def get_psf_secondpeak_old(fn, neighborhood_size=5, threshold=0.01):
from scipy import ndimage
from scipy.ndimage import filters
if fn.endswith('fits'):
data = fits.getdata(fn)
else:
from casatools import image
ia = image()
ia.open(fn)
data = ia.getchunk()
ia.close()
if data.ndim > 2:
data = data.squeeze()
if data.ndim > 2:
data = data[0, :, :]
data_max = filters.maximum_filter(data, neighborhood_size)
maxima = (data == data_max)
data_min = filters.minimum_filter(data, neighborhood_size)
diff = ((data_max - data_min) > threshold)
maxima[diff == 0] = 0
labeled, num_objects = ndimage.label(maxima)
slices = ndimage.find_objects(labeled)
pkval = [data[slc].max() for slc in slices]
if len(pkval) >= 2:
secondmax = sorted(pkval)[-2]
return secondmax
else:
return np.nan
def assert_header_correct(casa_filename):
fits_filename = tempfile.mktemp()
# Use CASA to convert back to FITS and use that header as the reference
ia = image()
ia.open(casa_filename)
ia.tofits(fits_filename, stokeslast=False)
ia.done()
ia.close()
# Parse header with WCS - for the purposes of this function
# we are not interested in keywords/values not in WCS
reference_wcs = WCS(fits_filename)
reference_header = reference_wcs.to_header()
# Now use our wcs_casa2astropy function to create the header and compare
# the results.
desc = getdesc(casa_filename)
actual_wcs = wcs_casa2astropy(desc['_keywords_']['coords'])
actual_header = actual_wcs.to_header()
assert sorted(actual_header) == sorted(reference_header)
for key in reference_header:
if isinstance(actual_header[key], str):
assert actual_header[key] == reference_header[key]
else:
assert_allclose(actual_header[key], reference_header[key])
def data_vda_beams_image(tmp_path):
d, h = prepare_adv_data()
d, h = transpose(d, h, [2, 0, 1])
d, h = transpose(d, h, [2, 1, 0])
h['BUNIT'] = ' Jy / beam '
del h['BMAJ'], h['BMIN'], h['BPA']
beams = prepare_4_beams()
hdul = fits.HDUList([fits.PrimaryHDU(data=d, header=h), beams])
hdul.writeto(tmp_path / 'vda_beams.fits')
from casatools import image
ia = image()
ia.fromfits(infile=tmp_path / 'vda_beams.fits',
outfile=tmp_path / 'vda_beams.image',
overwrite=True)
for (bmaj, bmin, bpa, chan, pol) in beams.data:
ia.setrestoringbeam(major={
'unit': 'arcsec',
'value': bmaj
},
minor={
'unit': 'arcsec',
'value': bmin
},
pa={
'unit': 'deg',
'value': bpa
},
channel=chan,
polarization=pol)
ia.close()
return tmp_path / 'vda_beams.image'
def make_casa_testimage(infile, outname):
infile = str(infile)
outname = str(outname)
if not CASA_INSTALLED:
raise Exception("Attempted to make a CASA test image in a non-CASA "
"environment")
ia = image()
ia.fromfits(infile=infile, outfile=outname, overwrite=True)
ia.unlock()
ia.close()
ia.done()
cube = SpectralCube.read(infile)
if isinstance(cube, VaryingResolutionSpectralCube):
ia.open(outname)
# populate restoring beam emptily
ia.setrestoringbeam(
major={
'value': 1.0,
'unit': 'arcsec'
},
minor={
'value': 1.0,
'unit': 'arcsec'
},
pa={
'value': 90.0,
'unit': 'deg'
},
channel=len(cube.beams) - 1,
polarization=-1,
)
# populate each beam (hard assumption of 1 poln)
for channum, beam in enumerate(cube.beams):
casabdict = {
'major': {
'value': beam.major.to(u.deg).value,
'unit': 'deg'
},
'minor': {
'value': beam.minor.to(u.deg).value,
'unit': 'deg'
},
'positionangle': {
'value': beam.pa.to(u.deg).value,
'unit': 'deg'
}
}
ia.setrestoringbeam(beam=casabdict,
channel=channum,
polarization=0)
ia.unlock()
ia.close()
ia.done()
def image_summary(image):
"""
Print image summary and returns data and headers
"""
ia = tools.image()
ia.open(image)
# summarize image
dd = ia.summary()
npixx, npixy, nch, npol = dd['shape']
print('Image shape: {0}'.format(dd['shape']))
imvals = ia.getchunk(0, int(npixx))[:, :, 0, 0]
ra_inc = dd['incr'][0] * units.radian
dec_inc = dd['incr'][1] * units.radian
c = ia.coordmeasures()
direction = c['measure']['direction']
az = direction['m0']['value']
el = direction['m1']['value']
co0 = coordinates.SkyCoord(ra=np.degrees(az),
dec=np.degrees(el),
unit=(units.deg, units.deg))
co0_str = co0.to_string('hmsdms')
refpix = (dd['refpix'][0], dd['refpix'][1])
print(f'Coordinates at reference pixel {refpix} are {co0_str}')
ra_inc = dd['incr'][0] * units.radian
dec_inc = dd['incr'][1] * units.radian
print(
f'RA, Dec increment: {ra_inc.to(units.arcsecond).value}", {dec_inc.to(units.arcsecond).value}"'
)
peakx, peaky = np.where(imvals.max() == imvals)
peakx, peaky = peakx[0], peaky[0]
print('Peak SNR at pix ({0},{1}) = {2}'.format(peakx, peaky,
imvals.max() /
imvals.std()))
rapeak = co0.ra + (peakx - refpix[0]) * ra_inc
decpeak = co0.dec + (peaky - refpix[1]) * dec_inc
copeak = coordinates.SkyCoord(ra=rapeak, dec=decpeak).to_string('hmsdms')
print(f'Rough coordinates of peak pixel are {copeak}')
return imvals, dd, c
## Data summary
# import sdmpy
# file = ''
# sdm = sdmpy.SDM(file, use_xsd=False)
# scan = sdm.scan(1)
# tab = sdm['SpectralWindow']
# for row in tab:
# print(row.getchildren())
def estimate_noise(imagename, linefree_range,
noise_function=np.nanstd):
'''
Calculate the noise in a cube in line-free channels.
