Added new stress test

run_all.sh

@@ -0,0 +1,123 @@
+#!/bin/bash
+
+set -e
+
+find . -name testing.out -delete
+find . -name build_testing.out -delete
+find . -name tallies_testing.out -delete
+
+# conda activate openmc-env
+
+BACK_DIR=${PWD}
+
+OPENMC_BIN=${CONDA_PREFIX}/bin/openmc
+PYTHON_BIN=${CONDA_PREFIX}/bin/python
+OPENMC_CROSS_SECTIONS=/opt/xdata/endfb-vii.1-hdf5/cross_sections.xml
+export OPENMC_CROSS_SECTIONS
+
+for dir in AP1000/*/
+do
+    cd "$dir"
+    ${PYTHON_BIN} *.py | tee testing.out
+
+    cd ${BACK_DIR}
+done
+
+for dir in ARC700/*/
+do
+    cd "$dir"
+    ${OPENMC_BIN} problem | tee testing.out
+
+    cd ${BACK_DIR}
+done
+
+cd BEAVRS
+./make_beavrs.py --optimal
+cd ${BACK_DIR}
+
+cd BWR
+rm -f *.xml *.h5
+jupyter nbconvert --execute BWR.ipynb --inplace | tee testing.out
+cd ${BACK_DIR}
+
+cd Depletion
+rm -f *.h5 [d-z]*.xml tallies.out
+jupyter nbconvert --execute depletion.ipynb --inplace | tee testing.out
+cd ${BACK_DIR}
+
+cd examples/hexagon
+jupyter nbconvert --execute hexagonal-lattice.ipynb --inplace | tee testing.out
+cd ../post_process
+jupyter nbconvert --execute post-processing.ipynb --inplace | tee testing.out
+cd ${BACK_DIR}
+
+cd RBMK
+jupyter nbconvert --execute RBMK.ipynb --inplace | tee testing.out
+cd ${BACK_DIR}
+
+#python3 Rep_Na_3_55.py
+
+cd SFR
+jupyter nbconvert --execute SFR.ipynb --inplace | tee testing.out
+cd ${BACK_DIR}
+
+cd stress-test/benchmark-1
+${OPENMC_BIN} | tee testing.out
+cd ../benchmark-2/case1_1
+
+cd ../case1_1
+${OPENMC_BIN} | tee testing.out
+cd ../case1_2
+${OPENMC_BIN} | tee testing.out
+cd ../case1_3
+${OPENMC_BIN} | tee testing.out
+cd ../case2_1
+${OPENMC_BIN} | tee testing.out
+cd ../case2_2
+${OPENMC_BIN} | tee testing.out
+cd ../case2_3
+${OPENMC_BIN} | tee testing.out
+
+cd ../../lattice-computations
+jupyter nbconvert --execute BEAVRS.ipynb --inplace | tee testing.out
+
+cd ../mc-performance
+${PYTHON_BIN} build.py | tee build_testing.out
+${PYTHON_BIN} generate_tallies.py | tee tallies_testing.out
+${OPENMC_BIN}
+
+#### TO DO: Add stress-test testing
+cd ${BACK_DIR}
+
+cd TRIGA
+jupyter nbconvert --execute TRIGA.ipynb --inplace | tee testing.out
+cd ${BACK_DIR}
+
+cd VHTR
+jupyter nbconvert --execute VHTR.ipynb --inplace | tee testing.out
+cd ${BACK_DIR}
+
+cd VVER
+jupyter nbconvert --execute VVER.ipynb --inplace | tee testing.out
+cd ${BACK_DIR}
+
+for dir in VVER/*/
+do
+    cd "$dir"
+    ${OPENMC_BIN} problem | tee testing.out
+
+    cd ${BACK_DIR}
+done
+
+cd WBN1
+for file in *.ipynb
+do
+    filename=$(basename -- "$file")
+    filename="${filename%.*}"
+    jupyter nbconvert --execute ${file} --inplace | tee ${filename}_testing.out
+done
+cd ${BACK_DIR}
+
+
+printf "ALL TESTS COMPLETED SUCCESSFULLY!\n"
+

setup.sh

@@ -86,7 +86,7 @@ openmc -g -n 1000000 -s 32 -t -e problem
 # tar -xzvf hdf5-1.8.13.tar.gz
 # cd hdf5-1.8.13
 # mkdir build && cd build
-# ../configure --enable-shared \
+# CC=${MPI_ROOT}/bin/mpicc ../configure --enable-shared \
 # 			 --enable-parallel \
 # 			 --prefix=${HDF5_DIR}
 # ${MAKE} -j16

stress-test/benchmarking/benchmark.py

@@ -0,0 +1,205 @@
+import argparse
+import os
+from pathlib import Path
+import re
+import shutil
+import subprocess
+import time
+
+import openmc
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-l', '--benchmarks', type=Path,
+                        default=Path('benchmarks/lists/pst-short'),
+                        help='List of benchmarks to run.')
+    parser.add_argument('-c', '--code', choices=['openmc', 'mcnp'],
+                        default='openmc',
+                        help='Code used to run benchmarks.')
+    parser.add_argument('-x', '--cross-sections', type=str,
+                        default=os.getenv('OPENMC_CROSS_SECTIONS'),
+                        help='OpenMC cross sections XML file.')
+    parser.add_argument('-s', '--suffix', type=str, default='80c',
+                        help='MCNP cross section suffix')
+    parser.add_argument('--suffix-thermal', type=str, default='20t',
+                        help='MCNP thermal scattering suffix')
+    parser.add_argument('-p', '--particles', type=int, default=10000,
+                        help='Number of source particles.')
+    parser.add_argument('-b', '--batches', type=int, default=150,
+                        help='Number of batches.')
+    parser.add_argument('-i', '--inactive', type=int, default=50,
+                        help='Number of inactive batches.')
+    parser.add_argument('-m', '--max-batches', type=int, default=10000,
+                        help='Maximum number of batches.')
+    parser.add_argument('--threads', type=int, default=None,
+                        help='Number of OpenMP threads')
+    parser.add_argument('-t', '--threshold', type=float, default=0.0001,
+                        help='Value of the standard deviation trigger on eigenvalue.')
+    parser.add_argument('--mpi-args', default="",
+                        help="MPI execute command and any additional MPI arguments")
+    args = parser.parse_args()
+
+    # Create timestamp
+    timestamp = time.strftime("%Y-%m-%d-%H%M%S")
+
+    # Check that executable exists
+    executable = 'mcnp6' if args.code == 'mcnp' else 'openmc'
+    if not shutil.which(executable, os.X_OK):
+        msg = f'Unable to locate executable {executable} in path.'
+        raise IOError(msg)
+    mpi_args = args.mpi_args.split()
+
+    # Create directory and set filename for results
+    results_dir = Path('results')
+    results_dir.mkdir(exist_ok=True)
+    outfile = results_dir / f'{timestamp}.csv'
+
+    # Get a copy of the benchmarks repository
+    if not Path('benchmarks').is_dir():
+        repo = 'https://github.com/mit-crpg/benchmarks.git'
+        subprocess.run(['git', 'clone', repo], check=True)
+
+    # Get the list of benchmarks to run
+    if not args.benchmarks.is_file():
+        msg = f'Unable to locate benchmark list {args.benchmarks}.'
