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new file: archive/f/Forge/Forge-18.2.3.eb
new file: archive/f/Forge/Forge-19.1.1.eb new file: archive/f/Forge/Forge-5.1-43967.eb new file: archive/f/Forge/Forge-6.1.2.eb new file: archive/f/Forge/Forge-7.0.0.eb new file: archive/f/Forge/Forge-7.0.1.eb new file: archive/f/Forge/Forge-7.0.2.eb new file: archive/f/Forge/Forge-7.0.3.eb new file: archive/f/Forge/Forge-7.0.4.eb new file: archive/f/Forge/Forge-7.0.5.eb new file: archive/f/Forge/Forge-7.0.6.eb new file: archive/f/Forge/Forge-7.1.eb new file: b/BEEF/BEEF-0.1.1-intel-2018a.eb new file: b/BEEF/BEEF-0.1.1-intel-2019a.eb new file: b/BEEF/BEEF-0.1.1-intel-2020.eb modified: f/Forge/Forge-20.1.1.eb new file: t/TRIQS-hubbardI/TRIQS-hubbardI-3.0.0-foss-2020a-Python-3.8.2.eb deleted: f/Forge/Forge-18.2.3.eb deleted: f/Forge/Forge-19.1.1.eb deleted: f/Forge/Forge-5.1-43967.eb deleted: f/Forge/Forge-6.1.2.eb deleted: f/Forge/Forge-7.0.0.eb deleted: f/Forge/Forge-7.0.1.eb deleted: f/Forge/Forge-7.0.2.eb deleted: f/Forge/Forge-7.0.3.eb deleted: f/Forge/Forge-7.0.4.eb deleted: f/Forge/Forge-7.0.5.eb deleted: f/Forge/Forge-7.0.6.eb deleted: f/Forge/Forge-7.1.eb
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b/BEEF/BEEF-0.1.1-intel-2018a.eb
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# IT4Innovations 2020
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# LK
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easyblock = 'ConfigureMake'
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name = 'BEEF'
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version = '0.1.1'
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homepage = 'http://suncat.stanford.edu/facility/software/functional'
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description = """BEEF is a library-based implementation of the Bayesian
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Error Estimation Functional, suitable for linking against by Fortran-
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or C-based DFT codes. A description of BEEF can be found at
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http://dx.doi.org/10.1103/PhysRevB.85.235149."""
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toolchain = {'name': 'intel', 'version': '2020a'}
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toolchainopts = {'openmp': False, 'usempi': False}
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source_urls = ['https://confluence.slac.stanford.edu/download/attachments/146704476']
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sources = ['libbeef-%(version)s.tar.gz']
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configopts = 'CC="$CC"'
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sanity_check_paths = {
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'files': ['bin/bee', 'lib/libbeef.a'],
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'dirs': [],
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}
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moduleclass = 'chem'
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b/BEEF/BEEF-0.1.1-intel-2019a.eb
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# IT4Innovations 2020
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# LK
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easyblock = 'ConfigureMake'
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name = 'BEEF'
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version = '0.1.1'
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homepage = 'http://suncat.stanford.edu/facility/software/functional'
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description = """BEEF is a library-based implementation of the Bayesian
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Error Estimation Functional, suitable for linking against by Fortran-
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or C-based DFT codes. A description of BEEF can be found at
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http://dx.doi.org/10.1103/PhysRevB.85.235149."""
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toolchain = {'name': 'intel', 'version': '2019a'}
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toolchainopts = {'openmp': False, 'usempi': False}
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source_urls = ['https://confluence.slac.stanford.edu/download/attachments/146704476']
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sources = ['libbeef-%(version)s.tar.gz']
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configopts = 'CC="$CC"'
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sanity_check_paths = {
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'files': ['bin/bee', 'lib/libbeef.a'],
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'dirs': [],
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}
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moduleclass = 'chem'
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b/BEEF/BEEF-0.1.1-intel-2020.eb
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# IT4Innovations 2020
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# LK
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easyblock = 'ConfigureMake'
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name = 'BEEF'
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version = '0.1.1'
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homepage = 'http://suncat.stanford.edu/facility/software/functional'
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description = """BEEF is a library-based implementation of the Bayesian
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Error Estimation Functional, suitable for linking against by Fortran-
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or C-based DFT codes. A description of BEEF can be found at
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http://dx.doi.org/10.1103/PhysRevB.85.235149."""
