TurbuStat

See the documentation at http://turbustat.readthedocs.org/.

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Statistics of Turbulence

This package is aimed at facilitating comparisons between spectral line data cubes. Included in this package are several techniques described in the literature which aim to describe some property of a data cube (or its moments/integrated intensity). We extend these techniques to be used as comparisons.

Distance Metrics

Ideally, we require a distance metric to satisfy several properties. A full description is shown in Yeremi et al. (2014). The key properties are: * cubes with similar physics should have a small distance * unaffected by coordinate shifts * sensitive to differences in physical scale * independent of noise levels in the data

Installing

The newest release of TurbuStat can be installed via pip:

>>> pip install turbustat

To install from the repository, use:

>>> python setup.py install

Package Dependencies

Requires:

• astropy>=2.0
• numpy>=1.7
• matplotlib>=1.2
• scipy>=0.12
• sklearn>=0.13.0
• statsmodels>=0.4.0
• scikit-image>=0.12

Recommended:

• spectral-cube (>v0.4.4) - Efficient handling of PPV cubes. Required for calculating moment arrays in turbustat.data_reduction.Mask_and_Moments
• astrodendro-development - Required for calculating dendrograms in turbustat.statistics.dendrograms
• radio_beam - A class for handling radio beams and useful utilities. Required for correcting for the beam shape in spatial power spectra. Automatically installed with spectral-cube.

Optional:

• emcee - Affine Invariant MCMC. Used for fitting the size-line width relation in PCA and fitting PDFs.
• pyfftw - Wrapper for the FFTW libraries. Allows FFTs to be run in parallel.

Credits

If you make use of this package in a publication, please cite our accompanying paper:

@ARTICLE{Koch2019AJ....158....1K,
     author = {{Koch}, Eric W. and {Rosolowsky}, Erik W. and {Boyden}, Ryan D. and
       {Burkhart}, Blakesley and {Ginsburg}, Adam and {Loeppky}, Jason L. and
       {Offner}, Stella S.~R.},
      title = "{TURBUSTAT: Turbulence Statistics in Python}",
    journal = {\aj},
   keywords = {methods: data analysis, methods: statistical, turbulence, Astrophysics - Instrumentation and Methods for Astrophysics},
       year = "2019",
      month = "Jul",
     volume = {158},
     number = {1},
        eid = {1},
      pages = {1},
        doi = {10.3847/1538-3881/ab1cc0},
     eprint = {1904.10484},
     adsurl = {https://ui.adsabs.harvard.edu/abs/2019AJ....158....1K},
    adsnote = {Provided by the SAO/NASA Astrophysics Data System}
    }

If your work makes use of the distance metrics, please cite the following:

@ARTICLE{Koch2017,
 author = {{Koch}, E.~W. and {Ward}, C.~G. and {Offner}, S. and {Loeppky}, J.~L. and {Rosolowsky}, E.~W.},
 title = "{Identifying tools for comparing simulations and observations of spectral-line data cubes}",
 journal = {\mnras},
 archivePrefix = "arXiv",
 eprint = {1707.05415},
 keywords = {methods: statistical, ISM: clouds, radio lines: ISM},
 year = 2017,
 month = oct,
 volume = 471,
 pages = {1506-1530},
 doi = {10.1093/mnras/stx1671},
 adsurl = {http://adsabs.harvard.edu/abs/2017MNRAS.471.1506K},
 adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

Citations courtesy of ADS.

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