def convert_griddata_to_image(gd):
""" Convert griddata to an image
:param gd:
:return:
"""
return create_image_from_array(gd.data, gd.grid_wcs, gd.polarisation_frame)
def convert_convolutionfunction_to_image(cf):
""" Convert ConvolutionFunction to an image
:param cf:
:return:
"""
return create_image_from_array(cf.data, cf.grid_wcs, cf.polarisation_frame)
def create_box_convolutionfunction(im, oversampling=1, support=1):
""" Fill a box car function into a ConvolutionFunction
Also returns the griddata correction function as an image
:param im: Image template
:param oversampling: Oversampling of the convolution function in uv space
:return: griddata correction Image, griddata kernel as ConvolutionFunction
"""
assert isinstance(im, Image)
cf = create_convolutionfunction_from_image(im, oversampling=1, support=4)
nchan, npol, _, _ = im.shape
cf.data[...] = 0.0 + 0.0j
cf.data[..., 2, 2] = 1.0 + 0.0j
# Now calculate the griddata correction function as an image with the same coordinates as the image
# which is necessary so that the correction function can be applied directly to the image
nchan, npol, ny, nx = im.data.shape
nu = numpy.abs(coordinates(nx))
gcf1d = numpy.sinc(nu)
gcf = numpy.outer(gcf1d, gcf1d)
gcf = 1.0 / gcf
gcf_data = numpy.zeros_like(im.data)
gcf_data[...] = gcf[numpy.newaxis, numpy.newaxis, ...]
gcf_image = create_image_from_array(gcf_data, cf.projection_wcs,
im.polarisation_frame)
return gcf_image, cf
def image_gather_channels(image_list: List[Image],
im: Image = None,
subimages=0) -> Image:
"""Gather a list of subimages back into an image using the channel_iterator
If the template image is not given then it will be formed assuming that the list has
been generated by image_scatter_channels with subimages = number of channels
:param image_list: List of subimages
:param im: Output image
:param subimages: Number of image partitions on each axis (2)
:return: list of subimages
"""
if im is None:
nchan = len(image_list)
_, npol, ny, nx = image_list[0].shape
im_shape = nchan, npol, ny, ny
im = create_image_from_array(
numpy.zeros(im_shape, dtype=image_list[0].data.dtype),
image_list[0].wcs, image_list[0].polarisation_frame)
assert image_is_canonical(im)
if subimages == 0:
subimages = len(image_list)
for i, slab in enumerate(image_channel_iter(im, subimages=subimages)):
slab.data[...] = image_list[i].data[...]
return im
def create_low_test_beam(model: Image, use_local=True) -> Image:
"""Create a test power beam for LOW using an image from OSKAR
This is not fit for anything except the most basic testing. It does not include any form of elevation/pa dependence.
:param model: Template image
:return: Image
"""
beam = import_image_from_fits(
rascil_path('data/models/SKA1_LOW_beam.fits'))
# Scale the image cellsize to account for the different in frequencies. Eventually we will want to
# use a frequency cube
log.debug(
"create_low_test_beam: LOW voltage pattern is defined at %.3f MHz" %
(beam.wcs.wcs.crval[2] * 1e-6))
nchan, npol, ny, nx = model.shape
# We need to interpolate each frequency channel separately. The beam is assumed to just scale with
# frequency.
reprojected_beam = create_empty_image_like(model)
for chan in range(nchan):
model2dwcs = model.wcs.sub(2).deepcopy()
model2dshape = [model.shape[2], model.shape[3]]
beam2dwcs = beam.wcs.sub(2).deepcopy()
# The frequency axis is the second to last in the beam
frequency = model.wcs.sub(['spectral']).wcs_pix2world([chan], 0)[0]
fscale = beam.wcs.wcs.crval[2] / frequency
beam2dwcs.wcs.cdelt = fscale * beam.wcs.sub(2).wcs.cdelt
beam2dwcs.wcs.crpix = beam.wcs.sub(2).wcs.crpix
beam2dwcs.wcs.crval = model.wcs.sub(2).wcs.crval
beam2dwcs.wcs.ctype = model.wcs.sub(2).wcs.ctype
model2dwcs.wcs.crpix = [
model.shape[2] // 2 + 1, model.shape[3] // 2 + 1
]
beam2d = create_image_from_array(beam.data[0, 0, :, :], beam2dwcs,
model.polarisation_frame)
reprojected_beam2d, footprint = reproject_image(beam2d,
model2dwcs,
shape=model2dshape)
assert numpy.max(
footprint.data) > 0.0, "No overlap between beam and model"
reprojected_beam2d.data[footprint.data <= 0.0] = 0.0
for pol in range(npol):
reprojected_beam.data[chan,
pol, :, :] = reprojected_beam2d.data[:, :]
set_pb_header(reprojected_beam, use_local=use_local)
return reprojected_beam
def create_pswf_convolutionfunction(im, oversampling=8, support=6):
""" Fill an Anti-Aliasing filter into a ConvolutionFunction
Fill the Prolate Spheroidal Wave Function into a GriData with the specified oversampling. Only the inner
non-zero part is retained
Also returns the griddata correction function as an image
:param im: Image template
:param oversampling: Oversampling of the convolution function in uv space
:return: griddata correction Image, griddata kernel as ConvolutionFunction
"""
assert isinstance(im, Image), im
# Calculate the convolution kernel. We oversample in u,v space by the factor oversampling
cf = create_convolutionfunction_from_image(im,
oversampling=oversampling,
support=support)
kernel = numpy.zeros([oversampling, support])
for grid in range(support):
for subsample in range(oversampling):
nu = ((grid - support // 2) -
(subsample - oversampling // 2) / oversampling)
kernel[subsample, grid] = grdsf([nu / (support // 2)])[1]
kernel /= numpy.sum(numpy.real(kernel[oversampling // 2, :]))
nchan, npol, _, _ = im.shape
cf.data = numpy.zeros(
[nchan, npol, 1, oversampling, oversampling, support,
support]).astype('complex')
for y in range(oversampling):
for x in range(oversampling):
cf.data[:, :, 0, y,
x, :, :] = numpy.outer(kernel[y, :],
kernel[x, :])[numpy.newaxis,
numpy.newaxis, ...]