Should be run on *non-*pb corrected images so the spatial noise
is flat!
Estimate will also skip empty channels that do not have any data.
'''
if imagename.endswith(".image"):
try:
# CASA 6
import casatools
ia = casatools.image()
except ImportError:
try:
from taskinit import iatool
iatool = iatool()
except ImportError:
raise ImportError("Could not import CASA (casac).")
ia.open(imagename)
data = ia.getchunk().squeeze()
ia.close()
ia.done()
elif imagename.lower().endswith(".fits"):
cube = SpectralCube.read(imagename)
data = cube.filled_data[:].value
del cube
else:
raise ValueError("Unsure what kind of file this is.")
noise_data = np.empty((1,))
for chan_range in linefree_range:
for chan in range(chan_range[0], chan_range[1]):
if chan >= data.shape[-1]:
continue
# Skip empty channels
if np.isnan(data[..., chan]).all():
continue
if (data[..., chan] == 0.).all():
continue
valid_mask = np.logical_and(data[..., chan] != 0.,
np.isfinite(data[..., chan]))
noise_data = np.append(noise_data, data[..., chan][valid_mask])
return noise_function(noise_data)
def display(imname=None):
""" Show an image
"""
im = tools.image()
im.open(imname)
data = im.getchunk()
fig = plt.figure(figsize=(10, 8))
plt.imshow(data.squeeze(), origin='bottom', interpolation='nearest')
plt.show()
def __init__(self):
import os
from casatools import image, synthesisimager, synthesisdeconvolver
from casatools import synthesisnormalizer, iterbotsink
from casatasks import casalog
from casatasks.private.imagerhelpers.input_parameters import ImagerParameters
self.ia = image()
self.si = synthesisimager()
self.sd = synthesisdeconvolver()
self.sn = synthesisnormalizer()
self.ib = iterbotsink()
self.imagename = 'imtry'
print("Initialize")
os.system('rm -rf '+self.imagename+'.*')
self.niter=100
self.cycleniter=10
self.itercount=0
self.finished=False
self.stopcode=0
params = ImagerParameters(
msname ='point.ms',
spw='0:10',
imagename=self.imagename,
imsize=100,
cell='5.0arcsec',
specmode='mfs',
gridder='standard',
niter=self.niter,
cycleniter=self.cycleniter,
loopgain=0.05,
deconvolver='hogbom'
)
self.selpars = params.getSelPars()['ms0']
self.impars = params.getImagePars()['0']
self.gridpars = params.getGridPars()['0']
self.decpars = params.getDecPars()['0']
self.normpars = params.getNormPars()['0']
self.weightpars = params.getWeightPars()
self.iterpars = params.getIterPars()
self.iters=[]
self.peaks=[]
self.peaks_in_mask=[]
self.fluxes=[]
self.majcycle=[]
def test_wcs_casa2astropy_linear(tmp_path):
# Test that things work properly when the WCS coordinates aren't set
casa_filename = str(tmp_path / 'test.image')
data = np.random.random((3, 4, 5, 6, 7))
ia = image()
ia.fromarray(outfile=casa_filename, pixels=data, log=False)
ia.close()
assert_header_correct(casa_filename)
def test_casa_mask(data_adv, tmp_path):
# This tests the make_casa_mask function which can be used to create a mask
# file in an existing image.
cube = SpectralCube.read(data_adv)
mask_array = np.array([[True, False], [False, False], [True, True]])
bool_mask = BooleanArrayMask(mask=mask_array,
wcs=cube._wcs,
shape=cube.shape)
cube = cube.with_mask(bool_mask)
make_casa_mask(cube,
str(tmp_path / 'casa.mask'),
add_stokes=False,
append_to_image=False,
overwrite=True)
ia = casatools.image()
ia.open(str(tmp_path / 'casa.mask'))
casa_mask = ia.getchunk()
coords = ia.coordsys()
ia.unlock()
ia.close()
ia.done()
# Test masks
# Mask array is broadcasted to the cube shape. Mimic this, switch to ints,
# and transpose to match CASA image.
compare_mask = np.tile(mask_array, (4, 1, 1)).astype('int16').T
assert np.all(compare_mask == casa_mask)
