arrakis.wsclean_rmsynth

Classes

RMSynthParams

Functions

`_clean_loop`(phis, rmsf, fdf_residual, fdf_clean, ...)
`_gauss`(→ numpy.ndarray)	Gaussion function
`deconvolve`(residual, model, psf, meta)
`get_rmsynth_params`(→ RMSynthParams)	Get parameters for RMSynth
`proper_rm_clean`(→ numpy.ndarray)
`rmsynth2d`(→ numpy.ndarray)	1D RMSynth
`rmsynth3d`(→ numpy.ndarray)	1D RMSynth
`rmsynth_1d`(→ numpy.ndarray)	1D RMSynth
`simple_clean`(→ numpy.ndarray)	Simple clean

Module Contents

class arrakis.wsclean_rmsynth.RMSynthParams[source]

Bases: NamedTuple

ays: numpy.ndarray[source]

fwhm: float[source]

lsq: numpy.ndarray[source]

lsq_0: float[source]

phis: numpy.ndarray[source]

phis_double: numpy.ndarray[source]

rmsf: numpy.ndarray[source]

arrakis.wsclean_rmsynth._clean_loop(phis: numpy.ndarray, rmsf: numpy.ndarray, fdf_residual: numpy.ndarray, fdf_clean: numpy.ndarray, cc_vec: numpy.ndarray, idx_max_rmsf: int, n_phi_pad: int, fwhm: float, cutoff: float, gain: float, max_iter: int)[source]

arrakis.wsclean_rmsynth._gauss(x: numpy.ndarray, amp: float, mu: float, sigma: float) → numpy.ndarray[source]

Gaussion function

Parameters:

x (np.ndarray) – X values
amp (float) – Maximum value
mu (float) – Mean
sigma (float) – Standard deviation

Returns:

Gaussian data

Return type:

np.ndarray

arrakis.wsclean_rmsynth.deconvolve(residual: numpy.ndarray, model: numpy.ndarray, psf: numpy.ndarray, meta: dict)[source]

arrakis.wsclean_rmsynth.get_rmsynth_params(freqs: numpy.ndarray, weights: numpy.ndarray, nsamp: int = 10) → RMSynthParams[source]

Get parameters for RMSynth

Parameters:

freqs (np.ndarray) – Frequencies in Hz
weights (np.ndarray) – Weights

Returns:

RMSynth parameters

Return type:

RMSynthParams

arrakis.wsclean_rmsynth.proper_rm_clean(phis: numpy.ndarray, phis_double: numpy.ndarray, fdf_dirty: numpy.ndarray, rmsf: numpy.ndarray, ays: numpy.ndarray, fwhm: float, cutoff: float = 0.1, gain: float = 0.1, max_iter: int = 1000) → numpy.ndarray[source]

arrakis.wsclean_rmsynth.rmsynth2d(stokes_q: numpy.ndarray, stokes_u: numpy.ndarray, weights: numpy.ndarray, ays: numpy.ndarray, phis: numpy.ndarray) → numpy.ndarray[source]

1D RMSynth

Parameters:

stokes_q (np.ndarray) – Stokes Q [nchan, pix]
stokes_u (np.ndarray) – Stokes U [nchan, pix]
weights (np.ndarray) – Weights [nchan]
ays (np.ndarray) – Lsq - Lsq_0 [nchan]
phis (np.ndarray) – Faraday depths [nphi]

Returns:

FDF [nphi, y, x]

Return type:

np.ndarray

arrakis.wsclean_rmsynth.rmsynth3d(stokes_q: numpy.ndarray, stokes_u: numpy.ndarray, weights: numpy.ndarray, ays: numpy.ndarray, phis: numpy.ndarray) → numpy.ndarray[source]

1D RMSynth

Parameters:

stokes_q (np.ndarray) – Stokes Q [nchan, y, x]
stokes_u (np.ndarray) – Stokes U [nchan, y, x]
weights (np.ndarray) – Weights [nchan]
ays (np.ndarray) – Lsq - Lsq_0 [nchan]
phis (np.ndarray) – Faraday depths [nphi]

Returns:

FDF [nphi, y, x]

Return type:

np.ndarray

arrakis.wsclean_rmsynth.rmsynth_1d(stokes_q: numpy.ndarray, stokes_u: numpy.ndarray, weights: numpy.ndarray, ays: numpy.ndarray, phis: numpy.ndarray) → numpy.ndarray[source]

1D RMSynth

Parameters:

stokes_q (np.ndarray) – Stokes Q
stokes_u (np.ndarray) – Stokes U
weights (np.ndarray) – Weights
ays (np.ndarray) – Lsq - Lsq_0
phis (np.ndarray) – Faraday depths

Returns:

FDF

Return type:

np.ndarray

arrakis.wsclean_rmsynth.simple_clean(phis: numpy.ndarray, fdf: numpy.ndarray, ays: numpy.ndarray, fwhm: float) → numpy.ndarray[source]

Simple clean

Parameters:

phis (np.ndarray) – Faraday depths
fdf (np.ndarray) – FDF
ays (np.ndarray) – Lsq - Lsq_0
fwhm (float) – FWHM

Returns:

Spectrum

Return type:

np.ndarray