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ASCII\nmeasurement data files.\n\n:Author: `Christoph Gohlke \u003chttps://www.cgohlke.com\u003e`_\n:License: BSD-3-Clause\n:Version: 2026.1.8\n:DOI: `10.5281/zenodo.17905094 \u003chttps://doi.org/10.5281/zenodo.17905094\u003e`_\n\nQuickstart\n----------\n\nInstall the fcsfiles package and all dependencies from the\n`Python Package Index \u003chttps://pypi.org/project/fcsfiles/\u003e`_::\n\n    python -m pip install -U fcsfiles\n\nSee `Examples`_ for using the programming interface.\n\nSource code and support are available on\n`GitHub \u003chttps://github.com/cgohlke/fcsfiles\u003e`_.\n\nRequirements\n------------\n\nThis revision was tested with the following requirements and dependencies\n(other versions may work):\n\n- `CPython \u003chttps://www.python.org\u003e`_ 3.11.9, 3.12.10, 3.13.11, 3.14.2 64-bit\n- `NumPy \u003chttps://pypi.org/project/numpy/\u003e`_ 2.4.0\n\nRevisions\n---------\n\n2026.1.8\n\n- Improve code quality.\n\n2025.12.12\n\n- Drop support for Python 3.10, support Python 3.14.\n\n2025.1.1\n\n- Improve type hints.\n- Drop support for Python 3.9, support Python 3.13.\n\n2024.5.24\n\n- …\n\nRefer to the CHANGES file for older revisions.\n\nNotes\n-----\n\n\"Carl Zeiss\" and \"ConfoCor\" are registered trademarks of Carl Zeiss, Inc.\n\nThe use of this implementation may be subject to patent or license\nrestrictions.\n\nThe API is not stable yet and is expected to change between revisions.\n\nThis module does *not* read flow cytometry standard FCS files.\n\nExamples\n--------\n\nRead the CountRateArray from a ConfoCor3 ASCII file as a numpy array:\n\n.. code-block:: python\n\n    \u003e\u003e\u003e fcs = ConfoCor3Fcs('ConfoCor3.fcs')\n    \u003e\u003e\u003e fcs['FcsData']['FcsEntry'][0]['FcsDataSet']['CountRateArray'].shape\n    (60000, 2)\n    \u003e\u003e\u003e print(fcs)  # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE\n    Carl Zeiss ConfoCor3 - measurement data file - version 3.0 ANSI\n    BEGIN FcsData 30000\n            Name = Fluorescein\n            Comment =\n            AverageFlags = Repeat|Position|Average_Fit_Results\n            SortOrder = Channel-Repeat-Position-Kinetics\n            BEGIN FcsEntry1 10000\n    ...\n\nRead data and metadata from a ConfoCor3 RAW file:\n\n.. code-block:: python\n\n    \u003e\u003e\u003e fcs = ConfoCor3Raw('ConfoCor3.raw')\n    \u003e\u003e\u003e fcs.filename\n    'f5ee4f36488fca2f89cb6b8626111006_R1_P1_K1_Ch1.raw'\n    \u003e\u003e\u003e fcs.frequency\n    20000000\n    \u003e\u003e\u003e times = fcs.asarray()\n    \u003e\u003e\u003e int(times[10858])\n    1199925494\n    \u003e\u003e\u003e times, bincounts = fcs.asarray(bins=1000)\n    \u003e\u003e\u003e times.shape\n    (1000,)\n    \u003e\u003e\u003e int(bincounts[618])\n    23\n    \u003e\u003e\u003e fcs.close()\n\nRead data and metadata from a ConfoCor2 RAW file:\n\n.. code-block:: python\n\n    \u003e\u003e\u003e fcs = ConfoCor2Raw('ConfoCor2.raw')\n    \u003e\u003e\u003e fcs.frequency\n    20000000\n    \u003e\u003e\u003e ch0, ch1 = fcs.asarray()\n    \u003e\u003e\u003e int(ch1[4812432])\n    999999833\n    \u003e\u003e\u003e times, ch0, ch1 = fcs.asarray(bins=1000)\n    \u003e\u003e\u003e times.shape\n    (1000,)\n    \u003e\u003e\u003e int(ch1[428])\n    10095\n    \u003e\u003e\u003e fcs.close()","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcgohlke%2Ffcsfiles","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcgohlke%2Ffcsfiles","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcgohlke%2Ffcsfiles/lists"}