norm = numpy.sum(numpy.real(cf.data[0, 0, 0, 0, 0, :, :]))
cf.data /= norm
# Now calculate the griddata correction function as an image with the same coordinates as the image
# which is necessary so that the correction function can be applied directly to the image
nchan, npol, ny, nx = im.data.shape
nu = numpy.abs(2.0 * coordinates(nx))
gcf1d = grdsf(nu)[0]
gcf = numpy.outer(gcf1d, gcf1d)
gcf[gcf > 0.0] = gcf.max() / gcf[gcf > 0.0]
gcf_data = numpy.zeros_like(im.data)
gcf_data[...] = gcf[numpy.newaxis, numpy.newaxis, ...]
gcf_image = create_image_from_array(gcf_data, cf.projection_wcs,
im.polarisation_frame)
return gcf_image, cf
def image_voronoi_iter(im: Image,
components: Skycomponent) -> collections.Iterable:
"""Iterate through Voronoi decomposition, returning a generator yielding fullsize images
:param im: Image
:param components: Components to define Voronoi decomposition
"""
if len(components) == 1:
mask = numpy.ones(im.data.shape)
yield create_image_from_array(mask,
wcs=im.wcs,
polarisation_frame=im.polarisation_frame)
else:
vor, vertex_array = voronoi_decomposition(im, components)
nregions = numpy.max(vertex_array) + 1
for region in range(nregions):
mask = numpy.zeros(im.data.shape)
mask[(vertex_array == region)[numpy.newaxis, numpy.newaxis,
...]] = 1.0
yield create_image_from_array(
mask, wcs=im.wcs, polarisation_frame=im.polarisation_frame)
def fft_griddata_to_image(griddata, gcf=None, imaginary=False):
""" FFT griddata after applying gcf
:param griddata:
:param gcf: Grid correction image
:return:
"""
projected = numpy.sum(griddata.data, axis=2)
ny, nx = projected.data.shape[-2], projected.data.shape[-1]
if gcf is None:
im_data = ifft(projected) * float(nx) * float(ny)
else:
im_data = ifft(projected) * gcf.data * float(nx) * float(ny)
im_real = create_image_from_array(im_data.real, griddata.projection_wcs, griddata.polarisation_frame)
if imaginary:
im_imag = create_image_from_array(im_data.imag, griddata.projection_wcs, griddata.polarisation_frame)
return im_real, im_imag
else:
return im_real
def image_channel_iter(im: Image, subimages=1) -> collections.Iterable:
"""Create a image_channel_iter generator, returning images
The WCS is adjusted appropriately for each raster element. Hence this is a coordinate-aware
way to iterate through an image.
Provided we don't break reference semantics, memory should be conserved
To update the image in place::
for r in image_channel_iter(im, subimages=nchan):
r.data[...] = numpy.sqrt(r.data[...])
:param im: Image
:param subimages: Number of subimages
:returns: Generator of images
See also
:py:func:`rascil.processing_components.image.image_gather_channels`
:py:func:`rascil.processing_components.image.image_scatter_channels`
"""
assert image_is_canonical(im)
nchan, npol, ny, nx = im.shape
assert subimages <= nchan, "More subimages %d than channels %d" % (
subimages, nchan)
step = nchan // subimages
channels = numpy.array(range(0, nchan, step), dtype='int')
assert len(
channels
) == subimages, "subimages %d does not match length of channels %d" % (
subimages, len(channels))
for i, channel in enumerate(channels):
if i + 1 < len(channels):
channel_max = channels[i + 1]
else:
channel_max = nchan
# Adjust WCS
wcs = im.wcs.deepcopy()
wcs.wcs.crpix[3] -= channel
# Yield image from slice (reference!)
yield create_image_from_array(im.data[channel:channel_max, ...], wcs,
im.polarisation_frame)
def fft_griddata_to_image(griddata, gcf=None):
""" FFT griddata after applying gcf
If imaginary is true the data array is complex
:param griddata:
:param gcf: Grid correction image
:return:
"""
projected = numpy.sum(griddata.data, axis=2)
ny, nx = projected.data.shape[-2], projected.data.shape[-1]
if gcf is None:
im_data = ifft(projected) * float(nx) * float(ny)
else:
im_data = ifft(projected) * gcf.data * float(nx) * float(ny)
return create_image_from_array(im_data, griddata.projection_wcs, griddata.polarisation_frame)
def setUp(self):
from rascil.data_models.parameters import rascil_path
self.dir = rascil_path('test_results')
self.persist = os.getenv("RASCIL_PERSIST", False)
self.niter = 1000
self.lowcore = create_named_configuration('LOWBD2-CORE')
self.nchan = 5
self.times = (numpy.pi / 12.0) * numpy.linspace(-3.0, 3.0, 7)
self.frequency = numpy.linspace(0.9e8, 1.1e8, self.nchan)
self.channel_bandwidth = numpy.array(self.nchan * [self.frequency[1] - self.frequency[0]])
self.phasecentre = SkyCoord(ra=+0.0 * u.deg, dec=-45.0 * u.deg, frame='icrs', equinox='J2000')
self.vis = create_visibility(self.lowcore, self.times, self.frequency, self.channel_bandwidth,
phasecentre=self.phasecentre, weight=1.0,
polarisation_frame=PolarisationFrame('stokesI'), zerow=True)
self.vis.data['vis'] *= 0.0
# Create model
self.test_model = create_low_test_image_from_gleam(npixel=512, cellsize=0.001,
phasecentre=self.vis.phasecentre,
frequency=self.frequency,
channel_bandwidth=self.channel_bandwidth,
flux_limit=1.0)
beam = create_low_test_beam(self.test_model)
if self.persist: export_image_to_fits(beam, "%s/test_deconvolve_mmclean_beam.fits" % self.dir)
self.test_model.data *= beam.data
if self.persist: export_image_to_fits(self.test_model, "%s/test_deconvolve_mmclean_model.fits" % self.dir)
self.vis = predict_2d(self.vis, self.test_model)
assert numpy.max(numpy.abs(self.vis.vis)) > 0.0
self.model = create_image_from_visibility(self.vis, npixel=512, cellsize=0.001,
polarisation_frame=PolarisationFrame('stokesI'))
self.dirty, sumwt = invert_2d(self.vis, self.model)
self.psf, sumwt = invert_2d(self.vis, self.model, dopsf=True)
if self.persist: export_image_to_fits(self.dirty, "%s/test_deconvolve_mmclean-dirty.fits" % self.dir)
if self.persist: export_image_to_fits(self.psf, "%s/test_deconvolve_mmclean-psf.fits" % self.dir)
window = numpy.ones(shape=self.model.shape, dtype=numpy.bool)
window[..., 129:384, 129:384] = True
self.innerquarter = create_image_from_array(window, self.model.wcs, polarisation_frame=PolarisationFrame('stokesI'))
def image_raster_iter(im: Image,
facets=1,
overlap=0,
taper='flat',
make_flat=False) -> collections.Iterable:
"""Create an image_raster_iter generator, returning images, optionally with overlaps
The WCS is adjusted appropriately for each raster element. Hence this is a coordinate-aware
way to iterate through an image.
