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Remove several data generation scripts (will move to data repository)
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Paul Romano
parent
d95fcbc559
commit
a489fe22d0
3 changed files with 0 additions and 199 deletions
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#!/usr/bin/env python3
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"""
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Download ENDF/B-VII.1 ENDF data from NNDC for photo-atomic and atomic
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relaxation data and convert it to an HDF5 library for use with OpenMC.
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This data is used for photon transport in OpenMC.
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"""
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import argparse
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import os
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from pathlib import Path
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import zipfile
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import openmc.data
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from openmc._utils import download
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class CustomFormatter(argparse.ArgumentDefaultsHelpFormatter,
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argparse.RawDescriptionHelpFormatter):
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pass
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parser = argparse.ArgumentParser(
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description=__doc__,
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formatter_class=CustomFormatter
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)
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parser.add_argument('-c', '--cross-sections',
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help='cross_sections.xml file to append libraries to')
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args = parser.parse_args()
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base_url = 'https://www.nndc.bnl.gov/endf/b7.1/zips/'
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files = ['ENDF-B-VII.1-photoat.zip', 'ENDF-B-VII.1-atomic_relax.zip']
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block_size = 16384
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# ==============================================================================
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# DOWNLOAD FILES FROM NNDC SITE
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output = Path('photon_hdf5')
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output.mkdir(exist_ok=True)
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for f in files:
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download(base_url + f)
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# ==============================================================================
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# EXTRACT FILES
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for f in files:
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print('Extracting {}...'.format(f))
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zipfile.ZipFile(f).extractall()
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# ==============================================================================
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# GENERATE HDF5 DATA LIBRARY
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# If previous cross_sections.xml was specified, load it in
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if args.cross_sections is not None:
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lib_path = args.cross_sections
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library = openmc.data.DataLibrary.from_xml(lib_path)
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else:
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lib_path = output / 'cross_sections.xml'
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library = openmc.data.DataLibrary()
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# Iterate over each natural element from Z=1 to Z=100
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for z in range(1, 101):
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element = openmc.data.ATOMIC_SYMBOL[z]
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print('Generating HDF5 file for Z={} ({})...'.format(z, element))
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# Generate instance of IncidentPhoton
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photo_file = os.path.join('photoat', 'photoat-{:03}_{}_000.endf'.format(z, element))
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atom_file = os.path.join('atomic_relax', 'atom-{:03}_{}_000.endf'.format(z, element))
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data = openmc.data.IncidentPhoton.from_endf(photo_file, atom_file)
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# Write HDF5 file and register it
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hdf5_file = output / (element + '.h5')
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data.export_to_hdf5(hdf5_file, 'w')
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library.register_file(hdf5_file)
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library.export_to_xml(lib_path)
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#!/usr/bin/env python
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import os
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import tarfile
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import numpy as np
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import h5py
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from openmc._utils import download
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base_url = 'http://geant4.cern.ch/support/source/'
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filename = 'G4EMLOW.6.48.tar.gz'
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# ==============================================================================
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# DOWNLOAD FILES FROM GEANT4 SITE
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download(base_url + filename)
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# ==============================================================================
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# EXTRACT FILES FROM TGZ
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if not os.path.isdir('G4EMLOW6.48'):
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with tarfile.open(filename, 'r') as tgz:
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print('Extracting {}...'.format(filename))
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tgz.extractall()
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# ==============================================================================
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# GENERATE COMPTON PROFILE HDF5 FILE
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print('Generating compton_profiles.h5...')
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shell_file = os.path.join('G4EMLOW6.48', 'doppler', 'shell-doppler.dat')
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with open(shell_file, 'r') as shell, h5py.File('compton_profiles.h5', 'w') as f:
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# Read/write electron momentum values
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pz = np.loadtxt(os.path.join('G4EMLOW6.48', 'doppler', 'p-biggs.dat'))
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f.create_dataset('pz', data=pz)
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for z in range(1, 101):
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# Create group for this element
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group = f.create_group('{:03}'.format(z))
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# Read data into one long array
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path = os.path.join('G4EMLOW6.48', 'doppler', 'profile-{}.dat'.format(z))
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with open(path, 'r') as profile:
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j = np.fromstring(profile.read(), sep=' ')
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# Determine number of electron shells and reshape. Profiles are
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# tabulated against a grid of 31 momentum values.
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n_shells = j.size // 31
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j.shape = (n_shells, 31)
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# Write Compton profile for this Z
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group.create_dataset('J', data=j)
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# Determine binding energies and number of electrons for each shell
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num_electrons = []
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binding_energy = []
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while True:
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words = shell.readline().split()
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if words[0] == '-1':
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break
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num_electrons.append(float(words[0]))
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binding_energy.append(float(words[1]))
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# Write binding energies and number of electrons
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group.create_dataset('num_electrons', data=num_electrons)
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group.create_dataset('binding_energy', data=binding_energy)
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#!/usr/bin/env python
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from urllib.parse import urlencode
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from urllib.request import urlopen
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from lxml import html
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import numpy as np
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import h5py
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from openmc.data import ATOMIC_SYMBOL
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base_url = 'https://physics.nist.gov/cgi-bin/Star/e_table-t.pl'
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energies = np.logspace(-3, 3, 200)
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data = {'matno': '', 'Energies': '\n'.join(str(x) for x in energies)}
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columns = {1: 's_collision', 2: 's_radiative'}
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# ==============================================================================
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# SCRAPE DATA FROM ESTAR SITE AND GENERATE STOPPING POWER HDF5 FILE
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print('Generating stopping_powers.h5...')
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with h5py.File('stopping_powers.h5', 'w') as f:
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# Write energies
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f.create_dataset('energy', data=energies)
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# Look over atomic number; ESTAR only goes up to Z=98 (Californium)
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for Z in range(1, 99):
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print('Processing {} data...'.format(ATOMIC_SYMBOL[Z]))
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# Update form-encoded data to send in POST request for this element
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data['matno'] = '{:03}'.format(Z)
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payload = urlencode(data).encode("utf-8")
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# Retrieve data from ESTAR site
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with urlopen(url=base_url, data=payload) as response:
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r = response.read()
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# Remove text and reformat data -- omit first 12 and last 5 lines to get
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# only data in table
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r = html.fromstring(r).xpath('//pre//text()')
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values = np.fromstring(' '.join(r[12:-5]), sep=' ').reshape((-1, 5)).T
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# Create group for this element
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group = f.create_group('{:03}'.format(Z))
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# Write the mean excitation energy
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attributes = np.fromstring(r[3], sep=' ')
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group.attrs['I'] = attributes[2]
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# Write collision and radiative stopping powers
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for i in columns:
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group.create_dataset(columns[i], data=values[i])
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