skills/flowio/SKILL.md
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 2026-07-23.
FlowIO is appropriate for:
FlowIO does not perform compensation, logicle/biexponential transforms, gating, clustering, or FlowJo workspace processing. Use FlowKit or another analysis package for those tasks.
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.
only_text=True for metadata-only
work, especially with large or unfamiliar files.as_array(preprocess=True) for
gain/log/time scaling from FCS metadata, or preprocess=False for values as
encoded in the DATA segment. Record the choice.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.
flow.events is the unprocessed, flattened one-dimensional event array.flow.as_array() returns shape (event_count, channel_count) as a NumPy
float64 array.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 encoded event values without
those scaling steps.as_array() creates another in-memory array. FlowIO does not provide chunked
or memory-mapped event access.
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.create_fcs() requires:
metadata_dictIt 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.
from pathlib import Path
from flowio import FlowData
flow = FlowData(Path("sample.fcs"))
events = flow.as_array(preprocess=True)
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.
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)
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.
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": "23-JUL-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.
Use write_fcs() when the event data does not need to change:
from flowio import FlowData
flow = FlowData("source.fcs")
# Preserve selected source metadata (cyt, date, and spill/spillover when present).
flow.write_fcs("copy.fcs")
# Write only required metadata plus the custom fields supplied here.
flow.write_fcs("deidentified.fcs", metadata={"src": "Deidentified export"})
Passing metadata=None preserves FlowIO's selected defaults. Passing any
dictionary, including {}, replaces those defaults rather than merging with
them. write_fcs() always produces FCS 3.1 floating-point output; non-float
source events are preprocessed before writing. It opens the destination for
overwrite, so reject an existing output path before calling it unless
replacement is intentional. For floating-point sources it can preserve encoded
events while dropping PnG or timestep, changing later
as_array(preprocess=True) results. Validate both raw and preprocessed
round-trips.
Use create_fcs() instead when event values, event count, or channel layout
changes.
scripts/inspect_fcs.py inventories one or more datasets without network
access. By default it reads metadata only, emits structural fields and channel
labels without full TEXT/ANALYSIS values, and refuses files above a
configurable size limit.
Set FLOWIO_SKILL_DIR to the installed skill directory. From this repository's
root, use skills/flowio:
FLOWIO_SKILL_DIR="skills/flowio"
# Metadata and channel inventory
uv run --no-project --with "flowio==1.4.0" \
python "$FLOWIO_SKILL_DIR/scripts/inspect_fcs.py" sample.fcs
# Include all normalized TEXT metadata; review output for identifiers
uv run --no-project --with "flowio==1.4.0" \
python "$FLOWIO_SKILL_DIR/scripts/inspect_fcs.py" sample.fcs --include-text
# Load events and compute finite-value statistics using FlowIO preprocessing
uv run --no-project --with "flowio==1.4.0" \
python "$FLOWIO_SKILL_DIR/scripts/inspect_fcs.py" sample.fcs --stats
# Compute statistics from encoded values instead
uv run --no-project --with "flowio==1.4.0" \
python "$FLOWIO_SKILL_DIR/scripts/inspect_fcs.py" sample.fcs --stats --raw
Use --help for output files, input/array memory limits, null-channel labels,
and controlled offset-recovery options.
Read only the reference needed for the current task:
references/api_reference.md — exact FlowIO 1.4.0 public API and signaturesreferences/workflows.md — inventory, DataFrame/CSV, batch, write, and
round-trip patternsreferences/fcs_semantics.md — FCS structure, metadata normalization,
preprocessing equations, indexing, and writer behaviorreferences/troubleshooting.md — offset failures, multi-dataset files,
memory limits, validation, security, and privacyreferences/sources.md — authoritative upstream docs, release notes, source,
and FCS 3.1 publications used for this refreshas_array(preprocess=True) as raw acquisition values.create_fcs().$ or uppercase spelling.