diff --git a/docs/source/devguide/index.rst b/docs/source/devguide/index.rst index e73d8ba70..37b17bc0f 100644 --- a/docs/source/devguide/index.rst +++ b/docs/source/devguide/index.rst @@ -16,5 +16,4 @@ as debugging. styleguide workflow xml-parsing - voxel docbuild diff --git a/docs/source/devguide/voxel.rst b/docs/source/devguide/voxel.rst deleted file mode 100644 index 98f5cb73b..000000000 --- a/docs/source/devguide/voxel.rst +++ /dev/null @@ -1,52 +0,0 @@ -.. _devguide_voxel: - -===================================== -Voxel Plot Binary File Specifications -===================================== - -The current revision of the voxel plot binary file is 1. - -**integer(4) n_voxels_x** - - Number of voxels in the x direction - -**integer(4) n_voxels_y** - - Number of voxels in the y direction - -**integer(4) n_voxels_z** - - Number of voxels in the z direction - -**real(8) width_voxel_x** - - Width of voxels in the x direction - -**real(8) width_voxel_y** - - Width of voxels in the y direction - -**real(8) width_voxel_z** - - Width of voxels in the z direction - -**real(8) lower_left_x** - - Lower left x point of the voxel grid - -**real(8) lower_left_y** - - Lower left y point of the voxel grid - -**real(8) lower_left_z** - - Lower left z point of the voxel grid - -*do x = 1, n_voxels_x* - *do y = 1, n_voxels_y* - *do z = 1, n_voxels_z* - - **integer(4) id** - - Cell or material id number at this voxel center. Set to -1 when - cell not_found. diff --git a/docs/source/methods/physics.rst b/docs/source/methods/physics.rst index d0a6a2c99..e25057488 100644 --- a/docs/source/methods/physics.rst +++ b/docs/source/methods/physics.rst @@ -1027,14 +1027,19 @@ probability distribution function can be found by integrating equation Let us call the normalization factor in the denominator of equation :eq:`target-pdf-1` :math:`C`. -It is normally assumed that :math:`\sigma (v_r)` is constant over the range of + +Constant Cross Section Model +---------------------------- + +It is often assumed that :math:`\sigma (v_r)` is constant over the range of relative velocities of interest. This is a good assumption for almost all cases since the elastic scattering cross section varies slowly with velocity for light nuclei, and for heavy nuclei where large variations can occur due to resonance scattering, the moderating effect is rather small. Nonetheless, this assumption may cause incorrect answers in systems with low-lying resonances that can cause a significant amount of up-scatter that would be ignored by this assumption -(e.g. U-238 in commercial light-water reactors). Nevertheless, with this +(e.g. U-238 in commercial light-water reactors). We will revisit this assumption +later in :ref:`energy_dependent_xs_model`. For now, continuing with the assumption, we write :math:`\sigma (v_r) = \sigma_s` which simplifies :eq:`target-pdf-1` to @@ -1232,6 +1237,35 @@ If is not accepted, then we repeat the process and resample a target speed and cosine until a combination is found that satisfies equation :eq:`freegas-accept-2`. +.. _energy_dependent_xs_model: + +Energy-Dependent Cross Section Model +------------------------------------ + +As was noted earlier, assuming that the elastic scattering cross section is +constant in :eq:`reaction-rate` is not strictly correct, especially when +low-lying resonances are present in the cross sections for heavy nuclides. To +correctly account for energy dependence of the scattering cross section entails +performing another rejection step. The most common method is to sample +:math:`\mu` and :math:`v_T` as in the constant cross section approximation and +then perform a rejection on the ratio of the 0 K elastic scattering cross +section at the relative velocity to the maximum 0 K elastic scattering cross +section over the range of velocities considered: + +.. math:: + :label: dbrc + + p_{dbrc} = \frac{\sigma_s(v_r)}{\sigma_{s,max}} + +where it should be noted that the maximum is taken over the range :math:`[v_n - +4/\beta, 4_n + 4\beta]`. This method is known as Doppler broadening rejection +correction (DBRC) and was first introduced by `Becker et al.`_. OpenMC has an +implementation of DBRC as well as an accelerated sampling method that are +described fully in `Walsh et al.`_ + +.. _Becker et al.: http://dx.doi.org/10.1016/j.anucene.2008.12.001 +.. _Walsh et al.: http://dx.doi.org/10.1016/j.anucene.2014.01.017 + .. _sab_tables: ------------ diff --git a/docs/source/pythonapi/examples/pandas-dataframes.ipynb b/docs/source/pythonapi/examples/pandas-dataframes.ipynb index 24c5c00a0..cb63e2ac3 100644 --- a/docs/source/pythonapi/examples/pandas-dataframes.ipynb +++ b/docs/source/pythonapi/examples/pandas-dataframes.ipynb @@ -374,7 +374,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "" ] @@ -429,7 +429,7 @@ "source": [ "# Instantiate a tally Mesh\n", "mesh = openmc.Mesh(mesh_id=1)\n", - "mesh.type = 'rectangular'\n", + "mesh.type = 'regular'\n", "mesh.dimension = [17, 17]\n", "mesh.lower_left = [-10.71, -10.71]\n", "mesh.width = [1.26, 1.26]\n", @@ -562,9 +562,9 @@ "\n", " Copyright: 2011-2015 Massachusetts Institute of Technology\n", " License: http://mit-crpg.github.io/openmc/license.html\n", - " Version: 0.6.2\n", - " Date/Time: 2015-08-11 13:40:43\n", - " MPI Processes: 4\n", + " Version: 0.7.0\n", + " Git SHA1: b167d70c877c516deca785801b9fa6f53fb0985b\n", + " Date/Time: 2015-09-21 10:27:06\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -590,38 +590,39 @@ "\n", " Bat./Gen. k Average k \n", " ========= ======== ==================== \n", - " 1/1 0.60069 \n", - " 2/1 0.62857 \n", - " 3/1 0.69431 \n", - " 4/1 0.65935 \n", - " 5/1 0.68092 \n", - " 6/1 0.64791 \n", - " 7/1 0.65859 0.65325 +/- 0.00534\n", - " 8/1 0.67381 0.66010 +/- 0.00752\n", - " 9/1 0.74149 0.68045 +/- 0.02103\n", - " 10/1 0.68244 0.68085 +/- 0.01629\n", - " 11/1 0.68068 0.68082 +/- 0.01330\n", - " 12/1 0.70394 0.68412 +/- 0.01172\n", - " 13/1 0.68624 0.68439 +/- 0.01015\n", - " 14/1 0.65667 0.68131 +/- 0.00947\n", - " 15/1 0.70080 0.68326 +/- 0.00869\n", - " 16/1 0.69639 0.68445 +/- 0.00795\n", - " 17/1 0.68786 0.68474 +/- 0.00726\n", - " 18/1 0.63698 0.68106 +/- 0.00762\n", - " 19/1 0.62785 0.67726 +/- 0.00802\n", - " 20/1 0.65759 0.67595 +/- 0.00758\n", - " Triggers unsatisfied, max unc./thresh. is 1.20713 for absorption in tally 10002\n", - " The estimated number of batches is 27\n", + " 1/1 0.59998 \n", + " 2/1 0.65473 \n", + " 3/1 0.67452 \n", + " 4/1 0.66458 \n", + " 5/1 0.70093 \n", + " 6/1 0.70726 \n", + " 7/1 0.65977 0.68351 +/- 0.02375\n", + " 8/1 0.68457 0.68387 +/- 0.01372\n", + " 9/1 0.70024 0.68796 +/- 0.01053\n", + " 10/1 0.64895 0.68016 +/- 0.01128\n", + " 11/1 0.68744 0.68137 +/- 0.00929\n", + " 12/1 0.68037 0.68123 +/- 0.00786\n", + " 13/1 0.64865 0.67715 +/- 0.00793\n", + " 14/1 0.71415 0.68127 +/- 0.00811\n", + " 15/1 0.65717 0.67886 +/- 0.00764\n", + " 16/1 0.71598 0.68223 +/- 0.00769\n", + " 17/1 0.67285 0.68145 +/- 0.00707\n", + " 18/1 0.69329 0.68236 +/- 0.00656\n", + " 19/1 0.65696 0.68055 +/- 0.00634\n", + " 20/1 0.65500 0.67884 +/- 0.00615\n", + " Triggers unsatisfied, max unc./thresh. is 1.21110 for absorption in tally 10002\n", + " The estimated number of batches is 28\n", " Creating state point statepoint.020.h5...\n", - " 21/1 0.68391 0.67645 +/- 0.00711\n", - " 22/1 0.69243 0.67739 +/- 0.00674\n", - " 23/1 0.65491 0.67614 +/- 0.00648\n", - " 24/1 0.64021 0.67425 +/- 0.00641\n", - " 25/1 0.72281 0.67668 +/- 0.00655\n", - " 26/1 0.71261 0.67839 +/- 0.00646\n", - " 27/1 0.69503 0.67914 +/- 0.00621\n", - " Triggers satisfied for batch 27\n", - " Creating state point statepoint.027.h5...\n", + " 21/1 0.67090 0.67835 +/- 0.00577\n", + " 22/1 0.69025 0.67905 +/- 0.00546\n", + " 23/1 0.66113 0.67805 +/- 0.00525\n", + " 24/1 0.67934 0.67812 +/- 0.00496\n", + " 25/1 0.67203 0.67781 +/- 0.00472\n", + " 26/1 0.66928 0.67741 +/- 0.00451\n", + " 27/1 0.70271 0.67856 +/- 0.00445\n", + " 28/1 0.70233 0.67959 +/- 0.00437\n", + " Triggers satisfied for batch 28\n", + " Creating state point statepoint.028.h5...\n", "\n", " ===========================================================================\n", " ======================> SIMULATION FINISHED <======================\n", @@ -630,28 +631,28 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 9.7300E-01 seconds\n", - " Reading cross sections = 3.0300E-01 seconds\n", - " Total time in simulation = 5.9130E+00 seconds\n", - " Time in transport only = 5.4000E+00 seconds\n", - " Time in inactive batches = 7.7300E-01 seconds\n", - " Time in active batches = 5.1400E+00 seconds\n", - " Time synchronizing fission bank = 4.4600E-01 seconds\n", - " Sampling source sites = 0.0000E+00 seconds\n", - " SEND/RECV source sites = 0.0000E+00 seconds\n", - " Time accumulating tallies = 9.0000E-03 seconds\n", + " Total time for initialization = 4.4100E-01 seconds\n", + " Reading cross sections = 1.7900E-01 seconds\n", + " Total time in simulation = 1.2656E+01 seconds\n", + " Time in transport only = 1.2642E+01 seconds\n", + " Time in inactive batches = 2.0300E+00 seconds\n", + " Time in active batches = 1.0626E+01 seconds\n", + " Time synchronizing fission bank = 4.0000E-03 seconds\n", + " Sampling source sites = 3.0000E-03 seconds\n", + " SEND/RECV source sites = 1.0000E-03 seconds\n", + " Time accumulating tallies = 0.0000E+00 seconds\n", " Total time for finalization = 0.0000E+00 seconds\n", - " Total time elapsed = 6.8870E+00 seconds\n", - " Calculation Rate (inactive) = 16170.8 neutrons/second\n", - " Calculation Rate (active) = 7295.72 neutrons/second\n", + " Total time elapsed = 1.3110E+01 seconds\n", + " Calculation Rate (inactive) = 6157.64 neutrons/second\n", + " Calculation Rate (active) = 3529.08 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 0.68117 +/- 0.00597\n", - " k-effective (Track-length) = 0.67914 +/- 0.00621\n", - " k-effective (Absorption) = 0.67898 +/- 0.00471\n", - " Combined k-effective = 0.67922 +/- 0.00479\n", - " Leakage Fraction = 0.34264 +/- 0.00301\n", + " k-effective (Collision) = 0.68196 +/- 0.00427\n", + " k-effective (Track-length) = 0.67959 +/- 0.00437\n", + " k-effective (Absorption) = 0.67957 +/- 0.00402\n", + " Combined k-effective = 0.67943 +/- 0.00295\n", + " Leakage Fraction = 0.34370 +/- 0.00201\n", "\n" ] }, @@ -671,7 +672,7 @@ "!rm statepoint.*\n", "\n", "# Run OpenMC with MPI!\n", - "executor.run_simulation(mpi_procs=4)" + "executor.run_simulation()" ] }, { @@ -694,9 +695,7 @@ "statepoints = glob.glob('statepoint.*.h5')\n", "\n", "# Load the last statepoint file\n", - "sp = StatePoint(statepoints[-1])\n", - "sp.read_results()\n", - "sp.compute_stdev()" + "sp = StatePoint(statepoints[-1])" ] }, { @@ -738,7 +737,7 @@ " \t\tmesh\t[1]\n", " \t\tenergy\t[ 0.00000000e+00 6.25000000e-07 2.00000000e+01]\n", "\tNuclides =\ttotal \n", - "\tScores =\t['fission', 'nu-fission']\n", + "\tScores =\t[u'fission', u'nu-fission']\n", "\tEstimator =\ttracklength\n", "\n" ] @@ -770,13 +769,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.14583021]]\n", + "[[[ 0.15044911]]\n", "\n", - " [[ 0.07846909]]\n", + " [[ 0.09149973]]\n", "\n", - " [[ 0.33705448]]\n", + " [[ 0.27611475]]\n", "\n", - " [[ 0.15150059]]]\n" + " [[ 0.12476673]]]\n" ] } ], @@ -840,7 +839,7 @@ " 0.0e+00 - 6.3e-07\n", " fission\n", " 0.000236\n", - " 0.000034\n", + " 0.000035\n", " \n", " \n", " 1\n", @@ -849,8 +848,8 @@ " 1\n", " 0.0e+00 - 6.3e-07\n", " nu-fission\n", - " 0.000576\n", - " 0.000084\n", + " 0.000574\n", + " 0.000086\n", " \n", " \n", " 2\n", @@ -859,8 +858,8 @@ " 1\n", " 6.3e-07 - 2.0e+01\n", " fission\n", - " 0.000069\n", - " 0.000004\n", + " 0.000072\n", + " 0.000006\n", " \n", " \n", " 3\n", @@ -869,8 +868,8 @@ " 1\n", " 6.3e-07 - 2.0e+01\n", " nu-fission\n", - " 0.000183\n", - " 0.000011\n", + " 0.000190\n", + " 0.000014\n", " \n", " \n", " 4\n", @@ -879,8 +878,8 @@ " 1\n", " 0.0e+00 - 6.3e-07\n", " fission\n", - " 0.000366\n", - " 0.000052\n", + " 0.000451\n", + " 0.000058\n", " \n", " \n", " 5\n", @@ -889,8 +888,8 @@ " 1\n", " 0.0e+00 - 6.3e-07\n", " nu-fission\n", - " 0.000892\n", - " 0.000127\n", + " 0.001100\n", + " 0.000141\n", " \n", " \n", " 6\n", @@ -899,8 +898,8 @@ " 1\n", " 6.3e-07 - 2.0e+01\n", " fission\n", - " 0.000109\n", - " 0.000009\n", + " 0.000095\n", + " 0.000006\n", " \n", " \n", " 7\n", @@ -909,8 +908,8 @@ " 1\n", " 6.3e-07 - 2.0e+01\n", " nu-fission\n", - " 0.000284\n", - " 0.000021\n", + " 0.000250\n", + " 0.000016\n", " \n", " \n", " 8\n", @@ -919,8 +918,8 @@ " 1\n", " 0.0e+00 - 6.3e-07\n", " fission\n", - " 0.000540\n", - " 0.000058\n", + " 0.000575\n", + " 0.000080\n", " \n", " \n", " 9\n", @@ -929,8 +928,8 @@ " 1\n", " 0.0e+00 - 6.3e-07\n", " nu-fission\n", - " 0.001316\n", - " 0.000141\n", + " 0.001401\n", + " 0.000194\n", " \n", " \n", " 10\n", @@ -939,8 +938,8 @@ " 1\n", " 6.3e-07 - 2.0e+01\n", " fission\n", - " 0.000144\n", - " 0.000017\n", + " 0.000134\n", + " 0.000011\n", " \n", " \n", " 11\n", @@ -949,8 +948,8 @@ " 1\n", " 6.3e-07 - 2.0e+01\n", " nu-fission\n", - " 0.000376\n", - " 0.000041\n", + " 0.000353\n", + " 0.000028\n", " \n", " \n", " 12\n", @@ -959,8 +958,8 @@ " 1\n", " 0.0e+00 - 6.3e-07\n", " fission\n", - " 0.000830\n", - " 0.000085\n", + " 0.000655\n", + " 0.000071\n", " \n", " \n", " 13\n", @@ -969,8 +968,8 @@ " 1\n", " 0.0e+00 - 6.3e-07\n", " nu-fission\n", - " 0.002022\n", - " 0.000207\n", + " 0.001596\n", + " 0.000174\n", " \n", " \n", " 14\n", @@ -979,8 +978,8 @@ " 1\n", " 6.3e-07 - 2.0e+01\n", " fission\n", - " 0.000168\n", - " 0.000013\n", + " 0.000149\n", + " 0.000009\n", " \n", " \n", " 15\n", @@ -989,8 +988,8 @@ " 1\n", " 6.3e-07 - 2.0e+01\n", " nu-fission\n", - " 0.000434\n", - " 0.000034\n", + " 0.000391\n", + " 0.000023\n", " \n", " \n", " 16\n", @@ -999,8 +998,8 @@ " 1\n", " 0.0e+00 - 6.3e-07\n", " fission\n", - " 0.000738\n", - " 0.000043\n", + " 0.000781\n", + " 0.000078\n", " \n", " \n", " 17\n", @@ -1009,8 +1008,8 @@ " 1\n", " 0.0e+00 - 6.3e-07\n", " nu-fission\n", - " 0.001799\n", - " 0.000105\n", + " 0.001903\n", + " 0.000191\n", " \n", " \n", " 18\n", @@ -1019,8 +1018,8 @@ " 1\n", " 6.3e-07 - 2.0e+01\n", " fission\n", - " 0.000186\n", - " 0.000010\n", + " 0.000185\n", + " 0.000009\n", " \n", " \n", " 19\n", @@ -1029,8 +1028,8 @@ " 1\n", " 6.3e-07 - 2.0e+01\n", " nu-fission\n", - " 0.000486\n", - " 0.000026\n", + " 0.000484\n", + " 0.000024\n", " \n", " \n", "\n", @@ -1040,26 +1039,26 @@ " mesh 1 energy [MeV] score mean std. dev.\n", " x y z \n", "bin \n", - "0 1 1 1 0.0e+00 - 6.3e-07 fission 0.000236 0.000034\n", - "1 1 1 1 0.0e+00 - 6.3e-07 nu-fission 0.000576 0.000084\n", - "2 1 1 1 6.3e-07 - 2.0e+01 fission 0.000069 0.000004\n", - "3 1 1 1 6.3e-07 - 2.0e+01 nu-fission 0.000183 0.000011\n", - "4 1 2 1 0.0e+00 - 6.3e-07 fission 0.000366 0.000052\n", - "5 1 2 1 0.0e+00 - 6.3e-07 nu-fission 0.000892 0.000127\n", - "6 1 2 1 6.3e-07 - 2.0e+01 fission 0.000109 0.000009\n", - "7 1 2 1 6.3e-07 - 2.0e+01 nu-fission 0.000284 0.000021\n", - "8 1 3 1 0.0e+00 - 6.3e-07 fission 0.000540 0.000058\n", - "9 1 3 1 0.0e+00 - 6.3e-07 nu-fission 0.001316 0.000141\n", - "10 1 3 1 6.3e-07 - 2.0e+01 fission 0.000144 0.000017\n", - "11 1 3 1 6.3e-07 - 2.0e+01 nu-fission 0.000376 0.000041\n", - "12 1 4 1 0.0e+00 - 6.3e-07 fission 0.000830 0.000085\n", - "13 1 4 1 0.0e+00 - 6.3e-07 nu-fission 0.002022 0.000207\n", - "14 1 4 1 6.3e-07 - 2.0e+01 fission 0.000168 0.000013\n", - "15 1 4 1 6.3e-07 - 2.0e+01 nu-fission 0.000434 0.000034\n", - "16 1 5 1 0.0e+00 - 6.3e-07 fission 0.000738 0.000043\n", - "17 1 5 1 0.0e+00 - 6.3e-07 nu-fission 0.001799 0.000105\n", - "18 1 5 1 6.3e-07 - 2.0e+01 fission 0.000186 0.000010\n", - "19 1 5 1 6.3e-07 - 2.0e+01 nu-fission 0.000486 0.000026" + "0 1 1 1 0.0e+00 - 6.3e-07 fission 0.000236 0.000035\n", + "1 1 1 1 0.0e+00 - 6.3e-07 nu-fission 0.000574 0.000086\n", + "2 1 1 1 6.3e-07 - 2.0e+01 fission 0.000072 0.000006\n", + "3 1 1 1 6.3e-07 - 2.0e+01 nu-fission 0.000190 0.000014\n", + "4 1 2 1 0.0e+00 - 6.3e-07 fission 0.000451 0.000058\n", + "5 1 2 1 0.0e+00 - 6.3e-07 nu-fission 0.001100 0.000141\n", + "6 1 2 1 6.3e-07 - 2.0e+01 fission 0.000095 0.000006\n", + "7 1 2 1 6.3e-07 - 2.0e+01 nu-fission 0.000250 0.000016\n", + "8 1 3 1 0.0e+00 - 6.3e-07 fission 0.000575 0.000080\n", + "9 1 3 1 0.0e+00 - 6.3e-07 nu-fission 0.001401 0.000194\n", + "10 1 3 1 6.3e-07 - 2.0e+01 fission 0.000134 0.000011\n", + "11 1 3 1 6.3e-07 - 2.0e+01 nu-fission 0.000353 0.000028\n", + "12 1 4 1 0.0e+00 - 6.3e-07 fission 0.000655 0.000071\n", + "13 1 4 1 0.0e+00 - 6.3e-07 nu-fission 0.001596 0.000174\n", + "14 1 4 1 6.3e-07 - 2.0e+01 fission 0.000149 0.000009\n", + "15 1 4 1 6.3e-07 - 2.0e+01 nu-fission 0.000391 0.000023\n", + "16 1 5 1 0.0e+00 - 6.3e-07 fission 0.000781 0.000078\n", + "17 1 5 1 0.0e+00 - 6.3e-07 nu-fission 0.001903 0.000191\n", + "18 1 5 1 6.3e-07 - 2.0e+01 fission 0.000185 0.000009\n", + "19 1 5 1 6.3e-07 - 2.0e+01 nu-fission 0.000484 0.000024" ] }, "execution_count": 25, @@ -1084,9 +1083,9 @@ "outputs": [ { "data": { - "image/png": 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EhKT+8u1AS0TslPR24E5Jp0XEcwXbYWZmwygvaWwDWkrmW8h6DLXqTEl1jqhQ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\nAAAAAElFTkSuQmCC\n", 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BuoCVABGxG9idph+U9DjQDDxYHtTe3t4/3dLSQmtra86uWJ7u7u5GN8FsQM4881i6un7Z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\neLpkfksqK1JnUo3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FJ00za7/qq1HOBLojoicieoGbgLkNdc4CrgeIiAeBsZImNIuNiBUR8XhBe3OB\nGyOiNyJ6gO78e0o5aZpZ+/W2+NrXJLIZY/dYmZe1UmdiC7GNJrL32uj9xgzja5pmNmJtrxwZLdZL\nvzjepj44aZpZ+1UfcrQKmNzn82T2PhIsqnN0Xmd0C7H9tXd0XlbKp+dm1n7VT8+XAF2SpkgaQ3aT\nZmFDnYXABQCSZgEbI2Jti7Gw91HqQuD3JI2RNBXoAn7WbNd8pGlm7VdxyFFE7JQ0H7iDbNjQdRGx\nXNK8fPuCiFgkaY6kbmALcHGzWABJZwNXkq2I9S+SlkbEGRGxTNLNwDKy4+NLIsKn52Y2xAbwRFBE\nLAYWN5QtaPg8v9XYvPxW4NaSmCuAK1rtn5OmmbWfH6M0M0vgxyjNzBJUH3I07Dlpmln7+fS8Di+V\nlL9Ssm1thTa2poesOb1CO8CazekxKw8tLn+F7EnaImPTm6kUc3SFGKj2G9ddUr62ybay8mbWpIe8\n+PclE4Y0U/Vf3Wkl5esonajloe73VmxsgHx6bmaWwDO3m5kl8Om5mVkCJ00zswS+pmlmlsBDjszM\nEvj03MwsgU/PzcwSeMiRmVkCn56bmSVw0jQzS+BrmmZmCTr4SNNrBJnZsCJptqQVkp6QdGlJnSvz\n7Y9ImtFfrKRxku6S9LikOyWNzcunSNoqaWn+urq//g3jI82ekvKyKV2OrNDGmyrEPFQhBmBcekjZ\nLEe7gY0lMVX+h3+4QkxVEyrElK0nuAZ4vGTbKxXaqTLbU4WZkRhfIQbK/0n0AM+UbKs6G1VNJI0C\nrgJOJ1sV8ueSFu5Z6yevMweYFhFdkt4DXAPM6if2s8BdEfG1PJl+Nn8BdEfEa4m3Pz7SNLPhZCZZ\nEuuJiF7gJmBuQ52zgOsBIuJBYKykCf3EvhaT//nBqh0ctKQp6duS1kp6tE/Z5ZJW9jkUnj1Y7ZtZ\nnSqv4TsJeK7P55V5WSt1JjaJPTJf5heymVj7nppOzfPRPZL6nYB0ME/PvwN8C/i/fcoC+EZEfGMQ\n2zWz2lW+E9R0+dw+1H8VVPR9ERGS9pSvBiZHxAZJJwO3SToxIkpnDR+0I82IuBfYULCplZ01sxGt\n8pHmKmByn8+T2feqdmOdo/M6ReWr8vdr81N4JB0FvAAQETsiYkP+/iHgSaCr2Z7VcU3zE/kdr+v2\n3MEys06ztcXXPpYAXfld7THAucDChjoLgQsAJM0CNuan3s1iFwIX5u8vBG7L48fnN5CQdCxZwnyq\n2Z4N9d0nHu9vAAAFBElEQVTza4Av5u+/BPwN8NHiqv/Y5/1b8xfsfcmir2crdKfKrc8xFWIADkoP\n2X1EcXncl91BL/JqejNDqsKyTKV/TRvvK4/ZVqGdKj+7TRViqp65lo0IWNfk59Df0lTrl8H65f1U\nqqLa6PaI2ClpPnAHMAq4LiKWS5qXb18QEYskzZHUDWwBLm4Wm3/1V4CbJX2UbLzBh/Py3wC+KKmX\n7F/VvIgoG5sCgCJavYSQTtIU4PaIeFfitoDPl3zro8A+IVQbclT0Pf2pMkwJKg05OmBqcfnuG+AN\n5xdva+eQnsFQpX/vLClfcwNMKPk5dOKQo7K4nhtgSsnPIXXI0V+LiBjQJbTs3+/TLdaeOuD2htqQ\nHmlKOioins8/nk35mopmNqJ17nOUg5Y0Jd0IvA8YL+k5skPH0ySdRHZH62lg3mC1b2Z16tznKAct\naUbEeQXF3x6s9sxsOPGRpplZgip3/EYGJ00zGwQ+PR8BqpwO9FSIWdV/lUKNT4K1YGfZUJKfwu6S\nMUcrp6S3Q8nEIE1VHEWwpkLcmrK/2xfgl2V3aaekt5ONd05U5Z9Q1REYZUdvm+GB9cWbxr6lYlsD\n5dNzM7MEPtI0M0vgI00zswQ+0jQzS+AjTTOzBB5yZGaWwEeaZmYJfE3TzCyBjzSHkRfr7sAwUHWA\nfafprrsDw8RjdXeggI80hxEnzWxZE3PS3KNsHeM6+UjTzCyBjzTNzBJ07pCjQV3uoqo+y2ua2RBr\nz3IXQ9feUBuWSdPMbLiqYwlfM7MRy0nTzCzBiEmakmZLWiHpCUmX1t2fukjqkfTvkpZK+lnd/RkK\nkr4taa2kR/uUjZN0l6THJd0pqcoCvCNKyc/hckkr89+HpZJm19nH/cGISJqSRgFXAbOB6cB5kk6o\nt1e1CeC0iJgRETPr7swQ+Q7Z331fnwXuioi3Az/MP3e6op9DAN/Ifx9mRMS/1tCv/cqISJrATKA7\nInoiohe4CZhbc5/qNKLuNg5URNwLbGgoPgu4Pn9/PfDBIe1UDUp+DrCf/T7UbaQkzUnAc30+r6TS\nojsdIYC7JS2R9Id1d6ZGR0bE2vz9WuDIOjtTs09IekTSdfvDZYq6jZSk6XFRrzs1ImYAZwAfl/Tr\ndXeobpGNm9tff0euAaYCJwHPA39Tb3c630hJmquAyX0+TyY72tzvRMTz+Z8vAreSXbrYH62VNAFA\n0lFUW0pyxIuIFyIHXMv++/swZEZK0lwCdEmaImkMcC6wsOY+DTlJb5Z0SP7+IOADwKPNozrWQuDC\n/P2FwG019qU2+X8Ye5zN/vv7MGRGxLPnEbFT0nzgDmAUcF1ELK+5W3U4ErhVEmR/d/8QEXfW26XB\nJ+lG4H3AeEnPAX8BfAW4WdJHyRaw/3B9PRwaBT+HzwOnSTqJ7PLE08C8Gru4X/BjlGZmCUbK6bmZ\n2bDgpGlmlsBJ08wsgZOmmVkCJ00zswROmmZmCZw0zcwSOGmamSVw0rS2kPSr+Uw7B0o6SNIvJU2v\nu19m7eYngqxtJH0JeCPwJuC5iPhqzV0yazsnTWsbSaPJJlfZCvzn8C+XdSCfnls7jQcOAg4mO9o0\n6zg+0rS2kbQQuAE4FjgqIj5Rc5fM2m5ETA1nw5+kC4DtEXGTpDcAP5V0WkTcU3PXzNrKR5pmZgl8\nTdPMLIGTpplZAidNM7METppmZgmcNM3MEjhpmpklcNI0M0vgpGlmluD/A3ovfji/2DWLAAAAAElF\nTkSuQmCC\n", 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z0jSz9utr8bWnI4B1De/X52Wt1JnaQmyzqey+NvqAMSP4mqaZjVrbake2Ort1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\nEvx/rHWCrxSlro8AAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1166,7 +1165,7 @@ "\tFilters =\t\n", " \t\tcell\t[10000]\n", "\tNuclides =\tU-235 U-238 \n", - "\tScores =\t['scatter-Y0,0', 'scatter-Y1,-1', 'scatter-Y1,0', 'scatter-Y1,1', 'scatter-Y2,-2', 'scatter-Y2,-1', 'scatter-Y2,0', 'scatter-Y2,1', 'scatter-Y2,2']\n", + "\tScores =\t[u'scatter-Y0,0', u'scatter-Y1,-1', u'scatter-Y1,0', u'scatter-Y1,1', u'scatter-Y2,-2', u'scatter-Y2,-1', u'scatter-Y2,0', u'scatter-Y2,1', u'scatter-Y2,2']\n", "\tEstimator =\tanalog\n", "\n" ] @@ -1217,143 +1216,143 @@ " U-235\n", " scatter-Y0,0\n", " 0.036453\n", - " 0.000941\n", + " 0.001219\n", " \n", " \n", " 1\n", " 10000\n", " U-235\n", " scatter-Y1,-1\n", - " -0.000725\n", - " 0.000261\n", + " 0.000302\n", + " 0.000314\n", " \n", " \n", " 2\n", " 10000\n", " U-235\n", " scatter-Y1,0\n", - " -0.000088\n", - " 0.000408\n", + " -0.000006\n", + " 0.000347\n", " \n", " \n", " 3\n", " 10000\n", " U-235\n", " scatter-Y1,1\n", - " 0.000986\n", - " 0.000412\n", + " 0.000244\n", + " 0.000286\n", " \n", " \n", " 4\n", " 10000\n", " U-235\n", " scatter-Y2,-2\n", - " 0.000098\n", - " 0.000204\n", + " 0.000184\n", + " 0.000211\n", " \n", " \n", " 5\n", " 10000\n", " U-235\n", " scatter-Y2,-1\n", - " -0.000358\n", - " 0.000247\n", + " 0.000067\n", + " 0.000173\n", " \n", " \n", " 6\n", " 10000\n", " U-235\n", " scatter-Y2,0\n", - " 0.000197\n", - " 0.000140\n", + " 0.000353\n", + " 0.000210\n", " \n", " \n", " 7\n", " 10000\n", " U-235\n", " scatter-Y2,1\n", - " -0.000084\n", - " 0.000196\n", + " -0.000266\n", + " 0.000263\n", " \n", " \n", " 8\n", " 10000\n", " U-235\n", " scatter-Y2,2\n", - " 0.000052\n", - " 0.000168\n", + " -0.000246\n", + " 0.000153\n", " \n", " \n", " 9\n", " 10000\n", " U-238\n", " scatter-Y0,0\n", - " 2.325600\n", - " 0.015545\n", + " 2.315893\n", + " 0.008243\n", " \n", " \n", " 10\n", " 10000\n", " U-238\n", " scatter-Y1,-1\n", - " -0.030089\n", - " 0.002460\n", + " -0.022028\n", + " 0.002316\n", " \n", " \n", " 11\n", " 10000\n", " U-238\n", " scatter-Y1,0\n", - " -0.004451\n", - " 0.003663\n", + " -0.003426\n", + " 0.002651\n", " \n", " \n", " 12\n", " 10000\n", " U-238\n", " scatter-Y1,1\n", - " 0.020832\n", - " 0.002831\n", + " 0.026620\n", + " 0.002084\n", " \n", " \n", " 13\n", " 10000\n", " U-238\n", " scatter-Y2,-2\n", - " -0.004149\n", - " 0.001530\n", + " -0.001295\n", + " 0.001627\n", " \n", " \n", " 14\n", " 10000\n", " U-238\n", " scatter-Y2,-1\n", - " 0.000735\n", - " 0.001729\n", + " 0.000759\n", + " 0.001426\n", " \n", " \n", " 15\n", " 10000\n", " U-238\n", " scatter-Y2,0\n", - " 0.003431\n", - " 0.002098\n", + " 0.005513\n", + " 0.001983\n", " \n", " \n", " 16\n", " 10000\n", " U-238\n", " scatter-Y2,1\n", - " 0.000385\n", - " 0.001263\n", + " 0.000431\n", + " 0.001862\n", " \n", " \n", " 17\n", " 10000\n", " U-238\n", " scatter-Y2,2\n", - " 0.000002\n", - " 0.001718\n", + " -0.001962\n", + " 0.001222\n", " \n", " \n", "\n", @@ -1362,24 +1361,24 @@ "text/plain": [ " cell nuclide score mean std. dev.\n", "bin \n", - "0 10000 U-235 scatter-Y0,0 0.036453 0.000941\n", - "1 10000 U-235 scatter-Y1,-1 -0.000725 0.000261\n", - "2 10000 U-235 scatter-Y1,0 -0.000088 0.000408\n", - "3 10000 U-235 scatter-Y1,1 0.000986 0.000412\n", - "4 10000 U-235 scatter-Y2,-2 0.000098 0.000204\n", - "5 10000 U-235 scatter-Y2,-1 -0.000358 0.000247\n", - "6 10000 U-235 scatter-Y2,0 0.000197 0.000140\n", - "7 10000 U-235 scatter-Y2,1 -0.000084 0.000196\n", - "8 10000 U-235 scatter-Y2,2 0.000052 0.000168\n", - "9 10000 U-238 scatter-Y0,0 2.325600 0.015545\n", - "10 10000 U-238 scatter-Y1,-1 -0.030089 0.002460\n", - "11 10000 U-238 scatter-Y1,0 -0.004451 0.003663\n", - "12 10000 U-238 scatter-Y1,1 0.020832 0.002831\n", - "13 10000 U-238 scatter-Y2,-2 -0.004149 0.001530\n", - "14 10000 U-238 scatter-Y2,-1 0.000735 0.001729\n", - "15 10000 U-238 scatter-Y2,0 0.003431 0.002098\n", - "16 10000 U-238 scatter-Y2,1 0.000385 0.001263\n", - "17 10000 U-238 scatter-Y2,2 0.000002 0.001718" + "0 10000 U-235 scatter-Y0,0 0.036453 0.001219\n", + "1 10000 U-235 scatter-Y1,-1 0.000302 0.000314\n", + "2 10000 U-235 scatter-Y1,0 -0.000006 0.000347\n", + "3 10000 U-235 scatter-Y1,1 0.000244 0.000286\n", + "4 10000 U-235 scatter-Y2,-2 0.000184 0.000211\n", + "5 10000 U-235 scatter-Y2,-1 0.000067 0.000173\n", + "6 10000 U-235 scatter-Y2,0 0.000353 0.000210\n", + "7 10000 U-235 scatter-Y2,1 -0.000266 0.000263\n", + "8 10000 U-235 scatter-Y2,2 -0.000246 0.000153\n", + "9 10000 U-238 scatter-Y0,0 2.315893 0.008243\n", + "10 10000 U-238 scatter-Y1,-1 -0.022028 0.002316\n", + "11 10000 U-238 scatter-Y1,0 -0.003426 0.002651\n", + "12 10000 U-238 scatter-Y1,1 0.026620 0.002084\n", + "13 10000 U-238 scatter-Y2,-2 -0.001295 0.001627\n", + "14 10000 U-238 scatter-Y2,-1 0.000759 0.001426\n", + "15 10000 U-238 scatter-Y2,0 0.005513 0.001983\n", + "16 10000 U-238 scatter-Y2,1 0.000431 0.001862\n", + "17 10000 U-238 scatter-Y2,2 -0.001962 0.001222" ] }, "execution_count": 29, @@ -1413,8 +1412,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.00171834 0.01554515]\n", - " [ 0.00016768 0.00094081]]]\n" + "[[[ 0.00122163 0.00824348]\n", + " [ 0.00015287 0.00121882]]]\n" ] } ], @@ -1450,7 +1449,7 @@ "\tFilters =\t\n", " \t\tdistribcell\t[10002]\n", "\tNuclides =\ttotal \n", - "\tScores =\t['absorption', 'scatter']\n", + "\tScores =\t[u'absorption', u'scatter']\n", "\tEstimator =\ttracklength\n", "\n" ] @@ -1482,25 +1481,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.04682759]]\n", + "[[[ 0.0400168 ]]\n", "\n", - " [[ 0.03205271]]\n", + " [[ 0.05233031]]\n", "\n", - " [[ 0.03592433]]\n", + " [[ 0.03819276]]\n", "\n", - " [[ 0.02417979]]\n", + " [[ 0.02900783]]\n", "\n", - " [[ 0.02524314]]\n", + " [[ 0.03176394]]\n", "\n", - " [[ 0.02390359]]\n", + " [[ 0.03046477]]\n", "\n", - " [[ 0.0274475 ]]\n", + " [[ 0.03864163]]\n", "\n", - " [[ 0.02827721]]\n", + " [[ 0.02455132]]\n", "\n", - " [[ 0.0231313 ]]\n", + " [[ 0.02282716]]\n", "\n", - " [[ 0.01898386]]]\n" + " [[ 0.02162945]]]\n" ] } ], @@ -1552,141 +1551,141 @@ " 558\n", " 279\n", " absorption\n", - " 0.000095\n", - " 0.000009\n", + " 0.000085\n", + " 0.000008\n", " \n", " \n", " 559\n", " 279\n", " scatter\n", - " 0.013611\n", - " 0.000544\n", + " 0.013429\n", + " 0.000449\n", " \n", " \n", " 560\n", " 280\n", " absorption\n", - " 0.000096\n", - " 0.000009\n", + " 0.000095\n", + " 0.000014\n", " \n", " \n", " 561\n", " 280\n", " scatter\n", - " 0.013999\n", - " 0.000568\n", + " 0.014770\n", + " 0.000783\n", " \n", " \n", " 562\n", " 281\n", " absorption\n", - " 0.000117\n", - " 0.000015\n", + " 0.000107\n", + " 0.000013\n", " \n", " \n", " 563\n", " 281\n", " scatter\n", - " 0.015951\n", - " 0.000682\n", + " 0.015044\n", + " 0.000605\n", " \n", " \n", " 564\n", " 282\n", " absorption\n", - " 0.000104\n", - " 0.000011\n", + " 0.000110\n", + " 0.000010\n", " \n", " \n", " 565\n", " 282\n", " scatter\n", - " 0.016057\n", - " 0.000574\n", + " 0.016090\n", + " 0.000795\n", " \n", " \n", " 566\n", " 283\n", " absorption\n", - " 0.000117\n", - " 0.000013\n", + " 0.000121\n", + " 0.000012\n", " \n", " \n", " 567\n", " 283\n", " scatter\n", - " 0.015997\n", - " 0.000706\n", + " 0.017010\n", + " 0.000793\n", " \n", " \n", " 568\n", " 284\n", " absorption\n", - " 0.000108\n", + " 0.000110\n", " 0.000007\n", " \n", " \n", " 569\n", " 284\n", " scatter\n", - " 0.016720\n", - " 0.000639\n", + " 0.017010\n", + " 0.000430\n", " \n", " \n", " 570\n", " 285\n", " absorption\n", - " 0.000116\n", - " 0.000008\n", + " 0.000112\n", + " 0.000007\n", " \n", " \n", " 571\n", " 285\n", " scatter\n", - " 0.017764\n", - " 0.000639\n", + " 0.017499\n", + " 0.000615\n", " \n", " \n", " 572\n", " 286\n", " absorption\n", - " 0.000111\n", - " 0.000014\n", + " 0.000127\n", + " 0.000016\n", " \n", " \n", " 573\n", " 286\n", " scatter\n", - " 0.018101\n", - " 0.000766\n", + " 0.017716\n", + " 0.000690\n", " \n", " \n", " 574\n", " 287\n", " absorption\n", - " 0.000115\n", - " 0.000012\n", + " 0.000119\n", + " 0.000013\n", " \n", " \n", " 575\n", " 287\n", " scatter\n", - " 0.018411\n", - " 0.000655\n", + " 0.018041\n", + " 0.000702\n", " \n", " \n", " 576\n", " 288\n", " absorption\n", - " 0.000138\n", - " 0.000014\n", + " 0.000125\n", + " 0.000013\n", " \n", " \n", " 577\n", " 288\n", " scatter\n", - " 0.019154\n", - " 0.000763\n", + " 0.018212\n", + " 0.000715\n", " \n", " \n", "\n", @@ -1695,26 +1694,26 @@ "text/plain": [ " distribcell score mean std. dev.\n", "bin \n", - "558 279 absorption 0.000095 0.000009\n", - "559 279 scatter 0.013611 0.000544\n", - "560 280 absorption 0.000096 0.000009\n", - "561 280 scatter 0.013999 0.000568\n", - "562 281 absorption 0.000117 0.000015\n", - "563 281 scatter 0.015951 0.000682\n", - "564 282 absorption 0.000104 0.000011\n", - "565 282 scatter 0.016057 0.000574\n", - "566 283 absorption 0.000117 0.000013\n", - "567 283 scatter 0.015997 0.000706\n", - "568 284 absorption 0.000108 0.000007\n", - "569 284 scatter 0.016720 0.000639\n", - "570 285 absorption 0.000116 0.000008\n", - "571 285 scatter 0.017764 0.000639\n", - "572 286 absorption 0.000111 0.000014\n", - "573 286 scatter 0.018101 0.000766\n", - "574 287 absorption 0.000115 0.000012\n", - "575 287 scatter 0.018411 0.000655\n", - "576 288 absorption 0.000138 0.000014\n", - "577 288 scatter 0.019154 0.000763" + "558 279 absorption 0.000085 0.000008\n", + "559 279 scatter 0.013429 0.000449\n", + "560 280 absorption 0.000095 0.000014\n", + "561 280 scatter 0.014770 0.000783\n", + "562 281 absorption 0.000107 0.000013\n", + "563 281 scatter 0.015044 0.000605\n", + "564 282 absorption 0.000110 0.000010\n", + "565 282 scatter 0.016090 0.000795\n", + "566 283 absorption 0.000121 0.000012\n", + "567 283 scatter 0.017010 0.000793\n", + "568 284 absorption 0.000110 0.000007\n", + "569 284 scatter 0.017010 0.000430\n", + "570 285 absorption 0.000112 0.000007\n", + "571 285 scatter 0.017499 0.000615\n", + "572 286 absorption 0.000127 0.000016\n", + "573 286 scatter 0.017716 0.000690\n", + "574 287 absorption 0.000119 0.000013\n", + "575 287 scatter 0.018041 0.000702\n", + "576 288 absorption 0.000125 0.000013\n", + "577 288 scatter 0.018212 0.000715" ] }, "execution_count": 33, @@ -1816,8 +1815,8 @@ " 10000\n", " 0\n", " absorption\n", - " 0.000122\n", - " 0.000010\n", + " 0.000136\n", + " 0.000017\n", " \n", " \n", " 1\n", @@ -1831,8 +1830,8 @@ " 10000\n", " 0\n", " scatter\n", - " 0.018596\n", - " 0.000871\n", + " 0.018504\n", + " 0.000740\n", " \n", " \n", " 2\n", @@ -1846,8 +1845,8 @@ " 10000\n", " 1\n", " absorption\n", - " 0.000206\n", - " 0.000014\n", + " 0.000231\n", + " 0.000031\n", " \n", " \n", " 3\n", @@ -1861,8 +1860,8 @@ " 10000\n", " 1\n", " scatter\n", - " 0.029733\n", - " 0.000953\n", + " 0.029149\n", + " 0.001525\n", " \n", " \n", " 4\n", @@ -1876,8 +1875,8 @@ " 10000\n", " 2\n", " absorption\n", - " 0.000280\n", - " 0.000018\n", + " 0.000306\n", + " 0.000032\n", " \n", " \n", " 5\n", @@ -1891,8 +1890,8 @@ " 10000\n", " 2\n", " scatter\n", - " 0.038494\n", - " 0.001383\n", + " 0.039770\n", + " 0.001519\n", " \n", " \n", " 6\n", @@ -1906,8 +1905,8 @@ " 10000\n", " 3\n", " absorption\n", - " 0.000384\n", - " 0.000026\n", + " 0.000339\n", + " 0.000028\n", " \n", " \n", " 7\n", @@ -1921,8 +1920,8 @@ " 10000\n", " 3\n", " scatter\n", - " 0.048839\n", - " 0.001181\n", + " 0.046708\n", + " 0.001355\n", " \n", " \n", " 8\n", @@ -1936,8 +1935,8 @@ " 10000\n", " 4\n", " absorption\n", - " 0.000457\n", - " 0.000023\n", + " 0.000433\n", + " 0.000031\n", " \n", " \n", " 9\n", @@ -1951,8 +1950,8 @@ " 10000\n", " 4\n", " scatter\n", - " 0.058061\n", - " 0.001466\n", + " 0.056359\n", + " 0.001790\n", " \n", " \n", " 10\n", @@ -1966,8 +1965,8 @@ " 10000\n", " 5\n", " absorption\n", - " 0.000494\n", - " 0.000026\n", + " 0.000538\n", + " 0.000028\n", " \n", " \n", " 11\n", @@ -1981,8 +1980,8 @@ " 10000\n", " 5\n", " scatter\n", - " 0.065874\n", - " 0.001575\n", + " 0.064943\n", + " 0.001978\n", " \n", " \n", " 12\n", @@ -1996,8 +1995,8 @@ " 10000\n", " 6\n", " absorption\n", - " 0.000490\n", - " 0.000032\n", + " 0.000588\n", + " 0.000028\n", " \n", " \n", " 13\n", @@ -2011,8 +2010,8 @@ " 10000\n", " 6\n", " scatter\n", - " 0.072420\n", - " 0.001988\n", + " 0.070231\n", + " 0.002714\n", " \n", " \n", " 14\n", @@ -2026,8 +2025,8 @@ " 10000\n", " 7\n", " absorption\n", - " 0.000605\n", - " 0.000042\n", + " 0.000670\n", + " 0.000041\n", " \n", " \n", " 15\n", @@ -2041,8 +2040,8 @@ " 10000\n", " 7\n", " scatter\n", - " 0.078802\n", - " 0.002228\n", + " 0.075852\n", + " 0.001862\n", " \n", " \n", " 16\n", @@ -2056,8 +2055,8 @@ " 10000\n", " 8\n", " absorption\n", - " 0.000627\n", - " 0.000037\n", + " 0.000745\n", + " 0.000039\n", " \n", " \n", " 17\n", @@ -2071,8 +2070,8 @@ " 10000\n", " 8\n", " scatter\n", - " 0.083684\n", - " 0.001936\n", + " 0.086234\n", + " 0.001968\n", " \n", " \n", " 18\n", @@ -2086,8 +2085,8 @@ " 10000\n", " 9\n", " absorption\n", - " 0.000711\n", - " 0.000040\n", + " 0.000731\n", + " 0.000039\n", " \n", " \n", " 19\n", @@ -2101,8 +2100,8 @@ " 10000\n", " 9\n", " scatter\n", - " 0.088989\n", - " 0.001689\n", + " 0.090448\n", + " 0.001956\n", " \n", " \n", "\n", @@ -2138,26 +2137,26 @@ " \n", " \n", "bin \n", - "0 0.000122 0.000010 \n", - "1 0.018596 0.000871 \n", - "2 0.000206 0.000014 \n", - "3 0.029733 0.000953 \n", - "4 0.000280 0.000018 \n", - "5 0.038494 0.001383 \n", - "6 0.000384 0.000026 \n", - "7 0.048839 0.001181 \n", - "8 0.000457 0.000023 \n", - "9 0.058061 0.001466 \n", - "10 0.000494 0.000026 \n", - "11 0.065874 0.001575 \n", - "12 0.000490 0.000032 \n", - "13 0.072420 0.001988 \n", - "14 0.000605 0.000042 \n", - "15 0.078802 0.002228 \n", - "16 0.000627 0.000037 \n", - "17 0.083684 0.001936 \n", - "18 0.000711 0.000040 \n", - "19 0.088989 0.001689 " + "0 0.000136 0.000017 \n", + "1 0.018504 0.000740 \n", + "2 0.000231 0.000031 \n", + "3 0.029149 0.001525 \n", + "4 0.000306 0.000032 \n", + "5 0.039770 0.001519 \n", + "6 0.000339 0.000028 \n", + "7 0.046708 0.001355 \n", + "8 0.000433 0.000031 \n", + "9 0.056359 0.001790 \n", + "10 0.000538 0.000028 \n", + "11 0.064943 0.001978 \n", + "12 0.000588 0.000028 \n", + "13 0.070231 0.002714 \n", + "14 0.000670 0.000041 \n", + "15 0.075852 0.001862 \n", + "16 0.000745 0.000039 \n", + "17 0.086234 0.001968 \n", + "18 0.000731 0.000039 \n", + "19 0.090448 0.001956 " ] }, "execution_count": 34, @@ -2210,38 +2209,38 @@ " \n", " \n", " mean\n", - " 0.000414\n", + " 0.000416\n", " 0.000025\n", " \n", " \n", " std\n", - " 0.000241\n", - " 0.000010\n", + " 0.000238\n", + " 0.000011\n", " \n", " \n", " min\n", - " 0.000013\n", - " 0.000003\n", + " 0.000023\n", + " 0.000004\n", " \n", " \n", " 25%\n", - " 0.000204\n", + " 0.000206\n", " 0.000017\n", " \n", " \n", " 50%\n", - " 0.000387\n", + " 0.000391\n", " 0.000024\n", " \n", " \n", " 75%\n", - " 0.000594\n", + " 0.000626\n", " 0.000031\n", " \n", " \n", " max\n", - " 0.000919\n", - " 0.000060\n", + " 0.000928\n", + " 0.000061\n", " \n", " \n", "\n", @@ -2252,13 +2251,13 @@ " \n", " \n", "count 289.000000 289.000000\n", - "mean 0.000414 0.000025\n", - "std 0.000241 0.000010\n", - "min 0.000013 0.000003\n", - "25% 0.000204 0.000017\n", - "50% 0.000387 0.000024\n", - "75% 0.000594 0.000031\n", - "max 0.000919 0.000060" + "mean 0.000416 0.000025\n", + "std 0.000238 0.000011\n", + "min 0.000023 0.000004\n", + "25% 0.000206 0.000017\n", + "50% 0.000391 0.000024\n", + "75% 0.000626 0.000031\n", + "max 0.000928 0.000061" ] }, "execution_count": 35, @@ -2293,7 +2292,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mann-Whitney Test p-value: 0.378626583393\n" + "Mann-Whitney Test p-value: 0.474494586047\n" ] } ], @@ -2331,7 +2330,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mann-Whitney Test p-value: 7.18782749267e-43\n" + "Mann-Whitney Test p-value: 1.364780046e-41\n" ] } ], @@ -2377,7 +2376,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 38, @@ -2386,9 +2385,9 @@ }, { "data": { - "image/png": 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YPOCYT4pyCyLDPks89Li7+284/vgJiTtkPvbYY0ydGmyWesYZ7+Kkk4qzFW3e\nvHlAYb788stFQQqLF38uNUBgMKJtR/vfvHlz+P+4iocegrvvPodjjz2Sc86ZnyhjvanVZzNrJGd1\nbNmyha1bt2bbaLVTn6G+CHKWRc1iS4k59QnMYudEjh8hWIDx9wQzmieAHQQ5z76a0EdGk8TaUuup\ncm9vb8T3kTNbjXWYGZrMCk1ggW9jeapZrLe3t8BX09o6bsCklOycL/TdxM1PCxcu9NbW13lr6+v8\ntNNOSzDpnehwTKJ5Lec7ams7fKBOW9vh3tY2pqSZrNh0WGwyTPLtFD7TWT52bId3dc0s20yX/Ixm\nyKFfJZIzW2hyn0sr8DiBQ7+NwR36M4g59MPzs5DPpSx6e3vDqK7C6LG8Eikc8FpbD/WOjpMHBtCc\ncgmizIp9Lsm+np6CAba4zGs97tA/7bTTfPbss0JZu0MFNStW7lCHzvA+bvGurpkDDv2urpmDKori\nQb7HA19TedFgUWUG7d7WNqakAimM0EuPjivnfzicQQDNMhhKzmzJQrnUzSzm7vvM7CICe8wI4CZ3\n32pmF4bXr3f3O8zsdDPbRjA7uSCtueGRurmZO3cub37zNNavn0+QjzRH9PH1AV8AXuDww8ezffuT\n7N37ZXbvhocfXsypp57KU089XdT2U089nbBxWH4vmNy6kvh+L8Hk9GNETWl3330Zvb3/H0uXXsHu\n3RsIzGEAm4GLCPxEf0PggzmHwEfUyl133c6aNWu45ZboTpl9wHXcf/8L9PX1lVjbMhV4E3AdY8e+\nwJo16etghrItwO7dh7N+/Xza2i6lrS2/Pie3/gieS5ErcifDmB9NiKqpVjs18gvNXIoonDn0xMxi\nueOoiWxsZJZzS/jre2asTLt3dc0sq/+g7owCc1laNFnyr/wZCcdjB2YOuRDf4B6LI96ia2QKZ1Dj\nB2ZBg80g0kxbuXrFbUcj3oJZ1sSJU8P1Oz1FslXSbzmznWpoll/akjNbaPJoMVEHCiPJvkPggL8K\nuJXAod9JNEoM/pEgWivPihWfpq1tH8Gs4zra2vaxYsWnB+27r6+Phx9+lGCmcjywANgK/C35FPtL\nCAIPyuVFYBR7957I0qVXDNzjsmUX09r6NeIRb7lZVe45dHXdTEtLD3Ae8BwjRy5h1qzpJRNK9vQs\noq3tk0S3BWhre2QgAq44Wm8h0Zlie/t4rrzycu64YzWzZz/B7NlrWbbsYlauvEERZGL/oVrt1Mgv\nNHMpSfFpAmASAAAZmklEQVQv4RmpM4nAUd5ecnV8lKTrhZkDor/sx3jge8mvwl++fHnolM/PPMwO\n9ZaW0ZF6cX/NoX7ccZN9+fLl4cyh+F5KLcDMZQSIzjpaWg7z5cuXJ9bp6DjZW1tf56NGHZlYJlcu\nybkf/Z+Xu04nq/U8ldAsv7QlZ7bQzA794XhJuZQmPlgFK/YPLRjQW1vHDTjLcw79StvNDYJ5M1ex\neaej4+QCZVSoiM4KFcVxoXyHhcdTEhVhPiquUInllFYppZhkesqlsSnnHtOeR6k0NZWYu+TQTyYn\nZ6NnPWiW5ynlIuVSNfEvYy5sefToY3306AkFYbblypk2WOZ9NYPPKJL9GjkfRc7vMiuhTG7F/zHh\n++WeC4OOz0ra2sZ4V9eslNlVocIqR75K/B9DVS6VkMVAW+1nc7gG+0JfW+Pma5Ny2U9eUi5DoxxT\nTimKnfa3DAwwwcA/0/PrSoJ1KUkzg6ScZHnTXa8H5rRDIudGexCePCZSLx8mXDiIL/cgWKEwIWZa\nv7VULrUYFAdrs9xBv5rP5nAM9tGccuWEoKfV10ywECkXKZeakDZwliNnqTUghQN3TzgTmZIaaZak\npMzGhr6YGWEb0b5yCy6L1+AU3ldvgXJLSqaZlok5ep/VDJzxZ1nOIFfJQFhK+VUiezWfzazNfUmz\n7Lh/LPhMlKdc5MNKR8pFyqUmVKNc0pJa5kib1SSRNHgsX748thg0bsJK3zIg3156+HNuAOvqmllk\nMkuSLz4g1mpGkDYQpvVXamCvZNBPkrPcexzsszDYvQ1WJmmmEvwoKE9ZKLQ7HSkXKZeakJQGf/ny\n5alyRgebtNX7adFYgw0AaQNZMAsaV9RX4IcpVEhRv1Fvb6+PHj0hcVBauHBhgUmsVNRWmky1mhGk\nDdRp/VWagqZc5VJpIEOpTAa5MsVZI8rzwSXtIRT9rGnd0NCRcpFyqQl530h+M7C0mUtSxFl0QGlt\nPdTNctFd+TDjLOzcgfkqat7KLQjtcbOxYb/dHkSQjRuY9bS0HOx5f0u3wxgfPfooz28/kD7IBQNm\nziwXpMjJRdMlKdak+jkfQSWznfTBtfj/lGuzq2tmmLpnVtlKMC5L/H9e6YBcaqYal6PUQtZylGta\n2HgaMoulI+Ui5VITKjGLJX/pc4PtTDcbExs8kjf7GirxNSpTprwtEpnWUzSL6eiY6vlQ61xGgvwv\n63yd5EEuKTtBzs+TNJOK1k8azMqdySXVDTZoK5Slo2Nq2WamJJNevF48/LxS5VKpeS5QRIcUKYm0\ne1q+fLmb5QMzkoJDSiGHfjJSLlIuNWGwaLFCM1h6hE6hAz23VuXEmpoeCrdiLvatFG5elr7FclD3\n4ILEnXkTTpKfxx16SprVSpt28s8oLcAhrkhHjz62qD2zw8qaQSW1m58J5etNmfK2grLx2WI5Zs3K\nMkQHMuR+oESd90lZqNPMsI1Ko33X08hCudRzPxfRoMQTUOaSTq5Zs6YoeWJb2ydpa7t0IBHjyJFL\n6OlZFWntQYKULl8Mjy9h1qzzan4PPT2LuOeeD9If20pu5MjX8NJL5bTwCHAQjz9+KfBddu/+PvPm\nncvYsYcklD0m/DuVadM6gZt56qmnOe64ExLKPgi8m2Dfu4PYu/e/iT+jTZsuK5lk85e/fIy77/4e\n7scUXXN/I9u2PVF0fufO5H1p8v/P8wj2zbkZ2Am8BDzLSy/9tqDslVf+M/39fwb8L+C/+au/+ouS\niTPTPkvB+0Xce+9C9uzJlc4l8VzPpk1b6O8P9hrasOEcgmf1JQD27MnvD5SWRFU0ANVqp0Z+oZlL\npqxevTrV9p0UYZXmdB+OmUtvb5CeJQg5zieHDNLK5HxCaWaxMR74X27xYD1MtMzBHg92CMxiPYOa\nuYoDJdq9pWV06BsqfkbxmUq+3RkROaNmscMdenzUqCO93MSief9a8lYJra1jYzONYlNjNaHTuRlJ\nNIln8WcmfdFtYBrMm8UqSaJaCVmZz5rlu45mLqIRaG8fR0/PosR08NOmTeGBB4ZXnvjsqqXlMqZN\n62TFiuBX8ymnnBLbFmAtO3fu4ne/O5Lf/OY7HHfcm4DWUO6bKd5dc3F4/iGCnTyn0tJyGcuW9bBh\nw8+KdrRcsODvOO64I9i2bXtRW/391zF69LNFs6mdO58vuId77rmM/v6/DuuuBTYCXybYO+8GglnH\nOEaOvJVJk07ggQdmhOUAFtLeXjybybORYNYUvce1wFXs28fAs7r//k3ATwrK9veTuNVAqe0B+vr6\nIs9/Ou3t45g27UTgPtrbn2DnzvI+M319fWzf/gJBclWAS2lp2QO0MmdONz09izLZjkBbHQyRarVT\nI7/QzCVTSqXYSHPclhuRk+Uvw2pDTHORVkEwQtIOmLnUMsWRWsl+hBM9Le1NzscSj3pKCpfOp73p\nDX+tF/tvkhYXDhYunBzSnd8SYfToYyM7e+buIe8jGjXqyKL2y/08RGdJuXQ8wYzz4EiZgwt2Pi31\nmQuc+4UBE0Ndi1Toi8pm9t0s33Xk0JdyGU5KJQcsNaAP9mXOMiS0WuVSKEuPw5943AzW2vraiMIo\nND0VD57tns+BFs8GXZi9oNA8lKSIomHXB3uwG2dyOHHaFsxJ9xs3Hwb32+15M2FuW+zl4T0U7/kT\nX7+S9j9IVr45RRbfR2im5xR33MGf1kd8v5/4osqhReeVl127HJrluy7lIuUyrJSSsxoFkeVitsES\nGA6m6JJk6ejo9LFjOwaSXwa+kzFF5XJRSsXRV9E2g9lAS0t70cBf2Hd8sG0PB/yxHmSDnhm+phTM\nWOL+i1Ir+ePPKbfgdPny5RHZo8rwsFCu5GzUUT9RV9fMgvVOpWYb+dlf0vn0z0Nc/mCm2VMkV/ra\noFkOJw48v/TPQeH/otofP82AlIuUy7AymJxDNW1lrVzSZClHARbL0uNjx3Yk/GIe/Ndsvr/iHTGj\n60fSQ4F7fMSIcR6Y4WZ62s6dXV0zw/U7+ZlMdK1Oktlt+fLliWG8uXsNrqXt7VMcgBCY92YVLaCN\nB3h0dc1MWfiavo9QOdsZTJw4NZxRRvf/KVY2ScEOSfnjyvkcVPP5bHSkXKRcKqYa30at5MzaLJZG\nOUqs2Cx2SJFc5UZNRc1THR2dBQNtVAmW8kEUL0LtLhicgwF1rCf7hoL7LV4P0xPWSVYS+b6L/TqB\nL6hwEIexbjYqVHDps7noc21pGeddXbMGfCLxmU5b2+EDprByPgtTprwtrJv3BXV0TE1YeHpy6nMq\nnHnNiviZslu9L+UyfIP/PIIFBY8BS1LKXBNe3wR0hecmAN8HHiYI2bkkpW5Gj7q2DNcHrtpBfKhy\nlhuSWutQz3JnSIM5cgtnJPnUMvE2Sj3rJUuWpC5cLJw9FPaf66s4A0LyL//85mpJJp54KPYYj6a+\n6ejoLFBuQYaDzqJBPOdvSnpeY8d2lP3sK3W0R8sdd9xkT0ozEy+bbpYrTidTqYIrBymX4VEsI4Bt\nwETgIODnwORYmdOBO8L3bwX+K3x/BHBy+H4U8It4XZdyKaJa81O5cqavz6h9/qZSMla6uryaIIXB\n6ra1xU0zxfnM0tYUJfcR99EcGlEOUbNcXAn1eLAqPsieEDe3ve51J3gwywnW8iSltc/t1FmYGieY\nHY0ePSF1UI/6inLKNOffyuVDiz/n4Nnl1ymZjfWOjqk+YkTU1FacIDNHYf28WSynSKr5fpSDlMvw\nKJe3Ab2R48uBy2NlrgPOjhw/AoxPaOs7wJ8nnM/iOdec/Um5JDmJK9ljo1pKZW5Om22kKYpaBSmk\nRzkVJl8crP8kv0BgojpsYHZTqHxmRAb/wr7jCUbjCUijPpxoBFZc3sCUdKJHN2xrazvcOzo6Y76W\n9oR+CmdSra2HFgUF5E1vyz0/I0vyQ81K/d/kk2nO8sCXNWNghiLlEtDsyuX9wI2R4/OAf46V+S7w\n9sjx3cCbY2UmAk8BoxL6yORB15r9ySxWTnhoPZTLUNfhDNVcV6rd5GdUvCvmYP0X+2uC2UrpfkYl\nmrqig3hwLt03EU9rH5+pFpvHcttOT/HizNNRxRCXNy03XG6jubR6Q0ummaXvL40DSbnUc4W+l1nO\n0uqZ2Sjgm8DH3f3lpMrd3d0D7ydPnkxnZ2eFYtaejRs3Dltfl1xyPuvWXQ/AGWecz65du1izZk1Z\ndcuRc8eOHUXnzB7FPcg31ta2mOnTP1J2n5WSJmOSXDt27GDx4s8VrahfvPhz7NqVz8V1/vnBZ6iS\nZwXpz3r69El873uf4NVXg3Jml+L+EeCqUIapBTKU6r+wj49x0kknFfSzYcPigbxvZpfy/ve/h9e/\n/vWROotYt+4H7N37qYFn0N8Pzz33vxLu6Fna2hYzZ85HOOmkkwD4/ve/z9VX38TevYHsGzYs5qij\njmT37lydPoJ8YVeFx5cCM4GhrW5vbW1h376bgTdEzi4i+G2aK/MJHn10PFOnvp0zznjXgKw54s8l\n95nctWtX6v9s8+bNrFv3g/B8cZvlMpzf9UrYsmULW7duzbbRarXTUF/ADArNYkuJOfUJzGLnRI4H\nzGIEfpo+4NISfWShxGtOs/yaGYpZLMv9W6qRMe1XaTV+lWrIOfRzjvlKzTHVOL/jJPt2isOXkxZk\nDl639GLQtrYxkdX3g5vFghT7OVNrdNb2Wu/qmlV2lFcl/9vhimZsJGhys1gr8DiBWauNwR36M8g7\n9A34KnD1IH1k9KhrS7N84Ibi0K+1Mokz2ELPcte+ZG0iifcdlbPSvrKWLQh0GOdxs1xvb+/A/jiV\nKKZoZFbStgAdHScXmNHym69NcRjpo0cfm+rQz8ubUzCB/+wDH/hASXnKIW7eyyv/WUNuM06zfNeb\nWrkE8vMegkivbcDS8NyFwIWRMteG1zcB08Nz7wD6Q4X0QPial9B+dk+7hjTLB64Z5ByKjEkDWJbO\n3SRlEN+EK+eryGUBKEUtZYNDfeHChQPXy1k4O5jPKr5+pXRQQnn3kqasixVBj48efWxZM7y09UZZ\nZvZuhu+Q+36gXGr9knLJlmaQMysZsxzAk9qKbsJV6Uyk1rLlQovdyzeFlpqplrqe1b3klUs8HLp4\nEWwS6etfAgVVSQh7OXI2OlkoF6XcFyKB+EZWxZugZcfKlTcUBRUkpbEfLtn6+yeV7D/O3LlzB90w\nbLjupb19PIEFfS2BsSO/xcFgzzWdqRx//JH85jdXAPCJT1ysdPtl0FJvAYRoRHI7KM6evZbZs9eW\n3L+jr6+POXO6mTOnm76+vqLrPT2LGDkyt8viKkaOXMIZZ7xrWGQbjJ6eRbS0XDYgW7Ab5Mwhy1Yp\nWd4L5J71rcB84PAK6+X/R3AJcDywira2S9m+/QV27/40u3d/miuv/OfE/7OIUe3Up5FfyCyWKc0g\n53DLWK5JK0uHftakOfTdm+N/7u5FzzMXhZeUmTkNOfTzILOYEPWlXJNW3DQUXa9Sap/54WDZsmWR\n3TmfGPb+syb6rKO7Xg52X/H/0bJlwd85c7pTaohSSLkI0QAM5rcYzv5zZj4IFhwuWLCgbnJVSxbP\ndTj9b/sTUi5CVMH+NvDE94vfsGExp556alPPZKql3jPLZkXKRYgq2N8GnriZb+/eoUZY7V/Ue2bZ\njEi5CFElGniEKEbKRQgxQNzM19a2mJ6eW+srlGhKpFyEEAPEzXzTp39EszIxJKRchBAFRM18tdoa\nQez/aIW+EEKIzJFyEUIIkTlSLkIIITJHykUIIUTmSLkIIYTIHCkXIYQQmSPlIoQQInPqqlzMbJ6Z\nPWJmj5nZkpQy14TXN5lZVyV1hRBC1Ie6KRczGwFcC8wDOoFzzWxyrMzpwAnuPglYBPxbuXWFEELU\nj3rOXN4CbHP3J939FeA24H2xMvMJ9hzF3X8MjDGzI8qsK4QQok7UU7kcDWyPHD8dniunzFFl1BVC\nCFEn6qlcvMxyVlMphBBCZE49E1c+A0yIHE8gmIGUKnNMWOagMuoC0N2d3/968uTJdHZ2Dl3iGrFx\n48Z6i1AWzSBnM8gIkjNrJGd1bNmyha1bt2baZj2Vy33AJDObCDwLnA2cGyuzFrgIuM3MZgAvuvvz\nZrarjLoA3H777bWQPXOaZZ/yZpCzGWQEyZk1kjM7zKo3GNVNubj7PjO7COgDRgA3uftWM7swvH69\nu99hZqeb2Tbg98AFperW506EEELEqet+Lu5+J3Bn7Nz1seOLyq0rhBCiMdAKfSGEEJkj5SKEECJz\npFyEEEJkjpSLEEKIzJFyEUIIkTlSLkIIITJHykUIIUTmSLkIIYTIHCkXIYQQmSPlIoQQInOkXIQQ\nQmSOlIsQQojMkXIRQgiROVIuQgghMkfKRQghROZIuQghhMgcKRchhBCZI+UihBAic6RchBBCZE5d\nlIuZjTWz9Wb2qJndZWZjUsrNM7NHzOwxM1sSOf8lM9tqZpvM7FtmdujwSS+EEGIw6jVzuRxY7+5v\nAO4JjwswsxHAtcA8oBM418wmh5fvAt7k7tOAR4GlwyJ1jdiyZUu9RSiLZpCzGWQEyZk1krPxqJdy\nmQ+sCt+vAv4yocxbgG3u/qS7vwLcBrwPwN3Xu3t/WO7HwDE1lrembN26td4ilEUzyNkMMoLkzBrJ\n2XjUS7mMd/fnw/fPA+MTyhwNbI8cPx2ei/PXwB3ZiieEEKIaWmvVsJmtB45IuLQseuDubmaeUC7p\nXLyPZcBed18zNCmFEELUgpopF3efnXbNzJ43syPc/TkzOxL4dUKxZ4AJkeMJBLOXXBvnA6cDf15K\nDjOrROy6ITmzoxlkBMmZNZKzsaiZchmEtcBC4Ivh3+8klLkPmGRmE4FngbOBcyGIIgM+Ccxy9z+k\ndeLuB8Z/UQghGgxzH9T6lH2nZmOB/wCOBZ4E/srdXzSzo4Ab3f2MsNx7gC8DI4Cb3H1FeP4xoA3Y\nHTb5I3f/2+G9CyGEEGnURbkIIYTYv2n6FfqNvCAzrc9YmWvC65vMrKuSuvWW08wmmNn3zexhM3vI\nzC5pRDkj10aY2QNm9t1GldPMxpjZN8PP5BYzm9Ggci4N/+8PmtkaM3tNPWQ0sxPN7Edm9gcz66mk\nbiPI2WjfoVLPM7xe/nfI3Zv6BfwD8Knw/RLgCwllRgDbgInAQcDPgcnhtdlAS/j+C0n1hyhXap+R\nMqcDd4Tv3wr8V7l1M3x+1ch5BHBy+H4U8ItGlDNy/RPAamBtDT+PVclJsO7rr8P3rcChjSZnWOeX\nwGvC468DC+sk4+HAKcByoKeSug0iZ6N9hxLljFwv+zvU9DMXGndBZmqfSbK7+4+BMWZ2RJl1s2Ko\nco539+fc/efh+ZeBrcBRjSYngJkdQzBYfgWoZaDHkOUMZ83vdPd/D6/tc/ffNpqcwO+AV4CDzawV\nOJggunPYZXT3F9z9vlCeiuo2gpyN9h0q8Twr/g7tD8qlURdkltNnWpmjyqibFUOVs0AJh1F9XQQK\nuhZU8zwBriaIMOyntlTzPI8HXjCzm83sZ2Z2o5kd3GByHu3uu4GVwK8IIjlfdPe76yRjLepWSiZ9\nNch3qBQVfYeaQrmEPpUHE17zo+U8mLc1yoLMciMl6h0uPVQ5B+qZ2Sjgm8DHw19ftWCocpqZvRf4\ntbs/kHA9a6p5nq3AdOBf3X068HsS8u5lxJA/n2bWAVxKYF45ChhlZh/MTrQBqok2Gs5Ipar7arDv\nUBFD+Q7Va51LRXiDLMiskJJ9ppQ5JixzUBl1s2Kocj4DYGYHAbcDt7p70nqlRpCzG5hvZqcDfwIc\nYmZfdfcPN5icBjzt7j8Nz3+T2imXauR8N/BDd98FYGbfAt5OYIsfbhlrUbdSquqrwb5DabydSr9D\ntXAcDeeLwKG/JHx/OckO/VbgcYJfWm0UOvTnAQ8D7RnLldpnpEzUYTqDvMN00LoNIqcBXwWuHob/\n85DljJWZBXy3UeUEfgC8IXz/WeCLjSYncDLwEDAy/AysAv6uHjJGyn6WQkd5Q32HSsjZUN+hNDlj\n18r6DtX0ZobjBYwF7iZIvX8XMCY8fxSwLlLuPQSRGNuApZHzjwFPAQ+Er3/NULaiPoELgQsjZa4N\nr28Cpg8mb42e4ZDkBN5BYH/9eeT5zWs0OWNtzKKG0WIZ/N+nAT8Nz3+LGkWLZSDnpwh+lD1IoFwO\nqoeMBNFW24HfAr8h8AONSqtbr2eZJmejfYdKPc9IG2V9h7SIUgghROY0hUNfCCFEcyHlIoQQInOk\nXIQQQmSOlIsQQojMkXIRQgiROVIuQgghMkfKRQghROZIuQghhMgcKRchqsTMJoYbMN1sZr8ws9Vm\nNsfMNlqwid2fmtlrzezfzezHYcbj+ZG6PzCz+8PX28Lz7zaz/2tm3wg3Dru1vncpRGVohb4QVRKm\nSn+MIOfWFsL0Le7+kVCJXBCe3+Luqy3YLfXHBOnVHeh39z+a2SRgjbv/qZm9G/gO0AnsADYCn3T3\njcN6c0IMkabIiixEE/CEuz8MYGYPE+S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173hBjjI6gc641zYK5+///mzWrr0LuBxnJNMznHXWaRkVSZSgt3+0UV3dM8B1\n/e6mmppr+PznZ7Ny5Z2sXHlnpFFdAxVdG06nOL3CW7FiiSsuA9fwTlLs7bWK0TDyJUrPYypwsYi8\niNM7AEdXKssBbORNtmGkmzd38tpraxg2rIaLLz6PNWvWpOWNOnM6c7TRfcBAwH769NasS5fkg/+a\nu3c3sG3b5MC0fgEcqkNrh+p9G3mQy6+FE7PIeBXqLyvGC4t5FA1/PCGK73vdunUBsYw1WeMWYeRT\nzoCNrQpjcvrp/fdUVzcm670GxVgKJSxuk891kvr9JHHffirpt58P1W4/pYh5qDvJzxjc5FpapBJ9\n396exe7dHwbuZcyYw0PnUPh7IlOmXEZzc3PovW7evKGo9xvUS1u69EtF73EVig0pNqJgq+oaBVEs\nN0e+5QRVdP75JP5lTFLng4YEp9i6dTsdHR1FrUSDgtE333xjxYu0YQRh4mEEks9qurt37wFO6K+g\n41SAcWZg+5d537DhIV588XXGjx9LS0tTYEseyGj1X3XVfObOnZtxr7CQvXvnlX1Wei527NjBmjXO\nfinTp0+hs/NxoPDl3m3peCMShfq9yvnCYh6JsmzZMh09eoKOHj1Bly1blvG51/5C5wcExVyC/O7+\n68BBCqP7j0VGuTGQ9NhJUExl0qQz0sodPXqCO/+kvaD4TbZ79D+jZcuW5fXc2tvbta5ujOc5HOLe\nd2FzM2yeRzSq3X6KEPMouwAUZLyJR2JEDZinKCRwnhnIPkLr6kYFXjvqZEPvcUoAncp1jvtqTROP\nzLLbFU7S2toP9E88LAbFCpgHPW/nvgoTvWINgMhFJf/2o1Dt9hdDPMxtNUSI64oI8s8vWbIiEReG\n/1rO3I87yDcO4F32Ha5i797LgReAu3CWWnHO19enlzfgvnrSTVvH/v3fZNs2mD3781x//ZcLdg0F\nxWgsQG1UJYWqTzlfWM8jEvm4IoJbtqPTWuHFclsFX2tqYOs3l9uqru4IXbZsmTY1zfH0NtRtlYe7\nrbz3MeC+8qZv1ZqawxJ350TF3Fblpdrtx9xWJh5RyKycW3X06AlZ3STO2lCHZa2c/PbnOz/AqQiP\n6L9Wbe3hoW4r/3WWLVumjY3TdPToCRnupfT7LlQ8Mt1jSbhz4rBo0aK051CsuRlxvsdC5qiUYj5J\nUph4mHiUlfKIR7tGmVCnqtrYOM2tNL0t+IGKs1j2O+Ixyr3WVK2rG1WUyjC9Fd3qCuDAfS9atChD\niBw7TlJyrUksAAAcJElEQVQ4OO05iYyuOPEo5u8nn4q8kF7KokWLKn7hyWyYeJh4lJXyuK2it6AH\n8gXnKZb9YUHaYrRM/eLgLc9fgYmM8LjAWhVG6LBhR2hj4/S8R0UlSTHFO597KyS4PmnSGRl5vSse\nl/vZ5sLEw8SjrJTyB5iqRB2XTPR/+NSS50H+/mLZ39g4PcOmCRM+GqtCy0doMiuwTJEcPXpCQddI\nkqTFO6l8qsHiMbCEfmWIczZMPEw8yko5foD5tjKDKs4g+/MZiuq4x7zB3zE6YsTRoS3Txsbp2tg4\nLdY6XEF25BaPVh058oORxaLYPaVcZZRbPIrptnIaJ5mu0UrFxMPEo6yU6wdYrBZ0UMA8n0lwTuWV\nPgcjqIfkbZk6YuNsXBXUc/FXPEG2XXDBBb6RW3+jcJgrIi3qj5Hk6vkU6taKW0ac30+277wQ23NN\nJM1mu9cmpwFRWTGlbJh4mHiUlWr/AfrtD2rBRnGTRRGdoJZpagRVlGuEzTAf2EXxJPUO+XVEJHpl\nVozJdXHLiNPzyyUOpQ6YR2l4mNsqOYohHolOEhSRmcCtODsJ3q2qNwWkWQWcDbwLzFfVbSLyNzgb\nQB2Is4Xtf6jqkiRtNUqPd+Li0qVforOzDRhY1+rUU0+NtBfH+PFHsW/forwWZxzYRbENWMzAXu13\nZKTdunU7M2a05D1BMOk1o8JW7b355ntzLr6Yz0TFYu46GGdtszjYOl0JUqj6hL1wBGMnzv4fB5B7\nD/PT8exhDhzk/q0Ffgl8MuAaRVXjUlPtrZdC3FZxW5qZw25HK0zSurpR/eVlazkHXS81T8Lbiwmb\nFJhrEl6u+4na+i/EbRXUcxFJueGK7xIauF67+/ymamPjtEh5S/HbT7I3U+3/u1Sy2wo4A2j3HC8G\nFvvS3AFc6Dl+BjjSl+Yg4NdAQ8A1ivpAS021/wALCZjn4+ZJjfxyFj8cmFEex83itSPldw/bUCpz\npnr2SjKbgEW930IC5mGrAsAyhUxRL0Zw35kXMyb291GK337Q8yjWcOBq/9+tdPH4O+Auz/HFwLd9\naR4EPuE5/gnwMR3ouTwBvAV8I+QaxX2iJabaf4CF2F+O4aF+UvanKuzGxmn9lYtX+BzxOElhYBa8\nyKj+EV9RKuKweFBY+igiEtTzS+8tpURxjit8U/sD28VqkUcdrBBlpF6xCXrmxRoO7F+ap5KGcEeh\nGOKRZMxDI6aToHyq+j7wURE5FOgQkU+r6v/1Z25pael/X19fT0NDQ37WloGurq7Eyt6xYwcbNz4C\nwDnnnMnJJxd/y/lC7J8yZSKdnQvdRRChrm4hU6Zcxvr167Pme+211wLP5coXhNf++fNb0j7bs2cP\nixcv5pZb7qG395vAN4H/Tcq/rwrbtt0BzObhh68CLgcm09l5Mddcc1nG8/bfb2rPkNmzM9Pv2LHD\nc11Cywx6/uPGHcWLL94BHAOsBV4HuoDXqavbyeWXX8Z9923MiFUsXPj10M2xsv2W3nuvNyO99/sI\nu5e33nor8FrFxP/MRa6mr+8yot53NlLPPsp3VYr/xVx0d3fT09NT3EILVZ+wFzCVdLfVEmCRL80d\nwOc8xxluK/f814CFAeeLJ8VlIMk9qIvRsszVoirU/myzv7PlKdbIoVz2p/v0D89oxXqXQI+yHPpA\nLyb7niFRe1dBPY/Gxulu67q1343knRMTp/xUmYXEcsKu5V2XK8nWelLDgVPPPtezrNRRZFS426oW\neB4nYF5H7oD5VNyAOTAGGOW+Hw48Anw24BpFf6ilJCnxKIZrJ8qPPunlMcJEoFhzFqKLR2oeindO\nyJg0AYi6l0aU7yYf8fDfd03NYdrYOC3y4IHc7rbweE/cWE9j47S0FYG9MZgg12GxKGZFHlU8iulm\nLSYVLR6OfZwNPIsz6mqJe+5K4EpPmtvcz7cDU9xzk4HHXcHZAVwXUn7RH2opqWTxiFJGmP1xK/2w\nCibp9ZZyPf+ByiY1WilVgU5SGOERkujLoUepwJYtW6beCYpwSMYEvPZ2Z4Z86lnG/c5TvRRnNeJg\nkVH1TuBMF6ZC5oIExUkGekvRFu3MFqfKZU+2fP7faNhvNtVzamycnnUF6Cg9k3LESypePJJ+mXgE\nU4wWVr7ika0XEWZTWDA5V4s3qt1hI2yyPf+BSma6TpjQEDBst0VhqtbUHK7z5s2LVQHkctcNVNgD\nM+5zuULiumSi/kacIHymyy5OY8RfQQaPCpuqQcvmh41ICxshF/X5R/mNhu1o6YwySx9hNmFCQ9q2\nAF6Rqq09PC3tQC8ru/AkiYmHiUcohbZo8nVbhYlONjEKb53Gb/FGrQDC7A+zx9k3xGmpT5gwOSOO\nkMoX55k7s9szF5zM9ayC4iaNjdNjNRji9FSijKjKRlBrPn0jq0Pd7zrzOo2N0zPKGrj//GIYcX6j\nQZuSZaZLnxOU/ptrVWfDMme7gdraQ9N+j373Z6lcWiYeJh6Jkk/APB/xCLrWQIs3vAUexe5sLfKw\n5x/We8k3cBz0HLO16KO2jB1hbU/LF1W8osQyotxbru/AiW8ckZH3ggsucO/fu47YSepfIDPlUgsq\ny1lCJvf8myjfb6onkJ94ZE7CHMiXW5CKsfd8XEw8TDzKSrHcVmFMmDA5sDKJQzbRiiMeudbPCrvO\nwNpZUxVa+0c/Be9WmN7DCHZnZVZEcSZKpnBa/9En+MVZADH9uw6+x8wVjVu1tvYD6m8spIt2UCU9\nIu0eamsPj907TfUs/c8jbEdLf88pqBEQXTxaFcZqahM0c1uZeERiMIqHavyAeRhBLcGRI8fFcsVl\nE604bqtcMYWwoH/wpL2p/WISxy0XLB5jIwlq0LOP6o6KK/zpdgaLavBmUJmDJNKfe9D95xeP8T+P\ngWeR3osJ+81ecMEF/WJ61lln5XBbHdL/XmS0u2RMq++zeKslFIqJh4lHYkSp6JO2P9wHHS+4GHYv\nUQLmXjdatuHEQcHPcDdIasb3mH4xqak5PFKLPkiMclWWYbYH2eePMajGH72Xnj5422P/fh51daPc\nfVrS92oJLislwIcpjM9LPDKfa3QRyozZpA+gWLZsWcagCH9DoqbmMB05clzBtueLiYeJRyJEbWkm\nbX+mj7+4wcW49ucSlJRLKlUBBrm6nLWmUvfg7FsSpyfld4NFEdFwH3/mJlxBvZi44pH5XEb1P5PU\nyDfv/vFhcZGgspw9Vw71VdzRWu9ho9yc5+Cfx3NoqJgHN2qyxy3ycYUmiYmHiUciRK0sSrUyalOT\nd3HC4v2jRbU/rOeSO7Ce7pYQGaU1NQdqym2Vr487Zc+kSWdEyp99EEPuAQlxhvUGVc7BQjsmaywn\nbDDFhAkfzUg7fPjRacNkw55Ztu/FH3iHk0IHPQT3KCeod/BClO8g37lMxcDEw8QjEZIUj3yHEOcT\ncM913Sj2Z7tutNbkQO/CCcoOtLDzDXSn7mPRokWR8xQ6iCHX95arrGy/qTg9m7BnHq/3lVn5i3g3\nAsscxebvSdXWetOnXGljQhsEudyecf8fCsXEw8QjEZJyWyUhAIVcN4r92Sq2uIH1uO6f8PtwfP4i\noyPFSVKumSgzqnOdDys/V88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i/Yqok0iKRFYUzGyemU0xs8lmNimqHJIiLT6DHzpHnUIqakMTmAFPT3k66iSS\nIlFuKThwkrt3dHd9WmS75trJnLG+gEe/eFSX2qwmou4+0lVWqosWk7STOVPNB8P4aP5HUSeRFIh6\nS2GcmX1uZrq6RzaruQEaz4Glh0WdRCqpX6d+PDb5sahjSArUiPC1u7r7IjPbGxhrZt+6+4fbnuzR\no0dsxrZt29KuXbsoMibFhAkToo6QVKXXb9WqVdBsJixtD1uD4S22bNkSVTSppBtPvhGuheG/Gw67\nOMm5sLAwdaEqIdv+94qKipg+fXpClxlZUXD3ReHPZWb2MtAZiBWFUaNGRRUtJXr37h11hKTatn5/\n/es/WbDHt9vtT6hRowabNkWVTCplncOCM6BtT5hyIeX1/GbC33UmZKwss6r3yEfSfWRm9cwsL7xf\nHzgFmBpFFkmB5t9qf0I2+LovHD486hSSZFHtU8gHPjSzr4BPgdfdfUxEWSTZWszQkUfZYEZ3aP45\n5OkC29ksku4jd58LHBHFa0tqbam5Ger/BMsPjjqKVNWWulDUAzo8C9nVNS+lRH1IqmS5DU3Wwo8H\ngWu47Kzw9UVw+LCoU0gSqShIUq1vshoWHhp1DEmUBV2DQ4ybRR1EkkVFQZJq/Z6rYYGKQtbwnODo\no8OjDiLJoqIgSbO1ZCvrG6/RlkK2mdIH2gM5Ot8kG6koSNIULSuixqZasL5R1FEkkVYcDMXAfuOj\nTiJJoKIgSTNx4UTqrcyLOoYkwxSgwzNRp5AkUFGQpJm4cCL1VjSIOoYkwzTg4Neg1tqok0iCqShI\n0ny84GMVhWy1DlhwHBz8atRJJMFUFCQplq9fzuK1i6mzul7UUSRZplwYnMgmWUVFQZLik4Wf0LlF\nZ0yXzMhe354FLT+G+kuiTiIJpKIgSfHR/I/o2rJr1DEkmTbXD8ZDav9c1EkkgVQUJCnem/ceJ7Y+\nMeoYkmxTLtRRSFlGRUESbmPJRqYtncYx+x4TdRRJtrndoMEC2HNG1EkkQVQUJOFmbphJp2adqFuz\nbtRRJNlKasC0XtrhnEVUFCThpm+YzkkFJ0UdQ1JFXUhZRUVBEm76hunan1CdLOoIW+pAy6iDSCKo\nKEhCrft5HfM3zefYlsdGHUVSxsKthahzSCKoKEhCTVw4kda1W1Ovpk5aq1am9oZD4eetP0edRKpI\nRUESauzssRxaT0NlVzurCmAZvDXrraiTSBWpKEhCvT37bTrUUz9CtTQFnpmiHc6ZTkVBEmbRmkXM\nL57P/nUvwTm0AAAK9ElEQVT2jzqKRKEo+FJQvLE46iRSBSoKkjBjZo+h237dyLXcqKNIFDZAt/26\nMWr6qKiTSBWoKEjCvD37bU7d/9SoY0iELjzsQnUhZTgVBUmIEi9h7JyxnHqAikJ19tuDfstXi79i\nQfGCqKNIJakoSEJ8svAT8uvn06phq6ijSITq1KhDj7Y9eHaqhr3IVCoKkhAvT3+Zc9ueG3UMSQNX\ndLqCoV8OpcRLoo4ilaCiIFXm7rz07Uucc8g5UUeRNNC5RWca1WnEmNljoo4ilaCiIFU2delUSryE\nI5oeEXUUSQNmxtVHXc2Qz4ZEHUUqQUVBquzl6S9zziHnYKZLb0qgV/teTFgwge9XfR91FKkgFQWp\nEnfnhaIXtD9BtlO/Vn36dujLo188GnUUqSAVBamSrxZ/xbrN6ziu5XFRR5E0c83R1zD0y6Gs+3ld\n1FGkAlQUpEqGfz2cvh36kmP6U5LtHbTnQZzQ+gQe+/KxqKNIBeg/WSpt89bNFE4rpG+HvlFHkTR1\nS9dbeOCTB9i8dXPUUSROKgpSaW9+9yb7N96fA/c8MOookqY6t+hMm8ZteO6b56KOInFSUZBKGzxp\nMP2P7h91DElzfz7+z9z9wd1sKdkSdRSJg4qCVErRsiK+WfYN5x96ftRRJM39us2vaZ7XnKe+eirq\nKBIHFQWplMGfDubKTldSK7dW1FEkzZkZ9/zqHu54/w42bN4QdRzZDRUFqbAfVv/A80XPc83R10Qd\nRTJEl3270LlFZx789MGoo8huqChIhd3z0T1cdsRl5O+RH3UUySD3/vpe7vv4Pp3lnOZUFKRCvl/1\nPSOmjeAPXf8QdRTJMAc0OYDfH/N7rv33tbh71HGkHCoKUiG/f/v33NDlBvapv0/UUSQD3dz1Zmav\nnM2IaSOijiLlUFGQuL353ZtMWzqNm7veHHUUyVC1cmvxzLnPcMNbNzBr5ayo48hOqChIXJavX85V\nr1/FkN8MoU6NOlHHkQzWqVknbjvhNnq+2FNHI6UhFQXZrRIv4ZJXLqFX+16cvP/JUceRLHBt52s5\nZK9D6DWql05qSzMqCrJL7s7AMQMp3lTMX7r9Jeo4kiXMjCfOeoL1m9dz5WtXsrVka9SRJKSiIOVy\ndwa9N4ixc8YyuudoaubWjDqSZJFaubV46YKXWLB6AT2e76GupDQRSVEws9PM7Fsz+87Mbokig+za\n+s3ruWz0Zbzx3RuM7TuWxnUbRx1JstAetfbgjd5vkFc7jy6PdWHqkqlRR6r2Ul4UzCwXeBg4DWgH\n9DKztqnOEaWioqKoI+zSO3PfoeOjHdm0ZRPvX/I+TfdoWqH26b5+kl5q5dZi+NnDGXDMALoN78Yt\nY2/hpw0/JeW19Le5e1FsKXQGZrn7PHffDIwEzoogR2SmT58edYQdbNyykReLXuSkp07iqtev4p5f\n3UNhj0Lq16pf4WWl4/pJejMzLu14KV9d9RUrN6xk/4f2p/8b/fnixy8SeqKb/jZ3r0YEr9kCWFDq\n8UKgSwQ5qiV3Z83Pa5i3ah6zV86maFkRHy34iIkLJnJk8yO5otMV9Gzfkxo5UfxpSHXXokELhnYf\nym0n3sYTk5+g16herN60ml/u90uOyD+Cw/IPo6BRAc32aEajOo0ws6gjZx1L9enmZtYDOM3d+4WP\nLwS6uPt1pebxbDwN/rZ3b+OLRV/wxRdf0LFTR9wdx7f7CewwLZ6fQLnPlXgJqzetpnhjMas3raZO\njTq0btSa/Rvvz8F7HkzXVl05vtXx7FVvr4SsZ48ePRg1ahQAnTqdwMyZW8jN3TP2/Pr149iyZSNQ\n+j22Mo/TfVq65EivbMn4v53z0xw++P4Dpi6ZytSlU1mwegE/rvmRTVs20aB2A+rXqk+9mvWoX7M+\nNXNrkmM55FgOuZYbu59jOeTm5PLlF19y5JFHJizb490fT6sxwMwMd69SpYyiKBwDDHL308LHfwJK\n3P3eUvNkX0UQEUmBTCwKNYAZwK+AH4FJQC93V2efiEjEUt5x7O5bzOxa4G0gF3hcBUFEJD2kfEtB\nRETSV2RnNJtZEzMba2YzzWyMmTUqZ74nzGyJmU2tTPuoVGD9dnoin5kNMrOFZjY5vJ2WuvTli+fE\nQzN7KHz+azPrWJG2Uarius0zsynhezUpdanjt7v1M7NDzGyimW00s5sq0jYdVHH9suH96xP+XU4x\nswlm1iHetttx90huwN+Am8P7twB/LWe+XwAdgamVaZ/O60fQfTYLKABqAl8BbcPnbgdujHo94s1b\nap7fAG+G97sAn8TbNlPXLXw8F2gS9XpUcf32Bo4C7gZuqkjbqG9VWb8sev+OBRqG90+r7P9elGMf\ndQeGhfeHAWfvbCZ3/xDY2emNcbWPUDz5dnciX7odhB3PiYex9Xb3T4FGZtY0zrZRquy6lT4eMd3e\nr9J2u37uvszdPwc2V7RtGqjK+m2T6e/fRHcvDh9+Cuwbb9vSoiwK+e6+JLy/BKjowb5VbZ9s8eTb\n2Yl8LUo9vi7cHHw8TbrHdpd3V/M0j6NtlKqybhActD/OzD43s35JS1l58axfMtqmSlUzZtv7dznw\nZmXaJvXoIzMbC+xs4JxbSz9wd6/KuQlVbV9ZCVi/XWV+BLgzvH8XcD/BGx2leH/H6fyNqzxVXbfj\n3f1HM9sbGGtm34ZbuemiKv8fmXA0SlUzdnX3Rdnw/pnZL4HLgK4VbQtJLgruXu4VWcKdx03dfbGZ\nNQOWVnDxVW1fZQlYvx+AlqUetySo4rh7bH4zewx4LTGpq6TcvLuYZ99wnppxtI1SZdftBwB3/zH8\nuczMXibYZE+nD5V41i8ZbVOlShndfVH4M6Pfv3Dn8lCCUSN+qkjbbaLsPhoNXBzevxh4JcXtky2e\nfJ8DB5pZgZnVAi4I2xEWkm3OAdJhTOFy85YyGrgIYmevrwq70eJpG6VKr5uZ1TOzvHB6feAU0uP9\nKq0iv/+yW0Pp/t5BFdYvW94/M2sFvARc6O6zKtJ2OxHuTW8CjANmAmOARuH05sAbpeYbQXDm8yaC\nfrFLd9U+XW4VWL/TCc7wngX8qdT04cAU4GuCgpIf9TqVlxe4Criq1DwPh89/DXTa3bqmy62y6wa0\nITii4ytgWjquWzzrR9AVugAoJji4Yz6wRya8d1VZvyx6/x4DVgCTw9ukXbUt76aT10REJEaX4xQR\nkRgVBRERiVFREBGRGBUFERGJUVEQEZEYFQUREYlRUZBqzcxKzOzpUo9rmNkyM0uHM8hFUk5FQaq7\ndcChZlYnfHwywRAAOoFHqiUVBZFgNMnfhvd7EZxFbxAMe2DBhZ4+NbMvzax7OL3AzD4wsy/C27Hh\n9JPM7D0ze8HMppvZM1GskEhlqSiIwHNATzOrDRxGMBb9NrcC4929C9AN+F8zq0cwHPrJ7n4k0BN4\nqFSbI4AbgHZAGzPrikiGSOooqSKZwN2nmlkBwVbCG2WePgU408wGho9rE4wyuRh42MwOB7YCB5Zq\nM8nDUVPN7CuCK15NSFZ+kURSURAJjAbuA04kuGxjaee6+3elJ5jZIGCRu/c1s1xgY6mnN5W6vxX9\nn0kGUfeRSOAJYJC7f1Nm+tv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v4iipYfl5UA04+t9hJ5E0VC3sAFI2ubm5XHHFtezd+924rQ02UrN5bXbm7wwv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\nCwBdwCNVkoqCSKQ3yR8Hjwc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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2459,7 +2458,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.8" + "version": "2.7.9" } }, "nbformat": 4, diff --git a/docs/source/pythonapi/examples/post-processing.ipynb b/docs/source/pythonapi/examples/post-processing.ipynb new file mode 100644 index 000000000..1bd7ee49a --- /dev/null +++ b/docs/source/pythonapi/examples/post-processing.ipynb @@ -0,0 +1,1121 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook demonstrates some basic post-processing tasks that can be performed with the Python API, such as plotting a 2D mesh tally and plotting neutron source sites from an eigenvalue calculation. The problem we will use is a simple reflected pin-cell." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from IPython.display import Image\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import openmc\n", + "from openmc.statepoint import StatePoint\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Input Files" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we need to define materials that will be used in the problem. Before defining a material, we must create nuclides that are used in the material." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Instantiate some Nuclides\n", + "h1 = openmc.Nuclide('H-1')\n", + "b10 = openmc.Nuclide('B-10')\n", + "o16 = openmc.Nuclide('O-16')\n", + "u235 = openmc.Nuclide('U-235')\n", + "u238 = openmc.Nuclide('U-238')\n", + "zr90 = openmc.Nuclide('Zr-90')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the nuclides we defined, we will now create three materials for the fuel, water, and cladding of the fuel pin." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# 1.6 enriched fuel\n", + "fuel = openmc.Material(name='1.6% Fuel')\n", + "fuel.set_density('g/cm3', 10.31341)\n", + "fuel.add_nuclide(u235, 3.7503e-4)\n", + "fuel.add_nuclide(u238, 2.2625e-2)\n", + "fuel.add_nuclide(o16, 4.6007e-2)\n", + "\n", + "# borated water\n", + "water = openmc.Material(name='Borated Water')\n", + "water.set_density('g/cm3', 0.740582)\n", + "water.add_nuclide(h1, 4.9457e-2)\n", + "water.add_nuclide(o16, 2.4732e-2)\n", + "water.add_nuclide(b10, 8.0042e-6)\n", + "\n", + "# zircaloy\n", + "zircaloy = openmc.Material(name='Zircaloy')\n", + "zircaloy.set_density('g/cm3', 6.55)\n", + "zircaloy.add_nuclide(zr90, 7.2758e-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With our three materials, we can now create a materials file object that can be exported to an actual XML file." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Instantiate a MaterialsFile, add Materials\n", + "materials_file = openmc.MaterialsFile()\n", + "materials_file.add_material(fuel)\n", + "materials_file.add_material(water)\n", + "materials_file.add_material(zircaloy)\n", + "materials_file.default_xs = '71c'\n", + "\n", + "# Export to \"materials.xml\"\n", + "materials_file.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's move on to the geometry. Our problem will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces -- in this case two cylinders and six reflective planes." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Create cylinders for the fuel and clad\n", + "fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, R=0.39218)\n", + "clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, R=0.45720)\n", + "\n", + "# Create boundary planes to surround the geometry\n", + "# Use both reflective and vacuum boundaries to make life interesting\n", + "min_x = openmc.XPlane(x0=-0.63, boundary_type='reflective')\n", + "max_x = openmc.XPlane(x0=+0.63, boundary_type='reflective')\n", + "min_y = openmc.YPlane(y0=-0.63, boundary_type='reflective')\n", + "max_y = openmc.YPlane(y0=+0.63, boundary_type='reflective')\n", + "min_z = openmc.ZPlane(z0=-0.63, boundary_type='reflective')\n", + "max_z = openmc.ZPlane(z0=+0.63, boundary_type='reflective')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the surfaces defined, we can now create cells that are defined by intersections of half-spaces created by the surfaces." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Create a Universe to encapsulate a fuel pin\n", + "pin_cell_universe = openmc.Universe(name='1.6% Fuel Pin')\n", + "\n", + "# Create fuel Cell\n", + "fuel_cell = openmc.Cell(name='1.6% Fuel')\n", + "fuel_cell.fill = fuel\n", + "fuel_cell.add_surface(fuel_outer_radius, halfspace=-1)\n", + "pin_cell_universe.add_cell(fuel_cell)\n", + "\n", + "# Create a clad Cell\n", + "clad_cell = openmc.Cell(name='1.6% Clad')\n", + "clad_cell.fill = zircaloy\n", + "clad_cell.add_surface(fuel_outer_radius, halfspace=+1)\n", + "clad_cell.add_surface(clad_outer_radius, halfspace=-1)\n", + "pin_cell_universe.add_cell(clad_cell)\n", + "\n", + "# Create a moderator Cell\n", + "moderator_cell = openmc.Cell(name='1.6% Moderator')\n", + "moderator_cell.fill = water\n", + "moderator_cell.add_surface(clad_outer_radius, halfspace=+1)\n", + "pin_cell_universe.add_cell(moderator_cell)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "OpenMC requires that there is a \"root\" universe. Let us create a root cell that is filled by the pin cell universe and then assign it to the root universe." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Create root Cell\n", + "root_cell = openmc.Cell(name='root cell')\n", + "root_cell.fill = pin_cell_universe\n", + "\n", + "# Add boundary planes\n", + "root_cell.add_surface(min_x, halfspace=+1)\n", + "root_cell.add_surface(max_x, halfspace=-1)\n", + "root_cell.add_surface(min_y, halfspace=+1)\n", + "root_cell.add_surface(max_y, halfspace=-1)\n", + "root_cell.add_surface(min_z, halfspace=+1)\n", + "root_cell.add_surface(max_z, halfspace=-1)\n", + "\n", + "# Create root Universe\n", + "root_universe = openmc.Universe(universe_id=0, name='root universe')\n", + "root_universe.add_cell(root_cell)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now must create a geometry that is assigned a root universe, put the geometry into a geometry file, and export it to XML." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Create Geometry and set root Universe\n", + "geometry = openmc.Geometry()\n", + "geometry.root_universe = root_universe" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Instantiate a GeometryFile\n", + "geometry_file = openmc.GeometryFile()\n", + "geometry_file.geometry = geometry\n", + "\n", + "# Export to \"geometry.xml\"\n", + "geometry_file.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the geometry and materials finished, we now just need to define simulation parameters. In this case, we will use 10 inactive batches and 90 active batches each with 5000 particles." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# OpenMC simulation parameters\n", + "batches = 100\n", + "inactive = 10\n", + "particles = 5000\n", + "\n", + "# Instantiate a SettingsFile\n", + "settings_file = openmc.SettingsFile()\n", + "settings_file.batches = batches\n", + "settings_file.inactive = inactive\n", + "settings_file.particles = particles\n", + "source_bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n", + "settings_file.set_source_space('box', source_bounds)\n", + "\n", + "# Export to \"settings.xml\"\n", + "settings_file.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us also create a plot file that we can use to verify that our pin cell geometry was created successfully." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Instantiate a Plot\n", + "plot = openmc.Plot(plot_id=1)\n", + "plot.filename = 'materials-xy'\n", + "plot.origin = [0, 0, 0]\n", + "plot.width = [1.26, 1.26]\n", + "plot.pixels = [250, 250]\n", + "plot.color = 'mat'\n", + "\n", + "# Instantiate a PlotsFile, add Plot, and export to \"plots.xml\"\n", + "plot_file = openmc.PlotsFile()\n", + "plot_file.add_plot(plot)\n", + "plot_file.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the plots.xml file, we can now generate and view the plot. OpenMC outputs plots in .ppm format, which can be converted into a compressed format like .png with the convert utility." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Run openmc in plotting mode\n", + "executor = openmc.Executor()\n", + "executor.plot_geometry(output=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nTlEz/gAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Convert OpenMC's funky ppm to png\n", + "!convert materials-xy.ppm materials-xy.png\n", + "\n", + "# Display the materials plot inline\n", + "Image(filename='materials-xy.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see from the plot, we have a nice pin cell with fuel, cladding, and water! Before we run our simulation, we need to tell the code what we want to tally. The following code shows how to create a 2D mesh tally." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Instantiate an empty TalliesFile\n", + "tallies_file = openmc.TalliesFile()\n", + "tallies_file.tallies = []" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Create mesh which will be used for tally\n", + "mesh = openmc.Mesh()\n", + "mesh.dimension = [100, 100]\n", + "mesh.lower_left = [-0.63, -0.63]\n", + "mesh.upper_right = [0.63, 0.63]\n", + "tallies_file.add_mesh(mesh)\n", + "\n", + "# Create mesh filter for tally\n", + "mesh_filter = openmc.Filter(type='mesh', bins=[1])\n", + "mesh_filter.mesh = mesh\n", + "\n", + "# Create mesh tally to score flux and fission rate\n", + "tally = openmc.Tally(name='flux')\n", + "tally.add_filter(mesh_filter)\n", + "tally.add_score('flux')\n", + "tally.add_score('fission')\n", + "tallies_file.add_tally(tally)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Export to \"tallies.xml\"\n", + "tallies_file.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we a have a complete set of inputs, so we can go ahead and run our simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " .d88888b. 888b d888 .d8888b.\n", + " d88P\" \"Y88b 8888b d8888 d88P Y88b\n", + " 888 888 88888b.d88888 888 888\n", + " 888 888 88888b. .d88b. 88888b. 888Y88888P888 888 \n", + " 888 888 888 \"88b d8P Y8b 888 \"88b 888 Y888P 888 888 \n", + " 888 888 888 888 88888888 888 888 888 Y8P 888 888 888\n", + " Y88b. .d88P 888 d88P Y8b. 888 888 888 \" 888 Y88b d88P\n", + " \"Y88888P\" 88888P\" \"Y8888 888 888 888 888 \"Y8888P\"\n", + "__________________888______________________________________________________\n", + " 888\n", + " 888\n", + "\n", + " Copyright: 2011-2015 Massachusetts Institute of Technology\n", + " License: http://mit-crpg.github.io/openmc/license.html\n", + " Version: 0.7.0\n", + " Git SHA1: 3df61825cc8c93656ed1458c34fca14000884e73\n", + " Date/Time: 2015-09-19 07:34:09\n", + " OpenMP Threads: 4\n", + "\n", + " ===========================================================================\n", + " ========================> INITIALIZATION <=========================\n", + " ===========================================================================\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading geometry XML file...\n", + " Reading materials XML file...\n", + " Reading tallies XML file...\n", + " Building neighboring cells lists for each surface...\n", + " Loading ACE cross section table: 92238.71c\n", + " Loading ACE cross section table: 8016.71c\n", + " Loading ACE cross section table: 92235.71c\n", + " Loading ACE cross section table: 5010.71c\n", + " Loading ACE cross section table: 1001.71c\n", + " Loading ACE cross section table: 40090.71c\n", + " Initializing source particles...\n", + "\n", + " ===========================================================================\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + " ===========================================================================\n", + "\n", + " Bat./Gen. k Average k \n", + " ========= ======== ==================== \n", + " 1/1 1.01593 \n", + " 2/1 1.05332 \n", + " 3/1 1.03858 \n", + " 4/1 1.03420 \n", + " 5/1 1.03004 \n", + " 6/1 1.03899 \n", + " 7/1 1.04639 \n", + " 8/1 1.03921 \n", + " 9/1 1.00410 \n", + " 10/1 1.06702 \n", + " 11/1 1.03401 \n", + " 12/1 1.05518 1.04460 +/- 0.01059\n", + " 13/1 1.03358 1.04092 +/- 0.00713\n", + " 14/1 1.00991 1.03317 +/- 0.00925\n", + " 15/1 1.04884 1.03631 +/- 0.00782\n", + " 16/1 1.03449 1.03600 +/- 0.00639\n", + " 17/1 1.04612 1.03745 +/- 0.00559\n", + " 18/1 1.07511 1.04216 +/- 0.00675\n", + " 19/1 1.01774 1.03944 +/- 0.00655\n", + " 20/1 1.04054 1.03955 +/- 0.00586\n", + " 21/1 1.01202 1.03705 +/- 0.00586\n", + " 22/1 1.04460 1.03768 +/- 0.00538\n", + " 23/1 1.04415 1.03818 +/- 0.00498\n", + " 24/1 1.04222 1.03846 +/- 0.00462\n", + " 25/1 1.04045 1.03860 +/- 0.00430\n", + " 26/1 1.04133 1.03877 +/- 0.00403\n", + " 27/1 1.03166 1.03835 +/- 0.00381\n", + " 28/1 1.00701 1.03661 +/- 0.00399\n", + " 29/1 1.04111 1.03685 +/- 0.00378\n", + " 30/1 1.05130 1.03757 +/- 0.00366\n", + " 31/1 1.02685 1.03706 +/- 0.00352\n", + " 32/1 1.03458 1.03695 +/- 0.00335\n", + " 33/1 1.05243 1.03762 +/- 0.00328\n", + " 34/1 1.05717 1.03843 +/- 0.00324\n", + " 35/1 1.07396 1.03985 +/- 0.00342\n", + " 36/1 1.01690 1.03897 +/- 0.00340\n", + " 37/1 1.03340 1.03877 +/- 0.00328\n", + " 38/1 1.04153 1.03886 +/- 0.00316\n", + " 39/1 1.01971 1.03820 +/- 0.00312\n", + " 40/1 1.01491 1.03743 +/- 0.00311\n", + " 41/1 1.02779 1.03712 +/- 0.00303\n", + " 42/1 1.03047 1.03691 +/- 0.00294\n", + " 43/1 1.02305 1.03649 +/- 0.00288\n", + " 44/1 1.07854 1.03773 +/- 0.00305\n", + " 45/1 1.04412 1.03791 +/- 0.00297\n", + " 46/1 1.05139 1.03828 +/- 0.00291\n", + " 47/1 1.05357 1.03870 +/- 0.00286\n", + " 48/1 1.06435 1.03937 +/- 0.00287\n", + " 49/1 1.02632 1.03904 +/- 0.00281\n", + " 50/1 1.05201 1.03936 +/- 0.00276\n", + " 51/1 1.04582 1.03952 +/- 0.00270\n", + " 52/1 1.02056 1.03907 +/- 0.00267\n", + " 53/1 1.06448 1.03966 +/- 0.00267\n", + " 54/1 1.03609 1.03958 +/- 0.00261\n", + " 55/1 1.02701 1.03930 +/- 0.00257\n", + " 56/1 1.04865 1.03950 +/- 0.00252\n", + " 57/1 1.06310 1.04000 +/- 0.00252\n", + " 58/1 1.02975 1.03979 +/- 0.00247\n", + " 59/1 1.03922 1.03978 +/- 0.00242\n", + " 60/1 1.07259 1.04043 +/- 0.00246\n", + " 61/1 1.04555 1.04053 +/- 0.00242\n", + " 62/1 1.01950 1.04013 +/- 0.00240\n", + " 63/1 1.04618 1.04024 +/- 0.00236\n", + " 64/1 1.02489 1.03996 +/- 0.00233\n", + " 65/1 1.06850 1.04048 +/- 0.00235\n", + " 66/1 1.03623 1.04040 +/- 0.00231\n", + " 67/1 0.99892 1.03967 +/- 0.00238\n", + " 68/1 1.05557 1.03995 +/- 0.00236\n", + " 69/1 1.01211 1.03948 +/- 0.00236\n", + " 70/1 1.04679 1.03960 +/- 0.00233\n", + " 71/1 1.03461 1.03952 +/- 0.00229\n", + " 72/1 1.01993 1.03920 +/- 0.00227\n", + " 73/1 1.04742 1.03933 +/- 0.00224\n", + " 74/1 1.05269 1.03954 +/- 0.00222\n", + " 75/1 1.05696 1.03981 +/- 0.00220\n", + " 76/1 1.05904 1.04010 +/- 0.00218\n", + " 77/1 1.05930 1.04039 +/- 0.00217\n", + " 78/1 1.03375 1.04029 +/- 0.00214\n", + " 79/1 1.07044 1.04073 +/- 0.00215\n", + " 80/1 1.04144 1.04074 +/- 0.00212\n", + " 81/1 1.06296 1.04105 +/- 0.00212\n", + " 82/1 1.04630 1.04112 +/- 0.00209\n", + " 83/1 1.03772 1.04108 +/- 0.00206\n", + " 84/1 1.03774 1.04103 +/- 0.00203\n", + " 85/1 1.03984 1.04101 +/- 0.00200\n", + " 86/1 1.03040 1.04087 +/- 0.00198\n", + " 87/1 1.03484 1.04080 +/- 0.00196\n", + " 88/1 1.03820 1.04076 +/- 0.00193\n", + " 89/1 1.04654 1.04084 +/- 0.00191\n", + " 90/1 1.03377 1.04075 +/- 0.00189\n", + " 91/1 1.03370 1.04066 +/- 0.00187\n", + " 92/1 1.04172 1.04067 +/- 0.00184\n", + " 93/1 1.04945 1.04078 +/- 0.00182\n", + " 94/1 1.03360 1.04069 +/- 0.00181\n", + " 95/1 1.06547 1.04099 +/- 0.00181\n", + " 96/1 1.04340 1.04101 +/- 0.00179\n", + " 97/1 1.07502 1.04140 +/- 0.00181\n", + " 98/1 1.05391 1.04155 +/- 0.00179\n", + " 99/1 1.05622 1.04171 +/- 0.00178\n", + " 100/1 1.01519 1.04142 +/- 0.00179\n", + " Creating state point statepoint.100.h5...\n", + "\n", + " ===========================================================================\n", + " ======================> SIMULATION FINISHED <======================\n", + " ===========================================================================\n", + "\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 3.6600E-01 seconds\n", + " Reading cross sections = 1.1500E-01 seconds\n", + " Total time in simulation = 8.1308E+01 seconds\n", + " Time in transport only = 8.1157E+01 seconds\n", + " Time in inactive batches = 2.1600E+00 seconds\n", + " Time in active batches = 7.9148E+01 seconds\n", + " Time synchronizing fission bank = 1.6000E-02 seconds\n", + " Sampling source sites = 9.0000E-03 seconds\n", + " SEND/RECV source sites = 7.0000E-03 seconds\n", + " Time accumulating tallies = 1.0000E-02 seconds\n", + " Total time for finalization = 1.6400E-01 seconds\n", + " Total time elapsed = 8.1856E+01 seconds\n", + " Calculation Rate (inactive) = 23148.1 neutrons/second\n", + " Calculation Rate (active) = 5685.55 neutrons/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.04100 +/- 0.00169\n", + " k-effective (Track-length) = 1.04142 +/- 0.00179\n", + " k-effective (Absorption) = 1.04380 +/- 0.00147\n", + " Combined k-effective = 1.04287 +/- 0.00130\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Run OpenMC!\n", + "executor.run_simulation()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tally Data Processing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our simulation ran successfully and created a statepoint file with all the tally data in it. We begin our analysis here loading the statepoint file and 'reading' the results. By default, data from the statepoint file is only read into memory when it is requested. This helps keep the memory use to a minimum even when a statepoint file may be huge." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [], + "source": [ + "# Load the statepoint file\n", + "sp = StatePoint('statepoint.100.h5')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we need to get the tally, which can be done with the ``StatePoint.get_tally(...)`` method." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tally\n", + "\tID =\t10000\n", + "\tName =\t\n", + "\tFilters =\t\n", + " \t\tmesh\t[10000]\n", + "\tNuclides =\t-1 \n", + "\tScores =\t['flux', 'fission']\n", + "\tEstimator =\ttracklength\n", + "\n" + ] + } + ], + "source": [ + "tally = sp.get_tally(scores=['flux'])\n", + "print(tally)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The statepoint file actually stores the sum and sum-of-squares for each tally bin from which the mean and variance can be calculated as described [here](http://mit-crpg.github.io/openmc/methods/tallies.html#variance). The sum and sum-of-squares can be accessed using the ``sum`` and ``sum_sq`` properties:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[ 0.41271426, 0. ]],\n", + "\n", + " [[ 0.40846766, 0. ]],\n", + "\n", + " [[ 0.4112029 , 0. ]],\n", + "\n", + " ..., \n", + " [[ 0.41437289, 0. ]],\n", + "\n", + " [[ 0.41376468, 0. ]],\n", + "\n", + " [[ 0.41312074, 0. ]]])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tally.sum" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "However, the mean and standard deviation of the mean are usually what you are more interested in. The Tally class also has properties ``mean`` and ``std_dev`` which automatically calculate these statistics on-the-fly." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(10000, 1, 2)\n" + ] + }, + { + "data": { + "text/plain": [ + "(array([[[ 0.00458571, 0. ]],\n", + " \n", + " [[ 0.00453853, 0. ]],\n", + " \n", + " [[ 0.00456892, 0. ]],\n", + " \n", + " ..., \n", + " [[ 0.00460414, 0. ]],\n", + " \n", + " [[ 0.00459739, 0. ]],\n", + " \n", + " [[ 0.00459023, 0. ]]]),\n", + " array([[[ 2.02702426e-05, 0.00000000e+00]],\n", + " \n", + " [[ 1.77108625e-05, 0.00000000e+00]],\n", + " \n", + " [[ 1.79568064e-05, 0.00000000e+00]],\n", + " \n", + " ..., \n", + " [[ 1.83114148e-05, 0.00000000e+00]],\n", + " \n", + " [[ 1.69970626e-05, 0.00000000e+00]],\n", + " \n", + " [[ 1.92143217e-05, 0.00000000e+00]]]))" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(tally.mean.shape)\n", + "(tally.mean, tally.std_dev)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The tally data has three dimensions: one for filter combinations, one for nuclides, and one for scores. We see that there are 10000 filter combinations (corresponding to the 100 x 100 mesh bins), a single nuclide (since none was specified), and two scores. If we only want to look at a single score, we can use the ``get_slice(...)`` method as follows." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tally\n", + "\tID =\t10000\n", + "\tName =\t\n", + "\tFilters =\t\n", + " \t\tmesh\t[10000]\n", + "\tNuclides =\t-1 \n", + "\tScores =\t['flux']\n", + "\tEstimator =\ttracklength\n", + "\n" + ] + } + ], + "source": [ + "flux = tally.get_slice(scores=['flux'])\n", + "fission = tally.get_slice(scores=['fission'])\n", + "print(flux)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get the bins into a form that we can plot, we can simply change the shape of the array since it is a numpy array." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "flux.std_dev.shape = (100, 100)\n", + "flux.mean.shape = (100, 100)\n", + "fission.std_dev.shape = (100, 100)\n", + "fission.mean.shape = (100, 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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KKmUirBJiO5wmemyHo5lruOsi7/3+SRrJAO5klS1xkOLrSQaLW3z2o18km0jwgvo0r914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\nw10n7Oqsk5U/G6H3hzboim7xJH/FR+tPc1sYZtMTJSptU8NNe8PdQf4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an6/9JXPyIN/W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\nfKEaI6E12nf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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.subplot(121)\n", + "fig.imshow(flux.mean)\n", + "fig2 = plt.subplot(122)\n", + "fig2.imshow(fission.mean)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's say we want to look at the distribution of relative errors of our tally bins for flux. First we create a new variable called ``relative_error`` and set it to the ratio of the standard deviation and the mean, being careful not to divide by zero in case some bins were never scored to." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Determine relative error\n", + "relative_error = np.zeros_like(flux.std_dev)\n", + "nonzero = flux.mean > 0\n", + "relative_error[nonzero] = flux.std_dev[nonzero] / flux.mean[nonzero]\n", + "\n", + "# distribution of relative errors\n", + "ret = plt.hist(relative_error[nonzero], bins=50)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Source Sites" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Source sites can be accessed from the ``source`` property. As shown below, the source sites are represented as a numpy array with a structured datatype." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ (1.0, [0.08159183470384083, 0.37187405724079425, -0.4569273259677805], [-0.5991379733562734, 0.6213299732428319, -0.5049581697849825], 1.4308796774550836),\n", + " (1.0, [0.08159183470384083, 0.37187405724079425, -0.4569273259677805], [0.6943502674814661, -0.18996972225593808, 0.694110373553384], 1.8499326750790277),\n", + " (1.0, [-0.2283457014858208, -0.3149356437736135, -0.6287339985223156], [0.22841158666373973, -0.9428738529578353, 0.24252225565130936], 2.8993105331976654),\n", + " ...,\n", + " (1.0, [-0.20844939420957254, 0.043779246455180054, -0.22209004880139005], [0.871391386295745, 0.3866181159860615, 0.30199914615933615], 2.2329770939373517),\n", + " (1.0, [-0.20844939420957254, 0.043779246455180054, -0.22209004880139005], [-0.4649777417907873, 0.38973845929247963, 0.7949211489119309], 1.6836109244016622),\n", + " (1.0, [-0.20844939420957254, 0.043779246455180054, -0.22209004880139005], [-0.4649777417907873, 0.38973845929247963, 0.7949211489119309], 1.6836109244016622)], \n", + " dtype=[('wgt', '" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create log-spaced energy bins from 1 keV to 100 MeV\n", + "energy_bins = np.logspace(-3,1)\n", + "\n", + "# Calculate pdf for source energies\n", + "probability, bin_edges = np.histogram(sp.source['E'], energy_bins, density=True)\n", + "\n", + "# Make sure integrating the PDF gives us unity\n", + "print(sum(probability*np.diff(energy_bins)))\n", + "\n", + "# Plot source energy PDF\n", + "plt.semilogx(energy_bins[:-1], probability*np.diff(energy_bins), linestyle='steps')\n", + "plt.xlabel('Energy (MeV)')\n", + "plt.ylabel('Probability/MeV')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's also look at the spatial distribution of the sites. To make the plot a little more interesting, we can also include the direction of the particle emitted from the source and color each source by the logarithm of its energy." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-0.5, 0.5)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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vwj8+RvQYhG1wW0wTq8HqhVjhxHXCQX1HfxJXHyc5ti2EXg+m+WhubUneqmhS\njx1GH2FANWwEyoG9KCfr0CQcQgp3g1cLWPwwqFSojUZyD4aQ0HYu+A1t/nPUbQWpuaeEdGacDoFC\nmftB1K4QWPAUbPwERr8NVgcsfAeqa5sHWFr7A6T1hjtehXZ98PgdLpPo52kTvpSS/gmGGHBmQmE/\nqHoBnP9qG6zA7Zr+y3VqSiDcD8YPQ7TMxvndclhgJ2b6FrCug1nXQEkZ7qn7McxZRIT3YOrebInx\n1lbo7k1F00dGjtEgqVrCuk9h+B2QlAc+xbi3jcVhuR3jsXrUFgGHHoPlt4EpC1poOR4i823nzqia\njMSqWuO7qD3+U99CDqgAQ1+Q4zHtclIbqCJowVLo3xupyxL0GYOQUm2UT7qNph5RaKKLCT1dhN73\nKOrO42nqvhXnU0+D0YeG/B+o0xpIbFpH6OE5NPXxQY6JR+3ngEY3dP0nJE4EWxksfA2WvgRV2dC6\nJ2LyCFyzBmJs7IF2X1sc3T+itkMMpoHj6F+6nrURqWzYPB/uvQOpQoupRxjy4CSktXVIql3w2PWQ\nshsl40Zyvg/BFWBGxLRE5Llo6BxAVb/eNJwIQdo+D1r1ROq1CEnvRWxgOSc0o3FWlSKbFdTjJyDf\nFIUYXI+tXwdEzwkQlgrZh9EY1dTkaKHzSfCOgcQXIeNRJPzgJz0klOrTeM3cSeD7X8KhDDD3hn2H\nYP0sqMiAaF+Y8CoowEvzmwPw//fwU49zu0x6R3iC8KVkSgRZB6pE8H0U6qdBw7fNNSPFiMvxAIpy\n5pEu/+q21u066D8eWtUh3GaIMMJNVaT3HwOdv0YkhmP5LhZrgRWaKtAl3IvGWYLVIkNUDwiSIPNT\n2PMsXD0U2kZA5R7cHfTYwwoxTI9HmhuJtNQFlgCITMKlaeBYv2ickX7cNmMTho018ME2KMgHcRCK\nymH7bOjYBymxAd/0GpyKG6bsQLPpQxyfr0ef2RvdiSVURGhw17aHAW+BnwZV5RywbsPm9Rk8tQzT\nkJsJy2ogefFJdLGfUTw2FmNEOI1mKE68Hra8Awufg7c6Q+mP4O8Fax6FsFZw8giqo/ugaTdy7j68\nVk7CV2fGnDONtruO8eq3H7CmZwjKO29Bm9uRQjPx81tC4Ag7rJ2CPXMtuLojCkNQyRHIy23w4jHq\n3jGgC44h5d3j6Fp1pMlohOShkO2GbVOwB6q4a+fzmIp8cLwzAseae7AHLsYRa0OXa0aqPQ2+wdBp\nPOql92JYLZvPAAAgAElEQVRw1YI2FGLfAp808OuBVFeKIvLB7YLt05H/3h9NjRdycCM88CS8vwWe\nnwkTXoZIPzj6JdSUQWzr5qFH7fWw97VLdSX/b/L0E/YAIHgElC8HVxREp4OuM5TdADsfBLxR3Gee\n6/bmbc3dnJx2CNyMIsfhzjShaRELu1cTG7oVpeQ+nBWV6AJ2Yh5rR8p8DUo3EFECRUELYOl+KOwI\n6S4YdwBqd0PGVyi6SOx93eiX2JAyTkCcHhKMoLFDm8l89tArVAyYQJu2j6ORLRCrA5UF5rqhTg2T\nVmCPC0Ip/4p6bzXqCifV3c0ocR2Qqo5iGNELqzUFc69DhK0KxBrphA37wWc2ks9gjCW1aKqsKMfu\ngOLpIGLAHYR7zT1Y/RVeCezIE2l/w6vVDdD7RThdA/F9IbEX9P47GA0oQUEoskKDnwrFkQsOI6yX\nkFb7YdpShlQg023lJl59/TmqJw9BfP0P3CY9lqYI1PKjOE3BcGIf7tl/o/5EBlEPHsGeq6dkTDiG\nFQ5Mx1UwLR1zTj3m/VUoc0ZAaT5cswzdUjciLhxtaSFKUy3qHRvQ6b/FWPwA8smlkP4MjpNfUO/f\nGmnYq/TqmAHbHoPcxVBxGvzHINeWItInw5RRoFJR/eVE5Idng39bsOb+/JppexNEdoaEdpCcBhFh\nsGgAiN/Qv9zjrPMIwpIkzQZ2AImSJBVIkvSHx0q/TFpF/sKChsLRe6DoB0h5vblrmK4z0qEItIXd\ncEcXgAaIiIecDNAeBkMe1tfcGJISEf5dcLl6oQ3ah+JjpLHvINzHwwhytIcud4EQGJbMobFHDTXa\nMvyi74big7DvXmiUcOaZcMTuQrtSi4i24Qxx4DY2ojEruNcquPLfoNs4C+b1Tpxqb+QJ76CKHwmn\n1zUPXJOzGbJ/4GhHFbvjb8di8qXQN4DWjUX0G5dJQt7VaDccwfrpIozJRrSmNmiL9sGhyRAfA+Gd\nkQpXo8/bg4j1w6nyRdtQieg2nvLUbJa7W2Dz0vJUw16889OoadqM14ur0Kx+GYr2Qv0H4LUE+fXl\nFKYGoFi9Oe0TwqGHh9AjMImkld8iQiUUv0bUboF3Yz7OYH+qI33xqrRgjCmCsifRDozGsS2CitUV\nWPYUo9w3CW9jCX6ba2iI9kfdOx15chJUV4F/BK7OxbgCjcjKXqQWjTh6+CBX1qNdJiO3HEnNu8+y\ns/NnbNtgILtIh0ar56F7VHRlK94JVjj4HfgPgoMzYOkrqMze0CsD7joJhiAMW59C1zsVBi6AzM9+\nfs1EdYG1jVBTCf6hYKtqvmknwtMm/LucRzODEOKmC5UNTxC+1FRGQAbhhMP3QI9NkD8eKrsimaKQ\njfMR5jSkeD/48WHomY5r17PgU4SkA8vCTNxHN2N+QIJ6Ez4/LEcJSoOiwxCRBHs/gpDriciZwpGP\n40hdPIdAyQWNCYjd7+O8xYdCJhLTdi9K2gtQeABN5ke4fdIQvfKwJDdRG6Fj6/Vd6KvbQkLlexic\nwWiSr4GobrB6IpyYQlq1TMdlh9nRZRAqh5XUskgs11XQUNiIub4BQxd/mj5/D9NgM9QWgUkPIcGw\n/y7QREC3dKidgFx8EE5b2NnrbtY4W3DLztnEBDegPqTBHpPI8RH1dJWCYdCL8EkSvLYLsk3Y7Haq\nJgbRLulLote+T+rGz9nRZwRLRvbB7D+Qrv0W0HHWMaxd7kGjauDIoCGkFN1F8BErNbF34V+gQ7N4\nCiGBAuWfJuzHv8VhclA61QvdMYUiezLGtGrk01ryOiXjr8sm0/kdQXku7BURdJSK2FHQgoWuVMa8\nt5FDYR3xi3Nwx+N3Eld2F5JPb6iYCuYJbJsfQ185B2qfhPwiiBuIFBQGeRnwURyMXYFxXQn0Brzj\noCYLFnwCRScQsoIIC0ReuR5+SIHUvrCvGobOBv+US3wx/4+5TKLfZZKNv7Bt6yD2arCcgsZT0HAE\nrOmQpoa95eAVAnVvQpAJGjIRhxMQma9g6G2EbAdalYL6zigqqnU0WjtgTj5BoH8QVDlg2VOIO5dQ\nrF1JkSMKWVbI7WxCVRWMLn893BOL1vdzttavISnkazC0BOkY1F+FaupamLGHQP9AzM6x3Kvcx/tN\n/bhl9Vpit90JKgkSQ6FjGIQ9A/tz4eB3RI8EjtZifnwejdpnqU2twjxuB1pHI9Zr+yOkLCRnLbQx\nQvq3EPMsHJ4Mhz6Fnquxe8/ga6/dqOV0njy0ktycdmjWr6C2py+Fffaj4ECuzIZH+zXf0WcGR8++\n5AZkkFjqB3PGQysteksxAxZNZUCLDuS2hO2D41n1RG8K1HGMrtvLoNPjUYQNl1GDwb0A13cNECsh\nKwJ5nwWNj4vauT5oB9yNX9BcFI0PdaVd0F99O4mhKdhEBnE1U/lknBdTDvZhefQsOrSTaDuiiMC4\nGfR75m1EcA7u3I1I1nwINjbXaiWJcuub8Nm3MPd56JEILbtD/yehZi6suQemXwOVGnjjOqhuhIrD\nUL4CxeyFMDQgG9pA204Q3RN8fEGbBz7RIHlaF38X/X9PcjF4ztqF4nb+sfUmvwKNiVC9EUoXwf7r\nQDSCpgB8WiHVt0DUCERxJUpLO4opHSm1EckYAgY36tanwalHX1tBixOLcUk25LxSDiSrEToj0s5v\nMa3XEvhZIeoyCUVjwB2QSFVwMVrzx1Qb2hLk3xfMLaHmAEr2j4hPp2OX6nH7eSO5FZLUz2Lya8Uk\nVzIzx48m54Ol8MxLYD8BC5xwUAfDxyFSw1FzgJDjp9h95C1Cd1lRV+Uhdt6CdPR5dD2rcRzSI3wC\nYLsKqoNAsYE9F05+jLTzQ5zpVVy/fDFjFBeG6KchX4bbtyC6t8XlyiE6T0akfwsTJ8HwZ1FkQePx\nHQQ11qJPmAQ97oLrx0CfNIhtCyo9kfluxk1ewUOH19Ih8xjHirzZsbkN8kJvhOYGsgPGoWo7EGdt\nMK5CgdMUj7NUjde71YSteged9xhMhUcJr1bQ55ejebI1PnfeTYuX1zO5ewYNj3xEr6Z6QrUmAsMG\nIhx52OI74nrmEaQcCfrNBVvt2cdEIUHbPjDudagJgg63QNV3kD8O6rrCUVVzr4dje6GpFAaMQRjc\nODo0osT7Il7+AEZNhIYyKF0Ofqebuzt6/D6XSe8IT034QlDcsPt26P79Tz5ov1FdDUz+J9ymx627\nASWvFE1iPlhPQ4eHYetDuLtVIUL9EXUS0jYJlVc8tBmC2PceIt5NvfldvinO4KbGeeR2aEGIZTB+\na2ZTXFxK4P6dVJfG4PvEA/jmfE5jQwrVaYEkvLYdqUM4xw2nSZYSYONMxOm/4/zWQuW1EfhVBKDb\nuBbys/Cb9jYktcM4pBeTCufzzrBhjLUfIClARpo0A95/EPzHIQW0QvidRB3Xge2dbiKcZIJooqzr\nQszVOtSRe6lduIfA1jIq2sKWpWCMhOyW4J0FOT/gGxqEMOmQHt6GNPlNDrfyo1VKb3zzwikOisS3\n5UCKWi4giAnoRAJFmYvxqsqEthFIZXWQ0AbihoBzB8Qfh4B3Uda+hS0uFp9Dm7jPdgyaBKL/M0hH\nBVqHAam8AKWmEf3rX9CkCsb6wrV4RQlMm4JRbnsUtzoP7cwK3LVz2CY5yBh1LbF1Gq7eugSkvehd\nTVSP7I87oS9eW5Zg/ehzdPd/g3pvNtITd8HB+Oba6r/IMorLhZzcGQpPwtpZEL0JVvWAfYcRtfXQ\n6yGkYeFgLYasIkSgBq67B5LH4FQWo5PvhUN3g0kNvgLiBRiU5j/nZM/H+je5TA6TpyZ8IbhtcGIu\nlC78/es++QYkt4OIm3AXKqjCQGjicepaYPd/AVfHUlSHFHA/gPjmbuTVbVEV+yIOfY7UpCDvGYrQ\nuqmUUjCqdURwF6WL/Kn9zMl6uR9SWgiaEX7UuFfj56wkqiKRkGxBwfBo+Ed3CtZNJva6vogdD1P7\nRQPlyTpK74rEaEyETbMhoTWNo65jxfvtWToog+LhHXmqrI6vknqxcEIXStYOhWgjoAX/ZEyqEORR\nb3I37fmYfaynEZ0cx8nA96mItCH7GnA3OBC9j4NeC/sWwYBnoDgQNGakPnMRY4dDUDXc1RY/VzYA\nNXorfnTDzFDCeIdqvuGYdRJ+6XWY/ELRq5OwSfNwH50Bn0yCk11BngSzxtDYoRRHey84UAsOBe7+\nDmnT51B9HCKHkDhtL3ZXNg2dQ2j46EPM/dqgM0o0rNHQOPZrGLsKt68RtaWRAXIuMYZ6jrfVs2hw\nN6r8tZSMDUBTXYbq1ilITQfx+lSPdtT1SLIC/3gCZr/f3Me3ZCWO2lqknBxyPv8cvhoHmUtwrXwB\nZh+DVtfjmDAGW7g/R5TjHI0biXAfxa3fh5Lihy75fSS5B0KUofhZwWmEchvk6OD5vnBfPLxwB3zz\nDuxaB7Vnxi4WAhrrL9jlfsW4TLqoXSbfBf/j3DaQBGR9AmGjf9+6A4bD4pkQPBGhfxkC3SiShgal\nJcrpDDZX34c5qSVdvn4CaZ0JXVg5CAlprw+SMRoRs47tcn/avroeEVFL7ZgvCb39Ptr/MJeQw1PZ\nbUwkvKqRlvNMSI8/ARpffHbtpaaFjurEcIYvWoUGP0SvCShHplE7Po4E413YXxtD/spHyW+1B3q0\nR6neTEdbFyK1oZB3E6/VJ/DM4LsxtduDStOe4A3vIBVuBv86xLUd0FjraZObi71hAX6ZDpDAnuhL\n4w8DkL9ai6pbB3zrsyA6G/Z+AwExUBkIk3shPfQV7pdiUC0LI2HGDLj5GSpMTcQqfZqrDVXFBK7c\nQ0j2RpRQMwiB9kglituOHH4d1G5CpKcjbdWA2gfvpSXQdBxCtNB+OCz7Aq7+EMoexz3/Dixhegr6\nxxD82NcYYrYjCpuwlmhRTxyIiLuF/AmD8OsSR+DL16JZeYgR2bkMOPIDtk5GsgvD0b9nQxfdG/+v\n7kOuGwqNddA4H3IPw/HX4K57oKAE8magbZ0MXl7o9Q4ITgErpEc10eGAC7d1MTTkoA800rqhhLp1\nI1naYxRDdh3G0FRBU95YDrccTwds2JX7MLTsBf0bYfFxGLMfoh+D4JcgOxOOp8P6RVBX3fzr7OA2\nGPcg3PQgGDxjUQCXzTPmPDXhC0HjA23uBJ/uv56uvuSX84RAlBbirL0DUqqRAoJQuR/Dv+WPBIoa\nRqmm0+X456zU9GHdve3ZG92GZVdtxZpyN+66fBpXtEbzwIcEzlsAkpm2zwQRMno0doeVUtN28vy6\nQ8f21JbXYH11CzjLoEkhOvkhKtuZ8d5UhUispeHbWeQ80JOY+izSlXx2MBvVoNvo8/5OBlZfzfDl\nc4h0RkHsOLg+C51d4ZUVc9jJIGaHmDjY7jgOcQxdRjGWD0KQp4/hpg/foK9vJlUdb8Ivrw+hXdcR\nqh5G/f39UOUfpyk4Hgp8IK4S9u8Fixek3Yj0+njI3EBNrx34tDiN7f5EFDmXItf9OCimMKCEbb1l\n0qvbYhUxNKXpsIWDpiAI+v2dpuru2Ct7Qt8H4aUsKu67lqZGsBZJsHYVbN4HM75AqTfiqnMjt0lE\nVMfj9N+MV4cRaKgk97Ou5Lu3URVVQcInY1H5pGJ7+RvsRXugaA9eZhvqf1aTODUD021aNj4WwFzn\np5Q7R4I1EA68CeZE6DYPOn+OElAGcc/A9ltR9+5AYJAVgkMhbxXaloMpf2Io7qu3o7r5G6SkCOTO\nJvw2SwyZOQXtgRqc5WpUR5YSUlWBVv0uAjtYsyE6BQa2hU0jwGcAFN0Chrdh1Dh47lN4ezZMeheG\n3AhqNWTs/XM+A/+LLpOasCcIXwgqNUQOh7Ltv57ux6eh9sxtyULAj4tg4iik6Fik7TfiXjQaNCVQ\n3vwMMmG8GzHdhk/RHq7PX8yo+vW0Tk0kav6XTFJaMvLJ0zzWcxzORz7E+3RPNB102IMiqdk4iYzK\nZ0g57WC4+ICpwddinLUOuXofTYe/wj74NM66DwicfYrDC3vQJEVBSBXB9fvxqiyluzSO/txJrLYn\nmjvfhjeuRY65FVqeqeV7x4E6Bq/TGXTNr2at1kx5VCjSaJmSrqHUPTqAqnsikPrl4V9RTMBzE+G2\nJ8BZjuHUG7S034rGFkaD4QTiyxKI+QRM8bBvHawuRLpqOPL2k/isdNM0Tqb0b1ZCHy8leFUFWouZ\nPPtS0p5LxzevHlv3YmSvfhjm1MHRHJxjrkWZ8hWavhNg+Lug90E5dhS9xoSsd2JJjYcBUTj7BWM7\nUY4Y/TZsCyBk5o/UPXQVquWrkXU+RAs/zFdfT7ahhDJjOX6nd6KL80JobDhkGeWUBkeQAe2N/gS0\njWOEl4qhBfvZYpZYEjWQJkM+WHKanylXvgFRm4Wy5Q7YsouREXPQHvkSnNvB0Q99sC/ZmqUgS8ir\nRkPtHlivxR2cSs5IHyRJizWxPTXGIGyr51Mi+SPLz+EKLUA52gQjFkHaNbBgLwQ/BrYTkD367HCZ\nweHwxLvwt8ehU98/6UPwP8gThK8w3m3AVv7raWQNfHcNbFwKD4yCylL4aD7c9zQsm4+mewvQtkCU\nLkdYKpGSJiPf+z3uAwacoT4opk7o85bT7uAXfFZZwCNyNWnGPArjulD3Yzuk9HIKTm4lT7eedn63\no/epwigcTCyaSGVta5ikRpNhR/2WBe0zFSx69WrCVwtK2zZiS5OJWpQLoh+qdx+AyXdBXQW0SIHE\neFi7CWzVZ8vSegIY7Azb9iUvOLfjdNhBNiD7GClRKjDW3YyjixF3vgr8/cE/GGQtWIqRHC701XUE\nNPnQlGagLONRnM4a3DFGLLcMwvX9Dho7CkrbbiP641y8ckyYOvTD+OBWnMNCiXtrNeYTlXjfZMN9\n0o9CKY/GW9NwJWloDExD9cE8VNffCbIMThuGk2WodHY0oQE4dmdhcfujFK1A73ag2/QiBstGQsKq\niZ79LW51JVJ4K0zBM/AJc9DZkk7I6o0UjTZjS6tBE+6H6B1K3gdj0KTqwV6Hz8It6Oa8idleyeiS\nGvpWuGlUeYFdgSUTYOZgpKwGXLo8RL3Aka9DanAjtqoR6xfi3v4lkt4K2RLC1x8OB8JjX+KY+AxR\nXzXh9FVzpH9PAnz0xFvqSVe+pr72PuxZVkRtO6g8CroM+D/23js6iiPt2746TJ7RzChnISEkEBIZ\njMjZJIMDJjiD0zrhdfY64bWN4zrnuDhhY2MwYILJOQqBSAKUszTK0mhyd39/aL9vH7/P7vt4lw3e\n5/N1Tp/T3dXTVX266tc1ddd9l84Au8uh7ylI/QyUlr9SEX8F6I6i9nO3fyK/ivA/CkMCyCJ0Vvzl\n9LMnoNYKW11QVAivLIerbgO9HtUchnZgF1Ly99Dph5ImhK6jAAgXTUL6tBNtxECass8Q8HQhDAMt\n8iMmnuvHbe7t3NboQlVNHHigH0F7LD1TLsf/4V14Mixo+FEEE0VFaYTar0JXE4NYK+D1+cjefxpF\nbEETuwglZqL2cMLJEjDZ4KaXwR7VXfbLHwJXE2z/GPze7nPOHLDbEeKzSSw7wnhfHK64sSRk3kcv\nNYIKZx7NbSk8WX4dW8fPBWsYCAaIuRYK74GYHKSOOrxLH0FJr6dl/iBCqSbc4atoW1RGVYoVoSrA\nrok3oeo1dKZsQmNHInXoieqswf1AJKbORApnJSP1jse0P56QV4/z0eswOUrh8+vg0wXw5mgcJ9oR\n4xMRnBFYE/Uc31SP15iG2CcbwduKbtLNMPF9zOcSkBweiGtEECT0e00onYX4Mh0omWNoyMykMceB\n0VSJI7gZ+SYB8yQFoSuA0GBD3G5EXbUT+0f7if7OBWfegJJY1GobwjER/WtNUCqihgQIC9D+QB/8\nvSXSF2wgMeRDqByIvzoSz2gLob33I+79GimqLyEBhj/4HoI/i8CMZsZWvo9jox9zbzc1ztVoX4+H\naOCah+H0XjiyBXSx3duv/HV+IT3hXw1z/yhEA9gjoW4b2Bb9NO10PiycDPZweGQK9Mj4qXFEdxYx\nqwm0cPhEQbssnJXWDq4EmvmOgOiiNdtK5u866by8N0G1Gn1LA9JJBfFwBYL9Hqwzu4g6qJHYFYNZ\nl4F2zkd95AiiXyyl7p6RJBVUIn32JUEMyDkSXsVCn11FdIyNRh8fjeiMQRwZC4NegT3TYUM6TNkD\n9t5g7wtXvwFP3gLGZJgwD+zpoAugpo0klFGLoaoYNfxiJGERZlUkruUqch68hYzEQu787TK62IxF\nnARJD0DjAYifgKCoRMY/iKdiFaFAIa7JJmJ3fUfQn4Y1GM2y8PG0ZBlZYxqIbEvEN7wvyaZDXGv7\nFPanUTnRh9XrIvndWtwr2nHeKyIU/g7awsEyDK55Ed6djBgVgxaXSrOlBHtnOMOHjSH/pa/o1d+C\nmWEUn9jKp/2iMN2yhOjG3USJ1UTXfU/bkBto3d/G9BmjiZDPYsnzE8hroXp+DLRLJFsyUPvuR9yU\nDIoL0VpPaMRsfOHnMSbdjuDOh/GTCAWdSLtOIq4/iXLNIOQfjqNE90I49yKilo6mbCNSfz+SbzNS\n42Garo7B0+MwkZ/txVemI9zdRWBsf9z9TmAracGYsBa14TW0cBfxET3AdxKsyyDfDz3Owyd3w7lr\n4aqHuv8N/Mpf5heifr++oX8kYdFQv+en50IhKC+CDzfA2gLo3QVFrwOgqU1oLTchlC8hGIxEPD8I\noXcUQvi16KSTbGEDlTxBA2tIWpeBlHkVDmMnBlsKTRfbUaNkQo0+lOBOovbLZAjzMa9ZAQfWIage\n4t7ZiKATcYc3Ik8cR4MtmurUcKgI4s6ORkoYjb7eS6xDwhkIIWiNIAdg7GpIvRbyHoLti2Hna2ir\n70LLFNFWPtod0U1vAkcSHdlObMpw/EIzTnESgiARClzPA0vPcvPAj/nj8PXEmj7CwwHahM/A0hsG\nfgNaLXR4QJCRkm6mdqYJfVUXUtgQTBd/THJ8OA+eX83d933Nyxffw9NPX8OiDT8wLbAB166+NIfb\n8UXocG5upH29ivOGdIQYP4hVcLICZt0LO1+ALpHg0NE0p3RhGLIUnV1COn6IgbmRFP+o0ZWVS5Y5\nnue3vMCDtS1MiLARHdtIg/MrtmhH+XTUpTxvG0JtWQWB5jO0jpPA5kTqI6BJ21ECfhoDTSBFQs5c\n5JEPYyy0Ibx6H4GAE61+JUriFBrrWhFiREhPQvQoaI0N2Jr7om9pRVNWYZSuQUKC9BSibG8Q2XoH\nrskGgok6Qjkawek1+FsldC4doYP30hp5kI4pmQTEcpjyOBg7QGuFhHgYL0H+I/Dt9d1R2X7lL/Or\ns8Z/KHu/g7KTYHXApOvB5vxzmqSHoLvb6Pb/Om3IMsyY172vtKIFdoEtGVqeAs9m6LwOYUsAvec4\nlOhgXDxCxxRmhK3jKamc3lzOnIJsDNpzMLMvbIpBX3+UuLz+aMkuhOtbEVtHoWvzwb4/Qng8RGRB\nXBSYPHQmOUjYcZaED/fzyYczKfEM4Pn3fotzXxdnc4L0PpVKe3w9ETWnobcX9mZCrQGqdGhCBErS\nKQI9axAnGdF6XoZm+x4htBj0CehiocXxLdZ2Gy3WAHGaSPP5vdz0rMziMd8gj2on8eQ4BCSiWEIb\nH9HEs0RsjEaoeAOqNdgyCUNkPFEXSYSfrEUwB2Hne7DYgrS7CmuYiRMfTqd2gIEeRxvpXfgEotFJ\n48YlpD9TTdDuJ/i4HaGmAhoMIMfA1Sndc4A3Pw7BJFx3jiHS+DiGIFDqgfI25Jl3MfDeRRy7eABp\nuXacWjjmwvfJ7L2M9PxtKF1buMQiYIhZiJb3LV1f1hHKTqVlikZG/SREpQy1Noa2yBIcXV645Hrw\n14DnE4SMGoIpl6OeexdVGYq4dil5v8li5hsa0hkbSpiM2OmmM/NuwpR7EUKxCAYDCCLCjCdRDVuw\nyg9zyLubQVMOEKowI++NI8qaStWkPIzVNQTGWbCqRozyCATrNGARaCHIuQeyPZB0FvY/A6tSIfUK\nGPRSt03iV/7ML0T9fu0J/63kzu4OJ/nNC/DBvZC3CZQ/BdPWR4E9FVpP/8WfaoIRmhRoKIENv4et\nBWgr34fQJsQYLwwcAvnNaN42xLs/Zsq337LNl4i+/WaY/Hs42xeiRsPIZxCzZiHlrkLUD4O2A5jP\nNcOEOTAgGla9AKPuhNSxuCMMZDxbBG/sRJ/sYVr1Wo7Omc+qh2eS6TuG3q7gWNXcHfpxcidEPwNx\nvwVHXzC2IrmrMR4LINd60J9ei9ioogY+QaeNQzGmoxNykGpLsZxooO67V7n+xRheulVmvMlLz9AM\ndg4+gptuA5GJwRiEHFxzz6DN/yOMGA7eKjqG12PMHYbQTwdpkfCHZaiqRKCvm+K5Cbj6OEhd6yVr\nzjb47mvobCdi0iMYhrdivL2Tuh7xEPcpdIyG4Y+A0g9OXQlGIxj9JLRehmHzNnj2agjWA21wYiuS\n1cDAuRbKttdzYl0VatpoBP83yLV+DIcFTC2TEJ0XI3WGY410UDE8juQP3cjOo4iaFyV5Hl2NEegy\nIkA2QK0R3j8HlnfRDXsDOXM8akUT1X0CtMda8N39PUJLCEGvgr+J8sZvKJ8Tg/jDcfjkaqjIR/jy\nVbSydykomE+/4EFkxYTxmAV/i4uDFzVz3J6L7eMgccY9hG+rQxz4EATzwTAFAkGQTXD2Jrjoarir\nEKblobpPEjiTiN87F0U9/i9qKP8B/Dom/B+KJMOi5+CS28Fogd3fwLNzIa4n5MgQnQm1WyE8+yc/\n07QQdF0HniCE+iP48tHUTLS8eoiWQW+Bjc+jlbnwFhchZ/Ri5Mm9tA1xEIhJwBA9G6bN/u/lCUyB\no4c5PXg2MZbBkPc2hHeCMR+8Z4lX/WiXyAgPz2ZSvIG9t47FJjpY8O5BdBY36tRyhM19EDqOo5YU\nIpo0mLoUpkndq54FAwgv3YzYchw6ziCIepSgH828gC5DDBEVl2Lc8A7bTRN468QDLHsulsiV98Ki\nryMuRiAAACAASURBVIitrMG87hj7F39GP6Zh4QwSUcAc6ju+JE7MJ3CFnYCxiAjfG+BaCVPD0VbG\nUmVNo3zuMFISi8n2Lsa07U3aPxiFe3AZ8ft3Ip76HC0thJIjEVVug+0/wO03g/sMjHgWjveBptu6\nwz0+Mhbq2gm9tIKAKRyjKRKxIQ9W9kMKNJCYIXN0o4ijIpmUmK/A2gbhmbD1UYjLgMG30FyxDsew\nRxAPvkfweCPy9E/wyncSa3cjbM2ArU+AJxWWHAWDEU7fTGh9O22IRPYYSXKbgLBtCagC7VGR2EPN\nRK2t4uRDaVhfiCdy9v0IbfsQEgbSnrgDX00e1q+GYJ72FN6Eu3HXtaNzQLihk8DbIbzNIwnLuhe9\nbATPETDfDNr3gAreImjeBFGzUXUqwZEZhIJ5GI5uRQqbCGEV0KO7LmleL9qp44hD/4c57v8b+dVZ\n4z+YoA+iksAWDjN+A49/B1NvgiNN8NUqqC//6fWBZjj/LLQ9hpA5C2FcAoxMQZh5Oc1JTnwBFTVn\nPoErMznz/i3II2ehvyoWYf7dzNy/CX2gBNZOhNIf/jzGp2mw6jlobYErHyPt9C448SYMboZ0oMEN\n3lz4YxtCfpCuUdewfuFViBaBS75Zgb68gFBxGnKwESnehzriVoTTl0Ko66fBYHR6eORTeGghLOyH\neKkLXcJ2VEMDBr8R3dYvcNXF8XrRCzz/upfIPUtg2mNgtCKER2Cu6mAid1DMAapUG+7AF0Tv/RSt\nYT/e5B4oTomIEfsRXlgMbgMt0jT2jcok1KOFUQVH4d4UhMY1NF9yFo8fdK2N0PlHNFlDswiIrTFE\nHipEnfItWsk8OP572JcDtR/DThEcTrgqEt79EmHAHHb1mMNjt6ZyNCsBtjSAEE2MU8fEHTcRLHwS\nVbOAUg2dKsRq8Nl8Autfp3milbjdxzHf9DChxhg6H1pOR4GIHAxB713QYga5AfQylG+gqqKRTxf1\nxZhgx5AYS18tCsO8VaipOYg2L+0hB6a9VfS5v5BmZx7Ccw/Cxn2wdTm1AT05721FnnYN/l3388aU\nyahWGVUfRVpwHqBDsjbTYn4PtnwEwSJoru0OhypHQtpSNL2eQPs9BIO/QyctxmQ6hdx3G9TdAycu\nhfLbwbUD9eFJaMfzu6tU1xnUz/ujfTQULf/j7jr2vxnj37D9E/m1J/z3sP4FmPX4Ty3PiRnQLIHZ\nBdY/OTW4z8P5RyDQhnAiGSqfhNsb4IyJk04j2xMq6TErlamvdtBg3IW12UTWdxUIl1wL/mKUZQeR\nHnkHjiyBkmpQ3oWyYug5DdY9B/0mweDL4PXbwabCjAfg/CjY+Uh3WMwUC8LocFpHj+ezKBfTY26k\nquIBOFmMMGAwgdJs5JAXws8j9H0EbfcXCD0WQtlOOPpB9zMIIhiCYCiFGjskPA0pUejrhuAeqHD3\n8ZeI6vKwcvIWtlSfJ3PyMwhhkQBoDgcKEnp0jNznp73pN7TnOjhjmg+5ifjFj0heGUKITMD30G2c\nqn0ayb+RYWcC6Pe3wRU2oi4twXjwONpBAcfAw4iHgwQSDeiMftQ4jZCvieCIMEQJRGRkh4pgb0FQ\nSmFeJPgiICoCXI8gSclMM+eS+8O3tEXG0JzaE0tTE95Jl+IwVZP++31QXwC6JGj7AQQNTdNTlnWA\nHqc6EA69DlHpmBZEE9jYE3v2MnzB69DlxMDut6BXFrT9DvW5z9l390QiAnocFz2HWnETBurAvB1l\nwndUWtbRZ2UxrpMK7PehFt1G69WTcH7yMmz7hpxt4ShJXqTVr7Bq4uWUOqOIr68n6tOeSB13IyWn\noJS5UG+vpz39DsKqkxGU9dByHA1Q9qxH3LgEXaMZ4bLbwLQGTBY05TPU8T60FkD5Bq36XdSRMlpE\nLYFVS9HcbgxuN4ERuehzsv+/RUf/1/ILUb9fSDH+wzi5AeIyYdg8OLKhO3JVeByc+h4yZUhLhuNz\nUVp344sMYv5iOML8GyA5H6p/T1VbD471m0UERqYda8Zr6SRyUy1SbyfCVXMhOhntVWjOP0e0dTRC\n75FwTgIhAzbcC+oncPtHUHiM0BdLkMYPI2b3e/DpE3DSDZWRMKYv7eZGOkSZbbEK04PfEanVoUUU\n0zFHj16rQZdSha/DjXe4TCD+bgxRKdgtsUi2DEgd1/2sSh24rgfhXeh4DdRWqN+FEDWdhu+2c92E\np8lpNWGY+iExHSsoXHEfWSmTYcI8REmGQD5svhQh7wxh/S4mb+0ulPjPyaiQaQ3rSVLnCc7mz6c5\nvo7slWXYlU6I6gs1IjS1csx1K6NGfY7J1gmddkgdjCFyPBQdJhTcin55CGny5ag9v0RtT0doikCo\nOA1KPMSMhiOHITYWwm1wdgEUx+Kob8Bx9Rnw3ISyeTVHcp2Y81ViDi8hzHeEsJM54BwDw4qouzIT\n28H9GI020Lkg7wsoyqLr7AZMF0v4/3AO3aDxGONmwahFcPRuintFE+8VGLxuOYw8hubMIGTqia4j\nhLJvAGltCrIlmsj7Fbytc9Ddno/4iAuGz4PCQwiBWjSiocZEi7eRu1ZuRfIo6HqFEZrehfJ+K4bq\nVvTFXxBQHsKfUY4WPIscPwhFvA3dvKcQzUugYypMugLF+wa0rkFrDuA2LEDfthN9WT1t9EBs9iPW\nOGgfM4PGQRH0VqZgk/v+O1vXv45fyHDEBYuwIAhTgdfofqSPNE174f9Ivxp4EBCATuA2TdNOXGi+\n/1ZEGYr2wUXzIX0QLL0cSgvQkoIIMSFo+AK0CMT2VPymIrpePI1TciC07WJH9Qj82QLzj/ZCnDYf\noWMeVmsSyqkyxJUl0H4Slt6J1wjeXU1oBbMRonvB4lVwUxbUaDDEAx8/CMNn4UuoxPz69+gDCoxK\ngTdfgWMH0JobWD7LTK37HLd++wFRdS7oXEmiw4KoqBgv+wP1WSFsc27HnCxj7eyJcH4qQtsHUH6k\nexjiludBuguiPwCpB1z+GeRfAkok2HrQZ+gCunR6WnN/h7Xicwbai7giaj4r86ajRb2HrPZEzqqB\nE31g9iM0uNyU37KMkXviaNyWQvugdDoyRmMzHyezKRXBVAwdMmwugqQBUH+IPuoGaAqDWA/EtXfH\n6TBcg3r0R9ovysE/WiBBHozYtAnxXArYR0LqpZA2BU79DsoEqMuFUffDqTuhYxXMnQgn7oND3yMN\nimX0lja0H7+ldvB4tk0ZhG/eUHLOVJBiK6ZNV0nvxHY4IoIP2H8CrfgcUY5O2n2zsWbOpH3uXKRX\nr0d37gNavCUcvn0u+koZy8hJIJzCmzCcztbNiE0CXQMGcHbHRKbrvsYQnoVh8R+wtbYRXD6Yjq9X\nY7v9GgQ36PK30ZkUR78WI1m9JAhmQuW3KPmRFA8yk7pwDeY+F6M//hahndV0Td9CwBiPVdyOJCRC\nTgbEtOPrWkB7Ui32uhYaIq/EcVDBWOQCczKRSUlQexjhUAzOqbPp4ckES9i/u3X967hA9fuftO/n\nckFjwoIgSMBbwFQgC1ggCML/ucZKKTBG07R+wNPABxeS578EVxEc+fKvLyF+9RsQntS9Hx4Hz++E\n25agmiSUqmQ4swcK/Aj3HUTwTUfviqOufR7LbUNJDdMz89vD6MfMRn7nYaSKs0g3PY4QY0XdsQNe\neA6mPQSjn8ExOQct6nnoOgLBKgjLhphU2FcCcVXgvw951Ul8koT3Uhv03IDGY2j9Pia/7RsCcg2X\nbzpGWISdJs1B048agUoL1Ghor95AYN8b6EZaMK5wYmg7iTxqEkqlC9ROcPwAa/rDijo4cgrcTXBy\nIWqwlkBOIqFIMxxYTLDiDqyRHkKOBxDbPkRTfbQPy0Z+/yzCZwXIo8rQjDJkDaW9uISBD15FZOYw\n+ky/lUzDGErbm4hcu4WulVGoqZlo2T1ghgiDT0Oymc7waAjrAfY7Ies8nBoN04ehXp2OwfkkwtRH\nIXcq9Lwc5q6HrGnQHIR3fwtLNoJOg5794cvb6azaTEBMAv3NkHcS6vxQnoB2YBfK1GwSckdw2Wk3\nl284giezlt2Zo4g7HY1o0GDai6DqIN9LV6oBuUbBWZiM6eoriVg9DznqC9SmbziWMZx9xt6k+09D\noAG104Du+08Iy69BH9uGs7mOhMh93ePmtcUgSkgRERj6ZyHqZZo+PYJy6QMEn/yczQvGMvh8BcKb\neQj7SqDdjr60g4hSM1W9E7vrX+AMkj6Avfg6jJvsdDISr/YiONPArSEckzgmX4bkCydlYx72llLE\niSHEWU+BuxliFZiZBC8s/P+XAMOFLvT5c7TvZ3GhhrlhQLGmaeWapgWBr4GfmPA1TTugaVr7nw4P\nAYkXmOc/n+hecPgzeGEgtFb99/SEbKg59edjnQHqTyGkTqL6tjS0ns/C1n0ELgvDnLeZsE+rif2k\nmLlfbya95ChURsPi4dC/P0REQeU3yNMmElzyIFrTLqjLw/jG1VhTw5CSp4AhB47MhkGZYLfAJydg\ncTH0egn1xizq3x2MPCgE8ZcibFXAOp5eFYdZXLeMAbMvQhwyC/dFsUjxZjxVOvYvHUPLXQ6cxkak\nnuH4auLgTBeCfTXa+UoYeilsb4NeXhguQM1yeDQR7dPVhNq6CHZ9hVrxBX5gr3MMhtoozhlGcSZh\nOR3qOD7MXkzDki8RdDJClALyVwSWDKHqwycZNFpC8hXQHPsESa4aWsZ0oh85AdNICf/pcLzySbzF\no1HXp8J3KhHRpZDyMWS8hrusk6plz6FNG4wW7UET0wGBZt8WNMNMECVIGQKOgdBphamXwygX7FsC\n1ZtQ9G62PxRBYcGdaMePgc0AVgv+N65AuSQF8l5Eq9qHkHOUfraJzLC+z6viZZS6h0HUFeDtgzpI\nj5bbQtOSFPwrNtDSdAw5XoKuOziYnMv5pCuYKA5hQJ/30MQyPCv6EJo2C3WKH39iNLaI91FLrd2u\n4X4XtFRC7Vpoysc62I7zNxMJPH4RNe/Np8/ZfOQ5S1Df+wPaJBuaZkNIGUxMs5m471dBwU4EMYj4\nzQCEWh0GSythKxpQGl7D27QKrXoPvsQi+tQOpzMqrvtdnqyBM2GwYxOUl6PpZ0DxaUjL/u/1/H87\nF+as8T9q38/lQkU4AfivKlX9p3N/jRuBDReY57+G2S+htjTjfX48XTs/+WmaztA9V7ixrPvY04ZW\nvhdm3Yz9kBW3fyOMzaTlioH47T0IKQHah6ViOLULiqug7hxawxmC6s34xx2kbWshjb9fC62VaFV+\n+PYV2vwRiDl18KYTtdOFtiEOcgtgUDrYzHBuG3jDMHtziNp/Gr9qwz3pGfakZeArlbCNfhYOXws6\nO2rVF7iT9NjvuRj/uQaM+XYEfSSWjlaCPVvwdTWj1SYj1L+KOOAMbfZ4tMd3gusu2GIC51q4IgCL\nZiIZr6Pz0ylUrKhFUnSMFBXMEen0aemimH3ktzoo77TS5NDAEkD7SIZKkeIaPQMG9EM4omI/nIvk\nVdEiS4glA58QRBJOY7rtRXT9eqMPyYilJ1Fj/YhGFU2fwfmlS9k1eASOTBF1zC7ERh3lPiNPt6fw\nR0FA0I8GTye8fAecPgiPLQNjPRR1QcsB6CXgmL6ctPVB3OlOWhLNKH7QZi4kaFyDFjqCNiQE0/zI\n5T6MVVnQWsW9az/gZW0+ynN3wp1vEgqPwJyuUjXhd6x/sjem+2bhc/1IHsc47uhJpC/ETIbiLd5G\nc1FP2i9qosKTSqHlJor9qezRPqB8kMY6yz5Kh8fC6hnw3myU8+1g7EDe/gf0v/+Rc80JlO93Qnw/\nNK0AzHUwxQbT5yHMvIOwz9+E+8ZDWRfctAROiBD3CqLZiXVrE8ayE2j+AxhL/CQ11uAy6yB0DuKj\noEKFdcfQ2nsjKD+AcQjc9vK/uoX9+7mw2RF/q/b9VS50TPhnz2ERBGE8sAgYeYF5/mtI7If4dBHq\no2lI224jVHEXctwEGP8VCHooPQzNlRCVCi8MQDGJdFQ8hP1IHS3WSAxKPOiiKZ/clw6aUIzV5Axv\nIGxPJ9pUCTXXieoIEni6DaW2lfBxTqT6OoJtDvQ3OQi9V4HY4YdicEvFtF+toskyTGmA4NXIYb3Q\nhYWjM+ZTK45BJ5xgGe8wtfd4vLW3Y+r5Gbz9Kox+CU/LmzSOdaALTSRm2S7UW9Zhf0VBlHTonY/i\nG3yE5vhDROhUNOtpvKuuxPJEBbqcsVC2Bzb8DnWwlxCb8SZuwdk7RIxfRtjrRH4qSGhsOobDx2n6\n4EeG9QyxOG4iaY8OAHMLVGp4c8yETygnasARMNmR9qwm+vXv0dwr6TNyEV1yGaawgdBahFARjVie\nD7c8hii+i0nXQrBiNa17fiDtnnuwjYslmPEWUo2VZxzNVKgGXvXugK8OQ2El3PQ0pPeDt+ZAeR5I\nOohOhVHXg+tKep1LQ8v9hq62bDa/PJ4Sq58bGrvQr3UQGNSGzqVHDE+DmrlQnokjq4gH3niNbcOH\nM+Xsa8jTW6B5AIM+XUmO3YI4xoR/lYHdzw+hzWimPqwK79HbifOXo0QkEKc2EH24CI6cIm9UDjli\nBsldy3FGVRGm+KHzPGrGRBocZ4gZvAl5z2s0bXqEj+bez8IX3kdduxKGbgbFiVCaBdqTYL4P7nkf\najfBjg9QFm1Ds6xGK1yFzukBl4AW9BOyCmiVMuqJd4k2+dASMxB6NME2J9x4J9quDxFdCiSUQOiv\nDL39b+bCDHP/sPl7FyrCNUDSfzlOovuL8BMEQegHfAhM1TSt9a/d7Ior/rwqRZ8+fcjKyrrA4v1l\n9u37C3F//6ur8Z8wW+oJ3DWD1O01ZJw7hKftBP6ayTg6qgnz1LJjyybkrWsZ01oBnTJtNVm45sai\n96uYw/PgbBh+uZx+JyqQB3sQ9wehUUN3sZ/SfXqiVrVgsMiIqT7OxeZSNew6Jux6juBhF0JIoOpU\nGpHldejyQ7hX6NEEgbi+HThuqaZxXSmCGCCYIkF6J55BOi4PrcKS/y1duY1YrhpJS1cG/t/Owdrf\nj1hhZk2BwCXhHSTPDxFcI9JsSqF90xr2JWYw+YUWDi0KY3BOG7ZP9Gw5+BadLakomkxUbi/S5P3E\ntAQJaOEg+XHpo3EYajg0cSj+plP0inOwYHFPEqKGIeb5MIbqKA2NwjK/DktKCWHHRJqvG0P5hFyC\nFgNDxwbpbI9AOvcpxmg3gboK2tYUIDX7CY02E1S+x1DsoM0s0PzulTTNuYeg7Kb4yHdYHEHucN5M\n/4JC/nDuafT1lbhq0tg+6lEiDq1iwPJ52Fpc1IcNwNjRzgHnb7DvrmWkzYOu5DCdD+VisAj02F9J\nZMZrbAoMZ8zwPegVM23nHRQbhlFtuZWpkY9hs7tJHO9h7YArEBtKmJgawH2mAqmhBK1DRI7xsn3m\nZYhlRu69+30CVhlbvy5kT5AOXTSBeIF11/ZBNU0m+bQZt6mGxpVmhCETGDzsQ7Q0OwerepEeXs6O\nvD1EeOIZ2/IlS0ur+GDcAkZ9eSdSlY/m1nQKIoYRH6PD5t9C9P43sHQ0Eeilp9qwA2erD88AA3Gt\nPvaMmMEQeRP2r7z4vUHcapCShSlkNBUTFmjF3TuCho4VmCs7kdoyCU8+RelzCynotwBFr+/+2P+j\n2tU/iDNnzlBYWPiPvemFqd/P0r6fg6BdwIRsQRBk4BwwEagFDgMLNE0r/C/XJAPbgWs0TTv4f7mX\ndiFl+VtYvnw5Vy1YALtXwPbPoPIMzH8MEnt3O2GEx4Oso047yyae5arWr9CfvBLP2XY6SitxOC2Y\nEuOhugBc5SBJ0GsMWuIAVNdrCGUybePDaR0qU6Drx4jKszh/7ED/WQda7wAttXaMTj+mYBAxGEK4\noS90laA1aNAVhnubFy02DGvmZYjfvI0WZkK4Zx6EmsF1HrIUiPBAj/UUqjXsjgwx/Nxz5PT4ACXM\nQFfBzYQ9e6p74UtEyIoiFNcbISqRoP57dBk26m5oxvmHL7GOj2ZJ8AC3LHyMsBscWNISUDefRej0\nUzU+hZWj7iFLGshkTxBd40uwKxxIghlZaHsfJjgql4/bBtB+uo2FO74g0tqB6PMRiMrEMG4xjVVv\nQGQpkb5chIJ6qBMgrQUtsR9CphuttJiymATsP9YS0SCgPvggqrwFed0+QpUB8tYo9P2tiM0VBR0d\nKNYAL9/wOLni5Yx+43HIKgC5FtKGQ1I9mM7CbidELIUNS2HgTAh5oWobCFH49KfRhvZC87hRNQst\nPZqxtXoQAxpNjkTCE62Icjtemwx1TbQft3I+PpvO6EiCpjDm1p/G9M4xWJgFHXupSLmcjY3R3HDj\nMtQeKZhvnQclT0HcldSWBtk9wchFEVNJ7XU9mqahNRyj/YPZOHOTYdg7qBXv0GXehdLpxdp3OztK\nvmHM7neQDYmsK47nkik/IB4xoSXdj2iLgGPrwLsbIkU0YyeNox0EbcnE7r0WLeExaLaAMxXF7MWw\nxQlmPfx2K2c6r6P3Nz4I20koIkhbuBHDFg/Wvg+iHi1FaFhG6EhfdC++jjR06N9lpFu+fDlXXXXV\nP7q5/kUEQUDTtL97IrMgCJqW9zdcP4Sf5PdztO/nckHfAk3TQoIg3An8SHfn/mNN0woFQbj1T+nv\nA08ATuDdP03+DmqaNuxC8v2HIAgwZh5EJHQLsSMGyk7A4R+gpRaUEEIChI+T8O4eh15uwGyow5gY\nR2d9Hc2tDhLVGIiLgY4DYN2GUFWGZk9BvSQHR30ZklJKlH4GGuNwZX2JbgEI1X6MiSas29vonJFK\n7Rw9qbsqkKt8iLEOiNHh39aC0+5GcH2ElhML0xfCJXdA2StQUAIjX4S2ZKiootfw6fRGZJ/8HqK/\nCLFpOLqyIOKoK2HzahSnGeUWPdqZNhpzKhHVSKJDqcQ+WIB/8xy0Urg7yY7noXhsZgmKT6FNvY3g\nt19i3qFwy3AjYVImeE9DXQws/AR2vwPGwQjhM9G7E/jN0X14zp9H19UBZomFT3zHwrONjDr6Fga5\nGPNZC4K1ipCvCqnPOIQfavE+NQlPrzoiyotJXV1BV5LE6R9CiPdvIHHxIYyFiTQ0FdHvBh3m6D4w\nzEf9/kQemPo4vztTTdbIeGjcAC0ipBlhTy2q1oUY3wsuegKWPQoxKRARA+YK8DXD+FzUwvN40n0c\nc/bEZ5DpWRDA0z8cx/4I9vfoS73ZSP82NzbBhWKwYx0nMkqVsOb/QNOwy3m2xzPc4Z9L7PYkmuVE\nfsiM5+KGEH88fDs3WJ4C105C6YvZ1XMUnZ4WLntoGYb41bDAjVC/lbrPG7CGGgiMvRG9vT9iqBfi\nuTyCPZIIhO6jwZGFLuBAE2rpMqcgfiJABAj73oRxvWB6Cih3oZ19HjSwSNPpai/G53kaVdEzd8JX\nPOb/PYPsO6H4cQhVwuqR6LNrCWRaMTiG40ruhePDZVhqPLjHvof+sruRNg3HoMtD+O43EPkqZM/s\n/ncYrAFdwt++ivh/Ahegfn9N+/6ee11QT/gfyb+8J/wzv9jb1XeY0HopNFah1R+CXR8j9BqGd8wi\nTCseg1Qr6NvAfRi0SLTjzWhqAAIK3oWJlOSMxJuvY9jpbLTY7+naeAJPlg01xUZkYyeUdRAUJc7d\nmELGYx2Ysm+h6fNviNKdAyWAUgVCrz4IT1+H6Pkd1A6DXY1Q64LlpyC2BwDrz17HVFMP1ORRtKs3\nYm70oe8IIAXcqEYBcb0ezecHgwnB6UeLiqBtvxuj0oWcPYB3B03n7lNvQSgRejwFPQfBhu9g0hWQ\nmIqGirD3adj/CUSPAUc+6JqgOBEGPwTbH8VzpBbmDUaedYo15vcZvukp5FATUXktSE4NX6+Z+M0e\nHBVxaF99he/eMIxfN8LkMILnzQhn6ik4Fk3yay0UPQh9b53O7qgxzIo/jqpv5cd6MyMPbyDMEgs2\nKygeaCtBu0jEq4uHMTdgrs0gsOYxAuOS0UU70ZOEUNmG399F2cS7qD76IuY0C5YaO/ExiZQVbuV0\nagpaVCxeYzaFaishRc9lOo2xgVnoG25AiP6KkGsEaqSdHVIO31XP4PV37uGTW+9ggVCJrmMV7v1x\ntF/xGGWug+THyoR5QhxMGYrV7+WKtRuYVGpEeuCPKAdupOi6r5HSR5H+3v0I5Rtokwoxyi1oCWGE\n2luxvlMFHZ0UR6TR01uMYAFGJyCcESG5L8Tp0GIL4WQVyrq+CBeV4ylsRbbF4nuugw2heewuu5aX\nTmwhbOJCtJZPKbN8jb5Kw2ZqQSYdy2dHIFpEjelF62U1iLITx3cSgq8Vbi6H2puhaw8kvA2On7d4\n7X9cT/jk33B9DheU3/+NXz3mANpbofA4nDkGky+DpFQANE1BE5oJOlYhhy+Cqv0w/WE01z6M749H\n08dA7+FQXwJ6AaHDBxNeJ2BajK4IDPn19D22goqByQQiNmLYE4mtCGxX6lB39Ec4vwmtXsMfYSDq\nsxbqZ5qJ27kbMTkaLq2EhkTE4myEm+9GSDkLwo9wfAOE1qHZg9BZiOJ/GdW/g/72Vnyyiqx0IKuJ\nCDWNhOhJMLULXXEpgZkTkULnEUKPUfb8jehrmtAJKgZbFHLv/iR1KlRd9CNJXQ9BWyus/QNUb4QV\nGyBhAFr7SZotPsLNCYgpe+FHHQz3QHgjbLsTIqIxfVpI++23IYRdyaVjN9NsChDe92V8EddhapCR\nqg/jGxFPfZ82YvU9Mb5ShnpDOM3jE4i6YhfC+jvJefUZWs9mExYbonZHEGY2oYx7jtDGZ+gxZBtS\now0iZ8DMJ0BQYc27dA36AF90G+H6u6BXJNrdgwhKJ2kXqqilDPO6AipuHUICuxl44jyW+LsRCt7k\n4Kx5lPTtg6nBzIikARiJoMjnoqb2FCnpj2LUpRDUTiNvWoBuxHIw5ZLe1cjCt+fz/dwrcafn8nIg\nmuimGOSxTdTZzpCgepl1+BBRYSHiLSrZ9iwy9UaEjAnwyEVI9yzBeH0ellAG1XfcSeKXB6n2BVJi\nNAAAIABJREFUv0mf05/gV6oxem9D67cCociAKcULJhFBrwPfRLh3Kbw5ADwhhIQ3oekzpHFFUNuC\n4ZSA2LMRzZPAJc5J9LK+QmF9J2310Uypf47WftmEBukYkgdS+3mI0qPFpCJMWI7drqJU3Isq5CNa\njQhn54DNBInvg/3vmnX1n8EvRP1+IcX4N+OqhR+/g+8/g4JDIMt/EuBSuCEMbd8hQo6vkc6WoFyc\niUgx4mwVjlWCqwrCBKhWYXkAIfVODPNiUQfWonqtCFYPshZEammH834474G3FYg7DoZUtNEOlIfC\niDd8RpVazfmaK0kzlUKLSODi/ki6VgSDCynmdij4EVathzc30dL+ODp5AaYqE7I3kYrabML5AUNC\nOb40N7rCMiTZiBD3NlrDrQhiI5JwmIPf3EvToUaCrTB6zf1Y2ldDazVTjrbjOTkXauthRjNctR+K\ne8PmxdDegRjqie3IeroyNCw+D3RIaK7xSE27QK9A/2sRwpOx3PckLbm5hD3xGLHDulCXrELKEFAG\nKXj3BInwn6ImJ5zqkILugV7orTosa0og+UVQFGo/XoTjmghOXTeWUcO2E/3EMboOvEnRy70x+axY\ndKVoje9xpjyC3hs/QMusQVMMhFXegJjeHbPC1xnktP8sQaWT3lubiH3uDFmm+Sh6D6EDrYjl7yDs\nKCbrwWfJECyEf7uWTlKo4FpSTV1kWGuI0J5DCzUQFNrRbHl4jW9jKjlPzxe+IeViE3H9f8sgoRPD\n8e9x2gfzZZqLwTXnmVy7C5vRCxYfV/gvpU0241GPYMxyInXlwtKPiYusxND8AR36SMrGZ1O79Tb6\nKF0YI79BizmJsLELweonMqIVLSCA7jHwfQ1bx8K8VLjdBesfgNkehHoFt70nxocFtAMK+sUxmPou\nYWhsOJr+KO66o+zsPRrJFkfStkqkt32oV8mQY8E/SMaUPKjbH8E3BnSHIDYajmyHK06D/e/yPfjP\n4Z+8dtzP5VcRBujVF5a8Bfc91x2Ux2xBKHoMwflbZGE9QuY0Qgd3oUuYiHgoCA1NcPkfQHkZHDmw\ncyesqIVmHdrcbOqGzca55lX0ZWNouqmU8N3nKeg1lAHjAgj+AoRBIYQoCaFjKBxdg9bgRdg4lBS5\ngWiXj8bUFE5OyCUQqiHbchaz7jNMxwzw0WOQW474Ri5hsSZO3xZPdIub2NMn6fn+SZQ7NDi8B3Ho\nJag9+iCnPoKmT0RrSQXrBFwuN43fFzP0JglTUhh2TxVkLAXbR1iDe1g5/vfMP1qAPvQjwts9UCNy\n8SbeR/DkZtTasxjMIv54icp1VtJNKrLdAI1hENLB56/AkXXIV63FtvQZfF98jGmoFXHSTjRjPMqw\nuUj99eiqJVK0HE4OfZ7UfSXYTItgzBCwzIb1t5GYdZbV1XNJmeIi4kg6NlMx7v7RCN93YmiKofzi\n2ewNNzGw+DiC0YaYD5ZaBffm1fhGFBA27zpkTwIDC7qw+I3w/eru2B71LciTopFbffgrgnRW6dEs\nOsI/20TR8MPA48TgJSB0YXePRuIcmt2JoSYWpXcjuq2rYNdhOh/vg76jFIPxWQ5hYupFdjx0csOm\n05gi/IjnF8KN90PVIoT2IpwNX6HUNtChfIkxZMRoS0c6HYQHPyZMF4207nWa5r5DKFtDd3QBQkoI\nIbwTZFBazbT5w4jtehPCgxAfDoZ8eHYR3PUtaCPQMo+h7PMhT3oAPG+hTXsCdfFcuKUOd7aRhphw\nUg39We6aR8dkibtm9se8ZRD2mkKE8jDYvxQGnYGTh2BWPvgbwfItFH8BQ5b+u1vmP5dfiPr9Qorx\nC6B8P0RlQPVxKNkJ1eugYyn6y6/HG9OfQ/ExZI4aTfKe12HOMtj8PHhEyNsNOysgdQjqkGr8STU4\ny1209Y7H3V8k5WwxmAI06gxoci2CrIM2L4LYCrqNSG4/oXOpkH2GMkt/SiwRFIWnsuD9TVisEXQ2\nOSiKb8ApLiZugQedN4Rq7ETXHCD1cw/eWAHVlYwxqQxjYwhaZaSjG1C7wsBxGqFvfxTTZE7PvA9v\nShyDnuiLoeQoWkgimO5Al3IFqONQar5k9NoPkBtO09aajumKIP58FZM9D3O8G8FZB74QhjEb2JOy\nDGXRj+REFsLFT0DiONA5QDoK/vvQ7p2CbrKM0tqM1OVDGPUhen0Cfs+XcHIvwpl3MP3+Ouqzm5CP\nHUE/4FJEu4vdwwbiqshiZtgK5OYAulMD2XPdHNL7BxmwLhFXZm82Va/EancT++YPNNxhxrwMTEIs\nxlQzslGEI2uwhHaBXusetlACMGcsRG8nUFZAx3IDUr90dF8PwJddT5HpZgxEE8fdmJuHUOfcRZ11\nBenbZiLMPASyhu6daKS6EN530vDvCREc/UeOcIopTMOGglmrQtFWoZUChg1wYBToG2FdL/D2RQpr\nwOFqwzMuE6XjAN4EC9aPl8IbeSj9v6Tf/C+p/u2rxKS3YzncDsEqtLAuDNVeSuU0YrNOQZcV1nVB\n4kRI2wmrh4P3JqrXniV61k6oWwOnmxBKFyD+P+y9d3Ac5brt/evuyUkjjXKOlixZknPOOWFswGQD\nBpPD3uQcTLLJG2zAJAPGgDE2tgEbnHOOsmUrWcnKoxwmT3ffP7S/e+536qtT+9TdG/jOZlXNH9M1\nVfNUT69V7/vM8651pZ+OdD0XJmTQ57sKHEXVPGV+k/boWN4aeow+jnu4fMVfsCxogtrnYacNrnui\nN0sQIHI8tJz6fXj4W+IPon5/+gm318BX18DyUbB2EbRVwqAFMOgaiApHTyfKiW8ZaxY4VfozDdev\ngdiBUH4Apj0L+6vALRFMa6VjUTSGmh4MtYcRHGFYnEfA6UZTL2KTXTSHRyA0aBAEM/y1DsT+uPpm\nUndSx85+r7A6fxYfXHsbCck52MMNaEelENbkJH9vOaZ+sziafzvHYkfTobWi2k2EtJsIMY6ibZKd\nYI8RzS4RWpIQN3uQXd1w9Cu8L0+k7uO3iL8qneg5Vuqc7TQMjSBgUyk17MTjfBOkKNS6WEJC+/LR\nVfdw8YFXME47gP0+O/o7FyOOexahU0awK2i/f5lx/Z6ncfkElAIn1HwIlSvAYkM1jcMdMhWh9UnM\njhIkXwco4+C5a+B8HUtd41mSuYCue/cgS0YSS9IoneymwbqLlw+fpKZSYl5nCfqyZASdHffQLCJj\nD5Dy6Q7UU3s4PMfH6KGXM+qtQqTl2TiaZmJ84SKafgvQzXoIsdQOCVeAPA16JoA+HSbEEoyw0f7s\nWVrXmvGtNON5U0PT4BoajdHENyaRIW/AwnxErwbj3z6nLbyB7ggrHHwecVkbwfEj8JtsVJ4xIHkP\n0ij9lXDVRBhRCP5f0KpRGMtiEZ1mlOgylB0zUX0ueOwN1Dc+QQnVI+j0mBskJLdI60QTtffm0vFa\nDup+O4ZzhSSnO2l+/zydTTIMWYjQptIyL5Gz/fJQrQZo7gGNGX7ZBz/pYbUVtfkjbDHL0Q19DdUU\nipruRg3pJJCh4OwfRtb2akIbhyEbY+m5rR/ipApeLKzm6g+eRYoPRWkOouaPhqkCBAsh4PoPXoQP\n/N0o+Zvhz2SN3xdKWxver1ejHPoarXQaggbkrg7kbT+hSHtACGDJ8SOdr8Tj9WEf/znDEobwkNrO\nR61ObM1tsG4FNIcSGK7gmmcjtKEAhnyPcvoGpH6RhB6pZeeiiZiCRrQ9Erv7RXL9mwWQcx3Iu6D8\nGN6gSnSKkbON9SjWgdze6WfqiZehjwUa68ERgrDofSKiBhGhC8PfcwstNhty4140mVMw+vU4qz0w\nPZTwUdfBjjqkCyWI3d20CU2UxBpofXoCEQ21JF8sIs9ZTbPJQfC8QGpMNUVJawkWlxOWNpu44auo\nEg6Tce4YdGWBXAKudyhW7yKLIJQmQE85YdV5pIhxdGXHEpr9OJxbjNJRTlB/DmX8YkxlkxBsv4I7\nHK5fDXGPgH4hr3SL/Cy9wlRzMoM8Y3hn+H1oW2ZwPsRNpiecq1kBSgZqdioqW2mR9hF9SKJnSi7n\n49oY2PUJjgIZ5b3RmIS/ounvgbQ0MF4Om1+FnBjY/B4MkCBsKEpHALmmE7luM+YMBekVG94QL2XB\nTmo7E5n7RRn6tP4wU+odMopLIOyUgb6PnqFn6gBsXxyAh03I2TkY3/iR3G9cdCz+G/HyPaSrh5GD\n21Dl42h0t/U+VJoMSO5ETDqK3H0B5fQo8DbTNFCHptZCFMNhcB78UoJ/aCHFT0ZhbDqNI8eBNf8G\nYmdtwe+bBDMehZQsbPrTzDm3Hv9ZDTpFQvBWQlw4xDtQSzeitApY4iT4dRhqnA110GSE6i1odDKZ\nJ7Oh5BfUYICOR+vxSz8RzhHEuREY4vsiV9+GkipB5X7UrCcR+j0Hou73pORvDvV/ipXl/x8her14\nPv0U/+7dSEE/6g3LEM/vQbtzL3pnA+KgRIT8bvB7MFkiCWS1gFhFFIO5X/6Fxe0Kr7bq0V36la5J\nSQRnDyIUI3KigDNxCUaXnpAvPbRX2bloSmJK425inB6qgqMhXQt92qB1AWrAx5mZt1A2OMicX35g\nTnMxId2AJxcmPwvH10J+Bbj3Q/ku8Leia9lPbJuMEudHFfZAzH2Y79tF5fpsYrYdxdvVSvXAOI5d\n1RfzMTcTm0MYoZ8P/h+hpww1P5tQYxmNN+ShtLpJfKWa2reCxOz5mhNps8kYdBc9OongZ9ORF6wk\n2H0fWVtngBLaO5Lub4DUEWR011I+wIpatwXtuLlYNi5DG5qKbsurYNPAIT1kT0b+diFS2lkQoqHY\nwtT2xRR6RL5NG8UVZQ7YtpJnbzczzvEkaGLgmgMIqorUto3ozhMET79MyzQ/cfIDxL99FNcdHRh8\nc9AUroHSInjxOOToQbGA9zR0aGHqIuririK44DKic8MxhF1C1gvoQpPpUetIKk5k5NbDCIMX9k5Z\nAGpbK7S1wrSJ2JbvxrL/EKz+Drnlfvx3fIk+NgHcFeyRWpmsK0GQ16N0rEbyzYbWEnC7EeJyETR3\n0mFficX1DRqDDN4uIhtCOHpFKu7znaT0/YRA3SgOD72R0ZrZxMXGogg+uq07aXVE4zd9hpZt2Efd\njXHDJSpvSaKNUCyt7cT3fZXofdtQW7dATBCigITx0PdXxMBBOHUTHpsVozwLNGcgdDL+KfMw7DtI\nSPoPiMGLEG0EhxXR8QwYX0U5mkDQ6UWf9w8IsCqD+yKYM/915PwNIf9B1O8PUsZvC8VgwPzYY5gf\newz1uREIV1wJt9zZG9i582dItsDGaRCIR3/Oi08EpfIGekbfznCbl9DtuXR59ITn9BAc7CUQUUl3\nbTOFebMJVp9meFkX4lERU6yGRQc7CeaMQvb/wIgfWuCBxfDLV9Bsgbzr2T8jHVOHn8jobDRNu8E1\nEeLHQuQkmDUJlr0EyXdDWDh0NMLaEfDgNtTK5Ti1hYS8W4jxihtwOi2cuL+Bpg4rhqIaLluxE658\nlfMTW9B/+yw90+4j6uqluLXVuI7MIbniApohy1F/uBbx5WuonOEl9eIp7JElaDuduHXdXDrwBJ/F\nPs7Y4U3M+/lv4G6ERAMUlsGbX5BY+RK1F48QYU9GsKZCZw1IMhjeBvvnMOgBArkzEQ0bEEp6wPca\nOq/I49+/wLUTIzh8PhezOY0hjUMQI2aCuB883XDwK8ifQiC2ii13XMHsJ/ZgND6Jb5ABXdNAdA0H\nwJQI/vdgSBKsKQNLKKwrgBeGg5rH0fgwYpfdTOKFxcjrINgahuYBM5HGTATfDxAqEFy6AjaV9D4U\ngQDqxu8hzIo02Io4yQW6PUgpQ5Ee9BF8cBdSbjyzPy1C82ACcrASUbMYVnwN27Ig0gr9uujKDudk\n2lkmdM9CaGiHs2PRXWtiyKU9tOkPc2ndaBxVDVyz5hj6G5/u9bYAjJ6BRLS10UoKDWyi1vo1/iu9\niEEzpkAoqZV+hB1P4XSkEZGzDH/VYrSaw6ihOgRRBH8unHHSnpGCMdZJuTmL1EYf+jMWeH8fPD8K\nnIXQfABOv4Uw7CGES1rEnOtQD31H4OA8tKP+C1sX2QvnboDEv/wpwv9k/EHK+P0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toHwWfDeugc3Ve+SUK3DeGHJ2DMU5B/BZIgEK07yLMnX0VXoUEZGUfwPJhOtdD2oBf7958iTRnY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\nguvUm7i1KqzyeXbrR1Az0sTcczGkiDiUo21czK2j9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/civFJCeH+DKurwJDroll5M+HOF\n1gHdSUnrA4uXols8GMOVbZjLa1HiIpRUBHDccg0c2Qj7vgFfF2peIeZ3MhFTr8SYWMn5+40kBe6g\noeYIGaGniRyOwZgxlPj1B2nq7yT4+HoMT8xFincijAfhmQ7o8xZa3xa0LBOWi0lU5UaRaauCAjPa\nxWj8gz7Fc/5ppHWriJLikYdciXncQowdHXQ+9hjSxi3on7iVWGMOHdnTUVq2E+RDYmxDcIZr4Px3\nGOU25Lpkols60Hqb0Q5UoWtNQowbQtTnX6H1jSYyKp3MdXUYfjyP+249IVsMpnUKoMC0hZdaMk19\n5l8sDV1aGkp1NQQ+hMhPCHkgbL0V9BYwDIe7ciDohTuXQN/JMGM9ouYM0jcqxl71SObLaRWLkJoz\nMGqj6J5nRT0TQbsli2BbLQ1yEv57xhEwB+j38gk8TU1YdSqN0ybitVeQXhPC5IzDkNUT+Y9/RIt1\nYtxbS0x+KqpjDEJ3BnvCrZjGKUxauhhOxaEcLmbl7iU8+OE6nuq5lnmT96LcqiPqOT9tz/TCWv8b\nyHoVCr7AUryBpux0DKX7kStldGMm4Rd76FTnEd2/AX91GgZfiLSvDsAwUFPKoDUd6UIBImsWeud+\nugoSkb87calWRuNQArpoApZadLE3QtU3oNmh8zjETfm7iPfP5ef4hIUQ24HEf+PSY5qmrf/TmMeB\nkKZpK//Svf5xlXBnPXy1EMI+GHoL5P1ZjVMhgSEX6j2QnABpNoiKhZqVEDsXBsZA2AxPX4fu0c8Q\n0TGgzEaKnYNlyIOgdMCKeERUGurG/YhxfRDFK+h52IWob8M3XkdsuBbnYR3mgB+lLwTNBuSm3xFJ\nNqMvbEBzSZAQQoir0RxrEGEN7QKEVqhoLoFu2kG0mXq0rxTEGCOm9wshsJaIezHV4fF035WJ8uUy\nlFoZhQihvT/h1wRhxxCUl/YiDzhK8TrB4D45ZI4ZDkSgYDwsexbVKRBD74Dxo3C1fYoWKMd2YCXD\ni44RmC6jzvBhrBuMZiiE7Ey0yyvp8pbgMM2AmpshMYRGJZpJIB3uTrSvmEDgJUgYA4tepjN4J5ru\nKcwfjkRfeQ9Vb75Nx2/fxjlxIlm//z2O99+n5ft1nBlzCzzYDfN0HSa/RJ26hvb2tTg/tqBO1rM0\n9XYsdDCs9mYiP2xEZziFOONDzEuE7mMI9OqBPqoY69150NGBbdt59KPrYIATrA4I1cG5LZdiVnte\nBr0mgD0NyW6HUPMly7cxFeKqUJMXIe3YA407YPid0HgIKgth5ydgioa8MUjGm9C2v40+ZxXxcQcI\nJX8Dx58B5wSkgk60ukq8vW4g6vyPxC/7FnPiKITLir2jC8+NH2L54EX8oVQMN43EkDsXre0BAuNB\nd7YJxSLj+qkRf9pyyPmKWLeB6pQPiUosgIOricx8g8sK7uPsh6CX70BxL8H4igV9cRvZy4q5Kfdz\nHi9cRKIZbNUhHI1mgsa1yHc/CXuLsVbOIyVjFZI3jH64G/FRNcrVWfgmzMAvu4m40onevA3rLfcj\nnt6OorYgxSTA6Glg7oXv+JN0M8ZD799Awkg4MQv+rcpx/034OSFqmqZN/kvXhRCLgOnAxP/oXv+4\n7ghrHNy2He7a968VMICvDg7eBQXvQeqLkDwCImUQdz00LbnUCvyUFW5+BvHcQqirhJR+cPgLqLYD\nR8CSiIi5iLAeRKvYRiBbQrXpUDItyAc0zAfA2BpGVIIsJ6O/YRC6aS3I8fWIeRrS7RqysQmMGyAu\nBVWnQ+kOhlcFphcaMF41huhyHTZ7O6SnEjj/IZ7jy1BPhMkoyyK8cwvK3CCdt+dzzhhNzepv8J07\nh+/KLhyZSfi7JuO6L5eUxfchLX8J3nwY1n2GOnU2mFMptJyn7PxujI3ldNvfSUg9y4reN2L9yoWt\nOYhWXUKoVwpoVbgfjGA4YEJta8EXeATv/IuE58topRLifBMsaCYhMgnm3YkSJxPIrSfcrR37hxqW\njB5kv/YaCRPH4i0u5sjv3+S3a2u4py4G7Qk3fZ3r6beik5wVMPDRkzh3VdDZ+COnXzdyKDGPa85u\nRpy8H3nyQqhMIOycgvZCO3Qbj/bTGkKiE83UQOjF87S/FE2kWAc72kByQPvXMPIWuPZTMDvgm8tg\nyeXwwx/QKorgzFkoOkDnBxdRGkpgylxwt0FJMRzfC5s2Qe0uEI1wsRa2vIUQPRHfbUNX9CpW/cOI\nvoXQFYJIA2JfIjHVYey9EzBEhRCnfoALfiJJqRzt14x9honSPpNh0/ewexdaRxe0SmgFoPbTkKxd\n6NtKCH0/Bl3hk9g6vYQHjIQYO0bTUhJWziMuGmTrt6iJUyBXYLM4mHSwkE9T6lFdVyFK9uE7vZfa\n2iZaZ1kI6Jx0TXgZjq5F9gxFpIUQfYbAlKHoDvXC7nyS+A0SyWsTsR5xw2s3Q+d56DqNsAsYMo+m\nXrnUz74X2W+GVdNBigLFCdIvNxnjP+JvtTH3p0YXDwGzNU0L/Efj/3EtYd2ftVfRNPjhCyjcwfCy\nUhBPQ/IgcPYFTYW4R6HuOsh6Fkqvh+F3w9f3QXYB3PM4PDERslRI8UPGXEhQIakXrddeiXHhfVhK\nzuN7zo4t5yOUrW+hNP1IfbVEVKITk70Bf3sv9GuPE64W6LwykYMqwbndsBaV4W9JR58VQEtVCO0X\nWIcJhF1GHZWAvK2R4CQj2FMoC17A1FJOD18Xqrme4OR7MHe+RfxQmZaei7nsuuuJUEsHVYSb5hD3\nyXNUxUskzjHDjPcupSkf+xH38d+gq6ojlFtJx6GdpEjVGGUJ2acSmRFGrLMhtnrRkvcikuJoHWEj\n2/pH5Kl/QNm5BN2sKNoHKtirvAT75mJtbERqdKMZ5hOouplAXi76VhdaQg9EjwHw3kNIl49C7dbG\nZ5Yb8Pj13GteTb/rC0B7kq6otfh6XEfNo4uIbWghWh2BvaGcPvpdTF8Thc3VCokGxK63EC47jTMX\nYji7Dd/X5zD2iKaqfDF7Miah63KSn7qFNY/eTvLBTPLfX4Ovh4Pt060MrDrFxOxxYJ90qetweTP2\nm64k3L0fNasXYrlORdvwJvx6Any67dKzemw4tNVArheEDOUroD0Ebjuipjdq7Aq0nccR9hwwHoSk\nIExKgNLPiTic6MUQkHahRqkEolXGHvsIEZOGKb0Txi2E3RcQDxShz7XS8WhfzvQyMnLXCer6dqfG\nZcGQ1o7R34hyvoxEYxfazjOI5EpCWybi792KsTwbf3oQa69G9Enz4KUbyXnle+iKhshFrFPvIKr9\nDsrCz/DA2WnY4j7mbX8TsbFPouM6xKBBiOQQvPkSWMrBshksOtRvv0O5Khuc/aBwG5rqpphtjDTd\nBlHvgz4f1s8C1ygImP9N8fvvwN8wTvgdwABsF5fStg9omnbnvzf4H1cJ/zlCwITrQQnjPLED9gcg\n3gttb0F2HpR8CGOvg4rLwTwUUl+G6bPR9ixC7K6AUf1h6w40vx9Fv5ZAWhIR3zqCZ/ej9fWi2gw4\nyjR0J+YTKW6jqUwPgyxEht9Le6iO6DXvIiQNHRIiewy6rEQkRzHirAPTovn4ly4lODYa97AYgtUe\n7LVd6O97DhElCPaPRdVVEm0uIzazG6JbJzrlO6J7qrDaBZ1NxOgqAehkKVGRBfgOP4vWVU9CWQ6U\nLQHX8Ev9yPqNxpBfgGneISzmcro1VVARm0jd8ERstWE6ouJoeqE7rrrJsPt9pPluHNo4DLufB0cz\n5GmEt3pxxXanyCKhd/YgYWE0onQYwhjAaBiMo2I02lkZ9+xW2jJLaIqU8fpnvdBxDfesfYpe91yN\nWLMWJt8Nsh6d5w2M4atIWVyA93A28aNzEBkXeOXoUgaeLwXPEJT+/fghT7AzksmN2+7DPyyT1rkD\nyCvaTfKGJbRN3o2pupVgXzt9UhYQ43sJ06gcYvdVccuiP2COMqI5PAgtBGkWCIQRI1ycv/099A8+\nSuDQNoLWOuSHLkPcNx0x6A4YeiX4mkApAXM51ObD7Z1w/CBiwTtIoSbUYSuQTtcj1HGwrxD6XaBz\n6k3UusrI/WE8odxjaEf8WBt9qOdVdHlDSKk9AfpZMHE0SucqxFEjykUTrr6zEaqbzGNNpPd+hMbn\n1uB4/0PqezxO/Le11I1xoWYaCE4JIJRHaDGvZsAhF1JWDoTPgbMW3h0OhjpIm0CC/TDofMTTxEeJ\nU9jftADp7LvIPpXzfT4mp//v0ZWugcKLcPEAXNcL7TYb6kOnCA0qRq/1YlfVfIap88lLGItccSdg\nhuF3QfWr8NN+0K2A6b//+8n1z+BvFSesadpflaHyP0cJw6Wd7ik3c6Chhqk3PAYqUHIETuyAonrY\nexL66SB3H0hVaLMGQsXnMHs3VG8Gy2HC58KIUBhTpx5ddE+iaooRvkZUl4auMYTq04joNBx9bTQt\nyiLaMov6wh+xuGR0oTjkKAeMTYSjAjo94PVSmucgLr2LwJUy0eVzqZ50BNePAZK3HEQxRRM43BNz\nXD4WeT9K6348sTokQypS3FCkfq8hBcxkKHuIaJ0o7IWTzWiH1tASSSHj3BHwPQyrMlB/TEek5qMt\nKEFntJNXfhq2CHrpL5JTV8M382dxrLUft5z+lPaWfQivhXIpjZjvvQRLwdD3KLSqaJsk6HMGt30M\nqZ8VY+0bxH9DHKLKjXdYIkr5cixKFzuL57L2zCQSAzfytHUqybs7UVN7wdjL4dReWPIs3PoCiiUZ\nKacNW08dtiFNsGkw+4Y+T9nwWSzYdCWvjb+a1j79mF/1Ci9KXyLNCsCRYti1A8bGI1Q/3V7YATFt\nhE6m0OvJAkomuOj22hqYex9qUyGt8QnUnDNhHTaM+NtvRzv2AL4PXsQxMANTSRXGr0qx+TqIBEA8\nugXdfU2I0DE474crZkCwA+p2QUUXJMjQdDsiGECq9qFmpyI96UVEXDB9OJbqFUSRRmv0ByQYO6i4\nbgJdpRfpubkdXcNSMhQv1Pth5GxEjY7gTDfutBA5kQmExYdIzRr69HEkDTqM997ZZPTpRiCjP8mT\nn+di+s3EXHThSXXhdiXQnl9C7M5KZDUPHtoCD/SFrB4w9VboPhJalxNx3Yo7/lvm1zyCiBjQrApW\nOci9vgaeCx/D6ayCswJVF0D5uh75OgXzO17CaWswhLLw6jtxNhRBsRVtykRE8njo+h6Gj4PCtRBs\ngUG/RmjK31e+/0r+ViFqfy3/uD7hv0CbPhsk/aWOsL1HwPVPwZNrId0HnV1wJB2t/To4+QXhouGQ\nlQ01G6FPK+LXNrTRLchmHaK0GmlHGGWvQGyS0GoyaTkzgUBqIo6eHrSoZro+7kPi9ofQgrF05Q2E\n8Yugthgteiuaz0/X9FF0le2gdmw8olSjuKeedvcQjGsaabsphbPLe+LNbsX+8LvY3i3CulHG8nEP\njL8Lo1u5AU2KEE70kdRvHx61J3LkCL4en6K/1o7dbkD0HQR7z8IpHTg7Uaq2ITproV9vhMuIWHkE\n8nMI9kxha8ZUqkIZdNXYsHT5iFbC5G04hyNUijK8jPae90F0Pv6P5qI2yAx4uZCohfX4Jg3Bdvom\nHF93EryiAlntw5HeTiLVGreN/ZoXMr8gdcrdSNOvRSQ70bxdoAdCftSTGwhLp/CLty99OaZ4imL7\ns9P3FjfL91P9UBo3dV/Di2XvknfajXS6L2LPBNpyY6AhgPjQCzFRMHYadIzFcMSH1N4BCVkEnt4N\nF84hJcfh8h8he05vTMNHUPLgCxx4+AzerChSWluJu3sJJdlj0IqqkD74PbqZEuzdRWBtHFqLFWK6\ngUsP4YEQcztEboTC8aAYEGnDkNQ7oK4DhuXDSQt06Uh9pxzTdg9uOYsotYr8gzaMZa1gc1CmjIOr\nX4XNb6NTOwgrOcSXVOLeMRuf1okSCcCPS9DNvB3d42upPSxwz7DjL30Vx3ozsX+oJWNfDVPckGAD\n2ZMPsaMulUa96V20oirY+xEggf0Z4qvH0X1ZF0rzQEhMwe90kVrdwDOdz3B6egrNd8WgfaAhDBXI\ncyyIeS+gumXef+wB2jKi0fWfg1oRRon8hD97BVrNRxA/G4Y+gGawoDmT4LNx9G/4i0EAvzj+i0LU\n/kP+Z1nCfwlrAtx0AFZPhSmjYMmnaMWg9p0EJd9CRQ3a1JsJj7mI+dNOCuuuoJ9zG0roIJGJiRjq\nfLSc6sRkqid0VR/UYx6s3hocRlCaQ8i33Elk8yfQWgwImH4docGfYSyPZsDj39L1q8lou8KMTLsf\nHrkC1d+O4u3FSb0dx7AMKrt30v3patQhMkq4GjErB+QG5FIdkmsoFyqspPXqwPz+aWTFiThYhprQ\nA5b/BGEFTr+IVHYS7auDGF5vRwtXIgYYYdkCIkopcmk0GXjwnDqHst9NIMdIl1VP1Cdd2BfUETyT\nhvXdd4lIPkwfXyD8go3ISLC+5MbXeRR3Sg1WzU7ngYOEr6imr7WZgU8uwG85jPHiZyDrYFJ3JIcP\njo2EnnGQEYe0/gkMTjO6KBUsXr7RaRQOGszwyE56igRKNsTgPHUEzF6YcC/k5IF+M40xuegHpxK1\n71v49gg4/aBKcKgNJmXS/YrJNN8skVofhOnXQMNp1KpzHPC9SWh7Kflfvo2663VKj1uR511BqCuE\nOP4cUvsKWLAKln6PXhSh1DdDx9XIzr5g+ANc/6eQz51z4ERf6MqGdS/D0Ai+IYkY177D6QGj6db3\nKM433IhTARhohtowJNmgqg7J1h0qDwB+NIfg7M3jGbR8BWHVjWgzo0uUCJ/9nI75MjExj+O86Uaa\nt7+B+epxRJ/PQFzjhfdvgygn3GKBmgoQSbDtY7S5j+KJjUYesB3xRXcMkTHoTn+CZdodRCYORm5Z\nivmTWtikEnN9B/0Hqzzhf5VuJUe5p2Y58mVz0ZytiAf13Nn5FieMfdB5DhJKLEKPQFfSAYGtqMOu\nQz31HrqqIjpmnUeOiyd6dyXa+U2IntP/npL8n+aXUsryf6Ql/O8SnY4angPr34UMDZ95HsYHH4e3\nfoCcCURGj0SWZiLmLeOtMwNQx6YRsoYwXmigvSEBXf84LFfI2L4rJny+BIu7E1EHIrc7IvprlB4O\nAs5MmPkWojkeQ40F2ZYOA8cRyfNjNo2HfRvh2D4kl532/HL8FoVevv6kSb3Rnv8IqSOfcEUuavkC\nDPZX0B3rQtQXofe1Y//6EMbUachNEcJ6A5I5Gt4ZDU8vhOJNoI5DN3EhUqeOgLsBxeaharBCOEZH\nrd/E2Jmv8PiiW/Gs89H8dYjGQ2HqJIn6t6Npf7WWpiI/LV063GkKSqQXjupHaR45i0NbvsTVvwFL\nQjs9Z0kk2hpp1RtoXb0cw14dGCSIfwgcNTD8TdSD6QREPu7lWwkkR9BvOI3y7Wv4Ps5h3Iaree7w\nGXJ+6MT5bR15dd9BfApE50HPKeD5BqU4jk5bkNa8UiINBhg2HCZPA89x6G0DRcVgz0bsXA9JGTD7\nKSL5E2mK0ej5dgm9pqXTY8Jc0hZOovuzIbxnThHTsgvPmUqYvA3qNsKcTHSZTuS8TOQeibB+FVzZ\nDSQdwb0voWQugJtXEPziS87MiaFzghH2f4S/K4Hey+qw1foRMwUcCcFbbihTYd6voeAa0g2HoexN\nKIjhfEYBJdVp4PZgCiVi7jUdbdjdaIEwth9W4j+zmM6rDpAcdR7b3g8IJXzB+QHnCLoEFDWg+pvQ\nGsrh+HcQ9iP2f4btioeRKiSU6/10NDXibg4hVj+C6bPPobwKgQ+6GWlLjaU2KoNXd6whHT8LJ7xL\nY2ECYdsi/OtVftw8kREtDRiPOQlPBf+MBPQ/KkSMJfh07xDuFQMGC4a427GcDdFs6IPImfb3luL/\nNH/DtOW/il/GT8EvgBAhVvA9TVeozDojsWPwbH497GrEMw/BotvRapcQVpagaL/F2zKaruAfCH21\nEiUTahu6EUr3YJlaQHjZAWSvl2APE8KnEekuoU+8ATIfJsr2OdKXT6JmG5EMQ5CXtCBc36Atvgwl\nvAdDVwGseQ8SnXQufAib6W1yPUPQ4vYjJ36EJNLgqZtgZAHB3U9hXP4BQp6E5ttOwkYvumtiEGsP\nQlUVsisd4+KJIG9Hs5lRgsnIg++G3Z+hO5GCFmPCt6mSss/m4MlopM9Pq8nIyeblm59j3vJXcIWK\n8V2fSEzIhz71VZg0D+W+YYgkM+rkY8ilVbD8UdKjXCi/vwGtqQZhGA9xJjr6tZI2UQZPX6CJiM2K\naDuJqDbCR7cQCORTvbST+LpWOvssxt5RhGSLQR0+GXt+ASI5m6zTG2HFAoRPgolPw9674A8TISED\nMaA38XVGorULNI2JwZ3rQZ6VTmzivTiWfol0+22IniOI+foHIrcMQ/ZWIQZdgzOkJ2HSG0hbT8Od\n4+D9m5ErfiLvSQsluwfjM83AljgGkTQWqjaC4wPwJ8LvRsFta6D9TbQDE2ja4KahqJa4SQl4L0th\n+dBreWr3Umqd3Yn8JpGea1ci6jQ0D4jhEoRj0IakQ/HniO1RyP2CoNWDJY54umg+1MVNPZZhUSUm\nhqpJkY9QYImiundv1CQLOVtXYZoaQdsFyicqkVQHFbelkJbhxOItJHjt5Zik0XBqM5RuR1R0w9Bw\nJfIPa5DvrEVMEYS+T6bzlXWYpg7Fdr2GzpdKXJIbe/0WtJzBzCn/mgHKHu4c+RqLP3iboSGZ7ZZ7\nmVH+ALJxD5Y3rYRvzEdES+hzXkZPbzABl1uxmhdBbhYZu58FJQSy8e8qy/9Z/n8py18Qbrr4lDU0\n087gzgp6tCVxnz4W9h+AjEzIjBBp3YB83IdFq0JVbSSYutBK/YQTdJgbK0jpL0FhC6IujDYqmZYE\nHbENoLvi19B2Fl6eh2XodDRpFIrvBypSDpMx7Wl0DRVoP/yIaVQARBHUnqRpuJXm5A+xiyAJZ7/C\nvDQboX8AjJc2Esy9uxNor0a8tICLMf1wIWEd4aOGVNqGpBKvE1T16seW/LlcX/4jjvZDeLsU2nfM\nxmWMInWIwJziIpJUy4ivXsKQpEOLDuOb9yANh+PpHBdLWmsM/kovnXNyia38LXTmItLTQUlG6I/A\nlC/BsQux5HVkoxH2K/wv9t47Oo4q2/f/nKrO3VJLLbVylmVJluScc8LYGIPBNhjbpDEDA5g05MwA\nhmEIAwwDJpiMDRgMDtjGOecsW7YsK+ccOoeqen9o7rv39+5v5s1dc+fCvJnPWmetrtW7q/p01/6u\nU+ecvTcDXcjvbcA/LBZmj4dv34WnPiAYKEbftg95h4SUnYN5yqPk1tUgtq8kesmdSPYo+M1C6Ncf\nkv60sFxZAmfCWE64wbAbEiLA0AIXy5AW9gHNS4T5aRyf30Pi3LG4IwK0Tx5BbUE7WpKEfftreK5N\nIatzBbrDVyHbJiNfugYSMtAiHkRcqIfrHsX72avoEjZgM8zHd3ItYtUuyC4E83hoGQdV6yBCgy+v\nQpueTrCwDEe3SkXuULBewpf3Xcoth9/BioGu/HiGPPAtUpIMuWGQQbPloN19Bz3Pvkyk0454+g1a\ndt5LQuJFcHfgCGlcVXqG2Wc+JHb5KtYobeRt3MuphELWnr6cq/dtQEcKlZUOUhsP0dMYQczdYSLi\nsnG/eyOq6yl0J2LRdr6DSBkKD+2HnFGItmbkh+uQ/thM+LAQr+0AACAASURBVLp2pPlGvNI1RFwo\np+vFAVj8pzFN6YOp/jTapi0wNIOsmgZWyi/x1KSnqcwIMP3wlxgmTIJ1YaRjp9Ad2Ip297sQ+R8K\n8A6/qTfwqehXbD8ZYra7DqKy/7OT/Qz5lwj/jLATwf3cDP42OLsapBugJAwlp+F3f0TTgoQGmzAF\nciFqIZLjTuKXfcnZqWmY0kLEurrQ1ndBtJ5gfDTS3nZicuOw5tei7f0YzepGJBeB9DVi8AL8J8qJ\ncqYj/fAkWnwa3sk5mJudYDmF1s9A272xRESEcOmMxNVmIsxdUOmDR38Lw8cgqSq6+4ei9deRePIk\nypkwJf0uY6AujrSkkyjzVhG3bjmFMX0JRXyKsfJxxPEDtBTcjj37coTnQ9j5LPo0GZ1DQgsCViPm\n/beyMf0I14zUaE3sx/uecVz1+nLEgkTs/nokuxH1fIju+KE4osfBzgfBq5FU0cqFWdnkFkxClHyP\nTusm3NSAbrwFKpZgnrCeUOVlaHjRzOcRzibU7cuQplQjvroPuoNgCMITM6BwLIyeBTnjYOkKSja8\nS8EDb8KhmbCnB/qaevd73zgcmoKwcCDi3veIeOAWIgpfgYQ5qNWltJ39gerxVroNMeQ4HyHu62Uo\nrvkEfqxDcxuwfPQN4plJVL64glO/G0Rs/sc4ihPJuOQKKH8EvvoQT99kjHm3oev2Q/0KxNYyjNcn\nYkwdwITISr49dJahP8p0B6vRVl1kUJ8LMEJCRJhR8tKhQSZUVsHFfcsJPDaYIa+1QsjOqYiFJKR8\nAPJsgvWfEirfRW6+AXFsIzfa48FcxDjHWZKJYp1SxB+jbsEU42OYZSwLRlgQF85gKLwGy6JPsL6R\nR9Mlo4k6vglD+zZ0LfNg2/NQehrOliImXYa+7jheMQvDiFfQPbqVmFe+Rqt0IZSzUCkh1DCkZkNj\nNYamg7zgX8IXKcM5VhXLhOffwK76wGmEmABC2Q3dE3sT1sO/50sGvHLMP4wAAwT4eYzY/yXC/5G2\nFRB7ORw8DGvb4YOvQAjC2o/ITU6CfS5gEm9CaCLR/Upwbu0hJbeFnkEWwiMMdDoteIYNpL7HheoL\nUGToxhSoRu+/CkPLD7A3ATVuLXLoEI78xRCbiprYQDCrE0vRWrq+f5dwRhd9NlXSM+ASgo565Be2\nQ50CCx+Crz6DI/thQBeG2Eq0sxFo+01QG8YwRoMYByhVyDYTCEGEkMCQDFU/Ym1pxRnsAl8XbHkN\nztZCWiwibRbh7skE738OQ3YLN7z8MSlSPXG5DzLWPIGI1r2UrDEz5poPESYNrVujM2YY0T/eD3UX\n4BdF6FxhWoIy2bteQ3dqFDHXXIEW7wWXHjKWIOrKkQ8Fob8KdT0I3zIk62m0GjPCtBemLoZwJzQ0\nw9mjMOpyuHAY2upJKz0MXw2EIzL8GASdAvNPEN18Bp3uUxgKfP4q3PsEPHMfPLwUcWADsTsrGRsV\nhTz6KYJfLcf1lQ8x3oP+7tnoDR5EzauwpIjMHwUrageQGKeSeW41F2p3ka21I5vS6RqTQWKpAls+\npuuW17Hf9yBCvgDj57IlezT2uk3E3vI0OZeHYLwBpVKhe2oBsdXFyNVt4AyhpumIPtNJ0BZAm+xG\nfPgCthHJ4Avi0iXi7Yqm70QP4tIn4HgxHPgQ3DbU7H7EhIpp7VPEQtMHRPm9HE+eR0vZegJ94lBn\nTiFmRBMW3/3of29AV63SOiGaiJNL0BfegfHWL6G+CnQN0HoEafUK2jan45j6KaH70tHdpQePA5Kb\nYeTtcMVbkPcJdC5GilrAosfWcaSgmX2XX8GMVavpih3CTlMmV1WshpLPYNC9MOopMPxjR8z9HPiX\nCP8bahC8W6FuIzSmwasHwGAgrO1CqX0FY1U/diZOYvS5PRi7LuWWWAX3wERk2omu8KAaUnHPuBW5\nazWD285T7UymLC6DeMNlxBka0EcnIKQa3Aikq1cg1pyBkRZEeAz2Vw8gpJm4Y6Pp1tmwbe9NVagb\nbcXzcBFRO+rhVy+ALMPXn6G9sBZKu+CV3yBpT8MJcCfHQ1QRSAoU/woY0NsvIdAioxCZWb0BK59e\nCQkBaIyEoisBF3JyDKLvQAJnN7N473qMo26A2rNMStxEaHwp2vYYznxhpKh/GZyOIelZFWobIR24\neBDVMoL01h5K0/Io6NiJNaiH+PuhMQwXq+HE20iXmaFMJZSRhmHEAbR3Z0C+DmFcD4eeACNw9SIo\naYH4WBgzB4SgTF9DQdVeREUH+lgV9d4cpPIyOrpSsHU4kY3tULgcceYdaPbCrPUEH9QTfEslnHIA\nDo5HNyYfU2oM4QU9KNoKhHwjeul9ROU1WCYk8fy2pfwh/2E2TdaTX3yQ+j52krQytG4Vya9BVA41\nOeM5/+vHGbBnDW7fj2ywX8bLgV2oNwc5vyJE9v0OjNl2ujuiiM16CarPgj1EVWoZ6d/3oNP0CFMp\namQjw4t0qBYV9+Y3iB81AKl6B3T8GsKjYeJAtH170cqrsckKzza8SLBMQgqpDNUfgUyB2mRgq5ZJ\nRUSACYXRfN9/BFMbz5F+QgdDEwideIuO0Hbsk9cg738D9DlodY3kD+vBYHiDsG85gfvbMB5NR5xt\nhul1oJShxb+P+kcLmns5OtnI8L3lsLkdutxYq06RkaJHu3ILIjoHTFE/mav+d/FzEeF/7Y74NyQD\npNwLQx8G5xCQ6uHoUsQnl2P8bA/Ub6PwTD3VydfC5O3URTyMGtONNs6JppMQwVqyt7vJ3NiJvipE\n9rZqChpqcDWv5mTDORov2KmcMIrmCenYnvs9hDwQ24RiP00oMgIaPcSc6sB2+a1U/qovp+6YTaR7\nFC5LIzi74OCtUL8ZbeZMtMQ2xCw9InQMAoOg4HIu2i6Bg5uh/1JoOw/etv/dtVOZE2H6eui6CPEm\nmP04LLwBKj+FuqNI2SOxrPsR86IEEux+HFk3we4QoRInGKxkPKdgVDrpqTBAgUzTcDPhWDvqLQOg\n0oY2ehQJBXbSlWq0TIGqk1EPfYDqPYZ24Gs0FegzDmGMQG+tQNsQj0jwoLQmoe7sT9i6kGB8Er6c\nUQQun0OwbQXebVNo+SqLqI4DnB8SQfNyM5UfzaEhM4cwFpQYA16/RqjCgBIELScL5dEitAdDiCo7\n0idjsK5JIrJRxXD+IvI5D0Z5LWZDKYaTCjw5g57icqo9J6iNl7nJu4lx3ef4w7hFWMobCYRt+KVI\n/ME2tGtfoigczRy9B3ukm9NaJq+8+wy43WBWSb9BUP5tG+47Z6FT6vFOiCHUtAN/6XFCfSZh0BuR\ntIVg/SWiWqP5PivFL+lIuLyZDrkabUAILoTg/V0or59B+7EHTOnII6YgDR6MaWoc3VOjCPv1SFVm\njLoUZk68iyvcjdgbsrjFe5hUTxvUlCC6OzDG9sW+oQTpq1/Aqe9h3Qc0diagG5QN+95Bd6gLw2YP\namQX2GUIR6JuHcSW0lhCeVNwR9hhyAiwpMDRdRD2YygqoMOTgvLu7RD6iXz0v5l/7RP+ORKOhNbP\noawaTvohbT7ByYMxds9GiplEdFIhG7V19C3dTlpiMU+vOcKLA99Dsz2FcIDQfQVVVRjt/SHsQ3c6\nm74RO/AnGagpykNf0UxkrcqBefFk9bxBfHEm+nHXoN3aifr25xhyRpFWrtDUlUJnXiNNeblIpgzC\nwoyufEdvHoOec0hXZcH5WOg5j5Z3DtH/OowXOsFsg6wl4PPB7mXgc0Plu2TUbkO13I5U+CSYI+HH\npRBvgahkGPUNfPMQKGFkIcDUAievRr5sGeGP5uMdFoulfRRZV75L+yca5rCOlBc60BYXIr8vQbkH\nOWERsr8Hr9xGsygkYc4KtBNbYNszUF2LNioDsUaCgstQpMOEYwswZVbT0/ccPl000SsP0HlrOkoq\nSJqGqcqO6bM6yq+OYdft44hQvCR21BHvCpBY6cU3YzGGhGosvjR0oRLo2YkiX0pg+y6ENQr9FDeG\nfiVI0nw82zdjDNUhRgpE1UuQeDtopYihViI3yYhfPkNZWjnm0lNcGnGQwevKeXboY9y2bTn5D5xE\n0xsJ2+eixjqotes5k5LPnENnkAMCLVJF06kYnEYi5w+i7aZvqb9nPPYDH4CzB+PuarLmPwTxByHw\nMRgz0WZEUvJbwbBZOrRiA6quD97u8xhdNmSzG0VfhzbQhnz/x9DcAyW7wP8ZMV09tE+LINRuJKnD\nBhVboDMAgSTEmdXokvqjxaai2IejG+pHNkyEwyvBaiR89xr0dw1AqvHDqUfBlonUJEEoBsY0gH8l\n0jfDcC57lKNTFfpOXgRXNYE0G87uhM56GHQ134SeYci0XxPx1ATkxe9B/tDep7N/UP61T/jniGwH\nnQ8efh9GLYOcBRiTPkQ68TEEmtC5api45xWCBhsi+2GqmuMJlW1EDL4TenTQWgXjAPsZcDegIwpT\njMCyZwzZW86RojYgCt1kqOcQ8V7wlKAZXeiNS5Dvu4g85xuEzYm16hSxxumU5h1HBNJwXf9bmPoV\njFiKWFgLUz+FnA6IO46wuRAnP2GY50OY9QtQvCCckO6BXXPBdZGwyYESNsLRlyBmIBhGgH8IdLnh\n+1ng3wA9lZBnBZ8HdJFQ9jz6TgshytDc76GMDuFYNpKwTu2tnDX/I+jshiF22DgU2n2YlAK8cg3V\nrk8Q036BcMQjIkHSmRCNa2HbWuRjF+GrI2ixvyBmwm5SIu7B0t5N8rKTpH2tkrp4E86SeGx3T2RU\n3lRSvxvAbC2R6av3kF5ThjIpB0/6CmKkH6hJWMvFKZW0Th2Bd5QZw5P3YUxOR64ZjPy8Qvip5Zii\nrkK35DzCOwOS74P6NyC0EyXmEOF7riTi4FEGv1+MkwLsBh/Z/cw83nmELxfPZfnC6zllycXvs1Cp\nWtkfPZzmqDSsU+9CnfgYgTY73r4j0SfMID2qGV9PGsabv0HankX1hIVgicO0Zx88sBUOpKKc7MQj\nXIx7qAP7KC/CHCAmRqG9BCp3+yEZdOMtGPskIb9wPXz9PGQNg/xJVE0YjXXc7TTdnIXHYsC7ZyWa\nbES79mt4rAlK7SiTnsKVMoZTP6wmuO5FaC7D296A5+l+JPYLQ6QF0gZC2ABD/NB2AOJleNYAv3mC\nQfpRtNFM4JoINFcZ3HR7r8jaVEgZyPA8OOh5Fd8LU1DWP4N211S4WPzT+uzfwM9ln/C/RPjfUELw\nwz0Q0wIZg8CSBD21SJuehs7zsOMy2D4X4ZhOFSE2vreX+A130V5hRURchdAUvBf6waEcKJfB7Yf6\nnVAfj7AK6gdFYytxkRhoJMLmpS3bga89H+ErQxhTwfinbFQjb6ZxUCI5H+9haM9L+GwxuBregoQh\naDEONM+9EH4cGAeRD6FszQSlkNT841B6Ixy+A8I9cN4GmTdBRwtBXQr+iBHQaILPHoYTa+HIKjjh\nhwOAnA+hNnAmgTsZTLlQtx5pkg39aSshswW9WIMuYhdMmomWmI7hszsgWYXLxkDu49DSjKlsLwne\nFrzHvsXz6WWEho4Fkw6SRvSWoI+0I0wCnaoSjO7p7W+THpGSBSf9aG8+hbZgFtzxACL7l5D7IkLR\nk1zSg7lREOOqJbtiIM71GXRpUfhMDn5v+w0dEW9zyjiRcvkHaqPbEZuP4ctzII/KwjBiPrIc3zsf\nrk8C2+XQ0gB6DanrDbjCCjPvgOWvgz0fUfQ4DksiT/z4WzbNvYKnV7xL430jaH64ENcwBwtLDsPq\n7fi/+5Jguxnb5x6EdSBKHzPJ17bhsxg41/8I3dIJan93B62RF2jeXEi7dICOnSe4MKA/Z4sGU3so\nGfVwAOHfSFwSmArChDJNaGM8aDddoOuVawg9+D6Un4Qz29k1ooiOrkMMaBrL4YeyaQ224tu7mq6X\nimheOoYj5+ppfGA+YtlSnE02tEH5aEkCsxMiCyLQ5c6Eca/CFe+C5zwMd0FaMuxPg6k3g3cVnLiW\ny4rXoKb7cR0JQFQsjJ4H7aUQlczIfnCwRGA1v4xnsUAJn4VHrwGv+ydx2b+Vn4sI/zzG4z8F3e3w\n2t2w6zsYPAHyImD8/WCRQP+nbTaRqTD5VfBtB0UF+2gSdFlUNnxOUeNJJLWH2KvXwtf34nNptK9r\nxHLXJDAlQbcXpCrCwSb0pdVkN+oRMWaUqlR80zxEBp3U5UCfujqEM9S7oIaCJlnoTO2Le+AdRKx8\nHttlz9Joe5xQaV90qTPAfDdCzoHwB/DFK0iJ8YjxY6gNekiMsCAnjUPo56LpXoHqI4iuGGI2fI8w\nl0BiHriMMOBqUI/BmFehcBJ8OB/u2gI9ByHLDOc/h4S7wOzFfvwI/lFGqPoG0b0SU3gfHmMNnv1B\nPvzjJ7RYHDz75YMYLWOgfQcWt45smnHldlI3qT+5m+Lg7DaYeQMs3w33zkRa2US45mPUb5qQigMw\n7xmE9TZU42S01e8juZsRVz707/+V4kYMn4bh+HcEW1/CtGATasMi/GlhIvQVOAPfk2I0EA4UczSr\ngOjrmrFctg5d0NpbmXrQZ72pTNu3QkcxYZ2dzlwnuqpOonv2QeAULCyC3W3QvA+5Zxma3cYT0lrO\nebM5lpnHoKijZOYdJGCR6Bk8GB8pGPxZqNsb0R/+I+aSFoxDnBTdAt4DdTjKA0iVr6P/pRXdpgZa\nTkhEtIeI+30/oif9AMfjoC+ILjCckuhuUXEUhjEYNbR6icjgH6h3nEGZpMdR1p++XTa6ZCPJOz+k\n6JwFT66KwatgiJKR7z1I/E2N8P7vaLl7JNayEozHfkCLFNDjg/mD0HaXwrYdCGcq9NXBJ2HI89Oz\npYnIpdEQuwByRmHYtZD4YBSr5w5lftthJC0A4xeAv5rcKAOltQMACaPzFnreKyeibin6hkroU/RT\nePHfRCD480jg888rwvYYeOZz+PELOPgmNMjw6TIYlgfDOyD5T6u/WgBmFUPgLK4zN+C3naY9Khtx\nyEtioQl9xkjUnnqavpDRp1mhcicEOyGkEUJPyyAz9kt1WE5ZUfsGCWe1YlIfxvjxG6Qc7IIJEkQZ\nwFwEcZ8jDP0Z2PcPHNU+oO/8XJzfXEH0gP70pF5JzHdNUHSSwMAkQpEqNlcn0u2vwuCZnNupxzzy\nD0S3HUfpXEf949kkh4ZjGDWZ6lFjSXhsObpUO9LdT0FtNew6BDuXEz58P7J5OKpnJ9SsRqo4ivAZ\nIDEfTnyLFLJiro0hVGTHkPg6YucKrDtuJhxuZ+7Hn6A2HqPa6cQgrUW9qS/e5F/S55P3sIlUzlNG\nSl4M1pYLEAAcl4KjHck+kK55BowvNBPddQzKfTAoA7kzAkUMQNmzD13bErj6sV7x1GqgqgUp9VLU\non0oxk7S/1hNZDiSCYuP4lGPoOubgJ7b0FduQJyV0U9PBIOMv/A2DGduQcIMmhWt8gm82fHo2jtR\nLBKBhvMYpXZonwOTZkCXB/aqaEV+rJKe2e43MbZlIhe3o/XkEBAn6Bh8ghCjyTL9HnGZAObA+HVY\nR9bQ3LOCunPrSX1zHWqjgvRBD6Tm48hvQqfaSE7ch6jrRp3Zg5wM1IAYoJKr07H3PcHEegPingnI\nvqOkNe1EqcjCeyKINc9B9/hGzqQUkffdOWqGOEBzkrwrBmGI6s3FMf8GOssfoU+JAulhRKkMxhi0\n790wtAei2glvbUfulBGaFT7xU33fTIrSxkPwALhroP0YpsBgMo4EKOFlClt0vQErcYlIOVegAWgC\no7gaA9MIpRwD/vEEGEAJ/zzk7597OkJToXUnzH0GnlkPz30J2VfD6nfgmctgzTB4fxFcngo3zMNa\nn4ehxoAsOZAtXhKettBQPRv3lD7oLBrOaS6UWUthXAqYBVXlBtQDPVgcdqTZNyG0bsKxGkrjBqTx\neligQa4FNunpkm5HNRQAYCKSMYH52Ho2cO6aqzE09kM6d5SQ2crhzlf4XFvC4cjNVI0poGKwk07q\naWvOxSRepsZZhifkIqMDDBkzIcKJXjLQ/OoStKMXUFauguGz4cFStFv/gCaq0Qomwr4H0S6uQGu+\ngH9YmHDrUZT+E9EmzIRj09A3VEDl1/DN/dADOk3Duq2a9EGx9HVYybh+FckBH1qDjf3DZ7M5cyht\ne+yUX6LgHlyItmUk/OIW6GyBQRPQXqjGk1sD0jgwRkN7NKhVyOIkQkiESxvgnlwSWoqh32BYsgox\n/22MfgchFsKchwn5PAz8qATHxXhc7g582gHiLnhpiYmBAx/i5RAt+reQkm5GDW4kcOQBXDkJWF1d\nqIqGqjOgdDfB8SRIKofRi2DMNXgWzcPQmkHC7osEjIKOTDNabTQMvRche0AbSiq3IRC991GWDEfG\ngTCSvn8l+TorIqMvUh8NLcuOWHQl+hnxkFtFsK4OykE+CBwFNgErJPQdCSRVy7SVKXCoEDJLIe4Q\n7ppuus83kf3D97R1jWNl5qWcuu9rimy/5vxIB22mFDqf/zWoKtqmZ3B6a5D9pyBhNEyZAk9VIx4+\nQTjubo43zKFM1x+mPQ1JY6C/j7SovdA0D/wHQX8JOKZB9UGGdJ0m2FJPbU4+yvW7YdJvQWvlnvF3\n0162CDybENgwMOEncNz/HpSw/Fe3vyf/vCLs6YSVd8CAK6HwT1mfZBn6j4ZFU2HWBYirhr5G+P0O\nyCtAOm3E/nmQtEVfIt+Uj72xk6iSZjzk4xsbR+2CaMoGrqc1J0DAKWja5+esbxCSToL6N9BMDkxn\nC9GcZUgOFZIEJLlAUmhyrYXAXghXQ2Av4uJiLPG7yfR5qRtxGG3Ic+ivXkZR3D1cdSidwefSST4Z\nQIQVKjlI1JQ11HZ/TjSDifzBjEh9Fuqug2AtxpRBBBKdyLfPAk1DefQ+tIpDsOoOvLPChIbqkK+6\niNxzLeKHJAxfxSIdqUdVPWhR++HM5wh5KhxYBJ0tiMd/h7juQRx9L6AUn0Cb/TacvxFj5EiKijcy\necxSZuXdwcyvPsfoM7BjRD5t3jLILYCOZrSC0UgnNCJLUyF0hp6CkVz8xUNw5ZOQrSBTgag/j5ow\nlv7nv+kN5JAiwZaKHIhHQ8VfNJXOfjkkVFVjq04idoMOT7ie6MZ6WgYNRg220OS6FYsnFjp3IPQd\ntOXU0p2VjxSQMEU9jOjxIXwRcOcJGL4Sqq5GrZuAllPKzlevo3xoFj5riAumfoT81SiiA4ETS7OL\nCPJ775nunWjW/qgpHYCGnDSL8icvUNZcj9YEIt+GunIrypYIgi0ShgkK2iyBMtwMjQIckZCowtEO\ncgr8nDmuoH79e/hmIXzSD7sURPV60BtDDDvyA46GTspbN6KPjCalxkf1AA/nXn2fYPERetJ0eFOv\nhD3REDkYQs1w5gY4cR2+E6+QUraTpOhBiAE3gtmKFq2nLbkvNOXDsUjYdBd4NbT8IeiHVVEUPMV5\nDrDHVgldb6K1LiHV9iOvb7wSrNN/Ks/9b+NfIvwTYvM1wTO5kDYY+s/6zwZR02DIRRjV0lvSu2gE\nTEuA36zC++gbNPs7sXW4CR3TYdrdjG6rg7ThBrJLq0g9ug/qjHSnRBGVb0E/Yxia1ApqGK1RRa5p\nRTVORdI/BJudKE2Z+MYsJHH/BSTfSXAvg857IHIT1I7EHPMcydZHEV13Uhn8I/qCuRweAYene+hK\nEjh1MtHhNeSfLicv8Ar2jgWo7mLUmHxIehcq7sCoaAS1bgDkGxYiJkxBe/FGKN6NwfILdIyDyv2I\nxESEwYrYe4rQ48/TM6MST7wHkhW0JU/A76Igfhy0dELJuxDVSjgcQ8cNg9Dc1bD3Isx4pTeUtaYC\nXcBA5v4W+nR1cf6ZXHySDwyXI5Iz0VkCRB7cD4Y8IkzDUEtfRKl+D6yLoV1CVqpQ5FTa7alojesJ\nFd9G2L8D5HT05rvoCj/NwbGX4hk2BYIxmPxhUktLUCbJhHLbCE9agNmfg1ZVhlb7GqpdoItS8XfX\ngBrCunYVflIwfO9GLVkFhmRUx2A0s5fwtxpjF71EwYpSTD49/c6VIzJzCPb8QENOKgkVvedA06Dh\nTVRHG0S1QaASIjPIunYyxR910+DPQ33eS9g9grqGZLxjvkU5pqfrohXRqkCsAS4NwxKgvw8pXTD8\nWgjlAJU7QO0DnvHE9ZlI7e8kEjZd5N77X8fk8rPbsw9H40V0lQeRzYLgO8/SUWjGkXMvRGdDWx6k\nfA4DVlKqm8cZ6xg8VzyCve5HKB0BQ06jTkgm/pNS6PJDzBnIAjz7EUXT0WJuQqm1khm8iMP3Bpqh\nEJHwLeXiMKea5/1PuuvfjXBI/qvb35N/ShHuX/0t5E6GvD9TMFWIf3+tt4K7oTeYQzZQvWwV6S+/\nhbhEj3SHQLu0Df/WZZhig4gOA9aSJJxxHxDX1Ac8LiYEvsDj0ePTRVFfPZ7fDrgTj99LKGoUK0bd\nydPpd3FODRBIHgmHmyHqRYhdDerL4BoGIhaddTrRSXuJ6fyR2varSSYXfyycXhBFu/sL0vd2c6Lx\nNkTcKMTWe9HVe/C2342mi4dKgVGyE+Nb1dufsIeWMU7Cdg/hb9oRNXaU4FHY8gJc9hza5aPApsNQ\nmoFd24XBMxNtkA5cHsJjsuCSe2HPYdjuRV2rQU07Fqef8O4m1I5y8OwCxQ9pLZCciaaLwxtTR3qg\nlOrmB6DwFsLbPySq9XhvWZ3BBYiGR3AaxtOxvRa63fDOfugzHZ2jAYO9DcVYh+7wRuQTW1BEGEle\nQISvkqqUDPTBBuiogUMRSMpwkl7rQArUU6MuxuK8jVB6AWqHwJdqxGctILLcjKaG0YydpNxxBmWc\ngrb9l7BvGNK5g8jbhuI4W4F+/HAMmSEiPAEsRw+j1yDcfgZhjUWOvBSq34WuTagWFaHvgzifhtZz\nCE2KxeE6xahJY6hq0RGeNoe2i2XYb7qZ6GkzCPcfg1Eo0KFAcBhUOuFEFAgL9I3E0gSlP0CoSQV9\nFTiKMd32PIZYJ+0VRuR8hSuOleDu1uO1F5Ae6SFtsRobcwAAIABJREFUqA9vyVFoasB85ChMnEZt\nySfw0n3s9bfzdv4wBs64gSTLt4RsNjpHfo8WHo8aYUNqBUa+AYnZkPkYNCVDxBlkbSu6QdM59+YQ\njPGf0mYpAiExYbCdm/7xB8EAqIrur27/FYQQzwkhTgkhTgohtgkhUv+S/c9jZvp/kqCX88kzyLjl\nib/OPqYISj+C1BkEG0+SUricCAR2I3S5JeqqnVicYfBMhFgB2nn4w3UE5R70VhMVBddiCGwhaHDS\nJ7Wbg+mFyN4ypPrVDPP7uW7jcdbffgkDqzJh9S0gFJj0LGQ8AMfngNreu+XMkENk4npcPY+TWH8H\nsjMGhzaVs8FOKJpMRve3sHkfuM8hxnownaxF9fRH1pwE9t1FQnA9ius0cs13xHd24jV2cfGN68ld\nU4Io3wNTXkSTmwhfthHmt8OJJeg2X46u/QIIJ1pEJ1J/P0y/ErItqOdsuF5aR/SSpYRbPWiHn8RX\n7Me4/RaY9wy6y54H4ySMnZH0/WgvrYujyetIhQ3XI39RjFIwFPKrIecYxC4hetnvuDjLhK1gJGaT\nBTVCInBjPM5396JdNOBT56HPGY+yawX6bbcS7jeK/KTVuOwNmNoboKkNvtIjEg34xCBsa1Zj/fgK\njP0ctC2OJbash/SjekJDIlDDRkLHGwhMTEHp14PSZSAyLGFyzoMhu2BwNrhP9Qbs5CZi9FaDfBhr\nRwqBNDeKXId86jBaUgta4bVI3Iry+VOIyDshKgopPZGNi1ZSu/YLAivfZXR+LOZJk+D0t/g7a4lK\n8oEAzXMQUamHMbdB1SaY0YA4oCPpkjCuIwYccxfC6Q/RPplGwvkezpisxGZMRJzZwbSO45Q6YjHZ\nZaKHGKla20Lf19tg0F2oGQuonZjC+jwv58Kneb7zKbrNNqokA+mp0QjpOKHsebhORUBUKRbHBIjo\nB58OA58TfjTDwPdg6AS0z35JLn3/Py5x9bj/frf8Sfj7TTP8TtO0JwGEEHcBTwO3/Dnjf76RsMFC\nR0TWX29vT4Wq7yB1Gp0/PI2hz2AwxSIqQI1fjG+zwDbSjXpgG/Qb2ptUPDEPV4pGREEGfWq3YG91\nER0OstM8jOeeXULKEZhXspkRjo/xREiYsSJlTIFfHYeLO2HVHNh4J2hOqJ8Nmvd/f53IyJmEnQ+R\ndaEHxXia8ZZ76VIucGpqEuWXPIyWdQOkj0cesxpN141yyUv4hs5FzRd0DtDjzRqFSO2Pub+P6BHn\nUW9OQ+1TSsj5W5TzdyJ97UFXqUOf0orY+SG8fQg+rUY4VaTDDbDuDZC9hA/8gCoKoGwzuhNPoXeE\nsZhl3HOyqZg3gHJnKZ6CKKhdgc3sJ/nbAHz/DjT30BE/GaWghi3zc9lk8nKy4mHUKQHikvpzzPoD\nnVyPd8ZWdOe+QxsEYXcC4VQnxA7GFx+H5K7CvmkrU7cdoviKXFxJYXCkQ3kIrc3DlJtexLy/A5Ge\ngecWM4pDIeCPxNO/jQ5HM00pmXTMHEXz/ZFokRJKWhilUyFQMB/6/x6iJkP6070RhY4CSH8SomYj\nW5qIqalDWvMuWqAMvC0Iz4OE5k9HuNxolZlczNtAFQ1MzpnNnUPfIueqMZxt6kBbOJTg+48Q7JtI\nlzcF0vQoqg4NHxx7HaIvwEEdWMLEZubiDZkJx9yONrcMtc2BYcdeUrL6UBkThrz70LVnk+ZpJqa0\nk7BThzfZTMgMHHUTqvqCnoZ2fpwylecqFuOLv4nI6MXkt4+i86oACrUYUmcg159BssfAnq/h1Zuh\nuRm0U9D/JiiaRseFC8Tk5f0nl/iPD4r/0Ph1f337L6Bpmus/HNqAtj9nC/+MI+H/Ks4RvZvbj40g\nbkQr4jRgtsCUT4m2T6Zh3jHMNYfQxrRByATVLTAzGdfH4IxSkZRmbHEmXCEZ74yR5BxdQ/6etVRO\nHgElJTRMO0E81/ReyxINEx+Hiu291W+7TqDZQ6C5IHwGJCcRniQiNn5PVfRViKwt6GvnMiA8l9IL\ndnaNfgVz/H4SXUMRdTuQEwpwWV4jSfuUWu0TOrR24mKOomXWYnErJHprCRafJZhhx/ZWLcIYhk4V\nzivQ1w6LcnsrNE8LQmd/aPLAtvfQjl9EOa1iDZTCmRKIUuHarYgLzUQ9fyNRkbMJzPkVbY7bMA6o\noS47TMwfijEMHkfdTEHpZUE69CNojnNQWFFKtnwaEQoiLEaSDfVovslYqzMRujCNpyX0jz+Bm3Mo\nvI4uQcJW20a4GyL9Exi7dAOaN0zY34FOgBot48vQ0TVahztrBAHzdoLdkYTTY0lqPo2lE6Tg5ZD2\nSwI1zyM1leKPjETprqZSuYo068tEBpsgcQFaQQAMx1FnPIDa9BBhux/D4Qqwq2hZboJOGen0Zejk\nMkJ32gmFLmJ5ewJOQzdqVS1qlpmYGw6iM9Zx/scIDNmxJMWMRb/5ACJVjzzYBlv8aH3MiPNhNDkS\nLWckIrs/KXcdR1t2B0pHDPJja/D1gfbP3AQWbKP99V1YQiMwpN2I1/0+Z+tyMI9po7XYRWogxPLU\n26iLHc37+9/DbPSgyQfQ2oz0xBwjUp5OAg8SJkzZ4EEUHtwIv10Bix6D7btgbn/I7FXZg6+8gj0j\n46fyvL8/4b/fqYUQS4HrAS8w8i/Z/kuE/290FkOcEyKqEbV2sFrBG4DuaKRImdgRBhSjhFyuhxVv\nwcSJkJGGu6mR9P4XISIDvdaDLk5PvOFpLj4cTZ+mR8lZ/gjdfWUSjtTjT15JbcunJG1tQL7/PTj+\nMUx4DAY+DXWLIfAwePdDcwZahQf2OjCNXsMLQ9/jTUs2VLyGubYAiOFIbCfD1E7iL7yEFF2CzvsL\nQlxBaqCGlFATqiEHzT8H5Y9vIBdMQ9HV47UXI02cjbH2BySHnpBjGqHaAmTPBfQzDyDKjcgLNsOp\naZCvIE5rBEJOInZehDdGg6UfpEyBFKD/OPhwOsaL15L8/GYCC2TK+mSw46OxWNpDpLbVMcAr43x6\nNb5xc5EfeYd6/zK8gU1EtB8lsXosim8lIvG3sPojYtrPYuUaqDgFG94mcOQsPUNMRB7xEGzcjWGw\nD1dDNJbObghLyJ0KnvR4zDlhrK5qTDuGIkduh6MyWoKAUADKVoNlLcYogSIrmHQuRIqZvhviCA4+\njWoeilTyKprLh8oWJGkNMlcgbXmP8BXTkd2HEGfiMb1Wglp/Gr/Vxvmv0rH+ykrX/HHYv1yLK2Iu\nCZNkRMk5ktJ3Y17cQN2qTsrPv0nKOIkoowVhyEYb14biVpGH9YXys9BajfLlBiSPDWLtyMp5lOYP\naHceB6GQPkyl8yYvxrEXaVOcJLUkYO2v0VabgbyvFNutOvoY2/jlH5YjfCU0P94HzVVCUOsgMWYF\nJrUIUb0Lzn+HrBzBUFIGSQOh6iDMWQqX3g2BUgDazp3D0bfvn/eNf3T+BhEWQmwBEv5/3npM07R1\nmqY9DjwuhHgE+D1w8587179E+C/R8SW03gtp7SgtkcjZt0Pbt2C/GQ6/DhVnscYWUuuYTsbqNTC6\nAnLfBfcbRBda0Ab+Gp/3Y8KaD1d2AcayGvI/jURufxWi8zBvrqApM5b4rHXQ5ylqr7+AY8NtWK7/\nBt3+dyCvAlE/BW1VFfSPgUAzjF0KOZ/jDJWxxP8kP5imka130RQHWdI1nLfXMrH8OMG0Fky2EG7v\nKkrbh6Gkz6OuvpasWj1D64tR8qKoGZTEscJ+xEoK3qwGosL3E9lQTcra94nsWU/tjCWY/MOIMW1B\nvng16CIg4ddoEX6Cr/weWQuBOxWyddBZ3VuVODYV1y1/oPT4I1QvnYkUDVll5YxxdWN2XIoY+CEc\n2gFLV2LOnktQd5qQrRHF8yssFz4GaQ1hg0Tg+CMYm+20OHPJ7GmF9EKq+nWxc8FsZq+vRH+pC/3c\nF9Da3ySg99JZ58V2qppIl5m4Cj2B9iCm/J0QOxGsfdBe3gHfTEZEn4GJT0P1cojQoxlLEZpKU+ZA\nUlbvRW+OQlxoh+itiNAgwthQ2jowH3wSMcKJXL0JaY0FsbsENU7Hd55xvNr2S7b1vQmvPovchj2o\nBaOJHH5zb5BDewVMuoISczEjPCcIFgVoXh/C4wthK+yHXW1AnDPQ+MAEEt+yEmxrQHX3EAgqBJM6\nkKYYIPAJSrmNnC9VdCJMh09HdbmetLh65Pw55BsO896j19BQupam09VcmvQJodpU1GAP1tcF7U8o\npHTcirH5bQh6IGMi5M7Duf8gIgvovgiXfgZDZvfe96Y88LlIGDiQYXfd9VN539+fv0GENU37M6v6\n/4kVwIa/ZPA3ibAQwgF8RW9m2SrgGk3Tuv4Pm1TgUyAO0ID3NE1782+57v8I/hKovbU3I5k5jFze\nDdbPYMR5KLsPEnaB/AtsX37Flim52CdeTbRjNXz7EL4Zefjm+qjI3UnSBrCphUT2TMJY/APuOD32\nvNFw7b2c/n4KmW3RVKWn4PS9TfqQnfjaX8d7ejG+Gb8hzr8BMfR3iD47UWtrkUbcCUYHjVVRNPpP\nMKhGJsc5B6/+SQb1+5ou91rGhQPobacwKEWUhIfxWtQcIhQ3c2Qfk3tWERETS8/EUajeowTNx5gl\n3kFhIB3KPsIbNpF53k/H9R+wLuYbnN0uBuw+RCjnHvQb18GCXLBdQvhEM1J8PHz4Gxg+BI68AENu\n6BXhxuNUB48RLVsYeGg7ui06mJMMi46CpO/9bcdeAdrlcGEh3rMlHBg1gOtqt0F0GKIWgOpHn/Ut\nWpkfvzEb1jxDyF3DrmvtJNQ2EFVRDJOegOK7EVlvEb/9OrTdPfRMSuL0NRNJ199A/LffQc0XED4B\nLh20XgVZaSDZ0dofR6uzIg2Zj+RvJGwfiLGmAS3CA84v0SxxECVAvYBoEagdjxA0qBi+CiCVAcNU\ntKv0SOtCWKdczctjS+FzKzFbG+DseaScTjj8HPiMqJmD6fKcIM7iQHfrSnRPLiE9sRolM5nKzw5Q\n6RpO/k1lmJ86gL8wgLEtgBqvQ7lRw5z7OcopPbXW58h67zRawIRqgbAw0rGtmtz5V0HOKEwxV7GY\nHFbNPkLsnA20z7oS1/2lOKJT6I6pIOX1ZtSGt1FuX4Y8bQqc/hq+upFkTytSshPohLp3IdoILmDv\nJ5BSyPhnn+2tqOXt7J0q+3+Nv1NKTiFEjqZpZX86vBI48Zfs/9aFuUeALZqm9QW2/en4/yQE3Kdp\nWgG9cyN3CiHy/8br/v2p3QLVg6FL7o1sGq5AWTv8mNGbsDvmNZBKkaQmjIYQH12XhiculvKZTbSY\n9pCUNIY++h+xWPOQGjtA20fkJBNHxuoIXfEr2PMtKafbcPzqe3K9GagtTjp2TsfS/iERnYkYu/dS\nSz09+ia8GZcQ3KPgu/9R1NZW2prWk5b3KGLIUoRuJFbfOGr2zGOIeQ39LjQjTBohdwQFunyWd/bl\n5fVnGScuJSm6P7asVdi1hZgbJVpDfmpYRFf9G5gqP0MkpFN67y04EiczXH8b7SYXhy4fjyxbQYqA\nZW1QPJ3grlcxFP3pL/S4YdBNvQmQgJ4z19Nvxz6yD+1GJ/kgHARTJBx4FX5zGwR9vZ9rrwFlNkJp\n4qot32NAgYg86PcpcsFyaqOmEcwJEYi2oRma2TMzjXHabDKCbaDpoKsGErLh0Dto3UH+F3vnHR3F\nke7tp3pylkY5ZyEEiJxzTgZMtnE2OAeM0zqHdVyccQTnBAaMbYJxAEwOIgsBAmWUwyjPjCZ2f3+I\nb+93791v93q93t276+ecOqenT3V3nZ6q91S/9dbvlfqDKfsa+pf6qTaeo0yzB/JUMMwLwga5p/A3\nGHFt2YniAIkO2Pcu0q7haH70Eu5pRU6Lh91hdJjn4NybQPBoANUH4FpvQLwVwEMihEchku5EtPlh\npkS/4EriXTUYRi+Be9bBIDXUOgAJinKp6Wan0esg6WQ+lNig2gayQNNWSqavgh43T6fq4FD8ySeo\n+rIQheauJYBuEtqvN1Gf8AappttQj52CpoeTgEND2jRw3dQXd+osGDQD0gaix0xsphkpXCD5ziCH\nO3EZikhcU4faaECb6kd1+FF4ZRxsfQp8MlLMfND1ApMWdp/sypix7XpoKYWMfpi/uQI+XwQ68z9g\nAP4dCP6M8vN4TgiRL4Q4CYwB7vlzlX+pO2Im/HHf4sfALv6LIVYUpQ6ou3jsFEIUALFAwS989q+H\nswZ+fBJskTB5PzROhYYAxLdAoQkIAcc3XTOECA2ZUh3tfiudWhPJVU2o9kmQpoIhuZAyFwo+g5JK\ntJnh+FqbqH79KZL3vkb07a93JUm89H7C9oQTPHAHcpwRKWceIUUvYHKM4fxl26gsPUzvJh9Rt7xE\nw0NLUV9aTZg6qmtTxIVt4HWTc+InQpNMYCnDHZxEkbGN/h4P1O9GkzEEWurBZkeYohHBQ3jKTGSf\nyyG0sIW2/h4aRkRgSislYVcpRBQTa4xhgiuJql5WDnQXDHnOjeHkfhC9kSJ3oG3NgKu3wJtz4M51\nXe6ImkMYnDrcmmbMig/GrEExLANdPaLgHQiPg/Wzuj4DG6uhswpb/4Eovp0QaIX4q0BREBUnCMsz\nEdBAMBQOzL+MOCmROJKoZjiEfg4jZfAPRpl+NWJVDxTJjiZkBE7tanqfHE9uehSe6GSyavfCmCy4\ndSuIL+jItWKw68HrhuZuKImxiKmTofptVJrpKBUvojn2BVQFUZVGo9aFYhTnqJ8ege5gAEWoMRYf\nh5nD6YwsxZG8mGzNTeDIh4hsaI2C1FA4sRWyF9Ac0kZLIJzM3KOwczkkptGpr8JUqoUBATRfLCZ+\n6K04K1IJm1VD27nxWOPK0KhyCHYbQdTqpWi19SCdIDDRjmafwKnzYb0iEqNzOt6ifpxNvRlncx6D\n83NpDJFoT+xANuiJ3dSIYrAjSxo02XdB6XEwtULOfIgbBgE/7F4NxTsgMhrym2D2Ssh/EfY/DEEj\nXPUFqDT/6BH56/ArLcwpijLv59T/pUY4SlGU+ovH9UDUn6sshEgG+gK5v/C5vx6KAgWfw4R3IH4E\n6ASoP4Xml2Hg+9D2EvS9EtobYN9bUOYktb4CyezmXGgGw4rLYGQW/v1HEIE9qGbuIdh9O6qqbxGN\nKhJr+tJhPoJ88wtIw2f/8bGi5kfcQwdgyt0D7vsgNgSNy0BycD7GtFROjP6W4d++ydHl8+m3+ws6\nZ4xHt2Q0qopVYInA1BCAkFWgeglr6k20H59Dx8GNGEP60DzyXiz1H6GvWAtIeLtXYt7ViMH5Lcod\nm1AnHSYhz0eL4T3KEyCy+izmwyV0zLqK5MAtZNxyN56Am+LfzSJi4B14Bk/GOqoe9QcjES3FsO32\nLsU5VxHqC6Eo9fuR5z1JfWci4Z521MIJ0kCYd19XzHOgHSpzodYJ5jKERwt1R6DqNfCvI5g0FPP4\nt6k2ltNa/xjhWOnGIJwUorENAPfHyJ4KvCH90cq/RxKZCGc5nNuA6KajOfN3OKVrif62BI8pEU3H\nXqQeKjQxY4i8djf+Q+1o/VpEmhvXHRfQFV9A7TxHSz8bypSZhJ4rhWYHyopDiIrPMJ/Ow/jDFmSr\nl9LYHuQ+uJmFnq20V39CdusuCJ8Bx38HafMgWAcDH4DDv4P89zAkjiP7fD3Cr0DNMfjDdwRfmwTd\nxoKqEC6fgC5vL75kL8qQBKxpJ/GfljC1PIBw/ICm3osSVURgejzUdCDJHZwYMginIZLc9KV4Og/R\n/fDd5LQIfGV+EswBOh2t1IaMg2nLkCuOEAzxoSmvgAvrYe4bMP62rk5X+hPEtcLl93cJwM+8B6qP\ngpwBgwdC0adw8F4Y/2HXZOFfDc8/ugFd/MU3K4TYdnFq/V/LzP+3nqIoCl0+3//ffczAl8BSRVH+\neQVIhYCB90G3BWCKBXUMhE6GgetBFw/jHkbe+we83YfjXngrimRELUWQUFBFtGzkUNpU+OEs6i3H\nUB04i/vWHBrMLoLVepTQPsREgX3AefwZRSDVdxl9gJId6Ey9aJ+lxROiQVH3BGsU5mevJlW+DMOM\npZwwVTNk+XPEdEZh0B9C5D1HoNmEkpCO7dJqiF8B6XeDZMJKX4xVZRSEVrI7bD/6HishOxMlzYun\n/CQ6I8hJdgKXT0V11ZPodjiIMr6HLS4WubaYqmExmHesQz11DJw7isbUi/g9Kuo+ehq/MwixdgLn\nTCi374Wpq2DU81CSj8ivg9HdaemThEln5MJHLgiR8TlsEHUp2OeAbT4M/xjMOpRuz8DQfCiI6kq1\nM38N7cNG02g+Tovko7atJ72bNwLQzmnkoBvZHELAeYYG9SO01/yEsr8Gut0LoT3QfOWkRRMCzvOE\nzkiFIj+qjzoQl6oR+t1ItXGotFkEaoCys+jvzUV1wzba43tg8xgJG/EakiqWpoxwTu66GXafQORc\nh2rVBdT37yG9rZ7xH47g9WYtNt1IiFgJ5XNBbYCD94CcAgXnYdab0NFJ+ncbUSfagQTIage9ilZt\nLO0zrqMlPAdn42Faxw2ker6VziYJxVRDp83Fhe1XUd/wBt4EQTArHeFoQu3wkf90FsWTUzEpZfTY\nvoLx2/YTVuwnuLsB3YlGtGYweo2cy0wErY7AoJ7QYzgUfwWTnoG2izrO+V9A/hrwJYLKCD4HvDsN\n/J2w8H3odSOkL+hSVst97D/66b8SgZ9RfkWE8gterhDiHDBGUZQ6IUQMsFNRlP8W3S2E0ABbgO8U\nRXn1/3MvZc6cOX/83b17d7Kzs//qtv059u/fz/Dhw/+6i00uLDnf40qR0TSa6aXdh+WsH1uli4ao\nLDbHT+SyfW8RGmjFk2KlqGIi5yyTmML9qAv8+LyCnXcMx3CiJ4ltJYRIFbTIydiri2lSp6GPqyIu\n7jQFz42gdUI/hp9+h0M9bqJ2gJNjQ6J5ct4riHooGzmCkOgqCt1jGRX2IuosaHYks83zBDIabHWl\nTPI9ycm+PThjvJq4I21k9f+WCpFJmn03vi/CiQoU4ZEtSGY/cqGGFmsyqvp2QjxV4AK1S0aEyTRN\nTeeQ/Ra8qhDSv1mLPS8X+b5Qwo+3UFHcg5K5cxh96iW0BieSSqa0+2jaRvtIXFRB3IUz+COCHE8x\nUj77LYRazZhdz2PKceAN0XN+yEAiP2nDllRJvnEeyaoDnDTNQD/1W+or+nHixXZuXOCgzR1PZ04N\n2oCTPvvyMKU4cMQmUvHpCLqf+Z6qHn0Iaa1B527nyA1J9OpxCsMrEu2OdJLdBxEq8Nu11Fdn0dkR\nRksbJDoLiVBXo2SraRkWQ6l9PMkcxPxjA1sGTKGhRxgJZ9NxiwgAQlznUPoVYC03Ele9j29HTWfQ\n6Qp6iH3oE9toKM/EZqhBkoM408MxlLdiOtlCx8gwDN+3I1LBX6rnVHg8teMGkOI9Snyeg7a4cFQJ\nrXh/jCAmtpTzCcMwlFdjNrcgVfqJ7u/A67TToLVRnpREQIZeh0pQqTT4zlqQPW3E55XhWGTDutVN\n+wwz268aw4QDB5E1EoZ8L9XxPdGclTkTNo9Ez2G0wQ5qNH3odWIdEeklqDR+XPXhbOr92n/r8pLi\nQ0GN8hdmw79oXP0Fzp49S0HBf3gwv/rqKxRF+au3jQghFDb+DNs3S/yi5/3ZtvxCI7wcaFIU5Q8X\n4+FCFEV54L/UEXT5i5sURVn2Z+6l/JK2/BxWr17NokWL/vobBLzU7pyPetyzuOUNONo24pE1SGo7\npvoIzoUGmfPDWtTaEEhIhopRBEQj0uEv8EWrOZ2ViTzjUgzODnxmMxHtOhLK6xEFa8CcQ+eAKoJf\nX49wSZhuuhYOfsBx+Xv2TZrOrJM7iFvpRqUrRlz/NvS+hMAUFe40Czp/B5pL+yL1uhair0FeMZQj\n2VEMGBmNtLc/lHxMfbqbsA4bGqUQ9ibib6+BlmhUKfVdKZfChtI6JQTTrTtR97oE6dpLuvSR636A\n5Gl4Xl6LqrIIaUwaneoqvIk52N4+gOrmbERZK/SNhz6TaA6LIjD/ZSJmG+DbDpx5R2kMhBMzcy4G\n7fsowQDMSyYo6VBOtKDxRcNteVD8BheMbRzWnGWMfhHbNrex6PKF0DCPirBhGEs7CGvbhqw+hNBc\ngiQegAOrILgRBg5GLijjcJadfvlleOdEYN49HHa/B9osxCUqeKQKbp+Esm8nnu1+/I97sQ6bhlyw\nDY6rkLJvAuU7nhr2AE0aePXoelrHLKNeHKauNZ/kMyYakwJYT+1j7uQPubOzhBvK14JyAhpqURqN\n4JLgqi+hZhNsWwF1akjRQp0bJAj0k1BbzBCvhbNNkBvAq9Og7zkY8vdBr/nw2RZ8gyEQ1olebUCS\nwkFdBedmUty9lrQDh1GsA+k4WEEw1YBjNhja7IScjUYndXBkskRW8XlsdQFYsg+Rd5zOTY+gnnMj\nFGxE3xoNERqo2gnCDpNvBut4iPvrtYF/8bj6GQjxy4yiEEJhw8+wN3N/PSP8Sx09zwMThRCFwLiL\nvxFCxAohvr1YZzhwJTBWCHHiYvnfJQES9EPRT1CyG8oPokgqYqKuIuLIPpKqp9Dvq2qGnTrNwIaZ\nxKpiiAnIHBo/mf0jRuAw58CFtahf/BxRL6PNE0Ray+n/wcv0rOqD3melxVFERYcbuUwPgx5FCv+M\n9ls6UDo66Pz8E3CdoighjPlfPMfuRDsNj11AKfagvPM6HFmF6jIdmlofDVPfxvv9AIIOLdyciLS/\nlkHrDiH5+yCm38vZO9cSnJiOekEu2OagzHLifHQwmgnhSINvRUTejfg2n5AZG5HDgyhL+oIpGpAg\n/Wb46gF0xt2oB7Sh2robc0szO8Yswn9vKnJrHkpKCEr+ZihcRsiRV1D6N8PoRMSra7HMTSD6hrn4\nv/4Mn9uCmL0IdvpRGYpQ9Wunea4HL8WQdhtmy3CmNN2AN20haZ9+iuLxgf1V7E1bsJesAE0+aCyI\npnaoLwXH92C5FPaYqXW4iG2tQhvnxHC+gmCuWRhOAAAgAElEQVT4Z5AZ2hWj81oD3BoJ3+QjXKFo\nr3TjfNtLZ14VwXSJjlsUapMOUTZhDL34ikHBXM4PUHHa8xTlge/o+W0ucSX1NNs6kTY2co0cysfm\nTNp7jkHpVQrNaoSqBRGaglANRcQ9hVD3RgS8COvdCK8O0W0m7ppwREEi2N4isDcdx6Be6APhKDXV\nKCKsa0GsuhPOSKiT9QhPAOw5UKQB6Tjhh8uhSY3/3AlaHzLhWKxgrwoQeTgcQ7GTtsFX0O07hbDM\n1+kcPRTVffeiWv8JneOjCLaV0j59KhcWRxAMnocbj8HVu+HTPaAL+wcPtL8z/p9RfkV+0cKcoijN\nwIQ/cb4GmH7xeB//yzUqFJUan+Yw6jXPgM9DcMJlKGEpSLvfQLLkIE1/CdHiQV1aSnjn94yMuBRG\nPQk1R5D79IOoAyjJi6GyCLGrE9vTQTw3hmKUk+kx4Q4wW2FoKgy5As7t5mXrYOZYc7GP1eJ7rQZv\ndhiaIUOICW1G8qRh+eY76NcJO7ajPLodZWEKJ+wZyOmZJIydAk/MhfihKD1PgMUPN7xHcHKAvGsj\nmC1FIFCDFIuvORpLfBXMmQfHToF9OkpJO43XRyLfFIL5/ArMZ8KhtRTaosEVg+jdCt6OrviWIpmU\ncyW0q0KxZ60gcOxe1BFWVLreCJFL6MgU/IWn0HofhvJ2jM1bUN4fTkDeQ8Vnx0lod4JzIMHZY9Hs\nehvH7FeIaXyQsPgxkAWNvxuMZVUR3kMHkcdacZmaUScp6OhEVIaBIR+l4CGEUQ17ciGjGkNHAoa0\nZBTVSFTuAlyxNRhdLYijJ1GGCyj1IkIy4fZHUG25Ft2UAPXzc7E+H4J+zgCsIXnYNUuocxuRyz+g\no2ccEa4MhukXIfZcCqF7SMjpTUmrmZvOfMjMHnHUqUPR7bgFXVMqxOdD9xmw/244Ugah+TD4UTjy\nDnSfBe5CStrG0jcjCrHuVpoHhGHuOQNOHEQ5vguh7QF7P0bpMxS571HUtekodMCe76BKRjZ4OBQz\nh17tOzHeJxPt60vzPhn3a/vxVPyIxqanY3ISBlrgxoWYkqyIEQNg1O2YeibjEkeJZDEB2mi87AN8\nvE6k8Ub0llC4ZQSsK/kXEof4C/z80LNfhd92zP0lOhsQdfvRNrQRGDcHr74UbcEppLMHEQr4jWfx\nGjajRBtA2gR6EMk1aFmPIXY+kqKA2YqYthXl894og9207hKEn/UQ3DoLdZ8BoK+C3vHQWcun5PCH\nxAnclzSQJuPVWEzNHNFYSD+qRTEbGa7NYP/osUypc4HBjLJyA/KXVXz2xO08+fj1ICohPQ1seaAJ\ngteOPAPk/GdYsDCIPN2GEnMFoqkAQoqQnP2h42lIuwyW3ghJFsyWdjTrJDqGOlEqBQIX1HTA8EXg\nDelKhdM7HBr60r/sDYpDjET2cuJ6fzydh7/B8m407jUhmG/3UhXdh/inLQh9H5jdCP4WNLvtJIhi\ndnQMoNeU5wjfehmGBoVW308EFixDg4KPYrTLhuO40kfb4Zs5/UkW3euCaFN8kNMbny4CHXuhogUi\nQyEziJzhQ+e5ANYAoiYCbN0xNCbh8WzHUBEOtg5Ia0VZMg+x5nqC196L5FmB9l0vYoMWQ+9T+KM8\nSMWPESenUthdkLJuF+EpzyAGOkBXAnG9Sdp1gtMLsuhY/x6pzUFEpRlpSxuMNkGjGiL2QO0B6OmH\n1iTobwbzEKg6iRJfT2phKYH3opEGj8M35SiGuveRCxJQahWkUY0wZw3BxB2IF44iXX0OxaOFDoFc\nE4J0wEGK8Qe8ukiiPMdxHWjBs82MvjlIeKQa+vSnbcJ1OA1boUKNtPhp0LdCxU8YGEljr9MAqLER\nzTL8NNIgVqLcaSXyUTPac0eh+8B/7Jj7e/ErL7j9T/nNCP8l1GbwtSMKVqNxVKKJ7gOR3br8aYqM\nuuIQ+o154GyGDidwFcoNj6BE2LquFwKKnwBPEqKzG7hK0YdE0zEuAffQE1juqkJ/rAbqilHKyzi8\nbCBTawpQhyej39OCe7iD8n4TyBj7NL73I0g07ufHfs+SlzOe3sOjEbWnCUr9uOuVVYT5KlBUCp5x\nk9EXnYCEs4hZjbRvmIXNWIJrVTqGLzMQq1sJ9GvE2T8WS3spWrcd2bEBabSA1nZ0VVZUcUOw1jsI\nLroD9ctL4IWTYI2AZ7uB0Qbp6TD3IaTNM3AmxdPYvJzI/n0I9JxH65LtaCfHoNQXEnffl9Q/MJZo\n240oZbfiXnsJem07qqk1jLItZM/nbzJGaUDpAFPGpTTq30R4XejrrNilHCxvvYHVpWaoqKZNeJC1\nfuqrw9HbClAlR6PuVoZib0HIkQRtPQiOKELzkxpSPZBwL6o3UtCXe5D9nUiD7gfXa9C2FtwOJEcl\n2iQf1hUxdBysxR/eD3X1BaoH5HDSkE74YQPWYxZUOUuh2ArxRoTSF+2gRagsu7kQOYbYdesgeTBE\nl4HsgPQAtCaA2QyBbjDuBRSjhiAKwYqzaFPasPUPItwB/A0SdrUOpciDUnIKVaaAa39A2XsnSkgb\nlGkQYQK0AdpWWLFmtuOTFbaNm8Kw2YtJiXgF6+AVtN/7JFHOZ5GWXY4y4zJOhJwhYvpQusVPhTP7\nYNFjkLUQ8dFsRHYqssqLhA4ADRHE8QheUyUNy3VIbZ9h8DRj1Q9Hxb/oJo3/y/+WELV/ezRG0GZA\nzBIY/znM3AqT1nUdT1gD15aAvR8EvMjhE1CmLEY8cjtSXpcICo5SOJkPrSvANhLRLQtzfz3tb5Xg\nbMqk8L4wAs9eTuCaBEhPoxteVleOhXe7Yc7sRnuvLLQeN+Efz8T7dSTB4JW4cHCyczW0fgt3LYVb\n78cRH4HwKrgq7ag/+wZ5qgO6rwFJ4uC8Wzl/z1Z07m6ojlfC4iDBIieWh2rwrWmlSmMnf9QlVNz1\nNPLkS1DFA4kXUEsJqL/6HGbf22WAATReiE8ANJTqTkHsGFLy+nLCdytKXC6qI58jAvWYpihIaybi\nWvky7uxSZNcLuJ91oYnqQHXpzWDQo06bxeB2H5KsEBiXie78PmLOTCH2if3YH/4I49cfIywQGJKO\n0X6WaMqQamUslko0Kc20eJ0EFDXkCpSjifh1flo/1qGhP3x7FgpOw5dVSHEOvDeC3FCLwIJoq4CR\nEmL7R6gZgTT2AA2XZ1IYksCBof1prvTQp7IXqTXlKK3HkZKd4LXBpVtQnB7UF7zE6pNoSdPAgjdh\nSzl4KqBzMOi04N0MI3NxD7mHhm0P4bzmafyHFbT6Hojv+6LkA6ckVJY2AjUOAi/pUQ1UIcwKSuAM\ngaSjqLacRW2X4eNwaA1iS26hjXDUS8KoWRJNRu0RlL3b8MjV6MlCMpth1BTE5HmoUWPDDtlD4MJJ\nqC8HSQXjHsBYUIP74i5aORjE09hI65kztOwsJrChL23bLVwILuXY0T4cvO1q3DU1f/8x9/finyRE\n7beZ8P+ElGFd5U/RUgySGq7LQ/5hP6KuCdWra+GJW2HNneCpgQGXQ0Y45ERAbg0GkYpct53I9lIi\nAx6UeD9KSAtCZeHW0hUoJzQwqhnRsobzdZcQ/l0emg/y0OguQ7FP5gZ3Kl9bTkLz4xA+nbPh0ygY\n052hI0fhf+VD9LUOpISXEPrJAAwSgwgPsaNUnkKc2YLyvQo5x42YuxjTzWvQH6nB+3kSLttmatJd\nxHnDkHLehM+XwejrYchFPeqAE5L9XRoR/nh2mMsIHXEvtmvnopezOHB7Cj19ZqzDC5H0Z+EPX2Gx\nxqLPXY77oZNoE7Vok1Qw+S7kb1bC9T0x40EMScQQcQmkTIKNj0GSGk63orRc4Hj6IhJn60gt6QXV\n36MY+hN8tojgkt5ExB1AUcNxpR+ZogHJYKRzewJiyI9Q6IAPnofrn4eQjRg+Og2TjRBsgpi7odfd\n0PoAWvcVEBGNknUJF7zHMbTJZFli0P30KrI7gEsVA1EFiAgVvuumoZq1CNWZTQzqezOHYxxwpAWK\nz8CEaJiWhtIk4y8eTFHgQV44OZ6e8Uu4Z8ByRMl2wAehycjnNYhxSciZ5bRt7M8fHrycpw88iLAa\noeJdVJ4+uMqqMPaxI6rDkUubEWMD2FKzUJ3P5KGvX8f0XBPBZ3S4A7sxaS72zWmzQacjnjSyGQin\nV8PQ4fDpo7BsFYVr12B0Haey5C7c69KQJBlLqJp4dR4mvQ59ZA9M/a+i7adwgiHVhL88DKMu9tcf\nX/8ofnNH/ItgS4IZnwAg9XQS/HEN0rhauH8wvHYKoZ8BC54G2Q3nFkPqaKTt3xB15ZuYUqJoqJmE\nKQBK81CEbxvIrSh33IRsKSTgb6U5STBsdTGidj9kzELE98Py0UAuvewbaO8LqfMJKV3ABNEE437k\nTPQ+hn5dA7qLecAUhfCOBih5CKHJgFk3IE+/FJofQ29vhk0TUZqs2OvbsORdjuvO6+kc3AvjdxMR\nQ3pCpBsUJ5w+BAm9wGyDfisI7L2bxpC+/OB/j9Ev3UC3FV8jFY/ANnE1yq5u0KjA/X1QFtrwvqVB\ne+1taJMGwVfPEPzgBYKyCs2q5YiHl0LEEKirgkEJ8NAh5KPrYOcNtG90YtMeRbXFhdOmQQmkENRG\noxqSg3bwRLz5EyA9SHqiGZ+/kOYvzTji/CTdU4D+xFL4uB6uvh/8NyMKE2DLR3ClCzKugj1PQPlK\nxMm18MgxpNBEctqjSMh9BKJCYegylA8fR5VyI8LRgPLK7Xi37EDvaUd1+z1IWZMYUF0A51+FnhIM\nzUYpfhVfTSbffdTJG7c/wgM3u5kQMw72rodTeVCohe5elAQZRT6FqJEIqWmidW44yl4v9BqKOFCN\nWJaP/5uFSLPvgBemIE5J0G8OUuhplB6XYSp6D34PKq0X/b7lGMyj4XwV1DTC5AkMUKlQiZ2Q9z5Y\nkyFcDV8uJSOuGnyDCb3wPcZxyQiNEcJToTYIsT1g1C1gshPB+H/UaPr78psR/hdBrfvjocjshvJK\nBaimgGoR3BONUqODB2cj7nm9S/A9ahMo5wntHg3aZGqTb8QeuAT14XvgTVCeeAVvQisB52p87khM\nJg/xV8so+Vch2rIhEAFaC9Y9T0CfS+FAPgfaejIgUA7nB5Bs0sED9yGEgDWvQmIlNL2Df+gXaCJm\ngP4UYs86dNduA1kCtQ0NEOR1/LvzMQ40ohiLUDp1KJkjkOTnofYTiFpF5+YrUUc6KAxdSZK/iuFV\nozAHehLjb6Xl2U34iubidyxF1dodceIEyjAPrqdd6Cb1RpPjhh4L4Px3SJteRrrGiah7GdJtBK9/\nhabPLiPC04Yo3YlS8CmBHgvQRDqpCUkn5al0dGveQzN/G6i7dAxcbOBCIJbEDe3oFznQtEeyfUQm\npQfT2GDs5NmYDPTjusGG92DGaDBEo7QUIdqNoI8CdRv+yfNQ6k+h2TSW1IAT2Z4F3V4H98vQ+jaB\nCwHU+nVQmITS1oqkU0NlAWz/EnZsQG0ww77VcKUMLXvoyDVxf/yjWOfXsCnkBwyxjwMCDPOhbwVo\nqyAsjY5qGdPgRsQHHsxXn2HFS1cj2Vxg0UNOdzjwFvbXLuYEjLoaYTmO0i8KzMvAtxBaEgnaqlHH\n9+dIVDJjjqXB1s8hYxD0fBSV7ANfB3yyCuIDkDkU1H5E1HQCWZdQKRdhL3cR2e3TLl0IRfn3iYj4\nf/mVQ8/+p/xmhP+GCI0GAgGEZiSKNa9rBpl2Fn5Xg3J6FJAEJXsRoT7obARbMjYyaS3Yh329Hvez\nCfjSHkJSa5BaJTzbJXJG+3FOsmBtHo6q+HiXYE5cd/DuhSnvIj/Xk4ER0QQzk+ANGet9LqT6R6FE\ngfXL8d+bTem4mcTqo9EAnXYfhuLzIIX+pxWBMG7BcyYNdawWeocSHPI1wdUL0Ax14xwUgUdzF4bU\neqTGGLJ5HjGqgr7le9ltL6P/tk+wuN5EFdtBIKIM+QO5K7xyjwbtNC2avq3g/AhuXQ9jr0fcdA/E\n1iBve58Ds8dQqHmIcZILdj6MZ1AD0uU3oivoja78NBb5DKYdT6FWxf7RAAOoiMVcbECqbEH2+RC5\nlczNqKIhai6x7QdAFMIQGZ7/AMLvRil3Ii8RqKoF7B8AvT5GEz4EWThw8TLu4DEs53qhOfsJtLWA\nrg4pOoCUlgyHHkUaeiPigkBzzXSYf19XI9wdcOp9HO1ZnGoawMupC3n4zOsMrdsJBRooL4RON5zZ\nBilj4Kn3Ye8krLTAYzIiEaQjQcRUJ3KehFLSibphf1eKIb8Oek6BQQNAHYpS8hVS2g0ovlTkIfuR\nJC005yPF9oX1v+/6L69/B3ThXW0zAP3GQqEdSq0wzAMZU1HrEzG3dEe5sAWMRyF56L+nAYZ/mhC1\n3xbm/tYYDCguF0KyI1SJCO0URORaGHoSimtBBiVcjRJsR6GTULkaJf1hWl4uJBDVhF5MwFgvsK5p\nJLbJRUpnb0SIDSk8AUaug8GZUFYJdMDygSht7SRWlOJTjKAoFPon4rb/CK8+g7x8M+eGpeMxqbG4\nY/DTQYn71S4BnfaW/2hzUx2iMBedy4/PqiDbbKhzeqOdOApR50SEPEiEcQMWnwXdOTViyysQkYR1\n4JW4kzKg12zUEZCbNJKGyAjkoILnnIJGr6Bp1kJYD7DEw9BhUFMIBVsJ9vo9x3oPpr33GAZK80kq\n68DPLvxxF9B8dQTqDiOX72Bo87uoK1oQQx/tUoK7iJ6h2KotKNPb0BeWIrIHImpCCc/YBv5W8AqQ\nusEVL8HdXoQbghY9CgGweEDcBQ4TkmMpZsd0gqppeHrU45qQhdzvcQLKYjq3qyDCCPoOFNf7iMYz\nMO2m/3hvnQ0cqxxMH8tuNiX1Z820Oxj66FtgkUGo4NQuOLkVnAL6jITNb0OTE1UwgKKVkWwJcNCI\n2GxBMij4LsmDcRLkpKPsfhp51ZVw8kc4vglpXyHKrlshPx/fMRBxQXDY6bZlG8RnweM7IK3vf+6L\nM1+DJ1dC797wwmdw9gIA0aGvoc26Hsr2/Xrj4H8Dnp9RfkV+mwn/jRHZPVEKziAGDPrP5/VheCe+\nQ9Ppd4hpOIO/4ko64jLR5NdgybOhvvpLVDsfR+7+HVJuKIx7HMyNiPjLMNAbEfwa/GrInASZCnzz\nNXSUUpGRji4khPrObLB+jS6sJ96Hb8B0zSqc6Xq0hKPy5FEuzSfANMJirgHvFbB+OgxMwH8oD6m1\nAtX4iYi+tWj8An9NHSpFQViSACuWYDcovw4szdDTDhufQ+k2AWEIMOKHF5FHPos06k761/5Eid1C\nRM5NWHVAdn9ETj/oeR1U3AUzR4GvhLqjlRwvXEwO/ekvliIF3QR6aJBq4zFqn0KMk5FPrUF4tiGa\noSZDQ0ThPWhzY2DJuxCZCEBwvhPDi90Ryhm4+0s6/AVoT92ONvQdONcEUVthdQAitdAiIZxaFEs7\nosIOUWPgmASl60CzF4tJQt8uIykyoupt5AgzgRIFJT0Cht2A8lUrwvddV3qki9Tuz+Xua17iktid\n3K6cwXxSj+J4GYEGhsyHw/ldsdpDx0LJ++AVKGGRBI5UoRo5AHHjH7qyLK98HuUcnGoawgBrOYqv\nJ876EiyJRUh3rAe1D3l7CkKXhn9jAZpwAe0+fHECf4EMy4+C3vTfO6Pl4qJadjTMrIMfVsIPX6Ke\nsoCQ3k9BWOt/v+bfiX8Sn/BvM+G/MVLP3sj5eSjB//KtIwTanIk0LupN/cLBqCpaCXn8DNb3Vej7\nvo66WQdNhxEVAuJ6wJB7wV8LkX3RylOhUwN5r4JxJJSchROloERgCLYTYUsl2nkWMfsldD8dxDdu\nLN4JI6niQ9J5nNQddrRNfprJxyeto2NiPPLuSppe1tL0aAVS1KPQZy1i9yBIWILPlIRy/i7ovRQS\nx6A4nkZxbkM2NhMQScheI/5XR+DZNY8Lo/rTbm0DcyqabjeTVRGDpo8fRa9DPPJpVz4+w0DQpuAL\nv4v9cRMom7mYicu+If7Lr5H2zMPnmgByDaq6WlSuRpTy3QTEWaqzR3F09kDU7RFosleDLR0emgSt\njbiVWlz6EKSmDAKXXIvSUYRyYDWN/pugOQdCL8CPTdBfBVcrkGRArbEQSBDgUEHBJjDPhIHPwYjH\nYcxSlJt2It1ejHiwEGnCdLQToT15G0HXKmTDeqTZM8Fs6Po/WzZhqbiJbesn8ZarhLQP3ye4tRqM\n4ZA9DOYshpjzML0HTBwD9x2Hpwqhezca1NlIt6+ArNEw5TnkHnHU7NPgNt7Pofv11LxyBMttH6KW\nTLBxPqK0DmmDGn7Yi/uQQL7nU2SHDpWqCtHDDt9fA7kvdEXq/CnMaRDVDx5eDRPnwuKJiKfvBMuf\nVZ791+efZNvyb0b4b4xyvgD/Y7+D+rNQsR3Kt4LjDACirJCsFbXUXKhGKstCNcAHz++AtjPwZj+U\ncCui1/sIQw50loA1p+s6325wJ8G5D2H10/DuTugRBtdfjV4yovHK6AJOOF2JthWaLwtSwtOk8SAq\n9EiacGKKx6OhO4niHaSJTxNsryOQ+xnaz+5FWXgTlFdAuoKUsBhzhhYqW1CaH0KOOg5r38bfrCFY\nbEL1ziaEoRJNdjy+6Fb6rvoQ3ft/QHlgCbz/BLz7GCLjOpQkFUgy+Lq+5SoskewIvkoGQxjaMQlN\ndDo8vxK/fAC5UoXU403QJ+BrVVOaepjTYyajKWln4PNHiUqZjPDshphasMkQ9NEiH8OlasHHFgL9\nBkB4H+RDn0PFUQKeQQQPhiD3gKCxBermwpgnECGhyMkSiuoseAPgaYITeVDXgbn0LBpVJGgNEJGB\nOmYRugUxeG424UweS7AhFam3DJVvwd5YqHgb8xYfmrAReDwBGp5MxXlGB4e+QBm+AHY+C/Y0mPIS\nOKpBrQdnM6LtNHndLofUri8luaWe6m1thM810/fR2wieryUstR71+itg4Ytw4hxsfAZihiDOlWIc\nGInL/TbSDj+d58ZwZtI0mLWuS/s6b1VXBpP/ii4Kejzb5fvtNwLe+xFsdtj7/d9lTPzT8utl1vhZ\n/OaO+FuiKEjDeyJlBhBN26CjCX5YDufDurSJo+PQhZ8lThlK2T2Xk3reCxvvgM4OmPQsomQnYs1y\nGHYL1H4DsRcTL3p/gtPVUKWG8COQnQCXRYG1kaA1HEo2o8SOgg1r8G79Pa3ydyTJMlq1vev6sEyc\ncZFYcCFQIW1146tUETEWOgbGUqt/HHVHAfZUD+rqpSihfmTt18iWCaiODUY0N6Aefhjp+SuQrW4U\nbU9UV2/CemcycoUFnBfwjqinJjEb3eZ2YodOQBR/gFz/I960WvbUziWss4ZJh3NRFf0esrUwWwO7\np6JWayhqjcQrXkA/TIdHrCLmeAMpm48gSRE0Z8ZjH/c01OeB7QcCvTrxrM3CsTCOuOI6NI5QpHwX\nJJyhos1A8MfdJB5Zi2+gFtGuIhCqJ3DHdVg0oxHBW9CUvUwg6QPUsXeiBLYh3jsK3mZEaBWMVAEQ\npIWgAYIhJgI7m5GfG4nidSFldgfvV2AbheI8jzLFQtn4KFKmPIF+skTtPQmoSvwYh0xGPvI4rts9\naMwfYtSEIc5vg50vwpDptJ5PAkB2u6lZfAsRTzyFrv1VpMhe9E07jpTVB3pPA91KmDkAvtqNb8hr\neNbmYkivx/yZAdfYcIi+hCBVXYLrcUO7yp9CCIie1HWs0cDA0V3l351/EnfEb0b4b4qCFOJGszAG\n8l+A+nTYpwNTAGbZoHk3ilqPalsRdXTQ9oWDpOuvQZvRD5XTj67+JCKiHd5cDDMGQ1sZzLkTSrZA\n9VS47RQ8sgje2IrSupvmylW4GlsIk4yk7dwDb/yeVnGMDjlIVEMs7rrrMAXmg3stbl8q0WeycZ9b\nj+vxuwl/6g+IH5/G9v472DKSkTkEsUGctRq8LROxlCSiGzEZaj8hMGkcjjMvEhJUo8paisZUAq8/\nA3UhSBlWECF4bniC02kvMCH9HKJtfld6p+LHcGrT6N00jI4mFy2Jkwj76CdEUQ+Y9yDKpg+omhTB\nmqlxjFI5iCqykbPiPFIriEw7nHYQKNSj3H4d/uFuPMPrODegN+nLG5BrU5D0GUjlh2HVc3DbNkKG\nuDGdUCFGh6EL+JA1flCPQPXDfThSrAQJEHbgNIFJvRDF9fiSqpAyDDTfnw41DaB/+OK/6KUj7Dv8\nI7VoF2ej6ziLtuIEwqiChA0oQkW76wNUjkfQyjvxvjoYbUElUc+5UD15H2LfXqTsh9GbAgiseMwv\nod76HLKIQ2O5DCngQ964kprHlxP21qfohw2DH75F2/N6+GoD2rg7IHwd2N+GilwIPYrU8SrGKR7k\nECvaK76m89QgdFn9kDn/D+7z/4v5zQj/CyIkSJiGuH4SeOvAGAfLBMhBaC0CfyvCXYmp6DDKk2uo\nXGyn/eFnsTxjQCQLot2dRFWH0rQoHY27Bss5H6qnhsE8K6RGwzP3woJhyEeuwBHipuOCE5U3CMdb\n6BidhL0ogl7VtRRn5KBeuhkp0IDr68EYD9iILPkYpd5O83MGwjf/iEjIgF3rofEk9CtA0gTBNQal\neTzVNy4n6YZhaH+4n4o7TPh0eqKeOkCwvje6Hk3w9Y/QKwF6pQEKNJVjfXIKE2do0KVqaAu9D6nz\nI8x1LURsPgFvrCRq43b8WWVUje9L/KFTiFgt3geWIG66nyVfthAx04ihuRviUB3c8yTY1yJnGvCG\nSLhyDuFzZfJF5DVM1c/AfnU9FzQvIKfGQ3dgQTGYtxMxbQW+CT1hSG/4MRvJHYbWGYS0K4nYsQxP\nz0uoXXApoQd3EEiXMIe/jwgsIpYVkH8FxHblG/BThcYXg015kpXPjmT89A0Ew3SUJ8loeB0VJnzB\ncgzhiegjrQjPfnypYeh2eRFPvgqL5iBuexV98wmCB/5A8zNl6JNlDEMaEUuWMmFsKDUrawl57XUM\nwy7udjNEgS0SOTMVyl6HwZ+Do4Lg2rM7OR8AACAASURBVHuovXY40bVHkUu0aCe8Cq8txOBy4I6Y\nSo+CBJT+rQhdSNd9Ak4o/xDCRkBITleUxm/8aX5lX68Q4h7gBSD8ouLkn+Q3I/wrINRqUMf/xwlJ\nBfb/m3BkCIbkeQzf+hmVDMSzbjjR1WOp2/x7lN3bqYuoo1qbQISzjKarQO2Nx6y0YD2/HI0tFH9S\nXxytTuxtiejqTqCud4ItmoMRNzP9xQcxvrScXq9fB2lDELduxWP4HXQWoi41QK2LsJW9EKHNUJsL\n2Z24/QEMbgVnlhF1/KWYCs/S84tp+PceonyGHlN1J7Ff26nKCZC8rglxahdkBYHTUBcJWX1Bl0RA\nbcAQWwrbZCwR7Yio2xG2l6DvYEjNQVzxHNpzC7AnLONc0sdk3DULvyGRqKlVqC1exOZIqNsHKRaU\nmi34IisIjEzBVlhFe0kPvsqaxJX+YYSYEwl2i0ac7kQ0uyE9G0w7wHQVpkG3YhICWo7htY/HZ9uL\npc4O5ftg0FPoZTfRppsJXvgG37jDVFU9SUiCB3NnGULVtenGW1dHzep1CPV6rPMkEkUy6oVh6D9R\n6FZ5gfaEW2jkHG5vEUn6UXRGL6DdtAH/8Y2EpzjQpUXDsTPw5efQPxPv+Q7ayz1YJ6ShHpiJ0mij\n+cvvKHl7LgPc3wCH0DAeraIgjn2Dv7eMVj0D6ad74btj1KYn4AqUE1TLaN0aRNPzMDoE6gXeNA3m\n4w20H5+GTUq52L8UqP4aIkZD2m0Qc8m/bxzwX8L7l6v8tQghEoCJwIW/VPc3I/yPoK0OFJmEgQ9z\nli14VLmkn2xGXPMOVNUScmATNZN8yJoODLZmxAYf9QNM+C810WY/QaruHZQ2B80tj5B41gFX3oc9\ntxjaW+GltxFj50CgGeH3EnJDG766atRhAaTuSxAng1A4HyVcBd3Tqd8cTlSZD71IoDnlWdSZvTFt\nO4kYbiW2cz6+1zYjd5YSVq1DPPMa9JoNL0wCWzVUJoKtJywYhdbze2hbDcefQVo2Eda9BU1NkFQE\nzyyD+BTk/9PeecdHVWwP/Dt3+2aTzaZXSAIJJSE06b0IgiAodhSxo1ieig1sP9RnefqUp6JPbKAg\nKvgAGwpIky41BEJNQkJ6b9t3fn9sfKJSojwI6P1+PvvJnbtn7j1nd/Zk7pmZMwHxGP51M8kBQXja\nWgkoPARlwBYt7K6G3m2gbToibTT6nFvRF+Txbbd+VNrSuT1/E7r4/tBQSH3dSgJiB2P+5H1Iux6K\nB0DIUIT2AAQmw/b/UJdaR9AGDTjzwdIBej5GLRuoLJ1GTOgVSE0hcRkFuGKuoXTJcjz7qih552q0\nVisx116L9QIPInsdl0RPZd1Ve9F2ySCoroKVvIsWPaOdI1EMuZhFT3CH41g2h40v9CPFdjdR5QcQ\nb7+I8/k8aqKSafndR2g2TMO3ay/Fm6PZ+spoBpVthZUd8dSlob3+INJ3BLFjBUq8xJvxKcpOBYoc\n5E0JJtldgTM8GvLq0BsHIkJ7oVtaQVCiICvGSfJ8oEs/6HOFf6g9/WUwRTdzIz8POLPhiH8CDwGL\nTyWoOuHmoKoA7vsaXB4SZ26iumwZFffOIdQyEJZPxjx9Ma23XI9s6ER9/lOUd4nAFy6Inn2U8HQ7\nNb1fosKai6djA+7lLjRzn6FzmQ8UHSS3hxwXZB+BvSNRtjUgeyq4SgMwOjMgaCCETYWge/EppZgH\nasjPMxJbUY1hSTC64o00jLcSIB5E9/Fc9DXF7B2ZTLwvH8+h+WgxQeoQyPwCSlbBgW+RPV7G2XIc\nhliJGNUfbLHw+lLIWo0s/ABPYXfcc2ejhLRE370/ii0KceNkKt7rg6VwL+4OCdT930NUle6npn0a\n0boUWtT2pqIqG2tJDUMaXkfndsK+nWDpRh1lBHokhgIFwtZB+rWgs0L2PNj6KdJsxqOxo6sogbGL\nkd8/QUnd87gDJHHb26F0GYSiJOM4cD8H3n2dwhwnaXcNof3fL0MfPwLqtuGVGxH2WITQ0Mb0JAfa\nv4Rt3yLaHo0iOvbvKDXfU1v0A9W1O4nL2oOuWEPHGzIpSn2CwLS/UZfVCVOijsgD2+HwHLwVpdRs\nacA5BGzJJiKDNMiVa6ncsIyGteXoOoMsN6HZFII3UY8uWME3tgOO+P5olWLMFU9Qd2gQ+q+3QZ+v\nIDwag3kginkbTH0bNm6CZy8B6YMnvmne9n2+cIbCEUKIMUC+lHKXaMJTiDpF7WwjJcS3gTALTB+H\ncd067PdOpyh0O159CdJt9z8+GmMQncZhyYhE5+tN9JcFeIf3x6KtI/rbJTiLfFT4WuHobEbWeJHx\nGpgQD1YzrPgEsvNxd7qI2olmanoGUu1NpPLF7XjaXg4hqxD2K9GkF6CsTiQqUuDqrsVY50OxJGGe\nodCQ8QAV126lqrvEcZEBs96HY/tivB9MAOtWOLQb2pmQNwZQn9gFb7gVYWgNnT+EdW8jvV5cK77G\nvWojdtHA3kWTKPrwMXLu60bt0LfIzb6QrCuHUxgUQ35sKFWrX8F6cBupeTri936JtG/BHBNLTHYl\n79pu5F+d3sbeejhoFlMbUkBg0UGUUVdDfih43LB9BmxbCo4o7G3TMBVXQH0gvl3P4PV8T/iiZ4n7\nZiFK1lJI6IEgFIokLacNps9n44kqLccXtRjqtiMLXwN0/kUXQARtkAyl1qYhOWMPxkUbObrsTRr+\ns4GgKR8gD63Avr8CNpYQ8u99lDz3MAbvUayvz0dcMQheyMSrGNBHRLF7TEfSlxxE6P6Okt6bkK6A\nx8zRNwTOUgXNBdfhCQqHumxK23QjnDSszML+zKsE3DYeYTfA/pYQ5UZ4skndVYPZFww9x8Il90FK\nD/j0aXDam7WZnxecxhS1U+xC/yjw5LHiJ1ND7QmfTaSEuk+g7AHILIKrrkF0v45Ecy+qyKFGPIhR\nLMLLDAICWiLsBfgixhK2eQ261G6Ie3Nh7g+IvAeora1lQNZiNJpI6KPgteugoRTWLkWOvgXP1+/h\nW7MKXXQDQQFaTHofvsFR+B4ZgSPMTn5aD6rstxA5vhjPYRe6nAqqfXq8HfvgGRuNu+JjakM1WKfW\nYK0vQYh6AuzgNGpwz1iHvv94lKHr8VV2RLv4azQ3vgCaMNDZwLMB8e3TaFqaccfGUSbmEbSqFF9a\nJ/TRU9GEP0liyT0kLa7AtcuN7tttiCg3DNsH8gowxUFdJcbB/2Z97nqu3TGHpdUwO6o9w8OX0xD4\nGubUfHA8z/4rhpISPRE2vwFY4MfV1F8cSrAiqNS4qehnIT77JbQrn4BWQ8C8HVzlCGM4Jr3AdOVH\nMDsEIjvi1hqp3/cuDcYNuI6EYNyiJbT+H7i2zid1cwxVPRVKhhVi+M8dWFO6Yho+HSW6PeLz8Zjz\nK7A7sqg95MJzr43awEPYZoyA3KPIBxQ8h6zUDIGgCB22qCr47gHQtEBkVxAUKamtguINGuLSr8e7\n9k1k74c5EplMW4bg3bcfabej6z4AchbD5jwYdguYB6P5z3XwQxiEj4cbn4c+lzd3Kz9/OI1whJTy\nwuOdF0KkAYnAzsZecBywVQjRXUpZcrw6qhM+mwgBgVdDwGgIWwT6dHBsgvIHCfZVI0UAPlcETmcu\ndaYfMTRkoavzoB89Cu++RYjCQli2Es+wN6h1P0jB7n6Em2oRnaLxlGyH1oWQUAk/rqYuJZzNvdvT\nxp6HLAojwbuWFZf8DY3FTXi7dei8uUTk2tHtNEGdD02HyzCk5KHtOA3tgYVosl2UV0ZTYTMQTiTV\nl8QQmLsTY+IIPNoD0G0RMn4+MvtKDHUaWD4Gki/0zwjpswXq30YT1g5NmY+kWUegzoUcmosYp0DE\njaAfBAPGoOkkkHPrEIfcsAbY9BDEpYOSCN/OpHVRJUFpd3D5nrU4n3uYLbcMI0iXx9HuLxJeNw9q\n18GcTqCEQsUW6vvY8BkkwlFF5ahIWqxzoWMbtJsEtWYY9BQYGxPUez3+XNBrdDAwG/0iB6IuB1Ns\nHZUmA/rUanzGh7FHBFL0YndqDheQat+D5+ZxWHISICYZDEEQ1pbqXl1xeFcTO1yhdoudwol1ePbX\noh0TgVK9E7OnmGVtxzBioRHHBwGYhgVD8C7oGYv2+yCi76wlb7eLhu8G4bxQh7syk7r6/QSWLKX6\nwXVY7mgDO6ZD/i4oBH6YA8HBkHoPsABiDkPGddBxJgQknrAJqhzDGYgJSyl3A/9diiiEyAa6qrMj\nzjWUAAgc7z82dAD8SdOFrxaNcinmhgikPRBfxWI8BbW4tjpx7XFR7I4hcvZ72PM3w8UeDPoGXNKH\ncdsugqMLoSQM4gZRaMmnfH0IrQuL2ZuaTsldrQl/w8qwnI+gsxG5PBLfknzQxeE2xJLTsRdx5XkE\nJNkpid+Htmwmiwd+SFr5I2hCO9J68y5EfSdqb7GhaMBy+BAieBK+fRej8dUhWmngANDtEqg7APa1\nUB0ACXeCxQdvj4ajhxC5X8G/bgVDa+gRCrohaKrL4P5KKC8GVy9Yb4LcnVBVDj/OITHMCmuOoNRX\nYEoKou/Sb1HsdbgWDWfL6Mvp9cNh0GjhUAYEG6m/MQ2PeS9is4GEr6tQnJ9DMGDUQwVgHQW5H0PO\nVti/Cx4NgVA7UI+oLkMajQipIcTcCsJ6IgRY8t4kYdn7VLkjcKVasOZ8TF1ZOKVlh9HqE7Bk7kcG\npBI1+yOEx41hcCJt3C8ibCvg3aWguDn4wou0qFyNoWIZtYMtEJkIRMLKbTAkAG1wJ+r3ryZg0Os0\nGG6iNvAWgvcvxvn6PrTOIDSFXfw26POhZQiUVsPVj/r/sTfcCutvBlcVrB8JfVeA6U+cjP1/xdlJ\nZSlPJaA64XMJJRA0kWB7BBF4F5rK+1Ccy9CG5qK90ErY3UMJjB2A98hrRLu8BCsmdCkhCF82pcXJ\nhIdeQFHntvjMA+kw/00IyiKRUuzKKsoH2BB5IwiYvZqCd3sS9sB72Ekh87bR5PWNZ20hRLYy0ydr\nAgUpL3GpvgfVxkgOaQrwKP0xOJdjvacQ10fzIa4Tcskz+PokoK1ygqsStkbAiM7IoL7UlGzFql2J\nb+VkfKI1mgsSEUGhkD4MrJ9C1HUw9w7kDg/0SMaXYkSEOhBrP0N8Ew1fboWqSrzTH6Rs42bCht4G\nl1wJQmA/+BmV3m3EybGkLhnD3lYdKep7G/2yvkL/YxZC5BNR7kNTcwcE7oTq1ZDngToHjhQdRs1c\neFEBlwaq3FBgQrolcrcNcVcV1LihRiA6P0pJaATBWwfirfagyQsiKn0c3qLl5LcOIji2lBa+S/EU\nZyMuuQN9u3F+h1iQQ0MdGN96AS6pgyGRZLfqR0ZrD2M+LgOfh+yI3oTtqIDKHXBZD1jzDUqPIP+c\nXvMlWHx3cjD/ZaJWxVC3uYzgb76BuDjYvxk+ewY6j4SFH/gz4VlDwBwNsRf7e/gtRoOrvLlb8vnB\nGZyi9hNSyqRTyahO+Fzjp9FUrxOcJYjEGEgchiFjE1rDeByaj2mIg/V1Y+n09Uf4dhYjwgIJsuRT\nbQoj5NWV6EvqoO9AkEUomlACtu8mYIsBT/0X7B4YR+iTn/LhrW2go5uEjml0z6ulZnMJnazLwFFG\n6D9uhqhnqX7xdtpXbcJQUQgLQ+GaOvSZk/FFX4gnxopu7iHYJWCUBe5/E+x1rCt7H+vgB2hZuBFX\npRNHYRnRO1agKd+NL6KQnPq9eB03ETXKh+nOwYiCesR3uxH7ghFHG2ByJhR+gcORxv5lmzg4dixt\nx1wFgO/IETQHddhKYnG3dVBw67Wk5vdB88MSlsZHMSoyH9vOw2jTp0FVBmTmQ2Ug2Mxgzafw+nDi\nJOheToE92+HjKuh+M2gknuBYxNGnoI8LzcZA3Ns+5rCSSVn7XlSmX8eF/57OijblJHtaUBrVln65\ns9hVMJ2WG6rQWS5A/8rd4DYiDSZMiYp/h+NVxWDLZeuwFriLduMb+Q4adxX532+j2+Zp8HB3qKmF\negfuJC/6WBf2reMxtkynIjCYhJVf4ItIQjHmgtMJSa1h0PUwshtERcHRbL8TBmgzCVZeBu4aSLml\nWZrueYe6Yk7lN3jcfidcWwXuQ1C4xL99UsDVkNQSTcYhAqKfwut7mOvDr0fz3jj49jnkV/NwCQPu\n6HqsIh0mToCOY+Dx2+Af8+DwarwuQcMblxAZfxRzppubX5uCCAlDJvTEvWQBZWl2aJEMGQ4YPBi2\nrSb8kx8IsCbDpjehf2eIj0XWZeCsWoyhpDvCokALO9y8GYyBHC3ZwOK2oVyjzEVW9SN0z1fsj41g\nSXotY+dsRIx4m4gdX7D+sffwzboNGeBA27oVtrDrsU55B+2dD0PBTDh4MyXz++MpK8WRkgKeetgx\nCRHUCs3Rg4gtn+MmmPijCp5Di0kJ1tImvyVUV6Bd6wDzYzDoXrA6oNO/QL4KbzrwhGspLBS0mFcD\nBUGQ3wC2HRDiRPRUEOJ6iF2Fq89g6qct4YJhVeCJQ0nti1KlYfyMLGTaVryDL6Ay5C1aOe6B0FB0\nGRnQ/24YcweunDzsX3yJSW6B7EzkpVZkm1TGzt2H7tZU2LqGYYufgnah/sTrB3KhWwdqOvRDKYyk\n+qCPrE4m6upNyOhEgh/XIeR6qKgBbxUMbQDn49BbgtkGsiMILcjG+HbWG6oTbirqzhoqv8FeD2sW\nQ8u2MHEahPQG9xFwVkGbkTD3DbjoRgJ9CoIW0EIL/R9CbM5Hm7WdwKgbILQAdiyBjG+gaA+y4C1E\n+C40//4S0wUKugAw9YuGxLaw93tE9A9obykhJgmkz4voeht0egFK8whw2eGDZyDPCfp9IK7HPjoJ\nva8dyuQnYMsq2PUtBATj3X8vgVUfcd9nCehvXERQRByiuhWxrRNw7NmH012KduUs9DVFpH/RD1fA\nbHTVwwkRN1E9exLZr/XHa96KpWYEQa9nUVO5lNT575NdUAaZd0DxXESlQNchCTnia7KCN9PSeD1K\nxmq8W1fgzQpG0y4LxW1GZIDIeR98Rti0BmGLgg5OQiojcddZkTc/jfjoCkjsgLdsB+V3x+COL8dU\n6iCgtC3K8rkYeg5FJK9G5GvwbLkT8n3oju7B5wsB378JuXwLvuo70NbNRbycCTp/iktX5rfoDy6B\nv02HOTPxHEpldItMjOFpcDQTlt6Jr7sWCqphXSXEmKH/42gDAnGUHqHspQV4r+hKbFkM1ttvRIQ7\nwLUELDMbE/VIcGSCsd0vlyRrDDDgU9j8N7CXgCmiedrw+YS6s4bKbwgMhl4jIL2Pv6xcBHV6qNoP\nIe3BUQ/OxQjXEvDuhsIjcNcocHooDWkLY++HK16C2z+BG95F9tfg0j4LriXIqHocI1PRFZtwDhkD\nW6th2OfwTRy8BL5pBsRzcbAuD2bfAvcNhOcmwsa1MCAB+unwFm5Gt+goWv0kv35Z2/H06UG1ZgoO\n0xECS3xER+dge+5yqKuC0iQCAoJJaaimqp+Z/C5rKL2sE1bjPQTX/Zs9Vx2lfvAgIuIvI9n8Im2O\n3oB18QZyrDrq32lF5oD5EFYLHecgF8Yjc4dAq6eh7l84xFbM0oKSnIpueBLGJ59BNzwNTb+bEFVd\nIc2ObC2R9ZHQ/nnoaiPkaB+cLY2IhRPhUAIYatAobsKXKoTu64n12+7oJ36PjDRimtwLJa4VjsMX\nIxuOIqKq8F1+Lb6qlghnJb4nU/HO+ABvfQS+jfPAVQrl/8H16QvoPUWw8BW4/Ql0Cz/GaJ0I6R5Y\n8S6MDOLLxJeov6YV3vK9NOha4N2yAJ2uPfpueryV9QQrbjpmtUDo9GAYCbrBUP8geAv8T0qmtOPn\nhFA00ONfoAs8G631/Efd8l7luIy+Gdr38B9XboO9ddDWAxqTf0S9uo1/NoCmPdTug892QHAonn/2\n/eV1dAZcsj2equvRR19HRf9JWMNfQ5O2BeePT6O5+T20zz0M9Ufw9e2M3L4Dz+gctJsLEYcqQIYj\nduZAggF06cghL+G+cC6G4ofg7QeRgQbqEtbjaz0AC4+gqdkGm/VQsQsZuZu6jLY4LwsHqxFjfTbh\n+5MQ646ytk8BQVUz6dRhBv31R/mh3E6atQPh70xB2GvxhI0lJOsIgfsqEKHdOOzT4HPbqT9gwzJi\nKN6doRz25OIONOLL741i644HLcL1KZocDXLhbBhej6gYiHAI6FMKB2dAx1sQKY+jyPvw5e5BycyA\nSe/CkvcRgZsxGp6EA2+ARUEz9HVE7UyEJxFzu9V4cm3UVtRiLfkQzeMfoayYgMZiR5vUGmk6iHf3\nLbh/1OKyhuPMcWKLr8IRBa5WGRgTXBTmL0MftR1zt33sjbqSbFsZJTUeoiPgQLeBdJj3Crq1X0C/\nVCInd6GlSEXjyfd/zwDGK6F2GVR1h5B9II6zi8ZPCAFa0xlpmn861JiwynHpfuHPg3OhtdBxIBz9\nCso2Qlp/2L0dej8PQg8pHfxytQW4jZbfXKohvhBrXhRlKc9iCByJlmjoMhrTqrepGvgUgXE+dJoA\ntJWHkLUS3qrDN7AOeZ0AbwUCDR6LgmQnyufXYBBahHEj3nZmPL4qLDtLEHVaaP0otHwWj95B5bQk\nNNruGHbbsL20AKXjRoTVCsFe0AYwoLoT++vXsyrjBvq2qKFfq3h+mHwlbac8Ttj4qZRMnEjytBSU\n+PGw/mE8VffTcOvteEvrEUd2IH2x1LQIxaRtRU1uLo7ytRxJNPNC3lA6hqRzx00bCduuQNYmyK6D\nJCOMcMPGSojfR4BtO3UtXATGhsP2pYgwD3SfBQH9oeAt+L9n0Xo2I+0eMA2A/pPR7p+Pbv1BCOqA\n2HI7xPug0oPYvBvx8Ex88TF4C7/C+9BsHAedVAXF4XqzjrqL/k3cbaUErdpJ7aSbMVW+RlfbZJyr\n3ycmvCP6kqN0fH4RPPsBfDeDqJ25WCbNQvg04M3x92x/wvwo+CrA/iaYp5zhRvgXQY0JqxyXnxyw\nsxACw2DAZMjcB2E9wVQN7z0Eg9/6ZZ28VRQb2tPmmFNOMhGtO+KqzKWOBQQwwv84W/cEYthuAlcX\nIWo8cGEwfBwCWgfS5EJZD25vNM5uPsrSNNSmBhPgEugdXmJ+rEJTWI04WIum1TAcW77FOHwiIiQd\nlr+EhjjC4t5FICAKfNUDYcENyFgvIukIRCuw73FSfFOJ3PYxq2/qTrfttXQd2JUtM98ieNVbpKQl\no6zdAzXd8Epob51DyVgj7qldORyajcYyC1d5JSbFTdnlKeh8nYhfv4CLLd+ghHjZb0jm9nbTeaDV\no3RZmo3xukcQ9V9A1jY4GIHFVk5pnygs+WNh7yyE1QVxF8NDN8KdD8KhFWCbjfDaodXL0KCAUVCe\nEIZ591ZE90mQEAu+FVC1CWa/gjalG1q3EeXRrxC7J2B56j28W2YQUfQDHvpg7lVGcIUXYXsURAfi\nTVswFFqhXUdYXgGJ3VFkAzVdW2Fd/CqioRqCWoHmmMTrmiSwLgTP7jPdAv86nIUpak1BdcLnKnvv\nAncRxHUDMc3vnC3B0FDtH6D5ibpCOPwNxYb+v6hezUeEJE+hjs+JZA5m2R/sT4FrPiImDu270dj7\npqB8uB2uewHPJ7dSmGrFPWoAwd/sJCQ3j8AFGmRyHG6rhfp2kvL0vtj07al/exbOzZ8S3EePCGgD\nxbOg5FvEoAc5dpm8culVyM9vxLe3DuWqrxG5K+HQdNBOx5ragYFFtey7tgqpr6TDRdPZd9cyNm7L\nZuD0S9HaeiC+eY74oiLMNQFQmUZBkkKC8VY8RU+i/fQAvotT8fRpj8/pYKJ7PpVrw9g68iXahni4\nruJzvKPMLBB3kdpyPgHjFkDJ4+i/CsTVKQj7R3mY3bXI0dMQn7wH2Zvgn5ngLoHrdKD3wTsvQ8Im\nWG0hxOKiOtmCbdn7MLA7GFdDjw4wdy8yUouY9BU6rxnbo4+iHzwEUmaC7wL0kbPwZryFr+oplMSd\niOpVGBtqIHoYtIsEsQukD0/lfrzlBmonfkjQjMdgzZtQXwsPvP/LdqFNO3Nt7q/GORKOUAfmzmUS\nHvLvTdbimHwAtigoyv65XHUY9swlxJXz31NeqpDY0RJNEBMwMwi8e0CWg2EqHLgP4bBhXpiJfXwS\n9sxHqJ5yP7YZ1SQtTCIsuy1KlAculog7pqG/7yNsmlCsawqpv24q+vG3Y73hYgwTboXM5RB3P1gc\nUPOxP479E0KARY+s0eJ9/XUY9yToUyC4G9IZgZKznbaf19BqwyDCHlpI0rARVO7aTeaiSkjujQht\nS70nEN/NRiomaCH9KoT7KbTDv0KaDSjvvIFhzfsYWwYhNpgI3VvIsH0HeTZyONlxtWT57mF3zTDa\nHoqgY/HVfLuzD772PTAkDkCEroc9TmSeB/btgnmfQBcXJHig7xvQbz2MM8ON38C8tVg6XwamYIgW\nkLEZ9qfgq9BQFjkM92E7suIIvllvYPpyAa7JE5F1PSH+WzC0RJMQjwh9HKf3Znxlf8dWfRg63ggx\n/aBHIGgU5O0LUDw+LJYLYNpyaH0RxKec+Tb2V0bd6FPlpERdDRFj/Mc/hSikhNI8ePWmn+V0Zkge\nQ7Ex9b+naviUQK4EQKFxpFzbAQJnwgs/wvT74JrHIKwc8yebCOj1T8K32rBc0M6/S/QzCyEqDawN\n8OwEUMx4ht+Hd8QqAsdZMV9+Kca8HRDVGbp0gS0P+GOqZYFw5CP4YhhseQ1+/AARqkFz8w2I3v2R\nnzwCIx+CxFjElOUoo+ehqUzBtH0/4vqriFn7KZc90QFzck/qG56gocdBAieUojeHsNt3JXGuLNgf\nhchaCP3b4Rs9FJmbBUvzkNH3ImNjoGgH4l/tYOtjmFNe4qa2N5K35x7e+ORSckfGM13Xnpwf1uMY\nYfGHYmY8g+zihP0bwJMEIeHwwZ2w6R6I6gMhHcASjoiyUtC9BT5bC6TGjq/8IPbdRQSExKPJq8D9\nwAPIvCOI1inopr+MaPug/3ura3eozAAAEjxJREFUmA3Vn6ME3YRBOweXdTeOvlqkJQYq9sC2F6Du\nKLqY7ugGP4Hy08Npn3EwYtKZbmV/bdSNPlVOSuRxsmEJAUMnwhev/XzOFAqDX4Gv1gPgYj8ONhHM\nrb+t/9Vsf14GSwDEtQadC1GuhU/eg7F3oszdjk9KqNgLQWlgzIaWJnipA85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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.quiver(sp.source['xyz'][:,0], sp.source['xyz'][:,1],\n", + " sp.source['uvw'][:,0], sp.source['uvw'][:,1],\n", + " np.log(sp.source['E']), cmap='jet', scale=20.0)\n", + "plt.colorbar()\n", + "plt.xlim((-0.5,0.5))\n", + "plt.ylim((-0.5,0.5))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/source/pythonapi/examples/post-processing.rst