| import pandas as pd |
| from pathlib import Path |
| from datasets import Features, Value, Sequence |
| from datasets import DatasetInfo, GeneratorBasedBuilder |
| import pickle |
| import pyarrow as pa |
| import pyarrow.parquet as pq |
|
|
|
|
| class fcsParquetIO: |
| def __init__(self): |
| pass |
|
|
| def write_parquets(self, tables: dict[str, pa.Table], dump_dir: Path): |
| if not dump_dir.exists(): |
| dump_dir.mkdir(parents=True, exist_ok=True) |
| for parquet_filename, table in tables.items(): |
| pq.write_table(table, dump_dir / f"{parquet_filename}.parquet") |
|
|
| def read_parquets(self, read_dir: Path) -> tuple[dict, pd.DataFrame]: |
| parquet_filepaths = list(read_dir.glob("*.parquet")) |
| table = {"meta": None, "event": pd.DataFrame()} |
| for parquet_filepath in parquet_filepaths: |
| table = pq.read_table(parquet_filepath) |
|
|
| parquet_filename = str( |
| parquet_filepath.name).replace(".parquet", '') |
| if "part_001" in parquet_filename: |
| raw_meta = table.schema.metadata or {} |
| table["meta"] = {k.decode('utf8'): v.decode('utf8') |
| for k, v in raw_meta.items()} |
|
|
| table["event"] = pd.concat( |
| [table["event"], table.to_pandas()], axis=0, ignore_index=True) |
|
|
| return table["meta"], table["event"] |
|
|
|
|
| features = Features({ |
| "flow_id": Value("string"), |
| "original_id": Value("string"), |
| "specimen": Value("string"), |
| "purpose": Value("string"), |
| "site": Value("string"), |
| "machine": Value("string"), |
| "color_counts": Value("int16"), |
| "sampling_date": Value("date32"), |
| "measuring_date": Value("date32"), |
| "panel": Value("string"), |
| "label": Value("string"), |
| "sublabel": Sequence(Value("string")), |
| "RDP": Value("float64"), |
|
|
| "tube_counts": Value("int16"), |
| "tube_event_counts": Value("string"), |
| "tube_channels": Value("string"), |
|
|
| "data": Value("binary"), |
| }) |
|
|
|
|
| class BD_LST(GeneratorBasedBuilder): |
| def _info(self): |
| return DatasetInfo( |
| features=features, |
| description="This dataset is a project of BD LST data from datalake with various training conditions, contained in different branches.", |
| homepage="", |
| citation="", |
| license="" |
| ) |
|
|
| def _generate_examples(self, dataset_dir: Path): |
| sample_metas = pd.read_csv(dataset_dir / "sample_metas.csv") |
|
|
| data_dir = dataset_dir / "fcs" |
| for sample_dir in sorted(list(data_dir.glob('*'))): |
| if sample_dir.is_dir(): |
| sample_meta = sample_metas[sample_metas["flow_id"] |
| == sample_dir.name].to_dict() |
| sample_tubes = {} |
| for tube_dir in sorted(list(sample_dir.glob('*'))): |
| if tube_dir.is_dir(): |
| meta, event = fcsParquetIO().read_parquets(tube_dir) |
| sample_tubes[tube_dir.name] = { |
| "meta": meta, "event": event} |
| sample_data = pickle.dumps(sample_tubes) |
|
|
| yield sample_meta | sample_data |
|
|