Provided we don't break reference semantics, memory should be conserved. However make_flat
creates a new set of images and thus reference semantics dont hold.
To update the image in place::
for r in image_raster_iter(im, facets=2):
r.data[...] = numpy.sqrt(r.data[...])
If the overlap is greater than zero, we choose to keep all images the same size so the
other ring of facets are ignored. So if facets=4 and overlap > 0 then the iterator returns
(facets-2)**2 = 4 images.
A taper is applied in the overlap regions. None implies a constant value, linear is a ramp, and
quadratic is parabolic at the ends.
:param im: Image
:param facets: Number of image partitions on each axis (2)
:param overlap: overlap in pixels
:param taper: method of tapering at the edges: 'flat' or 'linear' or 'quadratic' or 'tukey'
:param make_flat: Make the flat images
:returns: Generator of images
See also
:py:func:`rascil.processing_components.image.image_gather_facets`
:py:func:`rascil.processing_components.image.image_scatter_facets`
"""
assert image_is_canonical(im)
nchan, npol, ny, nx = im.shape
assert facets <= ny, "Cannot have more raster elements than pixels"
assert facets <= nx, "Cannot have more raster elements than pixels"
assert facets >= 1, "Facets cannot be zero or less"
assert overlap >= 0, "Overlap must be zero or greater"
if facets == 1:
yield im
else:
assert overlap < (nx // facets), "Overlap in facets is too large"
assert overlap < (ny // facets), "Overlap in facets is too large"
# Step between facets
sx = nx // facets + overlap
sy = ny // facets + overlap
# Size of facet
dx = sx + overlap
dy = sy + overlap
# Step between facets
sx = nx // facets + overlap
sy = ny // facets + overlap
# Size of facet
dx = nx // facets + 2 * overlap
dy = nx // facets + 2 * overlap
def taper_linear():
t = numpy.ones(dx)
ramp = numpy.arange(0, overlap).astype(float) / float(overlap)
t[:overlap] = ramp
t[(dx - overlap):dx] = 1.0 - ramp
result = numpy.outer(t, t)
return result
def taper_quadratic():
t = numpy.ones(dx)
ramp = numpy.arange(0, overlap).astype(float) / float(overlap)
quadratic_ramp = numpy.ones(overlap)
quadratic_ramp[0:overlap // 2] = 2.0 * ramp[0:overlap // 2]**2
quadratic_ramp[overlap //
2:] = 1 - 2.0 * ramp[overlap // 2:0:-1]**2
t[:overlap] = quadratic_ramp
t[(dx - overlap):dx] = 1.0 - quadratic_ramp
result = numpy.outer(t, t)
return result
def taper_tukey():
xs = numpy.arange(dx) / float(dx)
r = 2 * overlap / dx
t = [tukey_filter(x, r) for x in xs]
result = numpy.outer(t, t)
return result
i = 0
for fy in range(facets):
y = ny // 2 + sy * (fy - facets // 2) - overlap // 2
for fx in range(facets):
x = nx // 2 + sx * (fx - facets // 2) - overlap // 2
if (x >= 0) and (x + dx) <= nx and (y >= 0) and (y + dy) <= ny:
# Adjust WCS
wcs = im.wcs.deepcopy()
wcs.wcs.crpix[0] -= x
wcs.wcs.crpix[1] -= y
# yield image from slice (reference!)
subim = create_image_from_array(
im.data[..., y:y + dy, x:x + dx], wcs,
im.polarisation_frame)
if overlap > 0 and make_flat:
flat = create_empty_image_like(subim)
if taper == 'linear':
flat.data[..., :, :] = taper_linear()
elif taper == 'quadratic':
flat.data[..., :, :] = taper_quadratic()
elif taper == 'tukey':
flat.data[..., :, :] = taper_tukey()
else:
flat.data[...] = 1.0
yield flat
else:
yield subim
i += 1
def convert_image_to_kernel(im: Image, oversampling, kernelwidth):
""" Convert an image to a griddata kernel
:param im: Image to be converted
:param oversampling: Oversampling of Image spatially
:param kernelwidth: Kernel width to be extracted
:return: numpy.ndarray[nchan, npol, oversampling, oversampling, kernelwidth, kernelwidth]
"""
naxis = len(im.shape)
assert numpy.max(numpy.abs(im.data)) > 0.0, "Image is empty"
nchan, npol, ny, nx = im.shape
assert nx % oversampling == 0, "Oversampling must be even"
assert ny % oversampling == 0, "Oversampling must be even"
assert kernelwidth < nx and kernelwidth < ny, "Specified kernel width %d too large"
