diff --git a/src/allotropy/parsers/cytiva_biacore_insight/constants.py b/src/allotropy/parsers/cytiva_biacore_insight/constants.py index 8d79f7782..39281b9f3 100644 --- a/src/allotropy/parsers/cytiva_biacore_insight/constants.py +++ b/src/allotropy/parsers/cytiva_biacore_insight/constants.py @@ -6,3 +6,4 @@ REQUIRED_SHEETS = ("Properties", "Report point table") COMPARTMENT_TEMP_REGEX = r"Sample compartment temperature (\d+),.*" +RUN_HEADER_REGEX = r"Run \d+$" diff --git a/src/allotropy/parsers/cytiva_biacore_insight/cytiva_biacore_insight_structure.py b/src/allotropy/parsers/cytiva_biacore_insight/cytiva_biacore_insight_structure.py index 60c6c3e4f..dbe8be959 100644 --- a/src/allotropy/parsers/cytiva_biacore_insight/cytiva_biacore_insight_structure.py +++ b/src/allotropy/parsers/cytiva_biacore_insight/cytiva_biacore_insight_structure.py @@ -239,17 +239,75 @@ class RunMetadata: def create_runs(runs_data: pd.DataFrame) -> list[RunMetadata]: runs_data = runs_data.reset_index(drop=True) run, rest = split_dataframe( - runs_data, lambda row: str(row[0]).startswith("Run") + runs_data, + lambda row: bool(re.match(constants.RUN_HEADER_REGEX, str(row[0]))), ) runs = [run] while rest is not None: - run, rest = split_dataframe(rest, lambda row: str(row[0]).startswith("Run")) + run, rest = split_dataframe( + rest, + lambda row: bool(re.match(constants.RUN_HEADER_REGEX, str(row[0]))), + ) runs.append(run) if len(runs) > 1: msg = "Instrument file contains multiple runs. Only single runs are supported at the moment." raise AllotropeConversionError(msg) return [RunMetadata.create(run) for run in runs if not run.empty] + @staticmethod + def _build_run_table_from_transposed_sections( + run_data: pd.DataFrame, section_starts: list[int], method_summary_idx: int + ) -> pd.DataFrame: + """Build a flat run_table from transposed 'Flow cell N' sections. + + In newer Biacore Insight formats, flow cell data is stored as: + Flow cell 1 | ch1 | ch2 | ch3 | ... + Include | Yes | Yes | Yes | ... + Ligand | ... | ... | ... | ... + + This transposes each section into rows matching the old columnar format: + Flow cell | Channel | Include | Sensorgram type | Ligand | ... + """ + rows: list[dict[str, Any]] = [] + for i, start_idx in enumerate(section_starts): + end_idx = ( + section_starts[i + 1] + if i + 1 < len(section_starts) + else method_summary_idx + ) + section = run_data.loc[start_idx : end_idx - 1].dropna(how="all") + if section.empty: + continue + # First row is "Flow cell N | ch1 | ch2 | ..." + header_row = section.iloc[0] + flow_cell_label = str(header_row.iloc[0]) + flow_cell_num = flow_cell_label.replace("Flow cell", "").strip() + channels = [ + str(v) for v in header_row.iloc[1:] if v is not None and str(v) != "nan" + ] + # Remaining rows are attribute | val_ch1 | val_ch2 | ... + attr_rows = section.iloc[1:] + for ch_idx, channel in enumerate(channels): + row_dict: dict[str, Any] = { + "Flow cell": flow_cell_num, + "Channel": channel, + } + for _, attr_row in attr_rows.iterrows(): + attr_name = str(attr_row.iloc[0]) + if attr_name == "nan": + continue + # Map "Include" to "Included" for consistency with old format + if attr_name == "Include": + attr_name = "Included" + val = ( + attr_row.iloc[ch_idx + 1] + if ch_idx + 1 < len(attr_row) + else None + ) + row_dict[attr_name] = val + rows.append(row_dict) + return pd.DataFrame(rows) + @staticmethod def create(run_data: pd.DataFrame) -> RunMetadata: def get_run_information(idx: int) -> SeriesData: @@ -260,6 +318,7 @@ def get_run_information(idx: int) -> SeriesData: return df_to_series_data(parse_header_row(pd.concat((rd1, rd2, rd3)).T)) run_table_start = 0 + flow_cell_section_starts: list[int] = [] chip_data = SeriesData() run_information = SeriesData() run_table = pd.DataFrame() @@ -273,13 +332,22 @@ def get_run_information(idx: int) -> SeriesData: run_information = get_run_information(idx) elif row[0] == "Flow cell": run_table_start = idx + elif re.match(r"Flow cell \d+$", str(row[0])): + flow_cell_section_starts.append(idx) elif row[0] == "Method summary": - run_table = ( - parse_header_row(run_data.loc[run_table_start : idx - 1]) - .dropna(how="all") - .reset_index(drop=True) - ) - run_table = run_table.loc[:, ~run_table.columns.str.contains("^nan$")] + if flow_cell_section_starts: + run_table = RunMetadata._build_run_table_from_transposed_sections( + run_data, flow_cell_section_starts, idx + ) + else: + run_table = ( + parse_header_row(run_data.loc[run_table_start : idx - 1]) + .dropna(how="all") + .reset_index(drop=True) + ) + run_table = run_table.loc[ + :, ~run_table.columns.str.contains("^nan$") + ] elif row[0] == "Set temperatures 1": if match := re.match(constants.COMPARTMENT_TEMP_REGEX, row[1]): compartment_temperature = float(match.group(1)) @@ -317,7 +385,8 @@ def create(reader: CytivaBiacoreInsightReader) -> BiacoreInsightMetadata: properties = reader.data["Properties"] properties, runs_data = split_dataframe( - properties, lambda row: str(row[0]).startswith("Run") + properties, + lambda row: bool(re.match(constants.RUN_HEADER_REGEX, str(row[0]))), ) if runs_data is None or runs_data.empty: msg = "No run data found in the properties section of instrument file." diff --git a/tests/parsers/cytiva_biacore_insight/testdata/Biacore_Insight_Transposed_FlowCells.json b/tests/parsers/cytiva_biacore_insight/testdata/Biacore_Insight_Transposed_FlowCells.json new file mode 100644 index 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