def iterative_zoom(
image,
mindiff=1.0,
zoomshape=[10, 10],
return_zoomed=False,
zoomstep=2,
verbose=False,
minmax=np.min,
ploteach=False,
return_center=True,
):
"""
Iteratively zoom in on the *minimum* position in an image until the
delta-peak value is below `mindiff`
Parameters
----------
image : np.ndarray
Two-dimensional image with a *minimum* to zoom in on (or maximum, if
specified using `minmax`)
mindiff : float
Minimum difference that must be present in image before zooming is done
zoomshape : [int,int]
Shape of the "mini" image to create. Smaller is faster, but a bit less
accurate. [10,10] seems to work well in preliminary tests (though unit
tests have not been written)
return_zoomed : bool
Return the zoomed image in addition to the measured offset?
zoomstep : int
Amount to increase the zoom factor by on each iteration. Probably best to
stick with small integers (2-5ish).
verbose : bool
Print out information about zoom factor, offset at each iteration
minmax : np.min or np.max
Can zoom in on the minimum or maximum of the image
ploteach : bool
Primarily a debug tool, and to be used with extreme caution! Will open
a new figure at each iteration showing the next zoom level.
return_center : bool
Return the center position in original image coordinates? If False,
will retern the *offset from center* instead (but beware the
conventions associated with the concept of 'center' for even images).
Returns
-------
The y,x offsets (following numpy convention) of the center position of the
original image. If `return_zoomed`, returns (zoomed_image, zoom_factor,
offsets) because you can't interpret the zoomed image without the zoom
factor.
"""
image_zoom = image
argminmax = np.argmin if "min" in minmax.__name__ else np.argmax
zf = 1.0 # "zoom factor" initialized to 1 for the base shift measurement
offset = np.array([0] * image.ndim, dtype="float") # center offset
delta_image = image_zoom - minmax(image_zoom)
xaxzoom = np.indices(image.shape)
if ploteach:
ii = 1
pl.figure(ii)
pl.clf()
pl.pcolor(np.arange(image.shape[0] + 1) - 0.5, np.arange(image.shape[1] + 1) - 0.5, image)
minpos = np.unravel_index(argminmax(image_zoom), image_zoom.shape)
pl.plot(minpos[1], minpos[0], "wx")
# check to make sure the smallest *nonzero* difference > mindiff
while np.abs(delta_image[np.abs(delta_image) > 0]).min() > mindiff:
minpos = np.unravel_index(argminmax(image_zoom), image_zoom.shape)
center = xaxzoom[0][minpos], xaxzoom[1][minpos]
offset = xaxzoom[0][minpos] - (image.shape[0] - 1) / 2, xaxzoom[1][minpos] - (image.shape[1] - 1) / 2
zf *= zoomstep
xaxzoom, image_zoom = zoom.zoom_on_pixel(image, center, usfac=zf, outshape=zoomshape, return_xouts=True)
delta_image = image_zoom - minmax(image_zoom)
# base case: in case you can't do any better...
# (at this point, you're all the way zoomed)
if np.all(delta_image == 0):
if verbose:
print "Can't zoom any further. zf=%i" % zf
break
if verbose:
print (
"Zoom factor %6i, center = %30s, offset=%30s, minpos=%30s, min|diff|=%15g"
% (
zf,
",".join(["%15g" % c for c in center]),
",".join(["%15g" % c for c in offset]),
",".join(["%5i" % c for c in minpos]),
np.abs(delta_image[np.abs(delta_image) > 0]).min(),
)
)
if ploteach:
ii += 1
pl.figure(ii)
pl.clf()
pl.pcolor(centers_to_edges(xaxzoom[1][0, :]), centers_to_edges(xaxzoom[0][:, 0]), image_zoom)
pl.contour(xaxzoom[1], xaxzoom[0], image_zoom - image_zoom.min(), levels=[1, 5, 15], cmap=pl.cm.gray)
pl.plot(center[1], center[0], "wx")
minpos = np.unravel_index(argminmax(image_zoom), image_zoom.shape)
pl.plot(xaxzoom[1][minpos], xaxzoom[0][minpos], "w+")
pl.arrow(
center[1],
center[0],
xaxzoom[1][minpos] - center[1],
xaxzoom[0][minpos] - center[0],
color="w",
head_width=0.1 / zf,
linewidth=1.0 / zf,
length_includes_head=True,
)
pl.figure(1)
# pl.contour(xaxzoom[1],xaxzoom[0],image_zoom-image_zoom.min(),levels=[1,5,15],cmap=pl.cm.gray)
pl.arrow(
center[1],
center[0],
xaxzoom[1][minpos] - center[1],
xaxzoom[0][minpos] - center[0],
color="w",
head_width=0.1 / zf,
linewidth=1.0 / zf,
length_includes_head=True,
)
if return_center:
result = center
else:
result = offset
if return_zoomed:
return image_zoom, zf, result
else:
return result
def iterative_zoom_1d(
data,
mindiff=1.0,
zoomshape=(10,),
return_zoomed=False,
zoomstep=2,
verbose=False,
minmax=np.min,
return_center=True,
):
"""
Iteratively zoom in on the *minimum* position in a spectrum or timestream
until the delta-peak value is below `mindiff`
Parameters
----------
data : np.ndarray
One-dimensional array with a *minimum* (or maximum, as specified by
minmax) to zoom in on
mindiff : float
Minimum difference that must be present in image before zooming is done
zoomshape : int
Shape of the "mini" image to create. Smaller is faster, but a bit less
accurate. 10 seems to work well in preliminary tests (though unit
tests have not been written)
return_zoomed : bool
Return the zoomed image in addition to the measured offset?
