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