convolve_fft#
- astropy.convolution.convolve_fft(array, kernel, boundary='fill', fill_value=0.0, nan_treatment='interpolate', normalize_kernel=True, normalization_zero_tol=1e-08, preserve_nan=False, mask=None, crop=True, return_fft=False, fft_pad=None, psf_pad=None, min_wt=0.0, allow_huge=False, fftn=<function fftn>, ifftn=<function ifftn>, complex_dtype=<class 'complex'>, dealias=False)[source]#
Convolve an ndarray with an nd-kernel. Returns a convolved image with
shape = array.shape
. Assumes kernel is centered.convolve_fft
is very similar toconvolve
in that it replacesNaN
values in the original image with interpolated values using the kernel as an interpolation function. However, it also includes many additional options specific to the implementation.convolve_fft
differs fromscipy.signal.fftconvolve
in a few ways:It can treat
NaN
values as zeros or interpolate over them.inf
values are treated asNaN
It optionally pads to the nearest faster sizes to improve FFT speed. These sizes are optimized for the numpy and scipy implementations, and
fftconvolve
uses them by default as well; when using other external functions (see below), results may vary.Its only valid
mode
is ‘same’ (i.e., the same shape array is returned)It lets you use your own fft, e.g., pyFFTW or pyFFTW3 , which can lead to performance improvements, depending on your system configuration. pyFFTW3 is threaded, and therefore may yield significant performance benefits on multi-core machines at the cost of greater memory requirements. Specify the
fftn
andifftn
keywords to override the default, which isnumpy.fft.fftn
andnumpy.fft.ifftn
. Thescipy.fft
functions also offer somewhat better performance and a multi-threaded option.
- Parameters:
- array
numpy.ndarray
Array to be convolved with
kernel
. It can be of any dimensionality, though only 1, 2, and 3d arrays have been tested.- kernel
numpy.ndarray
orastropy.convolution.Kernel
The convolution kernel. The number of dimensions should match those for the array. The dimensions do not have to be odd in all directions, unlike in the non-fft
convolve
function. The kernel will be normalized ifnormalize_kernel
is set. It is assumed to be centered (i.e., shifts may result if your kernel is asymmetric)- boundary{‘fill’, ‘wrap’}, optional
A flag indicating how to handle boundaries:
‘fill’: set values outside the array boundary to fill_value (default)
‘wrap’: periodic boundary
The
None
and ‘extend’ parameters are not supported for FFT-based convolution.- fill_value
float
, optional The value to use outside the array when using boundary=’fill’.
- nan_treatment{‘interpolate’, ‘fill’}, optional
- The method used to handle NaNs in the input
array
: 'interpolate'
:NaN
values are replaced with interpolated values using the kernel as an interpolation function. Note that if the kernel has a sum equal to zero, NaN interpolation is not possible and will raise an exception.'fill'
:NaN
values are replaced byfill_value
prior to convolution.
- The method used to handle NaNs in the input
- normalize_kernel
callable()
or bool, optional If specified, this is the function to divide kernel by to normalize it. e.g.,
normalize_kernel=np.sum
means that kernel will be modified to be:kernel = kernel / np.sum(kernel)
. If True, defaults tonormalize_kernel = np.sum
.- normalization_zero_tol
float
, optional The absolute tolerance on whether the kernel is different than zero. If the kernel sums to zero to within this precision, it cannot be normalized. Default is “1e-8”.
- preserve_nanbool, optional
After performing convolution, should pixels that were originally NaN again become NaN?
- mask
None
orndarray
, optional A “mask” array. Shape must match
array
, and anything that is masked (i.e., not 0/False
) will be set to NaN for the convolution. IfNone
, no masking will be performed unlessarray
is a masked array. Ifmask
is notNone
andarray
is a masked array, a pixel is masked if it is masked in eithermask
orarray.mask
.- cropbool, optional
Default on. Return an image of the size of the larger of the input image and the kernel. If the image and kernel are asymmetric in opposite directions, will return the largest image in both directions. For example, if an input image has shape [100,3] but a kernel with shape [6,6] is used, the output will be [100,6].