+        raise IOError(msg)
+    with open(args.benchmarks) as f:
+        benchmarks = [Path(line) for line in f.read().split()]
+
+    # Set cross sections
+    if args.cross_sections is not None:
+        os.environ["OPENMC_CROSS_SECTIONS"] = args.cross_sections
+
+    # Prepare and run benchmarks
+    for i, benchmark in enumerate(benchmarks):
+        print(f"{i + 1} {benchmark} ", end="", flush=True)
+
+        path = 'benchmarks' / benchmark
+
+        if args.code == 'openmc':
+            openmc.reset_auto_ids()
+
+            # Remove old statepoint files
+            for f in path.glob('statepoint.*.h5'):
+                os.remove(f)
+
+            # Modify settings
+            settings = openmc.Settings.from_xml(path / 'settings.xml')
+            settings.particles = args.particles
+            settings.inactive = args.inactive
+            settings.batches = args.batches
+            settings.keff_trigger = {'type': 'std_dev',
+                                     'threshold': args.threshold}
+            settings.trigger_active = True
+            settings.trigger_max_batches = args.max_batches
+            settings.output = {'tallies': False}
+            settings.export_to_xml(path)
+
+            # Re-generate materials if Python script is present
+            genmat_script = path / "generate_materials.py"
+            if genmat_script.is_file():
+                subprocess.run(["python", "generate_materials.py"], cwd=path)
+
+            # Run benchmark
+            arg_list = mpi_args + ['openmc']
+            if args.threads is not None:
+                arg_list.extend(['-s', f'{args.threads}'])
+            proc = subprocess.run(
+                arg_list,
+                cwd=path,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.STDOUT,
+                universal_newlines=True,
+            )
+
+            # Determine last statepoint
+            t_last = 0
+            last_statepoint = None
+            for sp in path.glob('statepoint.*.h5'):
+                mtime = sp.stat().st_mtime
+                if mtime >= t_last:
+                    t_last = mtime
+                    last_statepoint = sp
+
+            # Read k-effective mean and standard deviation from statepoint
+            if last_statepoint is not None:
+                with openmc.StatePoint(last_statepoint) as sp:
+                    mean = sp.keff.nominal_value
+                    stdev = sp.keff.std_dev
+
+        else:
+            # Read input file
+            with open(path / 'input', 'r') as f:
+                lines = f.readlines()
+
+            # Update criticality source card
+            line = f'kcode {args.particles} 1 {args.inactive} {args.batches}\n'
+            for i in range(len(lines)):
+                if lines[i].strip().startswith('kcode'):
+                    lines[i] = line
+                    break
+
+            # Update cross section suffix
+            match = '(7[0-4]c)|(8[0-6]c)'
+            if not re.match(match, args.suffix):
+                msg = f'Unsupported cross section suffix {args.suffix}.'
+                raise ValueError(msg)
+            lines = [re.sub(match, args.suffix, x) for x in lines]
+
+            # Update thermal cross section suffix
+            match = r'\.[1-9][0-9]t'
+            lines = [re.sub(match, f'.{args.suffix_thermal}', x) for x in lines]
+
+            # Write new input file
+            with open(path / 'input', 'w') as f:
+                f.write(''.join(lines))
+
+            # Remove old MCNP output files
+            for f in ('outp', 'runtpe', 'srctp'):
+                try:
+                    os.remove(path / f)
+                except OSError:
+                    pass
+
+            # Run benchmark and capture and print output
+            arg_list = mpi_args + [executable, 'inp=input']
+            if args.threads is not None:
+                arg_list.extend(['tasks', f'{args.threads}'])
+            proc = subprocess.run(
+                arg_list,
+                cwd=path,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.STDOUT,
+                universal_newlines=True
+            )
+
+            # Read k-effective mean and standard deviation from output
+            with open(path / 'outp', 'r') as f:
+                for line in f:
+                    if line.strip().startswith('col/abs/trk len'):
+                        words = line.split()
+                        mean = float(words[2])
+                        stdev = float(words[3])
+                        break
+                else:
+                    mean = stdev = ""
+
+        # Write output to file
+        with open(path / f"output_{timestamp}", "w") as fh:
+            fh.write(proc.stdout)
+
+        if proc.returncode != 0:
+            mean = stdev = ""
+            print()
+        else:
+            # Display k-effective
+            print(f"{mean:.5f} ± {stdev:.5f}" if mean else "")
+
+        # Write results
+        words = str(benchmark).split('/')
+        name = words[1]
+        case = '/' + words[3] if len(words) > 3 else ''
+        line = f'{name}{case},{mean},{stdev}\n'
+        with open(outfile, 'a') as f:
+            f.write(line)

stress-test/benchmarking/plot.py

@@ -0,0 +1,194 @@
+from argparse import ArgumentParser
+from fnmatch import fnmatch
+from math import sqrt
+import os
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+from matplotlib.patches import Polygon
+import numpy as np
+
+from .results import get_result_dataframe, get_icsbep_dataframe, abbreviated_name
+
+
+def plot(files, labels=None, plot_type='keff', match=None, show_mean=True,
+         show_shaded=True, show_uncertainties=True):
+    """For all benchmark cases, produce a plot comparing the k-effective mean
+    from the calculation to the experimental value along with uncertainties.
+
+    Parameters
+    ----------
+    files : iterable of str
+        Name of a results file produced by the benchmarking script.
+    labels: iterable of str
+        Labels for each dataset to use in legend
+    plot_type : {'keff', 'diff'}
+        Type of plot to produce. A 'keff' plot shows the ratio of the
+        calculation k-effective mean to the experimental value (C/E). A 'diff'
+        plot shows the difference between C/E values for different
+        calculations. Default is 'keff'.