b/docs/source/pythonapi/examples/post-processing.rst new file mode 100644 index 000000000..b488d15ff --- /dev/null +++ b/docs/source/pythonapi/examples/post-processing.rst @@ -0,0 +1,13 @@ +.. _notebook_post_processing: + +=============== +Post Processing +=============== + +.. only:: html + + .. notebook:: post-processing.ipynb + +.. only:: latex + + IPython notebooks must be viewed in the online HTML documentation. diff --git a/docs/source/pythonapi/examples/tally-arithmetic.ipynb b/docs/source/pythonapi/examples/tally-arithmetic.ipynb index 2b1205204..2f32f3d9a 100644 --- a/docs/source/pythonapi/examples/tally-arithmetic.ipynb +++ b/docs/source/pythonapi/examples/tally-arithmetic.ipynb @@ -358,7 +358,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "" ] @@ -569,7 +569,8 @@ " Copyright: 2011-2015 Massachusetts Institute of Technology\n", " License: http://mit-crpg.github.io/openmc/license.html\n", " Version: 0.7.0\n", - " Date/Time: 2015-08-15 10:52:49\n", + " Git SHA1: b167d70c877c516deca785801b9fa6f53fb0985b\n", + " Date/Time: 2015-09-21 10:25:26\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -595,26 +596,26 @@ "\n", " Bat./Gen. k Average k \n", " ========= ======== ==================== \n", - " 1/1 1.00465 \n", - " 2/1 1.05814 \n", - " 3/1 1.05114 \n", - " 4/1 1.09189 \n", - " 5/1 1.03731 \n", - " 6/1 1.03510 \n", - " 7/1 1.09378 1.06444 +/- 0.02934\n", - " 8/1 1.04522 1.05803 +/- 0.01811\n", - " 9/1 1.06557 1.05992 +/- 0.01294\n", - " 10/1 1.05757 1.05945 +/- 0.01004\n", - " 11/1 1.04858 1.05764 +/- 0.00839\n", - " 12/1 1.01832 1.05202 +/- 0.00905\n", - " 13/1 1.05822 1.05279 +/- 0.00787\n", - " 14/1 1.07684 1.05547 +/- 0.00744\n", - " 15/1 1.00349 1.05027 +/- 0.00844\n", - " 16/1 1.06969 1.05203 +/- 0.00784\n", - " 17/1 1.06377 1.05301 +/- 0.00722\n", - " 18/1 1.02897 1.05116 +/- 0.00690\n", - " 19/1 1.00685 1.04800 +/- 0.00713\n", - " 20/1 1.02644 1.04656 +/- 0.00679\n", + " 1/1 1.00279 \n", + " 2/1 1.03320 \n", + " 3/1 1.04467 \n", + " 4/1 1.09693 \n", + " 5/1 1.05008 \n", + " 6/1 1.08426 \n", + " 7/1 1.05363 1.06894 +/- 0.01531\n", + " 8/1 0.97961 1.03917 +/- 0.03106\n", + " 9/1 1.06444 1.04549 +/- 0.02285\n", + " 10/1 1.08345 1.05308 +/- 0.01926\n", + " 11/1 1.06871 1.05568 +/- 0.01594\n", + " 12/1 1.03183 1.05228 +/- 0.01390\n", + " 13/1 1.04486 1.05135 +/- 0.01207\n", + " 14/1 1.06468 1.05283 +/- 0.01075\n", + " 15/1 1.04185 1.05173 +/- 0.00968\n", + " 16/1 1.01268 1.04818 +/- 0.00944\n", + " 17/1 1.04129 1.04761 +/- 0.00864\n", + " 18/1 1.01127 1.04481 +/- 0.00843\n", + " 19/1 1.03738 1.04428 +/- 0.00782\n", + " 20/1 1.04410 1.04427 +/- 0.00728\n", " Creating state point statepoint.20.h5...\n", "\n", " ===========================================================================\n", @@ -624,27 +625,27 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.4100E-01 seconds\n", - " Reading cross sections = 1.1300E-01 seconds\n", - " Total time in simulation = 1.8418E+01 seconds\n", - " Time in transport only = 1.8403E+01 seconds\n", - " Time in inactive batches = 2.1070E+00 seconds\n", - " Time in active batches = 1.6311E+01 seconds\n", - " Time synchronizing fission bank = 2.0000E-03 seconds\n", - " Sampling source sites = 2.0000E-03 seconds\n", + " Total time for initialization = 9.1800E-01 seconds\n", + " Reading cross sections = 6.5800E-01 seconds\n", + " Total time in simulation = 1.7037E+01 seconds\n", + " Time in transport only = 1.7024E+01 seconds\n", + " Time in inactive batches = 2.8600E+00 seconds\n", + " Time in active batches = 1.4177E+01 seconds\n", + " Time synchronizing fission bank = 4.0000E-03 seconds\n", + " Sampling source sites = 4.0000E-03 seconds\n", " SEND/RECV source sites = 0.0000E+00 seconds\n", " Time accumulating tallies = 0.0000E+00 seconds\n", " Total time for finalization = 1.0000E-03 seconds\n", - " Total time elapsed = 1.8861E+01 seconds\n", - " Calculation Rate (inactive) = 5932.61 neutrons/second\n", - " Calculation Rate (active) = 2299.06 neutrons/second\n", + " Total time elapsed = 1.7971E+01 seconds\n", + " Calculation Rate (inactive) = 4370.63 neutrons/second\n", + " Calculation Rate (active) = 2645.13 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.04599 +/- 0.00622\n", - " k-effective (Track-length) = 1.04656 +/- 0.00679\n", - " k-effective (Absorption) = 1.04614 +/- 0.00461\n", - " Combined k-effective = 1.04651 +/- 0.00368\n", + " k-effective (Collision) = 1.04044 +/- 0.00527\n", + " k-effective (Track-length) = 1.04427 +/- 0.00728\n", + " k-effective (Absorption) = 1.04794 +/- 0.00535\n", + " Combined k-effective = 1.04628 +/- 0.00467\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -692,8 +693,7 @@ "outputs": [], "source": [ "# Load the statepoint file\n", - "sp = StatePoint('statepoint.20.h5')\n", - "sp.read_results()" + "sp = StatePoint('statepoint.20.h5')" ] }, { @@ -759,8 +759,8 @@ " 0\n", " total\n", " (nu-fission / absorption)\n", - " 1.042726\n", - " 0.008661\n", + " 1.046353\n", + " 0.00935\n", " \n", " \n", "\n", @@ -769,7 +769,7 @@ "text/plain": [ " nuclide score mean std. dev.\n", "bin \n", - "0 total (nu-fission / absorption) 1.042726 0.008661" + "0 total (nu-fission / absorption) 1.046353 0.00935" ] }, "execution_count": 26, @@ -827,17 +827,17 @@ " 0\n", " total\n", " absorption\n", - " 0.958874\n", - " 0.007146\n", + " 0.95873\n", + " 0.00774\n", " \n", " \n", "\n", "" ], "text/plain": [ - " nuclide score mean std. dev.\n", - "bin \n", - "0 total absorption 0.958874 0.007146" + " nuclide score mean std. dev.\n", + "bin \n", + "0 total absorption 0.95873 0.00774" ] }, "execution_count": 27, @@ -893,17 +893,17 @@ " 0\n", " total\n", " nu-fission\n", - " 1.09186\n", - " 0.010424\n", + " 1.091622\n", + " 0.011163\n", " \n", " \n", "\n", "" ], "text/plain": [ - " nuclide score mean std. dev.\n", - "bin \n", - "0 total nu-fission 1.09186 0.010424" + " nuclide score mean std. dev.\n", + "bin \n", + "0 total nu-fission 1.091622 0.011163" ] }, "execution_count": 28, @@ -966,8 +966,8 @@ " 10000\n", " total\n", " absorption\n", - " 0.802921\n", - " 0.006109\n", + " 0.802012\n", + " 0.006609\n", " \n", " \n", "\n", @@ -976,7 +976,7 @@ "text/plain": [ " energy [MeV] cell nuclide score mean std. dev.\n", "bin \n", - "0 0.0e+00 - 6.2e-01 10000 total absorption 0.802921 0.006109" + "0 0.0e+00 - 6.2e-01 10000 total absorption 0.802012 0.006609" ] }, "execution_count": 29, @@ -1037,8 +1037,8 @@ " 10000\n", " total\n", " (nu-fission / absorption)\n", - " 1.240421\n", - " 0.010978\n", + " 1.246604\n", + " 0.011825\n", " \n", " \n", "\n", @@ -1047,11 +1047,11 @@ "text/plain": [ " energy [MeV] cell nuclide score mean \\\n", "bin \n", - "0 0.0e+00 - 6.2e-01 10000 total (nu-fission / absorption) 1.240421 \n", + "0 0.0e+00 - 6.2e-01 10000 total (nu-fission / absorption) 1.246604 \n", "\n", " std. dev. \n", "bin \n", - "0 0.010978 " + "0 0.011825 " ] }, "execution_count": 30, @@ -1105,8 +1105,8 @@ " 0\n", " total\n", " (((absorption * nu-fission) * absorption) * (n...\n", - " 1.042726\n", - " 0.017538\n", + " 1.046353\n", + " 0.01894\n", " \n", " \n", "\n", @@ -1115,11 +1115,11 @@ "text/plain": [ " nuclide score mean \\\n", "bin \n", - "0 total (((absorption * nu-fission) * absorption) * (n... 1.042726 \n", + "0 total (((absorption * nu-fission) * absorption) * (n... 1.046353 \n", "\n", " std. dev. \n", "bin \n", - "0 0.017538 " + "0 0.01894 " ] }, "execution_count": 31, @@ -1197,7 +1197,7 @@ " (U-238 / total)\n", " (nu-fission / flux)\n", " 0.000001\n", - " 6.985151e-09\n", + " 6.859257e-09\n", " \n", " \n", " 1\n", @@ -1205,8 +1205,8 @@ " 0.0e+00 - 6.3e-07\n", " (U-238 / total)\n", " (scatter / flux)\n", - " 0.209988\n", - " 2.206753e-03\n", + " 0.209986\n", + " 1.966887e-03\n", " \n", " \n", " 2\n", @@ -1214,8 +1214,8 @@ " 0.0e+00 - 6.3e-07\n", " (U-235 / total)\n", " (nu-fission / flux)\n", - " 0.355276\n", - " 3.741612e-03\n", + " 0.355667\n", + " 3.717881e-03\n", " \n", " \n", " 3\n", @@ -1224,7 +1224,7 @@ " (U-235 / total)\n", " (scatter / flux)\n", " 0.005555\n", - " 5.842517e-05\n", + " 5.218094e-05\n", " \n", " \n", " 4\n", @@ -1232,8 +1232,8 @@ " 6.3e-07 - 2.0e+01\n", " (U-238 / total)\n", " (nu-fission / flux)\n", - " 0.007229\n", - " 5.951357e-05\n", + " 0.007165\n", + " 5.625590e-05\n", " \n", " \n", " 5\n", @@ -1241,8 +1241,8 @@ " 6.3e-07 - 2.0e+01\n", " (U-238 / total)\n", " (scatter / flux)\n", - " 0.227642\n", - " 9.496469e-04\n", + " 0.227653\n", + " 8.544314e-04\n", " \n", " \n", " 6\n", @@ -1250,8 +1250,8 @@ " 6.3e-07 - 2.0e+01\n", " (U-235 / total)\n", " (nu-fission / flux)\n", - " 0.008076\n", - " 5.699123e-05\n", + " 0.008089\n", + " 5.080374e-05\n", " \n", " \n", " 7\n", @@ -1259,8 +1259,8 @@ " 6.3e-07 - 2.0e+01\n", " (U-235 / total)\n", " (scatter / flux)\n", - " 0.003369\n", - " 1.369755e-05\n", + " 0.003370\n", + " 1.361116e-05\n", " \n", " \n", "\n", @@ -1270,24 +1270,24 @@ " cell energy [MeV] nuclide score mean \\\n", "bin \n", "0 10000 0.0e+00 - 6.3e-07 (U-238 / total) (nu-fission / flux) 0.000001 \n", - "1 10000 0.0e+00 - 6.3e-07 (U-238 / total) (scatter / flux) 0.209988 \n", - "2 10000 0.0e+00 - 6.3e-07 (U-235 / total) (nu-fission / flux) 0.355276 \n", + "1 10000 0.0e+00 - 6.3e-07 (U-238 / total) (scatter / flux) 0.209986 \n", + "2 10000 0.0e+00 - 6.3e-07 (U-235 / total) (nu-fission / flux) 0.355667 \n", "3 10000 0.0e+00 - 6.3e-07 (U-235 / total) (scatter / flux) 0.005555 \n", - "4 10000 6.3e-07 - 2.0e+01 (U-238 / total) (nu-fission / flux) 0.007229 \n", - "5 10000 6.3e-07 - 2.0e+01 (U-238 / total) (scatter / flux) 0.227642 \n", - "6 10000 6.3e-07 - 2.0e+01 (U-235 / total) (nu-fission / flux) 0.008076 \n", - "7 10000 6.3e-07 - 2.0e+01 (U-235 / total) (scatter / flux) 0.003369 \n", + "4 10000 6.3e-07 - 2.0e+01 (U-238 / total) (nu-fission / flux) 0.007165 \n", + "5 10000 6.3e-07 - 2.0e+01 (U-238 / total) (scatter / flux) 0.227653 \n", + "6 10000 6.3e-07 - 2.0e+01 (U-235 / total) (nu-fission / flux) 0.008089 \n", + "7 10000 6.3e-07 - 2.0e+01 (U-235 / total) (scatter / flux) 0.003370 \n", "\n", " std. dev. \n", "bin \n", - "0 6.985151e-09 \n", - "1 2.206753e-03 \n", - "2 3.741612e-03 \n", - "3 5.842517e-05 \n", - "4 5.951357e-05 \n", - "5 9.496469e-04 \n", - "6 5.699123e-05 \n", - "7 1.369755e-05 " + "0 6.859257e-09 \n", + "1 1.966887e-03 \n", + "2 3.717881e-03 \n", + "3 5.218094e-05 \n", + "4 5.625590e-05 \n", + "5 8.544314e-04 \n", + "6 5.080374e-05 \n", + "7 1.361116e-05 " ] }, "execution_count": 33, @@ -1318,11 +1318,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 6.63809296e-07]\n", - " [ 3.55275544e-01]]\n", + "[[[ 6.64174599e-07]\n", + " [ 3.55666541e-01]]\n", "\n", - " [[ 7.22895528e-03]\n", - " [ 8.07565148e-03]]]\n" + " [[ 7.16505734e-03]\n", + " [ 8.08949336e-03]]]\n" ] } ], @@ -1350,9 +1350,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.00555505]]\n", + "[[[ 0.00555465]]\n", "\n", - " [[ 0.0033688 ]]]\n" + " [[ 0.00337011]]]\n" ] } ], @@ -1374,8 +1374,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.2276418]\n", - " [ 0.0033688]]]\n" + "[[[ 0.22765348]\n", + " [ 0.00337011]]]\n" ] } ], @@ -1434,7 +1434,7 @@ " U-238\n", " nu-fission\n", " 0.000002\n", - " 1.211808e-08\n", + " 1.284890e-08\n", " \n", " \n", " 1\n", @@ -1442,8 +1442,8 @@ " 0.0e+00 - 6.3e-07\n", " U-235\n", " nu-fission\n", - " 0.870360\n", - " 6.496431e-03\n", + " 0.867982\n", + " 7.022256e-03\n", " \n", " \n", " 2\n", @@ -1451,8 +1451,8 @@ " 6.3e-07 - 2.0e+01\n", " U-238\n", " nu-fission\n", - " 0.083226\n", - " 6.367951e-04\n", + " 0.082801\n", + " 6.087096e-04\n", " \n", " \n", " 3\n", @@ -1460,8 +1460,8 @@ " 6.3e-07 - 2.0e+01\n", " U-235\n", " nu-fission\n", - " 0.092974\n", - " 5.921990e-04\n", + " 0.093484\n", + " 5.275039e-04\n", " \n", " \n", "\n", @@ -1470,10 +1470,10 @@ "text/plain": [ " cell energy [MeV] nuclide score mean std. dev.\n", "bin \n", - "0 10000 0.0e+00 - 6.3e-07 U-238 nu-fission 0.000002 1.211808e-08\n", - "1 10000 0.0e+00 - 6.3e-07 U-235 nu-fission 0.870360 6.496431e-03\n", - "2 10000 6.3e-07 - 2.0e+01 U-238 nu-fission 0.083226 6.367951e-04\n", - "3 10000 6.3e-07 - 2.0e+01 U-235 nu-fission 0.092974 5.921990e-04" + "0 10000 0.0e+00 - 6.3e-07 U-238 nu-fission 0.000002 1.284890e-08\n", + "1 10000 0.0e+00 - 6.3e-07 U-235 nu-fission 0.867982 7.022256e-03\n", + "2 10000 6.3e-07 - 2.0e+01 U-238 nu-fission 0.082801 6.087096e-04\n", + "3 10000 6.3e-07 - 2.0e+01 U-235 nu-fission 0.093484 5.275039e-04" ] }, "execution_count": 37, @@ -1526,8 +1526,8 @@ " 1.0e-08 - 1.1e-07\n", " H-1\n", " scatter\n", - " 4.638428\n", - " 0.034134\n", + " 4.620525\n", + " 0.038249\n", " \n", " \n", " 1\n", @@ -1535,8 +1535,8 @@ " 1.1e-07 - 1.2e-06\n", " H-1\n", " scatter\n", - " 2.050818\n", - " 0.010745\n", + " 2.036841\n", + " 0.013203\n", " \n", " \n", " 2\n", @@ -1544,8 +1544,8 @@ " 1.2e-06 - 1.3e-05\n", " H-1\n", " scatter\n", - " 1.656905\n", - " 0.009480\n", + " 1.659916\n", + " 0.010107\n", " \n", " \n", " 3\n", @@ -1553,8 +1553,8 @@ " 1.3e-05 - 1.4e-04\n", " H-1\n", " scatter\n", - " 1.870808\n", - " 0.011883\n", + " 1.861546\n", + " 0.013328\n", " \n", " \n", " 4\n", @@ -1562,8 +1562,8 @@ " 1.4e-04 - 1.5e-03\n", " H-1\n", " scatter\n", - " 2.045621\n", - " 0.011414\n", + " 2.049664\n", + " 0.008215\n", " \n", " \n", " 5\n", @@ -1571,8 +1571,8 @@ " 1.5e-03 - 1.6e-02\n", " H-1\n", " scatter\n", - " 2.163297\n", - " 0.008725\n", + " 2.162157\n", + " 0.010245\n", " \n", " \n", " 6\n", @@ -1580,8 +1580,8 @@ " 1.6e-02 - 1.7e-01\n", " H-1\n", " scatter\n", - " 2.202045\n", - " 0.013500\n", + " 2.224496\n", + " 0.013796\n", " \n", " \n", " 7\n", @@ -1589,8 +1589,8 @@ " 1.7e-01 - 1.9e+00\n", " H-1\n", " scatter\n", - " 1.996977\n", - " 0.010791\n", + " 1.997585\n", + " 0.009161\n", " \n", " \n", " 8\n", @@ -1598,8 +1598,8 @@ " 1.9e+00 - 2.0e+01\n", " H-1\n", " scatter\n", - " 0.370890\n", - " 0.003597\n", + " 0.373472\n", + " 0.003922\n", " \n", " \n", "\n", @@ -1608,15 +1608,15 @@ "text/plain": [ " cell energy [MeV] nuclide score mean std. dev.\n", "bin \n", - "0 10002 1.0e-08 - 1.1e-07 H-1 scatter 4.638428 0.034134\n", - "1 10002 1.1e-07 - 1.2e-06 H-1 scatter 2.050818 0.010745\n", - "2 10002 1.2e-06 - 1.3e-05 H-1 scatter 1.656905 0.009480\n", - "3 10002 1.3e-05 - 1.4e-04 H-1 scatter 1.870808 0.011883\n", - "4 10002 1.4e-04 - 1.5e-03 H-1 scatter 2.045621 0.011414\n", - "5 10002 1.5e-03 - 1.6e-02 H-1 scatter 2.163297 0.008725\n", - "6 10002 1.6e-02 - 1.7e-01 H-1 scatter 2.202045 0.013500\n", - "7 10002 1.7e-01 - 1.9e+00 H-1 scatter 1.996977 0.010791\n", - "8 10002 1.9e+00 - 2.0e+01 H-1 scatter 0.370890 0.003597" + "0 10002 1.0e-08 - 1.1e-07 H-1 scatter 4.620525 0.038249\n", + "1 10002 1.1e-07 - 1.2e-06 H-1 scatter 2.036841 0.013203\n", + "2 10002 1.2e-06 - 1.3e-05 H-1 scatter 1.659916 0.010107\n", + "3 10002 1.3e-05 - 1.4e-04 H-1 scatter 1.861546 0.013328\n", + "4 10002 1.4e-04 - 1.5e-03 H-1 scatter 2.049664 0.008215\n", + "5 10002 1.5e-03 - 1.6e-02 H-1 scatter 2.162157 0.010245\n", + "6 10002 1.6e-02 - 1.7e-01 H-1 scatter 2.224496 0.013796\n", + "7 10002 1.7e-01 - 1.9e+00 H-1 scatter 1.997585 0.009161\n", + "8 10002 1.9e+00 - 2.0e+01 H-1 scatter 0.373472 0.003922" ] }, "execution_count": 38, @@ -1649,7 +1649,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.8" + "version": "2.7.9" } }, "nbformat": 4, diff --git a/docs/source/pythonapi/index.rst b/docs/source/pythonapi/index.rst index 8c42f2d7e..12baf937d 100644 --- a/docs/source/pythonapi/index.rst +++ b/docs/source/pythonapi/index.rst @@ -62,6 +62,7 @@ on a given module or class. .. toctree:: :maxdepth: 1 + examples/post-processing examples/pandas-dataframes examples/tally-arithmetic diff --git a/docs/source/usersguide/input.rst b/docs/source/usersguide/input.rst index 93e8236ec..b4d153f18 100644 --- a/docs/source/usersguide/input.rst +++ b/docs/source/usersguide/input.rst @@ -79,14 +79,13 @@ Message Description [VALID] XML file matches RelaxNG. ======================== =================================== -As an example, if OpenMC is installed in the directory -``/opt/openmc/0.6.2`` and the current working directory is where -OpenMC XML input files are located, they can be validated using -the following command: +As an example, if OpenMC is installed in the directory ``/opt/openmc/`` and the +current working directory is where OpenMC XML input files are located, they can +be validated using the following command: .. code-block:: bash - /opt/openmc/0.6.2/bin/xml_validate + /opt/openmc/bin/openmc-validate-xml -------------------------------------- Settings Specification -- settings.xml @@ -1278,14 +1277,16 @@ The ```` element accepts the following sub-elements: *Default*: total :estimator: - The estimator element is used to force the use of either ``analog`` or - ``tracklength`` tally estimation. ''analog'' is generally less efficient - though it can be used with every score type. ''tracklength'' is generally - the most efficient, though its usage is restricted to tallies that do not - score particle information which requires a collision to have occured, such - as a scattering tally which utilizes outgoing energy filters. + The estimator element is used to force the use of either ``analog``, + ``collision``, or ``tracklength`` tally estimation. ``analog`` is generally + the least efficient though it can be used with every score type. + ``tracklength`` is generally the most efficient, but neither ``tracklength`` + nor ``collision`` can be used to score a tally that requires post-collision + information. For example, a scattering tally with outgoing energy filters + cannot be used with ``tracklength`` or ``collision`` because the code will + not know the outgoing energy distribution. - *Default*: ``tracklength`` but will revert to analog if necessary. + *Default*: ``tracklength`` but will revert to ``analog`` if necessary. :scores: A space-separated list of the desired responses to be accumulated. Accepted @@ -1296,7 +1297,9 @@ The ```` element accepts the following sub-elements: physical quantities: :flux: - Total flux in particle-cm per source particle. + Total flux in particle-cm per source particle. Note: The ``analog`` + estimator is actually identical to the ``collision`` estimator for the + flux score. :total: Total reaction rate in reactions per source particle. @@ -1423,8 +1426,7 @@ a separate element with the tag name ````. This element has the following attributes/sub-elements: :type: - The type of structured mesh. Valid options include "rectangular" and - "hexagonal". + The type of structured mesh. The only valid option is "regular". :dimension: The number of mesh cells in each direction. @@ -1526,16 +1528,16 @@ sub-elements: *Default*: None - Required entry :type: - Keyword for type of plot to be produced. Currently only "slice" and - "voxel" plots are implemented. The "slice" plot type creates 2D pixel - maps saved in the PPM file format. PPM files can be displayed in most - viewers (e.g. the default Gnome viewer, IrfanView, etc.). The "voxel" - plot type produces a binary datafile containing voxel grid positioning and - the cell or material (specified by the ``color`` tag) at the center of each - voxel. These datafiles can be processed into 3D SILO files using the - ``voxel.py`` utility provided with the OpenMC source, and subsequently - viewed with a 3D viewer such as VISIT or Paraview. See the - :ref:`devguide_voxel` for information about the datafile structure. + Keyword for type of plot to be produced. Currently only "slice" and "voxel" + plots are implemented. The "slice" plot type creates 2D pixel maps saved in + the PPM file format. PPM files can be displayed in most viewers (e.g. the + default Gnome viewer, IrfanView, etc.). The "voxel" plot type produces a + binary datafile containing voxel grid positioning and the cell or material + (specified by the ``color`` tag) at the center of each voxel. These + datafiles can be processed into 3D SILO files using the + ``openmc-voxel-to-silovtk`` utility provided with the OpenMC source, and + subsequently viewed with a 3D viewer such as VISIT or Paraview. See the + :ref:`usersguide_voxel` for information about the datafile structure. .. note:: Since the PPM format is saved without any kind of compression, the resulting file sizes can be quite large. Saving the image in diff --git a/docs/source/usersguide/output/index.rst b/docs/source/usersguide/output/index.rst index 1eb85e9d5..31bd1da91 100644 --- a/docs/source/usersguide/output/index.rst +++ b/docs/source/usersguide/output/index.rst @@ -10,5 +10,7 @@ Output File Formats statepoint source + summary particle_restart track + voxel diff --git a/docs/source/usersguide/output/particle_restart.rst b/docs/source/usersguide/output/particle_restart.rst index 12ab3237f..e0d89a515 100644 --- a/docs/source/usersguide/output/particle_restart.rst +++ b/docs/source/usersguide/output/particle_restart.rst @@ -6,11 +6,9 @@ Particle Restart File Format The current revision of the particle restart file format is 1. -**/filetype** (*int*) +**/filetype** (*char[]*) - Flags what type of file this is. A value of -1 indicates a statepoint file, - a value of -2 indicates a particle restart file, a value of -3 indicates a - source file, and a value of -4 indicates a track file. + String indicating the type of file. **/revision** (*int*) diff --git a/docs/source/usersguide/output/source.rst b/docs/source/usersguide/output/source.rst index cc8e71a67..2981b0f66 100644 --- a/docs/source/usersguide/output/source.rst +++ b/docs/source/usersguide/output/source.rst @@ -8,11 +8,9 @@ Normally, source data is stored in a state point file. However, it is possible to request that the source be written separately, in which case the format used is that documented here. -**/filetype** (*int*) +**/filetype** (*char[]*) - Flags what type of file this is. A value of -1 indicates a statepoint file, - a value of -2 indicates a particle restart file, a value of -3 indicates a - source file, and a value of -4 indicates a track file. + String indicating the type of file. **/source_bank** (Compound type) diff --git a/docs/source/usersguide/output/statepoint.rst b/docs/source/usersguide/output/statepoint.rst index b17bdca02..d1ebc7231 100644 --- a/docs/source/usersguide/output/statepoint.rst +++ b/docs/source/usersguide/output/statepoint.rst @@ -6,11 +6,9 @@ State Point File Format The current revision of the statepoint file format is 13. -**/filetype** (*int*) +**/filetype** (*char[]*) - Flags what type of file this is. A value of -1 indicates a statepoint file, - a value of -2 indicates a particle restart file, a value of -3 indicates a - source file, and a value of -4 indicates a track file. + String indicating the type of file. **/revision** (*int*) @@ -29,11 +27,11 @@ The current revision of the statepoint file format is 13. Release version number for OpenMC -**/time_stamp** (*char[19]*) +**/date_and_time** (*char[]*) Date and time the state point was written. -**/path** (*char[255]*) +**/path** (*char[]*) Absolute path to directory containing input files. @@ -41,7 +39,7 @@ The current revision of the statepoint file format is 13. Pseudo-random number generator seed. -**/run_mode** (*int*) +**/run_mode** (*char[]*) Run mode used. A value of 1 indicates a fixed-source run and a value of 2 indicates an eigenvalue run. @@ -58,7 +56,7 @@ The current revision of the statepoint file format is 13. The number of batches already simulated. -if (run_mode == MODE_EIGENVALUE) +if run_mode == 'k-eigenvalue': **/n_inactive** (*int*) @@ -136,37 +134,27 @@ if (run_mode == MODE_EIGENVALUE) **/tally/meshes/keys** (*int[]*) - User-identified unique ID of each mesh + User-identified unique ID of each mesh. -*do i = 1, n_meshes* +**/tallies/meshes/mesh /type** (*char[]*) - **/tallies/meshes/mesh i/id** (*int*) + Type of mesh. - Unique identifier of the mesh. +**/tallies/meshes/mesh /dimension** (*int*) - **/tallies/meshes/mesh i/type** (*int*) + Number of mesh cells in each dimension. - Type of mesh. +**/tallies/meshes/mesh /lower_left** (*double[]*) - **/tallies/meshes/mesh i/n_dimension** (*int*) + Coordinates of lower-left corner of mesh. - Number of dimensions for mesh (2 or 3). +**/tallies/meshes/mesh /upper_right** (*double[]*) - **/tallies/meshes/mesh i/dimension** (*int*) + Coordinates of upper-right corner of mesh. - Number of mesh cells in each dimension. +**/tallies/meshes/mesh /width** (*double[]*) - **/tallies/meshes/mesh i/lower_left** (*double[]*) - - Coordinates of lower-left corner of mesh. - - **/tallies/meshes/mesh i/upper_right** (*double[]*) - - Coordinates of upper-right corner of mesh. - - **/tallies/meshes/mesh i/width** (*double[]*) - - Width of each mesh cell in each dimension. + Width of each mesh cell in each dimension. **/tallies/n_tallies** (*int*) @@ -180,65 +168,66 @@ if (run_mode == MODE_EIGENVALUE) User-identified unique ID of each tally. -*do i = 1, n_tallies* +**/tallies/tally /estimator** (*char[]*) - **/tallies/tally i/estimator** (*int*) + Type of tally estimator, either 'analog', 'tracklength', or 'collision'. - Type of tally estimator: analog (1) or tracklength (2). +**/tallies/tally /n_realizations** (*int*) - **/tallies/tally i/n_realizations** (*int*) + Number of realizations. - Number of realizations. +**/tallies/tally /n_filters** (*int*) - **/tallies/tally i/n_filters** (*int*) + Number of filters used. - Number of filters used. +**/tallies/tally /filter /type** (*char[]*) - *do j = 1, tallies(i) % n_filters* + Type of the j-th filter. Can be 'universe', 'material', 'cell', 'cellborn', + 'surface', 'mesh', 'energy', 'energyout', or 'distribcell'. - **/tallies/tally i/filter j/type** (*int*) +**/tallies/tally /filter /offset** (*int*) - Type of tally filter. + Filter offset (used for distribcell filter). - **/tallies/tally i/filter j/offset** (*int*) +**/tallies/tally /filter /n_bins** (*int*) - Filter offset (used for distribcell). + Number of bins for the j-th filter. - **/tallies/tally i/filter j/n_bins** (*int*) +**/tallies/tally /filter /bins** (*int[]* or *double[]*) - Number of bins for filter. + Value for each filter bin of this type. - **/tallies/tally i/filter j/bins** (*int[]* or *double[]*) +**/tallies/tally /nuclides** (*char[][]*) - Value for each filter bin of this type. + Array of nuclides to tally. Note that if no nuclide is specified in the user + input, a single 'total' nuclide appears here. - **/tallies/tally i/n_nuclides** (*int*) +**/tallies/tally /n_score_bins** (*int*) - Number of nuclide bins. If none are specified, this is just one. + Number of scoring bins for a single nuclide. In general, this can be greater + than the number of user-specified scores since each score might have + multiple scoring bins, e.g., scatter-PN. - **/tallies/tally i/nuclides** (*int[]*) +**/tallies/tally /score_bins** (*char[][]*) - Values of specified nuclide bins (ZAID identifiers) + Values of specified scores. - **/tallies/tally i/n_score_bins** (*int*) +**/tallies/tally /n_user_scores** (*int*) - Number of scoring bins. + Number of scores without accounting for those added by expansions, + e.g. scatter-PN. - **/tallies/tally i/score_bins** (*int*) +**/tallies/tally /moment_orders** (*char[][]*) - Values of specified scoring bins (e.g. SCORE_FLUX). + Tallying moment orders for Legendre and spherical harmonic tally expansions + (*e.g.*, 'P2', 'Y1,2', etc.). - **/tallies/tally i/n_user_score_bins** +**/tallies/tally /results** (Compound type) - Number of scoring bins without accounting for those added by - expansions, e.g. scatter-PN. - - *do J = 1, total number of moments* - - **/tallies/tally i/moments/orderJ** (*char[8]*) - - Tallying moment order for Legendre and spherical - harmonic tally expansions (*e.g.*, 'P2', 'Y1,2', etc.). + Accumulated sum and sum-of-squares for each bin of the i-th tally. This is a + two-dimensional array, the first dimension of which represents combinations + of filter bins and the second dimensions of which represents scoring + bins. Each element of the array has fields 'sum' and 'sum_sq'. **/source_present** (*int*) @@ -261,13 +250,7 @@ if (run_mode == MODE_EIGENVALUE) Flag indicated if tallies are present in the file. -*do i = 1, n_tallies* - -**/tallies/tally i/results** (Compound type) - - Accumulated sum and sum-of-squares for each bin of the tally i-th tally - -if (run_mode == MODE_EIGENVALUE and source_present) +if (run_mode == 'k-eigenvalue' and source_present > 0) **/source_bank** (Compound type) diff --git a/docs/source/usersguide/output/summary.rst b/docs/source/usersguide/output/summary.rst new file mode 100644 index 000000000..9ced8448a --- /dev/null +++ b/docs/source/usersguide/output/summary.rst @@ -0,0 +1,310 @@ +.. _usersguide_summary: + +=================== +Summary File Format +=================== + +The current revision of the summary file format is 1. + +**/filetype** (*char[]*) + + String indicating the type of file. + +**/revision** (*int*) + + Revision of the summary file format. Any time a change is made in the + format, this integer is incremented. + +**/version_major** (*int*) + + Major version number for OpenMC + +**/version_minor** (*int*) + + Minor version number for OpenMC + +**/version_release** (*int*) + + Release version number for OpenMC + +**/date_and_time** (*char[]*) + + Date and time the summary was written. + +**/n_procs** (*int*) + + Number of MPI processes used. + +**/n_particles** (*int8_t*) + + Number of particles used per generation. + +**/n_batches** (*int*) + + Number of batches to simulate. + +**/n_inactive** (*int*) + + Number of inactive batches. Only present if /run_mode is set to + 'k-eigenvalue'. + +**/n_active** (*int*) + + Number of active batches. Only present if /run_mode is set to + 'k-eigenvalue'. + +**/gen_per_batch** (*int*) + + Number of generations per batch. Only present if /run_mode is set to + 'k-eigenvalue'. + +**/geometry/n_cells** (*int*) + + Number of cells in the problem. + +**/geometry/n_surfaces** (*int*) + + Number of surfaces in the problem. + +**/geometry/n_universes** (*int*) + + Number of unique universes in the problem. + +**/geometry/n_lattices** (*int*) + + Number of lattices in the problem. + +**/geometry/cells/cell /index** (*int*) + + Index in cells array used internally in OpenMC. + +**/geometry/cells/cell /name** (*char[]*) + + Name of the cell. + +**/geometry/cells/cell /universe** (*int*) + + Universe assigned to the cell. If none is specified, the default + universe (0) is assigned. + +**/geometry/cells/cell /fill_type** (*char[]*) + + Type of fill for the cell. Can be 'normal', 'universe', or 'lattice'. + +**/geometry/cells/cell /material** (*int*) + + Unique ID of the material assigned to the cell. This dataset is present only + if fill_type is set to 'normal'. + +**/geometry/cells/cell /offset** (*int[]*) + + Offsets used for distribcell tally filter. This dataset is present only if + fill_type is set to 'universe'. + +**/geometry/cells/cell /translation** (*double[3]*) + + Translation applied to the fill universe. This dataset is present only if + fill_type is set to 'universe'. + +**/geometry/cells/cell /rotation** (*double[3]*) + + Angles in degrees about the x-, y-, and z-axes for which the fill universe + should be rotated. This dataset is present only if fill_type is set to + 'universe'. + +**/geometry/cells/cell /lattice** (*int*) + + Unique ID of the lattice which fills the cell. Only present if fill_type is + set to 'lattice'. + +**/geometry/cells/cell /surfaces** (*int[]*) + + Surface specification for the cell. + +**/geometry/surfaces/surface /index** (*int*) + + Index in surfaces array used internally in OpenMC. + +**/geometry/surfaces/surface /name** (*char[]*) + + Name of the surface. + +**/geometry/surfaces/surface /type** (*char[]*) + + Type of the surface. Can be 'x-plane', 'y-plane', 'z-plane', 'plane', + 'x-cylinder', 'y-cylinder', 'sphere', 'x-cone', 'y-cone', or 'z-cone'. + +**/geometry/surfaces/surface /coefficients** (*double[]*) + + Array of coefficients that define the surface. See :ref:`surface_element` + for what coefficients are defined for each surface type. + +**/geometry/surfaces/surface /boundary_condition** (*char[]*) + + Boundary condition applied to the surface. Can be 'transmission', 'vacuum', + 'reflective', or 'periodic'. + +**/geometry/universes/universe /index** (*int*) + + Index in the universes array used internally in OpenMC. + +**/geometry/universes/universe /cells** (*int[]*) + + Array of unique IDs of cells that appear in the universe. + +**/geometry/lattices/lattice /index** (*int*) + + Index in the lattices array used internally in OpenMC. + +**/geometry/lattices/lattice /name** (*char[]*) + + Name of the lattice. + +**/geometry/lattices/lattice /type** (*char[]*) + + Type of the lattice, either 'rectangular' or 'hexagonal'. + +**/geometry/lattices/lattice /pitch** (*double[]*) + + Pitch of the lattice. + +**/geometry/lattices/lattice /outer** (*int*) + + Outer universe assigned to lattice cells outside the defined range. + +**/geometry/lattices/lattice /offsets** (*int[]*) + + Offsets used for distribcell tally filter. + +**/geometry/lattices/lattice /universes** (*int[]*) + + Three-dimensional array of universes assigned to each cell of the lattice. + +**/geometry/lattices/lattice /dimension** (*int[]*) + + The number of lattice cells in each direction. This dataset is present only + when the 'type' dataset is set to 'rectangular'. + +**/geometry/lattices/lattice /lower_left** (*double[]*) + + The coordinates of the lower-left corner of the lattice. This dataset is + present only when the 'type' dataset is set to 'rectangular'. + +**/geometry/lattices/lattice /n_rings** (*int*) + + Number of radial ring positions in the xy-plane. This dataset is present + only when the 'type' dataset is set to 'hexagonal'. + +**/geometry/lattices/lattice /n_axial** (*int*) + + Number of lattice positions along the z-axis. This dataset is present only + when the 'type' dataset is set to 'hexagonal'. + +**/geometry/lattices/lattice /center** (*double[]*) + + Coordinates of the center of the lattice. This dataset is present only when + the 'type' dataset is set to 'hexagonal'. + +**/n_materials** (*int*) + + Number of materials in the problem. + +**/materials/material /index** (*int*) + + Index in materials array used internally in OpenMC. + +**/materials/material /name** (*char[]*) + + Name of the material. + +**/materials/material /atom_density** (*double[]*) + + Total atom density of the material in atom/b-cm. + +**/materials/material /nuclides** (*char[][]*) + + Array of nuclides present in the material, e.g., 'U-235.71c'. + +**/materials/material /nuclide_densities** (*double[]*) + + Atom density of each nuclide. + +**/materials/material /sab_names** (*char[][]*) + + Names of S(:math:`\alpha`,:math:`\beta`) tables assigned to the material. + +**/tallies/n_tallies** (*int*) + + Number of tallies in the problem. + +**/tallies/n_meshes** (*int*) + + Number of meshes in the problem. + +**/tallies/mesh /index** (*int*) + + Index in the meshes array used internally in OpenMC. + +**/tallies/mesh /type** (*char[]*) + + Type of the mesh. The only valid option is currently 'regular'. + +**/tallies/mesh /dimension** (*int[]*) + + Number of mesh cells in each direction. + +**/tallies/mesh /lower_left** (*double[]*) + + Coordinates of the lower-left corner of the mesh. + +**/tallies/mesh /upper_right** (*double[]*) + + Coordinates of the upper-right corner of the mesh. + +**/tallies/mesh /width** (*double[]*) + + Width of a single mesh cell in each direction. + +**/tallies/tally /index** (*int*) + + Index in tallies array used internally in OpenMC. + +**/tallies/tally /name** (*char[]*) + + Name of the tally. + +**/tallies/tally /n_filters** (*int*) + + Number of filters applied to the tally. + +**/tallies/tally /filter /type** (*char[]*) + + Type of the j-th filter. Can be 'universe', 'material', 'cell', 'cellborn', + 'surface', 'mesh', 'energy', 'energyout', or 'distribcell'. + +**/tallies/tally /filter /offset** (*int*) + + Filter offset (used for distribcell filter). + +**/tallies/tally /filter /n_bins** (*int*) + + Number of bins for the j-th filter. + +**/tallies/tally /filter /bins** (*int[]* or *double[]*) + + Value for each filter bin of this type. + +**/tallies/tally /nuclides** (*char[][]*) + + Array of nuclides to tally. Note that if no nuclide is specified in the user + input, a single 'total' nuclide appears here. + +**/tallies/tally /n_score_bins** (*int*) + + Number of scoring bins for a single nuclide. In general, this can be greater + than the number of user-specified scores since each score might have + multiple scoring bins, e.g., scatter-PN. + +**/tallies/tally /score_bins** (*char[][]*) + + Scoring bins for the tally. diff --git a/docs/source/usersguide/output/track.rst b/docs/source/usersguide/output/track.rst index 9a85ac7ea..d3c7a27d8 100644 --- a/docs/source/usersguide/output/track.rst +++ b/docs/source/usersguide/output/track.rst @@ -6,11 +6,9 @@ Track File Format The current revision of the particle track file format is 1. -**/filetype** (*int*) +**/filetype** (*char[]*) - Flags what type of file this is. A value of -1 indicates a statepoint file, - a value of -2 indicates a particle restart file, a value of -3 indicates a - source file, and a value of -4 indicates a track file. + String indicating the type of file. **/revision** (*int*) diff --git a/docs/source/usersguide/output/voxel.rst b/docs/source/usersguide/output/voxel.rst new file mode 100644 index 000000000..1da501fb5 --- /dev/null +++ b/docs/source/usersguide/output/voxel.rst @@ -0,0 +1,25 @@ +.. _usersguide_voxel: + +====================== +Voxel Plot File Format +====================== + +**/filetype** (*char[]*) + + String indicating the type of file. + +**/num_voxels** (*int[3]*) + + Number of voxels in the x-, y-, and z- directions. + +**/voxel_width** (*double[3]*) + + Width of a voxel in centimeters. + +**/lower_left** (*double[3]*) + + Cartesian coordinates of the lower-left corner of the plot. + +**/data** (*int[][][]*) + + Data for each voxel that represents a material or cell ID. diff --git a/docs/source/usersguide/processing.rst b/docs/source/usersguide/processing.rst index a6e9875ce..b18569ec6 100644 --- a/docs/source/usersguide/processing.rst +++ b/docs/source/usersguide/processing.rst @@ -6,31 +6,34 @@ Data Processing and Visualization This section is intended to explain in detail the recommended procedures for carrying out common post-processing tasks with OpenMC. While several utilities -of varying complexity are provided to help automate the process, in many cases -it will be extremely beneficial to do some coding in Python to quickly obtain -results. In these cases, and for many of the provided utilities, it is necessary -for your Python installation to contain: +of varying complexity are provided to help automate the process, the most +powerful capabilities for post-processing derive from use of the :ref:`Python +API `. Both the provided scripts and the Python API rely on a number +third-party Python packages, including: -* [1]_ `Numpy `_ -* [1]_ `Scipy `_ -* [2]_ `h5py `_ -* [3]_ `Matplotlib `_ -* [3]_ `Silomesh `_ -* [3]_ `VTK `_ +* [1]_ `NumPy `_ +* [2]_ `h5py `_ +* [3]_ `pandas `_ +* [4]_ `matplotlib `_ +* [4]_ `Silomesh `_ +* [4]_ `VTK `_ +* [4]_ `lxml `_ -Most of these are easily obtainable in Ubuntu through the package manager, or -are easily installed with distutils. +Most of these are can easily be installed with `pip `_ +or alternatively obtaining through a package manager. -.. [1] Required for tally data extraction from statepoints with statepoint.py -.. [2] Required only if reading HDF5 statepoint files. -.. [3] Optional for plotting utilities +.. [1] Required for most post-processing tasks +.. [2] Required for reading HDF5 output files +.. [3] Optional dependency for advanced features in Python API +.. [4] Not used directly by the Python API, but are optional dependencies for a + number of scripts. ---------------------- Geometry Visualization ---------------------- Geometry plotting is carried out by creating a plots.xml, specifying plots, and -running OpenMC with the -plot or -p command-line option (See +running OpenMC with the --plot or -p command-line option (See :ref:`usersguide_plotting`). Plotting in 2D @@ -128,27 +131,26 @@ capabilities of 3D voxel plots. Voxel plots are built the same way 2D slice plots are, by determining the cell or material id of a particle at the center of each voxel. In this example, the space covered is the cube between the points (-5,-5,-5) and (5,5,5), with voxel -centers 10/500 = 0.02 cm apart. The binary VOXEL files that are produced do not +centers 10/500 = 0.02 cm apart. The HDF5 voxel files that are produced do not specify any color - instead containing only material or cell ids (material id in this example) - and thus the ``background``, ``col_spec``, and ``mask`` elements are not used. If no cell is found at a voxel center, an id of -1 is stored. -The binary VOXEL files output by OpenMC can not be viewed directly by any -existing viewers. In order to view them, they must be converted into a standard -mesh format that can be viewed in ParaView, Visit, etc. This typically will -compress the size of the file significantly. The provided utility voxel.py -accomplishes this for SILO: +The voxel plot data is written to an HDF5 file. The voxel file can subsequently +be converted into a standard mesh format that can be viewed in ParaView, Visit, +etc. This typically will compress the size of the file significantly. The +provided utility openmc-voxel-to-silovtk accomplishes this for SILO: .. code-block:: sh - /src/utils/voxel.py myplot.voxel -o output.silo + openmc-voxel-to-silovtk myplot.voxel -o output.silo and VTK file formats: .. code-block:: sh - /src/utils/voxel.py myplot.voxel --vtk -o output.vti + openmc-voxel-to-silovtk myplot.voxel --vtk -o output.vti To use this utility you need either @@ -156,11 +158,10 @@ To use this utility you need either or -* `VTK `_ with python bindings - On Ubuntu, these are - easily obtained with ``sudo apt-get install python-vtk`` +* `VTK `_ with python bindings. On debian derivatives, + these are easily obtained with ``sudo apt-get install python-vtk`` -Users can process the binary into any other format if desired by following the -example of voxel.py. For the binary file structure, see :ref:`devguide_voxel`. +For the HDF5 file structure, see :ref:`usersguide_voxel`. Once processed into a standard 3D file format, colors and masks can be defined using the stored id numbers to better explore the geometry. The process for @@ -183,150 +184,38 @@ doing this will depend on the 3D viewer, but should be straightforward. Tally Visualization ------------------- -Tally results are saved in both a text file (tallies.out) as well as a binary +Tally results are saved in both a text file (tallies.out) as well as an HDF5 statepoint file. While the tallies.out file may be fine for simple tallies, in -many cases the user requires more information about the tally or the run, or -has to deal with a large number of result values (e.g. for mesh tallies). In -these cases, extracting data from the statepoint file via Python scripting is -the preferred method of data analysis and visualization. +many cases the user requires more information about the tally or the run, or has +to deal with a large number of result values (e.g. for mesh tallies). In these +cases, extracting data from the statepoint file via the :ref:`pythonapi` is the +preferred method of data analysis and visualization. Data Extraction --------------- A great deal of information is available in statepoint files (See -:ref:`usersguide_statepoint`), most of which is easily extracted by the provided -utility statepoint.py. This utility provides a Python class to load statepoints -and extract data - it is used in many of the provided plotting utilities, and -can be used in user-created scripts to carry out manipulations of the data. To -read tallies using this utility, make sure statepoint.py is in your PYTHONPATH, -and then import the class, instantiate it, and call read_results: +:ref:`usersguide_statepoint`), all of which is accessible through the Python +API. The ``openmc.statepoint`` module (see :ref:`pythonapi_statepoint`) provides +a class to load statepoints and access data as requested; it is used in many of +the provided plotting utilities, OpenMC's regression test suite, and can be used +in user-created scripts to carry out manipulations of the data. -.. code-block:: python - - from statepoint import StatePoint - sp = StatePoint('statepoint.100.binary') - sp.read_results() - -At this point the user can extract entire scores from tallies into a data -dictionary containing numpy arrays: - -.. code-block:: python - - tallyid = 1 - score = 'flux' - data = sp.extract_results(tallyid, score) - means = data['means'] - print data.keys() - -The results from this function contain all filter bins (all mesh points, all -energy groups, etc.), which can be reshaped with the bin ordering also contained -in the output dictionary. This is the best choice of output for easily -integrating ranges of data. - -Alternatively the user can extract specific values for a single score/filter -combination: - -.. code-block:: python - - tallyid = 1 - score = 'flux' - filters = [('mesh', (1, 1, 5)), ('energyin', 0)] - value, error = sp.get_value(tallyid, filters, score) - -In the future more documentation may become available here for statepoint.py and -the data extraction functions of StatePoint objects. However, for now it is up -to the user to explore the classes in statepoint.py to discover what data is -available in StatePoint objects (we highly recommend interactively exploring -with `IPython `_). Many examples can be found by looking -through the other utilities that use statepoint.py, and a few common -visualization tasks will be described here in the following sections. +An :ref:`example IPython notebook ` demonstrates how +to extract data from a statepoint using the Python API. Plotting in 2D -------------- +The :ref:`IPython notebook example ` also demonstrates +how to plot a mesh tally in two dimensions using the Python API. Note, however, +that there is also a script distributed with OpenMC, ``openmc-plot-mesh-tally``, +that provides an interactive GUI to explore and plot mesh tallies for any scores +and filter bins. + .. image:: ../_images/plotmeshtally.png :height: 200px -For simple viewing of 2D slices of a mesh plot, the utility plot_mesh_tally.py -is provided. This utility provides an interactive GUI to explore and plot -mesh tallies for any scores and filter bins. It requires statepoint.py. - -.. image:: ../_images/fluxplot.png - :height: 200px - -Alternatively, the user can write their own Python script to manipulate the data -appropriately. Consider a run where the first tally contains a 105x105x20 mesh -over a small core, with a flux score and two energyin filter bins. To explicitly -extract the data and create a plot with gnuplot, the following script can be -used. The script operates in several steps for clarity, and is not necessarily -the most efficient way to extract data from large mesh tallies. This creates the -two heatmaps in the previous figure. - -.. code-block:: python - - #!