zoomstep : int
Amount to increase the zoom factor by on each iteration. Probably best to
stick with small integers (2-5ish).
verbose : bool
Print out information about zoom factor, offset at each iteration
minmax : np.min or np.max
Can zoom in on the minimum or maximum of the image
return_center : bool
Return the center position in original image coordinates? If False,
will retern the *offset from center* instead (but beware the
conventions associated with the concept of 'center' for even images).
Returns
-------
The x offsets of the center position of the original spectrum. If
`return_zoomed`, returns (zoomed_image, zoom_factor, offsets) because you
can't interpret the zoomed spectrum without the zoom factor.
"""
data_zoom = data
argminmax = np.argmin if "min" in minmax.__name__ else np.argmax
zf = 1.0 # "zoom factor" initialized to 1 for the base shift measurement
offset = 0.0
delta_data = data_zoom - minmax(data_zoom)
xaxzoom = np.arange(data.size)
# check to make sure the smallest *nonzero* difference > mindiff
while np.abs(delta_data[np.abs(delta_data) > 0]).min() > mindiff:
minpos = argminmax(data_zoom)
center = (xaxzoom.squeeze()[minpos],)
offset = (xaxzoom.squeeze()[minpos] - (data.size - 1) / 2,)
zf *= zoomstep
xaxzoom, data_zoom = zoom.zoom_on_pixel(data, center, usfac=zf, outshape=zoomshape, return_xouts=True)
delta_data = data_zoom - minmax(data_zoom)
# base case: in case you can't do any better...
# (at this point, you're all the way zoomed)
if np.all(delta_data == 0):
if verbose:
print "Can't zoom any further. zf=%i" % zf
break
if verbose:
print (
"Zoom factor %6i, center = %30s, offset=%30s, minpos=%30s, mindiff=%30s"
% (
zf,
"%15g" % center,
"%15g" % offset,
"%15g" % minpos,
"%15g" % np.abs(delta_data[np.abs(delta_data) > 0]).min(),
)
)
if return_center:
result = center
else:
result = offset
if return_zoomed:
return data_zoom, zf, result
else:
return result
def fourier_combine(highresfitsfile, lowresfitsfile,
matching_scale=60*u.arcsec, scale=False,
return_hdu=False,
):
"""
Simple reimplementation of 'feather' for 2D images
"""
raise "Obsolete"
f1 = fits.open(highresfitsfile)
w1 = wcs.WCS(f1[0].header)
f2 = fits.open(lowresfitsfile)
w2 = wcs.WCS(f2[0].header)
nax1,nax2 = f1[0].header['NAXIS1'], f1[0].header['NAXIS2']
# We take care of zooming later...
#if not(nax1 == f2[0].header['NAXIS1'] and nax2 == f2[0].header['NAXIS2']):
# raise ValueError("Images are not in the same pixel space; reproject "
# "them to common pixel space first.")
pixscale1 = w1.wcs.get_cdelt()[1]
pixscale2 = w2.wcs.get_cdelt()[1]
center = w1.sub([wcs.WCSSUB_CELESTIAL]).wcs_pix2world([nax1/2.],
[nax2/2.],
1)
frame = 'icrs' if w1.celestial.wcs.ctype[0][:2] == 'RA' else 'galactic'
if w2.celestial.wcs.ctype[0][:2] == 'RA':
center = coordinates.SkyCoord(*(center*u.deg), frame=frame).fk5
cxy = center.ra.deg,center.dec.deg
elif w2.celestial.wcs.ctype[0][:4] == 'GLON':
center = coordinates.SkyCoord(*(center*u.deg), frame=frame).galactic
cxy = center.l.deg,center.b.deg
im1 = f1[0].data.squeeze()
im1[np.isnan(im1)] = 0
shape = im1.shape
im2raw = f2[0].data.squeeze()
im2raw[np.isnan(im2raw)] = 0
if len(shape) != im2raw.ndim:
raise ValueError("Different # of dimensions in the interferometer and "
"single-dish images")
if len(shape) == 3:
if shape[0] != im2raw.shape[0]:
raise ValueError("Spectral dimensions of cubes do not match.")
