pyopmspe11.visualization.data module

Script to write the benchmark data

class pyopmspe11.visualization.data.BenchmarkConfig(outfol: str, case: str, mode: str, lower: bool, deckfol: str, flowfol: str, where: str, nxyz: ndarray[tuple[Any, ...], dtype[_ScalarT]], dims: list, denset: ndarray[tuple[Any, ...], dtype[_ScalarT]], sparset: float, nocellsrepgrid: int)

Bases: object

Paths and benchmark settings

case: str

deckfol: str

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

dims: list

flowfol: str

lower: bool

mode: str

nocellsrepgrid: int

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

outfol: str

sparset: float

where: str

class pyopmspe11.visualization.data.SimulationData(simres: str, unrst: ERst, init: EclFile, egrid: EGrid, smspec: ESmry, times: list, timesumary: list, timeini: float, noskiprst: int, norst: int, porv: ndarray[tuple[Any, ...], dtype[_ScalarT]], porva: ndarray[tuple[Any, ...], dtype[_ScalarT]], actind: list, immiscible: bool, isothermal: bool, cornpoint: bool, nocellst: int, nocellsa: int, dof: int, nocellsxz: int, simdim: list)

Bases: object

Simulation data

actind: list

cornpoint: bool

dof: int

egrid: EGrid

immiscible: bool

init: EclFile

isothermal: bool

nocellsa: int

nocellst: int

nocellsxz: int

norst: int

noskiprst: int

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

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

simdim: list

simres: str

smspec: ESmry

timeini: float

times: list

timesumary: list

unrst: ERst

pyopmspe11.visualization.data.build_config_from_args(cmdargs: dict) → BenchmarkConfig: Prepare the methods according to the spe11x case

pyopmspe11.visualization.data.build_dense_reference_grid(cfg: BenchmarkConfig) → tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]]]: Build reporting grid coordinates

pyopmspe11.visualization.data.build_dense_schedule(cfg: BenchmarkConfig, sim: SimulationData) → tuple[list, int]: Compute restart indices for dense output

pyopmspe11.visualization.data.build_dense_static(cfg: BenchmarkConfig, sim: SimulationData) → tuple[list, int, tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]]], tuple[list[list[list[int | float]]], ndarray[tuple[Any, ...], dtype[_ScalarT]]], ndarray[tuple[Any, ...], dtype[_ScalarT]]]: Build static dense grid data

pyopmspe11.visualization.data.build_dense_step(cfg: BenchmarkConfig, sim: SimulationData, mapping: tuple[list[list[list[int | float]]], ndarray[tuple[Any, ...], dtype[_ScalarT]]], restart_index: int, actindr: ndarray[tuple[Any, ...], dtype[_ScalarT]]) → dict: Build dense data for one restart

pyopmspe11.visualization.data.build_detailed_series(sim: SimulationData, metrics: dict, detail_info: ndarray[tuple[Any, ...], dtype[_ScalarT]], infotimes: ndarray[tuple[Any, ...], dtype[_ScalarT]], interp_fgmip: interp1d, cpu: list) → list: Build detailed time series rows

pyopmspe11.visualization.data.build_fast_dense_mapping(cfg: BenchmarkConfig, sim: SimulationData, dx: ndarray[tuple[Any, ...], dtype[_ScalarT]], dz: ndarray[tuple[Any, ...], dtype[_ScalarT]]) → tuple[list[list[list[int | float]]], ndarray[tuple[Any, ...], dtype[_ScalarT]]]: Fast sim-to-report mapping for aligned grids

pyopmspe11.visualization.data.build_general_dense_mapping(cfg: BenchmarkConfig, sim: SimulationData, refgrid: tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]]], geometry: tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], list]) → tuple[list[list[list[int | float]]], ndarray[tuple[Any, ...], dtype[_ScalarT]]]: General sim-to-report mapping using geometry

pyopmspe11.visualization.data.build_performance_data(cfg: BenchmarkConfig, sim: SimulationData) → dict: Build performance CSV data

pyopmspe11.visualization.data.build_sparse_data(cfg: BenchmarkConfig, sim: SimulationData) → dict: Build sparse benchmark data

