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

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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.

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

The newest release of TurbuStat can be installed via pip:

>>> pip install turbustat

To install from the repository, use:

>>> python setup.py install

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.

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.