Genus¶

class turbustat.statistics.Genus(img, min_value=None, max_value=None, lowdens_percent=0, highdens_percent=100, numpts=100, thresholds=None, enable_smoothing=True, smoothing_radii=None, distance=None)[source]¶

Bases: BaseStatisticMixIn

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

Parameters:

imgnumpy.ndarray or astropy.io.fits.PrimaryHDU or astropy.io.fits.ImageHDU or spectral_cube.Projection or spectral_cube.Slice: 2D image.
min_valueQuantity 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_valueQuantity 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_percentfloat, 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_percentfloat, 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.
numptsint, optional: Number of thresholds to calculate statistic at.
thresholdsnumpy.ndarray or Quantity, optional: Pass a custom set of thresholds to compute the Genus statistic at. Note that this overrides the min/max_value, low/highdens_percent, and numpts keywords.
enable_smoothingbool, optional: When True, uses the set of smoothing_radii to convolve the input data with 2D Gaussian kernels. When False, no smoothing is applied to the data.
smoothing_radiinp.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.
distanceQuantity, 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]  
>>> genus = Genus(moment0, lowdens_percent=15, highdens_percent=85)  
>>> genus.run()  

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`.
`smoothed_means`	Means from the smoothed images.
`smoothed_stds`	Standard deviations from the smoothed images.
`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`([enable_small_removal, ...])	Smooth the data with a Gaussian kernel to create the genus curve from at the specified thresholds.
`make_genus_stats_table`()	Returns an astropy table with genus curve and thresholds with 1 table per smoothed image.
`plot_fit`([save_name, color, symbol, standardize])	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.

smoothed_means¶: Means from the smoothed images. Needed to convert the thresholds into standardized values.

smoothed_stds¶: Standard deviations from the smoothed images. Needed to convert the thresholds into standardized values.

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:

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

make_genus_curve(enable_small_removal=True, use_beam=False, min_size=4, connectivity=2, keep_smoothed_images=False, match_kernel=False, **convolution_kwargs)[source]¶

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

Parameters:

enable_small_removalbool, optional: Make use of the small region removal. Default is True. If False, the use_beam and min_size keywords are not used.
use_beambool, 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_sizeint 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_imagesbool, optional: Keep the convolved images in the smoothed_images list. Default is False.
match_kernelbool, 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`.

make_genus_stats_table()[source]¶: Returns an astropy table with genus curve and thresholds with 1 table per smoothed image.

plot_fit(save_name=None, color='r', symbol='o', standardize=False)[source]¶

Plot the Genus curves.

Parameters:

save_namestr,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', standardize_intensity=False, **kwargs)[source]¶

Run the whole statistic.

Parameters:

verbosebool, optional: Enables plotting.
save_namestr,optional: Save the figure when a file name is given. Must have verbose enabled for plotting.
kwargsSee 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_namestr: Name of the outputted pickle file.
keep_databool, optional: Save the data cube in the pickle file when enabled.