diff --git a/src/allotropy/parsers/cytiva_biacore_insight/constants.py b/src/allotropy/parsers/cytiva_biacore_insight/constants.py index a42dcf09a..8d79f7782 100644 --- a/src/allotropy/parsers/cytiva_biacore_insight/constants.py +++ b/src/allotropy/parsers/cytiva_biacore_insight/constants.py @@ -3,5 +3,6 @@ DEVICE_TYPE = "Binding Affinity Analyzer" DETECTION_TYPE = "Surface Plasmon Resonance" PRODUCT_MANUFACTURER = "Cytiva" +REQUIRED_SHEETS = ("Properties", "Report point table") COMPARTMENT_TEMP_REGEX = r"Sample compartment temperature (\d+),.*" diff --git a/src/allotropy/parsers/cytiva_biacore_insight/cytiva_biacore_insight_data_creator.py b/src/allotropy/parsers/cytiva_biacore_insight/cytiva_biacore_insight_data_creator.py index f81e868fd..57cb9f761 100644 --- a/src/allotropy/parsers/cytiva_biacore_insight/cytiva_biacore_insight_data_creator.py +++ b/src/allotropy/parsers/cytiva_biacore_insight/cytiva_biacore_insight_data_creator.py @@ -1,8 +1,12 @@ from pathlib import Path +from typing import Any + +import numpy as np from allotropy.allotrope.models.shared.definitions.custom import ( TQuantityValueHertz, TQuantityValueNumber, + TQuantityValueResponseUnit, TQuantityValueSecondTime, TQuantityValueSquareResponseUnit, TQuantityValueUnitless, @@ -35,6 +39,29 @@ from allotropy.parsers.utils.values import quantity_or_none +def _clean_custom_info(custom_info: dict[str, Any]) -> dict[str, Any]: + """Remove None and NaN values from custom_info dictionaries.""" + + def is_valid_value(value: Any) -> bool: + if value is None: + return False + # Check for NaN in plain float values + if isinstance(value, float) and np.isnan(value): + return False + # Check for NaN in quantity objects (objects with 'value' attribute or dicts with 'value' key) + if hasattr(value, "value"): + val = value.value + if isinstance(val, float) and np.isnan(val): + return False + elif isinstance(value, dict) and "value" in value: + val = value["value"] + if isinstance(val, float) and np.isnan(val): + return False + return True + + return {key: value for key, value in custom_info.items() if is_valid_value(value)} + + def create_metadata(metadata: BiacoreInsightMetadata, file_path: str) -> Metadata: path = Path(file_path) run_metadata = metadata.runs[0] @@ -107,15 +134,23 @@ def _get_measurements( maximum_binding_capacity__rmax_=( measurement.kinetics.maximum_binding_capacity ), - processed_data_custom_info=( + processed_data_custom_info=_clean_custom_info( { - "Kinetics Chi squared": quantity_or_none( + ( + "Affinity Chi squared" + if measurement.kinetics.is_affinity_measurement + else "Kinetics Chi squared" + ): quantity_or_none( TQuantityValueSquareResponseUnit, measurement.kinetics.kinetics_chi_squared, ), "tc": quantity_or_none( TQuantityValueUnitless, measurement.kinetics.tc ), + "offset": quantity_or_none( + TQuantityValueResponseUnit, + measurement.kinetics.offset, + ), } ), report_point_data=[ @@ -125,21 +160,26 @@ def _get_measurements( absolute_resonance=rp.absolute_resonance, time_setting=rp.time_setting, relative_resonance=rp.relative_resonance, - custom_info={ - "Step name": rp.step_name, - "Step purpose": rp.step_purpose, - "Window": quantity_or_none(TQuantityValueSecondTime, rp.window), - "Baseline": rp.baseline, - }, + custom_info=_clean_custom_info( + { + "Step purpose": rp.step_purpose, + "Window": quantity_or_none( + TQuantityValueSecondTime, rp.window + ), + "Baseline": rp.baseline, + } + ), ) for rp in measurement.report_point_data ], - data_processing_document={ - **data_processing_document, - "Acceptance State": measurement.kinetics.acceptance_state, - "Curve Markers": measurement.kinetics.curve_markers, - "Kinetics