pofff.jobs.data module

Write benchmark time-series and spatial (dense) data from OPM simulations.

class pofff.jobs.data.SimulationContext(resolution: ~numpy.ndarray[tuple[~typing.Any, ...], ~numpy.dtype[~numpy._typing._array_like._ScalarT]], dense_t: ~numpy.ndarray[tuple[~typing.Any, ...], ~numpy.dtype[~numpy._typing._array_like._ScalarT]], maps: ~pathlib.Path, where: ~pathlib.Path = PosixPath('.'), sparse_t: float = 600.0, dims: ~numpy.ndarray[tuple[~typing.Any, ...], ~numpy.dtype[~numpy._typing._array_like._ScalarT]] = <factory>, sim: ~pathlib.Path | None = None, unrst: ~opm.opmcommon_python.ERst | None = None, ini: ~opm.opmcommon_python.EclFile | None = None, egrid: ~opm.opmcommon_python.EGrid | None = None, smspec: ~opm.opmcommon_python.ESmry | None = None, times: list[float] = <factory>, times_summary: ~numpy.ndarray[tuple[~typing.Any, ...], ~numpy.dtype[~numpy._typing._array_like._ScalarT]] | None = None, time_initial: float = 0.0, porv: ~numpy.ndarray[tuple[~typing.Any, ...], ~numpy.dtype[~numpy._typing._array_like._ScalarT]] | None = None, actind: ~numpy.ndarray[tuple[~typing.Any, ...], ~numpy.dtype[~numpy._typing._array_like._ScalarT]] | None = None, gxyz: tuple[int, int, int] | None = None, norst: int | None = None)

Bases: object

Simulation-wide state, file handles, and grid information.

actind: ndarray[tuple[Any, ...], dtype[_ScalarT]] | None = None

dense_t: ndarray[tuple[Any, ...], dtype[_ScalarT]]

dims: ndarray[tuple[Any, ...], dtype[_ScalarT]]

egrid: EGrid | None = None

gxyz: tuple[int, int, int] | None = None

ini: EclFile | None = None

maps: Path

norst: int | None = None

property nxz: ndarray[tuple[Any, ...], dtype[_ScalarT]]: Alias for resolution to preserve original naming.

porv: ndarray[tuple[Any, ...], dtype[_ScalarT]] | None = None

resolution: ndarray[tuple[Any, ...], dtype[_ScalarT]]

sim: Path | None = None

smspec: ESmry | None = None

sparse_t: float = 600.0

time_initial: float = 0.0

times: list[float]

times_summary: ndarray[tuple[Any, ...], dtype[_ScalarT]] | None = None

unrst: ERst | None = None

where: Path = PosixPath('.')

class pofff.jobs.data.SparseResults(ctx: ~pofff.jobs.data.SimulationContext, m_c_series: list[float] = <factory>, m_c: ~numpy.ndarray[tuple[~typing.Any, ...], ~numpy.dtype[~numpy._typing._array_like._ScalarT]] | None = None)

Bases: object

Container for sparse (time-series) benchmark quantities.

ctx: SimulationContext

dissa: ndarray[tuple[Any, ...], dtype[_ScalarT]]

dissb: ndarray[tuple[Any, ...], dtype[_ScalarT]]

dx: ndarray[tuple[Any, ...], dtype[_ScalarT]]

dz: ndarray[tuple[Any, ...], dtype[_ScalarT]]

fipnum: ndarray[tuple[Any, ...], dtype[_ScalarT]]

imma: ndarray[tuple[Any, ...], dtype[_ScalarT]]

immb: ndarray[tuple[Any, ...], dtype[_ScalarT]]

m_c: ndarray[tuple[Any, ...], dtype[_ScalarT]] | None = None

m_c_series: list[float]

moba: ndarray[tuple[Any, ...], dtype[_ScalarT]]

mobb: ndarray[tuple[Any, ...], dtype[_ScalarT]]

pop1: ndarray[tuple[Any, ...], dtype[_ScalarT]]

pop2: ndarray[tuple[Any, ...], dtype[_ScalarT]]

seala: ndarray[tuple[Any, ...], dtype[_ScalarT]]

sealb: ndarray[tuple[Any, ...], dtype[_ScalarT]]

sealt: ndarray[tuple[Any, ...], dtype[_ScalarT]]

times_data: ndarray[tuple[Any, ...], dtype[_ScalarT]]

pofff.jobs.data.compute_m_c(ctx: SimulationContext, res: SparseResults) → None: Compute mass-center migration metric.

pofff.jobs.data.create_from_summary(ctx: SimulationContext, res: SparseResults) → None: Extract sparse quantities from OPM summary vectors.

pofff.jobs.data.dense_data(ctx: SimulationContext) → None: Write spatial maps for selected dense output times.

pofff.jobs.data.find_simulation_base(path: Path) → Path: Locate simulation base name from .UNRST file.

pofff.jobs.data.interpolate_sparse(ctx: SimulationContext, res: SparseResults) → None: Interpolate sparse quantities onto uniform time grid.

pofff.jobs.data.main(argv=None) → None: Entry point for benchmark postprocessing.

pofff.jobs.data.read_opm(ctx: SimulationContext) → None: Load OPM restart, grid, and summary data.

pofff.jobs.data.sparse_data(ctx: SimulationContext) → None: Compute and write sparse time-series benchmark data.

pofff.jobs.data.write_dense_data(ctx: SimulationContext, refxz, sgas, cco2, hours) → None: Write single spatial map CSV.

pofff.jobs.data.write_sparse_data(res: SparseResults) → None: Write sparse time-series values to CSV.