SCF¶

class turbustat.statistics.SCF(cube, header=None, size=11, roll_lags=None, distance=None)[source]¶

Bases: BaseStatisticMixIn

Computes the Spectral Correlation Function of a data cube (Rosolowsky et al, 1999).

Parameters:

cubenumpy.ndarray or astropy.io.fits.PrimaryHDU or astropy.io.fits.ImageHDU or SpectralCube: Data cube.
headerFITS header, optional: Header for the cube.
sizeint, optional: The total size of the lags used in one dimension in pixels. The maximum lag size will be (size - 1) / 2 in each direction.
roll_lagsndarray or Quantity, optional: Pass a custom array of lag values. An odd number of lags, centered at 0, must be given. If no units are given, it is assumed that the lags are in pixels. The lags should have symmetric positive and negative values (e.g., [-1, 0, 1]).
distanceQuantity, optional: Physical distance to the region in the data.

Examples

>>> from spectral_cube import SpectralCube
>>> from turbustat.statistics import SCF
>>> cube = SpectralCube.read("Design4.13co.fits")  
>>> scf = SCF(cube)  
>>> scf.run(verbose=True)  

Attributes Summary

`data`
`distance`
`ellip2D`	Fitted ellipticity of the 2D power-law.
`ellip2D_err`	Ellipticity standard error of the 2D power-law.
`header`
`lags`	Values of the lags, in pixels, to compute SCF at
`need_header_flag`
`no_data_flag`
`roll_lags`	Pixel values that the cube is rolled by to compute the SCF correlation surface.
`scf_spectrum`	Azimuthally averaged 1D SCF spectrum
`scf_spectrum_stddev`	Standard deviation of the `scf_spectrum`
`scf_surface`	SCF correlation array
`slope`	SCF spectrum slope
`slope2D`	Fitted slope of the 2D power-law.
`slope2D_err`	Slope standard error of the 2D power-law.
`slope_err`	1-sigma error on the SCF spectrum slope
`theta2D`	Fitted position angle of the 2D power-law.
`theta2D_err`	Position angle standard error of the 2D power-law.
`xhigh`	Upper limit for lags to consider in fits.
`xlow`	Lower limit for lags to consider in fits.

Methods Summary

`compute_spectrum`(**kwargs)	Compute the 1D spectrum as a function of lag.
`compute_surface`([boundary, show_progress])	Computes the SCF up to the given lag value.
`fit_2Dplaw`([fit_method, p0, xlow, xhigh, ...])	Model the 2D power-spectrum surface with an elliptical power-law model.
`fit_plaw`([xlow, xhigh, verbose, bootstrap])	Fit a power-law to the SCF spectrum.
`fitted_model`(xvals)	Computes the modelled power-law using the given x values.
`input_data_header`(data, header[, need_copy])	Check if the header is given separately from the data type.
`load_beam`([beam])	Try loading the beam from the header or a given object.
`load_results`(pickle_file)	Load in a saved pickle file.
`plot_fit`([save_name, show_radial, ...])	Plot the SCF surface, radial profiles, and associated fits.
`run`([boundary, show_progress, xlow, xhigh, ...])	Computes all SCF outputs.
`save_results`(output_name[, keep_data])	Save the results of the SCF to avoid re-computing.

Attributes Documentation

data¶

distance¶

ellip2D¶: Fitted ellipticity of the 2D power-law.

ellip2D_err¶: Ellipticity standard error of the 2D power-law.

header¶

lags¶: Values of the lags, in pixels, to compute SCF at

need_header_flag = True¶

no_data_flag = False¶

roll_lags¶: Pixel values that the cube is rolled by to compute the SCF correlation surface.

scf_spectrum¶: Azimuthally averaged 1D SCF spectrum

scf_spectrum_stddev¶: Standard deviation of the scf_spectrum

scf_surface¶: SCF correlation array

slope¶: SCF spectrum slope

slope2D¶: Fitted slope of the 2D power-law.

slope2D_err¶: Slope standard error of the 2D power-law.

slope_err¶: 1-sigma error on the SCF spectrum slope

theta2D¶: Fitted position angle of the 2D power-law.

theta2D_err¶: Position angle standard error of the 2D power-law.

xhigh¶: Upper limit for lags to consider in fits.

xlow¶: Lower limit for lags to consider in fits.

Methods Documentation

compute_spectrum(**kwargs)[source]¶

Compute the 1D spectrum as a function of lag. Can optionally use log-spaced bins. kwargs are passed into the pspec function, which provides many options. The default settings are applicable in nearly all use cases.

Parameters:

kwargspassed to turbustat.statistics.psds.pspec

compute_surface(boundary='continuous', show_progress=True)[source]¶

Computes the SCF up to the given lag value. This is an expensive operation and could take a long time to calculate.

