aces.sefd.sefd__cc

Module Contents

Classes

ASKAP_array

SEFD

Functions

baseline_2vec(bas)

baseline_2v(a1, a2)

baseline_len(a1, a2)

antenna_decompose(products, arr, b_flg)

find_factor(n_avg)

box_mean(x, n)

delfunc(x, f, t, z)

bl_scales(n_avg, zvis, flux1934)

bl_noise(zvis, scale, btau, n_avg, diff)

param zvis:

bl_noise_fft(zvis, scale, btau, n_avg)

fftmeth(sig)

remove_mean(z)

decomp(prod, lpols, arr, flags)

_plot0_per_ant(sefd_per_ant, chan_freq, ants, pltname)

medfilt(x, k)

Apply a length-k median filter to a 1D array x.

Attributes

__author__

logger

pols

pi

antrel01

ASKAP_antennas

aces.sefd.sefd__cc.__author__ = 'Dave McConnell <david.mcconnell@csiro.au>'[source]
aces.sefd.sefd__cc.logger[source]
aces.sefd.sefd__cc.pols = [0, 1, 2, 3][source]
aces.sefd.sefd__cc.pi[source]
aces.sefd.sefd__cc.antrel01[source]
aces.sefd.sefd__cc.ASKAP_antennas[source]
class aces.sefd.sefd__cc.ASKAP_array(name)[source]

Bases: object

select_antennas(antennas)[source]
gen_baselines()[source]
get_ant_names()[source]
aces.sefd.sefd__cc.baseline_2vec(bas)[source]
aces.sefd.sefd__cc.baseline_2v(a1, a2)[source]
aces.sefd.sefd__cc.baseline_len(a1, a2)[source]
aces.sefd.sefd__cc.antenna_decompose(products, arr, b_flg)[source]
aces.sefd.sefd__cc.find_factor(n_avg)[source]
aces.sefd.sefd__cc.box_mean(x, n)[source]
aces.sefd.sefd__cc.delfunc(x, f, t, z)[source]
class aces.sefd.sefd__cc.SEFD(sbid, file_name=None, mset_inx=0, dch=1, ch0=0, ncf=0, scan=-1, dnt=120, nstart=3, nct=0, uvmin=0.0)[source]

Bases: object

summary()[source]
calc_sefd(difference='none')[source]
Parameters:

difference – Options are ‘none’, ‘time’, ‘frequency’

decompose(what='SEFD')[source]
compare(other)[source]
save_pickle(file_name)[source]
save_hdf5(file_name, per_ant=True)[source]
plot_per_ant(plot_file)[source]
get_meta()[source]
aces.sefd.sefd__cc.bl_scales(n_avg, zvis, flux1934)[source]
aces.sefd.sefd__cc.bl_noise(zvis, scale, btau, n_avg, diff)[source]
Parameters:

  • zvis –

  • scale –

  • btau –

  • n_avg –

  • diff – Options are ‘none’, ‘time’, ‘frequency’

Returns:

aces.sefd.sefd__cc.bl_noise_fft(zvis, scale, btau, n_avg)[source]
aces.sefd.sefd__cc.fftmeth(sig)[source]
aces.sefd.sefd__cc.remove_mean(z)[source]
aces.sefd.sefd__cc.decomp(prod, lpols, arr, flags)[source]
aces.sefd.sefd__cc._plot0_per_ant(sefd_per_ant, chan_freq, ants, pltname)[source]
aces.sefd.sefd__cc.medfilt(x, k)[source]

Apply a length-k median filter to a 1D array x. Boundaries are extended by repeating endpoints.