Flowio

---
name: flowio
description: Read, inspect, and write Flow Cytometry Standard (FCS) 2.0, 3.0, and 3.1 files with FlowIO. Use for low-level FCS metadata and channel inspection, NumPy event extraction, multi-dataset files, table export, and FCS 3.1 creation; use FlowKit for compensation, cytometry transforms, gating, or FlowJo workspaces.
license: BSD-3-Clause license
compatibility: Requires Python 3.9-3.13, uv, and FlowIO 1.4.0. NumPy is installed with FlowIO; pandas is optional for DataFrame workflows. Runtime parsing is local and needs no credentials or network access.
allowed-tools: Read Write Bash
metadata:
  version: '2.1'
  category: life-sciences
  maintainer: Kalaris Labs
---

# 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 2026-07-23.

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:

```bash
uv pip install "flowio==1.4.0"
```

Confirm the runtime version:

```bash
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 values as
   encoded in the DATA segment. 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:

```python
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 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.

### 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

```python
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:

```python
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:

```python
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.

## Quick Start: Create an FCS 3.1 File

```python
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.

## Copy or Rewrite an Existing File

Use `write_fcs()` when the event data does not need to change:

```python
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.

## Bundled Inspector

`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`:

```bash
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.

## References

Read only the reference needed for the current task:

- `references/api_reference.md` — exact FlowIO 1.4.0 public API and signatures
- `references/workflows.md` — inventory, DataFrame/CSV, batch, write, and
  round-trip patterns
- `references/fcs_semantics.md` — FCS structure, metadata normalization,
  preprocessing equations, indexing, and writer behavior
- `references/troubleshooting.md` — offset failures, multi-dataset files,
  memory limits, validation, security, and privacy
- `references/sources.md` — authoritative upstream docs, release notes, source,
  and FCS 3.1 publications used for this refresh

## Non-Negotiable Checks

- Never claim FlowIO applies compensation or gating.
- Never treat `as_array(preprocess=True)` as raw acquisition values.
- Never pass a two-dimensional array or a path directly to `create_fcs()`.
- Never assume TEXT keys retain `$` or uppercase spelling.
- Never silence offset errors without documenting why and validating the data.
- Never describe FlowIO event loading as streaming or chunked.

## Agent operating procedure

1. **Check the environment.** Confirm tool versions, the reference genome/annotation build and the input formats (FASTQ, BAM, VCF, h5ad).
2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
3. **Run a small version first.** Run the pipeline on a small subset (one sample, one chromosome, a few thousand cells) first.
4. **Execute the full task** using the instructions and references above.
5. **Validate the result.** Check QC metrics, sample identities, genome build consistency and batch effects before interpreting results.
6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.

| If this happens | Do this |
|---|---|
| Genome builds or identifiers do not match between inputs | Stop and harmonize (liftover, ID mapping) before continuing. |
| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |
| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |

**Integrity rules**

- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
- Do not interpret biological significance beyond what the statistics support; report multiple-testing correction.
- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.