Parameters:

boundary{“continuous”, “cut”}: Treat the boundary as continuous (wrap-around) or cut values beyond the edge (i.e., for most observational data).
show_progressbool, optional: Show a progress bar when computing the surface. =

fit_2Dplaw(fit_method='LevMarq', p0=(), xlow=None, xhigh=None, bootstrap=True, niters=100, use_azimmask=False)[source]¶

Model the 2D power-spectrum surface with an elliptical power-law model.

Parameters:

fit_methodstr, optional: The algorithm fitting to use. Only ‘LevMarq’ is currently available.
p0tuple, optional: Initial parameters for fitting. If no values are given, the initial parameters start from the 1D fit parameters.
xlowQuantity, optional: Lower lag value limit to consider in the fit.
xhighQuantity, optional: Upper lag value limit to consider in the fit.
bootstrapbool, optional: Bootstrap using the model residuals to estimate the parameter standard errors. This tends to give more realistic intervals than the covariance matrix.
nitersint, optional: Number of bootstrap iterations.
use_azimmaskbool, optional: Use the azimuthal mask defined for the 1D spectrum, when azimuthal limit have been given.

fit_plaw(xlow=None, xhigh=None, verbose=False, bootstrap=False, **bootstrap_kwargs)[source]¶

Fit a power-law to the SCF spectrum.

Parameters:

xlowQuantity, optional: Lower lag value limit to consider in the fit.
xhighQuantity, optional: Upper lag value limit to consider in the fit.
verbosebool, optional: Show fit summary when enabled.

fitted_model(xvals)[source]¶

Computes the modelled power-law using the given x values.

Parameters:

xvalsQuantity: Values of lags to compute the model at.

Returns:

model_valuesndarray: Values of the model at the given values. Equivalent to log values of the SCF spectrum.

input_data_header(data, header, need_copy=False)¶: Check if the header is given separately from the data type.

load_beam(beam=None)¶

Try loading the beam from the header or a given object.

Parameters:

beamBeam, optional: The beam.

static load_results(pickle_file)¶

Load in a saved pickle file.

Parameters:

pickle_filestr: Name of filename to load in.

Returns:

selfSave statistic class: Statistic instance with saved results.

Examples

Load saved results. >>> stat = Statistic.load_results(“stat_saved.pkl”) # doctest: +SKIP

plot_fit(save_name=None, show_radial=True, show_residual=True, show_surface=True, contour_color='k', cmap='viridis', data_color='r', fit_color='k', xunit=Unit('pix'))[source]¶

Plot the SCF surface, radial profiles, and associated fits.

Parameters:

save_namestr, optional: Save name for the figure. Enables saving the plot.
show_radialbool, optional: Show the azimuthally-averaged 1D SCF spectrum and fit.
show_surfacebool, optional: Show the SCF surface and (if performed) fit.
show_residualbool, optional: Plot the residuals for the 1D SCF fit.
contour_color{str, RGB tuple}, optional: Color of the 2D fit contours.
cmap{str, matplotlib color map}, optional: Colormap to use in the plots. Default is viridis.
data_color{str, RGB tuple}, optional: Color of the azimuthally-averaged data.
fit_color{str, RGB tuple}, optional: Color of the 1D fit.
xunitUnit, optional: Choose the angular unit to convert to when ang_units is enabled.

run(boundary='continuous', show_progress=True, xlow=None, xhigh=None, fit_kwargs={}, fit_2D=True, fit_2D_kwargs={}, radialavg_kwargs={}, verbose=False, xunit=Unit('pix'), save_name=None)[source]¶

Computes all SCF outputs.

Parameters:

boundary{“continuous”, “cut”}: Treat the boundary as continuous (wrap-around) or cut values beyond the edge (i.e., for most observational data).
show_progressbool, optional: Show a progress bar during the creation of the covariance matrix.
xlowQuantity, optional: See fit_plaw.
xhighQuantity, optional: See fit_plaw.
fit_kwargsdict, optional: Keyword arguments for SCF.fit_plaw. Use the xlow and xhigh keywords to provide fit limits.
fit_2Dbool, optional: Fit an elliptical power-law model to the 2D spectrum.
fit_2D_kwargsdict, optional: Keyword arguments for SCF.fit_2Dplaw. Use the xlow and xhigh keywords to provide fit limits.
radialavg_kwargsdict, optional: Passed to compute_spectrum.
verbosebool, optional: Enables plotting.
xunitUnit, optional: Choose the angular unit to convert to when ang_units is enabled.
save_namestr, optional: Save the figure when a file name is given.

save_results(output_name, keep_data=False)¶

Save the results of the SCF to avoid re-computing. The pickled file will not include the data cube by default.

Parameters:

output_namestr: Name of the outputted pickle file.
keep_databool, optional: Save the data cube in the pickle file when enabled.