# Test WCS info
# Convert back to an astropy wcs object so transforms are dealt with.
casa_wcs = coordsys_to_astropy_wcs(coords.torecord())
header = casa_wcs.to_header() # Invokes transform
# Compare some basic properties EXCLUDING the spectral axis
assert_allclose(cube.wcs.wcs.crval[:2], casa_wcs.wcs.crval[:2])
assert_allclose(cube.wcs.wcs.cdelt[:2], casa_wcs.wcs.cdelt[:2])
assert np.all(list(cube.wcs.wcs.cunit)[:2] == list(casa_wcs.wcs.cunit)[:2])
assert np.all(list(cube.wcs.wcs.ctype)[:2] == list(casa_wcs.wcs.ctype)[:2])
assert_allclose(cube.wcs.wcs.crpix, casa_wcs.wcs.crpix)
def getimage(image):
"""
Loads and summarize the image, Peak SNR and position
"""
# load image
ia = tools.image()
ia.open(image)
# summarize image
dd = ia.summary()
npixx, npixy, nch, npol = dd['shape']
print('Image shape: {0}'.format(dd['shape']))
imvals = ia.getchunk(0, int(npixx))[:, :, 0, 0]
return imvals
def __init__(self):
from casatasks.private.imagerhelpers.imager_base import PySynthesisImager
from casatasks.private.imagerhelpers.input_parameters import ImagerParameters
from casatools import image
from casatasks import casalog
import os
self.ia = image()
self.imagename = 'imtry'
self.niter=100
self.cycleniter=10
self.itercount=0
print("Initialize")
os.system('rm -rf '+self.imagename+'.*')
## (2) Set up Input Parameters
paramList = ImagerParameters(
msname ='point.ms',
spw='0:10',
imagename=self.imagename,
imsize=100,
cell='5.0arcsec',
specmode='mfs',
gridder='standard',
niter=self.niter,
cycleniter=self.cycleniter,
loopgain=0.05,
deconvolver='hogbom'
)
## (3) Construct the PySynthesisImager object, with all input parameters
self.imager = PySynthesisImager(params=paramList)
self.finished=False
self.stopcode=0
self.iters=[]
self.peaks=[]
self.peaks_in_mask=[]
self.fluxes=[]
self.majcycle=[]
def test_getdminfo(tmp_path, shape):
filename = str(tmp_path / 'test.image')
data = np.random.random(shape)
ia = image()
ia.fromarray(outfile=filename, pixels=data, log=False)
ia.close()
tb = table()
tb.open(filename)
reference = tb.getdminfo()
tb.close()
actual = getdminfo(filename)
# We include information about endian-ness in the dminfo but CASA doesn't
actual['*1'].pop('BIGENDIAN')
# The easiest way to compare the output is simply to compare the output
# from pformat (checking for dictionary equality doesn't work because of
# the Numpy arrays inside).
assert pformat(actual) == pformat(reference)
import os.path
# set up
msname = 'sim_output.ms'
conf_file = 'LWA352_coordinates_mod.cfg'
if len(sys.argv) > 1:
fitsimage = sys.argv[1]
else:
fitsimage = 'SKAMid_B2_8h_v3.fits'
assert os.path.exists(fitsimage)
assert 'fits' in fitsimage
image = fitsimage.replace('.fits', '.image')
if not os.path.exists(image):
print('creating ms image to be used as sky model')
im = casatools.image()
im.fromfits(infile=fitsimage, outfile=image)
# get antenna positions
tabname = 'antenna_positions_' + conf_file.split('.cfg')[0] + '.tab'
tb = casatools.table()
tb.fromascii(tabname,
conf_file,
firstline=3,
sep=' ',
columnnames=['X', 'Y', 'Z', 'DIAM', 'NAME'],
datatypes=['D', 'D', 'D', 'D', 'A'])
xx = tb.getcol('X')
yy = tb.getcol('Y')
zz = tb.getcol('Z')
diam = tb.getcol('DIAM')
def create_model(snu_ff,
nu0,
dust_ff_ratio,
bandwidth=7.5,
startfreq=35.0,
offset_position=[0.0, 0.0],
direction="J2000 10h00m00.0s -30d00m00.0s",
modelname='skymodel_test'):
'''
create an ms with a point source with the given spectrum.
'''
from casatasks.private import simutil
u = simutil.simutil()
from casatools import quanta
from casatools import componentlist
from casatools import image
from casatools import measures
qa = quanta()
cl = componentlist()
me = measures()
ia = image()
obs_freq = np.linspace(startfreq, startfreq + bandwidth, 1000)
ff_spect = calc_ff_spect(snu_ff, nu0, obs_freq)
dust_spect = calc_dust_spect(snu_ff, nu0, dust_ff_ratio, obs_freq)
comb_spect = ff_spect + dust_spect
xx = u.direction_splitter(direction)
qra = xx[1]
qdec = xx[2]
qra1 = qa.add(qra, str(offset_position[0]) + "arcsec")
qdec1 = qa.add(qdec, str(offset_position[1]) + "arcsec")
xx1 = xx[0] + " " + qa.formxxx(qra1, format='hms',
prec=3) + " " + qa.formxxx(
qdec1, format='dms', prec=4)
cl.done() #close any open component list
cl.addcomponent(flux=1.0, dir=xx1, shape='point')
# tabularfreq in Hz
# tabularflux in Jy
obs_freq_Hz = obs_freq * 1e9
cl.setspectrum(which=0,
type='tabular',
tabularfreqs=obs_freq_Hz,
tabularflux=comb_spect)
filename = modelname + '.cl'
if os.path.exists(filename):
shutil.rmtree(filename)
cl.rename(filename)
