How to use pyradiosky - 5 common examples

if self.flux_unit is not None:
            stokes_use *= units.Unit(self.flux_unit)

        other = {}
        if self.spectral_type in ['full', 'subband']:
            other['freq_array'] = self.freq_array * units.Hz
        if self.spectral_type == 'spectral_index':
            other['reference_frequency'] = self.reference_frequency * units.Hz
            other['spectral_index'] = self.spectral_index
        if self.component_type == 'healpix':
            other['nside'] = self.nside
            other['hpx_inds'] = self.hpx_inds
        else:
            other['name'] = self.name
        return pyradiosky.SkyModel(
            ra=ra_use, dec=dec_use, stokes=stokes_use,
            spectral_type=self.spectral_type, **other
        )

if isinstance(array_location, MoonLocation):
        localframe = 'lunartopo'
        mock_keywords['world'] = 'moon'
    else:
        localframe = 'altaz'
        mock_keywords['world'] = 'earth'
    source_coord = SkyCoord(alt=Angle(alts, unit=units.deg), az=Angle(azs, unit=units.deg),
                            obstime=time, frame=localframe, location=array_location)
    icrs_coord = source_coord.transform_to('icrs')

    ra = icrs_coord.ra
    dec = icrs_coord.dec
    names = np.array(['src' + str(si) for si in range(Nsrcs)])
    stokes = np.zeros((4, 1, Nsrcs))
    stokes[0, :] = fluxes
    catalog = pyradiosky.SkyModel(names, ra, dec, stokes, 'flat')
    if return_data:
        catalog = SkyModelData(catalog)
    if get_rank() == 0 and save:
        np.savez('mock_catalog_' + arrangement, ra=ra.rad, dec=dec.rad, alts=alts, azs=azs,
                 fluxes=fluxes)

    return catalog, mock_keywords

warnings.warn(
                    "Warning: No julian date given for mock catalog. Defaulting to first time step."
                )
            else:
                raise ValueError(
                    "input_uv must be supplied if using mock catalog without specified julian date"
                )

        time = mock_keywords.pop('time')

        sky, mock_keywords = create_mock_catalog(time, **mock_keywords)
        mock_keyvals = [str(key) + str(val) for key, val in mock_keywords.items()]
        source_list_name = 'mock_' + "_".join(mock_keyvals)
    elif isinstance(catalog, str):
        source_list_name = os.path.basename(catalog)
        sky = pyradiosky.SkyModel()
        if not os.path.isfile(catalog):
            catalog = os.path.join(param_dict['config_path'], catalog)
        if catalog.endswith("txt"):
            sky.read_text_catalog(catalog)
        elif catalog.endswith('vot'):
            if 'gleam' in catalog:
                if "spectral_type" in source_params:
                    spectral_type = source_params["spectral_type"]
                else:
                    warnings.warn("No spectral_type specified for GLEAM, using 'flat'.")
                    spectral_type = "flat"
                sky.read_gleam_catalog(
                    catalog, spectral_type=spectral_type
                )
            else:
                vo_params = {}

for a1 in range(Nants):
        for a2 in range(a1, Nants):
            if bi >= Nbls:
                break
            blsel.append('({},{})'.format(a1, a2))
            bi += 1

    # ----------------
    # Sky Model setup
    #   Full frequency HEALPix shell.
    # ----------------

    fmin, fmax = 0.0, 1.0   # K (fluxes)
    skydat = np.random.uniform(fmin, fmax, (Nfreqs, Nsrcs))

    write_healpix_hdf5(os.path.join(confdir, hpx_fname), skydat, range(Nsrcs), freqs)

    # ----------------
    # Make config dictionaries
    # ----------------

    filedict = {
        'outdir': outdir,
        'outfile_name': 'benchmark',
        'output_format': 'uvh5'
    }

    freqdict = freq_array_to_params(freqs)

    srcdict = {
        'catalog': hpx_fname
    }

if bi >= Nbls:
            break
        blsel.append('({},{})'.format(a1, a2))
        bi += 1


# ----------------
# Sky Model setup
#   Full frequency HEALPix shell.
# ----------------

fmin, fmax = 0.0, 1.0   # K (fluxes)
skydat = np.random.uniform(fmin, fmax, (Nfreqs, Nsrcs))

hpx_fname = 'benchmark_skymodel.hdf5'
write_healpix_hdf5(os.path.join(confdir, hpx_fname), skydat, range(Nsrcs), freqs)

# ----------------
# Make config dictionaries
# ----------------

filedict = {
    'outdir': outdir,
    'outfile_name': 'benchmark',
    'output_format': 'uvh5'
}

freqdict = freq_array_to_params(freqs)


srcdict = {
    'catalog': hpx_fname