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toolchain = {'name': 'intel', 'version': '2018a'}
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toolchainopts = {'openmp': False, 'usempi': False}
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source_urls = ['https://confluence.slac.stanford.edu/download/attachments/146704476']
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sources = ['libbeef-%(version)s.tar.gz']
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configopts = 'CC="$CC"'
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sanity_check_paths = {
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'files': ['bin/bee', 'lib/libbeef.a'],
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'dirs': [],
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}
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moduleclass = 'chem'
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# IT4Innovations 2020
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# IT4Innovations 2020
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# !!! --include-easyblocks /apps/easybuild/it4i-easyblocks/easyblocks/a/allineabase.py !!!
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# !!! --include-easyblocks /apps/easybuild/it4i-easyblocks/easyblocks/a/allineabase.py !!!
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# LK
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easyblock = 'AllineaBase'
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easyblock = 'AllineaBase'
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name = 'Forge'
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name = 'Forge'
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@ -25,7 +27,8 @@ sources = ['arm-forge-20.1.1-Redhat-7.0-x86_64.tar']
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skipsteps = ['configure', 'build']
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skipsteps = ['configure', 'build']
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postinstallcmds = [
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postinstallcmds = [
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'ln -s /apps/licenses/Allinea/Licence %(installdir)s/licences/Licence',
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'ln -s /apps/licenses/Arm/Licence %(installdir)s/licences/Licence.16312',
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'ln -s /apps/licenses/PerformanceReports/Licence %(installdir)s/licences/Licence.16313',
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]
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]
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sanity_check_paths = {
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sanity_check_paths = {
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# JH 2020
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easyblock = 'CMakeMake'
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name = 'TRIQS-hubbardI'
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version = '3.0.0'
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versionsuffix = '-Python-%(pyver)s'
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homepage = 'https://triqs.github.io/hubbardI'
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description = """
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TRIQS (Toolbox for Research on Interacting Quantum Systems) is a
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scientific project providing a set of C++ and Python libraries to
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develop new tools for the study of interacting quantum systems.
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hubbardI = An application using TRIQS to implement a Hubbard-I solver.
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"""
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docurls = ['https://triqs.github.io/hubbardI/%(version_major_minor)s.x/']
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software_license = 'LicenseGPLv3'
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toolchain = {'name': 'foss', 'version': '2020a'}
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toolchainopts = {'pic': True, 'usempi': True}
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source_urls = ['https://github.com/TRIQS/hubbardI/archive']
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sources = ['%(version)s.tar.gz']
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dependencies = [
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('Python', '3.8.2'),
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('TRIQS', '3.0.0', versionsuffix),
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('NFFT', '3.5.2')
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]
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builddependencies = [
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('CMake', '3.16.4')
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]
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separate_build_dir = True
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runtest = 'test'
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sanity_check_paths = {
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'files': [],
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'dirs': ['lib', 'lib/python%(pyshortver)s/site-packages', 'share'],
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}
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sanity_check_commands = ["python -c 'import triqs_hubbardI'"]
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modextrapaths = {
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'PYTHONPATH': 'lib/python%(pyshortver)s/site-packages',
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'CMAKE_PREFIX_PATH': 'lib/cmake/triqs_hubbardI',
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}
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modextravars = {
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'TRIQS_HUBBARDI_ROOT': '%(installdir)s',
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'TRIQS_HUBBARDI_VERSION': '%(version)s',
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}
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moduleclass = 'phys'
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