# make a skymodel from the component list since simobserve
# doesn't handle the component lists with frequency dependence.
# Don't need header info. can set that in simobserve
ia.done()
filename = modelname + ".image"
if os.path.exists(filename):
shutil.rmtree(filename)
ia.fromshape(filename, [300, 300, 1, len(obs_freq)], overwrite=True)
cs = ia.coordsys()
cs.setunits(['deg', 'deg', '', 'GHz'])
cell_rad = qa.convert(qa.quantity("0.1arcsec"), "deg")['value']
cs.setincrement([-cell_rad, cell_rad], 'direction')
cs.setreferencepixel(0, type='Spectral')
cs.setreferencevalue(str(obs_freq[0]) + "GHz", 'Spectral')
cs.setincrement("%.5fGHz" % np.diff(obs_freq)[0], 'spectral')
tmp = cs.referencevalue(format='q')
tmp['quantity']['*1'] = xx[1]
tmp['quantity']['*2'] = xx[2]
cs.setreferencevalue(value=tmp)
ia.setcoordsys(cs.torecord())
ia.setbrightnessunit("Jy/pixel")
ia.modify(cl.torecord(), subtract=False)
ia.done()
cl.done()
def makePB(
vis="",
field="",
spw="",
timerange="",
uvrange="",
antenna="",
observation="",
intent="",
scan="",
imtemplate="",
outimage="",
pblimit=0.2,
stokes="",
):
"""
(modified from casarecipes.makepb to support multiple stokes)
Make a PB image using the imager tool, onto a specified image coordinate system
This function can be used along with tclean to make .pb images for gridders that
do not already do it (i.e. other than mosaic, awproject)
This script takes an image to use as a template coordinate system,
attempts to set up an identical coordinate system with the old imager tool,
makes a PB for the telescope listed in the MS observation subtable, and
regrids it (just in case) to the target coordinate system). This can be used for
single fields and mosaics.
"""
tb = casatools.table()
im = casatools.imager()
ia = casatools.image()
me = casatools.measures()
qa = casatools.quanta()
print("MAKEPB : Making a PB image using the imager tool")
tb.open(vis + "/OBSERVATION")
tel = tb.getcol("TELESCOPE_NAME")[0]
tb.close()
tb.open(vis + "/SPECTRAL_WINDOW")
mfreqref = tb.getcol("MEAS_FREQ_REF")[0]
tb.close()
if mfreqref == 64:
print(
"MAKEPB : This function is using old imager tool, Undefined frame may not be handled properly."
)
print("MAKEPB : Making PB for ", tel)
ia.open(imtemplate)
csysa = ia.coordsys()
csys = csysa.torecord()
shp = ia.shape()
ia.close()
dirs = csys["direction0"]
phasecenter = me.direction(
dirs["system"],
qa.quantity(dirs["crval"][0], dirs["units"][0]),
qa.quantity(dirs["crval"][1], dirs["units"][1]),
)
cellx = qa.quantity(fabs(dirs["cdelt"][0]), dirs["units"][0])
celly = qa.quantity(fabs(dirs["cdelt"][1]), dirs["units"][1])
nchan = shp[3]
start = qa.quantity(csysa.referencevalue()["numeric"][3],
csysa.units()[3]) # assumes refpix is zero
mestart = me.frequency("LSRK", start)
step = qa.quantity(csysa.increment()["numeric"][3], csysa.units()[3])
smode = "mfs"
if nchan > 1:
smode = "frequency"
print("MAKEPB : Starting imager tool")
im.open(vis)
im.selectvis(
field=field,
spw=spw,
time=timerange,
intent=intent,
scan=scan,
uvrange=uvrange,
baseline=antenna,
observation=observation,
)
im.defineimage(
nx=shp[0],
ny=shp[0],
phasecenter=phasecenter,
cellx=qa.tos(cellx),
celly=qa.tos(celly),
nchan=nchan,
start=mestart,
step=step,
mode=smode,
stokes=stokes,
)
im.setvp(dovp=True, telescope=tel)
im.makeimage(type="pb", image=outimage + ".tmp")
im.close()
if mfreqref == 64: # skip this step if the frame is 'Undefined'
shutil.copytree(outimage + ".tmp", outimage)
else:
print("MAKEPB : Regrid to desired coordinate system")
imregrid(
imagename=outimage + ".tmp",
template=imtemplate,
output=outimage,
overwrite=True,
asvelocity=False,
)
shutil.rmtree(outimage + ".tmp")
print("MAKEPB : Set mask to pblimit")
ia.open(outimage)
ia.calcmask("'" + outimage + "'>" + str(pblimit))
ia.close()
def test_casa_masking():
with tempfile.TemporaryDirectory() as tmpdir:
# SIMULATE SOME DATA SET
# Define antennas
diam = [25, 25, 25, 25, 25]
xx = [50, 100, 150, 200, 250]
yy = [2, -5, -20, -50, -100]
zz = [-0.5, -1.0, -1.5, -2.0, -2.5]
sm = simulator()
sm.open(tmpdir + '/SIM.ms')
# do configuration
posvla = me.observatory('VLA')
sm.setconfig(telescopename='VLA',
x=xx,
y=yy,
z=zz,
dishdiameter=diam,
mount='alt-az',
antname='VLA',
coordsystem='local',
referencelocation=posvla)
# Initialize the spectral windows
sm.setspwindow(spwname='CBand',
freq='5GHz',
deltafreq='50MHz',
freqresolution='50MHz',
nchannels=1,
stokes='RR RL LR LL')
# Initialize the source and calibrater
sm.setfield(sourcename='My cal',
sourcedirection=['J2000', '00h0m0.0', '+45.0.0.000'],
calcode='A')
sm.setfield(sourcename='My source',
sourcedirection=['J2000', '01h0m0.0', '+47.0.0.000'])
sm.setlimits(shadowlimit=0.001, elevationlimit='8.0deg')
sm.setauto(autocorrwt=0.0)
sm.settimes(integrationtime='10s',
usehourangle=False,
referencetime=me.epoch('utc', 'today'))
sm.observe('My cal', 'CBand', starttime='720s', stoptime='1020s')
sm.observe('My source', 'CBand', starttime='1030s', stoptime='1500s')
sm.close()
# Create mask to use during clean
reg = EllipseSkyRegion(center=coordinates.SkyCoord(0.0 * u.deg,
45.0 * u.deg,
frame='fk5'),
width=1.0 * u.arcmin,
height=2.0 * u.arcmin,
angle=45 * u.deg)
write_crtf([reg], tmpdir + '/SIM.crtf', 'fk5', '.6f', 'deg')
# Image the dataset
tclean(vis=tmpdir + '/SIM.ms',
imagename=tmpdir + '/SIM',
imsize=100,
cell='5arcsec',
niter=1,
mask=tmpdir + '/SIM.crtf',
interactive=False)
ia = image()
ia.open(tmpdir + '/SIM.mask')
mask_array = ia.getregion()
ia.close()
with open('data/binary_mask.pkl', 'rb') as f:
ref_mask = pickle.load(f)
assert all(mask_array == ref_mask)
def load_casa_image(filename, skipdata=False,
skipvalid=False, skipcs=False, **kwargs):
"""
Load a cube (into memory?) from a CASA image. By default it will transpose
the cube into a 'python' order and drop degenerate axes. These options can
be suppressed. The object holds the coordsys object from the image in
memory.
