fcsfiles 2026.1.8

Quickstart

Install the fcsfiles package and all dependencies from the Python Package Index:

python -m pip install -U fcsfiles

See Examples for using the programming interface.

Source code and support are available on GitHub.

Requirements

This revision was tested with the following requirements and dependencies (other versions may work):

• CPython 3.11.9, 3.12.10, 3.13.11, 3.14.2 64-bit

• NumPy 2.4.0

Revisions

2026.1.8

• Improve code quality.

2025.12.12

• Drop support for Python 3.10, support Python 3.14.

2025.1.1

• Improve type hints.

• Drop support for Python 3.9, support Python 3.13.

2024.5.24

• …

Refer to the CHANGES file for older revisions.

Notes

“Carl Zeiss” and “ConfoCor” are registered trademarks of Carl Zeiss, Inc.

The use of this implementation may be subject to patent or license restrictions.

The API is not stable yet and is expected to change between revisions.

This module does not read flow cytometry standard FCS files.

Examples

Read the CountRateArray from a ConfoCor3 ASCII file as a numpy array:

>>> fcs = ConfoCor3Fcs('ConfoCor3.fcs')
>>> fcs['FcsData']['FcsEntry'][0]['FcsDataSet']['CountRateArray'].shape
(60000, 2)
>>> print(fcs)  # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
Carl Zeiss ConfoCor3 - measurement data file - version 3.0 ANSI
BEGIN FcsData 30000
        Name = Fluorescein
        Comment =
        AverageFlags = Repeat|Position|Average_Fit_Results
        SortOrder = Channel-Repeat-Position-Kinetics
        BEGIN FcsEntry1 10000
...

Read data and metadata from a ConfoCor3 RAW file:

>>> fcs = ConfoCor3Raw('ConfoCor3.raw')
>>> fcs.filename
'f5ee4f36488fca2f89cb6b8626111006_R1_P1_K1_Ch1.raw'
>>> fcs.frequency
20000000
>>> times = fcs.asarray()
>>> int(times[10858])
1199925494
>>> times, bincounts = fcs.asarray(bins=1000)
>>> times.shape
(1000,)
>>> int(bincounts[618])
23
>>> fcs.close()

Read data and metadata from a ConfoCor2 RAW file:

>>> fcs = ConfoCor2Raw('ConfoCor2.raw')
>>> fcs.frequency
20000000
>>> ch0, ch1 = fcs.asarray()
>>> int(ch1[4812432])
999999833
>>> times, ch0, ch1 = fcs.asarray(bins=1000)
>>> times.shape
(1000,)
>>> int(ch1[428])
10095
>>> fcs.close()