/usr/bin/env python - - import os - - import statepoint - - # load and parse the statepoint file - sp = statepoint.StatePoint('statepoint.300.binary') - sp.read_results() - - tallyid = 0 # This is tally 1 - score = 0 # This corresponds to flux (see tally.scores) - - # get mesh dimensions - meshid = sp.tallies[tallyid].filters['mesh'].bins[0] - for i,m in enumerate(sp.meshes): - if m.id == meshid: - mesh = m - break - nx,ny,nz = mesh.dimension - - # loop through mesh and extract values to python dictionaries - thermal = {} - fast = {} - for x in range(1,nx+1): - for y in range(1,ny+1): - for z in range(1,nz+1): - val,err = sp.get_value(tallyid, - [('mesh',(x,y,z)),('energyin',0)], - score) - thermal[(x,y,z)] = val - val,err = sp.get_value(tallyid, - [('mesh',(x,y,z)),('energyin',1)], - score) - fast[(x,y,z)] = val - - # sum up the axial values and write datafile for gnuplot - with open('meshdata.dat','w') as fh: - for x in range(1,nx+1): - for y in range(1,ny+1): - thermalval = 0. - fastval = 0. - for z in range(1,nz+1): - thermalval += thermal[(x,y,z)] - fastval += fast[(x,y,z)] - fh.write("{} {} {} {}\n".format(x,y,thermalval,fastval)) - - # write gnuplot file - with open('tmp.gnuplot','w') as fh: - fh.write(r"""set terminal png size 1000 400 - set output 'fluxplot.png' - set nokey - set autoscale fix - set multiplot layout 1,2 title "Pin Mesh Flux Tally" - set title "Thermal" - plot 'meshdata.dat' using 1:2:3 with image - set title "Fast" - plot 'meshdata.dat' using 1:2:4 with image - """) - - # make plot - os.system("gnuplot < tmp.gnuplot") - Plotting in 3D -------------- @@ -334,22 +223,23 @@ Plotting in 3D :height: 200px As with 3D plots of the geometry, meshtally data needs to be put into a standard -format for viewing. The utility statepoint_3d.py is provided to accomplish this -for both VTK and SILO. By default statepoint_3d.py processes a statepoint into a -3D file with all mesh tallies and filter/score combinations, +format for viewing. The utility ``openmc-statepoint-3d`` is provided to +accomplish this for both VTK and SILO. By default ``openmc-statepoint-3d`` +processes a statepoint into a 3D file with all mesh tallies and filter/score +combinations, .. code-block:: sh - /src/utils/statepoint_3d.py -o output.silo - /src/utils/statepoint_3d.py --vtk -o output.vtm + openmc-statepoint-3d -o output.silo + openmc-statepoint-3d --vtk -o output.vtm but it also provides several command-line options to selectively process only certain data arrays in order to keep file sizes down. .. code-block:: sh - statepoint_3d.py --tallies 2,4 --scores 4.1,4.3 -o output.silo - statepoint_3d.py --filters 2.energyin.1 --vtk -o output.vtm + openmc-statepoint-3d --tallies 2,4 --scores 4.1,4.3 -o output.silo + openmc-statepoint-3d --filters 2.energyin.1 --vtk -o output.vtm All available options for specifying a subset of tallies, scores, and filters can be listed with the ``--list`` or ``-l`` command line options. @@ -426,13 +316,11 @@ Getting Data into MATLAB ------------------------ There is currently no front-end utility to dump tally data to MATLAB files, but -the process is straightforward. First extract the data using a custom Python -script with statepoint.py, put the data into appropriately-shaped numpy arrays, -and then use the `Scipy MATLAB IO routines +the process is straightforward. First extract the data using the Python API via +``openmc.statepoint`` and then use the `Scipy MATLAB IO routines `_ to save to a MAT -file. Note that the data contained in the output from -``StatePoint.extract_result`` is already in a Numpy array that can be reshaped -and dumped to MATLAB in one step. +file. Note that all arrays that are accessible in a statepoint are already in +NumPy arrays that can be reshaped and dumped to MATLAB in one step. ---------------------------- Particle Track Visualization @@ -463,15 +351,15 @@ particle numbers, respectively. For example, to output the tracks for particles After running OpenMC, the directory should contain a file of the form -"track_(batch #)_(generation #)_(particle #).(binary or h5)" for each particle -tracked. These track files can be converted into VTK poly data files with the -"track.py" utility. The usage of track.py is of the form "track.py [-o OUT] IN" -where OUT is the optional output filename and IN is one or more filenames -describing track files. The default output name is "track.pvtp". A common -usage of track.py is "track.py track*.binary" which will use the data from all -binary track files in the directory to write a "track.pvtp" VTK output file. -The .pvtp file can then be read and plotted by 3d visualization programs such as -ParaView. +"track_(batch #)_(generation #)_(particle #).h5" for each particle tracked. +These track files can be converted into VTK poly data files with the +``openmc-track-to-vtk`` utility. The usage of ``openmc-track-to-vtk`` is of the +form "openmc-track-to-vtk [-o OUT] IN" where OUT is the optional output filename +and IN is one or more filenames describing track files. The default output name +is "track.pvtp". A common usage of track.py is "openmc-track-to-vtk track*.h5" +which will use the data from all binary track files in the directory to write a +"track.pvtp" VTK output file. The .pvtp file can then be read and plotted by 3d +visualization programs such as ParaView. ---------------------- Source Site Processing @@ -480,43 +368,6 @@ Source Site Processing For eigenvalue problems, OpenMC will store information on the fission source sites in the statepoint file by default. For each source site, the weight, position, sampled direction, and sampled energy are stored. To extract this data -from a statepoint file, the statepoint.py Python module can be used. Below is an -example of an interactive ipython session using the statepoint.py Python module: - -.. code-block:: python - - In [1]: import statepoint - - In [2]: sp = statepoint.StatePoint('statepoint.100.h5') - - In [3]: sp.read_source() - - In [4]: len(sp.source) - Out[4]: 1000 - - In [5]: sp.source[0:10] - Out[5]: - [, - , - , - , - , - , - , - , - , - ] - - In [6]: site = sp.source[0] - - In [7]: site.weight - Out[7]: 1.0 - - In [8]: site.xyz - Out[8]: array([ 2.21980946, -8.92686048, 87.93720485]) - - In [9]: site.uvw - Out[9]: array([ 0.06740523, 0.50612814, 0.85982024]) - - In [10]: site.E - Out[10]: 0.93292326356564159 +from a statepoint file, the ``openmc.statepoint`` module can be used. An +:ref:`example IPython notebook ` demontrates how to +analyze and plot source information. diff --git a/docs/source/usersguide/troubleshoot.rst b/docs/source/usersguide/troubleshoot.rst index 10ac12184..c5e4e7c1e 100644 --- a/docs/source/usersguide/troubleshoot.rst +++ b/docs/source/usersguide/troubleshoot.rst @@ -31,21 +31,6 @@ f951: error: unrecognized command line option "-fbacktrace" You are probably using a version of the gfortran compiler that is too old. Download and install the latest version of gfortran_. - -make[1]: ifort: Command not found -********************************* - -You tried compiling with the Intel Fortran compiler and it was not found on your -:envvar:`PATH`. If you have the Intel compiler installed, make sure the shell -can locate it (this can be tested with :program:`which ifort`). - -make[1]: pgf90: Command not found -********************************* - -You tried compiling with the PGI Fortran compiler and it was not found on your -:envvar:`PATH`. If you have the PGI compiler installed, make sure the shell can -locate it (this can be tested with :program:`which pgf90`). - ------------------------- Problems with Simulations ------------------------- @@ -56,13 +41,13 @@ Segmentation Fault A segmentation fault occurs when the program tries to access a variable in memory that was outside the memory allocated for the program. The best way to debug a segmentation fault is to re-compile OpenMC with debug options turned -on. First go to your ``openmc/src`` directory where OpenMC was compiled and type -the following commands: +on. Create a new build directory and type the following commands: .. code-block:: sh - make distclean - make DEBUG=yes + mkdir build-debug && cd build-debug + cmake -Ddebug=on /path/to/openmc + make Now when you re-run your problem, it should report exactly where the program failed. If after reading the debug output, you are still unsure why the program diff --git a/examples/python/lattice/nested/build-xml.py b/examples/python/lattice/nested/build-xml.py index ce6766542..24b5554c0 100644 --- a/examples/python/lattice/nested/build-xml.py +++ b/examples/python/lattice/nested/build-xml.py @@ -168,7 +168,7 @@ plot_file.export_to_xml() # Instantiate a tally mesh mesh = openmc.Mesh(mesh_id=1) -mesh.type = 'rectangular' +mesh.type = 'regular' mesh.dimension = [4, 4] mesh.lower_left = [-2, -2] mesh.width = [1, 1] diff --git a/examples/python/lattice/simple/build-xml.py b/examples/python/lattice/simple/build-xml.py index 675c7e08b..57e1f1729 100644 --- a/examples/python/lattice/simple/build-xml.py +++ b/examples/python/lattice/simple/build-xml.py @@ -157,7 +157,7 @@ plot_file.export_to_xml() # Instantiate a tally mesh mesh = openmc.Mesh(mesh_id=1) -mesh.type = 'rectangular' +mesh.type = 'regular' mesh.dimension = [4, 4] mesh.lower_left = [-2, -2] mesh.width = [1, 1] diff --git a/examples/python/pincell/build-xml.py b/examples/python/pincell/build-xml.py index 9338aff0e..fc9663b90 100644 --- a/examples/python/pincell/build-xml.py +++ b/examples/python/pincell/build-xml.py @@ -189,7 +189,7 @@ settings_file.export_to_xml() # Instantiate a tally mesh mesh = openmc.Mesh(mesh_id=1) -mesh.type = 'rectangular' +mesh.type = 'regular' mesh.dimension = [100, 100, 1] mesh.lower_left = [-0.62992, -0.62992, -1.e50] mesh.upper_right = [0.62992, 0.62992, 1.e50] diff --git a/examples/xml/lattice/nested/tallies.xml b/examples/xml/lattice/nested/tallies.xml index 5730e6b12..89c0774f1 100644 --- a/examples/xml/lattice/nested/tallies.xml +++ b/examples/xml/lattice/nested/tallies.xml @@ -2,7 +2,7 @@ - rectangular + regular 4 4 -2.0 -2.0 1.0 1.0 diff --git a/examples/xml/lattice/simple/tallies.xml b/examples/xml/lattice/simple/tallies.xml index 5730e6b12..89c0774f1 100644 --- a/examples/xml/lattice/simple/tallies.xml +++ b/examples/xml/lattice/simple/tallies.xml @@ -2,7 +2,7 @@ - rectangular + regular 4 4 -2.0 -2.0 1.0 1.0 diff --git a/examples/xml/pincell/tallies.xml b/examples/xml/pincell/tallies.xml index bbfd58836..73242b913 100644 --- a/examples/xml/pincell/tallies.xml +++ b/examples/xml/pincell/tallies.xml @@ -1,7 +1,7 @@ - + 100 100 1 -0.62992 -0.62992 -1.e50 0.62992 0.62992 1.e50 @@ -13,4 +13,4 @@ flux fission nu-fission - \ No newline at end of file + diff --git a/openmc/constants.py b/openmc/constants.py deleted file mode 100644 index a6b535e6d..000000000 --- a/openmc/constants.py +++ /dev/null @@ -1,123 +0,0 @@ -"""Dictionaries of integer-to-string mappings from openmc/src/constants.F90""" - -SURFACE_TYPES = {1: 'x-plane', - 2: 'y-plane', - 3: 'z-plane', - 4: 'plane', - 5: 'x-cylinder', - 6: 'y-cylinder', - 7: 'z-cylinder', - 8: 'sphere', - 9: 'x-cone', - 10: 'y-cone', - 11: 'z-cone'} - -BC_TYPES = {0: 'transmission', - 1: 'vacuum', - 2: 'reflective', - 3: 'periodic'} - -FILL_TYPES = {1: 'normal', - 2: 'fill', - 3: 'lattice'} - -LATTICE_TYPES = {1: 'rectangular', - 2: 'hexagonal'} - -ESTIMATOR_TYPES = {1: 'analog', - 2: 'tracklength'} - -FILTER_TYPES = {1: 'universe', - 2: 'material', - 3: 'cell', - 4: 'cellborn', - 5: 'surface', - 6: 'mesh', - 7: 'energy', - 8: 'energyout', - 9: 'distribcell'} - -SCORE_TYPES = {-1: 'flux', - -2: 'total', - -3: 'scatter', - -4: 'nu-scatter', - -5: 'scatter-n', - -6: 'scatter-pn', - -7: 'nu-scatter-n', - -8: 'nu-scatter-pn', - -9: 'transport', - -10: 'n1n', - -11: 'absorption', - -12: 'fission', - -13: 'nu-fission', - -14: 'kappa-fission', - -15: 'current', - -16: 'flux-yn', - -17: 'total-yn', - -18: 'scatter-yn', - -19: 'nu-scatter-yn', - -20: 'events', - 1: '(n,total)', - 2: '(n,elastic)', - 4: '(n,level)', - 11: '(n,2nd)', - 16: '(n,2n)', - 17: '(n,3n)', - 18: '(n,fission)', - 19: '(n,f)', - 20: '(n,nf)', - 21: '(n,2nf)', - 22: '(n,na)', - 23: '(n,n3a)', - 24: '(n,2na)', - 25: '(n,3na)', - 28: '(n,np)', - 29: '(n,n2a)', - 30: '(n,2n2a)', - 32: '(n,nd)', - 33: '(n,nt)', - 34: '(n,nHe-3)', - 35: '(n,nd2a)', - 36: '(n,nt2a)', - 37: '(n,4n)', - 38: '(n,3nf)', - 41: '(n,2np)', - 42: '(n,3np)', - 44: '(n,n2p)', - 45: '(n,npa)', - 91: '(n,nc)', - 101: '(n,disappear)', - 102: '(n,gamma)', - 103: '(n,p)', - 104: '(n,d)', - 105: '(n,t)', - 106: '(n,3He)', - 107: '(n,a)', - 108: '(n,2a)', - 109: '(n,3a)', - 111: '(n,2p)', - 112: '(n,pa)', - 113: '(n,t2a)', - 114: '(n,d2a)', - 115: '(n,pd)', - 116: '(n,pt)', - 117: '(n,da)', - 201: '(n,Xn)', - 202: '(n,Xgamma)', - 203: '(n,Xp)', - 204: '(n,Xd)', - 205: '(n,Xt)', - 206: '(n,X3He)', - 207: '(n,Xa)', - 444: '(damage)', - 649: '(n,pc)', - 699: '(n,dc)', - 749: '(n,tc)', - 799: '(n,3Hec)', - 849: '(n,tc)'} -SCORE_TYPES.update({MT: '(n,n' + str(MT-50) + ')' for MT in range(51,91)}) -SCORE_TYPES.update({MT: '(n,p' + str(MT-600) + ')' for MT in range(600,649)}) -SCORE_TYPES.update({MT: '(n,d' + str(MT-650) + ')' for MT in range(650,699)}) -SCORE_TYPES.update({MT: '(n,t' + str(MT-700) + ')' for MT in range(700,749)}) -SCORE_TYPES.update({MT: '(n,3He' + str(MT-750) + ')' for MT in range(750,649)}) -SCORE_TYPES.update({MT: '(n,a' + str(MT-800) + ')' for MT in range(800,849)}) diff --git a/openmc/filter.py b/openmc/filter.py index 8a4c219ef..fdeebaf8a 100644 --- a/openmc/filter.py +++ b/openmc/filter.py @@ -15,6 +15,9 @@ if sys.version_info[0] >= 3: basestring = str +_FILTER_TYPES = ['universe', 'material', 'cell', 'cellborn', 'surface', + 'mesh', 'energy', 'energyout', 'distribcell'] + class Filter(object): """A filter used to constrain a tally to a specific criterion, e.g. only tally events when the particle is in a certain cell and energy range. @@ -135,7 +138,9 @@ class Filter(object): @type.setter def type(self, type): - if type not in FILTER_TYPES.values(): + if type is None: + self._type = type + elif type not in _FILTER_TYPES: msg = 'Unable to set Filter type to "{0}" since it is not one ' \ 'of the supported types'.format(type) raise ValueError(msg) diff --git a/openmc/mesh.py b/openmc/mesh.py index 961b1519f..3c5bef848 100644 --- a/openmc/mesh.py +++ b/openmc/mesh.py @@ -56,7 +56,7 @@ class Mesh(object): # Initialize Mesh class attributes self.id = mesh_id self.name = name - self._type = 'rectangular' + self._type = 'regular' self._dimension = None self._lower_left = None self._upper_right = None @@ -158,7 +158,7 @@ class Mesh(object): cv.check_type('type for mesh ID="{0}"'.format(self._id), meshtype, basestring) cv.check_value('type for mesh ID="{0}"'.format(self._id), - meshtype, ['rectangular', 'hexagonal']) + meshtype, ['regular']) self._type = meshtype @dimension.setter diff --git a/openmc/mgxs/mgxs.py b/openmc/mgxs/mgxs.py index d7d6af807..332eb7190 100644 --- a/openmc/mgxs/mgxs.py +++ b/openmc/mgxs/mgxs.py @@ -246,9 +246,6 @@ class MultiGroupXS(object): cv.check_type('statepoint', statepoint, openmc.statepoint.StatePoint) - # Ensure that tally metadata has been loaded from the statepoint file - statepoint.read_results() - # Create Tallies to search for in StatePoint self.create_tallies() @@ -1304,4 +1301,4 @@ class Chi(MultiGroupXS): nu_fission_out = self.tallies['nu-fission-out'] self._xs_tally = nu_fission_out / nu_fission_in self._xs_tally._mean = np.nan_to_num(self.xs_tally.mean) - self._xs_tally._std_dev = np.nan_to_num(self.xs_tally.std_dev) \ No newline at end of file + self._xs_tally._std_dev = np.nan_to_num(self.xs_tally.std_dev) diff --git a/openmc/particle_restart.py b/openmc/particle_restart.py index 846c7d566..72bf3ac3d 100644 --- a/openmc/particle_restart.py +++ b/openmc/particle_restart.py @@ -12,10 +12,6 @@ class Particle(object): Attributes ---------- - filetype : int - Integer indicating the file type - revision : int - Revision of the particle restart format current_batch : int The batch containing the particle gen_per_batch : int @@ -43,28 +39,52 @@ class Particle(object): import h5py self._f = h5py.File(filename, 'r') - # Read all metadata - self._read_data() + # Ensure filetype and revision are correct + if 'filetype' not in self._f or self._f[ + 'filetype'].value.decode() != 'particle restart': + raise IOError('{} is not a particle restart file.'.format(filename)) + if self._f['revision'].value != 1: + raise IOError('Particle restart file has a file revision of {} ' + 'which is not consistent with the revision this ' + 'version of OpenMC expects ({}).'.format( + self._f['revision'].value, 1)) - def _read_data(self): - # Read filetype - self.filetype = self._f['filetype'].value + @property + def current_batch(self): + return self._f['current_batch'].value - # Read statepoint revision - self.revision = self._f['revision'].value + @property + def current_gen(self): + return self._f['current_gen'].value - # Read current batch - self.current_batch = self._f['current_batch'].value + @property + def energy(self): + return self._f['energy'].value - # Read run information - self.gen_per_batch = self._f['gen_per_batch'].value - self.current_gen = self._f['current_gen'].value - self.n_particles = self._f['n_particles'].value - self.run_mode = self._f['run_mode'].value + @property + def gen_per_batch(self): + return self._f['gen_per_batch'].value - # Read particle properties - self.id = self._f['id'].value - self.weight = self._f['weight'].value - self.energy = self._f['energy'].value - self.xyz = self._f['xyz'].value - self.uvw = self._f['uvw'].value + @property + def id(self): + return self._f['id'].value + + @property + def n_particles(self): + return self._f['n_particles'].value + + @property + def run_mode(self): + return self._f['run_mode'].value.decode() + + @property + def uvw(self): + return self._f['uvw'].value + + @property + def weight(self): + return self._f['weight'].value + + @property + def xyz(self): + return self._f['xyz'].value diff --git a/openmc/statepoint.py b/openmc/statepoint.py index 545b53a1a..34ed09ad4 100644 --- a/openmc/statepoint.py +++ b/openmc/statepoint.py @@ -2,62 +2,13 @@ import copy import sys import numpy as np -import scipy.stats import openmc -from openmc.constants import * if sys.version > '3': long = int -class SourceSite(object): - """A single source site produced from fission. - - Attributes - ---------- - weight : float - Weight of the particle arising from the site - xyz : list of float - Cartesian coordinates of the site - uvw : list of float - Directional cosines for particles emerging from the site - E : float - Energy of the emerging particle in MeV - - """ - - def __init__(self): - self._weight = None - self._xyz = None - self._uvw = None - self._E = None - - def __repr__(self): - string = 'SourceSite\n' - string += '{0: <16}{1}{2}\n'.format('\tweight', '=\t', self._weight) - string += '{0: <16}{1}{2}\n'.format('\tE', '=\t', self._E) - string += '{0: <16}{1}{2}\n'.format('\t(x,y,z)', '=\t', self._xyz) - string += '{0: <16}{1}{2}\n'.format('\t(u,v,w)', '=\t', self._uvw) - return string - - @property - def weight(self): - return self._weight - - @property - def xyz(self): - return self._xyz - - @property - def uvw(self): - return self._uvw - - @property - def E(self): - return self._E - - class StatePoint(object): """State information on a simulation at a certain point in time (at the end of a given batch). Statepoints can be used to analyze tally results as well as @@ -65,28 +16,74 @@ class StatePoint(object): Attributes ---------- + cmfd_on : bool + Indicate whether CMFD is active + cmfd_balance : ndarray + Residual neutron balance for each batch + cmfd_dominance + Dominance ratio for each batch + cmfd_entropy : ndarray + Shannon entropy of CMFD fission source for each batch + cmfd_indices : ndarray + Number of CMFD mesh cells and energy groups. The first three indices + correspond to the x-, y-, and z- spatial directions and the fourth index + is the number of energy groups. + cmfd_srccmp : ndarray + Root-mean-square difference between OpenMC and CMFD fission source for + each batch + cmfd_src : ndarray + CMFD fission source distribution over all mesh cells and energy groups. + current_batch : int + Number of batches simulated + date_and_time : str + Date and time when simulation began + entropy : ndarray + Shannon entropy of fission source at each batch + gen_per_batch : int + Number of fission generations per batch + global_tallies : ndarray of compound datatype + Global tallies for k-effective estimates and leakage. The compound + datatype has fields 'name', 'sum', 'sum_sq', 'mean', and 'std_dev'. k_combined : list Combined estimator for k-effective and its uncertainty - n_particles : int - Number of particles per generation + k_col_abs : float + Cross-product of collision and absorption estimates of k-effective + k_col_tra : float + Cross-product of collision and tracklength estimates of k-effective + k_abs_tra : float + Cross-product of absorption and tracklength estimates of k-effective + k_generation : ndarray + Estimate of k-effective for each batch/generation + meshes : dict + Dictionary whose keys are mesh IDs and whose values are Mesh objects n_batches : int Number of batches - current_batch : - Number of batches simulated - results : bool - Indicate whether tally results have been read - source : ndarray of SourceSite - Array of source sites - with_summary : bool - Indicate whether statepoint data has been linked against a summary file + n_inactive : int + Number of inactive batches + n_particles : int + Number of particles per generation + n_realizations : int + Number of tally realizations + path : str + Working directory for simulation + run_mode : str + Simulation run mode, e.g. 'k-eigenvalue' + seed : int + Pseudorandom number generator seed + source : ndarray of compound datatype + Array of source sites. The compound datatype has fields 'wgt', 'xyz', + 'uvw', and 'E' corresponding to the weight, position, direction, and + energy of the source site. + source_present : bool + Indicate whether source sites are present tallies : dict Dictionary whose keys are tally IDs and whose values are Tally objects tallies_present : bool Indicate whether user-defined tallies are present - global_tallies : ndarray - Global tallies and their uncertainties - n_realizations : int - Number of tally realizations + version: tuple of int + Version of OpenMC + with_summary : bool + Indicate whether statepoint data has been linked against a summary file """ @@ -94,474 +91,375 @@ class StatePoint(object): import h5py self._f = h5py.File(filename, 'r') + # Ensure filetype and revision are correct + if 'filetype' not in self._f or self._f[ + 'filetype'].value.decode() != 'statepoint': + raise IOError('{} is not a statepoint file.'.format(filename)) + if self._f['revision'].value != 14: + raise IOError('Statepoint file has a file revision of {} ' + 'which is not consistent with the revision this ' + 'version of OpenMC expects ({}).'.format( + self._f['revision'].value, 14)) + # Set flags for what data has been read - self._results = False - self._source = False + self._meshes_read = False + self._tallies_read = False self._with_summary = False - - # Read all metadata - self._read_metadata() - - # Read information about tally meshes - self._read_meshes() - - # Read tally metadata - self._read_tallies() + self._global_tallies = None def close(self): self._f.close() @property - def k_combined(self): - return self._k_combined + def cmfd_on(self): + return self._f['cmfd_on'].value > 0 @property - def n_particles(self): - return self._n_particles + def cmfd_balance(self): + if self.cmfd_on: + return self._f['cmfd/cmfd_balance'].value + else: + return None @property - def n_batches(self): - return self._n_batches + def cmfd_dominance(self): + if self.cmfd_on: + return self._f['cmfd/cmfd_dominance'].value + else: + return None + + @property + def cmfd_entropy(self): + if self.cmfd_on: + return self._f['cmfd/cmfd_entropy'].value + else: + return None + + @property + def cmfd_indices(self): + if self.cmfd_on: + return self._f['cmfd/indices'].value + else: + return None + + @property + def cmfd_src(self): + if self.cmfd_on: + data = self._f['cmfd/cmfd_src'].value + return np.reshape(data, tuple(self.cmfd_indices), order='F') + else: + return None + + @property + def cmfd_srccmp(self): + if self.cmfd_on: + return self._f['cmfd/cmfd_srccmp'].value + else: + return None @property def current_batch(self): - return self._current_batch + return self._f['current_batch'].value @property - def results(self): - return self._results + def date_and_time(self): + return self._f['date_and_time'].value.decode() + + @property + def entropy(self): + if self.run_mode == 'k-eigenvalue': + return self._f['entropy'].value + else: + return None + + @property + def gen_per_batch(self): + if self.run_mode == 'k-eigenvalue': + return self._f['gen_per_batch'].value + else: + return None + + @property + def global_tallies(self): + if self._global_tallies is None: + data = self._f['global_tallies'].value + gt = np.zeros_like(data, dtype=[ + ('name', 'a14'), ('sum', 'f8'), ('sum_sq', 'f8'), + ('mean', 'f8'), ('std_dev', 'f8')]) + gt['name'] = ['k-collision', 'k-absorption', 'k-tracklength', + 'leakage'] + gt['sum'] = data['sum'] + gt['sum_sq'] = data['sum_sq'] + + # Calculate mean and sample standard deviation of mean + n = self.n_realizations + gt['mean'] = gt['sum']/n + gt['std_dev'] = np.sqrt((gt['sum_sq']/n - gt['mean']**2)/(n - 1)) + + self._global_tallies = gt + + return self._global_tallies + + @property + def k_cmfd(self): + if self.cmfd_on: + return self._f['cmfd/k_cmfd'].value + else: + return None + + @property + def k_generation(self): + if self.run_mode == 'k-eigenvalue': + return self._f['k_generation'].value + else: + return None + + @property + def k_combined(self): + if self.run_mode == 'k-eigenvalue': + return self._f['k_combined'].value + else: + return None + + @property + def k_col_abs(self): + if self.run_mode == 'k-eigenvalue': + return self._f['k_col_abs'].value + else: + return None + + @property + def k_col_tra(self): + if self.run_mode == 'k-eigenvalue': + return self._f['k_col_tra'].value + else: + return None + + @property + def k_abs_tra(self): + if self.run_mode == 'k-eigenvalue': + return self._f['k_abs_tra'].value + else: + return None + + @property + def meshes(self): + if not self._meshes_read: + # Initialize dictionaries for the Meshes + # Keys - Mesh IDs + # Values - Mesh objects + self._meshes = {} + + # Read the number of Meshes + n_meshes = self._f['tallies/meshes/n_meshes'].value + + # Read a list of the IDs for each Mesh + if n_meshes > 0: + # User-defined Mesh IDs + mesh_keys = self._f['tallies/meshes/keys'].value + else: + mesh_keys = [] + + # Build dictionary of Meshes + base = 'tallies/meshes/mesh ' + + # Iterate over all Meshes + for mesh_key in mesh_keys: + # Read the mesh type + mesh_type = self._f['{0}{1}/type'.format(base, mesh_key)].value.decode() + + # Read the mesh dimensions, lower-left coordinates, + # upper-right coordinates, and width of each mesh cell + dimension = self._f['{0}{1}/dimension'.format(base, mesh_key)].value + lower_left = self._f['{0}{1}/lower_left'.format(base, mesh_key)].value + upper_right = self._f['{0}{1}/upper_right'.format(base, mesh_key)].value + width = self._f['{0}{1}/width'.format(base, mesh_key)].value + + # Create the Mesh and assign properties to it + mesh = openmc.Mesh(mesh_key) + mesh.dimension = dimension + mesh.width = width + mesh.lower_left = lower_left + mesh.upper_right = upper_right + mesh.type = mesh_type + + # Add mesh to the global dictionary of all Meshes + self._meshes[mesh_key] = mesh + + self._meshes_read = True + + return self._meshes + + @property + def n_batches(self): + return self._f['n_batches'].value + + @property + def n_inactive(self): + if self.run_mode == 'k-eigenvalue': + return self._f['n_inactive'].value + else: + return None + + @property + def n_particles(self): + return self._f['n_particles'].value + + @property + def n_realizations(self): + return self._f['n_realizations'].value + + @property + def path(self): + return self._f['path'].value.decode() + + @property + def run_mode(self): + return self._f['run_mode'].value.decode() + + @property + def seed(self): + return self._f['seed'].value @property def source(self): - return self._source + if self.source_present: + return self._f['source_bank'].value + else: + return None @property - def with_summary(self): - return self._with_summary + def source_present(self): + return self._f['source_present'].value > 0 @property def tallies(self): + if not self._tallies_read: + # Initialize dictionary for tallies + self._tallies = {} + + # Read the number of tallies + n_tallies = self._f['tallies/n_tallies'].value + + # Read a list of the IDs for each Tally + if n_tallies > 0: + # OpenMC Tally IDs (redefined internally from user definitions) + tally_keys = self._f['tallies/keys'].value + else: + tally_keys = [] + + base = 'tallies/tally ' + + # Iterate over all Tallies + for tally_key in tally_keys: + + # Read the Tally size specifications + n_realizations = self._f['{0}{1}/n_realizations'.format(base, tally_key)].value + + # Create Tally object and assign basic properties + tally = openmc.Tally(tally_id=tally_key) + tally._statepoint = self + tally.estimator = self._f['{0}{1}/estimator'.format( + base, tally_key)].value.decode() + tally.num_realizations = n_realizations + + # Read the number of Filters + n_filters = self._f['{0}{1}/n_filters'.format(base, tally_key)].value + + subbase = '{0}{1}/filter '.format(base, tally_key) + + # Initialize all Filters + for j in range(1, n_filters+1): + + # Read the Filter type + filter_type = self._f['{0}{1}/type'.format(subbase, j)].value.decode() + + # Read the Filter offset + offset = self._f['{0}{1}/offset'.format(subbase, j)].value + + n_bins = self._f['{0}{1}/n_bins'.format(subbase, j)].value + + # Read the bin values + bins = self._f['{0}{1}/bins'.format(subbase, j)].value + + # Create Filter object + filter = openmc.Filter(filter_type, bins) + filter.offset = offset + filter.num_bins = n_bins + + if filter_type == 'mesh': + mesh_ids = self._f['tallies/meshes/ids'].value + mesh_keys = self._f['tallies/meshes/keys'].value + + key = mesh_keys[mesh_ids == bins][0] + filter.mesh = self.meshes[key] + + # Add Filter to the Tally + tally.add_filter(filter) + + # Read Nuclide bins + nuclide_names = self._f['{0}{1}/nuclides'.format(base, tally_key)].value + + # Add all Nuclides to the Tally + for name in nuclide_names: + nuclide = openmc.Nuclide(name.decode().strip()) + tally.add_nuclide(nuclide) + + # Read score bins + n_score_bins = self._f['{0}{1}/n_score_bins'.format(base, tally_key)].value + + tally.num_score_bins = n_score_bins + + scores = self._f['{0}{1}/score_bins'.format( + base, tally_key)].value + n_user_scores = self._f['{0}{1}/n_user_score_bins' + .format(base, tally_key)].value + + # Compute and set the filter strides + for i in range(n_filters): + filter = tally.filters[i] + filter.stride = n_score_bins * len(nuclide_names) + + for j in range(i+1, n_filters): + filter.stride *= tally.filters[j].num_bins + + # Read scattering moment order strings (e.g., P3, Y-1,2, etc.) + moments = self._f['{0}{1}/moment_orders'.format( + base, tally_key)].value + + # Add the scores to the Tally + for j, score in enumerate(scores): + score = score.decode() + # If this is a scattering moment, insert the scattering order + if '-n' in score: + score = score.replace('-n', '-' + moments[j].decode()) + elif '-pn' in score: + score = score.replace('-pn', '-' + moments[j].decode()) + elif '-yn' in score: + score = score.replace('-yn', '-' + moments[j].decode()) + + tally.add_score(score) + + # Add Tally to the global dictionary of all Tallies + self._tallies[tally_key] = tally + + self._tallies_read = True + return self._tallies @property def tallies_present(self): - return self._tallies_present + return self._f['tallies/tallies_present'].value @property - def global_tallies(self): - return self._global_tallies + def version(self): + return (self._f['version_major'].value, + self._f['version_minor'].value, + self._f['version_release'].value) @property - def n_realizations(self): - return self._n_realizations - - def _read_metadata(self): - # Read filetype - self._filetype = self._f['filetype'].value - - # Read statepoint revision - self._revision = self._f['revision'].value - if self._revision != 13: - raise Exception('Statepoint Revision is not consistent.') - - # Read OpenMC version - self._version = [self._f['version_major'].value, - self._f['version_minor'].value, - self._f['version_release'].value] - - # Read date and time - self._date_and_time = self._f['date_and_time'].value[0] - - # Read path - self._path = self._f['path'].value[0].strip() - - # Read random number seed - self._seed = self._f['seed'].value - - # Read run information - self._run_mode = self._f['run_mode'].value - self._n_particles = self._f['n_particles'].value - self._n_batches = self._f['n_batches'].value - - # Read current batch - self._current_batch = self._f['current_batch'].value - - # Read whether or not the source site distribution is present - self._source_present = self._f['source_present'].value - - # Read criticality information - if self._run_mode == 2: - self._read_criticality() - - def _read_criticality(self): - # Read criticality information - if self._run_mode == 2: - - self._n_inactive = self._f['n_inactive'].value - self._gen_per_batch = self._f['gen_per_batch'].value - self._k_generation = self._f['k_generation'].value - self._entropy = self._f['entropy'].value - - self._k_col_abs = self._f['k_col_abs'].value - self._k_col_tra = self._f['k_col_tra'].value - self._k_abs_tra = self._f['k_abs_tra'].value - self._k_combined = self._f['k_combined'].value - - # Read CMFD information (if used) - self._read_cmfd() - - def _read_cmfd(self): - base = 'cmfd' - - # Read CMFD information - self._cmfd_on = self._f['cmfd_on'].value - - if self._cmfd_on == 1: - self._cmfd_indices = self._f['{0}/indices'.format(base)].value - self._k_cmfd = self._f['{0}/k_cmfd'.format(base)].value - self._cmfd_src = self._f['{0}/cmfd_src'.format(base)].value - self._cmfd_src = np.reshape(self._cmfd_src, tuple(self._cmfd_indices), - order='F') - self._cmfd_entropy = self._f['{0}/cmfd_entropy'.format(base)].value - self._cmfd_balance = self._f['{0}/cmfd_balance'.format(base)].value - self._cmfd_dominance = self._f['{0}/cmfd_dominance'.format(base)].value - self._cmfd_srccmp = self._f['{0}/cmfd_srccmp'.format(base)].value - - def _read_meshes(self): - # Initialize dictionaries for the Meshes - # Keys - Mesh IDs - # Values - Mesh objects - self._meshes = {} - - # Read the number of Meshes - self._n_meshes = self._f['tallies/meshes/n_meshes'].value - - # Read a list of the IDs for each Mesh - if self._n_meshes > 0: - - # OpenMC Mesh IDs (redefined internally from user definitions) - self._mesh_ids = self._f['tallies/meshes/ids'].value - - # User-defined Mesh IDs - self._mesh_keys = self._f['tallies/meshes/keys'].value - - else: - self._mesh_keys = [] - self._mesh_ids = [] - - # Build dictionary of Meshes - base = 'tallies/meshes/mesh ' - - # Iterate over all Meshes - for mesh_key in self._mesh_keys: - - # Read the user-specified Mesh ID and type - mesh_id = self._f['{0}{1}/id'.format(base, mesh_key)].value - mesh_type = self._f['{0}{1}/type'.format(base, mesh_key)].value - - # Get the Mesh dimension - n_dimension = self._f['{0}{1}/n_dimension'.format(base, mesh_key)].value - - # Read the mesh dimensions, lower-left coordinates, - # upper-right coordinates, and width of each mesh cell - dimension = self._f['{0}{1}/dimension'.format(base, mesh_key)].value - lower_left = self._f['{0}{1}/lower_left'.format(base, mesh_key)].value - upper_right = self._f['{0}{1}/upper_right'.format(base, mesh_key)].value - width = self._f['{0}{1}/width'.format(base, mesh_key)].value - - # Create the Mesh and assign properties to it - mesh = openmc.Mesh(mesh_id) - - mesh.dimension = dimension - mesh.width = width - mesh.lower_left = lower_left - mesh.upper_right = upper_right - - #FIXME: Set the mesh type to 'rectangular' by default - mesh.type = 'rectangular' - - # Add mesh to the global dictionary of all Meshes - self._meshes[mesh_id] = mesh - - def _read_tallies(self): - # Initialize dictionaries for the Tallies - # Keys - Tally IDs - # Values - Tally objects - self._tallies = {} - - # Read the number of tallies - self._n_tallies = self._f['/tallies/n_tallies'].value - - # Read a list of the IDs for each Tally - if self._n_tallies > 0: - - # OpenMC Tally IDs (redefined internally from user definitions) - self._tally_ids = self._f['tallies/ids'].value - - # User-defined Tally IDs - self._tally_keys = self._f['tallies/keys'].value - - else: - self._tally_keys = [] - self._tally_ids = [] - - base = 'tallies/tally ' - - # Iterate over all Tallies - for tally_key in self._tally_keys: - - # Read integer Tally estimator type code (analog or tracklength) - estimator_type = self._f['{0}{1}/estimator'.format(base, tally_key)].value - - # Read the Tally size specifications - n_realizations = self._f['{0}{1}/n_realizations'.format(base, tally_key)].value - - # Create Tally object and assign basic properties - tally = openmc.Tally(tally_key) - tally.estimator = ESTIMATOR_TYPES[estimator_type] - tally.num_realizations = n_realizations - - # Read the number of Filters - n_filters = self._f['{0}{1}/n_filters'.format(base, tally_key)].value - - subbase = '{0}{1}/filter '.format(base, tally_key) - - # Initialize all Filters - for j in range(1, n_filters+1): - - # Read the integer Filter type code - filter_type = self._f['{0}{1}/type'.format(subbase, j)].value - - # Read the Filter offset - offset = self._f['{0}{1}/offset'.format(subbase, j)].value - - n_bins = self._f['{0}{1}/n_bins'.format(subbase, j)].value - - if n_bins <= 0: - msg = 'Unable to create Filter "{0}" for Tally ID="{1}" ' \ - 'since no bins were specified'.format(j, tally_key) - raise ValueError(msg) - - # Read the bin values - if FILTER_TYPES[filter_type] in ['energy', 'energyout']: - bins = self._f['{0}{1}/bins'.format(subbase, j)].value - - elif FILTER_TYPES[filter_type] in ['mesh', 'distribcell']: - bins = self._f['{0}{1}/bins'.format(subbase, j)].value - - else: - bins = self._f['{0}{1}/bins'.format(subbase, j)].value - - # Create Filter object - filter = openmc.Filter(FILTER_TYPES[filter_type], bins) - filter.offset = offset - filter.num_bins = n_bins - - if FILTER_TYPES[filter_type] == 'mesh': - key = self._mesh_keys[self._mesh_ids == bins][0] - filter.mesh = self._meshes[key] - - # Add Filter to the Tally - tally.add_filter(filter) - - # Read Nuclide bins - n_nuclides = self._f['{0}{1}/n_nuclides'.format(base, tally_key)].value - - nuclide_zaids = self._f['{0}{1}/nuclides'.format(base, tally_key)].value - - # Add all Nuclides to the Tally - for nuclide_zaid in nuclide_zaids: - tally.add_nuclide(nuclide_zaid) - - # Read score bins - n_score_bins = self._f['{0}{1}/n_score_bins'.format(base, tally_key)].value - - tally.num_score_bins = n_score_bins - - score_bins = self._f['{0}{1}/score_bins'.format( - base, tally_key)].value - scores = [SCORE_TYPES[score] for score in score_bins] - n_user_scores = self._f['{0}{1}/n_user_score_bins' - .format(base, tally_key)].value - - # Compute and set the filter strides - for i in range(n_filters): - filter = tally.filters[i] - filter.stride = n_score_bins * n_nuclides - - for j in range(i+1, n_filters): - filter.stride *= tally.filters[j].num_bins - - # Read scattering moment order strings (e.g., P3, Y-1,2, etc.) - moments = [] - subbase = '{0}{1}/moments/'.format(base, tally_key) - - # Extract the moment order string for each score - for k in range(len(scores)): - moment = str(self._f['{0}order{1}'.format( - subbase, k+1)].value[0]) - moment = moment.lstrip('[\'') - moment = moment.rstrip('\']') - - # Remove extra whitespace - moment.replace(" ", "") - moments.append(moment) - - # Add the scores to the Tally - for j, score in enumerate(scores): - # If this is a scattering moment, insert the scattering order - if '-n' in score: - score = score.replace('-n', '-' + str(moments[j])) - elif '-pn' in score: - score = score.replace('-pn', '-' + str(moments[j])) - elif '-yn' in score: - score = score.replace('-yn', '-' + str(moments[j])) - - tally.add_score(score) - - # Add Tally to the global dictionary of all Tallies - self.tallies[tally_key] = tally - - def read_results(self): - """Read tally results and store them in the ``tallies`` attribute. No results - are read when the statepoint is instantiated. - - """ - - # Number of realizations for global Tallies - self._n_realizations = self._f['n_realizations'].value - - # Read global Tallies - n_global_tallies = self._f['n_global_tallies'].value - - data = self._f['global_tallies'].value - self._global_tallies = np.column_stack((data['sum'], data['sum_sq'])) - - # Flag indicating if Tallies are present - self._tallies_present = self._f['tallies/tallies_present'].value - - base = 'tallies/tally ' - - # Read Tally results - if self._tallies_present: - - # Iterate over and extract the results for all Tallies - for tally_key in self._tally_keys: - - # Get this Tally - tally = self._tallies[tally_key] - - # Compute the total number of bins for this Tally - num_tot_bins = tally.num_bins - - # Extract Tally data from the file - data = self._f['{0}{1}/results'.format(base, tally_key)].value - sum = data['sum'] - sum_sq = data['sum_sq'] - - # Define a routine to convert 0 to 1 - def nonzero(val): - return 1 if not val else val - - # Reshape the results arrays - new_shape = (nonzero(tally.num_filter_bins), - nonzero(tally.num_nuclides), - nonzero(tally.num_score_bins)) - sum = np.reshape(sum, new_shape) - sum_sq = np.reshape(sum_sq, new_shape) - - # Set the data for this Tally - tally.sum = sum - tally.sum_sq = sum_sq - - # Indicate that Tally results have been read - self._results = True - - def read_source(self): - """Read and store source sites from the statepoint file. By default, source - sites are not loaded upon initialization. - - """ - - # Check whether Tally results have been read - if not self._results: - self.read_results() - - # Check if source bank is in statepoint - if not self._source_present: - print('Unable to read source since it is not in statepoint file') - return - - # Initialize a NumPy array for the source sites - self._source = np.empty(self._n_particles, dtype=SourceSite) - - # For HDF5 state points, copy entire bank - source_sites = self._f['source_bank'].value - - # Initialize SourceSite object for each particle - for i in range(self._n_particles): - # Initialize new source site - site = SourceSite() - - # Read position, angle, and energy - site._weight, site._xyz, site._uvw, site._E = source_sites[i] - - # Store the source site in the NumPy array - self._source[i] = site - - def compute_ci(self, confidence=0.95): - """Computes confidence intervals for each Tally bin. - - This method is equivalent to calling compute_stdev(...) when the - confidence is known as opposed to its corresponding t value. - - Parameters - ---------- - confidence : float, optional - Confidence level. Defaults to 0.95. - - """ - - # Determine significance level and percentile for two-sided CI - alpha = 1 - confidence - percentile = 1 - alpha/2 - - # Calculate t-value - t_value = scipy.stats.t.ppf(percentile, self._n_realizations - 1) - self.compute_stdev(t_value) - - def compute_stdev(self, t_value=1.0): - """Computes the sample mean and the standard deviation of the mean - for each Tally bin. - - Parameters - ---------- - t_value : float, optional - Student's t-value applied to the uncertainty. Defaults to 1.0, - meaning the reported value is the sample standard deviation. - - """ - - # Determine number of realizations - n = self._n_realizations - - # Calculate the standard deviation for each global tally - for i in range(len(self._global_tallies)): - - # Get sum and sum of squares - s, s2 = self._global_tallies[i] - - # Calculate sample mean and replace value - s /= n - self._global_tallies[i, 0] = s - - # Calculate standard deviation - if s != 0.0: - self._global_tallies[i, 1] = t_value * np.sqrt((s2 / n - s**2) / (n-1)) - - # Calculate sample mean and standard deviation for user-defined Tallies - for tally_id, tally in self.tallies.items(): - tally.compute_std_dev(t_value) + def with_summary(self): + return self._with_summary def get_tally(self, scores=[], filters=[], nuclides=[], name=None, id=None, estimator=None): @@ -706,15 +604,6 @@ class StatePoint(object): tally.name = summary.tallies[tally_id].name tally.with_summary = True - nuclide_zaids = copy.deepcopy(tally.nuclides) - - for nuclide_zaid in nuclide_zaids: - tally.remove_nuclide(nuclide_zaid) - if nuclide_zaid == -1: - tally.add_nuclide(openmc.Nuclide('total')) - else: - tally.add_nuclide(summary.nuclides[nuclide_zaid]) - for filter in tally.filters: if filter.type == 'surface': surface_ids = [] diff --git a/openmc/summary.py b/openmc/summary.py index 6fb487414..2ae746484 100644 --- a/openmc/summary.py +++ b/openmc/summary.py @@ -46,7 +46,6 @@ class Summary(object): def _read_geometry(self): # Read in and initialize the Materials and Geometry - self._read_nuclides() self._read_materials() self._read_surfaces() self._read_cells() @@ -54,35 +53,6 @@ class Summary(object): self._read_lattices() self._finalize_geometry() - def _read_nuclides(self): - self.n_nuclides = self._f['nuclides/n_nuclides'] - - # Initialize dictionary for each Nuclide - # Keys - Nuclide ZAIDs - # Values - Nuclide objects - self.nuclides = {} - - for key in self._f['nuclides'].keys(): - if key == 'n_nuclides': - continue - - index = self._f['nuclides'][key]['index'].value - alias = self._f['nuclides'][key]['alias'][0] - zaid = self._f['nuclides'][key]['zaid'].value - - # Read the Nuclide's name (e.g., 'H-1' or 'U-235') - name = alias.split('.')[0] - - # Read the Nuclide's cross-section identifier (e.g., '70c') - xs = alias.split('.')[1] - - # Initialize this Nuclide and add to global dictionary of Nuclides - if 'nat' in name: - self.nuclides[zaid] = openmc.Element(name=name, xs=xs) - else: - self.nuclides[zaid] = openmc.Nuclide(name=name, xs=xs) - self.nuclides[zaid].zaid = zaid - def _read_materials(self): self.n_materials = self._f['n_materials'].value @@ -97,23 +67,17 @@ class Summary(object): material_id = int(key.lstrip('material ')) index = self._f['materials'][key]['index'].value - name = self._f['materials'][key]['name'][0] + name = self._f['materials'][key]['name'].value.decode() density = self._f['materials'][key]['atom_density'].value nuc_densities = self._f['materials'][key]['nuclide_densities'][...] - nuclides = self._f['materials'][key]['nuclides'][...] - n_sab = self._f['materials'][key]['n_sab'].value + nuclides = self._f['materials'][key]['nuclides'].value - sab_names = [] - sab_xs = [] - - # Read the names of the S(a,b) tables for this Material - for i in range(1, n_sab+1): - sab_table = \ - self._f['materials'][key]['sab_tables'][str(i)].value - - # Read the cross-section identifiers for each S(a,b) table - sab_names.append(sab_table.split('.')[0]) - sab_xs.append(sab_table.split('.')[1]) + # Read the names of the S(a,b) tables for this Material and add them + if 'sab_names' in self._f['materials'][key]: + sab_tables = self._f['materials'][key]['sab_names'].value + for sab_table in sab_tables: + name, xs = sab_table.decode().split('.') + material.add_s_alpha_beta(name, xs) # Create the Material material = openmc.Material(material_id=material_id, name=name) @@ -121,21 +85,17 @@ class Summary(object): # Set the Material's density to g/cm3 - this is what is used in OpenMC material.set_density(density=density, units='g/cm3') - # Add all Nuclides to the Material - for i, zaid in enumerate(nuclides): - nuclide = self.get_nuclide_by_zaid(zaid) - density = nuc_densities[i] + # Add all nuclides to the Material + for fullname, density in zip(nuclides, nuc_densities): + fullname = fullname.decode().strip() + name, xs = fullname.split('.') - if isinstance(nuclide, openmc.Nuclide): - material.add_nuclide(nuclide, percent=density, percent_type='ao') - elif isinstance(nuclide, openmc.Element): - material.add_element(nuclide, percent=density, percent_type='ao') - - # Add S(a,b) table(s?) to the Material - for i in range(n_sab): - name = sab_names[i] - xs = sab_xs[i] - material.add_s_alpha_beta(name, xs) + if 'nat' in name: + material.add_element(openmc.Element(name=name, xs=xs), + percent=density, percent_type='ao') + else: + material.add_nuclide(openmc.Nuclide(name=name, xs=xs), + percent=density, percent_type='ao') # Add the Material to the global dictionary of all Materials self.materials[index] = material @@ -154,67 +114,67 @@ class Summary(object): surface_id = int(key.lstrip('surface ')) index = self._f['geometry/surfaces'][key]['index'].value - name = self._f['geometry/surfaces'][key]['name'][0] - surf_type = self._f['geometry/surfaces'][key]['type'][...] - bc = self._f['geometry/surfaces'][key]['boundary_condition'][...][0] + name = self._f['geometry/surfaces'][key]['name'].value.decode() + surf_type = self._f['geometry/surfaces'][key]['type'].value.decode() + bc = self._f['geometry/surfaces'][key]['boundary_condition'].value.decode() coeffs = self._f['geometry/surfaces'][key]['coefficients'][...] # Create the Surface based on its type - if surf_type == 'X Plane': + if surf_type == 'x-plane': x0 = coeffs[0] surface = openmc.XPlane(surface_id, bc, x0, name) - elif surf_type == 'Y Plane': + elif surf_type == 'y-plane': y0 = coeffs[0] surface = openmc.YPlane(surface_id, bc, y0, name) - elif surf_type == 'Z Plane': + elif surf_type == 'z-plane': z0 = coeffs[0] surface = openmc.ZPlane(surface_id, bc, z0, name) - elif surf_type == 'Plane': + elif surf_type == 'plane': A = coeffs[0] B = coeffs[1] C = coeffs[2] D = coeffs[3] surface = openmc.Plane(surface_id, bc, A, B, C, D, name) - elif surf_type == 'X Cylinder': + elif surf_type == 'x-cylinder': y0 = coeffs[0] z0 = coeffs[1] R = coeffs[2] surface = openmc.XCylinder(surface_id, bc, y0, z0, R, name) - elif surf_type == 'Y Cylinder': + elif surf_type == 'y-cylinder': x0 = coeffs[0] z0 = coeffs[1] R = coeffs[2] surface = openmc.YCylinder(surface_id, bc, x0, z0, R, name) - elif surf_type == 'Z Cylinder': + elif surf_type == 'z-cylinder': x0 = coeffs[0] y0 = coeffs[1] R = coeffs[2] surface = openmc.ZCylinder(surface_id, bc, x0, y0, R, name) - elif surf_type == 'Sphere': + elif surf_type == 'sphere': x0 = coeffs[0] y0 = coeffs[1] z0 = coeffs[2] R = coeffs[3] surface = openmc.Sphere(surface_id, bc, x0, y0, z0, R, name) - elif surf_type in ['X Cone', 'Y Cone', 'Z Cone']: + elif surf_type in ['x-cone', 'y-cone', 'z-cone']: x0 = coeffs[0] y0 = coeffs[1] z0 = coeffs[2] R2 = coeffs[3] - if surf_type == 'X Cone': + if surf_type == 'x-cone': surface = openmc.XCone(surface_id, bc, x0, y0, z0, R2, name) - if surf_type == 'Y Cone': + if surf_type == 'y-cone': surface = openmc.YCone(surface_id, bc, x0, y0, z0, R2, name) - if surf_type == 'Z Cone': + if surf_type == 'z-cone': surface = openmc.ZCone(surface_id, bc, x0, y0, z0, R2, name) # Add Surface to global dictionary of all Surfaces @@ -242,8 +202,8 @@ class Summary(object): cell_id = int(key.lstrip('cell ')) index = self._f['geometry/cells'][key]['index'].value - name = self._f['geometry/cells'][key]['name'][0] - fill_type = self._f['geometry/cells'][key]['fill_type'][...][0] + name = self._f['geometry/cells'][key]['name'].value.decode() + fill_type = self._f['geometry/cells'][key]['fill_type'].value.decode() if fill_type == 'normal': fill = self._f['geometry/cells'][key]['material'].value @@ -261,21 +221,17 @@ class Summary(object): cell = openmc.Cell(cell_id=cell_id, name=name) if fill_type == 'universe': - maps = self._f['geometry/cells'][key]['maps'].value - - if maps > 0: + if 'offset' in self._f['geometry/cells'][key]: offset = self._f['geometry/cells'][key]['offset'][...] cell.offsets = offset - translated = self._f['geometry/cells'][key]['translated'].value - if translated: + if 'translation' in self._f['geometry/cells'][key]: translation = \ self._f['geometry/cells'][key]['translation'][...] translation = np.asarray(translation, dtype=np.float64) cell.translation = translation - rotated = self._f['geometry/cells'][key]['rotated'].value - if rotated: + if 'rotation' in self._f['geometry/cells'][key]: rotation = \ self._f['geometry/cells'][key]['rotation'][...] rotation = np.asarray(rotation, dtype=np.int) @@ -288,8 +244,8 @@ class Summary(object): for surface_halfspace in surfaces: halfspace = np.sign(surface_halfspace) - surface_id = np.abs(surface_halfspace) - surface = self.surfaces[surface_id] + surface_id = abs(surface_halfspace) + surface = self.get_surface_by_id(surface_id) cell.add_surface(surface, halfspace) # Add the Cell to the global dictionary of all Cells @@ -336,13 +292,13 @@ class Summary(object): lattice_id = int(key.lstrip('lattice ')) index = self._f['geometry/lattices'][key]['index'].value - name = self._f['geometry/lattices'][key]['name'][...][0] - lattice_type = self._f['geometry/lattices'][key]['type'][...][0] - maps = self._f['geometry/lattices'][key]['maps'].value - offset_size = self._f['geometry/lattices'][key]['offset_size'].value + name = self._f['geometry/lattices'][key]['name'].value.decode() + lattice_type = self._f['geometry/lattices'][key]['type'].value.decode() - if offset_size > 0: + if 'offsets' in self._f['geometry/lattices'][key]: offsets = self._f['geometry/lattices'][key]['offsets'][...] + else: + offsets = None if lattice_type == 'rectangular': dimension = self._f['geometry/lattices'][key]['dimension'][...] @@ -383,7 +339,7 @@ class Summary(object): universes = universes[:, ::-1, :] lattice.universes = universes - if offset_size > 0: + if offsets is not None: offsets = np.swapaxes(offsets, 0, 1) offsets = np.swapaxes(offsets, 1, 2) lattice.offsets = offsets @@ -477,7 +433,7 @@ class Summary(object): # Lattice is 2D; extract the only axial level lattice.universes = universes[0] - if offset_size > 0: + if offsets is not None: lattice.offsets = offsets # Add the Lattice to the global dictionary of all Lattices @@ -532,26 +488,19 @@ class Summary(object): # Iterate over all Tallies for tally_key in tally_keys: - tally_id = int(tally_key.strip('tally ')) subbase = '{0}{1}'.format(base, tally_id) # Read Tally name metadata - name_size = self._f['{0}/name_size'.format(subbase)][...] - if (name_size > 0): - tally_name = self._f['{0}/name'.format(subbase)][...][0] - tally_name = tally_name.lstrip('[\'') - tally_name = tally_name.rstrip('\']') - else: - tally_name = '' + tally_name = self._f['{0}/name'.format(subbase)].value.decode() # Create Tally object and assign basic properties tally = openmc.Tally(tally_id, tally_name) # Read score metadata - score_bins = self._f['{0}/score_bins'.format(subbase)][...] - for score_bin in score_bins: - tally.add_score(openmc.SCORE_TYPES[score_bin]) + scores = self._f['{0}/score_bins'.format(subbase)].value + for score in scores: + tally.add_score(score.decode()) num_score_bins = self._f['{0}/n_score_bins'.format(subbase)][...] tally.num_score_bins = num_score_bins @@ -560,12 +509,10 @@ class Summary(object): # Initialize all Filters for j in range(1, num_filters+1): - subsubbase = '{0}/filter {1}'.format(subbase, j) # Read filter type (e.g., "cell", "energy", etc.) - filter_type_code = self._f['{0}/type'.format(subsubbase)].value - filter_type = openmc.FILTER_TYPES[filter_type_code] + filter_type = self._f['{0}/type'.format(subsubbase)].value.decode() # Read the filter bins num_bins = self._f['{0}/n_bins'.format(subsubbase)].value @@ -597,29 +544,6 @@ class Summary(object): if self.opencg_geometry is None: self.opencg_geometry = get_opencg_geometry(self.openmc_geometry) - def get_nuclide_by_zaid(self, zaid): - """Return a Nuclide object given the 'zaid' identifier for the nuclide. - - Parameters - ---------- - zaid : int - 1000*Z + A, where Z is the atomic number of the nuclide and A is the - mass number. For example, the zaid for U-235 is 92235. - - Returns - ------- - nuclide : openmc.nuclide.Nuclide or None - Nuclide matching the specified zaid, or None if no matching object - is found. - - """ - - for index, nuclide in self.nuclides.items(): - if nuclide._zaid == zaid: - return nuclide - - return None - def get_material_by_id(self, material_id): """Return a Material object given the material id diff --git a/openmc/surface.py b/openmc/surface.py index d5d258fa7..653754d30 100644 --- a/openmc/surface.py +++ b/openmc/surface.py @@ -4,7 +4,6 @@ from xml.etree import ElementTree as ET import sys from openmc.checkvalue import check_type, check_value, check_greater_than -from openmc.constants import BC_TYPES if sys.version_info[0] >= 3: basestring = str @@ -12,6 +11,8 @@ if sys.version_info[0] >= 3: # A static variable for auto-generated Surface IDs AUTO_SURFACE_ID = 10000 +_BC_TYPES = ['transmission', 'vacuum', 'reflective', 'periodic'] + def reset_auto_surface_id(): global AUTO_SURFACE_ID @@ -106,7 +107,7 @@ class Surface(object): @boundary_type.setter def boundary_type(self, boundary_type): check_type('boundary type', boundary_type, basestring) - check_value('boundary type', boundary_type, BC_TYPES.values()) + check_value('boundary type', boundary_type, _BC_TYPES) self._boundary_type = boundary_type def __repr__(self): @@ -134,7 +135,7 @@ class Surface(object): element.set("type", self._type) element.set("boundary", self._boundary_type) - element.set("coeffs", ' '.join([str(self._coeffs[key]) + element.set("coeffs", ' '.join([str(self._coeffs.setdefault(key, 0.0)) for key in self._coeff_keys])) return element diff --git a/openmc/tallies.py b/openmc/tallies.py index a62043132..3a2f77679 100644 --- a/openmc/tallies.py +++ b/openmc/tallies.py @@ -52,7 +52,7 @@ class Tally(object): List of nuclides to score results for scores : list of str List of defined scores, e.g. 