assert im.wcs.wcs.ctype[
0] == 'UU', 'Axis type %s inappropriate for construction of kernel' % im.wcs.wcs.ctype[
0]
assert im.wcs.wcs.ctype[
1] == 'VV', 'Axis type %s inappropriate for construction of kernel' % im.wcs.wcs.ctype[
1]
newwcs = WCS(naxis=naxis + 2)
for axis in range(2):
newwcs.wcs.ctype[axis] = im.wcs.wcs.ctype[axis]
newwcs.wcs.crpix[axis] = kernelwidth // 2
newwcs.wcs.crval[axis] = 0.0
newwcs.wcs.cdelt[axis] = im.wcs.wcs.cdelt[axis] * oversampling
newwcs.wcs.ctype[axis + 2] = im.wcs.wcs.ctype[axis]
newwcs.wcs.crpix[axis + 2] = oversampling // 2
newwcs.wcs.crval[axis + 2] = 0.0
newwcs.wcs.cdelt[axis + 2] = im.wcs.wcs.cdelt[axis]
# Now do Stokes and Frequency
newwcs.wcs.ctype[axis + 4] = im.wcs.wcs.ctype[axis + 2]
newwcs.wcs.crpix[axis + 4] = im.wcs.wcs.crpix[axis + 2]
newwcs.wcs.crval[axis + 4] = im.wcs.wcs.crval[axis + 2]
newwcs.wcs.cdelt[axis + 4] = im.wcs.wcs.cdelt[axis + 2]
newdata_shape = [
nchan, npol, oversampling, oversampling, kernelwidth, kernelwidth
]
newdata = numpy.zeros(newdata_shape, dtype=im.data.dtype)
assert oversampling * kernelwidth < ny
assert oversampling * kernelwidth < nx
ystart = ny // 2 - oversampling * kernelwidth // 2
xstart = nx // 2 - oversampling * kernelwidth // 2
yend = ny // 2 + oversampling * kernelwidth // 2
xend = nx // 2 + oversampling * kernelwidth // 2
for chan in range(nchan):
for pol in range(npol):
for y in range(oversampling):
slicey = slice(yend + y, ystart + y, -oversampling)
for x in range(oversampling):
slicex = slice(xend + x, xstart + x, -oversampling)
newdata[chan, pol, y, x, ...] = im.data[chan, pol, slicey,
slicex]
return create_image_from_array(newdata,
newwcs,
polarisation_frame=im.polarisation_frame)
filename = 'low_screen_%.1fr0_%.3frate.fits' % (r0, rate)
my_screens = ArScreens.ArScreens(n, m, pscale, rate, lowparamcube,
alpha_mag)
my_screens.run(ntimes, verbose=True)
from astropy.wcs import WCS
nfreqwin = 1
npol = 1
frequency = [1e8]
channel_bandwidth = [0.1e8]
w = WCS(naxis=4)
cellsize = pscale
w.wcs.cdelt = [cellsize, cellsize, 1.0 / rate, channel_bandwidth[0]]
w.wcs.crpix = [npixel // 2 + 1, npixel // 2 + 1, ntimes // 2 + 1, 1.0]
w.wcs.ctype = ['XX', 'YY', 'TIME', 'FREQ']
w.wcs.crval = [0.0, 0.0, 0.0, frequency[0]]
w.naxis = 4
w.wcs.radesys = 'ICRS'
w.wcs.equinox = 2000.0
data = numpy.zeros([nfreqwin, ntimes, npixel, npixel])
for i, screen in enumerate(my_screens.screens[0]):
data[:, i, ...] = screen[numpy.newaxis, ...]
im = create_image_from_array(
data, wcs=w, polarisation_frame=PolarisationFrame("stokesI"))
print(im)
export_image_to_fits(im, filename)
def create_test_image_from_s3(npixel=16384,
polarisation_frame=PolarisationFrame("stokesI"),
cellsize=0.000015,
frequency=numpy.array([1e8]),
channel_bandwidth=numpy.array([1e6]),
phasecentre=None,
fov=20,
flux_limit=1e-3) -> Image:
"""Create MID test image from S3
The input catalog was generated at http://s-cubed.physics.ox.ac.uk/s3_sex using the following query::
Database: s3_sex
SQL: select * from Galaxies where (pow(10,itot_151)*1000 > 1.0) and (right_ascension between -5 and 5) and (declination between -5 and 5);;
Number of rows returned: 29966
For frequencies < 610MHz, there are three tables to use::
data/models/S3_151MHz_10deg.csv, use fov=10
data/models/S3_151MHz_20deg.csv, use fov=20
data/models/S3_151MHz_40deg.csv, use fov=40
For frequencies > 610MHz, there are three tables:
data/models/S3_1400MHz_1mJy_10deg.csv, use flux_limit>= 1e-3
data/models/S3_1400MHz_100uJy_10deg.csv, use flux_limit < 1e-3
data/models/S3_1400MHz_1mJy_18deg.csv, use flux_limit>= 1e-3
data/models/S3_1400MHz_100uJy_18deg.csv, use flux_limit < 1e-3
The component spectral index is calculated from the 610MHz and 151MHz or 1400MHz and 610MHz, and then calculated
for the specified frequencies.
If polarisation_frame is not stokesI then the image will a polarised axis but the values will be zero.