+    match : str
+        Pattern to match benchmark names to
+    show_mean : bool
+        Whether to show bar/line indicating mean value
+    show_shaded : bool
+        Whether to show shaded region indicating uncertainty of mean
+    show_uncertainties : bool
+        Whether to show uncertainties for individual cases
+
+    Returns
+    -------
+    matplotlib.axes.Axes
+        A matplotlib.axes.Axes object
+
+    """
+    if labels is None:
+        labels = [Path(f).name for f in files]
+
+    # Read data from spreadsheets
+    dataframes = {}
+    for csvfile, label in zip(files, labels):
+        dataframes[label] = get_result_dataframe(csvfile).dropna()
+
+    # Get model keff and uncertainty from ICSBEP
+    icsbep = get_icsbep_dataframe()
+
+    # Determine common benchmarks
+    base = labels[0]
+    index = dataframes[base].index
+    for df in dataframes.values():
+        index = index.intersection(df.index)
+
+    # Applying matching as needed
+    if match is not None:
+        cond = index.map(lambda x: fnmatch(x, match))
+        index = index[cond]
+
+    # Setup x values (integers) and corresponding tick labels
+    n = index.size
+    x = np.arange(1, n + 1)
+    xticklabels = index.map(abbreviated_name)
+
+    fig, ax = plt.subplots(figsize=(17, 6))
+
+    if plot_type == 'diff':
+        # Check that two results files are specified
+        if len(files) < 2:
+            raise ValueError('Must provide two or more files to create a "diff" plot')
+
+        kwargs = {'mec': 'black', 'mew': 0.15, 'fmt': 'o'}
+
+        keff0 = dataframes[base]['keff'].loc[index]
+        stdev0 = 1.96*dataframes[base]['stdev'].loc[index]
+        for i, label in enumerate(labels[1:]):
+            df = dataframes[label]
+            keff_i = df['keff'].loc[index]
+            stdev_i = 1.96*df['stdev'].loc[index]
+
+            diff = (keff_i - keff0) * 1e5
+            err = np.sqrt(stdev_i**2 + stdev0**2) * 1e5
+            kwargs['label'] = labels[i + 1] + ' - ' + labels[0]
+            if show_uncertainties:
+                ax.errorbar(x, diff, yerr=err, color=f'C{i}', **kwargs)
+            else:
+                ax.plot(x, diff, color=f'C{i}', **kwargs)
+
+            # Plot mean difference
+            if show_mean:
+                mu = diff.mean()
+                if show_shaded:
+                    sigma = diff.std() / sqrt(n)
+                    verts = [(0, mu - sigma), (0, mu + sigma), (n+1, mu + sigma), (n+1, mu - sigma)]
+                    poly = Polygon(verts, facecolor=f'C{i}', alpha=0.5)
+                    ax.add_patch(poly)
+                else:
+                    ax.plot([-1, n], [mu, mu], '-', color=f'C{i}', lw=1.5)
+
+        # Define y-axis label
+        ylabel = r'$\Delta k_\mathrm{eff}$ [pcm]'
+
+    else:
+        for i, (label, df) in enumerate(dataframes.items()):
+            # Calculate keff C/E and its standard deviation
+            coe = (df['keff'] / icsbep['keff']).loc[index]
+            stdev = 1.96 * df['stdev'].loc[index]
+
+            # Plot keff C/E
+            kwargs = {'color': f'C{i}', 'mec': 'black', 'mew': 0.15, 'label': label}
+            if show_uncertainties:
+                ax.errorbar(x, coe, yerr=stdev, fmt='o', **kwargs)
+            else:
+                ax.plot(x, coe, 'o', **kwargs)
+
+            # Plot mean C/E
+            if show_mean:
+                mu = coe.mean()
+                sigma = coe.std() / sqrt(n)
+                if show_shaded:
+                    verts = [(0, mu - sigma), (0, mu + sigma), (n+1, mu + sigma), (n+1, mu - sigma)]
+                    poly = Polygon(verts, facecolor=f'C{i}', alpha=0.5)
+                    ax.add_patch(poly)
+                else:
+                    ax.plot([-1, n], [mu, mu], '-', color=f'C{i}', lw=1.5)
+
+        # Show shaded region of benchmark model uncertainties
+        unc = icsbep['stdev'].loc[index]
+        vert = np.block([[x, x[::-1]], [1 + unc, 1 - unc[::-1]]]).T
+        poly = Polygon(vert, facecolor='gray', edgecolor=None, alpha=0.2)
+        ax.add_patch(poly)
+
+        # Define axes labels and title
+        ylabel = r'$k_\mathrm{eff}$ C/E'
+
+    # Configure plot
+    ax.set_axisbelow(True)
+    ax.set_xlim((0, n+1))
+    ax.set_xticks(x)
+    ax.set_xticklabels(xticklabels, rotation='vertical')
+    ax.tick_params(axis='x', which='major', labelsize=10)
+    ax.tick_params(axis='y', which='major', labelsize=14)
+    ax.set_xlabel('Benchmark case', fontsize=18)
+    ax.set_ylabel(ylabel, fontsize=18)
+    ax.grid(True, which='both', color='lightgray', ls='-', alpha=0.7)
+    ax.legend(numpoints=1)
+    return ax
+
+
+def main():
+    """Produce plot of benchmark results"""
+    parser = ArgumentParser()
+    parser.add_argument('files', nargs='+', help='Result CSV files')
+    parser.add_argument('--labels', help='Comma-separated list of dataset labels')
+    parser.add_argument('--plot-type', choices=['keff', 'diff'], default='keff')
+    parser.add_argument('--match', help='Pattern to match benchmark names to')
+    parser.add_argument('--show-mean', action='store_true', help='Show line/bar indicating mean')
+    parser.add_argument('--no-show-mean', dest='show_mean', action='store_false',
+                        help='Do not show line/bar indicating mean')
+    parser.add_argument('--show-uncertainties', action='store_true',
+                        help='Show uncertainty bars on individual cases')
+    parser.add_argument('--no-show-uncertainties', dest='show_uncertainties', action='store_false',
+                        help='Do not show uncertainty bars on individual cases')
+    parser.add_argument('--show-shaded', action='store_true',
+                        help='Show shaded region indicating uncertainty of mean C/E')
+    parser.add_argument('--no-show-shaded', dest='show_shaded', action='store_false',
+                        help='Do not show shaded region indicating uncertainty of mean C/E')
+    parser.add_argument('-o', '--output', help='Filename to save to')
+    parser.set_defaults(show_uncertainties=True, show_shaded=True, show_mean=True)
+    args = parser.parse_args()
+
+    if args.labels is not None:
+        args.labels = args.labels.split(',')
+
+    ax = plot(
+        args.files,
+        labels=args.labels,
+        plot_type=args.plot_type,
+        match=args.match,
+        show_mean=args.show_mean,
+        show_shaded=args.show_shaded,
+        show_uncertainties=args.show_uncertainties,
+    )
+    if args.output is not None:
+        plt.savefig(args.output, bbox_inches='tight', transparent=True)
+    else:
+        plt.show()

stress-test/benchmarking/report.py

@@ -0,0 +1,119 @@
+from argparse import ArgumentParser
+from fnmatch import fnmatch
+from pathlib import Path
+
+from openmc import __version__
+
+from .results import get_result_dataframe, get_icsbep_dataframe
+
+def write_document(result, output, match=None):
+    """Write LaTeX document section with preamble, run info, and table
+    entries for all benchmark data comparing the calculated and
+    experimental values along with uncertainties.
+
+    Parameters
+    ----------
+    result : str
+        Name of a result csv file produced by the benchmarking script.
+    output : str
+        Name of the file to be written, ideally a .tex file
+    match : str
+        Pattern to match benchmark names to
+
+    """
+    # Define document preamble
+    preamble = [
+        r'\documentclass[12pt]{article}',
+        r'\usepackage[letterpaper, margin=1in]{geometry}',
+        r'\usepackage{dcolumn}',
+        r'\usepackage{tabularx}',
+        r'\usepackage{booktabs}',
+        r'\usepackage{longtable}',
+        r'\usepackage{fancyhdr}',
+        r'\usepackage{siunitx}',
+        r'\setlength\LTcapwidth{5.55in}',
+        r'\setlength\LTleft{0.5in}',
+        r'\setlength\LTright{0.5in}'
+    ]
+
+    # Define document start and end snippets
+    doc_start = [r'\begin{document}', r'\part*{Benchmark Results}']
+    doc_end = [r'\end{document}']
+
+    # Convert from list to string
+    result = result[0]
+
+    label = Path(result).name
+
+    # Read data from spreadsheet
+    dataframes = {}
+    dataframes[label] = get_result_dataframe(result).dropna()
+
+    # Get model keff and uncertainty from ICSBEP
+    icsbep = get_icsbep_dataframe()
+
+    # Determine ICSBEP case names
+    base = label
+    index = dataframes[base].index
+
+    df = dataframes[label]
+
+    # Applying matching as needed
+    if match is not None:
+        cond = index.map(lambda x: fnmatch(x, match))
+        index = index[cond]
+
+    # Custom Table Description and Caption
+    desc = (r'Table \ref{tab:1} uses (nuclear data info here) and openmc '
+            f'version {__version__} to evaluate ICSBEP benchmarks.')