center_pixel = w2.sub([wcs.WCSSUB_CELESTIAL]).wcs_world2pix(cxy[0], cxy[1],
0)[::-1]
zoomed = zoom_on_pixel(np.nan_to_num(im2raw),
center_pixel,
usfac=np.abs(pixscale2/pixscale1),
outshape=shape)
im2 = zoomed
xax,psd1 = fft_psd_tools.PSD2(im1, oned=True)
xax,psd2 = fft_psd_tools.PSD2(im2, oned=True)
xax_as = (pixscale1/xax*u.deg).to(u.arcsec)
if scale:
closest_point = np.argmin(np.abs(xax_as-matching_scale))
scale_2to1 = (psd1[closest_point] / psd2[closest_point])**0.5
else:
scale_2to1 = 1
fft1 = np.fft.fft2(im1)
fft2 = np.fft.fft2(im2) * scale_2to1
xgrid,ygrid = (np.indices(shape)-np.array([(shape[0]-1.)/2,(shape[1]-1.)/2.])[:,None,None])
sigma = np.abs(shape[0]/((matching_scale/(pixscale1*u.deg)).decompose().value)) / np.sqrt(8*np.log(2))
kernel = np.fft.fftshift(np.exp(-(xgrid**2+ygrid**2)/(2*sigma**2)))
kernel/=kernel.max()
fftsum = kernel*fft2 + (1-kernel)*fft1
combo = np.fft.ifft2(fftsum)
if not return_hdu:
return combo
elif return_hdu:
combo_hdu = fits.PrimaryHDU(data=np.abs(combo), header=w1.to_header())
return combo_hdu
def fourier_combine(
highresfitsfile,
lowresfitsfile,
matching_scale=60 * u.arcsec,
scale=False,
return_hdu=False,
):
"""
Simple reimplementation of 'feather' for 2D images
"""
raise "Obsolete"
f1 = fits.open(highresfitsfile)
w1 = wcs.WCS(f1[0].header)
f2 = fits.open(lowresfitsfile)
w2 = wcs.WCS(f2[0].header)
nax1, nax2 = f1[0].header['NAXIS1'], f1[0].header['NAXIS2']
# We take care of zooming later...
#if not(nax1 == f2[0].header['NAXIS1'] and nax2 == f2[0].header['NAXIS2']):
# raise ValueError("Images are not in the same pixel space; reproject "
# "them to common pixel space first.")
pixscale1 = w1.wcs.get_cdelt()[1]
pixscale2 = w2.wcs.get_cdelt()[1]
center = w1.sub([wcs.WCSSUB_CELESTIAL]).wcs_pix2world([nax1 / 2.],
[nax2 / 2.], 1)
frame = 'icrs' if w1.celestial.wcs.ctype[0][:2] == 'RA' else 'galactic'
if w2.celestial.wcs.ctype[0][:2] == 'RA':
center = coordinates.SkyCoord(*(center * u.deg), frame=frame).fk5
cxy = center.ra.deg, center.dec.deg
elif w2.celestial.wcs.ctype[0][:4] == 'GLON':
center = coordinates.SkyCoord(*(center * u.deg), frame=frame).galactic
cxy = center.l.deg, center.b.deg
im1 = f1[0].data.squeeze()
im1[np.isnan(im1)] = 0
shape = im1.shape
im2raw = f2[0].data.squeeze()
im2raw[np.isnan(im2raw)] = 0
if len(shape) != im2raw.ndim:
raise ValueError("Different # of dimensions in the interferometer and "
"single-dish images")
if len(shape) == 3:
if shape[0] != im2raw.shape[0]:
raise ValueError("Spectral dimensions of cubes do not match.")
