def irfftn(a, axes=None, lastsize=0, inorm=0, nthreads=1):
return fft.c2r(a,
axes=axes,
lastsize=lastsize,
forward=False,
inorm=inorm,
nthreads=nthreads)
def _hessian(x, psfhat, padding, nthreads, unpad_x, unpad_y, lastsize):
xhat = iFs(np.pad(x, padding, mode='constant'), axes=(1, 2))
xhat = r2c(xhat, axes=(1, 2), nthreads=nthreads, forward=True, inorm=0)
xhat = c2r(xhat * psfhat,
axes=(1, 2),
forward=False,
lastsize=lastsize,
inorm=2,
nthreads=nthreads)
return Fs(xhat, axes=(1, 2))[:, unpad_x, unpad_y]
def convolveb(self, x):
xhat = iFs(np.pad(x, self.paddingb, mode='constant'), axes=self.ax)
xhat = r2c(xhat,
axes=self.ax,
nthreads=self.nthreads,
forward=True,
inorm=0)
xhat = c2r(xhat * self.psfhatb,
axes=self.ax,
forward=False,
lastsize=self.lastsizeb,
inorm=2,
nthreads=self.nthreads)
return Fs(xhat, axes=self.ax)[:, self.unpad_xb, self.unpad_yb]
def convolve2gaussres(image, xx, yy, gaussparf, nthreads, gausspari=None, pfrac=0.5, norm_kernel=False):
"""
Convolves the image to a specified resolution.
Parameters
----------
Image - (nband, nx, ny) array to convolve
xx/yy - coordinates on the grid in the same units as gaussparf.
gaussparf - tuple containing Gaussian parameters of desired resolution (emaj, emin, pa).
gausspari - initial resolution . By default it is assumed that the image is a clean component image with no associated resolution.
If beampari is specified, it must be a tuple containing gausspars for each imaging band in the same format.
nthreads - number of threads to use for the FFT's.
pfrac - padding used for the FFT based convolution. Will pad by pfrac/2 on both sides of image
"""
nband, nx, ny = image.shape
padding, unpad_x, unpad_y = get_padding_info(nx, ny, pfrac)
ax = (1, 2) # axes over which to perform fft
lastsize = ny + np.sum(padding[-1])
gausskern = Gaussian2D(xx, yy, gaussparf, normalise=norm_kernel)
gausskern = np.pad(gausskern[None], padding, mode='constant')
gausskernhat = r2c(iFs(gausskern, axes=ax), axes=ax, forward=True, nthreads=nthreads, inorm=0)
image = np.pad(image, padding, mode='constant')
imhat = r2c(iFs(image, axes=ax), axes=ax, forward=True, nthreads=nthreads, inorm=0)
# convolve to desired resolution
if gausspari is None:
imhat *= gausskernhat
else:
for i in range(nband):
thiskern = Gaussian2D(xx, yy, gausspari[i], normalise=norm_kernel)
thiskern = np.pad(thiskern[None], padding, mode='constant')
thiskernhat = r2c(iFs(thiskern, axes=ax), axes=ax, forward=True, nthreads=nthreads, inorm=0)
convkernhat = np.where(np.abs(thiskernhat)>0.0, gausskernhat/thiskernhat, 0.0)
imhat[i] *= convkernhat[0]
image = Fs(c2r(imhat, axes=ax, forward=False, lastsize=lastsize, inorm=2, nthreads=nthreads), axes=ax)[:, unpad_x, unpad_y]
return image, gausskern[:, unpad_x, unpad_y]