Genus

class turbustat.statistics.Genus(img, min_value=None, max_value=None, lowdens_percent=0, highdens_percent=100, numpts=100, smoothing_radii=None, distance=None)

Bases: turbustat.statistics.base_statistic.BaseStatisticMixIn

Genus Statistics based off of Chepurnov et al. (2008).

Parameters:

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

2D image.

min_value : Quantity or float, optional

Minimum value in the data to consider. If None, the minimum is used. When img has an attached brightness unit, min_value must have the same units.

max_value : Quantity or float, optional

Maximum value in the data to consider. If None, the maximum is used. When img has an attached brightness unit, min_value must have the same units.

lowdens_percent : float, optional

Lower percentile of the data to use. Defaults to the minimum value. Overrides min_value when the value of this percentile is greater than min_value.

highdens_percent : float, optional

Upper percentile of the data to use. Defaults to the maximum value. Overrides max_value when the value of this percentile is lower than max_value.

numpts : int, optional

Number of thresholds to calculate statistic at.

smoothing_radii : np.ndarray or astropy.units.Quantity, optional

Kernel radii to smooth data to. If units are not attached, the radii are assumed to be in pixels. If no radii are given, 5 smoothing radii will be used ranging from 1 pixel to one-tenth the smallest dimension size.

distance : Quantity, optional

Physical distance to the region in the data.

Examples

>>> from turbustat.statistics import Genus
>>> from astropy.io import fits
>>> import astropy.units as u
>>> import numpy as np
>>> moment0 = fits.open("Design4_21_0_0_flatrho_0021_13co.moment0.fits")[0]  # doctest: +SKIP
>>> genus = Genus(moment0, lowdens_percent=15, highdens_percent=85)  # doctest: +SKIP
>>> genus.run()  # doctest: +SKIP

Attributes Summary

data
distance
genus_stats	Array of genus statistic values for all smoothed images (0th axis) and all threshold values (1st axis).
header
need_header_flag
no_data_flag
smoothed_images	List of smoothed versions of the image, using the radii in smoothing_radii.
smoothing_radii	Pixel radii used to smooth the data.
thresholds	Values of the data to compute the Genus statistics at.

Methods Summary

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.
make_genus_curve([use_beam, min_size, …])	Smooth the data with a Gaussian kernel to create the genus curve from at the specified thresholds.
plot_fit([save_name, color, symbol])	Plot the Genus curves.
run([verbose, save_name, color, symbol])	Run the whole statistic.
save_results(output_name[, keep_data])	Save the results of the SCF to avoid re-computing.

Attributes Documentation

data

distance

genus_stats

Array of genus statistic values for all smoothed images (0th axis) and all threshold values (1st axis).

header

need_header_flag = True

no_data_flag = False

smoothed_images

List of smoothed versions of the image, using the radii in smoothing_radii.

smoothing_radii

Pixel radii used to smooth the data.

thresholds

Values of the data to compute the Genus statistics at.

Methods Documentation

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:

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

make_genus_curve(use_beam=False, min_size=4, connectivity=1, keep_smoothed_images=False, match_kernel=False, **convolution_kwargs)

Smooth the data with a Gaussian kernel to create the genus curve from at the specified thresholds.

Parameters:

use_beam : bool, optional

When enabled, will use the given beam_fwhm or try to load it from the header. When disabled, the minimum size is set by min_size.

min_size : int or Quantity, optional

Directly specify the minimum area a region must have to be counted. Integer values with no units are assumed to be in pixels.

connectivity : {1, 2}, optional

Connectivity used when removing regions below min_size.

keep_smoothed_images : bool, optional

Keep the convolved images in the smoothed_images list. Default is False.

match_kernel : bool, optional

Match kernel shape to the data shape when convolving. Default is False. Enable to reproduce behaviour of Genus prior to version 1.0 of TurbuStat.

convolution_kwargs: Passed to `~astropy.convolve.convolve_fft`.

plot_fit(save_name=None, color='r', symbol='o')

Plot the Genus curves.

Parameters:

save_name : str,optional

Save the figure when a file name is given.

color : {str, RGB tuple}, optional

Color to show the Genus curves in.

run(verbose=False, save_name=None, color='r', symbol='o', **kwargs)

Run the whole statistic.

Parameters:

verbose : bool, optional

Enables plotting.

save_name : str,optional

Save the figure when a file name is given. Must have verbose enabled for plotting.

kwargs : See make_genus_curve.

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_name : str

Name of the outputted pickle file.

keep_data : bool, optional

Save the data cube in the pickle file when enabled.