'flux', 'fission', etc. - estimator : {'analog', 'tracklength'} + estimator : {'analog', 'tracklength', 'collision'} Type of estimator for the tally triggers : list of openmc.trigger.Trigger List of tally triggers @@ -103,6 +103,9 @@ class Tally(object): self._with_batch_statistics = False self._derived = False + self._statepoint = None + self._results_read = False + def __deepcopy__(self, memo): existing = memo.get(id(self)) @@ -121,6 +124,8 @@ class Tally(object): clone._with_summary = self.with_summary clone._with_batch_statistics = self.with_batch_statistics clone._derived = self.derived + clone._statepoint = self._statepoint + clone._results_read = self._results_read clone._filters = [] for filter in self.filters: @@ -265,24 +270,69 @@ class Tally(object): @property def sum(self): + if not self._statepoint: + return None + + if not self._results_read: + # Extract Tally data from the file + data = self._statepoint._f['tallies/tally {0}/results'.format( + self.id)].value + sum = data['sum'] + sum_sq = data['sum_sq'] + + # Define a routine to convert 0 to 1 + def nonzero(val): + return 1 if not val else val + + # Reshape the results arrays + new_shape = (nonzero(self.num_filter_bins), + nonzero(self.num_nuclides), + nonzero(self.num_score_bins)) + + sum = np.reshape(sum, new_shape) + sum_sq = np.reshape(sum_sq, new_shape) + + # Set the data for this Tally + self._sum = sum + self._sum_sq = sum_sq + + # Indicate that Tally results have been read + self._results_read = True + return self._sum @property def sum_sq(self): + if not self._statepoint: + return None + + if not self._results_read: + # Force reading of sum and sum_sq + self.sum + return self._sum_sq @property def mean(self): - # Compute the mean if needed if self._mean is None: - self.compute_mean() + if not self._statepoint: + return None + + self._mean = self.sum / self.num_realizations return self._mean @property def std_dev(self): - # Compute the standard deviation if needed if self._std_dev is None: - self.compute_std_dev() + if not self._statepoint: + return None + + n = self.num_realizations + nonzero = np.abs(self.mean) > 0 + self._std_dev = np.zeros_like(self.mean) + self._std_dev[nonzero] = np.sqrt((self.sum_sq[nonzero]/n - + self.mean[nonzero]**2)/(n - 1)) + self.with_batch_statistics = True return self._std_dev @property @@ -295,7 +345,8 @@ class Tally(object): @estimator.setter def estimator(self, estimator): - cv.check_value('estimator', estimator, ['analog', 'tracklength']) + cv.check_value('estimator', estimator, + ['analog', 'tracklength', 'collision']) self._estimator = estimator def add_trigger(self, trigger): @@ -462,30 +513,6 @@ class Tally(object): self._nuclides.remove(nuclide) - def compute_mean(self): - """Compute the sample mean for each bin in the tally""" - - # Calculate sample mean - self._mean = self.sum / self.num_realizations - - def compute_std_dev(self, t_value=1.0): - """Compute the sample standard deviation for each bin in the tally - - Parameters - ---------- - t_value : float, optional - Student's t-value applied to the uncertainty. Defaults to 1.0, - meaning the reported value is the sample standard deviation. - - """ - - # Calculate sample standard deviation - self.compute_mean() - self._std_dev = np.sqrt((self.sum_sq / self.num_realizations - - self.mean**2) / (self.num_realizations - 1)) - self._std_dev *= t_value - self.with_batch_statistics = True - def __repr__(self): string = 'Tally\n' string += '{0: <16}{1}{2}\n'.format('\tID', '=\t', self.id) diff --git a/scripts/openmc-plot-mesh-tally b/scripts/openmc-plot-mesh-tally index 1559ea328..04f1f06c6 100755 --- a/scripts/openmc-plot-mesh-tally +++ b/scripts/openmc-plot-mesh-tally @@ -273,8 +273,6 @@ class MeshPlotter(tk.Frame): def get_file_data(self, filename): # Create StatePoint object and read in data self.datafile = StatePoint(filename) - self.datafile.read_results() - self.datafile.compute_stdev() # Find which tallies are mesh tallies self.meshTallies = [] diff --git a/scripts/openmc-statepoint-histogram b/scripts/openmc-statepoint-histogram deleted file mode 100755 index 26e8bdae6..000000000 --- a/scripts/openmc-statepoint-histogram +++ /dev/null @@ -1,43 +0,0 @@ -#!/usr/bin/env python - -from __future__ import print_function -from sys import argv -from math import sqrt - -import numpy as np -import scipy.stats -import matplotlib.pyplot as plt - -from openmc.statepoint import StatePoint - -# Get filename -filename = argv[1] - -# Create StatePoint object -sp = StatePoint(filename) -sp.read_results() -sp.compute_ci() - -# Check if tallies are present -if not sp.tallies_present: - raise Exception("No tally data in state point!") - -# Loop over all tallies -for i, t in sp.tallies.items(): - # Determine relative error and fraction of bins with less than 1% half-width - # of CI - n_bins = t.mean.size - relative_error = t.std_dev[t.mean > 0.] / t.mean[t.mean > 0.] - fraction = float(sum(relative_error < 0.01))/n_bins - - # Display results - print("Tally " + str(i)) - print(" Fraction under 1% = {0}".format(fraction)) - print(" Min relative error = {0}".format(min(relative_error))) - print(" Max relative error = {0}".format(max(relative_error))) - print(" Non-scoring bins = {0}".format( - 1.0 - float(relative_error.size)/n_bins)) - - # Plot histogram - plt.hist(relative_error, 100) - plt.show() diff --git a/scripts/openmc-voxel-to-silovtk b/scripts/openmc-voxel-to-silovtk index eb052c75f..1329b6b6c 100755 --- a/scripts/openmc-voxel-to-silovtk +++ b/scripts/openmc-voxel-to-silovtk @@ -1,9 +1,11 @@ -#!/usr/bin/env python2 +#!/usr/bin/env python from __future__ import division, print_function import struct import sys +import numpy as np +import h5py def parse_options(): """Process command line arguments""" @@ -22,14 +24,16 @@ def parse_options(): return parsed -def main(file_, o): - print(file_) - fh = open(file_, 'rb') - header = get_header(fh) - meshparms = (header['dimension'] + header['lower_left'] + - header['upper_right']) - nx, ny, nz = meshparms[:3] - ll = header['lower_left'] +def main(filename, o): + # Read data from voxel file + fh = h5py.File(filename, 'r') + dimension = fh['num_voxels'].value + width = fh['voxel_width'].value + lower_left = fh['lower_left'].value + voxel_data = fh['data'].value + + nx, ny, nz = dimension + upper_right = lower_left + width*dimension if o.vtk: try: @@ -40,13 +44,10 @@ def main(file_, o): 'See: http://www.vtk.org/') return - origin = [(l + w*n/2.) for n, l, w in - zip((nx, ny, nz), ll, header['width'])] - grid = vtk.vtkImageData() grid.SetDimensions(nx+1, ny+1, nz+1) - grid.SetOrigin(*ll) - grid.SetSpacing(*header['width']) + grid.SetOrigin(*lower_left) + grid.SetSpacing(*width) data = vtk.vtkDoubleArray() data.SetName("id") @@ -57,8 +58,7 @@ def main(file_, o): for y in range(ny): for z in range(nz): i = z*nx*ny + y*nx + x - id_ = get_int(fh)[0] - data.SetValue(i, id_) + data.SetValue(i, voxel_data[x,y,z]) grid.GetCellData().AddArray(data) writer = vtk.vtkXMLImageDataWriter() @@ -81,44 +81,23 @@ def main(file_, o): if not o.output.endswith(".silo"): o.output += ".silo" silomesh.init_silo(o.output) - silomesh.init_mesh('plot', *meshparms) + meshparams = list(map(int, dimension)) + list(map(float, lower_left)) + \ + list(map(float, upper_right)) + silomesh.init_mesh('plot', *meshparams) silomesh.init_var("id") - for x in range(1, nx+1): + for x in range(nx): sys.stdout.write(" {0}%\r".format(int(x/nx*100))) sys.stdout.flush() - for y in range(1, ny+1): - for z in range(1, nz+1): - id_ = get_int(fh)[0] - silomesh.set_value(float(id_), x, y, z) + for y in range(ny): + for z in range(nz): + silomesh.set_value(float(voxel_data[x,y,z]), + x + 1, y + 1, z + 1) print() silomesh.finalize_var() silomesh.finalize_mesh() silomesh.finalize_silo() -def get_header(file_): - nx, ny, nz = get_int(file_, 3) - wx, wy, wz = get_double(file_, 3) - lx, ly, lz = get_double(file_, 3) - header = {'dimension': [nx, ny, nz], 'width': [wx, wy, wz], - 'lower_left': [lx, ly, lz], - 'upper_right': [lx+wx*nx, ly+wy*ny, lz+wz*nz]} - return header - - -def get_data(file_, n, typeCode, size): - return list(struct.unpack('={0}{1}'.format(n, typeCode), - file_.read(n*size))) - - -def get_int(file_, n=1, path=None): - return get_data(file_, n, 'i', 4) - - -def get_double(file_, n=1, path=None): - return get_data(file_, n, 'd', 8) - - if __name__ == '__main__': (options, args) = parse_options() if args: diff --git a/setup.py b/setup.py index 39fe67c22..0c3d5c116 100644 --- a/setup.py +++ b/setup.py @@ -32,7 +32,7 @@ kwargs = {'name': 'openmc', if have_setuptools: kwargs.update({ # Required dependencies - 'install_requires': ['numpy', 'scipy', 'h5py', 'matplotlib'], + 'install_requires': ['numpy', 'h5py', 'matplotlib'], # Optional dependencies 'extras_require': { diff --git a/src/ace.F90 b/src/ace.F90 index 0dd92ea2c..cccc57092 100644 --- a/src/ace.F90 +++ b/src/ace.F90 @@ -234,7 +234,7 @@ contains integer :: location ! location of ACE table integer :: entries ! number of entries on each record integer :: length ! length of ACE table - integer :: in = 7 ! file unit + integer :: unit_ace ! file unit integer :: zaids(16) ! list of ZAIDs (only used for S(a,b)) integer :: filetype ! filetype (ASCII or BINARY) real(8) :: kT ! temperature of table @@ -277,14 +277,14 @@ contains ! READ ACE TABLE IN ASCII FORMAT ! Find location of table - open(UNIT=in, FILE=filename, STATUS='old', ACTION='read') - rewind(UNIT=in) + open(NEWUNIT=unit_ace, FILE=filename, STATUS='old', ACTION='read') + rewind(UNIT=unit_ace) do i = 1, location - 1 - read(UNIT=in, FMT=*) + read(UNIT=unit_ace, FMT=*) end do ! Read first line of header - read(UNIT=in, FMT='(A10,2G12.0,1X,A10)') name, awr, kT, date_ + read(UNIT=unit_ace, FMT='(A10,2G12.0,1X,A10)') name, awr, kT, date_ ! Check that correct xs was found -- if cross_sections.xml is broken, the ! location of the table may be wrong @@ -294,7 +294,7 @@ contains end if ! Read more header and NXS and JXS - read(UNIT=in, FMT=100) comment, mat, & + read(UNIT=unit_ace, FMT=100) comment, mat, & (zaids(i), awrs(i), i=1,16), NXS, JXS 100 format(A70,A10/4(I7,F11.0)/4(I7,F11.0)/4(I7,F11.0)/4(I7,F11.0)/& ,8I9/8I9/8I9/8I9/8I9/8I9) @@ -304,21 +304,21 @@ contains allocate(XSS(length)) ! Read XSS array - read(UNIT=in, FMT='(4G20.0)') XSS + read(UNIT=unit_ace, FMT='(4G20.0)') XSS ! Close ACE file - close(UNIT=in) + close(UNIT=unit_ace) elseif (filetype == BINARY) then ! ======================================================================= ! READ ACE TABLE IN BINARY FORMAT ! Open ACE file - open(UNIT=in, FILE=filename, STATUS='old', ACTION='read', & + open(NEWUNIT=unit_ace, FILE=filename, STATUS='old', ACTION='read', & ACCESS='direct', RECL=record_length) ! Read all header information - read(UNIT=in, REC=location) name, awr, kT, date_, & + read(UNIT=unit_ace, REC=location) name, awr, kT, date_, & comment, mat, (zaids(i), awrs(i), i=1,16), NXS, JXS ! determine table length @@ -329,11 +329,11 @@ contains do i = 1, (length + entries - 1)/entries j1 = 1 + (i-1)*entries j2 = min(length, j1 + entries - 1) - read(UNIT=IN, REC=location + i) (XSS(j), j=j1,j2) + read(UNIT=UNIT_ACE, REC=location + i) (XSS(j), j=j1,j2) end do ! Close ACE file - close(UNIT=in) + close(UNIT=unit_ace) end if ! ========================================================================== diff --git a/src/cmfd_data.F90 b/src/cmfd_data.F90 index 2b1778419..19fe39572 100644 --- a/src/cmfd_data.F90 +++ b/src/cmfd_data.F90 @@ -57,7 +57,7 @@ contains use global, only: cmfd, n_cmfd_tallies, cmfd_tallies, meshes,& matching_bins use mesh, only: mesh_indices_to_bin - use mesh_header, only: StructuredMesh + use mesh_header, only: RegularMesh use string, only: to_str use tally_header, only: TallyObject @@ -79,8 +79,8 @@ contains integer :: i_filter_eout ! index for outgoing energy filter integer :: i_filter_surf ! index for surface filter real(8) :: flux ! temp variable for flux - type(TallyObject), pointer :: t => null() ! pointer for tally object - type(StructuredMesh), pointer :: m => null() ! pointer for mesh object + type(TallyObject), pointer :: t ! pointer for tally object + type(RegularMesh), pointer :: m ! pointer for mesh object ! Extract spatial and energy indices from object nx = cmfd % indices(1) diff --git a/src/cmfd_execute.F90 b/src/cmfd_execute.F90 index c55d206cb..4dfe99d77 100644 --- a/src/cmfd_execute.F90 +++ b/src/cmfd_execute.F90 @@ -217,7 +217,7 @@ contains use error, only: warning, fatal_error use global, only: meshes, source_bank, work, n_user_meshes, cmfd, & master - use mesh_header, only: StructuredMesh + use mesh_header, only: RegularMesh use mesh, only: count_bank_sites, get_mesh_indices use search, only: binary_search use string, only: to_str @@ -239,8 +239,7 @@ contains integer :: n_groups ! number of energy groups logical :: outside ! any source sites outside mesh logical :: in_mesh ! source site is inside mesh - - type(StructuredMesh), pointer :: m ! point to mesh + type(RegularMesh), pointer :: m ! point to mesh ! Associate pointer m => meshes(n_user_meshes + 1) diff --git a/src/cmfd_input.F90 b/src/cmfd_input.F90 index 2d44d3e9b..dac74c9c3 100644 --- a/src/cmfd_input.F90 +++ b/src/cmfd_input.F90 @@ -247,7 +247,7 @@ contains use constants, only: MAX_LINE_LEN use error, only: fatal_error, warning - use mesh_header, only: StructuredMesh + use mesh_header, only: RegularMesh use string use tally, only: setup_active_cmfdtallies use tally_header, only: TallyObject, TallyFilter @@ -264,10 +264,10 @@ contains integer :: i_filter_mesh ! index for mesh filter integer :: iarray3(3) ! temp integer array real(8) :: rarray3(3) ! temp double array - type(TallyObject), pointer :: t => null() - type(StructuredMesh), pointer :: m => null() + type(TallyObject), pointer :: t + type(RegularMesh), pointer :: m type(TallyFilter) :: filters(N_FILTER_TYPES) ! temporary filters - type(Node), pointer :: node_mesh => null() + type(Node), pointer :: node_mesh ! Set global variables if they are 0 (this can happen if there is no tally ! file) diff --git a/src/constants.F90 b/src/constants.F90 index 9a89542af..53891cdaa 100644 --- a/src/constants.F90 +++ b/src/constants.F90 @@ -11,16 +11,10 @@ module constants integer, parameter :: VERSION_RELEASE = 0 ! Revision numbers for binary files - integer, parameter :: REVISION_STATEPOINT = 13 + integer, parameter :: REVISION_STATEPOINT = 14 integer, parameter :: REVISION_PARTICLE_RESTART = 1 integer, parameter :: REVISION_TRACK = 1 - - ! Binary file types - integer, parameter :: & - FILETYPE_STATEPOINT = -1, & - FILETYPE_PARTICLE_RESTART = -2, & - FILETYPE_SOURCE = -3, & - FILETYPE_TRACK = -4 + integer, parameter :: REVISION_SUMMARY = 1 ! ============================================================================ ! ADJUSTABLE PARAMETERS @@ -251,7 +245,8 @@ module constants ! Tally estimator types integer, parameter :: & ESTIMATOR_ANALOG = 1, & - ESTIMATOR_TRACKLENGTH = 2 + ESTIMATOR_TRACKLENGTH = 2, & + ESTIMATOR_COLLISION = 3 ! Event types for tallies integer, parameter :: & @@ -317,6 +312,10 @@ module constants FILTER_ENERGYOUT = 8, & FILTER_DISTRIBCELL = 9 + ! Mesh types + integer, parameter :: & + MESH_REGULAR = 1 + ! Tally surface current directions integer, parameter :: & IN_RIGHT = 1, & @@ -332,7 +331,7 @@ module constants RELATIVE_ERROR = 2, & STANDARD_DEVIATION = 3 - ! Global tallY parameters + ! Global tally parameters integer, parameter :: N_GLOBAL_TALLIES = 4 integer, parameter :: & K_COLLISION = 1, & @@ -394,14 +393,6 @@ module constants MODE_PLOTTING = 3, & ! Plotting mode MODE_PARTICLE = 4 ! Particle restart mode - ! Unit numbers - integer, parameter :: UNIT_SUMMARY = 11 ! unit # for writing summary file - integer, parameter :: UNIT_TALLY = 12 ! unit # for writing tally file - integer, parameter :: UNIT_PLOT = 13 ! unit # for writing plot file - integer, parameter :: UNIT_XS = 14 ! unit # for writing xs summary file - integer, parameter :: UNIT_PARTICLE = 15 ! unit # for writing particle restart - integer, parameter :: UNIT_OUTPUT = 16 ! unit # for writing output - !============================================================================= ! CMFD CONSTANTS diff --git a/src/eigenvalue.F90 b/src/eigenvalue.F90 index 33d63b7cc..403347caa 100644 --- a/src/eigenvalue.F90 +++ b/src/eigenvalue.F90 @@ -9,7 +9,7 @@ module eigenvalue use global use math, only: t_percentile use mesh, only: count_bank_sites - use mesh_header, only: StructuredMesh + use mesh_header, only: RegularMesh use particle_header, only: Particle use random_lcg, only: prn, set_particle_seed, prn_skip use search, only: binary_search @@ -304,7 +304,7 @@ contains integer :: i, j, k ! index for bank sites integer :: n ! # of boxes in each dimension logical :: sites_outside ! were there sites outside entropy box? - type(StructuredMesh), pointer :: m => null() + type(RegularMesh), pointer :: m ! Get pointer to entropy mesh m => entropy_mesh diff --git a/src/global.F90 b/src/global.F90 index 0c5e38212..398b72e14 100644 --- a/src/global.F90 +++ b/src/global.F90 @@ -8,7 +8,7 @@ module global use dict_header, only: DictCharInt, DictIntInt use geometry_header, only: Cell, Universe, Lattice, LatticeContainer, Surface use material_header, only: Material - use mesh_header, only: StructuredMesh + use mesh_header, only: RegularMesh use plot_header, only: ObjectPlot use set_header, only: SetInt use source_header, only: ExtSource @@ -90,7 +90,7 @@ module global ! ============================================================================ ! TALLY-RELATED VARIABLES - type(StructuredMesh), allocatable, target :: meshes(:) + type(RegularMesh), allocatable, target :: meshes(:) type(TallyObject), allocatable, target :: tallies(:) integer, allocatable :: matching_bins(:) @@ -106,9 +106,11 @@ module global type(SetInt) :: active_analog_tallies type(SetInt) :: active_tracklength_tallies type(SetInt) :: active_current_tallies + type(SetInt) :: active_collision_tallies type(SetInt) :: active_tallies !$omp threadprivate(active_analog_tallies, active_tracklength_tallies, & -!$omp& active_current_tallies, active_tallies) +!$omp& active_current_tallies, active_collision_tallies, & +!$omp& active_tallies) ! Global tallies ! 1) collision estimate of k-eff @@ -201,11 +203,11 @@ module global logical :: entropy_on = .false. real(8), allocatable :: entropy(:) ! shannon entropy at each generation real(8), allocatable :: entropy_p(:,:,:,:) ! % of source sites in each cell - type(StructuredMesh), pointer :: entropy_mesh + type(RegularMesh), pointer :: entropy_mesh ! Uniform fission source weighting logical :: ufs = .false. - type(StructuredMesh), pointer :: ufs_mesh => null() + type(RegularMesh), pointer :: ufs_mesh => null() real(8), allocatable :: source_frac(:,:,:,:) ! Write source at end of simulation @@ -487,6 +489,7 @@ contains call active_analog_tallies % clear() call active_tracklength_tallies % clear() call active_current_tallies % clear() + call active_collision_tallies % clear() call active_tallies % clear() ! Deallocate track_identifiers diff --git a/src/hdf5_interface.F90 b/src/hdf5_interface.F90 index 5e0734648..656039d19 100644 --- a/src/hdf5_interface.F90 +++ b/src/hdf5_interface.F90 @@ -41,6 +41,7 @@ module hdf5_interface module procedure write_integer_4D module procedure write_long module procedure write_string + module procedure write_string_1D module procedure write_tally_result_1D module procedure write_tally_result_2D end interface write_dataset @@ -58,6 +59,7 @@ module hdf5_interface module procedure read_integer_4D module procedure read_long module procedure read_string + module procedure read_string_1D module procedure read_tally_result_1D module procedure read_tally_result_2D end interface read_dataset @@ -329,7 +331,7 @@ contains end subroutine read_double !=============================================================================== -! WRITE_DOUBLE_1DARRAY writes double precision 1-D array data +! WRITE_DOUBLE_1D writes double precision 1-D array data !=============================================================================== subroutine write_double_1D(group_id, name, buffer, indep) @@ -391,7 +393,7 @@ contains end subroutine write_double_1D_explicit !=============================================================================== -! READ_DOUBLE_1DARRAY reads double precision 1-D array data +! READ_DOUBLE_1D reads double precision 1-D array data !=============================================================================== subroutine read_double_1D(group_id, name, buffer, indep) @@ -449,7 +451,7 @@ contains end subroutine read_double_1D_explicit !=============================================================================== -! WRITE_DOUBLE_2DARRAY writes double precision 2-D array data +! WRITE_DOUBLE_2D writes double precision 2-D array data !=============================================================================== subroutine write_double_2D(group_id, name, buffer, indep) @@ -511,7 +513,7 @@ contains end subroutine write_double_2D_explicit !=============================================================================== -! READ_DOUBLE_2DARRAY reads double precision 2-D array data +! READ_DOUBLE_2D reads double precision 2-D array data !=============================================================================== subroutine read_double_2D(group_id, name, buffer, indep) @@ -569,7 +571,7 @@ contains end subroutine read_double_2D_explicit !=============================================================================== -! WRITE_DOUBLE_3DARRAY writes double precision 3-D array data +! WRITE_DOUBLE_3D writes double precision 3-D array data !=============================================================================== subroutine write_double_3D(group_id, name, buffer, indep) @@ -631,7 +633,7 @@ contains end subroutine write_double_3D_explicit !=============================================================================== -! READ_DOUBLE_3DARRAY reads double precision 3-D array data +! READ_DOUBLE_3D reads double precision 3-D array data !=============================================================================== subroutine read_double_3D(group_id, name, buffer, indep) @@ -689,7 +691,7 @@ contains end subroutine read_double_3D_explicit !=============================================================================== -! WRITE_DOUBLE_4DARRAY writes double precision 4-D array data +! WRITE_DOUBLE_4D writes double precision 4-D array data !=============================================================================== subroutine write_double_4D(group_id, name, buffer, indep) @@ -751,7 +753,7 @@ contains end subroutine write_double_4D_explicit !=============================================================================== -! READ_DOUBLE_4DARRAY reads double precision 4-D array data +! READ_DOUBLE_4D reads double precision 4-D array data !=============================================================================== subroutine read_double_4D(group_id, name, buffer, indep) @@ -896,7 +898,7 @@ contains end subroutine read_integer !=============================================================================== -! WRITE_INTEGER_1DARRAY writes integer precision 1-D array data +! WRITE_INTEGER_1D writes integer precision 1-D array data !=============================================================================== subroutine write_integer_1D(group_id, name, buffer, indep) @@ -958,7 +960,7 @@ contains end subroutine write_integer_1D_explicit !=============================================================================== -! READ_INTEGER_1DARRAY reads integer precision 1-D array data +! READ_INTEGER_1D reads integer precision 1-D array data !=============================================================================== subroutine read_integer_1D(group_id, name, buffer, indep) @@ -1016,7 +1018,7 @@ contains end subroutine read_integer_1D_explicit !=============================================================================== -! WRITE_INTEGER_2DARRAY writes integer precision 2-D array data +! WRITE_INTEGER_2D writes integer precision 2-D array data !=============================================================================== subroutine write_integer_2D(group_id, name, buffer, indep) @@ -1078,7 +1080,7 @@ contains end subroutine write_integer_2D_explicit !=============================================================================== -! READ_INTEGER_2DARRAY reads integer precision 2-D array data +! READ_INTEGER_2D reads integer precision 2-D array data !=============================================================================== subroutine read_integer_2D(group_id, name, buffer, indep) @@ -1136,7 +1138,7 @@ contains end subroutine read_integer_2D_explicit !=============================================================================== -! WRITE_INTEGER_3DARRAY writes integer precision 3-D array data +! WRITE_INTEGER_3D writes integer precision 3-D array data !=============================================================================== subroutine write_integer_3D(group_id, name, buffer, indep) @@ -1198,7 +1200,7 @@ contains end subroutine write_integer_3D_explicit !=============================================================================== -! READ_INTEGER_3DARRAY reads integer precision 3-D array data +! READ_INTEGER_3D reads integer precision 3-D array data !=============================================================================== subroutine read_integer_3D(group_id, name, buffer, indep) @@ -1256,7 +1258,7 @@ contains end subroutine read_integer_3D_explicit !=============================================================================== -! WRITE_INTEGER_4DARRAY writes integer precision 4-D array data +! WRITE_INTEGER_4D writes integer precision 4-D array data !=============================================================================== subroutine write_integer_4D(group_id, name, buffer, indep) @@ -1318,7 +1320,7 @@ contains end subroutine write_integer_4D_explicit !=============================================================================== -! READ_INTEGER_4DARRAY reads integer precision 4-D array data +! READ_INTEGER_4D reads integer precision 4-D array data !=============================================================================== subroutine read_integer_4D(group_id, name, buffer, indep) @@ -1469,10 +1471,9 @@ contains subroutine write_string(group_id, name, buffer, indep) integer(HID_T), intent(in) :: group_id character(*), intent(in) :: name ! name for data - character(*), intent(in) :: buffer ! read data to here + character(*), intent(in), target :: buffer ! read data to here logical, intent(in), optional :: indep ! independent I/O - integer :: n integer :: hdf5_err integer :: data_xfer_mode #ifdef PHDF5 @@ -1480,9 +1481,10 @@ contains #endif integer(HID_T) :: dset ! data set handle integer(HID_T) :: dspace ! data or file space handle - integer(HSIZE_T) :: dims1(1) - integer(HSIZE_T) :: dims2(2) - character(len=len_trim(buffer)), dimension(1) :: str_tmp + integer(HID_T) :: filetype + integer(HID_T) :: memtype + integer(HSIZE_T) :: n + type(c_ptr) :: f_ptr ! Set up collective vs. independent I/O data_xfer_mode = H5FD_MPIO_COLLECTIVE_F @@ -1490,37 +1492,37 @@ contains if (indep) data_xfer_mode = H5FD_MPIO_INDEPENDENT_F end if - ! Insert null character at end of string when writing - call h5tset_strpad_f(H5T_STRING, H5T_STR_NULLPAD_F, hdf5_err) - - ! Create the dataspace and dataset - dims1(1) = 1 - call h5screate_simple_f(1, dims1, dspace, hdf5_err) - call h5dcreate_f(group_id, trim(name), H5T_STRING, dspace, dset, hdf5_err) - - ! Set up dimesnions of string to write + ! Create datatype for HDF5 file based on C char n = len_trim(buffer) - dims2(:) = [n, 1] ! full array of strings to write - dims1(1) = n ! length of string + call h5tcopy_f(H5T_C_S1, filetype, hdf5_err) + call h5tset_size_f(filetype, n + 1, hdf5_err) - ! Copy over string buffer to a rank 1 array - str_tmp(1) = buffer + ! Create datatype in memory based on Fortran character + call h5tcopy_f(H5T_FORTRAN_S1, memtype, hdf5_err) + if (n > 0) call h5tset_size_f(memtype, n, hdf5_err) + + ! Create dataspace/dataset + call h5screate_f(H5S_SCALAR_F, dspace, hdf5_err) + call h5dcreate_f(group_id, trim(name), filetype, dspace, dset, hdf5_err) + + ! Get pointer to start of string + f_ptr = c_loc(buffer(1:1)) if (using_mpio_device(group_id)) then #ifdef PHDF5 call h5pcreate_f(H5P_DATASET_XFER_F, plist, hdf5_err) call h5pset_dxpl_mpio_f(plist, data_xfer_mode, hdf5_err) - call h5dwrite_vl_f(dset, H5T_STRING, str_tmp, dims2, dims1, hdf5_err, & - mem_space_id=dspace, xfer_prp=plist) + if (n > 0) call h5dwrite_f(dset, memtype, f_ptr, hdf5_err, xfer_prp=plist) call h5pclose_f(plist, hdf5_err) #endif else - call h5dwrite_vl_f(dset, H5T_STRING, str_tmp, dims2, dims1, hdf5_err, & - mem_space_id=dspace) + if (n > 0) call h5dwrite_f(dset, memtype, f_ptr, hdf5_err) end if call h5dclose_f(dset, hdf5_err) call h5sclose_f(dspace, hdf5_err) + call h5tclose_f(memtype, hdf5_err) + call h5tclose_f(filetype, hdf5_err) end subroutine write_string !=============================================================================== @@ -1529,11 +1531,10 @@ contains subroutine read_string(group_id, name, buffer, indep) integer(HID_T), intent(in) :: group_id - character(*), intent(in) :: name ! name for data - character(*), intent(inout) :: buffer ! read data to here - logical, intent(in), optional :: indep ! independent I/O + character(*), intent(in) :: name ! name for data + character(*), intent(inout), target :: buffer ! read data to here + logical, intent(in), optional :: indep ! independent I/O - integer :: n integer :: hdf5_err integer :: data_xfer_mode #ifdef PHDF5 @@ -1541,9 +1542,11 @@ contains #endif integer(HID_T) :: dset ! data set handle integer(HID_T) :: dspace ! data or file space handle - integer(HSIZE_T) :: dims1(1) - integer(HSIZE_T) :: dims2(2) - character(len=len_trim(buffer)), dimension(1) :: str_tmp + integer(HID_T) :: filetype + integer(HID_T) :: memtype + integer(HSIZE_T) :: size + integer(HSIZE_T) :: n + type(c_ptr) :: f_ptr ! Set up collective vs. independent I/O data_xfer_mode = H5FD_MPIO_COLLECTIVE_F @@ -1551,34 +1554,206 @@ contains if (indep) data_xfer_mode = H5FD_MPIO_INDEPENDENT_F end if - ! Set up dimesnions of string to write - n = len_trim(buffer) - dims2(:) = [n, 1] ! full array of strings to write - dims1(1) = n ! length of string - + ! Get dataset and dataspace call h5dopen_f(group_id, trim(name), dset, hdf5_err) call h5dget_space_f(dset, dspace, hdf5_err) + ! Make sure buffer is large enough + call h5dget_type_f(dset, filetype, hdf5_err) + call h5tget_size_f(filetype, size, hdf5_err) + if (size > len(buffer) + 1) then + call fatal_error("Character buffer is not long enough to & + &read HDF5 string.") + end if + + ! Get datatype in memory based on Fortran character + n = len(buffer) + call h5tcopy_f(H5T_FORTRAN_S1, memtype, hdf5_err) + call h5tset_size_f(memtype, n, hdf5_err) + + ! Get pointer to start of string + f_ptr = c_loc(buffer(1:1)) + if (using_mpio_device(group_id)) then #ifdef PHDF5 call h5pcreate_f(H5P_DATASET_XFER_F, plist, hdf5_err) call h5pset_dxpl_mpio_f(plist, data_xfer_mode, hdf5_err) - call h5dread_vl_f(dset, H5T_STRING, str_tmp, dims2, dims1, hdf5_err, & - mem_space_id=dspace, xfer_prp=plist) + call h5dread_f(dset, memtype, f_ptr, hdf5_err, mem_space_id=dspace, & + xfer_prp=plist) call h5pclose_f(plist, hdf5_err) #endif else - call h5dread_vl_f(dset, H5T_STRING, str_tmp, dims2, dims1, hdf5_err, & - mem_space_id=dspace) + call h5dread_f(dset, memtype, f_ptr, hdf5_err, mem_space_id=dspace) end if - ! Copy over buffer - buffer = str_tmp(1) - - ! Close dataset call h5dclose_f(dset, hdf5_err) + call h5sclose_f(dspace, hdf5_err) + call h5tclose_f(filetype, hdf5_err) + call h5tclose_f(memtype, hdf5_err) end subroutine read_string +!=============================================================================== +! WRITE_STRING_1D writes string 1-D array data +!=============================================================================== + + subroutine write_string_1D(group_id, name, buffer, indep) + integer(HID_T), intent(in) :: group_id + character(*), intent(in) :: name ! name for data + character(*), intent(in), target :: buffer(:) ! read data to here + logical, intent(in), optional :: indep ! independent I/O + + integer(HSIZE_T) :: dims(1) + + dims(:) = shape(buffer) + if (present(indep)) then + call write_string_1D_explicit(group_id, dims, name, buffer, indep) + else + call write_string_1D_explicit(group_id, dims, name, buffer) + end if + end subroutine write_string_1D + + subroutine write_string_1D_explicit(group_id, dims, name, buffer, indep) + integer(HID_T), intent(in) :: group_id + integer(HSIZE_T), intent(in) :: dims(1) + character(*), intent(in) :: name + character(*), intent(in), target :: buffer(dims(1)) + logical, intent(in), optional :: indep ! independent I/O + + integer :: hdf5_err + integer :: data_xfer_mode +#ifdef PHDF5 + integer(HID_T) :: plist ! property list +#endif + integer(HID_T) :: dset ! data set handle + integer(HID_T) :: dspace ! data or file space handle + integer(HID_T) :: filetype + integer(HID_T) :: memtype + integer(HSIZE_T) :: n + type(c_ptr) :: f_ptr + + ! Set up collective vs. independent I/O + data_xfer_mode = H5FD_MPIO_COLLECTIVE_F + if (present(indep)) then + if (indep) data_xfer_mode = H5FD_MPIO_INDEPENDENT_F + end if + + ! Create datatype for HDF5 file based on C char + n = maxval(len_trim(buffer)) + call h5tcopy_f(H5T_C_S1, filetype, hdf5_err) + call h5tset_size_f(filetype, n + 1, hdf5_err) + + ! Create datatype in memory based on Fortran character + call h5tcopy_f(H5T_FORTRAN_S1, memtype, hdf5_err) + call h5tset_size_f(memtype, int(len(buffer(1)), HSIZE_T), hdf5_err) + + ! Create dataspace/dataset + call h5screate_simple_f(1, dims, dspace, hdf5_err) + call h5dcreate_f(group_id, trim(name), filetype, dspace, dset, hdf5_err) + + ! Get pointer to start of string + f_ptr = c_loc(buffer(1)(1:1)) + + if (using_mpio_device(group_id)) then +#ifdef PHDF5 + call h5pcreate_f(H5P_DATASET_XFER_F, plist, hdf5_err) + call h5pset_dxpl_mpio_f(plist, data_xfer_mode, hdf5_err) + if (n > 0) call h5dwrite_f(dset, memtype, f_ptr, hdf5_err, xfer_prp=plist) + call h5pclose_f(plist, hdf5_err) +#endif + else + if (n > 0) call h5dwrite_f(dset, memtype, f_ptr, hdf5_err) + end if + + call h5dclose_f(dset, hdf5_err) + call h5sclose_f(dspace, hdf5_err) + call h5tclose_f(memtype, hdf5_err) + call h5tclose_f(filetype, hdf5_err) + end subroutine write_string_1D_explicit + +!=============================================================================== +! READ_STRING_1D reads string 1-D array data +!=============================================================================== + + subroutine read_string_1D(group_id, name, buffer, indep) + integer(HID_T), intent(in) :: group_id + character(*), intent(in) :: name + character(*), intent(inout), target :: buffer(:) + logical, intent(in), optional :: indep ! independent I/O + + integer(HSIZE_T) :: dims(1) + + dims(:) = shape(buffer) + if (present(indep)) then + call read_string_1D_explicit(group_id, dims, name, buffer, indep) + else + call read_string_1D_explicit(group_id, dims, name, buffer) + end if + end subroutine read_string_1D + + subroutine read_string_1D_explicit(group_id, dims, name, buffer, indep) + integer(HID_T), intent(in) :: group_id + integer(HSIZE_T), intent(in) :: dims(1) + character(*), intent(in) :: name + character(*), intent(inout), target :: buffer(dims(1)) + logical, intent(in), optional :: indep ! independent I/O + + integer :: hdf5_err + integer :: data_xfer_mode +#ifdef PHDF5 + integer(HID_T) :: plist ! property list +#endif + integer(HID_T) :: dset ! data set handle + integer(HID_T) :: dspace ! data or file space handle + integer(HID_T) :: filetype + integer(HID_T) :: memtype + integer(HSIZE_T) :: size + integer(HSIZE_T) :: n + type(c_ptr) :: f_ptr + + ! Set up collective vs. independent I/O + data_xfer_mode = H5FD_MPIO_COLLECTIVE_F + if (present(indep)) then + if (indep) data_xfer_mode = H5FD_MPIO_INDEPENDENT_F + end if + + ! Get dataset and dataspace + call h5dopen_f(group_id, trim(name), dset, hdf5_err) + call h5dget_space_f(dset, dspace, hdf5_err) + + ! Make sure buffer is large enough + call h5dget_type_f(dset, filetype, hdf5_err) + call h5tget_size_f(filetype, size, hdf5_err) + if (size > len(buffer(1)) + 1) then + call fatal_error("Character buffer is not long enough to & + &read HDF5 string array.") + end if + + ! Get datatype in memory based on Fortran character + n = len(buffer(1)) + call h5tcopy_f(H5T_FORTRAN_S1, memtype, hdf5_err) + call h5tset_size_f(memtype, n, hdf5_err) + + ! Get pointer to start of string + f_ptr = c_loc(buffer(1)(1:1)) + + if (using_mpio_device(group_id)) then +#ifdef PHDF5 + call h5pcreate_f(H5P_DATASET_XFER_F, plist, hdf5_err) + call h5pset_dxpl_mpio_f(plist, data_xfer_mode, hdf5_err) + call h5dread_f(dset, memtype, f_ptr, hdf5_err, mem_space_id=dspace, & + xfer_prp=plist) + call h5pclose_f(plist, hdf5_err) +#endif + else + call h5dread_f(dset, memtype, f_ptr, hdf5_err, mem_space_id=dspace) + end if + + call h5dclose_f(dset, hdf5_err) + call h5sclose_f(dspace, hdf5_err) + call h5tclose_f(filetype, hdf5_err) + call h5tclose_f(memtype, hdf5_err) + end subroutine read_string_1D_explicit + !=============================================================================== ! WRITE_ATTRIBUTE_STRING !=============================================================================== diff --git a/src/initialize.F90 b/src/initialize.F90 index 48985a718..86242c753 100644 --- a/src/initialize.F90 +++ b/src/initialize.F90 @@ -321,7 +321,7 @@ contains integer :: i ! loop index integer :: argc ! number of command line arguments integer :: last_flag ! index of last flag - integer :: filetype + character(MAX_WORD_LEN) :: filetype integer(HID_T) :: file_id character(MAX_WORD_LEN), allocatable :: argv(:) ! command line arguments @@ -366,10 +366,10 @@ contains ! Set path and flag for type of run select case (filetype) - case (FILETYPE_STATEPOINT) + case ('statepoint') path_state_point = argv(i) restart_run = .true. - case (FILETYPE_PARTICLE_RESTART) + case ('particle restart') path_particle_restart = argv(i) particle_restart_run = .true. case default @@ -389,7 +389,7 @@ contains file_id = file_open(argv(i), 'r', parallel=.true.) call read_dataset(file_id, 'filetype', filetype) call file_close(file_id) - if (filetype /= FILETYPE_SOURCE) then + if (filetype /= 'source') then call fatal_error("Second file after restart flag must be a & &source file") end if diff --git a/src/input_xml.F90 b/src/input_xml.F90 index 5c939a394..3899d43f0 100644 --- a/src/input_xml.F90 +++ b/src/input_xml.F90 @@ -8,7 +8,7 @@ module input_xml use geometry_header, only: Cell, Surface, Lattice, RectLattice, HexLattice use global use list_header, only: ListChar, ListReal - use mesh_header, only: StructuredMesh + use mesh_header, only: RegularMesh use output, only: write_message use plot_header use random_lcg, only: prn @@ -2091,9 +2091,9 @@ contains character(MAX_WORD_LEN) :: temp_str character(MAX_WORD_LEN), allocatable :: sarray(:) type(DictCharInt) :: trigger_scores - type(ElemKeyValueCI), pointer :: pair_list => null() - type(TallyObject), pointer :: t => null() - type(StructuredMesh), pointer :: m => null() + type(ElemKeyValueCI), pointer :: pair_list + type(TallyObject), pointer :: t + type(RegularMesh), pointer :: m type(TallyFilter), allocatable :: filters(:) ! temporary filters type(Node), pointer :: doc => null() type(Node), pointer :: node_mesh => null() @@ -2188,9 +2188,11 @@ contains call get_node_value(node_mesh, "type", temp_str) select case (to_lower(temp_str)) case ('rect', 'rectangle', 'rectangular') - m % type = LATTICE_RECT - case ('hex', 'hexagon', 'hexagonal') - m % type = LATTICE_HEX + call warning("Mesh type '" // trim(temp_str) // "' is deprecated. & + &Please use 'regular' instead.") + m % type = MESH_REGULAR + case ('regular') + m % type = MESH_REGULAR case default call fatal_error("Invalid mesh type: " // trim(temp_str)) end select @@ -2730,7 +2732,7 @@ contains t % moment_order(j : j + n_bins - 1) = n_order j = j + n_bins - 1 - case ('total') + case ('total', '(n,total)') t % score_bins(j) = SCORE_TOTAL if (t % find_filter(FILTER_ENERGYOUT) > 0) then call fatal_error("Cannot tally total reaction rate with an & @@ -2816,13 +2818,13 @@ contains ! Set tally estimator to analog t % estimator = ESTIMATOR_ANALOG - case ('n2n') + case ('n2n', '(n,2n)') t % score_bins(j) = N_2N - case ('n3n') + case ('n3n', '(n,3n)') t % score_bins(j) = N_3N - case ('n4n') + case ('n4n', '(n,4n)') t % score_bins(j) = N_4N case ('absorption') @@ -2903,6 +2905,79 @@ contains case ('events') t % score_bins(j) = SCORE_EVENTS + case ('elastic', '(n,elastic)') + t % score_bins(j) = ELASTIC + case ('(n,2nd)') + t % score_bins(j) = N_2ND + case ('(n,na)') + t % score_bins(j) = N_2NA + case ('(n,n3a)') + t % score_bins(j) = N_N3A + case ('(n,2na)') + t % score_bins(j) = N_2NA + case ('(n,3na)') + t % score_bins(j) = N_3NA + case ('(n,np)') + t % score_bins(j) = N_NP + case ('(n,n2a)') + t % score_bins(j) = N_N2A + case ('(n,2n2a)') + t % score_bins(j) = N_2N2A + case ('(n,nd)') + t % score_bins(j) = N_ND + case ('(n,nt)') + t % score_bins(j) = N_NT + case ('(n,nHe-3)') + t % score_bins(j) = N_N3HE + case ('(n,nd2a)') + t % score_bins(j) = N_ND2A + case ('(n,nt2a)') + t % score_bins(j) = N_NT2A + case ('(n,3nf)') + t % score_bins(j) = N_3NF + case ('(n,2np)') + t % score_bins(j) = N_2NP + case ('(n,3np)') + t % score_bins(j) = N_3NP + case ('(n,n2p)') + t % score_bins(j) = N_N2P + case ('(n,npa)') + t % score_bins(j) = N_NPA + case ('(n,n1)') + t % score_bins(j) = N_N1 + case ('(n,nc)') + t % score_bins(j) = N_NC + case ('(n,gamma)') + t % score_bins(j) = N_GAMMA + case ('(n,p)') + t % score_bins(j) = N_P + case ('(n,d)') + t % score_bins(j) = N_D + case ('(n,t)') + t % score_bins(j) = N_T + case ('(n,3He)') + t % score_bins(j) = N_3HE + case ('(n,a)') + t % score_bins(j) = N_A + case ('(n,2a)') + t % score_bins(j) = N_2A + case ('(n,3a)') + t % score_bins(j) = N_3A + case ('(n,2p)') + t % score_bins(j) = N_2P + case ('(n,pa)') + t % score_bins(j) = N_PA + case ('(n,t2a)') + t % score_bins(j) = N_T2A + case ('(n,d2a)') + t % score_bins(j) = N_D2A + case ('(n,pd)') + t % score_bins(j) = N_PD + case ('(n,pt)') + t % score_bins(j) = N_PT + case ('(n,da)') + t % score_bins(j) = N_DA + case default ! Assume that user has specified an MT number MT = int(str_to_int(score_name)) @@ -3132,15 +3207,26 @@ contains ! tally needs post-collision information if (t % estimator == ESTIMATOR_ANALOG) then call fatal_error("Cannot use track-length estimator for tally " & - &// to_str(t % id)) + // to_str(t % id)) end if ! Set estimator to track-length estimator t % estimator = ESTIMATOR_TRACKLENGTH + case ('collision') + ! If the estimator was set to an analog estimator, this means the + ! tally needs post-collision information + if (t % estimator == ESTIMATOR_ANALOG) then + call fatal_error("Cannot use collision estimator for tally " & + // to_str(t % id)) + end if + + ! Set estimator to collision estimator + t % estimator = ESTIMATOR_COLLISION + case default call fatal_error("Invalid estimator '" // trim(temp_str) & - &// "' on tally " // to_str(t % id)) + // "' on tally " // to_str(t % id)) end select end if diff --git a/src/mesh.F90 b/src/mesh.F90 index b9d85f439..3d0235d18 100644 --- a/src/mesh.F90 +++ b/src/mesh.F90 @@ -20,7 +20,7 @@ contains subroutine get_mesh_bin(m, xyz, bin) - type(StructuredMesh), pointer :: m ! mesh pointer + type(RegularMesh), pointer :: m ! mesh pointer real(8), intent(in) :: xyz(:) ! coordinates integer, intent(out) :: bin ! tally bin @@ -73,7 +73,7 @@ contains subroutine get_mesh_indices(m, xyz, ijk, in_mesh) - type(StructuredMesh), pointer :: m + type(RegularMesh), pointer :: m real(8), intent(in) :: xyz(:) ! coordinates to check integer, intent(out) :: ijk(:) ! indices in mesh logical, intent(out) :: in_mesh ! were given coords in mesh? @@ -98,7 +98,7 @@ contains function mesh_indices_to_bin(m, ijk, surface_current) result(bin) - type(StructuredMesh), pointer :: m + type(RegularMesh), pointer :: m integer, intent(in) :: ijk(:) logical, optional :: surface_current integer :: bin @@ -132,7 +132,7 @@ contains subroutine bin_to_mesh_indices(m, bin, ijk) - type(StructuredMesh), pointer :: m + type(RegularMesh), pointer :: m integer, intent(in) :: bin integer, intent(out) :: ijk(:) @@ -163,7 +163,7 @@ contains subroutine count_bank_sites(m, bank_array, cnt, energies, size_bank, & sites_outside) - type(StructuredMesh), pointer :: m ! mesh to count sites + type(RegularMesh), pointer :: m ! mesh to count sites type(Bank), intent(in) :: bank_array(:) ! fission or source bank real(8), intent(out) :: cnt(:,:,:,:) ! weight of sites in each ! cell and energy group @@ -264,7 +264,7 @@ contains function mesh_intersects_2d(m, xyz0, xyz1) result(intersects) - type(StructuredMesh), pointer :: m + type(RegularMesh), pointer :: m real(8), intent(in) :: xyz0(2) real(8), intent(in) :: xyz1(2) logical :: intersects @@ -330,7 +330,7 @@ contains function mesh_intersects_3d(m, xyz0, xyz1) result(intersects) - type(StructuredMesh), pointer :: m + type(RegularMesh), pointer :: m real(8), intent(in) :: xyz0(3) real(8), intent(in) :: xyz1(3) logical :: intersects diff --git a/src/mesh_header.F90 b/src/mesh_header.F90 index 9aa57df90..9da06f813 100644 --- a/src/mesh_header.F90 +++ b/src/mesh_header.F90 @@ -7,7 +7,7 @@ module mesh_header ! congruent squares or cubes !=============================================================================== - type StructuredMesh + type RegularMesh integer :: id ! user-specified id integer :: type ! rectangular, hexagonal integer :: n_dimension ! rank of mesh @@ -16,6 +16,6 @@ module mesh_header real(8), allocatable :: lower_left(:) ! lower-left corner of mesh real(8), allocatable :: upper_right(:) ! upper-right corner of mesh real(8), allocatable :: width(:) ! width of each mesh cell - end type StructuredMesh + end type RegularMesh end module mesh_header diff --git a/src/output.F90 b/src/output.F90 index 2ddbf6499..1a841a777 100644 --- a/src/output.F90 +++ b/src/output.F90 @@ -10,7 +10,7 @@ module output HexLattice, BASE_UNIVERSE use global use math, only: t_percentile - use mesh_header, only: StructuredMesh + use mesh_header, only: RegularMesh use mesh, only: mesh_indices_to_bin, bin_to_mesh_indices use particle_header, only: LocalCoord, Particle use plot_header @@ -315,694 +315,6 @@ contains end subroutine print_particle -!=============================================================================== -! PRINT_REACTION displays the attributes of a reaction -!=============================================================================== - - subroutine print_reaction(rxn) - - type(Reaction), pointer :: rxn - - write(ou,*) 'Reaction ' // reaction_name(rxn % MT) - write(ou,*) ' MT = ' // to_str(rxn % MT) - write(ou,*) ' Q-value = ' // to_str(rxn % Q_value) - write(ou,*) ' Multiplicity = ' // to_str(rxn % multiplicity) - write(ou,*) ' Threshold = ' // to_str(rxn % threshold) - if (rxn % has_energy_dist) then - write(ou,*) ' Energy: Law ' // to_str(rxn % edist % law) - end if - write(ou,*) - - end subroutine print_reaction - -!=============================================================================== -! PRINT_CELL displays the attributes of a cell -!=============================================================================== - - subroutine print_cell(c, unit) - - type(Cell), pointer :: c - integer, optional :: unit ! specified unit to write to - - integer :: index_cell ! index in cells array - integer :: i ! loop index for surfaces - integer :: index_surf ! index in surfaces array - integer :: unit_ ! unit to write to - character(MAX_LINE_LEN) :: string - type(Universe), pointer :: u => null() - class(Lattice), pointer :: l => null() - type(Material), pointer :: m => null() - - ! Set unit to stdout if not already set - if (present(unit)) then - unit_ = unit - else - unit_ = OUTPUT_UNIT - end if - - ! Write user-specified id for cell - write(unit_,*) 'Cell ' // to_str(c % id) - - ! Write user-specified name for cell - write(unit_,*) ' Name = ' // c % name - - ! Find index in cells array and write - index_cell = cell_dict % get_key(c % id) - write(unit_,*) ' Array Index = ' // to_str(index_cell) - - ! Write what universe this cell is in - u => universes(c % universe) - write(unit_,*) ' Universe = ' // to_str(u % id) - - ! Write information on fill for cell - select case (c % type) - case (CELL_NORMAL) - write(unit_,*) ' Fill = NONE' - case (CELL_FILL) - u => universes(c % fill) - write(unit_,*) ' Fill = Universe ' // to_str(u % id) - case (CELL_LATTICE) - l => lattices(c % fill) % obj - write(unit_,*) ' Fill = Lattice ' // to_str(l % id) - end select - - ! Write information on material - if (c % material == 0) then - write(unit_,*) ' Material = NONE' - elseif (c % material == MATERIAL_VOID) then - write(unit_,*) ' Material = Void' - else - m => materials(c % material) - write(unit_,*) ' Material = ' // to_str(m % id) - end if - - ! Write surface specification - string = "" - do i = 1, c % n_surfaces - select case (c % surfaces(i)) - case (OP_LEFT_PAREN) - string = trim(string) // ' (' - case (OP_RIGHT_PAREN) - string = trim(string) // ' )' - case (OP_UNION) - string = trim(string) // ' :' - case (OP_DIFFERENCE) - string = trim(string) // ' !' - case default - index_surf = abs(c % surfaces(i)) - string = trim(string) // ' ' // to_str(sign(& - surfaces(index_surf) % id, c % surfaces(i))) - end select - end do - write(unit_,*) ' Surface Specification:' // trim(string) - write(unit_,*) - - end subroutine print_cell - -!=============================================================================== -! PRINT_UNIVERSE displays the attributes of a universe -!=============================================================================== - - subroutine print_universe(univ, unit) - - type(Universe), pointer :: univ - integer, optional :: unit - - integer :: i ! loop index for cells in this universe - integer :: unit_ ! unit to write to - character(MAX_LINE_LEN) :: string - type(Cell), pointer :: c => null() - type(Universe), pointer :: base_u => null() - - ! Set default unit to stdout if not specified - if (present(unit)) then - unit_ = unit - else - unit_ = OUTPUT_UNIT - end if - - ! Get a pointer to the base universe - base_u => universes(BASE_UNIVERSE) - - ! Write user-specified id for this universe - write(unit_,*) 'Universe ' // to_str(univ % id) - - ! If this is the base universe, indicate so - if (associated(univ, base_u)) then - write(unit_,*) ' Base Universe' - end if - - ! Write list of cells in this universe - string = "" - do i = 1, univ % n_cells - c => cells(univ % cells(i)) - string = trim(string) // ' ' // to_str(c % id) - end do - write(unit_,*) ' Cells =' // trim(string) - write(unit_,*) - - end subroutine print_universe - -!=============================================================================== -! PRINT_LATTICE displays the attributes of a lattice -!