+    caption = r'\caption{\label{tab:1} Criticality (' + label + r') Benchmark Results}\\'
+
+    # Define Table Entry
+    table = [
+        desc,
+        r'\begin{longtable}{lcccc}',
+        caption,
+        r'\endfirsthead',
+        r'\midrule',
+        r'\multicolumn{5}{r}{Continued on Next Page}\\',
+        r'\midrule',
+        r'\endfoot',
+        r'\bottomrule',
+        r'\endlastfoot',
+        r'\toprule',
+        r'& Exp. $k_{\textrm{eff}}$&Exp. unc.& Calc. $k_{\textrm{eff}}$&Calc. unc.\\',
+        r'\midrule',
+        '% DATA',
+        r'\end{longtable}'
+    ]
+
+    for case in index:
+        # Obtain and format calculated values
+        keff = '{:.6f}'.format(df['keff'].loc[case])
+        keff = r'\num{' + keff + '}'
+
+        stdev = '{:.6f}'.format(df['stdev'].loc[case])
+        stdev = r'\num{' + stdev + '}'
+
+        # Obtain and format experimental values
+        icsbep_keff = '{:.4f}'.format(icsbep['keff'].loc[case])
+        icsbep_stdev = '{:.4f}'.format(icsbep['stdev'].loc[case])
+
+        # Insert data values into table as separate entries
+        table.insert(-1, rf'{case}&{icsbep_keff}&{icsbep_stdev}&{keff}&{stdev}\\')
+
+    # Write all accumulated lines
+    with open(output, 'w') as tex:
+        tex.writelines('\n'.join(preamble + doc_start + table + doc_end))
+
+
+def main():
+    """Produce LaTeX document with tabulated benchmark results"""
+
+    parser = ArgumentParser()
+    parser.add_argument('result', nargs='+', help='Result CSV file')
+    parser.add_argument('--match', help='Pattern to match benchmark names to')
+    parser.add_argument('-o', '--output', default='report.tex', help='Filename to save to')
+    args = parser.parse_args()
+
+    write_document(args.result, args.output, args.match)

stress-test/benchmarking/results.py

@@ -0,0 +1,75 @@
+import csv
+from pathlib import Path
+
+import pandas as pd
+
+
+def abbreviated_name(name):
+    """Return short name for ICSBEP benchmark cases
+
+    Parameters
+    ----------
+    name : str
+        ICSBEP benchmark name, e.g. "pu-met-fast-021/case-2"
+
+    Returns
+    -------
+    str
+        Abbreviated name, e.g. "pmf21-2"
+
+    """
+    model, *case = name.split('/')
+    volume, form, spectrum, number = model.split('-')
+    abbreviation = volume[0] + form[0] + spectrum[0]
+    if case:
+        casenum = case[0].replace('case', '')
+    else:
+        casenum = ''
+    return f'{abbreviation}{int(number)}{casenum}'
+
+
+def get_result_dataframe(filename):
+    """Read the data from a file produced by the benchmarking script.
+
+    Parameters
+    ----------
+    filename : str
+        Name of a results file produced by the benchmarking script.
+
+    Returns
+    -------
+    pandas.DataFrame
+        Dataframe with 'keff' and 'stdev' columns. The benchmark name is used as
+        the index in the dataframe.
+
+    """
+    return pd.read_csv(
+        filename,
+        header=None,
+        names=['name', 'keff', 'stdev'],
+        usecols=[0, 1, 2],
+        index_col="name",
+    )
+
+
+def get_icsbep_dataframe():
+    """Read the benchmark model k-effective means and uncertainties.
+
+    Returns
+    -------
+    pandas.DataFrame
+        Dataframe with 'keff' and 'stdev' columns. The benchmark name is used as
+        the index in the dataframe.
+
+    """
+    cwd = Path(__file__).parent
+    index = []
+    keff = []
+    stdev = []
+    with open(cwd / 'uncertainties.csv', 'r') as csvfile:
+        reader = csv.reader(csvfile, skipinitialspace=True)
+        for benchmark, case, mean, uncertainty in reader:
+            index.append(f'{benchmark}/{case}' if case else benchmark)
+            keff.append(float(mean))
+            stdev.append(float(uncertainty))
+    return pd.DataFrame({'keff': keff, 'stdev': stdev}, index=index)

stress-test/benchmarking/uncertainties.csv

@@ -0,0 +1,487 @@
+heu-comp-inter-003, case-1, 1.0, 0.0057
+heu-comp-inter-003, case-2, 1.0, 0.0061
+heu-comp-inter-003, case-3, 1.0, 0.0056
+heu-comp-inter-003, case-4, 1.0, 0.0055
+heu-comp-inter-003, case-5, 1.0, 0.0047
+heu-comp-inter-003, case-6, 1.0, 0.0047
+heu-comp-inter-003, case-7, 1.0, 0.0050
+heu-met-fast-001, case-1, 1.0, 0.001
+heu-met-fast-001, case-2, 1.0, 0.001
+heu-met-fast-002, case-1, 1.0, 0.003
+heu-met-fast-002, case-2, 1.0, 0.003
+heu-met-fast-002, case-3, 1.0, 0.003
+heu-met-fast-002, case-4, 1.0, 0.003
+heu-met-fast-002, case-5, 1.0, 0.003
+heu-met-fast-002, case-6, 1.0, 0.003
+heu-met-fast-003, case-1, 1.0, 0.005
+heu-met-fast-003, case-2, 1.0, 0.005
+heu-met-fast-003, case-3, 1.0, 0.005
+heu-met-fast-003, case-4, 1.0, 0.003
+heu-met-fast-003, case-5, 1.0, 0.003
