MVC_Distance¶

class turbustat.statistics.MVC_Distance(data1, data2, weight_by_error=False, low_cut=None, high_cut=<Quantity 0.5 1 / pix>, breaks=None, pspec_kwargs={}, pspec2_kwargs=None)[source] [edit on github]¶

Bases: object

Distance metric for MVC.

Parameters:

Parameters:	data1 : dict or `MVC` A dictionary containing the centroid, moment 0, and linewidth arrays of a spectral cube. This output is created by Moments.to_dict. The minimum expected keys are ‘centroid’, ‘moment0’ and ‘linewidth’. If weight_by_error is enabled, the dictionary should also contain the error arrays, where the keys are the three above with ‘_error’ appended to the end. An `MVC` class may also be passed which may be pre-computed. data2 : dict or `MVC` See `data1` above. weight_by_error : bool, optional When enabled, the property arrays are weighted by the inverse squared of the error arrays. low_cut : `Quantity` or np.ndarray, optional The lower frequency fitting limit. An array with 2 elements can be passed to give separate lower limits for the datasets. high_cut : `Quantity` or np.ndarray, optional The upper frequency fitting limit. See `low_cut` above. Defaults to 0.5. breaks : `Quantity`, list or array, optional Specify where the break point is with appropriate units. If none is given, no break point will be used in the fit. pspec_kwargs : dict, optional Passed to `radial_pspec_kwargs` in `run`. pspec2_kwargs : dict or None, optional Passed to `radial_pspec_kwargs` in `run` for `data2`. When `None` is given, setting from `pspec_kwargs` are used for `data2`.

data1 : dict or MVC: A dictionary containing the centroid, moment 0, and linewidth arrays of a spectral cube. This output is created by Moments.to_dict. The minimum expected keys are ‘centroid’, ‘moment0’ and ‘linewidth’. If weight_by_error is enabled, the dictionary should also contain the error arrays, where the keys are the three above with ‘_error’ appended to the end. An MVC class may also be passed which may be pre-computed.
data2 : dict or MVC: See data1 above.
weight_by_error : bool, optional: When enabled, the property arrays are weighted by the inverse squared of the error arrays.
low_cut : Quantity or np.ndarray, optional: The lower frequency fitting limit. An array with 2 elements can be passed to give separate lower limits for the datasets.
high_cut : Quantity or np.ndarray, optional: The upper frequency fitting limit. See low_cut above. Defaults to 0.5.
breaks : Quantity, list or array, optional: Specify where the break point is with appropriate units. If none is given, no break point will be used in the fit.
pspec_kwargs : dict, optional: Passed to radial_pspec_kwargs in run.
pspec2_kwargs : dict or None, optional: Passed to radial_pspec_kwargs in run for data2. When None is given, setting from pspec_kwargs are used for data2.

Methods Summary

distance_metric(self[, verbose, xunit, …]) Implements the distance metric for 2 MVC transforms.

Methods Documentation

distance_metric(self, verbose=False, xunit=Unit("1 / pix"), save_name=None, plot_kwargs1={}, plot_kwargs2={}, use_wavenumber=False)[source] [edit on github]¶

Implements the distance metric for 2 MVC transforms. We fit the linear portion of the transform to represent the powerlaw A linear model with an interaction term is fit to the two powerlaws. The distance is the t-statistic of the interaction.

Parameters:

Parameters:	verbose : bool, optional Enables plotting. xunit : `Unit`, optional Unit of the x-axis in the plot in pixel, angular, or physical units. save_name : str, optional Name of the save file. Enables saving the figure. plot_kwargs1 : dict, optional Pass kwargs to `plot_fit` for `data1`. plot_kwargs2 : dict, optional Pass kwargs to `plot_fit` for `data2`. use_wavenumber : bool, optional Plot the x-axis as the wavenumber rather than spatial frequency.

verbose : bool, optional: Enables plotting.
xunit : Unit, optional: Unit of the x-axis in the plot in pixel, angular, or physical units.
save_name : str, optional: Name of the save file. Enables saving the figure.
plot_kwargs1 : dict, optional: Pass kwargs to plot_fit for data1.
plot_kwargs2 : dict, optional: Pass kwargs to plot_fit for data2.
use_wavenumber : bool, optional: Plot the x-axis as the wavenumber rather than spatial frequency.