flowio

name flowio description Parse FCS (Flow Cytometry Standard) files v2.0-3.1. Extract events as NumPy arrays, read metadata/channels, convert to CSV/DataFrame, for flow cytometry data preprocessing. FlowIO: Flow Cytometry Standard File Handler Overview FlowIO is a lightweight Python library for reading and writing Flow Cytometry Standard (FCS) files. Parse FCS metadata, extract event data, and create new FCS files with minimal dependencies. The library supports FCS versions 2.0, 3.0, and 3.1, making it ideal for backend services, data pipelines, and basic cytometry file operations. When to Use This Skill This skill should be used when: FCS files requiring parsing or metadata extraction Flow cytometry data needing conversion to NumPy arrays Event data requiring export to FCS format Multi-dataset FCS files needing separation Channel information extraction (scatter, fluorescence, time) Cytometry file validation or inspection Pre-processing workflows before advanced analysis Related Tools: For advanced flow cytometry analysis including compensation, gating, and FlowJo/GatingML support, recommend FlowKit library as a companion to FlowIO. Installation uv pip install flowio Requires Python 3.9 or later. Quick Start Basic File Reading from flowio import FlowData # Read FCS file flow_data = FlowData( 'experiment.fcs' ) # Access basic information print ( f"FCS Version: {flow_data.version} " ) print ( f"Events: {flow_data.event_count} " ) print ( f"Channels: {flow_data.pnn_labels} " ) # Get event data as NumPy array events = flow_data.as_array() # Shape: (events, channels) Creating FCS Files import numpy as np from flowio import create_fcs # Prepare data data = np.array([[ 100 , 200 , 50 ], [ 150 , 180 , 60 ]]) # 2 events, 3 channels channels = [ 'FSC-A' , 'SSC-A' , 'FL1-A' ] # Create FCS file create_fcs( 'output.fcs' , data, channels) Core Workflows Reading and Parsing FCS Files The FlowData class provides the primary interface for reading FCS files. Standard Reading: from flowio import FlowData # Basic reading flow = FlowData( 'sample.fcs' ) # Access attributes version = flow.version # '3.0', '3.1', etc. event_count = flow.event_count # Number of events channel_count = flow.channel_count # Number of channels pnn_labels = flow.pnn_labels # Short channel names pns_labels = flow.pns_labels # Descriptive stain names # Get event data events = flow.as_array() # Preprocessed (gain, log scaling applied) raw_events = flow.as_array(preprocess= False ) # Raw data Memory-Efficient Metadata Reading: When only metadata is needed (no event data): # Only parse TEXT segment, skip DATA and ANALYSIS flow = FlowData( 'sample.fcs' , only_text= True ) # Access metadata metadata = flow.text # Dictionary of TEXT segment keywords print (metadata.get( '$DATE' )) # Acquisition date print (metadata.get( '$CYT' )) # Instrument name Handling Problematic Files: Some FCS files have offset discrepancies or errors: # Ignore offset discrepancies between HEADER and TEXT sections flow = FlowData( 'problematic.fcs' , ignore_offset_discrepancy= True ) # Use HEADER offsets instead of TEXT offsets flow = FlowData( 'problematic.fcs' , use_header_offsets= True ) # Ignore offset errors entirely flow = FlowData( 'problematic.fcs' , ignore_offset_error= True ) Excluding Null Channels: # Exclude specific channels during parsing flow = FlowData( 'sample.fcs' , null_channel_list=[ 'Time' , 'Null' ]) Extracting Metadata and Channel Information FCS files contain rich metadata in the TEXT segment. Common Metadata Keywords: flow = FlowData( 'sample.fcs' ) # File-level metadata text_dict = flow.text