center_pixel = w2.sub([wcs.WCSSUB_CELESTIAL
]).wcs_world2pix(cxy[0], cxy[1], 0)[::-1]
zoomed = zoom_on_pixel(np.nan_to_num(im2raw),
center_pixel,
usfac=np.abs(pixscale2 / pixscale1),
outshape=shape)
im2 = zoomed
xax, psd1 = fft_psd_tools.PSD2(im1, oned=True)
xax, psd2 = fft_psd_tools.PSD2(im2, oned=True)
xax_as = (pixscale1 / xax * u.deg).to(u.arcsec)
if scale:
closest_point = np.argmin(np.abs(xax_as - matching_scale))
scale_2to1 = (psd1[closest_point] / psd2[closest_point])**0.5
else:
scale_2to1 = 1
fft1 = np.fft.fft2(im1)
fft2 = np.fft.fft2(im2) * scale_2to1
xgrid, ygrid = (np.indices(shape) -
np.array([(shape[0] - 1.) / 2,
(shape[1] - 1.) / 2.])[:, None, None])
sigma = np.abs(shape[0] / (
(matching_scale /
(pixscale1 * u.deg)).decompose().value)) / np.sqrt(8 * np.log(2))
kernel = np.fft.fftshift(np.exp(-(xgrid**2 + ygrid**2) / (2 * sigma**2)))
kernel /= kernel.max()
fftsum = kernel * fft2 + (1 - kernel) * fft1
combo = np.fft.ifft2(fftsum)
if not return_hdu:
return combo
elif return_hdu:
combo_hdu = fits.PrimaryHDU(data=np.abs(combo), header=w1.to_header())
return combo_hdu
def iterative_zoom(image,
mindiff=1.,
zoomshape=[10, 10],
return_zoomed=False,
zoomstep=2,
verbose=False,
minmax=np.min,
ploteach=False,
return_center=True):
"""
Iteratively zoom in on the *minimum* position in an image until the
delta-peak value is below `mindiff`
Parameters
----------
image : np.ndarray
Two-dimensional image with a *minimum* to zoom in on (or maximum, if
specified using `minmax`)
mindiff : float
Minimum difference that must be present in image before zooming is done
zoomshape : [int,int]
Shape of the "mini" image to create. Smaller is faster, but a bit less
accurate. [10,10] seems to work well in preliminary tests (though unit
tests have not been written)
return_zoomed : bool
Return the zoomed image in addition to the measured offset?
zoomstep : int
Amount to increase the zoom factor by on each iteration. Probably best to
stick with small integers (2-5ish).
verbose : bool
Print out information about zoom factor, offset at each iteration
minmax : np.min or np.max
Can zoom in on the minimum or maximum of the image
ploteach : bool
Primarily a debug tool, and to be used with extreme caution! Will open
a new figure at each iteration showing the next zoom level.
return_center : bool
Return the center position in original image coordinates? If False,
will retern the *offset from center* instead (but beware the
conventions associated with the concept of 'center' for even images).
Returns
-------
The y,x offsets (following numpy convention) of the center position of the
original image. If `return_zoomed`, returns (zoomed_image, zoom_factor,
offsets) because you can't interpret the zoomed image without the zoom
factor.
"""
image_zoom = image
argminmax = np.argmin if "min" in minmax.__name__ else np.argmax
zf = 1. # "zoom factor" initialized to 1 for the base shift measurement
offset = np.array([0] * image.ndim, dtype='float') # center offset
delta_image = (image_zoom - minmax(image_zoom))
xaxzoom = np.indices(image.shape)
if ploteach:
ii = 1
pl.figure(ii)
pl.clf()
pl.pcolor(
np.arange(image.shape[0] + 1) - 0.5,
np.arange(image.shape[1] + 1) - 0.5, image)
minpos = np.unravel_index(argminmax(image_zoom), image_zoom.shape)
pl.plot(minpos[1], minpos[0], 'wx')
# check to make sure the smallest *nonzero* difference > mindiff
while np.abs(delta_image[np.abs(delta_image) > 0]).min() > mindiff:
minpos = np.unravel_index(argminmax(image_zoom), image_zoom.shape)
center = xaxzoom[0][minpos], xaxzoom[1][minpos]
offset = xaxzoom[0][minpos] - (image.shape[0] - 1) / 2, xaxzoom[1][
minpos] - (image.shape[1] - 1) / 2
zf *= zoomstep
xaxzoom, image_zoom = zoom.zoom_on_pixel(image,
center,
usfac=zf,
outshape=zoomshape,
return_xouts=True)
delta_image = image_zoom - minmax(image_zoom)