+heu-met-fast-003, case-6, 1.0, 0.003
+heu-met-fast-003, case-7, 1.0, 0.003
+heu-met-fast-003, case-8, 1.0, 0.005
+heu-met-fast-003, case-9, 1.0, 0.005
+heu-met-fast-003, case-10, 1.0, 0.005
+heu-met-fast-003, case-11, 1.0, 0.005
+heu-met-fast-003, case-12, 1.0, 0.003
+heu-met-fast-004, case-1, 1.0020, 0.0
+heu-met-fast-004, case-2, 0.9985, 0.0
+heu-met-fast-008, , 0.9989, 0.0016
+heu-met-fast-009, case-1, 0.9992, 0.0015
+heu-met-fast-009, case-2, 0.9992, 0.0015
+heu-met-fast-011, , 0.9989, 0.0015
+heu-met-fast-012, , 0.9992, 0.0018
+heu-met-fast-013, , 0.9990, 0.0015
+heu-met-fast-014, , 0.9989, 0.0017
+heu-met-fast-015, , 0.9996, 0.0017
+heu-met-fast-018, case-1, 1.0, 0.0014
+heu-met-fast-018, case-2, 1.0, 0.0014
+heu-met-fast-019, case-1, 1.0, 0.0028
+heu-met-fast-019, case-2, 1.0, 0.0028
+heu-met-fast-020, case-1, 1.0, 0.0028
+heu-met-fast-020, case-2, 1.0, 0.0028
+heu-met-fast-021, case-1, 1.0, 0.0024
+heu-met-fast-021, case-2, 1.0, 0.0024
+heu-met-fast-022, case-1, 1.0, 0.0019
+heu-met-fast-022, case-2, 1.0, 0.0019
+heu-met-fast-026, b-1, 0.9982, 0.0042
+heu-met-fast-026, b-2, 0.9982, 0.0042
+heu-met-fast-026, b-3, 1.0, 0.0038
+heu-met-fast-026, b-4, 1.0, 0.0038
+heu-met-fast-026, b-5, 1.0, 0.0038
+heu-met-fast-026, b-6, 0.9982, 0.0042
+heu-met-fast-026, b-7, 0.9982, 0.0042
+heu-met-fast-026, b-8, 1.0, 0.0038
+heu-met-fast-026, b-9, 1.0, 0.0038
+heu-met-fast-026, b-10, 1.0, 0.0038
+heu-met-fast-026, c-1, 0.9982, 0.0042
+heu-met-fast-026, c-2, 0.9982, 0.0042
+heu-met-fast-026, c-3, 0.9982, 0.0042
+heu-met-fast-026, c-4, 1.0, 0.0038
+heu-met-fast-026, c-5, 1.0, 0.0038
+heu-met-fast-026, c-6, 1.0, 0.0038
+heu-met-fast-026, c-7, 1.0, 0.0038
+heu-met-fast-026, c-8, 0.9982, 0.0042
+heu-met-fast-026, c-9, 0.9982, 0.0042
+heu-met-fast-026, c-10, 1.0, 0.0038
+heu-met-fast-026, c-11, 1.0, 0.0038
+heu-met-fast-026, c-12, 1.0, 0.0038
+heu-met-fast-026, d-1, 0.9982, 0.0042
+heu-met-fast-026, d-2, 0.9982, 0.0042
+heu-met-fast-026, d-3, 1.0, 0.0038
+heu-met-fast-026, d-4, 1.0, 0.0038
+heu-met-fast-026, d-5, 1.0, 0.0038
+heu-met-fast-026, d-6, 0.9982, 0.0042
+heu-met-fast-026, d-7, 0.9982, 0.0042
+heu-met-fast-026, d-8, 1.0, 0.0038
+heu-met-fast-026, d-9, 1.0, 0.0038
+heu-met-fast-026, d-10, 1.0, 0.0038
+heu-met-fast-027, , 1.0, 0.0025
+heu-met-fast-028, , 1.0, 0.0030
+heu-met-fast-029, , 1.0, 0.0020
+heu-met-fast-032, case-1, 1.0, 0.0016
+heu-met-fast-032, case-2, 1.0, 0.0027
+heu-met-fast-032, case-3, 1.0, 0.0017
+heu-met-fast-032, case-4, 1.0, 0.0017
+heu-met-fast-034, case-1, 0.9990, 0.0012
+heu-met-fast-034, case-2, 0.9990, 0.0012
+heu-met-fast-034, case-3, 0.9990, 0.0012
+heu-met-fast-041, case-1, 1.0013, 0.0030
+heu-met-fast-041, case-2, 1.0022, 0.0043
+heu-met-fast-041, case-3, 1.0006, 0.0029
+heu-met-fast-041, case-4, 1.0006, 0.0025
+heu-met-fast-041, case-5, 1.0006, 0.0031
+heu-met-fast-041, case-6, 1.0006, 0.0045
+heu-met-fast-058, case-1, 1.0, 0.0025
+heu-met-fast-058, case-2, 1.0, 0.0035
+heu-met-fast-058, case-3, 1.0, 0.0027
+heu-met-fast-058, case-4, 1.0, 0.0021
+heu-met-fast-058, case-5, 1.0, 0.0033
+heu-met-fast-066, case-1, 1.0030, 0.0033
+heu-met-fast-066, case-2, 1.0023, 0.0029
+heu-met-fast-066, case-3, 1.0023, 0.0026
+heu-met-fast-066, case-4, 1.0043, 0.0043
+heu-met-fast-066, case-5, 1.0030, 0.0033
+heu-met-fast-066, case-6, 1.0028, 0.0030
+heu-met-fast-066, case-7, 1.0048, 0.0039
+heu-met-fast-066, case-8, 1.0039, 0.0040
+heu-met-fast-066, case-9, 1.0027, 0.0036
+heu-met-fast-069, case-1, 0.9998, 0.0004
+heu-met-fast-069, case-2, 0.9994, 0.0004
+heu-met-inter-006, case-1, 0.9977, 0.0008
+heu-met-inter-006, case-2, 1.0001, 0.0008
+heu-met-inter-006, case-3, 1.0015, 0.0009
+heu-met-inter-006, case-4, 1.0016, 0.0008
+heu-met-therm-001, case-1, 1.0010, 0.0060
+heu-met-therm-001, case-2, 1.0010, 0.0060
+heu-met-therm-014, case-1, 0.9931, 0.0015
+heu-met-therm-014, case-2, 0.9939, 0.0015
+heu-sol-therm-001, case-1, 1.0004, 0.0060
+heu-sol-therm-001, case-2, 1.0021, 0.0072
+heu-sol-therm-001, case-3, 1.0003, 0.0035
+heu-sol-therm-001, case-4, 1.0008, 0.0053
+heu-sol-therm-001, case-5, 1.0001, 0.0049
+heu-sol-therm-001, case-6, 1.0002, 0.0046
+heu-sol-therm-001, case-7, 1.0008, 0.0040
+heu-sol-therm-001, case-8, 0.9998, 0.0038
+heu-sol-therm-001, case-9, 1.0008, 0.0054
+heu-sol-therm-001, case-10, 0.9993, 0.0054
+heu-sol-therm-004, case-1, 1.0, 0.00325
+heu-sol-therm-004, case-2, 1.0, 0.00355
+heu-sol-therm-004, case-3, 1.0, 0.0039
+heu-sol-therm-004, case-4, 1.0, 0.00455
+heu-sol-therm-004, case-5, 1.0, 0.0052
+heu-sol-therm-004, case-6, 1.0, 0.00585
+heu-sol-therm-009, case-1, 0.9990, 0.0043
+heu-sol-therm-009, case-2, 1.0, 0.0039
+heu-sol-therm-009, case-3, 1.0, 0.0036
+heu-sol-therm-009, case-4, 0.9986, 0.0035