"""
try:
import casatools
ia = casatools.image()
except ImportError:
try:
from taskinit import iatool
ia = iatool()
except ImportError:
raise ImportError("Could not import CASA (casac) and therefore cannot read CASA .image files")
# use the ia tool to get the file contents
ia.open(filename)
# read in the data
if not skipdata:
# CASA data are apparently transposed.
data = ia.getchunk().transpose()
# CASA stores validity of data as a mask
if not skipvalid:
valid = ia.getchunk(getmask=True).transpose()
# transpose is dealt with within the cube object
# read in coordinate system object
casa_cs = ia.coordsys()
wcs = wcs_casa2astropy(casa_cs)
unit = ia.brightnessunit()
beam_ = ia.restoringbeam()
if 'major' in beam_:
beam = Beam(major=u.Quantity(beam_['major']['value'], unit=beam_['major']['unit']),
minor=u.Quantity(beam_['minor']['value'], unit=beam_['minor']['unit']),
pa=u.Quantity(beam_['positionangle']['value'], unit=beam_['positionangle']['unit']),
)
elif 'beams' in beam_:
bdict = beam_['beams']
if beam_['nStokes'] > 1:
raise NotImplementedError()
nbeams = len(bdict)
assert nbeams == beam_['nChannels']
stokesidx = '*0'
majors = [u.Quantity(bdict['*{0}'.format(ii)][stokesidx]['major']['value'],
bdict['*{0}'.format(ii)][stokesidx]['major']['unit']) for ii in range(nbeams)]
minors = [u.Quantity(bdict['*{0}'.format(ii)][stokesidx]['minor']['value'],
bdict['*{0}'.format(ii)][stokesidx]['minor']['unit']) for ii in range(nbeams)]
pas = [u.Quantity(bdict['*{0}'.format(ii)][stokesidx]['positionangle']['value'],
bdict['*{0}'.format(ii)][stokesidx]['positionangle']['unit']) for ii in range(nbeams)]
beams = Beams(major=u.Quantity(majors),
minor=u.Quantity(minors),
pa=u.Quantity(pas))
else:
warnings.warn("No beam information found in CASA image.",
BeamWarning)
# don't need this yet
# stokes = get_casa_axis(temp_cs, wanttype="Stokes", skipdeg=False,)
# if stokes == None:
# order = np.arange(self.data.ndim)
# else:
# order = []
# for ax in np.arange(self.data.ndim+1):
# if ax == stokes:
# continue
# order.append(ax)
# self.casa_cs = ia.coordsys(order)
# This should work, but coordsys.reorder() has a bug
# on the error checking. JIRA filed. Until then the
# axes will be reversed from the original.
# if transpose == True:
# new_order = np.arange(self.data.ndim)
# new_order = new_order[-1*np.arange(self.data.ndim)-1]
# print new_order
# self.casa_cs.reorder(new_order)
# close the ia tool
ia.close()
meta = {'filename': filename,
'BUNIT': unit}
if wcs.naxis == 3:
mask = BooleanArrayMask(np.logical_not(valid), wcs)
if 'beam' in locals():
cube = SpectralCube(data, wcs, mask, meta=meta, beam=beam)
elif 'beams' in locals():
cube = VaryingResolutionSpectralCube(data, wcs, mask, meta=meta, beams=beams)
else:
cube = SpectralCube(data, wcs, mask, meta=meta)
# we've already loaded the cube into memory because of CASA
# limitations, so there's no reason to disallow operations
cube.allow_huge_operations = True
elif wcs.naxis == 4:
data, wcs = cube_utils._split_stokes(data, wcs)
mask = {}
for component in data:
data_, wcs_slice = cube_utils._orient(data[component], wcs)
mask[component] = LazyMask(np.isfinite, data=data[component],
wcs=wcs_slice)
if 'beam' in locals():
data[component] = SpectralCube(data_, wcs_slice, mask[component],
meta=meta, beam=beam)
elif 'beams' in locals():
data[component] = VaryingResolutionSpectralCube(data_,
wcs_slice,
mask[component],
meta=meta,
beams=beams)
else:
data[component] = SpectralCube(data_, wcs_slice, mask[component],
meta=meta)
data[component].allow_huge_operations = True
cube = StokesSpectralCube(stokes_data=data)
return cube
def make_casa_mask(SpecCube,
outname,
append_to_image=True,
img=None,
add_stokes=True,
stokes_posn=None,
overwrite=False):
'''
Outputs the mask attached to the SpectralCube object as a CASA image, or
optionally appends the mask to a preexisting CASA image.
