# IT4Innovations 2022
# JK

easyblock = 'Tarball'

name = 'Cordax'
version = '20220830'

homepage = 'https://cordax.switchlab.org/'
description = """CORDAX is an aggregation propensity predictor based on predicted packing energies."""

toolchain = {'name': 'foss', 'version': '2020b'}

sources = ['%(namelower)s-%(version)s.tar.gz']
patches = ['Cordax-20220830-set-proper-paths.patch']
checksums = [
    'bc2b2e84f3df84c1ecabc774fed247998d1aeb38bade04127e47cd585130160d',  # cordax-20220830.tar.gz
    'df8261e08da5d4e698f8621284d22bab99a0064f27d7735933c0d15c9ed730ff',  # Cordax-20220830-set-proper-paths.patch
]

dependencies = [
    ('Python', '2.7.18'),
    ('matplotlib', '2.2.5', '-Python-%(pyver)s'),
    ('SciPy-bundle', '2020.11', '-Python-%(pyver)s'),
    ('scikit-learn', '0.20.4', '-Python-%(pyver)s'),
    ('Biopython', '1.72', '-Python-%(pyver)s'),
    ('FoldX', '5.0', '', True),
]

sanity_check_paths = {
    'files': ['parser.py', 'precomputed_dg.py', 'predictor.py', 'propensity.py', 'run_foldx.py',
    'standalone.py', 'utils.py'],
    'dirs': ['dataset', 'foldx', 'test', 'DATADIR'],
}

modextrapaths = {'PATH': ''}

modloadmsg = "Precalculated models are available at $EBROOTCORDAX/DATADIR"

moduleclass = 'bio'