- return_fftbool, optional
Return the
fft(image)*fft(kernel)
instead of the convolution (which isifft(fft(image)*fft(kernel))
). Useful for making PSDs.- fft_padbool, optional
Default on. Zero-pad image to the nearest size supporting more efficient execution of the FFT, generally values factorizable into the first 3-5 prime numbers. With
boundary='wrap'
, this will be disabled.- psf_padbool, optional
Zero-pad image to be at least the sum of the image sizes to avoid edge-wrapping when smoothing. This is enabled by default with
boundary='fill'
, but it can be overridden with a boolean option.boundary='wrap'
andpsf_pad=True
are not compatible.- min_wt
float
, optional If ignoring
NaN
/ zeros, force all grid points with a weight less than this value toNaN
(the weight of a grid point with no ignored neighbors is 1.0). Ifmin_wt
is zero, then all zero-weight points will be set to zero instead ofNaN
(which they would be otherwise, because 1/0 = nan). See the examples below.- allow_hugebool, optional
Allow huge arrays in the FFT? If False, will raise an exception if the array or kernel size is >1 GB.
- fftn
callable()
, optional The fft function. Can be overridden to use your own ffts, e.g. an fftw3 wrapper or scipy’s fftn,
fft=scipy.fftpack.fftn
.- ifftn
callable()
, optional The inverse fft function. Can be overridden the same way
fttn
.- complex_dtype
complex
type, optional Which complex dtype to use.
numpy
has a range of options, from 64 to 256.- dealias: bool, optional
Default off. Zero-pad image to enable explicit dealiasing of convolution. With
boundary='wrap'
, this will be disabled. Note that for an input of nd dimensions this will increase the size of the temporary arrays by at least1.5**nd
. This may result in significantly more memory usage.
- array
- Returns:
- default
ndarray
array
convolved withkernel
. Ifreturn_fft
is set, returnsfft(array) * fft(kernel)
. If crop is not set, returns the image, but with the fft-padded size instead of the input size.
- default
- Raises:
ValueError
If the array is bigger than 1 GB after padding, will raise this exception unless
allow_huge
is True.
See also
convolve
Convolve is a non-fft version of this code. It is more memory efficient and for small kernels can be faster.
Notes
With
psf_pad=True
and a large PSF, the resulting data can become large and consume a lot of memory. See Issue astropy/astropy#4366 and the update in astropy/astropy#11533 for further details.Dealiasing of pseudospectral convolutions is necessary for numerical stability of the underlying algorithms. A common method for handling this is to zero pad the image by at least 1/2 to eliminate the wavenumbers which have been aliased by convolution. This is so that the aliased 1/3 of the results of the convolution computation can be thrown out. See https://doi.org/10.1175/1520-0469(1971)028%3C1074:OTEOAI%3E2.0.CO;2 https://iopscience.iop.org/article/10.1088/1742-6596/318/7/072037
Note that if dealiasing is necessary to your application, but your process is memory constrained, you may want to consider using FFTW++: dealias/fftwpp. It includes python wrappers for a pseudospectral convolution which will implicitly dealias your convolution without the need for additional padding. Note that one cannot use FFTW++’s convlution directly in this method as in handles the entire convolution process internally. Additionally, FFTW++ includes other useful pseudospectral methods to consider.
Examples
>>> convolve_fft([1, 0, 3], [1, 1, 1]) array([0.33333333, 1.33333333, 1. ])
>>> convolve_fft([1, np.nan, 3], [1, 1, 1]) array([0.5, 2. , 1.5])
>>> convolve_fft([1, 0, 3], [0, 1, 0]) array([ 1.00000000e+00, -3.70074342e-17, 3.00000000e+00])
>>> convolve_fft([1, 2, 3], [1]) array([1., 2., 3.])
>>> convolve_fft([1, np.nan, 3], [0, 1, 0], nan_treatment='interpolate') array([1., 0., 3.])
>>> convolve_fft([1, np.nan, 3], [0, 1, 0], nan_treatment='interpolate', ... min_wt=1e-8) array([ 1., nan, 3.])
>>> convolve_fft([1, np.nan, 3], [1, 1, 1], nan_treatment='interpolate') array([0.5, 2. , 1.5])
>>> convolve_fft([1, np.nan, 3], [1, 1, 1], nan_treatment='interpolate', ... normalize_kernel=True) array([0.5, 2. , 1.5])
>>> import scipy.fft # optional - requires scipy >>> convolve_fft([1, np.nan, 3], [1, 1, 1], nan_treatment='interpolate', ... normalize_kernel=True, ... fftn=scipy.fft.fftn, ifftn=scipy.fft.ifftn) array([0.5, 2. , 1.5])
>>> fft_mp = lambda a: scipy.fft.fftn(a, workers=-1) # use all available cores >>> ifft_mp = lambda a: scipy.fft.ifftn(a, workers=-1) >>> convolve_fft([1, np.nan, 3], [1, 1, 1], nan_treatment='interpolate', ... normalize_kernel=True, fftn=fft_mp, ifftn=ifft_mp) array([0.5, 2. , 1.5])