:param npixel: Number of pixels
:param polarisation_frame: Polarisation frame (default PolarisationFrame("stokesI"))
:param cellsize: cellsize in radians
:param frequency:
:param channel_bandwidth: Channel width (Hz)
:param phasecentre: phasecentre (SkyCoord)
:param fov: fov 10 | 20 | 40
:param flux_limit: Minimum flux (Jy)
:return: Image
"""
check_data_directory()
ras = []
decs = []
fluxes = []
if phasecentre is None:
phasecentre = SkyCoord(ra=+180.0 * u.deg,
dec=-60.0 * u.deg,
frame='icrs',
equinox='J2000')
if polarisation_frame is None:
polarisation_frame = PolarisationFrame("stokesI")
npol = polarisation_frame.npol
nchan = len(frequency)
shape = [nchan, npol, npixel, npixel]
w = WCS(naxis=4)
# The negation in the longitude is needed by definition of RA, DEC
w.wcs.cdelt = [
-cellsize * 180.0 / numpy.pi, cellsize * 180.0 / numpy.pi, 1.0,
channel_bandwidth[0]
]
w.wcs.crpix = [npixel // 2 + 1, npixel // 2 + 1, 1.0, 1.0]
w.wcs.ctype = ["RA---SIN", "DEC--SIN", 'STOKES', 'FREQ']
w.wcs.crval = [phasecentre.ra.deg, phasecentre.dec.deg, 1.0, frequency[0]]
w.naxis = 4
w.wcs.radesys = 'ICRS'
w.wcs.equinox = 2000.0
model = create_image_from_array(numpy.zeros(shape),
w,
polarisation_frame=polarisation_frame)
if numpy.max(frequency) > 6.1E8:
if fov > 10:
fovstr = '18'
else:
fovstr = '10'
if flux_limit >= 1e-3:
csvfilename = rascil_data_path('models/S3_1400MHz_1mJy_%sdeg.csv' %
fovstr)
else:
csvfilename = rascil_data_path(
'models/S3_1400MHz_100uJy_%sdeg.csv' % fovstr)
log.info('create_test_image_from_s3: Reading S3 sources from %s ' %
csvfilename)
else:
assert fov in [
10, 20, 40
], "Field of view invalid: use one of %s" % ([10, 20, 40])
csvfilename = rascil_data_path('models/S3_151MHz_%ddeg.csv' % (fov))
log.info('create_test_image_from_s3: Reading S3 sources from %s ' %
csvfilename)
with open(csvfilename) as csvfile:
readCSV = csv.reader(csvfile, delimiter=',')
r = 0
for row in readCSV:
# Skip first row
if r > 0:
ra = float(row[4]) + phasecentre.ra.deg
dec = float(row[5]) + phasecentre.dec.deg
if numpy.max(frequency) > 6.1E8:
alpha = (float(row[11]) - float(row[10])) / numpy.log10(
1400.0 / 610.0)
flux = numpy.power(10, float(row[10])) * numpy.power(
frequency / 1.4e9, alpha)
else:
alpha = (float(row[10]) - float(row[9])) / numpy.log10(
610.0 / 151.0)
flux = numpy.power(10, float(row[9])) * numpy.power(
frequency / 1.51e8, alpha)
if numpy.max(flux) > flux_limit:
ras.append(ra)
decs.append(dec)
fluxes.append(flux)
r += 1
csvfile.close()
assert len(fluxes) > 0, "No sources found above flux limit %s" % flux_limit
log.info('create_test_image_from_s3: %d sources read' % (len(fluxes)))
p = w.sub(2).wcs_world2pix(numpy.array(ras), numpy.array(decs), 1)
fluxes = numpy.array(fluxes)
total_flux = numpy.sum(fluxes)
ip = numpy.round(p).astype('int')
ok = numpy.where((0 <= ip[0, :]) & (npixel > ip[0, :]) & (0 <= ip[1, :])
& (npixel > ip[1, :]))[0]
ps = ip[:, ok]
fluxes = fluxes[ok]
actual_flux = numpy.sum(fluxes)
log.info('create_test_image_from_s3: %d sources inside the image' %
(ps.shape[1]))
log.info(
'create_test_image_from_s3: average channel flux in S3 model = %.3f, actual average channel flux in '
'image = %.3f' %
(total_flux / float(nchan), actual_flux / float(nchan)))
for chan in range(nchan):
for iflux, flux in enumerate(fluxes):
model.data[chan, 0, ps[1, iflux], ps[0, iflux]] = flux[chan]
return model
def deconvolve_cube(dirty: Image,
psf: Image,
prefix='',
**kwargs) -> (Image, Image):
""" Clean using a variety of algorithms
The algorithms available are:
hogbom: Hogbom CLEAN See: Hogbom CLEAN A&A Suppl, 15, 417, (1974)
hogbom-complex: Complex Hogbom CLEAN of stokesIQUV image
msclean: MultiScale CLEAN See: Cornwell, T.J., Multiscale CLEAN (IEEE Journal of Selected Topics in Sig Proc,
2008 vol. 2 pp. 793-801)
mfsmsclean, msmfsclean, mmclean: MultiScale Multi-Frequency See: U. Rau and T. J. Cornwell,
“A multi-scale multi-frequency deconvolution algorithm for synthesis imaging in radio interferometry,” A&A 532,
A71 (2011).
For example::
comp, residual = deconvolve_cube(dirty, psf, niter=1000, gain=0.7, algorithm='msclean',
scales=[0, 3, 10, 30], threshold=0.01)
For the MFS clean, the psf must have number of channels >= 2 * nmoment
:param dirty: Image dirty image
:param psf: Image Point Spread Function
:param window_shape: Window image (Bool) - clean where True
:param mask: Window in the form of an image, overrides window_shape
:param algorithm: Cleaning algorithm: 'msclean'|'hogbom'|'mfsmsclean'
:param gain: loop gain (float) 0.7
:param threshold: Clean threshold (0.0)
:param fractional_threshold: Fractional threshold (0.01)
:param scales: Scales (in pixels) for multiscale ([0, 3, 10, 30])
:param nmoment: Number of frequency moments (default 3)
:param findpeak: Method of finding peak in mfsclean: 'Algorithm1'|'ASKAPSoft'|'CASA'|'RASCIL', Default is RASCIL.