# base case: in case you can't do any better...
# (at this point, you're all the way zoomed)
if np.all(delta_image == 0):
if verbose:
print "Can't zoom any further. zf=%i" % zf
break
if verbose:
print(
"Zoom factor %6i, center = %30s, offset=%30s, minpos=%30s, min|diff|=%15g"
% (zf, ",".join(["%15g" % c for c in center]), ",".join(
["%15g" % c
for c in offset]), ",".join(["%5i" % c for c in minpos]),
np.abs(delta_image[np.abs(delta_image) > 0]).min()))
if ploteach:
ii += 1
pl.figure(ii)
pl.clf()
pl.pcolor(centers_to_edges(xaxzoom[1][0, :]),
centers_to_edges(xaxzoom[0][:, 0]), image_zoom)
pl.contour(xaxzoom[1],
xaxzoom[0],
image_zoom - image_zoom.min(),
levels=[1, 5, 15],
cmap=pl.cm.gray)
pl.plot(center[1], center[0], 'wx')
minpos = np.unravel_index(argminmax(image_zoom), image_zoom.shape)
pl.plot(xaxzoom[1][minpos], xaxzoom[0][minpos], 'w+')
pl.arrow(center[1],
center[0],
xaxzoom[1][minpos] - center[1],
xaxzoom[0][minpos] - center[0],
color='w',
head_width=0.1 / zf,
linewidth=1. / zf,
length_includes_head=True)
pl.figure(1)
#pl.contour(xaxzoom[1],xaxzoom[0],image_zoom-image_zoom.min(),levels=[1,5,15],cmap=pl.cm.gray)
pl.arrow(center[1],
center[0],
xaxzoom[1][minpos] - center[1],
xaxzoom[0][minpos] - center[0],
color='w',
head_width=0.1 / zf,
linewidth=1. / zf,
length_includes_head=True)
if return_center:
result = center
else:
result = offset
if return_zoomed:
return image_zoom, zf, result
else:
return result
def iterative_zoom_1d(data,
mindiff=1.,
zoomshape=(10, ),
return_zoomed=False,
zoomstep=2,
verbose=False,
minmax=np.min,
return_center=True):
"""
Iteratively zoom in on the *minimum* position in a spectrum or timestream
until the delta-peak value is below `mindiff`
Parameters
----------
data : np.ndarray
One-dimensional array with a *minimum* (or maximum, as specified by
minmax) to zoom in on
mindiff : float
Minimum difference that must be present in image before zooming is done
zoomshape : int
Shape of the "mini" image to create. Smaller is faster, but a bit less
accurate. 10 seems to work well in preliminary tests (though unit
tests have not been written)
return_zoomed : bool
Return the zoomed image in addition to the measured offset?
zoomstep : int
Amount to increase the zoom factor by on each iteration. Probably best to
stick with small integers (2-5ish).
verbose : bool
Print out information about zoom factor, offset at each iteration
minmax : np.min or np.max
Can zoom in on the minimum or maximum of the image
return_center : bool
Return the center position in original image coordinates? If False,
will retern the *offset from center* instead (but beware the
conventions associated with the concept of 'center' for even images).
Returns
-------
The x offsets of the center position of the original spectrum. If
`return_zoomed`, returns (zoomed_image, zoom_factor, offsets) because you
can't interpret the zoomed spectrum without the zoom factor.
"""
data_zoom = data
argminmax = np.argmin if "min" in minmax.__name__ else np.argmax
zf = 1. # "zoom factor" initialized to 1 for the base shift measurement
offset = 0.
delta_data = (data_zoom - minmax(data_zoom))
xaxzoom = np.arange(data.size)
# check to make sure the smallest *nonzero* difference > mindiff
while np.abs(delta_data[np.abs(delta_data) > 0]).min() > mindiff:
minpos = argminmax(data_zoom)
center = xaxzoom.squeeze()[minpos],
offset = xaxzoom.squeeze()[minpos] - (data.size - 1) / 2,
zf *= zoomstep
xaxzoom, data_zoom = zoom.zoom_on_pixel(data,
center,
usfac=zf,
outshape=zoomshape,
return_xouts=True)
delta_data = data_zoom - minmax(data_zoom)
# base case: in case you can't do any better...
# (at this point, you're all the way zoomed)
if np.all(delta_data == 0):
if verbose:
print "Can't zoom any further. zf=%i" % zf
break
if verbose:
print(
"Zoom factor %6i, center = %30s, offset=%30s, minpos=%30s, mindiff=%30s"
% (
zf,
"%15g" % center,
"%15g" % offset,
"%15g" % minpos,
"%15g" % np.abs(delta_data[np.abs(delta_data) > 0]).min(),
))
if return_center:
result = center
else:
result = offset
if return_zoomed:
return data_zoom, zf, result
else:
return result