Model": measurement.kinetics.kinetics_model, - }, + data_processing_document=_clean_custom_info( + { + **data_processing_document, + "Acceptance State": measurement.kinetics.acceptance_state, + "Curve Markers": measurement.kinetics.curve_markers, + "Kinetics Model": measurement.kinetics.kinetics_model, + } + ), ) for measurement in measurement_data ] diff --git a/src/allotropy/parsers/cytiva_biacore_insight/cytiva_biacore_insight_reader.py b/src/allotropy/parsers/cytiva_biacore_insight/cytiva_biacore_insight_reader.py index f5301ef96..ad33bece4 100644 --- a/src/allotropy/parsers/cytiva_biacore_insight/cytiva_biacore_insight_reader.py +++ b/src/allotropy/parsers/cytiva_biacore_insight/cytiva_biacore_insight_reader.py @@ -4,10 +4,9 @@ from allotropy.exceptions import AllotropeConversionError from allotropy.named_file_contents import NamedFileContents +from allotropy.parsers.cytiva_biacore_insight.constants import REQUIRED_SHEETS from allotropy.parsers.utils.pandas import read_multisheet_excel -REQUIRED_SHEETS = ("Properties", "Report point table", "Evaluation - Kinetics") - class CytivaBiacoreInsightReader: SUPPORTED_EXTENSIONS = "xlsx, xlsm" 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 e47d8cb06..195d22f6a 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 @@ -9,6 +9,7 @@ from allotropy.allotrope.models.shared.definitions.custom import ( TQuantityValueDalton, + TQuantityValueMicrogramPerMilliliter, TQuantityValueMicroliterPerMinute, TQuantityValueNanomolar, TQuantityValueResponseUnit, @@ -38,6 +39,68 @@ from allotropy.types import DictType +def _clean_custom_info(custom_info: dict[str, Any]) -> dict[str, Any]: + """Remove None and NaN values from custom_info dictionaries.""" + + def is_valid_value(value: Any) -> bool: + if value is None: + return False + # Check for NaN in plain float values + if isinstance(value, float) and np.isnan(value): + return False + # Check for NaN in quantity objects (objects with 'value' attribute or dicts with 'value' key) + if hasattr(value, "value"): + val = value.value + if isinstance(val, float) and np.isnan(val): + return False + elif isinstance(value, dict) and "value" in value: + val = value["value"] + if isinstance(val, float) and np.isnan(val): + return False + return True + + return {key: value for key, value in custom_info.items() if is_valid_value(value)} + + +def _get_capture_custom_info( + channel_data: pd.DataFrame, capture_1_solution: str | None +) -> dict[str, Any | None]: + """ + Dynamically extract all Capture columns (Capture 1-5) that exist in the data. + + Args: + channel_data: DataFrame with channel data + capture_1_solution: Pre-extracted Capture 1 Solution value (for backwards compatibility) + + Returns: + Dictionary with all available Capture fields + """ + capture_info: dict[str, Any | None] = {} + + # OPTIMIZATION: Get column set once for fast lookups + columns_set = set(channel_data.columns) + + # Check for Capture 1-5 (some files have multiple Capture sets) + for capture_num in range(1, 6): + solution_col = f"Capture {capture_num} Solution" + + # Special handling for Capture 1 Solution (use pre-extracted value if provided) + if capture_num == 1 and capture_1_solution is not None: + capture_info["Capture 1 Solution"] = capture_1_solution + elif solution_col in columns_set: + capture_info[solution_col] = _first_not_null_or_none( + channel_data[solution_col] + ) + + # Add other Capture fields if they exist + for field in ["Plate id", "Position", "Control type"]: + col_name = f"Capture {capture_num} {field}" + if col_name in columns_set: + capture_info[col_name] = _first_not_null_or_none(channel_data[col_name]) + + return