Parameters
----------
SpecCube : SpectralCube
SpectralCube object containing mask.
outname : str
Name of the outputted mask file.
append_to_image : bool, optional
Appends the mask to a given image.
img : str, optional
Image to be appended to. Must be specified if append_to_image is
enabled.
add_stokes: bool, optional
Adds a Stokes axis onto the wcs from SpecCube.
stokes_posn : int, optional
Sets the position of the new Stokes axis. Defaults to the last axis.
overwrite : bool, optional
Overwrite the image and mask files if they exist?
'''
try:
from casatools import image
ia = image()
except ImportError:
try:
from taskinit import ia
except ImportError:
raise ImportError(
"Cannot import casa. Must be run in a CASA environment.")
# the 'mask name' is distinct from the mask _path_
maskname = os.path.split(outname)[1]
maskpath = outname
# Get the header info from the image
# There's not wcs_astropy2casa (yet), so create a temporary file for
# CASA to open.
temp = tempfile.NamedTemporaryFile()
# CASA is closing this file at some point so set it to manual delete.
temp2 = tempfile.NamedTemporaryFile(delete=False)
# Grab wcs
# Optionally re-add on the Stokes axis
if add_stokes:
my_wcs = SpecCube.wcs
if stokes_posn is None:
stokes_posn = my_wcs.wcs.naxis
new_wcs = add_stokes_axis_to_wcs(my_wcs, stokes_posn)
header = new_wcs.to_header()
# Transpose the shape so we're adding the axis at the place CASA will
# recognize. Then transpose back.
shape = SpecCube.shape[::-1]
shape = shape[:stokes_posn] + (1, ) + shape[stokes_posn:]
shape = shape[::-1]
else:
# Just grab the header from SpecCube
header = SpecCube.header
shape = SpecCube.shape
hdu = fits.PrimaryHDU(header=header, data=np.empty(shape, dtype='int16'))
hdu.writeto(temp.name)
ia.fromfits(infile=temp.name, outfile=temp2.name, overwrite=overwrite)
temp.close()
cs = ia.coordsys()
ia.close()
temp2.close()
mask_arr = SpecCube.mask.include()
# Reshape mask with possible Stokes axis
mask_arr = mask_arr.reshape(shape)
# Transpose to match CASA axes
mask_arr = mask_arr.T
ia.newimagefromarray(outfile=maskpath,
pixels=mask_arr.astype('int16'),
overwrite=overwrite)
ia.close()
ia.open(maskpath)
ia.setcoordsys(cs.torecord())
ia.close()
if append_to_image:
if img is None:
raise TypeError(
"img argument must be specified to append the mask.")
ia.open(maskpath)
ia.calcmask(maskname + ">0.5")
ia.close()
ia.open(img)
ia.maskhandler('copy', [maskpath + ":mask0", maskname])
ia.maskhandler('set', maskname)
ia.close()
import pylab as pl
import os
import time
import numpy as np
from astropy.io import fits
from astropy import units as u
from astropy.stats import mad_std
import pylab as pl
import radio_beam
import glob
from spectral_cube import SpectralCube, DaskSpectralCube
from spectral_cube.lower_dimensional_structures import Projection
from casatools import image
ia = image()
if os.getenv('NO_PROGRESSBAR') is None:
from dask.diagnostics import ProgressBar
pbar = ProgressBar()
pbar.register()
nthreads = 1
scheduler = 'synchronous'
os.environ['TEMPDIR'] = '/blue/adamginsburg/adamginsburg/tmp/'
if os.getenv('DASK_THREADS') is not None:
try:
nthreads = int(os.getenv('DASK_THREADS'))
if nthreads > 1:
def fitimage(image,
outname='tmp.',
xmin=None,
xmax=None,
ymin=None,
ymax=None,
displaywindow=300,
fitwindow=100,
returnimfit=False,
estimates=''):
"""
CASA imfit on the image. Reports peak RA, DEC and errors.
