DeltaVariance¶

class turbustat.statistics.DeltaVariance(img, header=None, weights=None, diam_ratio=1.5, lags=None, nlags=25, distance=None)[source]¶

Bases: BaseStatisticMixIn

The delta-variance technique as described in Ossenkopf et al. (2008).

Parameters:

imgnumpy.ndarray or astropy.io.fits.PrimaryHDU or astropy.io.fits.ImageHDU or spectral_cube.Projection or spectral_cube.Slice: The image calculate the delta-variance of.
headerFITS header, optional: Image header. Required when img is a ndarray.
weightsnumpy.ndarray or astropy.io.fits.PrimaryHDU or astropy.io.fits.ImageHDU or spectral_cube.Projection or spectral_cube.Slice: Weights to be used.
diam_ratiofloat, optional: The ratio between the kernel sizes.
lagsnumpy.ndarray or list, optional: The pixel scales to compute the delta-variance at.
nlagsint, optional: Number of lags to use.
distanceQuantity, optional: Physical distance to the region in the data.

Examples

>>> from turbustat.statistics import DeltaVariance
>>> from astropy.io import fits
>>> moment0 = fits.open("2D.fits") 
>>> delvar = DeltaVariance(moment0) 
>>> delvar.run(verbose=True) 

Attributes Summary

`brk`	Fitted break point.
`brk_err`	1-sigma on the break point in the segmented linear model.
`convolve_arrays`
`convolve_weights`
`data`
`delta_var`	Delta Variance values.
`delta_var_error`	1-sigma errors on the Delta variance values.
`distance`
`fit_range`	Range of lags used in the fit.
`header`
`lags`	Lag values.
`need_header_flag`
`no_data_flag`
`slope`	Fitted slope.
`slope_err`	Standard error on the fitted slope.
`weights`	Array of weights.

Methods Summary

`compute_deltavar`([allow_huge, boundary, ...])	Perform the convolution and calculate the delta variance at all lags.
`fit_plaw`([xlow, xhigh, brk, verbose, ...])	Fit a power-law to the Delta-variance spectrum.
`fitted_model`(xvals)	Computes the fitted power-law in log-log space 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, xunit, symbol, color, ...])	Plot the delta-variance curve and the fit.
`run`([show_progress, verbose, xunit, ...])	Compute the delta-variance.
`save_results`(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 in the segmented linear model.

convolve_arrays¶

convolve_weights¶

data¶

delta_var¶: Delta Variance values.

delta_var_error¶: 1-sigma errors on the Delta variance values.

distance¶

fit_range¶: Range of lags used in the fit.

header¶

lags¶: Lag values.

need_header_flag = True¶

no_data_flag = False¶

slope¶: Fitted slope.

slope_err¶: Standard error on the fitted slope.

weights¶: Array of weights.

Methods Documentation

compute_deltavar(allow_huge=False, boundary='wrap', min_weight_frac=0.01, nan_treatment='fill', preserve_nan=False, use_pyfftw=False, threads=1, pyfftw_kwargs={}, show_progress=True, keep_convolve_arrays=False)[source]¶

Perform the convolution and calculate the delta variance at all lags.

Parameters:

allow_hugebool, optional: Passed to convolve_fft. Allows operations on images larger than 1 Gb.
boundary{“wrap”, “fill”}, optional: Use “wrap” for periodic boundaries, and “fill” for non-periodic.
min_weight_fracfloat, optional: Set the fraction of the peak of the weight array to mask below. Default is 0.01. This will remove most edge artifacts, but is not guaranteed to! Increase this value if artifacts are encountered (this typically results in large spikes in the delta-variance curve).
nan_treatmentbool, optional: Enable to interpolate over NaNs in the convolution. Default is True.
use_pyfftwbool, optional: Enable to use pyfftw, if it is installed.
threadsint, optional: Number of threads to use in FFT when using pyfftw.
pyfftw_kwargsPassed to: See here for a list of accepted kwargs.
show_progressbool, optional: Show a progress bar while convolving the image at each lag.
keep_convolve_arraysbool, optional: Keeps the convolved arrays at each lag. Disabled by default to minimize memory usage.

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

Fit a power-law to the Delta-variance spectrum.

Parameters:

xlowQuantity, optional: Lower lag value to consider in the fit.
xhighQuantity, optional: Upper lag value to consider in the fit.
brkQuantity, optional: Give an initial guess for a break point. This enables fitting with a turbustat.statistics.Lm_Seg.
bootstrapbool, optional: Bootstrap using the model residuals to estimate the standard errors.
bootstrap_kwargsdict, optional: Pass keyword arguments to residual_bootstrap.
verbosebool, optional: Show fit summary when enabled.

fitted_model(xvals)[source]¶

Computes the fitted power-law in log-log space using the given x values.

Parameters:

xvalsndarray: Values of log(lags) to compute the model at (base 10 log).

Returns:

model_valuesndarray: Values of the model at the given values.

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, xunit=Unit('pix'), symbol='o', color='r', fit_color='k', label=None, show_residual=True)[source]¶

Plot the delta-variance curve and the fit.

Parameters:

save_namestr,optional: Save the figure when a file name is given.
xunitu.Unit, optional: The unit to show the x-axis in.
symbolstr, optional: Shape to plot the data points with.
color{str, RGB tuple}, optional: Color to show the delta-variance curve in.
fit_color{str, RGB tuple}, optional: Color of the fitted line. Defaults to color when no input is given.
labelstr, optional: Label to later be used in a legend.
show_residualbool, optional: Plot the fit residuals.

run(show_progress=True, verbose=False, xunit=Unit('pix'), nan_treatment='fill', preserve_nan=False, allow_huge=False, boundary='wrap', use_pyfftw=False, threads=1, pyfftw_kwargs={}, xlow=None, xhigh=None, brk=None, fit_kwargs={}, save_name=None)[source]¶

Compute the delta-variance.

Parameters:

show_progressbool, optional: Show a progress bar during the creation of the covariance matrix.
verbosebool, optional: Plot delta-variance transform.
xunitu.Unit, optional: The unit to show the x-axis in.
allow_hugebool, optional: See do_convolutions.
nan_treatmentbool, optional: Enable to interpolate over NaNs in the convolution. Default is True.
boundary{“wrap”, “fill”}, optional: Use “wrap” for periodic boundaries, and “cut” for non-periodic.
use_pyfftwbool, optional: Enable to use pyfftw, if it is installed.
threadsint, optional: Number of threads to use in FFT when using pyfftw.
pyfftw_kwargsPassed to: See here for a list of accepted kwargs.
xlowQuantity, optional: Lower lag value to consider in the fit.
xhighQuantity, optional: Upper lag value to consider in the fit.
brkQuantity, optional: Give an initial break point guess. Enables fitting a segmented linear model.
fit_kwargsdict, optional: Passed to fit_model when using a broken linear fit.
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.