pyopmspe11.visualization.data.build_summary_data(cfg: BenchmarkConfig, sim: SimulationData, fipnum: list, groups: dict) → dict: Build sparse summary time series

pyopmspe11.visualization.data.build_time_series(sim: SimulationData, times_data: ndarray[tuple[Any, ...], dtype[_ScalarT]], metrics: dict, map_info: ndarray[tuple[Any, ...], dtype[_ScalarT]], map_summary: ndarray[tuple[Any, ...], dtype[_ScalarT]], interp_fgmip: interp1d, cpu: list) → list: Build time series rows

pyopmspe11.visualization.data.can_use_fast_dense_mapping(cfg: BenchmarkConfig, sim: SimulationData, dx: ndarray[tuple[Any, ...], dtype[_ScalarT]], dz: ndarray[tuple[Any, ...], dtype[_ScalarT]]) → bool: Check if fast dense mapping can be used

pyopmspe11.visualization.data.compute_cpu_times(cfg: BenchmarkConfig, sim: SimulationData, times_det: ndarray[tuple[Any, ...], dtype[_ScalarT]]) → tuple[list, ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]]]: Compute CPU times

pyopmspe11.visualization.data.compute_m_c(cfg: BenchmarkConfig, sim: SimulationData, fipnum: list, dx: ndarray[tuple[Any, ...], dtype[_ScalarT]], dy: ndarray[tuple[Any, ...], dtype[_ScalarT]], dz: ndarray[tuple[Any, ...], dtype[_ScalarT]]) → list: Compute Box C variation

pyopmspe11.visualization.data.dense_data(cfg: BenchmarkConfig, sim: SimulationData) → None: Generate dense benchmark data

pyopmspe11.visualization.data.extract_boundary_pressures(cfg: BenchmarkConfig, sim: SimulationData, fipnum: list) → tuple[list, list]: Extract boundary pressures

pyopmspe11.visualization.data.extract_sim_geometry(cfg: BenchmarkConfig, sim: SimulationData) → tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], list, ndarray[tuple[Any, ...], dtype[_ScalarT]]]: Extract simulation geometry

pyopmspe11.visualization.data.extract_solver_metrics(infosteps: ndarray[tuple[Any, ...], dtype[_ScalarT]], tags: list) → dict: Extract solver metrics

pyopmspe11.visualization.data.generate_arrays(cfg: BenchmarkConfig, sim: SimulationData, names: list, restart_index: int, actindr: ndarray[tuple[Any, ...], dtype[_ScalarT]]) → dict: Populate dense arrays for one restart

pyopmspe11.visualization.data.get_fip_groups(cfg: BenchmarkConfig) → dict: Define FIP groups

pyopmspe11.visualization.data.get_header(cfg: BenchmarkConfig, sim: SimulationData, i: int) → tuple[str, list[str]]: Get the CSV header for spe11x

pyopmspe11.visualization.data.handle_inactive_mapping(cfg: BenchmarkConfig, sim: SimulationData, cell_ind: list[list[list[int | float]]]) → ndarray[tuple[Any, ...], dtype[_ScalarT]]: Identify inactive reporting-grid cells

pyopmspe11.visualization.data.handle_performance_spatial(cfg: BenchmarkConfig, sim: SimulationData, rstno: list, refgrid: tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]]], mapping: tuple[list, ndarray[tuple[Any, ...], dtype[_ScalarT]]], actindr: ndarray[tuple[Any, ...], dtype[_ScalarT]]) → None: Create performance spatial maps

pyopmspe11.visualization.data.handle_post_mapping(cfg: BenchmarkConfig, sim: SimulationData, refgrid: tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]]], mapping: tuple[list[list[list[int | float]]], ndarray[tuple[Any, ...], dtype[_ScalarT]]], simycent: ndarray[tuple[Any, ...], dtype[_ScalarT]], satnum: ndarray[tuple[Any, ...], dtype[_ScalarT]]) → tuple[list[list[list[int | float]]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]]]: Post-process mapping

