def test_run_paramdict_uvsim():
# Running a simulation from parameter dictionary.
params = pyuvsim.simsetup._config_str_to_dict(
os.path.join(SIM_DATA_PATH, 'test_config',
'param_1time_1src_testcat.yaml'))
pyuvsim.run_uvsim(params, return_uv=True)
def test_run_gleam_uvsim(spectral_type):
params = pyuvsim.simsetup._config_str_to_dict(
os.path.join(SIM_DATA_PATH, 'test_config',
'param_1time_1src_testgleam.yaml'))
params["sources"]["spectral_type"] = spectral_type
params["sources"].pop("min_flux")
params["sources"].pop("max_flux")
pyuvsim.run_uvsim(params, return_uv=True)
def test_run_uvsim():
hera_uv = UVData()
hera_uv.read_uvfits(EW_uvfits_file)
beam = UVBeam()
beam.read_cst_beam(beam_files,
beam_type='efield',
frequency=[100e6, 123e6],
telescope_name='HERA',
feed_name='PAPER',
feed_version='0.1',
feed_pol=['x'],
model_name='E-field pattern - Rigging height 4.9m',
model_version='1.0')
beam_list = [beam]
mock_keywords = {"Nsrcs": 3}
uv_out = pyuvsim.run_uvsim(hera_uv,
beam_list,
catalog_file=None,
mock_keywords=mock_keywords,
uvdata_file=EW_uvfits_file)
if rank == 0:
nt.assert_true(
np.allclose(uv_out.data_array, hera_uv.data_array, atol=5e-3))
def test_zenith_spectral_sim(spectral_type, tmpdir):
# Make a power law source at zenith in three ways.
# Confirm that simulated visibilities match expectation.
params = pyuvsim.simsetup._config_str_to_dict(
os.path.join(SIM_DATA_PATH, 'test_config',
'param_1time_1src_testcat.yaml'))
alpha = -0.5
ref_freq = 111e6
Nfreqs = 20
freqs = np.linspace(110e6, 115e6, Nfreqs)
freq_params = pyuvsim.simsetup.freq_array_to_params(freqs)
freqs = pyuvsim.simsetup.parse_frequency_params(freq_params)['freq_array'][
0, :]
freqs *= units.Hz
spectrum = (freqs.value / ref_freq)**alpha
source, kwds = pyuvsim.create_mock_catalog(Time.now(),
arrangement='zenith',
Nsrcs=1)
source.spectral_type = spectral_type
if spectral_type == 'spectral_index':
source.reference_frequency = np.array([ref_freq]) * units.Hz
source.spectral_index = np.array([alpha])
else:
source.Nfreqs = Nfreqs
source.freq_array = freqs
source.stokes = np.repeat(source.stokes, Nfreqs, axis=1)
source.stokes[0, :, 0] *= spectrum
source.coherency_radec = stokes_to_coherency(source.stokes)
catpath = str(tmpdir.join('spectral_test_catalog.txt'))
source.write_text_catalog(catpath)
params['sources'] = {"catalog": catpath}
params['filing']['outdir'] = str(tmpdir)
params['freq'] = freq_params
params['time']['start_time'] = kwds['time']
params['select'] = {'antenna_nums': [1, 2]}
uv_out = pyuvsim.run_uvsim(params, return_uv=True)
for ii in range(uv_out.Nbls):
assert np.allclose(uv_out.data_array[ii, 0, :, 0], spectrum / 2)
def test_analytic_diffuse(model, tmpdir):
# Generate the given model and simulate for a few baselines.
# Import from analytic_diffuse (consider moving to rasg_affiliates?)
pytest.importorskip('analytic_diffuse')
pytest.importorskip('astropy_healpix')
import analytic_diffuse
modname = model
use_w = False
params = {}
if model == 'quaddome':
modname = 'polydome'
params['n'] = 2
elif model == 'monopole-nonflat':
modname = 'monopole'
use_w = True
params[
'order'] = 30 # Expansion order for the non-flat monopole solution.
# Making configuration files for this simulation.
template_path = os.path.join(SIM_DATA_PATH, 'test_config',
'obsparam_diffuse_sky.yaml')
obspar_path = str(tmpdir.join('obsparam_diffuse_sky.yaml'))
layout_path = str(tmpdir.join('threeant_layout.csv'))
herauniform_path = str(tmpdir.join('hera_uniform.yaml'))
teleconfig = {
'beam_paths': {
0: 'uniform'
},
'telescope_location': "(-30.72153, 21.42830, 1073.0)",
'telescope_name': 'HERA'
}
if not use_w:
antpos_enu = np.array([[0, 0, 0], [0, 3, 0], [5, 0, 0]], dtype=float)
else:
antpos_enu = np.array([[0, 0, 0], [0, 3, 0], [0, 3, 5]], dtype=float)
pyuvsim.simsetup._write_layout_csv(layout_path, antpos_enu,
np.arange(3).astype(str), np.arange(3))
with open(herauniform_path, 'w') as ofile:
yaml.dump(teleconfig, ofile, default_flow_style=False)
with open(template_path, 'r') as yfile:
obspar = yaml.safe_load(yfile)
obspar['telescope']['array_layout'] = layout_path
obspar['telescope']['telescope_config_name'] = herauniform_path
obspar['sources']['diffuse_model'] = modname
obspar['sources'].update(params)
obspar['filing']['outfile_name'] = 'diffuse_sim.uvh5'
obspar['filing']['output_format'] = 'uvh5'
obspar['filing']['outdir'] = str(tmpdir)
with open(obspar_path, 'w') as ofile:
yaml.dump(obspar, ofile, default_flow_style=False)
uv_out = pyuvsim.run_uvsim(obspar_path, return_uv=True)
# Convert from Jy to K sr
dat = uv_out.data_array[:, 0, 0, 0] * jy_to_ksr(uv_out.freq_array[0,
0]).value
# Evaluate the solution and compare to visibilities.
soln = analytic_diffuse.get_solution(modname)
uvw_lam = uv_out.uvw_array * uv_out.freq_array[0, 0] / c_ms
ana = soln(uvw_lam, **params)
assert np.allclose(ana / 2, dat, atol=1e-2)