"""Extracts grid information at each level and saves to file."""
import os
import sys
import pandas as pd
import yt
sys.path.append(os.path.abspath(os.path.join(sys.argv[0], "../../")))
import ytscripts.utilities as utils # noqa: E402
import ytscripts.ytargs as ytargs # noqa: E402
[docs]
def get_parser():
"""Get the parser."""
ytparse = ytargs.ytExtractArgs()
# Add in the arguments for the extract grid info
ytparse.grid_args()
return ytparse
[docs]
def get_base_parser():
"""Get the base level parser primarily for documentation."""
return get_parser().get_parser()
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def get_args(parser):
"""Get the arguments from the parser."""
args = parser.parse_args()
# Get the initial set of arguments
init_args = parser.parse_args()
# Override the command-line arguments with the input file
if init_args.ifile:
args = parser.override_args(init_args, init_args.ifile)
else:
args = vars(init_args)
# Return the parsed arguments as a dict
return args
[docs]
def main():
"""Main function for grid info extraction."""
# Parse the input arguments
parser = get_parser()
args = get_args(parser)
# Create the output directory
if args["outpath"]:
outpath = args["outpath"]
else:
outpath = os.path.abspath(
os.path.join(sys.argv[0], "../../outdata", "grid_info")
)
os.makedirs(outpath, exist_ok=True)
# Override the units if needed
if args["SI"]:
units_override = {
"length_unit": (1.0, "m"),
"time_unit": (1.0, "s"),
"mass_unit": (1.0, "kg"),
"velocity_unit": (1.0, "m/s"),
}
else:
units_override = None
# Load data files into dataset series
ts, _ = utils.load_dataseries(
datapath=args["datapath"], pname=args["pname"], units_override=units_override
)
base_attributes = utils.get_attributes(ds=ts[0])
if args["verbose"]:
print(f"""The fields in this dataset are: {base_attributes["field_list"]}""")
# Loop over the dataseries
yt.enable_parallelism()
data_dict = {}
for sto, ds in ts.piter(storage=data_dict, dynamic=True):
sto.result_id = float(ds.current_time)
level_data = ds.index.level_stats[0 : ds.index.max_level + 1]
tmp_df = pd.DataFrame(
data=level_data,
index=level_data["level"],
columns=["numgrids", "numcells"],
)
sto.result = tmp_df
if yt.is_root():
# Convert into a pandas dataframe for storage
df = pd.DataFrame({"time": data_dict.keys(), "grid_data": data_dict.values()})
# Sort the dataframe by time
df.sort_values(by="time", inplace=True, ignore_index=True)
# Add some metadata
df.attrs["base_attributes"] = base_attributes
# Save the data for later
df.to_pickle(os.path.join(outpath, f"""{args["name"]}.pkl"""))
if __name__ == "__main__":
main()