pyopmspe11.visualization.data.handle_yaxis_mapping_extensive(cfg: BenchmarkConfig, sim: SimulationData, cell_ind: list[list[list[int | float]]], simycent: ndarray[tuple[Any, ...], dtype[_ScalarT]], refyvert: ndarray[tuple[Any, ...], dtype[_ScalarT]]) → list[list[list[int | float]]]: Extend indices for y direction (extensive)

pyopmspe11.visualization.data.handle_yaxis_mapping_intensive(cfg: BenchmarkConfig, sim: SimulationData, cell_cent: ndarray[tuple[Any, ...], dtype[_ScalarT]], refycent: ndarray[tuple[Any, ...], dtype[_ScalarT]], simycent: ndarray[tuple[Any, ...], dtype[_ScalarT]]) → ndarray[tuple[Any, ...], dtype[_ScalarT]]: Extend representative cell indices for y direction (intensive)

pyopmspe11.visualization.data.init_performance_arrays(sim: SimulationData, names: tuple[str, str, str, str]) → dict: Initialize performance arrays

pyopmspe11.visualization.data.interpolate_sparse(times_data: ndarray[tuple[Any, ...], dtype[_ScalarT]], sim: SimulationData, summary: dict, m_c: list) → dict: Interpolate sparse outputs

pyopmspe11.visualization.data.main(argv=None) → None: Postprocess simulation results into benchmark CSVs

pyopmspe11.visualization.data.map_performance_to_report_grid(cfg: BenchmarkConfig, sim: SimulationData, arrays: dict, cell_ind: list, delta_t: float, pore_volume: ndarray[tuple[Any, ...], dtype[_ScalarT]], valid: ndarray[tuple[Any, ...], dtype[_ScalarT]]) → dict: Map simulation grid to reporting grid

pyopmspe11.visualization.data.map_to_report_grid(sim: SimulationData, mapping: tuple[list[list[list[int | float]]], ndarray[tuple[Any, ...], dtype[_ScalarT]]], arrays: dict) → None: Map simulation arrays to reporting grid

pyopmspe11.visualization.data.performance(cfg: BenchmarkConfig, sim: SimulationData) → None: Generate benchmark performance data

pyopmspe11.visualization.data.populate_performance_arrays(sim: SimulationData, arrays: dict, step_index: int) → None: Populate performance arrays

pyopmspe11.visualization.data.read_infostep(cfg: BenchmarkConfig, sim: SimulationData) → tuple[list, ndarray[tuple[Any, ...], dtype[_ScalarT]]]: Read INFOSTEP file

pyopmspe11.visualization.data.read_simulations(cfg: BenchmarkConfig) → SimulationData: Use opm Python package to read the results

pyopmspe11.visualization.data.sparse_data(cfg: BenchmarkConfig, sim: SimulationData) → None: Generate sparse benchmark data

pyopmspe11.visualization.data.static_map_performance_spatial(cfg: BenchmarkConfig, sim: SimulationData, cell_ind: list, counter: ndarray[tuple[Any, ...], dtype[_ScalarT]], pore_volume: ndarray[tuple[Any, ...], dtype[_ScalarT]]) → tuple[list, ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]]]: Map static quantities for performance spatial data

pyopmspe11.visualization.data.write_dense_csv(cfg: BenchmarkConfig, sim: SimulationData, refgrid: tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]]], dense_step: dict, step_index: int) → None: Write dense spatial CSV

pyopmspe11.visualization.data.write_dense_performance_spatial(cfg: BenchmarkConfig, refg: dict, cvol_refg: ndarray[tuple[Any, ...], dtype[_ScalarT]], arat_refg: ndarray[tuple[Any, ...], dtype[_ScalarT]], refxcent: ndarray[tuple[Any, ...], dtype[_ScalarT]], refycent: ndarray[tuple[Any, ...], dtype[_ScalarT]], refzcent: ndarray[tuple[Any, ...], dtype[_ScalarT]], i: int) → None: Write dense performance spatial CSV

pyopmspe11.visualization.data.write_performance_csv(cfg: BenchmarkConfig, perf: dict) → None: Write performance CSV files

pyopmspe11.visualization.data.write_sparse_csv(cfg: BenchmarkConfig, sparse: dict) → None: Write sparse CSV