPN8Z0NIV01L8kIncDjwA7UxtV9fWS3VU3sLh2A3OeXEs4FCCWUK4/Y0zeocKF1myMMaY9FRJUvuz+e5NnmwInFf92ugdvJ33KnKVrmTz2wJyjulL9K7Mz5qzYKDBjTEeSN6i4WX+XqOovynQ/3UJrhwpb+nxjTEdXSJ/K1DLdS7fRlqYsS59vjOnIChlS/DcRuVtEjhORI1JfJb+zLixzqLCtBGmM6SqsT6WdWFOWMaYrajaoqOqJ5biR7qh/ZcSCiTGmS2m2+UtEDhCR+0XkKff1mIxVGE0ZWPp7Y0xnUEifym9xlgQe7L5+G/hxIRcXkcki8paI1IvI1T77RUTucvev9vbViMgDIvKxiKzJOOcGEdkgIrXu1xTPvp+413pLRL5eyD12BotrNzDplme54L5XLD2LMaZDKySoDFDVheCMf3XXnk80d5I7HHkucCowBjhXRMZkHHYqMMr9mgHc49n3W2Byjsv/QlUnuF/L3Pcbg7N2/Vj3vF+799CpWfp7Y0xnUkhQ2Ski/XE65xGRo4FtBZx3FFCvqutUNQY8THbKl2nAfHWsAPqKyCAAVX0B+LTAcqSu9bCqRlX1PaDevYdOrW7jNgIiadssPYsxpqMqJKhcBiwBDhGRl4D5wKUFnDcEWO953eBua+kxfma5zWUPiEi/llxLRGaISI2I1GzevLmAtyovb9/J4toNXDy/hl2x9IqhpWcxxnRUhYz+el1EvgJ8ARDgLVVtLODa4rNNW3FMpnuAOe5xc4A7gO8Xei1VnQfMA6iurm7uvcrKu2hXNB4noZBInyNJJGRzWowxHVdBKz+6/Sh1Lbx2AzDU87oK2NiKYzLv5aPU9yLyG2Bpa6/VkXj7TrzpW7z2qQhy77eP5PjRA8t8d8YYU5hSrvy4EhglIiNEJIzTib4k45glwIXuKLCjgW2quinfRVN9Lq4zgdTosCXAOSISEZEROJ3/rxajIOXgt2hXpiTK2MG9y3RHxhjTciVbo15V4yIyC2c4chB4QFXrRGSmu/9eYBkwBadTfRfwvdT5IvIQcAIwQEQagOtV9X7gVhGZgNO09T7wr+716kRkIbAWiAOXuLnLOoWqfj2JJXLfbjgo1uxljOnwRLVDdSuUVXV1tdbU1LT3bQBOf8oVj66iMeH/+/h/Z47ja3lS4+eyZUfUUsEYY4pKRF5T1Wq/fa1do94W6CqiVH9KroACMGfpGy2e+GiTJo0x5daq5i9VtSzFReS3vkqmXY1O09jsRasZM6g3O2MJeoWD7IwlfGshfh3/sxetZtLIAVZjMcaUTMn6VEzh/NZXAehZEWB3Y/b2KXe9iADRhNKjwqls3nrWYWnLEbd2ITBjjGmLnM1fIvK5iGx3vz73vP5cRLaX8ya7Or/1VW4+cxz/+e0jiYTSp9/saUwSSyhRt6lsT2MyLXVLavJkr3DQ1rQ3xpRdzpqKqu5bzhvp7nKtr3Lb2eOb1qWPJpKI7g0oXhWBAAte+ZBfP1/ftIb99OoqFtY02Jr2xpiyKWj0l4gcC4xS1f8SkQHAvm5+rU6tI43+yic1gqtXOMjpdy9nj0+TWCQUAJRoXNO2PXnpsTn7XYwxpjXaNPpLRK4HrgJ+4m4KAw8W7/ZMc1Lr0o88YN+mZrJI0GkW61ERIBIKcNYRQ6gIpv86o/EkT635p61pb4wpm2ZrKiJSCxwOvK6qh7vbVqvqYaW/vdLqLDWVTN6ay7I1/2Tuc+8QCgTYGcuePBkJCX+7+mQLKsaYoslXUylk9FdMVVVEUqnvexX17kyLpZYh3rIjyq+frycaV6I5lrgJB4M24ssYUzaFTH5cKCL/ibPWycXAn4HflPa2TCHqNm4n4JuceS8b8WWMKae8NRUREeAR4FBgO076++tU9Zky3JvJYcuOKAte+ZC7n32HWI5Z+BUBIRBw5q8AvPD2x4AwdnBvq7UYY0omb1Bxm70eV9UjAQskHcDi2g3Mfmw10Xju2fcAjUklLELNB59yuSenWCgAd06fkDZR0hhjiqWQ5q8VIvKlkt+JaVYq9UpzASUlllDmv/xhWk6xeBKufGxVyde4965gaYzpPgrpqD8R+FcR+QDYibPConaF0V+dTSE5wgoRQHjuzY858dD9S9IU5l3BMjXp0mpGxnQPhdRUTgUOAU4CzgBOd/81ZZYrR1hKIH+ffZPd8SQ3PFFXkszF3kSWn0fjaSlkjDFdX7NBRVU/8Psqx82ZdKkcYc7s+WzJFiyNsyOaKMkD328Fy1QiS2NM11fK5YRNCUydMIS/XX0S5x81rCjXC4hQt3FbUa4F/rUpG9ZsTPdR0qAiIpNF5C0RqReRq332i4jc5e5fLSJHePY9ICIfi8iajHNuE5E33eP/KCJ93e3DRWS3iNS6X/eWsmztqX9lhMu+NjpnjaUldsUSXPS7lSxYUZzKp1/GZUtkaUz3UbLlhEUkCLwNnAI0ACuBc1V1reeYKcClOOvUTwR+qaoT3X3HAzuA+ao6znPO14BnVTUuIrcAqOpVIjIcWOo9tjmdNU1LypLaDcxetJqACLt8UrS01M1njuP8iQcVZQliW8bYmK6rrWlaWusooF5V17k38TAwDVjrOWYaTtBQnKHLfUVkkKpuUtUX3ECRRlX/5Hm5Aji7ZCXo4FLp8us2buPi+TVpGYrzCQWEZFKzxpDd+MRaUJjz5No2j9xKpZIxxnQvpWz+GgKs97xucLe19Jh8vg885Xk9QkT+LiJ/FZHj/E4QkRkiUiMiNZs3b27BW3VM/SsjHD96f247e3xa9uL8sgMKQFDgxqVrizpyy+arGNO9lLKm4vd0y/woXcgx/hcXuQaIAwvcTZuAYaq6RUSOBB4XkbGqmrZKparOA+aB0/xVyHt1Bt5Fvv74egO/fTl3H0muuZONSSUSDBDzbGvtEsSpVDJzn3uHcDBo81WM6SZKGVQagKGe11XAxlYck0VEvoMzX+Zkt+kMVY0CUff710TkXWA00Hk7TVoo9eB/aOX6Zo70N2XcgSxZtSltW+bIrUL6SjJTyUTjcQBmL1rNpJEDrFnMmC6slM1fK4FRIjJCRMLAOcCSjGOWABe6o8COBrap6qbMC3mJyGScRcOmquouz/aB7uAARORgYBSwrnjF6Rwatu6moqAmMMc+FQHCQeGnUw7lT2s/yto/dfzgpiCwuHYDk255lgvueyXnxMl8qWRsvooxXV/Jairu6KxZwNNAEHhAVetEZKa7/15gGc7Ir3pgF/