capture_info + + def _get_table_from_dataframe(df: pd.DataFrame, split_on: str) -> pd.DataFrame: data_table = drop_df_rows_while(df, lambda row: row[0] != split_on) return parse_header_row( @@ -221,19 +284,33 @@ class KineticsData: maximum_binding_capacity: float | None = None kinetics_chi_squared: float | None = None tc: float | None = None + offset: float | None = None # Affinity-specific field + is_affinity_measurement: bool = False # Track if this is from Affinity analysis @staticmethod def create(kinetics_data: SeriesData) -> KineticsData: + # Detect if this is affinity data by checking which chi-squared column is present + kinetics_chi = kinetics_data.get(float, "Kinetics Chi² (RU²)") + affinity_chi = kinetics_data.get(float, "Affinity Chi² (RU²)") + is_affinity = kinetics_chi is None and affinity_chi is not None + return KineticsData( acceptance_state=kinetics_data.get(str, "Acceptance state"), curve_markers=kinetics_data.get(str, "Curve markers"), - kinetics_model=kinetics_data.get(str, "Kinetics model"), + # Try "Kinetics model" first, fall back to "Affinity model" + kinetics_model=( + kinetics_data.get(str, "Kinetics model") + or kinetics_data.get(str, "Affinity model") + ), binding_on_rate_measurement_datum=kinetics_data.get(float, "ka (1/Ms)"), binding_off_rate_measurement_datum=kinetics_data.get(float, "kd (1/s)"), equilibrium_dissociation_constant=kinetics_data.get(float, "KD (M)"), maximum_binding_capacity=kinetics_data.get(float, "Rmax (RU)"), - kinetics_chi_squared=kinetics_data.get(float, "Kinetics Chi² (RU²)"), + # Try "Kinetics Chi²" first, fall back to "Affinity Chi²" + kinetics_chi_squared=kinetics_chi or affinity_chi, tc=kinetics_data.get(float, "tc"), + offset=kinetics_data.get(float, "offset (RU)"), # Affinity-specific + is_affinity_measurement=is_affinity, ) @@ -242,16 +319,24 @@ class EvaluationKinetics: def __init__(self, kinetics_table: pd.DataFrame) -> None: def _get_key(row: pd.Series[Any]) -> str: - return f'{row["Channel"]} {row["Capture 1 Solution"]} {row["Analyte 1 Solution"]}' - - self._data = { - _get_key(row): KineticsData.create(SeriesData(row)) - for _, row in kinetics_table.iterrows() - } + # Handle both "Channel" and "Flow cell" columns + channel_or_flowcell = row.get("Channel", row.get("Flow cell", "")) + # For affinity data, we might have multiple capture solutions + # Build key with all available capture solutions + capture_solution = row.get("Capture 1 Solution", "") + analyte_solution = row.get("Analyte 1 Solution", "") + return f"{channel_or_flowcell} {capture_solution} {analyte_solution}" + + self._data = {} + if not kinetics_table.empty and len(kinetics_table.columns) > 0: + self._data = { + _get_key(row): KineticsData.create(SeriesData(row)) + for _, row in kinetics_table.iterrows() + } def get_data( self, - channel: int, + channel: int | str, capture_solution: str | None, analyte_solution: str | None, ) -> KineticsData: @@ -310,21 +395,37 @@ def create( channel_data: pd.DataFrame, metadata: BiacoreInsightMetadata, evaluation_kinetics: EvaluationKinetics, + grouping_column: str = "Channel", ) -> MeasurementData: identifier = random_uuid_str() run_metadata = metadata.runs[0] first_row_data = SeriesData(channel_data.iloc[0]) run = first_row_data[int, "Run"] cycle_number = first_row_data[int, "Cycle"] - channel = first_row_data[int, "Channel"] + + # Handle both "Channel" and "Flow cell" as grouping columns + # When using Flow cell, convert to string since it can be "2-1" format + channel: int | str + if grouping_column == "Channel": + channel = first_row_data[int, "Channel"] + else: + # Flow cell can be string like "2-1", store as string + channel = str(first_row_data.get(str, grouping_column, "")) run_table = run_metadata.run_table device_control_document = [] for flow_cell, flow_cell_data in channel_data.groupby("Flow cell"): - run_data = run_table[ - (run_table["Flow cell"].astype(str) == str(flow_cell)) - & (run_table["Channel"] == channel) - ] + # Filter run_table using the appropriate column + if grouping_column == "Channel" and "Channel" in run_table.columns: + run_data = run_table[ + (run_table["Flow cell"].astype(str) == str(flow_cell)) + & (run_table["Channel"] == channel) + ] + else: + # For new format, just filter by flow cell + run_data = run_table[ + run_table["Flow cell"].astype(str) == str(flow_cell) + ] run_info = SeriesData() if run_data.empty else df_to_series_data(run_data) # All the info needed in the device control document is the same for all flow cell data flow_cell_info = SeriesData(flow_cell_data.iloc[0]) @@ -335,46 +436,57 @@ def create( sample_temperature_setting=flow_cell_info.get( float, "Temperature (°C)" ), - device_control_custom_info={ - "Analyte 1 Contact time": quantity_or_none( - TQuantityValueSecondTime, - flow_cell_info.get(float, "Analyte 1 Contact time (s)"), - ), - "Analyte 1 Dissociation time": quantity_or_none( - TQuantityValueSecondTime, - flow_cell_info.get( - float, "Analyte 1 Dissociation time (s)" + device_control_custom_info=_clean_custom_info( + { + "Analyte 1 Contact time": quantity_or_none( + TQuantityValueSecondTime, + flow_cell_info.get(float, "Analyte 1 Contact time (s)"), + ), + "Analyte 1 Dissociation time": quantity_or_none( + TQuantityValueSecondTime, + flow_cell_info.get( + float, "Analyte 1 Dissociation time (s)" + ), + ), + "Analyte 1 Flow rate": quantity_or_none( + TQuantityValueMicroliterPerMinute, + flow_cell_info.get( + float, "Analyte 1 Flow rate (µl/min)" + ), ), - ), - "Analyte 1 Flow rate": quantity_or_none( - TQuantityValueMicroliterPerMinute, - flow_cell_info.get(float, "Analyte 1 Flow rate (µl/min)"), - ), - "Regeneration 1 Contact time": quantity_or_none( - TQuantityValueSecondTime, - flow_cell_info.get( - float, "Regeneration 1 Contact time (s)" + "Regeneration 1 Contact time": quantity_or_none( + TQuantityValueSecondTime, + flow_cell_info.get( + float, "Regeneration 1 Contact time (s)" + ), ), - ), - "Regeneration 1 Flow rate": quantity_or_none( - TQuantityValueMicroliterPerMinute, - flow_cell_info.get( - float, "Regeneration 1 Flow rate (µl/min)" + "Regeneration 1 Flow rate": quantity_or_none( + TQuantityValueMicroliterPerMinute, + flow_cell_info.get( + float, "Regeneration 1 Flow rate (µl/min)" + ), ), - ), - "Included": run_info.get(str, "Included"), - "Sensorgram type": flow_cell_info.get( - str, "Sensorgram type", run_info.get(str, "Sensorgram type") - ), - "Level": quantity_or_none( - TQuantityValueResponseUnit, - run_info.get(float, "Level (RU)"), - ), - }, + "Included": run_info.get(str, "Included"), + "Sensorgram type": flow_cell_info.get( + str, + "Sensorgram type", + run_info.get(str, "Sensorgram type"), + ), + "Level": quantity_or_none( + TQuantityValueResponseUnit, + run_info.get(float, "Level (RU)"), + ), + } + ), ) ) - capture_solution = _first_not_null_or_none(channel_data["Capture 1 Solution"]) + # Capture columns may not exist in all file formats + capture_solution = None + if "Capture 1 Solution" in channel_data.columns: + capture_solution = _first_not_null_or_none( + channel_data["Capture 1 Solution"] + ) analyte_solution = first_row_data.get(str, "Analyte 1 Solution") return MeasurementData( @@ -383,49 +495,51 @@ def create( method_name=run_metadata.method_name, ligand_identifier=run_info.get(str, "Ligand"), device_control_document=device_control_document, - sample_custom_info={ - "Run": run, - "Cycle": cycle_number, - "Channel": channel, - "Analyte 1 Solution": analyte_solution, - "Analyte 1 Plate id": first_row_data.get(str, "Analyte 1 Plate id"), - "Analyte 1 Position": first_row_data.get(str, "Analyte 1 Position"), - "Analyte 1 Control type": first_row_data.get( - str, "Analyte 1 Control type" - ), - "Regeneration 1 Solution": first_row_data.get( - str, "Regeneration 1 Solution" - ), - "Regeneration 1 Plate id": first_row_data.get( - str, "Regeneration 1 Plate id" - ), - "Regeneration 1 Position": first_row_data.get( - str, "Regeneration 1 Position" - ), - "Regeneration 1 Control type": first_row_data.get( - str, "Regeneration 1 Control type" - ), - # Capture data is not reported for the Reference flow cell, - # so we have to look for the first non-null value - "Capture 1 Solution": capture_solution, - "Capture 1 Plate id": _first_not_null_or_none( - channel_data["Capture 1 Plate id"] - ), - "Capture 1 Position": _first_not_null_or_none( - channel_data["Capture 1 Position"] - ), - "Capture 1 Control type": _first_not_null_or_none( - channel_data["Capture 1 Control type"] - ), - "Analyte 1 Concentration": quantity_or_none( - TQuantityValueNanomolar, - first_row_data.get(float, "Analyte 1 Concentration (nM)"), - ), - "Analyte 1 Molecular weight": quantity_or_none( - TQuantityValueDalton, - first_row_data.get(float, "Analyte 1 Molecular weight (Da)"), - ), - }, + sample_custom_info=_clean_custom_info( + { + "Run": run, + "Cycle": cycle_number, + "Channel": channel, + "Analyte 1 Solution": analyte_solution, + "Analyte 1 Plate id": first_row_data.get(str, "Analyte 1 Plate id"), + "Analyte 1 Position": first_row_data.get(str, "Analyte 1 Position"), + "Analyte 1 Control type": first_row_data.get( + str, "Analyte 1 Control type" + ), + "Regeneration 1 Solution": first_row_data.get( + str, "Regeneration 1 Solution" + ), + "Regeneration 1 Plate id": first_row_data.get( + str, "Regeneration 1 Plate id" + ), + "Regeneration 1 Position": first_row_data.get( + str, "Regeneration 1 Position" + ), + "Regeneration 1 Control type": first_row_data.get( + str, "Regeneration 1 Control type" + ), + # Capture data may not be present in all file formats + # Dynamically add all Capture columns that exist (Capture 1-5) + **_get_capture_custom_info(channel_data, capture_solution), + "Analyte 1 Concentration": ( + # Try nM first, then µg/ml + quantity_or_none( + TQuantityValueNanomolar, + first_row_data.get(float, "Analyte 1 Concentration (nM)"), + ) + or quantity_or_none( + TQuantityValueMicrogramPerMilliliter, + first_row_data.get( + float, "Analyte 1 Concentration (µg/ml)" + ), + ) + ), + "Analyte 1 Molecular weight": quantity_or_none( + TQuantityValueDalton, + first_row_data.get(float, "Analyte 1 Molecular weight (Da)"), + ), + } + ), kinetics=evaluation_kinetics.get_data( channel, capture_solution, analyte_solution ), @@ -441,12 +555,60 @@ class Data: @staticmethod def create(reader: CytivaBiacoreInsightReader) -> Data: metadata = BiacoreInsightMetadata.create(reader) + + # OPTIMIZATION: Parse report point table once, not per cycle + report_point_table = _get_table_from_dataframe( + reader.data["Report point table"], split_on="Run" + ) + + # OPTIMIZATION: Determine grouping column once + grouping_column = ( + "Channel" if "Channel" in report_point_table.columns else "Flow cell" + ) + + # OPTIMIZATION: Parse evaluation/affinity data once, not per cycle + evaluation_kinetics = None + if "Evaluation - Kinetics" in reader.data: + evaluation_kinetics_table = _get_table_from_dataframe( + reader.data["Evaluation - Kinetics"], split_on="Group" + ) + evaluation_kinetics = EvaluationKinetics(evaluation_kinetics_table) + else: + # Look for Affinity sheets (e.g., "Affinity 1", "Affinity 2", etc.) + affinity_sheets = [ + sheet for sheet in reader.data.keys() if sheet.startswith("Affinity") + ] + if affinity_sheets: + # Use the first Affinity sheet found + affinity_table = _get_table_from_dataframe( + reader.data[affinity_sheets[0]], split_on="Group" + ) + evaluation_kinetics = EvaluationKinetics(affinity_table) + + # If no kinetics or affinity data found, create empty + if evaluation_kinetics is None: + evaluation_kinetics = EvaluationKinetics(pd.DataFrame()) + + # OPTIMIZATION: Group all data by cycle once using pandas groupby + # This is much faster than filtering per cycle + cycles_dict = {} + for cycle_number, cycle_group in report_point_table.groupby("Cycle"): + # Convert cycle_number to int safely + # pandas groupby can return various numeric types + if isinstance(cycle_number, int | np.integer): + cycle_int = int(cycle_number) + else: + cycle_int = int(float(str(cycle_number))) + cycles_dict[cycle_int] = [ + MeasurementData.create( + channel_data, metadata, evaluation_kinetics, grouping_column + ) + for _, channel_data in cycle_group.groupby(grouping_column) + ] + return Data( metadata=metadata, - cycles={ - cycle: Data.create_measurements_for_cycle(cycle, metadata, reader) - for cycle in range(1, int(metadata.runs[0].number_of_cycles) + 1) - }, + cycles=cycles_dict, ) @staticmethod @@ -455,17 +617,47 @@ def create_measurements_for_cycle( metadata: BiacoreInsightMetadata, reader: CytivaBiacoreInsightReader, ) -> list[MeasurementData]: + """ + DEPRECATED: This method is no longer used for performance reasons. + All cycle processing is now done in Data.create() to avoid redundant parsing. + Keeping this method for backward compatibility in case it's called externally. + """ # Report point table is the entry point report_point_table = _get_table_from_dataframe( reader.data["Report point table"], split_on="Run" ) cycle_data = report_point_table[report_point_table["Cycle"] == cycle_number] - evaluation_kinetics_table = _get_table_from_dataframe( - reader.data["Evaluation - Kinetics"], split_on="Group" - ) - evaluation_kinetics = EvaluationKinetics(evaluation_kinetics_table) + + # Handle optional kinetics/affinity data + # Try "Evaluation - Kinetics" first, then look for "Affinity" sheets + evaluation_kinetics = None + if "Evaluation - Kinetics" in reader.data: + evaluation_kinetics_table = _get_table_from_dataframe( + reader.data["Evaluation - Kinetics"], split_on="Group" + ) + evaluation_kinetics = EvaluationKinetics(evaluation_kinetics_table) + else: + # Look for Affinity sheets (e.g., "Affinity 1", "Affinity 2", etc.) + affinity_sheets = [ + sheet for sheet in reader.data.keys() if sheet.startswith("Affinity") + ] + if affinity_sheets: + # Use the first Affinity sheet found + affinity_table = _get_table_from_dataframe( + reader.data[affinity_sheets[0]], split_on="Group" + ) + evaluation_kinetics = EvaluationKinetics(affinity_table) + + # If no kinetics or affinity data found, create empty + if evaluation_kinetics is None: + evaluation_kinetics = EvaluationKinetics(pd.DataFrame()) + + # Determine grouping column: use "Channel" if available, otherwise "Flow cell" + grouping_column = "Channel" if "Channel" in cycle_data.columns else "Flow cell" return [ - MeasurementData.create(channel_data, metadata, evaluation_kinetics) - for _, channel_data in cycle_data.groupby("Channel") + MeasurementData.create( + channel_data, metadata, evaluation_kinetics, grouping_column + ) + for _, channel_data in cycle_data.groupby(grouping_column) ] diff --git a/tests/parsers/cytiva_biacore_insight/testdata/Affinity.json b/tests/parsers/cytiva_biacore_insight/testdata/Affinity.json new file mode 100644 index 000000000..ff8f21bc1 --- /dev/null +++ b/tests/parsers/cytiva_biacore_insight/testdata/Affinity.json @@ -0,0 +1,94742 @@ +{ + "$asm.manifest": "http://purl.allotrope.org/manifests/binding-affinity-analyzer/WD/2024/12/binding-affinity-analyzer.manifest", + "binding affinity analyzer aggregate document": { + "binding affinity analyzer document": [ + { + "measurement aggregate document": { + "measurement document": [ + { + "device control aggregate document": { + "device control document": [ + { + "device type": "Binding Affinity Analyzer", + "sample temperature setting": { + "value": 25.0, + "unit": "degC" + }, + "flow cell identifier": "1", + "custom information document": { + "Analyte 1 Contact time": { + "value": 60.0, + "unit": "s" + }, + "Analyte 1 Dissociation time": { + "value": 120.0, + "unit": "s" + }, + "Analyte 1 Flow rate": { + "value": 40.0, + "unit": "µL/min" + }, + "Regeneration 1 Contact time": { + "value": 30.0, + "unit": "s" + }, + "Regeneration 1 Flow rate": { + "value": 30.0, + "unit": "µL/min" + }, + "Included": "Yes", + "Sensorgram type": "Reference", + "Level": { + "value": 3293.93126, + "unit": "RU" + } + } + } + ] + }, + "measurement identifier": "CYTIVA_BIACORE_INSIGHT_TEST_ID_0", + "sample document": { + "sample identifier": "Run1_Cycle1", + "custom information document": { + "Run": 1, + "Cycle": 1, + "Channel": "1", + "Analyte 1 Solution": "Buffer", + "Analyte 1 Plate id": "Rack 1", + "Analyte 1 Position": "A12", + "Analyte 1 Control type": "Not a control", + "Regeneration 1 Solution": "Gly pH 1.5", + "Regeneration 1 Plate id": "Rack 1", + "Regeneration 1 Position": "D7", + "Regeneration 1 Control type": "Not a control" + } + }, + "detection type": "Surface Plasmon Resonance", + "processed data aggregate document": { + "processed data document": [ + { + "report point aggregate document": { + "report point document": [ + { + "absolute resonance": { + "value": 27887.6692, + "unit": "RU" + }, + "report point identifier": "CYTIVA_BIACORE_INSIGHT_TEST_ID_1", + "identifier role": "Capture baseline all", + "time setting": { + "value": 10.0, + "unit": "s" + }, + "custom information document": { + "Step purpose": "Startup", + "Window": { + "value": 5.0, + "unit": "s" + }, + "Baseline": "No" + } + }, + { + "absolute resonance": { + "value": 27889.2513, + "unit": "RU" + }, + "report point identifier": "CYTIVA_BIACORE_INSIGHT_TEST_ID_2", + "identifier role": "Capture level all_1", + "time setting": { + "value": 523.799987792969, + "unit": "s" + }, + "custom information document": { + "Step purpose": "Startup", + "Window": { + "value": 5.0, + "unit": "s" + }, + "Baseline": "No" + } + }, + { + "absolute resonance": { + "value": 27889.2416, + "unit": "RU" + }, + "report point identifier": "CYTIVA_BIACORE_INSIGHT_TEST_ID_3", + "identifier role": "Analyte baseline_1", + "relative resonance": { + "value": 0.0, + "unit": "RU" + }, + "time setting": { + "value": 528.799987792969, + "unit": "s" + }, + "custom information document": { + "Step purpose": "Startup", + "Window": { + "value": 5.0, + "unit": "s" + }, + "Baseline": "Yes" + } + }, + { + "absolute resonance": { + "value": 27841.7992, + "unit": "RU" + }, + "report point identifier": "CYTIVA_BIACORE_INSIGHT_TEST_ID_4", + "identifier role": "Analyte binding early_1", + "relative resonance": { + "value": -47.4423999999999, + "unit": "RU" + }, + "time setting": { + "value": 539.799987792969, + "unit": "s" + }, + "custom information 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