flowio

# 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 ```bash uv pip install flowio ``` Requires Python 3.9 or later. ## Quick Start ### Basic File Reading ```python 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 ```python 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:** ```python 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): ```python # 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: ```python # Ignore offset discrepancies between HEADER and TEXT sections flow = FlowData('problematic.f