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 2026-07-23.

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_lab