"""
# load image
ia = tools.image()
ia.open(image)
# summarize image
dd = ia.summary()
npixx, npixy, nch, npol = dd['shape']
if xmin is None:
xmin = 0
if xmax is None:
xmax = npixx
if ymin is None:
ymin = 0
if ymax is None:
ymax = npixy
print('Image shape: {0}'.format(dd['shape']))
imvals = ia.getchunk(0, int(npixx))[xmin:xmax, ymin:ymax, 0, 0]
peakx, peaky = np.where(imvals.max() == imvals)
print('Peak SNR at ({0},{1}) = {2}'.format(peakx[0], peaky[0],
imvals.max() / imvals.std()))
# print('Beam shape: {0}'.format(ia.history()[1].split('\n')[10].split(':')[5]))
# fit component and write residual image
box = '{0},{1},{2},{3}'.format(xmin + peakx[0] - fitwindow // 2,
ymin + peaky[0] - fitwindow // 2,
xmin + peakx[0] + fitwindow // 2,
ymin + peaky[0] + fitwindow // 2)
imfit = ia.fitcomponents(box=box,
residual=outname + 'fitresid',
estimates=estimates)
# report on fit
if imfit['converged']:
print('{0} element(s) fit'.format(imfit['results']['nelements']))
direction = imfit['results']['component0']['shape']['direction']
az = direction['m0']['value']
el = direction['m1']['value']
az_err = direction['error']['longitude']['value']
el_err = direction['error']['latitude']['value']
print(direction)
co0 = coordinates.SkyCoord(ra=np.degrees(az),
dec=np.degrees(el),
unit=(units.deg, units.deg))
# peak_ra = qa.unit(qa.angle(qa.quantity(az, unitname='rad'), prec=13)[0], unitname='deg')['value']
# peak_dec = qa.unit(qa.angle(qa.quantity(el, unitname='rad'), prec=13)[0], unitname='deg')['value']
print('{0} +- {1}"'.format(co0.ra.degree, az_err))
print('{0} +- {1}"'.format(co0.dec.degree, el_err))
print('Fitpeak flux: {0} Jy'.format(
imfit['results']['component0']['peak']['value']))
print(co0.to_string('hmsdms'))
else:
print('fitcomponents did not converge')
# load residuals
ia = tools.image()
ia.open(outname + 'fitresid')
dd = ia.summary()
npixy, npixx, nch, npol = dd['shape']
residvals = ia.getchunk(0, int(npixx))[xmin:xmax, ymin:ymax, 0, 0]
peakx_resid, peaky_resid = np.where(residvals.max() == residvals)
print('Residual SNR at ({0},{1}) = {2}'.format(
peakx_resid[0], peaky_resid[0],
residvals.max() / residvals.std()))
# show results
plt.figure(figsize=(25, 15))
plt.subplot(131)
plt.imshow(imvals.transpose() - imvals.min(),
interpolation='nearest',
origin='bottom')
plt.colorbar()
plt.subplot(132)
plt.imshow(
imvals[peakx[0] - displaywindow // 2:peakx[0] + displaywindow // 2,
peaky[0] - displaywindow // 2:peaky[0] +
displaywindow // 2].transpose(),
interpolation='nearest',
origin='bottom')
plt.colorbar()
plt.subplot(133)
plt.imshow(
residvals[peakx[0] - displaywindow // 2:peakx[0] + displaywindow // 2,
peaky[0] - displaywindow // 2:peaky[0] +
displaywindow // 2].transpose(),
interpolation='nearest',
origin='bottom')
plt.colorbar()
if returnimfit:
return imfit
else:
return az, el, az_err, el_err
prefix = '/lustre/cv/users/rloomis/research_tickets/mod_pcwd/'
filenames = [
prefix + 'r-2_c0.02_pcwdT.psf', prefix + 'r0_c0.02_pcwdT.psf',
prefix + 'r0.5_c0.02_pcwdT.psf', prefix + 'r1_c0.02_pcwdT.psf',
prefix + 'r2_c0.02_pcwdT.psf', prefix + 'r-2_c0.02_pcwdMOD.psf',
prefix + 'r0_c0.02_pcwdMOD.psf', prefix + 'r0.5_c0.02_pcwdMOD.psf',
prefix + 'r1_c0.02_pcwdMOD.psf', prefix + 'r2_c0.02_pcwdMOD.psf',
prefix + 'r-2_c0.02_pcwdF.psf', prefix + 'r0_c0.02_pcwdF.psf',
prefix + 'r0.5_c0.02_pcwdF.psf', prefix + 'r1_c0.02_pcwdF.psf',
prefix + 'r2_c0.02_pcwdF.psf'
]
#filenames=[prefix + 'r2_c0.02_pcwdF.psf']
ia = casatools.image()
npix_window = 31
# ellipse fitting code from: nicky.vanforeest.com/misc/fitEllipse/fitEllipse.html
# moded a number of typos from original source
def fitEllipse(x, y):
x = x[:, np.newaxis]
y = y[:, np.newaxis]
D = np.hstack((x * x, x * y, y * y, x, y, np.ones_like(x)))
S = np.dot(D.T, D)
C = np.zeros([6, 6])
C[0, 2] = C[2, 0] = 2
C[1, 1] = -1
E, V = linalg.eig(np.dot(linalg.inv(S), C))
n = np.argmax(E)
import pytest
import numpy as np
from numpy.testing import assert_allclose
import os
from astropy import units as u
from ..io.casa_masks import make_casa_mask
from ..io.casa_image import wcs_casa2astropy
from .. import SpectralCube, BooleanArrayMask, VaryingResolutionSpectralCube
from . import path
try:
import casatools
ia = casatools.image()
casaOK = True
except ImportError:
try:
from taskinit import ia
casaOK = True
except ImportError:
print("Run in CASA environment.")
casaOK = False
def make_casa_testimage(infile, outname):
if not casaOK:
raise Exception("Attempted to make a CASA test image in a non-CASA "
"environment")
def test_casa_image_dask_reader(tmpdir, memmap, shape):
# Unit tests for the low-level casa_image_dask_reader function which can
# read a CASA image or mask to a Dask array.
reference = np.random.random(shape).astype(np.float32)
# CASA seems to have precision issues when computing masks with values
# very close to e.g. 0.5 in >0.5. To avoid this, we filter out random
# values close to the boundaries that we use below.
reference[np.isclose(reference, 0.2)] += 0.05
reference[np.isclose(reference, 0.5)] += 0.05
reference[np.isclose(reference, 0.8)] += 0.05
os.chdir(tmpdir.strpath)
# Start off with a simple example with no mask. Note that CASA requires
# the array to be transposed in order to match what we would expect.
ia = image()
ia.fromarray('basic.image', pixels=reference.T, log=False)
ia.close()
array1 = casa_image_dask_reader('basic.image', memmap=memmap)
assert array1.dtype == np.float32
assert_allclose(array1, reference)
# Check slicing
assert_allclose(array1[:2, :1, :3], reference[:2, :1, :3])
# Try and get a mask - this should fail since there isn't one.
with pytest.raises(FileNotFoundError):
casa_image_dask_reader('basic.image', mask=True, memmap=memmap)