:return: component image, residual image
See also
:py:func:`rascil.processing_components.arrays.cleaners.hogbom`
:py:func:`rascil.processing_components.arrays.cleaners.hogbom_complex`
:py:func:`rascil.processing_components.arrays.cleaners.msclean`
:py:func:`rascil.processing_components.arrays.cleaners.msmfsclean`
"""
assert isinstance(dirty, Image), dirty
assert image_is_canonical(dirty)
assert isinstance(psf, Image), psf
assert image_is_canonical(psf)
window_shape = get_parameter(kwargs, 'window_shape', None)
if window_shape == 'quarter':
log.info("deconvolve_cube %s: window is inner quarter" % prefix)
qx = dirty.shape[3] // 4
qy = dirty.shape[2] // 4
window = numpy.zeros_like(dirty.data)
window[..., (qy + 1):3 * qy, (qx + 1):3 * qx] = 1.0
log.info(
'deconvolve_cube %s: Cleaning inner quarter of each sky plane' %
prefix)
elif window_shape == 'no_edge':
edge = get_parameter(kwargs, 'window_edge', 16)
nx = dirty.shape[3]
ny = dirty.shape[2]
window = numpy.zeros_like(dirty.data)
window[..., (edge + 1):(ny - edge), (edge + 1):(nx - edge)] = 1.0
log.info(
'deconvolve_cube %s: Window omits %d-pixel edge of each sky plane'
% (prefix, edge))
elif window_shape is None:
log.info("deconvolve_cube %s: Cleaning entire image" % prefix)
window = None
else:
raise ValueError("Window shape %s is not recognized" % window_shape)
mask = get_parameter(kwargs, 'mask', None)
if isinstance(mask, Image):
if window is not None:
log.warning(
'deconvolve_cube %s: Overriding window_shape with mask image' %
(prefix))
window = mask.data
psf_support = get_parameter(kwargs, 'psf_support',
max(dirty.shape[2] // 2, dirty.shape[3] // 2))
if (psf_support <= psf.shape[2] // 2) and (
(psf_support <= psf.shape[3] // 2)):
centre = [psf.shape[2] // 2, psf.shape[3] // 2]
psf.data = psf.data[..., (centre[0] - psf_support):(centre[0] +
psf_support),
(centre[1] - psf_support):(centre[1] +
psf_support)]
log.info('deconvolve_cube %s: PSF support = +/- %d pixels' %
(prefix, psf_support))
log.info('deconvolve_cube %s: PSF shape %s' %
(prefix, str(psf.data.shape)))
algorithm = get_parameter(kwargs, 'algorithm', 'msclean')
if algorithm == 'msclean':
log.info(
"deconvolve_cube %s: Multi-scale clean of each polarisation and channel separately"
% prefix)
gain = get_parameter(kwargs, 'gain', 0.7)
assert 0.0 < gain < 2.0, "Loop gain must be between 0 and 2"
thresh = get_parameter(kwargs, 'threshold', 0.0)
assert thresh >= 0.0
niter = get_parameter(kwargs, 'niter', 100)
assert niter > 0
scales = get_parameter(kwargs, 'scales', [0, 3, 10, 30])
fracthresh = get_parameter(kwargs, 'fractional_threshold', 0.01)
assert 0.0 < fracthresh < 1.0
comp_array = numpy.zeros_like(dirty.data)
residual_array = numpy.zeros_like(dirty.data)
for channel in range(dirty.data.shape[0]):
for pol in range(dirty.data.shape[1]):
if psf.data[channel, pol, :, :].max():
log.info(
"deconvolve_cube %s: Processing pol %d, channel %d" %
(prefix, pol, channel))
if window is None:
comp_array[channel, pol, :, :], residual_array[channel, pol, :, :] = \
msclean(dirty.data[channel, pol, :, :], psf.data[channel, pol, :, :],
None, gain, thresh, niter, scales, fracthresh, prefix)
else:
comp_array[channel, pol, :, :], residual_array[channel, pol, :, :] = \
msclean(dirty.data[channel, pol, :, :], psf.data[channel, pol, :, :],
window[channel, pol, :, :], gain, thresh, niter, scales, fracthresh,
prefix)
else:
log.info(
"deconvolve_cube %s: Skipping pol %d, channel %d" %
(prefix, pol, channel))
comp_image = create_image_from_array(comp_array, dirty.wcs,
dirty.polarisation_frame)
residual_image = create_image_from_array(residual_array, dirty.wcs,
dirty.polarisation_frame)
elif algorithm == 'msmfsclean' or algorithm == 'mfsmsclean' or algorithm == 'mmclean':
findpeak = get_parameter(kwargs, "findpeak", 'RASCIL')
log.info(
"deconvolve_cube %s: Multi-scale multi-frequency clean of each polarisation separately"
% prefix)
nmoment = get_parameter(kwargs, "nmoment", 3)
assert nmoment >= 1, "Number of frequency moments must be greater than or equal to one"
nchan = dirty.shape[0]
assert nchan > 2 * (nmoment -
1), "Require nchan %d > 2 * (nmoment %d - 1)" % (
nchan, 2 * (nmoment - 1))
dirty_taylor = calculate_image_frequency_moments(dirty,
nmoment=nmoment)
if nmoment > 1:
psf_taylor = calculate_image_frequency_moments(psf,
nmoment=2 * nmoment)
else:
psf_taylor = calculate_image_frequency_moments(psf, nmoment=1)
psf_peak = numpy.max(psf_taylor.data)
dirty_taylor.data /= psf_peak
psf_taylor.data /= psf_peak
log.info("deconvolve_cube %s: Shape of Dirty moments image %s" %
(prefix, str(dirty_taylor.shape)))
log.info("deconvolve_cube %s: Shape of PSF moments image %s" %
(prefix, str(psf_taylor.shape)))
gain = get_parameter(kwargs, 'gain', 0.7)
assert 0.0 < gain < 2.0, "Loop gain must be between 0 and 2"
thresh = get_parameter(kwargs, 'threshold', 0.0)
assert thresh >= 0.0
niter = get_parameter(kwargs, 'niter', 100)
assert niter > 0
scales = get_parameter(kwargs, 'scales', [0, 3, 10, 30])
fracthresh = get_parameter(kwargs, 'fractional_threshold', 0.1)
assert 0.0 < fracthresh < 1.0
comp_array = numpy.zeros(dirty_taylor.data.shape)
residual_array = numpy.zeros(dirty_taylor.data.shape)
for pol in range(dirty_taylor.data.shape[1]):
# Always use the Stokes I PSF
if psf_taylor.data[0, 0, :, :].max():
log.info("deconvolve_cube %s: Processing pol %d" %
(prefix, pol))
if window is None:
comp_array[:, pol, :, :], residual_array[:, pol, :, :] = \
msmfsclean(dirty_taylor.data[:, pol, :, :], psf_taylor.data[:, 0, :, :],
None, gain, thresh, niter, scales, fracthresh, findpeak, prefix)
