Metadata-Version: 2.1
Name: dtscalibration
Version: 0.7.2
Summary: A Python package to load raw DTS files, perform a calibration, and plot the result
Home-page: https://github.com/dtscalibration/python-dts-calibration
Author: Bas des Tombe, Bart Schilperoort
Author-email: bdestombe@gmail.com
License: BSD 3-Clause License
Description: ========
        Overview
        ========
        
        
        
        A Python package to load raw DTS files, perform a calibration, and plot the result
        
        * Free software: BSD 3-Clause License
        
        
        Installation
        ============
        
        ::
        
            pip install dtscalibration
            
        Or the development version directly from GitHub
        
        ::
        
            pip install https://github.com/dtscalibration/python-dts-calibration/zipball/master --upgrade
        
        Devices currently supported
        ===========================
        Silixa Ltd.:
        
        * Ultima .xml files (up to version 7.0)
        * XT-DTS .xml files (up to version 7.0)
        
        Sensornet Ltd.:
        
        * Oryx .ddf files
        * Halo .ddf files
        
        AP Sensing:
        
        * CP320 .xml files (single ended only)
        
        SensorTran:
        
        * SensorTran 5100 .dat binary files (single ended only)
        
        Learn by examples
        =================
        Interactively run the example notebooks online by clicking the launch-binder button.
        
        Documentation
        =============
        
        https://python-dts-calibration.readthedocs.io/
        
        
        
        Changelog
        =========
        0.7.2 (2019-11-22)
        ------------------
        * Set alpha and or gamma to known value, with corresponding variance.
        * Bug in computation of the weights for single and double-ended calibration
        * Added notebook about merging two single ended measurements
        * Added example notebook on how to create a custom datastore
        * Added notebook examples for loading in data from the different manufa..
        * Loading AP Sensing files and tests
        * Loading Sensortran files
        
        0.7.0 (2019-11-07)
        ------------------
        * Ensure order of dimension upon initialization of DataStore. Resamplingwould lead to issues
        * Bug in section definition (reported by Robert Law)
        * Rewritten calibration solvers to align with article of this package
        * Removed old calibration solvers
        * New possibilities of saving and loading large DataStores saved to multiplenetCDF files
        
        0.6.7 (2019-11-01)
        ------------------
        * Use twine to check if the compiled package meets all the requirements of Pypi
        
        0.6.6 (2019-11-01)
        ------------------
        * Use twine to check if the compiled package meets all the requirements of Pypi
        
        0.6.5 (2019-11-01)
        ------------------
        * Major bug fix version.
        * More flexibility in defining the time and space dimensions
        * Fixed unsave yaml loading
        * Added support for Silixa 7 files
        * Start using `__slots__` as it is something new
        * xarray doesn't have the attribute `._initialized` anymore. Rewritten teststo make more sense by checking the sum of the Stokes instead.
        * Support for double ended Sensornet files and tests
        * Bug fixing
        
        0.6.4 (2019-04-09)
        ------------------
        * More flexibility in defining the time dimension
        * Cleanup of some plotting functions
        
        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 mybinder.org 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.
        
Keywords: DTS,Calibration
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: Unix
Classifier: Operating System :: POSIX
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Utilities
Requires-Python: >= 3.6
Description-Content-Type: text/x-rst
