xpypact 0.12.2

"Python workflow framework for FISPACT."

Contents

• Description

• Implemented functionality

• Installation

• Examples

• Contributing

• References

Note

This document is in progress.

Description

The 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 functionality

• export to DuckDB

• export to parquet files

Note

Currently 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.

Installation

From PyPI

pip install xpypact

As dependency

poetry add xpypact

From source

pip install htpps://github.com/MC-kit/xpypact.git

Examples

from 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()

Contributing

Just follow ordinary practice:

• Commit message

• Conventional commits

References

Note

add references to FISPACT, pypact and used tools: poetry etc