C91Pki8hBwAjBARBqA61X1fuBuIAI84yRRZoWqzgSOB24SkTiQAGaq6qelKl9HtWbDNnZE/UeC9agIkEwqkZDTHHXt6WMYN7gPVf160rB1N0HJDkaP127kqsmHAjTNlE+lifGreeRLJWPzVYzp+krZ/IWqLsMJHN5t93q+V+CSHOeem2P7yBzbFwGLWn2zXcCWHVHmPLk2535VWPbD43KuWd+Y8KldBKWpdpEZLPz6W3KlkgkKNl/FmG7AZtR3IX4pUrxmnTiSkQfs67tmff/KCNefMTbrnERSqerXk17hINF4eg3Ir+bRvzLCZV8dnXWdUDDApJEDWlIcY0wnZEGlC8mXcDISEs6bmD+1y/lHH8TNZ44jHArQKxJsmg2/vP4TTr97OQE3Y2UkKDlnyi+u3cDtz7ydde1w0PpTjOkOStr8Zcqrf2WEa08bwzWPr8nad93pYwtqejp/4kFMHntg0wgvgEm3PMuexr3BSkV4ctax9OsVZtX6z5qa0lKd9DGfTnrrTzGme7Cg0sWMG9KHykgwrbO+VyTIuCF9Cr6Gdzb8qvWfZfWlRIIBlq35J79+vj5t5v1B/Xv5dtKHg2L9KcZ0E9b81cVU9etJPCMHfqpfpLXXy2xSiyWSzH3unayZ973CQd/mtyu+/gWb9GhMN2FBpYspRpZgb2oVv+vNOnEk4WAw7ZygCLXrP/PtpL/zmbctTYsx3YQ1f3VB3pQtLc0SnLkU8GWnjGa/fcI8+P2jqAgFm2o8c5+vTztvZyzBtYvXEE8q4QDEPBWW1qZ6aS3LkGxM+7Gg0kW1JkuwdyngVL/Iz5a92bT/wmOGcdO0fwGcOSezF60mKMJON+3+7kb/kWel7qT3BpHl9Z+kBUXLN2ZMeVlQMU3yzYYHmP/yh1x49HBGHrBvU23oiVUbueGJ7AmXFQHoURFqerCXqsbgrVnFEkkSySTxJHln/RtjSseCimmSb55LyvL6T9Jm5B88sJfvcb/41uH07lkBKIP79EwbelwsfjWrTOVuejOmu7OgYpqkOuVnL1qN4N+cdfOTb9Cjws0ddtoYhu7Xk1AgPZ1+KCBs+Gw3Vzy2ClWIxpOEg0IgIGnNUW3t+2iuZgU2P8aYcivZcsKdQWdfTrhUtuyI8tybH3PVY6tpbpHiykiQaDxJIqEEAuLbUe/VoyLAS1edVJS+jy07olkTMyuCQkCwNVyMKaH2Wk7YdFL9KyOceOj+VFQESOTofE/xTrJMuvNjcgUUcIYe123cXlDG40Luc3p1FfNf/rBp27lHDeVHJ4+20V/GtBObp2J8pZrCwi1Ym6UQTiZkzUp82dxaK37LEm/ZEWVhTUPacanXfkkzi2HLjigvvP0xL7y92ebeGOPDaiomp6kThjBmUG+m/Gq5bz6v1rj+jLGMHdwna0BAvr6PzLkzqXVgtu2OFZSOv1gW127gikdX0ZhwamShANw5fYI1rxnjYTUVk9fIA/bl9rP3zqiPhITLTxnNT089lHAokLMmEwkJ06uHEA4K+4QDhEMBbj5zHJPHOckqLztlNOGg0Cu8Nxsy4FsbSTWVpVLCXPPHNZz3mxVcPL+GPQWk4y+GLTuizH5sdVNAAWdwwpWPrbIaizEeJa2puEv//hJn5cf7VPXnGfvF3T8FZ+XH76rq6+6+B3DWof9YVcd5ztkPeAQYDrwPTFfVre6+nwAX4az8+ENVfbqU5esuMmfo7+1kh52x7IEep//LgUweN4hjDunPVZO/mHbepFueRZNKNKGEg0JjIskVX/sC72/ZxZWP/SWrgz3XCK/UhMugQCQUIBwMlHROTMPW3QQD2QE0KDZk2RivkgUVd734ucApQAOwUkSWqKp3ptypOGvJjwImAve4/wL8Fmfp4PkZl74a+Iuq/lxErnZfXyUiY4BzgLHAYODPIjJaVZsbwGQKkJqh76055LL0H/9k6T/+SUVQuOOb45k6YYjveTH3U//Pnto7az8ajwNODWDMoN5s2x0jlsj9K0wofONfBjHt8MGMHdynoId7a4YyV/XrSSKZHUATakOWjfEqZfPXUUC9qq5T1RjwMDAt45hpwHx1rAD6isggAFV9AfBbY34a8Dv3+98B3/Bsf1hVo6r6Hs6690cVs0Cm+dUlvRoTypWPrW56iBd6HkA0rnz9ly8y88HXSarTf7FP2P/8RX/fwMXzX+Ol+k+ave7i2g1MuuVZLrjvFSbd8ixLajcUdD/9KyPcdvZhVHia+0IBuO3s8UWf0JnZBGhMZ1LK5q8hwHrP6wb21kLyHTME2JTnugeo6iYAVd0kIvt7rrXC51qmiAqZde8VDEhTraAl54GTsn+X28wVEIgnnLVZYonsGkM0nmx2WLLfDPyWDGVONQPWbdwGCGMH9y5qQMkckGBzbExnVMqail8PbubToJBjivl+iMgMEakRkZrNmze38q26r8xU+KGAM+GwZ4X/n1JqLZfUeZFQ6/7kkuqs4+IXUFICIu4D359fbam5ocyZ+ldGOH70/hw/emDJUs5416ixGovpbEpZU2kAhnpeVwEbW3FMpo9EZJBbSxkEfNySa6nqPGAeODPqmyuEyZbZcQ/OA3vNhm1cv2RNU8qWiqBw29l7O85T5/3+lQ/51bNvEwoEicYTBAJCKCA5sxwXalcswcXza7jt7PG+n/D9aku5RouVO32+34AEy1tmOqNSBpWVwCgRGQFswOlEPy/jmCXALBF5GKdpbFuqaSuPJcB3gJ+7/y72bP+9iNyJ01E/Cni1GAUx2TJT6/evjDB+aF8mjzuQuo3bAfXtOO9fGeHSk0dx3sRhaUGpbuM2vvdfK8lTEckiOM1r3pUuo3HN26R1yQkjufu5d9JGmWUel6sZqpSBpiUBz5iOrGRBRVXjIjILeBpnSPEDqlonIjPd/fcCy3CGE9fjDCn+Xup8EXkIOAEYICINwPWqej9OMFkoIhcBHwLfdK9XJyILgbVAHLjERn6Vn9M8NLCg47wP5uNH78/5E4cxf8WHacftUxEkoUm+9aWhPLKygahnEqbipIbpEQqwJ57/E743UIAw4/iDOW/isKzgkKvf5fM9ceY8ubZk/R3eZJ7e97BaiulsLKGkJZTsEPySQ0ZCAX5zYXVTh/jSVRuZ9dDfs84NQNosllTSytQDecuOKF/++V+Ixvf+rUdC4l47vTa1av1nXHDfK3wejTdt6xUO0pjUtKwCme9RLKWsDdmKmKZYLKGk6fDqNm4jIOljLcLBAH16VjT7APQGlFCArE/49724Li2ggNNM5gxX1rRah18zVDSRJBIMEPNsK1V/R+p6qcEDxbq+jSwz5WJpWky7W1y7gYvn1zQNH07J7FNwFv3KLxgIMGnkgKbXC1Z8wD1/Xed77K5YImuUlXd0Ww93RFtAtWkGf657K5bWzqPJx0aWmXKyoGLaVeqBl1mTiIQCWTWO9Z/uavZ6qXkxqWvf+ERds+dkDiueOmEIS2cd65vKv1dkb66yUjRPleLhX4yh1MYUypq/TLvyG0q7T0WQe799ZFqH/5YdUeY8udbvEmlS82IA6jZuJyhCc1OfGpNJeoWDaUse74wliISCxBJ7+1b2CQe4aNIIpo4fzMgD9m1hSZtXqmHFNrLMlJPVVEy78nvgJVHGDu6dtq2QNC/eeTGpJrXdeVL2p2od06urOP3u5WlNTn73tSuW5P7l73H63cuL0iyVqVQP/8wJq6WqaRkDNvrLRn91AEtqN2QNpc3sRPYbHZZSERR+fPJIvj52ECMP2DfvsZGQICJce9oYxg3pQ69wkNPvXp52bGpk16LXGvh/T73pW88p1eivQn4WrbFlR7Rk6WVM92Ojv0yHljlD3++Bl/q0fcWjq7JStTQmlLnPreNXz73LrWcdxkH9e2U3qYWD3HrWYQzdb5+091i1/jPfJqf/8/ganlrzz5z3XKrRX4X8LFrKRn6ZcrLmL9MhpGbk53uITp0whGU/PI6wT/6wXY17R3I580oymtRUOeaQ/k3vkcoG7HdsNJ7IG1Agu1mqmNmFC/lZFKqUI78so7LxYzUV06mkVqKcvWg1AZGsYcgVgQA7Y4m8s9MzP7lPr65iYU1D0+uzDq9iwasf+r094DSh5bteZk0g16TDckxGLFXn/+LaDcx+bBVBCZDQZM58a6b7saBiOh1vCvqL59ekDUdO1SDGD+3r24zkl4ZlYU0DS2cdy85Ygqp+PXlv8w7foBIOCpeeNIpTxx3Ixm27eeHtzQzu0yPrelc86iwwNvKAfVmw4gNuXLqWcNDJUZYKOOVqkipF5/+WHVEuX1jrJg51gvplC2sLXkLAdG0WVEynlEpBf9vZ43PWSDLzi4HzyV0zVnDUpDO5cfzQvgAseCU7oAQF7vvOl9i6K8apd73YtFZ9UCCUscxwLKFM+dVyvjFhEAtrnFFiMXdk8uxFqxkzqDezH1tNNL43EKVWukwFtmI9nEuRU6xu43YyB9XFk872QvK+ma7Ngorp1Frasd0rHCSa0dEfTSiN8URTH8vc597JOi8UDDC4Tw8unl/TFFDAWc444ZNaORZPNgUUr2BAeOCl99MSY4KTNmbyL1+kZ0Ww6DWX4nf+5xox2rqRpJaTrPxK+TO3oGI6Pb8aSS47Ywl6VATShhCHAnDe/a8SCQaIJpKIz7Mxnkjy1Jp/Egz4rQVXuFg8yUM5+mviSW1KZNmSFSkL0ZKfUXPGDu5DRVDSgmtFUBg7uE+Lr2Uj08qv1D9zG/1luhW/voR40nnYfx6NE4sns2oy4NRI7n7uHRLJts3riie0oM/zzaVRac+RV/0rI9zxzfFEQgH2CQeJhALc8c3xLQ5alpOs/MrxM7eaiulWMvsYUitPZk5+jCeSWf0G4WCQGccfzF3PvtP0KT0ABDIWCgNnhNi3vjSUhTUNBEWIJZIkk1rwImT5OtM7wqf7YjSp2WqX5VeOn7kFFdPteB+IqRn1mR6++GjOu//VtDVUGpNJzps4jP16hbnhiTpUlXgSAm7do0dFAFWYdeLIpgXAfnTyaOo2bmfd5h3c9vRbWdmO/YSD6WlUvO3fgO8iYqUaeZWv7b2tTWpV/XqyuzGetm13Y9xykpVQOfLAWVAx3ZL3geg3Oqp6RP+m+TDe7QBznlyb1p+QijvJpPL7H0ykIhRs2re8/hOuWrSaUEAKCigACfc//ZYdURa88iFzn6snHHTu4ZITRvp+0qzbuJ0+PSuK2vFajhqRZCT8FGlbn5XJrxwrjJY095eITAZ+ibOc8H2q+vOM/eLun4KznPB3VfX1fOeKyCPAF9xL9AU+U9UJIjIceAN4y923QlVn5rs/y/3VNbVmZEtmbcDv+/6VEd+VIVN6VARIJpVIyBnBde1pY5jz5NqsHGS9wkHiySTnHjWM3/7tA9/7CQUgIJKVkiYSCgCaNjcnFHDWkQkHA8QSybSaUmv55U8rdr4zv5/lvpEQD/5gYtPw7u6i3CPg2vp+7ZL7S0SCwFzgFKABWCkiS1TVm7/8VGCU+zURuAeYmO9cVf2W5z3uALZ5rveuqk4oVZlMx9faT9epmktz5/s1H6SkHsCpdPk3PlGXlVKmVyTIjWeM5cRD96d/ZYSkKvNfzh4N5tR+sj/whYMBjhs1gGWeNDJJhXg82TRM+Y5n3ubu595p0yz3crS9l2piZmselu05rLkcNcLM8hVzNGCmUjZ/HQXUq+o6ABF5GJgGeIPKNGC+OtWlFSLSV0QGAcObO9et5UwHTiphGUwn4jdbviX9DYWc720+0KQSTahTQ1EQ1bSRYxXBQFZNI5HUpoACcNO0f6FfzzC/fLa+oDJG4wmeXvtR2ja/AWnRuHLlY04qm949Q4wd3KdFD5FcD/zMdWfaophNMXubCt8hHGzZXJ/2HPjQ1r/ZzGv5BcZyl6+UQWUIsN7zugGnNtLcMUMKPPc44CNV9c5UGyEifwe2A/9HVV/MvCkRmQHMABg2bFjBhTEdX1s/XRd6fmZH/85YYm+Hf9rESOWKU0Zz+zNvUxEUEm6alsz8X//5YvZyx6EAWaPPKoJCPKH415OyReNJZj3096Zz7/hm7pqL3yfZzAf+9COddWeK+XAqxigyJw/Z6qaaWjRe+FyfYjzU21LLKVaNMFfgKGbQKlQpg4pfj1vmZ6pcxxRy7rnAQ57Xm4BhqrpFRI4EHheRsaq6Pe0iqvOAeeD0qeS5f9PJtLU5pSXn+zUfZD2Eq6u4889vUxEQGuNJrj9jbNZD2O+h0isSZObxhzD3+XoqAgFiiQTnfmkYC179MGdAaW59y8aEcvmjq3wfJqmHcjDgBL7bznYeSH6j5LwPpysfK87DqS1NMXuXo/ZZZ6eAh3NbH+ptrQU09zdXSMDKFzjaY9h2KSc/NgBDPa+rgI0FHpP3XBEJAf8LeCS1TVWjqrrF/f414F1gdJtLYTqNtq5w2Nbzp04YwktXncSDP5jI0lnHsrCmgT2NSXbGEsQSypwn12ZNMvN7qCSSynkTh/HSVScx9/wj+P6kESx45YO0EWeZCvl01JhQd6GuvbbsiHLFo6uIxpPsiiWIxpNc/uiqpvvsXxmhql9Patd/lpXjLBpP8nufPGmFKNbkzXwrghbygaItH0SKMZEw39/c4toNTLrl2bQVSf34/QxSgaM9lpIuZU1lJTBKREYAG4BzgPMyjlkCzHL7TCYC21R1k4hsbubcrwJvqmpDaoOIDAQ+VdWEiByM0/mf3a5gurS2Nqe09fzUp+5ci39lfkLsXxnh2tPGcOMTdVQEAyRU0x4q3mYdPxUBSAKJAtvEXlm3Ja1/pW7jtqxglQo+x4/ev+mTeFD8h0Tf/dw7nDfRaUYu5BN1w9bdrNmwjTlPri1KM1qugROZyxPk0r8ywvTqqrTBEtOrq4raXNocv7+5QputtuyIsm13jFgi/XcTjSf45PM9vPPR51x2ymjufObtkg0hzlSyoKKqcRGZBTyNMyz4AVWtE5GZ7v57gWU4w4nrcYYUfy/fuZ7Ln0N60xfA8cBNIhLHycc9U1U/LVX5TMfV1pEtxRgZU+gnxMW1G5jz5FrCIadT//ozxqS1hecKKBVB4YcnjWL80D7824OvFzwHZu7z67j3hff4xfRU/4r/vJDtu+MsXbWBKx9dTSxPxAoHgyx45UN+7TbV5QoSqQAZENjtjpIrRht/Zt9PLJHk+5OGc8wh/QvKRbZlR5SFNQ1p2xbWNPCjk0c3ez/FrAVk/s0VErC8TW9JdYeWizgJUhPKRfNfazp3enUV5088qCyj20o6+VFVl+EEDu+2ez3fK3BJoed69n3XZ9siYFEbbteYoilkZJP302jKnKVrmTz2QN+HSko4KCz74XGMPGBftuyIkvCZaxYJBUiq+uYaSyS1qT9k7ODeWYMCBPj3hbV5m9tSYokEc5+rT0vjnxkkUk1sua4XEGmqGbWG95N+qhb04IoPC6oFtaW2UezRa96aSiF9LZk1mdTvHLKbQxfWNDDjuIPLMlzaZtQbUyLNNaXle6Dla9a57ezxjDxgXyD9wRYUoTGR5IqvfYGJB/enql9PHli+jrnP+7cCN2zdzfihfblz+gSudFdxjCcTKFJQQAH4/qQRPLjiw7QaVeZD2a+JzWtXLMHF82vaNK8m9V7fmvdyi0Y6+f2cY4lEwbWNYo1e8+vszxew/P52ggEhqEJjwr/WWrv+s6a/m1KyoGJMCeVrSsv3adSvWSfXTPlcD7bFtRuY9+J7vu+dVG16cE6dMIQxg3pTu/4zelQEmb1odc4Hk1evSJBjDhnAbzKGRO+KxekV3puq5m/vbmn2WtG4tnmoa2tqHamf82VNK1k6835eqv+k2QCXql14y9pS+fpO8gWsqn492RNP/x01JpLkW5lhQpmyFFhQMaadNNd80pJPwZnBK/WwylVDuGHq2KYOYe+kwVgi4TuZ0kkbA96um0RSGdynR1b+roTCqb98gRumjmPiiP24f3l2YBOc5po9eWo4mZobXtvaPo5JIwcQDASIu+c2JpoPcKnaBTiZFCJBQQLCtaeNYdyQPk3zlzKXss68/+YCYb4PJZkptlSV66aOY87StTTGk2kZsS88ZlhZailgQcWYdtVc4GjtoIFcfTLBANw0dRznTzwo56TBVIevt6/mvInDqD5ov6wAuDOWoEcoSGMiPRdaYxKueXxN09yXTDNPOJj/eun9jHMKT/efenh7f2Yt6ePwPuAbtu6GzAd0UnMGOL++sGhCIaFc8/gaIqEA0fjeQHPrWYeh0HT/3lpnawNhw9bdREJB4p4o37MixLjBfXjpqpNo2LqbxniC97fsYsLQvmULKGBBxZh2V4o8TH4Pq8wO/lyjyyKhILFEEm8L2CMr1/PVLx7A0lnHpn0C37IjmjMXGuAbUCKhAD849mC+eGDvtL6ga08b0+xDPBUkr3l8Db3CQRqTzoi58yceBGQH6a07YzxWsz7twZoZoC47ZbTvEtO5mrXyDaIA9gZpN9Bc+dgqQNIGM9zxzNvc9Ze3mV49jMu+OtqdJNt8Z38qGK5YtyVrxF9mMBoxsJLqEf19r1NKFlSM6YJyfWpPPVjzPRgbE0kioUBav0o0rsx88HWS7jyaVBbh/pURrj19DNf8cU1B9xUOCred7Tw0p04Ywud74ty41BlSPefJtezbI+SbdUB9glPqoXrNH9ewc0+8aXBCKkhf9/g/mL9i7/yTC48Zxo9OHp0VoG57+s2m2kVKj4oAG7ftzmrCgvxJRf0EJeA7crsxCQvcpaWnVw/h/InDm4KCX3615uYMXXvamKalFtpzATcLKsZ0Uc119OYaXXbd6WOZ8+TarH273AfZFY+tZsyg3k0BatzgvX0I+YRDAZZdemzTeVt2RJnz5Fpi8SQxt/XsysdW03efMGMH9266317hoO8Sz14/e+pNKiNB4m5+tTGDeqcFFID5L3/IUcP7ZwXTxgSEAtmJPy/67UrCoWDThNTU/KGGrbu59vQxzFnq/Iz2NCYJCcRz3GJCk6D514lZWLOBGccdkjMo+DW5efWKBBm6X09m/PdraQHzikdXpf2uysGCijFdWK6mteZGl+3bI8TsRasJIOxqTA8WsXiSKXe9yO1ugsqqfj1958oExVlquUcomFVTAv/aUjSeZOZ/v0ZCk8w6cRTnTRzm9NtUBHI+UFN2RJ37nL1oNVdPPtT3mE927MmafQ5OJ3ck5KxJsyeeaBrg0Bjbe83P98TTMgFce9oYtuyMcfez7xAKBtBGZ2nqoLs8dSQoIMKsE0eyX2WYm55Ymzc7wvL6T/j5/7xZcA4vr1g8yfbdcQIZVaJYQpnyq+Xcfnb5aiwWVIzppvLVZFL76jZu5+L5NVkPw1jGCKn0AJVoCgiQO31LrtpSKog568LUc90ZY1pUroAIPUL++cCOHTmQWScmuOOZt9O27xMOMff8IwDlot9lL9wXAG5c6tSqUg/2m5auBZRYQpsCVUjg9rPHA878nAdeeo95L6yjMZnkutPH8OnOGHf95W384uOAykhWfrXm5i2lxN2EoX5BKxZPljwzsVcpE0oaYzq4/pURxg/tm7M2c/zogdx29mFZi43B3gcepCfT/NvVJ3PpyaOaakm5rg9wyQkjiYQC7JOjUzwaTzJn6Vqmjh9UcJl2xRJct6SOCUN7p20PBoS1m7Zz3sRh7gqaezUmk4wd3Js+PcNZ+wBiiWRWt4iq21/iISJctrCWqxat4p6/riMa16Zkk3OeXMt5E4ex4qdf5bRxB6adN726ijUbtzXVtrz3lepncX5Wwj4V2T8rhWZyxO39XZWaBRVjTF5TJwxh2aXHEg6mP1YzRxs1F0C8mYlTGXjnvbAOUL5zzEFEQv79DsGA8PjfMxOcO6PILj9lNDefOS4rEMQSSu36tFUvSCSd2hXAbWfvzQwcCQW45ISRgFN7ivsMCrjkhJE+tbVk09yWlD2NSWIJZWfMPxV/3cbtNGzdzU3fGMef//14bj/7MH566qEsrt3AvX/Nznxw7elO5/uXf/4s9/z1XUD4zpcP8g18+ZQ6M7FXSdeo7+hsjXpjCrekdkPWaLJC2+m9w3hjiSSJZDIt31iPigDXnj6Gm56oI5rR4x0OBQgJ7PK0Ge1TEeTebx/J8aMHAvDC25v51/+uaUpWmcu+kRAP/mAi44f2TZv4GRQhlkgy++uHcmCfHs5Q54CTrub6M8YwtF9PLnxgZdb1/u0rB/Nff3ufikCAaCIJqlmrfaaEAhAMOP02qZ/fpJEDmHTLs779Rb3CQe654Egu+t3KtEmsFUHhhjPGNvXvpCasZh4TEFq8Cmah2mWNemNM19LaPFd+80wyVQQCjBvch79dfTK/f+VD7n6uvunhO3X84KxMwkmUsYP3Nm+t/3RXswEFsj+x//r5ejeIOQ/knz31JtOrq5omEKbK+cLbm32vd8whA/jBcQc3pWuZclfWYrNu+QBx5qqkajyzF61m3rerc3bAJ1TZuHWX79IEQ/frmXaPL9V/khXw25qTrLUsqBhjCtaaiZrNjVwCZ/2PXuEg/SsjXHqy08nvXXEyk3eiZGpocj69IsGs5Zwbtu72bf9PZfQd78mV5ZfNORSgaehz6prXnzGWax5Pn7MTDgW485vj+ckf/pGWecBZWEtzDO0OML26imsX55r/I2nvmyvglzOYpFifijGmpPxGLlUEhUhInGG3OEOPT797edPqhqn+mZ2xRNaqhr0iQcYN2btWSq7VH/cJB+lREeDmb4zj9z84mpeuOimtCaiqX8+ca8XUrv8s7XX/ygh3Tp/Q1FEeDgo/Ojl7Ydnzjz6Im88cRzgUoFfEef/bzz6MYw7p75uOZezgPmkrP0ZCwuWnjObJS52VQ/0qX6lglqm5Pq1ysZqKMaakcs3uHzOoN1N+tRzQpj6FzKGvuZZb9jZh+R0TCQn3XnBE2iqXfvc1++uH8rOn3sza55fRN1UbSPXDzHthHXOfr8/qrzh/4kFNa+J4aw258pL51TL8Vg4FJxjf8c3x7R448rGgYowpuVwPzkgwQCxPpuJCkkTmOqaQRb9mfOUQ6jfvSOuzaS6jb6ofJpWA028OiF8zYb4+qczjm8vd1pGVNKiIyGTglzhLAt+nqj/P2C/u/ik4ywl/V1Vfz3euiNwAXAykes5+6q4SiYj8BLgIZznhH6rq06UsnzGmcIU8OP2GvhYyQKAti2XdevZ4Zhx3MLXrP2s2o29b16UvtE+qudxtHVnJgoqIBIG5wClAA7BSRJaoqrdH7VRglPs1EbgHmFjAub9Q1dsz3m8Mztr1Y4HBwJ9FZLSqFrZ4tzGmrFqSqr6Qh3Fbsj2PPGDfgh7YxVyXvjnFWFWyPZSypnIUUK+q6wBE5GFgGuANKtOA+e5a9StEpK+IDAKGF3BupmnAw6oaBd4TkXr3Hl4ubrGMMcXS2R6cxVyXvtD36+g/k0ylDCpDgPWe1w04tZHmjhlSwLmzRORCoAa4XFW3uues8LlWGhGZAcwAGDZsWAuKY4wphc724OxsgbDcSjmk2C/nQuZU01zH5Dv3HuAQYAKwCbijBe+Hqs5T1WpVrR44cKDPKcYYk19HGb7bEZWyptIADPW8rgIyE/jkOiac61xV/Si1UUR+AyxtwfsZY4wpoVLWVFYCo0RkhIiEcTrRl2QcswS4UBxHA9tUdVO+c90+l5QzgTWea50jIhERGYHT+f9qqQpnjDEmW8lqKqoaF5FZwNM4w4IfUNU6EZnp7r8XWIYznLgeZ0jx9/Kd6176VhGZgNO09T7wr+45dSKyEKczPw5cYiO/jDGmvCxLsWUpNsaYFsmXpdhyfxljjCmabl1TEZHNwAc+uwYAn5T5dsrNytg1WBm7hs5WxoNU1Xf4bLcOKrmISE2uql1XYWXsGqyMXUNXKqM1fxljjCkaCyrGGGOKxoKKv3ntfQNlYGXsGqyMXUOXKaP1qRhjjCkaq6kYY4wpGgsqxhhjiqbbBRURmSwib4lIvYhc7bNfROQud/9qETmi0HM7itaWUUSGishzIvKGiNSJyI/Kf/eFacvv0d0fFJG/i8jSzHM7ijb+rfYVkcdE5E3393lMee++MG0s47+7f6drROQhEelR3rsvTAFlPFREXhaRqIhc0ZJzOyRV7TZfOHnE3gUOxsmEvAoYk3HMFOApnFT6RwOvFHpuR/hqYxkHAUe43+8LvN3VyujZfxnwe2Bpe5enFGUEfgf8wP0+DPRt7zIV+W91CPAe0NN9vRBnOfJ2L1cryrg/8CXgZuCKlpzbEb+6W02laTVKVY0BqRUlvZpWo1TVFUBqNcpCzu0IWl1GVd2kqq8DqOrnwBv4LHTWAbTl94iIVAGnAfeV86ZbqNVlFJHewPHA/QCqGlPVz8p474Vq0+8RJyFuTxEJAfvQMZe6aLaMqvqxqq4EGlt6bkfU3YJKrpUmCzmmkHM7graUsYmIDAcOB14p/i22WVvL+B/AbCBJx9WWMh4MbAb+y23iu09EepXyZlup1WVU1Q3A7cCHOIv1bVPVP5XwXlurLc+NzvLMSdPdgkqpVqPsSNpSRmenSCWwCPixqm4v4r0VS6vLKCKnAx+r6mvFv62iasvvMQQcAdyjqocDO4GO2B7flt9jP5xP7SOAwUAvEbmgyPdXDG15bnSWZ06a7hZU2rIaZWdZWbItZUREKnACygJV/UMJ77Mt2lLGScBUEXkfpznhJBF5sHS32mpt/VttUNVULfMxnCDT0bSljF8F3lPVzaraCPwB+HIJ77W12vLc6CzPnHTt3alTzi+cT3DrcD7dpDq+xmYccxrpHYOvFnpuR/hqYxkFmA/8R3uXo1RlzDjmBDpuR32bygi8CHzB/f4G4Lb2LlMxywhMBOpw+lIEZ2DCpe1dptaU0XPsDaR31HeKZ05WOdr7BtrhlzwFZ1TTu8A17raZwEz3ewHmuvv/AVTnO7cjfrW2jMCxONXr1UCt+zWlvctT7N+j5xodNqgU4W91AlDj/i4fB/q1d3lKUMYbgTdxlhT/byDS3uVpZRkPxKmVbAc+c7/vnevcjv5laVqMMcYUTXfrUzHGGFNCFlSMMcYUjQUVY4wxRWNBxRhjTNFYUDHGGFM0FlSMMcYUjQUVY4wxRWNBxZgSE5Hh7rom97lrfywQka+KyEsi8o6IHCUivUTkARFZ6SaBnOY590URed39+rK7/QQRed6zZsoCEfHLFWVMWdnkR2NKzM34XI+T9bkOWImTcuMiYCrwPWAtsFZVHxSRvsCr7vEKJFV1j4iMAh5S1WoROQFYDIzFyQf1EnClqi4vX8mMyRZq7xswppt4T1X/ASAidcBfVFVF5B/AcJxkgVM9K//1AIbhBIy7RWQCkABGe675qqo2uNesda9jQcW0KwsqxpRH1PN90vM6ifP/MAGcpapveU8SkRuAj4DxOM3Ve3JcM4H9fzYdgPWpGNMxPA1cmuoXEZHD3e19gE2qmgS+jbPErDEdlgUVYzqGOUAFsFpE1rivAX4NfEdEVuA0fe1sp/szpiDWUW+MMaZorKZijDGmaCyoGGOMKRoLKsYYY4rGgooxxpiisaBijDGmaCyoGGOMKRoLKsYYY4rm/wNRsNTdTDAaIwAAAABJRU5ErkJggg==\n", 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\n", 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\n", "text/plain": [ "
" ] @@ -2508,7 +2687,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/examples/jupyter/pincell.ipynb b/examples/jupyter/pincell.ipynb index 7d0f4e65a9..0f11ca6559 100644 --- a/examples/jupyter/pincell.ipynb +++ b/examples/jupyter/pincell.ipynb @@ -168,22 +168,13 @@ "cell_type": "code", "execution_count": 7, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/sam/openmc/openmc/openmc/mixin.py:71: IDWarning: Another Material instance already exists with id=2.\n", - " warn(msg, IDWarning)\n" - ] - } - ], + "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)" @@ -218,7 +209,7 @@ "metadata": {}, "outputs": [], "source": [ - "mats = openmc.Materials([uo2, zirconium, water])" + "materials = openmc.Materials([uo2, zirconium, water])" ] }, { @@ -245,10 +236,10 @@ } ], "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)" ] }, { @@ -275,7 +266,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -283,7 +274,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -294,7 +285,7 @@ } ], "source": [ - "mats.export_to_xml()\n", + "materials.export_to_xml()\n", "!cat materials.xml" ] }, @@ -330,7 +321,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -338,7 +329,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -353,7 +344,7 @@ "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 +377,14 @@ "text": [ "\n", "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " ...\n", " \n", " \n", @@ -493,7 +484,7 @@ "metadata": {}, "outputs": [], "source": [ - "sph = openmc.Sphere(r=1.0)" + "sphere = openmc.Sphere(r=1.0)" ] }, { @@ -511,8 +502,8 @@ "metadata": {}, "outputs": [], "source": [ - "inside_sphere = -sph\n", - "outside_sphere = +sph" + "inside_sphere = -sphere\n", + "outside_sphere = +sphere" ] }, { @@ -555,7 +546,7 @@ "outputs": [], "source": [ "z_plane = openmc.ZPlane(z0=0)\n", - "northern_hemisphere = -sph & +z_plane" + "northern_hemisphere = -sphere & +z_plane" ] }, { @@ -663,7 +654,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 24, @@ -672,7 +663,7 @@ }, { "data": { - "image/png": 