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gnu: python-dolfin-adjoint: Adjust package style.

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* gnu/packages/simulation.scm (python-dolfin-adjoint): Move inputs after
arguments.
[description]: Place it on a new line, fix indentation.

Change-Id: I3d971e48b4072258ed0b41af5c202e64af9de8f3
This commit is contained in:
Sharlatan Hellseher 2024-07-01 21:46:20 +01:00
parent 5a33a71e67
commit 9da142540e
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1 changed files with 20 additions and 20 deletions
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40
gnu/packages/simulation.scm
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@ -1204,17 +1204,6 @@ (define-public python-dolfin-adjoint
"tests/migration/viscoelasticity/test-results")
#t))))
(build-system python-build-system)
(inputs
(list fenics openmpi pybind11))
(native-inputs
(list pkg-config
python-coverage
python-decorator
python-flake8
python-pkgconfig
python-pytest))
(propagated-inputs
`(("scipy" ,python-scipy)))
(arguments
`(#:phases
(modify-phases %standard-phases
@ -1245,17 +1234,28 @@ (define-public python-dolfin-adjoint
;; fails with an ImportError if it sees that the backend module
;; has already been loaded.
(delete 'sanity-check))))
(inputs
(list fenics openmpi pybind11))
(native-inputs
(list pkg-config
python-coverage
python-decorator
python-flake8
python-pkgconfig
python-pytest))
(propagated-inputs
(list python-scipy))
(home-page "https://www.dolfin-adjoint.org")
(synopsis "Automatic differentiation library")
(description "@code{python-dolfin-adjoint} is a solver of
differential equations associated with a governing system and a
functional of interest. Working from the forward model the solver
automatically derives the discrete adjoint and tangent linear models.
These additional models are key ingredients in many algorithms such as
data assimilation, optimal control, sensitivity analysis, design
optimisation and error estimation. The dolfin-adjoint project
provides the necessary tools and data structures for cases where the
forward model is implemented in @code{fenics} or
(description
"@code{python-dolfin-adjoint} is a solver of differential equations
associated with a governing system and a functional of interest. Working from
the forward model the solver automatically derives the discrete adjoint and
tangent linear models. These additional models are key ingredients in many
algorithms such as data assimilation, optimal control, sensitivity analysis,
design optimisation and error estimation. The dolfin-adjoint project provides
the necessary tools and data structures for cases where the forward model is
implemented in @code{fenics} or
@url{https://firedrakeproject.org,firedrake}.")
(license license:lgpl3)))