=============================================================================== - - subroutine print_lattice(lat, unit) - - class(Lattice), pointer :: lat - integer, optional :: unit - - integer :: unit_ ! unit to write to - - ! set default unit if not specified - if (present(unit)) then - unit_ = unit - else - unit_ = OUTPUT_UNIT - end if - - ! Write information about lattice - write(unit_,*) 'Lattice ' // to_str(lat % id) - - ! Write user-specified name for lattice - write(unit_,*) ' Name = ' // lat % name - - select type(lat) - type is (RectLattice) - ! Write dimension of lattice. - if (lat % is_3d) then - write(unit_, *) ' Dimension = ' // to_str(lat % n_cells(1)) & - &// ' ' // to_str(lat % n_cells(2)) // ' ' & - &// to_str(lat % n_cells(3)) - else - write(unit_, *) ' Dimension = ' // to_str(lat % n_cells(1)) & - &// ' ' // to_str(lat % n_cells(2)) - end if - - ! Write lower-left coordinates of lattice. - if (lat % is_3d) then - write(unit_, *) ' Lower-left = ' // to_str(lat % lower_left(1)) & - &// ' ' // to_str(lat % lower_left(2)) // ' ' & - &// to_str(lat % lower_left(3)) - else - write(unit_, *) ' Lower-left = ' // to_str(lat % lower_left(1)) & - &// ' ' // to_str(lat % lower_left(2)) - end if - - ! Write lattice pitch along each axis. - if (lat % is_3d) then - write(unit_, *) ' Pitch = ' // to_str(lat % pitch(1)) & - &// ' ' // to_str(lat % pitch(2)) // ' ' & - &// to_str(lat % pitch(3)) - else - write(unit_, *) ' Pitch = ' // to_str(lat % pitch(1)) & - &// ' ' // to_str(lat % pitch(2)) - end if - write(unit_,*) - - type is (HexLattice) - ! Write dimension of lattice. - write(unit_,*) ' N-rings = ' // to_str(lat % n_rings) - if (lat % is_3d) write(unit_,*) ' N-axial = ' // to_str(lat % n_axial) - - ! Write center coordinates of lattice. - if (lat % is_3d) then - write(unit_, *) ' Center = ' // to_str(lat % center(1)) & - &// ' ' // to_str(lat % center(2)) // ' ' & - &// to_str(lat % center(3)) - else - write(unit_, *) ' Center = ' // to_str(lat % center(1)) & - &// ' ' // to_str(lat % center(2)) - end if - - ! Write lattice pitch along each axis. - if (lat % is_3d) then - write(unit_, *) ' Pitch = ' // to_str(lat % pitch(1)) & - &// ' ' // to_str(lat % pitch(2)) - else - write(unit_, *) ' Pitch = ' // to_str(lat % pitch(1)) - end if - write(unit_,*) - end select - - - end subroutine print_lattice - -!=============================================================================== -! PRINT_SURFACE displays the attributes of a surface -!=============================================================================== - - subroutine print_surface(surf, unit) - - type(Surface), pointer :: surf - integer, optional :: unit ! specified unit to write to - - integer :: i ! loop index for coefficients - integer :: unit_ ! unit to write to - character(MAX_LINE_LEN) :: string - type(Cell), pointer :: c => null() - - ! set default unit if not specified - if (present(unit)) then - unit_ = unit - else - unit_ = OUTPUT_UNIT - end if - - ! Write user-specified id of surface - write(unit_,*) 'Surface ' // to_str(surf % id) - - ! Write user-specified name for surface - write(unit_,*) ' Name = ' // surf % name - - ! Write type of surface - select case (surf % type) - case (SURF_PX) - string = "X Plane" - case (SURF_PY) - string = "Y Plane" - case (SURF_PZ) - string = "Z Plane" - case (SURF_PLANE) - string = "Plane" - case (SURF_CYL_X) - string = "X Cylinder" - case (SURF_CYL_Y) - string = "Y Cylinder" - case (SURF_CYL_Z) - string = "Z Cylinder" - case (SURF_SPHERE) - string = "Sphere" - case (SURF_CONE_X) - string = "X Cone" - case (SURF_CONE_Y) - string = "Y Cone" - case (SURF_CONE_Z) - string = "Z Cone" - end select - write(unit_,*) ' Type = ' // trim(string) - - ! Write coefficients for this surface - string = "" - do i = 1, size(surf % coeffs) - string = trim(string) // ' ' // to_str(surf % coeffs(i), 4) - end do - write(unit_,*) ' Coefficients = ' // trim(string) - - ! Write neighboring cells on positive side of this surface - string = "" - if (allocated(surf % neighbor_pos)) then - do i = 1, size(surf % neighbor_pos) - c => cells(abs(surf % neighbor_pos(i))) - string = trim(string) // ' ' // to_str(& - sign(c % id, surf % neighbor_pos(i))) - end do - end if - write(unit_,*) ' Positive Neighbors = ' // trim(string) - - ! Write neighboring cells on negative side of this surface - string = "" - if (allocated(surf % neighbor_neg)) then - do i = 1, size(surf % neighbor_neg) - c => cells(abs(surf % neighbor_neg(i))) - string = trim(string) // ' ' // to_str(& - sign(c % id, surf % neighbor_neg(i))) - end do - end if - write(unit_,*) ' Negative Neighbors =' // trim(string) - - ! Write boundary condition for this surface - select case (surf % bc) - case (BC_TRANSMIT) - write(unit_,*) ' Boundary Condition = Transmission' - case (BC_VACUUM) - write(unit_,*) ' Boundary Condition = Vacuum' - case (BC_REFLECT) - write(unit_,*) ' Boundary Condition = Reflective' - case (BC_PERIODIC) - write(unit_,*) ' Boundary Condition = Periodic' - end select - write(unit_,*) - - end subroutine print_surface - -!=============================================================================== -! PRINT_MATERIAL displays the attributes of a material -!=============================================================================== - - subroutine print_material(mat, unit) - - type(Material), pointer :: mat - integer, optional :: unit - - integer :: i ! loop index for nuclides - integer :: unit_ ! unit to write to - real(8) :: density ! density in atom/b-cm - character(MAX_LINE_LEN) :: string - type(Nuclide), pointer :: nuc => null() - - ! set default unit to stdout if not specified - if (present(unit)) then - unit_ = unit - else - unit_ = OUTPUT_UNIT - end if - - ! Write identifier for material - write(unit_,*) 'Material ' // to_str(mat % id) - - ! Write user-specified name for material - write(unit_,*) ' Name = ' // mat % name - - ! Write total atom density in atom/b-cm - write(unit_,*) ' Atom Density = ' // trim(to_str(mat % density)) & - // ' atom/b-cm' - - ! Write atom density for each nuclide in material - write(unit_,*) ' Nuclides:' - do i = 1, mat % n_nuclides - nuc => nuclides(mat % nuclide(i)) - density = mat % atom_density(i) - string = ' ' // trim(nuc % name) // ' = ' // & - trim(to_str(density)) // ' atom/b-cm' - write(unit_,*) trim(string) - end do - - ! Write information on S(a,b) table - if (mat % n_sab > 0) then - write(unit_,*) ' S(a,b) tables:' - do i = 1, mat % n_sab - write(unit_,*) ' ' // trim(& - sab_tables(mat % i_sab_tables(i)) % name) - end do - end if - write(unit_,*) - - end subroutine print_material - -!=============================================================================== -! PRINT_TALLY displays the attributes of a tally -!=============================================================================== - - subroutine print_tally(t, unit) - - type(TallyObject), pointer :: t - integer, optional :: unit - - integer :: i ! index for filter or score bins - integer :: j ! index in filters array - integer :: id ! user-specified id - integer :: unit_ ! unit to write to - integer :: n ! moment order to include in name - character(MAX_LINE_LEN) :: string - character(MAX_WORD_LEN) :: pn_string - type(Cell), pointer :: c => null() - type(Surface), pointer :: s => null() - type(Universe), pointer :: u => null() - type(Material), pointer :: m => null() - type(StructuredMesh), pointer :: sm => null() - - ! set default unit to stdout if not specified - if (present(unit)) then - unit_ = unit - else - unit_ = OUTPUT_UNIT - end if - - ! Write user-specified id of tally - write(unit_,*) 'Tally ' // to_str(t % id) - - ! Write the type of tally - select case(t % type) - case (TALLY_VOLUME) - write(unit_,*) ' Type: Volume' - case (TALLY_SURFACE_CURRENT) - write(unit_,*) ' Type: Surface Current' - end select - - ! Write the estimator used - select case(t % estimator) - case(ESTIMATOR_ANALOG) - write(unit_,*) ' Estimator: Analog' - case(ESTIMATOR_TRACKLENGTH) - write(unit_,*) ' Estimator: Track-length' - end select - - ! Write any cells bins if present - j = t % find_filter(FILTER_DISTRIBCELL) - if (j > 0) then - string = "" - id = t % filters(j) % int_bins(1) - c => cells(id) - string = trim(string) // ' ' // trim(to_str(c % id)) - write(unit_, *) ' Distribcell Bins:' // trim(string) - end if - - ! Write any cells bins if present - j = t % find_filter(FILTER_CELL) - if (j > 0) then - string = "" - do i = 1, t % filters(j) % n_bins - id = t % filters(j) % int_bins(i) - c => cells(id) - string = trim(string) // ' ' // trim(to_str(c % id)) - end do - write(unit_, *) ' Cell Bins:' // trim(string) - end if - - ! Write any surface bins if present - j = t % find_filter(FILTER_SURFACE) - if (j > 0) then - string = "" - do i = 1, t % filters(j) % n_bins - id = t % filters(j) % int_bins(i) - s => surfaces(id) - string = trim(string) // ' ' // trim(to_str(s % id)) - end do - write(unit_, *) ' Surface Bins:' // trim(string) - end if - - ! Write any universe bins if present - j = t % find_filter(FILTER_UNIVERSE) - if (j > 0) then - string = "" - do i = 1, t % filters(j) % n_bins - id = t % filters(j) % int_bins(i) - u => universes(id) - string = trim(string) // ' ' // trim(to_str(u % id)) - end do - write(unit_, *) ' Universe Bins:' // trim(string) - end if - - ! Write any material bins if present - j = t % find_filter(FILTER_MATERIAL) - if (j > 0) then - string = "" - do i = 1, t % filters(j) % n_bins - id = t % filters(j) % int_bins(i) - m => materials(id) - string = trim(string) // ' ' // trim(to_str(m % id)) - end do - write(unit_, *) ' Material Bins:' // trim(string) - end if - - ! Write any mesh bins if present - j = t % find_filter(FILTER_MESH) - if (j > 0) then - string = "" - id = t % filters(j) % int_bins(1) - sm => meshes(id) - string = trim(string) // ' ' // trim(to_str(sm % dimension(1))) - do i = 2, sm % n_dimension - string = trim(string) // ' x ' // trim(to_str(sm % dimension(i))) - end do - write(unit_, *) ' Mesh Bins:' // trim(string) - end if - - ! Write any birth region bins if present - j = t % find_filter(FILTER_CELLBORN) - if (j > 0) then - string = "" - do i = 1, t % filters(j) % n_bins - id = t % filters(j) % int_bins(i) - c => cells(id) - string = trim(string) // ' ' // trim(to_str(c % id)) - end do - write(unit_, *) ' Birth Region Bins:' // trim(string) - end if - - ! Write any incoming energy bins if present - j = t % find_filter(FILTER_ENERGYIN) - if (j > 0) then - string = "" - do i = 1, t % filters(j) % n_bins + 1 - string = trim(string) // ' ' // trim(to_str(& - t % filters(j) % real_bins(i))) - end do - write(unit_,*) ' Incoming Energy Bins:' // trim(string) - end if - - ! Write any outgoing energy bins if present - j = t % find_filter(FILTER_ENERGYOUT) - if (j > 0) then - string = "" - do i = 1, t % filters(j) % n_bins + 1 - string = trim(string) // ' ' // trim(to_str(& - t % filters(j) % real_bins(i))) - end do - write(unit_,*) ' Outgoing Energy Bins:' // trim(string) - end if - - ! Write nuclides bins - write(unit_,fmt='(1X,A)',advance='no') ' Nuclide Bins:' - do i = 1, t % n_nuclide_bins - if (t % nuclide_bins(i) == -1) then - write(unit_,fmt='(A)',advance='no') ' total' - else - write(unit_,fmt='(A)',advance='no') ' ' // trim(adjustl(& - nuclides(t % nuclide_bins(i)) % name)) - end if - if (mod(i,4) == 0 .and. i /= t % n_nuclide_bins) & - write(unit_,'(/18X)',advance='no') - end do - write(unit_,*) - - ! Write score bins - string = "" - j = 0 - do i = 1, t % n_user_score_bins - j = j + 1 - select case (t % score_bins(j)) - case (SCORE_FLUX) - string = trim(string) // ' flux' - case (SCORE_FLUX_YN) - pn_string = ' flux' - string = trim(string) // pn_string - do n = 1, t % moment_order(j) - pn_string = ' flux-y' // trim(to_str(n)) - string = trim(string) // pn_string - end do - j = j + n - 1 - case (SCORE_TOTAL) - string = trim(string) // ' total' - case (SCORE_TOTAL_YN) - pn_string = ' total' - string = trim(string) // pn_string - do n = 1, t % moment_order(j) - pn_string = ' total-y' // trim(to_str(n)) - string = trim(string) // pn_string - end do - j = j + n - 1 - case (SCORE_SCATTER) - string = trim(string) // ' scatter' - case (SCORE_NU_SCATTER) - string = trim(string) // ' nu-scatter' - case (SCORE_SCATTER_N) - pn_string = ' scatter-' // trim(to_str(t % moment_order(j))) - string = trim(string) // pn_string - case (SCORE_SCATTER_PN) - pn_string = ' scatter' - string = trim(string) // pn_string - do n = 1, t % moment_order(j) - pn_string = ' scatter-p' // trim(to_str(n)) - string = trim(string) // pn_string - end do - j = j + n - 1 - case (SCORE_NU_SCATTER_N) - pn_string = ' nu-scatter-' // trim(to_str(t % moment_order(j))) - string = trim(string) // pn_string - case (SCORE_NU_SCATTER_PN) - pn_string = ' nu-scatter' - string = trim(string) // pn_string - do n = 1, t % moment_order(j) - pn_string = ' nu-scatter-p' // trim(to_str(n)) - string = trim(string) // pn_string - end do - j = j + n - 1 - case (SCORE_SCATTER_YN) - pn_string = ' scatter' - string = trim(string) // pn_string - do n = 1, t % moment_order(j) - pn_string = ' scatter-y' // trim(to_str(n)) - string = trim(string) // pn_string - end do - j = j + n - 1 - case (SCORE_NU_SCATTER_YN) - pn_string = ' nu-scatter' - string = trim(string) // pn_string - do n = 1, t % moment_order(j) - pn_string = ' nu-scatter-y' // trim(to_str(n)) - string = trim(string) // pn_string - end do - j = j + n - 1 - case (SCORE_TRANSPORT) - string = trim(string) // ' transport' - case (SCORE_N_1N) - string = trim(string) // ' n1n' - case (SCORE_ABSORPTION) - string = trim(string) // ' absorption' - case (SCORE_FISSION) - string = trim(string) // ' fission' - case (SCORE_NU_FISSION) - string = trim(string) // ' nu-fission' - case (SCORE_KAPPA_FISSION) - string = trim(string) // ' kappa-fission' - case (SCORE_CURRENT) - string = trim(string) // ' current' - case default - string = trim(string) // ' ' // reaction_name(t % score_bins(j)) - end select - end do - write(unit_,*) ' Scores:' // trim(string) - write(unit_,*) - - end subroutine print_tally - -!=============================================================================== -! PRINT_GEOMETRY displays the attributes of all cells, surfaces, universes, -! surfaces, and lattices read in the input files. -!=============================================================================== - - subroutine print_geometry() - - integer :: i ! loop index for various arrays - type(Surface), pointer :: s => null() - type(Cell), pointer :: c => null() - type(Universe), pointer :: u => null() - class(Lattice), pointer :: l => null() - - ! print summary of surfaces - call header("SURFACE SUMMARY", unit=UNIT_SUMMARY) - do i = 1, n_surfaces - s => surfaces(i) - call print_surface(s, unit=UNIT_SUMMARY) - end do - - ! print summary of cells - call header("CELL SUMMARY", unit=UNIT_SUMMARY) - do i = 1, n_cells - c => cells(i) - call print_cell(c, unit=UNIT_SUMMARY) - end do - - ! print summary of universes - call header("UNIVERSE SUMMARY", unit=UNIT_SUMMARY) - do i = 1, n_universes - u => universes(i) - call print_universe(u, unit=UNIT_SUMMARY) - end do - - ! print summary of lattices - if (n_lattices > 0) then - call header("LATTICE SUMMARY", unit=UNIT_SUMMARY) - do i = 1, n_lattices - l => lattices(i) % obj - call print_lattice(l, unit=UNIT_SUMMARY) - end do - end if - - end subroutine print_geometry - !=============================================================================== ! PRINT_NUCLIDE displays information about a continuous-energy neutron ! cross_section table and its reactions and secondary angle/energy distributions @@ -1212,7 +524,8 @@ contains subroutine write_xs_summary() - integer :: i ! loop index + integer :: i ! loop index + integer :: unit_xs ! cross_sections.out file unit character(MAX_FILE_LEN) :: path ! path of summary file type(Nuclide), pointer :: nuc => null() type(SAlphaBeta), pointer :: sab => null() @@ -1221,17 +534,17 @@ contains path = trim(path_output) // "cross_sections.out" ! Open log file for writing - open(UNIT=UNIT_XS, FILE=path, STATUS='replace', ACTION='write') + open(NEWUNIT=unit_xs, FILE=path, STATUS='replace', ACTION='write') ! Write header - call header("CROSS SECTION TABLES", unit=UNIT_XS) + call header("CROSS SECTION TABLES", unit=unit_xs) NUCLIDE_LOOP: do i = 1, n_nuclides_total ! Get pointer to nuclide nuc => nuclides(i) ! Print information about nuclide - call print_nuclide(nuc, unit=UNIT_XS) + call print_nuclide(nuc, unit=unit_xs) end do NUCLIDE_LOOP SAB_TABLES_LOOP: do i = 1, n_sab_tables @@ -1239,11 +552,11 @@ contains sab => sab_tables(i) ! Print information about S(a,b) table - call print_sab_table(sab, unit=UNIT_XS) + call print_sab_table(sab, unit=unit_xs) end do SAB_TABLES_LOOP ! Close cross section summary file - close(UNIT_XS) + close(unit_xs) end subroutine write_xs_summary @@ -1624,6 +937,7 @@ contains integer :: i_listing ! index in xs_listings array integer :: n_order ! loop index for moment orders integer :: nm_order ! loop index for Ynm moment orders + integer :: unit_tally ! tallies.out file unit real(8) :: t_value ! t-values for confidence intervals real(8) :: alpha ! significance level for CI character(MAX_FILE_LEN) :: filename ! name of output file @@ -1672,7 +986,7 @@ contains filename = trim(path_output) // "tallies.out" ! Open tally file for writing - open(FILE=filename, UNIT=UNIT_TALLY, STATUS='replace', ACTION='write') + open(FILE=filename, NEWUNIT=unit_tally, STATUS='replace', ACTION='write') ! Calculate t-value for confidence intervals if (confidence_intervals) then @@ -1696,16 +1010,16 @@ contains ! Write header block if (t % name == "") then - call header("TALLY " // trim(to_str(t % id)), unit=UNIT_TALLY, & + call header("TALLY " // trim(to_str(t % id)), unit=unit_tally, & level=3) else call header("TALLY " // trim(to_str(t % id)) // ": " & - // trim(t % name), unit=UNIT_TALLY, level=3) + // trim(t % name), unit=unit_tally, level=3) endif ! Handle surface current tallies separately if (t % type == TALLY_SURFACE_CURRENT) then - call write_surface_current(t) + call write_surface_current(t, unit_tally) cycle end if @@ -1748,7 +1062,7 @@ contains ! Print current filter information type = t % filters(j) % type - write(UNIT=UNIT_TALLY, FMT='(1X,2A,1X,A)') repeat(" ", indent), & + write(UNIT=unit_tally, FMT='(1X,2A,1X,A)') repeat(" ", indent), & trim(filter_name(type)), trim(get_label(t, j)) indent = indent + 2 j = j + 1 @@ -1759,7 +1073,7 @@ contains ! Print filter information if (t % n_filters > 0) then type = t % filters(j) % type - write(UNIT=UNIT_TALLY, FMT='(1X,2A,1X,A)') repeat(" ", indent), & + write(UNIT=unit_tally, FMT='(1X,2A,1X,A)') repeat(" ", indent), & trim(filter_name(type)), trim(get_label(t, j)) end if @@ -1780,11 +1094,11 @@ contains ! Write label for nuclide i_nuclide = t % nuclide_bins(n) if (i_nuclide == -1) then - write(UNIT=UNIT_TALLY, FMT='(1X,2A,1X,A)') repeat(" ", indent), & + write(UNIT=unit_tally, FMT='(1X,2A,1X,A)') repeat(" ", indent), & "Total Material" else i_listing = nuclides(i_nuclide) % listing - write(UNIT=UNIT_TALLY, FMT='(1X,2A,1X,A)') repeat(" ", indent), & + write(UNIT=unit_tally, FMT='(1X,2A,1X,A)') repeat(" ", indent), & trim(xs_listings(i_listing) % alias) end if @@ -1797,7 +1111,7 @@ contains case (SCORE_SCATTER_N, SCORE_NU_SCATTER_N) score_name = 'P' // trim(to_str(t % moment_order(k))) // " " // & score_names(abs(t % score_bins(k))) - write(UNIT=UNIT_TALLY, FMT='(1X,2A,1X,A,"+/- ",A)') & + write(UNIT=unit_tally, FMT='(1X,2A,1X,A,"+/- ",A)') & repeat(" ", indent), score_name, & to_str(t % results(score_index,filter_index) % sum), & trim(to_str(t % results(score_index,filter_index) % sum_sq)) @@ -1807,7 +1121,7 @@ contains score_index = score_index + 1 score_name = 'P' // trim(to_str(n_order)) // " " //& score_names(abs(t % score_bins(k))) - write(UNIT=UNIT_TALLY, FMT='(1X,2A,1X,A,"+/- ",A)') & + write(UNIT=unit_tally, FMT='(1X,2A,1X,A,"+/- ",A)') & repeat(" ", indent), score_name, & to_str(t % results(score_index,filter_index) % sum), & trim(to_str(t % results(score_index,filter_index) & @@ -1823,7 +1137,7 @@ contains score_name = 'Y' // trim(to_str(n_order)) // ',' // & trim(to_str(nm_order)) // " " & // score_names(abs(t % score_bins(k))) - write(UNIT=UNIT_TALLY, FMT='(1X,2A,1X,A,"+/- ",A)') & + write(UNIT=unit_tally, FMT='(1X,2A,1X,A,"+/- ",A)') & repeat(" ", indent), score_name, & to_str(t % results(score_index,filter_index) % sum), & trim(to_str(t % results(score_index,filter_index)& @@ -1837,7 +1151,7 @@ contains else score_name = score_names(abs(t % score_bins(k))) end if - write(UNIT=UNIT_TALLY, FMT='(1X,2A,1X,A,"+/- ",A)') & + write(UNIT=unit_tally, FMT='(1X,2A,1X,A,"+/- ",A)') & repeat(" ", indent), score_name, & to_str(t % results(score_index,filter_index) % sum), & trim(to_str(t % results(score_index,filter_index) % sum_sq)) @@ -1854,7 +1168,7 @@ contains end do TALLY_LOOP - close(UNIT=UNIT_TALLY) + close(UNIT=unit_tally) end subroutine write_tallies @@ -1863,9 +1177,9 @@ contains ! tallies.out file. !=============================================================================== - subroutine write_surface_current(t) - + subroutine write_surface_current(t, unit_tally) type(TallyObject), pointer :: t + integer, intent(in) :: unit_tally integer :: i ! mesh index for x integer :: j ! mesh index for y @@ -1880,7 +1194,7 @@ contains integer :: filter_index ! index in results array for filters logical :: print_ebin ! should incoming energy bin be displayed? character(MAX_LINE_LEN) :: string - type(StructuredMesh), pointer :: m => null() + type(RegularMesh), pointer :: m ! Get pointer to mesh i_filter_mesh = t % find_filter(FILTER_MESH) @@ -1909,7 +1223,7 @@ contains do k = 1, m % dimension(3) ! Write mesh cell index string = string(1:len2+1) // trim(to_str(k)) // ")" - write(UNIT=UNIT_TALLY, FMT='(1X,A)') trim(string) + write(UNIT=unit_tally, FMT='(1X,A)') trim(string) do l = 1, n if (print_ebin) then @@ -1917,7 +1231,7 @@ contains matching_bins(i_filter_ein) = l ! Write incoming energy bin - write(UNIT=UNIT_TALLY, FMT='(3X,A,1X,A)') & + write(UNIT=unit_tally, FMT='(3X,A,1X,A)') & "Incoming Energy", trim(get_label(t, i_filter_ein)) end if @@ -1926,14 +1240,14 @@ contains mesh_indices_to_bin(m, (/ i-1, j, k /) + 1, .true.) matching_bins(i_filter_surf) = IN_RIGHT filter_index = sum((matching_bins(1:t%n_filters) - 1) * t % stride) + 1 - write(UNIT=UNIT_TALLY, FMT='(5X,A,T35,A,"+/- ",A)') & + write(UNIT=unit_tally, FMT='(5X,A,T35,A,"+/- ",A)') & "Outgoing Current to Left", & to_str(t % results(1,filter_index) % sum), & trim(to_str(t % results(1,filter_index) % sum_sq)) matching_bins(i_filter_surf) = OUT_RIGHT filter_index = sum((matching_bins(1:t%n_filters) - 1) * t % stride) + 1 - write(UNIT=UNIT_TALLY, FMT='(5X,A,T35,A,"+/- ",A)') & + write(UNIT=unit_tally, FMT='(5X,A,T35,A,"+/- ",A)') & "Incoming Current from Left", & to_str(t % results(1,filter_index) % sum), & trim(to_str(t % results(1,filter_index) % sum_sq)) @@ -1943,14 +1257,14 @@ contains mesh_indices_to_bin(m, (/ i, j, k /) + 1, .true.) matching_bins(i_filter_surf) = IN_RIGHT filter_index = sum((matching_bins(1:t%n_filters) - 1) * t % stride) + 1 - write(UNIT=UNIT_TALLY, FMT='(5X,A,T35,A,"+/- ",A)') & + write(UNIT=unit_tally, FMT='(5X,A,T35,A,"+/- ",A)') & "Incoming Current from Right", & to_str(t % results(1,filter_index) % sum), & trim(to_str(t % results(1,filter_index) % sum_sq)) matching_bins(i_filter_surf) = OUT_RIGHT filter_index = sum((matching_bins(1:t%n_filters) - 1) * t % stride) + 1 - write(UNIT=UNIT_TALLY, FMT='(5X,A,T35,A,"+/- ",A)') & + write(UNIT=unit_tally, FMT='(5X,A,T35,A,"+/- ",A)') & "Outgoing Current to Right", & to_str(t % results(1,filter_index) % sum), & trim(to_str(t % results(1,filter_index) % sum_sq)) @@ -1960,14 +1274,14 @@ contains mesh_indices_to_bin(m, (/ i, j-1, k /) + 1, .true.) matching_bins(i_filter_surf) = IN_FRONT filter_index = sum((matching_bins(1:t%n_filters) - 1) * t % stride) + 1 - write(UNIT=UNIT_TALLY, FMT='(5X,A,T35,A,"+/- ",A)') & + write(UNIT=unit_tally, FMT='(5X,A,T35,A,"+/- ",A)') & "Outgoing Current to Back", & to_str(t % results(1,filter_index) % sum), & trim(to_str(t % results(1,filter_index) % sum_sq)) matching_bins(i_filter_surf) = OUT_FRONT filter_index = sum((matching_bins(1:t%n_filters) - 1) * t % stride) + 1 - write(UNIT=UNIT_TALLY, FMT='(5X,A,T35,A,"+/- ",A)') & + write(UNIT=unit_tally, FMT='(5X,A,T35,A,"+/- ",A)') & "Incoming Current from Back", & to_str(t % results(1,filter_index) % sum), & trim(to_str(t % results(1,filter_index) % sum_sq)) @@ -1977,14 +1291,14 @@ contains mesh_indices_to_bin(m, (/ i, j, k /) + 1, .true.) matching_bins(i_filter_surf) = IN_FRONT filter_index = sum((matching_bins(1:t%n_filters) - 1) * t % stride) + 1 - write(UNIT=UNIT_TALLY, FMT='(5X,A,T35,A,"+/- ",A)') & + write(UNIT=unit_tally, FMT='(5X,A,T35,A,"+/- ",A)') & "Incoming Current from Front", & to_str(t % results(1,filter_index) % sum), & trim(to_str(t % results(1,filter_index) % sum_sq)) matching_bins(i_filter_surf) = OUT_FRONT filter_index = sum((matching_bins(1:t%n_filters) - 1) * t % stride) + 1 - write(UNIT=UNIT_TALLY, FMT='(5X,A,T35,A,"+/- ",A)') & + write(UNIT=unit_tally, FMT='(5X,A,T35,A,"+/- ",A)') & "Outgoing Current to Front", & to_str(t % results(1,filter_index) % sum), & trim(to_str(t % results(1,filter_index) % sum_sq)) @@ -1994,14 +1308,14 @@ contains mesh_indices_to_bin(m, (/ i, j, k-1 /) + 1, .true.) matching_bins(i_filter_surf) = IN_TOP filter_index = sum((matching_bins(1:t%n_filters) - 1) * t % stride) + 1 - write(UNIT=UNIT_TALLY, FMT='(5X,A,T35,A,"+/- ",A)') & + write(UNIT=unit_tally, FMT='(5X,A,T35,A,"+/- ",A)') & "Outgoing Current to Bottom", & to_str(t % results(1,filter_index) % sum), & trim(to_str(t % results(1,filter_index) % sum_sq)) matching_bins(i_filter_surf) = OUT_TOP filter_index = sum((matching_bins(1:t%n_filters) - 1) * t % stride) + 1 - write(UNIT=UNIT_TALLY, FMT='(5X,A,T35,A,"+/- ",A)') & + write(UNIT=unit_tally, FMT='(5X,A,T35,A,"+/- ",A)') & "Incoming Current from Bottom", & to_str(t % results(1,filter_index) % sum), & trim(to_str(t % results(1,filter_index) % sum_sq)) @@ -2011,14 +1325,14 @@ contains mesh_indices_to_bin(m, (/ i, j, k /) + 1, .true.) matching_bins(i_filter_surf) = IN_TOP filter_index = sum((matching_bins(1:t%n_filters) - 1) * t % stride) + 1 - write(UNIT=UNIT_TALLY, FMT='(5X,A,T35,A,"+/- ",A)') & + write(UNIT=unit_tally, FMT='(5X,A,T35,A,"+/- ",A)') & "Incoming Current from Top", & to_str(t % results(1,filter_index) % sum), & trim(to_str(t % results(1,filter_index) % sum_sq)) matching_bins(i_filter_surf) = OUT_TOP filter_index = sum((matching_bins(1:t%n_filters) - 1) * t % stride) + 1 - write(UNIT=UNIT_TALLY, FMT='(5X,A,T35,A,"+/- ",A)') & + write(UNIT=unit_tally, FMT='(5X,A,T35,A,"+/- ",A)') & "Outgoing Current to Top", & to_str(t % results(1,filter_index) % sum), & trim(to_str(t % results(1,filter_index) % sum_sq)) @@ -2047,8 +1361,8 @@ contains integer, allocatable :: ijk(:) ! indices in mesh real(8) :: E0 ! lower bound for energy bin real(8) :: E1 ! upper bound for energy bin - type(StructuredMesh), pointer :: m => null() - type(Universe), pointer :: univ => null() + type(RegularMesh), pointer :: m + type(Universe), pointer :: univ bin = matching_bins(i_filter) diff --git a/src/particle_restart.F90 b/src/particle_restart.F90 index c8874b071..2e0523d48 100644 --- a/src/particle_restart.F90 +++ b/src/particle_restart.F90 @@ -71,6 +71,7 @@ contains integer :: int_scalar integer(HID_T) :: file_id + character(MAX_WORD_LEN) :: mode ! Write meessage call write_message("Loading particle restart file " & @@ -86,7 +87,13 @@ contains call read_dataset(file_id, 'gen_per_batch', gen_per_batch) call read_dataset(file_id, 'current_gen', current_gen) call read_dataset(file_id, 'n_particles', n_particles) - call read_dataset(file_id, 'run_mode', previous_run_mode) + call read_dataset(file_id, 'run_mode', mode) + select case (mode) + case ('k-eigenvalue') + previous_run_mode = MODE_EIGENVALUE + case ('fixed source') + previous_run_mode = MODE_FIXEDSOURCE + end select call read_dataset(file_id, 'id', p%id) call read_dataset(file_id, 'weight', p%wgt) call read_dataset(file_id, 'energy', p%E) diff --git a/src/particle_restart_write.F90 b/src/particle_restart_write.F90 index 0c010b64c..edd779df0 100644 --- a/src/particle_restart_write.F90 +++ b/src/particle_restart_write.F90 @@ -40,13 +40,20 @@ contains src => source_bank(current_work) ! Write data to file - call write_dataset(file_id, 'filetype', FILETYPE_PARTICLE_RESTART) + call write_dataset(file_id, 'filetype', 'particle restart') call write_dataset(file_id, 'revision', REVISION_PARTICLE_RESTART) call write_dataset(file_id, 'current_batch', current_batch) call write_dataset(file_id, 'gen_per_batch', gen_per_batch) call write_dataset(file_id, 'current_gen', current_gen) call write_dataset(file_id, 'n_particles', n_particles) - call write_dataset(file_id, 'run_mode', run_mode) + select case(run_mode) + case (MODE_FIXEDSOURCE) + call write_dataset(file_id, 'run_mode', 'fixed source') + case (MODE_EIGENVALUE) + call write_dataset(file_id, 'run_mode', 'k-eigenvalue') + case (MODE_PARTICLE) + call write_dataset(file_id, 'run_mode', 'particle restart') + end select call write_dataset(file_id, 'id', p%id) call write_dataset(file_id, 'weight', src%wgt) call write_dataset(file_id, 'energy', src%E) diff --git a/src/plot.F90 b/src/plot.F90 index e507b6093..a5497bc20 100644 --- a/src/plot.F90 +++ b/src/plot.F90 @@ -5,7 +5,9 @@ module plot use geometry, only: find_cell, check_cell_overlap use geometry_header, only: Cell, BASE_UNIVERSE use global + use hdf5_interface use mesh, only: get_mesh_indices + use mesh_header, only: RegularMesh use output, only: write_message use particle_header, only: Particle, LocalCoord use plot_header @@ -14,6 +16,8 @@ module plot use progress_header, only: ProgressBar use string, only: to_str + use hdf5 + implicit none contains @@ -212,7 +216,7 @@ contains real(8) :: xyz_ur_plot(3) ! upper right xyz of plot image real(8) :: xyz_ll(3) ! lower left xyz real(8) :: xyz_ur(3) ! upper right xyz - type(StructuredMesh), pointer :: m => null() + type(RegularMesh), pointer :: m m => pl % meshlines_mesh @@ -305,25 +309,26 @@ contains integer :: i ! loop index for height integer :: j ! loop index for width + integer :: unit_plot ! Open PPM file for writing - open(UNIT=UNIT_PLOT, FILE=pl % path_plot) + open(NEWUNIT=unit_plot, FILE=pl % path_plot) ! Write header - write(UNIT_PLOT, '(A2)') 'P6' - write(UNIT_PLOT, '(I0,'' '',I0)') img%width, img%height - write(UNIT_PLOT, '(A)') '255' + write(unit_plot, '(A2)') 'P6' + write(unit_plot, '(I0,'' '',I0)') img%width, img%height + write(unit_plot, '(A)') '255' ! Write color for each pixel do j = 1, img % height do i = 1, img % width - write(UNIT_PLOT, '(3A1)', advance='no') achar(img%red(i,j)), & + write(unit_plot, '(3A1)', advance='no') achar(img%red(i,j)), & achar(img%green(i,j)), achar(img%blue(i,j)) end do end do ! Close plot file - close(UNIT=UNIT_PLOT) + close(UNIT=unit_plot) end subroutine output_ppm @@ -346,10 +351,20 @@ contains integer :: x, y, z ! voxel location indices integer :: rgb(3) ! colors (red, green, blue) from 0-255 integer :: id ! id of cell or material + integer :: hdf5_err + integer, target :: data(pl%pixels(3),pl%pixels(2)) + integer(HID_T) :: file_id + integer(HID_T) :: dspace + integeR(HID_T) :: memspace + integer(HID_T) :: dset + integer(HSIZE_T) :: dims(3) + integer(HSIZE_T) :: dims_slab(3) + integer(HSIZE_T) :: offset(3) real(8) :: vox(3) ! x, y, and z voxel widths real(8) :: ll(3) ! lower left starting point for each sweep direction type(Particle) :: p type(ProgressBar) :: progress + type(c_ptr) :: f_ptr ! compute voxel widths in each direction vox = pl % width/dble(pl % pixels) @@ -364,11 +379,30 @@ contains p % coord(1) % universe = BASE_UNIVERSE ! Open binary plot file for writing - open(UNIT=UNIT_PLOT, FILE=pl % path_plot, STATUS='replace', & - ACCESS='stream') + file_id = file_create(pl%path_plot) ! write plot header info - write(UNIT_PLOT) pl % pixels, vox, ll + call write_dataset(file_id, "filetype", 'voxel') + call write_dataset(file_id, "num_voxels", pl%pixels) + call write_dataset(file_id, "voxel_width", vox) + call write_dataset(file_id, "lower_left", ll) + + ! Create dataset for voxel data -- note that the dimensions are reversed + ! since we want the order in the file to be z, y, x + dims(:) = [pl%pixels(3), pl%pixels(2), pl%pixels(1)] + call h5screate_simple_f(3, dims, dspace, hdf5_err) + call h5dcreate_f(file_id, "data", H5T_NATIVE_INTEGER, dspace, dset, hdf5_err) + + ! Create another dataspace for 2D array in memory + dims_slab(1) = pl%pixels(3) + dims_slab(2) = pl%pixels(2) + dims_slab(3) = 1 + call h5screate_simple_f(2, dims_slab(1:2), memspace, hdf5_err) + + ! Initialize offset and get pointer to data + offset(:) = 0 + call h5sselect_hyperslab_f(dspace, H5S_SELECT_SET_F, offset, dims_slab, hdf5_err) + f_ptr = c_loc(data) ! move to center of voxels ll = ll + vox / TWO @@ -377,22 +411,19 @@ contains call progress % set_value(dble(x)/dble(pl % pixels(1))*100) do y = 1, pl % pixels(2) do z = 1, pl % pixels(3) - ! get voxel color call position_rgb(p, pl, rgb, id) ! write to plot file - write(UNIT_PLOT) id + data(z,y) = id ! advance particle in z direction p % coord(1) % xyz(3) = p % coord(1) % xyz(3) + vox(3) - end do ! advance particle in y direction p % coord(1) % xyz(2) = p % coord(1) % xyz(2) + vox(2) p % coord(1) % xyz(3) = ll(3) - end do ! advance particle in y direction @@ -400,9 +431,17 @@ contains p % coord(1) % xyz(2) = ll(2) p % coord(1) % xyz(3) = ll(3) + ! Write to HDF5 dataset + offset(3) = x - 1 + call h5soffset_simple_f(dspace, offset, hdf5_err) + call h5dwrite_f(dset, H5T_NATIVE_INTEGER, f_ptr, hdf5_err, & + mem_space_id=memspace, file_space_id=dspace) end do - close(UNIT_PLOT) + call h5dclose_f(dset, hdf5_err) + call h5sclose_f(dspace, hdf5_err) + call h5sclose_f(memspace, hdf5_err) + call file_close(file_id) end subroutine create_3d_dump diff --git a/src/plot_header.F90 b/src/plot_header.F90 index 8dc725d9d..a6ea9a580 100644 --- a/src/plot_header.F90 +++ b/src/plot_header.F90 @@ -1,7 +1,7 @@ module plot_header use constants - use mesh_header, only: StructuredMesh + use mesh_header, only: RegularMesh implicit none @@ -28,7 +28,7 @@ module plot_header integer :: pixels(3) ! pixel width/height of plot slice integer :: meshlines_width ! pixel width of meshlines integer :: level ! universe depth to plot the cells of - type(StructuredMesh), pointer :: meshlines_mesh => null() ! mesh to plot + type(RegularMesh), pointer :: meshlines_mesh => null() ! mesh to plot type(ObjectColor) :: meshlines_color ! Color for meshlines type(ObjectColor) :: not_found ! color for positions where no cell found type(ObjectColor), allocatable :: colors(:) ! colors of cells/mats diff --git a/src/relaxng/geometry.rnc b/src/relaxng/geometry.rnc index cebbc5b6a..82f1f15b3 100644 --- a/src/relaxng/geometry.rnc +++ b/src/relaxng/geometry.rnc @@ -6,7 +6,7 @@ element geometry { (element universe { xsd:int } | attribute universe { xsd:int })? & ( (element fill { xsd:int } | attribute fill { xsd:int }) | - (element material { ( xsd:int | "void" ) } | + (element material { ( xsd:int | "void" ) } | attribute material { ( xsd:int | "void" ) }) ) & (element surfaces { list { xsd:int* } } | attribute surfaces { list { xsd:int* } })? & @@ -18,7 +18,7 @@ element geometry { (element id { xsd:int } | attribute id { xsd:int }) & (element name { xsd:string { maxLength="52" } } | attribute name { xsd:string { maxLength="52" } })? & - (element type { xsd:string { maxLength = "15" } } | + (element type { xsd:string { maxLength = "15" } } | attribute type { xsd:string { maxLength = "15" } }) & (element coeffs { list { xsd:double+ } } | attribute coeffs { list { xsd:double+ } }) & (element boundary { ( "transmit" | "reflective" | "vacuum" ) } | @@ -29,12 +29,12 @@ element geometry { (element id { xsd:int } | attribute id { xsd:int }) & (element name { xsd:string { maxLength="52" } } | attribute name { xsd:string { maxLength="52" } })? & - (element dimension { list { xsd:positiveInteger+ } } | + (element dimension { list { xsd:positiveInteger+ } } | attribute dimension { list { xsd:positiveInteger+ } }) & (element lower_left { list { xsd:double+ } } | attribute lower_left { list { xsd:double+ } }) & (element pitch { list { xsd:double+ } } | attribute pitch { list { xsd:double+ } }) & (element universes { list { xsd:int+ } } | attribute universes { list { xsd:int+ } }) & - (element outside { xsd:int } | attribute outside { xsd:int })? + (element outer { xsd:int } | attribute outer { xsd:int })? }* & element hex_lattice { diff --git a/src/relaxng/geometry.rng b/src/relaxng/geometry.rng index fdbf74cbd..9bd573b34 100644 --- a/src/relaxng/geometry.rng +++ b/src/relaxng/geometry.rng @@ -282,10 +282,10 @@ - + - + diff --git a/src/relaxng/tallies.rnc b/src/relaxng/tallies.rnc index c2e0860b8..ee93d273c 100644 --- a/src/relaxng/tallies.rnc +++ b/src/relaxng/tallies.rnc @@ -1,8 +1,8 @@ element tallies { element mesh { (element id { xsd:int } | attribute id { xsd:int }) & - (element type { ( "rectangular" | "hexagonal" ) } | - attribute type { ( "rectangular" | "hexagonal" ) }) & + (element type { ( "regular" ) } | + attribute type { ( "regular" ) }) & (element dimension { list { xsd:positiveInteger+ } } | attribute dimension { list { xsd:positiveInteger+ } }) & (element lower_left { list { xsd:double+ } } | @@ -32,7 +32,7 @@ element tallies { element nuclides { list { xsd:string { maxLength = "12" }+ } }? & - element scores { + element scores { list { xsd:string { maxLength = "20" }+ } } & element trigger { diff --git a/src/relaxng/tallies.rng b/src/relaxng/tallies.rng index 9ea941fea..76973e855 100644 --- a/src/relaxng/tallies.rng +++ b/src/relaxng/tallies.rng @@ -14,16 +14,10 @@ - - rectangular - hexagonal - + regular - - rectangular - hexagonal - + regular diff --git a/src/source.F90 b/src/source.F90 index 285332ef8..c46174947 100644 --- a/src/source.F90 +++ b/src/source.F90 @@ -32,8 +32,8 @@ contains integer(8) :: i ! loop index over bank sites integer(8) :: id ! particle id - integer(4) :: itmp ! temporary integer integer(HID_T) :: file_id + character(MAX_WORD_LEN) :: filetype character(MAX_FILE_LEN) :: filename type(Bank), pointer :: src ! source bank site @@ -50,10 +50,10 @@ contains file_id = file_open(path_source, 'r', parallel=.true.) ! Read the file type - call read_dataset(file_id, "filetype", itmp) + call read_dataset(file_id, "filetype", filetype) ! Check to make sure this is a source file - if (itmp /= FILETYPE_SOURCE) then + if (filetype /= 'source') then call fatal_error("Specified starting source file not a source file & &type.") end if diff --git a/src/state_point.F90 b/src/state_point.F90 index 691d0567e..64ba7ef55 100644 --- a/src/state_point.F90 +++ b/src/state_point.F90 @@ -13,13 +13,14 @@ module state_point use constants + use endf, only: reaction_name use error, only: fatal_error, warning use global use hdf5_interface use output, only: write_message, time_stamp use string, only: to_str, zero_padded, count_digits use tally_header, only: TallyObject - use mesh_header, only: StructuredMesh + use mesh_header, only: RegularMesh use dict_header, only: ElemKeyValueII, ElemKeyValueCI #ifdef MPI @@ -40,19 +41,20 @@ contains subroutine write_state_point() - integer :: i, j, k - integer :: n_order ! loop index for moment orders - integer :: nm_order ! loop index for Ynm moment orders - integer, allocatable :: id_array(:) - integer, allocatable :: key_array(:) + integer :: i, j, k + integer :: i_list, i_xs + integer :: n_order ! loop index for moment orders + integer :: nm_order ! loop index for Ynm moment orders + integer, allocatable :: id_array(:) + integer, allocatable :: key_array(:) integer(HID_T) :: file_id integer(HID_T) :: cmfd_group integer(HID_T) :: tallies_group, tally_group integer(HID_T) :: meshes_group, mesh_group - integer(HID_T) :: filter_group, moments_group - character(8) :: moment_name ! name of moment (e.g, P3) - character(MAX_FILE_LEN) :: filename - type(StructuredMesh), pointer :: meshp + integer(HID_T) :: filter_group + character(20), allocatable :: str_array(:) + character(MAX_FILE_LEN) :: filename + type(RegularMesh), pointer :: meshp type(TallyObject), pointer :: tally type(ElemKeyValueII), pointer :: current type(ElemKeyValueII), pointer :: next @@ -70,7 +72,7 @@ contains file_id = file_create(filename) ! Write file type - call write_dataset(file_id, "filetype", FILETYPE_STATEPOINT) + call write_dataset(file_id, "filetype", 'statepoint') ! Write revision number for state point file call write_dataset(file_id, "revision", REVISION_STATEPOINT) @@ -90,7 +92,12 @@ contains call write_dataset(file_id, "seed", seed) ! Write run information - call write_dataset(file_id, "run_mode", run_mode) + select case(run_mode) + case (MODE_FIXEDSOURCE) + call write_dataset(file_id, "run_mode", "fixed source") + case (MODE_EIGENVALUE) + call write_dataset(file_id, "run_mode", "k-eigenvalue") + end select call write_dataset(file_id, "n_particles", n_particles) call write_dataset(file_id, "n_batches", n_batches) @@ -169,9 +176,10 @@ contains meshp => meshes(id_array(i)) mesh_group = create_group(meshes_group, "mesh " // trim(to_str(meshp%id))) - call write_dataset(mesh_group, "id", meshp%id) - call write_dataset(mesh_group, "type", meshp%type) - call write_dataset(mesh_group, "n_dimension", meshp%n_dimension) + select case (meshp%type) + case (MESH_REGULAR) + call write_dataset(mesh_group, "type", "regular") + end select call write_dataset(mesh_group, "dimension", meshp%dimension) call write_dataset(mesh_group, "lower_left", meshp%lower_left) call write_dataset(mesh_group, "upper_right", meshp%upper_right) @@ -214,7 +222,14 @@ contains tally_group = create_group(tallies_group, "tally " // & trim(to_str(tally%id))) - call write_dataset(tally_group, "estimator", tally%estimator) + select case(tally%estimator) + case (ESTIMATOR_ANALOG) + call write_dataset(tally_group, "estimator", "analog") + case (ESTIMATOR_TRACKLENGTH) + call write_dataset(tally_group, "estimator", "tracklength") + case (ESTIMATOR_COLLISION) + call write_dataset(tally_group, "estimator", "collision") + end select call write_dataset(tally_group, "n_realizations", tally%n_realizations) call write_dataset(tally_group, "n_filters", tally%n_filters) @@ -223,7 +238,28 @@ contains filter_group = create_group(tally_group, "filter " // & trim(to_str(j))) - call write_dataset(filter_group, "type", tally%filters(j)%type) + ! Write name of type + select case (tally%filters(j)%type) + case(FILTER_UNIVERSE) + call write_dataset(filter_group, "type", "universe") + case(FILTER_MATERIAL) + call write_dataset(filter_group, "type", "material") + case(FILTER_CELL) + call write_dataset(filter_group, "type", "cell") + case(FILTER_CELLBORN) + call write_dataset(filter_group, "type", "cellborn") + case(FILTER_SURFACE) + call write_dataset(filter_group, "type", "surface") + case(FILTER_MESH) + call write_dataset(filter_group, "type", "mesh") + case(FILTER_ENERGYIN) + call write_dataset(filter_group, "type", "energy") + case(FILTER_ENERGYOUT) + call write_dataset(filter_group, "type", "energyout") + case(FILTER_DISTRIBCELL) + call write_dataset(filter_group, "type", "distribcell") + end select + call write_dataset(filter_group, "offset", tally%filters(j)%offset) call write_dataset(filter_group, "n_bins", tally%filters(j)%n_bins) if (tally%filters(j)%type == FILTER_ENERGYIN .or. & @@ -238,59 +274,112 @@ contains call close_group(filter_group) end do FILTER_LOOP - call write_dataset(tally_group, "n_nuclides", tally%n_nuclide_bins) - ! Set up nuclide bin array and then write - allocate(key_array(tally%n_nuclide_bins)) + allocate(str_array(tally%n_nuclide_bins)) NUCLIDE_LOOP: do j = 1, tally%n_nuclide_bins if (tally%nuclide_bins(j) > 0) then - key_array(j) = nuclides(tally%nuclide_bins(j))%zaid + ! Get index in cross section listings for this nuclide + i_list = nuclides(tally%nuclide_bins(j))%listing + + ! Determine position of . in alias string (e.g. "U-235.71c"). If + ! no . is found, just use the entire string. + i_xs = index(xs_listings(i_list)%alias, '.') + if (i_xs > 0) then + str_array(j) = xs_listings(i_list)%alias(1:i_xs - 1) + else + str_array(j) = xs_listings(i_list)%alias + end if else - key_array(j) = tally%nuclide_bins(j) + str_array(j) = 'total' end if end do NUCLIDE_LOOP - call write_dataset(tally_group, "nuclides", key_array) - deallocate(key_array) + call write_dataset(tally_group, "nuclides", str_array) + deallocate(str_array) call write_dataset(tally_group, "n_score_bins", tally%n_score_bins) - call write_dataset(tally_group, "score_bins", tally%score_bins) + allocate(str_array(size(tally%score_bins))) + do j = 1, size(tally%score_bins) + select case(tally%score_bins(j)) + case (SCORE_FLUX) + str_array(j) = "flux" + case (SCORE_TOTAL) + str_array(j) = "total" + case (SCORE_SCATTER) + str_array(j) = "scatter" + case (SCORE_NU_SCATTER) + str_array(j) = "nu-scatter" + case (SCORE_SCATTER_N) + str_array(j) = "scatter-n" + case (SCORE_SCATTER_PN) + str_array(j) = "scatter-pn" + case (SCORE_NU_SCATTER_N) + str_array(j) = "nu-scatter-n" + case (SCORE_NU_SCATTER_PN) + str_array(j) = "nu-scatter-pn" + case (SCORE_TRANSPORT) + str_array(j) = "transport" + case (SCORE_N_1N) + str_array(j) = "n1n" + case (SCORE_ABSORPTION) + str_array(j) = "absorption" + case (SCORE_FISSION) + str_array(j) = "fission" + case (SCORE_NU_FISSION) + str_array(j) = "nu-fission" + case (SCORE_KAPPA_FISSION) + str_array(j) = "kappa-fission" + case (SCORE_CURRENT) + str_array(j) = "current" + case (SCORE_FLUX_YN) + str_array(j) = "flux-yn" + case (SCORE_TOTAL_YN) + str_array(j) = "total-yn" + case (SCORE_SCATTER_YN) + str_array(j) = "scatter-yn" + case (SCORE_NU_SCATTER_YN) + str_array(j) = "nu-scatter-yn" + case (SCORE_EVENTS) + str_array(j) = "events" + case default + str_array(j) = reaction_name(tally%score_bins(j)) + end select + end do + call write_dataset(tally_group, "score_bins", str_array) call write_dataset(tally_group, "n_user_score_bins", tally%n_user_score_bins) + deallocate(str_array) + ! Write explicit moment order strings for each score bin - moments_group = create_group(tally_group, "moments") k = 1 + allocate(str_array(tally%n_score_bins)) MOMENT_LOOP: do j = 1, tally%n_user_score_bins select case(tally%score_bins(k)) case (SCORE_SCATTER_N, SCORE_NU_SCATTER_N) - moment_name = 'P' // trim(to_str(tally%moment_order(k))) - call write_dataset(moments_group, "order" // trim(to_str(k)), moment_name) + str_array(k) = 'P' // trim(to_str(tally%moment_order(k))) k = k + 1 case (SCORE_SCATTER_PN, SCORE_NU_SCATTER_PN) do n_order = 0, tally%moment_order(k) - moment_name = 'P' // trim(to_str(n_order)) - call write_dataset(moments_group, "order" // trim(to_str(k)), moment_name) + str_array(k) = 'P' // trim(to_str(n_order)) k = k + 1 end do case (SCORE_SCATTER_YN, SCORE_NU_SCATTER_YN, SCORE_FLUX_YN, & - SCORE_TOTAL_YN) + SCORE_TOTAL_YN) do n_order = 0, tally%moment_order(k) do nm_order = -n_order, n_order - moment_name = 'Y' // trim(to_str(n_order)) // ',' // & + str_array(k) = 'Y' // trim(to_str(n_order)) // ',' // & trim(to_str(nm_order)) - call write_dataset(moments_group, "order" // & - trim(to_str(k)), moment_name) - k = k + 1 + k = k + 1 end do end do case default - moment_name = '' - call write_dataset(moments_group, "order" // trim(to_str(k)), & - moment_name) + str_array(k) = '' k = k + 1 end select end do MOMENT_LOOP - call close_group(moments_group) + call write_dataset(tally_group, "moment_orders", str_array) + deallocate(str_array) + call close_group(tally_group) end do TALLY_METADATA @@ -379,7 +468,7 @@ contains ! Create separate source file if (master .or. parallel) then file_id = file_create(filename, parallel=.true.) - call write_dataset(file_id, "filetype", FILETYPE_SOURCE) + call write_dataset(file_id, "filetype", 'source') end if else filename = trim(path_output) // 'statepoint.' // & @@ -401,7 +490,7 @@ contains call write_message("Creating source file " // trim(filename) // "...", 1) if (master .or. parallel) then file_id = file_create(filename, parallel=.true.) - call write_dataset(file_id, "filetype", FILETYPE_SOURCE) + call write_dataset(file_id, "filetype", 'source') end if call write_source_bank(file_id) @@ -582,25 +671,15 @@ contains subroutine load_state_point() - integer :: i, j, k - integer :: int_array(3) - integer :: curr_key - integer :: n_order ! loop index for moment orders - integer :: nm_order ! loop index for Ynm moment orders - integer, allocatable :: id_array(:) - integer, allocatable :: key_array(:) - integer, allocatable :: temp_array(:) + integer :: i + integer :: int_array(3) integer(HID_T) :: file_id integer(HID_T) :: cmfd_group - integer(HID_T) :: tallies_group, tally_group - integer(HID_T) :: meshes_group, mesh_group - integer(HID_T) :: filter_group, moments_group - real(8) :: real_array(3) - logical :: source_present - character(MAX_FILE_LEN) :: path_temp - character(19) :: current_time - character(8) :: moment_name ! name of moment (e.g, P3, Y-1,1) - type(StructuredMesh), pointer :: meshp + integer(HID_T) :: tallies_group + integer(HID_T) :: tally_group + real(8) :: real_array(3) + logical :: source_present + character(MAX_WORD_LEN) :: word type(TallyObject), pointer :: tally ! Write message @@ -621,27 +700,17 @@ contains &in OpenMC.") end if - ! Read OpenMC version - call read_dataset(file_id, "version_major", int_array(1)) - call read_dataset(file_id, "version_minor", int_array(2)) - call read_dataset(file_id, "version_release", int_array(3)) - if (int_array(1) /= VERSION_MAJOR .or. int_array(2) /= VERSION_MINOR & - .or. int_array(3) /= VERSION_RELEASE) then - if (master) call warning("State point file was created with a different & - &version of OpenMC.") - end if - - ! Read date and time - call read_dataset(file_id, "date_and_time", current_time) - - ! Read path to input - call read_dataset(file_id, "path", path_temp) - ! Read and overwrite random number seed call read_dataset(file_id, "seed", seed) ! Read and overwrite run information except number of batches - call read_dataset(file_id, "run_mode", run_mode) + call read_dataset(file_id, "run_mode", word) + select case(word) + case ('fixed source') + run_mode = MODE_FIXEDSOURCE + case ('k-eigenvalue') + run_mode = MODE_EIGENVALUE + end select call read_dataset(file_id, "n_particles", n_particles) call read_dataset(file_id, "n_batches", int_array(1)) @@ -701,142 +770,6 @@ contains end if end if - ! Read number of meshes - tallies_group = open_group(file_id, "tallies") - meshes_group = open_group(tallies_group, "meshes") - call read_dataset(meshes_group, "n_meshes", n_meshes) - - if (n_meshes > 0) then - - ! Read list of mesh keys-> IDs - allocate(id_array(n_meshes)) - allocate(key_array(n_meshes)) - - call read_dataset(meshes_group, "ids", id_array) - call read_dataset(meshes_group, "keys", key_array) - - ! Read and overwrite mesh information - MESH_LOOP: do i = 1, n_meshes - - meshp => meshes(id_array(i)) - curr_key = key_array(id_array(i)) - - mesh_group = open_group(meshes_group, "mesh " // & - trim(to_str(curr_key))) - call read_dataset(mesh_group, "id", meshp%id) - call read_dataset(mesh_group, "type", meshp%type) - call read_dataset(mesh_group, "n_dimension", meshp%n_dimension) - call read_dataset(mesh_group, "dimension", meshp%dimension) - call read_dataset(mesh_group, "lower_left", meshp%lower_left) - call read_dataset(mesh_group, "upper_right", meshp%upper_right) - call read_dataset(mesh_group, "width", meshp%width) - call close_group(mesh_group) - end do MESH_LOOP - - deallocate(id_array) - deallocate(key_array) - - end if - - call close_group(meshes_group) - - ! Read and overwrite number of tallies - call read_dataset(tallies_group, "n_tallies", n_tallies) - - ! Read list of tally keys-> IDs - allocate(id_array(n_tallies)) - allocate(key_array(n_tallies)) - - call read_dataset(tallies_group, "ids", id_array) - call read_dataset(tallies_group, "keys", key_array) - - ! Read in tally metadata - TALLY_METADATA: do i = 1, n_tallies - - ! Get pointer to tally - tally => tallies(i) - curr_key = key_array(id_array(i)) - tally_group = open_group(tallies_group, "tally " // & - trim(to_str(curr_key))) - - call read_dataset(tally_group, "estimator", tally%estimator) - call read_dataset(tally_group, "n_realizations", tally%n_realizations) - call read_dataset(tally_group, "n_filters", tally%n_filters) - - FILTER_LOOP: do j = 1, tally%n_filters - filter_group = open_group(tally_group, "filter " // trim(to_str(j))) - - call read_dataset(filter_group, "type", tally%filters(j)%type) - call read_dataset(filter_group, "offset", tally%filters(j)%offset) - call read_dataset(filter_group, "n_bins", tally%filters(j)%n_bins) - if (tally%filters(j)%type == FILTER_ENERGYIN .or. & - tally%filters(j)%type == FILTER_ENERGYOUT) then - call read_dataset(filter_group, "bins", tally%filters(j)%real_bins) - else - call read_dataset(filter_group, "bins", tally%filters(j)%int_bins) - end if - - call close_group(filter_group) - end do FILTER_LOOP - - call read_dataset(tally_group, "n_nuclides", tally%n_nuclide_bins) - - ! Set up nuclide bin array and then read - allocate(temp_array(tally%n_nuclide_bins)) - call read_dataset(tally_group, "nuclides", temp_array) - - NUCLIDE_LOOP: do j = 1, tally%n_nuclide_bins - if (temp_array(j) > 0) then - tally%nuclide_bins(j) = temp_array(j) - else - tally%nuclide_bins(j) = temp_array(j) - end if - end do NUCLIDE_LOOP - - deallocate(temp_array) - - ! Write number of score bins, score bins, user score bins - call read_dataset(tally_group, "n_score_bins", tally%n_score_bins) - call read_dataset(tally_group, "score_bins", tally%score_bins) - call read_dataset(tally_group, "n_user_score_bins", tally%n_user_score_bins) - - ! Read explicit moment order strings for each score bin - k = 1 - moments_group = open_group(tally_group, "moments") - MOMENT_LOOP: do j = 1, tally%n_user_score_bins - select case(tally%score_bins(k)) - case (SCORE_SCATTER_N, SCORE_NU_SCATTER_N) - call read_dataset(moments_group, "order" // trim(to_str(k)), & - moment_name) - k = k + 1 - case (SCORE_SCATTER_PN, SCORE_NU_SCATTER_PN) - do n_order = 0, tally%moment_order(k) - call read_dataset(moments_group, "order" // trim(to_str(k)), & - moment_name) - k = k + 1 - end do - case (SCORE_SCATTER_YN, SCORE_NU_SCATTER_YN, SCORE_FLUX_YN, & - SCORE_TOTAL_YN) - do n_order = 0, tally%moment_order(k) - do nm_order = -n_order, n_order - call read_dataset(moments_group, "order" // trim(to_str(k)), & - moment_name) - k = k + 1 - end do - end do - case default - call read_dataset(moments_group, "order" // trim(to_str(k)), & - moment_name) - k = k + 1 - end select - - end do MOMENT_LOOP - - call close_group(moments_group) - call close_group(tally_group) - - end do TALLY_METADATA - ! Check to make sure source bank is present if (path_source_point == path_state_point .and. .not. source_present) then call fatal_error("Source bank must be contained in statepoint restart & @@ -849,13 +782,6 @@ contains ! Read number of realizations for global tallies call read_dataset(file_id, "n_realizations", n_realizations, indep=.true.) - ! Read number of global tallies - call read_dataset(file_id, "n_global_tallies", int_array(1), indep=.false.) - if (int_array(1) /= N_GLOBAL_TALLIES) then - call fatal_error("Number of global tallies does not match in state & - &point.") - end if - ! Read global tally data call read_dataset(file_id, "global_tallies", global_tallies) @@ -866,14 +792,12 @@ contains ! Read in sum and sum squared if (int_array(1) == 1) then TALLY_RESULTS: do i = 1, n_tallies - ! Set pointer to tally tally => tallies(i) - curr_key = key_array(id_array(i)) ! Read sum and sum_sq for each bin tally_group = open_group(tallies_group, "tally " // & - trim(to_str(curr_key))) + trim(to_str(tally%id))) call read_dataset(tally_group, "results", tally%results) call close_group(tally_group) end do TALLY_RESULTS @@ -882,8 +806,6 @@ contains call close_group(tallies_group) end if - deallocate(id_array) - deallocate(key_array) ! Read source if in eigenvalue mode if (run_mode == MODE_EIGENVALUE) then diff --git a/src/summary.F90 b/src/summary.F90 index 0147230a0..b93bf120c 100644 --- a/src/summary.F90 +++ b/src/summary.F90 @@ -8,7 +8,7 @@ module summary use global use hdf5_interface use material_header, only: Material - use mesh_header, only: StructuredMesh + use mesh_header, only: RegularMesh use output, only: time_stamp use string, only: to_str use tally_header, only: TallyObject @@ -62,7 +62,6 @@ contains call write_geometry(file_id) call write_materials(file_id) - call write_nuclides(file_id) if (n_tallies > 0) then call write_tallies(file_id) end if @@ -79,6 +78,10 @@ contains subroutine write_header(file_id) integer(HID_T), intent(in) :: file_id + ! Write filetype and revision + call write_dataset(file_id, "filetype", "summary") + call write_dataset(file_id, "revision", REVISION_SUMMARY) + ! Write version information call write_dataset(file_id, "version_major", VERSION_MAJOR) call write_dataset(file_id, "version_minor", VERSION_MINOR) @@ -103,6 +106,7 @@ contains integer :: i, j, k, m integer, allocatable :: lattice_universes(:,:,:) + integer, allocatable :: surface_ids(:) integer(HID_T) :: geom_group integer(HID_T) :: cells_group, cell_group integer(HID_T) :: surfaces_group, surface_group @@ -153,23 +157,15 @@ contains case (CELL_FILL) call write_dataset(cell_group, "fill_type", "universe") call write_dataset(cell_group, "fill", universes(c%fill)%id) - call write_dataset(cell_group, "maps", size(c%offset)) if (size(c%offset) > 0) then call write_dataset(cell_group, "offset", c%offset) end if if (allocated(c%translation)) then - call write_dataset(cell_group, "translated", 1) call write_dataset(cell_group, "translation", c%translation) - else - call write_dataset(cell_group, "translated", 0) end if - if (allocated(c%rotation)) then - call write_dataset(cell_group, "rotated", 1) call write_dataset(cell_group, "rotation", c%rotation) - else - call write_dataset(cell_group, "rotated", 0) end if case (CELL_LATTICE) @@ -179,7 +175,13 @@ contains ! Write list of bounding surfaces if (c%n_surfaces > 0) then - call write_dataset(cell_group, "surfaces", c%surfaces) + allocate(surface_ids(c%n_surfaces)) + do j = 1, c%n_surfaces + k = c%surfaces(j) + surface_ids(j) = sign(surfaces(abs(k))%id, k) + end do + call write_dataset(cell_group, "surfaces", surface_ids) + deallocate(surface_ids) end if call close_group(cell_group) @@ -208,42 +210,32 @@ contains ! Write surface type select case (s%type) case (SURF_PX) - call write_dataset(surface_group, "type", "X Plane") + call write_dataset(surface_group, "type", "x-plane") case (SURF_PY) - call write_dataset(surface_group, "type", "Y Plane") + call write_dataset(surface_group, "type", "y-plane") case (SURF_PZ) - call write_dataset(surface_group, "type", "Z Plane") + call write_dataset(surface_group, "type", "z-plane") case (SURF_PLANE) - call write_dataset(surface_group, "type", "Plane") + call write_dataset(surface_group, "type", "plane") case (SURF_CYL_X) - call write_dataset(surface_group, "type", "X Cylinder") + call write_dataset(surface_group, "type", "x-cylinder") case (SURF_CYL_Y) - call write_dataset(surface_group, "type", "Y Cylinder") + call write_dataset(surface_group, "type", "y-cylinder") case (SURF_CYL_Z) - call write_dataset(surface_group, "type", "Z Cylinder") + call write_dataset(surface_group, "type", "z-cylinder") case (SURF_SPHERE) - call write_dataset(surface_group, "type", "Sphere") + call write_dataset(surface_group, "type", "sphere") case (SURF_CONE_X) - call write_dataset(surface_group, "type", "X Cone") + call write_dataset(surface_group, "type", "x-cone") case (SURF_CONE_Y) - call write_dataset(surface_group, "type", "Y Cone") + call write_dataset(surface_group, "type", "y-cone") case (SURF_CONE_Z) - call write_dataset(surface_group, "type", "Z Cone") + call write_dataset(surface_group, "type", "z-cone") end select ! Write coefficients for surface call write_dataset(surface_group, "coefficients", s%coeffs) - ! Write positive neighbors - if (allocated(s%neighbor_pos)) then - call write_dataset(surface_group, "neighbors_positive", s%neighbor_pos) - end if - - ! Write negative neighbors - if (allocated(s%neighbor_neg)) then - call write_dataset(surface_group, "neighbors_negative", s%neighbor_neg) - end if - ! Write boundary condition select case (s%bc) case (BC_TRANSMIT) @@ -298,10 +290,16 @@ contains ! Write internal OpenMC index for this lattice call write_dataset(lattice_group, "index", i) - ! Write name for this lattice + ! Write name, pitch, and outer universe call write_dataset(lattice_group, "name", lat%name) + call write_dataset(lattice_group, "pitch", lat%pitch) + call write_dataset(lattice_group, "outer", lat%outer) + + ! Write distribcell offsets if present + if (size(lat%offset) > 0) then + call write_dataset(lattice_group, "offsets", lat%offset) + end if - ! Write lattice type select type (lat) type is (RectLattice) ! Write lattice type. @@ -310,15 +308,6 @@ contains ! Write lattice dimensions, lower left corner, and pitch call write_dataset(lattice_group, "dimension", lat%n_cells) call write_dataset(lattice_group, "lower_left", lat%lower_left) - call write_dataset(lattice_group, "pitch", lat%pitch) - - call write_dataset(lattice_group, "outer", lat%outer) - call write_dataset(lattice_group, "offset_size", size(lat%offset)) - call write_dataset(lattice_group, "maps", size(lat%offset,1)) - - if (size(lat%offset) > 0) then - call write_dataset(lattice_group, "offsets", lat%offset) - end if ! Write lattice universes. allocate(lattice_universes(lat%n_cells(1), lat%n_cells(2), & @@ -330,8 +319,6 @@ contains end do end do end do - call write_dataset(lattice_group, "universes", lattice_universes) - deallocate(lattice_universes) type is (HexLattice) ! Write lattice type. @@ -341,17 +328,8 @@ contains call write_dataset(lattice_group, "n_rings", lat%n_rings) call write_dataset(lattice_group, "n_axial", lat%n_axial) - ! Write lattice center, pitch and outer universe. + ! Write lattice center call write_dataset(lattice_group, "center", lat%center) - call write_dataset(lattice_group, "pitch", lat%pitch) - - call write_dataset(lattice_group, "outer", lat%outer) - call write_dataset(lattice_group, "offset_size", size(lat%offset)) - call write_dataset(lattice_group, "maps", size(lat%offset,1)) - - if (size(lat%offset) > 0) then - call write_dataset(lattice_group, "offsets", lat%offset) - end if ! Write lattice universes. allocate(lattice_universes(2*lat%n_rings - 1, 2*lat%n_rings - 1, & @@ -372,10 +350,12 @@ contains end do end do end do - call write_dataset(lattice_group, "universes", lattice_universes) - deallocate(lattice_universes) end select + ! Write lattice universes + call write_dataset(lattice_group, "universes", lattice_universes) + deallocate(lattice_universes) + call close_group(lattice_group) end do LATTICE_LOOP @@ -391,12 +371,12 @@ contains subroutine write_materials(file_id) integer(HID_T), intent(in) :: file_id - integer :: i - integer :: j - integer, allocatable :: zaids(:) + integer :: i + integer :: j + integer :: i_list + character(12), allocatable :: nucnames(:) integer(HID_T) :: materials_group integer(HID_T) :: material_group - integer(HID_T) :: sab_group type(Material), pointer :: m materials_group = create_group(file_id, "materials") @@ -422,32 +402,23 @@ contains "atom/b-cm") ! Copy ZAID for each nuclide to temporary array - allocate(zaids(m%n_nuclides)) + allocate(nucnames(m%n_nuclides)) do j = 1, m%n_nuclides - zaids(j) = nuclides(m%nuclide(j))%zaid + i_list = nuclides(m%nuclide(j))%listing + nucnames(j) = xs_listings(i_list)%alias end do ! Write temporary array to 'nuclides' - call write_dataset(material_group, "nuclides", zaids) + call write_dataset(material_group, "nuclides", nucnames) ! Deallocate temporary array - deallocate(zaids) + deallocate(nucnames) ! Write atom densities call write_dataset(material_group, "nuclide_densities", m%atom_density) - ! Write S(a,b) information if present - call write_dataset(material_group, "n_sab", m%n_sab) - if (m%n_sab > 0) then - call write_dataset(material_group, "i_sab_nuclides", m%i_sab_nuclides) - call write_dataset(material_group, "i_sab_tables", m%i_sab_tables) - - sab_group = create_group(material_group, "sab_tables") - do j = 1, m%n_sab - call write_dataset(sab_group, to_str(j), m%sab_names(j)) - end do - call close_group(sab_group) + call write_dataset(material_group, "sab_names", m%sab_names) end if call close_group(material_group) @@ -464,13 +435,14 @@ contains subroutine write_tallies(file_id) integer(HID_T), intent(in) :: file_id - integer :: i, j - integer, allocatable :: temp_array(:) ! nuclide bin array + integer :: i, j + integer :: i_list, i_xs integer(HID_T) :: tallies_group integer(HID_T) :: mesh_group integer(HID_T) :: tally_group integer(HID_T) :: filter_group - type(StructuredMesh), pointer :: m + character(20), allocatable :: str_array(:) + type(RegularMesh), pointer :: m type(TallyObject), pointer :: t tallies_group = create_group(file_id, "tallies") @@ -483,9 +455,11 @@ contains m => meshes(i) mesh_group = create_group(tallies_group, "mesh " // trim(to_str(m%id))) + ! Write internal OpenMC index for this mesh + call write_dataset(mesh_group, "index", i) + ! Write type and number of dimensions - call write_dataset(mesh_group, "type", m%type) - call write_dataset(mesh_group, "n_dimension", m%n_dimension) + call write_dataset(mesh_group, "type", "regular") ! Write mesh information call write_dataset(mesh_group, "dimension", m%dimension) @@ -504,15 +478,11 @@ contains t => tallies(i) tally_group = create_group(tallies_group, "tally " // trim(to_str(t%id))) - ! Write the name for this tally - call write_dataset(tally_group, "name_size", len(t%name)) - if (len(t%name) > 0) then - call write_dataset(tally_group, "name", t%name) - endif + ! Write internal OpenMC index for this tally + call write_dataset(tally_group, "index", i) - ! Write size of each tally - call write_dataset(tally_group, "total_score_bins", t%total_score_bins) - call write_dataset(tally_group, "total_filter_bins", t%total_filter_bins) + ! Write the name for this tally + call write_dataset(tally_group, "name", t%name) ! Write number of filters call write_dataset(tally_group, "n_filters", t%n_filters) @@ -520,10 +490,8 @@ contains FILTER_LOOP: do j = 1, t%n_filters filter_group = create_group(tally_group, "filter " // trim(to_str(j))) - ! Write type of filter - call write_dataset(filter_group, "type", t%filters(j)%type) - ! Write number of bins for this filter + call write_dataset(filter_group, "offset", t%filters(j)%offset) call write_dataset(filter_group, "n_bins", t%filters(j)%n_bins) ! Write filter bins @@ -537,46 +505,100 @@ contains ! Write name of type select case (t%filters(j)%type) case(FILTER_UNIVERSE) - call write_dataset(filter_group, "type_name", "universe") + call write_dataset(filter_group, "type", "universe") case(FILTER_MATERIAL) - call write_dataset(filter_group, "type_name", "material") + call write_dataset(filter_group, "type", "material") case(FILTER_CELL) - call write_dataset(filter_group, "type_name", "cell") + call write_dataset(filter_group, "type", "cell") case(FILTER_CELLBORN) - call write_dataset(filter_group, "type_name", "cellborn") + call write_dataset(filter_group, "type", "cellborn") case(FILTER_SURFACE) - call write_dataset(filter_group, "type_name", "surface") + call write_dataset(filter_group, "type", "surface") case(FILTER_MESH) - call write_dataset(filter_group, "type_name", "mesh") + call write_dataset(filter_group, "type", "mesh") case(FILTER_ENERGYIN) - call write_dataset(filter_group, "type_name", "energy") + call write_dataset(filter_group, "type", "energy") case(FILTER_ENERGYOUT) - call write_dataset(filter_group, "type_name", "energyout") + call write_dataset(filter_group, "type", "energyout") + case(FILTER_DISTRIBCELL) + call write_dataset(filter_group, "type", "distribcell") end select call close_group(filter_group) end do FILTER_LOOP - ! Write number of nuclide bins - call write_dataset(tally_group, "n_nuclide_bins", t%n_nuclide_bins) - ! Create temporary array for nuclide bins - allocate(temp_array(t%n_nuclide_bins)) + allocate(str_array(t%n_nuclide_bins)) NUCLIDE_LOOP: do j = 1, t%n_nuclide_bins if (t%nuclide_bins(j) > 0) then - temp_array(j) = nuclides(t%nuclide_bins(j))%zaid + i_list = nuclides(t%nuclide_bins(j))%listing + i_xs = index(xs_listings(i_list)%alias, '.') + if (i_xs > 0) then + str_array(j) = xs_listings(i_list)%alias(1:i_xs - 1) + else + str_array(j) = xs_listings(i_list)%alias + end if else - temp_array(j) = t%nuclide_bins(j) + str_array(j) = 'total' end if end do NUCLIDE_LOOP ! Write and deallocate nuclide bins - call write_dataset(tally_group, "nuclide_bins", temp_array) - deallocate(temp_array) + call write_dataset(tally_group, "nuclides", str_array) + deallocate(str_array) ! Write number of score bins call write_dataset(tally_group, "n_score_bins", t%n_score_bins) - call write_dataset(tally_group, "score_bins", t%score_bins) + allocate(str_array(size(t%score_bins))) + do j = 1, size(t%score_bins) + select case(t%score_bins(j)) + case (SCORE_FLUX) + str_array(j) = "flux" + case (SCORE_TOTAL) + str_array(j) = "total" + case (SCORE_SCATTER) + str_array(j) = "scatter" + case (SCORE_NU_SCATTER) + str_array(j) = "nu-scatter" + case (SCORE_SCATTER_N) + str_array(j) = "scatter-n" + case (SCORE_SCATTER_PN) + str_array(j) = "scatter-pn" + case (SCORE_NU_SCATTER_N) + str_array(j) = "nu-scatter-n" + case (SCORE_NU_SCATTER_PN) + str_array(j) = "nu-scatter-pn" + case (SCORE_TRANSPORT) + str_array(j) = "transport" + case (SCORE_N_1N) + str_array(j) = "n1n" + case (SCORE_ABSORPTION) + str_array(j) = "absorption" + case (SCORE_FISSION) + str_array(j) = "fission" + case (SCORE_NU_FISSION) + str_array(j) = "nu-fission" + case (SCORE_KAPPA_FISSION) + str_array(j) = "kappa-fission" + case (SCORE_CURRENT) + str_array(j) = "current" + case (SCORE_FLUX_YN) + str_array(j) = "flux-yn" + case (SCORE_TOTAL_YN) + str_array(j) = "total-yn" + case (SCORE_SCATTER_YN) + str_array(j) = "scatter-yn" + case (SCORE_NU_SCATTER_YN) + str_array(j) = "nu-scatter-yn" + case (SCORE_EVENTS) + str_array(j) = "events" + case default + str_array(j) = reaction_name(t%score_bins(j)) + end select + end do + call write_dataset(tally_group, "score_bins", str_array) + + deallocate(str_array) call close_group(tally_group) end do TALLY_METADATA @@ -585,115 +607,6 @@ contains end subroutine write_tallies -!=============================================================================== -! WRITE_NUCLIDES -!=============================================================================== - - subroutine write_nuclides(file_id) - integer(HID_T), intent(in) :: file_id - - integer :: i, j - integer :: size_total - integer :: size_xs - integer :: size_angle - integer :: size_energy - integer(HID_T) :: nuclides_group, nuclide_group - integer(HID_T) :: reactions_group, rxn_group - type(Nuclide), pointer :: nuc - type(Reaction), pointer :: rxn - type(UrrData), pointer :: urr - - nuclides_group = create_group(file_id, "nuclides") - - ! write number of nuclides - call write_dataset(nuclides_group, "n_nuclides", n_nuclides_total) - - ! Write information on each nuclide - NUCLIDE_LOOP: do i = 1, n_nuclides_total - nuc => nuclides(i) - nuclide_group = create_group(nuclides_group, nuc%name) - - ! Write internal OpenMC index for this nuclide - call write_dataset(nuclide_group, "index", i) - - ! Determine size of cross-sections - size_xs = (5 + nuc%n_reaction) * nuc%n_grid * 8 - size_total = size_xs - - ! Write some basic attributes - call write_dataset(nuclide_group, "zaid", nuc%zaid) - call write_dataset(nuclide_group, "alias", xs_listings(nuc%listing)%alias) - call write_dataset(nuclide_group, "awr", nuc%awr) - call write_dataset(nuclide_group, "kT", nuc%kT) - call write_dataset(nuclide_group, "n_grid", nuc%n_grid) - call write_dataset(nuclide_group, "n_reactions", nuc%n_reaction) - call write_dataset(nuclide_group, "n_fission", nuc%n_fission) - call write_dataset(nuclide_group, "size_xs", size_xs) - - ! ======================================================================= - ! WRITE INFORMATION ON EACH REACTION - - ! Create overall group for reactions and close it - reactions_group = create_group(nuclide_group, "reactions") - - RXN_LOOP: do j = 1, nuc%n_reaction - ! Information on each reaction - rxn => nuc%reactions(j) - rxn_group = create_group(reactions_group, trim(reaction_name(rxn%MT))) - - ! Determine size of angle distribution - if (rxn%has_angle_dist) then - size_angle = rxn%adist%n_energy * 16 + size(rxn%adist%data) * 8 - else - size_angle = 0 - end if - - ! Determine size of energy distribution - if (rxn%has_energy_dist) then - size_energy = size(rxn%edist%data) * 8 - else - size_energy = 0 - end if - - ! Write information on reaction - call write_dataset(rxn_group, "Q_value", rxn%Q_value) - call write_dataset(rxn_group, "multiplicity", rxn%multiplicity) - call write_dataset(rxn_group, "threshold", rxn%threshold) - call write_dataset(rxn_group, "size_angle", size_angle) - call write_dataset(rxn_group, "size_energy", size_energy) - - ! Accumulate data size - size_total = size_total + size_angle + size_energy - - call close_group(rxn_group) - end do RXN_LOOP - - call close_group(reactions_group) - - ! ======================================================================= - ! WRITE INFORMATION ON URR PROBABILITY TABLES - - if (nuc%urr_present) then - urr => nuc%urr_data - call write_dataset(nuclide_group, "urr_n_energy", urr%n_energy) - call write_dataset(nuclide_group, "urr_n_prob", urr%n_prob) - call write_dataset(nuclide_group, "urr_interp", urr%interp) - call write_dataset(nuclide_group, "urr_inelastic", urr%inelastic_flag) - call write_dataset(nuclide_group, "urr_absorption", urr%absorption_flag) - call write_dataset(nuclide_group, "urr_min_E", urr%energy(1)) - call write_dataset(nuclide_group, "urr_max_E", urr%energy(urr%n_energy)) - end if - - ! Write total memory used - call write_dataset(nuclide_group, "size_total", size_total) - - call close_group(nuclide_group) - end do NUCLIDE_LOOP - - call close_group(nuclides_group) - - end subroutine write_nuclides - !=============================================================================== ! WRITE_TIMING !=============================================================================== diff --git a/src/tally.F90 b/src/tally.F90 index e78e58f28..33e452a4c 100644 --- a/src/tally.F90 +++ b/src/tally.F90 @@ -9,7 +9,7 @@ module tally use mesh, only: get_mesh_bin, bin_to_mesh_indices, & get_mesh_indices, mesh_indices_to_bin, & mesh_intersects_2d, mesh_intersects_3d - use mesh_header, only: StructuredMesh + use mesh_header, only: RegularMesh use output, only: header use particle_header, only: LocalCoord, Particle use search, only: binary_search @@ -81,8 +81,8 @@ contains case (SCORE_FLUX, SCORE_FLUX_YN) if (t % estimator == ESTIMATOR_ANALOG) then ! All events score to a flux bin. We actually use a collision - ! estimator since there is no way to count 'events' exactly for - ! the flux + ! estimator in place of an analog one since there is no way to count + ! 'events' exactly for the flux if (survival_biasing) then ! We need to account for the fact that some weight was already ! absorbed @@ -92,7 +92,7 @@ contains end if score = score / material_xs % total - else if (t % estimator == ESTIMATOR_TRACKLENGTH) then + else ! For flux, we need no cross section score = flux end if @@ -111,7 +111,7 @@ contains score = p % last_wgt end if - else if (t % estimator == ESTIMATOR_TRACKLENGTH) then + else if (i_nuclide > 0) then score = micro_xs(i_nuclide) % total * atom_density * flux else @@ -129,8 +129,8 @@ contains ! reaction rate score = p % last_wgt - else if (t % estimator == ESTIMATOR_TRACKLENGTH) then - ! Note SCORE_SCATTER_N not available for tracklength. + else + ! Note SCORE_SCATTER_N not available for tracklength/collision. if (i_nuclide > 0) then score = (micro_xs(i_nuclide) % total & - micro_xs(i_nuclide) % absorption) * atom_density * flux @@ -240,7 +240,7 @@ contains score = p % last_wgt end if - else if (t % estimator == ESTIMATOR_TRACKLENGTH) then + else if (i_nuclide > 0) then score = micro_xs(i_nuclide) % absorption * atom_density * flux else @@ -271,7 +271,7 @@ contains / micro_xs(p % event_nuclide) % absorption end if - else if (t % estimator == ESTIMATOR_TRACKLENGTH) then + else if (i_nuclide > 0) then score = micro_xs(i_nuclide) % fission * atom_density * flux else @@ -314,7 +314,7 @@ contains score = keff * p % wgt_bank end if - else if (t % estimator == ESTIMATOR_TRACKLENGTH) then + else if (i_nuclide > 0) then score = micro_xs(i_nuclide) % nu_fission * atom_density * flux else @@ -347,7 +347,7 @@ contains micro_xs(p % event_nuclide) % absorption end if - else if (t % estimator == ESTIMATOR_TRACKLENGTH) then + else if (i_nuclide > 0) then score = micro_xs(i_nuclide) % kappa_fission * atom_density * flux else @@ -360,6 +360,19 @@ contains ! Simply count number of scoring events score = ONE + case (ELASTIC) + if (t % estimator == ESTIMATOR_ANALOG) then + ! Check if event MT matches + if (p % event_MT /= ELASTIC) cycle SCORE_LOOP + score = p % last_wgt + + else + if (i_nuclide > 0) then + score = micro_xs(i_nuclide) % elastic * atom_density * flux + else + score = material_xs % elastic * flux + end if + end if case default if (t % estimator == ESTIMATOR_ANALOG) then @@ -368,7 +381,7 @@ contains if (p % event_MT /= score_bin) cycle SCORE_LOOP score = p % last_wgt - else if (t % estimator == ESTIMATOR_TRACKLENGTH) then + else ! Any other cross section has to be calculated on-the-fly. For ! cross sections that are used often (e.g. n2n, ngamma, etc. for ! depletion), it might make sense to optimize this section or @@ -484,7 +497,8 @@ contains case(SCORE_FLUX_YN, SCORE_TOTAL_YN) score_index = score_index - 1 num_nm = 1 - if (t % estimator == ESTIMATOR_ANALOG) then + if (t % estimator == ESTIMATOR_ANALOG .or. & + t % estimator == ESTIMATOR_COLLISION) then uvw = p % last_uvw else if (t % estimator == ESTIMATOR_TRACKLENGTH) then uvw = p % coord(1) % uvw @@ -536,6 +550,59 @@ contains end do SCORE_LOOP end subroutine score_general +!=============================================================================== +! SCORE_ALL_NUCLIDES tallies individual nuclide reaction rates specifically when +! the user requests all. +!=============================================================================== + + subroutine score_all_nuclides(p, i_tally, flux, filter_index) + + type(Particle), intent(in) :: p + integer, intent(in) :: i_tally + real(8), intent(in) :: flux + integer, intent(in) :: filter_index + + integer :: i ! loop index for nuclides in material + integer :: i_nuclide ! index in nuclides array + real(8) :: atom_density ! atom density of single nuclide in atom/b-cm + type(TallyObject), pointer :: t + type(Material), pointer :: mat + + ! Get pointer to tally + t => tallies(i_tally) + + ! Get pointer to current material. We need this in order to determine what + ! nuclides are in the material + mat => materials(p % material) + + ! ========================================================================== + ! SCORE ALL INDIVIDUAL NUCLIDE REACTION RATES + + NUCLIDE_LOOP: do i = 1, mat % n_nuclides + + ! Determine index in nuclides array and atom density for i-th nuclide in + ! current material + i_nuclide = mat % nuclide(i) + atom_density = mat % atom_density(i) + + ! Determine score for each bin + call score_general(p, t, (i_nuclide-1)*t % n_score_bins, filter_index, & + i_nuclide, atom_density, flux) + + end do NUCLIDE_LOOP + + ! ========================================================================== + ! SCORE TOTAL MATERIAL REACTION RATES + + i_nuclide = -1 + atom_density = ZERO + + ! Determine score for each bin + call score_general(p, t, n_nuclides_total*t % n_score_bins, filter_index, & + i_nuclide, atom_density, flux) + + end subroutine score_all_nuclides + !=============================================================================== ! SCORE_ANALOG_TALLY keeps track of how many events occur in a specified cell, ! energy range, etc. Note that since these are "analog" tallies, they are only @@ -819,59 +886,6 @@ contains end subroutine score_tracklength_tally -!=============================================================================== -! SCORE_ALL_NUCLIDES tallies individual nuclide reaction rates specifically when -! the user requests all. -!=============================================================================== - - subroutine score_all_nuclides(p, i_tally, flux, filter_index) - - type(Particle), intent(in) :: p - integer, intent(in) :: i_tally - real(8), intent(in) :: flux - integer, intent(in) :: filter_index - - integer :: i ! loop index for nuclides in material - integer :: i_nuclide ! index in nuclides array - real(8) :: atom_density ! atom density of single nuclide in atom/b-cm - type(TallyObject), pointer :: t - type(Material), pointer :: mat - - ! Get pointer to tally - t => tallies(i_tally) - - ! Get pointer to current material. We need this in order to determine what - ! nuclides are in the material - mat => materials(p % material) - - ! ========================================================================== - ! SCORE ALL INDIVIDUAL NUCLIDE REACTION RATES - - NUCLIDE_LOOP: do i = 1, mat % n_nuclides - - ! Determine index in nuclides array and atom density for i-th nuclide in - ! current material - i_nuclide = mat % nuclide(i) - atom_density = mat % atom_density(i) - - ! Determine score for each bin - call score_general(p, t, (i_nuclide-1)*t % n_score_bins, filter_index, & - i_nuclide, atom_density, flux) - - end do NUCLIDE_LOOP - - ! ========================================================================== - ! SCORE TOTAL MATERIAL REACTION RATES - - i_nuclide = -1 - atom_density = ZERO - - ! Determine score for each bin - call score_general(p, t, n_nuclides_total*t % n_score_bins, filter_index, & - i_nuclide, atom_density, flux) - - end subroutine score_all_nuclides - !=============================================================================== ! SCORE_TL_ON_MESH calculate fluxes and reaction rates based on the track-length ! estimate of the flux specifically for tallies that have mesh filters. For @@ -907,7 +921,7 @@ contains logical :: start_in_mesh ! starting coordinates inside mesh? logical :: end_in_mesh ! ending coordinates inside mesh? type(TallyObject), pointer :: t - type(StructuredMesh), pointer :: m + type(RegularMesh), pointer :: m type(Material), pointer :: mat t => tallies(i_tally) @@ -1119,6 +1133,118 @@ contains end subroutine score_tl_on_mesh +!=============================================================================== +! SCORE_COLLISION_TALLY calculates fluxes and reaction rates based on the +! 1/Sigma_t estimate of the flux. This is triggered after every collision. It +! is invalid for tallies that require post-collison information because it can +! score reactions that didn't actually occur, and we don't a priori know what +! the outcome will be for reactions that we didn't sample. +!=============================================================================== + + subroutine score_collision_tally(p) + + type(Particle), intent(in) :: p + + integer :: i + integer :: i_tally + integer :: j ! loop index for scoring bins + integer :: k ! loop index for nuclide bins + integer :: filter_index ! single index for single bin + integer :: i_nuclide ! index in nuclides array (from bins) + real(8) :: flux ! collision estimate of flux + real(8) :: atom_density ! atom density of single nuclide + ! in atom/b-cm + logical :: found_bin ! scoring bin found? + type(TallyObject), pointer :: t + type(Material), pointer :: mat + + ! Determine collision estimate of flux + if (survival_biasing) then + ! We need to account for the fact that some weight was already absorbed + flux = (p % last_wgt + p % absorb_wgt) / material_xs % total + else + flux = p % last_wgt / material_xs % total + end if + + ! A loop over all tallies is necessary because we need to simultaneously + ! determine different filter bins for the same tally in order to score to it + + TALLY_LOOP: do i = 1, active_collision_tallies % size() + ! Get index of tally and pointer to tally + i_tally = active_collision_tallies % get_item(i) + t => tallies(i_tally) + + ! ======================================================================= + ! DETERMINE SCORING BIN COMBINATION + + call get_scoring_bins(p, i_tally, found_bin) + if (.not. found_bin) cycle + + ! ======================================================================= + ! CALCULATE RESULTS AND ACCUMULATE TALLY + + ! If we have made it here, we have a scoring combination of bins for this + ! tally -- now we need to determine where in the results array we should + ! be accumulating the tally values + + ! Determine scoring index for this filter combination + filter_index = sum((matching_bins(1:t%n_filters) - 1) * t % stride) + 1 + + if (t % all_nuclides) then + if (p % material /= MATERIAL_VOID) then + call score_all_nuclides(p, i_tally, flux, filter_index) + end if + else + + NUCLIDE_BIN_LOOP: do k = 1, t % n_nuclide_bins + ! Get index of nuclide in nuclides array + i_nuclide = t % nuclide_bins(k) + + if (i_nuclide > 0) then + if (p % material /= MATERIAL_VOID) then + ! Get pointer to current material + mat => materials(p % material) + + ! Determine if nuclide is actually in material + NUCLIDE_MAT_LOOP: do j = 1, mat % n_nuclides + ! If index of nuclide matches the j-th nuclide listed in the + ! material, break out of the loop + if (i_nuclide == mat % nuclide(j)) exit + + ! If we've reached the last nuclide in the material, it means + ! the specified nuclide to be tallied is not in this material + if (j == mat % n_nuclides) then + cycle NUCLIDE_BIN_LOOP + end if + end do NUCLIDE_MAT_LOOP + + atom_density = mat % atom_density(j) + else + atom_density = ZERO + end if + end if + + ! Determine score for each bin + call score_general(p, t, (k-1)*t % n_score_bins, filter_index, & + i_nuclide, atom_density, flux) + + end do NUCLIDE_BIN_LOOP + end if + + ! If the user has specified that we can assume all tallies are spatially + ! separate, this implies that once a tally has been scored to, we needn't + ! check the others. This cuts down on overhead when there are many + ! tallies specified + + if (assume_separate) exit TALLY_LOOP + + end do TALLY_LOOP + + ! Reset tally map positioning + position = 0 + + end subroutine score_collision_tally + !=============================================================================== ! GET_SCORING_BINS determines a combination of filter bins that should be scored ! for a tally based on the particle's current attributes. @@ -1136,7 +1262,7 @@ contains integer :: offset ! offset for distribcell real(8) :: E ! particle energy type(TallyObject), pointer :: t - type(StructuredMesh), pointer :: m + type(RegularMesh), pointer :: m found_bin = .true. t => tallies(i_tally) @@ -1289,7 +1415,7 @@ contains logical :: y_same ! same starting/ending y index (j) logical :: z_same ! same starting/ending z index (k) type(TallyObject), pointer :: t - type(StructuredMesh), pointer :: m + type(RegularMesh), pointer :: m TALLY_LOOP: do i = 1, active_current_tallies % size() ! Copy starting and ending location of particle @@ -1873,6 +1999,8 @@ contains call active_analog_tallies % add(i_user_tallies + i) elseif (user_tallies(i) % estimator == ESTIMATOR_TRACKLENGTH) then call active_tracklength_tallies % add(i_user_tallies + i) + elseif (user_tallies(i) % estimator == ESTIMATOR_COLLISION) then + call active_collision_tallies % add(i_user_tallies + i) end if elseif (user_tallies(i) % type == TALLY_SURFACE_CURRENT) then call active_current_tallies % add(i_user_tallies + i) diff --git a/src/track_output.F90 b/src/track_output.F90 index f4018cde3..1665ac25e 100644 --- a/src/track_output.F90 +++ b/src/track_output.F90 @@ -114,7 +114,7 @@ contains !$omp critical (FinalizeParticleTrack) file_id = file_create(fname) - call write_dataset(file_id, 'filetype', FILETYPE_TRACK) + call write_dataset(file_id, 'filetype', 'track') call write_dataset(file_id, 'revision', REVISION_TRACK) call write_dataset(file_id, 'n_particles', n_particle_tracks) call write_dataset(file_id, 'n_coords', n_coords) diff --git a/src/tracking.F90 b/src/tracking.F90 index 173babd2f..81f01b411 100644 --- a/src/tracking.F90 +++ b/src/tracking.F90 @@ -13,7 +13,7 @@ module tracking use random_lcg, only: prn use string, only: to_str use tally, only: score_analog_tally, score_tracklength_tally, & - score_surface_current + score_collision_tally, score_surface_current use track_output, only: initialize_particle_track, write_particle_track, & add_particle_track, finalize_particle_track @@ -157,6 +157,7 @@ contains ! has occurred rather than before because we need information on the ! outgoing energy for any tallies with an outgoing energy filter + if (active_collision_tallies % size() > 0) call score_collision_tally(p) if (active_analog_tallies % size() > 0) call score_analog_tally(p) ! Reset banked weight during collision diff --git a/src/trigger.F90 b/src/trigger.F90 index 4a16cd5ca..a74a64be0 100644 --- a/src/trigger.F90 +++ b/src/trigger.F90 @@ -9,6 +9,7 @@ module trigger use string, only: to_str use output, only: warning, write_message use mesh, only: mesh_indices_to_bin + use mesh_header, only: RegularMesh use trigger_header, only: TriggerObject use tally, only: TallyObject @@ -315,7 +316,7 @@ contains real(8) :: std_dev = ZERO ! temporary standard deviration of result type(TallyObject), pointer :: t ! surface current tally type(TriggerObject) :: trigger ! surface current tally trigger - type(StructuredMesh), pointer :: m ! surface current mesh + type(RegularMesh), pointer :: m ! surface current mesh ! Get pointer to mesh i_filter_mesh = t % find_filter(FILTER_MESH) diff --git a/tests/test_cmfd_feed/tallies.xml b/tests/test_cmfd_feed/tallies.xml index b20c0ad61..37edcecc2 100644 --- a/tests/test_cmfd_feed/tallies.xml +++ b/tests/test_cmfd_feed/tallies.xml @@ -2,7 +2,7 @@ - rectangular + regular -10 -1 -1 10 1 1 10 1 1 diff --git a/tests/test_cmfd_nofeed/tallies.xml b/tests/test_cmfd_nofeed/tallies.xml index b20c0ad61..37edcecc2 100644 --- a/tests/test_cmfd_nofeed/tallies.xml +++ b/tests/test_cmfd_nofeed/tallies.xml @@ -2,7 +2,7 @@ - rectangular + regular -10 -1 -1 10 1 1 10 1 1 diff --git a/tests/test_entropy/test_entropy.py b/tests/test_entropy/test_entropy.py index 9b13fd3dd..43c17da14 100644 --- a/tests/test_entropy/test_entropy.py +++ b/tests/test_entropy/test_entropy.py @@ -14,7 +14,6 @@ class EntropyTestHarness(TestHarness): # Read the statepoint file. statepoint = glob.glob(os.path.join(os.getcwd(), self._sp_name))[0] sp = StatePoint(statepoint) - sp.read_results() # Write out k-combined. outstr = 'k-combined:\n' @@ -23,7 +22,7 @@ class EntropyTestHarness(TestHarness): # Write out entropy data. outstr += 'entropy:\n' - results = ['{0:12.6E}'.format(x) for x in sp._entropy] + results = ['{0:12.6E}'.format(x) for x in sp.entropy] outstr += '\n'.join(results) + '\n' return outstr diff --git a/tests/test_filter_mesh_2d/tallies.xml b/tests/test_filter_mesh_2d/tallies.xml index e046549de..de3fa6553 100644 --- a/tests/test_filter_mesh_2d/tallies.xml +++ b/tests/test_filter_mesh_2d/tallies.xml @@ -2,7 +2,7 @@ - rectangular + regular -182.07 -182.07 182.07 182.07 17 17 @@ -13,4 +13,4 @@ total - \ No newline at end of file + diff --git a/tests/test_filter_mesh_3d/tallies.xml b/tests/test_filter_mesh_3d/tallies.xml index b2be27279..cd7f925e8 100644 --- a/tests/test_filter_mesh_3d/tallies.xml +++ b/tests/test_filter_mesh_3d/tallies.xml @@ -2,7 +2,7 @@ - rectangular + regular -182.07 -182.07 -183.00 182.07 182.07 183.00 17 17 17 @@ -13,4 +13,4 @@ total - \ No newline at end of file + diff --git a/tests/test_fixed_source/test_fixed_source.py b/tests/test_fixed_source/test_fixed_source.py index 1f154a465..c3bd34856 100644 --- a/tests/test_fixed_source/test_fixed_source.py +++ b/tests/test_fixed_source/test_fixed_source.py @@ -14,26 +14,26 @@ class FixedSourceTestHarness(TestHarness): # Read the statepoint file. statepoint = glob.glob(os.path.join(os.getcwd(), self._sp_name))[0] sp = StatePoint(statepoint) - sp.read_results() # Write out tally data. outstr = '' if self._tallies: tally_num = 1 - for tally_ind in sp._tallies: - tally = sp._tallies[tally_ind] - results = np.zeros((tally._sum.size*2, )) - results[0::2] = tally._sum.ravel() - results[1::2] = tally._sum_sq.ravel() + for tally_ind in sp.tallies: + tally = sp.tallies[tally_ind] + results = np.zeros((tally.sum.size*2, )) + results[0::2] = tally.sum.ravel() + results[1::2] = tally.sum_sq.ravel() results = ['{0:12.6E}'.format(x) for x in results] outstr += 'tally ' + str(tally_num) + ':\n' outstr += '\n'.join(results) + '\n' tally_num += 1 + gt = sp.global_tallies outstr += 'leakage:\n' - outstr += '{0:12.6E}'.format(sp._global_tallies[3][0]) + '\n' - outstr += '{0:12.6E}'.format(sp._global_tallies[3][1]) + '\n' + outstr += '{0:12.6E}'.format(gt[gt['name'] == b'leakage'][0]['sum']) + '\n' + outstr += '{0:12.6E}'.format(gt[gt['name'] == b'leakage'][0]['sum_sq']) + '\n' return outstr diff --git a/tests/test_particle_restart_eigval/results_true.dat b/tests/test_particle_restart_eigval/results_true.dat index bbc23fb6e..f34397853 100644 --- a/tests/test_particle_restart_eigval/results_true.dat +++ b/tests/test_particle_restart_eigval/results_true.dat @@ -5,7 +5,7 @@ current gen: particle id: 5.550000E+02 run mode: -2.000000E+00 +k-eigenvalue particle weight: 1.000000E+00 particle energy: diff --git a/tests/test_particle_restart_fixed/results_true.dat b/tests/test_particle_restart_fixed/results_true.dat index 701c3e133..de42a0c68 100644 --- a/tests/test_particle_restart_fixed/results_true.dat +++ b/tests/test_particle_restart_fixed/results_true.dat @@ -5,7 +5,7 @@ current gen: particle id: 9.280000E+02 run mode: -1.000000E+00 +fixed source particle weight: 1.000000E+00 particle energy: diff --git a/tests/test_score_MT/results_true.dat b/tests/test_score_MT/results_true.dat index 248f6657d..4b1c1af26 100644 --- a/tests/test_score_MT/results_true.dat +++ b/tests/test_score_MT/results_true.dat @@ -33,3 +33,69 @@ tally 1: 2.080857E-09 6.101318E-02 8.452067E-04 +tally 2: +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +3.000000E-01 +2.440000E-02 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +4.000000E-02 +6.000000E-04 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +tally 3: +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +5.724026E-03 +1.684945E-05 +5.724026E-03 +1.684945E-05 +3.250298E-01 +2.370870E-02 +1.083784E+00 +2.568556E-01 +4.449887E-05 +1.980149E-09 +4.449887E-05 +1.980149E-09 +3.526275E-02 +2.863085E-04 +1.417358E-02 +4.375519E-05 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +5.106469E-05 +8.605176E-10 +6.204277E-02 +8.555398E-04 diff --git a/tests/test_score_MT/tallies.xml b/tests/test_score_MT/tallies.xml index ef7ff8dd8..5e66ae929 100644 --- a/tests/test_score_MT/tallies.xml +++ b/tests/test_score_MT/tallies.xml @@ -6,4 +6,16 @@ n2n 16 51 102 - \ No newline at end of file + + + n2n 16 51 102 + analog + + + + + n2n 16 51 102 + collision + + + diff --git a/tests/test_score_absorption/results_true.dat b/tests/test_score_absorption/results_true.dat index bacbf26a3..9370a146f 100644 --- a/tests/test_score_absorption/results_true.dat +++ b/tests/test_score_absorption/results_true.dat @@ -18,3 +18,12 @@ tally 2: 0.000000E+00 4.000000E-01 4.240000E-02 +tally 3: +0.000000E+00 +0.000000E+00 +1.990713E+00 +8.557870E-01 +1.427399E-02 +4.420707E-05 +2.968053E-01 +1.960663E-02 diff --git a/tests/test_score_absorption/tallies.xml b/tests/test_score_absorption/tallies.xml index 8cbcef251..8b2dc2931 100644 --- a/tests/test_score_absorption/tallies.xml +++ b/tests/test_score_absorption/tallies.xml @@ -12,4 +12,10 @@ absorption - \ No newline at end of file + + + collision + absorption + + + diff --git a/tests/test_score_current/tallies.xml b/tests/test_score_current/tallies.xml index a740949fa..3f496d43a 100644 --- a/tests/test_score_current/tallies.xml +++ b/tests/test_score_current/tallies.xml @@ -2,7 +2,7 @@ - rectangular + regular -182.07 -182.07 -183.00 182.07 182.07 183.00 17 17 17 @@ -19,4 +19,4 @@ current - \ No newline at end of file + diff --git a/tests/test_score_fission/results_true.dat b/tests/test_score_fission/results_true.dat index 904c0d889..86b31aaaf 100644 --- a/tests/test_score_fission/results_true.dat +++ b/tests/test_score_fission/results_true.dat @@ -18,3 +18,12 @@ tally 2: 0.000000E+00 9.923196E-01 2.067216E-01 +tally 3: +9.036254E-01 +1.746552E-01 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +9.912744E-01 +2.192705E-01 diff --git a/tests/test_score_fission/tallies.xml b/tests/test_score_fission/tallies.xml index d56614bdf..a8b57f911 100644 --- a/tests/test_score_fission/tallies.xml +++ b/tests/test_score_fission/tallies.xml @@ -12,4 +12,10 @@ fission - \ No newline at end of file + + + collision + fission + + + diff --git a/tests/test_score_flux/results_true.dat b/tests/test_score_flux/results_true.dat index 31e1d9292..31ca238d4 100644 --- a/tests/test_score_flux/results_true.dat +++ b/tests/test_score_flux/results_true.dat @@ -13,3 +13,29 @@ tally 1: 2.880575E+01 5.605671E+01 6.804062E+02 +tally 2: +3.077754E+01 +2.017424E+02 +1.172238E+01 +3.139127E+01 +5.231699E+01 +5.870469E+02 +3.259142E+01 +2.336719E+02 +1.040924E+01 +2.432332E+01 +5.709679E+01 +6.978668E+02 +tally 3: +3.077754E+01 +2.017424E+02 +1.172238E+01 +3.139127E+01 +5.231699E+01 +5.870469E+02 +3.259142E+01 +2.336719E+02 +1.040924E+01 +2.432332E+01 +5.709679E+01 +6.978668E+02 diff --git a/tests/test_score_flux/tallies.xml b/tests/test_score_flux/tallies.xml index 30bef7174..bcde40c75 100644 --- a/tests/test_score_flux/tallies.xml +++ b/tests/test_score_flux/tallies.xml @@ -6,4 +6,16 @@ flux - \ No newline at end of file + + + flux + analog + + + + + flux + collision + + + diff --git a/tests/test_score_flux_yn/results_true.dat b/tests/test_score_flux_yn/results_true.dat index 2e5df97cf..937f810ea 100644 --- a/tests/test_score_flux_yn/results_true.dat +++ b/tests/test_score_flux_yn/results_true.dat @@ -446,3 +446,869 @@ tally 2: 1.093913E-01 2.235961E-01 5.449263E-02 +tally 3: +3.077754E+01 +2.017424E+02 +-4.040800E-01 +5.606719E-01 +-5.238239E-01 +4.460714E-01 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+5.060214E-02 +-2.914577E-01 +2.233355E-01 +1.053098E-01 +6.693123E-02 +4.541315E-01 +1.328340E-01 +-6.290732E-02 +8.910616E-02 +7.371592E-01 +1.980732E-01 +5.804701E-01 +1.711949E-01 +-6.642202E-01 +1.131866E-01 +1.779818E-01 +6.530190E-02 +-5.224739E-01 +2.534102E-01 +-1.824905E-01 +2.910309E-02 diff --git a/tests/test_score_flux_yn/tallies.xml b/tests/test_score_flux_yn/tallies.xml index 71301d0e7..e9f08bd4e 100644 --- a/tests/test_score_flux_yn/tallies.xml +++ b/tests/test_score_flux_yn/tallies.xml @@ -10,5 +10,17 @@ flux-y5 + + + + flux-y5 + analog + + + + + flux-y5 + collision + diff --git a/tests/test_score_kappafission/results_true.dat b/tests/test_score_kappafission/results_true.dat index dadcdd281..e992b4b68 100644 --- a/tests/test_score_kappafission/results_true.dat +++ b/tests/test_score_kappafission/results_true.dat @@ -9,3 +9,21 @@ tally 1: 0.000000E+00 2.035912E+02 8.999693E+03 +tally 2: +1.765331E+02 +6.974050E+03 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +1.938441E+02 +7.888708E+03 +tally 3: +1.770125E+02 +6.702714E+03 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +1.942246E+02 +8.416720E+03 diff --git a/tests/test_score_kappafission/tallies.xml b/tests/test_score_kappafission/tallies.xml index 1b63fdeeb..8fe5d5c84 100644 --- a/tests/test_score_kappafission/tallies.xml +++ b/tests/test_score_kappafission/tallies.xml @@ -6,4 +6,16 @@ kappa-fission - \ No newline at end of file + + + kappa-fission + analog + + + + + kappa-fission + collision + + + diff --git a/tests/test_score_nufission/results_true.dat b/tests/test_score_nufission/results_true.dat index 5d0b44662..33c4ff9fb 100644 --- a/tests/test_score_nufission/results_true.dat +++ b/tests/test_score_nufission/results_true.dat @@ -9,3 +9,21 @@ tally 1: 0.000000E+00 2.733038E+00 1.616903E+00 +tally 2: +2.296157E+00 +1.167084E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +2.679940E+00 +1.498454E+00 +tally 3: +2.381373E+00 +1.213497E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +2.607077E+00 +1.513932E+00 diff --git a/tests/test_score_nufission/tallies.xml b/tests/test_score_nufission/tallies.xml index d3e6963f3..2812a00b5 100644 --- a/tests/test_score_nufission/tallies.xml +++ b/tests/test_score_nufission/tallies.xml @@ -6,4 +6,16 @@ nu-fission - \ No newline at end of file + + + nu-fission + analog + + + + + nu-fission + collision + + + diff --git a/tests/test_score_scatter/results_true.dat b/tests/test_score_scatter/results_true.dat index 5f0ae8b1d..21d7d83c2 100644 --- a/tests/test_score_scatter/results_true.dat +++ b/tests/test_score_scatter/results_true.dat @@ -9,3 +9,21 @@ tally 1: 1.814004E+00 4.059013E+01 3.609499E+02 +tally 2: +0.000000E+00 +0.000000E+00 +1.169000E+01 +2.915330E+01 +3.200000E+00 +2.342600E+00 +4.064000E+01 +3.595168E+02 +tally 3: +0.000000E+00 +0.000000E+00 +1.172929E+01 +2.935812E+01 +3.185726E+00 +2.323517E+00 +4.074319E+01 +3.614902E+02 diff --git a/tests/test_score_scatter/tallies.xml b/tests/test_score_scatter/tallies.xml index a4425c0f2..b6eb73b50 100644 --- a/tests/test_score_scatter/tallies.xml +++ b/tests/test_score_scatter/tallies.xml @@ -6,4 +6,16 @@ scatter - \ No newline at end of file + + + scatter + analog + + + + + scatter + collision + + + diff --git a/tests/test_score_total/results_true.dat b/tests/test_score_total/results_true.dat index f3aa5d89b..e782fd9d0 100644 --- a/tests/test_score_total/results_true.dat +++ b/tests/test_score_total/results_true.dat @@ -9,3 +9,21 @@ tally 1: 1.831649E+00 4.088282E+01 3.662539E+02 +tally 2: +0.000000E+00 +0.000000E+00 +1.372000E+01 +4.018980E+01 +3.200000E+00 +2.342600E+00 +4.104000E+01 +3.668254E+02 +tally 3: +0.000000E+00 +0.000000E+00 +1.372000E+01 +4.018980E+01 +3.200000E+00 +2.342600E+00 +4.104000E+01 +3.668254E+02 diff --git a/tests/test_score_total/tallies.xml b/tests/test_score_total/tallies.xml index 815b84c14..02286f96c 100644 --- a/tests/test_score_total/tallies.xml +++ b/tests/test_score_total/tallies.xml @@ -6,4 +6,16 @@ total - \ No newline at end of file + + + total + analog + + + + + total + collision + + + diff --git a/tests/test_score_total_yn/results_true.dat b/tests/test_score_total_yn/results_true.dat index bcfdb9080..28a4b1627 100644 --- a/tests/test_score_total_yn/results_true.dat +++ b/tests/test_score_total_yn/results_true.dat @@ -410,3 +410,805 @@ tally 2: 3.130205E-02 -3.695818E-01 4.703480E-02 +tally 3: +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 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b/tests/test_score_total_yn/tallies.xml @@ -11,5 +11,19 @@ total-y4 U-235 total + + + + total-y4 + U-235 total + analog + + + + + total-y4 + U-235 total + collision + diff --git a/tests/test_sourcepoint_batch/test_sourcepoint_batch.py b/tests/test_sourcepoint_batch/test_sourcepoint_batch.py index 521e3bb4b..a306b9aa7 100644 --- a/tests/test_sourcepoint_batch/test_sourcepoint_batch.py +++ b/tests/test_sourcepoint_batch/test_sourcepoint_batch.py @@ -21,14 +21,12 @@ class SourcepointTestHarness(TestHarness): # Read the statepoint file. statepoint = glob.glob(os.path.join(os.getcwd(), self._sp_name))[0] sp = StatePoint(statepoint) - sp.read_results() - sp.read_source() # Get the eigenvalue information. outstr = TestHarness._get_results(self) # Add the source information. - xyz = sp._source[0]._xyz + xyz = sp.source[0]['xyz'] outstr += ' '.join(['{0:12.6E}'.format(x) for x in xyz]) outstr += "\n" diff --git a/tests/test_sourcepoint_interval/test_sourcepoint_interval.py b/tests/test_sourcepoint_interval/test_sourcepoint_interval.py index 521e3bb4b..a306b9aa7 100644 --- a/tests/test_sourcepoint_interval/test_sourcepoint_interval.py +++ b/tests/test_sourcepoint_interval/test_sourcepoint_interval.py @@ -21,14 +21,12 @@ class SourcepointTestHarness(TestHarness): # Read the statepoint file. statepoint = glob.glob(os.path.join(os.getcwd(), self._sp_name))[0] sp = StatePoint(statepoint) - sp.read_results() - sp.read_source() # Get the eigenvalue information. outstr = TestHarness._get_results(self) # Add the source information. - xyz = sp._source[0]._xyz + xyz = sp.source[0]['xyz'] outstr += ' '.join(['{0:12.6E}'.format(x) for x in xyz]) outstr += "\n" diff --git a/tests/test_sourcepoint_restart/tallies.xml b/tests/test_sourcepoint_restart/tallies.xml index 1704f56e1..67a1b50a9 100644 --- a/tests/test_sourcepoint_restart/tallies.xml +++ b/tests/test_sourcepoint_restart/tallies.xml @@ -2,7 +2,7 @@ - rectangular + regular 5 3 4 -10. -5. 0. 10. 4. 9. @@ -20,4 +20,4 @@ fission absorption total flux - \ No newline at end of file + diff --git a/tests/test_statepoint_restart/tallies.xml b/tests/test_statepoint_restart/tallies.xml index 1704f56e1..67a1b50a9 100644 --- a/tests/test_statepoint_restart/tallies.xml +++ b/tests/test_statepoint_restart/tallies.xml @@ -2,7 +2,7 @@ - rectangular + regular 5 3 4 -10. -5. 0. 10. 4. 9. @@ -20,4 +20,4 @@ fission absorption total flux - \ No newline at end of file + diff --git a/tests/testing_harness.py b/tests/testing_harness.py index 2059c46da..3dcbee667 100644 --- a/tests/testing_harness.py +++ b/tests/testing_harness.py @@ -93,7 +93,6 @@ class TestHarness(object): # Read the statepoint file. statepoint = glob.glob(os.path.join(os.getcwd(), self._sp_name))[0] sp = StatePoint(statepoint) - sp.read_results() # Write out k-combined. outstr = 'k-combined:\n' @@ -103,11 +102,11 @@ class TestHarness(object): # Write out tally data. if self._tallies: tally_num = 1 - for tally_ind in sp._tallies: - tally = sp._tallies[tally_ind] - results = np.zeros((tally._sum.size*2, )) - results[0::2] = tally._sum.ravel() - results[1::2] = tally._sum_sq.ravel() + for tally_ind in sp.tallies: + tally = sp.tallies[tally_ind] + results = np.zeros((tally.sum.size*2, )) + results[0::2] = tally.sum.ravel() + results[1::2] = tally.sum_sq.ravel() results = ['{0:12.6E}'.format(x) for x in results] outstr += 'tally ' + str(tally_num) + ':\n' @@ -204,26 +203,25 @@ class CMFDTestHarness(TestHarness): # Read the statepoint file. statepoint = glob.glob(os.path.join(os.getcwd(), self._sp_name))[0] sp = StatePoint(statepoint) - sp.read_results() # Write out the eigenvalue and tallies. outstr = TestHarness._get_results(self) # Write out CMFD data. outstr += 'cmfd indices\n' - outstr += '\n'.join(['{0:12.6E}'.format(x) for x in sp._cmfd_indices]) + outstr += '\n'.join(['{0:12.6E}'.format(x) for x in sp.cmfd_indices]) outstr += '\nk cmfd\n' - outstr += '\n'.join(['{0:12.6E}'.format(x) for x in sp._k_cmfd]) + outstr += '\n'.join(['{0:12.6E}'.format(x) for x in sp.k_cmfd]) outstr += '\ncmfd entropy\n' - outstr += '\n'.join(['{0:12.6E}'.format(x) for x in sp._cmfd_entropy]) + outstr += '\n'.join(['{0:12.6E}'.format(x) for x in sp.cmfd_entropy]) outstr += '\ncmfd balance\n' - outstr += '\n'.join(['{0:12.6E}'.format(x) for x in sp._cmfd_balance]) + outstr += '\n'.join(['{0:12.6E}'.format(x) for x in sp.cmfd_balance]) outstr += '\ncmfd dominance ratio\n' - outstr += '\n'.join(['{0:10.3E}'.format(x) for x in sp._cmfd_dominance]) + outstr += '\n'.join(['{0:10.3E}'.format(x) for x in sp.cmfd_dominance]) outstr += '\ncmfd openmc source comparison\n' - outstr += '\n'.join(['{0:12.6E}'.format(x) for x in sp._cmfd_srccmp]) + outstr += '\n'.join(['{0:12.6E}'.format(x) for x in sp.cmfd_srccmp]) outstr += '\ncmfd source\n' - cmfdsrc = np.reshape(sp._cmfd_src, np.product(sp._cmfd_indices), + cmfdsrc = np.reshape(sp.cmfd_src, np.product(sp.cmfd_indices), order='F') outstr += '\n'.join(['{0:12.6E}'.format(x) for x in cmfdsrc]) outstr += '\n' @@ -256,7 +254,7 @@ class ParticleRestartTestHarness(TestHarness): outstr += 'particle id:\n' outstr += "{0:12.6E}\n".format(p.id) outstr += 'run mode:\n' - outstr += "{0:12.6E}\n".format(p.run_mode) + outstr += "{0}\n".format(p.run_mode) outstr += 'particle weight:\n' outstr += "{0:12.6E}\n".format(p.weight) outstr += 'particle energy:\n'