StatMoments¶

class turbustat.statistics.StatMoments(img, header=None, weights=None, radius=<Quantity 5. pix>, nbins=None, distance=None)[source]¶

Bases: BaseStatisticMixIn

Statistical Moments of an image. See Burkhart et al. (2010) for the methods used. By specifying the radius of circular mask, the mean, variance, skewness, and kurtosis are calculated within the circular mask for every pixel in the image. The distributions of these moments can be compared between data sets.

Parameters:

imgnumpy.ndarray or astropy.io.fits.PrimaryHDU or astropy.io.fits.ImageHDU or spectral_cube.Projection or spectral_cube.Slice: 2D image.
headerFITS header, optional: The image header. Needed for the pixel scale.
weightsnumpy.ndarray or astropy.io.fits.PrimaryHDU or astropy.io.fits.ImageHDU or spectral_cube.Projection or spectral_cube.Slice: 2D array of weights. Uniform weights are used if none are given.
radiusQuantity, optional: Radius of circle to use when computing moments. When angular or physical units are given, they will be rounded down to the nearest pixel size.
nbinsarray or int, optional: Number of bins to use in the histogram.
distanceQuantity, optional: Physical distance to the region in the data.

Attributes Summary

`data`
`distance`
`header`
`kurtosis_array`	The array of local kurtosiss.
`kurtosis_extrema`	The extrema of the kurtosis array.
`kurtosis_hist`	The histogram bins and values for the kurtosis array.
`mean_array`	The array of local means.
`mean_extrema`	The extrema of the mean array.
`mean_hist`	The histogram bins and values for the mean array.
`need_header_flag`
`no_data_flag`
`radius`
`skewness_array`	The array of local skewnesss.
`skewness_extrema`	The extrema of the skewness array.
`skewness_hist`	The histogram bins and values for the skewness array.
`variance_array`	The array of local variances.
`variance_extrema`	The extrema of the variance array.
`variance_hist`	The histogram bins and values for the variance array.

Methods Summary

`array_moments`()	Moments over the entire image.
`compute_spatial_distrib`([radius, periodic, ...])	Compute the moments over circular region with the specified radius.
`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_spatial_histograms`([mean_bins, ...])	Create histograms of the moments.
`plot_histograms`([new_figure, save_name, ...])	Plot the histograms of each moment.
`plot_maps`([save_name, cmap, contour_cmap])	Plot the maps of locally-estimated moments.
`run`([show_progress, verbose, save_name, ...])	Compute the entire method.
`save_results`(output_name[, keep_data])	Save the results of the SCF to avoid re-computing.

Attributes Documentation

data¶

distance¶

header¶

kurtosis_array¶: The array of local kurtosiss.

kurtosis_extrema¶: The extrema of the kurtosis array.

kurtosis_hist¶: The histogram bins and values for the kurtosis array. The first element is the array of bins, and the second contains the values.

mean_array¶: The array of local means.

mean_extrema¶: The extrema of the mean array.

mean_hist¶: The histogram bins and values for the mean array. The first element is the array of bins, and the second contains the values.

need_header_flag = True¶

no_data_flag = False¶

radius¶

skewness_array¶: The array of local skewnesss.

skewness_extrema¶: The extrema of the skewness array.

skewness_hist¶: The histogram bins and values for the skewness array. The first element is the array of bins, and the second contains the values.

variance_array¶: The array of local variances.

variance_extrema¶: The extrema of the variance array.

variance_hist¶: The histogram bins and values for the variance array. The first element is the array of bins, and the second contains the values.

Methods Documentation

array_moments()[source]¶: Moments over the entire image.

compute_spatial_distrib(radius=None, periodic=True, min_frac=0.8, show_progress=True)[source]¶

Compute the moments over circular region with the specified radius.

Parameters:

radiusQuantity, optional: Override the radius size of the region.
periodicbool, optional: Specify whether the boundaries can be wrapped. Default is True.
min_fracfloat, optional: A number between 0 and 1 that sets the minimum fraction of data in each region that are finite. A value of 1.0 requires that no NaNs be in the region.
show_progressbool, optional: Show a progress bar during the creation of the covariance matrix.

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_spatial_histograms(mean_bins=None, variance_bins=None, skewness_bins=None, kurtosis_bins=None)[source]¶

Create histograms of the moments. If an optional set of bins is not given, \(\sqrt{N}\) equally-size bins will be created, where \(N\) is the number of elements in the array. The histogram values are normalized so that the sum of the values in the bins, multiplied by the bin width is 1.

Parameters:

mean_binsarray, optional: Bins to use for the histogram of the mean array.
variance_binsarray, optional: Bins to use for the histogram of the variance array.
skewness_binsarray, optional: Bins to use for the histogram of the skewness array.
kurtosis_binsarray, optional: Bins to use for the histogram of the kurtosis array.

plot_histograms(new_figure=True, save_name=None, hist_color='r', face_color='k')[source]¶

Plot the histograms of each moment.

Parameters:

new_figurebool, optional: Creates a new matplotlib figure.
save_namestr, optional: The filename to save the plot as. This enables saving of the plot.

plot_maps(save_name=None, cmap='binary', contour_cmap='viridis')[source]¶

Plot the maps of locally-estimated moments.

Parameters:

save_namestr, optional: Save name for the figure. Enables saving the plot.
cmap{str, matplotlib colormap}, optional: Colormap for the images.
contour_cmap{str, matplotlib colormap}, optional: Colormap for the contours.

run(show_progress=True, verbose=False, save_name=None, radius=None, periodic=True, min_frac=0.8, **hist_kwargs)[source]¶

Compute the entire method.

Parameters:

show_progressbool, optional: Show a progress bar during the creation of the covariance matrix.
verbosebool, optional: Enables plotting.
save_namestr,optional: Save the figure when a file name is given.
radiusQuantity, optional: Override the radius size of the region.
periodicbool, optional: Specify whether the boundaries can be wrapped. Default is True.
min_fracfloat, optional: A number between 0 and 1 that sets the minimum fraction of data in each region that are finite. A value of 1.0 requires that no NaNs be in the region.
hist_kwargsPassed to make_spatial_histograms.

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.