+heu-sol-therm-010, case-1, 1.0, 0.0029
+heu-sol-therm-010, case-2, 1.0, 0.0029
+heu-sol-therm-010, case-3, 1.0, 0.0029
+heu-sol-therm-010, case-4, 0.9992, 0.0029
+heu-sol-therm-011, case-1, 1.0, 0.0023
+heu-sol-therm-011, case-2, 1.0, 0.0023
+heu-sol-therm-012, , 0.9999, 0.0058
+heu-sol-therm-013, case-1, 1.0012, 0.0026
+heu-sol-therm-013, case-2, 1.0007, 0.0036
+heu-sol-therm-013, case-3, 1.0009, 0.0036
+heu-sol-therm-013, case-4, 1.0003, 0.0036
+heu-sol-therm-020, case-1, 0.9966, 0.0116
+heu-sol-therm-020, case-2, 0.9956, 0.0093
+heu-sol-therm-020, case-3, 0.9957, 0.0079
+heu-sol-therm-020, case-4, 0.9955, 0.0078
+heu-sol-therm-020, case-5, 0.9959, 0.0077
+heu-sol-therm-032, , 1.0015, 0.0026
+heu-sol-therm-042, case-1, 0.9957, 0.0039
+heu-sol-therm-042, case-2, 0.9965, 0.0036
+heu-sol-therm-042, case-3, 0.9994, 0.0028
+heu-sol-therm-042, case-4, 1.0, 0.0034
+heu-sol-therm-042, case-5, 1.0, 0.0034
+heu-sol-therm-042, case-6, 1.0, 0.0037
+heu-sol-therm-042, case-7, 1.0, 0.0036
+heu-sol-therm-042, case-8, 1.0, 0.0035
+heu-sol-therm-043, case-1, 0.9986, 0.0031
+heu-sol-therm-043, case-2, 0.9995, 0.0026
+heu-sol-therm-043, case-3, 0.9990, 0.0025
+ieu-met-fast-001, case-1, 0.9988, 0.0009
+ieu-met-fast-001, case-2, 0.9997, 0.0009
+ieu-met-fast-001, case-3, 0.9993, 0.0003
+ieu-met-fast-001, case-4, 1.0002, 0.0003
+ieu-met-fast-002, , 1.0, 0.0030
+ieu-met-fast-003, case-1, 1.0, 0.0017
+ieu-met-fast-003, case-2, 1.0, 0.0017
+ieu-met-fast-004, case-1, 1.0, 0.0030
+ieu-met-fast-004, case-2, 1.0, 0.0030
+ieu-met-fast-005, case-1, 1.0, 0.0021
+ieu-met-fast-005, case-2, 1.0, 0.0021
+ieu-met-fast-006, case-1, 1.0, 0.0023
+ieu-met-fast-006, case-2, 1.0, 0.0023
+ieu-met-fast-007, case-1, 1.0045, 0.0007
+ieu-met-fast-007, case-2, 1.0049, 0.0008
+ieu-met-fast-007, case-4, 1.0049, 0.0008
+ieu-met-fast-009, , 1.0, 0.0053
+leu-comp-therm-008, case-1, 1.0007, 0.0012
+leu-comp-therm-008, case-2, 1.0007, 0.0012
+leu-comp-therm-008, case-3, 1.0007, 0.0012
+leu-comp-therm-008, case-4, 1.0007, 0.0012
+leu-comp-therm-008, case-5, 1.0007, 0.0012
+leu-comp-therm-008, case-6, 1.0007, 0.0012
+leu-comp-therm-008, case-7, 1.0007, 0.0012
+leu-comp-therm-008, case-8, 1.0007, 0.0012
+leu-comp-therm-008, case-9, 1.0007, 0.0012
+leu-comp-therm-008, case-10, 1.0007, 0.0012
+leu-comp-therm-008, case-11, 1.0007, 0.0012
+leu-comp-therm-008, case-12, 1.0007, 0.0012
+leu-comp-therm-008, case-13, 1.0007, 0.0012
+leu-comp-therm-008, case-14, 1.0007, 0.0012
+leu-comp-therm-008, case-15, 1.0007, 0.0012
+leu-comp-therm-008, case-16, 1.0007, 0.0012
+leu-comp-therm-008, case-17, 1.0007, 0.0012
+leu-sol-therm-001, , 0.9991, 0.0029
+leu-sol-therm-002, case-1, 1.0038, 0.0040
+leu-sol-therm-002, case-2, 1.0024, 0.0037
+leu-sol-therm-002, case-3, 1.0024, 0.0044
+leu-sol-therm-003, case-1, 0.9997, 0.0039
+leu-sol-therm-003, case-2, 0.9993, 0.0042
+leu-sol-therm-003, case-3, 0.9995, 0.0042
+leu-sol-therm-003, case-4, 0.9995, 0.0042
+leu-sol-therm-003, case-5, 0.9997, 0.0048
+leu-sol-therm-003, case-6, 0.9999, 0.0049
+leu-sol-therm-003, case-7, 0.9994, 0.0049
+leu-sol-therm-003, case-8, 0.9993, 0.0052
+leu-sol-therm-003, case-9, 0.9996, 0.0052
+leu-sol-therm-004, case-1, 0.9994, 0.0008
+leu-sol-therm-004, case-29, 0.9999, 0.0009
+leu-sol-therm-004, case-33, 0.9999, 0.0009
+leu-sol-therm-004, case-34, 0.9999, 0.0010
+leu-sol-therm-004, case-46, 0.9999, 0.0010
+leu-sol-therm-004, case-51, 0.9994, 0.0011
+leu-sol-therm-004, case-54, 0.9996, 0.0011
+leu-sol-therm-007, case-14, 0.9961, 0.0009
+leu-sol-therm-007, case-30, 0.9973, 0.0009
+leu-sol-therm-007, case-32, 0.9985, 0.0010
+leu-sol-therm-007, case-36, 0.9988, 0.0011
+leu-sol-therm-007, case-49, 0.9983, 0.0011
+leu-sol-therm-016, case-105, 0.9996, 0.0013
+leu-sol-therm-016, case-113, 0.9999, 0.0013
+leu-sol-therm-016, case-125, 0.9994, 0.0014
+leu-sol-therm-016, case-129, 0.9996, 0.0014
+leu-sol-therm-016, case-131, 0.9995, 0.0014
+leu-sol-therm-016, case-140, 0.9992, 0.0015
+leu-sol-therm-016, case-196, 0.9994, 0.0015
+mix-comp-therm-002, pnl-30d, 1.0010, 0.0059
+mix-comp-therm-002, pnl-31d, 1.0009, 0.0045
+mix-comp-therm-002, pnl-32d, 1.0024, 0.0029
+mix-comp-therm-002, pnl-33d, 1.0024, 0.0021
+mix-comp-therm-002, pnl-34d, 1.0038, 0.0022
+mix-comp-therm-002, pnl-35d, 1.0029, 0.0024
+mix-comp-therm-002, pnl-30, 1.0024, 0.0060
+mix-comp-therm-002, pnl-31, 1.0009, 0.0047
+mix-comp-therm-002, pnl-32, 1.0042, 0.0031
+mix-comp-therm-002, pnl-33, 1.0024, 0.0024
+mix-comp-therm-002, pnl-34, 1.0038, 0.0025
+mix-comp-therm-002, pnl-35, 1.0029, 0.0027
+mix-comp-therm-004, case-1, 1.0, 0.0046
+mix-comp-therm-004, case-2, 1.0, 0.0046
+mix-comp-therm-004, case-3, 1.0, 0.0046
+mix-comp-therm-004, case-4, 1.0, 0.0039
