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A Python package to load raw DTS files, perform a calibration, and plot the result

Project description

A Python package to load raw DTS files, perform a calibration, and plot the result

  • Free software: BSD 3-Clause License


pip install dtscalibration

Or the development version directly from GitHub

pip install --upgrade

Learn by examples

Interactively run the example notebooks online by clicking the launch-binder button.



0.6.3 (2019-04-03)

  • Added reading support for zipped silixa files. Still rarely fails due to upstream bug.

  • pretty __repr__

  • Reworked double ended calibration procedure. Integrated differential attenuation outside of reference sections is now calculated seperately.

  • New approach for estimation of Stokes variance. Not restricted to a decaying exponential

  • Bug in averaging TMPF and TMPB to TMPW

  • Modified residuals plot, especially useful for long fibers (Great work Bart!)

  • Example notebooks updatred accordingly

  • Bug in to_netcdf when passing encodings

  • Better support for sections that are not related to a timeseries.

0.6.2 (2019-02-26)

  • Double-ended weighted calibration procedure is rewritten so that the integrated differential attenuation outside of the reference sections is calculated seperately. Better memory usage and faster

  • Other calibration routines cleaned up

  • Official support for Python 3.7

  • Coverage figures are now trustworthy

  • String representation improved

  • Include test for aligning double ended measurements

  • Example for aligning double ended measurements

0.6.1 (2019-01-04)

  • Many examples were shown in the documentation

  • Fixed verbose settings of solvers

  • Revised example notebooks

  • Moved to 80 characters per line (PEP)

  • More Python formatting using YAPF

  • Use example of plot_residuals_reference_sections function in Stokes variance example notebook

  • Support Python 3.7

0.6.0 (2018-12-08)

  • Reworked the double-ended calibration routine and the routine for confidence intervals. The integrated differential attenuation is not zero at x=0 anymore.

  • Verbose commands carpentry

  • Bug fixed that would make the read_silixa routine crash if there are copies of the same file in the same folder

  • Routine to read sensornet files. Only single-ended configurations supported for now. Anyone has double-ended measurements?

  • Lazy calculation of the confidence intervals

  • Bug solved. The x-coordinates where not calculated correctly. The bug only appeared for measurements along long cables.

  • Example notebook of importing a timeseries. For example, importing measurments from an external temperature sensor for calibration.

  • Updated documentation

0.5.3 (2018-10-26)

  • No changes

0.5.2 (2018-10-26)

  • New resample_datastore method (see basic usage notebook)

  • New notebook on basic usage of DataStore

  • Support for Silixa v4 (Windows xp based system) and Silixa v6 (Windows 7) measurement files

  • The representation string now includes the sections

  • Reorganized the IO related files

  • CI: Add appveyor to continuesly test on Windows platform

  • Auto load Silixa files to memory option, if size is small

0.5.1 (2018-10-19)

  • Rewritten the routine that reads Silixa measurement files

  • dts-calibration is now citable

  • Refractored the MC confidence interval routine

  • MC confidence interval routine speed up, with full dask support

  • Link to to try the example notebooks online

  • Added a few missing dependencies

  • The routine to read the Silixa files is completely refractored. Faster, smarter. Supports both the path to a directory and a list of file paths.

  • Changed imports from dtscalibration to be relative

0.4.0 (2018-09-06)

  • Single ended calibration

  • Confidence intervals for single ended calibration

  • Example notebooks have figures embedded

  • Several bugs squashed

  • Reorganized DataStore functions

0.2.0 (2018-08-16)

  • Double ended calibration

  • Confidence intervals for double ended calibration

0.1.0 (2018-08-01)

  • First release on PyPI.

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