# Smoothing¶

There are two types of smoothing routine available in spectral_cube: spatial and spectral.

## Spatial Smoothing¶

The convolve_to method will convolve each plane of the cube to a common resolution, assuming the cube’s resolution is known in advanced and stored in the cube’s beam or beams attribute.

A simple example:

import radio_beam
from spectral_cube import SpectralCube
from astropy import units as u

new_cube = cube.convolve_to(beam)


Note that the convolve_to() method will work for both VaryingResolutionSpectralCube instances and single-resolution SpectralCube instances, but for a VaryingResolutionSpectralCube, the convolution kernel will be different for each slice.

### Common Beam selection¶

You may want to convolve your cube to the smallest beam that is still larger than all contained beams. To do this, you can use the common_beam tool. For example:

common_beam = cube.beams.common_beam()
new_cube = cube.convolve_to(common_beam)


Sometimes, you’ll encounter the error “Could not find common beam to deconvolve all beams.” This is a real issue, as the algorithms we have in hand so far do not always converge on a common containing beam. There are two ways to get the algorithm to converge to a valid common beam:

1. Changing the tolerance. - You can try to change the tolerance used in the getMinVolEllipse code by passing tolerance=1e-5 to the common beam function:

cube.beams.common_beam(tolerance=1e-5)


Convergence may be met by either increasing or decreasing the tolerance; it depends on having the algorithm not step within the minimum enclosing ellipse, leading to the error. Note that decreasing the tolerance by an order of magnitude will require an order of magnitude more iterations for the algorithm to converge and will take longer to run.

2. Changing epsilon - A second parameter epsilon controls the fraction to overestimate the beam size, ensuring that solutions that are marginally smaller than the common beam will not be found by the algorithm:

cube.beams.common_beam(epsilon=1e-3)


The default value of epsilon=1e-3 will sample points 0.1% larger than the edge of each beam in the set. Increasing epsilon ensures that a valid common beam can be found, avoiding the tolerance issue, but will result in overestimating the common beam area. For most radio data sets, where the beam is oversampled by $$\sim 3 \mbox{-5}$$ pixels, moderate increases in epsilon will increase the common beam area far less than a pixel area, making the overestimation negligible.

We recommend testing different values of tolerance to find convergence, and if the error persists, to then slowly increase epsilon until a valid common beam is found. More information can be found in the radio-beam documentation.

### Alternative approach to spatial smoothing¶

There is an alternative way to spatially smooth a data cube, which is using the spatial_smooth() method. This is an example of how you can do this:

from spectral_cube import SpectralCube
from astropy.io import fits
from astropy.convolution import Gaussian2DKernel

kernel = Gaussian2DKernel(x_stddev=1)
new_cube = cube.spatial_smooth(kernel)
new_cube.write('/some_path/some_other_file.fits')


x_stddev specifies the width of the Gaussian kernel. Any astropy convolution is acceptable.

## Spectral Smoothing¶

Only SpectralCube instances with a consistent beam can be spectrally smoothed, so if you have a VaryingResolutionSpectralCube, you must convolve each slice in it to a common resolution before spectrally smoothing. spectral_smooth() will apply a convolution kernel to each spectrum in turn. As of July 2016, a parallelized version is partly written but incomplete.

Example:

import radio_beam
from spectral_cube import SpectralCube
from astropy import units as u
from astropy.convolution import Gaussian1DKernel

kernel = Gaussian1DKernel(2.5)
new_cube = cube.spectral_smooth(kernel)


This can be useful if you want to interpolate onto a coarser grid but maintain Nyquist sampling. You can then use the spectral_interpolate method to regrid your smoothed spectrum onto a new grid.

Say, for example, you have a cube with 0.5 km/s resolution, but you want to resample it onto a 2 km/s grid. You might then choose to smooth by a factor of 4, then downsample by the same factor:

# cube.spectral_axis is np.arange(0,10,0.5) for this example
new_axis = np.arange(0,10,2)*u.km/u.s
fwhm_factor = np.sqrt(8*np.log(2))

smcube = cube.spectral_smooth(Gaussian1DKernel(4/fwhm_factor))
interp_Cube = smcube.spectral_interpolate(new_axis,
suppress_smooth_warning=True)


We include the suppress_smooth_warning override because there is no way for SpectralCube to know if you’ve done the appropriate smoothing (i.e., making sure that your new grid nyquist samples the data) prior to the interpolation step. If you don’t specify this, it will still work, but you’ll be warned that you should preserve Nyquist sampling.

If you have a cube with 0.1 km/s resolution (where we assume resolution corresponds to the fwhm of a gaussian), and you want to smooth it to 0.25 km/s resolution, you can smooth the cube with a Gaussian Kernel that has a width of (0.25^2 - 0.1^2)^0.5 = 0.229 km/s. For simplicity, it can be done in the unit of pixel. In our example, each channel is 0.1 km/s wide:

import numpy as np
from astropy import units as u
from spectral_cube import SpectralCube
from astropy.convolution import Gaussian1DKernel


gaussian_width is in pixel units but is defined as a unitless Quantity. By using gaussian_width.value, we convert the pixel width into a float.