+mix-comp-therm-004, case-5, 1.0, 0.0039
+mix-comp-therm-004, case-6, 1.0, 0.0039
+mix-comp-therm-004, case-7, 1.0, 0.0040
+mix-comp-therm-004, case-8, 1.0, 0.0040
+mix-comp-therm-004, case-9, 1.0, 0.0040
+mix-comp-therm-004, case-10, 1.0, 0.0051
+mix-comp-therm-004, case-11, 1.0, 0.0051
+mix-met-fast-001, , 1.0, 0.0016
+mix-met-fast-003, , 0.9993, 0.0016
+mix-met-fast-007, case-1, 1.0, 0.0045
+mix-met-fast-007, case-2, 1.0, 0.0023
+mix-met-fast-007, case-3, 1.0, 0.0028
+mix-met-fast-007, case-4, 1.0, 0.0028
+mix-met-fast-007, case-5, 1.0, 0.0032
+mix-met-fast-007, case-6, 1.0, 0.0035
+mix-met-fast-007, case-7, 1.0, 0.0032
+mix-met-fast-007, case-8, 1.0, 0.0030
+mix-met-fast-007, case-9, 1.0, 0.0028
+mix-met-fast-007, case-10, 1.0, 0.0027
+mix-met-fast-007, case-11, 1.0, 0.0026
+mix-met-fast-007, case-12, 1.0, 0.0030
+mix-met-fast-007, case-13, 1.0, 0.0033
+mix-met-fast-007, case-14, 1.0, 0.0032
+mix-met-fast-007, case-15, 1.0, 0.0032
+mix-met-fast-007, case-16, 1.0, 0.0028
+mix-met-fast-007, case-17, 1.0, 0.0028
+mix-met-fast-007, case-18, 1.0, 0.0030
+mix-met-fast-007, case-19, 1.0, 0.0034
+mix-met-fast-007, case-20, 1.0, 0.0030
+mix-met-fast-007, case-21, 1.0, 0.0031
+mix-met-fast-007, case-22, 1.0, 0.0030
+mix-met-fast-007, case-23, 1.0, 0.0028
+mix-met-fast-008, 8a-2, 0.9992, 0.0063
+mix-met-fast-008, 8b, 1.0010, 0.0023
+mix-met-fast-008, 8c-2, 0.9860, 0.0044
+mix-met-fast-008, 8d, 0.9730, 0.0045
+mix-met-fast-008, 8e, 1.0060, 0.0069
+mix-met-fast-008, 8f-2, 0.9710, 0.0042
+mix-met-fast-008, 8h, 1.0300, 0.0025
+pu-comp-inter-001, , 1.0, 0.0110
+pu-met-fast-001, , 1.0, 0.0020
+pu-met-fast-002, , 1.0, 0.0020
+pu-met-fast-003, case-101, 1.0, 0.0030
+pu-met-fast-003, case-102, 1.0, 0.0030
+pu-met-fast-003, case-103, 1.0, 0.0030
+pu-met-fast-003, case-104, 1.0, 0.0030
+pu-met-fast-003, case-105, 1.0, 0.0030
+pu-met-fast-005, , 1.0, 0.0013
+pu-met-fast-006, , 1.0, 0.0030
+pu-met-fast-008, case-1, 1.0, 0.0006
+pu-met-fast-008, case-2, 1.0, 0.0006
+pu-met-fast-009, , 1.0, 0.0027
+pu-met-fast-010, , 1.0, 0.0018
+pu-met-fast-011, , 1.0, 0.0010
+pu-met-fast-012, case-1, 1.0009, 0.0021
+pu-met-fast-015, case-1, 1.0041, 0.0026
+pu-met-fast-018, , 1.0, 0.0030
+pu-met-fast-019, , 0.9992, 0.0015
+pu-met-fast-020, , 0.9993, 0.0017
+pu-met-fast-021, case-1, 1.0, 0.0026
+pu-met-fast-021, case-2, 1.0, 0.0026
+pu-met-fast-022, case-1, 1.0, 0.0021
+pu-met-fast-022, case-2, 1.0, 0.0021
+pu-met-fast-023, case-1, 1.0, 0.0020
+pu-met-fast-023, case-2, 1.0, 0.0020
+pu-met-fast-024, case-1, 1.0, 0.0020
+pu-met-fast-024, case-2, 1.0, 0.0020
+pu-met-fast-025, case-1, 1.0, 0.0020
+pu-met-fast-025, case-2, 1.0, 0.0020
+pu-met-fast-026, case-1, 1.0, 0.0024
+pu-met-fast-026, case-2, 1.0, 0.0024
+pu-met-fast-029, case-1, 1.0, 0.0020
+pu-met-fast-029, case-2, 1.0, 0.0020
+pu-met-fast-032, case-1, 1.0, 0.0020
+pu-met-fast-032, case-2, 1.0, 0.0020
+pu-met-fast-035, case-1, 1.0, 0.0016
+pu-met-fast-036, case-1, 1.0, 0.0031
+pu-met-fast-039, case-1, 1.0, 0.0022
+pu-met-fast-040, case-1, 1.0, 0.0038
+pu-met-fast-044, case-1, 0.9977, 0.0021
+pu-met-fast-044, case-2, 0.9980, 0.0022
+pu-met-fast-044, case-3, 0.9977, 0.0021
+pu-met-fast-044, case-4, 0.9978, 0.0026
+pu-met-fast-044, case-5, 0.9977, 0.0024
+pu-sol-therm-001, case-1, 1.0, 0.0050
+pu-sol-therm-001, case-2, 1.0, 0.0050
+pu-sol-therm-001, case-3, 1.0, 0.0050
+pu-sol-therm-001, case-4, 1.0, 0.0050
+pu-sol-therm-001, case-5, 1.0, 0.0050
+pu-sol-therm-001, case-6, 1.0, 0.0050
+pu-sol-therm-002, case-1, 1.0, 0.0047
+pu-sol-therm-002, case-2, 1.0, 0.0047
+pu-sol-therm-002, case-3, 1.0, 0.0047
+pu-sol-therm-002, case-4, 1.0, 0.0047
+pu-sol-therm-002, case-5, 1.0, 0.0047
+pu-sol-therm-002, case-6, 1.0, 0.0047
+pu-sol-therm-002, case-7, 1.0, 0.0047
+pu-sol-therm-003, case-1, 1.0, 0.0047
+pu-sol-therm-003, case-2, 1.0, 0.0047
+pu-sol-therm-003, case-3, 1.0, 0.0047
+pu-sol-therm-003, case-4, 1.0, 0.0047
+pu-sol-therm-003, case-5, 1.0, 0.0047
+pu-sol-therm-003, case-6, 1.0, 0.0047
+pu-sol-therm-003, case-7, 1.0, 0.0047
+pu-sol-therm-003, case-8, 1.0, 0.0047
+pu-sol-therm-004, case-1, 1.0, 0.0047
+pu-sol-therm-004, case-2, 1.0, 0.0047
+pu-sol-therm-004, case-3, 1.0, 0.0047
+pu-sol-therm-004, case-4, 1.0, 0.0047
+pu-sol-therm-004, case-5, 1.0, 0.0047
+pu-sol-therm-004, case-6, 1.0, 0.0047
+pu-sol-therm-004, case-7, 1.0, 0.0047
+pu-sol-therm-004, case-8, 1.0, 0.0047
+pu-sol-therm-004, case-9, 1.0, 0.0047
+pu-sol-therm-004, case-10, 1.0, 0.0047
+pu-sol-therm-004, case-11, 1.0, 0.0047
+pu-sol-therm-004, case-12, 1.0, 0.0047
+pu-sol-therm-004, case-13, 1.0, 0.0047
+pu-sol-therm-005, case-1, 1.0, 0.0047
+pu-sol-therm-005, case-2, 1.0, 0.0047
+pu-sol-therm-005, case-3, 1.0, 0.0047
+pu-sol-therm-005, case-4, 1.0, 0.0047
