VCS

class turbustat.statistics.VCS(cube, header=None)

Bases: turbustat.statistics.base_statistic.BaseStatisticMixIn

The VCS technique (Lazarian & Pogosyan, 2004).

Parameters:

cube : numpy.ndarray or astropy.io.fits.PrimaryHDU or astropy.io.fits.ImageHDU or SpectralCube

Data cube.

header : FITS header, optional

Corresponding FITS header.

vel_units : bool, optional

Convert frequencies to the spectral unit in the header.

Attributes Summary

brk	Fitted break point.
brk_err	1-sigma on the break point.
data
distance
header
need_header_flag
no_data_flag
ps1D	The 1D VCS spectrum.
slope	Power spectrum slope(s).
slope_err	1-sigma error on the power spectrum slope(s).

Methods Summary

compute_pspec(self[, use_pyfftw, threads])	Take the FFT of each spectrum in the velocity dimension and average.
fit_pspec(self[, breaks, log_break, …])	Fit the 1D Power spectrum using a segmented linear model.
input_data_header(self, data, header[, …])	Check if the header is given separately from the data type.
load_beam(self[, beam])	Try loading the beam from the header or a given object.
load_results(pickle_file)	Load in a saved pickle file.
plot_fit(self[, save_name, xunit, color, …])	Plot the VCS curve and the associated fit.
run(self[, verbose, save_name, xunit, …])	Run the entire computation.
save_results(self, output_name[, keep_data])	Save the results of the SCF to avoid re-computing.

Attributes Documentation

brk

Fitted break point.

brk_err

1-sigma on the break point.

data

distance

header

need_header_flag = True

no_data_flag = False

ps1D

The 1D VCS spectrum.

slope

Power spectrum slope(s).

slope_err

1-sigma error on the power spectrum slope(s).

Methods Documentation

compute_pspec(self, use_pyfftw=False, threads=1, **pyfftw_kwargs)

Take the FFT of each spectrum in the velocity dimension and average.

Parameters:

use_pyfftw : bool, optional

Enable to use pyfftw, if it is installed.

threads : int, optional

Number of threads to use in FFT when using pyfftw.

pyfftw_kwargs : Passed to

rfft_to_fft. See here for a list of accepted kwargs.

fit_pspec(self, breaks=None, log_break=True, low_cut=None, high_cut=None, fit_verbose=False, bootstrap=False, **bootstrap_kwargs)

Fit the 1D Power spectrum using a segmented linear model. Note that the current implementation allows for only 1 break point in the model. If the break point is estimated via a spline, the breaks are tested, starting from the largest, until the model finds a good fit.

Parameters:

breaks : float or None, optional

Guesses for the break points. If given as a list, the length of the list sets the number of break points to be fit. If a choice is outside of the allowed range from the data, Lm_Seg will raise an error. If None, a spline is used to estimate the breaks.

log_break : bool, optional

Sets whether the provided break estimates are log-ed values.

low_cut : Quantity, optional

Lowest frequency to consider in the fit.

high_cut : Quantity, optional

Highest frequency to consider in the fit.

fit_verbose : bool, optional

Enables verbose mode in Lm_Seg.

bootstrap : bool, optional

Bootstrap using the model residuals to estimate the standard errors.

bootstrap_kwargs : dict, optional

Pass keyword arguments to residual_bootstrap.

input_data_header(self, data, header, need_copy=False)

Check if the header is given separately from the data type.

load_beam(self, beam=None)

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

Parameters:

beam : Beam, optional

The beam.

static load_results(pickle_file)

Load in a saved pickle file.

Parameters:

pickle_file : str

Name of filename to load in.

Returns:

self : Save statistic class

Statistic instance with saved results.

Examples

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

plot_fit(self, save_name=None, xunit=Unit("1 / pix"), color='r', symbol='o', fit_color='k', label=None, show_residual=True)

Plot the VCS curve and the associated fit.

Parameters:

save_name : str, optional

Save name for the figure. Enables saving the plot.

xunit : Unit, optional

Choose the angular unit to convert to when ang_units is enabled.

color : {str, RGB tuple}, optional

Color to plot the VCS curve.

symbol : str, optional

Symbol to use for the data.

fit_color : {str, RGB tuple}, optional

Color of the 1D fit.

label : str, optional

Label to later be used in a legend.

show_residual : bool, optional

Plot the residuals for the 1D power-spectrum fit.

run(self, verbose=False, save_name=None, xunit=Unit("1 / pix"), use_pyfftw=False, threads=1, pyfftw_kwargs={}, **fit_kwargs)

Run the entire computation.

Parameters:

verbose: bool, optional

Enables plotting.

save_name : str,optional

Save the figure when a file name is given.

xunit : u.Unit, optional

Choose the unit to convert the x-axis in the plot to.

use_pyfftw : bool, optional

Enable to use pyfftw, if it is installed.

threads : int, optional

Number of threads to use in FFT when using pyfftw.

pyfftw_kwargs : Passed to

rfft_to_fft. See here for a list of accepted kwargs.

fit_kwargs : Passed to fit_pspec.

save_results(self, 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_name : str

Name of the outputted pickle file.

keep_data : bool, optional

Save the data cube in the pickle file when enabled.