def actualSetUp(self, add_errors=False, freqwin=1, block=False, dospectral=True, dopol=False): self.npixel = 256 self.low = create_named_configuration('LOWBD2', rmax=750.0) self.freqwin = freqwin self.vis_list = list() self.ntimes = 5 self.times = numpy.linspace(-3.0, +3.0, self.ntimes) * numpy.pi / 12.0 self.frequency = numpy.linspace(0.8e8, 1.2e8, self.freqwin) if freqwin > 1: self.channelwidth = numpy.array(freqwin * [self.frequency[1] - self.frequency[0]]) else: self.channelwidth = numpy.array([1e6]) if dopol: self.vis_pol = PolarisationFrame('linear') self.image_pol = PolarisationFrame('stokesIQUV') f = numpy.array([100.0, 20.0, -10.0, 1.0]) else: self.vis_pol = PolarisationFrame('stokesI') self.image_pol = PolarisationFrame('stokesI') f = numpy.array([100.0]) if dospectral: flux = numpy.array([f * numpy.power(freq / 1e8, -0.7) for freq in self.frequency]) else: flux = numpy.array([f]) self.phasecentre = SkyCoord(ra=+180.0 * u.deg, dec=-60.0 * u.deg, frame='icrs', equinox='J2000') self.vis = ingest_unittest_visibility(self.low, self.frequency, self.channelwidth, self.times, self.vis_pol, self.phasecentre, block=block) self.model = create_unittest_model(self.vis, self.image_pol, npixel=self.npixel)
def actualSetUp(self, freqwin=1, block=False, dospectral=True, dopol=False, zerow=False): self.npixel = 512 self.low = create_named_configuration('LOWBD2', rmax=750.0) self.freqwin = freqwin self.vis = list() self.ntimes = 5 self.times = numpy.linspace(-3.0, +3.0, self.ntimes) * numpy.pi / 12.0 if freqwin > 1: self.frequency = numpy.linspace(0.8e8, 1.2e8, self.freqwin) self.channelwidth = numpy.array(freqwin * [self.frequency[1] - self.frequency[0]]) else: self.frequency = numpy.array([1e8]) self.channelwidth = numpy.array([1e6]) if dopol: self.vis_pol = PolarisationFrame('linear') self.image_pol = PolarisationFrame('stokesIQUV') f = numpy.array([100.0, 20.0, -10.0, 1.0]) else: self.vis_pol = PolarisationFrame('stokesI') self.image_pol = PolarisationFrame('stokesI') f = numpy.array([100.0]) if dospectral: flux = numpy.array([f * numpy.power(freq / 1e8, -0.7) for freq in self.frequency]) else: flux = numpy.array([f]) self.phasecentre = SkyCoord(ra=+180.0 * u.deg, dec=-60.0 * u.deg, frame='icrs', equinox='J2000') self.vis = ingest_unittest_visibility(self.low, [self.frequency], [self.channelwidth], self.times, self.vis_pol, self.phasecentre, block=block, zerow=zerow) self.model = create_unittest_model(self.vis, self.image_pol, npixel=self.npixel) self.components = create_unittest_components(self.model, flux) self.model = insert_skycomponent(self.model, self.components) self.vis = predict_skycomponent_visibility(self.vis, self.components) # Calculate the model convolved with a Gaussian. self.cmodel = smooth_image(self.model) export_image_to_fits(self.model, '%s/test_imaging_model.fits' % self.dir) export_image_to_fits(self.cmodel, '%s/test_imaging_cmodel.fits' % self.dir)
def actualSetUp(self, zerow=True): self.doplot = False self.npixel = 256 self.cellsize = 0.0009 self.low = create_named_configuration('LOWBD2', rmax=750.0) self.freqwin = 1 self.vis_list = list() self.ntimes = 3 self.times = numpy.linspace(-2.0, +2.0, self.ntimes) * numpy.pi / 12.0 if self.freqwin == 1: self.frequency = numpy.array([1e8]) self.channelwidth = numpy.array([4e7]) else: self.frequency = numpy.linspace(0.8e8, 1.2e8, self.freqwin) self.channelwidth = numpy.array(self.freqwin * [self.frequency[1] - self.frequency[0]]) self.vis_pol = PolarisationFrame('linear') self.image_pol = PolarisationFrame('stokesIQUV') f = numpy.array([100.0, 20.0, -10.0, 1.0]) flux = numpy.array([f * numpy.power(freq / 1e8, -0.7) for freq in self.frequency]) self.phasecentre = SkyCoord(ra=+180.0 * u.deg, dec=-60.0 * u.deg, frame='icrs', equinox='J2000') self.vis = ingest_unittest_visibility(self.low, self.frequency, self.channelwidth, self.times, self.vis_pol, self.phasecentre, block=False, zerow=zerow) self.model = create_unittest_model(self.vis, self.image_pol, cellsize=self.cellsize, npixel=self.npixel, nchan=self.freqwin) self.components = create_unittest_components(self.model, flux, applypb=False, scale=0.5, single=False, symmetric=True) self.model = insert_skycomponent(self.model, self.components) self.vis = predict_skycomponent_visibility(self.vis, self.components) # Calculate the model convolved with a Gaussian. self.cmodel = smooth_image(self.model) if self.persist: export_image_to_fits(self.model, '%s/test_gridding_model.fits' % self.dir) export_image_to_fits(self.cmodel, '%s/test_gridding_cmodel.fits' % self.dir) pb = create_pb_generic(self.model, diameter=35.0, blockage=0.0) self.cmodel.data *= pb.data if self.persist: export_image_to_fits(self.cmodel, '%s/test_gridding_cmodel_pb.fits' % self.dir) self.peak = numpy.unravel_index(numpy.argmax(numpy.abs(self.cmodel.data)), self.cmodel.shape)