BDEPEND=network? ( <dev-python/joblib-2.0.0[python_targets_python3_10(-)?,python_targets_python3_11(-)?] ) >=dev-python/jsonpickle-3.0.1[python_targets_python3_10(-)?,python_targets_python3_11(-)?] viz? ( dev-python/kaleido[python_targets_python3_10(-)?,python_targets_python3_11(-)?] ) <dev-python/matplotlib-4.0[python_targets_python3_10(-)?,python_targets_python3_11(-)?] network? ( <dev-python/networkx-4.0[python_targets_python3_10(-)?,python_targets_python3_11(-)?] ) dev-python/periodictable[python_targets_python3_10(-)?,python_targets_python3_11(-)?] <dev-python/plotly-6.0[python_targets_python3_10(-)?,python_targets_python3_11(-)?] <dev-python/pooch-2.0.0[python_targets_python3_10(-)?,python_targets_python3_11(-)?] <dev-python/pyyaml-7.0[python_targets_python3_10(-)?,python_targets_python3_11(-)?] estimation? ( >=dev-python/scikit-learn-1.0[python_targets_python3_10(-)?,python_targets_python3_11(-)?] ) <dev-python/scipy-2.0.0[python_targets_python3_10(-)?,python_targets_python3_11(-)?] >dev-python/seaborn-0.11[python_targets_python3_10(-)?,python_targets_python3_11(-)?] viz? ( >=dev-python/statsmodels-0.14.0[python_targets_python3_10(-)?,python_targets_python3_11(-)?] ) network? ( <dev-python/tqdm-5.0.0[python_targets_python3_10(-)?,python_targets_python3_11(-)?] ) >=dev-python/xarray-2022.6.0[python_targets_python3_10(-)?,python_targets_python3_11(-)?] python_targets_python3_10? ( dev-lang/python:3.10 ) python_targets_python3_11? ( dev-lang/python:3.11 ) >=dev-python/gpep517-15[python_targets_python3_10(-)?,python_targets_python3_11(-)?]
DEFINED_PHASES=compile configure install prepare test unpack
DESCRIPTION=For managing multi-dimensional mass-composition datasets, supporting weighted mathematical operations and visualisation.
EAPI=8
HOMEPAGE=https://github.com/elphick/mass-composition
INHERIT=python-r1 gs-pypi
IUSE=estimation network viz python_targets_python3_10 python_targets_python3_11
KEYWORDS=~amd64 ~x86
RDEPEND=network? ( <dev-python/joblib-2.0.0[python_targets_python3_10(-)?,python_targets_python3_11(-)?] ) >=dev-python/jsonpickle-3.0.1[python_targets_python3_10(-)?,python_targets_python3_11(-)?] viz? ( dev-python/kaleido[python_targets_python3_10(-)?,python_targets_python3_11(-)?] ) <dev-python/matplotlib-4.0[python_targets_python3_10(-)?,python_targets_python3_11(-)?] network? ( <dev-python/networkx-4.0[python_targets_python3_10(-)?,python_targets_python3_11(-)?] ) dev-python/periodictable[python_targets_python3_10(-)?,python_targets_python3_11(-)?] <dev-python/plotly-6.0[python_targets_python3_10(-)?,python_targets_python3_11(-)?] <dev-python/pooch-2.0.0[python_targets_python3_10(-)?,python_targets_python3_11(-)?] <dev-python/pyyaml-7.0[python_targets_python3_10(-)?,python_targets_python3_11(-)?] estimation? ( >=dev-python/scikit-learn-1.0[python_targets_python3_10(-)?,python_targets_python3_11(-)?] ) <dev-python/scipy-2.0.0[python_targets_python3_10(-)?,python_targets_python3_11(-)?] >dev-python/seaborn-0.11[python_targets_python3_10(-)?,python_targets_python3_11(-)?] viz? ( >=dev-python/statsmodels-0.14.0[python_targets_python3_10(-)?,python_targets_python3_11(-)?] ) network? ( <dev-python/tqdm-5.0.0[python_targets_python3_10(-)?,python_targets_python3_11(-)?] ) >=dev-python/xarray-2022.6.0[python_targets_python3_10(-)?,python_targets_python3_11(-)?] python_targets_python3_10? ( dev-lang/python:3.10 ) python_targets_python3_11? ( dev-lang/python:3.11 )
REQUIRED_USE=|| ( python_targets_python3_10 python_targets_python3_11 )
RESTRICT=test
SLOT=0
SRC_URI=https://files.pythonhosted.org/packages/source/m/mass-composition/mass_composition-0.6.8.tar.gz
_eclasses_=out-of-source-utils	1a9007554652a6e627edbccb3c25a439	multibuild	d67e78a235f541871c7dfe4cf7931489	multiprocessing	30ead54fa2e2b5f9cd4e612ffc34d0fe	toolchain-funcs	e56c7649b804f051623c8bc1a1c44084	multilib	c19072c3cd7ac5cb21de013f7e9832e0	python-utils-r1	042f4cc53680245bf99a84669b94155a	python-r1	8a28fa6d3e3bc96ff8a7eff2badbe71f	flag-o-matic	24c947ff5f858625cf0b33c15eed4425	ninja-utils	2df4e452cea39a9ec8fb543ce059f8d6	gs-distutils-r1	8d3844b6b903f08bc4e83bd198f8a72f	g-sorcery	692c9ad583c66d03e7c0e8b3b106a435	gs-pypi	1ce6ce880db2f1f1e7842a13bd058366
_md5_=0488e5576cf969f6f16a28db5c2604f7