Flowio

FlowIO

Purpose

Use FlowIO as a lightweight, low-level reader and writer for Flow Cytometry Standard files. Examples in this skill target FlowIO 1.4.0, the current stable release verified on 2026-09-30. Standalone FlowIO checks used Python 3.13, NumPy 2.5.3, and pandas 3.0.6.

FlowIO is appropriate for:

• Reading FCS 2.0, 3.0, and 3.1 files
• Inspecting HEADER, TEXT, ANALYSIS, and channel metadata
• Retrieving event data as a two-dimensional NumPy array
• Reading legacy files that contain multiple datasets
• Writing list-mode, single-precision FCS 3.1 files
• Preparing data for pandas, machine-learning, or downstream cytometry tools

FlowIO does not perform compensation, logicle/biexponential transforms, gating, clustering, or FlowJo workspace processing. Use FlowKit or another analysis package for those tasks.

Install

Create or activate a Python environment, then install the verified release:

uv pip install "flowio==1.4.0"

Confirm the runtime version:

uv run python -c "import flowio; print(flowio.__version__)"

FlowIO 1.4.0 supports Python 3.9 through 3.13 and depends on NumPy.

Operating Workflow

1. Clarify the operation. Distinguish metadata inventory, event extraction, file repair, conversion, and downstream biological analysis.
2. Inspect before loading events. Use only_text=True for metadata-only work, especially with large or unfamiliar files.
3. Choose event semantics explicitly. Use as_array(preprocess=True) for gain/log/time scaling from FCS metadata, or preprocess=False for decoded DATA values without those scaling steps. Record the choice.
4. Keep parsing strict by default. Do not automatically suppress offset errors. Relax checks only for a known vendor-format defect, and review the resulting event data.
5. Treat metadata as potentially sensitive. FCS TEXT values can include sample, subject, operator, and instrument identifiers. Export only fields needed for the task.
6. Validate writes by reopening them. Check event/channel counts, labels, metadata, and representative values after any FCS export.

Critical Semantics

TEXT keys are normalized

FlowData.text stores keys in lowercase and strips the leading $ from standard FCS keywords:

from flowio import FlowData

flow = FlowData("sample.fcs", only_text=True)
acquisition_date = flow.text.get("date")
instrument = flow.text.get("cyt")
next_dataset = int(flow.text.get("nextdata", "0"))

Do not look up "$DATE", "$CYT", or other uppercase dollar-prefixed keys. TEXT values remain strings. FlowIO 1.4.0 also removes every $ character from the decoded TEXT segment, including $ characters inside values; preserve the original file when exact metadata fidelity matters.

Events have two representations

• flow.events is the decoded, flattened one-dimensional event array. Integer parsing already applies PnR range masks; this is not a byte-level copy of the original DATA words.
• flow.as_array() returns a NumPy float64 array with one column per channel. For valid input its shape is (event_count, channel_count), but FlowIO infers rows from DATA and does not enforce $TOT; check the shape.
• flow.as_array(preprocess=True) applies FCS gain, logarithmic, and time scaling. It does not apply compensation or logicle/biexponential display transforms.
• flow.as_array(preprocess=False) reshapes the decoded event values without those scaling steps. A recognized, non-null Time channel (case-insensitive) has its gain forced to 1.0 by FlowIO; timestep still applies when preprocessing.

as_array() creates another in-memory array. FlowIO does not provide chunked or memory-mapped event access.

Channel numbering uses two conventions

• NumPy columns and fluoro_indices, scatter_indices, and time_index use zero-based indices.
• flow.channels uses FCS parameter numbers beginning at 1.
• null_channels contains the PnN label strings supplied through null_channel_list, including supplied labels that were not found.
• pns_labels always matches pnn_labels in length; missing optional PnS labels appear as empty strings.

Writing is intentionally limited

create_fcs() requires:

• An already-open binary file handle
• Flattened one-dimensional event data in row-major event/channel order
• One PnN name per channel
• Optional PnS names and string-valued metadata via metadata_dict

It writes FCS 3.1 list-mode ($MODE=L) single-precision float ($DATATYPE=F) data. Required interpretation keywords are generated by FlowIO and cannot be overridden through metadata.

Quick Start: Read an FCS File

from pathlib import Path

from flowio import FlowData

flow = FlowData(Path("sample.fcs"))
events = flow.as_array(preprocess=True)
if events.shape != (flow.event_count, flow.channel_count):
    raise ValueError("DATA shape disagrees with declared $TOT/$PAR")

print(
    {
        "version": flow.version,
        "events": flow.event_count,
        "channels": flow.channel_count,
        "shape": events.shape,
        "pnn": flow.pnn_labels,
        "pns": flow.pns_labels,
        "date": flow.text.get("date"),
        "instrument": flow.text.get("cyt"),
    }
)

For metadata only:

from flowio import FlowData

flow = FlowData("sample.fcs", only_text=True)
print(flow.version, flow.event_count, flow.pnn_labels)

Do not call as_array() on a metadata-only instance because its event data was not loaded.

Prefer a path or Path over a caller-owned file handle. FlowData closes a provided handle after parsing. In FlowIO 1.4.0, read_multiple_data_sets(handle) can fail after the first dataset because the handle has been closed; pass a filesystem path for multi-dataset files.

Quick Start: Read Multiple Datasets

Use the standalone helper rather than manually interpreting $NEXTDATA offsets:

from flowio import read_multiple_data_sets

datasets = read_multiple_data_sets("legacy-multi-dataset.fcs")
for index, dataset in enumerate(datasets):
    values = dataset.as_array(preprocess=True)
    if values.shape != (dataset.event_count, dataset.channel_count):
        raise ValueError(f"Dataset {index}: DATA shape disagrees with $TOT/$PAR")
    print(index, dataset.event_count, dataset.pnn_labels, values.shape)

The FCS 3.1 specification deprecated multiple datasets in one file, but FlowIO can read legacy files that use them.

Quick Start: Create an FCS 3.1 File

from pathlib import Path

import numpy as np
from flowio import FlowData, create_fcs

values = np.asarray(
    [[100.0, 200.0, 50.0], [150.0, 180.0, 60.0]],
    dtype=np.float32,
)
pnn_labels = ["FSC-A", "SSC-A", "FITC-A"]
pns_labels = ["Forward scatter", "Side scatter", "CD3"]

output = Path("output.fcs")
with output.open("xb") as handle:
    create_fcs(
        handle,
        values.ravel(order="C"),
        pnn_labels,
        opt_channel_names=pns_labels,
        metadata_dict={
            "date": "30-SEP-2026",
            "cyt": "Example instrument",
            "src": "Validated NumPy array",
        },
    )

roundtrip = FlowData(output)
assert roundtrip.event_count == values.shape[0]
assert roundtrip.pnn_labels == pnn_labels
np.testing.assert_allclose(
    roundtrip.as_array(preprocess=False),
    values,
    rtol=1e-6,
    atol=1e-6,
)

Metadata keys may be supplied in mixed case or with $, but lowercase keys without $ match FlowIO's normalized representation and are less error-prone. Metadata values must be strings.