else:
log.info(
'deconvolve_cube %s: Clean window has %d valid pixels'
% (prefix, int(numpy.sum(window[0, pol]))))
comp_array[:, pol, :, :], residual_array[:, pol, :, :] = \
msmfsclean(dirty_taylor.data[:, pol, :, :], psf_taylor.data[:, 0, :, :],
window[0, pol, :, :], gain, thresh, niter, scales, fracthresh,
findpeak, prefix)
else:
log.info("deconvolve_cube %s: Skipping pol %d" % (prefix, pol))
comp_image = create_image_from_array(comp_array, dirty_taylor.wcs,
dirty.polarisation_frame)
residual_image = create_image_from_array(residual_array,
dirty_taylor.wcs,
dirty.polarisation_frame)
return_moments = get_parameter(kwargs, "return_moments", False)
if not return_moments:
log.info("deconvolve_cube %s: calculating spectral cubes" % prefix)
comp_image = calculate_image_from_frequency_moments(
dirty, comp_image)
residual_image = calculate_image_from_frequency_moments(
dirty, residual_image)
else:
log.info("deconvolve_cube %s: constructed moment cubes" % prefix)
elif algorithm == 'hogbom':
log.info(
"deconvolve_cube %s: Hogbom clean of each polarisation and channel separately"
% prefix)
gain = get_parameter(kwargs, 'gain', 0.1)
assert 0.0 < gain < 2.0, "Loop gain must be between 0 and 2"
thresh = get_parameter(kwargs, 'threshold', 0.0)
assert thresh >= 0.0
niter = get_parameter(kwargs, 'niter', 100)
assert niter > 0
fracthresh = get_parameter(kwargs, 'fractional_threshold', 0.1)
assert 0.0 < fracthresh < 1.0
comp_array = numpy.zeros(dirty.data.shape)
residual_array = numpy.zeros(dirty.data.shape)
for channel in range(dirty.data.shape[0]):
for pol in range(dirty.data.shape[1]):
if psf.data[channel, pol, :, :].max():
log.info(
"deconvolve_cube %s: Processing pol %d, channel %d" %
(prefix, pol, channel))
if window is None:
comp_array[channel, pol, :, :], residual_array[channel, pol, :, :] = \
hogbom(dirty.data[channel, pol, :, :], psf.data[channel, pol, :, :],
None, gain, thresh, niter, fracthresh, prefix)
else:
comp_array[channel, pol, :, :], residual_array[channel, pol, :, :] = \
hogbom(dirty.data[channel, pol, :, :], psf.data[channel, pol, :, :],
window[channel, pol, :, :], gain, thresh, niter, fracthresh, prefix)
else:
log.info(
"deconvolve_cube %s: Skipping pol %d, channel %d" %
(prefix, pol, channel))
comp_image = create_image_from_array(comp_array, dirty.wcs,
dirty.polarisation_frame)
residual_image = create_image_from_array(residual_array, dirty.wcs,
dirty.polarisation_frame)
elif algorithm == 'hogbom-complex':
log.info(
"deconvolve_cube_complex: Hogbom-complex clean of each polarisation and channel separately"
)
gain = get_parameter(kwargs, 'gain', 0.1)
assert 0.0 < gain < 2.0, "Loop gain must be between 0 and 2"
thresh = get_parameter(kwargs, 'threshold', 0.0)
assert thresh >= 0.0
niter = get_parameter(kwargs, 'niter', 100)
assert niter > 0
fracthresh = get_parameter(kwargs, 'fractional_threshold', 0.1)
assert 0.0 <= fracthresh < 1.0
comp_array = numpy.zeros(dirty.data.shape)
residual_array = numpy.zeros(dirty.data.shape)
for channel in range(dirty.data.shape[0]):
for pol in range(dirty.data.shape[1]):
if pol == 0 or pol == 3:
if psf.data[channel, pol, :, :].max():
log.info(
"deconvolve_cube_complex: Processing pol %d, channel %d"
% (pol, channel))
if window is None:
comp_array[channel, pol, :, :], residual_array[channel, pol, :, :] = \
hogbom(dirty.data[channel, pol, :, :], psf.data[channel, pol, :, :],
None, gain, thresh, niter, fracthresh)
else:
comp_array[channel, pol, :, :], residual_array[channel, pol, :, :] = \
hogbom(dirty.data[channel, pol, :, :], psf.data[channel, pol, :, :],
window[channel, pol, :, :], gain, thresh, niter, fracthresh)
else:
log.info(
"deconvolve_cube_complex: Skipping pol %d, channel %d"
% (pol, channel))
if pol == 1:
if psf.data[channel, 1:2, :, :].max():
log.info(
"deconvolve_cube_complex: Processing pol 1 and 2, channel %d"
% (channel))
if window is None:
comp_array[channel, 1, :, :], comp_array[
channel, 2, :, :], residual_array[
channel, 1, :, :], residual_array[
channel, 2, :, :] = hogbom_complex(
dirty.data[channel, 1, :, :],
dirty.data[channel, 2, :, :],
psf.data[channel, 1, :, :],
psf.data[channel, 2, :, :], None,
gain, thresh, niter, fracthresh)
else:
comp_array[channel, 1, :, :], comp_array[
channel, 2, :, :], residual_array[
channel, 1, :, :], residual_array[
channel, 2, :, :] = hogbom_complex(
dirty.data[channel, 1, :, :],
dirty.data[channel, 2, :, :],
psf.data[channel, 1, :, :],
psf.data[channel, 2, :, :],
window[channel, pol, :, :], gain,
thresh, niter, fracthresh)
else:
log.info(
"deconvolve_cube_complex: Skipping pol 1 and 2, channel %d"
% (channel))
if pol == 2:
continue
comp_image = create_image_from_array(
comp_array,
dirty.wcs,
polarisation_frame=PolarisationFrame('stokesIQUV'))
residual_image = create_image_from_array(
residual_array,
dirty.wcs,
polarisation_frame=PolarisationFrame('stokesIQUV'))
else:
raise ValueError('deconvolve_cube %s: Unknown algorithm %s' %
(prefix, algorithm))
return comp_image, residual_image
def create_low_test_skymodel_from_gleam(npixel=512,
polarisation_frame=PolarisationFrame(
"stokesI"),
cellsize=0.000015,
frequency=numpy.array([1e8]),
channel_bandwidth=numpy.array([1e6]),
phasecentre=None,
kind='cubic',
applybeam=True,
flux_limit=0.1,
flux_max=numpy.inf,
flux_threshold=1.0,
insert_method='Nearest',
telescope='LOW') -> SkyModel:
"""Create LOW test skymodel from the GLEAM survey
Stokes I is estimated from a cubic spline fit to the measured fluxes. The polarised flux is always zero.
See http://www.mwatelescope.org/science/gleam-survey The catalog is available from Vizier.
