Package: xpypact
ContentsDescriptionImplemented functionalityInstallationExamplesContributingReferencesNoteThis document is in progress.DescriptionThe module loads FISPACT JSON output files and converts to Polars dataframes
with minor data normalization.
This allows efficient data extraction and aggregation.
Multiple JSON files can be combined using simple additional identification for different
FISPACT runs. So far we use just two-dimensional identification by material
and case. The case usually identifies certain neutron flux.Implemented functionalityexport to DuckDBexport to parquet filesNoteCurrently available FISPACT v.5 API uses rather old python version (3.6).
That prevents direct use of their API in our package (>=3.10).
Check if own python integration with FISPACT is reasonable and feasible.
Or provide own FISPACT python binding.InstallationFrom PyPIpip install xpypactAs dependencypoetry add xpypactFrom sourcepip install htpps://github.com/MC-kit/xpypact.gitExamplesfrom xpypact import FullDataCollector, Inventory

def get_material_id(p: Path) -> int:
    ...

def get_case_id(p: Path) -> int:
    ...

jsons = [path1, path2, ...]
material_ids = {p: get_material_id(p) for p in jsons }
case_ids = {c: get_case_id(p) for p in jsons }

collector = FullDataCollector()

if sequential_load:
    for json in jsons:
        inventory = Inventory.from_json(json)
        collector.append(inventory, material_id=material_ids[json], case_id=case_ids[json])

else:  # multithreading is allowed for collector as well

    task_list = ...  # list of tuples[directory, case_id, tasks_sequence]
    threads = 16  # whatever

    def _find_path(arg) -> tuple[int, int, Path]:
        _case, path, inventory = arg
        json_path: Path = (Path(path) / inventory).with_suffix(".json")
        if not json_path.exists():
            msg = f"Cannot find file {json_path}"
            raise FindPathError(msg)
        try:
            material_id = int(inventory[_LEN_INVENTORY:])
            case_str = json_path.parent.parts[-1]
            case_id = int(case_str[_LEN_CASE:])
        except (ValueError, IndexError) as x:
            msg = f"Cannot define material_id and case_id from {json_path}"
            raise FindPathError(msg) from x
        if case_id != _case:
            msg = f"Contradicting values of case_id in case path and database: {case_id} != {_case}"
            raise FindPathError(msg)
        return material_id, case_id, json_path

    with futures.ThreadPoolExecutor(max_workers=threads) as executor:
        mcp_futures = [
            executor.submit(_find_path, arg)
            for arg in (
                (task_case[0], task_case[1], task)
                for task_case in task_list
                for task in task_case[2].split(",")
                if task.startswith("inventory-")
            )
        ]

    mips = [x.result() for x in futures.as_completed(mcp_futures)]
    mips.sort(key=lambda x: x[0:2])  # sort by material_id, case_id

    def _load_json(arg) -> None:
        collector, material_id, case_id, json_path = arg
        collector.append(from_json(json_path.read_text(encoding="utf8")), material_id, case_id)

    with futures.ThreadPoolExecutor(max_workers=threads) as executor:
        executor.map(_load_json, ((collector, *mip) for mip in mips))


collected = collector.get_result()

# save to parquet files

collected.save_to_parquets(Path.cwd() / "parquets")

# or use DuckDB database

import from xpypact.dao save
import duckdb as db

con = db.connect()
save(con, collected)

gamma_from_db = con.sql(
    """
    select
    g, rate
    from timestep_gamma
    where material_id = 1 and case_id = 54 and time_step_number = 7
    order by g
    """,
).fetchall()ContributingJust follow ordinary practice:Commit messageConventional commitsReferencesNoteadd references to FISPACT, pypact and used tools:  poetry etc