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Load Distributed Temperature Sensing (DTS) files, calibrate the temperature and estimate its uncertainty.

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PyPI Package latest release Supported versions Commits since latest release


It would be greatly appreciated if you could cite this package in eg articles presentations

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A Python package to load Distributed Temperature Sensing files, perform a calibration, and plot the result. A detailed description of the calibration procedure can be found at .

Do you have questions, ideas or just want to say hi? Please leave a message on the ` discussions page <>`_!


pip install dtscalibration

Or the development version directly from GitHub

pip install --upgrade

Package features

DTS measures temperature by calibrating backscatter measurements to sections with a known temperature. DTS devices provide a simple interface to perform a limited calibration. Re-calibrating your measurements with this Python package gives you better temperature estimates and additional options.

Devices currently supported

  • Silixa Ltd.: Ultima & XT-DTS .xml files (up to version 8.1)

  • Sensornet Ltd.: Oryx, Halo & Sentinel .ddf files

  • AP Sensing: CP320 .xml files (single ended only)

  • SensorTran: SensorTran 5100 .dat binary files (single ended only)


How to cite

The following article explains and discusses the calibration procedure:

des Tombe, B., Schilperoort, B., & Bakker, M. (2020). Estimation of Temperature and Associated Uncertainty from Fiber-Optic Raman-Spectrum Distributed Temperature Sensing. Sensors, 20(8), 2235.

Cite the specific implementation / repository via Zenodo:

  1. Check the version of dtscalibration that is used in your Python console with:

    >>> # The following line introduces the .dts accessor for xarray datasets
    >>> import dtscalibration  # noqa: E401
    >>> dtscalibration.__version__
  2. Go to Zenodo and follow the link to the version of interest.

  3. The citation is found on the bottom right of the page.

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dtscalibration-3.0.3.tar.gz (9.1 MB view hashes)

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dtscalibration-3.0.3-py3-none-any.whl (82.3 kB view hashes)

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