"""Post-process with data catenation and plotting."""
import argparse
import datetime
import pathlib
import waves
import yaml
import modsim_package.utilities
from modsim_package import __version__
TODAY = datetime.datetime.now(tz=datetime.timezone.utc).date()
DEFAULT_SELECTION_DICT = {
"E values": "E22",
"S values": "S22",
"elements": 1,
"step": "Step-1",
"integration point": 0,
}
[docs]
def main(
input_file: pathlib.Path,
output_file: pathlib.Path,
group_path: str,
strain_units: str,
stress_units: str,
selection_dict: dict,
):
"""Calculate QOIs for ``input_file`` datasets.
:param input_file: The input file h5netcdf files containing an Xarray Dataset
:param output_file: The QOI CSV file name. Relative or absolute path.
:param group_path: The h5netcdf group path locating the Xarray Dataset in the input files.
:param strain_units: The independent (x-axis) units
:param stress_units: The dependent (y-axis) units
:param selection_dict: Dictionary to define the down selection of data for QOI calculation. Dictionary
``key: value`` pairs must match the data variables and coordinates of the expected Xarray Dataset object.
"""
concat_coord = waves.parameter_generators.SET_COORDINATE_KEY
# Build single dataset along the "set_name" dimension
combined_data = modsim_package.utilities.combine_data([input_file], group_path, concat_coord)
# Add units
combined_data["E"].attrs["units"] = strain_units
combined_data["S"].attrs["units"] = stress_units
# Calculate QOIs
qoi_data = combined_data.sel(selection_dict).isel({"time": -1}).squeeze()
strain = waves.qoi.create_qoi(
name="strain",
calculated=qoi_data["E"].item(),
long_name="true strain",
description=f"true strain tensor component {selection_dict['E values']} from final increment",
group=qoi_data["set_name"].item(),
version=__version__,
date=TODAY,
units=strain_units,
)
stress = waves.qoi.create_qoi(
name="stress",
calculated=qoi_data["S"].item(),
long_name="true stress",
description=f"true stress tensor component {selection_dict['S values']} from final increment",
group=qoi_data["set_name"].item(),
version=__version__,
date=TODAY,
units=stress_units,
)
qoi_set = waves.qoi.create_qoi_set([strain, stress])
waves.qoi.write_qoi_set_to_csv(qoi_set, output_file)
# Clean up open files
combined_data.close()
[docs]
def get_parser() -> argparse.ArgumentParser:
"""Return parser for CLI options."""
script_name = pathlib.Path(__file__)
default_output_file = f"{script_name.name}.csv"
default_group_path = "RECTANGLE/FieldOutputs/ALL_ELEMENTS"
prog = f"python {script_name.name} "
cli_description = "Read Xarray Dataset and calculate QOIs. Save to ``output_file``."
parser = argparse.ArgumentParser(description=cli_description, prog=prog)
required_named = parser.add_argument_group("required named arguments")
required_named.add_argument(
"-i",
"--input-file",
type=pathlib.Path,
required=True,
help="The Xarray Dataset file",
)
required_named.add_argument(
"--strain-units",
type=str,
required=True,
help="The strain units string.",
)
required_named.add_argument(
"--stress-units",
type=str,
required=True,
help="The stress units string.",
)
parser.add_argument(
"-o",
"--output-file",
type=pathlib.Path,
default=default_output_file,
help="The QOI CSV output file (default: %(default)s)",
)
parser.add_argument(
"-g",
"--group-path",
type=str,
default=default_group_path,
help="The h5py group path to the dataset object (default: %(default)s)",
)
parser.add_argument(
"-s",
"--selection-dict",
type=pathlib.Path,
default=None,
help=(
"The YAML formatted dictionary file to define the down selection of data to be plotted. "
"Dictionary key: value pairs must match the data variables and coordinates of the "
"expected Xarray Dataset object. If no file is provided, the a default selection dict "
f"will be used (default: {DEFAULT_SELECTION_DICT})"
),
)
return parser
if __name__ == "__main__":
parser = get_parser()
args = parser.parse_args()
if not args.selection_dict:
selection_dict = DEFAULT_SELECTION_DICT
else:
with args.selection_dict.open(mode="r") as input_yaml:
selection_dict = yaml.safe_load(input_yaml)
main(
input_file=args.input_file,
output_file=args.output_file,
group_path=args.group_path,
strain_units=args.strain_units,
stress_units=args.stress_units,
selection_dict=selection_dict,
)
