pyopmspe11.visualization.plotting module

Script to plot the results

class pyopmspe11.visualization.plotting.CaseConfig(case: str, tlabel: str, dims: int, tscale: float, lower: bool)

Bases: object

Case configuration

case: str

dims: int

lower: bool

tlabel: str

tscale: float

class pyopmspe11.visualization.plotting.GridState(times: list, xmsh: ndarray, zmsh: ndarray, xmx: ndarray, ymy: ndarray, zmz: ndarray, kinds: list, cmaps: list, dims: int)

Bases: object

Grid configuration

cmaps: list

dims: int

kinds: list

times: list

xmsh: ndarray

xmx: ndarray

ymy: ndarray

zmsh: ndarray

zmz: ndarray

class pyopmspe11.visualization.plotting.RunConfig(folders: list, generate: str, compare: str, where: str, dataf: str, colors: list, linestyles: list, props: dict)

Bases: object

Runtime configuration

colors: list

compare: str

dataf: str

folders: list

generate: str

linestyles: list

props: dict

where: str

pyopmspe11.visualization.plotting.configure_matplotlib() → None: Parameters for the figures

pyopmspe11.visualization.plotting.dense_data(case_cfg: CaseConfig, run_cfg: RunConfig, grid: GridState) → None: Plot the dense data

pyopmspe11.visualization.plotting.generate_grid(folder: str, dataf: str, tlabel: str, dims: int, time) → tuple[list, 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]]]: Create the meshgrid

pyopmspe11.visualization.plotting.load_csv(path: str) → ndarray[tuple[Any, ...], dtype[_ScalarT]]: Load the csv

pyopmspe11.visualization.plotting.load_performance(folder: str, case: str, kind: str) → tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]] | None, bool]: Read the performance_time_series*.csv

pyopmspe11.visualization.plotting.load_spatial(folder: str, dataf: str, case: str, kind: str, time: str, tlabel: str) → ndarray[tuple[Any, ...], dtype[_ScalarT]]: Read the spatial_map_*.csv

pyopmspe11.visualization.plotting.load_time_series(folder: str, case: str) → tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]] | None, bool]: Read the time_series.csv

pyopmspe11.visualization.plotting.main(argv: list[str] | None) → None: Entry point

pyopmspe11.visualization.plotting.performance(case_cfg: CaseConfig, run_cfg: RunConfig) → None: Plot the performance

pyopmspe11.visualization.plotting.performance_label(csv: ndarray[tuple[Any, ...], dtype[_ScalarT]], index: int, folder: str) → str: Set the metrics in the labels for the performance plots

pyopmspe11.visualization.plotting.plot_results(args: dict) → None: Orchestrate the plotting

pyopmspe11.visualization.plotting.sparse_data(case_cfg: CaseConfig, run_cfg: RunConfig) → None: Plot the sparse data