Wavelet

class turbustat.statistics.Wavelet(data, header=None, scales=None, num=50, distance=None)

Bases: turbustat.statistics.base_statistic.BaseStatisticMixIn

Compute the wavelet transform of a 2D array.

Parameters:

array : numpy.ndarray or astropy.io.fits.PrimaryHDU or astropy.io.fits.ImageHDU or spectral_cube.Projection or spectral_cube.Slice

2D data.

header : FITS header, optional

Header for the array.

scales : numpy.ndarray or list

The scales where the transform is calculated.

num : int, optional

Number of scales to compute the transform at.

distance : Quantity, optional

Physical distance to the region in the data.

Attributes Summary

Wf	The wavelet transforms of the image.
brk	Break point in the segmented linear model.
brk_err	1-sigma on the break point in the segmented linear model.
data
distance
fit_range	Range of scales used in the fit.
header
need_header_flag
no_data_flag
scales	Wavelet scales.
slope	Fitted slope.
slope_err	Standard error on the fitted slope.
stddev	Standard deviation of the 1-dimensional wavelet transform.
values	The 1-dimensional wavelet transform.

Methods Summary

compute_transform(self[, show_progress, …])	Compute the wavelet transform at each scale.
fit_transform(self[, xlow, xhigh, brk, …])	Perform a fit to the transform in log-log space.
fitted_model(self, xvals)	Computes the fitted power-law in log-log space using the given x values.
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_transform(self[, save_name, xunit, …])	Plot the transform and the fit.
run(self[, show_progress, verbose, xunit, …])	Compute the Wavelet transform.
save_results(self, output_name[, keep_data])	Save the results of the SCF to avoid re-computing.

Attributes Documentation

Wf

The wavelet transforms of the image. Each plane is the transform at different wavelet sizes.

brk

Break point in the segmented linear model.

brk_err

1-sigma on the break point in the segmented linear model.

data

distance

fit_range

Range of scales used in the fit.

header

need_header_flag = True

no_data_flag = False

scales

Wavelet scales.

slope

Fitted slope.

slope_err

Standard error on the fitted slope.

stddev

Standard deviation of the 1-dimensional wavelet transform.

values

The 1-dimensional wavelet transform.

Methods Documentation

compute_transform(self, show_progress=True, scale_normalization=True, keep_convolved_arrays=False, convolve_kwargs={}, use_pyfftw=False, threads=1, pyfftw_kwargs={})

Compute the wavelet transform at each scale.

Parameters:

show_progress : bool, optional

Show a progress bar during the creation of the covariance matrix.

scale_normalization: bool, optional

Compute the transform with the correct scale-invariant normalization.

keep_convolved_arrays: bool, optional

Keep the image convolved at all wavelet scales. For large images, this can require a large amount memory. Default is False.

convolve_kwargs : dict, optional

Passed to convolve_fft.

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

See here for a list of accepted kwargs.

fit_transform(self, xlow=None, xhigh=None, brk=None, min_fits_pts=3, weighted_fit=False, bootstrap=False, bootstrap_kwargs={}, **fit_kwargs)

Perform a fit to the transform in log-log space.

Parameters:

xlow : Quantity, optional

Lower scale value to consider in the fit.

xhigh : Quantity, optional

Upper scale value to consider in the fit.

brk : Quantity, optional

Give an initial guess for a break point. This enables fitting with a turbustat.statistics.Lm_Seg.

min_fits_pts : int, optional

Minimum number of points required above or below the fitted break for it to be considered a valid fit. Only used when a segmented line is fit, i.e. when a value for brk is given.

weighted_fit: bool, optional

Use the stddev to perform a weighted fit.

bootstrap : bool, optional

Bootstrap using the model residuals to estimate the standard errors.

bootstrap_kwargs : dict, optional

Pass keyword arguments to residual_bootstrap.

fit_kwargs : Passed to turbustat.statistics.Lm_Seg.fit_model

fitted_model(self, xvals)

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

Parameters:

xvals : ndarray

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

Returns:

model_values : ndarray

Values of the model at the given values.

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

Plot the transform and the 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 wavelet 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 fit residuals.

run(self, show_progress=True, verbose=False, xunit=Unit("pix"), convolve_kwargs={}, use_pyfftw=False, threads=1, pyfftw_kwargs={}, scale_normalization=True, xlow=None, xhigh=None, brk=None, fit_kwargs={}, save_name=None, **plot_kwargs)

Compute the Wavelet transform.

Parameters:

show_progress : bool, optional

Show a progress bar during the creation of the covariance matrix.

verbose : bool, optional

Plot wavelet transform.

xunit : u.Unit, optional

Choose the unit to convert to when ang_units is enabled.

convolve_kwargs : dict, optional

Passed to convolve_fft.

scale_normalization: bool, optional

Compute the transform with the correct scale-invariant normalization.

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

See here for a list of accepted kwargs.

scale_normalization: bool, optional

Multiply the wavelet transform by the correct normalization factor.

xlow : Quantity, optional

Lower scale value to consider in the fit.

xhigh : Quantity, optional

Upper scale value to consider in the fit.

brk : Quantity, optional

Give an initial guess for a break point. This enables fitting with a turbustat.statistics.Lm_Seg.

fit_kwargs : dict, optional

Passed to fit_transform

save_name : str,optional

Save the figure when a file name is given.

plot_kwargs : Passed to plot_transform.

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.