# Now create an array with a simple uniform mask.
ia = image()
ia.fromarray('scalar_mask.image', pixels=reference.T, log=False)
ia.calcmask(mask='T')
ia.close()
array2 = casa_image_dask_reader('scalar_mask.image', memmap=memmap)
assert_allclose(array2, reference)
mask2 = casa_image_dask_reader('scalar_mask.image',
mask=True,
memmap=memmap)
assert mask2.dtype is np.dtype('bool')
assert mask2.shape == array2.shape
assert np.all(mask2)
# Check with a full 3-d mask
ia = image()
ia.fromarray('array_mask.image', pixels=reference.T, log=False)
ia.calcmask(mask='array_mask.image>0.5')
ia.close()
array3 = casa_image_dask_reader('array_mask.image', memmap=memmap)
assert_allclose(array3, reference)
mask3 = casa_image_dask_reader('array_mask.image',
mask=True,
memmap=memmap)
assert_allclose(mask3, reference > 0.5)
# Check slicing
assert_allclose(mask3[:2, :1, :3], (reference > 0.5)[:2, :1, :3])
# Test specifying the mask name
ia = image()
ia.fromarray('array_masks.image', pixels=reference.T, log=False)
ia.calcmask(mask='array_masks.image>0.5')
ia.calcmask(mask='array_masks.image>0.2')
ia.calcmask(mask='array_masks.image>0.8', name='gt08')
ia.close()
array4 = casa_image_dask_reader('array_masks.image', memmap=memmap)
assert_allclose(array4, reference)
mask4 = casa_image_dask_reader('array_masks.image',
mask=True,
memmap=memmap)
assert_allclose(mask4, reference > 0.5)
mask5 = casa_image_dask_reader('array_masks.image',
mask='mask0',
memmap=memmap)
assert_allclose(mask5, reference > 0.5)
mask6 = casa_image_dask_reader('array_masks.image',
mask='mask1',
memmap=memmap)
assert_allclose(mask6, reference > 0.2)
mask7 = casa_image_dask_reader('array_masks.image',
mask='gt08',
memmap=memmap)
assert_allclose(mask7, reference > 0.8)
# Check that things still work if we write the array out with doubles
reference = np.random.random(shape).astype(np.float64)
ia = image()
ia.fromarray('double.image', pixels=reference.T, type='d', log=False)
ia.close()
array8 = casa_image_dask_reader('double.image', memmap=memmap)
assert array8.dtype == np.float64
assert_allclose(array8, reference)
def make_casa_mask(SpecCube, outname, append_to_image=True,
img=None, add_stokes=True, stokes_posn=None,
overwrite=False
):
'''
Outputs the mask attached to the SpectralCube object as a CASA image, or
optionally appends the mask to a preexisting CASA image.
Parameters
----------
SpecCube : SpectralCube
SpectralCube object containing mask.
outname : str
Name of the outputted mask file.
append_to_image : bool, optional
Appends the mask to a given image.
img : str, optional
Image to be appended to. Must be specified if append_to_image is
enabled.
add_stokes: bool, optional
Adds a Stokes axis onto the wcs from SpecCube.
stokes_posn : int, optional
Sets the position of the new Stokes axis. Defaults to the last axis.
overwrite : bool, optional
Overwrite the image and mask files if they exist?
'''
try:
from casatools import image
ia = image()
except ImportError:
try:
from taskinit import ia
except ImportError:
raise ImportError("Cannot import casa. Must be run in a CASA environment.")
# the 'mask name' is distinct from the mask _path_
maskname = os.path.split(outname)[1]
maskpath = outname
# Get the header info from the image
# There's not wcs_astropy2casa (yet), so create a temporary file for
# CASA to open.
temp = tempfile.NamedTemporaryFile()
# CASA is closing this file at some point so set it to manual delete.
temp2 = tempfile.NamedTemporaryFile(delete=False)
# Grab wcs
# Optionally re-add on the Stokes axis
if add_stokes:
my_wcs = SpecCube.wcs
if stokes_posn is None:
stokes_posn = my_wcs.wcs.naxis
new_wcs = add_stokes_axis_to_wcs(my_wcs, stokes_posn)
header = new_wcs.to_header()
# Transpose the shape so we're adding the axis at the place CASA will
# recognize. Then transpose back.
shape = SpecCube.shape[::-1]
shape = shape[:stokes_posn] + (1,) + shape[stokes_posn:]
shape = shape[::-1]
else:
# Just grab the header from SpecCube
header = SpecCube.header
shape = SpecCube.shape
hdu = fits.PrimaryHDU(header=header,
data=np.empty(shape, dtype='int16'))
hdu.writeto(temp.name)
ia.fromfits(infile=temp.name, outfile=temp2.name, overwrite=overwrite)
temp.close()
cs = ia.coordsys()
ia.close()
temp2.close()
mask_arr = SpecCube.mask.include()
# Reshape mask with possible Stokes axis
mask_arr = mask_arr.reshape(shape)
# Transpose to match CASA axes
mask_arr = mask_arr.T
ia.newimagefromarray(outfile=maskpath,
pixels=mask_arr.astype('int16'),
overwrite=overwrite)
ia.close()
ia.open(maskpath)
ia.setcoordsys(cs.torecord())
ia.close()
if append_to_image:
if img is None:
raise TypeError("img argument must be specified to append the mask.")
ia.open(maskpath)
ia.calcmask(maskname+">0.5")
ia.close()
ia.open(img)
ia.maskhandler('copy', [maskpath+":mask0", maskname])
ia.maskhandler('set', maskname)
ia.close()