acquisition_date = text_dict.get( '$DATE' , 'Unknown' ) instrument = text_dict.get( '$CYT' , 'Unknown' ) data_type = flow.data_type # 'I', 'F', 'D', 'A' # Channel metadata for i in range (flow.channel_count): pnn = flow.pnn_labels[i] # Short name (e.g., 'FSC-A') pns = flow.pns_labels[i] # Descriptive name (e.g., 'Forward Scatter') pnr = flow.pnr_values[i] # Range/max value print ( f"Channel {i} : {pnn} ( {pns} ), Range: {pnr} " ) Channel Type Identification: FlowIO automatically categorizes channels: # Get indices by channel type scatter_idx = flow.scatter_indices # [0, 1] for FSC, SSC fluoro_idx = flow.fluoro_indices # [2, 3, 4] for FL channels time_idx = flow.time_index # Index of time channel (or None) # Access specific channel types events = flow.as_array() scatter_data = events[:, scatter_idx] fluorescence_data = events[:, fluoro_idx] ANALYSIS Segment: If present, access processed results: if flow.analysis: analysis_keywords = flow.analysis # Dictionary of ANALYSIS keywords print (analysis_keywords) Creating New FCS Files Generate FCS files from NumPy arrays or other data sources. Basic Creation: import numpy as np from flowio import create_fcs # Create event data (rows=events, columns=channels) events = np.random.rand( 10000 , 5 ) * 1000 # Define channel names channel_names = [ 'FSC-A' , 'SSC-A' , 'FL1-A' , 'FL2-A' , 'Time' ] # Create FCS file create_fcs( 'output.fcs' , events, channel_names) With Descriptive Channel Names: # Add optional descriptive names (PnS) channel_names = [ 'FSC-A' , 'SSC-A' , 'FL1-A' , 'FL2-A' , 'Time' ] descriptive_names = [ 'Forward Scatter' , 'Side Scatter' , 'FITC' , 'PE' , 'Time' ] create_fcs( 'output.fcs' , events, channel_names, opt_channel_names=descriptive_names) With Custom Metadata: # Add TEXT segment metadata metadata = { '$SRC' : 'Python script' , '$DATE' : '19-OCT-2025' , '$CYT' : 'Synthetic Instrument' , '$INST' : 'Laboratory A' } create_fcs( 'output.fcs' , events, channel_names, opt_channel_names=descriptive_names, metadata=metadata) Note: FlowIO exports as FCS 3.1 with single-precision floating-point data. Exporting Modified Data Modify existing FCS files and re-export them. Approach 1: Using write_fcs() Method: from flowio import FlowData # Read original file flow = FlowData( 'original.fcs' ) # Write with updated metadata flow.write_fcs( 'modified.fcs' , metadata={ '$SRC' : 'Modified data' }) Approach 2: Extract, Modify, and Recreate: For modifying event data: from flowio import FlowData, create_fcs # Read and extract data flow = FlowData( 'original.fcs' ) events = flow.as_array(preprocess= False ) # Modify event data events[:, 0 ] = events[:, 0 ] * 1.5 # Scale first channel # Create new FCS file with modified data create_fcs( 'modified.fcs' , events, flow.pnn_labels, opt_channel_names=flow.pns_labels, metadata=flow.text) Handling Multi-Dataset FCS Files Some FCS files contain multiple datasets in a single file. Detecting Multi-Dataset Files: from flowio import FlowData, MultipleDataSetsError try : flow = FlowData( 'sample.fcs' ) except MultipleDataSetsError: print ( "File contains multiple datasets" ) # Use read_multiple_data_sets() instead Reading All Datasets: from flowio import read_multiple_data_sets # Read all datasets from file datasets = read_multiple_data_sets( 'multi_dataset.fcs' ) print ( f"Found { len (datasets)} datasets" ) # Process each dataset for i, dataset in enumerate (datasets): print ( f"\nDataset {i} :" ) print ( f" Events: {dataset.event_count} " ) print ( f" Channels: {dataset.pnn_labels} " ) # Get event data for this dataset events = dataset.as_array() print ( f" Shape: {events.shape} " ) print ( f" Mean values: {events.mean(axis= 0 )} " ) Reading Specific Dataset: from flowio import FlowData # Read first dataset (nextdata_offset=0) first_dataset = FlowData( 'multi.fcs' , nextdata_offset= 0 ) # Read second dataset using NEXTDATA offset from first next_offset = int (first_dataset.text[ '$NEXTDATA' ]) if next_offset > 0 : second_dataset = FlowData( 'multi.fcs' , nextdata_offset=next_offset) Data Preprocessing FlowIO applies standard FCS preprocessing transformations when preprocess=True . Preprocessing Steps: Gain Scaling: Multiply values by PnG (gain) keyword Logarithmic Transformation: Apply PnE exponential transformation if present Formula: value = a * 10^(b * raw_value) where PnE = "a,b" Time Scaling: Convert time values to appropriate units Controlling Preprocessing: # Preprocessed data (default) preprocessed = flow.as_array(preprocess= True ) # Raw data (no transformations) raw = flow.as_array(preprocess= False ) Error Handling Handle common FlowIO exceptions appropriately. from flowio import ( FlowData, FCSParsingError, DataOffsetDiscrepancyError, MultipleDataSetsError ) try : flow = FlowData( 'sample.fcs' ) events = flow.as_array() except FCSParsingError as e: print ( f"Failed to parse FCS file: {e} " ) # Try with relaxed parsing flow = FlowData( 'sample.fcs' , ignore_offset_error= True ) except DataOffsetDiscrepancyError as e: print ( f"Offset discrepancy detected: {e} " ) # Use ignore_offset_discrepancy parameter flow = FlowData( 'sample.fcs' , ignore_offset_discrepancy= True ) except MultipleDataSetsError as e: print ( f"Multiple datasets detected: {e} " ) # Use read_multiple_data_sets instead from flowio import read_multiple_data_sets datasets = read_multiple_data_sets( 'sample.fcs' ) except Exception as e: print ( f"Unexpected error: {e} " ) Common Use Cases Inspecting FCS File Contents Quick exploration of FCS file structure: from flowio import FlowData flow = FlowData( 'unknown.fcs' ) print ( "=" * 50 ) print ( f"File: {flow.name} " ) print ( f"Version: {flow.version} " ) print ( f"Size: {flow.file_size:,} bytes" ) print ( "=" * 50 ) print ( f"\nEvents: {flow.event_count:,} " ) print ( f"Channels: {flow.channel_count} " ) print ( "\nChannel Information:" ) for i, (pnn, pns) in enumerate ( zip (flow.pnn_labels, flow.pns_labels)): ch_type = "scatter" if i in flow.scatter_indices else \ "fluoro" if i in flow.fluoro_indices else \ "time" if i == flow.time_index else "other" print ( f" [ {i} ] {pnn:10s} | {pns:30s} | {ch_type} " ) print ( "\nKey Metadata:" ) for key in [ '$DATE' , '$BTIM' , '$ETIM' , '$CYT' , '$INST' , '$SRC' ]: value = flow.text.get(key, 'N/A' ) print ( f" {key:15s} : {value} " ) Batch Processing Multiple Files Process a directory of FCS files: from pathlib import Path from flowio import FlowData import pandas as pd # Find all FCS files fcs_files = list (Path( 'data/' ).glob( '*.fcs' )) # Extract summary information summaries = [] for fcs_path in fcs_files: try : flow = FlowData( str (fcs_path), only_text= True ) summaries.append({ 'filename' : fcs_path.name, 'version' : flow.version, 'events' : flow.event_count, 'channels' : flow.channel_count, 'date' : flow.text.get( '$DATE' , 'N/A' ) }) except Exception as e: print ( f"Error processing {fcs_path.name} : {e} " ) # Create summary DataFrame df = pd.DataFrame(summaries) print (df) Converting FCS to CSV Export event data to CSV format: from flowio import FlowData import pandas as pd # Read FCS file flow = FlowData( 'sample.fcs' ) # Convert to DataFrame df = pd.DataFrame( flow.as_array(), columns=flow.pnn_labels ) # Add metadata as attributes