diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 2cfa874..5b9fecb 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -25,7 +25,7 @@ jobs: shell: bash -el {0} run: | conda install -c conda-forge "openmc>=0.15.2" jupyter - python -m pip install pypact + python -m pip install git+https://github.com/fispact/pypact python download_fns_fusion_decay.py mkdir -p ~/nuclear_data wget -q -O - https://anl.box.com/shared/static/uhbxlrx7hvxqw27psymfbhi7bx7s6u6a.xz | tar -C ~/nuclear_data -xJ diff --git a/compare.ipynb b/compare.ipynb index 6eaf19e..ae1283b 100644 --- a/compare.ipynb +++ b/compare.ipynb @@ -21,8 +21,7 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import openmc\n", - "import openmc.deplete\n", - "import pypact as pp # can be installed with pip install pypact\n", + "import pypact as pp # needs latest version which can be installed with pip install git+https://github.com/fispact/pypact\n", "\n", "from openmc_activator import OpenmcActivator" ] @@ -101,7 +100,17 @@ "outputs": [ { "data": { - "image/png": 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", + "text/plain": [ + "Text(0, 0.5, 'Flux [n/cm$^2$/s]')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" ] @@ -114,7 +123,9 @@ "# in this case we plot the silver Ag experiment spectrum but you could plot others\n", "plt.stairs(values=flux_dict['Ag']['2000exp_5min'], edges=ebins)\n", "plt.yscale('log')\n", - "plt.xscale('log')\n" + "plt.xscale('log')\n", + "plt.xlabel('Energy [eV]') #TODO check these units\n", + "plt.ylabel('Flux [n/cm$^2$/s]') #TODO check these units" ] }, { @@ -217,26 +228,10 @@ " flux_mag_list.append(flux_mag)\n", " return days_list, flux_mag_list\n", "\n", - "# TODO consider replacing with pypact\n", "def read_mat_setup(filepath):\n", - " lines = open(filepath).readlines()\n", - " for indx, line in enumerate(lines):\n", - " spl = line.strip().split()\n", - " if 'MASS' in line:\n", - " assert(len(spl) == 3)\n", - " assert(spl[0] == 'MASS')\n", - " mass = float(spl[1]) # kg\n", - " num_entry = int(spl[2])\n", - " break\n", - " comp_dict = {}\n", - " for i in range(num_entry):\n", - " line = lines[indx+i+1]\n", - " spl = line.strip().split()\n", - " assert(len(spl) == 2)\n", - " comp_dict[spl[0]] = float(spl[1])\n", - " tot = sum(comp_dict.values())\n", - " mass_dict = {k:v/tot * mass for k,v in comp_dict.items()}\n", - " return mass_dict\n", + " ff = pp.InputData()\n", + " pp.from_file(ff, filepath)\n", + " return ff._inventorymass.entries\n", "\n", "# TODO consider replacing with pypact\n", "def read_density(filepath):\n", @@ -258,8 +253,7 @@ " input_path = str(input_path.absolute())\n", " days, flux_mag = read_irr_setup(input_path)\n", " days = np.cumsum(days)\n", - " # kg to grams\n", - " mass_dict = {k:v*1e3 for k,v in read_mat_setup(input_path).items()}\n", + " mass_dict = {k:v/100 for k,v in read_mat_setup(input_path)}\n", " setup_dict['days'][k][exp] = days\n", " setup_dict['flux_mag'][k][exp] = flux_mag\n", " setup_dict['mass'][k][exp] = mass_dict\n", @@ -611,7 +605,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": ".neutronicsworkshop2", "language": "python", "name": "python3" },