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BD_LST.py

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+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

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