+pu-sol-therm-005, case-5, 1.0, 0.0047
+pu-sol-therm-005, case-6, 1.0, 0.0047
+pu-sol-therm-005, case-7, 1.0, 0.0047
+pu-sol-therm-005, case-8, 1.0, 0.0047
+pu-sol-therm-005, case-9, 1.0, 0.0047
+pu-sol-therm-006, case-1, 1.0, 0.0035
+pu-sol-therm-006, case-2, 1.0, 0.0035
+pu-sol-therm-006, case-3, 1.0, 0.0035
+pu-sol-therm-007, case-2, 1.0, 0.0047
+pu-sol-therm-007, case-3, 1.0, 0.0047
+pu-sol-therm-007, case-5, 1.0, 0.0047
+pu-sol-therm-007, case-6, 1.0, 0.0047
+pu-sol-therm-007, case-7, 1.0, 0.0047
+pu-sol-therm-007, case-8, 1.0, 0.0047
+pu-sol-therm-007, case-9, 1.0, 0.0047
+pu-sol-therm-007, case-10, 1.0, 0.0047
+pu-sol-therm-009, case-1, 1.0000, 0.0033
+pu-sol-therm-009, case-1a, 1.0003, 0.0033
+pu-sol-therm-009, case-2, 1.0000, 0.0033
+pu-sol-therm-009, case-2a, 1.0003, 0.0033
+pu-sol-therm-009, case-3, 1.0000, 0.0033
+pu-sol-therm-009, case-3a, 1.0003, 0.0033
+pu-sol-therm-011, case-16-1, 1.0, 0.0052
+pu-sol-therm-011, case-16-2, 1.0, 0.0052
+pu-sol-therm-011, case-16-3, 1.0, 0.0052
+pu-sol-therm-011, case-16-4, 1.0, 0.0052
+pu-sol-therm-011, case-16-5, 1.0, 0.0052
+pu-sol-therm-011, case-18-1, 1.0, 0.0052
+pu-sol-therm-011, case-18-2, 1.0, 0.0052
+pu-sol-therm-011, case-18-3, 1.0, 0.0052
+pu-sol-therm-011, case-18-4, 1.0, 0.0052
+pu-sol-therm-011, case-18-5, 1.0, 0.0052
+pu-sol-therm-011, case-18-6, 1.0, 0.0052
+pu-sol-therm-011, case-18-7, 1.0, 0.0052
+pu-sol-therm-012, case-1, 1.0, 0.0043
+pu-sol-therm-012, case-2, 1.0, 0.0043
+pu-sol-therm-012, case-3, 1.0, 0.0058
+pu-sol-therm-012, case-4, 1.0, 0.0058
+pu-sol-therm-012, case-5, 1.0, 0.0058
+pu-sol-therm-012, case-6, 1.0, 0.0007
+pu-sol-therm-012, case-7, 1.0, 0.0013
+pu-sol-therm-012, case-8, 1.0, 0.0013
+pu-sol-therm-012, case-9, 1.0, 0.0043
+pu-sol-therm-012, case-10, 1.0, 0.0043
+pu-sol-therm-012, case-11, 1.0, 0.0043
+pu-sol-therm-012, case-12, 1.0, 0.0043
+pu-sol-therm-012, case-13, 1.0, 0.0058
+pu-sol-therm-012, case-14, 1.0, 0.0013
+pu-sol-therm-012, case-15, 1.0, 0.0043
+pu-sol-therm-012, case-16, 1.0, 0.0043
+pu-sol-therm-012, case-17, 1.0, 0.0043
+pu-sol-therm-012, case-18, 1.0, 0.0043
+pu-sol-therm-012, case-19, 1.0, 0.0043
+pu-sol-therm-012, case-20, 1.0, 0.0058
+pu-sol-therm-012, case-21, 1.0, 0.0058
+pu-sol-therm-012, case-22, 1.0, 0.0058
+pu-sol-therm-012, case-23, 1.0, 0.0058
+pu-sol-therm-018, case-1, 1.0, 0.0034
+pu-sol-therm-018, case-2, 1.0, 0.0034
+pu-sol-therm-018, case-3, 1.0, 0.0032
+pu-sol-therm-018, case-4, 1.0, 0.0030
+pu-sol-therm-018, case-5, 1.0, 0.0030
+pu-sol-therm-018, case-6, 1.0, 0.0031
+pu-sol-therm-018, case-7, 1.0, 0.0032
+pu-sol-therm-018, case-8, 1.0, 0.0033
+pu-sol-therm-018, case-9, 1.0, 0.0034
+pu-sol-therm-021, case-1, 1.0, 0.0032
+pu-sol-therm-021, case-2, 1.0, 0.0032
+pu-sol-therm-021, case-3, 1.0, 0.0065
+pu-sol-therm-021, case-4, 1.0, 0.0025
+pu-sol-therm-021, case-5, 1.0, 0.0025
+pu-sol-therm-021, case-6, 1.0, 0.0044
+pu-sol-therm-021, case-7, 1.0, 0.0032
+pu-sol-therm-021, case-8, 1.0, 0.0065
+pu-sol-therm-021, case-9, 1.0, 0.0032
+pu-sol-therm-021, case-10, 1.0, 0.0025
+pu-sol-therm-034, case-1, 1.0, 0.0062
+pu-sol-therm-034, case-2, 1.0, 0.0044
+pu-sol-therm-034, case-3, 1.0, 0.0040
+pu-sol-therm-034, case-4, 1.0, 0.0039
+pu-sol-therm-034, case-5, 1.0, 0.0040
+pu-sol-therm-034, case-6, 1.0, 0.0042
+pu-sol-therm-034, case-7, 1.0, 0.0057
+pu-sol-therm-034, case-8, 1.0, 0.0055
+pu-sol-therm-034, case-9, 1.0, 0.0052
+pu-sol-therm-034, case-10, 1.0, 0.0052
+pu-sol-therm-034, case-11, 1.0, 0.0048
+pu-sol-therm-034, case-12, 1.0, 0.0042
+pu-sol-therm-034, case-13, 1.0, 0.0043
+pu-sol-therm-034, case-14, 1.0, 0.0044
+pu-sol-therm-034, case-15, 1.0, 0.0042
+u233-comp-therm-001, case-1, 1.0006, 0.0027
+u233-comp-therm-001, case-2, 1.0015, 0.0025
+u233-comp-therm-001, case-3, 1.0000, 0.0024
+u233-comp-therm-001, case-4, 1.0007, 0.0025
+u233-comp-therm-001, case-5, 1.0015, 0.0026
+u233-comp-therm-001, case-6, 1.0015, 0.0028
+u233-comp-therm-001, case-7, 0.9995, 0.0027
+u233-comp-therm-001, case-8, 1.0004, 0.0028
+u233-met-fast-001, , 1.0, 0.0010
+u233-met-fast-002, case-1, 1.0, 0.0010
+u233-met-fast-002, case-2, 1.0, 0.0011
+u233-met-fast-003, case-1, 1.0, 0.0010
+u233-met-fast-003, case-2, 1.0, 0.0010
+u233-met-fast-004, case-1, 1.0, 0.0007
+u233-met-fast-004, case-2, 1.0, 0.0008
+u233-met-fast-005, case-1, 1.0, 0.0030
+u233-met-fast-005, case-2, 1.0, 0.0030
+u233-met-fast-006, , 1.0, 0.0014
+u233-sol-inter-001, case-1, 1.0, 0.0083
+u233-sol-therm-001, case-1, 1.0, 0.0031
+u233-sol-therm-001, case-2, 1.0005, 0.0033
+u233-sol-therm-001, case-3, 1.0006, 0.0033
+u233-sol-therm-001, case-4, 0.9998, 0.0033
+u233-sol-therm-001, case-5, 0.9999, 0.0033
+u233-sol-therm-008, , 1.0006, 0.0029