VIII/100 GaLactic and Extragalactic All-sky MWA survey (Hurley-Walker+, 2016)
GaLactic and Extragalactic All-sky Murchison Wide Field Array (GLEAM) survey. I: A low-frequency extragalactic
catalogue. Hurley-Walker N., et al., Mon. Not. R. Astron. Soc., 464, 1146-1167 (2017), 2017MNRAS.464.1146H
:param telescope:
:param npixel: Number of pixels
:param polarisation_frame: Polarisation frame (default PolarisationFrame("stokesI"))
:param cellsize: cellsize in radians
:param frequency:
:param channel_bandwidth: Channel width (Hz)
:param phasecentre: phasecentre (SkyCoord)
:param kind: Kind of interpolation (see scipy.interpolate.interp1d) Default: cubic
:param applybeam: Apply the primary beam?
:param flux_limit: Weakest component
:param flux_max: Maximum strength component to be included in components
:param flux_threshold: Split between components (brighter) and image (weaker)
:param insert_method: Nearest | PSWF | Lanczos
:return:
:return: SkyModel
"""
check_data_directory()
if phasecentre is None:
phasecentre = SkyCoord(ra=+15.0 * u.deg,
dec=-35.0 * u.deg,
frame='icrs',
equinox='J2000')
radius = npixel * cellsize
sc = create_low_test_skycomponents_from_gleam(
flux_limit=flux_limit,
polarisation_frame=polarisation_frame,
frequency=frequency,
phasecentre=phasecentre,
kind=kind,
radius=radius)
sc = filter_skycomponents_by_flux(sc, flux_max=flux_max)
if polarisation_frame is None:
polarisation_frame = PolarisationFrame("stokesI")
npol = polarisation_frame.npol
nchan = len(frequency)
shape = [nchan, npol, npixel, npixel]
w = WCS(naxis=4)
# The negation in the longitude is needed by definition of RA, DEC
w.wcs.cdelt = [
-cellsize * 180.0 / numpy.pi, cellsize * 180.0 / numpy.pi, 1.0,
channel_bandwidth[0]
]
w.wcs.crpix = [npixel // 2 + 1, npixel // 2 + 1, 1.0, 1.0]
w.wcs.ctype = ["RA---SIN", "DEC--SIN", 'STOKES', 'FREQ']
w.wcs.crval = [phasecentre.ra.deg, phasecentre.dec.deg, 1.0, frequency[0]]
w.naxis = 4
w.wcs.radesys = 'ICRS'
w.wcs.equinox = 2000.0
model = create_image_from_array(numpy.zeros(shape),
w,
polarisation_frame=polarisation_frame)
if applybeam:
beam = create_pb(model, telescope=telescope, use_local=False)
sc = apply_beam_to_skycomponent(sc, beam)
weaksc = filter_skycomponents_by_flux(sc, flux_max=flux_threshold)
brightsc = filter_skycomponents_by_flux(sc, flux_min=flux_threshold)
model = insert_skycomponent(model, weaksc, insert_method=insert_method)
log.info(
'create_low_test_skymodel_from_gleam: %d bright sources above flux threshold %.3f, %d weak sources below '
% (len(brightsc), flux_threshold, len(weaksc)))
return SkyModel(components=brightsc,
image=model,
mask=None,
gaintable=None)
def create_low_test_image_from_gleam(npixel=512,
polarisation_frame=PolarisationFrame(
"stokesI"),
cellsize=0.000015,
frequency=numpy.array([1e8]),
channel_bandwidth=numpy.array([1e6]),
phasecentre=None,
kind='cubic',
applybeam=False,
flux_limit=0.1,
flux_max=numpy.inf,
flux_min=-numpy.inf,
radius=None,
insert_method='Nearest') -> Image:
"""Create LOW test image from the GLEAM survey
Stokes I is estimated from a cubic spline fit to the measured fluxes. The polarised flux is always zero.
See http://www.mwatelescope.org/science/gleam-survey The catalog is available from Vizier.
VIII/100 GaLactic and Extragalactic All-sky MWA survey (Hurley-Walker+, 2016)
GaLactic and Extragalactic All-sky Murchison Wide Field Array (GLEAM) survey. I: A low-frequency extragalactic
catalogue. Hurley-Walker N., et al., Mon. Not. R. Astron. Soc., 464, 1146-1167 (2017), 2017MNRAS.464.1146H
:param npixel: Number of pixels
:param polarisation_frame: Polarisation frame (default PolarisationFrame("stokesI"))
:param cellsize: cellsize in radians
:param frequency:
:param channel_bandwidth: Channel width (Hz)
:param phasecentre: phasecentre (SkyCoord)
:param kind: Kind of interpolation (see scipy.interpolate.interp1d) Default: linear
:return: Image
"""
check_data_directory()
if phasecentre is None:
phasecentre = SkyCoord(ra=+15.0 * u.deg,
dec=-35.0 * u.deg,
frame='icrs',
equinox='J2000')
if radius is None:
radius = npixel * cellsize / numpy.sqrt(2.0)
sc = create_low_test_skycomponents_from_gleam(
flux_limit=flux_limit,
polarisation_frame=polarisation_frame,
frequency=frequency,
phasecentre=phasecentre,
kind=kind,
radius=radius)
sc = filter_skycomponents_by_flux(sc, flux_min=flux_min, flux_max=flux_max)
if polarisation_frame is None:
polarisation_frame = PolarisationFrame("stokesI")
npol = polarisation_frame.npol
nchan = len(frequency)
shape = [nchan, npol, npixel, npixel]
w = WCS(naxis=4)
# The negation in the longitude is needed by definition of RA, DEC
w.wcs.cdelt = [
-cellsize * 180.0 / numpy.pi, cellsize * 180.0 / numpy.pi, 1.0,
channel_bandwidth[0]
]
w.wcs.crpix = [npixel // 2 + 1, npixel // 2 + 1, 1.0, 1.0]
w.wcs.ctype = ["RA---SIN", "DEC--SIN", 'STOKES', 'FREQ']
w.wcs.crval = [phasecentre.ra.deg, phasecentre.dec.deg, 1.0, frequency[0]]
w.naxis = 4
w.wcs.radesys = 'ICRS'
w.wcs.equinox = 2000.0
model = create_image_from_array(numpy.zeros(shape),
w,
polarisation_frame=polarisation_frame)
model = insert_skycomponent(model, sc, insert_method=insert_method)
if applybeam:
beam = create_pb(model, telescope='LOW', use_local=False)
model.data[...] *= beam.data[...]
return model
