fft_psd_tools package¶
Submodules¶
fft_psd_tools.convolve_nd module¶
fft_psd_tools.convolve_nd Module¶
- ND fft convolution with NaN masking:
- This has been merged into astropy, so it’s probably best to use that version
Functions¶
convolvend(array, kernel[, boundary, ...]) |
Convolve an ndarray with an nd-kernel. |
fftn(a[, s, axes, norm]) |
Compute the N-dimensional discrete Fourier Transform. |
ifftn(a[, s, axes, norm]) |
Compute the N-dimensional inverse discrete Fourier Transform. |
test_3d(psf_pad, use_numpy_fft, ...[, ...]) |
fft_psd_tools.correlate2d module¶
fft_psd_tools.correlate2d Module¶
Functions¶
convolve(array, kernel[, boundary, ...]) |
Convolve an ndarray with an nd-kernel. |
correlate2d(im1, im2[, boundary]) |
Cross-correlation of two images of arbitrary size. |
fft_psd_tools.fast_ffts module¶
fft_psd_tools.fast_ffts Module¶
fft_psd_tools.psds module¶
fft_psd_tools.psds Module¶
Power Spectra
Functions¶
PSD2(image[, image2, oned, fft_pad, real, ...]) |
Two-dimensional Power Spectral Density. |
azimuthalAverageBins(image, azbins[, ...]) |
Compute the azimuthal average over a limited range of angles |
correlate2d(im1, im2[, boundary]) |
Cross-correlation of two images of arbitrary size. |
hanning2d(M, N) |
A 2D hanning window, as per IDL’s hanning function. |
power_spectrum(*args, **kwargs) |
Thin wrapper of PSD2. |
pspec(psd2[, return_index, wavenumber, ...]) |
Create a Power Spectrum (radial profile of a PSD) from a Power Spectral Density image |
radialAverageBins(image, radbins[, corners, ...]) |
Compute the radial average over a limited range of radii |
fft_psd_tools.shift module¶
fft_psd_tools.shift Module¶
fft_psd_tools.smooth_tools module¶
fft_psd_tools.smooth_tools Module¶
Functions¶
convolve(array, kernel[, boundary, ...]) |
Convolve an ndarray with an nd-kernel. |
downsample_2d(myarr, factor[, estimator]) |
Downsample a 2D array by averaging over factor pixels in each axis. |
make_kernel(kernelshape[, kernelwidth, ...]) |
Create a smoothing kernel for use with convolve or convolve_fft |
smooth(image[, kernelwidth, kerneltype, ...]) |
Returns a smoothed image using a gaussian, boxcar, or tophat kernel |
fft_psd_tools.upsample module¶
fft_psd_tools.upsample Module¶
Functions¶
dftups(inp[, nor, noc, usfac, roff, coff]) |
translated from matlab |
dftups1d(inp[, nor, usfac, roff]) |
1D upsampling... not exactly dft becuase I still don’t understand it =( |
upsample_image(image[, upsample_factor, ...]) |
Use dftups to upsample an image (but takes an image and returns an image with all reals) |
Module contents¶
fft_psd_tools Package¶
Wrappers around numpy, scipy, and pyfftw tools to perform 2D convolution in general, smoothing with a set of ‘standard’ kernels, and computing power spectra and PSDs.
Functions¶
PSD2(image[, image2, oned, fft_pad, real, ...]) |
Two-dimensional Power Spectral Density. |
convolve(array, kernel[, boundary, ...]) |
Convolve an ndarray with an nd-kernel. |
convolvend(array, kernel[, boundary, ...]) |
Convolve an ndarray with an nd-kernel. |
correlate2d(im1, im2[, boundary]) |
Cross-correlation of two images of arbitrary size. |
dftups(inp[, nor, noc, usfac, roff, coff]) |
translated from matlab |
shift(data, deltax, deltay[, phase, ...]) |
FFT-based sub-pixel image shift |
smooth(image[, kernelwidth, kerneltype, ...]) |
Returns a smoothed image using a gaussian, boxcar, or tophat kernel |
upsample_image(image[, upsample_factor, ...]) |
Use dftups to upsample an image (but takes an image and returns an image with all reals) |