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PBet/SdJWYCuwEvjQhO1phK52Fsuny31ExaWQdpmbm4v5+fm6mzESnw5ZWVvCRNKWiJgbZ11/9mfKfK3FoD1hkoPfpl+RWepUdrhZ2/cOlQrNWuey2dznPv2pmE+HZsMshkmPRypmlpVHKjXxiKWbZnmE0uORSs3cCbvD+7LgkUoDeNTSXg6SIzlUGqTcQR0wzeYwGcynPw3lTttc3jfDeaTSYD4tahaHyWgcKi3gcKmXw2RpHCot4msu1XGQjM+h0lIOmPwcJHk4VDrAATM+B0l+DpWO8fWX0ThMpscvKZtZVh6pdJRPiY7k0Uk1HCozoN/B1PWgcYDUZ6JQkfRm4O+B04FzIqLvF8dKugD4Z+Ao4PqI6H1B9qnARuB4YAvwZxHxi0naZKNZeNC1PWQcIs0x6UhlG/AnwKcGLSDpKODjwGuBXcC9kjZFxIPAtcB1EbFR0ieBS4FPTNgmG0ObRjMOkGabKFQi4iGAomzPQOcAOyJiZ1p2I7BO0kMUBdvfnpa7gWLU41BpiMUO3mmFjkOj3aq4pnIS8KPS413AKyhOeX4WEQdK008a9CSSLgMuSw/3S2p94bE+VgI/rbsRUzLytt00/J9U03R1n7143BUXDRVJtwPP6TPrqogYVpEwq3LZU0nz49YkabKubhd0d9u6vF3jrjtRLeUR7aaoTtjzvDTtCeA4SUen0Upvupm1WBVvfrsXOE3SqZKOAdYDm6IojXgXcFFabrFazGbWAhOFiqQ3StoF/D7wdUm3punPlXQLQBqFXA7cCjwEfDkiHkhP8X7gPZJ2UFxj+fSIv3rDJO1usK5uF3R327xdC7SylrKZNZc/+2NmWTlUzCyrVoSKpDdLekDSM5IGvnwn6QJJD0vaIemKKts4DkkrJN0maXv6uXzAcgcl3Zdum6pu56gW+/tLWibppjT/HkmnVN/K8YywbZdI2lPaT++qo51LIekzkh4f9J4vFT6Wtvl+SaO92zEiGn+j+GzRi4G7gbkByxwFPAqsBo4Bvg+cUXfbF9mujwBXpPtXANcOWO7/6m7rCNuy6N8f+Evgk+n+euCmutudcdsuAf6l7rYucbteBZwNbBsw/w3ANwABrwTuGeV5WzFSiYiHIuLhRRY79HGAKD6UuBFYN/3WTWQdxccTSD8vrLEtkxrl71/e3puB12iRz3g0RBv71qIi4lvA3iGLrAM+H4XNFO8rW7XY87YiVEbU7+MAA9/23xAnRsRj6f6PgRMHLHespHlJmyU1NXhG+fsfWiaKtxo8RfFWgqYbtW+9KZ0m3Czp+X3mt81Yx1Rjvk+lKR8HyG3YdpUfRERIGvT6/skRsVvSauBOSVsj4tHcbbWJfA24MSL2S/pzihHZq2tuUy0aEyoxvY8D1GrYdkn6iaRVEfFYGlY+PuA5dqefOyXdDZxFcY7fJKP8/XvL7JJ0NPBsio9rNN2i2xYR5e24nuJ6WduNdUx16fSn78cBam7TYjZRfDwBBnxMQdJyScvS/ZXAucCDlbVwdKP8/cvbexFwZ6Qrgg236LYtuNawluLd4223CXhHehXolcBTpdP1weq+Aj3iVeo3UpzP7Qd+Atyapj8XuGXB1epHKP6LX1V3u0fYruOBO4DtwO3AijR9juIb8gD+ANhK8YrDVuDSuts9ZHuO+PsDVwNr0/1jga8AO4DvAqvrbnPGbftH4IG0n+4C1tTd5hG26UbgMeCX6fi6FHg38O40XxRfsPZo6nt9X3ldePPb9M0sqy6d/phZAzhUzCwrh4qZZeVQMbOsHCpmlpVDxcyycqiYWVb/D5MtR6Xki1ByAAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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" ] @@ -702,7 +693,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 25, @@ -711,7 +702,7 @@ }, { "data": { - "image/png": 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" ] @@ -741,7 +732,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 26, @@ -750,7 +741,7 @@ }, { "data": { - "image/png": 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\n", "text/plain": [ "
" ] @@ -787,9 +778,9 @@ "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 +796,9 @@ "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" ] }, { @@ -821,27 +812,16 @@ "cell_type": "code", "execution_count": 29, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/sam/openmc/openmc/openmc/mixin.py:71: 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", - " warn(msg, IDWarning)\n" - ] - } - ], + "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" ] @@ -879,9 +859,9 @@ "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 = openmc.Cell(name='moderator')\n", "moderator.fill = water\n", "moderator.region = water_region" ] @@ -928,7 +908,7 @@ "metadata": {}, "outputs": [], "source": [ - "water_region = box & +clad_or" + "water_region = box & +clad_outer_radius" ] }, { @@ -949,10 +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", @@ -965,14 +945,14 @@ } ], "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 +971,9 @@ "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)" ] }, { @@ -1009,7 +990,7 @@ "outputs": [], "source": [ "settings = openmc.Settings()\n", - "settings.source = src\n", + "settings.source = source\n", "settings.batches = 100\n", "settings.inactive = 10\n", "settings.particles = 1000" @@ -1065,8 +1046,8 @@ "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 +1063,8 @@ "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)']" ] }, { @@ -1105,7 +1086,7 @@ "\r\n", "\r\n", " \r\n", - " 1\r\n", + " 3\r\n", " \r\n", " \r\n", " 1\r\n", @@ -1117,7 +1098,7 @@ } ], "source": [ - "tallies = openmc.Tallies([t])\n", + "tallies = openmc.Tallies([tally])\n", "tallies.export_to_xml()\n", "!cat tallies.xml" ] @@ -1168,165 +1149,164 @@ "\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 | 2020-08-25 14:58:51\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 /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 = 6.9022e-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", - " 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" ] @@ -1354,12 +1334,12 @@ "text": [ " ============================> TALLY 1 <============================\r\n", "\r\n", - " Cell 1\r\n", + " Cell 3\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 +1362,12 @@ "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'}" ] }, { @@ -1413,14 +1393,14 @@ " 1.26 1.26\r\n", " 200 200\r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", "\r\n" ] } ], "source": [ - "plots = openmc.Plots([p])\n", + "plots = openmc.Plots([plot])\n", "plots.export_to_xml()\n", "!cat plots.xml" ] @@ -1467,12 +1447,11 @@ "\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 | 2020-08-25 14:58:54\n", + " OpenMP Threads | 4\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", @@ -1534,7 +1513,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "" ] @@ -1563,7 +1542,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "" ] @@ -1574,7 +1553,7 @@ } ], "source": [ - "p.to_ipython_image()" + "plot.to_ipython_image()" ] } ], @@ -1595,7 +1574,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/examples/jupyter/post-processing.ipynb b/examples/jupyter/post-processing.ipynb index 889e16877a..44a2c2d4e8 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()" ] }, { @@ -208,23 +207,18 @@ "outputs": [], "source": [ "# OpenMC simulation parameters\n", - "batches = 100\n", - "inactive = 10\n", - "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 = 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", "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()" ] }, { @@ -271,7 +265,7 @@ "outputs": [], "source": [ "# Instantiate an empty Tallies object\n", - "tallies_file = openmc.Tallies()" + "tallies = openmc.Tallies()" ] }, { @@ -293,7 +287,7 @@ "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 +297,7 @@ "outputs": [], "source": [ "# Export to \"tallies.xml\"\n", - "tallies_file.export_to_xml()" + "tallies.export_to_xml()" ] }, { @@ -977,7 +971,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/examples/jupyter/search.ipynb b/examples/jupyter/search.ipynb index bfdc206955..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", @@ -95,24 +96,25 @@ " 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", - " # 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", + " \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", @@ -129,7 +131,7 @@ "\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 +139,27 @@ "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 +178,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -344,7 +225,7 @@ "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,